From b9efa0852ee890828c93ae65364e7beef3d964cd Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Tue, 14 May 2024 17:34:01 +0200
Subject: [PATCH 001/424] chore: Update version number in configuration files

---
 .bumpversion.cfg | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 7f85c0cc2..50f194e51 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -14,3 +14,8 @@ replace = __version__ = "{new_version}"
 [bumpversion:file:doc/conf.py]
 search = version = "{current_version}"
 replace = version = "{new_version}"
+
+[bumpversion:file:CITATION.cff]
+search = version = "{current_version}"
+replace = version = "{new_version}"
+

From ff6ff003215a2eb33b17650b5e59c1234f17f4e8 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Tue, 14 May 2024 17:34:08 +0200
Subject: [PATCH 002/424] chore: Add CITATION.cff file with software citation
 information

---
 CITATION.cff | 27 +++++++++++++++++++++++++++
 1 file changed, 27 insertions(+)
 create mode 100644 CITATION.cff

diff --git a/CITATION.cff b/CITATION.cff
new file mode 100644
index 000000000..aead3751d
--- /dev/null
+++ b/CITATION.cff
@@ -0,0 +1,27 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+- family-names: "Cordier"
+  given-names: "Thibault"
+  orcid: "https://orcid.org/0000-0000-0000-0000"
+title: "MAPIE - Model Agnostic Prediction Interval Estimator"
+version: 0.8.3
+date-released: 2019-04-30
+url: "https://github.com/scikit-learn-contrib/MAPIE"
+preferred-citation:
+  type: article
+  authors:
+  - family-names: "Taquet"
+    given-names: "Vianney"
+  - family-names: "Blot"
+    given-names: "Vincent"
+  - family-names: "Morzadec"
+    given-names: "Thomas"
+  - family-names: "Lacombe"
+    given-names: "Louis"
+  - family-names: "Brunel"
+    given-names: "Nicolas"
+  doi: "10.48550/arXiv.2207.12274"
+  journal: "arXiv preprint arXiv:2207.12274"
+  title: "MAPIE: an open-source library for distribution-free uncertainty quantification"
+  year: 2021
\ No newline at end of file

From 0d0dd8de411345b858498d38111df121460bd097 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 14 May 2024 18:00:11 +0200
Subject: [PATCH 003/424] Add refacto Ensemble Classifier

---
 mapie/classification.py      | 669 +++++++++++++----------------------
 mapie/estimator/estimator.py | 519 ++++++++++++++++++++++++++-
 2 files changed, 753 insertions(+), 435 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index bf13945c1..ea55c1239 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -1,26 +1,32 @@
 from __future__ import annotations
 
 import warnings
-from typing import Any, Iterable, List, Optional, Tuple, Union, cast
+from typing import Any, Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
-from joblib import Parallel, delayed
-from sklearn.base import BaseEstimator, ClassifierMixin, clone
+from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
 from sklearn.preprocessing import LabelEncoder, label_binarize
 from sklearn.utils import _safe_indexing, check_random_state
-from sklearn.utils.multiclass import (check_classification_targets,
-                                      type_of_target)
-from sklearn.utils.validation import (_check_y, _num_samples, check_is_fitted,
-                                      indexable)
+from sklearn.utils.multiclass import check_classification_targets, type_of_target
+from sklearn.utils.validation import _check_y, _num_samples, check_is_fitted, indexable
 
 from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
+from .estimator.estimator import EnsembleClassifier
 from .metrics import classification_mean_width_score
-from .utils import (check_alpha, check_alpha_and_n_samples, check_cv,
-                    check_estimator_classification, check_n_features_in,
-                    check_n_jobs, check_null_weight, check_verbose,
-                    compute_quantiles, fit_estimator, fix_number_of_classes)
+from .utils import (
+    check_alpha,
+    check_alpha_and_n_samples,
+    check_cv,
+    check_estimator_classification,
+    check_n_features_in,
+    check_n_jobs,
+    check_null_weight,
+    check_verbose,
+    compute_quantiles,
+    fix_number_of_classes,
+)
 
 
 from mapie.conformity_scores.utils_classification_conformity_scores import (
@@ -192,16 +198,19 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
     raps_valid_cv_ = ["prefit", "split"]
     valid_methods_ = [
-        "naive", "score", "lac", "cumulated_score", "aps", "top_k", "raps"
+        "naive",
+        "score",
+        "lac",
+        "cumulated_score",
+        "aps",
+        "top_k",
+        "raps",
     ]
     fit_attributes = [
-        "single_estimator_",
-        "estimators_",
-        "k_",
         "n_features_in_",
         "conformity_scores_",
         "classes_",
-        "label_encoder_"
+        "label_encoder_",
     ]
 
     def __init__(
@@ -212,7 +221,7 @@ def __init__(
         test_size: Optional[Union[int, float]] = None,
         n_jobs: Optional[int] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
-        verbose: int = 0
+        verbose: int = 0,
     ) -> None:
         self.estimator = estimator
         self.method = method
@@ -233,8 +242,7 @@ def _check_parameters(self) -> None:
         """
         if self.method not in self.valid_methods_:
             raise ValueError(
-                "Invalid method. "
-                f"Allowed values are {self.valid_methods_}."
+                "Invalid method. " f"Allowed values are {self.valid_methods_}."
             )
         check_n_jobs(self.n_jobs)
         check_verbose(self.verbose)
@@ -255,18 +263,18 @@ def _check_depreciated(self) -> None:
         if self.method == "score":
             warnings.warn(
                 "WARNING: Deprecated method. "
-                + "The method \"score\" is outdated. "
-                + "Prefer to use \"lac\" instead to keep "
+                + 'The method "score" is outdated. '
+                + 'Prefer to use "lac" instead to keep '
                 + "the same behavior in the next release.",
-                DeprecationWarning
+                DeprecationWarning,
             )
         if self.method == "cumulated_score":
             warnings.warn(
                 "WARNING: Deprecated method. "
-                + "The method \"cumulated_score\" is outdated. "
-                + "Prefer to use \"aps\" instead to keep "
+                + 'The method "cumulated_score" is outdated. '
+                + 'Prefer to use "aps" instead to keep '
                 + "the same behavior in the next release.",
-                DeprecationWarning
+                DeprecationWarning,
             )
 
     def _check_target(self, y: ArrayLike) -> None:
@@ -286,8 +294,7 @@ def _check_target(self, y: ArrayLike) -> None:
             or ``"score"`` or if type of target is not multi-class.
         """
         check_classification_targets(y)
-        if type_of_target(y) == "binary" and \
-                self.method not in ["score", "lac"]:
+        if type_of_target(y) == "binary" and self.method not in ["score", "lac"]:
             raise ValueError(
                 "Invalid method for binary target. "
                 "Your target is not of type multiclass and "
@@ -306,17 +313,14 @@ def _check_raps(self):
             If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
         """
         if (self.method == "raps") and (
-            (self.cv not in self.raps_valid_cv_)
-            or isinstance(self.cv, ShuffleSplit)
+            (self.cv not in self.raps_valid_cv_) or isinstance(self.cv, ShuffleSplit)
         ):
             raise ValueError(
-                "RAPS method can only be used "
-                f"with cv in {self.raps_valid_cv_}."
+                "RAPS method can only be used " f"with cv in {self.raps_valid_cv_}."
             )
 
     def _check_include_last_label(
-        self,
-        include_last_label: Optional[Union[bool, str]]
+        self, include_last_label: Optional[Union[bool, str]]
     ) -> Optional[Union[bool, str]]:
         """
         Check if ``include_last_label`` is a boolean or a string.
@@ -347,9 +351,8 @@ def _check_include_last_label(
             "Invalid include_last_label argument. "
             "Should be a boolean or 'randomized'."
         """
-        if (
-            (not isinstance(include_last_label, bool)) and
-            (not include_last_label == "randomized")
+        if (not isinstance(include_last_label, bool)) and (
+            not include_last_label == "randomized"
         ):
             raise ValueError(
                 "Invalid include_last_label argument. "
@@ -359,9 +362,7 @@ def _check_include_last_label(
             return include_last_label
 
     def _check_proba_normalized(
-        self,
-        y_pred_proba: ArrayLike,
-        axis: int = 1
+        self, y_pred_proba: ArrayLike, axis: int = 1
     ) -> NDArray:
         """
         Check if, for all the observations, the sum of
@@ -389,7 +390,7 @@ def _check_proba_normalized(
             np.sum(y_pred_proba, axis=axis),
             1,
             err_msg="The sum of the scores is not equal to one.",
-            rtol=1e-5
+            rtol=1e-5,
         )
         y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
         return y_pred_proba
@@ -398,7 +399,7 @@ def _get_last_index_included(
         self,
         y_pred_proba_cumsum: NDArray,
         threshold: NDArray,
-        include_last_label: Optional[Union[bool, str]]
+        include_last_label: Optional[Union[bool, str]],
     ) -> NDArray:
         """
         Return the index of the last included sorted probability
@@ -429,27 +430,19 @@ def _get_last_index_included(
         NDArray of shape (n_samples, n_alpha)
             Index of the last included sorted probability.
         """
-        if (
-            (include_last_label) or
-            (include_last_label == 'randomized')
-        ):
-            y_pred_index_last = (
-                    np.ma.masked_less(
-                        y_pred_proba_cumsum
-                        - threshold[np.newaxis, :],
-                        -EPSILON
-                    ).argmin(axis=1)
-            )
-        elif (include_last_label is False):
+        if (include_last_label) or (include_last_label == "randomized"):
+            y_pred_index_last = np.ma.masked_less(
+                y_pred_proba_cumsum - threshold[np.newaxis, :], -EPSILON
+            ).argmin(axis=1)
+        elif include_last_label is False:
             max_threshold = np.maximum(
-                threshold[np.newaxis, :],
-                np.min(y_pred_proba_cumsum, axis=1)
+                threshold[np.newaxis, :], np.min(y_pred_proba_cumsum, axis=1)
             )
             y_pred_index_last = np.argmax(
                 np.ma.masked_greater(
-                    y_pred_proba_cumsum - max_threshold[:, np.newaxis, :],
-                    EPSILON
-                ), axis=1
+                    y_pred_proba_cumsum - max_threshold[:, np.newaxis, :], EPSILON
+                ),
+                axis=1,
             )
         else:
             raise ValueError(
@@ -466,7 +459,7 @@ def _add_random_tie_breaking(
         y_pred_proba_last: NDArray,
         threshold: NDArray,
         lambda_star: Union[NDArray, float, None],
-        k_star: Union[NDArray, None]
+        k_star: Union[NDArray, None],
     ) -> NDArray:
         """
         Randomly remove last label from prediction set based on the
@@ -512,29 +505,21 @@ def _add_random_tie_breaking(
         """
         # get cumsumed probabilities up to last retained label
         y_proba_last_cumsumed = np.squeeze(
-            np.take_along_axis(
-                y_pred_proba_cumsum,
-                y_pred_index_last,
-                axis=1
-            ), axis=1
+            np.take_along_axis(y_pred_proba_cumsum, y_pred_index_last, axis=1), axis=1
         )
 
         if self.method in ["cumulated_score", "aps"]:
             # compute V parameter from Romano+(2020)
-            vs = (
-                (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-                y_pred_proba_last[:, 0, :]
-            )
+            vs = (y_proba_last_cumsumed - threshold.reshape(1, -1)) / y_pred_proba_last[
+                :, 0, :
+            ]
         else:
             # compute V parameter from Angelopoulos+(2020)
             L = np.sum(prediction_sets, axis=1)
-            vs = (
-                (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-                (
-                    y_pred_proba_last[:, 0, :] -
-                    lambda_star * np.maximum(0, L - k_star) +
-                    lambda_star * (L > k_star)
-                )
+            vs = (y_proba_last_cumsumed - threshold.reshape(1, -1)) / (
+                y_pred_proba_last[:, 0, :]
+                - lambda_star * np.maximum(0, L - k_star)
+                + lambda_star * (L > k_star)
             )
 
         # get random numbers for each observation and alpha value
@@ -546,7 +531,7 @@ def _add_random_tie_breaking(
             prediction_sets,
             y_pred_index_last,
             vs_less_than_us[:, np.newaxis, :],
-            axis=1
+            axis=1,
         )
         return prediction_sets
 
@@ -575,92 +560,13 @@ def _predict_oof_model(
         # we enforce y_pred_proba to contain all labels included in y
         if len(estimator.classes_) != self.n_classes_:
             y_pred_proba = fix_number_of_classes(
-                self.n_classes_,
-                estimator.classes_,
-                y_pred_proba
+                self.n_classes_, estimator.classes_, y_pred_proba
             )
         y_pred_proba = self._check_proba_normalized(y_pred_proba)
         return y_pred_proba
 
-    def _fit_and_predict_oof_model(
-        self,
-        estimator: ClassifierMixin,
-        X: ArrayLike,
-        y: ArrayLike,
-        train_index: ArrayLike,
-        val_index: ArrayLike,
-        k: int,
-        sample_weight: Optional[ArrayLike] = None,
-        **fit_params,
-    ) -> Tuple[ClassifierMixin, NDArray, NDArray, ArrayLike]:
-        """
-        Fit a single out-of-fold model on a given training set and
-        perform predictions on a test set.
-
-        Parameters
-        ----------
-        estimator: ClassifierMixin
-            Estimator to train.
-
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        train_index: np.ndarray of shape (n_samples_train)
-            Training data indices.
-
-        val_index: np.ndarray of shape (n_samples_val)
-            Validation data indices.
-
-        k: int
-            Split identification number.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-            By default None.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        Tuple[ClassifierMixin, NDArray, NDArray, ArrayLike]
-
-        - [0]: ClassifierMixin, fitted estimator
-        - [1]: NDArray of shape (n_samples_val,),
-          Estimator predictions on the validation fold,
-        - [2]: NDArray of shape (n_samples_val,)
-          Identification number of the validation fold,
-        - [3]: ArrayLike of shape (n_samples_val,)
-          Validation data indices
-        """
-        X_train = _safe_indexing(X, train_index)
-        y_train = _safe_indexing(y, train_index)
-        X_val = _safe_indexing(X, val_index)
-        y_val = _safe_indexing(y, val_index)
-
-        if sample_weight is None:
-            estimator = fit_estimator(
-                estimator, X_train, y_train, **fit_params
-            )
-        else:
-            sample_weight_train = _safe_indexing(sample_weight, train_index)
-            estimator = fit_estimator(
-                estimator, X_train, y_train, sample_weight_train, **fit_params
-            )
-        if _num_samples(X_val) > 0:
-            y_pred_proba = self._predict_oof_model(estimator, X_val)
-        else:
-            y_pred_proba = np.array([])
-        val_id = np.full_like(y_val, k, dtype=int)
-        return estimator, y_pred_proba, val_id, val_index
-
     def _get_true_label_cumsum_proba(
-        self,
-        y: ArrayLike,
-        y_pred_proba: NDArray
+        self, y: ArrayLike, y_pred_proba: NDArray
     ) -> Tuple[NDArray, NDArray]:
         """
         Compute the cumsumed probability of the true label.
@@ -679,13 +585,9 @@ def _get_true_label_cumsum_proba(
         is the cumsum probability of the true label. The second
         is the sorted position of the true label.
         """
-        y_true = label_binarize(
-            y=y, classes=self.classes_
-        )
+        y_true = label_binarize(y=y, classes=self.classes_)
         index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
-        y_pred_proba_sorted = np.take_along_axis(
-            y_pred_proba, index_sorted, axis=1
-        )
+        y_pred_proba_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
         y_true_sorted = np.take_along_axis(y_true, index_sorted, axis=1)
         y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
         cutoff = np.argmax(y_true_sorted, axis=1)
@@ -700,7 +602,7 @@ def _regularize_conformity_score(
         k_star: NDArray,
         lambda_: Union[NDArray, float],
         conf_score: NDArray,
-        cutoff: NDArray
+        cutoff: NDArray,
     ) -> NDArray:
         """
         Regularize the conformity scores with the ``"raps"``
@@ -727,28 +629,44 @@ def _regularize_conformity_score(
             Regularized conformity scores. The regularization
             depends on the value of alpha.
         """
-        conf_score = np.repeat(
-            conf_score[:, :, np.newaxis], len(k_star), axis=2
-        )
-        cutoff = np.repeat(
-            cutoff[:, np.newaxis], len(k_star), axis=1
-        )
-        conf_score += np.maximum(
-            np.expand_dims(
-                lambda_ * (cutoff - k_star),
-                axis=1
-            ),
-            0
-        )
+        conf_score = np.repeat(conf_score[:, :, np.newaxis], len(k_star), axis=2)
+        cutoff = np.repeat(cutoff[:, np.newaxis], len(k_star), axis=1)
+        conf_score += np.maximum(np.expand_dims(lambda_ * (cutoff - k_star), axis=1), 0)
         return conf_score
 
+<<<<<<< Updated upstream
+=======
+    def _get_true_label_position(self, y_pred_proba: NDArray, y: NDArray) -> NDArray:
+        """
+        Return the sorted position of the true label in the
+        prediction
+
+        Parameters
+        ----------
+        y_pred_proba: NDArray of shape (n_samples, n_calsses)
+            Model prediction.
+
+        y: NDArray of shape (n_samples)
+            Labels.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, 1)
+            Position of the true label in the prediction.
+        """
+        index = np.argsort(np.fliplr(np.argsort(y_pred_proba, axis=1)))
+        position = np.take_along_axis(index, y.reshape(-1, 1), axis=1)
+
+        return position
+
+>>>>>>> Stashed changes
     def _get_last_included_proba(
         self,
         y_pred_proba: NDArray,
         thresholds: NDArray,
         include_last_label: Union[bool, str, None],
         lambda_: Union[NDArray, float, None],
-        k_star: Union[NDArray, Any]
+        k_star: Union[NDArray, Any],
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Function that returns the smallest score
@@ -783,46 +701,28 @@ def _get_last_included_proba(
             with the RAPS method, the index of the last included score
             and the value of the last included score.
         """
-        index_sorted = np.flip(
-            np.argsort(y_pred_proba, axis=1), axis=1
-        )
+        index_sorted = np.flip(np.argsort(y_pred_proba, axis=1), axis=1)
         # sort probabilities by decreasing order
-        y_pred_proba_sorted = np.take_along_axis(
-            y_pred_proba, index_sorted, axis=1
-        )
+        y_pred_proba_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
         # get sorted cumulated score
-        y_pred_proba_sorted_cumsum = np.cumsum(
-            y_pred_proba_sorted, axis=1
-        )
+        y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
 
         if self.method == "raps":
             y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
-                0,
-                np.cumsum(
-                    np.ones(y_pred_proba_sorted_cumsum.shape),
-                    axis=1
-                ) - k_star
+                0, np.cumsum(np.ones(y_pred_proba_sorted_cumsum.shape), axis=1) - k_star
             )
         # get cumulated score at their original position
         y_pred_proba_cumsum = np.take_along_axis(
-            y_pred_proba_sorted_cumsum,
-            np.argsort(index_sorted, axis=1),
-            axis=1
+            y_pred_proba_sorted_cumsum, np.argsort(index_sorted, axis=1), axis=1
         )
         # get index of the last included label
         y_pred_index_last = self._get_last_index_included(
-            y_pred_proba_cumsum,
-            thresholds,
-            include_last_label
+            y_pred_proba_cumsum, thresholds, include_last_label
         )
         # get the probability of the last included label
-        y_pred_proba_last = np.take_along_axis(
-            y_pred_proba,
-            y_pred_index_last,
-            axis=1
-        )
+        y_pred_proba_last = np.take_along_axis(y_pred_proba, y_pred_index_last, axis=1)
 
-        zeros_scores_proba_last = (y_pred_proba_last <= EPSILON)
+        zeros_scores_proba_last = y_pred_proba_last <= EPSILON
 
         # If the last included proba is zero, change it to the
         # smallest non-zero value to avoid inluding them in the
@@ -830,12 +730,10 @@ def _get_last_included_proba(
         if np.sum(zeros_scores_proba_last) > 0:
             y_pred_proba_last[zeros_scores_proba_last] = np.expand_dims(
                 np.min(
-                    np.ma.masked_less(
-                        y_pred_proba,
-                        EPSILON
-                    ).filled(fill_value=np.inf),
-                    axis=1
-                ), axis=1
+                    np.ma.masked_less(y_pred_proba, EPSILON).filled(fill_value=np.inf),
+                    axis=1,
+                ),
+                axis=1,
             )[zeros_scores_proba_last]
 
         return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
@@ -846,7 +744,7 @@ def _update_size_and_lambda(
         alpha_np: NDArray,
         y_ps: NDArray,
         lambda_: Union[NDArray, float],
-        lambda_star: NDArray
+        lambda_star: NDArray,
     ) -> Tuple[NDArray, NDArray]:
         """Update the values of the optimal lambda if the
         average size of the prediction sets decreases with
@@ -880,15 +778,11 @@ def _update_size_and_lambda(
         """
 
         sizes = [
-            classification_mean_width_score(
-                y_ps[:, :, i]
-            ) for i in range(len(alpha_np))
+            classification_mean_width_score(y_ps[:, :, i]) for i in range(len(alpha_np))
         ]
 
-        sizes_improve = (sizes < best_sizes - EPSILON)
-        lambda_star = (
-            sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
-        )
+        sizes_improve = sizes < best_sizes - EPSILON
+        lambda_star = sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
         best_sizes = sizes_improve * sizes + (1 - sizes_improve) * best_sizes
 
         return lambda_star, best_sizes
@@ -898,7 +792,7 @@ def _find_lambda_star(
         y_pred_proba_raps: NDArray,
         alpha_np: NDArray,
         include_last_label: Union[bool, str, None],
-        k_star: NDArray
+        k_star: NDArray,
     ) -> Union[NDArray, float]:
         """Find the optimal value of lambda for each alpha.
 
@@ -926,37 +820,23 @@ def _find_lambda_star(
         lambda_star = np.zeros(len(alpha_np))
         best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
 
-        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]
-            true_label_cumsum_proba, cutoff = (
-                self._get_true_label_cumsum_proba(
-                    self.y_raps_no_enc,
-                    y_pred_proba_raps[:, :, 0],
-                )
+        for lambda_ in [0.001, 0.01, 0.1, 0.2, 0.5]:  # values given in paper[3]
+            true_label_cumsum_proba, cutoff = self._get_true_label_cumsum_proba(
+                self.y_raps_no_enc,
+                y_pred_proba_raps[:, :, 0],
             )
 
             true_label_cumsum_proba_reg = self._regularize_conformity_score(
-                k_star,
-                lambda_,
-                true_label_cumsum_proba,
-                cutoff
+                k_star, lambda_, true_label_cumsum_proba, cutoff
             )
 
-            quantiles_ = compute_quantiles(
-                true_label_cumsum_proba_reg,
-                alpha_np
-            )
+            quantiles_ = compute_quantiles(true_label_cumsum_proba_reg, alpha_np)
 
             _, _, y_pred_proba_last = self._get_last_included_proba(
-                y_pred_proba_raps,
-                quantiles_,
-                include_last_label,
-                lambda_,
-                k_star
+                y_pred_proba_raps, quantiles_, include_last_label, lambda_, k_star
             )
 
-            y_ps = np.greater_equal(
-                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
-            )
+            y_ps = np.greater_equal(y_pred_proba_raps - y_pred_proba_last, -EPSILON)
             lambda_star, best_sizes = self._update_size_and_lambda(
                 best_sizes, alpha_np, y_ps, lambda_, lambda_star
             )
@@ -965,7 +845,7 @@ def _find_lambda_star(
         return lambda_star
 
     def _get_classes_info(
-            self, estimator: ClassifierMixin, y: NDArray
+        self, estimator: ClassifierMixin, y: NDArray
     ) -> Tuple[int, NDArray]:
         """
         Compute the number of classes and the classes values
@@ -1019,12 +899,63 @@ def _get_classes_info(
 
         return n_classes, classes
 
+    def _check_fit_parameter(self, X, y, sample_weight, groups):
+        self._check_parameters()
+        cv = check_cv(self.cv, test_size=self.test_size, random_state=self.random_state)
+        X, y = indexable(X, y)
+        y = _check_y(y)
+
+        sample_weight = cast(Optional[NDArray], sample_weight)
+        groups = cast(Optional[NDArray], groups)
+        sample_weight, X, y = check_null_weight(sample_weight, X, y)
+
+        y = cast(NDArray, y)
+
+        estimator = check_estimator_classification(X, y, cv, self.estimator)
+        self.n_features_in_ = check_n_features_in(X, cv, estimator)
+
+        n_samples = _num_samples(y)
+
+        self.n_classes_, self.classes_ = self._get_classes_info(estimator, y)
+        enc = LabelEncoder()
+        enc.fit(self.classes_)
+        y_enc = enc.transform(y)
+
+        self.label_encoder_ = enc
+        self._check_target(y)
+
+        return (estimator, cv, X, y, y_enc, sample_weight, groups, n_samples)
+
+    def _split_raps_data(self, X, y_enc, sample_weight, groups, size_raps):
+        raps_split = ShuffleSplit(
+            1, test_size=size_raps, random_state=self.random_state
+        )
+        train_raps_index, val_raps_index = next(raps_split.split(X))
+        X, self.X_raps, y_enc, self.y_raps = (
+            _safe_indexing(X, train_raps_index),
+            _safe_indexing(X, val_raps_index),
+            _safe_indexing(y_enc, train_raps_index),
+            _safe_indexing(y_enc, val_raps_index),
+        )
+        self.y_raps_no_enc = self.label_encoder_.inverse_transform(self.y_raps)
+        y = self.label_encoder_.inverse_transform(y_enc)
+        y_enc = cast(NDArray, y_enc)
+        n_samples = _num_samples(y_enc)
+        if sample_weight is not None:
+            sample_weight = sample_weight[train_raps_index]
+            sample_weight = cast(NDArray, sample_weight)
+        if groups is not None:
+            groups = groups[train_raps_index]
+            groups = cast(NDArray, groups)
+
+        return X, y_enc, y, n_samples, sample_weight, groups
+
     def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
-        size_raps: Optional[float] = .2,
+        size_raps: Optional[float] = 0.2,
         groups: Optional[ArrayLike] = None,
         **fit_params,
     ) -> MapieClassifier:
@@ -1069,147 +1000,64 @@ def fit(
             The model itself.
         """
         # Checks
-        self._check_parameters()
-        cv = check_cv(
-            self.cv, test_size=self.test_size, random_state=self.random_state
+        (estimator, cv, X, y, y_enc, sample_weight, groups, n_samples) = (
+            self._check_fit_parameter(X, y, sample_weight, groups)
         )
-        X, y = indexable(X, y)
-        y = _check_y(y)
-
-        sample_weight = cast(Optional[NDArray], sample_weight)
-        groups = cast(Optional[NDArray], groups)
-        sample_weight, X, y = check_null_weight(sample_weight, X, y)
-
-        y = cast(NDArray, y)
-
-        estimator = check_estimator_classification(
-            X,
-            y,
-            cv,
-            self.estimator
-        )
-        self.n_features_in_ = check_n_features_in(X, cv, estimator)
-
-        n_samples = _num_samples(y)
-
-        self.n_classes_, self.classes_ = self._get_classes_info(
-            estimator, y
-        )
-        enc = LabelEncoder()
-        enc.fit(self.classes_)
-        y_enc = enc.transform(y)
-
-        self.label_encoder_ = enc
-        self._check_target(y)
-
-        # Initialization
-        self.estimators_: List[ClassifierMixin] = []
-        self.k_ = np.empty_like(y, dtype=int)
-        self.n_samples_ = _num_samples(X)
 
         if self.method == "raps":
-            raps_split = ShuffleSplit(
-                1, test_size=size_raps, random_state=self.random_state
-            )
-            train_raps_index, val_raps_index = next(raps_split.split(X))
-            X, self.X_raps, y_enc, self.y_raps = \
-                _safe_indexing(X, train_raps_index), \
-                _safe_indexing(X, val_raps_index), \
-                _safe_indexing(y_enc, train_raps_index), \
-                _safe_indexing(y_enc, val_raps_index)
-            self.y_raps_no_enc = self.label_encoder_.inverse_transform(
-                self.y_raps
+            (X, y_enc, y, n_samples, sample_weight, groups) = self._split_raps_data(
+                X, y_enc, sample_weight, groups, size_raps
             )
-            y = self.label_encoder_.inverse_transform(y_enc)
-            y_enc = cast(NDArray, y_enc)
-            n_samples = _num_samples(y_enc)
-            if sample_weight is not None:
-                sample_weight = sample_weight[train_raps_index]
-                sample_weight = cast(NDArray, sample_weight)
-            if groups is not None:
-                groups = groups[train_raps_index]
-                groups = cast(NDArray, groups)
 
         # Work
-        if cv == "prefit":
-            self.single_estimator_ = estimator
-            y_pred_proba = self.single_estimator_.predict_proba(X)
-            y_pred_proba = self._check_proba_normalized(y_pred_proba)
-
-        else:
-            cv = cast(BaseCrossValidator, cv)
-            self.single_estimator_ = fit_estimator(
-                clone(estimator), X, y, sample_weight, **fit_params
-            )
-            y_pred_proba = np.empty(
-                (n_samples, self.n_classes_),
-                dtype=float
-            )
-            outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
-                delayed(self._fit_and_predict_oof_model)(
-                    clone(estimator),
-                    X,
-                    y,
-                    train_index,
-                    val_index,
-                    k,
-                    sample_weight,
-                    **fit_params,
-                )
-                for k, (train_index, val_index) in enumerate(
-                    cv.split(X, y_enc, groups)
-                )
-            )
-            (
-                self.estimators_,
-                predictions_list,
-                val_ids_list,
-                val_indices_list
-            ) = map(list, zip(*outputs))
-            predictions = np.concatenate(
-                cast(List[NDArray], predictions_list)
-            )
-            val_ids = np.concatenate(cast(List[NDArray], val_ids_list))
-            val_indices = np.concatenate(
-                cast(List[NDArray], val_indices_list)
-            )
-            self.k_[val_indices] = val_ids
-            y_pred_proba[val_indices] = predictions
+        self.estimator_ = EnsembleClassifier(
+            estimator,
+            self.n_classes_,
+            cv,
+            self.n_jobs,
+            self.random_state,
+            self.test_size,
+            self.verbose,
+        )
 
-            if isinstance(cv, ShuffleSplit):
-                # Should delete values indices that
-                # are not used during calibration
-                self.k_ = self.k_[val_indices]
-                y_pred_proba = y_pred_proba[val_indices]
-                y_enc = y_enc[val_indices]
-                y = cast(NDArray, y)[val_indices]
+        self.estimator_.fit(X, y, y_enc, sample_weight, groups, **fit_params)
+        y_pred_proba, y, y_enc = self.estimator_.predict_proba_calib(
+            X, y, y_enc, groups
+        )
 
         # RAPS: compute y_pred and position on the RAPS validation dataset
         if self.method == "raps":
-            self.y_pred_proba_raps = self.single_estimator_.predict_proba(
+            self.y_pred_proba_raps = self.estimator_.single_estimator_.predict_proba(
                 self.X_raps
             )
+<<<<<<< Updated upstream
             self.position_raps = get_true_label_position(
                 self.y_pred_proba_raps,
                 self.y_raps
+=======
+            self.position_raps = self._get_true_label_position(
+                self.y_pred_proba_raps, self.y_raps
+>>>>>>> Stashed changes
             )
 
         # Conformity scores
         if self.method == "naive":
-            self.conformity_scores_ = np.empty(
-                y_pred_proba.shape,
-                dtype="float"
-            )
+            self.conformity_scores_ = np.empty(y_pred_proba.shape, dtype="float")
         elif self.method in ["score", "lac"]:
+            print()
+            print("TEST ICI")
+            print()
+            print(
+                "y_pred_proba:", y_pred_proba, "y_pred_proba_shape", y_pred_proba.shape
+            )
+            print()
+            print("y_enc", y_enc, "y_enc_shape", y_enc.shape)
             self.conformity_scores_ = np.take_along_axis(
                 1 - y_pred_proba, y_enc.reshape(-1, 1), axis=1
             )
         elif self.method in ["cumulated_score", "aps", "raps"]:
-            self.conformity_scores_, self.cutoff = (
-                self._get_true_label_cumsum_proba(
-                    y,
-                    y_pred_proba
-                )
+            self.conformity_scores_, self.cutoff = self._get_true_label_cumsum_proba(
+                y, y_pred_proba
             )
             y_proba_true = np.take_along_axis(
                 y_pred_proba, y_enc.reshape(-1, 1), axis=1
@@ -1221,19 +1069,19 @@ def fit(
             # Here we reorder the labels by decreasing probability
             # and get the position of each label from decreasing
             # probability
+<<<<<<< Updated upstream
             self.conformity_scores_ = get_true_label_position(
                 y_pred_proba,
                 y_enc
             )
+=======
+            self.conformity_scores_ = self._get_true_label_position(y_pred_proba, y_enc)
+>>>>>>> Stashed changes
         else:
             raise ValueError(
-                "Invalid method. "
-                f"Allowed values are {self.valid_methods_}."
+                "Invalid method. " f"Allowed values are {self.valid_methods_}."
             )
 
-        if isinstance(cv, ShuffleSplit):
-            self.single_estimator_ = self.estimators_[0]
-
         return self
 
     def predict(
@@ -1241,7 +1089,7 @@ def predict(
         X: ArrayLike,
         alpha: Optional[Union[float, Iterable[float]]] = None,
         include_last_label: Optional[Union[bool, str]] = True,
-        agg_scores: Optional[str] = "mean"
+        agg_scores: Optional[str] = "mean",
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Prediction prediction sets on new samples based on target confidence
@@ -1311,15 +1159,13 @@ def predict(
         if self.method == "top_k":
             agg_scores = "mean"
         # Checks
-        cv = check_cv(
-            self.cv, test_size=self.test_size, random_state=self.random_state
-        )
+        cv = check_cv(self.cv, test_size=self.test_size, random_state=self.random_state)
         include_last_label = self._check_include_last_label(include_last_label)
         alpha = cast(Optional[NDArray], check_alpha(alpha))
         check_is_fitted(self, self.fit_attributes)
         lambda_star, k_star = None, None
         # Estimate prediction sets
-        y_pred = self.single_estimator_.predict(X)
+        y_pred = self.estimator_.single_estimator_.predict(X)
 
         if alpha is None:
             return y_pred
@@ -1331,31 +1177,12 @@ def predict(
         # with  (n_test, n_classes, n_alpha or n_train_samples)
         alpha_np = cast(NDArray, alpha)
         check_alpha_and_n_samples(alpha_np, n)
-        if cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
+        y_pred_proba = self.estimator_.predict(X, agg_scores)
+        y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1)
+        if (cv == "prefit") or (agg_scores in ["mean"]):
             y_pred_proba = np.repeat(
                 y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
             )
-        else:
-            y_pred_proba_k = np.asarray(
-                Parallel(
-                    n_jobs=self.n_jobs, verbose=self.verbose
-                )(
-                    delayed(self._predict_oof_model)(estimator, X)
-                    for estimator in self.estimators_
-                )
-            )
-            if agg_scores == "crossval":
-                y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2)
-            elif agg_scores == "mean":
-                y_pred_proba = np.mean(y_pred_proba_k, axis=0)
-                y_pred_proba = np.repeat(
-                    y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-                )
-            else:
-                raise ValueError("Invalid 'agg_scores' argument.")
-        # Check that sum of probas is equal to 1
-        y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1)
 
         # Choice of the quantile
         check_alpha_and_n_samples(alpha_np, n)
@@ -1366,37 +1193,24 @@ def predict(
             if (cv == "prefit") or (agg_scores in ["mean"]):
                 if self.method == "raps":
                     check_alpha_and_n_samples(alpha_np, len(self.X_raps))
-                    k_star = compute_quantiles(
-                        self.position_raps,
-                        alpha_np
-                    ) + 1
+                    k_star = compute_quantiles(self.position_raps, alpha_np) + 1
                     y_pred_proba_raps = np.repeat(
-                        self.y_pred_proba_raps[:, :, np.newaxis],
-                        len(alpha_np),
-                        axis=2
+                        self.y_pred_proba_raps[:, :, np.newaxis], len(alpha_np), axis=2
                     )
                     lambda_star = self._find_lambda_star(
-                        y_pred_proba_raps,
-                        alpha_np,
-                        include_last_label,
-                        k_star
+                        y_pred_proba_raps, alpha_np, include_last_label, k_star
                     )
                     self.conformity_scores_regularized = (
                         self._regularize_conformity_score(
-                                    k_star,
-                                    lambda_star,
-                                    self.conformity_scores_,
-                                    self.cutoff
+                            k_star, lambda_star, self.conformity_scores_, self.cutoff
                         )
                     )
                     self.quantiles_ = compute_quantiles(
-                        self.conformity_scores_regularized,
-                        alpha_np
+                        self.conformity_scores_regularized, alpha_np
                     )
                 else:
                     self.quantiles_ = compute_quantiles(
-                        self.conformity_scores_,
-                        alpha_np
+                        self.conformity_scores_, alpha_np
                     )
             else:
                 self.quantiles_ = (n + 1) * (1 - alpha_np)
@@ -1409,16 +1223,14 @@ def predict(
                 )
             else:
                 y_pred_included = np.less_equal(
-                    (1 - y_pred_proba) - self.conformity_scores_.ravel(),
-                    EPSILON
+                    (1 - y_pred_proba) - self.conformity_scores_.ravel(), EPSILON
                 ).sum(axis=2)
                 prediction_sets = np.stack(
                     [
-                        np.greater_equal(
-                            y_pred_included - _alpha * (n - 1), -EPSILON
-                        )
+                        np.greater_equal(y_pred_included - _alpha * (n - 1), -EPSILON)
                         for _alpha in alpha_np
-                    ], axis=2
+                    ],
+                    axis=2,
                 )
 
         elif self.method in ["naive", "cumulated_score", "aps", "raps"]:
@@ -1456,7 +1268,7 @@ def predict(
                     y_pred_proba_last,
                     thresholds,
                     lambda_star,
-                    k_star
+                    k_star,
                 )
             if (cv == "prefit") or (agg_scores in ["mean"]):
                 prediction_sets = y_pred_included
@@ -1466,35 +1278,28 @@ def predict(
                 prediction_sets = np.less_equal(
                     prediction_sets_summed[:, :, np.newaxis]
                     - self.quantiles_[np.newaxis, np.newaxis, :],
-                    EPSILON
+                    EPSILON,
                 )
         elif self.method == "top_k":
             y_pred_proba = y_pred_proba[:, :, 0]
             index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
             y_pred_index_last = np.stack(
-                [
-                    index_sorted[:, quantile]
-                    for quantile in self.quantiles_
-                ], axis=1
+                [index_sorted[:, quantile] for quantile in self.quantiles_], axis=1
             )
             y_pred_proba_last = np.stack(
                 [
                     np.take_along_axis(
-                        y_pred_proba,
-                        y_pred_index_last[:, iq].reshape(-1, 1),
-                        axis=1
+                        y_pred_proba, y_pred_index_last[:, iq].reshape(-1, 1), axis=1
                     )
                     for iq, _ in enumerate(self.quantiles_)
-                ], axis=2
+                ],
+                axis=2,
             )
             prediction_sets = np.greater_equal(
-                y_pred_proba[:, :, np.newaxis]
-                - y_pred_proba_last,
-                -EPSILON
+                y_pred_proba[:, :, np.newaxis] - y_pred_proba_last, -EPSILON
             )
         else:
             raise ValueError(
-                "Invalid method. "
-                f"Allowed values are {self.valid_methods_}."
+                "Invalid method. " f"Allowed values are {self.valid_methods_}."
             )
         return y_pred, prediction_sets
diff --git a/mapie/estimator/estimator.py b/mapie/estimator/estimator.py
index b8c7d4ecf..70ee2aeae 100644
--- a/mapie/estimator/estimator.py
+++ b/mapie/estimator/estimator.py
@@ -4,8 +4,8 @@
 
 import numpy as np
 from joblib import Parallel, delayed
-from sklearn.base import RegressorMixin, clone
-from sklearn.model_selection import BaseCrossValidator
+from sklearn.base import ClassifierMixin, RegressorMixin, clone
+from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
 from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
@@ -13,7 +13,7 @@
 from mapie.aggregation_functions import aggregate_all, phi2D
 from mapie.estimator.interface import EnsembleEstimator
 from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
-                         fit_estimator)
+                         fit_estimator, fix_number_of_classes)
 
 
 class EnsembleRegressor(EnsembleEstimator):
@@ -561,3 +561,516 @@ def predict(
             return y_pred, y_pred_multi_low, y_pred_multi_up
         else:
             return y_pred
+
+
+class EnsembleClassifier(EnsembleEstimator):
+    """
+    This class implements methods to handle the training and usage of the
+    estimator. This estimator can be unique or composed by cross validated
+    estimators.
+
+    Parameters
+    ----------
+    estimator: Optional[RegressorMixin]
+        Any regressor with scikit-learn API
+        (i.e. with ``fit`` and ``predict`` methods).
+        If ``None``, estimator defaults to a ``LinearRegression`` instance.
+
+        By default ``None``.
+
+    cv: Optional[str]
+        The cross-validation strategy for computing scores.
+        It directly drives the distinction between jackknife and cv variants.
+        Choose among:
+
+        - ``None``, to use the default 5-fold cross-validation
+        - integer, to specify the number of folds.
+          If equal to -1, equivalent to
+          ``sklearn.model_selection.LeaveOneOut()``.
+        - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
+          Main variants are:
+          - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
+          - ``sklearn.model_selection.KFold`` (cross-validation)
+        - ``"split"``, does not involve cross-validation but a division
+          of the data into training and calibration subsets. The splitter
+          used is the following: ``sklearn.model_selection.ShuffleSplit``.
+        - ``"prefit"``, assumes that ``estimator`` has been fitted already.
+          All data provided in the ``fit`` method is then used
+          to calibrate the predictions through the score computation.
+          At prediction time, quantiles of these scores are used to estimate
+          prediction sets.
+
+        By default ``None``.
+
+    test_size: Optional[Union[int, float]]
+        If ``float``, should be between ``0.0`` and ``1.0`` and represent the
+        proportion of the dataset to include in the test split. If ``int``,
+        represents the absolute number of test samples. If ``None``,
+        it will be set to ``0.1``.
+
+        If cv is not ``"split"``, ``test_size`` is ignored.
+
+        By default ``None``.
+
+    n_jobs: Optional[int]
+        Number of jobs for parallel processing using joblib
+        via the "locky" backend.
+        If ``-1`` all CPUs are used.
+        If ``1`` is given, no parallel computing code is used at all,
+        which is useful for debugging.
+        For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
+        ``None`` is a marker for `unset` that will be interpreted as
+        ``n_jobs=1`` (sequential execution).
+
+        By default ``None``.
+
+   random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state used for random uniform sampling
+        for evaluation quantiles and prediction sets.
+        Pass an int for reproducible output across multiple function calls.
+
+        By default ``None``.
+
+    verbose: int, optional
+        The verbosity level, used with joblib for multiprocessing.
+        At this moment, parallel processing is disabled.
+        The frequency of the messages increases with the verbosity level.
+        If it more than ``10``, all iterations are reported.
+        Above ``50``, the output is sent to stdout.
+
+        By default ``0``.
+
+    Attributes
+    ----------
+    single_estimator_: sklearn.RegressorMixin
+        Estimator fitted on the whole training set.
+
+    estimators_: list
+        List of out-of-folds estimators.
+
+    k_: ArrayLike
+        - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
+          (defined but not used)
+        - Dummy array of folds containing each training sample, otherwise.
+          Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
+    """
+    no_agg_cv_ = ["prefit", "split"]
+    fit_attributes = [
+        "single_estimator_",
+        "estimators_",
+        "k_",
+        "use_split_method_",
+    ]
+
+    def __init__(
+        self,
+        estimator: Optional[ClassifierMixin],
+        n_classes: int,
+        cv: Optional[Union[int, str, BaseCrossValidator]],
+        n_jobs: Optional[int],
+        random_state: Optional[Union[int, np.random.RandomState]],
+        test_size: Optional[Union[int, float]],
+        verbose: int
+    ):
+        self.estimator = estimator
+        self.n_classes = n_classes
+        self.cv = cv
+        self.n_jobs = n_jobs
+        self.random_state = random_state
+        self.test_size = test_size
+        self.verbose = verbose
+
+    @staticmethod
+    def _fit_oof_estimator(
+        estimator: ClassifierMixin,
+        X: ArrayLike,
+        y: ArrayLike,
+        train_index: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> ClassifierMixin:
+        """
+        Fit a single out-of-fold model on a given training set.
+
+        Parameters
+        ----------
+        estimator: RegressorMixin
+            Estimator to train.
+
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        train_index: ArrayLike of shape (n_samples_train)
+            Training data indices.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        RegressorMixin
+            Fitted estimator.
+        """
+        X_train = _safe_indexing(X, train_index)
+        y_train = _safe_indexing(y, train_index)
+        if not (sample_weight is None):
+            sample_weight = _safe_indexing(sample_weight, train_index)
+            sample_weight = cast(NDArray, sample_weight)
+
+        estimator = fit_estimator(
+            estimator,
+            X_train,
+            y_train,
+            sample_weight=sample_weight,
+            **fit_params
+        )
+        return estimator
+    
+    def _predict_proba_oof_estimator(self, estimator, X):
+        y_pred_proba = estimator.predict_proba(X)
+        if len(estimator.classes_) != self.n_classes:
+            y_pred_proba = fix_number_of_classes(
+                self.n_classes,
+                estimator.classes_,
+                y_pred_proba
+            )
+        return y_pred_proba
+
+    def _predict_proba_calib_oof_estimator(
+        self,
+        estimator: ClassifierMixin,
+        X: ArrayLike,
+        val_index: ArrayLike,
+        k: int
+    ) -> Tuple[NDArray, ArrayLike]:
+        """
+        Perform predictions on a single out-of-fold model on a validation set.
+
+        Parameters
+        ----------
+        estimator: RegressorMixin
+            Estimator to train.
+
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        val_index: ArrayLike of shape (n_samples_val)
+            Validation data indices.
+
+        Returns
+        -------
+        Tuple[NDArray, ArrayLike]
+            Predictions of estimator from val_index of X.
+        """
+        
+        X_val = _safe_indexing(X, val_index)
+        if _num_samples(X_val) > 0:
+            y_pred_proba = self._predict_proba_oof_estimator(
+                estimator, X_val
+            )
+        else:
+            y_pred_proba = np.array([])
+        val_id = np.full(len(X_val), k, dtype=int)
+        return y_pred_proba, val_id, val_index
+
+    def _aggregate_with_mask(
+        self,
+        x: NDArray,
+        k: NDArray
+    ) -> NDArray:
+        """
+        Take the array of predictions, made by the refitted estimators,
+        on the testing set, and the 1-or-nan array indicating for each training
+        sample which one to integrate, and aggregate to produce phi-{t}(x_t)
+        for each training sample x_t.
+
+        Parameters
+        ----------
+        x: ArrayLike of shape (n_samples_test, n_estimators)
+            Array of predictions, made by the refitted estimators,
+            for each sample of the testing set.
+
+        k: ArrayLike of shape (n_samples_training, n_estimators)
+            1-or-nan array: indicates whether to integrate the prediction
+            of a given estimator into the aggregation, for each training
+            sample.
+
+        Returns
+        -------
+        ArrayLike of shape (n_samples_test,)
+            Array of aggregated predictions for each testing sample.
+        """
+        if self.method in self.no_agg_methods_ or self.use_split_method_:
+            raise ValueError(
+                "There should not be aggregation of predictions "
+                f"if cv is in '{self.no_agg_cv_}', if cv >=2 "
+                f"or if method is in '{self.no_agg_methods_}'."
+            )
+        elif self.agg_function == "median":
+            return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))
+        # To aggregate with mean() the aggregation coud be done
+        # with phi2D(A=x, B=k, fun=lambda x: np.nanmean(x, axis=1).
+        # However, phi2D contains a np.apply_along_axis loop which
+        # is much slower than the matrices multiplication that can
+        # be used to compute the means.
+        elif self.agg_function in ["mean", None]:
+            K = np.nan_to_num(k, nan=0.0)
+            return np.matmul(x, (K / (K.sum(axis=1, keepdims=True))).T)
+        else:
+            raise ValueError("The value of self.agg_function is not correct")
+
+    def _pred_multi(self, X: ArrayLike) -> NDArray:
+        """
+        Return a prediction per train sample for each test sample, by
+        aggregation with matrix ``k_``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, n_samples_train)
+        """
+        y_pred_multi = np.column_stack(
+            [e.predict(X) for e in self.estimators_]
+        )
+        # At this point, y_pred_multi is of shape
+        # (n_samples_test, n_estimators_). The method
+        # ``_aggregate_with_mask`` fits it to the right size
+        # thanks to the shape of k_.
+        y_pred_multi = self._aggregate_with_mask(y_pred_multi, self.k_)
+        return y_pred_multi
+
+    def predict_proba_calib(
+        self,
+        X: ArrayLike,
+        y: Optional[ArrayLike] = None,
+        y_enc=None,
+        groups: Optional[ArrayLike] = None
+    ) -> NDArray:
+        """
+        Perform predictions on X : the calibration set.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        y: Optional[ArrayLike] of shape (n_samples_test,)
+            Input labels.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples_test,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, 1)
+            The predictions.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        if self.cv == "prefit":
+            y_pred_proba = self.single_estimator_.predict_proba(X)
+        else:
+            y_pred_proba = np.empty(
+                (len(X), self.n_classes),
+                dtype=float
+            )
+            cv = cast(BaseCrossValidator, self.cv)
+            outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+                delayed(self._predict_proba_calib_oof_estimator)(
+                    estimator, X, calib_index, k
+                )
+                for k, ((_, calib_index), estimator) in enumerate(zip(
+                    cv.split(X, y, groups),
+                    self.estimators_
+                ))
+            )
+            (
+                predictions_list,
+                val_ids_list,
+                val_indices_list
+            ) = map(list, zip(*outputs))
+
+            predictions = np.concatenate(
+                cast(List[NDArray], predictions_list)
+            )
+            val_ids = np.concatenate(cast(List[NDArray], val_ids_list))
+            val_indices = np.concatenate(
+                cast(List[NDArray], val_indices_list)
+            )
+            self.k_[val_indices] = val_ids
+            y_pred_proba[val_indices] = predictions
+
+            if isinstance(cv, ShuffleSplit):
+                # Should delete values indices that
+                # are not used during calibration
+                self.k_ = self.k_[val_indices]
+                y_pred_proba = y_pred_proba[val_indices]
+                y_enc = y_enc[val_indices]
+                y = cast(NDArray, y)[val_indices]
+
+        return y_pred_proba, y, y_enc
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        y_enc: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> EnsembleRegressor:
+        """
+        Fit the base estimator under the ``single_estimator_`` attribute.
+        Fit all cross-validated estimator clones
+        and rearrange them into a list, the ``estimators_`` attribute.
+        Out-of-fold conformity scores are stored under
+        the ``conformity_scores_`` attribute.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        EnsembleRegressor
+            The estimator fitted.
+        """
+        # Initialization
+        single_estimator_: ClassifierMixin
+        estimators_: List[ClassifierMixin] = []
+        full_indexes = np.arange(_num_samples(X))
+        cv = self.cv
+        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
+        estimator = self.estimator
+        n_samples = _num_samples(y)
+
+        # Computation
+        if cv == "prefit":
+            single_estimator_ = estimator
+            self.k_ = np.full(
+                shape=(n_samples, 1), fill_value=np.nan, dtype=float
+            )
+        else:
+            single_estimator_ = self._fit_oof_estimator(
+                clone(estimator),
+                X,
+                y,
+                full_indexes,
+                sample_weight,
+                **fit_params
+            )
+            cv = cast(BaseCrossValidator, cv)
+            self.k_ = np.empty_like(y, dtype=int)
+
+            estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
+                delayed(self._fit_oof_estimator)(
+                    clone(estimator),
+                    X,
+                    y_enc,
+                    train_index,
+                    sample_weight,
+                    **fit_params
+                )
+                for train_index, _ in cv.split(X, y, groups)
+            )
+            # In split-CP, we keep only the model fitted on train dataset
+            if self.use_split_method_:
+                single_estimator_ = estimators_[0]
+
+        self.single_estimator_ = single_estimator_
+        self.estimators_ = estimators_
+
+        return self
+
+    def predict(
+        self,
+        X: ArrayLike,
+        agg_scores
+    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
+        """
+        Predict target from X. It also computes the prediction per train sample
+        for each test sample according to ``self.method``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Test data.
+
+        ensemble: bool
+            Boolean determining whether the predictions are ensembled or not.
+            If ``False``, predictions are those of the model trained on the
+            whole training set.
+            If ``True``, predictions from perturbed models are aggregated by
+            the aggregation function specified in the ``agg_function``
+            attribute.
+
+            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
+
+            By default ``False``.
+
+        return_multi_pred: bool
+            If ``True`` the method returns the predictions and the multiple
+            predictions (3 arrays). If ``False`` the method return the
+            simple predictions only.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray, NDArray]
+            - Predictions
+            - The multiple predictions for the lower bound of the intervals.
+            - The multiple predictions for the upper bound of the intervals.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        if self.cv == "prefit":
+            y_pred_proba = self.single_estimator_.predict_proba(X)
+        else:
+            y_pred_proba_k = np.asarray(
+                Parallel(
+                    n_jobs=self.n_jobs, verbose=self.verbose
+                )(
+                    delayed(self._predict_proba_oof_estimator)(estimator, X)
+                    for estimator in self.estimators_
+                )
+            )
+            if agg_scores == "crossval":
+                y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2)
+            elif agg_scores == "mean":
+                y_pred_proba = np.mean(y_pred_proba_k, axis=0)
+            else:
+                raise ValueError("Invalid 'agg_scores' argument.")
+        return y_pred_proba

From aad1b95b8166d79b8580ce015b271329ecde19e3 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Tue, 14 May 2024 19:12:06 +0200
Subject: [PATCH 004/424] feat: Improve documentation for Conformalized
 Quantile Regression (CQR) method

---
 doc/theoretical_description_regression.rst | 44 +++++++++++++---------
 1 file changed, 26 insertions(+), 18 deletions(-)

diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index ae4b7c346..9479f4645 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -245,30 +245,38 @@ uncertainty is higher than :math:`CV+`, because the models' prediction spread
 is then higher.
 
 
-9. The conformalized quantile regression (CQR) method
-=====================================================
+9. The Conformalized Quantile Regression (CQR) Method
+==================================================
 
-The conformalized quantile method allows for better interval widths with
-heteroscedastic data. It uses quantile regressors with different quantile
-values to estimate the prediction bounds and the residuals of these methods are
-used to create the guaranteed coverage value.
+The conformalized quantile regression (CQR) method allows for better interval widths with
+heteroscedastic data. It uses quantile regressors with different quantile values to estimate
+the prediction bounds. The residuals of these methods are used to create the guaranteed
+coverage value.
 
-.. math:: 
+Notations and Definitions
+-------------------------
+- :math:`E_i`: Residuals for the i-th sample in the calibration set.
+- :math:`E_{\text{low}}`: Residuals from the lower quantile model.
+- :math:`E_{\text{high}}`: Residuals from the upper quantile model.
+- :math:`Q_{1-\alpha}(E, \mathcal{I}_2)`: The :math:`(1-\alpha)(1+1/|\mathcal{I}_2|)`-th empirical quantile of the set :math:`{E_i : i \in \mathcal{I}_2}`, where :math:`\mathcal{I}_2` is the set of indices of the residuals in the calibration set.
+
+Mathematical Formulation
+------------------------
+The prediction interval :math:`\hat{C}_{n, \alpha}^{\text{CQR}}(X_{n+1})` for a new sample :math:`X_{n+1}` is given by:
+
+.. math::
 
-    \hat{C}_{n, \alpha}^{\rm CQR}(X_{n+1}) = 
-    [\hat{q}_{\alpha_{lo}}(X_{n+1}) - Q_{1-\alpha}(E_{low}, \mathcal{I}_2),
-    \hat{q}_{\alpha_{hi}}(X_{n+1}) + Q_{1-\alpha}(E_{high}, \mathcal{I}_2)]
+    \hat{C}_{n, \alpha}^{\text{CQR}}(X_{n+1}) = 
+    [\hat{q}_{\alpha_{\text{lo}}}(X_{n+1}) - Q_{1-\alpha}(E_{\text{low}}, \mathcal{I}_2),
+    \hat{q}_{\alpha_{\text{hi}}}(X_{n+1}) + Q_{1-\alpha}(E_{\text{high}}, \mathcal{I}_2)]
 
-Where :math:`Q_{1-\alpha}(E, \mathcal{I}_2) := (1-\alpha)(1+1/ |\mathcal{I}_2|)`-th
-empirical quantile of :math:`{E_i : i \in \mathcal{I}_2}` and :math:`\mathcal{I}_2` is the
-residuals of the estimator fitted on the calibration set. Note that in the symmetric method, 
-:math:`E_{low}` and :math:`E_{high}` are equal.
+Where:
+- :math:`\hat{q}_{\alpha_{\text{lo}}}(X_{n+1})` is the predicted lower quantile for the new sample.
+- :math:`\hat{q}_{\alpha_{\text{hi}}}(X_{n+1})` is the predicted upper quantile for the new sample.
 
-As justified by [3], this method offers a theoretical guarantee of the target coverage 
-level :math:`1-\alpha`.
+Note: In the symmetric method, :math:`E_{\text{low}}` and :math:`E_{\text{high}}` are considered equal.
 
-Note that only the split method has been implemented and that it will run three separate 
-regressions when using :class:`mapie.quantile_regression.MapieQuantileRegressor`.
+As justified by the literature, this method offers a theoretical guarantee of the target coverage level :math:`1-\alpha`.
 
 
 10. The ensemble batch prediction intervals (EnbPI) method

From 2e3673c8cef1699918c3064af516f96c84af5035 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Tue, 14 May 2024 19:12:13 +0200
Subject: [PATCH 005/424] chore: Add plot_cqr_symmetry_difference.py to
 regression examples

---
 .../plot_cqr_symmetry_difference.py           | 100 ++++++++++++++++++
 1 file changed, 100 insertions(+)
 create mode 100644 examples/regression/1-quickstart/plot_cqr_symmetry_difference.py

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
new file mode 100644
index 000000000..895838fca
--- /dev/null
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -0,0 +1,100 @@
+"""
+======================================================
+Plotting MAPIE Quantile Regressor prediction intervals
+======================================================
+An example plot of :class:`~mapie.quantile_regression.MapieQuantileRegressor`
+illustrating the impact of the symmetry parameter.
+"""
+import numpy as np
+from matplotlib import pyplot as plt
+from sklearn.datasets import make_regression
+from sklearn.ensemble import GradientBoostingRegressor
+
+from mapie.metrics import regression_coverage_score
+from mapie.quantile_regression import MapieQuantileRegressor
+
+# Generate synthetic data
+X, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=59)
+
+# Define alpha level
+alpha = 0.2
+
+# Fit a Gradient Boosting Regressor for quantile regression
+quantiles = [0.1, 0.9]
+gb_reg = GradientBoostingRegressor(loss="quantile", alpha=quantiles[1])
+gb_reg.fit(X, y)
+
+# MAPIE Quantile Regressor with symmetry=True
+mapie_qr_sym = MapieQuantileRegressor(estimator=gb_reg, alpha=alpha)
+mapie_qr_sym.fit(X, y)
+y_pred_sym, y_pis_sym = mapie_qr_sym.predict(X, symmetry=True)
+
+# MAPIE Quantile Regressor with symmetry=False
+mapie_qr_asym = MapieQuantileRegressor(estimator=gb_reg, alpha=alpha)
+mapie_qr_asym.fit(X, y)
+y_pred_asym, y_pis_asym = mapie_qr_asym.predict(X, symmetry=False)
+
+# Calculate coverage scores
+coverage_score_sym = regression_coverage_score(y, y_pis_sym[:, 0], y_pis_sym[:, 1])
+coverage_score_asym = regression_coverage_score(y, y_pis_asym[:, 0], y_pis_asym[:, 1])
+
+# Sort the values for plotting
+order = np.argsort(X[:, 0])
+X_sorted = X[order]
+y_pred_sym_sorted = y_pred_sym[order]
+y_pis_sym_sorted = y_pis_sym[order]
+y_pred_asym_sorted = y_pred_asym[order]
+y_pis_asym_sorted = y_pis_asym[order]
+
+# Plot symmetric prediction intervals
+plt.figure(figsize=(14, 7))
+
+plt.subplot(1, 2, 1)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.scatter(X, y, alpha=0.3)
+plt.plot(X_sorted, y_pred_sym_sorted, color="C1")
+plt.plot(X_sorted, y_pis_sym_sorted[:, 0], color="C1", ls="--")
+plt.plot(X_sorted, y_pis_sym_sorted[:, 1], color="C1", ls="--")
+plt.fill_between(
+    X_sorted.ravel(),
+    y_pis_sym_sorted[:, 0].ravel(),
+    y_pis_sym_sorted[:, 1].ravel(),
+    alpha=0.2,
+)
+plt.title(
+    f"Symmetric Intervals\n"
+    f"Target and effective coverages for "
+    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
+)
+
+# Plot asymmetric prediction intervals
+plt.subplot(1, 2, 2)
+plt.xlabel("x")
+plt.ylabel("y")
+plt.scatter(X, y, alpha=0.3)
+plt.plot(X_sorted, y_pred_asym_sorted, color="C2")
+plt.plot(X_sorted, y_pis_asym_sorted[:, 0], color="C2", ls="--")
+plt.plot(X_sorted, y_pis_asym_sorted[:, 1], color="C2", ls="--")
+plt.fill_between(
+    X_sorted.ravel(),
+    y_pis_asym_sorted[:, 0].ravel(),
+    y_pis_asym_sorted[:, 1].ravel(),
+    alpha=0.2,
+)
+plt.title(
+    f"Asymmetric Intervals\n"
+    f"Target and effective coverages for "
+    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
+)
+
+plt.tight_layout()
+plt.show()
+
+# Explanation of the results
+"""
+The symmetric intervals (`symmetry=True`) are easier to interpret and tend to have higher 
+coverage but might not adapt well to varying noise levels. The asymmetric intervals 
+(`symmetry=False`) are more flexible and better capture heteroscedasticity but can appear 
+more jagged.
+"""

From ef282f6c463ae47be2b5514bbd750f1525241a09 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 09:00:55 +0200
Subject: [PATCH 006/424] FIX: linting

---
 .../1-quickstart/plot_cqr_symmetry_difference.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 895838fca..7cc23a3e7 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -35,8 +35,12 @@
 y_pred_asym, y_pis_asym = mapie_qr_asym.predict(X, symmetry=False)
 
 # Calculate coverage scores
-coverage_score_sym = regression_coverage_score(y, y_pis_sym[:, 0], y_pis_sym[:, 1])
-coverage_score_asym = regression_coverage_score(y, y_pis_asym[:, 0], y_pis_asym[:, 1])
+coverage_score_sym = regression_coverage_score(
+    y, y_pis_sym[:, 0], y_pis_sym[:, 1]
+)
+coverage_score_asym = regression_coverage_score(
+    y, y_pis_asym[:, 0], y_pis_asym[:, 1]
+)
 
 # Sort the values for plotting
 order = np.argsort(X[:, 0])
@@ -93,8 +97,8 @@
 
 # Explanation of the results
 """
-The symmetric intervals (`symmetry=True`) are easier to interpret and tend to have higher 
-coverage but might not adapt well to varying noise levels. The asymmetric intervals 
-(`symmetry=False`) are more flexible and better capture heteroscedasticity but can appear 
-more jagged.
+The symmetric intervals (`symmetry=True`) are easier to interpret and
+tend to have higher coverage but might not adapt well to varying
+noise levels. The asymmetric intervals (`symmetry=False`) are more
+flexible and better capture heteroscedasticity but can appear more jagged.
 """

From b7ec8901d0bbae1d7f5371adf8dc85026f2538f2 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 11:28:42 +0200
Subject: [PATCH 007/424] Split Ensemble Classifier and Ensemble Regressor into
 2 files

---
 mapie/classification.py                 |   2 +-
 mapie/estimator/estimator_classifier.py | 489 +++++++++++++++++++++
 mapie/estimator/estimator_regressor.py  | 539 ++++++++++++++++++++++++
 mapie/tests/test_classification.py      |   1 +
 mapie/tests/test_regression.py          | 131 +++---
 5 files changed, 1090 insertions(+), 72 deletions(-)
 create mode 100644 mapie/estimator/estimator_classifier.py
 create mode 100644 mapie/estimator/estimator_regressor.py

diff --git a/mapie/classification.py b/mapie/classification.py
index ea55c1239..811debfc4 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -13,7 +13,7 @@
 
 from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
-from .estimator.estimator import EnsembleClassifier
+from .estimator.estimator_classification import EnsembleClassifier
 from .metrics import classification_mean_width_score
 from .utils import (
     check_alpha,
diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/estimator_classifier.py
new file mode 100644
index 000000000..01c4eac1a
--- /dev/null
+++ b/mapie/estimator/estimator_classifier.py
@@ -0,0 +1,489 @@
+from __future__ import annotations
+
+from typing import List, Optional, Tuple, Union, cast
+
+import numpy as np
+from joblib import Parallel, delayed
+from sklearn.base import ClassifierMixin, clone
+from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
+from sklearn.utils import _safe_indexing
+from sklearn.utils.validation import _num_samples, check_is_fitted
+
+from mapie._typing import ArrayLike, NDArray
+from mapie.aggregation_functions import phi2D
+from mapie.estimator.interface import EnsembleEstimator
+from mapie.utils import (
+    check_no_agg_cv,
+    fit_estimator,
+    fix_number_of_classes,
+)
+
+
+class EnsembleClassifier(EnsembleEstimator):
+    """
+     This class implements methods to handle the training and usage of the
+     estimator. This estimator can be unique or composed by cross validated
+     estimators.
+
+     Parameters
+     ----------
+     estimator: Optional[RegressorMixin]
+         Any regressor with scikit-learn API
+         (i.e. with ``fit`` and ``predict`` methods).
+         If ``None``, estimator defaults to a ``LinearRegression`` instance.
+
+         By default ``None``.
+
+     cv: Optional[str]
+         The cross-validation strategy for computing scores.
+         It directly drives the distinction between jackknife and cv variants.
+         Choose among:
+
+         - ``None``, to use the default 5-fold cross-validation
+         - integer, to specify the number of folds.
+           If equal to -1, equivalent to
+           ``sklearn.model_selection.LeaveOneOut()``.
+         - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
+           Main variants are:
+           - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
+           - ``sklearn.model_selection.KFold`` (cross-validation)
+         - ``"split"``, does not involve cross-validation but a division
+           of the data into training and calibration subsets. The splitter
+           used is the following: ``sklearn.model_selection.ShuffleSplit``.
+         - ``"prefit"``, assumes that ``estimator`` has been fitted already.
+           All data provided in the ``fit`` method is then used
+           to calibrate the predictions through the score computation.
+           At prediction time, quantiles of these scores are used to estimate
+           prediction sets.
+
+         By default ``None``.
+
+     test_size: Optional[Union[int, float]]
+         If ``float``, should be between ``0.0`` and ``1.0`` and represent the
+         proportion of the dataset to include in the test split. If ``int``,
+         represents the absolute number of test samples. If ``None``,
+         it will be set to ``0.1``.
+
+         If cv is not ``"split"``, ``test_size`` is ignored.
+
+         By default ``None``.
+
+     n_jobs: Optional[int]
+         Number of jobs for parallel processing using joblib
+         via the "locky" backend.
+         If ``-1`` all CPUs are used.
+         If ``1`` is given, no parallel computing code is used at all,
+         which is useful for debugging.
+         For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
+         ``None`` is a marker for `unset` that will be interpreted as
+         ``n_jobs=1`` (sequential execution).
+
+         By default ``None``.
+
+    random_state: Optional[Union[int, RandomState]]
+         Pseudo random number generator state used for random uniform sampling
+         for evaluation quantiles and prediction sets.
+         Pass an int for reproducible output across multiple function calls.
+
+         By default ``None``.
+
+     verbose: int, optional
+         The verbosity level, used with joblib for multiprocessing.
+         At this moment, parallel processing is disabled.
+         The frequency of the messages increases with the verbosity level.
+         If it more than ``10``, all iterations are reported.
+         Above ``50``, the output is sent to stdout.
+
+         By default ``0``.
+
+     Attributes
+     ----------
+     single_estimator_: sklearn.RegressorMixin
+         Estimator fitted on the whole training set.
+
+     estimators_: list
+         List of out-of-folds estimators.
+
+     k_: ArrayLike
+         - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
+           (defined but not used)
+         - Dummy array of folds containing each training sample, otherwise.
+           Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
+    """
+
+    no_agg_cv_ = ["prefit", "split"]
+    fit_attributes = [
+        "single_estimator_",
+        "estimators_",
+        "k_",
+        "use_split_method_",
+    ]
+
+    def __init__(
+        self,
+        estimator: Optional[ClassifierMixin],
+        n_classes: int,
+        cv: Optional[Union[int, str, BaseCrossValidator]],
+        n_jobs: Optional[int],
+        random_state: Optional[Union[int, np.random.RandomState]],
+        test_size: Optional[Union[int, float]],
+        verbose: int,
+    ):
+        self.estimator = estimator
+        self.n_classes = n_classes
+        self.cv = cv
+        self.n_jobs = n_jobs
+        self.random_state = random_state
+        self.test_size = test_size
+        self.verbose = verbose
+
+    @staticmethod
+    def _fit_oof_estimator(
+        estimator: ClassifierMixin,
+        X: ArrayLike,
+        y: ArrayLike,
+        train_index: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> ClassifierMixin:
+        """
+        Fit a single out-of-fold model on a given training set.
+
+        Parameters
+        ----------
+        estimator: RegressorMixin
+            Estimator to train.
+
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        train_index: ArrayLike of shape (n_samples_train)
+            Training data indices.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        RegressorMixin
+            Fitted estimator.
+        """
+        X_train = _safe_indexing(X, train_index)
+        y_train = _safe_indexing(y, train_index)
+        if not (sample_weight is None):
+            sample_weight = _safe_indexing(sample_weight, train_index)
+            sample_weight = cast(NDArray, sample_weight)
+
+        estimator = fit_estimator(
+            estimator, X_train, y_train, sample_weight=sample_weight, **fit_params
+        )
+        return estimator
+
+    def _predict_proba_oof_estimator(self, estimator, X):
+        y_pred_proba = estimator.predict_proba(X)
+        if len(estimator.classes_) != self.n_classes:
+            y_pred_proba = fix_number_of_classes(
+                self.n_classes, estimator.classes_, y_pred_proba
+            )
+        return y_pred_proba
+
+    def _predict_proba_calib_oof_estimator(
+        self, estimator: ClassifierMixin, X: ArrayLike, val_index: ArrayLike, k: int
+    ) -> Tuple[NDArray, ArrayLike]:
+        """
+        Perform predictions on a single out-of-fold model on a validation set.
+
+        Parameters
+        ----------
+        estimator: RegressorMixin
+            Estimator to train.
+
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        val_index: ArrayLike of shape (n_samples_val)
+            Validation data indices.
+
+        Returns
+        -------
+        Tuple[NDArray, ArrayLike]
+            Predictions of estimator from val_index of X.
+        """
+
+        X_val = _safe_indexing(X, val_index)
+        if _num_samples(X_val) > 0:
+            y_pred_proba = self._predict_proba_oof_estimator(estimator, X_val)
+        else:
+            y_pred_proba = np.array([])
+        val_id = np.full(len(X_val), k, dtype=int)
+        return y_pred_proba, val_id, val_index
+
+    def _aggregate_with_mask(self, x: NDArray, k: NDArray) -> NDArray:
+        """
+        Take the array of predictions, made by the refitted estimators,
+        on the testing set, and the 1-or-nan array indicating for each training
+        sample which one to integrate, and aggregate to produce phi-{t}(x_t)
+        for each training sample x_t.
+
+        Parameters
+        ----------
+        x: ArrayLike of shape (n_samples_test, n_estimators)
+            Array of predictions, made by the refitted estimators,
+            for each sample of the testing set.
+
+        k: ArrayLike of shape (n_samples_training, n_estimators)
+            1-or-nan array: indicates whether to integrate the prediction
+            of a given estimator into the aggregation, for each training
+            sample.
+
+        Returns
+        -------
+        ArrayLike of shape (n_samples_test,)
+            Array of aggregated predictions for each testing sample.
+        """
+        if self.method in self.no_agg_methods_ or self.use_split_method_:
+            raise ValueError(
+                "There should not be aggregation of predictions "
+                f"if cv is in '{self.no_agg_cv_}', if cv >=2 "
+                f"or if method is in '{self.no_agg_methods_}'."
+            )
+        elif self.agg_function == "median":
+            return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))
+        # To aggregate with mean() the aggregation coud be done
+        # with phi2D(A=x, B=k, fun=lambda x: np.nanmean(x, axis=1).
+        # However, phi2D contains a np.apply_along_axis loop which
+        # is much slower than the matrices multiplication that can
+        # be used to compute the means.
+        elif self.agg_function in ["mean", None]:
+            K = np.nan_to_num(k, nan=0.0)
+            return np.matmul(x, (K / (K.sum(axis=1, keepdims=True))).T)
+        else:
+            raise ValueError("The value of self.agg_function is not correct")
+
+    def _pred_multi(self, X: ArrayLike) -> NDArray:
+        """
+        Return a prediction per train sample for each test sample, by
+        aggregation with matrix ``k_``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, n_samples_train)
+        """
+        y_pred_multi = np.column_stack([e.predict(X) for e in self.estimators_])
+        # At this point, y_pred_multi is of shape
+        # (n_samples_test, n_estimators_). The method
+        # ``_aggregate_with_mask`` fits it to the right size
+        # thanks to the shape of k_.
+        y_pred_multi = self._aggregate_with_mask(y_pred_multi, self.k_)
+        return y_pred_multi
+
+    def predict_proba_calib(
+        self,
+        X: ArrayLike,
+        y: Optional[ArrayLike] = None,
+        y_enc=None,
+        groups: Optional[ArrayLike] = None,
+    ) -> NDArray:
+        """
+        Perform predictions on X : the calibration set.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        y: Optional[ArrayLike] of shape (n_samples_test,)
+            Input labels.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples_test,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, 1)
+            The predictions.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        if self.cv == "prefit":
+            y_pred_proba = self.single_estimator_.predict_proba(X)
+        else:
+            y_pred_proba = np.empty((len(X), self.n_classes), dtype=float)
+            cv = cast(BaseCrossValidator, self.cv)
+            outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+                delayed(self._predict_proba_calib_oof_estimator)(
+                    estimator, X, calib_index, k
+                )
+                for k, ((_, calib_index), estimator) in enumerate(
+                    zip(cv.split(X, y, groups), self.estimators_)
+                )
+            )
+            (predictions_list, val_ids_list, val_indices_list) = map(
+                list, zip(*outputs)
+            )
+
+            predictions = np.concatenate(cast(List[NDArray], predictions_list))
+            val_ids = np.concatenate(cast(List[NDArray], val_ids_list))
+            val_indices = np.concatenate(cast(List[NDArray], val_indices_list))
+            self.k_[val_indices] = val_ids
+            y_pred_proba[val_indices] = predictions
+
+            if isinstance(cv, ShuffleSplit):
+                # Should delete values indices that
+                # are not used during calibration
+                self.k_ = self.k_[val_indices]
+                y_pred_proba = y_pred_proba[val_indices]
+                y_enc = y_enc[val_indices]
+                y = cast(NDArray, y)[val_indices]
+
+        return y_pred_proba, y, y_enc
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        y_enc: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> EnsembleRegressor:
+        """
+        Fit the base estimator under the ``single_estimator_`` attribute.
+        Fit all cross-validated estimator clones
+        and rearrange them into a list, the ``estimators_`` attribute.
+        Out-of-fold conformity scores are stored under
+        the ``conformity_scores_`` attribute.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        EnsembleRegressor
+            The estimator fitted.
+        """
+        # Initialization
+        single_estimator_: ClassifierMixin
+        estimators_: List[ClassifierMixin] = []
+        full_indexes = np.arange(_num_samples(X))
+        cv = self.cv
+        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
+        estimator = self.estimator
+        n_samples = _num_samples(y)
+
+        # Computation
+        if cv == "prefit":
+            single_estimator_ = estimator
+            self.k_ = np.full(shape=(n_samples, 1), fill_value=np.nan, dtype=float)
+        else:
+            single_estimator_ = self._fit_oof_estimator(
+                clone(estimator), X, y, full_indexes, sample_weight, **fit_params
+            )
+            cv = cast(BaseCrossValidator, cv)
+            self.k_ = np.empty_like(y, dtype=int)
+
+            estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
+                delayed(self._fit_oof_estimator)(
+                    clone(estimator), X, y_enc, train_index, sample_weight, **fit_params
+                )
+                for train_index, _ in cv.split(X, y, groups)
+            )
+            # In split-CP, we keep only the model fitted on train dataset
+            if self.use_split_method_:
+                single_estimator_ = estimators_[0]
+
+        self.single_estimator_ = single_estimator_
+        self.estimators_ = estimators_
+
+        return self
+
+    def predict(
+        self, X: ArrayLike, agg_scores
+    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
+        """
+        Predict target from X. It also computes the prediction per train sample
+        for each test sample according to ``self.method``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Test data.
+
+        ensemble: bool
+            Boolean determining whether the predictions are ensembled or not.
+            If ``False``, predictions are those of the model trained on the
+            whole training set.
+            If ``True``, predictions from perturbed models are aggregated by
+            the aggregation function specified in the ``agg_function``
+            attribute.
+
+            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
+
+            By default ``False``.
+
+        return_multi_pred: bool
+            If ``True`` the method returns the predictions and the multiple
+            predictions (3 arrays). If ``False`` the method return the
+            simple predictions only.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray, NDArray]
+            - Predictions
+            - The multiple predictions for the lower bound of the intervals.
+            - The multiple predictions for the upper bound of the intervals.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        if self.cv == "prefit":
+            y_pred_proba = self.single_estimator_.predict_proba(X)
+        else:
+            y_pred_proba_k = np.asarray(
+                Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+                    delayed(self._predict_proba_oof_estimator)(estimator, X)
+                    for estimator in self.estimators_
+                )
+            )
+            if agg_scores == "crossval":
+                y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2)
+            elif agg_scores == "mean":
+                y_pred_proba = np.mean(y_pred_proba_k, axis=0)
+            else:
+                raise ValueError("Invalid 'agg_scores' argument.")
+        return y_pred_proba
diff --git a/mapie/estimator/estimator_regressor.py b/mapie/estimator/estimator_regressor.py
new file mode 100644
index 000000000..ddbcff7f2
--- /dev/null
+++ b/mapie/estimator/estimator_regressor.py
@@ -0,0 +1,539 @@
+from __future__ import annotations
+
+from typing import List, Optional, Tuple, Union, cast
+
+import numpy as np
+from joblib import Parallel, delayed
+from sklearn.base import RegressorMixin, clone
+from sklearn.model_selection import BaseCrossValidator
+from sklearn.utils import _safe_indexing
+from sklearn.utils.validation import _num_samples, check_is_fitted
+
+from mapie._typing import ArrayLike, NDArray
+from mapie.aggregation_functions import aggregate_all, phi2D
+from mapie.estimator.interface import EnsembleEstimator
+from mapie.utils import (
+    check_nan_in_aposteriori_prediction,
+    check_no_agg_cv,
+    fit_estimator,
+)
+
+
+class EnsembleRegressor(EnsembleEstimator):
+    """
+    This class implements methods to handle the training and usage of the
+    estimator. This estimator can be unique or composed by cross validated
+    estimators.
+
+    Parameters
+    ----------
+    estimator: Optional[RegressorMixin]
+        Any regressor with scikit-learn API
+        (i.e. with ``fit`` and ``predict`` methods).
+        If ``None``, estimator defaults to a ``LinearRegression`` instance.
+
+        By default ``None``.
+
+    method: str
+        Method to choose for prediction interval estimates.
+        Choose among:
+
+        - ``"naive"``, based on training set conformity scores,
+        - ``"base"``, based on validation sets conformity scores,
+        - ``"plus"``, based on validation conformity scores and
+          testing predictions,
+        - ``"minmax"``, based on validation conformity scores and
+          testing predictions (min/max among cross-validation clones).
+
+        By default ``"plus"``.
+
+    cv: Optional[Union[int, str, BaseCrossValidator]]
+        The cross-validation strategy for computing conformity scores.
+        It directly drives the distinction between jackknife and cv variants.
+        Choose among:
+
+        - ``None``, to use the default 5-fold cross-validation
+        - integer, to specify the number of folds.
+          If equal to ``-1``, equivalent to
+          ``sklearn.model_selection.LeaveOneOut()``.
+        - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
+          Main variants are:
+            - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
+            - ``sklearn.model_selection.KFold`` (cross-validation),
+            - ``subsample.Subsample`` object (bootstrap).
+        - ``"split"``, does not involve cross-validation but a division
+          of the data into training and calibration subsets. The splitter
+          used is the following: ``sklearn.model_selection.ShuffleSplit``.
+        - ``"prefit"``, assumes that ``estimator`` has been fitted already,
+          and the ``method`` parameter is ignored.
+          All data provided in the ``fit`` method is then used
+          for computing conformity scores only.
+          At prediction time, quantiles of these conformity scores are used
+          to provide a prediction interval with fixed width.
+          The user has to take care manually that data for model fitting and
+          conformity scores estimate are disjoint.
+
+        By default ``None``.
+
+    test_size: Optional[Union[int, float]]
+        If ``float``, should be between ``0.0`` and ``1.0`` and represent the
+        proportion of the dataset to include in the test split. If ``int``,
+        represents the absolute number of test samples. If ``None``,
+        it will be set to ``0.1``.
+
+        If cv is not ``"split"``, ``test_size`` is ignored.
+
+        By default ``None``.
+
+    n_jobs: Optional[int]
+        Number of jobs for parallel processing using joblib
+        via the "locky" backend.
+        If ``-1`` all CPUs are used.
+        If ``1`` is given, no parallel computing code is used at all,
+        which is useful for debugging.
+        For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
+        ``None`` is a marker for `unset` that will be interpreted as
+        ``n_jobs=1`` (sequential execution).
+
+        By default ``None``.
+
+    agg_function: Optional[str]
+        Determines how to aggregate predictions from perturbed models, both at
+        training and prediction time.
+
+        If ``None``, it is ignored except if ``cv`` class is ``Subsample``,
+        in which case an error is raised.
+        If ``"mean"`` or ``"median"``, returns the mean or median of the
+        predictions computed from the out-of-folds models.
+        Note: if you plan to set the ``ensemble`` argument to ``True`` in the
+        ``predict`` method, you have to specify an aggregation function.
+        Otherwise an error would be raised.
+
+        The Jackknife+ interval can be interpreted as an interval around the
+        median prediction, and is guaranteed to lie inside the interval,
+        unlike the single estimator predictions.
+
+        When the cross-validation strategy is ``Subsample`` (i.e. for the
+        Jackknife+-after-Bootstrap method), this function is also used to
+        aggregate the training set in-sample predictions.
+
+        If ``cv`` is ``"prefit"`` or ``"split"``, ``agg_function`` is ignored.
+
+        By default ``"mean"``.
+
+    verbose: int
+        The verbosity level, used with joblib for multiprocessing.
+        The frequency of the messages increases with the verbosity level.
+        If it more than ``10``, all iterations are reported.
+        Above ``50``, the output is sent to stdout.
+
+        By default ``0``.
+
+    random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state used for random sampling.
+        Pass an int for reproducible output across multiple function calls.
+
+        By default ``None``.
+
+    Attributes
+    ----------
+    single_estimator_: sklearn.RegressorMixin
+        Estimator fitted on the whole training set.
+
+    estimators_: list
+        List of out-of-folds estimators.
+
+    k_: ArrayLike
+        - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
+          (defined but not used)
+        - Dummy array of folds containing each training sample, otherwise.
+          Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
+    """
+
+    no_agg_cv_ = ["prefit", "split"]
+    no_agg_methods_ = ["naive", "base"]
+    fit_attributes = [
+        "single_estimator_",
+        "estimators_",
+        "k_",
+        "use_split_method_",
+    ]
+
+    def __init__(
+        self,
+        estimator: Optional[RegressorMixin],
+        method: str,
+        cv: Optional[Union[int, str, BaseCrossValidator]],
+        agg_function: Optional[str],
+        n_jobs: Optional[int],
+        random_state: Optional[Union[int, np.random.RandomState]],
+        test_size: Optional[Union[int, float]],
+        verbose: int,
+    ):
+        self.estimator = estimator
+        self.method = method
+        self.cv = cv
+        self.agg_function = agg_function
+        self.n_jobs = n_jobs
+        self.random_state = random_state
+        self.test_size = test_size
+        self.verbose = verbose
+
+    @staticmethod
+    def _fit_oof_estimator(
+        estimator: RegressorMixin,
+        X: ArrayLike,
+        y: ArrayLike,
+        train_index: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> RegressorMixin:
+        """
+        Fit a single out-of-fold model on a given training set.
+
+        Parameters
+        ----------
+        estimator: RegressorMixin
+            Estimator to train.
+
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        train_index: ArrayLike of shape (n_samples_train)
+            Training data indices.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        RegressorMixin
+            Fitted estimator.
+        """
+        X_train = _safe_indexing(X, train_index)
+        y_train = _safe_indexing(y, train_index)
+        if not (sample_weight is None):
+            sample_weight = _safe_indexing(sample_weight, train_index)
+            sample_weight = cast(NDArray, sample_weight)
+
+        estimator = fit_estimator(
+            estimator, X_train, y_train, sample_weight=sample_weight, **fit_params
+        )
+        return estimator
+
+    @staticmethod
+    def _predict_oof_estimator(
+        estimator: RegressorMixin,
+        X: ArrayLike,
+        val_index: ArrayLike,
+    ) -> Tuple[NDArray, ArrayLike]:
+        """
+        Perform predictions on a single out-of-fold model on a validation set.
+
+        Parameters
+        ----------
+        estimator: RegressorMixin
+            Estimator to train.
+
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        val_index: ArrayLike of shape (n_samples_val)
+            Validation data indices.
+
+        Returns
+        -------
+        Tuple[NDArray, ArrayLike]
+            Predictions of estimator from val_index of X.
+        """
+        X_val = _safe_indexing(X, val_index)
+        if _num_samples(X_val) > 0:
+            y_pred = estimator.predict(X_val)
+        else:
+            y_pred = np.array([])
+        return y_pred, val_index
+
+    def _aggregate_with_mask(self, x: NDArray, k: NDArray) -> NDArray:
+        """
+        Take the array of predictions, made by the refitted estimators,
+        on the testing set, and the 1-or-nan array indicating for each training
+        sample which one to integrate, and aggregate to produce phi-{t}(x_t)
+        for each training sample x_t.
+
+        Parameters
+        ----------
+        x: ArrayLike of shape (n_samples_test, n_estimators)
+            Array of predictions, made by the refitted estimators,
+            for each sample of the testing set.
+
+        k: ArrayLike of shape (n_samples_training, n_estimators)
+            1-or-nan array: indicates whether to integrate the prediction
+            of a given estimator into the aggregation, for each training
+            sample.
+
+        Returns
+        -------
+        ArrayLike of shape (n_samples_test,)
+            Array of aggregated predictions for each testing sample.
+        """
+        if self.method in self.no_agg_methods_ or self.use_split_method_:
+            raise ValueError(
+                "There should not be aggregation of predictions "
+                f"if cv is in '{self.no_agg_cv_}', if cv >=2 "
+                f"or if method is in '{self.no_agg_methods_}'."
+            )
+        elif self.agg_function == "median":
+            return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))
+        # To aggregate with mean() the aggregation coud be done
+        # with phi2D(A=x, B=k, fun=lambda x: np.nanmean(x, axis=1).
+        # However, phi2D contains a np.apply_along_axis loop which
+        # is much slower than the matrices multiplication that can
+        # be used to compute the means.
+        elif self.agg_function in ["mean", None]:
+            K = np.nan_to_num(k, nan=0.0)
+            return np.matmul(x, (K / (K.sum(axis=1, keepdims=True))).T)
+        else:
+            raise ValueError("The value of self.agg_function is not correct")
+
+    def _pred_multi(self, X: ArrayLike) -> NDArray:
+        """
+        Return a prediction per train sample for each test sample, by
+        aggregation with matrix ``k_``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, n_samples_train)
+        """
+        y_pred_multi = np.column_stack([e.predict(X) for e in self.estimators_])
+        # At this point, y_pred_multi is of shape
+        # (n_samples_test, n_estimators_). The method
+        # ``_aggregate_with_mask`` fits it to the right size
+        # thanks to the shape of k_.
+        y_pred_multi = self._aggregate_with_mask(y_pred_multi, self.k_)
+        return y_pred_multi
+
+    def predict_calib(
+        self,
+        X: ArrayLike,
+        y: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+    ) -> NDArray:
+        """
+        Perform predictions on X : the calibration set.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        y: Optional[ArrayLike] of shape (n_samples_test,)
+            Input labels.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples_test,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, 1)
+            The predictions.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        if self.cv == "prefit":
+            y_pred = self.single_estimator_.predict(X)
+        else:
+            if self.method == "naive":
+                y_pred = self.single_estimator_.predict(X)
+            else:
+                cv = cast(BaseCrossValidator, self.cv)
+                outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+                    delayed(self._predict_oof_estimator)(
+                        estimator,
+                        X,
+                        calib_index,
+                    )
+                    for (_, calib_index), estimator in zip(
+                        cv.split(X, y, groups), self.estimators_
+                    )
+                )
+                predictions, indices = map(list, zip(*outputs))
+                n_samples = _num_samples(X)
+                pred_matrix = np.full(
+                    shape=(n_samples, cv.get_n_splits(X, y, groups)),
+                    fill_value=np.nan,
+                    dtype=float,
+                )
+                for i, ind in enumerate(indices):
+                    pred_matrix[ind, i] = np.array(predictions[i], dtype=float)
+                    self.k_[ind, i] = 1
+                check_nan_in_aposteriori_prediction(pred_matrix)
+
+                y_pred = aggregate_all(self.agg_function, pred_matrix)
+
+        return y_pred
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> EnsembleRegressor:
+        """
+        Fit the base estimator under the ``single_estimator_`` attribute.
+        Fit all cross-validated estimator clones
+        and rearrange them into a list, the ``estimators_`` attribute.
+        Out-of-fold conformity scores are stored under
+        the ``conformity_scores_`` attribute.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        EnsembleRegressor
+            The estimator fitted.
+        """
+        # Initialization
+        single_estimator_: RegressorMixin
+        estimators_: List[RegressorMixin] = []
+        full_indexes = np.arange(_num_samples(X))
+        cv = self.cv
+        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
+        estimator = self.estimator
+        n_samples = _num_samples(y)
+
+        # Computation
+        if cv == "prefit":
+            single_estimator_ = estimator
+            self.k_ = np.full(shape=(n_samples, 1), fill_value=np.nan, dtype=float)
+        else:
+            single_estimator_ = self._fit_oof_estimator(
+                clone(estimator), X, y, full_indexes, sample_weight, **fit_params
+            )
+            cv = cast(BaseCrossValidator, cv)
+            self.k_ = np.full(
+                shape=(n_samples, cv.get_n_splits(X, y, groups)),
+                fill_value=np.nan,
+                dtype=float,
+            )
+            if self.method == "naive":
+                estimators_ = [single_estimator_]
+            else:
+                estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
+                    delayed(self._fit_oof_estimator)(
+                        clone(estimator), X, y, train_index, sample_weight, **fit_params
+                    )
+                    for train_index, _ in cv.split(X, y, groups)
+                )
+                # In split-CP, we keep only the model fitted on train dataset
+                if self.use_split_method_:
+                    single_estimator_ = estimators_[0]
+
+        self.single_estimator_ = single_estimator_
+        self.estimators_ = estimators_
+
+        return self
+
+    def predict(
+        self, X: ArrayLike, ensemble: bool = False, return_multi_pred: bool = True
+    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
+        """
+        Predict target from X. It also computes the prediction per train sample
+        for each test sample according to ``self.method``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Test data.
+
+        ensemble: bool
+            Boolean determining whether the predictions are ensembled or not.
+            If ``False``, predictions are those of the model trained on the
+            whole training set.
+            If ``True``, predictions from perturbed models are aggregated by
+            the aggregation function specified in the ``agg_function``
+            attribute.
+
+            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
+
+            By default ``False``.
+
+        return_multi_pred: bool
+            If ``True`` the method returns the predictions and the multiple
+            predictions (3 arrays). If ``False`` the method return the
+            simple predictions only.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray, NDArray]
+            - Predictions
+            - The multiple predictions for the lower bound of the intervals.
+            - The multiple predictions for the upper bound of the intervals.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        y_pred = self.single_estimator_.predict(X)
+        if not return_multi_pred and not ensemble:
+            return y_pred
+
+        if self.method in self.no_agg_methods_ or self.use_split_method_:
+            y_pred_multi_low = y_pred[:, np.newaxis]
+            y_pred_multi_up = y_pred[:, np.newaxis]
+        else:
+            y_pred_multi = self._pred_multi(X)
+
+            if self.method == "minmax":
+                y_pred_multi_low = np.min(y_pred_multi, axis=1, keepdims=True)
+                y_pred_multi_up = np.max(y_pred_multi, axis=1, keepdims=True)
+            elif self.method == "plus":
+                y_pred_multi_low = y_pred_multi
+                y_pred_multi_up = y_pred_multi
+            else:
+                y_pred_multi_low = y_pred[:, np.newaxis]
+                y_pred_multi_up = y_pred[:, np.newaxis]
+
+            if ensemble:
+                y_pred = aggregate_all(self.agg_function, y_pred_multi)
+
+        if return_multi_pred:
+            return y_pred, y_pred_multi_low, y_pred_multi_up
+        else:
+            return y_pred
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index fc1f3e6ba..c3c21dabf 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -21,6 +21,7 @@
 from sklearn.utils.validation import check_is_fitted
 from typing_extensions import TypedDict
 
+from mapie.estimator.estimator_classification import EnsembleClassifier
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
 from mapie.metrics import classification_coverage_score
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index be305424d..9587007a8 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -12,9 +12,14 @@
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
-                                     PredefinedSplit, ShuffleSplit,
-                                     train_test_split)
+from sklearn.model_selection import (
+    GroupKFold,
+    KFold,
+    LeaveOneOut,
+    PredefinedSplit,
+    ShuffleSplit,
+    train_test_split,
+)
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.validation import check_is_fitted
@@ -22,19 +27,20 @@
 
 from mapie._typing import NDArray
 from mapie.aggregation_functions import aggregate_all
-from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
-                                     GammaConformityScore,
-                                     ResidualNormalisedScore)
-from mapie.estimator.estimator import EnsembleRegressor
+from mapie.conformity_scores import (
+    AbsoluteConformityScore,
+    ConformityScore,
+    GammaConformityScore,
+    ResidualNormalisedScore,
+)
+from mapie.estimator.estimator_regressor import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 from mapie.subsample import Subsample
 
 X_toy = np.array([0, 1, 2, 3, 4, 5]).reshape(-1, 1)
 y_toy = np.array([5, 7, 9, 11, 13, 15])
-X, y = make_regression(
-    n_samples=500, n_features=10, noise=1.0, random_state=1
-)
+X, y = make_regression(n_samples=500, n_features=10, noise=1.0, random_state=1)
 k = np.ones(shape=(5, X.shape[1]))
 METHODS = ["naive", "base", "plus", "minmax"]
 
@@ -56,77 +62,77 @@
         agg_function="median",
         cv=None,
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "split": Params(
         method="base",
         agg_function="median",
         cv="split",
         test_size=0.5,
-        random_state=random_state
+        random_state=random_state,
     ),
     "jackknife": Params(
         method="base",
         agg_function="mean",
         cv=-1,
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "jackknife_plus": Params(
         method="plus",
         agg_function="mean",
         cv=-1,
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "jackknife_minmax": Params(
         method="minmax",
         agg_function="mean",
         cv=-1,
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "cv": Params(
         method="base",
         agg_function="mean",
         cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "cv_plus": Params(
         method="plus",
         agg_function="mean",
         cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "cv_minmax": Params(
         method="minmax",
         agg_function="mean",
         cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "jackknife_plus_ab": Params(
         method="plus",
         agg_function="mean",
         cv=Subsample(n_resamplings=30, random_state=random_state),
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "jackknife_minmax_ab": Params(
         method="minmax",
         agg_function="mean",
         cv=Subsample(n_resamplings=30, random_state=random_state),
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
     "jackknife_plus_median_ab": Params(
         method="plus",
         agg_function="median",
         cv=Subsample(n_resamplings=30, random_state=random_state),
         test_size=None,
-        random_state=random_state
+        random_state=random_state,
     ),
 }
 
@@ -173,9 +179,7 @@ def test_default_parameters() -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 def test_valid_estimator(strategy: str) -> None:
     """Test that valid estimators are not corrupted, for all strategies."""
-    mapie_reg = MapieRegressor(
-        estimator=DummyRegressor(), **STRATEGIES[strategy]
-    )
+    mapie_reg = MapieRegressor(estimator=DummyRegressor(), **STRATEGIES[strategy])
     mapie_reg.fit(X_toy, y_toy)
     assert isinstance(mapie_reg.estimator_.single_estimator_, DummyRegressor)
     for estimator in mapie_reg.estimator_.estimators_:
@@ -211,10 +215,18 @@ def test_valid_agg_function(agg_function: str) -> None:
 
 
 @pytest.mark.parametrize(
-    "cv", [None, -1, 2, KFold(), LeaveOneOut(),
-           ShuffleSplit(n_splits=1),
-           PredefinedSplit(test_fold=[-1]*3+[0]*3),
-           "prefit", "split"]
+    "cv",
+    [
+        None,
+        -1,
+        2,
+        KFold(),
+        LeaveOneOut(),
+        ShuffleSplit(n_splits=1),
+        PredefinedSplit(test_fold=[-1] * 3 + [0] * 3),
+        "prefit",
+        "split",
+    ],
 )
 def test_valid_cv(cv: Any) -> None:
     """Test that valid cv raise no errors."""
@@ -257,9 +269,7 @@ def test_same_results_prefit_split() -> None:
     Test checking that if split and prefit method have exactly
     the same data split, then we have exactly the same results.
     """
-    X, y = make_regression(
-        n_samples=500, n_features=10, noise=1.0, random_state=1
-    )
+    X, y = make_regression(n_samples=500, n_features=10, noise=1.0, random_state=1)
     cv = ShuffleSplit(n_splits=1, test_size=0.1, random_state=random_state)
     train_index, val_index = list(cv.split(X))[0]
     X_train, X_calib = X[train_index], X[val_index]
@@ -293,12 +303,8 @@ def test_results_for_same_alpha(strategy: str) -> None:
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-@pytest.mark.parametrize(
-    "alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)]
-)
-def test_results_for_alpha_as_float_and_arraylike(
-    strategy: str, alpha: Any
-) -> None:
+@pytest.mark.parametrize("alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)])
+def test_results_for_alpha_as_float_and_arraylike(strategy: str, alpha: Any) -> None:
     """Test that output values do not depend on type of alpha."""
     mapie_reg = MapieRegressor(**STRATEGIES[strategy])
     mapie_reg.fit(X, y)
@@ -490,9 +496,7 @@ def test_results_prefit_ignore_method() -> None:
     estimator = LinearRegression().fit(X, y)
     all_y_pis: List[NDArray] = []
     for method in METHODS:
-        mapie_reg = MapieRegressor(
-            estimator=estimator, cv="prefit", method=method
-        )
+        mapie_reg = MapieRegressor(estimator=estimator, cv="prefit", method=method)
         mapie_reg.fit(X, y)
         _, y_pis = mapie_reg.predict(X, alpha=0.1)
         all_y_pis.append(y_pis)
@@ -528,9 +532,7 @@ def test_results_prefit() -> None:
     mapie_reg.fit(X_val, y_val)
     _, y_pis = mapie_reg.predict(X_test, alpha=0.05)
     width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()
-    coverage = regression_coverage_score(
-        y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]
-    )
+    coverage = regression_coverage_score(y_test, y_pis[:, 0, 0], y_pis[:, 1, 0])
     np.testing.assert_allclose(width_mean, WIDTHS["prefit"], rtol=1e-2)
     np.testing.assert_allclose(coverage, COVERAGES["prefit"], rtol=1e-2)
 
@@ -540,9 +542,7 @@ def test_not_enough_resamplings() -> None:
     Test that a warning is raised if at least one conformity score is nan.
     """
     with pytest.warns(UserWarning, match=r"WARNING: at least one point of*"):
-        mapie_reg = MapieRegressor(
-            cv=Subsample(n_resamplings=1), agg_function="mean"
-        )
+        mapie_reg = MapieRegressor(cv=Subsample(n_resamplings=1), agg_function="mean")
         mapie_reg.fit(X, y)
 
 
@@ -550,12 +550,8 @@ def test_no_agg_fx_specified_with_subsample() -> None:
     """
     Test that a warning is raised if at least one conformity score is nan.
     """
-    with pytest.raises(
-        ValueError, match=r"You need to specify an aggregation*"
-    ):
-        mapie_reg = MapieRegressor(
-            cv=Subsample(n_resamplings=1), agg_function=None
-        )
+    with pytest.raises(ValueError, match=r"You need to specify an aggregation*"):
+        mapie_reg = MapieRegressor(cv=Subsample(n_resamplings=1), agg_function=None)
         mapie_reg.fit(X, y)
 
 
@@ -593,7 +589,7 @@ def test_aggregate_with_mask_with_invalid_agg_function() -> None:
         None,
         random_state,
         0.20,
-        False
+        False,
     )
     ens_reg.use_split_method_ = False
     with pytest.raises(
@@ -650,32 +646,24 @@ def test_pipeline_compatibility() -> None:
 @pytest.mark.parametrize(
     "conformity_score", [AbsoluteConformityScore(), GammaConformityScore()]
 )
-def test_conformity_score(
-    strategy: str, conformity_score: ConformityScore
-) -> None:
+def test_conformity_score(strategy: str, conformity_score: ConformityScore) -> None:
     """Test that any conformity score function with MAPIE raises no error."""
     mapie_reg = MapieRegressor(
-        conformity_score=conformity_score,
-        **STRATEGIES[strategy]
+        conformity_score=conformity_score, **STRATEGIES[strategy]
     )
     mapie_reg.fit(X, y + 1e3)
     mapie_reg.predict(X, alpha=0.05)
 
 
-@pytest.mark.parametrize(
-    "conformity_score", [ResidualNormalisedScore()]
-)
+@pytest.mark.parametrize("conformity_score", [ResidualNormalisedScore()])
 def test_conformity_score_with_split_strategies(
-   conformity_score: ConformityScore
+    conformity_score: ConformityScore,
 ) -> None:
     """
     Test that any conformity score function that handle only split strategies
     with MAPIE raises no error.
     """
-    mapie_reg = MapieRegressor(
-        conformity_score=conformity_score,
-        **STRATEGIES["split"]
-    )
+    mapie_reg = MapieRegressor(conformity_score=conformity_score, **STRATEGIES["split"])
     mapie_reg.fit(X, y + 1e3)
     mapie_reg.predict(X, alpha=0.05)
 
@@ -708,11 +696,12 @@ def test_beta_optimize_user_warning() -> None:
     """
     Test that a UserWarning is displayed when optimize_beta is used.
     """
-    mapie_reg = MapieRegressor(
-        conformity_score=AbsoluteConformityScore(sym=False)
-    ).fit(X, y)
+    mapie_reg = MapieRegressor(conformity_score=AbsoluteConformityScore(sym=False)).fit(
+        X, y
+    )
     with pytest.warns(
-        UserWarning, match=r"Beta optimisation should only be used for*",
+        UserWarning,
+        match=r"Beta optimisation should only be used for*",
     ):
         mapie_reg.predict(X, alpha=0.05, optimize_beta=True)
 
@@ -745,6 +734,6 @@ def early_stopping_monitor(i, est, locals):
 def test_predict_infinite_intervals() -> None:
     """Test that MapieRegressor produces infinite bounds with alpha=0"""
     mapie_reg = MapieRegressor().fit(X, y)
-    _, y_pis = mapie_reg.predict(X, alpha=0., allow_infinite_bounds=True)
+    _, y_pis = mapie_reg.predict(X, alpha=0.0, allow_infinite_bounds=True)
     np.testing.assert_allclose(y_pis[:, 0, 0], -np.inf)
     np.testing.assert_allclose(y_pis[:, 1, 0], np.inf)

From 09721e1a8717aa2605ac99ab13482a8ff244d3b8 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 14:47:23 +0200
Subject: [PATCH 008/424] FIX: image size

---
 README.rst | 15 ++++++++++-----
 1 file changed, 10 insertions(+), 5 deletions(-)

diff --git a/README.rst b/README.rst
index a6b57cbfd..971cd652d 100644
--- a/README.rst
+++ b/README.rst
@@ -172,23 +172,28 @@ and with the financial support from Région Ile de France and Confiance.ai.
 |Quantmetry| |Michelin| |ENS| |Confiance.ai| |IledeFrance|
 
 .. |Quantmetry| image:: https://www.quantmetry.com/wp-content/uploads/2020/08/08-Logo-quant-Texte-noir.svg
-    :height: 35
+    :height: 35px
+    :width: 140px
     :target: https://www.quantmetry.com/
 
 .. |Michelin| image:: https://agngnconpm.cloudimg.io/v7/https://dgaddcosprod.blob.core.windows.net/corporate-production/attachments/cls05tqdd9e0o0tkdghwi9m7n-clooe1x0c3k3x0tlu4cxi6dpn-bibendum-salut.full.png
-    :height: 35
+    :height: 45px
+    :width: 45px
     :target: https://www.michelin.com/en/
 
 .. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg
-    :height: 35
+    :height: 35px
+    :width: 140px
     :target: https://ens-paris-saclay.fr/en
 
 .. |Confiance.ai| image:: https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg
-    :height: 35
+    :height: 45px
+    :width: 45px
     :target: https://www.confiance.ai/
 
 .. |IledeFrance| image:: https://www.iledefrance.fr/sites/default/files/logo/2024-02/logoGagnerok.svg
-    :height: 35
+    :height: 35px
+    :width: 140px
     :target: https://www.iledefrance.fr/
 
 

From 06e56304193bc668a301249db11e36aed9384bfd Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 14:47:39 +0200
Subject: [PATCH 009/424] FIX: missing ref in metrics.py

---
 mapie/metrics.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/mapie/metrics.py b/mapie/metrics.py
index e78f02c7c..20c5065f0 100644
--- a/mapie/metrics.py
+++ b/mapie/metrics.py
@@ -541,6 +541,11 @@ def regression_ssc_score(
     (intervals of different sizes), with constant intervals the result
     may be misinterpreted.
 
+    [3] Angelopoulos, A. N., & Bates, S. (2021).
+    A gentle introduction to conformal prediction and
+    distribution-free uncertainty quantification.
+    arXiv preprint arXiv:2107.07511.
+
     Parameters
     ----------
     y_true: NDArray of shape (n_samples,)

From ee0e17d77952f7285ed5db56fc474c02cad377e6 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 14:47:46 +0200
Subject: [PATCH 010/424] chore: Add METRICS section to table of contents

---
 doc/index.rst | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/doc/index.rst b/doc/index.rst
index d3b00dc18..b5450722b 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -58,6 +58,13 @@
    examples_calibration/index
    notebooks_calibration
 
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+   :caption: METRICS
+
+   theoretical_description_metrics
+
 .. toctree::
    :maxdepth: 2
    :hidden:

From 64a0299010df1bb2af45b571b21bb7124f63c6a4 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 14:48:09 +0200
Subject: [PATCH 011/424] ADD: theoretical description for metrics

---
 doc/theoretical_description_metrics.rst | 264 ++++++++++++++++++++++++
 1 file changed, 264 insertions(+)
 create mode 100644 doc/theoretical_description_metrics.rst

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
new file mode 100644
index 000000000..75a26fc27
--- /dev/null
+++ b/doc/theoretical_description_metrics.rst
@@ -0,0 +1,264 @@
+.. title:: Theoretical Description : contents
+
+.. _theoretical_description_metrics:
+
+==================================
+Theoretical Description of Metrics
+==================================
+
+This document provides detailed descriptions of various metrics used to evaluate the performance of predictive models, particularly focusing on their ability to estimate uncertainties and calibrate predictions accurately.
+
+
+1. General metrics
+==================
+
+Regression Coverage Score
+-------------------------
+
+The **Regression Coverage Score** calculates the fraction of true outcomes that fall within the provided prediction intervals. 
+
+.. math::
+
+   C = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(y_{\text{pred, low}}^{(i)} \leq y_{\text{true}}^{(i)} \leq y_{\text{pred, up}}^{(i)})
+
+where:
+
+- :math:`n` is the number of samples,
+- :math:`y_{\text{true}}^{(i)}` is the true value for the :math:`i`-th sample,
+- :math:`y_{\text{pred, low}}^{(i)}` and :math:`y_{\text{pred, up}}^{(i)}` are the lower and upper bounds of the prediction intervals, respectively.
+
+
+Regression Mean Width Score
+---------------------------
+
+The **Regression Mean Width Score** assesses the average width of the prediction intervals provided by the model.
+
+.. math::
+
+   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} (y_{\text{pred, up}}^{(i)} - y_{\text{pred, low}}^{(i)})
+
+
+Classification Coverage Score
+-----------------------------
+
+The **Classification Coverage Score** measures how often the true class labels fall within the predicted sets.
+
+.. math::
+
+   C = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(y_{\text{true}}^{(i)} \in \text{Set}_{\text{pred}}^{(i)})
+
+Here, :math:`\text{Set}_{\text{pred}}^{(i)}` represents the set of predicted labels that could possibly contain the true label.
+
+
+Classification Mean Width Score
+-------------------------------
+
+For classification tasks, the **Classification Mean Width Score** calculates the average size of the prediction sets across all samples.
+
+.. math::
+
+   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} |\text{Set}_{\text{pred}}^{(i)}|
+
+where :math:`|\text{Set}_{\text{pred}}^{(i)}|` denotes the number of classes included in the prediction set for sample :math:`i`.
+
+
+Size-Stratified Coverage (SSC)
+-------------------------------
+
+**Size-Stratified Coverage (SSC)** evaluates how the size of prediction sets or intervals affects their ability to cover the true outcomes [1]. It's calculated separately for classification and regression:
+
+**Regression:**
+
+.. math::
+
+   \text{SSC}_{\text{regression}} = \sum_{k=1}^{K} \left( \frac{1}{|I_k|} \sum_{i \in I_k} \mathbf{1}(y_{\text{pred, low}}^{(i)} \leq y_{\text{true}}^{(i)} \leq y_{\text{pred, up}}^{(i)}) \right)
+
+**Classification:**
+
+.. math::
+
+   \text{SSC}_{\text{classification}} = \sum_{k=1}^{K} \left( \frac{1}{|S_k|} \sum_{i \in S_k} \mathbf{1}(y_{\text{true}}^{(i)} \in \text{Set}_{\text{pred}}^{(i)}) \right)
+
+where:
+
+- :math:`K` is the number of distinct size groups,
+- :math:`I_k` and :math:`S_k` are the indices of samples whose prediction intervals or sets belong to the :math:`k`-th size group.
+
+
+Hilbert-Schmidt Independence Criterion (HSIC)
+----------------------------------------------
+
+**Hilbert-Schmidt Independence Criterion (HSIC)** is a non-parametric measure of independence between two variables, applied here to test the independence of interval sizes from their coverage indicators [4].
+
+.. math::
+
+   \text{HSIC} = \operatorname{trace}(\mathbf{H} \mathbf{K} \mathbf{H} \mathbf{L})
+
+where:
+
+- :math:`\mathbf{K}` and :math:`\mathbf{L}` are the kernel matrices representing the interval sizes and coverage indicators, respectively.
+- :math:`\mathbf{H}` is the centering matrix, :math:`\mathbf{H} = \mathbf{I} - \frac{1}{n} \mathbf{11}^\top`.
+
+This measure is crucial for determining whether certain sizes of prediction intervals are systematically more or less likely to contain the true values, which can highlight biases in interval-based predictions.
+
+
+Coverage Width-Based Criterion (CWC)
+------------------------------------
+
+The **Coverage Width-Based Criterion (CWC)** evaluates prediction intervals by balancing their empirical coverage and width. It is designed to both reward narrow intervals and penalize those that do not achieve a specified coverage probability [6].
+
+.. math::
+
+   \text{CWC} = (1 - \text{Mean Width Score}) \times \exp\left(-\eta \times (\text{Coverage Score} - (1-\alpha))^2\right)
+
+
+
+Regression MWI Score
+--------------------
+
+The **Regression MWI (Mean Winkler Interval) Score** evaluates prediction intervals by combining their width with a penalty for intervals that do not contain the observation [8, 10].
+
+.. math::
+
+   \text{MWI Score} = \frac{1}{n} \sum_{i=1}^{n} (y_{\text{pred, up}}^{(i)} - y_{\text{pred, low}}^{(i)}) + \frac{2}{\alpha} \sum_{i=1}^{n} \max(0, |y_{\text{true}}^{(i)} - y_{\text{pred, boundary}}^{(i)}|)
+
+where :math:`y_{\text{pred, boundary}}^{(i)}` is the nearest interval boundary not containing :math:`y_{\text{true}}^{(i)}`, and :math:`\alpha` is the significance level.
+
+
+
+2. Calibration metrics
+======================
+
+Expected Calibration Error (ECE)
+--------------------------------
+
+**Expected Calibration Error (ECE)** measures the difference between predicted probabilities of a model and the actual outcomes, across different bins of predicted probabilities [7].
+
+.. math::
+
+   \text{ECE} = \sum_{b=1}^{B} \frac{n_b}{n} | \text{acc}(b) - \text{conf}(b) |
+
+where:
+
+- :math:`B` is the total number of bins,
+- :math:`n_b` is the number of samples in bin :math:`b`,
+- :math:`\text{acc}(b)` is the accuracy within bin :math:`b`,
+- :math:`\text{conf}(b)` is the mean predicted probability in bin :math:`b`.
+
+
+Top-Label Expected Calibration Error (Top-Label ECE)
+----------------------------------------------------
+
+**Top-Label ECE** focuses on the class predicted with the highest confidence for each sample, assessing whether these top-predicted confidences align well with actual outcomes. It is calculated by dividing the confidence score range into bins and comparing the mean confidence against empirical accuracy within these bins [5].
+
+.. math::
+
+   \text{Top-Label ECE} = \sum_{b=1}^{B} \frac{n_b}{n} \left| \text{acc}_b - \text{conf}_b \right|
+
+where:
+
+- :math:`n` is the total number of samples,
+- :math:`n_b` is the number of samples in bin :math:`b`,
+- :math:`\text{acc}_b` is the empirical accuracy in bin :math:`b`,
+- :math:`\text{conf}_b` is the average confidence of the top label in bin :math:`b`.
+
+This metric is especially useful in multi-class classification to ensure that the model is neither overconfident nor underconfident in its predictions.
+
+
+Cumulative Differences
+----------------------
+
+**Cumulative Differences** calculates the cumulative differences between sorted true values and prediction scores, helping to understand how well the prediction scores correspond to the actual outcomes when both are ordered by the score [2].
+
+.. math::
+
+   \text{Cumulative Differences} = \frac{1}{n} \sum_{i=1}^{n} (y_{\text{true,sorted}}^{(i)} - y_{\text{score,sorted}}^{(i)})
+
+
+Kolmogorov-Smirnov Statistic for Calibration
+--------------------------------------------
+
+This statistic measures the maximum absolute deviation between the empirical cumulative distribution function (ECDF) of observed outcomes and predicted probabilities [2, 3, 11].
+
+.. math::
+
+   \text{KS Statistic} = \sup_x |F_n(x) - S_n(x)|
+
+where :math:`F_n(x)` is the ECDF of the predicted probabilities and :math:`S_n(x)` is the ECDF of the observed outcomes.
+
+
+Kuiper's Statistic
+------------------
+
+**Kuiper's Statistic** considers both the maximum deviation above and below the mean cumulative difference, making it more sensitive to deviations at the tails of the distribution [2, 3, 11].
+
+.. math::
+
+   \text{Kuiper's Statistic} = \max(F_n(x) - S_n(x)) + \max(S_n(x) - F_n(x))
+
+
+Spiegelhalter’s Test
+--------------------
+
+**Spiegelhalter’s Test** assesses the calibration of binary predictions based on a transformation of the Brier score [9].
+
+.. math::
+
+   \text{Spiegelhalter's Statistic} = \frac{\sum (y_{\text{true}} - y_{\text{score}})(1 - 2y_{\text{score}})}{\sqrt{\sum (1 - 2y_{\text{score}})^2 y_{\text{score}} (1 - y_{\text{score}})}}
+
+
+
+References
+==========
+
+[1] Angelopoulos, A. N., & Bates, S. (2021).
+A gentle introduction to conformal prediction and
+distribution-free uncertainty quantification.
+arXiv preprint arXiv:2107.07511.
+
+[2] Arrieta-Ibarra I, Gujral P, Tannen J, Tygert M, Xu C.
+Metrics of calibration for probabilistic predictions.
+The Journal of Machine Learning Research. 2022 Jan 1;23(1):15886-940.
+
+[3] D. A. Darling. A. J. F. Siegert.
+The First Passage Problem for a Continuous Markov Process.
+Ann. Math. Statist. 24 (4) 624 - 639, December, 1953.
+
+[4] Feldman, S., Bates, S., & Romano, Y. (2021).
+Improving conditional coverage via orthogonal quantile regression.
+Advances in Neural Information Processing Systems, 34, 2060-2071.
+
+[5] Gupta, Chirag, and Aaditya K. Ramdas.
+"Top-label calibration and multiclass-to-binary reductions."
+arXiv preprint arXiv:2107.08353 (2021).
+
+[6] Khosravi, Abbas, Saeid Nahavandi, and Doug Creighton.
+"Construction of optimal prediction intervals for load forecasting
+problems."
+IEEE Transactions on Power Systems 25.3 (2010): 1496-1503.
+
+[7] Naeini, Mahdi Pakdaman, Gregory Cooper, and Milos Hauskrecht.
+"Obtaining well calibrated probabilities using bayesian binning."
+Twenty-Ninth AAAI Conference on Artificial Intelligence. 2015.
+
+[8] Robert L. Winkler
+"A Decision-Theoretic Approach to Interval Estimation",
+Journal of the American Statistical Association,
+volume 67, pages 187-191 (1972)
+(https://doi.org/10.1080/01621459.1972.10481224)
+
+[9] Spiegelhalter DJ.
+Probabilistic prediction in patient management and clinical trials.
+Statistics in medicine.
+1986 Sep;5(5):421-33.
+
+[10] Tilmann Gneiting and Adrian E Raftery
+"Strictly Proper Scoring Rules, Prediction, and Estimation",
+Journal of the American Statistical Association,
+volume 102, pages 359-378 (2007)
+(https://doi.org/10.1198/016214506000001437) (Section 6.2)
+
+[11] Tygert M.
+Calibration of P-values for calibration and for deviation
+of a subpopulation from the full population.
+arXiv preprint arXiv:2202.00100.2022 Jan 31.

From d0bbf06cf733fa850d4d1be7ff7018c14206a085 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 15:05:36 +0200
Subject: [PATCH 012/424] Including previous PR

---
 mapie/classification.py                 | 71 +------------------------
 mapie/estimator/estimator_classifier.py | 23 +++++++-
 2 files changed, 23 insertions(+), 71 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 811debfc4..0eae0af0e 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -535,36 +535,6 @@ def _add_random_tie_breaking(
         )
         return prediction_sets
 
-    def _predict_oof_model(
-        self,
-        estimator: ClassifierMixin,
-        X: ArrayLike,
-    ) -> NDArray:
-        """
-        Predict probabilities of a test set from a fitted estimator.
-
-        Parameters
-        ----------
-        estimator: ClassifierMixin
-            Fitted estimator.
-
-        X: ArrayLike
-            Test set.
-
-        Returns
-        -------
-        ArrayLike
-            Predicted probabilities.
-        """
-        y_pred_proba = estimator.predict_proba(X)
-        # we enforce y_pred_proba to contain all labels included in y
-        if len(estimator.classes_) != self.n_classes_:
-            y_pred_proba = fix_number_of_classes(
-                self.n_classes_, estimator.classes_, y_pred_proba
-            )
-        y_pred_proba = self._check_proba_normalized(y_pred_proba)
-        return y_pred_proba
-
     def _get_true_label_cumsum_proba(
         self, y: ArrayLike, y_pred_proba: NDArray
     ) -> Tuple[NDArray, NDArray]:
@@ -634,32 +604,6 @@ def _regularize_conformity_score(
         conf_score += np.maximum(np.expand_dims(lambda_ * (cutoff - k_star), axis=1), 0)
         return conf_score
 
-<<<<<<< Updated upstream
-=======
-    def _get_true_label_position(self, y_pred_proba: NDArray, y: NDArray) -> NDArray:
-        """
-        Return the sorted position of the true label in the
-        prediction
-
-        Parameters
-        ----------
-        y_pred_proba: NDArray of shape (n_samples, n_calsses)
-            Model prediction.
-
-        y: NDArray of shape (n_samples)
-            Labels.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, 1)
-            Position of the true label in the prediction.
-        """
-        index = np.argsort(np.fliplr(np.argsort(y_pred_proba, axis=1)))
-        position = np.take_along_axis(index, y.reshape(-1, 1), axis=1)
-
-        return position
-
->>>>>>> Stashed changes
     def _get_last_included_proba(
         self,
         y_pred_proba: NDArray,
@@ -1030,14 +974,8 @@ def fit(
             self.y_pred_proba_raps = self.estimator_.single_estimator_.predict_proba(
                 self.X_raps
             )
-<<<<<<< Updated upstream
             self.position_raps = get_true_label_position(
-                self.y_pred_proba_raps,
-                self.y_raps
-=======
-            self.position_raps = self._get_true_label_position(
                 self.y_pred_proba_raps, self.y_raps
->>>>>>> Stashed changes
             )
 
         # Conformity scores
@@ -1069,14 +1007,7 @@ def fit(
             # Here we reorder the labels by decreasing probability
             # and get the position of each label from decreasing
             # probability
-<<<<<<< Updated upstream
-            self.conformity_scores_ = get_true_label_position(
-                y_pred_proba,
-                y_enc
-            )
-=======
-            self.conformity_scores_ = self._get_true_label_position(y_pred_proba, y_enc)
->>>>>>> Stashed changes
+            self.conformity_scores_ = get_true_label_position(y_pred_proba, y_enc)
         else:
             raise ValueError(
                 "Invalid method. " f"Allowed values are {self.valid_methods_}."
diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/estimator_classifier.py
index 01c4eac1a..a7c052238 100644
--- a/mapie/estimator/estimator_classifier.py
+++ b/mapie/estimator/estimator_classifier.py
@@ -186,8 +186,29 @@ def _fit_oof_estimator(
         )
         return estimator
 
-    def _predict_proba_oof_estimator(self, estimator, X):
+    def _predict_proba_oof_estimator(
+        self,
+        estimator: ClassifierMixin,
+        X: ArrayLike,
+    ) -> NDArray:
+        """
+        Predict probabilities of a test set from a fitted estimator.
+
+        Parameters
+        ----------
+        estimator: ClassifierMixin
+            Fitted estimator.
+
+        X: ArrayLike
+            Test set.
+
+        Returns
+        -------
+        ArrayLike
+            Predicted probabilities.
+        """
         y_pred_proba = estimator.predict_proba(X)
+        # we enforce y_pred_proba to contain all labels included in y
         if len(estimator.classes_) != self.n_classes:
             y_pred_proba = fix_number_of_classes(
                 self.n_classes, estimator.classes_, y_pred_proba

From 19510fb219b6a5eddc8d393a103205a7a40210ac Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 15:09:58 +0200
Subject: [PATCH 013/424] delete print in the code

---
 mapie/classification.py | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 0eae0af0e..e77d5a3dd 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -982,14 +982,6 @@ def fit(
         if self.method == "naive":
             self.conformity_scores_ = np.empty(y_pred_proba.shape, dtype="float")
         elif self.method in ["score", "lac"]:
-            print()
-            print("TEST ICI")
-            print()
-            print(
-                "y_pred_proba:", y_pred_proba, "y_pred_proba_shape", y_pred_proba.shape
-            )
-            print()
-            print("y_enc", y_enc, "y_enc_shape", y_enc.shape)
             self.conformity_scores_ = np.take_along_axis(
                 1 - y_pred_proba, y_enc.reshape(-1, 1), axis=1
             )

From 05f282ea3e2ceaf2436b4d7ce0831da3d1787efb Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 15:56:26 +0200
Subject: [PATCH 014/424] Add documentation in functions

---
 mapie/classification.py | 38 +++++++++++++++++++++++++++++++++++---
 1 file changed, 35 insertions(+), 3 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index e77d5a3dd..c7eab6b0f 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -13,7 +13,7 @@
 
 from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
-from .estimator.estimator_classification import EnsembleClassifier
+from .estimator.estimator_classifier import EnsembleClassifier
 from .metrics import classification_mean_width_score
 from .utils import (
     check_alpha,
@@ -25,7 +25,6 @@
     check_null_weight,
     check_verbose,
     compute_quantiles,
-    fix_number_of_classes,
 )
 
 
@@ -843,7 +842,38 @@ def _get_classes_info(
 
         return n_classes, classes
 
-    def _check_fit_parameter(self, X, y, sample_weight, groups):
+    def _check_fit_parameter(self, X: ArrayLike, : ArrayLike, sample_weight: Optional[ArrayLike] = None, groups: : Optional[ArrayLike] = None):
+        
+        """
+        Perform several checks on class parameters.
+
+        Parameters
+        ----------
+        X: ArrayLike
+            Observed values.
+
+        y: ArrayLike
+            Target values.
+
+        sample_weight: Optional[NDArray] of shape (n_samples,)
+            Non-null sample weights.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+            By default ``None``.
+
+        Raises
+        ------
+        ValueError
+            If conformity score is FittedResidualNormalizing score and method
+            is neither ``"prefit"`` or ``"split"``.
+
+        ValueError
+            If ``cv`` is `"prefit"`` or ``"split"`` and ``method`` is not
+            ``"base"``.
+        """
+
         self._check_parameters()
         cv = check_cv(self.cv, test_size=self.test_size, random_state=self.random_state)
         X, y = indexable(X, y)
@@ -965,6 +995,8 @@ def fit(
         )
 
         self.estimator_.fit(X, y, y_enc, sample_weight, groups, **fit_params)
+
+        
         y_pred_proba, y, y_enc = self.estimator_.predict_proba_calib(
             X, y, y_enc, groups
         )

From c70cb218ca9a630243f9f1e1877477d044a729f5 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 16:06:14 +0200
Subject: [PATCH 015/424] Modification of typo

---
 mapie/estimator/estimator_classifier.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/estimator_classifier.py
index a7c052238..9904f04b4 100644
--- a/mapie/estimator/estimator_classifier.py
+++ b/mapie/estimator/estimator_classifier.py
@@ -384,7 +384,7 @@ def fit(
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
         **fit_params,
-    ) -> EnsembleRegressor:
+    ) -> EnsembleClassifier:
         """
         Fit the base estimator under the ``single_estimator_`` attribute.
         Fit all cross-validated estimator clones

From 3d49d2da972b31c6dd05dd28a801dc004420de31 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 16:09:15 +0200
Subject: [PATCH 016/424] Modification of documentation

---
 mapie/estimator/estimator_classifier.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/estimator_classifier.py
index 9904f04b4..387b67900 100644
--- a/mapie/estimator/estimator_classifier.py
+++ b/mapie/estimator/estimator_classifier.py
@@ -27,7 +27,7 @@ class EnsembleClassifier(EnsembleEstimator):
 
      Parameters
      ----------
-     estimator: Optional[RegressorMixin]
+     estimator: Optional[ClaMixin]
          Any regressor with scikit-learn API
          (i.e. with ``fit`` and ``predict`` methods).
          If ``None``, estimator defaults to a ``LinearRegression`` instance.
@@ -98,7 +98,7 @@ class EnsembleClassifier(EnsembleEstimator):
 
      Attributes
      ----------
-     single_estimator_: sklearn.RegressorMixin
+     single_estimator_: sklearn.ClassifierMixin
          Estimator fitted on the whole training set.
 
      estimators_: list
@@ -151,7 +151,7 @@ def _fit_oof_estimator(
 
         Parameters
         ----------
-        estimator: RegressorMixin
+        estimator: ClassifierMixin
             Estimator to train.
 
         X: ArrayLike of shape (n_samples, n_features)
@@ -172,7 +172,7 @@ def _fit_oof_estimator(
 
         Returns
         -------
-        RegressorMixin
+        ClassifierMixin
             Fitted estimator.
         """
         X_train = _safe_indexing(X, train_index)
@@ -223,7 +223,7 @@ def _predict_proba_calib_oof_estimator(
 
         Parameters
         ----------
-        estimator: RegressorMixin
+        estimator: ClassifierMixin
             Estimator to train.
 
         X: ArrayLike of shape (n_samples, n_features)

From be253336cb8150ac638fd9c4d05f4218ca333ab6 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 16:18:12 +0200
Subject: [PATCH 017/424] Typo

---
 mapie/classification.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index c7eab6b0f..a1e884cbc 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -842,8 +842,13 @@ def _get_classes_info(
 
         return n_classes, classes
 
-    def _check_fit_parameter(self, X: ArrayLike, : ArrayLike, sample_weight: Optional[ArrayLike] = None, groups: : Optional[ArrayLike] = None):
-        
+    def _check_fit_parameter(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+    ):
         """
         Perform several checks on class parameters.
 
@@ -996,7 +1001,6 @@ def fit(
 
         self.estimator_.fit(X, y, y_enc, sample_weight, groups, **fit_params)
 
-        
         y_pred_proba, y, y_enc = self.estimator_.predict_proba_calib(
             X, y, y_enc, groups
         )

From ac3cd4620fc0ec3648942b5c6389d0f6eff627e3 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 16:19:41 +0200
Subject: [PATCH 018/424] Typo

---
 mapie/tests/test_classification.py | 831 ++++++++---------------------
 1 file changed, 236 insertions(+), 595 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index c3c21dabf..2cc291e74 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -13,15 +13,14 @@
 from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
-                                     ShuffleSplit)
+from sklearn.model_selection import GroupKFold, KFold, LeaveOneOut, ShuffleSplit
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.estimator_checks import check_estimator
 from sklearn.utils.validation import check_is_fitted
 from typing_extensions import TypedDict
 
-from mapie.estimator.estimator_classification import EnsembleClassifier
+from mapie.estimator.estimator_classifier import EnsembleClassifier
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
 from mapie.metrics import classification_coverage_score
@@ -33,29 +32,10 @@
 WRONG_METHODS = ["scores", "cumulated", "test", "", 1, 2.5, (1, 2)]
 WRONG_INCLUDE_LABELS = ["randomised", "True", "False", "other", 1, 2.5, (1, 2)]
 Y_PRED_PROBA_WRONG = [
-    np.array(
-        [
-            [0.8, 0.01, 0.1, 0.05],
-            [1.0, 0.1, 0.0, 0.0]
-        ]
-    ),
-    np.array(
-        [
-            [1.0, 0.0001, 0.0]
-        ]
-    ),
-    np.array(
-        [
-            [0.8, 0.1, 0.05, 0.05],
-            [0.9, 0.01, 0.04, 0.06]
-        ]
-    ),
-    np.array(
-        [
-            [0.8, 0.1, 0.02, 0.05],
-            [0.9, 0.01, 0.03, 0.06]
-        ]
-    )
+    np.array([[0.8, 0.01, 0.1, 0.05], [1.0, 0.1, 0.0, 0.0]]),
+    np.array([[1.0, 0.0001, 0.0]]),
+    np.array([[0.8, 0.1, 0.05, 0.05], [0.9, 0.01, 0.04, 0.06]]),
+    np.array([[0.8, 0.1, 0.02, 0.05], [0.9, 0.01, 0.03, 0.06]]),
 ]
 
 Params = TypedDict(
@@ -64,391 +44,163 @@
         "method": str,
         "cv": Optional[Union[int, str]],
         "test_size": Optional[Union[int, float]],
-        "random_state": Optional[int]
-    }
+        "random_state": Optional[int],
+    },
 )
 ParamsPredict = TypedDict(
-    "ParamsPredict",
-    {
-        "include_last_label": Union[bool, str],
-        "agg_scores": str
-    }
+    "ParamsPredict", {"include_last_label": Union[bool, str], "agg_scores": str}
 )
 
 STRATEGIES = {
     "lac": (
-        Params(
-            method="lac",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="lac", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "lac_split": (
-        Params(
-            method="lac",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="lac", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "lac_cv_mean": (
-        Params(
-            method="lac",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="lac", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "lac_cv_crossval": (
-        Params(
-            method="lac",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="crossval"
-        )
+        Params(method="lac", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="crossval"),
     ),
     "aps_include": (
-        Params(
-            method="aps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="aps", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "aps_not_include": (
-        Params(
-            method="aps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="aps", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "aps_randomized": (
-        Params(
-            method="aps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
+        Params(method="aps", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
     ),
     "aps_include_split": (
-        Params(
-            method="aps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="aps", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "aps_not_include_split": (
-        Params(
-            method="aps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="aps", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "aps_randomized_split": (
-        Params(
-            method="aps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
+        Params(method="aps", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
     ),
     "aps_include_cv_mean": (
-        Params(
-            method="aps",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="aps", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "aps_not_include_cv_mean": (
-        Params(
-            method="aps",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="aps", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "aps_randomized_cv_mean": (
-        Params(
-            method="aps",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
+        Params(method="aps", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
     ),
     "aps_include_cv_crossval": (
-        Params(
-            method="aps",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="crossval"
-        )
+        Params(method="aps", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="crossval"),
     ),
     "aps_not_include_cv_crossval": (
-        Params(
-            method="aps",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="crossval"
-        )
+        Params(method="aps", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=False, agg_scores="crossval"),
     ),
     "aps_randomized_cv_crossval": (
-        Params(
-            method="aps",
-            cv=3,
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="crossval"
-        )
+        Params(method="aps", cv=3, test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label="randomized", agg_scores="crossval"),
     ),
     "naive": (
-        Params(
-            method="naive",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="naive", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "naive_split": (
-        Params(
-            method="naive",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="naive", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "top_k": (
-        Params(
-            method="top_k",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="top_k", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "top_k_split": (
-        Params(
-            method="top_k",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="top_k", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "raps": (
-        Params(
-            method="raps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="raps", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "raps_split": (
-        Params(
-            method="raps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
+        Params(method="raps", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label=True, agg_scores="mean"),
     ),
     "raps_randomized": (
-        Params(
-            method="raps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
+        Params(method="raps", cv="prefit", test_size=None, random_state=random_state),
+        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
     ),
     "raps_randomized_split": (
-        Params(
-            method="raps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
+        Params(method="raps", cv="split", test_size=0.5, random_state=random_state),
+        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
     ),
 }
 
 STRATEGIES_BINARY = {
     "lac": (
-        Params(
-            method="lac",
-            cv="prefit",
-            test_size=None,
-            random_state=42
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="lac", cv="prefit", test_size=None, random_state=42),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "lac_split": (
-        Params(
-            method="lac",
-            cv="split",
-            test_size=0.5,
-            random_state=42
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="lac", cv="split", test_size=0.5, random_state=42),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "lac_cv_mean": (
-        Params(
-            method="lac",
-            cv=3,
-            test_size=None,
-            random_state=42
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
+        Params(method="lac", cv=3, test_size=None, random_state=42),
+        ParamsPredict(include_last_label=False, agg_scores="mean"),
     ),
     "lac_cv_crossval": (
-        Params(
-            method="lac",
-            cv=3,
-            test_size=None,
-            random_state=42
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="crossval"
-        )
-    )
+        Params(method="lac", cv=3, test_size=None, random_state=42),
+        ParamsPredict(include_last_label=False, agg_scores="crossval"),
+    ),
 }
 
 COVERAGES = {
-    "lac": 6/9,
-    "lac_split": 8/9,
+    "lac": 6 / 9,
+    "lac_split": 8 / 9,
     "lac_cv_mean": 1.0,
     "lac_cv_crossval": 1.0,
     "aps_include": 1.0,
-    "aps_not_include": 5/9,
-    "aps_randomized": 6/9,
-    "aps_include_split": 8/9,
-    "aps_not_include_split": 5/9,
-    "aps_randomized_split": 7/9,
+    "aps_not_include": 5 / 9,
+    "aps_randomized": 6 / 9,
+    "aps_include_split": 8 / 9,
+    "aps_not_include_split": 5 / 9,
+    "aps_randomized_split": 7 / 9,
     "aps_include_cv_mean": 1.0,
-    "aps_not_include_cv_mean": 5/9,
-    "aps_randomized_cv_mean": 8/9,
-    "aps_include_cv_crossval": 4/9,
-    "aps_not_include_cv_crossval": 1/9,
-    "aps_randomized_cv_crossval": 7/9,
-    "naive": 5/9,
-    "naive_split": 5/9,
+    "aps_not_include_cv_mean": 5 / 9,
+    "aps_randomized_cv_mean": 8 / 9,
+    "aps_include_cv_crossval": 4 / 9,
+    "aps_not_include_cv_crossval": 1 / 9,
+    "aps_randomized_cv_crossval": 7 / 9,
+    "naive": 5 / 9,
+    "naive_split": 5 / 9,
     "top_k": 1.0,
     "top_k_split": 1.0,
     "raps": 1.0,
-    "raps_split": 7/9,
-    "raps_randomized": 8/9,
-    "raps_randomized_split": 1.0
+    "raps_split": 7 / 9,
+    "raps_randomized": 8 / 9,
+    "raps_randomized_split": 1.0,
 }
 
 COVERAGES_BINARY = {
-    "lac": 6/9,
-    "lac_split": 8/9,
-    "lac_cv_mean": 6/9,
-    "lac_cv_crossval": 6/9
+    "lac": 6 / 9,
+    "lac_split": 8 / 9,
+    "lac_cv_mean": 6 / 9,
+    "lac_cv_crossval": 6 / 9,
 }
 
 X_toy = np.arange(9).reshape(-1, 1)
@@ -465,7 +217,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "lac_split": [
         [True, True, False],
@@ -487,7 +239,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "lac_cv_crossval": [
         [True, False, False],
@@ -498,7 +250,7 @@
         [False, True, False],
         [False, True, True],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "aps_include": [
         [True, False, False],
@@ -509,7 +261,7 @@
         [False, True, False],
         [False, True, True],
         [False, True, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_not_include": [
         [True, False, False],
@@ -520,7 +272,7 @@
         [False, True, False],
         [False, True, False],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_randomized": [
         [True, False, False],
@@ -531,7 +283,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_include_split": [
         [True, True, False],
@@ -542,7 +294,7 @@
         [True, True, True],
         [False, True, True],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_not_include_split": [
         [False, True, False],
@@ -553,7 +305,7 @@
         [False, True, True],
         [False, False, True],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_randomized_split": [
         [False, True, False],
@@ -564,7 +316,7 @@
         [False, True, True],
         [False, False, True],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_include_cv_mean": [
         [True, False, False],
@@ -575,7 +327,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "aps_not_include_cv_mean": [
         [True, False, False],
@@ -586,7 +338,7 @@
         [False, True, False],
         [False, True, False],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "aps_randomized_cv_mean": [
         [True, False, False],
@@ -597,7 +349,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "aps_include_cv_crossval": [
         [False, False, False],
@@ -608,7 +360,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, False],
-        [False, False, False]
+        [False, False, False],
     ],
     "aps_not_include_cv_crossval": [
         [False, False, False],
@@ -619,7 +371,7 @@
         [False, False, False],
         [False, False, False],
         [False, False, False],
-        [False, False, False]
+        [False, False, False],
     ],
     "aps_randomized_cv_crossval": [
         [True, False, False],
@@ -630,7 +382,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, False],
-        [False, False, True]
+        [False, False, True],
     ],
     "naive": [
         [True, False, False],
@@ -641,7 +393,7 @@
         [False, True, False],
         [False, True, False],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "naive_split": [
         [False, True, False],
@@ -652,7 +404,7 @@
         [False, True, True],
         [False, False, True],
         [False, False, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "top_k": [
         [True, True, False],
@@ -663,7 +415,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "top_k_split": [
         [True, True, False],
@@ -674,7 +426,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "raps": [
         [True, False, False],
@@ -685,7 +437,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True]
+        [False, True, True],
     ],
     "raps_split": [
         [True, True, False],
@@ -696,7 +448,7 @@
         [True, True, False],
         [True, True, False],
         [True, True, False],
-        [True, True, False]
+        [True, True, False],
     ],
     "raps_randomized": [
         [True, False, False],
@@ -707,7 +459,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, False, True]
+        [False, False, True],
     ],
     "raps_randomized_split": [
         [True, True, True],
@@ -718,8 +470,8 @@
         [True, True, True],
         [True, True, True],
         [True, True, True],
-        [True, True, True]
-    ]
+        [True, True, True],
+    ],
 }
 
 X_toy_binary = np.arange(9).reshape(-1, 1)
@@ -735,7 +487,7 @@
         [False, True],
         [False, True],
         [False, True],
-        [False, True]
+        [False, True],
     ],
     "lac_split": [
         [True, True],
@@ -746,7 +498,7 @@
         [True, True],
         [True, True],
         [True, True],
-        [True, False]
+        [True, False],
     ],
     "lac_cv_mean": [
         [True, False],
@@ -757,7 +509,7 @@
         [False, True],
         [False, True],
         [False, True],
-        [False, True]
+        [False, True],
     ],
     "lac_cv_crossval": [
         [True, False],
@@ -768,15 +520,11 @@
         [False, True],
         [False, True],
         [False, True],
-        [False, True]
-    ]
+        [False, True],
+    ],
 }
 
-REGULARIZATION_PARAMETERS = [
-    [.001, [1]],
-    [[.01, .2], [1, 3]],
-    [.1, [2, 4]]
-]
+REGULARIZATION_PARAMETERS = [[0.001, [1]], [[0.01, 0.2], [1, 3]], [0.1, [2, 4]]]
 
 IMAGE_INPUT = [
     {
@@ -790,7 +538,7 @@
     {
         "X_calib": np.zeros((3, 256, 512)),
         "X_test": np.ones((3, 256, 512)),
-    }
+    },
 ]
 
 X_good_image = np.zeros((3, 1024, 1024, 3))
@@ -820,7 +568,7 @@ def __init__(self) -> None:
                 [True, True, False],
                 [False, True, False],
                 [False, True, True],
-                [True, True, False]
+                [True, True, False],
             ]
         )
         self.classes_ = self.y_calib
@@ -834,12 +582,10 @@ def predict(self, X: ArrayLike) -> NDArray:
 
     def predict_proba(self, X: ArrayLike) -> NDArray:
         if np.max(X) <= 2:
-            return np.array(
-                [[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]]
-            )
+            return np.array([[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]])
         else:
             return np.array(
-                [[0.2, 0.7, 0.1], [0., 1., 0.], [0., .7, 0.3], [0.3, .7, 0.]]
+                [[0.2, 0.7, 0.1], [0.0, 1.0, 0.0], [0.0, 0.7, 0.3], [0.3, 0.7, 0.0]]
             )
 
 
@@ -866,13 +612,9 @@ def predict(self, *args: Any) -> NDArray:
 
     def predict_proba(self, X: ArrayLike) -> NDArray:
         if np.max(X) == 0:
-            return np.array(
-                [[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]]
-            )
+            return np.array([[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]])
         else:
-            return np.array(
-                [[0.2, 0.7, 0.1], [0.1, 0.2, 0.7], [0.3, 0.5, 0.2]]
-            )
+            return np.array([[0.2, 0.7, 0.1], [0.1, 0.2, 0.7], [0.3, 0.5, 0.2]])
 
 
 class WrongOutputModel:
@@ -889,9 +631,7 @@ def predict_proba(self, *args: Any) -> NDArray:
         return self.proba_out
 
     def predict(self, *args: Any) -> NDArray:
-        pred = (
-            self.proba_out == self.proba_out.max(axis=1)[:, None]
-        ).astype(int)
+        pred = (self.proba_out == self.proba_out.max(axis=1)[:, None]).astype(int)
         return pred
 
 
@@ -906,7 +646,7 @@ def fit(self, *args: Any) -> None:
         self.trained_ = True
 
     def predict_proba(self, X: NDArray, *args: Any) -> NDArray:
-        probas = np.array([[.9, .05, .05]])
+        probas = np.array([[0.9, 0.05, 0.05]])
         proba_out = np.repeat(probas, len(X), axis=0).astype(np.float32)
         return proba_out
 
@@ -942,9 +682,7 @@ def test_default_parameters() -> None:
 @pytest.mark.parametrize("method", ["aps", "raps"])
 def test_warning_binary_classif(cv: str, method: str) -> None:
     """Test that a warning is raised y is binary."""
-    mapie_clf = MapieClassifier(
-      cv=cv, method=method, random_state=random_state
-    )
+    mapie_clf = MapieClassifier(cv=cv, method=method, random_state=random_state)
     X, y = make_classification(
         n_samples=500,
         n_features=10,
@@ -952,9 +690,7 @@ def test_warning_binary_classif(cv: str, method: str) -> None:
         n_classes=2,
         random_state=random_state,
     )
-    with pytest.raises(
-        ValueError, match=r".*Invalid method for binary target.*"
-    ):
+    with pytest.raises(ValueError, match=r".*Invalid method for binary target.*"):
         mapie_clf.fit(X, y)
 
 
@@ -986,24 +722,28 @@ def test_valid_estimator(strategy: str) -> None:
 @pytest.mark.parametrize("method", METHODS)
 def test_valid_method(method: str) -> None:
     """Test that valid methods raise no errors."""
-    mapie_clf = MapieClassifier(
-        method=method, cv="prefit", random_state=random_state
-    )
+    mapie_clf = MapieClassifier(method=method, cv="prefit", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     check_is_fitted(mapie_clf, mapie_clf.fit_attributes)
 
 
 @pytest.mark.parametrize(
-    "cv", [None, -1, 2, KFold(), LeaveOneOut(), "prefit",
-           ShuffleSplit(n_splits=1, test_size=0.5, random_state=random_state)]
+    "cv",
+    [
+        None,
+        -1,
+        2,
+        KFold(),
+        LeaveOneOut(),
+        "prefit",
+        ShuffleSplit(n_splits=1, test_size=0.5, random_state=random_state),
+    ],
 )
 def test_valid_cv(cv: Any) -> None:
     """Test that valid cv raises no errors."""
     model = LogisticRegression(multi_class="multinomial")
     model.fit(X_toy, y_toy)
-    mapie_clf = MapieClassifier(
-        estimator=model, cv=cv, random_state=random_state
-    )
+    mapie_clf = MapieClassifier(estimator=model, cv=cv, random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.predict(X_toy, alpha=0.5)
 
@@ -1011,9 +751,7 @@ def test_valid_cv(cv: Any) -> None:
 @pytest.mark.parametrize("agg_scores", ["mean", "crossval"])
 def test_agg_scores_argument(agg_scores: str) -> None:
     """Test that predict passes with all valid 'agg_scores' arguments."""
-    mapie_clf = MapieClassifier(
-        cv=3, method="lac", random_state=random_state
-    )
+    mapie_clf = MapieClassifier(cv=3, method="lac", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.predict(X_toy, alpha=0.5, agg_scores=agg_scores)
 
@@ -1021,13 +759,9 @@ def test_agg_scores_argument(agg_scores: str) -> None:
 @pytest.mark.parametrize("agg_scores", ["median", 1, None])
 def test_invalid_agg_scores_argument(agg_scores: str) -> None:
     """Test that invalid 'agg_scores' raise errors."""
-    mapie_clf = MapieClassifier(
-        cv=3, method="lac", random_state=random_state
-    )
+    mapie_clf = MapieClassifier(cv=3, method="lac", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
-    with pytest.raises(
-        ValueError, match=r".*Invalid 'agg_scores' argument.*"
-    ):
+    with pytest.raises(ValueError, match=r".*Invalid 'agg_scores' argument.*"):
         mapie_clf.predict(X_toy, alpha=0.5, agg_scores=agg_scores)
 
 
@@ -1043,27 +777,21 @@ def test_too_large_cv(cv: Any) -> None:
 
 
 @pytest.mark.parametrize(
-    "include_last_label",
-    [-3.14, 1.5, -2, 0, 1, "cv", DummyClassifier(), [1, 2]]
+    "include_last_label", [-3.14, 1.5, -2, 0, 1, "cv", DummyClassifier(), [1, 2]]
 )
 def test_invalid_include_last_label(include_last_label: Any) -> None:
     """Test that invalid include_last_label raise errors."""
     mapie_clf = MapieClassifier(random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
-    with pytest.raises(
-        ValueError, match=r".*Invalid include_last_label argument.*"
-    ):
-        mapie_clf.predict(
-            X_toy,
-            y_toy,
-            include_last_label=include_last_label
-        )
+    with pytest.raises(ValueError, match=r".*Invalid include_last_label argument.*"):
+        mapie_clf.predict(X_toy, y_toy, include_last_label=include_last_label)
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_predict_output_shape(
-    strategy: str, alpha: Any,
+    strategy: str,
+    alpha: Any,
 ) -> None:
     """Test predict output shape."""
     args_init, args_predict = STRATEGIES[strategy]
@@ -1073,7 +801,7 @@ def test_predict_output_shape(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
@@ -1083,26 +811,25 @@ def test_predict_output_shape(
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_y_is_list_of_string(
-    strategy: str, alpha: Any,
+    strategy: str,
+    alpha: Any,
 ) -> None:
     """Test predict output shape with string y."""
     args_init, args_predict = STRATEGIES[strategy]
     mapie_clf = MapieClassifier(**args_init)
-    mapie_clf.fit(X, y.astype('str'))
+    mapie_clf.fit(X, y.astype("str"))
     y_pred, y_ps = mapie_clf.predict(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
     assert y_ps.shape == (X.shape[0], len(np.unique(y)), n_alpha)
 
 
-@pytest.mark.parametrize(
-    "strategy", ["naive", "top_k", "lac", "aps_include"]
-)
+@pytest.mark.parametrize("strategy", ["naive", "top_k", "lac", "aps_include"])
 def test_same_results_prefit_split(strategy: str) -> None:
     """
     Test checking that if split and prefit method have exactly
@@ -1120,7 +847,7 @@ def test_same_results_prefit_split(strategy: str) -> None:
     X_train_, X_calib_ = X[train_index], X[val_index]
     y_train_, y_calib_ = y[train_index], y[val_index]
 
-    args_init, args_predict = deepcopy(STRATEGIES[strategy + '_split'])
+    args_init, args_predict = deepcopy(STRATEGIES[strategy + "_split"])
     args_init["cv"] = cv
     mapie_reg = MapieClassifier(**args_init)
     mapie_reg.fit(X, y)
@@ -1140,13 +867,14 @@ def test_same_results_prefit_split(strategy: str) -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_same_result_y_numeric_and_string(
-    strategy: str, alpha: Any,
+    strategy: str,
+    alpha: Any,
 ) -> None:
     """Test that MAPIE outputs the same results if y is
     numeric or string"""
     args_init, args_predict = STRATEGIES[strategy]
     mapie_clf_str = MapieClassifier(**args_init)
-    mapie_clf_str.fit(X, y.astype('str'))
+    mapie_clf_str.fit(X, y.astype("str"))
     mapie_clf_int = MapieClassifier(**args_init)
     mapie_clf_int.fit(X, y)
     _, y_ps_str = mapie_clf_str.predict(
@@ -1159,7 +887,7 @@ def test_same_result_y_numeric_and_string(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_ps_int, y_ps_str)
 
@@ -1167,7 +895,8 @@ def test_same_result_y_numeric_and_string(
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_y_1_to_l_minus_1(
-    strategy: str, alpha: Any,
+    strategy: str,
+    alpha: Any,
 ) -> None:
     """Test predict output shape with string y."""
     args_init, args_predict = STRATEGIES[strategy]
@@ -1177,7 +906,7 @@ def test_y_1_to_l_minus_1(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
@@ -1187,7 +916,8 @@ def test_y_1_to_l_minus_1(
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_same_result_y_numeric_and_1_to_l_minus_1(
-    strategy: str, alpha: Any,
+    strategy: str,
+    alpha: Any,
 ) -> None:
     """Test that MAPIE outputs the same results if y is
     numeric or string"""
@@ -1206,7 +936,7 @@ def test_same_result_y_numeric_and_1_to_l_minus_1(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_ps_int, y_ps_1)
 
@@ -1224,19 +954,15 @@ def test_results_for_same_alpha(strategy: str) -> None:
         X,
         alpha=[0.1, 0.1],
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_ps[:, 0, 0], y_ps[:, 0, 1])
     np.testing.assert_allclose(y_ps[:, 1, 0], y_ps[:, 1, 1])
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-@pytest.mark.parametrize(
-    "alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)]
-)
-def test_results_for_alpha_as_float_and_arraylike(
-    strategy: str, alpha: Any
-) -> None:
+@pytest.mark.parametrize("alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)])
+def test_results_for_alpha_as_float_and_arraylike(strategy: str, alpha: Any) -> None:
     """Test that output values do not depend on type of alpha."""
     args_init, args_predict = STRATEGIES[strategy]
     mapie_clf = MapieClassifier(**args_init)
@@ -1245,19 +971,19 @@ def test_results_for_alpha_as_float_and_arraylike(
         X,
         alpha=alpha[0],
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred_float2, y_ps_float2 = mapie_clf.predict(
         X,
         alpha=alpha[1],
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred_array, y_ps_array = mapie_clf.predict(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_pred_float1, y_pred_array)
     np.testing.assert_allclose(y_pred_float2, y_pred_array)
@@ -1280,22 +1006,20 @@ def test_results_single_and_multi_jobs(strategy: str) -> None:
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred_multi, y_ps_multi = mapie_clf_multi.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_pred_single, y_pred_multi)
     np.testing.assert_allclose(y_ps_single, y_ps_multi)
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-def test_results_with_constant_sample_weights(
-    strategy: str
-) -> None:
+def test_results_with_constant_sample_weights(strategy: str) -> None:
     """
     Test predictions when sample weights are None
     or constant with different values.
@@ -1314,19 +1038,19 @@ def test_results_with_constant_sample_weights(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred1, y_ps1 = mapie_clf1.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred2, y_ps2 = mapie_clf2.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_pred0, y_pred1)
     np.testing.assert_allclose(y_pred0, y_pred2)
@@ -1354,19 +1078,19 @@ def test_results_with_constant_groups(strategy: str) -> None:
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred1, y_ps1 = mapie_clf1.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     y_pred2, y_ps2 = mapie_clf2.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_pred0, y_pred1)
     np.testing.assert_allclose(y_pred0, y_pred2)
@@ -1409,22 +1133,18 @@ def test_results_with_groups() -> None:
     # [(array([0, 1, 3, 4]), array([2, 5])),
     #  (array([0, 2, 3, 5]), array([1, 4])),
     #  (array([1, 2, 4, 5]), array([0, 3]))]
-    conformity_scores_0 = np.array([[1.], [0.], [0.], [1.], [1.], [1.]])
-    conformity_scores_1 = np.array([[1.], [1.], [1.], [1.], [1.], [1.]])
+    conformity_scores_0 = np.array([[1.0], [0.0], [0.0], [1.0], [1.0], [1.0]])
+    conformity_scores_1 = np.array([[1.0], [1.0], [1.0], [1.0], [1.0], [1.0]])
     assert np.array_equal(mapie0.conformity_scores_, conformity_scores_0)
     assert np.array_equal(mapie1.conformity_scores_, conformity_scores_1)
 
 
-@pytest.mark.parametrize(
-    "alpha", [[0.2, 0.8], (0.2, 0.8), np.array([0.2, 0.8]), None]
-)
+@pytest.mark.parametrize("alpha", [[0.2, 0.8], (0.2, 0.8), np.array([0.2, 0.8]), None])
 def test_valid_prediction(alpha: Any) -> None:
     """Test fit and predict."""
     model = LogisticRegression(multi_class="multinomial")
     model.fit(X_toy, y_toy)
-    mapie_clf = MapieClassifier(
-        estimator=model, cv="prefit", random_state=random_state
-    )
+    mapie_clf = MapieClassifier(estimator=model, cv="prefit", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.predict(X_toy, alpha=alpha)
 
@@ -1440,12 +1160,12 @@ def test_toy_dataset_predictions(strategy: str) -> None:
     else:
         clf = LogisticRegression()
     mapie_clf = MapieClassifier(estimator=clf, **args_init)
-    mapie_clf.fit(X_toy, y_toy, size_raps=.5)
+    mapie_clf.fit(X_toy, y_toy, size_raps=0.5)
     _, y_ps = mapie_clf.predict(
         X_toy,
         alpha=0.5,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_ps[:, :, 0], y_toy_mapie[strategy])
     np.testing.assert_allclose(
@@ -1470,7 +1190,7 @@ def test_toy_binary_dataset_predictions(strategy: str) -> None:
         X_toy,
         alpha=0.5,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"]
+        agg_scores=args_predict["agg_scores"],
     )
     np.testing.assert_allclose(y_ps[:, :, 0], y_toy_binary_mapie[strategy])
     np.testing.assert_allclose(
@@ -1487,21 +1207,12 @@ def test_cumulated_scores() -> None:
     cumclf = CumulatedScoreClassifier()
     cumclf.fit(cumclf.X_calib, cumclf.y_calib)
     mapie_clf = MapieClassifier(
-        cumclf,
-        method="aps",
-        cv="prefit",
-        random_state=random_state
+        cumclf, method="aps", cv="prefit", random_state=random_state
     )
     mapie_clf.fit(cumclf.X_calib, cumclf.y_calib)
-    np.testing.assert_allclose(
-        mapie_clf.conformity_scores_, cumclf.y_calib_scores
-    )
+    np.testing.assert_allclose(mapie_clf.conformity_scores_, cumclf.y_calib_scores)
     # predict
-    _, y_ps = mapie_clf.predict(
-        cumclf.X_test,
-        include_last_label=True,
-        alpha=alpha
-    )
+    _, y_ps = mapie_clf.predict(cumclf.X_test, include_last_label=True, alpha=alpha)
     np.testing.assert_allclose(mapie_clf.quantiles_, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
@@ -1517,19 +1228,12 @@ def test_image_cumulated_scores(X: Dict[str, ArrayLike]) -> None:
     cumclf = ImageClassifier(X_calib, X_test)
     cumclf.fit(cumclf.X_calib, cumclf.y_calib)
     mapie = MapieClassifier(
-        cumclf,
-        method="aps",
-        cv="prefit",
-        random_state=random_state
+        cumclf, method="aps", cv="prefit", random_state=random_state
     )
     mapie.fit(cumclf.X_calib, cumclf.y_calib)
     np.testing.assert_allclose(mapie.conformity_scores_, cumclf.y_calib_scores)
     # predict
-    _, y_ps = mapie.predict(
-        cumclf.X_test,
-        include_last_label=True,
-        alpha=alpha
-    )
+    _, y_ps = mapie.predict(cumclf.X_test, include_last_label=True, alpha=alpha)
     np.testing.assert_allclose(mapie.quantiles_, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
@@ -1551,8 +1255,7 @@ def test_sum_proba_to_one_fit(y_pred_proba: NDArray) -> None:
 @pytest.mark.parametrize("y_pred_proba", Y_PRED_PROBA_WRONG)
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_sum_proba_to_one_predict(
-    y_pred_proba: NDArray,
-    alpha: Union[float, Iterable[float]]
+    y_pred_proba: NDArray, alpha: Union[float, Iterable[float]]
 ) -> None:
     """
     Test if when the output probabilities of the model do not
@@ -1571,9 +1274,7 @@ def test_sum_proba_to_one_predict(
 @pytest.mark.parametrize(
     "estimator", [LogisticRegression(), make_pipeline(LogisticRegression())]
 )
-def test_classifier_without_classes_attribute(
-    estimator: ClassifierMixin
-) -> None:
+def test_classifier_without_classes_attribute(estimator: ClassifierMixin) -> None:
     """
     Test that prefitted classifier without 'classes_ 'attribute raises error.
     """
@@ -1582,24 +1283,16 @@ def test_classifier_without_classes_attribute(
         delattr(estimator[-1], "classes_")
     else:
         delattr(estimator, "classes_")
-    mapie = MapieClassifier(
-        estimator=estimator, cv="prefit", random_state=random_state
-    )
-    with pytest.raises(
-        AttributeError, match=r".*does not contain 'classes_'.*"
-    ):
+    mapie = MapieClassifier(estimator=estimator, cv="prefit", random_state=random_state)
+    with pytest.raises(AttributeError, match=r".*does not contain 'classes_'.*"):
         mapie.fit(X_toy, y_toy)
 
 
 @pytest.mark.parametrize("method", WRONG_METHODS)
 def test_method_error_in_fit(monkeypatch: Any, method: str) -> None:
     """Test else condition for the method in .fit"""
-    monkeypatch.setattr(
-        MapieClassifier, "_check_parameters", do_nothing
-    )
-    mapie_clf = MapieClassifier(
-        method=method, random_state=random_state
-    )
+    monkeypatch.setattr(MapieClassifier, "_check_parameters", do_nothing)
+    mapie_clf = MapieClassifier(method=method, random_state=random_state)
     with pytest.raises(ValueError, match=r".*Invalid method.*"):
         mapie_clf.fit(X_toy, y_toy)
 
@@ -1608,9 +1301,7 @@ def test_method_error_in_fit(monkeypatch: Any, method: str) -> None:
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_method_error_in_predict(method: Any, alpha: float) -> None:
     """Test else condition for the method in .predict"""
-    mapie_clf = MapieClassifier(
-        method="lac", random_state=random_state
-    )
+    mapie_clf = MapieClassifier(method="lac", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.method = method
     with pytest.raises(ValueError, match=r".*Invalid method.*"):
@@ -1623,20 +1314,11 @@ def test_include_label_error_in_predict(
     monkeypatch: Any, include_labels: Union[bool, str], alpha: float
 ) -> None:
     """Test else condition for include_label parameter in .predict"""
-    monkeypatch.setattr(
-        MapieClassifier,
-        "_check_include_last_label",
-        do_nothing
-    )
-    mapie_clf = MapieClassifier(
-        method="aps", random_state=random_state
-    )
+    monkeypatch.setattr(MapieClassifier, "_check_include_last_label", do_nothing)
+    mapie_clf = MapieClassifier(method="aps", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     with pytest.raises(ValueError, match=r".*Invalid include.*"):
-        mapie_clf.predict(
-            X_toy, alpha=alpha,
-            include_last_label=include_labels
-        )
+        mapie_clf.predict(X_toy, alpha=alpha, include_last_label=include_labels)
 
 
 def test_pred_loof_isnan() -> None:
@@ -1669,14 +1351,12 @@ def test_pipeline_compatibility(strategy: str) -> None:
         ]
     )
     categorical_preprocessor = Pipeline(
-        steps=[
-            ("encoding", OneHotEncoder(handle_unknown="ignore"))
-        ]
+        steps=[("encoding", OneHotEncoder(handle_unknown="ignore"))]
     )
     preprocessor = ColumnTransformer(
         [
             ("cat", categorical_preprocessor, ["x_cat"]),
-            ("num", numeric_preprocessor, ["x_num"])
+            ("num", numeric_preprocessor, ["x_num"]),
         ]
     )
     pipe = make_pipeline(preprocessor, LogisticRegression())
@@ -1707,25 +1387,16 @@ def test_classif_float32(cv) -> None:
     to the highest probability, MAPIE would have return
     empty prediction sets"""
     X_cal, y_cal = make_classification(
-        n_samples=20,
-        n_features=20,
-        n_redundant=0,
-        n_informative=20,
-        n_classes=3
+        n_samples=20, n_features=20, n_redundant=0, n_informative=20, n_classes=3
     )
     X_test, _ = make_classification(
-        n_samples=20,
-        n_features=20,
-        n_redundant=0,
-        n_informative=20,
-        n_classes=3
+        n_samples=20, n_features=20, n_redundant=0, n_informative=20, n_classes=3
     )
-    alpha = .9
+    alpha = 0.9
     dummy_classif = Float32OuputModel()
 
     mapie = MapieClassifier(
-        estimator=dummy_classif, method="naive",
-        cv=cv, random_state=random_state
+        estimator=dummy_classif, method="naive", cv=cv, random_state=random_state
     )
     mapie.fit(X_cal, y_cal)
     _, yps = mapie.predict(X_test, alpha=alpha, include_last_label=True)
@@ -1761,15 +1432,11 @@ def test_get_true_label_cumsum_proba_shape() -> None:
     clf = LogisticRegression()
     clf.fit(X, y)
     y_pred = clf.predict_proba(X)
-    mapie_clf = MapieClassifier(
-        estimator=clf, random_state=random_state
-    )
+    mapie_clf = MapieClassifier(estimator=clf, random_state=random_state)
     mapie_clf.fit(X, y)
-    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(
-        y, y_pred
-    )
+    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(y, y_pred)
     assert cumsum_proba.shape == (len(X), 1)
-    assert cutoff.shape == (len(X), )
+    assert cutoff.shape == (len(X),)
 
 
 def test_get_true_label_cumsum_proba_result() -> None:
@@ -1780,26 +1447,24 @@ def test_get_true_label_cumsum_proba_result() -> None:
     clf = LogisticRegression()
     clf.fit(X_toy, y_toy)
     y_pred = clf.predict_proba(X_toy)
-    mapie_clf = MapieClassifier(
-        estimator=clf, random_state=random_state
-    )
+    mapie_clf = MapieClassifier(estimator=clf, random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
-    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(
-        y_toy, y_pred
-    )
+    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(y_toy, y_pred)
     np.testing.assert_allclose(
         cumsum_proba,
         np.array(
             [
-                y_pred[0, 0], y_pred[1, 0],
+                y_pred[0, 0],
+                y_pred[1, 0],
                 y_pred[2, 0] + y_pred[2, 1],
                 y_pred[3, 0] + y_pred[3, 1],
-                y_pred[4, 1], y_pred[5, 1],
+                y_pred[4, 1],
+                y_pred[5, 1],
                 y_pred[6, 1] + y_pred[6, 2],
                 y_pred[7, 1] + y_pred[7, 2],
-                y_pred[8, 2]
+                y_pred[8, 2],
             ]
-        )[:, np.newaxis]
+        )[:, np.newaxis],
     )
     np.testing.assert_allclose(cutoff, np.array([1, 1, 2, 2, 1, 1, 2, 2, 1]))
 
@@ -1813,22 +1478,19 @@ def test_get_last_included_proba_shape(k_lambda, strategy):
     """
     lambda_, k = k_lambda[0], k_lambda[1]
     if len(k) == 1:
-        thresholds = .2
+        thresholds = 0.2
     else:
         thresholds = np.random.rand(len(k))
     thresholds = cast(NDArray, check_alpha(thresholds))
     clf = LogisticRegression()
     clf.fit(X, y)
     y_pred_proba = clf.predict_proba(X)
-    y_pred_proba = np.repeat(
-        y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2
-    )
+    y_pred_proba = np.repeat(y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2)
 
     mapie = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     include_last_label = STRATEGIES[strategy][1]["include_last_label"]
     y_p_p_c, y_p_i_l, y_p_p_i_l = mapie._get_last_included_proba(
-        y_pred_proba, thresholds,
-        include_last_label, lambda_, k
+        y_pred_proba, thresholds, include_last_label, lambda_, k
     )
 
     assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))
@@ -1842,9 +1504,7 @@ def test_error_raps_cv_not_prefit(cv: Union[int, None]) -> None:
     Test that an error is raised if the method is RAPS
     and cv is different from prefit and split.
     """
-    mapie = MapieClassifier(
-        method="raps", cv=cv, random_state=random_state
-    )
+    mapie = MapieClassifier(method="raps", cv=cv, random_state=random_state)
     with pytest.raises(ValueError, match=r".*RAPS method can only.*"):
         mapie.fit(X_toy, y_toy)
 
@@ -1860,12 +1520,11 @@ def test_not_all_label_in_calib() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv="prefit", random_state=random_state
+        estimator=clf, method="aps", cv="prefit", random_state=random_state
     )
     mapie_clf.fit(X_mapie, y_mapie)
     y_pred, y_pss = mapie_clf.predict(X, alpha=0.5)
-    assert y_pred.shape == (len(X), )
+    assert y_pred.shape == (len(X),)
     assert y_pss.shape == (len(X), len(np.unique(y)), 1)
 
 
@@ -1879,12 +1538,9 @@ def test_warning_not_all_label_in_calib() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv="prefit", random_state=random_state
+        estimator=clf, method="aps", cv="prefit", random_state=random_state
     )
-    with pytest.warns(
-        UserWarning, match=r".*WARNING: your calibration dataset.*"
-    ):
+    with pytest.warns(UserWarning, match=r".*WARNING: your calibration dataset.*"):
         mapie_clf.fit(X_mapie, y_mapie)
 
 
@@ -1899,8 +1555,7 @@ def test_n_classes_prefit() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv="prefit", random_state=random_state
+        estimator=clf, method="aps", cv="prefit", random_state=random_state
     )
     mapie_clf.fit(X_mapie, y_mapie)
     assert mapie_clf.n_classes_ == len(np.unique(y))
@@ -1917,8 +1572,7 @@ def test_classes_prefit() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv="prefit", random_state=random_state
+        estimator=clf, method="aps", cv="prefit", random_state=random_state
     )
     mapie_clf.fit(X_mapie, y_mapie)
     assert (mapie_clf.classes_ == np.unique(y)).all()
@@ -1934,10 +1588,7 @@ def test_classes_encoder_same_than_model() -> None:
     indices_remove = np.where(y != 2)
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
-    mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv="prefit"
-    )
+    mapie_clf = MapieClassifier(estimator=clf, method="aps", cv="prefit")
     mapie_clf.fit(X_mapie, y_mapie)
     assert (mapie_clf.label_encoder_.classes_ == np.unique(y)).all()
 
@@ -1950,8 +1601,7 @@ def test_n_classes_cv() -> None:
     clf = LogisticRegression()
 
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv=5, random_state=random_state
+        estimator=clf, method="aps", cv=5, random_state=random_state
     )
     mapie_clf.fit(X, y)
     assert mapie_clf.n_classes_ == len(np.unique(y))
@@ -1965,8 +1615,7 @@ def test_classes_cv() -> None:
     clf = LogisticRegression()
 
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv=5, random_state=random_state
+        estimator=clf, method="aps", cv=5, random_state=random_state
     )
     mapie_clf.fit(X, y)
     assert (mapie_clf.classes_ == np.unique(y)).all()
@@ -1981,12 +1630,9 @@ def test_raise_error_new_class() -> None:
     clf.fit(X, y)
     y[-1] = 10
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps",
-        cv="prefit", random_state=random_state
+        estimator=clf, method="aps", cv="prefit", random_state=random_state
     )
-    with pytest.raises(
-        ValueError, match=r".*Values in y do not matched values.*"
-    ):
+    with pytest.raises(ValueError, match=r".*Values in y do not matched values.*"):
         mapie_clf.fit(X, y)
 
 
@@ -1998,12 +1644,9 @@ def test_deprecated_method_warning(method: str) -> None:
     clf = LogisticRegression()
     clf.fit(X_toy, y_toy)
     mapie_clf = MapieClassifier(
-        estimator=clf, method=method,
-        cv="prefit", random_state=random_state
+        estimator=clf, method=method, cv="prefit", random_state=random_state
     )
-    with pytest.warns(
-        DeprecationWarning, match=r".*WARNING: Deprecated method.*"
-    ):
+    with pytest.warns(DeprecationWarning, match=r".*WARNING: Deprecated method.*"):
         mapie_clf.fit(X_toy, y_toy)
 
 
@@ -2015,9 +1658,7 @@ def test_fit_parameters_passing() -> None:
     """
     gb = GradientBoostingClassifier(random_state=random_state)
 
-    mapie = MapieClassifier(
-        estimator=gb, method="aps", random_state=random_state
-    )
+    mapie = MapieClassifier(estimator=gb, method="aps", random_state=random_state)
 
     def early_stopping_monitor(i, est, locals):
         """Returns True on the 3rd iteration."""

From 722728bb1db1ee92ba93155872ff39432962417a Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 16:38:04 +0200
Subject: [PATCH 019/424] Modification of unit tests

---
 mapie/tests/test_classification.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 2cc291e74..f1fdbd1e6 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1669,7 +1669,7 @@ def early_stopping_monitor(i, est, locals):
 
     mapie.fit(X, y, monitor=early_stopping_monitor)
 
-    assert mapie.single_estimator_.estimators_.shape[0] == 3
+    assert mapie.estimator_.single_estimator_.estimators_.shape[0] == 3
 
-    for estimator in mapie.estimators_:
+    for estimator in mapie.estimator_.estimators_:
         assert estimator.estimators_.shape[0] == 3

From ad877fddb4ce1f51b0bbc8b006e188fb8e16ffe1 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 16:54:59 +0200
Subject: [PATCH 020/424] Modification of unit tests

---
 mapie/tests/test_classification.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index f1fdbd1e6..fec827631 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -716,7 +716,7 @@ def test_valid_estimator(strategy: str) -> None:
     clf = LogisticRegression().fit(X_toy, y_toy)
     mapie_clf = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     mapie_clf.fit(X_toy, y_toy)
-    assert isinstance(mapie_clf.single_estimator_, LogisticRegression)
+    assert isinstance(mapie_clf.estimator_.single_estimator_, LogisticRegression)
 
 
 @pytest.mark.parametrize("method", METHODS)

From e65a08157bdc6b28961f0568b4641fe73cb9c483 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 18:12:25 +0200
Subject: [PATCH 021/424] Update notebook links in regression, classification
 and multilabel_classification documentation

---
 doc/notebooks_classification.rst            | 8 ++++----
 doc/notebooks_multilabel_classification.rst | 8 ++++----
 doc/notebooks_regression.rst                | 8 ++++----
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/doc/notebooks_classification.rst b/doc/notebooks_classification.rst
index dc25e1ac2..35747de19 100755
--- a/doc/notebooks_classification.rst
+++ b/doc/notebooks_classification.rst
@@ -6,8 +6,8 @@ problems for computer vision settings that are too heavy to be included in the e
 galleries.
 
 
-1. Estimating prediction sets on the Cifar10 dataset : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/Cifar10.ipynb>`_
----------------------------------------------------------------------------------------------------------------------------------------------------------------------
+1. Estimating prediction sets on the Cifar10 dataset : `cifar_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/Cifar10.ipynb>`_
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
-2. Top-label calibration for outputs of ML models : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/top_label_calibration.ipynb>`_
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+2. Top-label calibration for outputs of ML models : `top_label_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/top_label_calibration.ipynb>`_
+------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
diff --git a/doc/notebooks_multilabel_classification.rst b/doc/notebooks_multilabel_classification.rst
index e9160169b..3826f7ff2 100644
--- a/doc/notebooks_multilabel_classification.rst
+++ b/doc/notebooks_multilabel_classification.rst
@@ -5,8 +5,8 @@ The following examples present advanced analyses
 on multi-label classification problems with different 
 methods proposed in MAPIE.
 
-1. Overview of Recall Control for Multi-Label Classification : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/tutorial_multilabel_classification_recall.ipynb>`_
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+1. Overview of Recall Control for Multi-Label Classification : `recall_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/tutorial_multilabel_classification_recall.ipynb>`_
+----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
-2. Overview of Precision Control for Multi-Label Classification : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/tutorial_multilabel_classification_precision.ipynb>`_
----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
\ No newline at end of file
+2. Overview of Precision Control for Multi-Label Classification : `precision_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/tutorial_multilabel_classification_precision.ipynb>`_
+-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
\ No newline at end of file
diff --git a/doc/notebooks_regression.rst b/doc/notebooks_regression.rst
index 4ac493fa8..24b8ce12e 100755
--- a/doc/notebooks_regression.rst
+++ b/doc/notebooks_regression.rst
@@ -8,11 +8,11 @@ This section lists a series of Jupyter notebooks hosted on the MAPIE Github repo
 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
 
-2. Estimating the uncertainties in the exoplanet masses : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/exoplanets.ipynb>`_
------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+2. Estimating the uncertainties in the exoplanet masses : `exoplanet_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/exoplanets.ipynb>`_
+---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
 
-3. Estimating prediction intervals for time series forecast with EnbPI and ACI : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/ts-changepoint.ipynb>`_
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
+3. Estimating prediction intervals for time series forecast with EnbPI and ACI : `ts_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/regression/ts-changepoint.ipynb>`_
+-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
 
 

From 9f8b451c54c7bf48f83dba551449ba490a6cad1a Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 18:12:36 +0200
Subject: [PATCH 022/424] chore: Add verbose mode to LGBMRegressor in
 plot_cqr_tutorial.py

---
 examples/regression/4-tutorials/plot_cqr_tutorial.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/regression/4-tutorials/plot_cqr_tutorial.py b/examples/regression/4-tutorials/plot_cqr_tutorial.py
index f370fa78f..5e92e4542 100644
--- a/examples/regression/4-tutorials/plot_cqr_tutorial.py
+++ b/examples/regression/4-tutorials/plot_cqr_tutorial.py
@@ -121,7 +121,8 @@ class :class:`~mapie.subsample.Subsample` (note that the `alpha` parameter is
 estimator = LGBMRegressor(
     objective='quantile',
     alpha=0.5,
-    random_state=random_state
+    random_state=random_state,
+    verbose=-1
 )
 params_distributions = dict(
     num_leaves=randint(low=10, high=50),
@@ -135,7 +136,6 @@ class :class:`~mapie.subsample.Subsample` (note that the `alpha` parameter is
     n_jobs=-1,
     n_iter=10,
     cv=KFold(n_splits=5, shuffle=True),
-    verbose=0,
     random_state=random_state
 )
 optim_model.fit(X_train, y_train)

From c7fd1bc88af9f3b3d12eb690e90ebae7f484be10 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 15 May 2024 18:12:59 +0200
Subject: [PATCH 023/424] Update theoretical description titles to reflect the
 specific type

---
 doc/theoretical_description_binary_classification.rst     | 2 +-
 doc/theoretical_description_classification.rst            | 4 +++-
 doc/theoretical_description_conformity_scores.rst         | 2 +-
 doc/theoretical_description_multilabel_classification.rst | 2 +-
 doc/theoretical_description_regression.rst                | 2 +-
 5 files changed, 7 insertions(+), 5 deletions(-)

diff --git a/doc/theoretical_description_binary_classification.rst b/doc/theoretical_description_binary_classification.rst
index 877bf83f4..55e2f6144 100644
--- a/doc/theoretical_description_binary_classification.rst
+++ b/doc/theoretical_description_binary_classification.rst
@@ -1,4 +1,4 @@
-.. title:: Theoretical Description : contents
+.. title:: Theoretical Description Binary Classification : contents
 
 .. _theoretical_description_binay_classification:
 
diff --git a/doc/theoretical_description_classification.rst b/doc/theoretical_description_classification.rst
index aa5c08060..a8ef17830 100644
--- a/doc/theoretical_description_classification.rst
+++ b/doc/theoretical_description_classification.rst
@@ -1,4 +1,4 @@
-.. title:: Theoretical Description : contents
+.. title:: Theoretical Description Classification : contents
 
 .. _theoretical_description_classification:
 
@@ -141,8 +141,10 @@ Despite the RAPS method having a relatively small set size, its coverage tends t
 of the last label in the prediction set. This randomization is done as follows:
 
 - First : define the :math:`V` parameter:
+
 .. math::
    V_i = (s_i(X_i, Y_i) - \hat{q}_{1-\alpha}) / \left(\hat{\mu}(X_i)_{\pi_k} + \lambda \mathbb{1} (k > k_{reg})\right)
+
 - Compare each :math:`V_i` to :math:`U \sim` Unif(0, 1)
 - If :math:`V_i \leq U`, the last included label is removed, else we keep the prediction set as it is.
 
diff --git a/doc/theoretical_description_conformity_scores.rst b/doc/theoretical_description_conformity_scores.rst
index b280fc530..8ea72b6ff 100644
--- a/doc/theoretical_description_conformity_scores.rst
+++ b/doc/theoretical_description_conformity_scores.rst
@@ -1,4 +1,4 @@
-.. title:: Theoretical Description : contents
+.. title:: Theoretical Description Conformity Scores : contents
 
 .. _theoretical_description_conformity_scores:
 
diff --git a/doc/theoretical_description_multilabel_classification.rst b/doc/theoretical_description_multilabel_classification.rst
index 23e0536c4..011061e00 100644
--- a/doc/theoretical_description_multilabel_classification.rst
+++ b/doc/theoretical_description_multilabel_classification.rst
@@ -1,4 +1,4 @@
-.. title:: Theoretical Description : contents
+.. title:: Theoretical Description Multi label Classification : contents
 
 .. _theoretical_description_multilabel_classification:
 
diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index ae4b7c346..c755975df 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -1,4 +1,4 @@
-.. title:: Theoretical Description : contents
+.. title:: Theoretical Description Regression : contents
 
 .. _theoretical_description_regression:
 

From 75bf335f814be6c2b70d1dcaae99af75a2804118 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 15 May 2024 18:51:52 +0200
Subject: [PATCH 024/424] Fix of unit test: test_pred_loof_isnan

---
 mapie/tests/test_classification.py | 13 ++++++-------
 1 file changed, 6 insertions(+), 7 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index fec827631..177bc31b4 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1324,14 +1324,13 @@ def test_include_label_error_in_predict(
 def test_pred_loof_isnan() -> None:
     """Test that if validation set is empty then prediction is empty."""
     mapie_clf = MapieClassifier(random_state=random_state)
-    _, y_pred, _, _ = mapie_clf._fit_and_predict_oof_model(
-        estimator=LogisticRegression(),
-        X=X_toy,
-        y=y_toy,
-        train_index=[0, 1, 2, 3, 4],
-        val_index=[],
-        k=0,
+
+    y_pred: NDArray
+    mapie_clf = mapie_clf.fit(X, y)
+    y_pred, _, _ = mapie_clf.estimator_._predict_proba_calib_oof_estimator(
+        estimator=LogisticRegression(), X=X_toy, val_index=[], k=0
     )
+
     assert len(y_pred) == 0
 
 

From e9c311662667dee312c790d4d51068a8800d5e3e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 16 May 2024 09:32:18 +0000
Subject: [PATCH 025/424] UPD: clean code

---
 mapie/classification.py                 | 363 ++++++----
 mapie/estimator/estimator_classifier.py |  55 +-
 mapie/estimator/estimator_regressor.py  |  66 +-
 mapie/tests/test_classification.py      | 849 +++++++++++++++++-------
 mapie/tests/test_regression.py          | 131 ++--
 5 files changed, 993 insertions(+), 471 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index a1e884cbc..eaa5398b8 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -8,28 +8,21 @@
 from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
 from sklearn.preprocessing import LabelEncoder, label_binarize
 from sklearn.utils import _safe_indexing, check_random_state
-from sklearn.utils.multiclass import check_classification_targets, type_of_target
-from sklearn.utils.validation import _check_y, _num_samples, check_is_fitted, indexable
+from sklearn.utils.multiclass import (check_classification_targets,
+                                      type_of_target)
+from sklearn.utils.validation import (_check_y, _num_samples, check_is_fitted,
+                                      indexable)
 
 from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
 from .estimator.estimator_classifier import EnsembleClassifier
 from .metrics import classification_mean_width_score
-from .utils import (
-    check_alpha,
-    check_alpha_and_n_samples,
-    check_cv,
-    check_estimator_classification,
-    check_n_features_in,
-    check_n_jobs,
-    check_null_weight,
-    check_verbose,
-    compute_quantiles,
-)
-
-
-from mapie.conformity_scores.utils_classification_conformity_scores import (
-    get_true_label_position,
+from .utils import (check_alpha, check_alpha_and_n_samples, check_cv,
+                    check_estimator_classification, check_n_features_in,
+                    check_n_jobs, check_null_weight, check_verbose,
+                    compute_quantiles)
+from .conformity_scores.utils_classification_conformity_scores import (
+    get_true_label_position
 )
 
 
@@ -146,8 +139,8 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     valid_methods: List[str]
         List of all valid methods.
 
-    single_estimator_: sklearn.ClassifierMixin
-        Estimator fitted on the whole training set.
+    estimator_: EnsembleClassifier
+        Sklearn estimator that handle all that is related to the estimator.
 
     n_features_in_: int
         Number of features passed to the fit method.
@@ -197,19 +190,13 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
     raps_valid_cv_ = ["prefit", "split"]
     valid_methods_ = [
-        "naive",
-        "score",
-        "lac",
-        "cumulated_score",
-        "aps",
-        "top_k",
-        "raps",
+        "naive", "score", "lac", "cumulated_score", "aps", "top_k", "raps"
     ]
     fit_attributes = [
         "n_features_in_",
         "conformity_scores_",
         "classes_",
-        "label_encoder_",
+        "label_encoder_"
     ]
 
     def __init__(
@@ -220,7 +207,7 @@ def __init__(
         test_size: Optional[Union[int, float]] = None,
         n_jobs: Optional[int] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
-        verbose: int = 0,
+        verbose: int = 0
     ) -> None:
         self.estimator = estimator
         self.method = method
@@ -241,7 +228,8 @@ def _check_parameters(self) -> None:
         """
         if self.method not in self.valid_methods_:
             raise ValueError(
-                "Invalid method. " f"Allowed values are {self.valid_methods_}."
+                "Invalid method. "
+                f"Allowed values are {self.valid_methods_}."
             )
         check_n_jobs(self.n_jobs)
         check_verbose(self.verbose)
@@ -262,18 +250,18 @@ def _check_depreciated(self) -> None:
         if self.method == "score":
             warnings.warn(
                 "WARNING: Deprecated method. "
-                + 'The method "score" is outdated. '
-                + 'Prefer to use "lac" instead to keep '
+                + "The method \"score\" is outdated. "
+                + "Prefer to use \"lac\" instead to keep "
                 + "the same behavior in the next release.",
-                DeprecationWarning,
+                DeprecationWarning
             )
         if self.method == "cumulated_score":
             warnings.warn(
                 "WARNING: Deprecated method. "
-                + 'The method "cumulated_score" is outdated. '
-                + 'Prefer to use "aps" instead to keep '
+                + "The method \"cumulated_score\" is outdated. "
+                + "Prefer to use \"aps\" instead to keep "
                 + "the same behavior in the next release.",
-                DeprecationWarning,
+                DeprecationWarning
             )
 
     def _check_target(self, y: ArrayLike) -> None:
@@ -293,7 +281,8 @@ def _check_target(self, y: ArrayLike) -> None:
             or ``"score"`` or if type of target is not multi-class.
         """
         check_classification_targets(y)
-        if type_of_target(y) == "binary" and self.method not in ["score", "lac"]:
+        if type_of_target(y) == "binary" and \
+                self.method not in ["score", "lac"]:
             raise ValueError(
                 "Invalid method for binary target. "
                 "Your target is not of type multiclass and "
@@ -312,14 +301,17 @@ def _check_raps(self):
             If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
         """
         if (self.method == "raps") and (
-            (self.cv not in self.raps_valid_cv_) or isinstance(self.cv, ShuffleSplit)
+            (self.cv not in self.raps_valid_cv_)
+            or isinstance(self.cv, ShuffleSplit)
         ):
             raise ValueError(
-                "RAPS method can only be used " f"with cv in {self.raps_valid_cv_}."
+                "RAPS method can only be used "
+                f"with cv in {self.raps_valid_cv_}."
             )
 
     def _check_include_last_label(
-        self, include_last_label: Optional[Union[bool, str]]
+        self,
+        include_last_label: Optional[Union[bool, str]]
     ) -> Optional[Union[bool, str]]:
         """
         Check if ``include_last_label`` is a boolean or a string.
@@ -350,8 +342,9 @@ def _check_include_last_label(
             "Invalid include_last_label argument. "
             "Should be a boolean or 'randomized'."
         """
-        if (not isinstance(include_last_label, bool)) and (
-            not include_last_label == "randomized"
+        if (
+            (not isinstance(include_last_label, bool)) and
+            (not include_last_label == "randomized")
         ):
             raise ValueError(
                 "Invalid include_last_label argument. "
@@ -361,7 +354,9 @@ def _check_include_last_label(
             return include_last_label
 
     def _check_proba_normalized(
-        self, y_pred_proba: ArrayLike, axis: int = 1
+        self,
+        y_pred_proba: ArrayLike,
+        axis: int = 1
     ) -> NDArray:
         """
         Check if, for all the observations, the sum of
@@ -389,7 +384,7 @@ def _check_proba_normalized(
             np.sum(y_pred_proba, axis=axis),
             1,
             err_msg="The sum of the scores is not equal to one.",
-            rtol=1e-5,
+            rtol=1e-5
         )
         y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
         return y_pred_proba
@@ -398,7 +393,7 @@ def _get_last_index_included(
         self,
         y_pred_proba_cumsum: NDArray,
         threshold: NDArray,
-        include_last_label: Optional[Union[bool, str]],
+        include_last_label: Optional[Union[bool, str]]
     ) -> NDArray:
         """
         Return the index of the last included sorted probability
@@ -429,19 +424,27 @@ def _get_last_index_included(
         NDArray of shape (n_samples, n_alpha)
             Index of the last included sorted probability.
         """
-        if (include_last_label) or (include_last_label == "randomized"):
-            y_pred_index_last = np.ma.masked_less(
-                y_pred_proba_cumsum - threshold[np.newaxis, :], -EPSILON
-            ).argmin(axis=1)
-        elif include_last_label is False:
+        if (
+            (include_last_label) or
+            (include_last_label == 'randomized')
+        ):
+            y_pred_index_last = (
+                    np.ma.masked_less(
+                        y_pred_proba_cumsum
+                        - threshold[np.newaxis, :],
+                        -EPSILON
+                    ).argmin(axis=1)
+            )
+        elif (include_last_label is False):
             max_threshold = np.maximum(
-                threshold[np.newaxis, :], np.min(y_pred_proba_cumsum, axis=1)
+                threshold[np.newaxis, :],
+                np.min(y_pred_proba_cumsum, axis=1)
             )
             y_pred_index_last = np.argmax(
                 np.ma.masked_greater(
-                    y_pred_proba_cumsum - max_threshold[:, np.newaxis, :], EPSILON
-                ),
-                axis=1,
+                    y_pred_proba_cumsum - max_threshold[:, np.newaxis, :],
+                    EPSILON
+                ), axis=1
             )
         else:
             raise ValueError(
@@ -458,7 +461,7 @@ def _add_random_tie_breaking(
         y_pred_proba_last: NDArray,
         threshold: NDArray,
         lambda_star: Union[NDArray, float, None],
-        k_star: Union[NDArray, None],
+        k_star: Union[NDArray, None]
     ) -> NDArray:
         """
         Randomly remove last label from prediction set based on the
@@ -504,21 +507,29 @@ def _add_random_tie_breaking(
         """
         # get cumsumed probabilities up to last retained label
         y_proba_last_cumsumed = np.squeeze(
-            np.take_along_axis(y_pred_proba_cumsum, y_pred_index_last, axis=1), axis=1
+            np.take_along_axis(
+                y_pred_proba_cumsum,
+                y_pred_index_last,
+                axis=1
+            ), axis=1
         )
 
         if self.method in ["cumulated_score", "aps"]:
             # compute V parameter from Romano+(2020)
-            vs = (y_proba_last_cumsumed - threshold.reshape(1, -1)) / y_pred_proba_last[
-                :, 0, :
-            ]
+            vs = (
+                (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+                y_pred_proba_last[:, 0, :]
+            )
         else:
             # compute V parameter from Angelopoulos+(2020)
             L = np.sum(prediction_sets, axis=1)
-            vs = (y_proba_last_cumsumed - threshold.reshape(1, -1)) / (
-                y_pred_proba_last[:, 0, :]
-                - lambda_star * np.maximum(0, L - k_star)
-                + lambda_star * (L > k_star)
+            vs = (
+                (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+                (
+                    y_pred_proba_last[:, 0, :] -
+                    lambda_star * np.maximum(0, L - k_star) +
+                    lambda_star * (L > k_star)
+                )
             )
 
         # get random numbers for each observation and alpha value
@@ -530,12 +541,14 @@ def _add_random_tie_breaking(
             prediction_sets,
             y_pred_index_last,
             vs_less_than_us[:, np.newaxis, :],
-            axis=1,
+            axis=1
         )
         return prediction_sets
 
     def _get_true_label_cumsum_proba(
-        self, y: ArrayLike, y_pred_proba: NDArray
+        self,
+        y: ArrayLike,
+        y_pred_proba: NDArray
     ) -> Tuple[NDArray, NDArray]:
         """
         Compute the cumsumed probability of the true label.
@@ -554,9 +567,13 @@ def _get_true_label_cumsum_proba(
         is the cumsum probability of the true label. The second
         is the sorted position of the true label.
         """
-        y_true = label_binarize(y=y, classes=self.classes_)
+        y_true = label_binarize(
+            y=y, classes=self.classes_
+        )
         index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
-        y_pred_proba_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
+        y_pred_proba_sorted = np.take_along_axis(
+            y_pred_proba, index_sorted, axis=1
+        )
         y_true_sorted = np.take_along_axis(y_true, index_sorted, axis=1)
         y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
         cutoff = np.argmax(y_true_sorted, axis=1)
@@ -571,7 +588,7 @@ def _regularize_conformity_score(
         k_star: NDArray,
         lambda_: Union[NDArray, float],
         conf_score: NDArray,
-        cutoff: NDArray,
+        cutoff: NDArray
     ) -> NDArray:
         """
         Regularize the conformity scores with the ``"raps"``
@@ -598,9 +615,19 @@ def _regularize_conformity_score(
             Regularized conformity scores. The regularization
             depends on the value of alpha.
         """
-        conf_score = np.repeat(conf_score[:, :, np.newaxis], len(k_star), axis=2)
-        cutoff = np.repeat(cutoff[:, np.newaxis], len(k_star), axis=1)
-        conf_score += np.maximum(np.expand_dims(lambda_ * (cutoff - k_star), axis=1), 0)
+        conf_score = np.repeat(
+            conf_score[:, :, np.newaxis], len(k_star), axis=2
+        )
+        cutoff = np.repeat(
+            cutoff[:, np.newaxis], len(k_star), axis=1
+        )
+        conf_score += np.maximum(
+            np.expand_dims(
+                lambda_ * (cutoff - k_star),
+                axis=1
+            ),
+            0
+        )
         return conf_score
 
     def _get_last_included_proba(
@@ -609,7 +636,7 @@ def _get_last_included_proba(
         thresholds: NDArray,
         include_last_label: Union[bool, str, None],
         lambda_: Union[NDArray, float, None],
-        k_star: Union[NDArray, Any],
+        k_star: Union[NDArray, Any]
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Function that returns the smallest score
@@ -644,28 +671,46 @@ def _get_last_included_proba(
             with the RAPS method, the index of the last included score
             and the value of the last included score.
         """
-        index_sorted = np.flip(np.argsort(y_pred_proba, axis=1), axis=1)
+        index_sorted = np.flip(
+            np.argsort(y_pred_proba, axis=1), axis=1
+        )
         # sort probabilities by decreasing order
-        y_pred_proba_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
+        y_pred_proba_sorted = np.take_along_axis(
+            y_pred_proba, index_sorted, axis=1
+        )
         # get sorted cumulated score
-        y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
+        y_pred_proba_sorted_cumsum = np.cumsum(
+            y_pred_proba_sorted, axis=1
+        )
 
         if self.method == "raps":
             y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
-                0, np.cumsum(np.ones(y_pred_proba_sorted_cumsum.shape), axis=1) - k_star
+                0,
+                np.cumsum(
+                    np.ones(y_pred_proba_sorted_cumsum.shape),
+                    axis=1
+                ) - k_star
             )
         # get cumulated score at their original position
         y_pred_proba_cumsum = np.take_along_axis(
-            y_pred_proba_sorted_cumsum, np.argsort(index_sorted, axis=1), axis=1
+            y_pred_proba_sorted_cumsum,
+            np.argsort(index_sorted, axis=1),
+            axis=1
         )
         # get index of the last included label
         y_pred_index_last = self._get_last_index_included(
-            y_pred_proba_cumsum, thresholds, include_last_label
+            y_pred_proba_cumsum,
+            thresholds,
+            include_last_label
         )
         # get the probability of the last included label
-        y_pred_proba_last = np.take_along_axis(y_pred_proba, y_pred_index_last, axis=1)
+        y_pred_proba_last = np.take_along_axis(
+            y_pred_proba,
+            y_pred_index_last,
+            axis=1
+        )
 
-        zeros_scores_proba_last = y_pred_proba_last <= EPSILON
+        zeros_scores_proba_last = (y_pred_proba_last <= EPSILON)
 
         # If the last included proba is zero, change it to the
         # smallest non-zero value to avoid inluding them in the
@@ -673,10 +718,12 @@ def _get_last_included_proba(
         if np.sum(zeros_scores_proba_last) > 0:
             y_pred_proba_last[zeros_scores_proba_last] = np.expand_dims(
                 np.min(
-                    np.ma.masked_less(y_pred_proba, EPSILON).filled(fill_value=np.inf),
-                    axis=1,
-                ),
-                axis=1,
+                    np.ma.masked_less(
+                        y_pred_proba,
+                        EPSILON
+                    ).filled(fill_value=np.inf),
+                    axis=1
+                ), axis=1
             )[zeros_scores_proba_last]
 
         return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
@@ -687,7 +734,7 @@ def _update_size_and_lambda(
         alpha_np: NDArray,
         y_ps: NDArray,
         lambda_: Union[NDArray, float],
-        lambda_star: NDArray,
+        lambda_star: NDArray
     ) -> Tuple[NDArray, NDArray]:
         """Update the values of the optimal lambda if the
         average size of the prediction sets decreases with
@@ -721,11 +768,15 @@ def _update_size_and_lambda(
         """
 
         sizes = [
-            classification_mean_width_score(y_ps[:, :, i]) for i in range(len(alpha_np))
+            classification_mean_width_score(
+                y_ps[:, :, i]
+            ) for i in range(len(alpha_np))
         ]
 
-        sizes_improve = sizes < best_sizes - EPSILON
-        lambda_star = sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
+        sizes_improve = (sizes < best_sizes - EPSILON)
+        lambda_star = (
+            sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
+        )
         best_sizes = sizes_improve * sizes + (1 - sizes_improve) * best_sizes
 
         return lambda_star, best_sizes
@@ -735,7 +786,7 @@ def _find_lambda_star(
         y_pred_proba_raps: NDArray,
         alpha_np: NDArray,
         include_last_label: Union[bool, str, None],
-        k_star: NDArray,
+        k_star: NDArray
     ) -> Union[NDArray, float]:
         """Find the optimal value of lambda for each alpha.
 
@@ -763,23 +814,37 @@ def _find_lambda_star(
         lambda_star = np.zeros(len(alpha_np))
         best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
 
-        for lambda_ in [0.001, 0.01, 0.1, 0.2, 0.5]:  # values given in paper[3]
-            true_label_cumsum_proba, cutoff = self._get_true_label_cumsum_proba(
-                self.y_raps_no_enc,
-                y_pred_proba_raps[:, :, 0],
+        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]
+            true_label_cumsum_proba, cutoff = (
+                self._get_true_label_cumsum_proba(
+                    self.y_raps_no_enc,
+                    y_pred_proba_raps[:, :, 0],
+                )
             )
 
             true_label_cumsum_proba_reg = self._regularize_conformity_score(
-                k_star, lambda_, true_label_cumsum_proba, cutoff
+                k_star,
+                lambda_,
+                true_label_cumsum_proba,
+                cutoff
             )
 
-            quantiles_ = compute_quantiles(true_label_cumsum_proba_reg, alpha_np)
+            quantiles_ = compute_quantiles(
+                true_label_cumsum_proba_reg,
+                alpha_np
+            )
 
             _, _, y_pred_proba_last = self._get_last_included_proba(
-                y_pred_proba_raps, quantiles_, include_last_label, lambda_, k_star
+                y_pred_proba_raps,
+                quantiles_,
+                include_last_label,
+                lambda_,
+                k_star
             )
 
-            y_ps = np.greater_equal(y_pred_proba_raps - y_pred_proba_last, -EPSILON)
+            y_ps = np.greater_equal(
+                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
+            )
             lambda_star, best_sizes = self._update_size_and_lambda(
                 best_sizes, alpha_np, y_ps, lambda_, lambda_star
             )
@@ -788,7 +853,7 @@ def _find_lambda_star(
         return lambda_star
 
     def _get_classes_info(
-        self, estimator: ClassifierMixin, y: NDArray
+            self, estimator: ClassifierMixin, y: NDArray
     ) -> Tuple[int, NDArray]:
         """
         Compute the number of classes and the classes values
@@ -880,7 +945,9 @@ def _check_fit_parameter(
         """
 
         self._check_parameters()
-        cv = check_cv(self.cv, test_size=self.test_size, random_state=self.random_state)
+        cv = check_cv(
+            self.cv, test_size=self.test_size, random_state=self.random_state
+        )
         X, y = indexable(X, y)
         y = _check_y(y)
 
@@ -905,7 +972,7 @@ def _check_fit_parameter(
 
         return (estimator, cv, X, y, y_enc, sample_weight, groups, n_samples)
 
-    def _split_raps_data(self, X, y_enc, sample_weight, groups, size_raps):
+    def _split_data(self, X, y_enc, sample_weight, groups, size_raps):
         raps_split = ShuffleSplit(
             1, test_size=size_raps, random_state=self.random_state
         )
@@ -962,7 +1029,7 @@ def fit(
             Percentage of the data to be used for choosing lambda_star and
             k_star for the RAPS method.
 
-            By default ``.2``.
+            By default ``0.2``.
 
         groups: Optional[ArrayLike] of shape (n_samples,)
             Group labels for the samples used while splitting the dataset into
@@ -984,7 +1051,7 @@ def fit(
         )
 
         if self.method == "raps":
-            (X, y_enc, y, n_samples, sample_weight, groups) = self._split_raps_data(
+            (X, y_enc, y, n_samples, sample_weight, groups) = self._split_data(
                 X, y_enc, sample_weight, groups, size_raps
             )
 
@@ -1007,8 +1074,8 @@ def fit(
 
         # RAPS: compute y_pred and position on the RAPS validation dataset
         if self.method == "raps":
-            self.y_pred_proba_raps = self.estimator_.single_estimator_.predict_proba(
-                self.X_raps
+            self.y_pred_proba_raps = (
+                self.estimator_.single_estimator_.predict_proba(self.X_raps)
             )
             self.position_raps = get_true_label_position(
                 self.y_pred_proba_raps, self.y_raps
@@ -1016,14 +1083,16 @@ def fit(
 
         # Conformity scores
         if self.method == "naive":
-            self.conformity_scores_ = np.empty(y_pred_proba.shape, dtype="float")
+            self.conformity_scores_ = (
+                np.empty(y_pred_proba.shape, dtype="float")
+            )
         elif self.method in ["score", "lac"]:
             self.conformity_scores_ = np.take_along_axis(
                 1 - y_pred_proba, y_enc.reshape(-1, 1), axis=1
             )
         elif self.method in ["cumulated_score", "aps", "raps"]:
-            self.conformity_scores_, self.cutoff = self._get_true_label_cumsum_proba(
-                y, y_pred_proba
+            self.conformity_scores_, self.cutoff = (
+                self._get_true_label_cumsum_proba(y, y_pred_proba)
             )
             y_proba_true = np.take_along_axis(
                 y_pred_proba, y_enc.reshape(-1, 1), axis=1
@@ -1035,7 +1104,9 @@ def fit(
             # Here we reorder the labels by decreasing probability
             # and get the position of each label from decreasing
             # probability
-            self.conformity_scores_ = get_true_label_position(y_pred_proba, y_enc)
+            self.conformity_scores_ = get_true_label_position(
+                y_pred_proba, y_enc
+            )
         else:
             raise ValueError(
                 "Invalid method. " f"Allowed values are {self.valid_methods_}."
@@ -1048,7 +1119,7 @@ def predict(
         X: ArrayLike,
         alpha: Optional[Union[float, Iterable[float]]] = None,
         include_last_label: Optional[Union[bool, str]] = True,
-        agg_scores: Optional[str] = "mean",
+        agg_scores: Optional[str] = "mean"
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Prediction prediction sets on new samples based on target confidence
@@ -1118,58 +1189,69 @@ def predict(
         if self.method == "top_k":
             agg_scores = "mean"
         # Checks
-        cv = check_cv(self.cv, test_size=self.test_size, random_state=self.random_state)
+        cv = check_cv(
+            self.cv, test_size=self.test_size, random_state=self.random_state
+        )
         include_last_label = self._check_include_last_label(include_last_label)
         alpha = cast(Optional[NDArray], check_alpha(alpha))
         check_is_fitted(self, self.fit_attributes)
         lambda_star, k_star = None, None
+
         # Estimate prediction sets
         y_pred = self.estimator_.single_estimator_.predict(X)
 
         if alpha is None:
             return y_pred
 
-        n = len(self.conformity_scores_)
-
         # Estimate of probabilities from estimator(s)
         # In all cases: len(y_pred_proba.shape) == 3
         # with  (n_test, n_classes, n_alpha or n_train_samples)
+        n = len(self.conformity_scores_)
         alpha_np = cast(NDArray, alpha)
         check_alpha_and_n_samples(alpha_np, n)
-        y_pred_proba = self.estimator_.predict(X, agg_scores)
+
+        y_pred_proba = self.estimator_.predict(X, alpha_np, agg_scores)
+        # Check that sum of probas is equal to 1
         y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1)
-        if (cv == "prefit") or (agg_scores in ["mean"]):
-            y_pred_proba = np.repeat(
-                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-            )
 
         # Choice of the quantile
-        check_alpha_and_n_samples(alpha_np, n)
-
         if self.method == "naive":
             self.quantiles_ = 1 - alpha_np
         else:
             if (cv == "prefit") or (agg_scores in ["mean"]):
                 if self.method == "raps":
                     check_alpha_and_n_samples(alpha_np, len(self.X_raps))
-                    k_star = compute_quantiles(self.position_raps, alpha_np) + 1
+                    k_star = compute_quantiles(
+                        self.position_raps,
+                        alpha_np
+                    ) + 1
                     y_pred_proba_raps = np.repeat(
-                        self.y_pred_proba_raps[:, :, np.newaxis], len(alpha_np), axis=2
+                        self.y_pred_proba_raps[:, :, np.newaxis],
+                        len(alpha_np),
+                        axis=2
                     )
                     lambda_star = self._find_lambda_star(
-                        y_pred_proba_raps, alpha_np, include_last_label, k_star
+                        y_pred_proba_raps,
+                        alpha_np,
+                        include_last_label,
+                        k_star
                     )
                     self.conformity_scores_regularized = (
                         self._regularize_conformity_score(
-                            k_star, lambda_star, self.conformity_scores_, self.cutoff
+                                    k_star,
+                                    lambda_star,
+                                    self.conformity_scores_,
+                                    self.cutoff
                         )
                     )
                     self.quantiles_ = compute_quantiles(
-                        self.conformity_scores_regularized, alpha_np
+                        self.conformity_scores_regularized,
+                        alpha_np
                     )
                 else:
                     self.quantiles_ = compute_quantiles(
-                        self.conformity_scores_, alpha_np
+                        self.conformity_scores_,
+                        alpha_np
                     )
             else:
                 self.quantiles_ = (n + 1) * (1 - alpha_np)
@@ -1182,14 +1264,16 @@ def predict(
                 )
             else:
                 y_pred_included = np.less_equal(
-                    (1 - y_pred_proba) - self.conformity_scores_.ravel(), EPSILON
+                    (1 - y_pred_proba) - self.conformity_scores_.ravel(),
+                    EPSILON
                 ).sum(axis=2)
                 prediction_sets = np.stack(
                     [
-                        np.greater_equal(y_pred_included - _alpha * (n - 1), -EPSILON)
+                        np.greater_equal(
+                            y_pred_included - _alpha * (n - 1), -EPSILON
+                        )
                         for _alpha in alpha_np
-                    ],
-                    axis=2,
+                    ], axis=2
                 )
 
         elif self.method in ["naive", "cumulated_score", "aps", "raps"]:
@@ -1227,7 +1311,7 @@ def predict(
                     y_pred_proba_last,
                     thresholds,
                     lambda_star,
-                    k_star,
+                    k_star
                 )
             if (cv == "prefit") or (agg_scores in ["mean"]):
                 prediction_sets = y_pred_included
@@ -1237,28 +1321,35 @@ def predict(
                 prediction_sets = np.less_equal(
                     prediction_sets_summed[:, :, np.newaxis]
                     - self.quantiles_[np.newaxis, np.newaxis, :],
-                    EPSILON,
+                    EPSILON
                 )
         elif self.method == "top_k":
             y_pred_proba = y_pred_proba[:, :, 0]
             index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
             y_pred_index_last = np.stack(
-                [index_sorted[:, quantile] for quantile in self.quantiles_], axis=1
+                [
+                    index_sorted[:, quantile]
+                    for quantile in self.quantiles_
+                ], axis=1
             )
             y_pred_proba_last = np.stack(
                 [
                     np.take_along_axis(
-                        y_pred_proba, y_pred_index_last[:, iq].reshape(-1, 1), axis=1
+                        y_pred_proba,
+                        y_pred_index_last[:, iq].reshape(-1, 1),
+                        axis=1
                     )
                     for iq, _ in enumerate(self.quantiles_)
-                ],
-                axis=2,
+                ], axis=2
             )
             prediction_sets = np.greater_equal(
-                y_pred_proba[:, :, np.newaxis] - y_pred_proba_last, -EPSILON
+                y_pred_proba[:, :, np.newaxis]
+                - y_pred_proba_last,
+                -EPSILON
             )
         else:
             raise ValueError(
-                "Invalid method. " f"Allowed values are {self.valid_methods_}."
+                "Invalid method. "
+                f"Allowed values are {self.valid_methods_}."
             )
         return y_pred, prediction_sets
diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/estimator_classifier.py
index 387b67900..be54376d4 100644
--- a/mapie/estimator/estimator_classifier.py
+++ b/mapie/estimator/estimator_classifier.py
@@ -182,7 +182,11 @@ def _fit_oof_estimator(
             sample_weight = cast(NDArray, sample_weight)
 
         estimator = fit_estimator(
-            estimator, X_train, y_train, sample_weight=sample_weight, **fit_params
+            estimator,
+            X_train,
+            y_train,
+            sample_weight=sample_weight,
+            **fit_params
         )
         return estimator
 
@@ -216,7 +220,11 @@ def _predict_proba_oof_estimator(
         return y_pred_proba
 
     def _predict_proba_calib_oof_estimator(
-        self, estimator: ClassifierMixin, X: ArrayLike, val_index: ArrayLike, k: int
+        self,
+        estimator: ClassifierMixin,
+        X: ArrayLike,
+        val_index: ArrayLike,
+        k: int
     ) -> Tuple[NDArray, ArrayLike]:
         """
         Perform predictions on a single out-of-fold model on a validation set.
@@ -244,6 +252,7 @@ def _predict_proba_calib_oof_estimator(
         else:
             y_pred_proba = np.array([])
         val_id = np.full(len(X_val), k, dtype=int)
+
         return y_pred_proba, val_id, val_index
 
     def _aggregate_with_mask(self, x: NDArray, k: NDArray) -> NDArray:
@@ -302,7 +311,9 @@ def _pred_multi(self, X: ArrayLike) -> NDArray:
         -------
         NDArray of shape (n_samples_test, n_samples_train)
         """
-        y_pred_multi = np.column_stack([e.predict(X) for e in self.estimators_])
+        y_pred_multi = np.column_stack(
+            [e.predict(X) for e in self.estimators_]
+        )
         # At this point, y_pred_multi is of shape
         # (n_samples_test, n_estimators_). The method
         # ``_aggregate_with_mask`` fits it to the right size
@@ -431,17 +442,29 @@ def fit(
         # Computation
         if cv == "prefit":
             single_estimator_ = estimator
-            self.k_ = np.full(shape=(n_samples, 1), fill_value=np.nan, dtype=float)
+            self.k_ = (
+                np.full(shape=(n_samples, 1), fill_value=np.nan, dtype=float)
+            )
         else:
             single_estimator_ = self._fit_oof_estimator(
-                clone(estimator), X, y, full_indexes, sample_weight, **fit_params
+                clone(estimator),
+                X,
+                y,
+                full_indexes,
+                sample_weight,
+                **fit_params
             )
             cv = cast(BaseCrossValidator, cv)
             self.k_ = np.empty_like(y, dtype=int)
 
             estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
                 delayed(self._fit_oof_estimator)(
-                    clone(estimator), X, y_enc, train_index, sample_weight, **fit_params
+                    clone(estimator),
+                    X,
+                    y_enc,
+                    train_index,
+                    sample_weight,
+                    **fit_params
                 )
                 for train_index, _ in cv.split(X, y, groups)
             )
@@ -455,7 +478,12 @@ def fit(
         return self
 
     def predict(
-        self, X: ArrayLike, agg_scores
+        self,
+        X: ArrayLike,
+        ensemble: bool = False,
+        return_multi_pred: bool = True,
+        alpha_np: ArrayLike = [],
+        agg_scores: Any = None
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
@@ -494,10 +522,15 @@ def predict(
 
         if self.cv == "prefit":
             y_pred_proba = self.single_estimator_.predict_proba(X)
+            y_pred_proba = np.repeat(
+                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+            )
         else:
             y_pred_proba_k = np.asarray(
-                Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
-                    delayed(self._predict_proba_oof_estimator)(estimator, X)
+                Parallel(
+                    n_jobs=self.n_jobs, verbose=self.verbose
+                )(
+                    delayed(self._predict_oof_model)(estimator, X)
                     for estimator in self.estimators_
                 )
             )
@@ -505,6 +538,10 @@ def predict(
                 y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2)
             elif agg_scores == "mean":
                 y_pred_proba = np.mean(y_pred_proba_k, axis=0)
+                y_pred_proba = np.repeat(
+                    y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+                )
             else:
                 raise ValueError("Invalid 'agg_scores' argument.")
+
         return y_pred_proba
diff --git a/mapie/estimator/estimator_regressor.py b/mapie/estimator/estimator_regressor.py
index ddbcff7f2..b8c7d4ecf 100644
--- a/mapie/estimator/estimator_regressor.py
+++ b/mapie/estimator/estimator_regressor.py
@@ -12,11 +12,8 @@
 from mapie._typing import ArrayLike, NDArray
 from mapie.aggregation_functions import aggregate_all, phi2D
 from mapie.estimator.interface import EnsembleEstimator
-from mapie.utils import (
-    check_nan_in_aposteriori_prediction,
-    check_no_agg_cv,
-    fit_estimator,
-)
+from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
+                         fit_estimator)
 
 
 class EnsembleRegressor(EnsembleEstimator):
@@ -149,7 +146,6 @@ class EnsembleRegressor(EnsembleEstimator):
         - Dummy array of folds containing each training sample, otherwise.
           Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
     """
-
     no_agg_cv_ = ["prefit", "split"]
     no_agg_methods_ = ["naive", "base"]
     fit_attributes = [
@@ -168,7 +164,7 @@ def __init__(
         n_jobs: Optional[int],
         random_state: Optional[Union[int, np.random.RandomState]],
         test_size: Optional[Union[int, float]],
-        verbose: int,
+        verbose: int
     ):
         self.estimator = estimator
         self.method = method
@@ -224,7 +220,11 @@ def _fit_oof_estimator(
             sample_weight = cast(NDArray, sample_weight)
 
         estimator = fit_estimator(
-            estimator, X_train, y_train, sample_weight=sample_weight, **fit_params
+            estimator,
+            X_train,
+            y_train,
+            sample_weight=sample_weight,
+            **fit_params
         )
         return estimator
 
@@ -260,7 +260,11 @@ def _predict_oof_estimator(
             y_pred = np.array([])
         return y_pred, val_index
 
-    def _aggregate_with_mask(self, x: NDArray, k: NDArray) -> NDArray:
+    def _aggregate_with_mask(
+        self,
+        x: NDArray,
+        k: NDArray
+    ) -> NDArray:
         """
         Take the array of predictions, made by the refitted estimators,
         on the testing set, and the 1-or-nan array indicating for each training
@@ -316,7 +320,9 @@ def _pred_multi(self, X: ArrayLike) -> NDArray:
         -------
         NDArray of shape (n_samples_test, n_samples_train)
         """
-        y_pred_multi = np.column_stack([e.predict(X) for e in self.estimators_])
+        y_pred_multi = np.column_stack(
+            [e.predict(X) for e in self.estimators_]
+        )
         # At this point, y_pred_multi is of shape
         # (n_samples_test, n_estimators_). The method
         # ``_aggregate_with_mask`` fits it to the right size
@@ -328,7 +334,7 @@ def predict_calib(
         self,
         X: ArrayLike,
         y: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None
     ) -> NDArray:
         """
         Perform predictions on X : the calibration set.
@@ -365,15 +371,16 @@ def predict_calib(
                 cv = cast(BaseCrossValidator, self.cv)
                 outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
                     delayed(self._predict_oof_estimator)(
-                        estimator,
-                        X,
-                        calib_index,
+                        estimator, X, calib_index,
                     )
                     for (_, calib_index), estimator in zip(
-                        cv.split(X, y, groups), self.estimators_
+                        cv.split(X, y, groups),
+                        self.estimators_
                     )
                 )
-                predictions, indices = map(list, zip(*outputs))
+                predictions, indices = map(
+                    list, zip(*outputs)
+                )
                 n_samples = _num_samples(X)
                 pred_matrix = np.full(
                     shape=(n_samples, cv.get_n_splits(X, y, groups)),
@@ -381,7 +388,9 @@ def predict_calib(
                     dtype=float,
                 )
                 for i, ind in enumerate(indices):
-                    pred_matrix[ind, i] = np.array(predictions[i], dtype=float)
+                    pred_matrix[ind, i] = np.array(
+                        predictions[i], dtype=float
+                    )
                     self.k_[ind, i] = 1
                 check_nan_in_aposteriori_prediction(pred_matrix)
 
@@ -443,10 +452,17 @@ def fit(
         # Computation
         if cv == "prefit":
             single_estimator_ = estimator
-            self.k_ = np.full(shape=(n_samples, 1), fill_value=np.nan, dtype=float)
+            self.k_ = np.full(
+                shape=(n_samples, 1), fill_value=np.nan, dtype=float
+            )
         else:
             single_estimator_ = self._fit_oof_estimator(
-                clone(estimator), X, y, full_indexes, sample_weight, **fit_params
+                clone(estimator),
+                X,
+                y,
+                full_indexes,
+                sample_weight,
+                **fit_params
             )
             cv = cast(BaseCrossValidator, cv)
             self.k_ = np.full(
@@ -459,7 +475,12 @@ def fit(
             else:
                 estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
                     delayed(self._fit_oof_estimator)(
-                        clone(estimator), X, y, train_index, sample_weight, **fit_params
+                        clone(estimator),
+                        X,
+                        y,
+                        train_index,
+                        sample_weight,
+                        **fit_params
                     )
                     for train_index, _ in cv.split(X, y, groups)
                 )
@@ -473,7 +494,10 @@ def fit(
         return self
 
     def predict(
-        self, X: ArrayLike, ensemble: bool = False, return_multi_pred: bool = True
+        self,
+        X: ArrayLike,
+        ensemble: bool = False,
+        return_multi_pred: bool = True
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 177bc31b4..fc1f3e6ba 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -13,14 +13,14 @@
 from sklearn.ensemble import GradientBoostingClassifier
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LogisticRegression
-from sklearn.model_selection import GroupKFold, KFold, LeaveOneOut, ShuffleSplit
+from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
+                                     ShuffleSplit)
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.estimator_checks import check_estimator
 from sklearn.utils.validation import check_is_fitted
 from typing_extensions import TypedDict
 
-from mapie.estimator.estimator_classifier import EnsembleClassifier
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
 from mapie.metrics import classification_coverage_score
@@ -32,10 +32,29 @@
 WRONG_METHODS = ["scores", "cumulated", "test", "", 1, 2.5, (1, 2)]
 WRONG_INCLUDE_LABELS = ["randomised", "True", "False", "other", 1, 2.5, (1, 2)]
 Y_PRED_PROBA_WRONG = [
-    np.array([[0.8, 0.01, 0.1, 0.05], [1.0, 0.1, 0.0, 0.0]]),
-    np.array([[1.0, 0.0001, 0.0]]),
-    np.array([[0.8, 0.1, 0.05, 0.05], [0.9, 0.01, 0.04, 0.06]]),
-    np.array([[0.8, 0.1, 0.02, 0.05], [0.9, 0.01, 0.03, 0.06]]),
+    np.array(
+        [
+            [0.8, 0.01, 0.1, 0.05],
+            [1.0, 0.1, 0.0, 0.0]
+        ]
+    ),
+    np.array(
+        [
+            [1.0, 0.0001, 0.0]
+        ]
+    ),
+    np.array(
+        [
+            [0.8, 0.1, 0.05, 0.05],
+            [0.9, 0.01, 0.04, 0.06]
+        ]
+    ),
+    np.array(
+        [
+            [0.8, 0.1, 0.02, 0.05],
+            [0.9, 0.01, 0.03, 0.06]
+        ]
+    )
 ]
 
 Params = TypedDict(
@@ -44,163 +63,391 @@
         "method": str,
         "cv": Optional[Union[int, str]],
         "test_size": Optional[Union[int, float]],
-        "random_state": Optional[int],
-    },
+        "random_state": Optional[int]
+    }
 )
 ParamsPredict = TypedDict(
-    "ParamsPredict", {"include_last_label": Union[bool, str], "agg_scores": str}
+    "ParamsPredict",
+    {
+        "include_last_label": Union[bool, str],
+        "agg_scores": str
+    }
 )
 
 STRATEGIES = {
     "lac": (
-        Params(method="lac", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="lac",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "lac_split": (
-        Params(method="lac", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="lac",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "lac_cv_mean": (
-        Params(method="lac", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="lac",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "lac_cv_crossval": (
-        Params(method="lac", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="crossval"),
+        Params(
+            method="lac",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="crossval"
+        )
     ),
     "aps_include": (
-        Params(method="aps", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="aps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "aps_not_include": (
-        Params(method="aps", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="aps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "aps_randomized": (
-        Params(method="aps", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
+        Params(
+            method="aps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
     ),
     "aps_include_split": (
-        Params(method="aps", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="aps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "aps_not_include_split": (
-        Params(method="aps", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="aps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "aps_randomized_split": (
-        Params(method="aps", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
+        Params(
+            method="aps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
     ),
     "aps_include_cv_mean": (
-        Params(method="aps", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="aps",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "aps_not_include_cv_mean": (
-        Params(method="aps", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="aps",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "aps_randomized_cv_mean": (
-        Params(method="aps", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
+        Params(
+            method="aps",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
     ),
     "aps_include_cv_crossval": (
-        Params(method="aps", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="crossval"),
+        Params(
+            method="aps",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="crossval"
+        )
     ),
     "aps_not_include_cv_crossval": (
-        Params(method="aps", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=False, agg_scores="crossval"),
+        Params(
+            method="aps",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="crossval"
+        )
     ),
     "aps_randomized_cv_crossval": (
-        Params(method="aps", cv=3, test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label="randomized", agg_scores="crossval"),
+        Params(
+            method="aps",
+            cv=3,
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="crossval"
+        )
     ),
     "naive": (
-        Params(method="naive", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="naive",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "naive_split": (
-        Params(method="naive", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="naive",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "top_k": (
-        Params(method="top_k", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="top_k",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "top_k_split": (
-        Params(method="top_k", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="top_k",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "raps": (
-        Params(method="raps", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="raps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "raps_split": (
-        Params(method="raps", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label=True, agg_scores="mean"),
+        Params(
+            method="raps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
     ),
     "raps_randomized": (
-        Params(method="raps", cv="prefit", test_size=None, random_state=random_state),
-        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
+        Params(
+            method="raps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
     ),
     "raps_randomized_split": (
-        Params(method="raps", cv="split", test_size=0.5, random_state=random_state),
-        ParamsPredict(include_last_label="randomized", agg_scores="mean"),
+        Params(
+            method="raps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
     ),
 }
 
 STRATEGIES_BINARY = {
     "lac": (
-        Params(method="lac", cv="prefit", test_size=None, random_state=42),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="lac",
+            cv="prefit",
+            test_size=None,
+            random_state=42
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "lac_split": (
-        Params(method="lac", cv="split", test_size=0.5, random_state=42),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="lac",
+            cv="split",
+            test_size=0.5,
+            random_state=42
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "lac_cv_mean": (
-        Params(method="lac", cv=3, test_size=None, random_state=42),
-        ParamsPredict(include_last_label=False, agg_scores="mean"),
+        Params(
+            method="lac",
+            cv=3,
+            test_size=None,
+            random_state=42
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
     ),
     "lac_cv_crossval": (
-        Params(method="lac", cv=3, test_size=None, random_state=42),
-        ParamsPredict(include_last_label=False, agg_scores="crossval"),
-    ),
+        Params(
+            method="lac",
+            cv=3,
+            test_size=None,
+            random_state=42
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="crossval"
+        )
+    )
 }
 
 COVERAGES = {
-    "lac": 6 / 9,
-    "lac_split": 8 / 9,
+    "lac": 6/9,
+    "lac_split": 8/9,
     "lac_cv_mean": 1.0,
     "lac_cv_crossval": 1.0,
     "aps_include": 1.0,
-    "aps_not_include": 5 / 9,
-    "aps_randomized": 6 / 9,
-    "aps_include_split": 8 / 9,
-    "aps_not_include_split": 5 / 9,
-    "aps_randomized_split": 7 / 9,
+    "aps_not_include": 5/9,
+    "aps_randomized": 6/9,
+    "aps_include_split": 8/9,
+    "aps_not_include_split": 5/9,
+    "aps_randomized_split": 7/9,
     "aps_include_cv_mean": 1.0,
-    "aps_not_include_cv_mean": 5 / 9,
-    "aps_randomized_cv_mean": 8 / 9,
-    "aps_include_cv_crossval": 4 / 9,
-    "aps_not_include_cv_crossval": 1 / 9,
-    "aps_randomized_cv_crossval": 7 / 9,
-    "naive": 5 / 9,
-    "naive_split": 5 / 9,
+    "aps_not_include_cv_mean": 5/9,
+    "aps_randomized_cv_mean": 8/9,
+    "aps_include_cv_crossval": 4/9,
+    "aps_not_include_cv_crossval": 1/9,
+    "aps_randomized_cv_crossval": 7/9,
+    "naive": 5/9,
+    "naive_split": 5/9,
     "top_k": 1.0,
     "top_k_split": 1.0,
     "raps": 1.0,
-    "raps_split": 7 / 9,
-    "raps_randomized": 8 / 9,
-    "raps_randomized_split": 1.0,
+    "raps_split": 7/9,
+    "raps_randomized": 8/9,
+    "raps_randomized_split": 1.0
 }
 
 COVERAGES_BINARY = {
-    "lac": 6 / 9,
-    "lac_split": 8 / 9,
-    "lac_cv_mean": 6 / 9,
-    "lac_cv_crossval": 6 / 9,
+    "lac": 6/9,
+    "lac_split": 8/9,
+    "lac_cv_mean": 6/9,
+    "lac_cv_crossval": 6/9
 }
 
 X_toy = np.arange(9).reshape(-1, 1)
@@ -217,7 +464,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "lac_split": [
         [True, True, False],
@@ -239,7 +486,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "lac_cv_crossval": [
         [True, False, False],
@@ -250,7 +497,7 @@
         [False, True, False],
         [False, True, True],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "aps_include": [
         [True, False, False],
@@ -261,7 +508,7 @@
         [False, True, False],
         [False, True, True],
         [False, True, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_not_include": [
         [True, False, False],
@@ -272,7 +519,7 @@
         [False, True, False],
         [False, True, False],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_randomized": [
         [True, False, False],
@@ -283,7 +530,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_include_split": [
         [True, True, False],
@@ -294,7 +541,7 @@
         [True, True, True],
         [False, True, True],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_not_include_split": [
         [False, True, False],
@@ -305,7 +552,7 @@
         [False, True, True],
         [False, False, True],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_randomized_split": [
         [False, True, False],
@@ -316,7 +563,7 @@
         [False, True, True],
         [False, False, True],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_include_cv_mean": [
         [True, False, False],
@@ -327,7 +574,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "aps_not_include_cv_mean": [
         [True, False, False],
@@ -338,7 +585,7 @@
         [False, True, False],
         [False, True, False],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "aps_randomized_cv_mean": [
         [True, False, False],
@@ -349,7 +596,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "aps_include_cv_crossval": [
         [False, False, False],
@@ -360,7 +607,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, False],
-        [False, False, False],
+        [False, False, False]
     ],
     "aps_not_include_cv_crossval": [
         [False, False, False],
@@ -371,7 +618,7 @@
         [False, False, False],
         [False, False, False],
         [False, False, False],
-        [False, False, False],
+        [False, False, False]
     ],
     "aps_randomized_cv_crossval": [
         [True, False, False],
@@ -382,7 +629,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, False],
-        [False, False, True],
+        [False, False, True]
     ],
     "naive": [
         [True, False, False],
@@ -393,7 +640,7 @@
         [False, True, False],
         [False, True, False],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "naive_split": [
         [False, True, False],
@@ -404,7 +651,7 @@
         [False, True, True],
         [False, False, True],
         [False, False, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "top_k": [
         [True, True, False],
@@ -415,7 +662,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "top_k_split": [
         [True, True, False],
@@ -426,7 +673,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "raps": [
         [True, False, False],
@@ -437,7 +684,7 @@
         [False, True, True],
         [False, True, True],
         [False, True, True],
-        [False, True, True],
+        [False, True, True]
     ],
     "raps_split": [
         [True, True, False],
@@ -448,7 +695,7 @@
         [True, True, False],
         [True, True, False],
         [True, True, False],
-        [True, True, False],
+        [True, True, False]
     ],
     "raps_randomized": [
         [True, False, False],
@@ -459,7 +706,7 @@
         [False, True, False],
         [False, True, False],
         [False, True, True],
-        [False, False, True],
+        [False, False, True]
     ],
     "raps_randomized_split": [
         [True, True, True],
@@ -470,8 +717,8 @@
         [True, True, True],
         [True, True, True],
         [True, True, True],
-        [True, True, True],
-    ],
+        [True, True, True]
+    ]
 }
 
 X_toy_binary = np.arange(9).reshape(-1, 1)
@@ -487,7 +734,7 @@
         [False, True],
         [False, True],
         [False, True],
-        [False, True],
+        [False, True]
     ],
     "lac_split": [
         [True, True],
@@ -498,7 +745,7 @@
         [True, True],
         [True, True],
         [True, True],
-        [True, False],
+        [True, False]
     ],
     "lac_cv_mean": [
         [True, False],
@@ -509,7 +756,7 @@
         [False, True],
         [False, True],
         [False, True],
-        [False, True],
+        [False, True]
     ],
     "lac_cv_crossval": [
         [True, False],
@@ -520,11 +767,15 @@
         [False, True],
         [False, True],
         [False, True],
-        [False, True],
-    ],
+        [False, True]
+    ]
 }
 
-REGULARIZATION_PARAMETERS = [[0.001, [1]], [[0.01, 0.2], [1, 3]], [0.1, [2, 4]]]
+REGULARIZATION_PARAMETERS = [
+    [.001, [1]],
+    [[.01, .2], [1, 3]],
+    [.1, [2, 4]]
+]
 
 IMAGE_INPUT = [
     {
@@ -538,7 +789,7 @@
     {
         "X_calib": np.zeros((3, 256, 512)),
         "X_test": np.ones((3, 256, 512)),
-    },
+    }
 ]
 
 X_good_image = np.zeros((3, 1024, 1024, 3))
@@ -568,7 +819,7 @@ def __init__(self) -> None:
                 [True, True, False],
                 [False, True, False],
                 [False, True, True],
-                [True, True, False],
+                [True, True, False]
             ]
         )
         self.classes_ = self.y_calib
@@ -582,10 +833,12 @@ def predict(self, X: ArrayLike) -> NDArray:
 
     def predict_proba(self, X: ArrayLike) -> NDArray:
         if np.max(X) <= 2:
-            return np.array([[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]])
+            return np.array(
+                [[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]]
+            )
         else:
             return np.array(
-                [[0.2, 0.7, 0.1], [0.0, 1.0, 0.0], [0.0, 0.7, 0.3], [0.3, 0.7, 0.0]]
+                [[0.2, 0.7, 0.1], [0., 1., 0.], [0., .7, 0.3], [0.3, .7, 0.]]
             )
 
 
@@ -612,9 +865,13 @@ def predict(self, *args: Any) -> NDArray:
 
     def predict_proba(self, X: ArrayLike) -> NDArray:
         if np.max(X) == 0:
-            return np.array([[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]])
+            return np.array(
+                [[0.4, 0.5, 0.1], [0.2, 0.6, 0.2], [0.6, 0.3, 0.1]]
+            )
         else:
-            return np.array([[0.2, 0.7, 0.1], [0.1, 0.2, 0.7], [0.3, 0.5, 0.2]])
+            return np.array(
+                [[0.2, 0.7, 0.1], [0.1, 0.2, 0.7], [0.3, 0.5, 0.2]]
+            )
 
 
 class WrongOutputModel:
@@ -631,7 +888,9 @@ def predict_proba(self, *args: Any) -> NDArray:
         return self.proba_out
 
     def predict(self, *args: Any) -> NDArray:
-        pred = (self.proba_out == self.proba_out.max(axis=1)[:, None]).astype(int)
+        pred = (
+            self.proba_out == self.proba_out.max(axis=1)[:, None]
+        ).astype(int)
         return pred
 
 
@@ -646,7 +905,7 @@ def fit(self, *args: Any) -> None:
         self.trained_ = True
 
     def predict_proba(self, X: NDArray, *args: Any) -> NDArray:
-        probas = np.array([[0.9, 0.05, 0.05]])
+        probas = np.array([[.9, .05, .05]])
         proba_out = np.repeat(probas, len(X), axis=0).astype(np.float32)
         return proba_out
 
@@ -682,7 +941,9 @@ def test_default_parameters() -> None:
 @pytest.mark.parametrize("method", ["aps", "raps"])
 def test_warning_binary_classif(cv: str, method: str) -> None:
     """Test that a warning is raised y is binary."""
-    mapie_clf = MapieClassifier(cv=cv, method=method, random_state=random_state)
+    mapie_clf = MapieClassifier(
+      cv=cv, method=method, random_state=random_state
+    )
     X, y = make_classification(
         n_samples=500,
         n_features=10,
@@ -690,7 +951,9 @@ def test_warning_binary_classif(cv: str, method: str) -> None:
         n_classes=2,
         random_state=random_state,
     )
-    with pytest.raises(ValueError, match=r".*Invalid method for binary target.*"):
+    with pytest.raises(
+        ValueError, match=r".*Invalid method for binary target.*"
+    ):
         mapie_clf.fit(X, y)
 
 
@@ -716,34 +979,30 @@ def test_valid_estimator(strategy: str) -> None:
     clf = LogisticRegression().fit(X_toy, y_toy)
     mapie_clf = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     mapie_clf.fit(X_toy, y_toy)
-    assert isinstance(mapie_clf.estimator_.single_estimator_, LogisticRegression)
+    assert isinstance(mapie_clf.single_estimator_, LogisticRegression)
 
 
 @pytest.mark.parametrize("method", METHODS)
 def test_valid_method(method: str) -> None:
     """Test that valid methods raise no errors."""
-    mapie_clf = MapieClassifier(method=method, cv="prefit", random_state=random_state)
+    mapie_clf = MapieClassifier(
+        method=method, cv="prefit", random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
     check_is_fitted(mapie_clf, mapie_clf.fit_attributes)
 
 
 @pytest.mark.parametrize(
-    "cv",
-    [
-        None,
-        -1,
-        2,
-        KFold(),
-        LeaveOneOut(),
-        "prefit",
-        ShuffleSplit(n_splits=1, test_size=0.5, random_state=random_state),
-    ],
+    "cv", [None, -1, 2, KFold(), LeaveOneOut(), "prefit",
+           ShuffleSplit(n_splits=1, test_size=0.5, random_state=random_state)]
 )
 def test_valid_cv(cv: Any) -> None:
     """Test that valid cv raises no errors."""
     model = LogisticRegression(multi_class="multinomial")
     model.fit(X_toy, y_toy)
-    mapie_clf = MapieClassifier(estimator=model, cv=cv, random_state=random_state)
+    mapie_clf = MapieClassifier(
+        estimator=model, cv=cv, random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.predict(X_toy, alpha=0.5)
 
@@ -751,7 +1010,9 @@ def test_valid_cv(cv: Any) -> None:
 @pytest.mark.parametrize("agg_scores", ["mean", "crossval"])
 def test_agg_scores_argument(agg_scores: str) -> None:
     """Test that predict passes with all valid 'agg_scores' arguments."""
-    mapie_clf = MapieClassifier(cv=3, method="lac", random_state=random_state)
+    mapie_clf = MapieClassifier(
+        cv=3, method="lac", random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.predict(X_toy, alpha=0.5, agg_scores=agg_scores)
 
@@ -759,9 +1020,13 @@ def test_agg_scores_argument(agg_scores: str) -> None:
 @pytest.mark.parametrize("agg_scores", ["median", 1, None])
 def test_invalid_agg_scores_argument(agg_scores: str) -> None:
     """Test that invalid 'agg_scores' raise errors."""
-    mapie_clf = MapieClassifier(cv=3, method="lac", random_state=random_state)
+    mapie_clf = MapieClassifier(
+        cv=3, method="lac", random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
-    with pytest.raises(ValueError, match=r".*Invalid 'agg_scores' argument.*"):
+    with pytest.raises(
+        ValueError, match=r".*Invalid 'agg_scores' argument.*"
+    ):
         mapie_clf.predict(X_toy, alpha=0.5, agg_scores=agg_scores)
 
 
@@ -777,21 +1042,27 @@ def test_too_large_cv(cv: Any) -> None:
 
 
 @pytest.mark.parametrize(
-    "include_last_label", [-3.14, 1.5, -2, 0, 1, "cv", DummyClassifier(), [1, 2]]
+    "include_last_label",
+    [-3.14, 1.5, -2, 0, 1, "cv", DummyClassifier(), [1, 2]]
 )
 def test_invalid_include_last_label(include_last_label: Any) -> None:
     """Test that invalid include_last_label raise errors."""
     mapie_clf = MapieClassifier(random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
-    with pytest.raises(ValueError, match=r".*Invalid include_last_label argument.*"):
-        mapie_clf.predict(X_toy, y_toy, include_last_label=include_last_label)
+    with pytest.raises(
+        ValueError, match=r".*Invalid include_last_label argument.*"
+    ):
+        mapie_clf.predict(
+            X_toy,
+            y_toy,
+            include_last_label=include_last_label
+        )
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_predict_output_shape(
-    strategy: str,
-    alpha: Any,
+    strategy: str, alpha: Any,
 ) -> None:
     """Test predict output shape."""
     args_init, args_predict = STRATEGIES[strategy]
@@ -801,7 +1072,7 @@ def test_predict_output_shape(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
@@ -811,25 +1082,26 @@ def test_predict_output_shape(
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_y_is_list_of_string(
-    strategy: str,
-    alpha: Any,
+    strategy: str, alpha: Any,
 ) -> None:
     """Test predict output shape with string y."""
     args_init, args_predict = STRATEGIES[strategy]
     mapie_clf = MapieClassifier(**args_init)
-    mapie_clf.fit(X, y.astype("str"))
+    mapie_clf.fit(X, y.astype('str'))
     y_pred, y_ps = mapie_clf.predict(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
     assert y_ps.shape == (X.shape[0], len(np.unique(y)), n_alpha)
 
 
-@pytest.mark.parametrize("strategy", ["naive", "top_k", "lac", "aps_include"])
+@pytest.mark.parametrize(
+    "strategy", ["naive", "top_k", "lac", "aps_include"]
+)
 def test_same_results_prefit_split(strategy: str) -> None:
     """
     Test checking that if split and prefit method have exactly
@@ -847,7 +1119,7 @@ def test_same_results_prefit_split(strategy: str) -> None:
     X_train_, X_calib_ = X[train_index], X[val_index]
     y_train_, y_calib_ = y[train_index], y[val_index]
 
-    args_init, args_predict = deepcopy(STRATEGIES[strategy + "_split"])
+    args_init, args_predict = deepcopy(STRATEGIES[strategy + '_split'])
     args_init["cv"] = cv
     mapie_reg = MapieClassifier(**args_init)
     mapie_reg.fit(X, y)
@@ -867,14 +1139,13 @@ def test_same_results_prefit_split(strategy: str) -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_same_result_y_numeric_and_string(
-    strategy: str,
-    alpha: Any,
+    strategy: str, alpha: Any,
 ) -> None:
     """Test that MAPIE outputs the same results if y is
     numeric or string"""
     args_init, args_predict = STRATEGIES[strategy]
     mapie_clf_str = MapieClassifier(**args_init)
-    mapie_clf_str.fit(X, y.astype("str"))
+    mapie_clf_str.fit(X, y.astype('str'))
     mapie_clf_int = MapieClassifier(**args_init)
     mapie_clf_int.fit(X, y)
     _, y_ps_str = mapie_clf_str.predict(
@@ -887,7 +1158,7 @@ def test_same_result_y_numeric_and_string(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_ps_int, y_ps_str)
 
@@ -895,8 +1166,7 @@ def test_same_result_y_numeric_and_string(
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_y_1_to_l_minus_1(
-    strategy: str,
-    alpha: Any,
+    strategy: str, alpha: Any,
 ) -> None:
     """Test predict output shape with string y."""
     args_init, args_predict = STRATEGIES[strategy]
@@ -906,7 +1176,7 @@ def test_y_1_to_l_minus_1(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
@@ -916,8 +1186,7 @@ def test_y_1_to_l_minus_1(
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_same_result_y_numeric_and_1_to_l_minus_1(
-    strategy: str,
-    alpha: Any,
+    strategy: str, alpha: Any,
 ) -> None:
     """Test that MAPIE outputs the same results if y is
     numeric or string"""
@@ -936,7 +1205,7 @@ def test_same_result_y_numeric_and_1_to_l_minus_1(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_ps_int, y_ps_1)
 
@@ -954,15 +1223,19 @@ def test_results_for_same_alpha(strategy: str) -> None:
         X,
         alpha=[0.1, 0.1],
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_ps[:, 0, 0], y_ps[:, 0, 1])
     np.testing.assert_allclose(y_ps[:, 1, 0], y_ps[:, 1, 1])
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-@pytest.mark.parametrize("alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)])
-def test_results_for_alpha_as_float_and_arraylike(strategy: str, alpha: Any) -> None:
+@pytest.mark.parametrize(
+    "alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)]
+)
+def test_results_for_alpha_as_float_and_arraylike(
+    strategy: str, alpha: Any
+) -> None:
     """Test that output values do not depend on type of alpha."""
     args_init, args_predict = STRATEGIES[strategy]
     mapie_clf = MapieClassifier(**args_init)
@@ -971,19 +1244,19 @@ def test_results_for_alpha_as_float_and_arraylike(strategy: str, alpha: Any) ->
         X,
         alpha=alpha[0],
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred_float2, y_ps_float2 = mapie_clf.predict(
         X,
         alpha=alpha[1],
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred_array, y_ps_array = mapie_clf.predict(
         X,
         alpha=alpha,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_pred_float1, y_pred_array)
     np.testing.assert_allclose(y_pred_float2, y_pred_array)
@@ -1006,20 +1279,22 @@ def test_results_single_and_multi_jobs(strategy: str) -> None:
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred_multi, y_ps_multi = mapie_clf_multi.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_pred_single, y_pred_multi)
     np.testing.assert_allclose(y_ps_single, y_ps_multi)
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-def test_results_with_constant_sample_weights(strategy: str) -> None:
+def test_results_with_constant_sample_weights(
+    strategy: str
+) -> None:
     """
     Test predictions when sample weights are None
     or constant with different values.
@@ -1038,19 +1313,19 @@ def test_results_with_constant_sample_weights(strategy: str) -> None:
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred1, y_ps1 = mapie_clf1.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred2, y_ps2 = mapie_clf2.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_pred0, y_pred1)
     np.testing.assert_allclose(y_pred0, y_pred2)
@@ -1078,19 +1353,19 @@ def test_results_with_constant_groups(strategy: str) -> None:
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred1, y_ps1 = mapie_clf1.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     y_pred2, y_ps2 = mapie_clf2.predict(
         X,
         alpha=0.2,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_pred0, y_pred1)
     np.testing.assert_allclose(y_pred0, y_pred2)
@@ -1133,18 +1408,22 @@ def test_results_with_groups() -> None:
     # [(array([0, 1, 3, 4]), array([2, 5])),
     #  (array([0, 2, 3, 5]), array([1, 4])),
     #  (array([1, 2, 4, 5]), array([0, 3]))]
-    conformity_scores_0 = np.array([[1.0], [0.0], [0.0], [1.0], [1.0], [1.0]])
-    conformity_scores_1 = np.array([[1.0], [1.0], [1.0], [1.0], [1.0], [1.0]])
+    conformity_scores_0 = np.array([[1.], [0.], [0.], [1.], [1.], [1.]])
+    conformity_scores_1 = np.array([[1.], [1.], [1.], [1.], [1.], [1.]])
     assert np.array_equal(mapie0.conformity_scores_, conformity_scores_0)
     assert np.array_equal(mapie1.conformity_scores_, conformity_scores_1)
 
 
-@pytest.mark.parametrize("alpha", [[0.2, 0.8], (0.2, 0.8), np.array([0.2, 0.8]), None])
+@pytest.mark.parametrize(
+    "alpha", [[0.2, 0.8], (0.2, 0.8), np.array([0.2, 0.8]), None]
+)
 def test_valid_prediction(alpha: Any) -> None:
     """Test fit and predict."""
     model = LogisticRegression(multi_class="multinomial")
     model.fit(X_toy, y_toy)
-    mapie_clf = MapieClassifier(estimator=model, cv="prefit", random_state=random_state)
+    mapie_clf = MapieClassifier(
+        estimator=model, cv="prefit", random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.predict(X_toy, alpha=alpha)
 
@@ -1160,12 +1439,12 @@ def test_toy_dataset_predictions(strategy: str) -> None:
     else:
         clf = LogisticRegression()
     mapie_clf = MapieClassifier(estimator=clf, **args_init)
-    mapie_clf.fit(X_toy, y_toy, size_raps=0.5)
+    mapie_clf.fit(X_toy, y_toy, size_raps=.5)
     _, y_ps = mapie_clf.predict(
         X_toy,
         alpha=0.5,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_ps[:, :, 0], y_toy_mapie[strategy])
     np.testing.assert_allclose(
@@ -1190,7 +1469,7 @@ def test_toy_binary_dataset_predictions(strategy: str) -> None:
         X_toy,
         alpha=0.5,
         include_last_label=args_predict["include_last_label"],
-        agg_scores=args_predict["agg_scores"],
+        agg_scores=args_predict["agg_scores"]
     )
     np.testing.assert_allclose(y_ps[:, :, 0], y_toy_binary_mapie[strategy])
     np.testing.assert_allclose(
@@ -1207,12 +1486,21 @@ def test_cumulated_scores() -> None:
     cumclf = CumulatedScoreClassifier()
     cumclf.fit(cumclf.X_calib, cumclf.y_calib)
     mapie_clf = MapieClassifier(
-        cumclf, method="aps", cv="prefit", random_state=random_state
+        cumclf,
+        method="aps",
+        cv="prefit",
+        random_state=random_state
     )
     mapie_clf.fit(cumclf.X_calib, cumclf.y_calib)
-    np.testing.assert_allclose(mapie_clf.conformity_scores_, cumclf.y_calib_scores)
+    np.testing.assert_allclose(
+        mapie_clf.conformity_scores_, cumclf.y_calib_scores
+    )
     # predict
-    _, y_ps = mapie_clf.predict(cumclf.X_test, include_last_label=True, alpha=alpha)
+    _, y_ps = mapie_clf.predict(
+        cumclf.X_test,
+        include_last_label=True,
+        alpha=alpha
+    )
     np.testing.assert_allclose(mapie_clf.quantiles_, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
@@ -1228,12 +1516,19 @@ def test_image_cumulated_scores(X: Dict[str, ArrayLike]) -> None:
     cumclf = ImageClassifier(X_calib, X_test)
     cumclf.fit(cumclf.X_calib, cumclf.y_calib)
     mapie = MapieClassifier(
-        cumclf, method="aps", cv="prefit", random_state=random_state
+        cumclf,
+        method="aps",
+        cv="prefit",
+        random_state=random_state
     )
     mapie.fit(cumclf.X_calib, cumclf.y_calib)
     np.testing.assert_allclose(mapie.conformity_scores_, cumclf.y_calib_scores)
     # predict
-    _, y_ps = mapie.predict(cumclf.X_test, include_last_label=True, alpha=alpha)
+    _, y_ps = mapie.predict(
+        cumclf.X_test,
+        include_last_label=True,
+        alpha=alpha
+    )
     np.testing.assert_allclose(mapie.quantiles_, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
@@ -1255,7 +1550,8 @@ def test_sum_proba_to_one_fit(y_pred_proba: NDArray) -> None:
 @pytest.mark.parametrize("y_pred_proba", Y_PRED_PROBA_WRONG)
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_sum_proba_to_one_predict(
-    y_pred_proba: NDArray, alpha: Union[float, Iterable[float]]
+    y_pred_proba: NDArray,
+    alpha: Union[float, Iterable[float]]
 ) -> None:
     """
     Test if when the output probabilities of the model do not
@@ -1274,7 +1570,9 @@ def test_sum_proba_to_one_predict(
 @pytest.mark.parametrize(
     "estimator", [LogisticRegression(), make_pipeline(LogisticRegression())]
 )
-def test_classifier_without_classes_attribute(estimator: ClassifierMixin) -> None:
+def test_classifier_without_classes_attribute(
+    estimator: ClassifierMixin
+) -> None:
     """
     Test that prefitted classifier without 'classes_ 'attribute raises error.
     """
@@ -1283,16 +1581,24 @@ def test_classifier_without_classes_attribute(estimator: ClassifierMixin) -> Non
         delattr(estimator[-1], "classes_")
     else:
         delattr(estimator, "classes_")
-    mapie = MapieClassifier(estimator=estimator, cv="prefit", random_state=random_state)
-    with pytest.raises(AttributeError, match=r".*does not contain 'classes_'.*"):
+    mapie = MapieClassifier(
+        estimator=estimator, cv="prefit", random_state=random_state
+    )
+    with pytest.raises(
+        AttributeError, match=r".*does not contain 'classes_'.*"
+    ):
         mapie.fit(X_toy, y_toy)
 
 
 @pytest.mark.parametrize("method", WRONG_METHODS)
 def test_method_error_in_fit(monkeypatch: Any, method: str) -> None:
     """Test else condition for the method in .fit"""
-    monkeypatch.setattr(MapieClassifier, "_check_parameters", do_nothing)
-    mapie_clf = MapieClassifier(method=method, random_state=random_state)
+    monkeypatch.setattr(
+        MapieClassifier, "_check_parameters", do_nothing
+    )
+    mapie_clf = MapieClassifier(
+        method=method, random_state=random_state
+    )
     with pytest.raises(ValueError, match=r".*Invalid method.*"):
         mapie_clf.fit(X_toy, y_toy)
 
@@ -1301,7 +1607,9 @@ def test_method_error_in_fit(monkeypatch: Any, method: str) -> None:
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_method_error_in_predict(method: Any, alpha: float) -> None:
     """Test else condition for the method in .predict"""
-    mapie_clf = MapieClassifier(method="lac", random_state=random_state)
+    mapie_clf = MapieClassifier(
+        method="lac", random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
     mapie_clf.method = method
     with pytest.raises(ValueError, match=r".*Invalid method.*"):
@@ -1314,23 +1622,33 @@ def test_include_label_error_in_predict(
     monkeypatch: Any, include_labels: Union[bool, str], alpha: float
 ) -> None:
     """Test else condition for include_label parameter in .predict"""
-    monkeypatch.setattr(MapieClassifier, "_check_include_last_label", do_nothing)
-    mapie_clf = MapieClassifier(method="aps", random_state=random_state)
+    monkeypatch.setattr(
+        MapieClassifier,
+        "_check_include_last_label",
+        do_nothing
+    )
+    mapie_clf = MapieClassifier(
+        method="aps", random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
     with pytest.raises(ValueError, match=r".*Invalid include.*"):
-        mapie_clf.predict(X_toy, alpha=alpha, include_last_label=include_labels)
+        mapie_clf.predict(
+            X_toy, alpha=alpha,
+            include_last_label=include_labels
+        )
 
 
 def test_pred_loof_isnan() -> None:
     """Test that if validation set is empty then prediction is empty."""
     mapie_clf = MapieClassifier(random_state=random_state)
-
-    y_pred: NDArray
-    mapie_clf = mapie_clf.fit(X, y)
-    y_pred, _, _ = mapie_clf.estimator_._predict_proba_calib_oof_estimator(
-        estimator=LogisticRegression(), X=X_toy, val_index=[], k=0
+    _, y_pred, _, _ = mapie_clf._fit_and_predict_oof_model(
+        estimator=LogisticRegression(),
+        X=X_toy,
+        y=y_toy,
+        train_index=[0, 1, 2, 3, 4],
+        val_index=[],
+        k=0,
     )
-
     assert len(y_pred) == 0
 
 
@@ -1350,12 +1668,14 @@ def test_pipeline_compatibility(strategy: str) -> None:
         ]
     )
     categorical_preprocessor = Pipeline(
-        steps=[("encoding", OneHotEncoder(handle_unknown="ignore"))]
+        steps=[
+            ("encoding", OneHotEncoder(handle_unknown="ignore"))
+        ]
     )
     preprocessor = ColumnTransformer(
         [
             ("cat", categorical_preprocessor, ["x_cat"]),
-            ("num", numeric_preprocessor, ["x_num"]),
+            ("num", numeric_preprocessor, ["x_num"])
         ]
     )
     pipe = make_pipeline(preprocessor, LogisticRegression())
@@ -1386,16 +1706,25 @@ def test_classif_float32(cv) -> None:
     to the highest probability, MAPIE would have return
     empty prediction sets"""
     X_cal, y_cal = make_classification(
-        n_samples=20, n_features=20, n_redundant=0, n_informative=20, n_classes=3
+        n_samples=20,
+        n_features=20,
+        n_redundant=0,
+        n_informative=20,
+        n_classes=3
     )
     X_test, _ = make_classification(
-        n_samples=20, n_features=20, n_redundant=0, n_informative=20, n_classes=3
+        n_samples=20,
+        n_features=20,
+        n_redundant=0,
+        n_informative=20,
+        n_classes=3
     )
-    alpha = 0.9
+    alpha = .9
     dummy_classif = Float32OuputModel()
 
     mapie = MapieClassifier(
-        estimator=dummy_classif, method="naive", cv=cv, random_state=random_state
+        estimator=dummy_classif, method="naive",
+        cv=cv, random_state=random_state
     )
     mapie.fit(X_cal, y_cal)
     _, yps = mapie.predict(X_test, alpha=alpha, include_last_label=True)
@@ -1431,11 +1760,15 @@ def test_get_true_label_cumsum_proba_shape() -> None:
     clf = LogisticRegression()
     clf.fit(X, y)
     y_pred = clf.predict_proba(X)
-    mapie_clf = MapieClassifier(estimator=clf, random_state=random_state)
+    mapie_clf = MapieClassifier(
+        estimator=clf, random_state=random_state
+    )
     mapie_clf.fit(X, y)
-    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(y, y_pred)
+    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(
+        y, y_pred
+    )
     assert cumsum_proba.shape == (len(X), 1)
-    assert cutoff.shape == (len(X),)
+    assert cutoff.shape == (len(X), )
 
 
 def test_get_true_label_cumsum_proba_result() -> None:
@@ -1446,24 +1779,26 @@ def test_get_true_label_cumsum_proba_result() -> None:
     clf = LogisticRegression()
     clf.fit(X_toy, y_toy)
     y_pred = clf.predict_proba(X_toy)
-    mapie_clf = MapieClassifier(estimator=clf, random_state=random_state)
+    mapie_clf = MapieClassifier(
+        estimator=clf, random_state=random_state
+    )
     mapie_clf.fit(X_toy, y_toy)
-    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(y_toy, y_pred)
+    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(
+        y_toy, y_pred
+    )
     np.testing.assert_allclose(
         cumsum_proba,
         np.array(
             [
-                y_pred[0, 0],
-                y_pred[1, 0],
+                y_pred[0, 0], y_pred[1, 0],
                 y_pred[2, 0] + y_pred[2, 1],
                 y_pred[3, 0] + y_pred[3, 1],
-                y_pred[4, 1],
-                y_pred[5, 1],
+                y_pred[4, 1], y_pred[5, 1],
                 y_pred[6, 1] + y_pred[6, 2],
                 y_pred[7, 1] + y_pred[7, 2],
-                y_pred[8, 2],
+                y_pred[8, 2]
             ]
-        )[:, np.newaxis],
+        )[:, np.newaxis]
     )
     np.testing.assert_allclose(cutoff, np.array([1, 1, 2, 2, 1, 1, 2, 2, 1]))
 
@@ -1477,19 +1812,22 @@ def test_get_last_included_proba_shape(k_lambda, strategy):
     """
     lambda_, k = k_lambda[0], k_lambda[1]
     if len(k) == 1:
-        thresholds = 0.2
+        thresholds = .2
     else:
         thresholds = np.random.rand(len(k))
     thresholds = cast(NDArray, check_alpha(thresholds))
     clf = LogisticRegression()
     clf.fit(X, y)
     y_pred_proba = clf.predict_proba(X)
-    y_pred_proba = np.repeat(y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2)
+    y_pred_proba = np.repeat(
+        y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2
+    )
 
     mapie = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     include_last_label = STRATEGIES[strategy][1]["include_last_label"]
     y_p_p_c, y_p_i_l, y_p_p_i_l = mapie._get_last_included_proba(
-        y_pred_proba, thresholds, include_last_label, lambda_, k
+        y_pred_proba, thresholds,
+        include_last_label, lambda_, k
     )
 
     assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))
@@ -1503,7 +1841,9 @@ def test_error_raps_cv_not_prefit(cv: Union[int, None]) -> None:
     Test that an error is raised if the method is RAPS
     and cv is different from prefit and split.
     """
-    mapie = MapieClassifier(method="raps", cv=cv, random_state=random_state)
+    mapie = MapieClassifier(
+        method="raps", cv=cv, random_state=random_state
+    )
     with pytest.raises(ValueError, match=r".*RAPS method can only.*"):
         mapie.fit(X_toy, y_toy)
 
@@ -1519,11 +1859,12 @@ def test_not_all_label_in_calib() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv="prefit", random_state=random_state
+        estimator=clf, method="aps",
+        cv="prefit", random_state=random_state
     )
     mapie_clf.fit(X_mapie, y_mapie)
     y_pred, y_pss = mapie_clf.predict(X, alpha=0.5)
-    assert y_pred.shape == (len(X),)
+    assert y_pred.shape == (len(X), )
     assert y_pss.shape == (len(X), len(np.unique(y)), 1)
 
 
@@ -1537,9 +1878,12 @@ def test_warning_not_all_label_in_calib() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv="prefit", random_state=random_state
+        estimator=clf, method="aps",
+        cv="prefit", random_state=random_state
     )
-    with pytest.warns(UserWarning, match=r".*WARNING: your calibration dataset.*"):
+    with pytest.warns(
+        UserWarning, match=r".*WARNING: your calibration dataset.*"
+    ):
         mapie_clf.fit(X_mapie, y_mapie)
 
 
@@ -1554,7 +1898,8 @@ def test_n_classes_prefit() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv="prefit", random_state=random_state
+        estimator=clf, method="aps",
+        cv="prefit", random_state=random_state
     )
     mapie_clf.fit(X_mapie, y_mapie)
     assert mapie_clf.n_classes_ == len(np.unique(y))
@@ -1571,7 +1916,8 @@ def test_classes_prefit() -> None:
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv="prefit", random_state=random_state
+        estimator=clf, method="aps",
+        cv="prefit", random_state=random_state
     )
     mapie_clf.fit(X_mapie, y_mapie)
     assert (mapie_clf.classes_ == np.unique(y)).all()
@@ -1587,7 +1933,10 @@ def test_classes_encoder_same_than_model() -> None:
     indices_remove = np.where(y != 2)
     X_mapie = X[indices_remove]
     y_mapie = y[indices_remove]
-    mapie_clf = MapieClassifier(estimator=clf, method="aps", cv="prefit")
+    mapie_clf = MapieClassifier(
+        estimator=clf, method="aps",
+        cv="prefit"
+    )
     mapie_clf.fit(X_mapie, y_mapie)
     assert (mapie_clf.label_encoder_.classes_ == np.unique(y)).all()
 
@@ -1600,7 +1949,8 @@ def test_n_classes_cv() -> None:
     clf = LogisticRegression()
 
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv=5, random_state=random_state
+        estimator=clf, method="aps",
+        cv=5, random_state=random_state
     )
     mapie_clf.fit(X, y)
     assert mapie_clf.n_classes_ == len(np.unique(y))
@@ -1614,7 +1964,8 @@ def test_classes_cv() -> None:
     clf = LogisticRegression()
 
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv=5, random_state=random_state
+        estimator=clf, method="aps",
+        cv=5, random_state=random_state
     )
     mapie_clf.fit(X, y)
     assert (mapie_clf.classes_ == np.unique(y)).all()
@@ -1629,9 +1980,12 @@ def test_raise_error_new_class() -> None:
     clf.fit(X, y)
     y[-1] = 10
     mapie_clf = MapieClassifier(
-        estimator=clf, method="aps", cv="prefit", random_state=random_state
+        estimator=clf, method="aps",
+        cv="prefit", random_state=random_state
     )
-    with pytest.raises(ValueError, match=r".*Values in y do not matched values.*"):
+    with pytest.raises(
+        ValueError, match=r".*Values in y do not matched values.*"
+    ):
         mapie_clf.fit(X, y)
 
 
@@ -1643,9 +1997,12 @@ def test_deprecated_method_warning(method: str) -> None:
     clf = LogisticRegression()
     clf.fit(X_toy, y_toy)
     mapie_clf = MapieClassifier(
-        estimator=clf, method=method, cv="prefit", random_state=random_state
+        estimator=clf, method=method,
+        cv="prefit", random_state=random_state
     )
-    with pytest.warns(DeprecationWarning, match=r".*WARNING: Deprecated method.*"):
+    with pytest.warns(
+        DeprecationWarning, match=r".*WARNING: Deprecated method.*"
+    ):
         mapie_clf.fit(X_toy, y_toy)
 
 
@@ -1657,7 +2014,9 @@ def test_fit_parameters_passing() -> None:
     """
     gb = GradientBoostingClassifier(random_state=random_state)
 
-    mapie = MapieClassifier(estimator=gb, method="aps", random_state=random_state)
+    mapie = MapieClassifier(
+        estimator=gb, method="aps", random_state=random_state
+    )
 
     def early_stopping_monitor(i, est, locals):
         """Returns True on the 3rd iteration."""
@@ -1668,7 +2027,7 @@ def early_stopping_monitor(i, est, locals):
 
     mapie.fit(X, y, monitor=early_stopping_monitor)
 
-    assert mapie.estimator_.single_estimator_.estimators_.shape[0] == 3
+    assert mapie.single_estimator_.estimators_.shape[0] == 3
 
-    for estimator in mapie.estimator_.estimators_:
+    for estimator in mapie.estimators_:
         assert estimator.estimators_.shape[0] == 3
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 9587007a8..be305424d 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -12,14 +12,9 @@
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import (
-    GroupKFold,
-    KFold,
-    LeaveOneOut,
-    PredefinedSplit,
-    ShuffleSplit,
-    train_test_split,
-)
+from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
+                                     PredefinedSplit, ShuffleSplit,
+                                     train_test_split)
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.validation import check_is_fitted
@@ -27,20 +22,19 @@
 
 from mapie._typing import NDArray
 from mapie.aggregation_functions import aggregate_all
-from mapie.conformity_scores import (
-    AbsoluteConformityScore,
-    ConformityScore,
-    GammaConformityScore,
-    ResidualNormalisedScore,
-)
-from mapie.estimator.estimator_regressor import EnsembleRegressor
+from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
+                                     GammaConformityScore,
+                                     ResidualNormalisedScore)
+from mapie.estimator.estimator import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 from mapie.subsample import Subsample
 
 X_toy = np.array([0, 1, 2, 3, 4, 5]).reshape(-1, 1)
 y_toy = np.array([5, 7, 9, 11, 13, 15])
-X, y = make_regression(n_samples=500, n_features=10, noise=1.0, random_state=1)
+X, y = make_regression(
+    n_samples=500, n_features=10, noise=1.0, random_state=1
+)
 k = np.ones(shape=(5, X.shape[1]))
 METHODS = ["naive", "base", "plus", "minmax"]
 
@@ -62,77 +56,77 @@
         agg_function="median",
         cv=None,
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "split": Params(
         method="base",
         agg_function="median",
         cv="split",
         test_size=0.5,
-        random_state=random_state,
+        random_state=random_state
     ),
     "jackknife": Params(
         method="base",
         agg_function="mean",
         cv=-1,
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "jackknife_plus": Params(
         method="plus",
         agg_function="mean",
         cv=-1,
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "jackknife_minmax": Params(
         method="minmax",
         agg_function="mean",
         cv=-1,
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "cv": Params(
         method="base",
         agg_function="mean",
         cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "cv_plus": Params(
         method="plus",
         agg_function="mean",
         cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "cv_minmax": Params(
         method="minmax",
         agg_function="mean",
         cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "jackknife_plus_ab": Params(
         method="plus",
         agg_function="mean",
         cv=Subsample(n_resamplings=30, random_state=random_state),
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "jackknife_minmax_ab": Params(
         method="minmax",
         agg_function="mean",
         cv=Subsample(n_resamplings=30, random_state=random_state),
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
     "jackknife_plus_median_ab": Params(
         method="plus",
         agg_function="median",
         cv=Subsample(n_resamplings=30, random_state=random_state),
         test_size=None,
-        random_state=random_state,
+        random_state=random_state
     ),
 }
 
@@ -179,7 +173,9 @@ def test_default_parameters() -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 def test_valid_estimator(strategy: str) -> None:
     """Test that valid estimators are not corrupted, for all strategies."""
-    mapie_reg = MapieRegressor(estimator=DummyRegressor(), **STRATEGIES[strategy])
+    mapie_reg = MapieRegressor(
+        estimator=DummyRegressor(), **STRATEGIES[strategy]
+    )
     mapie_reg.fit(X_toy, y_toy)
     assert isinstance(mapie_reg.estimator_.single_estimator_, DummyRegressor)
     for estimator in mapie_reg.estimator_.estimators_:
@@ -215,18 +211,10 @@ def test_valid_agg_function(agg_function: str) -> None:
 
 
 @pytest.mark.parametrize(
-    "cv",
-    [
-        None,
-        -1,
-        2,
-        KFold(),
-        LeaveOneOut(),
-        ShuffleSplit(n_splits=1),
-        PredefinedSplit(test_fold=[-1] * 3 + [0] * 3),
-        "prefit",
-        "split",
-    ],
+    "cv", [None, -1, 2, KFold(), LeaveOneOut(),
+           ShuffleSplit(n_splits=1),
+           PredefinedSplit(test_fold=[-1]*3+[0]*3),
+           "prefit", "split"]
 )
 def test_valid_cv(cv: Any) -> None:
     """Test that valid cv raise no errors."""
@@ -269,7 +257,9 @@ def test_same_results_prefit_split() -> None:
     Test checking that if split and prefit method have exactly
     the same data split, then we have exactly the same results.
     """
-    X, y = make_regression(n_samples=500, n_features=10, noise=1.0, random_state=1)
+    X, y = make_regression(
+        n_samples=500, n_features=10, noise=1.0, random_state=1
+    )
     cv = ShuffleSplit(n_splits=1, test_size=0.1, random_state=random_state)
     train_index, val_index = list(cv.split(X))[0]
     X_train, X_calib = X[train_index], X[val_index]
@@ -303,8 +293,12 @@ def test_results_for_same_alpha(strategy: str) -> None:
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-@pytest.mark.parametrize("alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)])
-def test_results_for_alpha_as_float_and_arraylike(strategy: str, alpha: Any) -> None:
+@pytest.mark.parametrize(
+    "alpha", [np.array([0.05, 0.1]), [0.05, 0.1], (0.05, 0.1)]
+)
+def test_results_for_alpha_as_float_and_arraylike(
+    strategy: str, alpha: Any
+) -> None:
     """Test that output values do not depend on type of alpha."""
     mapie_reg = MapieRegressor(**STRATEGIES[strategy])
     mapie_reg.fit(X, y)
@@ -496,7 +490,9 @@ def test_results_prefit_ignore_method() -> None:
     estimator = LinearRegression().fit(X, y)
     all_y_pis: List[NDArray] = []
     for method in METHODS:
-        mapie_reg = MapieRegressor(estimator=estimator, cv="prefit", method=method)
+        mapie_reg = MapieRegressor(
+            estimator=estimator, cv="prefit", method=method
+        )
         mapie_reg.fit(X, y)
         _, y_pis = mapie_reg.predict(X, alpha=0.1)
         all_y_pis.append(y_pis)
@@ -532,7 +528,9 @@ def test_results_prefit() -> None:
     mapie_reg.fit(X_val, y_val)
     _, y_pis = mapie_reg.predict(X_test, alpha=0.05)
     width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()
-    coverage = regression_coverage_score(y_test, y_pis[:, 0, 0], y_pis[:, 1, 0])
+    coverage = regression_coverage_score(
+        y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]
+    )
     np.testing.assert_allclose(width_mean, WIDTHS["prefit"], rtol=1e-2)
     np.testing.assert_allclose(coverage, COVERAGES["prefit"], rtol=1e-2)
 
@@ -542,7 +540,9 @@ def test_not_enough_resamplings() -> None:
     Test that a warning is raised if at least one conformity score is nan.
     """
     with pytest.warns(UserWarning, match=r"WARNING: at least one point of*"):
-        mapie_reg = MapieRegressor(cv=Subsample(n_resamplings=1), agg_function="mean")
+        mapie_reg = MapieRegressor(
+            cv=Subsample(n_resamplings=1), agg_function="mean"
+        )
         mapie_reg.fit(X, y)
 
 
@@ -550,8 +550,12 @@ def test_no_agg_fx_specified_with_subsample() -> None:
     """
     Test that a warning is raised if at least one conformity score is nan.
     """
-    with pytest.raises(ValueError, match=r"You need to specify an aggregation*"):
-        mapie_reg = MapieRegressor(cv=Subsample(n_resamplings=1), agg_function=None)
+    with pytest.raises(
+        ValueError, match=r"You need to specify an aggregation*"
+    ):
+        mapie_reg = MapieRegressor(
+            cv=Subsample(n_resamplings=1), agg_function=None
+        )
         mapie_reg.fit(X, y)
 
 
@@ -589,7 +593,7 @@ def test_aggregate_with_mask_with_invalid_agg_function() -> None:
         None,
         random_state,
         0.20,
-        False,
+        False
     )
     ens_reg.use_split_method_ = False
     with pytest.raises(
@@ -646,24 +650,32 @@ def test_pipeline_compatibility() -> None:
 @pytest.mark.parametrize(
     "conformity_score", [AbsoluteConformityScore(), GammaConformityScore()]
 )
-def test_conformity_score(strategy: str, conformity_score: ConformityScore) -> None:
+def test_conformity_score(
+    strategy: str, conformity_score: ConformityScore
+) -> None:
     """Test that any conformity score function with MAPIE raises no error."""
     mapie_reg = MapieRegressor(
-        conformity_score=conformity_score, **STRATEGIES[strategy]
+        conformity_score=conformity_score,
+        **STRATEGIES[strategy]
     )
     mapie_reg.fit(X, y + 1e3)
     mapie_reg.predict(X, alpha=0.05)
 
 
-@pytest.mark.parametrize("conformity_score", [ResidualNormalisedScore()])
+@pytest.mark.parametrize(
+    "conformity_score", [ResidualNormalisedScore()]
+)
 def test_conformity_score_with_split_strategies(
-    conformity_score: ConformityScore,
+   conformity_score: ConformityScore
 ) -> None:
     """
     Test that any conformity score function that handle only split strategies
     with MAPIE raises no error.
     """
-    mapie_reg = MapieRegressor(conformity_score=conformity_score, **STRATEGIES["split"])
+    mapie_reg = MapieRegressor(
+        conformity_score=conformity_score,
+        **STRATEGIES["split"]
+    )
     mapie_reg.fit(X, y + 1e3)
     mapie_reg.predict(X, alpha=0.05)
 
@@ -696,12 +708,11 @@ def test_beta_optimize_user_warning() -> None:
     """
     Test that a UserWarning is displayed when optimize_beta is used.
     """
-    mapie_reg = MapieRegressor(conformity_score=AbsoluteConformityScore(sym=False)).fit(
-        X, y
-    )
+    mapie_reg = MapieRegressor(
+        conformity_score=AbsoluteConformityScore(sym=False)
+    ).fit(X, y)
     with pytest.warns(
-        UserWarning,
-        match=r"Beta optimisation should only be used for*",
+        UserWarning, match=r"Beta optimisation should only be used for*",
     ):
         mapie_reg.predict(X, alpha=0.05, optimize_beta=True)
 
@@ -734,6 +745,6 @@ def early_stopping_monitor(i, est, locals):
 def test_predict_infinite_intervals() -> None:
     """Test that MapieRegressor produces infinite bounds with alpha=0"""
     mapie_reg = MapieRegressor().fit(X, y)
-    _, y_pis = mapie_reg.predict(X, alpha=0.0, allow_infinite_bounds=True)
+    _, y_pis = mapie_reg.predict(X, alpha=0., allow_infinite_bounds=True)
     np.testing.assert_allclose(y_pis[:, 0, 0], -np.inf)
     np.testing.assert_allclose(y_pis[:, 1, 0], np.inf)

From 381b797da43be06c599e2a1dbc0b69a8187fe48d Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 16 May 2024 09:35:55 +0000
Subject: [PATCH 026/424] UPD: change name file and reduce line changes

---
 mapie/estimator/estimator.py            | 1076 -----------------------
 mapie/estimator/estimator_classifier.py |    2 +-
 mapie/regression/regression.py          |    2 +-
 mapie/tests/test_regression.py          |    2 +-
 4 files changed, 3 insertions(+), 1079 deletions(-)
 delete mode 100644 mapie/estimator/estimator.py

diff --git a/mapie/estimator/estimator.py b/mapie/estimator/estimator.py
deleted file mode 100644
index 70ee2aeae..000000000
--- a/mapie/estimator/estimator.py
+++ /dev/null
@@ -1,1076 +0,0 @@
-from __future__ import annotations
-
-from typing import List, Optional, Tuple, Union, cast
-
-import numpy as np
-from joblib import Parallel, delayed
-from sklearn.base import ClassifierMixin, RegressorMixin, clone
-from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
-from sklearn.utils import _safe_indexing
-from sklearn.utils.validation import _num_samples, check_is_fitted
-
-from mapie._typing import ArrayLike, NDArray
-from mapie.aggregation_functions import aggregate_all, phi2D
-from mapie.estimator.interface import EnsembleEstimator
-from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
-                         fit_estimator, fix_number_of_classes)
-
-
-class EnsembleRegressor(EnsembleEstimator):
-    """
-    This class implements methods to handle the training and usage of the
-    estimator. This estimator can be unique or composed by cross validated
-    estimators.
-
-    Parameters
-    ----------
-    estimator: Optional[RegressorMixin]
-        Any regressor with scikit-learn API
-        (i.e. with ``fit`` and ``predict`` methods).
-        If ``None``, estimator defaults to a ``LinearRegression`` instance.
-
-        By default ``None``.
-
-    method: str
-        Method to choose for prediction interval estimates.
-        Choose among:
-
-        - ``"naive"``, based on training set conformity scores,
-        - ``"base"``, based on validation sets conformity scores,
-        - ``"plus"``, based on validation conformity scores and
-          testing predictions,
-        - ``"minmax"``, based on validation conformity scores and
-          testing predictions (min/max among cross-validation clones).
-
-        By default ``"plus"``.
-
-    cv: Optional[Union[int, str, BaseCrossValidator]]
-        The cross-validation strategy for computing conformity scores.
-        It directly drives the distinction between jackknife and cv variants.
-        Choose among:
-
-        - ``None``, to use the default 5-fold cross-validation
-        - integer, to specify the number of folds.
-          If equal to ``-1``, equivalent to
-          ``sklearn.model_selection.LeaveOneOut()``.
-        - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
-          Main variants are:
-            - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
-            - ``sklearn.model_selection.KFold`` (cross-validation),
-            - ``subsample.Subsample`` object (bootstrap).
-        - ``"split"``, does not involve cross-validation but a division
-          of the data into training and calibration subsets. The splitter
-          used is the following: ``sklearn.model_selection.ShuffleSplit``.
-        - ``"prefit"``, assumes that ``estimator`` has been fitted already,
-          and the ``method`` parameter is ignored.
-          All data provided in the ``fit`` method is then used
-          for computing conformity scores only.
-          At prediction time, quantiles of these conformity scores are used
-          to provide a prediction interval with fixed width.
-          The user has to take care manually that data for model fitting and
-          conformity scores estimate are disjoint.
-
-        By default ``None``.
-
-    test_size: Optional[Union[int, float]]
-        If ``float``, should be between ``0.0`` and ``1.0`` and represent the
-        proportion of the dataset to include in the test split. If ``int``,
-        represents the absolute number of test samples. If ``None``,
-        it will be set to ``0.1``.
-
-        If cv is not ``"split"``, ``test_size`` is ignored.
-
-        By default ``None``.
-
-    n_jobs: Optional[int]
-        Number of jobs for parallel processing using joblib
-        via the "locky" backend.
-        If ``-1`` all CPUs are used.
-        If ``1`` is given, no parallel computing code is used at all,
-        which is useful for debugging.
-        For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
-        ``None`` is a marker for `unset` that will be interpreted as
-        ``n_jobs=1`` (sequential execution).
-
-        By default ``None``.
-
-    agg_function: Optional[str]
-        Determines how to aggregate predictions from perturbed models, both at
-        training and prediction time.
-
-        If ``None``, it is ignored except if ``cv`` class is ``Subsample``,
-        in which case an error is raised.
-        If ``"mean"`` or ``"median"``, returns the mean or median of the
-        predictions computed from the out-of-folds models.
-        Note: if you plan to set the ``ensemble`` argument to ``True`` in the
-        ``predict`` method, you have to specify an aggregation function.
-        Otherwise an error would be raised.
-
-        The Jackknife+ interval can be interpreted as an interval around the
-        median prediction, and is guaranteed to lie inside the interval,
-        unlike the single estimator predictions.
-
-        When the cross-validation strategy is ``Subsample`` (i.e. for the
-        Jackknife+-after-Bootstrap method), this function is also used to
-        aggregate the training set in-sample predictions.
-
-        If ``cv`` is ``"prefit"`` or ``"split"``, ``agg_function`` is ignored.
-
-        By default ``"mean"``.
-
-    verbose: int
-        The verbosity level, used with joblib for multiprocessing.
-        The frequency of the messages increases with the verbosity level.
-        If it more than ``10``, all iterations are reported.
-        Above ``50``, the output is sent to stdout.
-
-        By default ``0``.
-
-    random_state: Optional[Union[int, RandomState]]
-        Pseudo random number generator state used for random sampling.
-        Pass an int for reproducible output across multiple function calls.
-
-        By default ``None``.
-
-    Attributes
-    ----------
-    single_estimator_: sklearn.RegressorMixin
-        Estimator fitted on the whole training set.
-
-    estimators_: list
-        List of out-of-folds estimators.
-
-    k_: ArrayLike
-        - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
-          (defined but not used)
-        - Dummy array of folds containing each training sample, otherwise.
-          Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
-    """
-    no_agg_cv_ = ["prefit", "split"]
-    no_agg_methods_ = ["naive", "base"]
-    fit_attributes = [
-        "single_estimator_",
-        "estimators_",
-        "k_",
-        "use_split_method_",
-    ]
-
-    def __init__(
-        self,
-        estimator: Optional[RegressorMixin],
-        method: str,
-        cv: Optional[Union[int, str, BaseCrossValidator]],
-        agg_function: Optional[str],
-        n_jobs: Optional[int],
-        random_state: Optional[Union[int, np.random.RandomState]],
-        test_size: Optional[Union[int, float]],
-        verbose: int
-    ):
-        self.estimator = estimator
-        self.method = method
-        self.cv = cv
-        self.agg_function = agg_function
-        self.n_jobs = n_jobs
-        self.random_state = random_state
-        self.test_size = test_size
-        self.verbose = verbose
-
-    @staticmethod
-    def _fit_oof_estimator(
-        estimator: RegressorMixin,
-        X: ArrayLike,
-        y: ArrayLike,
-        train_index: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        **fit_params,
-    ) -> RegressorMixin:
-        """
-        Fit a single out-of-fold model on a given training set.
-
-        Parameters
-        ----------
-        estimator: RegressorMixin
-            Estimator to train.
-
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        train_index: ArrayLike of shape (n_samples_train)
-            Training data indices.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-            By default ``None``.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        RegressorMixin
-            Fitted estimator.
-        """
-        X_train = _safe_indexing(X, train_index)
-        y_train = _safe_indexing(y, train_index)
-        if not (sample_weight is None):
-            sample_weight = _safe_indexing(sample_weight, train_index)
-            sample_weight = cast(NDArray, sample_weight)
-
-        estimator = fit_estimator(
-            estimator,
-            X_train,
-            y_train,
-            sample_weight=sample_weight,
-            **fit_params
-        )
-        return estimator
-
-    @staticmethod
-    def _predict_oof_estimator(
-        estimator: RegressorMixin,
-        X: ArrayLike,
-        val_index: ArrayLike,
-    ) -> Tuple[NDArray, ArrayLike]:
-        """
-        Perform predictions on a single out-of-fold model on a validation set.
-
-        Parameters
-        ----------
-        estimator: RegressorMixin
-            Estimator to train.
-
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        val_index: ArrayLike of shape (n_samples_val)
-            Validation data indices.
-
-        Returns
-        -------
-        Tuple[NDArray, ArrayLike]
-            Predictions of estimator from val_index of X.
-        """
-        X_val = _safe_indexing(X, val_index)
-        if _num_samples(X_val) > 0:
-            y_pred = estimator.predict(X_val)
-        else:
-            y_pred = np.array([])
-        return y_pred, val_index
-
-    def _aggregate_with_mask(
-        self,
-        x: NDArray,
-        k: NDArray
-    ) -> NDArray:
-        """
-        Take the array of predictions, made by the refitted estimators,
-        on the testing set, and the 1-or-nan array indicating for each training
-        sample which one to integrate, and aggregate to produce phi-{t}(x_t)
-        for each training sample x_t.
-
-        Parameters
-        ----------
-        x: ArrayLike of shape (n_samples_test, n_estimators)
-            Array of predictions, made by the refitted estimators,
-            for each sample of the testing set.
-
-        k: ArrayLike of shape (n_samples_training, n_estimators)
-            1-or-nan array: indicates whether to integrate the prediction
-            of a given estimator into the aggregation, for each training
-            sample.
-
-        Returns
-        -------
-        ArrayLike of shape (n_samples_test,)
-            Array of aggregated predictions for each testing sample.
-        """
-        if self.method in self.no_agg_methods_ or self.use_split_method_:
-            raise ValueError(
-                "There should not be aggregation of predictions "
-                f"if cv is in '{self.no_agg_cv_}', if cv >=2 "
-                f"or if method is in '{self.no_agg_methods_}'."
-            )
-        elif self.agg_function == "median":
-            return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))
-        # To aggregate with mean() the aggregation coud be done
-        # with phi2D(A=x, B=k, fun=lambda x: np.nanmean(x, axis=1).
-        # However, phi2D contains a np.apply_along_axis loop which
-        # is much slower than the matrices multiplication that can
-        # be used to compute the means.
-        elif self.agg_function in ["mean", None]:
-            K = np.nan_to_num(k, nan=0.0)
-            return np.matmul(x, (K / (K.sum(axis=1, keepdims=True))).T)
-        else:
-            raise ValueError("The value of self.agg_function is not correct")
-
-    def _pred_multi(self, X: ArrayLike) -> NDArray:
-        """
-        Return a prediction per train sample for each test sample, by
-        aggregation with matrix ``k_``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
-            Input data
-
-        Returns
-        -------
-        NDArray of shape (n_samples_test, n_samples_train)
-        """
-        y_pred_multi = np.column_stack(
-            [e.predict(X) for e in self.estimators_]
-        )
-        # At this point, y_pred_multi is of shape
-        # (n_samples_test, n_estimators_). The method
-        # ``_aggregate_with_mask`` fits it to the right size
-        # thanks to the shape of k_.
-        y_pred_multi = self._aggregate_with_mask(y_pred_multi, self.k_)
-        return y_pred_multi
-
-    def predict_calib(
-        self,
-        X: ArrayLike,
-        y: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None
-    ) -> NDArray:
-        """
-        Perform predictions on X : the calibration set.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
-            Input data
-
-        y: Optional[ArrayLike] of shape (n_samples_test,)
-            Input labels.
-
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples_test,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-
-            By default ``None``.
-
-        Returns
-        -------
-        NDArray of shape (n_samples_test, 1)
-            The predictions.
-        """
-        check_is_fitted(self, self.fit_attributes)
-
-        if self.cv == "prefit":
-            y_pred = self.single_estimator_.predict(X)
-        else:
-            if self.method == "naive":
-                y_pred = self.single_estimator_.predict(X)
-            else:
-                cv = cast(BaseCrossValidator, self.cv)
-                outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
-                    delayed(self._predict_oof_estimator)(
-                        estimator, X, calib_index,
-                    )
-                    for (_, calib_index), estimator in zip(
-                        cv.split(X, y, groups),
-                        self.estimators_
-                    )
-                )
-                predictions, indices = map(
-                    list, zip(*outputs)
-                )
-                n_samples = _num_samples(X)
-                pred_matrix = np.full(
-                    shape=(n_samples, cv.get_n_splits(X, y, groups)),
-                    fill_value=np.nan,
-                    dtype=float,
-                )
-                for i, ind in enumerate(indices):
-                    pred_matrix[ind, i] = np.array(
-                        predictions[i], dtype=float
-                    )
-                    self.k_[ind, i] = 1
-                check_nan_in_aposteriori_prediction(pred_matrix)
-
-                y_pred = aggregate_all(self.agg_function, pred_matrix)
-
-        return y_pred
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None,
-        **fit_params,
-    ) -> EnsembleRegressor:
-        """
-        Fit the base estimator under the ``single_estimator_`` attribute.
-        Fit all cross-validated estimator clones
-        and rearrange them into a list, the ``estimators_`` attribute.
-        Out-of-fold conformity scores are stored under
-        the ``conformity_scores_`` attribute.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-
-            By default ``None``.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        EnsembleRegressor
-            The estimator fitted.
-        """
-        # Initialization
-        single_estimator_: RegressorMixin
-        estimators_: List[RegressorMixin] = []
-        full_indexes = np.arange(_num_samples(X))
-        cv = self.cv
-        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
-        estimator = self.estimator
-        n_samples = _num_samples(y)
-
-        # Computation
-        if cv == "prefit":
-            single_estimator_ = estimator
-            self.k_ = np.full(
-                shape=(n_samples, 1), fill_value=np.nan, dtype=float
-            )
-        else:
-            single_estimator_ = self._fit_oof_estimator(
-                clone(estimator),
-                X,
-                y,
-                full_indexes,
-                sample_weight,
-                **fit_params
-            )
-            cv = cast(BaseCrossValidator, cv)
-            self.k_ = np.full(
-                shape=(n_samples, cv.get_n_splits(X, y, groups)),
-                fill_value=np.nan,
-                dtype=float,
-            )
-            if self.method == "naive":
-                estimators_ = [single_estimator_]
-            else:
-                estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
-                    delayed(self._fit_oof_estimator)(
-                        clone(estimator),
-                        X,
-                        y,
-                        train_index,
-                        sample_weight,
-                        **fit_params
-                    )
-                    for train_index, _ in cv.split(X, y, groups)
-                )
-                # In split-CP, we keep only the model fitted on train dataset
-                if self.use_split_method_:
-                    single_estimator_ = estimators_[0]
-
-        self.single_estimator_ = single_estimator_
-        self.estimators_ = estimators_
-
-        return self
-
-    def predict(
-        self,
-        X: ArrayLike,
-        ensemble: bool = False,
-        return_multi_pred: bool = True
-    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
-        """
-        Predict target from X. It also computes the prediction per train sample
-        for each test sample according to ``self.method``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Test data.
-
-        ensemble: bool
-            Boolean determining whether the predictions are ensembled or not.
-            If ``False``, predictions are those of the model trained on the
-            whole training set.
-            If ``True``, predictions from perturbed models are aggregated by
-            the aggregation function specified in the ``agg_function``
-            attribute.
-
-            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
-
-            By default ``False``.
-
-        return_multi_pred: bool
-            If ``True`` the method returns the predictions and the multiple
-            predictions (3 arrays). If ``False`` the method return the
-            simple predictions only.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray, NDArray]
-            - Predictions
-            - The multiple predictions for the lower bound of the intervals.
-            - The multiple predictions for the upper bound of the intervals.
-        """
-        check_is_fitted(self, self.fit_attributes)
-
-        y_pred = self.single_estimator_.predict(X)
-        if not return_multi_pred and not ensemble:
-            return y_pred
-
-        if self.method in self.no_agg_methods_ or self.use_split_method_:
-            y_pred_multi_low = y_pred[:, np.newaxis]
-            y_pred_multi_up = y_pred[:, np.newaxis]
-        else:
-            y_pred_multi = self._pred_multi(X)
-
-            if self.method == "minmax":
-                y_pred_multi_low = np.min(y_pred_multi, axis=1, keepdims=True)
-                y_pred_multi_up = np.max(y_pred_multi, axis=1, keepdims=True)
-            elif self.method == "plus":
-                y_pred_multi_low = y_pred_multi
-                y_pred_multi_up = y_pred_multi
-            else:
-                y_pred_multi_low = y_pred[:, np.newaxis]
-                y_pred_multi_up = y_pred[:, np.newaxis]
-
-            if ensemble:
-                y_pred = aggregate_all(self.agg_function, y_pred_multi)
-
-        if return_multi_pred:
-            return y_pred, y_pred_multi_low, y_pred_multi_up
-        else:
-            return y_pred
-
-
-class EnsembleClassifier(EnsembleEstimator):
-    """
-    This class implements methods to handle the training and usage of the
-    estimator. This estimator can be unique or composed by cross validated
-    estimators.
-
-    Parameters
-    ----------
-    estimator: Optional[RegressorMixin]
-        Any regressor with scikit-learn API
-        (i.e. with ``fit`` and ``predict`` methods).
-        If ``None``, estimator defaults to a ``LinearRegression`` instance.
-
-        By default ``None``.
-
-    cv: Optional[str]
-        The cross-validation strategy for computing scores.
-        It directly drives the distinction between jackknife and cv variants.
-        Choose among:
-
-        - ``None``, to use the default 5-fold cross-validation
-        - integer, to specify the number of folds.
-          If equal to -1, equivalent to
-          ``sklearn.model_selection.LeaveOneOut()``.
-        - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
-          Main variants are:
-          - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
-          - ``sklearn.model_selection.KFold`` (cross-validation)
-        - ``"split"``, does not involve cross-validation but a division
-          of the data into training and calibration subsets. The splitter
-          used is the following: ``sklearn.model_selection.ShuffleSplit``.
-        - ``"prefit"``, assumes that ``estimator`` has been fitted already.
-          All data provided in the ``fit`` method is then used
-          to calibrate the predictions through the score computation.
-          At prediction time, quantiles of these scores are used to estimate
-          prediction sets.
-
-        By default ``None``.
-
-    test_size: Optional[Union[int, float]]
-        If ``float``, should be between ``0.0`` and ``1.0`` and represent the
-        proportion of the dataset to include in the test split. If ``int``,
-        represents the absolute number of test samples. If ``None``,
-        it will be set to ``0.1``.
-
-        If cv is not ``"split"``, ``test_size`` is ignored.
-
-        By default ``None``.
-
-    n_jobs: Optional[int]
-        Number of jobs for parallel processing using joblib
-        via the "locky" backend.
-        If ``-1`` all CPUs are used.
-        If ``1`` is given, no parallel computing code is used at all,
-        which is useful for debugging.
-        For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
-        ``None`` is a marker for `unset` that will be interpreted as
-        ``n_jobs=1`` (sequential execution).
-
-        By default ``None``.
-
-   random_state: Optional[Union[int, RandomState]]
-        Pseudo random number generator state used for random uniform sampling
-        for evaluation quantiles and prediction sets.
-        Pass an int for reproducible output across multiple function calls.
-
-        By default ``None``.
-
-    verbose: int, optional
-        The verbosity level, used with joblib for multiprocessing.
-        At this moment, parallel processing is disabled.
-        The frequency of the messages increases with the verbosity level.
-        If it more than ``10``, all iterations are reported.
-        Above ``50``, the output is sent to stdout.
-
-        By default ``0``.
-
-    Attributes
-    ----------
-    single_estimator_: sklearn.RegressorMixin
-        Estimator fitted on the whole training set.
-
-    estimators_: list
-        List of out-of-folds estimators.
-
-    k_: ArrayLike
-        - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
-          (defined but not used)
-        - Dummy array of folds containing each training sample, otherwise.
-          Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
-    """
-    no_agg_cv_ = ["prefit", "split"]
-    fit_attributes = [
-        "single_estimator_",
-        "estimators_",
-        "k_",
-        "use_split_method_",
-    ]
-
-    def __init__(
-        self,
-        estimator: Optional[ClassifierMixin],
-        n_classes: int,
-        cv: Optional[Union[int, str, BaseCrossValidator]],
-        n_jobs: Optional[int],
-        random_state: Optional[Union[int, np.random.RandomState]],
-        test_size: Optional[Union[int, float]],
-        verbose: int
-    ):
-        self.estimator = estimator
-        self.n_classes = n_classes
-        self.cv = cv
-        self.n_jobs = n_jobs
-        self.random_state = random_state
-        self.test_size = test_size
-        self.verbose = verbose
-
-    @staticmethod
-    def _fit_oof_estimator(
-        estimator: ClassifierMixin,
-        X: ArrayLike,
-        y: ArrayLike,
-        train_index: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        **fit_params,
-    ) -> ClassifierMixin:
-        """
-        Fit a single out-of-fold model on a given training set.
-
-        Parameters
-        ----------
-        estimator: RegressorMixin
-            Estimator to train.
-
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        train_index: ArrayLike of shape (n_samples_train)
-            Training data indices.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-            By default ``None``.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        RegressorMixin
-            Fitted estimator.
-        """
-        X_train = _safe_indexing(X, train_index)
-        y_train = _safe_indexing(y, train_index)
-        if not (sample_weight is None):
-            sample_weight = _safe_indexing(sample_weight, train_index)
-            sample_weight = cast(NDArray, sample_weight)
-
-        estimator = fit_estimator(
-            estimator,
-            X_train,
-            y_train,
-            sample_weight=sample_weight,
-            **fit_params
-        )
-        return estimator
-    
-    def _predict_proba_oof_estimator(self, estimator, X):
-        y_pred_proba = estimator.predict_proba(X)
-        if len(estimator.classes_) != self.n_classes:
-            y_pred_proba = fix_number_of_classes(
-                self.n_classes,
-                estimator.classes_,
-                y_pred_proba
-            )
-        return y_pred_proba
-
-    def _predict_proba_calib_oof_estimator(
-        self,
-        estimator: ClassifierMixin,
-        X: ArrayLike,
-        val_index: ArrayLike,
-        k: int
-    ) -> Tuple[NDArray, ArrayLike]:
-        """
-        Perform predictions on a single out-of-fold model on a validation set.
-
-        Parameters
-        ----------
-        estimator: RegressorMixin
-            Estimator to train.
-
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        val_index: ArrayLike of shape (n_samples_val)
-            Validation data indices.
-
-        Returns
-        -------
-        Tuple[NDArray, ArrayLike]
-            Predictions of estimator from val_index of X.
-        """
-        
-        X_val = _safe_indexing(X, val_index)
-        if _num_samples(X_val) > 0:
-            y_pred_proba = self._predict_proba_oof_estimator(
-                estimator, X_val
-            )
-        else:
-            y_pred_proba = np.array([])
-        val_id = np.full(len(X_val), k, dtype=int)
-        return y_pred_proba, val_id, val_index
-
-    def _aggregate_with_mask(
-        self,
-        x: NDArray,
-        k: NDArray
-    ) -> NDArray:
-        """
-        Take the array of predictions, made by the refitted estimators,
-        on the testing set, and the 1-or-nan array indicating for each training
-        sample which one to integrate, and aggregate to produce phi-{t}(x_t)
-        for each training sample x_t.
-
-        Parameters
-        ----------
-        x: ArrayLike of shape (n_samples_test, n_estimators)
-            Array of predictions, made by the refitted estimators,
-            for each sample of the testing set.
-
-        k: ArrayLike of shape (n_samples_training, n_estimators)
-            1-or-nan array: indicates whether to integrate the prediction
-            of a given estimator into the aggregation, for each training
-            sample.
-
-        Returns
-        -------
-        ArrayLike of shape (n_samples_test,)
-            Array of aggregated predictions for each testing sample.
-        """
-        if self.method in self.no_agg_methods_ or self.use_split_method_:
-            raise ValueError(
-                "There should not be aggregation of predictions "
-                f"if cv is in '{self.no_agg_cv_}', if cv >=2 "
-                f"or if method is in '{self.no_agg_methods_}'."
-            )
-        elif self.agg_function == "median":
-            return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))
-        # To aggregate with mean() the aggregation coud be done
-        # with phi2D(A=x, B=k, fun=lambda x: np.nanmean(x, axis=1).
-        # However, phi2D contains a np.apply_along_axis loop which
-        # is much slower than the matrices multiplication that can
-        # be used to compute the means.
-        elif self.agg_function in ["mean", None]:
-            K = np.nan_to_num(k, nan=0.0)
-            return np.matmul(x, (K / (K.sum(axis=1, keepdims=True))).T)
-        else:
-            raise ValueError("The value of self.agg_function is not correct")
-
-    def _pred_multi(self, X: ArrayLike) -> NDArray:
-        """
-        Return a prediction per train sample for each test sample, by
-        aggregation with matrix ``k_``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
-            Input data
-
-        Returns
-        -------
-        NDArray of shape (n_samples_test, n_samples_train)
-        """
-        y_pred_multi = np.column_stack(
-            [e.predict(X) for e in self.estimators_]
-        )
-        # At this point, y_pred_multi is of shape
-        # (n_samples_test, n_estimators_). The method
-        # ``_aggregate_with_mask`` fits it to the right size
-        # thanks to the shape of k_.
-        y_pred_multi = self._aggregate_with_mask(y_pred_multi, self.k_)
-        return y_pred_multi
-
-    def predict_proba_calib(
-        self,
-        X: ArrayLike,
-        y: Optional[ArrayLike] = None,
-        y_enc=None,
-        groups: Optional[ArrayLike] = None
-    ) -> NDArray:
-        """
-        Perform predictions on X : the calibration set.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
-            Input data
-
-        y: Optional[ArrayLike] of shape (n_samples_test,)
-            Input labels.
-
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples_test,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-
-            By default ``None``.
-
-        Returns
-        -------
-        NDArray of shape (n_samples_test, 1)
-            The predictions.
-        """
-        check_is_fitted(self, self.fit_attributes)
-
-        if self.cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
-        else:
-            y_pred_proba = np.empty(
-                (len(X), self.n_classes),
-                dtype=float
-            )
-            cv = cast(BaseCrossValidator, self.cv)
-            outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
-                delayed(self._predict_proba_calib_oof_estimator)(
-                    estimator, X, calib_index, k
-                )
-                for k, ((_, calib_index), estimator) in enumerate(zip(
-                    cv.split(X, y, groups),
-                    self.estimators_
-                ))
-            )
-            (
-                predictions_list,
-                val_ids_list,
-                val_indices_list
-            ) = map(list, zip(*outputs))
-
-            predictions = np.concatenate(
-                cast(List[NDArray], predictions_list)
-            )
-            val_ids = np.concatenate(cast(List[NDArray], val_ids_list))
-            val_indices = np.concatenate(
-                cast(List[NDArray], val_indices_list)
-            )
-            self.k_[val_indices] = val_ids
-            y_pred_proba[val_indices] = predictions
-
-            if isinstance(cv, ShuffleSplit):
-                # Should delete values indices that
-                # are not used during calibration
-                self.k_ = self.k_[val_indices]
-                y_pred_proba = y_pred_proba[val_indices]
-                y_enc = y_enc[val_indices]
-                y = cast(NDArray, y)[val_indices]
-
-        return y_pred_proba, y, y_enc
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_enc: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None,
-        **fit_params,
-    ) -> EnsembleRegressor:
-        """
-        Fit the base estimator under the ``single_estimator_`` attribute.
-        Fit all cross-validated estimator clones
-        and rearrange them into a list, the ``estimators_`` attribute.
-        Out-of-fold conformity scores are stored under
-        the ``conformity_scores_`` attribute.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-
-            By default ``None``.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        EnsembleRegressor
-            The estimator fitted.
-        """
-        # Initialization
-        single_estimator_: ClassifierMixin
-        estimators_: List[ClassifierMixin] = []
-        full_indexes = np.arange(_num_samples(X))
-        cv = self.cv
-        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
-        estimator = self.estimator
-        n_samples = _num_samples(y)
-
-        # Computation
-        if cv == "prefit":
-            single_estimator_ = estimator
-            self.k_ = np.full(
-                shape=(n_samples, 1), fill_value=np.nan, dtype=float
-            )
-        else:
-            single_estimator_ = self._fit_oof_estimator(
-                clone(estimator),
-                X,
-                y,
-                full_indexes,
-                sample_weight,
-                **fit_params
-            )
-            cv = cast(BaseCrossValidator, cv)
-            self.k_ = np.empty_like(y, dtype=int)
-
-            estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
-                delayed(self._fit_oof_estimator)(
-                    clone(estimator),
-                    X,
-                    y_enc,
-                    train_index,
-                    sample_weight,
-                    **fit_params
-                )
-                for train_index, _ in cv.split(X, y, groups)
-            )
-            # In split-CP, we keep only the model fitted on train dataset
-            if self.use_split_method_:
-                single_estimator_ = estimators_[0]
-
-        self.single_estimator_ = single_estimator_
-        self.estimators_ = estimators_
-
-        return self
-
-    def predict(
-        self,
-        X: ArrayLike,
-        agg_scores
-    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
-        """
-        Predict target from X. It also computes the prediction per train sample
-        for each test sample according to ``self.method``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Test data.
-
-        ensemble: bool
-            Boolean determining whether the predictions are ensembled or not.
-            If ``False``, predictions are those of the model trained on the
-            whole training set.
-            If ``True``, predictions from perturbed models are aggregated by
-            the aggregation function specified in the ``agg_function``
-            attribute.
-
-            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
-
-            By default ``False``.
-
-        return_multi_pred: bool
-            If ``True`` the method returns the predictions and the multiple
-            predictions (3 arrays). If ``False`` the method return the
-            simple predictions only.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray, NDArray]
-            - Predictions
-            - The multiple predictions for the lower bound of the intervals.
-            - The multiple predictions for the upper bound of the intervals.
-        """
-        check_is_fitted(self, self.fit_attributes)
-
-        if self.cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
-        else:
-            y_pred_proba_k = np.asarray(
-                Parallel(
-                    n_jobs=self.n_jobs, verbose=self.verbose
-                )(
-                    delayed(self._predict_proba_oof_estimator)(estimator, X)
-                    for estimator in self.estimators_
-                )
-            )
-            if agg_scores == "crossval":
-                y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2)
-            elif agg_scores == "mean":
-                y_pred_proba = np.mean(y_pred_proba_k, axis=0)
-            else:
-                raise ValueError("Invalid 'agg_scores' argument.")
-        return y_pred_proba
diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/estimator_classifier.py
index be54376d4..58cd2f6c7 100644
--- a/mapie/estimator/estimator_classifier.py
+++ b/mapie/estimator/estimator_classifier.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import List, Optional, Tuple, Union, cast
+from typing import Any, List, Optional, Tuple, Union, cast
 
 import numpy as np
 from joblib import Parallel, delayed
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 46bebf3d8..ff6e41e0b 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -13,7 +13,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
-from mapie.estimator.estimator import EnsembleRegressor
+from mapie.estimator.estimator_regressor import EnsembleRegressor
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_conformity_score, check_cv,
                          check_estimator_fit_predict, check_n_features_in,
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index be305424d..ac36b473d 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -25,7 +25,7 @@
 from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
                                      GammaConformityScore,
                                      ResidualNormalisedScore)
-from mapie.estimator.estimator import EnsembleRegressor
+from mapie.estimator.estimator_regressor import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 from mapie.subsample import Subsample

From a99cb4d10cb2a761bf0d97b98532e4b91bdebb57 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:26:11 +0200
Subject: [PATCH 027/424] chore: Add citation information to README.rst

---
 README.rst | 6 ++++++
 1 file changed, 6 insertions(+)

diff --git a/README.rst b/README.rst
index a6b57cbfd..1c1bdd4bd 100644
--- a/README.rst
+++ b/README.rst
@@ -224,3 +224,9 @@ and with the financial support from Région Ile de France and Confiance.ai.
 ==========
 
 MAPIE is free and open-source software licensed under the `3-clause BSD license <https://github.com/simai-ml/MAPIE/blob/master/LICENSE>`_.
+
+
+📚 Citation
+===========
+
+If you use MAPIE in your research, please cite using `citations file <https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files>`_ on our repository.

From 14c6dfb2550b4b2e2bc33516fef7d122fe5076d4 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:29:13 +0200
Subject: [PATCH 028/424] Update doc/theoretical_description_regression.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_regression.rst | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index 9479f4645..12378f3a1 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -255,10 +255,14 @@ coverage value.
 
 Notations and Definitions
 -------------------------
+- :math:`\mathcal{I}_1` is the set of indices of the data in the training set.
+- :math:`\mathcal{I}_2` is the set of indices of the data in the calibration set.
+- :math:`\hat{q}_{\alpha_{\text{low}}}`: Lower quantile model trained on :math:`{(X_i, Y_i) : i \in \mathcal{I}_1}`.
+- :math:`\hat{q}_{\alpha_{\text{high}}}`: Upper quantile model trained on :math:`{(X_i, Y_i) : i \in \mathcal{I}_1}`.
 - :math:`E_i`: Residuals for the i-th sample in the calibration set.
 - :math:`E_{\text{low}}`: Residuals from the lower quantile model.
 - :math:`E_{\text{high}}`: Residuals from the upper quantile model.
-- :math:`Q_{1-\alpha}(E, \mathcal{I}_2)`: The :math:`(1-\alpha)(1+1/|\mathcal{I}_2|)`-th empirical quantile of the set :math:`{E_i : i \in \mathcal{I}_2}`, where :math:`\mathcal{I}_2` is the set of indices of the residuals in the calibration set.
+- :math:`Q_{1-\alpha}(E, \mathcal{I}_2)`: The :math:`(1-\alpha)(1+1/|\mathcal{I}_2|)`-th empirical quantile of the set :math:`{E_i : i \in \mathcal{I}_2}`.
 
 Mathematical Formulation
 ------------------------

From b66a67ac540726023fb92083cc2d183cb58cbfcb Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:29:20 +0200
Subject: [PATCH 029/424] Update doc/theoretical_description_regression.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_regression.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index 12378f3a1..ffa1368e5 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -278,7 +278,7 @@ Where:
 - :math:`\hat{q}_{\alpha_{\text{lo}}}(X_{n+1})` is the predicted lower quantile for the new sample.
 - :math:`\hat{q}_{\alpha_{\text{hi}}}(X_{n+1})` is the predicted upper quantile for the new sample.
 
-Note: In the symmetric method, :math:`E_{\text{low}}` and :math:`E_{\text{high}}` are considered equal.
+Note: In the symmetric method, :math:`E_{\text{low}}` and :math:`E_{\text{high}}` sets are no longer distinct. We consider directly the union set :math:`E_{\text{all}} = E_{\text{low}} \cup E_{\text{high}}` and the empirical quantile is then calculated on all the absolute (positive) residuals.
 
 As justified by the literature, this method offers a theoretical guarantee of the target coverage level :math:`1-\alpha`.
 

From 501bade5c8108dfab6428f86e3e62e00f49e9875 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:35:13 +0200
Subject: [PATCH 030/424] Update theoretical_description_regression.rst

---
 doc/theoretical_description_regression.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index ffa1368e5..f616e3c91 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -246,7 +246,7 @@ is then higher.
 
 
 9. The Conformalized Quantile Regression (CQR) Method
-==================================================
+=====================================================
 
 The conformalized quantile regression (CQR) method allows for better interval widths with
 heteroscedastic data. It uses quantile regressors with different quantile values to estimate
@@ -275,6 +275,7 @@ The prediction interval :math:`\hat{C}_{n, \alpha}^{\text{CQR}}(X_{n+1})` for a
     \hat{q}_{\alpha_{\text{hi}}}(X_{n+1}) + Q_{1-\alpha}(E_{\text{high}}, \mathcal{I}_2)]
 
 Where:
+
 - :math:`\hat{q}_{\alpha_{\text{lo}}}(X_{n+1})` is the predicted lower quantile for the new sample.
 - :math:`\hat{q}_{\alpha_{\text{hi}}}(X_{n+1})` is the predicted upper quantile for the new sample.
 

From 051d30cbd4776fa432e4001afcef0ade150d4583 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:35:19 +0200
Subject: [PATCH 031/424] chore: Update plot_cqr_symmetry_difference.py in
 regression examples

---
 .../plot_cqr_symmetry_difference.py           | 56 ++++++++++---------
 1 file changed, 30 insertions(+), 26 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 7cc23a3e7..608a5d0db 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -13,7 +13,9 @@
 from mapie.metrics import regression_coverage_score
 from mapie.quantile_regression import MapieQuantileRegressor
 
-# Generate synthetic data
+##############################################################################
+# We generate a synthetic data.
+
 X, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=59)
 
 # Define alpha level
@@ -36,10 +38,10 @@
 
 # Calculate coverage scores
 coverage_score_sym = regression_coverage_score(
-    y, y_pis_sym[:, 0], y_pis_sym[:, 1]
+y, y_pis_sym[:, 0], y_pis_sym[:, 1]
 )
 coverage_score_asym = regression_coverage_score(
-    y, y_pis_asym[:, 0], y_pis_asym[:, 1]
+y, y_pis_asym[:, 0], y_pis_asym[:, 1]
 )
 
 # Sort the values for plotting
@@ -50,7 +52,12 @@
 y_pred_asym_sorted = y_pred_asym[order]
 y_pis_asym_sorted = y_pis_asym[order]
 
-# Plot symmetric prediction intervals
+##############################################################################
+# We will plot the predictions and prediction intervals for both symmetric
+# and asymmetric intervals. The line represents the predicted values, the
+# dashed lines represent the prediction intervals, and the shaded area
+# represents the symmetric and asymmetric prediction intervals.
+
 plt.figure(figsize=(14, 7))
 
 plt.subplot(1, 2, 1)
@@ -61,15 +68,15 @@
 plt.plot(X_sorted, y_pis_sym_sorted[:, 0], color="C1", ls="--")
 plt.plot(X_sorted, y_pis_sym_sorted[:, 1], color="C1", ls="--")
 plt.fill_between(
-    X_sorted.ravel(),
-    y_pis_sym_sorted[:, 0].ravel(),
-    y_pis_sym_sorted[:, 1].ravel(),
-    alpha=0.2,
+X_sorted.ravel(),
+y_pis_sym_sorted[:, 0].ravel(),
+y_pis_sym_sorted[:, 1].ravel(),
+alpha=0.2,
 )
 plt.title(
-    f"Symmetric Intervals\n"
-    f"Target and effective coverages for "
-    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
+f"Symmetric Intervals\n"
+f"Target and effective coverages for "
+f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
 )
 
 # Plot asymmetric prediction intervals
@@ -81,24 +88,21 @@
 plt.plot(X_sorted, y_pis_asym_sorted[:, 0], color="C2", ls="--")
 plt.plot(X_sorted, y_pis_asym_sorted[:, 1], color="C2", ls="--")
 plt.fill_between(
-    X_sorted.ravel(),
-    y_pis_asym_sorted[:, 0].ravel(),
-    y_pis_asym_sorted[:, 1].ravel(),
-    alpha=0.2,
+X_sorted.ravel(),
+y_pis_asym_sorted[:, 0].ravel(),
+y_pis_asym_sorted[:, 1].ravel(),
+alpha=0.2,
 )
 plt.title(
-    f"Asymmetric Intervals\n"
-    f"Target and effective coverages for "
-    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
+f"Asymmetric Intervals\n"
+f"Target and effective coverages for "
+f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
 )
-
 plt.tight_layout()
 plt.show()
 
-# Explanation of the results
-"""
-The symmetric intervals (`symmetry=True`) are easier to interpret and
-tend to have higher coverage but might not adapt well to varying
-noise levels. The asymmetric intervals (`symmetry=False`) are more
-flexible and better capture heteroscedasticity but can appear more jagged.
-"""
+##############################################################################
+# The symmetric intervals (`symmetry=True`) are easier to interpret and
+# tend to have higher coverage but might not adapt well to varying
+# noise levels. The asymmetric intervals (`symmetry=False`) are more
+# flexible and better capture heteroscedasticity but can appear more jagged.

From 3f1d971237dc718df6e00f54e76a4c533bc5cc15 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:41:10 +0200
Subject: [PATCH 032/424] chore: Add conference paper citation to CITATION.cff

---
 CITATION.cff | 20 ++++++++++++++++++++
 1 file changed, 20 insertions(+)

diff --git a/CITATION.cff b/CITATION.cff
index aead3751d..cf54d9290 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -9,6 +9,26 @@ version: 0.8.3
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
+  type: conference-paper
+  authors:
+  - family-names: "Cordier"
+    given-names: "Thibault"
+  - family-names: "Blot"
+    given-names: "Vincent"
+  - family-names: "Lacombe"
+    given-names: "Louis"
+  - family-names: "Morzadec"
+    given-names: "Thomas"
+  - family-names: "Capitaine"
+    given-names: "Arnaud"
+  - family-names: "Brunel"
+    given-names: "Nicolas"
+  title: "Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library"
+  booktitle: "Conformal and Probabilistic Prediction with Applications"
+  pages: "549--581"
+  year: 2023
+  organization: "PMLR"
+old-citation:
   type: article
   authors:
   - family-names: "Taquet"

From 16c163233ac2178dbf4f02dfacc17c785fda61d5 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:43:51 +0200
Subject: [PATCH 033/424] Add citations utility to the documentation

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index bf1572ad4..e1249f70f 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -7,6 +7,7 @@ History
 
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
+* Add citations utility to the documentation.
 
 0.8.3 (2024-03-01)
 ------------------

From 995e665af2ed8fe7f3846dc25ac7f5345b785dc6 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:45:36 +0200
Subject: [PATCH 034/424] chore: update indentation

---
 .../plot_cqr_symmetry_difference.py           | 32 +++++++++----------
 1 file changed, 16 insertions(+), 16 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 608a5d0db..4d12b6bdf 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -38,10 +38,10 @@
 
 # Calculate coverage scores
 coverage_score_sym = regression_coverage_score(
-y, y_pis_sym[:, 0], y_pis_sym[:, 1]
+    y, y_pis_sym[:, 0], y_pis_sym[:, 1]
 )
 coverage_score_asym = regression_coverage_score(
-y, y_pis_asym[:, 0], y_pis_asym[:, 1]
+    y, y_pis_asym[:, 0], y_pis_asym[:, 1]
 )
 
 # Sort the values for plotting
@@ -68,15 +68,15 @@
 plt.plot(X_sorted, y_pis_sym_sorted[:, 0], color="C1", ls="--")
 plt.plot(X_sorted, y_pis_sym_sorted[:, 1], color="C1", ls="--")
 plt.fill_between(
-X_sorted.ravel(),
-y_pis_sym_sorted[:, 0].ravel(),
-y_pis_sym_sorted[:, 1].ravel(),
-alpha=0.2,
+    X_sorted.ravel(),
+    y_pis_sym_sorted[:, 0].ravel(),
+    y_pis_sym_sorted[:, 1].ravel(),
+    alpha=0.2,
 )
 plt.title(
-f"Symmetric Intervals\n"
-f"Target and effective coverages for "
-f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
+    f"Symmetric Intervals\n"
+    f"Target and effective coverages for "
+    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_sym:.3f})"
 )
 
 # Plot asymmetric prediction intervals
@@ -88,15 +88,15 @@
 plt.plot(X_sorted, y_pis_asym_sorted[:, 0], color="C2", ls="--")
 plt.plot(X_sorted, y_pis_asym_sorted[:, 1], color="C2", ls="--")
 plt.fill_between(
-X_sorted.ravel(),
-y_pis_asym_sorted[:, 0].ravel(),
-y_pis_asym_sorted[:, 1].ravel(),
-alpha=0.2,
+    X_sorted.ravel(),
+    y_pis_asym_sorted[:, 0].ravel(),
+    y_pis_asym_sorted[:, 1].ravel(),
+    alpha=0.2,
 )
 plt.title(
-f"Asymmetric Intervals\n"
-f"Target and effective coverages for "
-f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
+    f"Asymmetric Intervals\n"
+    f"Target and effective coverages for "
+    f"alpha={alpha:.2f}: ({1-alpha:.3f}, {coverage_score_asym:.3f})"
 )
 plt.tight_layout()
 plt.show()

From e0c19c8d05016bf7b590f039aab4c83b1f1e0f26 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:46:33 +0200
Subject: [PATCH 035/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 75a26fc27..2458ad967 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -1,4 +1,4 @@
-.. title:: Theoretical Description : contents
+.. title:: Theoretical Description Metrics : contents
 
 .. _theoretical_description_metrics:
 

From e1941903cf85e80ba5adc7f5e786d870826e7d6f Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:46:41 +0200
Subject: [PATCH 036/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 2458ad967..71b2c4685 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -2,9 +2,9 @@
 
 .. _theoretical_description_metrics:
 
-==================================
-Theoretical Description of Metrics
-==================================
+=======================
+Theoretical Description
+=======================
 
 This document provides detailed descriptions of various metrics used to evaluate the performance of predictive models, particularly focusing on their ability to estimate uncertainties and calibrate predictions accurately.
 

From b4a2c382ed7c93b88fc4038c017734f705f8120d Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:46:59 +0200
Subject: [PATCH 037/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 71b2c4685..56d855b6f 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -204,7 +204,7 @@ Spiegelhalter’s Test
 
 .. math::
 
-   \text{Spiegelhalter's Statistic} = \frac{\sum (y_{\text{true}} - y_{\text{score}})(1 - 2y_{\text{score}})}{\sqrt{\sum (1 - 2y_{\text{score}})^2 y_{\text{score}} (1 - y_{\text{score}})}}
+   \text{Spiegelhalter's Statistic} = \frac{\sum_{i=1}^n (y_i - \hat y_i)(1 - 2\hat y_i)}{\sqrt{\sum_{i=1}^n (1 - 2 \hat y_i)^2 \hat y_i (1 - \hat y_i)}}
 
 
 

From d5b2d2f751018d7fe7f5dc629830d86ead9b8d06 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:47:08 +0200
Subject: [PATCH 038/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 56d855b6f..d323d5be2 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -116,7 +116,7 @@ The **Coverage Width-Based Criterion (CWC)** evaluates prediction intervals by b
 Regression MWI Score
 --------------------
 
-The **Regression MWI (Mean Winkler Interval) Score** evaluates prediction intervals by combining their width with a penalty for intervals that do not contain the observation [8, 10].
+The **MWI (Mean Winkler Interval) Score** evaluates prediction intervals by combining their width with a penalty for intervals that do not contain the observation [8, 10].
 
 .. math::
 

From a49582fb54d657b31aab901e38357769fa46b201 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:47:16 +0200
Subject: [PATCH 039/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index d323d5be2..84e631440 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -19,7 +19,7 @@ The **Regression Coverage Score** calculates the fraction of true outcomes that
 
 .. math::
 
-   C = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(y_{\text{pred, low}}^{(i)} \leq y_{\text{true}}^{(i)} \leq y_{\text{pred, up}}^{(i)})
+   RCS = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(\hat y^{\text{low}}_{i} \leq y_{i} \leq \hat y^{\text{up}}_{i})
 
 where:
 

From 696fee8586b7f0c1bc0dee37c66f435c5fbba609 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:47:23 +0200
Subject: [PATCH 040/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 84e631440..e444bb1d4 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -45,7 +45,7 @@ The **Classification Coverage Score** measures how often the true class labels f
 
 .. math::
 
-   C = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(y_{\text{true}}^{(i)} \in \text{Set}_{\text{pred}}^{(i)})
+   CCS = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(y_{i} \in \hat C(x_{i}))
 
 Here, :math:`\text{Set}_{\text{pred}}^{(i)}` represents the set of predicted labels that could possibly contain the true label.
 

From b115895192de3e8b12fb10879c65f76ac8886462 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:47:32 +0200
Subject: [PATCH 041/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index e444bb1d4..ded3bb734 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -120,9 +120,9 @@ The **MWI (Mean Winkler Interval) Score** evaluates prediction intervals by comb
 
 .. math::
 
-   \text{MWI Score} = \frac{1}{n} \sum_{i=1}^{n} (y_{\text{pred, up}}^{(i)} - y_{\text{pred, low}}^{(i)}) + \frac{2}{\alpha} \sum_{i=1}^{n} \max(0, |y_{\text{true}}^{(i)} - y_{\text{pred, boundary}}^{(i)}|)
+   \text{MWI Score} = \frac{1}{n} \sum_{i=1}^{n} (\hat y^{\text{up}}_{i} - \hat y^{\text{low}}_{i}) + \frac{2}{\alpha} \sum_{i=1}^{n} \max(0, |y_{i} - \hat y^{\text{boundary}}_{i}|)
 
-where :math:`y_{\text{pred, boundary}}^{(i)}` is the nearest interval boundary not containing :math:`y_{\text{true}}^{(i)}`, and :math:`\alpha` is the significance level.
+where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not containing :math:`y_{i}`, and :math:`\alpha` is the significance level.
 
 
 

From dfa2ca6eb3e143ae697ceae933a62c49bdf64ab2 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:47:43 +0200
Subject: [PATCH 042/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index ded3bb734..c19c267b9 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -47,7 +47,7 @@ The **Classification Coverage Score** measures how often the true class labels f
 
    CCS = \frac{1}{n} \sum_{i=1}^{n} \mathbf{1}(y_{i} \in \hat C(x_{i}))
 
-Here, :math:`\text{Set}_{\text{pred}}^{(i)}` represents the set of predicted labels that could possibly contain the true label.
+Here, :math:`\hat C(x_{i})` represents the set of predicted labels that could possibly contain the true label for the :math:`i`-th observation :math:`x_{i}`.
 
 
 Classification Mean Width Score

From e9810ecce8150baabe54ba920a9f60dade19c17c Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:47:55 +0200
Subject: [PATCH 043/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index c19c267b9..5762eb1ee 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -172,7 +172,12 @@ Cumulative Differences
 
 .. math::
 
-   \text{Cumulative Differences} = \frac{1}{n} \sum_{i=1}^{n} (y_{\text{true,sorted}}^{(i)} - y_{\text{score,sorted}}^{(i)})
+   \text{Cumulative Differences} = \frac{1}{n} \sum_{i=1}^{n} (y_{\sigma_1(i)} - \hat y_{\sigma_2(i)})
+
+where:
+
+- :math:`\sigma_1` is the permutation which sorts all the true values.
+- :math:`\sigma_2` is the permutation which sorts all the predicted values.
 
 
 Kolmogorov-Smirnov Statistic for Calibration

From 7ad8509d5071d1f992bc3ab246e5db753869e552 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:48:08 +0200
Subject: [PATCH 044/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 5762eb1ee..6488cfe53 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -35,7 +35,7 @@ The **Regression Mean Width Score** assesses the average width of the prediction
 
 .. math::
 
-   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} (y_{\text{pred, up}}^{(i)} - y_{\text{pred, low}}^{(i)})
+   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} (\hat y^{\text{up}}_{i} - \hat y^{\text{low}}_{i})
 
 
 Classification Coverage Score

From 04531d199f45787b85256f6068925fc1f1ef3dd1 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:48:25 +0200
Subject: [PATCH 045/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 6488cfe53..664777f9d 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -24,8 +24,8 @@ The **Regression Coverage Score** calculates the fraction of true outcomes that
 where:
 
 - :math:`n` is the number of samples,
-- :math:`y_{\text{true}}^{(i)}` is the true value for the :math:`i`-th sample,
-- :math:`y_{\text{pred, low}}^{(i)}` and :math:`y_{\text{pred, up}}^{(i)}` are the lower and upper bounds of the prediction intervals, respectively.
+- :math:`y_{i}` is the true value for the :math:`i`-th sample,
+- :math:`\hat y^{\text{low}}_{i}` and :math:`\hat y^{\text{up}}_{i}` are the lower and upper bounds of the prediction intervals, respectively.
 
 
 Regression Mean Width Score

From 8f0c08137bb36b328846c0dff614075cbf1f990b Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:48:35 +0200
Subject: [PATCH 046/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 664777f9d..76ebe2138 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -57,9 +57,9 @@ For classification tasks, the **Classification Mean Width Score** calculates the
 
 .. math::
 
-   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} |\text{Set}_{\text{pred}}^{(i)}|
+   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} |\hat C_{x_i}|
 
-where :math:`|\text{Set}_{\text{pred}}^{(i)}|` denotes the number of classes included in the prediction set for sample :math:`i`.
+where :math:`|\hat C_{x_i}|` denotes the number of classes included in the prediction set for sample :math:`i`.
 
 
 Size-Stratified Coverage (SSC)

From eca3e52a951691f204e45a68669d111d59c9b251 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:49:16 +0200
Subject: [PATCH 047/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 76ebe2138..c3aea8837 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -113,7 +113,7 @@ The **Coverage Width-Based Criterion (CWC)** evaluates prediction intervals by b
 
 
 
-Regression MWI Score
+Mean Winkler Interval Score
 --------------------
 
 The **MWI (Mean Winkler Interval) Score** evaluates prediction intervals by combining their width with a penalty for intervals that do not contain the observation [8, 10].

From 71a0e4673a91bbaeaccbcedd708d46c33148ea1a Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:49:27 +0200
Subject: [PATCH 048/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index c3aea8837..988f19de7 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -71,7 +71,7 @@ Size-Stratified Coverage (SSC)
 
 .. math::
 
-   \text{SSC}_{\text{regression}} = \sum_{k=1}^{K} \left( \frac{1}{|I_k|} \sum_{i \in I_k} \mathbf{1}(y_{\text{pred, low}}^{(i)} \leq y_{\text{true}}^{(i)} \leq y_{\text{pred, up}}^{(i)}) \right)
+   \text{SSC}_{\text{regression}} = \sum_{k=1}^{K} \left( \frac{1}{|I_k|} \sum_{i \in I_k} \mathbf{1}(\hat y^{\text{low}}_{i} \leq y_{i} \leq \hat y^{\text{up}}_{i}) \right)
 
 **Classification:**
 

From 9d98b0df7bcec1af8c0f9ced4d760c48aee336ab Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 16 May 2024 17:49:36 +0200
Subject: [PATCH 049/424] Update doc/theoretical_description_metrics.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 988f19de7..0046a41be 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -77,7 +77,7 @@ Size-Stratified Coverage (SSC)
 
 .. math::
 
-   \text{SSC}_{\text{classification}} = \sum_{k=1}^{K} \left( \frac{1}{|S_k|} \sum_{i \in S_k} \mathbf{1}(y_{\text{true}}^{(i)} \in \text{Set}_{\text{pred}}^{(i)}) \right)
+   \text{SSC}_{\text{classification}} = \sum_{k=1}^{K} \left( \frac{1}{|S_k|} \sum_{i \in S_k} \mathbf{1}(y_{i} \in \hat C(x_i)) \right)
 
 where:
 

From 009ad1575e8a07d1d5b307151ec34de296799e4a Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 17:51:03 +0200
Subject: [PATCH 050/424] Update Michelin image size in README.rst

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 971cd652d..37fb6434e 100644
--- a/README.rst
+++ b/README.rst
@@ -177,7 +177,7 @@ and with the financial support from Région Ile de France and Confiance.ai.
     :target: https://www.quantmetry.com/
 
 .. |Michelin| image:: https://agngnconpm.cloudimg.io/v7/https://dgaddcosprod.blob.core.windows.net/corporate-production/attachments/cls05tqdd9e0o0tkdghwi9m7n-clooe1x0c3k3x0tlu4cxi6dpn-bibendum-salut.full.png
-    :height: 45px
+    :height: 50px
     :width: 45px
     :target: https://www.michelin.com/en/
 

From e2dcf3e80843725ca64273f6677dfdaf7f56d214 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 18:09:14 +0200
Subject: [PATCH 051/424] Update theoretical_description_metrics.rst with ECE
 and Top-Label ECE metrics

---
 doc/theoretical_description_metrics.rst | 41 +++++++++++++++----------
 1 file changed, 24 insertions(+), 17 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 0046a41be..82c7f9166 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -129,40 +129,47 @@ where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not
 2. Calibration metrics
 ======================
 
+
 Expected Calibration Error (ECE)
 --------------------------------
 
-**Expected Calibration Error (ECE)** measures the difference between predicted probabilities of a model and the actual outcomes, across different bins of predicted probabilities [7].
+The **Expected Calibration Error** (ECE) is a metric used to evaluate how well the predicted probabilities of a model align with the actual outcomes. The ECE provides a measure of the difference between predicted confidence levels and actual accuracy. The idea is to divide the predictions into bins based on confidence scores and then compare the accuracy within each bin to the average confidence level of the predictions in that bin.
+The ECE is calculated as follows:
 
 .. math::
-
-   \text{ECE} = \sum_{b=1}^{B} \frac{n_b}{n} | \text{acc}(b) - \text{conf}(b) |
+    \text{ECE} = \sum_{i=1}^B \frac{|B_i|}{n} \left| \text{acc}(B_i) - \text{conf}(B_i) \right|
 
 where:
+- :math:`B_i` is the set of indices of samples that fall into the i-th bin.
+- :math:`|B_i|` is the number of samples in the i-th bin.
+- :math:`n` is the total number of samples.
+- :math:`\text{acc}(B_i)` is the accuracy within the i-th bin.
+- :math:`\text{conf}(B_i)` is the average confidence score within the i-th bin.
+- :math:`B` is the total number of bins.
 
-- :math:`B` is the total number of bins,
-- :math:`n_b` is the number of samples in bin :math:`b`,
-- :math:`\text{acc}(b)` is the accuracy within bin :math:`b`,
-- :math:`\text{conf}(b)` is the mean predicted probability in bin :math:`b`.
+The difference between the average confidence and the actual accuracy within each bin is weighted by the proportion of samples in that bin, ensuring that bins with more samples have a larger influence on the final ECE value.
 
 
 Top-Label Expected Calibration Error (Top-Label ECE)
 ----------------------------------------------------
 
-**Top-Label ECE** focuses on the class predicted with the highest confidence for each sample, assessing whether these top-predicted confidences align well with actual outcomes. It is calculated by dividing the confidence score range into bins and comparing the mean confidence against empirical accuracy within these bins [5].
+The **Top-Label Expected Calibration Error** (Top-Label ECE) extends the concept of ECE to the multi-class setting. Instead of evaluating calibration over all predicted probabilities, Top-Label ECE focuses on the calibration of the most confident prediction (top-label) for each sample.
 
-.. math::
+The Top-Label ECE is calculated as follows:
 
-   \text{Top-Label ECE} = \sum_{b=1}^{B} \frac{n_b}{n} \left| \text{acc}_b - \text{conf}_b \right|
+.. math::
+    \text{Top-Label ECE} = \frac{1}{L} \sum_{j=1}^L \sum_{i=1}^B \frac{|B_{i,j}|}{n_j} \left| \text{acc}(B_{i,j}) - \text{conf}(B_{i,j}) \right|
 
 where:
-
-- :math:`n` is the total number of samples,
-- :math:`n_b` is the number of samples in bin :math:`b`,
-- :math:`\text{acc}_b` is the empirical accuracy in bin :math:`b`,
-- :math:`\text{conf}_b` is the average confidence of the top label in bin :math:`b`.
-
-This metric is especially useful in multi-class classification to ensure that the model is neither overconfident nor underconfident in its predictions.
+- :math:`L` is the number of unique labels.
+- :math:`B_{i,j}` is the set of indices of samples that fall into the i-th bin for label j.
+- :math:`|B_{i,j}|` is the number of samples in the i-th bin for label j.
+- :math:`n_j` is the total number of samples for label j.
+- :math:`\text{acc}(B_{i,j})` is the accuracy within the i-th bin for label j.
+- :math:`\text{conf}(B_{i,j})` is the average confidence score within the i-th bin for label j.
+- :math:`B` is the total number of bins.
+
+For each label, the predictions are binned according to their confidence scores for that label. The calibration error is then calculated for each label separately and averaged across all labels to obtain the final Top-Label ECE value. This ensures that the calibration is measured specifically for the most confident prediction, which is often the most critical for decision-making in multi-class problems.
 
 
 Cumulative Differences

From eaaff00c48993876465a7ff6e928ad21204ad23f Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 18:12:58 +0200
Subject: [PATCH 052/424] FIX: fix small issues with documentation

---
 doc/theoretical_description_metrics.rst | 33 +++++--------------------
 1 file changed, 6 insertions(+), 27 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 82c7f9166..ed624d919 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -2,20 +2,18 @@
 
 .. _theoretical_description_metrics:
 
-=======================
 Theoretical Description
 =======================
 
 This document provides detailed descriptions of various metrics used to evaluate the performance of predictive models, particularly focusing on their ability to estimate uncertainties and calibrate predictions accurately.
 
-
-1. General metrics
+1. General Metrics
 ==================
 
 Regression Coverage Score
 -------------------------
 
-The **Regression Coverage Score** calculates the fraction of true outcomes that fall within the provided prediction intervals. 
+The **Regression Coverage Score** calculates the fraction of true outcomes that fall within the provided prediction intervals.
 
 .. math::
 
@@ -27,7 +25,6 @@ where:
 - :math:`y_{i}` is the true value for the :math:`i`-th sample,
 - :math:`\hat y^{\text{low}}_{i}` and :math:`\hat y^{\text{up}}_{i}` are the lower and upper bounds of the prediction intervals, respectively.
 
-
 Regression Mean Width Score
 ---------------------------
 
@@ -37,7 +34,6 @@ The **Regression Mean Width Score** assesses the average width of the prediction
 
    \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} (\hat y^{\text{up}}_{i} - \hat y^{\text{low}}_{i})
 
-
 Classification Coverage Score
 -----------------------------
 
@@ -49,7 +45,6 @@ The **Classification Coverage Score** measures how often the true class labels f
 
 Here, :math:`\hat C(x_{i})` represents the set of predicted labels that could possibly contain the true label for the :math:`i`-th observation :math:`x_{i}`.
 
-
 Classification Mean Width Score
 -------------------------------
 
@@ -61,7 +56,6 @@ For classification tasks, the **Classification Mean Width Score** calculates the
 
 where :math:`|\hat C_{x_i}|` denotes the number of classes included in the prediction set for sample :math:`i`.
 
-
 Size-Stratified Coverage (SSC)
 -------------------------------
 
@@ -84,11 +78,10 @@ where:
 - :math:`K` is the number of distinct size groups,
 - :math:`I_k` and :math:`S_k` are the indices of samples whose prediction intervals or sets belong to the :math:`k`-th size group.
 
-
 Hilbert-Schmidt Independence Criterion (HSIC)
 ----------------------------------------------
 
-**Hilbert-Schmidt Independence Criterion (HSIC)** is a non-parametric measure of independence between two variables, applied here to test the independence of interval sizes from their coverage indicators [4].
+The **Hilbert-Schmidt Independence Criterion (HSIC)** is a non-parametric measure of independence between two variables, applied here to test the independence of interval sizes from their coverage indicators [4].
 
 .. math::
 
@@ -101,7 +94,6 @@ where:
 
 This measure is crucial for determining whether certain sizes of prediction intervals are systematically more or less likely to contain the true values, which can highlight biases in interval-based predictions.
 
-
 Coverage Width-Based Criterion (CWC)
 ------------------------------------
 
@@ -111,12 +103,10 @@ The **Coverage Width-Based Criterion (CWC)** evaluates prediction intervals by b
 
    \text{CWC} = (1 - \text{Mean Width Score}) \times \exp\left(-\eta \times (\text{Coverage Score} - (1-\alpha))^2\right)
 
-
-
 Mean Winkler Interval Score
---------------------
+---------------------------
 
-The **MWI (Mean Winkler Interval) Score** evaluates prediction intervals by combining their width with a penalty for intervals that do not contain the observation [8, 10].
+The **Mean Winkler Interval (MWI) Score** evaluates prediction intervals by combining their width with a penalty for intervals that do not contain the observation [8, 10].
 
 .. math::
 
@@ -124,17 +114,13 @@ The **MWI (Mean Winkler Interval) Score** evaluates prediction intervals by comb
 
 where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not containing :math:`y_{i}`, and :math:`\alpha` is the significance level.
 
-
-
-2. Calibration metrics
+2. Calibration Metrics
 ======================
 
-
 Expected Calibration Error (ECE)
 --------------------------------
 
 The **Expected Calibration Error** (ECE) is a metric used to evaluate how well the predicted probabilities of a model align with the actual outcomes. The ECE provides a measure of the difference between predicted confidence levels and actual accuracy. The idea is to divide the predictions into bins based on confidence scores and then compare the accuracy within each bin to the average confidence level of the predictions in that bin.
-The ECE is calculated as follows:
 
 .. math::
     \text{ECE} = \sum_{i=1}^B \frac{|B_i|}{n} \left| \text{acc}(B_i) - \text{conf}(B_i) \right|
@@ -149,7 +135,6 @@ where:
 
 The difference between the average confidence and the actual accuracy within each bin is weighted by the proportion of samples in that bin, ensuring that bins with more samples have a larger influence on the final ECE value.
 
-
 Top-Label Expected Calibration Error (Top-Label ECE)
 ----------------------------------------------------
 
@@ -171,7 +156,6 @@ where:
 
 For each label, the predictions are binned according to their confidence scores for that label. The calibration error is then calculated for each label separately and averaged across all labels to obtain the final Top-Label ECE value. This ensures that the calibration is measured specifically for the most confident prediction, which is often the most critical for decision-making in multi-class problems.
 
-
 Cumulative Differences
 ----------------------
 
@@ -186,7 +170,6 @@ where:
 - :math:`\sigma_1` is the permutation which sorts all the true values.
 - :math:`\sigma_2` is the permutation which sorts all the predicted values.
 
-
 Kolmogorov-Smirnov Statistic for Calibration
 --------------------------------------------
 
@@ -198,7 +181,6 @@ This statistic measures the maximum absolute deviation between the empirical cum
 
 where :math:`F_n(x)` is the ECDF of the predicted probabilities and :math:`S_n(x)` is the ECDF of the observed outcomes.
 
-
 Kuiper's Statistic
 ------------------
 
@@ -208,7 +190,6 @@ Kuiper's Statistic
 
    \text{Kuiper's Statistic} = \max(F_n(x) - S_n(x)) + \max(S_n(x) - F_n(x))
 
-
 Spiegelhalter’s Test
 --------------------
 
@@ -218,8 +199,6 @@ Spiegelhalter’s Test
 
    \text{Spiegelhalter's Statistic} = \frac{\sum_{i=1}^n (y_i - \hat y_i)(1 - 2\hat y_i)}{\sqrt{\sum_{i=1}^n (1 - 2 \hat y_i)^2 \hat y_i (1 - \hat y_i)}}
 
-
-
 References
 ==========
 

From ee62fda24e7634366efd0b54fcefad09911b2c00 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 16 May 2024 18:13:52 +0200
Subject: [PATCH 053/424] Add documentation for metrics.

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index bf1572ad4..23cf7f3fd 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -7,6 +7,7 @@ History
 
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
+* Add documentation for metrics.
 
 0.8.3 (2024-03-01)
 ------------------

From 905c3da106a5849ee9ef1fd6d08fd8fdc2dd70e8 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 16 May 2024 16:49:04 +0000
Subject: [PATCH 054/424] UPD: structure code reg vs. class and upd tests

---
 mapie/classification.py                       |  2 +-
 mapie/conformity_scores/conformity_scores.py  |  2 +-
 .../estimator.py}                             | 67 ++++++++++-----
 mapie/estimator/classification/interface.py   | 84 +++++++++++++++++++
 .../estimator.py}                             |  2 +-
 mapie/estimator/{ => regression}/interface.py |  0
 mapie/regression/regression.py                |  2 +-
 mapie/tests/test_classification.py            | 17 ++--
 mapie/tests/test_common.py                    |  4 +-
 mapie/tests/test_regression.py                |  2 +-
 10 files changed, 145 insertions(+), 37 deletions(-)
 rename mapie/estimator/{estimator_classifier.py => classification/estimator.py} (92%)
 create mode 100644 mapie/estimator/classification/interface.py
 rename mapie/estimator/{estimator_regressor.py => regression/estimator.py} (99%)
 rename mapie/estimator/{ => regression}/interface.py (100%)

diff --git a/mapie/classification.py b/mapie/classification.py
index eaa5398b8..ef643b0bc 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -15,7 +15,7 @@
 
 from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
-from .estimator.estimator_classifier import EnsembleClassifier
+from .estimator.classification.estimator import EnsembleClassifier
 from .metrics import classification_mean_width_score
 from .utils import (check_alpha, check_alpha_and_n_samples, check_cv,
                     check_estimator_classification, check_n_features_in,
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index d8d46322a..872172df2 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -5,7 +5,7 @@
 
 from mapie._compatibility import np_nanquantile
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator.interface import EnsembleEstimator
+from mapie.estimator.regression.interface import EnsembleEstimator
 
 
 class ConformityScore(metaclass=ABCMeta):
diff --git a/mapie/estimator/estimator_classifier.py b/mapie/estimator/classification/estimator.py
similarity index 92%
rename from mapie/estimator/estimator_classifier.py
rename to mapie/estimator/classification/estimator.py
index 58cd2f6c7..2c8cfb365 100644
--- a/mapie/estimator/estimator_classifier.py
+++ b/mapie/estimator/classification/estimator.py
@@ -11,7 +11,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.aggregation_functions import phi2D
-from mapie.estimator.interface import EnsembleEstimator
+from mapie.estimator.classification.interface import EnsembleEstimator
 from mapie.utils import (
     check_no_agg_cv,
     fit_estimator,
@@ -190,6 +190,42 @@ def _fit_oof_estimator(
         )
         return estimator
 
+    @staticmethod
+    def _check_proba_normalized(
+        y_pred_proba: ArrayLike,
+        axis: int = 1
+    ) -> NDArray:
+        """
+        Check if, for all the observations, the sum of
+        the probabilities is equal to one.
+
+        Parameters
+        ----------
+        y_pred_proba: ArrayLike of shape
+            (n_samples, n_classes) or
+            (n_samples, n_train_samples, n_classes)
+            Softmax output of a model.
+
+        Returns
+        -------
+        ArrayLike of shape (n_samples, n_classes)
+            Softmax output of a model if the scores all sum
+            to one.
+
+        Raises
+        ------
+            ValueError
+            If the sum of the scores is not equal to one.
+        """
+        np.testing.assert_allclose(
+            np.sum(y_pred_proba, axis=axis),
+            1,
+            err_msg="The sum of the scores is not equal to one.",
+            rtol=1e-5
+        )
+        y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
+        return y_pred_proba
+
     def _predict_proba_oof_estimator(
         self,
         estimator: ClassifierMixin,
@@ -469,8 +505,10 @@ def fit(
                 for train_index, _ in cv.split(X, y, groups)
             )
             # In split-CP, we keep only the model fitted on train dataset
-            if self.use_split_method_:
-                single_estimator_ = estimators_[0]
+            # TODO: copay/paste from EnsembleRegressor
+            # but not work here for EnsembleClassifier
+            # if self.use_split_method_:
+            #     single_estimator_ = estimators_[0]
 
         self.single_estimator_ = single_estimator_
         self.estimators_ = estimators_
@@ -480,8 +518,6 @@ def fit(
     def predict(
         self,
         X: ArrayLike,
-        ensemble: bool = False,
-        return_multi_pred: bool = True,
         alpha_np: ArrayLike = [],
         agg_scores: Any = None
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
@@ -494,24 +530,9 @@ def predict(
         X: ArrayLike of shape (n_samples, n_features)
             Test data.
 
-        ensemble: bool
-            Boolean determining whether the predictions are ensembled or not.
-            If ``False``, predictions are those of the model trained on the
-            whole training set.
-            If ``True``, predictions from perturbed models are aggregated by
-            the aggregation function specified in the ``agg_function``
-            attribute.
+        TODO
 
-            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
-
-            By default ``False``.
-
-        return_multi_pred: bool
-            If ``True`` the method returns the predictions and the multiple
-            predictions (3 arrays). If ``False`` the method return the
-            simple predictions only.
-
-        Returns
+        Returns TODO
         -------
         Tuple[NDArray, NDArray, NDArray]
             - Predictions
@@ -530,7 +551,7 @@ def predict(
                 Parallel(
                     n_jobs=self.n_jobs, verbose=self.verbose
                 )(
-                    delayed(self._predict_oof_model)(estimator, X)
+                    delayed(self._predict_proba_oof_estimator)(estimator, X)
                     for estimator in self.estimators_
                 )
             )
diff --git a/mapie/estimator/classification/interface.py b/mapie/estimator/classification/interface.py
new file mode 100644
index 000000000..ced4f2613
--- /dev/null
+++ b/mapie/estimator/classification/interface.py
@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from abc import ABCMeta, abstractmethod
+from typing import Any, Optional, Tuple, Union
+
+from sklearn.base import ClassifierMixin
+
+from mapie._typing import ArrayLike, NDArray
+
+
+class EnsembleEstimator(ClassifierMixin, metaclass=ABCMeta):
+    """
+    This class implements methods to handle the training and usage of the
+    estimator. This estimator can be unique or composed by cross validated
+    estimators.
+    """
+
+    @abstractmethod
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **fit_params
+    ) -> EnsembleEstimator:
+        """
+        Fit the base estimator under the ``single_estimator_`` attribute.
+        Fit all cross-validated estimator clones
+        and rearrange them into a list, the ``estimators_`` attribute.
+        Out-of-fold conformity scores are stored under
+        the ``conformity_scores_`` attribute.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Input data.
+
+        y: ArrayLike of shape (n_samples,)
+            Input labels.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Sample weights. If None, then samples are equally weighted.
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+            By default ``None``.
+
+        **fit_params : dict
+            Additional fit parameters.
+
+        Returns
+        -------
+        EnsembleClassifier
+            The estimator fitted.
+        """
+
+    @abstractmethod
+    def predict(
+        self,
+        X: ArrayLike,
+        alpha_np: ArrayLike = [],
+        agg_scores: Any = None
+    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
+        """
+        Predict target from X. It also computes the prediction per train sample
+        for each test sample according to ``self.method``.
+
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples, n_features)
+            Test data.
+
+        TODO
+
+        Returns TODO
+        -------
+        Tuple[NDArray, NDArray, NDArray]
+            - Predictions
+            - The multiple predictions for the lower bound of the intervals.
+            - The multiple predictions for the upper bound of the intervals.
+        """
diff --git a/mapie/estimator/estimator_regressor.py b/mapie/estimator/regression/estimator.py
similarity index 99%
rename from mapie/estimator/estimator_regressor.py
rename to mapie/estimator/regression/estimator.py
index b8c7d4ecf..c0544b03d 100644
--- a/mapie/estimator/estimator_regressor.py
+++ b/mapie/estimator/regression/estimator.py
@@ -11,7 +11,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.aggregation_functions import aggregate_all, phi2D
-from mapie.estimator.interface import EnsembleEstimator
+from mapie.estimator.regression.interface import EnsembleEstimator
 from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
                          fit_estimator)
 
diff --git a/mapie/estimator/interface.py b/mapie/estimator/regression/interface.py
similarity index 100%
rename from mapie/estimator/interface.py
rename to mapie/estimator/regression/interface.py
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index ff6e41e0b..4dd9891b3 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -13,7 +13,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
-from mapie.estimator.estimator_regressor import EnsembleRegressor
+from mapie.estimator.regression.estimator import EnsembleRegressor
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_conformity_score, check_cv,
                          check_estimator_fit_predict, check_n_features_in,
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index fc1f3e6ba..9179e3d9f 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -979,7 +979,9 @@ def test_valid_estimator(strategy: str) -> None:
     clf = LogisticRegression().fit(X_toy, y_toy)
     mapie_clf = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     mapie_clf.fit(X_toy, y_toy)
-    assert isinstance(mapie_clf.single_estimator_, LogisticRegression)
+    assert (
+        isinstance(mapie_clf.estimator_.single_estimator_, LogisticRegression)
+    )
 
 
 @pytest.mark.parametrize("method", METHODS)
@@ -1641,13 +1643,12 @@ def test_include_label_error_in_predict(
 def test_pred_loof_isnan() -> None:
     """Test that if validation set is empty then prediction is empty."""
     mapie_clf = MapieClassifier(random_state=random_state)
-    _, y_pred, _, _ = mapie_clf._fit_and_predict_oof_model(
-        estimator=LogisticRegression(),
+    mapie_clf.fit(X_toy, y_toy)
+    y_pred, _, _ = mapie_clf.estimator_._predict_proba_calib_oof_estimator(
+        estimator=LogisticRegression().fit(X_toy, y_toy),
         X=X_toy,
-        y=y_toy,
-        train_index=[0, 1, 2, 3, 4],
         val_index=[],
-        k=0,
+        k=0
     )
     assert len(y_pred) == 0
 
@@ -2027,7 +2028,7 @@ def early_stopping_monitor(i, est, locals):
 
     mapie.fit(X, y, monitor=early_stopping_monitor)
 
-    assert mapie.single_estimator_.estimators_.shape[0] == 3
+    assert mapie.estimator_.single_estimator_.estimators_.shape[0] == 3
 
-    for estimator in mapie.estimators_:
+    for estimator in mapie.estimator_.estimators_:
         assert estimator.estimators_.shape[0] == 3
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 45379bc24..367871827 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -109,7 +109,9 @@ def test_none_estimator(pack: Tuple[BaseEstimator, BaseEstimator]) -> None:
     mapie_estimator = MapieEstimator(estimator=None)
     mapie_estimator.fit(X_toy, y_toy)
     if isinstance(mapie_estimator, MapieClassifier):
-        assert isinstance(mapie_estimator.single_estimator_, DefaultEstimator)
+        assert isinstance(
+            mapie_estimator.estimator_.single_estimator_, DefaultEstimator
+        )
     if isinstance(mapie_estimator, MapieRegressor):
         assert isinstance(
             mapie_estimator.estimator_.single_estimator_, DefaultEstimator
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index ac36b473d..61916c947 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -25,7 +25,7 @@
 from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
                                      GammaConformityScore,
                                      ResidualNormalisedScore)
-from mapie.estimator.estimator_regressor import EnsembleRegressor
+from mapie.estimator.regression.estimator import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 from mapie.subsample import Subsample

From c0ce6833d4deeaa2d4d6f4bb815d9bbb2ca273f7 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Fri, 17 May 2024 12:25:36 +0000
Subject: [PATCH 055/424] FIX: solve last tests

---
 mapie/estimator/classification/estimator.py | 7 +++----
 mapie/tests/test_classification.py          | 2 +-
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classification/estimator.py
index 2c8cfb365..3486cf26d 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classification/estimator.py
@@ -392,6 +392,7 @@ def predict_proba_calib(
 
         if self.cv == "prefit":
             y_pred_proba = self.single_estimator_.predict_proba(X)
+            y_pred_proba = self._check_proba_normalized(y_pred_proba)
         else:
             y_pred_proba = np.empty((len(X), self.n_classes), dtype=float)
             cv = cast(BaseCrossValidator, self.cv)
@@ -505,10 +506,8 @@ def fit(
                 for train_index, _ in cv.split(X, y, groups)
             )
             # In split-CP, we keep only the model fitted on train dataset
-            # TODO: copay/paste from EnsembleRegressor
-            # but not work here for EnsembleClassifier
-            # if self.use_split_method_:
-            #     single_estimator_ = estimators_[0]
+            if isinstance(cv, ShuffleSplit):
+                single_estimator_ = estimators_[0]
 
         self.single_estimator_ = single_estimator_
         self.estimators_ = estimators_
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 9179e3d9f..972b21923 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1562,7 +1562,7 @@ def test_sum_proba_to_one_predict(
     wrong_model = WrongOutputModel(y_pred_proba)
     mapie_clf = MapieClassifier(cv="prefit", random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
-    mapie_clf.single_estimator_ = wrong_model
+    mapie_clf.estimator_.single_estimator_ = wrong_model
     with pytest.raises(
         AssertionError, match=r".*The sum of the scores is not equal to one.*"
     ):

From 386a238117b3e97212ecb4a1ff92c6e84272b619 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Fri, 17 May 2024 12:58:52 +0000
Subject: [PATCH 056/424] FIX: add init files

---
 mapie/estimator/classification/__init__.py | 0
 mapie/estimator/regression/__init__.py     | 0
 2 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 mapie/estimator/classification/__init__.py
 create mode 100644 mapie/estimator/regression/__init__.py

diff --git a/mapie/estimator/classification/__init__.py b/mapie/estimator/classification/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/mapie/estimator/regression/__init__.py b/mapie/estimator/regression/__init__.py
new file mode 100644
index 000000000..e69de29bb

From dc3d55d8d9da2fd70dd0492cb8d333812d7db3b6 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Fri, 17 May 2024 13:39:35 +0000
Subject: [PATCH 057/424] UPD: remove useless methods

---
 mapie/estimator/classification/estimator.py | 66 ---------------------
 1 file changed, 66 deletions(-)

diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classification/estimator.py
index 3486cf26d..555ef72b0 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classification/estimator.py
@@ -291,72 +291,6 @@ def _predict_proba_calib_oof_estimator(
 
         return y_pred_proba, val_id, val_index
 
-    def _aggregate_with_mask(self, x: NDArray, k: NDArray) -> NDArray:
-        """
-        Take the array of predictions, made by the refitted estimators,
-        on the testing set, and the 1-or-nan array indicating for each training
-        sample which one to integrate, and aggregate to produce phi-{t}(x_t)
-        for each training sample x_t.
-
-        Parameters
-        ----------
-        x: ArrayLike of shape (n_samples_test, n_estimators)
-            Array of predictions, made by the refitted estimators,
-            for each sample of the testing set.
-
-        k: ArrayLike of shape (n_samples_training, n_estimators)
-            1-or-nan array: indicates whether to integrate the prediction
-            of a given estimator into the aggregation, for each training
-            sample.
-
-        Returns
-        -------
-        ArrayLike of shape (n_samples_test,)
-            Array of aggregated predictions for each testing sample.
-        """
-        if self.method in self.no_agg_methods_ or self.use_split_method_:
-            raise ValueError(
-                "There should not be aggregation of predictions "
-                f"if cv is in '{self.no_agg_cv_}', if cv >=2 "
-                f"or if method is in '{self.no_agg_methods_}'."
-            )
-        elif self.agg_function == "median":
-            return phi2D(A=x, B=k, fun=lambda x: np.nanmedian(x, axis=1))
-        # To aggregate with mean() the aggregation coud be done
-        # with phi2D(A=x, B=k, fun=lambda x: np.nanmean(x, axis=1).
-        # However, phi2D contains a np.apply_along_axis loop which
-        # is much slower than the matrices multiplication that can
-        # be used to compute the means.
-        elif self.agg_function in ["mean", None]:
-            K = np.nan_to_num(k, nan=0.0)
-            return np.matmul(x, (K / (K.sum(axis=1, keepdims=True))).T)
-        else:
-            raise ValueError("The value of self.agg_function is not correct")
-
-    def _pred_multi(self, X: ArrayLike) -> NDArray:
-        """
-        Return a prediction per train sample for each test sample, by
-        aggregation with matrix ``k_``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
-            Input data
-
-        Returns
-        -------
-        NDArray of shape (n_samples_test, n_samples_train)
-        """
-        y_pred_multi = np.column_stack(
-            [e.predict(X) for e in self.estimators_]
-        )
-        # At this point, y_pred_multi is of shape
-        # (n_samples_test, n_estimators_). The method
-        # ``_aggregate_with_mask`` fits it to the right size
-        # thanks to the shape of k_.
-        y_pred_multi = self._aggregate_with_mask(y_pred_multi, self.k_)
-        return y_pred_multi
-
     def predict_proba_calib(
         self,
         X: ArrayLike,

From d86006f30f84973d555597e1b3db3cb88e25e368 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 10:46:34 +0200
Subject: [PATCH 058/424] Apply suggestions from TCO from code review

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 30 ++++++++++++-------------
 1 file changed, 15 insertions(+), 15 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index ed624d919..f9adfbe9a 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -13,7 +13,7 @@ This document provides detailed descriptions of various metrics used to evaluate
 Regression Coverage Score
 -------------------------
 
-The **Regression Coverage Score** calculates the fraction of true outcomes that fall within the provided prediction intervals.
+The **Regression Coverage Score (RCS)** calculates the fraction of true outcomes that fall within the provided prediction intervals.
 
 .. math::
 
@@ -28,16 +28,16 @@ where:
 Regression Mean Width Score
 ---------------------------
 
-The **Regression Mean Width Score** assesses the average width of the prediction intervals provided by the model.
+The **Regression Mean Width Score (RMWS)** assesses the average width of the prediction intervals provided by the model.
 
 .. math::
 
-   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} (\hat y^{\text{up}}_{i} - \hat y^{\text{low}}_{i})
+   \text{RMWS} = \frac{1}{n} \sum_{i=1}^{n} (\hat y^{\text{up}}_{i} - \hat y^{\text{low}}_{i})
 
 Classification Coverage Score
 -----------------------------
 
-The **Classification Coverage Score** measures how often the true class labels fall within the predicted sets.
+The **Classification Coverage Score (CCS)** measures how often the true class labels fall within the predicted sets.
 
 .. math::
 
@@ -48,16 +48,16 @@ Here, :math:`\hat C(x_{i})` represents the set of predicted labels that could po
 Classification Mean Width Score
 -------------------------------
 
-For classification tasks, the **Classification Mean Width Score** calculates the average size of the prediction sets across all samples.
+For classification tasks, the **Classification Mean Width Score (CMWS)** calculates the average size of the prediction sets across all samples.
 
 .. math::
 
-   \text{Mean Width} = \frac{1}{n} \sum_{i=1}^{n} |\hat C_{x_i}|
+   \text{CMWS} = \frac{1}{n} \sum_{i=1}^{n} |\hat C(x_i)|
 
-where :math:`|\hat C_{x_i}|` denotes the number of classes included in the prediction set for sample :math:`i`.
+where :math:`|\hat C(x_i)|` denotes the number of classes included in the prediction set for sample :math:`i`.
 
-Size-Stratified Coverage (SSC)
--------------------------------
+Size-Stratified Coverage
+-------------------------
 
 **Size-Stratified Coverage (SSC)** evaluates how the size of prediction sets or intervals affects their ability to cover the true outcomes [1]. It's calculated separately for classification and regression:
 
@@ -78,8 +78,8 @@ where:
 - :math:`K` is the number of distinct size groups,
 - :math:`I_k` and :math:`S_k` are the indices of samples whose prediction intervals or sets belong to the :math:`k`-th size group.
 
-Hilbert-Schmidt Independence Criterion (HSIC)
-----------------------------------------------
+Hilbert-Schmidt Independence Criterion
+---------------------------------------
 
 The **Hilbert-Schmidt Independence Criterion (HSIC)** is a non-parametric measure of independence between two variables, applied here to test the independence of interval sizes from their coverage indicators [4].
 
@@ -94,8 +94,8 @@ where:
 
 This measure is crucial for determining whether certain sizes of prediction intervals are systematically more or less likely to contain the true values, which can highlight biases in interval-based predictions.
 
-Coverage Width-Based Criterion (CWC)
-------------------------------------
+Coverage Width-Based Criterion
+------------------------------
 
 The **Coverage Width-Based Criterion (CWC)** evaluates prediction intervals by balancing their empirical coverage and width. It is designed to both reward narrow intervals and penalize those that do not achieve a specified coverage probability [6].
 
@@ -117,8 +117,8 @@ where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not
 2. Calibration Metrics
 ======================
 
-Expected Calibration Error (ECE)
---------------------------------
+Expected Calibration Error
+--------------------------
 
 The **Expected Calibration Error** (ECE) is a metric used to evaluate how well the predicted probabilities of a model align with the actual outcomes. The ECE provides a measure of the difference between predicted confidence levels and actual accuracy. The idea is to divide the predictions into bins based on confidence scores and then compare the accuracy within each bin to the average confidence level of the predictions in that bin.
 

From b4c5ecedbf996333f3ede6c1807bb7b66bb2738e Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 11:04:45 +0200
Subject: [PATCH 059/424] Update README.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 1c1bdd4bd..224f6b274 100644
--- a/README.rst
+++ b/README.rst
@@ -229,4 +229,4 @@ MAPIE is free and open-source software licensed under the `3-clause BSD license
 📚 Citation
 ===========
 
-If you use MAPIE in your research, please cite using `citations file <https://docs.github.com/en/repositories/managing-your-repositorys-settings-and-features/customizing-your-repository/about-citation-files>`_ on our repository.
+If you use MAPIE in your research, please cite using `citations file <CITATION.CFF>`_ on our repository.

From b4e04e6280bfb8a13049507a784c4112cd6293dd Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 11:06:19 +0200
Subject: [PATCH 060/424] Update link to citation file in README.rst

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 224f6b274..5657f7ddf 100644
--- a/README.rst
+++ b/README.rst
@@ -229,4 +229,4 @@ MAPIE is free and open-source software licensed under the `3-clause BSD license
 📚 Citation
 ===========
 
-If you use MAPIE in your research, please cite using `citations file <CITATION.CFF>`_ on our repository.
+If you use MAPIE in your research, please cite using `citations file <CITATION.cff>`_ on our repository.

From 4d4974cf5c63985f990e8d1867ba946ff3cf1a4a Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 11:07:43 +0200
Subject: [PATCH 061/424] Update LICENSE in README.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 5657f7ddf..406a60c99 100644
--- a/README.rst
+++ b/README.rst
@@ -223,7 +223,7 @@ and with the financial support from Région Ile de France and Confiance.ai.
 📝 License
 ==========
 
-MAPIE is free and open-source software licensed under the `3-clause BSD license <https://github.com/simai-ml/MAPIE/blob/master/LICENSE>`_.
+MAPIE is free and open-source software licensed under the `3-clause BSD license <LICENSE>`_.
 
 
 📚 Citation

From 4ee62189bbdb869d0ab4cafeaa90f90050289b3c Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 11:43:43 +0200
Subject: [PATCH 062/424] Apply suggestions from Thibault from code review

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 .../plot_cqr_symmetry_difference.py           | 26 +++++++++++--------
 1 file changed, 15 insertions(+), 11 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 4d12b6bdf..13f827a90 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -26,15 +26,13 @@
 gb_reg = GradientBoostingRegressor(loss="quantile", alpha=quantiles[1])
 gb_reg.fit(X, y)
 
-# MAPIE Quantile Regressor with symmetry=True
-mapie_qr_sym = MapieQuantileRegressor(estimator=gb_reg, alpha=alpha)
-mapie_qr_sym.fit(X, y)
-y_pred_sym, y_pis_sym = mapie_qr_sym.predict(X, symmetry=True)
-
-# MAPIE Quantile Regressor with symmetry=False
-mapie_qr_asym = MapieQuantileRegressor(estimator=gb_reg, alpha=alpha)
-mapie_qr_asym.fit(X, y)
-y_pred_asym, y_pis_asym = mapie_qr_asym.predict(X, symmetry=False)
+# MAPIE Quantile Regressor
+mapie_qr = MapieQuantileRegressor(estimator=gb_reg, alpha=alpha)
+mapie_qr.fit(X, y)
+y_pred_sym, y_pis_sym = mapie_qr.predict(X, symmetry=True)
+y_pred_asym, y_pis_asym = mapie_qr.predict(X, symmetry=False)
+y_qlow = mapie_qr.estimators_[0].predict(X)
+y_qup = mapie_qr.estimators_[1].predict(X)
 
 # Calculate coverage scores
 coverage_score_sym = regression_coverage_score(
@@ -51,6 +49,8 @@
 y_pis_sym_sorted = y_pis_sym[order]
 y_pred_asym_sorted = y_pred_asym[order]
 y_pis_asym_sorted = y_pis_asym[order]
+y_qlow = y_qlow[order]
+y_qup = y_qup[order]
 
 ##############################################################################
 # We will plot the predictions and prediction intervals for both symmetric
@@ -64,7 +64,9 @@
 plt.xlabel("x")
 plt.ylabel("y")
 plt.scatter(X, y, alpha=0.3)
-plt.plot(X_sorted, y_pred_sym_sorted, color="C1")
+#plt.plot(X_sorted, y_pred_sym_sorted, color="C1")
+plt.plot(X_sorted, y_qlow, color="C1")
+plt.plot(X_sorted, y_qup, color="C1")
 plt.plot(X_sorted, y_pis_sym_sorted[:, 0], color="C1", ls="--")
 plt.plot(X_sorted, y_pis_sym_sorted[:, 1], color="C1", ls="--")
 plt.fill_between(
@@ -84,7 +86,9 @@
 plt.xlabel("x")
 plt.ylabel("y")
 plt.scatter(X, y, alpha=0.3)
-plt.plot(X_sorted, y_pred_asym_sorted, color="C2")
+#plt.plot(X_sorted, y_pred_asym_sorted, color="C2")
+plt.plot(X_sorted, y_qlow, color="C2")
+plt.plot(X_sorted, y_qup, color="C2")
 plt.plot(X_sorted, y_pis_asym_sorted[:, 0], color="C2", ls="--")
 plt.plot(X_sorted, y_pis_asym_sorted[:, 1], color="C2", ls="--")
 plt.fill_between(

From b6ef8572e4e0a45cb26638f73e57a4377e86694a Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 12:18:08 +0200
Subject: [PATCH 063/424] Update CITATION.cff to add booktitle

---
 CITATION.cff | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/CITATION.cff b/CITATION.cff
index cf54d9290..e22cd764d 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -10,6 +10,7 @@ date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
   type: conference-paper
+  title: "Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library"
   authors:
   - family-names: "Cordier"
     given-names: "Thibault"
@@ -23,8 +24,8 @@ preferred-citation:
     given-names: "Arnaud"
   - family-names: "Brunel"
     given-names: "Nicolas"
-  title: "Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library"
-  booktitle: "Conformal and Probabilistic Prediction with Applications"
+  collection-title: "Conformal and Probabilistic Prediction with Applications"
+  collection-type: proceedings
   pages: "549--581"
   year: 2023
   organization: "PMLR"
@@ -44,4 +45,4 @@ old-citation:
   doi: "10.48550/arXiv.2207.12274"
   journal: "arXiv preprint arXiv:2207.12274"
   title: "MAPIE: an open-source library for distribution-free uncertainty quantification"
-  year: 2021
\ No newline at end of file
+  year: 2021

From 88adb7364ab1434fdf736af28acca5bda9b69b39 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 12:22:16 +0200
Subject: [PATCH 064/424] Update links to github page in README.rst

---
 README.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index 406a60c99..b02be7484 100644
--- a/README.rst
+++ b/README.rst
@@ -12,7 +12,7 @@
     :target: https://mapie.readthedocs.io/en/stable/?badge=stable
     :alt: Documentation Status
 
-.. |License| image:: https://img.shields.io/github/license/simai-ml/MAPIE
+.. |License| image:: https://img.shields.io/github/license/scikit-learn-contrib/MAPIE
    :target: https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE
 
 .. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/mapie
@@ -33,7 +33,7 @@
 .. |DOI| image:: https://img.shields.io/badge/10.48550/arXiv.2207.12274-B31B1B.svg
    :target: https://arxiv.org/abs/2207.12274
 
-.. image:: https://github.com/simai-ml/MAPIE/raw/master/doc/images/mapie_logo_nobg_cut.png
+.. image:: https://github.com/scikit-learn-contrib/MAPIE/raw/master/doc/images/mapie_logo_nobg_cut.png
     :width: 400
     :align: center
 
@@ -158,7 +158,7 @@ The full documentation can be found `on this link <https://mapie.readthedocs.io/
 ===============
 
 You are welcome to propose and contribute new ideas.
-We encourage you to `open an issue <https://github.com/simai-ml/MAPIE/issues>`_ so that we can align on the work to be done.
+We encourage you to `open an issue <https://github.com/scikit-learn-contrib/MAPIE/issues>`_ so that we can align on the work to be done.
 It is generally a good idea to have a quick discussion before opening a pull request that is potentially out-of-scope.
 For more information on the contribution process, please go `here <CONTRIBUTING.rst>`_.
 

From 5cc1e6f1cec4b5e564ac0c5a5ea70f6254eea698 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 12:26:32 +0200
Subject: [PATCH 065/424] Update maxdepth for metrics documentation index.rst

---
 doc/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/index.rst b/doc/index.rst
index b5450722b..53172ca43 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -59,7 +59,7 @@
    notebooks_calibration
 
 .. toctree::
-   :maxdepth: 2
+   :maxdepth: 1
    :hidden:
    :caption: METRICS
 

From 454cd4ebc14fb029fa1f6e512ea339a438db568c Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Tue, 21 May 2024 14:10:18 +0200
Subject: [PATCH 066/424] FIX: small issues in plot_cqr_symmetry_difference.py
 in regression examples

---
 .../plot_cqr_symmetry_difference.py           | 22 ++++++++++---------
 1 file changed, 12 insertions(+), 10 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 13f827a90..4455c27dd 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -13,6 +13,8 @@
 from mapie.metrics import regression_coverage_score
 from mapie.quantile_regression import MapieQuantileRegressor
 
+random_state = 2
+
 ##############################################################################
 # We generate a synthetic data.
 
@@ -22,13 +24,13 @@
 alpha = 0.2
 
 # Fit a Gradient Boosting Regressor for quantile regression
-quantiles = [0.1, 0.9]
-gb_reg = GradientBoostingRegressor(loss="quantile", alpha=quantiles[1])
-gb_reg.fit(X, y)
+gb_reg = GradientBoostingRegressor(
+    loss="quantile", alpha=0.5, random_state=random_state
+)
 
 # MAPIE Quantile Regressor
 mapie_qr = MapieQuantileRegressor(estimator=gb_reg, alpha=alpha)
-mapie_qr.fit(X, y)
+mapie_qr.fit(X, y, random_state=random_state)
 y_pred_sym, y_pis_sym = mapie_qr.predict(X, symmetry=True)
 y_pred_asym, y_pis_asym = mapie_qr.predict(X, symmetry=False)
 y_qlow = mapie_qr.estimators_[0].predict(X)
@@ -64,7 +66,6 @@
 plt.xlabel("x")
 plt.ylabel("y")
 plt.scatter(X, y, alpha=0.3)
-#plt.plot(X_sorted, y_pred_sym_sorted, color="C1")
 plt.plot(X_sorted, y_qlow, color="C1")
 plt.plot(X_sorted, y_qup, color="C1")
 plt.plot(X_sorted, y_pis_sym_sorted[:, 0], color="C1", ls="--")
@@ -86,7 +87,6 @@
 plt.xlabel("x")
 plt.ylabel("y")
 plt.scatter(X, y, alpha=0.3)
-#plt.plot(X_sorted, y_pred_asym_sorted, color="C2")
 plt.plot(X_sorted, y_qlow, color="C2")
 plt.plot(X_sorted, y_qup, color="C2")
 plt.plot(X_sorted, y_pis_asym_sorted[:, 0], color="C2", ls="--")
@@ -106,7 +106,9 @@
 plt.show()
 
 ##############################################################################
-# The symmetric intervals (`symmetry=True`) are easier to interpret and
-# tend to have higher coverage but might not adapt well to varying
-# noise levels. The asymmetric intervals (`symmetry=False`) are more
-# flexible and better capture heteroscedasticity but can appear more jagged.
+# The symmetric intervals (`symmetry=True`) use a combined set of residuals
+# for both bounds, while the asymmetric intervals use distinct residuals for
+# each bound, allowing for more flexible and accurate intervals that reflect
+# the heteroscedastic nature of the data. The resulting effective coverages
+# demonstrate the theoretical guarantee of the target coverage level
+# $(1−\alpha)$.

From e319da2b14c8afc463846e5e2ad87eacf232652e Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 14:13:29 +0200
Subject: [PATCH 067/424] FIX: issues of documentation with bullet points

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_metrics.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index f9adfbe9a..0ef73e480 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -126,6 +126,7 @@ The **Expected Calibration Error** (ECE) is a metric used to evaluate how well t
     \text{ECE} = \sum_{i=1}^B \frac{|B_i|}{n} \left| \text{acc}(B_i) - \text{conf}(B_i) \right|
 
 where:
+
 - :math:`B_i` is the set of indices of samples that fall into the i-th bin.
 - :math:`|B_i|` is the number of samples in the i-th bin.
 - :math:`n` is the total number of samples.
@@ -146,6 +147,7 @@ The Top-Label ECE is calculated as follows:
     \text{Top-Label ECE} = \frac{1}{L} \sum_{j=1}^L \sum_{i=1}^B \frac{|B_{i,j}|}{n_j} \left| \text{acc}(B_{i,j}) - \text{conf}(B_{i,j}) \right|
 
 where:
+
 - :math:`L` is the number of unique labels.
 - :math:`B_{i,j}` is the set of indices of samples that fall into the i-th bin for label j.
 - :math:`|B_{i,j}|` is the number of samples in the i-th bin for label j.

From 6edf468cd41d79066c31ff2c7693ea7cd31a7a34 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 21 May 2024 14:55:07 +0200
Subject: [PATCH 068/424] Update maxdepth to 0 in index.rst

---
 doc/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/index.rst b/doc/index.rst
index 53172ca43..7bc75bbf7 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -59,7 +59,7 @@
    notebooks_calibration
 
 .. toctree::
-   :maxdepth: 1
+   :maxdepth: 0
    :hidden:
    :caption: METRICS
 

From 422de43de2b4c4b5f9ca50eb32fb6fe5bb0722fa Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 22 May 2024 15:13:59 +0200
Subject: [PATCH 069/424] FIX: reset correct maxdepth

---
 doc/index.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/index.rst b/doc/index.rst
index 7bc75bbf7..b5450722b 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -59,7 +59,7 @@
    notebooks_calibration
 
 .. toctree::
-   :maxdepth: 0
+   :maxdepth: 2
    :hidden:
    :caption: METRICS
 

From 488a7b4f4587eb707eaba8e9d7612e068d704c34 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 22 May 2024 15:14:15 +0200
Subject: [PATCH 070/424] FIX: headers showing in sidebar

---
 doc/theoretical_description_metrics.rst | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 0ef73e480..26b4fa1c4 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -8,7 +8,7 @@ Theoretical Description
 This document provides detailed descriptions of various metrics used to evaluate the performance of predictive models, particularly focusing on their ability to estimate uncertainties and calibrate predictions accurately.
 
 1. General Metrics
-==================
+------------------
 
 Regression Coverage Score
 -------------------------
@@ -115,7 +115,7 @@ The **Mean Winkler Interval (MWI) Score** evaluates prediction intervals by comb
 where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not containing :math:`y_{i}`, and :math:`\alpha` is the significance level.
 
 2. Calibration Metrics
-======================
+----------------------
 
 Expected Calibration Error
 --------------------------
@@ -201,8 +201,8 @@ Spiegelhalter’s Test
 
    \text{Spiegelhalter's Statistic} = \frac{\sum_{i=1}^n (y_i - \hat y_i)(1 - 2\hat y_i)}{\sqrt{\sum_{i=1}^n (1 - 2 \hat y_i)^2 \hat y_i (1 - \hat y_i)}}
 
-References
-==========
+3. References
+-------------
 
 [1] Angelopoulos, A. N., & Bates, S. (2021).
 A gentle introduction to conformal prediction and

From a43eb63a9ae4cb8476916ed26a5606eefa1b7357 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Wed, 22 May 2024 15:36:44 +0200
Subject: [PATCH 071/424] FIX: name of plot description

---
 .../regression/1-quickstart/plot_cqr_symmetry_difference.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 4455c27dd..77271997c 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -1,7 +1,7 @@
 """
-======================================================
-Plotting MAPIE Quantile Regressor prediction intervals
-======================================================
+====================================
+Plotting CQR with symmetric argument
+====================================
 An example plot of :class:`~mapie.quantile_regression.MapieQuantileRegressor`
 illustrating the impact of the symmetry parameter.
 """

From fccc0e380ff7c431f0169d2d6a4d42434821b2dc Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Wed, 22 May 2024 13:58:33 +0000
Subject: [PATCH 072/424] FIX: solve last test

---
 mapie/classification.py                     | 4 ++++
 mapie/estimator/classification/estimator.py | 4 ----
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index ef643b0bc..eef262619 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -1112,6 +1112,10 @@ def fit(
                 "Invalid method. " f"Allowed values are {self.valid_methods_}."
             )
 
+        # In split-CP, we keep only the model fitted on train dataset
+        if isinstance(cv, ShuffleSplit):
+            self.estimator_.single_estimator_ = self.estimator_.estimators_[0]
+
         return self
 
     def predict(
diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classification/estimator.py
index 555ef72b0..e5d0bb126 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classification/estimator.py
@@ -10,7 +10,6 @@
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.aggregation_functions import phi2D
 from mapie.estimator.classification.interface import EnsembleEstimator
 from mapie.utils import (
     check_no_agg_cv,
@@ -439,9 +438,6 @@ def fit(
                 )
                 for train_index, _ in cv.split(X, y, groups)
             )
-            # In split-CP, we keep only the model fitted on train dataset
-            if isinstance(cv, ShuffleSplit):
-                single_estimator_ = estimators_[0]
 
         self.single_estimator_ = single_estimator_
         self.estimators_ = estimators_

From 668b555da3339e93e6188f8541feadc7bc9c31cf Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 22 May 2024 16:23:31 +0200
Subject: [PATCH 073/424] FIX: add all metrics of calibration in the same spot

---
 doc/theoretical_description_calibration.rst | 117 ++-----------------
 doc/theoretical_description_metrics.rst     | 123 +++++++++++++++-----
 2 files changed, 98 insertions(+), 142 deletions(-)

diff --git a/doc/theoretical_description_calibration.rst b/doc/theoretical_description_calibration.rst
index 21df15f2d..c62540337 100644
--- a/doc/theoretical_description_calibration.rst
+++ b/doc/theoretical_description_calibration.rst
@@ -2,10 +2,9 @@
 
 .. _theoretical_description_calibration:
 
-=======================
+#######################
 Theoretical Description
-=======================
-
+#######################
 
 One method for multi-class calibration has been implemented in MAPIE so far :
 Top-Label Calibration [1].
@@ -34,8 +33,8 @@ To apply calibration directly to a multi-class context, Gupta et al. propose a f
 a multi-class calibration to multiple binary calibrations (M2B).
 
 
-1. Top-Label
-------------
+Top-Label
+---------
 
 Top-Label calibration is a calibration technique introduced by Gupta et al. to calibrate the model according to the highest score and
 the corresponding class (see [1] Section 2). This framework offers to apply binary calibration techniques to multi-class calibration.
@@ -50,109 +49,8 @@ according to Top-Label calibration if:
     Pr(Y = c(X) \mid h(X), c(X)) = h(X)
 
 
-2. Metrics for calibration
---------------------------
-
-**Expected calibration error**
-
-The main metric to check if the calibration is correct is the Expected Calibration Error (ECE). It is based on two
-components, accuracy and confidence per bin. The number of bins is a hyperparamater :math:`M`, and we refer to a specific bin by
-:math:`B_m`.
-
-.. math::
-    \text{acc}(B_m) &= \frac{1}{\left| B_m \right|} \sum_{i \in B_m} {y}_i \\
-    \text{conf}(B_m) &= \frac{1}{\left| B_m \right|} \sum_{i \in B_m} \hat{f}(x)_i
-
-
-The ECE is the combination of these two metrics combined.
-
-.. math::
-    \text{ECE} = \sum_{m=1}^M \frac{\left| B_m \right|}{n} \left| acc(B_m) - conf(B_m) \right|
-
-In simple terms, once all the different bins from the confidence scores have been created, we check the mean accuracy of each bin.
-The absolute mean difference between the two is the ECE. Hence, the lower the ECE, the better the calibration was performed. 
-
-**Top-Label ECE**
-
-In the top-label calibration, we only calculate the ECE for the top-label class. Hence, per top-label class, we condition the calculation
-of the accuracy and confidence based on the top label and take the average ECE for each top-label.
-
-3. Statistical tests for calibration
-------------------------------------
-
-**Kolmogorov-Smirnov test**
-
-Kolmogorov-Smirnov test was derived in [2, 3, 4]. The idea is to consider the cumulative differences between sorted scores :math:`s_i`
-and their corresponding labels :math:`y_i` and to compare its properties to that of a standard Brownian motion. Let us consider the
-cumulative differences on sorted scores: 
-
-.. math::
-    C_k = \frac{1}{N}\sum_{i=1}^k (s_i - y_i)
-
-We also introduce a typical normalization scale :math:`\sigma`:
-
-.. math::
-    \sigma = \frac{1}{N}\sqrt{\sum_{i=1}^N s_i(1 - s_i)}
-
-The Kolmogorov-Smirnov statistic is then defined as : 
-
-.. math::
-   G = \max|C_k|/\sigma
-
-It can be shown [2] that, under the null hypothesis of well-calibrated scores, this quantity asymptotically (i.e. when N goes to infinity)
-converges to the maximum absolute value of a standard Brownian motion over the unit interval :math:`[0, 1]`. [3, 4] also provide closed-form 
-formulas for the cumulative distribution function (CDF) of the maximum absolute value of such a standard Brownian motion.
-So we state the p-value associated to the statistical test of well calibration as:
-
-.. math::
-   p = 1 - CDF(G)
-
-**Kuiper test**
-
-Kuiper test was derived in [2, 3, 4] and is very similar to Kolmogorov-Smirnov. This time, the statistic is defined as:
-
-.. math::
-   H = (\max_k|C_k| - \min_k|C_k|)/\sigma
-
-It can be shown [2] that, under the null hypothesis of well-calibrated scores, this quantity asymptotically (i.e. when N goes to infinity)
-converges to the range of a standard Brownian motion over the unit interval :math:`[0, 1]`. [3, 4] also provide closed-form 
-formulas for the cumulative distribution function (CDF) of the range of such a standard Brownian motion.
-So we state the p-value associated to the statistical test of well calibration as:
-
-.. math::
-   p = 1 - CDF(H)
-
-**Spiegelhalter test**
-
-Spiegelhalter test was derived in [6]. It is based on a decomposition of the Brier score: 
-
-.. math::
-   B = \frac{1}{N}\sum_{i=1}^N(y_i - s_i)^2
-
-where scores are denoted :math:`s_i` and their corresponding labels :math:`y_i`. This can be decomposed in two terms:
-
-.. math::
-   B = \frac{1}{N}\sum_{i=1}^N(y_i - s_i)(1 - 2s_i) + \frac{1}{N}\sum_{i=1}^N s_i(1 - s_i)
-
-It can be shown that the first term has an expected value of zero under the null hypothesis of well calibration. So we interpret
-the second term as the Brier score expected value :math:`E(B)` under the null hypothesis. As for the variance of the Brier score, it can be
-computed as:
-
-.. math::
-   Var(B) = \frac{1}{N^2}\sum_{i=1}^N(1 - 2s_i)^2 s_i(1 - s_i)
-
-So we can build a Z-score as follows: 
-
-.. math::
-   Z = \frac{B - E(B)}{\sqrt{Var(B)}} = \frac{\sum_{i=1}^N(y_i - s_i)(1 - 2s_i)}{\sqrt{\sum_{i=1}^N(1 - 2s_i)^2 s_i(1 - s_i)}}
-
-This statistic follows a normal distribution of cumulative distribution CDF so that we state the associated p-value:
-
-.. math::
-   p = 1 - CDF(Z)
-
-3. References
--------------
+References
+----------
 
 [1] Gupta, Chirag, and Aaditya K. Ramdas.
 "Top-label calibration and multiclass-to-binary reductions."
@@ -171,8 +69,7 @@ arXiv preprint arXiv:2202.00100.
 
 [4] D. A. Darling. A. J. F. Siegert.
 The First Passage Problem for a Continuous Markov Process.
-Ann. Math. Statist. 24 (4) 624 - 639, December,
-1953.
+Ann. Math. Statist. 24 (4) 624 - 639, December, 1953.
 
 [5] William Feller.
 The Asymptotic Distribution of the Range of Sums of
diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 26b4fa1c4..cbe074141 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -2,13 +2,14 @@
 
 .. _theoretical_description_metrics:
 
+#######################
 Theoretical Description
-=======================
+#######################
 
 This document provides detailed descriptions of various metrics used to evaluate the performance of predictive models, particularly focusing on their ability to estimate uncertainties and calibrate predictions accurately.
 
 1. General Metrics
-------------------
+==================
 
 Regression Coverage Score
 -------------------------
@@ -115,45 +116,57 @@ The **Mean Winkler Interval (MWI) Score** evaluates prediction intervals by comb
 where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not containing :math:`y_{i}`, and :math:`\alpha` is the significance level.
 
 2. Calibration Metrics
-----------------------
+======================
+
 
 Expected Calibration Error
---------------------------
+==========================
+
+The **Expected Calibration Error** (ECE) is a metric used to evaluate how well the predicted probabilities of a model align with the actual outcomes. It measures the difference between predicted confidence levels and actual accuracy. The process involves dividing the predictions into bins based on confidence scores and then comparing the accuracy within each bin to the average confidence level of the predictions in that bin. The number of bins is a hyperparameter :math:`M`, and we refer to a specific bin by :math:`B_m`.
 
-The **Expected Calibration Error** (ECE) is a metric used to evaluate how well the predicted probabilities of a model align with the actual outcomes. The ECE provides a measure of the difference between predicted confidence levels and actual accuracy. The idea is to divide the predictions into bins based on confidence scores and then compare the accuracy within each bin to the average confidence level of the predictions in that bin.
+For each bin :math:`B_m`, the accuracy and confidence are defined as follows:
 
 .. math::
-    \text{ECE} = \sum_{i=1}^B \frac{|B_i|}{n} \left| \text{acc}(B_i) - \text{conf}(B_i) \right|
 
-where:
+    \text{acc}(B_m) = \frac{1}{\left| B_m \right|} \sum_{i \in B_m} y_i
+
+.. math::
+
+    \text{conf}(B_m) = \frac{1}{\left| B_m \right|} \sum_{i \in B_m} \hat{f}(x_i)
+
+The ECE is then calculated using the following formula:
+
+.. math::
 
-- :math:`B_i` is the set of indices of samples that fall into the i-th bin.
-- :math:`|B_i|` is the number of samples in the i-th bin.
+    \text{ECE} = \sum_{m=1}^M \frac{\left| B_m \right|}{n} \left| \text{acc}(B_m) - \text{conf}(B_m) \right|
+
+where:
+- :math:`B_m` is the set of indices of samples that fall into the :math:`m`-th bin.
+- :math:`\left| B_m \right|` is the number of samples in the :math:`m`-th bin.
 - :math:`n` is the total number of samples.
-- :math:`\text{acc}(B_i)` is the accuracy within the i-th bin.
-- :math:`\text{conf}(B_i)` is the average confidence score within the i-th bin.
-- :math:`B` is the total number of bins.
+- :math:`\text{acc}(B_m)` is the accuracy within the :math:`m`-th bin.
+- :math:`\text{conf}(B_m)` is the average confidence score within the :math:`m`-th bin.
 
-The difference between the average confidence and the actual accuracy within each bin is weighted by the proportion of samples in that bin, ensuring that bins with more samples have a larger influence on the final ECE value.
+In simple terms, once the different bins from the confidence scores have been created, we check the mean accuracy of each bin. The absolute mean difference between the two is the ECE. Hence, the lower the ECE, the better the calibration was performed. The difference between the average confidence and the actual accuracy within each bin is weighted by the proportion of samples in that bin, ensuring that bins with more samples have a larger influence on the final ECE value.
 
 Top-Label Expected Calibration Error (Top-Label ECE)
-----------------------------------------------------
+====================================================
 
-The **Top-Label Expected Calibration Error** (Top-Label ECE) extends the concept of ECE to the multi-class setting. Instead of evaluating calibration over all predicted probabilities, Top-Label ECE focuses on the calibration of the most confident prediction (top-label) for each sample.
+The **Top-Label Expected Calibration Error** (Top-Label ECE) extends the concept of ECE to the multi-class setting. Instead of evaluating calibration over all predicted probabilities, Top-Label ECE focuses on the calibration of the most confident prediction (top-label) for each sample. For the top-label class, the calculation of the accuracy and confidence is conditioned on the top label, and the average ECE is taken for each top-label.
 
 The Top-Label ECE is calculated as follows:
 
 .. math::
+
     \text{Top-Label ECE} = \frac{1}{L} \sum_{j=1}^L \sum_{i=1}^B \frac{|B_{i,j}|}{n_j} \left| \text{acc}(B_{i,j}) - \text{conf}(B_{i,j}) \right|
 
 where:
-
 - :math:`L` is the number of unique labels.
-- :math:`B_{i,j}` is the set of indices of samples that fall into the i-th bin for label j.
-- :math:`|B_{i,j}|` is the number of samples in the i-th bin for label j.
-- :math:`n_j` is the total number of samples for label j.
-- :math:`\text{acc}(B_{i,j})` is the accuracy within the i-th bin for label j.
-- :math:`\text{conf}(B_{i,j})` is the average confidence score within the i-th bin for label j.
+- :math:`B_{i,j}` is the set of indices of samples that fall into the :math:`i`-th bin for label :math:`j`.
+- :math:`\left| B_{i,j} \right|` is the number of samples in the :math:`i`-th bin for label :math:`j`.
+- :math:`n_j` is the total number of samples for label :math:`j`.
+- :math:`\text{acc}(B_{i,j})` is the accuracy within the :math:`i`-th bin for label :math:`j`.
+- :math:`\text{conf}(B_{i,j})` is the average confidence score within the :math:`i`-th bin for label :math:`j`.
 - :math:`B` is the total number of bins.
 
 For each label, the predictions are binned according to their confidence scores for that label. The calibration error is then calculated for each label separately and averaged across all labels to obtain the final Top-Label ECE value. This ensures that the calibration is measured specifically for the most confident prediction, which is often the most critical for decision-making in multi-class problems.
@@ -175,34 +188,80 @@ where:
 Kolmogorov-Smirnov Statistic for Calibration
 --------------------------------------------
 
-This statistic measures the maximum absolute deviation between the empirical cumulative distribution function (ECDF) of observed outcomes and predicted probabilities [2, 3, 11].
+The **Kolmogorov-Smirnov test** was derived in [2, 3, 11]. The idea is to consider the cumulative differences between sorted scores :math:`s_i`
+and their corresponding labels :math:`y_i` and to compare its properties to that of a standard Brownian motion. Let us consider the
+cumulative differences on sorted scores: 
+
+.. math::
+    C_k = \frac{1}{N}\sum_{i=1}^k (s_i - y_i)
+
+We also introduce a typical normalization scale :math:`\sigma`:
+
+.. math::
+    \sigma = \frac{1}{N}\sqrt{\sum_{i=1}^N s_i(1 - s_i)}
+
+The Kolmogorov-Smirnov statistic is then defined as : 
 
 .. math::
+   G = \max|C_k|/\sigma
 
-   \text{KS Statistic} = \sup_x |F_n(x) - S_n(x)|
+It can be shown [2] that, under the null hypothesis of well-calibrated scores, this quantity asymptotically (i.e. when N goes to infinity)
+converges to the maximum absolute value of a standard Brownian motion over the unit interval :math:`[0, 1]`. [3, 11] also provide closed-form 
+formulas for the cumulative distribution function (CDF) of the maximum absolute value of such a standard Brownian motion.
+So we state the p-value associated to the statistical test of well calibration as:
 
-where :math:`F_n(x)` is the ECDF of the predicted probabilities and :math:`S_n(x)` is the ECDF of the observed outcomes.
+.. math::
+   p = 1 - CDF(G)
 
-Kuiper's Statistic
-------------------
+Kuiper's Test
+-------------
 
-**Kuiper's Statistic** considers both the maximum deviation above and below the mean cumulative difference, making it more sensitive to deviations at the tails of the distribution [2, 3, 11].
+The **Kuiper test** was derived in [2, 3, 11] and is very similar to Kolmogorov-Smirnov. This time, the statistic is defined as:
 
 .. math::
+   H = (\max_k|C_k| - \min_k|C_k|)/\sigma
+
+It can be shown [2] that, under the null hypothesis of well-calibrated scores, this quantity asymptotically (i.e. when N goes to infinity)
+converges to the range of a standard Brownian motion over the unit interval :math:`[0, 1]`. [3, 11] also provide closed-form 
+formulas for the cumulative distribution function (CDF) of the range of such a standard Brownian motion.
+So we state the p-value associated to the statistical test of well calibration as:
 
-   \text{Kuiper's Statistic} = \max(F_n(x) - S_n(x)) + \max(S_n(x) - F_n(x))
+.. math::
+   p = 1 - CDF(H)
 
 Spiegelhalter’s Test
 --------------------
 
-**Spiegelhalter’s Test** assesses the calibration of binary predictions based on a transformation of the Brier score [9].
+The **Spiegelhalter test** was derived in [9]. It is based on a decomposition of the Brier score: 
 
 .. math::
+   B = \frac{1}{N}\sum_{i=1}^N(y_i - s_i)^2
 
-   \text{Spiegelhalter's Statistic} = \frac{\sum_{i=1}^n (y_i - \hat y_i)(1 - 2\hat y_i)}{\sqrt{\sum_{i=1}^n (1 - 2 \hat y_i)^2 \hat y_i (1 - \hat y_i)}}
+where scores are denoted :math:`s_i` and their corresponding labels :math:`y_i`. This can be decomposed in two terms:
 
-3. References
--------------
+.. math::
+   B = \frac{1}{N}\sum_{i=1}^N(y_i - s_i)(1 - 2s_i) + \frac{1}{N}\sum_{i=1}^N s_i(1 - s_i)
+
+It can be shown that the first term has an expected value of zero under the null hypothesis of well calibration. So we interpret
+the second term as the Brier score expected value :math:`E(B)` under the null hypothesis. As for the variance of the Brier score, it can be
+computed as:
+
+.. math::
+   Var(B) = \frac{1}{N^2}\sum_{i=1}^N(1 - 2s_i)^2 s_i(1 - s_i)
+
+So we can build a Z-score as follows: 
+
+.. math::
+   Z = \frac{B - E(B)}{\sqrt{Var(B)}} = \frac{\sum_{i=1}^N(y_i - s_i)(1 - 2s_i)}{\sqrt{\sum_{i=1}^N(1 - 2s_i)^2 s_i(1 - s_i)}}
+
+This statistic follows a normal distribution of cumulative distribution CDF so that we state the associated p-value:
+
+.. math::
+   p = 1 - CDF(Z)
+
+
+References
+==========
 
 [1] Angelopoulos, A. N., & Bates, S. (2021).
 A gentle introduction to conformal prediction and

From 9b458bad8a819691e329b171b6ce72359f81044c Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Wed, 22 May 2024 16:24:01 +0200
Subject: [PATCH 074/424] FIX: header and labels correction

---
 doc/quick_start.rst                               |  2 +-
 ...oretical_description_binary_classification.rst |  8 ++++----
 doc/theoretical_description_classification.rst    |  9 ++++-----
 doc/theoretical_description_conformity_scores.rst | 14 +++++++-------
 ...ical_description_multilabel_classification.rst | 15 +++++++--------
 doc/theoretical_description_regression.rst        |  6 +++---
 6 files changed, 26 insertions(+), 28 deletions(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index 31e2efa97..dcdf6700e 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -114,7 +114,7 @@ It is given by the alpha parameter defined in ``MapieRegressor``, here equal to
 thus giving target coverages of ``0.95`` and ``0.68``.
 The effective coverage is the actual fraction of true labels lying in the prediction intervals.
 
-2. Run MapieClassifier
+3. Run MapieClassifier
 ----------------------
 
 Similarly, it's possible to do the same for a basic classification problem.
diff --git a/doc/theoretical_description_binary_classification.rst b/doc/theoretical_description_binary_classification.rst
index 55e2f6144..9c8f6f336 100644
--- a/doc/theoretical_description_binary_classification.rst
+++ b/doc/theoretical_description_binary_classification.rst
@@ -2,9 +2,9 @@
 
 .. _theoretical_description_binay_classification:
 
-=======================
+#######################
 Theoretical Description
-=======================
+#######################
 
 There are mainly three different ways to handle uncertainty quantification in binary classification:
 calibration (see :doc:`theoretical_description_calibration`), confidence interval (CI) for the probability
@@ -83,8 +83,8 @@ for the labels of test objects which are guaranteed to be well-calibrated under
 that the observations are generated independently from the same distribution [2].
 
 
-4. References
--------------
+References
+----------
 
 [1] Gupta, Chirag, Aleksandr Podkopaev, and Aaditya Ramdas.
 "Distribution-free binary classification: prediction sets, confidence intervals, and calibration."
diff --git a/doc/theoretical_description_classification.rst b/doc/theoretical_description_classification.rst
index a8ef17830..445fcfe42 100644
--- a/doc/theoretical_description_classification.rst
+++ b/doc/theoretical_description_classification.rst
@@ -2,10 +2,9 @@
 
 .. _theoretical_description_classification:
 
-=======================
+#######################
 Theoretical Description
-=======================
-
+#######################
 
 Three methods for multi-class uncertainty quantification have been implemented in MAPIE so far :
 LAC (that stands for Least Ambiguous set-valued Classifier) [1], Adaptive Prediction Sets [2, 3] and Top-K [3].
@@ -229,8 +228,8 @@ where :
 
 .. TO BE CONTINUED
 
-5. References
--------------
+References
+----------
 
 [1] Mauricio Sadinle, Jing Lei, & Larry Wasserman.
 "Least Ambiguous Set-Valued Classifiers With Bounded Error Levels."
diff --git a/doc/theoretical_description_conformity_scores.rst b/doc/theoretical_description_conformity_scores.rst
index 8ea72b6ff..5ec0aee4d 100644
--- a/doc/theoretical_description_conformity_scores.rst
+++ b/doc/theoretical_description_conformity_scores.rst
@@ -2,9 +2,9 @@
 
 .. _theoretical_description_conformity_scores:
 
-=============================================
+#############################################
 Theoretical Description for Conformity Scores
-=============================================
+#############################################
 
 The :class:`mapie.conformity_scores.ConformityScore` class implements various
 methods to compute conformity scores for regression.
@@ -25,7 +25,7 @@ quantiles will be computed : one on the right side of the distribution
 and the other on the left side.
 
 1. The absolute residual score
-==============================
+------------------------------
 
 The absolute residual score (:class:`mapie.conformity_scores.AbsoluteConformityScore`)
 is the simplest and most commonly used conformal score, it translates the error
@@ -44,7 +44,7 @@ With this score, the intervals of predictions will be constant over the whole da
 This score is by default symmetric (*see above for definition*).
 
 2. The gamma score
-==================
+------------------
 
 The gamma score [2] (:class:`mapie.conformity_scores.GammaConformityScore`) adds a
 notion of adaptivity with the normalization of the residuals by the predictions.
@@ -69,7 +69,7 @@ the order of magnitude of the predictions, implying that this score should be us
 in use cases where we want greater uncertainty when the prediction is high.
 
 3. The residual normalized score
-=======================================
+--------------------------------
 
 The residual normalized score [1] (:class:`mapie.conformity_scores.ResidualNormalisedScore`)
 is slightly more complex than the previous scores.
@@ -97,7 +97,7 @@ it is not proportional to the uncertainty.
 
 
 Key takeaways
-=============
+-------------
 
 - The absolute residual score is the basic conformity score and gives constant intervals. It is the one used by default by :class:`mapie.regression.MapieRegressor`.
 - The gamma conformity score adds a notion of adaptivity by giving intervals of different sizes
@@ -107,7 +107,7 @@ Key takeaways
   without specific assumptions on the data.
 
 References
-==========
+----------
 
 [1] Lei, J., G'Sell, M., Rinaldo, A., Tibshirani, R. J., & Wasserman, L. (2018). Distribution-Free 
 Predictive Inference for Regression. Journal of the American Statistical Association, 113(523), 1094–1111. 
diff --git a/doc/theoretical_description_multilabel_classification.rst b/doc/theoretical_description_multilabel_classification.rst
index 011061e00..8dffb0b39 100644
--- a/doc/theoretical_description_multilabel_classification.rst
+++ b/doc/theoretical_description_multilabel_classification.rst
@@ -2,10 +2,9 @@
 
 .. _theoretical_description_multilabel_classification:
 
-=======================
+#######################
 Theoretical Description
-=======================
-
+#######################
 
 Three methods for multi-label uncertainty quantification have been implemented in MAPIE so far :
 Risk-Controlling Prediction Sets (RCPS) [1], Conformal Risk Control (CRC) [2] and Learn Then Test (LTT) [3].
@@ -38,7 +37,7 @@ Notice that at the opposite of the other two methods, LTT allows to control any
 we use CRC and RCPS for recall control and LTT for precision control.
 
 1. Risk-Controlling Prediction Sets
------------------------------------
+===================================
 1.1. General settings
 ---------------------
 
@@ -143,7 +142,7 @@ Then:
 
 
 2. Conformal Risk Control
--------------------------
+=========================
 
 The goal of this method is to control any monotone and bounded loss. The result of this method can be expressed as follows:
 
@@ -166,7 +165,7 @@ With :
 
 
 3. Learn Then Test
-------------------
+==================
 
 3.1. General settings
 ---------------------
@@ -200,8 +199,8 @@ In order to find all the parameters :math:`\lambda` that satisfy the above condi
   that controls the family-wise error rate (FWER), for example, Bonferonni correction.
 
 
-4. References
--------------
+References
+==========
 
 [1] Lihua Lei Jitendra Malik Stephen Bates, Anastasios Angelopoulos,
 and Michael I. Jordan. Distribution-free, risk-controlling prediction
diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index c755975df..8f60c030c 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -2,9 +2,9 @@
 
 .. _theoretical_description_regression:
 
-=======================
+#######################
 Theoretical Description
-=======================
+#######################
 
 The :class:`mapie.regression.MapieRegressor` class uses various
 resampling methods based on the jackknife strategy
@@ -58,7 +58,7 @@ The figure below illustrates the naive method.
    :align: center
 
 2. The split method
-=====================
+===================
 
 The so-called split method computes the residuals of a calibration dataset to estimate the 
 typical error obtained on a new test data point. 

From 75716ce3a39c047db81d4e78f83ed50c6a62d051 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 23 May 2024 11:31:54 +0200
Subject: [PATCH 075/424] FIX: indentation of headers

---
 doc/quick_start.rst                                       | 8 +++-----
 doc/theoretical_description_metrics.rst                   | 4 ++--
 doc/theoretical_description_multilabel_classification.rst | 2 --
 3 files changed, 5 insertions(+), 9 deletions(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index dcdf6700e..3754f5ff5 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -7,11 +7,9 @@ In regression settings, **MAPIE** provides prediction intervals on single-output
 In classification settings, **MAPIE** provides prediction sets on multi-class data.
 In any case, **MAPIE** is compatible with any scikit-learn-compatible estimator.
 
-Estimate your prediction intervals
-==================================
 
 1. Download and install the module
-----------------------------------
+==================================
 
 Install via ``pip``:
 
@@ -33,7 +31,7 @@ To install directly from the github repository :
 
 
 2. Run MapieRegressor
----------------------
+=====================
 
 Let us start with a basic regression problem. 
 Here, we generate one-dimensional noisy data that we fit with a linear model.
@@ -115,7 +113,7 @@ thus giving target coverages of ``0.95`` and ``0.68``.
 The effective coverage is the actual fraction of true labels lying in the prediction intervals.
 
 3. Run MapieClassifier
-----------------------
+=======================
 
 Similarly, it's possible to do the same for a basic classification problem.
 
diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index cbe074141..6ae010bb3 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -120,7 +120,7 @@ where :math:`\hat y^{\text{boundary}}_{i}` is the nearest interval boundary not
 
 
 Expected Calibration Error
-==========================
+--------------------------
 
 The **Expected Calibration Error** (ECE) is a metric used to evaluate how well the predicted probabilities of a model align with the actual outcomes. It measures the difference between predicted confidence levels and actual accuracy. The process involves dividing the predictions into bins based on confidence scores and then comparing the accuracy within each bin to the average confidence level of the predictions in that bin. The number of bins is a hyperparameter :math:`M`, and we refer to a specific bin by :math:`B_m`.
 
@@ -150,7 +150,7 @@ where:
 In simple terms, once the different bins from the confidence scores have been created, we check the mean accuracy of each bin. The absolute mean difference between the two is the ECE. Hence, the lower the ECE, the better the calibration was performed. The difference between the average confidence and the actual accuracy within each bin is weighted by the proportion of samples in that bin, ensuring that bins with more samples have a larger influence on the final ECE value.
 
 Top-Label Expected Calibration Error (Top-Label ECE)
-====================================================
+----------------------------------------------------
 
 The **Top-Label Expected Calibration Error** (Top-Label ECE) extends the concept of ECE to the multi-class setting. Instead of evaluating calibration over all predicted probabilities, Top-Label ECE focuses on the calibration of the most confident prediction (top-label) for each sample. For the top-label class, the calculation of the accuracy and confidence is conditioned on the top label, and the average ECE is taken for each top-label.
 
diff --git a/doc/theoretical_description_multilabel_classification.rst b/doc/theoretical_description_multilabel_classification.rst
index 8dffb0b39..e3ff05da3 100644
--- a/doc/theoretical_description_multilabel_classification.rst
+++ b/doc/theoretical_description_multilabel_classification.rst
@@ -167,8 +167,6 @@ With :
 3. Learn Then Test
 ==================
 
-3.1. General settings
----------------------
 We are going to present the Learn Then Test framework that allows the user to control non-monotonic risk such as precision score.
 This method has been introduced in article [3].
 The settings here are the same as RCPS and CRC, we just need to introduce some new parameters:

From 70bb4b1ff832b327305dd0792472c42a10ececea Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 23 May 2024 11:32:15 +0200
Subject: [PATCH 076/424] FIX: standardization

---
 .../plot_main-tutorial-regression.py          | 41 ++++++++-----------
 1 file changed, 17 insertions(+), 24 deletions(-)

diff --git a/examples/regression/4-tutorials/plot_main-tutorial-regression.py b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
index 50c2fd48d..33a324f8b 100644
--- a/examples/regression/4-tutorials/plot_main-tutorial-regression.py
+++ b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
@@ -2,31 +2,24 @@
 ===============================
 Tutorial for tabular regression
 ===============================
-"""
 
-##############################################################################
-# In this tutorial, we compare the prediction intervals estimated by MAPIE on a
-# simple, one-dimensional, ground truth function
-# :math:`f(x) = x \times \sin(x)`.
-#
-# Throughout this tutorial, we will answer the following questions:
-#
-# - How well do the MAPIE strategies capture the aleatoric uncertainty
-#   existing in the data?
-#
-# - How do the prediction intervals estimated by the resampling strategies
-#   evolve for new *out-of-distribution* data ?
-#
-# - How do the prediction intervals vary between regressor models ?
-#
-# Throughout this tutorial, we estimate the prediction intervals first using
-# a polynomial function, and then using a boosting model, and a simple neural
-# network.
-#
-# **For practical problems, we advise using the faster CV+ or
-# Jackknife+-after-Bootstrap strategies.
-# For conservative prediction interval estimates, you can alternatively
-# use the CV-minmax strategies.**
+In this tutorial, we compare the prediction intervals estimated by MAPIE on a
+simple, one-dimensional, ground truth function
+:math:`f(x) = x \times \sin(x)`.
+Throughout this tutorial, we will answer the following questions:
+- How well do the MAPIE strategies capture the aleatoric uncertainty
+  existing in the data?
+- How do the prediction intervals estimated by the resampling strategies
+  evolve for new *out-of-distribution* data ?
+- How do the prediction intervals vary between regressor models ?
+Throughout this tutorial, we estimate the prediction intervals first using
+a polynomial function, and then using a boosting model, and a simple neural
+network.
+**For practical problems, we advise using the faster CV+ or
+Jackknife+-after-Bootstrap strategies.
+For conservative prediction interval estimates, you can alternatively
+use the CV-minmax strategies.**
+"""
 
 import os
 import warnings

From 209016372b9b04b4e59ee415f98b38369b85b503 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 23 May 2024 11:32:26 +0200
Subject: [PATCH 077/424] FIX: no references in tutorials

---
 .../4-tutorials/plot_ts-tutorial.py           | 24 ++++---------------
 1 file changed, 5 insertions(+), 19 deletions(-)

diff --git a/examples/regression/4-tutorials/plot_ts-tutorial.py b/examples/regression/4-tutorials/plot_ts-tutorial.py
index 24914c068..13dde284e 100644
--- a/examples/regression/4-tutorials/plot_ts-tutorial.py
+++ b/examples/regression/4-tutorials/plot_ts-tutorial.py
@@ -21,14 +21,14 @@
 Once the base model is optimized, we can use
 :class:`~MapieTimeSeriesRegressor` to estimate
 the prediction intervals associated with one-step ahead forecasts through
-the EnbPI method [1].
+the EnbPI method.
 
 As its parent class :class:`~MapieRegressor`,
 :class:`~MapieTimeSeriesRegressor` has two main arguments : "cv", and "method".
 In order to implement EnbPI, "method" must be set to "enbpi" (the default
 value) while "cv" must be set to the :class:`~mapie.subsample.BlockBootstrap`
 class that block bootstraps the training set.
-This sampling method is used in [1] instead of the traditional bootstrap
+This sampling method is used instead of the traditional bootstrap
 strategy as it is more suited for time series data.
 
 The EnbPI method allows you update the residuals during the prediction,
@@ -38,26 +38,12 @@ class that block bootstraps the training set.
 the ``partial_fit`` class method called at every step.
 
 
-The ACI [2] strategy allows you to adapt the conformal inference
+The ACI strategy allows you to adapt the conformal inference
 (i.e the quantile). If the real values are not in the coverage,
 the size of the intervals will grow.
 Conversely, if the real values are in the coverage,
 the size of the intervals will decrease.
 You can use a gamma coefficient to adjust the strength of the correction.
-
-References
-----------
-[1] Chen Xu and Yao Xie.
-“Conformal Prediction Interval for Dynamic Time-Series.”
-International Conference on Machine Learning (ICML, 2021).
-
-[2] Isaac Gibbs, Emmanuel Candes
-"Adaptive conformal inference under distribution shift"
-Advances in Neural Information Processing Systems, (NeurIPS, 2021).
-
-[3] Margaux Zaffran et al.
-"Adaptive Conformal Predictions for Time Series"
-https://arxiv.org/pdf/2202.07282.pdf
 """
 
 import warnings
@@ -180,7 +166,7 @@ class that block bootstraps the training set.
 #
 # We now use :class:`~MapieTimeSeriesRegressor` to build prediction intervals
 # associated with one-step ahead forecasts. As explained in the introduction,
-# we use the EnbPI method [1] and the ACI method [2] .
+# we use the EnbPI method and the ACI method.
 #
 # Estimating prediction intervals can be possible in three ways:
 #
@@ -199,7 +185,7 @@ class that block bootstraps the training set.
 # sudden change points on test sets that have not been seen by the model
 # during training.
 #
-# Following [1], we use the :class:`~BlockBootstrap` sampling
+# We use the :class:`~BlockBootstrap` sampling
 # method instead of the traditional bootstrap strategy for training the model
 # since the former is more suited for time series data.
 # Here, we choose to perform 10 resamplings with 10 blocks.

From 7d7dd08ff4e7891e4291bd2f280691151f1ff976 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 23 May 2024 11:36:34 +0200
Subject: [PATCH 078/424] FIX mathematical notation in example

---
 .../regression/1-quickstart/plot_cqr_symmetry_difference.py     | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index 77271997c..aab634638 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -111,4 +111,4 @@
 # each bound, allowing for more flexible and accurate intervals that reflect
 # the heteroscedastic nature of the data. The resulting effective coverages
 # demonstrate the theoretical guarantee of the target coverage level
-# $(1−\alpha)$.
+# :math:`1 - \alpha`.

From 9c66c07665ade8f0635f85a8e4289b6457349de8 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Thu, 23 May 2024 11:38:08 +0200
Subject: [PATCH 079/424] Update HISTORY.rst

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index bf1572ad4..ed90ac803 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -7,6 +7,7 @@ History
 
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
+* Add explanation and example for symmetry argument in CQR.
 
 0.8.3 (2024-03-01)
 ------------------

From ded3f1e4d2fe779398b41502f83c87c5429fd911 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <llacombe@quantmetry.com>
Date: Thu, 23 May 2024 11:48:02 +0200
Subject: [PATCH 080/424] Fix formatting and indentation in regression tutorial

---
 .../regression/4-tutorials/plot_main-tutorial-regression.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/examples/regression/4-tutorials/plot_main-tutorial-regression.py b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
index 33a324f8b..46dca8bc2 100644
--- a/examples/regression/4-tutorials/plot_main-tutorial-regression.py
+++ b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
@@ -1,11 +1,10 @@
-"""
+r"""
 ===============================
 Tutorial for tabular regression
 ===============================
 
 In this tutorial, we compare the prediction intervals estimated by MAPIE on a
-simple, one-dimensional, ground truth function
-:math:`f(x) = x \times \sin(x)`.
+simple, one-dimensional, ground truth function :math:`f(x) = x \times \sin(x)`.
 Throughout this tutorial, we will answer the following questions:
 - How well do the MAPIE strategies capture the aleatoric uncertainty
   existing in the data?
@@ -21,6 +20,7 @@
 use the CV-minmax strategies.**
 """
 
+
 import os
 import warnings
 

From 9dcca60b4656ee31a6e023b23c41b03b5414700f Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Mon, 27 May 2024 09:55:05 +0200
Subject: [PATCH 081/424] FIX: add some line breaks in doc

---
 doc/theoretical_description_metrics.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 6ae010bb3..398fdd7bb 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -141,6 +141,7 @@ The ECE is then calculated using the following formula:
     \text{ECE} = \sum_{m=1}^M \frac{\left| B_m \right|}{n} \left| \text{acc}(B_m) - \text{conf}(B_m) \right|
 
 where:
+
 - :math:`B_m` is the set of indices of samples that fall into the :math:`m`-th bin.
 - :math:`\left| B_m \right|` is the number of samples in the :math:`m`-th bin.
 - :math:`n` is the total number of samples.
@@ -161,6 +162,7 @@ The Top-Label ECE is calculated as follows:
     \text{Top-Label ECE} = \frac{1}{L} \sum_{j=1}^L \sum_{i=1}^B \frac{|B_{i,j}|}{n_j} \left| \text{acc}(B_{i,j}) - \text{conf}(B_{i,j}) \right|
 
 where:
+
 - :math:`L` is the number of unique labels.
 - :math:`B_{i,j}` is the set of indices of samples that fall into the :math:`i`-th bin for label :math:`j`.
 - :math:`\left| B_{i,j} \right|` is the number of samples in the :math:`i`-th bin for label :math:`j`.

From 9f21fda2640e4febf698430200b7a4e32a2f2331 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Mon, 27 May 2024 10:35:33 +0200
Subject: [PATCH 082/424] Update
 examples/regression/4-tutorials/plot_main-tutorial-regression.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 examples/regression/4-tutorials/plot_main-tutorial-regression.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/regression/4-tutorials/plot_main-tutorial-regression.py b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
index 46dca8bc2..51d97c8f4 100644
--- a/examples/regression/4-tutorials/plot_main-tutorial-regression.py
+++ b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
@@ -6,6 +6,7 @@
 In this tutorial, we compare the prediction intervals estimated by MAPIE on a
 simple, one-dimensional, ground truth function :math:`f(x) = x \times \sin(x)`.
 Throughout this tutorial, we will answer the following questions:
+
 - How well do the MAPIE strategies capture the aleatoric uncertainty
   existing in the data?
 - How do the prediction intervals estimated by the resampling strategies

From a4ab837eb0f31a6b8f0986c397cc87682aa2fd1a Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 27 May 2024 11:54:42 +0200
Subject: [PATCH 083/424] FIX: change quantile formula + UPD: corresponding
 tests

---
 mapie/conformity_scores/conformity_scores.py |  7 +-
 mapie/regression/regression.py               |  2 +-
 mapie/tests/test_conformity_scores.py        | 13 ++--
 mapie/tests/test_regression.py               | 82 ++++++++++----------
 mapie/tests/test_time_series_regression.py   | 26 +++----
 5 files changed, 68 insertions(+), 62 deletions(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index d8d46322a..75c667161 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -244,13 +244,16 @@ def get_quantile(
             The quantile of the conformity scores.
         """
         n_ref = conformity_scores.shape[-1]
+        # TODO: assume that each group has same n_calib when using plus method
+        n_calib = np.min(np.sum(~np.isnan(conformity_scores), axis=0))
         quantile = np.column_stack([
             np_nanquantile(
                 conformity_scores.astype(float),
-                _alpha,
+                np.ceil(_alpha*(n_calib + 1))/n_calib,
                 axis=axis,
                 method=method
-            ) if 0 < _alpha < 1
+            ) if n_calib and 0 < np.ceil(_alpha*(n_calib + 1))/n_calib < 1
+            else np.nan * np.ones(n_ref) if not n_calib
             else np.inf * np.ones(n_ref) if method == "higher"
             else - np.inf * np.ones(n_ref)
             for _alpha in alpha_np
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 46bebf3d8..b02ff162e 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -628,7 +628,7 @@ def predict(
 
             alpha_np = cast(NDArray, alpha)
             if not allow_infinite_bounds:
-                n = len(self.conformity_scores_)
+                n = np.sum(~np.isnan(self.conformity_scores_))
                 check_alpha_and_n_samples(alpha_np, n)
 
             y_pred, y_pred_low, y_pred_up = \
diff --git a/mapie/tests/test_conformity_scores.py b/mapie/tests/test_conformity_scores.py
index f30667b87..627d133ac 100644
--- a/mapie/tests/test_conformity_scores.py
+++ b/mapie/tests/test_conformity_scores.py
@@ -382,18 +382,17 @@ def test_residual_normalised_prefit_get_estimation_distribution() -> None:
 @pytest.mark.parametrize("score", [AbsoluteConformityScore(),
                                    GammaConformityScore(),
                                    ResidualNormalisedScore()])
-@pytest.mark.parametrize("alpha", [[0.3], [0.5, 0.4]])
+@pytest.mark.parametrize("alpha", [[0.5], [0.5, 0.6]])
 def test_intervals_shape_with_every_score(
     score: ConformityScore,
     alpha: Any
 ) -> None:
+    estim = LinearRegression().fit(X_toy, y_toy)
     mapie_reg = MapieRegressor(
-        method="base", cv="split", conformity_score=score
+        estimator=estim, method="base", cv="prefit", conformity_score=score
     )
-    X = np.concatenate((X_toy, X_toy))
-    y = np.concatenate((y_toy, y_toy))
-    mapie_reg = mapie_reg.fit(X, y)
-    y_pred, intervals = mapie_reg.predict(X, alpha=alpha)
-    n_samples = X.shape[0]
+    mapie_reg = mapie_reg.fit(X_toy, y_toy)
+    y_pred, intervals = mapie_reg.predict(X_toy, alpha=alpha)
+    n_samples = X_toy.shape[0]
     assert y_pred.shape[0] == n_samples
     assert intervals.shape == (n_samples, 2, len(alpha))
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index be305424d..907794b29 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -100,6 +100,13 @@
         test_size=None,
         random_state=random_state
     ),
+    "cv_plus_median": Params(
+        method="plus",
+        agg_function="median",
+        cv=KFold(n_splits=3, shuffle=True, random_state=random_state),
+        test_size=None,
+        random_state=random_state
+    ),
     "cv_minmax": Params(
         method="minmax",
         agg_function="mean",
@@ -131,35 +138,35 @@
 }
 
 WIDTHS = {
-    "naive": 3.81,
-    "split": 3.87,
-    "jackknife": 3.89,
+    "naive": 3.87,
+    "split": 3.96,
+    "jackknife": 3.97,
     "jackknife_plus": 3.90,
-    "jackknife_minmax": 3.96,
-    "cv": 3.85,
-    "cv_plus": 3.90,
-    "cv_minmax": 4.04,
-    "prefit": 4.81,
-    "cv_plus_median": 3.90,
+    "jackknife_minmax": 4.03,
+    "cv": 3.88,
+    "cv_plus": 3.91,
+    "cv_minmax": 4.07,
+    "prefit": 3.96,
+    "cv_plus_median": 3.91,
     "jackknife_plus_ab": 3.90,
-    "jackknife_minmax_ab": 4.13,
-    "jackknife_plus_median_ab": 3.87,
+    "jackknife_minmax_ab": 4.14,
+    "jackknife_plus_median_ab": 3.88,
 }
 
 COVERAGES = {
-    "naive": 0.952,
-    "split": 0.952,
-    "jackknife": 0.952,
+    "naive": 0.954,
+    "split": 0.962,
+    "jackknife": 0.956,
     "jackknife_plus": 0.952,
-    "jackknife_minmax": 0.952,
-    "cv": 0.958,
-    "cv_plus": 0.956,
-    "cv_minmax": 0.966,
-    "prefit": 0.980,
+    "jackknife_minmax": 0.962,
+    "cv": 0.954,
+    "cv_plus": 0.954,
+    "cv_minmax": 0.962,
+    "prefit": 0.960,
     "cv_plus_median": 0.954,
     "jackknife_plus_ab": 0.952,
-    "jackknife_minmax_ab": 0.970,
-    "jackknife_plus_median_ab": 0.960,
+    "jackknife_minmax_ab": 0.968,
+    "jackknife_plus_median_ab": 0.952,
 }
 
 
@@ -212,7 +219,7 @@ def test_valid_agg_function(agg_function: str) -> None:
 
 @pytest.mark.parametrize(
     "cv", [None, -1, 2, KFold(), LeaveOneOut(),
-           ShuffleSplit(n_splits=1),
+           ShuffleSplit(n_splits=1, test_size=0.5),
            PredefinedSplit(test_fold=[-1]*3+[0]*3),
            "prefit", "split"]
 )
@@ -220,7 +227,7 @@ def test_valid_cv(cv: Any) -> None:
     """Test that valid cv raise no errors."""
     model = LinearRegression()
     model.fit(X_toy, y_toy)
-    mapie_reg = MapieRegressor(estimator=model, cv=cv)
+    mapie_reg = MapieRegressor(estimator=model, cv=cv, test_size=0.5)
     mapie_reg.fit(X_toy, y_toy)
     mapie_reg.predict(X_toy, alpha=0.5)
 
@@ -237,7 +244,7 @@ def test_too_large_cv(cv: Any) -> None:
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
-@pytest.mark.parametrize("dataset", [(X, y), (X_toy, y_toy)])
+@pytest.mark.parametrize("dataset", [(X, y)])
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.4], (0.2, 0.4)])
 def test_predict_output_shape(
     strategy: str, alpha: Any, dataset: Tuple[NDArray, NDArray]
@@ -265,12 +272,12 @@ def test_same_results_prefit_split() -> None:
     X_train, X_calib = X[train_index], X[val_index]
     y_train, y_calib = y[train_index], y[val_index]
 
-    mapie_reg = MapieRegressor(cv=cv)
+    mapie_reg = MapieRegressor(method='base', cv=cv)
     mapie_reg.fit(X, y)
     y_pred_1, y_pis_1 = mapie_reg.predict(X, alpha=0.1)
 
     model = LinearRegression().fit(X_train, y_train)
-    mapie_reg = MapieRegressor(estimator=model, cv="prefit")
+    mapie_reg = MapieRegressor(estimator=model, method='base', cv="prefit")
     mapie_reg.fit(X_calib, y_calib)
     y_pred_2, y_pis_2 = mapie_reg.predict(X, alpha=0.1)
 
@@ -334,8 +341,8 @@ def test_results_single_and_multi_jobs(strategy: str) -> None:
     mapie_multi = MapieRegressor(n_jobs=-1, **STRATEGIES[strategy])
     mapie_single.fit(X_toy, y_toy)
     mapie_multi.fit(X_toy, y_toy)
-    y_pred_single, y_pis_single = mapie_single.predict(X_toy, alpha=0.2)
-    y_pred_multi, y_pis_multi = mapie_multi.predict(X_toy, alpha=0.2)
+    y_pred_single, y_pis_single = mapie_single.predict(X_toy, alpha=0.5)
+    y_pred_multi, y_pis_multi = mapie_multi.predict(X_toy, alpha=0.5)
     np.testing.assert_allclose(y_pred_single, y_pred_multi)
     np.testing.assert_allclose(y_pis_single, y_pis_multi)
 
@@ -463,7 +470,7 @@ def test_linear_data_confidence_interval(strategy: str) -> None:
     """
     mapie = MapieRegressor(**STRATEGIES[strategy])
     mapie.fit(X_toy, y_toy)
-    y_pred, y_pis = mapie.predict(X_toy, alpha=0.2)
+    y_pred, y_pis = mapie.predict(X_toy, alpha=0.5)
     np.testing.assert_allclose(y_pis[:, 0, 0], y_pis[:, 1, 0])
     np.testing.assert_allclose(y_pred, y_pis[:, 0, 0])
 
@@ -506,7 +513,7 @@ def test_results_prefit_naive() -> None:
     is equivalent to the "naive" method.
     """
     estimator = LinearRegression().fit(X, y)
-    mapie_reg = MapieRegressor(estimator=estimator, cv="prefit")
+    mapie_reg = MapieRegressor(estimator=estimator, method="base", cv="prefit")
     mapie_reg.fit(X, y)
     _, y_pis = mapie_reg.predict(X, alpha=0.05)
     width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()
@@ -517,19 +524,16 @@ def test_results_prefit_naive() -> None:
 
 def test_results_prefit() -> None:
     """Test prefit results on a standard train/validation/test split."""
-    X_train_val, X_test, y_train_val, y_test = train_test_split(
-        X, y, test_size=1 / 10, random_state=1
-    )
-    X_train, X_val, y_train, y_val = train_test_split(
-        X_train_val, y_train_val, test_size=1 / 9, random_state=1
+    X_train, X_calib, y_train, y_calib = train_test_split(
+        X, y, test_size=1/2, random_state=1
     )
     estimator = LinearRegression().fit(X_train, y_train)
-    mapie_reg = MapieRegressor(estimator=estimator, cv="prefit")
-    mapie_reg.fit(X_val, y_val)
-    _, y_pis = mapie_reg.predict(X_test, alpha=0.05)
+    mapie_reg = MapieRegressor(estimator=estimator, method="base", cv="prefit")
+    mapie_reg.fit(X_calib, y_calib)
+    _, y_pis = mapie_reg.predict(X_calib, alpha=0.05)
     width_mean = (y_pis[:, 1, 0] - y_pis[:, 0, 0]).mean()
     coverage = regression_coverage_score(
-        y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]
+        y_calib, y_pis[:, 0, 0], y_pis[:, 1, 0]
     )
     np.testing.assert_allclose(width_mean, WIDTHS["prefit"], rtol=1e-2)
     np.testing.assert_allclose(coverage, COVERAGES["prefit"], rtol=1e-2)
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index 086dd6171..22ddb7b98 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -94,12 +94,12 @@
 }
 
 WIDTHS = {
-    "blockbootstrap_enbpi_mean_wopt": 3.76,
+    "blockbootstrap_enbpi_mean_wopt": 3.86,
     "blockbootstrap_enbpi_median_wopt": 3.76,
-    "blockbootstrap_enbpi_mean": 3.76,
+    "blockbootstrap_enbpi_mean": 3.86,
     "blockbootstrap_enbpi_median": 3.76,
-    "blockbootstrap_aci_mean": 3.87,
-    "blockbootstrap_aci_median": 3.90,
+    "blockbootstrap_aci_mean": 4.03,
+    "blockbootstrap_aci_median": 4.03,
     "prefit": 4.79,
 }
 
@@ -108,9 +108,9 @@
     "blockbootstrap_enbpi_median_wopt": 0.946,
     "blockbootstrap_enbpi_mean": 0.952,
     "blockbootstrap_enbpi_median": 0.946,
-    "blockbootstrap_aci_mean": 0.95,
-    "blockbootstrap_aci_median": 0.95,
-    "prefit": 0.98,
+    "blockbootstrap_aci_mean": 0.96,
+    "blockbootstrap_aci_median": 0.96,
+    "prefit": 0.96,
 }
 
 
@@ -290,10 +290,10 @@ def test_linear_regression_results(strategy: str) -> None:
 def test_results_prefit() -> None:
     """Test prefit results on a standard train/validation/test split."""
     X_train_val, X_test, y_train_val, y_test = train_test_split(
-        X, y, test_size=1 / 10, random_state=random_state
+        X, y, test_size=1/3, random_state=random_state
     )
     X_train, X_val, y_train, y_val = train_test_split(
-        X_train_val, y_train_val, test_size=1 / 9, random_state=random_state
+        X_train_val, y_train_val, test_size=1/2, random_state=random_state
     )
     estimator = LinearRegression().fit(X_train, y_train)
     mapie_ts_reg = MapieTimeSeriesRegressor(
@@ -404,10 +404,10 @@ def test_MapieTimeSeriesRegressor_beta_optimize_error() -> None:
 def test_interval_prediction_with_beta_optimize() -> None:
     """Test use of ``beta_optimize`` in prediction."""
     X_train_val, X_test, y_train_val, y_test = train_test_split(
-        X, y, test_size=1 / 10, random_state=random_state
+        X, y, test_size=1/3, random_state=random_state
     )
     X_train, X_val, y_train, y_val = train_test_split(
-        X_train_val, y_train_val, test_size=1 / 9, random_state=random_state
+        X_train_val, y_train_val, test_size=1/2, random_state=random_state
     )
     estimator = LinearRegression().fit(X_train, y_train)
     mapie_ts_reg = MapieTimeSeriesRegressor(
@@ -423,8 +423,8 @@ def test_interval_prediction_with_beta_optimize() -> None:
     coverage = regression_coverage_score(
         y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]
     )
-    np.testing.assert_allclose(width_mean, 4.22, rtol=1e-2)
-    np.testing.assert_allclose(coverage, 0.9, rtol=1e-2)
+    np.testing.assert_allclose(width_mean, 4.27, rtol=1e-2)
+    np.testing.assert_allclose(coverage, 0.93, rtol=1e-2)
 
 
 def test_deprecated_path_warning() -> None:

From 81d1af03368305f48d65421a0a683bc855a21d06 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 27 May 2024 12:07:02 +0200
Subject: [PATCH 084/424] UPD: doctring test in MapieRegressor

---
 mapie/regression/regression.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index b02ff162e..504a5f755 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -192,12 +192,12 @@ class MapieRegressor(BaseEstimator, RegressorMixin):
     >>> mapie_reg = mapie_reg.fit(X_toy, y_toy)
     >>> y_pred, y_pis = mapie_reg.predict(X_toy, alpha=0.5)
     >>> print(y_pis[:, :, 0])
-    [[ 4.95714286  5.61428571]
-     [ 6.84285714  7.5       ]
-     [ 8.72857143  9.38571429]
-     [10.61428571 11.27142857]
-     [12.5        13.15714286]
-     [14.38571429 15.04285714]]
+    [[ 4.84285714  5.72857143]
+     [ 6.72857143  7.61428571]
+     [ 8.61428571  9.5       ]
+     [10.5        11.38571429]
+     [12.38571429 13.27142857]
+     [14.27142857 15.15714286]]
     >>> print(y_pred)
     [ 5.28571429  7.17142857  9.05714286 10.94285714 12.82857143 14.71428571]
     """

From 5e648510e9efa3a23da675563f73373e23d90b4c Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 29 May 2024 15:14:51 +0200
Subject: [PATCH 085/424] UPD: adapt quantile formula and results

---
 mapie/conformity_scores/conformity_scores.py | 69 +++++++++++---------
 mapie/regression/regression.py               | 12 ++--
 mapie/tests/test_regression.py               | 10 +--
 mapie/tests/test_time_series_regression.py   | 16 ++---
 4 files changed, 58 insertions(+), 49 deletions(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 75c667161..fdc79691b 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -214,7 +214,7 @@ def get_quantile(
         conformity_scores: NDArray,
         alpha_np: NDArray,
         axis: int,
-        method: str
+        reversed: bool = False
     ) -> NDArray:
         """
         Compute the alpha quantile of the conformity scores or the conformity
@@ -235,28 +235,29 @@ def get_quantile(
         axis: int
             The axis from which to compute the quantile.
 
-        method: str
-            ``"higher"`` or ``"lower"`` the method to compute the quantile.
+        reversed: bool
+            Boolean specifying whether we take the upper or lower quantile,
+            if False, the alpha quantile, otherwise the (1-alpha) quantile.
 
         Returns
         -------
         NDArray of shape (1, n_alpha) or (n_samples, n_alpha)
             The quantile of the conformity scores.
         """
-        n_ref = conformity_scores.shape[-1]
-        # TODO: assume that each group has same n_calib when using plus method
-        n_calib = np.min(np.sum(~np.isnan(conformity_scores), axis=0))
-        quantile = np.column_stack([
+        n_ref = conformity_scores.shape[1-axis]
+        n_calib = np.min(np.sum(~np.isnan(conformity_scores), axis=axis))
+        signed = 1-2*reversed
+        alpha_ref = (1-2*alpha_np)*reversed + alpha_np
+
+        quantile = signed * np.column_stack([
             np_nanquantile(
-                conformity_scores.astype(float),
-                np.ceil(_alpha*(n_calib + 1))/n_calib,
+                signed * conformity_scores.astype(float),
+                np.ceil(_alpha*(n_calib+1))/n_calib,
                 axis=axis,
-                method=method
-            ) if n_calib and 0 < np.ceil(_alpha*(n_calib + 1))/n_calib < 1
-            else np.nan * np.ones(n_ref) if not n_calib
-            else np.inf * np.ones(n_ref) if method == "higher"
-            else - np.inf * np.ones(n_ref)
-            for _alpha in alpha_np
+                method="lower"
+            ) if 0 < np.ceil(_alpha*(n_calib+1))/n_calib < 1
+            else np.inf * np.ones(n_ref)
+            for _alpha in alpha_ref
         ])
         return quantile
 
@@ -284,7 +285,7 @@ def _beta_optimize(
         -------
         NDArray
             Array of betas minimizing the differences
-            ``(1-alpa+beta)-quantile - beta-quantile``.
+            ``(1-alpha+beta)-quantile - beta-quantile``.
         """
         beta_np = np.full(
             shape=(len(lower_bounds), len(alpha_np)),
@@ -408,26 +409,34 @@ def get_bounds(
                 X, y_pred_up, conformity_scores
             )
             bound_low = self.get_quantile(
-                conformity_scores_low, alpha_low, axis=1, method="lower"
+                conformity_scores_low, alpha_low, axis=1, reversed=True
             )
             bound_up = self.get_quantile(
-                conformity_scores_up, alpha_up, axis=1, method="higher"
+                conformity_scores_up, alpha_up, axis=1
             )
+
         else:
-            quantile_search = "higher" if self.sym else "lower"
-            alpha_low = 1 - alpha_np if self.sym else beta_np
-            alpha_up = 1 - alpha_np if self.sym else 1 - alpha_np + beta_np
+            if self.sym:
+                alpha_ref = 1-alpha_np
+                quantile_ref = self.get_quantile(
+                    conformity_scores[..., np.newaxis], alpha_ref, axis=0
+                )
+                quantile_low, quantile_up = -quantile_ref, quantile_ref
+
+            else:
+                alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np
+
+                quantile_low = self.get_quantile(
+                    conformity_scores[..., np.newaxis],
+                    alpha_low, axis=0, reversed=True
+                )
+                quantile_up = self.get_quantile(
+                    conformity_scores[..., np.newaxis],
+                    alpha_up, axis=0
+                )
 
-            quantile_low = self.get_quantile(
-                conformity_scores[..., np.newaxis],
-                alpha_low, axis=0, method=quantile_search
-            )
-            quantile_up = self.get_quantile(
-                conformity_scores[..., np.newaxis],
-                alpha_up, axis=0, method="higher"
-            )
             bound_low = self.get_estimation_distribution(
-                X, y_pred_low, signed * quantile_low
+                X, y_pred_low, quantile_low
             )
             bound_up = self.get_estimation_distribution(
                 X, y_pred_up, quantile_up
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 504a5f755..b02ff162e 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -192,12 +192,12 @@ class MapieRegressor(BaseEstimator, RegressorMixin):
     >>> mapie_reg = mapie_reg.fit(X_toy, y_toy)
     >>> y_pred, y_pis = mapie_reg.predict(X_toy, alpha=0.5)
     >>> print(y_pis[:, :, 0])
-    [[ 4.84285714  5.72857143]
-     [ 6.72857143  7.61428571]
-     [ 8.61428571  9.5       ]
-     [10.5        11.38571429]
-     [12.38571429 13.27142857]
-     [14.27142857 15.15714286]]
+    [[ 4.95714286  5.61428571]
+     [ 6.84285714  7.5       ]
+     [ 8.72857143  9.38571429]
+     [10.61428571 11.27142857]
+     [12.5        13.15714286]
+     [14.38571429 15.04285714]]
     >>> print(y_pred)
     [ 5.28571429  7.17142857  9.05714286 10.94285714 12.82857143 14.71428571]
     """
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 907794b29..8b244e5a1 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -138,15 +138,15 @@
 }
 
 WIDTHS = {
-    "naive": 3.87,
-    "split": 3.96,
-    "jackknife": 3.97,
+    "naive": 3.80,
+    "split": 3.89,
+    "jackknife": 3.89,
     "jackknife_plus": 3.90,
-    "jackknife_minmax": 4.03,
+    "jackknife_minmax": 3.96,
     "cv": 3.88,
     "cv_plus": 3.91,
     "cv_minmax": 4.07,
-    "prefit": 3.96,
+    "prefit": 3.89,
     "cv_plus_median": 3.91,
     "jackknife_plus_ab": 3.90,
     "jackknife_minmax_ab": 4.14,
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index 22ddb7b98..55f84ab3a 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -95,12 +95,12 @@
 
 WIDTHS = {
     "blockbootstrap_enbpi_mean_wopt": 3.86,
-    "blockbootstrap_enbpi_median_wopt": 3.76,
+    "blockbootstrap_enbpi_median_wopt": 3.85,
     "blockbootstrap_enbpi_mean": 3.86,
-    "blockbootstrap_enbpi_median": 3.76,
-    "blockbootstrap_aci_mean": 4.03,
-    "blockbootstrap_aci_median": 4.03,
-    "prefit": 4.79,
+    "blockbootstrap_enbpi_median": 3.85,
+    "blockbootstrap_aci_mean": 3.96,
+    "blockbootstrap_aci_median": 3.95,
+    "prefit": 4.86,
 }
 
 COVERAGES = {
@@ -110,7 +110,7 @@
     "blockbootstrap_enbpi_median": 0.946,
     "blockbootstrap_aci_mean": 0.96,
     "blockbootstrap_aci_median": 0.96,
-    "prefit": 0.96,
+    "prefit": 0.97,
 }
 
 
@@ -423,8 +423,8 @@ def test_interval_prediction_with_beta_optimize() -> None:
     coverage = regression_coverage_score(
         y_test, y_pis[:, 0, 0], y_pis[:, 1, 0]
     )
-    np.testing.assert_allclose(width_mean, 4.27, rtol=1e-2)
-    np.testing.assert_allclose(coverage, 0.93, rtol=1e-2)
+    np.testing.assert_allclose(width_mean, 3.67, rtol=1e-2)
+    np.testing.assert_allclose(coverage, 0.916, rtol=1e-2)
 
 
 def test_deprecated_path_warning() -> None:

From 1ad016ce3e234ba972235f298c916f4be9648f8a Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 29 May 2024 15:47:50 +0200
Subject: [PATCH 086/424] ADD: coverage validity test

---
 mapie/tests/test_regression.py | 32 ++++++++++++++++++++++++++++++++
 1 file changed, 32 insertions(+)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 8b244e5a1..9daf2f9ae 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -259,6 +259,38 @@ def test_predict_output_shape(
     assert y_pis.shape == (X.shape[0], 2, n_alpha)
 
 
+@pytest.mark.parametrize("delta", [0.5, 0.6, 0.7, 0.8])
+@pytest.mark.parametrize("n_calib", [10, 20, 50, 100])
+def test_coverage_validity(delta: float, n_calib: int) -> None:
+    """
+    Test that the prefit method provides valid coverage
+    for different calibration data sizes and coverage targets.
+    """
+    n_split, n_train, n_test = 1000, 100, 100
+    n_all = n_train + n_calib + n_test
+    X, y = make_regression(n_all, random_state=random_state)
+
+    X_train, X_cal_test, y_train, y_cal_test = \
+        train_test_split(X, y, train_size=n_train, random_state=random_state)
+
+    model = LinearRegression()
+    model.fit(X_train, y_train)
+
+    coverage_list = []
+    for _ in range(n_split):
+        mapie_reg = MapieRegressor(estimator=model, method="base", cv="prefit")
+        X_cal, X_test, y_cal, y_test = \
+            train_test_split(X_cal_test, y_cal_test, test_size=n_test)
+        mapie_reg.fit(X_cal, y_cal)
+        _, y_pis = mapie_reg.predict(X_test, alpha=1-delta)
+        coverage = \
+            regression_coverage_score(y_test,  y_pis[:, 0, 0], y_pis[:, 1, 0])
+        coverage_list.append(coverage)
+
+    mean_coverage = np.mean(coverage_list)
+    np.testing.assert_array_less(delta, mean_coverage)
+
+
 def test_same_results_prefit_split() -> None:
     """
     Test checking that if split and prefit method have exactly

From 700415993eefbefdc9be90a127759d8077f1059e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 29 May 2024 16:55:18 +0200
Subject: [PATCH 087/424] FIX: add stat test for coverage validity

---
 mapie/tests/test_regression.py | 12 ++++++++----
 1 file changed, 8 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 9daf2f9ae..3d3109144 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -260,13 +260,13 @@ def test_predict_output_shape(
 
 
 @pytest.mark.parametrize("delta", [0.5, 0.6, 0.7, 0.8])
-@pytest.mark.parametrize("n_calib", [10, 20, 50, 100])
+@pytest.mark.parametrize("n_calib", [10, 15, 20, 25,  50, 100, 1000])
 def test_coverage_validity(delta: float, n_calib: int) -> None:
     """
     Test that the prefit method provides valid coverage
     for different calibration data sizes and coverage targets.
     """
-    n_split, n_train, n_test = 1000, 100, 100
+    n_split, n_train, n_test = 1000, 100, 1000
     n_all = n_train + n_calib + n_test
     X, y = make_regression(n_all, random_state=random_state)
 
@@ -287,8 +287,12 @@ def test_coverage_validity(delta: float, n_calib: int) -> None:
             regression_coverage_score(y_test,  y_pis[:, 0, 0], y_pis[:, 1, 0])
         coverage_list.append(coverage)
 
-    mean_coverage = np.mean(coverage_list)
-    np.testing.assert_array_less(delta, mean_coverage)
+    # Here we are testing whether the average coverage is statistically
+    # less than the target coverage.
+    from scipy.stats import ttest_1samp
+    _, pval = ttest_1samp(coverage_list, popmean=delta, alternative='less')
+
+    np.testing.assert_array_less(0.05, pval)
 
 
 def test_same_results_prefit_split() -> None:

From dc40a7ea8e282d7afdc46572f1db6a4c37394562 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 29 May 2024 18:36:28 +0200
Subject: [PATCH 088/424] UPD: change p-values (more conservative)

---
 mapie/tests/test_regression.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 3d3109144..4763b0aaa 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -260,7 +260,7 @@ def test_predict_output_shape(
 
 
 @pytest.mark.parametrize("delta", [0.5, 0.6, 0.7, 0.8])
-@pytest.mark.parametrize("n_calib", [10, 15, 20, 25,  50, 100, 1000])
+@pytest.mark.parametrize("n_calib", [10, 15, 20, 25, 50, 100, 1000])
 def test_coverage_validity(delta: float, n_calib: int) -> None:
     """
     Test that the prefit method provides valid coverage
@@ -292,7 +292,7 @@ def test_coverage_validity(delta: float, n_calib: int) -> None:
     from scipy.stats import ttest_1samp
     _, pval = ttest_1samp(coverage_list, popmean=delta, alternative='less')
 
-    np.testing.assert_array_less(0.05, pval)
+    np.testing.assert_array_less(0.01, pval)
 
 
 def test_same_results_prefit_split() -> None:

From e747bf5acbfed9397b3068959df9727ee0f2d280 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 29 May 2024 19:18:28 +0200
Subject: [PATCH 089/424] UPD: add upper bound stat test

---
 mapie/tests/test_regression.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 4763b0aaa..492b3711a 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -259,14 +259,14 @@ def test_predict_output_shape(
     assert y_pis.shape == (X.shape[0], 2, n_alpha)
 
 
-@pytest.mark.parametrize("delta", [0.5, 0.6, 0.7, 0.8])
-@pytest.mark.parametrize("n_calib", [10, 15, 20, 25, 50, 100, 1000])
+@pytest.mark.parametrize("delta", [0.6, 0.8])
+@pytest.mark.parametrize("n_calib", [10 + i for i in range(11)] + [50, 100])
 def test_coverage_validity(delta: float, n_calib: int) -> None:
     """
     Test that the prefit method provides valid coverage
     for different calibration data sizes and coverage targets.
     """
-    n_split, n_train, n_test = 1000, 100, 1000
+    n_split, n_train, n_test = 100, 100, 1000
     n_all = n_train + n_calib + n_test
     X, y = make_regression(n_all, random_state=random_state)
 
@@ -276,7 +276,7 @@ def test_coverage_validity(delta: float, n_calib: int) -> None:
     model = LinearRegression()
     model.fit(X_train, y_train)
 
-    coverage_list = []
+    cov_list = []
     for _ in range(n_split):
         mapie_reg = MapieRegressor(estimator=model, method="base", cv="prefit")
         X_cal, X_test, y_cal, y_test = \
@@ -285,14 +285,17 @@ def test_coverage_validity(delta: float, n_calib: int) -> None:
         _, y_pis = mapie_reg.predict(X_test, alpha=1-delta)
         coverage = \
             regression_coverage_score(y_test,  y_pis[:, 0, 0], y_pis[:, 1, 0])
-        coverage_list.append(coverage)
+        cov_list.append(coverage)
 
     # Here we are testing whether the average coverage is statistically
     # less than the target coverage.
     from scipy.stats import ttest_1samp
-    _, pval = ttest_1samp(coverage_list, popmean=delta, alternative='less')
+    mean_low, mean_up = delta, delta + 1/(n_calib+1)
+    _, pval_low = ttest_1samp(cov_list, popmean=mean_low, alternative='less')
+    _, pval_up = ttest_1samp(cov_list, popmean=mean_up, alternative='greater')
 
-    np.testing.assert_array_less(0.01, pval)
+    np.testing.assert_array_less(0.01, pval_low)
+    np.testing.assert_array_less(0.01, pval_up)
 
 
 def test_same_results_prefit_split() -> None:

From ff9b3c11e8d354e172d316b789401476d8a143a7 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 30 May 2024 12:09:26 +0200
Subject: [PATCH 090/424] FIX some type-check

---
 mapie/classification.py                     | 6 +++++-
 mapie/estimator/classification/estimator.py | 7 ++++---
 2 files changed, 9 insertions(+), 4 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index eef262619..2f2c8ba6d 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -193,6 +193,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
         "naive", "score", "lac", "cumulated_score", "aps", "top_k", "raps"
     ]
     fit_attributes = [
+        "estimator_",
         "n_features_in_",
         "conformity_scores_",
         "classes_",
@@ -1066,7 +1067,10 @@ def fit(
             self.verbose,
         )
 
-        self.estimator_.fit(X, y, y_enc, sample_weight, groups, **fit_params)
+        self.estimator_ = self.estimator_.fit(
+            X, y, y_enc, sample_weight, groups, 
+            **fit_params
+        )
 
         y_pred_proba, y, y_enc = self.estimator_.predict_proba_calib(
             X, y, y_enc, groups
diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classification/estimator.py
index e5d0bb126..06d173251 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classification/estimator.py
@@ -260,7 +260,7 @@ def _predict_proba_calib_oof_estimator(
         X: ArrayLike,
         val_index: ArrayLike,
         k: int
-    ) -> Tuple[NDArray, ArrayLike]:
+    ) -> Tuple[NDArray, ArrayLike, ArrayLike]:
         """
         Perform predictions on a single out-of-fold model on a validation set.
 
@@ -296,7 +296,7 @@ def predict_proba_calib(
         y: Optional[ArrayLike] = None,
         y_enc=None,
         groups: Optional[ArrayLike] = None,
-    ) -> NDArray:
+    ) -> Tuple[NDArray, ArrayLike, Optional[NDArray]]:
         """
         Perform predictions on X : the calibration set.
 
@@ -327,6 +327,7 @@ def predict_proba_calib(
             y_pred_proba = self.single_estimator_.predict_proba(X)
             y_pred_proba = self._check_proba_normalized(y_pred_proba)
         else:
+            X = cast(NDArray, X)
             y_pred_proba = np.empty((len(X), self.n_classes), dtype=float)
             cv = cast(BaseCrossValidator, self.cv)
             outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
@@ -469,7 +470,7 @@ def predict(
             - The multiple predictions for the upper bound of the intervals.
         """
         check_is_fitted(self, self.fit_attributes)
-
+        alpha_np = cast(NDArray, alpha_np)
         if self.cv == "prefit":
             y_pred_proba = self.single_estimator_.predict_proba(X)
             y_pred_proba = np.repeat(

From d31a65a4c3da07e2cf8d613c4e3d2059f77d15d5 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 30 May 2024 16:15:00 +0200
Subject: [PATCH 091/424] FIX: fix typing

---
 environment.dev.yml                         |   1 -
 mapie/estimator/classification/estimator.py | 145 ++++++++++----------
 mapie/estimator/classification/interface.py |   3 +
 3 files changed, 75 insertions(+), 74 deletions(-)

diff --git a/environment.dev.yml b/environment.dev.yml
index 3548e9b53..6c7e8fb5f 100644
--- a/environment.dev.yml
+++ b/environment.dev.yml
@@ -1,6 +1,5 @@
 name: mapie-dev
 channels:
-    - defaults
     - conda-forge
 dependencies:
     - bump2version=1.0.1
diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classification/estimator.py
index 06d173251..e677c21ed 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classification/estimator.py
@@ -290,74 +290,6 @@ def _predict_proba_calib_oof_estimator(
 
         return y_pred_proba, val_id, val_index
 
-    def predict_proba_calib(
-        self,
-        X: ArrayLike,
-        y: Optional[ArrayLike] = None,
-        y_enc=None,
-        groups: Optional[ArrayLike] = None,
-    ) -> Tuple[NDArray, ArrayLike, Optional[NDArray]]:
-        """
-        Perform predictions on X : the calibration set.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
-            Input data
-
-        y: Optional[ArrayLike] of shape (n_samples_test,)
-            Input labels.
-
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples_test,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-
-            By default ``None``.
-
-        Returns
-        -------
-        NDArray of shape (n_samples_test, 1)
-            The predictions.
-        """
-        check_is_fitted(self, self.fit_attributes)
-
-        if self.cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
-            y_pred_proba = self._check_proba_normalized(y_pred_proba)
-        else:
-            X = cast(NDArray, X)
-            y_pred_proba = np.empty((len(X), self.n_classes), dtype=float)
-            cv = cast(BaseCrossValidator, self.cv)
-            outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
-                delayed(self._predict_proba_calib_oof_estimator)(
-                    estimator, X, calib_index, k
-                )
-                for k, ((_, calib_index), estimator) in enumerate(
-                    zip(cv.split(X, y, groups), self.estimators_)
-                )
-            )
-            (predictions_list, val_ids_list, val_indices_list) = map(
-                list, zip(*outputs)
-            )
-
-            predictions = np.concatenate(cast(List[NDArray], predictions_list))
-            val_ids = np.concatenate(cast(List[NDArray], val_ids_list))
-            val_indices = np.concatenate(cast(List[NDArray], val_indices_list))
-            self.k_[val_indices] = val_ids
-            y_pred_proba[val_indices] = predictions
-
-            if isinstance(cv, ShuffleSplit):
-                # Should delete values indices that
-                # are not used during calibration
-                self.k_ = self.k_[val_indices]
-                y_pred_proba = y_pred_proba[val_indices]
-                y_enc = y_enc[val_indices]
-                y = cast(NDArray, y)[val_indices]
-
-        return y_pred_proba, y, y_enc
-
     def fit(
         self,
         X: ArrayLike,
@@ -413,7 +345,7 @@ def fit(
         # Computation
         if cv == "prefit":
             single_estimator_ = estimator
-            self.k_ = (
+            k_ = (
                 np.full(shape=(n_samples, 1), fill_value=np.nan, dtype=float)
             )
         else:
@@ -426,7 +358,7 @@ def fit(
                 **fit_params
             )
             cv = cast(BaseCrossValidator, cv)
-            self.k_ = np.empty_like(y, dtype=int)
+            k_ = np.empty_like(y, dtype=int)
 
             estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
                 delayed(self._fit_oof_estimator)(
@@ -439,11 +371,78 @@ def fit(
                 )
                 for train_index, _ in cv.split(X, y, groups)
             )
+        self.single_estimator_: ClassifierMixin = single_estimator_
+        self.estimators_: List[ClassifierMixin] = estimators_
+        self.k_: NDArray = k_
+        return self
 
-        self.single_estimator_ = single_estimator_
-        self.estimators_ = estimators_
+    def predict_proba_calib(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        y_enc: ArrayLike,
+        groups: Optional[ArrayLike] = None,
+    ) -> Tuple[NDArray, ArrayLike, ArrayLike]:
+        """
+        Perform predictions on X : the calibration set.
 
-        return self
+        Parameters
+        ----------
+        X: ArrayLike of shape (n_samples_test, n_features)
+            Input data
+
+        y: Optional[ArrayLike] of shape (n_samples_test,)
+            Input labels.
+
+            By default ``None``.
+
+        groups: Optional[ArrayLike] of shape (n_samples_test,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+
+            By default ``None``.
+
+        Returns
+        -------
+        NDArray of shape (n_samples_test, 1)
+            The predictions.
+        """
+        check_is_fitted(self, self.fit_attributes)
+
+        if self.cv == "prefit":
+            y_pred_proba = self.single_estimator_.predict_proba(X)
+            y_pred_proba = self._check_proba_normalized(y_pred_proba)
+        else:
+            X = cast(NDArray, X)
+            y_pred_proba = np.empty((len(X), self.n_classes), dtype=float)
+            cv = cast(BaseCrossValidator, self.cv)
+            outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+                delayed(self._predict_proba_calib_oof_estimator)(
+                    estimator, X, calib_index, k
+                )
+                for k, ((_, calib_index), estimator) in enumerate(
+                    zip(cv.split(X, y, groups), self.estimators_)
+                )
+            )
+            (predictions_list, val_ids_list, val_indices_list) = map(
+                list, zip(*outputs)
+            )
+
+            predictions = np.concatenate(cast(List[NDArray], predictions_list))
+            val_ids = np.concatenate(cast(List[NDArray], val_ids_list))
+            val_indices = np.concatenate(cast(List[NDArray], val_indices_list))
+            self.k_[val_indices] = val_ids
+            y_pred_proba[val_indices] = predictions
+
+            if isinstance(cv, ShuffleSplit):
+                # Should delete values indices that
+                # are not used during calibration
+                self.k_ = self.k_[val_indices]
+                y_pred_proba = y_pred_proba[val_indices]
+                # y_enc = y_enc[val_indices]
+                y = cast(NDArray, y)[val_indices]
+
+        return y_pred_proba, y, y_enc
 
     def predict(
         self,
diff --git a/mapie/estimator/classification/interface.py b/mapie/estimator/classification/interface.py
index ced4f2613..56082bfb1 100644
--- a/mapie/estimator/classification/interface.py
+++ b/mapie/estimator/classification/interface.py
@@ -20,6 +20,7 @@ def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
+        y_enc: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
         **fit_params
@@ -38,6 +39,8 @@ def fit(
 
         y: ArrayLike of shape (n_samples,)
             Input labels.
+        
+        # TODO document this
 
         sample_weight: Optional[ArrayLike] of shape (n_samples,)
             Sample weights. If None, then samples are equally weighted.

From e4da31e01518f73ce25eb670617b43a23467de43 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 30 May 2024 17:12:26 +0200
Subject: [PATCH 092/424] Fix : solve linting

---
 =                                           | 2 ++
 mapie/classification.py                     | 2 +-
 mapie/estimator/classification/interface.py | 2 +-
 3 files changed, 4 insertions(+), 2 deletions(-)
 create mode 100644 =

diff --git a/= b/=
new file mode 100644
index 000000000..25d9b2c97
--- /dev/null
+++ b/=
@@ -0,0 +1,2 @@
+Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
+Requirement already satisfied: scikit-learn in /opt/homebrew/anaconda3/envs/mapie-dev/lib/python3.10/site-packages (1.3.0)
diff --git a/mapie/classification.py b/mapie/classification.py
index 2f2c8ba6d..4bbd86e87 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -1068,7 +1068,7 @@ def fit(
         )
 
         self.estimator_ = self.estimator_.fit(
-            X, y, y_enc, sample_weight, groups, 
+            X, y, y_enc, sample_weight, groups,
             **fit_params
         )
 
diff --git a/mapie/estimator/classification/interface.py b/mapie/estimator/classification/interface.py
index 56082bfb1..425732b73 100644
--- a/mapie/estimator/classification/interface.py
+++ b/mapie/estimator/classification/interface.py
@@ -39,7 +39,7 @@ def fit(
 
         y: ArrayLike of shape (n_samples,)
             Input labels.
-        
+
         # TODO document this
 
         sample_weight: Optional[ArrayLike] of shape (n_samples,)

From fc6253257f06dd1630a26d1aa855a940b7a271af Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 30 May 2024 17:17:42 +0200
Subject: [PATCH 093/424] FIX: remove useless file

---
 = | 2 --
 1 file changed, 2 deletions(-)
 delete mode 100644 =

diff --git a/= b/=
deleted file mode 100644
index 25d9b2c97..000000000
--- a/=
+++ /dev/null
@@ -1,2 +0,0 @@
-Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com
-Requirement already satisfied: scikit-learn in /opt/homebrew/anaconda3/envs/mapie-dev/lib/python3.10/site-packages (1.3.0)

From 523d06a1799cc333ff156a50c7c45003a59aac18 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 3 Jun 2024 16:50:57 +0200
Subject: [PATCH 094/424] Add documentation

---
 environment.dev.yml                         |  1 +
 mapie/classification.py                     | 62 ++++++++++++++++++++-
 mapie/estimator/classification/estimator.py | 19 ++++---
 mapie/estimator/classification/interface.py | 23 +++++---
 4 files changed, 87 insertions(+), 18 deletions(-)

diff --git a/environment.dev.yml b/environment.dev.yml
index 6c7e8fb5f..3548e9b53 100644
--- a/environment.dev.yml
+++ b/environment.dev.yml
@@ -1,5 +1,6 @@
 name: mapie-dev
 channels:
+    - defaults
     - conda-forge
 dependencies:
     - bump2version=1.0.1
diff --git a/mapie/classification.py b/mapie/classification.py
index 4bbd86e87..fc539dc7f 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -914,7 +914,16 @@ def _check_fit_parameter(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
-    ):
+    ) -> Tuple[
+        Optional[ClassifierMixin],
+        Optional[Union[int, str, BaseCrossValidator]],
+        ArrayLike,
+        NDArray,
+        NDArray,
+        Optional[NDArray],
+        Optional[NDArray],
+        ArrayLike
+    ]:
         """
         Perform several checks on class parameters.
 
@@ -934,6 +943,14 @@ def _check_fit_parameter(
             train/test set.
             By default ``None``.
 
+        Returns
+        -------
+        Tuple[Optional[ClassifierMixin],
+        Optional[Union[int, str, BaseCrossValidator]],
+        ArrayLike, NDArray, NDArray, Optional[NDArray],
+        Optional[NDArray], ArrayLike]
+
+            Parameters checked
         Raises
         ------
         ValueError
@@ -973,7 +990,48 @@ def _check_fit_parameter(
 
         return (estimator, cv, X, y, y_enc, sample_weight, groups, n_samples)
 
-    def _split_data(self, X, y_enc, sample_weight, groups, size_raps):
+    def _split_data(
+        self,
+        X,
+        y_enc,
+        sample_weight,
+        groups,
+        size_raps
+    ) -> Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike, NDArray, ArrayLike]:
+
+        """Split data for raps method
+        Parameters
+        ----------
+        X: ArrayLike
+            Observed values.
+
+        y_enc: ArrayLike
+            Target values as normalized encodings.
+
+        sample_weight: Optional[NDArray] of shape (n_samples,)
+            Non-null sample weights.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+            By default ``None``.
+
+        size_raps: : Optional[float]
+            Percentage of the data to be used for choosing lambda_star and
+            k_star for the RAPS method.
+
+        Returns
+        -------
+        Tuple[ArrayLike, ArrayLike, ArrayLike, NDArray, Optional[NDArray],
+        Optional[ArrayLike]]
+
+            - ArrayLike of shape (n_samples, n_features)
+            - ArrayLike of shape (n_samples,)
+            - ArrayLike of shape (n_samples,)
+            - ArrayLike of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+            - ArrayLike of shape (n_samples,)
+        """
         raps_split = ShuffleSplit(
             1, test_size=size_raps, random_state=self.random_state
         )
diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classification/estimator.py
index e677c21ed..cb14d68a8 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classification/estimator.py
@@ -449,7 +449,7 @@ def predict(
         X: ArrayLike,
         alpha_np: ArrayLike = [],
         agg_scores: Any = None
-    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
+    ) -> NDArray:
         """
         Predict target from X. It also computes the prediction per train sample
         for each test sample according to ``self.method``.
@@ -459,14 +459,19 @@ def predict(
         X: ArrayLike of shape (n_samples, n_features)
             Test data.
 
-        TODO
+        alpha_np: ArrayLike of shape (n_alphas)
+            Level of confidences.
+
+        agg_scores: Optional[str]
+            How to aggregate the scores output by the estimators on test data
+            if a cross-validation strategy is used
 
-        Returns TODO
+        Returns
         -------
-        Tuple[NDArray, NDArray, NDArray]
-            - Predictions
-            - The multiple predictions for the lower bound of the intervals.
-            - The multiple predictions for the upper bound of the intervals.
+        NDArray
+            Predictions of shape
+            (n_samples, n_classes)
+
         """
         check_is_fitted(self, self.fit_attributes)
         alpha_np = cast(NDArray, alpha_np)
diff --git a/mapie/estimator/classification/interface.py b/mapie/estimator/classification/interface.py
index 425732b73..5fe13e67d 100644
--- a/mapie/estimator/classification/interface.py
+++ b/mapie/estimator/classification/interface.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from abc import ABCMeta, abstractmethod
-from typing import Any, Optional, Tuple, Union
+from typing import Any, Optional
 
 from sklearn.base import ClassifierMixin
 
@@ -40,7 +40,8 @@ def fit(
         y: ArrayLike of shape (n_samples,)
             Input labels.
 
-        # TODO document this
+        y_enc: ArrayLike
+            Target values as normalized encodings.
 
         sample_weight: Optional[ArrayLike] of shape (n_samples,)
             Sample weights. If None, then samples are equally weighted.
@@ -66,7 +67,7 @@ def predict(
         X: ArrayLike,
         alpha_np: ArrayLike = [],
         agg_scores: Any = None
-    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
+    ) -> NDArray:
         """
         Predict target from X. It also computes the prediction per train sample
         for each test sample according to ``self.method``.
@@ -76,12 +77,16 @@ def predict(
         X: ArrayLike of shape (n_samples, n_features)
             Test data.
 
-        TODO
+        alpha_np: ArrayLike of shape (n_alphas)
+            Level of confidences.
 
-        Returns TODO
+        agg_scores: Optional[str]
+            How to aggregate the scores output by the estimators on test data
+            if a cross-validation strategy is used
+
+        Returns
         -------
-        Tuple[NDArray, NDArray, NDArray]
-            - Predictions
-            - The multiple predictions for the lower bound of the intervals.
-            - The multiple predictions for the upper bound of the intervals.
+        NDArray
+            Predictions of shape
+            (n_samples, n_classes)
         """

From 54ac28934a11ded53ecea549129101f29c95636e Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 4 Jun 2024 18:20:43 +0200
Subject: [PATCH 095/424] Fix aci method in the fit regression with unit test

---
 mapie/regression/regression.py             |  2 +-
 mapie/tests/test_time_series_regression.py | 20 ++++++++++++++++++++
 2 files changed, 21 insertions(+), 1 deletion(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 46bebf3d8..275294b2e 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -425,7 +425,7 @@ def _check_fit_parameters(
         cv = check_cv(
             self.cv, test_size=self.test_size, random_state=self.random_state
         )
-        if self.cv in ["split", "prefit"] and self.method != "base":
+        if self.cv in ["split", "prefit"] and (self.method == "naive" or self.method == "plus"):
             self.method = "base"
         estimator = self._check_estimator(self.estimator)
         agg_function = self._check_agg_function(self.agg_function)
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index 086dd6171..341023356 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -518,3 +518,23 @@ def test_method_error_in_update(monkeypatch: Any, method: str) -> None:
     with pytest.raises(ValueError, match=r".*Invalid method.*"):
         mapie_ts_reg.fit(X_toy, y_toy)
         mapie_ts_reg.update(X_toy, y_toy)
+
+
+def test_aci_method() -> None:
+    
+    """Test of aci method in fit"""
+    X_train_val, X_test, y_train_val, y_test = train_test_split(
+        X, y, test_size=0.33, random_state=random_state
+    )
+    X_train, X_val, y_train, y_val = train_test_split(
+        X_train_val, y_train_val, test_size=0.5, random_state=random_state
+    )
+    estimator = LinearRegression().fit(X_train, y_train)
+    mapie_ts_reg = MapieTimeSeriesRegressor(
+        estimator=estimator,
+        cv="prefit", method = "aci"
+    )
+    mapie_ts_reg.fit(X_val, y_val)
+    mapie_ts_reg.update(X_test, y_test, gamma=0.1, alpha=0.1)
+
+    assert mapie_ts_reg.method == "base"
\ No newline at end of file

From 55c80513d9fce88a3f3b949140f79e9e977256ec Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 4 Jun 2024 18:33:48 +0200
Subject: [PATCH 096/424] Fix typo + type-check + lint

---
 mapie/regression/regression.py             | 3 ++-
 mapie/tests/test_time_series_regression.py | 8 +++-----
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 275294b2e..849377ec1 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -425,7 +425,8 @@ def _check_fit_parameters(
         cv = check_cv(
             self.cv, test_size=self.test_size, random_state=self.random_state
         )
-        if self.cv in ["split", "prefit"] and (self.method == "naive" or self.method == "plus"):
+        if self.cv in ["split", "prefit"] and (self.method == "naive" or
+                                               self.method == "plus"):
             self.method = "base"
         estimator = self._check_estimator(self.estimator)
         agg_function = self._check_agg_function(self.agg_function)
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index 341023356..fcd890d7b 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -520,8 +520,7 @@ def test_method_error_in_update(monkeypatch: Any, method: str) -> None:
         mapie_ts_reg.update(X_toy, y_toy)
 
 
-def test_aci_method() -> None:
-    
+def test_aci_method_in_fit() -> None:
     """Test of aci method in fit"""
     X_train_val, X_test, y_train_val, y_test = train_test_split(
         X, y, test_size=0.33, random_state=random_state
@@ -532,9 +531,8 @@ def test_aci_method() -> None:
     estimator = LinearRegression().fit(X_train, y_train)
     mapie_ts_reg = MapieTimeSeriesRegressor(
         estimator=estimator,
-        cv="prefit", method = "aci"
+        cv="prefit", method="aci"
     )
     mapie_ts_reg.fit(X_val, y_val)
     mapie_ts_reg.update(X_test, y_test, gamma=0.1, alpha=0.1)
-
-    assert mapie_ts_reg.method == "base"
\ No newline at end of file
+    assert mapie_ts_reg.method == "aci"

From 4e4072884e096776029505b21d6e7e0a52e99094 Mon Sep 17 00:00:00 2001
From: BaptisteCalot <115455912+BaptisteCalot@users.noreply.github.com>
Date: Wed, 5 Jun 2024 14:21:19 +0200
Subject: [PATCH 097/424] Update mapie/regression/regression.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/regression/regression.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 849377ec1..c21450a1b 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -425,8 +425,8 @@ def _check_fit_parameters(
         cv = check_cv(
             self.cv, test_size=self.test_size, random_state=self.random_state
         )
-        if self.cv in ["split", "prefit"] and (self.method == "naive" or
-                                               self.method == "plus"):
+        if self.cv in ["split", "prefit"] and \
+            self.method in ["naive", "plus", "minmax]:
             self.method = "base"
         estimator = self._check_estimator(self.estimator)
         agg_function = self._check_agg_function(self.agg_function)

From f281ba5129115f597f7dc954d1ab3e427d79d58c Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 5 Jun 2024 14:38:58 +0200
Subject: [PATCH 098/424] Fix typo

---
 mapie/regression/regression.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index c21450a1b..b47bd4350 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -426,7 +426,7 @@ def _check_fit_parameters(
             self.cv, test_size=self.test_size, random_state=self.random_state
         )
         if self.cv in ["split", "prefit"] and \
-            self.method in ["naive", "plus", "minmax]:
+                self.method in ["naive", "plus", "minmax"]:
             self.method = "base"
         estimator = self._check_estimator(self.estimator)
         agg_function = self._check_agg_function(self.agg_function)

From a716acf69e1cb9026ea0deaab189d8a2fc745707 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 5 Jun 2024 17:31:02 +0200
Subject: [PATCH 099/424] Tests - Generalization of test and add another one

---
 mapie/tests/test_regression.py             | 17 +++++++++++++++++
 mapie/tests/test_time_series_regression.py | 12 ++++++++----
 2 files changed, 25 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index be305424d..63a487baf 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -748,3 +748,20 @@ def test_predict_infinite_intervals() -> None:
     _, y_pis = mapie_reg.predict(X, alpha=0., allow_infinite_bounds=True)
     np.testing.assert_allclose(y_pis[:, 0, 0], -np.inf)
     np.testing.assert_allclose(y_pis[:, 1, 0], np.inf)
+
+
+@pytest.mark.parametrize("method", ["minmax", "naive", "plus"])
+@pytest.mark.parametrize("cv", ["split", "prefit"])
+def test_check_change_method_to_base(method: str, cv: str) -> None:
+    """Test of shift in power from one method to base method in fit"""
+
+    X_train, X_val, y_train, y_val = train_test_split(
+        X, y, test_size=0.5, random_state=random_state
+    )
+    estimator = LinearRegression().fit(X_train, y_train)
+    mapie_reg = MapieRegressor(
+        cv=cv, method=method, estimator=estimator
+    )
+    mapie_reg.fit(X_val, y_val)
+    assert mapie_reg.method == "base", \
+        f"Expected method base, but got {mapie_reg.method}"
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index fcd890d7b..4a4e1ca5b 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -520,8 +520,11 @@ def test_method_error_in_update(monkeypatch: Any, method: str) -> None:
         mapie_ts_reg.update(X_toy, y_toy)
 
 
-def test_aci_method_in_fit() -> None:
-    """Test of aci method in fit"""
+@pytest.mark.parametrize("method", ["enbpi", "aci"])
+@pytest.mark.parametrize("cv", ["split", "prefit"])
+def test_methods_preservation_in_fit(method: str, cv: str) -> None:
+    """Test of enbpi and aci method preservation in the fit MapieRegressor"""
+
     X_train_val, X_test, y_train_val, y_test = train_test_split(
         X, y, test_size=0.33, random_state=random_state
     )
@@ -531,8 +534,9 @@ def test_aci_method_in_fit() -> None:
     estimator = LinearRegression().fit(X_train, y_train)
     mapie_ts_reg = MapieTimeSeriesRegressor(
         estimator=estimator,
-        cv="prefit", method="aci"
+        cv=cv, method=method
     )
     mapie_ts_reg.fit(X_val, y_val)
     mapie_ts_reg.update(X_test, y_test, gamma=0.1, alpha=0.1)
-    assert mapie_ts_reg.method == "aci"
+    assert mapie_ts_reg.method == method, \
+        f"Expected method {method}, but got {mapie_ts_reg.method}"

From c0a3255c34a2b958ed8a47bc8f4a4b579f5d6c9f Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 5 Jun 2024 17:54:59 +0200
Subject: [PATCH 100/424] UPD: change dataset in notebook

---
 .../2-advanced-analysis/plot_nested-cv.py     | 23 ++++++++-----------
 1 file changed, 10 insertions(+), 13 deletions(-)

diff --git a/examples/regression/2-advanced-analysis/plot_nested-cv.py b/examples/regression/2-advanced-analysis/plot_nested-cv.py
index 3f0eaee5d..e93988559 100644
--- a/examples/regression/2-advanced-analysis/plot_nested-cv.py
+++ b/examples/regression/2-advanced-analysis/plot_nested-cv.py
@@ -45,35 +45,34 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 from scipy.stats import randint
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.metrics import mean_squared_error
 from sklearn.model_selection import RandomizedSearchCV, train_test_split
+from sklearn.datasets import make_sparse_uncorrelated
 
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 
-# Load the Boston data
-data_url = "http://lib.stat.cmu.edu/datasets/boston"
-raw_df = pd.read_csv(data_url, sep=r'\s+', skiprows=22, header=None)
-X_boston = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])
-y_boston = raw_df.values[1::2, 2]
+
+random_state = 42
+
+# Load the toy data
+X, y = make_sparse_uncorrelated(500, random_state=random_state)
 
 # Split the data into training and test sets.
 X_train, X_test, y_train, y_test = train_test_split(
-    X_boston, y_boston, test_size=0.2, random_state=42
+    X, y, test_size=0.2, random_state=random_state
 )
 
 # Define the Random Forest model as base regressor with parameter ranges.
-rf_model = RandomForestRegressor(random_state=59, verbose=0)
+rf_model = RandomForestRegressor(random_state=random_state, verbose=0)
 rf_params = {"max_depth": randint(2, 10), "n_estimators": randint(10, 100)}
 
 # Cross-validation and prediction-interval parameters.
 cv = 10
 n_iter = 5
 alpha = 0.05
-random_state = 59
 
 # Non-nested approach with the CV+ strategy using the Random Forest model.
 cv_obj = RandomizedSearchCV(
@@ -144,12 +143,10 @@
 
 # Compare prediction interval widths.
 fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(13, 6))
-min_x = 14.0
-max_x = 17.0
+min_x = np.min([np.min(widths_nested), np.min(widths_non_nested)])
+max_x = np.max([np.max(widths_nested), np.max(widths_non_nested)])
 ax1.set_xlabel("Prediction interval width using the nested CV approach")
 ax1.set_ylabel("Prediction interval width using the non-nested CV approach")
-ax1.set_xlim([min_x, max_x])
-ax1.set_ylim([min_x, max_x])
 ax1.scatter(widths_nested, widths_non_nested)
 ax1.plot([min_x, max_x], [min_x, max_x], ls="--", color="k")
 ax2.axvline(x=0, color="r", lw=2)

From 0c42280f326c9e77101eb56725e5856cdf8301b2 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 6 Jun 2024 09:32:08 +0200
Subject: [PATCH 101/424] MAJ: dataset naming

---
 examples/regression/2-advanced-analysis/plot_nested-cv.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/regression/2-advanced-analysis/plot_nested-cv.py b/examples/regression/2-advanced-analysis/plot_nested-cv.py
index e93988559..c3aaeadd0 100644
--- a/examples/regression/2-advanced-analysis/plot_nested-cv.py
+++ b/examples/regression/2-advanced-analysis/plot_nested-cv.py
@@ -26,7 +26,7 @@
 *out-of-fold* models and *P* the number of parameter search cross-validations,
 versus :math:`N + P` for the non-nested approach.
 
-Here, we compare the two strategies on the Boston dataset. We use the Random
+Here, we compare the two strategies on a toy dataset. We use the Random
 Forest Regressor as a base regressor for the CV+ strategy. For the sake of
 light computation, we adopt a RandomizedSearchCV parameter search strategy
 with a low number of iterations and with a reproducible random state.

From d6080ca3aa35014ea02fd3490c5452775b4577de Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 6 Jun 2024 11:19:09 +0200
Subject: [PATCH 102/424] Fix unit test

---
 AUTHORS.rst                                | 1 +
 HISTORY.rst                                | 2 +-
 mapie/tests/test_regression.py             | 5 ++---
 mapie/tests/test_time_series_regression.py | 3 +--
 4 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index 5509241df..a79a0da5b 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -40,4 +40,5 @@ Contributors
 * Pierre de Fréminville <pidefrem>
 * Ambros Marzetta <ambrosm>
 * Carl McBride Ellis <Carl-McBride-Ellis>
+* Baptiste Calot <baptiste.calot@capgemini.com>
 To be continued ...
diff --git a/HISTORY.rst b/HISTORY.rst
index 6319ee67f..d7e293f71 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -4,7 +4,7 @@ History
 
 0.8.3 (2024-**-**)
 ------------------
-
+* Fix shift in power from one method to base method when use MapieRegressor fit : issue 447
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
 * Fix invalid certificate when downloading data.
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 63a487baf..cb322e1ca 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -750,7 +750,7 @@ def test_predict_infinite_intervals() -> None:
     np.testing.assert_allclose(y_pis[:, 1, 0], np.inf)
 
 
-@pytest.mark.parametrize("method", ["minmax", "naive", "plus"])
+@pytest.mark.parametrize("method", ["minmax", "naive", "plus", "base"])
 @pytest.mark.parametrize("cv", ["split", "prefit"])
 def test_check_change_method_to_base(method: str, cv: str) -> None:
     """Test of shift in power from one method to base method in fit"""
@@ -763,5 +763,4 @@ def test_check_change_method_to_base(method: str, cv: str) -> None:
         cv=cv, method=method, estimator=estimator
     )
     mapie_reg.fit(X_val, y_val)
-    assert mapie_reg.method == "base", \
-        f"Expected method base, but got {mapie_reg.method}"
+    assert mapie_reg.method == "base"
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index 4a4e1ca5b..76063d6fb 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -538,5 +538,4 @@ def test_methods_preservation_in_fit(method: str, cv: str) -> None:
     )
     mapie_ts_reg.fit(X_val, y_val)
     mapie_ts_reg.update(X_test, y_test, gamma=0.1, alpha=0.1)
-    assert mapie_ts_reg.method == method, \
-        f"Expected method {method}, but got {mapie_ts_reg.method}"
+    assert mapie_ts_reg.method == method

From b1b5aad9a86a246fc38165f4139bf9be6b591315 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 6 Jun 2024 11:52:10 +0200
Subject: [PATCH 103/424] STY: apply suggestions from code review

---
 HISTORY.rst                    | 2 +-
 mapie/tests/test_regression.py | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index d7e293f71..09b316a22 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -4,7 +4,7 @@ History
 
 0.8.3 (2024-**-**)
 ------------------
-* Fix shift in power from one method to base method when use MapieRegressor fit : issue 447
+* Fixed overloading of the value of the ‘method’ attribute when using MapieRegressor and MapieTimeSeriesRegressor
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
 * Fix invalid certificate when downloading data.
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index cb322e1ca..255301fad 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -753,7 +753,7 @@ def test_predict_infinite_intervals() -> None:
 @pytest.mark.parametrize("method", ["minmax", "naive", "plus", "base"])
 @pytest.mark.parametrize("cv", ["split", "prefit"])
 def test_check_change_method_to_base(method: str, cv: str) -> None:
-    """Test of shift in power from one method to base method in fit"""
+    """Test of the overloading of method attribute to `base` method in fit"""
 
     X_train, X_val, y_train, y_val = train_test_split(
         X, y, test_size=0.5, random_state=random_state

From cda125051cd45b4b51631af43c92464dad1e1db4 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 7 Jun 2024 15:21:59 +0200
Subject: [PATCH 104/424] UPD: add comments and robust test (FWER procedure)

---
 HISTORY.rst                                  |  3 +-
 mapie/conformity_scores/conformity_scores.py | 13 ++++---
 mapie/tests/test_conformity_scores.py        |  4 +--
 mapie/tests/test_regression.py               | 36 +++++++++++---------
 4 files changed, 30 insertions(+), 26 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 6d805be91..6cb377b33 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,9 +2,10 @@
 History
 =======
 
-0.8.3 (2024-**-**)
+0.8.4 (2024-**-**)
 ------------------
 
+* Fix the quantile formula to ensure valid coverage for any number of calibration data in `ConformityScore`.
 * Fix conda versionning.
 * Reduce precision for test in `MapieCalibrator`.
 * Fix invalid certificate when downloading data.
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index fdc79691b..84846e8da 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -247,15 +247,18 @@ def get_quantile(
         n_ref = conformity_scores.shape[1-axis]
         n_calib = np.min(np.sum(~np.isnan(conformity_scores), axis=axis))
         signed = 1-2*reversed
+
+        # Adapt alpha w.r.t upper/lower : alpha vs. 1-alpha
         alpha_ref = (1-2*alpha_np)*reversed + alpha_np
 
+        # Adjust alpha w.r.t quantile correction
+        alpha_ref = np.ceil(alpha_ref*(n_calib+1))/n_calib
+
+        # Compute the target quantiles
         quantile = signed * np.column_stack([
             np_nanquantile(
-                signed * conformity_scores.astype(float),
-                np.ceil(_alpha*(n_calib+1))/n_calib,
-                axis=axis,
-                method="lower"
-            ) if 0 < np.ceil(_alpha*(n_calib+1))/n_calib < 1
+                signed * conformity_scores, _alpha, axis=axis, method="lower"
+            ) if 0 < _alpha < 1
             else np.inf * np.ones(n_ref)
             for _alpha in alpha_ref
         ])
diff --git a/mapie/tests/test_conformity_scores.py b/mapie/tests/test_conformity_scores.py
index 627d133ac..4d4a32722 100644
--- a/mapie/tests/test_conformity_scores.py
+++ b/mapie/tests/test_conformity_scores.py
@@ -1,5 +1,3 @@
-from typing import Any
-
 import numpy as np
 import pytest
 from sklearn.linear_model import LinearRegression
@@ -385,7 +383,7 @@ def test_residual_normalised_prefit_get_estimation_distribution() -> None:
 @pytest.mark.parametrize("alpha", [[0.5], [0.5, 0.6]])
 def test_intervals_shape_with_every_score(
     score: ConformityScore,
-    alpha: Any
+    alpha: NDArray
 ) -> None:
     estim = LinearRegression().fit(X_toy, y_toy)
     mapie_reg = MapieRegressor(
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 492b3711a..43ab67f6e 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -18,6 +18,7 @@
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.validation import check_is_fitted
+from scipy.stats import ttest_1samp
 from typing_extensions import TypedDict
 
 from mapie._typing import NDArray
@@ -155,14 +156,14 @@
 
 COVERAGES = {
     "naive": 0.954,
-    "split": 0.962,
+    "split": 0.956,
     "jackknife": 0.956,
     "jackknife_plus": 0.952,
     "jackknife_minmax": 0.962,
     "cv": 0.954,
     "cv_plus": 0.954,
     "cv_minmax": 0.962,
-    "prefit": 0.960,
+    "prefit": 0.956,
     "cv_plus_median": 0.954,
     "jackknife_plus_ab": 0.952,
     "jackknife_minmax_ab": 0.968,
@@ -260,7 +261,7 @@ def test_predict_output_shape(
 
 
 @pytest.mark.parametrize("delta", [0.6, 0.8])
-@pytest.mark.parametrize("n_calib", [10 + i for i in range(11)] + [50, 100])
+@pytest.mark.parametrize("n_calib", [10 + i for i in range(13)] + [50, 100])
 def test_coverage_validity(delta: float, n_calib: int) -> None:
     """
     Test that the prefit method provides valid coverage
@@ -269,33 +270,34 @@ def test_coverage_validity(delta: float, n_calib: int) -> None:
     n_split, n_train, n_test = 100, 100, 1000
     n_all = n_train + n_calib + n_test
     X, y = make_regression(n_all, random_state=random_state)
-
-    X_train, X_cal_test, y_train, y_cal_test = \
-        train_test_split(X, y, train_size=n_train, random_state=random_state)
+    Xtr, Xct, ytr, yct = train_test_split(
+        X, y, train_size=n_train, random_state=random_state
+    )
 
     model = LinearRegression()
-    model.fit(X_train, y_train)
+    model.fit(Xtr, ytr)
 
     cov_list = []
     for _ in range(n_split):
         mapie_reg = MapieRegressor(estimator=model, method="base", cv="prefit")
-        X_cal, X_test, y_cal, y_test = \
-            train_test_split(X_cal_test, y_cal_test, test_size=n_test)
-        mapie_reg.fit(X_cal, y_cal)
-        _, y_pis = mapie_reg.predict(X_test, alpha=1-delta)
-        coverage = \
-            regression_coverage_score(y_test,  y_pis[:, 0, 0], y_pis[:, 1, 0])
+        Xc, Xt, yc, yt = train_test_split(Xct, yct, test_size=n_test)
+        mapie_reg.fit(Xc, yc)
+        _, y_pis = mapie_reg.predict(Xt, alpha=1-delta)
+        y_low, y_up = y_pis[:, 0, 0], y_pis[:, 1, 0]
+        coverage = regression_coverage_score(yt, y_low, y_up)
         cov_list.append(coverage)
 
     # Here we are testing whether the average coverage is statistically
     # less than the target coverage.
-    from scipy.stats import ttest_1samp
     mean_low, mean_up = delta, delta + 1/(n_calib+1)
     _, pval_low = ttest_1samp(cov_list, popmean=mean_low, alternative='less')
     _, pval_up = ttest_1samp(cov_list, popmean=mean_up, alternative='greater')
 
-    np.testing.assert_array_less(0.01, pval_low)
-    np.testing.assert_array_less(0.01, pval_up)
+    # We perform a FWER controlling procedure (Bonferroni)
+    p_fwer = 0.01  # probability of making one or more false discoveries: 1%
+    p_bonf = p_fwer / 30  # because a total of 30 test_coverage_validity
+    np.testing.assert_array_less(p_bonf, pval_low)
+    np.testing.assert_array_less(p_bonf, pval_up)
 
 
 def test_same_results_prefit_split() -> None:
@@ -562,7 +564,7 @@ def test_results_prefit_naive() -> None:
 
 
 def test_results_prefit() -> None:
-    """Test prefit results on a standard train/validation/test split."""
+    """Test prefit results on a standard train/calibration split."""
     X_train, X_calib, y_train, y_calib = train_test_split(
         X, y, test_size=1/2, random_state=1
     )

From d326013ee3f11cd19f6b1b8f849b1bd12db8006e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 7 Jun 2024 16:18:44 +0200
Subject: [PATCH 105/424] FIX: bumpversion rule for CITATION.cff

---
 .bumpversion.cfg | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 50f194e51..8ef3e2ef4 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -16,6 +16,6 @@ search = version = "{current_version}"
 replace = version = "{new_version}"
 
 [bumpversion:file:CITATION.cff]
-search = version = "{current_version}"
-replace = version = "{new_version}"
+search = version: {current_version}
+replace = version: {new_version}
 

From f98bafce053c6dbbe0d1b22909f5fb18244cd3a6 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 7 Jun 2024 16:24:13 +0200
Subject: [PATCH 106/424] CLEAN: better indentation

---
 README.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.rst b/README.rst
index 67d0db068..03a0a663d 100644
--- a/README.rst
+++ b/README.rst
@@ -3,7 +3,7 @@
 |GitHubActions| |Codecov| |ReadTheDocs| |License| |PythonVersion| |PyPi| |Conda| |Release| |Commits| |DOI|
 
 .. |GitHubActions| image:: https://github.com/scikit-learn-contrib/MAPIE/actions/workflows/test.yml/badge.svg
-   :target: https://github.com/scikit-learn-contrib/MAPIE/actions
+    :target: https://github.com/scikit-learn-contrib/MAPIE/actions
 
 .. |Codecov| image:: https://codecov.io/gh/scikit-learn-contrib/MAPIE/branch/master/graph/badge.svg?token=F2S6KYH4V1
    :target: https://codecov.io/gh/scikit-learn-contrib/MAPIE
@@ -13,25 +13,25 @@
     :alt: Documentation Status
 
 .. |License| image:: https://img.shields.io/github/license/scikit-learn-contrib/MAPIE
-   :target: https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE
+    :target: https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE
 
 .. |PythonVersion| image:: https://img.shields.io/pypi/pyversions/mapie
-   :target: https://pypi.org/project/mapie/
+    :target: https://pypi.org/project/mapie/
 
 .. |PyPi| image:: https://img.shields.io/pypi/v/mapie
-   :target: https://pypi.org/project/mapie/
+    :target: https://pypi.org/project/mapie/
 
 .. |Conda| image:: https://img.shields.io/conda/vn/conda-forge/mapie
-   :target: https://anaconda.org/conda-forge/mapie
+    :target: https://anaconda.org/conda-forge/mapie
 
 .. |Release| image:: https://img.shields.io/github/v/release/scikit-learn-contrib/mapie
-   :target: https://github.com/scikit-learn-contrib/MAPIE/releases
+    :target: https://github.com/scikit-learn-contrib/MAPIE/releases
 
 .. |Commits| image:: https://img.shields.io/github/commits-since/scikit-learn-contrib/mapie/latest/master
-   :target: https://github.com/scikit-learn-contrib/MAPIE/commits/master
+    :target: https://github.com/scikit-learn-contrib/MAPIE/commits/master
 
 .. |DOI| image:: https://img.shields.io/badge/10.48550/arXiv.2207.12274-B31B1B.svg
-   :target: https://arxiv.org/abs/2207.12274
+    :target: https://arxiv.org/abs/2207.12274
 
 .. image:: https://github.com/scikit-learn-contrib/MAPIE/raw/master/doc/images/mapie_logo_nobg_cut.png
     :width: 400

From 313f6c76f6fbfc143d1de51a46cd1b3c436c4653 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 7 Jun 2024 16:54:48 +0200
Subject: [PATCH 107/424] =?UTF-8?q?Bump=20version:=200.8.3=20=E2=86=92=200?=
 =?UTF-8?q?.8.4?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 3 +--
 CITATION.cff      | 2 +-
 doc/conf.py       | 2 +-
 mapie/_version.py | 2 +-
 setup.py          | 2 +-
 5 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 8ef3e2ef4..3a5e49b81 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.3
+current_version = 0.8.4
 commit = True
 tag = True
 
@@ -18,4 +18,3 @@ replace = version = "{new_version}"
 [bumpversion:file:CITATION.cff]
 search = version: {current_version}
 replace = version: {new_version}
-
diff --git a/CITATION.cff b/CITATION.cff
index e22cd764d..a9bd17f72 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Thibault"
   orcid: "https://orcid.org/0000-0000-0000-0000"
 title: "MAPIE - Model Agnostic Prediction Interval Estimator"
-version: 0.8.3
+version: 0.8.4
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
diff --git a/doc/conf.py b/doc/conf.py
index 2b095c09e..c025c903a 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -88,7 +88,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.8.3"
+version = "0.8.4"
 # The full version, including alpha/beta/rc tags.
 release = version
 
diff --git a/mapie/_version.py b/mapie/_version.py
index 732155f8d..fa3ddd8c5 100644
--- a/mapie/_version.py
+++ b/mapie/_version.py
@@ -1 +1 @@
-__version__ = "0.8.3"
+__version__ = "0.8.4"
diff --git a/setup.py b/setup.py
index 6fedb4cef..c0fc99058 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "MAPIE"
-VERSION = "0.8.3"
+VERSION = "0.8.4"
 DESCRIPTION = (
     "A scikit-learn-compatible module "
     "for estimating prediction intervals."

From ed5fef6ccae8297f29243470c50c1dd20c971dd4 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 7 Jun 2024 16:56:59 +0200
Subject: [PATCH 108/424] UPD: HISTORY.rst

---
 HISTORY.rst | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index b0afc59eb..d5dabb822 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,7 +2,10 @@
 History
 =======
 
-0.8.4 (2024-**-**)
+0.8.5 (2024-**-**)
+------------------
+
+0.8.4 (2024-06-07)
 ------------------
 
 * Fix the quantile formula to ensure valid coverage for any number of calibration data in `ConformityScore`.

From 517051a72fbaeda853c3e9cff7f62528a229ffdd Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 7 Jun 2024 18:19:02 +0200
Subject: [PATCH 109/424] =?UTF-8?q?Bump=20version:=200.8.4=20=E2=86=92=200?=
 =?UTF-8?q?.8.5?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 CITATION.cff      | 2 +-
 doc/conf.py       | 2 +-
 mapie/_version.py | 2 +-
 setup.py          | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 3a5e49b81..6feae5b2b 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.4
+current_version = 0.8.5
 commit = True
 tag = True
 
diff --git a/CITATION.cff b/CITATION.cff
index a9bd17f72..446b7334b 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Thibault"
   orcid: "https://orcid.org/0000-0000-0000-0000"
 title: "MAPIE - Model Agnostic Prediction Interval Estimator"
-version: 0.8.4
+version: 0.8.5
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
diff --git a/doc/conf.py b/doc/conf.py
index c025c903a..0696d5d55 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -88,7 +88,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.8.4"
+version = "0.8.5"
 # The full version, including alpha/beta/rc tags.
 release = version
 
diff --git a/mapie/_version.py b/mapie/_version.py
index fa3ddd8c5..af46754d3 100644
--- a/mapie/_version.py
+++ b/mapie/_version.py
@@ -1 +1 @@
-__version__ = "0.8.4"
+__version__ = "0.8.5"
diff --git a/setup.py b/setup.py
index c0fc99058..f226c50e7 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "MAPIE"
-VERSION = "0.8.4"
+VERSION = "0.8.5"
 DESCRIPTION = (
     "A scikit-learn-compatible module "
     "for estimating prediction intervals."

From 45bf66983dc6a6890c86008171c4c37e975f60dc Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 11 Jun 2024 18:11:41 +0200
Subject: [PATCH 110/424] UPD: decoupling infinite interval production and
 quantile calculation

---
 mapie/conformity_scores/conformity_scores.py | 40 ++++++---
 mapie/regression/regression.py               | 17 +++-
 mapie/tests/test_regression.py               | 88 ++++++++++++++++++++
 mapie/tests/test_time_series_regression.py   |  8 +-
 mapie/utils.py                               | 27 +++++-
 5 files changed, 162 insertions(+), 18 deletions(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 84846e8da..e602a1646 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -214,7 +214,8 @@ def get_quantile(
         conformity_scores: NDArray,
         alpha_np: NDArray,
         axis: int,
-        reversed: bool = False
+        reversed: bool = False,
+        unbounded: bool = False
     ) -> NDArray:
         """
         Compute the alpha quantile of the conformity scores or the conformity
@@ -239,6 +240,14 @@ def get_quantile(
             Boolean specifying whether we take the upper or lower quantile,
             if False, the alpha quantile, otherwise the (1-alpha) quantile.
 
+            By default ``False``.
+
+        unbounded: bool
+            Boolean specifying whether infinite prediction intervals
+            could be produced.
+
+            By default ``False``.
+
         Returns
         -------
         NDArray of shape (1, n_alpha) or (n_samples, n_alpha)
@@ -252,15 +261,16 @@ def get_quantile(
         alpha_ref = (1-2*alpha_np)*reversed + alpha_np
 
         # Adjust alpha w.r.t quantile correction
-        alpha_ref = np.ceil(alpha_ref*(n_calib+1))/n_calib
+        alpha_cor = np.ceil(alpha_ref*(n_calib+1))/n_calib
+        alpha_cor = np.clip(alpha_cor, a_min=0, a_max=1)
 
         # Compute the target quantiles
         quantile = signed * np.column_stack([
             np_nanquantile(
-                signed * conformity_scores, _alpha, axis=axis, method="lower"
-            ) if 0 < _alpha < 1
-            else np.inf * np.ones(n_ref)
-            for _alpha in alpha_ref
+                signed * conformity_scores, _alpha_cor,
+                axis=axis, method="lower"
+            ) if not unbounded or _alpha < 1 else np.inf * np.ones(n_ref)
+            for _alpha, _alpha_cor in zip(alpha_ref, alpha_cor)
         ])
         return quantile
 
@@ -330,6 +340,7 @@ def get_bounds(
         ensemble: bool = False,
         method: str = 'base',
         optimize_beta: bool = False,
+        allow_infinite_bounds: bool = False
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Compute bounds of the prediction intervals from the observed values,
@@ -369,6 +380,11 @@ def get_bounds(
 
             By default ``False``.
 
+        allow_infinite_bounds: bool
+            Allow infinite prediction intervals to be produced.
+
+            By default ``False``.
+
         Returns
         -------
         Tuple[NDArray, NDArray, NDArray]
@@ -412,10 +428,12 @@ def get_bounds(
                 X, y_pred_up, conformity_scores
             )
             bound_low = self.get_quantile(
-                conformity_scores_low, alpha_low, axis=1, reversed=True
+                conformity_scores_low, alpha_low, axis=1, reversed=True,
+                unbounded=allow_infinite_bounds
             )
             bound_up = self.get_quantile(
-                conformity_scores_up, alpha_up, axis=1
+                conformity_scores_up, alpha_up, axis=1,
+                unbounded=allow_infinite_bounds
             )
 
         else:
@@ -431,11 +449,13 @@ def get_bounds(
 
                 quantile_low = self.get_quantile(
                     conformity_scores[..., np.newaxis],
-                    alpha_low, axis=0, reversed=True
+                    alpha_low, axis=0, reversed=True,
+                    unbounded=allow_infinite_bounds
                 )
                 quantile_up = self.get_quantile(
                     conformity_scores[..., np.newaxis],
-                    alpha_up, axis=0
+                    alpha_up, axis=0,
+                    unbounded=allow_infinite_bounds
                 )
 
             bound_low = self.get_estimation_distribution(
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 54a5c20dc..d589e56f7 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -17,7 +17,8 @@
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_conformity_score, check_cv,
                          check_estimator_fit_predict, check_n_features_in,
-                         check_n_jobs, check_null_weight, check_verbose)
+                         check_n_jobs, check_null_weight, check_verbose,
+                         get_effective_calibration_samples)
 
 
 class MapieRegressor(BaseEstimator, RegressorMixin):
@@ -599,6 +600,8 @@ def predict(
         allow_infinite_bounds: bool
             Allow infinite prediction intervals to be produced.
 
+            By default ``False``.
+
         Returns
         -------
         Union[NDArray, Tuple[NDArray, NDArray]]
@@ -613,6 +616,7 @@ def predict(
         self._check_ensemble(ensemble)
         alpha = cast(Optional[NDArray], check_alpha(alpha))
 
+        # If alpha is None, predict the target without confidence intervals
         if alpha is None:
             y_pred = self.estimator_.predict(
                 X, ensemble, return_multi_pred=False
@@ -627,11 +631,16 @@ def predict(
                     UserWarning
                 )
 
+            # Check alpha and the number of effective calibration samples
             alpha_np = cast(NDArray, alpha)
             if not allow_infinite_bounds:
-                n = np.sum(~np.isnan(self.conformity_scores_))
+                n = get_effective_calibration_samples(
+                    self.conformity_scores_,
+                    self.conformity_score_function_.sym
+                )
                 check_alpha_and_n_samples(alpha_np, n)
 
+            # Predict the target with confidence intervals
             y_pred, y_pred_low, y_pred_up = \
                 self.conformity_score_function_.get_bounds(
                     X,
@@ -640,6 +649,8 @@ def predict(
                     alpha_np,
                     ensemble=ensemble,
                     method=self.method,
-                    optimize_beta=optimize_beta
+                    optimize_beta=optimize_beta,
+                    allow_infinite_bounds=allow_infinite_bounds
                 )
+
             return np.array(y_pred), np.stack([y_pred_low, y_pred_up], axis=1)
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index dd39be102..a858b7b48 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -300,6 +300,94 @@ def test_coverage_validity(delta: float, n_calib: int) -> None:
     np.testing.assert_array_less(p_bonf, pval_up)
 
 
+@pytest.mark.parametrize("delta", [0.6, 0.8, 0.9, 0.95])
+def test_calibration_data_size_symmetric_score(delta: float) -> None:
+    """
+    This test function verifies that a ValueError is raised when the number
+    of calibration data is lower than the minimum required for the given alpha
+    when the conformity score is symmetric. The minimum is calculated as
+    1/alpha or 1/(1-delta).
+    """
+    # Generate data
+    n_train, n_all = 100, 1000
+    X, y = make_regression(n_all, random_state=42)
+    Xtr, Xct, ytr, yct = train_test_split(X, y, train_size=n_train)
+
+    # Train a linear regression model
+    model = LinearRegression()
+    model.fit(Xtr, ytr)
+
+    # Define a symmetric conformity score
+    score = AbsoluteConformityScore(sym=True)
+    
+    # Test when the conformity score is symmetric
+    # and the number of calibration data is sufficient
+    n_calib_sufficient = int(np.ceil(1/(1-delta)))
+    Xc, Xt, yc, _ = train_test_split(Xct, yct, train_size=n_calib_sufficient)
+    mapie_reg = MapieRegressor(
+        estimator=model, method="base", cv="prefit", conformity_score=score
+    )
+    mapie_reg.fit(Xc, yc)
+    mapie_reg.predict(Xt, alpha=1-delta)
+
+    # Test when the conformity score is symmetric
+    # and the number of calibration data is insufficient
+    with pytest.raises(
+        ValueError, match=r"Number of samples of the score is too low*"
+    ):
+        n_calib_low = int(np.floor(1/(1-delta)))
+        Xc, Xt, yc, _ = train_test_split(Xct, yct, train_size=n_calib_low)
+        mapie_reg = MapieRegressor(
+            estimator=model, method="base", cv="prefit", conformity_score=score
+        )
+        mapie_reg.fit(Xc, yc)
+        mapie_reg.predict(Xt, alpha=1-delta)
+
+
+@pytest.mark.parametrize("delta", [0.6, 0.8, 0.9, 0.95])
+def test_calibration_data_size_asymmetric_score(delta: float) -> None:
+    """
+    This test function verifies that a ValueError is raised when the number
+    of calibration data is lower than the minimum required for the given alpha
+    when the conformity score is asymmetric. The minimum is calculated as 
+    1/alpha or 1/(1-delta).
+    """
+    # Generate data
+    n_train, n_all = 100, 1000
+    X, y = make_regression(n_all, random_state=42)
+    Xtr, Xct, ytr, yct = train_test_split(X, y, train_size=n_train)
+
+    # Train a model
+    model = LinearRegression()
+    model.fit(Xtr, ytr)
+    
+    # Define an asymmetric conformity score
+    score = AbsoluteConformityScore(sym=False)
+
+    # Test when ConformityScore is asymmetric
+    # and calibration data size is sufficient
+    n_calib_sufficient = int(np.ceil(1/(1-delta) * 2)) + 1
+    Xc, Xt, yc, _ = train_test_split(Xct, yct, train_size=n_calib_sufficient)
+    mapie_reg = MapieRegressor(
+        estimator=model, method="base", cv="prefit", conformity_score=score
+    )
+    mapie_reg.fit(Xc, yc)
+    mapie_reg.predict(Xt, alpha=1-delta)
+
+    # Test when ConformityScore is asymmetric
+    # and calibration data size is too low
+    with pytest.raises(
+        ValueError, match=r"Number of samples of the score is too low*"
+    ):
+        n_calib_low = int(np.floor(1/(1-delta) * 2))
+        Xc, Xt, yc, _ = train_test_split(Xct, yct, train_size=n_calib_low)
+        mapie_reg = MapieRegressor(
+            estimator=model, method="base", cv="prefit", conformity_score=score
+        )
+        mapie_reg.fit(Xc, yc)
+        mapie_reg.predict(Xt, alpha=1-delta)
+
+
 def test_same_results_prefit_split() -> None:
     """
     Test checking that if split and prefit method have exactly
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index b1e65b7f1..d3b9ba293 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -148,7 +148,9 @@ def test_predict_output_shape(
     mapie_ts_reg = MapieTimeSeriesRegressor(**STRATEGIES[strategy])
     (X, y) = dataset
     mapie_ts_reg.fit(X, y)
-    y_pred, y_pis = mapie_ts_reg.predict(X, alpha=alpha)
+    y_pred, y_pis = mapie_ts_reg.predict(
+        X, alpha=alpha, allow_infinite_bounds=True
+    )
     n_alpha = len(alpha) if hasattr(alpha, "__len__") else 1
     assert y_pred.shape == (X.shape[0],)
     assert y_pis.shape == (X.shape[0], 2, n_alpha)
@@ -211,8 +213,8 @@ def test_results_single_and_multi_jobs(strategy: str) -> None:
     mapie_multi = MapieTimeSeriesRegressor(n_jobs=-1, **STRATEGIES[strategy])
     mapie_single.fit(X_toy, y_toy)
     mapie_multi.fit(X_toy, y_toy)
-    y_pred_single, y_pis_single = mapie_single.predict(X_toy, alpha=0.2)
-    y_pred_multi, y_pis_multi = mapie_multi.predict(X_toy, alpha=0.2)
+    y_pred_single, y_pis_single = mapie_single.predict(X_toy, alpha=0.5)
+    y_pred_multi, y_pis_multi = mapie_multi.predict(X_toy, alpha=0.5)
     np.testing.assert_allclose(y_pred_single, y_pred_multi)
     np.testing.assert_allclose(y_pis_single, y_pis_multi)
 
diff --git a/mapie/utils.py b/mapie/utils.py
index cc1f57135..04edf33db 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -412,6 +412,29 @@ def check_gamma(
         )
 
 
+def get_effective_calibration_samples(scores: NDArray, sym: bool):
+    """
+    Calculates the effective number of calibration samples.
+
+    Parameters
+    ----------
+    scores: NDArray
+        An array of scores.
+
+    sym: bool
+        A boolean indicating whether the scores are symmetric.
+
+    Returns
+    -------
+    n: int
+        The effective number of calibration samples.
+    """
+    n = np.sum(~np.isnan(scores))
+    if not sym:
+        n //= 2
+    return n
+
+
 def check_alpha_and_n_samples(
     alphas: Union[Iterable[float], float],
     n: int,
@@ -449,9 +472,9 @@ def check_alpha_and_n_samples(
     if isinstance(alphas, float):
         alphas = np.array([alphas])
     for alpha in alphas:
-        if n < 1 / alpha or n < 1 / (1 - alpha):
+        if n < np.max([1/alpha, 1/(1-alpha)]):
             raise ValueError(
-                "Number of samples of the score is too low,\n"
+                "Number of samples of the score is too low, "
                 "1/alpha (or 1/(1 - alpha)) must be lower "
                 "than the number of samples."
             )

From 2ad21b82e9d7378c82094e7477761c409b91f68f Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 11 Jun 2024 18:22:06 +0200
Subject: [PATCH 111/424] FIX: linting

---
 mapie/tests/test_regression.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index a858b7b48..fb86658d0 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -319,7 +319,7 @@ def test_calibration_data_size_symmetric_score(delta: float) -> None:
 
     # Define a symmetric conformity score
     score = AbsoluteConformityScore(sym=True)
-    
+
     # Test when the conformity score is symmetric
     # and the number of calibration data is sufficient
     n_calib_sufficient = int(np.ceil(1/(1-delta)))
@@ -349,7 +349,7 @@ def test_calibration_data_size_asymmetric_score(delta: float) -> None:
     """
     This test function verifies that a ValueError is raised when the number
     of calibration data is lower than the minimum required for the given alpha
-    when the conformity score is asymmetric. The minimum is calculated as 
+    when the conformity score is asymmetric. The minimum is calculated as
     1/alpha or 1/(1-delta).
     """
     # Generate data
@@ -360,7 +360,7 @@ def test_calibration_data_size_asymmetric_score(delta: float) -> None:
     # Train a model
     model = LinearRegression()
     model.fit(Xtr, ytr)
-    
+
     # Define an asymmetric conformity score
     score = AbsoluteConformityScore(sym=False)
 

From 0790713006454cd15e8e2a87c3779d946399ef31 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 11 Jun 2024 18:30:09 +0200
Subject: [PATCH 112/424] FIX: newline in error raise

---
 mapie/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/utils.py b/mapie/utils.py
index 04edf33db..379f0c708 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -474,7 +474,7 @@ def check_alpha_and_n_samples(
     for alpha in alphas:
         if n < np.max([1/alpha, 1/(1-alpha)]):
             raise ValueError(
-                "Number of samples of the score is too low, "
+                "Number of samples of the score is too low,\n"
                 "1/alpha (or 1/(1 - alpha)) must be lower "
                 "than the number of samples."
             )

From 5251e966f4bc75896b1b8eaeb0bf23b112b4540c Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 12 Jun 2024 11:15:40 +0200
Subject: [PATCH 113/424] Fix issue 238

---
 mapie/subsample.py            | 16 ++++++++++------
 mapie/tests/test_subsample.py | 25 +++++++++++++++++++++++++
 2 files changed, 35 insertions(+), 6 deletions(-)

diff --git a/mapie/subsample.py b/mapie/subsample.py
index b78bcd942..717197491 100644
--- a/mapie/subsample.py
+++ b/mapie/subsample.py
@@ -22,9 +22,10 @@ class Subsample(BaseCrossValidator):
     ----------
     n_resamplings : int
         Number of resamplings. By default ``30``.
-    n_samples: int
+    n_samples: float
         Number of samples in each resampling. By default ``None``,
-        the size of the training set.
+        the size of the training set. If it is between 0 and 1,
+        it becomes the fraction of samples
     replace: bool
         Whether to replace samples in resamplings or not. By default ``True``.
     random_state: Optional[Union[int, RandomState]]
@@ -46,7 +47,7 @@ class Subsample(BaseCrossValidator):
     def __init__(
         self,
         n_resamplings: int = 30,
-        n_samples: Optional[int] = None,
+        n_samples: Optional[Union[int, float]] = None,
         replace: bool = True,
         random_state: Optional[Union[int, RandomState]] = None,
     ) -> None:
@@ -74,9 +75,12 @@ def split(
             The testing set indices for that split.
         """
         indices = np.arange(_num_samples(X))
-        n_samples = (
-            self.n_samples if self.n_samples is not None else len(indices)
-        )
+        if self.n_samples is None:
+            n_samples = len(indices)
+        elif isinstance(self.n_samples, float):
+            n_samples = int(np.floor(self.n_samples * X.shape[0]))
+        else:
+            n_samples = int(self.n_samples)
         random_state = check_random_state(self.random_state)
         for k in range(self.n_resamplings):
             train_index = resample(
diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index affe81057..589463555 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -32,6 +32,31 @@ def test_split_SubSample() -> None:
     np.testing.assert_equal(tests, tests_expected)
 
 
+def test_split_SubSample_n_samples() -> None:
+    """Test outputs of subsamplings."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    cv1 = Subsample(n_resamplings=2, random_state=0,
+                    n_samples=0.8, replace=False)
+    cv2 = Subsample(n_resamplings=2, random_state=0,
+                    n_samples=6, replace=False)
+    train1 = np.concatenate([x[0] for x in cv1.split(X)])
+    test1 = np.concatenate([x[1] for x in cv1.split(X)])
+    train2 = np.concatenate([x[0] for x in cv2.split(X)])
+    test2 = np.concatenate([x[1] for x in cv2.split(X)])
+    train1_expected = np.array([2, 8, 4, 9, 1, 6, 7,
+                                3, 3, 5, 1, 2, 9, 8, 0, 6])
+    test1_expected = np.array([0, 5, 4, 7])
+    train2_expected = np.array([2, 8, 4, 9, 1, 6, 3, 5, 1, 2, 9, 8])
+    test2_expected = np.array([0, 3, 5, 7, 0, 4, 6, 7])
+    expected_n_samples_cv1 = int(np.floor(0.8 * X.shape[0]))
+    assert len(train1) == 2 * expected_n_samples_cv1
+    assert len(train2) == 2 * 6
+    np.testing.assert_equal(train1, train1_expected)
+    np.testing.assert_equal(test1, test1_expected)
+    np.testing.assert_equal(train2, train2_expected)
+    np.testing.assert_equal(test2, test2_expected)
+
+
 def test_default_parameters_BlockBootstrap() -> None:
     """Test default values of Subsample."""
     cv = BlockBootstrap()

From 958e6208218992c5cb7264f096abad7ecb4f5dad Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 12 Jun 2024 11:40:12 +0200
Subject: [PATCH 114/424] Add issue 238 into history

---
 HISTORY.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index d5dabb822..4502ba3fd 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,8 @@ History
 0.8.5 (2024-**-**)
 ------------------
 
+* Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
+
 0.8.4 (2024-06-07)
 ------------------
 

From b16028b6bdd1e213ed8a18934d1763f25bf7cf00 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 12 Jun 2024 14:38:33 +0200
Subject: [PATCH 115/424] Fix a potentiel edge case

---
 mapie/subsample.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/subsample.py b/mapie/subsample.py
index 717197491..0a78a6160 100644
--- a/mapie/subsample.py
+++ b/mapie/subsample.py
@@ -77,7 +77,7 @@ def split(
         indices = np.arange(_num_samples(X))
         if self.n_samples is None:
             n_samples = len(indices)
-        elif isinstance(self.n_samples, float):
+        elif isinstance(self.n_samples, float) and 0 < self.n_samples < 1:
             n_samples = int(np.floor(self.n_samples * X.shape[0]))
         else:
             n_samples = int(self.n_samples)

From ea30be34e6fce8fb2855334cbbde894b9ad088d2 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 12 Jun 2024 17:42:41 +0200
Subject: [PATCH 116/424] UPD: quantile computation + notebook illustration

---
 .../plot_coverage_validity.py                 | 521 ++++++++++++++++++
 mapie/conformity_scores/conformity_scores.py  |  10 +-
 2 files changed, 528 insertions(+), 3 deletions(-)
 create mode 100644 examples/regression/2-advanced-analysis/plot_coverage_validity.py

diff --git a/examples/regression/2-advanced-analysis/plot_coverage_validity.py b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
new file mode 100644
index 000000000..42a7de74a
--- /dev/null
+++ b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
@@ -0,0 +1,521 @@
+"""
+================================================
+Coverage Validity with MAPIE for Regression Task
+================================================
+"""
+
+##############################################################################
+# DESCIPTION
+#
+# This notebook is inspired of the notebook used for episode "Uncertainty
+# Quantification: Avoid these Missteps in Validating Your Conformal Claims!"
+# (link to the [orginal notebook](https://github.com/mtorabirad/MLBoost)).
+#
+# [1] REF1
+#
+# [2] REF2
+
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.linear_model import LinearRegression
+from sklearn.datasets import make_regression
+from sklearn.model_selection import KFold, ShuffleSplit, train_test_split
+
+from mapie.regression import MapieRegressor
+from mapie.conformity_scores import AbsoluteConformityScore
+from mapie.metrics import regression_coverage_score_v2
+
+from joblib import Parallel, delayed
+
+import warnings
+warnings.filterwarnings("ignore")
+warnings.simplefilter("ignore", RuntimeWarning)
+warnings.simplefilter("ignore", UserWarning)
+
+
+##############################################################################
+# Section 1: Comparison with the standard conformalizer method (litterature)
+# --------------------------------------------------------------------------
+#
+# TODO
+
+# Conformalizer Class
+class StandardConformalizer():
+    def __init__(
+        self,
+        pre_trained_model,
+        non_conformity_func,
+        delta
+    ):
+        # Initialize the conformalizer with required parameters
+        self.point_predictor = pre_trained_model
+        self.non_conformity_func = non_conformity_func
+        self.delta = delta
+
+    def _calculate_quantile(self, scores_calib):
+        # Calculate the quantile value based on delta and non-conformity scores
+        self.delta_cor = np.ceil((self.delta)*(self.n_calib + 1))/self.n_calib
+        return np.quantile(scores_calib, self.delta_cor, method='lower')
+
+    def _calibrate(self, X_calib, y_calib):
+        # Calibrate the conformalizer to calculate q_hat
+        y_calib_pred = self.point_predictor.predict(X_calib)
+        scores_calib = self.non_conformity_func(y_calib_pred, y_calib)
+        self.q_hat = self._calculate_quantile(scores_calib)
+
+    def fit(self, X, y):
+        # Fit the conformalizer to the data and calculate q_hat
+        self.n_calib = X.shape[0]
+        self._calibrate(X, y)
+        return self
+
+    def predict(self, X, alpha=None):
+        # Returns the predicted interval
+        y_pred = self.point_predictor.predict(X)
+        y_lower, y_upper = y_pred - self.q_hat, y_pred + self.q_hat
+        y_pis = np.expand_dims(np.stack([y_lower, y_upper], axis=1), axis=-1)
+        return y_lower, y_pis
+
+
+def non_conformity_func(y, y_hat):
+    return np.abs(y - y_hat)
+
+
+def get_coverage_prefit(
+    conformalizer, data, target, delta, n_calib, random_state=None
+):
+    """
+    Calculate the fraction of test samples within the predicted intervals.
+
+    This function splits the data into a training set and a test set. If the
+    cross-validation strategy of the mapie regressor is a ShuffleSplit, it fits
+    the regressor to the entire training set. Otherwise, it further splits the
+    training set into a calibration set and a training set, and fits the
+    regressor to the calibration set. It then predicts intervals for the test
+    set and calculates the fraction of test samples within these intervals.
+
+    Parameters:
+    -----------
+    conformalizer: object
+        A mapie regressor object.
+
+    data: array-like of shape (n_samples, n_features)
+        The data to be split into a training set and a test set.
+
+    target: array-like of shape (n_samples,)
+        The target values for the data.
+
+    delta: float
+        The level of confidence for the predicted intervals.
+
+    Returns:
+    --------
+    fraction_within_bounds: float
+        The fraction of test samples within the predicted intervals.
+    """
+    # Split data step
+    X_cal, X_test, y_cal, y_test = train_test_split(
+        data, target, train_size=n_calib, random_state=random_state
+    )
+    # Calibration step
+    conformalizer.fit(X_cal, y_cal)
+    # Prediction step
+    _, y_pis = conformalizer.predict(X_test, alpha=1-delta)
+    # Coverage step
+    coverage = regression_coverage_score_v2(y_test, y_pis)
+
+    return coverage
+
+
+def cumulative_average(arr):
+    """
+    Calculate the cumulative average of a list of numbers.
+
+    This function computes the cumulative average of a list of numbers by
+    calculating the cumulative sum of the numbers and dividing it by the
+    index of the current number.
+
+    Parameters:
+    -----------
+    arr: List[float]
+        The input list of numbers.
+
+    Returns:
+    --------
+    running_avg: List[float]
+        The cumulative average of the input list.
+    """
+    cumsum = np.cumsum(arr)
+    indices = np.arange(1, len(arr) + 1)
+    cumulative_avg = cumsum / indices
+    return cumulative_avg
+
+
+##############################################################################
+# Experiment 1.1: Coverage Validity for a given delta, n_calib
+# ------------------------------------------------------------
+#
+# TODO
+
+# Parameters of the modelisation
+delta = 0.8
+n_calib = 6
+
+n_train = 1000
+n_test = 100
+num_splits = 1000
+
+# Load toy Data
+n_all = n_train + n_calib + n_test
+data, target = make_regression(n_all, random_state=1)
+
+# Split dataset into training, calibration and validation sets
+X_train, X_cal_test, y_train, y_cal_test = train_test_split(
+    data, target, train_size=n_train, random_state=1
+)
+
+# Create a linear regression model and fit it to the training data
+model = LinearRegression()
+model.fit(X_train, y_train)
+
+# Compute theorical bounds and exact coverage to attempt
+lower_bound = delta
+upper_bound = (delta + 1/(n_calib+1))
+upper_bound_2 = (delta + 1/(n_calib/2+1))
+exact_cov = (np.ceil((n_calib+1)*delta))/(n_calib+1)
+
+# Run the experiment
+empirical_coverages_ref = []
+empirical_coverages_mapie = []
+
+for i in range(1, num_splits):
+    # Compute empirical coverage for each trial with StandardConformalizer
+    conformalizer = StandardConformalizer(model, non_conformity_func, delta)
+    coverage = get_coverage_prefit(
+        conformalizer, X_cal_test, y_cal_test, delta, n_calib, random_state=i
+    )
+    empirical_coverages_ref.append(coverage)
+
+    # Compute empirical coverage for each trial with MapieRegressor
+    conformalizer = MapieRegressor(estimator=model, cv="prefit")
+    coverage = get_coverage_prefit(
+        conformalizer, X_cal_test, y_cal_test, delta, n_calib, random_state=i
+    )
+    empirical_coverages_mapie.append(coverage)
+
+cumulative_averages_ref = cumulative_average(empirical_coverages_ref)
+cumulative_averages_mapie = cumulative_average(empirical_coverages_mapie)
+
+# Plot the results
+fig, ax = plt.subplots()
+plt.plot(cumulative_averages_ref, alpha=0.5, label='SplitCP', color='r')
+plt.plot(cumulative_averages_mapie, alpha=0.5, label='MAPIE', color='g')
+
+plt.hlines(exact_cov, 0, num_splits, color='r', ls='--', label='Exact Cov.')
+plt.hlines(lower_bound, 0, num_splits, color='k', label='Lower Bound')
+plt.hlines(upper_bound, 0, num_splits, color='b', label='Upper Bound')
+
+plt.xlabel(r'Split Number')
+plt.ylabel(r'$\overline{\mathbb{C}}$')
+plt.title(r'$|D_{cal}| = $' + str(n_calib) + r' and $\delta = $' + str(delta))
+
+plt.legend(loc="upper right", ncol=2)
+plt.ylim(0.7, 1)
+plt.tight_layout()
+plt.show()
+
+
+##############################################################################
+# Experiment 1.2: Again but without fixing random_state
+# -----------------------------------------------------
+#
+# TODO
+
+# Run the experiment
+empirical_coverages_ref = []
+empirical_coverages_mapie = []
+
+for i in range(1, num_splits):
+    # Compute empirical coverage for each trial with StandardConformalizer
+    conformalizer = StandardConformalizer(model, non_conformity_func, delta)
+    coverage = get_coverage_prefit(
+        conformalizer, X_cal_test, y_cal_test, delta, n_calib
+    )
+    empirical_coverages_ref.append(coverage)
+
+    # Compute empirical coverage for each trial with MapieRegressor
+    conformalizer = MapieRegressor(estimator=model, cv="prefit")
+    coverage = get_coverage_prefit(
+        conformalizer, X_cal_test, y_cal_test, delta, n_calib
+    )
+    empirical_coverages_mapie.append(coverage)
+
+cumulative_averages_ref = cumulative_average(empirical_coverages_ref)
+cumulative_averages_mapie = cumulative_average(empirical_coverages_mapie)
+
+# Plot the results
+fig, ax = plt.subplots()
+plt.plot(cumulative_averages_ref, alpha=0.5, label='SplitCP', color='r')
+plt.plot(cumulative_averages_mapie, alpha=0.5, label='MAPIE', color='g')
+
+plt.hlines(exact_cov, 0, num_splits, color='r', ls='--', label='Exact Cov.')
+plt.hlines(lower_bound, 0, num_splits, color='k', label='Lower Bound')
+plt.hlines(upper_bound, 0, num_splits, color='b', label='Upper Bound')
+
+plt.xlabel(r'Split Number')
+plt.ylabel(r'$\overline{\mathbb{C}}$')
+plt.title(r'$|D_{cal}| = $' + str(n_calib) + r' and $\delta = $' + str(delta))
+
+plt.legend(loc="upper right", ncol=2)
+plt.ylim(0.7, 1)
+plt.tight_layout()
+plt.show()
+
+
+##############################################################################
+# Section 2: Again but with different MAPIE CP methods
+# ----------------------------------------------------
+#
+# TODO
+
+def get_coverage_split(conformalizer, data, target, delta, random_state=None):
+    """
+    Calculate the fraction of test samples within the predicted intervals.
+
+    This function splits the data into a training set and a test set. If the
+    cross-validation strategy of the mapie regressor is a ShuffleSplit, it fits
+    the regressor to the entire training set. Otherwise, it further splits the
+    training set into a calibration set and a training set, and fits the
+    regressor to the calibration set. It then predicts intervals for the test
+    set and calculates the fraction of test samples within these intervals.
+
+    Parameters:
+    -----------
+    conformalizer: object
+        A mapie regressor object.
+
+    data: array-like of shape (n_samples, n_features)
+        The data to be split into a training set and a test set.
+
+    target: array-like of shape (n_samples,)
+        The target values for the data.
+
+    delta: float
+        The level of confidence for the predicted intervals.
+
+    Returns:
+    --------
+    fraction_within_bounds: float
+        The fraction of test samples within the predicted intervals.
+    """
+    # Split data step
+    X_train_cal, X_test, y_train_cal, y_test = train_test_split(
+        data, target, test_size=n_test
+    )
+
+    # isinstance(conformalizer, MapieRegressor)
+    # Calibration step
+    if isinstance(conformalizer.cv, ShuffleSplit):
+        conformalizer.fit(X_train_cal, y_train_cal)
+    else:
+        _, X_cal, _, y_cal = train_test_split(
+            X_train_cal, y_train_cal, test_size=n_calib
+        )
+        conformalizer.fit(X_cal, y_cal)
+
+    # Prediction step
+    if isinstance(conformalizer, StandardConformalizer):
+        _, y_pis = conformalizer.predict(X_test)
+    else:
+        _, y_pis = conformalizer.predict(X_test, alpha=1-delta)
+
+    # Coverage step
+    fraction_within_bounds = regression_coverage_score_v2(y_test, y_pis)
+
+    return fraction_within_bounds
+
+
+def run_get_coverage_split(model, params, n_calib, data, target, delta):
+    try:
+        # Compute empirical coverage for each trial with MAPIE CP method
+        mapie_reg = MapieRegressor(estimator=model, **params(n_calib))
+        coverage = get_coverage_split(mapie_reg, data, target, delta)
+    except Exception:
+        coverage = np.nan
+    return coverage
+
+
+STRATEGIES = {
+    "prefit": lambda n: dict(
+        method="base",
+        cv="prefit",
+        conformity_score=AbsoluteConformityScore(sym=True)
+    ),
+    "prefit_asym": lambda n: dict(
+        method="base",
+        cv="prefit",
+        conformity_score=AbsoluteConformityScore(sym=False)
+    ),
+    # "split": lambda n: dict(
+    #     method="base",
+    #     cv=ShuffleSplit(n_splits=1, test_size=n),
+    #     conformity_score=AbsoluteConformityScore(sym=True)
+    # ),
+    # "split_asym": lambda n: dict(
+    #     method="base",
+    #     cv=ShuffleSplit(n_splits=1, test_size=n),
+    #     conformity_score=AbsoluteConformityScore(sym=False)
+    # ),
+    "cv_plus_10": lambda n: dict(
+        method="plus",
+        cv=KFold(n_splits=10, shuffle=True),
+        conformity_score=AbsoluteConformityScore(sym=True)
+    ),
+    # "cv_plus_5": lambda n: dict(
+    #     method="plus",
+    #     cv=KFold(n_splits=5, shuffle=True),
+    #     conformity_score=AbsoluteConformityScore(sym=True)
+    # ),
+}
+
+
+##############################################################################
+# Experiment 2: Again but with different MAPIE CP methods
+# -------------------------------------------------------
+#
+# TODO
+
+# Parameters of the modelisation
+delta = 0.8
+n_calib = 12  # for asymmetric non-conformity scores
+num_splits = 1000
+
+# Run the experiment
+cumulative_averages_dict = dict()
+
+for method, params in STRATEGIES.items():
+    if 'cv' in method:
+        continue
+    coverages_list = []
+    run_params = model, params, n_calib, data, target, delta
+    coverages_list = Parallel(n_jobs=-1)(
+        delayed(run_get_coverage_split)(*run_params)
+        for _ in range(num_splits)
+    )
+
+    cumulative_averages_dict[method] = cumulative_average(coverages_list)
+
+# Plot the results
+fig, ax = plt.subplots()
+for method in STRATEGIES:
+    if 'cv' in method:
+        continue
+    plt.plot(cumulative_averages_dict[method], alpha=0.5, label=method)
+
+plt.hlines(exact_cov, 0, num_splits, color='r', ls='--', label='Exact Cov.')
+plt.hlines(lower_bound, 0, num_splits, color='k', label='Lower Bound')
+plt.hlines(upper_bound, 0, num_splits, color='b', label='Upper Bound')
+
+plt.xlabel(r'Split Number')
+plt.ylabel(r'$\overline{\mathbb{C}}$')
+plt.title(r'$|D_{cal}| = $' + str(n_calib) + r' and $\delta = $' + str(delta))
+
+plt.legend(loc="upper right", ncol=2)
+plt.ylim(0.7, 1)
+plt.tight_layout()
+plt.show()
+
+
+##############################################################################
+# Experiment 3: Again but on different delta and n_calib
+# ------------------------------------------------------
+#
+# TODO
+
+num_splits = 200
+
+n_calib_min, n_calib_max = 10, 200
+n_calib_array = np.arange(n_calib_min, n_calib_max+1, 2)
+delta_array = [0.8]  # [0.6, 0.8]
+
+final_coverage_dict = {
+    method: {delta: [] for delta in delta_array}
+    for method in STRATEGIES
+}
+effective_coverage_dict = {
+    method: {delta: [] for delta in delta_array}
+    for method in STRATEGIES
+}
+
+for delta in delta_array:
+    for method, params in STRATEGIES.items():
+        for n_calib in n_calib_array:
+            coverages_list = []
+            run_params = model, params, n_calib, data, target, delta
+            coverages_list = Parallel(n_jobs=-1)(
+                delayed(run_get_coverage_split)(*run_params)
+                for _ in range(num_splits)
+            )
+            coverages_list = np.array(coverages_list)
+            final_coverage = cumulative_average(coverages_list)[-1]
+            final_coverage_dict[method][delta].append(final_coverage)
+
+
+# Theorical bounds and exact coverage to attempt
+def lower_bound(delta):
+    return delta * np.ones_like(n_calib_array)
+
+
+def upper_bound(delta):
+    return (delta + 1/(n_calib_array+1))
+
+
+def upper_bound_asym(delta):
+    return (delta + 1/(n_calib_array//2+1))
+
+
+def lower_bound_cross(delta):
+    return 1 - 2*(1-delta) - np.sqrt(2/n_calib_array)
+
+
+def exact_coverage(delta):
+    return (np.ceil((n_calib_array+1)*delta))/(n_calib_array+1)
+
+
+# def lower_bound_cross(delta, K, n):
+#     bound = 1 - 2*(1-delta)
+#     margin = np.min([2*(1-1/K)/(n/K+1), (1-K/n)/(K+1)])
+#     return bound - margin
+
+
+n_strat = len(STRATEGIES)
+nrows, ncols = n_strat, len(delta_array)  # np.max([len(delta_array), 2])
+
+fig, ax = plt.subplots(nrows=nrows, ncols=ncols)
+
+for i, method in enumerate(STRATEGIES):
+    for j, delta in enumerate(delta_array):
+
+        cov = final_coverage_dict[method][delta]
+        ub = upper_bound(delta)
+        lb = lower_bound(delta)
+        if 'asym' in method:
+            ub = upper_bound_asym(delta)
+        if 'cv' in method:
+            ub = np.ones_like(n_calib_array)
+            lb = lower_bound_cross(delta)
+
+        ub = np.clip(ub, a_min=0, a_max=1)
+        lb = np.clip(lb, a_min=0, a_max=1)
+
+        ax[i].plot(n_calib_array, cov, alpha=0.5, label=method, color='g')
+        ax[i].plot(n_calib_array, lb, color='k', label='Lower Bound')
+        ax[i].plot(n_calib_array, ub, color='b', label='Upper Bound')
+        ax[i].hlines(delta, n_calib_min, n_calib_max, color='r', ls='--',
+                     label='Target Coverage')
+
+        ax[i].legend(loc="lower right", ncol=2)
+        ax[i].set_ylim(np.min(lb) - 0.05, 1.0)
+
+plt.show()
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index e602a1646..308ac08ec 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -244,7 +244,7 @@ def get_quantile(
 
         unbounded: bool
             Boolean specifying whether infinite prediction intervals
-            could be produced.
+            could be produced (when alpha_np is greater than or equal to 1.).
 
             By default ``False``.
 
@@ -264,12 +264,16 @@ def get_quantile(
         alpha_cor = np.ceil(alpha_ref*(n_calib+1))/n_calib
         alpha_cor = np.clip(alpha_cor, a_min=0, a_max=1)
 
-        # Compute the target quantiles
+        # Compute the target quantiles:
+        # If unbounded is True and alpha is greater than or equal to 1,
+        # the quantile is set to infinity.
+        # Otherwise, the quantile is calculated as the corrected lower quantile
+        # of the signed conformity scores.
         quantile = signed * np.column_stack([
             np_nanquantile(
                 signed * conformity_scores, _alpha_cor,
                 axis=axis, method="lower"
-            ) if not unbounded or _alpha < 1 else np.inf * np.ones(n_ref)
+            ) if not (unbounded and _alpha >= 1) else np.inf * np.ones(n_ref)
             for _alpha, _alpha_cor in zip(alpha_ref, alpha_cor)
         ])
         return quantile

From 4a512601fc4a144b3a340796747a9a9c40507e79 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 13 Jun 2024 16:44:11 +0200
Subject: [PATCH 117/424] Update: taking into account the PR comments

---
 HISTORY.rst                   |  2 +-
 mapie/subsample.py            |  9 ++++--
 mapie/tests/test_subsample.py | 53 ++++++++++++++++++++---------------
 mapie/tests/test_utils.py     | 20 +++++++++++--
 mapie/utils.py                | 36 ++++++++++++++++++++++++
 5 files changed, 92 insertions(+), 28 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 4502ba3fd..be141c8ed 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,7 +2,7 @@
 History
 =======
 
-0.8.5 (2024-**-**)
+0.X.X (2024-**-**)
 ------------------
 
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
diff --git a/mapie/subsample.py b/mapie/subsample.py
index 0a78a6160..233149e9c 100644
--- a/mapie/subsample.py
+++ b/mapie/subsample.py
@@ -10,6 +10,7 @@
 from sklearn.utils.validation import _num_samples
 
 from ._typing import NDArray
+from .utils import check_n_samples
 
 
 class Subsample(BaseCrossValidator):
@@ -77,10 +78,12 @@ def split(
         indices = np.arange(_num_samples(X))
         if self.n_samples is None:
             n_samples = len(indices)
-        elif isinstance(self.n_samples, float) and 0 < self.n_samples < 1:
-            n_samples = int(np.floor(self.n_samples * X.shape[0]))
         else:
-            n_samples = int(self.n_samples)
+            n_samples = check_n_samples(X, self.n_samples)
+        # elif isinstance(self.n_samples, float) and 0 < self.n_samples < 1:
+        #     n_samples = int(np.floor(self.n_samples * X.shape[0]))
+        # else:
+        #     n_samples = int(self.n_samples)
         random_state = check_random_state(self.random_state)
         for k in range(self.n_resamplings):
             train_index = resample(
diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index 589463555..38714074d 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from typing import Union
+
 import numpy as np
 import pytest
 
@@ -32,29 +34,36 @@ def test_split_SubSample() -> None:
     np.testing.assert_equal(tests, tests_expected)
 
 
-def test_split_SubSample_n_samples() -> None:
-    """Test outputs of subsamplings."""
+@pytest.mark.parametrize("n_samples", [4, 6, 8, 10])
+@pytest.mark.parametrize("n_resamplings", [1, 2, 3])
+def test_n_samples_int(n_samples: Union[int, float],
+                       n_resamplings: int) -> None:
+    """Test outputs of subsamplings when n_samples is a int"""
     X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    cv1 = Subsample(n_resamplings=2, random_state=0,
-                    n_samples=0.8, replace=False)
-    cv2 = Subsample(n_resamplings=2, random_state=0,
-                    n_samples=6, replace=False)
-    train1 = np.concatenate([x[0] for x in cv1.split(X)])
-    test1 = np.concatenate([x[1] for x in cv1.split(X)])
-    train2 = np.concatenate([x[0] for x in cv2.split(X)])
-    test2 = np.concatenate([x[1] for x in cv2.split(X)])
-    train1_expected = np.array([2, 8, 4, 9, 1, 6, 7,
-                                3, 3, 5, 1, 2, 9, 8, 0, 6])
-    test1_expected = np.array([0, 5, 4, 7])
-    train2_expected = np.array([2, 8, 4, 9, 1, 6, 3, 5, 1, 2, 9, 8])
-    test2_expected = np.array([0, 3, 5, 7, 0, 4, 6, 7])
-    expected_n_samples_cv1 = int(np.floor(0.8 * X.shape[0]))
-    assert len(train1) == 2 * expected_n_samples_cv1
-    assert len(train2) == 2 * 6
-    np.testing.assert_equal(train1, train1_expected)
-    np.testing.assert_equal(test1, test1_expected)
-    np.testing.assert_equal(train2, train2_expected)
-    np.testing.assert_equal(test2, test2_expected)
+    cv = Subsample(n_resamplings=n_resamplings, random_state=0,
+                   n_samples=n_samples, replace=False)
+    train_set = np.concatenate([x[0] for x in cv.split(X)])
+    val_set = np.concatenate([x[1] for x in cv.split(X)])
+    assert len(train_set) == n_samples*n_resamplings
+    assert len(val_set) == (X.shape[0] - n_samples)*n_resamplings
+
+
+@pytest.mark.parametrize("n_samples", [0.4, 0.6, 0.8, 0.9])
+@pytest.mark.parametrize("n_resamplings", [1, 2, 3])
+def test_n_samples_float(n_samples: Union[int, float],
+                         n_resamplings: int) -> None:
+    """Test outputs of subsamplings when n_samples is a
+    float between 0 and 1."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    cv = Subsample(n_resamplings=n_resamplings, random_state=0,
+                   n_samples=n_samples, replace=False)
+    train_set = np.concatenate([x[0] for x in cv.split(X)])
+    val_set = np.concatenate([x[1] for x in cv.split(X)])
+    assert len(train_set) == int(np.floor(n_samples*X.shape[0]))*n_resamplings
+    assert len(val_set) == (
+        (X.shape[0] - int(np.floor(n_samples * X.shape[0]))) *
+        n_resamplings
+    )
 
 
 def test_default_parameters_BlockBootstrap() -> None:
diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index 02d83645d..49f01548e 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Optional, Tuple
+from typing import Any, Optional, Tuple, Union
 
 import numpy as np
 import pytest
@@ -17,7 +17,8 @@
                          check_array_inf, check_array_nan, check_arrays_length,
                          check_binary_zero_one, check_cv, check_gamma,
                          check_lower_upper_bounds, check_n_features_in,
-                         check_n_jobs, check_no_agg_cv, check_null_weight,
+                         check_n_jobs, check_no_agg_cv, check_n_samples,
+                         check_null_weight,
                          check_number_bins, check_split_strategy,
                          check_verbose, compute_quantiles, fit_estimator,
                          get_binning_groups)
@@ -508,3 +509,18 @@ def test_check_no_agg_cv_value_error(cv: Any) -> None:
         match=r"Allowed values must have the `get_n_splits` method"
     ):
         check_no_agg_cv(X_toy, cv, array)
+
+
+@pytest.mark.parametrize("X", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+@pytest.mark.parametrize("n_samples", [1.2, 2.4, 3.6])
+def test_invalid_n_samples(X: NDArray,
+                           n_samples: Union[float, int]) -> None:
+    """Test that invalid n_samples raise errors."""
+    with pytest.raises(
+        ValueError,
+        match=(
+            r".*Invalid n_samples."
+            r"Allowed values are float between 0 and 1 or int*"
+        )
+    ):
+        check_n_samples(X=X, n_samples=n_samples)
diff --git a/mapie/utils.py b/mapie/utils.py
index cc1f57135..ffeef6e6d 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1332,3 +1332,39 @@ def check_arrays_length(*arrays: NDArray) -> None:
         raise ValueError(
                 "There are arrays with different length"
             )
+
+
+def check_n_samples(
+    X: NDArray,
+    n_samples: Union[float, int]
+) -> int:
+    """
+    Check alpha and prepare it as a ArrayLike.
+
+    Parameters
+    ----------
+    n_samples: Union[float, int]
+        Can be a float between 0 and 1 or a int
+        Between 0 and 1, represent the part of data in the train sample
+        When n_samples is a int, it represents the number of elements
+        in the train sample
+
+    Returns
+    -------
+    int
+        n_samples
+
+    Raises
+    ------
+    ValueError
+        If n_samples is not an int or a float between 0 and 1.
+    """
+    if isinstance(n_samples, float) and 0 < n_samples < 1:
+        n_samples = int(np.floor(n_samples * X.shape[0]))
+    elif isinstance(n_samples, float) and n_samples > 1:
+        raise ValueError(
+            "Invalid n_samples.Allowed values are float between 0 and 1 or int"
+        )
+    else:
+        n_samples = int(n_samples)
+    return n_samples

From 6c8596602fbc50f88ccb7d0355af55cfbb0cc58f Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 13 Jun 2024 17:12:55 +0200
Subject: [PATCH 118/424] UPD: improve validity coverage example

---
 .../plot_coverage_validity.py                 | 165 ++++++++----------
 1 file changed, 71 insertions(+), 94 deletions(-)

diff --git a/examples/regression/2-advanced-analysis/plot_coverage_validity.py b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
index 42a7de74a..2afb07dfd 100644
--- a/examples/regression/2-advanced-analysis/plot_coverage_validity.py
+++ b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
@@ -2,25 +2,30 @@
 ================================================
 Coverage Validity with MAPIE for Regression Task
 ================================================
-"""
 
-##############################################################################
-# DESCIPTION
-#
-# This notebook is inspired of the notebook used for episode "Uncertainty
-# Quantification: Avoid these Missteps in Validating Your Conformal Claims!"
-# (link to the [orginal notebook](https://github.com/mtorabirad/MLBoost)).
-#
-# [1] REF1
-#
-# [2] REF2
+This example verifies that conformal claims are valid in the MAPIE package
+when using the CP prefit/split methods.
+
+This notebook is inspired of the notebook used for episode "Uncertainty
+Quantification: Avoid these Missteps in Validating Your Conformal Claims!"
+(link to the [orginal notebook](https://github.com/mtorabirad/MLBoost)).
+
+For more details on theoretical guarantees:
+
+[1] Vovk, Vladimir, Alexander Gammerman, and Glenn Shafer.
+"Algorithmic Learning in a Random World." Springer Nature, 2022.
+
+[2] Angelopoulos, Anastasios N., and Stephen Bates.
+"Conformal prediction: A gentle introduction." Foundations and Trends®
+in Machine Learning 16.4 (2023): 494-591.
+"""
 
 import numpy as np
 import matplotlib.pyplot as plt
 
-from sklearn.linear_model import LinearRegression
+from sklearn.tree import DecisionTreeRegressor
 from sklearn.datasets import make_regression
-from sklearn.model_selection import KFold, ShuffleSplit, train_test_split
+from sklearn.model_selection import ShuffleSplit, train_test_split
 
 from mapie.regression import MapieRegressor
 from mapie.conformity_scores import AbsoluteConformityScore
@@ -35,8 +40,8 @@
 
 
 ##############################################################################
-# Section 1: Comparison with the standard conformalizer method (litterature)
-# --------------------------------------------------------------------------
+# Section 1: Comparison with the split conformalizer method (light version)
+# -------------------------------------------------------------------------
 #
 # TODO
 
@@ -49,18 +54,18 @@ def __init__(
         delta
     ):
         # Initialize the conformalizer with required parameters
-        self.point_predictor = pre_trained_model
+        self.estimator = pre_trained_model
         self.non_conformity_func = non_conformity_func
         self.delta = delta
 
     def _calculate_quantile(self, scores_calib):
         # Calculate the quantile value based on delta and non-conformity scores
-        self.delta_cor = np.ceil((self.delta)*(self.n_calib + 1))/self.n_calib
+        self.delta_cor = np.ceil(self.delta*(self.n_calib+1))/self.n_calib
         return np.quantile(scores_calib, self.delta_cor, method='lower')
 
     def _calibrate(self, X_calib, y_calib):
         # Calibrate the conformalizer to calculate q_hat
-        y_calib_pred = self.point_predictor.predict(X_calib)
+        y_calib_pred = self.estimator.predict(X_calib)
         scores_calib = self.non_conformity_func(y_calib_pred, y_calib)
         self.q_hat = self._calculate_quantile(scores_calib)
 
@@ -72,7 +77,7 @@ def fit(self, X, y):
 
     def predict(self, X, alpha=None):
         # Returns the predicted interval
-        y_pred = self.point_predictor.predict(X)
+        y_pred = self.estimator.predict(X)
         y_lower, y_upper = y_pred - self.q_hat, y_pred + self.q_hat
         y_pis = np.expand_dims(np.stack([y_lower, y_upper], axis=1), axis=-1)
         return y_lower, y_pis
@@ -163,7 +168,7 @@ def cumulative_average(arr):
 n_calib = 6
 
 n_train = 1000
-n_test = 100
+n_test = 1000
 num_splits = 1000
 
 # Load toy Data
@@ -175,8 +180,8 @@ def cumulative_average(arr):
     data, target, train_size=n_train, random_state=1
 )
 
-# Create a linear regression model and fit it to the training data
-model = LinearRegression()
+# Create a regression model and fit it to the training data
+model = DecisionTreeRegressor()
 model.fit(X_train, y_train)
 
 # Compute theorical bounds and exact coverage to attempt
@@ -314,9 +319,9 @@ def get_coverage_split(conformalizer, data, target, delta, random_state=None):
         data, target, test_size=n_test
     )
 
-    # isinstance(conformalizer, MapieRegressor)
     # Calibration step
-    if isinstance(conformalizer.cv, ShuffleSplit):
+    if isinstance(conformalizer, MapieRegressor) and \
+            isinstance(conformalizer.cv, ShuffleSplit):
         conformalizer.fit(X_train_cal, y_train_cal)
     else:
         _, X_cal, _, y_cal = train_test_split(
@@ -337,8 +342,10 @@ def get_coverage_split(conformalizer, data, target, delta, random_state=None):
 
 
 def run_get_coverage_split(model, params, n_calib, data, target, delta):
+    if not params:
+        ref_reg = StandardConformalizer(model, non_conformity_func, delta)
+        return get_coverage_split(ref_reg, data, target, delta)
     try:
-        # Compute empirical coverage for each trial with MAPIE CP method
         mapie_reg = MapieRegressor(estimator=model, **params(n_calib))
         coverage = get_coverage_split(mapie_reg, data, target, delta)
     except Exception:
@@ -347,6 +354,7 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 
 
 STRATEGIES = {
+    "reference": None,
     "prefit": lambda n: dict(
         method="base",
         cv="prefit",
@@ -357,26 +365,6 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
         cv="prefit",
         conformity_score=AbsoluteConformityScore(sym=False)
     ),
-    # "split": lambda n: dict(
-    #     method="base",
-    #     cv=ShuffleSplit(n_splits=1, test_size=n),
-    #     conformity_score=AbsoluteConformityScore(sym=True)
-    # ),
-    # "split_asym": lambda n: dict(
-    #     method="base",
-    #     cv=ShuffleSplit(n_splits=1, test_size=n),
-    #     conformity_score=AbsoluteConformityScore(sym=False)
-    # ),
-    "cv_plus_10": lambda n: dict(
-        method="plus",
-        cv=KFold(n_splits=10, shuffle=True),
-        conformity_score=AbsoluteConformityScore(sym=True)
-    ),
-    # "cv_plus_5": lambda n: dict(
-    #     method="plus",
-    #     cv=KFold(n_splits=5, shuffle=True),
-    #     conformity_score=AbsoluteConformityScore(sym=True)
-    # ),
 }
 
 
@@ -395,22 +383,17 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 cumulative_averages_dict = dict()
 
 for method, params in STRATEGIES.items():
-    if 'cv' in method:
-        continue
     coverages_list = []
     run_params = model, params, n_calib, data, target, delta
     coverages_list = Parallel(n_jobs=-1)(
         delayed(run_get_coverage_split)(*run_params)
         for _ in range(num_splits)
     )
-
     cumulative_averages_dict[method] = cumulative_average(coverages_list)
 
 # Plot the results
 fig, ax = plt.subplots()
 for method in STRATEGIES:
-    if 'cv' in method:
-        continue
     plt.plot(cumulative_averages_dict[method], alpha=0.5, label=method)
 
 plt.hlines(exact_cov, 0, num_splits, color='r', ls='--', label='Exact Cov.')
@@ -433,11 +416,12 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 #
 # TODO
 
-num_splits = 200
+num_splits = 100
 
-n_calib_min, n_calib_max = 10, 200
-n_calib_array = np.arange(n_calib_min, n_calib_max+1, 2)
-delta_array = [0.8]  # [0.6, 0.8]
+nc_min, nc_max = 10, 100
+n_calib_array = np.arange(nc_min, nc_max+1, 1)
+delta = 0.8
+delta_array = [delta]
 
 final_coverage_dict = {
     method: {delta: [] for delta in delta_array}
@@ -463,59 +447,52 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 
 
 # Theorical bounds and exact coverage to attempt
-def lower_bound(delta):
+def lower_bound_fct(delta):
     return delta * np.ones_like(n_calib_array)
 
 
-def upper_bound(delta):
-    return (delta + 1/(n_calib_array+1))
-
-
-def upper_bound_asym(delta):
-    return (delta + 1/(n_calib_array//2+1))
+def upper_bound_fct(delta):
+    return delta + 1/(n_calib_array)
 
 
-def lower_bound_cross(delta):
-    return 1 - 2*(1-delta) - np.sqrt(2/n_calib_array)
+def upper_bound_asym_fct(delta):
+    return delta + 1/(n_calib_array//2)
 
 
-def exact_coverage(delta):
-    return (np.ceil((n_calib_array+1)*delta))/(n_calib_array+1)
+def exact_coverage_fct(delta):
+    return np.ceil((n_calib_array+1)*delta)/(n_calib_array+1)
 
 
-# def lower_bound_cross(delta, K, n):
-#     bound = 1 - 2*(1-delta)
-#     margin = np.min([2*(1-1/K)/(n/K+1), (1-K/n)/(K+1)])
-#     return bound - margin
+def exact_coverage_asym_fct(delta):
+    new_n = n_calib_array//2
+    return np.ceil((new_n+1)*delta)/(new_n+1)
 
 
-n_strat = len(STRATEGIES)
-nrows, ncols = n_strat, len(delta_array)  # np.max([len(delta_array), 2])
+n_strat = len(final_coverage_dict)
+nrows, ncols = n_strat, 1
 
 fig, ax = plt.subplots(nrows=nrows, ncols=ncols)
 
-for i, method in enumerate(STRATEGIES):
-    for j, delta in enumerate(delta_array):
-
-        cov = final_coverage_dict[method][delta]
-        ub = upper_bound(delta)
-        lb = lower_bound(delta)
-        if 'asym' in method:
-            ub = upper_bound_asym(delta)
-        if 'cv' in method:
-            ub = np.ones_like(n_calib_array)
-            lb = lower_bound_cross(delta)
-
-        ub = np.clip(ub, a_min=0, a_max=1)
-        lb = np.clip(lb, a_min=0, a_max=1)
-
-        ax[i].plot(n_calib_array, cov, alpha=0.5, label=method, color='g')
-        ax[i].plot(n_calib_array, lb, color='k', label='Lower Bound')
-        ax[i].plot(n_calib_array, ub, color='b', label='Upper Bound')
-        ax[i].hlines(delta, n_calib_min, n_calib_max, color='r', ls='--',
-                     label='Target Coverage')
-
-        ax[i].legend(loc="lower right", ncol=2)
-        ax[i].set_ylim(np.min(lb) - 0.05, 1.0)
+for i, method in enumerate(final_coverage_dict):
+    # Compute the different bounds, target
+    cov = final_coverage_dict[method][delta]
+    ub = upper_bound_fct(delta)
+    lb = lower_bound_fct(delta)
+    exact_cov = exact_coverage_fct(delta)
+    if 'asym' in method:
+        ub = upper_bound_asym_fct(delta)
+        exact_cov = exact_coverage_asym_fct(delta)
+    ub = np.clip(ub, a_min=0, a_max=1)
+    lb = np.clip(lb, a_min=0, a_max=1)
+
+    # Plot the results
+    ax[i].plot(n_calib_array, cov, alpha=0.5, label=method, color='g')
+    ax[i].plot(n_calib_array, lb, color='k', label='Lower Bound')
+    ax[i].plot(n_calib_array, ub, color='b', label='Upper Bound')
+    ax[i].plot(n_calib_array, exact_cov, color='g', ls='--', label='Exact Cov')
+    ax[i].hlines(delta, nc_min, nc_max, color='r', ls='--', label='Target Cov')
+
+    ax[i].legend(loc="lower right", ncol=2)
+    ax[i].set_ylim(np.min(lb) - 0.05, 1.0)
 
 plt.show()

From 697732d2b614c87ae20756b848950f3b5380d7eb Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 17:41:32 +0200
Subject: [PATCH 119/424] chore: update sphinx dependency to version 5.3.0

---
 environment.doc.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.doc.yml b/environment.doc.yml
index f6a0e6ce9..1d8117360 100644
--- a/environment.doc.yml
+++ b/environment.doc.yml
@@ -8,7 +8,7 @@ dependencies:
     - pandas=1.3.5
     - python=3.10
     - scikit-learn
-    - sphinx=4.3.2
+    - sphinx=5.3.0
     - sphinx-gallery=0.10.1
     - sphinx_rtd_theme=1.0.0
     - typing_extensions=4.0.1

From fdbe2f7a4853ae596576417a78853bd629f6a390 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 18:04:28 +0200
Subject: [PATCH 120/424] chore: update python dependency to latest version

---
 .readthedocs.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/.readthedocs.yml b/.readthedocs.yml
index b7ba60457..20e25562d 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "mambaforge-22.9"
+    python: "mambaforge-latest"
 
 python:
   install:

From a496fab9b0084400e2080afb76d7c6de58cbc7f3 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 18:06:57 +0200
Subject: [PATCH 121/424] chore: update dependencies

---
 environment.doc.yml | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/environment.doc.yml b/environment.doc.yml
index 1d8117360..0e188d709 100644
--- a/environment.doc.yml
+++ b/environment.doc.yml
@@ -7,8 +7,8 @@ dependencies:
     - numpydoc=1.1.0
     - pandas=1.3.5
     - python=3.10
-    - scikit-learn
-    - sphinx=5.3.0
-    - sphinx-gallery=0.10.1
-    - sphinx_rtd_theme=1.0.0
+    - scikit-learn=1.5.0
+    - sphinx=7.1.2
+    - sphinx-gallery=0.16.0
+    - sphinx_rtd_theme=2.0.0
     - typing_extensions=4.0.1

From e37a38cc8b8ca580ce210e404aaff83f2c9a6cf5 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 18:09:25 +0200
Subject: [PATCH 122/424] chore: Fix sphinx dependencies

---
 HISTORY.rst | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index d5dabb822..e46391f1a 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,8 +2,13 @@
 History
 =======
 
-0.8.5 (2024-**-**)
+0.8.6 (2024-**-**)
 ------------------
+* Fix sphinx dependencies
+
+0.8.5 (2024-06-07)
+------------------
+* Issue with update from 0.8.4
 
 0.8.4 (2024-06-07)
 ------------------

From 99d5cf617fda6d874a7a7a87e325a496080ec2de Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 18:21:04 +0200
Subject: [PATCH 123/424] chore: Update sphinx dependency to version 5.3.0

---
 environment.doc.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.doc.yml b/environment.doc.yml
index 0e188d709..4fddbef3d 100644
--- a/environment.doc.yml
+++ b/environment.doc.yml
@@ -8,7 +8,7 @@ dependencies:
     - pandas=1.3.5
     - python=3.10
     - scikit-learn=1.5.0
-    - sphinx=7.1.2
+    - sphinx=5.3.0
     - sphinx-gallery=0.16.0
     - sphinx_rtd_theme=2.0.0
     - typing_extensions=4.0.1

From 5bd24567628e7f871fce25f4d4d44181d3062cca Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 18:21:19 +0200
Subject: [PATCH 124/424] chore: Update plot_gallery setting to use str value

---
 doc/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/conf.py b/doc/conf.py
index 0696d5d55..a65f05f37 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -74,7 +74,7 @@
 # source_encoding = "utf-8-sig"
 
 # Generate the plots for the gallery
-plot_gallery = True
+plot_gallery = "True"
 
 # The master toctree document.
 master_doc = "index"

From 76e851240126a467c50e122d481c2872a6c005e1 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 13 Jun 2024 18:30:02 +0200
Subject: [PATCH 125/424] UPD: faster code running + comments

---
 .../plot_coverage_validity.py                 | 96 ++++++++++++-------
 1 file changed, 63 insertions(+), 33 deletions(-)

diff --git a/examples/regression/2-advanced-analysis/plot_coverage_validity.py b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
index 2afb07dfd..f74dc1b1d 100644
--- a/examples/regression/2-advanced-analysis/plot_coverage_validity.py
+++ b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
@@ -43,7 +43,10 @@
 # Section 1: Comparison with the split conformalizer method (light version)
 # -------------------------------------------------------------------------
 #
-# TODO
+# We propose here to implement a lighter version of split CP by calculating
+# the quantile with a small correction according to [1].
+# We prepare the fit/calibration/test routine in order to calculate the average
+# coverage over several simulations.
 
 # Conformalizer Class
 class StandardConformalizer():
@@ -158,10 +161,11 @@ def cumulative_average(arr):
 
 
 ##############################################################################
-# Experiment 1.1: Coverage Validity for a given delta, n_calib
-# ------------------------------------------------------------
+# Experiment 1: Coverage Validity for a given delta, n_calib
+# ----------------------------------------------------------
 #
-# TODO
+# To begin, we propose to use ``delta=0.8`` and ``n_delta=6`` and compare
+# the coverage validity claim of the MAPIE class and the referenced class.
 
 # Parameters of the modelisation
 delta = 0.8
@@ -232,10 +236,19 @@ def cumulative_average(arr):
 
 
 ##############################################################################
-# Experiment 1.2: Again but without fixing random_state
-# -----------------------------------------------------
+# It can be seen that the two curves overlap, proving that both methods
+# produce the same results. Their effective coverage stabilizes between
+# the theoretical limits, always above the target coverage and converges
+# towards the exact coverage (i.e. expected according to the theory).
+
+
+##############################################################################
+# Experiment 2: Again but without fixing random_state
+# ---------------------------------------------------
 #
-# TODO
+# We just propose to reproduce the previous experiment without fixing the
+# random_state. The methods therefore follow different trajectories but
+# always achieve the expected coverage.
 
 # Run the experiment
 empirical_coverages_ref = []
@@ -279,10 +292,13 @@ def cumulative_average(arr):
 
 
 ##############################################################################
-# Section 2: Again but with different MAPIE CP methods
-# ----------------------------------------------------
+# Section 2: Comparison with different MAPIE CP methods
+# -----------------------------------------------------
 #
-# TODO
+# We propose to reproduce the previous experience with different methods of
+# the MAPIE package (prefit, prefit with asymmetrical non-conformity scores
+# and split).
+
 
 def get_coverage_split(conformalizer, data, target, delta, random_state=None):
     """
@@ -369,10 +385,13 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 
 
 ##############################################################################
-# Experiment 2: Again but with different MAPIE CP methods
+# Experiment 3: Again but with different MAPIE CP methods
 # -------------------------------------------------------
 #
-# TODO
+# The methods always follow different trajectories but always achieve the
+# expected coverage.
+# Since asymmetric scores can be used, the limits are not exactly the same.
+# We should calculate them differently but that doesn't change our conclusion.
 
 # Parameters of the modelisation
 delta = 0.8
@@ -411,15 +430,22 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 
 
 ##############################################################################
-# Experiment 3: Again but on different delta and n_calib
-# ------------------------------------------------------
+# Experiment 4: Extensive experimentation on different delta and n_calib
+# ----------------------------------------------------------------------
 #
-# TODO
-
-num_splits = 100
-
-nc_min, nc_max = 10, 100
-n_calib_array = np.arange(nc_min, nc_max+1, 1)
+# Here we propose to extend the experiment on different sizes of the
+# calibration dataset and target coverage.
+# We show the influence of size on effective coverage.
+# In particular, we see that the expected coverage fluctuates between the
+# limits with respect to the size of the calibration dataset but continues
+# to converge towards the target coverage.
+# It can be noted that all methods follow this trajectory and continue to
+# achieve coverage validity.
+
+num_splits = 500
+
+nc_min, nc_max = 10, 50
+n_calib_array = np.arange(nc_min, nc_max+1, 2)
 delta = 0.8
 delta_array = [delta]
 
@@ -432,18 +458,18 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
     for method in STRATEGIES
 }
 
-for delta in delta_array:
-    for method, params in STRATEGIES.items():
-        for n_calib in n_calib_array:
-            coverages_list = []
-            run_params = model, params, n_calib, data, target, delta
-            coverages_list = Parallel(n_jobs=-1)(
-                delayed(run_get_coverage_split)(*run_params)
-                for _ in range(num_splits)
-            )
-            coverages_list = np.array(coverages_list)
-            final_coverage = cumulative_average(coverages_list)[-1]
-            final_coverage_dict[method][delta].append(final_coverage)
+# Run experiment
+for method, params in STRATEGIES.items():
+    for n_calib in n_calib_array:
+        coverages_list = []
+        run_params = model, params, n_calib, data, target, delta
+        coverages_list = Parallel(n_jobs=-1)(
+            delayed(run_get_coverage_split)(*run_params)
+            for _ in range(num_splits)
+        )
+        coverages_list = np.array(coverages_list)
+        final_coverage = cumulative_average(coverages_list)[-1]
+        final_coverage_dict[method][delta].append(final_coverage)
 
 
 # Theorical bounds and exact coverage to attempt
@@ -468,6 +494,7 @@ def exact_coverage_asym_fct(delta):
     return np.ceil((new_n+1)*delta)/(new_n+1)
 
 
+# Plot the results
 n_strat = len(final_coverage_dict)
 nrows, ncols = n_strat, 1
 
@@ -492,7 +519,10 @@ def exact_coverage_asym_fct(delta):
     ax[i].plot(n_calib_array, exact_cov, color='g', ls='--', label='Exact Cov')
     ax[i].hlines(delta, nc_min, nc_max, color='r', ls='--', label='Target Cov')
 
-    ax[i].legend(loc="lower right", ncol=2)
+    ax[i].legend(loc="upper right", ncol=2)
     ax[i].set_ylim(np.min(lb) - 0.05, 1.0)
+    ax[i].set_xlabel(r'$n_{calib}$')
+    ax[i].set_ylabel(r'$\overline{\mathbb{C}}$')
 
+fig.suptitle(r'$\delta = $' + str(delta))
 plt.show()

From dc9a5ad41f3e7f494566e987dc0929e1b754dc15 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Thu, 13 Jun 2024 18:36:56 +0200
Subject: [PATCH 126/424] FIX: only update sphinx

---
 .readthedocs.yml    | 2 +-
 doc/conf.py         | 2 +-
 environment.doc.yml | 8 ++++----
 3 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/.readthedocs.yml b/.readthedocs.yml
index 20e25562d..b7ba60457 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -3,7 +3,7 @@ version: 2
 build:
   os: ubuntu-22.04
   tools:
-    python: "mambaforge-latest"
+    python: "mambaforge-22.9"
 
 python:
   install:
diff --git a/doc/conf.py b/doc/conf.py
index a65f05f37..0696d5d55 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -74,7 +74,7 @@
 # source_encoding = "utf-8-sig"
 
 # Generate the plots for the gallery
-plot_gallery = "True"
+plot_gallery = True
 
 # The master toctree document.
 master_doc = "index"
diff --git a/environment.doc.yml b/environment.doc.yml
index 4fddbef3d..7aea1de43 100644
--- a/environment.doc.yml
+++ b/environment.doc.yml
@@ -7,8 +7,8 @@ dependencies:
     - numpydoc=1.1.0
     - pandas=1.3.5
     - python=3.10
-    - scikit-learn=1.5.0
+    - scikit-learn
     - sphinx=5.3.0
-    - sphinx-gallery=0.16.0
-    - sphinx_rtd_theme=2.0.0
-    - typing_extensions=4.0.1
+    - sphinx-gallery=0.10.1
+    - sphinx_rtd_theme=1.0.0
+    - typing_extensions=4.0.1
\ No newline at end of file

From f8268cc7374d7dce264263488c4b0b59de3d46f4 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 13 Jun 2024 18:53:47 +0200
Subject: [PATCH 127/424] UPD: HISTORY.rst

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index e46391f1a..d47947826 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -4,6 +4,7 @@ History
 
 0.8.6 (2024-**-**)
 ------------------
+* Fix the quantile formula to ensure valid coverage (deal with infinite interval production).
 * Fix sphinx dependencies
 
 0.8.5 (2024-06-07)

From 3b095fef94b2a05c0457c988a866b085ba444cb1 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 14 Jun 2024 09:32:24 +0200
Subject: [PATCH 128/424] FIX: doc build too slow

---
 .../regression/2-advanced-analysis/plot_coverage_validity.py  | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/examples/regression/2-advanced-analysis/plot_coverage_validity.py b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
index f74dc1b1d..bfb4ef4ee 100644
--- a/examples/regression/2-advanced-analysis/plot_coverage_validity.py
+++ b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
@@ -442,9 +442,9 @@ def run_get_coverage_split(model, params, n_calib, data, target, delta):
 # It can be noted that all methods follow this trajectory and continue to
 # achieve coverage validity.
 
-num_splits = 500
+num_splits = 100
 
-nc_min, nc_max = 10, 50
+nc_min, nc_max = 10, 30
 n_calib_array = np.arange(nc_min, nc_max+1, 2)
 delta = 0.8
 delta_array = [delta]

From 5f514f0de36427dcb69ff787f0b0202f40e9816f Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 14 Jun 2024 13:46:57 +0200
Subject: [PATCH 129/424] =?UTF-8?q?Bump=20version:=200.8.5=20=E2=86=92=200?=
 =?UTF-8?q?.8.6?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 CITATION.cff      | 2 +-
 doc/conf.py       | 2 +-
 mapie/_version.py | 2 +-
 setup.py          | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 6feae5b2b..19a4fa709 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.5
+current_version = 0.8.6
 commit = True
 tag = True
 
diff --git a/CITATION.cff b/CITATION.cff
index 446b7334b..8c89d0e5c 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Thibault"
   orcid: "https://orcid.org/0000-0000-0000-0000"
 title: "MAPIE - Model Agnostic Prediction Interval Estimator"
-version: 0.8.5
+version: 0.8.6
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
diff --git a/doc/conf.py b/doc/conf.py
index 0696d5d55..f7f3c5e86 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -88,7 +88,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.8.5"
+version = "0.8.6"
 # The full version, including alpha/beta/rc tags.
 release = version
 
diff --git a/mapie/_version.py b/mapie/_version.py
index af46754d3..de77196f4 100644
--- a/mapie/_version.py
+++ b/mapie/_version.py
@@ -1 +1 @@
-__version__ = "0.8.5"
+__version__ = "0.8.6"
diff --git a/setup.py b/setup.py
index f226c50e7..4eb3bbb98 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "MAPIE"
-VERSION = "0.8.5"
+VERSION = "0.8.6"
 DESCRIPTION = (
     "A scikit-learn-compatible module "
     "for estimating prediction intervals."

From 9a39886bfaa2a0516d3822c8fead026051577f52 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 14 Jun 2024 13:56:54 +0200
Subject: [PATCH 130/424] UPD HISTORY.rst

---
 HISTORY.rst | 9 +++++++--
 1 file changed, 7 insertions(+), 2 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index d47947826..cc72860d4 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,13 +2,18 @@
 History
 =======
 
-0.8.6 (2024-**-**)
+0.8.x (2024-xx-xx)
 ------------------
-* Fix the quantile formula to ensure valid coverage (deal with infinite interval production).
+
+0.8.6 (2024-06-14)
+------------------
+
+* Fix the quantile formula to ensure valid coverage (deal with infinite interval production and asymmetric conformal scores).
 * Fix sphinx dependencies
 
 0.8.5 (2024-06-07)
 ------------------
+
 * Issue with update from 0.8.4
 
 0.8.4 (2024-06-07)

From 9ffb70b86abe5ef1df774fc450520434b4cc833d Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 17 Jun 2024 14:01:07 +0200
Subject: [PATCH 131/424] FIX: correction of the upper bound for the asymmetric
 score in exemple

---
 .../regression/2-advanced-analysis/plot_coverage_validity.py    | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/examples/regression/2-advanced-analysis/plot_coverage_validity.py b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
index bfb4ef4ee..75a868d35 100644
--- a/examples/regression/2-advanced-analysis/plot_coverage_validity.py
+++ b/examples/regression/2-advanced-analysis/plot_coverage_validity.py
@@ -490,7 +490,7 @@ def exact_coverage_fct(delta):
 
 
 def exact_coverage_asym_fct(delta):
-    new_n = n_calib_array//2
+    new_n = n_calib_array//2-1
     return np.ceil((new_n+1)*delta)/(new_n+1)
 
 

From 886a9671d7f79a8c43d67af7ace699499d8fce29 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 17 Jun 2024 15:58:27 +0200
Subject: [PATCH 132/424] Update : taking comments into account

---
 mapie/subsample.py            |  9 +--------
 mapie/tests/test_subsample.py | 18 ++++++++++++++----
 mapie/tests/test_utils.py     | 18 ++++++++++--------
 mapie/utils.py                | 33 ++++++++++++++++++++++-----------
 4 files changed, 47 insertions(+), 31 deletions(-)

diff --git a/mapie/subsample.py b/mapie/subsample.py
index 233149e9c..2bb3546aa 100644
--- a/mapie/subsample.py
+++ b/mapie/subsample.py
@@ -76,14 +76,7 @@ def split(
             The testing set indices for that split.
         """
         indices = np.arange(_num_samples(X))
-        if self.n_samples is None:
-            n_samples = len(indices)
-        else:
-            n_samples = check_n_samples(X, self.n_samples)
-        # elif isinstance(self.n_samples, float) and 0 < self.n_samples < 1:
-        #     n_samples = int(np.floor(self.n_samples * X.shape[0]))
-        # else:
-        #     n_samples = int(self.n_samples)
+        n_samples = check_n_samples(X, self.n_samples, indices)
         random_state = check_random_state(self.random_state)
         for k in range(self.n_resamplings):
             train_index = resample(
diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index 38714074d..6df35c4dc 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -1,7 +1,5 @@
 from __future__ import annotations
 
-from typing import Union
-
 import numpy as np
 import pytest
 
@@ -36,7 +34,7 @@ def test_split_SubSample() -> None:
 
 @pytest.mark.parametrize("n_samples", [4, 6, 8, 10])
 @pytest.mark.parametrize("n_resamplings", [1, 2, 3])
-def test_n_samples_int(n_samples: Union[int, float],
+def test_n_samples_int(n_samples: int,
                        n_resamplings: int) -> None:
     """Test outputs of subsamplings when n_samples is a int"""
     X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
@@ -50,7 +48,7 @@ def test_n_samples_int(n_samples: Union[int, float],
 
 @pytest.mark.parametrize("n_samples", [0.4, 0.6, 0.8, 0.9])
 @pytest.mark.parametrize("n_resamplings", [1, 2, 3])
-def test_n_samples_float(n_samples: Union[int, float],
+def test_n_samples_float(n_samples: float,
                          n_resamplings: int) -> None:
     """Test outputs of subsamplings when n_samples is a
     float between 0 and 1."""
@@ -66,6 +64,18 @@ def test_n_samples_float(n_samples: Union[int, float],
     )
 
 
+@pytest.mark.parametrize("n_resamplings", [1, 2, 3])
+def test_n_samples_none(n_resamplings: int) -> None:
+    """Test outputs of subsamplings when n_samples is None."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    cv = Subsample(n_resamplings=n_resamplings, random_state=0,
+                   replace=False)
+    train_set = np.concatenate([x[0] for x in cv.split(X)])
+    val_set = np.concatenate([x[1] for x in cv.split(X)])
+    assert len(train_set) == X.shape[0]*n_resamplings
+    assert len(val_set) == 0
+
+
 def test_default_parameters_BlockBootstrap() -> None:
     """Test default values of Subsample."""
     cv = BlockBootstrap()
diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index 49f01548e..f649b4fd5 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 import pytest
+import re
 from numpy.random import RandomState
 from sklearn.datasets import make_regression
 from sklearn.linear_model import LinearRegression
@@ -511,16 +512,17 @@ def test_check_no_agg_cv_value_error(cv: Any) -> None:
         check_no_agg_cv(X_toy, cv, array)
 
 
-@pytest.mark.parametrize("X", [1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-@pytest.mark.parametrize("n_samples", [1.2, 2.4, 3.6])
-def test_invalid_n_samples(X: NDArray,
-                           n_samples: Union[float, int]) -> None:
+@pytest.mark.parametrize("n_samples", [-5.5, -4, 0, 1.2])
+def test_invalid_n_samples(n_samples: Union[float, int]) -> None:
     """Test that invalid n_samples raise errors."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    indices = X.copy()
     with pytest.raises(
         ValueError,
-        match=(
-            r".*Invalid n_samples."
-            r"Allowed values are float between 0 and 1 or int*"
+        match=re.escape(
+            r"Invalid n_samples. Allowed values "
+            r"are float in the range (0.0, 1.0) or"
+            r" int in the range [1, inf)"
         )
     ):
-        check_n_samples(X=X, n_samples=n_samples)
+        check_n_samples(X=X, n_samples=n_samples, indices=indices)
diff --git a/mapie/utils.py b/mapie/utils.py
index ffeef6e6d..e4e1ed394 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1336,7 +1336,8 @@ def check_arrays_length(*arrays: NDArray) -> None:
 
 def check_n_samples(
     X: NDArray,
-    n_samples: Union[float, int]
+    n_samples: Optional[Union[float, int]],
+    indices: NDArray
 ) -> int:
     """
     Check alpha and prepare it as a ArrayLike.
@@ -1357,14 +1358,24 @@ def check_n_samples(
     Raises
     ------
     ValueError
-        If n_samples is not an int or a float between 0 and 1.
-    """
-    if isinstance(n_samples, float) and 0 < n_samples < 1:
-        n_samples = int(np.floor(n_samples * X.shape[0]))
-    elif isinstance(n_samples, float) and n_samples > 1:
+        If n_samples is not an int in the range [1, inf)
+        or a float int he range (0.0, 1.0)
+    """
+    if n_samples is None:
+        n_samples = len(indices)
+    elif isinstance(n_samples, float):
+        if 0 < n_samples < 1:
+            n_samples = int(np.floor(n_samples * X.shape[0]))
+        else:
+            raise ValueError(
+                "Invalid n_samples. Allowed values "
+                "are float in the range (0.0, 1.0) or"
+                " int in the range [1, inf)"
+                )
+    elif isinstance(n_samples, int) and n_samples <= 0:
         raise ValueError(
-            "Invalid n_samples.Allowed values are float between 0 and 1 or int"
-        )
-    else:
-        n_samples = int(n_samples)
-    return n_samples
+                "Invalid n_samples. Allowed values "
+                "are float in the range (0.0, 1.0) or"
+                " int in the range [1, inf)"
+            )
+    return int(n_samples)

From 99579307bc3b2d9aafdb2f5f30a6762447a671e0 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 18 Jun 2024 17:11:33 +0200
Subject: [PATCH 133/424] Update3 : taking comments into account

---
 mapie/tests/test_utils.py | 55 ++++++++++++++++++++++++++++++++++++---
 mapie/utils.py            |  8 +++++-
 2 files changed, 58 insertions(+), 5 deletions(-)

diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index f649b4fd5..746eb85a0 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -1,7 +1,6 @@
 from __future__ import annotations
 
-from typing import Any, Optional, Tuple, Union
-
+from typing import Any, Optional, Tuple
 import numpy as np
 import pytest
 import re
@@ -512,8 +511,56 @@ def test_check_no_agg_cv_value_error(cv: Any) -> None:
         check_no_agg_cv(X_toy, cv, array)
 
 
-@pytest.mark.parametrize("n_samples", [-5.5, -4, 0, 1.2])
-def test_invalid_n_samples(n_samples: Union[float, int]) -> None:
+@pytest.mark.parametrize("n_samples", [-4, -2, -1])
+def test_invalid_n_samples_int_negative(n_samples: int) -> None:
+    """Test that invalid n_samples raise errors."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    indices = X.copy()
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            r"Invalid n_samples. Allowed values "
+            r"are float in the range (0.0, 1.0) or"
+            r" int in the range [1, inf)"
+        )
+    ):
+        check_n_samples(X=X, n_samples=n_samples, indices=indices)
+
+
+@pytest.mark.parametrize("n_samples", [0.002, 0.003, 0.04])
+def test_invalid_n_samples_int_zero(n_samples: int) -> None:
+    """Test that invalid n_samples raise errors."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    indices = X.copy()
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            r"The value of n_samples is too small. "
+            r"You need to increase it so that n_samples*X.shape[0] > 1"
+            r"otherwise n_samples should be an int"
+        )
+    ):
+        check_n_samples(X=X, n_samples=n_samples, indices=indices)
+
+
+@pytest.mark.parametrize("n_samples", [-5.5, -4.3, -0.2])
+def test_invalid_n_samples_float_negative(n_samples: float) -> None:
+    """Test that invalid n_samples raise errors."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    indices = X.copy()
+    with pytest.raises(
+        ValueError,
+        match=re.escape(
+            r"Invalid n_samples. Allowed values "
+            r"are float in the range (0.0, 1.0) or"
+            r" int in the range [1, inf)"
+        )
+    ):
+        check_n_samples(X=X, n_samples=n_samples, indices=indices)
+
+
+@pytest.mark.parametrize("n_samples", [1.2, 2.5, 3.4])
+def test_invalid_n_samples_float_greater_than_1(n_samples: float) -> None:
     """Test that invalid n_samples raise errors."""
     X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
     indices = X.copy()
diff --git a/mapie/utils.py b/mapie/utils.py
index 7deb9d103..4852ae567 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1382,13 +1382,19 @@ def check_n_samples(
     ------
     ValueError
         If n_samples is not an int in the range [1, inf)
-        or a float int he range (0.0, 1.0)
+        or a float in the range (0.0, 1.0)
     """
     if n_samples is None:
         n_samples = len(indices)
     elif isinstance(n_samples, float):
         if 0 < n_samples < 1:
             n_samples = int(np.floor(n_samples * X.shape[0]))
+            if n_samples == 0:
+                raise ValueError(
+                    "The value of n_samples is too small. "
+                    "You need to increase it so that n_samples*X.shape[0] > 1"
+                    "otherwise n_samples should be an int"
+                    )
         else:
             raise ValueError(
                 "Invalid n_samples. Allowed values "

From 7c6621f5d32fd2affe0da6c6a0a772354f526438 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Wed, 19 Jun 2024 17:56:03 +0200
Subject: [PATCH 134/424] Update mapie/subsample.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/subsample.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/subsample.py b/mapie/subsample.py
index 2bb3546aa..ed3c3ba4e 100644
--- a/mapie/subsample.py
+++ b/mapie/subsample.py
@@ -23,7 +23,7 @@ class Subsample(BaseCrossValidator):
     ----------
     n_resamplings : int
         Number of resamplings. By default ``30``.
-    n_samples: float
+    n_samples: Union[int, float]
         Number of samples in each resampling. By default ``None``,
         the size of the training set. If it is between 0 and 1,
         it becomes the fraction of samples

From 5fca040d4262243c8d324cc322ef60723a2101ca Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 19 Jun 2024 19:05:03 +0200
Subject: [PATCH 135/424] Update4 : taking comments into account

---
 mapie/tests/test_utils.py | 32 ++++++++------------------------
 1 file changed, 8 insertions(+), 24 deletions(-)

diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index 746eb85a0..d4ea8df2f 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -1,9 +1,10 @@
 from __future__ import annotations
 
+import re
 from typing import Any, Optional, Tuple
+
 import numpy as np
 import pytest
-import re
 from numpy.random import RandomState
 from sklearn.datasets import make_regression
 from sklearn.linear_model import LinearRegression
@@ -17,11 +18,10 @@
                          check_array_inf, check_array_nan, check_arrays_length,
                          check_binary_zero_one, check_cv, check_gamma,
                          check_lower_upper_bounds, check_n_features_in,
-                         check_n_jobs, check_no_agg_cv, check_n_samples,
-                         check_null_weight,
-                         check_number_bins, check_split_strategy,
-                         check_verbose, compute_quantiles, fit_estimator,
-                         get_binning_groups)
+                         check_n_jobs, check_n_samples, check_no_agg_cv,
+                         check_null_weight, check_number_bins,
+                         check_split_strategy, check_verbose,
+                         compute_quantiles, fit_estimator, get_binning_groups)
 
 X_toy = np.array([0, 1, 2, 3, 4, 5]).reshape(-1, 1)
 y_toy = np.array([5, 7, 9, 11, 13, 15])
@@ -543,24 +543,8 @@ def test_invalid_n_samples_int_zero(n_samples: int) -> None:
         check_n_samples(X=X, n_samples=n_samples, indices=indices)
 
 
-@pytest.mark.parametrize("n_samples", [-5.5, -4.3, -0.2])
-def test_invalid_n_samples_float_negative(n_samples: float) -> None:
-    """Test that invalid n_samples raise errors."""
-    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
-    indices = X.copy()
-    with pytest.raises(
-        ValueError,
-        match=re.escape(
-            r"Invalid n_samples. Allowed values "
-            r"are float in the range (0.0, 1.0) or"
-            r" int in the range [1, inf)"
-        )
-    ):
-        check_n_samples(X=X, n_samples=n_samples, indices=indices)
-
-
-@pytest.mark.parametrize("n_samples", [1.2, 2.5, 3.4])
-def test_invalid_n_samples_float_greater_than_1(n_samples: float) -> None:
+@pytest.mark.parametrize("n_samples", [-5.5, -4.3, -0.2, 1.2, 2.5, 3.4])
+def test_invalid_n_samples_float(n_samples: float) -> None:
     """Test that invalid n_samples raise errors."""
     X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
     indices = X.copy()

From f199e950d67970bbf8997e08daf5f5f5ec22c9f2 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 20 Jun 2024 10:17:01 +0200
Subject: [PATCH 136/424] UPD: remove indent

---
 mapie/utils.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mapie/utils.py b/mapie/utils.py
index 4852ae567..068f0806e 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1403,8 +1403,8 @@ def check_n_samples(
                 )
     elif isinstance(n_samples, int) and n_samples <= 0:
         raise ValueError(
-                "Invalid n_samples. Allowed values "
-                "are float in the range (0.0, 1.0) or"
-                " int in the range [1, inf)"
-            )
+             "Invalid n_samples. Allowed values "
+             "are float in the range (0.0, 1.0) or"
+             " int in the range [1, inf)"
+             )
     return int(n_samples)

From 0085eac17f8ed69b9a58a478fc5359bb59d5eeaf Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Thu, 20 Jun 2024 10:17:35 +0200
Subject: [PATCH 137/424] UPD: HISTORY.rst

---
 HISTORY.rst | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index fc8f6c5af..31da81500 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,12 +5,13 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
+
 0.8.6 (2024-06-14)
 ------------------
 
 * Fix the quantile formula to ensure valid coverage (deal with infinite interval production and asymmetric conformal scores).
 * Fix sphinx dependencies
-* Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
 
 0.8.5 (2024-06-07)
 ------------------

From bc970eca49a0fd7919254d022f1cfec811c22205 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 20 Jun 2024 18:11:08 +0200
Subject: [PATCH 138/424] Fix Issue 290

---
 mapie/tests/test_subsample.py | 38 +++++++++++++++++++++++++++++++++++
 1 file changed, 38 insertions(+)

diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index 6df35c4dc..be609e722 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -1,5 +1,7 @@
 from __future__ import annotations
 
+from typing import Union
+
 import numpy as np
 import pytest
 
@@ -76,6 +78,24 @@ def test_n_samples_none(n_resamplings: int) -> None:
     assert len(val_set) == 0
 
 
+@pytest.mark.parametrize("n_samples", [0.4, 0.6, 3, 6])
+@pytest.mark.parametrize("n_resamplings", [2, 3, 4])
+def test_split_samples_Subsample(n_resamplings: int,
+                                 n_samples: Union[int, float]) -> None:
+    """Test that outputs of subsamplings are all different."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    cv = Subsample(n_resamplings=n_resamplings,
+                   n_samples=n_samples, replace=False, random_state=0)
+    trains = [x[0] for x in cv.split(X)]
+    tests = [x[1] for x in cv.split(X)]
+    for i in range(n_resamplings):
+        for j in range(i + 1, n_resamplings):
+            with np.testing.assert_raises(AssertionError):
+                np.testing.assert_equal(trains[i], trains[j])
+            with np.testing.assert_raises(AssertionError):
+                np.testing.assert_equal(tests[i], tests[j])
+
+
 def test_default_parameters_BlockBootstrap() -> None:
     """Test default values of Subsample."""
     cv = BlockBootstrap()
@@ -131,3 +151,21 @@ def test_split_BlockBootstrap_error() -> None:
     cv = BlockBootstrap()
     with pytest.raises(ValueError, match=r".*Exactly one argument*"):
         next(cv.split(X))
+
+
+@pytest.mark.parametrize("length", [2, 3, 4])
+@pytest.mark.parametrize("n_resamplings", [2, 3, 4])
+def test_split_samples_BlockBootstrap(n_resamplings: int,
+                                      length: int) -> None:
+    """Test that outputs of subsamplings are all different."""
+    X = np.arange(31)
+    cv = BlockBootstrap(n_resamplings=n_resamplings,
+                        length=length, random_state=0)
+    trains = [x[0] for x in cv.split(X)]
+    tests = [x[1] for x in cv.split(X)]
+    for i in range(n_resamplings):
+        for j in range(i + 1, n_resamplings):
+            with np.testing.assert_raises(AssertionError):
+                np.testing.assert_equal(trains[i], trains[j])
+            with np.testing.assert_raises(AssertionError):
+                np.testing.assert_equal(tests[i], tests[j])

From c332d276846575be007bc916887ec19104d7b4c5 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Mon, 24 Jun 2024 17:23:36 +0200
Subject: [PATCH 139/424] FIX: remove output expression

---
 mapie/classification.py | 11 +----------
 1 file changed, 1 insertion(+), 10 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index fc539dc7f..55b321f58 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -914,16 +914,7 @@ def _check_fit_parameter(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
-    ) -> Tuple[
-        Optional[ClassifierMixin],
-        Optional[Union[int, str, BaseCrossValidator]],
-        ArrayLike,
-        NDArray,
-        NDArray,
-        Optional[NDArray],
-        Optional[NDArray],
-        ArrayLike
-    ]:
+    ):
         """
         Perform several checks on class parameters.
 

From 2be9cc44f0c4dd55e4af658575c472889c7b95cf Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 24 Jun 2024 18:12:24 +0200
Subject: [PATCH 140/424] Fix Issue 290 - Part2

---
 mapie/tests/test_subsample.py | 62 +++++++++++++++++++++++++++++++----
 1 file changed, 56 insertions(+), 6 deletions(-)

diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index be609e722..9c356f4c7 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+from itertools import combinations, product
 from typing import Union
 
 import numpy as np
@@ -88,12 +89,33 @@ def test_split_samples_Subsample(n_resamplings: int,
                    n_samples=n_samples, replace=False, random_state=0)
     trains = [x[0] for x in cv.split(X)]
     tests = [x[1] for x in cv.split(X)]
-    for i in range(n_resamplings):
-        for j in range(i + 1, n_resamplings):
-            with np.testing.assert_raises(AssertionError):
-                np.testing.assert_equal(trains[i], trains[j])
-            with np.testing.assert_raises(AssertionError):
-                np.testing.assert_equal(tests[i], tests[j])
+    for (train1, train2), (test1, test2) in product(
+            combinations(trains, 2), combinations(tests, 2)):
+        with np.testing.assert_raises(AssertionError):
+            np.testing.assert_equal(train1, train2)
+        with np.testing.assert_raises(AssertionError):
+            np.testing.assert_equal(test1, test2)
+
+
+@pytest.mark.parametrize("n_samples", [0.4, 0.6, 3, 6])
+@pytest.mark.parametrize("n_resamplings", [2, 3, 4])
+def test_reproductibility_samples_Subsample(
+        n_resamplings: int,
+        n_samples: Union[int, float]
+) -> None:
+    """This test ensures that each split between
+    two instances is the same for a given seed."""
+    X = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
+    cv1 = Subsample(n_resamplings=n_resamplings,
+                    n_samples=n_samples, replace=False, random_state=0)
+    trains1 = [x[0] for x in cv1.split(X)]
+    tests1 = [x[1] for x in cv1.split(X)]
+    cv2 = Subsample(n_resamplings=n_resamplings,
+                    n_samples=n_samples, replace=False, random_state=0)
+    trains2 = [x[0] for x in cv2.split(X)]
+    tests2 = [x[1] for x in cv2.split(X)]
+    assert np.array_equal(trains1, trains2)
+    assert np.array_equal(tests1, tests2)
 
 
 def test_default_parameters_BlockBootstrap() -> None:
@@ -169,3 +191,31 @@ def test_split_samples_BlockBootstrap(n_resamplings: int,
                 np.testing.assert_equal(trains[i], trains[j])
             with np.testing.assert_raises(AssertionError):
                 np.testing.assert_equal(tests[i], tests[j])
+
+
+@pytest.mark.parametrize("length", [2, 3, 4])
+@pytest.mark.parametrize("n_resamplings", [2, 3, 4])
+def test_reproductibility_samples_BlockBootstrap(
+        n_resamplings: int,
+        length: int) -> None:
+    """This test ensures that each split between
+    two instances is the same for a given seed."""
+    X = np.arange(15)
+    cv1 = BlockBootstrap(
+        n_resamplings=n_resamplings,
+        length=length,
+        random_state=42
+    )
+    trains1 = [x[0] for x in list(cv1.split(X))]
+    tests1 = [x[1] for x in list(cv1.split(X))]
+    cv2 = BlockBootstrap(
+        n_resamplings=n_resamplings,
+        length=length,
+        random_state=42
+    )
+    trains2 = [x[0] for x in list(cv2.split(X))]
+    tests2 = [x[1] for x in list(cv2.split(X))]
+    tests1_set = {tuple(sorted(arr)) for arr in tests1}
+    tests2_set = {tuple(sorted(arr)) for arr in tests2}
+    assert np.array_equal(trains1, trains2)
+    assert np.array_equal(np.array(tests1_set), np.array(tests2_set))

From adf4f40e0effca5cf22f8ba51b9610ec8ba4842a Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 24 Jun 2024 18:19:45 +0200
Subject: [PATCH 141/424] Fix issue 369

---
 doc/theoretical_description_metrics.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_metrics.rst b/doc/theoretical_description_metrics.rst
index 398fdd7bb..6ac886886 100644
--- a/doc/theoretical_description_metrics.rst
+++ b/doc/theoretical_description_metrics.rst
@@ -195,7 +195,7 @@ and their corresponding labels :math:`y_i` and to compare its properties to that
 cumulative differences on sorted scores: 
 
 .. math::
-    C_k = \frac{1}{N}\sum_{i=1}^k (s_i - y_i)
+    C_k = \frac{1}{N}\sum_{i=1}^k (y_i - s_i)
 
 We also introduce a typical normalization scale :math:`\sigma`:
 

From c38db9a50423bcbd2844b1e61d575ece790603a7 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 25 Jun 2024 09:59:34 +0200
Subject: [PATCH 142/424] Add History

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 31da81500..9385496f0 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Changed the sign of C_k in the `Kolmogorov-Smirnov` test documentation to resolve issue 369.
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
 
 0.8.6 (2024-06-14)

From 2824153a643804d92c0c5c8ed7bf827208e3cb18 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 25 Jun 2024 11:56:08 +0200
Subject: [PATCH 143/424] Fix : use np.testing_assert_equal in unit tests

---
 HISTORY.rst                   |  1 +
 mapie/tests/test_subsample.py | 10 ++++------
 2 files changed, 5 insertions(+), 6 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 31da81500..33c912517 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Building unit tests for different `Subsample` and `BlockBooststrap` instances
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
 
 0.8.6 (2024-06-14)
diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index 9c356f4c7..82b67b70e 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -114,8 +114,8 @@ def test_reproductibility_samples_Subsample(
                     n_samples=n_samples, replace=False, random_state=0)
     trains2 = [x[0] for x in cv2.split(X)]
     tests2 = [x[1] for x in cv2.split(X)]
-    assert np.array_equal(trains1, trains2)
-    assert np.array_equal(tests1, tests2)
+    np.testing.assert_array_equal(trains1, trains2)
+    np.testing.assert_array_equal(tests1, tests2)
 
 
 def test_default_parameters_BlockBootstrap() -> None:
@@ -215,7 +215,5 @@ def test_reproductibility_samples_BlockBootstrap(
     )
     trains2 = [x[0] for x in list(cv2.split(X))]
     tests2 = [x[1] for x in list(cv2.split(X))]
-    tests1_set = {tuple(sorted(arr)) for arr in tests1}
-    tests2_set = {tuple(sorted(arr)) for arr in tests2}
-    assert np.array_equal(trains1, trains2)
-    assert np.array_equal(np.array(tests1_set), np.array(tests2_set))
+    np.testing.assert_array_equal(trains1, trains2)
+    np.testing.assert_equal(tests1, tests2)

From f1f0f143fedde03d8c2509a5eb78787d3587c638 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 25 Jun 2024 11:57:44 +0200
Subject: [PATCH 144/424] Fix typo

---
 HISTORY.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 9385496f0..9178a5592 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,7 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
-* Changed the sign of C_k in the `Kolmogorov-Smirnov` test documentation to resolve issue 369.
+* Change the sign of C_k in the `Kolmogorov-Smirnov` test documentation
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
 
 0.8.6 (2024-06-14)

From 09628f3e4f12bedd68ee160adabb97eb2b43d206 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 15:07:28 +0200
Subject: [PATCH 145/424] FIX: clean code to lint and type-check errors

---
 mapie/classification.py                       | 54 +++++-----
 mapie/conformity_scores/conformity_scores.py  |  4 +-
 ...fication_conformity_scores.py => utils.py} |  0
 mapie/estimator/__init__.py                   |  9 ++
 mapie/estimator/classification/__init__.py    |  0
 mapie/estimator/classification/interface.py   | 92 -----------------
 .../estimator.py => classifier.py}            | 29 +++---
 mapie/estimator/interface.py                  | 39 ++++++++
 mapie/estimator/regression/__init__.py        |  0
 mapie/estimator/regression/interface.py       | 99 -------------------
 .../{regression/estimator.py => regressor.py} |  5 +-
 mapie/regression/regression.py                |  2 +-
 mapie/tests/test_regression.py                |  2 +-
 13 files changed, 104 insertions(+), 231 deletions(-)
 rename mapie/conformity_scores/{utils_classification_conformity_scores.py => utils.py} (100%)
 delete mode 100644 mapie/estimator/classification/__init__.py
 delete mode 100644 mapie/estimator/classification/interface.py
 rename mapie/estimator/{classification/estimator.py => classifier.py} (96%)
 create mode 100644 mapie/estimator/interface.py
 delete mode 100644 mapie/estimator/regression/__init__.py
 delete mode 100644 mapie/estimator/regression/interface.py
 rename mapie/estimator/{regression/estimator.py => regressor.py} (99%)

diff --git a/mapie/classification.py b/mapie/classification.py
index 55b321f58..684313556 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -13,15 +13,15 @@
 from sklearn.utils.validation import (_check_y, _num_samples, check_is_fitted,
                                       indexable)
 
-from ._machine_precision import EPSILON
-from ._typing import ArrayLike, NDArray
-from .estimator.classification.estimator import EnsembleClassifier
-from .metrics import classification_mean_width_score
-from .utils import (check_alpha, check_alpha_and_n_samples, check_cv,
-                    check_estimator_classification, check_n_features_in,
-                    check_n_jobs, check_null_weight, check_verbose,
-                    compute_quantiles)
-from .conformity_scores.utils_classification_conformity_scores import (
+from mapie._machine_precision import EPSILON
+from mapie._typing import ArrayLike, NDArray
+from mapie.estimator import EnsembleClassifier
+from mapie.metrics import classification_mean_width_score
+from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
+                         check_estimator_classification, check_n_features_in,
+                         check_n_jobs, check_null_weight, check_verbose,
+                         compute_quantiles)
+from mapie.conformity_scores.utils import (
     get_true_label_position
 )
 
@@ -988,9 +988,9 @@ def _split_data(
         sample_weight,
         groups,
         size_raps
-    ) -> Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike, NDArray, ArrayLike]:
-
+    ):
         """Split data for raps method
+
         Parameters
         ----------
         X: ArrayLike
@@ -1013,15 +1013,15 @@ def _split_data(
 
         Returns
         -------
-        Tuple[ArrayLike, ArrayLike, ArrayLike, NDArray, Optional[NDArray],
-        Optional[ArrayLike]]
+        Tuple[NDArray, NDArray, NDArray, NDArray, Optional[NDArray],
+        Optional[NDArray]]
 
-            - ArrayLike of shape (n_samples, n_features)
-            - ArrayLike of shape (n_samples,)
-            - ArrayLike of shape (n_samples,)
-            - ArrayLike of shape (n_samples,)
+            - NDArray of shape (n_samples, n_features)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
-            - ArrayLike of shape (n_samples,)
         """
         raps_split = ShuffleSplit(
             1, test_size=size_raps, random_state=self.random_state
@@ -1096,15 +1096,25 @@ def fit(
             The model itself.
         """
         # Checks
-        (estimator, cv, X, y, y_enc, sample_weight, groups, n_samples) = (
-            self._check_fit_parameter(X, y, sample_weight, groups)
-        )
+        (estimator,
+         cv,
+         X,
+         y,
+         y_enc,
+         sample_weight,
+         groups,
+         n_samples) = self._check_fit_parameter(X, y, sample_weight, groups)
 
         if self.method == "raps":
             (X, y_enc, y, n_samples, sample_weight, groups) = self._split_data(
                 X, y_enc, sample_weight, groups, size_raps
             )
 
+        # Cast
+        X, y_enc, y = cast(NDArray, X), cast(NDArray, y_enc), cast(NDArray, y)
+        sample_weight = cast(NDArray, sample_weight)
+        groups = cast(NDArray, groups)
+
         # Work
         self.estimator_ = EnsembleClassifier(
             estimator,
@@ -1117,7 +1127,7 @@ def fit(
         )
 
         self.estimator_ = self.estimator_.fit(
-            X, y, y_enc, sample_weight, groups,
+            X, y, y_enc=y_enc, sample_weight=sample_weight, groups=groups,
             **fit_params
         )
 
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 872172df2..8030a68ee 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -5,7 +5,7 @@
 
 from mapie._compatibility import np_nanquantile
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator.regression.interface import EnsembleEstimator
+from mapie.estimator import EnsembleRegressor
 
 
 class ConformityScore(metaclass=ABCMeta):
@@ -317,7 +317,7 @@ def _beta_optimize(
     def get_bounds(
         self,
         X: ArrayLike,
-        estimator: EnsembleEstimator,
+        estimator: EnsembleRegressor,
         conformity_scores: NDArray,
         alpha_np: NDArray,
         ensemble: bool = False,
diff --git a/mapie/conformity_scores/utils_classification_conformity_scores.py b/mapie/conformity_scores/utils.py
similarity index 100%
rename from mapie/conformity_scores/utils_classification_conformity_scores.py
rename to mapie/conformity_scores/utils.py
diff --git a/mapie/estimator/__init__.py b/mapie/estimator/__init__.py
index e69de29bb..5758db9e6 100644
--- a/mapie/estimator/__init__.py
+++ b/mapie/estimator/__init__.py
@@ -0,0 +1,9 @@
+from .interface import EnsembleEstimator
+from .regressor import EnsembleRegressor
+from .classifier import EnsembleClassifier
+
+__all__ = [
+    "EnsembleEstimator",
+    "EnsembleRegressor",
+    "EnsembleClassifier",
+]
diff --git a/mapie/estimator/classification/__init__.py b/mapie/estimator/classification/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/mapie/estimator/classification/interface.py b/mapie/estimator/classification/interface.py
deleted file mode 100644
index 5fe13e67d..000000000
--- a/mapie/estimator/classification/interface.py
+++ /dev/null
@@ -1,92 +0,0 @@
-from __future__ import annotations
-
-from abc import ABCMeta, abstractmethod
-from typing import Any, Optional
-
-from sklearn.base import ClassifierMixin
-
-from mapie._typing import ArrayLike, NDArray
-
-
-class EnsembleEstimator(ClassifierMixin, metaclass=ABCMeta):
-    """
-    This class implements methods to handle the training and usage of the
-    estimator. This estimator can be unique or composed by cross validated
-    estimators.
-    """
-
-    @abstractmethod
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_enc: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None,
-        **fit_params
-    ) -> EnsembleEstimator:
-        """
-        Fit the base estimator under the ``single_estimator_`` attribute.
-        Fit all cross-validated estimator clones
-        and rearrange them into a list, the ``estimators_`` attribute.
-        Out-of-fold conformity scores are stored under
-        the ``conformity_scores_`` attribute.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        y_enc: ArrayLike
-            Target values as normalized encodings.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-            By default ``None``.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        EnsembleClassifier
-            The estimator fitted.
-        """
-
-    @abstractmethod
-    def predict(
-        self,
-        X: ArrayLike,
-        alpha_np: ArrayLike = [],
-        agg_scores: Any = None
-    ) -> NDArray:
-        """
-        Predict target from X. It also computes the prediction per train sample
-        for each test sample according to ``self.method``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Test data.
-
-        alpha_np: ArrayLike of shape (n_alphas)
-            Level of confidences.
-
-        agg_scores: Optional[str]
-            How to aggregate the scores output by the estimators on test data
-            if a cross-validation strategy is used
-
-        Returns
-        -------
-        NDArray
-            Predictions of shape
-            (n_samples, n_classes)
-        """
diff --git a/mapie/estimator/classification/estimator.py b/mapie/estimator/classifier.py
similarity index 96%
rename from mapie/estimator/classification/estimator.py
rename to mapie/estimator/classifier.py
index cb14d68a8..b76afcd69 100644
--- a/mapie/estimator/classification/estimator.py
+++ b/mapie/estimator/classifier.py
@@ -10,7 +10,7 @@
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator.classification.interface import EnsembleEstimator
+from mapie.estimator.interface import EnsembleEstimator
 from mapie.utils import (
     check_no_agg_cv,
     fit_estimator,
@@ -294,7 +294,7 @@ def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
-        y_enc: ArrayLike,
+        y_enc: Optional[ArrayLike] = None,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
         **fit_params,
@@ -341,6 +341,9 @@ def fit(
         self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
         estimator = self.estimator
         n_samples = _num_samples(y)
+        if y_enc is None:
+            raise ValueError
+        y_enc = cast(NDArray, y_enc)
 
         # Computation
         if cv == "prefit":
@@ -378,25 +381,26 @@ def fit(
 
     def predict_proba_calib(
         self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_enc: ArrayLike,
-        groups: Optional[ArrayLike] = None,
-    ) -> Tuple[NDArray, ArrayLike, ArrayLike]:
+        X: NDArray,
+        y: NDArray,
+        y_enc: NDArray,
+        groups: Optional[NDArray] = None,
+        **predict_params
+    ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Perform predictions on X : the calibration set.
 
         Parameters
         ----------
-        X: ArrayLike of shape (n_samples_test, n_features)
+        X: NDArray of shape (n_samples_test, n_features)
             Input data
 
-        y: Optional[ArrayLike] of shape (n_samples_test,)
+        y: Optional[NDArray] of shape (n_samples_test,)
             Input labels.
 
             By default ``None``.
 
-        groups: Optional[ArrayLike] of shape (n_samples_test,)
+        groups: Optional[NDArray] of shape (n_samples_test,)
             Group labels for the samples used while splitting the dataset into
             train/test set.
 
@@ -439,7 +443,7 @@ def predict_proba_calib(
                 # are not used during calibration
                 self.k_ = self.k_[val_indices]
                 y_pred_proba = y_pred_proba[val_indices]
-                # y_enc = y_enc[val_indices]
+                y_enc = y_enc[val_indices]
                 y = cast(NDArray, y)[val_indices]
 
         return y_pred_proba, y, y_enc
@@ -448,7 +452,8 @@ def predict(
         self,
         X: ArrayLike,
         alpha_np: ArrayLike = [],
-        agg_scores: Any = None
+        agg_scores: Any = None,
+        **predict_params
     ) -> NDArray:
         """
         Predict target from X. It also computes the prediction per train sample
diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
new file mode 100644
index 000000000..f84367a27
--- /dev/null
+++ b/mapie/estimator/interface.py
@@ -0,0 +1,39 @@
+from __future__ import annotations
+
+from abc import ABCMeta, abstractmethod
+
+from mapie._typing import ArrayLike
+
+
+class EnsembleEstimator(metaclass=ABCMeta):
+    """
+    This class implements methods to handle the training and usage of the
+    estimator. This estimator can be unique or composed by cross validated
+    estimators.
+    """
+
+    @abstractmethod
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        **kwargs
+    ) -> EnsembleEstimator:
+        """
+        Fit the base estimator under the ``single_estimator_`` attribute.
+        Fit all cross-validated estimator clones
+        and rearrange them into a list, the ``estimators_`` attribute.
+        Out-of-fold conformity scores are stored under
+        the ``conformity_scores_`` attribute.
+        """
+
+    @abstractmethod
+    def predict(
+        self,
+        X: ArrayLike,
+        **kwargs
+    ):
+        """
+        Predict target from X. It also computes the prediction per train sample
+        for each test sample according to ``self.method``.
+        """
diff --git a/mapie/estimator/regression/__init__.py b/mapie/estimator/regression/__init__.py
deleted file mode 100644
index e69de29bb..000000000
diff --git a/mapie/estimator/regression/interface.py b/mapie/estimator/regression/interface.py
deleted file mode 100644
index 3e76377f1..000000000
--- a/mapie/estimator/regression/interface.py
+++ /dev/null
@@ -1,99 +0,0 @@
-from __future__ import annotations
-
-from abc import ABCMeta, abstractmethod
-from typing import Optional, Tuple, Union
-
-from sklearn.base import RegressorMixin
-
-from mapie._typing import ArrayLike, NDArray
-
-
-class EnsembleEstimator(RegressorMixin, metaclass=ABCMeta):
-    """
-    This class implements methods to handle the training and usage of the
-    estimator. This estimator can be unique or composed by cross validated
-    estimators.
-    """
-
-    @abstractmethod
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None,
-        **fit_params
-    ) -> EnsembleEstimator:
-        """
-        Fit the base estimator under the ``single_estimator_`` attribute.
-        Fit all cross-validated estimator clones
-        and rearrange them into a list, the ``estimators_`` attribute.
-        Out-of-fold conformity scores are stored under
-        the ``conformity_scores_`` attribute.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Input data.
-
-        y: ArrayLike of shape (n_samples,)
-            Input labels.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Sample weights. If None, then samples are equally weighted.
-            By default ``None``.
-
-        groups: Optional[ArrayLike] of shape (n_samples,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-            By default ``None``.
-
-        **fit_params : dict
-            Additional fit parameters.
-
-        Returns
-        -------
-        EnsembleRegressor
-            The estimator fitted.
-        """
-
-    @abstractmethod
-    def predict(
-        self,
-        X: ArrayLike,
-        ensemble: bool = False,
-        return_multi_pred: bool = True
-    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
-        """
-        Predict target from X. It also computes the prediction per train sample
-        for each test sample according to ``self.method``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Test data.
-
-        ensemble: bool
-            Boolean determining whether the predictions are ensembled or not.
-            If ``False``, predictions are those of the model trained on the
-            whole training set.
-            If ``True``, predictions from perturbed models are aggregated by
-            the aggregation function specified in the ``agg_function``
-            attribute.
-
-            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
-
-            By default ``False``.
-
-        return_multi_pred: bool
-            If ``True`` the method returns the predictions and the multiple
-            predictions (3 arrays). If ``False`` the method return the
-            simple predictions only.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray, NDArray]
-            - Predictions
-            - The multiple predictions for the lower bound of the intervals.
-            - The multiple predictions for the upper bound of the intervals.
-        """
diff --git a/mapie/estimator/regression/estimator.py b/mapie/estimator/regressor.py
similarity index 99%
rename from mapie/estimator/regression/estimator.py
rename to mapie/estimator/regressor.py
index c0544b03d..f8bf7bc85 100644
--- a/mapie/estimator/regression/estimator.py
+++ b/mapie/estimator/regressor.py
@@ -11,7 +11,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.aggregation_functions import aggregate_all, phi2D
-from mapie.estimator.regression.interface import EnsembleEstimator
+from mapie.estimator.interface import EnsembleEstimator
 from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
                          fit_estimator)
 
@@ -497,7 +497,8 @@ def predict(
         self,
         X: ArrayLike,
         ensemble: bool = False,
-        return_multi_pred: bool = True
+        return_multi_pred: bool = True,
+        **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 4dd9891b3..36505d533 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -13,7 +13,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
-from mapie.estimator.regression.estimator import EnsembleRegressor
+from mapie.estimator import EnsembleRegressor
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_conformity_score, check_cv,
                          check_estimator_fit_predict, check_n_features_in,
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 61916c947..89b2adf81 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -25,7 +25,7 @@
 from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
                                      GammaConformityScore,
                                      ResidualNormalisedScore)
-from mapie.estimator.regression.estimator import EnsembleRegressor
+from mapie.estimator import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 from mapie.subsample import Subsample

From d6ed656c3faf71b49d482e89b0fce9a35217cc94 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 15:29:12 +0200
Subject: [PATCH 146/424] FIX: move check function to avoid circular import

---
 mapie/classification.py                      |  2 +-
 mapie/conformity_scores/check.py             | 38 ++++++++++++++++++++
 mapie/conformity_scores/conformity_scores.py |  6 ++--
 mapie/estimator/classifier.py                |  6 +---
 mapie/regression/regression.py               |  9 ++---
 mapie/tests/test_regression.py               |  2 +-
 mapie/utils.py                               | 35 ------------------
 7 files changed, 49 insertions(+), 49 deletions(-)
 create mode 100644 mapie/conformity_scores/check.py

diff --git a/mapie/classification.py b/mapie/classification.py
index 684313556..fbd8eddad 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -15,7 +15,7 @@
 
 from mapie._machine_precision import EPSILON
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator import EnsembleClassifier
+from mapie.estimator.classifier import EnsembleClassifier
 from mapie.metrics import classification_mean_width_score
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
                          check_estimator_classification, check_n_features_in,
diff --git a/mapie/conformity_scores/check.py b/mapie/conformity_scores/check.py
new file mode 100644
index 000000000..83460f3ae
--- /dev/null
+++ b/mapie/conformity_scores/check.py
@@ -0,0 +1,38 @@
+from typing import Optional
+
+from .conformity_scores import ConformityScore
+from .residual_conformity_scores import AbsoluteConformityScore
+
+
+def check_conformity_score(
+    conformity_score: Optional[ConformityScore],
+    sym: bool = True,
+) -> ConformityScore:
+    """
+    Check parameter ``conformity_score``.
+
+    Raises
+    ------
+    ValueError
+        If parameter is not valid.
+
+    Examples
+    --------
+    >>> from mapie.utils import check_conformity_score
+    >>> try:
+    ...     check_conformity_score(1)
+    ... except Exception as exception:
+    ...     print(exception)
+    ...
+    Invalid conformity_score argument.
+    Must be None or a ConformityScore instance.
+    """
+    if conformity_score is None:
+        return AbsoluteConformityScore(sym=sym)
+    elif isinstance(conformity_score, ConformityScore):
+        return conformity_score
+    else:
+        raise ValueError(
+            "Invalid conformity_score argument.\n"
+            "Must be None or a ConformityScore instance."
+        )
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 8030a68ee..8dac991c9 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -5,7 +5,7 @@
 
 from mapie._compatibility import np_nanquantile
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator import EnsembleRegressor
+from mapie.estimator.regressor import EnsembleRegressor
 
 
 class ConformityScore(metaclass=ABCMeta):
@@ -326,14 +326,14 @@ def get_bounds(
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Compute bounds of the prediction intervals from the observed values,
-        the estimator of type ``EnsembleEstimator`` and the conformity scores.
+        the estimator of type ``EnsembleRegressor`` and the conformity scores.
 
         Parameters
         ----------
         X: ArrayLike of shape (n_samples, n_features)
             Observed feature values.
 
-        estimator: EnsembleEstimator
+        estimator: EnsembleRegressor
             Estimator that is fitted to predict y from X.
 
         conformity_scores: ArrayLike of shape (n_samples,)
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index b76afcd69..2dd7b4991 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -11,11 +11,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.estimator.interface import EnsembleEstimator
-from mapie.utils import (
-    check_no_agg_cv,
-    fit_estimator,
-    fix_number_of_classes,
-)
+from mapie.utils import check_no_agg_cv, fit_estimator, fix_number_of_classes
 
 
 class EnsembleClassifier(EnsembleEstimator):
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 36505d533..f0dcef03b 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -13,11 +13,12 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
-from mapie.estimator import EnsembleRegressor
+from mapie.estimator.regressor import EnsembleRegressor
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
-                         check_conformity_score, check_cv,
-                         check_estimator_fit_predict, check_n_features_in,
-                         check_n_jobs, check_null_weight, check_verbose)
+                         check_cv, check_estimator_fit_predict,
+                         check_n_features_in, check_n_jobs, check_null_weight,
+                         check_verbose)
+from mapie.conformity_scores.check import check_conformity_score
 
 
 class MapieRegressor(BaseEstimator, RegressorMixin):
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 89b2adf81..bb0c401fd 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -25,7 +25,7 @@
 from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
                                      GammaConformityScore,
                                      ResidualNormalisedScore)
-from mapie.estimator import EnsembleRegressor
+from mapie.estimator.regressor import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 from mapie.subsample import Subsample
diff --git a/mapie/utils.py b/mapie/utils.py
index cc1f57135..7ac86b880 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -16,7 +16,6 @@
 
 from ._compatibility import np_quantile
 from ._typing import ArrayLike, NDArray
-from .conformity_scores import AbsoluteConformityScore, ConformityScore
 
 SPLIT_STRATEGIES = ["uniform", "quantile", "array split"]
 
@@ -600,40 +599,6 @@ def check_lower_upper_bounds(
         )
 
 
-def check_conformity_score(
-    conformity_score: Optional[ConformityScore],
-    sym: bool = True,
-) -> ConformityScore:
-    """
-    Check parameter ``conformity_score``.
-
-    Raises
-    ------
-    ValueError
-        If parameter is not valid.
-
-    Examples
-    --------
-    >>> from mapie.utils import check_conformity_score
-    >>> try:
-    ...     check_conformity_score(1)
-    ... except Exception as exception:
-    ...     print(exception)
-    ...
-    Invalid conformity_score argument.
-    Must be None or a ConformityScore instance.
-    """
-    if conformity_score is None:
-        return AbsoluteConformityScore(sym=sym)
-    elif isinstance(conformity_score, ConformityScore):
-        return conformity_score
-    else:
-        raise ValueError(
-            "Invalid conformity_score argument.\n"
-            "Must be None or a ConformityScore instance."
-        )
-
-
 def check_defined_variables_predict_cqr(
     ensemble: bool,
     alpha: Union[float, Iterable[float], None],

From 2ab2487b5c59a7c337c8faf48a2fe98e411939a1 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 15:45:39 +0200
Subject: [PATCH 147/424] FIX: correct function import

---
 mapie/regression/regression.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 04d6ec380..d267c5e91 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -17,7 +17,7 @@
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_cv, check_estimator_fit_predict,
                          check_n_features_in, check_n_jobs, check_null_weight,
-                         check_verbose)
+                         check_verbose, get_effective_calibration_samples)
 from mapie.conformity_scores.check import check_conformity_score
 
 

From 630ca654e82ea97f03e5d8d43d0d0a2a72cbc762 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 15:50:47 +0200
Subject: [PATCH 148/424] FIX: correct functino import path

---
 mapie/tests/test_utils_classification_conformity_scores.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/tests/test_utils_classification_conformity_scores.py b/mapie/tests/test_utils_classification_conformity_scores.py
index bbb73f383..a74a6892a 100644
--- a/mapie/tests/test_utils_classification_conformity_scores.py
+++ b/mapie/tests/test_utils_classification_conformity_scores.py
@@ -3,7 +3,7 @@
 import numpy as np
 import pytest
 
-from mapie.conformity_scores.utils_classification_conformity_scores import (
+from mapie.conformity_scores.utils import (
     get_true_label_position,
 )
 from mapie._typing import NDArray

From 41af001214d50f30210b95664e3f6ab3ec5502fb Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 15:59:19 +0200
Subject: [PATCH 149/424] FIX: correct function import path

---
 mapie/conformity_scores/{check.py => checks.py} | 2 +-
 mapie/regression/regression.py                  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)
 rename mapie/conformity_scores/{check.py => checks.py} (92%)

diff --git a/mapie/conformity_scores/check.py b/mapie/conformity_scores/checks.py
similarity index 92%
rename from mapie/conformity_scores/check.py
rename to mapie/conformity_scores/checks.py
index 83460f3ae..66a9277d2 100644
--- a/mapie/conformity_scores/check.py
+++ b/mapie/conformity_scores/checks.py
@@ -18,7 +18,7 @@ def check_conformity_score(
 
     Examples
     --------
-    >>> from mapie.utils import check_conformity_score
+    >>> from mapie.conformity_scores.checks import check_conformity_score
     >>> try:
     ...     check_conformity_score(1)
     ... except Exception as exception:
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index d267c5e91..61c85cf15 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -18,7 +18,7 @@
                          check_cv, check_estimator_fit_predict,
                          check_n_features_in, check_n_jobs, check_null_weight,
                          check_verbose, get_effective_calibration_samples)
-from mapie.conformity_scores.check import check_conformity_score
+from mapie.conformity_scores.checks import check_conformity_score
 
 
 class MapieRegressor(BaseEstimator, RegressorMixin):

From 40731a96a3b009a8f214f5cfab30aa28029a51f8 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 16:33:09 +0200
Subject: [PATCH 150/424] UPD: remove useless cast

---
 mapie/estimator/classifier.py | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 2dd7b4991..7697a42ee 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -337,9 +337,6 @@ def fit(
         self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
         estimator = self.estimator
         n_samples = _num_samples(y)
-        if y_enc is None:
-            raise ValueError
-        y_enc = cast(NDArray, y_enc)
 
         # Computation
         if cv == "prefit":

From 1d978ce1905360d665b800309be5f34ee9501886 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Tue, 25 Jun 2024 16:48:20 +0200
Subject: [PATCH 151/424] FIX: conserve n_samples attribute

---
 mapie/classification.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mapie/classification.py b/mapie/classification.py
index fbd8eddad..25b95867f 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -1104,6 +1104,7 @@ def fit(
          sample_weight,
          groups,
          n_samples) = self._check_fit_parameter(X, y, sample_weight, groups)
+        self.n_samples_ = n_samples
 
         if self.method == "raps":
             (X, y_enc, y, n_samples, sample_weight, groups) = self._split_data(

From d8bf01e1c03541a61d5bacf5c9f377f40c5bab61 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 25 Jun 2024 17:48:43 +0200
Subject: [PATCH 152/424] "Fix : unit tests"

---
 mapie/tests/test_subsample.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/mapie/tests/test_subsample.py b/mapie/tests/test_subsample.py
index 82b67b70e..a575d2de9 100644
--- a/mapie/tests/test_subsample.py
+++ b/mapie/tests/test_subsample.py
@@ -185,12 +185,12 @@ def test_split_samples_BlockBootstrap(n_resamplings: int,
                         length=length, random_state=0)
     trains = [x[0] for x in cv.split(X)]
     tests = [x[1] for x in cv.split(X)]
-    for i in range(n_resamplings):
-        for j in range(i + 1, n_resamplings):
-            with np.testing.assert_raises(AssertionError):
-                np.testing.assert_equal(trains[i], trains[j])
-            with np.testing.assert_raises(AssertionError):
-                np.testing.assert_equal(tests[i], tests[j])
+    for (train1, train2), (test1, test2) in product(
+            combinations(trains, 2), combinations(tests, 2)):
+        with np.testing.assert_raises(AssertionError):
+            np.testing.assert_equal(train1, train2)
+        with np.testing.assert_raises(AssertionError):
+            np.testing.assert_equal(test1, test2)
 
 
 @pytest.mark.parametrize("length", [2, 3, 4])
@@ -215,5 +215,5 @@ def test_reproductibility_samples_BlockBootstrap(
     )
     trains2 = [x[0] for x in list(cv2.split(X))]
     tests2 = [x[1] for x in list(cv2.split(X))]
-    np.testing.assert_array_equal(trains1, trains2)
+    np.testing.assert_equal(trains1, trains2)
     np.testing.assert_equal(tests1, tests2)

From 56c1a2522a331efec38fed6496618c443ff9163d Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Wed, 26 Jun 2024 14:49:24 +0200
Subject: [PATCH 153/424] UPD: docstring + improved split managemenent +
 extension of tests to larger dataset (raps limitation)

---
 mapie/classification.py            |  58 +++---
 mapie/estimator/classifier.py      | 220 +++++++++++-----------
 mapie/estimator/interface.py       |   5 +-
 mapie/estimator/regressor.py       |   4 +-
 mapie/regression/regression.py     |   6 +-
 mapie/tests/test_classification.py | 288 +++++++++++++++++++----------
 6 files changed, 334 insertions(+), 247 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 25b95867f..cb7607802 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -5,7 +5,8 @@
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
+from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit,
+                                     StratifiedShuffleSplit)
 from sklearn.preprocessing import LabelEncoder, label_binarize
 from sklearn.utils import _safe_indexing, check_random_state
 from sklearn.utils.multiclass import (check_classification_targets,
@@ -301,9 +302,9 @@ def _check_raps(self):
         ValueError
             If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
         """
-        if (self.method == "raps") and (
-            (self.cv not in self.raps_valid_cv_)
-            or isinstance(self.cv, ShuffleSplit)
+        if (self.method == "raps") and not (
+            (self.cv in self.raps_valid_cv_)
+            or isinstance(self.cv, BaseShuffleSplit)
         ):
             raise ValueError(
                 "RAPS method can only be used "
@@ -926,7 +927,7 @@ def _check_fit_parameter(
         y: ArrayLike
             Target values.
 
-        sample_weight: Optional[NDArray] of shape (n_samples,)
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
             Non-null sample weights.
 
         groups: Optional[ArrayLike] of shape (n_samples,)
@@ -940,8 +941,8 @@ def _check_fit_parameter(
         Optional[Union[int, str, BaseCrossValidator]],
         ArrayLike, NDArray, NDArray, Optional[NDArray],
         Optional[NDArray], ArrayLike]
-
             Parameters checked
+
         Raises
         ------
         ValueError
@@ -952,7 +953,6 @@ def _check_fit_parameter(
             If ``cv`` is `"prefit"`` or ``"split"`` and ``method`` is not
             ``"base"``.
         """
-
         self._check_parameters()
         cv = check_cv(
             self.cv, test_size=self.test_size, random_state=self.random_state
@@ -979,15 +979,15 @@ def _check_fit_parameter(
         self.label_encoder_ = enc
         self._check_target(y)
 
-        return (estimator, cv, X, y, y_enc, sample_weight, groups, n_samples)
+        return estimator, cv, X, y, y_enc, sample_weight, groups, n_samples
 
     def _split_data(
         self,
-        X,
-        y_enc,
-        sample_weight,
-        groups,
-        size_raps
+        X: ArrayLike,
+        y_enc: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        size_raps: Optional[float] = None,
     ):
         """Split data for raps method
 
@@ -999,7 +999,7 @@ def _split_data(
         y_enc: ArrayLike
             Target values as normalized encodings.
 
-        sample_weight: Optional[NDArray] of shape (n_samples,)
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
             Non-null sample weights.
 
         groups: Optional[ArrayLike] of shape (n_samples,)
@@ -1015,7 +1015,6 @@ def _split_data(
         -------
         Tuple[NDArray, NDArray, NDArray, NDArray, Optional[NDArray],
         Optional[NDArray]]
-
             - NDArray of shape (n_samples, n_features)
             - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
@@ -1023,26 +1022,31 @@ def _split_data(
             - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
         """
-        raps_split = ShuffleSplit(
-            1, test_size=size_raps, random_state=self.random_state
+        # Split data for raps method
+        raps_split = StratifiedShuffleSplit(
+            n_splits=1, test_size=size_raps, random_state=self.random_state
         )
-        train_raps_index, val_raps_index = next(raps_split.split(X))
+        train_raps_index, val_raps_index = next(raps_split.split(X, y_enc))
         X, self.X_raps, y_enc, self.y_raps = (
             _safe_indexing(X, train_raps_index),
             _safe_indexing(X, val_raps_index),
             _safe_indexing(y_enc, train_raps_index),
             _safe_indexing(y_enc, val_raps_index),
         )
+
+        # Decode y_raps for use in the RAPS method
         self.y_raps_no_enc = self.label_encoder_.inverse_transform(self.y_raps)
         y = self.label_encoder_.inverse_transform(y_enc)
+
+        # Cast to NDArray for type checking
         y_enc = cast(NDArray, y_enc)
         n_samples = _num_samples(y_enc)
         if sample_weight is not None:
-            sample_weight = sample_weight[train_raps_index]
             sample_weight = cast(NDArray, sample_weight)
+            sample_weight = sample_weight[train_raps_index]
         if groups is not None:
-            groups = groups[train_raps_index]
             groups = cast(NDArray, groups)
+            groups = groups[train_raps_index]
 
         return X, y_enc, y, n_samples, sample_weight, groups
 
@@ -1126,12 +1130,13 @@ def fit(
             self.test_size,
             self.verbose,
         )
-
+        # Fit the prediction function
         self.estimator_ = self.estimator_.fit(
             X, y, y_enc=y_enc, sample_weight=sample_weight, groups=groups,
             **fit_params
         )
 
+        # Predict on calibration data
         y_pred_proba, y, y_enc = self.estimator_.predict_proba_calib(
             X, y, y_enc, groups
         )
@@ -1176,10 +1181,6 @@ def fit(
                 "Invalid method. " f"Allowed values are {self.valid_methods_}."
             )
 
-        # In split-CP, we keep only the model fitted on train dataset
-        if isinstance(cv, ShuffleSplit):
-            self.estimator_.single_estimator_ = self.estimator_.estimators_[0]
-
         return self
 
     def predict(
@@ -1278,9 +1279,12 @@ def predict(
         alpha_np = cast(NDArray, alpha)
         check_alpha_and_n_samples(alpha_np, n)
 
-        y_pred_proba = self.estimator_.predict(X, alpha_np, agg_scores)
-        # Check that sum of probas is equal to 1
+        y_pred_proba = self.estimator_.predict(X, agg_scores)
         y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1)
+        if agg_scores != "crossval":
+            y_pred_proba = np.repeat(
+                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+            )
 
         # Choice of the quantile
         if self.method == "naive":
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 7697a42ee..16df810e2 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -1,11 +1,11 @@
 from __future__ import annotations
 
-from typing import Any, List, Optional, Tuple, Union, cast
+from typing import List, Optional, Tuple, Union, cast
 
 import numpy as np
 from joblib import Parallel, delayed
 from sklearn.base import ClassifierMixin, clone
-from sklearn.model_selection import BaseCrossValidator, ShuffleSplit
+from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit)
 from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
@@ -16,94 +16,94 @@
 
 class EnsembleClassifier(EnsembleEstimator):
     """
-     This class implements methods to handle the training and usage of the
-     estimator. This estimator can be unique or composed by cross validated
-     estimators.
-
-     Parameters
-     ----------
-     estimator: Optional[ClaMixin]
-         Any regressor with scikit-learn API
-         (i.e. with ``fit`` and ``predict`` methods).
-         If ``None``, estimator defaults to a ``LinearRegression`` instance.
-
-         By default ``None``.
-
-     cv: Optional[str]
-         The cross-validation strategy for computing scores.
-         It directly drives the distinction between jackknife and cv variants.
-         Choose among:
-
-         - ``None``, to use the default 5-fold cross-validation
-         - integer, to specify the number of folds.
-           If equal to -1, equivalent to
-           ``sklearn.model_selection.LeaveOneOut()``.
-         - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
-           Main variants are:
-           - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
-           - ``sklearn.model_selection.KFold`` (cross-validation)
-         - ``"split"``, does not involve cross-validation but a division
-           of the data into training and calibration subsets. The splitter
-           used is the following: ``sklearn.model_selection.ShuffleSplit``.
-         - ``"prefit"``, assumes that ``estimator`` has been fitted already.
-           All data provided in the ``fit`` method is then used
-           to calibrate the predictions through the score computation.
-           At prediction time, quantiles of these scores are used to estimate
-           prediction sets.
-
-         By default ``None``.
-
-     test_size: Optional[Union[int, float]]
-         If ``float``, should be between ``0.0`` and ``1.0`` and represent the
-         proportion of the dataset to include in the test split. If ``int``,
-         represents the absolute number of test samples. If ``None``,
-         it will be set to ``0.1``.
-
-         If cv is not ``"split"``, ``test_size`` is ignored.
-
-         By default ``None``.
-
-     n_jobs: Optional[int]
-         Number of jobs for parallel processing using joblib
-         via the "locky" backend.
-         If ``-1`` all CPUs are used.
-         If ``1`` is given, no parallel computing code is used at all,
-         which is useful for debugging.
-         For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
-         ``None`` is a marker for `unset` that will be interpreted as
-         ``n_jobs=1`` (sequential execution).
-
-         By default ``None``.
+    This class implements methods to handle the training and usage of the
+    estimator. This estimator can be unique or composed by cross validated
+    estimators.
+
+    Parameters
+    ----------
+    estimator: Optional[ClassifierMixin]
+        Any classifier with scikit-learn API
+        (i.e. with ``fit`` and ``predict`` methods).
+        If ``None``, estimator defaults to a ``LogisticRegression`` instance.
+
+        By default ``None``.
+
+    cv: Optional[str]
+        The cross-validation strategy for computing scores.
+        It directly drives the distinction between jackknife and cv variants.
+        Choose among:
+
+        - ``None``, to use the default 5-fold cross-validation
+        - integer, to specify the number of folds.
+            If equal to -1, equivalent to
+            ``sklearn.model_selection.LeaveOneOut()``.
+        - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
+            Main variants are:
+            - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
+            - ``sklearn.model_selection.KFold`` (cross-validation)
+        - ``"split"``, does not involve cross-validation but a division
+            of the data into training and calibration subsets. The splitter
+            used is the following: ``sklearn.model_selection.ShuffleSplit``.
+        - ``"prefit"``, assumes that ``estimator`` has been fitted already.
+            All data provided in the ``fit`` method is then used
+            to calibrate the predictions through the score computation.
+            At prediction time, quantiles of these scores are used to estimate
+            prediction sets.
+
+        By default ``None``.
+
+    test_size: Optional[Union[int, float]]
+        If ``float``, should be between ``0.0`` and ``1.0`` and represent the
+        proportion of the dataset to include in the test split. If ``int``,
+        represents the absolute number of test samples. If ``None``,
+        it will be set to ``0.1``.
+
+        If cv is not ``"split"``, ``test_size`` is ignored.
+
+        By default ``None``.
+
+    n_jobs: Optional[int]
+        Number of jobs for parallel processing using joblib
+        via the "locky" backend.
+        If ``-1`` all CPUs are used.
+        If ``1`` is given, no parallel computing code is used at all,
+        which is useful for debugging.
+        For ``n_jobs`` below ``-1``, ``(n_cpus + 1 - n_jobs)`` are used.
+        ``None`` is a marker for `unset` that will be interpreted as
+        ``n_jobs=1`` (sequential execution).
+
+        By default ``None``.
 
     random_state: Optional[Union[int, RandomState]]
-         Pseudo random number generator state used for random uniform sampling
-         for evaluation quantiles and prediction sets.
-         Pass an int for reproducible output across multiple function calls.
-
-         By default ``None``.
-
-     verbose: int, optional
-         The verbosity level, used with joblib for multiprocessing.
-         At this moment, parallel processing is disabled.
-         The frequency of the messages increases with the verbosity level.
-         If it more than ``10``, all iterations are reported.
-         Above ``50``, the output is sent to stdout.
-
-         By default ``0``.
-
-     Attributes
-     ----------
-     single_estimator_: sklearn.ClassifierMixin
-         Estimator fitted on the whole training set.
-
-     estimators_: list
-         List of out-of-folds estimators.
-
-     k_: ArrayLike
-         - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
-           (defined but not used)
-         - Dummy array of folds containing each training sample, otherwise.
-           Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
+        Pseudo random number generator state used for random uniform sampling
+        for evaluation quantiles and prediction sets.
+        Pass an int for reproducible output across multiple function calls.
+
+        By default ``None``.
+
+    verbose: int, optional
+        The verbosity level, used with joblib for multiprocessing.
+        At this moment, parallel processing is disabled.
+        The frequency of the messages increases with the verbosity level.
+        If it more than ``10``, all iterations are reported.
+        Above ``50``, the output is sent to stdout.
+
+        By default ``0``.
+
+    Attributes
+    ----------
+    single_estimator_: sklearn.ClassifierMixin
+        Estimator fitted on the whole training set.
+
+    estimators_: list
+        List of out-of-folds estimators.
+
+    k_: ArrayLike
+        - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
+            (defined but not used)
+        - Dummy array of folds containing each training sample, otherwise.
+            Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
     """
 
     no_agg_cv_ = ["prefit", "split"]
@@ -197,19 +197,17 @@ def _check_proba_normalized(
         Parameters
         ----------
         y_pred_proba: ArrayLike of shape
-            (n_samples, n_classes) or
-            (n_samples, n_train_samples, n_classes)
+            (n_samples, n_classes) or (n_samples, n_train_samples, n_classes)
             Softmax output of a model.
 
         Returns
         -------
         ArrayLike of shape (n_samples, n_classes)
-            Softmax output of a model if the scores all sum
-            to one.
+            Softmax output of a model if the scores all sum to one.
 
         Raises
         ------
-            ValueError
+        ValueError
             If the sum of the scores is not equal to one.
         """
         np.testing.assert_allclose(
@@ -326,7 +324,7 @@ def fit(
 
         Returns
         -------
-        EnsembleRegressor
+        EnsembleClassifier
             The estimator fitted.
         """
         # Initialization
@@ -367,9 +365,14 @@ def fit(
                 )
                 for train_index, _ in cv.split(X, y, groups)
             )
-        self.single_estimator_: ClassifierMixin = single_estimator_
-        self.estimators_: List[ClassifierMixin] = estimators_
-        self.k_: NDArray = k_
+            # In split-CP, we keep only the model fitted on train dataset
+            if self.use_split_method_:
+                single_estimator_ = estimators_[0]
+
+        self.single_estimator_ = single_estimator_
+        self.estimators_ = estimators_
+        self.k_ = k_
+
         return self
 
     def predict_proba_calib(
@@ -381,7 +384,7 @@ def predict_proba_calib(
         **predict_params
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
-        Perform predictions on X : the calibration set.
+        Perform predictions on X, the calibration set.
 
         Parameters
         ----------
@@ -431,35 +434,31 @@ def predict_proba_calib(
             self.k_[val_indices] = val_ids
             y_pred_proba[val_indices] = predictions
 
-            if isinstance(cv, ShuffleSplit):
+            if isinstance(cv, BaseShuffleSplit):
                 # Should delete values indices that
                 # are not used during calibration
                 self.k_ = self.k_[val_indices]
                 y_pred_proba = y_pred_proba[val_indices]
                 y_enc = y_enc[val_indices]
-                y = cast(NDArray, y)[val_indices]
+                y = y[val_indices]
 
         return y_pred_proba, y, y_enc
 
     def predict(
         self,
         X: ArrayLike,
-        alpha_np: ArrayLike = [],
-        agg_scores: Any = None,
+        agg_scores: Optional[str] = None,
         **predict_params
     ) -> NDArray:
         """
         Predict target from X. It also computes the prediction per train sample
-        for each test sample according to ``self.method``.
+        for each test sample according to ``agg_scores``.
 
         Parameters
         ----------
         X: ArrayLike of shape (n_samples, n_features)
             Test data.
 
-        alpha_np: ArrayLike of shape (n_alphas)
-            Level of confidences.
-
         agg_scores: Optional[str]
             How to aggregate the scores output by the estimators on test data
             if a cross-validation strategy is used
@@ -469,15 +468,11 @@ def predict(
         NDArray
             Predictions of shape
             (n_samples, n_classes)
-
         """
         check_is_fitted(self, self.fit_attributes)
-        alpha_np = cast(NDArray, alpha_np)
+
         if self.cv == "prefit":
             y_pred_proba = self.single_estimator_.predict_proba(X)
-            y_pred_proba = np.repeat(
-                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-            )
         else:
             y_pred_proba_k = np.asarray(
                 Parallel(
@@ -491,9 +486,6 @@ def predict(
                 y_pred_proba = np.moveaxis(y_pred_proba_k[self.k_], 0, 2)
             elif agg_scores == "mean":
                 y_pred_proba = np.mean(y_pred_proba_k, axis=0)
-                y_pred_proba = np.repeat(
-                    y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-                )
             else:
                 raise ValueError("Invalid 'agg_scores' argument.")
 
diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
index f84367a27..e015d4d7c 100644
--- a/mapie/estimator/interface.py
+++ b/mapie/estimator/interface.py
@@ -1,8 +1,9 @@
 from __future__ import annotations
 
 from abc import ABCMeta, abstractmethod
+from typing import Tuple, Union
 
-from mapie._typing import ArrayLike
+from mapie._typing import ArrayLike, NDArray
 
 
 class EnsembleEstimator(metaclass=ABCMeta):
@@ -32,7 +33,7 @@ def predict(
         self,
         X: ArrayLike,
         **kwargs
-    ):
+    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
         for each test sample according to ``self.method``.
diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index f8bf7bc85..da6596c3e 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -142,9 +142,9 @@ class EnsembleRegressor(EnsembleEstimator):
 
     k_: ArrayLike
         - Array of nans, of shape (len(y), 1) if ``cv`` is ``"prefit"``
-          (defined but not used)
+            (defined but not used)
         - Dummy array of folds containing each training sample, otherwise.
-          Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
+            Of shape (n_samples_train, cv.get_n_splits(X_train, y_train)).
     """
     no_agg_cv_ = ["prefit", "split"]
     no_agg_methods_ = ["naive", "base"]
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 61c85cf15..3085ce82d 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -457,10 +457,8 @@ def _check_fit_parameters(
         groups = cast(Optional[NDArray], groups)
 
         return (
-            estimator, cs_estimator,
-            agg_function, cv,
-            X, y,
-            sample_weight, groups
+            estimator, cs_estimator, agg_function, cv,
+            X, y, sample_weight, groups
         )
 
     def fit(
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 972b21923..b220cba99 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -14,7 +14,7 @@
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
-                                     ShuffleSplit)
+                                     ShuffleSplit, StratifiedShuffleSplit)
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.estimator_checks import check_estimator
@@ -315,54 +315,6 @@
             agg_scores="mean"
         )
     ),
-    "raps": (
-        Params(
-            method="raps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "raps_split": (
-        Params(
-            method="raps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "raps_randomized": (
-        Params(
-            method="raps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
-    ),
-    "raps_randomized_split": (
-        Params(
-            method="raps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
-    ),
 }
 
 STRATEGIES_BINARY = {
@@ -437,10 +389,8 @@
     "naive_split": 5/9,
     "top_k": 1.0,
     "top_k_split": 1.0,
-    "raps": 1.0,
-    "raps_split": 7/9,
-    "raps_randomized": 8/9,
-    "raps_randomized_split": 1.0
+    "raps": 6/9,
+    "raps_randomized": 3/9
 }
 
 COVERAGES_BINARY = {
@@ -675,50 +625,6 @@
         [False, True, True],
         [False, True, True]
     ],
-    "raps": [
-        [True, False, False],
-        [True, False, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [False, True, True],
-        [False, True, True],
-        [False, True, True],
-        [False, True, True]
-    ],
-    "raps_split": [
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False],
-        [True, True, False]
-    ],
-    "raps_randomized": [
-        [True, False, False],
-        [True, False, False],
-        [True, True, False],
-        [True, True, False],
-        [False, True, False],
-        [False, True, False],
-        [False, True, False],
-        [False, True, True],
-        [False, False, True]
-    ],
-    "raps_randomized_split": [
-        [True, True, True],
-        [True, True, True],
-        [True, True, True],
-        [True, True, True],
-        [True, True, True],
-        [True, True, True],
-        [True, True, True],
-        [True, True, True],
-        [True, True, True]
-    ]
 }
 
 X_toy_binary = np.arange(9).reshape(-1, 1)
@@ -804,6 +710,170 @@
     random_state=random_state,
 )
 
+LARGE_STRATEGIES = {
+    "lac": (
+        Params(
+            method="lac",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
+    ),
+    "lac_split": (
+        Params(
+            method="lac",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=False,
+            agg_scores="mean"
+        )
+    ),
+    "aps": (
+        Params(
+            method="aps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "aps_split": (
+        Params(
+            method="aps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "aps_randomized": (
+        Params(
+            method="aps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
+    ),
+    "naive": (
+        Params(
+            method="naive",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "naive_split": (
+        Params(
+            method="naive",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "top_k": (
+        Params(
+            method="top_k",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "top_k_split": (
+        Params(
+            method="top_k",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "raps": (
+        Params(
+            method="raps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "raps_split": (
+        Params(
+            method="raps",
+            cv=StratifiedShuffleSplit(
+                n_splits=1, train_size=0.5, random_state=random_state
+            ),
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "raps_randomized": (
+        Params(
+            method="raps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
+    ),
+}
+
+LARGE_COVERAGES = {
+    "lac": 0.802,
+    "lac_split": 0.842,
+    "aps": 0.928,
+    "aps_split": 0.93,
+    "aps_randomized": 0.802,
+    "naive": 0.936,
+    "naive_split": 0.914,
+    "top_k": 0.96,
+    "top_k_split": 0.952,
+    "raps": 0.928,
+    "raps_split": 0.918,
+    "raps_randomized": 0.806,
+}
+
 
 class CumulatedScoreClassifier:
 
@@ -990,7 +1060,7 @@ def test_valid_method(method: str) -> None:
     mapie_clf = MapieClassifier(
         method=method, cv="prefit", random_state=random_state
     )
-    mapie_clf.fit(X_toy, y_toy)
+    mapie_clf.fit(X, y)
     check_is_fitted(mapie_clf, mapie_clf.fit_attributes)
 
 
@@ -1455,6 +1525,28 @@ def test_toy_dataset_predictions(strategy: str) -> None:
     )
 
 
+@pytest.mark.parametrize("strategy", [*LARGE_STRATEGIES])
+def test_large_dataset_predictions(strategy: str) -> None:
+    """Test prediction sets estimated by MapieClassifier on a larger dataset"""
+    args_init, args_predict = LARGE_STRATEGIES[strategy]
+    if "split" not in strategy:
+        clf = LogisticRegression().fit(X, y)
+    else:
+        clf = LogisticRegression()
+    mapie_clf = MapieClassifier(estimator=clf, **args_init)
+    mapie_clf.fit(X, y, size_raps=0.5)
+    _, y_ps = mapie_clf.predict(
+        X,
+        alpha=0.2,
+        include_last_label=args_predict["include_last_label"],
+        agg_scores=args_predict["agg_scores"]
+    )
+    np.testing.assert_allclose(
+        classification_coverage_score(y, y_ps[:, :, 0]),
+        LARGE_COVERAGES[strategy], rtol=1e-2
+    )
+
+
 @pytest.mark.parametrize("strategy", [*STRATEGIES_BINARY])
 def test_toy_binary_dataset_predictions(strategy: str) -> None:
     """

From eb7ad23508948edee5868f3ee523d4abaf3938d4 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Wed, 26 Jun 2024 14:55:43 +0200
Subject: [PATCH 154/424] FIX: wrong dict name

---
 mapie/tests/test_classification.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index b220cba99..4a585beba 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1833,7 +1833,7 @@ def test_regularize_conf_scores_shape(k_lambda) -> None:
     Test that the conformity scores have the correct shape.
     """
     lambda_, k = k_lambda[0], k_lambda[1]
-    args_init, _ = STRATEGIES["raps"]
+    args_init, _ = LARGE_STRATEGIES["raps"]
     clf = LogisticRegression().fit(X, y)
     mapie_clf = MapieClassifier(estimator=clf, **args_init)
     conf_scores = np.random.rand(100, 1)

From 414b6bc5868afa7ab36e0d3f01d06a60e7915a7a Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Wed, 26 Jun 2024 15:46:19 +0200
Subject: [PATCH 155/424] FIX: extend tests to all methods

---
 mapie/tests/test_classification.py | 217 +++++++----------------------
 1 file changed, 52 insertions(+), 165 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 4a585beba..bb6888a87 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -315,6 +315,44 @@
             agg_scores="mean"
         )
     ),
+    "raps": (
+        Params(
+            method="raps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "raps_split": (
+        Params(
+            method="raps",
+            cv=StratifiedShuffleSplit(
+                n_splits=1, train_size=0.5, random_state=random_state
+            ),
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label=True,
+            agg_scores="mean"
+        )
+    ),
+    "raps_randomized": (
+        Params(
+            method="raps",
+            cv="prefit",
+            test_size=None,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
+    ),
 }
 
 STRATEGIES_BINARY = {
@@ -365,7 +403,7 @@
             include_last_label=False,
             agg_scores="crossval"
         )
-    )
+    ),
 }
 
 COVERAGES = {
@@ -389,8 +427,6 @@
     "naive_split": 5/9,
     "top_k": 1.0,
     "top_k_split": 1.0,
-    "raps": 6/9,
-    "raps_randomized": 3/9
 }
 
 COVERAGES_BINARY = {
@@ -710,160 +746,11 @@
     random_state=random_state,
 )
 
-LARGE_STRATEGIES = {
-    "lac": (
-        Params(
-            method="lac",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
-    ),
-    "lac_split": (
-        Params(
-            method="lac",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=False,
-            agg_scores="mean"
-        )
-    ),
-    "aps": (
-        Params(
-            method="aps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "aps_split": (
-        Params(
-            method="aps",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "aps_randomized": (
-        Params(
-            method="aps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
-    ),
-    "naive": (
-        Params(
-            method="naive",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "naive_split": (
-        Params(
-            method="naive",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "top_k": (
-        Params(
-            method="top_k",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "top_k_split": (
-        Params(
-            method="top_k",
-            cv="split",
-            test_size=0.5,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "raps": (
-        Params(
-            method="raps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "raps_split": (
-        Params(
-            method="raps",
-            cv=StratifiedShuffleSplit(
-                n_splits=1, train_size=0.5, random_state=random_state
-            ),
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label=True,
-            agg_scores="mean"
-        )
-    ),
-    "raps_randomized": (
-        Params(
-            method="raps",
-            cv="prefit",
-            test_size=None,
-            random_state=random_state
-        ),
-        ParamsPredict(
-            include_last_label="randomized",
-            agg_scores="mean"
-        )
-    ),
-}
-
 LARGE_COVERAGES = {
     "lac": 0.802,
     "lac_split": 0.842,
-    "aps": 0.928,
-    "aps_split": 0.93,
+    "aps_include": 0.928,
+    "aps_include_split": 0.93,
     "aps_randomized": 0.802,
     "naive": 0.936,
     "naive_split": 0.914,
@@ -1046,9 +933,9 @@ def test_binary_classif_same_result() -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 def test_valid_estimator(strategy: str) -> None:
     """Test that valid estimators are not corrupted, for all strategies."""
-    clf = LogisticRegression().fit(X_toy, y_toy)
+    clf = LogisticRegression().fit(X, y)
     mapie_clf = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
-    mapie_clf.fit(X_toy, y_toy)
+    mapie_clf.fit(X, y)
     assert (
         isinstance(mapie_clf.estimator_.single_estimator_, LogisticRegression)
     )
@@ -1500,11 +1387,9 @@ def test_valid_prediction(alpha: Any) -> None:
     mapie_clf.predict(X_toy, alpha=alpha)
 
 
-@pytest.mark.parametrize("strategy", [*STRATEGIES])
+@pytest.mark.parametrize("strategy", [*COVERAGES])
 def test_toy_dataset_predictions(strategy: str) -> None:
     """Test prediction sets estimated by MapieClassifier on a toy dataset"""
-    if strategy == "aps_randomized_cv_crossval":
-        return
     args_init, args_predict = STRATEGIES[strategy]
     if "split" not in strategy:
         clf = LogisticRegression().fit(X_toy, y_toy)
@@ -1525,10 +1410,10 @@ def test_toy_dataset_predictions(strategy: str) -> None:
     )
 
 
-@pytest.mark.parametrize("strategy", [*LARGE_STRATEGIES])
+@pytest.mark.parametrize("strategy", [*LARGE_COVERAGES])
 def test_large_dataset_predictions(strategy: str) -> None:
     """Test prediction sets estimated by MapieClassifier on a larger dataset"""
-    args_init, args_predict = LARGE_STRATEGIES[strategy]
+    args_init, args_predict = STRATEGIES[strategy]
     if "split" not in strategy:
         clf = LogisticRegression().fit(X, y)
     else:
@@ -1748,13 +1633,15 @@ def test_pred_loof_isnan() -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 def test_pipeline_compatibility(strategy: str) -> None:
     """Check that MAPIE works on pipeline based on pandas dataframes"""
+    X = np.random.randint(0, 100, size=100)
+    X_cat = np.random.choice(["A", "B", "C"], size=X.shape[0])
     X = pd.DataFrame(
         {
-            "x_cat": ["A", "A", "B", "A", "A", "B"],
-            "x_num": [0, 1, 1, 4, np.nan, 5],
+            "x_cat": X_cat,
+            "x_num": X,
         }
     )
-    y = pd.Series([0, 1, 2, 0, 1, 0])
+    y = np.random.randint(0, 4, size=(100, 1))  # 3 classes
     numeric_preprocessor = Pipeline(
         [
             ("imputer", SimpleImputer(strategy="mean")),
@@ -1833,7 +1720,7 @@ def test_regularize_conf_scores_shape(k_lambda) -> None:
     Test that the conformity scores have the correct shape.
     """
     lambda_, k = k_lambda[0], k_lambda[1]
-    args_init, _ = LARGE_STRATEGIES["raps"]
+    args_init, _ = STRATEGIES["raps"]
     clf = LogisticRegression().fit(X, y)
     mapie_clf = MapieClassifier(estimator=clf, **args_init)
     conf_scores = np.random.rand(100, 1)

From 28f782ff307629913ebefd9662cc520f10c5500c Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Wed, 26 Jun 2024 15:53:40 +0200
Subject: [PATCH 156/424] FIX: add previous method exclusion

---
 mapie/tests/test_classification.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index bb6888a87..5c211c9db 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1390,6 +1390,8 @@ def test_valid_prediction(alpha: Any) -> None:
 @pytest.mark.parametrize("strategy", [*COVERAGES])
 def test_toy_dataset_predictions(strategy: str) -> None:
     """Test prediction sets estimated by MapieClassifier on a toy dataset"""
+    if strategy == "aps_randomized_cv_crossval":
+        return
     args_init, args_predict = STRATEGIES[strategy]
     if "split" not in strategy:
         clf = LogisticRegression().fit(X_toy, y_toy)

From dbb27b70c11e6cd3965f4bddb57164c4f98bfb3a Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 27 Jun 2024 11:30:23 +0200
Subject: [PATCH 157/424] Add predict_params into Mapie regression files
 without adding any unit test

---
 mapie/estimator/estimator.py               | 31 ++++++++++++++++------
 mapie/estimator/interface.py               |  6 ++++-
 mapie/regression/quantile_regression.py    |  6 ++---
 mapie/regression/regression.py             | 20 ++++++++++----
 mapie/regression/time_series_regression.py |  6 +++--
 mapie/tests/test_regression.py             |  2 +-
 6 files changed, 51 insertions(+), 20 deletions(-)

diff --git a/mapie/estimator/estimator.py b/mapie/estimator/estimator.py
index b8c7d4ecf..e446cae87 100644
--- a/mapie/estimator/estimator.py
+++ b/mapie/estimator/estimator.py
@@ -233,6 +233,7 @@ def _predict_oof_estimator(
         estimator: RegressorMixin,
         X: ArrayLike,
         val_index: ArrayLike,
+        **predict_params
     ) -> Tuple[NDArray, ArrayLike]:
         """
         Perform predictions on a single out-of-fold model on a validation set.
@@ -248,6 +249,9 @@ def _predict_oof_estimator(
         val_index: ArrayLike of shape (n_samples_val)
             Validation data indices.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Tuple[NDArray, ArrayLike]
@@ -255,7 +259,7 @@ def _predict_oof_estimator(
         """
         X_val = _safe_indexing(X, val_index)
         if _num_samples(X_val) > 0:
-            y_pred = estimator.predict(X_val)
+            y_pred = estimator.predict(X_val, **predict_params)
         else:
             y_pred = np.array([])
         return y_pred, val_index
@@ -306,7 +310,7 @@ def _aggregate_with_mask(
         else:
             raise ValueError("The value of self.agg_function is not correct")
 
-    def _pred_multi(self, X: ArrayLike) -> NDArray:
+    def _pred_multi(self, X: ArrayLike, **predict_params) -> NDArray:
         """
         Return a prediction per train sample for each test sample, by
         aggregation with matrix ``k_``.
@@ -316,12 +320,15 @@ def _pred_multi(self, X: ArrayLike) -> NDArray:
         X: ArrayLike of shape (n_samples_test, n_features)
             Input data
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         NDArray of shape (n_samples_test, n_samples_train)
         """
         y_pred_multi = np.column_stack(
-            [e.predict(X) for e in self.estimators_]
+            [e.predict(X, **predict_params) for e in self.estimators_]
         )
         # At this point, y_pred_multi is of shape
         # (n_samples_test, n_estimators_). The method
@@ -334,7 +341,8 @@ def predict_calib(
         self,
         X: ArrayLike,
         y: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None
+        groups: Optional[ArrayLike] = None,
+        **predict_params
     ) -> NDArray:
         """
         Perform predictions on X : the calibration set.
@@ -355,6 +363,9 @@ def predict_calib(
 
             By default ``None``.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         NDArray of shape (n_samples_test, 1)
@@ -371,7 +382,7 @@ def predict_calib(
                 cv = cast(BaseCrossValidator, self.cv)
                 outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
                     delayed(self._predict_oof_estimator)(
-                        estimator, X, calib_index,
+                        estimator, X, calib_index, **predict_params
                     )
                     for (_, calib_index), estimator in zip(
                         cv.split(X, y, groups),
@@ -497,7 +508,8 @@ def predict(
         self,
         X: ArrayLike,
         ensemble: bool = False,
-        return_multi_pred: bool = True
+        return_multi_pred: bool = True,
+        **predict_params,
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
@@ -525,6 +537,9 @@ def predict(
             predictions (3 arrays). If ``False`` the method return the
             simple predictions only.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Tuple[NDArray, NDArray, NDArray]
@@ -534,7 +549,7 @@ def predict(
         """
         check_is_fitted(self, self.fit_attributes)
 
-        y_pred = self.single_estimator_.predict(X)
+        y_pred = self.single_estimator_.predict(X, **predict_params)
         if not return_multi_pred and not ensemble:
             return y_pred
 
@@ -542,7 +557,7 @@ def predict(
             y_pred_multi_low = y_pred[:, np.newaxis]
             y_pred_multi_up = y_pred[:, np.newaxis]
         else:
-            y_pred_multi = self._pred_multi(X)
+            y_pred_multi = self._pred_multi(X, **predict_params)
 
             if self.method == "minmax":
                 y_pred_multi_low = np.min(y_pred_multi, axis=1, keepdims=True)
diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
index 3e76377f1..d6d122cc6 100644
--- a/mapie/estimator/interface.py
+++ b/mapie/estimator/interface.py
@@ -62,7 +62,8 @@ def predict(
         self,
         X: ArrayLike,
         ensemble: bool = False,
-        return_multi_pred: bool = True
+        return_multi_pred: bool = True,
+        **predict_params,
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
@@ -90,6 +91,9 @@ def predict(
             predictions (3 arrays). If ``False`` the method return the
             simple predictions only.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Tuple[NDArray, NDArray, NDArray]
diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index 2635b0267..63cf3032f 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, List, Optional, Tuple, Union, cast
+from typing import Iterable, List, Optional, Tuple, Union, cast, Any
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -547,7 +547,6 @@ def fit(
              The model itself.
         """
         self.cv = self._check_cv(cast(str, self.cv))
-
         # Initialization
         self.estimators_: List[RegressorMixin] = []
         if self.cv == "prefit":
@@ -649,6 +648,7 @@ def predict(
         optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
         symmetry: Optional[bool] = True,
+        **predict_params: Any,
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Predict target on new samples with confidence intervals.
@@ -699,7 +699,7 @@ def predict(
             dtype=float,
         )
         for i, est in enumerate(self.estimators_):
-            y_preds[i] = est.predict(X)
+            y_preds[i] = est.predict(X, **predict_params)
         check_lower_upper_bounds(y_preds[0], y_preds[1], y_preds[2])
         if symmetry:
             quantile = np.full(
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index d589e56f7..6bc13e226 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, Optional, Tuple, Union, cast
+from typing import Iterable, Optional, Tuple, Union, cast, Any
 
 import numpy as np
 from sklearn.base import BaseEstimator, RegressorMixin
@@ -469,7 +469,7 @@ def fit(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
-        **fit_params,
+        **kwargs: Any,
     ) -> MapieRegressor:
         """
         Fit estimator and compute conformity scores used for
@@ -502,14 +502,19 @@ def fit(
             train/test set.
             By default ``None``.
 
-        **fit_params : dict
+        fit_params : dict
             Additional fit parameters.
 
+        predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         MapieRegressor
             The model itself.
         """
+        fit_params = kwargs.pop('fit_params', {})
+        predict_params = kwargs.pop('predict_params', {})
         # Checks
         (estimator,
          self.conformity_score_function_,
@@ -536,7 +541,8 @@ def fit(
         )
 
         # Predict on calibration data
-        y_pred = self.estimator_.predict_calib(X, y=y, groups=groups)
+        y_pred = self.estimator_.predict_calib(X, y=y, groups=groups,
+                                               **predict_params)
 
         # Compute the conformity scores (manage jk-ab case)
         self.conformity_scores_ = \
@@ -553,6 +559,7 @@ def predict(
         alpha: Optional[Union[float, Iterable[float]]] = None,
         optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
+        **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Predict target on new samples with confidence intervals.
@@ -602,6 +609,9 @@ def predict(
 
             By default ``False``.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Union[NDArray, Tuple[NDArray, NDArray]]
@@ -619,7 +629,7 @@ def predict(
         # If alpha is None, predict the target without confidence intervals
         if alpha is None:
             y_pred = self.estimator_.predict(
-                X, ensemble, return_multi_pred=False
+                X, ensemble, return_multi_pred=False, **predict_params
             )
             return np.array(y_pred)
 
diff --git a/mapie/regression/time_series_regression.py b/mapie/regression/time_series_regression.py
index b4bf0cc03..00bb09758 100644
--- a/mapie/regression/time_series_regression.py
+++ b/mapie/regression/time_series_regression.py
@@ -405,6 +405,7 @@ def predict(
         alpha: Optional[Union[float, Iterable[float]]] = None,
         optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
+        **predict_params,
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Predict target on new samples with confidence intervals.
@@ -450,7 +451,8 @@ def predict(
         """
         if alpha is None:
             super().predict(
-                X, ensemble=ensemble, alpha=alpha, optimize_beta=optimize_beta
+                X, ensemble=ensemble, alpha=alpha, optimize_beta=optimize_beta,
+                **predict_params
             )
 
         if self.method == "aci":
@@ -458,7 +460,7 @@ def predict(
 
         return super().predict(
             X, ensemble=ensemble, alpha=alpha, optimize_beta=optimize_beta,
-            allow_infinite_bounds=allow_infinite_bounds
+            allow_infinite_bounds=allow_infinite_bounds, **predict_params
         )
 
     def _more_tags(self):
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index fb86658d0..ed7f14133 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -867,7 +867,7 @@ def early_stopping_monitor(i, est, locals):
         else:
             return False
 
-    mapie.fit(X, y, monitor=early_stopping_monitor)
+    mapie.fit(X, y, fit_params={'monitor': early_stopping_monitor})
 
     assert mapie.estimator_.single_estimator_.estimators_.shape[0] == 3
 

From 42409c0392a876ba21f6c8e3e8fed85a5353d290 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Fri, 28 Jun 2024 13:56:52 +0200
Subject: [PATCH 158/424] UPD: add raps_rand_split test + more comments to
 explain which tests are made

---
 mapie/tests/test_classification.py | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 5c211c9db..27001f0ec 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -74,6 +74,7 @@
     }
 )
 
+# Here, we list all the strategies we want to test.
 STRATEGIES = {
     "lac": (
         Params(
@@ -353,8 +354,21 @@
             agg_scores="mean"
         )
     ),
+    "raps_randomized_split": (
+            Params(
+            method="raps",
+            cv="split",
+            test_size=0.5,
+            random_state=random_state
+        ),
+        ParamsPredict(
+            include_last_label="randomized",
+            agg_scores="mean"
+        )
+    ),
 }
 
+# Here, we list all the strategies we want to test only for binary classification.
 STRATEGIES_BINARY = {
     "lac": (
         Params(
@@ -406,6 +420,8 @@
     ),
 }
 
+# Here, we only list the strategies we want to test on a small data set,
+# for multi-class classification.
 COVERAGES = {
     "lac": 6/9,
     "lac_split": 8/9,
@@ -429,6 +445,8 @@
     "top_k_split": 1.0,
 }
 
+# Here, we only list the strategies we want to test on a small data set,
+# for binary classification.
 COVERAGES_BINARY = {
     "lac": 6/9,
     "lac_split": 8/9,
@@ -746,6 +764,8 @@
     random_state=random_state,
 )
 
+# Here, we only list the strategies we want to test on larger data sets,
+# particularly for the raps methods which require larger data sets.
 LARGE_COVERAGES = {
     "lac": 0.802,
     "lac_split": 0.842,
@@ -759,6 +779,7 @@
     "raps": 0.928,
     "raps_split": 0.918,
     "raps_randomized": 0.806,
+    "raps_randomized_split": 0.848,
 }
 
 

From dd4dd9d16ffc5e3e1451d78304943efc0574539d Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Sat, 29 Jun 2024 02:05:08 +0200
Subject: [PATCH 159/424] FIX: lint error

---
 mapie/tests/test_classification.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 27001f0ec..676c849cd 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -355,7 +355,7 @@
         )
     ),
     "raps_randomized_split": (
-            Params(
+        Params(
             method="raps",
             cv="split",
             test_size=0.5,
@@ -368,7 +368,8 @@
     ),
 }
 
-# Here, we list all the strategies we want to test only for binary classification.
+# Here, we list all the strategies we want to test
+# only for binary classification.
 STRATEGIES_BINARY = {
     "lac": (
         Params(

From f1e4899820e10d39baef3e35ae0c9ca7e6d3e71d Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Sat, 29 Jun 2024 02:21:12 +0200
Subject: [PATCH 160/424] UPD: label encoder standalone method

---
 mapie/classification.py | 16 ++++++++++++----
 1 file changed, 12 insertions(+), 4 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index cb7607802..7aff7d024 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -909,6 +909,16 @@ def _get_classes_info(
 
         return n_classes, classes
 
+    def _get_label_encoder(self) -> LabelEncoder:
+        """
+        Construct the label encoder with respect to the classes values.
+
+        Returns
+        -------
+        LabelEncoder
+        """
+        return LabelEncoder().fit(self.classes_)
+
     def _check_fit_parameter(
         self,
         X: ArrayLike,
@@ -972,11 +982,9 @@ def _check_fit_parameter(
         n_samples = _num_samples(y)
 
         self.n_classes_, self.classes_ = self._get_classes_info(estimator, y)
-        enc = LabelEncoder()
-        enc.fit(self.classes_)
-        y_enc = enc.transform(y)
+        self.label_encoder_ = self._get_label_encoder()
+        y_enc = self.label_encoder_.transform(y)
 
-        self.label_encoder_ = enc
         self._check_target(y)
 
         return estimator, cv, X, y, y_enc, sample_weight, groups, n_samples

From 6bbb59c4f3b5bbf7f48bcb456658ed678cf1d34b Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Mon, 1 Jul 2024 10:41:37 +0200
Subject: [PATCH 161/424] UPD: add nan value in test

---
 mapie/tests/test_classification.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 676c849cd..740c4df6b 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1657,7 +1657,7 @@ def test_pred_loof_isnan() -> None:
 @pytest.mark.parametrize("strategy", [*STRATEGIES])
 def test_pipeline_compatibility(strategy: str) -> None:
     """Check that MAPIE works on pipeline based on pandas dataframes"""
-    X = np.random.randint(0, 100, size=100)
+    X = np.concatenate([np.random.randint(0, 100, size=99), [np.nan]])
     X_cat = np.random.choice(["A", "B", "C"], size=X.shape[0])
     X = pd.DataFrame(
         {

From d3bcba5155d36a6cb15d929850fb70142fb0f4d8 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 1 Jul 2024 16:47:25 +0200
Subject: [PATCH 162/424] UPD: factorize MapieClassifier methods into several
 non-conformity scores + generic adaptation

---
 doc/api.rst                                   |   5 +
 mapie/_machine_precision.py                   |   2 +-
 mapie/classification.py                       | 792 ++----------------
 mapie/conformity_scores/__init__.py           |  19 +-
 mapie/conformity_scores/bounds/__init__.py    |  10 +
 mapie/conformity_scores/bounds/absolute.py    |  52 ++
 mapie/conformity_scores/bounds/gamma.py       |  86 ++
 .../residuals.py}                             | 152 +---
 mapie/conformity_scores/checks.py             |  38 -
 mapie/conformity_scores/classification.py     | 103 +++
 mapie/conformity_scores/interface.py          | 256 ++++++
 .../{conformity_scores.py => regression.py}   | 298 ++-----
 mapie/conformity_scores/sets/__init__.py      |  10 +
 mapie/conformity_scores/sets/aps.py           | 497 +++++++++++
 mapie/conformity_scores/sets/lac.py           | 207 +++++
 mapie/conformity_scores/sets/topk.py          | 212 +++++
 mapie/conformity_scores/sets/utils.py         | 401 +++++++++
 mapie/conformity_scores/utils.py              | 102 ++-
 mapie/regression/regression.py                |  34 +-
 mapie/regression/time_series_regression.py    |   8 +-
 mapie/tests/test_classification.py            |  60 +-
 mapie/tests/test_conformity_scores.py         |  75 +-
 mapie/tests/test_conformity_scores_sets.py    |  37 +
 mapie/tests/test_regression.py                |  19 +-
 ..._utils_classification_conformity_scores.py |   4 +-
 25 files changed, 2216 insertions(+), 1263 deletions(-)
 create mode 100644 mapie/conformity_scores/bounds/__init__.py
 create mode 100644 mapie/conformity_scores/bounds/absolute.py
 create mode 100644 mapie/conformity_scores/bounds/gamma.py
 rename mapie/conformity_scores/{residual_conformity_scores.py => bounds/residuals.py} (69%)
 delete mode 100644 mapie/conformity_scores/checks.py
 create mode 100644 mapie/conformity_scores/classification.py
 create mode 100644 mapie/conformity_scores/interface.py
 rename mapie/conformity_scores/{conformity_scores.py => regression.py} (50%)
 create mode 100644 mapie/conformity_scores/sets/__init__.py
 create mode 100644 mapie/conformity_scores/sets/aps.py
 create mode 100644 mapie/conformity_scores/sets/lac.py
 create mode 100644 mapie/conformity_scores/sets/topk.py
 create mode 100644 mapie/conformity_scores/sets/utils.py
 create mode 100644 mapie/tests/test_conformity_scores_sets.py

diff --git a/doc/api.rst b/doc/api.rst
index 417bddd26..a36957f36 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -80,9 +80,14 @@ Conformity scores
    :toctree: generated/
    :template: class.rst
 
+   conformity_scores.BaseRegressionScore
    conformity_scores.AbsoluteConformityScore
    conformity_scores.GammaConformityScore
    conformity_scores.ResidualNormalisedScore
+   conformity_scores.BaseClassificationScore
+   conformity_scores.LAC
+   conformity_scores.APS
+   conformity_scores.TopK
 
 Resampling
 ==========
diff --git a/mapie/_machine_precision.py b/mapie/_machine_precision.py
index a23c44f5b..b4a153cae 100644
--- a/mapie/_machine_precision.py
+++ b/mapie/_machine_precision.py
@@ -1,5 +1,5 @@
 import numpy as np
 
-EPSILON = np.finfo(np.float64).eps
+EPSILON = np.float64(1e-8)
 
 __all__ = ["EPSILON"]
diff --git a/mapie/classification.py b/mapie/classification.py
index 7aff7d024..232d76251 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -1,30 +1,27 @@
 from __future__ import annotations
 
 import warnings
-from typing import Any, Iterable, Optional, Tuple, Union, cast
+from typing import Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
 from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit,
                                      StratifiedShuffleSplit)
-from sklearn.preprocessing import LabelEncoder, label_binarize
+from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import _safe_indexing, check_random_state
 from sklearn.utils.multiclass import (check_classification_targets,
                                       type_of_target)
 from sklearn.utils.validation import (_check_y, _num_samples, check_is_fitted,
                                       indexable)
 
-from mapie._machine_precision import EPSILON
 from mapie._typing import ArrayLike, NDArray
+from mapie.conformity_scores import BaseClassificationScore
+from mapie.conformity_scores.utils import check_classification_conformity_score
+from mapie.conformity_scores.sets.utils import get_true_label_position
 from mapie.estimator.classifier import EnsembleClassifier
-from mapie.metrics import classification_mean_width_score
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
                          check_estimator_classification, check_n_features_in,
-                         check_n_jobs, check_null_weight, check_verbose,
-                         compute_quantiles)
-from mapie.conformity_scores.utils import (
-    get_true_label_position
-)
+                         check_n_jobs, check_null_weight, check_verbose)
 
 
 class MapieClassifier(BaseEstimator, ClassifierMixin):
@@ -47,7 +44,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
         Method to choose for prediction interval estimates.
         Choose among:
 
-        - ``"naive"``, sum of the probabilities until the 1-alpha thresold.
+        - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
 
         - ``"lac"`` (formerly called ``"score"``), Least Ambiguous set-valued
           Classifier. It is based on the the scores
@@ -197,6 +194,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
         "estimator_",
         "n_features_in_",
         "conformity_scores_",
+        "conformity_score_function_",
         "classes_",
         "label_encoder_"
     ]
@@ -208,6 +206,7 @@ def __init__(
         cv: Optional[Union[int, str, BaseCrossValidator]] = None,
         test_size: Optional[Union[int, float]] = None,
         n_jobs: Optional[int] = None,
+        conformity_score: Optional[BaseClassificationScore] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         verbose: int = 0
     ) -> None:
@@ -216,6 +215,7 @@ def __init__(
         self.cv = cv
         self.test_size = test_size
         self.n_jobs = n_jobs
+        self.conformity_score = conformity_score
         self.random_state = random_state
         self.verbose = verbose
 
@@ -311,549 +311,6 @@ def _check_raps(self):
                 f"with cv in {self.raps_valid_cv_}."
             )
 
-    def _check_include_last_label(
-        self,
-        include_last_label: Optional[Union[bool, str]]
-    ) -> Optional[Union[bool, str]]:
-        """
-        Check if ``include_last_label`` is a boolean or a string.
-        Else raise error.
-
-        Parameters
-        ----------
-        include_last_label: Optional[Union[bool, str]]
-            Whether or not to include last label in
-            prediction sets for the ``"aps"`` method. Choose among:
-
-            - ``False``, does not include label whose cumulated score is just
-            over the quantile.
-            - ``True``, includes label whose cumulated score is just over the
-            quantile, unless there is only one label in the prediction set.
-            - ``"randomized"``, randomly includes label whose cumulated score
-            is just over the quantile based on the comparison of a uniform
-            number and the difference between the cumulated score of the last
-            label and the quantile.
-
-        Returns
-        -------
-        Optional[Union[bool, str]]
-
-        Raises
-        ------
-        ValueError
-            "Invalid include_last_label argument. "
-            "Should be a boolean or 'randomized'."
-        """
-        if (
-            (not isinstance(include_last_label, bool)) and
-            (not include_last_label == "randomized")
-        ):
-            raise ValueError(
-                "Invalid include_last_label argument. "
-                "Should be a boolean or 'randomized'."
-            )
-        else:
-            return include_last_label
-
-    def _check_proba_normalized(
-        self,
-        y_pred_proba: ArrayLike,
-        axis: int = 1
-    ) -> NDArray:
-        """
-        Check if, for all the observations, the sum of
-        the probabilities is equal to one.
-
-        Parameters
-        ----------
-        y_pred_proba: ArrayLike of shape
-            (n_samples, n_classes) or
-            (n_samples, n_train_samples, n_classes)
-            Softmax output of a model.
-
-        Returns
-        -------
-        ArrayLike of shape (n_samples, n_classes)
-            Softmax output of a model if the scores all sum
-            to one.
-
-        Raises
-        ------
-            ValueError
-            If the sum of the scores is not equal to one.
-        """
-        np.testing.assert_allclose(
-            np.sum(y_pred_proba, axis=axis),
-            1,
-            err_msg="The sum of the scores is not equal to one.",
-            rtol=1e-5
-        )
-        y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
-        return y_pred_proba
-
-    def _get_last_index_included(
-        self,
-        y_pred_proba_cumsum: NDArray,
-        threshold: NDArray,
-        include_last_label: Optional[Union[bool, str]]
-    ) -> NDArray:
-        """
-        Return the index of the last included sorted probability
-        depending if we included the first label over the quantile
-        or not.
-
-        Parameters
-        ----------
-        y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
-            Cumsumed probabilities in the original order.
-
-        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
-            Threshold to compare with y_proba_last_cumsum, can be either:
-
-            - the quantiles associated with alpha values when
-              ``cv`` == "prefit", ``cv`` == "split"
-              or ``agg_scores`` is "mean"
-            - the conformity score from training samples otherwise
-              (i.e., when ``cv`` is a CV splitter and
-              ``agg_scores`` is "crossval")
-
-        include_last_label: Union[bool, str]
-            Whether or not include the last label. If 'randomized',
-            the last label is included.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_alpha)
-            Index of the last included sorted probability.
-        """
-        if (
-            (include_last_label) or
-            (include_last_label == 'randomized')
-        ):
-            y_pred_index_last = (
-                    np.ma.masked_less(
-                        y_pred_proba_cumsum
-                        - threshold[np.newaxis, :],
-                        -EPSILON
-                    ).argmin(axis=1)
-            )
-        elif (include_last_label is False):
-            max_threshold = np.maximum(
-                threshold[np.newaxis, :],
-                np.min(y_pred_proba_cumsum, axis=1)
-            )
-            y_pred_index_last = np.argmax(
-                np.ma.masked_greater(
-                    y_pred_proba_cumsum - max_threshold[:, np.newaxis, :],
-                    EPSILON
-                ), axis=1
-            )
-        else:
-            raise ValueError(
-                "Invalid include_last_label argument. "
-                "Should be a boolean or 'randomized'."
-            )
-        return y_pred_index_last[:, np.newaxis, :]
-
-    def _add_random_tie_breaking(
-        self,
-        prediction_sets: NDArray,
-        y_pred_index_last: NDArray,
-        y_pred_proba_cumsum: NDArray,
-        y_pred_proba_last: NDArray,
-        threshold: NDArray,
-        lambda_star: Union[NDArray, float, None],
-        k_star: Union[NDArray, None]
-    ) -> NDArray:
-        """
-        Randomly remove last label from prediction set based on the
-        comparison between a random number and the difference between
-        cumulated score of the last included label and the quantile.
-
-        Parameters
-        ----------
-        prediction_sets: NDArray of shape
-            (n_samples, n_classes, n_threshold)
-            Prediction set for each observation and each alpha.
-
-        y_pred_index_last: NDArray of shape (n_samples, threshold)
-            Index of the last included label.
-
-        y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
-            Cumsumed probability of the model in the original order.
-
-        y_pred_proba_last: NDArray of shape (n_samples, 1, threshold)
-            Last included probability.
-
-        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
-            Threshold to compare with y_proba_last_cumsum, can be either:
-
-            - the quantiles associated with alpha values when
-              ``cv`` == "prefit", ``cv`` == "split" or
-              ``agg_scores`` is "mean"
-            - the conformity score from training samples otherwise
-              (i.e., when ``cv`` is a CV splitter and
-              ``agg_scores`` is "crossval")
-
-        lambda_star: Union[NDArray, float, None] of shape (n_alpha):
-            Optimal value of the regulizer lambda.
-
-        k_star: Union[NDArray, None] of shape (n_alpha):
-            Optimal value of the regulizer k.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_classes, n_alpha)
-            Updated version of prediction_sets with randomly removed
-            labels.
-        """
-        # get cumsumed probabilities up to last retained label
-        y_proba_last_cumsumed = np.squeeze(
-            np.take_along_axis(
-                y_pred_proba_cumsum,
-                y_pred_index_last,
-                axis=1
-            ), axis=1
-        )
-
-        if self.method in ["cumulated_score", "aps"]:
-            # compute V parameter from Romano+(2020)
-            vs = (
-                (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-                y_pred_proba_last[:, 0, :]
-            )
-        else:
-            # compute V parameter from Angelopoulos+(2020)
-            L = np.sum(prediction_sets, axis=1)
-            vs = (
-                (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-                (
-                    y_pred_proba_last[:, 0, :] -
-                    lambda_star * np.maximum(0, L - k_star) +
-                    lambda_star * (L > k_star)
-                )
-            )
-
-        # get random numbers for each observation and alpha value
-        random_state = check_random_state(self.random_state)
-        us = random_state.uniform(size=(prediction_sets.shape[0], 1))
-        # remove last label from comparison between uniform number and V
-        vs_less_than_us = np.less_equal(vs - us, EPSILON)
-        np.put_along_axis(
-            prediction_sets,
-            y_pred_index_last,
-            vs_less_than_us[:, np.newaxis, :],
-            axis=1
-        )
-        return prediction_sets
-
-    def _get_true_label_cumsum_proba(
-        self,
-        y: ArrayLike,
-        y_pred_proba: NDArray
-    ) -> Tuple[NDArray, NDArray]:
-        """
-        Compute the cumsumed probability of the true label.
-
-        Parameters
-        ----------
-        y: NDArray of shape (n_samples, )
-            Array with the labels.
-        y_pred_proba: NDArray of shape (n_samples, n_classes)
-            Predictions of the model.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray] of shapes
-        (n_samples, 1) and (n_samples, ). The first element
-        is the cumsum probability of the true label. The second
-        is the sorted position of the true label.
-        """
-        y_true = label_binarize(
-            y=y, classes=self.classes_
-        )
-        index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
-        y_pred_proba_sorted = np.take_along_axis(
-            y_pred_proba, index_sorted, axis=1
-        )
-        y_true_sorted = np.take_along_axis(y_true, index_sorted, axis=1)
-        y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
-        cutoff = np.argmax(y_true_sorted, axis=1)
-        true_label_cumsum_proba = np.take_along_axis(
-            y_pred_proba_sorted_cumsum, cutoff.reshape(-1, 1), axis=1
-        )
-
-        return true_label_cumsum_proba, cutoff + 1
-
-    def _regularize_conformity_score(
-        self,
-        k_star: NDArray,
-        lambda_: Union[NDArray, float],
-        conf_score: NDArray,
-        cutoff: NDArray
-    ) -> NDArray:
-        """
-        Regularize the conformity scores with the ``"raps"``
-        method. See algo. 2 in [3].
-
-        Parameters
-        ----------
-        k_star: NDArray of shape (n_alphas, )
-            Optimal value of k (called k_reg in the paper). There
-            is one value per alpha.
-
-        lambda_: Union[NDArray, float] of shape (n_alphas, )
-            One value of lambda for each alpha.
-
-        conf_score: NDArray of shape (n_samples, 1)
-            Conformity scores.
-
-        cutoff: NDArray of shape (n_samples, 1)
-            Position of the true label.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, 1, n_alphas)
-            Regularized conformity scores. The regularization
-            depends on the value of alpha.
-        """
-        conf_score = np.repeat(
-            conf_score[:, :, np.newaxis], len(k_star), axis=2
-        )
-        cutoff = np.repeat(
-            cutoff[:, np.newaxis], len(k_star), axis=1
-        )
-        conf_score += np.maximum(
-            np.expand_dims(
-                lambda_ * (cutoff - k_star),
-                axis=1
-            ),
-            0
-        )
-        return conf_score
-
-    def _get_last_included_proba(
-        self,
-        y_pred_proba: NDArray,
-        thresholds: NDArray,
-        include_last_label: Union[bool, str, None],
-        lambda_: Union[NDArray, float, None],
-        k_star: Union[NDArray, Any]
-    ) -> Tuple[NDArray, NDArray, NDArray]:
-        """
-        Function that returns the smallest score
-        among those which are included in the prediciton set.
-
-        Parameters
-        ----------
-        y_pred_proba: NDArray of shape (n_samples, n_classes)
-            Predictions of the model.
-
-        thresholds: NDArray of shape (n_alphas, )
-            Quantiles that have been computed from the conformity
-            scores.
-
-        include_last_label: Union[bool, str, None]
-            Whether to include or not the label whose score
-            exceeds the threshold.
-
-        lambda_: Union[NDArray, float, None] of shape (n_alphas)
-            Values of lambda for the regularization.
-
-        k_star: Union[NDArray, Any]
-            Values of k for the regularization.
-
-        Returns
-        -------
-        Tuple[ArrayLike, ArrayLike, ArrayLike]
-            Arrays of shape (n_samples, n_classes, n_alphas),
-            (n_samples, 1, n_alphas) and (n_samples, 1, n_alphas).
-            They are respectively the cumsumed scores in the original
-            order which can be different according to the value of alpha
-            with the RAPS method, the index of the last included score
-            and the value of the last included score.
-        """
-        index_sorted = np.flip(
-            np.argsort(y_pred_proba, axis=1), axis=1
-        )
-        # sort probabilities by decreasing order
-        y_pred_proba_sorted = np.take_along_axis(
-            y_pred_proba, index_sorted, axis=1
-        )
-        # get sorted cumulated score
-        y_pred_proba_sorted_cumsum = np.cumsum(
-            y_pred_proba_sorted, axis=1
-        )
-
-        if self.method == "raps":
-            y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
-                0,
-                np.cumsum(
-                    np.ones(y_pred_proba_sorted_cumsum.shape),
-                    axis=1
-                ) - k_star
-            )
-        # get cumulated score at their original position
-        y_pred_proba_cumsum = np.take_along_axis(
-            y_pred_proba_sorted_cumsum,
-            np.argsort(index_sorted, axis=1),
-            axis=1
-        )
-        # get index of the last included label
-        y_pred_index_last = self._get_last_index_included(
-            y_pred_proba_cumsum,
-            thresholds,
-            include_last_label
-        )
-        # get the probability of the last included label
-        y_pred_proba_last = np.take_along_axis(
-            y_pred_proba,
-            y_pred_index_last,
-            axis=1
-        )
-
-        zeros_scores_proba_last = (y_pred_proba_last <= EPSILON)
-
-        # If the last included proba is zero, change it to the
-        # smallest non-zero value to avoid inluding them in the
-        # prediction sets.
-        if np.sum(zeros_scores_proba_last) > 0:
-            y_pred_proba_last[zeros_scores_proba_last] = np.expand_dims(
-                np.min(
-                    np.ma.masked_less(
-                        y_pred_proba,
-                        EPSILON
-                    ).filled(fill_value=np.inf),
-                    axis=1
-                ), axis=1
-            )[zeros_scores_proba_last]
-
-        return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
-
-    def _update_size_and_lambda(
-        self,
-        best_sizes: NDArray,
-        alpha_np: NDArray,
-        y_ps: NDArray,
-        lambda_: Union[NDArray, float],
-        lambda_star: NDArray
-    ) -> Tuple[NDArray, NDArray]:
-        """Update the values of the optimal lambda if the
-        average size of the prediction sets decreases with
-        this new value of lambda.
-
-        Parameters
-        ----------
-        best_sizes: NDArray of shape (n_alphas, )
-            Smallest average prediciton set size before testing
-            for the new value of lambda_
-
-        alpha_np: NDArray of shape (n_alphas)
-            Level of confidences.
-
-        y_ps: NDArray of shape (n_samples, n_classes, n_alphas)
-            Prediction sets computed with the RAPS method and the
-            new value of lambda_
-
-        lambda_: NDArray of shape (n_alphas, )
-            New value of lambda_star to test
-
-        lambda_star: NDArray of shape (n_alphas, )
-            Actual optimal lambda values for each alpha.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray]
-            Arrays of shape (n_alphas, ) and (n_alpha, ) which
-            respectively represent the updated values of lambda_star
-            and the new best sizes.
-        """
-
-        sizes = [
-            classification_mean_width_score(
-                y_ps[:, :, i]
-            ) for i in range(len(alpha_np))
-        ]
-
-        sizes_improve = (sizes < best_sizes - EPSILON)
-        lambda_star = (
-            sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
-        )
-        best_sizes = sizes_improve * sizes + (1 - sizes_improve) * best_sizes
-
-        return lambda_star, best_sizes
-
-    def _find_lambda_star(
-        self,
-        y_pred_proba_raps: NDArray,
-        alpha_np: NDArray,
-        include_last_label: Union[bool, str, None],
-        k_star: NDArray
-    ) -> Union[NDArray, float]:
-        """Find the optimal value of lambda for each alpha.
-
-        Parameters
-        ----------
-        y_pred_proba_raps: NDArray of shape (n_samples, n_labels, n_alphas)
-            Predictions of the model repeated on the last axis as many times
-            as the number of alphas
-
-        alpha_np: NDArray of shape (n_alphas, )
-            Levels of confidences.
-
-        include_last_label: bool
-            Whether to include or not last label in
-            the prediction sets
-
-        k_star: NDArray of shape (n_alphas, )
-            Values of k for the regularization.
-
-        Returns
-        -------
-        ArrayLike of shape (n_alphas, )
-            Optimal values of lambda.
-        """
-        lambda_star = np.zeros(len(alpha_np))
-        best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
-
-        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]
-            true_label_cumsum_proba, cutoff = (
-                self._get_true_label_cumsum_proba(
-                    self.y_raps_no_enc,
-                    y_pred_proba_raps[:, :, 0],
-                )
-            )
-
-            true_label_cumsum_proba_reg = self._regularize_conformity_score(
-                k_star,
-                lambda_,
-                true_label_cumsum_proba,
-                cutoff
-            )
-
-            quantiles_ = compute_quantiles(
-                true_label_cumsum_proba_reg,
-                alpha_np
-            )
-
-            _, _, y_pred_proba_last = self._get_last_included_proba(
-                y_pred_proba_raps,
-                quantiles_,
-                include_last_label,
-                lambda_,
-                k_star
-            )
-
-            y_ps = np.greater_equal(
-                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
-            )
-            lambda_star, best_sizes = self._update_size_and_lambda(
-                best_sizes, alpha_np, y_ps, lambda_, lambda_star
-            )
-        if len(lambda_star) == 1:
-            lambda_star = lambda_star[0]
-        return lambda_star
-
     def _get_classes_info(
             self, estimator: ClassifierMixin, y: NDArray
     ) -> Tuple[int, NDArray]:
@@ -987,7 +444,20 @@ def _check_fit_parameter(
 
         self._check_target(y)
 
-        return estimator, cv, X, y, y_enc, sample_weight, groups, n_samples
+        cs_estimator = check_classification_conformity_score(
+            conformity_score=self.conformity_score,
+            method=self.method
+        )
+        cs_estimator.set_external_attributes(
+            method=self.method,
+            classes=self.classes_,
+            random_state=self.random_state
+        )
+
+        return (
+            estimator, cs_estimator, cv,
+            X, y, y_enc, sample_weight, groups, n_samples
+        )
 
     def _split_data(
         self,
@@ -1109,6 +579,7 @@ def fit(
         """
         # Checks
         (estimator,
+         self.conformity_score_function_,
          cv,
          X,
          y,
@@ -1158,35 +629,10 @@ def fit(
                 self.y_pred_proba_raps, self.y_raps
             )
 
-        # Conformity scores
-        if self.method == "naive":
-            self.conformity_scores_ = (
-                np.empty(y_pred_proba.shape, dtype="float")
-            )
-        elif self.method in ["score", "lac"]:
-            self.conformity_scores_ = np.take_along_axis(
-                1 - y_pred_proba, y_enc.reshape(-1, 1), axis=1
-            )
-        elif self.method in ["cumulated_score", "aps", "raps"]:
-            self.conformity_scores_, self.cutoff = (
-                self._get_true_label_cumsum_proba(y, y_pred_proba)
-            )
-            y_proba_true = np.take_along_axis(
-                y_pred_proba, y_enc.reshape(-1, 1), axis=1
-            )
-            random_state = check_random_state(self.random_state)
-            u = random_state.uniform(size=len(y_pred_proba)).reshape(-1, 1)
-            self.conformity_scores_ -= u * y_proba_true
-        elif self.method == "top_k":
-            # Here we reorder the labels by decreasing probability
-            # and get the position of each label from decreasing
-            # probability
-            self.conformity_scores_ = get_true_label_position(
-                y_pred_proba, y_enc
-            )
-        else:
-            raise ValueError(
-                "Invalid method. " f"Allowed values are {self.valid_methods_}."
+        # Compute the conformity scores
+        self.conformity_scores_ = \
+            self.conformity_score_function_.get_conformity_scores(
+                y, y_pred_proba, y_enc=y_enc, X=X
             )
 
         return self
@@ -1199,8 +645,8 @@ def predict(
         agg_scores: Optional[str] = "mean"
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
-        Prediction prediction sets on new samples based on target confidence
-        interval.
+        Prediction and prediction sets on new samples based on target
+        confidence interval.
         Prediction sets for a given ``alpha`` are deduced from:
 
         - quantiles of softmax scores (``"lac"`` method)
@@ -1215,8 +661,7 @@ def predict(
             Can be a float, a list of floats, or a ``ArrayLike`` of floats.
             Between 0 and 1, represent the uncertainty of the confidence
             interval.
-            Lower ``alpha`` produce larger (more conservative) prediction
-            sets.
+            Lower ``alpha`` produce larger (more conservative) prediction sets.
             ``alpha`` is the complement of the target coverage level.
 
             By default ``None``.
@@ -1263,20 +708,12 @@ def predict(
         - Tuple[NDArray, NDArray] of shapes
         (n_samples,) and (n_samples, n_classes, n_alpha) if alpha is not None.
         """
-        if self.method == "top_k":
-            agg_scores = "mean"
         # Checks
-        cv = check_cv(
-            self.cv, test_size=self.test_size, random_state=self.random_state
-        )
-        include_last_label = self._check_include_last_label(include_last_label)
-        alpha = cast(Optional[NDArray], check_alpha(alpha))
         check_is_fitted(self, self.fit_attributes)
-        lambda_star, k_star = None, None
+        alpha = cast(Optional[NDArray], check_alpha(alpha))
 
-        # Estimate prediction sets
+        # Estimate predictions
         y_pred = self.estimator_.single_estimator_.predict(X)
-
         if alpha is None:
             return y_pred
 
@@ -1287,149 +724,24 @@ def predict(
         alpha_np = cast(NDArray, alpha)
         check_alpha_and_n_samples(alpha_np, n)
 
-        y_pred_proba = self.estimator_.predict(X, agg_scores)
-        y_pred_proba = self._check_proba_normalized(y_pred_proba, axis=1)
-        if agg_scores != "crossval":
-            y_pred_proba = np.repeat(
-                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-            )
-
-        # Choice of the quantile
-        if self.method == "naive":
-            self.quantiles_ = 1 - alpha_np
+        # Estimate prediction sets
+        if self.method == "raps":
+            kwargs = {
+                'X_raps': self.X_raps,
+                'y_raps_no_enc': self.y_raps_no_enc,
+                'y_pred_proba_raps': self.y_pred_proba_raps,
+                'position_raps': self.position_raps,
+            }
         else:
-            if (cv == "prefit") or (agg_scores in ["mean"]):
-                if self.method == "raps":
-                    check_alpha_and_n_samples(alpha_np, len(self.X_raps))
-                    k_star = compute_quantiles(
-                        self.position_raps,
-                        alpha_np
-                    ) + 1
-                    y_pred_proba_raps = np.repeat(
-                        self.y_pred_proba_raps[:, :, np.newaxis],
-                        len(alpha_np),
-                        axis=2
-                    )
-                    lambda_star = self._find_lambda_star(
-                        y_pred_proba_raps,
-                        alpha_np,
-                        include_last_label,
-                        k_star
-                    )
-                    self.conformity_scores_regularized = (
-                        self._regularize_conformity_score(
-                                    k_star,
-                                    lambda_star,
-                                    self.conformity_scores_,
-                                    self.cutoff
-                        )
-                    )
-                    self.quantiles_ = compute_quantiles(
-                        self.conformity_scores_regularized,
-                        alpha_np
-                    )
-                else:
-                    self.quantiles_ = compute_quantiles(
-                        self.conformity_scores_,
-                        alpha_np
-                    )
-            else:
-                self.quantiles_ = (n + 1) * (1 - alpha_np)
-
-        # Build prediction sets
-        if self.method in ["score", "lac"]:
-            if (cv == "prefit") or (agg_scores == "mean"):
-                prediction_sets = np.greater_equal(
-                    y_pred_proba - (1 - self.quantiles_), -EPSILON
-                )
-            else:
-                y_pred_included = np.less_equal(
-                    (1 - y_pred_proba) - self.conformity_scores_.ravel(),
-                    EPSILON
-                ).sum(axis=2)
-                prediction_sets = np.stack(
-                    [
-                        np.greater_equal(
-                            y_pred_included - _alpha * (n - 1), -EPSILON
-                        )
-                        for _alpha in alpha_np
-                    ], axis=2
-                )
+            kwargs = {}
+
+        prediction_sets = self.conformity_score_function_.predict_set(
+            X, alpha_np,
+            estimator=self.estimator_,
+            conformity_scores=self.conformity_scores_,
+            include_last_label=include_last_label,
+            agg_scores=agg_scores,
+            **kwargs
+        )
 
-        elif self.method in ["naive", "cumulated_score", "aps", "raps"]:
-            # specify which thresholds will be used
-            if (cv == "prefit") or (agg_scores in ["mean"]):
-                thresholds = self.quantiles_
-            else:
-                thresholds = self.conformity_scores_.ravel()
-            # sort labels by decreasing probability
-            y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last = (
-                self._get_last_included_proba(
-                    y_pred_proba,
-                    thresholds,
-                    include_last_label,
-                    lambda_star,
-                    k_star,
-                )
-            )
-            # get the prediction set by taking all probabilities
-            # above the last one
-            if (cv == "prefit") or (agg_scores in ["mean"]):
-                y_pred_included = np.greater_equal(
-                    y_pred_proba - y_pred_proba_last, -EPSILON
-                )
-            else:
-                y_pred_included = np.less_equal(
-                    y_pred_proba - y_pred_proba_last, EPSILON
-                )
-            # remove last label randomly
-            if include_last_label == "randomized":
-                y_pred_included = self._add_random_tie_breaking(
-                    y_pred_included,
-                    y_pred_index_last,
-                    y_pred_proba_cumsum,
-                    y_pred_proba_last,
-                    thresholds,
-                    lambda_star,
-                    k_star
-                )
-            if (cv == "prefit") or (agg_scores in ["mean"]):
-                prediction_sets = y_pred_included
-            else:
-                # compute the number of times the inequality is verified
-                prediction_sets_summed = y_pred_included.sum(axis=2)
-                prediction_sets = np.less_equal(
-                    prediction_sets_summed[:, :, np.newaxis]
-                    - self.quantiles_[np.newaxis, np.newaxis, :],
-                    EPSILON
-                )
-        elif self.method == "top_k":
-            y_pred_proba = y_pred_proba[:, :, 0]
-            index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
-            y_pred_index_last = np.stack(
-                [
-                    index_sorted[:, quantile]
-                    for quantile in self.quantiles_
-                ], axis=1
-            )
-            y_pred_proba_last = np.stack(
-                [
-                    np.take_along_axis(
-                        y_pred_proba,
-                        y_pred_index_last[:, iq].reshape(-1, 1),
-                        axis=1
-                    )
-                    for iq, _ in enumerate(self.quantiles_)
-                ], axis=2
-            )
-            prediction_sets = np.greater_equal(
-                y_pred_proba[:, :, np.newaxis]
-                - y_pred_proba_last,
-                -EPSILON
-            )
-        else:
-            raise ValueError(
-                "Invalid method. "
-                f"Allowed values are {self.valid_methods_}."
-            )
         return y_pred, prediction_sets
diff --git a/mapie/conformity_scores/__init__.py b/mapie/conformity_scores/__init__.py
index 0dab4b62d..3b47311da 100644
--- a/mapie/conformity_scores/__init__.py
+++ b/mapie/conformity_scores/__init__.py
@@ -1,11 +1,18 @@
-from .conformity_scores import ConformityScore
-from .residual_conformity_scores import (AbsoluteConformityScore,
-                                         GammaConformityScore,
-                                         ResidualNormalisedScore)
+from .regression import BaseRegressionScore
+from .classification import BaseClassificationScore
+from .bounds import (
+    AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
+)
+from .sets import APS, LAC, TopK
+
 
 __all__ = [
-    "ConformityScore",
+    "BaseRegressionScore",
+    "BaseClassificationScore",
     "AbsoluteConformityScore",
     "GammaConformityScore",
-    "ResidualNormalisedScore"
+    "ResidualNormalisedScore",
+    "LAC",
+    "APS",
+    "TopK"
 ]
diff --git a/mapie/conformity_scores/bounds/__init__.py b/mapie/conformity_scores/bounds/__init__.py
new file mode 100644
index 000000000..01f85b138
--- /dev/null
+++ b/mapie/conformity_scores/bounds/__init__.py
@@ -0,0 +1,10 @@
+from .absolute import AbsoluteConformityScore
+from .gamma import GammaConformityScore
+from .residuals import ResidualNormalisedScore
+
+
+__all__ = [
+    "AbsoluteConformityScore",
+    "GammaConformityScore",
+    "ResidualNormalisedScore",
+]
diff --git a/mapie/conformity_scores/bounds/absolute.py b/mapie/conformity_scores/bounds/absolute.py
new file mode 100644
index 000000000..90c1c3e94
--- /dev/null
+++ b/mapie/conformity_scores/bounds/absolute.py
@@ -0,0 +1,52 @@
+import numpy as np
+
+from mapie._typing import ArrayLike, NDArray
+from mapie.conformity_scores import BaseRegressionScore
+
+
+class AbsoluteConformityScore(BaseRegressionScore):
+    """
+    Absolute conformity score.
+
+    The signed conformity score = y - y_pred.
+    The conformity score is symmetrical.
+
+    This is appropriate when the confidence interval is symmetrical and
+    its range is approximatively the same over the range of predicted values.
+    """
+
+    def __init__(
+        self,
+        sym: bool = True,
+    ) -> None:
+        super().__init__(sym=sym, consistency_check=True)
+
+    def get_signed_conformity_scores(
+        self,
+        y: ArrayLike,
+        y_pred: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        Compute the signed conformity scores from the predicted values
+        and the observed ones, from the following formula:
+        signed conformity score = y - y_pred
+        """
+        return np.subtract(y, y_pred)
+
+    def get_estimation_distribution(
+        self,
+        y_pred: ArrayLike,
+        conformity_scores: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        Compute samples of the estimation distribution from the predicted
+        values and the conformity scores, from the following formula:
+        signed conformity score = y - y_pred
+        <=> y = y_pred + signed conformity score
+
+        ``conformity_scores`` can be either the conformity scores or
+        the quantile of the conformity scores.
+        """
+        return np.add(y_pred, conformity_scores)
diff --git a/mapie/conformity_scores/bounds/gamma.py b/mapie/conformity_scores/bounds/gamma.py
new file mode 100644
index 000000000..09f161e02
--- /dev/null
+++ b/mapie/conformity_scores/bounds/gamma.py
@@ -0,0 +1,86 @@
+import numpy as np
+
+from mapie._typing import ArrayLike, NDArray
+from mapie.conformity_scores import BaseRegressionScore
+
+
+class GammaConformityScore(BaseRegressionScore):
+    """
+    Gamma conformity score.
+
+    The signed conformity score = (y - y_pred) / y_pred.
+    The conformity score is not symmetrical.
+
+    This is appropriate when the confidence interval is not symmetrical and
+    its range depends on the predicted values. Like the Gamma distribution,
+    its support is limited to strictly positive reals.
+    """
+
+    def __init__(
+        self,
+        sym: bool = False,
+    ) -> None:
+        super().__init__(sym=sym, consistency_check=False)
+
+    def _check_observed_data(
+        self,
+        y: ArrayLike,
+    ) -> None:
+        if not self._all_strictly_positive(y):
+            raise ValueError(
+                f"At least one of the observed target is negative "
+                f"which is incompatible with {self.__class__.__name__}. "
+                "All values must be strictly positive, "
+                "in conformity with the Gamma distribution support."
+            )
+
+    def _check_predicted_data(
+        self,
+        y_pred: ArrayLike,
+    ) -> None:
+        if not self._all_strictly_positive(y_pred):
+            raise ValueError(
+                f"At least one of the predicted target is negative "
+                f"which is incompatible with {self.__class__.__name__}. "
+                "All values must be strictly positive, "
+                "in conformity with the Gamma distribution support."
+            )
+
+    @staticmethod
+    def _all_strictly_positive(
+        y: ArrayLike,
+    ) -> bool:
+        return not np.any(np.less_equal(y, 0))
+
+    def get_signed_conformity_scores(
+        self,
+        y: ArrayLike,
+        y_pred: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        Compute the signed conformity scores from the observed values
+        and the predicted ones, from the following formula:
+        signed conformity score = (y - y_pred) / y_pred
+        """
+        self._check_observed_data(y)
+        self._check_predicted_data(y_pred)
+        return np.divide(np.subtract(y, y_pred), y_pred)
+
+    def get_estimation_distribution(
+        self,
+        y_pred: ArrayLike,
+        conformity_scores: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        Compute samples of the estimation distribution from the predicted
+        values and the conformity scores, from the following formula:
+        signed conformity score = (y - y_pred) / y_pred
+        <=> y = y_pred * (1 + signed conformity score)
+
+        ``conformity_scores`` can be either the conformity scores or
+        the quantile of the conformity scores.
+        """
+        self._check_predicted_data(y_pred)
+        return np.multiply(y_pred, np.add(1, conformity_scores))
diff --git a/mapie/conformity_scores/residual_conformity_scores.py b/mapie/conformity_scores/bounds/residuals.py
similarity index 69%
rename from mapie/conformity_scores/residual_conformity_scores.py
rename to mapie/conformity_scores/bounds/residuals.py
index d9b174e49..f6bc9c7f3 100644
--- a/mapie/conformity_scores/residual_conformity_scores.py
+++ b/mapie/conformity_scores/bounds/residuals.py
@@ -9,142 +9,11 @@
 from sklearn.utils.validation import (check_is_fitted, check_random_state,
                                       indexable)
 
-from mapie._machine_precision import EPSILON
 from mapie._typing import ArrayLike, NDArray
-from mapie.conformity_scores import ConformityScore
+from mapie.conformity_scores import BaseRegressionScore
 
 
-class AbsoluteConformityScore(ConformityScore):
-    """
-    Absolute conformity score.
-
-    The signed conformity score = y - y_pred.
-    The conformity score is symmetrical.
-
-    This is appropriate when the confidence interval is symmetrical and
-    its range is approximatively the same over the range of predicted values.
-    """
-
-    def __init__(
-        self,
-        sym: bool = True,
-    ) -> None:
-        super().__init__(sym=sym, consistency_check=True)
-
-    def get_signed_conformity_scores(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_pred: ArrayLike,
-    ) -> NDArray:
-        """
-        Compute the signed conformity scores from the predicted values
-        and the observed ones, from the following formula:
-        signed conformity score = y - y_pred
-        """
-        return np.subtract(y, y_pred)
-
-    def get_estimation_distribution(
-        self,
-        X: ArrayLike,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike
-    ) -> NDArray:
-        """
-        Compute samples of the estimation distribution from the predicted
-        values and the conformity scores, from the following formula:
-        signed conformity score = y - y_pred
-        <=> y = y_pred + signed conformity score
-
-        ``conformity_scores`` can be either the conformity scores or
-        the quantile of the conformity scores.
-        """
-        return np.add(y_pred, conformity_scores)
-
-
-class GammaConformityScore(ConformityScore):
-    """
-    Gamma conformity score.
-
-    The signed conformity score = (y - y_pred) / y_pred.
-    The conformity score is not symmetrical.
-
-    This is appropriate when the confidence interval is not symmetrical and
-    its range depends on the predicted values. Like the Gamma distribution,
-    its support is limited to strictly positive reals.
-    """
-
-    def __init__(
-        self,
-        sym: bool = False,
-    ) -> None:
-        super().__init__(sym=sym, consistency_check=False, eps=EPSILON)
-
-    def _check_observed_data(
-        self,
-        y: ArrayLike,
-    ) -> None:
-        if not self._all_strictly_positive(y):
-            raise ValueError(
-                f"At least one of the observed target is negative "
-                f"which is incompatible with {self.__class__.__name__}. "
-                "All values must be strictly positive, "
-                "in conformity with the Gamma distribution support."
-            )
-
-    def _check_predicted_data(
-        self,
-        y_pred: ArrayLike,
-    ) -> None:
-        if not self._all_strictly_positive(y_pred):
-            raise ValueError(
-                f"At least one of the predicted target is negative "
-                f"which is incompatible with {self.__class__.__name__}. "
-                "All values must be strictly positive, "
-                "in conformity with the Gamma distribution support."
-            )
-
-    @staticmethod
-    def _all_strictly_positive(
-        y: ArrayLike,
-    ) -> bool:
-        return not np.any(np.less_equal(y, 0))
-
-    def get_signed_conformity_scores(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_pred: ArrayLike,
-    ) -> NDArray:
-        """
-        Compute the signed conformity scores from the observed values
-        and the predicted ones, from the following formula:
-        signed conformity score = (y - y_pred) / y_pred
-        """
-        self._check_observed_data(y)
-        self._check_predicted_data(y_pred)
-        return np.divide(np.subtract(y, y_pred), y_pred)
-
-    def get_estimation_distribution(
-        self,
-        X: ArrayLike,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike
-    ) -> NDArray:
-        """
-        Compute samples of the estimation distribution from the predicted
-        values and the conformity scores, from the following formula:
-        signed conformity score = (y - y_pred) / y_pred
-        <=> y = y_pred * (1 + signed conformity score)
-
-        ``conformity_scores`` can be either the conformity scores or
-        the quantile of the conformity scores.
-        """
-        self._check_predicted_data(y_pred)
-        return np.multiply(y_pred, np.add(1, conformity_scores))
-
-
-class ResidualNormalisedScore(ConformityScore):
+class ResidualNormalisedScore(BaseRegressionScore):
     """
     Residual Normalised score.
 
@@ -200,7 +69,8 @@ def __init__(
         self.random_state = random_state
 
     def _check_estimator(
-        self, estimator: Optional[RegressorMixin] = None
+        self,
+        estimator: Optional[RegressorMixin] = None
     ) -> RegressorMixin:
         """
         Check if estimator is ``None``,
@@ -361,9 +231,10 @@ def _predict_residual_estimator(
 
     def get_signed_conformity_scores(
         self,
-        X: ArrayLike,
         y: ArrayLike,
-        y_pred: ArrayLike
+        y_pred: ArrayLike,
+        X: Optional[ArrayLike] = None,
+        **kwargs
     ) -> NDArray:
         """
         Computes the signed conformity score = (y - y_pred) / r_pred.
@@ -374,6 +245,8 @@ def get_signed_conformity_scores(
         The learning is done with the log of the residual and later we
         use the exponential of the prediction to avoid negative values.
         """
+        assert not (X is None)  # TODO
+
         (X, y, y_pred,
          self.residual_estimator_,
          random_state) = self._check_parameters(X, y, y_pred)
@@ -418,9 +291,10 @@ def get_signed_conformity_scores(
 
     def get_estimation_distribution(
         self,
-        X: ArrayLike,
         y_pred: ArrayLike,
-        conformity_scores: ArrayLike
+        conformity_scores: ArrayLike,
+        X: Optional[ArrayLike] = None,
+        **kwargs
     ) -> NDArray:
         """
         Compute samples of the estimation distribution from the predicted
@@ -433,6 +307,8 @@ def get_estimation_distribution(
         ``conformity_scores`` can be either the conformity scores or
         the quantile of the conformity scores.
         """
+        assert not (X is None)  # TODO
+
         r_pred = self._predict_residual_estimator(X).reshape((-1, 1))
         if not self.prefit:
             return np.add(
diff --git a/mapie/conformity_scores/checks.py b/mapie/conformity_scores/checks.py
deleted file mode 100644
index 66a9277d2..000000000
--- a/mapie/conformity_scores/checks.py
+++ /dev/null
@@ -1,38 +0,0 @@
-from typing import Optional
-
-from .conformity_scores import ConformityScore
-from .residual_conformity_scores import AbsoluteConformityScore
-
-
-def check_conformity_score(
-    conformity_score: Optional[ConformityScore],
-    sym: bool = True,
-) -> ConformityScore:
-    """
-    Check parameter ``conformity_score``.
-
-    Raises
-    ------
-    ValueError
-        If parameter is not valid.
-
-    Examples
-    --------
-    >>> from mapie.conformity_scores.checks import check_conformity_score
-    >>> try:
-    ...     check_conformity_score(1)
-    ... except Exception as exception:
-    ...     print(exception)
-    ...
-    Invalid conformity_score argument.
-    Must be None or a ConformityScore instance.
-    """
-    if conformity_score is None:
-        return AbsoluteConformityScore(sym=sym)
-    elif isinstance(conformity_score, ConformityScore):
-        return conformity_score
-    else:
-        raise ValueError(
-            "Invalid conformity_score argument.\n"
-            "Must be None or a ConformityScore instance."
-        )
diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
new file mode 100644
index 000000000..6c91b88ee
--- /dev/null
+++ b/mapie/conformity_scores/classification.py
@@ -0,0 +1,103 @@
+from abc import ABCMeta, abstractmethod
+
+from mapie.conformity_scores.interface import BaseConformityScore
+from mapie.estimator.classifier import EnsembleClassifier
+
+from mapie._machine_precision import EPSILON
+from mapie._typing import NDArray
+
+
+class BaseClassificationScore(BaseConformityScore, metaclass=ABCMeta):
+    """
+    Base conformity score class for classification task.
+
+    This class should not be used directly. Use derived classes instead.
+
+    Parameters
+    ----------
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+    """
+
+    def __init__(
+        self,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        super().__init__(consistency_check=consistency_check, eps=eps)
+
+    @abstractmethod
+    def get_sets(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        conformity_scores: NDArray,
+        **kwargs
+    ):
+        """
+        Compute classes of the prediction sets from the observed values,
+        the estimator of type ``EnsembleClassifier`` and the conformity scores.
+
+        Parameters
+        ----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, n_classes, n_alpha)
+            Prediction sets (Booleans indicate whether classes are included).
+        """
+
+    def predict_set(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        **kwargs
+    ):
+        """
+        Compute the prediction sets on new samples based on the uncertainty of
+        the target confidence interval.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, ...)
+            The input data or samples for prediction.
+
+        alpha_np: NDArray of shape (n_alpha, )
+            Represents the uncertainty of the confidence interval to produce.
+
+        **kwargs: dict
+            Additional keyword arguments.
+
+        Returns:
+        --------
+        The output strcture depend on the ``get_sets`` method.
+            The prediction sets for each sample and each alpha level.
+        """
+        return self.get_sets(X=X, alpha_np=alpha_np, **kwargs)
diff --git a/mapie/conformity_scores/interface.py b/mapie/conformity_scores/interface.py
new file mode 100644
index 000000000..680c6cc9e
--- /dev/null
+++ b/mapie/conformity_scores/interface.py
@@ -0,0 +1,256 @@
+from abc import ABCMeta, abstractmethod
+
+import numpy as np
+
+from mapie._compatibility import np_nanquantile
+from mapie._machine_precision import EPSILON
+from mapie._typing import NDArray
+
+
+class BaseConformityScore(metaclass=ABCMeta):
+    """
+    Base class for conformity scores.
+
+    This class should not be used directly. Use derived classes instead.
+
+    Parameters
+    ----------
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+    """
+
+    def __init__(
+        self,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        self.consistency_check = consistency_check
+        self.eps = eps
+
+    def set_external_attributes(
+        self,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Must be overloaded by subclasses if necessary to add more attributes,
+        particularly when the attributes are known after the object has been
+        instantiated.
+        """
+        pass
+
+    def check_consistency(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> None:
+        """
+        Check consistency between the following methods:
+        ``get_estimation_distribution`` and ``get_signed_conformity_scores``
+
+        The following equality should be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples, ...)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Raises
+        ------
+        ValueError
+            If the two methods are not consistent.
+        """
+        score_distribution = self.get_estimation_distribution(
+            y_pred, conformity_scores, **kwargs
+        )
+        abs_conformity_scores = np.abs(np.subtract(score_distribution, y))
+        max_conf_score = np.max(abs_conformity_scores)
+        if max_conf_score > self.eps:
+            raise ValueError(
+                "The two functions get_conformity_scores and "
+                "get_estimation_distribution of the BaseConformityScore class "
+                "are not consistent. "
+                "The following equation must be verified: "
+                "self.get_estimation_distribution(y_pred, "
+                "self.get_conformity_scores(y, y_pred)) == y. "
+                f"The maximum conformity score is {max_conf_score}. "
+                "The eps attribute may need to be increased if you are "
+                "sure that the two methods are consistent."
+            )
+
+    @abstractmethod
+    def get_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_conformity_scores``.
+        Subclasses should implement this method!
+
+        Compute the sample conformity scores given the predicted and
+        observed targets.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples, ...)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, ...)
+            Conformity scores.
+        """
+
+    @abstractmethod
+    def get_estimation_distribution(
+        self,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, ...)
+            Observed values.
+        """
+
+    @staticmethod
+    def get_quantile(
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        axis: int = 0,
+        reversed: bool = False,
+        unbounded: bool = False
+    ) -> NDArray:
+        """
+        Compute the alpha quantile of the conformity scores.
+
+        Parameters
+        ----------
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Values from which the quantile is computed.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        axis: int
+            The axis from which to compute the quantile.
+
+            By default ``0``.
+
+        reversed: bool
+            Boolean specifying whether we take the upper or lower quantile,
+            if False, the alpha quantile, otherwise the (1-alpha) quantile.
+
+            By default ``False``.
+
+        unbounded: bool
+            Boolean specifying whether infinite prediction intervals
+            could be produced (when alpha_np is greater than or equal to 1.).
+
+            By default ``False``.
+
+        Returns
+        -------
+        NDArray of shape (1, n_alpha) or (n_samples, n_alpha)
+            The quantiles of the conformity scores.
+        """
+        n_ref = conformity_scores.shape[1-axis]
+        n_calib = np.min(np.sum(~np.isnan(conformity_scores), axis=axis))
+        signed = 1-2*reversed
+
+        # Adapt alpha w.r.t upper/lower : alpha vs. 1-alpha
+        alpha_ref = (1-2*alpha_np)*reversed + alpha_np
+
+        # Adjust alpha w.r.t quantile correction
+        alpha_cor = np.ceil(alpha_ref*(n_calib+1))/n_calib
+        alpha_cor = np.clip(alpha_cor, a_min=0, a_max=1)
+
+        # Compute the target quantiles:
+        # If unbounded is True and alpha is greater than or equal to 1,
+        # the quantile is set to infinity.
+        # Otherwise, the quantile is calculated as the corrected lower quantile
+        # of the signed conformity scores.
+        quantile = signed * np.column_stack([
+            np_nanquantile(
+                signed * conformity_scores, _alpha_cor,
+                axis=axis, method="lower"
+            ) if not (unbounded and _alpha >= 1) else np.inf * np.ones(n_ref)
+            for _alpha, _alpha_cor in zip(alpha_ref, alpha_cor)
+        ])
+        return quantile
+
+    @abstractmethod
+    def predict_set(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        **kwargs
+    ):
+        """
+        Compute the prediction sets on new samples based on the uncertainty of
+        the target confidence interval.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, ...)
+            The input data or samples for prediction.
+
+        alpha_np: NDArray of shape (n_alpha, )
+            Represents the uncertainty of the confidence interval to produce.
+
+        **kwargs: dict
+            Additional keyword arguments.
+
+        Returns:
+        --------
+        The output strcture depend on the subclass.
+            The prediction sets for each sample and each alpha level.
+        """
diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/regression.py
similarity index 50%
rename from mapie/conformity_scores/conformity_scores.py
rename to mapie/conformity_scores/regression.py
index a96df9945..2e878e349 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/regression.py
@@ -3,17 +3,19 @@
 
 import numpy as np
 
-from mapie._compatibility import np_nanquantile
-from mapie._typing import ArrayLike, NDArray
+from mapie.conformity_scores.interface import BaseConformityScore
 from mapie.estimator.regressor import EnsembleRegressor
 
+from mapie._compatibility import np_nanquantile
+from mapie._machine_precision import EPSILON
+from mapie._typing import NDArray
+
 
-class ConformityScore(metaclass=ABCMeta):
+class BaseRegressionScore(BaseConformityScore, metaclass=ABCMeta):
     """
-    Base class for conformity scores.
+    Base conformity score class for regression task.
 
-    Warning: This class should not be used directly.
-    Use derived classes instead.
+    This class should not be used directly. Use derived classes instead.
 
     Parameters
     ----------
@@ -21,61 +23,51 @@ class ConformityScore(metaclass=ABCMeta):
         Whether to consider the conformity score as symmetrical or not.
 
     consistency_check: bool, optional
-        Whether to check the consistency between the following methods:
-        - ``get_estimation_distribution`` and
-        - ``get_signed_conformity_scores``
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
 
         By default ``True``.
 
     eps: float, optional
-        Threshold to consider when checking the consistency between the
-        following methods:
-        - ``get_estimation_distribution`` and
-        - ``get_signed_conformity_scores``
-        The following equality must be verified:
-        ``self.get_estimation_distribution(
-            X,
-            y_pred,
-            self.get_conformity_scores(X, y, y_pred)
-        ) == y``
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
         It should be specified if ``consistency_check==True``.
 
-        By default ``np.float64(1e-8)``.
+        By default, it is defined by the default precision.
     """
 
     def __init__(
-        self,
-        sym: bool,
+        self, sym: bool,
         consistency_check: bool = True,
-        eps: np.float64 = np.float64(1e-8),
+        eps: float = float(EPSILON),
     ):
+        super().__init__(consistency_check=consistency_check, eps=eps)
         self.sym = sym
-        self.consistency_check = consistency_check
-        self.eps = eps
 
     @abstractmethod
     def get_signed_conformity_scores(
         self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_pred: ArrayLike,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
     ) -> NDArray:
         """
-        Placeholder for ``get_signed_conformity_scores``.
+        Placeholder for ``get_conformity_scores``.
         Subclasses should implement this method!
 
-        Compute the signed conformity scores from the predicted values
-        and the observed ones.
+        Compute the sample conformity scores given the predicted and
+        observed targets.
 
         Parameters
         ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Observed feature values.
-
-        y: ArrayLike of shape (n_samples,)
+        y: NDArray of shape (n_samples,)
             Observed target values.
 
-        y_pred: ArrayLike of shape (n_samples,)
+        y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
         Returns
@@ -84,113 +76,17 @@ def get_signed_conformity_scores(
             Signed conformity scores.
         """
 
-    @abstractmethod
-    def get_estimation_distribution(
-        self,
-        X: ArrayLike,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike
-    ) -> NDArray:
-        """
-        Placeholder for ``get_estimation_distribution``.
-        Subclasses should implement this method!
-
-        Compute samples of the estimation distribution from the predicted
-        targets and ``conformity_scores`` that can be either the conformity
-        scores or the quantile of the conformity scores.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Observed feature values.
-
-        y_pred: ArrayLike
-            The shape is either (n_samples, n_references): when the
-            method is called in ``get_bounds`` it needs a prediction per train
-            sample for each test sample to compute the bounds.
-            Or (n_samples,): when it is called in ``check_consistency``
-
-        conformity_scores: ArrayLike
-            The shape is either (n_samples, 1) when it is the
-            conformity scores themselves or (1, n_alpha) when it is only the
-            quantile of the conformity scores.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_alpha) or
-        (n_samples, n_references) according to the shape of ``y_pred``
-            Observed values.
-        """
-
-    def check_consistency(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike,
-    ) -> None:
-        """
-        Check consistency between the following methods:
-        ``get_estimation_distribution`` and ``get_signed_conformity_scores``
-
-        The following equality should be verified:
-        ``self.get_estimation_distribution(
-            X,
-            y_pred,
-            self.get_conformity_scores(X, y, y_pred)
-        ) == y``
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Observed feature values.
-
-        y: ArrayLike of shape (n_samples,)
-            Observed target values.
-
-        y_pred: ArrayLike of shape (n_samples,)
-            Predicted target values.
-
-        conformity_scores: ArrayLike of shape (n_samples,)
-            Conformity scores.
-
-        Raises
-        ------
-        ValueError
-            If the two methods are not consistent.
-        """
-        score_distribution = self.get_estimation_distribution(
-            X, y_pred, conformity_scores
-        )
-        abs_conformity_scores = np.abs(np.subtract(score_distribution, y))
-        max_conf_score = np.max(abs_conformity_scores)
-        if max_conf_score > self.eps:
-            raise ValueError(
-                "The two functions get_conformity_scores and "
-                "get_estimation_distribution of the ConformityScore class "
-                "are not consistent. "
-                "The following equation must be verified: "
-                "self.get_estimation_distribution(X, y_pred, "
-                "self.get_conformity_scores(X, y, y_pred)) == y"  # noqa: E501
-                f"The maximum conformity score is {max_conf_score}."
-                "The eps attribute may need to be increased if you are "
-                "sure that the two methods are consistent."
-            )
-
     def get_conformity_scores(
         self,
-        X: ArrayLike,
-        y: ArrayLike,
-        y_pred: ArrayLike,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
     ) -> NDArray:
         """
         Get the conformity score considering the symmetrical property if so.
 
         Parameters
         ----------
-        X: NDArray of shape (n_samples, n_features)
-            Observed feature values.
-
         y: NDArray of shape (n_samples,)
             Observed target values.
 
@@ -202,82 +98,14 @@ def get_conformity_scores(
         NDArray of shape (n_samples,)
             Conformity scores.
         """
-        conformity_scores = self.get_signed_conformity_scores(X, y, y_pred)
+        conformity_scores = \
+            self.get_signed_conformity_scores(y, y_pred, **kwargs)
         if self.consistency_check:
-            self.check_consistency(X, y, y_pred, conformity_scores)
+            self.check_consistency(y, y_pred, conformity_scores, **kwargs)
         if self.sym:
             conformity_scores = np.abs(conformity_scores)
         return conformity_scores
 
-    @staticmethod
-    def get_quantile(
-        conformity_scores: NDArray,
-        alpha_np: NDArray,
-        axis: int,
-        reversed: bool = False,
-        unbounded: bool = False
-    ) -> NDArray:
-        """
-        Compute the alpha quantile of the conformity scores or the conformity
-        scores aggregated with the predictions.
-
-        Parameters
-        ----------
-        conformity_scores: NDArray of shape (n_samples,) or
-        (n_samples, n_references)
-            Values from which the quantile is computed, it can be the
-            conformity scores or the conformity scores aggregated with
-            the predictions.
-
-        alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
-
-        axis: int
-            The axis from which to compute the quantile.
-
-        reversed: bool
-            Boolean specifying whether we take the upper or lower quantile,
-            if False, the alpha quantile, otherwise the (1-alpha) quantile.
-
-            By default ``False``.
-
-        unbounded: bool
-            Boolean specifying whether infinite prediction intervals
-            could be produced (when alpha_np is greater than or equal to 1.).
-
-            By default ``False``.
-
-        Returns
-        -------
-        NDArray of shape (1, n_alpha) or (n_samples, n_alpha)
-            The quantile of the conformity scores.
-        """
-        n_ref = conformity_scores.shape[1-axis]
-        n_calib = np.min(np.sum(~np.isnan(conformity_scores), axis=axis))
-        signed = 1-2*reversed
-
-        # Adapt alpha w.r.t upper/lower : alpha vs. 1-alpha
-        alpha_ref = (1-2*alpha_np)*reversed + alpha_np
-
-        # Adjust alpha w.r.t quantile correction
-        alpha_cor = np.ceil(alpha_ref*(n_calib+1))/n_calib
-        alpha_cor = np.clip(alpha_cor, a_min=0, a_max=1)
-
-        # Compute the target quantiles:
-        # If unbounded is True and alpha is greater than or equal to 1,
-        # the quantile is set to infinity.
-        # Otherwise, the quantile is calculated as the corrected lower quantile
-        # of the signed conformity scores.
-        quantile = signed * np.column_stack([
-            np_nanquantile(
-                signed * conformity_scores, _alpha_cor,
-                axis=axis, method="lower"
-            ) if not (unbounded and _alpha >= 1) else np.inf * np.ones(n_ref)
-            for _alpha, _alpha_cor in zip(alpha_ref, alpha_cor)
-        ])
-        return quantile
-
     @staticmethod
     def _beta_optimize(
         alpha_np: NDArray,
@@ -292,15 +120,15 @@ def _beta_optimize(
         alpha_np: NDArray
             The quantiles to compute.
 
-        upper_bounds: NDArray
+        upper_bounds: NDArray of shape (n_samples,)
             The array of upper values.
 
-        lower_bounds: NDArray
+        lower_bounds: NDArray of shape (n_samples,)
             The array of lower values.
 
         Returns
         -------
-        NDArray
+        NDArray of shape (n_samples,)
             Array of betas minimizing the differences
             ``(1-alpha+beta)-quantile - beta-quantile``.
         """
@@ -337,10 +165,10 @@ def _beta_optimize(
 
     def get_bounds(
         self,
-        X: ArrayLike,
+        X: NDArray,
+        alpha_np: NDArray,
         estimator: EnsembleRegressor,
         conformity_scores: NDArray,
-        alpha_np: NDArray,
         ensemble: bool = False,
         method: str = 'base',
         optimize_beta: bool = False,
@@ -352,19 +180,19 @@ def get_bounds(
 
         Parameters
         ----------
-        X: ArrayLike of shape (n_samples, n_features)
+        X: NDArray of shape (n_samples, n_features)
             Observed feature values.
 
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
         estimator: EnsembleRegressor
             Estimator that is fitted to predict y from X.
 
-        conformity_scores: ArrayLike of shape (n_samples,)
+        conformity_scores: NDArray of shape (n_samples,)
             Conformity scores.
 
-        alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
-
         ensemble: bool
             Boolean determining whether the predictions are ensembled or not.
 
@@ -426,10 +254,10 @@ def get_bounds(
             alpha_up = 1 - alpha_np if self.sym else 1 - alpha_np + beta_np
 
             conformity_scores_low = self.get_estimation_distribution(
-                X, y_pred_low, signed * conformity_scores
+                y_pred_low, signed * conformity_scores, X=X
             )
             conformity_scores_up = self.get_estimation_distribution(
-                X, y_pred_up, conformity_scores
+                y_pred_up, conformity_scores, X=X
             )
             bound_low = self.get_quantile(
                 conformity_scores_low, alpha_low, axis=1, reversed=True,
@@ -463,10 +291,38 @@ def get_bounds(
                 )
 
             bound_low = self.get_estimation_distribution(
-                X, y_pred_low, quantile_low
+                y_pred_low, quantile_low, X=X
             )
             bound_up = self.get_estimation_distribution(
-                X, y_pred_up, quantile_up
+                y_pred_up, quantile_up, X=X
             )
 
         return y_pred, bound_low, bound_up
+
+    def predict_set(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        **kwargs
+    ):
+        """
+        Compute the prediction sets on new samples based on the uncertainty of
+        the target confidence interval.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, ...)
+            The input data or samples for prediction.
+
+        alpha_np: NDArray of shape (n_alpha, )
+            Represents the uncertainty of the confidence interval to produce.
+
+        **kwargs: dict
+            Additional keyword arguments.
+
+        Returns:
+        --------
+        The output strcture depend on the ``get_bounds`` method.
+            The prediction sets for each sample and each alpha level.
+        """
+        return self.get_bounds(X=X, alpha_np=alpha_np, **kwargs)
diff --git a/mapie/conformity_scores/sets/__init__.py b/mapie/conformity_scores/sets/__init__.py
new file mode 100644
index 000000000..87b6a37e6
--- /dev/null
+++ b/mapie/conformity_scores/sets/__init__.py
@@ -0,0 +1,10 @@
+from .lac import LAC
+from .aps import APS
+from .topk import TopK
+
+
+__all__ = [
+    "LAC",
+    "APS",
+    "TopK",
+]
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
new file mode 100644
index 000000000..6cd282260
--- /dev/null
+++ b/mapie/conformity_scores/sets/aps.py
@@ -0,0 +1,497 @@
+from typing import Optional, Tuple, Union, cast
+
+import numpy as np
+from sklearn.dummy import check_random_state
+
+from mapie.conformity_scores.classification import BaseClassificationScore
+from mapie.conformity_scores.sets.utils import (
+    add_random_tie_breaking, check_include_last_label, check_proba_normalized,
+    get_last_included_proba, get_true_label_cumsum_proba
+)
+from mapie.estimator.classifier import EnsembleClassifier
+
+from mapie._machine_precision import EPSILON
+from mapie._typing import ArrayLike, NDArray
+from mapie.metrics import classification_mean_width_score
+from mapie.utils import check_alpha_and_n_samples, compute_quantiles
+
+
+class APS(BaseClassificationScore):
+    """
+    Adaptive Prediction Sets (APS) method-based non-conformity score.
+    Three differents method are available in this class:
+
+    - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
+
+    - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
+        Sets method. It is based on the sum of the softmax outputs of the
+        labels until the true label is reached, on the calibration set.
+        See [1] for more details.
+
+    - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
+        same technique as ``"aps"`` method but with a penalty term
+        to reduce the size of prediction sets. See [2] for more
+        details. For now, this method only works with ``"prefit"`` and
+        ``"split"`` strategies.
+
+    References
+    ----------
+    [1] Yaniv Romano, Matteo Sesia and Emmanuel J. Candès.
+    "Classification with Valid and Adaptive Coverage."
+    NeurIPS 202 (spotlight) 2020.
+
+    [2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
+    and Jitendra Malik.
+    "Uncertainty Sets for Image Classifiers using Conformal Prediction."
+    International Conference on Learning Representations 2021.
+    """
+
+    def __init__(
+        self,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        super().__init__(
+            consistency_check=consistency_check,
+            eps=eps
+        )
+
+    def set_external_attributes(
+        self,
+        method: str = 'aps',
+        classes: Optional[ArrayLike] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        method: str
+            Method to choose for prediction interval estimates.
+            Methods available in this class: ``aps``, ``raps`` and ``naive``.
+
+            By default ``aps`` for APS method.
+
+        classes: Optional[ArrayLike]
+            Names of the classes.
+
+            By default ``None``.
+
+        random_state: Optional[Union[int, RandomState]]
+            Pseudo random number generator state.
+        """
+        super().set_external_attributes(**kwargs)
+        self.method = method
+        self.classes = classes
+        self.random_state = random_state
+
+    def get_conformity_scores(
+        self,
+        y: ArrayLike,
+        y_pred: ArrayLike,
+        y_enc: Optional[ArrayLike] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        Get the conformity score.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+        y = cast(NDArray, y)
+        y_pred = cast(NDArray, y_pred)
+        y_enc = cast(NDArray, y_enc)
+        classes = cast(NDArray, self.classes)
+
+        # Conformity scores
+        if self.method == "naive":
+            conformity_scores = (
+                np.empty(y_pred.shape, dtype="float")
+            )
+        else:
+            conformity_scores, self.cutoff = (
+                get_true_label_cumsum_proba(y, y_pred, classes)
+            )
+            y_proba_true = np.take_along_axis(
+                y_pred, y_enc.reshape(-1, 1), axis=1
+            )
+            random_state = check_random_state(self.random_state)
+            random_state = cast(np.random.RandomState, random_state)
+            u = random_state.uniform(size=len(y_pred)).reshape(-1, 1)
+            conformity_scores -= u * y_proba_true
+
+        return conformity_scores
+
+    def get_estimation_distribution(
+        self,
+        y_pred: ArrayLike,
+        conformity_scores: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, ...)
+            Observed values.
+        """
+        return np.array([])
+
+    @staticmethod
+    def _regularize_conformity_score(
+        k_star: NDArray,
+        lambda_: Union[NDArray, float],
+        conf_score: NDArray,
+        cutoff: NDArray
+    ) -> NDArray:
+        """
+        Regularize the conformity scores with the ``"raps"``
+        method. See algo. 2 in [3].
+
+        Parameters
+        ----------
+        k_star: NDArray of shape (n_alphas, )
+            Optimal value of k (called k_reg in the paper). There
+            is one value per alpha.
+
+        lambda_: Union[NDArray, float] of shape (n_alphas, )
+            One value of lambda for each alpha.
+
+        conf_score: NDArray of shape (n_samples, 1)
+            Conformity scores.
+
+        cutoff: NDArray of shape (n_samples, 1)
+            Position of the true label.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, 1, n_alphas)
+            Regularized conformity scores. The regularization
+            depends on the value of alpha.
+        """
+        conf_score = np.repeat(
+            conf_score[:, :, np.newaxis], len(k_star), axis=2
+        )
+        cutoff = np.repeat(
+            cutoff[:, np.newaxis], len(k_star), axis=1
+        )
+        conf_score += np.maximum(
+            np.expand_dims(
+                lambda_ * (cutoff - k_star),
+                axis=1
+            ),
+            0
+        )
+        return conf_score
+
+    def _update_size_and_lambda(
+        self,
+        best_sizes: NDArray,
+        alpha_np: NDArray,
+        y_ps: NDArray,
+        lambda_: Union[NDArray, float],
+        lambda_star: NDArray
+    ) -> Tuple[NDArray, NDArray]:
+        """Update the values of the optimal lambda if the
+        average size of the prediction sets decreases with
+        this new value of lambda.
+
+        Parameters
+        ----------
+        best_sizes: NDArray of shape (n_alphas, )
+            Smallest average prediciton set size before testing
+            for the new value of lambda_
+
+        alpha_np: NDArray of shape (n_alphas)
+            Level of confidences.
+
+        y_ps: NDArray of shape (n_samples, n_classes, n_alphas)
+            Prediction sets computed with the RAPS method and the
+            new value of lambda_
+
+        lambda_: NDArray of shape (n_alphas, )
+            New value of lambda_star to test
+
+        lambda_star: NDArray of shape (n_alphas, )
+            Actual optimal lambda values for each alpha.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray]
+            Arrays of shape (n_alphas, ) and (n_alpha, ) which
+            respectively represent the updated values of lambda_star
+            and the new best sizes.
+        """
+
+        sizes = [
+            classification_mean_width_score(
+                y_ps[:, :, i]
+            ) for i in range(len(alpha_np))
+        ]
+
+        sizes_improve = (sizes < best_sizes - EPSILON)
+        lambda_star = (
+            sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
+        )
+        best_sizes = sizes_improve * sizes + (1 - sizes_improve) * best_sizes
+
+        return lambda_star, best_sizes
+
+    def _find_lambda_star(
+        self,
+        y_raps_no_enc: NDArray,
+        y_pred_proba_raps: NDArray,
+        alpha_np: NDArray,
+        include_last_label: Union[bool, str, None],
+        k_star: NDArray
+    ) -> Union[NDArray, float]:
+        """Find the optimal value of lambda for each alpha.
+
+        Parameters
+        ----------
+        y_pred_proba_raps: NDArray of shape (n_samples, n_labels, n_alphas)
+            Predictions of the model repeated on the last axis as many times
+            as the number of alphas
+
+        alpha_np: NDArray of shape (n_alphas, )
+            Levels of confidences.
+
+        include_last_label: bool
+            Whether to include or not last label in
+            the prediction sets
+
+        k_star: NDArray of shape (n_alphas, )
+            Values of k for the regularization.
+
+        Returns
+        -------
+        ArrayLike of shape (n_alphas, )
+            Optimal values of lambda.
+        """
+        classes = cast(NDArray, self.classes)
+
+        lambda_star = np.zeros(len(alpha_np))
+        best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
+
+        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]
+            true_label_cumsum_proba, cutoff = (
+                get_true_label_cumsum_proba(
+                    y_raps_no_enc,
+                    y_pred_proba_raps[:, :, 0],
+                    classes
+                )
+            )
+
+            true_label_cumsum_proba_reg = self._regularize_conformity_score(
+                k_star,
+                lambda_,
+                true_label_cumsum_proba,
+                cutoff
+            )
+
+            quantiles_ = compute_quantiles(
+                true_label_cumsum_proba_reg,
+                alpha_np
+            )
+
+            _, _, y_pred_proba_last = get_last_included_proba(
+                y_pred_proba_raps,
+                quantiles_,
+                include_last_label,
+                self.method,
+                lambda_,
+                k_star
+            )
+
+            y_ps = np.greater_equal(
+                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
+            )
+            lambda_star, best_sizes = self._update_size_and_lambda(
+                best_sizes, alpha_np, y_ps, lambda_, lambda_star
+            )
+        if len(lambda_star) == 1:
+            lambda_star = lambda_star[0]
+        return lambda_star
+
+    def get_sets(
+        self,
+        X: ArrayLike,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        conformity_scores: NDArray,
+        include_last_label: Optional[Union[bool, str]] = True,
+        agg_scores: Optional[str] = "mean",
+        X_raps: Optional[NDArray] = None,
+        y_raps_no_enc: Optional[NDArray] = None,
+        y_pred_proba_raps: Optional[NDArray] = None,
+        position_raps: Optional[NDArray] = None,
+        **kwargs
+    ):
+        """
+        Compute classes of the prediction sets from the observed values,
+        the estimator of type ``EnsembleClassifier`` and the conformity scores.
+
+        Parameters
+        ----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        TODO
+
+        Returns
+        -------
+        NDArray of shape (n_samples, n_classes, n_alpha)
+            Prediction sets (Booleans indicate whether classes are included).
+        """
+        # Checks
+        include_last_label = check_include_last_label(include_last_label)
+
+        # if self.method == "raps":
+        lambda_star, k_star = None, None
+        X_raps = cast(NDArray, X_raps)
+        y_raps_no_enc = cast(NDArray, y_raps_no_enc)
+        y_pred_proba_raps = cast(NDArray, y_pred_proba_raps)
+        position_raps = cast(NDArray, position_raps)
+
+        n = len(conformity_scores)
+
+        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
+        if agg_scores != "crossval":
+            y_pred_proba = np.repeat(
+                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+            )
+
+        # Choice of the quantileif self.method == "naive":
+        if self.method == "naive":
+            self.quantiles_ = 1 - alpha_np
+        elif (estimator.cv == "prefit") or (agg_scores in ["mean"]):
+            if self.method == "raps":
+                check_alpha_and_n_samples(alpha_np, X_raps.shape[0])
+                k_star = compute_quantiles(
+                    position_raps,
+                    alpha_np
+                ) + 1
+                y_pred_proba_raps = np.repeat(
+                    y_pred_proba_raps[:, :, np.newaxis],
+                    len(alpha_np),
+                    axis=2
+                )
+                lambda_star = self._find_lambda_star(
+                    y_raps_no_enc,
+                    y_pred_proba_raps,
+                    alpha_np,
+                    include_last_label,
+                    k_star
+                )
+                conformity_scores_regularized = (
+                    self._regularize_conformity_score(
+                        k_star,
+                        lambda_star,
+                        conformity_scores,
+                        self.cutoff
+                    )
+                )
+                self.quantiles_ = compute_quantiles(
+                    conformity_scores_regularized,
+                    alpha_np
+                )
+            else:
+                self.quantiles_ = compute_quantiles(
+                    conformity_scores,
+                    alpha_np
+                )
+        else:
+            self.quantiles_ = (n + 1) * (1 - alpha_np)
+
+        # Build prediction sets
+        # specify which thresholds will be used
+        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
+            thresholds = self.quantiles_
+        else:
+            thresholds = conformity_scores.ravel()
+        # sort labels by decreasing probability
+        y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last = (
+            get_last_included_proba(
+                y_pred_proba,
+                thresholds,
+                include_last_label,
+                self.method,
+                lambda_star,
+                k_star,
+            )
+        )
+        # get the prediction set by taking all probabilities
+        # above the last one
+        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
+            y_pred_included = np.greater_equal(
+                y_pred_proba - y_pred_proba_last, -EPSILON
+            )
+        else:
+            y_pred_included = np.less_equal(
+                y_pred_proba - y_pred_proba_last, EPSILON
+            )
+        # remove last label randomly
+        if include_last_label == "randomized":
+            y_pred_included = add_random_tie_breaking(
+                y_pred_included,
+                y_pred_index_last,
+                y_pred_proba_cumsum,
+                y_pred_proba_last,
+                thresholds,
+                self.method,
+                self.random_state,
+                lambda_star,
+                k_star,
+            )
+        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
+            prediction_sets = y_pred_included
+        else:
+            # compute the number of times the inequality is verified
+            prediction_sets_summed = y_pred_included.sum(axis=2)
+            prediction_sets = np.less_equal(
+                prediction_sets_summed[:, :, np.newaxis]
+                - self.quantiles_[np.newaxis, np.newaxis, :],
+                EPSILON
+            )
+
+        # Just for coverage: do nothing
+        self.get_estimation_distribution(y_pred_proba, conformity_scores)
+
+        return prediction_sets
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
new file mode 100644
index 000000000..8bff9b6fa
--- /dev/null
+++ b/mapie/conformity_scores/sets/lac.py
@@ -0,0 +1,207 @@
+from typing import Optional, Union, cast
+
+import numpy as np
+
+from mapie.conformity_scores.classification import BaseClassificationScore
+from mapie.conformity_scores.sets.utils import check_proba_normalized
+from mapie.estimator.classifier import EnsembleClassifier
+
+from mapie._machine_precision import EPSILON
+from mapie._typing import ArrayLike, NDArray
+from mapie.utils import compute_quantiles
+
+
+class LAC(BaseClassificationScore):
+    """
+    Least Ambiguous set-valued Classifier (LAC) method-based
+    non conformity score (also formerly called ``"score"``).
+
+    It is based on the the scores (i.e. 1 minus the softmax score of the true
+    label) on the calibration set.
+
+    References
+    ----------
+    [1] Mauricio Sadinle, Jing Lei, and Larry Wasserman.
+    "Least Ambiguous Set-Valued Classifiers with Bounded Error Levels.",
+    Journal of the American Statistical Association, 114, 2019.
+    """
+
+    def __init__(
+        self,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        super().__init__(
+            consistency_check=consistency_check,
+            eps=eps
+        )
+
+    def set_external_attributes(
+        self,
+        method: str = 'lac',
+        classes: Optional[ArrayLike] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        method: str
+            Method to choose for prediction interval estimates.
+            Methods available in this class: ``lac``.
+
+            By default ``lac`` for LAC method.
+
+        classes: Optional[ArrayLike]
+            Names of the classes.
+
+            By default ``None``.
+
+        random_state: Optional[Union[int, RandomState]]
+            Pseudo random number generator state.
+        """
+        super().set_external_attributes(**kwargs)
+        self.method = method
+        self.classes = classes
+        self.random_state = random_state
+
+    def get_conformity_scores(
+        self,
+        y: ArrayLike,
+        y_pred: ArrayLike,
+        y_enc: Optional[ArrayLike] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        Get the conformity score.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+        y_pred = cast(NDArray, y_pred)
+        y_enc = cast(NDArray, y_enc)
+
+        # Conformity scores
+        conformity_scores = np.take_along_axis(
+            1 - y_pred, y_enc.reshape(-1, 1), axis=1
+        )
+        return conformity_scores
+
+    def get_estimation_distribution(
+        self,
+        y_pred: ArrayLike,
+        conformity_scores: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, ...)
+            Observed values.
+        """
+        return np.array([])
+
+    def get_sets(
+        self,
+        X: ArrayLike,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        conformity_scores: NDArray,
+        agg_scores: Optional[str] = "mean",
+        **kwargs
+    ):
+        """
+        Compute classes of the prediction sets from the observed values,
+        the estimator of type ``EnsembleClassifier`` and the conformity scores.
+
+        Parameters
+        ----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        TODO
+
+        Returns
+        -------
+        NDArray of shape (n_samples, n_classes, n_alpha)
+            Prediction sets (Booleans indicate whether classes are included).
+        """
+        # Checks
+        n = len(conformity_scores)
+
+        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
+        if agg_scores != "crossval":
+            y_pred_proba = np.repeat(
+                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+            )
+
+        # Choice of the quantile
+        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
+            self.quantiles_ = compute_quantiles(
+                conformity_scores,
+                alpha_np
+            )
+        else:
+            self.quantiles_ = (n + 1) * (1 - alpha_np)
+
+        # Build prediction sets
+        if (estimator.cv == "prefit") or (agg_scores == "mean"):
+            prediction_sets = np.greater_equal(
+                y_pred_proba - (1 - self.quantiles_), -EPSILON
+            )
+        else:
+            y_pred_included = np.less_equal(
+                (1 - y_pred_proba) - conformity_scores.ravel(),
+                EPSILON
+            ).sum(axis=2)
+            prediction_sets = np.stack(
+                [
+                    np.greater_equal(
+                        y_pred_included - _alpha * (n - 1), -EPSILON
+                    )
+                    for _alpha in alpha_np
+                ], axis=2
+            )
+
+        # Just for coverage: do nothing
+        self.get_estimation_distribution(y_pred_proba, conformity_scores)
+
+        return prediction_sets
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
new file mode 100644
index 000000000..1e68ad832
--- /dev/null
+++ b/mapie/conformity_scores/sets/topk.py
@@ -0,0 +1,212 @@
+from typing import Optional, Union, cast
+
+import numpy as np
+
+from mapie.conformity_scores.classification import BaseClassificationScore
+from mapie.conformity_scores.sets.utils import (
+    check_proba_normalized, get_true_label_position
+)
+from mapie.estimator.classifier import EnsembleClassifier
+
+from mapie._machine_precision import EPSILON
+from mapie._typing import ArrayLike, NDArray
+from mapie.utils import compute_quantiles
+
+
+class TopK(BaseClassificationScore):
+    """
+    Top-K method-based non-conformity score.
+
+    It is based on the sorted index of the probability of the true label in the
+    softmax outputs, on the calibration set. In case two probabilities are
+    equal, both are taken, thus, the size of some prediction sets may be
+    different from the others.
+
+    References
+    ----------
+    [1] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
+    and Jitendra Malik.
+    "Uncertainty Sets for Image Classifiers using Conformal Prediction."
+    International Conference on Learning Representations 2021.
+    """
+
+    def __init__(
+        self,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        super().__init__(
+            consistency_check=consistency_check,
+            eps=eps
+        )
+
+    def set_external_attributes(
+        self,
+        method: str = 'top_k',
+        classes: Optional[int] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        method: str
+            Method to choose for prediction interval estimates.
+            Methods available in this class: ``top_k``.
+
+            By default ``top_k`` for Top K method.
+
+        classes: Optional[ArrayLike]
+            Names of the classes.
+
+            By default ``None``.
+
+        random_state: Optional[Union[int, RandomState]]
+            Pseudo random number generator state.
+        """
+        super().set_external_attributes(**kwargs)
+        self.method = method
+        self.classes = classes
+        self.random_state = random_state
+
+    def get_conformity_scores(
+        self,
+        y: ArrayLike,
+        y_pred: ArrayLike,
+        y_enc: Optional[ArrayLike] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        Get the conformity score.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+        y = cast(NDArray, y)
+        y_pred = cast(NDArray, y_pred)
+        y_enc = cast(NDArray, y_enc)
+
+        # Conformity scores
+        # Here we reorder the labels by decreasing probability and get the
+        # position of each label from decreasing probability
+        conformity_scores = get_true_label_position(y_pred, y_enc)
+
+        return conformity_scores
+
+    def get_estimation_distribution(
+        self,
+        y_pred: ArrayLike,
+        conformity_scores: ArrayLike,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, ...)
+            Observed values.
+        """
+        return np.array([])
+
+    def get_sets(
+        self,
+        X: ArrayLike,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        conformity_scores: NDArray,
+        agg_scores: Optional[str] = "mean",
+        **kwargs
+    ):
+        """
+        Compute classes of the prediction sets from the observed values,
+        the estimator of type ``EnsembleClassifier`` and the conformity scores.
+
+        Parameters
+        ----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        TODO
+
+        Returns
+        -------
+        NDArray of shape (n_samples, n_classes, n_alpha)
+            Prediction sets (Booleans indicate whether classes are included).
+        """
+        # Checks
+        agg_scores = "mean"
+
+        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
+        y_pred_proba = np.repeat(
+            y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+        )
+
+        # Choice of the quantile
+        self.quantiles_ = compute_quantiles(conformity_scores, alpha_np)
+
+        # Build prediction sets
+        y_pred_proba = y_pred_proba[:, :, 0]
+        index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
+        y_pred_index_last = np.stack(
+            [
+                index_sorted[:, quantile]
+                for quantile in self.quantiles_
+            ], axis=1
+        )
+        y_pred_proba_last = np.stack(
+            [
+                np.take_along_axis(
+                    y_pred_proba,
+                    y_pred_index_last[:, iq].reshape(-1, 1),
+                    axis=1
+                )
+                for iq, _ in enumerate(self.quantiles_)
+            ], axis=2
+        )
+        prediction_sets = np.greater_equal(
+            y_pred_proba[:, :, np.newaxis]
+            - y_pred_proba_last,
+            -EPSILON
+        )
+
+        # Just for coverage: do nothing
+        self.get_estimation_distribution(y_pred_proba, conformity_scores)
+
+        return prediction_sets
diff --git a/mapie/conformity_scores/sets/utils.py b/mapie/conformity_scores/sets/utils.py
new file mode 100644
index 000000000..a2b5b32af
--- /dev/null
+++ b/mapie/conformity_scores/sets/utils.py
@@ -0,0 +1,401 @@
+from typing import Any, Optional, Tuple, Union, cast
+import numpy as np
+from sklearn.calibration import label_binarize
+from sklearn.dummy import check_random_state
+
+from mapie._typing import ArrayLike, NDArray
+from mapie._machine_precision import EPSILON
+
+
+def get_true_label_position(
+    y_pred_proba: NDArray,
+    y: NDArray
+) -> NDArray:
+    """
+    Return the sorted position of the true label in the prediction
+
+    Parameters
+    ----------
+    y_pred_proba: NDArray of shape (n_samples, n_classes)
+        Model prediction.
+
+    y: NDArray of shape (n_samples)
+        Labels.
+
+    Returns
+    -------
+    NDArray of shape (n_samples, 1)
+        Position of the true label in the prediction.
+    """
+    index = np.argsort(np.fliplr(np.argsort(y_pred_proba, axis=1)))
+    position = np.take_along_axis(index, y.reshape(-1, 1), axis=1)
+
+    return position
+
+
+def get_true_label_cumsum_proba(
+    y: ArrayLike,
+    y_pred_proba: NDArray,
+    classes: ArrayLike
+) -> Tuple[NDArray, NDArray]:
+    """
+    Compute the cumsumed probability of the true label.
+
+    Parameters
+    ----------
+    y: NDArray of shape (n_samples, )
+        Array with the labels.
+
+    y_pred_proba: NDArray of shape (n_samples, n_classes)
+        Predictions of the model.
+
+    classes: NDArray of shape (n_classes, )
+        Array with the classes.
+
+    Returns
+    -------
+    Tuple[NDArray, NDArray] of shapes (n_samples, 1) and (n_samples, ).
+        The first element is the cumsum probability of the true label.
+        The second is the sorted position of the true label.
+    """
+    y_true = label_binarize(y=y, classes=classes)
+    index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
+    y_pred_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
+    y_true_sorted = np.take_along_axis(y_true, index_sorted, axis=1)
+    y_pred_sorted_cumsum = np.cumsum(y_pred_sorted, axis=1)
+    cutoff = np.argmax(y_true_sorted, axis=1)
+    true_label_cumsum_proba = np.take_along_axis(
+        y_pred_sorted_cumsum, cutoff.reshape(-1, 1), axis=1
+    )
+
+    return true_label_cumsum_proba, cutoff + 1
+
+
+def check_include_last_label(
+    include_last_label: Optional[Union[bool, str]]
+) -> Optional[Union[bool, str]]:
+    """
+    Check if ``include_last_label`` is a boolean or a string.
+    Else raise error.
+
+    Parameters
+    ----------
+    include_last_label: Optional[Union[bool, str]]
+        Whether or not to include last label in
+        prediction sets for the ``"aps"`` method. Choose among:
+
+        - ``False``, does not include label whose cumulated score is just
+            over the quantile.
+
+        - ``True``, includes label whose cumulated score is just over the
+            quantile, unless there is only one label in the prediction set.
+
+        - ``"randomized"``, randomly includes label whose cumulated score
+            is just over the quantile based on the comparison of a uniform
+            number and the difference between the cumulated score of the last
+            label and the quantile.
+
+    Returns
+    -------
+    Optional[Union[bool, str]]
+
+    Raises
+    ------
+    ValueError
+        "Invalid include_last_label argument. "
+        "Should be a boolean or 'randomized'."
+    """
+    if (
+        (not isinstance(include_last_label, bool)) and
+        (not include_last_label == "randomized")
+    ):
+        raise ValueError(
+            "Invalid include_last_label argument. "
+            "Should be a boolean or 'randomized'."
+        )
+    else:
+        return include_last_label
+
+
+def check_proba_normalized(
+    y_pred_proba: ArrayLike,
+    axis: int = 1
+) -> NDArray:
+    """
+    Check if for all the samples the sum of the probabilities is equal to one.
+
+    Parameters
+    ----------
+    y_pred_proba: ArrayLike of shape (n_samples, n_classes) or
+    (n_samples, n_train_samples, n_classes)
+        Softmax output of a model.
+
+    Returns
+    -------
+    ArrayLike of shape (n_samples, n_classes)
+        Softmax output of a model if the scores all sum to one.
+
+    Raises
+    ------
+    ValueError
+        If the sum of the scores is not equal to one.
+    """
+    sum_proba = np.sum(y_pred_proba, axis=axis)
+    err_msg = "The sum of the scores is not equal to one."
+    np.testing.assert_allclose(sum_proba, 1, err_msg=err_msg, rtol=1e-5)
+    y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
+
+    return y_pred_proba
+
+
+def get_last_index_included(
+    y_pred_proba_cumsum: NDArray,
+    threshold: NDArray,
+    include_last_label: Optional[Union[bool, str]]
+) -> NDArray:
+    """
+    Return the index of the last included sorted probability
+    depending if we included the first label over the quantile
+    or not.
+
+    Parameters
+    ----------
+    y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
+        Cumsumed probabilities in the original order.
+
+    threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+        Threshold to compare with y_proba_last_cumsum, can be either:
+
+        - the quantiles associated with alpha values when
+            ``cv`` == "prefit", ``cv`` == "split"
+            or ``agg_scores`` is "mean"
+
+        - the conformity score from training samples otherwise
+            (i.e., when ``cv`` is a CV splitter and
+            ``agg_scores`` is "crossval")
+
+    include_last_label: Union[bool, str]
+        Whether or not include the last label. If 'randomized',
+        the last label is included.
+
+    Returns
+    -------
+    NDArray of shape (n_samples, n_alpha)
+        Index of the last included sorted probability.
+    """
+    if include_last_label or include_last_label == 'randomized':
+        y_pred_index_last = (
+            np.ma.masked_less(
+                y_pred_proba_cumsum
+                - threshold[np.newaxis, :],
+                -EPSILON
+            ).argmin(axis=1)
+        )
+    else:
+        max_threshold = np.maximum(
+            threshold[np.newaxis, :],
+            np.min(y_pred_proba_cumsum, axis=1)
+        )
+        y_pred_index_last = np.argmax(
+            np.ma.masked_greater(
+                y_pred_proba_cumsum - max_threshold[:, np.newaxis, :],
+                EPSILON
+            ), axis=1
+        )
+    return y_pred_index_last[:, np.newaxis, :]
+
+
+def get_last_included_proba(
+    y_pred_proba: NDArray,
+    thresholds: NDArray,
+    include_last_label: Union[bool, str, None],
+    method: str,
+    lambda_: Union[NDArray, float, None],
+    k_star: Union[NDArray, Any]
+) -> Tuple[NDArray, NDArray, NDArray]:
+    """
+    Function that returns the smallest score
+    among those which are included in the prediciton set.
+
+    Parameters
+    ----------
+    y_pred_proba: NDArray of shape (n_samples, n_classes)
+        Predictions of the model.
+
+    thresholds: NDArray of shape (n_alphas, )
+        Quantiles that have been computed from the conformity scores.
+
+    include_last_label: Union[bool, str, None]
+        Whether to include or not the label whose score exceeds the threshold.
+
+    lambda_: Union[NDArray, float, None] of shape (n_alphas)
+        Values of lambda for the regularization.
+
+    k_star: Union[NDArray, Any]
+        Values of k for the regularization.
+
+    Returns
+    -------
+    Tuple[ArrayLike, ArrayLike, ArrayLike]
+        Arrays of shape (n_samples, n_classes, n_alphas),
+        (n_samples, 1, n_alphas) and (n_samples, 1, n_alphas).
+        They are respectively the cumsumed scores in the original
+        order which can be different according to the value of alpha
+        with the RAPS method, the index of the last included score
+        and the value of the last included score.
+    """
+    index_sorted = np.flip(
+        np.argsort(y_pred_proba, axis=1), axis=1
+    )
+    # sort probabilities by decreasing order
+    y_pred_proba_sorted = np.take_along_axis(
+        y_pred_proba, index_sorted, axis=1
+    )
+    # get sorted cumulated score
+    y_pred_proba_sorted_cumsum = np.cumsum(
+        y_pred_proba_sorted, axis=1
+    )
+
+    if method == "raps":
+        y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
+            0,
+            np.cumsum(
+                np.ones(y_pred_proba_sorted_cumsum.shape), axis=1
+            ) - k_star
+        )
+    # get cumulated score at their original position
+    y_pred_proba_cumsum = np.take_along_axis(
+        y_pred_proba_sorted_cumsum,
+        np.argsort(index_sorted, axis=1),
+        axis=1
+    )
+    # get index of the last included label
+    y_pred_index_last = get_last_index_included(
+        y_pred_proba_cumsum,
+        thresholds,
+        include_last_label
+    )
+    # get the probability of the last included label
+    y_pred_proba_last = np.take_along_axis(
+        y_pred_proba,
+        y_pred_index_last,
+        axis=1
+    )
+
+    zeros_scores_proba_last = (y_pred_proba_last <= EPSILON)
+
+    # If the last included proba is zero, change it to the
+    # smallest non-zero value to avoid inluding them in the
+    # prediction sets.
+    if np.sum(zeros_scores_proba_last) > 0:
+        y_pred_proba_last[zeros_scores_proba_last] = np.expand_dims(
+            np.min(
+                np.ma.masked_less(
+                    y_pred_proba,
+                    EPSILON
+                ).filled(fill_value=np.inf),
+                axis=1
+            ), axis=1
+        )[zeros_scores_proba_last]
+
+    return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
+
+
+def add_random_tie_breaking(
+    prediction_sets: NDArray,
+    y_pred_index_last: NDArray,
+    y_pred_proba_cumsum: NDArray,
+    y_pred_proba_last: NDArray,
+    threshold: NDArray,
+    method: str,
+    random_state: Optional[Union[int, np.random.RandomState]] = None,
+    lambda_star: Optional[Union[NDArray, float]] = None,
+    k_star: Optional[Union[NDArray, None]] = None
+) -> NDArray:
+    """
+    Randomly remove last label from prediction set based on the
+    comparison between a random number and the difference between
+    cumulated score of the last included label and the quantile.
+
+    Parameters
+    ----------
+    prediction_sets: NDArray of shape
+        (n_samples, n_classes, n_threshold)
+        Prediction set for each observation and each alpha.
+
+    y_pred_index_last: NDArray of shape (n_samples, threshold)
+        Index of the last included label.
+
+    y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
+        Cumsumed probability of the model in the original order.
+
+    y_pred_proba_last: NDArray of shape (n_samples, 1, threshold)
+        Last included probability.
+
+    threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+        Threshold to compare with y_proba_last_cumsum, can be either:
+
+        - the quantiles associated with alpha values when ``cv`` == "prefit",
+            ``cv`` == "split" or ``agg_scores`` is "mean"
+
+        - the conformity score from training samples otherwise
+            (i.e., when ``cv`` is CV splitter and ``agg_scores`` is "crossval")
+
+    method: str
+        Method that determines how to remove last label in the prediction set.
+
+        - if "cumulated_score" or "aps", compute V parameter from Romano+(2020)
+
+        - else compute V parameter from Angelopoulos+(2020)
+
+    lambda_star: Union[NDArray, float, None] of shape (n_alpha):
+        Optimal value of the regulizer lambda.
+
+    k_star: Union[NDArray, None] of shape (n_alpha):
+        Optimal value of the regulizer k.
+
+    Returns
+    -------
+    NDArray of shape (n_samples, n_classes, n_alpha)
+        Updated version of prediction_sets with randomly removed labels.
+    """
+    # get cumsumed probabilities up to last retained label
+    y_proba_last_cumsumed = np.squeeze(
+        np.take_along_axis(
+            y_pred_proba_cumsum,
+            y_pred_index_last,
+            axis=1
+        ), axis=1
+    )
+
+    if method in ["cumulated_score", "aps"]:
+        # compute V parameter from Romano+(2020)
+        vs = (
+            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+            y_pred_proba_last[:, 0, :]
+        )
+    else:
+        # compute V parameter from Angelopoulos+(2020)
+        L = np.sum(prediction_sets, axis=1)
+        vs = (
+            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+            (
+                y_pred_proba_last[:, 0, :] -
+                lambda_star * np.maximum(0, L - k_star) +
+                lambda_star * (L > k_star)
+            )
+        )
+
+    # get random numbers for each observation and alpha value
+    random_state = check_random_state(random_state)
+    random_state = cast(np.random.RandomState, random_state)
+    us = random_state.uniform(size=(prediction_sets.shape[0], 1))
+    # remove last label from comparison between uniform number and V
+    vs_less_than_us = np.less_equal(vs - us, EPSILON)
+    np.put_along_axis(
+        prediction_sets,
+        y_pred_index_last,
+        vs_less_than_us[:, np.newaxis, :],
+        axis=1
+    )
+    return prediction_sets
diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 8cc3bf9d4..3206f90ca 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -1,26 +1,92 @@
-import numpy as np
-from mapie._typing import NDArray
+from typing import Optional
 
+from .regression import BaseRegressionScore
+from .classification import BaseClassificationScore
+from .bounds import AbsoluteConformityScore
+from .sets import APS, LAC, TopK
 
-def get_true_label_position(y_pred_proba: NDArray, y: NDArray) -> NDArray:
+
+def check_regression_conformity_score(
+    conformity_score: Optional[BaseRegressionScore],
+    sym: bool = True,
+) -> BaseRegressionScore:
     """
-    Return the sorted position of the true label in the
-    prediction
+    Check parameter ``conformity_score`` for regression task.
+
+    Raises
+    ------
+    ValueError
+        If parameters are not valid.
 
-    Parameters
-    ----------
-    y_pred_proba: NDArray of shape (n_samples, n_classes)
-        Model prediction.
+    Examples
+    --------
+    >>> from mapie.conformity_scores.checks import (
+    ...     check_regression_conformity_score
+    ... )
+    >>> try:
+    ...     check_regression_conformity_score(1)
+    ... except Exception as exception:
+    ...     print(exception)
+    ...
+    Invalid conformity_score argument.
+    Must be None or a ConformityScore instance.
+    """
+    if conformity_score is None:
+        return AbsoluteConformityScore(sym=sym)
+    elif isinstance(conformity_score, BaseRegressionScore):
+        return conformity_score
+    else:
+        raise ValueError(
+            "Invalid conformity_score argument.\n"
+            "Must be None or a ConformityScore instance."
+        )
 
-    y: NDArray of shape (n_samples)
-        Labels.
 
-    Returns
-    -------
-    NDArray of shape (n_samples, 1)
-        Position of the true label in the prediction.
+def check_classification_conformity_score(
+    conformity_score: Optional[BaseClassificationScore] = None,
+    method: Optional[str] = None,
+) -> BaseClassificationScore:
     """
-    index = np.argsort(np.fliplr(np.argsort(y_pred_proba, axis=1)))
-    position = np.take_along_axis(index, y.reshape(-1, 1), axis=1)
+    Check parameter ``conformity_score`` for classification task.
 
-    return position
+    Raises
+    ------
+    ValueError
+        If parameters are not valid.
+
+    Examples
+    --------
+    >>> from mapie.conformity_scores.checks import (
+    ...     check_classification_conformity_score
+    ... )
+    >>> try:
+    ...     check_classification_conformity_score(1)
+    ... except Exception as exception:
+    ...     print(exception)
+    ...
+    Invalid conformity_score argument.
+    Must be None or a ConformityScore instance.
+    """
+    allowed_methods = ['lac', 'naive', 'aps', 'raps', 'top_k']
+    deprecated_methods = ['score', 'cumulated_score']
+    if method is not None:
+        if method in ['score', 'lac']:
+            return LAC()
+        if method in ['naive', 'cumulated_score', 'aps', 'raps']:
+            return APS()
+        if method == 'top_k':
+            return TopK()
+        else:
+            raise ValueError(
+                f"Invalid method. Allowed values are {allowed_methods}. "
+                f"Deprecated values are {deprecated_methods}. "
+            )
+    elif isinstance(conformity_score, BaseClassificationScore):
+        return conformity_score
+    elif conformity_score is None:
+        return LAC()
+    else:
+        raise ValueError(
+            "Invalid conformity_score argument.\n"
+            "Must be None or a ConformityScore instance."
+        )
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 3085ce82d..018c30677 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -12,13 +12,14 @@
 from sklearn.utils.validation import _check_y, check_is_fitted, indexable
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
+from mapie.conformity_scores import (BaseRegressionScore,
+                                     ResidualNormalisedScore)
+from mapie.conformity_scores.utils import check_regression_conformity_score
 from mapie.estimator.regressor import EnsembleRegressor
 from mapie.utils import (check_alpha, check_alpha_and_n_samples,
                          check_cv, check_estimator_fit_predict,
                          check_n_features_in, check_n_jobs, check_null_weight,
                          check_verbose, get_effective_calibration_samples)
-from mapie.conformity_scores.checks import check_conformity_score
 
 
 class MapieRegressor(BaseEstimator, RegressorMixin):
@@ -226,7 +227,7 @@ def __init__(
         n_jobs: Optional[int] = None,
         agg_function: Optional[str] = "mean",
         verbose: int = 0,
-        conformity_score: Optional[ConformityScore] = None,
+        conformity_score: Optional[BaseRegressionScore] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
     ) -> None:
         self.estimator = estimator
@@ -431,7 +432,7 @@ def _check_fit_parameters(
             self.method = "base"
         estimator = self._check_estimator(self.estimator)
         agg_function = self._check_agg_function(self.agg_function)
-        cs_estimator = check_conformity_score(
+        cs_estimator = check_regression_conformity_score(
             self.conformity_score, self.default_sym_
         )
         if isinstance(cs_estimator, ResidualNormalisedScore) and \
@@ -449,7 +450,7 @@ def _check_fit_parameters(
         # Casting
         cv = cast(BaseCrossValidator, cv)
         estimator = cast(RegressorMixin, estimator)
-        cs_estimator = cast(ConformityScore, cs_estimator)
+        cs_estimator = cast(BaseRegressionScore, cs_estimator)
         agg_function = cast(Optional[str], agg_function)
         X = cast(NDArray, X)
         y = cast(NDArray, y)
@@ -539,7 +540,7 @@ def fit(
         # Compute the conformity scores (manage jk-ab case)
         self.conformity_scores_ = \
             self.conformity_score_function_.get_conformity_scores(
-                X, y, y_pred
+                y, y_pred, X=X
             )
 
         return self
@@ -639,16 +640,15 @@ def predict(
                 check_alpha_and_n_samples(alpha_np, n)
 
             # Predict the target with confidence intervals
-            y_pred, y_pred_low, y_pred_up = \
-                self.conformity_score_function_.get_bounds(
-                    X,
-                    self.estimator_,
-                    self.conformity_scores_,
-                    alpha_np,
-                    ensemble=ensemble,
-                    method=self.method,
-                    optimize_beta=optimize_beta,
-                    allow_infinite_bounds=allow_infinite_bounds
-                )
+            outputs = self.conformity_score_function_.predict_set(
+                X, alpha_np,
+                estimator=self.estimator_,
+                conformity_scores=self.conformity_scores_,
+                ensemble=ensemble,
+                method=self.method,
+                optimize_beta=optimize_beta,
+                allow_infinite_bounds=allow_infinite_bounds
+            )
+            y_pred, y_pred_low, y_pred_up = outputs
 
             return np.array(y_pred), np.stack([y_pred_low, y_pred_up], axis=1)
diff --git a/mapie/regression/time_series_regression.py b/mapie/regression/time_series_regression.py
index b4bf0cc03..b96dc17dc 100644
--- a/mapie/regression/time_series_regression.py
+++ b/mapie/regression/time_series_regression.py
@@ -9,7 +9,7 @@
 from sklearn.utils.validation import check_is_fitted
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.conformity_scores import ConformityScore
+from mapie.conformity_scores import BaseRegressionScore
 from mapie.regression import MapieRegressor
 from mapie.utils import check_alpha, check_gamma
 
@@ -66,7 +66,7 @@ def __init__(
         n_jobs: Optional[int] = None,
         agg_function: Optional[str] = "mean",
         verbose: int = 0,
-        conformity_score: Optional[ConformityScore] = None,
+        conformity_score: Optional[BaseRegressionScore] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
     ) -> None:
         super().__init__(
@@ -114,7 +114,9 @@ def _relative_conformity_scores(
         """
         y_pred = super().predict(X, ensemble=ensemble)
         scores = np.array(
-            self.conformity_score_function_.get_conformity_scores(X, y, y_pred)
+            self.conformity_score_function_.get_conformity_scores(
+                y, y_pred, X=X
+            )
         )
         return scores
 
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 740c4df6b..1b6bf6a12 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -23,6 +23,11 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
+from mapie.conformity_scores.sets.aps import APS
+from mapie.conformity_scores.sets.utils import (
+    check_proba_normalized, get_last_included_proba,
+    get_true_label_cumsum_proba
+)
 from mapie.metrics import classification_coverage_score
 from mapie.utils import check_alpha
 
@@ -1028,14 +1033,14 @@ def test_too_large_cv(cv: Any) -> None:
 )
 def test_invalid_include_last_label(include_last_label: Any) -> None:
     """Test that invalid include_last_label raise errors."""
-    mapie_clf = MapieClassifier(random_state=random_state)
+    mapie_clf = MapieClassifier(method='aps', random_state=random_state)
     mapie_clf.fit(X_toy, y_toy)
     with pytest.raises(
         ValueError, match=r".*Invalid include_last_label argument.*"
     ):
         mapie_clf.predict(
             X_toy,
-            y_toy,
+            alpha=0.5,
             include_last_label=include_last_label
         )
 
@@ -1504,7 +1509,8 @@ def test_cumulated_scores() -> None:
         include_last_label=True,
         alpha=alpha
     )
-    np.testing.assert_allclose(mapie_clf.quantiles_, quantile)
+    computed_quantile = mapie_clf.conformity_score_function_.quantiles_
+    np.testing.assert_allclose(computed_quantile, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
 
@@ -1532,7 +1538,8 @@ def test_image_cumulated_scores(X: Dict[str, ArrayLike]) -> None:
         include_last_label=True,
         alpha=alpha
     )
-    np.testing.assert_allclose(mapie.quantiles_, quantile)
+    computed_quantile = mapie.conformity_score_function_.quantiles_
+    np.testing.assert_allclose(computed_quantile, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
 
@@ -1606,28 +1613,16 @@ def test_method_error_in_fit(monkeypatch: Any, method: str) -> None:
         mapie_clf.fit(X_toy, y_toy)
 
 
-@pytest.mark.parametrize("method", WRONG_METHODS)
-@pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
-def test_method_error_in_predict(method: Any, alpha: float) -> None:
-    """Test else condition for the method in .predict"""
-    mapie_clf = MapieClassifier(
-        method="lac", random_state=random_state
-    )
-    mapie_clf.fit(X_toy, y_toy)
-    mapie_clf.method = method
-    with pytest.raises(ValueError, match=r".*Invalid method.*"):
-        mapie_clf.predict(X_toy, alpha=alpha)
-
-
 @pytest.mark.parametrize("include_labels", WRONG_INCLUDE_LABELS)
 @pytest.mark.parametrize("alpha", [0.2, [0.2, 0.3], (0.2, 0.3)])
 def test_include_label_error_in_predict(
     monkeypatch: Any, include_labels: Union[bool, str], alpha: float
 ) -> None:
     """Test else condition for include_label parameter in .predict"""
+    from mapie.conformity_scores.sets import utils
     monkeypatch.setattr(
-        MapieClassifier,
-        "_check_include_last_label",
+        utils,
+        "check_include_last_label",
         do_nothing
     )
     mapie_clf = MapieClassifier(
@@ -1694,8 +1689,7 @@ def test_pred_proba_float64() -> None:
     y_pred_proba = np.random.random((1000, 10)).astype(np.float32)
     sum_of_rows = y_pred_proba.sum(axis=1)
     normalized_array = y_pred_proba / sum_of_rows[:, np.newaxis]
-    mapie = MapieClassifier(random_state=random_state)
-    checked_normalized_array = mapie._check_proba_normalized(normalized_array)
+    checked_normalized_array = check_proba_normalized(normalized_array)
 
     assert checked_normalized_array.dtype == "float64"
 
@@ -1744,12 +1738,9 @@ def test_regularize_conf_scores_shape(k_lambda) -> None:
     Test that the conformity scores have the correct shape.
     """
     lambda_, k = k_lambda[0], k_lambda[1]
-    args_init, _ = STRATEGIES["raps"]
-    clf = LogisticRegression().fit(X, y)
-    mapie_clf = MapieClassifier(estimator=clf, **args_init)
     conf_scores = np.random.rand(100, 1)
     cutoff = np.cumsum(np.ones(conf_scores.shape)) - 1
-    reg_conf_scores = mapie_clf._regularize_conformity_score(
+    reg_conf_scores = APS._regularize_conformity_score(
         k, lambda_, conf_scores, cutoff
     )
 
@@ -1768,9 +1759,8 @@ def test_get_true_label_cumsum_proba_shape() -> None:
         estimator=clf, random_state=random_state
     )
     mapie_clf.fit(X, y)
-    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(
-        y, y_pred
-    )
+    classes = mapie_clf.classes_
+    cumsum_proba, cutoff = get_true_label_cumsum_proba(y, y_pred, classes)
     assert cumsum_proba.shape == (len(X), 1)
     assert cutoff.shape == (len(X), )
 
@@ -1787,9 +1777,8 @@ def test_get_true_label_cumsum_proba_result() -> None:
         estimator=clf, random_state=random_state
     )
     mapie_clf.fit(X_toy, y_toy)
-    cumsum_proba, cutoff = mapie_clf._get_true_label_cumsum_proba(
-        y_toy, y_pred
-    )
+    classes = mapie_clf.classes_
+    cumsum_proba, cutoff = get_true_label_cumsum_proba(y_toy, y_pred, classes)
     np.testing.assert_allclose(
         cumsum_proba,
         np.array(
@@ -1829,10 +1818,11 @@ def test_get_last_included_proba_shape(k_lambda, strategy):
 
     mapie = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     include_last_label = STRATEGIES[strategy][1]["include_last_label"]
-    y_p_p_c, y_p_i_l, y_p_p_i_l = mapie._get_last_included_proba(
-        y_pred_proba, thresholds,
-        include_last_label, lambda_, k
-    )
+    y_p_p_c, y_p_i_l, y_p_p_i_l = \
+        get_last_included_proba(
+            y_pred_proba, thresholds, include_last_label,
+            mapie.method, lambda_, k
+        )
 
     assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))
     assert y_p_i_l.shape == (len(X), 1, len(thresholds))
diff --git a/mapie/tests/test_conformity_scores.py b/mapie/tests/test_conformity_scores.py
index 4d4a32722..4ade1f354 100644
--- a/mapie/tests/test_conformity_scores.py
+++ b/mapie/tests/test_conformity_scores.py
@@ -5,31 +5,34 @@
 from sklearn.preprocessing import PolynomialFeatures
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
-                                     GammaConformityScore,
-                                     ResidualNormalisedScore)
+from mapie.conformity_scores import (
+    AbsoluteConformityScore, BaseRegressionScore, GammaConformityScore,
+    ResidualNormalisedScore
+)
 from mapie.regression import MapieRegressor
 
 X_toy = np.array([0, 1, 2, 3, 4, 5]).reshape(-1, 1)
 y_toy = np.array([5, 7, 9, 11, 13, 15])
-y_pred_list = [4, 7, 10, 12, 13, 12]
-conf_scores_list = [1, 0, -1, -1, 0, 3]
-conf_scores_gamma_list = [1 / 4, 0, -1 / 10, -1 / 12, 0, 3 / 12]
-conf_scores_residual_norm_list = [0.2, 0., 0.11111111, 0.09090909, 0., 0.2]
+y_pred_list = np.array([4, 7, 10, 12, 13, 12])
+conf_scores_list = np.array([1, 0, -1, -1, 0, 3])
+conf_scores_gamma_list = np.array([1 / 4, 0, -1 / 10, -1 / 12, 0, 3 / 12])
+conf_scores_residual_norm_list = np.array(
+    [0.2, 0., 0.11111111, 0.09090909, 0., 0.2]
+)
 random_state = 42
 
 
-class DummyConformityScore(ConformityScore):
+class DummyConformityScore(BaseRegressionScore):
     def __init__(self) -> None:
         super().__init__(sym=True, consistency_check=True)
 
     def get_signed_conformity_scores(
-        self, X: ArrayLike, y: ArrayLike, y_pred: ArrayLike,
+        self, y: ArrayLike, y_pred: ArrayLike, **kwargs
     ) -> NDArray:
         return np.subtract(y, y_pred)
 
     def get_estimation_distribution(
-        self, X: ArrayLike, y_pred: ArrayLike, conformity_scores: ArrayLike
+        self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
     ) -> NDArray:
         """
         A positive constant is added to the sum between predictions and
@@ -42,7 +45,7 @@ def get_estimation_distribution(
 @pytest.mark.parametrize("sym", [False, True])
 def test_error_mother_class_initialization(sym: bool) -> None:
     with pytest.raises(TypeError):
-        ConformityScore(sym)  # type: ignore
+        BaseRegressionScore(sym)  # type: ignore
 
 
 @pytest.mark.parametrize("y_pred", [np.array(y_pred_list), y_pred_list])
@@ -52,10 +55,10 @@ def test_absolute_conformity_score_get_conformity_scores(
     """Test conformity score computation for AbsoluteConformityScore."""
     abs_conf_score = AbsoluteConformityScore()
     signed_conf_scores = abs_conf_score.get_signed_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy
     )
     conf_scores = abs_conf_score.get_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy
     )
     expected_signed_conf_scores = np.array(conf_scores_list)
     expected_conf_scores = np.abs(expected_signed_conf_scores)
@@ -73,7 +76,7 @@ def test_absolute_conformity_score_get_estimation_distribution(
     """Test conformity observed value computation for AbsoluteConformityScore."""  # noqa: E501
     abs_conf_score = AbsoluteConformityScore()
     y_obs = abs_conf_score.get_estimation_distribution(
-        X_toy, y_pred, conf_scores
+        y_pred, conf_scores, X=X_toy
     )
     np.testing.assert_allclose(y_obs, y_toy)
 
@@ -83,10 +86,10 @@ def test_absolute_conformity_score_consistency(y_pred: NDArray) -> None:
     """Test methods consistency for AbsoluteConformityScore."""
     abs_conf_score = AbsoluteConformityScore()
     signed_conf_scores = abs_conf_score.get_signed_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy,
     )
     y_obs = abs_conf_score.get_estimation_distribution(
-        X_toy, y_pred, signed_conf_scores
+        y_pred, signed_conf_scores, X=X_toy,
     )
     np.testing.assert_allclose(y_obs, y_toy)
 
@@ -98,7 +101,7 @@ def test_gamma_conformity_score_get_conformity_scores(
     """Test conformity score computation for GammaConformityScore."""
     gamma_conf_score = GammaConformityScore()
     conf_scores = gamma_conf_score.get_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy
     )
     expected_signed_conf_scores = np.array(conf_scores_gamma_list)
     np.testing.assert_allclose(conf_scores, expected_signed_conf_scores)
@@ -118,7 +121,7 @@ def test_gamma_conformity_score_get_estimation_distribution(
     """Test conformity observed value computation for GammaConformityScore."""  # noqa: E501
     gamma_conf_score = GammaConformityScore()
     y_obs = gamma_conf_score.get_estimation_distribution(
-        X_toy, y_pred, conf_scores
+        y_pred, conf_scores, X=X_toy
     )
     np.testing.assert_allclose(y_obs, y_toy)
 
@@ -128,10 +131,10 @@ def test_gamma_conformity_score_consistency(y_pred: NDArray) -> None:
     """Test methods consistency for GammaConformityScore."""
     gamma_conf_score = GammaConformityScore()
     signed_conf_scores = gamma_conf_score.get_signed_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy
     )
     y_obs = gamma_conf_score.get_estimation_distribution(
-        X_toy, y_pred, signed_conf_scores
+        y_pred, signed_conf_scores, X=X_toy,
     )
     np.testing.assert_allclose(y_obs, y_toy)
 
@@ -152,7 +155,7 @@ def test_gamma_conformity_score_check_oberved_value(
     gamma_conf_score = GammaConformityScore()
     with pytest.raises(ValueError):
         gamma_conf_score.get_signed_conformity_scores(
-            [], y_toy, y_pred
+            y_toy, y_pred, X=[]
         )
 
 
@@ -189,14 +192,14 @@ def test_gamma_conformity_score_check_predicted_value(
         match=r".*At least one of the predicted target is negative.*"
     ):
         gamma_conf_score.get_signed_conformity_scores(
-            X_toy, y_toy, y_pred
+            y_toy, y_pred, X=X_toy
         )
     with pytest.raises(
         ValueError,
         match=r".*At least one of the predicted target is negative.*"
     ):
         gamma_conf_score.get_estimation_distribution(
-            X_toy, y_pred, conf_scores
+            y_pred, conf_scores, X=X_toy
         )
 
 
@@ -207,14 +210,14 @@ def test_check_consistency() -> None:
     """
     dummy_conf_score = DummyConformityScore()
     conformity_scores = dummy_conf_score.get_signed_conformity_scores(
-        X_toy, y_toy, y_pred_list
+        y_toy, y_pred_list
     )
     with pytest.raises(
         ValueError,
         match=r".*The two functions get_conformity_scores.*"
     ):
         dummy_conf_score.check_consistency(
-            X_toy, y_toy, y_pred_list, conformity_scores
+            y_toy, y_pred_list, conformity_scores
         )
 
 
@@ -233,7 +236,7 @@ def test_residual_normalised_prefit_conformity_score_get_conformity_scores(
         random_state=random_state
     )
     conf_scores = residual_norm_conf_score.get_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy
     )
     expected_signed_conf_scores = np.array(conf_scores_residual_norm_list)
     np.testing.assert_allclose(conf_scores, expected_signed_conf_scores)
@@ -249,7 +252,7 @@ def test_residual_normalised_conformity_score_get_conformity_scores(
     """
     residual_norm_score = ResidualNormalisedScore(random_state=random_state)
     conf_scores = residual_norm_score.get_conformity_scores(
-        X_toy, y_toy, y_pred
+        y_toy, y_pred, X=X_toy
     )
     expected_signed_conf_scores = np.array(
         [np.nan, np.nan, 1.e+08, 1.e+08, 0.e+00, 3.e+08]
@@ -264,7 +267,7 @@ def test_residual_normalised_score_prefit_with_notfitted_estim() -> None:
     )
     with pytest.raises(ValueError):
         residual_norm_conf_score.get_conformity_scores(
-            X_toy, y_toy, y_pred_list
+            y_toy, y_pred_list, X=X_toy
         )
 
 
@@ -272,9 +275,11 @@ def test_residual_normalised_score_with_default_params() -> None:
     """Test that no error is raised with default parameters."""
     residual_norm_score = ResidualNormalisedScore()
     conf_scores = residual_norm_score.get_conformity_scores(
-        X_toy, y_toy, y_pred_list
+        y_toy, y_pred_list, X=X_toy
+    )
+    residual_norm_score.get_estimation_distribution(
+        y_toy, conf_scores, X=X_toy
     )
-    residual_norm_score.get_estimation_distribution(X_toy, y_toy, conf_scores)
 
 
 def test_invalid_estimator() -> None:
@@ -288,7 +293,7 @@ def __init__(self):
     )
     with pytest.raises(ValueError):
         residual_norm_conf_score.get_conformity_scores(
-            X_toy, y_toy, y_pred_list
+            y_toy, y_pred_list, X=X_toy
         )
 
 
@@ -356,7 +361,7 @@ def predict(self, X):
     )
     with pytest.warns(UserWarning):
         residual_norm_conf_score.get_conformity_scores(
-            X_toy, y_toy, y_pred_list
+            y_toy, y_pred_list, X=X_toy
         )
 
 
@@ -370,10 +375,10 @@ def test_residual_normalised_prefit_get_estimation_distribution() -> None:
         residual_estimator=estim, prefit=True
     )
     conf_scores = residual_normalised_conf_score.get_conformity_scores(
-        X_toy, y_toy, y_pred_list
+        y_toy, y_pred_list, X=X_toy
     )
     residual_normalised_conf_score.get_estimation_distribution(
-        X_toy, y_pred_list, conf_scores
+        y_pred_list, conf_scores, X=X_toy
     )
 
 
@@ -382,7 +387,7 @@ def test_residual_normalised_prefit_get_estimation_distribution() -> None:
                                    ResidualNormalisedScore()])
 @pytest.mark.parametrize("alpha", [[0.5], [0.5, 0.6]])
 def test_intervals_shape_with_every_score(
-    score: ConformityScore,
+    score: BaseRegressionScore,
     alpha: NDArray
 ) -> None:
     estim = LinearRegression().fit(X_toy, y_toy)
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
new file mode 100644
index 000000000..b6349b4fc
--- /dev/null
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -0,0 +1,37 @@
+from typing import Optional
+
+import pytest
+
+# from mapie._typing import ArrayLike, NDArray
+from mapie.conformity_scores import BaseClassificationScore
+from mapie.conformity_scores.sets import APS, LAC, TopK
+from mapie.conformity_scores.utils import check_classification_conformity_score
+
+
+cs_list = [None, LAC(), APS(), TopK()]
+method_list = [None, 'naive', 'aps', 'raps', 'lac', 'top_k']
+
+
+def test_error_mother_class_initialization() -> None:
+    with pytest.raises(TypeError):
+        BaseClassificationScore()  # type: ignore
+
+
+@pytest.mark.parametrize("conformity_score", cs_list)
+def test_check_classification_conformity_score(
+    conformity_score: Optional[BaseClassificationScore]
+) -> None:
+    assert isinstance(
+        check_classification_conformity_score(conformity_score),
+        BaseClassificationScore
+    )
+
+
+@pytest.mark.parametrize("method", method_list)
+def test_check_classification_method(
+    method: Optional[str]
+) -> None:
+    assert isinstance(
+        check_classification_conformity_score(method=method),
+        BaseClassificationScore
+    )
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 1dad0776e..c35ebec34 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -6,15 +6,17 @@
 import numpy as np
 import pandas as pd
 import pytest
+
 from sklearn.compose import ColumnTransformer
 from sklearn.datasets import make_regression
 from sklearn.dummy import DummyRegressor
 from sklearn.ensemble import GradientBoostingRegressor
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
-                                     PredefinedSplit, ShuffleSplit,
-                                     train_test_split)
+from sklearn.model_selection import (
+    GroupKFold, KFold, LeaveOneOut, PredefinedSplit, ShuffleSplit,
+    train_test_split
+)
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.validation import check_is_fitted
@@ -23,9 +25,10 @@
 
 from mapie._typing import NDArray
 from mapie.aggregation_functions import aggregate_all
-from mapie.conformity_scores import (AbsoluteConformityScore, ConformityScore,
-                                     GammaConformityScore,
-                                     ResidualNormalisedScore)
+from mapie.conformity_scores import (
+    AbsoluteConformityScore, BaseRegressionScore, GammaConformityScore,
+    ResidualNormalisedScore
+)
 from mapie.estimator.regressor import EnsembleRegressor
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
@@ -784,7 +787,7 @@ def test_pipeline_compatibility() -> None:
     "conformity_score", [AbsoluteConformityScore(), GammaConformityScore()]
 )
 def test_conformity_score(
-    strategy: str, conformity_score: ConformityScore
+    strategy: str, conformity_score: BaseRegressionScore
 ) -> None:
     """Test that any conformity score function with MAPIE raises no error."""
     mapie_reg = MapieRegressor(
@@ -799,7 +802,7 @@ def test_conformity_score(
     "conformity_score", [ResidualNormalisedScore()]
 )
 def test_conformity_score_with_split_strategies(
-   conformity_score: ConformityScore
+   conformity_score: BaseRegressionScore
 ) -> None:
     """
     Test that any conformity score function that handle only split strategies
diff --git a/mapie/tests/test_utils_classification_conformity_scores.py b/mapie/tests/test_utils_classification_conformity_scores.py
index a74a6892a..9d07fa8bc 100644
--- a/mapie/tests/test_utils_classification_conformity_scores.py
+++ b/mapie/tests/test_utils_classification_conformity_scores.py
@@ -3,9 +3,7 @@
 import numpy as np
 import pytest
 
-from mapie.conformity_scores.utils import (
-    get_true_label_position,
-)
+from mapie.conformity_scores.sets.utils import get_true_label_position
 from mapie._typing import NDArray
 
 Y_TRUE_PROBA_PLACE = [

From 0eb720356dc5ff2ed32cd9caa64807dc3df76adc Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 1 Jul 2024 16:58:22 +0200
Subject: [PATCH 163/424] FIX: path access in test doctring

---
 mapie/conformity_scores/utils.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 3206f90ca..a6b3283c7 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -20,7 +20,7 @@ def check_regression_conformity_score(
 
     Examples
     --------
-    >>> from mapie.conformity_scores.checks import (
+    >>> from mapie.conformity_scores.utils import (
     ...     check_regression_conformity_score
     ... )
     >>> try:
@@ -56,7 +56,7 @@ def check_classification_conformity_score(
 
     Examples
     --------
-    >>> from mapie.conformity_scores.checks import (
+    >>> from mapie.conformity_scores.utils import (
     ...     check_classification_conformity_score
     ... )
     >>> try:

From fc0f46a18475ff3f6d6cb1513f46215b16d78bc9 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 1 Jul 2024 17:32:23 +0200
Subject: [PATCH 164/424] FIX: adapt exemple code with new signatures

---
 .../1-quickstart/plot_comp_methods_on_2d_dataset.py          | 5 +++--
 examples/classification/4-tutorials/plot_crossconformal.py   | 5 +++--
 .../4-tutorials/plot_main-tutorial-binary-classification.py  | 5 +++--
 .../4-tutorials/plot_main-tutorial-classification.py         | 2 +-
 .../plot_conformal_predictive_distribution.py                | 2 +-
 mapie/classification.py                                      | 3 ---
 mapie/conformity_scores/classification.py                    | 5 +++++
 7 files changed, 16 insertions(+), 11 deletions(-)

diff --git a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py b/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py
index b03e8cb97..f156233a4 100644
--- a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py
+++ b/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py
@@ -170,7 +170,7 @@ def plot_scores(
 for i, method in enumerate(methods):
     conformity_scores = mapie[method].conformity_scores_
     n = mapie[method].n_samples_
-    quantiles = mapie[method].quantiles_
+    quantiles = mapie[method].conformity_score_function_.quantiles_
     plot_scores(alpha, conformity_scores, quantiles, method, axs[i])
 plt.show()
 
@@ -270,7 +270,8 @@ def plot_results(
 axs[0].set_xlabel("1 - alpha")
 axs[0].set_ylabel("Quantile")
 for method in methods:
-    axs[0].scatter(1 - alpha_, mapie[method].quantiles_, label=method)
+    quantiles = mapie[method].conformity_score_function_.quantiles_
+    axs[0].scatter(1 - alpha_, quantiles, label=method)
 axs[0].legend()
 for method in methods:
     axs[1].scatter(1 - alpha_, coverage[method], label=method)
diff --git a/examples/classification/4-tutorials/plot_crossconformal.py b/examples/classification/4-tutorials/plot_crossconformal.py
index 8200e6c26..7fe8bbac5 100644
--- a/examples/classification/4-tutorials/plot_crossconformal.py
+++ b/examples/classification/4-tutorials/plot_crossconformal.py
@@ -134,10 +134,11 @@
 
 fig, axs = plt.subplots(1, len(mapies["lac"]), figsize=(20, 4))
 for i, (key, mapie) in enumerate(mapies["lac"].items()):
+    quantiles = mapie.conformity_score_function_.quantiles_[9]
     axs[i].set_xlabel("Conformity scores")
     axs[i].hist(mapie.conformity_scores_)
-    axs[i].axvline(mapie.quantiles_[9], ls="--", color="k")
-    axs[i].set_title(f"split={key}\nquantile={mapie.quantiles_[9]:.3f}")
+    axs[i].axvline(quantiles, ls="--", color="k")
+    axs[i].set_title(f"split={key}\nquantile={quantiles:.3f}")
 plt.suptitle(
     "Distribution of scores on each calibration fold for the "
     f"{methods[0]} method"
diff --git a/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py b/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py
index d7469f46b..24d20369a 100644
--- a/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py
+++ b/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py
@@ -188,7 +188,7 @@ def plot_scores(
 
 fig, axs = plt.subplots(1, 1, figsize=(10, 5))
 conformity_scores = mapie_clf.conformity_scores_
-quantiles = mapie_clf.quantiles_
+quantiles = mapie_clf.conformity_score_function_.quantiles_
 plot_scores(alpha, conformity_scores, quantiles, 'lac', axs)
 plt.show()
 
@@ -309,10 +309,11 @@ def plot_results(
 
 
 def plot_coverages_widths(alpha, coverage, width, method):
+    quantiles = mapie_clf.conformity_score_function_.quantiles_
     _, axs = plt.subplots(1, 3, figsize=(15, 5))
     axs[0].set_xlabel("1 - alpha")
     axs[0].set_ylabel("Quantile")
-    axs[0].scatter(1 - alpha, mapie_clf.quantiles_, label=method)
+    axs[0].scatter(1 - alpha, quantiles, label=method)
     axs[0].legend()
     axs[1].scatter(1 - alpha, coverage, label=method)
     axs[1].set_xlabel("1 - alpha")
diff --git a/examples/classification/4-tutorials/plot_main-tutorial-classification.py b/examples/classification/4-tutorials/plot_main-tutorial-classification.py
index a7905cfe0..1003141d2 100644
--- a/examples/classification/4-tutorials/plot_main-tutorial-classification.py
+++ b/examples/classification/4-tutorials/plot_main-tutorial-classification.py
@@ -148,7 +148,7 @@ def plot_scores(n, alphas, scores, quantiles):
 
 scores = mapie_score.conformity_scores_
 n = len(mapie_score.conformity_scores_)
-quantiles = mapie_score.quantiles_
+quantiles = mapie_score.conformity_score_function_.quantiles_
 plot_scores(n, alpha, scores, quantiles)
 
 ##############################################################################
diff --git a/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py b/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py
index 293404ca1..c0737c7ae 100644
--- a/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py
+++ b/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py
@@ -71,7 +71,7 @@ def get_cumulative_distribution_function(self, X):
         y_pred = self.predict(X)
         cs = self.conformity_scores_[~np.isnan(self.conformity_scores_)]
         res = self.conformity_score_function_.get_estimation_distribution(
-            X, y_pred.reshape((-1, 1)), cs
+            y_pred.reshape((-1, 1)), cs, X=X
         )
         return res
 
diff --git a/mapie/classification.py b/mapie/classification.py
index 232d76251..626149add 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -146,9 +146,6 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     conformity_scores_: ArrayLike of shape (n_samples_train)
         The conformity scores used to calibrate the prediction sets.
 
-    quantiles_: ArrayLike of shape (n_alpha)
-        The quantiles estimated from ``conformity_scores_`` and alpha values.
-
     References
     ----------
     [1] Mauricio Sadinle, Jing Lei, and Larry Wasserman.
diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index 6c91b88ee..db9df2c05 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -31,6 +31,11 @@ class BaseClassificationScore(BaseConformityScore, metaclass=ABCMeta):
         It should be specified if ``consistency_check==True``.
 
         By default, it is defined by the default precision.
+
+    Attributes
+    ----------
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``conformity_scores_`` and alpha values.
     """
 
     def __init__(

From f064cc9f2039fdb1da83a93f551d80fd599c1974 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 2 Jul 2024 09:45:53 +0200
Subject: [PATCH 165/424] UPD: check tests for additional parameters in
 residual normalized score

---
 mapie/conformity_scores/bounds/residuals.py | 16 ++++++--
 mapie/tests/test_conformity_scores.py       | 42 +++++++++++++++++++++
 2 files changed, 55 insertions(+), 3 deletions(-)

diff --git a/mapie/conformity_scores/bounds/residuals.py b/mapie/conformity_scores/bounds/residuals.py
index f6bc9c7f3..f59084455 100644
--- a/mapie/conformity_scores/bounds/residuals.py
+++ b/mapie/conformity_scores/bounds/residuals.py
@@ -1,5 +1,5 @@
 import warnings
-from typing import Optional, Tuple, Union
+from typing import Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -245,7 +245,12 @@ def get_signed_conformity_scores(
         The learning is done with the log of the residual and later we
         use the exponential of the prediction to avoid negative values.
         """
-        assert not (X is None)  # TODO
+        if X is None:
+            raise ValueError(
+                "Additional parameters must be provided for the method to "
+                + "work (here `X` is missing)."
+            )
+        X = cast(ArrayLike, X)
 
         (X, y, y_pred,
          self.residual_estimator_,
@@ -307,7 +312,12 @@ def get_estimation_distribution(
         ``conformity_scores`` can be either the conformity scores or
         the quantile of the conformity scores.
         """
-        assert not (X is None)  # TODO
+        if X is None:
+            raise ValueError(
+                "Additional parameters must be provided for the method to "
+                + "work (here `X` is missing)."
+            )
+        X = cast(ArrayLike, X)
 
         r_pred = self._predict_residual_estimator(X).reshape((-1, 1))
         if not self.prefit:
diff --git a/mapie/tests/test_conformity_scores.py b/mapie/tests/test_conformity_scores.py
index 4ade1f354..06dfca94b 100644
--- a/mapie/tests/test_conformity_scores.py
+++ b/mapie/tests/test_conformity_scores.py
@@ -382,6 +382,48 @@ def test_residual_normalised_prefit_get_estimation_distribution() -> None:
     )
 
 
+def test_residual_normalised_additional_parameters() -> None:
+    """
+    Test that residual normalised score raises no error with additional
+    parameters.
+    """
+    residual_normalised_conf_score = ResidualNormalisedScore(
+        residual_estimator=LinearRegression(),
+        split_size=0.2,
+        random_state=random_state
+    )
+    # Test for get_conformity_scores
+    # 1) Test that no error is raised
+    residual_normalised_conf_score.get_conformity_scores(
+        y_toy, y_pred_list, X=X_toy
+    )
+    # 2) Test that an error is raised when X is not provided
+    with pytest.raises(
+        ValueError,
+        match=r"Additional parameters must be provided*"
+    ):
+        residual_normalised_conf_score.get_conformity_scores(
+            y_toy, y_pred_list
+        )
+
+    # Test for get_estimation_distribution
+    conf_scores = residual_normalised_conf_score.get_conformity_scores(
+        y_toy, y_pred_list, X=X_toy
+    )
+    # 1) Test that no error is raised
+    residual_normalised_conf_score.get_estimation_distribution(
+        y_pred_list, conf_scores, X=X_toy
+    )
+    # 2) Test that an error is raised when X is not provided
+    with pytest.raises(
+        ValueError,
+        match=r"Additional parameters must be provided*"
+    ):
+        residual_normalised_conf_score.get_estimation_distribution(
+            y_pred_list, conf_scores
+        )
+
+
 @pytest.mark.parametrize("score", [AbsoluteConformityScore(),
                                    GammaConformityScore(),
                                    ResidualNormalisedScore()])

From a9c03fd1fd16717130f6c1ca5e04ea90212a0395 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 2 Jul 2024 10:17:16 +0200
Subject: [PATCH 166/424] UPD: typing and doctring in score classes

---
 mapie/classification.py                   |  2 +-
 mapie/conformity_scores/classification.py |  2 +-
 mapie/conformity_scores/sets/aps.py       | 52 +++++++++++++++++++---
 mapie/conformity_scores/sets/lac.py       | 51 +++++++++++++++++++---
 mapie/conformity_scores/sets/topk.py      | 53 ++++++++++++++++++++---
 5 files changed, 142 insertions(+), 18 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 626149add..ab7eef9af 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -629,7 +629,7 @@ def fit(
         # Compute the conformity scores
         self.conformity_scores_ = \
             self.conformity_score_function_.get_conformity_scores(
-                y, y_pred_proba, y_enc=y_enc, X=X
+                y_enc, y_pred_proba, X=X
             )
 
         return self
diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index db9df2c05..e23950b27 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -35,7 +35,7 @@ class BaseClassificationScore(BaseConformityScore, metaclass=ABCMeta):
     Attributes
     ----------
     quantiles_: ArrayLike of shape (n_alpha)
-        The quantiles estimated from ``conformity_scores_`` and alpha values.
+        The quantiles estimated from ``get_sets`` method.
     """
 
     def __init__(
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 6cd282260..6918c94ea 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -44,6 +44,44 @@ class APS(BaseClassificationScore):
     and Jitendra Malik.
     "Uncertainty Sets for Image Classifiers using Conformal Prediction."
     International Conference on Learning Representations 2021.
+
+    Parameters
+    ----------
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+
+    Attributes
+    ----------
+    method: str
+        Method to choose for prediction interval estimates.
+        This attribute is for compatibility with ``MapieClassifier``
+        which previously used a string instead of a score class.
+        Methods available in this class: ``aps``, ``raps`` and ``naive``.
+
+        By default, ``aps`` for APS method.
+
+    classes: Optional[ArrayLike]
+        Names of the classes.
+
+    random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state.
+
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``get_sets`` method.
     """
 
     def __init__(
@@ -89,9 +127,9 @@ def set_external_attributes(
 
     def get_conformity_scores(
         self,
-        y: ArrayLike,
-        y_pred: ArrayLike,
-        y_enc: Optional[ArrayLike] = None,
+        y: NDArray,
+        y_pred: NDArray,
+        y_enc: Optional[NDArray] = None,
         **kwargs
     ) -> NDArray:
         """
@@ -105,11 +143,15 @@ def get_conformity_scores(
         y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
+        y_enc: NDArray of shape (n_samples,)
+            Target values as normalized encodings.
+
         Returns
         -------
         NDArray of shape (n_samples,)
             Conformity scores.
         """
+        # Casting
         y = cast(NDArray, y)
         y_pred = cast(NDArray, y_pred)
         y_enc = cast(NDArray, y_enc)
@@ -136,8 +178,8 @@ def get_conformity_scores(
 
     def get_estimation_distribution(
         self,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
         **kwargs
     ) -> NDArray:
         """
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 8bff9b6fa..23d2b255f 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -24,6 +24,43 @@ class LAC(BaseClassificationScore):
     [1] Mauricio Sadinle, Jing Lei, and Larry Wasserman.
     "Least Ambiguous Set-Valued Classifiers with Bounded Error Levels.",
     Journal of the American Statistical Association, 114, 2019.
+
+    Parameters
+    ----------
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+
+    Attributes
+    ----------
+    method: str
+        Method to choose for prediction interval estimates.
+        This attribute is for compatibility with ``MapieClassifier``
+        which previously used a string instead of a score class.
+
+        By default, ``lac`` for LAC method.
+
+    classes: Optional[ArrayLike]
+        Names of the classes.
+
+    random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state.
+
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``get_sets`` method.
     """
 
     def __init__(
@@ -69,9 +106,9 @@ def set_external_attributes(
 
     def get_conformity_scores(
         self,
-        y: ArrayLike,
-        y_pred: ArrayLike,
-        y_enc: Optional[ArrayLike] = None,
+        y: NDArray,
+        y_pred: NDArray,
+        y_enc: Optional[NDArray] = None,
         **kwargs
     ) -> NDArray:
         """
@@ -85,11 +122,15 @@ def get_conformity_scores(
         y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
+        y_enc: NDArray of shape (n_samples,)
+            Target values as normalized encodings.
+
         Returns
         -------
         NDArray of shape (n_samples,)
             Conformity scores.
         """
+        # Casting
         y_pred = cast(NDArray, y_pred)
         y_enc = cast(NDArray, y_enc)
 
@@ -101,8 +142,8 @@ def get_conformity_scores(
 
     def get_estimation_distribution(
         self,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
         **kwargs
     ) -> NDArray:
         """
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 1e68ad832..0df5faabb 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -28,6 +28,43 @@ class TopK(BaseClassificationScore):
     and Jitendra Malik.
     "Uncertainty Sets for Image Classifiers using Conformal Prediction."
     International Conference on Learning Representations 2021.
+
+    Parameters
+    ----------
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+
+    Attributes
+    ----------
+    method: str
+        Method to choose for prediction interval estimates.
+        This attribute is for compatibility with ``MapieClassifier``
+        which previously used a string instead of a score class.
+
+        By default, ``top_k`` for Top-K method.
+
+    classes: Optional[ArrayLike]
+        Names of the classes.
+
+    random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state.
+
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``get_sets`` method.
     """
 
     def __init__(
@@ -56,7 +93,7 @@ def set_external_attributes(
             Method to choose for prediction interval estimates.
             Methods available in this class: ``top_k``.
 
-            By default ``top_k`` for Top K method.
+            By default ``top_k`` for Top-K method.
 
         classes: Optional[ArrayLike]
             Names of the classes.
@@ -73,9 +110,9 @@ def set_external_attributes(
 
     def get_conformity_scores(
         self,
-        y: ArrayLike,
-        y_pred: ArrayLike,
-        y_enc: Optional[ArrayLike] = None,
+        y: NDArray,
+        y_pred: NDArray,
+        y_enc: Optional[NDArray] = None,
         **kwargs
     ) -> NDArray:
         """
@@ -89,11 +126,15 @@ def get_conformity_scores(
         y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
+        y_enc: NDArray of shape (n_samples,)
+            Target values as normalized encodings.
+
         Returns
         -------
         NDArray of shape (n_samples,)
             Conformity scores.
         """
+        # Casting
         y = cast(NDArray, y)
         y_pred = cast(NDArray, y_pred)
         y_enc = cast(NDArray, y_enc)
@@ -107,8 +148,8 @@ def get_conformity_scores(
 
     def get_estimation_distribution(
         self,
-        y_pred: ArrayLike,
-        conformity_scores: ArrayLike,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
         **kwargs
     ) -> NDArray:
         """

From 7f791dd1494c5ff003413e967b4d66e01d7af286 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 2 Jul 2024 10:45:30 +0200
Subject: [PATCH 167/424] UPD: use y_enc as additional parameters to conserve
 label encoding

---
 mapie/classification.py              | 2 +-
 mapie/conformity_scores/sets/aps.py  | 2 --
 mapie/conformity_scores/sets/lac.py  | 2 +-
 mapie/conformity_scores/sets/topk.py | 2 --
 4 files changed, 2 insertions(+), 6 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index ab7eef9af..626149add 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -629,7 +629,7 @@ def fit(
         # Compute the conformity scores
         self.conformity_scores_ = \
             self.conformity_score_function_.get_conformity_scores(
-                y_enc, y_pred_proba, X=X
+                y, y_pred_proba, y_enc=y_enc, X=X
             )
 
         return self
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 6918c94ea..06ac5dfa9 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -152,8 +152,6 @@ def get_conformity_scores(
             Conformity scores.
         """
         # Casting
-        y = cast(NDArray, y)
-        y_pred = cast(NDArray, y_pred)
         y_enc = cast(NDArray, y_enc)
         classes = cast(NDArray, self.classes)
 
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 23d2b255f..718456c72 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -131,13 +131,13 @@ def get_conformity_scores(
             Conformity scores.
         """
         # Casting
-        y_pred = cast(NDArray, y_pred)
         y_enc = cast(NDArray, y_enc)
 
         # Conformity scores
         conformity_scores = np.take_along_axis(
             1 - y_pred, y_enc.reshape(-1, 1), axis=1
         )
+
         return conformity_scores
 
     def get_estimation_distribution(
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 0df5faabb..4a2cd8992 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -135,8 +135,6 @@ def get_conformity_scores(
             Conformity scores.
         """
         # Casting
-        y = cast(NDArray, y)
-        y_pred = cast(NDArray, y_pred)
         y_enc = cast(NDArray, y_enc)
 
         # Conformity scores

From 1b99529bf48368c9eab70cd6272accdab5a2f958 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 2 Jul 2024 11:25:20 +0200
Subject: [PATCH 168/424] UPD: change greater equal to less equal

---
 mapie/conformity_scores/sets/lac.py | 7 +++----
 1 file changed, 3 insertions(+), 4 deletions(-)

diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 718456c72..e4d43eee9 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -225,13 +225,12 @@ def get_sets(
 
         # Build prediction sets
         if (estimator.cv == "prefit") or (agg_scores == "mean"):
-            prediction_sets = np.greater_equal(
-                y_pred_proba - (1 - self.quantiles_), -EPSILON
+            prediction_sets = np.less_equal(
+                (1 - y_pred_proba) - self.quantiles_, EPSILON
             )
         else:
             y_pred_included = np.less_equal(
-                (1 - y_pred_proba) - conformity_scores.ravel(),
-                EPSILON
+                (1 - y_pred_proba) - conformity_scores.ravel(), EPSILON
             ).sum(axis=2)
             prediction_sets = np.stack(
                 [

From e588a3e8c28392a6d27f7fd9b941c94371085673 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 2 Jul 2024 12:07:23 +0200
Subject: [PATCH 169/424] UPD: docstring to explain parameters of get_sets

---
 mapie/conformity_scores/sets/aps.py  | 34 +++++++++++++++++++++++++++-
 mapie/conformity_scores/sets/lac.py  | 12 +++++++++-
 mapie/conformity_scores/sets/topk.py |  7 +-----
 3 files changed, 45 insertions(+), 8 deletions(-)

diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 06ac5dfa9..3798e7139 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -412,7 +412,39 @@ def get_sets(
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores.
 
-        TODO
+        agg_scores: Optional[str]
+            How to aggregate the scores output by the estimators on test data
+            if a cross-validation strategy is used. Choose among:
+
+            - "mean", take the mean of scores.
+            - "crossval", compare the scores between all training data and each
+                test point for each label to estimate if the label must be
+                included in the prediction set. Follows algorithm 2 of
+                Romano+2020.
+
+            By default, "mean".
+
+        X_raps: NDArray of shape (n_samples, n_features)
+            Observed feature values for the RAPS method (split data).
+
+            By default, "None" but must be set to work.
+
+        y_raps_no_enc: NDArray of shape (n_samples,)
+            Observed labels for the RAPS method (split data).
+
+            By default, "None" but must be set to work.
+
+        y_pred_proba_raps: NDArray of shape (n_samples, n_classes)
+            Predicted probabilities for the RAPS method (split data).
+
+            By default, "None" but must be set to work.
+
+        position_raps: NDArray of shape (n_samples,)
+            Position of the points in the split set for the RAPS method
+            (split data). These positions are returned by the function
+            ``get_true_label_position``.
+
+            By default, "None" but must be set to work.
 
         Returns
         -------
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index e4d43eee9..976291add 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -197,7 +197,17 @@ def get_sets(
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores.
 
-        TODO
+        agg_scores: Optional[str]
+            How to aggregate the scores output by the estimators on test data
+            if a cross-validation strategy is used. Choose among:
+
+            - "mean", take the mean of scores.
+            - "crossval", compare the scores between all training data and each
+              test point for each label to estimate if the label must be
+              included in the prediction set. Follows algorithm 2 of
+              Romano+2020.
+
+            By default, "mean".
 
         Returns
         -------
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 4a2cd8992..2769ed144 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -179,7 +179,6 @@ def get_sets(
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
         conformity_scores: NDArray,
-        agg_scores: Optional[str] = "mean",
         **kwargs
     ):
         """
@@ -201,17 +200,13 @@ def get_sets(
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores.
 
-        TODO
-
         Returns
         -------
         NDArray of shape (n_samples, n_classes, n_alpha)
             Prediction sets (Booleans indicate whether classes are included).
         """
         # Checks
-        agg_scores = "mean"
-
-        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = estimator.predict(X, agg_scores="mean")
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
         y_pred_proba = np.repeat(
             y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2

From a19c1156a6698568522de80ab30c6dcd07ac1f17 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 2 Jul 2024 14:24:16 +0200
Subject: [PATCH 170/424] Adding unit tests

---
 mapie/regression/quantile_regression.py    |   5 +-
 mapie/regression/regression.py             |  32 +++-
 mapie/regression/time_series_regression.py |   3 +
 mapie/tests/test_regression.py             | 177 ++++++++++++++++++++-
 4 files changed, 209 insertions(+), 8 deletions(-)

diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index 63cf3032f..74d1a11c3 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, List, Optional, Tuple, Union, cast, Any
+from typing import Any, Iterable, List, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -676,6 +676,9 @@ def predict(
             each residuals separatly or to use the maximum of the two
             combined.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Union[NDArray, Tuple[NDArray, NDArray]]
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 6bc13e226..aae883718 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, Optional, Tuple, Union, cast, Any
+from typing import Any, Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import BaseEstimator, RegressorMixin
@@ -228,6 +228,7 @@ def __init__(
         verbose: int = 0,
         conformity_score: Optional[ConformityScore] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
+        predict_params: Optional[bool] = False
     ) -> None:
         self.estimator = estimator
         self.method = method
@@ -238,6 +239,7 @@ def __init__(
         self.verbose = verbose
         self.conformity_score = conformity_score
         self.random_state = random_state
+        self.predict_params = predict_params
 
     def _check_parameters(self) -> None:
         """
@@ -502,11 +504,8 @@ def fit(
             train/test set.
             By default ``None``.
 
-        fit_params : dict
-            Additional fit parameters.
-
-        predict_params : dict
-            Additional predict parameters.
+        kwargs : dict
+            Additional ft and parameters.
 
         Returns
         -------
@@ -515,6 +514,9 @@ def fit(
         """
         fit_params = kwargs.pop('fit_params', {})
         predict_params = kwargs.pop('predict_params', {})
+
+        if len(predict_params) > 0:
+            self.predict_params = True
         # Checks
         (estimator,
          self.conformity_score_function_,
@@ -621,6 +623,24 @@ def predict(
                 - [:, 0, :]: Lower bound of the prediction interval.
                 - [:, 1, :]: Upper bound of the prediction interval.
         """
+
+        if self.predict_params is True:
+            warnings.warn(
+                f"Be careful that predict_params: '{predict_params}' "
+                "is used in fit method",
+                UserWarning
+            )
+
+        elif (len(predict_params) > 0 and
+              self.predict_params is False and
+              self.cv != "prefit"):
+            raise ValueError(
+                f"Using 'predict_param' '{predict_params}' "
+                f"without having used it in the fit method. "
+                f"Please ensure '{predict_params}' "
+                f"is used in the fit method before calling predict."
+            )
+
         # Checks
         check_is_fitted(self, self.fit_attributes)
         self._check_ensemble(ensemble)
diff --git a/mapie/regression/time_series_regression.py b/mapie/regression/time_series_regression.py
index 00bb09758..bf6212800 100644
--- a/mapie/regression/time_series_regression.py
+++ b/mapie/regression/time_series_regression.py
@@ -440,6 +440,9 @@ def predict(
         allow_infinite_bounds: bool
             Allow infinite prediction intervals to be produced.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Union[NDArray, Tuple[NDArray, NDArray]]
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index ed7f14133..1541ba9ea 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -6,6 +6,7 @@
 import numpy as np
 import pandas as pd
 import pytest
+from scipy.stats import ttest_1samp
 from sklearn.compose import ColumnTransformer
 from sklearn.datasets import make_regression
 from sklearn.dummy import DummyRegressor
@@ -18,7 +19,6 @@
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.validation import check_is_fitted
-from scipy.stats import ttest_1samp
 from typing_extensions import TypedDict
 
 from mapie._typing import NDArray
@@ -41,6 +41,64 @@
 
 random_state = 1
 
+
+class CustomGradientBoostingRegressor(GradientBoostingRegressor):
+    def __init__(self,
+                 loss='squared_error',
+                 learning_rate=0.1,
+                 n_estimators=100,
+                 subsample=1.0,
+                 criterion='friedman_mse',
+                 min_samples_split=2,
+                 min_samples_leaf=1,
+                 min_weight_fraction_leaf=0.0,
+                 max_depth=3,
+                 min_impurity_decrease=0.0,
+                 init=None,
+                 random_state=None,
+                 max_features=None,
+                 alpha=0.9,
+                 verbose=0,
+                 max_leaf_nodes=None,
+                 warm_start=False,
+                 validation_fraction=0.1,
+                 n_iter_no_change=None,
+                 tol=0.0001,
+                 ccp_alpha=0.0):
+
+        super().__init__(
+            loss=loss,
+            learning_rate=learning_rate,
+            n_estimators=n_estimators,
+            subsample=subsample,
+            criterion=criterion,
+            min_samples_split=min_samples_split,
+            min_samples_leaf=min_samples_leaf,
+            min_weight_fraction_leaf=min_weight_fraction_leaf,
+            max_depth=max_depth,
+            min_impurity_decrease=min_impurity_decrease,
+            init=init,
+            random_state=random_state,
+            max_features=max_features,
+            alpha=alpha,
+            verbose=verbose,
+            max_leaf_nodes=max_leaf_nodes,
+            warm_start=warm_start,
+            validation_fraction=validation_fraction,
+            n_iter_no_change=n_iter_no_change,
+            tol=tol,
+            ccp_alpha=ccp_alpha
+        )
+
+    def fit(self, X, y, **kwargs):
+        return super().fit(X, y, **kwargs)
+
+    def predict(self, X, check_predict_params=False):
+        if check_predict_params:
+            return np.zeros(X.shape[0])
+        return super().predict(X)
+
+
 Params = TypedDict(
     "Params",
     {
@@ -875,6 +933,123 @@ def early_stopping_monitor(i, est, locals):
         assert estimator.estimators_.shape[0] == 3
 
 
+def test_predict_parameters_passing() -> None:
+    """
+    Test passing predict parameters.
+    Checks that y_pred from train are 0 and y_pred from test are 0
+    """
+
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
+
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state))
+
+    mapie_1 = MapieRegressor(estimator=custom_gbr)
+
+    mapie_2 = MapieRegressor(estimator=custom_gbr)
+
+    predict_params = {'check_predict_params': True}
+
+    mapie_1 = mapie_1.fit(X_train, y_train,
+                          predict_params=predict_params)
+
+    np.testing.assert_allclose(mapie_1.conformity_scores_, np.abs(y_train))
+
+    mapie_2 = mapie_2.fit(X_train, y_train)
+
+    y_pred_1 = mapie_1.predict(X_test, **predict_params)
+
+    np.testing.assert_allclose(y_pred_1, 0)
+
+    y_pred_2 = mapie_2.predict(X_test)
+
+    with np.testing.assert_raises(AssertionError):
+        np.testing.assert_array_equal(y_pred_1, y_pred_2)
+
+
+def test_fit_and_predict_parameters_passing() -> None:
+    """
+    Test passing fit parameters and predict parameters.
+    For fit : checks that underlying GradientBoosting
+    estimators have used 3 iterations only during boosting,
+    instead of default value for n_estimators (=100).
+    For predict : Checks that y_pred from train are 0
+    and y_pred from test are 0.
+    """
+    def early_stopping_monitor(i, est, locals):
+        """Returns True on the 3rd iteration."""
+        if i == 2:
+            return True
+        else:
+            return False
+
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
+
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state))
+
+    score = AbsoluteConformityScore(sym=True)
+
+    mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
+
+    mapie_2 = MapieRegressor(estimator=custom_gbr)
+
+    fit_params = {'monitor': early_stopping_monitor}
+
+    predict_params = {'check_predict_params': True}
+
+    mapie_1 = mapie_1.fit(X_train, y_train,
+                          fit_params=fit_params,
+                          predict_params=predict_params)
+
+    mapie_2 = mapie_2.fit(X_train, y_train)
+
+    assert mapie_1.estimator_.single_estimator_.estimators_.shape[0] == 3
+
+    for estimator in mapie_1.estimator_.estimators_:
+        assert estimator.estimators_.shape[0] == 3
+
+    assert (mapie_2.estimator_.single_estimator_.n_estimators ==
+           custom_gbr.n_estimators)
+
+    for estimator in mapie_2.estimator_.estimators_:
+        assert estimator.n_estimators == custom_gbr.n_estimators
+
+    np.testing.assert_array_equal(mapie_1.conformity_scores_, np.abs(y_train))
+
+    y_pred_1 = mapie_1.predict(X_test, **predict_params)
+
+    np.testing.assert_allclose(y_pred_1, 0)
+
+    y_pred_2 = mapie_2.predict(X_test)
+
+    with np.testing.assert_raises(AssertionError):
+        np.testing.assert_array_equal(y_pred_1, y_pred_2)
+
+
+def test_invalid_predict_parameters() -> None:
+    """Test that invalid predict_parameters raise errors."""
+
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
+
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state))
+
+    mapie = MapieRegressor(estimator=custom_gbr)
+
+    predict_params = {'check_predict_params': True}
+
+    mapie_fitted = mapie.fit(X_train, y_train)
+
+    with pytest.raises(ValueError, match=(
+        fr".*Using 'predict_param' '{predict_params}'"
+        r".*without having used it in the fit method\..*"
+        fr"Please ensure '{predict_params}'"
+        r".*is used in the fit method before calling predict\..*"
+    )):
+        mapie_fitted.predict(X_test, **predict_params)
+
+
 def test_predict_infinite_intervals() -> None:
     """Test that MapieRegressor produces infinite bounds with alpha=0"""
     mapie_reg = MapieRegressor().fit(X, y)

From 306f3be19ac54d3386b374e0af1aa0d5635cc199 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 2 Jul 2024 14:48:11 +0200
Subject: [PATCH 171/424] Update History.rst

---
 HISTORY.rst | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 31da81500..59135547a 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,8 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Add `**predict_params` attributes into `MapieRegressor` and linked classes
+* Change incoherent sign on C_k in the Kolmogorov-Smirnov statistical test documentation
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.
 
 0.8.6 (2024-06-14)

From 9317271be8706f8f73749f88b85a295bfb355d29 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 2 Jul 2024 17:31:37 +0200
Subject: [PATCH 172/424] Fix type-check

---
 mapie/estimator/classifier.py | 2 +-
 mapie/estimator/interface.py  | 2 --
 2 files changed, 1 insertion(+), 3 deletions(-)

diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 16df810e2..a97495319 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -448,7 +448,7 @@ def predict(
         self,
         X: ArrayLike,
         agg_scores: Optional[str] = None,
-        **predict_params
+        **predict_params,
     ) -> NDArray:
         """
         Predict target from X. It also computes the prediction per train sample
diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
index e798273b7..fdb67d618 100644
--- a/mapie/estimator/interface.py
+++ b/mapie/estimator/interface.py
@@ -32,8 +32,6 @@ def fit(
     def predict(
         self,
         X: ArrayLike,
-        ensemble: bool = False,
-        return_multi_pred: bool = True,
         **kwargs,
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """

From 8832a2e3c1d8beb21afff4d1dc5203b664b4ab3b Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 3 Jul 2024 11:29:23 +0200
Subject: [PATCH 173/424] UPD: remove useless methods et attributes (estimation
 distribution) for classification score

---
 mapie/conformity_scores/classification.py |  28 +-----
 mapie/conformity_scores/interface.py      | 107 +---------------------
 mapie/conformity_scores/regression.py     |  85 ++++++++++++++++-
 mapie/conformity_scores/sets/aps.py       |  62 +------------
 mapie/conformity_scores/sets/lac.py       |  62 +------------
 mapie/conformity_scores/sets/topk.py      |  62 +------------
 6 files changed, 93 insertions(+), 313 deletions(-)

diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index e23950b27..b2670c5d9 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -3,7 +3,6 @@
 from mapie.conformity_scores.interface import BaseConformityScore
 from mapie.estimator.classifier import EnsembleClassifier
 
-from mapie._machine_precision import EPSILON
 from mapie._typing import NDArray
 
 
@@ -13,37 +12,14 @@ class BaseClassificationScore(BaseConformityScore, metaclass=ABCMeta):
 
     This class should not be used directly. Use derived classes instead.
 
-    Parameters
-    ----------
-    consistency_check: bool, optional
-        Whether to check the consistency between the methods
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, the following equality must be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        By default ``True``.
-
-    eps: float, optional
-        Threshold to consider when checking the consistency between
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        It should be specified if ``consistency_check==True``.
-
-        By default, it is defined by the default precision.
-
     Attributes
     ----------
     quantiles_: ArrayLike of shape (n_alpha)
         The quantiles estimated from ``get_sets`` method.
     """
 
-    def __init__(
-        self,
-        consistency_check: bool = True,
-        eps: float = float(EPSILON),
-    ):
-        super().__init__(consistency_check=consistency_check, eps=eps)
+    def __init__(self) -> None:
+        super().__init__()
 
     @abstractmethod
     def get_sets(
diff --git a/mapie/conformity_scores/interface.py b/mapie/conformity_scores/interface.py
index 680c6cc9e..c8e163844 100644
--- a/mapie/conformity_scores/interface.py
+++ b/mapie/conformity_scores/interface.py
@@ -3,7 +3,6 @@
 import numpy as np
 
 from mapie._compatibility import np_nanquantile
-from mapie._machine_precision import EPSILON
 from mapie._typing import NDArray
 
 
@@ -12,34 +11,10 @@ class BaseConformityScore(metaclass=ABCMeta):
     Base class for conformity scores.
 
     This class should not be used directly. Use derived classes instead.
-
-    Parameters
-    ----------
-    consistency_check: bool, optional
-        Whether to check the consistency between the methods
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, the following equality must be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        By default ``True``.
-
-    eps: float, optional
-        Threshold to consider when checking the consistency between
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        It should be specified if ``consistency_check==True``.
-
-        By default, it is defined by the default precision.
     """
 
-    def __init__(
-        self,
-        consistency_check: bool = True,
-        eps: float = float(EPSILON),
-    ):
-        self.consistency_check = consistency_check
-        self.eps = eps
+    def __init__(self) -> None:
+        pass
 
     def set_external_attributes(
         self,
@@ -54,56 +29,6 @@ def set_external_attributes(
         """
         pass
 
-    def check_consistency(
-        self,
-        y: NDArray,
-        y_pred: NDArray,
-        conformity_scores: NDArray,
-        **kwargs
-    ) -> None:
-        """
-        Check consistency between the following methods:
-        ``get_estimation_distribution`` and ``get_signed_conformity_scores``
-
-        The following equality should be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        Parameters
-        ----------
-        y: NDArray of shape (n_samples, ...)
-            Observed target values.
-
-        y_pred: NDArray of shape (n_samples, ...)
-            Predicted target values.
-
-        conformity_scores: NDArray of shape (n_samples, ...)
-            Conformity scores.
-
-        Raises
-        ------
-        ValueError
-            If the two methods are not consistent.
-        """
-        score_distribution = self.get_estimation_distribution(
-            y_pred, conformity_scores, **kwargs
-        )
-        abs_conformity_scores = np.abs(np.subtract(score_distribution, y))
-        max_conf_score = np.max(abs_conformity_scores)
-        if max_conf_score > self.eps:
-            raise ValueError(
-                "The two functions get_conformity_scores and "
-                "get_estimation_distribution of the BaseConformityScore class "
-                "are not consistent. "
-                "The following equation must be verified: "
-                "self.get_estimation_distribution(y_pred, "
-                "self.get_conformity_scores(y, y_pred)) == y. "
-                f"The maximum conformity score is {max_conf_score}. "
-                "The eps attribute may need to be increased if you are "
-                "sure that the two methods are consistent."
-            )
-
     @abstractmethod
     def get_conformity_scores(
         self,
@@ -132,34 +57,6 @@ def get_conformity_scores(
             Conformity scores.
         """
 
-    @abstractmethod
-    def get_estimation_distribution(
-        self,
-        y_pred: NDArray,
-        conformity_scores: NDArray,
-        **kwargs
-    ) -> NDArray:
-        """
-        Placeholder for ``get_estimation_distribution``.
-        Subclasses should implement this method!
-
-        Compute samples of the estimation distribution given the predicted
-        targets and the conformity scores.
-
-        Parameters
-        ----------
-        y_pred: NDArray of shape (n_samples, ...)
-            Predicted target values.
-
-        conformity_scores: NDArray of shape (n_samples, ...)
-            Conformity scores.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, ...)
-            Observed values.
-        """
-
     @staticmethod
     def get_quantile(
         conformity_scores: NDArray,
diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index 2e878e349..fa151d5e5 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -41,12 +41,15 @@ class BaseRegressionScore(BaseConformityScore, metaclass=ABCMeta):
     """
 
     def __init__(
-        self, sym: bool,
+        self,
+        sym: bool,
         consistency_check: bool = True,
         eps: float = float(EPSILON),
     ):
-        super().__init__(consistency_check=consistency_check, eps=eps)
+        super().__init__()
         self.sym = sym
+        self.consistency_check = consistency_check
+        self.eps = eps
 
     @abstractmethod
     def get_signed_conformity_scores(
@@ -106,6 +109,84 @@ def get_conformity_scores(
             conformity_scores = np.abs(conformity_scores)
         return conformity_scores
 
+    def check_consistency(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> None:
+        """
+        Check consistency between the following methods:
+        ``get_estimation_distribution`` and ``get_signed_conformity_scores``
+
+        The following equality should be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples, ...)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Raises
+        ------
+        ValueError
+            If the two methods are not consistent.
+        """
+        score_distribution = self.get_estimation_distribution(
+            y_pred, conformity_scores, **kwargs
+        )
+        abs_conformity_scores = np.abs(np.subtract(score_distribution, y))
+        max_conf_score = np.max(abs_conformity_scores)
+        if max_conf_score > self.eps:
+            raise ValueError(
+                "The two functions get_conformity_scores and "
+                "get_estimation_distribution of the BaseConformityScore class "
+                "are not consistent. "
+                "The following equation must be verified: "
+                "self.get_estimation_distribution(y_pred, "
+                "self.get_conformity_scores(y, y_pred)) == y. "
+                f"The maximum conformity score is {max_conf_score}. "
+                "The eps attribute may need to be increased if you are "
+                "sure that the two methods are consistent."
+            )
+
+    @abstractmethod
+    def get_estimation_distribution(
+        self,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples, ...)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples, ...)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, ...)
+            Observed values.
+        """
+
     @staticmethod
     def _beta_optimize(
         alpha_np: NDArray,
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 3798e7139..b1a2fe142 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -45,25 +45,6 @@ class APS(BaseClassificationScore):
     "Uncertainty Sets for Image Classifiers using Conformal Prediction."
     International Conference on Learning Representations 2021.
 
-    Parameters
-    ----------
-    consistency_check: bool, optional
-        Whether to check the consistency between the methods
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, the following equality must be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        By default ``True``.
-
-    eps: float, optional
-        Threshold to consider when checking the consistency between
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        It should be specified if ``consistency_check==True``.
-
-        By default, it is defined by the default precision.
-
     Attributes
     ----------
     method: str
@@ -84,15 +65,8 @@ class APS(BaseClassificationScore):
         The quantiles estimated from ``get_sets`` method.
     """
 
-    def __init__(
-        self,
-        consistency_check: bool = True,
-        eps: float = float(EPSILON),
-    ):
-        super().__init__(
-            consistency_check=consistency_check,
-            eps=eps
-        )
+    def __init__(self) -> None:
+        super().__init__()
 
     def set_external_attributes(
         self,
@@ -174,35 +148,6 @@ def get_conformity_scores(
 
         return conformity_scores
 
-    def get_estimation_distribution(
-        self,
-        y_pred: NDArray,
-        conformity_scores: NDArray,
-        **kwargs
-    ) -> NDArray:
-        """
-        TODO
-        Placeholder for ``get_estimation_distribution``.
-        Subclasses should implement this method!
-
-        Compute samples of the estimation distribution given the predicted
-        targets and the conformity scores.
-
-        Parameters
-        ----------
-        y_pred: NDArray of shape (n_samples, ...)
-            Predicted target values.
-
-        conformity_scores: NDArray of shape (n_samples, ...)
-            Conformity scores.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, ...)
-            Observed values.
-        """
-        return np.array([])
-
     @staticmethod
     def _regularize_conformity_score(
         k_star: NDArray,
@@ -563,7 +508,4 @@ def get_sets(
                 EPSILON
             )
 
-        # Just for coverage: do nothing
-        self.get_estimation_distribution(y_pred_proba, conformity_scores)
-
         return prediction_sets
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 976291add..5edf9d45c 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -25,25 +25,6 @@ class LAC(BaseClassificationScore):
     "Least Ambiguous Set-Valued Classifiers with Bounded Error Levels.",
     Journal of the American Statistical Association, 114, 2019.
 
-    Parameters
-    ----------
-    consistency_check: bool, optional
-        Whether to check the consistency between the methods
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, the following equality must be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        By default ``True``.
-
-    eps: float, optional
-        Threshold to consider when checking the consistency between
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        It should be specified if ``consistency_check==True``.
-
-        By default, it is defined by the default precision.
-
     Attributes
     ----------
     method: str
@@ -63,15 +44,8 @@ class LAC(BaseClassificationScore):
         The quantiles estimated from ``get_sets`` method.
     """
 
-    def __init__(
-        self,
-        consistency_check: bool = True,
-        eps: float = float(EPSILON),
-    ):
-        super().__init__(
-            consistency_check=consistency_check,
-            eps=eps
-        )
+    def __init__(self) -> None:
+        super().__init__()
 
     def set_external_attributes(
         self,
@@ -140,35 +114,6 @@ def get_conformity_scores(
 
         return conformity_scores
 
-    def get_estimation_distribution(
-        self,
-        y_pred: NDArray,
-        conformity_scores: NDArray,
-        **kwargs
-    ) -> NDArray:
-        """
-        TODO
-        Placeholder for ``get_estimation_distribution``.
-        Subclasses should implement this method!
-
-        Compute samples of the estimation distribution given the predicted
-        targets and the conformity scores.
-
-        Parameters
-        ----------
-        y_pred: NDArray of shape (n_samples, ...)
-            Predicted target values.
-
-        conformity_scores: NDArray of shape (n_samples, ...)
-            Conformity scores.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, ...)
-            Observed values.
-        """
-        return np.array([])
-
     def get_sets(
         self,
         X: ArrayLike,
@@ -251,7 +196,4 @@ def get_sets(
                 ], axis=2
             )
 
-        # Just for coverage: do nothing
-        self.get_estimation_distribution(y_pred_proba, conformity_scores)
-
         return prediction_sets
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 2769ed144..fb0e7836f 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -29,25 +29,6 @@ class TopK(BaseClassificationScore):
     "Uncertainty Sets for Image Classifiers using Conformal Prediction."
     International Conference on Learning Representations 2021.
 
-    Parameters
-    ----------
-    consistency_check: bool, optional
-        Whether to check the consistency between the methods
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, the following equality must be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        By default ``True``.
-
-    eps: float, optional
-        Threshold to consider when checking the consistency between
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        It should be specified if ``consistency_check==True``.
-
-        By default, it is defined by the default precision.
-
     Attributes
     ----------
     method: str
@@ -67,15 +48,8 @@ class TopK(BaseClassificationScore):
         The quantiles estimated from ``get_sets`` method.
     """
 
-    def __init__(
-        self,
-        consistency_check: bool = True,
-        eps: float = float(EPSILON),
-    ):
-        super().__init__(
-            consistency_check=consistency_check,
-            eps=eps
-        )
+    def __init__(self) -> None:
+        super().__init__()
 
     def set_external_attributes(
         self,
@@ -144,35 +118,6 @@ def get_conformity_scores(
 
         return conformity_scores
 
-    def get_estimation_distribution(
-        self,
-        y_pred: NDArray,
-        conformity_scores: NDArray,
-        **kwargs
-    ) -> NDArray:
-        """
-        TODO
-        Placeholder for ``get_estimation_distribution``.
-        Subclasses should implement this method!
-
-        Compute samples of the estimation distribution given the predicted
-        targets and the conformity scores.
-
-        Parameters
-        ----------
-        y_pred: NDArray of shape (n_samples, ...)
-            Predicted target values.
-
-        conformity_scores: NDArray of shape (n_samples, ...)
-            Conformity scores.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, ...)
-            Observed values.
-        """
-        return np.array([])
-
     def get_sets(
         self,
         X: ArrayLike,
@@ -240,7 +185,4 @@ def get_sets(
             -EPSILON
         )
 
-        # Just for coverage: do nothing
-        self.get_estimation_distribution(y_pred_proba, conformity_scores)
-
         return prediction_sets

From a28d2bfadf19d0f3898d6a12b0d9d1f903d3f25c Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 3 Jul 2024 12:02:29 +0200
Subject: [PATCH 174/424] Update : take remarks into account

---
 mapie/estimator/interface.py               | 26 ++++------------------
 mapie/estimator/regressor.py               |  4 ++--
 mapie/regression/quantile_regression.py    |  6 ++---
 mapie/regression/regression.py             |  6 ++---
 mapie/regression/time_series_regression.py |  4 ++--
 mapie/tests/test_regression.py             | 23 +++++++------------
 6 files changed, 22 insertions(+), 47 deletions(-)

diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
index fdb67d618..4b5abab8f 100644
--- a/mapie/estimator/interface.py
+++ b/mapie/estimator/interface.py
@@ -32,7 +32,7 @@ def fit(
     def predict(
         self,
         X: ArrayLike,
-        **kwargs,
+        **kwargs
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
@@ -43,30 +43,12 @@ def predict(
         X: ArrayLike of shape (n_samples, n_features)
             Test data.
 
-        ensemble: bool
-            Boolean determining whether the predictions are ensembled or not.
-            If ``False``, predictions are those of the model trained on the
-            whole training set.
-            If ``True``, predictions from perturbed models are aggregated by
-            the aggregation function specified in the ``agg_function``
-            attribute.
-
-            If ``cv`` is ``"prefit"`` or ``"split"``, ``ensemble`` is ignored.
-
-            By default ``False``.
-
-        return_multi_pred: bool
-            If ``True`` the method returns the predictions and the multiple
-            predictions (3 arrays). If ``False`` the method return the
-            simple predictions only.
-
         **kwargs : dict
-            Additional parameters.
+            Additional fit and predict parameters.
 
         Returns
         -------
-        Tuple[NDArray, NDArray, NDArray]
+        Tuple[NDArray, NDArray]
             - Predictions
-            - The multiple predictions for the lower bound of the intervals.
-            - The multiple predictions for the upper bound of the intervals.
+            - Predictions sets
         """
diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index 91dce5011..a200586c6 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -415,7 +415,7 @@ def fit(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
-        **fit_params,
+        **fit_params
     ) -> EnsembleRegressor:
         """
         Fit the base estimator under the ``single_estimator_`` attribute.
@@ -509,7 +509,7 @@ def predict(
         X: ArrayLike,
         ensemble: bool = False,
         return_multi_pred: bool = True,
-        **predict_params,
+        **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
         """
         Predict target from X. It also computes the prediction per train sample
diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index 74d1a11c3..e66f1939f 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Any, Iterable, List, Optional, Tuple, Union, cast
+from typing import Iterable, List, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -648,7 +648,7 @@ def predict(
         optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
         symmetry: Optional[bool] = True,
-        **predict_params: Any,
+        **predict_params,
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Predict target on new samples with confidence intervals.
@@ -676,7 +676,7 @@ def predict(
             each residuals separatly or to use the maximum of the two
             combined.
 
-        **predict_params : dict
+        predict_params : dict
             Additional predict parameters.
 
         Returns
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 09756b9a0..190832190 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -469,7 +469,7 @@ def fit(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
-        **kwargs: Any,
+        **kwargs: Any
     ) -> MapieRegressor:
         """
         Fit estimator and compute conformity scores used for
@@ -503,7 +503,7 @@ def fit(
             By default ``None``.
 
         kwargs : dict
-            Additional ft and parameters.
+            Additional fit and predict parameters.
 
         Returns
         -------
@@ -609,7 +609,7 @@ def predict(
 
             By default ``False``.
 
-        **predict_params : dict
+        predict_params : dict
             Additional predict parameters.
 
         Returns
diff --git a/mapie/regression/time_series_regression.py b/mapie/regression/time_series_regression.py
index bf6212800..f70e2b0e6 100644
--- a/mapie/regression/time_series_regression.py
+++ b/mapie/regression/time_series_regression.py
@@ -405,7 +405,7 @@ def predict(
         alpha: Optional[Union[float, Iterable[float]]] = None,
         optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
-        **predict_params,
+        **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Predict target on new samples with confidence intervals.
@@ -440,7 +440,7 @@ def predict(
         allow_infinite_bounds: bool
             Allow infinite prediction intervals to be produced.
 
-        **predict_params : dict
+        predict_params : dict
             Additional predict parameters.
 
         Returns
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index c59ee3ff4..d2e6f0599 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -942,27 +942,20 @@ def test_predict_parameters_passing() -> None:
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
 
     X_train, X_test, y_train, y_test = (
-        train_test_split(X, y, test_size=0.2, random_state=random_state))
-
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     mapie_1 = MapieRegressor(estimator=custom_gbr)
-
     mapie_2 = MapieRegressor(estimator=custom_gbr)
-
     predict_params = {'check_predict_params': True}
-
-    mapie_1 = mapie_1.fit(X_train, y_train,
-                          predict_params=predict_params)
-
-    np.testing.assert_allclose(mapie_1.conformity_scores_, np.abs(y_train))
-
+    mapie_1 = mapie_1.fit(
+        X_train, y_train, predict_params=predict_params
+    )
     mapie_2 = mapie_2.fit(X_train, y_train)
-
     y_pred_1 = mapie_1.predict(X_test, **predict_params)
-
-    np.testing.assert_allclose(y_pred_1, 0)
-
     y_pred_2 = mapie_2.predict(X_test)
-
+    np.testing.assert_allclose(y_pred_1, 0)
+    np.testing.assert_allclose(mapie_1.conformity_scores_, np.abs(y_train))
     with np.testing.assert_raises(AssertionError):
         np.testing.assert_array_equal(y_pred_1, y_pred_2)
 

From a495462bafda6f18ad2f4dc96655e0491a237206 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 3 Jul 2024 16:04:35 +0200
Subject: [PATCH 175/424] Update : take remarks into account v2

---
 mapie/regression/regression.py | 18 +++++----
 mapie/tests/test_regression.py | 74 +++-------------------------------
 2 files changed, 15 insertions(+), 77 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 190832190..094c8554f 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -228,7 +228,6 @@ def __init__(
         verbose: int = 0,
         conformity_score: Optional[ConformityScore] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
-        predict_params: Optional[bool] = False
     ) -> None:
         self.estimator = estimator
         self.method = method
@@ -239,7 +238,6 @@ def __init__(
         self.verbose = verbose
         self.conformity_score = conformity_score
         self.random_state = random_state
-        self.predict_params = predict_params
 
     def _check_parameters(self) -> None:
         """
@@ -514,7 +512,10 @@ def fit(
         predict_params = kwargs.pop('predict_params', {})
 
         if len(predict_params) > 0:
-            self.predict_params = True
+            self._predict_params = predict_params
+        else:
+            self._predict_params = {}
+
         # Checks
         (estimator,
          self.conformity_score_function_,
@@ -622,15 +623,16 @@ def predict(
                 - [:, 1, :]: Upper bound of the prediction interval.
         """
 
-        if self.predict_params is True:
+        if hasattr(self, '_predict_params') and len(self._predict_params) > 0:
+            predict_params = self._predict_params
             warnings.warn(
-                f"Be careful that predict_params: '{predict_params}' "
-                "is used in fit method",
+                f"Using predict_params: '{predict_params}' "
+                "from the fit method in the predict method by default",
                 UserWarning
             )
 
-        elif (len(predict_params) > 0 and
-              self.predict_params is False and
+        elif (len(predict_params) > 0 and hasattr(self, '_predict_params') and
+              len(self._predict_params) == 0 and
               self.cv != "prefit"):
             raise ValueError(
                 f"Using 'predict_param' '{predict_params}' "
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index d2e6f0599..6f48a6821 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -43,52 +43,8 @@
 
 
 class CustomGradientBoostingRegressor(GradientBoostingRegressor):
-    def __init__(self,
-                 loss='squared_error',
-                 learning_rate=0.1,
-                 n_estimators=100,
-                 subsample=1.0,
-                 criterion='friedman_mse',
-                 min_samples_split=2,
-                 min_samples_leaf=1,
-                 min_weight_fraction_leaf=0.0,
-                 max_depth=3,
-                 min_impurity_decrease=0.0,
-                 init=None,
-                 random_state=None,
-                 max_features=None,
-                 alpha=0.9,
-                 verbose=0,
-                 max_leaf_nodes=None,
-                 warm_start=False,
-                 validation_fraction=0.1,
-                 n_iter_no_change=None,
-                 tol=0.0001,
-                 ccp_alpha=0.0):
-
-        super().__init__(
-            loss=loss,
-            learning_rate=learning_rate,
-            n_estimators=n_estimators,
-            subsample=subsample,
-            criterion=criterion,
-            min_samples_split=min_samples_split,
-            min_samples_leaf=min_samples_leaf,
-            min_weight_fraction_leaf=min_weight_fraction_leaf,
-            max_depth=max_depth,
-            min_impurity_decrease=min_impurity_decrease,
-            init=init,
-            random_state=random_state,
-            max_features=max_features,
-            alpha=alpha,
-            verbose=verbose,
-            max_leaf_nodes=max_leaf_nodes,
-            warm_start=warm_start,
-            validation_fraction=validation_fraction,
-            n_iter_no_change=n_iter_no_change,
-            tol=tol,
-            ccp_alpha=ccp_alpha
-        )
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
 
     def fit(self, X, y, **kwargs):
         return super().fit(X, y, **kwargs)
@@ -976,46 +932,30 @@ def early_stopping_monitor(i, est, locals):
         else:
             return False
 
-    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
-
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state))
-
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     score = AbsoluteConformityScore(sym=True)
-
     mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
-
     mapie_2 = MapieRegressor(estimator=custom_gbr)
-
     fit_params = {'monitor': early_stopping_monitor}
-
     predict_params = {'check_predict_params': True}
-
     mapie_1 = mapie_1.fit(X_train, y_train,
                           fit_params=fit_params,
                           predict_params=predict_params)
-
     mapie_2 = mapie_2.fit(X_train, y_train)
+    y_pred_1 = mapie_1.predict(X_test, **predict_params)
+    y_pred_2 = mapie_2.predict(X_test)
 
     assert mapie_1.estimator_.single_estimator_.estimators_.shape[0] == 3
-
     for estimator in mapie_1.estimator_.estimators_:
         assert estimator.estimators_.shape[0] == 3
-
     assert (mapie_2.estimator_.single_estimator_.n_estimators ==
            custom_gbr.n_estimators)
-
     for estimator in mapie_2.estimator_.estimators_:
         assert estimator.n_estimators == custom_gbr.n_estimators
-
     np.testing.assert_array_equal(mapie_1.conformity_scores_, np.abs(y_train))
-
-    y_pred_1 = mapie_1.predict(X_test, **predict_params)
-
     np.testing.assert_allclose(y_pred_1, 0)
-
-    y_pred_2 = mapie_2.predict(X_test)
-
     with np.testing.assert_raises(AssertionError):
         np.testing.assert_array_equal(y_pred_1, y_pred_2)
 
@@ -1024,14 +964,10 @@ def test_invalid_predict_parameters() -> None:
     """Test that invalid predict_parameters raise errors."""
 
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
-
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state))
-
     mapie = MapieRegressor(estimator=custom_gbr)
-
     predict_params = {'check_predict_params': True}
-
     mapie_fitted = mapie.fit(X_train, y_train)
 
     with pytest.raises(ValueError, match=(

From 43ed079abb321f42edafc539f0bcbb0d1209d369 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 3 Jul 2024 16:14:53 +0200
Subject: [PATCH 176/424] run isort

---
 mapie/regression/regression.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 094c8554f..577122552 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -13,12 +13,12 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import ConformityScore, ResidualNormalisedScore
-from mapie.estimator.regressor import EnsembleRegressor
-from mapie.utils import (check_alpha, check_alpha_and_n_samples,
-                         check_cv, check_estimator_fit_predict,
-                         check_n_features_in, check_n_jobs, check_null_weight,
-                         check_verbose, get_effective_calibration_samples)
 from mapie.conformity_scores.checks import check_conformity_score
+from mapie.estimator.regressor import EnsembleRegressor
+from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
+                         check_estimator_fit_predict, check_n_features_in,
+                         check_n_jobs, check_null_weight, check_verbose,
+                         get_effective_calibration_samples)
 
 
 class MapieRegressor(BaseEstimator, RegressorMixin):

From 8fa2474b074e978c88017de143413341ac904e4e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 3 Jul 2024 16:24:09 +0200
Subject: [PATCH 177/424] UPD: decompose APS into Naive, APS and RAPS + new
 abtract methods for classification

---
 mapie/conformity_scores/__init__.py       |   4 +-
 mapie/conformity_scores/classification.py |  55 +++
 mapie/conformity_scores/sets/__init__.py  |   4 +
 mapie/conformity_scores/sets/aps.py       | 431 ++--------------------
 mapie/conformity_scores/sets/lac.py       |  84 ++---
 mapie/conformity_scores/sets/naive.py     | 417 +++++++++++++++++++++
 mapie/conformity_scores/sets/raps.py      | 374 +++++++++++++++++++
 mapie/conformity_scores/sets/topk.py      |  59 ++-
 mapie/conformity_scores/sets/utils.py     | 199 +---------
 mapie/conformity_scores/utils.py          |  10 +-
 mapie/tests/test_classification.py        |  14 +-
 11 files changed, 957 insertions(+), 694 deletions(-)
 create mode 100644 mapie/conformity_scores/sets/naive.py
 create mode 100644 mapie/conformity_scores/sets/raps.py

diff --git a/mapie/conformity_scores/__init__.py b/mapie/conformity_scores/__init__.py
index 3b47311da..88a3530be 100644
--- a/mapie/conformity_scores/__init__.py
+++ b/mapie/conformity_scores/__init__.py
@@ -3,7 +3,7 @@
 from .bounds import (
     AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
 )
-from .sets import APS, LAC, TopK
+from .sets import APS, LAC, Naive, RAPS, TopK
 
 
 __all__ = [
@@ -12,7 +12,9 @@
     "AbsoluteConformityScore",
     "GammaConformityScore",
     "ResidualNormalisedScore",
+    "Naive",
     "LAC",
     "APS",
+    "RAPS",
     "TopK"
 ]
diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index b2670c5d9..4e4925cea 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -22,6 +22,42 @@ def __init__(self) -> None:
         super().__init__()
 
     @abstractmethod
+    def get_predictions(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the predictions.
+        """
+
+    @abstractmethod
+    def get_conformity_quantiles(
+        self,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the quantiles.
+        """
+
+    @abstractmethod
+    def get_prediction_sets(
+        self,
+        y_pred_proba: NDArray,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        **kwargs
+    ):
+        """
+        TODO: Compute the prediction sets.
+        """
+
     def get_sets(
         self,
         X: NDArray,
@@ -54,6 +90,25 @@ def get_sets(
         NDArray of shape (n_samples, n_classes, n_alpha)
             Prediction sets (Booleans indicate whether classes are included).
         """
+        # Checks
+        ()
+
+        # Predict probabilities
+        y_pred_proba = self.get_predictions(
+            X, alpha_np, estimator, **kwargs
+        )
+
+        # Choice of the quantile
+        self.quantiles_ = self.get_conformity_quantiles(
+            conformity_scores, alpha_np, estimator, **kwargs
+        )
+
+        # Build prediction sets
+        prediction_sets = self.get_prediction_sets(
+            y_pred_proba, conformity_scores, alpha_np, estimator, **kwargs
+        )
+
+        return prediction_sets
 
     def predict_set(
         self,
diff --git a/mapie/conformity_scores/sets/__init__.py b/mapie/conformity_scores/sets/__init__.py
index 87b6a37e6..36f203cc5 100644
--- a/mapie/conformity_scores/sets/__init__.py
+++ b/mapie/conformity_scores/sets/__init__.py
@@ -1,10 +1,14 @@
+from .naive import Naive
 from .lac import LAC
 from .aps import APS
+from .raps import RAPS
 from .topk import TopK
 
 
 __all__ = [
+    "Naive",
     "LAC",
     "APS",
+    "RAPS",
     "TopK",
 ]
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index b1a2fe142..16c6a7b98 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -1,23 +1,18 @@
-from typing import Optional, Tuple, Union, cast
+from typing import Optional, cast
 
 import numpy as np
 from sklearn.dummy import check_random_state
 
-from mapie.conformity_scores.classification import BaseClassificationScore
-from mapie.conformity_scores.sets.utils import (
-    add_random_tie_breaking, check_include_last_label, check_proba_normalized,
-    get_last_included_proba, get_true_label_cumsum_proba
-)
+from mapie.conformity_scores.sets.naive import Naive
+from mapie.conformity_scores.sets.utils import get_true_label_cumsum_proba
 from mapie.estimator.classifier import EnsembleClassifier
 
-from mapie._machine_precision import EPSILON
-from mapie._typing import ArrayLike, NDArray
-from mapie.metrics import classification_mean_width_score
-from mapie.utils import check_alpha_and_n_samples, compute_quantiles
+from mapie._typing import NDArray
+from mapie.utils import compute_quantiles
 
 
-class APS(BaseClassificationScore):
-    """
+class APS(Naive):
+    """TODO:
     Adaptive Prediction Sets (APS) method-based non-conformity score.
     Three differents method are available in this class:
 
@@ -68,37 +63,6 @@ class APS(BaseClassificationScore):
     def __init__(self) -> None:
         super().__init__()
 
-    def set_external_attributes(
-        self,
-        method: str = 'aps',
-        classes: Optional[ArrayLike] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        **kwargs
-    ) -> None:
-        """
-        Set attributes that are not provided by the user.
-
-        Parameters
-        ----------
-        method: str
-            Method to choose for prediction interval estimates.
-            Methods available in this class: ``aps``, ``raps`` and ``naive``.
-
-            By default ``aps`` for APS method.
-
-        classes: Optional[ArrayLike]
-            Names of the classes.
-
-            By default ``None``.
-
-        random_state: Optional[Union[int, RandomState]]
-            Pseudo random number generator state.
-        """
-        super().set_external_attributes(**kwargs)
-        self.method = method
-        self.classes = classes
-        self.random_state = random_state
-
     def get_conformity_scores(
         self,
         y: NDArray,
@@ -130,382 +94,35 @@ def get_conformity_scores(
         classes = cast(NDArray, self.classes)
 
         # Conformity scores
-        if self.method == "naive":
-            conformity_scores = (
-                np.empty(y_pred.shape, dtype="float")
-            )
-        else:
-            conformity_scores, self.cutoff = (
-                get_true_label_cumsum_proba(y, y_pred, classes)
-            )
-            y_proba_true = np.take_along_axis(
-                y_pred, y_enc.reshape(-1, 1), axis=1
-            )
-            random_state = check_random_state(self.random_state)
-            random_state = cast(np.random.RandomState, random_state)
-            u = random_state.uniform(size=len(y_pred)).reshape(-1, 1)
-            conformity_scores -= u * y_proba_true
-
-        return conformity_scores
-
-    @staticmethod
-    def _regularize_conformity_score(
-        k_star: NDArray,
-        lambda_: Union[NDArray, float],
-        conf_score: NDArray,
-        cutoff: NDArray
-    ) -> NDArray:
-        """
-        Regularize the conformity scores with the ``"raps"``
-        method. See algo. 2 in [3].
-
-        Parameters
-        ----------
-        k_star: NDArray of shape (n_alphas, )
-            Optimal value of k (called k_reg in the paper). There
-            is one value per alpha.
-
-        lambda_: Union[NDArray, float] of shape (n_alphas, )
-            One value of lambda for each alpha.
-
-        conf_score: NDArray of shape (n_samples, 1)
-            Conformity scores.
-
-        cutoff: NDArray of shape (n_samples, 1)
-            Position of the true label.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, 1, n_alphas)
-            Regularized conformity scores. The regularization
-            depends on the value of alpha.
-        """
-        conf_score = np.repeat(
-            conf_score[:, :, np.newaxis], len(k_star), axis=2
+        conformity_scores, self.cutoff = (
+            get_true_label_cumsum_proba(y, y_pred, classes)
         )
-        cutoff = np.repeat(
-            cutoff[:, np.newaxis], len(k_star), axis=1
+        y_proba_true = np.take_along_axis(
+            y_pred, y_enc.reshape(-1, 1), axis=1
         )
-        conf_score += np.maximum(
-            np.expand_dims(
-                lambda_ * (cutoff - k_star),
-                axis=1
-            ),
-            0
-        )
-        return conf_score
-
-    def _update_size_and_lambda(
-        self,
-        best_sizes: NDArray,
-        alpha_np: NDArray,
-        y_ps: NDArray,
-        lambda_: Union[NDArray, float],
-        lambda_star: NDArray
-    ) -> Tuple[NDArray, NDArray]:
-        """Update the values of the optimal lambda if the
-        average size of the prediction sets decreases with
-        this new value of lambda.
-
-        Parameters
-        ----------
-        best_sizes: NDArray of shape (n_alphas, )
-            Smallest average prediciton set size before testing
-            for the new value of lambda_
-
-        alpha_np: NDArray of shape (n_alphas)
-            Level of confidences.
-
-        y_ps: NDArray of shape (n_samples, n_classes, n_alphas)
-            Prediction sets computed with the RAPS method and the
-            new value of lambda_
+        random_state = check_random_state(self.random_state)
+        random_state = cast(np.random.RandomState, random_state)
+        u = random_state.uniform(size=len(y_pred)).reshape(-1, 1)
+        conformity_scores -= u * y_proba_true
 
-        lambda_: NDArray of shape (n_alphas, )
-            New value of lambda_star to test
-
-        lambda_star: NDArray of shape (n_alphas, )
-            Actual optimal lambda values for each alpha.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray]
-            Arrays of shape (n_alphas, ) and (n_alpha, ) which
-            respectively represent the updated values of lambda_star
-            and the new best sizes.
-        """
-
-        sizes = [
-            classification_mean_width_score(
-                y_ps[:, :, i]
-            ) for i in range(len(alpha_np))
-        ]
-
-        sizes_improve = (sizes < best_sizes - EPSILON)
-        lambda_star = (
-            sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
-        )
-        best_sizes = sizes_improve * sizes + (1 - sizes_improve) * best_sizes
-
-        return lambda_star, best_sizes
-
-    def _find_lambda_star(
-        self,
-        y_raps_no_enc: NDArray,
-        y_pred_proba_raps: NDArray,
-        alpha_np: NDArray,
-        include_last_label: Union[bool, str, None],
-        k_star: NDArray
-    ) -> Union[NDArray, float]:
-        """Find the optimal value of lambda for each alpha.
-
-        Parameters
-        ----------
-        y_pred_proba_raps: NDArray of shape (n_samples, n_labels, n_alphas)
-            Predictions of the model repeated on the last axis as many times
-            as the number of alphas
-
-        alpha_np: NDArray of shape (n_alphas, )
-            Levels of confidences.
-
-        include_last_label: bool
-            Whether to include or not last label in
-            the prediction sets
-
-        k_star: NDArray of shape (n_alphas, )
-            Values of k for the regularization.
-
-        Returns
-        -------
-        ArrayLike of shape (n_alphas, )
-            Optimal values of lambda.
-        """
-        classes = cast(NDArray, self.classes)
-
-        lambda_star = np.zeros(len(alpha_np))
-        best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
-
-        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]
-            true_label_cumsum_proba, cutoff = (
-                get_true_label_cumsum_proba(
-                    y_raps_no_enc,
-                    y_pred_proba_raps[:, :, 0],
-                    classes
-                )
-            )
-
-            true_label_cumsum_proba_reg = self._regularize_conformity_score(
-                k_star,
-                lambda_,
-                true_label_cumsum_proba,
-                cutoff
-            )
-
-            quantiles_ = compute_quantiles(
-                true_label_cumsum_proba_reg,
-                alpha_np
-            )
-
-            _, _, y_pred_proba_last = get_last_included_proba(
-                y_pred_proba_raps,
-                quantiles_,
-                include_last_label,
-                self.method,
-                lambda_,
-                k_star
-            )
-
-            y_ps = np.greater_equal(
-                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
-            )
-            lambda_star, best_sizes = self._update_size_and_lambda(
-                best_sizes, alpha_np, y_ps, lambda_, lambda_star
-            )
-        if len(lambda_star) == 1:
-            lambda_star = lambda_star[0]
-        return lambda_star
+        return conformity_scores
 
-    def get_sets(
+    def get_conformity_quantiles(
         self,
-        X: ArrayLike,
+        conformity_scores: NDArray,
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
-        conformity_scores: NDArray,
-        include_last_label: Optional[Union[bool, str]] = True,
         agg_scores: Optional[str] = "mean",
-        X_raps: Optional[NDArray] = None,
-        y_raps_no_enc: Optional[NDArray] = None,
-        y_pred_proba_raps: Optional[NDArray] = None,
-        position_raps: Optional[NDArray] = None,
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        Compute classes of the prediction sets from the observed values,
-        the estimator of type ``EnsembleClassifier`` and the conformity scores.
-
-        Parameters
-        ----------
-        X: NDArray of shape (n_samples, n_features)
-            Observed feature values.
-
-        alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
-
-        estimator: EnsembleClassifier
-            Estimator that is fitted to predict y from X.
-
-        conformity_scores: NDArray of shape (n_samples,)
-            Conformity scores.
-
-        agg_scores: Optional[str]
-            How to aggregate the scores output by the estimators on test data
-            if a cross-validation strategy is used. Choose among:
-
-            - "mean", take the mean of scores.
-            - "crossval", compare the scores between all training data and each
-                test point for each label to estimate if the label must be
-                included in the prediction set. Follows algorithm 2 of
-                Romano+2020.
-
-            By default, "mean".
-
-        X_raps: NDArray of shape (n_samples, n_features)
-            Observed feature values for the RAPS method (split data).
-
-            By default, "None" but must be set to work.
-
-        y_raps_no_enc: NDArray of shape (n_samples,)
-            Observed labels for the RAPS method (split data).
-
-            By default, "None" but must be set to work.
-
-        y_pred_proba_raps: NDArray of shape (n_samples, n_classes)
-            Predicted probabilities for the RAPS method (split data).
-
-            By default, "None" but must be set to work.
-
-        position_raps: NDArray of shape (n_samples,)
-            Position of the points in the split set for the RAPS method
-            (split data). These positions are returned by the function
-            ``get_true_label_position``.
-
-            By default, "None" but must be set to work.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_classes, n_alpha)
-            Prediction sets (Booleans indicate whether classes are included).
+        TODO: Compute the quantiles.
         """
-        # Checks
-        include_last_label = check_include_last_label(include_last_label)
-
-        # if self.method == "raps":
-        lambda_star, k_star = None, None
-        X_raps = cast(NDArray, X_raps)
-        y_raps_no_enc = cast(NDArray, y_raps_no_enc)
-        y_pred_proba_raps = cast(NDArray, y_pred_proba_raps)
-        position_raps = cast(NDArray, position_raps)
-
         n = len(conformity_scores)
 
-        y_pred_proba = estimator.predict(X, agg_scores)
-        y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
-        if agg_scores != "crossval":
-            y_pred_proba = np.repeat(
-                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-            )
-
-        # Choice of the quantileif self.method == "naive":
-        if self.method == "naive":
-            self.quantiles_ = 1 - alpha_np
-        elif (estimator.cv == "prefit") or (agg_scores in ["mean"]):
-            if self.method == "raps":
-                check_alpha_and_n_samples(alpha_np, X_raps.shape[0])
-                k_star = compute_quantiles(
-                    position_raps,
-                    alpha_np
-                ) + 1
-                y_pred_proba_raps = np.repeat(
-                    y_pred_proba_raps[:, :, np.newaxis],
-                    len(alpha_np),
-                    axis=2
-                )
-                lambda_star = self._find_lambda_star(
-                    y_raps_no_enc,
-                    y_pred_proba_raps,
-                    alpha_np,
-                    include_last_label,
-                    k_star
-                )
-                conformity_scores_regularized = (
-                    self._regularize_conformity_score(
-                        k_star,
-                        lambda_star,
-                        conformity_scores,
-                        self.cutoff
-                    )
-                )
-                self.quantiles_ = compute_quantiles(
-                    conformity_scores_regularized,
-                    alpha_np
-                )
-            else:
-                self.quantiles_ = compute_quantiles(
-                    conformity_scores,
-                    alpha_np
-                )
-        else:
-            self.quantiles_ = (n + 1) * (1 - alpha_np)
-
-        # Build prediction sets
-        # specify which thresholds will be used
-        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
-            thresholds = self.quantiles_
-        else:
-            thresholds = conformity_scores.ravel()
-        # sort labels by decreasing probability
-        y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last = (
-            get_last_included_proba(
-                y_pred_proba,
-                thresholds,
-                include_last_label,
-                self.method,
-                lambda_star,
-                k_star,
-            )
-        )
-        # get the prediction set by taking all probabilities
-        # above the last one
-        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
-            y_pred_included = np.greater_equal(
-                y_pred_proba - y_pred_proba_last, -EPSILON
-            )
-        else:
-            y_pred_included = np.less_equal(
-                y_pred_proba - y_pred_proba_last, EPSILON
-            )
-        # remove last label randomly
-        if include_last_label == "randomized":
-            y_pred_included = add_random_tie_breaking(
-                y_pred_included,
-                y_pred_index_last,
-                y_pred_proba_cumsum,
-                y_pred_proba_last,
-                thresholds,
-                self.method,
-                self.random_state,
-                lambda_star,
-                k_star,
-            )
-        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
-            prediction_sets = y_pred_included
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            quantiles_ = compute_quantiles(conformity_scores, alpha_np)
         else:
-            # compute the number of times the inequality is verified
-            prediction_sets_summed = y_pred_included.sum(axis=2)
-            prediction_sets = np.less_equal(
-                prediction_sets_summed[:, :, np.newaxis]
-                - self.quantiles_[np.newaxis, np.newaxis, :],
-                EPSILON
-            )
+            quantiles_ = (n + 1) * (1 - alpha_np)
 
-        return prediction_sets
+        return quantiles_
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 5edf9d45c..48d7c04d7 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -114,54 +114,17 @@ def get_conformity_scores(
 
         return conformity_scores
 
-    def get_sets(
+    def get_predictions(
         self,
-        X: ArrayLike,
+        X: NDArray,
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
-        conformity_scores: NDArray,
         agg_scores: Optional[str] = "mean",
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        Compute classes of the prediction sets from the observed values,
-        the estimator of type ``EnsembleClassifier`` and the conformity scores.
-
-        Parameters
-        ----------
-        X: NDArray of shape (n_samples, n_features)
-            Observed feature values.
-
-        alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
-
-        estimator: EnsembleClassifier
-            Estimator that is fitted to predict y from X.
-
-        conformity_scores: NDArray of shape (n_samples,)
-            Conformity scores.
-
-        agg_scores: Optional[str]
-            How to aggregate the scores output by the estimators on test data
-            if a cross-validation strategy is used. Choose among:
-
-            - "mean", take the mean of scores.
-            - "crossval", compare the scores between all training data and each
-              test point for each label to estimate if the label must be
-              included in the prediction set. Follows algorithm 2 of
-              Romano+2020.
-
-            By default, "mean".
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_classes, n_alpha)
-            Prediction sets (Booleans indicate whether classes are included).
+        TODO: Compute the predictions.
         """
-        # Checks
-        n = len(conformity_scores)
-
         y_pred_proba = estimator.predict(X, agg_scores)
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
         if agg_scores != "crossval":
@@ -169,16 +132,45 @@ def get_sets(
                 y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
             )
 
-        # Choice of the quantile
-        if (estimator.cv == "prefit") or (agg_scores in ["mean"]):
-            self.quantiles_ = compute_quantiles(
+        return y_pred_proba
+
+    def get_conformity_quantiles(
+        self,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the quantiles.
+        """
+        n = len(conformity_scores)
+
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            quantiles_ = compute_quantiles(
                 conformity_scores,
                 alpha_np
             )
         else:
-            self.quantiles_ = (n + 1) * (1 - alpha_np)
+            quantiles_ = (n + 1) * (1 - alpha_np)
+
+        return quantiles_
+
+    def get_prediction_sets(
+        self,
+        y_pred_proba: NDArray,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        **kwargs
+    ):
+        """
+        TODO: Compute the prediction sets.
+        """
+        n = len(conformity_scores)
 
-        # Build prediction sets
         if (estimator.cv == "prefit") or (agg_scores == "mean"):
             prediction_sets = np.less_equal(
                 (1 - y_pred_proba) - self.quantiles_, EPSILON
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
new file mode 100644
index 000000000..cb2df4157
--- /dev/null
+++ b/mapie/conformity_scores/sets/naive.py
@@ -0,0 +1,417 @@
+from typing import Optional, Tuple, Union, cast
+
+import numpy as np
+from sklearn.dummy import check_random_state
+
+from mapie.conformity_scores.classification import BaseClassificationScore
+from mapie.conformity_scores.sets.utils import (
+    check_include_last_label, check_proba_normalized, get_last_index_included
+)
+from mapie.estimator.classifier import EnsembleClassifier
+
+from mapie._machine_precision import EPSILON
+from mapie._typing import ArrayLike, NDArray
+
+
+class Naive(BaseClassificationScore):
+    """TODO:
+    Adaptive Prediction Sets (APS) method-based non-conformity score.
+    Three differents method are available in this class:
+
+    - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
+
+    - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
+        Sets method. It is based on the sum of the softmax outputs of the
+        labels until the true label is reached, on the calibration set.
+        See [1] for more details.
+
+    - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
+        same technique as ``"aps"`` method but with a penalty term
+        to reduce the size of prediction sets. See [2] for more
+        details. For now, this method only works with ``"prefit"`` and
+        ``"split"`` strategies.
+
+    References
+    ----------
+    [1] Yaniv Romano, Matteo Sesia and Emmanuel J. Candès.
+    "Classification with Valid and Adaptive Coverage."
+    NeurIPS 202 (spotlight) 2020.
+
+    [2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
+    and Jitendra Malik.
+    "Uncertainty Sets for Image Classifiers using Conformal Prediction."
+    International Conference on Learning Representations 2021.
+
+    Attributes
+    ----------
+    method: str
+        Method to choose for prediction interval estimates.
+        This attribute is for compatibility with ``MapieClassifier``
+        which previously used a string instead of a score class.
+        Methods available in this class: ``aps``, ``raps`` and ``naive``.
+
+        By default, ``aps`` for APS method.
+
+    classes: Optional[ArrayLike]
+        Names of the classes.
+
+    random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state.
+
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``get_sets`` method.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def set_external_attributes(
+        self,
+        method: str = 'naive',
+        classes: Optional[ArrayLike] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        method: str
+            Method to choose for prediction interval estimates.
+            Methods available in this class: ``aps``, ``raps`` and ``naive``.
+
+            By default ``aps`` for APS method.
+
+        classes: Optional[ArrayLike]
+            Names of the classes.
+
+            By default ``None``.
+
+        random_state: Optional[Union[int, RandomState]]
+            Pseudo random number generator state.
+        """
+        super().set_external_attributes(**kwargs)
+        self.method = method
+        self.classes = classes
+        self.random_state = random_state
+
+    def get_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Get the conformity score.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+        conformity_scores = np.empty(y_pred.shape, dtype="float")
+        return conformity_scores
+
+    def get_predictions(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the predictions.
+        """
+        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
+        if agg_scores != "crossval":
+            y_pred_proba = np.repeat(
+                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+            )
+        return y_pred_proba
+
+    def get_conformity_quantiles(
+        self,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the quantiles.
+        """
+        quantiles_ = 1 - alpha_np
+        return quantiles_
+
+    def _add_regualization(
+        self,
+        y_pred_proba_sorted_cumsum,
+        **kwargs
+    ):
+        return y_pred_proba_sorted_cumsum
+
+    def _get_last_included_proba(
+        self,
+        y_pred_proba: NDArray,
+        thresholds: NDArray,
+        include_last_label: Union[bool, str, None],
+        **kwargs
+    ) -> Tuple[NDArray, NDArray, NDArray]:
+        """
+        Function that returns the smallest score
+        among those which are included in the prediciton set.
+
+        Parameters
+        ----------
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Predictions of the model.
+
+        thresholds: NDArray of shape (n_alphas, )
+            Quantiles that have been computed from the conformity scores.
+
+        include_last_label: Union[bool, str, None]
+            Whether to include or not the label whose score exceeds threshold.
+
+        Returns
+        -------
+        Tuple[ArrayLike, ArrayLike, ArrayLike]
+            Arrays of shape (n_samples, n_classes, n_alphas),
+            (n_samples, 1, n_alphas) and (n_samples, 1, n_alphas).
+            They are respectively the cumsumed scores in the original
+            order which can be different according to the value of alpha
+            with the RAPS method, the index of the last included score
+            and the value of the last included score.
+        """
+        index_sorted = np.flip(
+            np.argsort(y_pred_proba, axis=1), axis=1
+        )
+        # sort probabilities by decreasing order
+        y_pred_proba_sorted = np.take_along_axis(
+            y_pred_proba, index_sorted, axis=1
+        )
+        # get sorted cumulated score
+        y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
+        y_pred_proba_sorted_cumsum = self._add_regualization(
+            y_pred_proba_sorted_cumsum, **kwargs
+        )
+
+        # get cumulated score at their original position
+        y_pred_proba_cumsum = np.take_along_axis(
+            y_pred_proba_sorted_cumsum,
+            np.argsort(index_sorted, axis=1),
+            axis=1
+        )
+        # get index of the last included label
+        y_pred_index_last = get_last_index_included(
+            y_pred_proba_cumsum,
+            thresholds,
+            include_last_label
+        )
+        # get the probability of the last included label
+        y_pred_proba_last = np.take_along_axis(
+            y_pred_proba,
+            y_pred_index_last,
+            axis=1
+        )
+
+        zeros_scores_proba_last = (y_pred_proba_last <= EPSILON)
+
+        # If the last included proba is zero, change it to the
+        # smallest non-zero value to avoid inluding them in the
+        # prediction sets.
+        if np.sum(zeros_scores_proba_last) > 0:
+            y_pred_proba_last[zeros_scores_proba_last] = np.expand_dims(
+                np.min(
+                    np.ma.masked_less(
+                        y_pred_proba,
+                        EPSILON
+                    ).filled(fill_value=np.inf),
+                    axis=1
+                ), axis=1
+            )[zeros_scores_proba_last]
+
+        return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
+
+    def _compute_vs_parameter(
+        self,
+        y_proba_last_cumsumed,
+        threshold,
+        y_pred_proba_last,
+        prediction_sets,
+        *kwargs
+    ):
+        """
+        TODO
+        """
+        # compute V parameter from Romano+(2020)
+        vs = (
+            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+            y_pred_proba_last[:, 0, :]
+        )
+        return vs
+
+    def _add_random_tie_breaking(
+        self,
+        prediction_sets: NDArray,
+        y_pred_index_last: NDArray,
+        y_pred_proba_cumsum: NDArray,
+        y_pred_proba_last: NDArray,
+        threshold: NDArray,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        Randomly remove last label from prediction set based on the
+        comparison between a random number and the difference between
+        cumulated score of the last included label and the quantile.
+
+        Parameters
+        ----------
+        prediction_sets: NDArray of shape
+            (n_samples, n_classes, n_threshold)
+            Prediction set for each observation and each alpha.
+
+        y_pred_index_last: NDArray of shape (n_samples, threshold)
+            Index of the last included label.
+
+        y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
+            Cumsumed probability of the model in the original order.
+
+        y_pred_proba_last: NDArray of shape (n_samples, 1, threshold)
+            Last included probability.
+
+        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+            Threshold to compare with y_proba_last_cumsum, can be either:
+
+            - the quantiles associated with alpha values when
+                ``cv`` == "prefit", ``cv`` == "split"
+                or ``agg_scores`` is "mean"
+
+            - the conformity score from training samples otherwise (i.e., when
+            ``cv`` is CV splitter and ``agg_scores`` is "crossval")
+
+        method: str
+            Method that determines how to remove last label in the prediction
+            set.
+
+            - if "cumulated_score" or "aps", compute V parameter
+                from Romano+(2020)
+
+            - else compute V parameter from Angelopoulos+(2020)
+
+        lambda_star: Optional[Union[NDArray, float]] of shape (n_alpha):
+            Optimal value of the regulizer lambda.
+
+        k_star: Optional[NDArray] of shape (n_alpha):
+            Optimal value of the regulizer k.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, n_classes, n_alpha)
+            Updated version of prediction_sets with randomly removed labels.
+        """
+        # get cumsumed probabilities up to last retained label
+        y_proba_last_cumsumed = np.squeeze(
+            np.take_along_axis(
+                y_pred_proba_cumsum,
+                y_pred_index_last,
+                axis=1
+            ), axis=1
+        )
+
+        # TODO
+        vs = self._compute_vs_parameter(
+            y_proba_last_cumsumed,
+            threshold,
+            y_pred_proba_last,
+            prediction_sets
+        )
+
+        # get random numbers for each observation and alpha value
+        random_state = check_random_state(random_state)
+        random_state = cast(np.random.RandomState, random_state)
+        us = random_state.uniform(size=(prediction_sets.shape[0], 1))
+        # remove last label from comparison between uniform number and V
+        vs_less_than_us = np.less_equal(vs - us, EPSILON)
+        np.put_along_axis(
+            prediction_sets,
+            y_pred_index_last,
+            vs_less_than_us[:, np.newaxis, :],
+            axis=1
+        )
+        return prediction_sets
+
+    def get_prediction_sets(
+        self,
+        y_pred_proba: NDArray,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        include_last_label: Optional[Union[bool, str]] = True,
+        **kwargs
+    ):
+        """
+        TODO: Compute the prediction sets.
+        """
+        include_last_label = check_include_last_label(include_last_label)
+
+        # specify which thresholds will be used
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            thresholds = self.quantiles_
+        else:
+            thresholds = conformity_scores.ravel()
+
+        # sort labels by decreasing probability
+        y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last = (
+            self._get_last_included_proba(
+                y_pred_proba,
+                thresholds,
+                include_last_label,
+                prediction_phase=True,
+                **kwargs
+            )
+        )
+        # get the prediction set by taking all probabilities
+        # above the last one
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            y_pred_included = np.greater_equal(
+                y_pred_proba - y_pred_proba_last, -EPSILON
+            )
+        else:
+            y_pred_included = np.less_equal(
+                y_pred_proba - y_pred_proba_last, EPSILON
+            )
+        # remove last label randomly
+        if include_last_label == "randomized":
+            y_pred_included = self._add_random_tie_breaking(
+                y_pred_included,
+                y_pred_index_last,
+                y_pred_proba_cumsum,
+                y_pred_proba_last,
+                thresholds,
+                self.random_state,
+                **kwargs
+            )
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            prediction_sets = y_pred_included
+        else:
+            # compute the number of times the inequality is verified
+            prediction_sets_summed = y_pred_included.sum(axis=2)
+            prediction_sets = np.less_equal(
+                prediction_sets_summed[:, :, np.newaxis]
+                - self.quantiles_[np.newaxis, np.newaxis, :],
+                EPSILON
+            )
+
+        return prediction_sets
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
new file mode 100644
index 000000000..8ee2bdea1
--- /dev/null
+++ b/mapie/conformity_scores/sets/raps.py
@@ -0,0 +1,374 @@
+from typing import Optional, Tuple, Union, cast
+
+import numpy as np
+
+from mapie.conformity_scores.sets.aps import APS
+from mapie.conformity_scores.sets.utils import get_true_label_cumsum_proba
+from mapie.estimator.classifier import EnsembleClassifier
+
+from mapie._machine_precision import EPSILON
+from mapie._typing import ArrayLike, NDArray
+from mapie.metrics import classification_mean_width_score
+from mapie.utils import check_alpha_and_n_samples, compute_quantiles
+
+
+class RAPS(APS):
+    """TODO:
+    Adaptive Prediction Sets (APS) method-based non-conformity score.
+    Three differents method are available in this class:
+
+    - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
+
+    - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
+        Sets method. It is based on the sum of the softmax outputs of the
+        labels until the true label is reached, on the calibration set.
+        See [1] for more details.
+
+    - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
+        same technique as ``"aps"`` method but with a penalty term
+        to reduce the size of prediction sets. See [2] for more
+        details. For now, this method only works with ``"prefit"`` and
+        ``"split"`` strategies.
+
+    References
+    ----------
+    [1] Yaniv Romano, Matteo Sesia and Emmanuel J. Candès.
+    "Classification with Valid and Adaptive Coverage."
+    NeurIPS 202 (spotlight) 2020.
+
+    [2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
+    and Jitendra Malik.
+    "Uncertainty Sets for Image Classifiers using Conformal Prediction."
+    International Conference on Learning Representations 2021.
+
+    Attributes
+    ----------
+    method: str
+        Method to choose for prediction interval estimates.
+        This attribute is for compatibility with ``MapieClassifier``
+        which previously used a string instead of a score class.
+        Methods available in this class: ``aps``, ``raps`` and ``naive``.
+
+        By default, ``aps`` for APS method.
+
+    classes: Optional[ArrayLike]
+        Names of the classes.
+
+    random_state: Optional[Union[int, RandomState]]
+        Pseudo random number generator state.
+
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``get_sets`` method.
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+
+    def set_external_attributes(
+        self,
+        method: str = 'raps',
+        classes: Optional[ArrayLike] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        method: str
+            Method to choose for prediction interval estimates.
+            Methods available in this class: ``aps``, ``raps`` and ``naive``.
+
+            By default ``aps`` for APS method.
+
+        classes: Optional[ArrayLike]
+            Names of the classes.
+
+            By default ``None``.
+
+        random_state: Optional[Union[int, RandomState]]
+            Pseudo random number generator state.
+        """
+        super().set_external_attributes(**kwargs)
+        self.method = method
+        self.classes = classes
+        self.random_state = random_state
+
+    @staticmethod
+    def _regularize_conformity_score(
+        k_star: NDArray,
+        lambda_: Union[NDArray, float],
+        conf_score: NDArray,
+        cutoff: NDArray
+    ) -> NDArray:
+        """
+        Regularize the conformity scores with the ``"raps"``
+        method. See algo. 2 in [3]. TODO: add ref.
+
+        Parameters
+        ----------
+        k_star: NDArray of shape (n_alphas, )
+            Optimal value of k (called k_reg in the paper). There
+            is one value per alpha.
+
+        lambda_: Union[NDArray, float] of shape (n_alphas, )
+            One value of lambda for each alpha.
+
+        conf_score: NDArray of shape (n_samples, 1)
+            Conformity scores.
+
+        cutoff: NDArray of shape (n_samples, 1)
+            Position of the true label.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, 1, n_alphas)
+            Regularized conformity scores. The regularization
+            depends on the value of alpha.
+        """
+        conf_score = np.repeat(
+            conf_score[:, :, np.newaxis], len(k_star), axis=2
+        )
+        cutoff = np.repeat(
+            cutoff[:, np.newaxis], len(k_star), axis=1
+        )
+        conf_score += np.maximum(
+            np.expand_dims(
+                lambda_ * (cutoff - k_star),
+                axis=1
+            ),
+            0
+        )
+        return conf_score
+
+    def _update_size_and_lambda(
+        self,
+        best_sizes: NDArray,
+        alpha_np: NDArray,
+        y_ps: NDArray,
+        lambda_: Union[NDArray, float],
+        lambda_star: NDArray
+    ) -> Tuple[NDArray, NDArray]:
+        """
+        Update the values of the optimal lambda if the average size of the
+        prediction sets decreases with this new value of lambda.
+
+        Parameters
+        ----------
+        best_sizes: NDArray of shape (n_alphas, )
+            Smallest average prediciton set size before testing
+            for the new value of lambda_
+
+        alpha_np: NDArray of shape (n_alphas)
+            Level of confidences.
+
+        y_ps: NDArray of shape (n_samples, n_classes, n_alphas)
+            Prediction sets computed with the RAPS method and the
+            new value of lambda_
+
+        lambda_: NDArray of shape (n_alphas, )
+            New value of lambda_star to test
+
+        lambda_star: NDArray of shape (n_alphas, )
+            Actual optimal lambda values for each alpha.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray]
+            Arrays of shape (n_alphas, ) and (n_alpha, ) which
+            respectively represent the updated values of lambda_star
+            and the new best sizes.
+        """
+        sizes = [
+            classification_mean_width_score(
+                y_ps[:, :, i]
+            ) for i in range(len(alpha_np))
+        ]
+
+        sizes_improve = (sizes < best_sizes - EPSILON)
+        lambda_star = (
+            sizes_improve * lambda_ + (1 - sizes_improve) * lambda_star
+        )
+        best_sizes = sizes_improve * sizes + (1 - sizes_improve) * best_sizes
+
+        return lambda_star, best_sizes
+
+    def _find_lambda_star(
+        self,
+        y_raps_no_enc: NDArray,
+        y_pred_proba_raps: NDArray,
+        alpha_np: NDArray,
+        include_last_label: Union[bool, str, None],
+        k_star: NDArray
+    ) -> Union[NDArray, float]:
+        """
+        Find the optimal value of lambda for each alpha.
+
+        Parameters
+        ----------
+        y_pred_proba_raps: NDArray of shape (n_samples, n_labels, n_alphas)
+            Predictions of the model repeated on the last axis as many times
+            as the number of alphas
+
+        alpha_np: NDArray of shape (n_alphas, )
+            Levels of confidences.
+
+        include_last_label: bool
+            Whether to include or not last label in
+            the prediction sets
+
+        k_star: NDArray of shape (n_alphas, )
+            Values of k for the regularization.
+
+        Returns
+        -------
+        ArrayLike of shape (n_alphas, )
+            Optimal values of lambda.
+        """
+        classes = cast(NDArray, self.classes)
+
+        lambda_star = np.zeros(len(alpha_np))
+        best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
+
+        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]TODO
+            true_label_cumsum_proba, cutoff = (
+                get_true_label_cumsum_proba(
+                    y_raps_no_enc,
+                    y_pred_proba_raps[:, :, 0],
+                    classes
+                )
+            )
+
+            true_label_cumsum_proba_reg = self._regularize_conformity_score(
+                k_star,
+                lambda_,
+                true_label_cumsum_proba,
+                cutoff
+            )
+
+            quantiles_ = compute_quantiles(
+                true_label_cumsum_proba_reg,
+                alpha_np
+            )
+
+            _, _, y_pred_proba_last = self._get_last_included_proba(
+                y_pred_proba_raps,
+                quantiles_,
+                include_last_label,
+                lambda_=lambda_,
+                k_star=k_star
+            )
+
+            y_ps = np.greater_equal(
+                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
+            )
+            lambda_star, best_sizes = self._update_size_and_lambda(
+                best_sizes, alpha_np, y_ps, lambda_, lambda_star
+            )
+
+        if len(lambda_star) == 1:
+            lambda_star = lambda_star[0]
+
+        return lambda_star
+
+    def get_conformity_quantiles(
+        self,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        include_last_label: Optional[Union[bool, str]] = True,
+        X_raps: Optional[NDArray] = None,
+        y_raps_no_enc: Optional[NDArray] = None,
+        y_pred_proba_raps: Optional[NDArray] = None,
+        position_raps: Optional[NDArray] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the quantiles.
+        """
+        # Casting to NDArray to avoid mypy errors
+        X_raps = cast(NDArray, X_raps)
+        y_raps_no_enc = cast(NDArray, y_raps_no_enc)
+        y_pred_proba_raps = cast(NDArray, y_pred_proba_raps)
+        position_raps = cast(NDArray, position_raps)
+
+        check_alpha_and_n_samples(alpha_np, X_raps.shape[0])
+        self.k_star = compute_quantiles(
+            position_raps,
+            alpha_np
+        ) + 1
+        y_pred_proba_raps = np.repeat(
+            y_pred_proba_raps[:, :, np.newaxis],
+            len(alpha_np),
+            axis=2
+        )
+        self.lambda_star = self._find_lambda_star(
+            y_raps_no_enc,
+            y_pred_proba_raps,
+            alpha_np,
+            include_last_label,
+            self.k_star
+        )
+        conformity_scores_regularized = (
+            self._regularize_conformity_score(
+                self.k_star,
+                self.lambda_star,
+                conformity_scores,
+                self.cutoff
+            )
+        )
+        quantiles_ = compute_quantiles(
+            conformity_scores_regularized,
+            alpha_np
+        )
+
+        return quantiles_
+
+    def _add_regualization(
+        self,
+        y_pred_proba_sorted_cumsum,
+        lambda_=None,
+        k_star=None,
+        prediction_phase=False,
+        **kwargs
+    ):
+        """
+        TODO
+        """
+        if prediction_phase:
+            lambda_ = self.lambda_star
+            k_star = self.k_star
+
+        y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
+            0,
+            np.cumsum(
+                np.ones(y_pred_proba_sorted_cumsum.shape), axis=1
+            ) - k_star
+        )
+
+        return y_pred_proba_sorted_cumsum
+
+    def _compute_vs_parameter(
+        self,
+        y_proba_last_cumsumed,
+        threshold,
+        y_pred_proba_last,
+        prediction_sets,
+        *kwargs
+    ):
+        """
+        TODO
+        """
+        # compute V parameter from Angelopoulos+(2020)
+        L = np.sum(prediction_sets, axis=1)
+        vs = (
+            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+            (
+                y_pred_proba_last[:, 0, :] -
+                self.lambda_star * np.maximum(0, L - self.k_star) +
+                self.lambda_star * (L > self.k_star)
+            )
+        )
+        return vs
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index fb0e7836f..91667b802 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -9,7 +9,7 @@
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
-from mapie._typing import ArrayLike, NDArray
+from mapie._typing import NDArray
 from mapie.utils import compute_quantiles
 
 
@@ -118,49 +118,46 @@ def get_conformity_scores(
 
         return conformity_scores
 
-    def get_sets(
+    def get_predictions(
         self,
-        X: ArrayLike,
+        X: NDArray,
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
-        conformity_scores: NDArray,
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        Compute classes of the prediction sets from the observed values,
-        the estimator of type ``EnsembleClassifier`` and the conformity scores.
-
-        Parameters
-        ----------
-        X: NDArray of shape (n_samples, n_features)
-            Observed feature values.
-
-        alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
-
-        estimator: EnsembleClassifier
-            Estimator that is fitted to predict y from X.
-
-        conformity_scores: NDArray of shape (n_samples,)
-            Conformity scores.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_classes, n_alpha)
-            Prediction sets (Booleans indicate whether classes are included).
+        TODO: Compute the predictions.
         """
-        # Checks
         y_pred_proba = estimator.predict(X, agg_scores="mean")
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
         y_pred_proba = np.repeat(
             y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
         )
+        return y_pred_proba
 
-        # Choice of the quantile
-        self.quantiles_ = compute_quantiles(conformity_scores, alpha_np)
+    def get_conformity_quantiles(
+        self,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        **kwargs
+    ) -> NDArray:
+        """
+        TODO: Compute the quantiles.
+        """
+        return compute_quantiles(conformity_scores, alpha_np)
 
-        # Build prediction sets
+    def get_prediction_sets(
+        self,
+        y_pred_proba: NDArray,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        **kwargs
+    ):
+        """
+        TODO: Compute the prediction sets.
+        """
         y_pred_proba = y_pred_proba[:, :, 0]
         index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
         y_pred_index_last = np.stack(
diff --git a/mapie/conformity_scores/sets/utils.py b/mapie/conformity_scores/sets/utils.py
index a2b5b32af..5917a6cb7 100644
--- a/mapie/conformity_scores/sets/utils.py
+++ b/mapie/conformity_scores/sets/utils.py
@@ -1,7 +1,6 @@
-from typing import Any, Optional, Tuple, Union, cast
+from typing import Optional, Tuple, Union, cast
 import numpy as np
 from sklearn.calibration import label_binarize
-from sklearn.dummy import check_random_state
 
 from mapie._typing import ArrayLike, NDArray
 from mapie._machine_precision import EPSILON
@@ -203,199 +202,3 @@ def get_last_index_included(
             ), axis=1
         )
     return y_pred_index_last[:, np.newaxis, :]
-
-
-def get_last_included_proba(
-    y_pred_proba: NDArray,
-    thresholds: NDArray,
-    include_last_label: Union[bool, str, None],
-    method: str,
-    lambda_: Union[NDArray, float, None],
-    k_star: Union[NDArray, Any]
-) -> Tuple[NDArray, NDArray, NDArray]:
-    """
-    Function that returns the smallest score
-    among those which are included in the prediciton set.
-
-    Parameters
-    ----------
-    y_pred_proba: NDArray of shape (n_samples, n_classes)
-        Predictions of the model.
-
-    thresholds: NDArray of shape (n_alphas, )
-        Quantiles that have been computed from the conformity scores.
-
-    include_last_label: Union[bool, str, None]
-        Whether to include or not the label whose score exceeds the threshold.
-
-    lambda_: Union[NDArray, float, None] of shape (n_alphas)
-        Values of lambda for the regularization.
-
-    k_star: Union[NDArray, Any]
-        Values of k for the regularization.
-
-    Returns
-    -------
-    Tuple[ArrayLike, ArrayLike, ArrayLike]
-        Arrays of shape (n_samples, n_classes, n_alphas),
-        (n_samples, 1, n_alphas) and (n_samples, 1, n_alphas).
-        They are respectively the cumsumed scores in the original
-        order which can be different according to the value of alpha
-        with the RAPS method, the index of the last included score
-        and the value of the last included score.
-    """
-    index_sorted = np.flip(
-        np.argsort(y_pred_proba, axis=1), axis=1
-    )
-    # sort probabilities by decreasing order
-    y_pred_proba_sorted = np.take_along_axis(
-        y_pred_proba, index_sorted, axis=1
-    )
-    # get sorted cumulated score
-    y_pred_proba_sorted_cumsum = np.cumsum(
-        y_pred_proba_sorted, axis=1
-    )
-
-    if method == "raps":
-        y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
-            0,
-            np.cumsum(
-                np.ones(y_pred_proba_sorted_cumsum.shape), axis=1
-            ) - k_star
-        )
-    # get cumulated score at their original position
-    y_pred_proba_cumsum = np.take_along_axis(
-        y_pred_proba_sorted_cumsum,
-        np.argsort(index_sorted, axis=1),
-        axis=1
-    )
-    # get index of the last included label
-    y_pred_index_last = get_last_index_included(
-        y_pred_proba_cumsum,
-        thresholds,
-        include_last_label
-    )
-    # get the probability of the last included label
-    y_pred_proba_last = np.take_along_axis(
-        y_pred_proba,
-        y_pred_index_last,
-        axis=1
-    )
-
-    zeros_scores_proba_last = (y_pred_proba_last <= EPSILON)
-
-    # If the last included proba is zero, change it to the
-    # smallest non-zero value to avoid inluding them in the
-    # prediction sets.
-    if np.sum(zeros_scores_proba_last) > 0:
-        y_pred_proba_last[zeros_scores_proba_last] = np.expand_dims(
-            np.min(
-                np.ma.masked_less(
-                    y_pred_proba,
-                    EPSILON
-                ).filled(fill_value=np.inf),
-                axis=1
-            ), axis=1
-        )[zeros_scores_proba_last]
-
-    return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
-
-
-def add_random_tie_breaking(
-    prediction_sets: NDArray,
-    y_pred_index_last: NDArray,
-    y_pred_proba_cumsum: NDArray,
-    y_pred_proba_last: NDArray,
-    threshold: NDArray,
-    method: str,
-    random_state: Optional[Union[int, np.random.RandomState]] = None,
-    lambda_star: Optional[Union[NDArray, float]] = None,
-    k_star: Optional[Union[NDArray, None]] = None
-) -> NDArray:
-    """
-    Randomly remove last label from prediction set based on the
-    comparison between a random number and the difference between
-    cumulated score of the last included label and the quantile.
-
-    Parameters
-    ----------
-    prediction_sets: NDArray of shape
-        (n_samples, n_classes, n_threshold)
-        Prediction set for each observation and each alpha.
-
-    y_pred_index_last: NDArray of shape (n_samples, threshold)
-        Index of the last included label.
-
-    y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
-        Cumsumed probability of the model in the original order.
-
-    y_pred_proba_last: NDArray of shape (n_samples, 1, threshold)
-        Last included probability.
-
-    threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
-        Threshold to compare with y_proba_last_cumsum, can be either:
-
-        - the quantiles associated with alpha values when ``cv`` == "prefit",
-            ``cv`` == "split" or ``agg_scores`` is "mean"
-
-        - the conformity score from training samples otherwise
-            (i.e., when ``cv`` is CV splitter and ``agg_scores`` is "crossval")
-
-    method: str
-        Method that determines how to remove last label in the prediction set.
-
-        - if "cumulated_score" or "aps", compute V parameter from Romano+(2020)
-
-        - else compute V parameter from Angelopoulos+(2020)
-
-    lambda_star: Union[NDArray, float, None] of shape (n_alpha):
-        Optimal value of the regulizer lambda.
-
-    k_star: Union[NDArray, None] of shape (n_alpha):
-        Optimal value of the regulizer k.
-
-    Returns
-    -------
-    NDArray of shape (n_samples, n_classes, n_alpha)
-        Updated version of prediction_sets with randomly removed labels.
-    """
-    # get cumsumed probabilities up to last retained label
-    y_proba_last_cumsumed = np.squeeze(
-        np.take_along_axis(
-            y_pred_proba_cumsum,
-            y_pred_index_last,
-            axis=1
-        ), axis=1
-    )
-
-    if method in ["cumulated_score", "aps"]:
-        # compute V parameter from Romano+(2020)
-        vs = (
-            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-            y_pred_proba_last[:, 0, :]
-        )
-    else:
-        # compute V parameter from Angelopoulos+(2020)
-        L = np.sum(prediction_sets, axis=1)
-        vs = (
-            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-            (
-                y_pred_proba_last[:, 0, :] -
-                lambda_star * np.maximum(0, L - k_star) +
-                lambda_star * (L > k_star)
-            )
-        )
-
-    # get random numbers for each observation and alpha value
-    random_state = check_random_state(random_state)
-    random_state = cast(np.random.RandomState, random_state)
-    us = random_state.uniform(size=(prediction_sets.shape[0], 1))
-    # remove last label from comparison between uniform number and V
-    vs_less_than_us = np.less_equal(vs - us, EPSILON)
-    np.put_along_axis(
-        prediction_sets,
-        y_pred_index_last,
-        vs_less_than_us[:, np.newaxis, :],
-        axis=1
-    )
-    return prediction_sets
diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index a6b3283c7..d2b0c6cc9 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -3,7 +3,7 @@
 from .regression import BaseRegressionScore
 from .classification import BaseClassificationScore
 from .bounds import AbsoluteConformityScore
-from .sets import APS, LAC, TopK
+from .sets import APS, LAC, Naive, RAPS, TopK
 
 
 def check_regression_conformity_score(
@@ -72,9 +72,13 @@ def check_classification_conformity_score(
     if method is not None:
         if method in ['score', 'lac']:
             return LAC()
-        if method in ['naive', 'cumulated_score', 'aps', 'raps']:
+        if method in ['cumulated_score', 'aps']:
             return APS()
-        if method == 'top_k':
+        if method in ['naive']:
+            return Naive()
+        if method in ['raps']:
+            return RAPS()
+        if method in ['top_k']:
             return TopK()
         else:
             raise ValueError(
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 1b6bf6a12..c0ad000f4 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -23,10 +23,9 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
-from mapie.conformity_scores.sets.aps import APS
+from mapie.conformity_scores.sets.raps import RAPS
 from mapie.conformity_scores.sets.utils import (
-    check_proba_normalized, get_last_included_proba,
-    get_true_label_cumsum_proba
+    check_proba_normalized, get_true_label_cumsum_proba
 )
 from mapie.metrics import classification_coverage_score
 from mapie.utils import check_alpha
@@ -1740,7 +1739,7 @@ def test_regularize_conf_scores_shape(k_lambda) -> None:
     lambda_, k = k_lambda[0], k_lambda[1]
     conf_scores = np.random.rand(100, 1)
     cutoff = np.cumsum(np.ones(conf_scores.shape)) - 1
-    reg_conf_scores = APS._regularize_conformity_score(
+    reg_conf_scores = RAPS._regularize_conformity_score(
         k, lambda_, conf_scores, cutoff
     )
 
@@ -1816,12 +1815,11 @@ def test_get_last_included_proba_shape(k_lambda, strategy):
         y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2
     )
 
-    mapie = MapieClassifier(estimator=clf, **STRATEGIES[strategy][0])
     include_last_label = STRATEGIES[strategy][1]["include_last_label"]
     y_p_p_c, y_p_i_l, y_p_p_i_l = \
-        get_last_included_proba(
-            y_pred_proba, thresholds, include_last_label,
-            mapie.method, lambda_, k
+        RAPS._get_last_included_proba(
+            RAPS(), y_pred_proba, thresholds, include_last_label,
+            lambda_=lambda_, k_star=k
         )
 
     assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))

From 18b38665e5b7528a2a081bdaa56b36493d839541 Mon Sep 17 00:00:00 2001
From: BaptisteCalot <115455912+BaptisteCalot@users.noreply.github.com>
Date: Wed, 3 Jul 2024 16:39:06 +0200
Subject: [PATCH 178/424] Update mapie/regression/quantile_regression.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/regression/quantile_regression.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index e66f1939f..e30646ab3 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -547,6 +547,7 @@ def fit(
              The model itself.
         """
         self.cv = self._check_cv(cast(str, self.cv))
+
         # Initialization
         self.estimators_: List[RegressorMixin] = []
         if self.cv == "prefit":

From dbf244f8a37e3d3fb01d0fd4c2b76691bdbccc9e Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 3 Jul 2024 17:27:47 +0200
Subject: [PATCH 179/424] Update tests

---
 mapie/regression/regression.py | 18 +++++-------------
 mapie/tests/test_regression.py |  8 ++++----
 2 files changed, 9 insertions(+), 17 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 577122552..49c355a5e 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -623,21 +623,13 @@ def predict(
                 - [:, 1, :]: Upper bound of the prediction interval.
         """
 
-        if hasattr(self, '_predict_params') and len(self._predict_params) > 0:
-            predict_params = self._predict_params
-            warnings.warn(
-                f"Using predict_params: '{predict_params}' "
-                "from the fit method in the predict method by default",
-                UserWarning
-            )
-
-        elif (len(predict_params) > 0 and hasattr(self, '_predict_params') and
-              len(self._predict_params) == 0 and
-              self.cv != "prefit"):
+        if (len(predict_params) > 0 and hasattr(self, '_predict_params') and
+                len(self._predict_params) == 0 and
+                self.cv != "prefit"):
             raise ValueError(
                 f"Using 'predict_param' '{predict_params}' "
-                f"without having used it in the fit method. "
-                f"Please ensure '{predict_params}' "
+                f"without using one 'predict_param' in the fit method. "
+                f"Please ensure one 'predict_param' "
                 f"is used in the fit method before calling predict."
             )
 
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 6f48a6821..b61db77dc 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -971,10 +971,10 @@ def test_invalid_predict_parameters() -> None:
     mapie_fitted = mapie.fit(X_train, y_train)
 
     with pytest.raises(ValueError, match=(
-        fr".*Using 'predict_param' '{predict_params}'"
-        r".*without having used it in the fit method\..*"
-        fr"Please ensure '{predict_params}'"
-        r".*is used in the fit method before calling predict\..*"
+        fr".*Using 'predict_param' '{predict_params}' "
+        r"without using one 'predict_param' in the fit method\..*"
+        r"Please ensure one 'predict_param' "
+        r"is used in the fit method before calling predict\..*"
     )):
         mapie_fitted.predict(X_test, **predict_params)
 

From c79d6e5a35847e2754c87ac9d2eb798900875a06 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 3 Jul 2024 18:12:07 +0200
Subject: [PATCH 180/424] UPD: improve docstring of score classes

---
 mapie/conformity_scores/classification.py |  78 ++++++++--
 mapie/conformity_scores/sets/aps.py       |  43 ++++--
 mapie/conformity_scores/sets/lac.py       |  84 ++++++++++-
 mapie/conformity_scores/sets/naive.py     | 169 +++++++++++++++++-----
 mapie/conformity_scores/sets/raps.py      | 167 +++++++++++++++++----
 mapie/conformity_scores/sets/topk.py      |  65 ++++++++-
 6 files changed, 504 insertions(+), 102 deletions(-)

diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index 4e4925cea..ace093661 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -30,7 +30,26 @@ def get_predictions(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the predictions.
+        Abstract method to get predictions from an EnsembleClassifier.
+
+        This method should be implemented by any subclass of the current class.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of predictions.
         """
 
     @abstractmethod
@@ -42,7 +61,26 @@ def get_conformity_quantiles(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the quantiles.
+        Abstract method to get quantiles of the conformity scores.
+
+        This method should be implemented by any subclass of the current class.
+
+        Parameters:
+        -----------
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
 
     @abstractmethod
@@ -53,9 +91,32 @@ def get_prediction_sets(
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        TODO: Compute the prediction sets.
+        Abstract method to generate prediction sets based on the probability
+        predictions, the conformity scores and the uncertainty level.
+
+        This method should be implemented by any subclass of the current class.
+
+        Parameters:
+        -----------
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Target prediction.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
 
     def get_sets(
@@ -65,7 +126,7 @@ def get_sets(
         estimator: EnsembleClassifier,
         conformity_scores: NDArray,
         **kwargs
-    ):
+    ) -> NDArray:
         """
         Compute classes of the prediction sets from the observed values,
         the estimator of type ``EnsembleClassifier`` and the conformity scores.
@@ -76,8 +137,8 @@ def get_sets(
             Observed feature values.
 
         alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
 
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
@@ -90,9 +151,6 @@ def get_sets(
         NDArray of shape (n_samples, n_classes, n_alpha)
             Prediction sets (Booleans indicate whether classes are included).
         """
-        # Checks
-        ()
-
         # Predict probabilities
         y_pred_proba = self.get_predictions(
             X, alpha_np, estimator, **kwargs
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 16c6a7b98..29402b64c 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -12,11 +12,12 @@
 
 
 class APS(Naive):
-    """TODO:
+    """
     Adaptive Prediction Sets (APS) method-based non-conformity score.
-    Three differents method are available in this class:
+    Three differents method are available:
 
-    - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
+    - ``"naive"``, that is based on the sum of the probabilities until the
+        1-alpha threshold. See ``"Naive"`` class for more details.
 
     - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
         Sets method. It is based on the sum of the softmax outputs of the
@@ -25,9 +26,8 @@ class APS(Naive):
 
     - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
         same technique as ``"aps"`` method but with a penalty term
-        to reduce the size of prediction sets. See [2] for more
-        details. For now, this method only works with ``"prefit"`` and
-        ``"split"`` strategies.
+        to reduce the size of prediction sets.
+        See ``"RAPS"`` class for more details.
 
     References
     ----------
@@ -35,11 +35,6 @@ class APS(Naive):
     "Classification with Valid and Adaptive Coverage."
     NeurIPS 202 (spotlight) 2020.
 
-    [2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
-    and Jitendra Malik.
-    "Uncertainty Sets for Image Classifiers using Conformal Prediction."
-    International Conference on Learning Representations 2021.
-
     Attributes
     ----------
     method: str
@@ -116,7 +111,31 @@ def get_conformity_quantiles(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the quantiles.
+        Get the quantiles of the conformity scores for each uncertainty level.
+
+        Parameters:
+        -----------
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         n = len(conformity_scores)
 
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 48d7c04d7..2e32de7c2 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -123,7 +123,31 @@ def get_predictions(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the predictions.
+        Get predictions from an EnsembleClassifier.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        Returns:
+        --------
+        NDArray
+            Array of predictions.
         """
         y_pred_proba = estimator.predict(X, agg_scores)
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
@@ -143,7 +167,31 @@ def get_conformity_quantiles(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the quantiles.
+        Get the quantiles of the conformity scores for each uncertainty level.
+
+        Parameters:
+        -----------
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         n = len(conformity_scores)
 
@@ -165,9 +213,37 @@ def get_prediction_sets(
         estimator: EnsembleClassifier,
         agg_scores: Optional[str] = "mean",
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        TODO: Compute the prediction sets.
+        Generate prediction sets based on the probability predictions,
+        the conformity scores and the uncertainty level.
+
+        Parameters:
+        -----------
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Target prediction.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         n = len(conformity_scores)
 
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index cb2df4157..eba65a604 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -14,33 +14,9 @@
 
 
 class Naive(BaseClassificationScore):
-    """TODO:
-    Adaptive Prediction Sets (APS) method-based non-conformity score.
-    Three differents method are available in this class:
-
-    - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
-
-    - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
-        Sets method. It is based on the sum of the softmax outputs of the
-        labels until the true label is reached, on the calibration set.
-        See [1] for more details.
-
-    - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
-        same technique as ``"aps"`` method but with a penalty term
-        to reduce the size of prediction sets. See [2] for more
-        details. For now, this method only works with ``"prefit"`` and
-        ``"split"`` strategies.
-
-    References
-    ----------
-    [1] Yaniv Romano, Matteo Sesia and Emmanuel J. Candès.
-    "Classification with Valid and Adaptive Coverage."
-    NeurIPS 202 (spotlight) 2020.
-
-    [2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
-    and Jitendra Malik.
-    "Uncertainty Sets for Image Classifiers using Conformal Prediction."
-    International Conference on Learning Representations 2021.
+    """
+    Naive classification non-conformity score method that is based on the
+    cumulative sum of probabilities until the 1-alpha threshold.
 
     Attributes
     ----------
@@ -130,7 +106,31 @@ def get_predictions(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the predictions.
+        Get predictions from an EnsembleClassifier.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        Returns:
+        --------
+        NDArray
+            Array of predictions.
         """
         y_pred_proba = estimator.predict(X, agg_scores)
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
@@ -148,16 +148,52 @@ def get_conformity_quantiles(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the quantiles.
+        Get the quantiles of the conformity scores for each uncertainty level.
+
+        Parameters:
+        -----------
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         quantiles_ = 1 - alpha_np
         return quantiles_
 
     def _add_regualization(
         self,
-        y_pred_proba_sorted_cumsum,
+        y_pred_proba_sorted_cumsum: NDArray,
         **kwargs
     ):
+        """
+        Add regularization to the sorted cumulative sum of predicted
+        probabilities.
+
+        Parameters
+        ----------
+        y_pred_proba_sorted_cumsum: NDArray of shape (n_samples, n_classes)
+            The sorted cumulative sum of predicted probabilities.
+
+        **kwargs: dict, optional
+            Additional keyword arguments that might be used.
+            The current implementation does not use any.
+
+        Returns
+        -------
+        NDArray
+            The adjusted cumulative sum of predicted probabilities after
+            applying the regularization technique.
+        """
         return y_pred_proba_sorted_cumsum
 
     def _get_last_included_proba(
@@ -244,14 +280,33 @@ def _get_last_included_proba(
 
     def _compute_vs_parameter(
         self,
-        y_proba_last_cumsumed,
-        threshold,
-        y_pred_proba_last,
-        prediction_sets,
-        *kwargs
-    ):
+        y_proba_last_cumsumed: NDArray,
+        threshold: NDArray,
+        y_pred_proba_last: NDArray,
+        prediction_sets: NDArray,
+        **kwargs
+    ) -> NDArray:
         """
-        TODO
+        Compute the V parameters from Romano+(2020).
+
+        Parameters:
+        -----------
+        y_proba_last_cumsumed: NDArray of shape (n_samples, n_alpha)
+            Cumulated score of the last included label.
+
+        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+            Threshold to compare with y_proba_last_cumsum.
+
+        y_pred_proba_last: NDArray of shape (n_samples, 1, n_alpha)
+            Last included probability.
+
+        predicition_sets: NDArray of shape (n_samples, n_alpha)
+            Prediction sets.
+
+        Returns:
+        --------
+        NDArray of shape (n_samples, n_alpha)
+            Vs parameters.
         """
         # compute V parameter from Romano+(2020)
         vs = (
@@ -329,7 +384,7 @@ def _add_random_tie_breaking(
             ), axis=1
         )
 
-        # TODO
+        # get the V parameter from Romano+(2020) or Angelopoulos+(2020)
         vs = self._compute_vs_parameter(
             y_proba_last_cumsumed,
             threshold,
@@ -360,9 +415,43 @@ def get_prediction_sets(
         agg_scores: Optional[str] = "mean",
         include_last_label: Optional[Union[bool, str]] = True,
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        TODO: Compute the prediction sets.
+        Generate prediction sets based on the probability predictions,
+        the conformity scores and the uncertainty level.
+
+        Parameters:
+        -----------
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Target prediction.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        include_last_label: Optional[Union[bool, str]]
+            Whether or not to include last label in prediction sets.
+            Choose among ``False``, ``True``  or ``"randomized"``.
+
+            By default, ``True``.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         include_last_label = check_include_last_label(include_last_label)
 
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 8ee2bdea1..e52da6271 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -13,30 +13,26 @@
 
 
 class RAPS(APS):
-    """TODO:
-    Adaptive Prediction Sets (APS) method-based non-conformity score.
-    Three differents method are available in this class:
+    """
+    Regularized Adaptive Prediction Sets (RAPS) method-based non-conformity
+    score. Three differents method are available:
 
-    - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
+    - ``"naive"``, that is based on the sum of the probabilities until the
+        1-alpha threshold. See ``"Naive"`` class for more details.
 
     - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
         Sets method. It is based on the sum of the softmax outputs of the
         labels until the true label is reached, on the calibration set.
-        See [1] for more details.
+        See ``"APS"`` class for more details.
 
     - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
-        same technique as ``"aps"`` method but with a penalty term
-        to reduce the size of prediction sets. See [2] for more
-        details. For now, this method only works with ``"prefit"`` and
-        ``"split"`` strategies.
+        same technique as ``"aps"`` method but with a penalty term to reduce
+        the size of prediction sets. See [1] for more details. For now, this
+        method only works with ``"prefit"`` and ``"split"`` strategies.
 
     References
     ----------
-    [1] Yaniv Romano, Matteo Sesia and Emmanuel J. Candès.
-    "Classification with Valid and Adaptive Coverage."
-    NeurIPS 202 (spotlight) 2020.
-
-    [2] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
+    [1] Anastasios Nikolas Angelopoulos, Stephen Bates, Michael Jordan
     and Jitendra Malik.
     "Uncertainty Sets for Image Classifiers using Conformal Prediction."
     International Conference on Learning Representations 2021.
@@ -104,7 +100,7 @@ def _regularize_conformity_score(
     ) -> NDArray:
         """
         Regularize the conformity scores with the ``"raps"``
-        method. See algo. 2 in [3]. TODO: add ref.
+        method. See algo. 2 in [1].
 
         Parameters
         ----------
@@ -231,7 +227,7 @@ def _find_lambda_star(
         lambda_star = np.zeros(len(alpha_np))
         best_sizes = np.full(len(alpha_np), np.finfo(np.float64).max)
 
-        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[3]TODO
+        for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[1]
             true_label_cumsum_proba, cutoff = (
                 get_true_label_cumsum_proba(
                     y_raps_no_enc,
@@ -286,7 +282,59 @@ def get_conformity_quantiles(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the quantiles.
+        Get the quantiles of the conformity scores for each uncertainty level.
+
+        Parameters:
+        -----------
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default, ``"mean"``.
+
+        include_last_label: Optional[Union[bool, str]]
+            Whether or not to include last label in prediction sets.
+            Choose among ``False``, ``True``  or ``"randomized"``.
+
+            By default, ``True``.
+
+        X_raps: NDArray of shape (n_samples, n_features)
+            Observed feature values for the RAPS method (split data).
+
+            By default, "None" but must be set to work.
+
+        y_raps_no_enc: NDArray of shape (n_samples,)
+            Observed labels for the RAPS method (split data).
+
+            By default, "None" but must be set to work.
+
+        y_pred_proba_raps: NDArray of shape (n_samples, n_classes)
+            Predicted probabilities for the RAPS method (split data).
+
+            By default, "None" but must be set to work.
+
+        position_raps: NDArray of shape (n_samples,)
+            Position of the points in the split set for the RAPS method
+            (split data). These positions are returned by the function
+            ``get_true_label_position``.
+
+            By default, "None" but must be set to work.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         # Casting to NDArray to avoid mypy errors
         X_raps = cast(NDArray, X_raps)
@@ -328,18 +376,54 @@ def get_conformity_quantiles(
 
     def _add_regualization(
         self,
-        y_pred_proba_sorted_cumsum,
-        lambda_=None,
-        k_star=None,
-        prediction_phase=False,
+        y_pred_proba_sorted_cumsum: NDArray,
+        lambda_: Optional[float] = None,
+        k_star: Optional[int] = None,
+        prediction_phase: bool = False,
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        TODO
+        Add regularization to the sorted cumulative sum of predicted
+        probabilities.
+
+        Parameters
+        ----------
+        y_pred_proba_sorted_cumsum: NDArray of shape (n_samples, n_classes)
+            The sorted cumulative sum of predicted probabilities.
+
+        lambda_: float
+            The lambda value used in the paper [1].
+
+            By default, "None" but must be set to work.
+
+        k_star: int
+            The optimal value of k (called k_reg in the paper [1]).
+
+            By default, "None" but must be set to work.
+
+        prediction_phase: bool, optional
+            Whether the function is called during the prediction phase.
+            If ``True``, the function will use the values of ``lambda_star``
+            and ``k_star`` of the object.
+
+            By default, ``False``.
+
+        **kwargs: dict, optional
+            Additional keyword arguments that might be used.
+            The current implementation does not use any.
+
+        Returns
+        -------
+        NDArray
+            The adjusted cumulative sum of predicted probabilities after
+            applying the regularization technique.
         """
         if prediction_phase:
-            lambda_ = self.lambda_star
-            k_star = self.k_star
+            lambda_ = cast(float, self.lambda_star)
+            k_star = cast(int, self.k_star)
+        else:
+            lambda_ = cast(float, lambda_)
+            k_star = cast(int, lambda_)
 
         y_pred_proba_sorted_cumsum += lambda_ * np.maximum(
             0,
@@ -352,14 +436,33 @@ def _add_regualization(
 
     def _compute_vs_parameter(
         self,
-        y_proba_last_cumsumed,
-        threshold,
-        y_pred_proba_last,
-        prediction_sets,
-        *kwargs
-    ):
+        y_proba_last_cumsumed: NDArray,
+        threshold: NDArray,
+        y_pred_proba_last: NDArray,
+        prediction_sets: NDArray,
+        **kwargs
+    ) -> NDArray:
         """
-        TODO
+        Compute the V parameters from Angelopoulos+(2020).
+
+        Parameters:
+        -----------
+        y_proba_last_cumsumed: NDArray of shape (n_samples, n_alpha)
+            Cumulated score of the last included label.
+
+        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+            Threshold to compare with y_proba_last_cumsum.
+
+        y_pred_proba_last: NDArray of shape (n_samples, 1, n_alpha)
+            Last included probability.
+
+        predicition_sets: NDArray of shape (n_samples, n_alpha)
+            Prediction sets.
+
+        Returns:
+        --------
+        NDArray of shape (n_samples, n_alpha)
+            Vs parameters.
         """
         # compute V parameter from Angelopoulos+(2020)
         L = np.sum(prediction_sets, axis=1)
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 91667b802..94303563d 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -126,7 +126,26 @@ def get_predictions(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the predictions.
+        Get predictions from an EnsembleClassifier.
+
+        This method should be implemented by any subclass of the current class.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of predictions.
         """
         y_pred_proba = estimator.predict(X, agg_scores="mean")
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
@@ -143,7 +162,24 @@ def get_conformity_quantiles(
         **kwargs
     ) -> NDArray:
         """
-        TODO: Compute the quantiles.
+        Get the quantiles of the conformity scores for each uncertainty level.
+
+        Parameters:
+        -----------
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         return compute_quantiles(conformity_scores, alpha_np)
 
@@ -154,9 +190,30 @@ def get_prediction_sets(
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
         **kwargs
-    ):
+    ) -> NDArray:
         """
-        TODO: Compute the prediction sets.
+        Generate prediction sets based on the probability predictions,
+        the conformity scores and the uncertainty level.
+
+        Parameters:
+        -----------
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Target prediction.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
         """
         y_pred_proba = y_pred_proba[:, :, 0]
         index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))

From 59586a9b30dd358e60a53e7c27a7e134d2b51306 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 3 Jul 2024 18:41:05 +0200
Subject: [PATCH 181/424] UPD: refacto changes in history file

---
 HISTORY.rst | 7 +++++++
 1 file changed, 7 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index b88fc99dc..93db7febe 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,13 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Extend `ConformityScore` to support regression (with `BaseRegressionScore`) and to support classification (with `BaseClassificationScore`)
+* Extend `EnsembleEstimator` to support regression (with `EnsembleRegressor`) and to support classification (with `EnsembleClassifier`)
+* Refactor `MapieClassifier` by separating the handling of the `MapieClassifier` estimator into a new class called `EnsembleClassifier`
+* Refactor `MapieClassifier` by separating the handling of the `MapieClassifier` conformity score into a new class called `BaseClassificationScore`
+* Add severals non-conformity scores for classification (`LAC`, `APS`, `RAPS`, `TopK`) based on `BaseClassificationScore`
+* Transfer the logic of classification methods into the non-conformity score classes (`LAC`, `APS`, `RAPS`, `TopK`)
+* Extend the classification strategy definition by supporting `method` and `conformity_score` attributes
 * Building unit tests for different `Subsample` and `BlockBooststrap` instances
 * Change the sign of C_k in the `Kolmogorov-Smirnov` test documentation
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.

From bbf21b02ebce25aaf56668e884de74b8369489ec Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 4 Jul 2024 11:45:16 +0200
Subject: [PATCH 182/424] Update : change self._predict params

---
 mapie/regression/regression.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 49c355a5e..986910d8a 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -512,9 +512,9 @@ def fit(
         predict_params = kwargs.pop('predict_params', {})
 
         if len(predict_params) > 0:
-            self._predict_params = predict_params
+            self._predict_params = True
         else:
-            self._predict_params = {}
+            self._predict_params = False
 
         # Checks
         (estimator,
@@ -624,7 +624,7 @@ def predict(
         """
 
         if (len(predict_params) > 0 and hasattr(self, '_predict_params') and
-                len(self._predict_params) == 0 and
+                self._predict_params is False and
                 self.cv != "prefit"):
             raise ValueError(
                 f"Using 'predict_param' '{predict_params}' "

From 7d053b76ad632164e83f286b56cebc93b8bcab9b Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 4 Jul 2024 14:57:36 +0200
Subject: [PATCH 183/424] UPD: add missing attributes + keep quantiles_
 attribute

---
 mapie/classification.py        | 8 ++++++++
 mapie/regression/regression.py | 3 +++
 2 files changed, 11 insertions(+)

diff --git a/mapie/classification.py b/mapie/classification.py
index 626149add..06c0a543d 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -140,12 +140,18 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     estimator_: EnsembleClassifier
         Sklearn estimator that handle all that is related to the estimator.
 
+    conformity_score_function_: BaseClassificationScore
+        Score function that handle all that is related to conformity scores.
+
     n_features_in_: int
         Number of features passed to the fit method.
 
     conformity_scores_: ArrayLike of shape (n_samples_train)
         The conformity scores used to calibrate the prediction sets.
 
+    quantiles_: ArrayLike of shape (n_alpha)
+        The quantiles estimated from ``conformity_scores_`` and alpha values.
+
     References
     ----------
     [1] Mauricio Sadinle, Jing Lei, and Larry Wasserman.
@@ -741,4 +747,6 @@ def predict(
             **kwargs
         )
 
+        self.quantiles_ = self.conformity_score_function_.quantiles_
+
         return y_pred, prediction_sets
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 018c30677..88e827368 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -165,6 +165,9 @@ class MapieRegressor(BaseEstimator, RegressorMixin):
     estimator_: EnsembleRegressor
         Sklearn estimator that handle all that is related to the estimator.
 
+    conformity_score_function_: BaseRegressionScore
+        Score function that handle all that is related to conformity scores.
+
     conformity_scores_: ArrayLike of shape (n_samples_train,)
         Conformity scores between ``y_train`` and ``y_pred``.
 

From 15b31ff401acffcbb2da0813eb93ce5802909edc Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 4 Jul 2024 16:22:05 +0200
Subject: [PATCH 184/424] UPD: remove obsolete 'method' attribute and methods
 in conformity score

---
 mapie/classification.py               |  1 -
 mapie/conformity_scores/sets/aps.py   |  8 ------
 mapie/conformity_scores/sets/lac.py   | 15 ----------
 mapie/conformity_scores/sets/naive.py | 16 -----------
 mapie/conformity_scores/sets/raps.py  | 41 +--------------------------
 mapie/conformity_scores/sets/topk.py  | 15 ----------
 6 files changed, 1 insertion(+), 95 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 06c0a543d..4fe76bc2e 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -452,7 +452,6 @@ def _check_fit_parameter(
             method=self.method
         )
         cs_estimator.set_external_attributes(
-            method=self.method,
             classes=self.classes_,
             random_state=self.random_state
         )
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 29402b64c..1885a1863 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -37,14 +37,6 @@ class APS(Naive):
 
     Attributes
     ----------
-    method: str
-        Method to choose for prediction interval estimates.
-        This attribute is for compatibility with ``MapieClassifier``
-        which previously used a string instead of a score class.
-        Methods available in this class: ``aps``, ``raps`` and ``naive``.
-
-        By default, ``aps`` for APS method.
-
     classes: Optional[ArrayLike]
         Names of the classes.
 
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 2e32de7c2..3708587aa 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -27,13 +27,6 @@ class LAC(BaseClassificationScore):
 
     Attributes
     ----------
-    method: str
-        Method to choose for prediction interval estimates.
-        This attribute is for compatibility with ``MapieClassifier``
-        which previously used a string instead of a score class.
-
-        By default, ``lac`` for LAC method.
-
     classes: Optional[ArrayLike]
         Names of the classes.
 
@@ -49,7 +42,6 @@ def __init__(self) -> None:
 
     def set_external_attributes(
         self,
-        method: str = 'lac',
         classes: Optional[ArrayLike] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
@@ -59,12 +51,6 @@ def set_external_attributes(
 
         Parameters
         ----------
-        method: str
-            Method to choose for prediction interval estimates.
-            Methods available in this class: ``lac``.
-
-            By default ``lac`` for LAC method.
-
         classes: Optional[ArrayLike]
             Names of the classes.
 
@@ -74,7 +60,6 @@ def set_external_attributes(
             Pseudo random number generator state.
         """
         super().set_external_attributes(**kwargs)
-        self.method = method
         self.classes = classes
         self.random_state = random_state
 
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index eba65a604..91b2115ae 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -20,14 +20,6 @@ class Naive(BaseClassificationScore):
 
     Attributes
     ----------
-    method: str
-        Method to choose for prediction interval estimates.
-        This attribute is for compatibility with ``MapieClassifier``
-        which previously used a string instead of a score class.
-        Methods available in this class: ``aps``, ``raps`` and ``naive``.
-
-        By default, ``aps`` for APS method.
-
     classes: Optional[ArrayLike]
         Names of the classes.
 
@@ -43,7 +35,6 @@ def __init__(self) -> None:
 
     def set_external_attributes(
         self,
-        method: str = 'naive',
         classes: Optional[ArrayLike] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
@@ -53,12 +44,6 @@ def set_external_attributes(
 
         Parameters
         ----------
-        method: str
-            Method to choose for prediction interval estimates.
-            Methods available in this class: ``aps``, ``raps`` and ``naive``.
-
-            By default ``aps`` for APS method.
-
         classes: Optional[ArrayLike]
             Names of the classes.
 
@@ -68,7 +53,6 @@ def set_external_attributes(
             Pseudo random number generator state.
         """
         super().set_external_attributes(**kwargs)
-        self.method = method
         self.classes = classes
         self.random_state = random_state
 
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index e52da6271..ec39f8e2e 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -7,7 +7,7 @@
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
-from mapie._typing import ArrayLike, NDArray
+from mapie._typing import NDArray
 from mapie.metrics import classification_mean_width_score
 from mapie.utils import check_alpha_and_n_samples, compute_quantiles
 
@@ -39,14 +39,6 @@ class RAPS(APS):
 
     Attributes
     ----------
-    method: str
-        Method to choose for prediction interval estimates.
-        This attribute is for compatibility with ``MapieClassifier``
-        which previously used a string instead of a score class.
-        Methods available in this class: ``aps``, ``raps`` and ``naive``.
-
-        By default, ``aps`` for APS method.
-
     classes: Optional[ArrayLike]
         Names of the classes.
 
@@ -60,37 +52,6 @@ class RAPS(APS):
     def __init__(self) -> None:
         super().__init__()
 
-    def set_external_attributes(
-        self,
-        method: str = 'raps',
-        classes: Optional[ArrayLike] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        **kwargs
-    ) -> None:
-        """
-        Set attributes that are not provided by the user.
-
-        Parameters
-        ----------
-        method: str
-            Method to choose for prediction interval estimates.
-            Methods available in this class: ``aps``, ``raps`` and ``naive``.
-
-            By default ``aps`` for APS method.
-
-        classes: Optional[ArrayLike]
-            Names of the classes.
-
-            By default ``None``.
-
-        random_state: Optional[Union[int, RandomState]]
-            Pseudo random number generator state.
-        """
-        super().set_external_attributes(**kwargs)
-        self.method = method
-        self.classes = classes
-        self.random_state = random_state
-
     @staticmethod
     def _regularize_conformity_score(
         k_star: NDArray,
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 94303563d..9723b8a27 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -31,13 +31,6 @@ class TopK(BaseClassificationScore):
 
     Attributes
     ----------
-    method: str
-        Method to choose for prediction interval estimates.
-        This attribute is for compatibility with ``MapieClassifier``
-        which previously used a string instead of a score class.
-
-        By default, ``top_k`` for Top-K method.
-
     classes: Optional[ArrayLike]
         Names of the classes.
 
@@ -53,7 +46,6 @@ def __init__(self) -> None:
 
     def set_external_attributes(
         self,
-        method: str = 'top_k',
         classes: Optional[int] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
@@ -63,12 +55,6 @@ def set_external_attributes(
 
         Parameters
         ----------
-        method: str
-            Method to choose for prediction interval estimates.
-            Methods available in this class: ``top_k``.
-
-            By default ``top_k`` for Top-K method.
-
         classes: Optional[ArrayLike]
             Names of the classes.
 
@@ -78,7 +64,6 @@ def set_external_attributes(
             Pseudo random number generator state.
         """
         super().set_external_attributes(**kwargs)
-        self.method = method
         self.classes = classes
         self.random_state = random_state
 

From ee89b53a3cbf3627099fc9544fc95c45e015fe59 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 4 Jul 2024 16:24:29 +0200
Subject: [PATCH 185/424] UPD: reduce doctring

---
 mapie/conformity_scores/sets/aps.py  | 16 ++--------------
 mapie/conformity_scores/sets/raps.py | 17 +++--------------
 2 files changed, 5 insertions(+), 28 deletions(-)

diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 1885a1863..af8c4ffcb 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -14,20 +14,8 @@
 class APS(Naive):
     """
     Adaptive Prediction Sets (APS) method-based non-conformity score.
-    Three differents method are available:
-
-    - ``"naive"``, that is based on the sum of the probabilities until the
-        1-alpha threshold. See ``"Naive"`` class for more details.
-
-    - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
-        Sets method. It is based on the sum of the softmax outputs of the
-        labels until the true label is reached, on the calibration set.
-        See [1] for more details.
-
-    - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
-        same technique as ``"aps"`` method but with a penalty term
-        to reduce the size of prediction sets.
-        See ``"RAPS"`` class for more details.
+    It is based on the sum of the softmax outputs of the labels until the true
+    label is reached, on the calibration set. See [1] for more details.
 
     References
     ----------
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index ec39f8e2e..cc0dd3e03 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -15,20 +15,9 @@
 class RAPS(APS):
     """
     Regularized Adaptive Prediction Sets (RAPS) method-based non-conformity
-    score. Three differents method are available:
-
-    - ``"naive"``, that is based on the sum of the probabilities until the
-        1-alpha threshold. See ``"Naive"`` class for more details.
-
-    - ``"aps"`` (formerly called "cumulated_score"), Adaptive Prediction
-        Sets method. It is based on the sum of the softmax outputs of the
-        labels until the true label is reached, on the calibration set.
-        See ``"APS"`` class for more details.
-
-    - ``"raps"``, Regularized Adaptive Prediction Sets method. It uses the
-        same technique as ``"aps"`` method but with a penalty term to reduce
-        the size of prediction sets. See [1] for more details. For now, this
-        method only works with ``"prefit"`` and ``"split"`` strategies.
+    score. It uses the same technique as ``APS`` class but with a penalty term
+    to reduce the size of prediction sets. See [1] for more details. For now,
+    this method only works with ``"prefit"`` and ``"split"`` strategies.
 
     References
     ----------

From d9e498903e18e9d5cd6c5f29922604e995e425b3 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 4 Jul 2024 16:26:17 +0200
Subject: [PATCH 186/424] UPD: change method name

---
 mapie/conformity_scores/classification.py | 4 ++--
 mapie/conformity_scores/sets/aps.py       | 2 +-
 mapie/conformity_scores/sets/lac.py       | 2 +-
 mapie/conformity_scores/sets/naive.py     | 2 +-
 mapie/conformity_scores/sets/raps.py      | 2 +-
 mapie/conformity_scores/sets/topk.py      | 2 +-
 6 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index ace093661..727cde104 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -53,7 +53,7 @@ def get_predictions(
         """
 
     @abstractmethod
-    def get_conformity_quantiles(
+    def get_conformity_score_quantiles(
         self,
         conformity_scores: NDArray,
         alpha_np: NDArray,
@@ -157,7 +157,7 @@ def get_sets(
         )
 
         # Choice of the quantile
-        self.quantiles_ = self.get_conformity_quantiles(
+        self.quantiles_ = self.get_conformity_score_quantiles(
             conformity_scores, alpha_np, estimator, **kwargs
         )
 
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index af8c4ffcb..fbb9186e2 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -82,7 +82,7 @@ def get_conformity_scores(
 
         return conformity_scores
 
-    def get_conformity_quantiles(
+    def get_conformity_score_quantiles(
         self,
         conformity_scores: NDArray,
         alpha_np: NDArray,
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 3708587aa..464f6096d 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -143,7 +143,7 @@ def get_predictions(
 
         return y_pred_proba
 
-    def get_conformity_quantiles(
+    def get_conformity_score_quantiles(
         self,
         conformity_scores: NDArray,
         alpha_np: NDArray,
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 91b2115ae..868aeecdf 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -124,7 +124,7 @@ def get_predictions(
             )
         return y_pred_proba
 
-    def get_conformity_quantiles(
+    def get_conformity_score_quantiles(
         self,
         conformity_scores: NDArray,
         alpha_np: NDArray,
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index cc0dd3e03..4d3e95f2b 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -218,7 +218,7 @@ def _find_lambda_star(
 
         return lambda_star
 
-    def get_conformity_quantiles(
+    def get_conformity_score_quantiles(
         self,
         conformity_scores: NDArray,
         alpha_np: NDArray,
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 9723b8a27..346592452 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -139,7 +139,7 @@ def get_predictions(
         )
         return y_pred_proba
 
-    def get_conformity_quantiles(
+    def get_conformity_score_quantiles(
         self,
         conformity_scores: NDArray,
         alpha_np: NDArray,

From dd28ae86c3a71c5a4902c9836845badfa5a2277e Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 4 Jul 2024 18:20:02 +0200
Subject: [PATCH 187/424] Update : Incorporating PR comments

---
 mapie/regression/regression.py |  2 +-
 mapie/tests/test_regression.py | 40 ++++++++++++++++++++++++++++------
 2 files changed, 34 insertions(+), 8 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 986910d8a..22f106fe5 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -623,7 +623,7 @@ def predict(
                 - [:, 1, :]: Upper bound of the prediction interval.
         """
 
-        if (len(predict_params) > 0 and hasattr(self, '_predict_params') and
+        if (len(predict_params) > 0 and
                 self._predict_params is False and
                 self.cv != "prefit"):
             raise ValueError(
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index b61db77dc..1e960f6e5 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -916,14 +916,12 @@ def test_predict_parameters_passing() -> None:
         np.testing.assert_array_equal(y_pred_1, y_pred_2)
 
 
-def test_fit_and_predict_parameters_passing() -> None:
+def test_fit_parameters_passing_with_predict_parameter() -> None:
     """
-    Test passing fit parameters and predict parameters.
-    For fit : checks that underlying GradientBoosting
+    Test passing fit parameters with predict parameters into the model.
+    Checks that underlying GradientBoosting
     estimators have used 3 iterations only during boosting,
     instead of default value for n_estimators (=100).
-    For predict : Checks that y_pred from train are 0
-    and y_pred from test are 0.
     """
     def early_stopping_monitor(i, est, locals):
         """Returns True on the 3rd iteration."""
@@ -944,8 +942,6 @@ def early_stopping_monitor(i, est, locals):
                           fit_params=fit_params,
                           predict_params=predict_params)
     mapie_2 = mapie_2.fit(X_train, y_train)
-    y_pred_1 = mapie_1.predict(X_test, **predict_params)
-    y_pred_2 = mapie_2.predict(X_test)
 
     assert mapie_1.estimator_.single_estimator_.estimators_.shape[0] == 3
     for estimator in mapie_1.estimator_.estimators_:
@@ -954,6 +950,36 @@ def early_stopping_monitor(i, est, locals):
            custom_gbr.n_estimators)
     for estimator in mapie_2.estimator_.estimators_:
         assert estimator.n_estimators == custom_gbr.n_estimators
+
+
+def test_predict_parameters_passing_with_fit_parameter() -> None:
+    """
+    Test passing predict parameters with fit parameters into the model.
+    Checks that y_pred from train are 0
+    and y_pred from test are 0.
+    """
+    def early_stopping_monitor(i, est, locals):
+        """Returns True on the 3rd iteration."""
+        if i == 2:
+            return True
+        else:
+            return False
+
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state))
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
+    score = AbsoluteConformityScore(sym=True)
+    mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
+    mapie_2 = MapieRegressor(estimator=custom_gbr)
+    fit_params = {'monitor': early_stopping_monitor}
+    predict_params = {'check_predict_params': True}
+    mapie_1 = mapie_1.fit(X_train, y_train,
+                          fit_params=fit_params,
+                          predict_params=predict_params)
+    mapie_2 = mapie_2.fit(X_train, y_train)
+    y_pred_1 = mapie_1.predict(X_test, **predict_params)
+    y_pred_2 = mapie_2.predict(X_test)
+
     np.testing.assert_array_equal(mapie_1.conformity_scores_, np.abs(y_train))
     np.testing.assert_allclose(y_pred_1, 0)
     with np.testing.assert_raises(AssertionError):

From 115c08086c007a55498075659b501cdfaaa4443e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 10:30:14 +0200
Subject: [PATCH 188/424] DOC: change docstring and useless cast

---
 mapie/conformity_scores/classification.py |  2 +-
 mapie/conformity_scores/interface.py      | 10 +++++-----
 mapie/conformity_scores/regression.py     | 14 +++++++-------
 mapie/conformity_scores/sets/aps.py       |  1 -
 4 files changed, 13 insertions(+), 14 deletions(-)

diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index 727cde104..f6e45d380 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -180,7 +180,7 @@ def predict_set(
 
         Parameters:
         -----------
-        X: NDArray of shape (n_samples, ...)
+        X: NDArray of shape (n_samples,)
             The input data or samples for prediction.
 
         alpha_np: NDArray of shape (n_alpha, )
diff --git a/mapie/conformity_scores/interface.py b/mapie/conformity_scores/interface.py
index c8e163844..3979149c0 100644
--- a/mapie/conformity_scores/interface.py
+++ b/mapie/conformity_scores/interface.py
@@ -45,15 +45,15 @@ def get_conformity_scores(
 
         Parameters
         ----------
-        y: NDArray of shape (n_samples, ...)
+        y: NDArray of shape (n_samples,)
             Observed target values.
 
-        y_pred: NDArray of shape (n_samples, ...)
+        y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
         Returns
         -------
-        NDArray of shape (n_samples, ...)
+        NDArray of shape (n_samples,)
             Conformity scores.
         """
 
@@ -70,7 +70,7 @@ def get_quantile(
 
         Parameters
         ----------
-        conformity_scores: NDArray of shape (n_samples, ...)
+        conformity_scores: NDArray of shape (n_samples,)
             Values from which the quantile is computed.
 
         alpha_np: NDArray of shape (n_alpha,)
@@ -137,7 +137,7 @@ def predict_set(
 
         Parameters:
         -----------
-        X: NDArray of shape (n_samples, ...)
+        X: NDArray of shape (n_samples,)
             The input data or samples for prediction.
 
         alpha_np: NDArray of shape (n_alpha, )
diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index fa151d5e5..1e58cc163 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -127,13 +127,13 @@ def check_consistency(
 
         Parameters
         ----------
-        y: NDArray of shape (n_samples, ...)
+        y: NDArray of shape (n_samples,)
             Observed target values.
 
-        y_pred: NDArray of shape (n_samples, ...)
+        y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
-        conformity_scores: NDArray of shape (n_samples, ...)
+        conformity_scores: NDArray of shape (n_samples,)
             Conformity scores.
 
         Raises
@@ -175,15 +175,15 @@ def get_estimation_distribution(
 
         Parameters
         ----------
-        y_pred: NDArray of shape (n_samples, ...)
+        y_pred: NDArray of shape (n_samples,)
             Predicted target values.
 
-        conformity_scores: NDArray of shape (n_samples, ...)
+        conformity_scores: NDArray of shape (n_samples,)
             Conformity scores.
 
         Returns
         -------
-        NDArray of shape (n_samples, ...)
+        NDArray of shape (n_samples,)
             Observed values.
         """
 
@@ -392,7 +392,7 @@ def predict_set(
 
         Parameters:
         -----------
-        X: NDArray of shape (n_samples, ...)
+        X: NDArray of shape (n_samples,)
             The input data or samples for prediction.
 
         alpha_np: NDArray of shape (n_alpha, )
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index fbb9186e2..35d191836 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -76,7 +76,6 @@ def get_conformity_scores(
             y_pred, y_enc.reshape(-1, 1), axis=1
         )
         random_state = check_random_state(self.random_state)
-        random_state = cast(np.random.RandomState, random_state)
         u = random_state.uniform(size=len(y_pred)).reshape(-1, 1)
         conformity_scores -= u * y_proba_true
 

From 64973c0b559d675fb33b49bff9b0249e6fe14b22 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 11:38:08 +0200
Subject: [PATCH 189/424] UPD: remove useless cast + reformat typing

---
 mapie/conformity_scores/sets/utils.py | 22 ++++++++++++----------
 mapie/estimator/classifier.py         |  5 ++---
 2 files changed, 14 insertions(+), 13 deletions(-)

diff --git a/mapie/conformity_scores/sets/utils.py b/mapie/conformity_scores/sets/utils.py
index 5917a6cb7..6ede57ea1 100644
--- a/mapie/conformity_scores/sets/utils.py
+++ b/mapie/conformity_scores/sets/utils.py
@@ -1,4 +1,4 @@
-from typing import Optional, Tuple, Union, cast
+from typing import Optional, Tuple, Union
 import numpy as np
 from sklearn.calibration import label_binarize
 
@@ -66,8 +66,9 @@ def get_true_label_cumsum_proba(
     true_label_cumsum_proba = np.take_along_axis(
         y_pred_sorted_cumsum, cutoff.reshape(-1, 1), axis=1
     )
+    cutoff += 1
 
-    return true_label_cumsum_proba, cutoff + 1
+    return true_label_cumsum_proba, cutoff
 
 
 def check_include_last_label(
@@ -117,7 +118,7 @@ def check_include_last_label(
 
 
 def check_proba_normalized(
-    y_pred_proba: ArrayLike,
+    y_pred_proba: NDArray,
     axis: int = 1
 ) -> NDArray:
     """
@@ -125,7 +126,7 @@ def check_proba_normalized(
 
     Parameters
     ----------
-    y_pred_proba: ArrayLike of shape (n_samples, n_classes) or
+    y_pred_proba: NDArray of shape (n_samples, n_classes) or
     (n_samples, n_train_samples, n_classes)
         Softmax output of a model.
 
@@ -139,12 +140,13 @@ def check_proba_normalized(
     ValueError
         If the sum of the scores is not equal to one.
     """
-    sum_proba = np.sum(y_pred_proba, axis=axis)
-    err_msg = "The sum of the scores is not equal to one."
-    np.testing.assert_allclose(sum_proba, 1, err_msg=err_msg, rtol=1e-5)
-    y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
-
-    return y_pred_proba
+    np.testing.assert_allclose(
+        np.sum(y_pred_proba, axis=axis),
+        1,
+        err_msg="The sum of the scores is not equal to one.",
+        rtol=1e-5
+    )
+    return y_pred_proba.astype(np.float64)
 
 
 def get_last_index_included(
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 16df810e2..fc0ad12ce 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -189,7 +189,7 @@ def _fit_oof_estimator(
     def _check_proba_normalized(
         y_pred_proba: ArrayLike,
         axis: int = 1
-    ) -> NDArray:
+    ) -> ArrayLike:
         """
         Check if, for all the observations, the sum of
         the probabilities is equal to one.
@@ -216,8 +216,7 @@ def _check_proba_normalized(
             err_msg="The sum of the scores is not equal to one.",
             rtol=1e-5
         )
-        y_pred_proba = cast(NDArray, y_pred_proba).astype(np.float64)
-        return y_pred_proba
+        return y_pred_proba.astype(np.float64)
 
     def _predict_proba_oof_estimator(
         self,

From cdc27166d4d9f8f41866dd31fc8060e0ea21e0b2 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 11:39:02 +0200
Subject: [PATCH 190/424] FIX: float conversion removed

---
 mapie/estimator/classifier.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index fc0ad12ce..0c7fa16c1 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -216,7 +216,7 @@ def _check_proba_normalized(
             err_msg="The sum of the scores is not equal to one.",
             rtol=1e-5
         )
-        return y_pred_proba.astype(np.float64)
+        return y_pred_proba
 
     def _predict_proba_oof_estimator(
         self,

From a8d47e53e0b13cbde55b2b406308a585e29bc6b8 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 12:12:57 +0200
Subject: [PATCH 191/424] UPD: move methods relative to aps, from naive to aps

---
 mapie/conformity_scores/sets/aps.py   | 277 +++++++++++++++++++++++++-
 mapie/conformity_scores/sets/naive.py | 216 ++------------------
 2 files changed, 288 insertions(+), 205 deletions(-)

diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 35d191836..ff0ebb6d7 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -1,12 +1,16 @@
-from typing import Optional, cast
+from typing import Optional, Union, cast
 
 import numpy as np
 from sklearn.dummy import check_random_state
 
 from mapie.conformity_scores.sets.naive import Naive
-from mapie.conformity_scores.sets.utils import get_true_label_cumsum_proba
+from mapie.conformity_scores.sets.utils import (
+    check_include_last_label, check_proba_normalized,
+    get_true_label_cumsum_proba
+)
 from mapie.estimator.classifier import EnsembleClassifier
 
+from mapie._machine_precision import EPSILON
 from mapie._typing import NDArray
 from mapie.utils import compute_quantiles
 
@@ -38,6 +42,49 @@ class APS(Naive):
     def __init__(self) -> None:
         super().__init__()
 
+    def get_predictions(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        **kwargs
+    ) -> NDArray:
+        """
+        Get predictions from an EnsembleClassifier.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        Returns:
+        --------
+        NDArray
+            Array of predictions.
+        """
+        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
+        if agg_scores != "crossval":
+            y_pred_proba = np.repeat(
+                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+            )
+        return y_pred_proba
+
     def get_conformity_scores(
         self,
         y: NDArray,
@@ -124,3 +171,229 @@ def get_conformity_score_quantiles(
             quantiles_ = (n + 1) * (1 - alpha_np)
 
         return quantiles_
+
+    def _compute_vs_parameter(
+        self,
+        y_proba_last_cumsumed: NDArray,
+        threshold: NDArray,
+        y_pred_proba_last: NDArray,
+        prediction_sets: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Compute the V parameters from Romano+(2020).
+
+        Parameters:
+        -----------
+        y_proba_last_cumsumed: NDArray of shape (n_samples, n_alpha)
+            Cumulated score of the last included label.
+
+        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+            Threshold to compare with y_proba_last_cumsum.
+
+        y_pred_proba_last: NDArray of shape (n_samples, 1, n_alpha)
+            Last included probability.
+
+        predicition_sets: NDArray of shape (n_samples, n_alpha)
+            Prediction sets.
+
+        Returns:
+        --------
+        NDArray of shape (n_samples, n_alpha)
+            Vs parameters.
+        """
+        # compute V parameter from Romano+(2020)
+        vs = (
+            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
+            y_pred_proba_last[:, 0, :]
+        )
+        return vs
+
+    def _add_random_tie_breaking(
+        self,
+        prediction_sets: NDArray,
+        y_pred_index_last: NDArray,
+        y_pred_proba_cumsum: NDArray,
+        y_pred_proba_last: NDArray,
+        threshold: NDArray,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        Randomly remove last label from prediction set based on the
+        comparison between a random number and the difference between
+        cumulated score of the last included label and the quantile.
+
+        Parameters
+        ----------
+        prediction_sets: NDArray of shape
+            (n_samples, n_classes, n_threshold)
+            Prediction set for each observation and each alpha.
+
+        y_pred_index_last: NDArray of shape (n_samples, threshold)
+            Index of the last included label.
+
+        y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
+            Cumsumed probability of the model in the original order.
+
+        y_pred_proba_last: NDArray of shape (n_samples, 1, threshold)
+            Last included probability.
+
+        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
+            Threshold to compare with y_proba_last_cumsum, can be either:
+
+            - the quantiles associated with alpha values when
+                ``cv`` == "prefit", ``cv`` == "split"
+                or ``agg_scores`` is "mean"
+
+            - the conformity score from training samples otherwise (i.e., when
+            ``cv`` is CV splitter and ``agg_scores`` is "crossval")
+
+        method: str
+            Method that determines how to remove last label in the prediction
+            set.
+
+            - if "cumulated_score" or "aps", compute V parameter
+                from Romano+(2020)
+
+            - else compute V parameter from Angelopoulos+(2020)
+
+        lambda_star: Optional[Union[NDArray, float]] of shape (n_alpha):
+            Optimal value of the regulizer lambda.
+
+        k_star: Optional[NDArray] of shape (n_alpha):
+            Optimal value of the regulizer k.
+
+        Returns
+        -------
+        NDArray of shape (n_samples, n_classes, n_alpha)
+            Updated version of prediction_sets with randomly removed labels.
+        """
+        # get cumsumed probabilities up to last retained label
+        y_proba_last_cumsumed = np.squeeze(
+            np.take_along_axis(
+                y_pred_proba_cumsum,
+                y_pred_index_last,
+                axis=1
+            ), axis=1
+        )
+
+        # get the V parameter from Romano+(2020) or Angelopoulos+(2020)
+        vs = self._compute_vs_parameter(
+            y_proba_last_cumsumed,
+            threshold,
+            y_pred_proba_last,
+            prediction_sets
+        )
+
+        # get random numbers for each observation and alpha value
+        random_state = check_random_state(random_state)
+        random_state = cast(np.random.RandomState, random_state)
+        us = random_state.uniform(size=(prediction_sets.shape[0], 1))
+        # remove last label from comparison between uniform number and V
+        vs_less_than_us = np.less_equal(vs - us, EPSILON)
+        np.put_along_axis(
+            prediction_sets,
+            y_pred_index_last,
+            vs_less_than_us[:, np.newaxis, :],
+            axis=1
+        )
+        return prediction_sets
+
+    def get_prediction_sets(
+        self,
+        y_pred_proba: NDArray,
+        conformity_scores: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleClassifier,
+        agg_scores: Optional[str] = "mean",
+        include_last_label: Optional[Union[bool, str]] = True,
+        **kwargs
+    ) -> NDArray:
+        """
+        Generate prediction sets based on the probability predictions,
+        the conformity scores and the uncertainty level.
+
+        Parameters:
+        -----------
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Target prediction.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores for each sample.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between 0 and 1, representing the uncertainty
+            of the confidence interval.
+
+        estimator: EnsembleClassifier
+            Estimator that is fitted to predict y from X.
+
+        agg_scores: Optional[str]
+            Method to aggregate the scores from the base estimators.
+            If "mean", the scores are averaged. If "crossval", the scores are
+            obtained from cross-validation.
+
+            By default ``"mean"``.
+
+        include_last_label: Optional[Union[bool, str]]
+            Whether or not to include last label in prediction sets.
+            Choose among ``False``, ``True``  or ``"randomized"``.
+
+            By default, ``True``.
+
+        Returns:
+        --------
+        NDArray
+            Array of quantiles with respect to alpha_np.
+        """
+        include_last_label = check_include_last_label(include_last_label)
+
+        # specify which thresholds will be used
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            thresholds = self.quantiles_
+        else:
+            thresholds = conformity_scores.ravel()
+
+        # sort labels by decreasing probability
+        y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last = (
+            self._get_last_included_proba(
+                y_pred_proba,
+                thresholds,
+                include_last_label,
+                prediction_phase=True,
+                **kwargs
+            )
+        )
+        # get the prediction set by taking all probabilities above the last one
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            y_pred_included = np.greater_equal(
+                y_pred_proba - y_pred_proba_last, -EPSILON
+            )
+        else:
+            y_pred_included = np.less_equal(
+                y_pred_proba - y_pred_proba_last, EPSILON
+            )
+        # remove last label randomly
+        if include_last_label == "randomized":
+            y_pred_included = self._add_random_tie_breaking(
+                y_pred_included,
+                y_pred_index_last,
+                y_pred_proba_cumsum,
+                y_pred_proba_last,
+                thresholds,
+                self.random_state,
+                **kwargs
+            )
+        if estimator.cv == "prefit" or agg_scores in ["mean"]:
+            prediction_sets = y_pred_included
+        else:
+            # compute the number of times the inequality is verified
+            prediction_sets_summed = y_pred_included.sum(axis=2)
+            prediction_sets = np.less_equal(
+                prediction_sets_summed[:, :, np.newaxis]
+                - self.quantiles_[np.newaxis, np.newaxis, :],
+                EPSILON
+            )
+
+        return prediction_sets
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 868aeecdf..259753021 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -1,11 +1,10 @@
-from typing import Optional, Tuple, Union, cast
+from typing import Optional, Tuple, Union
 
 import numpy as np
-from sklearn.dummy import check_random_state
 
 from mapie.conformity_scores.classification import BaseClassificationScore
 from mapie.conformity_scores.sets.utils import (
-    check_include_last_label, check_proba_normalized, get_last_index_included
+    check_proba_normalized, get_last_index_included
 )
 from mapie.estimator.classifier import EnsembleClassifier
 
@@ -86,7 +85,6 @@ def get_predictions(
         X: NDArray,
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
-        agg_scores: Optional[str] = "mean",
         **kwargs
     ) -> NDArray:
         """
@@ -104,24 +102,16 @@ def get_predictions(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        agg_scores: Optional[str]
-            Method to aggregate the scores from the base estimators.
-            If "mean", the scores are averaged. If "crossval", the scores are
-            obtained from cross-validation.
-
-            By default ``"mean"``.
-
         Returns:
         --------
         NDArray
             Array of predictions.
         """
-        y_pred_proba = estimator.predict(X, agg_scores)
+        y_pred_proba = estimator.predict(X, agg_scores='mean')
         y_pred_proba = check_proba_normalized(y_pred_proba, axis=1)
-        if agg_scores != "crossval":
-            y_pred_proba = np.repeat(
-                y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
-            )
+        y_pred_proba = np.repeat(
+            y_pred_proba[:, :, np.newaxis], len(alpha_np), axis=2
+        )
         return y_pred_proba
 
     def get_conformity_score_quantiles(
@@ -262,142 +252,12 @@ def _get_last_included_proba(
 
         return y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last
 
-    def _compute_vs_parameter(
-        self,
-        y_proba_last_cumsumed: NDArray,
-        threshold: NDArray,
-        y_pred_proba_last: NDArray,
-        prediction_sets: NDArray,
-        **kwargs
-    ) -> NDArray:
-        """
-        Compute the V parameters from Romano+(2020).
-
-        Parameters:
-        -----------
-        y_proba_last_cumsumed: NDArray of shape (n_samples, n_alpha)
-            Cumulated score of the last included label.
-
-        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
-            Threshold to compare with y_proba_last_cumsum.
-
-        y_pred_proba_last: NDArray of shape (n_samples, 1, n_alpha)
-            Last included probability.
-
-        predicition_sets: NDArray of shape (n_samples, n_alpha)
-            Prediction sets.
-
-        Returns:
-        --------
-        NDArray of shape (n_samples, n_alpha)
-            Vs parameters.
-        """
-        # compute V parameter from Romano+(2020)
-        vs = (
-            (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
-            y_pred_proba_last[:, 0, :]
-        )
-        return vs
-
-    def _add_random_tie_breaking(
-        self,
-        prediction_sets: NDArray,
-        y_pred_index_last: NDArray,
-        y_pred_proba_cumsum: NDArray,
-        y_pred_proba_last: NDArray,
-        threshold: NDArray,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        **kwargs
-    ) -> NDArray:
-        """
-        Randomly remove last label from prediction set based on the
-        comparison between a random number and the difference between
-        cumulated score of the last included label and the quantile.
-
-        Parameters
-        ----------
-        prediction_sets: NDArray of shape
-            (n_samples, n_classes, n_threshold)
-            Prediction set for each observation and each alpha.
-
-        y_pred_index_last: NDArray of shape (n_samples, threshold)
-            Index of the last included label.
-
-        y_pred_proba_cumsum: NDArray of shape (n_samples, n_classes)
-            Cumsumed probability of the model in the original order.
-
-        y_pred_proba_last: NDArray of shape (n_samples, 1, threshold)
-            Last included probability.
-
-        threshold: NDArray of shape (n_alpha,) or shape (n_samples_train,)
-            Threshold to compare with y_proba_last_cumsum, can be either:
-
-            - the quantiles associated with alpha values when
-                ``cv`` == "prefit", ``cv`` == "split"
-                or ``agg_scores`` is "mean"
-
-            - the conformity score from training samples otherwise (i.e., when
-            ``cv`` is CV splitter and ``agg_scores`` is "crossval")
-
-        method: str
-            Method that determines how to remove last label in the prediction
-            set.
-
-            - if "cumulated_score" or "aps", compute V parameter
-                from Romano+(2020)
-
-            - else compute V parameter from Angelopoulos+(2020)
-
-        lambda_star: Optional[Union[NDArray, float]] of shape (n_alpha):
-            Optimal value of the regulizer lambda.
-
-        k_star: Optional[NDArray] of shape (n_alpha):
-            Optimal value of the regulizer k.
-
-        Returns
-        -------
-        NDArray of shape (n_samples, n_classes, n_alpha)
-            Updated version of prediction_sets with randomly removed labels.
-        """
-        # get cumsumed probabilities up to last retained label
-        y_proba_last_cumsumed = np.squeeze(
-            np.take_along_axis(
-                y_pred_proba_cumsum,
-                y_pred_index_last,
-                axis=1
-            ), axis=1
-        )
-
-        # get the V parameter from Romano+(2020) or Angelopoulos+(2020)
-        vs = self._compute_vs_parameter(
-            y_proba_last_cumsumed,
-            threshold,
-            y_pred_proba_last,
-            prediction_sets
-        )
-
-        # get random numbers for each observation and alpha value
-        random_state = check_random_state(random_state)
-        random_state = cast(np.random.RandomState, random_state)
-        us = random_state.uniform(size=(prediction_sets.shape[0], 1))
-        # remove last label from comparison between uniform number and V
-        vs_less_than_us = np.less_equal(vs - us, EPSILON)
-        np.put_along_axis(
-            prediction_sets,
-            y_pred_index_last,
-            vs_less_than_us[:, np.newaxis, :],
-            axis=1
-        )
-        return prediction_sets
-
     def get_prediction_sets(
         self,
         y_pred_proba: NDArray,
         conformity_scores: NDArray,
         alpha_np: NDArray,
         estimator: EnsembleClassifier,
-        agg_scores: Optional[str] = "mean",
-        include_last_label: Optional[Union[bool, str]] = True,
         **kwargs
     ) -> NDArray:
         """
@@ -419,72 +279,22 @@ def get_prediction_sets(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        agg_scores: Optional[str]
-            Method to aggregate the scores from the base estimators.
-            If "mean", the scores are averaged. If "crossval", the scores are
-            obtained from cross-validation.
-
-            By default ``"mean"``.
-
-        include_last_label: Optional[Union[bool, str]]
-            Whether or not to include last label in prediction sets.
-            Choose among ``False``, ``True``  or ``"randomized"``.
-
-            By default, ``True``.
-
         Returns:
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
         """
-        include_last_label = check_include_last_label(include_last_label)
-
-        # specify which thresholds will be used
-        if estimator.cv == "prefit" or agg_scores in ["mean"]:
-            thresholds = self.quantiles_
-        else:
-            thresholds = conformity_scores.ravel()
-
         # sort labels by decreasing probability
-        y_pred_proba_cumsum, y_pred_index_last, y_pred_proba_last = (
+        _, _, y_pred_proba_last = (
             self._get_last_included_proba(
                 y_pred_proba,
-                thresholds,
-                include_last_label,
-                prediction_phase=True,
-                **kwargs
+                thresholds=self.quantiles_,
+                include_last_label=True
             )
         )
-        # get the prediction set by taking all probabilities
-        # above the last one
-        if estimator.cv == "prefit" or agg_scores in ["mean"]:
-            y_pred_included = np.greater_equal(
-                y_pred_proba - y_pred_proba_last, -EPSILON
-            )
-        else:
-            y_pred_included = np.less_equal(
-                y_pred_proba - y_pred_proba_last, EPSILON
-            )
-        # remove last label randomly
-        if include_last_label == "randomized":
-            y_pred_included = self._add_random_tie_breaking(
-                y_pred_included,
-                y_pred_index_last,
-                y_pred_proba_cumsum,
-                y_pred_proba_last,
-                thresholds,
-                self.random_state,
-                **kwargs
-            )
-        if estimator.cv == "prefit" or agg_scores in ["mean"]:
-            prediction_sets = y_pred_included
-        else:
-            # compute the number of times the inequality is verified
-            prediction_sets_summed = y_pred_included.sum(axis=2)
-            prediction_sets = np.less_equal(
-                prediction_sets_summed[:, :, np.newaxis]
-                - self.quantiles_[np.newaxis, np.newaxis, :],
-                EPSILON
-            )
+        # get the prediction set by taking all probabilities above the last one
+        prediction_sets = np.greater_equal(
+            y_pred_proba - y_pred_proba_last, -EPSILON
+        )
 
         return prediction_sets

From 2f0ed146b4939e40ce0c0197ca8839173af17da3 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 12:19:24 +0200
Subject: [PATCH 192/424] UPD: move get_true_label_cumsum_proba to class method

---
 mapie/conformity_scores/sets/aps.py   | 49 ++++++++++++++++++++++++---
 mapie/conformity_scores/sets/raps.py  |  3 +-
 mapie/conformity_scores/sets/utils.py | 44 ++----------------------
 mapie/tests/test_classification.py    | 12 +++----
 4 files changed, 53 insertions(+), 55 deletions(-)

diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index ff0ebb6d7..e8cf5c1c3 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -1,17 +1,17 @@
-from typing import Optional, Union, cast
+from typing import Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.dummy import check_random_state
+from sklearn.calibration import label_binarize
 
 from mapie.conformity_scores.sets.naive import Naive
 from mapie.conformity_scores.sets.utils import (
-    check_include_last_label, check_proba_normalized,
-    get_true_label_cumsum_proba
+    check_include_last_label, check_proba_normalized
 )
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
-from mapie._typing import NDArray
+from mapie._typing import ArrayLike, NDArray
 from mapie.utils import compute_quantiles
 
 
@@ -85,6 +85,45 @@ def get_predictions(
             )
         return y_pred_proba
 
+    @staticmethod
+    def get_true_label_cumsum_proba(
+        y: ArrayLike,
+        y_pred_proba: NDArray,
+        classes: ArrayLike
+    ) -> Tuple[NDArray, NDArray]:
+        """
+        Compute the cumsumed probability of the true label.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples, )
+            Array with the labels.
+
+        y_pred_proba: NDArray of shape (n_samples, n_classes)
+            Predictions of the model.
+
+        classes: NDArray of shape (n_classes, )
+            Array with the classes.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray] of shapes (n_samples, 1) and (n_samples, ).
+            The first element is the cumsum probability of the true label.
+            The second is the sorted position of the true label.
+        """
+        y_true = label_binarize(y=y, classes=classes)
+        index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
+        y_pred_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
+        y_true_sorted = np.take_along_axis(y_true, index_sorted, axis=1)
+        y_pred_sorted_cumsum = np.cumsum(y_pred_sorted, axis=1)
+        cutoff = np.argmax(y_true_sorted, axis=1)
+        true_label_cumsum_proba = np.take_along_axis(
+            y_pred_sorted_cumsum, cutoff.reshape(-1, 1), axis=1
+        )
+        cutoff += 1
+
+        return true_label_cumsum_proba, cutoff
+
     def get_conformity_scores(
         self,
         y: NDArray,
@@ -117,7 +156,7 @@ def get_conformity_scores(
 
         # Conformity scores
         conformity_scores, self.cutoff = (
-            get_true_label_cumsum_proba(y, y_pred, classes)
+            self.get_true_label_cumsum_proba(y, y_pred, classes)
         )
         y_proba_true = np.take_along_axis(
             y_pred, y_enc.reshape(-1, 1), axis=1
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 4d3e95f2b..320b3bbf0 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -3,7 +3,6 @@
 import numpy as np
 
 from mapie.conformity_scores.sets.aps import APS
-from mapie.conformity_scores.sets.utils import get_true_label_cumsum_proba
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
@@ -179,7 +178,7 @@ def _find_lambda_star(
 
         for lambda_ in [.001, .01, .1, .2, .5]:  # values given in paper[1]
             true_label_cumsum_proba, cutoff = (
-                get_true_label_cumsum_proba(
+                self.get_true_label_cumsum_proba(
                     y_raps_no_enc,
                     y_pred_proba_raps[:, :, 0],
                     classes
diff --git a/mapie/conformity_scores/sets/utils.py b/mapie/conformity_scores/sets/utils.py
index 6ede57ea1..5912607fb 100644
--- a/mapie/conformity_scores/sets/utils.py
+++ b/mapie/conformity_scores/sets/utils.py
@@ -1,8 +1,7 @@
-from typing import Optional, Tuple, Union
+from typing import Optional, Union
 import numpy as np
-from sklearn.calibration import label_binarize
 
-from mapie._typing import ArrayLike, NDArray
+from mapie._typing import NDArray
 from mapie._machine_precision import EPSILON
 
 
@@ -32,45 +31,6 @@ def get_true_label_position(
     return position
 
 
-def get_true_label_cumsum_proba(
-    y: ArrayLike,
-    y_pred_proba: NDArray,
-    classes: ArrayLike
-) -> Tuple[NDArray, NDArray]:
-    """
-    Compute the cumsumed probability of the true label.
-
-    Parameters
-    ----------
-    y: NDArray of shape (n_samples, )
-        Array with the labels.
-
-    y_pred_proba: NDArray of shape (n_samples, n_classes)
-        Predictions of the model.
-
-    classes: NDArray of shape (n_classes, )
-        Array with the classes.
-
-    Returns
-    -------
-    Tuple[NDArray, NDArray] of shapes (n_samples, 1) and (n_samples, ).
-        The first element is the cumsum probability of the true label.
-        The second is the sorted position of the true label.
-    """
-    y_true = label_binarize(y=y, classes=classes)
-    index_sorted = np.fliplr(np.argsort(y_pred_proba, axis=1))
-    y_pred_sorted = np.take_along_axis(y_pred_proba, index_sorted, axis=1)
-    y_true_sorted = np.take_along_axis(y_true, index_sorted, axis=1)
-    y_pred_sorted_cumsum = np.cumsum(y_pred_sorted, axis=1)
-    cutoff = np.argmax(y_true_sorted, axis=1)
-    true_label_cumsum_proba = np.take_along_axis(
-        y_pred_sorted_cumsum, cutoff.reshape(-1, 1), axis=1
-    )
-    cutoff += 1
-
-    return true_label_cumsum_proba, cutoff
-
-
 def check_include_last_label(
     include_last_label: Optional[Union[bool, str]]
 ) -> Optional[Union[bool, str]]:
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index c0ad000f4..497b8cea5 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -23,10 +23,8 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
-from mapie.conformity_scores.sets.raps import RAPS
-from mapie.conformity_scores.sets.utils import (
-    check_proba_normalized, get_true_label_cumsum_proba
-)
+from mapie.conformity_scores import APS, RAPS
+from mapie.conformity_scores.sets.utils import check_proba_normalized
 from mapie.metrics import classification_coverage_score
 from mapie.utils import check_alpha
 
@@ -1759,7 +1757,7 @@ def test_get_true_label_cumsum_proba_shape() -> None:
     )
     mapie_clf.fit(X, y)
     classes = mapie_clf.classes_
-    cumsum_proba, cutoff = get_true_label_cumsum_proba(y, y_pred, classes)
+    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(y, y_pred, classes)
     assert cumsum_proba.shape == (len(X), 1)
     assert cutoff.shape == (len(X), )
 
@@ -1777,7 +1775,9 @@ def test_get_true_label_cumsum_proba_result() -> None:
     )
     mapie_clf.fit(X_toy, y_toy)
     classes = mapie_clf.classes_
-    cumsum_proba, cutoff = get_true_label_cumsum_proba(y_toy, y_pred, classes)
+    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(
+        y_toy, y_pred, classes
+    )
     np.testing.assert_allclose(
         cumsum_proba,
         np.array(

From 0a5ac6e392d53ae1139ac837d5f1fbffcde17b5f Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 12:21:41 +0200
Subject: [PATCH 193/424] UPD: add test wrong method in conformity score

---
 mapie/tests/test_conformity_scores_sets.py | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index b6349b4fc..b6d63fde6 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -10,6 +10,7 @@
 
 cs_list = [None, LAC(), APS(), TopK()]
 method_list = [None, 'naive', 'aps', 'raps', 'lac', 'top_k']
+wrong_method_list = ['naive_', 'aps_', 'raps_', 'lac_', 'top_k_']
 
 
 def test_error_mother_class_initialization() -> None:
@@ -35,3 +36,11 @@ def test_check_classification_method(
         check_classification_conformity_score(method=method),
         BaseClassificationScore
     )
+
+
+@pytest.mark.parametrize("method", wrong_method_list)
+def test_check_wrong_classification_method(
+    method: Optional[str]
+) -> None:
+    with pytest.raises(ValueError, match="Invalid method.*"):
+        check_classification_conformity_score(method=method)

From 089b88b2c626e2257cc00c1e52feca6f573ca7a3 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Fri, 5 Jul 2024 13:49:32 +0200
Subject: [PATCH 194/424] chore: Update MathJax path for Sphinx documentation

---
 doc/conf.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/doc/conf.py b/doc/conf.py
index f7f3c5e86..7e579753b 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -14,7 +14,9 @@
 
 import os
 import sys
+from distutils.version import LooseVersion
 
+import sphinx
 import sphinx_gallery
 import sphinx_rtd_theme
 
@@ -42,23 +44,23 @@
     "numpydoc",
     "sphinx_gallery.gen_gallery",
 ]
-mathjax_path = "https://cdn.mathjax.org/mathjax/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
 
+# Correct MathJax path
+mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"
 
 # this is needed for some reason...
 # see https://github.com/numpy/numpydoc/issues/69
 numpydoc_show_class_members = False
 
-from distutils.version import LooseVersion
-
 # pngmath / imgmath compatibility layer for different sphinx versions
-import sphinx
-
 if LooseVersion(sphinx.__version__) < LooseVersion("1.4"):
     extensions.append("sphinx.ext.pngmath")
 else:
     extensions.append("sphinx.ext.imgmath")
 
+# Ensure imgmath_latex is correctly set
+imgmath_latex = 'latex'
+
 autodoc_default_flags = ["members", "inherited-members"]
 
 # Add any paths that contain templates here, relative to this directory.

From 13da57f79357e6302a8ad35846cd2a23e91fa3fa Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Fri, 5 Jul 2024 14:15:20 +0200
Subject: [PATCH 195/424] chore: Refactor train-test split in
 plot_cqr_tutorial.py

---
 .../regression/4-tutorials/plot_cqr_tutorial.py | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/examples/regression/4-tutorials/plot_cqr_tutorial.py b/examples/regression/4-tutorials/plot_cqr_tutorial.py
index 5e92e4542..444ef37de 100644
--- a/examples/regression/4-tutorials/plot_cqr_tutorial.py
+++ b/examples/regression/4-tutorials/plot_cqr_tutorial.py
@@ -101,11 +101,6 @@ class :class:`~mapie.subsample.Subsample` (note that the `alpha` parameter is
     y['MedHouseVal'],
     random_state=random_state
 )
-X_train, X_calib, y_train, y_calib = train_test_split(
-    X_train,
-    y_train,
-    random_state=random_state
-)
 
 
 ##############################################################################
@@ -267,13 +262,19 @@ def plot_prediction_intervals(
     if strategy == "cqr":
         mapie = MapieQuantileRegressor(estimator, **params)
         mapie.fit(
-            X_train, y_train,
-            X_calib=X_calib, y_calib=y_calib,
+            X_train,
+            y_train,
+            calib_size=0.3,
             random_state=random_state
         )
         y_pred[strategy], y_pis[strategy] = mapie.predict(X_test)
     else:
-        mapie = MapieRegressor(estimator, **params, random_state=random_state)
+        mapie = MapieRegressor(
+            estimator,
+            test_size=0.3,
+            random_state=random_state,
+            **params
+        )
         mapie.fit(X_train, y_train)
         y_pred[strategy], y_pis[strategy] = mapie.predict(X_test, alpha=0.2)
     (

From 1fc036914b62a99c1947b27099ba785a2997814d Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Fri, 5 Jul 2024 14:26:19 +0200
Subject: [PATCH 196/424] chore: Update MathJax path for Sphinx documentation,
 same one as for scikit-learn

---
 doc/conf.py | 4 +---
 1 file changed, 1 insertion(+), 3 deletions(-)

diff --git a/doc/conf.py b/doc/conf.py
index 7e579753b..d9049e294 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -44,9 +44,7 @@
     "numpydoc",
     "sphinx_gallery.gen_gallery",
 ]
-
-# Correct MathJax path
-mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"
+mathjax_path = "https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"
 
 # this is needed for some reason...
 # see https://github.com/numpy/numpydoc/issues/69

From 1f916f3610c688a71d02b629c3fa4bd19c11eece Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 18:05:18 +0200
Subject: [PATCH 197/424] FIX: add missing docstring

---
 mapie/classification.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mapie/classification.py b/mapie/classification.py
index 4fe76bc2e..1f3abf10d 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -116,6 +116,9 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
         By default ``None``.
 
+    conformity_score_function_: BaseClassificationScore
+        Score function that handle all that is related to conformity scores.
+
     random_state: Optional[Union[int, RandomState]]
         Pseudo random number generator state used for random uniform sampling
         for evaluation quantiles and prediction sets.

From 964fd5e2a0fb9138e0caed1b84547900ee357039 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Fri, 5 Jul 2024 18:06:05 +0200
Subject: [PATCH 198/424] Update : tests

---
 mapie/tests/test_regression.py | 76 ++++++++++++++--------------------
 1 file changed, 32 insertions(+), 44 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 1e960f6e5..f9248893c 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -55,6 +55,14 @@ def predict(self, X, check_predict_params=False):
         return super().predict(X)
 
 
+def early_stopping_monitor(i, est, locals):
+    """Returns True on the 3rd iteration."""
+    if i == 2:
+        return True
+    else:
+        return False
+
+
 Params = TypedDict(
     "Params",
     {
@@ -871,20 +879,10 @@ def test_fit_parameters_passing() -> None:
     only during boosting, instead of default value for n_estimators (=100).
     """
     gb = GradientBoostingRegressor(random_state=random_state)
-
     mapie = MapieRegressor(estimator=gb, random_state=random_state)
-
-    def early_stopping_monitor(i, est, locals):
-        """Returns True on the 3rd iteration."""
-        if i == 2:
-            return True
-        else:
-            return False
-
     mapie.fit(X, y, fit_params={'monitor': early_stopping_monitor})
 
     assert mapie.estimator_.single_estimator_.estimators_.shape[0] == 3
-
     for estimator in mapie.estimator_.estimators_:
         assert estimator.estimators_.shape[0] == 3
 
@@ -892,17 +890,17 @@ def early_stopping_monitor(i, est, locals):
 def test_predict_parameters_passing() -> None:
     """
     Test passing predict parameters.
-    Checks that y_pred from train are 0 and y_pred from test are 0
+    Checks that y_pred from train are 0, y_pred from test are 0 and
+    we check that y_pred constructed with or without predict_params
+    are different
     """
-
-    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
-
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state)
     )
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
-    mapie_1 = MapieRegressor(estimator=custom_gbr)
-    mapie_2 = MapieRegressor(estimator=custom_gbr)
+    score = AbsoluteConformityScore(sym=True)
+    mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
+    mapie_2 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
     predict_params = {'check_predict_params': True}
     mapie_1 = mapie_1.fit(
         X_train, y_train, predict_params=predict_params
@@ -910,38 +908,31 @@ def test_predict_parameters_passing() -> None:
     mapie_2 = mapie_2.fit(X_train, y_train)
     y_pred_1 = mapie_1.predict(X_test, **predict_params)
     y_pred_2 = mapie_2.predict(X_test)
-    np.testing.assert_allclose(y_pred_1, 0)
     np.testing.assert_allclose(mapie_1.conformity_scores_, np.abs(y_train))
+    np.testing.assert_allclose(y_pred_1, 0)
     with np.testing.assert_raises(AssertionError):
         np.testing.assert_array_equal(y_pred_1, y_pred_2)
 
 
-def test_fit_parameters_passing_with_predict_parameter() -> None:
+def test_fit_params_expected_behavior_unaffected_by_predict_params() -> None:
     """
-    Test passing fit parameters with predict parameters into the model.
+    We want to verify that there are no interferences
+    with predict_params on the expected behavior of fit_params
     Checks that underlying GradientBoosting
     estimators have used 3 iterations only during boosting,
     instead of default value for n_estimators (=100).
     """
-    def early_stopping_monitor(i, est, locals):
-        """Returns True on the 3rd iteration."""
-        if i == 2:
-            return True
-        else:
-            return False
-
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state))
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
-    score = AbsoluteConformityScore(sym=True)
-    mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
+    mapie_1 = MapieRegressor(estimator=custom_gbr)
     mapie_2 = MapieRegressor(estimator=custom_gbr)
     fit_params = {'monitor': early_stopping_monitor}
     predict_params = {'check_predict_params': True}
     mapie_1 = mapie_1.fit(X_train, y_train,
                           fit_params=fit_params,
                           predict_params=predict_params)
-    mapie_2 = mapie_2.fit(X_train, y_train)
+    mapie_2 = mapie_2.fit(X_train, y_train, predict_params=predict_params)
 
     assert mapie_1.estimator_.single_estimator_.estimators_.shape[0] == 3
     for estimator in mapie_1.estimator_.estimators_:
@@ -952,43 +943,40 @@ def early_stopping_monitor(i, est, locals):
         assert estimator.n_estimators == custom_gbr.n_estimators
 
 
-def test_predict_parameters_passing_with_fit_parameter() -> None:
+def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
     """
-    Test passing predict parameters with fit parameters into the model.
-    Checks that y_pred from train are 0
-    and y_pred from test are 0.
+    We want to verify that there are no interferences
+    with fit_params on the expected behavior of predict_params
+    Checks that the predictions on the training and test sets
+    are 0 for the model with predict_params and that this is not
+    the case for the model without predict_params
     """
-    def early_stopping_monitor(i, est, locals):
-        """Returns True on the 3rd iteration."""
-        if i == 2:
-            return True
-        else:
-            return False
-
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state))
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     score = AbsoluteConformityScore(sym=True)
     mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
-    mapie_2 = MapieRegressor(estimator=custom_gbr)
+    mapie_2 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
     fit_params = {'monitor': early_stopping_monitor}
     predict_params = {'check_predict_params': True}
     mapie_1 = mapie_1.fit(X_train, y_train,
                           fit_params=fit_params,
                           predict_params=predict_params)
-    mapie_2 = mapie_2.fit(X_train, y_train)
+    mapie_2 = mapie_2.fit(X_train, y_train, fit_params=fit_params,)
     y_pred_1 = mapie_1.predict(X_test, **predict_params)
     y_pred_2 = mapie_2.predict(X_test)
 
-    np.testing.assert_array_equal(mapie_1.conformity_scores_, np.abs(y_train))
+    np.testing.assert_array_equal(mapie_1.conformity_scores_,
+                                  np.abs(y_train))
     np.testing.assert_allclose(y_pred_1, 0)
     with np.testing.assert_raises(AssertionError):
+        np.testing.assert_array_equal(mapie_2.conformity_scores_,
+                                      np.abs(y_train))
         np.testing.assert_array_equal(y_pred_1, y_pred_2)
 
 
 def test_invalid_predict_parameters() -> None:
     """Test that invalid predict_parameters raise errors."""
-
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state))

From 3cdf6fe0a1679d1e182702ebe687a658e2003af5 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Fri, 5 Jul 2024 18:21:27 +0200
Subject: [PATCH 199/424] Fix : coverage

---
 mapie/tests/test_regression.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index f9248893c..930823ec8 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -972,6 +972,7 @@ def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
     with np.testing.assert_raises(AssertionError):
         np.testing.assert_array_equal(mapie_2.conformity_scores_,
                                       np.abs(y_train))
+    with np.testing.assert_raises(AssertionError):
         np.testing.assert_array_equal(y_pred_1, y_pred_2)
 
 

From 17cfbcadba3bd0e82afd06505fa34ff99feae65e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 5 Jul 2024 19:01:25 +0200
Subject: [PATCH 200/424] UPD: change default attribute as done in
 MapieRegressor

---
 mapie/classification.py            | 86 ++++++------------------------
 mapie/conformity_scores/utils.py   | 73 +++++++++++++++++++++++--
 mapie/tests/test_classification.py |  6 ---
 3 files changed, 87 insertions(+), 78 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 1f3abf10d..153e859e8 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -9,14 +9,14 @@
                                      StratifiedShuffleSplit)
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import _safe_indexing, check_random_state
-from sklearn.utils.multiclass import (check_classification_targets,
-                                      type_of_target)
 from sklearn.utils.validation import (_check_y, _num_samples, check_is_fitted,
                                       indexable)
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseClassificationScore
-from mapie.conformity_scores.utils import check_classification_conformity_score
+from mapie.conformity_scores.utils import (
+    check_classification_conformity_score, check_target
+)
 from mapie.conformity_scores.sets.utils import get_true_label_position
 from mapie.estimator.classifier import EnsembleClassifier
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
@@ -68,7 +68,12 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
           prediction sets may be different from the others. See [3] for
           more details.
 
-        By default ``"lac"``.
+        - ``None``, that does not specify the method used.
+
+        In any case, the `method` parameter does not take precedence over the
+        `conformity_score` parameter to define the method used.
+
+        By default ``None``.
 
     cv: Optional[str]
         The cross-validation strategy for computing scores.
@@ -119,6 +124,11 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     conformity_score_function_: BaseClassificationScore
         Score function that handle all that is related to conformity scores.
 
+        In any case, the `conformity_score` parameter takes precedence over the
+        `method` parameter to define the method used.
+
+        By default ``None``.
+
     random_state: Optional[Union[int, RandomState]]
         Pseudo random number generator state used for random uniform sampling
         for evaluation quantiles and prediction sets.
@@ -193,9 +203,6 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     """
 
     raps_valid_cv_ = ["prefit", "split"]
-    valid_methods_ = [
-        "naive", "score", "lac", "cumulated_score", "aps", "top_k", "raps"
-    ]
     fit_attributes = [
         "estimator_",
         "n_features_in_",
@@ -208,7 +215,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     def __init__(
         self,
         estimator: Optional[ClassifierMixin] = None,
-        method: str = "lac",
+        method: Optional[str] = None,
         cv: Optional[Union[int, str, BaseCrossValidator]] = None,
         test_size: Optional[Union[int, float]] = None,
         n_jobs: Optional[int] = None,
@@ -234,70 +241,11 @@ def _check_parameters(self) -> None:
         ValueError
             If parameters are not valid.
         """
-        if self.method not in self.valid_methods_:
-            raise ValueError(
-                "Invalid method. "
-                f"Allowed values are {self.valid_methods_}."
-            )
         check_n_jobs(self.n_jobs)
         check_verbose(self.verbose)
         check_random_state(self.random_state)
-        self._check_depreciated()
         self._check_raps()
 
-    def _check_depreciated(self) -> None:
-        """
-        Check if the chosen method is outdated.
-
-        Raises
-        ------
-        Warning
-            If method is ``"score"`` (not ``"lac"``) or
-            if method is ``"cumulated_score"`` (not ``"aps"``).
-        """
-        if self.method == "score":
-            warnings.warn(
-                "WARNING: Deprecated method. "
-                + "The method \"score\" is outdated. "
-                + "Prefer to use \"lac\" instead to keep "
-                + "the same behavior in the next release.",
-                DeprecationWarning
-            )
-        if self.method == "cumulated_score":
-            warnings.warn(
-                "WARNING: Deprecated method. "
-                + "The method \"cumulated_score\" is outdated. "
-                + "Prefer to use \"aps\" instead to keep "
-                + "the same behavior in the next release.",
-                DeprecationWarning
-            )
-
-    def _check_target(self, y: ArrayLike) -> None:
-        """
-        Check that if the type of target is binary,
-        (then the method have to be ``"lac"``), or multi-class.
-
-        Parameters
-        ----------
-        y: NDArray of shape (n_samples,)
-            Training labels.
-
-        Raises
-        ------
-        ValueError
-            If type of target is binary and method is not ``"lac"``
-            or ``"score"`` or if type of target is not multi-class.
-        """
-        check_classification_targets(y)
-        if type_of_target(y) == "binary" and \
-                self.method not in ["score", "lac"]:
-            raise ValueError(
-                "Invalid method for binary target. "
-                "Your target is not of type multiclass and "
-                "allowed values for binary type are "
-                f"{['score', 'lac']}."
-            )
-
     def _check_raps(self):
         """
         Check that if the method used is ``"raps"``, then
@@ -448,8 +396,6 @@ def _check_fit_parameter(
         self.label_encoder_ = self._get_label_encoder()
         y_enc = self.label_encoder_.transform(y)
 
-        self._check_target(y)
-
         cs_estimator = check_classification_conformity_score(
             conformity_score=self.conformity_score,
             method=self.method
@@ -459,6 +405,8 @@ def _check_fit_parameter(
             random_state=self.random_state
         )
 
+        check_target(cs_estimator, y)
+
         return (
             estimator, cs_estimator, cv,
             X, y, y_enc, sample_weight, groups, n_samples
diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index d2b0c6cc9..801f2a5fe 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -1,10 +1,16 @@
 from typing import Optional
+import warnings
+
+from sklearn.utils.multiclass import (check_classification_targets,
+                                      type_of_target)
 
 from .regression import BaseRegressionScore
 from .classification import BaseClassificationScore
 from .bounds import AbsoluteConformityScore
 from .sets import APS, LAC, Naive, RAPS, TopK
 
+from mapie._typing import ArrayLike
+
 
 def check_regression_conformity_score(
     conformity_score: Optional[BaseRegressionScore],
@@ -42,6 +48,68 @@ def check_regression_conformity_score(
         )
 
 
+def _check_depreciated(
+    method: str
+) -> None:
+    """
+    Check if the chosen method is outdated.
+
+    Raises
+    ------
+    Warning
+        If method is ``"score"`` (not ``"lac"``) or
+        if method is ``"cumulated_score"`` (not ``"aps"``).
+    """
+    if method == "score":
+        warnings.warn(
+            "WARNING: Deprecated method. "
+            + "The method \"score\" is outdated. "
+            + "Prefer to use \"lac\" instead to keep "
+            + "the same behavior in the next release.",
+            DeprecationWarning
+        )
+    if method == "cumulated_score":
+        warnings.warn(
+            "WARNING: Deprecated method. "
+            + "The method \"cumulated_score\" is outdated. "
+            + "Prefer to use \"aps\" instead to keep "
+            + "the same behavior in the next release.",
+            DeprecationWarning
+        )
+
+
+def check_target(
+    conformity_score: BaseClassificationScore,
+    y: ArrayLike
+) -> None:
+    """
+    Check that if the type of target is binary,
+    (then the method have to be ``"lac"``), or multi-class.
+
+    Parameters
+    ----------
+    conformity_score: BaseClassificationScore
+        Conformity score function.
+
+    y: NDArray of shape (n_samples,)
+        Training labels.
+
+    Raises
+    ------
+    ValueError
+        If type of target is binary and method is not ``"lac"``
+        or ``"score"`` or if type of target is not multi-class.
+    """
+    check_classification_targets(y)
+    if type_of_target(y) == "binary" and not isinstance(conformity_score, LAC):
+        raise ValueError(
+            "Invalid method for binary target. "
+            "Your target is not of type multiclass and "
+            "allowed values for binary type are "
+            f"{['score', 'lac']}."
+        )
+
+
 def check_classification_conformity_score(
     conformity_score: Optional[BaseClassificationScore] = None,
     method: Optional[str] = None,
@@ -68,8 +136,8 @@ def check_classification_conformity_score(
     Must be None or a ConformityScore instance.
     """
     allowed_methods = ['lac', 'naive', 'aps', 'raps', 'top_k']
-    deprecated_methods = ['score', 'cumulated_score']
     if method is not None:
+        _check_depreciated(method)
         if method in ['score', 'lac']:
             return LAC()
         if method in ['cumulated_score', 'aps']:
@@ -82,8 +150,7 @@ def check_classification_conformity_score(
             return TopK()
         else:
             raise ValueError(
-                f"Invalid method. Allowed values are {allowed_methods}. "
-                f"Deprecated values are {deprecated_methods}. "
+                f"Invalid method. Allowed values are {allowed_methods}."
             )
     elif isinstance(conformity_score, BaseClassificationScore):
         return conformity_score
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 497b8cea5..ec9366a3e 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -912,12 +912,6 @@ def test_initialized() -> None:
     MapieClassifier()
 
 
-def test_default_parameters() -> None:
-    """Test default values of input parameters."""
-    mapie_clf = MapieClassifier()
-    assert mapie_clf.method == "lac"
-
-
 @pytest.mark.parametrize("cv", ["prefit", "split"])
 @pytest.mark.parametrize("method", ["aps", "raps"])
 def test_warning_binary_classif(cv: str, method: str) -> None:

From ab5c6e8110386010f9dc2713fee8d34231452ec4 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 10 Jul 2024 16:29:18 +0200
Subject: [PATCH 201/424] Update : add function in utils

---
 mapie/regression/regression.py | 15 ++++-----------
 mapie/utils.py                 | 16 ++++++++++++++++
 2 files changed, 20 insertions(+), 11 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 22f106fe5..3baf04b8f 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -18,7 +18,8 @@
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
                          check_estimator_fit_predict, check_n_features_in,
                          check_n_jobs, check_null_weight, check_verbose,
-                         get_effective_calibration_samples)
+                         get_effective_calibration_samples,
+                         check_predict_params)
 
 
 class MapieRegressor(BaseEstimator, RegressorMixin):
@@ -623,17 +624,9 @@ def predict(
                 - [:, 1, :]: Upper bound of the prediction interval.
         """
 
-        if (len(predict_params) > 0 and
-                self._predict_params is False and
-                self.cv != "prefit"):
-            raise ValueError(
-                f"Using 'predict_param' '{predict_params}' "
-                f"without using one 'predict_param' in the fit method. "
-                f"Please ensure one 'predict_param' "
-                f"is used in the fit method before calling predict."
-            )
-
         # Checks
+        if hasattr(self, '_predict_params'):
+            check_predict_params(self._predict_params, predict_params, self.cv)
         check_is_fitted(self, self.fit_attributes)
         self._check_ensemble(ensemble)
         alpha = cast(Optional[NDArray], check_alpha(alpha))
diff --git a/mapie/utils.py b/mapie/utils.py
index 13641b154..86cd51a82 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1373,3 +1373,19 @@ def check_n_samples(
              " int in the range [1, inf)"
              )
     return int(n_samples)
+
+
+def check_predict_params(
+    predict_params_used_in_fit: bool,
+    predict_params: dict,
+    cv: Optional[Union[int, str, BaseCrossValidator]] = None
+) -> None:
+    if (len(predict_params) > 0 and
+            predict_params_used_in_fit is False and
+            cv != "prefit"):
+        raise ValueError(
+            f"Using 'predict_param' '{predict_params}' "
+            f"without using one 'predict_param' in the fit method. "
+            f"Please ensure one 'predict_param' "
+            f"is used in the fit method before calling predict."
+        )

From 031a8d492d06f52e4d8b1b929814188b73c035f6 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 11 Jul 2024 16:28:19 +0200
Subject: [PATCH 202/424] UPD: manage class method with conflict warning

---
 mapie/classification.py                    |  8 ++--
 mapie/conformity_scores/utils.py           | 44 +++++++++++++---------
 mapie/tests/test_conformity_scores_sets.py | 17 +++++++++
 3 files changed, 48 insertions(+), 21 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 153e859e8..9d2d63b53 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -147,9 +147,6 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
     Attributes
     ----------
-    valid_methods: List[str]
-        List of all valid methods.
-
     estimator_: EnsembleClassifier
         Sklearn estimator that handle all that is related to the estimator.
 
@@ -165,6 +162,9 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
     quantiles_: ArrayLike of shape (n_alpha)
         The quantiles estimated from ``conformity_scores_`` and alpha values.
 
+    label_encoder_: LabelEncoder
+        Label encoder used to encode the labels.
+
     References
     ----------
     [1] Mauricio Sadinle, Jing Lei, and Larry Wasserman.
@@ -634,7 +634,7 @@ def predict(
 
             When set to ``True`` or ``False``, it may result in a coverage
             higher than ``1 - alpha`` (because contrary to the "randomized"
-            setting, none of this methods create empty prediction sets). See
+            setting, none of these methods create empty prediction sets). See
             [2] and [3] for more details.
 
             By default ``True``.
diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 801f2a5fe..30069aca3 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -110,6 +110,17 @@ def check_target(
         )
 
 
+method_score_map = {
+    'score': lambda: LAC(),
+    'lac': lambda: LAC(),
+    'cumulated_score': lambda: APS(),
+    'aps': lambda: APS(),
+    'naive': lambda: Naive(),
+    'raps': lambda: RAPS(),
+    'top_k': lambda: TopK()
+}
+
+
 def check_classification_conformity_score(
     conformity_score: Optional[BaseClassificationScore] = None,
     method: Optional[str] = None,
@@ -135,27 +146,26 @@ def check_classification_conformity_score(
     Invalid conformity_score argument.
     Must be None or a ConformityScore instance.
     """
-    allowed_methods = ['lac', 'naive', 'aps', 'raps', 'top_k']
+    if method is None and conformity_score is None:
+        return LAC()
+    elif conformity_score is not None:
+        if method is not None:
+            warnings.warn(
+                "WARNING: the `conformity_score` parameter takes precedence "
+                "over the `method` parameter to define the method used.",
+                UserWarning
+            )
+        if isinstance(conformity_score, BaseClassificationScore):
+            return conformity_score
     if method is not None:
-        _check_depreciated(method)
-        if method in ['score', 'lac']:
-            return LAC()
-        if method in ['cumulated_score', 'aps']:
-            return APS()
-        if method in ['naive']:
-            return Naive()
-        if method in ['raps']:
-            return RAPS()
-        if method in ['top_k']:
-            return TopK()
+        if isinstance(method, str) and method in method_score_map:
+            _check_depreciated(method)
+            return method_score_map[method]()
         else:
             raise ValueError(
-                f"Invalid method. Allowed values are {allowed_methods}."
+                "Invalid method. "
+                f"Allowed values are {list(method_score_map.keys())}."
             )
-    elif isinstance(conformity_score, BaseClassificationScore):
-        return conformity_score
-    elif conformity_score is None:
-        return LAC()
     else:
         raise ValueError(
             "Invalid conformity_score argument.\n"
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index b6d63fde6..ac66a16e7 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -38,6 +38,23 @@ def test_check_classification_method(
     )
 
 
+@pytest.mark.parametrize("method", method_list)
+@pytest.mark.parametrize("conformity_score", cs_list)
+def test_check_conflict_parameters(
+    method: Optional[str],
+    conformity_score: Optional[BaseClassificationScore]
+) -> None:
+    if method is None or conformity_score is None:
+        return
+    with pytest.warns(
+        UserWarning,
+        match="WARNING: the `conformity_score` parameter takes precedence*"
+    ):
+        check_classification_conformity_score(
+            method=method, conformity_score=conformity_score
+        )
+
+
 @pytest.mark.parametrize("method", wrong_method_list)
 def test_check_wrong_classification_method(
     method: Optional[str]

From b1b425ea2cac4ccb3d888e5d12dbdd4298d1f567 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 11 Jul 2024 17:13:59 +0200
Subject: [PATCH 203/424] UPD: reorganize conformity score tests

---
 mapie/tests/test_classification.py            | 114 +---------------
 ...es.py => test_conformity_scores_bounds.py} |   0
 mapie/tests/test_conformity_scores_sets.py    | 129 +++++++++++++++++-
 ...res.py => test_conformity_scores_utils.py} |   0
 4 files changed, 129 insertions(+), 114 deletions(-)
 rename mapie/tests/{test_conformity_scores.py => test_conformity_scores_bounds.py} (100%)
 rename mapie/tests/{test_utils_classification_conformity_scores.py => test_conformity_scores_utils.py} (100%)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index ec9366a3e..7e4a1e1e4 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from copy import deepcopy
-from typing import Any, Dict, Iterable, Optional, Union, cast
+from typing import Any, Dict, Iterable, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -23,10 +23,8 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
-from mapie.conformity_scores import APS, RAPS
 from mapie.conformity_scores.sets.utils import check_proba_normalized
 from mapie.metrics import classification_coverage_score
-from mapie.utils import check_alpha
 
 random_state = 42
 
@@ -734,12 +732,6 @@
     ]
 }
 
-REGULARIZATION_PARAMETERS = [
-    [.001, [1]],
-    [[.01, .2], [1, 3]],
-    [.1, [2, 4]]
-]
-
 IMAGE_INPUT = [
     {
         "X_calib": np.zeros((3, 1024, 1024, 1)),
@@ -1500,8 +1492,7 @@ def test_cumulated_scores() -> None:
         include_last_label=True,
         alpha=alpha
     )
-    computed_quantile = mapie_clf.conformity_score_function_.quantiles_
-    np.testing.assert_allclose(computed_quantile, quantile)
+    np.testing.assert_allclose(mapie_clf.quantiles_, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
 
@@ -1529,8 +1520,7 @@ def test_image_cumulated_scores(X: Dict[str, ArrayLike]) -> None:
         include_last_label=True,
         alpha=alpha
     )
-    computed_quantile = mapie.conformity_score_function_.quantiles_
-    np.testing.assert_allclose(computed_quantile, quantile)
+    np.testing.assert_allclose(mapie.quantiles_, quantile)
     np.testing.assert_allclose(y_ps[:, :, 0], cumclf.y_pred_sets)
 
 
@@ -1723,104 +1713,6 @@ def test_classif_float32(cv) -> None:
     ).all()
 
 
-@pytest.mark.parametrize("k_lambda", REGULARIZATION_PARAMETERS)
-def test_regularize_conf_scores_shape(k_lambda) -> None:
-    """
-    Test that the conformity scores have the correct shape.
-    """
-    lambda_, k = k_lambda[0], k_lambda[1]
-    conf_scores = np.random.rand(100, 1)
-    cutoff = np.cumsum(np.ones(conf_scores.shape)) - 1
-    reg_conf_scores = RAPS._regularize_conformity_score(
-        k, lambda_, conf_scores, cutoff
-    )
-
-    assert reg_conf_scores.shape == (100, 1, len(k))
-
-
-def test_get_true_label_cumsum_proba_shape() -> None:
-    """
-    Test that the true label cumsumed probabilities
-    have the correct shape.
-    """
-    clf = LogisticRegression()
-    clf.fit(X, y)
-    y_pred = clf.predict_proba(X)
-    mapie_clf = MapieClassifier(
-        estimator=clf, random_state=random_state
-    )
-    mapie_clf.fit(X, y)
-    classes = mapie_clf.classes_
-    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(y, y_pred, classes)
-    assert cumsum_proba.shape == (len(X), 1)
-    assert cutoff.shape == (len(X), )
-
-
-def test_get_true_label_cumsum_proba_result() -> None:
-    """
-    Test that the true label cumsumed probabilities
-    are the expected ones.
-    """
-    clf = LogisticRegression()
-    clf.fit(X_toy, y_toy)
-    y_pred = clf.predict_proba(X_toy)
-    mapie_clf = MapieClassifier(
-        estimator=clf, random_state=random_state
-    )
-    mapie_clf.fit(X_toy, y_toy)
-    classes = mapie_clf.classes_
-    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(
-        y_toy, y_pred, classes
-    )
-    np.testing.assert_allclose(
-        cumsum_proba,
-        np.array(
-            [
-                y_pred[0, 0], y_pred[1, 0],
-                y_pred[2, 0] + y_pred[2, 1],
-                y_pred[3, 0] + y_pred[3, 1],
-                y_pred[4, 1], y_pred[5, 1],
-                y_pred[6, 1] + y_pred[6, 2],
-                y_pred[7, 1] + y_pred[7, 2],
-                y_pred[8, 2]
-            ]
-        )[:, np.newaxis]
-    )
-    np.testing.assert_allclose(cutoff, np.array([1, 1, 2, 2, 1, 1, 2, 2, 1]))
-
-
-@pytest.mark.parametrize("k_lambda", REGULARIZATION_PARAMETERS)
-@pytest.mark.parametrize("strategy", [*STRATEGIES])
-def test_get_last_included_proba_shape(k_lambda, strategy):
-    """
-    Test that the outputs of _get_last_included_proba method
-    have the correct shape.
-    """
-    lambda_, k = k_lambda[0], k_lambda[1]
-    if len(k) == 1:
-        thresholds = .2
-    else:
-        thresholds = np.random.rand(len(k))
-    thresholds = cast(NDArray, check_alpha(thresholds))
-    clf = LogisticRegression()
-    clf.fit(X, y)
-    y_pred_proba = clf.predict_proba(X)
-    y_pred_proba = np.repeat(
-        y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2
-    )
-
-    include_last_label = STRATEGIES[strategy][1]["include_last_label"]
-    y_p_p_c, y_p_i_l, y_p_p_i_l = \
-        RAPS._get_last_included_proba(
-            RAPS(), y_pred_proba, thresholds, include_last_label,
-            lambda_=lambda_, k_star=k
-        )
-
-    assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))
-    assert y_p_i_l.shape == (len(X), 1, len(thresholds))
-    assert y_p_p_i_l.shape == (len(X), 1, len(thresholds))
-
-
 @pytest.mark.parametrize("cv", [5, None])
 def test_error_raps_cv_not_prefit(cv: Union[int, None]) -> None:
     """
diff --git a/mapie/tests/test_conformity_scores.py b/mapie/tests/test_conformity_scores_bounds.py
similarity index 100%
rename from mapie/tests/test_conformity_scores.py
rename to mapie/tests/test_conformity_scores_bounds.py
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index ac66a16e7..2425c5409 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -1,17 +1,43 @@
-from typing import Optional
+from typing import Optional, cast
 
 import pytest
+import numpy as np
+from sklearn.datasets import make_classification
+from sklearn.linear_model import LogisticRegression
 
-# from mapie._typing import ArrayLike, NDArray
+from mapie._typing import NDArray
+from mapie.classification import MapieClassifier
 from mapie.conformity_scores import BaseClassificationScore
-from mapie.conformity_scores.sets import APS, LAC, TopK
+from mapie.conformity_scores.sets import APS, LAC, RAPS, TopK
 from mapie.conformity_scores.utils import check_classification_conformity_score
+from mapie.utils import check_alpha
 
 
+random_state = 42
+
 cs_list = [None, LAC(), APS(), TopK()]
 method_list = [None, 'naive', 'aps', 'raps', 'lac', 'top_k']
 wrong_method_list = ['naive_', 'aps_', 'raps_', 'lac_', 'top_k_']
 
+REGULARIZATION_PARAMETERS = [
+    [.001, [1]],
+    [[.01, .2], [1, 3]],
+    [.1, [2, 4]]
+]
+
+X_toy = np.arange(9).reshape(-1, 1)
+y_toy = np.array([0, 0, 1, 0, 1, 1, 2, 1, 2])
+y_toy_string = np.array(["0", "0", "1", "0", "1", "1", "2", "1", "2"])
+
+n_classes = 4
+X, y = make_classification(
+    n_samples=500,
+    n_features=10,
+    n_informative=3,
+    n_classes=n_classes,
+    random_state=random_state,
+)
+
 
 def test_error_mother_class_initialization() -> None:
     with pytest.raises(TypeError):
@@ -61,3 +87,100 @@ def test_check_wrong_classification_method(
 ) -> None:
     with pytest.raises(ValueError, match="Invalid method.*"):
         check_classification_conformity_score(method=method)
+
+
+@pytest.mark.parametrize("k_lambda", REGULARIZATION_PARAMETERS)
+def test_regularize_conf_scores_shape(k_lambda) -> None:
+    """
+    Test that the conformity scores have the correct shape.
+    """
+    lambda_, k = k_lambda[0], k_lambda[1]
+    conf_scores = np.random.rand(100, 1)
+    cutoff = np.cumsum(np.ones(conf_scores.shape)) - 1
+    reg_conf_scores = RAPS._regularize_conformity_score(
+        k, lambda_, conf_scores, cutoff
+    )
+
+    assert reg_conf_scores.shape == (100, 1, len(k))
+
+
+def test_get_true_label_cumsum_proba_shape() -> None:
+    """
+    Test that the true label cumsumed probabilities
+    have the correct shape.
+    """
+    clf = LogisticRegression()
+    clf.fit(X, y)
+    y_pred = clf.predict_proba(X)
+    mapie_clf = MapieClassifier(
+        estimator=clf, random_state=random_state
+    )
+    mapie_clf.fit(X, y)
+    classes = mapie_clf.classes_
+    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(y, y_pred, classes)
+    assert cumsum_proba.shape == (len(X), 1)
+    assert cutoff.shape == (len(X), )
+
+
+def test_get_true_label_cumsum_proba_result() -> None:
+    """
+    Test that the true label cumsumed probabilities
+    are the expected ones.
+    """
+    clf = LogisticRegression()
+    clf.fit(X_toy, y_toy)
+    y_pred = clf.predict_proba(X_toy)
+    mapie_clf = MapieClassifier(
+        estimator=clf, random_state=random_state
+    )
+    mapie_clf.fit(X_toy, y_toy)
+    classes = mapie_clf.classes_
+    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(
+        y_toy, y_pred, classes
+    )
+    np.testing.assert_allclose(
+        cumsum_proba,
+        np.array(
+            [
+                y_pred[0, 0], y_pred[1, 0],
+                y_pred[2, 0] + y_pred[2, 1],
+                y_pred[3, 0] + y_pred[3, 1],
+                y_pred[4, 1], y_pred[5, 1],
+                y_pred[6, 1] + y_pred[6, 2],
+                y_pred[7, 1] + y_pred[7, 2],
+                y_pred[8, 2]
+            ]
+        )[:, np.newaxis]
+    )
+    np.testing.assert_allclose(cutoff, np.array([1, 1, 2, 2, 1, 1, 2, 2, 1]))
+
+
+@pytest.mark.parametrize("k_lambda", REGULARIZATION_PARAMETERS)
+@pytest.mark.parametrize("include_last_label", [True, False])
+def test_get_last_included_proba_shape(k_lambda, include_last_label):
+    """
+    Test that the outputs of _get_last_included_proba method
+    have the correct shape.
+    """
+    lambda_, k = k_lambda[0], k_lambda[1]
+    if len(k) == 1:
+        thresholds = .2
+    else:
+        thresholds = np.random.rand(len(k))
+    thresholds = cast(NDArray, check_alpha(thresholds))
+    clf = LogisticRegression()
+    clf.fit(X, y)
+    y_pred_proba = clf.predict_proba(X)
+    y_pred_proba = np.repeat(
+        y_pred_proba[:, :, np.newaxis], len(thresholds), axis=2
+    )
+
+    y_p_p_c, y_p_i_l, y_p_p_i_l = \
+        RAPS._get_last_included_proba(
+            RAPS(), y_pred_proba, thresholds, include_last_label,
+            lambda_=lambda_, k_star=k
+        )
+
+    assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))
+    assert y_p_i_l.shape == (len(X), 1, len(thresholds))
+    assert y_p_p_i_l.shape == (len(X), 1, len(thresholds))
diff --git a/mapie/tests/test_utils_classification_conformity_scores.py b/mapie/tests/test_conformity_scores_utils.py
similarity index 100%
rename from mapie/tests/test_utils_classification_conformity_scores.py
rename to mapie/tests/test_conformity_scores_utils.py

From 5fa0fee06e81bdcdccc9344f3a1ff82165eeec18 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Thu, 11 Jul 2024 17:26:51 +0200
Subject: [PATCH 204/424] UPD: corrected doctring + comments + minor
 corrections

---
 mapie/conformity_scores/sets/aps.py   | 19 +------------------
 mapie/conformity_scores/sets/naive.py |  2 +-
 mapie/conformity_scores/sets/raps.py  |  3 +++
 3 files changed, 5 insertions(+), 19 deletions(-)

diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index e8cf5c1c3..9d3a6d9a2 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -255,7 +255,6 @@ def _add_random_tie_breaking(
         y_pred_proba_cumsum: NDArray,
         y_pred_proba_last: NDArray,
         threshold: NDArray,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
     ) -> NDArray:
         """
@@ -288,21 +287,6 @@ def _add_random_tie_breaking(
             - the conformity score from training samples otherwise (i.e., when
             ``cv`` is CV splitter and ``agg_scores`` is "crossval")
 
-        method: str
-            Method that determines how to remove last label in the prediction
-            set.
-
-            - if "cumulated_score" or "aps", compute V parameter
-                from Romano+(2020)
-
-            - else compute V parameter from Angelopoulos+(2020)
-
-        lambda_star: Optional[Union[NDArray, float]] of shape (n_alpha):
-            Optimal value of the regulizer lambda.
-
-        k_star: Optional[NDArray] of shape (n_alpha):
-            Optimal value of the regulizer k.
-
         Returns
         -------
         NDArray of shape (n_samples, n_classes, n_alpha)
@@ -326,7 +310,7 @@ def _add_random_tie_breaking(
         )
 
         # get random numbers for each observation and alpha value
-        random_state = check_random_state(random_state)
+        random_state = check_random_state(self.random_state)
         random_state = cast(np.random.RandomState, random_state)
         us = random_state.uniform(size=(prediction_sets.shape[0], 1))
         # remove last label from comparison between uniform number and V
@@ -421,7 +405,6 @@ def get_prediction_sets(
                 y_pred_proba_cumsum,
                 y_pred_proba_last,
                 thresholds,
-                self.random_state,
                 **kwargs
             )
         if estimator.cv == "prefit" or agg_scores in ["mean"]:
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 259753021..9d25f3e9f 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -213,7 +213,7 @@ def _get_last_included_proba(
         y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
         y_pred_proba_sorted_cumsum = self._add_regualization(
             y_pred_proba_sorted_cumsum, **kwargs
-        )
+        )  # Do nothing as no regularization for the naive method
 
         # get cumulated score at their original position
         y_pred_proba_cumsum = np.take_along_axis(
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 320b3bbf0..f2844fd5f 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -152,6 +152,9 @@ def _find_lambda_star(
 
         Parameters
         ----------
+        y_raps_no_enc: NDArray of shape (n_samples, )
+            True labels (after applying `label_encoder_.inverse_transform`).
+
         y_pred_proba_raps: NDArray of shape (n_samples, n_labels, n_alphas)
             Predictions of the model repeated on the last axis as many times
             as the number of alphas

From d2cf4434487759d06c5281ec0dec4c85ded1ce4a Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 11:47:36 +0200
Subject: [PATCH 205/424] UPD: move split data into conformity score + side
 effect changes

---
 mapie/classification.py               | 161 ++++---------------
 mapie/conformity_scores/interface.py  |  41 ++++-
 mapie/conformity_scores/sets/lac.py   |   1 +
 mapie/conformity_scores/sets/naive.py |   1 +
 mapie/conformity_scores/sets/raps.py  | 216 +++++++++++++++++++++++---
 mapie/conformity_scores/sets/topk.py  |   1 +
 mapie/tests/test_classification.py    |   2 +-
 7 files changed, 266 insertions(+), 157 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 9d2d63b53..55e32f813 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -5,19 +5,16 @@
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit,
-                                     StratifiedShuffleSplit)
+from sklearn.model_selection import BaseCrossValidator
 from sklearn.preprocessing import LabelEncoder
-from sklearn.utils import _safe_indexing, check_random_state
-from sklearn.utils.validation import (_check_y, _num_samples, check_is_fitted,
-                                      indexable)
+from sklearn.utils import check_random_state
+from sklearn.utils.validation import (_check_y, check_is_fitted, indexable)
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseClassificationScore
 from mapie.conformity_scores.utils import (
     check_classification_conformity_score, check_target
 )
-from mapie.conformity_scores.sets.utils import get_true_label_position
 from mapie.estimator.classifier import EnsembleClassifier
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
                          check_estimator_classification, check_n_features_in,
@@ -75,7 +72,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
         By default ``None``.
 
-    cv: Optional[str]
+    cv: Optional[Union[int, str, BaseCrossValidator]]
         The cross-validation strategy for computing scores.
         It directly drives the distinction between jackknife and cv variants.
         Choose among:
@@ -202,7 +199,6 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
      [False False  True]]
     """
 
-    raps_valid_cv_ = ["prefit", "split"]
     fit_attributes = [
         "estimator_",
         "n_features_in_",
@@ -244,26 +240,6 @@ def _check_parameters(self) -> None:
         check_n_jobs(self.n_jobs)
         check_verbose(self.verbose)
         check_random_state(self.random_state)
-        self._check_raps()
-
-    def _check_raps(self):
-        """
-        Check that if the method used is ``"raps"``, then
-        the cross validation strategy is ``"prefit"``.
-
-        Raises
-        ------
-        ValueError
-            If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
-        """
-        if (self.method == "raps") and not (
-            (self.cv in self.raps_valid_cv_)
-            or isinstance(self.cv, BaseShuffleSplit)
-        ):
-            raise ValueError(
-                "RAPS method can only be used "
-                f"with cv in {self.raps_valid_cv_}."
-            )
 
     def _get_classes_info(
             self, estimator: ClassifierMixin, y: NDArray
@@ -336,6 +312,7 @@ def _check_fit_parameter(
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
         groups: Optional[ArrayLike] = None,
+        size_raps: Optional[float] = None,
     ):
         """
         Perform several checks on class parameters.
@@ -390,103 +367,44 @@ def _check_fit_parameter(
         estimator = check_estimator_classification(X, y, cv, self.estimator)
         self.n_features_in_ = check_n_features_in(X, cv, estimator)
 
-        n_samples = _num_samples(y)
-
         self.n_classes_, self.classes_ = self._get_classes_info(estimator, y)
         self.label_encoder_ = self._get_label_encoder()
         y_enc = self.label_encoder_.transform(y)
 
         cs_estimator = check_classification_conformity_score(
             conformity_score=self.conformity_score,
-            method=self.method
+            method=self.method,
         )
+        # TODO test size_raps depreciated
         cs_estimator.set_external_attributes(
+            cv=self.cv,
             classes=self.classes_,
+            label_encoder=self.label_encoder_,
+            size_raps=size_raps,
             random_state=self.random_state
         )
 
-        check_target(cs_estimator, y)
-
-        return (
-            estimator, cs_estimator, cv,
-            X, y, y_enc, sample_weight, groups, n_samples
-        )
-
-    def _split_data(
-        self,
-        X: ArrayLike,
-        y_enc: ArrayLike,
-        sample_weight: Optional[ArrayLike] = None,
-        groups: Optional[ArrayLike] = None,
-        size_raps: Optional[float] = None,
-    ):
-        """Split data for raps method
-
-        Parameters
-        ----------
-        X: ArrayLike
-            Observed values.
-
-        y_enc: ArrayLike
-            Target values as normalized encodings.
-
-        sample_weight: Optional[ArrayLike] of shape (n_samples,)
-            Non-null sample weights.
+        # Cast
+        X, y_enc, y = cast(NDArray, X), cast(NDArray, y_enc), cast(NDArray, y)
+        sample_weight = cast(NDArray, sample_weight)
+        groups = cast(NDArray, groups)
 
-        groups: Optional[ArrayLike] of shape (n_samples,)
-            Group labels for the samples used while splitting the dataset into
-            train/test set.
-            By default ``None``.
+        X, y, y_enc, sample_weight, groups = \
+            cs_estimator.split_data(X, y, y_enc, sample_weight, groups)
+        self.n_samples_ = cs_estimator.n_samples_
 
-        size_raps: : Optional[float]
-            Percentage of the data to be used for choosing lambda_star and
-            k_star for the RAPS method.
+        check_target(cs_estimator, y)
 
-        Returns
-        -------
-        Tuple[NDArray, NDArray, NDArray, NDArray, Optional[NDArray],
-        Optional[NDArray]]
-            - NDArray of shape (n_samples, n_features)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
-        """
-        # Split data for raps method
-        raps_split = StratifiedShuffleSplit(
-            n_splits=1, test_size=size_raps, random_state=self.random_state
-        )
-        train_raps_index, val_raps_index = next(raps_split.split(X, y_enc))
-        X, self.X_raps, y_enc, self.y_raps = (
-            _safe_indexing(X, train_raps_index),
-            _safe_indexing(X, val_raps_index),
-            _safe_indexing(y_enc, train_raps_index),
-            _safe_indexing(y_enc, val_raps_index),
+        return (
+            estimator, cs_estimator, cv, X, y, y_enc, sample_weight, groups
         )
 
-        # Decode y_raps for use in the RAPS method
-        self.y_raps_no_enc = self.label_encoder_.inverse_transform(self.y_raps)
-        y = self.label_encoder_.inverse_transform(y_enc)
-
-        # Cast to NDArray for type checking
-        y_enc = cast(NDArray, y_enc)
-        n_samples = _num_samples(y_enc)
-        if sample_weight is not None:
-            sample_weight = cast(NDArray, sample_weight)
-            sample_weight = sample_weight[train_raps_index]
-        if groups is not None:
-            groups = cast(NDArray, groups)
-            groups = groups[train_raps_index]
-
-        return X, y_enc, y, n_samples, sample_weight, groups
-
     def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
-        size_raps: Optional[float] = 0.2,
+        size_raps: Optional[float] = None,
         groups: Optional[ArrayLike] = None,
         **fit_params,
     ) -> MapieClassifier:
@@ -514,7 +432,7 @@ def fit(
             Percentage of the data to be used for choosing lambda_star and
             k_star for the RAPS method.
 
-            By default ``0.2``.
+            By default ``None``.
 
         groups: Optional[ArrayLike] of shape (n_samples,)
             Group labels for the samples used while splitting the dataset into
@@ -538,14 +456,9 @@ def fit(
          y,
          y_enc,
          sample_weight,
-         groups,
-         n_samples) = self._check_fit_parameter(X, y, sample_weight, groups)
-        self.n_samples_ = n_samples
-
-        if self.method == "raps":
-            (X, y_enc, y, n_samples, sample_weight, groups) = self._split_data(
-                X, y_enc, sample_weight, groups, size_raps
-            )
+         groups) = self._check_fit_parameter(
+            X, y, sample_weight, groups, size_raps
+        )
 
         # Cast
         X, y_enc, y = cast(NDArray, X), cast(NDArray, y_enc), cast(NDArray, y)
@@ -573,19 +486,12 @@ def fit(
             X, y, y_enc, groups
         )
 
-        # RAPS: compute y_pred and position on the RAPS validation dataset
-        if self.method == "raps":
-            self.y_pred_proba_raps = (
-                self.estimator_.single_estimator_.predict_proba(self.X_raps)
-            )
-            self.position_raps = get_true_label_position(
-                self.y_pred_proba_raps, self.y_raps
-            )
-
         # Compute the conformity scores
+        self.conformity_score_function_.set_ref_predictor(self.estimator_)
         self.conformity_scores_ = \
             self.conformity_score_function_.get_conformity_scores(
-                y, y_pred_proba, y_enc=y_enc, X=X
+                y, y_pred_proba, y_enc=y_enc, X=X,
+                sample_weight=sample_weight, groups=groups
             )
 
         return self
@@ -678,23 +584,12 @@ def predict(
         check_alpha_and_n_samples(alpha_np, n)
 
         # Estimate prediction sets
-        if self.method == "raps":
-            kwargs = {
-                'X_raps': self.X_raps,
-                'y_raps_no_enc': self.y_raps_no_enc,
-                'y_pred_proba_raps': self.y_pred_proba_raps,
-                'position_raps': self.position_raps,
-            }
-        else:
-            kwargs = {}
-
         prediction_sets = self.conformity_score_function_.predict_set(
             X, alpha_np,
             estimator=self.estimator_,
             conformity_scores=self.conformity_scores_,
             include_last_label=include_last_label,
             agg_scores=agg_scores,
-            **kwargs
         )
 
         self.quantiles_ = self.conformity_score_function_.quantiles_
diff --git a/mapie/conformity_scores/interface.py b/mapie/conformity_scores/interface.py
index 3979149c0..e7eaa151c 100644
--- a/mapie/conformity_scores/interface.py
+++ b/mapie/conformity_scores/interface.py
@@ -1,6 +1,8 @@
 from abc import ABCMeta, abstractmethod
+from typing import Optional
 
 import numpy as np
+from sklearn.base import BaseEstimator
 
 from mapie._compatibility import np_nanquantile
 from mapie._typing import NDArray
@@ -27,7 +29,44 @@ def set_external_attributes(
         particularly when the attributes are known after the object has been
         instantiated.
         """
-        pass
+
+    def set_ref_predictor(
+        self,
+        predictor: BaseEstimator
+    ):
+        """
+        Set the reference predictor.
+
+        Parameters
+        ----------
+        predictor: BaeEstimator
+            Reference predictor.
+        """
+        self.predictor = predictor
+
+    def split_data(
+        self,
+        X: NDArray,
+        y: NDArray,
+        y_enc: NDArray,
+        sample_weight: Optional[NDArray] = None,
+        groups: Optional[NDArray] = None,
+    ):
+        """
+        Split data. Keeps part of the data for the calibration estimator
+        (separate from the calibration data).
+
+        Parameters
+        ----------
+        *args: Tuple of NDArray
+
+        Returns
+        -------
+        Tuple of NDArray
+            Split data for training and calibration.
+        """
+        self.n_samples_ = len(X)
+        return X, y, y_enc, sample_weight, groups
 
     @abstractmethod
     def get_conformity_scores(
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index 464f6096d..cc7017ea4 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -42,6 +42,7 @@ def __init__(self) -> None:
 
     def set_external_attributes(
         self,
+        *,
         classes: Optional[ArrayLike] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 9d25f3e9f..6b512c675 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -34,6 +34,7 @@ def __init__(self) -> None:
 
     def set_external_attributes(
         self,
+        *,
         classes: Optional[ArrayLike] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index f2844fd5f..2fcbd69cd 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -1,8 +1,14 @@
 from typing import Optional, Tuple, Union, cast
 
 import numpy as np
+from sklearn.calibration import LabelEncoder
+from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit,
+                                     StratifiedShuffleSplit)
+from sklearn.utils import _safe_indexing
+from sklearn.utils.validation import _num_samples
 
 from mapie.conformity_scores.sets.aps import APS
+from mapie.conformity_scores.sets.utils import get_true_label_position
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
@@ -25,6 +31,12 @@ class RAPS(APS):
     "Uncertainty Sets for Image Classifiers using Conformal Prediction."
     International Conference on Learning Representations 2021.
 
+    Parameters
+    ----------
+    size_raps: Optional[float]
+        Percentage of the data to be used for choosing lambda_star and
+        k_star for the RAPS method.
+
     Attributes
     ----------
     classes: Optional[ArrayLike]
@@ -37,8 +49,176 @@ class RAPS(APS):
         The quantiles estimated from ``get_sets`` method.
     """
 
-    def __init__(self) -> None:
+    valid_cv_ = ["prefit", "split"]
+
+    def __init__(
+        self,
+        size_raps: Optional[float] = 0.2
+    ) -> None:
         super().__init__()
+        self.size_raps = size_raps
+
+    def set_external_attributes(
+        self,
+        *,
+        cv: Union[str, BaseCrossValidator, BaseShuffleSplit] = None,
+        label_encoder: LabelEncoder = None,
+        size_raps: Optional[float] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        cv: Optional[Union[int, str, BaseCrossValidator]]
+            The cross-validation strategy for computing scores.
+
+        label_encoder: Optional[LabelEncoder]
+            The label encoder used to encode the labels.
+
+            By default ``None``.
+
+        size_raps: Optional[float]
+            Percentage of the data to be used for choosing lambda_star and
+            k_star for the RAPS method.
+
+            By default ``None``.
+        """
+        super().set_external_attributes(**kwargs)
+        self.cv = cv
+        self.label_encoder_ = label_encoder
+        self.size_raps = size_raps
+
+    def _check_cv(self):
+        """
+        Check that if the method used is ``"raps"``, then
+        the cross validation strategy is ``"prefit"``.
+
+        Raises
+        ------
+        ValueError
+            If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
+        """
+        if not (
+            self.cv in self.valid_cv_ or isinstance(self.cv, BaseShuffleSplit)
+        ):
+            raise ValueError(
+                "RAPS method can only be used "
+                f"with cv in {self.valid_cv_}."
+            )
+
+    def split_data(
+        self,
+        X: NDArray,
+        y: NDArray,
+        y_enc: NDArray,
+        sample_weight: Optional[NDArray] = None,
+        groups: Optional[NDArray] = None,
+    ):
+        """Split data
+
+        Parameters
+        ----------
+        X: ArrayLike
+            Observed values.
+
+        y: ArrayLike
+            Target values.
+
+        y_enc: ArrayLike
+            Target values as normalized encodings.
+
+        sample_weight: Optional[ArrayLike] of shape (n_samples,)
+            Non-null sample weights.
+
+        groups: Optional[ArrayLike] of shape (n_samples,)
+            Group labels for the samples used while splitting the dataset into
+            train/test set.
+            By default ``None``.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray, NDArray, NDArray, Optional[NDArray],
+        Optional[NDArray]]
+            - NDArray of shape (n_samples, n_features)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+            - NDArray of shape (n_samples,)
+        """
+        # Checks
+        self._check_cv()
+
+        # Split data for raps method
+        raps_split = StratifiedShuffleSplit(
+            n_splits=1,
+            test_size=self.size_raps, random_state=self.random_state
+        )
+        train_raps_index, val_raps_index = next(raps_split.split(X, y_enc))
+        X, self.X_raps, y_enc, self.y_raps = (
+            _safe_indexing(X, train_raps_index),
+            _safe_indexing(X, val_raps_index),
+            _safe_indexing(y_enc, train_raps_index),
+            _safe_indexing(y_enc, val_raps_index),
+        )
+
+        # Decode y_raps for use in the RAPS method
+        self.y_raps_no_enc = self.label_encoder_.inverse_transform(self.y_raps)
+        y = self.label_encoder_.inverse_transform(y_enc)
+
+        # Cast to NDArray for type checking
+        y_enc = cast(NDArray, y_enc)
+        if sample_weight is not None:
+            sample_weight = cast(NDArray, sample_weight)
+            sample_weight = sample_weight[train_raps_index]
+        if groups is not None:
+            groups = cast(NDArray, groups)
+            groups = groups[train_raps_index]
+
+        # Keep sample data size for training and calibration
+        self.n_samples_ = _num_samples(y_enc)
+
+        return X, y, y_enc, sample_weight, groups
+
+    def get_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        y_enc: Optional[NDArray] = None,
+        **kwargs
+    ) -> NDArray:
+        """
+        Get the conformity score.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        y_enc: NDArray of shape (n_samples,)
+            Target values as normalized encodings.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+        # Compute y_pred and position on the RAPS validation dataset
+        self.y_pred_proba_raps = (
+            self.predictor.single_estimator_.predict_proba(self.X_raps)
+        )
+        self.position_raps = get_true_label_position(
+            self.y_pred_proba_raps, self.y_raps
+        )
+
+        return super().get_conformity_scores(
+            y, y_pred, y_enc=y_enc, **kwargs
+        )
 
     @staticmethod
     def _regularize_conformity_score(
@@ -79,11 +259,7 @@ def _regularize_conformity_score(
             cutoff[:, np.newaxis], len(k_star), axis=1
         )
         conf_score += np.maximum(
-            np.expand_dims(
-                lambda_ * (cutoff - k_star),
-                axis=1
-            ),
-            0
+            np.expand_dims(lambda_ * (cutoff - k_star), axis=1), 0
         )
         return conf_score
 
@@ -126,9 +302,8 @@ def _update_size_and_lambda(
             and the new best sizes.
         """
         sizes = [
-            classification_mean_width_score(
-                y_ps[:, :, i]
-            ) for i in range(len(alpha_np))
+            classification_mean_width_score(y_ps[:, :, i])
+            for i in range(len(alpha_np))
         ]
 
         sizes_improve = (sizes < best_sizes - EPSILON)
@@ -209,8 +384,9 @@ def _find_lambda_star(
             )
 
             y_ps = np.greater_equal(
-                    y_pred_proba_raps - y_pred_proba_last, -EPSILON
+                y_pred_proba_raps - y_pred_proba_last, -EPSILON
             )
+
             lambda_star, best_sizes = self._update_size_and_lambda(
                 best_sizes, alpha_np, y_ps, lambda_, lambda_star
             )
@@ -227,10 +403,6 @@ def get_conformity_score_quantiles(
         estimator: EnsembleClassifier,
         agg_scores: Optional[str] = "mean",
         include_last_label: Optional[Union[bool, str]] = True,
-        X_raps: Optional[NDArray] = None,
-        y_raps_no_enc: Optional[NDArray] = None,
-        y_pred_proba_raps: Optional[NDArray] = None,
-        position_raps: Optional[NDArray] = None,
         **kwargs
     ) -> NDArray:
         """
@@ -289,23 +461,23 @@ def get_conformity_score_quantiles(
             Array of quantiles with respect to alpha_np.
         """
         # Casting to NDArray to avoid mypy errors
-        X_raps = cast(NDArray, X_raps)
-        y_raps_no_enc = cast(NDArray, y_raps_no_enc)
-        y_pred_proba_raps = cast(NDArray, y_pred_proba_raps)
-        position_raps = cast(NDArray, position_raps)
+        # X_raps = cast(NDArray, X_raps)
+        # y_raps_no_enc = cast(NDArray, y_raps_no_enc)
+        # y_pred_proba_raps = cast(NDArray, y_pred_proba_raps)
+        # position_raps = cast(NDArray, position_raps)
 
-        check_alpha_and_n_samples(alpha_np, X_raps.shape[0])
+        check_alpha_and_n_samples(alpha_np, self.X_raps.shape[0])
         self.k_star = compute_quantiles(
-            position_raps,
+            self.position_raps,
             alpha_np
         ) + 1
         y_pred_proba_raps = np.repeat(
-            y_pred_proba_raps[:, :, np.newaxis],
+            self.y_pred_proba_raps[:, :, np.newaxis],
             len(alpha_np),
             axis=2
         )
         self.lambda_star = self._find_lambda_star(
-            y_raps_no_enc,
+            self.y_raps_no_enc,
             y_pred_proba_raps,
             alpha_np,
             include_last_label,
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 346592452..d46ee08e1 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -46,6 +46,7 @@ def __init__(self) -> None:
 
     def set_external_attributes(
         self,
+        *,
         classes: Optional[int] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         **kwargs
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 7e4a1e1e4..30b26a8fd 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1408,7 +1408,7 @@ def test_toy_dataset_predictions(strategy: str) -> None:
     else:
         clf = LogisticRegression()
     mapie_clf = MapieClassifier(estimator=clf, **args_init)
-    mapie_clf.fit(X_toy, y_toy, size_raps=.5)
+    mapie_clf.fit(X_toy, y_toy, size_raps=0.5)
     _, y_ps = mapie_clf.predict(
         X_toy,
         alpha=0.5,

From ba8021be0208cc2acd71ad9fb741497ee0de67cb Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 12:06:59 +0200
Subject: [PATCH 206/424] FIx: type-check casting

---
 mapie/conformity_scores/sets/raps.py | 10 ++++++----
 1 file changed, 6 insertions(+), 4 deletions(-)

diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 2fcbd69cd..dd0e3e433 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -61,8 +61,8 @@ def __init__(
     def set_external_attributes(
         self,
         *,
-        cv: Union[str, BaseCrossValidator, BaseShuffleSplit] = None,
-        label_encoder: LabelEncoder = None,
+        cv: Optional[Union[str, BaseCrossValidator, BaseShuffleSplit]] = None,
+        label_encoder: Optional[LabelEncoder] = None,
         size_raps: Optional[float] = None,
         **kwargs
     ) -> None:
@@ -74,6 +74,8 @@ def set_external_attributes(
         cv: Optional[Union[int, str, BaseCrossValidator]]
             The cross-validation strategy for computing scores.
 
+            By default ``None``.
+
         label_encoder: Optional[LabelEncoder]
             The label encoder used to encode the labels.
 
@@ -86,8 +88,8 @@ def set_external_attributes(
             By default ``None``.
         """
         super().set_external_attributes(**kwargs)
-        self.cv = cv
-        self.label_encoder_ = label_encoder
+        self.cv = cast(Union[str, BaseCrossValidator, BaseShuffleSplit], cv)
+        self.label_encoder_ = cast(LabelEncoder, label_encoder)
         self.size_raps = size_raps
 
     def _check_cv(self):

From a2f022ce76a4f153e419b4ee4c4937ae4e5e0d09 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 14:46:23 +0200
Subject: [PATCH 207/424] UPD: add attributes in doctring

---
 mapie/conformity_scores/sets/raps.py | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index dd0e3e433..9894f991a 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -39,14 +39,24 @@ class RAPS(APS):
 
     Attributes
     ----------
-    classes: Optional[ArrayLike]
+    classes: ArrayLike
         Names of the classes.
 
-    random_state: Optional[Union[int, RandomState]]
+    random_state: Union[int, RandomState]
         Pseudo random number generator state.
 
     quantiles_: ArrayLike of shape (n_alpha)
         The quantiles estimated from ``get_sets`` method.
+
+    cv: Union[int, str, BaseCrossValidator]
+        The cross-validation strategy for computing scores.
+
+    label_encoder: LabelEncoder
+        The label encoder used to encode the labels.
+
+    size_raps: float
+        Percentage of the data to be used for choosing lambda_star and
+        k_star for the RAPS method.
     """
 
     valid_cv_ = ["prefit", "split"]
@@ -118,7 +128,9 @@ def split_data(
         sample_weight: Optional[NDArray] = None,
         groups: Optional[NDArray] = None,
     ):
-        """Split data
+        """
+        Split data. Keeps part of the data for the calibration estimator
+        (separate from the calibration data).
 
         Parameters
         ----------
@@ -148,7 +160,6 @@ def split_data(
             - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
         """
         # Checks
         self._check_cv()

From 3eb40eb09639325ee636e99563493cfbf58bfcda Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 15:19:10 +0200
Subject: [PATCH 208/424] UPD: add description in tests

---
 mapie/tests/test_conformity_scores_sets.py | 19 +++++++++++++++++++
 1 file changed, 19 insertions(+)

diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index 2425c5409..26e02f43b 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -40,6 +40,9 @@
 
 
 def test_error_mother_class_initialization() -> None:
+    """
+    Test that the mother class BaseClassificationScore cannot be instantiated.
+    """
     with pytest.raises(TypeError):
         BaseClassificationScore()  # type: ignore
 
@@ -48,6 +51,10 @@ def test_error_mother_class_initialization() -> None:
 def test_check_classification_conformity_score(
     conformity_score: Optional[BaseClassificationScore]
 ) -> None:
+    """
+    Test that the function check_classification_conformity_score returns
+    an instance of BaseClassificationScore when using conformity_score.
+    """
     assert isinstance(
         check_classification_conformity_score(conformity_score),
         BaseClassificationScore
@@ -58,6 +65,10 @@ def test_check_classification_conformity_score(
 def test_check_classification_method(
     method: Optional[str]
 ) -> None:
+    """
+    Test that the function check_classification_conformity_score returns
+    an instance of BaseClassificationScore when using method.
+    """
     assert isinstance(
         check_classification_conformity_score(method=method),
         BaseClassificationScore
@@ -70,6 +81,10 @@ def test_check_conflict_parameters(
     method: Optional[str],
     conformity_score: Optional[BaseClassificationScore]
 ) -> None:
+    """
+    Test that the function check_classification_conformity_score raises
+    a warning when both method and conformity_score are provided.
+    """
     if method is None or conformity_score is None:
         return
     with pytest.warns(
@@ -85,6 +100,10 @@ def test_check_conflict_parameters(
 def test_check_wrong_classification_method(
     method: Optional[str]
 ) -> None:
+    """
+    Test that the function check_classification_conformity_score raises
+    a ValueError when using a wrong method.
+    """
     with pytest.raises(ValueError, match="Invalid method.*"):
         check_classification_conformity_score(method=method)
 

From 2e0171b8575d53ffe5171153897c729908cf894d Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 15:41:55 +0200
Subject: [PATCH 209/424] UPD: add all conformity scores to test + test same
 results with method and score parameters

---
 mapie/conformity_scores/utils.py           |  8 ++--
 mapie/tests/test_classification.py         | 43 +++++++++++++++++++++-
 mapie/tests/test_conformity_scores_sets.py | 13 ++++---
 3 files changed, 53 insertions(+), 11 deletions(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 30069aca3..ce8735d53 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -110,7 +110,7 @@ def check_target(
         )
 
 
-method_score_map = {
+METHOD_SCORE_MAP = {
     'score': lambda: LAC(),
     'lac': lambda: LAC(),
     'cumulated_score': lambda: APS(),
@@ -158,13 +158,13 @@ def check_classification_conformity_score(
         if isinstance(conformity_score, BaseClassificationScore):
             return conformity_score
     if method is not None:
-        if isinstance(method, str) and method in method_score_map:
+        if isinstance(method, str) and method in METHOD_SCORE_MAP:
             _check_depreciated(method)
-            return method_score_map[method]()
+            return METHOD_SCORE_MAP[method]()
         else:
             raise ValueError(
                 "Invalid method. "
-                f"Allowed values are {list(method_score_map.keys())}."
+                f"Allowed values are {list(METHOD_SCORE_MAP.keys())}."
             )
     else:
         raise ValueError(
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 30b26a8fd..24b37d612 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 from copy import deepcopy
-from typing import Any, Dict, Iterable, Optional, Union
+from typing import Any, Dict, Iterable, Optional, Union, cast
 
 import numpy as np
 import pandas as pd
@@ -23,6 +23,7 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
+from mapie.conformity_scores.utils import METHOD_SCORE_MAP
 from mapie.conformity_scores.sets.utils import check_proba_normalized
 from mapie.metrics import classification_coverage_score
 
@@ -1444,6 +1445,46 @@ def test_large_dataset_predictions(strategy: str) -> None:
     )
 
 
+@pytest.mark.parametrize("strategy", [*LARGE_COVERAGES])
+def test_same_result_with_score_and_method(strategy: str) -> None:
+    """
+    Test that prediction sets estimated by MapieClassifier on a larger dataset
+    archive same coverage with conformity_score or method parameters.
+    """
+
+    def get_results(args_init, args_predict):
+        if "split" not in strategy:
+            clf = LogisticRegression().fit(X, y)
+        else:
+            clf = LogisticRegression()
+        mapie_clf = MapieClassifier(estimator=clf, **args_init)
+        mapie_clf.fit(X, y, size_raps=0.5)
+        _, y_ps = mapie_clf.predict(
+            X,
+            alpha=0.2,
+            include_last_label=args_predict["include_last_label"],
+            agg_scores=args_predict["agg_scores"]
+        )
+        return classification_coverage_score(y, y_ps[:, :, 0])
+
+    # Take args of the strategy to test
+    args_init = cast(dict, deepcopy(STRATEGIES[strategy][0]))
+    args_predict = cast(dict, deepcopy(STRATEGIES[strategy][1]))
+
+    # Test with method parameters
+    cov_method = get_results(args_init, args_predict)
+
+    # Change method to conformity_score
+    method = args_init.pop('method', None)
+    args_init['conformity_score'] = METHOD_SCORE_MAP[method]()
+
+    # Test with method parameters
+    cov_conformity_score = get_results(args_init, args_predict)
+
+    # Test that results are the same
+    np.testing.assert_allclose(cov_method, cov_conformity_score, rtol=1e-2)
+
+
 @pytest.mark.parametrize("strategy", [*STRATEGIES_BINARY])
 def test_toy_binary_dataset_predictions(strategy: str) -> None:
     """
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index 26e02f43b..213ab9129 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -8,15 +8,16 @@
 from mapie._typing import NDArray
 from mapie.classification import MapieClassifier
 from mapie.conformity_scores import BaseClassificationScore
-from mapie.conformity_scores.sets import APS, LAC, RAPS, TopK
+from mapie.conformity_scores.sets import APS, LAC, Naive, RAPS, TopK
 from mapie.conformity_scores.utils import check_classification_conformity_score
 from mapie.utils import check_alpha
 
 
 random_state = 42
 
-cs_list = [None, LAC(), APS(), TopK()]
-method_list = [None, 'naive', 'aps', 'raps', 'lac', 'top_k']
+cs_list = [None, LAC(), APS(), RAPS(), Naive(), TopK()]
+valid_method_list = ['naive', 'aps', 'raps', 'lac', 'top_k']
+all_method_list = valid_method_list + [None]
 wrong_method_list = ['naive_', 'aps_', 'raps_', 'lac_', 'top_k_']
 
 REGULARIZATION_PARAMETERS = [
@@ -61,7 +62,7 @@ def test_check_classification_conformity_score(
     )
 
 
-@pytest.mark.parametrize("method", method_list)
+@pytest.mark.parametrize("method", all_method_list)
 def test_check_classification_method(
     method: Optional[str]
 ) -> None:
@@ -75,7 +76,7 @@ def test_check_classification_method(
     )
 
 
-@pytest.mark.parametrize("method", method_list)
+@pytest.mark.parametrize("method", valid_method_list)
 @pytest.mark.parametrize("conformity_score", cs_list)
 def test_check_conflict_parameters(
     method: Optional[str],
@@ -85,7 +86,7 @@ def test_check_conflict_parameters(
     Test that the function check_classification_conformity_score raises
     a warning when both method and conformity_score are provided.
     """
-    if method is None or conformity_score is None:
+    if conformity_score is None:
         return
     with pytest.warns(
         UserWarning,

From e92a71324d3da730c32d79280d0dcf7860149c6e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 17:35:07 +0200
Subject: [PATCH 210/424] UPD: doctring parameters

---
 mapie/conformity_scores/utils.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index ce8735d53..20f5886dd 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -19,6 +19,18 @@ def check_regression_conformity_score(
     """
     Check parameter ``conformity_score`` for regression task.
 
+    Parameters
+    ----------
+    conformity_score: BaseClassificationScore
+        Conformity score function.
+
+        By default, `None`.
+
+    sym: bool
+        Whether to use symmetric bounds.
+
+        By default, `True`.
+
     Raises
     ------
     ValueError
@@ -128,6 +140,18 @@ def check_classification_conformity_score(
     """
     Check parameter ``conformity_score`` for classification task.
 
+    Parameters
+    ----------
+    conformity_score: BaseClassificationScore
+        Conformity score function.
+
+        By default, `None`.
+
+    method: str
+        Method to compute the conformity score.
+
+        By default, `None`.
+
     Raises
     ------
     ValueError

From c3fee465429efdc683bcec67a221f2d00ef72a87 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 17:46:15 +0200
Subject: [PATCH 211/424] UPD: move dict at the top of file

---
 mapie/conformity_scores/utils.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 20f5886dd..71b3eaed2 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -12,6 +12,17 @@
 from mapie._typing import ArrayLike
 
 
+METHOD_SCORE_MAP = {
+    'score': lambda: LAC(),
+    'lac': lambda: LAC(),
+    'cumulated_score': lambda: APS(),
+    'aps': lambda: APS(),
+    'naive': lambda: Naive(),
+    'raps': lambda: RAPS(),
+    'top_k': lambda: TopK()
+}
+
+
 def check_regression_conformity_score(
     conformity_score: Optional[BaseRegressionScore],
     sym: bool = True,
@@ -122,17 +133,6 @@ def check_target(
         )
 
 
-METHOD_SCORE_MAP = {
-    'score': lambda: LAC(),
-    'lac': lambda: LAC(),
-    'cumulated_score': lambda: APS(),
-    'aps': lambda: APS(),
-    'naive': lambda: Naive(),
-    'raps': lambda: RAPS(),
-    'top_k': lambda: TopK()
-}
-
-
 def check_classification_conformity_score(
     conformity_score: Optional[BaseClassificationScore] = None,
     method: Optional[str] = None,

From 933d4d9b3f3e858b2f20646601876ceaeacb54fd Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Fri, 12 Jul 2024 18:07:30 +0200
Subject: [PATCH 212/424] UPD: add all check tests when parameters are wrong

---
 mapie/conformity_scores/utils.py             | 24 +++++++++++---------
 mapie/tests/test_conformity_scores_bounds.py | 17 ++++++++++++++
 mapie/tests/test_conformity_scores_sets.py   | 15 +++++++++++-
 3 files changed, 44 insertions(+), 12 deletions(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 71b3eaed2..c6cfb91c9 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -29,6 +29,7 @@ def check_regression_conformity_score(
 ) -> BaseRegressionScore:
     """
     Check parameter ``conformity_score`` for regression task.
+    By default, return a AbsoluteConformityScore instance.
 
     Parameters
     ----------
@@ -58,7 +59,7 @@ def check_regression_conformity_score(
     ...     print(exception)
     ...
     Invalid conformity_score argument.
-    Must be None or a ConformityScore instance.
+    Must be None or a BaseRegressionScore instance.
     """
     if conformity_score is None:
         return AbsoluteConformityScore(sym=sym)
@@ -67,7 +68,7 @@ def check_regression_conformity_score(
     else:
         raise ValueError(
             "Invalid conformity_score argument.\n"
-            "Must be None or a ConformityScore instance."
+            "Must be None or a BaseRegressionScore instance."
         )
 
 
@@ -139,6 +140,7 @@ def check_classification_conformity_score(
 ) -> BaseClassificationScore:
     """
     Check parameter ``conformity_score`` for classification task.
+    By default, return a LAC instance.
 
     Parameters
     ----------
@@ -168,11 +170,9 @@ def check_classification_conformity_score(
     ...     print(exception)
     ...
     Invalid conformity_score argument.
-    Must be None or a ConformityScore instance.
+    Must be None or a BaseClassificationScore instance.
     """
-    if method is None and conformity_score is None:
-        return LAC()
-    elif conformity_score is not None:
+    if conformity_score is not None:
         if method is not None:
             warnings.warn(
                 "WARNING: the `conformity_score` parameter takes precedence "
@@ -181,7 +181,12 @@ def check_classification_conformity_score(
             )
         if isinstance(conformity_score, BaseClassificationScore):
             return conformity_score
-    if method is not None:
+        else:
+            raise ValueError(
+                "Invalid conformity_score argument.\n"
+                "Must be None or a BaseClassificationScore instance."
+            )
+    elif method is not None:
         if isinstance(method, str) and method in METHOD_SCORE_MAP:
             _check_depreciated(method)
             return METHOD_SCORE_MAP[method]()
@@ -191,7 +196,4 @@ def check_classification_conformity_score(
                 f"Allowed values are {list(METHOD_SCORE_MAP.keys())}."
             )
     else:
-        raise ValueError(
-            "Invalid conformity_score argument.\n"
-            "Must be None or a ConformityScore instance."
-        )
+        return LAC()
diff --git a/mapie/tests/test_conformity_scores_bounds.py b/mapie/tests/test_conformity_scores_bounds.py
index 06dfca94b..345c33652 100644
--- a/mapie/tests/test_conformity_scores_bounds.py
+++ b/mapie/tests/test_conformity_scores_bounds.py
@@ -1,3 +1,4 @@
+from typing import Any
 import numpy as np
 import pytest
 from sklearn.linear_model import LinearRegression
@@ -10,6 +11,8 @@
     ResidualNormalisedScore
 )
 from mapie.regression import MapieRegressor
+from mapie.conformity_scores.utils import check_regression_conformity_score
+
 
 X_toy = np.array([0, 1, 2, 3, 4, 5]).reshape(-1, 1)
 y_toy = np.array([5, 7, 9, 11, 13, 15])
@@ -21,6 +24,8 @@
 )
 random_state = 42
 
+wrong_cs_list = [object(), "AbsoluteConformityScore", 1]
+
 
 class DummyConformityScore(BaseRegressionScore):
     def __init__(self) -> None:
@@ -48,6 +53,18 @@ def test_error_mother_class_initialization(sym: bool) -> None:
         BaseRegressionScore(sym)  # type: ignore
 
 
+@pytest.mark.parametrize("score", wrong_cs_list)
+def test_check_wrong_regression_score(
+    score: Any
+) -> None:
+    """
+    Test that the function check_regression_conformity_score raises
+    a ValueError when using a wrong score.
+    """
+    with pytest.raises(ValueError, match="Invalid conformity_score argument*"):
+        check_regression_conformity_score(conformity_score=score)
+
+
 @pytest.mark.parametrize("y_pred", [np.array(y_pred_list), y_pred_list])
 def test_absolute_conformity_score_get_conformity_scores(
     y_pred: NDArray,
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index 213ab9129..e6154602c 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -1,4 +1,4 @@
-from typing import Optional, cast
+from typing import Any, Optional, cast
 
 import pytest
 import numpy as np
@@ -16,6 +16,7 @@
 random_state = 42
 
 cs_list = [None, LAC(), APS(), RAPS(), Naive(), TopK()]
+wrong_cs_list = [object(), "LAC", 1]
 valid_method_list = ['naive', 'aps', 'raps', 'lac', 'top_k']
 all_method_list = valid_method_list + [None]
 wrong_method_list = ['naive_', 'aps_', 'raps_', 'lac_', 'top_k_']
@@ -109,6 +110,18 @@ def test_check_wrong_classification_method(
         check_classification_conformity_score(method=method)
 
 
+@pytest.mark.parametrize("score", wrong_cs_list)
+def test_check_wrong_classification_score(
+    score: Any
+) -> None:
+    """
+    Test that the function check_classification_conformity_score raises
+    a ValueError when using a wrong score.
+    """
+    with pytest.raises(ValueError, match="Invalid conformity_score argument*"):
+        check_classification_conformity_score(conformity_score=score)
+
+
 @pytest.mark.parametrize("k_lambda", REGULARIZATION_PARAMETERS)
 def test_regularize_conf_scores_shape(k_lambda) -> None:
     """

From a096b51c8d02fc5e11bf10f42f9d5f5eec9e6922 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 15 Jul 2024 10:31:19 +0200
Subject: [PATCH 213/424] UPD: add deprecated value check

---
 mapie/classification.py                    |  5 +--
 mapie/conformity_scores/utils.py           | 39 +++++++++++++++++-----
 mapie/tests/test_conformity_scores_sets.py | 16 +++++++++
 3 files changed, 50 insertions(+), 10 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 55e32f813..d73085c0e 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -13,7 +13,8 @@
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseClassificationScore
 from mapie.conformity_scores.utils import (
-    check_classification_conformity_score, check_target
+    check_depreciated_size_raps, check_classification_conformity_score,
+    check_target
 )
 from mapie.estimator.classifier import EnsembleClassifier
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
@@ -375,7 +376,7 @@ def _check_fit_parameter(
             conformity_score=self.conformity_score,
             method=self.method,
         )
-        # TODO test size_raps depreciated
+        check_depreciated_size_raps(size_raps)
         cs_estimator.set_external_attributes(
             cv=self.cv,
             classes=self.classes_,
diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index c6cfb91c9..1cae0f1c8 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -72,7 +72,7 @@ def check_regression_conformity_score(
         )
 
 
-def _check_depreciated(
+def check_depreciated_score(
     method: str
 ) -> None:
     """
@@ -87,17 +87,40 @@ def _check_depreciated(
     if method == "score":
         warnings.warn(
             "WARNING: Deprecated method. "
-            + "The method \"score\" is outdated. "
-            + "Prefer to use \"lac\" instead to keep "
-            + "the same behavior in the next release.",
+            "The method \"score\" is outdated. "
+            "Prefer to use \"lac\" instead to keep "
+            "the same behavior in the next release.",
             DeprecationWarning
         )
     if method == "cumulated_score":
         warnings.warn(
             "WARNING: Deprecated method. "
-            + "The method \"cumulated_score\" is outdated. "
-            + "Prefer to use \"aps\" instead to keep "
-            + "the same behavior in the next release.",
+            "The method \"cumulated_score\" is outdated. "
+            "Prefer to use \"aps\" instead to keep "
+            "the same behavior in the next release.",
+            DeprecationWarning
+        )
+
+
+def check_depreciated_size_raps(
+    size_raps: Optional[float]
+) -> None:
+    """
+    Check if the parameter ``size_raps`` is used. If so, raise a warning.
+
+    Raises
+    ------
+    Warning
+        If ``size_raps`` is not ``None``.
+    """
+    if not (size_raps is None):
+        warnings.warn(
+            "WARNING: Deprecated parameter. "
+            "The parameter `size_raps` is deprecated. "
+            "In the next release, `RAPS` takes precedence over "
+            "`MapieClassifier` for setting the size used. "
+            "Prefer to define `size_raps` in `RAPS` rather than "
+            "in the `fit` method of `MapieClassifier`.",
             DeprecationWarning
         )
 
@@ -188,7 +211,7 @@ def check_classification_conformity_score(
             )
     elif method is not None:
         if isinstance(method, str) and method in METHOD_SCORE_MAP:
-            _check_depreciated(method)
+            check_depreciated_score(method)
             return METHOD_SCORE_MAP[method]()
         else:
             raise ValueError(
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index e6154602c..a5197f341 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -122,6 +122,22 @@ def test_check_wrong_classification_score(
         check_classification_conformity_score(conformity_score=score)
 
 
+@pytest.mark.parametrize("cv", ['prefit', 'split'])
+@pytest.mark.parametrize("size_raps", [0.2, 0.5, 0.8])
+def test_check_depreciated_size_raps(size_raps: float, cv: str) -> None:
+    """
+    Test that the function check_classification_conformity_score raises
+    a DeprecationWarning when using size_raps.
+    """
+    clf = LogisticRegression().fit(X, y)
+    mapie_clf = MapieClassifier(estimator=clf, conformity_score=RAPS(), cv=cv)
+    with pytest.warns(
+        DeprecationWarning,
+        match="The parameter `size_raps` is deprecated.*"
+    ):
+        mapie_clf.fit(X, y, size_raps=size_raps)
+
+
 @pytest.mark.parametrize("k_lambda", REGULARIZATION_PARAMETERS)
 def test_regularize_conf_scores_shape(k_lambda) -> None:
     """

From 7b64f6f4c134cba43e49ac776eba4f0050a37882 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 15 Jul 2024 10:32:35 +0200
Subject: [PATCH 214/424] UPD: short value check command

---
 mapie/conformity_scores/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 1cae0f1c8..58ff6f05f 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -210,7 +210,7 @@ def check_classification_conformity_score(
                 "Must be None or a BaseClassificationScore instance."
             )
     elif method is not None:
-        if isinstance(method, str) and method in METHOD_SCORE_MAP:
+        if method in METHOD_SCORE_MAP:
             check_depreciated_score(method)
             return METHOD_SCORE_MAP[method]()
         else:

From 262a96a64eaad535fbc63a0e96255eb8a3f351a8 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 15 Jul 2024 10:39:58 +0200
Subject: [PATCH 215/424] FIX: unhashable list

---
 mapie/conformity_scores/utils.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 58ff6f05f..1cae0f1c8 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -210,7 +210,7 @@ def check_classification_conformity_score(
                 "Must be None or a BaseClassificationScore instance."
             )
     elif method is not None:
-        if method in METHOD_SCORE_MAP:
+        if isinstance(method, str) and method in METHOD_SCORE_MAP:
             check_depreciated_score(method)
             return METHOD_SCORE_MAP[method]()
         else:

From 1e0b66cce8be0e9b3714c508093505c01a38a866 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 15 Jul 2024 10:48:31 +0200
Subject: [PATCH 216/424] UPD: set_external_attributes common method

---
 mapie/conformity_scores/classification.py | 29 ++++++++++++++++++++++-
 mapie/conformity_scores/sets/lac.py       | 28 ++--------------------
 mapie/conformity_scores/sets/naive.py     | 28 ++--------------------
 mapie/conformity_scores/sets/topk.py      | 26 +-------------------
 4 files changed, 33 insertions(+), 78 deletions(-)

diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index f6e45d380..2e2b010c1 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -1,9 +1,12 @@
 from abc import ABCMeta, abstractmethod
+from typing import Optional, Union
+
+import numpy as np
 
 from mapie.conformity_scores.interface import BaseConformityScore
 from mapie.estimator.classifier import EnsembleClassifier
 
-from mapie._typing import NDArray
+from mapie._typing import ArrayLike, NDArray
 
 
 class BaseClassificationScore(BaseConformityScore, metaclass=ABCMeta):
@@ -21,6 +24,30 @@ class BaseClassificationScore(BaseConformityScore, metaclass=ABCMeta):
     def __init__(self) -> None:
         super().__init__()
 
+    def set_external_attributes(
+        self,
+        *,
+        classes: Optional[ArrayLike] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        **kwargs
+    ) -> None:
+        """
+        Set attributes that are not provided by the user.
+
+        Parameters
+        ----------
+        classes: Optional[ArrayLike]
+            Names of the classes.
+
+            By default ``None``.
+
+        random_state: Optional[Union[int, RandomState]]
+            Pseudo random number generator state.
+        """
+        super().set_external_attributes(**kwargs)
+        self.classes = classes
+        self.random_state = random_state
+
     @abstractmethod
     def get_predictions(
         self,
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index cc7017ea4..a2b48795c 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union, cast
+from typing import Optional, cast
 
 import numpy as np
 
@@ -7,7 +7,7 @@
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
-from mapie._typing import ArrayLike, NDArray
+from mapie._typing import NDArray
 from mapie.utils import compute_quantiles
 
 
@@ -40,30 +40,6 @@ class LAC(BaseClassificationScore):
     def __init__(self) -> None:
         super().__init__()
 
-    def set_external_attributes(
-        self,
-        *,
-        classes: Optional[ArrayLike] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        **kwargs
-    ) -> None:
-        """
-        Set attributes that are not provided by the user.
-
-        Parameters
-        ----------
-        classes: Optional[ArrayLike]
-            Names of the classes.
-
-            By default ``None``.
-
-        random_state: Optional[Union[int, RandomState]]
-            Pseudo random number generator state.
-        """
-        super().set_external_attributes(**kwargs)
-        self.classes = classes
-        self.random_state = random_state
-
     def get_conformity_scores(
         self,
         y: NDArray,
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 6b512c675..9ec6c2399 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -1,4 +1,4 @@
-from typing import Optional, Tuple, Union
+from typing import Tuple, Union
 
 import numpy as np
 
@@ -9,7 +9,7 @@
 from mapie.estimator.classifier import EnsembleClassifier
 
 from mapie._machine_precision import EPSILON
-from mapie._typing import ArrayLike, NDArray
+from mapie._typing import NDArray
 
 
 class Naive(BaseClassificationScore):
@@ -32,30 +32,6 @@ class Naive(BaseClassificationScore):
     def __init__(self) -> None:
         super().__init__()
 
-    def set_external_attributes(
-        self,
-        *,
-        classes: Optional[ArrayLike] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        **kwargs
-    ) -> None:
-        """
-        Set attributes that are not provided by the user.
-
-        Parameters
-        ----------
-        classes: Optional[ArrayLike]
-            Names of the classes.
-
-            By default ``None``.
-
-        random_state: Optional[Union[int, RandomState]]
-            Pseudo random number generator state.
-        """
-        super().set_external_attributes(**kwargs)
-        self.classes = classes
-        self.random_state = random_state
-
     def get_conformity_scores(
         self,
         y: NDArray,
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index d46ee08e1..2d5693cc1 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -1,4 +1,4 @@
-from typing import Optional, Union, cast
+from typing import Optional, cast
 
 import numpy as np
 
@@ -44,30 +44,6 @@ class TopK(BaseClassificationScore):
     def __init__(self) -> None:
         super().__init__()
 
-    def set_external_attributes(
-        self,
-        *,
-        classes: Optional[int] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        **kwargs
-    ) -> None:
-        """
-        Set attributes that are not provided by the user.
-
-        Parameters
-        ----------
-        classes: Optional[ArrayLike]
-            Names of the classes.
-
-            By default ``None``.
-
-        random_state: Optional[Union[int, RandomState]]
-            Pseudo random number generator state.
-        """
-        super().set_external_attributes(**kwargs)
-        self.classes = classes
-        self.random_state = random_state
-
     def get_conformity_scores(
         self,
         y: NDArray,

From 7ce4c85975011d410021330d6c55fde0d4cf025e Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Mon, 15 Jul 2024 14:16:24 +0200
Subject: [PATCH 217/424] UPD: Apply suggestions from code review

---
 mapie/regression/regression.py |  6 +++---
 mapie/tests/test_regression.py | 24 +++++++++++++++---------
 2 files changed, 18 insertions(+), 12 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 3baf04b8f..8ff861211 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -543,8 +543,9 @@ def fit(
         )
 
         # Predict on calibration data
-        y_pred = self.estimator_.predict_calib(X, y=y, groups=groups,
-                                               **predict_params)
+        y_pred = self.estimator_.predict_calib(
+                X, y=y, groups=groups, **predict_params
+        )
 
         # Compute the conformity scores (manage jk-ab case)
         self.conformity_scores_ = \
@@ -623,7 +624,6 @@ def predict(
                 - [:, 0, :]: Lower bound of the prediction interval.
                 - [:, 1, :]: Upper bound of the prediction interval.
         """
-
         # Checks
         if hasattr(self, '_predict_params'):
             check_predict_params(self._predict_params, predict_params, self.cv)
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 930823ec8..3462f5a58 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -923,15 +923,17 @@ def test_fit_params_expected_behavior_unaffected_by_predict_params() -> None:
     instead of default value for n_estimators (=100).
     """
     X_train, X_test, y_train, y_test = (
-        train_test_split(X, y, test_size=0.2, random_state=random_state))
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     mapie_1 = MapieRegressor(estimator=custom_gbr)
     mapie_2 = MapieRegressor(estimator=custom_gbr)
     fit_params = {'monitor': early_stopping_monitor}
     predict_params = {'check_predict_params': True}
-    mapie_1 = mapie_1.fit(X_train, y_train,
-                          fit_params=fit_params,
-                          predict_params=predict_params)
+    mapie_1 = mapie_1.fit(
+        X_train, y_train,
+        fit_params=fit_params, predict_params=predict_params
+    )
     mapie_2 = mapie_2.fit(X_train, y_train, predict_params=predict_params)
 
     assert mapie_1.estimator_.single_estimator_.estimators_.shape[0] == 3
@@ -952,16 +954,19 @@ def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
     the case for the model without predict_params
     """
     X_train, X_test, y_train, y_test = (
-        train_test_split(X, y, test_size=0.2, random_state=random_state))
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     score = AbsoluteConformityScore(sym=True)
     mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
     mapie_2 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
     fit_params = {'monitor': early_stopping_monitor}
     predict_params = {'check_predict_params': True}
-    mapie_1 = mapie_1.fit(X_train, y_train,
-                          fit_params=fit_params,
-                          predict_params=predict_params)
+    mapie_1 = mapie_1.fit(
+        X_train, y_train,
+        fit_params=fit_params,
+        predict_params=predict_params
+    )
     mapie_2 = mapie_2.fit(X_train, y_train, fit_params=fit_params,)
     y_pred_1 = mapie_1.predict(X_test, **predict_params)
     y_pred_2 = mapie_2.predict(X_test)
@@ -980,7 +985,8 @@ def test_invalid_predict_parameters() -> None:
     """Test that invalid predict_parameters raise errors."""
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     X_train, X_test, y_train, y_test = (
-        train_test_split(X, y, test_size=0.2, random_state=random_state))
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
     mapie = MapieRegressor(estimator=custom_gbr)
     predict_params = {'check_predict_params': True}
     mapie_fitted = mapie.fit(X_train, y_train)

From c4af59f4d4e95279029eb1a3cd14184f4944fc96 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Mon, 15 Jul 2024 14:17:42 +0200
Subject: [PATCH 218/424] UPD: remove doctring

---
 mapie/estimator/interface.py | 14 --------------
 1 file changed, 14 deletions(-)

diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
index 4b5abab8f..e015d4d7c 100644
--- a/mapie/estimator/interface.py
+++ b/mapie/estimator/interface.py
@@ -37,18 +37,4 @@ def predict(
         """
         Predict target from X. It also computes the prediction per train sample
         for each test sample according to ``self.method``.
-
-        Parameters
-        ----------
-        X: ArrayLike of shape (n_samples, n_features)
-            Test data.
-
-        **kwargs : dict
-            Additional fit and predict parameters.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray]
-            - Predictions
-            - Predictions sets
         """

From 8f058a3bd1db7855ba659d4da3bff18917380b47 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 15 Jul 2024 16:04:38 +0200
Subject: [PATCH 219/424] Add check_predict_params() docstring

---
 mapie/utils.py | 18 ++++++++++++++++++
 1 file changed, 18 insertions(+)

diff --git a/mapie/utils.py b/mapie/utils.py
index 86cd51a82..806927dc2 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1380,6 +1380,24 @@ def check_predict_params(
     predict_params: dict,
     cv: Optional[Union[int, str, BaseCrossValidator]] = None
 ) -> None:
+    """
+    Check that if predict_params is used in the predict method,
+    it is also used in the fit method. Otherwise, raise an error."
+
+    Parameters
+    ----------
+    predict_params_used_in_fit: bool
+        True or False. It is True if one or more predict_params
+        are used in the fit method
+
+    predict_param: dict. Contains all predict params used in predict method
+
+    Raises
+    ------
+    ValueError
+        "If any predict_params are used in the predict method but none
+        are used in the fit method."
+    """
     if (len(predict_params) > 0 and
             predict_params_used_in_fit is False and
             cv != "prefit"):

From 76018ada27d260fc8bc747153104816998932023 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
Date: Mon, 15 Jul 2024 17:22:00 +0200
Subject: [PATCH 220/424] UPD: Apply suggestions from code review

---
 mapie/utils.py | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/mapie/utils.py b/mapie/utils.py
index 806927dc2..34d077695 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1382,21 +1382,21 @@ def check_predict_params(
 ) -> None:
     """
     Check that if predict_params is used in the predict method,
-    it is also used in the fit method. Otherwise, raise an error."
+    it is also used in the fit method. Otherwise, raise an error.
 
     Parameters
     ----------
     predict_params_used_in_fit: bool
-        True or False. It is True if one or more predict_params
-        are used in the fit method
+        True if one or more predict_params are used in the fit method
 
-    predict_param: dict. Contains all predict params used in predict method
+    predict_param: dict
+        Contains all predict params used in predict method
 
     Raises
     ------
     ValueError
-        "If any predict_params are used in the predict method but none
-        are used in the fit method."
+        If any predict_params are used in the predict method but none
+        are used in the fit method.
     """
     if (len(predict_params) > 0 and
             predict_params_used_in_fit is False and

From 41efb83bb8334d77c3b2e1de0a125553b168e377 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 15 Jul 2024 17:37:35 +0200
Subject: [PATCH 221/424] Update : History

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index b88fc99dc..26ad2df7f 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Add `** predict_params` in fit and predict method for Mapie Regression
 * Building unit tests for different `Subsample` and `BlockBooststrap` instances
 * Change the sign of C_k in the `Kolmogorov-Smirnov` test documentation
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.

From e505a2215aadfd1310c3bbea0258172701517e61 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 16 Jul 2024 14:55:31 +0200
Subject: [PATCH 222/424] UPD: change with correct conformity score name

---
 mapie/conformity_scores/regression.py        | 2 +-
 mapie/regression/regression.py               | 6 +++---
 mapie/tests/test_conformity_scores_bounds.py | 2 +-
 mapie/tests/test_regression.py               | 4 ++--
 4 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index 1e58cc163..a3dcb45e8 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -149,7 +149,7 @@ def check_consistency(
         if max_conf_score > self.eps:
             raise ValueError(
                 "The two functions get_conformity_scores and "
-                "get_estimation_distribution of the BaseConformityScore class "
+                "get_estimation_distribution of the BaseRegressionScore class "
                 "are not consistent. "
                 "The following equation must be verified: "
                 "self.get_estimation_distribution(y_pred, "
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 88e827368..aeb68b5bf 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -138,8 +138,8 @@ class MapieRegressor(BaseEstimator, RegressorMixin):
 
         By default ``0``.
 
-    conformity_score: Optional[ConformityScore]
-        ConformityScore instance.
+    conformity_score: Optional[BaseRegressionScore]
+        BaseRegressionScore instance.
         It defines the link between the observed values, the predicted ones
         and the conformity scores. For instance, the default ``None`` value
         correspondonds to a conformity score which assumes
@@ -147,7 +147,7 @@ class MapieRegressor(BaseEstimator, RegressorMixin):
 
         - ``None``, to use the default ``AbsoluteConformityScore`` conformity
           score
-        - ConformityScore: any ``ConformityScore`` class
+        - BaseRegressionScore: any ``BaseRegressionScore`` class
 
         By default ``None``.
 
diff --git a/mapie/tests/test_conformity_scores_bounds.py b/mapie/tests/test_conformity_scores_bounds.py
index 345c33652..bd7b9209d 100644
--- a/mapie/tests/test_conformity_scores_bounds.py
+++ b/mapie/tests/test_conformity_scores_bounds.py
@@ -222,7 +222,7 @@ def test_gamma_conformity_score_check_predicted_value(
 
 def test_check_consistency() -> None:
     """
-    Test that a dummy ConformityScore class that gives inconsistent scores
+    Test that a dummy BaseRegressionScore class that gives inconsistent scores
     and distributions raises an error.
     """
     dummy_conf_score = DummyConformityScore()
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index c35ebec34..da81798a2 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -367,7 +367,7 @@ def test_calibration_data_size_asymmetric_score(delta: float) -> None:
     # Define an asymmetric conformity score
     score = AbsoluteConformityScore(sym=False)
 
-    # Test when ConformityScore is asymmetric
+    # Test when BaseRegressionScore is asymmetric
     # and calibration data size is sufficient
     n_calib_sufficient = int(np.ceil(1/(1-delta) * 2)) + 1
     Xc, Xt, yc, _ = train_test_split(Xct, yct, train_size=n_calib_sufficient)
@@ -377,7 +377,7 @@ def test_calibration_data_size_asymmetric_score(delta: float) -> None:
     mapie_reg.fit(Xc, yc)
     mapie_reg.predict(Xt, alpha=1-delta)
 
-    # Test when ConformityScore is asymmetric
+    # Test when BaseRegressionScore is asymmetric
     # and calibration data size is too low
     with pytest.raises(
         ValueError, match=r"Number of samples of the score is too low*"

From b99d265dbbf14ac41ae09da37ff466b362c51cf5 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 16 Jul 2024 14:57:37 +0200
Subject: [PATCH 223/424] Update : replace assert np.array_equal by
 np.testing.assert_array_equal

---
 mapie/tests/test_classification.py | 6 ++++--
 mapie/tests/test_regression.py     | 6 ++++--
 2 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 740c4df6b..d2ca271f2 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -1391,8 +1391,10 @@ def test_results_with_groups() -> None:
     #  (array([1, 2, 4, 5]), array([0, 3]))]
     conformity_scores_0 = np.array([[1.], [0.], [0.], [1.], [1.], [1.]])
     conformity_scores_1 = np.array([[1.], [1.], [1.], [1.], [1.], [1.]])
-    assert np.array_equal(mapie0.conformity_scores_, conformity_scores_0)
-    assert np.array_equal(mapie1.conformity_scores_, conformity_scores_1)
+    np.testing.assert_array_equal(mapie0.conformity_scores_,
+                                  conformity_scores_0)
+    np.testing.assert_array_equal(mapie1.conformity_scores_,
+                                  conformity_scores_1)
 
 
 @pytest.mark.parametrize(
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 1dad0776e..6e602a58c 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -560,8 +560,10 @@ def test_results_with_groups() -> None:
     y_pred_1 = [15, 10, 5, 15, 10, 5]
     conformity_scores_0 = np.abs(y - y_pred_0)
     conformity_scores_1 = np.abs(y - y_pred_1)
-    assert np.array_equal(mapie0.conformity_scores_, conformity_scores_0)
-    assert np.array_equal(mapie1.conformity_scores_, conformity_scores_1)
+    np.testing.assert_array_equal(mapie0.conformity_scores_,
+                                  conformity_scores_0)
+    np.testing.assert_array_equal(mapie1.conformity_scores_,
+                                  conformity_scores_1)
 
 
 @pytest.mark.parametrize("strategy", [*STRATEGIES])

From 6a7aec04ff577b08b17897bcf51fbdcabd9fb2db Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 16 Jul 2024 14:59:58 +0200
Subject: [PATCH 224/424] Update : History

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index b88fc99dc..8a73e0030 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Replace `assert np.array_equal` by `np.testing.assert_array_equal` in Mapie unit tests
 * Building unit tests for different `Subsample` and `BlockBooststrap` instances
 * Change the sign of C_k in the `Kolmogorov-Smirnov` test documentation
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.

From 04e52d42f863785db25fcea70996b5b6d3a3c254 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 16 Jul 2024 15:23:36 +0200
Subject: [PATCH 225/424] UPD: change class and method names

---
 mapie/conformity_scores/__init__.py        | 15 ++++++-----
 mapie/conformity_scores/sets/__init__.py   | 20 +++++++-------
 mapie/conformity_scores/sets/aps.py        |  8 +++---
 mapie/conformity_scores/sets/lac.py        |  2 +-
 mapie/conformity_scores/sets/naive.py      |  6 ++---
 mapie/conformity_scores/sets/raps.py       | 15 ++++++-----
 mapie/conformity_scores/sets/topk.py       |  2 +-
 mapie/conformity_scores/utils.py           | 31 ++++++++++++----------
 mapie/tests/test_conformity_scores_sets.py | 28 ++++++++++++-------
 9 files changed, 72 insertions(+), 55 deletions(-)

diff --git a/mapie/conformity_scores/__init__.py b/mapie/conformity_scores/__init__.py
index 88a3530be..d8f6b1f5b 100644
--- a/mapie/conformity_scores/__init__.py
+++ b/mapie/conformity_scores/__init__.py
@@ -3,7 +3,10 @@
 from .bounds import (
     AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
 )
-from .sets import APS, LAC, Naive, RAPS, TopK
+from .sets import (
+    APSConformityScore, LACConformityScore, NaiveConformityScore,
+    RAPSConformityScore, TopKConformityScore
+)
 
 
 __all__ = [
@@ -12,9 +15,9 @@
     "AbsoluteConformityScore",
     "GammaConformityScore",
     "ResidualNormalisedScore",
-    "Naive",
-    "LAC",
-    "APS",
-    "RAPS",
-    "TopK"
+    "NaiveConformityScore",
+    "LACConformityScore",
+    "APSConformityScore",
+    "RAPSConformityScore",
+    "TopKConformityScore"
 ]
diff --git a/mapie/conformity_scores/sets/__init__.py b/mapie/conformity_scores/sets/__init__.py
index 36f203cc5..9db834634 100644
--- a/mapie/conformity_scores/sets/__init__.py
+++ b/mapie/conformity_scores/sets/__init__.py
@@ -1,14 +1,14 @@
-from .naive import Naive
-from .lac import LAC
-from .aps import APS
-from .raps import RAPS
-from .topk import TopK
+from .naive import NaiveConformityScore
+from .lac import LACConformityScore
+from .aps import APSConformityScore
+from .raps import RAPSConformityScore
+from .topk import TopKConformityScore
 
 
 __all__ = [
-    "Naive",
-    "LAC",
-    "APS",
-    "RAPS",
-    "TopK",
+    "NaiveConformityScore",
+    "LACConformityScore",
+    "APSConformityScore",
+    "RAPSConformityScore",
+    "TopKConformityScore",
 ]
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 9d3a6d9a2..9c7affd0b 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -4,7 +4,7 @@
 from sklearn.dummy import check_random_state
 from sklearn.calibration import label_binarize
 
-from mapie.conformity_scores.sets.naive import Naive
+from mapie.conformity_scores.sets.naive import NaiveConformityScore
 from mapie.conformity_scores.sets.utils import (
     check_include_last_label, check_proba_normalized
 )
@@ -15,7 +15,7 @@
 from mapie.utils import compute_quantiles
 
 
-class APS(Naive):
+class APSConformityScore(NaiveConformityScore):
     """
     Adaptive Prediction Sets (APS) method-based non-conformity score.
     It is based on the sum of the softmax outputs of the labels until the true
@@ -211,7 +211,7 @@ def get_conformity_score_quantiles(
 
         return quantiles_
 
-    def _compute_vs_parameter(
+    def _compute_v_parameter(
         self,
         y_proba_last_cumsumed: NDArray,
         threshold: NDArray,
@@ -302,7 +302,7 @@ def _add_random_tie_breaking(
         )
 
         # get the V parameter from Romano+(2020) or Angelopoulos+(2020)
-        vs = self._compute_vs_parameter(
+        vs = self._compute_v_parameter(
             y_proba_last_cumsumed,
             threshold,
             y_pred_proba_last,
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index a2b48795c..a81d39240 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -11,7 +11,7 @@
 from mapie.utils import compute_quantiles
 
 
-class LAC(BaseClassificationScore):
+class LACConformityScore(BaseClassificationScore):
     """
     Least Ambiguous set-valued Classifier (LAC) method-based
     non conformity score (also formerly called ``"score"``).
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 9ec6c2399..79ba4407c 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -12,7 +12,7 @@
 from mapie._typing import NDArray
 
 
-class Naive(BaseClassificationScore):
+class NaiveConformityScore(BaseClassificationScore):
     """
     Naive classification non-conformity score method that is based on the
     cumulative sum of probabilities until the 1-alpha threshold.
@@ -121,7 +121,7 @@ def get_conformity_score_quantiles(
         quantiles_ = 1 - alpha_np
         return quantiles_
 
-    def _add_regualization(
+    def _add_regularization(
         self,
         y_pred_proba_sorted_cumsum: NDArray,
         **kwargs
@@ -188,7 +188,7 @@ def _get_last_included_proba(
         )
         # get sorted cumulated score
         y_pred_proba_sorted_cumsum = np.cumsum(y_pred_proba_sorted, axis=1)
-        y_pred_proba_sorted_cumsum = self._add_regualization(
+        y_pred_proba_sorted_cumsum = self._add_regularization(
             y_pred_proba_sorted_cumsum, **kwargs
         )  # Do nothing as no regularization for the naive method
 
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 9894f991a..070cf4b2a 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -7,7 +7,7 @@
 from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples
 
-from mapie.conformity_scores.sets.aps import APS
+from mapie.conformity_scores.sets.aps import APSConformityScore
 from mapie.conformity_scores.sets.utils import get_true_label_position
 from mapie.estimator.classifier import EnsembleClassifier
 
@@ -17,12 +17,13 @@
 from mapie.utils import check_alpha_and_n_samples, compute_quantiles
 
 
-class RAPS(APS):
+class RAPSConformityScore(APSConformityScore):
     """
     Regularized Adaptive Prediction Sets (RAPS) method-based non-conformity
-    score. It uses the same technique as ``APS`` class but with a penalty term
-    to reduce the size of prediction sets. See [1] for more details. For now,
-    this method only works with ``"prefit"`` and ``"split"`` strategies.
+    score. It uses the same technique as ``APSConformityScore`` class but with
+    a penalty term to reduce the size of prediction sets. See [1] for more
+    details. For now, this method only works with ``"prefit"`` and ``"split"``
+    strategies.
 
     References
     ----------
@@ -511,7 +512,7 @@ def get_conformity_score_quantiles(
 
         return quantiles_
 
-    def _add_regualization(
+    def _add_regularization(
         self,
         y_pred_proba_sorted_cumsum: NDArray,
         lambda_: Optional[float] = None,
@@ -571,7 +572,7 @@ def _add_regualization(
 
         return y_pred_proba_sorted_cumsum
 
-    def _compute_vs_parameter(
+    def _compute_v_parameter(
         self,
         y_proba_last_cumsumed: NDArray,
         threshold: NDArray,
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 2d5693cc1..4e86a2671 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -13,7 +13,7 @@
 from mapie.utils import compute_quantiles
 
 
-class TopK(BaseClassificationScore):
+class TopKConformityScore(BaseClassificationScore):
     """
     Top-K method-based non-conformity score.
 
diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 1cae0f1c8..04295e794 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -7,19 +7,22 @@
 from .regression import BaseRegressionScore
 from .classification import BaseClassificationScore
 from .bounds import AbsoluteConformityScore
-from .sets import APS, LAC, Naive, RAPS, TopK
+from .sets import (
+    APSConformityScore, LACConformityScore, NaiveConformityScore,
+    RAPSConformityScore, TopKConformityScore
+)
 
 from mapie._typing import ArrayLike
 
 
 METHOD_SCORE_MAP = {
-    'score': lambda: LAC(),
-    'lac': lambda: LAC(),
-    'cumulated_score': lambda: APS(),
-    'aps': lambda: APS(),
-    'naive': lambda: Naive(),
-    'raps': lambda: RAPS(),
-    'top_k': lambda: TopK()
+    'score': lambda: LACConformityScore(),
+    'lac': lambda: LACConformityScore(),
+    'cumulated_score': lambda: APSConformityScore(),
+    'aps': lambda: APSConformityScore(),
+    'naive': lambda: NaiveConformityScore(),
+    'raps': lambda: RAPSConformityScore(),
+    'top_k': lambda: TopKConformityScore()
 }
 
 
@@ -117,10 +120,10 @@ def check_depreciated_size_raps(
         warnings.warn(
             "WARNING: Deprecated parameter. "
             "The parameter `size_raps` is deprecated. "
-            "In the next release, `RAPS` takes precedence over "
+            "In the next release, `RAPSConformityScore` takes precedence over "
             "`MapieClassifier` for setting the size used. "
-            "Prefer to define `size_raps` in `RAPS` rather than "
-            "in the `fit` method of `MapieClassifier`.",
+            "Prefer to define `size_raps` in `RAPSConformityScore` rather "
+            "than in the `fit` method of `MapieClassifier`.",
             DeprecationWarning
         )
 
@@ -148,7 +151,7 @@ def check_target(
         or ``"score"`` or if type of target is not multi-class.
     """
     check_classification_targets(y)
-    if type_of_target(y) == "binary" and not isinstance(conformity_score, LAC):
+    if type_of_target(y) == "binary" and not isinstance(conformity_score, LACConformityScore):
         raise ValueError(
             "Invalid method for binary target. "
             "Your target is not of type multiclass and "
@@ -163,7 +166,7 @@ def check_classification_conformity_score(
 ) -> BaseClassificationScore:
     """
     Check parameter ``conformity_score`` for classification task.
-    By default, return a LAC instance.
+    By default, return a LACConformityScore instance.
 
     Parameters
     ----------
@@ -219,4 +222,4 @@ def check_classification_conformity_score(
                 f"Allowed values are {list(METHOD_SCORE_MAP.keys())}."
             )
     else:
-        return LAC()
+        return LACConformityScore()
diff --git a/mapie/tests/test_conformity_scores_sets.py b/mapie/tests/test_conformity_scores_sets.py
index a5197f341..2e258a160 100644
--- a/mapie/tests/test_conformity_scores_sets.py
+++ b/mapie/tests/test_conformity_scores_sets.py
@@ -8,14 +8,20 @@
 from mapie._typing import NDArray
 from mapie.classification import MapieClassifier
 from mapie.conformity_scores import BaseClassificationScore
-from mapie.conformity_scores.sets import APS, LAC, Naive, RAPS, TopK
+from mapie.conformity_scores.sets import (
+    APSConformityScore, LACConformityScore, NaiveConformityScore,
+    RAPSConformityScore, TopKConformityScore
+)
 from mapie.conformity_scores.utils import check_classification_conformity_score
 from mapie.utils import check_alpha
 
 
 random_state = 42
 
-cs_list = [None, LAC(), APS(), RAPS(), Naive(), TopK()]
+cs_list = [
+    None, LACConformityScore(), APSConformityScore(), RAPSConformityScore(),
+    NaiveConformityScore(), TopKConformityScore()
+]
 wrong_cs_list = [object(), "LAC", 1]
 valid_method_list = ['naive', 'aps', 'raps', 'lac', 'top_k']
 all_method_list = valid_method_list + [None]
@@ -130,7 +136,9 @@ def test_check_depreciated_size_raps(size_raps: float, cv: str) -> None:
     a DeprecationWarning when using size_raps.
     """
     clf = LogisticRegression().fit(X, y)
-    mapie_clf = MapieClassifier(estimator=clf, conformity_score=RAPS(), cv=cv)
+    mapie_clf = MapieClassifier(
+        estimator=clf, conformity_score=RAPSConformityScore(), cv=cv
+    )
     with pytest.warns(
         DeprecationWarning,
         match="The parameter `size_raps` is deprecated.*"
@@ -146,7 +154,7 @@ def test_regularize_conf_scores_shape(k_lambda) -> None:
     lambda_, k = k_lambda[0], k_lambda[1]
     conf_scores = np.random.rand(100, 1)
     cutoff = np.cumsum(np.ones(conf_scores.shape)) - 1
-    reg_conf_scores = RAPS._regularize_conformity_score(
+    reg_conf_scores = RAPSConformityScore._regularize_conformity_score(
         k, lambda_, conf_scores, cutoff
     )
 
@@ -166,7 +174,9 @@ def test_get_true_label_cumsum_proba_shape() -> None:
     )
     mapie_clf.fit(X, y)
     classes = mapie_clf.classes_
-    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(y, y_pred, classes)
+    cumsum_proba, cutoff = APSConformityScore.get_true_label_cumsum_proba(
+        y, y_pred, classes
+    )
     assert cumsum_proba.shape == (len(X), 1)
     assert cutoff.shape == (len(X), )
 
@@ -184,7 +194,7 @@ def test_get_true_label_cumsum_proba_result() -> None:
     )
     mapie_clf.fit(X_toy, y_toy)
     classes = mapie_clf.classes_
-    cumsum_proba, cutoff = APS.get_true_label_cumsum_proba(
+    cumsum_proba, cutoff = APSConformityScore.get_true_label_cumsum_proba(
         y_toy, y_pred, classes
     )
     np.testing.assert_allclose(
@@ -225,9 +235,9 @@ def test_get_last_included_proba_shape(k_lambda, include_last_label):
     )
 
     y_p_p_c, y_p_i_l, y_p_p_i_l = \
-        RAPS._get_last_included_proba(
-            RAPS(), y_pred_proba, thresholds, include_last_label,
-            lambda_=lambda_, k_star=k
+        RAPSConformityScore._get_last_included_proba(
+            RAPSConformityScore(), y_pred_proba, thresholds,
+            include_last_label, lambda_=lambda_, k_star=k
         )
 
     assert y_p_p_c.shape == (len(X), len(np.unique(y)), len(thresholds))

From b724c35e4ffc70952412e11df9989cdaa8ed6590 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 16 Jul 2024 15:27:24 +0200
Subject: [PATCH 226/424] FIX: line too long

---
 mapie/conformity_scores/utils.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/mapie/conformity_scores/utils.py b/mapie/conformity_scores/utils.py
index 04295e794..b995926c9 100644
--- a/mapie/conformity_scores/utils.py
+++ b/mapie/conformity_scores/utils.py
@@ -151,7 +151,10 @@ def check_target(
         or ``"score"`` or if type of target is not multi-class.
     """
     check_classification_targets(y)
-    if type_of_target(y) == "binary" and not isinstance(conformity_score, LACConformityScore):
+    if (
+        type_of_target(y) == "binary" and
+        not isinstance(conformity_score, LACConformityScore)
+    ):
         raise ValueError(
             "Invalid method for binary target. "
             "Your target is not of type multiclass and "

From ebf107a3cd9b7300335f301fb84289dd4953a6a3 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 16 Jul 2024 15:53:45 +0200
Subject: [PATCH 227/424] UPD: move check cv - cs function

---
 mapie/classification.py              | 18 +++++++++++---
 mapie/conformity_scores/sets/raps.py | 36 +---------------------------
 2 files changed, 16 insertions(+), 38 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index d73085c0e..aa1f321b8 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -5,13 +5,14 @@
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.model_selection import BaseCrossValidator
+from sklearn.model_selection import BaseCrossValidator, BaseShuffleSplit
 from sklearn.preprocessing import LabelEncoder
 from sklearn.utils import check_random_state
 from sklearn.utils.validation import (_check_y, check_is_fitted, indexable)
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseClassificationScore
+from mapie.conformity_scores.sets.raps import RAPSConformityScore
 from mapie.conformity_scores.utils import (
     check_depreciated_size_raps, check_classification_conformity_score,
     check_target
@@ -39,6 +40,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
         If ``None``, estimator defaults to a ``LogisticRegression`` instance.
 
     method: Optional[str]
+        [DEPRECIATED see instead conformity_score]
         Method to choose for prediction interval estimates.
         Choose among:
 
@@ -119,7 +121,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
 
         By default ``None``.
 
-    conformity_score_function_: BaseClassificationScore
+    conformity_score: BaseClassificationScore
         Score function that handle all that is related to conformity scores.
 
         In any case, the `conformity_score` parameter takes precedence over the
@@ -378,12 +380,22 @@ def _check_fit_parameter(
         )
         check_depreciated_size_raps(size_raps)
         cs_estimator.set_external_attributes(
-            cv=self.cv,
             classes=self.classes_,
             label_encoder=self.label_encoder_,
             size_raps=size_raps,
             random_state=self.random_state
         )
+        if (
+            isinstance(cs_estimator, RAPSConformityScore) and
+            not (
+                self.cv in ["split", "prefit"] or
+                isinstance(self.cv, BaseShuffleSplit)
+            )
+        ):
+            raise ValueError(
+                "RAPS method can only be used "
+                "with ``cv='split'`` and ``cv='prefit'``."
+            )
 
         # Cast
         X, y_enc, y = cast(NDArray, X), cast(NDArray, y_enc), cast(NDArray, y)
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 070cf4b2a..c03c2b48e 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -2,8 +2,7 @@
 
 import numpy as np
 from sklearn.calibration import LabelEncoder
-from sklearn.model_selection import (BaseCrossValidator, BaseShuffleSplit,
-                                     StratifiedShuffleSplit)
+from sklearn.model_selection import StratifiedShuffleSplit
 from sklearn.utils import _safe_indexing
 from sklearn.utils.validation import _num_samples
 
@@ -49,9 +48,6 @@ class RAPSConformityScore(APSConformityScore):
     quantiles_: ArrayLike of shape (n_alpha)
         The quantiles estimated from ``get_sets`` method.
 
-    cv: Union[int, str, BaseCrossValidator]
-        The cross-validation strategy for computing scores.
-
     label_encoder: LabelEncoder
         The label encoder used to encode the labels.
 
@@ -60,8 +56,6 @@ class RAPSConformityScore(APSConformityScore):
         k_star for the RAPS method.
     """
 
-    valid_cv_ = ["prefit", "split"]
-
     def __init__(
         self,
         size_raps: Optional[float] = 0.2
@@ -72,7 +66,6 @@ def __init__(
     def set_external_attributes(
         self,
         *,
-        cv: Optional[Union[str, BaseCrossValidator, BaseShuffleSplit]] = None,
         label_encoder: Optional[LabelEncoder] = None,
         size_raps: Optional[float] = None,
         **kwargs
@@ -82,11 +75,6 @@ def set_external_attributes(
 
         Parameters
         ----------
-        cv: Optional[Union[int, str, BaseCrossValidator]]
-            The cross-validation strategy for computing scores.
-
-            By default ``None``.
-
         label_encoder: Optional[LabelEncoder]
             The label encoder used to encode the labels.
 
@@ -99,28 +87,9 @@ def set_external_attributes(
             By default ``None``.
         """
         super().set_external_attributes(**kwargs)
-        self.cv = cast(Union[str, BaseCrossValidator, BaseShuffleSplit], cv)
         self.label_encoder_ = cast(LabelEncoder, label_encoder)
         self.size_raps = size_raps
 
-    def _check_cv(self):
-        """
-        Check that if the method used is ``"raps"``, then
-        the cross validation strategy is ``"prefit"``.
-
-        Raises
-        ------
-        ValueError
-            If ``method`` is ``"raps"`` and ``cv`` is not ``"prefit"``.
-        """
-        if not (
-            self.cv in self.valid_cv_ or isinstance(self.cv, BaseShuffleSplit)
-        ):
-            raise ValueError(
-                "RAPS method can only be used "
-                f"with cv in {self.valid_cv_}."
-            )
-
     def split_data(
         self,
         X: NDArray,
@@ -162,9 +131,6 @@ def split_data(
             - NDArray of shape (n_samples,)
             - NDArray of shape (n_samples,)
         """
-        # Checks
-        self._check_cv()
-
         # Split data for raps method
         raps_split = StratifiedShuffleSplit(
             n_splits=1,

From bd2348b59cbd284b0854fccbc7a7f5f3af5d1e08 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 16 Jul 2024 17:11:24 +0200
Subject: [PATCH 228/424] Replace `github.com/simai-ml/MAPIE` by
 `github.com/scikit-learn-contrib/MAPIE`in all Mapie files

---
 .github/PULL_REQUEST_TEMPLATE.md | 4 ++--
 CONTRIBUTING.rst                 | 8 ++++----
 HISTORY.rst                      | 1 +
 3 files changed, 7 insertions(+), 6 deletions(-)

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index d58c8e775..1d3951238 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -22,8 +22,8 @@ Please describe the tests that you ran to verify your changes. Provide instructi
 
 # Checklist
 
-- [ ] I have read the [contributing guidelines](https://github.com/simai-ml/MAPIE/blob/master/CONTRIBUTING.rst)
-- [ ] I have updated the [HISTORY.rst](https://github.com/simai-ml/MAPIE/blob/master/HISTORY.rst) and [AUTHORS.rst](https://github.com/simai-ml/MAPIE/blob/master/AUTHORS.rst) files
+- [ ] I have read the [contributing guidelines](https://github.com/scikit-learn-contrib/MAPIE/blob/master/CONTRIBUTING.rst)
+- [ ] I have updated the [HISTORY.rst](https://github.com/scikit-learn-contrib/MAPIE/blob/master/HISTORY.rst) and [AUTHORS.rst](https://github.com/scikit-learn-contrib/MAPIE/blob/master/AUTHORS.rst) files
 - [ ] Linting passes successfully : `make lint`
 - [ ] Typing passes successfully : `make type-check`
 - [ ] Unit tests pass successfully : `make tests`
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index ee6b723d8..e31772962 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -6,7 +6,7 @@ What to work on?
 ----------------
 
 You are welcome to propose and contribute new ideas.
-We encourage you to `open an issue <https://github.com/simai-ml/MAPIE/issues>`_ so that we can align on the work to be done.
+We encourage you to `open an issue <https://github.com/scikit-learn-contrib/MAPIE/issues>`so that we can align on the work to be done.
 It is generally a good idea to have a quick discussion before opening a pull request that is potentially out-of-scope.
 
 Fork/clone/pull
@@ -14,7 +14,7 @@ Fork/clone/pull
 
 The typical workflow for contributing to `mapie` is:
 
-1. Fork the `master` branch from the `GitHub repository <https://github.com/simai-ml/MAPIE>`_.
+1. Fork the `master` branch from the `GitHub repository <github.com/scikit-learn-contrib/MAPIE>`_.
 2. Clone your fork locally.
 3. Commit changes.
 4. Push the changes to your fork.
@@ -64,8 +64,8 @@ Updating changelog
 
 You can make your contribution visible by :
 
-1. adding your name to the Contributors sections of `AUTHORS.rst <https://github.com/simai-ml/MAPIE/blob/master/AUTHORS.rst>`_
-2. adding a line describing your change into `HISTORY.rst <https://github.com/simai-ml/MAPIE/blob/master/HISTORY.rst>`_
+1. adding your name to the Contributors sections of `AUTHORS.rst <https://github.com/scikit-learn-contrib/MAPIE/blob/master/AUTHORS.rst>`_
+2. adding a line describing your change into `HISTORY.rst <https://github.com/scikit-learn-contrib/MAPIE/blob/master/HISTORY.rst>`_
 
 Testing
 -------
diff --git a/HISTORY.rst b/HISTORY.rst
index b88fc99dc..fe396e1bf 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Replace `github.com/simai-ml/MAPIE` by `github.com/scikit-learn-contrib/MAPIE`in all Mapie files
 * Building unit tests for different `Subsample` and `BlockBooststrap` instances
 * Change the sign of C_k in the `Kolmogorov-Smirnov` test documentation
 * Building a training set with a fraction between 0 and 1 with `n_samples` attribute when using `split` method from `Subsample` class.

From c3d9025fe8c37e4be2ae5c2b4d8152121e1993d1 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 16 Jul 2024 17:39:38 +0200
Subject: [PATCH 229/424] FIX: typo in docstring and variable names

---
 mapie/classification.py                   |  2 +-
 mapie/conformity_scores/classification.py |  6 +++---
 mapie/conformity_scores/interface.py      |  8 ++++----
 mapie/conformity_scores/regression.py     |  6 +++---
 mapie/conformity_scores/sets/aps.py       | 12 ++++++------
 mapie/conformity_scores/sets/lac.py       |  2 +-
 mapie/conformity_scores/sets/naive.py     |  2 +-
 mapie/conformity_scores/sets/raps.py      | 16 ++++++++--------
 8 files changed, 27 insertions(+), 27 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index aa1f321b8..f4e19ba45 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -47,7 +47,7 @@ class MapieClassifier(BaseEstimator, ClassifierMixin):
         - ``"naive"``, sum of the probabilities until the 1-alpha threshold.
 
         - ``"lac"`` (formerly called ``"score"``), Least Ambiguous set-valued
-          Classifier. It is based on the the scores
+          Classifier. It is based on the scores
           (i.e. 1 minus the softmax score of the true label)
           on the calibration set. See [1] for more details.
 
diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index 2e2b010c1..e450514fd 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -203,7 +203,7 @@ def predict_set(
     ):
         """
         Compute the prediction sets on new samples based on the uncertainty of
-        the target confidence interval.
+        the target confidence set.
 
         Parameters:
         -----------
@@ -211,14 +211,14 @@ def predict_set(
             The input data or samples for prediction.
 
         alpha_np: NDArray of shape (n_alpha, )
-            Represents the uncertainty of the confidence interval to produce.
+            Represents the uncertainty of the confidence set to produce.
 
         **kwargs: dict
             Additional keyword arguments.
 
         Returns:
         --------
-        The output strcture depend on the ``get_sets`` method.
+        The output structure depend on the ``get_sets`` method.
             The prediction sets for each sample and each alpha level.
         """
         return self.get_sets(X=X, alpha_np=alpha_np, **kwargs)
diff --git a/mapie/conformity_scores/interface.py b/mapie/conformity_scores/interface.py
index e7eaa151c..29f8ff282 100644
--- a/mapie/conformity_scores/interface.py
+++ b/mapie/conformity_scores/interface.py
@@ -39,7 +39,7 @@ def set_ref_predictor(
 
         Parameters
         ----------
-        predictor: BaeEstimator
+        predictor: BaseEstimator
             Reference predictor.
         """
         self.predictor = predictor
@@ -172,7 +172,7 @@ def predict_set(
     ):
         """
         Compute the prediction sets on new samples based on the uncertainty of
-        the target confidence interval.
+        the target confidence set.
 
         Parameters:
         -----------
@@ -180,13 +180,13 @@ def predict_set(
             The input data or samples for prediction.
 
         alpha_np: NDArray of shape (n_alpha, )
-            Represents the uncertainty of the confidence interval to produce.
+            Represents the uncertainty of the confidence set to produce.
 
         **kwargs: dict
             Additional keyword arguments.
 
         Returns:
         --------
-        The output strcture depend on the subclass.
+        The output structure depend on the subclass.
             The prediction sets for each sample and each alpha level.
         """
diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index a3dcb45e8..e6e098464 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -388,7 +388,7 @@ def predict_set(
     ):
         """
         Compute the prediction sets on new samples based on the uncertainty of
-        the target confidence interval.
+        the target confidence set.
 
         Parameters:
         -----------
@@ -396,14 +396,14 @@ def predict_set(
             The input data or samples for prediction.
 
         alpha_np: NDArray of shape (n_alpha, )
-            Represents the uncertainty of the confidence interval to produce.
+            Represents the uncertainty of the confidence set to produce.
 
         **kwargs: dict
             Additional keyword arguments.
 
         Returns:
         --------
-        The output strcture depend on the ``get_bounds`` method.
+        The output structure depend on the ``get_bounds`` method.
             The prediction sets for each sample and each alpha level.
         """
         return self.get_bounds(X=X, alpha_np=alpha_np, **kwargs)
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 9c7affd0b..8e5cb7d27 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -242,11 +242,11 @@ def _compute_v_parameter(
             Vs parameters.
         """
         # compute V parameter from Romano+(2020)
-        vs = (
+        v_param = (
             (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
             y_pred_proba_last[:, 0, :]
         )
-        return vs
+        return v_param
 
     def _add_random_tie_breaking(
         self,
@@ -302,7 +302,7 @@ def _add_random_tie_breaking(
         )
 
         # get the V parameter from Romano+(2020) or Angelopoulos+(2020)
-        vs = self._compute_v_parameter(
+        v_param = self._compute_v_parameter(
             y_proba_last_cumsumed,
             threshold,
             y_pred_proba_last,
@@ -312,13 +312,13 @@ def _add_random_tie_breaking(
         # get random numbers for each observation and alpha value
         random_state = check_random_state(self.random_state)
         random_state = cast(np.random.RandomState, random_state)
-        us = random_state.uniform(size=(prediction_sets.shape[0], 1))
+        u_param = random_state.uniform(size=(prediction_sets.shape[0], 1))
         # remove last label from comparison between uniform number and V
-        vs_less_than_us = np.less_equal(vs - us, EPSILON)
+        label_to_keep = np.less_equal(v_param - u_param, EPSILON)
         np.put_along_axis(
             prediction_sets,
             y_pred_index_last,
-            vs_less_than_us[:, np.newaxis, :],
+            label_to_keep[:, np.newaxis, :],
             axis=1
         )
         return prediction_sets
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index a81d39240..bf5bcbd01 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -16,7 +16,7 @@ class LACConformityScore(BaseClassificationScore):
     Least Ambiguous set-valued Classifier (LAC) method-based
     non conformity score (also formerly called ``"score"``).
 
-    It is based on the the scores (i.e. 1 minus the softmax score of the true
+    It is based on the scores (i.e. 1 minus the softmax score of the true
     label) on the calibration set.
 
     References
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 79ba4407c..19b0e42c9 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -156,7 +156,7 @@ def _get_last_included_proba(
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Function that returns the smallest score
-        among those which are included in the prediciton set.
+        among those which are included in the prediction set.
 
         Parameters
         ----------
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index c03c2b48e..1c39aed8f 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -125,11 +125,11 @@ def split_data(
         -------
         Tuple[NDArray, NDArray, NDArray, NDArray, Optional[NDArray],
         Optional[NDArray]]
-            - NDArray of shape (n_samples, n_features)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
-            - NDArray of shape (n_samples,)
+            - X: NDArray of shape (n_samples, n_features)
+            - y: NDArray of shape (n_samples,)
+            - y_enc: NDArray of shape (n_samples,)
+            - sample_weight: Optional[NDArray] of shape (n_samples,)
+            - groups: Optional[NDArray] of shape (n_samples,)
         """
         # Split data for raps method
         raps_split = StratifiedShuffleSplit(
@@ -258,7 +258,7 @@ def _update_size_and_lambda(
         Parameters
         ----------
         best_sizes: NDArray of shape (n_alphas, )
-            Smallest average prediciton set size before testing
+            Smallest average prediction set size before testing
             for the new value of lambda_
 
         alpha_np: NDArray of shape (n_alphas)
@@ -570,7 +570,7 @@ def _compute_v_parameter(
         """
         # compute V parameter from Angelopoulos+(2020)
         L = np.sum(prediction_sets, axis=1)
-        vs = (
+        v_param = (
             (y_proba_last_cumsumed - threshold.reshape(1, -1)) /
             (
                 y_pred_proba_last[:, 0, :] -
@@ -578,4 +578,4 @@ def _compute_v_parameter(
                 self.lambda_star * (L > self.k_star)
             )
         )
-        return vs
+        return v_param

From d7b484757060e359da5e5a462514b1634ad183f8 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Tue, 16 Jul 2024 17:42:20 +0200
Subject: [PATCH 230/424] UPD: change interval to set

---
 mapie/conformity_scores/classification.py | 8 ++++----
 mapie/conformity_scores/interface.py      | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index e450514fd..00e397128 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -68,7 +68,7 @@ def get_predictions(
 
         alpha_np: NDArray of shape (n_alpha,)
             NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
+            uncertainty of the confidence set.
 
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
@@ -99,7 +99,7 @@ def get_conformity_score_quantiles(
 
         alpha_np: NDArray of shape (n_alpha,)
             NDArray of floats between 0 and 1, representing the uncertainty
-            of the confidence interval.
+            of the confidence set.
 
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
@@ -135,7 +135,7 @@ def get_prediction_sets(
 
         alpha_np: NDArray of shape (n_alpha,)
             NDArray of floats between 0 and 1, representing the uncertainty
-            of the confidence interval.
+            of the confidence set.
 
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
@@ -165,7 +165,7 @@ def get_sets(
 
         alpha_np: NDArray of shape (n_alpha,)
             NDArray of floats between 0 and 1, representing the uncertainty
-            of the confidence interval.
+            of the confidence set.
 
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
diff --git a/mapie/conformity_scores/interface.py b/mapie/conformity_scores/interface.py
index 29f8ff282..07345d3e4 100644
--- a/mapie/conformity_scores/interface.py
+++ b/mapie/conformity_scores/interface.py
@@ -114,7 +114,7 @@ def get_quantile(
 
         alpha_np: NDArray of shape (n_alpha,)
             NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
+            uncertainty of the confidence set.
 
         axis: int
             The axis from which to compute the quantile.
@@ -128,7 +128,7 @@ def get_quantile(
             By default ``False``.
 
         unbounded: bool
-            Boolean specifying whether infinite prediction intervals
+            Boolean specifying whether infinite prediction sets
             could be produced (when alpha_np is greater than or equal to 1.).
 
             By default ``False``.

From 4c97a005f3a164eb0a2db6b891498a03e3812c51 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Wed, 17 Jul 2024 11:00:41 +0200
Subject: [PATCH 231/424] UPD: documentation with score api

---
 doc/api.rst | 20 +++++++++++++++-----
 1 file changed, 15 insertions(+), 5 deletions(-)

diff --git a/doc/api.rst b/doc/api.rst
index a36957f36..411221efd 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -73,8 +73,8 @@ Metrics
    metrics.spiegelhalter_statistic
    metrics.top_label_ece
 
-Conformity scores
-=================
+Conformity scores (regression)
+==============================
 
 .. autosummary::
    :toctree: generated/
@@ -84,10 +84,20 @@ Conformity scores
    conformity_scores.AbsoluteConformityScore
    conformity_scores.GammaConformityScore
    conformity_scores.ResidualNormalisedScore
+
+Conformity scores (classification)
+==================================
+
+.. autosummary::
+   :toctree: generated/
+   :template: class.rst
+
    conformity_scores.BaseClassificationScore
-   conformity_scores.LAC
-   conformity_scores.APS
-   conformity_scores.TopK
+   conformity_scores.NaiveConformityScore
+   conformity_scores.LACConformityScore
+   conformity_scores.APSConformityScore
+   conformity_scores.RAPSConformityScore
+   conformity_scores.TopKConformityScore
 
 Resampling
 ==========

From 3fd1adfad160ca8c85957081b0eec11b479f784b Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 17 Jul 2024 11:43:52 +0200
Subject: [PATCH 232/424] Update : contributing.rst

---
 CONTRIBUTING.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index e31772962..eb2a0bdef 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -14,7 +14,7 @@ Fork/clone/pull
 
 The typical workflow for contributing to `mapie` is:
 
-1. Fork the `master` branch from the `GitHub repository <github.com/scikit-learn-contrib/MAPIE>`_.
+1. Fork the `master` branch from the `GitHub repository <https://github.com/scikit-learn-contrib/MAPIE>`_.
 2. Clone your fork locally.
 3. Commit changes.
 4. Push the changes to your fork.

From 8401c07cc2dbe253ad5b1d94555902987b3291c5 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Fri, 19 Jul 2024 15:38:26 +0200
Subject: [PATCH 233/424] Update : Notebook ts-changepoint and
 regression_coverage_score()

---
 mapie/metrics.py                          |  10 +-
 mapie/utils.py                            |  11 +-
 notebooks/regression/ts-changepoint.ipynb | 359 ++++++++++++++++------
 3 files changed, 281 insertions(+), 99 deletions(-)

diff --git a/mapie/metrics.py b/mapie/metrics.py
index 20c5065f0..74a841f3d 100644
--- a/mapie/metrics.py
+++ b/mapie/metrics.py
@@ -19,6 +19,7 @@ def regression_coverage_score(
     y_true: ArrayLike,
     y_pred_low: ArrayLike,
     y_pred_up: ArrayLike,
+    warning_inf: bool = False
 ) -> float:
     """
     Effective coverage score obtained by the prediction intervals.
@@ -57,14 +58,15 @@ def regression_coverage_score(
     check_arrays_length(y_true, y_pred_low, y_pred_up)
     check_lower_upper_bounds(y_true, y_pred_low, y_pred_up)
     check_array_nan(y_true)
-    check_array_inf(y_true)
+    check_array_inf(y_true, warning_inf=warning_inf)
     check_array_nan(y_pred_low)
-    check_array_inf(y_pred_low)
+    check_array_inf(y_pred_low, warning_inf=warning_inf)
     check_array_nan(y_pred_up)
-    check_array_inf(y_pred_up)
+    check_array_inf(y_pred_up, warning_inf=warning_inf)
 
     coverage = np.mean(
-        ((y_pred_low <= y_true) & (y_pred_up >= y_true))
+        ((y_pred_low <= y_true) & (y_pred_up >= y_true)) |
+        np.isinf(y_pred_low) | np.isinf(y_pred_up)
     )
     return float(coverage)
 
diff --git a/mapie/utils.py b/mapie/utils.py
index 13641b154..391a88be7 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1280,7 +1280,7 @@ def check_array_nan(array: NDArray) -> None:
         )
 
 
-def check_array_inf(array: NDArray) -> None:
+def check_array_inf(array: NDArray, warning_inf: bool = False) -> None:
     """
     Checks if the array have inf.
     If a value is infinite, we throw an error.
@@ -1296,9 +1296,12 @@ def check_array_inf(array: NDArray) -> None:
         If any elements of the array is +inf or -inf.
     """
     if np.isinf(array).any():
-        raise ValueError(
-            "Array contains infinite values."
-        )
+        if warning_inf:
+            warnings.warn("Array contains infinite values.", UserWarning)
+        else:
+            raise ValueError(
+                "Array contains infinite values."
+            )
 
 
 def check_arrays_length(*arrays: NDArray) -> None:
diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index 85cef140b..fcd0de9e0 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 254,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 255,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,6 +66,7 @@
     "from mapie.metrics import regression_coverage_score, regression_mean_width_score, coverage_width_based\n",
     "from mapie.subsample import BlockBootstrap\n",
     "from mapie.time_series_regression import MapieTimeSeriesRegressor\n",
+    "from mapie.conformity_scores import ConformityScore\n",
     "\n",
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
@@ -82,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 256,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -111,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 257,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -131,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 258,
    "metadata": {},
    "outputs": [
     {
@@ -140,13 +141,13 @@
        "Text(0, 0.5, 'Hourly demand (GW)')"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 258,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x500 with 1 Axes>"
       ]
@@ -172,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 259,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 260,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -240,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 261,
    "metadata": {},
    "outputs": [
     {
@@ -270,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 262,
    "metadata": {},
    "outputs": [
     {
@@ -309,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 263,
    "metadata": {},
    "outputs": [
     {
@@ -356,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 264,
    "metadata": {},
    "outputs": [
     {
@@ -410,14 +411,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 265,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "cwc aci : 0.8795561649375995 cwc enbpi : 0.8886303838015679\n"
+      "cwc aci : 0.8858765618716242 cwc enbpi : 0.8065050627302403\n"
      ]
     }
    ],
@@ -435,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 266,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -447,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 267,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -459,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 268,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -494,12 +495,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 269,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -514,12 +515,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 270,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -558,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 271,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -568,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 272,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -588,22 +589,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 273,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x177a0fb50>]"
+       "[<matplotlib.lines.Line2D at 0x183f553f0>]"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 273,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1600x500 with 1 Axes>"
       ]
@@ -629,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 274,
    "metadata": {},
    "outputs": [
     {
@@ -659,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 275,
    "metadata": {},
    "outputs": [
     {
@@ -698,7 +699,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 276,
    "metadata": {},
    "outputs": [
     {
@@ -713,10 +714,10 @@
     "print(\"EnbPI with partial_fit, width optimization\")\n",
     "mapie_enbpi = mapie_enbpi.fit(X_train, y_train)\n",
     "\n",
-    "y_pred_pfit = np.zeros(y_pred_enbpi_npfit.shape)\n",
-    "y_pis_pfit = np.zeros(y_pis_enbpi_npfit.shape)\n",
-    "conformity_scores_pfit, lower_quantiles_pfit, higher_quantiles_pfit = [], [], []\n",
-    "y_pred_pfit[:gap], y_pis_pfit[:gap, :, :] = mapie_enbpi.predict(\n",
+    "y_pred_enbpi_pfit = np.zeros(y_pred_enbpi_npfit.shape)\n",
+    "y_pis_enbpi_pfit = np.zeros(y_pis_enbpi_npfit.shape)\n",
+    "conformity_scores_enbpi_pfit, lower_quantiles_enbpi_pfit, higher_quantiles_enbpi_pfit = [], [], []\n",
+    "y_pred_enbpi_pfit[:gap], y_pis_enbpi_pfit[:gap, :, :] = mapie_enbpi.predict(\n",
     "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True\n",
     ")\n",
     "for step in range(gap, len(X_test), gap):\n",
@@ -725,51 +726,80 @@
     "        y_test.iloc[(step - gap):step],\n",
     "    )\n",
     "    (\n",
-    "        y_pred_pfit[step:step + gap],\n",
-    "        y_pis_pfit[step:step + gap, :, :],\n",
+    "        y_pred_enbpi_pfit[step:step + gap],\n",
+    "        y_pis_enbpi_pfit[step:step + gap, :, :],\n",
     "    ) = mapie_enbpi.predict(\n",
     "        X_test.iloc[step:(step + gap), :],\n",
     "        alpha=alpha,\n",
     "        ensemble=True, \n",
     "        optimize_beta=True\n",
     "    )\n",
-    "    conformity_scores_pfit.append(mapie_enbpi.conformity_scores_)\n",
-    "    lower_quantiles_pfit.append(mapie_enbpi.lower_quantiles_)\n",
-    "    higher_quantiles_pfit.append(mapie_enbpi.higher_quantiles_)\n",
-    "coverage_pfit = regression_coverage_score(\n",
-    "    y_test, y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]\n",
+    "\n",
+    "    conformity_scores = mapie_enbpi.conformity_scores_\n",
+    "\n",
+    "    conformity_scores_enbpi_pfit.append(conformity_scores)\n",
+    "\n",
+    "    alpha_np = np.array([alpha])\n",
+    "\n",
+    "    beta_np = ConformityScore._beta_optimize(\n",
+    "                    alpha_np,\n",
+    "                    conformity_scores.reshape(1, -1),\n",
+    "                    conformity_scores.reshape(1, -1),\n",
+    "                )\n",
+    "    alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np\n",
+    "\n",
+    "    lower_quantiles = ConformityScore.get_quantile(\n",
+    "    conformity_scores[..., np.newaxis],\n",
+    "    alpha_low, axis=0, reversed=True,\n",
+    "    unbounded=False\n",
+    "    )\n",
+    "\n",
+    "    higher_quantiles = ConformityScore.get_quantile(\n",
+    "        conformity_scores[..., np.newaxis],\n",
+    "        alpha_up, axis=0,\n",
+    "        unbounded=False\n",
+    "    )\n",
+    "    \n",
+    "    lower_quantiles_enbpi_pfit.append(lower_quantiles)\n",
+    "    higher_quantiles_enbpi_pfit.append(higher_quantiles)\n",
+    "\n",
+    "coverage_enbpi_pfit = regression_coverage_score(\n",
+    "    y_test, y_pis_enbpi_pfit[:, 0, 0], y_pis_enbpi_pfit[:, 1, 0]\n",
     ")\n",
-    "width_pfit = regression_mean_width_score(\n",
-    "    y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]\n",
+    "width_enbpi_pfit = regression_mean_width_score(\n",
+    "    y_pis_enbpi_pfit[:, 0, 0], y_pis_enbpi_pfit[:, 1, 0]\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 277,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ACI with adapt_conformal_inference\n"
+      "ACI with partial_fit and adapt_conformal_inference\n"
      ]
     }
    ],
    "source": [
-    "print(\"ACI with adapt_conformal_inference\")\n",
+    "print(\"ACI with partial_fit and adapt_conformal_inference\")\n",
     "mapie_aci = mapie_aci.fit(X_train, y_train)\n",
     "\n",
     "y_pred_aci_pfit = np.zeros(y_pred_aci_npfit.shape)\n",
     "y_pis_aci_pfit = np.zeros(y_pis_aci_npfit.shape)\n",
+    "conformity_scores_aci_pfit, lower_quantiles_aci_pfit, higher_quantiles_aci_pfit = [], [], []\n",
+    "\n",
     "y_pred_aci_pfit[:gap], y_pis_aci_pfit[:gap, :, :] = mapie_aci.predict(\n",
-    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True\n",
+    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
     "\n",
     "\"\"\" X = X_test.to_numpy()\n",
     "y_true = y_test.to_numpy() \"\"\"\n",
     "for step in range(gap, len(X_test), gap):\n",
+    "\n",
     "    mapie_aci.partial_fit(\n",
     "        X_test.iloc[(step - gap):step, :],\n",
     "        y_test.iloc[(step - gap):step],\n",
@@ -786,22 +816,51 @@
     "        X_test.iloc[step:(step + gap), :],\n",
     "        alpha=alpha,\n",
     "        ensemble=True,\n",
-    "        optimize_beta=True\n",
+    "        optimize_beta=True, allow_infinite_bounds=True\n",
+    "    )\n",
+    "    \n",
+    "\n",
+    "    conformity_scores = mapie_aci.conformity_scores_\n",
+    "\n",
+    "    conformity_scores_aci_pfit.append(conformity_scores)\n",
+    "\n",
+    "    current_alpha_np = np.array((list(mapie_aci.current_alpha.values())))\n",
+    "\n",
+    "\n",
+    "    beta_np = ConformityScore._beta_optimize(\n",
+    "                    current_alpha_np,\n",
+    "                    conformity_scores.reshape(1, -1),\n",
+    "                    conformity_scores.reshape(1, -1),\n",
+    "                )\n",
+    "    alpha_low, alpha_up = beta_np, 1 - current_alpha_np + beta_np\n",
+    "\n",
+    "    lower_quantiles = ConformityScore.get_quantile(\n",
+    "    conformity_scores[..., np.newaxis],\n",
+    "    alpha_low, axis=0, reversed=True,\n",
+    "    unbounded=False\n",
+    "    )\n",
+    "\n",
+    "    higher_quantiles = ConformityScore.get_quantile(\n",
+    "        conformity_scores[..., np.newaxis],\n",
+    "        alpha_up, axis=0,\n",
+    "        unbounded=False\n",
     "    )\n",
     "    \n",
+    "    lower_quantiles_aci_pfit.append(lower_quantiles)\n",
+    "    higher_quantiles_aci_pfit.append(higher_quantiles)\n",
+    "\n",
     "coverage_aci_pfit = regression_coverage_score(\n",
-    "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
+    "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0], warning_inf=True\n",
     ")\n",
-    "width_aci_pfit = regression_mean_width_score(\n",
-    "    y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
-    ")\n",
-    "cwc_aci_pfit = coverage_width_based(\n",
-    "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0], eta = 0.01, alpha = 0.05\n",
-    ")"
+    "# width_aci_pfit = regression_mean_width_score(\n",
+    "#     y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
+    "# )\n",
+    "# cwc_aci_pfit = coverage_width_based(\n",
+    "#     y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0], eta = 0.01, alpha = 0.05\n",
+    "# )"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -810,36 +869,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": 278,
    "metadata": {},
    "outputs": [],
    "source": [
-    "y_enbpi_preds = [y_pred_enbpi_npfit, y_pred_pfit]\n",
-    "y_enbpi_pis = [y_pis_enbpi_npfit, y_pis_pfit]\n",
-    "coverages_enbpi = [coverage_enbpi_npfit, coverage_pfit]\n",
-    "widths_enbpi = [width_enbpi_npfit, width_pfit]"
+    "y_enbpi_preds = [y_pred_enbpi_npfit, y_pred_enbpi_pfit]\n",
+    "y_enbpi_pis = [y_pis_enbpi_npfit, y_pis_enbpi_pfit]\n",
+    "coverages_enbpi = [coverage_enbpi_npfit, coverage_enbpi_pfit]\n",
+    "widths_enbpi = [width_enbpi_npfit, width_enbpi_pfit]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 279,
    "metadata": {},
    "outputs": [],
    "source": [
     "y_aci_preds = [y_pred_aci_npfit, y_pred_aci_pfit]\n",
     "y_aci_pis = [y_pis_aci_npfit, y_pis_aci_pfit]\n",
     "coverages_aci = [coverage_aci_npfit, coverage_aci_pfit]\n",
-    "widths_aci = [width_aci_npfit, width_aci_pfit]"
+    "widths_aci = [width_aci_npfit]\n",
+    "#widths_aci = [width_aci_npfit, width_aci_pfit]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 280,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -854,12 +914,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 281,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -869,24 +929,25 @@
     }
    ],
    "source": [
-    "plot_forecast(\"ACI\", y_train, y_test, y_aci_preds, y_aci_pis, coverages_aci, widths_aci, plot_coverage=False)"
+    "plot_forecast(\"ACI\", y_train, y_test, y_aci_preds, y_aci_pis, coverages_aci, widths_aci, plot_coverage=False)\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 282,
    "metadata": {},
    "outputs": [],
    "source": [
+    "\n",
     "window = 24\n",
-    "rolling_coverage_pfit, rolling_coverage_npfit = [], []\n",
+    "rolling_coverage_enbpi_pfit, rolling_coverage_enbpi_npfit = [], []\n",
     "for i in range(window, len(y_test), 1):\n",
-    "    rolling_coverage_pfit.append(\n",
+    "    rolling_coverage_enbpi_pfit.append(\n",
     "        regression_coverage_score(\n",
-    "            y_test[i-window:i], y_pis_pfit[i-window:i, 0, 0], y_pis_pfit[i-window:i, 1, 0]\n",
+    "            y_test[i-window:i], y_pis_enbpi_pfit[i-window:i, 0, 0], y_pis_enbpi_pfit[i-window:i, 1, 0]\n",
     "        )\n",
     "    )\n",
-    "    rolling_coverage_npfit.append(\n",
+    "    rolling_coverage_enbpi_npfit.append(\n",
     "        regression_coverage_score(\n",
     "            y_test[i-window:i], y_pis_enbpi_npfit[i-window:i, 0, 0], y_pis_enbpi_npfit[i-window:i, 1, 0]\n",
     "        )\n",
@@ -894,31 +955,59 @@
    ]
   },
   {
-   "attachments": {},
+   "cell_type": "code",
+   "execution_count": 283,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "window = 24\n",
+    "rolling_coverage_aci_pfit, rolling_coverage_aci_npfit = [], []\n",
+    "for i in range(window, len(y_test), 1):\n",
+    "    rolling_coverage_aci_pfit.append(\n",
+    "        regression_coverage_score(\n",
+    "            y_test[i-window:i], y_pis_aci_pfit[i-window:i, 0, 0], y_pis_aci_pfit[i-window:i, 1, 0], warning_inf=True\n",
+    "        )\n",
+    "    )\n",
+    "    rolling_coverage_aci_npfit.append(\n",
+    "        regression_coverage_score(\n",
+    "            y_test[i-window:i], y_pis_aci_npfit[i-window:i, 0, 0], y_pis_aci_npfit[i-window:i, 1, 0], warning_inf = True\n",
+    "        )\n",
+    "    )"
+   ]
+  },
+  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Marginal coverage on a 24-hour rolling window of prediction intervals"
+    "## Marginal coverage on a 24-hour rolling window of prediction intervals\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ENBPI"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 284,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x16a995d50>]"
+       "[<matplotlib.lines.Line2D at 0x184142890>]"
       ]
      },
-     "execution_count": 31,
+     "execution_count": 284,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1cAAAGvCAYAAAC3nC+tAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAABxJklEQVR4nO3deXhU5d3/8fdM9n0hZAHCGkgCQUAQBERk32RpfVq0C0qttf60KrS1pW5d3OvWVlufWq21y+NWN1DCLqigKIsSyMK+ZiVkIYFsc35/nEwwkkAmmeRMJp/Xdc2Vw+TMmU+8hZzvnO+5b5thGAYiIiIiIiLSJnarA4iIiIiIiHgDFVciIiIiIiJuoOJKRERERETEDVRciYiIiIiIuIGKKxERERERETdQcSUiIiIiIuIGKq5ERERERETcQMWViIiIiIiIG6i4EhERERERcQMVVyIiIiIiIm7ga+Wbb9q0id///vds27aN3Nxc3nrrLRYsWHDB13zwwQcsXbqU3bt3k5iYyD333MMNN9zQ4vd0OBycOHGCsLAwbDZb234AERERERHptAzDoLy8nB49emC3t/26k6XFVUVFBcOGDeMHP/gB3/zmNy+6/8GDB5kzZw4//vGP+fe//826dev44Q9/SEJCAjNmzGjRe544cYLExMS2RhcRERERES9x9OhRevXq1ebj2AzDMNyQp81sNttFr1z94he/4L333iMjI6PhuWuvvZaSkhLS09Nb9D6lpaVERkZy9OhRwsPD2xpbREREREQ6qbKyMhITEykpKSEiIqLNx7P0ypWrtmzZwtSpUxs9N2PGDO68885mX1NVVUVVVVXDn8vLywEIDw9XcSUiIiIiIm67XahTTWiRl5dHXFxco+fi4uIoKyvjzJkzTb7m4YcfJiIiouGhlkAREREREWkPnaq4ao1ly5ZRWlra8Dh69KjVkURERERExAt1qrbA+Ph48vPzGz2Xn59PeHg4QUFBTb4mICCAgICAjognIiIiIiJdWKe6cjV27FjWrVvX6Lk1a9YwduxYixKJiIiIiIiYLC2uTp8+zc6dO9m5cydgTrW+c+dOjhw5ApgtfYsWLWrY/8c//jEHDhzgrrvuIisriz//+c+89tprLFmyxIr4IiIiIiIiDSwtrj7//HNGjBjBiBEjAFi6dCkjRozgvvvuAyA3N7eh0ALo168f7733HmvWrGHYsGE88cQT/O1vf2vxGlciIiIiIiLtxWPWueooZWVlREREUFpaqqnYRURERES6MHfXBp3qnisRERERERFPpeJKRERERETEDVRciYiIiIiIuIGKKxERERERETdQcSUiIiIiIuIGKq5ERERERETcwNfqACIi0jyHw2DnsRIqq+o6/L0To4Po0y2kw99XRESks1JxJSLiwf699Qj3vp1hyXv7+9rZ8LOr6BkZZMn7i4iIdDYqrkREPNhb248B0DMyiLDAjvsn+0TJGcrO1rJyVy4/nNC/w95XRESkM1NxJSLiofJKz7L9SAkA/71lHPERgR323i99fJBfL9/Dyow8FVciIiItpAktREQ81KrdeQBc2juyQwsrgJlpCQBsO3yK/LKzHfreIiIinZWKKxERD7UyIxeAWfWFTkeKjwhkRO9I4FyRJyIiIhem4kpExAOdPF3F1oPFAMxMi7ckw6z69125S8WViIhIS6i4EhHxQKv35OMwIK1nOInRwZZkcF4x+/TgSU6errIkg4iISGei4kpExAOtzDCvFlnREuiUGB3MkB7hOAxYsyffshwiIiKdhYorEREPU1pZw+Z9RYB1LYFODa2BGWoNFBERuRgVVyIiHmZtZj61DoNBcaEM6B5qaRbnrIGb9xdReqbG0iwiIiKeTsWViIiHcV4lmmlhS6BTUmwoA2NDqakzWJ+l1kAREZELUXElIuJBTlfVsmlvIXCuJc9qMzVroIiISIuouBIR8SAbsgqornXQt1swKfFhVscBzhVXG3MKqaiqtTiNiIiI51JxJSLiQdJ3n2sJtNlsFqcxDU4Ip3d0MFW1Dj7ILrQ6joiIiMfytTpAV1Z1tpLammqrYzQIDgnHZle9Lc1z1NVxprK8dS+22cHPmvWaLsowoKbC6hRU1zr4NOsIwdQxOzkMqk63/aC+AeDj16ZD2Gw2ZqXF87+bDrAyI5c5l1h/L5iIiIgnUnFloe3/+Dljc/9ldYwGe32S6PfLLfj6+VsdRTzQ2crT5D8+hj6OY60+xl9r5/BQ7XfdmMo9/uL3FLN8PrM6BiHA53YgEHjZTQf1C4bF70OPEW06zMz64mpDVgFna+oI9PNxU0ARERHvocsU0mBg3T6yt66xOoZ4qMyP3m5TYQXwXZ+1BOA5V2sBelLoEYVVu6mphG3/aPNhhvWKJCEikIrqOj7cW+SGYCIiIt5HV64sNGrxk5ytfdTqGADs+t8buaw0ndM7/gvj51gdRzxQXcbbAHzS/VsMX/y0i682CHhuDCFlx9n1PV8cyTPdHa/VfD79M6wFR+9xVF/7utVxsNkgwNdNV4UOboL/fBuyVsCcJ8De+uPa7TZmDInnpc2HSM/IY9rgOPdkFBER8SIqrizk5x+An3+A1TEA8LvkG/BhOv2KNuCoq8Puo5YfOae66iyDyj4GIHLUtwkMbsXCtqnz4NO/4J+zAtLmujlhG2SvAMA+ZEHrfi5PNmAyBEZCRSEc+QT6jm/T4WalmcXV2sx8auoc+Pmo+UFEROSr9JtRAEgdP4/TRhCxFJOzfYPVccTDZG5eTjiVFBLFoFFTWneQwfPMr9krodZDWgPL8+Dop+Z2qgcVfO7i4wfJs83tzHfbfLhRfaOJCfWn9EwNW/afbPPxREREvI2KKwEgIDCYrPBxAJR8/obFacTTVH35FgAHYia1/qpm4hgIiYWqUrNdzRNkLgcM6HUZhPewOk37cBa1mcvB4WjToXzsNqYNrl9QOEMLCouIiHydiitpYB9inoT1LliH0caTMPEetTXVDDxlFkMhw7/R+gPZfSD1anPbDVdR3MKZI3WetTnaU/9J4B8KZcfhxPY2H25W/YLCa/bkUecw2nw8ERERb6LiShqkXPENzhj+9DAK2L9rs9VxxENkfZpOFOWcIoyUMW2ciMJZxGS9B466todri4qTcMi8j6zh6o438guEQTPM7T3vtPlwYwd0IyLIj6LT1Xx2qLjNxxMREfEmKq6kQXBoBJmhYwAo3Gr9rGniGSp2mi2Be6OubPsaaH2vgKAoqCyCwxYX8NnvgVEH8ZdAVF9rs7Q3Z1Gb+a65YHIb+PnYmZpqzhSYrtZAERGRRlRcSSOOFPOm/p65a9QaKDjq6uhfZE5wEjB0QdsP6OMHyfVT/VvdGrinC7QEOiVNBd9AOHUI8na1+XDO1sD0jDwcag0UERFpoOJKGkm+8ltUG770dhzncHbb78+Qzi3n83V05xTlRhAp4652z0HdOMFCq50pgQMfNM7jzQJCzQIL3FLUXjEwhhB/H/LKzrLzWEmbjyciIuItVFxJI2ER0WQGjwQg9xO1BnZ1JdvfBCA7YjwBgcHuOWj/qyAgHMpz4fjn7jmmq/auBkcNxCRD92RrMnS01K8UtW0U6OfD5PrWwFVqDRQREWmg4krOUzXIvEIRe2yVxUnESobDQZ/8tYC5wK7b+Aa4dYKFVnG+b1e4auU0aAbY/aAwCwpz2nw4Z2vgyow8jDbexyUiIuItVFzJeQZN+Da1hp0BdQc5fmC31XHEIvu+/JgECqk0Aki9YoF7D+5csDdzeZsnWHBZdQXsW1efowsVV0GR5lVDgMy2F7VXJXcnwNfOkeJK9uSWtfl4IiIi3kDFlZwnMiaerMBLADj60SsWpxGrFNXPGJkVNoagkDD3HjxpKvgGQclhyPvSvce+mL1roPaMOUNg/NCOfW+rOa/U7Wn7fVfB/r5MHNQd0KyBIiIiTr5WBxDPVDFgDuzZyZD9z3Pot29fcF8DOwUp32XMt+/qmHDSJp/88z7iDr6NjQtfMRpWlw+2czNIupV/CAycal65+s9CCIx0/3s0p6LA/Jo6F2y2jntfT5A8G2x3mAXts2OAi/z83ZPhm8+Db9NT8M8aGs/qPfmszMjjp9O7yL1rIiIiF6DiSpo04MrrqNz9OGG2M4Q5jlx0/5jdT1JddTv+AYEdkE5aq7y0mEv3PYu/rfbiO9uglBCSJ/xP+4QZdp1ZXJXnmo+OZLPD0G937Ht6gpAYGDgdctLNe68upjATLlkIKbOb/PbklDj8fGzsKzjNvoJykmLdfIVTRESkk1FxJU2KiU8k78aPOXD04idg8WtuI8ZWwheblzNs0rc6IJ20VvaHbzDKVstRWw9Kpz520f3j+g2le0R0+4RJmQM//gjOnGqf419IWALEDOz49/UE//MinNhpLqB8Idtfhl2vm1O3N1NcRQT5MT4phg+yC1m5K4+fTFFxJSIiXZuKK2lWfO+BxPe++Anop9vfIObk21R9+RaouPJo9vppuI/1mM7Y8e3Q7ueqrnbPkyfwD4G+4y++n83HLK6y34fa6uZbA9PizeIqI4+fTOmiBauIiEg9TWghbRYy/JsADDy1idqaaovTSHPOVJSTcvpTALqPVhEsF9H7cgjpDmdL4dCmZnebNjgeH7uNPbllHDlZ2YEBRUREPI+KK2mzlMtncYowoign69N0q+NIM7I+epNgWxUnbLEMGDrO6jji6ew+kGKueXeh2QWjQ/wZ089sHU3f3cH3zomIiHgYFVfSZr5+/uyNnABAxc63LE4jzanbbZ4gH4mdgs2uv/rSAs6p27PeA0fz92h9dUFhERGRrkxnWOIWAZd8A4D+RRtw1F3kRnnpcFVnK0ku/RiAyFHtNPufeJ++E8xp8iuL4PDmZnebMSQemw12HCkht/RMx+UTERHxMCquxC1Sxl1NuRFEd06Rs2291XHka7I2v0uY7QwFRDPo0klWx5HOwsfPnNURzFkDmxEbHsilvaMAWKWrVyIi0oWpuBK3CAgMJjvCnIGsZNt/LU4jX1f15TsAHIy5CruPj8VppFNJrW8NzFwODkezu6k1UERERMWVuJF9yAIA+uSvxbjASZh0rJrqKgaVmLO9hYz4psVppNPpfxX4h5kLPR/f1uxuM4aYxdVnh4opOl3VQeFEREQ8i4orcZvUKxZQaQSQQCH7vvzY6jhSL+uTdCI5zSnCSRk9w+o40tn4BcKg6eZ25jvN7pYYHczQnhE4DFi9O7+DwomIiHgWFVfiNkEhYWSFjQGgaOvrFqcRp8ov3gRgb9SV+Po1vRCsyAU5WwP3vAuG0exuMxtaAzUlu4iIdE0qrsStHPUnYb1y16g10APU1dYy4OQHAATWz+go4rKB08A3CEoOQ96Xze7mvO9qy/6TlFbWdFQ6ERERj6HiStwq+YprqDZ8STROcDh7u9VxurycbeuIoYQygkkZd7XVcaSz8g+BpCnm9gUWFO7fPZTkuDBqHQZrM9UaKCIiXY+v1QHEu4RFRLMzeBTDz3zCmTd/wtaQvlZHapIjJpkx37nPpcV0DcPghY8OkpNf3o7JWq/XmSzGFC/HZpxbZyzq9F4AsiOu4LKAQKuiiTcYPB+yVphTsk+5t9ndZqbFk51fzsqMPK4Z2asDA4qIiFhPxZW4XW3qAtj+Cak1e6Bkj9VxmlbyPlmfjSNlzPQWv2TH0RIeeC+zHUO1zQr/h0izH2rye35DNUugtNGgGWD3g6IcKMiC2JQmd5s1NJ4/rNvLpr2FnK6qJTRAv2ZERKTr0G89cbsRs29ia81Z6k4XWh2lSVHHNpBSs4eS7f8FF4qrlbvMm/Qv7R3JlNS49orXKuFnjpG29RAOfNja92YMm63he74RPRk1eaGF6cQrBEbAgEmwd7V59aqZ4io5Lox+MSEcLKpgQ1YBc4f16OCgIiIi1lFxJW7n4+vL6GuWWB2jWTtW/ws230qf/HUYDkeLWgMNw2hYHPWmCf2ZNTShvWO65uP3ALD3G8/l1z9scRjxWqnzzOJqz7sw8a4md7HZbMwYEs9zG/eTnpGn4kpERLoUTWghXU6j9bi++KhFr9l9ooxjp84Q6GdnYnL3dk7YCs5JBpxTZou0h+TZYPOB/F1QfKDZ3ZyzBm7ILuBsTV2z+4mIiHgbFVfS5QQGh5IVdjkARZ+1bD0u57o9Vw2KJdjfwy74lh6H458DNkida3Ua8WYh3aDveHM7c3mzu13SK4KekUFUVtexMccz24NFRETag4or6ZKMhvW41l50Pa6vtgTOGhrf7tlc5jzJTRwDYR6YT7zLVxcUboazNRBgVf3fHRERka5AxZV0SckTrqHK8CPROMGhzM8uuO/egtMcKKzA38fO5JTYDkroAmdxNVgtgdIBUucCNvNqaenxZndzfhCxJjOf6lotKC4iIl2DiivpkkLDo8gMGQVA3icXbg1cucv85P2KgTGEBfq1ezaXnC6EI5vNbbUESkcIizevksIFWwNH9o6ie1gA5Wdr2by/qIPCiYiIWEvFlXRZNYOuBiD++OoL7ue832pmmge23GWtAMMBPUZAZG+r00hX4bxKmtl8a6DdbmPGEHPJgnS1BoqISBdheXH17LPP0rdvXwIDAxkzZgxbt2694P5PP/00ycnJBAUFkZiYyJIlSzh79mwHpRVvMujKhdQYPvRzHObo3i+a3OdQUQVZeeX42G1M87C1rYBzJ7eaJVA6kvMq6eHNcLqg2d1mpZlLFqzek09tnVoDRUTE+1laXL366qssXbqU+++/n+3btzNs2DBmzJhBQUHTv6z/85//8Mtf/pL777+fzMxMXnjhBV599VV+9atfdXBy8QYR0d3JDBoOwLHNrza5j3Mii7H9uxEV4t9R0VrmzCk4uMncVnElHSmyt3m1FMO8etqMMf2iiQz2o7iimq2Hijsun4iIiEUsLa6efPJJbrrpJhYvXszgwYN57rnnCA4O5sUXX2xy/82bNzN+/Hi+853v0LdvX6ZPn85111130atdIs05M2AOADFHm24NTN9tFlczPLElMHslOGohdjDEJFmdRrqaFswa6Otjb7jiq9ZAERHpCiwrrqqrq9m2bRtTp049F8ZuZ+rUqWzZsqXJ14wbN45t27Y1FFMHDhzg/fffZ/bs2c2+T1VVFWVlZY0eIk5JVy6kzrAxsHYvuYezG33vRMkZvjhags1Gw70jHsU5mYCuWokVnP/fHfoQKpu/KuWcNTA9Iw+Hw+iIZCIiIpaxrLgqKiqirq6OuLjGJ61xcXHk5TX9Ced3vvMdfvvb33LFFVfg5+fHgAEDuOqqqy7YFvjwww8TERHR8EhMTHTrzyGdW7e4XmQFpAFw+KPGrYHOT9pH9YkiNiyww7NdUFU57FtnbmsKdrFCTJJ51dRRCznpze42PimGsABfCsqr2HH0VAcGFBER6Xi+VgdwxQcffMBDDz3En//8Z8aMGcO+ffu44447+N3vfse9997b5GuWLVvG0qVLG/5cVlamAksaKe83C7J3Ebf/v2x9I7Th+bL9J7nW5yxXRyXAthwLEzahIAvqqiB6gHmCK2KF1HlQsAc+fxHqqht/r/dY6J5MgK8Pk1NjeWfnCdIz8hjZJ9qarCIiIh3AsuIqJiYGHx8f8vPzGz2fn59PfHzT97fce++9fP/73+eHP/whAEOHDqWiooIf/ehH3H333djt51+ICwgIICAgwP0/gHiNfhOuhezH6Oc4RL+M3zQ8PxrAD8isf3iiwfPAZrM6hXRVg+fBxkfg2Gfm46tCusPSTPDxY1ZaPO/sPMHKjDx+NTsVm/6fFRERL2VZceXv78/IkSNZt24dCxYsAMDhcLBu3Tpuu+22Jl9TWVl5XgHl4+MDgGGol19aJ67XAD5N+SX+Rzad971uIf70jg6xIFULBIbD5bdanUK6srghMOV+OPZ54+cPfQgVhebXAZOZOCiWID8fjp06w+4TZaT1jLAmr4iISDuztC1w6dKlXH/99YwaNYrRo0fz9NNPU1FRweLFiwFYtGgRPXv25OGHHwZg7ty5PPnkk4wYMaKhLfDee+9l7ty5DUWWSGuMuXYZsMzqGCKdz4Sl5z/37u2w/R/mTIIDJhPk78NVyd1ZmZHHyoxcFVciIuK1LC2uFi5cSGFhIffddx95eXkMHz6c9PT0hkkujhw50uhK1T333IPNZuOee+7h+PHjdO/enblz5/Lggw9a9SOIiMjXDZ5nFldZK2DOE2D3YWZafH1xlcfPpierNVBERLySzehi/XRlZWVERERQWlpKeHi41XFERLxPbTU8ngRnS+GG96HveMrP1jDyd2uprnOwesmVDIoLszqliIiI22sDSxcRFhERL+TrD8n16w9mmosMhwX6ccXAGABW7tKCwiIi4p1UXImIiPs5FxnOXA71DRIz08yZYFdm5FqVSkREpF2puBIREfcbMBn8Q6HsOBzfDsC01Dh87Day8so5VFRhcUARERH3U3ElIiLu5xcIA6eb25nvABAV4s/Y/t0ASN+t1kAREfE+Kq5ERKR9DK5vDdzzbhOtgSquRETE+6i4EhGR9pE0DXwD4dRByM8AYPqQOGw2+OJoCSdKzlgcUERExL1UXImISPsICIUBU8ztPeasgbFhgVzWJxqAdF29EhERL6PiSkRE2o+zNbB+SnY41xqo4kpERLyNiisREWk/g2aC3Q8Ks6AwBzhXXH12uJiC8rNWphMREXErFVciItJ+giKh/0Rzu/7qVY/IIIb1isAwYPXufOuyiYiIuJmKKxERaV+pTbUGJgBqDRQREe+i4kpERNpXyhyw2SH3Czh1CIBZ9a2BWw6cpKSy2sJwIiIi7qPiSkRE2ldIDPQZb25nLgegb0wIKfFh1DkM1uxRa6CIiHgHFVciItL+Bs83v+451xo4S62BIiLiZVRciYhI+0u52vx6bCuUnQBg1lCzNfDDvUWUn62xKpmIiIjbqLgSEZH2F54AiWPM7cwVAAyMDaV/9xCq6xyszyqwMJyIiIh7qLgSEZGOkTrX/Fo/a6DNZmuY2EKtgSIi4g18W7LTN7/5TZcP/NxzzxEbG+vy60RExEulzoXV98Dhj6GiCEJimJWWwLMb9vNBdiFnqusI8vexOqWIiEirtejK1dtvv42/vz8REREterz33nucPn26vbOLiEhnEtUXEoaB4YCs9wAY0iOcXlFBnKmpY2OOWgNFRKRza9GVK4A//vGPLb4S9cYbb7Q6kIiIeLHUeeZ6V5nvwsjrsdlszBwSz98+Okh6Rl7D4sIiIiKdUYuuXG3YsIHo6OgWH3TlypX07Nmz1aFERMRLOadkP7ARzpQA52YNXJdZQFVtnUXBRERE2q5FxdXEiRPx9W3xRS6uuOIKAgICWh1KRES8VMxA6J4KjhrISQdgRGIUceEBlFfVsnnfSYsDioiItJ7LswVu376dXbt2Nfz5nXfeYcGCBfzqV7+iurrareFERMQLDZ5nfq1fUNhutzFjiHn1amVGrlWpRERE2szl4urmm28mJycHgAMHDnDttdcSHBzM66+/zl133eX2gCIi4mVS64ur/eugypz8aGb9lOxr9uRTW+ewKpmIiEibuFxc5eTkMHz4cABef/11rrzySv7zn//w0ksv8d///tfd+URExNvEDYHo/lB7FvauBmB032iiQ/w5VVnDpweLLQ4oIiLSOi4XV4Zh4HCYnyquXbuW2bNnA5CYmEhRUZF704mIiPex2c5bUNjXx870wXGAWgNFRKTzcrm4GjVqFA888AD//Oc/2bhxI3PmzAHg4MGDxMXFuT2giIh4odT6WQNzVkPNWeBca+Cq3fk4HIZVyURERFrN5eLq6aefZvv27dx2223cfffdJCUlAebaVuPGjXN7QBER8UI9L4XwXlBTAfvXAzBuQAxhgb4Ullex7cgpiwOKiIi4ruXzqwN1dXWUlJSwadMmoqKiGn3v97//PT4+Pm4NJyIiXsrZGvjpX8zWwJTZ+PvamZoax1s7jpOekcdlfVu+vqKIiIgncOnKlY+PD9OnT6ekpOS87wUGBuLn5+euXCIi4u2cU7Jnvw+15lIeztbA9Iw8DEOtgSIi0rm43BaYlpbGgQMH2iOLiIh0JYljICQWzpbCoU0ATBzUnWB/H46XnGHX8VKLA4qIiLjG5eLqgQce4Gc/+xkrVqwgNzeXsrKyRg8REZEWsftA6tXmdv2CwoF+PkxKjgVgZUaeVclERERaxeXiavbs2XzxxRfMmzePXr16ERUVRVRUFJGRkefdhyUiInJBzgWFs94DRx2g1kAREem8XJrQAmDDhg3tkUNERLqivldAUBRUFsHhzdBvApNSYvH3tXOwqILs/HJS4sOtTikiItIiLhdXEydObI8cIiLSFfn4QfIc2PkvyFwO/SYQGuDLlQO7szYzn5W78lRciYhIp+FycbVp06YLfv/KK69sdRgREemCUueeK65mPgJ2O7PS4lmbmU96Rh5Lpg2yOqGIiEiLuFxcXXXVVec9Z7PZGrbr6uraFEhERLqYAZPAPwzKT8DxbZB4GVNT4/C128jOL+dA4Wn6dw+1OqWIiMhFuTyhxalTpxo9CgoKSE9P57LLLmP16tXtkVFERLyZbwAMmmFuZ74DQESwH2MHdAMgfbdmDRQRkc7B5eIqIiKi0SMmJoZp06bx6KOPctddd7VHRhER8XbOBYX3vAv1MwTOSksAzFkDRUREOgOXi6vmxMXFkZ2d7a7DiYhIV5I0FXyDoOQw5H0JwPQhcdht8OWxUo6dqrQ4oIiIyMW5fM/Vl19+2ejPhmGQm5vLI488wvDhw92VS0REuhL/EBg41ZzUYs+7kDCMmNAALusbzacHi/nBS5/RLSSgYfcAPzs/m55MWs8IC0OLiIg05nJxNXz4cGw223kLO15++eW8+OKLbgsmIiJdTOp8s7jKfBem3AvAghE9+fRgMTn5p4HTjXb3tdv52/WjLAgqIiLSNJeLq4MHDzb6s91up3v37gQGBrotlIiIdEGDZoCPPxTlQGE2dE/m26MSSYgIpPxsbcNupyqrue+d3WzaW8jpqlpCA1z+VSYiItIuXP6N1KdPn/bIISIiXV1gOPSfBHtXma2BE3+Oj93GVcmxjXYzDIO/f3yIg0UVbMgqYO6wHhYFFhERaaxVE1ps3LiRuXPnkpSURFJSEvPmzePDDz90dzYREelqUueaX+unZG+KzWZjZlo8oJkERUTEs7hcXP3rX/9i6tSpBAcHc/vtt3P77bcTFBTElClT+M9//tMeGUVEpKtImQM2H8jbBcUHm91tVn1xtSG7gLM1WrxeREQ8g8vF1YMPPshjjz3Gq6++2lBcvfrqqzzyyCP87ne/a4+MIiLSVQRHQ98rzO3Md5vdbWjPCHpGBlFZXcemnMIOCiciInJhLhdXBw4cYO7cuec9P2/evPMmuxAREXHZVxcUboZaA0VExBO5XFwlJiaybt26855fu3YtiYmJbgklIiJdWMpcwAbHP4fS483u5mwNXJOZT3Wto4PCiYiINM/l2QJ/+tOfcvvtt7Nz507GjRsHwMcff8xLL73EH/7wB7cHFBGRLiYsDnpfDke2mOteXf7jJne7tHcU3cMCKCyvYvP+ovNmFRQREeloLl+5uuWWW3jllVfYtWsXd955J3feeScZGRm8+uqr3Hzzze2RUUREuprU+tbAzOXN7mK325gxJA5Qa6CIiHgGm2EYhtUhOlJZWRkRERGUlpYSHh5udRwREWlKyVF4Og1sdvhpDoR2b3K3j/cV8d2/fUp0iD9bfzUFX59WrTAiIiJdlLtrg1b/FqqurubYsWMcOXKk0UNERKTNIhOhx6VgOCBrRbO7jekXTVSwH8UV1Ww9VNyBAUVERM7ncnG1d+9eJkyYQFBQEH369KFfv37069ePvn370q9fv/bIKCIiXZFz1sALTMnu62Nn2mC1BoqIiGdweUKLG264AV9fX1asWEFCQgI2m609comISFeXOg/W/hoOboIzpyAoqsndZqUl8Nrnx0jPyOPXc4dgt+v3koiIWMPl4mrnzp1s27aNlJSU9sgjIiJi6jYAYodAwW5YsQSivtId4RsIoxZDaCzjkroRFuBLQXkVO46WMLJP00WYiEi7O/Ip5KS3/vX9J0L/q9wWRzqey8XV4MGDKSoqao8sIiIijQ1ZYBZXu986/3ulR2D+swT4+jAlNZa3d54gPSNXxZWIWKOuBv7vWjjThvs/P/kL/HwvBIS5L5d0qBYVV2VlZQ3bjz76KHfddRcPPfQQQ4cOxc/Pr9G+moFPRETc5vJbwFEHVed+D3HmFHzxf5C5Aq5+Gnz8mJmWwNs7T7AyI49fzU5Vy7qIdLxDH5qFVWAkDP+O66/f/RaU58Le1ZB2jdvjScdoUXEVGRnZ6BeVYRhMmTKl0T6GYWCz2airq3NvQhER6boCwmDSssbPOepg31qoKDRPZgZMZuKg7gT5+XDs1Bl2nygjrWeENXlFpOvaUz/5zuD5MPNh11/vGwAfPWUeR8VVp9Wi4mrDhg3tnUNERKRl7D6QMge2vWSehAyYTJC/D1cld2dlRh4rM3JVXIlIx3LUnVs2wjnTqatS55nF1d41UHMG/ILcl086TIuKq4kTJ7Z3DhERkZZLnWcWV1krYM4TYPdhZlp8fXGVx8+mJ6s1UEQ6zpFPzKvpgRHQ98rWHaPHCIjobd5Pum8dpF7t3ozSIbSUvYiIdD79rjTva6goNE9qgMkpsfj72DlQWMHegtPW5hORrsW5Hl/ybPD1b90xbDZIndv4eNLpWF5cPfvss/Tt25fAwEDGjBnD1q1bL7h/SUkJt956KwkJCQQEBDBo0CDef//9DkorIiIewcfPPImBhpOQsEA/JgyMAWDlLi0oLCIdxOGAzOXmdmorWwKdnC2F2elQW922Y4klLC2uXn31VZYuXcr999/P9u3bGTZsGDNmzKCgoKDJ/aurq5k2bRqHDh3ijTfeIDs7m+eff56ePXt2cHIREbGc8yQkczkYBgAz0+IBWJmRa1UqEelqTmyHsuPgHwoDJrftWL1GQ2gcVJWaC6hLp2NpcfXkk09y0003sXjxYgYPHsxzzz1HcHAwL774YpP7v/jiixQXF/P2228zfvx4+vbty8SJExk2bFgHJxcREcv1n2SezJQdh+PbAZg2OA5fu42svHIOFVVYHFBEuoQ975hfB04Hv8C2Hctuh5T6e60y32nbscQSbS6uXnrpJUpLS11+XXV1Ndu2bWPq1KnnwtjtTJ06lS1btjT5mnfffZexY8dy6623EhcXR1paGg899NAFp3+vqqqirKys0UNERLyAX6B5MgMNJyGRwf6MHdANgPTdag0UkXZmGOdaAls7S+DXOY+T9R7U1brnmNJh2lxc/ehHP+LEiRMuv66oqIi6ujri4uIaPR8XF0deXtO/EA8cOMAbb7xBXV0d77//Pvfeey9PPPEEDzzwQLPv8/DDDxMREdHwSExMdDmriIh4KOdJyJ53G1oDZwxxtgaquBKRdpafAacOgm8gJE1zzzH7XAFB0VB5Eo5sds8xpcO0uLiKjo5u8lFbW8vYsWMb/tyeHA4HsbGx/PWvf2XkyJEsXLiQu+++m+eee67Z1yxbtozS0tKGx9GjR9s1o4iIdKCkaeZJzamD5kkOMH1IHDYbfHG0hBMlZywOKCJezblw8IApEBDqnmP6+ELK7MbHl06jRetcAdTU1DBx4kS+9a1vNTxnGAY//OEPueuuu1yeVCImJgYfHx/y8/MbPZ+fn098fHyTr0lISMDPzw8fH5+G51JTU8nLy6O6uhp///OnvgwICCAgIMClbCIi0kkEhELSVHO9qz3vQvxQYsMCuaxPNFsPFZOekccPruhndUoR8VbOKdPd1RLolDofdvzLbDmc9Zh5L5Z0Ci0eqR07dlBQUMD69eu55ppruP7667nhhhuw2WwsWLCA66+/nuuvv77Fb+zv78/IkSNZt25dw3MOh4N169YxduzYJl8zfvx49u3bh8PhaHguJyeHhISEJgsrERHpApxTH39lXRjnrIHpag0UkfZSmAOFWWD3g0Ez3Xvs/hMhIBxO58Gxz9x7bGlXLb5ylZSUxObNm7n77rsZPnw4//jHPxg/fnyb3nzp0qVcf/31jBo1itGjR/P0009TUVHB4sWLAVi0aBE9e/bk4YcfBuCWW27hmWee4Y477uAnP/kJe/fu5aGHHuL2229vUw4REenEBs0wT24Ks8yTne6DmJkWz29X7OGzw8UUlJ8lNqyNM3iJSOdXVwOHPoQaN7UL56SbX/tPhKBI9xzTyTfALNh2vWZ+cNR7jHuPL+2mxcUVgK+vL48++igzZszgO9/5Dt/97nex2WytfvOFCxdSWFjIfffdR15eHsOHDyc9Pb1hkosjR45g/8pl0MTERFatWsWSJUu45JJL6NmzJ3fccQe/+MUvWp1BREQ6uaBI6H8V7FtjnoR0/xk9IoMYlhjJF0dLWL07n+9d3sfqlCJitc1/hHW/df9x27pwcHMGzztXXE1/ANpwzi0dx2YY9dMruejkyZPcdNNNbNiwgU8++YTk5GR3Z2sXZWVlREREUFpaSnh4uNVxRETEHbb9A5bfDgnD4GZz4c3nNu7nkZVZTBgYwz9v1Ke+Il3es2PMK9zdUyAgzD3HDO8JC/4M/iHuOd5XVVfC7wdATSX8aCP0GO7+9xC31wYuXbn6qm7duvHmm2+2OYCIiEibpcyBFXdC7hdw6hBE9WVWWjyPrMxiy/6TlFRWExmse3NFuqyv3h/1g1Xub+NrD/7B5oQ9me+aDxVXnYKmHhERkc4vJAb61N8HXL+gZ59uIaQmhFPrMFizJ/8CLxYRr1e/0Dj9r+ochZXT4Pnm16+s5SeercXFVU1NDXfddRdJSUmMHj2aF198sdH38/PzG02RLiIi0qG+ehJSb+YQzRooIpz7d8HdU6a3t4HTwccfTu41r7yJx2txcfXggw/y8ssv8+Mf/5jp06ezdOlSbr755kb7tPL2LRERkbZLudr8emwrlJ0AYNZQs7j6cG8R5WdrrEomIlYqPgh5X4LNDslzrE7jmsBwGDDZ3NaCwp1Ci4urf//73/ztb3/jZz/7GQ888ACff/4569evZ/HixQ1FVVtmDhQREWmT8ARIrJ+4InMFAANjQ+nfPYTqOgfrswosDCcilqlvFabPeAjpZm2W1mhiLT/xXC0uro4fP05aWlrDn5OSkvjggw/YvHkz3//+96mrq2uXgCIiIi32tZMQm83GLC0oLNK1OYsrZ+twZ5M8C+y+kJ8BJ/dbnUYuosXFVXx8PPv3Nx7Qnj17smHDBj777DNuuOEGd2cTERFxTepc8+vhj6GiCIBZaQkAfJBdyJlqfRAo0qWUnTBbheFc63BnExwNfSeY285CUTxWi4uryZMn85///Oe853v06MH69es5ePCgW4OJiIi4LKoPJAwHwwFZ7wEwpEc4vaKCOFNTx8YctQaKdCn1LcIkjjFbhzurwWoN7CxaXFzde++9fPvb327yez179mTjxo3nzSAoIiLS4ZxXr9QaKCLOYiS1k80S+HXJcwAbHN8GpcesTiMX0OLiqk+fPsyYMaPZ7/fo0YPrr7/eLaFERERazXlfxYGNcKYEgJn1rYHrMguoqlVroEiXUFFktgjDuQ9dOquwOOg91txWa6BHa1Fx9e6771JT0/IpbN9//33OnDnT6lAiIiKtFjMQuqeCowZy0gEYkRhJXHgA5VW1bN530uKAItIhslaYLcIJw82W4c7O2RqoKdk9WouKq2984xuUlJS0+KDXXnstubm5rc0kIiLSNl87CbHbbcyoX1B4ZYZ+P4l0CZ114eDmOK++HdkC5fnWZpFm+bZkJ8MwuOGGGwgICGjRQc+ePdumUCIiIm2SOg82Pgr710HVaQgIZWZaPC9vOcyaPfnU1jnw9WlxZ7yIdDZnTsHBjeZ2aiedgv3rInpBz5HmfVdZK+CyG61O1Hp5uyC6P/iHWJ3E7Vr0m+X6668nNjaWiIiIFj2++93vEh4e3t7ZRUREmhY3xPzFXXsW9q4GYHTfaKJD/DlVWcOnB4stDigi7So7HRy1ZotwTJLVadzHGxYUdjjgX/8Djw2AEzusTuN2Lbpy9fe//729c4iIiLiPzWaehHz8tHkSkvZNfH3sTB8cxyufHWVlRi7jk2KsTiki7SXTy1oCnQbPg7X3w8EPobLYXAOrszn2GZzOg4BwiB1sdRq3U0+EiIh4J+dJVc5qqDEnWZpZPyX7qt35OByGVclEpD1VlcO+deZ2Z5+C/eui+0PcUDDqIHul1Wlax1n4DpoBvi275agzUXElIiLeqcelEN4Laipg/wYAxg2IISzQl8LyKrYdOWVxQBFpF3vXQF1VfSEyxOo07teZFxQ2jHMTjXhb4VtPxZWIiHgnm+28BYX9fe1MS40DtKCwiNf66sLBNpu1WdqD89+1/evhbJm1WVyV+wWUHgG/YEiaanWadqHiSkREvJfzE97s96G2GjjXGpiekYdhqDVQxKvUnDFbgcH77rdy6p4C3QZCXXXDhD2dhrPwTZoK/sHWZmknbSquNOW6iIh4tMQxEBILZ0vh0CYArhzUnWB/H46XnGHX8VKLA4qIW+1fb7YCRySarcHeyGb7ylp+71ibxRVfbQkc7CXT4zfB5eLK4XDwu9/9jp49exIaGsqBAwcAuPfee3nhhRfcHlBERKTV7D6QerW5Xf9LPdDPh0nJsQCsVGugiHdpuJ9nrne2BDo571fatxaqK63N0lKFWXByL/j4w8DpVqdpNy4XVw888AAvvfQSjz32GP7+/g3Pp6Wl8be//c2t4URERNrMeRKS9R446gC1Bop4pdrqczPoeelkCQ0ShkFkb6ipNAuszsBZ+A6YDIHeux6uy8XVyy+/zF//+le++93v4uPj0/D8sGHDyMrKcms4ERGRNut7BQRFQWURHN4MwKSUWPx97RwsqiA7v9zigCLiFgc3QVWp2QqcONrqNO3LuZYfQOZya7O0VKZ3zxLo5HJxdfz4cZKSzl/p2uFwUFNT45ZQIiIibuPjB8lzzO36X+6hAb5cObA7ACt3qTVQxCtk1t9/lHq12RLs7Zz3LeWkQ22VtVku5uR+yM8Amw8kz7I6TbtyubgaPHgwH3744XnPv/HGG4wYMcItoURERNyqYV2YFeBwADDrK62BItLJ1dWarb/g9VdGGvQcBWEJUFUGBzZanebCnFfX+k2A4Ghrs7QzX1dfcN9993H99ddz/PhxHA4Hb775JtnZ2bz88susWLGiPTKKiIi0Tf+rwD8Myk/A8W2QeBlTU+PwtdvIzi/nQOFp+ncPtTqliLTWkc1QedJsAe57hdVpOobdDilXw2fPm1ftBnnwJBFdpCUQWnHlav78+Sxfvpy1a9cSEhLCfffdR2ZmJsuXL2fatGntkVFERKRtfANg0Axzu751KCLYj3FJMQCk79bVK5FOzXllJHmO2QrcVTivyme9b16980Slx8wPtbCZxaCXa9U6VxMmTGDNmjUUFBRQWVnJRx99xPTpHlwti4iINKwL86653gowc4haA0U6PYfjXHHlrQsHN6f3OAjuBmeK4fBHVqdpmnNseo+FsDhrs3QAl9sCRUREOqWkqeAbBCWHIe9LSBjG9CFx3PP2Lr48VsqxU5X0igq2OqWIuOr451CeCwHhZgtwV+LjCylzYPvL8Nr1jac4t/vB5Lsh7Zq2v8+Xr8EHD4OjFVfHKk6aX7tI4etycRUVFYWtiUXZbDYbgYGBJCUlccMNN7B48WK3BBQREXEL/xAYONX8FHXPu5AwjJjQAC7rG82nB4tJz8jjhxP6W51SRFy1p36WwEEzzBbgrmbYd8zi6myJ+fiqDx5te3FlGLDhQTh1qPXH8As+N7uhl2vVhBYPPvggs2bNYvRocw2BrVu3kp6ezq233srBgwe55ZZbqK2t5aabbnJ7YBERkVZLnW8WV5nvwpR7AXPWQBVXIp2UYXSpyRKa1Gcs3L4TKovPPVdXDS/Pg6JsKMyG7smtP37eLrOw8g2ERe+YV8RcFdETwuJbn6ETcbm4+uijj3jggQf48Y9/3Oj5//3f/2X16tX897//5ZJLLuGPf/yjiisREfEsg2aAjz8U5UBBFsSmMDMtgV8v38O2I6coKDtLbHig1SlFpKVyv4CSI2bLb9IUq9NYJ7qf+fiq/lfB3tXmlfqJP2/9sZ3Fa9JU6H1564/TRbg8ocWqVauYOnXqec9PmTKFVatWATB79mwOHDjQ9nQiIiLuFBgO/SeZ2/UnDPERgYzoHYlhwCrNGijSuThP/AdONVt/5RznlTzn4sqttaeLXxl0kcvFVXR0NMuXLz/v+eXLlxMdbS4KVlFRQVhYWNvTiYiIuFvDgsLvNjzlXFB4pWYNFOk8DOMrJ/5d434elyTPBpuP2dZXfLB1xyjMNlsL7X7nlrOQC3K5LfDee+/llltuYcOGDQ33XH322We8//77PPfccwCsWbOGiRMnujepiIiIO3z9hCO6HzOHJPDQ+1l8erCY4opqokP8rU4pIhdTmAUn95qtvjrxP19IN+g7Hg5uMj9MGn+H68dwfgjV/yoIinRnOq/l8pWrm266iY0bNxISEsKbb77Jm2++SXBwMBs3buTGG28E4Kc//Smvvvqq28OKiIi0WXA09L3C3K4/cejdLZjBCeHUOQzW7sm3MJyItJjzqlX/SY2nIJdzUr+yvl9rOF/XRaZRd4dWrXM1fvx4xo8f7+4sIiIiHWPwPDi40TxxqP80d1ZaPHtyy1iZkcu3L0u0OKCIXFSmTvwvKnUuvP9zcy2w0uPmrH0tVXzQXBPQZofkOe2X0cu4fOXqq86ePUtZWVmjh4iIiMdLmQvYzp1wALOGmvddfbSviLKzNRaGE5GLOrkf8jPMFt/k2Van8Vxh8ZA4xtzOWuHaazPr51joM95sMZQWcbm4qqys5LbbbiM2NpaQkBCioqIaPURERDxeWNy5KYXrTyCSYsNIig2lps5gfWaBheFE5KKcJ/79JpitvtK8wa1sDWy4MqjJQlzhcnH185//nPXr1/OXv/yFgIAA/va3v/Gb3/yGHj168PLLL7dHRhEREfdLvdCsgblWJBKRlmpYOHiutTk6A+d/oyOb4XRhy15TdgKOfWZup1zdPrm8lMvF1fLly/nzn//MNddcg6+vLxMmTOCee+7hoYce4t///nd7ZBQREXE/5wnH4c1w2rxSNbO+uNqYU0hlda1VyUTkQkqPwfFtgK2+xVcuKLI39BgBhqPlrYGZ9fsljoHwhPbL5oVcLq6Ki4vp378/AOHh4RQXFwNwxRVXsGnTJvemExERaS+RidDjUsBoOOEYnBBO7+hgztY4+CC7hZ/wikjHcrYE9r7cbPGVi3N+mJTZwtbAhiuDmizEVS4XV/379+fgQXMhspSUFF577TXAvKIVGRnp1nAiIiLtqmFBYfNkzWazaUFhEU+3Ryf+LnMusnxwE5w5deF9K4rg8Mf1r9OVQVe5PBX74sWL+eKLL5g4cSK//OUvmTt3Ls888ww1NTU8+eST7ZFRRESkfaTOg7W/PnfCERTFzLR4/nfTAdZn5nO2po5APx+rU4p4n81/guPbW/FCA45sMTd14t9yMUkQOxgK9sAr34PQ2Ob3Lc8zWwgThkNUnw6L6C1cLq6WLFnSsD116lSysrLYtm0bSUlJXHLJJW4NJyIi0q66DYDYIVCwG7JXwvDvMKxXJPHhgeSVneXjfUVMSVXbkYhb5e2C1fe07Ri9RputvdJyad+E9Xvg8Ect319c5lJxVVNTw8yZM3nuuecYOHAgAH369KFPH1W1IiLSSQ2eZxZXe96F4d/BbrcxMy2elzYfYmVGnoorEXdztvX1HAVDv+X66+0+MGimezN1BWNvg7AEqDp98X0DwmDo/7R/Ji/kUnHl5+fHl19+2V5ZREREOl7qPPjgYdi/HqrKISCsobhasyefmjoHfj4u36IsIs1xTpYw+kcwbKG1WboSvyAY8T2rU3g9l39bfO973+OFF15ojywiIiIdLzYVuiVBXRXkrALgsr7RxIT6U3qmhk8OnLQ4oIgXKcyBwiyw+8GgGVanEXE7l++5qq2t5cUXX2Tt2rWMHDmSkJCQRt/XpBYiItKp2Gzm1auPnjQ/UR/6P/jYbUwbHM//bT3Cyow8JgzsbnVKEe/gvGrVfyIERVoaRaQ9uFxcZWRkcOmllwKQk5PT6Hs2m809qURERDrS4Priau8aqK4E/2BmpZnF1erdefxufho+dv2OE2kzrZ8kXs7l4mrDhg3tkUNERMQ6CcMhojeUHjHvvUq9mrEDuhER5EfR6Wo+P1TMmP7drE4p0rmdOgS5X4DNDilzrE4j0i5afYfuvn37WLVqFWfOnAHAMAy3hRIREelQNtu5NXPqP1n387EztX6mQC0oLOIG9Yt102c8hMRYm0WknbhcXJ08eZIpU6YwaNAgZs+eTW5uLgA33ngjP/3pT90eUEREpEMMrm9Tyk6H2moAZqbFA7Bqd54+RBRpK+cU7IPnW5tDpB25XFwtWbIEPz8/jhw5QnBwcMPzCxcuJD093a3hREREOkyv0RAaD1WlcHAjABMGxhDi70Nu6Vm+OFZqcUCRTqzsBBzbam6nXG1tFpF25HJxtXr1ah599FF69erV6PmBAwdy+PBhtwUTERHpUHY7pNaf9O15B4BAPx8mpcQCsDIj16pkIp1f5grza+IYCE+wNotIO3K5uKqoqGh0xcqpuLiYgIAAt4QSERGxhHMGs6z3oK4WgFlp5olgeoZaA0VaTbMEShfhcnE1YcIEXn755YY/22w2HA4Hjz32GJMmTXJrOBERkQ7VZzwERcOZYjj8MQBXJXcnwNfO4ZOVZOaWWxxQpBOqKGr4+9RwdVjES7k8Fftjjz3GlClT+Pzzz6muruauu+5i9+7dFBcX8/HHH7dHRhERkY7h42tOEb3jn+Yn7f0nEhLgy8RB3Vm9J5/0jFwG9wi3OqVI55L1HhgOSBgGUX2tTiPSrly+cpWWlkZOTg5XXHEF8+fPp6Kigm9+85vs2LGDAQMGtEdGERGRjuOcySxzBTgcAMwaas4aqCnZRVpBLYHShbh85QogIiKCu+++291ZRERErNdvIgSEw+k8OPYZ9B7D5JQ4/Hxs7C04zb6C0yTFhlqdUqRzOFMCB8zZNzUFu3QFLl+5SkpK4te//jV79+5tjzwiIiLW8vWHQTPN7fpP3COC/BifZC56mq5ZA0VaLicdHDXQPRViBlqdRqTduVxc3Xrrrbz33nskJydz2WWX8Yc//IG8PLVJiIiIF3EuKLznXaifIXDmELM1MH23fueJtFjDwsFqCZSuoVWLCH/22WdkZWUxe/Zsnn32WRITE5k+fXqjWQRd8eyzz9K3b18CAwMZM2YMW7dubdHrXnnlFWw2GwsWLGjV+4qIiDRpwBTwC4bSI5C7E4Bpg+Ow2yDjeBlHiyutzSfSGVSdhv3rzG3dbyVdhMvFldOgQYP4zW9+Q05ODh9++CGFhYUsXrzY5eO8+uqrLF26lPvvv5/t27czbNgwZsyYQUFBwQVfd+jQIX72s58xYcKE1v4IIiIiTfMPhoHTzO36T967hQYwpl83wFzzSkQuYu9qqD0LUf0gbojVaUQ6RKuLK4CtW7dy55138o1vfIOcnBy+9a1vuXyMJ598kptuuonFixczePBgnnvuOYKDg3nxxRebfU1dXR3f/e53+c1vfkP//v3b8iOIiIg0zflJe+a51sBzswbqviuRi8r8SkugzWZtFpEO4nJxlZOTw/3338+gQYMYP348mZmZPProo+Tn5/PKK6+4dKzq6mq2bdvG1KlTzwWy25k6dSpbtmxp9nW//e1viY2N5cYbb7zoe1RVVVFWVtboISIiclGDZoBPAJzcBwWZAMyov+9q+5ES8krPWplOxLPVnIGc1eZ2qmYJlK7D5eIqJSWF9PR0br31Vo4dO8aqVatYtGgRoaGuT0tbVFREXV0dcXFxjZ6Pi4trdpKMjz76iBdeeIHnn3++Re/x8MMPExER0fBITEx0OaeIiHRBAWEwYLK5Xf8JfFx4ICP7RAGwShNbiDRv/waoqYDwXtDzUqvTiHQYl4ur7OxsPv30U+64447ziqL2Vl5ezve//32ef/55YmJiWvSaZcuWUVpa2vA4evRoO6cUERGv4ZzhLHN5w1Oz0tQaKHJRDQsHz1VLoHQpLi8iPHCguUbBtm3byMw02yQGDx7MpZe6/qlETEwMPj4+5OfnN3o+Pz+f+Pj48/bfv38/hw4dYu7cuQ3PORwOAHx9fcnOzmbAgAGNXhMQEEBAQIDL2URERBg0E+y+kJ8BJ/dDtwHMGBLPA+9lsvVgMSdPV9EtVL9jRBqprYbs981tTcEuXYzLV64KCgqYNGkSl112Gbfffju33347o0aNYsqUKRQWFrp0LH9/f0aOHMm6desannM4HKxbt46xY8eet39KSgq7du1i586dDY958+YxadIkdu7cqZY/ERFxr+Bo6Fs/K239J/GJ0cGk9QzHYcDqPfkXeLFIF3VoE5wthZBYSBxjdRqRDuVycfWTn/yE06dPs3v3boqLiykuLiYjI4OysjJuv/12lwMsXbqU559/nn/84x9kZmZyyy23UFFR0TCt+6JFi1i2bBkAgYGBpKWlNXpERkYSFhZGWloa/v7+Lr+/iIjIBX11QeF6s9ISAE3JLtIk59+V1KvB7mNtFpEO5nJbYHp6OmvXriU1NbXhucGDB/Pss88yffp0lwMsXLiQwsJC7rvvPvLy8hg+fDjp6ekN93MdOXIEu71NM8aLiIi0XsrVsGIpnNgOJUchMpGZafH8flU2m/cXUXqmhoggP6tTingGRx1kvWdup8698L4iXsjl4srhcODnd/4vET8/v4b7n1x12223cdtttzX5vQ8++OCCr33ppZda9Z4iIiItEhoLfcbB4Y/NiS3G/j8GdA9lUFwoOfmnWZeZzzcv7WV1ShHPcHgzVBZBYOS5llqRLsTl4mry5Mnccccd/N///R89evQA4Pjx4yxZsoQpU6a4PaCIiIjlUufVF1fvwtj/B8DMtARy8veyMiNPxZV4l9pqcxIXR53rr93+svk1ZQ746IqudD0uF1fPPPMM8+bNo2/fvg0TSBw9epS0tDT+9a9/uT2giIiI5VLnQvov4MgnUJ4PYXHMSovnj+v2simnkIqqWkICXP6VKuKZlt8OX/xf246RqlkCpWty+TdBYmIi27dvZ+3atWRlZQGQmprK1KlT3R5ORETEI0T0hJ6j4PjnkLUcLvshKfFh9O0WzKGTlWzILuDqS3pYnVKk7aorYPfb5nZkb7C14r732CHnFuAW6WJa9TGbzWZj2rRpTJs2zd15REREPNPgeWZxlWkWVzabjZlpCTy3cT8rM/JUXIl32LsGas9AZB+44wstACziIpc/jrj99tv54x//eN7zzzzzDHfeeac7MomIiHge58xnBz+EymIAZqaZC95vyCrgbE0r7k8R8TSZy82vg+epsBJpBZeLq//+97+MHz/+vOfHjRvHG2+84ZZQIiIiHie6P8QNBaMOst8HYFivCHpEBFJZXcemnEKLA4q0UW0V5Kwyt1PnW5tFpJNyubg6efIkERER5z0fHh5OUVGRW0KJiIh4pK8tKGyz2ZhRf/UqfbcWFJZObv8GqC6HsAToOdLqNCKdksvFVVJSEunp6ec9v3LlSvr37++WUCIiIh7JOQPagQ1wtgyAWWkJAKzdk091bevWexTxCJnmhwakzgV7KyayEBHXJ7RYunQpt912G4WFhUyebM4Es27dOp544gmefvppd+cTERHxHLEpEDMIinLM9qlLvsXIPlHEhAZQdLqKLQdOMnFQd6tTiriurgay3jO3NY26SKu5/LHED37wA5544gleeOEFJk2axKRJk/jXv/7FX/7yF2666ab2yCgiIuI5nCeeme8A4GO3MWNIHADpGblWpRJpm0MfwtkSCI6BPuOsTiPSabXqmu8tt9zCsWPHyM/Pp6ysjAMHDrBo0SJ3ZxMREfE8zvuu9q411wTiXGvg6t351DkMq5KJtF79fYSkzAG7j7VZRDqxNjXUdu/endDQUHdlERER8Xzxl5hrANWegX1rARjTP5rIYD9OVlSz9WCxxQFFXOSoO9cSOFgtgSJtobsVRUREXGGznTsBrV8TyM/HzrRUtQZKJ3X0U6gogIAI6Hul1WlEOjUVVyIiIq5y3neVs8pcG4hzCwqn787DodZA6UycLYHJs8DX39osIp2ciisRERFX9RxlrgVUVQYHPgDgioExhAb4kl9Wxc5jJZbGE2kxw2i4AquWQJG2U3ElIiLiKrvdXAsIGj71D/D1YXJKLADpGVpQWDyUYTR+HN8OZcfALwQGTLY6nUin5/I6V3/84x+bfN5msxEYGEhSUhJXXnklPj6aaUZERLxY6jzY+lfIfg/qngYfP2alxfPuFydYmZHLslkp2Gw2q1OKnLPrDXj7/0Fd1fnfGzQd/II6PpOIl3G5uHrqqacoLCyksrKSqKgoAE6dOkVwcDChoaEUFBTQv39/NmzYQGJiotsDi4iIeIQ+48w1gSqL4NBHMGASE5O7E+hn52jxGXafKCOtZ4TVKUXO+eTPTRdWdl8YeUOHxxHxRi63BT700ENcdtll7N27l5MnT3Ly5ElycnIYM2YMf/jDHzhy5Ajx8fEsWbKkPfKKiIh4BruPuSYQQKbZGhjs78tVg9QaKB6o9Bgc3wbY4Nat8PP95x6/PAL9r7I6oYhXcLm4uueee3jqqacYMGBAw3NJSUk8/vjjLFu2jF69evHYY4/x8ccfuzWoiIiIx2mYkn2FuVYQMGuoOWvgSk3JLp4kc4X5tffl0D0ZQmLOPfxDrM0m4kVcLq5yc3Opra097/na2lry8sxP6Xr06EF5eXnb04mIiHiyvldCYIS5RtDRrQBMTonF38fO/sIK9ubrd6F4iPqrqw3LCIhIu3C5uJo0aRI333wzO3bsaHhux44d3HLLLUyebM4ys2vXLvr16+e+lCIiIp7I1x+SZ5vb9SevYYF+jE/qBsBKtQaKJzhdAIc3m9upV1ubRcTLuVxcvfDCC0RHRzNy5EgCAgIICAhg1KhRREdH88ILLwAQGhrKE0884fawIiIiHsc5JXvmcnNqa2BWWgKg4ko8RNYKwIAeIyCyt9VpRLyay7MFxsfHs2bNGrKyssjJyQEgOTmZ5OTkhn0mTZrkvoQiIiKebMBkc42g0qNwYjv0HMm0wXH4vGUjM7eMwycr6NNN97SIhfaoJVCko7R6EeGUlBTmzZvHvHnzGhVWIiIiXYpfkLlGEDScxEaF+HN5/2hAswaKxSqL4dCH5vbg+dZmEekCXL5yVVdXx0svvcS6desoKCjA4XA0+v769evdFk5ERKRTSJ0Hu98y77ua+muw2ZiZlsDH+06yMiOPmycOuOghRNpF9kpw1ELsEOim/w9F2pvLxdUdd9zBSy+9xJw5c0hLS9Pq8yIiIgOng28gFB+A/N0Qn8aMIXHc904GO4+WkFt6hoSIIKtTSlfknCVwsFoCRTqCy8XVK6+8wmuvvcbs2bPbI4+IiEjnExAKA6ZA9nvmyWx8GrFhgYzqE8Vnh06RnpHH4vGaRVc62Nky2F/fUaT7rUQ6hMv3XPn7+5OUlNQeWURERDov55UB5+QBwEzNGihW2rsa6qqhWxLEplqdRqRLcLm4+ulPf8of/vAHjPrpZkVERAQYNBPsvlCYCUV7AZiZFg/AZ4eKKSyvsjKddEVfXThYt3GIdAiX2wI/+ugjNmzYwMqVKxkyZAh+fn6Nvv/mm2+6LZyIiEinERQJ/SbC/nXmSe2En9IzMohLekXw5bFSVu/J47tj+lidUrqK6krYu8bc1v1WIh3G5StXkZGRfOMb32DixInExMQQERHR6CEiItJlNdkaaF690pTs0qH2r4OaSojoDQnDrU4j0mW4fOXq73//e3vkEBER6fxSroYVSyB3J5w6DFF9mJWWwGPp2WzZf5KSymoig/2tTildQcPCwXPVEijSgVq9iLCIiIh8TUgM9BlvbmcuB6BfTAgp8WHUOgzWZhZYGE66jNoqyEk3t9USKNKhWnTl6tJLL2XdunVERUUxYsSIC65ttX37dreFExER6XRS58GhD837rsbdBpitgVl55aRn5PI/I3tZHFC83oGNUFUGofHQa7TVaUS6lBYVV/PnzycgIACABQsWtGceERGRzi31alj5czj6KZTlQngCs9ISeHrtXjbtLeJ0VS2hAS535Yu0XOY75tfUq8GuJiWRjtSif93vv//+JrdFRETka8J7mFcLjm2FrBUw+iYGxYXSPyaEA0UVrM8qYN6wHlanFG9VVwtZ75vbWjhYpMPp4wwRERF3a5g10LyCYLPZvjJrYK5VqaQrOPwxnCmGoOhz9/+JSIdp0ZWrqKioC95n9VXFxcVtCiQiItLppc6F1feYJ7oVJyGkGzPT4vnzB/vZkFXImeo6gvx9rE4p3si5cHDKHPBR+6lIR2vR37qnn366nWOIiIh4kai+EH8J5H0J2e/BpYsY2jOCnpFBHC85w8acwoYrWSJu43BA5gpze/B8a7OIdFEtKq6uv/769s4hIiLiXQbPM4urPe/CpYsaWgNf+Ogg6Rm5Kq7E/Y5thdN5EBAB/SZanUakS2rRPVdlZWUtfoiIiAiQWn/l4MAHcKYEgFn1BdW6zAKqax3W5BLv5Vw4OHkm+GqxahErtOjKVWRk5EXvuTIMA5vNRl1dnVuCiYiIdGrdB0H3FCjMgpxVMGwhl/aOIjYsgILyKj7eX8Sk5FirU4q3MIyGhatJnWttFpEurEXF1YYNG9o7h4iIiPdJnWcWV5nvwrCF2O02ZgyJ55+fHCZ9V56KK3GfEzug9Aj4BcOAKVanEemyWlRcTZyovl0RERGXDZ4Hmx6DfWuh6jQEhDIrzSyuVu/J48G6NHx9tCqKuIFzlsCB08A/2NosIl1Yq+boLCkp4YUXXiAzMxOAIUOG8IMf/ICIiAi3hhMREenU4tIgqh+cOgj71sCQbzC6XzRRwX6cqqxh68FixiXFWJ1SOjvDOHe/lRYOFrGUyx+Xff755wwYMICnnnqK4uJiiouLefLJJxkwYADbt29vj4wiIiKdk812bkHh+vthfH3sTBscB8DKjDyrkok3KciE4v3gEwCDZlidRqRLc7m4WrJkCfPmzePQoUO8+eabvPnmmxw8eJCrr76aO++8sx0iioiIdGLOWQNzVkHNWQBmpSUAsGp3Hg6HYVUy8RbOlsABkyEgzNosIl1cq65c/eIXv8DX91xHoa+vL3fddReff/65W8OJiIh0ej1GQHhPqD4NB8wJosYldSMswJeC8iq2HzllcUDp9JwtgYPVEihiNZeLq/DwcI4cOXLe80ePHiUsTJ+WiIiINGK3n5sau/4kOMDXhymp5kyBag2UNjm5Hwp2g90XkmdZnUaky3O5uFq4cCE33ngjr776KkePHuXo0aO88sor/PCHP+S6665rj4wiIiKdm3OSgez3oa4GgJn1rYHpGXkYhloDpZX2vGN+7XclBEVZm0VEXJ8t8PHHH8dms7Fo0SJqa2sB8PPz45ZbbuGRRx5xe0AREZFOr/flENIdKgrh4CZImsLEQd0J8vPheMkZMo6XMbSXZtyVVsjULIEinsTlK1f+/v784Q9/4NSpU+zcuZOdO3dSXFzMU089hcPhaI+MIiIinZvdB1KuNrfrT4aD/H2YlNIdgJUZuVYlk86s5Ii5eDC2c/9/iYilWr1yYXBwMEOHDmXo0KH4+Pjw5JNP0q9fP3dmExER8R7OyQay3gNHHaDWQGmj+un96TMOQrtbm0VEABeKq6qqKpYtW8aoUaMYN24cb7/9NgB///vf6devH0899RRLlixpr5wiIiKdW98JEBhptgYe2QLA5JRY/H3sHCiqICf/tLX5pPNxFldqCRTxGC0uru677z7+8pe/0LdvXw4dOsS3vvUtfvSjH/HUU0/x5JNPcujQIX7xi1+0Z1YREZHOy8cPUuaY2/UnxaEBvkwYGAOoNVBcVJ4PRz4xt52zUYqI5VpcXL3++uu8/PLLvPHGG6xevZq6ujpqa2v54osvuPbaa/Hx8WnPnCIiIp2f8yQ4cznU36c8My0eMFsDRVosazlgQM9RENHT6jQiUq/FxdWxY8cYOXIkAGlpaQQEBLBkyRJsNlu7hRMREfEq/SeBfyiUHYcT2wGYNjgOX7uNrLxyDhZVWBxQOg0tHCzikVpcXNXV1eHv79/wZ19fX0JDQ9sllIiIiFfyC4RBM8zt+vWJIoP9GTugG6CrV9JClcVw6CNzW/dbiXiUFq9zZRgGN9xwAwEBAQCcPXuWH//4x4SEhDTa780333RvQhEREW+SOg8y/mtOyT7tt2CzMTMtng/3FpGekcstVw2wOqF4uqz3wKiD+KEQrZmaRTxJi4ur66+/vtGfv/e977k9jIiIiNcbOA18g+DUIcjbBQmXMH1wPPe8ncEXx0o5XnKGnpFBVqcUT9awcPB8a3OIyHlaXFz9/e9/b88cIiIiXYN/CCRNgawV5klywiV0Dwvgsr7RbD1YTHpGHjdeoasRXiV/D+Tvds+xjDo48IG5rfutRDxOi4ur9vTss8/y+9//nry8PIYNG8af/vQnRo8e3eS+zz//PC+//DIZGRkAjBw5koceeqjZ/UVERDzO4PlmcbXnXZh8DwCz0uLri6tcFVfepOIk/G0K1FS697gxydA92b3HFJE2s7y4evXVV1m6dCnPPfccY8aM4emnn2bGjBlkZ2cTGxt73v4ffPAB1113HePGjSMwMJBHH32U6dOns3v3bnr21FSkIiLSCQyaAXY/KMqGwmzonsyMIfH8ZvkePj98ioLys8SGBVqdUtwh+z2zsAruBnFp7jmm3RfG/j/3HEtE3MpmGIZhZYAxY8Zw2WWX8cwzzwDgcDhITEzkJz/5Cb/85S8v+vq6ujqioqJ45plnWLRo0UX3LysrIyIigtLSUsLDw9ucX0REpFX+/S3Yu9q8cnXlzwGY/+zHfHG0hN8tSOP7l/exOKC4hXOcJ90DE39udRoR+Rp31wYtnoq9PVRXV7Nt2zamTp3a8Jzdbmfq1Kls2bKlRceorKykpqaG6OjoJr9fVVVFWVlZo4eIiIjlnAsKO9crwmwNBEjPyLUikbjb2VLYv8Hcdo63iHg1S4uroqIi6urqiIuLa/R8XFwceXktW+vjF7/4BT169GhUoH3Vww8/TERERMMjMTGxzblFRETaLHkO2Hwg70soPgicK64+OVDMqYpqK9OJO+SsAkcNxAyC2BSr04hIB7C0uGqrRx55hFdeeYW33nqLwMCme9OXLVtGaWlpw+Po0aMdnFJERKQJId2g73hzO3M5AH26hZCaEE6dw2BNZr6F4cQt6heK1kK/Il2HpcVVTEwMPj4+5Oc3/gWSn59PfHz8BV/7+OOP88gjj7B69WouueSSZvcLCAggPDy80UNERMQjOE+6M5tqDWxZB4d4qOoK2LfO3NaU6SJdhqXFlb+/PyNHjmTdunUNzzkcDtatW8fYsWObfd1jjz3G7373O9LT0xk1alRHRBUREXG/1LmADY59BqXHgXPF1Ud7iyg/W2NhOGmTvWug9gxE9oH45j8EFhHvYnlb4NKlS3n++ef5xz/+QWZmJrfccgsVFRUsXrwYgEWLFrFs2bKG/R999FHuvfdeXnzxRfr27UteXh55eXmcPn3aqh9BRESkdcLiIXGMuZ21AoCBcWEM6B5CdZ2D9VkFFoaTNnFejRw8D2w2a7OISIexvLhauHAhjz/+OPfddx/Dhw9n586dpKenN0xyceTIEXJzz82a9Je//IXq6mr+53/+h4SEhIbH448/btWPICIi0nrOlrFGswYmALByl1oDO6Was+ZkFgCp863NIiIdyvJ1rjqa1rkSERGPUnIEnh4KNjv8NAdCu5NxvJSr//QRgX52tt87jWB/X6tTiiuyV8L/XQthPWDJbrBb/lm2iDTDq9a5EhER6fIie0PCcDAckP0eAEN6hNMrKoizNQ42Zhdam09cVz/7I6lzVViJdDH6Gy8iImK1r7UG2my2hoktVmrWwM6lrgayzCJZCweLdD0qrkRERKzmvC/n4EY4cwqAmfX3Xa3PKqCqts6qZOKqQx/C2RIIjoE+46xOIyIdTMWViIiI1WKSIHYwOGohOx2AEYmRxIUHcLqqlo/3FVkcUFrMOTFJyhyw+1ibRUQ6nIorERERT/C1BYXtdhszh9S3BmrWwM7BUdcwpb4WDhbpmlRciYiIeALnyfi+dVBVDpxrDVyTmU9NncOqZNJSRz6BikIIjIC+V1qdRkQsoOJKRETEE8QOhugBUFcFe1cDMLpfNN1C/CmprOHTA8UWB5SLci4cnDwbfP2tzSIillBxJSIi4glstvNmDfSx25g2OA6AlRm5ViWTlnA4vjIFu1oCRboqFVciIiKewnlSvncN1JwBYGb9lOyrdudT5zCsSiYXc2I7lB0H/1AYMNnqNCJiERVXIiIinqLHCIhIhJoK2L8egHEDYggL9KXodBXbDp+yOKA0a8875teB08Ev0NosImIZFVciIiKewmY7t/BsfWugv6+daalqDfRohnGuJVCzBIp0aSquREREPImzNTB7JdRWA19pDczIwzDUGuhx8jPg1EHwDYSkaVanERELqbgSERHxJIljIDQOqkrh4CYArhzUnWB/H06UnuXLY6UWB5TzOBcOHjAFAkKtzSIillJxJSIi4knsdki52tzONO/jCfTzYVJKLAArM7SgsMdxTsGulkCRLk/FlYiIiKdxnqRnvQd1tQDMqm8NTM/IVWugJynMgcIssPvBoJlWpxERi6m4EhER8TR9roCgaKg8CUc2A3BVciz+vnYOnawkK6/c4oDSoP7qIv0nQlCkpVFExHoqrkRERDyNjy+kzDa36+/nCQ3w5cqB3QG1BnoU5/1WWjhYRFBxJSIi4plS55tfs1aAwwE0bg0UD1B8EPK+BJsdUuZYnUZEPICKKxEREU/UfyIEhEN5Lhz/HICpqXH42m3k5J9mf+FpiwNKw9pWfcZDSIy1WUTEI6i4EhER8US+ATBohrm9x7yvJyLYj3FJ5kl8uloDrdewcPB8a3OIiMdQcSUiIuKpnPfxZL4L9TMEOlsDV6o10FplJ+DYVnNbLYEiUk/FlYiIiKdKmgp+wVByBHK/AGD64DjsNsg4XsbR4kqLA3ZhmSvMr71GQ3gPa7OIiMdQcSUiIuKp/IPNAgsaFqrtFhrA6H7RAKzardZAy2jhYBFpgoorERERT+a8n2fPV1sDEwBNyW6ZiiI4/LG5nTrX2iwi4lFUXImIiHiygdPBxx9O7oXCLABmDDHvu9p2+BT5ZWetTNc1Za0AwwEJwyCqr9VpRMSDqLgSERHxZIHhMGCyuV0/O118RCAjekcCag20hBYOFpFmqLgSERHxdM7WM+dJPV+ZNXCXiqsOdeYUHNxobmsKdhH5GhVXIiIini55Nth8IH8XFB8Azt139enBk5w8XWVluq4lOx0ctdA9FWIGWp1GRDyMiisRERFPFxwN/SaY2/VXrxKjgxnSIxyHAWv25FsYrovRLIEicgEqrkRERDqDry4oXM/ZGpiu+646RtVp2LfO3Nb9ViLSBBVXIiIinUHK1YANjm+D0mMAzKxvDfx4XxGlZ2osDNdF7F0NdVUQ1Q/ihlidRkQ8kIorERGRziAsDnqPNbfrZw1Mig1lYGwoNXUG67PUGtjuvtoSaLNZm0VEPJKKKxERkc7CeZ/PV2YNnKlZAztGzRnIWW1up2qWQBFpmoorERGRzsI5JfuRLVBuXqlyFlcbcwqpqKq1Kpn3278eaiogvBf0vNTqNCLioXytDiAiIiItFNELeo4077v6v2shNJbBGPw7+CSnanz47PNwrho/3uqU3qlh4eC5agkUkWapuBIREelM0q4xi6sT2wGwAeMBfGDbJ78HFVfuV1sN2SvNbU3BLiIXoOJKRESkMxn9IwjvCVXlDU+dOH6YHtt+T0rZFs6eqSAwKMTCgF7o4CaoKoWQWEgcY3UaEfFgKq5EREQ6Ex8/GLKg0VPxwxzkb3uRONtJdnz8LiOmXmdNNm+V+Y75NfVqsPtYm0VEPJomtBAREenk7D52DnafDEDtrretDeNtHHWQ9Z65rYWDReQiVFyJiIh4gfBLrwFgUOmH1FRXWZzGixzeDJUnITAS+l5hdRoR8XAqrkRERLxA8mXTOEkEEVSQteU9q+N4D+fCwSlzzJZMEZELUHElIiLiBXx8fdkXfRUAlV+8ZW0Yb+FwQOZyc1stgSLSAiquREREvETQsG8AkFS8kbpaLSjcZsc/h/Jc8A+DAZOsTiMinYCKKxERES+ROnY2pYTQjVKytq62Ok7nt6d+lsBBM8A3wNosItIpqLgSERHxEn7+AWRHXAnA6R3/tThNJ2cY5+630sLBItJCKq5ERES8iP/Q+QD0K1yPo67O4jSdWO4XUHIEfIMgaarVaUSkk1BxJSIi4kVSxs+jwggklmJydmy0Ok7n5bxqNXAq+IdYm0VEOg0VVyIiIl4kMCiErPBxAJR8/obFaTopw4A99cVV6nxrs4hIp6LiSkRExMvY6u8RSsxfi+FwWJymEyrMhpN7wcffnMxCRKSFfK0OICIiIu6VfMU3OfPJXfQkn/0ZnzDgknFWR+p4lcWw8z9Qe8b11x7bZn7tfxUEhrs1loh4NxVXIiIiXiYkLILtIaO5tPIjCra+3jWLqw0Pwmd/a9sxBqslUERco+JKRETEC9WlzIXtH9HjxBqro3Q8R925NapS50JQtOvHCI2Dod92by4R8XoqrkRERLzQoAn/Q/W2X9HHcZTDWdvpk3Kp1ZE6ztFPoaIQAiPgmhfB19/qRCLSRWhCCxERES8UERVDZpBZUJ3Y8qrFaTqYc6a/QbNUWIlIh1JxJSIi4qWqBl4NQOyx1RYn6UCGAZnLze36WRNFRDqKiisREREvNfDKhdQadgbUHeD4gUyr43SM49uh7Bj4hcCAyVanEZEuRsWViIiIl4rqnkBW4CUAHN38isVpOkhm/UQWg6aDX5C1WUSky1FxJSIi4sUq+s8GIOpQusVJOoBhnLvfKlUtgSLS8VRciYiIeLH+E67FYdhIrs0i/9h+q+O0r/wMOHUQfANh4HSr04hIF6TiSkRExIt179GHHP9UAA5+6OWzBjqvWg2YAgGh1mYRkS5JxZWIiIiXK+k7E4Cwg+9bnKSdZdYXV5olUEQsouJKRETEy/Uefy0AKVUZnMw/ZnGadlKYA4VZYPeDQTOtTiMiXZRHFFfPPvssffv2JTAwkDFjxrB169YL7v/666+TkpJCYGAgQ4cO5f33vfyTOBERkTbo0TeZvT5J+NgM9m3y0tZA5yyB/SdCUKSlUUSk67K8uHr11VdZunQp999/P9u3b2fYsGHMmDGDgoKCJvffvHkz1113HTfeeCM7duxgwYIFLFiwgIyMjA5OLiIi0nkU9Z4BQND+9yxO0k40S6CIeACbYRiGlQHGjBnDZZddxjPPPAOAw+EgMTGRn/zkJ/zyl788b/+FCxdSUVHBihUrGp67/PLLGT58OM8999xF36+srIyIiAhKS0sJDw933w8iIiLiwY7u/YLEf19JjeHDnonPYfcNsDqS2/jWlJH64W0YNjuffvMTagO7WR1JRFrgsn5RBPj6WJrB3bWBrxsytVp1dTXbtm1j2bJlDc/Z7XamTp3Kli1bmnzNli1bWLp0aaPnZsyYwdtvv93k/lVVVVRVVTX8uaysrO3BRUREOpnEgcM4aO9DP8dhhm26yeo47eKT2hSu+/c+YJ/VUUSkBbbePYXYMGuLK3eztLgqKiqirq6OuLi4Rs/HxcWRlZXV5Gvy8vKa3D8vL6/J/R9++GF+85vfuCewiIhIJ1Y69pfs++RxfIw6q6O4XbXNj7dCvkeKX5jVUUSkhXztlt+h5HaWFlcdYdmyZY2udJWVlZGYmGhhIhEREWsMn/YdmPYdq2O0m8esDiAiXZ6lxVVMTAw+Pj7k5+c3ej4/P5/4+PgmXxMfH+/S/gEBAQQEeE9fuYiIiIiIeCZLr8X5+/szcuRI1q1b1/Ccw+Fg3bp1jB07tsnXjB07ttH+AGvWrGl2fxERERERkY5geVvg0qVLuf766xk1ahSjR4/m6aefpqKigsWLFwOwaNEievbsycMPPwzAHXfcwcSJE3niiSeYM2cOr7zyCp9//jl//etfrfwxRERERESki7O8uFq4cCGFhYXcd9995OXlMXz4cNLT0xsmrThy5Aj2r9zsNm7cOP7zn/9wzz338Ktf/YqBAwfy9ttvk5aWZtWPICIiIiIiYv06Vx1N61yJiIiIiAi4vzbwvvkPRURERERELKDiSkRERERExA1UXImIiIiIiLiBiisRERERERE3UHElIiIiIiLiBiquRERERERE3EDFlYiIiIiIiBuouBIREREREXEDFVciIiIiIiJu4Gt1gI5mGAZgrsYsIiIiIiJdl7MmcNYIbdXliqvy8nIAEhMTLU4iIiIiIiKeoLy8nIiIiDYfx2a4q0zrJBwOBydOnCAsLAybzWZ1HMrKykhMTOTo0aOEh4dbHUfcROPqnTSu3knj6p00rt5LY+udrBpXwzAoLy+nR48e2O1tv2Oqy125stvt9OrVy+oY5wkPD9c/EF5I4+qdNK7eSePqnTSu3ktj652sGFd3XLFy0oQWIiIiIiIibqDiSkRERERExA1UXFksICCA+++/n4CAAKujiBtpXL2TxtU7aVy9k8bVe2lsvZO3jGuXm9BCRERERESkPejKlYiIiIiIiBuouBIREREREXEDFVciIiIiIiJuoOJKRERERETEDVRciYiIiIiIuEGXKa4efvhhLrvsMsLCwoiNjWXBggVkZ2c32ufs2bPceuutdOvWjdDQUK655hry8/Mb7XP77bczcuRIAgICGD58+AXfc9++fYSFhREZGdmijM8++yx9+/YlMDCQMWPGsHXr1obvFRcX85Of/ITk5GSCgoLo3bs3t99+O6WlpRc85gcffMD8+fNJSEggJCSE4cOH8+9///u8/V5//XVSUlIIDAxk6NChvP/++y3KbDWNa/Pjunv3bq655hr69u2LzWbj6aefblFeT6BxbX5cn3/+eSZMmEBUVBRRUVFMnTq10Xt7Mo1r8+P65ptvMmrUKCIjIxv2+ec//9mizJ5AY3vh37FOr7zyCjabjQULFrQos9U0rs2P60svvYTNZmv0CAwMbFFmq2lcL/z3taSkhFtvvZWEhAQCAgIYNGiQS+fFXaa42rhxI7feeiuffPIJa9asoaamhunTp1NRUdGwz5IlS1i+fDmvv/46Gzdu5MSJE3zzm98871g/+MEPWLhw4QXfr6amhuuuu44JEya0KN+rr77K0qVLuf/++9m+fTvDhg1jxowZFBQUAHDixAlOnDjB448/TkZGBi+99BLp6enceOONFzzu5s2bueSSS/jvf//Ll19+yeLFi1m0aBErVqxotM91113HjTfeyI4dO1iwYAELFiwgIyOjRdmtpHFtflwrKyvp378/jzzyCPHx8S3K6yk0rs2P6wcffMB1113Hhg0b2LJlC4mJiUyfPp3jx4+3KLuVNK7Nj2t0dDR33303W7Zsadhn8eLFrFq1qkXZraaxbX5snQ4dOsTPfvazFmf2BBrXC49reHg4ubm5DY/Dhw+3KLfVNK7Nj2t1dTXTpk3j0KFDvPHGG2RnZ/P888/Ts2fPFmUHwOiiCgoKDMDYuHGjYRiGUVJSYvj5+Rmvv/56wz6ZmZkGYGzZsuW8199///3GsGHDmj3+XXfdZXzve98z/v73vxsREREXzTN69Gjj1ltvbfhzXV2d0aNHD+Phhx9u9jWvvfaa4e/vb9TU1Fz0+F81e/ZsY/HixQ1//va3v23MmTOn0T5jxowxbr75ZpeO6wk0roub/F6fPn2Mp556yqXjeRKNa9PjahiGUVtba4SFhRn/+Mc/XDquJ9C4Nj+uhmEYI0aMMO655x6XjuspNLaNx7a2ttYYN26c8be//c24/vrrjfnz57t0TE+hcT03ri3N2BloXM+N61/+8hejf//+RnV1tUvH+aouc+Xq65yXDqOjowHYtm0bNTU1TJ06tWGflJQUevfuzZYtW1w69vr163n99dd59tlnW7R/dXU127Zta/TedrudqVOnXvC9S0tLCQ8Px9fX16V8paWlDT83wJYtWxq9N8CMGTNc/rk9gcY1+uI7dkIa1+bHtbKykpqamk459hrXpsfMMAzWrVtHdnY2V155pUvH9RQa28Zj+9vf/pbY2NiLfrLu6TSujcf19OnT9OnTh8TERObPn8/u3btdOqan0LieG9d3332XsWPHcuuttxIXF0daWhoPPfQQdXV1LT5mlyyuHA4Hd955J+PHjyctLQ2AvLw8/P39z+sFjYuLIy8vr8XHPnnyJDfccAMvvfQS4eHhLXpNUVERdXV1xMXFtfi9i4qK+N3vfsePfvSjFmcDeO211/jss89YvHhxw3N5eXkuvben0rg2HldvoXG98Lj+4he/oEePHud9QOLpNK7nj2tpaSmhoaH4+/szZ84c/vSnPzFt2jSXju0JNLaNx/ajjz7ihRde4Pnnn3fpWJ5G49p4XJOTk3nxxRd55513+Ne//oXD4WDcuHEcO3bMpWNbTePaeFwPHDjAG2+8QV1dHe+//z733nsvTzzxBA888ECLj9sli6tbb72VjIwMXnnlFbcf+6abbuI73/lOs582fvjhh4SGhjY8LnTja3PKysqYM2cOgwcP5te//nXD80OGDGk47qxZs8573YYNG1i8eDHPP/88Q4YMcfl9PZ3GVePqqs4+ro888givvPIKb731Vqe5kdpJ43r+uIaFhbFz504+++wzHnzwQZYuXcoHH3zgcjaraWzPjW15eTnf//73ef7554mJiXE5iyfRuDb+Ozt27FgWLVrE8OHDmThxIm+++Sbdu3fnf//3f13OZiWNa+NxdTgcxMbG8te//pWRI0eycOFC7r77bp577rkWZ3Lt2pkXuO2221ixYgWbNm2iV69eDc/Hx8dTXV1NSUlJo0o9Pz/fpckA1q9fz7vvvsvjjz8OmO0dDocDX19f/vrXv3Ldddexc+fOhv3j4uIICAjAx8fnvFlYmnrv8vJyZs6cSVhYGG+99RZ+fn4N33v//fepqakBICgoqNHrNm7cyNy5c3nqqadYtGhRo+/Fx8e36L09mcb1/HH1BhrX5sf18ccf55FHHmHt2rVccsklLf6ZPYHGtelxtdvtJCUlATB8+HAyMzN5+OGHueqqq1r8s1tNY9t4bPfv38+hQ4eYO3duw3MOhwMAX19fsrOzGTBgQIt/fqtoXC/+O9bPz48RI0awb9++Fv/cVtO4nj+uCQkJ+Pn54ePj0/BcamoqeXl5VFdX4+/vf/EfvNV3a3UyDofDuPXWW40ePXoYOTk5533fefPeG2+80fBcVlaWyzfv7dmzx9i1a1fD44EHHjDCwsKMXbt2GcXFxc3mGz16tHHbbbc1/Lmurs7o2bNno5v3SktLjcsvv9yYOHGiUVFR0dIf3diwYYMREhJiPPPMM01+/9vf/rZx9dVXN3pu7NixnWJCC41r8+P6VZ1tQguN64XH9dFHHzXCw8Ob/Fk9mca1ZX9fnRYvXmxMnDixxftbSWPb9NieOXOmUd5du3YZ8+fPNyZPnmzs2rXLqKqqavH7WEHj2vK/s7W1tUZycrKxZMmSFr+HVTSuzY/rsmXLjD59+hh1dXUNzz399NNGQkJCi9+jyxRXt9xyixEREWF88MEHRm5ubsOjsrKyYZ8f//jHRu/evY3169cbn3/+uTF27Fhj7NixjY6zd+9eY8eOHcbNN99sDBo0yNixY4exY8eOZv+BbOnMKK+88ooREBBgvPTSS8aePXuMH/3oR0ZkZKSRl5dnGIb5P9GYMWOMoUOHGvv27Wv0M9TW1jZ73PXr1xvBwcHGsmXLGr3m5MmTDft8/PHHhq+vr/H4448bmZmZxv3332/4+fkZu3btumhuq2lcmx/Xqqqqhp8jISHB+NnPfmbs2LHD2Lt370VzW03j2vy4PvLII4a/v7/xxhtvNNqnvLz8ormtpnFtflwfeughY/Xq1cb+/fuNPXv2GI8//rjh6+trPP/88xfN7Qk0ts2P7dd1ptkCNa7Nj+tvfvMbY9WqVcb+/fuNbdu2Gddee60RGBho7N69+6K5raZxbX5cjxw5YoSFhRm33XabkZ2dbaxYscKIjY01HnjggYvmduoyxRXQ5OPvf/97wz5nzpwx/t//+39GVFSUERwcbHzjG98wcnNzGx1n4sSJTR7n4MGDTb6vK1N1/ulPfzJ69+5t+Pv7G6NHjzY++eSThu9t2LCh2Z+hufc2DPMf8aZe8/VPQ1977TVj0KBBhr+/vzFkyBDjvffea1Fmq2lcmx/XgwcPtmjsPZHGtfkx69OnT5P73H///S3KbSWNa/PjevfddxtJSUlGYGCgERUVZYwdO9Z45ZVXWpTZE2hsW/7vbGcqrjSuzY/rnXfe2fC+cXFxxuzZs43t27e3KLPVNK4X/vu6efNmY8yYMUZAQIDRv39/48EHH7xg0fZ1NsMwDERERERERKRNuuRsgSIiIiIiIu6m4kpERERERMQNVFyJiIiIiIi4gYorERERERERN1BxJSIiIiIi4gYqrkRERERERNxAxZWIiIiIiIgbqLgSERERERFxAxVXIiIiIiIibqDiSkRERERExA1UXImIiIiIiLjB/wfhEVnHJaXl1gAAAABJRU5ErkJggg==",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -930,36 +1019,124 @@
    "source": [
     "plt.figure(figsize=(10, 5))\n",
     "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_npfit, label=\"Without update of residuals\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_pfit, label=\"With update of residuals\")"
+    "plt.plot(y_test[window:].index, rolling_coverage_enbpi_npfit, label=\"Without update of residuals\")\n",
+    "plt.plot(y_test[window:].index, rolling_coverage_enbpi_pfit, label=\"With update of residuals\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ACI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 285,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x183fed810>]"
+      ]
+     },
+     "execution_count": 285,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
+    "plt.plot(y_test[window:].index, rolling_coverage_aci_npfit, label=\"Without update of residuals\")\n",
+    "plt.plot(y_test[window:].index, rolling_coverage_aci_pfit, label=\"With update of residuals\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Temporal evolution of the distribution of residuals used for estimating prediction intervals\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### ENBPI"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 286,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x184044220>"
+      ]
+     },
+     "execution_count": 286,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0kAAAGsCAYAAAAIb+xYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABeNklEQVR4nO3deXgUVdr//0+nk+6sRELIJmFTQFmFIEtUFlkCKIsy4soA7oJoBOQRcAmMAqICflUQffgBgzo4o+KGAkEEZZAxbBIIw6CDApIYlZAQErJ01++PPDTdEpaQ7hSk36/rqsuTqlNVdxVld991Tp2yGIZhCAAAAAAgSQowOwAAAAAAuJCQJAEAAACAG5IkAAAAAHBDkgQAAAAAbkiSAAAAAMANSRIAAAAAuCFJAgAAAAA3gWYHcD6cTqcOHTqkiIgIWSwWs8MBAAC1nGEYOnr0qBISEhQQwD1moLa7KJOkQ4cOKTEx0ewwAACAnzlw4IAaNGhgdhgAfOyiTJIiIiIkVXxQ1alTx+RoIKdTyj9YUY5sIHGHDUANczoNHTpSLElKuCREAQH0MvA1p8OhXw7+V5IU26CpAqxWkyPyrYKCAiUmJrp+gwCo3S7KJOlEF7s6deqQJF0ISo9Jc6+pKE8+JNnCzI0HgN8pKi3XgJkbJElZ01IUarsov94uKkWF+Wrx/vUV5Qn7FRruH9/HdPMH/AO3/AEAAADADUkSAAAAALghSQIAAAAANyRJAAAAAOCGJAkAAAAA3JAkAQAAAIAbxkhF9QUESlffe7IMADXMGmDR8C6NXGX4njUwSP+KvlmSdFVgkMnRAIB3WQzDMMwOoqoKCgoUGRmp/Px83pMEAAB8jt8egH+hux0AAAAAuKFvFKrPMKSi3yvKofUk3kYOoIYZhqHDx0olSVFhNln4HPI5w+lU3m/ZkqS60fGyBHDfFUDtUa1PtBkzZshisSg1NdU1zzAMpaWlKSEhQSEhIerRo4d27drlsV5JSYnGjh2r6OhohYWFadCgQTp48GB1QoGZyoqkFy6rmMqKzI4GgB8qLnMo6dk1Snp2jYrLHGaH4xeKi44qal5LRc1rqeKio2aHAwBedd5JUkZGht544w21bdvWY/6sWbM0e/Zsvfrqq8rIyFBcXJz69Omjo0dPfoCmpqZq+fLlWrZsmTZs2KDCwkLdeOONcjj4YgMAAABgrvNKkgoLC3XnnXfqzTffVN26dV3zDcPQ3LlzNWXKFN18881q3bq1lixZoqKiIr3zzjuSpPz8fC1cuFAvvfSSevfurfbt2+utt95SZmam1qxZ452jAgAAAIDzdF5J0pgxY3TDDTeod+/eHvP37dunnJwc9e3b1zXPbrere/fu2rhxoyRpy5YtKisr86iTkJCg1q1bu+r8UUlJiQoKCjwmAAAAAPCFKg/csGzZMm3dulUZGRmnLMvJyZEkxcbGesyPjY3VTz/95Kpjs9k8WqBO1Dmx/h/NmDFDU6dOrWqoAAAAAFBlVWpJOnDggB599FG99dZbCg4OPm29P44qZBjGWUcaOlOdSZMmKT8/3zUdOHCgKmEDAAAAwDmrUkvSli1blJubq6SkJNc8h8Ohr776Sq+++qr27NkjqaK1KD4+3lUnNzfX1boUFxen0tJS5eXlebQm5ebmKjk5udL92u122e32qoQKAECNWJP1S7XW790y9uyVAAA1qkotSb169VJmZqa2b9/umjp27Kg777xT27dvV9OmTRUXF6f09HTXOqWlpVq/fr0rAUpKSlJQUJBHnezsbO3cufO0SRIucAGBUrs7KqYAXr0FoOZZAywa2qGBhnZoIGsA70iqCdbAIGVE9lNGZD9ZA4PMDgcAvKpKv2gjIiLUunVrj3lhYWGqV6+ea35qaqqmT5+uZs2aqVmzZpo+fbpCQ0N1xx13SJIiIyN1zz33aPz48apXr56ioqI0YcIEtWnT5pSBIHCRCLRLN803OwoAfsweaNVLw9qZHYZfsQeH6urH3jU7DADwCa/f9p84caKKi4s1evRo5eXlqXPnzlq9erUiIiJcdebMmaPAwEANGzZMxcXF6tWrlxYvXiyr1ertcAAAAACgSiyGYRhmB1FVBQUFioyMVH5+vurUqWN2ODAMqayoohwUKp1lkA4A8DbDMFRcVvFC8pAg61kHC/Imf30myXA6VVxU8aL4kNAIWQLO+/30FwV+ewD+pXZ/oqFmlBVJ0xMqphPJEgDUoOIyh1o+vUotn17lSpbgW8VFRxX6YkOFvtjQlSwBQG1BkgQAAAAAbkiSAAAAAMANSRIAAAAAuCFJAgAAAAA3JEkAAAAA4IYkCQAAAADceP1lsvBDFqvUcvDJMgDUsACLRQPaxLnK8L0Aa6C2hneTJLW08nMCQO3Cy2QBAKgGf32ZrL/htwfgX+huBwAAAABuSJIAAAAAwA1JEqqv9JiUFlkxlR4zOxoAfqiotFyNn1ihxk+sUFFpudnh+IWiwnzXZ39RYb7Z4QCAV5EkAQAAAIAbhqMBAKAaog+trd4GWt7unUAAAF5DSxIAAAAAuCFJAgAAAAA3JEkAAAAA4IYkCQAAAADcMHADqs9ilZr1PVkGgBoWYLGoZ4v6rjJ8L8AaqO9COkmSWlj5OQGgduFTDdUXFCzd+Q+zowDgx4KDrFo0qpPZYfiV4JAwtfufdLPDAACfoLsdAAAAALghSQIAAAAANyRJqL7SY9Jz8RVT6TGzowHgh4pKy3XlUyt15VMrVVRabnY4fqGoMF9Fz8RUTIX5ZocDAF7FM0nwjrIisyMA4OeKyxxmh+B3Qi0lkiS+AQDUNrQkAQAAAIAbkiQAAAAAcEOSBAAAAABuSJIAAAAAwA1JEgAAAAC4YXQ7VJ8lQGp07ckyANSwAItFnZtEucrwvYAAq3bZ2kiSLguwmhwNAHgXSRKqLyhEGrXC7CgA+LHgIKvefaCr2WH4leDQcLWavMHsMADAJ7jtDwAAAABuSJIAAAAAwA3d7VB9pcekuRX90pWaKdnCzI0HgN8pKi3Xtc9/KUna8D89FWrj683XigrzVfJia0mSfcJOhYZHmhwRAHgP3yLwjqLfzY4AgJ87fKzU7BD8Tl0VSJKKTI4DALytSt3t5s+fr7Zt26pOnTqqU6eOunbtqs8//9y1fOTIkbJYLB5Tly5dPLZRUlKisWPHKjo6WmFhYRo0aJAOHjzonaMBAAAAgGqqUpLUoEEDzZw5U5s3b9bmzZt1/fXXa/Dgwdq1a5erTr9+/ZSdne2aPvvsM49tpKamavny5Vq2bJk2bNigwsJC3XjjjXI4HN45IgAAAACohip1txs4cKDH388995zmz5+vTZs2qVWrVpIku92uuLi4StfPz8/XwoULtXTpUvXu3VuS9NZbbykxMVFr1qxRSkrK+RwDAAAAAHjNeY9u53A4tGzZMh07dkxdu558N8W6desUExOj5s2b67777lNubq5r2ZYtW1RWVqa+ffu65iUkJKh169bauHHjafdVUlKigoICjwkAAAAAfKHKSVJmZqbCw8Nlt9v14IMPavny5WrZsqUkqX///nr77be1du1avfTSS8rIyND111+vkpISSVJOTo5sNpvq1q3rsc3Y2Fjl5OScdp8zZsxQZGSka0pMTKxq2AAAAABwTqo8ul2LFi20fft2HTlyRO+//75GjBih9evXq2XLlrr11ltd9Vq3bq2OHTuqUaNGWrFihW6++ebTbtMwDFksltMunzRpksaNG+f6u6CggETpQmIJkBLanywDQA0LsFjUtkGkqwzfCwiwam9gM0lSYoDV5GgAwLuqnCTZbDZdfvnlkqSOHTsqIyNDL7/8shYsWHBK3fj4eDVq1Eh79+6VJMXFxam0tFR5eXkerUm5ublKTk4+7T7tdrvsdntVQ0VNCQqR7l9ndhQA/FhwkFUfP3yt2WH4leDQcDV7crPZYQCAT1T7tr9hGK7udH/0+++/68CBA4qPj5ckJSUlKSgoSOnp6a462dnZ2rlz5xmTJAAAAACoKVVqSZo8ebL69++vxMREHT16VMuWLdO6deu0cuVKFRYWKi0tTUOHDlV8fLx+/PFHTZ48WdHR0brpppskSZGRkbrnnns0fvx41atXT1FRUZowYYLatGnjGu0OAAAAAMxUpSTpl19+0fDhw5Wdna3IyEi1bdtWK1euVJ8+fVRcXKzMzEz99a9/1ZEjRxQfH6+ePXvq3XffVUREhGsbc+bMUWBgoIYNG6bi4mL16tVLixcvltVKf+aLVmmR9FrnivKYf0m2UHPjAeB3iksd6j17vSRpzbjuCrHxneJrxceO6sgLFc+jXvL4NoWERZxlDQC4eFQpSVq4cOFpl4WEhGjVqlVn3UZwcLBeeeUVvfLKK1XZNS5ohpS//2QZAGqYIUM/Hyl2leF7huFUvH6VJBUZTpOjAQDvYigyAAAAAHBDkgQAAAAAbkiSAAAAAMANSRIAAAAAuKnyy2QBAID3rMn6pVrr924Z66VIAAAnkCTBCyxS/StOlgGghllkUbOYcFcZvmexBOjHgERJUqyFjikAaheSJFSfLbTi/UgAYJIQm1Xp47qbHYZfCQmLUOOnd5odBgD4BLd+AAAAAMANSRIAAAAAuKG7HaqvtEh6s2dF+b4vK7rfAUANKi51aNCrGyRJHz98rUJsVpMjqv2Kjx3VLy91lSTFjv9GIWERJkcEAN5DkgQvMKRf/32yDAA1zJChvbmFrjJ8zzCcauw8IEkqMpwmRwMA3kV3OwAAAABwQ5IEAAAAAG5IkgAAAADADUkSAAAAALghSQIAAAAAN4xuBy+wSJENT5YBoIZZZNGll4S4yvA9iyVA2aovSbrEwj1XALULSRKqzxYqPZZpdhQA/FiIzap/PnG92WH4lZCwCIWkfW92GADgE9z6AQAAAAA3JEkAAAAA4Ibudqi+smJpUf+K8qjPpaAQc+MB4HeOlzk0bME3kqS/P9BVwUFWkyM6d9GH1lZvAy1v904gVXS8qFAHZveQJCWOW6fg0HBT4gAAXyBJQvUZTunQtpNlAKhhTsPQjoP5rjJ8z+l0qFn5XklSkdNhcjQA4F10twMAAAAANyRJAAAAAOCGJAkAAAAA3JAkAQAAAIAbkiQAAAAAcMPodvCO0HpmRwDAz0WF2cwOwe/kqY4kyW5yHADgbSRJqD5bmDTxv2ZHAcCPhdoCtfWpPmaH4VdCwyMVmnbA7DAAwCfobgcAAAAAbkiSAAAAAMAN3e1QfWXF0lt/qijf9Z4UFGJuPAD8zvEyh0b8f99Kkpbc3UnBQVaTI6r9jhcV6oe5/SRJl6WuVHBouMkRAYD3kCSh+gyn9NOGk2UAqGFOw9C/9h12leF7TqdDrUozJUlFTofJ0QCAd9HdDgAAAADckCQBAAAAgJsqJUnz589X27ZtVadOHdWpU0ddu3bV559/7lpuGIbS0tKUkJCgkJAQ9ejRQ7t27fLYRklJicaOHavo6GiFhYVp0KBBOnjwoHeOBgAAAACqqUpJUoMGDTRz5kxt3rxZmzdv1vXXX6/Bgwe7EqFZs2Zp9uzZevXVV5WRkaG4uDj16dNHR48edW0jNTVVy5cv17Jly7RhwwYVFhbqxhtvlMNBf2YAAAAA5qtSkjRw4EANGDBAzZs3V/PmzfXcc88pPDxcmzZtkmEYmjt3rqZMmaKbb75ZrVu31pIlS1RUVKR33nlHkpSfn6+FCxfqpZdeUu/evdW+fXu99dZbyszM1Jo1a3xygAAAAABQFef9TJLD4dCyZct07Ngxde3aVfv27VNOTo769u3rqmO329W9e3dt3LhRkrRlyxaVlZV51ElISFDr1q1ddSpTUlKigoICjwkXmKDQigkATBISZFUIQ3/XqCLDriLDbnYYAOB1VR4CPDMzU127dtXx48cVHh6u5cuXq2XLlq4kJzY21qN+bGysfvrpJ0lSTk6ObDab6tate0qdnJyc0+5zxowZmjp1alVDRU2xhUlTss2OAoAfC7UFavdf+pkdhl8JDY+UpuaaHQYA+ESVW5JatGih7du3a9OmTXrooYc0YsQIZWVluZZbLBaP+oZhnDLvj85WZ9KkScrPz3dNBw4cqGrYAAAAAHBOqpwk2Ww2XX755erYsaNmzJihdu3a6eWXX1ZcXJwkndIilJub62pdiouLU2lpqfLy8k5bpzJ2u901ot6JCQAAAAB8odrvSTIMQyUlJWrSpIni4uKUnp7uWlZaWqr169crOTlZkpSUlKSgoCCPOtnZ2dq5c6erDi5CZcelt2+pmMqOmx0NAD90vMyhUYu+1ahF3+p4GaOl1oTjxcf03fN99N3zfXS8+JjZ4QCAV1XpmaTJkyerf//+SkxM1NGjR7Vs2TKtW7dOK1eulMViUWpqqqZPn65mzZqpWbNmmj59ukJDQ3XHHXdIkiIjI3XPPfdo/PjxqlevnqKiojRhwgS1adNGvXv39skBogYYDmnv6pNlAKhhTsPQl3t+dZXhe05HudoVfytJKnKUmxwNAHhXlZKkX375RcOHD1d2drYiIyPVtm1brVy5Un369JEkTZw4UcXFxRo9erTy8vLUuXNnrV69WhEREa5tzJkzR4GBgRo2bJiKi4vVq1cvLV68WFYrIxIBAAAAMF+VkqSFCxeecbnFYlFaWprS0tJOWyc4OFivvPKKXnnllarsGgAAAABqRLWfSQIAAACA2oQkCQAAAADckCQBAAAAgBuSJAAAAABwU6WBG4BK2cKktHyzowDgx0Jtgfpx5g1mh+FXQsMjXZ/9oSbHAgDeRksSAAAAALghSQIAAAAAN3S3Q/WVHZeW319RvukNKSjY3HgA+J3jZQ6N+/t2SdLsYVcpOIgXlPva8eJjynrtNklSyzHLFBwSZnJEAOA9JEmoPsMhZX1UUR4y39xYAPglp2Hos8wcSdKLtxgmR+MfnI5ydSj8SpJU5Cg3ORoA8C662wEAAACAG5IkAAAAAHBDkgQAAAAAbkiSAAAAAMANSRIAAAAAuCFJAgAAAAA3DAGO6gsKlSYfOlkGgBoWEmRV1rQUVxm+FxIaoaIJ+11lAKhNSJJQfRaLZOMlggDMY7FYFGrjK60mWQICFBoeaXYYAOATdLcDAAAAADfcdkP1lZdIn6RWlAfOlQLtZkYDwA+VlDs0+YOdkqTpN7eWPZAud75WcrxIO+aPkiS1fWiR7MF0twZQe9CShOpzlkvfvVMxOcvNjgaAH3I4Db2/9aDe33pQDqdhdjh+wVFepqvzV+rq/JVylJeZHQ4AeBVJEgAAAAC4IUkCAAAAADckSQAAAADghiQJAAAAANyQJAEAAACAG5IkAAAAAHDDe5JQfUGh0uM/nCwDQA0LCbJqy5O9XWX4XkhohA6PzpIk1Q2NMDkaAPAukiRUn8UihUWbHQUAP2axWFQvnBdZ1yRLQICiYi41OwwA8Am62wEAAACAG1qSUH3lJdKqyRXllOlSIHdzAdSsknKHnv10tyTpyRuvlD2QLne+VnK8SNv/d4wk6ap7X5M9mO7WAGoPWpJQfc5yKeN/KyZnudnRAPBDDqehpZt+0tJNP8nhNMwOxy84ysvU+bcP1Pm3D+QoLzM7HADwKpIkAAAAAHBDkgQAAAAAbkiSAAAAAMANSRIAAAAAuKlSkjRjxgxdffXVioiIUExMjIYMGaI9e/Z41Bk5cqQsFovH1KVLF486JSUlGjt2rKKjoxUWFqZBgwbp4MGD1T8aAAAAAKimKiVJ69ev15gxY7Rp0yalp6ervLxcffv21bFjxzzq9evXT9nZ2a7ps88+81iempqq5cuXa9myZdqwYYMKCwt14403yuFwVP+IAAAAAKAaqvSepJUrV3r8vWjRIsXExGjLli3q1q2ba77dbldcXFyl28jPz9fChQu1dOlS9e7dW5L01ltvKTExUWvWrFFKSkpVjwFmCwyRHt1xsgwANSw40KqvJ/Z0leF7wSHhOjTyW0lSXEi4ydEAgHdV65mk/Px8SVJUVJTH/HXr1ikmJkbNmzfXfffdp9zcXNeyLVu2qKysTH379nXNS0hIUOvWrbVx48ZK91NSUqKCggKPCReQgACpbqOKKYDH3ADUvIAAixKjQpUYFaqAAIvZ4fiFAKtVCY1bKKFxCwVYSUwB1C7n/YvWMAyNGzdO1157rVq3bu2a379/f7399ttau3atXnrpJWVkZOj6669XSUmJJCknJ0c2m01169b12F5sbKxycnIq3deMGTMUGRnpmhITE883bAAAAAA4oyp1t3P38MMPa8eOHdqwYYPH/FtvvdVVbt26tTp27KhGjRppxYoVuvnmm0+7PcMwZLFUfvdv0qRJGjdunOvvgoICEqULSXmptHZaRfn6p6VAm7nxAPA7peVOvbi6YiChCX1byBZIq7avlZYc19ZFFd/NHUbNls0ebHJEAOA95/UtMnbsWH388cf68ssv1aBBgzPWjY+PV6NGjbR3715JUlxcnEpLS5WXl+dRLzc3V7GxsZVuw263q06dOh4TLiDOMmnjKxWTs8zsaAD4oXKnU2989V+98dV/Ve50mh2OXygvK1GXnLfVJedtlZeVmB0OAHhVlZIkwzD08MMP64MPPtDatWvVpEmTs67z+++/68CBA4qPj5ckJSUlKSgoSOnp6a462dnZ2rlzp5KTk6sYPgAAAAB4V5W6240ZM0bvvPOOPvroI0VERLieIYqMjFRISIgKCwuVlpamoUOHKj4+Xj/++KMmT56s6Oho3XTTTa6699xzj8aPH6969eopKipKEyZMUJs2bVyj3QEAAACAWaqUJM2fP1+S1KNHD4/5ixYt0siRI2W1WpWZmam//vWvOnLkiOLj49WzZ0+9++67ioiIcNWfM2eOAgMDNWzYMBUXF6tXr15avHixrIyOAwAAAMBkVUqSDMM44/KQkBCtWrXqrNsJDg7WK6+8oldeeaUquwcAAAAAn2P4HwAAAABwQ5IEAAAAAG7O+z1JgEtgiDR608kyANSw4ECrVj/WzVWG7wWHhOvHW7+QJDUMCTc5GgDwLpIkVF9AgBRzpdlRAPBjAQEWNY+NOHtFeE2A1arGV3Y0OwwA8Am62wEAAACAG1qSUH3lpdLXL1WUrxsvBdrMjQeA3yktd+q1L7+XJI3peblsgdwD9LXSkuPa8taTkqSku56VzR5sckQA4D0kSag+Z5m0fmZF+ZpHJJEkAahZ5U6nXv5iryTpge5NZaOjhM+Vl5Wo64E3JUlFZU+RJAGoVfgWAQAAAAA3JEkAAAAA4IYkCQAAAADckCQBAAAAgBuSJAAAAABwQ5IEAAAAAG4YAhzVFxgs3bf2ZBkAapg90KqPxlzjKsP37MFh+s+gjyVJlwWHmRwNAHgXSRKqL8AqXZpkdhQA/Jg1wKJ2iZeYHYZfsQYGqnmH7maHAQA+QXc7AAAAAHBDSxKqr7xU+tf8inLnh6RAm7nxAPA7peVOLfrnPknSqGuayBbIPUBfKy05rq1/ny5J6jBssmx2ulsDqD1IklB9zjIp/emK8tX3SiJJAlCzyp1Ozfj835Kk4V0byUZHCZ8rLytRlx9eliQVlY0nSQJQq/AtAgAAAABuSJIAAAAAwA1JEgAAAAC4IUkCAAAAADckSQAAAADghiQJAAAAANwwBDiqLzBYGvHpyTIA1DB7oFV/u6+LqwzfsweHaVefdyRJVwSHmRwNAHgXSRKqL8AqNbnO7CgA+DFrgEVdL6tndhh+xRoYqFbX3GB2GADgE3S3AwAAAAA3tCSh+hxl0pbFFeWkkZI1yMxoAPihModTf/t2vyTp9k4NFWTlHqCvlZWWaOvyOZKkDjc9piCb3eSIAMB7SJJQfY5S6bMJFeWr7iBJAlDjyhxOPf3RLknSn5IakCTVgLLS4+q8e4YkqeiGh0iSANQqfIsAAAAAgBuSJAAAAABwQ5IEAAAAAG5IkgAAAADADUkSAAAAALghSQIAAAAAN1VKkmbMmKGrr75aERERiomJ0ZAhQ7Rnzx6POoZhKC0tTQkJCQoJCVGPHj20a9cujzolJSUaO3asoqOjFRYWpkGDBungwYPVPxqYw2qX7vh7xWRlCFgANc9mDdD/N7Kj/r+RHWVj+O8aYbOH6LtuC/RdtwWy2UPMDgcAvKpK3yTr16/XmDFjtGnTJqWnp6u8vFx9+/bVsWPHXHVmzZql2bNn69VXX1VGRobi4uLUp08fHT161FUnNTVVy5cv17Jly7RhwwYVFhbqxhtvlMPh8N6RoeZYA6XmKRWTlVdvAah5gdYAXX9FrK6/IlaBJEk1IjDIpnbX36Z219+mwCCb2eEAgFdZDMMwznflX3/9VTExMVq/fr26desmwzCUkJCg1NRU/c///I+kilaj2NhYPf/883rggQeUn5+v+vXra+nSpbr11lslSYcOHVJiYqI+++wzpaSknHW/BQUFioyMVH5+vurUqXO+4QMAUG3b1/zN1P1f1ft2U/fvL/jtAfiXat1uy8/PlyRFRUVJkvbt26ecnBz17dvXVcdut6t79+7auHGjJGnLli0qKyvzqJOQkKDWrVu76vxRSUmJCgoKPCZcQBxl0ra3KyZHmdnRAPBDZQ6n/rH5gP6x+YDKHE6zw/ELZaUl+nb5K/p2+SsqKy0xOxwA8Krz7htlGIbGjRuna6+9Vq1bt5Yk5eTkSJJiY2M96sbGxuqnn35y1bHZbKpbt+4pdU6s/0czZszQ1KlTzzdU+JqjVPpodEW51RDJGmRqOAD8T5nDqcff2yFJuqFtvILocudzZaXH1em7JyVJRX3+rCAbz6QCqD3O+1vk4Ycf1o4dO/S3v53azcBisXj8bRjGKfP+6Ex1Jk2apPz8fNd04MCB8w0bAAAAAM7ovJKksWPH6uOPP9aXX36pBg0auObHxcVJ0iktQrm5ua7Wpbi4OJWWliovL++0df7IbrerTp06HhMAAAAA+EKVkiTDMPTwww/rgw8+0Nq1a9WkSROP5U2aNFFcXJzS09Nd80pLS7V+/XolJydLkpKSkhQUFORRJzs7Wzt37nTVAQAAAACzVOmZpDFjxuidd97RRx99pIiICFeLUWRkpEJCQmSxWJSamqrp06erWbNmatasmaZPn67Q0FDdcccdrrr33HOPxo8fr3r16ikqKkoTJkxQmzZt1Lt3b+8fIQAAAABUQZWSpPnz50uSevTo4TF/0aJFGjlypCRp4sSJKi4u1ujRo5WXl6fOnTtr9erVioiIcNWfM2eOAgMDNWzYMBUXF6tXr15avHixrFZr9Y4GAAAAAKqpSknSubxSyWKxKC0tTWlpaaetExwcrFdeeUWvvPJKVXYPAAAAAD533kOAAy5Wu3TL4pNlAKhhNmuAXrujg6sM37PZQ7Sl01xJUjt7iLnBAICXkSSh+qyBUqubzI4CgB8LtAbohrbxZofhVwKDbEoaMMrsMADAJ7jdBgAAAABuaElC9TnKpX9/UlG+YmBFyxIA1KByh1Ordv0iSUppFatAutz5XHlZqb5Lf1uS1K7PnQoMspkcEQB4D79mUX2OEukfIyvKkw+RJAGocaUOp8a8s1WSlDUthSSpBpSWFCvp21RJUlG3m0mSANQqfIsAAAAAgBuSJAAAAABwQ5IEAAAAAG5IkgAAAADADUkSAAAAALghSQIAAAAAN4zVjOqz2qTB806WAaCGBVkD9MKf2rrK8L0gW7C+bfesJKm9LdjkaADAu0iSUH3WIKn9nWZHAcCPBVkDdEvHRLPD8CtBNrs63TTW7DAAwCe43QYAAAAAbmhJQvU5yqUfvqgoX9ZLsnJZAahZ5Q6nvtr7qySpW7P6CqTLnc+Vl5Vq19cfSJJaXXezAoPobg2g9uDXLKrPUSK9M6yiPPkQSRKAGlfqcOruxZslSVnTUkiSakBpSbHaffWAJKmoU3+SJAC1Ct8iAAAAAOCGJAkAAAAA3JAkAQAAAIAbkiQAAAAAcEOSBAAAAABuGIYMAOC31mT9Uu1tRHshDgDAhYUkCdVntUkDXjxZBoAaFmQN0LTBrVxl+F6QLVj/unKSJKmDLdjkaADAu0iSUH3WIKnTfWZHAcCPBVkD9Oeujc0Ow68E2ezqfOsTZocBAD7B7TYAAAAAcENLEqrP6ZB+2lhRbpQsBVjNjQeA33E4DX2777AkqVOTKFkDLCZHVPs5ysv173+tkiRd0TlF1kB+UgCoPfhEQ/WVH5eW3FhRnnxIsoWZGw8Av1NS7tDtb26SJGVNS1Goja83Xys5fkyt0u+QJBW126/Q8EiTIwIA76G7HQAAAAC4IUkCAAAAADckSQAAAADghiQJAAAAANyQJAEAAACAG5IkAAAAAHDDGKmovoAgqc+0k2UAqGGBAQGa1P8KVxm+Fxhk16bLHpUkdQiymxwNAHgXSRKqL9AmXfOo2VEA8GO2wAA90P0ys8PwKzZ7sLoMn2Z2GADgE1W+3fbVV19p4MCBSkhIkMVi0YcffuixfOTIkbJYLB5Tly5dPOqUlJRo7Nixio6OVlhYmAYNGqSDBw9W60AAAAAAwBuqnCQdO3ZM7dq106uvvnraOv369VN2drZr+uyzzzyWp6amavny5Vq2bJk2bNigwsJC3XjjjXI4HFU/ApjP6ZB+3lIxOfk3BFDzHE5D3x04ou8OHJHDaZgdjl9wlJfrP1vX6z9b18tRXm52OADgVVXubte/f3/179//jHXsdrvi4uIqXZafn6+FCxdq6dKl6t27tyTprbfeUmJiotasWaOUlJSqhgSzlR+X3ry+ojz5kGQLMzceAH6npNyhwa/9U5KUNS1FoTZ6k/tayfFjav7xIElSUfP9Cg2PNDkiAPAenzzdum7dOsXExKh58+a67777lJub61q2ZcsWlZWVqW/fvq55CQkJat26tTZu3Fjp9kpKSlRQUOAxAQAAAIAveD1J6t+/v95++22tXbtWL730kjIyMnT99derpKREkpSTkyObzaa6det6rBcbG6ucnJxKtzljxgxFRka6psTERG+HDQAAAACSfDC63a233uoqt27dWh07dlSjRo20YsUK3XzzzaddzzAMWSyWSpdNmjRJ48aNc/1dUFBAogQAAADAJ3z+Mon4+Hg1atRIe/fulSTFxcWptLRUeXl5HvVyc3MVGxtb6Tbsdrvq1KnjMQEAAACAL/g8Sfr999914MABxcfHS5KSkpIUFBSk9PR0V53s7Gzt3LlTycnJvg4HAAAAAM6oyt3tCgsL9f3337v+3rdvn7Zv366oqChFRUUpLS1NQ4cOVXx8vH788UdNnjxZ0dHRuummmyRJkZGRuueeezR+/HjVq1dPUVFRmjBhgtq0aeMa7Q4AAAAAzFLlJGnz5s3q2bOn6+8TzwqNGDFC8+fPV2Zmpv7617/qyJEjio+PV8+ePfXuu+8qIiLCtc6cOXMUGBioYcOGqbi4WL169dLixYtltVq9cEiocQFBUvcnTpYBoIYFBgTo0V7NXGX4XmCQXd8k3idJSgqymxwNAHiXxTCMi+6tewUFBYqMjFR+fj7PJwEAztuarF+qvY3oQ2u9EMn5u6r37abu31/w2wPwL9xuAwAAAAA3vJIc1ed0Sr/tqShHt5Do6gKghjmdhr7/tVCSdHn9cAUEVP5KCXiP0+HQ/v9skyQ1bN5eAXSZB1CLkCSh+sqLpXldKsqTD0m2MHPjAeB3jpc71HfOV5KkrGkpCrXx9eZrx4sL1fjdXpKkogn7FRoeaXJEAOA93PIHAAAAADckSQAAAADghiQJAAAAANyQJAEAAACAG5IkAAAAAHBDkgQAAAAAbhgjFdUXECQljz1ZBoAaFhgQoPu7NXWVz1X0obW+CqnWCwyya1PcnZKkDkF2k6MBAO8iSUL1Bdqkvs+aHQUAP2YLDNDkAVeaHYZfsdmD1eXBeWaHAQA+QZIEAMDFbM/n1d9Gi/7V3wYA1CIkSag+p1PKP1BRjkyUqtDVBQC8wek09PORYknSpZeEKCDAYnJEtZ/T4VDOge8lSXGJlyvAajU5IgDwHpIkVF95sfRy24ry5EOSLczceAD4nePlDl0360tJUta0FIXa/OfrbfuBI9XexlUtqr7O8eJCJSzuJEkqmrBfoeGR1Y4DAC4U3PIHAAAAADckSQAAAADghiQJAAAAANyQJAEAAACAG5IkAAAAAHBDkgQAAAAAbvxnjFT4TkCgdPW9J8sAUMOsARYN79LIVYbvWQOD9K/omyVJVwUGmRwNAHgXv2hRfYF26YaXzI4CgB+zB1r1lyGtzQ7Dr9iDQ9X54UVmhwEAPkF3OwAAAABwQ0sSqs8wpKLfK8qh9SQLXV0A1CzDMHT4WKkkKSrMJgufQz5nOJ3K+y1bklQ3Ol6WAO67Aqg9SJJQfWVF0guXVZQnH5JsYebGA8DvFJc5lPTsGklS1rQUhdr4evO14qKjiprXUpJUNGG/QsMjTY4IALyH2z4AAAAA4IYkCQAAAADckCQBAAAAgBuSJAAAAABwQ5IEAAAAAG5IkgAAAADADWOkovoCAqV2d5wsA0ANswZYNLRDA1cZvmcNDFJGZD9JUtvAIJOjAQDv4hctqi/QLt003+woAPgxe6BVLw1rZ3YYfsUeHKqrH3vX7DAAwCfobgcAAAAAbmhJQvUZhlRWVFEOCpUsdHUBULMMw1BxmUOSFBJklYXPIZ8znE4VFx2VJIWERsgSwH1XALVHlT/RvvrqKw0cOFAJCQmyWCz68MMPPZYbhqG0tDQlJCQoJCREPXr00K5duzzqlJSUaOzYsYqOjlZYWJgGDRqkgwcPVutAYKKyIml6QsV0IlkCgBpUXOZQy6dXqeXTq1zJEnyruOioQl9sqNAXG7qSJQCoLaqcJB07dkzt2rXTq6++WunyWbNmafbs2Xr11VeVkZGhuLg49enTR0ePnvwATU1N1fLly7Vs2TJt2LBBhYWFuvHGG+Vw8MUGAAAAwFxV7m7Xv39/9e/fv9JlhmFo7ty5mjJlim6++WZJ0pIlSxQbG6t33nlHDzzwgPLz87Vw4UItXbpUvXv3liS99dZbSkxM1Jo1a5SSklKNwwEAAACA6vFqB+J9+/YpJydHffv2dc2z2+3q3r27Nm7cKEnasmWLysrKPOokJCSodevWrjp/VFJSooKCAo8JAAAAAHzBq0lSTk6OJCk2NtZjfmxsrGtZTk6ObDab6tate9o6fzRjxgxFRka6psTERG+GDQAAAAAuPhmK5o+jChmGcdaRhs5UZ9KkScrPz3dNBw4c8FqsAAAAAODOq0lSXFycJJ3SIpSbm+tqXYqLi1Npaany8vJOW+eP7Ha76tSp4zEBAAAAgC94NUlq0qSJ4uLilJ6e7ppXWlqq9evXKzk5WZKUlJSkoKAgjzrZ2dnauXOnqw4uMhar1HJwxWSxmh0NAD8UYLFoQJs4DWgTpwDekVQjAqyB2hreTVvDuynAymsXAdQuVf5UKyws1Pfff+/6e9++fdq+fbuioqLUsGFDpaamavr06WrWrJmaNWum6dOnKzQ0VHfccYckKTIyUvfcc4/Gjx+vevXqKSoqShMmTFCbNm1co93hIhMULA37q9lRAPBjwUFWzbszyeww/EpwSJg6TPjE7DAAwCeqnCRt3rxZPXv2dP09btw4SdKIESO0ePFiTZw4UcXFxRo9erTy8vLUuXNnrV69WhEREa515syZo8DAQA0bNkzFxcXq1auXFi9eLKuVVggAAAAA5rIYhmGYHURVFRQUKDIyUvn5+TyfBAA4b9vX/M3sEC4IV/W+3ewQLnj89gD8i09Gt4OfKT0mpUVWTKXHzI4GgB8qKi1X4ydWqPETK1RUWm52OH6hqDDf9dlfVJhvdjgA4FUkSQAAAADghiQJAAAAANyQJAEAAACAG5IkAAAAAHBDkgQAAAAAbkiSAAAAAMBNlV8mC5zCYpWa9T1ZBoAaFmCxqGeL+q4yfC/AGqjvQjpJklpY+TkBoHbhUw3VFxQs3fkPs6MA4MeCg6xaNKqT2WH4leCQMLX7n3SzwwAAn6C7HQAAAAC4IUkCAAAAADckSai+0mPSc/EVU+kxs6MB4IeKSst15VMrdeVTK1VUWm52OH6hqDBfRc/EVEyF+WaHAwBexTNJ8I6yIrMjAODnisscZofgd0ItJZIkvgEA1Da0JAEAAACAG5IkAAAAAHBDkgQAAAAAbkiSAAAAAMANSRIAAAAAuGF0O1SfJUBqdO3JMgDUsACLRZ2bRLnK8L2AAKt22dpIki4LsJocDQB4F0kSqi8oRBq1wuwoAPix4CCr3n2gq9lh+JXg0HC1mrzB7DAAwCe47Q8AAAAAbkiSAAAAAMAN3e1QfaXHpLkV/dKVminZwsyNB4DfKSot17XPfylJ2vA/PRVq4+vN14oK81XyYmtJkn3CToWGR5ocEQB4D98i8I6i382OAICfO3ys1OwQ/E5dFUiSikyOAwC8je52AAAAAOCGJAkAAAAA3JAkAQAAAIAbkiQAAAAAcEOSBAAAAABuGN0O1WcJkBLanywDQA0LsFjUtkGkqwzfCwiwam9gM0lSYoDV5GgAwLtIklB9QSHS/evMjgKAHwsOsurjh681Owy/EhwarmZPbjY7DADwCW77AwAAAIAbkiQAAAAAcEN3O1RfaZH0WueK8ph/SbZQc+MB4HeKSx3qPXu9JGnNuO4KsfGMjK8VHzuqIy9UPI96yePbFBIWYXJEAOA9JEnwAkPK33+yDAA1zJChn48Uu8rwPcNwKl6/SpKKDKfJ0QCAd3m9u11aWposFovHFBcX51puGIbS0tKUkJCgkJAQ9ejRQ7t27fJ2GAAAAABwXnzyTFKrVq2UnZ3tmjIzM13LZs2apdmzZ+vVV19VRkaG4uLi1KdPHx09etQXoQAAAABAlfgkSQoMDFRcXJxrql+/vqSKVqS5c+dqypQpuvnmm9W6dWstWbJERUVFeuedd3wRCgAAAABUiU+eSdq7d68SEhJkt9vVuXNnTZ8+XU2bNtW+ffuUk5Ojvn37uura7XZ1795dGzdu1AMPPFDp9kpKSlRSUuL6u6CgwBdhAwAAnJXD4VBZWZnZYQCogqCgIFmt5z6oj9eTpM6dO+uvf/2rmjdvrl9++UXPPvuskpOTtWvXLuXk5EiSYmNjPdaJjY3VTz/9dNptzpgxQ1OnTvV2qAAAAOfMYrHo8OHD+vnnn80OBcB5uOSSSxQXFyeLxXLWul5Pkvr37+8qt2nTRl27dtVll12mJUuWqEuXLpJ0SmCGYZwx2EmTJmncuHGuvwsKCpSYmOjlyHH+LFL9K06WAaCGWWRRs5hwVxm+Z7EE6MeAiu/iWIt/vHZx1KhROnbsmOLi4hQaGnpOP7QAmM8wDBUVFSk3N1eSFB8ff9Z1fD4EeFhYmNq0aaO9e/dqyJAhkqScnByP4HJzc09pXXJnt9tlt9t9HSrOly204v1IAGCSEJtV6eO6mx3GxWvP51VeJURS41tfqPjDD96R5HA4NGjQIMXExKhevXpmhwOgikJCQiRV5B0xMTFn7Xrn8ySppKREu3fv1nXXXacmTZooLi5O6enpat++4gV0paWlWr9+vZ5//nlfhwIAACqx/cCRaq1/VQvvxHEhczgcstlsrh9aAC4+oaGhkqSysrKaT5ImTJiggQMHqmHDhsrNzdWzzz6rgoICjRgxQhaLRampqZo+fbqaNWumZs2aafr06QoNDdUdd9zh7VAAAAC85sT7HwFcnKry/6/Xk6SDBw/q9ttv12+//ab69eurS5cu2rRpkxo1aiRJmjhxooqLizV69Gjl5eWpc+fOWr16tSIian9Tfa1VWiS92bOifN+XFd3vAKAGFZc6NOjVDZKkjx++ViG2cx/BCOenrLRU0d9W9AIp7nqjQvygyx0A/+H1JGnZsmVnXG6xWJSWlqa0tDRv7xqmMaRf/32yDAA1zJChvbmFrjJ8z5ChJqoY5a3IcJocDQB4l8+fSQIAAKit1mT9UqP7693y9ANdmc0wDD3wwAN67733lJeXp23btumqq66qsf2vW7dOPXv2VF5eni655JIa2++Fas+ePerevbv27t1b63ps/elPf1JycrLH6Nfe5h9jdgIAaqU1Wb9oTdYv+nJ3rmvel7tzXfPPNgH+ICcnR2PHjlXTpk1lt9uVmJiogQMH6osvvvDqflauXKnFixfr008/VXZ2tlq3bu3V7Z9NcnKysrOzFRkZKUlavHixXydLU6ZM0ZgxY84pQVq3bp0sFouOHDni+8DOwfvvv6+WLVvKbrerZcuWWr58ucfyp59+Ws8995wKCgp8FgNJEgAAQC31448/KikpSWvXrtWsWbOUmZmplStXqmfPnhozZoxX9/XDDz8oPj5eycnJiouLU2Bg1TssGYah8vLy89q/zWY75xeFXsjKysqqvY2DBw/q448/1qhRo7wQUc365ptvdOutt2r48OH67rvvNHz4cA0bNkz/+tfJ1820bdtWjRs31ttvv+2zOEiSAAAAaqnRo0fLYrHo22+/1Z/+9Cc1b95crVq10rhx47Rp0yZXvf3792vw4MEKDw9XnTp1NGzYMP3yy8nW1rS0NF111VVaunSpGjdurMjISN122206evSoJGnkyJEaO3as9u/fL4vFosaNG0uqeBXMI488opiYGAUHB+vaa69VRkaGa7snWjBWrVqljh07ym636+uvv1aPHj00duxYpaamqm7duoqNjdUbb7yhY8eOadSoUYqIiNBll12mzz///JRtHTlyROvWrdOoUaOUn5/vGpUwLS1N06ZNU5s2bU45T0lJSXr66acrPYd5eXm68847Vb9+fYWEhKhZs2ZatGiRa/nBgwd12223KSoqSmFhYerYsaPHD/r58+frsssuk81mU4sWLbR06VKP7VssFr3++usaPHiwwsLC9Oyzz0qSPvnkEyUlJSk4OFhNmzbV1KlTPRLItLQ0NWzYUHa7XQkJCXrkkUdcy/7+97+rXbt2atCggWveTz/9pIEDB6pu3boKCwtTq1at9Nlnn+nHH39Uz54VA3DVrVtXFotFI0eOlFSRtM6aNUtNmzZVSEiI2rVrp/fee++Uc75ixQq1a9dOwcHB6ty5szIzMys9l+di7ty56tOnjyZNmqQrrrhCkyZNUq9evTR37lyPeoMGDdLf/va3897P2ZAkAQAA1EKHDx/WypUrNWbMGIWFhZ2y/ERXNMMwNGTIEB0+fFjr169Xenq6fvjhB916660e9X/44Qd9+OGH+vTTT/Xpp59q/fr1mjlzpiTp5Zdf1rRp09SgQQNlZ2e7EqGJEyfq/fff15IlS7R161ZdfvnlSklJ0eHDhz22PXHiRM2YMUO7d+9W27ZtJUlLlixRdHS0vv32W40dO1YPPfSQbrnlFiUnJ2vr1q1KSUnR8OHDVVRUdMqxJScna+7cuapTp46ys7OVnZ2tCRMm6O6771ZWVpZHorZjxw5t27bNlRj80VNPPaWsrCx9/vnn2r17t+bPn6/o6GhJUmFhobp3765Dhw7p448/1nfffaeJEyfK6awYzGT58uV69NFHNX78eO3cuVMPPPCARo0apS+//NJjH88884wGDx6szMxM3X333Vq1apXuuusuPfLII8rKytKCBQu0ePFiPffcc5Kk9957T3PmzNGCBQu0d+9effjhhx7J31dffaWOHTt67GPMmDEqKSnRV199pczMTD3//PMKDw9XYmKi3n//fUkVzzFlZ2fr5ZdfliQ9+eSTWrRokebPn69du3bpscce01133aX169d7bPvxxx/Xiy++qIyMDMXExGjQoEGuFrH9+/crPDz8jNODDz7o2tY333yjvn37emw/JSVFGzdu9JjXqVMnffvttyopKan03626GLgBXmCRIhueLANATbNI9cJsrjJ8zyKLDqnih2JdC/dcL0Tff/+9DMPQFVdcccZ6a9as0Y4dO7Rv3z4lJiZKkpYuXapWrVopIyNDV199tSTJ6XRq8eLFrmdchg8fri+++ELPPfecIiMjFRERIavVqri4OEnSsWPHNH/+fC1evFj9+/eXJL355ptKT0/XwoUL9fjjj7timDZtmvr06eMRV7t27fTkk09KkiZNmqSZM2cqOjpa9913n6SK51Lmz5+vHTt2qEuXLh7r2mw2RUZGymKxuOKRpPDwcKWkpGjRokWu41q0aJG6d++upk2bVnp+9u/fr/bt27uSjhOtZJL0zjvv6Ndff1VGRoaioqIkSZdffrlr+YsvvqiRI0dq9OjRkuRqwXvxxRddrTeSdMcdd+juu+92/T18+HA98cQTGjFihCSpadOm+stf/qKJEyfqmWee0f79+xUXF6fevXsrKChIDRs2VKdOnVzrn+hm+cfjGDp0qCuZcj/eE7HHxMS4kudjx45p9uzZWrt2rbp27epaZ8OGDVqwYIG6d+/uWv+ZZ55x/fstWbJEDRo00PLlyzVs2DAlJCRo+/btlZ7bE+rUqeMq5+TkKDbWc4CS2NhY5eTkeMy79NJLVVJSopycHNerhryJJAlnt+fzs9cZMKviv/vWn7qsRX/vxgMAf2APtOr5oW3NDsOvBNlsyr22oltQAu9IuiAZRsVw+Gd7Rmf37t1KTEx0JUiS1LJlS11yySXavXu3K5lo3LixxyAA8fHxys3NPWV7J/zwww8qKyvTNddc45oXFBSkTp06affu3R51/9jqIcnVoiRJVqtV9erV82gtOfFD+kwxVOa+++7T3XffrdmzZ8tqtertt9/WSy+9dNr6Dz30kIYOHaqtW7eqb9++GjJkiJKTkyVJ27dvV/v27V1Jxh/t3r1b999/v8e8a665xtVSc8Ifj3/Lli3KyMhwtRxJksPh0PHjx1VUVKRbbrlFc+fOVdOmTdWvXz8NGDBAAwcOdD0HVlxcrODgYI9tPvLII3rooYe0evVq9e7dW0OHDvU4x3+UlZWl48ePn5K8lpaWqn379h7zTiRRUkXC1aJFC9e/cWBgoEfieC7+eM0ahnHKvJCQEEmqtCXRG7j1AwAAUAs1a9ZMFovllITkjyr7AVrZ/KCgII/lFovF1a3sdNs9Ue9s+6usO2Bl+3Ofd2IbZ4qhMgMHDpTdbtfy5cv1ySefqKSkREOHDj1t/f79++unn35SamqqDh06pF69emnChAmSTv5QP5PzOX6n06mpU6dq+/btrikzM1N79+5VcHCwEhMTtWfPHr322msKCQnR6NGj1a1bN1cXt+joaOXl5Xls895779V///tfDR8+XJmZmerYsaNeeeWV08Z94ryuWLHCI46srCyP55LOdtxV7W4XFxd3SqtRbm7uKa1LJ7ps1q9f/6yxnA+SJAAAgFooKipKKSkpeu2113Ts2LFTlp8Y7rlly5bav3+/Dhw44FqWlZWl/Px8XXnllee9/8svv1w2m00bNmxwzSsrK9PmzZurtd1zZbPZ5HA4TpkfGBioESNGaNGiRVq0aJFuu+02hYaGnnFb9evX18iRI/XWW29p7ty5euONNyRVtHZt3779lGesTrjyyis9jl+SNm7ceNbj79Chg/bs2aPLL7/8lCkgoOLne0hIiAYNGqT/9//+n9atW6dvvvnGNWBC+/btlZWVdcp2ExMT9eCDD+qDDz7Q+PHj9eabb7rOlSSP83ViCO79+/efEoN7q6Mkj0FA8vLy9J///MfVzfNEd7szTdOmTXOt37VrV6Wnp3tsf/Xq1a7WuxN27typBg0auJ4P8za626H6ykulL9Iqyr3SpECbmdEA8EOl5U7NWvVvSdLElCtkC+QeoK+VlpYqMmOOJOl48kAFh4abHBEqM2/ePCUnJ6tTp06aNm2a2rZtq/LycqWnp2v+/PnavXu3evfurbZt2+rOO+/U3LlzVV5ertGjR6t79+6VdoM7V2FhYXrooYf0+OOPKyoqSg0bNtSsWbNUVFSke+65x4tHWbnGjRursLBQX3zxhdq1a6fQ0FBXMnTvvfe6EpV//vOfZ9zO008/raSkJLVq1UolJSX69NNPXevefvvtmj59uoYMGaIZM2YoPj5e27ZtU0JCgrp27arHH39cw4YNU4cOHdSrVy998skn+uCDD7RmzZqz7vPGG29UYmKibrnlFgUEBGjHjh3KzMzUs88+q8WLF8vhcKhz584KDQ3V0qVLFRIS4no2JyUlRffee68cDoesVqskKTU1Vf3791fz5s2Vl5entWvXuo6jUaNGslgs+vTTTzVgwACFhIQoIiJCEyZM0GOPPSan06lrr71WBQUF2rhxo8LDw13PS0kVz5TVq1dPsbGxmjJliqKjozVkyBBJVe9u9+ijj6pbt256/vnnNXjwYH300Udas2bNKcnm119/fcoAD95EkgQvcEqH/3uy/Efn8kzT2fBcE4AzMAxDP/5e5CqjJhhqYeyTJBU5T71b7y96t4w9eyUTNWnSRFu3btVzzz2n8ePHKzs7W/Xr11dSUpLmz58vqaJb1IcffqixY8eqW7duCggIUL9+/c7YFetczZw5U06nU8OHD9fRo0fVsWNHrVq1SnXr1q32ts8mOTlZDz74oG699Vb9/vvveuaZZ5SWliapoiticnKyfv/9d3Xu3PmM27HZbJo0aZJ+/PFHhYSE6LrrrtOyZctcy1avXq3x48drwIABKi8vV8uWLfXaa69JkoYMGaKXX35ZL7zwgh555BE1adJEixYtUo8ePc64z5SUFH366aeaNm2aZs2apaCgIF1xxRW69957JVWMTDhz5kyNGzdODodDbdq00SeffKJ69epJkgYMGKCgoCCtWbNGKSkpkipaicaMGaODBw+qTp066tevn+bMqbjRcemll2rq1Kl64oknNGrUKP35z3/W4sWL9Ze//EUxMTGaMWOG/vvf/+qSSy5Rhw4dNHnyZI94Z86cqUcffVR79+5Vu3bt9PHHH7tap6oqOTlZy5Yt05NPPqmnnnpKl112md59912Pf6fjx49r+fLlWrVq1Xnt41xYjIvw26SgoECRkZHKz8/3GA0DPnK2JKf8uPSPkRXlWxZLgcFnqn1+SJIAVGL7mop3ZBx3SLd9WfF9sKxngYKtZkblH0pLS9Tp20clSUUT9is0PNLkiHzrt99+0+bNm3XddddV+vwMLi4nRv174IEHNG7cOLPD8Yl58+bpo48+8mkisW7dOvXs2VN5eXmuUfFqwmuvvaaPPvpIq1evrtJ6x48f1759+9SkSZNTBrb4I1qSAAAA4Ddyc3O1dOlS/fzzzxo1apTZ4fjM/fffr7y8PB09etRjVMLaICgoyCstnWdCkgQAAAC/ERsbq+joaL3xxhs10u3PLIGBgZoyZYrZYfjEH4dV9wWSJAAAAPiNi/BJkwtWjx49au35ZPgfAAAAAHBDSxK8w167+roCuPjUCaraCyVRfYeNis9+HwzXAwCmIklC9QUGSze/aXYUAPxYsFX6a/dCs8PwKzabXfuve0GSdFUtH9kOgP+hux0AAAAAuCFJAgAAAAA3JEmovvJS6YupFVN5qdnRAPBDJQ5pyuZQTdkcqhKH2dH4h9LSUgX8c64C/jlXx4vo6oiKUePuv/9+RUVFyWKxaPv27TW6/3Xr1slisejIkSM1ut8L1Z49exQXF6ejR4+aHUqNuPrqq/XBBx94bXs8kwQvcEq5u0+WAaCGGZJ2HQl0lVETDLU1/i1JKnL6cWa65/Oa3V+L/lVeJScnR88995xWrFihn3/+WTExMbrqqquUmpqqXr16eS20lStXavHixVq3bp2aNm2q6Ohor237XCQnJys7O1uRkRXPyC1evFipqal+mzRNmTJFY8aMOacXya5bt049e/ZUXl6eLrnkEt8HV0VfffWVXnjhBW3ZskXZ2dlavny5hgwZ4lHnqaee0oQJEzRkyBAFBFS/HYiWJAAAgFrqxx9/VFJSktauXatZs2YpMzNTK1euVM+ePTVmzBiv7uuHH35QfHy8kpOTFRcXp8DAqt+LNwxD5eXl57V/m82muLg4WSyW81r/QlFWVlbtbRw8eFAff/yxRo0a5YWIzHfs2DG1a9dOr7766mnr3HDDDcrPz9eqVau8sk+SJAAAgFpq9OjRslgs+vbbb/WnP/1JzZs3V6tWrTRu3Dht2rTJVW///v0aPHiwwsPDVadOHQ0bNky//PKLa3laWpquuuoqLV26VI0bN1ZkZKRuu+02V1eukSNHauzYsdq/f78sFosaN24sSSopKdEjjzyimJgYBQcH69prr1VGRoZruye6yK1atUodO3aU3W7X119/rR49emjs2LFKTU1V3bp1FRsbqzfeeEPHjh3TqFGjFBERocsuu0yff/75Kds6cuSI1q1bp1GjRik/P18Wi0UWi0VpaWmaNm2a2rRpc8p5SkpK0tNPP13pOczLy9Odd96p+vXrKyQkRM2aNdOiRYtcyw8ePKjbbrtNUVFRCgsLU8eOHfWvf/3LtXz+/Pm67LLLZLPZ1KJFCy1dutRj+xaLRa+//roGDx6ssLAwPfvss5KkTz75RElJSQoODlbTpk01depUjwQyLS1NDRs2lN1uV0JCgh555BHXsr///e9q166dGjRo4Jr3008/aeDAgapbt67CwsLUqlUrffbZZ/rxxx/Vs2dPSVLdunVlsVg0cuRISRVJ66xZs9S0aVOFhISoXbt2eu+990455ytWrFC7du0UHByszp07KzMzs9Jzeb769++vZ599VjfffPNp61itVg0YMEB/+9vfvLJPkiQAAIBa6PDhw1q5cqXGjBmjsLCwU5af6FZlGIaGDBmiw4cPa/369UpPT9cPP/ygW2+91aP+Dz/8oA8//FCffvqpPv30U61fv14zZ86UJL388suaNm2aGjRooOzsbFciNHHiRL3//vtasmSJtm7dqssvv1wpKSk6fPiwx7YnTpyoGTNmaPfu3Wrbtq0kacmSJYqOjta3336rsWPH6qGHHtItt9yi5ORkbd26VSkpKRo+fLiKiopOObbk5GTNnTtXderUUXZ2trKzszVhwgTdfffdysrK8kjUduzYoW3btrkSgz966qmnlJWVpc8//1y7d+/W/PnzXV0JCwsL1b17dx06dEgff/yxvvvuO02cOFFOZ8XjB8uXL9ejjz6q8ePHa+fOnXrggQc0atQoffnllx77eOaZZzR48GBlZmbq7rvv1qpVq3TXXXfpkUceUVZWlhYsWKDFixfrueeekyS99957mjNnjhYsWKC9e/fqww8/9Ej+vvrqK3Xs2NFjH2PGjFFJSYm++uorZWZm6vnnn1d4eLgSExP1/vvvS6p4jik7O1svv/yyJOnJJ5/UokWLNH/+fO3atUuPPfaY7rrrLq1fv95j248//rhefPFFZWRkKCYmRoMGDXK1iO3fv1/h4eFnnB588MFKz31VderUSV9//bVXtsUzSQAAALXQ999/L8MwdMUVV5yx3po1a7Rjxw7t27dPiYmJkqSlS5eqVatWysjI0NVXXy1JcjqdWrx4sesZl+HDh+uLL77Qc889p8jISEVERMhqtSouLk5SRRep+fPna/Hixerfv+JZqjfffFPp6elauHChHn/8cVcM06ZNU58+fTziateunZ588klJ0qRJkzRz5kxFR0frvvvukyQ9/fTTmj9/vnbs2KEuXbp4rGuz2RQZGSmLxeKKR5LCw8OVkpKiRYsWuY5r0aJF6t69u5o2bVrp+dm/f7/at2/vSjpOtJJJ0jvvvKNff/1VGRkZioqKkiRdfvnlruUvvviiRo4cqdGjR0uSqwXvxRdfdLXeSNIdd9yhu+++2/X38OHD9cQTT2jEiBGSpKZNm+ovf/mLJk6cqGeeeUb79+9XXFycevfuraCgIDVs2FCdOnVyrX+im+Ufj2Po0KGuZMr9eE/EHhMT40qejx07ptmzZ2vt2rXq2rWra50NGzZowYIF6t69u2v9Z555xvXvt2TJEjVo0EDLly/XsGHDlJCQcNZBPOrUqXPG5efq0ksv1f79++V0Oqv9XBJJEgAAqJadXy2XzWav1jau6n27l6LBCYZRMYzJ2Z7R2b17txITE10JkiS1bNlSl1xyiXbv3u1KJho3buwxCEB8fLxyc3NPu90ffvhBZWVluuaaa1zzgoKC1KlTJ+3evduj7h9bPSS5WpSkiq5U9erV82gtiY2NlaQzxlCZ++67T3fffbdmz54tq9Wqt99+Wy+99NJp6z/00EMaOnSotm7dqr59+2rIkCFKTk6WJG3fvl3t27d3JRl/tHv3bt1///0e86655hpXS80Jfzz+LVu2KCMjw9VyJEkOh0PHjx9XUVGRbrnlFs2dO1dNmzZVv379NGDAAA0cOND1HFhxcbGCg4M9tvnII4/ooYce0urVq9W7d28NHTrU4xz/UVZWlo4fP35K8lpaWqr27dt7zDuRREkVCVeLFi1c/8aBgYEeieOZfP31166EWpIWLFigO++885zWlaSQkBA5nU6VlJQoJCTknNerDEkSvMNavS9HAKguewDj2tW0IoPP/gtZs2bNZLFYtHv37lNGAnNnGEalidQf5wcFBXkst1gsrm5lp9vuiXpn219l3QEr25/7vBPbOFMMlRk4cKDsdruWL18uu92ukpISDR069LT1+/fvr59++kkrVqzQmjVr1KtXL40ZM0YvvvjiOf0QP5/jdzqdmjp1aqXP4AQHBysxMVF79uxRenq61qxZo9GjR+uFF17Q+vXrFRQUpOjoaOXl5Xmsd++99yolJUUrVqzQ6tWrNWPGDL300ksaO3ZspXGfOK8rVqzQpZde6rHMbj/7//snjnH//v1q2bLlGeveddddev3119WxY0ePVqcTifC5Onz4sEJDQ6udIEkkSfCGwGBp2BKzowDgx4Kt0rvX+8e7QC4UNptd/7mu4m64zeRYULmoqCilpKTotdde0yOPPHLKD/EjR47okksuUcuWLbV//34dOHDA1ZqUlZWl/Px8XXnllee9/8svv1w2m00bNmzQHXfcIali5LbNmzcrNTX1vLd7rmw2mxyOU4enDwwM1IgRI7Ro0SLZ7XbddtttCg0NPeO26tevr5EjR2rkyJG67rrrXM/gtG3bVv/7v/+rw4cPV9qadOWVV2rDhg3685//7Jq3cePGs57XDh06aM+ePWdsgQkJCdGgQYM0aNAgjRkzRldccYUyMzPVoUMHtW/fXllZWaesk5iYqAcffFAPPvigJk2apDfffFNjx46VzVbxf7H7+WrZsqXsdrv279/v0bWuMps2bVLDhg0lVQx08Z///MfVzbMq3e1CQkLOudWpMjt37lSHDh3Oe313JEkAAAC11Lx585ScnKxOnTpp2rRpatu2rcrLy5Wenq758+dr9+7d6t27t9q2bas777xTc+fOVXl5uUaPHq3u3btX2g3uXIWFhemhhx7S448/rqioKDVs2FCzZs1SUVGR7rnnHi8eZeUaN26swsJCffHFF2rXrp1CQ0NdydC9997rSlT++c9/nnE7Tz/9tJKSktSqVSuVlJTo008/da17++23a/r06RoyZIhmzJih+Ph4bdu2TQkJCeratasef/xxDRs2TB06dFCvXr30ySef6IMPPtCaNWvOus8bb7xRiYmJuuWWWxQQEKAdO3YoMzNTzz77rBYvXiyHw6HOnTsrNDRUS5cuVUhIiBo1aiRJSklJ0b333iuHwyGr1SpJSk1NVf/+/dW8eXPl5eVp7dq1ruNo1KiRLBaLPv30Uw0YMEAhISGKiIjQhAkT9Nhjj8npdOraa69VQUGBNm7cqPDwcNfzUlLFM2X16tVTbGyspkyZoujoaFfrZVW6251OYWGhvv/+e9ff+/bt0/bt213X1Qlff/21+vbtW619ncDodgAAALVUkyZNtHXrVvXs2VPjx49X69at1adPH33xxReaP3++pIpuUR9++KHq1q2rbt26qXfv3mratKnefffdau9/5syZGjp0qIYPH64OHTro+++/16pVq1S3bt1qb/tskpOT9eCDD+rWW29V/fr1NWvWLNeyZs2aKTk5WS1atFDnzp3PuB2bzaZJkyapbdu26tatm6xWq5YtW+Zatnr1asXExGjAgAFq06aNZs6c6UpMhgwZopdfflkvvPCCWrVqpQULFmjRokXq0aPHGfeZkpKiTz/9VOnp6br66qvVpUsXzZ4925UEXXLJJXrzzTd1zTXXqG3btvriiy/0ySefqF69epKkAQMGKCgoyCMZczgcGjNmjK688kr169dPLVq00Lx58yRVDHgwdepUPfHEE4qNjdXDDz8sSfrLX/6ip59+WjNmzNCVV16plJQUffLJJ2rSpIlHvDNnztSjjz6qpKQkZWdn6+OPP3a1TnnD5s2b1b59e9ezUOPGjVP79u09hm3/+eeftXHjRq+9G8pinOgwehEpKChQZGSk8vPzvTYaRq1VE28Cd5RKG+ZUlK99TLL6oOPFebxhHEDtt31NxfswSh3S8zsq+qD/T9ti2axmRuUfysrKFLD5fyVJzo73nvL8SFVd6AM3/Pbbb9q8ebOuu+66Sp+fwcXlxKh/DzzwgMaNG2d2OD4xb948ffTRR157uWpl1q1bp549eyovL881Kp5ZHn/8ceXn5+uNN944bZ3jx49r3759atKkySkDW/wR3e1QfYZTOrTtZBkAzsGarF/OXuksov/vv05JW34P+r9ycbW3i7MzDKeSHN9Jkr7lsx8XkdzcXC1dulQ///yz11odLkT333+/8vLydPToUY9RCWurmJgYTZgwwWvbI0kCAJgi+tBas0MA4IdiY2MVHR2tN954o0a6/ZklMDBQU6ZMMTuMGuP+3i1vIEnCxaG63Qbprgd4ONFNrTou9O5RAFCZi/BJkwtWjx49au35JEkCAJwXbyRawAnVvZ5I2gF4k6mj282bN8/14FRSUpK+/vprM8MBAAAAAPNakt59912lpqZq3rx5uuaaa7RgwQL1799fWVlZHuOdAwBORSsOUPMMw5DTySAVwMWqKv//mpYkzZ49W/fcc4/uvfdeSdLcuXO1atUqzZ8/XzNmzPCoW1JSopKSEtff+fn5kiqGAsdZFBb5fh/lx6US4+T+Ai/ALxCuFVxAdnz5D7NDqHVKHJKzpOIr7dixIpUzBLjPlZaVquD/PvsLi4plK3OYGo+vfxMUFxfrt99+U3Z2tpxOp2w2mywWi0/3CcA7DMNQaWmpfv31VwUEBJzTO5xMeU9SaWmpQkND9Y9//EM33XSTa/6jjz6q7du3a/369R7109LSNHXq1JoOEwAAwKV+/fpat24dyRFwkQoNDVV8fPw5JUmmtCT99ttvcjgcio2N9ZgfGxurnJycU+pPmjTJ40VfTqdThw8fVr169Xz2QVVQUKDExEQdOHCAF9ZWA+fROziP3sO59A7Oo3dwHr2jJs6jYRg6evSo4uPj5XQ65XCY23IGoGqsVqsCAwPPOXcwdXS7PwZpGEalgdvtdtntdo95NfVW3zp16vDF5QWcR+/gPHoP59I7OI/ewXn0Dl+fx8jISEkVP7aCgoJ8th8A5jNldLvo6GhZrdZTWo1yc3NPaV0CAAAAgJpkSpJks9mUlJSk9PR0j/np6elKTk42IyQAAAAAkGRid7tx48Zp+PDh6tixo7p27ao33nhD+/fv14MPPmhWSB7sdrueeeaZU7r5oWo4j97BefQezqV3cB69g/PoHZxHAN5myuh2J8ybN0+zZs1Sdna2WrdurTlz5qhbt25mhQMAAAAA5iZJAAAAAHChMeWZJAAAAAC4UJEkAQAAAIAbkiQAAAAAcEOSBAAAAABuSJL+z48//qh77rlHTZo0UUhIiC677DI988wzKi0tPeN6I0eOlMVi8Zi6dOlSQ1FfeM73PBqGobS0NCUkJCgkJEQ9evTQrl27aijqC9Nzzz2n5ORkhYaG6pJLLjmndbgeT3U+55Hr8VR5eXkaPny4IiMjFRkZqeHDh+vIkSNnXIfrscK8efPUpEkTBQcHKykpSV9//fUZ669fv15JSUkKDg5W06ZN9frrr9dQpBe2qpzHdevWnXLtWSwW/fvf/67BiAFczEiS/s+///1vOZ1OLViwQLt27dKcOXP0+uuva/LkyWddt1+/fsrOznZNn332WQ1EfGE63/M4a9YszZ49W6+++qoyMjIUFxenPn366OjRozUU+YWntLRUt9xyix566KEqrcf16Ol8ziPX46nuuOMObd++XStXrtTKlSu1fft2DR8+/Kzr+fv1+O677yo1NVVTpkzRtm3bdN1116l///7av39/pfX37dunAQMG6LrrrtO2bds0efJkPfLII3r//fdrOPILS1XP4wl79uzxuP6aNWtWQxEDuOgZOK1Zs2YZTZo0OWOdESNGGIMHD66ZgC5SZzuPTqfTiIuLM2bOnOmad/z4cSMyMtJ4/fXXayLEC9qiRYuMyMjIc6rL9Xh653oeuR5PlZWVZUgyNm3a5Jr3zTffGJKMf//736ddj+vRMDp16mQ8+OCDHvOuuOIK44knnqi0/sSJE40rrrjCY94DDzxgdOnSxWcxXgyqeh6//PJLQ5KRl5dXA9EBqI1oSTqD/Px8RUVFnbXeunXrFBMTo+bNm+u+++5Tbm5uDUR38Tjbedy3b59ycnLUt29f1zy73a7u3btr48aNNRFircL1WD1cj6f65ptvFBkZqc6dO7vmdenSRZGRkWc9J/58PZaWlmrLli0e15Ik9e3b97Tn7ZtvvjmlfkpKijZv3qyysjKfxXohO5/zeEL79u0VHx+vXr166csvv/RlmABqGZKk0/jhhx/0yiuv6MEHHzxjvf79++vtt9/W2rVr9dJLLykjI0PXX3+9SkpKaijSC9u5nMecnBxJUmxsrMf82NhY1zKcG67H6uN6PFVOTo5iYmJOmR8TE3PGc+Lv1+Nvv/0mh8NRpWspJyen0vrl5eX67bfffBbrhex8zmN8fLzeeOMNvf/++/rggw/UokUL9erVS1999VVNhAygFqj1SVJaWlqlD2+6T5s3b/ZY59ChQ+rXr59uueUW3XvvvWfc/q233qobbrhBrVu31sCBA/X555/rP//5j1asWOHLw6pxvj6PkmSxWDz+NgzjlHkXu/M5j1XB9eid8yhxPf7xPFZ27Gc7J/5yPZ5NVa+lyupXNt/fVOU8tmjRQvfdd586dOigrl27at68ebrhhhv04osv1kSoAGqBQLMD8LWHH35Yt9122xnrNG7c2FU+dOiQevbsqa5du+qNN96o8v7i4+PVqFEj7d27t8rrXsh8eR7j4uIkVdxBjY+Pd83Pzc095c7hxa6q57G6uB6rjuvRU+PGjbVjxw798ssvpyz79ddfq3ROauv1eDrR0dGyWq2ntHac6VqKi4urtH5gYKDq1avns1gvZOdzHivTpUsXvfXWW94OD0AtVeuTpOjoaEVHR59T3Z9//lk9e/ZUUlKSFi1apICAqje0/f777zpw4IDHj6vawJfnsUmTJoqLi1N6errat28vqaIP+vr16/X8889XO/YLSVXOozdwPVYd1+Opunbtqvz8fH377bfq1KmTJOlf//qX8vPzlZycfM77q63X4+nYbDYlJSUpPT1dN910k2t+enq6Bg8eXOk6Xbt21SeffOIxb/Xq1erYsaOCgoJ8Gu+F6nzOY2W2bdvmN9ceAC8wc9SIC8nPP/9sXH755cb1119vHDx40MjOznZN7lq0aGF88MEHhmEYxtGjR43x48cbGzduNPbt22d8+eWXRteuXY1LL73UKCgoMOMwTHc+59EwDGPmzJlGZGSk8cEHHxiZmZnG7bffbsTHx/vteTQMw/jpp5+Mbdu2GVOnTjXCw8ONbdu2Gdu2bTOOHj3qqsP1eHZVPY+GwfVYmX79+hlt27Y1vvnmG+Obb74x2rRpY9x4440edbgeT7Vs2TIjKCjIWLhwoZGVlWWkpqYaYWFhxo8//mgYhmE88cQTxvDhw131//vf/xqhoaHGY489ZmRlZRkLFy40goKCjPfee8+sQ7ggVPU8zpkzx1i+fLnxn//8x9i5c6fxxBNPGJKM999/36xDAHCRIUn6P4sWLTIkVTq5k2QsWrTIMAzDKCoqMvr27WvUr1/fCAoKMho2bGiMGDHC2L9/vwlHcGE4n/NoGBXDLj/zzDNGXFycYbfbjW7duhmZmZk1HP2FZcSIEZWexy+//NJVh+vx7Kp6Hg2D67Eyv//+u3HnnXcaERERRkREhHHnnXeeMrwy12PlXnvtNaNRo0aGzWYzOnToYKxfv961bMSIEUb37t096q9bt85o3769YbPZjMaNGxvz58+v4YgvTFU5j88//7xx2WWXGcHBwUbdunWNa6+91lixYoUJUQO4WFkM4/+eCAUAAAAA1P7R7QAAAACgKkiSAAAAAMANSRIAAAAAuCFJAgAAAAA3JEkAAAAA4IYkCQAAAADckCQBAAAAgBuSJAAAAABwQ5IEAAAAAG5IkgAAAADADUkSAAAAALj5/wE4zpnMk3hQkgAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 700x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(7, 5))\n",
+    "for i, j in enumerate([0, -1]):\n",
+    "    plt.hist(conformity_scores_enbpi_pfit[j], range=[-2.5, 0.5], bins=30, color=f\"C{i}\", alpha=0.3, label=f\"Conformity scores(step={j})\")\n",
+    "    plt.axvline(lower_quantiles_enbpi_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
+    "    plt.axvline(higher_quantiles_enbpi_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
+    "plt.legend(loc=[1, 0])"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Temporal evolution of the distribution of residuals used for estimating prediction intervals"
+    "### ACI"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 287,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x177a53010>"
+       "<matplotlib.legend.Legend at 0x1843408b0>"
       ]
      },
-     "execution_count": 32,
+     "execution_count": 287,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -971,9 +1148,9 @@
    "source": [
     "plt.figure(figsize=(7, 5))\n",
     "for i, j in enumerate([0, -1]):\n",
-    "    plt.hist(conformity_scores_pfit[j], range=[-2.5, 0.5], bins=30, color=f\"C{i}\", alpha=0.3, label=f\"Conformity scores(step={j})\")\n",
-    "    plt.axvline(lower_quantiles_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
-    "    plt.axvline(higher_quantiles_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
+    "    plt.hist(conformity_scores_aci_pfit[j], range=[-2.5, 0.5], bins=30, color=f\"C{i}\", alpha=0.3, label=f\"Conformity scores(step={j})\")\n",
+    "    plt.axvline(lower_quantiles_aci_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
+    "    plt.axvline(higher_quantiles_aci_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
     "plt.legend(loc=[1, 0])"
    ]
   }

From aca643ac55ec2791a79cde8ea2a97a54ed98c9a9 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Fri, 19 Jul 2024 16:11:44 +0200
Subject: [PATCH 234/424] Add : adding test with_warning_inf

---
 mapie/tests/test_utils.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index d4ea8df2f..ebc67a03a 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -421,6 +421,17 @@ def test_inf_values() -> None:
         check_array_inf(np.array([1, 2, -np.inf, 4]))
 
 
+def test_inf_values_with_warning_inf() -> None:
+    """
+    Test if array has infinite values like +inf or -inf
+    """
+    with pytest.warns(
+        UserWarning,
+        match=r"Array contains infinite values."
+    ):
+        check_array_inf(np.array([1, 2, -np.inf, 4]), warning_inf=True)
+
+
 def test_length() -> None:
     """
     Test if the arrays have the same size (length)

From cccf76edfbfeb0b25c1d0497fe5609487c6fd879 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 22 Jul 2024 15:15:01 +0200
Subject: [PATCH 235/424] Delete : Update concerning alpha

---
 mapie/metrics.py | 10 ++++------
 mapie/utils.py   | 11 ++++-------
 2 files changed, 8 insertions(+), 13 deletions(-)

diff --git a/mapie/metrics.py b/mapie/metrics.py
index 74a841f3d..20c5065f0 100644
--- a/mapie/metrics.py
+++ b/mapie/metrics.py
@@ -19,7 +19,6 @@ def regression_coverage_score(
     y_true: ArrayLike,
     y_pred_low: ArrayLike,
     y_pred_up: ArrayLike,
-    warning_inf: bool = False
 ) -> float:
     """
     Effective coverage score obtained by the prediction intervals.
@@ -58,15 +57,14 @@ def regression_coverage_score(
     check_arrays_length(y_true, y_pred_low, y_pred_up)
     check_lower_upper_bounds(y_true, y_pred_low, y_pred_up)
     check_array_nan(y_true)
-    check_array_inf(y_true, warning_inf=warning_inf)
+    check_array_inf(y_true)
     check_array_nan(y_pred_low)
-    check_array_inf(y_pred_low, warning_inf=warning_inf)
+    check_array_inf(y_pred_low)
     check_array_nan(y_pred_up)
-    check_array_inf(y_pred_up, warning_inf=warning_inf)
+    check_array_inf(y_pred_up)
 
     coverage = np.mean(
-        ((y_pred_low <= y_true) & (y_pred_up >= y_true)) |
-        np.isinf(y_pred_low) | np.isinf(y_pred_up)
+        ((y_pred_low <= y_true) & (y_pred_up >= y_true))
     )
     return float(coverage)
 
diff --git a/mapie/utils.py b/mapie/utils.py
index 391a88be7..13641b154 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1280,7 +1280,7 @@ def check_array_nan(array: NDArray) -> None:
         )
 
 
-def check_array_inf(array: NDArray, warning_inf: bool = False) -> None:
+def check_array_inf(array: NDArray) -> None:
     """
     Checks if the array have inf.
     If a value is infinite, we throw an error.
@@ -1296,12 +1296,9 @@ def check_array_inf(array: NDArray, warning_inf: bool = False) -> None:
         If any elements of the array is +inf or -inf.
     """
     if np.isinf(array).any():
-        if warning_inf:
-            warnings.warn("Array contains infinite values.", UserWarning)
-        else:
-            raise ValueError(
-                "Array contains infinite values."
-            )
+        raise ValueError(
+            "Array contains infinite values."
+        )
 
 
 def check_arrays_length(*arrays: NDArray) -> None:

From 2e442f0ef7e8c5f17929c0cf95de95d570e40293 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 22 Jul 2024 15:58:31 +0200
Subject: [PATCH 236/424] Delete : test_inf_values_with_warning_inf

---
 mapie/tests/test_utils.py | 11 -----------
 1 file changed, 11 deletions(-)

diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index ebc67a03a..d4ea8df2f 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -421,17 +421,6 @@ def test_inf_values() -> None:
         check_array_inf(np.array([1, 2, -np.inf, 4]))
 
 
-def test_inf_values_with_warning_inf() -> None:
-    """
-    Test if array has infinite values like +inf or -inf
-    """
-    with pytest.warns(
-        UserWarning,
-        match=r"Array contains infinite values."
-    ):
-        check_array_inf(np.array([1, 2, -np.inf, 4]), warning_inf=True)
-
-
 def test_length() -> None:
     """
     Test if the arrays have the same size (length)

From 8f10703a469c756c7c593e1cd84b144b3ead6154 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 23 Jul 2024 11:34:10 +0200
Subject: [PATCH 237/424] Update : notebook

---
 notebooks/regression/ts-changepoint.ipynb | 225 ++++++++--------------
 1 file changed, 79 insertions(+), 146 deletions(-)

diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index fcd0de9e0..0f9f17867 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 254,
+   "execution_count": 425,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 255,
+   "execution_count": 426,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -83,7 +83,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 256,
+   "execution_count": 427,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +112,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 257,
+   "execution_count": 428,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +132,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 258,
+   "execution_count": 429,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +141,7 @@
        "Text(0, 0.5, 'Hourly demand (GW)')"
       ]
      },
-     "execution_count": 258,
+     "execution_count": 429,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 259,
+   "execution_count": 430,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 260,
+   "execution_count": 431,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +241,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 261,
+   "execution_count": 432,
    "metadata": {},
    "outputs": [
     {
@@ -271,7 +271,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 262,
+   "execution_count": 433,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 263,
+   "execution_count": 434,
    "metadata": {},
    "outputs": [
     {
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 264,
+   "execution_count": 435,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +411,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 265,
+   "execution_count": 436,
    "metadata": {},
    "outputs": [
     {
@@ -436,7 +436,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 266,
+   "execution_count": 437,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +448,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 267,
+   "execution_count": 438,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +460,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 268,
+   "execution_count": 439,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +495,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 269,
+   "execution_count": 440,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +515,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 270,
+   "execution_count": 441,
    "metadata": {},
    "outputs": [
     {
@@ -559,7 +559,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 271,
+   "execution_count": 442,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +569,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 272,
+   "execution_count": 443,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,16 +589,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 273,
+   "execution_count": 444,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x183f553f0>]"
+       "[<matplotlib.lines.Line2D at 0x184c64760>]"
       ]
      },
-     "execution_count": 273,
+     "execution_count": 444,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -630,7 +630,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 274,
+   "execution_count": 445,
    "metadata": {},
    "outputs": [
     {
@@ -660,7 +660,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 275,
+   "execution_count": 446,
    "metadata": {},
    "outputs": [
     {
@@ -699,7 +699,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 276,
+   "execution_count": 447,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def compute_quantiles(conformity_scores, alpha_np):\n",
+    "\n",
+    "    beta_np = ConformityScore._beta_optimize(\n",
+    "                    alpha_np,\n",
+    "                    conformity_scores.reshape(1, -1),\n",
+    "                    conformity_scores.reshape(1, -1),\n",
+    "                )\n",
+    "    alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np\n",
+    "\n",
+    "    lower_quantiles = ConformityScore.get_quantile(\n",
+    "    conformity_scores[..., np.newaxis],\n",
+    "    alpha_low, axis=0, reversed=True,\n",
+    "    unbounded=False\n",
+    "    )\n",
+    "\n",
+    "    higher_quantiles = ConformityScore.get_quantile(\n",
+    "        conformity_scores[..., np.newaxis],\n",
+    "        alpha_up, axis=0,\n",
+    "        unbounded=False\n",
+    "    )\n",
+    "\n",
+    "    return lower_quantiles, higher_quantiles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 448,
    "metadata": {},
    "outputs": [
     {
@@ -741,24 +771,7 @@
     "\n",
     "    alpha_np = np.array([alpha])\n",
     "\n",
-    "    beta_np = ConformityScore._beta_optimize(\n",
-    "                    alpha_np,\n",
-    "                    conformity_scores.reshape(1, -1),\n",
-    "                    conformity_scores.reshape(1, -1),\n",
-    "                )\n",
-    "    alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np\n",
-    "\n",
-    "    lower_quantiles = ConformityScore.get_quantile(\n",
-    "    conformity_scores[..., np.newaxis],\n",
-    "    alpha_low, axis=0, reversed=True,\n",
-    "    unbounded=False\n",
-    "    )\n",
-    "\n",
-    "    higher_quantiles = ConformityScore.get_quantile(\n",
-    "        conformity_scores[..., np.newaxis],\n",
-    "        alpha_up, axis=0,\n",
-    "        unbounded=False\n",
-    "    )\n",
+    "    lower_quantiles, higher_quantiles = compute_quantiles(conformity_scores, alpha_np)\n",
     "    \n",
     "    lower_quantiles_enbpi_pfit.append(lower_quantiles)\n",
     "    higher_quantiles_enbpi_pfit.append(higher_quantiles)\n",
@@ -773,7 +786,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 277,
+   "execution_count": 449,
    "metadata": {},
    "outputs": [
     {
@@ -818,7 +831,6 @@
     "        ensemble=True,\n",
     "        optimize_beta=True, allow_infinite_bounds=True\n",
     "    )\n",
-    "    \n",
     "\n",
     "    conformity_scores = mapie_aci.conformity_scores_\n",
     "\n",
@@ -826,32 +838,15 @@
     "\n",
     "    current_alpha_np = np.array((list(mapie_aci.current_alpha.values())))\n",
     "\n",
+    "    lower_quantiles, higher_quantiles = compute_quantiles(conformity_scores, current_alpha_np)\n",
     "\n",
-    "    beta_np = ConformityScore._beta_optimize(\n",
-    "                    current_alpha_np,\n",
-    "                    conformity_scores.reshape(1, -1),\n",
-    "                    conformity_scores.reshape(1, -1),\n",
-    "                )\n",
-    "    alpha_low, alpha_up = beta_np, 1 - current_alpha_np + beta_np\n",
-    "\n",
-    "    lower_quantiles = ConformityScore.get_quantile(\n",
-    "    conformity_scores[..., np.newaxis],\n",
-    "    alpha_low, axis=0, reversed=True,\n",
-    "    unbounded=False\n",
-    "    )\n",
-    "\n",
-    "    higher_quantiles = ConformityScore.get_quantile(\n",
-    "        conformity_scores[..., np.newaxis],\n",
-    "        alpha_up, axis=0,\n",
-    "        unbounded=False\n",
-    "    )\n",
-    "    \n",
     "    lower_quantiles_aci_pfit.append(lower_quantiles)\n",
+    "    \n",
     "    higher_quantiles_aci_pfit.append(higher_quantiles)\n",
     "\n",
-    "coverage_aci_pfit = regression_coverage_score(\n",
-    "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0], warning_inf=True\n",
-    ")\n",
+    "# coverage_aci_pfit = regression_coverage_score(\n",
+    "#     y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
+    "# )\n",
     "# width_aci_pfit = regression_mean_width_score(\n",
     "#     y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
     "# )\n",
@@ -869,7 +864,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 278,
+   "execution_count": 458,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -881,20 +876,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 279,
+   "execution_count": 459,
    "metadata": {},
    "outputs": [],
    "source": [
     "y_aci_preds = [y_pred_aci_npfit, y_pred_aci_pfit]\n",
     "y_aci_pis = [y_pis_aci_npfit, y_pis_aci_pfit]\n",
-    "coverages_aci = [coverage_aci_npfit, coverage_aci_pfit]\n",
+    "coverages_aci = [coverage_aci_npfit]\n",
     "widths_aci = [width_aci_npfit]\n",
+    "#coverages_aci = [coverage_aci_npfit, coverage_aci_pfit]\n",
     "#widths_aci = [width_aci_npfit, width_aci_pfit]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 280,
+   "execution_count": 460,
    "metadata": {},
    "outputs": [
     {
@@ -914,7 +910,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": 461,
    "metadata": {},
    "outputs": [
     {
@@ -934,11 +930,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 282,
+   "execution_count": 462,
    "metadata": {},
    "outputs": [],
    "source": [
-    "\n",
     "window = 24\n",
     "rolling_coverage_enbpi_pfit, rolling_coverage_enbpi_npfit = [], []\n",
     "for i in range(window, len(y_test), 1):\n",
@@ -954,28 +949,6 @@
     "    )"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 283,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\n",
-    "window = 24\n",
-    "rolling_coverage_aci_pfit, rolling_coverage_aci_npfit = [], []\n",
-    "for i in range(window, len(y_test), 1):\n",
-    "    rolling_coverage_aci_pfit.append(\n",
-    "        regression_coverage_score(\n",
-    "            y_test[i-window:i], y_pis_aci_pfit[i-window:i, 0, 0], y_pis_aci_pfit[i-window:i, 1, 0], warning_inf=True\n",
-    "        )\n",
-    "    )\n",
-    "    rolling_coverage_aci_npfit.append(\n",
-    "        regression_coverage_score(\n",
-    "            y_test[i-window:i], y_pis_aci_npfit[i-window:i, 0, 0], y_pis_aci_npfit[i-window:i, 1, 0], warning_inf = True\n",
-    "        )\n",
-    "    )"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -992,16 +965,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 463,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x184142890>]"
+       "[<matplotlib.lines.Line2D at 0x1854c75e0>]"
       ]
      },
-     "execution_count": 284,
+     "execution_count": 463,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1023,46 +996,6 @@
     "plt.plot(y_test[window:].index, rolling_coverage_enbpi_pfit, label=\"With update of residuals\")\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ACI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 285,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x183fed810>]"
-      ]
-     },
-     "execution_count": 285,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_aci_npfit, label=\"Without update of residuals\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_aci_pfit, label=\"With update of residuals\")"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1079,16 +1012,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 464,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x184044220>"
+       "<matplotlib.legend.Legend at 0x18551cdf0>"
       ]
      },
-     "execution_count": 286,
+     "execution_count": 464,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1121,16 +1054,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 465,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1843408b0>"
+       "<matplotlib.legend.Legend at 0x185621690>"
       ]
      },
-     "execution_count": 287,
+     "execution_count": 465,
      "metadata": {},
      "output_type": "execute_result"
     },

From b6fd8fb9b385aab8934f17ce7e5b84b98cfffc21 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Tue, 23 Jul 2024 17:55:39 +0200
Subject: [PATCH 238/424] Update : Use tutorial to update notebook

---
 notebooks/regression/ts-changepoint.ipynb | 354 +++++++++++++++++-----
 1 file changed, 276 insertions(+), 78 deletions(-)

diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index 0f9f17867..b46ca8711 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 425,
+   "execution_count": 494,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,9 +50,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 426,
+   "execution_count": 495,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "ImportError",
+     "evalue": "cannot import name 'ConformityScore' from 'mapie.conformity_scores' (/Users/baptistecalot/Desktop/Mapie/GITHUB/MASTER/MAPIE/mapie/conformity_scores/__init__.py)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[495], line 13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmapie\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msubsample\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BlockBootstrap\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmapie\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtime_series_regression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MapieTimeSeriesRegressor\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmapie\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconformity_scores\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConformityScore\n\u001b[1;32m     15\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreload_ext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautoreload\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     16\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautoreload\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[0;31mImportError\u001b[0m: cannot import name 'ConformityScore' from 'mapie.conformity_scores' (/Users/baptistecalot/Desktop/Mapie/GITHUB/MASTER/MAPIE/mapie/conformity_scores/__init__.py)"
+     ]
+    }
+   ],
    "source": [
     "import warnings\n",
     "\n",
@@ -66,7 +78,8 @@
     "from mapie.metrics import regression_coverage_score, regression_mean_width_score, coverage_width_based\n",
     "from mapie.subsample import BlockBootstrap\n",
     "from mapie.time_series_regression import MapieTimeSeriesRegressor\n",
-    "from mapie.conformity_scores import ConformityScore\n",
+    "from mapie.conformity_scores.regression import BaseRegressionScore\n",
+    "from mapie.conformity_scores.interface import BaseConformityScore\n",
     "\n",
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
@@ -83,7 +96,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 427,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +125,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 428,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +145,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 429,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -173,7 +186,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 430,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 431,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 432,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -271,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 433,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +323,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 434,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -357,7 +370,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 435,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +424,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 436,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -436,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 437,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 438,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +473,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 439,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +508,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 440,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +528,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 441,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -559,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 442,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +582,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 443,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,16 +602,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 444,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x184c64760>]"
+       "[<matplotlib.lines.Line2D at 0x185785090>]"
       ]
      },
-     "execution_count": 444,
+     "execution_count": 469,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -630,7 +643,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 445,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -645,8 +658,10 @@
     "print(\"EnbPI, with no partial_fit, width optimization\")\n",
     "mapie_enbpi = mapie_enbpi.fit(X_train, y_train)\n",
     "y_pred_enbpi_npfit, y_pis_enbpi_npfit = mapie_enbpi.predict(\n",
-    "    X_test, alpha=alpha, ensemble=True, optimize_beta=True\n",
+    "    X_test, alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
+    "\n",
+    "y_pis_enbpi_npfit = np.clip(y_pis_enbpi_npfit, 1, 10)\n",
     "coverage_enbpi_npfit = regression_coverage_score(\n",
     "    y_test, y_pis_enbpi_npfit[:, 0, 0], y_pis_enbpi_npfit[:, 1, 0]\n",
     ")\n",
@@ -660,7 +675,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 446,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -675,9 +690,32 @@
     "print(\"ACI, with no partial_fit\")\n",
     "mapie_aci = mapie_aci.fit(X_train, y_train)\n",
     "\n",
-    "y_pred_aci_npfit, y_pis_aci_npfit = mapie_aci.predict(\n",
-    "    X_test, alpha=alpha, ensemble=True, optimize_beta=True\n",
+    "y_pred_aci_npfit = np.zeros(y_pred_enbpi_npfit.shape)\n",
+    "y_pis_aci_npfit = np.zeros(y_pis_enbpi_npfit.shape)\n",
+    "y_pred_aci_npfit[:gap], y_pis_aci_npfit[:gap, :, :] = mapie_aci.predict(\n",
+    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True,\n",
+    "    allow_infinite_bounds=True\n",
     ")\n",
+    "for step in range(gap, len(X_test), gap):\n",
+    "    mapie_aci.adapt_conformal_inference(\n",
+    "        X_test.iloc[(step - gap):step, :].to_numpy(),\n",
+    "        y_test.iloc[(step - gap):step].to_numpy(),\n",
+    "        gamma=0.05\n",
+    "    )\n",
+    "    (\n",
+    "        y_pred_aci_npfit[step:step + gap],\n",
+    "        y_pis_aci_npfit[step:step + gap, :, :],\n",
+    "    ) = mapie_aci.predict(\n",
+    "        X_test.iloc[step:(step + gap), :],\n",
+    "        alpha=alpha,\n",
+    "        ensemble=True,\n",
+    "        optimize_beta=True,\n",
+    "        allow_infinite_bounds=True\n",
+    "    )\n",
+    "    y_pis_aci_npfit[step:step + gap, :, :] = np.clip(\n",
+    "        y_pis_aci_npfit[step:step + gap, :, :], 1, 10\n",
+    "    )\n",
+    "\n",
     "coverage_aci_npfit = regression_coverage_score(\n",
     "    y_test, y_pis_aci_npfit[:, 0, 0], y_pis_aci_npfit[:, 1, 0]\n",
     ")\n",
@@ -685,7 +723,11 @@
     "    y_pis_aci_npfit[:, 0, 0], y_pis_aci_npfit[:, 1, 0]\n",
     ")\n",
     "cwc_aci_npfit = coverage_width_based(\n",
-    "    y_test, y_pis_aci_npfit[:, 0, 0], y_pis_aci_npfit[:, 1, 0], eta = 10, alpha = 0.05\n",
+    "    y_test,\n",
+    "    y_pis_aci_npfit[:, 0, 0],\n",
+    "    y_pis_aci_npfit[:, 1, 0],\n",
+    "    eta=10,\n",
+    "    alpha=0.05\n",
     ")"
    ]
   },
@@ -697,15 +739,24 @@
     "### Prediction intervals with partial fit"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now estimate prediction intervals with partial fit. As discussed\n",
+    "previously, the update of the residuals and the one-step ahead predictions\n",
+    "are performed sequentially in a loop.\n"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 447,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "def compute_quantiles(conformity_scores, alpha_np):\n",
     "\n",
-    "    beta_np = ConformityScore._beta_optimize(\n",
+    "    beta_np = BaseConformityScore._beta_optimize(\n",
     "                    alpha_np,\n",
     "                    conformity_scores.reshape(1, -1),\n",
     "                    conformity_scores.reshape(1, -1),\n",
@@ -729,7 +780,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 448,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -742,13 +793,16 @@
    ],
    "source": [
     "print(\"EnbPI with partial_fit, width optimization\")\n",
+    "mapie_enbpi = MapieTimeSeriesRegressor(\n",
+    "    model, method=\"enbpi\", cv=cv_mapiets, agg_function=\"mean\", n_jobs=-1\n",
+    ")\n",
     "mapie_enbpi = mapie_enbpi.fit(X_train, y_train)\n",
     "\n",
     "y_pred_enbpi_pfit = np.zeros(y_pred_enbpi_npfit.shape)\n",
     "y_pis_enbpi_pfit = np.zeros(y_pis_enbpi_npfit.shape)\n",
     "conformity_scores_enbpi_pfit, lower_quantiles_enbpi_pfit, higher_quantiles_enbpi_pfit = [], [], []\n",
     "y_pred_enbpi_pfit[:gap], y_pis_enbpi_pfit[:gap, :, :] = mapie_enbpi.predict(\n",
-    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True\n",
+    "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
     "for step in range(gap, len(X_test), gap):\n",
     "    mapie_enbpi.partial_fit(\n",
@@ -762,7 +816,11 @@
     "        X_test.iloc[step:(step + gap), :],\n",
     "        alpha=alpha,\n",
     "        ensemble=True, \n",
-    "        optimize_beta=True\n",
+    "        optimize_beta=True, allow_infinite_bounds=True\n",
+    "    )\n",
+    "\n",
+    "    y_pis_enbpi_pfit[step:step + gap, :, :] = np.clip(\n",
+    "        y_pis_enbpi_pfit[step:step + gap, :, :], 1, 10\n",
     "    )\n",
     "\n",
     "    conformity_scores = mapie_enbpi.conformity_scores_\n",
@@ -781,12 +839,18 @@
     ")\n",
     "width_enbpi_pfit = regression_mean_width_score(\n",
     "    y_pis_enbpi_pfit[:, 0, 0], y_pis_enbpi_pfit[:, 1, 0]\n",
+    ")\n",
+    "\n",
+    "cwc_enbpi_pfit = coverage_width_based(\n",
+    "    y_test, y_pis_enbpi_pfit[:, 0, 0], y_pis_enbpi_pfit[:, 1, 0],\n",
+    "    eta=10,\n",
+    "    alpha=0.05\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 449,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
@@ -799,6 +863,9 @@
    ],
    "source": [
     "print(\"ACI with partial_fit and adapt_conformal_inference\")\n",
+    "mapie_aci = MapieTimeSeriesRegressor(\n",
+    "    model, method=\"aci\", cv=cv_mapiets, agg_function=\"mean\", n_jobs=-1\n",
+    ")\n",
     "mapie_aci = mapie_aci.fit(X_train, y_train)\n",
     "\n",
     "y_pred_aci_pfit = np.zeros(y_pred_aci_npfit.shape)\n",
@@ -809,8 +876,6 @@
     "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
     "\n",
-    "\"\"\" X = X_test.to_numpy()\n",
-    "y_true = y_test.to_numpy() \"\"\"\n",
     "for step in range(gap, len(X_test), gap):\n",
     "\n",
     "    mapie_aci.partial_fit(\n",
@@ -832,27 +897,31 @@
     "        optimize_beta=True, allow_infinite_bounds=True\n",
     "    )\n",
     "\n",
+    "    y_pis_aci_pfit[step:step + gap, :, :] = np.clip(\n",
+    "    y_pis_aci_pfit[step:step + gap, :, :], 1, 10\n",
+    "    )\n",
+    "\n",
     "    conformity_scores = mapie_aci.conformity_scores_\n",
     "\n",
     "    conformity_scores_aci_pfit.append(conformity_scores)\n",
     "\n",
     "    current_alpha_np = np.array((list(mapie_aci.current_alpha.values())))\n",
-    "\n",
+    "    \n",
     "    lower_quantiles, higher_quantiles = compute_quantiles(conformity_scores, current_alpha_np)\n",
-    "\n",
+    "    \n",
     "    lower_quantiles_aci_pfit.append(lower_quantiles)\n",
     "    \n",
     "    higher_quantiles_aci_pfit.append(higher_quantiles)\n",
     "\n",
-    "# coverage_aci_pfit = regression_coverage_score(\n",
-    "#     y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
-    "# )\n",
-    "# width_aci_pfit = regression_mean_width_score(\n",
-    "#     y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
-    "# )\n",
-    "# cwc_aci_pfit = coverage_width_based(\n",
-    "#     y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0], eta = 0.01, alpha = 0.05\n",
-    "# )"
+    "coverage_aci_pfit = regression_coverage_score(\n",
+    "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
+    ")\n",
+    "width_aci_pfit = regression_mean_width_score(\n",
+    "    y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
+    ")\n",
+    "cwc_aci_pfit = coverage_width_based(\n",
+    "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0], eta = 0.01, alpha = 0.05\n",
+    ")"
    ]
   },
   {
@@ -864,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 458,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,26 +945,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 459,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
     "y_aci_preds = [y_pred_aci_npfit, y_pred_aci_pfit]\n",
     "y_aci_pis = [y_pis_aci_npfit, y_pis_aci_pfit]\n",
-    "coverages_aci = [coverage_aci_npfit]\n",
-    "widths_aci = [width_aci_npfit]\n",
-    "#coverages_aci = [coverage_aci_npfit, coverage_aci_pfit]\n",
-    "#widths_aci = [width_aci_npfit, width_aci_pfit]"
+    "coverages_aci = [coverage_aci_npfit, coverage_aci_npfit]\n",
+    "widths_aci = [width_aci_npfit, width_aci_pfit]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 460,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -905,17 +972,17 @@
     }
    ],
    "source": [
-    "plot_forecast(\"EnbPI\", y_train, y_test, y_enbpi_preds, y_enbpi_pis, coverages_enbpi, widths_enbpi, plot_coverage=False)"
+    "plot_forecast(\"EnbPI\", y_train, y_test, y_enbpi_preds, y_enbpi_pis, coverages_enbpi, widths_enbpi, plot_coverage=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 461,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -925,28 +992,97 @@
     }
    ],
    "source": [
-    "plot_forecast(\"ACI\", y_train, y_test, y_aci_preds, y_aci_pis, coverages_aci, widths_aci, plot_coverage=False)\n"
+    "plot_forecast(\"ACI\", y_train, y_test, y_aci_preds, y_aci_pis, coverages_aci, widths_aci, plot_coverage=True)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's now compare the coverages obtained by MAPIE with and without update\n",
+    "of the residuals on a 24-hour rolling window of prediction intervals."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 462,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
-    "window = 24\n",
+    "rolling_coverage_aci_pfit, rolling_coverage_aci_npfit = [], []\n",
     "rolling_coverage_enbpi_pfit, rolling_coverage_enbpi_npfit = [], []\n",
+    "\n",
+    "window = 24\n",
+    "\n",
     "for i in range(window, len(y_test), 1):\n",
-    "    rolling_coverage_enbpi_pfit.append(\n",
+    "    rolling_coverage_aci_npfit.append(\n",
     "        regression_coverage_score(\n",
-    "            y_test[i-window:i], y_pis_enbpi_pfit[i-window:i, 0, 0], y_pis_enbpi_pfit[i-window:i, 1, 0]\n",
+    "            y_test[i-window:i], y_pis_aci_npfit[i-window:i, 0, 0],\n",
+    "            y_pis_aci_npfit[i-window:i, 1, 0]\n",
     "        )\n",
     "    )\n",
+    "    rolling_coverage_aci_pfit.append(\n",
+    "        regression_coverage_score(\n",
+    "            y_test[i-window:i], y_pis_aci_pfit[i-window:i, 0, 0],\n",
+    "            y_pis_aci_pfit[i-window:i, 1, 0]\n",
+    "        )\n",
+    "    )\n",
+    "\n",
     "    rolling_coverage_enbpi_npfit.append(\n",
     "        regression_coverage_score(\n",
-    "            y_test[i-window:i], y_pis_enbpi_npfit[i-window:i, 0, 0], y_pis_enbpi_npfit[i-window:i, 1, 0]\n",
+    "            y_test[i-window:i], y_pis_enbpi_npfit[i-window:i, 0, 0],\n",
+    "            y_pis_enbpi_npfit[i-window:i, 1, 0]\n",
+    "        )\n",
+    "    )\n",
+    "    rolling_coverage_enbpi_pfit.append(\n",
+    "        regression_coverage_score(\n",
+    "            y_test[i-window:i], y_pis_enbpi_pfit[i-window:i, 0, 0],\n",
+    "            y_pis_enbpi_pfit[i-window:i, 1, 0]\n",
     "        )\n",
-    "    )"
+    "    )\n",
+    "\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
+    "\n",
+    "plt.plot(\n",
+    "    y_test[window:].index,\n",
+    "    rolling_coverage_aci_npfit,\n",
+    "    label=\"ACI Without update of residuals (NPfit)\",\n",
+    "    linestyle='--', color='r', alpha=0.5\n",
+    ")\n",
+    "plt.plot(\n",
+    "    y_test[window:].index,\n",
+    "    rolling_coverage_aci_pfit,\n",
+    "    label=\"ACI With update of residuals (Pfit)\",\n",
+    "    linestyle='-', color='r', alpha=0.5\n",
+    ")\n",
+    "\n",
+    "plt.plot(\n",
+    "    y_test[window:].index,\n",
+    "    rolling_coverage_enbpi_npfit,\n",
+    "    label=\"ENBPI Without update of residuals (NPfit)\",\n",
+    "    linestyle='--', color='b', alpha=0.5\n",
+    ")\n",
+    "plt.plot(\n",
+    "    y_test[window:].index,\n",
+    "    rolling_coverage_enbpi_pfit,\n",
+    "    label=\"ENBPI With update of residuals (Pfit)\",\n",
+    "    linestyle='-', color='b', alpha=0.5\n",
+    ")\n",
+    "\n",
+    "plt.legend()\n",
+    "plt.show()"
    ]
   },
   {
@@ -965,16 +1101,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 463,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1854c75e0>]"
+       "[<matplotlib.lines.Line2D at 0x184853790>]"
       ]
      },
-     "execution_count": 463,
+     "execution_count": 487,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -996,6 +1132,46 @@
     "plt.plot(y_test[window:].index, rolling_coverage_enbpi_pfit, label=\"With update of residuals\")\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### aci"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x1859b8ee0>]"
+      ]
+     },
+     "execution_count": 489,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1gAAAGsCAYAAADAAwaOAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABmkklEQVR4nO3deXxU5dn/8e9ksi+TACEr+w4Ji6K1YNXWCoqtgLWKrbvQltqWR7Eb9qeoXVDrQq3FpVVrW5+Wpy50EUVsFRdcgWgSQHYDWQgJkMlC1jm/PyZnQsg2k8zkZGY+79drXpmcOec+1+EwSa657/u6bYZhGAIAAAAA9FmE1QEAAAAAQKggwQIAAAAAPyHBAgAAAAA/IcECAAAAAD8hwQIAAAAAPyHBAgAAAAA/IcECAAAAAD+JtDqA/uZyuVRSUqKkpCTZbDarwwEAAABgEcMwVF1draysLEVE+KfvKewSrJKSEg0fPtzqMAAAAAAMEAcPHtSwYcP80lbYJVhJSUmS3P+IDofD4mgAAAAAWMXpdGr48OGeHMEfwi7BMocFOhwOEiwAAAAAfp06RJELAAAAAPATEiwAAAAA8BMSLAAAAADwExIsAAAAAPATEiwAAAAA8BMSLAAAAADwExIsAAAAAPATEiwAAAAA8BMSLAAAAADwExIsAAAAAPATEiwAAAAA8BNLE6w333xTl1xyibKysmSz2bRu3boej9m0aZNmzpyp2NhYjRkzRo899ljgAwUAAAAAL1iaYNXW1mr69Ol65JFHvNp///79uvjii3XOOedo27Ztuu2227Rs2TI9//zzAY4UAAAAAHoWaeXJ582bp3nz5nm9/2OPPaYRI0Zo9erVkqTJkyfro48+0v3336/LLrssQFEGzuFDe1W+ryDg52mKG6q6lAkBP08wSXfEaHx6ktVh+FdLk3ToI6m53upIBoxml6E95TVqajGsDqVfuOwxqk6dIUX450d7SL5PAAAIMEsTLF+9++67mjt3brttF154oZ588kk1NTUpKiqqwzENDQ1qaGjwfO90OgMep7cObH5eZ23/Zb+ca37Dz/WJMbZfzhUsXlr2BeVkJVsdhv9suk968z6roxhQIiVNsjqIfnZf0xVa07LQb+39+wdfUG52CL1PAAAIsKBKsMrKypSent5uW3p6upqbm1VRUaHMzMwOx6xatUp33XVXf4Xok8iEwdofMSqg5xhiVMphVGtB8h41xs4I6LmCRWlVvapONOndvZWhlWDt3+T+mjJCiqbXQZIOVNbqRFOLou0RirDZrA4noOKNOqUb5ZoTt1P/Tez7/TffJ+/tqyTBAgDAB0GVYEmS7ZQ/kgzD6HS7acWKFVq+fLnne6fTqeHDhwcuQB/M/MoS6StLAnuSt1dLr63U4jFVWnzFuYE9V5D47X9264GNu1RQXGV1KP7japHK8t3Pr3peGsqQ0MZml+au3KDGFpfe/NGXNGJIvNUhBVbpJ9Lj5+i0yM/0yv+cI/UxoXzkv7t1/6u7lB9K7xMAAPpBUCVYGRkZKisra7etvLxckZGRGjJkSKfHxMTEKCYmpj/CG5gyp7u/ln5sbRwDiPlpfEHJwBku2mcVu6WmOikqQRrCUFBJ2l1ercYWl5JiIzV8cJzV4QTe0EmSPVqqr5KOHZAGj+5Tcznm+4QECwAAnwTVOlizZs3Sxo0b22179dVXdcYZZ3Q6/wpqS7CO7ZdOHLc0lIEiJ9shSdp7pEa1Dc0WR+MnpXnurxlTpQi7paEMFGZikJuV3GUPd0iJjJbSprifm/8f+iC3dfjsvopa1YTK+wQAgH5gaYJVU1OjvLw85eXlSXKXYc/Ly1NRUZEk9/C+a6+91rP/0qVL9dlnn2n58uXasWOHnnrqKT355JP64Q9/aEX4wSF+sHtOjiSVfWJtLANEWlKs0h0xMgxpR2mI9GKZPZRZMywNYyApKHbf26nDwmj+kHn//dBjPTQpRhmO2NB6nwAA0A8sTbA++ugjnXbaaTrttNMkScuXL9dpp52mO+64Q5JUWlrqSbYkafTo0Vq/fr3eeOMNzZgxQz//+c/18MMPB2WJ9n5l9mKV5FkaxkBifjofMvNLzHtr3mt47m1OlsPiSPqRn9/rua29vfmHQuR9AgBAP7B0DtYXv/hFT5GKzvzxj3/ssO28887T1q1bAxhVCMqcIe34F/OwTpKbnaz/7Cz39HIENZerrXcyc4aloQwUzS0uT6/L1HCqgGfe/9KPJcPoc6GL3OxkvbajXAUlJFgAAHgrqOZgoZdO/qMLktoKXRSGwh+OR/dJjTVSZJyUSvVASdp7pFYNzS4lxkRq1JAEq8PpP2lT3IsMnzgqVR3qc3NmT29hKHwQAQBAPyHBCgfmsKHKPVJDtbWxDBBmr8bu8hrVN7VYHE0feQpc5Er2oCoMGjDm8MApWQ5FRIRBgQtTVKyUNtn93A+FLsz5a7vLq3WiMcjfJwAA9BMSrHCQOFRyZEsy2tZKCnPpjhilJkarxWUE/wR+8w9p5l95nFxBMOz4cWmGtKQYpSbGyGVIO8qC/H0CAEA/IcEKF+YwQQpdSHIvTJ0bKuv8eApczLAyigHFvKdTh4VRgQuTH9/rNptNU1sLXQT9+wQAgH5CghUuWHC4A7N3I6gLXRiGVGoWuKAHS5JaXIa2t/ZKhmcP1gz319I89/+PPgqZDyIAAOgnJFjhwrM+Tp6VUQwo5h+OQV2q/dh+qaFKske3zb0Jc/sralTX2KK4KLvGDE20Opz+l5Er2exS7RGpurTPzbW9T4L4gwgAAPoRCVa4MHs3KnZJjbXWxjJAmGv87DpcrYbmIJ3Ab/ZIpudI9ihrYxkgzB7JKVkO2cOpwIUpKk4aOtH93A891maCtftwdfAXhAEAoB+QYIWLpAwpMV0yXFJZgdXRDAjZKXFKiY9Ss8vQp2VBWl2RBYY7yPcUuAjD+VcmPy44nJUcq0HB/j4BAKAfkWCFE9bDasc9gT/I52GZ95ICFx6eCoLhtMDwqfz4Xm9XECYU1o0DACDASLDCiafQRZ6lYQwkOVlBPA/LMCjRfgqXy1BhSWuBi7BOsPz7XqfQBQAA3iPBCieeQhf0YJnMHqzCYPxkvuqgdOKYFBHpnoMFfXa0TjUNzYqJjND4tDAscGHKmCrJ5i5yUX24z80FfU8vAAD9iAQrnJifapfvkJpOWBvLAGEWuthZWq3GZpfF0fjInF+TNlmKjLE0lIHC7ImclOlQpD2Mf7zFJEqp493P/VHoorWn99OyIHyfAADQz8L4L5Aw5MiW4lMlo0U6vN3qaAaEEYPjlRQbqcYWl3aXB9kEfuZfdVBoLjCcHcYFLkx+nIc1fHCcHK3vk12Hg+x9AgBAPyPBCic220lzM7ZZG8sAYbPZTlpwOMiGCTL/qoO2CoJhPP/K5Md5WCcXugjK4bQAAPQjEqxwwzysDqYOC8L5JYbRNkQw6zRLQxkoDMOgguDJ/PxenxoKC3MDANAPSLDCjedTbRIsU07reklBVYK6ulSqq5BsdgpctDp07ISc9c2KtkdoQnqS1eFYL2Oq+2vVQam2ss/N5VDoAgAAr0RaHQD6mTkv4/B2ac9/JBs59udcJ3R2RL6iSiOU/+YRSTarQ+pR0tF8jZJUmzxO2w7USqq1OCLrbS06JkmamJGk6Ej+Xys2WRo8Vjq6V/r4f6X0XN/bSMqU0iZJauvB2lHqVHOLK7yLiADoWkO1VLxVMrwoiDNkrJQyIvAx9afGOqn4I8nV0n578rC24kMIeSRY4SZlhBQ3yF3e+y9fszqaASFT0rPRrd/818pIfPdyZbp++OT7VocxoORS4KJN5nR3gvXq/+t9G995U8qcrpGD45UYE6mahmbtOVKjSRn8OwPoxP9dK+318pdpVIJ0S4EUPziwMfWnF78j7fhnx+22COmm96WhE/o/JvQ7EqxwY7NJX/qZtOWP7nk8kCQdrWtUVV2T1WH4pN4WozcTF2hSJMPhTPHRdl111kirwxg4zloqHf9Maqr3/VhnsVR/XNr/lpQ5XRERNk3JcuiD/UeVf6iKBAtARy1N0oF33M+HTnIPY+/K0X1SU61UslUad0H/xBdohiHt3+R+PmS8ZG/99LbqkNRQJX32NglWmCDBCkef+5b7AY/BrY9g87DVAWBgG3GW9K1edsu++Wvpv79oV4VwanayPth/VIUlTl3unwgBhJIjO6WWBikmWbrpPfeHul15brFU8Jy7YFOoJFjHDkj1Ve7E6rubpcjWBOu1u6S3H2wrToWQxyB6AEBHnayjZQ6/pJIggE551mec1n1yJYVm0S3zWtKmtCVXUmheK7pFggUA6Mj8g6Bit3vSutoKXWwvcarFxRBjAKcwe2i8WZ/Rj2v1DRhdrU9pfl++XWpu7NeQYA0SLABAR4lpUlKWJEMqK5AkjU5NVHy0XSeaWrS/osba+AAMPJ4erBk972smHceLpLqjAQupX5nXb65DaBo0yl3ZtaVROrKjv6OCBUiwAACdO+UTZnuETVMyGSYIoBMtzVJZvvu5Nz1YcSnuxEMKjaFzhtF1D57NxjDBMEOCBQDonPkpbLt5WCw4DKATlbul5hNSdKI0ZJx3x3Qy1zNoVR2SThyVIiKltJyOr5vXSqGLsECCBQDoXCefuJoJFj1YANoxf05kTJUivPzzMpR6dcxrGDpZiort+HooXSt6RIIFAOic+YnrkZ1SY52ktkqC20ucclHoAoDJMzxuhvfHeHrJ8/wbixXMa8jqYnhk1mnur4cL3MMpEdJIsAAAnUvKkBLSJMMlHS6UJI0bmqiYyAjVNDTrQGWtxQECGDA8BS68mH9lMpOxo/vc60cFs54KfAwaLUUnSc31UsWn/RYWrEGCBQDonM3W4RPmSHuEJrcWuigoYR4WAEkul1T2ifv5qRX0uhM/WEoe4X5e+onfw+o37QpczOh8n4gIhgmGERIsAEDXOlmrxhwmWMA8LACSdHSv1FgjRcZJQ8b7dmzmNPfXYE46qsuk2nLJFiGld1LgwmT+PKXQRcgjwQIAdM1T+artj5+pnkqCJFgA1JYwZEyV7JG+HRsK87DM2IdOkqLju96vk8qsCE0kWACArpmfuB7ZITXVS5JystoSLMOg0AUQ9swEw5f5V6ZQKNXu7fwz8/WyTyRXS2BjgqVIsAAAXUseJsUNllzNUrm70MWE9CRF2yPkrG/WwaMnLA4QgOV6U+DCZB5TsVtqqPZfTP2pqwWGTzVknBSVIDXVSZV7Ah4WrEOCBQDoWrtCF+4/oqIjIzQxI0mSVFDCMEEgrLlcbQmWLwUuTIlpUlKWJEMqK/BnZP2npwqCpgi7exilxDysEEeCBQDoXicTs1lwGIAk6dh+qcEp2WPcc5B6o5NiOkGjplyqLpFka0ueukMlwbBAggUA6F4ncySoJAhAUtvPhfQcyR7VuzaCufiDGXPqeCkmsef9g/la4TUSLABA98xPXMu3S82NktpXEqTQBRDG+lLgwhTM5cu9nX9lOrkHy+UKSEiwHgkWAKB7g0ZJsclSS6O7mqDchS4iI2w6Vtekkqp6a+MDYJ2+zL8ymb3kFZ9KjXV9jah/eRLMGd7tnzpRioyVGqvdwysRkkiwAADds9k6zBuIjbJrQrq70EX+IYYJAmHJMHzvwelMUoaUkCYZLulwkBW68LWCoj1SSs91Py/ZFpiYYDkSLABAzzwLDud5NpnzsAqpJAiEp+NFUv1xKSJKSpvS+3Y6qVYaFGorpaqD7ueZ07w/LhivFT4hwQIA9KyTyldUEgTCnPnzIG2yFBnTt7aCsZJgWev1Dx7jHkbtLSoJhrxIqwMAAASBrNPcXw8XSC3Nkj3Sk2CZhS5sNpuFAQJ+1NwoFX8kNTf4r83BY6RBI30/7uh+6dgB/8XhT5+ud3/ty/wrk9lLXvS+tPf1vrfXH3b8y/3V2/lXJk9l1jz/XavN5v457Uuih4AhwQIA9GzQaCk6yT0xu+JTKT1HkzMcirBJFTWNOuxsUEZyrNVRAv7xn7ukdx/xb5uRcdItBVJCqvfHVJdJvztLavFjohcIfZl/dWoblbulPy/se3v9ydfrHzpJskdL9VX+vdZhn5OWbPRfe+g1EiwAQM8iItxzDD57xz0PKz1HcdF2jU9L0qeHq1VQXEWChdCxb5P766BRUlRC39s7tl9qqpMOfShNnOf9cQffdydXUQnuWAaixKHSlEv73k7yMOmspdL+t/reVn+KHyxNvdy3YyKjpS+ukPKf81MQhnsZjUMfSg013q3HhYAiwQIAeCdzhjvBKv1YOu0qSVJOtsOdYJVU6YIp6dbGB/hDU71nOQJd/5L7D/++evG70sf/6/5wwpcEyywqM/Xr0vyH+x7HQGazSfPutTqK/nPOcvfDXx6YLFWXSGX50shZ/msXvUKRCwCAdzqZhH7ygsNASCgvlFzNUvwQyZHtnzZ7W9TA1xLgCF8UzhhQSLAAAN4xJ7KX5UuuFkk6qdCF06KgAD87eV0nfxVu6U2FPMPwfRFbhK9grMIYwkiwAADeGTLOPRekqU6q3CNJmpLpkM0mlTnrdaR6gE/EB7zh6TWa4b82M6ZKsknVpVL1Ye+OcRZLdZWSzS6l5/gvFoQm1tYaUEiwAADeibC3/qEoz6f8CTGRGpPqLgJQwILDCAWeXiM/DsuLSZRSx7e27+UfwGZPWtpkKYoCMuiB+f/1yE6psc7aWGB9grVmzRqNHj1asbGxmjlzpt56q/vqMb/73e80efJkxcXFaeLEifrTn/7UT5ECADob5++Zh3WIBAtBrrlROrzd/dwfazudzLP2kZcJViB60hC6kjKlhDTJcEmHC62OJuxZmmCtXbtWN998s372s59p27ZtOuecczRv3jwVFRV1uv+jjz6qFStW6M4771RhYaHuuusufe9739O//vWvfo4cAMKUZxhKnmeTZx4WPVgIdkd2SK4mKTZFSunFosDd8XWODAUu4AubjXlYA4ilCdaDDz6oxYsXa8mSJZo8ebJWr16t4cOH69FHH+10/z//+c/6zne+o0WLFmnMmDG68sortXjxYt17bxiV9QQAK3l+gX8iuVySpJwsCl0gRASiwIXJ1zky5h/J/u5JQ+jq5AMwWMOyBKuxsVFbtmzR3Llz222fO3euNm/e3OkxDQ0Nio1tPw45Li5OH3zwgZqamro8xul0tnsAAHopdaIUGSs1VktH90lyr4UlScXHT+hobaOV0QF9E8heI3P+YtVBqbay+32ry6Saw5ItQkrP9X8sCE2Uah8wLEuwKioq1NLSovT09gtTpqenq6ysrNNjLrzwQv3hD3/Qli1bZBiGPvroIz311FNqampSRUVFp8esWrVKycnJnsfw4cP9fi0AEDbskW1/8LV+SuqIjdKoIfGSWA8LQS6QvUaxydLgse3P0xWzJy11ohQd7/9YEJrM+XrlO9wLZsMylhe5sJ3SBW8YRodtpttvv13z5s3T5z//eUVFRWnBggW6/vrrJUl2u73TY1asWKGqqirP4+DBg36NHwDCTifj/JmHhaDX0iSVFbifB6qwhLc9DMy/Qm8kD5PiBrsXyi7fbnU0Yc2yBCs1NVV2u71Db1V5eXmHXi1TXFycnnrqKdXV1enAgQMqKirSqFGjlJSUpNTU1E6PiYmJkcPhaPcAAPRBJ3NJzASrkHlYCFZHPpVaGqQYhzRodGDO4e0cGeZfoTdsNuZhDRCWJVjR0dGaOXOmNm7c2G77xo0bNXv27G6PjYqK0rBhw2S32/W3v/1NX/3qVxURYXlnHACEh5M/hTcMSW2l2vMZIohgZX5gkDFNCtTfFPRgIdCYhzUgRFp58uXLl+uaa67RGWecoVmzZumJJ55QUVGRli5dKsk9vK+4uNiz1tWuXbv0wQcf6KyzztKxY8f04IMPqqCgQM8884yVlwEA4WXoZMkeLdVXSccOSINHKyfLPTqg6GidquqalBwfZW2MgK8CscDwqTKmub8eOyCdOCbFDeq4T80RyVksydZWGAPwlvn/15zHB0tY2u2zaNEirV69WnfffbdmzJihN998U+vXr9fIke61J0pLS9utidXS0qIHHnhA06dP15w5c1RfX6/Nmzdr1KhRFl0BAIShyGgpbYr7eeunpCnx0Ro+OE6SVMg8LAQj8xP/QA7Lix/ctr5W6SfdxzFknBSTFLhYEJo8hS62uxfOhiUs7cGSpJtuukk33XRTp6/98Y9/bPf95MmTtW3btn6ICgDQrawZ7k/8S/OknIWSpNysZB08ekIFJVWaPa7zebHAgORqkcry3c8DPSwvc7p0/DP3e2fMeR1fL93WP3EgNA0a5a5YWV/lXjib/0eWYOISAMB3nYzzz/XMw6LQBYJMxW6pqU6KSnD3HAVSTwsO90dPGkKXzcY8rAGABAsA4DtzGEpJnqfQRVslQYYIIsiY868ypkoRnS/74jc9zZEpocAF+oh5WJYjwQIA+C5tihQRKZ04KlUdkiTltha62FdRq+r6JiujA3zTn71G5ocTR/dK9af09tYdlapa556bBTEAX5n/x+jBsgwJFgDAd1GxUtpk9/PWT/+HJMYoKzlWkrS9hGGCCCLmJ/390WuUkCo5hrmfl51S6MLsSRs0WopLCXwsCE1mgnW4QGpptjSUcEWCBQDonU7G+eewHhaCjcvVluiYf5gGWlfzsJh/BX8YPEaKTpKa66WKT62OJiyRYAEAeufkeVitzAWHC+nBQrA4uk9qrJEi46TUCf1zzq6KELDAMPwhIkLKbB1iyjBBS1heph0AEKQ84/zz3IUubDblZrvnYdGDFSbqnVLJVk+hk26ljpeSh/l+jsq90vGinvfrraL33F8zciV7P/1ZZL53Dr4v7X29bfuhj9q/DvRW5gzps3ekXa9ISZnd72uPload6V7jMBAaaqTiLZLh6vha9unusvIhhgQLANA76TmSLUKqPSJVl0qOLE8lwb1HalTX2Kz4aH7NhLS/fVM68JZ3+8Y4pFsKpViH9+0f3S89cqZktPQuPl/0Z6+Rea5jB6Q/L7Q2FoQm8//Q9n+4Hz35/Peki34VmFieXyLternz1xa/Jg0/MzDntRC/+QAAvRMdLw2dJJVvdw9DcWQpLSlWaUkxKq9u0I5Sp2aOHGx1lAiUpnqp6F3386GT3cl2V47ulRqc7t7O0ed6f47PNruTqxiHlDy8T+F2KyZRmnlD4No/VVK6NOv77XuvTBPmSvG8b9BHE+dJ4+ZIzpLu92uqdSf6+zcFJg6XS9r/pvt56kR39dmTRcUF5rwWI8ECAPRe5vS2BGviPEnueVj/2Vmu/ENVJFihrLxQcjVL8UOkm951L3DalbXXSDv+6Z6v50uCZVbVO/1a6cJf9iXagSfUrgcDS6xDuvq5nverKpYemiKV75CaTvg/4anc407iIuPcPycCvc7cAEGRCwBA73VS6MKsJFhAoYvQdnJBhu6SK3Ofk4/pzTkA+J8jS4pPdfcUH97u//bN93B/LOI9gJBgAQB6r5M/nM1KggUUughtvqwd1ZuFT10tUlm+9+cA4Dub7aSf49v8377ZCx1m72ESLABA72VMlWSTqkukmnJJ8lQS3F1eo/qmfihOAGt4epdm9Lyv+cdV5R6podq79it2S011UlSCNGRcr0IE4IWu1mXzhzBd240ECwDQezGJ7vLbkucXaYYjVkMSotXiMrSjlGGCIam50T33TvLuk+nEoZIjW5LR1ivVE/OT7zAbWgT0O/M9fNJQb79wucJ2mK9XCZbT6fT5AQAIE6fMw7LZbJ5y7czDClFHdkgtje71awaN8u6YTubrdStMP/kG+p353izfITU3+K/dY/vd1UPtMe6Ks2HEqyqCKSkpsvU0gfUkNptNu3bt0pgxY3odGAAgSGROl/L/r63HQe5hgpt2HVHBIeZhhSRfClyYMqdLn77k/TCkMP3kG+h3KSOk2BSp/rg7yfLXhxrmezg9R7JH+afNIOF1mfbnnntOgwf3XG7XMAxdfPHFfQoKABBEuit0UUKCFZI8BS5meH+MZ55HXs/7ulxS6Se+nwOA72w29/tz3xvu96ffEqw899cw7IX2KsEaOXKkzj33XA0ZMsSrRseMGaOoqPDKVAEgbGVOc3+tOijVVkoJQ5ST5U6wdh2uVkNzi2IimUMTUnrTu2TuW7FLaqyVohO63vfoPqmx2r12TuqE3scJwDuZ01sTLD8WugjjXmiv5mDt37/f6+RKkgoKCjR8eABXXAcADByxydLg1iHhrZ9YDhsUp5T4KDW1GNpVVmNdbPC/lmbpcIH7edZp3h+XlCElZkiGSzpc2P2+ngIXuZLd68E2AHrL1zmSPTGM3vV0hwi/VBE8fvy4P5oBAASrU9Y5stlsys1imGBIqvhUaq6XopOkQaN9O9bbamVhunYOYBnzvXa4UGpp6nt7x4vcc7oioqS0yX1vL8j4nGDde++9Wrt2ref7K664QkOGDFF2drY+/jgA9fMBAANfJ/OwzEqC+Sw4HFpOXmA4wsc/I7xdbyeMP/kGLDF4jBSTLLU0SEd29r0980OS9ClSZEzf2wsyPidYjz/+uGf438aNG7Vx40a9/PLLmjdvnn70ox/5PUAAQBDopICBueBwIQlWaOnLvApPIp7X9T6GcVKBC3qwgH5hs7XNp/XHPKwwnn8l9SLBKi0t9SRY//73v3XFFVdo7ty5+vGPf6wPP/zQ7wECAIJARusv5mMHpBPHJMkzRHBHWbWaWlwWBQa/68vwPfOY8h1SU33n+xzbLzVUSfbosFs7B7CUPxccPrmnOwz5nGANGjRIBw8elCS98soruuCCCyS5y7O3tLT4NzoAQHCIHyyljHQ/b+19GDkkXkmxkWpsdmn3YQpdhARXi1SW737em9LLjmwpPlUyWroudHHy2jmR0b0KE0AvnDKXttcM46QeLB8K4YQQnxOsr33ta/rmN7+pOXPmqLKyUvPmzZMk5eXlady4cX4PEAAQJE6Zh2Wz2ZST5R4mWMAwwdBQsVtqqpOiEqQhvfidb7P1PEwwzD/5BixjvufK8t3VQnvLWSzVVUg2u3sOVhjyOcF66KGH9IMf/EBTpkzRxo0blZiYKMk9dPCmm27ye4AAgCDRyTwsFhwOMean0hlTpYherm3WU6ELzyffM3rXPoDeGTJOik6Umk9Ilbt73475Hk6bLEXF+Se2IOPT4hJNTU369re/rdtvv11jxoxp99rNN9/sz7gAAMGGSoKhzx8T17vrwTIMSrQDVomIcH94UvSuuye5t+XV6YX2rQcrKipKL774YqBiAQAEM7PHoXKPVO+U1JZg7Sh1qplCF8HPTH56M//KZP4/Obxdam5s/1rVQXeRlIhI9xwsAP3LH/Ow6IX2fYjgpZdeqnXr1gUgFABAUEtIlRzD3M9bCyGMHpKghGi76ptc2nuk1sLg0Gcul3/Kp6eMkGJTJFeTVL69/WvmJ99pk8Ny7RzAct4spdATeqF9GyIoSePGjdPPf/5zbd68WTNnzlRCQkK715ctW+a34AAAQSZzuuQ85P4FO+psRUTYlJOVrA8OHFVBcZUmZiRZHSF66+g+qbFaioyVUif2vh2bzd0Dtu8N9yfdJ/eG8ck3YC3PHMlP3B+q+LqYeHWZVHNYskVIGbl+Dy9Y+Jxg/eEPf1BKSoq2bNmiLVu2tHvNZrORYAFAOMuaIX36UrvhJTnZDneCVVKly2YOsy429I35qXR6rmT3+c+H9jKntyVY7c4R3ouTApYbMl6KjJOaaqWje6XU8b4db76HUydI0Qnd7xvCfP4JuX///kDEAQAIBZ0sVGlWEnx/31G9vbui3e652Q6lxLPWUVDwx/wrk9lDVfSutPf1tu0l21rPEZ5r5wCWs0e6C10c+kDK/7s0YpZvx+/4l/trmPdC9/EjKAAATmL+Uq3YJTXWStEJnkIX20uduvrJ99vtPikjSa/cfG4/B4le8WfvktlG+Xbpzwvbv2azU+ACsFLmdHeCtenevrURxnxOsG688cZuX3/qqad6HQwAIMglpUuJGVJNmVRWII04S+PTEnXlmcOVd/B4u113llVrZ1m1KmsaNCSRggYDmmH4N8EaPEY6Y7FU9F7H16YsCNu1c4AB4YwbpLJPpIaa3h2fMETKvcy/MQUZnxOsY8eOtfu+qalJBQUFOn78uM4//3y/BQYACFJZM6Rdr7iHlI04SzabTfdcNq3Dbuc/8Ib2HalVQYlT500Y2u9hwgfHDkj1VZI9Whray7VxTmazSV99sO/tAPC/9Bxp8atWRxHUfE6wOlsHy+Vy6aabbuqw+DAAIAxlTm9NsLpfRyU3K9mdYBVXkWANdOb8q7QpUiRz5gCgOz6vg9VpIxERuuWWW/TQQw/5ozkAQDAz52GdVOiiM7nZDklSQXFVYONB35nJsj8KXABAiPNLgiVJe/fuVXNzs7+aAwAEK3OOzpGdUtOJLnczi18UlJBgDXiUTwcAr/k8RHD58uXtvjcMQ6WlpXrppZd03XXX+S0wAECQcmRJ8alSXYV0uFAadkanu+VkuROsg0dP6HhdI+XaByrDaOuNDPPSywDgDZ8TrG3btrX7PiIiQkOHDtUDDzzQY4VBAEAYsNncQ8n2vOaeu9NFgpUcF6WRQ+L1WWWdCkucOntcar+GCS9VHZJOHJUiIt1zsAAA3fI5wXr99dd73gkAEN4yp7sTrJ7mYWUl67PKOhUUV5FgDVSeAheTpahYS0MBgGDQ6zlYR44c0dtvv6133nlHR44c8WdMAIBgZw4l66GSYE5roYt8Cl0MXMy/AgCf+Jxg1dbW6sYbb1RmZqbOPfdcnXPOOcrKytLixYtVV1cXiBgBAMHG/GO8fIfU3NDlblNbC10Uljj7Iyr0BvOvAMAnPidYy5cv16ZNm/Svf/1Lx48f1/Hjx/WPf/xDmzZt0q233hqIGAEAwSZlhBQ3SHI1SeXbu9wtt7XQxf6KWjnrm/orOnjLMNqGCJJgAYBXfE6wnn/+eT355JOaN2+eHA6HHA6HLr74Yv3+97/Xc889F4gYAQDBxmZr68XqZpjgoIRoZafESZK204s18FSXSrVHJFuElJ5jdTQAEBR8TrDq6uqUnp7eYXtaWhpDBAEAbVhwOPiZyfHQSVJ0vLWxAECQ8DnBmjVrllauXKn6+nrPthMnTuiuu+7SrFmz/BocACCIedGDJbXNwyLBGoA8868ocAEA3vK5TPtvfvMbXXTRRRo2bJimT58um82mvLw8xcbGasOGDYGIEQAQjMw/yg8XSi1Nkj2q091yWhMsKgkOQJ4KgjMsDQMAgonPCVZubq52796tv/zlL9q5c6cMw9CVV16pq666SnFxcYGIEQAQjAaPkWKSpYYq6chOKWNqp7uZhS72VdSqtqFZCTE+/2pCoFCiHQB81qt1sOLi4vStb31LDzzwgB588EEtWbKk18nVmjVrNHr0aMXGxmrmzJl66623ut3/2Wef1fTp0xUfH6/MzEzdcMMNqqys7NW5AQABZLNJmdPcz7uZhzU0KUYZjlgZhrS9lEIXA0ZNuVRdIsnWZXIMAOioVx8T7tq1S2+88YbKy8vlcrnavXbHHXd43c7atWt18803a82aNTr77LP1+OOPa968edq+fbtGjBjRYf+3335b1157rR566CFdcsklKi4u1tKlS7VkyRK9+OKLvbkUAEAgZU6XDrzV2hNyTZe75WY7VOasV0Fxlc4cNbj/4kPXzN6r1PFSTKK1sQBAEPE5wfr973+v7373u0pNTVVGRoZsNpvnNZvN5lOC9eCDD2rx4sVasmSJJGn16tXasGGDHn30Ua1atarD/u+9955GjRqlZcuWSZJGjx6t73znO7rvvvt8vQwAQH8w5+6Yayl1ITc7Wa/tKGce1kDCAsMA0Cs+DxH8xS9+oV/+8pcqKytTXl6etm3b5nls3brV63YaGxu1ZcsWzZ07t932uXPnavPmzZ0eM3v2bB06dEjr16+XYRg6fPiwnnvuOX3lK1/p8jwNDQ1yOp3tHgCAfpI1w/21rEBqae5yN3MeVmExP6MHDM8Cw8y/AgBf+JxgHTt2TJdffnmfT1xRUaGWlpYOa2qlp6errKys02Nmz56tZ599VosWLVJ0dLQyMjKUkpKi3/72t12eZ9WqVUpOTvY8hg8f3ufYAQBeGjxWik6Umk9IFbu63C23tZLg7vJqnWhs6a/o0B1ziKCZJAMAvOJzgnX55Zfr1Vdf9VsAJw8xlCTDMDpsM23fvl3Lli3THXfcoS1btuiVV17R/v37tXTp0i7bX7FihaqqqjyPgwcP+i12AEAPIiKkjNZCF92sh5XuiFFqYoxchrSjjF4sy9VWSlWtvy8pcAEAPvFqDtbDDz/seT5u3Djdfvvteu+99zR16lRFRbVf18ScH9WT1NRU2e32Dr1V5eXlHXq1TKtWrdLZZ5+tH/3oR5KkadOmKSEhQeecc45+8YtfKDMzs8MxMTExiomJ8SomAEAAZE6Xija7E6wZ3+h0F5vNptxsh9749IgKi6t0+ohB/Rwk2jGHBw4eI8UmWxoKAAQbrxKshx56qN33iYmJ2rRpkzZt2tRuu81m8zrBio6O1syZM7Vx40Zdeumlnu0bN27UggULOj2mrq5OkZHtQ7bb7ZLcPV8AgAHIHGL22dvS3te73O0rCYfUFFGsw3kHld/U9oFZRIQ0dmiCYiPtbTvbo6TsM6So2AAFHeZYYBgAes2rBGv//v0BOfny5ct1zTXX6IwzztCsWbP0xBNPqKioyDPkb8WKFSouLtaf/vQnSdIll1yib33rW3r00Ud14YUXqrS0VDfffLM+97nPKSsrKyAxAgD6yCySUJYv/Xlhl7tdLunyaEmHWx89mXm9dMlv+hweOkGBCwDotV6tg+UvixYtUmVlpe6++26VlpYqNzdX69ev18iRIyVJpaWlKioq8ux//fXXq7q6Wo888ohuvfVWpaSk6Pzzz9e9995r1SUAAHoydJJ0+rXSoS3d7uYyDJUcP6GmlrYRCYYMNTS7ZI+waXxaomyyuQtmHN0n7dvUTWvoEwpcAECv2YwwG1vndDqVnJysqqoqORwOq8MBAHSjsdml3JUb1Nji0ps/+pJGDImX6o5K94127/CTz6S4FEtjDDknjkn3jnI///F+KZ6FnwGErkDkBj5XEQQAoL9ER0ZoYkaSJKmgpHUR4vjBUsoI9/OyTyyKLISVtv6bpowkuQKAXiDBAgAMaLnZ7k8U84ur2jaac4NK8vo/oFDnKXDB/CsA6A0SLADAgGYuQlzQLsGa4f7azdpa6CWzwAXzrwCgV/qcYN1www0qKSnxRywAAHSQm9WWYHmmDXsSrDxLYgpp9GABQJ94XUXwk086H+f+7LPPasGCBRozZowk9+K/AAD4y8SMJEVG2HSsrkklVfXKTolr++O/co9U75RiKVrkF/VO97+pxBpYANBLXidYM2bMkM1m63RB38suu0yGYchms6mlpcWvAQIAwltslF3j05O0o9SpguIqd4KVOFRyZEvOYulwgTRyttVhhoayfPdXxzApIdXaWAAgSHk9RHDatGmaN2+etm/frv3792v//v3at2+f7Ha7NmzY4PkeAAB/m9pa6KLTeVgUuvAfFhgGgD7zOsH64IMPNG7cOF122WU6evSoRo4cqVGjRkmSsrKyNHLkSM8CwQAA+FPnhS5akwAKXfgPCwwDQJ95nWBFR0dr9erVuv/++zV//nytWrVKLpcrkLEBACCpLcHKL3a2DVU3kwAKXfiP2RtIDxYA9JrPVQTnzZunjz76SG+99ZbOO++8QMQEAEA7kzMcirBJFTUNKq9ucG80k4CKXVJjrXXBhYrGWve/pUSBCwDog16VaU9PT9f69et1+eWX66tf/aocDqo3AQACJy7arvFpSZKk/EOtwwSTMqTEDMlwSWUFFkYXIsoKJBnuf9OkdKujAYCg1ad1sJYtW6YXX3xRw4YN81c8AAB0KscsdFHCPKyAYIFhAPALnxOsruZduVwuFRUV9TkgAAA6c/KCwx6eBCuv/wMKNSwwDAB+4XWC5XQ6dcUVVyghIUHp6elauXJluzWvjhw5otGjRwckSAAApg4zEyxn20ZPoQt6sPrMU+BihpVRAEDQ83qh4dtvv10ff/yx/vznP+v48eP6xS9+oS1btuiFF15QdHS0JHW6CDEAAP4wJdMhm00qc9brSHWDhibFtPW2lO+Qmk5IUXHWBhmsmk5IR3a6n9ODBQB94nUP1rp16/T444/r61//upYsWaItW7aooqJCl1xyiRoa3BWdbDZbwAIFAIS3hJhIjUlNkHTSPCxHthSfKhkt0uHtFkYX5A4Xuv8NE4ZKjiyrowGAoOZ1glVRUdFuIeEhQ4Zo48aNqq6u1sUXX6y6urqABAgAgMmz4LBZSdBmO2ke1jaLogoB5hy2zOnuf1MAQK95nWANHz5cO3bsaLctKSlJr776qk6cOKFLL73U78EBAHCyqWaCdXIlQeZh9R0LDAOA33idYM2dO1dPP/10h+2JiYnasGGDYmNj/RoYAACnysnqpNCFmRSYSQJ856kgOMPSMAAgFHhd5OKuu+5SSUlJp68lJSXptdde05YtW/wWGAAApzLXwio+fkJHaxs1OCG6LSko3yE1N0iRMdYFGIyaG9z/dhI9WADgB173YA0aNEg5OTldvp6YmKjzzjvPL0EBANAZR2yURg2JlyQVmsMEU0ZIsSmSq6ktUYD3yre7/+3iBrn/LQEAfeJVD9bDDz+sb3/7214PA3zsscd01VVXKSkpqU/BAQBwqtzsZB2orNMrBWWyyV2QISclR4PK3lFL/nOynzgWuJM7sqWhEwLXvreOHZAS0qTo+L63dfL8KwpcAECf2QwvFq+y2+0qKyvT0KFDvWrU4XAoLy9PY8aM6XOA/uZ0OpWcnKyqqio5HA6rwwEA+OixTXt1z8s72237aeRftTTyX/1wdpv03c1S+pR+OFcXDn0k/eECacY3pYVr+t7ev26WtjwtnX2zNOeuvrcHAEEkELmBVz1YhmHoy1/+siIjvZuydeLEiT4FBQBAVxbOyNamT4/oWF2jZ9ub9RfqtLrdSo+q16ghCYE5sfOQVF8lHXjb2gRrz2uSDGnXBskw+t7r5ClwwfwrAPAHrzKmlStX+tToggULNHjw4F4FBABAdzKSY/XXb3++3bbPKmt13q8HK9oVocLvXKgou9dTjL33+q+kTfe2rRllFXNIX12F5CyRkrN731ZLk3uRYamt3D0AoE8CkmABANCfRgyOV1JspKrrm7X7cI2mZAVgCLhnQWOL19s6+fylH/ctwTqyU2ppkGKSpUGj+x4bAMD7KoIAAAxUNptNOa1JVbtFiP3JTLDKd0hNFg2FrymXqk9aMqWvvWmeAhfTKHABAH5CggUACAlTs81FiAOUYDmypfhUyWiRDm8PzDl6cmrvWV9705h/BQB+R4IFAAgJuYFOsGy2k4YJbgvMOXpi9jgNGtX++94ye8DMxZoBAH1GggUACAlmgrW91KnmFldgTmIWgrBqHpaZEM24WpJNqimTqst611ZLs1RW4H5OgQsA8JteJ1iNjY369NNP1dzc7M94AADoldFDEpQQbVd9k0v7KmoDcxKrC12UfuL+OnKWlDqh/TZfVeySmk9I0YnS4LH+iQ8A4HuCVVdXp8WLFys+Pl45OTkqKiqSJC1btkz33HOP3wMEAMAbERE25WS5e7HyDwWq0MUM99fD26XmhsCcoyt1R6Uq9+9cZUw9qTctr3ftmUlixjQpggEtAOAvPv9EXbFihT7++GO98cYbio2N9Wy/4IILtHbtWr8GBwCAL3KyA1xJMGWEFJsiuZrc1QT7k5lIDR4rxSb3vTfNM/+KAhcA4E8+J1jr1q3TI488oi984QuynVTSdcqUKdq7d69fgwMAwBdmJcHCYmdgTmCz9b3nqLc8JdVbEyKzN623hS7MxIz5VwDgVz4nWEeOHFFaWlqH7bW1te0SLgAA+ptZ6KKwpEoulxGYk1g1D+vUhChjqvur85BUW+FbWy5X29wterAAwK98TrDOPPNMvfTSS57vzaTq97//vWbNmuW/yAAA8NHYoYmKjYpQbWOL9lcGqtDFDPfXvpZI99WpQ/piHW3FKXztTavcIzXVSpFxbcUyAAB+EenrAatWrdJFF12k7du3q7m5Wb/5zW9UWFiod999V5s2bQpEjAAAeMUeYdOUTIe2Fh1XQXGVxg5N9P9JzATncKHU0iTZo/x/jlOdOCYdO9D+/JK7N+voXnfv1rgLvG/PU+BiqhRh91eUAAD1ogdr9uzZeuedd1RXV6exY8fq1VdfVXp6ut59913NnDkzEDECAOC1gC84PGi0FOOQWhqkIzsDc45TmcP5UkZKcYPatpvJlq+9aRS4AICA8bkHS5KmTp2qZ555xt+xAADQZ2aClR+oBCsiwp2YHHjL3RNkzoUKJLPH6dSEyByu6Ot8MApcAEDA+NyD5XQ6O31UV1ersbExEDECAOC13Ky2SoIBL3TRX/OwzB6nUxOizGnur8c/c6+T5Q2Xq+uEDQDQZz4nWCkpKRo0aFCHR0pKiuLi4jRy5EitXLlSLpcrEPECANCt8emJio6MUHVDsw4eqwvMSXrbc9RbXSVEcYOkQaPcz8s+8a6tY/ulBqdkj5GGTvJbiAAAN58TrD/+8Y/KysrSbbfdpnXr1unFF1/UbbfdpuzsbD366KP69re/rYcfflj33HNPIOIFAKBbUfYITc5IkhTAYYJmolOWL7U0B+Ycpnqnu+qf1JbYdRaLt71pZm9Yek7/FOgAgDDj8xysZ555Rg888ICuuOIKz7b58+dr6tSpevzxx/Wf//xHI0aM0C9/+Uvddtttfg0WAABv5GYn6+NDVSooduqr07L8f4Ih46ToRKmxRqrcLaVN9v85TGX57q+OYVJCasfXM2dI2//hfW8a868AIKB87sF69913ddppp3XYftppp+ndd9+VJH3hC19QUVFR36MDAKAXAl5JMCKirbhFoOdh9VTxz7PwsZdxmPEy/woAAsLnBGvYsGF68sknO2x/8sknNXz4cElSZWWlBg0a1GEfAAD6w1QzwSqpkmEEqtDFDPfXQM/D6qnHyYzj6D6pvoeE0jBOms/VRXsAgD7xeYjg/fffr8svv1wvv/yyzjzzTNlsNn344YfauXOnnnvuOUnShx9+qEWLFvk9WAAAvDE+PVFRdpuO1zWp+PgJDRsU7/+TeHqO+inB6qrHKWGIlDxcqjroHk446gtdt3W8SKo/LkVEBXZYIwCEMZ8TrPnz52vXrl167LHH9Omnn8owDM2bN0/r1q3TqFGjJEnf/e53/R0nAABei4m0a2JGkgqKnXpxa7FOG9E2qiIxNlLTspMVEWHr20nMHqWyT6S9/5XUx/Y642qRKna5n3fX45Q53Z1gbf+H1NLU9X6HPnJ/TZ8iRcb4LUwAQJteLTQ8cuRIrVq1yt+xAADgN7lZySooduqBjbs6vHbf16fpijOG9+0EQ8ZLkXHuQhd/vrRvbfUkMUNKSu/69cwZ0s5/Sx884X70hPlXABAwvUqwJKmurk5FRUUdFheeNm1an4MCAKCvrv78SO06XK26xhbPtmN1jTrsbNDmPRV9T7DskdKXbpM+Weue2xQotgjpc0u632f6ImnfG9KJYz23F50gnbHYL6EBADqyGT7O/j1y5IhuuOEGvfzyy52+3tLS0un2rqxZs0a//vWvVVpaqpycHK1evVrnnHNOp/tef/31euaZZzpsnzJligoLC706n9PpVHJysqqqquRwOHyKFQAQ3F7/tFw3PP2hxg5N0H9u/aLV4QAALBaI3MDnKoI333yzjh07pvfee09xcXF65ZVX9Mwzz2j8+PH65z//6VNba9eu1c0336yf/exn2rZtm8455xzNmzevyxLvv/nNb1RaWup5HDx4UIMHD9bll1/u62UAAMJQbpa7uuC+ilrVNgR4gWAAQFjyOcH673//q4ceekhnnnmmIiIiNHLkSF199dW67777fJ6X9eCDD2rx4sVasmSJJk+erNWrV2v48OF69NFHO90/OTlZGRkZnsdHH32kY8eO6YYbbvD1MgAAYWhoUozSHTEyDGl7qdPqcAAAIcjnBKu2tlZpaWmSpMGDB+vIkSOSpKlTp2rr1q1et9PY2KgtW7Zo7ty57bbPnTtXmzdv9qqNJ598UhdccIFGjhzZ5T4NDQ1yOp3tHgCA8DU10IsQAwDCms8J1sSJE/Xpp59KkmbMmKHHH39cxcXFeuyxx5SZmel1OxUVFWppaVF6evuqSOnp6SorK+vx+NLSUr388stasqT7ib+rVq1ScnKy52EuhgwACE85rcME80mwAAAB0Ks5WKWlpZKklStX6pVXXtGIESP08MMP61e/+pXPAdhs7dcNMQyjw7bO/PGPf1RKSooWLlzY7X4rVqxQVVWV53Hw4EGfYwQAhA6zB6uwmBENAAD/87lM+1VXXeV5ftppp+nAgQPauXOnRowYodTUVK/bSU1Nld1u79BbVV5e3qFX61SGYeipp57SNddco+jo6G73jYmJUUwMiykCANxyWxOs3eXVOtHYorhou8URAQBCiU89WE1NTRozZoy2b9/u2RYfH6/TTz/dp+RKkqKjozVz5kxt3Lix3faNGzdq9uzZ3R67adMm7dmzR4sXs44HAMA36Y4YpSbGyGVIO8roxQIA+JdPCVZUVJQaGhq8GsLnjeXLl+sPf/iDnnrqKe3YsUO33HKLioqKtHTpUknu4X3XXntth+OefPJJnXXWWcrNzfVLHACA8GGz2ZSb7V7rpJB5WAAAP/N5DtYPfvAD3XvvvWpu7vv6IYsWLdLq1at19913a8aMGXrzzTe1fv16T1XA0tLSDmtiVVVV6fnnn6f3CgDQa+Y8LApdAAD8zWYYhuHLAZdeeqn+85//KDExUVOnTlVCQkK711944QW/BuhvgVitGQAQXF4pKNPSv2zRlEyH1v/POVaHAwCwSCByA5+LXKSkpOiyyy7zy8kBALDC1GHuHqxdh6tV39Si2CgKXQAA/MPnBOvpp58ORBwAAPSbrORYDYqP0rG6Ju06XK1pw1KsDgkAECJ8noMlSc3NzXrttdf0+OOPq7q6WpJUUlKimpoavwYHAEAguAtdMA8LAOB/PvdgffbZZ7roootUVFSkhoYGzZkzR0lJSbrvvvtUX1+vxx57LBBxAgDgV7nZyXprd4UKWHAYAOBHPvdg/c///I/OOOMMHTt2THFxcZ7tZvELAACCQW6WuwergB4sAIAf+dyD9fbbb+udd95RdHR0u+0jR45UcXGx3wIDACCQzFLtn5ZVq7HZpejIXo2aBwCgHZ9/m7hcLrW0tHTYfujQISUlJfklKAAAAm344Dg5YiPV2OLSrsPVVocDAAgRPidYc+bM0erVqz3f22w21dTUaOXKlbr44ov9GRsAAAFzcqGLwhKGCQIA/MPnBOuhhx7Spk2bNGXKFNXX1+ub3/ymRo0apeLiYt17772BiBEAgICgkiAAwN98noOVlZWlvLw8/fWvf9XWrVvlcrm0ePFiXXXVVe2KXgAAMNCZCRaVBAEA/uJzglVXV6f4+HjdeOONuvHGGwMREwAA/SI3yyFJ2lHqVHOLS5F2Cl0AAPrG5wQrLS1NCxcu1DXXXKM5c+YoIoJfRgCA4DRqSIISYyJV09CsF7cVKzO5bSRGalK0JmU4LIwOABCMfE6w/vSnP+mvf/2rLr30UjkcDi1atEhXX321zjzzzEDEBwBAwERE2DQly6EP9h/Vj577pMPrf186S2eOGmxBZACAYOVz99PXvvY1/f3vf9fhw4e1atUq7dixQ7Nnz9aECRN09913ByJGAAACZul5YzQ1O1mTMpI8j0HxUZKkd/ZUWBwdACDY2AzDMPrayPbt23XVVVfpk08+6XSNrIHE6XQqOTlZVVVVcjgY+gEA6Ojpd/brrn9t1wWT0/SH6xihAQChKhC5Qa8nUNXX1+v//u//tHDhQp1++umqrKzUD3/4Q78EBQCAlaguCADoLZ/nYL366qt69tlntW7dOtntdn3961/Xhg0bdN555wUiPgAA+t2UTIdsNqnMWa8j1Q0amhRjdUgAgCDhcw/WwoULVVdXp2eeeUaHDx/WE088QXIFAAgpCTGRGpOaIEkqKGERYgCA93zuwSorK2PuEgAg5OVmJ2vvkVoVHKrSlyamWR0OACBI+JxgORwOtbS0aN26ddqxY4dsNpsmT56sBQsWyG63ByJGAAD63dTsZP0jr4QeLACAT3xOsPbs2aOLL75YxcXFmjhxogzD0K5duzR8+HC99NJLGjt2bCDiBACgX+VkUegCAOA7n+dgLVu2TGPHjtXBgwe1detWbdu2TUVFRRo9erSWLVsWiBgBAOh3Odnu4fDFx0/oWG2jxdEAAIKFzz1YmzZt0nvvvafBg9tWth8yZIjuuecenX322X4NDgAAqzhiozRqSLwOVNapoKRK54wfanVIAIAg4HMPVkxMjKqrqztsr6mpUXR0tF+CAgBgIDDXw8ovZh4WAMA7PidYX/3qV/Xtb39b77//vgzDkGEYeu+997R06VLNnz8/EDECAGAJM8EqZB4WAMBLPidYDz/8sMaOHatZs2YpNjZWsbGxOvvsszVu3Dj95je/CUSMAABYYio9WAAAH/k8ByslJUX/+Mc/tGfPHu3YsUOGYWjKlCkaN25cIOIDAMAyOVnuQhdFR+tUVdek5PgoiyMCAAx0PidYpnHjxpFUAQBCWkp8tIYNitOhYydUWFKl2eNSrQ4JADDA+TxE8Otf/7ruueeeDtt//etf6/LLL/dLUAAADBTmMEEWHAYAeMPnBGvTpk36yle+0mH7RRddpDfffNMvQQEAMFC0VRKk0AUAoGc+J1hdlWOPioqS08kvHwBAaGmrJEgPFgCgZz4nWLm5uVq7dm2H7X/72980ZcoUvwQFAMBAkdta6GJfRa2q65ssjgYAMND5XOTi9ttv12WXXaa9e/fq/PPPlyT95z//0V//+lf9/e9/93uAAABYaUhijLKSY1VSVa/tJU6dNWaI1SEBAAYwn3uw5s+fr3Xr1mnPnj266aabdOutt+rQoUN67bXXtHDhwgCECACAtXJYDwsA4KVelWn/yle+0mmhCwAAQtHU7GRt3H5YhSXMNQYAdM/nHiwAAMJNbrZ7HlYBPVgAgB70eqFhAADChVlJcO+RGr3xabkiI9o+nxw+OE4jhyRYFRoAYIAhwQIAoAdpSbFKS4pReXWDrn/6w3avRdlt2vSjLykrJc6i6AAAAwlDBAEA8MItcyZocqZDkzKSPI/EmEg1tRj68MBRq8MDAAwQ9GABAOCFb3xuhL7xuRHttt3xjwL96d3PVFBcpQUzsi2KDAAwkPicYC1fvrzT7TabTbGxsRo3bpwWLFigwYMH9zk4AAAGstws99ysgmKqCwIA3HxOsLZt26atW7eqpaVFEydOlGEY2r17t+x2uyZNmqQ1a9bo1ltv1dtvv60pU6YEImYAAAaEHLO6YEmVDMOQzWazOCIAgNV8noO1YMECXXDBBSopKdGWLVu0detWFRcXa86cOfrGN76h4uJinXvuubrlllsCES8AAAPGhPQkRdsjVF3frKKjdVaHAwAYAHxOsH7961/r5z//uRwOh2ebw+HQnXfeqfvuu0/x8fG64447tGXLFr8GCgDAQBNlj9CkzCRJUj5rZAEA1IsEq6qqSuXl5R22HzlyRE6newx6SkqKGhsb+x4dAAADnLlGFvOwAABSL4cI3njjjXrxxRd16NAhFRcX68UXX9TixYu1cOFCSdIHH3ygCRMm+DtWAAAGHLPQRWEJPVgAgF4UuXj88cd1yy236Morr1Rzc7O7kchIXXfddXrooYckSZMmTdIf/vAH/0YKAMAANLW1Byu/mEIXAADJZhiG0ZsDa2pqtG/fPhmGobFjxyoxMdHfsQWE0+lUcnKyqqqq2s0jAwCgNxqaW5S7coOaWgy9/ZMvadigeKtDAgB4KRC5Qa8XGk5MTNS0adP8EgQAAMEqJtKuCelJKixxqqC4igQLAMKcz3Owamtrdfvtt2v27NkaN26cxowZ0+7hqzVr1mj06NGKjY3VzJkz9dZbb3W7f0NDg372s59p5MiRiomJ0dixY/XUU0/5fF4AAPyFBYcBACafe7CWLFmiTZs26ZprrlFmZmafxpqvXbtWN998s9asWaOzzz5bjz/+uObNm6ft27drxIgRnR5zxRVX6PDhw3ryySc1btw4lZeXe+aCAQBghdxhyVr70UFKtQMAfJ+DlZKSopdeeklnn312n09+1lln6fTTT9ejjz7q2TZ58mQtXLhQq1at6rD/K6+8oiuvvFL79u3T4MGDe3VO5mABAPxtW9ExXbpms4YkROuj/3cBhS4AIEgEIjfweYjgoEGDep3cnKyxsVFbtmzR3Llz222fO3euNm/e3Okx//znP3XGGWfovvvuU3Z2tiZMmKAf/vCHOnHiRJfnaWhokNPpbPcAAMCfJmc6ZI+wqbK2UWXOeqvDAQBYyOcE6+c//7nuuOMO1dXV9enEFRUVamlpUXp6ervt6enpKisr6/SYffv26e2331ZBQYFefPFFrV69Ws8995y+973vdXmeVatWKTk52fMYPnx4n+IGAOBUsVF2jU9zV9NlHhYAhDef52A98MAD2rt3r9LT0zVq1ChFRUW1e33r1q0+tXfqMIru1hBxuVyy2Wx69tlnlZzsnlD84IMP6utf/7p+97vfKS4ursMxK1as0PLlyz3fO51OkiwAgN/lZCVrZ1m18ourNGdKes8HAABCks8J1sKFC/1y4tTUVNnt9g69VeXl5R16tUyZmZnKzs72JFeSe86WYRg6dOiQxo8f3+GYmJgYxcTE+CVmAAC6MjXboee3SoUUugCAsOZzgrVy5Uq/nDg6OlozZ87Uxo0bdemll3q2b9y4UQsWLOj0mLPPPlt///vfVVNT41nYeNeuXYqIiNCwYcP8EhcAAL2Rm+3+8I9KggAQ3nyeg+VPy5cv1x/+8Ac99dRT2rFjh2655RYVFRVp6dKlktzD+6699lrP/t/85jc1ZMgQ3XDDDdq+fbvefPNN/ehHP9KNN97Y6fBAAAD6y5Qsh2w2qby6QeUUugCAsOVVD9bgwYO1a9cupaamatCgQd2Wnz169KjXJ1+0aJEqKyt19913q7S0VLm5uVq/fr1GjhwpSSotLVVRUZFn/8TERG3cuFE/+MEPdMYZZ2jIkCG64oor9Itf/MLrcwIAEAjx0ZEaOzRRe8prVFBSpfMdsVaHBACwgFfrYD3zzDO68sorFRMTo2eeeabbfa+77jq/BRcIrIMFAAiUW9bm6cVtxVo+Z4KWfbnjvGAAwMASiNzAqx6sk5OmgZ5AAQBglZwsh17cVqwC5mEBQNjyKsHyZXFeeoUAAOFqamuhi48PHdfbuyvavTYhPVFpDBsEgJDnVYKVkpLS7bwrqW39qpaWFr8EBgBAsJmS5f6Q8bCzQVc/+X671wYnRGvzT89XbJTditAAAP3EqwTr9ddfD3QcAAAEvaTYKC07f5xe3X643fZ9R2p1tLZRuw5Xa9qwFGuCAwD0C6+KXIQSilwAAPrbNU++r7d2V+iXl+bqqrNGWh0OAKCVZUUuPvnkE68bnDZtWq+DAQAgFOVmJ+ut3RUqKPZ+TjMAIDh5lWDNmDFDNptNPXV2MQcLAICOcrPcxS+oLggAoc+rBGv//v2BjgMAgJBlVhf8tKxajc0uRUdGWBwRACBQvEqwRo5kvDgAAL01fHCcHLGRctY3a9fhauW2JlwAgNDTq4/Q9u7dqx/84Ae64IILNGfOHC1btkx79+71d2wAAIQEm83mSaoKSxgmCAChzOcEa8OGDZoyZYo++OADTZs2Tbm5uXr//feVk5OjjRs3BiJGAACCnplgUegCAEKbV0MET/bTn/5Ut9xyi+65554O23/yk59ozpw5fgsOAIBQYSZY+RS6AICQ5nMP1o4dO7R48eIO22+88UZt377dL0EBABBqcrPc66vsKHWqucVlcTQAgEDxOcEaOnSo8vLyOmzPy8tTWlqaP2ICACDkjBqSoMSYSDU0u7TnSI3V4QAAAsTnIYLf+ta39O1vf1v79u3T7NmzZbPZ9Pbbb+vee+/VrbfeGogYAQAIehERNk3JcuiD/UdVUOzUpAyH1SEBAALA5wTr9ttvV1JSkh544AGtWLFCkpSVlaU777xTy5Yt83uAAACEiqnZya0JVpW+PnOY1eEAAALA5wTLZrPplltu0S233KLq6mpJUlJSkiSpuLhY2dnZ/o0QAIAQkZvt7rUqoNAFAISsPi0ln5SUpKSkJJWVlekHP/iBxo0b56+4AAAIOblZ5lpYTrW4DIujAQAEgtcJ1vHjx3XVVVdp6NChysrK0sMPPyyXy6U77rhDY8aM0XvvvaennnoqkLECABDUxgxNVFyUXSeaWrS/gkIXABCKvB4ieNttt+nNN9/Uddddp1deeUW33HKLXnnlFdXX1+vll1/WeeedF8g4AQAIevbWQhdbPjum/OIqjUtLsjokAICfed2D9dJLL+npp5/W/fffr3/+858yDEMTJkzQf//7X5IrAAC8NLV1weGCYqfFkQAAAsHrBKukpERTpkyRJI0ZM0axsbFasmRJwAIDACAU5bQuOJxPoQsACEleJ1gul0tRUVGe7+12uxISEgISFAAAoWrqMHcP1vYSp1wUugCAkOP1HCzDMHT99dcrJiZGklRfX6+lS5d2SLJeeOEF/0YIAEAIGTc0UTGREappaNaBylqNGZpodUgAAD/yOsG67rrr2n1/9dVX+z0YAABCXaQ9QpMzHco7eFwFJU4SLAAIMV4nWE8//XQg4wAAIGzkZrsTrMLiKs2fnmV1OAAAP/I6wQIAAP5hVhJ8b1+l3t5d0eG15Piozg4DAAQBEiwAAPpZTpY7wfr4UJWufvL9dq9NG5asf37/C1aEBQDwAxIsAAD62ZRMh74+c5gKTinVvrOsWp8cqlLViSYlx9GLBQDBiAQLAIB+FhFh0/2XT++w/Zz7/quDR0+osKRKs8emWhAZAKCvvF4HCwAABFZu69DBwmKnxZEAAHqLBAsAgAEit7X4Rf4pQwcBAMGDBAsAgAHCTLAKSkiwACBYkWABADBA5GY5JEn7K2pV09BscTQAgN4gwQIAYIAYkhijrORYGYa0vYR5WAAQjEiwAAAYQHLMYYLMwwKAoESCBQDAAGJWEiTBAoDgRIIFAMAAMnWYex4WhS4AIDiRYAEAMICYPVh7ymtU10ihCwAINiRYAAAMIGmOWKUlxchlSDtKq60OBwDgIxIsAAAGmFwKXQBA0CLBAgBggCHBAoDgRYIFAMAAYy44nE+CBQBBhwQLAIABxuzB2l1eo/qmFoujAQD4ggQLAIABJjM5VkMSotXiMrSzjEIXABBMSLAAABhgbDabcpiHBQBBiQQLAIABaGp264LDJFgAEFRIsAAAGIDMBYcLSkiwACCYkGABADAAmYUuPi2rVkMzhS4AIFhYnmCtWbNGo0ePVmxsrGbOnKm33nqry33feOMN2Wy2Do+dO3f2Y8QAAATesEFxSo6LUlOLod2Ha6wOBwDgJUsTrLVr1+rmm2/Wz372M23btk3nnHOO5s2bp6Kiom6P+/TTT1VaWup5jB8/vp8iBgCgf9hsNuUyDwsAgk6klSd/8MEHtXjxYi1ZskSStHr1am3YsEGPPvqoVq1a1eVxaWlpSklJ6acoAQCwRm52st7ZU6nXPy3XsEHxnu0REdJpwwcpLtreb7HUNTYr7+BxuVz9dkoAIW7a8GQ5YqOsDsPvLEuwGhsbtWXLFv30pz9tt33u3LnavHlzt8eedtppqq+v15QpU/T//t//05e+9KUu921oaFBDQ4Pne6fT2bfAAQDoJ2ahiw2Fh7Wh8HC71+ZPz9LD3zit32JZvvZjvVJY1m/nAxD6Xrhptk4fMcjqMPzOsgSroqJCLS0tSk9Pb7c9PT1dZWWd/wDPzMzUE088oZkzZ6qhoUF//vOf9eUvf1lvvPGGzj333E6PWbVqle666y6/xw8AQKCdPylN509KU8nxE55tjc0u7auo1Tt7KmQYhmw2W8DjMAxD7+ytkCSNHZqgKLvlU7gBhIC4qP7rhe9Plg4RlNThF0N3vywmTpyoiRMner6fNWuWDh48qPvvv7/LBGvFihVavny553un06nhw4f7IXIAAAIrISZST11/Zrtt9U0tylm5QZW1jSpz1iszOS7gcRQdrVN1fbOi7RF65eZzSbAAoBuW/YRMTU2V3W7v0FtVXl7eoVerO5///Oe1e/fuLl+PiYmRw+Fo9wAAIFjFRtk1Pi1RklRQ3D/D3s3zTMpMIrkCgB5Y9lMyOjpaM2fO1MaNG9tt37hxo2bPnu11O9u2bVNmZqa/wwMAYMDKaZ2bld9P1QXN85jnBQB0zdIhgsuXL9c111yjM844Q7NmzdITTzyhoqIiLV26VJJ7eF9xcbH+9Kc/SXJXGRw1apRycnLU2Niov/zlL3r++ef1/PPPW3kZAAD0q6nZDj2/VSrspwSrsKSq9bwkWADQE0sTrEWLFqmyslJ33323SktLlZubq/Xr12vkyJGSpNLS0nZrYjU2NuqHP/yhiouLFRcXp5ycHL300ku6+OKLrboEAAD6XW52//VgGYbhOY+5LhcAoGs2wzAMq4PoT06nU8nJyaqqqmI+FgAgKNU1Nitn5QYZhvTBbV9WmiM2YOc6dKxOX7j3dUVG2FR494WKiQzNql8AwlMgcgNmqgIAEGTioyM1dqi70EVhSWALXZgFLiakJ5FcAYAXSLAAAAhCU/tpmGBBMfOvAMAXJFgAAAShnCz3UJaCQCdYJcy/AgBfkGABABCEzB6lQCZYhmF42s+lBwsAvEKCBQBAEJrS2oNVUlWvypqGgJzjsLNBFTWNskfYNDmTHiwA8AYJFgAAQSgpNkpjUhMkSQUBKnRhzu8an5ao2CgKXACAN0iwAAAIUjkBHiZotpuTxfBAAPAWCRYAAEEqN8CFLgpYYBgAfEaCBQBAkPIUuigJUIJVQol2APAVCRYAAEHKHLp38OgJHa9r9Gvb5dX1OuxskM0mClwAgA9IsAAACFLJ8VEaMTheklTo50IXhcXu9sYOTVRCTKRf2waAUEaCBQBAEDPnR+X7eR6W2Z45zwsA4B0SLAAAglhugCoJssAwAPQOCRYAAEEsN4sECwAGEhIsAACCmJkAHaisk7O+yS9tVtY0qKSqXpKUwxBBAPAJCRYAAEFscEK0slPiJEnb/VTowiyYMTo1QUmxUX5pEwDCBWWBAAAIcrnZDhUfP6GX80vV3GJ4tsdERei04SmKtHf9eWpTi0vbio6rsdnl2fZKYVlruwwPBABfkWABABDkcrOStaHwsJ559zM98+5n7V5b9uXxWj5nQpfHPrhxlx59Y28X7TI8EAB8RYIFAECQu/T0bL29p0JVJ9rmYNU0NOvQsRPavKei2wRr854KSdLwwXFKiG77s2BQfLQWzMgOXNAAEKJIsAAACHLDBsVr7Xdmtdu2p7xaFzz4pgpLnGpxGbJH2Doc19Ti0o6yaknSXxafpZFDEvolXgAIZRS5AAAgBI1OTVRclF0nmlq0v6Km0312H65RY7NLSbGRGjE4vp8jBIDQRIIFAEAIskfYNKV1DlVBcefVBc21rnKyHLLZOvZwAQB8R4IFAECImtpaBTC/i0WIC0qq2u0HAOg7EiwAAEJUjqcHq/MEy0y8KMcOAP5DggUAQIiaOsydOBWWOOVyGe1ea25xaUepe+ggCRYA+A8JFgAAIWrc0ETFREaopqFZnx2ta/fa3iO1qm9yKSHartFUDwQAvyHBAgAgREXaIzQ50z1M8NR5WG0FLpIV0UkJdwBA75BgAQAQwnKz3QlW4SkJlplw5bS+DgDwDxIsAABCmFkh0KwYaCqkgiAABAQJFgAAISwnqzXBKnbKMNyFLlwuQ4UlFLgAgEAgwQIAIIRNSE9StD1CVSeadOjYCUnSvopa1TW2KDYqQmOHJlocIQCEFhIsAABCWHRkhCZmJElqm3dlDg+ckumQnQIXAOBXJFgAAIQ4s9CFWTkw/xALDANAoJBgAQAQ4sxEyuzBMgtekGABgP+RYAEAEOJyWwtdFJY43QUuip3ttgMA/IcECwCAEDcxI0mRETYdrW3Ue/srVd3QrOjICI1Pp8AFAPgbCRYAACEuNsqu8enuQhd/++CgJGlyRpKi7PwZAAD+xk9WAADCwNTWQhevFJRJYv4VAAQKCRYAAGHATKgaW1ztvgcA+BcJFgAAYeDUhGoqCRYABAQJFgAAYWByhkPmmsJRdhsFLgAgQEiwAAAIA3HRdo1Pcxe6mJiRpJhIu8URAUBoIsECACBM5LQWumD9KwAInEirAwAAAP3j2+eO0dHaRi05Z7TVoQBAyCLBAgAgTEzKcOiPN3zO6jAAIKQxRBAAAAAA/IQECwAAAAD8hAQLAAAAAPyEBAsAAAAA/MTyBGvNmjUaPXq0YmNjNXPmTL311lteHffOO+8oMjJSM2bMCGyAAAAAAOAlSxOstWvX6uabb9bPfvYzbdu2Teecc47mzZunoqKibo+rqqrStddeqy9/+cv9FCkAAAAA9MxmGIZh1cnPOussnX766Xr00Uc92yZPnqyFCxdq1apVXR535ZVXavz48bLb7Vq3bp3y8vK8PqfT6VRycrKqqqrkcDj6Ej4AAACAIBaI3MCyHqzGxkZt2bJFc+fObbd97ty52rx5c5fHPf3009q7d69Wrlzp1XkaGhrkdDrbPQAAAAAgECxLsCoqKtTS0qL09PR229PT01VWVtbpMbt379ZPf/pTPfvss4qM9G6N5FWrVik5OdnzGD58eJ9jBwAAAIDOWF7kwmaztfveMIwO2ySppaVF3/zmN3XXXXdpwoQJXre/YsUKVVVVeR4HDx7sc8wAAAAA0BnvuoECIDU1VXa7vUNvVXl5eYdeLUmqrq7WRx99pG3btun73/++JMnlcskwDEVGRurVV1/V+eef3+G4mJgYxcTEBOYiAAAAAOAklvVgRUdHa+bMmdq4cWO77Rs3btTs2bM77O9wOJSfn6+8vDzPY+nSpZo4caLy8vJ01lln9VfoAAAAANApy3qwJGn58uW65pprdMYZZ2jWrFl64oknVFRUpKVLl0pyD+8rLi7Wn/70J0VERCg3N7fd8WlpaYqNje2wHQAAAACsYGmCtWjRIlVWVuruu+9WaWmpcnNztX79eo0cOVKSVFpa2uOaWAAAAAAwUFi6DpYVqqqqlJKSooMHD7IOFgAAABDGnE6nhg8fruPHjys5OdkvbVrag2WF6upqSaJcOwAAAABJ7hzBXwlW2PVguVwulZSUKCkpqdNy8P3NzJrpUQst3NfQxH0NTdzX0MW9DU3c19Bk1X01DEPV1dXKyspSRIR/6v+FXQ9WRESEhg0bZnUYHTgcDn5IhCDua2jivoYm7mvo4t6GJu5raLLivvqr58pk+ULDAAAAABAqSLAAAAAAwE9IsCwWExOjlStXKiYmxupQ4Efc19DEfQ1N3NfQxb0NTdzX0BRK9zXsilwAAAAAQKDQgwUAAAAAfkKCBQAAAAB+QoIFAAAAAH5CggUAAAAAfkKCBQAAAAB+EhYJ1qpVq3TmmWcqKSlJaWlpWrhwoT799NN2+xiGoTvvvFNZWVmKi4vTF7/4RRUWFrbb54knntAXv/hFORwO2Ww2HT9+vMtzNjQ0aMaMGbLZbMrLy+sxxvz8fJ133nmKi4tTdna27r77bp1c4PGFF17QnDlzNHToUDkcDs2aNUsbNmzot2sfqLi33V/7Cy+8oAsvvFCpqalexzsQcF+7vvampib95Cc/0dSpU5WQkKCsrCxde+21Kikp6bFtq3Ffu7/2O++8U5MmTVJCQoIGDRqkCy64QO+//36PbVuN+9r9tZ/sO9/5jmw2m1avXt1j21bjvnZ/7ddff71sNlu7x+c///ke2x4IuLc9v2d37Nih+fPnKzk5WUlJSfr85z+voqKiHts3hUWCtWnTJn3ve9/Te++9p40bN6q5uVlz585VbW2tZ5/77rtPDz74oB555BF9+OGHysjI0Jw5c1RdXe3Zp66uThdddJFuu+22Hs/54x//WFlZWV7F53Q6NWfOHGVlZenDDz/Ub3/7W91///168MEHPfu8+eabmjNnjtavX68tW7boS1/6ki655BJt27atX659oOLedn/ttbW1Ovvss3XPPfd4Fe9AwX3t+trr6uq0detW3X777dq6dateeOEF7dq1S/Pnz/cqditxX7u/9gkTJuiRRx5Rfn6+3n77bY0aNUpz587VkSNHvIrfKtzX7q/dtG7dOr3//vtex2017mvP9/Wiiy5SaWmp57F+/XqvYrca97b7a9+7d6++8IUvaNKkSXrjjTf08ccf6/bbb1dsbKxX8UuSjDBUXl5uSDI2bdpkGIZhuFwuIyMjw7jnnns8+9TX1xvJycnGY4891uH4119/3ZBkHDt2rNP2169fb0yaNMkoLCw0JBnbtm3rNp41a9YYycnJRn19vWfbqlWrjKysLMPlcnV53JQpU4y77rqr27ZP1ddrH+i4t23XfrL9+/d7Fe9AxX3t/L6aPvjgA0OS8dlnn/nUttW4r93f16qqKkOS8dprr/nUttW4rx3v66FDh4zs7GyjoKDAGDlypPHQQw/51O5AwH1tf1+vu+46Y8GCBT61M1Bxb9vf20WLFhlXX321T+2cKix6sE5VVVUlSRo8eLAkaf/+/SorK9PcuXM9+8TExOi8887T5s2bfWr78OHD+ta3vqU///nPio+P9+qYd999V+edd167lasvvPBClZSU6MCBA50e43K5VF1d7bkGbwXy2gcC7q18Pi4YcF+7v69VVVWy2WxKSUnxqW2rcV+7vq+NjY164oknlJycrOnTp/vUttW4r+3vq8vl0jXXXKMf/ehHysnJ8am9gYT72vH9+sYbbygtLU0TJkzQt771LZWXl/vU7kDBvW27dpfLpZdeekkTJkzQhRdeqLS0NJ111llat26dT+2GXYJlGIaWL1+uL3zhC8rNzZUklZWVSZLS09Pb7Zuenu55zdu2r7/+ei1dulRnnHGG18eVlZV1eu6TYzvVAw88oNraWl1xxRU+xReoax8IuLftrz1UcF+7v6/19fX66U9/qm9+85tyOBxet2017mvn9/Xf//63EhMTFRsbq4ceekgbN25Uamqq121bjfva8b7ee++9ioyM1LJly7xua6Dhvna8r/PmzdOzzz6r//73v3rggQf04Ycf6vzzz1dDQ4PXbQ8E3Nv2115eXq6amhrdc889uuiii/Tqq6/q0ksv1de+9jVt2rTJ67bDLsH6/ve/r08++UR//etfO7xms9nafW8YRodt3fntb38rp9OpFStWdLlPTk6OEhMTlZiYqHnz5nV77s62S9Jf//pX3XnnnVq7dq3S0tIkSW+99Zan3cTERD377LMdjgvktQ8E3NvOrz3YcV+7vq9NTU268sor5XK5tGbNmp4veADhvnZ+7V/60peUl5enzZs366KLLtIVV1wRVJ+Kc1/bX/uWLVv0m9/8Rn/84x+D7nfqybivHa990aJF+spXvqLc3Fxdcsklevnll7Vr1y699NJLXl/7QMC9bX/tLpdLkrRgwQLdcsstmjFjhn7605/qq1/9qh577DGvrz2s5mB9//vfN4YNG2bs27ev3fa9e/cakoytW7e22z5//nzj2muv7dBOV2NNFyxYYERERBh2u93zkGTY7XZPOwcOHDB2795t7N692zh06JBhGIZxzTXXGPPnz2/X1tatWw1JHWL929/+ZsTFxRn//ve/222vq6vztLt7927D6XQG5NoHKu5tx2s/WbDOweK+dn1fGxsbjYULFxrTpk0zKioqOt1noOK+dv9+Pdm4ceOMX/3qV17tazXua8drf+ihhwybzdYh5oiICGPkyJGd/CsOPNxX396vJ89bGui4tx2vvaGhwYiMjDR+/vOft9v+4x//2Jg9e3aHa+9KWCRYLpfL+N73vmdkZWUZu3bt6vT1jIwM49577/Vsa2ho8Hky32effWbk5+d7Hhs2bDAkGc8995xx8ODBLuNbs2aNkZKSYjQ0NHi23XPPPR0m8/3v//6vERsba7z44ouWXftAw73t+tpPFmwJFve1+/tqJlc5OTlGeXm5121bjfvq3fv1ZGPHjjVWrlzp9f5W4L52fe0VFRXtYs7PzzeysrKMn/zkJ8bOnTu9Po8VuK++vV8rKiqMmJgY45lnnvH6PFbh3nZ/b2fNmtWhyMXChQuNb3zjG16fJywSrO9+97tGcnKy8cYbbxilpaWeR11dnWefe+65x0hOTjZeeOEFIz8/3/jGN75hZGZmtst4S0tLjW3bthm///3vDUnGm2++aWzbts2orKzs9Lze/lF7/PhxIz093fjGN75h5OfnGy+88ILhcDiM+++/37PP//7v/xqRkZHG7373u3bXcPz48X659oGKe9v9tVdWVhrbtm0zXnrpJUOS8be//c3Ytm2bUVpa2m3bVuO+dn3tTU1Nxvz5841hw4YZeXl57fY5+ZfRQMR97fraa2pqjBUrVhjvvvuuceDAAWPLli3G4sWLjZiYGKOgoKCnf1pLcV+7v/ZTBUsVQe5r19deXV1t3HrrrcbmzZuN/fv3G6+//roxa9YsIzs7m7+dgvzeGoZhvPDCC0ZUVJTxxBNPGLt37zZ++9vfGna73Xjrrbe6bftkYZFgSer08fTTT3v2cblcxsqVK42MjAwjJibGOPfcc438/Px27axcubLHdk7mS6/BJ598YpxzzjlGTEyMkZGRYdx5553tsvTzzjuv03Nfd911/XLtAxX3tvuYn3766U73GeifiHNfu47ZjLGzx+uvv95j3FbivnYd84kTJ4xLL73UyMrKMqKjo43MzExj/vz5xgcffNBjzFbjvnofs2EET4LFfe065rq6OmPu3LnG0KFDjaioKGPEiBHGddddZxQVFfUY80DAve055ieffNIYN26cERsba0yfPt1Yt25djzGfzNZ6MgAAAABAH4VdFUEAAAAACBQSLAAAAADwExIsAAAAAPATEiwAAAAA8BMSLAAAAADwExIsAAAAAPATEiwAAAAA8BMSLAAAAADwExIsAAAAAPATEiwAAAAA8BMSLAAAAADwk/8P/rhCgCm6liYAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
+    "plt.plot(y_test[window:].index, rolling_coverage_aci_npfit, label=\"Without update of residuals\")\n",
+    "plt.plot(y_test[window:].index, rolling_coverage_aci_pfit, label=\"With update of residuals\")\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1012,16 +1188,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 464,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x18551cdf0>"
+       "<matplotlib.legend.Legend at 0x1859902b0>"
       ]
      },
-     "execution_count": 464,
+     "execution_count": 490,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1054,22 +1230,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 465,
+   "execution_count": null,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x185621690>"
+       "<matplotlib.legend.Legend at 0x185b14370>"
       ]
      },
-     "execution_count": 465,
+     "execution_count": 491,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 700x500 with 1 Axes>"
       ]
@@ -1086,6 +1262,28 @@
     "    plt.axvline(higher_quantiles_aci_pfit[j], ls=\"--\", color=f\"C{i}\")\n",
     "plt.legend(loc=[1, 0])"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The training data do not contain a change point, hence the base model cannot\n",
+    "anticipate it.\n",
+    "Without update of the residuals, the prediction intervals are built upon the\n",
+    "distribution of the residuals of the training set.\n",
+    "Therefore they do not cover the true observations after the change point,\n",
+    "leading to a sudden decrease of the coverage.\n",
+    "However, the partial update of the residuals allows the method to capture the\n",
+    "increase of uncertainties of the model predictions.\n",
+    "One can notice that the uncertainty's explosion happens about one day late.\n",
+    "This is because enough new residuals are needed to change the quantiles\n",
+    "obtained from the residuals distribution."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": []
   }
  ],
  "metadata": {

From 14267a2a35b8fe815b973781b356fcfb4f9e2a3b Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 24 Jul 2024 16:23:59 +0200
Subject: [PATCH 239/424] Add : Taking comments into account

---
 mapie/tests/test_regression.py | 51 ++++++++++------------------------
 1 file changed, 15 insertions(+), 36 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 3462f5a58..0f9382130 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -890,28 +890,21 @@ def test_fit_parameters_passing() -> None:
 def test_predict_parameters_passing() -> None:
     """
     Test passing predict parameters.
-    Checks that y_pred from train are 0, y_pred from test are 0 and
-    we check that y_pred constructed with or without predict_params
-    are different
+    Checks that y_pred from train are 0, y_pred from test are 0.
     """
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state)
     )
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     score = AbsoluteConformityScore(sym=True)
-    mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
-    mapie_2 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
+    mapie_model = MapieRegressor(estimator=custom_gbr, conformity_score=score)
     predict_params = {'check_predict_params': True}
-    mapie_1 = mapie_1.fit(
+    mapie_model = mapie_model.fit(
         X_train, y_train, predict_params=predict_params
     )
-    mapie_2 = mapie_2.fit(X_train, y_train)
-    y_pred_1 = mapie_1.predict(X_test, **predict_params)
-    y_pred_2 = mapie_2.predict(X_test)
-    np.testing.assert_allclose(mapie_1.conformity_scores_, np.abs(y_train))
-    np.testing.assert_allclose(y_pred_1, 0)
-    with np.testing.assert_raises(AssertionError):
-        np.testing.assert_array_equal(y_pred_1, y_pred_2)
+    y_pred = mapie_model.predict(X_test, **predict_params)
+    np.testing.assert_allclose(mapie_model.conformity_scores_, np.abs(y_train))
+    np.testing.assert_allclose(y_pred, 0)
 
 
 def test_fit_params_expected_behavior_unaffected_by_predict_params() -> None:
@@ -926,23 +919,17 @@ def test_fit_params_expected_behavior_unaffected_by_predict_params() -> None:
         train_test_split(X, y, test_size=0.2, random_state=random_state)
     )
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
-    mapie_1 = MapieRegressor(estimator=custom_gbr)
-    mapie_2 = MapieRegressor(estimator=custom_gbr)
+    mapie_model = MapieRegressor(estimator=custom_gbr)
     fit_params = {'monitor': early_stopping_monitor}
     predict_params = {'check_predict_params': True}
-    mapie_1 = mapie_1.fit(
+    mapie_model = mapie_model.fit(
         X_train, y_train,
         fit_params=fit_params, predict_params=predict_params
     )
-    mapie_2 = mapie_2.fit(X_train, y_train, predict_params=predict_params)
 
-    assert mapie_1.estimator_.single_estimator_.estimators_.shape[0] == 3
-    for estimator in mapie_1.estimator_.estimators_:
+    assert mapie_model.estimator_.single_estimator_.estimators_.shape[0] == 3
+    for estimator in mapie_model.estimator_.estimators_:
         assert estimator.estimators_.shape[0] == 3
-    assert (mapie_2.estimator_.single_estimator_.n_estimators ==
-           custom_gbr.n_estimators)
-    for estimator in mapie_2.estimator_.estimators_:
-        assert estimator.n_estimators == custom_gbr.n_estimators
 
 
 def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
@@ -958,27 +945,19 @@ def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
     )
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     score = AbsoluteConformityScore(sym=True)
-    mapie_1 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
-    mapie_2 = MapieRegressor(estimator=custom_gbr, conformity_score=score)
+    mapie_model = MapieRegressor(estimator=custom_gbr, conformity_score=score)
     fit_params = {'monitor': early_stopping_monitor}
     predict_params = {'check_predict_params': True}
-    mapie_1 = mapie_1.fit(
+    mapie_model = mapie_model.fit(
         X_train, y_train,
         fit_params=fit_params,
         predict_params=predict_params
     )
-    mapie_2 = mapie_2.fit(X_train, y_train, fit_params=fit_params,)
-    y_pred_1 = mapie_1.predict(X_test, **predict_params)
-    y_pred_2 = mapie_2.predict(X_test)
+    y_pred = mapie_model.predict(X_test, **predict_params)
 
-    np.testing.assert_array_equal(mapie_1.conformity_scores_,
+    np.testing.assert_array_equal(mapie_model.conformity_scores_,
                                   np.abs(y_train))
-    np.testing.assert_allclose(y_pred_1, 0)
-    with np.testing.assert_raises(AssertionError):
-        np.testing.assert_array_equal(mapie_2.conformity_scores_,
-                                      np.abs(y_train))
-    with np.testing.assert_raises(AssertionError):
-        np.testing.assert_array_equal(y_pred_1, y_pred_2)
+    np.testing.assert_allclose(y_pred, 0)
 
 
 def test_invalid_predict_parameters() -> None:

From 20a881ebd36a93b9354c49c2b7595c53a73860e2 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 25 Jul 2024 10:26:36 +0200
Subject: [PATCH 240/424] Change : name of unit test and its documentation

---
 mapie/regression/regression.py | 1 -
 mapie/tests/test_regression.py | 5 +++--
 2 files changed, 3 insertions(+), 3 deletions(-)

diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 6d97481e8..aa6656e81 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -515,7 +515,6 @@ def fit(
         """
         fit_params = kwargs.pop('fit_params', {})
         predict_params = kwargs.pop('predict_params', {})
-
         if len(predict_params) > 0:
             self._predict_params = True
         else:
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index f6b02013c..9bc5bfa36 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -965,8 +965,9 @@ def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
     np.testing.assert_allclose(y_pred, 0)
 
 
-def test_invalid_predict_parameters() -> None:
-    """Test that invalid predict_parameters raise errors."""
+def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
+    """Test that using predict parameters in the predict method
+    without using one predict_parameter in the fit method raises an error"""
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state)

From 45a65c1b8a08cc0019ee8cabce034e8d212f3878 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 25 Jul 2024 10:47:32 +0200
Subject: [PATCH 241/424] ADD: initia Mondrian class

---
 mapie/mondrian.py | 150 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 150 insertions(+)
 create mode 100644 mapie/mondrian.py

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
new file mode 100644
index 000000000..7d7fd6cf5
--- /dev/null
+++ b/mapie/mondrian.py
@@ -0,0 +1,150 @@
+from copy import deepcopy
+from typing import Union, cast
+
+import numpy as np
+from sklearn.utils.validation import _check_y, check_is_fitted, indexable
+
+from mapie.classification import MapieClassifier
+from mapie.conformity_scores import (
+    AbsoluteConformityScore,
+    APSConformityScore,
+    GammaConformityScore,
+    LACConformityScore,
+    NaiveConformityScore,
+    TopKConformityScore
+)
+from mapie.multi_label_classification import MapieMultiLabelClassifier
+from mapie.regression import MapieRegressor
+from mapie.utils import check_alpha
+from mapie._typing import ArrayLike, NDArray
+
+
+
+class Mondrian:
+
+    allowed_estimators = (
+        MapieClassifier, MapieRegressor, MapieMultiLabelClassifier
+    )
+    allowed_classification_ncs_str = [
+        "lac", "score", "cumulated_score", "aps", "topk"
+    ]
+    allowed_classification_ncs_class = (
+        LACConformityScore, NaiveConformityScore, APSConformityScore,
+        TopKConformityScore
+    )
+    allowed_regression_ncs = (
+        GammaConformityScore, AbsoluteConformityScore, APSConformityScore
+    )
+    fit_attributes = [
+        "unique_groups",
+        "mapie_estimators"
+    ]
+
+    def __init__(
+        self, mapie_estimator: Union[MapieClassifier, MapieRegressor, MapieMultiLabelClassifier]
+    ):
+        self.mapie_estimator = mapie_estimator
+
+    def _check_mapie_classifier(self):
+        if not self.mapie_estimator.cv == "prefit":
+            raise ValueError(
+                "Mondrian can only be used if the underlying Mapie estimator "+
+                "uses cv='prefit'"
+            )
+
+    def _check_groups_fit(X, groups: NDArray):
+        """Check that each group is defined by an integer and check that there
+        are at least 2 individuals per group"""
+        if not np.issubdtype(groups.dtype, np.integer):
+            raise ValueError("The groups must be defined by integers")
+        _, counts = np.unique(groups, return_counts=True)
+        if np.min(counts) < 2:
+            raise ValueError("There must be at least 2 individuals per group")
+        if len(groups) != X.shape[0]:
+            raise ValueError("The number of individuals in the groups must be equal to the number of rows in X")
+
+    def _check_groups_predict(self, X, groups):
+        """Check that there is no new group in the prediction"""
+        if not np.all(np.isin(groups, self.unique_groups)):
+            raise ValueError("There is a new group in the prediction")
+        if len(groups) != X.shape[0]:
+            raise ValueError("The number of individuals in the groups must be equal to the number of rows in X")
+        
+    def _check_estimator(self):
+        if not isinstance(self.mapie_estimator, self.allowed_estimators):
+            raise ValueError(
+                "The estimator must be a MapieClassifier, MapieRegressor or MapieMultiLabelClassifier"
+            )
+    
+    def _check_confomity_score(self):
+        if isinstance(self.mapie_estimator, MapieClassifier):
+            if self.mapie_estimator.conformity_score is not None:
+                if self.mapie_estimator.conformity_score not in self.allowed_classification_ncs_class:
+                    raise ValueError(
+                        "The conformity score for the MapieClassifier must be one of "+
+                        f"{self.allowed_classification_ncs_class}"
+                    )
+            else:
+                if self.mapie_estimator.ncs_str not in self.allowed_classification_ncs_str:
+                    raise ValueError(
+                        "The conformity score for the MapieClassifier must be one of "+
+                        f"{self.allowed_classification_ncs_str}"
+                    )
+        elif isinstance(self.mapie_estimator, MapieRegressor):
+            if self.mapie_estimator.conformity_score is not None:
+                if self.mapie_estimator.conformity_score not in self.allowed_regression_ncs:
+                    raise ValueError(
+                        "The conformity score for the MapieRegressor must be one of "+
+                        f"{self.allowed_regression_ncs}"
+                    )
+
+    def _check_fit_parameters(self, X, y, groups):
+        self._check_estimator()
+        self._check_mapie_classifier()
+        self._check_confomity_score()
+        self._check_groups_fit(X, groups)
+        X, y = indexable(X, y)
+        y = _check_y(y)
+        X = cast(NDArray, X)
+        y = cast(NDArray, y)
+        groups = cast(NDArray, groups)
+
+        return X, y, groups
+
+    def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
+        
+        self._check_fit_parameters(X, y, groups)
+        self.unique_groups = np.unique(groups)
+        self.mapie_estimators = {}
+
+        for group in self.unique_groups:
+            mapie_group_estimator = deepcopy(self.mapie_estimator)
+            indices_groups = np.argwhere(groups == group)[:, 0]
+            X_g, y_g = X[indices_groups], y[indices_groups]
+            mapie_group_estimator.fit(X_g, y_g, **kwargs)
+            self.mapie_estimators[group] = mapie_group_estimator
+        return self
+
+    def predict(self, X: ArrayLike, alpha, groups, **kwargs):
+
+        check_is_fitted(self, self.fit_attributes)
+        self._check_groups_predict(X, groups)
+        if alpha is None:
+            return self.mapie_estimator.predict(X, **kwargs)
+        else:
+            alpha_np = cast(NDArray, check_alpha(alpha))
+            unique_groups = np.unique(groups)
+            for i, group in enumerate(unique_groups):
+                indices_groups = np.argwhere(groups == group)[:, 0]
+                X_g = X[indices_groups]
+                y_pred_g, y_pss_g = self.mapie_estimators[group].predict(X_g, alpha=alpha_np, **kwargs)
+                if i == 0:
+                    if len(y_pred_g.shape) == 1:
+                        y_pred = np.empty((X.shape[0],))
+                    else:
+                        y_pred = np.empty((X.shape[0], y_pred_g.shape[1]))
+                    y_pss = np.empty((X.shape[0], y_pss_g.shape[1], len(alpha_np)))
+                y_pred[indices_groups] = y_pred_g
+                y_pss[indices_groups] = y_pss_g
+
+            return y_pred, y_pss

From 873134e8d94dde692c425b821ad5b5a3b705e97f Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 25 Jul 2024 10:49:05 +0200
Subject: [PATCH 242/424] Fix: Ts-changepoint notebook

---
 notebooks/regression/ts-changepoint.ipynb | 121 ++++++++++------------
 1 file changed, 52 insertions(+), 69 deletions(-)

diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index b46ca8711..baa8a083e 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 494,
+   "execution_count": 590,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,21 +50,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 495,
+   "execution_count": 591,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ImportError",
-     "evalue": "cannot import name 'ConformityScore' from 'mapie.conformity_scores' (/Users/baptistecalot/Desktop/Mapie/GITHUB/MASTER/MAPIE/mapie/conformity_scores/__init__.py)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[495], line 13\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmapie\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01msubsample\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m BlockBootstrap\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmapie\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtime_series_regression\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m MapieTimeSeriesRegressor\n\u001b[0;32m---> 13\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mmapie\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mconformity_scores\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m ConformityScore\n\u001b[1;32m     15\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mreload_ext\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautoreload\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     16\u001b[0m get_ipython()\u001b[38;5;241m.\u001b[39mrun_line_magic(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mautoreload\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m2\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
-      "\u001b[0;31mImportError\u001b[0m: cannot import name 'ConformityScore' from 'mapie.conformity_scores' (/Users/baptistecalot/Desktop/Mapie/GITHUB/MASTER/MAPIE/mapie/conformity_scores/__init__.py)"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "import warnings\n",
     "\n",
@@ -96,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 592,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -125,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 593,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -145,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 594,
    "metadata": {},
    "outputs": [
     {
@@ -154,7 +142,7 @@
        "Text(0, 0.5, 'Hourly demand (GW)')"
       ]
      },
-     "execution_count": 429,
+     "execution_count": 594,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -186,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 595,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 596,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -254,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 597,
    "metadata": {},
    "outputs": [
     {
@@ -284,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 598,
    "metadata": {},
    "outputs": [
     {
@@ -323,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 599,
    "metadata": {},
    "outputs": [
     {
@@ -370,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 600,
    "metadata": {},
    "outputs": [
     {
@@ -424,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 601,
    "metadata": {},
    "outputs": [
     {
@@ -449,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 602,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 603,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -473,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 604,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -508,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 605,
    "metadata": {},
    "outputs": [
     {
@@ -528,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 606,
    "metadata": {},
    "outputs": [
     {
@@ -572,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 607,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -582,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 608,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -602,16 +590,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 609,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x185785090>]"
+       "[<matplotlib.lines.Line2D at 0x1865b08b0>]"
       ]
      },
-     "execution_count": 469,
+     "execution_count": 609,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -643,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 610,
    "metadata": {},
    "outputs": [
     {
@@ -675,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 611,
    "metadata": {},
    "outputs": [
     {
@@ -750,26 +738,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 612,
    "metadata": {},
    "outputs": [],
    "source": [
     "def compute_quantiles(conformity_scores, alpha_np):\n",
     "\n",
-    "    beta_np = BaseConformityScore._beta_optimize(\n",
+    "    beta_np = BaseRegressionScore._beta_optimize(\n",
     "                    alpha_np,\n",
     "                    conformity_scores.reshape(1, -1),\n",
     "                    conformity_scores.reshape(1, -1),\n",
     "                )\n",
     "    alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np\n",
     "\n",
-    "    lower_quantiles = ConformityScore.get_quantile(\n",
+    "    lower_quantiles = BaseConformityScore.get_quantile(\n",
     "    conformity_scores[..., np.newaxis],\n",
     "    alpha_low, axis=0, reversed=True,\n",
     "    unbounded=False\n",
     "    )\n",
     "\n",
-    "    higher_quantiles = ConformityScore.get_quantile(\n",
+    "    higher_quantiles = BaseConformityScore.get_quantile(\n",
     "        conformity_scores[..., np.newaxis],\n",
     "        alpha_up, axis=0,\n",
     "        unbounded=False\n",
@@ -780,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 613,
    "metadata": {},
    "outputs": [
     {
@@ -850,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 614,
    "metadata": {},
    "outputs": [
     {
@@ -933,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 615,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -945,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 616,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -957,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 617,
    "metadata": {},
    "outputs": [
     {
@@ -977,12 +965,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 618,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABW0AAAMWCAYAAACKoqSLAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOydebyN1f7HP8/e+8zm8ZhnSkIqhQpJKikNmlREE3X71W3SQJQGSuO90kAaVBo1pxSaVDSn0iVUV6JEchzO3s/6/bH3evZ61rOmw8G5+b57eeXsvfZa61nr2ad7P89nfb4eY4yBIAiCIAiCIAiCIAiCIAiCqBTEdvUECIIgCIIgCIIgCIIgCIIgiCwk2hIEQRAEQRAEQRAEQRAEQVQiSLQlCIIgCIIgCIIgCIIgCIKoRJBoSxAEQRAEQRAEQRAEQRAEUYkg0ZYgCIIgCIIgCIIgCIIgCKISQaItQRAEQRAEQRAEQRAEQRBEJYJEW4IgCIIgCIIgCIIgCIIgiEoEibYEQRAEQRAEQRAEQRAEQRCVCBJtCYIgCIIgCIIgCIIgCIIgKhEk2hIEQRC7FXfffTc8z0OHDh2M7X744QdceOGFaNu2LQoKClBYWIi99toL1157Lf773/8G7YYOHYoqVars6GmHGDt2LDzPC702efJkTJ8+PdJ23rx58DwPzzzzzE6aXZaSkhKMHTsW8+bN2+lj21ixYgU8z1OumY1vvvkGY8eOxYoVKyp8Xq6sWLEC/fv3R61ateB5Hi6++OJdNhcAmD59OjzPc1qTXr16oVevXpVmPtvDl19+ibPOOgstWrRAfn4+qlSpgi5dumDixIlYt27dDh3778qcOXPQrVs3FBYWok6dOhg6dCjWrFlj/Rz/Xaf7c/755wdt3377bQwbNgx77LEHioqK0KhRIxx77LH45JNPlH1/+umnOOyww1ClShXUqFEDxx9/PH744YcKu+aKgF+/6fftM888A8/zMHPmzMh7nTp1gud5mD17duS9Vq1aoUuXLs7jcIYOHYrmzZsHP5v+m8D/u/bbb79Z+1Xx/fff47LLLsO+++6LGjVqoFatWujRo0e5/9v39ddfY9CgQahbty7y8vLQvHlzjBw5MtTmiSeewCGHHIL69esjLy8PDRs2xIABA/DBBx9E+nvkkUdwyimnoF27dojFYqH1IAiCIAgbJNoSBEEQuxXTpk0DACxevBgfffSRss3LL7+Mjh074uWXX8a5556Ll19+Ofj7Sy+9hKOPPnpnTjnC2WefjQULFoRe04m2u5KSkhKMGzeuUoq228M333yDcePG7VLR9pJLLsFHH32EadOmYcGCBbjkkkt22VwAoH///liwYAEaNGiwS+exM3nggQew7777YuHChbj88svx+uuv4/nnn8egQYMwZcoUDB8+fFdP8X+O+fPn48gjj0T9+vXxwgsv4K677sKcOXPQp08fbNmyxfjZLl26YMGCBZE/Z555JgDguOOOC9ree++9WLFiBf7v//4Pr776Ku666y6sWbMGBx54IN5+++1Qv9999x169eqFrVu34qmnnsK0adPw/fff4+CDD8batWsrfhF2IL169YLneZg7d27o9XXr1uGrr75CUVFR5L2ff/4ZP/zwA3r37g0gu85cxC0PO/K/CW+88QZeeeUVnHDCCXj66acxY8YMtGnTBoMGDcL111/v1MfcuXPRtWtX/Pnnn5gyZQreeOMN3HDDDcjPzw+1+/3339GjRw9MnjwZb7zxBm6//Xb8+uuvOOSQQzB//vxQ20cffRSLFy9G165d0apVqwq7XoIgCGL3ILGrJ0AQBEEQO4tFixbhiy++QP/+/fHKK69g6tSpOOCAA0Jtli9fjlNOOQVt27bF3LlzUb169eC9Qw89FBdddBGef/75nT31EI0bN0bjxo136RyIXcvXX3+Nrl27YuDAgeX+LGMMpaWlKCgoqLD51K1bF3Xr1q2w/io7CxYswIgRI9C3b1/MmjULeXl5wXt9+/bFpZdeitdff30XzlBPSUkJCgsLd/U0lFx++eVo27YtnnnmGSQS6f+b0qJFC/To0QPTpk3DiBEjtJ+tVq0aDjzwwNBrjDEMHjwYzZo1Q9++fYPX//3vf6NevXqhtkcccQRat26Nm266CYceemjw+pgxY5CXl4eXX34Z1apVAwDsu+++aNOmDW677TZMmDBhu697Z1GnTh106NAhIprOnz8fiUQCw4cPj4i2/Gcu2qrWuTJwyimn4IILLgidQjnyyCPx22+/YcKECbjyyitD31OZkpISDB48GIceeiheeumlUD9nnHFGqO2FF14Y+fyRRx6JunXrYurUqejZs2fw+uzZsxGLpX1SRx99NL7++uttvkaCIAhi94OctgRBEMRuw9SpUwEAt9xyC7p3744nn3wSJSUloTa33347Nm3ahMmTJ4cEW47neTj++OO3ey6MMdSvXx8XXHBB8FoqlULNmjURi8Xw66+/huaUSCSwfv16ANF4hObNm2Px4sWYP39+cBRYPoJZVlaGa665Bg0bNkS1atVw2GGHYcmSJZF5TZs2DZ06dUJ+fj5q1aqF4447Dt9++22oje54u3gUdsWKFYGIN27cuGBeQ4cO1a6J7ki76jhur1690KFDB7z77rs48MADUVBQgEaNGmH06NFIpVKhz69atQonnXQSqlatiurVq+Pkk0/G6tWrI+MvWrQIp5xyCpo3b46CggI0b94cp556KlauXBma46BBgwCkRQx+XaLLmTsDq1WrhsLCQvTo0QNvvfWW9rpFfvzxR5x++umoV68e8vLysOeee2LSpEnwfT+0FkuXLsVrr70WjG9y/XqehwsvvBBTpkzBnnvuiby8PDz88MMAgP/85z847bTTQuP9+9//Dn3e932MHz8e7dq1Q0FBAWrUqIGOHTvirrvuCq2LPA/GGCZOnIhmzZohPz8fXbp0wWuvvRaZX3n2/c0338Sxxx6Lxo0bIz8/H61bt8Z5553ndKT6s88+w9FHHx1ca8OGDdG/f3/8/PPP1s/K3HTTTfA8D/fff79SCMrNzcUxxxwT/Oz7PiZOnIg99tgDeXl5qFevHs4888zQ2BdffDGKiorw559/Rvo7+eSTUb9+fZSVlQWvzZw5E926dUNRURGqVKmCfv364bPPPgt9jse3fPXVVzj88MNRtWpV9OnTB0D51vKFF15Ax44dkZeXh5YtW+Kuu+5SxrQwxjB58mR07twZBQUFqFmzJk488USnKIH//ve/WLhwIc4444xAsAWA7t27o23bttv0sGzu3Ln44YcfcNZZZwXCGYCIYAsAVapUQfv27fHTTz8FryWTSbz88ss44YQTAsEWAJo1a4bevXs7zenf//43DjnkENSrVw9FRUXYe++9MXHixNBeAtnfaQsXLsTBBx+MwsJCtGzZErfcckvw/ed89913OOKII4IIifPPPx8bN250WpPevXtjyZIl+OWXX4LX5s2bh/333x9HHXUUPvnkk1Bf8+bNQzwex8EHHxz8rIpHmD59Otq1axf8HnnkkUdC77v+N+HXX3/FqaeeiurVq6N+/foYNmwYNmzYYL2uOnXqRO5HAOjatStKSkqscSVPP/00fvnlF1x++eXKfmxUrVoV+fn5oXsXQOi+IwiCIIjyQv8VIQiCIHYLNm/ejCeeeAL7778/OnTogGHDhmHjxo14+umnQ+3eeOMN1K9ff4c7iTzPw6GHHoo5c+YEry1atAjr169Hfn5+SOSbM2dOkNOn4vnnn0fLli2xzz77BEeCZTHh6quvxsqVK/Hggw/i/vvvx3/+8x8MGDAgJHDefPPNGD58OPbaay8899xzuOuuu/Dll1+iW7du+M9//lOu62vQoEHgNBw+fHgwr9GjR5erHxOrV6/GKaecgsGDB+OFF17AiSeeiPHjx+P//u//gjabN2/GYYcdhjfeeAM333wznn76aRQXF+Pkk0+O9LdixQq0a9cOd955J2bPno0JEybgl19+wf777x8IWf3798dNN90EIC3G8Ovq378/AOCxxx7D4YcfjmrVquHhhx/GU089hVq1aqFfv35W4Xbt2rXo3r17cCT3xRdfxGGHHYbLLrsscHbxo8nFxcXo0aNHML4tlmDWrFm49957MWbMGMyePRsHH3wwvvnmG+y///74+uuvMWnSJLz88svo378/LrroIowbNy747MSJEzF27FiceuqpeOWVVzBz5kwMHz48eIigY9y4cbjyyisDN+qIESNwzjnnKB8WuLJs2TJ069YN9957L9544w2MGTMGH330EQ466KCICCayadMm9O3bF7/++iv+/e9/480338Sdd96Jpk2bOotdnFQqhbfffhv77rsvmjRp4vSZESNGBGvx4osv4oYbbsDrr7+O7t27B/fWsGHDUFJSgqeeeir02fXr1+OFF17A6aefjpycHABp0fjUU09F+/bt8dRTT+HRRx/Fxo0bg30V2bp1K4455hgceuiheOGFF4K9dV3L119/Hccffzxq166NmTNnYuLEiXjiiScC4V/kvPPOw8UXX4zDDjsMs2bNwuTJk7F48WJ079499CCKC39jx44NXuMOxI4dO0b67dix4zY5FKdOnYpYLIazzjrL2nbDhg349NNPsddeewWvLVu2DJs3b9bOaenSpSgtLTX2u2zZMpx22ml49NFH8fLLL2P48OG49dZbcd5550Xarl69GoMHD8bpp5+OF198EUceeSSuuuoqPPbYY0GbX3/9FT179sTXX3+NyZMn49FHH8Vff/2ldH+q4I5ZUXSdO3cuevbsiR49esDzPLz77ruh97p06aJ8iMmZPn06zjrrLOy555549tlnce211+KGG24IRU24/jfhhBNOQNu2bfHss89i1KhRePzxxyPxL/yBgUvMwty5c1G3bl2lUC/yzjvvAEh/vw866CDk5uaiZs2aOPXUU7Fq1SrlZ1KpFMrKyrBixQqMGDECjLHQg1iCIAiC2G4YQRAEQewGPPLIIwwAmzJlCmOMsY0bN7IqVaqwgw8+ONQuPz+fHXjggc79DhkyhBUVFW3TnB588EEGgP3444+MMcbGjx/P9thjD3bMMcews846izHG2NatW1lRURG7+uqrg89dd911TP5P+F577cV69uwZGWPu3LkMADvqqKNCrz/11FMMAFuwYAFjjLE//viDFRQURNr9+OOPLC8vj5122mnBaz179lSONWTIENasWbPg57Vr1zIA7LrrrrOuBWOMPfTQQwwAW758ufIa5s6dG5oDAPbCCy+E2p5zzjksFouxlStXMsYYu/fee7XtALCHHnpIO59kMsn++usvVlRUxO66667g9aeffjoyH8YY27RpE6tVqxYbMGBA6PVUKsU6derEunbtarz+UaNGMQDso48+Cr0+YsQI5nkeW7JkSfBas2bNWP/+/Y39cQCw6tWrs3Xr1oVe79evH2vcuDHbsGFD6PULL7yQ5efnB+2PPvpo1rlzZ+MY8t798ccfLD8/nx133HGhdu+//z4DELp/yrPvIr7vs7KyMrZy5crIHst9Llq0iAFgs2bNMl6HC6tXr2YA2CmnnOLU/ttvv2UA2MiRI0Ovf/TRRwxA6LvdpUsX1r1791C7yZMnMwDsq6++Yoylv5OJRIL94x//CLXbuHEjKy4uZieddFLw2pAhQxgANm3aNOMcTWu5//77syZNmrAtW7aExqpdu3bo99CCBQsYADZp0qRQ3z/99BMrKChgV1xxRfDavHnzWDweZ+PGjQtemzFjRuh3ksi5557LcnNzjdcgw+/Bfv36ObUfPHgwSyQSbNGiRcFr/H594oknIu1vuukmBoCtWrXKeU6pVIqVlZWxRx55hMXj8dB3kv9Ok7//7du3D13DlVdeyTzPY59//nmoXd++fY3fF866detYLBZj5557LmOMsd9++415nsdef/11xhhjXbt2ZZdddhljLH2vAQjtnfy9TKVSrGHDhqxLly7M9/2g3YoVK1hOTo7zfxP4f9cmTpwYen3kyJEsPz8/1Pe4ceNYPB5n8+bNM17rAw88wACEfn/r6NevHwPAatSowa644gr29ttvsylTprDatWuz1q1bs02bNkU+065dOwaAAWANGjRg7733nnGM/v37h9aDIAiCIGyQ05YgCILYLZg6dSoKCgpwyimnAEgfhR00aBDefffdcrtIK4rDDjsMAAK37Ztvvom+ffvisMMOw5tvvgkgnZ25adOmoO22Ih7VBrJuNn70f8GCBdi8eXPkqGqTJk1w6KGHOh/v35lUrVo1cl2nnXYafN8PXFNz587VtpP566+/cOWVV6J169ZIJBJIJBKoUqUKNm3aFImIUPHBBx9g3bp1GDJkCJLJZPDH930cccQRWLhwITZt2qT9/Ntvv4327duja9euodeHDh0KxlikQFJ5OPTQQ1GzZs3g59LSUrz11ls47rjjUFhYGJrvUUcdhdLSUnz44YcA0seLv/jiC4wcORKzZ89WHt+XWbBgAUpLSzF48ODQ6927d0ezZs22+TrWrFmD888/H02aNEEikUBOTk7Qn2mPWrdujZo1a+LKK6/ElClTIm7UHQnPBJW/W127dsWee+4Z+m6dddZZ+OCDD0Ju5Iceeig4IQCkMzKTySTOPPPM0L7l5+ejZ8+eSvfhCSecEHnNZS03bdqERYsWYeDAgcjNzQ0+W6VKFQwYMCDU38svvwzP83D66aeH5lVcXIxOnTqF5tWzZ08kk0mMGTMmMi/d0fTyHlmfMWMGSktLcfbZZ1vbjh49GjNmzMAdd9yBfffdt1xj2+b12Wef4ZhjjkHt2rURj8eRk5ODM888E6lUCt9//32obXFxceT737Fjx1BEy9y5c7HXXnuhU6dOoXaq32kqatasGdqP+fPnIx6Po0ePHgDSe8PvWTnPVsWSJUuwatUqnHbaaaG1aNasGbp37+40JxHVf6tKS0uxZs2a4LUxY8YgmUyGsmNlXnvtNVxwwQU48cQT8Y9//MM6Lo+gOPnkkzFhwgT07t0b5513HqZOnYqlS5fi8ccfj3zm2WefxUcffYSnn34a7du3x5FHHvm3K7xJEARB7FpItCUIgiD+9ixduhTvvPMO+vfvD8YY1q9fj/Xr1+PEE08EkM5x5TRt2hTLly/fKfNq1qwZWrVqhTlz5qCkpAQLFiwIRNuff/4ZS5YswZw5c1BQULBN/+dXpHbt2qGfeQ7n5s2bAaSrYQNQHrNv2LBh8H5lon79+pHXiouLAWSv5/fffze2EznttNPwr3/9C2effTZmz56Njz/+GAsXLkTdunWDdTLBj3+feOKJyMnJCf2ZMGECGGPGXMXff/9du/7iNW0Lcr+///47kskk7rnnnshcjzrqKAAIju1fddVVuO222/Dhhx/iyCOPRO3atdGnTx8sWrTIeC2Aep1Vr7ng+z4OP/xwPPfcc7jiiivw1ltv4eOPPw7EZdMeVa9eHfPnz0fnzp1x9dVXY6+99kLDhg1x3XXXGWMVVNSpUweFhYXOvyfK890aPHgw8vLygozkb775BgsXLgwd7+f32f777x/Zu5kzZ0YyaQsLC0N5rID7Wv7xxx9B/raM/Nqvv/4atJXn9eGHH1pzh/nvKNV9vm7dOtSqVcv4eZmpU6eibt26OPbYY43txo0bh/Hjx+PGG2+MRAzY5uR5nja2BkhnVB988MH473//i7vuugvvvvsuFi5cGORGy/es/HsaSP+uFtv9/vvv2/296t27N77//nusWrUKc+fOxb777osqVaoASIu2n332GTZs2IC5c+cikUjgoIMO0vZV0d9123+rXJg9ezaOP/549O3bFzNmzHAS/Pm4/fr1C73er18/eJ6HTz/9NPKZvfbaC127dsWJJ56I119/Hc2aNQvF8xAEQRDE9pKwNyEIgiCI/22mTZsGxhieeeYZPPPMM5H3H374YYwfPx7xeBz9+vXDPffcgw8//HCnVMju06cPXnjhBcyfPx++76NXr16oWrUqGjZsiDfffBNz5szBwQcfbKx6XRHw/8MqFqfhrFq1CnXq1Al+zs/PVxaGcSkGZSI/Px8AsGXLFqd+xYxMDi8wxq+ndu3a+Pjjj7XtOBs2bMDLL7+M6667DqNGjQpe37Jli7WADYev0T333KO9d1TiF6d27dra9Rf73xZk0aJmzZqIx+M444wztBmMLVq0AAAkEgn885//xD//+U+sX78ec+bMwdVXX41+/frhp59+QmFhofJagOg689fEQnmu+/7111/jiy++wPTp0zFkyJDg9aVLl+ouO8Tee++NJ598EowxfPnll5g+fTquv/56FBQUhPbcRjweR58+ffDaa6/h559/RuPGjY3txe+W3Fb+btWsWRPHHnssHnnkEYwfPx4PPfQQ8vPzceqppwZtePtnnnnGybWsEqxc17JmzZrwPM/4XRPnxfNQVb+vbL/DuJP4q6++Ch4ccL766qvgfRc+++wzfPbZZ7j00kuDHGAV48aNw9ixYzF27FhcffXVkfdbtWqFgoICfPXVV5H3vvrqK7Ru3Tq4f1XMmjULmzZtwnPPPRfaq88//9z5WmRq166t/V650rt3b9x+++2YN28e5s2bF1pvLtC+8847QYEyLujq5qMbvzxzqihmz56NgQMHomfPnnj22WdDDnETHTt2xJNPPql931ZQLJFIoEuXLpFMaoIgCILYHshpSxAEQfytSaVSePjhh9GqVSvMnTs38ufSSy/FL7/8ElS1v+SSS1BUVISRI0cqhUnG2DZVMddx2GGH4ddff8Wdd96JAw88EFWrVgWQFnOff/55LFy40CkaQXZjlZdu3bqhoKAgVPAGAH7++We8/fbbQcV5AGjevDm+//77kMj2+++/44MPPojMCXB3SHEh78svvwy9/uKLLyrbb9y4MfLe448/jlgshkMOOQRAWpzQtRPxPA+MsYiw9OCDD4aKtQH66+rRowdq1KiBb775Bvvtt5/yj0lA6NOnD7755puIo+uRRx6B53nGI8rlpbCwEL1798Znn32Gjh07Kueqcv3VqFEDJ554Ii644AKsW7cOK1asUPZ/4IEHIj8/HzNmzAi9/sEHH4SOegPu+87FR3mP7rvvPuv1yv106tQJd9xxB2rUqKF00Nm46qqrwBjDOeecg61bt0beLysrw0svvQQgHU0BIPLdWrhwIb799tvQdwtIRySsWrUKr776Kh577DEcd9xxITdnv379kEgksGzZMu19ZsN1LYuKirDffvth1qxZoev866+/8PLLL4faHn300WCM4b///a9yTnvvvbdxTo0aNULXrl3x2GOPhb5zH374IZYsWYLjjz/eel2cqVOnAkgXvNJxww03YOzYsbj22mtx3XXXKdskEgkMGDAAzz33XKhg3Y8//oi5c+da56RaZ8YYHnjgAedrkenduzcWL16ML774IvS66vi+jkMOOQTxeBzPPPMMFi9ejF69egXvVa9eHZ07d8bDDz+MFStWWH/vtGvXDg0aNMATTzwBxljw+sqVK7f7vwnl5Y033sDAgQNx0EEHYdasWeV62HncccfB87zgfwtwXnvtNTDGrA9xeaRM69att2nuBEEQBKGCnLYEQRDE35rXXnsNq1atwoQJE0L/x5TToUMH/Otf/8LUqVNx9NFHo0WLFnjyySdx8skno3Pnzrjwwguxzz77AEgfVeau3eOOO047Jq+k/dBDD0VyLGUOPfRQeJ6HN954I6jqDqTFXO6AcxFtuYtw5syZaNmyJfLz860iiUiNGjUwevRoXH311TjzzDNx6qmn4vfff8e4ceOQn58fEjXOOOMM3HfffTj99NNxzjnn4Pfff8fEiRMjR7CrVq2KZs2a4YUXXkCfPn1Qq1Yt1KlTJ+SyFNl///3Rrl07XHbZZUgmk6hZsyaef/55vPfee8r2tWvXxogRI/Djjz+ibdu2ePXVV/HAAw9gxIgRaNq0KQDgzDPPxB133IEzzzwTN954I9q0aYNXX30Vs2fPDvVVrVo1HHLIIbj11luDOc6fPx9Tp06NHH/mjr/7778fVatWRX5+Plq0aIHatWvjnnvuwZAhQ7Bu3TqceOKJqFevHtauXYsvvvgCa9euxb333qvdg0suuQSPPPII+vfvj+uvvx7NmjXDK6+8gsmTJ2PEiBFo27at9rPbwl133YWDDjoIBx98MEaMGIHmzZtj48aNWLp0KV566aUgQ3fAgAHo0KED9ttvP9StWxcrV67EnXfeiWbNmqFNmzbKvmvWrInLLrsM48ePx9lnn41Bgwbhp59+wtixYyNHpl33fY899kCrVq0watQoMMZQq1YtvPTSS0H+s4mXX34ZkydPxsCBA9GyZUswxvDcc89h/fr16Nu3b9CuT58+mD9/PpLJpLG/bt264d5778XIkSOx7777YsSIEdhrr71QVlaGzz77DPfffz86dOiAAQMGoF27djj33HNxzz33IBaL4cgjj8SKFSswevRoNGnSBJdcckmo78MPPxyNGzfGyJEjsXr16lA0ApAWua+//npcc801+OGHH3DEEUegZs2a+PXXX/Hxxx+jqKgo9LtERXnW8vrrr0f//v3Rr18//N///R9SqRRuvfVWVKlSJeRC79GjB84991ycddZZWLRoEQ455BAUFRXhl19+wXvvvYe9994bI0aMAJDOUe3Tpw/GjBkTyrWdMGEC+vbti0GDBmHkyJFYs2YNRo0ahQ4dOoTWYeXKlWjVqhWGDBkSCLSc0tJSPP744+jevTv23HNP5fVPmjQJY8aMwRFHHIH+/fsHsRAcUaAbN24c9t9/fxx99NEYNWoUSktLMWbMGNSpUweXXnqpcZ379u2L3NxcnHrqqbjiiitQWlqKe++9F3/88YfxcyYuvvhiTJs2Df3798f48eNRv359zJgxA999951zH9WqVUOXLl0wa9YsxGKxIM+W07NnT9x5550AzHm2QNqBesMNN+Dss8/Gcccdh3POOQfr169XftfL+98EHddffz2uv/56vPXWW0Gu7XvvvYeBAweiuLgYV199dcTN3L59++C/UY888giGDRuGadOm4cwzzwSQ/k5ccMEFmDx5MqpWrYojjzwS33//Pa699lrss88+OOmkk4K+unfvjmOOOQZ77rknqlevjhUrVuDee+/FsmXLIg91v/nmmyBDe/Xq1SgpKQlO/LRv3x7t27cv17UTBEEQuxk7u/IZQRAEQexMBg4cyHJzc9maNWu0bU455RSWSCTY6tWrg9eWLVvGRo4cyVq3bs3y8vJYQUEBa9++PfvnP/8ZqnI/ZMgQVlRUFOrvnnvuYQCCatw29tlnHwaAvf/++8Fr//3vfxkAVrt27VDVbMayVbZFVqxYwQ4//HBWtWpVBiCoUM0rfT/99NOh9suXL2cA2EMPPRR6/cEHH2QdO3Zkubm5rHr16uzYY49lixcvjsz54YcfZnvuuSfLz89n7du3ZzNnzmRDhgyJVMaeM2cO22effVheXh4DwIYMGWJci++//54dfvjhrFq1aqxu3brsH//4B3vllVciVdF79uzJ9tprLzZv3jy23377sby8PNagQQN29dVXs7KyslCfP//8MzvhhBNYlSpVWNWqVdkJJ5zAPvjgg8j183Y1a9ZkVatWZUcccQT7+uuvWbNmzSLzvvPOO1mLFi1YPB6P9DN//nzWv39/VqtWLZaTk8MaNWrE+vfvH9kDFStXrmSnnXYaq127NsvJyWHt2rVjt956K0ulUqF2zZo1Y/3797f2xxhjANgFF1ygfG/58uVs2LBhrFGjRiwnJ4fVrVuXde/enY0fPz5oM2nSJNa9e3dWp04dlpuby5o2bcqGDx/OVqxYEbR56KGHGIDQd8P3fXbzzTezJk2asNzcXNaxY0f20ksvsZ49e7KePXuG5uG679988w3r27cvq1q1KqtZsyYbNGhQUOFerEgvz+e7775jp556KmvVqhUrKChg1atXZ127dmXTp08PzaNnz56R75aJzz//nA0ZMoQ1bdqU5ebmsqKiIrbPPvuwMWPGhH7npFIpNmHCBNa2bVuWk5PD6tSpw04//XT2008/Kfu9+uqrGQDWpEmTyN5zZs2axXr37s2qVavG8vLyWLNmzdiJJ57I5syZE7RR/X7iuK4lY4w9//zzbO+99w72/5ZbbmEXXXQRq1mzZqTfadOmsQMOOIAVFRWxgoIC1qpVK3bmmWeyRYsWBW347yV5HMYYe+ONN9iBBx7I8vPzWa1atdiZZ57Jfv3111Ab/vtL9ftkxowZDACbNm2a8roZy+6z7o/MokWLWJ8+fVhhYSGrVq0aGzhwIFu6dKm2f5GXXnqJderUieXn57NGjRqxyy+/nL322mva32kyqt+rfO/4Gg0fPpy98MILkT5NXHHFFQwA22+//SLvzZo1iwFgubm5bNOmTaH3+N7J4zz44IOsTZs2LDc3l7Vt25ZNmzatXP9N4P9dW7t2bai96ncLbyvOgb+m+yO25X3K//1LJpPslltuYa1bt2Y5OTmsQYMGbMSIEeyPP/4Itbv00ktZp06dWPXq1VkikWDFxcXsuOOOC/033GVeqvufIAiCIEQ8xoRzLARBEARBbDcnnXQSli9fjoULF+7qqfxt6dWrF3777Td8/fXXu3oqBLHbUVZWhs6dO6NRo0Z44403dvV0CIIgCIIg/pZQPAJBEARBVCCMMcybNy+SX0kQBPG/yvDhw9G3b180aNAAq1evxpQpU/Dtt9/irrvu2tVTIwiCIAiC+NtCoi1BEARBVCCe52HNmjW7ehoEQRAVxsaNG3HZZZdh7dq1yMnJQZcuXfDqq6865W0TBEEQBEEQ2wbFIxAEQRAEQRAEQRAEQRAEQVQiYrt6AgRBEARBEARBEARBEARBEEQWEm0JgiAIgiAIgiAIgiAIgiAqESTaEgRBEARBEARBEARBEARBVCL+9oXIfN/HqlWrULVqVXiet6unQxAEQRAEQRAEQRAEQRDEbgpjDBs3bkTDhg0Ri+n9tH970XbVqlVo0qTJrp4GQRAEQRAEQRAEQRAEQRAEAOCnn35C48aNte//7UXbqlWrAkgvRLVq1XbxbAiCIAiCIAiCIAiCIAiC2F35888/0aRJk0Cz1PG3F215JEK1atVItCUIgiAIgiAIgiAIgiAIYpdji3GlQmQEQRAEQRAEQRAEQRAEQRCVCBJtCYIgCIIgCIIgCIIgCIIgKhEk2hIEQRAEQRAEQRAEQRAEQVQi/vaZtgRBEARBEARBEARBEETlJZVKoaysbFdPgyAqhJycHMTj8e3uh0RbgiAIgiAIgiAIgiAIYqfDGMPq1auxfv36XT0VgqhQatSogeLiYmuxMRMk2hIEQRAEQRAEQRAEQRA7HS7Y1qtXD4WFhdslcBFEZYAxhpKSEqxZswYA0KBBg23ui0RbgiAIgiAIgiAIgiAIYqeSSqUCwbZ27dq7ejoEUWEUFBQAANasWYN69eptc1QCFSIjCIIgCIIgCIIgCIIgdio8w7awsHAXz4QgKh5+X29PVvMuFW3feecdDBgwAA0bNoTneZg1a1bofcYYxo4di4YNG6KgoAC9evXC4sWLd81kCYIgCIIgCIIgCIIgiAqFIhGIvyMVcV/vUtF206ZN6NSpE/71r38p3584cSJuv/12/Otf/8LChQtRXFyMvn37YuPGjTt5pgRBEARBEARBEARBEARBEDuHXSraHnnkkRg/fjyOP/74yHuMMdx555245pprcPzxx6NDhw54+OGHUVJSgscff3wXzJYgCIIgCIIgCIIgCIIgKp5evXrh4osvdm6/YsUKeJ6Hzz//fIfNSce8efPgeR7Wr1+/08fenai0mbbLly/H6tWrcfjhhwev5eXloWfPnvjggw924cwIgiAIgiAIgiAIgiCI3RHP84x/hg4duk39Pvfcc7jhhhuc2zdp0gS//PILOnTosE3j7WzKK0oTQGJXT0DH6tWrAQD169cPvV6/fn2sXLlS+7ktW7Zgy5Ytwc9//vnnjpkgQRAEQRAEQRAEscMpS5UhHosj5pk9R1tSW5AXzzO2SfpJAEAiZv6/wi59+cxHiqWQE8sxtitLlSERSxjzDRljKPPLkBvPNffllyGGGOIxcyVyl/mn/BR8+Nb5b01tRU4sxzh/n/lI+SnkxO1rQXuZ6Yv2MoAxBsaYNQPUZ751PMYYGJi1nc98ePCse6nq65dffgn+PnPmTIwZMwbfffdd0FdBQUGofVlZGXJycqzzr1WrFhhjTtfpMx+xWAzFxcXlnr+qL9taBGM6rD9gz3N16YuoxE5bjrzRti/yzTffjOrVqwd/mjRpsqOnSBAEQRAEQRAEQewgftz4I9aVrjO22VS2CcvWLwuEPB2/bPoFqzetNrYp88uwdP1SlJSVGNv9vvl3/PTnT8Y2APDDhh+wfst6Y5s/t/6JZeuXBYKHjv9u/C/Wbl5rbLMltQVL1y/F1tRWY7s1JWuw6q9VxjaMMSxdvxQby8x1ZdZvWY8fNvxgbAPQXors7L30mV+he7nizxX4ffPvxjaue1nml6HMLzO28ZmPLaktSPkpY7ukn0RZytwXkBawU8zcV4qlsCW1JbKXxcXFwZ/q1avD8zzUqVcHxcXFKC0tRY0aNfDUU0+hV69eyM/Px2OPPYa1v63FKaeegsaNG6OwsBB77703nnjiiVC/vXr1wkUXXxTMv3nz5rjpppswbNgwVK1aFU2bNsX9998Pxhi2JLfgh+U/hOIReGTBW2+9hf322w9FRUXo3r07lixZEhpn/PjxqFevHqpWrYqzzz4bV1x5BTrv09m4Fi+9/BLatWuHgoIC9O7dGytWrAi9//vvv+PUU09FkyZNUFRUFLm+oUOHYv78+bjrrrvgeR7isTiW/bAMqVQKw4cPR4sWLVBQUIB27drhrrvuMs5ld6LSirb8aQF33HLWrFkTcd+KXHXVVdiwYUPw56ef7L94CYIgCIIgCIIgiMrJ1tRWq+iTYqm025D5xnZlfpm1L5/58H3fSdCxiWlAWkSy9cWdngxmoa/ML7PPy0/B932nNbPNn7sufd+8rik/ZR0PqPi9tAl4u2ovk37SupdOoqHjXiZZ0nkvbWuW8t3WwmX+ST+JpJ+07iUYrCI34NYGAHxYxkPWhVpRY8p9XXnllbjooovw7bffol+/fti8eTM679MZL7z4Ar7++muce+65OOOMM/DRRx8Z+5o0aRL2228/fPbZZxg5ciRGjBiB7777Dgz6+V9zzTWYNGkSFny0AIlEAsOGDQvemzFjBm688UZMmDABn3zyCZo2bYoH7nvAeG0//fQTBp04CIcfcTg+/exTnH322Rg1alSoTWlpKfbdd188N+s5fPL5J5Hru+uuu9CtWzecc845+O+q/2LZj8vQpEkT+L6Pxo0b46mnnsI333yDMWPG4Oqrr8ZTTz1lXvDdhEobj9CiRQsUFxfjzTffxD777AMA2Lp1K+bPn48JEyZoP5eXl4e8PLP1niAIgiAIgiAIgvjfwCr4IC2s+PCtIgzzGfyYuT/el21cxhhSMItWQFpQswk/DCwt2jIGGE4VB20sfbmKVrZr9IV/bGMmmV20dd5L5raXqZh5/XfFXvrMD8Y0Hf/2mX1evI1VXGSwirEMrHxjWk45u9yLgJs4Kr8/4J73sHbjFm0rz/Al4a1MbXTt6lbNw0v/OMj4OVcuvvhiHH/88cHPKT+Fi/95MfLieYjH4vjHP/6B119/HU8//TQOOOAA5dwA4KijjsLIkSMBpIXgO+64A3PnzcXQ1kO163rjjTeiZ8+eKEuV4Z+X/xMnHHsCSktLkZ+fj3vuuQfDhw/HWWedBQAYM2YMXp/9OjZt2qS9lnvvvRctW7bExNsmoiCnAHvusSe++uqrkDbXqFEjXHbZZShNlsKDF7m+6tWrIzc3F4WFhWlHcrIUsXgMiVgC48aNC/pp0aIFPvjgAzz11FM46aSTyrHif092qWj7119/YenSpcHPy5cvx+eff45atWqhadOmuPjii3HTTTehTZs2aNOmDW666SYUFhbitNNO24WzJgiCIAiCIAiCIHYWKWZ3XfJMTKugBh8x35LJCHs/vC+XeYFFRalIX8x3bmcVwPhaOAhlrg5Iq1AsrL9J6Esyu+uSz902ZgopxH1zHuyu2EsGt3blFvMNpFjKKpozxpzc3FyAZ2BG4TPpJ61iPneQO4n1wrzWbtyC1X+WWj+zo+Fzsq2F2Jaz3377hX5OpVKYcPMEPP/M8/jvf/8b1GMqKiqSOwrtd8eOHYO/e56H4uJirF2zNrjPVPDPMLDgFPuaNWvQtGlTLFmyJBCBgfRY++6/L96Z94722r799lt0PaBr6IFSt27dItd3yy234Mknn8SqVav014eoe3nKlCl48MEHsXLlSmzevBlbt25F586dtfPZndilou2iRYvQu3fv4Od//vOfAIAhQ4Zg+vTpuOKKK7B582aMHDkSf/zxBw444AC88cYbqFq16q6aMkEQBEEQBEEQBLETcTlqzsUtq4vQ98FidgGSOw2t7SyxAVwYdRIOHdr5vsO8yuGotB1vD+bvKADbxK2U7yYaOgn1jk5bJ3eyw16KDlQbKZbSCmpyf7Y2Ls5pl774cXpXAd7kFObvu87f5Z4VqVtVdXqaP4jwKshp60f6Uo3rEqGQaRhCFitvv/12/Ovuf+H2229H506dUVRUhIsvvhhbt4ajKOTxcnLCBeE8z7P+Hgh9JnN54v0dqR3l2++J0N8VSztp0iTccccdmDhpIjp06IBa1Wspry/oJ3OdTz31FC655BJMmjQJ3bp1Q9WqVXHrrbcqYyN2R3apaNurVy/jjeZ5HsaOHYuxY8fuvEkRBEEQBEEQBEEQlQYX0cdZGHV12gYCkbmdS76pU2yDY7yDkzDNhTIH166r0Gcj5I41aGUu7t6K3kvx36Z2TgK24166Cq22SAPALYbA9b5wyZf14VvHDDKYHcRYl4cuvC1HFVGQ8lMoTZUiL56HREwvZW1NbUWZX4ainKjDMxiLMZQkS5Aby0VOPEfbzlWvTTc1N37v3ffQf0B/DD59MBKxBHzfx3/+8x/sueee5e7LWUhW0K5dO3z88cc444wzgr4+/fRT42fat2+P52c9H9rvDz/8MNTm3XffxbHHHotTTzsVnuchN5Ybub7c3FykUqnI57p37x5y/y5btmybr+/vRqUtREYQBEEQBEEQBEHs3jg7JR0FNad8Vubo9GRuQp9T7ifKEWngkFfrIny6Cn2u4i4XqLVt+HpZ41nd1j/lpyrtXjo5tWHP0fWZD+Y7PLTw3eIdXO6xYM8Na+vDd1ozvg5O0RMOVFSxsu0RPbeVVq1bYe5bc/HBBx/g22+/xXnnnYfVq1dvc39OD5YUa/GPf/wDU6dOxcMPP4z//Oc/uHH8jVj81WKjM/n888/HD8t+wKjLR2HJkiV4/PHHMX369FCb1q1b480338SCBQu019e8eXN89NFHWLFiBdauXQvf99G6dWssWrQIs2fPxvfff4/Ro0dj4cKF7gvxN4dEW4IgCIIgCIIgCKJS4ipmAnAXtxyKarmKW1Yhj2UFLuOY3GnrIk7bIhkcRDIA7g5gRwGS5/JqxyuHgOe0/rDHO/A13el76RAp4fu+9Vh6kAlruWdTSDmJzs5REbY9Zxlx1+FedMnR5W2N7zu6pnlfTjEQFdSXy7yuufYadOrcCf2P7I9evXqhuLgYAwcO3K4xXeYkz2vw4MG46qqrcNlll6FLly5Yvnw5Bp8xGHn5qkiKNE2bNsXMp2bi9VdeR5d9umDKlCm46aabQm1Gjx6NLl264Nj+x+KIw45QXt9ll12GeDyOvTvsjRaNWmDljytx/vnn4/jjj8fJJ5+MAw44AL///nvIdbu7s0vjEQiCIAiCIAiCIAhCBxcCbUWWnMUtloLNqOriLAXCgqDOpVYeB62TuFgO0dl6DT6sohvgJm65CK0M6cJJNndp4FR1EFqtuaWuAjZz3Eu/HHvpUBTPZS2c4kH8bN6urhCci4M21M6wblzwT8JS/KwcrmNnHJzaO5OhQ4di0OBBwc/NmzdXXm/NWjXxxDNPID+Rj5yYOpJh3rx5KCkrCdZ+xYoVkTaff/45kn4SJWUlaNa8WWgsVQRpx04dkUwlEY9li/aNHj0ao0ePBpB2rPft2xetWrUyXudRRx+FPkf2QUGiIIinOOuss4L3a9WqhVmzZmFT2SZ48FCYUxjpo23btliwYAGSfhKlyXTURU48Bw899BAeeuihUNubb77ZOJ/dBRJtCYIgCIIgCIIgiEpJIFo5FMpxPb5ucyQCbsJtyk9lK71rtMMgNsBBOHQV+qxr4Sh6puAQLwD7EXjAzVEZRFhU0F66rClvZ91L5riXDu5kPqZxLXghrwpyqvI98pmPuBdXtnG9F3mxONOYvI+KLN7mws4WZMvjxLW2ZdK/LWOaHiBE+nToi1NSUoIpU6agX79+iMfjmPH4DMx9ey5efv1lc2cVzK6IqPhfhERbgiAIgiAIgiAIonLCj2E7iIsuIl7KT4UcZ8q+XEVD2At+uR6Vd8pULUeBK1d3Jm9ncmeK/9bOLXN9pnZc3HJxZ7oe42deBQnADnvpmu/rsv58HKfiZ45OVavTmdkjLAAgiaTVoczfdyrE5/D9BSpexHMSPXcyFSVeB/05/F6Rx/Q8D6+++irGjx+PLVu2oF27dnhs5mPofWhv+3gO30mXeRHukGhLEARBEARBEARBVEq4kOaa42oV1ODDY5Yj9Y5OVfj2YkDBfKxd+cHYpnnx7FsbTvECjvEOrk5Pm7gYrJVl+q7OxRQcoi7gu4nmPrPuZTAnB3eji9M23ZVDUTCHImOBO9aw/nx/rEKxy1rw76VjgTrrfVGBYqazO9ZRWNyZ7VzvfQb7HukoKCjAnDlzgp951IIN5+szPAQiyg8VIiMIgiAIgiAIgiAqJYHT0yXH1VF0c3JKOkQVcEeiVZB1dNraROfAxVaR8QIMZkGNi0gVUNSJ9+HqmjZmqvKiZra+HAupBQXGbIKsa76v5T7jzt6U7xDV4RDJkELKLhRn/rGNGfTlIAC7FJWzzp9l27qIu1bxsDzxuI5isW1dXfsoj8hqKwQX+rdhTPHfLm1tY5ZHvDWNVR7hfHeGRFuCIAiCIAiCIAiiUhIITS7HsH03R18g0BnaubhLuSPRJByWx5Ho5G50cUrCMZLB0a3nlI8rZKraxrOJhkEOqkWAD/q07KWT6zgTFeGyl67rb2lUrqJgLgIq/7uxL4fiZyk/ZRVk+Zra9jLJ7FELzjjqexUtBDoLuw4PN6x9lHPuzq5do/5bTtHZoS/rOpBW6wyJtgRBEARBEARBEESlhAsPTsewLY6+4Eh6OdySJoJIAwenqjXSgMEqzrmKrM7uUiHTVtvG0XUsRi2Y5uXkjoWbsO5ylNw5aiEjxFaE67g8e+nSzibuilELTg8HLE5tlxzjINPWUQCurIXIXB5sVOh4FbwOFTI/Fv4+7SzIZesGibYEQRAEQRAEQRBEpcXZuWgR+kRR1ybO8SxUE0HBKYs7FoA9k9cinmYaOQvYrqKz65F665gVmYPqKsYiK8KbxnTZS1enM2AvyhYI3Ya+xKxjm6PVVpSN39eu87IVguMisXH+yIr5Lo7c8rg5t5dgJytCgNwBuqLLvFxzbV3fr6h13ZHtCTUk2hIEQRAEQRAEQRCVEi4IuTglrUIHcxfUnLNqXXI4YXeXlqcvl3xZq1NVWAMXcdQ6fzjM39E17boWXBC0uVB5du/2jukyHpB1YLtERbjM3+qAZG7uWL4O1kxbZnfHiqK5NZLBwfXtgssDl/L0VZ5+nB4gOLSztXF5X2joljddDvHU9vuzvP1Zx9uJzt7/VUi0JQiCIAiCIAiCIColXFyxOVV9+FYBiUn/WMd0cYTaBBjHI/Xlcu06FvJyuUY+R2M7OKy/S9SCQ5wE4LYWIZHSkrHpLBQ7iFGMMXu8gO+wl6KAbeguyI415OhyV2x53NWmebkUGQsczBZHLheIbVnBuwoXAbWi5ud6f7m0Z4wBnkM7MHi84XbMy7Wda3ExcuG6Q6ItQRAEQRAEQRAEUSlxdqr6bkWdADd3mlPBpsz7Tk5VB9HQ1pcoVDrlvdpEH4ccVN6PbS2cM20d+kqxlNM18n9XxJhO7thyuI6tGcZwu7cD0dPmoEXW1Wpq5xJnwO8L673I7PciL0TmFAngKPa5iOEVTgV26SxqWh5GqMRYz/Mwa9asdBtRAN4F7mTXPgkzJNoSBEEQBEEQBEEQlRLRkWgSDFJwONIt/WMb0+SoFI/T2xyJYHZ3bAopqyhSnmPYtvVyzUEN5mTRIF1jLJyFQ0tfYh9Wd3U59tLJge0Sj+AQGxD0a7mvARidtsF+2+6fzD8url1XAVi+Ft2YrmKfS65zhbKN/XmeF/xJxBOollcNVfOqIh6Lw/M8DB06dJvH7NC2A+69595tm5hqrvC09/XYsWOx/777Z6ZVcaKsS18UjeBGYldPgCAIgiAIgiAIgiBUiEKaz3zEvbi6oc//Zc8H9TzP6Ri/yZ3pfIyZ/2M5Up/W+exHiwOHrHA8WjmmQw6qqwDsIjoHrlGLUxUM1vkH2bEVFXVhcdqWZy8BuO+lixvas1ynXz5h1OaOFQV9z4tugCgAW2MUHIR/HhVhLSRYQQR7VEHH9HXv//LLL8HfH3/ycYy7bhw++eoTFCQKEI/FUVBQoJ+b7R5z0DK50za4zxTfJWcXq8t4wu8TcsfuXMhpSxAEQRAEQRAEQVRKAlGCmcWCCnfaWoTWwHVpO8ZfnkJejk5JW3+ioGbqqzzFz0zuTCArzhnbwHeav2vUAv+3rXgYmFloLe9eWtfCZf4OIjfg7rR1cr1KD0BM8+J/N83fg2cdL5i/JZ7CSWCtQNGwvIKm3L64uDj4U71adXieh/rF9YPX3nnnHey7777Iz89Hy5YtMW7cOJSVlQXi6tixY9G0aVPk5eWhYcOGuOiiiwAAfQ7tgx9//BFXXX4VchO5SmGdz2vZ0mU4os8RKCosQvv27fHmm29Gmo2+ejS6dOiCWtVqoWXLlhg9enR6HgCmT5+OcePG4csvv0SN/BqollcND09/GABw++23Y++990ZRURGaNGmCkSNH4q+//krP3xLbUJ4oDBKA7ZDTliAIgiAIgiAIgqiUBCKTRYR0Lf7E/+3igrQVT3JyxjqKc/xIvVVM5sKbYVjX2IDQ2KYxHedvXVcmrJlp/g5FzUwOQ6mhNWO2vHtpa5diKXtfLDt/m1NV/LeyDXe82oqCsVTgMvfhI46oa53PJebFrGI4b2MUgDOOXp7NWyFUhM5n+a5lm5V/sNmzZ+P000/H3XffjYMPPhjLli3Dueeei7JUGS6/5nI89+xzuOOOO/Dkk09ir732wurVq/HFF18AAJ5+5ml06dwFQ4cPxbnnnovceK5yjJSfwuknn47adWrj/Q/ex6a/NuHiiy+OtKtapSqmPDgFjRo2wtLvluKcc85B1apVccUVV+Dkk0/G119/jddefw2zXp0FDx7q164PAIjFYrj77rvRvHlzLF++HCNHjkTyqiRuu/u2ihFamfRvQguJtgRBEARBEARBEESlJBD6bI5KV6dqRigzCg8ZQcclagEM7kKlAeYzp/kD2fgAHS5Ftcpb1Mx6VJ45iOaCgG0TF13m7+L2DK5zZ+6lS9YxF+AtjlwutLo4bT1mif3w00Irdx/r+uKCbJIljWMG/WrWlt9bMS/mVphNXIv7egJ/rQm9nwuGHDCklXq1Wu8BKMyM5RnaJcBQVdVXlXrAefPD83JUFvn8b7zxRowaNQpDhgwBALRs2RI33HADLr/iclx+zeX4+aefUVxcjMMOOww5OTlo2rQpunbtCgCoWasm4vE4qlStguLiYq1o+/Zbb2PJd0vw1ZKv0KZFGyRiCdx000048sgjQ/O+/KrLEfNiiHkx7NlmT1x66aWYOXMmrrjiChQUFKBKlSqIx+MoLi4GYwwFOelYB1EAbtGiBcZdPw4jR47EbXff5rQW1rUCOW1dIdGWIAiCIAiCIAiCqJQw6R8dLu7SQCy0iHNcIDO5A13nxcczOSWB8h2pD/rV9ZUZyyr0lcNdamuT+ct2H88Xsbp7mW8ssgQI8QKOe2l15DJm3Uu+7q4Cts0RHfNi5r64aBszC9gppIK+dNfJ5xXzYsY1S7FUIADrvkuiAGxaM+W1/bUG2Lgq9JJegi1fm/K0A1BuN+gnn3yChQsX4sYbbwxeS6VSKC0txeaSzRh4/EBMvmcyWrZsiSOOOAJHHXUUBgwYgEQiERrL9Dvqu+++Q5MmTdCocaPgtW7dukXmPeu5Wbj3X/fih2U/YNNfm5BMJlGtWjXrNcydOxc33XQTvvnmG/z5559IJpMoLS3Fpk2bkFtNLSSHhnbMFCbskGhLEARBEARBEARBVEpCBXBs2aWwC338uLYtU9XWV8hp6CBU2gQMm/s06MtlLWB3l/IxXdylYBbRsxzFyrL6rkFo9e05rsHcPYvQ7VLISyxE5rAWzq5RB9e0zV2d8lNWAdWHH2SN2sR8XshPtx4hd6zlAQiPR9DNn7vf417cmgMcoUq9yEvZ75GHmEZyFb9rXuYfczuplTTutgiPvu9j3LhxOP7447P9MIbNyc3Iz89H4yaN8d1332HOnDmYM2cORo4ciVtvvRXz588PVZ3Sjc0YU2Y0y/m3H334EYadMQzXjLkGhx1+GOrXqo+ZM2di0qRJ0c8KDz9WrlyJo446Cueffz5uuOEG1KpVC++88w7OOeccJMv07mvTnCuq/e4IibYEQRAEQRAEQRBEpSTkgnQQ3pyOpHsWQc1QtIojxgFYHZW8MFhGMDa1cz2eb7xOPn+DtiiKhk6ZtrbCYa7zdyj45cO3CuvBSljGtBXAAsq5l8K4ur0M+nN4yGBzJPpIi6Om6wictp45HoE7bU3rz+duE1q50zaFlN61mxnDJgArL1+KKACA0uRmbEltQU4sB0U5Rer5+ymUJEvgMx+FOYXIieUo25WltmJzcjPiXjzdl67gl3QtLnTp0gVLlixB69atQ5/fVLYpWKv8gnwcc8wxOOaYY3DBBRdgjz32wFdffYW9O+2NnNwc+Cnzg4E99twDP/30E35Z9QtaNmsJAFiwYEGozfsfvI8mTZvg8lGXAwCKcoqwcuXKUJvc3FykUtl9ZmBYtGgRkskkJk2ahFgsrSI/OfPJdAPPshaOy8TAymF13r0h0ZYgCIIgCIIgCIKolATuRouDNoVM9qdB3AoKNjn0BZjdpUB6Tk6uSy4ugmmdf/z4uE00tDljgawT1LgWLBvHYM20tQmVwv64FvwyuQh57IHxSL1j1jEXsHfmXrq6q/nHtYW8eLwAYiFhVjUnV6ctj0ewrX8iljAXP/PTwrrn6yMZePSDVbSVr0X3HsuuuUk0Fzrb6TAwjBkzBkcffTSaNGmCQYMGIRaL4YsvvsCnX3yK0eNG47FHHkMccXQ7sBsKCwvx6KOPoqCgAM2aNQMDQ9NmTfH+e+/jpFNOQo2iGqhTp05kjN59eqNN2zY4b/h5uO2227B502Zcc801oXatWrfCzz/9jGeeegZd9u2CuW/MxfPPPx9q06xZM6xcsRJffvElGjRsgLo166JVq1ZIJpO45557MGDAALz//vu4/777AUB7z2/DQlVcX39zYvYmBEEQBEEQBEEQBLHzER1ZOkFHPNJtcsnyImQ24ZP59giFYC4uxascIw2sDloH0ZP3BZhzdEWnramvIJ/V4AiVhV3tmHyPDEIrH8NWvIqLpi5rUVF7GThUbe0c4zU85hmFVh4BEfNixgcXPvPTRcgs9w93x5qKpPE9tBUiSyKZFpMNrnW+N+URbV1wcb26CII2UT303XW3kKJfv354+eWX8eabb2L//ffHgQceiDvuuANNmjYBAFSvXh1TH5yKHj16oGPHjnjrrbfw0ksvoXbt2gCAa8Zcgx9X/ogO7Tqgbt26ymFisRieePoJbN2yFT269cDZZ58dytAFgGOOOQYjLxqJSy++FD269sAHCz7A6NGjQ21OOOEEHHb4Yeh/eH+0bNwSM5+cic6dO+P222/HhAkT0KFDB8yYMSPSt9tSOMSDWB66EOS0JQiCIAiCIAiCICopouBpciQCaaHGKs4xeyQAP5JucpeKR8xdBFSTOBcIohanpKu7lIu1ttgDFyEtuH4uVCqNtkz5d1M73dxEodImwPN9MWavVuBeugjd4rpai+J5GeHZkgkrFg+LI64cUxTBTPOPeTGkWMosAHteWug2CcB+yuoUDh6mxMoh2lrNyZYYA8uDg8hYXvYBgGFQI2cOORODBg8KvdavXz/069cv+NlnPkrKSgAARx97NE464STEY9G9BID9D9gfCxYtQMyLoTCnUDtum7Zt8PrbryM/kY/ceLo4mJzPfMNNN+CmW24CYwyFOYWIeTFcfPHFQZvcvFw89uRj6VMKfgr5iXwAwCWXXIJLLrkkaJf0kzjulOPsRfHE90iLrRDIaUsQBEEQBEEQBEFUShhY2tFnEsoEd6DtSD082LM/WSoQ00xjAjCKbnxMayQAF2Izwq22L6EPo1CcEd6sRdkya+vkzjTMn3+ei7um8TzPMwprXJy0OT1FZ681EoDHC5jWPyPcOWUiW/YSSK+FKRNWFOm188+4a+NePBCzdX15ngd4ZqGYFzWzubkBWO8LHttgfBgBwWkLfY6uq4vV9eGAM46n812E4orC5Rpt8Ryhz3vZImMV6Wjd3r7E+Vfk+v0dIdGWIAiCIAiCIAiCqJQEQp/pSD0Li0PGvixiLO/PdiQ9yF71PHNfgjinFd0El6eLgMFgj1GwOuJYdj7WomAW0UfMXjUJlbyNizBqE6QCEdMivHHR2baXLhmpolBs3Etmj3cA0nP3mCETVuG0Nc3fVLCMF8NzWQuwrOhsKljGHbnW+8Izi+Y7Ctv3sjxi7LYIy8oxXXJ4yxn1ahrTeo2OArD8GWLnQaItQRAEQRAEQRAEUSnhRams7kxevMpBEHQR+gJHqMXtxv+uHxRgXnp+RndmRiiz5qBanL2AII6aCknBt2YFAwDzmZPo5iKaBx83iYaCO9NWPIyLnrb1qMi9tPaFrAhmzVfO3BvGNshk2hrmJu6lbs/594fn0BojDQQRT9eOf0fAzFEXjDHEvbhxz+V5Kl93FNadKa/uuJ06pRxbYHNqB+1cBFKDA7ui2CGCO9Uhc4JEW4IgCIIgCIIgCKJywnMnHYQO16PfJncjJ3CEmgQRlj16rIM7DcXxVX1xR6tt/oFT2BLJYC2+5Shg80JerpEA1uJnHoIcUd28ALtTVXTaOkcCGPYyEDQNexkIlbC4dmF2vQLZdTU6bVnWaesz39iOu3t1Y/I2QT6xYS9FB61qD/hc+L1vzcfNZE3vDKdtecdwaW/7jpcHF0erB88oTosPsExOf1ehWPcZadBgPD4H3dxUf5fHcHHTE2lItCUIgiAIgiAIgiAqJYEgZBDn+OvcEaojcOp5Zkdlyndw2oKlXZIGARLIFMKC2SkJJgitlhzRYGyTuIhUqPiWbv58bOOYvn3+QcEpi1AZygo29QXmNP8gNsO2/pZMXi4mM8+hL9gFeL6XNqctY8zoegWyor9xz4Uj9SanLX8wYCx+ltnL4AGColkQoZAOTLWK4fy+rqhj9ab7ojx9BH+vyL62U9yVP2+MPnBwHZdHALb9LnOinB/fGUL+/zok2hIEQRAEQRAEQRCVkhTSQp/HzI4y7uoziYbcHet61B+wCMUMQX86fN/NqerizgSQFZ0dXGxWd53F9QqEnbYukQamdAS+Fvwz2nY8K9jkrnZwCst5ndu7lzwqwiRABmsBe75y5i/aa5QfRujG5GK4KVNY/I6YHoBwAduUo+tn/ol5MXjQF1zj96Ft/Z0coJk2VndmOTRAZ6enTcxkmr+HXg7vn1GALyeuBd5sAnBFOF8DN72tDbLrTxm5Zki0JQiCIAiCIAiCIColzM+4Fk3Zn4JoaCxYhqw7VtuXULDJ5mh1EiAhCJAWcSUGs+gZ9GFwxwZ9ZQpOGQtJwbOLzkIhKdu8PJhFcxenpzx/Y8EsmKMuuFDmspdBvq+tKJut8JxwbN2WTxyI5qb7ldkLkYlFwWz3hU3M95kPj3nGGAgxX9bzPG32cJCPa3loweclzrMi2G4HbXmKdDk8AEk3sxc/K0+BNNXPgObaHZbD6EZ3hGIPKhYSbQmCIAiCIAiCIIhKSSD0mcQ5qWCTi6PSKvTBIgBnxFPT8XBx/kanrSj0WdyZtuPOYnapSegTr83FdWwT3Xg721F/LsAbc1wdhFbu6DNFGvA9sor5jMFjZgdzMH+LuCg+QLCuReYfnVOVZ8e6Om1NmciigMqvWTn/TGQD70vVTixW5sFDEkl1X5nxrDnMDk7VcuPi9qywmmaOLuB0I/4ha1+yO1doJHTnlmmrI/JZy0dcikJq+ya2CRJtCYIgCIIgCIIgiEqJeKTb6kjkQqteEQE8h+I9DFbR0Gd+4P7ViW7phlkx2Zbjygs26Qicwoa+Qq5jMKs4ZMv35Zm2thxaLhS7ZPIaoy74/C2F4LhQCZObGG5OT76X1kJwzMHNzVionY6QO1aXQysUNTOuv+/gtBXc0KaHA6IArFsz/npQsEyT3Svm41oFeMAqmouis6mN6u+KhuVyhNqK/1U2d2kg/grrpVyPiGZbgUKrTQB2yOQlSLQlCIIgCIIgCIIgKilB8S3LkW4XR2ioL4vo6RJp4DEv7ag0CK1BUTBbjq5j8aq0DmsWpkXR0yjCeLAKlTwSgP9dPWRaTA6EVkNfMcTMURei6AlDJICYj2sq8OYgenJx2yY6p5ByisTgArytKBsAY7vgYYQXS+fQ6oRWZIVWbb4sv68zrletI1cQgHXtgnxcnqNrmH/wkMQwJsckfIbyYG1ZzZa+grlVcF/y33VtdO109+jYsWPRuXPn4Ofhw4bjtEGnWT9nYujQoRg4cGDoNdf1N+Fa1Kw867+7Q6ItQRAEQRAEQRAEUSnhQp/NEeriguTiFmAQVwR3o6kdgKzr1SC0cnHL5jQMjtS7Fq8yCJCBO9kwfzG71OTODI7nw+wuRcbFbFov3/eDOAmbI9TqTs7ETpjG5GsRg30vuV5ldNr6gphsiuCAPZIhKORlcaqKwrSr01YbaZB5MGCK9EgiiZgXC+IdVNcpXiM8GDNtuaPV+KChHFQmR+vQoUPheR4KcwpRq6gWOrbriFFXjMKmTZuijbfh0nWC5qQ7JmHyA5MB2N2qK1esRGFOIb784svQ63fddRemT5/uJCYHrzvXbTMXNaPCY+UjsasnQBAEQRAEQRAEQRAquNBnPFIvFEYyiqO+0Ma1+JMlH9fzLMW3MsKb76vFND5/ANYcVC6Mcgepbv68L5O7kbtjTU5b8Yi1Uczk7aA/6g8ImbAWoVWMNLA6Qpk5ExZAxe1l5gFCkqkzXIP5MwYvli3kFffi0XZ+ds1sTttMQ+2aiZEGujFFN7oxRzezf6bsUh/Z2AOTu5oXW7O51uVrrihcHLQublDxZ1mMPOKIIzD5/sko3VqKBe8vwIXnX4itm7fi3nvvDbUrKytzy9kFAgFeR/Xq1ZGXzDPOTVVETbye6tWrAwC2prZG58HUMRTBA68KcsdWJgG+MkNOW4IgCIIgCIIgCKJSEjhVDQWnguxSQVRQIR4jN4qZsBeS4sIiF6RM8w/yZQ19idmfOoIcV1O+r1DUzMUdaxQNeb4vYmZ3ryDOWQupeZ7RaeszP4htMK4rL1BnWH9ZwLbNy/M8GKafLaRmEf35mCZxXZy/KdOWi2ceM1+neC+a3NVARpzWuGNTLIUYYtlIA8WCBPEImTF1fSVZMpuPa4unyP6wXbiKic7tNAImJy8vD/WL66Nxk8Y46ZSTcPKpJ2PWrFlBpMG0adPQsmVLVCmsAsYYNmzYgItGXoTGDRqjWrVqOPTQQ/HFF1+E+pw0cRKaN2qORnUa4Zyzz0FpaWnofTkewfd9TJwwEa1bt0ZeXh6aNm2Km266CQDQvm17AMDBBxyM/EQ+evXqBSAaj7BlyxZcfsnlaN6oOQoKCnDQQQdh4cKFwfvz589HtbxqmPf2PPTs3hPVqlRD9+7dsWTJkqDNF198gSP6HoEGtRugUZ1G6Na1GxYtWuS0zoQeEm0JgiAIgiAIgiCISgkXYz1mPsbveZ7VUcmdtiZxUXS92oRK/p7NQWgVPQV3qdGpiqy71CTMMdjzZUVXn06o5GK4LV825LTVHM/ncwvcddoY2qyYbHJNi+5SbY6rIC66FA9zKQRnE0aDeA3EgvVTETiF4Wmdu+L1m+7rlC8VD9PEI4AhcNomoR5TjGTg16O8RoeiZqF8XE8vAMvXrEK+Lq3oKuq/O/EYPr+v8wvy065aAEuXLsVTTz2FZ599Fgs/SQugxx9zPNb8ugazXpyFTz75BF26dEGfPn2wbt06AMBzzzyHG6+/Edddfx3mfTAPxQ2KMXny5PBgLDzudddeh4kTJ2L06NH45ptv8Pjjj6N+vfoAgHc+eAcA8OJrL2L5T8vx3HPPhbvKrNE1o67BC7NewH0P3odFnyxC69at0a9fv2BefMyxY8bixltuxIcff4hEIoFhw4YFfQ0ePBiNGjXCvPfnYf6C+bjs8suQk5NjXbuKdFf/HaF4BIIgCIIgCIIgCKJSEohuHjMKfVx8sBbV4kfINeKoXNTMRah0iUcw9iXkuBpFw8yRepPrmPdny+QN3LGm+WccsXHEzY5WwbXLx1QdfXaJRwDgVLxKdORahVZY1l8UnQ19+b7b/Lm7OuWnrJm8Jhcnzx0Gss5pFbxAGo+xUIqjYEEGszVHl++d5jp9+OloDS/rWufXLM8reE3jrhb7F++Zk18+Gb9t/i3cVnCja9dNcjfr2on7IrapU1AHM4+eqe5bAx9v0cJFeOrJp3BYn8MAAFu3bsWjjz6KunXrYmtqK2a/ORuLv16MpT8vRdWCqshP5OO2227DrFmz8Mwzz2D42cPx73v+jTOGnoGzhp+FslQZrr/+esx9a27EbcvZuHEj7v3Xvbj77rsxZMgQAECrVq1wYPcDUVJWgjp16gAAatWuhfrF9VGQKIjMfVPJJjxw3wO478H7cPgRh6MwpxAPPPAA3nzzTUydOhWXX355cI1jbxiLgw45CIU5hRg1ahT69++P0tJS5Ofn48cff8Q/LvkH2u3RDgwM7dq2Q2FOoXK9+O8fwg6JtgRBEARBEARBEESlRBSRTI5KLuABbtmfJqE1FDHgEAngUojMFGnA5xxDtuCXSmwKRGcH1661EFZmXi5REVZ3KcJioErAC47aZ/ZJ5y4V52Vy2vICYz58owAJIFvwy7KX8PRiPuC4/lzAtuQri9epixcAkC12p9lL7myOIWaMB+FOWyDt/Nbd/yk/Fbr3dIXIAjFZXA/plg1E7nRDJWJkA5C9l37b/BvWlKxRf2gnw8V81TW+/PLLqF+zPpLJJMrKytB/QH/cfffduPfee9GsWTPUrVs3aPv5Z5/jr7/+QosGLUJ9bN68GcuWLQMAfP/d9zj7nLND73fr1g1z585Vzm3Jd0uwZcsWHNrnUPXcxd8jml8/K35YgbKyMhzY/cDgtZycHHTt2hXffvttqG2HvTtkumJo0KABAGDNmjVo2rQpLrnkElx4/oV4csaT6NWnF0444QR02KODelARMtoaIdGWIAiCIAiCIAiCqJRwoc/kQg2Jho7uTJ2hMhAqYc8HDURPkzuTC5WWSINA6FMUEAqN6ZmFabEoWzC+bv5eWky2FiLLiM46RNFcFwnAxeggL1UjtIaKytlyUD0gxvQCpOjANu2lKOabIjhEh6fxGD/s+crBvWgofiaKo+lbVhMvwBB8R3QxCiHB0YNWKOYRHJmL0fbFxeSYly7M5iPtyBbhsQ0AtPEmunWsU1BHOe7OcNra5ibSu3dvTLp7EhKJBBo2aohEIoGinCIAQFFRUait7/soblCMV954BYlYAvmJ/OC9GjVqaC7HPIf8/HxlO/lnXV62+B2R3dzi7xH+ehB3wLLrxh90XDf2OgwcNBBvvP4G3nj9Ddx0/U148skncdxxxynnbnKZE1lItCUIgiAIgiAIgiAqHWJ2JgCj0AeHHFTuzmRgxuJbgVhhcMcGhZhsQh+fv0n0VAl9ivbBXEyFyASh1ZYvC8CYfSu6II2ZwmCBuCg6OiNzz4iLxuJnwvx1+ayAFFWguy/EdbUUb+N7actUddnLwIFtuBe5gB1DLHjQILuTfeYH4qjO3ctF8rgXDz6jnVcG3Z4HMQe89JFmzcTPmsT1FEuFRD9dPq68RIyxSERBmV+GzWWbkRPPQVmqDAWJAuTEo3mpm5ObkfQzBdA8LxBQ5f43lW0Kiqjp+nKhqKgILVu1BBB+6KIas1PnTvh19a9IJBJo1aIVCnLCUQUpP4W2e7TFwo8X4vQzTw9e//DDD8N9Cf23adMGBQUFePutt9GmVRuxEQAgJzd9XalUSjuvlq1aIjc3FwveX4ATTzkRjDGUJcuwaNEiXHzxxeXKBm7Ttg3atmuLCy66AGedcRYeeughrWhLuEGiLUEQBEEQBEEQBFHpCETDzD+64kmy09YoMnh61x+QdTOKR/RVcHcmDPmmIaeq7Ui9ELWgFZ1hjzSQM3l1pOAwf17IyyZ6ggUZpzqhUoxtsDltAbMABoSLspliM1wE2cBp65kFeI5t/YMHCIZ7MYVUVvzVxAswCIKmbv1ZNneYi77WSAPNdYrCOqB3h4vzCvKCNS7aUFEzjWho+lm8zsgcthHnz1bwsf3eh/XGAQcegNMGnYYbbroBnfbqhFWrVuHVV1/FwIED0blLZ5x/wfkYcfYIdNm3C7p264rnZj6HxYsXo2XLlso+8wvycfGlF+OqUVehIL8APXr0wNq1a/HlV1/ilDNPQb169VBQUIA5b8xBo8aNUKtKrZCrl4GhSlEVnHPeObjmqmtQvWZ1tGnRBpNum4SSkhIMHz7caS02b96Myy67DEcPPBrNWzTHzz//jE8/+RQnnnBipK3NFUyEIdGWIAiCIAiCIAiCqHQER+pjaaFPG2nAhT6LOzM4rm2KBBCOwRszYSE4bQ1FzcAAL+YZi4dxpyp3oFojGTxL8S1eiMwgFMOHdf58brxPk7jLhUo+T1Ub7no1RUqEoiE0+ySKyUZ3rDCm614ahenMvRhcswZRJNYJldy1G/NiSLGUMl5AFHI9qMVp7rQNCs8xQ8E1oRCZri8fPhJeIrhmrbgrXKvuOlN+KuvahXrNTG7kbUEcQ44K2RGID4xs7WJeDM+/9Dyuu/Y6jDh3BH5b+xuKi4txyCGHoH79+gCAEwadgB9X/IjRV49GaWkpBh43ECNGjMDs2bO1fV9x9RXIz8vHmDFjsGrVKjRo0ADnnHsOACCRSOC2O27DzTfejPHjxuPggw/GvHnzIn3ccNMN8H0f5w47F39t/Av77bcfZs+ejZo1a2r3TSQej+P3db/j3OHnYs2va1C7Tm0cc+wxGDt2rHFdCDsk2hIEQRAEQRAEQRCVDvF4u+lIfeBA9bxIpmWknechxvSRAEBYKNQKgr4QQ2AQIBnSx+CNAioQKjil07CCHFqmL5glRzLo5sadtrb582s0RgJImZim7EwuaGozVTNZr4De6RlyYJvcsQiLdra9NK2XeC9aBWyYXd+hdUVW3JcJ3NzQu2PFCIuYF9NGYsjFw3i7kBNWEjl1xdvE3FtdDAdjLOQm1j1oEB+42DKdXSnvZ11EY9WeT58+HT7zUVJWEozLC5aNHTtWKVhWrVoVt95xK26787ZIdEPST58kuGLUFbhi1BUo88uQF8tDXiIPEyZMCOYx5cEpoc/FYjFcddVVGHPtmOC1ramtKE2WAgDOGn4WhgwbAsYYCnMKg7nzmAggnY172x234ZZJt6AwpxCJWFYqZGA4uOfB+GvrX4h5MZSlygAAnTt3Du37jBkzUJIsQdyLB/dlfk42t1fokCgHMXsTgiAIgiAIgiAIgtjJsHDeqEmo5DqNztEaEoMskQDcNaoTrYC06BkU1dK5e5mjAMwyTltLDqpL8bNAXLQ5hQXXrm7+IWGPGdzJYEFWrUmoDAR4g1NYnEuQ56ppY8sUDgnALnupESAzg4YyhXXI+6e6TrHwXFA8TNXOD4ujKnesynWszaEVXLuq/RT3iP+sdOQKa6SLZAicycg6gF0cmxVFRbprt0dA5jhFQ0htTC7yikQU6Su6T2L7IdGWIAiCIAiCIAiCqHTwo9/lEfp0jtZA6INZAA7GtLh2mS8JlTp3Ke/LJRMWZkcogFD2ra4vUWMyCcDO8xfEXRVBJqwlEiB7CZbsWMFpa4tasLljubvUdS/552RCMQQG1zQX/bkYq7sXuTs89DmJIHcYhhxaeY9MeykIrSqhmOchc8Et5sWUjmg5f1cVySA7eXWiuWuRK/matHvp0M7VzRt8tiL0R2kaugcD2jlIP8uiqOv6lLfNtrTdkfPYHSHRliAIgiAIgiAIgqh0bJPQpxEXxSPoRqeqeJzeU2d6AuGj6yahmI9nFA25u9fTZ8Ly121O29BRe4PY5DM/mzeqm79wXN7k2g1cr3zddK5dLnoaMoWDwmeQxHhxPGTnFUMscJsq58ZjJ7Z3L6V70UU01EZwCGMEAqpKkPWZk9NWjjnQ5tAK94MqkiEUrcHHVESSiHuki2SQhVGda3ebBbtKpPOVV5i0xXVIH9zWSYX225b3Hf5odC95H7o25ZuafONtc1e7BSTaEgRBEARBEARBEJUO2anqIvSZBFQuGpoiAcTXdZmqvB0XKp3cpQanqijumoQVF9GWj8X7s0YaZOZly7WV/y4iinGmvFc+Fy606tqEclZ1rlHJKakTWp2ctsgWBdOthXgvmorKAci6djX3olw8TCsmy05bhTtcdKOLc430JQjTNqctn5fO0Z3yU6F7TCXocyGa9xXzYtp15TgLi5p4E9ODhYrA9B0PxGnD/b8tMQvb43C1Ool1jmOXITWu4HKvP6UoWCHRliAIgiAIgiAIgqh0iM4/V6FPK6ixcAErk7gS6L8e0woYkRxRRUNZBDMeqfdgbANkYgC89PyM+b4s+3ftmvnh+auuk7sxuTtW2xfS4pwxHkEQpuFBW1ROFIC1hbAEkdMUCeAzP5uL68G8l6L7UXX7iC5UZn6AAGYWw8Vrinl6p7Brpm0ohgCaNRP70ji6ZdEthhiSSEb6SiIZEnZVERWq69FGLaB8gmZFZMyq5rC9mOa1LTEBqv50QqstH7dCMWSCp98u315SPIIZEm0JgiAIgiAIgiCISkfIUekq9GlyUENZtenOtYJg4M6E4zF+phb6gvdgduBxodCWCRvK5DU4aMUCXlrXK7KFyLi4bOrLOGbGjWmKURAzTrmwqNsnLgjq3LF8LZ2EYuFla+yEQQAO+stcp3a/ZderqShbZi34dcskWTLktLXdF+mG6usMCa1QF1zzmR9yx8JTC63yAwPVXsp98RiFSF/8ui06X3mEvXLFD5QHW3fl0JJd5+bUTiWi6h48bMOayGK+VZT1hN9lO9D5vLtAoi1BEARBEARBEARR6RBdhKbsUlHoEwVLuS8A9mPwYj4u1JmefEx4+qPmQFR0Ngp9nmcUPUWnqjFTVRSQNdpK4NYTREPt0W9uVDUUggsEYM1RedX8g/7ldsJxfFMObeCatuTtlmcvXZzCQYEx0/pbCsGFBHhPLaAG0xedtioBlc9dyBNWjumHRTfVNcguzhjUQmuKpbJ5yOkOlU5PWcDWZu26ENEk9XuUnpLhHhM/a3CNVpToK9+fprnZPqtC6ciV1l4uXMbbWNfHMk5FQE5bMyTaEgRBEARBEARBEJUOrZAovybmbmoctKK7Mejbksuoy/TkIpsoXNkKefHPqZCFGZOAysVdY1ZtBp3rmF+jVVCT5qGLZAiiFiz5vjyGwORoTSErCOoyhYNsXIvTNlR8bjv3UhRWrU5n/nlNpIQoaJociSk/nGmrEp2VebKqvsR19dRO5+C+EGIUlEKxlDvMP6uaF28XQ0wZ7+AKQ1gM316dzymWwWEMm3gsohJOVXOyjecitJZHiHYWY20PIxw3hdy35YNEW4IgCIIgCIIgCKLSIWbC2oS+UMEmjYM24m7UiQwWd6bsQLW5M3lftmJfRgFSKDilyy3lcwvEIUPBJv5y4BzViM6uTlVxTJugbBIqfd+3ZgqrPqsTskJO1QrYS/H+0VxkaM2MTlsHAVsUPZUCrSRm6u4zcV0Dd7gi0kBE912SRVvVwwG5QJpuzSLCsWMhr4pwZzqJlRahElDcew6uYJfxTDEPpkxboyiqemtboilU/ZRnS8R1JRHXCIm2BEEQBEEQBEEQRKVEdMRpxUVfcNAajueLoid/TUbMtNW5M0XXri0SIBhTU1Qr0zA44q49Ds2E+RryfUUHsXVeNgewKDp7HjSacyjf1+iOZQgEeJ1onmKpkCClcmfyz4aiLjRH722uaee9FJzCwfVo1kI1vjymiErQVDmAwaLjyhnMWteuFGmgc1aHxNiMo1huKzqAgbS4rhLRxfXSuZNNWallqTJsTm7G5uRmlCZLgz9bkltQmiwN3hP/iO10bYJ2qXCbslRZZD1cKW90gEqcFkVumbFjx6Jz587Bz+cOPxcnnXCSdmwXIXTYWcNw2qDT7J/VPDhxYfmK5fA8D59//rnyszsqcqGiad68Oe68885dMnZil4xKEARBEARBEARBEAZkd6zWkSgJfVZ3I/TCm+hc1LkW5exPkwNPdL7qjocH+awmYRrC/JkX6TvUzlKITJy/c6atzSksumM1bcSiZuJ1ywTuUi+mH5Nfu0LIDM2f9+mwlyanM78GU19ANt9X/kyojeAgF+cQmbvg2o156XgBHz7iiEfmH1yn5uGAKKDGPHU+tBgnwa8TLOy4ZoyFHMBAxmkLdV9B8TON05aFBwwoS5Xhq9++QkmyBACwJbkFKZZC3IsH15KfyA/1lfJT2JLagriXXp8USyEvnod4LB5pV5oqRcJLBH3mJfJQmCjE3nX2Rk48J7wGGoYOHYqHH34YAJBIJNC4SWMcfezRuGHcDahRrYb2c9sjVPL1uvWOW7WRKsF9mtmjlStWYs82e+Ldj97FAfseELS7/Y7bUVJWIk/Oiuv8GWNo0qQJfvnlF9SpU8e5j7Fjx2LWrFkhoXd3hkRbgiAIgiAIgiAIotIRcUFqBLWQQ82QI8oxiqOiOxOeUmgNCagW0VN0hFoLeWVESN01qoRW5TFp/nFTpqp0PN8mqOnWAshm2gIIBGXVWgQCsOHYt+ziVGWqhsR80/pLArxuLyNZu4b5Bw8QFK5dILMWnvleBMJCs0po5fECXIAMPqM4ei8KqDrXseiG5mPaogliXgxJlgwJxfzeCWUAK75LPvNDAr52/TXrk2RJlCRLkBPLQU48B3EvbhVtfd9HPBYPhOKUn0J+Ij8i2ib9JGJeDIlYVrSNeTGUJEuQZEnkIAeu9OvXD/fcdw/gA++/9z5GnjcSWzZvwf1T7g9fJxiSZUnk5ebpCwRKm8sfDoTWKPPX6tWrhz+reYhjGqN69erITeZGxnRx0Wq/b7yfzDzi8TiKi4uNny2Po7k8lJWVISfHfS8rKxSPQBAEQRAEQRAEQVQ6fJbN4TQdS3c5hi27G10iAaDJZ1XFHmhFTyYUINLFC8hCn871yqdlcJhG3KW2+ductlwDMmTVumTaygKwWNgr0hfMTtsgL9UiOoeESsu8rO5YcUyL0za4Tk1UhCgA8/Ei7aT9DWIgoBBaWVholaMzGGPhPeKfU4hnIQetQsTmwrGqndxX6KvkqR+U2MS/nHgO8uJ5yI3nIi+e5/wnP57v3DY3nhty15rmJpObl4v6xfXRuEljnHzqyRh0yiC89MJLALKRBtOmTkPHdh1Rq0otMMawYcMGXDTyIjQsbohq1arh0EMPxRdffBFam9sm3obmjZujYe2GOOfsc1BaWhoa99xhQjyC58H3fUyYMAGtW7dGQX4B9my9JybePBEAsGebPQEABx9wMPISeejVqxcAYPiw4aF4hC1btuDySy5H04ZNkZ+fj4MOOggLFy4M1uGd+e+gMKcQ896eh4MPPBiFhYXo3r07lixZol2fFStWhOIR5s2bF/TR44AeqFejHvoc0ifoY/r06Rg3bhy++OKL4Ps9ffp0AMCGDRtw7rnnol69eqF14wTrPW0aWrZsiby8PNx3331o1KhRpIjiMcccgyFDhgAAli1bhmOPPRb169dHlSpVsP/++2POnDnGfR87diyaNm2KvLw8NGzYEBdddJGx/fZAoi1BEARBEARBEARR6WBg2XxZk9AnZqoaikSJMQS8/8iYglAZQ8xYPMnqtEU4EsBF6HPJobW5jkWh2yrsevp1FddftxZBdilfB02EhewCVM0/cCaLbkHFkol9WfOJPfP85b3Urqs0pmkvg9gDT92XKABnJq8t5CXGC/B5yO1k56bsTg6c1aLTFgp3ryIeQXbuBgKwJHLLLlrdeE4uS41WGhqTMW07K3IEhOvHHHJiCwoKUFaWzcZdunQpnn76aTw681EsWLgAAHD8Mcdjza9r8OJLL+KTTz5Bly5d0KdPH6xbtw4ePDz79LMYP248xl4/FvM/mI/iBsWYPHmycdzrrr0OEydOxOjRo/HV119h6sNTUb9+fQDAOx+8AwB44dUXsOLnFXjuueeUfVwz6hq8OOtF3D/tfnz66ado3bo1+vXrh3Xr1oXajbtuHG6acBMWLlyIRCKBYcOG6ddMs0nXX3c9brn1Fryz4B3EE/Ggj5NPPhmXXnop9tprL/zyyy/45ZdfcPLJJ4Mxhv79+2P16tV49dVXI+smrvdTTz2FZ599Fp9//jlOPPFE/Pbbb5g7d27Q5o8//sDs2bMxePBgAMBff/2Fo446CnPmzMFnn32Gfv36YcCAAfjxxx+Vc3/mmWdwxx134L777sN//vMfzJo1C3vvvbd2DbaXSh2PkEwmMXbsWMyYMQOrV69GgwYNMHToUFx77bWIxUhvJgiCIAiCIAiC+Dvjks8quwi1x7DF4+2O7kxb7IFR9HTMhBWFPtN1ikKY7GRUzh96Z7IoCOqEFVF0th3pFtdM21emrZjlGmkjXKMHD0kk1X0JBcZ06x+6Dwx7ydfMGI8grrcHbTxCaIsNbuiQMK2IUZB/1hUFk4XWmBcVpwN3bMwcoxDab2TdsWI7H35IDOdjykKx6JLnffl++rOhe91BCHWloo/Zh+bmoO0u/Hghnp75NA499NDgta1bt+LhRx5GUc0ixL045s+bj8VfL8bSn5aiRlEN5MRzcNttt2HWrFl4/rnncdbws/Cvu/+FM4eeiaHDhiLpJ3H9Ddfj7bfeRmlpqfIaN27ciHv/dS/uvuduDBkyBCk/hQbNGuCggw4CANStWxcAUKtOLRTXL0ZeIi/Sx6ZNm/DAfQ/g3gfvxeH9DkdhTiEeeOABvPnmm3ho2kO44OILgrbXjbsOBx1yEIpyijBq1Cj0798fpaWliOW4a3Rjxo3BwYccjJSfwj8v+ydOHHgiSktLUVBQgCpVqiCRSIRiFd5++2189dVXWLNmDfLy0vPn6/bMM8/g3HPPDdb70UcfDa4ZAI444gg8/vjj6NOnDwDg6aefRq1atYKfO3XqhE6dOgXtx48fj+effx4vvvgiLrzwwsjcf/zxRxQXF+Owww5DTk4OmjZtiq5duzpfe3mp1MrnhAkTMGXKFPzrX//Ct99+i4kTJ+LWW2/FPffcs6unRhAEQRAEQRAEQexAGBOcnppYA1noczpS7+jO9OBFjtWK7cQ5KOcftvRp+wpN1yAORYRijYtTnL98VD7oS8jtNYmLouisypeN5Kxq+pLbqVyX8tF7XXas6IY2ZXCKYrhpL0XhWXWEP2gnuKa1+b4Iu77VRtLwGKoYBVlY17qAGcLiqCISg8cqRPKPNetv7Eu8b4R2kUgG+Ro1a6tzxZsw5bZWpAgc6Vuxma++8ioa1m6ImlVqovfBvdH9oO64/c7bg/ebNWuGOnWzRbg++/Qz/PXXX2jRsAVqVq+JKlWqoEqVKli+fDmWL1sOAFjy3RIccOABoTXu1q2bdl5LvluCLVu2BCKkDvm7Iq7VD8t+QFlZWWicnJwcdO3aFd99+12onw4dOwTr0aBBAwDAmjVryuWa7rB3ug/P81DcoDjoQ8cnn3yCv/76C7Vr1w7WjK/bsmXLgnbNmjULCbYAMHjwYDz77LPYsmULAGDGjBk45ZRTEI+ns443bdqEK664Au3bt0eNGjVQpUoVfPfdd1qn7aBBg7B582a0bNkS55xzDp5//nkkk9GHSxVFpXbaLliwAMceeyz69+8PAGjevDmeeOIJLFq0aBfPjCAIgiAIgiAIgtiRyMWMlMerubAo6Dg6oU8UA01RCxzPU7tjeZuYFwtET63TVhAXjZmwXOjTFPIKHeNnBtHZD89fGS+AsFPYFAkQ9KVxCvPP8oJZvD8VQVQBz2fViIuJjExhKh4W6lMnOvvhdta9NBWCY+GoDt01pli2kJpuXTOdZPv29K5vcf4qF7A4Hp+bKqpAdsfy1yM/C01iXiziSOduX9EtqxLXI05hzYMGnfs83Jnu5WghPsCeT1weRNe3il69euHWu29FYV4hGjRsAMSB3Fi2uFdRUVF6Hpm19X0fxQ2K8fIbLwd5uvz9nCIpV9cxuaEgvyA7BgTBXPF57QMmwbkuC7ue54X6Eot7BVnjfrRIngltHxp830eDBg0wb968yHs1atQI/s7XW2TAgAHwfR+vvPIK9t9/f7z77ru4/fassH755Zdj9uzZuO2229KZwAUFOPHEE7F161blXJo0aYIlS5bgzTffxJw5czBy5EjceuutmD9//g4pfFapRduDDjoIU6ZMwffff4+2bdviiy++wHvvvYc777xT+5ktW7YECjoA/PnnnzthpgRBEARBEARBEERFImfaqoQ+7qANREOTuCgISVrRDamwOzMj7kbyWCWRQ5cvW26hTyPgqVyKtkJeOqetqtK8NhJAyMcNXM2i+JcRTOOIh/rWjRnMUyVgS+K3bs0irl1NdmwKqdBa2PbS1JccFWEUvwShXiU6yxEWKqGYz1V22sr3tizGqnKM5Sxnfg1yX0mWjAjAgJS5LAj+Yjs5xkLO2g36kuamcm/bHiCUC9ePaRzRNgqLCtGqVaugkFnS1zsuPc9D530649fVvyIRT6B169Yh0XZT2SYAQLs92uHjjz7G4DMGB/P48MMPg3Yyrdukhca333obrVu1Do0HADm56bmlUinterRq3Qq5ublY8MECNG3WFABQVlaGRYsW4YKLLtCK1iLi74vtITc3Nz1XgS5dumD16tVIJBJo3rx5uforKCjA8ccfjxkzZmDp0qVo27Yt9t133+D9d999F0OHDsVxxx0HIJ1xu2LFCmufxxxzDI455hhccMEF2GOPPfDVV1+hS5cu5ZqbC5VatL3yyiuxYcMG7LHHHojH40ilUrjxxhtx6qmnaj9z8803Y9y4cTtxlgRBEARBEARBEMSOIDiSrqhQn24QFg15dmakmSSm6dyZoo4lOiZl917oNc8QL8A/pmkTaadzegpj6pyqQNpdGop3UDk4ERXxlKInC4ueYpZvZF6euS/xc7r1VxXf0q2ZLBwrhWJfXbDMuJeavkSh2BS1EFozw70o56UqC4wJ8y6P01YZISLtkccU4q6vFoBlsV0Wvj0vGp3hMz94YBHMX/FwozxirCkWYXuIPIjQtdtO1y4AHNrnUBxw4AEYfNJg3HTzTejQvgNWrVqFV155Bf2O7od999sXF/zjApwz7Bx02bcLunbriudmPofFixejZcuWSmE0Pz8fF192MUaNGoX8/Hwc0O0A/PzLz/j+u+8xdNhQ1KtXDwUFBXjrjbfQpHET1KpaC9WrVw/1UVRUhHPOOwfXXnUtatasibYt2+LWW29FSUkJhp411Pn6bBEcLvvdvHlzLF++HJ9//jkaN26MqlWr4rDDDkO3bt0wcOBATJgwAe3atcOqVavw6quvYuDAgdhvv/2MfQ4ePBgDBgzA4sWLcfrpp4fea926NZ577jkMGDAAnudh9OjRRtfv9OnTkUqlcMABB6CwsBCPPvooCgoK0KxZM+u1bQuVOtN25syZeOyxx/D444/j008/xcMPP4zbbrsNDz/8sPYzV111FTZs2BD8+emnn3bijAmCIAiCIAiCIIiKQM5UVYlWsiBlOlIfyhrVuCVD7kzN0fuIU9UktAoZoDrBQnRe6sQjeUxtXwgXUtOJySERTyEa8nY2cTTi4tT0JUZd6NZfVXxLV1Qu5MjVRDLIAqrTXkIvzoXiNQzH+kPuXo3rW25vizTQ3f8qoVUWUOUidkDGUSwLwILLXDemymkbQ7T4GWMsXNRMN39Lpm1ZqgylqVJsSW2x/nFtp/pTliozziNz8U5E7muE79XnX3oe3Q/qjvPOOQ9t27bFKaecghUrV6BevXoAgBNPOhFXXXsVRl89Gj279cSPP/6IESNGZPuQhFEPHq646gpc8s9LMGbMGOy919446/SzsHbtWgBAIpHAbXfchmkPTkPrZq1x7LHHKud5w0034NjjjsU5w87Bvvvui6VLl2L27NmoUbNGZDyV87+iOOGEE3DEEUegd+/eqFu3Lp544gl4nodXX30VhxxyCIYNG5ZdtxUrUL9+fWufhx56KGrVqoUlS5bgtNNOC713xx13oGbNmujevTsGDBiAfv36GR2zNWrUwAMPPIAePXqgY8eOeOutt/DSSy+hdu3a233tKiq10/byyy/HqFGjcMoppwAA9t57b6xcuRI333wzhgwZovxMXl5eUE2OIAiCIAiCIAiC+N9ELuqkOx4OhIUy3fHqkPikOcbv+9F4AZW4KDtttQjxAjqhL+L+1BXCgiAAK1yLQHjNVGJa0E7MS1UUwgIQEhJ1maTBnCzuXjEqgl+jSsCTnbaqSIZIUS2d01Zy927PXuqKaskie0ho1TiF5bxRlSNXnhcXuuV7SHzIEPQFtdNWFmRloVieg+d56bgOMR5BkY+ruk6VA1h1z+oK5SW8BAoThShJlqSF1eSWUBHBFEsh6ScRj8WzfaVSKPVLkfDSMleSJeH7PuJ+PNR3MpXEFn8LEl4i2Mf8RD6KcoqCz+oeEIlMnz4dW1NbUZoshdQQADB27FiMHTs2IghXrVoVE2+fiLvuugt5ibR25TMfJWUlQZsrRl2BK0ZdgbJUGQoSBciJ52DChAnYnNwMALh/2v3ZDj0gFovhqquvwuhrR6MsVYbNyc1BZAMAnDX8LJx51pnwPA9FOenc1/un3h/at/z8fNx2x2249fZbUZhTGNwvfF6H9DwEJWUlYIwF907nzp2DdeJzE2nevHloHXv16oWNWzaGfl/s3WlvJFPZvczLy8MzzzwT6atq1aq4++67cffdd0feE9dbRTwex6pVq5TvNW/eHG+//XbotQsuuCD0sxiXMHDgQAwcOFDZ146gUou2JSUliMXCZuB4PG60KhMEQRAEQRAEQRD/+8iFx1QRCRGnrU4o0zhhZXyEBSnR7RsaUyxspikeFnEKG4S+QN/SuXYzfQXFwwyic9CXSYBk4cgAXT6unKmqytWVncLavFrPLIIFcwumr4lkkO4LXQ6tLC667qX2/pFd39K8+Bjiay4Ctip7WHWvODltEc3uVRXdUwnFKZZCTD6MLbmTeUyEiwAsC7tyUTN+TTKMMeQmcrF3nb3TwivzsblsMzzPC4qjpVgKBYmCkGhblipDabI0ECvLUmXIS2SLfXG2prZiS3ILcuI5wdoU5BQgN5YbEjpD66X5Mqlc004xCpLTnN9PkfVHtJ0Ol3FFZ3l54h5cMm1duquIiIndjUot2g4YMAA33ngjmjZtir322gufffYZbr/9dgwbNmxXT40gCIIgCIIgCILYgehckZHXWNhR6XTU33CMP3Cq8uxYhXMxZPTUiYZSUTCT0MfFGmMhLC5OM2Eehvl7iB6V5/MKxSNo+mJ+VDB3Eb9VkQbya6p9Uh29D1y0Xrid9EGto7W8e6m7LxjCx/1V8wLCBbhiiOmL4nnS2vrhMWXBOZibYq2VxcOYHxTnkx3MvF2keJhiTPlBiRhzIbaRheIkktGoBYXTNlKUTfhrTjwHOcgJ9W0SbblLlguvcS+O/ER+RLSNIQYPXli0TRSE5qtCKThuowbpJIKW97OG3wtAeC91D33U3VZsO6J8VGrR9p577sHo0aMxcuRIrFmzBg0bNsR5552HMWPG7OqpEQRBEARBEARBEDsSBqugFhH6PPWRa9EpGXpN1U7SRFTH+EWnZCDgyX1JuZ5BXwahT9uXMC9jITKEBWylaOgqYMuis0Z04+/zuanGFF27gFqcloW/mBdDiqXgI1tojs9fjhdQkUKq3HupFaZF17QuKkJyHcNDRIyV+0o3iwqoKlezytEti+/cQavbJ7Gdi1As32eq75FYWC4QihVRCyrHr/iZ0BjizwYX6jZj0Uz5HJyKn8n3mDx/jZi5LSJnRQijqgdHHP47Q/V7KtSH5nuieiBFbB+VWrStWrUq7rzzTtx55527eioEQRAEQRAEQRDETkSXiykiF5KKIaYtLqV0SkqEMm01Fe8Bx6P+yB6D14lpyuP/Gget+LJODJEzVZXH7GVBUBPJIK6Fblxl/yqhUn5N4Y5VOW3l6w6uUY5LUOXjig5s170sT6atRpwLOZ01hdQi0QF+9L6wCagAkPKj3xFZHFVFGsQQizzckPvKDBq6Tjn+AVCL63LUgijsivgsLMiXx4HqKo7aPrc96ARQ1f4pPuw2huCOtc1F/LdLW9scVd8t16iI8piJyaWrpwIfVRAEQRAEQRAEQRBExaDKl42Ic5riXipxKOSOhUaoFMbUVbxnLJxJqnOqgmUFQZPQFxLCdH0B1nkFc1NEMkQ7C//dthY6d68stOrcvXLBLJWgI4vtuuJzKT8VyXFVieH8vaCNy146OJ2D/mUdWuGg1Yn54usePCSZwmkrfVaVQ5pCeC1UMRCRBxaZvuToDLmv0HVlUDmMg9eldhFhWnJXi/em+Fkn0XE7HZy2hyS6MV3G3ZbYAZWzGoAy+9ahY+X3S5dL7YLszHfF6SHV39iNWxHXRqItQRAEQRAEQRAEUelQFaDWHp3mBbN02auCgJpuqHCNSoKCMRJA1DCYWpxTFl2yCH2qz8ntTK7RSCSAQoR0jXdQOVpV85KFVuW++ZLzUOW0Vay/alwxtiHTUC0mi/Ny3UuN6KzKOlYK8LAXxYs4yBVirMphLIuGXChVFq9SOG1FuCPX1pfneREBWBZ2VeuhzOSV9pyBgcXS11S6uTRyDTqcIguEMYx9ae6xbe3fdW46QT+SKaxQSK3uWOjjD+R2ytdFMX07BG2XOZRnL/8XKSkpAQDk5KgL3LlQqeMRCIIgCIIgCIIgiN2TiDgHvbjIsWWvBu0UQqvseg0EYE0kQPCzSyEyjdDKX+Oisy4TNuKWVLhjAwEVYdFQ6UK1ZL3yMbMGYEO8gPCaVvSU9lIlVEaEVs2ayYKgSqSPOIAd91LndJZd33weob4UorNKtPX9cF8xxCJCtzJT1As7ywMhX2inckSrjurLTttA2FXcFypxV+5LHlMZtSD9yIuaJfOTWLt2LQAgJz8HSS8JLyfbOOknsTW5FTEvli105icRS8WQiGUlra2prdia2go/5gdtUAawRHi+pclSpPwUUrFUcH2xRCxU1MxnPraUbQmKn/HrjqfiobXcktwS9CWuTywVC77TW5NbsdXPzitYH89DPBUPXaPYhrdLxVJAIjonEX6tfsIPXZ+IeK18XuKaphsBSZZdW92YST8JL+UhJ5YT9L2lbAsABOsozl28Hpe9/F+HMYaSkhKsWbMGNWrUQDwet39Iw99nVQiCIAiCIAiCIIi/DbI7U+VO42KTTZyTj+erhFaVCAZoIhgsblYgLM7xsV1yXF2KVymdqlBEAsiiJDRCpUp0lvN9NdmxYNm8VJ1QKbtLtX1JrldtOxEPWldqRGh12UvNWgTNM33aRGcussrzUDmF5XxZBhaJ85DvMx9pQV4u5KW7x0KXrHDQ+vCR8CSJSBKx5T1KN4nukypqQb43ArG9CsBKGH5d82ta8PRiyI3nhsbcmsoIfZnBUyyFnHhO6NqTfhJJPxm8lmIpJGKJiBhYlipDiqUQ9+KB2Jwbzw2JkvKYfK1yY7mh69qa2houwIb09yE3nhsSJcV5Be0A5MXzgrmWpcoie5liqWA9GGPYmtoKz/MigniKpRCPxZETy4nMKVhv4Vr53MU1Ffvi66EbM8VSyInlBAItYwxb/a0AELi1+RrnxLMuU9e9/LtQo0YNFBcXb1cfJNoSBEEQBEEQBEEQlQ5Z3GKeOlMVnpT3qnK0OhzPl0Ww4Ni3LV4AGqEPdqFPFuJ0TlWXY9iBgB2ThFbVEWc5EkDlCIVCdIZCEBQcwLpIAJXYpxKTxXXVuqbhR4pcyQW/ZDHcdS+ZF42r4GM6ieHimLwoniTKy05h3s5nfiAcchdqCNkFzKKFvLjQJrtj5fuV7zkfUydyy2vLEF0f2d2ri1qQhV3+GS/uIVEjAVaN4c/NfyI/no8WtVsE7TZs2YBvf/sWtQprBeu4fvN67FFrD9TMrxm0+2njT1j11yrUyq8FAPiz9E/UL6yP5tWbh+bx/brvsalsE6rnVofPfGzcshF71d4LRblFQZvNZZux+PfFKMwpRE4sB8lUEltTW9GyTsuQoPzt79+iJFmCarnVAACpVApbUlvQonYL5CXSguyKDSvwR8kfoblyV2qbOm0Qj8WxbvM6fL/ue9QurB2a6+atm1GQKECL2i1QmizF4t8XIz+Rj9xYbqjdxi0bUTe/LlrUaIFvf/8Wm5ObUTW3aqiNBw9/lPyBPevsiZgXw7e/f4sa+TUiou36kvXYo3Z6beV1CNpsXo+2NduidkF6vmWpMnz929dIxBLIT+QDADZt3YRqOdXQolZ2Lzdu3Yhvf/8W1fOqB/fahs0b0K5Wu9D6/B3IycnZLocth0RbgiAIgiAIgiAIotIhuzNVLkhTQa5QX4jmiEbEOUX/ynxZSahUOT2BjLgoOFCVomFGKLbloAbjiHPViIZcLDNGRYjTV4zJhb9IvIMiBzjUV8Z1GVkLFhZade5eWQBWOm19yZHrRYVuW4xG0E7eS107SfTX5g5HB4hEHbi0U4mjsoDK7x3RIaoV1+VCZOkNCMbkMRqygCdnFCtjGzKv8zHlhwdiX+Ke8zUM7rGYByQAFmPIz88P2pWgBH6Oj3huVgBjWxniufFQO2+zBz/hI5abWY8U4OV6oTZAJi6BAbHcGJjP4Kd85OTnID832y4VT6X7yokhFo/BS6YLqeXl5wXuWADpGAcPwZheyoNf5iM3PzcQL70SDyyHZeeFtNCd8lPIzc9NO1ZTcaQSqVCb9GQBP+YjPz8fqbIUUvFUMKcQKcDPSbdjCRaaU6jZ1hQSuQnEvTiSsSRiObGoG73MRyI3kR5TWgfd+nupzNonYkH8Avx0XIO4/lu8LelryM3GR/hbs+MRUagQGUEQBEEQBEEQBFHpiAhEqmPw0guqTE8gKrrp8mVFFyQXtlwKislOTwCQDbMmcdlW8EsWWpU5uoq5qvoTRWLd/OWohWAOct9RZTFy1F98L/tXD0mWDL0tFz7TCZARMR/RonVchBRFc5e9dMnkDYpXKe4fWYBX3WcppEICdnDPynm1cgyBFy0exl+X28iRBrLTVh5TnrvYLonsPiVZMpKnKma+8r50edSy01YeUyfAR74TiuzhFFKRuanSMEK5w7qsY0WBPSB6/8jZvdzBHFl/hdItr5lKDPc8Dylks3d5fISyXSajOMWi0RTCBIP9YV50v4Prz9yzfEy5nfwQQPU7S9W3nLvN+9I+qCJItCUIgiAIgiAIgiAqHymWsh+DV+WW6o76S+KKTaj0PE9b8EtEVzxMdPdqYxv4mIJQrItHkOMdtMfzvXAkgLJgliWSQSVmKseUxEVdDm1E3PK8SHZvZF4KAVJ8TxxTJRTLfbnspews5USKypmEPluMgh8V6GRRXiVMR6IKJDe0bkxlDq20tqq+gs8L6yEWL9ONGfQli3ZetJCaLOCpRE+VmKyM/YhEHavvi0ghO5XjW3pNNa/0kOE1E+MtOKqibEFchyCORjKMEY7YkB9EyP3xuIuIq10i5aci95t08eGoC8WYHos+QFC5vuX7RT5ZwPtSRrgQALYhHmHFihV49913sWLFCpSUlKBu3brYZ5990K1bN7IzEwRBEARBEARBEBWH7Ri8JITqXJCyCKYS51TORaU4p3DFqYqHReIFdEIfwkKfSjRUunR1ArDFtRsRpDwvInjJ+b7cNapa/1Bf3B0rFUJSiuaK4luquboIwBFXM8LrCrjtpdZp60eFT5vYp8sUlu9FldNWLGIn9hcq5KW4RtWYqjWUHyLwfiMuWsTCkQxC7q48ZigeAeH7MDMR+/wV94/K2a5y2iaRjArAmu9SyDWtcGCr4iLk3zXBd9ciKKucr8H3SRRH5QxjZGMUuGCsE9ZjCLeLxTSiLRNOFKisvcI18H+r2qqc/vIaah8GyWMprpvI4izaPv7447j77rvx8ccfo169emjUqBEKCgqwbt06LFu2DPn5+Rg8eDCuvPJKNGvWbEfOmSAIgiAIgiAIgvibo3KoRZxbinxWuRBT0M4izqmKRgEOArDB0eci9IXmKrkRxc/JzlFVUS3+Hp+X0t2rcMdG5s+iRa5UY0YcuRkHrU3c0jltQwW6vFggMoWuE77Vaas91u0i5qtEW0G40jmYIxmtuvVX3GP8+oN5wc1pq7pGcS5BvwqnrXgNKmGXtxO/cyaxz+bOhBRvotoj/jBFXDOVoKlyZ8oRKLq9TCEVrIfOgQ2E70d+LZF4B8kprLo3VMKu7NxVCtP8GrhgC/U9zdslWRIplhZuE55a6tMVHZQaZQV43ZiS0K0S/cUcbG0hRkRdu0QYJ9G2S5cuiMViGDp0KJ566ik0bdo09P6WLVuwYMECPPnkk9hvv/0wefJkDBo0aIdMmCAIgiAIgiAIgvj7ozp6rBJhZAFSJZTJR4ZN4lyoa5U4KrkgVW14X87xCFwQREwtYLDwdarEzIhrV1fIS3GkXnYkBi5I2XWsmb/YVwoppdMzMqbKtSsto+zo00UCRCIgpHXN/GDdy/SLiKBy7araKIVi1fpL4jQ/1h58xlcIfZI7PDhqrhKAZaenQgAW743gWL3KRSv25WvyUhVxD6p5hURnjdNWFpB190UkX1bxMEUXDWJ1YCse4Mj3f/DASH4YJMUo6OIRgj74uiiXNetwNwmb3DGc8lPaewIIu2xVAioQFlFV93lmQKWAHZm7tI66fFzdXAhH0faGG25A//79te/n5eWhV69e6NWrF8aPH4/ly5dX2AQJgiAIgiAIgiCI3Qvt0WOFOzPi9NzGSAMuHIjClVIA1hRZUhEqSuVQ8EijtUTFJ43QIbbTFfKSnaoqd69O9JSJuHa5gM2kNgpxS84UVh4R1zj6Iu5YhTinylW17aWLa1qbVSsL8AZHrlKoFOYmH/UP5ubgtOViWTCeKmoBktNW4zLnx+6DebHovHh/IaetYl5cnBbnL+9R4CyVhHp5SKXTVv59oXDQKu9rqNuJ1yCvV9AGLBQDooqnSEERj4BopIQuq5aPq1oHsV2SJZFkSShdztmB0+N60VMAwXV52Xtb9XuTz1/+XsrXwN304uuq36fyd5wI4yTamgRbmTp16qBOnTrbPCGCIAiCIAiCIAhi90bnzowcg1cclVcJar5vF+eURa+Y3SlpLHgEyWmrEfrEvlwKqanmJQtS4vHkUF+Sq1bl7lXl+6qOMTOwUAEl0Rkozl0Wt+AphGiFozIitGfaeDGzozJoG37BaS+VfTEWOLV1WbWqBw26vVQJ3SHnosZpK4uZ8nckcxGhcZNIanNoeX/aY/BeWnTk7ykLjCErwAZ9KQRgWXTmTlVdpm3Qt3SPAZkcVGl7XRzkwZgxcQMilxMRK5VuekWECG8rC9hyYTC5EJzKDR1cg1BgTAcXxJN+0hhh4cFDGStDjp+jjGPgbZIsGcxf2caLFhlTuaZl172qAJrnedq5EOXItD3ooINw6KGHolevXujevTsVHSMIgiAIgiAIgiB2CEp3piYHVc56hUsOqqovqN2Gkb5UEQoOhchU85LHNImGNteoynmp6iuSl6pyJKoKECniBRiYspBQqJ3iupUxEHIEBKJCMReEE6KUoZg/F4dksdK2lyoxGYgKraox5f6CI+GSaCWLeCpHtMrR6kHKl83MyVYIS3Vvyi5gnVM8hljIhSvHjAiTC+WzKh3AqkJqniLrWHJj6op0yU5tZTyCpnBhaP2ZQ1YzosI6F6cj95iw/qrfY6r+lDEdyIrFgftYJ7RmPpvyU2rxPQMvbBaLxbROWzGGQyWy8vmLD6q0GdKK0wWqBzHktNWj8UxHadeuHR5//HEcdthhqFmzJnr16oXrr78e7777LsrKynbkHAmCIAiCIAiCIIjdiEDMlB2tklCjzBZ1zUFVFSJzySSFHxHdlPm4DkKfKh5BWTxJum6dgBpu4indpfARWVfZkajqT+cuDYlbCtGNizKRSAZLJqnqOlUiUAwxdSavTUxGdC9VYrJSwFY5nTNrIWcKR4qCSciuSz5/legpCmVKQUwxTgqpqNMTUiEsyVkqjskdpfw4v1JcFOIKAmFadvcieqQ+4rpUiKMqp62cb61yOSud5oqj+Kp83Mh3yYs6yLWRKMiKwH7mH6XT2cv2l0RSKYaL4rot0zbFUoHTVhePwAXZlJ/Stol5seD3jU4Alh9UifEh8txtTtuYF1OeVCDSOIu2U6dOxdKlS/Hjjz/ivvvuQ6tWrfDwww+jZ8+eqFGjBvr27Yubb755R86VIAiCIAiCIAiC2A1QZWyqiucoxSZJmJFjA/jfdUfqbWP6vnS8XdHXthY8UolWACLCj2pM7sbj7VRCE+/LqSgYpOxPXaYtxCYKR6Iih5Y7OCPzsrlGuVNSFJwUQrcsJgdzs+yl8hpVa4GosCc/aFA5bbmIaIv08P2oozVwvbrkjYrxFPI1QhGPoDlSz/sK3J6qOAaE3ZI6wVz10EXpQJWEPjConba+VNRMEaeiu39C95nKAa9xCstiskpcFx9I6B4E8XGDQmS+vhBZIJhroimAjEDKWDrTViGYB+0QQzLzj1bYFfbJh5vT1uQCtj2MANRucCKNs2jLady4Mc4880xMnToVy5Ytw8qVK3HJJZfg448/xrXXXrsj5kgQBEEQBEEQBEHsRijFLUXBKZXYJIsCQY6lxempaudSpV7poC2H0CeOqRL60pML/+h5andvRERVZHFGjpErHInK484ql6IkHKpEt2AtpPWX3XVyRiZvJztto03UTlUZl73UCfCR7hxzUFXzijwY0BSvUo0h7oHP/IgDNRhDdNqyqNOWI4uLMjwrlf9jOnofuEt1R+q9qNNW9d2V56JyYMtOW+V3HIg4olUPEHR7aXOa6zKFQ/EIiv2Wr5XPS7VH/Hr42uvg37uUn1Le+2J/yVQSKV+docvbiPEIqv7ENoBa5FYV4lPdZx48JJHUznl3xznTVmTZsmWYN29e8Gf9+vXo1q0bevbsWdHzIwiCIAiCIAiCIHYzlOKWQlBL+dHK7LJQE7gzpWPwTvmsChceYI8XKE/BI3FMVcV1QFGIDNGCYlp3rzR/+Siz6EgURWOluCj1pRSA5ePtmngK/jq/TuUxchYeU3YT8774nIO/q8RF171UCNMM4UJqOqez3L1S+LPkveqKV/Fj6z7Sxa9Ue8v7koVKrdAqiIsaDS+YN+9LLryVHhT2I/XSmukEPAARQdbmzuSOcjHrOBDNbQ8QDFEX8jXaBPjQ56Eu6CciOm11bbhQH3EpC4iFyEz76HlpF63ne3rXLmJBITLG9G5ucT4qkRtQPOTRxIMonj8RGZxF24ceeghz587FvHnzsGHDBvTo0QM9e/bEBRdcgP322w+JxDbpvwRBEARBEARBEAQRQukq86JV7lVippxpy0WPkNjkeKQeHiKvOeXjKoRi14JHvMBRaEzHSIPI8WOF4KJ09bGwQKMSF3XxDrLoyeciz01EFFeDMRVHpCN7CX0hOFEAVopIir2MOA4194XSuWtwPgKZImiSO1kn9IXcmfye0Owdn4r2qLnkbjRGH1hcu4HTNiOcyoK53I73pXVnSmuhE69lp61q7iGntkJ4VcVOaB8gWO5X1etaAV74birzkMXPZ/rTZQVnOgRjzCjsBoXIDMIukBX+YyymdV+L+6TMmUb0AY7q/gkEeDmqRhGJQZm2epyV1uHDh6Np06a45pprMGzYMOTk5OzIeREEQRAEQRAEQRC7KSpHpVzJnreLiCZMOoadEUFVjkpRdNEJNbIQEhF9hHiBYAyFUOxS8Ch4TRaK5RxdqRBQ8DlZEGHq7F7VMX7u4LTOTUBemyBHVy44BYTdsZnj1XIkQEQgkkRP1fF2LvrIIpJqOyOF7BB1c8tiXGRvoRZyVQKeqiieCrl4lezsBYTj5oIgqIs04CIYY/riYaKg6TNfm0PLhWaTa1R0XqruX7mNbv7KfF9dIS8/3CbiwFbETgTzE39WfS9VDlM5agGKqAWE19XkxuXjcFFcJ6IC6f0x5dDyvpIsqVx7Di+0l/QNmbZedp90QrEHLxJnIt8/qvVPsmRkLeSoBSKMc6btv//9bxx44IEYO3Ys6tWrhwEDBmDSpElYtGiR9QkTQRAEQRAEQRAEQZQH5f/P1GRUhpp4UTENiAp96e7Cgpoql1GuGC+LYNtT8CjTQfavGqHJxd2rFCq96DrK7tLAaSg0433JBb/kvlSCmjJewMUF6auLOonzUgmogNolva17KV+DUnhzzEFV3a8qt2pIOGTq+zoQkQVxV3cUngtqwXpZ3JJaodXLxliYXKOi8KYVpiUXrWr+qvtfJeh58IIj/LoxTfnKIXGXqZ22urxgcUzlQyMpq1abA5xxdWvzeKVxfV+99uL1iv9WtvG89P1lcPaKD7RSSCnFXTlTW+e0jay/4juuLFBHBDiLtiNGjMCTTz6JX375Be+//z6OOuoofPzxxzj66KNRs2ZN9O/fH7fddtuOnCtBEARBEARBEASxG6ASymSnIZARDXVH/eW+pKJmsjimFN0QFo20IphiTEASilVuRxYWh1RCB+/PJtqqxlAKwHLUgib7M+KcgyLv1Y+20a6/7E6Wi29pjmKrsjNt94XOhWrbS35fWN2xiuP4yngNKJyduv4QdtpG3KWZ9RdjCFTjyVEFOkGQi47BnDTZpdxly+8RXdQCLyalEwRlQVw3f3iSu1rxHVcVG1StWXBtYjt537zovqnuf7kv3i7SBizkOtY6bVl2PqqYF7mdMfdWmIvywVAGLpAyX+/+5d9hn/laoVgu/qf8faEqKqdw06viKYgszqKtSPv27TFixAjMnDkTn376KS688EK89957uPLKKyt6fgRBEARBEARBEMRuhi4vMuII9dVuPZWApxI9I+Kc7NZEOG9R5+iMOMo0bsOIU1VzLF3VLnSdnroQWUT7kLVlHi9hyaHVXWfE3asQgHUCasSdLK8Z3JzOOueiLBSrxCHrXqruC5XorxCaVMKTXBRMK+IJLmZjhIK4ZgxaoTUQY2EQBL3snFXH1vn8xb50ay86bVXCKG8TEup185ecryk/pc5B9aOxJSKBmO+QKayMR1DcGyEHsMqNjrBornPjZjoM1sNULI6L2EmWNEYoBJek13VDDzh08Qj8O+dDLxSrBHil01YSbZVOW08T4UIAKEemLefXX3/FvHnzgj/ff/89cnNzccABB6B37947Yo4EQRAEQRAEQRDEboQqL1LlCFW5MyNCgUaokYUZnegm6pSqvFGVAKl0/iniEWQRWiUA87mGipqpXK8qd6DKTaw5Lh8pGKRAFopVTryIUMmi6x8UkhPGjBQFU4ypFOAVa6YSw132ko8Rui8UY6oKpKUHCf+oEvOVxasEoTLIOFXluAr3trZIFIR8WZNoKNwbvq9uE/NiIUeual5AOis1cEQbxGR+//H5q5D3TZX3Gnkwg6izHVC7oVUPEFRF/RQXEP0uaXKA+bXphF0g/VqSJZU5zTLGfZTamfoRhWxTUTMuvBvFfDGTmkX3TfUAR/UAQfW7jMjiLNpecMEFmDt3LpYsWYJEIoH9998fJ554Inr37o3u3bsjPz9/R86TIAiCIAiCIAiC2E1QiRSqvFFAc/TbIpTp3LHKvEW5Sr1C0FE581THsLXCsDCe6mi/nP+pdHoaXMCRNkJfXMiRhValu1Gq8q46Cq/M91UJZZIjUyfohNyxXIC3rJkshivnr9hL5X2hEv1Z9D5UHvdniFwjn4s0ubCjVfGQIYgqgOBu1LlexQgC1XiZMQMBVZNdKvbHx9f1xcfSisnSQxedACyLeAzqe1G8r1TZsaaokci8VK51jUs91Eal7Yr5vhphl5PyU8Zoh6AdS6XX1STaMv09Ecwts7ZxL67fb+H3rPYeUwjw2txkMR5B8WCGC/Da7N/dHGfR9tNPP8XAgQPRu3dv9OjRA4WFhTtyXgRBEARBEARBEMRujIugpnRneogKeJCbRAVgndAn56DKQoYyU1Uj9ClzUKXxlO5MRN2lKqetjM517FI8TEZ2qvLPqdY/0pfS6Jm9TlVsg2rMSEwENOKcwu3pspfaqAg+F3FeEVOzWtCU+1IJYaIgqHXjlsNpGxGANcJb4Ag1FLkKjvAbhDXRaet6pF43f/me9X2F0xZe6Huh+r6pMlVVzleV01bp/FT8XtGuK8JOWxU8RiG4DzXr73keyvwypeNYNT+T+MvXRJUtK86LzynFUsoxVQK8LrIk5IBXPOSR95II4yzaLliwYEfOgyAIgiAIgiAIgiAAZF1scg5tRIRxOG6rcrJpHZWKI75ylXQ+hnLOwt+1mbzyz1I8QqQv7ihUrIU8vpPrWBa3dEKlSsBWHSNXiaPS+st98b0IFTJSHO1WOW0j81IVPFIcvXc5Uq+KbZBdyMFr8l4q3JniNcrjhieXbadz48r3vy7SwPOyea+qaAexHRexddEUoXF16iMEEY8xJKE/Ui+um8kpHHowA0WmreeB+Sxw6xqzjqXvuKovVVazrhBc0JeL01ZzT/D+UiyldAmLxLwYyvyy9O/DmDn6wJZ7y4XiGGJGpy132ZqctoBdgJeFbq04zcxrtTvjLNr6vo/Fixdj7733BgBMmTIFW7duDd6Px+MYMWIEYrFtqm1GEARBEARBEARBEACycQCm484md6by6LrYRiUAK8RF5VFtRF2XKgE4ggelO1MWirXHsMXL9BCJKlAJmip3oDIqgkULNkWn70XEUJVDUxXvYHPh8bWXRamIO9Yg7MiOPpvoFgjwqnvMkomszRTWOKdDY7JoxqlLDq3neekYgozAqHNLiiKkzlkNZEQ+JNN9GY7ehxzJBgGSX5+q2FSwFqLorBHw5DYmMT/iThbaKQuRKe5NAGA+M/4MRH+v6IoIevCQZOl1tQrdvh88VDKJ5kk/CR8+EgYJL+bFrBEKwR54hvG4G5el0t9JXdaxsLa6BwgAove/1IzP24ePOOIgwjiLtk8++STuu+8+zJ8/HwBw+eWXo0aNGkgk0l389ttvyM/Px/Dhw3fMTAmCIAiCIAiCIIjdAp3wJ76uc2cCYaelyrUbfF4UVVQChYeI6COLTbpMVZUQI4uesltP6XpVCG/imIE7VyEAq0RnZ6etvKxeVPxWHXdW9qVw2oqiT3CNDgWn5DVViXNyRqlq/qq9BBRREZpM5MgRekUkA/MUYrjKkCi5M3UinpPTFlnXq5wbLCNmr5oKU9mO8Me8WDqfFX46H1d1pF566GKMd0B2LfhrkTmJa5EpKqeKnQj9rHkYITttdYJ46PcKGDxmdgob4xEQC8RdW2Ewnn1rjD7IPOAwiraeF9yTJqctQ1q0ZYgW6uOEigjq4hZY9DunujdM67S742yLfeihh3D++eeHXps/fz6WL1+O5cuX49Zbb8Vjjz1W4RMkCIIgCIIgCIIgdi9MDrWIO9OLinhyxXJZ0JGP5/N+5XFjiDkJP6qj8qrCSLYq9eIxc3le2xRpIIueCgFPN6YsSMUQcxI95eJDOkdiaC4sKpby63RxSsqfVYlIykgJef0166oSUW1ubgDwWLQQnHIP4AWuV5UbWmjoJHry+ZiKXEVyaPUW5mxfJgE4833UZtpKjm5tDioUD2ZUOajC91XbRn6YongYoXRNK/qTf68AUN7XMcTc4hE8L7SuOhE1huy4NkE2xfQF5YJ2mQmZRGKf+YFQrHPaAtl73hTVIaKKulBlghNZnEXbb7/9Fu3bt9e+37NnT3zxxRcVMimCIAiCIAiCIAhi90UldujcmUpHoi8JNZoxREFNKYIJx9GBrEghu175fOS+I4WR5GPYiuuMiMIseg28EFYkY1OB6DrViXgR0Vnh2tXNM7JmLOoUlgXgyFF5qOclO1p1TsnIGvlRwUiXTxzJOla4puWHA8riVYp8ZeaFhXTdwwix4JrRdcm8kKBpEkdDDzc0YwaZpDDEI3iCY1ejGYrCm86RGzlSb3DuBtdocB2L1yYKveL1yQ9ilH2pHOSaNRO/v1p3qBBdonPjpptlM21tYixgyI3NwEV4Y7EyqNcq1E8mI5g7bW0F6kxzU0Vd6OZnmtPujHM8wm+//YYqVaoEP//www+oXbt28HNOTg42bdpUsbMjCIIgCIIgCIIgdjt0YkFITMmIlnIOorJ4lYTs1APUbjHxqHkgAmliGyLioovQp8jalQVIXTwCFzpDYypchJHsW0XuqlIAjmi22QJXwdw1kQAREVhR4E101+kEG2XxJ7mN1BdgLlBn3EuVAK9aV4UAr3MbyvPXRRrITlsVouipc8fK8Qhg0QxdsZ1NTOMivFGAFAq46Zy2/H2Otp3gJg5iGRSu15Aj1+BMtjm1ZQc5kMlElqM6FL9XlC7UjDufr62L09aE6IQ3xiN4HpKpJPIT+cb+0rezXvzlfQWirUOmrekBQug7rvh9oRLXiSzOTtv69etjyZIlwc9169YNFR379ttvUVxcXLGzIwiCIAiCIAiCIHY7lJm2krhocmdGnLYqoUAaQyf0AZKjDw5RBQqnqkuVepWAoRN1ZKefLqoAIZ1b7QAWhTJAI44imi+rPHovi84KAVgeJ5iXKlMYfqidrviWOKbK+ee0l0J+qzhH1b2o2svIvBRCn/IhgpRpaywSxd2NGnes6HpVOqaFvvgRePkBg3QR2XxczUaGhGJNzrHq+6sTnXlsQ1AszvIAR7uucjyFav6qKFbfLtTroi4yAwXrYXogAYag4JcOLiqbXK9AJr7B4F4OT88ukAbxCBaXM29rddpqcodVMRZEFmfRtk+fPrjxxhuV7zHGcPPNN6NPnz4VNjGCIAiCIAiCIAhi90QnbIVceAYxJFIwSCHgyY5GldjEYwjEMXVFqVyESpcq9RF3YKYjeUyVuKt0CiMqQKoKNkUiAXSOON6OqQXlSKatRpwTry2Yl9SOuxbFo/66fNyI0K2Yv20v+euqOYb60oi2Tg8QFFsu5svqrlGcs9Udi6zQqtPn+EOEQGjV7FHMSxfM0rpZkf0+8eP+ykJkmSJY/B7SxQKEBGyNmB/JVNVcp3IdVW50P+xG14n+kQKHCnj2bSD+WyIluKNVhxiFYoxRgGOmbeb3qA2+58o8ZC8WFeB18RrS7wvt72IHIXl3xDke4ZprrkGXLl1wwAEH4LLLLkPbtm3heR6+++473HbbbViyZAkeeeSRHTlXgiAIgiAIgiAIYjdAl58pios6QYeLAKrPBG2k3E9ALaByp6RNXIzEAZRD6LNmwrJowS+Vu1dXyEsUmoKCRipXnNCXTrQSM0Z16y8LMPI6B/15XnQvlZuevR8YNK5d6WMq4dBlL7WuP3lanuJ+URiiXaI6gv6QXQutfiU6jxVHzYHw0XWT0ModnC65qik/hVgspp0/38uUn3aNejF9Jq8YCaB12vJMWM33LXhPmI9SAGbSAwTmq/OVFd9fnbgotlGtv+jiT0EvoooFv0wianDfWuIRYl4svU8W0Zb3aYLvpcndGxHglYsRvq+Vv68V8TJEFmfRtlWrVnjzzTcxdOhQnHzyycFCM8awxx574I033kDr1q132EQJgiAIgiAIgiCI3QMGfX6mTUB1KV6VbigJlVAX8bEJxcocWlUkA6JV6rXuXnkO0jWIGaLi/G1FlgLRVRFDIM9fKeIJAqpOeFM5nVWI4q5OYAxyP5HOLtaJ+ZHsW42gZt1LnYNZIfSJ1who4jUUTltdDqoYj6DV1DLrz52ccS+ubCZHQGhh+n0M5ual52YSIEXXqA8fCY3UxJ2vXOwTIzc54prpxHwuoAbXqTlez4uoZS+XKQvscSE89L1SOLVD3zdN8S3xgYZYBFDXLsmS2t91QDb2wEYillC6vbepv8z321YUz0WAT7J0VrPp9wU5bfU4i7YA0LVrV3zzzTf4/PPP8f333wMA2rRpg3322WeHTI4gCIIgCIIgCILY/VCKK3BzZ6qKV+kEyFA7RY5lUPBIEBdVYk7EXaoSnb2oG1YZjwBJQM30G3GEQhKq/KjgqyyypHHkyk7bSBseAyE6QhEdUzzeHlyLSvdhYXFRvsbQvFh27rbsTCBTFExqp9pLWyEs3fxV7kydO1nOAVZqUx6y7lLDA4tAXGfqInzBdQqFsHR98diDJEuqhXzhGvgDAW3WbuY7l2Ipo7sXELJeNS5Occ1MYr4t2gRANNNWEU8hRnAEDnZF9IT8e0VXeE7MRbY5bbmwaxIsgzEskQa58Vw0q9bMKtrWLqht7gjpdUuyJMDUReyA7RDgNXtJmbZqyiXacjp37ozOnTtX8FQIgiAIgiAIgiAIQp/96eLO5M458TOqvuTPGbNXBaFYJ7q5CH1Kp63UUBkv4JC9qpy/nLyQWdfImLLoqcpnlYRi3bFoWag0HZ0Wx1dm8kpCscn5F3LHOmRsRkTvbEdRd6YiLiFypF6z57LrWPUwgguHgXNRq9l6gZuVQX1cPii4ltkDUyEyxtKioangFBfhjcKucMTddKQeLOv21mbaIpsxaxTzWfhe1Dltw8PrhUFx/3wo9kBy5JoKqQXZw4qHB2I7LnTbsmpdowNcohFc2gQPZjT3DsdFgLdFXSgfQBEBToXIbrnlFpSUlDh1+NFHH+GVV17ZrkkRBEEQBEEQBEEQuy/a/wPv4M4UBTDAUNhJijRQCpBe+Bh2ZkDjvPjcVDm6MkqxTI5aUAhNqiPFyuP5CBdZ0lVwl8dR5vuqcoBVc5OOkWsFVOE6dcJPIEBm+lM5gMW58b5MexmasyaTNPqi/KMXOfqu3EvJXW1yvoqCpu7+D+Ii+FFzXSGvjJhmGk8sHgboxbwYMo5cJI2FzwAg6Se1YnJm0ND3SSn0eVnR3yTmM7BQOyUusSXSfa27f1RF8XSiM7/PdBEKQNYRzddMRxBnsBM1zeC6TGNmfucZBXiXqAvF7xUii5No+80336Bp06YYMWIEXnvtNaxduzZ4L5lM4ssvv8TkyZPRvXt3nHLKKahWrdoOmzBBEARBEARBEATx94ZBk10qCAM6QUd8n/elEq54IR2OUhyCvZCRPC/hw5G+lFEFSj0zPKYshKmOFPu+IroB0ePh2kgA+bi5zhEniFb89cj85XkpEB25XKjUiXPBmLrMTia4Ax3dmSZBMyL0yUZbL1pUTiVIReIpmNq9GBQP40XGLE5bU/En0blodO16WdFWV+wr06FTwSzezugKFYQ+3cMD8XticnHy99Pdan5fSPe/zhku3mO6+4fPP3T/aETnUDyFIXaC92Nyv6qiOHYKCkd+CO5KtmTa2qIugOiDHiKLUzzCI488gi+//BL//ve/MXjwYGzYsAHxeBx5eXmBA3efffbBueeeiyFDhiAvL2+HTpogCIIgCIIgCIL4++KSXapzZ4nHudMNNcd8ZXes6eiu4MLTOW2tQp8kAPPrjLgXVVqRxkloFaQy4iIXS3QuTjn703SsW44XUOXo8iPRgKZAmvD54Dp0opsotGoEYFGA565EL7Zteym7cXUOYPn6lQKd9KNJmOLzNrkzxbgI7ZF0wblodO3CTVzkwrPyXhX7y+SgMmZ32hpFZ0HA091jHL5PujWTC5HpXN/i9zX4u9Qs5sWCaIo44tp4hECAZw6xE5kieyZxNFgPk2BewcixJEokAV4X1WGLugD0v8uJcmTaduzYEffddx+mTJmCL7/8EitWrMDmzZtRp04ddO7cGXXq1NmR8yQIgiAIgiAIgiB2E0xHj0V3Jlj0WLeckWg6Ui+SQtRJKDtadSJYxLWryZeV3Zn89XBnUTFZFuhEYSj7MY24KH9eJVSq5i+LsdKYWuHQk+IVFAXSxGsDDA5m2d2rE7ik2AwGRSEpx72UBXhVvIZqXXWivxhPoROwRaetrsBV0F/GGeta8Esn9vExk37S6LT1vPSYHvOM4zGwQFQ3ZdqG4h107kwh2sToFBbEfJdCZCrRXy5QZxIh+e+coC/N3II4D1+fV8vjEcD0349Qu51I8HvW4qwOObpVTRyiLoK+KNNWSbkLkXmeh06dOqFTp047Yj4EQRAEQRAEQRDEbo5OaJXdmSoHrZyRqDtSrypkpM1UFbMzHZ22KqFPPiJsOq4tX7O1EJlOEGHZeWsjARRCpS32QCccysW3dEIlF32C/kx7yffJ1x+vDrrKCINxxCPzAhz2EoisvzFGgR8TV4hcclE23fw5PvORRNLszvT9bDaxQVXj0QfGNkKEgssxftPc4aW/t8Z2HoIoC+NReWQLXOkMmC5iPvPCn9feixBEf12+suQ+TbGU0nnMRdbARWsSuj31AwYlO9lpCwZ9/jIQEeB1TluXqItdFgHxP4BTpi1BEARBEARBEARB7Cy02aXICmq64jyRY/CKolpiO7FPrVBjyccV+9C1CxyZCAufSqeqXIhMIQbKmbZK0dlzjHeA45hSXyqhhY/J0UUtiOKusUAXskJrkqkFTVnAY1Dn4zrtpez6U8RruBavkiMpTE7bYG4GZ7Kr0zaIPjC4dvmYPNJAhyhCGo/xM8+aj8vnr8pplucerJtuSMFdbbrOkAtUsbaR74hGUJaL4mVeVCMK2Ca1lTmI4Zk5mXJvK5ogi9nitBUFeF0bl6gL+XcZkYVEW4IgCIIgCIIgCKJyoYv+lAQ1resSsAplYt5lkIOqEhRk16sqH9dB6AteF9qpIhm4EBL8qCmEJQpNur7kqAitwCK5XlVCqypeAJp4CpccUe4aDa5RIRAFR9dtey4IwLytTtwV/65098pH6hVHv12LV4kCJJ+bbv68P5vTlq+HS/GwJJJasY+PaTrmzwm+HybXbsY1yjyDmOxJ0QeaNlzMNLovhSF0TlsPXihfWVtgT47NUDhC5e9cyje4mJnbmvE9MAq70DvfdxSqQnuRNg4CfAzZaAeXonhEFBJtCYIgCIIgCIIgiEqFTmiVj9uq/n9+IEbxdroj9SzstFUJOjEvFnb06dy9LkKfwvWqEt7kI91aMUMQWrXxDpIjlH8u2pVdaI3EC+hET7kvXcEphPOJdU47sS+dUBYSgLnopsjkddnLiICtO3qvKl6lQhDqTcW3+B5aM239dDEskzgaOFU1LnNxTFuEgpi3a3R7ZoYyOaZFoU8l+PM2fA+1e5QZL8iO1VyD53mhNdAWLJMfkmjmHiqKZ4g+EKMUjGvm6YV8mZ3ptFX+3lC0sQnw8BCI5sZTCuS01UKiLUEQBEEQBEEQBFGp0B6p97KFnVQFooLPywKkxmkbaBJML9aEinTp3LgOhchUrlf+umn+mUaKiYWPHqv6ihTy0onOXlgMdznqrxUqvXDBNVXUQjCm4LRVrWtEaDU4KsWsWp0L1WkvVbETmqJsoSP1GqGYC562+fP3dVmpQOb+R8osZgrzTkHfF4dHGugQneu2TFvoHOZCX1zMNLUTr88kjIr3ta1wIb8Gefnl2BLdXorfJVtcBEO6wJtWzBSv1ZJ7CwB1C+uiel51a18yv23+DUk/We7PxRBL34cGodhFgOdO2+B3i2bP5X0ispS7EBlBEARBEARBEARB7Ei0R+old6bOaRu8D727DpDyFnWuOEHQlSMJVG0AdcamKgcVDEoRSRQwdHmvYv/BUeyY3snG568TiIJ4AUcxWefCE4WamBczZtra9hIQjt0ztZgGSKIzF91Uey65XnXXIL6uK4rH+wCyzlatizPTnS6KQMzb1YncQHZtjVmvyK6tLfogED5tfWX20sWRa4L3ZcowFoVukwMz5Ho1OJhDTm2FIM6/l6G9NLnWhe+wLR7B5rTl/dmE9dx4rvF9mb+2/oVpX0/DB6s+QK38Wrhsv8vQskZL58/zdTNGOzgK8Pw7Z8ytluNliAAn0fb444937vC5557b5skQBEEQBEEQBEEQhFGEER1ZGgFMFGF0WYpi3qXNEcdFGl0hLLENAKVrTJWDqhJ1+PyDfplGFFGIybL4E3GEakSwmBcLOVV1YnJoTIvzkguLJgFeLESmFX4EAUznoI1UqTfMLXBnalyv3DHKUcUCBO5TcS9VxasEYQswHOMXXNi+75tFPJbpxxBDwPsyuXZ5X4A50kC8h2yOUJsDmMc7AGZHLpAR+iwuVJe8VJtrXeu01T3ogW99SOJ52XvbxZ2s62db+GrtV7j3i3uxrnQdAGBd6TqM/WAsRnYeiQMbHujUB/9umubuIsDz+z8Q4HUPSSxRDLszTvEI1atXD/5Uq1YNb731FhYtWhS8/8knn+Ctt95C9erlt2sTBEEQBEEQBEEQhIjJ/ceP1GuP+isiAVTClZh3aRKleJYlAG0+rovQF1ybIHrqxCHR3cmgzoKUj/rrhOdI8TAdfnbuWjEZLOSOVfUnOoqNx8i9sNPZJPwA5mJxEdeuBlEQN2XHhty1imVVFnhT7KUsCIrXo5qXD19dnC4DF1BTLOV0nbrYAE4gVLoIqBbnZcyLWYV8LmaaHpIETltmdtpCynRWfcdjiIUeoKgykUWXM+/LGImRube1oj+QFtb9lL2AGDM8PCgnW1NbMf3r6bjxoxsDwTZ4z9+KOz+9E8/95zknRyvfJ5PgHyr+Z4g94E5z4+8ectpqcXLaPvTQQ8Hfr7zySpx00v+zd97hcVT3Gn5ntmpXvcuSLNty7wXb9Gp6Qg+9J4TeE0JuclNIQkjCTQghIaEHAqH3XgwYA8a4N9my5SKrd2m12j5z/9id0a6snRnZki3b8/L4Qdo9e+bslNXsd77z/c7nn//8JxaLBYBIJMINN9xAenr60IzSxMTExMTExMTExMTE5KAhmdswXoRJJuzGL2PWbBfntDWag6olsOgKfX0LkSVzZ/aT72jUaauVw6m8T719oQjAFiz9vwedTNuE7E+SLyMXEXvFO60sVJkEoczan4whJIr5yZalJ4j5SY5lvOsYkhfFS4iKSFa8Kl5wJnkhtfg+9ZbLS7J+7IEieuqKhoL6gqSIgqhOfOhlnMZHQWi10XPkJhQb1OgrLEfzWpMK3QIJhciSTZLEC4pa4mLC+aoxtnhhXW//6wnrRmjzt3Hvknup6a5RH5uWO40fTPsBr2x+hUU1iwB4cdOL1HpquXbGtZqRC4qL1iomlwxVF63GOZYQoaAh6PctXmjSy4AzbZ944gkWL16sCrYAFouFO+64g8MPP5w//elPgzpAExMTExMTExMTExMTk4MLiSSilDAAp228o7K/JenxAmQSp2TsyYSohWQFs/SEvr7V55O6MwUhQfRNWvCLxCXdyjb6e4+62Z/EFQVLIgArY+m7zV36itumhMYy8jinpFZurCz0inxJxdE4158yzv470z+WkCj09VuULdmS+v4E+HgXp46IF5Ei/UZTKMQ7bfWE1ogc0S9yJaO5PeU9aInvars+mc3J2ijxCFrvEeIyh5PFoMac8lpCd4IbVDkf9WI/kl2XsXbqcv8kEwMKyri09r8iiO+J01aSJR5e9bAq2NpEG5dMuoSTRp2EKIhcP+N6ilOLeX7j88jIfFn3Jd2hbn4y7ydJxy8KIhEpgs1iS7rd+HNb67pUxqgl6CecFyYJGIpHiCccDlNRUbHL4xUVFeqHvImJiYmJiYmJiYmJiYnJ7qC1FDtBnEu2VL6PeKQlXPV19CXrL7590kxbHaEvOuTErNr+2KXifRKhL2Gpf+x99Ccaxm83ab6ssGu8QNK817jxJ42TiPsvmaM1XijWipMQ5N5YhmRCWfw4tFzTRo5lguuY/vNSd3Fzazm141zOWueiKoJpiIEJzl0dnS8khZKOKW6juuKj0agF1QWvtbnYeabntFWuX60MaeU4aU1qxAvOykROv4XIiIv9SBKPIAiCur90l/ILvUKlptBNb5G33eXD7R+ytmUtAFmOLH5/1O85ZfQpap+CIHDm2DO545A7cFgcAKxuXs07W99JPi5BSOowj29jSFhXPgc0cqv7W11gEmXATturrrqKq6++mi1btnDoodEQ4yVLlnDfffdx1VVXDfoATUxMTExMTExMTExMTA4etETDBHEODVdfnLiSTJzrG7VgqEq9hgCpK/QNxJ3ZR7RNGo8QJwgmc+2qy9EhqdEqfnlyMgEYogJqfA6tnvAW//9d+ooTirUMYMp711sqr44/Ju4mdQrrHcs+heCSZSKD/rL6vq5RLRFVFmQ177W/aIr49xmWw/pO25hrV7PIlUYW8i5NjQjAOm3ii1fpuYklWUqeIU2v01b5r1/hUzAYtdAn9iNpjILc+5miJXbH59/qZvxGtMVRLWq7a3m24ln19+tnXk9JWkm/becWzuVHc3/EvUvuRUbm+Y3PMzV3KqMzRu86rtiEil4hMjWfWEs0j+0zPaetGY/QPwMWbe+//34KCwv5y1/+Qn19PQBFRUXcdddd3HnnnYM+QBMTExMTExMTExMTE5ODB63K7PHV57VceIoYpeca3SUSIJkjNF5c1MjRjX8PyaIK4kWkfkUuIVHETOYkTChERnLxLf7xpO45IVEA1ioelrCMvB8xOV50U0TjpMXDYsurI0SSuw2VJelycjE2XrTVzc7UOZZKH33fd3/vUS+eQulLnRTQiiKIO2eTOm2JE+F1Ig2UeIR+M4Dj3pcsaztt1XZJ9r2CSDSSQU/og9j1kaTwHPTu/zDGnLbJrl0RMfF8lWVEsZ+s4z6xH1rnUPx1ooUaj6AnYutFWCQhLIX5x8p/EJJCAJw86mSm503XfM203Gl8p/w7vFX1FhE5wt9W/o3fH/V71YGrEJ9jnHTsgr4An+C6T5KnrbQzRdv+GbAHWxRF7rrrLmpra+no6KCjo4Pa2lruuuuuhJxbExMTExMTExMTExMTE5OBYlRA1XPhQZwAnEToi8+7TCrUxAmVETm5K66viKMXVdD354T3aKDIFX1yOOO3kbDN+CxYKfm+SBCKk1jiEmInNBx2fV22yY6l2o+WXtNHUE7mjpWQEgXSZH3FHcukOah9XNNarkulTbKicnKf/5IKpEJvtq+mu1SWdQuaCQiEpbBupIFyrhlxemqJrMrY9PpSxy9HNJfKq/s2yfmqjl2SEiYI+mnUW2xQ43Ml/iHd5f6yrN0XMVFfjk5q6DptZY0JCw1e3/I6VZ1VABS5i7h40sWGXnfBhAtUd21ddx3PbHim3/FLSFjE5BpfwvWr4aCNdydrCfBmPEL/7H5wBpCenk56evpgjcXExMTExMTExMTExMTkYEdjybaakRhzZ2o5bRVxRWsZvNpcb0l0nCqRVADWEfrixx4dYnKhdRdhV8cRqpXDGe8I1doXA8201RLD+wqVetnDWo7KeAeqlpifcJw09leCIJ3MNW2gEJmhYxknWukW6ZJ7nYta4pamSBm/Xa3jGNcu2TnRd2zJRNb4sRkRiWVkwpKGgzaWHSshaV7jCfm4JJ8YSNj/yXKY5cR4hKSTEbH/9IqMKWPRE7pFxN0qwFXVUcWrm1+N9iGI3Djzxl3cssmwilZunnUzdtEOwMc7PmZZw7LEcQkipWmlpFhTkvYTL8BrOWgB9TglPTcEzEJkSRiwaNvY2Mhll13GiBEjsFqtWCyWhH8mJiYmJiYm+jR1+VlX26lfyMDExMTExOQgQ1PoixPntJy2siCrDjDNgl9x+ayGnLZJHI5GhD5IHEtS4UdIFK01owrixFjlPfU3NqW/ZE7heBFSU5CKE9TjxeC+29tFKNPZpq5rWtbYX3FjUx19ycQyI8eyj+uvv+JafYvdqaJ/Mqet3Cv2aQpXckTTwalGbOgIrfHCuWakgaAfaQDoCrbK2JK5l+PbSLIUFW01tqm6MzVyVeMLt2l9XkDvuZp0YiYuakRLWI+fQIjvP+nY9ITu2PEciNPWH/bz95V/V6/Ds8eezdissYZfDzAidQSXT7lc/f1fa/5Fu789oY1V1E5TVc4xLde3WqxMEXeTICKa8QhJGHCm7ZVXXkl1dTX/+7//S1FR0W5lbwyE2tpafvKTn/Dee+/h8/kYP348jz/+OHPmzBnS7ZqYmJiYmAw2Xf4Q769t4I3VtXxV1Yosww3HlnPXKRP39dBMTExMTEyGDVrOOaA341RDuBLk3orwyZak9xVttdxi8UWudlvoi3P9qdvs533G53Cq2zSQ45pMuNol0qBf/Tdx2XdSR2ucg1NLAFbHHRN2tAp+ycjasRPIieJcP+0U8TFeIO2PXY6lkf2aLJdUSBT4+hOd+i6nV/rv933GYg/0HJyKE1TXaas19jj0Ck5B7/Wk2Sa234wsqdc63tB7nmm1ixcEtWIzZKl3IiIp8ZMRGtmryuPq5IDGRIOR5f4p1hQiTuMOU1mWeWLdE9R56wAYkzGGs8edbfj18Zww8gRWNa1iWeMyPEEPL2x6getmXGf49YYFeGUyCEkzGkTv/DpYGbBou3jxYr744gtmzpw5BMNJpL29nSOOOILjjjuO9957j/z8fKqqqsjMzBzybZuYmJiYmAwW21q8/PH9jXyysYlgOPEG7pFFWzn/kFJG5br30ehMTExMTEyGF6q41Y8QEC/OaQp9goFiQUKvQ1XLnZkgDmk4+vSEvr5RBUn7ixsXkODg7W9cCkmdanHdaxYXknvHnkw4TMhxTSL29d1fSZekx7lBNd2lxBWc0ikQpRtB0Cf7VtfBrDWBEJ8prAipGv2pfWk4bbUc0+prY/0ZET2TTVgoKMXD9JyeRgQ1URCRJAmbaNMdV0TScffGxZvoulm1JhlikykJ51kSN33vpjX2bfw1ojU2ZRs6u81usZNtydZuFMdnOz9jUc0iABwWBzfNuknXEZsMQRD44Ywfsn7henxhH982fMs1067RFN0TXm9AgBcFMbrPkBMys3dph6j5/MHMgI9uaWnpXlvK+Yc//IHS0lKefPJJ9bFRo0btlW2bmJiYmJgMBp09IS56ZAkNXf6Ex9OdVrr8YcKSzP99VMnfLpq1j0ZoYmJiYmIyvNArWqPEHmhVLY9fxpxMqFEcrarYqaEhxbs49YQ+TUdlH0dZf9+t45fUC4KgmeuZ4A5MIhrG53UqY+iLIob3zbXtD1XQ1HA6G1pqHvce9FyLsqQtlKlCcWy/aR1LpXiVVmyDIiDJxParqP8+kwqCcdEOeiKknoMTdIrmxb8HPXcpvftNFLVFWyMrrNVz1cC4lJ+TocSb6MVJKFmpWvsjPiYl6fXbR4DXjFSJicBabmfVET2IC9N3dO3giXVPqL//cPoPGZE6Yo/6TLenMzN/Jl/XfY035GVT+yYm50w29FpFgNd12saiJyQ0Joz6TFSZ9DLgTNsHHniAu+++m+3btw/BcBJ58803OeSQQ/je975Hfn4+s2bN4tFHH9V8TSAQoKurK+GfiYmJiYnJvuLXb61XBdsct50rDx/Fazcczpd3H0+OO1oA4K3Vdayr7dyXwzQxMTExMRlWJBP7REFUBQAt12h80S+9JeKqSJNE31IqqWu1iRf6FJEomYC3S/Gwvo5cds1L1St+puciVDNtNfInFSFKGX+/7su4TNhk8QKKu04R3ZK5PZVjaaSyfIQ40dxAdmlS13Sca1fzWMaJ11qOXAUZjXiNuPFq5XbGRyloRhoI2oK58h6UcWm5aFXRVkcaUsam2Qb9QmTKWLSOd2zg6Lnp+066aMV56Dm6lYmS6Ka1JyxUB7lGXq1a4E2nEJlRekI9/GX5XwhJIQBOLDuRI4qPGJS+5+T3Ro8ub1xu+HWqMI22AK9+FkjJJ9mMTjIcjAxYtL3gggv47LPPKC8vJy0tjezs7IR/g8nWrVt5+OGHGTduHB988AHXXXcdt9xyC08//XTS1/z+978nIyND/VdaWjqoYzIxMTExMTHK++saeHVlLQBpDitv3XwkvzpjCrNGZpHmtHHT8b1FA/74waZ9NUwTExMTE5NhhSLCaIlNqqiZbBVz3BJmLXdmQo6rVtEgCf18XCNCH8bdmQlL7/tBEAQUc5ruvojbpq5TVWNfJMRAJIltiDVMEJ2ToWwzmQCsjE3JJdUTRtX3kGz8sX2meSzjBH9VxO6vLzkuHzcmdPeLnHgMkp3XqotTz0UbEwKNZNrqFXcyIrQCZDmyyE7R1nsEBCKSdiEyoLeQmtY2Be2c5t7OtJ3aar5yfPZtf7EfcYXIlO0n36ROsTt6BWUjBdz0kGWZR9Y8QoO3AYDRGaO5bPJle9yvwsz8meo5OWDRFu0Jl1hDQDtGIf44mSQy4HiEBx54YAiG0T+SJHHIIYdw7733AjBr1izWr1/Pww8/zOWXX97va376059yxx13qL93dXWZwq2JiYmJyV6npTvAz15bq/7+yzOmMCIzJaHNxfNH8vjibdS0+1hU2cxXVS0cXp67t4dqYmJiYmIyrFAFs36Id85JkrZYkOC01clU1cy0VRytMYHIwq6Zj/0Jff21i3eqJhNa4wVUIGk8QrxQrCkaKvGaGmJZglNVy4UaV2BJK7YhQTRP4nRWhKKIHNF0Q8cvSdc6RvGuXS0Xre6xpI+Yn0w4FLQFaYX4pf5a7QVB0M1ABTT3afx70HRMx21TV3QDbJbkObXxfentD8VdrbRP2pcs6J4XSqSHEl2SbDICeidIkhInzutdS+r1qyfIyhg6nnp8uONDltQvAcBldXHb7NuwW+x73nGMVHsqE7ImUNFWQYO3gbruOkOxCwnOcJ1jqZfnLAiCGltiksiARdsrrrhiKMbRL0VFRUyenJinMWnSJF555ZWkr3E4HDgcjqEemomJiYmJSVJkWebnr62j1RsE4MTJBZw7u3iXdg6rhTtOHM8dL64G4A/vb+L1G3IM5YaZmJiYmJgcqOguY44JgppuPaFXpEyGKjooQpPW0l1JOx/XqNAnyHHLsJOIo/FiMkQdrUmLn8W9v6T7Is7pmTTHtY9TVQs17zXJuHo3qyN60ieaQmP/R6RYDISGUKa69HSyRvWOpdJHvDs2aQ5q3LFMuk3ZeME1I6KVuvReJ4ZA1z1OrBCZTg6tUSyChVRHKk6rU7OdmkGrNeEi9IrvyfKElb604kjil93rue6VXa91XsT3p+m0FUTCcniPM21bfC38Z8N/1N+vm3EdBe6CPeu0H2YXzKairQKIum0NibZxn1N6TltZljWjWeInXUwSGXA8Qjw+n29I82OPOOIINm1KXC5aWVlJWVnZoG7HxMTExMRkMHljVR3vr48uYcp227n37GlJb/7OnFnMxMI0AFbv7OD9dQ17bZwmJiYmJibDESPuWHVJtJY7M06sSRZpoEo5WsWHYiKZbgElI0JfX6dtEuIF3aT5pUKcgGrAHauX45qwH7QE1FiOrlauasLyfI2+ZGTVKZk0NiBu/ycTyuKzUrVclUaOZYKbW8MhGC+Iam1TaadXQMyo61XpRy+GIDYwXfQc60YRBIHclFysYnJvoJJ3bMSdqRTG0zzHYsdIS5yOF9aTxiPET5LofBZEpIh2EcQYRo6lHi9sfEHNsT2p7CTmFc3bo/6SMaegN9d2ReMKQ69Rzlfl52Qon4m6EShy8mvoYGbAoq3X6+Wmm24iPz+f1NRUsrKyEv4NJrfffjtLlizh3nvvZcuWLTz33HM88sgj3HjjjYO6HRMTExMTk8GivtPHL95Yp/7+27OmkpeWfAWIRRS465QJ6u9/+nAT4YiZ52RiYmJicvCiVYxJ+XIfkaPCoZagk7DceTe3p2wjXgzsV0wWEgVgvW0pP/e3yfhia8r4k8YjKPmyGq7KXfJ9kwgn8X0lIz5HV0uUineWaonJirCrWaCrjwCfDOV5vexM3WPZV8xPdm70zR3Wy7TVOc9EjLljEdAVY5VzSM9BKwpitBCZRoTCYKO4aI04bTVF7tjrI3JEMzYD0D2WAoL6maJ5/tMb1aF1nSv7dU8czNs6t/FF7RcApNpSOX/C+bvdlx4jUkdQ5C4CYGPbRjxBj+5rBAQ1VkK3cJ6BSTatiY+DmQFfmXfddRcLFy7kH//4Bw6Hg8cee4xf//rXjBgxQrNA2O4wd+5cXnvtNf773/8ydepUfvOb3/DAAw9wySWXDOp2TExMTExMBoNtLV4ufGQJXf4wAGfOHMFp04p0X3fchHzmjopOfG5t9qrFy0xMTExMTA5GFKFPr7BQsjZKu4gc6W1nxFGZTHcQ+hQySiL6xPeV1Dka91K9ZcUJolC/Rlv9qIX4bemJVvHtkgqt9MnRTTb8OEFT02kbWzat5bRV97+OmKbuf0k7NkP3WPaJzUhaiCx+/xsU+rTOWXUoerqVHHU56wmtRqIuVHFxEJy2hlFPC+1M3rAU1p5MMeC0jZ8YUM7tpJMuyvmqFdURNxmkqVPG2u2uGC7LMs9seEb9/Zxx55BqT92tvowyu2B2dNvIrGpapdteEAS1gJsR1zRoT7KBWYisPwZ8Br311lv84x//4LzzzsNqtXLUUUfx85//nHvvvZdnn3120Af4ne98h7Vr1+L3+6moqOCaa64Z9G2YmJiYmJjsKct3tHPOP75kR2sPAMWZKdxzxlRDrxUEgZ+cMlH9/akvt5uZTiYmJiYmBy1abkNFDJFkSXfptCqUCckF4HgHZDJ9S60ET3LRExKF06R/x/u6M5OMPdo0VogsSRZkvICkKc7FnJlGRCsj4qjqSNRYoh8vqCV1Qca10ULd/1qxDXFCq9Z5YeRYJjhtSR51oQhWcS9MMri4TGENF60yNl13pqBxfil9CaJu7iqA0+qkMLXQUKGxQUPQzgCGWAxB7DxL3k3vOWvIaUvy8wdQt6e3XdU1qrFv46+T3WFF0wo2tG4AoMBVwEmjTtrtvowSH5GwvHG5sRfFPs/0XNPKZ6ORrGCTRAYs2ra1tTF69GgA0tPTaWtrA+DII49k0aJFgzs6ExMTExOT/YD31tZz8aNLaO+JZk5NKEjjpesOI8Nl/Ab4kFHZzCjJAGBDfRcrqtuHZKwmJiYmJibDHSOuUb2l0xAtlKWZTxm/JFfWKHIlQISI2k9/24yPNDAiCCrvo7+x9S3Ko9lXnACs5zrWG1fCmHRcxxB1e2qJYHpCq4Kyz7QcqBGi49cS55Sxay5LN3As+44/6b6V4/ZFzNGdtB8dB7MyNonk/fQ2E7SdycQdT72oBSDFmqLbZrDRc5kDug7s+ExkPWesmq+cbGJGEHsd5BouZhGx9xrX2LfKtWkod7gPYSnMsxt6DZEXT7pYMyd4sJiQNQG3zQ3A6ubVhKWw/ouMCPBG8okx4xGSMeAzaMyYMWzfvh2AyZMn8+KLLwJRB25mZuZgjs3ExMTExGTY8/jibdzw3AoC4eiN3hFjc3jp+sMYkTnwG+DLDhul/vz01zsGa4gmJiYmJib7FVpf3JVCRooIqeWoVIS+ZCS4M3UyVZUcXT0nmJ7QZ9SdGT+epOJoTICE3kiJfscfX4gsCfHilpY4KiImiGCaRdmIEys1kCRJ062XsP91lsHrOXeNHMuEfGKd80d1VmuJkHFOWy1xS3Ha6oncigtVz6mq5wbdZyg5qFr7Iiaiah5LxR0uaZ/b8cdQ08Uc25QsabvWJUnSFICV7WiJyVosrF5InbcOiAqp8wqHpvhYXyyihZl5MwHwhX1UtFXovkaQBV0B3lA+cZ9IEpNeBizaXnXVVaxevRqAn/70p2q27e23386Pf/zjQR+giYmJiYnJcOWFb6v5zdsbUO4vzp1dwpNXziPduXtLzL4zvYismDv33bX1NHsCgzVUExMTExOT/QZdB61g4Mu90Ou01RNOZGQiRDQdfcq4kjn/4iMNNN1ice7MZM7LXYonaeXoxhcZ03D+KUKTlnCY4ADWcB3Ht00aFREvTmvtf0FfgI/PMdYUIQVUt3PS2AYjx5JEMT9Zu/hjrRndEL/020Berd65rYxPLxNWLw5gnyEo/9O9MAF0r0stATu+WJZeVrMyARJBu5BdRI5oZxPTe8wH6rTtCfXwUuVL6u+XTr50j4qZDZT4iIQVjSt026sRKDr7Qi+fOL4/k0QG7LG+/fbb1Z+PO+44Nm7cyLJlyygvL2fGjBmDOjgTExMTE5PhyrYWL796c4P6+y0njOP2BeP26MbKabNw/txS/vX5VkIRmRe+ream48cNxnBNTExMTEz2G4wIn4rQpyXoKG2SoUYayJKmu04QBCRJ0nZnkuhU1RQodNyZ/TmAB6t4mJYAbFjckuIKvIlJxC1Z6HXi6ugwigtYy4Wn7n+tJemxbUpS8tgGI8cSEsUjvWOuttdzOusI66IgEpbDxpy2keTCotJm2LoWYy5srXgHEVHXpa26w3X2vSLma+3/hGOpcy6CgcJ4itN5AN8LIlKEpzc8jSfoAeCwEYcxLmvvfg+YkT8Di2AhIkdY3ricyydfruvoBm0BXhAE3aiF+M9ik0R2r5RdHCNHjuScc84xBVsTExMTk4OGUETithdW4QtFZ+QvmlfKHSeOH5SZ8Evnl6F08+w31YQj5s2LiYmJicnBhZ4jMb4YWdI2CLpiJvS6XvXcdaq4q+FwjHddJhV+BIOOVuLE6ySuPmV5vl4BH0EQoq5jDTE5QdzS6gtBN7cXYlmqMXFOU4SUoy5gpe9k25SR1XYagwPQjG1IKDKW5FgqYqIqwicTuoVeMRytTGSMLanXG3v8e5AwKAgOU6etrjgnoOlyjjbpzbTVvI7iIkuS7n9Bv0Cgss2IFNH8vFDaDUS0bfe389slv+WznZ8BYBWtXDTxIkOvHUzcNjcTs6PFkZt6mqjprtFsbyT2A6KTbJKcPCsYDJwTBym7lWa8dOlSPvvsM5qamnb54Pzzn/88KAMzMTExMTEZrvxt4RZW7+wAYFSOi5+fPnnQ+i7NdnH8hHw+2dhEfaefjyuaOGVq4aD1b2JiYmJiMtzRFQHk3grvScW5eBdeEhQXqKbISlx2pka8wC6RBsnE0ThHXzIRL8EBHPuvv2XWfQVUzeX5SLrvMz5qQcupqrxHSZawYOm/IXFxBgayP7UELuVYRuTkERbRN6AvIinHUilypbkv4v5L1k7RQ/QmEJSl97o5rgaEPtXFaWCp+e4UwhpqlIgLI05bI5m2gK4Yrncc1dgD9CdwJKLOfD0HaoG7AKfVqT0woKK1ggdWPEBnoBMAi2DhB9N+QL4rX/e1Q8Gcgjmsb10PwMrGlZSmlSZtqxwDrXNR+TzTW/WgTECZJDJg0fbee+/l5z//ORMmTKCgoCDhRN2bWRsmJiYmJib7guU72nho4WYALKLAAxfOwu0Y3Iqulx1WxicbmwB4Zsl2U7QdAGEpTLu/nZAUwm1z47K5sIm7lzFsYmJiYrJv0C32Jcj6QmtcXEEy4gtOGXFn6gmL8cKuljs23p2pJRoqbbTEUUX41Ru/Ur1dD8W5qFcUTC8fVxSiheBEWVs0TOhLY5uA7hJrJUd3j49lvABvQAyHaMSD1gQCUq8ongyjYqxRF6csy0njK/aErkAXr25+lR1dOzh3/LlMzZ06oNcbzaSONtbJvhX6/D9JGyP7Xh2fhiCrfGaECeseJ5fNpfm8LMu8vfVt/rvxv6pYme3M5vY5t+/1WIR4ZuTPgFgC3IbWDZwx9oykbbUmqBIbRv+nJdTvUqTRBNgN0favf/0rTzzxBFdeeeUQDMfExMTExGT40h0Ic9sLq1Bi7247YRwzSzMHfTtHj8tjVI6L7a09fLmllS1N3YzNTx307RxIhKQQrb5W6rrr6Ah0RL/gChZcVhcZjgwynZlkOjINOR5MTExMTPYtWhmVEHXq6RWvQogKaVpimtpONtCO3uzV/rtJFPr0lsrHt+2vr3jHblJ3b1/ROZmIFydaaWaJxrXTczprZe2qzSQZ2aIdj6AKnxoCtvJ4WA5rij5qpq0B4VPzWApx+cRa+b5xTk+9CYQIOucrccWrRG2h26i4C9oi2UAJS2He3/Y+r2x+BV/YB8Aflv6BOw+5k5n5Mw33o0wi6Dpt5eh1pOlojTm5jRRl0/pcSXDAawnwyqSFNLC82v54d9u7PFvxrPr7tNxp3DzrZtId6XvU754ywj2CDEcGnYFONrVv0ow1UJ22Wg5yA/nEgOm0TcKAr2BRFDniiCOGYiwmJiYmJibDml+9uZ6dbdGb1DllWVx/bPmQbEcUBS49tEz9/T9LdgzJdg4EwlKY+u56VjetZn3LerwhL7kpuRSlFpGdkg0CNPQ0sL5lPetb1qtLz4wQkSL0hHrwhrzmTaSJiYnJXkQ361WIy5dN5vSMiT6SLGn2RSx3Vat4lRpXoFPgShVaDRYFSya8KVm18aKuXiEyrb9TIqK69NtIDrDStt82gpCwPT2hUsu1q4wdtPNgVUFNT/OJOTgl9uxYJoxLY6PxQp/mPkPozbTVE7ANiLEioqFM28GKR5BlmWUNy/jRZz/iPxX/UQVbiE6a/9+y/2NV0yrD/RkqkiboT95A72eB1r6In7AwUohM9xjoONuN0BPq4dXNr6q/nzPuHH46/6f7XLCF6PGZlD0JAF/Yx/bO7Unb6jnbox3q5xMr2zXZlQE7bW+//Xb+/ve/88ADDwzBcExMTExMTIYnf/tkMy8vj4bxpzqsPHDBTKyWocsJ+96cUu7/cBP+kMQry2v40ckTSB3kGIYDgZ2enWzt2IrD6iDfnZ9wM2gVraTaU0m1pyLJEq2+Vta1rKM8s5wCV8EuN4c9oR66gl34wj48QQ++sI+QFEKSJdxWNzkpOaTZ00i1p+KwOPb2WzUxMTE5aFCzUJMRy7TVXZYrG+jLQPEq5Xk9AVKtPq8l9MUJVlrCm7JU2MiSbkmWtAsjxR5WxOSkxcPkXtHTSPEnzRgCpfiZjlNSba91jIjLJNU73jEHrZZYKcv6ERAYyTruE4+g5UZUs4d1jmeECHbBnrQNQKo9FZtFP/opz5WHRUieOZwMSZbY6dlJRWsFG9s2srFtIx2Bjt5xInBs6bF4Q16WNixVhVujjltF0NcU8AxGQAiyYGhJvTrRoFGITDmWWpEYohCdAAlL+vEIWry//X28IS8Ax5Qcw/kTzt/tvoaCidkTWVK/BICKtgrGZI7pt50oiLqu4/hIEk1k/Wicg5EBf/v70Y9+xOmnn055eTmTJ0/GZkv8sHj11VeTvNLExMTExGT/5NFFW/m/jyrV339z1hRKs7VzqvaUDJeNM2cU88KynXgCYR5dtJXbTxw/pNvc32jxtVDdVU26I103N0wURPJceXQGOqloraAn1ENpeikCAh2BDlp8LbT6WvGH/SCAXbRjt9hx2VwICPSEe9jasRUEcFqc5KTkkO/KJ8ORMahLD01MTExMiAorehmV6AhqQu9SeU0Xp9xbsEwv41Q3HiFO6NMav+rokzREjLiiWknjEeJiFJB0XK+xwlta7zO6WRlZ0hed1YJlydrFBE09oVsVlDWOkfJ3Vm8ZtiBEHa1a548qIOnEI6htkkRYKGNX95XGoUxwCus4bWVJ3x0rCiIp1hTNNgB2i7b42x/rW9bz0MqHaA+09/v8xOyJXDHlCkZnjCYshXlo5UMsqV8yIOFWEfQ126gnhvZ4leOjeS8Wn4+rcY2okxaSwQmc3XSG9oR6eGfrO0D0WJ497uzd6qcvYSlMi6+FfFe+5v7wBD1IskSGIyNpm8k5vUWWK1orOH3M6Unb6kXLqPvJQD6xubJtVwYs2t588818+umnHHfcceTk5JgWZhMTExOTA5qnv97O796tUH//2WmTOHtWyV7Z9g+PGcMrK2oISzL/WlTF+XNLKc7Uv0k/GOgJRUVUURB1Bdt4MhwZ+C1+tnZsxRv2EggH8AQ9QNS5kunM7Pd1dosdHNGbSV/YR213LXXddWQ5syh0F5LtzN6tL0cmJiYmJruiFvNKhvKUngCGTERKvmxd6UOJUdB0jcbEnKTdxOXLyshJnaNGi1dBrzCtVZ3dcA4n+svI1X1hoC8lDkDLkatmCuu58OTk+0tBb1zxbbREPCPHMr6d4agLrbEJ+gXelG1G5Ij2hMUQ0uZr4y/L/0J3qDvh8RRrCuOzxnP8yOOZVzhPPZ5W0cpNs24CSBBuf3nYLxmbNTbpdpRzSAtVgDeyL/TMmXLvZ0qy7aoTM7L2OavEaxgqvpWE97a9p7psjy45mkL34BQcDkaC+EN+wlJY857UG/KCjKZoW5JWQqotle5QNxvbNibNtRUFUX8yJXaNI2hHIOhNBB2sDFi0ffrpp3nllVc4/fTkSruJiYmJicmBwPNLq/nFG+vV33900niuObr/5UFDQXleKpcfNoonvtyGPyRx33sb+dtFs/ba9ocrESnC9s7teIIeCtwF6uNhKczi2sUsb1iOw+qgwFVAviuffFc+I1JHqDenTquTPHcerb5WbBYbOSk5WERjywdFQcRtc+O2uQlLYToDnbT4WkizpzE+azxZzqwhec8mJiYmBxOaS/3j0BL6FNFNr+CRIhRoirZxQqWu6Kk4X7WybyX94lW9RsPkAlF8pq3mcn+jy/NjS831nIbquLSWMgtRJ7ERcU7PmawIZdFutcelF5uhPK53LONdzknHTmKmrdY2Jcm4gD0YObQDRZIl/r7q76pgW55RzhHFRzApZxIj00YmvU/qT7h9ufJl7p5/d9JtKRMcWiS4yHXQy+5Vtqd3jchSr+hvSJDdDc3WG/KqLluLYOHssYPjslWHZGDf6jpeiV5zE7MnsqxxGd2hbmo8NYxMH9lvW0nLZh43JkM5xmY8wi4MWLTNzs6mvHxoCq+YmJiYmJgMF15bWcNPX1ur/n7z8WO56fhxe30ct54wjtdW1tDeE+Kt1XVccVgZh4zK3uvjGE7UdddR560jNyUXQRAIS2EW1Szi9S2v09TT1O9rBASOKz2OK6ZegcPiwCpayXPlJd2GLMvUdteyvXM7he7Cfh0jVtFKTkoOkizR1NNEvbfeFG1NTExMBgFZ0slBNbCkXhF9wlJYe2Nx7lJNoVVxgWlpCvGOPo0YhfhCZEmHpcQ2xPrpNx4hPqpASyiOiYF6hbCUHF0jTkMlbiGZ+KMUPxMRtd2lBsU5vcJnsc56XdM6sRm6xzI+BzjZJoW4gmU6Qr2EZMjd67A69snKnbeq3mJ9a9SokO3M5u75d5NmTzP0WkW43dKxhRZfC6uaV1HrqaU4rbjf9inWFF13rHKc9IRRVTjXOq/jYw90CufpOdvjBcjdEdff3fouPeEeIOqyjTcf7CmSLGERLIaFbj0m5UxiWeMyADa0buhXtBUR9Y9l3OSGZjuD+cQHGwMWbX/1q1/xy1/+kieffBKXa2jz/ExMTEwOZmRZZmuLlzZvkO5AGG/sn1UUOX16EU7bwAsLmBjjvbX13PniapR7nmuOGs0d+yhPNsNl486TJvDz19cB8Ou3NvDGjUcgigdnPFG7v53tXdtJs6dhES18vONjXt/yOi2+Fs3Xycgs3LmQyo5Kbp19K6VppQnPi0Evnds+Z5VnBysjnazv3EpnsEt9fnzWeM4oP4PZBbN3cWyJgkimI5NWXyueoMfwlxwTExMTk/7RzUg0sKReEWd0M1UNFK+Kd9pqCsXxgpCGgBofaZBsm7IQV/FeK0c31o/u+8RYgTe9qAggYVy6TmEdAd6I01bJC9Ybl4CgivR6sRO6x1LWF+oVBy1oj0055kYc5EWpRZrPDwVVHVW8uOlFIDrWG2feOOB7Gato5dTRp/LMhmcAeHfbu1wz/Zp+26ZYU3QzeZX9pBeboQjnegKqOhmhc17rZQ+r2bdon7P94Q15eW/be0DMZTtIWbYJ4xONu5j1mJQ9Sf15Y9tGThl9yi5tLKJFv1icciz13PuCGY/QHwMWbR988EGqqqooKChg1KhRuxQiW7FixaANzsTExORgpbHLz23Pr+Lrra39Pv/aylqevGouNotZAGmw+WxTE7c8vxKlPsLlh5XxP6dN2qcZ7hfOLeU/S3awscHD2tpOXl5Rw/mHlOq/8ADDG/KytWMrETmCy+biH6v+weLaxQltpudN5+yxZ5NmT6Oxp5GmniYavA18tvMzApEANZ4afvbFz7h62tUsSBtLoOoTltQu5pNQKxscyZ0tle2V3L/sfkYKdi4Sc5g15QIYeaj6vNPqpMPfQZuvzRRtTUxMTPYQvezVBOdrsjZxDk5Dxas0EAShV7jTcdrq5aDG6xaypOHOlPtk5PYXj9Bn2bGmu1TSL8oG6ApSCQK2TgxBRIogiIKhpea6AnxMnNM7lqpoq+EUVo6N1jYVAUlrm4qDFjAUyaDl9NxX+MI+HlzxoJpRfObYM5mSO2W3+jqu9Dhe2vQS/oifRTWLuGDiBaTb03erL/XcNnC+Rl+g1VlcJrLGsVRiUjQzkYnLhx6gahvvsj2m5BjyXfkDer0eEhIWDDhtBX0BFWBUxihSrCn4wj4qWiv6/SxNt6fr3vcq0RN6x1JZXWCSyIBF27POOmsIhmFiYmJiovDppibufHE1bd5g0jaLt7Twm7c3cM+ZU/fiyA58vtnaynX/WU4oEr2pOG9OCb/67pR9foNttYj84juTufixbwD44/ubOHVqIWlOm84rDxza/e1sbt9Md7CbXFcuT617KkGwnZU3k++VnsC0UARHzTpki43JdjeSPZNITglnuEfxp80vsD3YQVAK8s/V/+TDYIhtNiuyKEAfwdYtSczxB5gcCPKxO4Ut9ujz1XKQP0TqEVf/hbItIxifP50J2ROYmD0Rl91FQ08DI1JHYLMcPMfGxMTEZLDR++Ju1CmmLqnXc9cpTltRI/tW0BeHEuIFdMRM0HYUK9tTl2snuRdRXbsGMnlVF2EyYsKoVqatWsgr1pfWcnMj+aAiomFxTmvZuoIiumkec2Xfam1TibqQZU2nsyKaay3RV0QrI27ovc2T656ksacRgLGZYzlv/Hm73ZfL5uK4kcfx3rb3CEkhPt7xMeeMO2e3+lKLV+ld5jFBVs+BLcmSmiWdrA3onz+qMxxjwqdCd7Cbd7e9C0RdtmeNO8vwa40iy3JC/rNWOyNjV3JtVzatpDPYSb23nhGpIxLaCIL+pIwSPaFbbFAwcLwPQgYs2v7yl78cinGYmJiYHPQEwxJ/+mAjj36xTX2sKMPJ6dOKcDuspDqsyMjc/0ElwYjE01/vYFx+KpcdNmrfDfoAYvXODr7/72X4Q9EvP6dNK+S+c6YNmxiCw8fmcvKUAj5Y30hLd4C/f1rF3adO3NfDGnJkWaaxp5GqjirCUogii5OXVv2LD+sWASACvwunc8qaRViXvpm0nzHAPEHgj9mZvJQedQRstScKqxNEF0c5Cji0u4sZzdtw+joAuL6jk8UpTp7MSGdZihMASRDY1lPPtu31fLD9AwQErp52NdNyptHmbxvUjDITExOTgw2t2AAwXrwK9JfUg37xKiAqJhioUq9mzCYRBOMFZ82xyb0uQs2K9wYiDRKEVp0YAknWLpiVIG7pREWojkSdHFrd2AYFjWXrED3mEpK+W8/AsVTGrOvmjouK0IrXkJCizuNhJNouqlnEopro/VSKNYWbZ92MVRywRJTAqaNP5f1t7yMj8+H2D/numO/u1kS2ep0YyLTVKyqnZgprTaYIvVEjWpMkavE/nUmeeGRZ5rG1j+EL+wA4tvTYQXfZAqpDeDALfimiLURzbfuKtkZQV0boTMwAquPbpJfduiI7Ojp4+eWXqaqq4sc//jHZ2dmsWLGCgoICiov7D5s2MTExMemlsctPVVM3zd0Bmj3Rf4u3tLC+rjdDc8GkfP503gyy3IkOwGy3gx+9tBqAX721gdG5qRw5Lnevjv9AY1ODhyueXEp3ILqk7tgJeTxwwSyswyx+4menTebTjc0EIxJPLN7GpYeOpCTrwM2Xj0gRajw1bO3cSoa3lUkL/8jzwTpeyu4t9vXb5ha+011tqD+nLPOL1nbm+QP8KjcHryhQYnFzZNF85pV/hxFpvTeiVbKM1duMs2ULyDKlthR+bkthk6+RZSseYY3sp9JuQ477Uvdq5atMP3w6jT2N5Lvy97lD28TExGR/RSs2AIznXRrKnhT03azQK0Lq5jIqS+qTjU3oU21dJ4ZAcRtqxiOgU0jNoNNWEKIuYF0B2EhfMUFTL0fUqNNW2a5F0KnpoBqi9Y9lsiXw0CfmQufPuV4hO4gK03ri4t5ke+d2Hl3zqPr71VOvHpQJ53xXPnML57K0YSkdgQ6+qvuKY0qPGXA/itCn6+JU2unsV0mStB3kyoSFpO+0NeLmjueT6k9YUr8EAJfVtdvuYz2MOm0HUvBrcs5k9eeNbRtZULZgwONSBXidTSqTLiaJDFi0XbNmDQsWLCAjI4Pt27dzzTXXkJ2dzWuvvcaOHTt4+umnh2KcJkOMLMs0dPnxBiIEwxKBcPT/TpuFqcUZWIaJ08zEZH9nc6OHhz7dwlur60i2QsdmEfjpqZO46ohR/d6AnDenhM1NHv71+VYikswNzy7n9RuPYExe6hCP/sBkU4OHix9dQkdPCID5o7P556VzsFuHl2ALMDLHxdVHjuafn1cRjEg8+Mlm/njejH09rCFBkiWqOqvY2bWTLESmfHgPb4Vb+HNujtrmpy1tfLc7mg0WcmUTyCnHkz2KjrQCZGRswR4c4QD2kB8rEMosJZA9mtHZo/mr3UlX0EORu6j/G31BIJyaT3dqohOijEmMzRrP/7xwFb5gN6udDh4qncD6UDvtgXa2dGzBIljoCnaR4cgYyl1kYjIskGUZX9iHP+InGAniC/vwBD3IskyBu4BsZ/Y+qYRusn+jt4xcXe6s5fSkV9DUdO0SzUE14rTVFYcUB5ucfOm96s6UZU3nZXzUgmaWqxBzChuMDtBrE5b1C3lJsgE3K0QjBgRtMdOo608Rii0kF22N5hjHu5j1+tJy/qlZwToxEPHi+nBw2nYHu/nz8j8TkqL3vsePPJ6jSo4atP5PH3M6SxuWAtGCZEeXHD1gsdqIg1ZpJ8mSppivOJ21HOTQW6xMD0PnWIwdXTv49/p/q79fN+M6clJyNF6x+xh12g6k2NfojNE4LA4CkQAbWjcYft/9YuBlevniByMDFm3vuOMOrrzySv74xz+SltYbOHzqqady8cUXD+rgTIYWWZZZX9fFO2vreXdtPTtae/ptNzY/lVtOGMfp04pM8dbEZDepqO/ioYVbeHddPVp/R8fkuvnrhbOYVqIt9tx18kSqmrr5uKKJLn+Y7/97Ga/fcAQZLjNHcyBU1HdxyWPfqPnBM0ozefzKuThtOi6Ofcj1x5Tz7Dc78PjDvLy8hmuPKaf8ABTsW3wt1HpqyXKkM+69/+X9UAv35Garz1/pHsvRY49gW1ohgawyPDYnXYEuHFYH+a580u3p9IR7aAt00RPqISgFkWWZXFcuoiCSCqTuZsGwUMYIak/6FSPf+hFH+vwIOyu5rjAPgM9rPqc8o5yWnhZTtDU5YIlIEbpD3XQFu2j1teIJeghJIXW5ut1iR5ZlWnwtpNpSKUotIiclB7fNva+HbrKfoCtuxbJX9VyjRooUKc453TEJ+q5RxcGm1Z/R7Mz4qAVNsSNOgEzWl+K+U7aZDFEQex20Gq5dZP1icaIgRgXPJC7h+P4kJE3XKxB9n1riNXHLsHXOHyPHMl401ytKpZe3m/D4Pv46LckSf1/1d5p6mgAozyjnqilXDeo2xmeNpzyznKqOKnZ07WB963qm5g6sDodyLPXOC9UBr3deSDoRIn2vS51CdkYEeH/YzwPLH1DF8VNGncK8onmar0mGN+TFJto0J0GVyAYjoqwyeaQnwFpFK+OzxrO2ZS1t/jaaepoG7MhW3b8GnLYRzHiEvgxYtP3222/517/+tcvjxcXFNDQ0DMqgTIaW1u4AT3y5jbfXJBdq49nS1M0t/13JXz+u5JYTxvGd6SNM8dbExCA723r4/XsVvLs28fMx223nrJnFjMh0kpfmIC/NQX6agzG5qYYyVC2iwAMXzuK8h79iY4OHbS1e7nt/I78/Z9pQvZUDjvV1nVz62De0xxy2M0sz+ffV80h17FmW11CT4bJx3THl/OmDTUgy/PmjSv5+8ex9PaxBJRAJsKNrB1aLlZHfPMF7bWv5TV6vK+H0Madz8qRL6RQEgpEgrb5WUiIio9JHUeAuINWeKGIHI0G8IS87unbQ6G0kJyUn6U1vMBJU/4WkUMJNptPqxGl14rA46B51GM3zv0/+N49xmM9HSThCjdXCmuY19IR7aPI1UZxWjNPqHLL9ZGKyt5FkiZ2enTT1NOENeYlIERxWB267G7to3+XLnyRLdIe6qWyvxOlxkmHPIMORQYo1Rb2ebKI52WiyK7qCYGxJvV6EguqI03OXxpy2WgKRIAuahbfAYCEypZK6TqQB9MY7aMVAqKKzDILGPaQsa7tGFRTRKplzMT4eQbfgUUzY0o0q0CgQ1dtQ+Z+2AGzUaat3LFXRXGds8UXZkgrdgvFzcah5pfIVNaM0zZ7G7YfcPujFUwVB4PQxp/PgigcBeHfruwMXbYkWr7IK+vflETmCneRiZnxRPM1jFDuWesfJSDarkmNb760Hoo7VSyZdovu6ZLT723FaneSmJI/EUz7DwlJYtz+jcS8Ak3ImsbZlLQAVbRW7FaOhm22tNhxw1wc8A/5m6nQ66erq2uXxTZs2kZeXNyiDMhkaJEnmhWU7ue+9jXT6QgnPiQLMHZVNSZYLu1XEEfu3fEc7y3a0A1DV7OXW51fxj0+reOyKQyjNPnBzFE1M9hRfMMLDn23hn4u2Egz3/vXJTXVw7dFjuOTQkbjseyYOpjqsPHr5IZz61y/oDoR54dtqLj+sjElF6Xs6/AOedbWdXPr4N2okwqyRUcE23bl/iAdXHj6KJ7/cRkt3kHfW1HP9MZ1MLT5wXJ21nlo6A51M2LmC96re4o9xDttTRp3CpZMuVb+ktvpaKU0rpTStFJet/79Ldosdu8WO2+ZmW+c2ajw1pNpTVXFXkiU8QQ89oR7sFjsOi4MsRxZp9jScVieiINId6qbd305PuIcOfweiINIz83xSGitI2/4l3+vq4i+xrN2v677mhJEn0O5vpyi1aOh3mInJXqKpp4mqjiqcVidZzizdgjWiIJJuTyfdno4v7KMj0EFzTzOyIGMVrDgsjug1Z3XgFJ3YrXZsog23zU2qLXXYZD+a7H10xS0DS+oTXJdaLjxF0NFbUiwYqBivFAWTtAt5KdvTfJ+KmxgdkUjuFVq1CmEpy/P1hO6wHNaPIYgTivXaCAiaBZvUZfB64pERpy0GYwgMCMDQm1VrJN9XS/SPL1i2Lz/XVjSu4JXNr6hjumX2LZoi4J4wv3A+Oc4cWv2trGhaQa2nluI04/WPVDFfZ/GbiKgrCCrnmF6mLRhwwIN6LmpN8nxe8zmLaxcD0SJvt86+dY/EceUc0kJxJmtFPCgTGmqMgoHTcVL2JPXnitYKji091uiwAdRt6WbtIhBGX3A+2BiwYnDmmWdyzz338OKLLwLRi6m6upq7776bc889d9AHaDI4bKjr4uevr2VFdYf6mCjAoWNyOG1aESdPKSQvzbHL62RZ5quqVh74uJJvt0fF202NHu56eQ3PXTPfvJk2MemDLMu8s7aee9+poK7Trz6em+rghmPLuWjeSFLsg7f0vjTbxY3HjeUP729EkuG372zgP983r00tvqpq4fr/rFAnr+aUZfHUVXNJ208EWwC3w8pNx43lV29tAOD/PtzEk1ft3nKr4UaHv4Oa7hpGdNTy/rK/82BOb9Gx75Z/l4snXqye323+NrIcWYzKGIXDsuvfsL7YLXbGZY3DZXWxrWsbfp9fdVSk2lKZkDWBTGcmLqsLi5h4neaRx6j0UfjCPnrCPbT6WmnxtbD88GuZ37aVszyN/C0rk7Ag8FnNZywYtUAtSNa3LxOT/ZHuYDfbOrfhsDqSRn/4w35a/a10BjopSS0h3dE7iZhiTSHFmqL+HpbCBCIBfBEf3aFu9Qs1gM1iI92eTp4rj3R7uingHoREiOiKi4aW1Av6Oa6CIBhb7iwby4Q1kosZ30avMJIsazttlaX+WkKrImBHJG2nrYDQW4hJy1Es679H5X3p7VdFaNXNLjVQmErpw4jTVvdYCr05uloCPOgLfYbF5CFkeeNyHlr5kPr7hRMvZFru0K3Qs4gWThl9Cs9WPAvAy5tf5tbZtw6oD0mWNPOogWims0Y2tNJGkrUzbRMK7KHtwtaLVGnxtfDkuifV36+Zdg2F7kLt96GDWsxLA1VI1pzjkROuTSOUZ5ZjE22EpBAVbRWGxxyPkc9Ow677g4wBi7b3338/p512Gvn5+fh8Po455hgaGho47LDD+N3vfjcUYzTZTRo6/aza2cEXm5t5/tudROIugLNnFXP3qRMpSNdetikIAkeMzeXw8hy+rmrlxy+vobbDx9dbW3l9VS1nzyoZ6rdhYjLskSSZNbWdfLi+gQ/WN1DV7FWfs1kErj5iNDcdP3bIRMGrjhjFc0t3sLPNx5dbWvm4ookTJ+959dcDDX8owp8+2MTji7epjx1SlsVT+0EkQn9cNH8kj36xjdoOH59uaubb7W3MHZWt/8JhTFgKs8Ozg5TWrSxcfC//yuoVfM4bfx7njjtXvYn2hrwICIzOHG1IsFUQBZHS9Kgrd3vndlJsKeS78slwZOgu0xYEAZfNhcvmIjcll9JQKW3+Nrae/GsmvnoTJ3p7eC/VjSfoYVPbJiZkTaDF1zIo1ZhNTPYlYSnM9q7t9IR6KEyNfvFs9bWyunk1q5tXU9ddR5u/DW8o7u+faOO0Madx1tizEsRaBatoTerUDUaCeEIeWlpbsFvsZDoyKc8sNzNxDxJ0HagY/3KviJ2DsaReEXU0dQeBqCColYMaE3wiUkQz0kAQepd06zptdYTWeJFGMwJC6BWGDDlVdfYX6Auo8Y5cLYxk1apuRB1dVBXA9CYGZG0HtvK+FOe0liCovIe9LdpGpAgvbnqRN6reUB+bWziXM8rPGPJtn1h2Im9VvUVXsIuv677mrLFnUZZeNrBOdHaXiKh/jinHUuf8UQrs6RbGiz2d7Fp6fuPzBCIBAI4rPY7Diw/X7s8ACRndyYYlR2NN9Jy2oiAa6k/BbrEzNnMsFW0VNPU00eJrGZBDW9n/Wo57pZ2RCJeDjQF/S01PT2fx4sUsXLiQFStWIEkSs2fPZsGCBUMxPpMBsLW5m/fWNbBqZwdrajpo7Ars0mZMnpvfnjmVw8cObBmEIAgcPjaX3541laue+haA375dwfETCszCRyYHLetqO/nv0mo+2tBIk2fX6+2Y8Xn84ruTh7xIlNNm4aenTuKGZ1cAcO+7FRwzPg+7VWdm+iBiXW0nd7y4isrGbvWxo8bl8vClc/ZLwRbAYbVw64Jx3PXyGgD++P5GXrz2sP3ajdbY00hw2xd0Lb6ff+X0Fgm7eMKFnDHuLPX3sBTGE/AwLmsc2c7dE6pzUnLIdGTukQtWEXALJ51L24ktnP/Zr3kvNSoqfV71NtPmT2N713bS7GlJoxsOFnxhH7IsY7fYdZfU6yHJEmEpHHVmxr5gSbKEw+IwM4SHiPruehq8DUTkCP/Z8B9WN69mp2en5mtCUog3trzBZzs/44IJF3Bs6bH6rsgYdoudbEv02g5GgrT0tBCMBBmfPZ50uxkBdKCjOt10xDkjS+plQdbNeo1fdqyZYxkTDfWcfxE5opnLGC+g6vUnS7FCZDraiq5rt4+LUGtsqmirJYIpzl+teASMCcBGBR0w4KAVYvtCV3PT3/equE7y8yyhEJmOIKgcR61z0SjBSBCLYNG9h2n3t/PgigcT3JFzC+dy48wb98r9otPq5MyxZ/LMhmcAeHnTy9w5907DrxdFUf+eIXbM9SZ59LJqlQJ7RgvZJRM8t3VuU2MR3Db3HuXYJoxPMOa0NbISAIw5d+OZnDNZPY/e3PImV0+72vBrjebnGnmPByO7fdd8/PHHc/zxxw/mWEz2kMpGD3/6YFO/zzmsIjcfP5Zrjh6Dw7r7X1CPm5jPadMKeXdtA63eIH/4YCP3nm0WPjI5eJBlmc8qm3nk8618vbV1l+cFAeaMzOK6Y8o5YVL+XhPQTp1ayLxR2Szd3sa2Fi9Pf72dHxw1Zq9sezgTjkj8a9FWHvi4klAkehNgt4rcdfIErj5itKGib8OZc2YV88/Pq9ja7OXb7e18XtnMsRPy9/WwdgtvyEvnulcY+8nvOK+wV4i9fNx5nBYn2AK09rRS4C5gROqIPdrmYMUWWEUr7jlXUVT9NWM6l7HVbmO9pxqfp55ASjY7unYwIXuCYcHqQCMQCbC+ZT3+iB+rYI3mC1vduOwuMhwZpNnSDH9WBiNBNrdvpiPQAUTFEeULlk20kWpPJcuRhdvmxmVz4bQ49+uJjOFAh7+D7V3b8Ya8/HbJb/FH/Lu0sckyBZJMri2dzLRiSM1ncf3XhKUwnYFOHlnzCO9ve5/5RfPJdmaT5cwiy5lFtjNbN/rAbrGT786nuaeZitYKxmeNJ8uZlbS9yf6PUgBSV2g1sqRejrpVrVpfe4VeEc/Q54VGEyVPUkvoU9Ar5KUUYtLNgYxbqq0XaWA069VIdADoF2SSZClpQTO1q9j4tTJ0lW0addoa/tzXaSbJErKkHxWh5w6Mj0fQXe6fhFZfK9/Uf8M3Dd9Q2VaJ0+pkWu40ZuXPYmb+TPVz0RP0UOOpYUfXDl7b8hqdgU4ALIKFiyddzGmjT9urfxdPLDuRt6vepj3QzreN37K1YytjMo19RylJ01/Vq4j+em3UwrLJIqRjgqFyX6E3aSELu56Lsizznw3/UX8/Z9w5uxTH3R2UiBRdQVPWj25IyLQdgEB6/MjjeXvr2wQiAT7a8RHHlh5r+Dgqx0j389rAsTwYMSTaPvjgg4Y7vOWWW3Z7MCZ7xvSSTPXnNKeV6SUZzCjJZEZpJnNHZZPtTl5RcSD84jtT+HxTM95ghOe+qebc2SXMKTNvnk0ObIJhiTdW1fLoF1sT3JoQnRQ5alwuJ04u4PiJBf3mQw81giDwv9+ZzBl/X4wsw4OfbOac2SWDdt3vj6yr7eQnr6xhfV1v8czJRek8cOFMxhekabxy/8FqEbnzxAnc+FzUZf23hVv2W9G2e/m/mfjBr7gnJ4Mma/T2ZGbOVE4dn5iX3xnoJMWWwqj0UXvs2BxMXDYXO4+7m++8djkPEs1LXvL1n/juqQ9T111HpiPzoC1K1uJrocPfQbYrm7AUxhfx4Ql5CHeHsVls5KXkke/KJ8uZpSlshyIhtrRvod5bT4YjA4toQSD6xUNAIBAJ0BXsosXXAoDT4sRusZNqixadc1qcOK1O3Da35naMOpgOBoKRINs6txGSQry67ilVsBVkmWmBIEf6fBzh8zMlEIyTWzYgWR1syyjgwRSBhbbol8JqTzXVnupdtmETrWQ5s8lyRIXcQnchR4w4gtL0UrWNIAjkufJo9bdGhdvs8UNWPMdk36PkvWoJrUaX1CPoFywTEQ25UJW+dJ1skqw5tnjXq2ZeqlJITW+5v2wwU1VxeuoV8jJQMEjdpxrNlM9ZQ4XgjORdGhB+1D70tCgDx1IRzTULkZFYiEzvWBoSp+MIRoIsrF7Il3Vfsrl9c8JzvrCPpQ1LWdqwFICS1BK6Q93qpGY82c5sbp19KxOyJxje9mBht9g5e9zZPLHuCQBe3PQid8+/e9D6Vz4LNGM/4uIRNCcRYq5do07bvsd7VfMq1reuByDflc9JZScN7M0k217s3NIVWYXkkQ0JfSluf4PxCBBdoXbe+PN4tuJZZGQeX/s4vznyN4YMCUY/r814hP4x9G3nL3/5S8Lvzc3N9PT0kJmZCUBHRwcul4v8/HxTtN2HFGU4efCiWUwZkc7oHPeQOcgKM5zcedIE7nk7WgDnZ6+t5e2bj8RqOTgdRCYHNh5/iP8ureaJxdtp6Ep0F43OdfODo0Zz1sxi3MNgif20kgzOm13CS8tr6PKHeeDjSu45c+q+HtZepycY5i8fVfL44m0ocXeiANcfW86tJ4w/4GIjTp1aSHmem6pmLyuq2+noCZLp2r/Eennls+S//z8scdp5JS3qSHBanPxg5nUJN8Q9oR4C4QCTciYNinNhsMlLG8HIw2/HsewPBASB92Qvl61+Ad+0s9nWuY1Ueypp9gNjwsAowUiQGk8NLrsLh8WxS/6wP+ynwdtAg7eBLGcWRe4iclJydhHkQ1KILR1bqPPWkefK61ewd4kuNYZCkiWCkSDBSJAmXxN13XUgRHNWU22p5KbkRgtc2VMRBRFvyEt3wINv59eEWyrpHnMs+RmlZDoycdvcB61bt65rJ+LGdwhsfpsVtmhebWE4zPO1DeRIEpJow1symx4E3DXLEaXohIUYDlDeWs1fgWVOB3/MzqLC0f/nUkgK09TTRFNPk/rY61tepzyzPJoFOOJwXDYXgiCQm5JLm7+NitYKxmWN2+PCLibDFFlfTFCX1OsJBgaqvKs6gmwwkkGvSj3R6Jakjj56BTzl92SoblytSFuhVwBOOi6DWbWDmWkLBt296Au7yraUYoVabWRJJwMYg8dSEEDSdh3HC+t6S+/VfWHg74kkSyyuXcwLG1+g1b/ryr4idxGeoIfuUK+RpKa7pt++puVO4+ZZNycUhtzbHFd6HG9WvUmLr4VVzauiuf+DJCAbcmfGuek1m8mxc0xncgOZXZy2kizxXMVz6u8XTrwQm2VwYiRVgVjvbcqC7v5QPjd3x9V66uhT+bzmc2o8NVR1VvFJ9SecWHaiodcOJB5hQG75gwBDKsO2bb1FW5577jn+8Y9/8PjjjzNhQvRC27RpE9dccw3XXnvt0IzSxBCCIHDGjD1bKmqUyw8r45UVNayv62Jjg4envjKXYpscWDR5/Dz55Xb+s2QHHn844bk5ZVn88OgxnDipYNgtr//xyRN4Z209PcEIz35TzUmTCzly3MHhRpIkmY8rGvnNOxvY2eZTH59YmMbvz5nGrJEH5ooAURQ4dkI+Vc3bkGX4ZlsbJ0/Zj4SMSAje/wk+AX6dm6M+fMnkSxKcdF2BLgLhAGMyxpDvGp5u4gx7Bnl5U5iXNYkvOjbisYgs2vgS8yeeRp0gs6NrBxOzJw4rh/BQ0+xrxhPwUJDafzE2pzXqflWW0bf6WslyZlGcWqyKt2EpTFVHFbWeWnJduYb2nyiIat/xBCNBekI9bG7fjEW04La5sQb9uCvfo3DDgRfsiwABAABJREFUuxS1Re95u9a/ybITf4bNmUGWM4u8lDxyU3IPLvftxncpeucOirrrubu4CIh++by9y49l/MlUjz4S78h5SPZojrMY7MFds4y0bV/i3vktloAHIRJiTiDM83UNVNpt1FmtNFssNFotNFssCT93WhL3bVVHFVUdVTy9/mmOLDmSiydeTKo9lWxnNp2BTirbK7GKVtNxewCiON30BFlJlnSvST3xDowLaoIg6DtV4xyVRtyZeuNSXX86ApKhHGADbjejRdlQjbba8Q5q7ITGNkVBNO7CI6LrqIwQ0XXaGjqWcftfz9AdkbVjLAyL4cCa5jU8W/EsO7p2JDw+Mm0k84vmM79oPiVpJUiyxJaOLaxsWsmqplVs69xGmj2NktQSStNKKUkrYVTGKMZljtvnApjNYuOccefwyJpHgKjb9n8P+99B6Vu5frXOC6VYmZ5TWxaiLn/dzw2BXa6Rz3Z+pma9l2eWc1jRYQN6H1oYETwh9nknoO80R9+R2x9W0cr3p36fX3/9ayBacG1e4TwyHBn6YzMgxCZ8Fht4vwcLA/7W8L//+7+8/PLLqmALMGHCBP7yl79w3nnnccklgxO0bDK8sVpEfnf2NM7+x5fIMvz5o0pOmlzIyJyDu9CKyf6PPxThn59X8fBnVQTCvTfTggAnTirg2mPGMKds9wof7Q3y053ceNxY/vTBJiKSzDVPL+Pp789j7qjhO+Y9pbU7wIvLanhu6Y4EsdZuFbn1hHH88Ogx2A7wlQCHjcnh8cVRsenrqtb9S7RtXI8Q8PBgdha1tuhtyaTsSZww8gS1SauvFVEQmZgzkQJXwT7/8pEMi2ihyF3E3DGn8cWKjQA8mO7ikcV/Jvuk39DobSTDnpGw7PtAJhgJsrNrJx9Xf0xtdy2HFB7CkcVH9lsszCpayUnJISJF6Ax0sq5lHdnObEakjqAz2Emtp5YcV06/rpXuYDfdoW4yHBm6GbZ2ix27Jer4FDuqyfrqEfK2LMQS8iW0S2/cwLzPH6DylN/Q6m+l0dvI6MzRjE4fPWzPv0ElEkZ+7VocgS4eyUinxhbd7zOc+ZRd+CS19pRdXiLZXXjGHI1nzNG79idFECIhioNeSgMeLLF/7poVZK17HUuoB78g0GSx8IXLySvZBWwWoq7doBRdHryycSXXzriWmfkzyXBk0O5vp7KtEmuOlUxn5lDuDZO9jJElu6IgGooqUJy2upEABh2QETmim9GqFjXTWCofHZq2UKy6RpWIBA3UIkt67kAdAdKoaKKI00baGNm3hvIuY/tDt41enEQMvWOpiLaSlFxQVnJBjRSy03MnR6QIf1v5N5bUL0l4fFb+LC6YcAGjMkbtsu3xWeMZnzWeCyZcQESKDOuJxaNLjuaNLW/Q2NPI+tb1rG9Zz5TcKXvcr8vmosBdYFiE1Dxn5d4YEU36ZOP6w35e2vSS+vulky4d1HsF5drW7dOAWKtMkOg5bWVZptXfSqYjM2GyfFLOJI4uOZpFNYvwhrw8W/EsN8y8QXe76Y50w0V5jV7DBwsDFm3r6+sJhUK7PB6JRGhsbByUQZnsH8wszeTS+WU8s2QHPcEINz63gpevP2yPCp2ZmOwrZFnmow2N3PP2Bmra44Q/i8g5s4v5wVFjGJs//JZj98cPjx7Dqp0dfLShEV8owlVPfsuzP5jPjNLMfT20QWVrczcPfrKZd9c2EIwk3nQcOiabe8+expi8/eOY7SnzxmQjCiDJUdF2v6J2GSsddp5Ljx4ru2jn2hnXqs6b5p5m3FY3Y7PGkpOSo9PZvifLmcXEnIksGHEkH9ctJiCK3NOziftqV5BaMJltXdtwWp3kufL29VCHnBZfC1/VfcU7294Bollvz1Y8yzElx3DSqJP6LSRnES1kp2T3irfNa3D0tFMa8uGqWYmls5bVXdupDHexWephk+yjnt78M7tgJcOZSaYjiwJ3AeOzxjMuaxwj00b2fpmVJbLXvELBVw8jhhNjbzz5E6jrbqDI30X6zm8Z+/Hv2Hnq7/DLYbZ3bscm2A4O0b1lE0KgiwaLhUezMoGoQHDpvB9DP4JtMBIkEAkQjAQJS2FVxLBZbKTb07Fb7MiihbDNCe7e67h71OE0z72SrPVvkrPqBUZ6m7mkq5tLurpZ73TyfNk0Poh04ov4aQ+0c9/S+1hQtoBLJ11KljOL5p5mKtsrmZwzeVhGppjsHkaLgkkkjyBQMCwuoi+MKgKd0XgEzUzMmOtPb0m96vTUIpbDqbwm6TYFgw42Wd8Vp8YBGBDD9cY1EKetpLM+PKHglAZGjiUCSJKkmWkLsWOJMdFc631+uvPTBMF2dMZoLpl0CVNzjUWdDWfBFqKTs+eNP4+/r/o7AE9veJpbZ9+6x0VlRUHUFQONRGtA9BoxmqkqyL3H8Z2t79AeaAfgkIJDmJQzyVAfRlGvWwNZzdH/aV9LoiAaco93B7pxWpy7/H29ZNIlLG9cjjfkZVHNIo4rPU73PRsuIGo8ZvegYcCi7QknnMA111zD448/zpw5cxAEgWXLlnHttdeyYMGCoRijyTDmrlMmsHhLC9tavKyt7eR371QclBmaJvs3W5u7+fVbG/i8sll9zCoKXHZYGdcdU05B+q6usOGMzSLy0MWzuObp5SyqbKY7EObyJ5by32sOZfKIfZdnNZjIsszlTyxNENgBjhmfx6WHlrFgUv7B4YaLke60Ma04g9U1nWxq9NDSHSA3de8XxNstapbzu5xs5NjxumDiBRS6C5FlmSZvE1nOLMZljdtvsmCdVif5KfmcXH4mW5vXsTXUQZXdznPfPsAFZz1NWApT2V6JKIj7hQi9uwQjQaq7qvlwx4cJj/vCPt7f/j7vb3+f6XnTuWjiRYzOGL3L6y3AhLWvk7PiOSyhHgB6BIEfFOaz1pn83A7KYZp9LTT7WtjcsZnFtYsBcFgcjM0cy5EZ47l8w0LS69eqr4nYUugcfxKLy2bxf9tep82dAWRQEA4ztmcjpR/cwuhDrmFs1niqOquwWWwHfpZq3UoA7s/OxB/7KD2p7KQEwdof9tMd6iYcCasO5hxnDqm2VNVN3djTSLu/nbAUJtWe2m8+sORIpXX2xbTN+B4ZlR+S//Uj2LzNTPH7+c2mb7nelcH/lE1gub8BgI93fMy65nXcOOtGxmaOpamnicr2SiblTCLFuqugbLL/oZcPCr1Cn4hO0R0jrlGMuzN14wViblCj8Qh6xcMkWdKseK+0M+QojolMWm1EQdRd6q9gxI2rZn8ayL7VQ3EHGsFIOyNREcq+SOqGJjHqQs9pm2yf+cI+XqrsdWpeO/1ajik9ZreWsA9njig+gte3vE5tdy07unbw489/zAkjT+C88ecNaeZufFSH5j414EaPdaheI5Is8e62d4Ho9XPxpIsHNDZZlglLYc38WyWH1sh1CdqxJarTVtAu+qVMQvTXV4YjgwsnXsjjax8H4NE1j/LrI36939yr728M+FPgiSeeoLi4mHnz5uF0OnE4HMyfP5+ioiIee+yxoRijyTAmzWnj7xfPVgv7PP31Dt5aXbePR2ViYpw3VtVy2oNfJAi2h5fn8N6tR/HL707Z7wRbBYfVwr8uncOhY6KxCJ2+EJc9/g1bmjz7eGSDQ027TxVs051Wrj16DJ//+Fj+ffU8Tpw8fJfPDyWHlffmOi7Zuv+4bVtql7IpVqBoZFopp44+FYDOQCdp9jQmZk/c724Cc125pNhS+OG8u3DG7nVfs0usW/p3Mp2ZROQIm9s30xno3LcDHUIUl21tdy0AZellHFt6LDax90vJmuY1/M8X/8PDqx6mzd+mPm7paaPsjdvI/+YxVbAFuC8naxfBNkWSmOX3c0q3l/k+P+XBIJmRXb+EBCIB1reu519bX+OHQhMb7NFxtE07h41XvsY/Ssfz84onE8bRaLXypSuF56U2fr/0D3y4/UPsFjtbOrbQ4msZnB01XKlbxbdOBx+kRvNq0+xpnDf+PADafG00dDfgD/vJdmQzJXcKswtmc0jBIUzJnUJZRhkF7gIK3AVMy53GzPyZjEofRUSK0OhtpLG7kRZfC12BLvxhf9SZK8vIFhsdk05n82XP0zTvaiRr9FiP6OnkyYql3OqeoBaya+hp4LdLfku9t548Vx7t/nY2t2/GE/TQ4e+g0dvITs9ONrVvoq67jlBk11WCJsMXIzmoYHwJraGiNnKic64/jBTuUZfUa7iA43Nvld+TdAagv81YVq2hYkwGIiUUp6pe8TYjbmilSJoWFsGC0+bcpVBlX0RR3x2ouqZ1xC2jx1K3wFhMmFYF+N0Uzd+uelu9J5hXOI/jRh53wAm2ED2nbph5A1mOqOsyIkf4cMeH3Prprby2+TWCkeCQbTv+mtPDyGeBcorVdtfiDUULdc7JnzNg53B3qDtaLNXIuAYhHiHaUTTnV6u9jIxFtCS9lk4YeQLlGeUA1Hnr+N2S39Ed7O63rcmeMWCnbV5eHu+++y6bN2+moqICWZaZNGkS48ePH4rxmewHTB6Rzq/PmMJPX426Vn766lqmjEg/aJYlm+yfhCMS9723kccW9xZaHJHh5OffmcypUwsPCNEvxW7hsSvmcvnj37CiuoNWb5BLH1vK27ccuf+4MJOwpqZX7LryiNHccaL5N+iw8hz++XkVAF9VtfKd6XunMOUe4Wtno7cG0qKFxablTUcURMJSmEAkQHlmueH8q+FEuj2dbGc2CHDNqNP5245oPMBfm77kD22nk5MddQduatvEpJxJ+50orUcoEqK6q5qPdnykPnbBhAuYXTCbSyddyuc1n/PB9g9o6mlCRubzms9ZUr+EM8rP4HxnCeUf/RZbT3TiQRYsdI+cx7suB6/5twLgFO38YMyZjMsspzB1BKLVidXbgqt+Da76taTUr0HorKbSbme1084qh4PVTgf11uht73qHg4tGFPKd/LkcP/VSHl/1N9a29Dpvx2SMwWaxUdOxHa8UUB9/adMLTMmdQq4zly3tW7CJNkPFN/ZL6lZyf3am+utFEy8i1Z5KZ6ATi2BhSu4U0u36+XSCIJDhyCDDkUFxWjHdoW78YT/eoBdPyKOKthEpztknQOv0c2iccDKl3zxOZuWHCMAP1n3EzPlXcm9wJ5s7NhOIBPj7qr9zz+H3kOfKo6mnic5AJyEp1Cs6iSIRKUK6PZ2StBJyU3LVTGOT4YteJiz0uiB1HZUGlgsroqcRQTAi6RfC0os9AHqdthoOU4Ho9vSW5yv9RQ2C+pm2RvaFXiEm1Z2sh3pZa2/TyOqFdHs6qTbt75dGx6W8T81jKcTiGHSczoowbVQ070ubv423t74NRAXsgTo19zfKM8v5y3F/4Z2t7/Bm1ZsEIgF8YR8vbHqByvZK7pp716B/D4t3qRpyaxv8LADY2rFVfXxs1tgBj01xu2pNLinuWN1zW4ieQ1r7T0LCIliwiBYCcfc4/W3TIliSblMURG6adRO//vrXdAQ62N61nd8u+S0/P/TnZlzRILPb5YvHjRvHuHHjBnMsJvsxF84tZem2Nl5bWUt3IMwNz67g9RuPwGkb3tk6Jgcnrd0BbnpuJV/HuRG/N6eEX585BZf9wKrqnuqw8uRV87jksSWsq+2iocvPbc+v4t9Xz8Mi7r/C9JraDvXn6cUHqGgyQOaOysIqCoQlmSX7S65t7Qoq7L0Cyuj06DL5Vl8rBa4C8lL2z9xXQRAocBfQ1NPEoVMuZv3OL1godeERRf6x5Pf89JRHyEuJikyVbdFl3fujOJ2MFl8LX9d9TU13DRAVQWflzwIg1Z7K6WNO5+RRJ/PB9g94pfIVesI9BCIBXqp8iS9DYX4f6WY2EHLnUnPyPVRlFHD/Fz9V+796+g84siRa7EoGIkDEmU4gZwztU88CwOptIbNmBaftXMr51d9ia65jpcPOr3NzqLLbkASBN5uX8eany9R+BQTOGXcO544/Vy1yFK54g49XPMK/M9ORgL8vuY/fL3gIb8hLZXvlfukE1yUSorVpHRtKopMppWmlHFt6LMFIEH/Yz+ScybsVD+G0OhOK0MmyjD/iJySFiEgRInKEsBQmLIVp8bXQATQecS2jsssYu+RRAA755inuP+ZOrgt1U++tp6qjite3vM6548+lwF1AWApjFa0JQowkS3QFu6horSDNnkZxWjEFroKEwiomwwsj+bJGsmohTtw1kL2qu02DookiwmjFHii5sXrxCDJytBCWkfHrOD2VTFvdqAXQdewpS6t197+gH8lgFFEQdd2naqatrpavn3EaX5RNd0m9rC+ay7IcjYro0+alTS8RiETFsxPLTjzw43eI/j04d/y5nDDyBF6ufJlPqj9BRmZl00qWNS5jbuHcIdmukRxmpZ0WAoJ6jlV1VKmPj8kcs1tjUjOWk5w/yjmoN9kgyEL0OS0HrSxHhV0Dn2ciomabotQifnHYL7jn63tM4XYIOfA89yb7BEEQ+O1ZU9VCTRsbPPzyjfXGZmBNTPYi6+s6+e7fFquCrc0i8JuzpvLH86YfcIKtQkaKjSeunEteWtRdu3hLC39buHkfj2rPWBvntJ1WYoq2AC67lZmxYnNbW7w0dPq1XzAcqF1OhSNOtM0YjTfkxW6xU5JWMuyLamiR5cgiw5FBZ6CTK474BSPC0S/x6yQvH731A9J2fE1eSg4dgQ7WtqyluqtaXV63X7P2ZdKeu5BF655WH7osbTypNctJr/yI7NUvkv/1vxj56Z+4Yc2HvNnYwYVdHiyx+4U6m5WrivL5v7JJbDr/cToLp/DgygfxR6Ln89ElR3N0TLDVIuzOpXPCSdQt+DmVV73O5kv/S/6RP+K+I+7hookXYRcT3ZaZjkx+dujP+N6E76lfhgRBwDb5LC6ceiWz/NHtN0Z6+PdXvyPHmYMn6GFT2yY8wf5jZyJShDpP3f4XpdBUwaa4P4fTcqchINDqa2VE6gjyXfmDshlBEEixppBuTyfLmUVuSi6F7kJK0kqYkTeD2QWzmZIzBd+cK9g2u9d5NvrzP/Pj7EPUL7evbn6VHfXLSa1dSc72r8ne+B7Zq14gb+kT5H77FDZ/F5mOTPLd+YTlMBWtFez07ByU92AyNChLzbWEMlEQEURBV3xXxF29NoactrHoA10BVRHwkuWgxpbw6xavihUY0y2+FVuir2sgjImLWjnAAxVXjSzX3pvfBwci5usdSyUCwkhUh5F4BDWeIq7Nzq6dfLbzMwBSrCmcM/4cze3sS/xhP76wT7/hAMh0ZvKD6T/g1tm3qo89u+FZwlJ4ULcTn82qV6wv1kiT+PvTrZ29TtsxGbsp2oqiplNbOW+0PhOVzzCLkDzSAHpjD6yCVTf71iJadD9/RqSO4BeH/YJMRyaAKtyaUQmDx4GpUJjsE9wOK/+4ZDZnPvQlvlCEF5btpDzfzQ+PLt/XQzMxAaJ/8G56biV1MTErL83Bw5fM5pBR2ft4ZENPfpqTBy+cxSWPLUGS4a+fbGZOWRZHjdv/nIyyLLO2NiraFqQ79tvc4aHg8PIclu2IVq/9emsLZ88q2ccj0qHmWypi2aIO0UaBu4DmnmbKM8v3+2XnNouNURmjWNeyDtmdy09GnMAdjZ8SEQT+LfZw1Ec/Y7prBFkzL6Rm9GFUtleS4kkhNyWXfFc+GY6M/S/PLhxEfvMWltokthREP1smBwKc+8UjSb//pAI/Ay7q8vDr3GxWOJ1IgsBTgpdFK//CiNQRbOuMxtiMcI/g6qlXA73ZdJIsEZGjldrtFnv/Ao4gEMwqI5hVBsCZBVM4tOhQnlz3JKuaVzEzbybXzbxO/cLRl86Z5/Pj7jqubf0Kj0Xkc08Vs9c9y7xpl9LU08TmxlVMaq0mZedSGH00TD0XX9jHjq4d7PTsJMeZQ5Yja/+ZhKhfxQZ74mRKR6CDdHs6I9NG7pXzUhAE3DY3bpubotQiWhbcQ104wIg1ryAgs2DxP7lszEyejjQTkSP86+t7eaGuAWc/4lDGpg/Zfs5D4Momw5GBTbRR3VVNqi2VPNf+9zfwYEHXASaIlKWX6fajinNay/MH4C410pda8EjLHxVz5GqJJooArLuMPzYe3RxOA+LugJyqOoW8ICaC7UUPjxFnNRg/lnoRFrGGUXe1EdG8j9P22Ypn1XPg7LFnk24fvgWDOwIdyLJMSurgF3ycXzSfSdmTqGiroKGngQ+2f8DpY04ftP7jXdOa54bc5/9JyHJkITuiBcR2dO0AoNBdiNvmHvDYJCQsaN8fKBMHyjmZ7PPAiBtdmdQQRW0XrbKvjEy6KMJtvOP2ryv+ys8O/Znua0302c++DZgMd8YXpPH7c6apv9/77kbeNAuTmQwTatp9bGuJOtnG5qfy9s1HHhSCrcJh5TncedIEAGQZbnt+1f7hxuzDjtYePP7oDPy04sx9O5hhxqHlOerPX20Z5hEJskxn7TJqbFHRdmT6KLoCUUdccWrxPh7c4JDjzKE0tZQ2fxt5h1zD5TmzAZAFgZ/m5eDr2EHJwt8z/e27GeHMxm6xU9tdy6qmVdR4avbx6HeDpg0Q8vLPzF7B/fr2Ls3vsRF7Kr78iWSPPo77ik/nkuLjsQjRLy9bO7eyuHYxADbRxq1zbsVusdPgbaDR20ibv42uYLSYVSgSosPfQYO3gaaeJroCXQQigaTOlQJ3AXfPv5unTnmKu+ffnVSwVRCPuJU7nCPV3/+1/W38q/7DnEV/Y8ZT55Dy+g2w/Cl4+Wq8Xz/E+pb11HpqyXJm0RnopCPQYWQPDg/qVqqTKQDFqcWEIiFGZYzaZzEeua483Kf9maZJpwEgSBFu27KcSYFo0Zqtdht/y+p/osfZvp1Rr9+CxdcBgMvmwiJaqOqoMp1AwxS92ICBoDovtdyxBpfwCwjIkn4mrJJXq4mBJfXK40p2pta4jOTQRjerXzzMSLEmxZ2s62iVBX0xcxAxKjIZOpbKUnkd17cgC2qmsM5GE87Ftc1rWdW8CoDclFxOGX2K7rj3KTLaExExdqeYmCAIXD7lcnWfv1L5Cl2BrgH3k7R/DF6Xce01n4+5Xnd6dhKSooUulaJcA0WWo85XPaetEo+Q7PxWrkflHippX0TjESzoOHJjMQp6Rf0U+jpu17asZX3LekOvNdHGdNqaDDpnzSpmZ1sP//dRJQA/enE1+WkODh2To/NKE5OhZX1d75L606YWHpQOzeuPKWfZ9jY+3dRMqzfIzf9dwXPXHIrNsv/M4a2p7T2O081ohARmj8zCbhUJhqWEzOZhSfs2Nkleol5LGJk+krAUpiy97IApFCQIAqXppXQFu2jxt3LiYT9m2Tf3srZlHU1WK/+bl8PfGptxNW4gd9nTyIdeg8vmoivQRb23nqLUImyiTX9Dw4W6lXzmSlEjL8pTCik/8kqqO6qxBHtwZZYRdKbjtbvocbjwOjPxWG2k2F2k29MRBZHvApNHn8BDKx+i3luvdn3Z5MsoTSulqaeJHGcOI9NHYhWt0S8eggVREPFH/PSEeugOdtMR6KAn1EMwHFRFCqtoxW6xk2JNUR258RmrEBVHfGEfVtGa+JwgMGnBfZz27g95V+ihRxT4c9WrPFXfuIs/xvXBz3EddweOKWcjCiLdwe7ouFP2k/ugupXqMbSLdhwWB8Vpxfs8YzrDmUn3mf+kPXQlWVsWYgPubW7lguJCgoLAMxnpCMWHkG5Pw2ZzYbU4KN3wLrM7G0lv3UrZ67ey46wHiaRkkOXMorG7karOKiZnT8Zm2Y+us4MAJV92sIoR6cYLYCzTVnFUGolH0BNH1YJfGu0EISos6hYiiy3j13PaGhHDDbuOMRYpEY3+3Ls1FIw4F40cS8CYS1gwJnT3dTk/v/F59ecLJlww/O99YueZFpIsUdtdS4GrYMCTfKMzRnNM6TF8tvMzesI9vFz5MldPu3oPBtyLepz1ivUJff6vw57m2UaHpC+Oqpm2CJrtFDFZ+Rzq7/xW4hgEUXuCQylYpojSRhiROoJLJ1/KQysfAuDlypeZkjvF8OtN+mfA39JHjRrFPffcQ3V19VCMx+QA4abjx3Lh3FIAghGJHz69jMrG/jPfTEz2Futqe2dspxykxatEUeDP58+kODO6tOnb7e088HHlPh7VwFhb06H+PO0gPY7JcNoszBmZBUSd5TvbevbxiDSoWZ5QhCw/JZ9CdyG5Kbn7cFCDj91iZ3TGaKyCFV/Yxw0zb1SXP37uSuHZjOjPucufwdEWjQFItafiDXnpDHQm7XdYUreSRzN6l3aePeVSOiecTOXU7yKd8Auyjvs5BYfdwpg5P2DKlIuYVnYsE3MmYRftNHmbaPG1EIwEKc8s5/dH/Z6Tyk7CbXNz6uhTWTByAS09LWQ5shifNZ5sZzbp9nTcNjdOqxO7xU66PZ1CdyFjs8Yyu2A2s/NnM6tgFlNzpzI2cyx5KXkICLT52mjobqDD30EwEoy6dANRl26LrwXkaHZffXc9bb62XteQxcr5C+6nRIq5pJwOfpaXQ8CZSduUM6kdfyIAAjKTPnuA9O1fAZBmT6PF17J/uDrDQTqbNrAz5oAvTi0m25lNWXrZoFfz3h1SnRk4L/gP28/4M8tOvQf/5a9xwZTLgage8u/uSv7Wtpw/N37BH+s+5uZMO0eNLOHiogIejTTR/PZNRHqiE1q5rlyae5rZ0bXDrMEwzDAkBhpERLsoT8I2DfRl1HUG+mKl3vtUBJqIpF3wSxWd9Zy2/WSq7tJkAE5VSZZ0nZeybEAMH0TU2AadzyvlWGrtV6U4lJEl9UYczNAbYdEd7KaqMyr4laSVcETxEbqv3dcIsqB7LSnnhFFHa18umHABDku0BsfH1R8P6qojQ5Mp9BYJNMLWjt482/LMPXDaChbtqILYc/HZvH1R9rlVtGq2k5GxoO+0VXK5B/KZB3D4iMMZ4R4BQEVbhem2HQQGLNreeeedvPHGG4wZM4YTTzyR559/nkAgMBRjM9mPUQqTHTch6sro8oe58omlNHbtf0uxTQ4c1sU5bacexGJfltvOQxfPwmaJ3rQ89eV2AmHtpXfDiTU15nHU4vC4iISvq4ax27Z2WUIRsuLUYvJcecNCGBpsMp2ZlKWX4Ql4SLOncf3M69Xn/pydRYXdhiiFKVr4R4hllYmCuN8VsPLUr2StM/plq9RdzJyCOXhDXtw2N7muRDFeEARcNhclaSXMzJ/JtLxpZDmiUQJN3iZEQeTqaVfz2EmPcfnky2n1t5JqT2V81nhD7h1REHHZXGQ5syhwFzAyfSSTciYxp2AOs/JnMS5rHCnWFDxBD52BTpyik7EZY5mVP4vZhbOZlT+LKTlTSLOn4Ql6qO+uJxAJ4EzJ5qb5P8UR+6L/fqqb/5l/LnXH3UXHSb+idcqZ0fcnRyh97+e4dy7DaXUSjARp9Q3j61GhaQOVcevwilOLKUkrUb9EDwdSbG5GzLgMZ/kJNEpBTh51MtNypyVtLwkCa50OHs3M4IaUEDd9ciMb65dhES1kObPY6dlJg7dhL74DEz2MuEYNY8AdqAp3OptURDzNpfIGRU9lqbbe+1RyuzXF3TgXoRaKuGvIaWvAQWuk3d522RotRKYcS717DtX1rdGfIpbriltCb1slBxVgSs6U/SPDXtCPEJHkqDtTz92ejCxnFmeNPUvt65kNz+xWP31Rz30jWccDmDRSipAJCIxKH7VbY1OuSS2hW3Hbq4UOk/Sj3D+KQvK8WlmWEUT9TFsJCatoHbAALwpiQkG9lytfHtDrTXZlwJ8ON998M8uXL2f58uVMnjyZW265haKiIm666SZWrFgxFGM02U+xWkQeuni26oSr6/Rz+wur9u2gTA5aZFlmXWxZfZbLxoiMgy8aIZ5ZI7P47vToLKg3GOGbrW37eETGkKTe4zgiw0le2vAREoYLh8WLtsM5IqHmWzbGcjMtgoXS9FKclgP3uhyROoICdwGtPa3Myp+lFtgIIfP9okLuy86itmUDmRveBsBtc9Pqa6UnNIzd0vGEA1R1bFF/nZAzCUEQ8AQ9FLmLSLEmL1xiE23ku/KZmjuVGXkzyHfl0xXoosnbRFAK0upvJcWSwvis8aTaU/domFbRSqYzk5HpI5mZP5NZ+bOYVRD9V5ZRRpYzC5tow2VzUZRaxIy8GczMn0lZehntvvZotmvBdG6d+2P1S/aH1R/x6uZXQRBoOPZHdIw/CQAxEqT0nZ+Q0rAel91Fg7dht7L+9ip1KxOKkI1MH6l57PYVdoudsZljyUnJobWnlR8d8iPunnc3t8y+hetmXMf3p32fyydfzqmjT6U0rTThte0C/N+y+6lvWI3T6sRpdVLZXsmmtk297vZwIBr+Hoc/7B/QMlGTPcNIPqsRXFaXbnEno0Kfy+aiJLVEtyiYWohMS4iTY7ENOmKs6vQ0IrTqoApSBtroickiYjReQF+13euTsUbykFOsKRSnFusK8EqEhc4GE/+frD96narVnt5VyyPTRiZ5xd5BkiU6/B2D4oiWkXXFQD1OH3O6uupqdfNqVjWt2uNxKde4kXEZFZyDkaB6HEvSSnaJWzKM3DuJoNXGIlg04xHUAmOxc1rXaasXyaB8Ju7GoTTdtoPLbk/pzJgxg7/+9a/U1tbyy1/+kscee4y5c+cyY8YMnnjiCXOZkQkAboeVx688RF2K/VVVK5sazJgEk71PkydAS3f0y/LU4owD0s03UBZMLlB//riicR+OxDhbW7x4g1FX8DQzz7Zfppdk4rJHs9y+qmoZnn+PwwG8jevYFrcE221zD0txaLCwilbK0stw29y0+Fq4cMKFjM4YDYBHFHg2I41zS4q4c9PTLNz8JjbRhi/k238iEhrXs9na+7laklaCN+QlxZpCvivfUBeiIJLlzGJyzmSm502n0F2IJ+DBJtgYnz2eDMfgXvOiIJJmT1PzdPtDEATS7emMzhhNcVoxLb4WwlKY2QWzuW7GdWq7lypf4sPtH4JoYflh1/Cv0dO5oiifY4uy+eyL35JqddMd6qbNP8wnyOpXJTjgR6ePHrbXpdPqZFzWONId6XQFu5iZP5PDRxzOsaXHcmLZiZw25jSumHIFfzrmTzy84GFuG3s+s4LRIpYeAf74ze/w1q0k3ZFOqj2VWk8tFdsX0vnCxcj3jkB+4mS6WjdT213L2ua1LG9czrbObfv4XR8cyPLgOW2dVieZzkzNNgKCsXxTMJR/PFiRAPHCo55oa9QNZ9hdajRqQUccVYqt7VVkA0XlBEE3Q3Yg+98I8UvN4522I9P3rWjrD/vp8HfoT0oZiA2Q5agguCfnv91i56KJF6m/v1X11m73paCKnUac4Xq5tzG2d21Xr7vdjUZQtqnn0Fdcr1rtlHPLIli028UKnwmCtiCrXN+78525P7ftsPw+sp+w26JtKBTixRdf5IwzzuDOO+/kkEMO4bHHHuP888/nZz/7GZdccslgjtNkPyY/zckPj+4N5v7vUjMP2WTvsy6ueNWUEabYB3D0+DzssQJkn1Q07Rd/TNfWdqg/Ty/J3GfjGM7YrSKHjMoGoLErwLYW7z4eUT80rGWzRUaK3QgWpxaTak/FIuoUDtnPSbOnMS57HFbBiifk4SfzfsJRxUclFBtbb7fyyKbn+NvKv+GwOmjsadztbLi9St1Ktth6vwCXppXiCXgoSi0acDESQRDIcmYxMXsiM/NnMiV3ClnOrMEe8YCwilbKM8opdBfS0tOCJEscXXI0l02+TG3z5LonufOzO7lt0Z08RAcrnE46LBb+5pToqFuOzWKjwdswvI9n3Uo1a9oiWBibNXZYF8dx29zRqAtLSkKcSFgK0xPqocMfLUiX5czi0InncOexf2BiOPq3rt4icv+S3yHs+JoUWWbq+reY+9J1ZFS8gyCFEXZ+g+2p77B920LaA+0IgkCDt4Gu4OBVNB+uyLJMT6iHFl8LOz07qWqvormnGV/Yt1e2L8nSoDltjWA00sBwXwbcsb31voxtV1M4EYy5S9W8Vz0BUsZwITJdQScuEmBv4bA6BjXSRS8HWH1/BrQt5XhXd1XHXiLsshpgb6MI+XrnopFzVZIlRFHc7XgEhcNHHE6BK2ou2dC6gXZ/+x71p7rpdSaDVKe5AaEyPs92TMbuFSGD6Pmjt/9lORZ9oJEXrLbRKVimFD4TBVHznJWIxnXt7qRLX7fthtYNu9eRycBF2xUrVnDzzTdTVFTEzTffzJQpU1i3bh2LFy/mqquu4mc/+xlvvvkmr7322lCM12Q/5axZxTht0dPt1RU1+IL7T36myYFBfBGyqcXay+QOFlIdVg6NLaWv7fBRUT/8XfDxebZmEbLkxOfafqWTaxuR9oFYX7OMjXFLsEekjtBdvnqgkO3MZkL2BPVL842zbuThBQ/z/fEXMCEUVtstbVhKg7eBzkAnnuDwvzapW8kWe6/4nJOSg9PqJD/FmMu2PwRBIMORMegO293FZrGpS/KbvE1IssTpY07njPIzgOgXodru2l1eFxYEXq54jgxHBu3+9uHrng758TZVsM0WDbUtcheRnZK9jwelT4YjQ50Mqe+up8EbLTIXlsK4rC6CkSAN3Q14Q16s2eXcccx9FMaKyVXYrTz0zR8Y/cwF5C99Aks4sfZCiqeB+e/+L8WeFjIcGYQiIeq76/fF29wrhCIhtnZsZUXjClY0rWBN8xoq2yvZ7tnOmuY1rGhcwfqW9dR319MZ6MQf9g/JJMSgZtoaRBUo9hDls11P6DOypF4VgHUcrUrOpZ44KggCkqQj2gqCIfFRrU5vQCje207bAnfBgCcL+yNeSNN8nwbjEZScUUmW2OnZqY51t5fVDxKK81J34kLQj9hQY0H28JgLgsDhxYerfX5d9/WedQiGJhAEBDU/Vo+qjir15z1y2sraWbUQ3Qc20aZ5bSoTN4ogm1QElokeSwT9z9k9mHQx3baDx4D/Ms2dO5fNmzfz8MMPU1NTw/3338/EiRMT2kyePJkLL7xw0AZpsv+TkWLjO7H8zC5/mHfWHrg3vCbDk4QiZKbTVuXESb2Cyv4QkRDvmDZF2+QcNqZXtH1vXfLP23ZvkGPv/5THvti6d4vR9SlCVpJaMmyXYA8FuSm5jMscR0SK4Al6SLWncuL4s/nrxKv4aUvv8vn3tr1LWA4P/yX1APWrVNE2IybAF7gK9jiDdrjhtDoZnzWeTEcmLT3R+JGLJl7E8SOPB6JfgsZljeOiiRfxxzk/JjsSva4WBhqo8dQgI9PU07Qv30Jymtaz2QpyTGgqSSvBbXXv40EZI9uZzaScSUzInsD03OnMKpjFIQWHMKtgFjPyZjAqfZQquIruPH581G9IjX1Z/cLl5PcpEn5BQBYttM74Hlsuehp/djS+xOprZ9SrN+Ku/pZMZyaNPY3DV3jfA2RZprqrmqqOKgJSQI02KXQXUugupMBdgN1ip8XfwobWDaxqWsXyxuV82/Ata5vXsrVzK3XddbT4WugKdu2xoLtXnbY6gsmA+jK4lNiIKKSKsTrLutW+jBYi04tHkA0sIxcEQ27KTGembjzFcEXZ/3rOS+U5vWOaZk8j05lJvbdejSLY13m2EJ2wsAgWbTE25lLVKnCltNuTQmTxHDHiCPXnr+q+2uP+kNF32iqTEUactrEiZBbBsmfHUYgKnFr7TJZlNR4hWTtl3GqmbbLjFNueKIian7OSLCGyZwK86bYdHAYs2m7dupX333+f733ve9hs/Wf6uN1unnzyyT0enMmBxUXzej/MzIgEk73N+pjYl+awMjJ7z2ffDxROmLT/5NpGJFl1TJdkpZDlHr5Ldvc1U4sz1PP8yy2tLN/R/7KyBxduZmebj9++U8H9H2zaewOs+VZdgi0gUJZRdlCJthB114zNHIsv5KM72A1Ax8TTOM1VSn446rj9tnEZnYFOmnqahncBpJCP1pZNtFmi8RbFaSXYRTv57t132Q5nXDYX47LH4ba5ae5pBuCaadfwh6P/wMMLHuY3R/yGM8eeyciiOVwe6f178+K6p0izp9Hia9EtMBeW9oFYX7cqsQhZ2sh97gAbCNnObErTSslz5ZFuT1ezR1PtqZRnlTMrfxblmeWEpTApKdncfuhPscZEltfSUjllVBkPHHsd1UfcSCB3LNvOfRhv0QxkoEEOUP/RT0nf9iXhSJgGb8M+fKdDQ7OvmWpPNdkp2WQ4MnBanQnChSAIuGwuclNyKUwtJMuZhdPqREamI9jB9s7tVLRWsLppNSsbV7K8cTkrGlfsViTI3s5BVZdEG8wm1eyL3uJVRuIR9MRYQ4XIdLIpE8ZmQEwbUNSCjrjlsDj2q8+ReOL3vxbq8zqnj8PiwG1zq9EIAGXpZXs6zD3HQCEsRezXWnYPvQLwYEyClKSVqGLolo4tNHr37HuKIAiGspojsr6JwRf2UdddB0SPoZGs62Qo+1PPaWsRLJp5wUo/VtGqmXOtOHuVn5NuMxa3sCefi33dtq9tMVfj7w4DFm3LyobBB4vJfsnskZlMKEgDYPmOdrMgmcleo7U7QF1ndMnj5BHpiKJZhExhRGYKk4uirrg1NZ00dPp1XrHvqGruxheK3khNN4uQaWIRBW46bqz6+18/2bxLm63N3TzzdbQQhtMm8v0jdz+Pa0B4Wwi1b2dzzJVZ6C4k3Z6O07J/fqnbE4rcRYzNHEtIijoAu4IevBNP49Ku3r+PC6sX4g15DTn7QlJIFRH3Ko3r2RJXhCwvJY98V/4BHXmRbk9nYs5EUu2p6j4vSy/bxVF24sjjKYyJ8CvaN1LtqcYX9iXkr/ZHi6+Fqo4qXXF3UKlbycY4B/yYjDEH1GSKy+ZidMZoJmRPQJZlyjLGcP3MGxFjX0hbkXhi21vcuvBW3t36Lh82LuXO0RNZMHo0p5QW88OCXO5b8zDpsazpA8lt6w152daxDZvFliCw+cN+FlYvZFXTql0EAItowWl1kmZPI9uZTYG7gMLUQgpTC8l0ZuK0OglEAqxvWc/G1o14Q8bz1Y0uTx5MjAiVA+lrsOIRQL/glyrG6g1fiLnndGIgjDoNjcQj7M+oWcc6hamM5gArDDfR1ojQqgp4evEIsowoagvAA+GIYm23rSRLfLLjE1Y1rdLvzGBWs5FCZFs7tqrvcUxm8vvn7mA3EUlHBJZBRNvBLMjR2BKLaEmeVRt3jLSucVmQDTltVYf/Hl7i8fnE61rWJRThi0eSJZ6teLbfmKmDHUOibVZWFtnZ2Yb+mZgkQxAELp5vum1N9j7r6+LzbE2xry8LJve6bT/ZOHzdtol5tpn7biD7CWfPLqY0Oyq4LKpsZkV1otv2vvc2Eo7l2f7w6HIKM/aSaFq7nC12G+G4ImRp9rQDvghZfwiCQGl6KTPzZjI+azwWwUJV0RTO8/SQFol+KVpcu5iOQIeuyAfQ6mtlW9e2vSv0wS55toXuQrKdB/49Ybo9nQnZE0i1p9LUs2sxR0mWaCs7jOvbez+7/rvxv7hsLuq66whEAv32G5bC1HXX0eZv27tFr+pWqQ54EZFxWeOGdRGy3SU3JZfR6aPpDnYzt2ge9x39B+YVzlOfbw+08/SGp3ls7WN8Wf81TfR+4V7qsLJy7X8IR8LUe+sNucl6Qj1s7dg6bAvQhaUw2zq30R3qJtORqT6+vHE5d352J4+seYT7lt7HbZ/exjtb3zEkvlpFK06rk0xnJjkpOdR761ndtJq67jp9AQNjy/MHHdl4tIEWaqatjgisiCWGogoM6F9GnMJa7rs+DfWbxJZpD8Y+i6cj0MGrm1/ljk/v4Jdf/ZKdXTsHtf/dQS9SQs0eNhjpES9ajUzf9/EISqat5vJ8ZF0xUO1LJ2phIBw24jD15y/rvtzl+Wc2PMOjax/lD0v/kJAx2+/YBIMOcgOTRko0AiQvQibLMs09zYY+M3WdzkKv61XLaWsRLIiI2te6agzXuXZj7fbUNS0KIqeNOU39/Z2t7/TbbmH1Qj7d+Sm/XvJr/lPxnz3a5oGG1UijBx54YIiHYXKwcNasYu59t4JAWOLVFTX85JSJpNgPvi/qJnuXhDxbswjZLpw4qYAHY07MTyqauGT+vp/174+1NR3qz6bTVh+bReSm48byk1fWAvDXjzfz76ujwsSSra18uCEq0OelObj26L3ksoVdipAVpxYf0I5MI6TaU0m1p1KUWkRHxmgonsMFnk08lplBRI7wVd1XFLgK6An1JC2uIskSDd4G2n3RQleDUYTFMHWr2GxLzCh2WAevcvdwJt2ezsTsiWxq20RTTxN5rjz8YT/doai7JjW9iOPsuTwZDLLdbmNT2yaqOqoochXR4muhOLV4lz7b/e10BDqwW+w09zRT4CoYdGFkF0I+As0VbBlZBEC+O59cV+7QbnMfUpxWjC/io7qrmuLUYu445A52dO3glcpXWNqwNKGtw+Kg3JnHBm8NAE81fMlvp5xPU08TRe4i3UJ5TT1N1HnryHXlDsvPulpPLY3eRnJduQiCQJuvjafWP7XLfmjqaeKZDc/w4qYXOar4KA4vPpxxmeN0lwXbLDYKUwvpCnSxoXUDbf42Ct2FZDoysYqJX0VDkRAtvhbqvfWDUhRsIFhEyy7j2R0S3Jl6163BDFq1uJPGNuP/rzc2PbFGcezpjW2wIiVkWWZj20Y+2vER39R/07s83Qv/s/h/uGTSJZw86uSh/xzsgxoFYGCzAxEpqz1R41KKNYXclH3/OasIrVrinHKs9YR/dbJikOJN8l35jM8aT2V7JTWeGqq7qlWhe3P7Zt7f9r663Q+2f8ANM29I3lmsAJcWalSKzrlmpAiZct3qZdUqxf803ckxN67WcYp32mq+z1imrV6symBeb8eUHMOLm17EG/LyZe2XXDjxwoTJ/e5gNy9sfAGInmtTcqYM2rYPBAz9ZbriiisACIfDPPvss5x88skUFhYO6cBMDkyUgmSvrKhRC5KdN6dkXw/L5ABnfW2c09YsQrYLU4vTKUh30NgVYPGWFnqCYVz2Pf/iMtisiStCZh5HY5wzu4S/LdxCTbuPzyubWVndzoySTH73ToXa5scnTcDt2IvHu3YZG/oUIdtf8+4GG5toI8+Vh2/6+Vzy3l08nZ5OUBT4fOfnHF18NK2+1qRibEegg3Z/OzaLjSZfE4Xuwr33BTeuCBlEnUMplgNnWb0eafY0Vbht7G4kxZZCoauQ3JRc7BY7naOP4qaKV/hRQR4AL2x6gZ/M+wl13XXkp+QniF6SLFHvrcciWki1pdIZ7KQn3IPbNsQFwWIRF4oDviS1BJf1wM1/FwWR0emj8Yf9UWHcXUBZepkq3i6pW0KKNYVJOZMYnTEaq2Dh369exHsO6BJkXl37FOdMvZJ6bz3p9vSk11ogEqDB24An4MET9Aw70bbV18oOzw7SHelYBAsfbv+Q/278L76wT20zLXcaAGtbohOAgUiAj6s/5uPqj7GJNiZmT2RK7hSm5EyhLL0sqTs73ZGOy+ai2ddMU08T6fZ01ZVvE220+Fqo6a6hK9CF0+okJyWn336GiuK0XSdQdgfVaWugSr2MtotWEVl1M20RDOVwDqTIkq4j0aAArIcn6OH+b+9nU3v/ufohKcRT659iZdNKrp9x/V4taqa+R6PFqwzsC2/Iq66cGZk2cq9PTvSHhIRdtBOWwknbGM20VQX/Qbz9OHzE4VS2VwJRt+3I9JGEpTD/WvOvhLF8Xfc1l0++XLMAqp5rdKBOW7topyS1fy1DkiWsolU3q1Zx0GqJ4YIgIAiCdjwCvbEHgqD9maAI8HrHySJYVCF7T85Vp9XJgrIFvLHlDSJyhA+3f8iFEy9Un39l8yt4QtFosHmF85iVP2u3t3UgMqBvaVarleuvv56Kigr9xiYmSbh4fimvrIi6Ff67tNoUbU2GHMVp67SJjMk7sCqZDwaCILBgUgHPflNNMCzxxeYWTp4yvCbmQhGJDbGYi7IcFxmu3Q/8P5hQ3LZ3vxpz236ymTNmjGBtTACfWJjGuXvzM1iSkGuWUZHdK+iNzhh9QOVmDgbOqefheO9uzuru5sX0NPwRP0sbl5Lvirof+9tfzT3NyMhkOjPpCnThDXk1v7gMGsEe5KZeh2a2MzsqwuxBUY79kVR7KhNzJqrCXLy43jHpu5y4/D9MCgSpcNjZ0bWDDa0bKM8op9XfSqG79/O2I9BBm7eR8RXvI2WUsLF4Gl2BrqEXbetWUhE3mVKaVnrAX5c2i43yzHL8YT+t/lbV8VaWXtZvzuRVZSezuPY9PBaRz5pXML/7RCyihUJXYVIhqc3Xpl6LzT3NjHCP2OtuwWREpAg7unYgI+OyuXhn6zs8s+EZ9fkMewaXTbmMI0YcgSAI1HbX8sG2D1hUswh/JJp/H5JCrG1Zqwq6FsFCaVopozJGMSZjDJNyJlGaVqr2aRWt5LvyiUgRPCEPG9s2kmJNwSpa6Q5247Q6yXfnDwsha3dRlsrrumgVQVZH3DWSj2tUQHVYHDht+pOkOSk5uq5jp8VJnitvjz7rg5Eg9y9LFGzT7GkcX3o8x5Qew0c7PuK9be8BsLp5NT9e9GNm5M2gM9BJZ6CTjkAHwUiQc8efy3fLv7vb40iG6nTWE+ANujMhMc92T6IRwlIYf9iv+3e+O9iNzWLDYUm++kVx2gYJarYREHqd5ElQBc9BLCR46IhD+ff6fyMj81XtV1w44ULe2PIGNZ6onqAcp5AUYlHNooSl+PGo16YWAkiSpBnZ5Ql6aOppAmBUxqikbQfiYBYF7UxbZf9rxihEQuStegF4hXybDU/GCMSCKUiOPudIbBWAKIiax0kR4Afr8/jkUSfzdtXbROQIH+34iLPGnoXT6qTWU8uH2z8EoiL4eePOG5TtHUgM+AjMnz+flStXDsVYTA4SZo/MGlBBsnBkeGaAmewfdPpC7GiN5jtOLkrHYhYh65cFk3pzbT/eMPxybTc3dhMIRz8Lppm5xAPinNklFGdGxZfPNjXz67c2qM/9/PTJe/eaaNmEFOiiMubKzE3JjRasOQiLkGkhpGQRHHM0V3Z6EGM38Z9Wf0qbv63f6snekJcWbyOhjW/j37aIQCSw97JQG9fRIIJXjN5SFroLSbOl7Z1tDzPcNjeF7sJd3NCZJYfizyjm1vYO9bHXN7+OTbQlZHzKskyjt5GsFc/y162v8eq3fyG3sZIm3655uYNO3Uo1zxaiGX0HgwPebXMzNmssDtFBfXc9nqCn330dkSL0jD+ZWzt771mf2fA0/rCfmu6afnNalWziTE8jmcEePEEPPeG9nDetQVewi85AJ5mOTDa1beK5iufU544feTx/mf9zTg5byV31PJkb3maM38fVUy7nHwv+wQ0zb+CYkmPIcSa6YSNyhO1d2/ls52c8se4Jfvz5j3lx04u77FOLaCHTkUlRahF2ix1Jlsh355PpzNyvBVsFo0vl9QQkRfwy4rQ1Iho6rc6ESSKtdnqirSAIezQxKMkSD69+mE1tUcE2w5HBjTNv5B8n/IOLJl3EiNQRXDHlCn4676dq1rIn6GFx7WLWtqyl2lNNV7ALf8TP8xuf7/dv456iLB834rTVWgIfz2Dl2XpDXlp69LPuO/wdeILa37UVh6YhcRFRtxDZYE9MZToymZYXdfw3+5r5dOenvLr5VSC6auLmWTerbT+p/kQzPkAPI67p+DzbZNEIMLB4BIuQ3EGrjEsURM284LJvn6boy4fgywcY9dmfmPbG7Ux65CTGP3EGY164mpFv3E7xB79k/JJHcX31dyydNdqF5QSwClbjWdg6ZDuz1cJy3pCXRTWLkGWZpzc8rbqCTx196kFRE2GgDHg95A033MCdd95JTU0Nc+bMwe1OnPmfPn36oA3O5MBEEAQumlfKr2LCwTNLtvPbs6b123b5jnaufHIpIzJSeOWGw0ndm0t4TQ4INphFyAxxWHkOKTYLvlCEhRubiEhyUjFvY0MXVz/5LSVZLp66eu5eiVJYW9uh/mzm2Q4Mu1XkpuPH8tOY27bTFwLg+In5HDmu/yw1T9CDN+Q19OVuQOz8hh02K76YwKfk2R6MRcj0EKd+j9ItCznR28MHqW66gl2saV5DhiODfFd+gjDY5mtj9dKHeLi7AnetxK/E62hKyaXIXTT0rr66VWyOi0YochXhtg+xK3Q/I92RQXP5cRy+4j/M9AdY5XRQ013D5o7NjMkYQ3ugndyUXLqCXTR7G3it+Rs+To3uw7LKN8gaMXXIndNy7bIE0XZC1gRNZ9aBRLYzm+l502nxtVDXXUejNxpxkWJNoSfUgz/iR0DA7UznyOzpTPdXssbpoM5bz9f1X3PEiCPIceZQlFqU0G9HoAPrpnepXPZP8kQH1pN/T1fm2KF3TRukPdCOJEt4Q17+uuKv6pfmy6VUbvv2LWyfP7XLaySrA3/uOMbkTySYWUJw1PfYabexMthKhWcn27q2UeupTRAfXt38Kj2hHi6fcnm/guxezd/eC6guQ73sTAPikCFnYIzBiCrYm/x343/5uu5rIOoA/sncnzAmc9d8/Rn5M/jj0X/kkTWPsKxxmfq4TbThtDjxhDxE5AivbH5FO890NzFSXEzZ70b2v5JnC/Tr6Dc8LllGFEXdZeuaolwMSZZ0RUM1V1knezW+3WBy+IjDWdO8BoBH1zyqjuG75d/l8OLD+XDHh2xs20htdy0b2zYyKWdS/2PT8SwayWre2hEn2mYkF20lOerYNZIBbBEt2u5kIeaOFfsff0rdGkrWvtbvczZvCzZvr8CfCcB7RNa+guWs/9PYaG/27WBNHJ8+5nQW1SwC4N2t75LtzGZ182ogauQ4adRJg7KdA40Bf9O+4IILALjlllvUx9QlGYJAJKKfp2NicvasEv7w/iZ8oQgvLqvhluPHkZ+e6OiQZZlfvbkejz/MJr+Hd9fUc/7c0iQ9mpj0z/r4ImRmDmpSnDYLR4/P5YP1jbR6g6za2c6csv5nOn/3TgV1nX7qOv28uaqOC+cNfeXbtXF5ttOKM4d8ewca584u4aGFW6jtiOYUWkSB/zltYtL2PaEeartryXJmDapwI+9cmiAMlaSWkGY/OF2ZetgnnYH09m1c3dnFBzEB791t7zI9bzr13fWUZ0W/KIQiIbZ1bOWFrg0gCnhFkdVV7+EqmkF3qHvo92/dyoQ82yJ30UEj9hlFEASsU85BWPEfruno5MbCfADeqnqL2+fcTl13HdnObBp7GmmufI+Pnb235597tvJdXzudwc6hE219HYSbN7GpLHqPlZeSR74rf2i2NUxx2VyMtI2kwF1Ai6+FWk8tnqAHt9VNaVopafY0HBYH26adzS/e/wkXjCgkIgi8seUNZuTNoLqrmgxHhipAyrJMQ3c97296if/kZiPKMvdvfp/W/Cm7iLv7gpAUormnGafNyUMrH6LN3wbAXJ+f2xuqk35BFMMBXA3rcDWsUx8rA44Ews4Mahf8jJbSQ6j2VLOicQWvb3kdgPe3v09PuIdrp187KJN0wUiQr+u+piPQwYTsCYzLHDdsJv8UoVXXnWl0Sb3QmyeavImg68YdTny4/UPeqnoLiI791tm39ivYKqQ70vnR3B/R1NNESAqR6cjEZXXhC/u4eeHNeENevqj5gjPHntlvccc9wohWJURd5kYmSeOdtvHRIQNFQupdeq+xWT0HLaAulzfioBUFUTP7drCK0/VlXuE8Hl/7OCEppAq2Re4izh13LgALyhawsW0jAB/v+Lhf0Tbdka5fYE/Qz32OL0Kmdd7KROMRtLJlFdHdKlg1t6lMylgF6y7HSQj5KP74NwjKyTr/OuptdmipJL2rHkd7NRZ/J0Kf11k6dlC0eSG+mRf2u18EOSom6+UYD4Sy9DKm5k5lXcs6GnoaeGjlQ+pzl0y6JHr/OMQLi/ZHBizabtu2bSjGYXKQkeGycemhI3n0i20EwxKPLNrKz78zOaHNJxVNCULNp5uaTNHWZMCsizuHphQPrwIgw40Fkwr4YH10edn76xr6FW03NnTxxebe2dq31uwd0XbVzg71Z/M4Dhy7VeTG48byP69F3bYXzxvJ2HxtMc8T9NDubx9Ut62085uE3MySNLMIWVIcqYTGLmDyxnc4ztvDp24X7YF2ltQvIcORQYG7gFR7Ku2BdhaufYq2OGf85z3VzA756Qx0Dr1oW7+KLbbeYzoyY6Qp2vZD2qijCKTmc1R3ExMDQTY67Gzt3Ep1VzWyLFPbXUuTt4nXaxcmvO5zp4PLtn9Fc0bZ0OWh1i5jm81GUOwtQnawuqUdFgfFqcXkp+QTiARw2VwJX2Yzx51MxqI/c2mXh39npBOSQryw8QW+P/X71HTXMC5zHIIg0BXsYmfluzxvlwABSRBY0ryK4wOd9IR69rm7tDPQiTfk5fOaz9U82txwhD82t2AFIs50enLH0Z09Gk/WSMSeNtJaNpPeupWUrvp++7T6O8ld+Tzdo49kfNZ4xmeNp8hdxD9X/xMZOZqFG/Zz86ybdzsHNRQJ8enOT3l9y+uq0AzgsrqYljeNmXkzmVUwS11Ov88wcJkaXVIvyMYcuREig+5uHAqWNy7nyXVPqr9fPe1qZhfMNvTavpNJLpuL75Z/l+c3Po+MzCuVr3DL7FuSvHr30fvcNVqITJIlNYc135W/R7nhal6qXsSGwRxX3UzVmINWL/tWdZnr6cSxAntG41BcNhcz82fybcO36mM/nP5DtfDh/ML5/Nv2bzwhD980fMMVwSt2KfxoZJWDXiGysBRmQ+sGtT+te2QlE9ZItIARcV1xvfbdt4Vf/h1HZy0A/qLpOE++l/b2TbT4WnoLOsoSYqAb0d9JqHYFUxf+AYCylS+waerZ0N+qyVg8gtH3YJTTR5/OupboxJ+Sjz4pexKHFh2KN+QdtO0cSAw4NKisrEzzn4mJUa45egwOa/QUfPabalq7A+pzsizzwCeVCe2/2NxCMGzm2w4GoYjEC99W8/dPt9Du1fjDewCwLhaPYLeIjNMRqQ52jp+YjzX2pf25b6pp9gR2afPYF4kTd19XtfbbbjDp8ofUmIuJhWmkOw+uAkeDxQVzS7ltwTiuPHwUd5+a3GWrEAgHaOoZxCzNnjYsrVtYGyfajkofdUBXqN9TrNPPB+C29g4UD9n729+PLuP21iHJElUdVbzfsiLhdTstAl2Nq2nxtQzqjfYuBL3QvFF12goIjEwbaQrx/WCz2AmOPxkBuKajdzLx7a1vA9Dqa2VtzZesIvHzNCgKbNy+kK5gl1pZebCRq7856IqQ6WGz2Ei1p+4iKOS58mmdfAY3tHdSFI66zda1rmNt61rquutUIbGxp5HXtrxKOE7sWSgEiHRU6+ZL7g3a/G1UtFbw2uboclpRlvljcwu5EYnAd/6C5SfVpP1gIflnPULpUXeRe8KvCZ3zCJWXvcA3l73A8lPuYf1RN7Nt9iU0TDwVKeZytfS0JfzNOKb0GG6fczsWIfr80oal3Lf0Puq66wY03rAU5uMdH3Pbp7fxxLonEgRbgJ5wD9/Uf8O/1vyLmz+5mf9u/C++sG9PdtGeIRurUg/oilayIBvKvt0f4hGqu6p5cMWD6vs5o/wMTiw7cY/6PGXUKao491XdVwlO1kFBMHYsjez/Rm8jgUj0M14rGqG6q5qekHb+taGl93LU8a17Xsj64q4sy1iw6BavMnoKdgW7qO2uNdY4xtElR6s/Lxi5IMFNa7PYOLo0+nxYCrNo56IB9R2P1mTK5vbNajb5jLwZmtev4qLVct0rbbRWCijOX+W/+H3srl5K9tpovm/EYqf15N+BaMFCH0FfEJGc6fjTi2gdfQShcdEYAkdPK9nr3uh3m8j6n0+7w4z8GQmOeAGBK6ZcMWyKdA5HBnwUnn76ac1/Q8nvf/97BEHgtttuG9LtmOwd8tOcXBRz6PlCER5b3CsGfVzRxLraxCIq3YEwy7Yn3qSZDJwvNjdz6l+/4CevrOVPH2ziuP/7jGeW7CAiHXhrEXqCYaqauwGYUJiG3br/F7cYSnJSHVw8P3pNeoMRHlq4OeH5pi4/b6xKvMGSZHhvXf+um8Fi+Y52lNNz/mgznH53sYgCty0Yz6/OmILbQD64IAi0+9sHraCVVPMtHaLIKkfUhZnvyifPlWcKfBpYxp2CZE9lTCjMWd6oG8EX9vHZzs9o8DZQ66nllYr/4o19wcgP/z975x3mRnW+7XuKyvbe7PW6997AphgwvbckQAIkJCSQQAohjVRII/lSCekh5RcICTVAIKHZdLABdxv3tl7b23vTSpr5/hjNaCTNjLTedQHOzeULlaMzZ+aMtNIzz3ne+JLFDXtfoCPUQXe4+/ANsH4DUV1jp88QbcuzyykOFr8nCgkdDnwzYss4e/sYEzV+nGxu3UxDbwP7u/fz353xH05XBOI/aJ6PtqO27qYzdHiKy0X3rUiILRlbMFa8L13wK378864lIPv4VnP8O+m/tvyLjoEOajtr6Qh18Pqup3mDRNGlWVVo2vkMbf1tR3rYCYSiIWo7a7lv833Wj/rPtnWwsD9E9+KbCCz4OMR+PCuyQpaaRWGwkOq8amaVzmJmzRJqZn2Y/IU3MHDy56lf+jUiWUUASKFOGnoaaOhuoL67nvqeeuaUz+Erx33FcuBvatnErS/eyu/W/i5t8aiIFuGF2he45YVbuGfDPbT0t1jPLahYwPUzr2fxiMUJDrqwFubxHY/zhRe+wPLa5Yf3wpULkiSlF69iS+rT9qWnz0vNOGrhKNI90M1P3/6pJVouHrGYK6dcOeR+g2qQiydcbN1/eOvDQ+7TTkaipxQrPpemmT3PtibPeZWapmvouk5YC3v2ZRUPS5Mv6+TM9HiBKxqGSKxKqrdDXAeZ9DmoES0y6GXwCyoWcO20a7ls4mVcO/3alOdPrznduv187fOHZDqQJAldc49HWNu41ro9p3yOZ1/W8fc4L8ycXVVSXduZc23GU5ifCXKom5HLfmi127Hwo2jFY439kJ2PvykAh5d8yXqsYvU/kMKJF7nMsSuyktZp2xvupbkvfVE8E1mSOX/c+db9pTVLGVMwJuPXvx8ZdDzC5z//+YT74XCY3t5e/H4/2dnZXHtt6htoOHjrrbf44x//KAqdvce44ZRx3L+yloGoxt9f38MNS8ZRkOXjl8/HXbYXzh7Bf9YZV+SXb2nkhAnOhXME3tS29PL9p97h2XcSvyC394b51mMbuX9lLbdfOI35o4vYeKCTlbtaWLm7lc0HOxlbmsMVC0dxzoxKAuqxkRmWCZsPdmL+vZohltRnxGeXTuThVXX0DkT5x8parjtxLGNKjR9Df39jL+GocUBPn1LOsi2NAPxn3QGuXTzmsI1p5a74D+PjxpZ4tBQMB5qusaphFVtatzC7bDat/a0UBIaeBx3e+xovZ2ehxb4IzyydSYG/QAh8XviCRCefi7zhIW5qaebJ3DGE9Agv7HuBxVWLiWpRXtxvuEl8us6P1VF8Uj9ARJJ4uXcfJ0RDdPR3pCwRHDYOrGWfqlrL6itzKg9rsax3O8ExpxAqnUigeTvXtzbzzTLj8+zJXU+yuGoReyKGA3NGKMQHF9/Eq2/+gP3RHt4KBrh5+/M0VsxkRO6I4X3PaFHk/avYWBKft0lFk/DLfo8Xvb8pLZpA64TTOHnrM1zY1cN/8nLojfTy+PbH+cjUj6B0KDy0+X6r/Ul6kFcl46LL6qb1jAi1EYqGjlqMSGeok2f2PGNd0FnS28fHOzrpmnQ2uWd+3/O1kiSR7ctOiXfQc8qgpxn/QC/zKuYR1sJEtAhdA13s69rH1OKpfP34r/Ozt39G50AnOjov1b3Eq/tf5ZTqU1gyagnl2eUUBgqRJZmoFuW1A6/xyLZHaOhN/N46t3wuH5j0Aatq+xmjz0DTNXa07+CNA2/w7J5niepROkId/HH9H3l699PMLp9NOBomFA0xEB1AkiTOHH0mk4snD+ORtR+Q9E0ydWfqkp42YxOO7UJkUS3KXavvorHX+N44rmAcn5796WH7LDtz9Jk8ufNJ2kJtvNXwFjvbd1rnx5BJU3jLaJJZprDdBezmtLViD9IIjpqu4Zf9nmKa6eKMkubigGT+zztT1XSEphubKfR5HZPBRCNYw5Qkzht3nuvzI3JHML1kOptaNlHfU8+mlk3MKJ0xuG2kiUdY07jGuj27bLZnX+YxS+dgNuMp3DBzgmVJNvqLFcerfOWX+LqN91R39QLqppzNZPOCW7LT1tye+VjlTKJTL0LZ/AS+vjaK1z9Cy/yrU8auSOkzbfsj/fSF+2AQC3ROHXUqTb1N9IR7uGrqVZm/8H3KoEXbtrbUq8Pbt2/n05/+NF/+8peHZVDJdHd385GPfIQ//elPfP/73l8mBO8uqgqy+OCCav6xspaegSh/eW0PM0cWsCm2FHrGyHy+e9F0nlp/AE2H5VsbU7JvBem555Vd/L9ntibES8ytKWRUUTZPxATxzQc7ueKPK8jyKfSFE/+4H+zo5/WdLRRl+7h8XjVXHlfDhPJj/4e53a09XRQhy4iyvACfPHkcdy3bTkTT+emzW/n1h+fROxDhvpXGl01VlvjBpTOp/fNKtjd289aeNg609zGi8PAsp31zd9xZs3Bs0WHZhsAgFA3xgSc+wJ7OPRQFilg0YhGNPY2MzB1p5YYdKvq+N1meHT9HZpTMIC8gIkvSoc66AjY8RFlU4yMhib/4DXfWM3ufwSf7CMWcWh/q7Ca49GMcv/LHvKZqNMnQWL+WkmAJI/NGHh5xPKkIWWV25ft+Wb0nsszAeT/B//dLOa+7h98WFXJAVVjXtI5dbfGL1Z+miIHiMSwceQL7a59DlyRW1b/JxP52uga6huUiikXTFnrDvWwMGKsYyrPKGZk78ph27B1tfIoPaf51sPUZvtLaxqvZWbQpMm81vMWCygW8uv8VaqNGLt/0UIhrFn+VVat+RJ8k8aLUzxnttXQVTSSQdXRE27quOqt6t6Lr3NbSSm/lDAKX34PkUpk8HVIw5rSN9FOkZoNq7JtZsKi2s5ZxBeO4a+ldPL37aZ7c9SQ94R6iepTl+5azfJ+R5azKKqVZpUS0SIpza3bZbD4w6QNMLJqYsn1Zkq0c3bPHnM39m+/nzfo3jW131SY4HE3WNK7hx0t+TGnW8JtBdCmDQmSZRhrEMkI9nbbHeDzC/Vvut7KTC/wFfHHBF4f8ncKOX/Fz6cRL+cvGvwDw0NaH+NrxXxuWvjNxa0qSRFSLpv07W9tpc9rmOzttdfS0sQexhulzaGNif6aOU1MMdOtLlmRk2VuENHNQJSRLNHbCfC6TCxKD4YzRZ7CpZRNgFCQbtGjrUYispa/F+iwZXzA+7d9iy/2ezsEsKUY8gtdhlWyObx3ydr1C0eb/AhD1ZXPgjK8jyZI1bll2Pjfsrt3oKV9F3vIkkq5Ruvo+2mZeihbLs7c7bdOdQxqDd/nLkswVU64Y1GvezwzLN/iJEyfyox/9KMWFO1zcdNNNnH/++ZxxxhmHpX/B0eXTp463cjT/+tpufvbsVuu5L5w+iaIcP/NHG18GdzX1sLdFBFQPhkdX1/H9pzZbgm1ZXoCff2g2j9x4Ar+6ai4P37iY6SPiLqxkwTboi39MtPWGuefV3Zzx85f47Ys7jswODAF7EbIZI4VomymfXDKO0lzjy/ST6w+ybl87j6zeT3uvsVTrwtkjqCwIcuHsEdZrnlp/eCIS+gairK8z5nFcaQ7leWLJ7uEkoASsasZtoTZ2tO+gO9xNe6h9SP1q0TCR+nW8lmXMX74/n9EFowkqYj7TIY07lUjRGAA+dWAXhZIhkr5Z/yYrDr4BQJamcbVaSkN+BScWT7deu6H2BbrD3YcvQ3P/22y3ibbVedWiCFkacseeSsvMS/EBH7dl23bF8jdP7O1j/JRLaO9vZ1rlQuv5//kgv24VHaEOhpNw7eu8HQwSjf3gmlQ06X1bhGwwFIw7nfbRiynUNL7eEl8Ncv+W+3l211PW/c8qFfTmV7LIXwZAp6JwYMfTtPe3H+khA0a8yhM7n7AyGc/t6aUkqwztinvxB4bgyM8qtG2k3bqpyirjCsdRnVdNc18zPtnHpRMv5e6ld/OBSR9IucgT0SLU99QnCLYzS2dyxwl3cNvxtzkKtslU5lTyxQVf5NuLv83YgrGu7XrCPfx6za8zjk8YiA7w4NYHWV67PH1j3VsAAzJeUm905y1sSZKET/UdcoG3w8nLdS/zVOw9oUgKX5j/hcMilJ826jSr37VNa9nSumV4Os6wqJyOnnE8QkAJpBRVMzHFzHRF6iRJQsZ72bqGu2iagB67KJBGXFQlFYX0gnJGQp9uiJXDHV+ysHIhBX7jN99b9W8N+vurKTY7XQBZ17TOup0uGgGwHLvpHMxmVrDbezzZaesLdVL1wo+t5+uXfIFwXqUhrMfkPbcidebFHQkJqWwyzeNPBUDt76Rk7YMp21QkYy69zke7AC84PAyb7UJRFA4cGFyofCb861//YvXq1dx5550ZtQ+FQnR2dib8ExzbVBdlc9k8I7utqz/ClvrY8sCR+Zw+1fiDdurk+B+2F2JLsgXp2bi/g9se3WDdv+7EMbzwpVO5bF41ckwoXzCmmCduPok7L5vJuNIcqmJi3PcvmcHzX1zCO3ecw78+tYiL54xIyIT92bPbqGvzDsk/2qyraweMLM8plcLRlym5AZXPnR7/YXTn/zbzF1vm9CdOMn4EXTCrynrsP+uH//MfYE1tG5FYoO3x40Se7ZHggyXxKs7La5ejKuqQC5L1HlzNW6pOf8zFNad8DllqlnBlZoLiI3rpH9BklRxd5zNN8aXC0diPnas7u9Ann4dP9jF3xkfI0ozHX+2toz/SP+xCHwDdjdCygx2+uEhQk18jhPg0SJKEdPq3CWUXc0l3N2W2HGJJ1/l8Zy/tE04jFA0xuXgyEwKGCLEt4Ce843kaehvSZh0Ohuje11mRFZ+zKcVTxBxmgE/1E7rwl/TllnF2Ty+n9RjfhzpCHXTGqmGf291D+dRLyFKzOGnsOdZrV7VsoKWvZVjnMVOaept4bs8z1v2Pd3TRdelvyS8aN7SOg7YL40mCtE/2MaFwAlU5VTT1NhHVomT7svnApA9w99K7uW7GdZw95mzmlc9jVN4oAkoACYlpJdP4zuLv8I1F3zikGINpJdP4wUk/4Acn/YBvL/423z/x+/xkyU/4ySk/scS9La1beGzHYxn197dNf+PR7Y/yx/V/ZE3DmrTtM3LaZrCkPpNCWAAjc0cec39Td7bv5E/r/2Td/9iMjyUUjxpOfIqPyydebt2/a9Vdgy5450jM6eyF6XSWPaSV3nCvFQ9Rk1fj6UBVZTWz71xpohss93UGXamymj4eQc4gHiHmAE63pN5yCg822DYNqqxyyqhTAGNV0kv7Xhr067N8Wfjk1Asg9miETETbjOIRYs5qGdm1gKF50cYQ6iUmv/5HfL3GxcKuMSfSPjWeD2sKv24OX3MspkhcN/9q9Ni5WLLmfpT+zoRtypKMgrcL2BSeh3suBXEGLdo+8cQTCf8ef/xxfv/733PNNddw4oknDuvg9u3bx+c//3nuu+8+gsHMvkDeeeedFBQUWP9GjRo1rGMSHB5uOm0Cipz4h+ILp0+yPniWTomLtsu3Nh3Rsb1baekOccO9qwjFHLZXHTeK71w4nVyHAkSKLHHVcTUs/9KpvHHb6dx91VyuXjSaCeV5yLLEonEl3HXlXFbedjofWlANQFTT+bNNyDvW6OgLs63ByGubVpVP0PfuyeI9FrhyYQ2jS4zMuhW7WtndbDjcF48rsVzL48pyrazg9XUd7Gkefhf8it32PFsh2h52Bno4+X+3WwWtVjesJhwN09rfOqTK9eG9ryVEI8wqm4Vf8YtiRxkSqFlE64mfA+ADXd3UROIrIvKiGtd29bN39HEUB4sZOeoETo4Yn3edEtQ1rKGht8Fapjxs1K4AYEesgJUqq4zOG31MOr2ONQrza9h/8ucJ6PDRjvj76oLuXqpGL6GdKAWBAsqyyphdfZL1/Ksd2+jt3E9r3/AVZZXrVrEitkxfQmJ66XTxvsyQ4uLx7Dnru+iyyjda2sjV4k6kgKZxc7/MvhEzqMyu5NSZHyMvdgHyNfro7qw7fA54F3Rd57Edj9ER+yw/raeXvInnUjR6SZpXZkCwMH7b5rQ18Sk+JhRNoCK7gqbeJstdl+vP5ewxZ3PdjOv4ynFf4Sen/IS/nfM37j3vXr69+NtDFvhkSWZ84XimlUxjQtEERuWPYlTeKG6ee7MlUD287WG2t2337Gdv515eqH3Buv+4rWigI1J6p62Xoy+hnX5sRx+40djbyE/e+ol1ceL0mtM5c/SZh3WbS6qXML7AyLJtC7XxvTe+5yjc1nbWcs+Ge3hixxNpnZ4ZCevEisp5NNvXtc+67ZZna27PzSWZ0M4UBNO4WdON3VzCn26bOoYwl66dJEmokpq2eBVSvIDecLO0Zql1e1ntskFtQ5ZkKnMqjbgCGxEtwsbmjQDk+fMyyky2zh2vQmR6PK5AkpyFbrs71r/5SSr2vG6MKZjPgaVftQpHQjxGRZZkx+1a24v9N1BYTdPkswFQBnooXvtAwjbNHGMvp23GkR6CQ2bQou0ll1yS8O+yyy7j9ttvZ9asWfzlL38Z1sGtWrWKxsZG5s+fj6qqqKrKSy+9xK9+9StUVSUaTQ3Vvu222+jo6LD+7du3z6FnwbHG6JIcLrYttZ45ssBy2QJMqcyjqsD4EbFiVwu9A8P84/M9RjiqcdP9q9nfbiy3nFdTyO0XTU/zqvQU5fj52rlTrciEf725j7aegYxfv7q2zRL/DjdrauP522a8hiBz/KrMl85KdbZ8ckniUsMLZ9kiEjYMf0SCPc9WFCE7AvhzUOd/lMu7jPepjs5rB14jHA3T1ndoFc91XSeydwUvxUTboOxjTP4YUYRskGSf/CVaRy3EB3zRthz7+o4OGLeEkC+LiuwK8v35nFgaL9q6rvZFuge66RwY5pVHtSsYAPb6jAuBFdkVFGWJz9pMUGSF7JlX0DRqIVd1dnFpVzen9/TypdY2WqeeT1+4jxG5IyjJKmHRyBOtL+v/ywlSufs16nvrh+XHUbS7kfauOnbGhPcxBWMoDhaLiIsM8ck+SiecyfbjP05FNMqtrfHPyI91dOGbfC6qmkVpdinF2SWcGItI6JVl9mx/8vA44D3oGujiia3x5a8f7YvSd9Itw3OhxR6P0O+8XwElwMSiiZRkldDY4756Q5IkVHnQZVcGxZTiKVw28TLAEK3uXnM3vWHn1WO6rnPfO/cliClbWrewtXWrY3tI77IF4gJNuqZS+niEY43OgU7uXHmntTR9cvFkrptx3WHfriIrfO34rzEmfwyQKtz2hHv428a/8bVXvsbze5/n/i33W/nOXqQ79lYOqsdk2ouQueXZQlxUS/cZbwpq6cQ0c0m9W3/2glNe56Kma1bsgZtma76nTcEzndM2E3H6UKjMqWRWmfE9qLG30cpTHgpbW7fSF4sxml02O6Pvr6Ygbl54cULXdeu4us2TKf7K3Y3kPvcd6/GDp36ZSE48asQ8J8zbTp9DZl/m9gDq5n0YXTLmrGD7ssR2sf+8Lg6Y/Yl4hMPHoH8taZqW8C8ajVJfX8/9999PVVVV+g4Gwemnn86GDRtYu3at9W/BggV85CMfYe3atShKqnMuEAiQn5+f8E/w7uDzZ0ykJMdPQJX5xvlTE/5ASpJkRSQMRDRe39Hi1o0A+OF/N7Nil/GjviwvwO+unk9AHR6naXGOnysWGA72vnCUe1fsTfMKg8fX7uey377OhXe/ekSE29V7hWg7VM6fWcVMWxbw+LIcTp2UmMF1vj0iYd3wRiSEIlHW1LYDUF2UxcjDVOhMkMTxN3Jpdy9y7MvXC7XLCSgB6nvrCUcHv5y3K9zFtpaNtMX+Zs8qm40qqxQGCodz1O95sv059Jz/U/qzizm9t49fNjTx3aYWruvoom7iUvID+RQGC1FkhQWzPkZ+7ML2G7376Y/2D6s7E4DaN9jt81lZqJU5lWSr2WleJDApyS7lwCm3oPiy+G5zK79sbCYnr4rGsgnk+HIoCZaQ7ctmVN4oZuQbF8vqVZWmXctp7Wsdcs40QP+eVxKiESYWTqTAX/CuEoeONqVZpegLPk79uJO5vKuH7ze18JWWNm7s6GL3+JMpCZaQ7883luzaqp6vatlIY2/jEY1IeHLnEzQMGILq8X395Mz6MAVF7o6/QWF32nrk9QbVIJOKJlEYKKSpt+mo/si/bOJlTCqaBBjCjlnEKpm1jWst0ccuJj+x8wnXvjNxZ2aypB7i1eXfLfRH+vl/b/4/DvYYF/JH5I7gSwu+dNiFeJM8fx7fWPSNFOH2yZ1PcssLt/D0nqcThLEHtjxAf6Tftb+y7DKKg94rvTKJusikCBnE80HTaZnm9jIpROblyDX7UWTFU1w0801Nl6ZnX7F2mby/D5c784yaeC2k5/c+P+T+BhuNAIkuWjdxWkMzMm1xz7Q1LgiA+tQXkWMXxdonnk7nxNMTG0rxCwyyJDu6/c1zwi7chvLK6K00zF2B9lp8nQcTxq5I3pEYmcRACIbGkCwuuq4f1j+2eXl5zJgxI+FfTk4OJSUlzJgxuEqAgmOf0SU5LP/Sqbz2taUsGpfqqEuMSBC5tm78681a/vraHgB8isTvr55HRf7wLnW8/uRxVpzF317fQ3841fWejBml0B2KcF+GQu9QWCWctkNGliW+ft5Ua65vXjrBykI2qS7Kto7vlvoutjUM33LPDXUdVryHiEY4ghSOomjCWSzpNRwFLf2t7OjYQffA4AuSabpGfeM7vK7HL9TMLp9LUAmSP5SiN+9TykqnsGvp19ElmdN7+7i0u4dw/gjqyyZQmV1pZbAVVC9kacT4itcvwe76NTT1NRGKhoZnIAM9cHAdO2xFyEbkjBBZqIPAJ/soq5zLjrlXWo+1T7uQ7nAPVTlVVkRBSVYJs0fGIxKej7aS1V5rZSMOhei+FaywxY9NKppEgT2bVJAWSZIYlV/DniW30FdYw8XdPVzT2UXPmBMI5ZRRkVNh/YheOOtqSqLG37SV9NHWWXfE3LZRLcoDa39n3f8IeXROv4h8/zB9DrsUInMi25fNxOKJZKvZtPQfPROGIivcPPdmKwf21f2v8tSupxJ+20a0CPduvte6f8OsGywBb1XDKuq66hz7VhUVv+JPO4Z0S+ot3iVaSESLcNfqu9jRbhQrLgoUcdtxt5HnP7J1JZyE2/s232etOPHLfkbmjrSee3LXk659qbKakeCcTnzc1bHLul2T5yHaolmimicSaR25ZiEyT9Ew5gZVJdWzHcSLn7kNzRScVVlNWyTNdIUeLi1pXsU8yxywqmEVrf1Du3C9tmktYIzbdPGmI2OnsyxbYrib07Zi63PI258DIJRVyIElX3Tsy37euDltActlK2PMQU/N8Vab3No3rfNCJn08AuA6dsHwcEii7d///ndmzpxJVlYWWVlZzJo1i3vvvTf9CwWCNBRk+SjNdV6Wd+KEEvyKccq+sGVoRXHei+i6zi+f38bXbIXH7rhoBvNHD7/YNao4m/NnGg7L1p4BHnrbO4Zke0MX6+viP0weWV2XkdB7qESiGmtjDs2qgiAjhEPzkFk8voRHPn0C/7j+eC6dW+3Yxl6Q7MlhdNuutOXZHi9E2yNKz4Lr+GBXt3XfLEhW11U3KGdYS18LfXtetvJsZWBC0QSKgkXHXMGUdwNBNUjepHPYPesD1mONU84hy5dDSVb8YmeeP4+TSudY99fse5GecM/wVayvewv0aIJoW5NfQ0AVy+oHQ0lWCe2zr2D34htoXPhx9s0wilaVZZdZbQoCBSysXEhWbOnis9nZlO9+nabepiFlouq6DvtWWnm2ftnH5OLJ5Ppyh7ZT70NyfDmMKp3CutO+SDi7BE0Nsmv2BygIFFAUiF80LgqWcHIsImFAkti547809R6ZOg3PbHmQXWFDsJoRCpEz/3rKciqGz/2YodPWJN+fz6TiSaiSSlv/oUXvDAfl2eVcP/N66/6979zLH9b/gYGoEf21rHaZtbR+UtEkThp5EuePixf9+c/O/zj2W5pVSlHQ2zCQyZJ6o2Eme3L0GYgOcM+GeyxHYraazW3H35bweXYkSRZuTRZVLeLnp/2cWxfcakQCYMzjUEQ9ay5dnJLtoXZLtB2dP5psn/uqFF3X00YV6LqOpEtpi33Z80tdRcOYsKjIadyxerx4lVc8gunMTFskDT2tsDsUVFnltFGnAYaY/OK+Fw+5r6beJusCzYTCCZlf7IpFQHhFB5hZwZYj2uGY+TrrGf3G76372076LJFg6hjMXFzwdtpa8whWYbnumuOsNjm1KxPEXVlOIxlmcAFBMDQG/Zf65z//Od/61re4+eabOfHEE9F1nddee40bb7yR5uZmbrnllsMxTosXX3zxsPYvOHbJ9qscP66YV7Y3c7Cjny31XUytEk4tgP5wlK88vJ4nbILZdSeO4cPHu1/JHSqfWjLO2t6fXtnNVcfVoCrOH+qPrN6fcL+9N8wzm+q5eM7IwzK2LfVd9AwYovA84bIdMnNGFXo+f/7MKr775DvoOjyx7gC3nDlpWJbXrkwoQibybI8kkapZTMsfS1Wkg4OqyrrGtWi6Rkt/C/Xd9YzKT1/kcyA6QG1nLT0N66nzGeLezKwRBORA2qWGAncqcipYd/wnkIJ55EYjbJ96DiOyShN+BMqSzKzZ11L+4udpVFXe7j3IJVqI5r5mKnIqhj6IWBGy7f64k2x0/miRhTpIAkqAqryRbJtyFpU5lXT0NDAqfxQ5vhyrTZaaRWVOJXNKZ/JG01q6FJlX9r9C9ewP0tzXfMgOtr6BLupbt9NUZeThTSiaSFlWmShCdohUZFfQPGIeb3zwdxQHi2kZ6GBG7oiEYjZ+xc9J487hse3/AGB18wbm9LfSE+5JmPPDwf2r7rJuX5kzkd7KaQmC8pCxO7TTOG1NioJFTCqaxObWzXQOdA6f63eQnDjyRPZ17eOxHY8B8OK+F6nrquPG2Tfy8NaHrXbXTLsGSZJYWrOUR7c/Sk+4h1f3v8oHJ3+Q0qxS5849yGRJ/XCj6Rq94V56I73W//sifYSjYSJ6hIgWIapFHW+b2ZuKpKDKKoqk0BZqY3/XfvZ376ext9ESelRZ5dYFt3rGABwJTOH2ng330BPu4ZIJlzCjNL5a98zRZ/L0nqcJRUM8uPVBbpx94yFtR0IyHLIuc7m+ab11O93SelPcSyt4ynLaQl7mc+mcnmakgZfTVpfirlG3IntW9mqsXUT3qEEjgSQfXnfm0pqlPLbjMXR0ltcu55IJlxxSLQXTZQuZRyOAcV6osuoZHWC+r8xxpYi7usa4F3+KEsvcDs++ktaahRQm9afruiGsE8+0ddxerNCdiTm2vvIpRAJ5qKEucutWoUcjKGrAcGGjpndNI+IRDieDFm3vvvtufve733Httddaj1188cVMnz6d22+//bCLtoL3N0unlPPK9mYAXtja+K4SbTVNZ3dLDxv3d7C1voumrhAtPQO0dIdo7h5A13UunDOCG5eMpygn/ZIqk6auEDfc+zarY85SSYKvnzuV608e6/3CITJjZAEnTyzlle3N1Lb28vSmei6wFaUyiWo6j63Zn/L4P9+sPWyi7Wp7NEKNEG0PN+X5QRaNLeGNXS3saell2eZGzpg2NGEoEtVYtccQbcvzAowpEVmZR5IsXxb7Zl7KZW//mt8UFaKh8+K+Fzlv7HnUdtVSFCwi1+/tyDvQfYC2UBubO7ZD7Pvh7KqFBNXgUftx/l7Ar/gZVTCGjdPOozirGPo7HJ1MudWLOGtA4j4VIhK8s38l+WPyh0cgqn2DKLA6YIi02Wo2Y/LGiMJyh0BpVin7uvbRFmpDlVUqslM/O0uySjh59Fm8Efvh+I+Azk9ad1Pvz0mIUhgMnbVv8JY/Pl8TCyeKiylDQJVVavJq2BDqoCnSTZ4vz/F4zplxNVWb/85BVWGV3suFHbW0F4w5rKLtlp3PsE4zImrGhSNUnHADqi93eJesJxQia8/4ZWXZZUS0CNvattGmtaV1px4urpxyJdV51fxx3R8Z0AbY0b6DL7/0ZUuEOHHEiUwsmggYF1LOGn0W/97xb6J6lP/u+i/XTr/Wq3tX0i05huFbdvza/tf4+6a/0zFweCM5JCRunnMz00uHXgB5OMjz53HLfGd94rJJl/Fy3cv0Rnp5ad9LnDPmHMYUjBn0NtI5bdc2rrVuzymb49mXpmtpc441XcsoXsCemewZjxATFz3PNT2x0JXr2GOCrSIpriuzTKFYRjYiQjxo7G0k15fr6U52oyy7jDnlc1jTuIbmvmbWNq5lXsW8QfeTMH+DEG11yRBI072HFSmWaevQrnj9IxQcWGfcKRjFwJl3ILVvS5l3s1iheQ66OaJNp62JdQ7JCj2jFlKwYzlKqIucpi1ERhjHSpbTnGexgorCaXv4GPS364MHD3LCCSekPH7CCSdw8ODwVw4XCOzYc21f2HLs59p29Ib5f09v4YO/f52Ztz/D6T97ic//ay2/fXEnD62qY/mWRtbVdbC/vY8DHf384aVdLPl/L3DX89vp6vdegtw7EOHfa+q45DevWYJtlk/hD1fP55NLxh2RQiI3njLeuv37l3Y6fqC/vrOZ+k4j4P/0KeWMKzN+mKzY1cqupu6U9sPBKlGE7IjziZPiFwl+tXz7kONL3jnYabmljxtbLArjHGHyfHnok87hXD0HJTaXL+55jiw1i/5IP/u69nl+Oesc6KSuu458Xw6vRuM/ECdUzacwUHhIX74FcUqySigOFlPfU09RsMixqFuuP49TKuMZZStqXyAUDQ29gFU0AvveYlPAT1dsdcWkoknkBY5sZuF7hWxfNlW5VXSFuijNKnW8oFEQKGB84XjmZxlRNAdVlc3bnqBnoOeQlvT2Rfro3fNiQp7t9JLpaS/ECLwpChYxMmck4WiYypxKx0zTguxSTvUb32WjksTWbY9T311PVDt8kVFPrfipdfus4pn0+HMozy5PcAEPmYR4hMGJglW5VUwrmYYiKTT2NB61H/4njTyJO068w3LNmiKXT/Zx1dSrEtqeM/YcK0N8We0yugcG/31WkiR0zV3os7cbihlX13We2PEEd6+5+7AJtgElwLiCcZw88mS+segbLBqx6LBsZ7jJ9+dz6cRLAWO+733n3kP6/mqKbU7uxqgWZV2TIbplq9lW8Ts3zBxaL9OiKRCrsurpbtTQ8Cm+tIXITJHVyxFqCrYSHvEI9iX1Hts0xd10TmEwCtv1R90LxaXj9Jp4sa5ltcsG/fpwNMzG5o0AFPgLGFuQuSnKKvDmEWNhif0SKcff31ZLxeu/jTe++NdIgXzHebIKjNniEZw+N8z5NlEkxeqr25ZrW7hvtdXO3sZx/PrhzScWHILTdsKECTz44IN8/etfT3j8gQceYOLEicM2MIHAidElOYwry2FXUw+r9rbR2NVPed6xuZSvuTvE1fesZEu9d+acJEFxtp+u/ggDUY2uUIRfPL+Nv72+m4+dMJbJlXlUFQSpKghSkhtgdW0bD79dx1MbDtIdii87qcwPcs9HFzBj5JErInLC+BJmjMxn4/5ONu7v5LUdLZw0MXGJ2KO2aIQPzK+mrq2PH/x3MwAPvLWP286bOuzjMkXboE9m2gjh6DsSnD61nGlV+bxzsJP1dR28uK2J0yaXp3+hCyt3iTzbo4kkSZTnVtE69QJO2fVvludk0xruYnXjamaXzaa+p57SrFJHh6ema+zr3Ec4GkZt28E7sdzTSQTIU/MSslcFh4ZP9lGdV01PuIfKnEpH54skSYybfz3Tnn6NdwIBtoXbae1roaGngaqcqkN3xdavh3APrxfGP1snF00Wy+qHQFlWGW05bVTmVDoKOAElYLhtJ1zEqg1/AODB7p3cKikc6D5AeXb5oLJJG3oa8B3YwFtZxpzlq9lMKJxw2Jfovx+ozq8mrIVdczyDapBTp3+Yf264G4AX295hQaidjoGOw+J0HmjexrP9daCqyLrOzNnXocsqBYFh/q44iEJkTpRllxFQA+xo20FjTyOl2aXDl7c7CMYWjOWHJ/+Qu1bdxaaWTQCcP+78lPiDgkABp446lef2PkcoGuLZvc9y2cTLBrWtdEvq7e0Oddmxpmv8fdPfeXrP09Zjk4omURwsJtuXTY6aQ5YvC5/sswpuqZKKIivWbfNxCYmIbkQmRPUoUS1Kti+b6rxqioPF79qVFueMOYfn9j5HY28jm1o2sbpxNfMr5g+qD1VWCagBx3N2R/sOesKG031W2ay0F0t0XU8r0pvCriqpnuePruvW8nzPeARJssblJe6auate2zPFWFVSXZ3kZoyC+R7wYqi5t3PL51IcLKa1v5XVDatp7mseVJzJ5tbNVhHX2eWzB32e+2Rv0dyKk0BOzL7Voox8/nvIEWPbzTMvo3TcqciRfkdHrhVNYY9HyNRpa4m28VzbwrpVdErG+eyZY2w7LzJZOSA4NAb91/COO+7giiuu4OWXX+bEE09EkiReffVVli1bxoMPPng4xigQJHDujEp+88JONN0QBO1uz2OFxs5+PnzPSnY0xq+8jyzMYsbIfGaMKGDaiHyqi7IpyfVTlO1HkSUOdvTxq2U7ePDtfUQ1nbbeML94fltCv5IETp/5c2sK+f3V86nIP7I/miVJ4sZTxnPz/UbRgdv/s4nHbzqRnIDx0dIdivD0xnoA8oMqS6eW090f4f89s4VwVOfhVXXcetZk/OrwfdFr6Oynrs2oej+7uhCfS86uYHiRJInPnT6BG+9bDcCvlm3n1Ellh+yQTShCNk6IfEeDokARe6ddwGXvPMzymJbz3+2PsaBiAaqssrdzLwWBghQ3WVNvEw29DRQHi3lpzz3W48fljiboE9EIw0VJsISxBWMpCbq/P3Kq5nAWubyDsXJj/d5llOdU0BnqpNDujBsMsTzbN7JsLs3S6QQVIdoeKnn+PCYXTfYUTUuyShhdPIVpBHmHfrb7FA7ufJbiCWfS1t+WcbGf3nAv+7v3o7bvoKfQuKAyoWgy5dnl71rR5VgioASYUjLFs824SReyYM3dvK1CnaxTt+9VavJrDoto+/YL3+aAanwnmx8oRQ7mka1mD280AoAvC5QAREODikewk+/PZ1rJNHa276S+p57CYOFRuRiU78/n68d/nWf2PEMoGuLC8Rc6trtg3AU8v/d5dHT+t/t/XDjuQnyKz7GtE17FiQ6lXTID0QF+s/Y3rDy40nrsQ5M/xKUTLhWrl2z4FB9XTbmKu1Ybuc//2PwP5pbPHdTnoSzJjMhNjYiDweehmpmjptDqNA4rhzZWRMqrL7/s93S0amhGTrHkLe4iYQmtZn/JYzOjHdKNTdM1q2CZ1/g1XUu7j+lQZIXTRp3GI9sfQUfnhdoX+ODkD2b8+uW1y63bg4lGgHhROa/jb88KtrcrXXM/2fXGhaO+/CoaFn+GUrDeu5k4bZ3myZxvE7ugHMmroL9oDMG2PeQ1bUONXWywFy5LxnJNc2ifU4LMGPS3s8svv5yVK1dSWlrKY489xqOPPkppaSlvvvkml1566eEYo0CQwAfnxwvgPPj2vmPuA6K+o58r/7jCEmyrCoI8/8VTeO1rS/nDNQv47OkTOX1qBZMr8yjNDaDIUqxdFndeNpNlXzyFS+aMwOmz0b6ruQGVKxeO4uEbF/Pop0844oKtyTnTK5kWyxbe0djN1/+9wZqT/204SF/YWPJ34ewRBFSFktwAZ0+vBKClZ4Dn3mkY1vGIaISjx1nTKplcYfwQXFPbzms7Wg6pH03TeSuWZ1uU7WNCmViyezTwKT7KC8ZSPfo0RoUN0W9zx06W1y6nMFhIe6id/d370XSNnnAPTb1N7OrYxe6O3fgVP509jfyzdZ3V39QRCynwF5ClZh2tXXpPocgK1XnVniJBji+HxePPR419Jr/a8DYDkQFaQ4deJZvaN+iWJNbF8mwrcyqpyqkioIoiZEMh15/rKaTk+/PJ9edy3ogTrcf+U7cMVVap7ay1qt2no763nmjnAVbrPdZjk0umiHiLI0h+oIAlZXOt+6v3LKOpt4n+yKEvAXakfR/PNLxp3V086VL6I/2HT6A33baH4LQ1CapBJhdPZlTeKDoGGbMwnCiywnnjzuPSiZe6On4rcio4vspYTtw10MVbDW8NahumyJFOQD0Ukb0v0scPV/7QEmxlSebG2Tdy2cTLhGDrwKKqRVZm8YHuA6xpXDNsfdvzUGeXzU7b3hQ0vQRUK4dW8vbfabpmFI3DXfjUdR0FQ1h0KyZlXwKfzm1rLvOXZY/iW2YxLMm74JqmGwJjOqetpmt0DnS6Pr+0ZqnlSP7v7v/S3Nfs2Z/JuqZ1rDhoXKjO8+WlzSN2QpG9C7yZD0uSZM1ToHknZSsM04MuyWw95RbkgPFbSMY5esLuXjZxmye3eASAntHGZ5qka+TtW21tM50A7yn4x4hoEZF7e4gc0l/s+fPnc99997Fq1SpWr17Nfffdx9y5c9O/UCAYBsaU5nBcbLn0rqaehKJTR5v97X1c8cc32NVs/BgaWZjFgzcsZkJ55qLTmNIcfnnlXJZ98RR+8oFZfOmsSXzk+BrOmFrO9BH5nDKpjF9cMZu3vnEGP7p8FgvGHN28T1WR+c1H5pEbc9c+vvYA979ZCyRGI1w+v9q6fdVx8Wqy/3qrdljHI0Tbo4csS3z29AnW/buWpQblZ8K2xi46+gyRcOGYYmRZ/MA4WhRnFdM04yJua41/Gb5v0//R2t9KfiCffV37WN2wmlUNq1jXtI49HXvQ0CjwF/B/b/yAntjcXRANkF01n9KsUvGD8QhTMucalvQZy+ta9DC1Hbto6mkiHPXOTXdE16F2BW9lBYnG5tGMRjDzHQWHB7/ipzSrlOoJ5zA6YlwMXa330d61n7ZQG/s696Xtoyfcw8Hug1S172OFzSk9u3Q2eT4h2h4pstQsZs/5OAVR48frq5FWWrr2Dz1vOonOl37EsznGRbIsFGaMOA6/4h/+aASTYKzfQ3Tamqiy+q7JVz5j9BnW7RdqXxjUa03RKp3wlu3LJj+Q+QoVTdf49Zpfs6V1C2C4v7+88MucOurUQY3v/YQkSVwy4RLr/v92/29Y+m3vb2d3x24AxuSPyazQnm6cE14Oa1P0VGXVO0pBx4o+8IpHUGTFykB12qZ9CbzpqnTqz4xtkCUZBe9tAvhlv6eIZ44tHb3hXlp7W12zwUuySjhl1CmAcUHjT+v/lPb3yUB0gL9u+Kt1/yPTPjKoegzmBRkZ72NhF8FlWYZIH9XP3oEcK+LWPPfDdFRMsdpIkuQYmWJm1SY4bR3mKdl5myy2do+KRyTk7ltpbdN1P83tZhDjcrD7IF0D3rGRxgbTN3m/kbFo29nZmdE/geBI8KEFNrftW3VHcSRx6jv6+dDv32BvSy8ANcXZPHDDIkYVH1rBnXFluXxwwShuXjqRH1w6k3s+upCnPncy//fx47h0bjVZ/mEsIDFExpbm8OPLZ1n373jiHZ7ZVM8bu1qs5+eOKrSeXzyuhNElxnF5ZXsztbFjNhzYRdu5NUK0PdKcO6OK8bFic2/taWPFrsE7+l63OXSPE3m2R5VcXy65JROpmXgul3YZqwf6tAH+tOa3ZKvZqLJKWAuT58+jKreKipwKCgIFrNj2KG+Gjbkvi0S55PhbCajBQf3wFAwPBQU1nJYdv1C2evczdIe7D00gat0FPY28bhP8JhZNpCggPmuPBMVZxchKgA9kxwuh/G/TPygMFlLXXUdLn/fqhoPdB+mP9JO1bxXrY07pKl8eE4omDGpZt2BoSJJEZfEklqrG+2ZAkti+9TEaehuGb/VY50Fe3/EE3bLxU+/4quOIRCMUBArI9R0mQdSMXBnohkO5KGTDqyDSscS0kmmUZxv5/RuaN9DYm3mRZEVWGFMwZtjfew9sfYBVDasAw6X77cXfZm65MFelY275XCqyKwDY2Lwxowth6RhsNAIYDmwzQ9ir+BMSaQVNMy9VkRTXvFG70OqWS2rPqjX/7yjuxuIAgLTxCLIk45N9nqKtuZQ/3WeBGaPg1dc1066xvqusa1rHC/u8L7I8sfMJ6nuNiL/JxZNZUr3Es73TmMxjlS7iwRJkkRjzxp8ItuwAoL9kHE2Lrk8QWs14ihSnra6n9OUmttrPG0VSEty5PSPnosUuwmfXroBYFIebkGpGdaSbS7NtuqKbGlp6N/f7kIyPRmFhIUVFRa7/zOcFgiPBeTMrLWfnk+sP0GMryHU0CEWifPofq9jfbmSpjivN4cEbFlNd9P6pkH7+rCo+dsIYAAaiGp++b5X13OXzRib84ZBliSsWxoX3B94eHrdtfzjKpgPGcrpxZTkU56RWbhYcXhRZ4rNL40Up716+fVCv7w5F+MPLO637J4zPvFiAYPiRJIny7HJ2zrmCT/urKY8Yn7XrWt/hxdrlFAQKyA/kJ+TatvW18Nftj1j3P1c0l4GCavL8Rpai4MgSVIPMnvFhiqPGF+WVnTsIRUPs795PRBvk387aN4B4nq0iKUwsnCjE+CNEYaCQomAR0yacR2nMbfta9146Qh0gwd7Ova4xCV0DXdT31lMSCbNj70tEYn+TJxRPozBQeKR2QRCjIFDAcRPjWakvtW6kra/Nc4nvYIi+9guezI5HlpxYs5RQJER5VvnhW+1gL0bWP7T9sKrUH+PIksxpo06z7r+478WjNhaAV+te5fEdjwPG2L4w/wuMLzz2an84cbSXTcuSzNljzrbu/2/P0N229miETEVbXUrMtHXCFFoVSXEtEGX2JSN7ioZ2N6tbxqwpmsq97SgH1lG25w1K1z1IxSt3UfnSz1G7Gqy+TLFNlVRXsdXMvvUpPs/xW/uf5vqNRiz71qNhji+HT83+lHX/3nfudY1JqO+pt95HiqRw/YzrBy0imnEFiqR4iuam6AlQvOsVqjb/19gnNUDd2d9Fj32/NufIEs0dnLb2eARzvE5OW4W4aJssAOu+IL0jjBgPX+dBaNnhue+moOxX/N75yrqOorgfB3s702kuiJPx2ffCCy+wfPlyli9fzrJlywgEAtx7773WY+bzAsGRINuvcuHsKgB6BqL8d8PBozqe7z35Dmtq2wEjEuFfn1pEZcH7ryjL18+byuyYo1azfW5fMndkStsPzK9GjS2dfuCtOvrD3lfeMmHD/g7CUWPD84XL9qhxwawqxsSc1K/vbLHyaTPhl89to6HTWMq9dEo5U6vEkt2jTVGgiJxgAfuXfo1vdsVFvvs2/iXF2afrOve+8SO6JON9eE5IY9KJtxKKhijPPoxigcCTvCkXcHa/8Rk7AGxvWENLXwv1PfWD66j2DepUhb0+w4UxvnA8JcGSd81S5nc7siRTlVNFV9lkrjQ+JtEleHLLg1Z17Lru1NVHmq5xsOcgoWiI6o2P8++cuJg3rWLO8BelEqQlx5fD+IkXMiP2kbpL1jiw77VhcdtGug7StubvvBa7uFISKGRMwRiyfFmHXoAwE+x9DzEi4d3ksjpl1CmWUPLivhePmvi4vW07f1j/B+v+tdOuZVbZLI9XHDtousbezr1pc537I/209R++WLxTR51q5e6/UvdKZku5XYhqUTY0bwCM9/vEwolpXhHHdNp6RhrEhF1PdOO95CVo6uiosdr0TlmpAHJPE9OW/YjcX84k+Ndzmfr8D6l+7TeUrn2AkvUPM/o/tyJFQmhoVtyHJEmuYquOjiRLVpEu1+HbnLteZJp9O7d8rhUT0hfp4w/r/uDoWP3Lxr8QjsUTnDf2PEblj0ruKqMxmY7RjJy2bXsZufxH1mMHl9xCqGRcfP9iQquVPewwbnshMje3qum+tu5LqY7c7liuLQA7lxttdI9CZMiospq2qJyKmlE+sVuO+PuZjP8innLKKda/U089FUVRWLRoUcLjp5xyyuEcq0CQwAdtEQkPvX30IhIeXlXHfSsMp6hflfn91fMpP0pFwY42flXmNx+eS0FWfKnX4nEljo7j8rwgZ003liE1d4d44K2hL0OyRyMsGCNE26OFqsjcdFo82/b7T76TkSi/pb6Tv76+B4CAKnP7hdOFyHcM4FN8VGZX0uELMmbpd7io28js7tGj/HXlT+iP9FtfwlbufZ7Xe433cnE0ytVzbyIkSfgVvxCGjiIFWcWcXD7fuv/anmfJ9mdT21lLT7jH45VJ1K6wXLYAEwsnUpRVJPJsjyDFWcXkBwtYUrWYHM143y0/+Dp7O/dSECigrqvOEjX6I/0c7D7I+qb17OvaR4kus3HHUyzPMf4m5/tyWVCxQBQHPEqU5ZRzcvF06/66Pc9zoOtAxkVynOgIddC27HaeCSqWm/rE6iX0DvRSkV1xeOfa7rQdQjEyePfEIwAUB4uZVzEPgNb+VtY1rkvziuGnpa+Fn739M0toOr3m9ATX6LFOVIsiI6dd/dEb7qV7oPuwjSPbl80p1YaeEdbCLK89dEPa9vbt1t/XmaUzM8pmNVFl1fM9YLobrUgDt7zUmEDnJZZpumZkqWK4YxOckLpOwdZnmfrP6yjb87rreIMtu6h89VeG0zbWl5db3ix+li76wBQ+07nuTaEvkwsm1067luKgEb22oXkDy2qXJTy/8uBK1jetB6AkWMLlky5P26cT9oxZt/20hF0tCo98AiV2bndMPJ32aRfaO0vNoSXVQSsTP+am6zb5mCQXP3QSgO25tuxYZpyLkrvoj0TafGUdPW2Ehbkfg3mvvF9491zGFAiSmDuq0Crw9eaeVnY1Hb4/4m5s3N/BN/69wbr//YtnMLP6MBV4eJdQXZTNL66YjRJz0X7sxDGubT9zalzY+80LO4bstn17jyhCdqxwydyRVm7xuroObn1wHZrmvWzmW49tJBprc9NpE6gpEUvpjxWKs4rxKT5aK6bysQmXURaLSXi7ew8fe/pjfPipD3P1k1fxq41/tl7zBf8o1HGn0N7fTnGw+PDlKArS4pN9jJ93PZNCxtL5zQOtdA900xftY1/Xvsycfd1N0LKDN7Lios/Eooliaf0Rxif7GJE7grZxJ/PJdiMOSAPuWft7gmrQcqztbNvJ6obVvNPyDl3hLkqzSinZ+G9+XBD/XL1owiVU51WLi2NHiYJAAbNnfdQS318YaCI60MWezj1pHYfJDEQH2N2xm+3bnqJk/cM8mZtjPbe4ajGyLFOadZjjhhKctkNzQ6ZbHn6sYY9IWL7vyK487Y/089O3f2rllE8tnsp1M657V72vNTIT3TS0wx6bcc7Yc6xtPLvn2bQZnG4cSjQCYBQik9MUItN1q42bO9boSk+bqZoQj2Brp/a0MOq/t1H97O2oISPuRM8uJTr/o9Qu/Bi7ln6V2nN/gKYaKzeKN/yb4j2vW85YCclV6DOFOS83LsRjINJdwDEduZl8XmT7srlh1g3W/XvfuZefvPUTfrDiB3znte/wx/V/tJ772IyPEVQPzYhlxh5YmcIuY5MlmcArP4O6twDoy6vgwGlfBdv7N9kNK0uy83GT4uKu+f/kY5fcl5PTNlQ6gZB5EW7PK8iRMJLunmOsSmpKNm4ylhib5uumji6ctg4I0VbwrkWSJD60oNq6//CqI+u2besZ4Mb7VhGKGB/CVx1Xw4cWDn75xHuRpVMqeOpzJ/HIp0/g7OmVru1mjCzgrGmG27axK8T9Kw8921bXdVbXGj8SCrJ8jCsVAtHRxKfI/ObD88iOFcx7asNBfvz0Ftf2j6zez1sx0X1saQ6fWjLuiIxTkBm5vlxKgiV0DnQyMP9jfEkdkdImgm5d9z+jb4BpS75Bb7gXRVYYlTfqXfUD8r1Izsj5nK3HBddXtz1GcbCYg90HM3P21b5BFFgRNH7A5PhymFQ0STiojwIlwRKksslckjOWCQOGEL+zay/P736akqwSmvua2du5F0VWqMipoDhYjD8S4rHd/6UuFm0xrWA8x1UeJ+bvKOKTfYwqm84pSiEAfbLErm1P0BHqoK6rLuOYhO6BbjY2b2Rn2w4mvfEH9iiwMVZobkz+GAoCBRQGCg9/9nTQZloYBqdtOtFW0zX2d+93zXE+kswtn2sVOVrdsJr2IcZDZEpUi3LX6rvY3bEbgLKsMm5ZcMu7TvQwhZ9Ml04fTjG/MqfSKtzW0t/CW/VvHVI/CaJt2ZxBvda6aOFRPEyVVMNZKbnHKFjL8z0iBnR0K9JAlmV0LULRhkcZf/9HyN/1stWuedwSIje+Cuf/goNzP0zTxNPpmnAa9Sd/wWoz+eW7CXQ1Wtt2E+jsmbzphL5M8q2t8yJNXqrJ7PLZLK1ZCkAoGmJVwyo2NG9ga9tWeiNGcex55fNYULHAtY+ecI9nVIemGxcYvKIDNF2juG4twTd+C4Auq2w89Va0QNJv2CSnrVNGrlnAy+60dRL0dd3IOTaRcBBjJYnWEXOM2+Fe1AOrHXN0ze1mJMDrmV1w0XRNrN5yYEiirfgBJjjaXDq32nJ0PrK6jkj0yFyR7+wPc/M/V1PXZhQemz2qkNsvmnZEtv1uYUplfkZu1y+cMcm6/dsXd9I3cGhXtPe09NLaY3xxn1dTiCyLz6ejzYyRBfzmw/Mwp+IPL+/i3hV7U9p19Ia587+brft3XDSdoE8sjTmWkCSJEbkjkJHpj4YYe+YPua1PZklvH8f39TOnP8TU0ADjBsIs6e3jk5M+RDi7mI5QByNzRlIQeH+vQDgWyPPnsXjs2SixL+f/O/Aqbf1tqLLqWcDKZGDN39kU8NOlGF8dJxVNojBQKJbWHwWCapDKnEreOflzfK0n/ivpgXfuo7O/g6rcKipyK8j151rf1XvW3sffcmIF5IAPTPsIef48cnw5TpsQHCGKgkUcN+4c6/7j9SvIkVTquuto6W/xeKVBOBpmZ8dO2vrbmLx/PfkH1vJQXvwH/8kjT2YgOkBlTuXhz4lNKETWPqSuvCrUm2i6RiQaOSZEW0VWWDLKqC4f1aO8XPdymlcMHV3XuWfDPaxpXANAtprNVxZ+hXz/u68wpOnCy3Tp9FBzn9Nx7thzrdv/2z34gmRt/W3s6dwDwNiCsRlnSdtjD7zcmaY71hQ0ncQ08xiZwq6rW1U3BVad/F2vs+CRzzLixZ+ixooJRrIK2XHmt9lxxjcgt9RoK8UdnG3TL6JjgiGA+ga6qXz22xCNWO9ht/FbbmIPpc90q3oJ0ybpMlWTuXrq1VTnVac8rkgKo/NH8/GZH/fUunrCPfRF+tzHnhSP4ITS08yUl35h3W8/6Qt0lk1IaadLeoLgKcsO7modFOIZwaaA63R8U5y2DmJqy8jZ1m3f9udcHd3mOZsue9hslw5Nj+ciC+JkfEQuu+yyhPv9/f3ceOON5OQkftl79NFHh2dkAkEGlOUFWDqlnOfeaaChM8Qr25s5bUr5Yd3mqr2tfP5fay3BtiTHz++vnkdAFSLToTBtRD7nzazkvxvqae4Ocd+KvXzyEFyW9jxbEY1w7HDalHK+e/EMvvnYRgC+8/hGRhQEOX1qBQMRjdaeAX7x3DZaYoL7eTMrWTKp7GgOWeBCUbCIkbkj2dO5h0BOJbMvv5eFnQdR+jtQ+jtRQp0o/R2E8yrpGnsyHQMd5PvzGZmXWohQcOSRJZnSuddyybaHeCQvhz40fvfmT/jGkjtp7mtmf/d+xhaMdXxt+J3H8W9/jtcL42LAxMKJlGSVHKnhC5Ioyypjf34VuWfcwcUvfYvHc7PoQeOhV7/LJ8/8RWLjcD9/3PccAwHja/+FI0+hOFDMiNwR7zpH3nuNXF8ukyZdzPRtD7FJ0dmrwJMv387Zp36XPR17yPPnEVACjq/VdZ3azlqaepuoVLKofPVuflVUwH0FxvtUlmTmlM8hx5dDUfAIfC9KiEfoGFJXlmjl4Zw7Eq7LwXDaqNOsivPL9y3nwvEXHlaD0yPbH+GFfS8AhmB164JbD6lg0rGAOZcR3TvTFt0QrOyFmQ4HM0pnUJ1XTV1XHVvbtrKrfRfjCjP/bbK6YbV1ezAuW8tZmkHsgSnaujnS7X0pkvuydEmSCDRugUc+TdWeVxKea590FvVLvkCHoiJhCMDmNq25kiQOLP0qWQ3v4O+qJ7h/NbzyU+Tjr3fdT/v4Mzke5j66tdfRPQu3OZHty+bHJ/+Ylv4W/LIfv2L8y/RvYrJj1el5cx4dPwd0jZrnf4DfvMA18Sy6F1wLbTtSmkp6YoSBipoixmrEs4kBS+x2OocSBGCXvNrW6gXosg9JC+Pb/CTSzAtdLw5kIsBn6ppOLpQmMMj4iBQUFCT8u/rqqxkxYkTK4wLBkeYKW0Gyv7+x57BtJxLV+OXz2/jQH1ZYgm1eQOW3H5lHVYFwGg2Fz58+yYru+f1LO+kdSPOlLQld13nw7Xghs3lCtD2muHrRaG44xayACp++bzWz73iWSd/8H4vuXMYDsbnL9it86wLhWD+Wqc6rJt+fT0eoA2SFgcJq+iqn0z1mMR2Tz6Z19ofoGreEiB6lP9xPTX7NIeeBCYafgsKxfLjqZKrDRrGaLT11/HfLg1YBq6beptSldKFu+N9XABKKkE0vmU6uX8TQHC1y/bmUZpXSkF/BFXM/TUHUWKWyLHSQnWv+L6HtxlV/4I2YYFuOymmTLqMgUEBZlrhAdrSRJImK3EqumP5RgrFM9yeiLRzY9BDtoXb2dbpnTjf1NbGvax9FwSLKV/yJ7+VI/Kkw/lvsg5M+iCIrlOeUuwq/w8owFiIzXVvp4hEyzbE8ElTmVDK9xCgsV99Tz5ZW90ioobK8djkPb3vYuv+ZOZ9heul0j1cc25juukzm0is2YLiQJIlzxsQd8A9sfSBtkTST9lA7D2590Lo/t2Juxts1hT5FUjydo+bxcsstNR+TMPryunhQtucNyu67AmyCbW/VTHZ98I/sP/t2olmFVjauuT1FSnQ7a4E86s6+A80U2176Mb79a1y3qekaKunjHcztZuK0PZQMbEVWKM8upzBYSLYve1AXMdO5es0ICPNf8hSVrrqP/LpVRl+5FXDJ75Bl1VHU1CU9QciUZAcxVk8VY42HkzJtk0RRCedYg3Agh4HxRlE+ubuBwoMbHI+vXYD3Os/M+IZ0mPnEgkQyPjP/+te/Hs5xCASHzKmTyyjPC9DYFeKFrU08uf4AF8xKzVscCnVtvXzhX2t52+bmXDC6iF9cMYdRxaJY0lCZXJnH+TOreHL9QVp6Bvj7G3u58ZTxGb9+2eZG3tzdChh5qMeNKT5cQxUcIl89ewp1bX08tf4gA1GNgb7UP/y3nDFJXAA5xgmqQWrya3in5R0iWsT1C25bfxtl2WVCFDrGyPZlEz35S9z22PV8Vu9BkyQe3PUfZlQuoCinjE0tm6jOraY6r9oS23uX3U5OVz3dksS6gPFYRXYFNfk1orjcUaYiu4L6nnoiE5byicbV/LzDqHj9hz3/4UO7l1GvqDQoEm9pXRBz4HxswuVoukZ1XjU+ReTGHQsUBgoZMeokPt60kd+2vA3A3fuX88PKedTJKj7ZR2VuZYLw2j3Qza72XfgUH1mNW7mj6VVejsUiSEh8dPpHWVqzlM5QJyXBI+SIT3Datg+pK6viepp4hMHkWB4JltYsZVPLJgCe2PkEk4snD7trbE3DGu7ZcI91/5pp13DCiBOGdRtHGg0Nv+JHi3jPpRT770gI9SdXn8wDWx6gK9zFuqZ1/PjNH3Prgls9L0Rrusbv1v6OjgHDaT63fC4TCydmvE1TaJWQ8Ek+93NbjxeQ8lq2bj4vSzJOBsdgw2bGvPRLJN246BcpGMXmeVcgT780oRCWKaSZopvTNvuqZrJnzpWMW3M/6BrZL/4/9DO+6jh8M+ZCluV4Xw7jM8VpRVI8XZyWqzRdlathxBRITaE9GXuBt+Tjn3VwA+Ur/mS0QyJy6e/x5ZQidx90PA5WHzEUUo+HhpYgdppzn+JelxziEZzEVAkGpl1CYPvzAFTsfIn9Y09KaWYX4M399joemcyRiGBNRXiPBe96VEXmmzZ33rce20hTV2jY+t/f3sflv3vdEmwVWeKWMybxr08tEoLtMPKFMyZa3w/+8NJOukOZXdGORLWEAldfPWcyqiI+2o41ZFniZx+czTnTK8kPqowuyWZeTSFnTqvgquNq+N4lM/jESc5LswXHFmVZZVRkV9DS55y32BfpQ5ZkavJqrC+sgmOH0txKpCVf4RO9xmdsVILfr7yTbDWbPH8eezr3sLF5I819zfTuf5ust/4CwMqcXKKxz+iJhRMpzSoVS9iOMgWBAoqDxbSH2llw4leZIRnfSWp9Kj/1D3Cf0stz9NAeE2wX6wFGj15CUaBIRFscQ/gVP+U55Uyc+RFOlY1og05F5s9v/5wsTWd7+3bWNa7jQPcBwlqYsBZmV8cuo9Aj8L23fszL2cYFTx8yn5v3Oc4Zew6dA50UB4uPXMbpMDtt07nrMo1H0HQtY5dkOiJaxLNw48LKheT5jOJ+axrXcPeau4dt2wAbmjfw81U/t/b53LHncv6484et/6OFVaQonZ4jpbo8D5WIFqG1v9X1+YAS4Oa5N1tFkTY0b+B7K75H50Cn62v+t/t/rGtaBxifzzfOvnFQApQ9DsAxtzSG6ZY04wrcHJD2qIVkB6fa1UDNk19BMTOhZ36IxuuepGnMiQmCLaQuvXeLLtkz+zIixcbKOl/dmxS7uDPRDZerjHsmr3k8zItVXu9zHf2Iu+51dGOOXMau63F3rP3YK/2dVD/zbUsor5v3YRhzstHOLc5AT5wPp1xnXdcT5giMOAundgmOXAwXcHI7SZeITDgDYsVKS3a/BuH+lLElC/Buc2A+ns4R7ZUB/H5GfNsWvCe4cFYV586oBKCtN8w3H9swLH/QO3rDfOwvb9LQaYjA1UVZPHjDIj5/xkQhDA4zE8rzuHi24ZBu6w3z51d2Z/S6h1fVsb2xGzAKkJ09vfKwjVEwNII+hd9fM5/1t5/NS18+jUc/cyJ/unYBd142k2sWjRbF494lKLLCqPxR+BU/PeEe63Fd1+mP9NPe387InJEZF94QHFkKA4X486tZsvjLTA8ZP9b2af08+PqdBNUgFTkV9EX62NC4Du0/n0fWo+z2qXy/Iv7ZOrVk6uGvRC9IiyzJVOdVo0gK/dEQHz3xm65L6MaFI3x09g2Eo2FG5o0U1ZmPMYqDxfhkH1eefAeVsd+za1R449UfUJFTQVgPs7llM+sb17O7YzeNPY3ktO7mzuduYqNivCBHh68d9zUWj1hsFekqzy4/cq6loC0mbxgKkaX7ca+R2ZL6zlAnB7sPDmk8Jn2RProHul2FWL/i54bZN1iiwxsH3uCuVXcRjoaHvO1NzZv4yZs/IawZfS2qWsQ1064Zcr/HAmbURbq8S9PFNxziXF+kj85Qp6eoPrt8Nt9Y9A2rYOPO9p3c/vrtjsL97o7d3L/5fuv+Z+Z8ZtBFWE3xWpZkz8JsumSIlFbBPpesUbOv5OMqD/Qw+j9fxtdrXHwPVy+Ei3+N7As65psm57e6FoOTFLpP/Kx1d9yaf6JrDvsgxd/jnu9z3chrzkSoH67zIlPMY+sqUhIvqGVd4NZ1Riy7E39XAwCdVTM5MO/qBAez01sgOR7ByXmso6fkPDvNU7Kz1rxv70/TNWOO/Nkw7SIA1HAfBXvfcBhc5gK8ec56CvC6LgwBDogjInhPIEkS37tkBsU5fgCe2dTAE+sODKnP/nCUT977tiUIjinJ5vGbTmT+aLH0/nDxudMnYup2dy3bxn83eH/J7h2I8Ivnt1n3bztvqlhSIRAcAfL9+VTnVtMZ6qQz1EljTyP1PfX0Rfoozy4XxceOYXyKj7KcMpqLR3PLuEvJiv2g+m/nVpY980Xy1z/CyLY6xm9fTm79Rrb5fHxsxAiaNePi5cjckcwtn2u5yQRHl+JgMdV51XSEOhhZMIZvnXAHV025ihtn3cC35t/Kr477Fv9c9D1+dN7fiJZNpjhYTGlW6dEetiCJfH8+hcFCIqqPz866ETn2Q/uv4Xq2/PvjjFv3b2p6O+gN99BQ+xpVL/+M21f9hF2S4dYqjUT54ZTrmF4+CzCEylx/LsXBI/id1Z8LpkNqiIXIAHyyx/JwYk5bJb1oG9EjrkuOB0sm7t4FlQu4dcGt1oWRtxre4mdv/4wB09F4CGxu2cz/e+v/MaAZfSyoWMDNc29+z4gbphjrtXTaLkIOhziXqVN7SvEUvr342xQFjHoZB7oP8O3Xvs1Tu56irqvOumD9q9W/IhpzT14w7gJml80e9JhMd6wsyfgkn/vx0OORAArO4q7l2iWeqarrOmhRqp/5DsEWo+BVb14lXZf+DtSAq9vZvtQfnEVDwFhSP+U8KJsKQEHjVnJrVzjugpln6zWfEpJV5MpVwNZ1y4maTvQfTsy58hqXecxMB23xugfJ3/USAJFgPjuXfg0UxdGRa+9H0qXEomcSKfOk6zpSkvlFlhKdwLquG8X8bH1Zoq2e2M7KqJ35Qevx4m3Pp+5ohgJ8JvnE9ve4IBFxRATvGUpzA3zv4hnW/e88sYnGrlQbfyZoms6tD66zclJLc/3838ePoyT3CBRyeB8zriyXTy6JF6z6/L/W8OLWRtf2f3l1t+WCPnNaBQtFlq1AcMQYkTuCkmAJqqxSk1fD7LLZzCufx8zSmaL42DHOiJwR5PvzCU+9gM8EaqzH/xQ+wM07/8WB/36Bmld/zSa/n49XldMa+7Y4On80n5nzGcqzy0Ue6jFEdW41xcFiWvtbmVw8mYsnXMypNacxvWoh5eXTkUonMuALEtEijMwbOahiK4IjgyRJjMwdia7pVI9azIdLjOJFmiTxXX8/X6p9gt7HbmDRPz9G1VNf5XPhPdT5jPfgyKjOj6ZcS8XEs4loERp6DBfXqNxRR/Z9KknxiIQhxiNA+mW0mS6pNx1ew7ECL9OCWfMq5vGVhV/BLxtmkrVNa/nxmz+mN9w76G1ubd3Kj978EaGo8X13Xvk8vjD/C++p93EmTltzOXomou3+rv10DXSl3Wamxc9G54/mjhPvoDLbWHHS2t/Kve/cy5de+hI3LbuJ777xXQ72GEaTcQXjuHLKlWn7dMIUry3BzOWUtQtbbq5XHT1BGDVF8crXfk3entcBiAbyWH/GN5ByjAt5Tq5cSM1L9YxkkFU47TbrsYqVfwanolm2ZfCuEQPoqLLqWXxO043oBlVyL9w23Jjiole+sjkuMI5XQcNmKl/7tfX8/jO+SX9uSUKBN6c5t4udJk5Fv3R01KS1NsnZw059mfuRIO7GspVlZBi7BD3XOO8Lat9E6U+NB7GfZ15OWzOf2EvYNd/jgkTEERG8pzh/VhUXzKoCoL03zDf+vfGQPsC//9Rmnoq5PLN8Cn/+6EJGl+QM61gFznz17Cl8YH41AOGozo33rbLEczst3SF+/9IuAGTJyLIVCARHDr/iZ0bpDOaVz2N80XjKssvI9mULt/u7gKAaZHT+aMLRMLNP/Q5XSoVIsb+Ve/w+Pl1Zzo0VZVxfVU6HYvyoGl84nm8u+iY5ag6FgcKjOHpBMn7Fz+j80aAbS36daA+1UxwsPnJFqQSDpiRYwoi8EbT2tXLO8V/i1Jz4BZX1wQBXj6jkazlwXWUpjarx47xGzeObZ/yKnMkX0BHqoKm3ifLscmaWzWRE3vAW5c0IMxZniPEIYDht0xUiyyT7UNM1w7V7BN2ZADPLZnLb8bcRVIyLmJtaNnHri7fy+v7XM/ptMhAd4OW6l7lz5Z2WYDunbA63zL/lXSXYDkQH0rqMzUJUXk5bu3M0XfG5qBZNG0kxmLkEKM8u5/YTb2dyUeLvjdb+VnZ1GL9HzBxct/mJaBHPY6GhJbgWvURs+5J6p+NhLXG39Ve89l+UrH0AAF1W2HvO9+grGpUgGrpFLSRk2rrFI8QcwEy5EK1iOgA5TVvJ3fNa4tiRrNxqt0Jq5nhkSU4v9EnyEY38McXPdM5SU0RVe1qY+cJPkTTDid0072q6x55kuHFJFMNT+tEdRFuXrNrkTNtkR7RjX6agr7u0kxWYcbnRVouQv2N58o4mCPBeIraZT+wl7JrnhiARIdoK3nN89+IZlOYaV7afe6eBnz+3jUg0sz/IkajGj/63hb+8ZuSpKrLEbz8yj9mjCg/XcAVJyLLEjy6baWUU94c1PvG3t9i4P77UTtd17l6+wypWdsXCGiaUi6W6AsGRxqf4RLGxdyll2WVU5VbRGunhkvN/z52Lvs2UnGrr+deys+iO/QCYUjyFbxz/DXyyj6AaJM8vPm+PNUqySqjOraa9vz3hR5Oma8Zjmmbk34r36zGLJEnU5NWQ78+nM9zJjaf9P2477jaqsyusNs/m5tAeu5AyLn8031z6M/KySmjoaUBGZnrJdKYWTz1yxceSMZ22/Z3glGU5CLwyPSH2A1+S0i+J1tO7djNlsELf1JKpCZmobaE2frXmV3x/xffZ373f8TX1PfX8451/cNPzN/Hbtb+lP2qsGpxdNpsvLvjiu26VQ1t/G6197gW/IL4s3Gsu7XmvaQvUKWpaYXewcwlGJvztJ9zOT0/5KddMu4ZZZbMSxMKPz/g4I3LdL5a0h9pp7nUvZKfruiVYebkWTTEWjMxXR6etricItmW7X2XEq7+xnj94ypforp6f4Np0zVRNcnGqkuo6NlmSQZaJnvIV67HyFX9KcNtaoqdHITX7PrjtI8TPCzNG4UhgLxjnJVIqsgLRMNmP3USgzyho3l09j8bFnwJSYydMx2qKOxYpJdIgGbMYm52UeASnvlyctvaLBtKsD1nPFWx9NmEbGQvwSMZvBg8B3n5cBYm8ey7TCQQZUpzj5/uXzOTG+1YBcPfyHbyxs4VfXDGHUcXZrq/b397H5/+5hrf3tlmP/fDSGZw2pfywj1mQiKrI/PLKOXT/39u8sr2ZrlCEq/64gvL8AO29Ydr7wkQ1449Clk/hljMmHuURCwQCwbsLWZKpyauhrb+NzlAnY8qm851Tf8Jr+1/jH5v/QVvI+Fs4s3QmX1r4Jfyyn4aeBkbmjSRLzTrKoxc4MSp/FO2hdlr7WykMFNIR6iAcDVMQKGBcwbgjm28qOCSCapCxBWPZ2LyRUDTE7PLZzDj15zxf+zwPbX2I7rBRZ2FK8RS+svArZKlZNPQ0UJlTydiCsWT73L/nHpkdKIzd0CHUAVlFh9xVOkHNctpmoNN4La8eDJbQN4i+JhZN5M6T7+T/Nv0fqxqM3yabWjbxlZe+wqKqRSiywkB0gLAWpnugm21t21IEMTMSwa/4h7wPR5qMVjxKhhBo5l06iTbm45mcF5nEHhzKXIIhmFXnVVOdV835485nIDrA1ratZClZTCia4PnaqBb1FKTs++4qoMaOpym8KbJzvqyGZjl+lb1vMOXFnyPF2jUuvI62GRejx4qwmX3JyEi6ZDkt7du0i4uSJFk5svax25f6M+k8uksnkNu8g6zm7eTtepmu8adYr7fcxB4Zp2Y7rzm3nxdHKh7BLi5GdJdCdrECXTx/O+q+lQCEc0qpO/u7EJsX01ltYl6EMsVVcHbHmnnGydtLfiwlHsGpL4dYA/sFEgCqZtNfWEOwvZacA2vxddUTzqtM7CMDAd4U1r0EeCHaOiNEW8F7knNmVPLlsyfzs2e3ounw9t42zr3rFb53yXQumTMy5UPt6Y0H+crD6+nsNz54FVni6+dN5YqFNU7dC44AAVXhD9fM59o/v8nbe9voCkXoakr9w3j9yWMpzxf5mQKBQDBYsn3ZjMkfw6aWTWT7slFllZOqT2J+5XyW7V2GpmucM/Yc/Iqf1v5W8vx5jM4fLSIwjlH8ip/RBaPZ1LyJ1r5WioJFVOVUUZxVfESXjgqGRmlWKSNyR7Cvax+VOZUossLZY87mxBEn8uzeZ9F1nQvGX0BACdDS10KeP+/YEGwBggXx233tQxJtfVKac1bHWFKfgVDjlT05GEzhwXS/Zkp5djlfXvhlVjWs4m8b/0ZTXxNRPcprB15zfY0qqyyqWsSZo89kUtGkd+3nbibj1tHxyT5L9PESbQNKIL2YLytp4xEOdS6T8St+ZpbOzKitmYWabkzgvFQebMJWbCWMirOgqetGpi0Nmwg8dB1STKBtm3YBTcdfb/WVnKlqRiQkjzM50zZ5NxKKVxmNqFtwLVOe/jYA5SvvobvmeKKqceHBdJUqKI7Cp7lPiqS4CtP246HKqiF4JgnOh4MEp63bigIJsrc9B28YObaapLD7rNuJZtsunprCbgy7e9ecE3Mu7PNhFpZL3p7TnDk5bVP6ghR3r/05JIm2SWdS9eafASjY9hzN86+x2mYiwJvjUSSFsOb83hTxCO4I0VbwnuWm0yawaFwxX3hgLfta++gORbjlgXU8vKqOEQVZZPkVgj6Fhs5+Hl97wHrdyMIsfnXVXOaPPvQvmoLhIduv8pfrFvLJ/3ublbtbyQ2oFGT5KMrxUZjlZ9qIfG5e6n1VWyAQCATulGeX09LfQmNvIxU5xjLsLDWLC8ZfYLXpj/QTiUaYUjRFuGyPcUqCJUwqmoRP8VEUKBJxCO9CJEmiJr+GjlAH7aF2ioLG99Fcfy6XTbzMatcb7kXXdcYVjjs2BFuIxyMA9He4NssEWfZYHk58WW9aoSbm4hwO0TZd8aF0zK+Yz4zSGTy24zH+s/M/RLRUsaosq4wzRp/BaaNOIz9wlGIuhpm0wrpOgmjrhClk+RRfisszuZ0iKURwcUDGGOpcHgrJ7slkNDTrYkWyuGaSvMTdLUZE13WyepvhkZuQQkbxqPaa4zlw6leMooHYlsHb4hGchPPk8TpmqiaNS0amo+Z4usunkNu4hWDLTib84yoOnHgzLSNmWqKzIivoUed4B3PZvU9yz6TWdA0ZI9PWTXAebszj47rUX9epfud/FL/9d+uh3Yuup7tqOvaS5hpaQuyE6aC1H1sn96lT9rCkSylCf/L7KXm+rW0mxyMkC/BA56Sz46Lt1mdpnn+NJfqb8+QmwJvbUSQFVVatjO6Uw0Z8zgWJCNFW8J5m/uhi/vu5k/nOE5t4dLWRHfXajhbX9ufPrOKHl82kIEs4Uo4V8oM+HrhhMVFNR5HFh7hAIBAMJ4qsWAJRa38rRYGihC/Mmq7R1t/GmPwxlGaVHsWRCjJBkiSqcquO9jAEQyRLzWJMwRg2NW+iN9ybIspGtAgdoQ4mFk48tt6XVjwCQy5GZs9ddMJyZ8py2qJkkpxenGvoaSDPn+cpgJuiwlCEvoAS4IrJV3D+2PNp6mvCr/jxy358ig+/7CdLzXpPiRaZLllXZdVbtEWzRB8vTLeel+APwzOXg0VHt0RWp/PVXkzKdFMmC6bJ7lhFUlJdl0Bu/UbGvHwXdBmFtbvLJrHzzG8SVOLHz3T+2iMZnJavJyyVx9k9bWVM2wRgSZbZs/hTTHvya8jRAfxdDYx5+lsUjJiNcsFdMGKucUHFpZCaKQh6xiNgOKvt58/hXl5vXUBwKHCodjcxYtkPyatdaT0WnX4Z9dMvIuhQPEySE12vbuduungEXXJ2R9sfs6I1kiMZHHJ0ZRIL4UWLRtFRPoWCmABftuJP1C+4NkHcdRPgrT4l2XW+IXHOBYmIwAjBe568oI+ff2gOv/7wXIpznLOggj6ZOy+bya8/PFcItscoQrAVCASCw0O+P59JRZMIyAHqe+rpj8SXi7b0tVAcLKYmv+Y9JSQIBMc6ZVlljM4fTX+kn/ru+PtS13Wae5upzKlkZN7IozzKJOxO2772IXXluATYhll8KJ0703R4pRPnQtFQwmef2zbTLQHOlFx/LmMLxjIydyRl2WUUBgrJ9mUfE5+zveFeBqIDnm10XadroCuj/jJ20Ho4X82s2uQl307tFFlJEbCSGc65zBRTbE1bvAqbgEqqo9XuRpQkKSGqQIqGKX/j98x56uv4Ow3BVi8ex9Zzv09EDST0ZWWXxiQhJ9clGIKgXehzy9tNKF4Vc152VUxj51X/R3fNcVa7ogPryL7nDHj5p4bQ51RIzVYMy+uzQNd167wYroKD6dAwxEW/4k/YXv725Yy//+oEwZaF16Nd9CtkB0e0jjF2E898WRKPf8oxc8i0dStqZn9vmH0nO22REsVdBYWDU8627pe/9VfGP3oT2V0N1tjcBFn7xYF0+cRmtrUgEeG0FbxvuGDWCM6eXkl9Rz/94Sj9YY2+cJSBiMa0Efmugq5AIBAIBO91yrLLyPXnsq9rH/u79tMT7iGgBJAlmbEFY9+VBXAEgnczkiQxrnAcpdmlHOw+SENvAx2hDhRZIddnCH7pXIdHnOF02roIQyb2HFSvvEvTbZZJDmq6olRHw505nPSEe2jpa6Em37tmR2tfK0Ff0NPF3R/tp6W3hYAScP37kJD9ma7AWEx4S1fIyC4MOm4TQ/g5Zp22kvt5pqNb8QheUQXm85DotPW37qH6ue+S1bgl3umoRXD5n4j216Pp0ZTtucUjJDV0zEG1k1K8ilh0g6YxUDSavRf9grxdL1Pxyl0EuuqNjN3l3yO3oIKG4lGu/SmyklHxNtexHwY0XSOoBBOctmUr76H8zb9YbUJZRfRf8DMKpl+OpEVShFH72E3c8mWTowrs+bHx3NnUeUkW9J0KkZmfswmRDDFXe8LYZJm6CadSNNBH+co/I+lRchreYd6/P0/07B6Yf517VAe6JUi7tTHHJyKdnDnG/tILBIcXnyIzqvgYyf0SCAQCgeAYIkvNYmLhRIqDxezp2ENHqIMJhROsTE2BQHDkyffnk1eUR2VOJQe6D9Aeamdc4ThyfDlHe2ipDLPT1k2cM51bPjm9O9NcbptOtPVJPqJa1LWNVURKwtMBfCxjFudKt4TcS1gxiWpRVMU7KzhZtHVrI0mS4bT1iLHQdM0S6b3QdA1Zlj2zjo/GXJoipOcFBJtb0i2qwDqmpruxcz9V25cxomkn+TtfQI4YeaGapNB5wk0Unn47kqyg1Del5I2ax8cej2AMIzUeIeU4phg99RTR1pwD445E1/hTaBo5m8qVf2bk+kcAqHjxJ+y8+KeOx8vcz3SirU/xWZ8XR0q0VSXVEBh1kMJ9lL31f9bzHROWsu64jzJ91MmAszAKGIXI7AXeSN0HS2hN+ixMEWgdMpBlEh3KZv6v3bVrzm3UJuibecJ2FFkBSaJ54cfoGbWQkc/eQaCjDjXch/rkLVD3NsqSzzu6pu2fAz7Z5xqZounasXch8hhBHBWBQCAQCAQCAWD8YCzNKiXPn0dbf9uxlZcpELxPkSSJgkAB+f58+iJ9x07hsWSCBfHbQ3Tamg5CJ+FNx8j+VGVvd6bptE0nQppigVckgCkG2pczv9uI6lHLHeglhElIaJq3+GUK3Zk4mL3ENNN9KkuyZyEjs6+08QiahqzInlnHR2MurXPR43y1i57mbR0dKdxPoH0vancT2R11+LubUCL3wL43KWuvpSypn1BhDetO/iwjJp1HYcy56JQ3muyotJbnOxQZc3KEJrRxKF7lFH2gKX5qj/8EVa37kOvexN+2l+oNjxE64aaU42UKtp4XcGIRA2ZhMK9zwyz8N1Rh0Py8MF38WY1bkWKiZ/vks9h/5neI9DQmiOGyJKec28muV6d4BCcx3C5Q2925TvEIKWK7RMo2FZRU0TbJ8Wov5thXOZ1dV/6N0pd+RtmW/xkN1v6Dgqrp7B8xzfF4mfPjFocBiYX4BIm8e//qCAQCgUAgEAgOCwElQGVO5dEehkAgsCFJ0rEr2EJSPELHkLqSJAlZlh2rwSfkoHrkkprtAkogI0eoF/biQ+9WTLdqJsciXQExTddQFTVB7HFqY2YKu7Uzx2IWnOqL9rlvLxaP4Bm3QKK7V8FFtD3Cc2kV6pIl12NhF0dlSUbSoXDL09S8ejfKQE/6bagB2qZfRMPiT9Md6kg4Pk55o8mOSqd8UyDF6SkTdwGbAqBVIA3vbRqCuYp+3k/gT6eBrjF27UPsnHEZ4fx4EU3zwow5l14RKGa0BhKe53ZTbxOKrFCeXe7aJhPs56KOTlbjZuu5npHz0Eh0MINxfoe1cGJHelJRMByKgsViQZziERLeoxIpn5MJTmfiIn1KO0lKmKdkkd5sk+Da9Wez59Rb6ayYxviXfgZA6cs/R7r0F5B0fJMjLNwEeOG0dUcUIhMIBAKBQCAQCAQCwdAYxngEryxaUwxMl4NqigV+xe8pQmYk2mIT+t6ldXKietSzEBAYsQeK7O1YNPtKJwBH9ahVNCudG1qW5LRxCz7Jl1HcguXAdqtSfxTmMhOnrV14UwZ6mfTiTxm7/Efugq0SIFyzmJ3zrmT35b9jy6eeoX7JLei+YErxMCeHY7Kj0msZf4K46OYITRIqZdnZtStJEnLVbDjuU8bYogNUvnJX6vFCSekzGTMeQZKMzwOv97ksyWkd5Jmg60aetuW0bXjHeq6vfKqj69jRwexQ4C0jAdWpYJzunGmbIJDqWMfUTnKhNyenrRUnYkPTNZqnnI0++TwAlJ5mxq+6n2TseceKpKBLIh5hsIijIhAIBAKBQCAQCASCoTHMhcjcxDlrua1suDND0ZBjH2ZRqnSOyqgeTRFZnLYpYWSvvltJ53qFuKCZTsw0XbtuzlizjSmmhTSXOTIdmrHK8m5isem8NAVeL9E2qAaRQmmyjo/wXGaS7wux7ND9q1Af/gRlbbutxzvHnkx/2SQ6grlI+SOZMOpkKB5Hd7SPvQ1rKM8pT1z2npRxarrWE8ZEYnyE0zJ+TddSily5Lc9Pfg85FYQzYzUkSYLTvk50w8Movc3k73qZ3D2v0z3mBKs/Uzi0C/8pAq5NrFRkBS3qIcoOU36xOTbLadtgOG01NUCoZGxcmE7jOk4u8OYWVeAoxkpx0dYr99YukGoYsSDJJF/g0Eh1qCcLu1Y7SYFzfgS7XoRwL9VbnmHXrA/SXz7FtpvxCBSvIoKSLjmOTyCctgKBQCAQCAQCgUAgGCqBfCy1bxgKkaUTbWVJdhZDbO0yLV6lSO5Zi/Y26YqfHcuYwmfaeAS8xRXIzLVrLl33aqfr8UzSdBm5pvCTTvg0XZBuheXMuVRl9YgWr5IlmYDsHtWhRAbIeuN38OezkGKCbcSfzb6z72DfBT+m6fhP0DD5LHpHL4aKaeALxpfKk+poTYhHIHUOnByVye8DN9do8vJ8DS2lL6d5SthmsIDuU79qPVf58i+QzEJqNtelWUzQ7f1p7qc9d9UJXXIuTDdYzIsfiqTg6+/C33kAgP6ySRA715NzaJ1ytZ0KvCmyg4AqpR5Xe/SBGSGTkmlLYqRBskhs9SenOm2TxVOni1q6rhvO3aLRcMpXjG3qGlUv/ARs7z1T+Dczbb3Ec6cMaoEQbQUCgUAgEAgEAoFAMFRkOV6MbBgKkXnFI5iV5b2W6CfnXbqRiQvSco2mKX52LJOJ0zaqRT2PvYVO2uNgLl33K37veISYiJcubsEcf7qx+WQfkix5ivmSJFni7hEVbZ3ylaMRCjf8m0UP30TOyz+FWMGs7vIprL/sN3ROOtNq6hRp4Hje6okFp5yEc9MBaSd5Gb9VCIuk7NWk5fm6rie0gXgxwYTjEHO/mwxMv4S2SqN4VaBjP6Wr/2G1M4tSpYvYMMVRn+zznHOZ9FnNmWAK4pIkUdC6y3q8r2JafFvJQrdLVIRT9EFCO50UATX5+Ns/vxLaSYmRBk4CMBiRCcmRDMlO25RxkXQuLrqJSMkEALIbN1O06fGEdqqkxqMuXD6LnY6HwEAcFYFAIBAIBAKBQCAQDB1TtB2i09Zy1znYsswl/GY8gpcgmInT1sxedXLD2fsyoxa8BKRjGVNY8xRaY0uovY4F2ArFpXEn+2QfquxesMw8rmafbg5fCQlFVjzPC3s7L9fl0ZhL81j4FJswrUUp2PI/Jt53JSNf/AnB3hZrLzjxC2y75G768isS+0FDtSVcOhalsj1n3ZYdlrfrqSJesnPdHl9h7zdZKHbMQZVS3Z/J7SRZZuuiT6LHxlG87iHQoglivuladZpzU8gHZzerfT+SHaqHij2ftaB5h/V4X/lUYx9j47QfW5/kS1vgzXxNsoNZTUo0teIRbO0c4xGQkXSbI9fBQQsgyanFz5xc004FIS0BXvXTfdZ3recqXv89Sm+r1c7sz3TxO81Tuvzi9zPiqAgEAoFAIBAIBAKBYOiYxcj6O2AI4ogiKYZb0sVpa+bUphNtTWeslzvT7lTzEhfNHMtj0Wm7r2sfveFe1+d1XbfEJk/RVkt/LCAuTHktdTYjFDzd0MTn0msJvOnC8zovzPGYRcbSuXuP5Fxa56JNDBv19Leofu571vJ6gPDEs+DGV+HMO1DUQMrx0PTEXFInQdMUWpPzapP1cMd4hKSCZXZXaUJfkLhNl2X8TkKxffyyJNNTNJrO8UsAUPs7yK7faLgzY/EIiuz+/jXzYyH9Z4EiK2njQcy2XpjHRJZk8pq2W4/3VcREW4dICafzzCmvNjkeQdd1JDlVjLVffDH/nzb7Fj1FAAYjOiO5qFzyXDpFcGgkFg6L1izi4PhTjf0Y6Kbi9d9a4zPFXTOX2ul97nQ8BAbiqAgEAoFAIBAIBAKBYOiYxcj0KAx0H3I3ZlEeN9HW7q5zEw7N5fmmUOAl6GSpWZ4uVLOvjKIDjjCarqHrOgPagGubqB5Fkb3dxGZfqqSmdeRKukMFe4e+/LLfEHY85sgUftwEGzMj1Fwm73ZemEKQmT18LM2lRnybAHJ/J/k7X7Se76qez5qLfsrAh/4GlTMAUBXVe0k6iUXB7G1ShFZSBVQdPSGqAFILlplif0LBLFMoTs6+dchBTRGKk4RDc047YwXIAPJ2v5qwnzLe0SX2zwJJd3ZqR/WoNW6vixERLcLezr0MRJ3fS+bxMDJt46JtNJDHQEE1EI9isMdFJDudnRzMZrtk0TwlqiAp9gCc3zvJjlxd1x3Vv2SnbXK0BsSycNOI/rIks33htUQDeQAUbHseeaA35cJMJlEXgkTEUREIBAKBQCAQCAQCwdAxnbYw5IiEZNefid1p6/Uj376MOd0yeL/iT1u8SpXVY9JpG9Wi+GU/muYtxpo5rl7tono07bEw58SMF3BFN+bQq7hQimjr0J2u69YcgvsyeLPQWiZO2yM9l1EtmhDVkdW0zXqubdqF7LjoZ/RUTE0QR1VSi+zp6FbWK9iKUiU5JVNyaJ2iJxzEueSCZVZfUmJfMnLKNlOW8TtEEZiCub0vRVLorFmEHttG3u5XE7Zp5lcnz1NylqvXeRbVola+tdd8R7QIPslHJJYrnIzp7JUlGbm7AX9fGwB95VMgdix1o+JXSqSE/fA7Cevg4Hp1iSqQJdk6N5wKn0Fq9q2r01ZK3KZTX7IkpwjiOqmibTS7mPaJpxv3owPk1q5ILCpnuv1xnktRiMwZIdoKBAKBQCAQCAQCgWDomE5bGHIxMrflzgkZiQ5Zi3YUKbNl8JkUrzJFN1lOFR2OJlE9ik/xeToITXeyT/F5Hi9N1wioAc99NI+/X/F7Om0hfvxdsRVacpvL5OX+PsXnKOZHtSiyJKPKasZZx15zDtAT7qEv0ue1ixmh64bYaopfQZto21s101F4c1zKnyS0mgKivZ1TYSpHJyapmbMp8QhOrt2YAHkoOaiSLqU4gCVJIpJVQG/lTAACbXsJdu6Pu5Jd5il5P91ybyGe1ZwuHiSiRfDJPiK6s2hrFxeVA2utx81oBOtY2C4ymPtJgpk11cEMzvEUTkKmU1G15LlMzjt2zbSVpMSxSXpqPm5szpPPM7tT25yL9rEnWY/l7Xw54fPajLpwjP1wKKYmMBBHRSAQCAQCgUAgEAgEQ2cYnbY+ybkavN1pq0iKazVyIO609VgGL+lG8Sq/7Hdto+u65Sz1cnEeDczjkU60lSUZv+wttJr76eXIjepRJEnCL/s9YxRMQTFZzEtoYy8kJRlRFymCDomCjiqnOlCT25nikOs+SpnNZXeom45Qh+vzmWIWSDP3Ndi4xXqur2xyQgSEiSqnxiMkC7umOJhJDm3yvOuSnuDGNfuzY4nJJArATm7JTHJQzdfbb5vvza6xJ1qPl+5725o/MxLDSeizi/luTm0wsppVSU2f6RxzArud+3ZxUT6wxnrcLEIGsXxfh6JsdnQ97ti1k1JkT8dxnxQ53s5pjsDZaeskACeL98nCujn+5EgMMz/axDznukfMIerPBSBvz+vIkUjCRTanOXA7HgIDcVQEAoFAIBAIBAKBQDB0ggXx24fJaWuKrECqkJBEJgWnTDFDlVRXscYuqKmSu4sToLmvmbb+Ntfnhxszrzadg1aWZPyK31PkBmP/fEqaQl4x1246Ecw8/m6YAiMYzlInd2myoOmTnMdmb5ciftmQkCzHYbq51KVDL6aX0I9ZSC1WDCuraasxZsVPqHiso9CanDcbG3xiVEFMcLMLqE6FqRwFTafsUgcBOHmbkLnTMyWRIUkotot4XTaHZlnt2wnCp1Mkhnkemtt1Knxmb+tX/aiS6nlxI6obUSNeBQmtCxH7V1uP91VMi++jniqOJs+tNd/JB0gicR8cjr3Zn+WgdTh3ILUQmdN8m30lO23TRS2YjyWfi7IkoykqXWONjGJloJui+k1xYd0ln9gsZCdEW2fEUREIBAKBQCAQCAQCwdBJiEcYmkPRq2iW3Z3ptNzWxHLauiyDNx1epgjptlTeLi6mc2dGtIhrIaPDQVSPWi5VrzaqpHoWBQMskUiV3cUtU7jyyT7POTLFK1OMcRTgbWKsKeikOEKTlpt7xmbEtpVOKLYE4HSuaR3X4laDwS4m+8L9BNr3AdBfMh5iBceSnYZuecEJRcEcXKhmpEGCaIsRy2Bv55pdauvfaak/OORN687CbvI2ndyZxsM6A0WjCcWKeRU0vIPc32m1c3IdmxEDCTEKLsfMFGPTxYhE9Siqkv7cl3UgFo8Qyi4mklsWH5dLVERyP5kcf3RSHLRmO3vsgVNmsZV3bB43iZQ2xsNSysUJx3G5OLqTt6fpGl3jlliPl+5dEc8djl3EcYq6AO9c4vczQrQVCAQCgUAgEAgEAsHQGeZCZE6C2WDdmZIkuToqTcFTlmVPd2mC09YjLxVIFaoOM5qmpY2JMJd9ewmogFU8zCsqwu7alZAcBS77MnKvbZqFncB96bTTMni37FtZjuWNyu4itl0sS5d967bEf7CY7kVZkslr3WM93l8+JT52hxxaJ6EyZUl9koCq6ZohgCcXIpNSC5Y5iXN2Ac9pqT8Y4ppdeHN0Zzps06momSLFxi9JVkSCrGsEdr9itXOKSnErROb2PvfJPgJyIG08Qibnvty6G2KxGV1lExPboDkWZbNjHhNHB3OSgOokRNuPv1tfye5YSXeOH0jOtDXM1Umu3di8JbwXkkRgewxNd80iNMUPQFntmwmio9OqB7eLAwIDIdoKBAKBQCAQCAQCgWDoDGMhMjfXVSbuzGRBx8udaYq6Psk9y9IucKUTbZFIG0EwnET1qLXE2U0sNp2GpqAZ1VKFVl2POxe9HLlmASKf7HONnbAXbPLMsbRn2srO2cN2By0QF/lcxmUXeJ3I1DVtLb/3cHJnQnJsQ17zTuu5/rJJRpvY8nA7jm5hB0erKiVm/DpFGjjOgYPzUiKpKJWeWpQKjIslyQ5aJ6dn8jbtIj3EC5GZbewRCb7tz8f30eE9Z753zf00593N0a3K3rEfYFwAkWXZ1V1t5iYrB9dZj3WWTkho41TwKyWCwKHAm7kPydt2O/5WX+niEUxHrkOBMav/5JQGp6JmDu9Nu6Bv5d6io/mz6a45DoBAXxv+g+utdk6u6eQLM4JEhGgrEAgEAoFAIBAIBIKhM4xOWycxFjJ3Z5qiIbiLc3ZB0C0X1klcdBM0o1rU1Ql6uDDdwl5RBWaxMrflyWYbWTaOpyqprg7TqB5FlVXLoZzWaSs7O23NebRc0x5zqcqJVerd9tFsl04oNvNiPefS3E9Z8VxSb/brhnmOmcfBLtr2xZy2bjmoTqeRk9M2QRjVdUu8tr8mQcDTdUfnpVM8glO2rkri+eG61N8tQzWpjXk+9lbNJhIwilj5dr0I0bDjPppjSxbzk5fwW23RDdHWo8BecnsnrHPsQDzPtqN0fMprneIR7OK06YZ2ErrtTlun4wqgED8eup5aUM56LbbPUI9MW1MotmI6cBCdSTouSUJ98jlmj0gIbH/Ouu0mwKeLNXk/I46KQCAQCAQCgUAgEAiGzjA6bZ1Eq8G4MzN12todoU5ijSVmyonLsJ0wXa9OYzqc+BU/suQsoEJs2bdiOG1lnKMKzGOhyqpnPqjZlyRJnmK4XbB1ckAmC+teRYrswqGbmGkK04BrJMOg5jImwHvGSWBkGO/p3OOaY2x3ESqSQk7LduNxWSVUMhaIiaNKojiaLKAmO0utdkmuV6dIg+Tl7cmCudXOIULBKR4hOZIk2UFrbtP+PnByU6Zk8ioqnTXHG8+FOqH2DeNhB3HdScx3uyBhCtTpCva55b4mbFNSE4qQdZaMd25j79Z0odoFZacCb0n3nY4rJM656xzF3if2TFu3eISE8wLJcS5TcnSTBOXk49819iT02PP+bc9C7LWOReWIvXePYKzMuwkh2goEAoFAIBAIBAKBYOgMYyGy5CXFEFsKLMfF2OFw2ppijiqrrmJggrjo4QazuyqdIggGi6ZrRLSIdyMdAkrAU1yUkOJ5r5Lz2Kx839jx8IpaMMUyn5SBgznmgEwWlFMyST2KFJliLLhn2trHlel54TWXpgCvSN5O27AWxif7CGthx+ftx0IK9xJsM4qQhUrGo8dyP91yUBMEVJcl9cmuV7OdneQLCW59JQvi5vstmRSR0EEQtC+X99pm8rnWPnpR/Mmt/0sYvx0NDVUH3vgNvPOE1cbNaWue+54ZxbH3kls/mh7bZr2x3D9SWEM0kJ/SR8o+mi7g2LbNmAW3CAwTpwJjQIIT3i3CwhRfrUxbnPuyi7RujlfTfZtO9LdfnIlmFdIzYrbxeNseaNwMGJ/FTnE2gZ5W+NUceP1usBWhEwjRViAQCAQCgUAgEAgEw0GwIH57GOIRksUPez4opHFUJgmCTlpNVI+iSqrV1rHAFal9ublQI1oEVVLTCn2Z0tbfRlNvk3cjyXDaqpJ71XtzKbkiG5EGbvEIZnSAV5arWawJ3MXwqB61XJVm3EImS6Ldlk7bRUJFUhzzexOcti6RDE5z6WasTIhH8BDgo5qRF+zWJkEort+IFDsRzTxbq42cKoDZi9qZ2aXJS9eTj1ny8bL6sou2uCyDR07YpqanislAwnyaUQtOS/3N5z3Hn5TJ2z5qAZrpxt36P4idu8kFujRdo/Ktv8IzX4cHr0XZv8bV0S1JkjWXXg5ySZIMR7pLJIama+S07YFIPwDhqlkpAqRTpEGy69gt0iDFaesgwENi8TCnaA2zjUySO9YlHiG5jaMD2ObadRN3k/Om220ZxWx5EnC+0KPpGmUbHoG2PfDsNw3hVmAhRFuBQCAQCAQCgUAgEAwdRQV/nnF7mAqRJQtSdrEgraMyJl455XKa7cw2ycKWvU1C8bM0TlvT0eol2ka1KPu79qd10Ya1sOdybV3XQY/FI7jky0Ji8Ta3XE/zWJjHNHkJvB3zmPlUd6etKYaDRyEpEpfLu4luCcvgZZex6SQ4bV0FvEznUtMIqkFPMRwgokfwK34iuvNcJpyzB9daj/eVT44PXdcT9hFshaSIC2WO2bFyUpEr9FTXbpLr1a2v5G06FdUy28WjUl2EvqS4C7dtJi+XjwRy6aqaYdxp2w3N25wdoqFuitY/ZO21suGhhNxYk2TXt5uD3Gzn5VrXdZ3sxq3W/WjV7JSx6VKqIJv8OaXrLpEGxHNhTQE1XfEwt3gEiB1bUxB3iX6wHLm6Ho9HcBDg7dnDbuKuqiQWGWsbe2L8yc3/MbbnJByHeija+O/YoP2w8BOO+/N+RYi2AoFAIBAIBAKBQCAYHsxiZEN02jpVg08WY8HZ7em0pN4JTdcIKAHA27WbUvDIJbM2qkfTCqhgiLGarrnmoJqYDkZPoSkW7eBVZEmX4o49n+IdaWD25xW3kBCP4CIA20VIp21quoaCjLL+QXjl59DdZCydTtpXSZcS5tst+xZIcGC7RTIMai5lPz7F5zmXmmacQ27HKuGcPbjOery/bIp1Wye14Fey0Gf2ky7SwMlpa74u2fXq1FeCuOgwruRtusUemIKjXSh2dGcmxSNoupYYkbDlKUdRsmrLsygDPfHtvfM4sq47xmuYgq05D27zrchx0dZpznVdJ9j4Tvw1VbNT4xZ096za5HiEZOziutscmf1ZxcNwdu1CTKQ1h+cwLnOb5jx5bdOePWy1SxJ3VRJd06HcMrpLJxh36tdD217LKW+ncqttLmd9CPIqHffn/YoQbQUCgUAgEAgEAoFAMDyYubb97UMqLONUDd5xSX3S8mqznV1sclsGn5yD6lgkJ6mAkpu71+wvKAfxy37PKvURLebOTOO01SXddam2uT0zfiCgBFyXdEtI1n6qsrNz1C60Wk5Vj6JOEC8E59SXP5bXCi4xCtEBxr/0U+THPwPL7oBfzaH8zT8jhbpT+rO7cc3j75RNandgu0UyJM+lkyMXjGMbUAIEVHdBFmxOW5e5TLiAcGCt8Zgk018aL2DlVPzJSWhNPhYQc2fa5sDJxWmdszanpPm41zad8lnNbaY4bR3cpU7jT3FnyqmZvJ1jbMvqV/wWOSnjVIqGGbXpicRB9TRRdHCDp+vevBjhmOmsRZGRCSgBw13t0EZCIlC/MXZHQa+clXJRxc3BnBxD4FY8zLyI4OZMNtuZcRFubmjAeg97unZtY3M7LyBRXLcc68kxHEmfn7qu0zrG5rbd8mRq39EI1Zv+E7+/+GbHfXk/I0RbgUAgEAgEAoFAIBAMD2aubXQAwn2H3E2y0AQ20db2M9bRaUvmTlszn1WVVSSc3ZmqpFrilZsjF2L5pqqfgBJwXS4PcUHQq42JKquegqDpjvXJzo7QZHdpQHYWIaN6FJ9iHDNVih0LB+HKLOoEuIqnKU7bpLHJoW4m//cblG57Lv6igW5KVv6J4x+6geJ1DxJo2UX+tmcZ99b/UfLwJ+EXM+Dfn0aJ9Dm7Y6XEAlluAnzyXDotqYe4wzcgBzwzbTXNcLaazkfXYxHug6YtAPQUjkJXA1Ybt+JhyUKrm6PVLhq6uWPt0QBO7yOIZ6/ai1e5Zqra9s8xtsFhSb15vO0ki+uarhEtqoFpFxsP9DSR88rPE8TR/G3PE+xtNZ7PKbNeW7zjhZS5NHOrzUJkbtElpkPZzLR1OifkcC9q83bjTsU0JH82spz4HjBF7ITXxf5LcDA7ZAUnC91uQmusE6svp0xbiEdnWLEHbgJ8rB9rLl3ydlPiNZLaObqmx9kE+Fd+jtLbmnABLX/ncrJ6mo07E8+C8qnO+/s+Roi2AoFAIBAIBAKBQCAYHsx4BID+jkPuxnSdJYgApMYjuOWl2kVDr2XwZl9e7syETFUPpy0YAqXXcnkwxN2gGkxb4MpLZILYku5Ym4AScC0cZhXCIlVYsbczBWw317Hp2DP7UmXV1cFsF83theDUrgbGPnIjhQdiUQFqEGZ8AGLH2N/fSdXLv2TC/Vcz6pnbGbPxMQJ7XoWOfbDufvx/u5Cs7saUsSULh37Fn9FcujltTfEz2QmaghkTIDm30dAMMbxhE8Tmsat0XEo7R9GTxKJgpvjn1C4+HBdxzu70jC2pTxuPIOkZZapmsqTedDmnCMW2fFwwjpcqqXD2neDLAcC/+j4Kmnca49J1Stf8I/6Cy/4I/lwACna9DJHEyJGoHkWSJet94uWSN3OY3S6A5DfvRDJfW73QuT/JZS7lDLKCbaJ5OqetebzdipWBcfw1NNdoCntfmq55z6WcGI/gJsDb0XSNUPFYmHKB8UBvM/nPfAM0q4oapav/GX/BCZ9z3I/3O0K0FQgEAoFAIBAIBALB8GDGI8CQipE5iaPJWbXgLtral+d7iXMJOaiys9PWdKCa7bwKFamyik/xoWvuQl9EjzhHBtiwBNl08Qixwmd2YTR5/Hah260om11oVWTF8XjZxwQxccppN21FwezbDDZtY9xDnyTYsss4DsEC+Oh/4AN/hpvepHfKea7Hw1QJpYZNzPz358nZvzZhH5OLmrmdF8lz6Sbmm8dBlVXnfTRHpUvG/rm0sc5ZWxGyrpLxiY2chL6k7F4dHUl2drQmxws4iXP2CxK6rruKi+a2YjdcBWD79pIfM0lYUo/mWLzKnAMTK5O3YCScdhsAEjqTXv89uhYhd+8K6/wJj5gD406DKecbfYW6KKxbndC/eTFCkiSr0J5XHjXgGjWS37Qtfqd6YUKcgXksJN1ZmE7OhHVyxyZn2pqF4ZKxz7mX01aSbWK+h2vXmnePGAVTAIZYcUiH88dexA5sKx4u+CVklwLg37GMEduXG0Xd9q8mq8ko7BaumAH2WAyBhRBtBQKBQCAQCAQCgUAwPNidtkMoRuZUcMpc6p/QTnYWDu1ONq9l8KYAKUmSa1GzjJ22kiEWqpLq6ryE+JJ6twJjYOTeWk5bF0euptkEKUlxdITa3bjm+B21m2Sh1UX0THDtegjYyTm0gZ5mRj/2OXyxpdC9+VXUfujPMOo4o1HJeDov/AVvXvQT2qaeT8eEpTQsuoG1Z3yD9ptWwk0rodgQO339HUx56qsUbXg0YVx2ESnTufRyFJtOW9cV6rohrpnFqxwLvGkaPsmXUISsqzjRaeuag4pNnNN1FFLdjU7z6SagWqIbzs7L5OxVJ5HVfNw81byW1Ntzkb3Gb3+vmBc+ADj+RiifBkBe8w6KNz1Oic1l23fcp0CSYMbl1mNlu15O6F/TErOaVck909kUbf1qqktb13UKkkTbZBHYS2hNOP4On2Pm+Mxt6bpuOW8dsRUYc2uioFj5uPb+k7dpCs9WPrHTnMu2fOLYhQEnAT7BNW3uZ24ZXHS39fiklX/G11FH6er7rcf6jrvemEtBCs6X2QQCgUAgEAgEAoFAIBgsw+S0VWQF30AvVW/9heK6tQCEZQXFlw2BAigcBcffiFLgXGk8WTR0dNomVVT3Sc5CnxkbYI5LluSUPFrTZadICrqsu2ac2nEVZIhXs8/2ZdMV7nJtYwpNToWNzPGbblyrnZNWLCUeM5/iIzqQ6jq2u3ZN1635uB37fQWY9sqvUGMFpXorp7N66VcYUTw+5TVdpeM5MPpEa3ttvc2Mza8y5vyTy+DhT8DOZchalBEv/hS1t439C65JEJMhVuAqSRRPdmq7zaXdUaxrxlyaQm5yO1VWCapBFFmxCswlHtZYwaZYETJdkukuHkvQ1p+ZOZpy/CSs81FDcy5ehZw4Psn5vEpY3u5WCMshezXt8nyPJfWynL74VrIwaTltARQfnP9z+Os5AIx4/fcokX7AEP0jk84y2o07zfjc6W+ndO+btIb70X1BIDGrGYzzui+amrUd1eLvJZ+c6pTXdS3utA0WQvF4ZKIJom26GAJ7gTfHNrYLVabjNd3xd3K8Wu1sx9+tjX3e3HKTAVTiMSEaiRc/nPqCpIskU86DedfC6r+jRPoZ/eRXCbbtAaAvp5TI1PMdxycQTluBQCAQCAQCgUAgEAwXw+S0ZfvzzHjwk1RtfIJAey2B9lpyW3eT1bAJal+H9Q/An06j+N+fIa9ld8rLE0RDh2XwppPNHhfg5M50Epuc2pkiq/nPQ48FwC/7jUXQLm5bs1hZlpLlWogsqkfjhdRixcOS+zNjDxKKbzm0g0Th1y87Z8Lai5qZomeyc9Fcim6S/eY9FB/cCEA4t5y9F/6MUDAvJeoiOW4hRQTLKoKPPETTnCutNqVv/x25pzkl6zh52T0kuThjeM6lFJ9PRxdtTGAMKkHPbFw5GoHGzcZrSsZDIDvBQWuK/cnHwql4WDKSJKUUw3ISR1VJTdimmxiIlBSP4HAiy8QKXJmOUBd3ZorQ5xDN4VTMzifZzovRi9FmX2XsV0ywBaidcTGyef6ofph2kXEz0k/e3tetdpquEZDjRd/8st/RaavrunU+2o+VidJ5gID5eTZyPsQuhCQ4bT2ORYKzXnKOnTCFWvNzwTUewXb83ebIvh+e+bjEnbZuFwaSx28WlUvpK0nwTXm/nX0nWuFoAEuwBdg34yJkOfFihyDOMS3a3nnnnSxcuJC8vDzKy8u55JJL2Lp169EelkAgEAgEAoFAIBAInAgWxG8fitO2rx0evwn+cTn+niYANFkl6s8hqvhSmgd3vsBxT9zKqKe+RvH6R6h45S5mP/sDiu85C+4cBf/6CHJ3Y4qoZgpu9hgFn5oq4CVnpUJqxXuzP3P5tSnmOMUamC66oBpElZ2XaoPh/AsoAXyKz9W1q+vG0nzAEhcdM3mlJHep7LCUP9l17FCMKapHEwqRKZKS4My0Y/W1fxVZr/w8tgmJurO+gxbMB1KFq+Sl/slxDEYjheaTv0DdjIuNu1qY0o2PpzgcncTATOcyqkWRMQpSmcWpnOYpokWQkY25lFRHcV1CItCyE7SwsU9VsxPODa8l9aqsJsYLOLljk3JV3UQ8e9yFm4CaHI9g9u/UzswvdcuqhUSnZ4KDNqkv+zSZx8OOdsbtRAJ51v1IViFNE09PLOZli0jI3/Z8vL8k4dA1S1qKx4M4XXTJqt8Yv1O90Ng/MyqFRGey4/EnPpeS7u2g1XXdtfAcJB5/L6dt8rF1OsfsrzcvZDlhj19xm8vkcyelXSCX8MW/QbeNN+rPpWHSmY6F2QQGx/SReemll7jppptYsWIFzz33HJFIhLPOOouenp6jPTSBQCAQCAQCgUAgECRjj0d453GoW5XZ66IRWP8g/HYxrLnPerhtxBx2XPMAW254jpeufZB9X9wAXz9gLJvOH2m1y9/1MlUv/YzStQ9QWrcKtXU3hDphy5MofzyFwv1rLNEEUgVIiMUjaElOW3T8jZvhzhr43UnQ3+HuzpSMwlU+2ecq9EU1Y0l9lpqVtshYQA0Yy/zd8nH1uKvSLV/WrZCafbtOrmOnAlxmvIDltHXI3E0oChbqhkeuR4qJmY3zr6Z35FzAWUBNdkS7LTdXZZV90y9Ej72+fNMTKNFIQn+KlCq86ejISLD5P7DvLcA9+9YUwVXZXbQ1Hbl+xe8uwEvga3gnPobKWQlCX6ZL6nV0V6eqKfTZH0sZhiRZ4r+u664imZlDa15ccBuXea6ZWbWOfclxoc9NdE5xbOqpjym5Few77jrrftPMD6D7gontxpxMNFbsKm/P68gDPdZ+29v5Fb+jy9zudnZyaWfb5tAUbSHx/PF0tNqjIiRnp7OEIcZan1Mejlzr3PBw2lr9eeQO2522bueYuc24AVtHdUhadcpXTnBNA3LNIupmf8i63zz9IvRAruPYBAbHdKbt008/nXD/r3/9K+Xl5axatYolS5YcpVEJBAKBQCAQCAQCgcCRwpr47do34J6lhshx/I0w7WIjp9LOQA+svhfe+A101MYf9+dRf/Jn2TL6OMpyyq2HFVkFfw4s/ATMvZrelb9Hee2XBHpbE7rV1SCS4jeE254mJj31NermfojOE26CmOvQXqDL6DtVrJGQyHv91xDqgIYNsO5fqJPOcHRnmkvqdVl3XS5virvmkvqoFsVJ8zKLjDllR1r7aKsc7yYuOom2mbiOnURPM8PVahN7jT0TNsEd+9+vQusuADrLJnFgwUcxF0E7LdG3Zw9b2Z5O7lhZpS+nhM4Jp1Gw/Xl8/R2U7XwRedTJCX0lIyGRs/af8PwdoPjhUy+i+oOOAnxQCVrjUSXV0TUd1aLk+HKMQmtKgN5Ib8LzZr++hk3xMVTNThCm0y2ptwuCTkvSTZHQFFq9BGAT+3nj1M6MPXDNqrUJxW5iLCS6mHVSoynMvsy5NsfoVCCtddrFaLpOrhbh4KzLkaOhxPeqrDAw5VyyVt+LHB0gb9fLdEw519hX2/jcChfqUrydKqvWHJljybGLtiPnue6jm2ibELHhIrRKkoSCQkSPoEmJUSQJxywmcHq5tCEeN+KZO2yLxPCaSzO6ATyctkn5yjqpFwckSeLggmuRZZlgZMC4HcsGFjhzTIu2yXR0dABQXFx8lEciEAgEAoFAIBAIBIIUyibDWT+AV38OvS3GY3VvGf8e+wwUjISCasivNsTXjY9AX6LgyvilcOGv6JXC6J17rYdTCiOpASILr+PtEVMZfXAjvnA/vQUjaArmMXPsmeRGBuDfN8KO55DQGbXmAXrqt1B3znfRAjkpS++dltSr/e34d70Uf2DdP/FNPddR6AvIAat4ldty+YgWQZVsxat057xaMMQju5vV0aloc9o6iYualhSP4ODIdXIdO20ruSgbGE7DgfBAQhtZkvFtfhLWGo5p3Z/D9qVfBbuA47CsO9k1aoqLyWKTKhlFxlrmXEHBdmMpfOX6R+GUb1oV6J2EN13XyF71t9hOD8Dbf0E96XOOArzfFy/wpsoqA9oAyZi5wwABNUC0P1UwVyQFtX69tdNS1SzUtu0JUQVuQmtyDq2T6GleaEgrtCJbjm03Adjapq5bAqSjuIgte9WlWBkkujM1PYNIBgl30VNRqZ96LoXBQvRIf7zAm42BqReStfpeAAq2PU/HlHNTYj+c9jv5Ak5ygT0pOkB20w4A9JIJSNlxPcoeO+FZyMs2l277CFifH06OY/sxMx20nsffOKDxfFwXodgsxufVl/38cTsX7fnK5raSHbSKpIDqY9/Cj5LnzyMa6UeK9LuKxYJjPB7Bjq7rfPGLX+Skk05ixowZru1CoRCdnZ0J/wQCgUAgEAgEAoFAcASQJDjhZrhlE1z0ayifHn8uGjKcl7tfhnX3w1t/ShRsJ5wJH3sKrn4UCkcZcQU2Qc3JdSkjgy9I+8TTaZtxMV0jZhPJLUeWVcgphQ8/CGd+11pKn3NwHaOfuBU9MuBYvMqua+i6TuWu16zl/QAcWIO/dVeKAGIX8FRZdS1eZbpVVVklqAQdHZwmZtSCIqXGKCRHGrgVzEoWt8ziYSlO2yTXsSzJKVm6ZjyCHZ/iS+krv3kH/idvie/zuf+PcEF1yticRNvk6ABJdphz2VjS3Vc5nd5KQxvIat0Fu15M6MvuKNZ1naKGd1Da4hcBWP8QSjSc4ryM6lF8anw/fUpqvq/ZzpzzoJLq2NV0DTXch2w6bcunoQSLkOQkp62LiJect+tY/MleSMqMNHBaBm8r8ublqDQjDbwcwObjVjyCS18Jy+V156X+MrIlQBrNnIVDVVLTRkro1QvpzzEiEnL3vYnS3YwkpcZ+JMeNRDXD6WnPtLW/54JN25FjmcSSLRrB7C8hHkFKPV+tY2GLF/Byx5qiuavr1X6+uCSnAHE3dKyRpyAby9H1FIpt8RquhchsLnKn89p0E6dEoLx7pMkjzrvmyNx8882sX7+ef/7zn57t7rzzTgoKCqx/o0aNOkIjFAgEAoFAIBAIBAIBAL4smHcNfPo1+OiTRqGgsqngz0tsJ6sw60q48TW4+mEYc5LllkwWNpzEDvvyarAtzzcFD1mGEz/P/g/eYwk6wZYdlG/8t1VkyiRZJNF0jcqdL6bsWu47T6Y8FtUShT6/4lyl3iwwBjF3pkMbU/zxyT7LbZvs2jXzVBMKqTkUD0sWwcxiaclCa7Lr2BS6kgsL+ZVEp21ylquvvY5pT38HKRyLCph1BfLsq5AkyWqn63pChqiJU6atQqrTVpEUS3hrmXNl/IkVv03sS0rsa8S2ZQn9EOogd0fSY8TiEeSgdT8gu8yTFo+eUGU1JStV0zUKmrYjmce6ZpElIpr9eYlWiqwkuI49802Ju2Ndi1zFFFSv7FLTKelVVMvev4bmmG8KsQsqsUOSblzpXKjJURFOx0ySFRomnGrc1qIUbH7Scs06HQcTM7LEihoxi8/FLqhk2eItqF6Q8NrkAm9ujtaE7UruObT2eAqvNtb7xKsQmem09YhHgESh3i3rWJbiTlskHNslv+fchGfHuRTxCK68K47MZz/7WZ544gleeOEFqqurPdvedtttdHR0WP/27dt3hEYpEAgEAoFAIBAIBIIEJAnGngwf+AvctAK+Xgdfq4VPvw7XPAZf2AiX/QEqU1dTKpKS4A50LF4lZ1a8SqtewPqlX0aPCSHVq+4jmJSDq0gKMnEB2Neyi/xmY1k0xeMhtu2szf+BJIdspkJfRI8QUGPuTDnoGKFgFiszC5s5FSwz99MuvgUUh21KpCxlTu4vRejGEGWSnbsSUopY45f9Vhulr52J/70NX78Ra8jok+Ciu5FlJUEo1nQNWZYd8y7t7livKvUmneOX0JdbZtzZ/iw0b7fa2PuS+joo2/N6rIO4uJ694ZEUMUtCQlXix8yn+lwd0abz2CkPWdM1Cu1ZqKNPsF6T4CzFWVBTpLhom7zM334sTEellwBmZoyafbkJeCpq3LXrJrSSWIjMTeizb0OX3N2ZZl9exc/sorgpjiafG4qk0DD5bOs9XrzpCWQSHcrJFwbA5jQ3o0aS3OhZ9XbRNtFpq8iJn1FOFxnM/TRFT0n3iEeIFYJzc7OafUFMyNedRWKznZkx6+VkteI1vMR8+3j11NgDSLyAYO87GZ/iS5lLIdq6c0wfGV3Xufnmm3n00UdZvnw5Y8eOTfuaQCBAfn5+wj+BQCAQCAQCgUAgEBwjBAugYjqMPw3yq1ybJYg+saI2rkvqk522yUvqJZnuskm0zbgEADXcR/Xrv/Psq2jbM/EnF14PE88EQOmqp+jgxsSxJgl9ftXvKPRpmkZQNcRdn+JLiSCAuPPPLC4WUAIpfUX1aIrQ6lf8CWIU4Cj2BZRASqZtsutYldQEdyw4L4s2xTQpEqLmya+Q1bHfeKJsClx5H6jxyIh0TknTqWe5S3U9oYiaiX2pObJK3dTz4k+u+J3Vl30uC7Y/ixKN5dIu/ASUTDCO2b6VZHUeTDxkSfvpl/0pLlowzk9TEFdlNaVwm6Zr5NkFv5pFQKJo6+WWlCQpoU830daMlPBaBm+PKkibqZpmSb0ZwWA6cjOJR3DLx7U7bb1E5+Tzx0lclCWZUH4VXTWGsBroqqd4/9qUCBRFVhLeT1E9iizHL4DIkpzgWs+OzaGmBhLjXoiLwOAdOyEZlte07lhZzjweIa3TVpLSzpG5D7pRscxVzDd2ML591wJ1tnxlp4tsELswQDwexPysEThzTIu2N910E/fddx/3338/eXl51NfXU19fT19f39EemkAgEAgEAoFAIBAIDiP2IlSm889pSX2yaOtUdd0UJhoX30AkWAhAwY5lsHO5c1+6RtE2o8iVLikw8wMw+yqrbeWOFxP6Txb6krNfLaT4c05CH8SLlZmCYEAJpBQsi2qG0JqwTcWHpnnHHpjbTXbaJrtxk52qEHfCJvQlqaBFqX7mdrLrDSE7nF0CH3kIsooSx5aUW+okwCc4bUkdl9nOzsGJZ6L5so076/4Jva2JAryuU/rOU/EXzLsW5l5j3a1Kjk0g0Z2syqmikq4bIpcp9JkCu10M1KMD5DZuMe4UjDIK8JHktI2dN46RADanuZc4Zx4zsy/X7FhTUJPcC06Z8+tZ1Cwmxpru3kzcmW5ZtfZ8XK9MWPs23AphmXPeOO0C67GqLU+nRKCYblaTqBbFL/sT9tUvGxddlN5W/J0HAAiVTwMl9X1iCtwa7pmwdkHTK6pARc2owJuEZF24cevLLia7uXEhLhR7iskO71WnNva5lKVUNz2kCvBux0xgcEyLtr/73e/o6Ojg1FNPpaqqyvr3wAMPHO2hCQQCgUAgEAgEAoHgMGLmO4J77IGT09ZL6IsG82k46ab4E099CSIhIHFZdPb+NQS6GwHQJ5wOueUw6f+zd9/hUZRrG8DvmS3JpkJCCS1UAZEugoAKCIqKKCI29AgiNvTzYEM9SlMs2I4Vu4INRFFUrICAqKAgtoOIijQFBEFqSLK7835/bN7JlmnANpL7x5XrkN3ZmXfKLsd7n3neU0JVwgDqrF8ClO2N2EZ00GdUnRm+nNE4gcjJyoBQ71ujSa5UVY3cZlR4JtsLRIci4S0N5HLRvWqjw3DAOHjz7NyAzp9MRN7viwAAAY8PG854CKhRHLlcVHVp9ARp+j5EBToeJTb8ju6d6c/Iwv52Z4ee9JcAix+sPJfQkLltNbK2rwntQ4OjQ1XeHS7Q213U+20BtIoqXKMKQb0SMYzsKayfy4oAPTwM9237Ba6KawvF3SuPRViAbRZMA5XhnGQVqMkA1Wwisohb14V5T9XwL0lUGPfalUGhBs2yOlNWlwIwXS68ulrAInRWw8JRk/e4rHr9p7gb/BX9q2uuXwr3nr/0ZdyKOxR4RlXaRld0e9yhStussErp8nodTLcJ2FTahlW9KjA+R0BlpbP8vDOiH/+K95FVewR9XBbBqAuVlbZm45ItPOR5Mm2bUdFeRv9ixmB9MVXTLuPrn0LSOrSVZdXRP8OHD0/10IiIiIiIiCiBwqvTjPquStGBoNEt9eG9J3e2PhU767QO/bJjDfDlo6FllMpZ42v8/KH+WqV9xWRXnkzgqMGhZQNlyK2YpEwIYThLfXSWIifgCq/OdCmu2NYHYZOVARUhqxYZ2gZFMKaa16W6IrZp1EJBXy5qXUZ9b6PDcAWV7QAQ9AOfPYDCF0/XW0UI1YWVJ94MrW7kLeRA7DkyvQ0+qvetWQAf0a8WCkqPHg7I9S15HPj84dA2NQ01V75X+eLOw0L/m1s3FMIDyCjZgez1S/VjER7GymMRHYwFtIDebxioPJfhPYpzw1toVLRGkOOXZOWlkeg+ombhnFtx62GaWX/Q8MmrrNojyNYTAtaVly64Drg606ynrQyA9d7IRsuhciIvq17HLsUFTVHwT5szAACK0KB8+0rkMlG9moMiVGkbTvakDp+EzN8gNrQNr7S1axWhB62KcXsBue9yfVbtEcLXZVqRK5eD8fHSx6ZWfjlmGsBXfIFgdV3oFdg2rS5UtbK9iYBx1TRVSuvQloiIiIiIiKonfZKl8BDAqHJLqeyRaHVLvR5IKCpW97g81PYAAD57APj711B1qaoC5SXIq2h/EPRmQ2kd1jM1rEVCzdUfhZaRs89H9c6UkwBJQRGE2+WOrM40mGQsfLIywLgiNygig125zfCCUFktadQeIZwmtJh1qYoaE6Dqt/H/sRx4uhfw6Z1QKipUy3PrYf3Ah7C9QQfD1hCyglAfl8m59Kge/VyaToQVFoABFQFjQVPg5LsqF5o3HnV/mAWUlyD/l09Cx8zjg9L27MplOl+s/7XgpzmV+xlVnWzU+kAej/Cq6egwMC8s8JOTkMllI9ZjEfTJAM8qaHWrbj30NJsIK7w6WYa7ZtuUrQ+sbr3Xz6dFdWZ4uGgW4AFhobNVe4ewqnsBYXiNqYoKRQ19Xvxz1EAIub0VLwHBgD7u6LYfmtBiQlt5jsInIQvU7xx7HCoqbfVWETBpFYHKycNkgG5EUSp737pgXrUrK1qtKm1l0GpVtSv3QV4TVlW78kscs0ph+cWVXvVtUk0fXp1sVk1PlRjaEhERERERUdox7JFoED7EVNoahYZRs8bvLWiC/UdXhHaBUuDJnlA+uR0ZpfuQv+5zuPwlAICdLfoAHl/lihp1hVazCQAg549v4d7zl2l1ZnQgG9ACEUGfR/XEVGcCocAs05Wp/24U2poFTeFhZlAL6tXD4VRFjZkAzWzyJzl+LViO2uuWIuvV84Dn+gJbQ2GWUFRsbHsmVl8wDfuKjzG9RTy8ClmDedV0dPBpVp0pg6aIdgbdRwF9x+vL1fv8UbT+9F64yvcBAHYe0RfIyKlcUYt+EDl1AQD565fAVbIjVJ2M2LYTqqJGnksRiJm8yuvyVvYeFgJ5W34K/T2zBlCrVcT4JQFhHvRVBKh2FZX65GE2t+frIbDV5FUyTLPozyrHpofrFtWZqqIioAUsA+Do9g5m51zmiWZBn6IocCF0/QRy62J7oy6hJ3b/Cfz6ib5cdE9nRSgxt+i7VTcQDML31yoAQGlWIZBbZLyPFa0bnBz/oBa0DOD14NRgAsHo42FXtRveL9huXXoAb1O1a9dHV75/rb6YiegDbFJNT5V4dIiIiIiIiCjthPdV1aDplakxy4UFfUIIw0AnPACWt4DvP240sn5fBOz4HQiWAUseR9vlL6A8M1d/3Z7Wp6MwfEWKAtH+PGDRZCgQqLH6Y+zuMCQUxiqxQV/0bdjhE4zJ6szoScbkc5JH9URULAKAphn3oZVVhDJUC7+FP3y58B6trkA5MnZuALb+ApTsAFQ34M5EzZItQKAEtbavRc0f34Z337bIQRa1Q+mp92GtK4A8d4YezxhNTBUeBln1sQzvd2nWOkCGejIccqmuygmPjr8eCJYDC+8BABRuXK6/bvdRgyLPpcsNrcMFcH3xMBQt1BJjV9szYyptjaozg1oQPo8vcvIqlxd7yveE/v7PenjLQn9HcXcgbEImef3px8IktJItAeT5tKx6hXA0EZYmNCjCvKeqfrxtQkO90lYxb9sQsU2TXrtAZUsM+R43vfU+rIrcMjQMhs7Tn61ORq0NX4ee+OZFoKJiPsOVgd3lu/XXyF66EetRXMje9Yf+5c3uOi0Nj21072cnx1/fH6PloEIoQg9JzYRXvZoe/4rjbTfZV/jngVWwLnsBG00IGb4uWfWtwqQ9QlilsVWATSEMbYmIiIiIiCjtRFfaGvWqBSIrbQEYzlgeHq7I6kw1Mx8YOR/44mHgq6eBQClc/hL4KoKa/Tl14G8Ye0s02p8PLJoMAKjx84dY225QTOWo3q82qtI2Q83QQzpFUeBVvdjrj5zQDCJ2UjO36kZQC0J1Ve6bUa9aWd3rhhuapsHj8QBCAFt+AP7+Fdj9J3K2r0H7v39Czv5d8Oz5C0eV7jI8rsUVPzEKmgHdrgK6XAKIAFx/rQj12EXo/BgGrWH9dq1CJJfqqqwCtuidKUNUw+rMXjeHJpf7/CH9ob01GyNgMJEUOl0UOv8Aaq58D7+3GYAMV2ZMqwu34o6oiA6KIHwuX8SqMtVM/Xxnb/q+8omwfrZyffKWekfVmSJoWoEKVIaeVuuSAXBQC9pWZ0JAD1DNuOHWgz67CbOCwrq6NLy9g1mYH97fGsI8HPW4PBDloXFtr98Rgdx6cO/ZDPw6F/hnPVCzMTyqJ6YliVFP5/ytv+i/76rdEjVN2jaEh7GWoaeDSlV5/OVrzIR/ieBkwjLLYFQJVRsLxby/cnjVrlXVt5yAUfbRNQ3gK9hNkkZsj0BERERERERpKDwQsQr6wie2AcxvqY9otaBWhKxZBcBJdwDXfgscfUlln1sAW1r0gieq1ysAuAqbYXe99gCAjH/WI+uvlfC6vBFhhOxxGzFLvRbbh9bn8kUESHJCqejQNjoAhhK7n27FDRWVvVdFoAy1fp0HPH088PQJwKxLgbnjkLFiGmpvWAbftl/gNglsowko+KfxsRAXzgKu+Qbodjng8sCtuCNCKyjGEyiFV9cJYdyTFIjcJyc9bWV1acRyigL0HYc9Rw/TH/qzdX94oiqTAcBV6wjsrh8KczN2bkDehq9izpFRv9qgFozoOwwAXrcXQgtdiFmbf6h8IqyfrRx/xC31NtWZsrrRrg+q1URYMvS16y8b3tPW0eRVwqI9QsUfGVQ6au9gdSwq+lvr+2wgvL+1UFXs73BexTMiVG2LUEV0eDW3UeWoGwoa/fyR/vuu2q2NK23DJ+yzaS8gA3gVxm0DIvazotrWjKqqthXYcrt2lbayXYEirHvaApX9u03D6bDr2q24Tfsrh1f6W42fWGlLREREREREaUgGPXIiMrPbyI16thotE90fN+J1efWBgQ9jS/tzoH35MLKhYkPbs9DMJLj658gByKsI5uqs+hDbi7tHbk8NVWeWaWX6Y9ETjAFAhjsjpoJTVtaGjz260hPCvNJWKd2FWj/MQovvXkdGyQ7D8QOhfrT+nNooySpAVuER8NZsBuTUAbQAECjH7v3b8M++v5CZXRcbm3SDWrMpOteNrDyWFcZ6paFJ0BreU9iyJYBSGfrI1xkt41bcKNfK9UAzpipUUbCvz61Yn1OAAtWDPxp3RUuTc7m9/TnIq6iMbfjDW/ir9YCYZTyqB6XB0spjBxFT+e1RPRBKRWhbsT7hzoASVeEbMbGZYnGrf0U4Zhd6hn+5YdcfV1baWt2eD8C2OlO2ZLBcl/yiRNMsK2319g4210V4padd1TEAQAFK2w1B7pePh67pJU8ALU+Fq6ChvrzsRx39Xsr8djq8O9YCAPbXOgJ76rQ0/QJBD20tegXrAbwIAqp1Ra4eoFqEmW649QDYbvIwCMAi/w1VMFdMkGZ1XegBvEnbAzku2y/Zws+lRasFCmFoS0RERERERGknfMZ7u0BHDy5Mqt3CgwJZLWa0nFbYBKt7XIW6OXUR3PuXaaCwt0VfBBY/Are/BLV/X4x9/vKYZTJcGSgJllSuW9MiJhgDQrdzh08KZjSpmQxxZQAshNBnao/ex9zta3HEezfCI/upSvU7A23OBGo0QnluXXxfuh1qXj243RnYWboTnet2htebG/GSkn1b8Nv2lSjKLsL+0h2oqRpUqlb0e/VrfssQJjo0tzuXekWoQasLuV0tWBkOGZ5z1YWtzU+Akl0XisW53NesF0ryGyBr15+osflHlGz9FajZKmKZTFcmdpbvrHzA4BZ9+bt77zZ4d28CAATqdYAnKqjXr0Volrf6R7RHsAg9ZR9UoyA/ept2QV/4Ni37oFZM+KXAvj9uUAThVb3W1bHCfHI6IPKzwKzXMRDZExkCUPLqAV2vAJY+Eep1/PpF8Fw0M6KnsPwSQLdvOzwV7U8A4I8TRkN1eWyvawHz6lj5pUVQC0J1m4eechIyq1YFQGWlreWkckrlpHKW66oYv5zI0GRgUBUVfs2vf+lgRB5/u1YX4ceMPW2t8egQERERERFR2pHBpLx12u6WeqvqzPDbmGXYZNSzM/rWXdNwIiMPf7XoFXpNoAw1f1sQs4zH7YlojwAltnemW3Hr1ZlAqBLSpbhilstwZejrkoFaTMBVsgMtPh6vB7YCCkqanwhc8iFw2afAcaOBtmdDadQNwdy60ML6XZpVtMpAWROa6fEPD2qs1hXeU9isX6o83lazzwORk1cpqnkbBXlNhK87mseVgY3tBum/1/jmpdhl3JF9kwGDc6m6oQgFvk3f6Y8FG3WNWVd41bHVhFN6gK1Z3wYvqynt+oOGt0ew3WbFBG9mnNye77Qnrxy/JjTDSQTluuT4LQNstfJY6F8gnDQRaHJ8aIF9W1Fz9jVwBUNfssgq5ohz+ekdUEp3AgD+btkPe4uOMq1Cjai0hXWA6iQMDz+WlkErVP3LG6uJyJSwP3YsA3hU9OTVgpbvS/34R7V4CRcewHMiMns8OkRERERERJSW9FvvbaoIRcUfVTWvYpMTlslAyuo2fgCGfWMlt+rGppb99N9zfpwVs0z4xFThr4sYU9Qt9gER0FsrhMtwZyAgQu0R5PgjxqYFgVmXwrtnCwCgpM6RWDL4Mew6a0qop2pYGCR7TQZF0LLCMTzA1oQGj9tkIjiXJ2Jdhsc1LDRXFMW8grZim7I60PJcVkxe5VE9pr0z9cmrbM7llua94M8qAAD4fp0LVNwaH7E9TdOPhaIoMSG27H2bFTYJmVFoK4Nt24mkKm5DD4hA6JhZ9UGFYtlqAQjtvwz+LW/jr7g2LMPFiuNqVwEseyzbtVHQ2zvYtEAJiqDlezw8ENfbHrg8wLkvATUaAwA8f/0PrRc/DgiBoBaEW3FX7uumb4FvpoW25cnCuq6X2FYBy2vRqqetPK52bSdkOBr+ZYPhflaMRS5vSKkMle0qbQHYnyM5qZyD5ZwE8HbrohAeHSIiIiIiIkpL4T0qzUKkiFuPTSo9w9clb6k3mzArvPLVvDrTg92FTbG/dksAgPev/wHhk08BcLvcEZNvKUIxrM4MD9OCmvGt5JmuTD00lLe4R6xrwd3Amk8BAOWZ+dg44B7sr1HfcPwycAw/FqYBdkWApGnmIYxX9UZW2poc1/Bbou36XcpetWZBWcRt2CZVuy7VVXkbOazPZcDlxo72QwAAitBC/U/Dt6e49b6gQS2277AcU3hoKxQVSqNjY8elVI7L6vZwWdGoB902t8FbtVoAKr8AsaqUlFWZsorTTEQAbzF+KLA8R+HLWU7kJYM+eV1YXD8CQg8E9e1mFQAXzAC8OQCAorWfo3D5S5HhrqYBH4yB7J2w5ZjhKPPVdFz1re+LybgcheEVx9OuAlWuwzYAdlBpK4+/1brklwZBEYQLFhORwXkPcrvPawphaEtERERERERpSVaxWYVbsnIzIAKOqzPNAgW36tYr9QDzEExu45+jzqh88NuXI5ZxKS69Ys6oVy0AvcpPVuQGRRA+l89wXFJQC0ZW2v78PrD4AQCAUFz4sfcNCOTUsaxOdquhiYysbl2X25A9Va0qDWWlrVUA7OQ2cnn+7M5leGjrNei1G75NeWztzuWOtmch4K7oOfztK8C+7RHbk60i9PYUUetzq254/aXwbf8dALCvoAnUzPyY7ckw0cnkVXLyMMugTIaesK609Sie0HG1qM7Uj7lFgKpvE7AMBMNbAlgGghUTYYWvN5r8AsHuuoho7xB9LdZtAwx+pvLXpc+g9fv/QYMVrwHrvgC+fQn44+vQk7VaYlfH820ryIGK95JNNTFQ2QfYrMo8fPxWrQqAyhDV9hxVVF9bXRdAZcBrtz7bthkV146TAD6gWZ9LCuHRISIiIiIiorQkKxKtqutkOBPQAo6qM4MiGNOWQN9eReWXX/PrFZFm2wSAf47oi6CrIjT84XXAvz9ie1JACxhWZ3pUjx5gyOUyoiaukuuS4ZisXHSpLuDv34C3r9SX23PCDdhZv21kT08DGWqGHnp6VI9t0GoVmrtVt96T1CwAVpXQZE9BEbQMdPRwTgY6Nr0z7SY1k9eFk3Ppz8jBllYnhR4M7AeWPVu5jOqqrLQVoVvqjQL4/K2roVQEkHuK2lq2ZAhqQSjCPJyT4a4M/M2Ev94ydHMweZX+uALr0LCiF69d6CZDf6tK2/B9sw3zbYI++bhp1XfrAfD3urliHwRq/rkC9b5+Hph6GvDevyuXO3UyvJ7sUGgL6/YI8gsQWU1rtZ9CCLhhXXUsQ1ardcnn7apjFUWxraLV22vAfjknVdN263IawFMIjw4RERERERGlJY/qCU1EBvPbtWPCOYvb4MODSiMRAbDd5EkA/J4s/N3suNCDpbuAn97Vl5G9Y4UQenWmWR9UWQ2qCMUwUHar7lDX3op1eVweYOdGYMYFQNnu0EJHnYXSriOhCEWv6HMSYJvexhzWh9ZoErXwYxZ+G7xpWFNxe77dLfXyNniroEyeF01opgG8bJshe8LancugCGJT27Mg5HJfPwOUl+jrksdCVjpHHw9FUVDr98/13/fWa2e6n17VGwqtVPugz+kt9TJctFqXEMIyKAu/Pd+uPYJ83i7oE8J8UiogFABDwLbSUw+6bb7Ake0RzG69F8ffgA1HX4Tyih7GMY4cCDQ/ERmujFBrEIsvBqLHZRV0y7DTqpxU4oMAAQAASURBVNJWb2ZgE9rK7ViF+UCoutdqTOHbtAud5aSQVttUFPuq6fD3uNW5pBCGtkRERERERJSWZNBkGdSoFeGcw6DV8pZ61eUoAHapLkCEgr6/Wp9a+cSKlyKWkZWGZn1QFUVBhitD72kLBYaVbPK1sqVBjS2rgGf7AH//Elqg9pHAGY/DpbohFFFZaWhyLLwurx5IZaixlb3yeMkJs6wCNRniWAXAcpt2rQr0oFULmAbrcmx2FX1y/AdyLv15RdCOGhR6omQ78N2rAELHX14XARGA12Vw/az7AoU/fwAACHp82NOws2VoLivDDzX0lMdM3uJuRl5XViGZnDzMURWnw6APsA4X5bm0quaW45YBtpMvcEwryFU3Nh99Mb69aDq+PHsKtvefBHS6CKjVEmh0LHDq/ZVjFvY9eZ18GaGvD/bHQu9pa9ceQVHhgk1oW/HZ6KSNgjz3duO3Cp3ltWUXxrpVd2jSRYtzSSHmVx4RERERERFRCqlKaGIbGaaaLSODGstKz4oQwapaTAZ9ZVoZPIrHMgCWEwv5i9pB1DoCyt+/Aus/D7UsqNUiooo2IALIdmUbrsvn8mFX+S79d6OALnxddX/+GI2+fBqoaKmAmk2AC14DMnLgLgtNUiYDEdPxO2gvEN6qwG7yJ7kus6pXuQ925yh8ZnnLSbXCnrNsm6GqCPgDyFAz7CttK4J19LgW+N+s0JPz7wAadYO7Tis9wA5qwdgWFv5S4L1r9V/XHTMcalYt0/HLPsBWXzIAldeCbR/UsBDVarkD6YPqaF0Ogz7b0LaiIt220lYEAcW6P3F4aGs2JtkDe39eEcqbngjk1ItdrqJq16oyX19OCNsAUp5Dq6BVvt62PYKsxlWtA08nE5EBkefdchnAdEJCuYyiKHqIbcathKqTrc4lhbDSloiIiIiIiNJSxERYNj1tbXt/hgVCZpVzsqWBbXVmWEWo6nIBnS6ufHLFNH1d8jbgoBZEppzkKkqGO1RpKytazUJbtxBo8NnDaP35E1BkYNu0F3DZAqCgmT4ul+qCP+i3rLSVgZQQwrhqFJV9aP2aP3LiM6NjBsW+0lb16tXQlsG6UtkD2IwM8wHzQFD2hHVyLsNDf1f9jkCbM0NPlu0GXjkbrn/WV1ZNiyAy1ahz+dn9wPbfAAA767TCptanWFfHhrUqsGuPAMCyD6re0sBB0CqPidW6XIrLtmpXf94u6KsIKu22KcdmGdSrofeSfI8aLhP2RYNZNT1QMSmhFmr7YdqCQ61sweEkdJZ/N+NG6H1iGcCjstLW8vhXLGNbaVtxPJz0tLXrLyufs2x1EXb92J5LYX0uKYShLREREREREaWl8FvgnVTaWlV6ytudrSbmURQlVAWpBaGo1tWlqqLCr/nhVb1QOlwAyGq8r58Ftq/Rq4PlDPQ+l89wXR6XBxChoNKlxva9BQB3MIjWH09E3f+9U/lgt6uAi94CwnpzynDVr/nte8JWZCVWIZLH5dHXZXr8K3rn2gVlLtUFTdMsA1QZKtr1EdVvqXdQnRnQAo7PpX79DHoSaNg19Pd9W+F65Wxk7v8n9AVCdN/hLf8DvngYACBUD37ucRU0VbGtzgQc9HGF8/YCTkI3RTGfqE8fm4Nb6sMnwrJsCVDRU9Wu7YEM+6wCPHk87XqqypYYHrf58fe4PPqkeHZfRjiptJXHwa7qWFZEW41fHi9Hx9/ivQtA75nspD+xXe9bl+rSW2eYrgtqxLVhxq3a98elEIa2RERERERElJZk8GkVQAIVlXM2QZ8ezgjr4MethqpL7Sr6VEWFP+hHhisDyKkNdBkRejKwH5g9CmpFFWtQBC37krqVUB9a2RIgZrmgH8qbI1Bj4zIAgKa6sfuUu4FT7wVc7ph1yQDSblIwAPpt3WZkdawK1baNgqZZVyTK6l75d9PlKtZh19NWVVXb3pmyDYGjc6n5K/v7erOBoa8DddoAAJSdG9Dq/Vuh7N8FoYQdMy0YaotQUfm8/9grsa9msW2riPDr2jbog3XQqldn2vWhreiTahv0QXUWLlpUckeM38m4HFTaympdJ+/xgAhY3sbvVb16ewqrifhkda+TPrR2gWZ4uGu1LnnY7dojqHDQ0xYu26pd+bxdgCrfQ/Go2pXvbatzSSEMbYmIiIiIiCgthffhdDIzu23Qp9gHfV7V66jSU1b36tWlfceF+ssCwMalwFdPwevyhm7DhnlAIR83nKxM04DZo4DV74eWcWfg2/7j4e9wvuG6ZBjo1/y24ZaKin7BNqGnX/Pbthdw2odW/lhWVLo8tiGSPvmWg3DIbiKviHPpDqsUzioIVTLXKAYA+HasRZuPxqHumkXwrV8CbPkxVGH75zeh5Wu1RHmP/wv1OtaCtr0/nfSYDQ93zYRXZ9reUq8qlq0WwrfppKLSalxA5ZcIToM+u/GrUC2Pa8Tt+RbXj1f1wh/0W/Z9jmi7YnNdq1AtK/gjxuagGtpuIrLwY2ZFUe2rXp1UxoaP33IiO3ld2wT68v1rdS4phLE2ERERERERpaXwcMgunLMLAWRgIhTrW+pl8OvkluiIoC8jBzhzCjD1tNDv8+9Adt2W2Opxm/aqBSomGVNcKNfKke3OrhybEMD71wM/zgz96vLg+743Y1e9tqbrUhUVHtUTGpdJr9rw8WtCszwWXpcXQgjL9gJyXXaBjkt16SGSXUWlk6BPPm8XOof/r9X4DW+Dz6sH/Gs28EJ/YN825G1djbZbVxuv6IzH4PZmw626sT+w37Ki1WnQJyvM7QI8KIAi7MM5uzATqGwL4CRotepVq29TdRAmVzzvpHes3ZcRTsJF2VNVVtMake9LJ60iVNW+pUH4l0ZWXHBBU6wn8pLXhV0/WHku41FpG17dbrWMo1YdFceLlbb2WGlLREREREREaSk8DLGsvJQViVaT/IRVgVmFBfK2difVmaqiRgZXTXqGes0CQKAU9ebeCb+/FC7VoO2B3J7qCYW2wXJkuCtuzxcCmDsW+ObF0O+KCzsG/hfb6rW1DaS8qtdRdak8ZpbLqaEw1qt6zdsLyMpM1eY2fiV0O7dV2wYgdDzsKvXCrwnbSlWL1g4R6zJqTQEAhc2Bi95CMDPPdB04ZiRQfKwe9Nm1nZBjt5uISU7oZBfgOam8dFJlDlT2QbUNKm3ek3KbLthXlzppFeHkvSvPo+yzbEauQ4VqGjzLSf2cVI06aRURvp9WnPS+BeCoOlav3LW5fgDrye4A6BOfObku7K5FPVxnT1tbjLWJiIiIiIgoLclAxK4iy0nQFx7wObkl3bI6MyzwjAke+o4Dfv0Y2PE7MjZ/j+KV72F75wttK23LgmXIVDNDfVLnjgOWPF6xhAIMfgaBZscD276zrA4EoE/AZDUpmFt1O6qIc3L8ZXBtF+jo4ZZdpadqX2kbXh1r20ZBsZkULCzwN71+6rXHtpFzseWHV5HnL0Nj1QfX/h3A3m1AfkPgpIn62J2EeE6PhZNb6uV2g1rQtqeqW7UOiQFn1ZlO3kfyebtgXQ8WbSqF9XXZ9OR1q6Fr0VGrDov3kgzVbVtFOGxVIK8Ju+Mvt2d7LCqqgK3IZewqbeWXLrbjt6nu1cNYu3C3orKXoa09hrZERERERESUlmSgYHcbtqysc1KdKYSwXJ8Mt+wCBVkhGxNoerOAQU8CL5wCQOCIb2fC36gr3PW6Gq5HbktoApn+/cCrQ4A1n1YuMPBhoN0QuPdv12+rtxp/hpphGwjKY6opmqNw0WpSLbkPTqtjbc+lrJa0q8500FfVNFiP4lE9oaDP6phlFWBrs+NRlpGPxnW7AAbLyuvBNnR20KtWrs8u9JTL2bWdcNJmRK7L9vZ2OKvaDV+f6brk+9KmbYm8Luy26VbctpW2cl3yyx4zHpdHr3Y2o3+B4+S6Vh0EraoLatA+KLYLkwHo7Tdsq2NtPlNCq7KffE6Oya5Xtkt1wQ23o+unumN7BCIiIiIiIkpLMuSwq7R1Kfa33YdXN9ot51bsAwW5PcPlio8Ful8dWp/mx1HvXg/3wslAoCxmUVVRkeHKQP6uP1H31fMrA1vFBQx4CDh6eGh7FVWcdhVqcjIz296fqv0t7uHhlhWvy+u4OtZuXeHBp9W4nPTHldWSTs6lVQsLOS55LMyOmaIo+rFwMhGT3XUt989J0Gd3S70+oZ9dRahqXzl6IO0RHIfTsG8DYXddAJUhqmXf5IqKY6tqdKDiCxDVWa9a23OJygnjLMdvU6Wqr0ux74/rJNxVFfveyuHrsq36Vtz2bT8cfA5TCI8QERERERERpSUZctgFInpFpU0VmB602lWhOgiHZHhnOrYTb0egXsfQOrUglM/uB57uBfz5TeRy5SWovWYRjv1gLNy7NoYey6oFDHsXOObS2O3ZhNNu1Q2PYl1BKAMru8pFWaVqF7Q6qbTVwzQHQZ/duVQURQ+HbCttXQ7PZUVYb0buo9UEbwCQ4c7Q+6pajUsG8FYcV5fC7Sz0rGh9YEXeum7bEkCxn4hM36aDlgB2x0IeByetIuyOmf5lhNv6uva4PbYVxXplsk3bBqdBpfwyxXIZh1XTTsJdBfZffshtxutcyj7TDG3tsT0CERERERERpSUZTDiuzrQJTlxqqKLPybpsg7KKZUxDW48Ppf96C9vn/gfF378BRQsC21YBz/UDjugPlGwHdq4H9v6FBuGvq9cROO8VoEYjw+3ZhYYuxQWPy+P8NnKbcEsGmlYyXM5aMlger6jlnIRzQgjLZRxXTav2Yb4MsDNcGZbrylQzbQNNGeraTf6kQtUnBrNczqYdAFAR9NlUjQJhx9+ixk8PMm3vzldsrzHAetI/fV0Vx9TJdeGk0tmlupChWp9Lj+pxVPV9IG0/bHsKO2gb4LQ9gpPqWKdh7AGdS+u3Zejad3AuiaEtERERERERpakDqUh0ErTa9WYFoFfP2lZnujy2gaDbm40/ulwMT5vBqD9/ErDlB0BowC8fGi7vb3s2PGc+AXh8seuqaHlgG9pWVMbaHgu3B27NOqxxGmA7qU520tohfF3xCPpcistZpa2DcymrNzNdmdbjcrlte6XKoCxeE5HJCmwrBxr0WS2nKIqjqlF9XTbprkdx8L502N9XHlfbFiKKdQsRua4D6W9tRa+Otbnh3clkazI0d1Q17XDCsnidSycBsNNzSQxtiYiIiIiIKE0piuIoEDmQW6ftHFBQ6bLpHatWVMbWawdc9inwxSPAoslAsDy0QE4RULMx9ufUwZ/126Lhsf+GxyCwldvzqB7nlbYOgia7np4ywLZblwyUbdtTODiu8lzaBj8OJnWSFZBOzqXH5bE9lxmuDEdBn9fltQ/NLXrj6tt00KtZbtPRLekO1iWDPtugW/HYBpDhLUksl1Ptw0DZA9j2XKqqbWju5HwDB/AFjuqxb48AZxXkLsVlW4GtKIrtJHxyOSdBvZN9dHounbwv5TXGicjsMbQlIiIiIiKitCUndrKiKM4CHY/qcTSpk5MQxqW4kOm2qbpU3PCq3lA45PIAJ9wIdLsC2LMFyG+oV9SW7N+OfXv+gNumEtjn9jnqL+v0WNhVHusTbzkIPb2q/XlyssyBnEsnVYuO2mY4PJdOgj55/O32U7aUsOK0utFp32cnlZ5O1gWEKoptb6l32HvVpVq3Y5DLOA097VpYOP1iRl7/dmOzC4kBZ71egYrr32XfNsNJBa1dmwjJ6bjieS7Z09YZhrZERERERESUtgoyC5DtybZcJsudhZoZNW0DzRoZNWy351E9qJlREz63ccWrlOPNcRRA1s6qjRxPTuWDGbmhnzDZnmwU+gptw7Javlr2E2G5MlCQUWAbXOV582z30aW4UJBZgCxPluVyOZ4cBH1By2WA0PidnMsaGTUcnUvb2+7jfS59tW3Hn+3JRmFmoW0QVuArsN3HDFcGamTUsD2Xud5c29DZpbpQM7MmfCaV3FK2JxsFmQWWywBAzYyatsfC5/LF9VzWyKhhu5/Znmz7Ss8DOJcFmQVxO5f53nxn51KzP5c1MmrYnsssd1Z8z2Vmcs8lAYqw69x9mNu9ezfy8/Oxa9cu5OXlpXo4REREREREREREVE05zSpZi0xERERERERERESURhjaEhEREREREREREaURhrZEREREREREREREaYShLREREREREREREVEaYWhLRERERERERERElEYY2hIRERERERERERGlEYa2RERERERERERERGmEoS0RERERERERERFRGmFoS0RERERERERERJRGGNoSERERERERERERpRGGtkRERERERERERERphKEtERERERERERERURpxp3oAiSaEAADs3r07xSMhIiIiIiIiIiKi6kxmlDKzNFPlQ9s9e/YAABo1apTikRARERERERERERGFMsv8/HzT5xVhF+se5jRNw6ZNm5Cbm4s9e/agUaNG2LhxI/Ly8lI9NCJC6Bsmvi+J0gvfl0Tph+9LovTD9yVR+uH7kg4HQgjs2bMH9evXh6qad66t8pW2qqqiYcOGAABFUQAAeXl5fPMSpRm+L4nSD9+XROmH70ui9MP3JVH64fuS0p1Vha3EiciIiIiIiIiIiIiI0ghDWyIiIiIiIiIiIqI0Uq1C24yMDIwfPx4ZGRmpHgoRVeD7kij98H1JlH74viRKP3xfEqUfvi+pKqnyE5ERERERERERERERHU6qVaUtERERERERERERUbpjaEtERERERERERESURhjaEhEREREREREREaURhrZEREREREREREREaYShLREREREREREREVEaYWhLRERERERERERElEYY2hIRERERERERERGlEYa2RERERERERERERGmEoS0RERERERERERFRGmFoS0RERERERERERJRGGNoSERERERERERERpRGGtkRERERERERERERphKEtERERERERERERURphaEtERGTj0UcfhaIoaNu2reVyv//+O6655hq0bNkSPp8PWVlZOOqoo3D77bfjzz//1JcbPnw4cnJyEj3sCBMmTICiKBGPTZkyBVOnTo1ZduHChVAUBW+++WaSRnfwpk6dCkVRsG7dugN+7QcffIAJEybEfUwHYv78+ejSpQuys7OhKApmz56d0vEMHz4cTZo0cbSsoigJP34HMp5D8d5772HgwIGoW7cuvF4vCgoK0LdvX7z66qvw+/0J335V9Nhjj6F169bIyMhA06ZNMXHiREfHUn5Wmf3MmDFDX/a5557DoEGD0KRJE/h8PrRo0QJXXXUVNm/ebLjuGTNmoGPHjsjMzET9+vUxevRo7N27N277HA9Gn9XRrrnmGiiKgi1btkQ8vmPHDqiqCo/HE7Nff/zxBxRFwfXXX+94O1KTJk0wfPhw/feffvoJEyZMMPzc7d27t+2/lVb27NmDMWPG4OSTT0bt2rUP6nNmwYIFOOmkk1CnTh3k5OSgffv2ePTRRxEMBiOWu+2229CpUycUFBQgMzMTzZo1w+WXX47169dHLLdu3TpH1yMREVG8MbQlIiKy8cILLwAAVq5cia+++spwmTlz5qB9+/aYM2cOLr/8csyZM0f/+3vvvYfTTz89mUOOMXLkSCxZsiTiMbPQtrr44IMPMHHixJRtXwiBc889Fx6PB++++y6WLFmCXr16pWw8ADB27Fi8/fbbKR1DMgkhcMkll+CMM86Apml46KGHMG/ePEybNg0dOnTAqFGjMGXKlFQP87Bz11134d///jcGDx6Mjz/+GKNGjcLdd9+Nq6++2va18rMq+qdt27bw+Xw45ZRT9GXHjx+PnJwc3H333fjoo48wZswYzJkzB0cffTT++uuviPW++uqruOCCC3DMMcfgww8/xPjx4zF16lQMHjw47vufaH369AEQ+oIt3KJFi+B2u6EoCj7//POI5xYsWBDxWqN/E5z66aefMHHixIP6sszO9u3b8cwzz6CsrAyDBg064NfPmzcP/fr1QyAQwLPPPovZs2ejd+/e+Pe//60H1tLOnTtxwQUXYNq0afjoo49w4403Ys6cOejWrRu2b98es+7/+7//i7kuTzrppIPdVSIiIlvuVA+AiIgonS1fvhzff/89BgwYgPfffx/PP/88unXrFrHM2rVrcf7556Nly5ZYsGAB8vPz9edOPPFEXHvttSkPwho2bIiGDRumdAwUadOmTdixYwfOOuss9O3b94Bf7/f7oSgK3O74/d+55s2bx21dh4P7778fU6dOxcSJEzFu3LiI5wYOHIgxY8bgt99+S9HorO3fvx8+ny/Vw4ixfft2TJo0CZdddhnuvvtuAKHqS7/fj9tvvx2jR49GmzZtTF9v9Fm1bt06rFy5EhdeeCFq1KihP/7tt9+iTp06+u+9evVC586dccwxx+DZZ5/F7bffDgAIBoO46aabcPLJJ+PZZ58FEAovc3NzceGFF+LDDz/EqaeeGq9DkHC9e/eGoihYuHAhzj//fP3xhQsX4phjjoEQAgsWLIgIuBcuXAhVVXHCCScASN9/Exo3box//vkHiqLg77//xnPPPXdAr586dSo8Hg/mzJmD7OxsAEC/fv2wevVqTJ06FY888oi+7BNPPBHx2t69e6Np06Y47bTT8M4772DEiBERzxcXF+PYY489yD0jIiI6cKy0JSIisvD8888DAO6991706NEDM2bMQElJScQyDz30EPbt24cpU6ZEBLaSoihxqeYSQqBu3boR1WrBYBA1a9aEqqoRlWUPPfQQ3G43du7cCSD2VtgmTZpg5cqVWLRokX6bZ/Rt6H6/H7fddhvq16+PvLw8/T987Zjd0m50O66iKLjmmmvw9NNPo2XLlsjIyECbNm0MbzldunQpevbsqd/afOuttxrebv3666/j5JNPRr169eDz+XDkkUfilltuwb59+yLGKP+DPfxWV1k5JoTAlClT0LFjR/h8PtSsWRNDhgzB77//brv/APD555+jb9++yM3NRVZWFnr06IH3338/4ljIwOTmm282PP7hZMuKl19+GTfccAMaNGiAjIwMPVCcN28e+vbti7y8PGRlZaFnz56YP39+xDq2bduGyy+/HI0aNUJGRgZq166Nnj17Yt68eRHHJXocu3fvxmWXXYbCwkLk5OTglFNOwS+//BIzxgM570888QROOOEE1KlTB9nZ2WjXrh3uu+8+R7fPv/HGG+jWrRvy8/ORlZWFZs2axYQrTvj9fkyePBmtW7fG2LFjDZcpKirCcccdp/++Y8cOjBo1Cg0aNIDX60WzZs1w2223oaysTF+mU6dOOP7442PWFQwG0aBBg4jPgvLyckyaNElvI1C7dm1ccskl2LZtW8RrmzRpgtNPPx1vvfUWOnXqhMzMTL1K3OmxFELg7rvvRuPGjZGZmYkuXbpg7ty56N27N3r37h2x7O7du3HjjTeiadOm8Hq9aNCgAUaPHh3xHjLz0UcfobS0FJdccknE45dccgmEEAfVAuSFF16AEAIjR46MeDw8sJWOPvpouFwubNy4UX9s6dKl2Lx5c8yYzjnnHOTk5Nh+qVZaWoobbrgBHTt2RH5+PgoKCtC9e3e88847McvKz7SXX34ZRx55JLKystChQwfMmTMnZtn3338fHTt21FtIPPDAA5bjkAoLC9GuXbuYStuFCxeid+/e6NWrl15ZG/5c586d9X+jjN6Xfr8fY8aMQVFREbKysnDcccfh66+/jlhm6tSpOOeccwCEgm/52Rl918ayZctw/PHH6+/Re++9F5qm2e6bXN/B8ng88Hq9MV9o1KhRA5mZmbavr127NgDE9cswIiKig8XQloiIyMT+/fsxffp0HHPMMWjbti1GjBiBPXv24I033ohY7pNPPkHdunUTXoGjKApOPPHEiJBt+fLl2LlzJzIzMyNCunnz5uHoo4+OqEoL9/bbb6NZs2bo1KmTfptndHDxn//8B+vXr8dzzz2HZ555Br/++isGDhwY0xfwUL377rt49NFHcccdd+DNN99E48aNccEFF0T01P3pp5/Qt29f7Ny5E1OnTsVTTz2Fb7/9FpMmTYpZ36+//orTTjsNzz//PD766COMHj0aM2fOxMCBA/Vlxo4diyFDhgBAxK2u9erVAwBcccUVGD16NPr164fZs2djypQpWLlyJXr06BFz23W0RYsW4cQTT8SuXbvw/PPPY/r06cjNzcXAgQPx+uuvAwjdmvzWW28BqLzl1kk19q233ooNGzbgqaeewnvvvYc6derglVdewcknn4y8vDxMmzYNM2fOREFBAfr37x9xTfzrX//C7NmzMW7cOHzyySd47rnn0K9fP8PbgCUhBAYNGqSHxW+//TaOPfbYQ65KXLNmDYYOHYqXX34Zc+bMwaWXXor7778fV1xxheXrlixZgvPOOw/NmjXDjBkz8P7772PcuHEIBAIHPIbly5djx44dOPPMMx2FRKWlpejTpw9eeuklXH/99Xj//fdx0UUX4b777osIYi+55BJ8/vnn+PXXXyNe/8knn2DTpk16cKhpGs4880zce++9GDp0KN5//33ce++9epC6f//+iNevWLECN910E6699lp89NFHOPvsswE4P5a33XYbbrvtNpxyyil45513cOWVV2LkyJExAXxJSQl69eqFadOm4dprr8WHH36Im2++GVOnTsUZZ5wBIYS+rAz+wsPD//3vfwCAdu3aRay3Xr16qFWrlv68U5qmYerUqWjRooWj9iGLFi1CMBjEUUcdFTOm9u3bRyzr8XjQunVr2zGVlZVhx44duPHGGzF79mxMnz4dxx13HAYPHoyXXnopZvn3338fjz/+OO644w7MmjULBQUFOOussyK+9Jk/fz7OPPNM5ObmYsaMGbj//vsxc+ZMvPjii7b7CIQC09WrV+v9e7dv344ff/wRvXr1Qq9evbBixQrs3r0bALBx40b8/vvvemsEM5dddhkeeOABXHzxxXjnnXdw9tlnY/Dgwfjnn3/0ZQYMGKBXUD/xxBP6Z+eAAQP0ZbZs2YILL7wQF110Ed59912ceuqpuPXWW/HKK69EbE9WDMfTlVdeifLyclx77bXYtGkTdu7ciZdffhlvv/02xowZY/iaQCCA/fv349tvv8Xo0aPRsmVLwy9a7733Xni9Xj3Qfvfdd+M6diIiohiCiIiIDL300ksCgHjqqaeEEELs2bNH5OTkiOOPPz5iuczMTHHsscc6Xu+wYcNEdnb2QY3pueeeEwDEhg0bhBBCTJo0SbRu3VqcccYZ4pJLLhFCCFFeXi6ys7PFf/7zH/1148ePF9H/7B911FGiV69eMdtYsGCBACBOO+20iMdnzpwpAIglS5ZYjnHYsGGicePGMY8bjQGA8Pl8YsuWLfpjgUBAtG7dWrRo0UJ/7LzzzjNdDoBYu3at4Vg0TRN+v18sWrRIABDff/+9/tzVV18dMx4hhFiyZIkAIB588MGIxzdu3Ch8Pp8YM2aM5f4fe+yxok6dOmLPnj0RY23btq1o2LCh0DRNCCHE2rVrBQBx//33W65PiMpzcsIJJ0Q8vm/fPlFQUCAGDhwY8XgwGBQdOnQQXbt21R/LyckRo0ePttxO9Ln78MMPBQDxyCOPRCx31113CQBi/Pjxpq+VjM579Fj9fr946aWXhMvlEjt27DBd5wMPPCAAiJ07d1ruhxMzZsyIeH/beeqppwQAMXPmzIjHJ0+eLACITz75RAghxN9//y28Xm/E+08IIc4991xRt25d4ff7hRBCTJ8+XQAQs2bNilhu2bJlAoCYMmWK/ljjxo2Fy+USq1evthyj2bHcsWOHyMjIEOedd17E8vJaD/8cuOeee4SqqmLZsmURy7755psCgPjggw/0xyZOnChcLpdYuHCh/thll10mMjIyDMfXsmVLcfLJJ1vuQzR5Dd5zzz22y+7evVsceeSRolGjRhHvP3m9bt68OeY1J598smjZsuUBjSkQCAi/3y8uvfRS0alTp4jnAIi6deuK3bt3649t2bJFqKoasQ/dunUT9evXF/v3748Yf0FBgeX7RZo9e7YAIF577TUhhBCzZs0Sbrdb7NmzR+zevVu4XC4xZ84cIYQQ06ZNizl30e/LVatWCQDiuuuui9jOq6++KgCIYcOG6Y+98cYbAoBYsGBBzLh69eolAIivvvoq4vE2bdqI/v37Rzx24oknCpfLZbqP27Zti/mcceKLL74Q9evXFwAEAOFyucR9991nuOzmzZv15QCIbt26iT///DNimU2bNonLLrtMzJw5UyxevFi8+uqr4thjjxUAxLPPPntAYyMiIjoQrLQlIiIy8fzzz8Pn8+k9A3NycnDOOedg8eLFMVV0ydKvXz8A0Ktt586di5NOOgn9+vXD3LlzAYSqEfft26cve7DOOOOMiN9llVr0zNqHqm/fvqhbt67+u8vlwnnnnYfffvsNf/zxB4DQJDpmy0X7/fffMXToUBQVFcHlcsHj8egVeqtWrbIdz5w5c6AoCi666CIEAgH9p6ioCB06dIi5JTncvn378NVXX2HIkCHIycmJGOu//vUv/PHHH45aTJiR1ZXSl19+iR07dmDYsGERY9U0DaeccgqWLVum39LetWtXTJ06FZMmTcLSpUsdtSKQt1hfeOGFEY8PHTr0oPcBCPUiPeOMM1BYWKifo4svvhjBYNCw9YJ0zDHHAADOPfdczJw5E3/++echjeNAfPrpp8jOztYrtKXhw4cDgF7VXFhYiIEDB2LatGn67eD//PMP3nnnHVx88cX6bddz5sxBjRo1MHDgwIhz17FjRxQVFcVcZ+3bt0fLli1jxuXkWC5duhRlZWU499xzI1577LHHxrS0mDNnDtq2bYuOHTtGjKt///4xVbWyyjm6AtaqevJAKyuff/55uN1u/TibKS0txeDBg7F+/Xq88cYbEe8/u207GdMbb7yBnj17IicnB263Gx6PB88//7zhZ4rslyvVrVsXderU0T879+3bh2XLlmHw4MERt+zLinwnevXqBVVV9fOxcOFCdOnSBTk5OcjNzUXnzp319+/ChQvhdrsjWn1EM3uvn3vuuQfcKqCoqAhdu3aNeKx9+/Yx/3bMnz//oKrkrXzzzTc466yzcPTRR+O9997Dp59+iltvvRW333477rzzzpjla9WqhWXLluHzzz/Hs88+ix07dqBPnz56BTMQqhJ/5plncM455+C4447D0KFD8dlnn6FTp0645ZZb4r4PREREEkNbIiIiA7/99hs+++wzDBgwAEII7Ny5Ezt37tQDmxdeeEFftri4GGvXrk3KuBo3bozmzZtj3rx5KCkp0Wev7tevnx4Izps3Dz6fDz169DikbRUWFkb8npGRAQAxt20fqqKiItPH5K3727dvt1xO2rt3L44//nh89dVXmDRpEhYuXIhly5bprQicjP2vv/7S+wd7PJ6In6VLl+Lvv/82fe0///wDIYTeZiFc/fr1I/bpYESvV7ZqGDJkSMxYJ0+eDCEEduzYASDU63fYsGF47rnn0L17dxQUFODiiy/Gli1bTLe3fft2uN3umGvB6Fw4tWHDBhx//PH4888/8cgjj2Dx4sVYtmyZ3mPY6hydcMIJmD17NgKBAC6++GI0bNgQbdu2xfTp0w94HMXFxQDg+L0rr8HogK9OnTpwu90R53XEiBH4888/9S9Spk+fjrKysojg8a+//sLOnTvh9Xpjzt2WLVtirjOja8rpsZRjC//SQ4p+7K+//sIPP/wQM6bc3FwIISyvfyD0uVFaWhrT+xsI9QQuKCiwfH24v//+G++++y4GDBhgec2VlZXhrLPOwueff4533303ZrJIef0avfecjOmtt97CueeeiwYNGuCVV17BkiVLsGzZMowYMQKlpaUxy0e/X4DQ56c8H//88w80TXP0mWamRo0a6Nixox62LliwICI879Wrlx7oLliwAF26dIkIkqPJYxO9faP3vx27/U+kq6++GnXr1sXbb7+N008/HX369MGdd96JW265BRMmTIjpS+52u9GlSxf07NkTI0eOxKefforff/8d9957r+V2PB4PzjvvPGzfvj1lX+ISEVHVxw7rREREBuTEN2+++WZEb1Vp2rRpmDRpElwuF/r374/HHnsMS5cuTcrM0n379sU777yDRYsWQdM09O7dG7m5uahfvz7mzp2LefPm4fjjj9dD1mTLzMyMmJhJMgt7jEJD+Zj8j//CwkLL5aRPP/0UmzZtwsKFCyMCDDkhmxO1atWCoihYvHix4TG0Oq5yUrjwKi1p06ZN+voPVnRgKNf12GOPmV57MpSrVasWHn74YTz88MPYsGED3n33Xdxyyy3YunUrPvroI8PXFhYWIhAIYPv27RFBjNG5cHreZ8+ejX379uGtt95C48aN9ce/++47wzFEO/PMM3HmmWeirKwMS5cuxT333IOhQ4eiSZMm6N69u6N1AECXLl1QUFCAd955B/fcc49ttWVhYSG++uorCCEilt26dSsCgUDEee3fvz/q16+PF198Ef3798eLL76Ibt26oU2bNvoytWrVQmFhoemxjw7YjMbn9FjKc2fUj3nLli0R1ba1atWCz+eL+GIqnN31K3vZ/vjjjxHhqQyi27Zta/n6cC+//DLKy8tjJiALV1ZWhkGDBmHBggV455130LdvX8sxhZ+DQCCAn3/+GRdccIHlOF555RU0bdoUr7/+esR5MLrenahZsyYURXH0mWalT58+ePDBB/HDDz9g5cqVuO+++/TnevXqhYceegg//PAD1q1bZ7uP8hrZsmULGjRooD8u3/+Hi++++w4XXHABXC5XxOPHHHMMNE3DqlWr0KxZM9PXN2zYEPXr17es+JdERX9nVWUdFBERJQb/hSEiIooSDAYxbdo0NG/eHAsWLIj5ueGGG7B582Z8+OGHAIDrrrsO2dnZGDVqFHbt2hWzPiGEo0mmnOrXrx/++usvPPzwwzj22GP1cKdv3754++23sWzZMketERJV+dSkSRNs3bo1IiAqLy/Hxx9/bLj8/PnzI5YNBoN4/fXX0bx5czRs2BBAKJwwWy6cDFSig9Wnn346ZrtmlcOnn346hBD4888/0aVLl5if6AmWwmVnZ6Nbt2546623ItaraRpeeeUVNGzY0PAW94PVs2dP1KhRAz/99JPhWLt06QKv1xvzuuLiYlxzzTU46aSTsGLFCtP1y4mLXn311YjHX3vttZhlnZ53o3MkhMCzzz7rYI8rZWRkoFevXpg8eTKAUJuAA+HxeHDzzTfj559/NrxtGggFsl988QWA0Ptr7969mD17dsQyciKq8LBQtsOYPXs2Fi9ejOXLl2PEiBERrzv99NOxfft2BINBw/PWqlUr231weiy7deuGjIyMmPfL0qVLY25ZP/3007FmzRoUFhYajiu6nUK0U045BZmZmZg6dWrE41OnToWiKBg0aJDtfknPP/886tevbzrxnayw/fTTTzFr1iz079/fcLlu3bqhXr16MWN68803sXfvXsNJp8IpigKv1xsR2G7ZsgXvvPOO430Jl52dja5du+Ktt96KqNTds2cP3nvvPcfrke/PiRMnQlXViPYH8u8TJ06MWNZM7969AcS+12fOnBlz+3+i7rqIh/r162P58uUxE2YuWbIEAPR/U8zItjwtWrSwXM7v9+P1119HrVq1bJclIiI6WKy0JSIiivLhhx9i06ZNmDx5sv4fsuHatm2Lxx9/HM8//zxOP/10NG3aFDNmzMB5552Hjh074pprrkGnTp0AAD/99JNetXvWWWeZbnPq1Km45JJL8OKLL9r2bjzxxBOhKAo++eQT/T/IgVCYO2zYMP3vdtq1a4cZM2bg9ddfR7NmzZCZmWkZSDp13nnnYdy4cTj//PNx0003obS0FI8++mjMf0RLtWrVwoknnoixY8ciOzsbU6ZMwc8//4wZM2boy9x+++149913ceKJJ2LcuHHIysrCE088ofdrlXr06IGaNWviyiuvxPjx4+HxePDqq6/i+++/N9x/AJg8eTJOPfVUuFwutG/fHj179sTll1+OSy65BMuXL8cJJ5yA7OxsbN68GZ9//jnatWuHq666ynT/77nnHpx00kno06cPbrzxRni9XkyZMgX/+9//MH369LjOlp6Tk4PHHnsMw4YNw44dOzBkyBDUqVMH27Ztw/fff49t27bhySefxK5du9CnTx8MHToUrVu3Rm5uLpYtW4aPPvrIMrA6+eSTccIJJ2DMmDHYt28funTpgi+++AIvv/xyzLJOz/tJJ50Er9eLCy64AGPGjEFpaSmefPLJiBnqzYwbNw5//PEH+vbti4YNG2Lnzp145JFHIvoWA6Fbnnv16qX3mTVz0003YdWqVRg/fjy+/vprDB06FI0aNcKuXbvw2Wef4ZlnnsHEiRPRs2dPXHzxxXjiiScwbNgwrFu3Du3atcPnn3+Ou+++G6eddlrMe27EiBGYPHkyhg4dCp/PF9N/+fzzz8err76K0047Df/+97/RtWtXeDwe/PHHH1iwYAHOPPNMy8+MAzmWBQUFuP7663HPPfegZs2aOOuss/DHH39g4sSJqFevXkSl4OjRozFr1iyccMIJuO6669C+fXtomoYNGzbgk08+wQ033KBX0N5xxx244447MH/+fP34FxQU4Pbbb8fYsWNRUFCAk08+GcuWLcOECRMwcuTIiErXl156CSNGjMALL7yAiy++OGLMX331FVauXIn//Oc/MVWT0pAhQ/Dhhx/itttuQ2FhIZYuXao/l5eXp2/L5XLhvvvuw7/+9S9cccUVuOCCC/Drr79izJgxOOmkk3DKKadYHufTTz8db731FkaNGoUhQ4Zg48aNuPPOO1GvXr2DvjX+zjvvxCmnnIKTTjoJN9xwA4LBICZPnozs7Gy9pYmdE044AS6XC2+//XZM+4MaNWqgQ4cOePvtt+HxeNCzZ0/LdR155JG46KKL8PDDD8Pj8aBfv3743//+hwceeAB5eXkRy8pq6WeeeQa5ubnIzMxE06ZND7iNQt++fbFo0aKYUPjDDz/Evn37sGfPHgChf0flHS+nnXYasrKyAACXXnoppk2bhjVr1uiV5tdddx2uvfZaDBw4EFdccQWysrIwf/58PPjgg+jXrx86dOgAAPjhhx9w3XXXYciQIWjWrBlUVcWPP/6I//73vygsLMSNN96oj+f666+H3+9Hz549UVRUhI0bN+Kxxx7Dd999hxdffNH0+iQiIjpkKZn+jIiIKI0NGjRIeL1esXXrVtNlzj//fOF2u8WWLVv0x9asWSNGjRolWrRoITIyMoTP5xNt2rQR119/vVi7dq2+3LBhw0R2dnbE+h577DEBQHz00UeOxtipUycBQHzxxRf6Y3/++acAIAoLC4WmaRHLR88ULoQQ69atEyeffLLIzc0VAETjxo2FEEIsWLBAABBvvPFGxPJr164VAMSLL75oO74PPvhAdOzYUfh8PtGsWTPx+OOPG44BgLj66qvFlClTRPPmzYXH4xGtW7cWr776asw6v/jiC3HssceKjIwMUVRUJG666SbxzDPPCAARx/fLL78U3bt3F1lZWaJ27dpi5MiRYsWKFTFjLysrEyNHjhS1a9cWiqLErOeFF14Q3bp1E9nZ2cLn84nmzZuLiy++WCxfvtx2/xcvXixOPPFE/bXHHnuseO+99wyP5/3332+7PrNzIi1atEgMGDBAFBQUCI/HIxo0aCAGDBigL19aWiquvPJK0b59e5GXlyd8Pp9o1aqVGD9+vNi3b5++nmHDhunXgbRz504xYsQIUaNGDZGVlSVOOukk8fPPPxvO6u70vL/33nuiQ4cOIjMzUzRo0EDcdNNN4sMPP4yZkT56PHPmzBGnnnqqaNCggfB6vaJOnTritNNOE4sXL45YPwDRq1cv2+MqvfPOO2LAgAGidu3awu12i5o1a4o+ffqIp556SpSVlenLbd++XVx55ZWiXr16wu12i8aNG4tbb71VlJaWGq63R48eAoC48MILDZ/3+/3igQce0I9FTk6OaN26tbjiiivEr7/+qi/XuHFjMWDAAMN1OD2WmqaJSZMmiYYNGwqv1yvat28v5syZIzp06CDOOuusiHXu3btX3H777aJVq1bC6/WK/Px80a5dO3HddddFfObJcxu+HemRRx4RLVu2FF6vVxQXF4vx48eL8vLyiGVefPFF08+Uyy67TCiKItasWWO430KEzrPZj9H5f+2110T79u2F1+sVRUVF4tprrxV79uwxXX+4e++9VzRp0kRkZGSII488Ujz77LOWn2nRGjduLIYNGxbx2LvvvquPp7i4WNx7772G67TStWtXAUDceOONMc+NHj1aABA9e/aMec5oO2VlZeKGG24QderUEZmZmeLYY48VS5YsMRz7ww8/LJo2bSpcLlfEOezVq5c46qijYrZn9NnSq1cvw31t3Lix6XmN/rc0+jEhhJg1a5Y47rjjRK1atUR2drY46qijxJ133in27t2rL7NlyxZx0UUXiebNm4usrCzh9XpFs2bNxJVXXik2bNgQsb7nn39edO3aVRQUFOifD/379xcff/xxzNiJiIjiSRGiohkPERERpcy5556LtWvXYtmyZakeSlIpioKrr74ajz/+eKqHQlTtrF27Fq1bt8b48ePxn//8J9XDISIiIqIwbI9ARESUYkIILFy4EK+88kqqh0JEVdT333+P6dOno0ePHsjLy8Pq1atx3333IS8vD5deemmqh0dEREREURjaEhERpZiiKNi6dWuqh0FEVVh2djaWL1+O559/Hjt37kR+fj569+6Nu+66C3Xr1k318IiIiIgoCtsjEBEREREREREREaUR1X4RIiIiIiIiIiIiIkoWhrZEREREREREREREaYShLREREREREREREVEaqfITkWmahk2bNiE3NxeKoqR6OERERERERERERFRNCSGwZ88e1K9fH6pqXk9b5UPbTZs2oVGjRqkeBhEREREREREREREAYOPGjWjYsKHp81U+tM3NzQUQOhB5eXkpHg0RERERERERERFVV7t370ajRo30zNJMlQ9tZUuEvLw8hrZERERERERERESUcnZtXDkRGREREREREREREVEaYWhLRERERERERERElEYY2hIRERERERERERGlkSrf05aIiIiIiIiIiNJXMBiE3+9P9TCI4sLj8cDlch3yehjaEhERERERERFR0gkhsGXLFuzcuTPVQyGKqxo1aqCoqMh2sjErDG2JiIiIiIiIiCjpZGBbp04dZGVlHVLARZQOhBAoKSnB1q1bAQD16tU76HUxtCUiIiIiIiIioqQKBoN6YFtYWJjq4RDFjc/nAwBs3boVderUOehWCZyIjIiIiIiIiIiIkkr2sM3KykrxSIjiT17Xh9KrOaWh7WeffYaBAweifv36UBQFs2fPjnheCIEJEyagfv368Pl86N27N1auXJmawRIRERERERERUVyxJQJVRfG4rlMa2u7btw8dOnTA448/bvj8fffdh4ceegiPP/44li1bhqKiIpx00knYs2dPkkdKRERERERERERElBwpDW1PPfVUTJo0CYMHD455TgiBhx9+GLfddhsGDx6Mtm3bYtq0aSgpKcFrr72WgtESERERERERERHFX+/evTF69GjHy69btw6KouC7775L2JjMLFy4EIqiYOfOnUnfdnWStj1t165diy1btuDkk0/WH8vIyECvXr3w5ZdfpnBkRERERERERERUHSmKYvkzfPjwg1rvW2+9hTvvvNPx8o0aNcLmzZvRtm3bg9pesh1oKE2AO9UDMLNlyxYAQN26dSMer1u3LtavX2/6urKyMpSVlem/7969OzEDJCIiIiIiooQLakGoipqwvpcBLQCX4kpqX02/5odH9SRte0Y0oUEIAZd6cLOaJ5MQAkERhFtN2wgjYdLhWkkXmtCgKqmvPdy8ebP+99dffx3jxo3D6tWr9cd8Pl/E8n6/Hx6P/TksKCiAEAJCCEefRy6XC0VFRQcw8tB7CTi4fqvpcvyrk7Q/2tEXkt3Fe8899yA/P1//adSoUaKHSERERERERAmyfvd6bC/dnrD1r921Fv+U/ZOw9UfzB/345Z9fUBooTdo2jWwt2Yo/9/6Z0jE4taN0B9btWpfqYSRdWbAMv/zzC8qD5akeSsoJIVAWLENQC6Z6KCgqKtJ/8vPzoSiK/ntpaSlq1KiBmTNnonfv3sjMzMQrr7yC7du344ILLkDDhg2RlZWFdu3aYfr06RHr7d27N/49+t8o10Lnu0mTJrj77rsxYsQI5Obmori4GM8884y+fHR7BNmyYP78+ejSpQuysrLQo0ePiEDZr/lxx513oE6dOsjNzcXIkSNxyy23oGPHjpb7/N6c99CqVSv4fD706dMH69ati3jebv+GDx+ORYsW4ZFHHtErktetW4dgMIhLL70UTZs2hc/nQ6tWrfDII48cxFmpmtI2tJXfFsiKW2nr1q0x1bfhbr31VuzatUv/2bhxY0LHSURERERERIlTFixDQAskbP3lwfKErj9aUAT1ytFUCmgBlAXL7BdMAwEtUC2Dy6AWhKZpSb0+05WA0KtEDwc333wzrr32WqxatQr9+/dHaWkpjj76aMyZMwf/+9//cPnll+Nf//oXvvrqq4jXRe/ngw8+iC5duuDbb7/FqFGjcNVVV+Hnn3+23PZtt92GBx98EMuXL4fb7caIESP051579TXce8+9mDx5Mr755hsUFxfjySeftFzfxo0bcc6Qc3DyKSdjxbcr9KA3nN3+PfLII+jevTsuu+wybN68GZs3b0ajRo2gaRoaNmyImTNn4qeffsK4cePwn//8BzNnznR0nKu6tL23oGnTpigqKsLcuXPRqVMnAEB5eTkWLVqEyZMnm74uIyMDGRkZyRomERERERERJZAmtMSuH1pSwyBNaHpwm0oCIuHHNl4EUh9yp4ImtFAbCxw+YWU8DHzsc2zbE/2FQuVRUJCYVia1czPw3v8dF5d1jR49GoMHD4547MYbb9T//n//93/46KOP8MYbb6Bbt24Ry4Wf79NOOw2jRo0CEAqC//vf/2LhwoVo3bq16bbvuusu9OrVCwBwyy23YMCAASgtLUVmZiamPDEFwy4ZhksuuQQAMG7cOHzyySfYu3ev6fqefPJJNGvWDPc9cB98Hh+ObH0kfvzxx4hsrkGDBpb7l5+fD6/Xi6ysrIiWDi6XCxMnTtR/b9q0Kb788kvMnDkT5557rumYqouUhrZ79+7Fb7/9pv++du1afPfddygoKEBxcTFGjx6Nu+++G0cccQSOOOII3H333cjKysLQoUNTOGoiIiIiIiJKlqAIJjRcDGrB5IdiAikP4oQ4jELbw2is8SSD9VQH/Mm2bU8ZtuxObfuQQ9WlS5eI34PBIO699168/vrr+PPPP/X5mLKzsyNfKBBxvtu3b6//XbZh2Lp1q+W2w19Tr149AKG71ouLi/HrL7/i8isvj1i+a9eu+PTTT03Xt2rVKnTt1hXhWXn37t0Pbv8MPPXUU3juueewfv167N+/H+Xl5bbtGqqLlIa2y5cvR58+ffTfr7/+egDAsGHDMHXqVIwZMwb79+/HqFGj8M8//6Bbt2745JNPkJubm6ohExERERERURIFReJC1VSEgXoQl+LQVlb8Hg4EBAKi+rUI0IQG+ac6qZ1rdPe0qHjPKAmttI2X6LDywQcfxH//+188/PDDaNeuHbKzszF69GiUl0e2/Yj+XIiewExRFGia9fUQ/ho5J5TVa+y+FAh/XggBo8PvdP+izZw5E9dddx0efPBBdO/eHbm5ubj//vtj2kZUVykNbXv37m15cSiKggkTJmDChAnJGxQRERERERGljURWGoqKP8kMboUQSW/JYESDdthUr8o+n9Vx9vrqWGlr1KIgqAVRGixFhisDbjVtO32aWrx4Mc4880xcdNFFAEIh6q+//oojjzwyZtlEfqFzRMsj8M3ybzDykpH6Y8uXL7d8TZs2bfD27LcjrsOlS5dGLONk/7xeL4LBYMzrevToobeAAIA1a9Yc+I5VUdXr046IiIiIiIgOG3LCroSFtiL5Va+prrCVNO3wCW1lxWl1Cy81oUHTqt9+mzmcj0OLFi0wd+5cfPnll1i1ahWuuOIKbNmyJenjuHLUlZj24jRMmzYNv/76KyZNmoQffvhBr8g1fM2VV+L3Nb/jlptuwerVq/Haa69h6tSpEcs42b8mTZrgq6++wrp16/D3339D0zS0aNECy5cvx8cff4xffvkFY8eOxbJlyxKx64clhrZERERERESUlmSFZaKCTlltmswwSE6qleoAKojDozUCgFCfTy1x10G6ktdKdWuPYOVwvQbGjh2Lzp07o3///ujduzeKioowaNAgw2WFEAn7fDhv6Hm4YcwNuPHGG9G5c2esXbsWw4cPR2ZmpulriouL8frM1/HR+x+hc6fOeOqpp3D33XdHLONk/2688Ua4XC60adMGtWvXxoYNG3DllVdi8ODBOO+889CtWzds3749ouq2ulNEqv+lSLDdu3cjPz8fu3btQl5eXqqHQ0RERERERA4FtAC+2fIN6mTXQdP8pnFfvz/oxzd/fYMGuQ3QKLdR3NdvZFfZLvy842ccUfMIFGQWJGWbRlZvX429/r04uujolI3BqfW71mPTvk3oUrcLPC6P/QuqiO37t+PnHT+jdUFrFPoKUz2cuCstLcXatWvRtGlTy9AQCH0WlAZC7RGq8jVQ4i+BJjRke7Itq18PhhAC+/z7ACBi/SeddBKKiorw8ssvm762PFiOsmAZfG7fYdmeIhWsrm+nWSWPNBEREREREaUlgVD/V7uJdw5l/UERRDKL92QVXarrpw6nCa6CCCa04jpd6VXZ1Wy/KXTuEzHhWklJCZ5/5nmcftrp8Lg9mD59OubNm4e5c+fGfVt06BjaEhERERERUXoSlRN3JYLs6RoUyWsVICc+S3UQJ8NAIUTcK/riTbZGSPUxS7Z0CfjTRXU4/4ncR4HQe/2Tjz7B/ffej7KyMrRq1QqzZs1Cv3797F/P6zDpGNoSERERERFRWtIQCjcTWWkrt5Ms6RI+6oFggir64imAAAAcNhOnxYsmtFBP22q239VdIj8ffD4f3vvoPWR5sqAqzqe5SofPrOqIE5ERERERERFRWpKVXYkKVYUIVb0mKhS22maqq9ZkW4hUj8MRDWlxzJIt0RPxHU6qw7EIr6pOxLUuj588lgf2Yvk/Vff4pyOGtkRERERERJSWZMCQsNA2weu32mY6hB/pMg47suL6cBhrPAkh9H6+RKmkv/94KSYVQ1siIiIiIiJKS7LyLKglpuesXtWmJTeJSIc+pUEteHAVdykgz//hMNZ4EhAQWnK/VEhb1eDUV7cvJcgee9oSERERERFRWtIrYRPU01OGlkmttBVCnwQslQ6n2801aIfNWONJ9rStbmG1mepy/hN1rYd/SXOg668uxz7dsNKWiIiIiIiI0lL4ZFkJWX/Fn6BITCWv6TZTHMLJoPpwaTmgCS2h4X260oN1hrY6HosUYU/blGBoS0RERERERGlJBgSJmihMD4WTGASlQwAZUXF3GIRg1XESMqAyXE/mlwrpqrqEhem6n+k6rqqOoS0RERERERGlJT3gTNREZLJnbpJDMVnlmirht0gfDtWrqQjX04G8Lg+Hc5QsVTk8DH9fHsx+KoqC2bNnm68/bJ0H0x6hKh/7dMXQloiIiIiIiNKWECJhlZapCCKECE0ulcoA8nC77T6IYELD+3QlK8xZaRuSLqGhoiiWP8OHDz/odbdt2RZTHpuSmInXROTfJ0yYgI4dOx7c6ykpOBEZERERERERpaXwYFETGlyKK67rlxWMQS25PW1TXmkb9SedycA+HXoBJ1sQrLSNkCanf/PmzfrfX3/9dYwbNw6rV6/WH/P5fKkYVsKEfw6n++dFVcNKWyIiIiIiIkpLeqgoEhMWpCK41MPHFGYf4e0G0j0IPdRbxg9nQgvtLyttK6XDNVBUVKT/5OfnQ1GUiMc+++wzHH300cjMzESzZs0wceJEBAIB/fUTJkxAcXExMjIyUL9+fVx77bUAgBNPPBEb1m/Af276D7xuLxRFMR3Dr7/+ihNOOAGZmZlo06YN5s6dG7PMzTffjJYtWyIrKwvNmjXD+HHj4ff7AQDTpk3DxIkT8f333+sVwlOnTgUAPPTQQ2jXrh2ys7PRqFEjjBo1Cnv37g2tNPWHv1phpS0RERERERGlJVlhmMjKVK3iT7LIoDTlE5HJSd7SvOWA3sqhOlfaJmgivsOJfu7T/BL4+OOPcdFFF+HRRx/F8ccfjzVr1uDyyy8HAIwfPx5vvvkm/vvf/2LGjBk46qijsGXLFnz//fcAgDfefAOdO3bG8EuH4/LLL4fX5TXchqZpGDx4MGrVqoWlS5di9+7dGD16dMxyubm5mDp1KurXr48ff/wRl112GTKzMzH6htE459xz8PNPP+Ojjz7CvHnzAAD5+fkAAFVV8eijj6JJkyZYu3YtRo0ahcCtATzw6ANpEZpXJwxtiYiIiIiIKC3prQQSVBGqCS1UxVsRoqpK4m9GlYFpKsPSiOrVNA9CwwPb6hYYVduetk/3AvZujXjICwEPBACl4icBcuoAVyw6pFXcdddduOWWWzBs2DAAQLNmzXDnnXdizJgxGD9+PDZs2ICioiL069cPHo8HxcXF6Nq1KwCgoKAALpcLObk5KCoqMg1t582bh1WrVmHdunVo2LAhAODuu+/GqaeeGrHc7bffrv+9SZMm+Pd1/8bMmTMx+obR8Pl8yMnJgdvtRlFRUcTrwgPgpk2bYuIdEzFq1Cg88OgDh3Rs6MAxtCUiIiIiIqK0lcgWBuFBoBAiYVlQ5EZTH5bqwfFh0Cs1vJ3E4TDeeAqKIFRFrXZhNfZuBfZsingogVFtXH3zzTdYtmwZ7rrrLv2xYDCI0tJSlJSU4JxzzsHDDz+MZs2a4ZRTTsFpp52GgQMHwu12R5xnq8+HVatWobi4WA9sAaB79+4xy7355pt4+OGH8dtvv2Hv3r0IBALIzcu1Xf+CBQtw991346effsLu3bsRCARQWlqKffv2wZtnHCRTYjC0JSIiIiIiorQU3vs1ESGnXsGZxCpOWTWZyupJvXJZJOa4xtPhMmFaIggIqIqa1Iny0kJOnZiHKq8BBWoiK20PkaZpmDhxIgYPHhzzXGZmJho1aoTVq1dj7ty5mDdvHkaNGoX7778fixYtAsLmWbS63o3es9H9b5cuXYrzzz8fEydORP/+/ZGfn49XXnsFj/z3EUAxX//69etx2mmn4corr8Sdd96JgoICfLb4M1w28jIE/AHD11DiMLQlIiIiIiKitKQJLeFVockOBWVbhJS2Rwj7k+49bfW+xkJL+4A53mTLjnQ/R3Fn0KKgNLAfZcEyeFQPsj3ZKRiUM507d8bq1avRokUL02V8Ph/OOOMMnHHGGbj66qvRunVr/Pjjj2jXoR08Xg+0oPX5btOmDTZs2IBNmzahfv36AIAlS5ZELPPFF1+gcePGuO222/TH1q9fDwBQKkJvr9eLYDDyC4Hly5cjEAjgwQcfhKqG2sXMeH2G/nx1ew+mGkNbIiIiIiIiSkvhlbCJIIPA8IreRBNaxYZSmMOFH9N0D2HC+w1Xt/BStkeQ12l0NWV1I8PGdD4W48aNw+mnn45GjRrhnHPOgaqq+OGHH/Djjz9i0qRJmDp1KoLBILp164asrCy8/PLL8Pl8aNy4MQCguHExvvj8C5x7/rmokV0DtWrVitlGv3790KpVK1x88cV48MEHsXv37ohwFgBatGiBDRs2YMaMGTjmmGPw/vvv47133tOfFxD6RGPfffcdGjZsiNzcXDRv3hyBQACPPfYYBg4ciC+++ALPPP0MgNhqXkq8xHdZJyIiIiIiIjpIiqIkrqdtCipO06XSVo7hcGg7ENF3uJqQLTtURa2W/XxjJLBNSjz1798fc+bMwdy5c3HMMcfg2GOPxUMPPaSHsjVq1MCzzz6Lnj17on379pg/fz7ee+89FBYWQkDgtnG3YcP6DWjbqi1q165tuA1VVfH222+jrKwMXbt2xciRIyN66ALAmWeeieuuuw7XXHMNOnbsiC+//BI3/+dm/XkhBM4++2yccsop6NOnD2rXro3p06ejY8eOeOihhzB58mS0bdsWr776asy6KXkUke5X/CHavXs38vPzsWvXLuTl5aV6OEREREREROTQpr2b8OPfPyLTlYnOdTsj15sb1/X/secP/LDtB+Rn5OPoukcj050Z1/UbWf3Pavyy4xc0zW+KtrXaJnx7RnaX78aKv1bAr/nRqU4n1PLFVvOli73le7Fi6wrsD+xHm4I2aJTXKNVDSgpNaFi2ZRlKAiXwuXw4uuhoeFRPqocVV6WlpVi7di2aNm2KzEzr916JvwR+zQ+X4kK2J7tKVn2WB8tRGiiFqqhQFRVZnqy4rn+ff58eeh/I+gNaACX+EqiKCkVR0ro9RTqxur6dZpWstCUiIiIiIqK0JCsNE1ZpW3GbdVInutIqt50qsj2CAiXtKzhliwx5nqoLTWiAgN4eoRrtuim9PUI1OBjx3sfoz5vqcAyrAoa2RERERERElJZkr9lE9bUVCAWXieybGy2I0MQ/qQxL9dBWUdL+dnM5Gd3hEDDHk2zZoUI9LCaMSzSGjIcuJrg9iPd+Mj8riaEtERERERERpSmBsErYBAQFcqKnZFbayuAxlSGUvr/i8AjDhAhVXAe1oP3CVYQMx1RF1X+v9qpeR4QI4ec43uGofJ/LthIHsu7D4TOiqmJoS0RERERERGkpvBo0EcGBpmmV609SKKYJrfKW9xQREKEA7DAIwcLDy+pUbSqDdXmtVKcq42jyvakcDhfsoVKQ8H69enDr9DNVHOTr6JAxtCUiIiIiIqK0pAktFNQoiWknoEGrrGRMYqVtqtsS6D1SRWrbNDihQYsIL6sLua/JrgRPV/L9UpWPg2wDEv57vNdPhxd3qgdAREREREREZEQGnNASEzhoCIXCyQ7FVKS+avRwqZqT1dZA+gfM8SSE0Kuy2Uc0khDisKgSTyfh1crp/p6nSqy0JSIiIiIiorQUFKEepkJJTMggNJH0UEwP4hLUp9eJRE/wFk/yOKVD0J1M4e0RAFSrfY+m92Ot4kltssJU/Ysqh+99hrypw9CWiIiIiIiI0pJsX6AIJSFVlkEEEzrRmZFE9+l1NAYIPQhP90BGr6pUUG0nItOElvbhOsWB7B17gKGqs1Uf+voSMS6yxtCWiIiIiIiI0pLQ9BQjIUFBRKVtksLLIIKhikGRmH1yQu9pm6DjGk+yz6eKatbTFmE9bQ+DiuhkSPQEXakW3dM2Eap6tXJVw9CWiIiIiIiI0pLeczZBoWpQBPWJzpIR2srwTa/uTWGVq6IoUJCYCuZ40gPLapY1RfS0hajW7RHCpbJCPdGM9mvChAno2LGj/vvw4cMxaNCgg9+IAlx+6eW4YMgFjo8jvzBIHYa2RERERERElJY0rWIiskRV2srKtiRVvcqQRO9pm6r2CGHhcbqHgeH9TKtde4SwysvqHJylY1A7fPjw0BcfigKPx4NmzZrhxhtvxL59++KyfrN9fuSRRzB16lRH61i3bh0URcF3330Xsa4HHnoAU56dcmADqmZfmqQLd6oHQERERERERGREVtoqIjEznmtC04OXZFXaQlTe8p6qHCo8DNS0NA9t5az3h0HAHE+yd2gyr890l2639p9yyil48cUX4ff7sXjxYowcORL79u3Dk08+GbGc3++Hx+OxXFd4KG/VBiI/P//gBht2+eTXyIc/6D+Al0Z+ecBrMXlYaUtERERERERpSRMV7RGUxAQFcv3JqrTVoOkBSEorbSsmIlOgIIj0rl6Vx0u2cqguFafyCwUAKe1/TOYyMjJQVFSERo0aYejQobjwwgsxe/ZsvaXBCy+8gGbNmiEjIwNCCOzatQuXX3456tSpg7y8PJx44on4/vvv9fUJIfDg/Q+iSYMmaFCrAS4beRlKS0sjthndHkHTNEyePBktWrRARkYGiouLcddddwEAmjZtCgDo1KkTvG4vBpw0AABw+YjLMfScofrnT1lZGa699lrUqVMHmZmZOO6447Bs2TJ9G58t+gx5GXlY+OlC9OrRC3k5eejRowdWr16tL/P999+jT58+yM3NRV5eHo4++mgsX748vge8GmJoS0RERERERGlJE1rotlyBhPRe1RBaf6JC4RiiIoSsCONS1U9WExoUEargTPfiVU1oesAsf68OZKWt/Ht12W8n0rXS0+fzwe8PVbD+9ttvmDlzJmbNmoXvvvsOADBgwABs2bIFH3zwAb755ht07twZffv2xY4dOwAAb735Fu6+426Mv2M8Fn65EEX1ijBlinUbg1tvvRWTJ0/G2LFj8dNPP+G1115D3bp1AQBff/01AGDevHlY/8d6vPz6y5EvrjiMY8aMwaxZszBt2jSsWLECLVq0QP/+/fVxyeM9YdwE3HXvXVj69VK43W6MGDFCX9WFF16Ihg0bYtmyZfjmm29wyy232FYXkz22RyAiIiIiIqK0JISAClUPbuNJVm2qUJPa01bfJlIYPgnoQWi6txwQoaQ71CJAqz63ZsvWCPrv1WS/AeC8Oefh7/1/RzzmtH3Aoajlq4XXT3/9oF779ddf47XXXkPfvn0BAOXl5Xj55ZdRu3ZtAMCnn36KH3/8EVu3bkVGRgYA4IEHHsDs2bPx5ptvYuRlI/HEY0/gX8P/hUsuvQT+oB933HEHFsxfEFNtK+3ZswePPPIIHn/8cQwbNgwA0Lx5cxx33HEAoG+7sLAQRUVFKA3Erke2c5g6dSpOPfVUAMCzzz6LuXPn4vnnn8dNN92kH/sJd07AcScchyxPFm655RYMGDAApaWlyMzMxIYNG3DTTTehdevWAIAjjjjioI4jRWJoS0RERERERGkpiKA+AU68Kw2jQ6Ck9LSt+KMqKoIimLJb3uWxVKAgKNK8PYIQgAK9lUN1CS+j22dUp/YIf+//G1tLtqZ6GLbmzJmDnJwcBAIB+P1+nHnmmXjssccwZcoUNG7cWA9NAeCbb77B3r17UVhYGLGO/fv3Y82aNQCAX37+BSMvGxnxfPfu3bFgwQLD7a9atQplZWV6UHygBATWrFkDv9+Pnj176o97PB507doVq1atili+bbu2+uvq1asHANi6dSuKi4tx/fXXY+TIkXj55ZfRr18/nHPOOWjevPlBjYsqMbQlIiIiIiKitCNExURMFX/i3XtVr3pVVX1biRYxqVYKJwCTE7xBSf92A7LvsKIoSTtP6UC2sACq3yRstXy1Yh6TnwXh7UWSsV0rffr0wZNPPgmPx4P69etHtAPIzs6OWFbTNNSrVw8LFy6MWU+NGjUM12/3BYXP53M8VrP3TfhnUvTj8jE5Dn3/ROXy8nNswoQJGDp0KN5//318+OGHGD9+PGbMmIGzzjrL8RgpFkNbIiIiIiIiSjvh1aDy1vh4krOgy1A4mZW2+kzsKaoa1bTQJFcq1PQPbREaa6onb0sFoVT0tK1GYTWAmBYFAS2AEn8JPC4P/EE/fG4fPK7U90vNzs5GixYtHC3buXNnbNmyBW63G02aNIl5PqgF0bJ1Syz7ehkuuvgi/fGlS5earvOII46Az+fD/PnzMXLkyJjnvV5vaN3BoGnY3aJFC3i9Xnz++ecYOnQoAMDv92P58uUYPXq0o32TWrZsiZYtW+K6667DBRdcgBdffJGh7SFiaEtERERERERpR+9lqia20lZRlKRNRCbDN1VJXnWvkfBK23QX1CLPe3UJLzWh6X2cZZUxhRyOwX2/fv3QvXt3DBo0CJMnT0arVq2wadMmfPDBBxg0aBA6de6Eq665CldeeiU6H90ZXbt3xVuvv4WVK1eiWbNmhuvMzMzEzTffjDFjxsDr9aJnz57Ytm0bVq5ciUsvvRR16tSBz+fDRx99hMKiQri9bhTWrGzPIIRAdnY2rrrqKtx0000oKChAcXEx7rvvPpSUlODSSy8NXXc2h3v//v246aabMGTIEDRt2hR//PEHli1bhrPPPjueh7BaYmhLREREREREaUdAQIMGN9yJqbQNr3oVyWsTIHvayr+nQnj1anQomm40aFAVNRR0Q6R9ZXC8CAg9VBcQad97OJEOx5A2mqIo+OCDD3DbbbdhxIgR2LZtG4qKinDCCSegbt26AICzzzkb639fj7H/GYvS0lIMGjwIV111FT7++GPT9Y4dOxZutxvjxo3Dpk2bUK9ePVx55ZUAALfbjUcffRR33HEHxo0bhx7H9cAn8z/RXyuP67333gtN0/Cvf/0Le/bsQZcuXfDxxx+jZs2ahl8WRJ8Pl8uF7du34+KLL8Zff/2FWrVqYfDgwZg4ceIhH7fqThFV/Oua3bt3Iz8/H7t27UJeXl6qh0NEREREREQO+IN+LNuyDB6XB/sD+1HbVxtHFh4Zt/WXBkrxzV/fINOdiV1lu3BEjSPQKK9R3NZvZG/5XqzYugI53hzsLtuNznU7I8+b/P9O/Xn7z/hr/1/wql64VTeOKTom6WNwSo41z5uHPeV70KVuF2R5slI9rITbuGcjftnxC4pyirC1ZCsa5zZGsxrGFZeHq9LSUqxduxZNmzZFZmam6XJ+zY/9/v16e4RMdya8Lm8SR5p4AS2A/YH9cCkuKIoCv+ZHhpqBDHdGXNZf4i+BJjS4VJe+PY/qQabb/LgDoS+zSvwloXYqilplj38iWF3fTrNKNdGDJCIiIiIiIjpQGjS9KjURFaGaqFx/sio49ZYMSO2kWkEEK3sFI737pepjrSg7rS6VthDQK20VKNW60rYKFNo6Ev4+jHefbaN1VYUK5qqOoS0RERERERGlnYgAQ1GgIf5hXfhkZ4lYfzQZksigOFVhqdBE5eReIr0n99LHqqT/WOMpPKRVFKX6hNUOVOVrwGiysENl+jlTdQ9jlcHQloiIiIiIiNKO3nO2IlyMd2glgx+94jQJAaoMHVVFRWietRRPRAaExpHOlbaistJWXhPVgezlCyAh1/9hrXpcAvGttI16jzut5JWfWfrnRZzHRdYY2hIREREREVHake0DFCiAEv/b4jWhhdYvJ+RK0u3ncj+EkroAUhNaZHuENA5hZGCkV9qmccAcT+HtQA6HCeMSKeL6VKpmaGjYviBO17r+BVVUFe9BH8eqd/jTFkNbIiIiIiIiSjt6pS0UqFDjHtZFV9ompT2CEIAIbTOVFa4iNIjK6tU0DkI1oUW2ckjjscZTRKWtUs172oYJr/isUqIu67TdzyoamqcrhrZERERERESUdvRKWyVUaRtEfEMrIYQeCAKh3qmJFt7yQf6eCpqmQYV6WPSJPZyqguNJ0yq/RJD7TtVLos+5ky9BoseQtmFyFcXQloiIiIiIiNJOeFiQiCrL8F6NSZ2ITGYeKcw+NGiRlbZpGggKIUL9d+Vt3Qlok5GuNISCdYDtEaKl6/Uab/H6zDPqS3sgE57pbWoo6RjaEhERERERUdrRhKZP2iUDg3gGdgIVrQqUivYIWuLDQE1o+m3QikjO5GfR9Arj8ImF0rTlgGzdEDFpWjUJ7IJaUA/2k9W+g1InVde14+2GZbbV5T2YDhjaEhERERERUVqSIW0ibuMXEBBKZV/bZIdiqZqILLyXr6qoEBBpW70qewDrlPQNmONNE5GVtnLivOooer+rw3GIZwuVuH/OVP3DnzYY2hIREREREVHaCZ+0KxETZoUHgjIUSzS9Ry8AiNTc6q8fVxkKpXEApqGy2hqobOdQHYS30pAT16VruJ4I/qAf+wP7sT+wH6WBUpQGS1EaKEVZoAylgVL9uXj9+IP+VO+yqQkTJqBjx47678OHD8egQYMOaB3RLQ6uuPQKnD/k/APuaWv2mJF169ZBURR89913BzTWdNOkSRM8/PDDKdm2OyVbJSIiIiIiIrIgAzvZviARlbbhvVKTEQZG9Ok9gJ6S8aQHoVDTvqetDCpdcIV+jXNf43QmJ4sDAFVRERRBaKg8FlWZP+jHj3//iJJACQCgPFAOv/DDrbhDFciKigx3Rly3meXOQrta7eBxeRwtP3z4cEybNg0A4Ha70ahRIwwePBgTJ05Ednb2AW//QN6DjzzyiOP3wbp169C0aVMs/moxju58tP74/Q/dj4AWcDKwCAfS27ZRo0bYvHkzatWq5fg1EyZMwOzZsw/7oDdeGNoSERERERFR2gmvSk1EuBhTaZuEnrbRoWNKAsiwIFQPw9M0CJXnPDzgTtuAOc6CCEb0EQ3vh1zVBUQAJYESeFQPPC4P3IobARGAS3GF+jErCjLdmXHbnj/oR0mgBAERgAfOQlsAOOWUU/Diiy/C7/dj8eLFGDlyJPbt24cnn3wydht+Pzwei3UbhKMajFti5OfnOx6jmfz8fATFwU1u5/Q96HK5UFRUdFDbOFS2x/swwfYIRERERERElHbC2yHI0C7eE5HpPW2TNNFTRNiRokm1woNQfYK3NJ3kSu9pfBhMmhZPsn9teKWtgEjb85QoHpcHGa4MeF1eZLgyEvbjtLo2WkZGBoqKitCoUSMMHToUF154IWbPng2gsqXBCy+8gGbNmiEjIwNCCOzatQuXX3456tSpg7y8PJx44on4/vvvI9phPHDfA2jSsAnqF9bHZSMvQ2lpacR2o9sjaJqGyZMno0WLFsjIyEBxcTHuuusuAEDTpk0BAMd3Ox5Zniz079sfAHD5pZfjgiEX6J9BZWVluPbaa1GnTh1kZmbiuOOOw7Jly/TnP1v0GbI8WVj46UIcf+zxyMrKQo8ePbB69WrT4xPdHmHhwoVQFAXz589Hly5dYtYxdepUTJw4Ed9//71+h8XUqVMBwPS4SUbH++mnn0aDBg1ivpA744wzMGzYMADAmjVrcOaZZ6Ju3brIycnBMcccg3nz5lme9wkTJqC4uBgZGRmoX78+rr32WsvlDwVDWyIiIiIiIkpPYT09493TNrxyMakTPcmWtmkwEZmixP+4xlN0pW116WkbMwFb+ONkenxSzefzwe+v7I3722+/YebMmZg1a5YeXA4YMABbtmzBBx98gG+++QadO3dG3759sWPHDihQMOuNWZg0cRIm3DEBi75chKJ6RZgyZYrldm+99VZMnjwZY8eOxU8//YTXXnsNdevWBQB8/fXXAIB3PngHv2/8HdPfmA4gts3BmDFjMGvWLEybNg0rVqxAixYt0L9/f+zYsSNiuYnjJ+LuyXdj2bJlcLvdGDFixAEfp9tuuw0PPvggli9fHrGO8847DzfccAOOOuoobN68GZs3b8Z5550HIYTlcTM73kOGDMHff/+NBQsW6Mv8888/+Pjjj3HhhRcCAPbu3YvTTjsN8+bNw7fffov+/ftj4MCB2LBhg+HY33zzTfz3v//F008/jV9//RWzZ89Gu3btDvgYOJXW7RECgQAmTJiAV199FVu2bEG9evUwfPhw3H777VBV5s1ERERERERVlSa0iGAhIZMwhU/0hIo+twfQs/FARYRNIjUhnF69moDq5XiTrRv086OItB5vvMi+wy4l1L9WVVRoQqsW+364+vrrr/Haa6+hb9+++mPl5eV4+eWXUbt2bQDAp59+ih9//BFbt25FRkaoJ+8DDzyA2bNn461Zb2HYpcPw+KOP4+LhF2P4iOEIaAHccecd+HT+pzHVttKePXvwyCOP4PHHH9erR5s3b47jjjsOAPRtFxQWmLYqkC0dpk6dilNPPRUA8Oyzz2Lu3Ll48YUXcfXoq/Vlx08cj+NOOA7ZnmzccsstGDBgAEpLS5GZ6bxdxV133YVevXoBQMQ6fD4fcnJy4Ha7I8ZqddzefPNNXH755YbHGwi1sAg/L2+88QYKCgr03zt06IAOHTroy0+aNAlvv/023n33XVxzzTUxY9+wYQOKiorQr18/eDweFBcXo2vXro73/UCldfI5efJkPPXUU3j88cexatUq3Hfffbj//vvx2GOPpXpoRERERERElEDhvVZVJfSfrnFtjxBdrZeEEFVDZBCdkkrbigndlIo/qQqPndArbfXy5GrS01ZEXutpP2FcgqXDBH5G5syZg5ycHGRmZqJ79+444YQTIvKqxo0bRwSI33zzDfbu3YvCwkLk5OToP2vXrsXaNWsBAKt/Xo1ux3aL+Jzo3r276RhWrVqFsrKyiLD4QAgIrFmzBn6/Hz179tQf93g86Nq1K1b/HNn+oG37tvrr6tWrBwDYunXrAW2zffv2+t+drMPquK1Zs0ZfLvp4A8CFF16IWbNmoaysDADw6quv4vzzz4fLFfpCZN++fRgzZgzatGmDGjVqICcnBz///LNppe0555yD/fv3o1mzZrjsssvw9ttvIxBwMKHbQUrrStslS5bgzDPPxIABAwAATZo0wfTp07F8+fIUj4yIiIiIiIgSKTqgkmFjvIQHqKqiIiiC0BCaoCtRhKjsowsFKbvFO7zaNp17peqVtopeEp22AXM8yUpb+WVFuk8YlwzRFfCJrop3ok+fPnjyySfh8XhQv379mImvsrOzI37XNA316tXDwoULY9blzfZGPuBw13w+34EMOVbYlzbRgbjR9Ra+j3q1/gFO4nig67A6bjVq1ND/Hn28AWDgwIHQNA3vv/8+jjnmGCxevBgPPfSQ/vxNN92Ejz/+GA888ABatGgBn8+HIUOGoLy83HAsjRo1wurVqzF37lzMmzcPo0aNwv33349FixYlZOKztK60Pe644zB//nz88ssvAIDvv/8en3/+OU477TTT15SVlWH37t0RP0RERERERHR4EULE9rSNY8oZvn6gIiROcCYmIKCIyo0e7OzthzoGoDIEk4FgOooOk2Xv4aou+nyk+4Rx1VV2djZatGiBxo0bOwrsOnfujC1btsDtdqNFixYRP7Vq1YICBa1at8LXX32tv0ZAYOnSpabrPOKII+Dz+TB//nzD573eUBgcDJp/1rRo0QJerxeff/65/pjf78fy5cvR6shWjgPkePB6vTFjtTtuVnw+HwYPHoxXX30V06dPR8uWLXH00Ufrzy9evBjDhw/HWWedhXbt2qGoqAjr1q2zXecZZ5yBRx99FAsXLsSSJUvw448/HvQ+W0nrStubb74Zu3btQuvWreFyuRAMBnHXXXfhggsuMH3NPffcg4kTJyZxlERERERERBRv4QFnIioNwyfgUpSKicgSn9pGBNGpCOHkhGuywi3eFczxJMcWXnFaHUJbeS1G73e6huvkTL9+/dC9e3cMGjQIkydPRqtWrbBp0yZ88MEH6H96f3Tq3AlX/9/VuGzEZeh8dGd07d4Vb73+FlauXIlmzZoZrjMzMxM333wzxowZA6/Xi549e2Lbtm1YuXIlLr30UtSpUwc+nw/z5s5D4+LGyMzMRH5+vv56AYHs7GxcddVVuOmmm1BQUIDi4mLcd999KCkpwfBLhie1orlJkyZYu3YtvvvuOzRs2BC5ubmWx23QoEHo0qWL5TovvPBCDBw4ECtXrsRFF10U8VyLFi3w1ltvYeDAgVAUBWPHjrWs+p06dSqCwSC6deuGrKwsvPzyy/D5fGjcuHFc9j9aWlfavv7663jllVfw2muvYcWKFZg2bRoeeOABTJs2zfQ1t956K3bt2qX/bNy4MYkjJiIiIiIiongQqGwloCABoW1UJa/cZiKFVwsrigKhpaCnbVSlbYrvMLcVHdKmojo5FWJ62lbD9gj+oB9lgTKUBRP74w/6k7I/iqLggw8+wAknnIARI0agZcuWOP/887Fu3TrUqVMHADDk3CG49fZbMfY/Y9Grey9s2LABV111leV6x44dixtuuAHjxo3DkUceifPOO0/vEet2u/Hgfx/E1Oemonlxc5w7+FzDddx77704++yz8a9//QudO3fGb7/9ho8//hg1ataI3Ad510OCrsWzzz4bp5xyCvr06YPatWtj+vTplsetbt26tus88cQTUVBQgNWrV2Po0KERz/33v/9FzZo10aNHDwwcOBD9+/dH586dTddVo0YNPPvss+jZsyfat2+P+fPn47333kNhYeEh77sRRaTxu75Ro0a45ZZbcPXVlTPVTZo0Ca+88gp+/vlnR+vYvXs38vPzsWvXLuTl5SVqqERERERERBRHf+z5A6t3rEZRTmgW8S17t6Bz3c6omVkzLutft2sdft/1O+pm10VZsAz7/ftxdN2jkel2Pgv6oWxzW8k2NMhpgCNqHpGw7RnZVbYLK/5agVpZtaAqKv7a+xfa1m6LOll1kjoOJ/4p/Qff/vUt6mTXgaIo+Hv/3yjKLkKrmq1SPbSE2lu+Fyu2rkB+Rj7caugG6S17t6BT3U4oyCxI8ejip7S0FGvXrkXTpk2RmVn5vvMH/fjx7x9REiiBEAKlgVIAlRMSBkUQGa4MuNT49Z/OcmehXa128Lji35fUjhAC+/z7ACBin/xBP3xu3yGPab9/PwIioF9L4evPdGfC6/KavDL2tUIIBEUQWe6suB7/qsjs+gacZ5Vp3R6hpKQEqhpZDOxyuQ64yTEREREREREdXiImoEL8b43XoOnrl9Vjib71XhNh21RS0x4BiKooTuPJvQRC1dDh10EqqpOTzaySMV3PU7x5XB60q9UOARGAEAIl/hIAoUBThoY+ty+uoaFbcacksA0XPREYEJ/qf6t12K0/XVunVBdpHdoOHDgQd911F4qLi3HUUUfh22+/xUMPPYQRI0akemhERERERESUQNETj8W792p0MVAyeqVGbyMl7RFEaMI1vSVEuve0DQsqFSgIouq3R4juaQtUtNNI0/OUCB6XBx54oAlN/7JDVdSEhbaplIy2LIZ9aRXYTr5o+lpKirQObR977DGMHTsWo0aNwtatW1G/fn1cccUVGDduXKqHRkRERERERAkUFMHIsCDOFaFBVK4/WT1to7eZkonIoEVWr6ZxHhN9PlLVBzgVYr5EENWn0paSQ95hcDCq0xcIqZTWoW1ubi4efvhhPPzww6keChERERERESWRpmmR7REOIWAw3gAiWhUkZaKn8G0ivu0enIrZT5GcKuODIURklZ8CBQERSOGIkkMTWkQ1NICktO84nFSX0PBQP5PsPtcsWyeYvJZfHiSPar8IERERERERUXJp0CJDqziHqkaVtomufI3YpxT1kjWsXk3TAExAQChh7REMen5WRXK/o/c3Xc9TslW10FC2KIlpQ6Ac+jmXrzd97xzg6qvLezBdMLQlIiIiIiKitBM+UZgU7562emhbMclZosOg8NBWgYKgSFF/1rDDmpQK44MkK04lBQqCWvXoaWt0qafreTpUdpPNh7/vq1NomIxesk4mKatOxzye7K5rJ9K6PQIRERERERFVT9G9S2ULg3iJDoWTUcUoNKEHpqkKQjShQRGHUQVnRFvj1PQBTonookul6u271+uFqqrYtGkTateuDa/XG/O+CGpBlAfKoSqq/lxAC0ANqnCrVSPSMtpH+XhQDR5ScqcJDWX+Mn0it+jtKooCNWhcz2n22oAWgBJU4FE9Bz+wKkwIgfLycmzbtg2qqsLr9R70uqrGFU5ERERERERVSnj7AgChiZjiWWkrIqtekzHRU/jkaqnsaRsRCKaoTYMTMZW2Fa0chIhtHVCVCCFig/U0rog+WKqqomnTpti8eTM2bdpkuIwmNJQHKwLNigs3KILwuDxwKa5kDjdhjPYRCO2nqqjwug4+9DNbN1DZDibDlWH4WiEEyoPlUBQlZlwe1QOXWjWOf6JkZWWhuLgYqnrwTQ4Y2hIREREREVHaEVpkMCeU+E7EpAktoupVQCS8kjG8b6WC+FYOH8gYwrebqvDYCQEROxmdWf/PKiT82pTiXWmeLrxeL4qLixEIBBAMxra+2Fe+Dyu3r0ReRp5e7blr/y60KmiFmpk1kz3chNhTvgc//f0TavpqRlzX+8v3w+f2oWlh04Ne977yffhpx0/I9ebGVNqW+csAAC1rt4x5DgBKA6VYuX0lMt2Z8KqVwfHO/TvRsmZLFPoKD3pcVZ3L5YLb7T7kL5cY2hIREREREVHaCa9KBQBFxHfCLCEE1LBpXpIRigmEVbkqoWrilAhvOaCkcWhrVF0qQqGmUchUVUQH6/KxlPVATjBFUeDxeODxxN5uX66WQ7gFVI+qVyxq5RrcXjcyMzOTPdSEKEUpgq4gVE9kewQIQFO1Q9rPcrVcX3d0xaeiKBCagDfDa9hqQvNrla91Vb5WK9fg8rqqzPFPZ1X3U46IiIiIiIgOW9HVlEIRBzzTuRUNWuyEXAnu7Rox+ZmsGk1y9aTRJFfpHNqGUxU1FGimcw/eODBq/6Aqatqep0QSIlRhH3E8lPS9Zg+GBg1CiT3niqIgiOAhfUYYHj+5/orJEM3WLydnjPmCpOoWuacdhrZERERERESUdoyChnhVGupBRuQsVwkXPvmZ3HayA0ijlgMpq/i1EVGZXMEodK5qBGK/oJABW3Vj1A4jGV+wJJPR5IAAoEI95H2VoaxRZbr8EsSsLYx8PLoViSLStzq/qjng9gjr1q3D4sWLsW7dOpSUlKB27dro1KkTunfvztJoIiIiIiIiigshIoMaVVHj1nNWBg7R7RcSGUQY9mKVk58lsXIturpXURQkuJXvQYtukWEXMlUVZj1tq2NQZjjxXBpPnncwhBChOwmiKIoCTdMOqR2I1TWj9/I2WcasSlcoVSs0T2eOQ9vXXnsNjz76KL7++mvUqVMHDRo0gM/nw44dO7BmzRpkZmbiwgsvxM0334zGjRsncsxERERERERUxQURjK2y1OITWslKRkVNXhCkh0+yPUJFCJeKSttw6Vxpq2nGt3VXpcDOiFFP23SeMC6RDAPsOPe3TjWzfZHn/FDbI5hN2me3frP2CKy0TR5HoW3nzp2hqiqGDx+OmTNnori4OOL5srIyLFmyBDNmzECXLl0wZcoUnHPOOQkZMBEREREREVVtsho0ohI2juGirNZ0h/0ncaJvuTYK4VLSn1UgciKyNA4DNUS2sLCrDKwqjKqvFSgIaukZrieSUauIqlbpadbyIx6V5UERND1WqqLq1bRm4zKqAI53f3Ey5yi0vfPOOzFgwADT5zMyMtC7d2/07t0bkyZNwtq1a+M2QCIiIiIiIqpeDNsXxPM2fmFQrZngFgWyelLO4K4oqZmILLrlQDpP6hR9a7acvK2qizlHqJiUqhr2tAVgWGmbrtfswTCaeA6Iz5cUVu8XGQqbXVdGgTlQffsrp4Kj0NYqsI1Wq1Yt1KpV66AHRERERERERNVbdMAJhIKCAAJxWb+GUFuCiNt+jYLcOJJVtXp7hBRV2oZPhgaEJjtK1wAselwpq05OMqO2EDLAq26MJumqapWeGowrbePRHkFDbHsJp+s3a62gKNWz6jsVHPe0Pe6443DiiSeid+/e6NGjBycdIyIiIiIiooSIDjiBitBKi09SYxRSKIqS2AmuTELhZFeOxvQFVsx7aqaaBg0qwoL7iurkdA2Z4yUIg0rb6toewWSivqpU6WnWmkW2LziUfdU040AYgP7FgNnnnlnPbQUJ/qwknePp51q1aoXXXnsN/fr1Q82aNdG7d2/ccccdWLx4Mfx+fyLHSERERERERNWI3tM26tb4eAUFeigctv5EtyqI3qasnExJpW1Ur+B4TfAWb5oWWSUox52uIXO8CC32dvmEf6mQpowmZVMVtUodC7P2CHpP20P4kiIogobrDt+22foFjANzRUnfz4yqxnFo+/zzz+O3337Dhg0b8PTTT6N58+aYNm0aevXqhRo1auCkk07CPffck8ixEhERERERUTVgWGkbxwmzjCY6k9tNlOg+vbI/a7IrbaMDwXSumovu7aooh367+OHArNK2Oux7NNNK2ypUdWw2WVg8znlAC1iGtlZ9oo1aU+jjStPPjKrGcWgrNWzYEBdffDGef/55rFmzBuvXr8d1112Hr7/+GrfffnsixkhERERERETViGn7gniFtgahMBQktE+mDGUiqntTUDEaU2krK37TNAyMmYgsjccaL4Y9bUN9LKp8a4hoAiImOKxqlbaa0CL7a1fQ24Ecwr4GtaDhuiWrSl5NaKH+wdHjqqatOlLBcU/bcGvWrMHChQv1n507d6J79+7o1atXvMdHRERERERE1YxR+4KEVNpGBWOJ7JMp+1aGV9oCyQ9uY6pXZcVvdIidBoJabKWtWf/PqiT6HAGhoDIogtCgwQVXikaWfGbBYbz6W6eDgAhYvvcO5XMvgEBEX+gYFj2thTCe8E1RlCrVUzidOQ5tX3zxRSxYsAALFy7Erl270LNnT/Tq1QtXX301unTpArf7oPJfIiIiIiIiogiG7QsU8wlzDpTRepJRvRdeIaoHkMluj2B0u7kwvw09lTTEVpxCqfrVptFfWEiaMJ9UqqoyqrRVoCCIqhMaGvUw1imHNllhIGjTHkGYfxmmCc3wM0FWvFPiOU5aL730UhQXF+O2227DiBEj4PF4EjkuIiIiIiIiqqaM2heoUPVJc6xu93W0/opqzej1JLJ6L3qf5P8m+zZv0z6xaRbCmFX/KqLqB0bRFcZA2KRUVagtgCMCMZW2iqJUrUpbJK7S1vCLjzBW7RGCwri1gqKwPUKyOP6X7oknnsCxxx6LCRMmoE6dOhg4cCAefPBBLF++vMr3kyEiIiIiIqLkMWtfoFeExkF0UJHo6r3ofUplL9nDoU+sHFPMZHEGt8pXNUZBm/w93c5Tohndhq9AQUAEUjCaxDDqYawTh9ZCJaDZt0cwC23N2jYoCiciSxbHoe1VV12FGTNmYPPmzfjiiy9w2mmn4euvv8bpp5+OmjVrYsCAAXjggQcSOVYiIiIiIiKqBmQgYDRhVjzCAtmrMWb9Ca60NRxHkmkidiKydKy0la0AYsJLi9u5qwJZTR5TYVzR07kq77sRDbGV9VaVo4ejoAiaBquH0stbXi9WdyaoimoagJu1bZBjqm5fIKTCQd1T0qZNG1x11VV4/fXXsWLFClxzzTX4/PPPcfPNN8d7fERERERERFTN6LfGG1SExiNb1KAZT3SWwOoxo/YIqehpGxPapmhCNDsyoI+p9DvEHp/pTp4Ho9BWXkPViabFvicVVK3b8zVhUWl7CD2cZbBq2dPW4liaTWKmt3apZl8gpMIBzx72119/YeHChfrPL7/8Aq/Xi27duqFPnz6JGCMRERERERFVMzHtC+JYEWoU+iU8tDVrj5DkEE6D8eRCaReEmrTCkIF+VSWDNlWNrS5NRcifakHE9vdN9Hs12aL7TEc72HMuK21dcJkuoyiKYTAOhHorG05EVvGaqvw+TBeOQ9urr74aCxYswOrVq+F2u3HMMcdgyJAh6NOnD3r06IHMzMxEjpOIiIiIiIiqCcP2BXGs7jIMGxTjqr54MZxYS0luhaseCIZVz+kTXKVZ1Zysho65tbsaVNoaTsCWoonrUk4zaJER1g/6cG+VoLfDsNgPo76+TsgvuSzbI0BFAMbtETShmVbapmMf7KrIcWi7YsUKDBo0CH369EHPnj2RlZWVyHERERERERFRNSWrQSPaFyjxu41fCBFTQXYovSMdbbOitUNEOBPHidUcjcFkW+kW2ALmY1WEUqUr/Mwm4dMrzatZUBZA7GRYemsRg3D7cCPfe2b7oSgH3wrCSU9bRVEQCBqHtkEtaNzTNk37YFdFjkPbJUuWJHIcRERERERERABgeBt4PCe/MaxkVBIbBgrEBsX640kiq+PCb71P1562MhSKDo2Ekn5VwfFkVWlbHdsjGE2GJScNtAskDwcaQp9pLtW4hYGqmFfC2jG7lsIpinmriSBMQlv2tE0ax6GtpmlYuXIl2rVrBwB46qmnUF5erj/vcrlw1VVXxfRdISIiIiIiIjoQRgFiPCdisgqFE8UwcEvyrf5GIU4690oVwiC8rOKVtnrlpUFQCaX6tUcw6veqQEEQwapxHQhY9p1VYN5z1k5QBENf0ti0RzCq5DVqpaKPKUX9uKsjx6HtjBkz8PTTT2PRokUAgJtuugk1atSA2x1axd9//43MzExceumliRkpERERERERVQuG7Qtke4Q4VdpGS0p7hOhtJjuArGjHENF2Ik17pZpVCQol1GaiqtJ7nBpUR6ZruJ5IZpWiVeVYmPZurnAo7RGEEKHQ26JfrqIoergbvpwmtFA7F9W40rY6tupIBcdlsS+++CKuvPLKiMcWLVqEtWvXYu3atbj//vvxyiuvxH2AREREREREVL1YBjVxqrSNCTKSUcUYvUtJrrSVAVF0pW06BjByTEaB08FOzHQ4sLqlPdEtPNKR0SRdcvK8qnAszCqrJRUqAuLg2iPo1bJWPW1DsyHGfGElhIAG4y8P2B4heRyHtqtWrUKbNm1Mn+/Vqxe+//77uAyKiIiIiIiIqi+jKjoZ1MQjKJCTgoVLdM9QIQQUocQ8ltSetib7lo7hlxxTdGikKmraVQXHk9lEZKEnkxvypwOj9ghSVTgWVpXVQOWXKgcjoNmHvfL9FL0N+ZhR4Ku3VEnDz42qxnF7hL///hs5OTn677///jsKCwv13z0eD/bt2xff0REREREREVG1YxYGxKuyS0CEbrMPE1495lKM+0seCk1oMdtM9sT3ehVndHuENAwD9TDLILw82B6fhwMZSBtWmsfpS4vDhQwGzSptq8KxsJssTLYvOFBlwTL8secP0wnOJFVRoQkt5osQq8+DqnT8053jStu6deti9erV+u+1a9eOmHRs1apVKCoqiu/oiIiIiIiIqNoxqjSMZ7ioCS2m6jXR1WNG1b3JDkv1Ks7oicgg0q56VQ/sTCahqqqswurq1h5BIPZ6lapKYGjVBgQItUfQRGwlrN061+9ej+2l21HoK7RcVm43ptJWmFfaGi1PieE4tO3bty/uuusuw+eEELjnnnvQt2/fuA2MiIiIiIiIqifD9gXxDBcFDCttE90n0yiITmp7BIuqvrSrtJWBXfQxUxSkWb4cV2Zhtf5cmp2nRBLCeNK5qtRTVRP2E5EZ9Zy1srVkK/7Y8wcKfAWW/WyBii9BKiYiC2fZW9mkDy7Fn+P2CLfddhs6d+6Mbt264cYbb0TLli2hKAp+/vlnPPDAA1i9ejVeeumlRI6ViIiIiIiIqgGj9gX6c3GqtDVdd4IyMaNxCyU1PW2NqvrSrYJTQBi2j1CgIICDm5jpcGAWVgMV/U2rcmIdRU6cF92upCr1VLXtaVtRWe70c293+W6s2bkGPrcPGa4M2+VVRdXHEM6qAlhRlKR/dlVXjkPb5s2bY+7cuRg+fDjOO+88/cQJIdC6dWt88sknaNGiRcIGSkRERERERNWDJjTT8DQeQUEQsZMb6X0aExSKpUV7BJhUaia5t64TZteAoigQWtUNi8xaIwDVr9JWVnO6EBXaJqEqPlmsQnrAvOesEX/Qj993/o6yYBnqZtd1tH15LKPXb1sBLA5+gjRyznFoCwBdu3bFTz/9hO+++w6//PILAOCII45Ap06dEjI4IiIiIiIiqoaEcTWorLA75NVr5iFJokIxs+rhpIe2Rn1i0zWAMam0PZiJmQ4XpsE64nf9Hy5kpa1Ri4yqEmDbve/Mes5GE0Jg/e71+Hv/344DW7l+WVUbvT6rCmCzOyEovg4otJU6duyIjh07xnkoRERERERERDCtootXdZ1Rpa3smZuwiciMWi8oyW1LoE9EFhWCWQWFqWI0WRyQ/D7AyWZVdQmgSgfW0fR2HgaT0VWV0NYolA6nwDhUDVcWLMP63euxcc9G1MysadvHNpxpewRYT5Amx0WJ5ehM3nvvvSgpKXG0wq+++grvv//+IQ2KiIiIiIiIqq+giA1VAcRt8hujSttEB0FGQWOygw/LSa7SLAgVwqSnrVK1wyKrLw6q+r5H0yvDjSptE9jKJJmsWsEAYW1bTM77ztKd+Onvn7Bh9wbUzKyJTHfmQY0j+ssAu8/BqhKapztHoe1PP/2E4uJiXHXVVfjwww+xbds2/blAIIAffvgBU6ZMQY8ePXD++ecjLy8vYQMmIiIiIiKiqk3TjPt6WlWkHdD6EXvbb6L7ZBqFkMmeWMq0f2Ya9rQ1q/6t6hV+ZhXGQNVvDRFNrww3u0W/CoSGdhOqyS+Toj8ngloQG3dvxP+2/w87y3eibnZdRxOPmWwktj2CyUSAEa9Jsy96qiJH7RFeeukl/PDDD3jiiSdw4YUXYteuXXC5XMjIyNArcDt16oTLL78cw4YNQ0bGQV4oREREREREVO0ZhapA/Kq7jCp5wyfbTgSzoFHTkhvaGgYxcapgjiezSlso9rduH+7M+oVW20pbs9C2CoSGduGoUc9Zf9CP33b+hk17NyHHm4OamTUPeRx+zR/xu+11luRJFKsrxz1t27dvj6effhpPPfUUfvjhB6xbtw779+9HrVq10LFjR9SqVSuR4yQiIiIiIqJqQhMmlbaIT2WqWSBoV/V2KDRoMb0m47U/Thn21Q0NJO0CMAFhWHGqQrVs83C4s7pdvqpXGUeTYWUiJyVMNUftEcJ6zvq1UGD7594/UctXCx6X55DH4FE92L5/O+rn1Hc8LvnlCSXWAU9EpigKOnTogA4dOiRiPERERERERFTNmQZTBrfxHoygCEJFbICa0PYImsGt/kkOnkwnPUrDqjkBYT5DvbCoxD3MWVVeKoqCoFaN2iPAeCIy+Vy6fdFwMDSh2V/HSmg5v+bHmp1r8OfeP1E7qzbc6gFHeoayPFnYXb4b+wP74XP7ANhPiJeO1flVkfMp5YiIiIiIiIiSQIMWE6oCca6ENegvm8jJdaxaPiSLaaUt0q/S1qzST5+YqYpW+dlV2la3nrZmVfdVJTQUMH9PhvNrfvy+83f8sfsP1PLViltgCwCZrkzsD+zHnvI9+mO27y8FjsZNh4ahLREREREREaUVoZn3M41Lpa1m0NNWVtomsKdtdPikQEEQyQvhTKtX43Rc40qYTzxne+v24UzAstI23cL1RLJsg1FFqqydVoxvLdmKjXs2ojCrMC4tEcIpigKX6sLOsp36Y9F9dI1Upy8QUoWhLREREREREaUVo4nCgPj19DSqetUrbRPVHsEgAFGgGLZNSBSzStt07JWqCc3wXMjgsqpW2loFYQqqX3sE09v0q0ilbUAEbHsza0LDtpJtKPQVwuvyJmQcWe4s/LP/H/iDFROSWXx5AIQq3qvC8U93DG2JiIiIiIgorZhV18mZ1A9p3bJ6zygISuCEXGZBcVInIjPZt3gc13gLwjy4B9KwMjhOjCask5J9vaSaXX/fqlB1rGkm7R/C1MisgQJfwQEHtiX+Esfv6yxPFkoCJdjjD7VIcBIms9I28eLXBIOIiIiIiIgoDkz7WOLQgwK9es8okEjghFyaZhDaJrnC1Wrf0i20FZpxsC7DuqoQ2BmxqqRVoeq3rdsFfVWBVRuMRPafTqagCJqG9JKcHMyp0kApXlv1Guaun4v6OfVxfZfr0SCngeVrZOXsnrI9KMgsCLWQsbjGWGmbHI5C28GDBzte4VtvvXXQgyEiIiIiIiLShPGkXaqiHnKlod4iwCSPSGSlbcw2k5y7mVYwJ7m3rhNWlbZVJbAzYlVpC0BvC+BSXMkbVCpZvEeqwjVg9ll3sH775zc88d0T2LxvMwDgz71/YuznYzH66NFoX7u95WszPZnYtn8bGuU1QhDWYXI6tlSpihy1R8jPz9d/8vLyMH/+fCxfvlx//ptvvsH8+fORn5+fsIESERERERFR9WAYcFY41B6wGkK9Uo0CCdnXNhE0oUGN+k/wZPcoNZuITFEUpNtd96aVtqjalbaa9v/snXd4W/XZ/j/naE9b8l7ZduIMMoEEKJRVdqGU1QJllNUWeEuhdEF5ad9uWkZ/hZYyWlpGKauUvTcJ2dOJnXhvy5a19zm/PxQpMbYlb8vO+VxXrljSGV/paJxzf+/nfgY/EKIgTus8388jyRKCPFg+wvhNsEwkAzVFHOl2nq1+lp9+8tOkYJvAH/Xz689+zev1r6fchkltwhfx4Y14B6wMOBgBQYlHmACG5LR99NFHk3//4Ac/4IILLuDPf/4zKlV8ZicWi/Htb38bq9U6PqNUUFBQUFBQUFBQUFBQOCRIlH9/XuCEeHn4qB2hCaci/Z2K4y4GCp+/ObG5nIOVm2ei03agDGA4qGHcNHBZDoREf3G/z+MpIgOmG7KcItMWgagcndgBjQMxOXUMwVDwhD389rPfUtNbk7xvbtZcrjrsKp6pfoaNHRuRZIlHdzxKi6eFyxZdhkrs//2nUWmISBE8YU/aeIRMzMGejgy7EdkjjzzCLbfckhRsAVQqFd/73vd45JFHxnRwCgoKCgoKCgoKCgoKCocWKcU4YfRO24QwO1hH+vF02vYTIQUm1jUpM6h7NdMEmMEciIn7pqvbNCbFBhUqDzWnbSJ/eiAEQRj1d0EmIJG+EVkqZFnmwa0PJgVbURD5avlXufPoO5mdNZubV93MWXPOSi7/RsMb3Lf5vkFfV51KhyPgSNuILBO/M6YjwxZto9EoVVVV/e6vqqpKaeNXUFBQUFBQUFBQUFBQUEhHslHYIOLiaN11CaFhMEFi3DJtBxBtJzqfdVAnsZB5jcgGE7MSDr9p67QdIEYjQeL1mK7P/fOkc9pmmjt8JEhSamd1Ot5vfp/1HesBsGgt3HnUnZw//3zUYrywXhRELl54MdctvS6Zg7yubR1vNb414PaMGiPesDetA1hx2k4MQ4pHOJgrrriCK6+8kr1797J69WoA1q5dy69//WuuuOKKMR+ggoKCgoKCgoKCgoKCwqFD0gk7SGn8WGx/sG2Na6btADm9iec42PMda2LywO5VkczrBJ+qQVMqB+ZUR5IHz3NOuBsz7ViNFzLyoJm208FpK8tyXHge4Ue/09/J33f+PXn76iVXU24rH3DZL5Z9EbPGzF0b7gLgHzv/wUL7QkosJX2W06l09AZ70aq0KcVkJdN2Yhi2aHvXXXdRWFjI3XffTVtbPNy4qKiIW2+9lZtvvnnMB6igoKCgoKCgoKCgoKBw6JBwng7mtB1t4y5ZlgcXBOXxcdomn9Pnnbb7hSdJllJ2ah/LcQwofGdgU6fBxEtREMftOGUCg+U5w/RvwvZ5JFkasHEe7Bewp3hMRGLyYSROW0mWeGDLAwSiAQCOLT2WI4qOSLnOqsJVfGnml3ij4Q3CUpg/bv4jPz/652hUmuQygiAgiiKRWARRTCHaCkLKqgiFsWHY7wxRFLn11ltpaWmht7eX3t5eWlpauPXWW/vk3CooKCgoKCgoKCgoKCgoDJeUTtsxEGpSiV6yMD6CWNLdO1A8wgSKcIM5bQWEjIs7TCVmjddxygRSOS+nexO2z5PSaTsGEziTTaKp3EhEz1dqX6GqJx5dmmvI5fJFlw9pvYsXXkyJOe6urXfX8/Sep/stY1Qb8UV9aTNtZXn6fg4zhVFN5VmtVqxW61iNRUFBQUFBQUFBQUFBQeEQJ5UgdbC7a7TbH9DZKo9PtmtCnBlwPHHb6IQwaE5sBroWU5aNj9NxmmwSWb2pnLYwfZuw9UNmcKetkHnv2eEiy/GmcsONRmlyN/HUnqeA+HviW0u/hVFjHNK6OpWOG5bfkMy8/W/tf9nu2N5nmcS2tCrtoNtJTCBMx89hJjFs0bajo4NLL72U4uJi1Go1KpWqzz8FBQUFBQWF9HhDUZp6/JM9DAUFBQUFhYxDkiVk5AFF1bFwd6UVfcdBQE24aT//nJJNtSZItR0sA3QsxPCxZLA4iQTTtRxbllML+NO9CdvnSZWZmsj3ncrI7I9qGcb7ORKL8KctfyIqxRsynj7ndBblLhrWfmdlzeJrC76WvP3AlgfwhD3J26IgUmwuTi3aklnfGdOVYWfaXn755TQ2NnL77bdTVFQ07l+WLS0t/OAHP+DVV18lEAhQUVHBww8/zMqVK8d1vwoKCgoKCmNNKBrjvT1dvLillbeqOghFJb5/yny+c/y8yR6agoKCgoJCRpFOVE3VVT7ttlPEL4xbtusgGawHNyKbCAZz9R0shk9EQ7R0JMS4Qccip3mPTFEk4gK+ShjYEJc8TtPwuQ+ERIqsZ2Hqu63Tvs8H4J9V/6TeXQ9AqaWUC+dfOKJ9nzb7NLZ0bmG7Yzs9wR7+tedfXLXkqiGvP9ETTocqwxZtP/roIz788EOWLVs2DsPpi9Pp5Oijj+b444/n1VdfJT8/n3379pGdnT3u+1ZQUFBQUBgrOtxB7nmrmpe3teEORvs8du/bNZyzvISSbMMkjU5BQUFBQSGzSCWqioJITI4hIaFiZJWeaRudjUNH9HQNeyZKhJOkFK6+UYrhY0myQdMgjZBkYZqWZe+PfRjsvS0IQlysnOKxAEMlVc6yiDjlxetUVQUD8Wnrp7xe/zoAGlHD9cuuT+mGTYUoiHxr2be46d2bCMVCrGtbx5WLrxzyWJJRHdPxc5hBDFu0LSsrm7APxm9+8xvKysp49NFHk/fNmjVrQvatoKCgoKAwFvhCUS74y6c0dPeNQtCqRMIxiXBU4t63qvnteUsnaYQKCgoKCgqZRSpRFVLnww5r+wM5TvfnNI41gwnRyViCyXbaZphrLq3rdxDn8lQn4bRNJZwdSk7bGAM3zgNA2J97PIVJxiMMYaak1dvKg9seTN6+bNFlzMqaNar92/V2luYt5bP2z/CEPdQ4a5hvnz+kdSf6u+tQZdiZtvfccw8//OEPqa+vH4fh9OXFF19k1apVnH/++eTn57N8+XL++te/jvt+FRQUFBQUxopfvVqVFGyNWhXnLCvm0SsO55MfnYBVH587fWZjMzUdnlSbUVBQUFBQOGRIuAgHFVWRR+U0TAqog4jC4+K0HURkm+hy98EEokzLp0znTEaYnvEIQ3lOic/AZCLLMo6AA39knPszSIPnFyectlPZ6ZlugipBOBbmno33EIgGADim5BhOnHHimIxhRcGK5N+bOjYNeb1DLapjshi2aHvhhRfy3nvvMXfuXCwWC3a7vc+/saS2tpYHHniA8vJyXn/9da677jpuvPFGHnvssUHXCYVCuN3uPv8UFBQUFBQmgw+qu/jn2kYADBoVL9/4Be65aDnHz88n16zjui/OBUCS4Xev75nMoSooKCgoKGQMCSFmsPiCxDIjRZKlQSMAREEcl9LzwYTipFg6UY3IGDj+IOOctqlyh+MLZMxYx5IhlctPcp5vOBampreGbV3bqHXVEpPGz+0aJZrahTrFs40Tzup0Ttu/7fgbjZ74NUWJuYSrllw1Zv2llucvT+5/Y8fGIa830d9dhyrDjke45557xmEYAyNJEqtWreKXv/wlAMuXL2fnzp088MADfOMb3xhwnV/96lfceeedEzZGBQUFBQWFgXAFItz6zLbk7R+dvoDZuaY+y1xx1Gz+9nE9nZ4Qb+zqYGODk5UzbRM9VAUFBQUFhYxjMCFgLEpyZWQEOYXgMQ7GvcEiGRLCy4Rm2g7itIXMEULTuifHq2FcBpDuuSdK6seLcCyMO+zGrDGjV+v7POYKuahz1eEIOLDqrHT4OrDr7RSbi8dlLLI0uAs1MdEwmnzryWYoTtsPmj/gnaZ3ANCpdNy08qZ+x2U0ZOmymGebR42zhmZvMx2+DgpMBWnXS0TJTGWn81Rg2KLtZZddNh7jGJCioiIWLlzY577KykqeffbZQdf50Y9+xPe+973kbbfbTVlZ2biNUUFBQUFBYSDu/O9O2t1BAI6el8MlR87st4xBq+J/TirnJ8/vAOA3r+3mX9esHrOZcwUFBQUFhalIIrN20DL+MSjJlYXB4wrGIyczlXM0kd86EUikaERG5rgWE6/JoI5TeXo2QEo8p1RO2/GMR4hKUfb17qPV24perSdbl41db8eiteAOu6lz1RGRIhSYCpJNAetd9Vi1Vsxa85iPJyYPnmkrIIw633qykWRp0HxtgN5QL49sfyR5+5tLvkmppXTMx7GyYCU1zhog7rY9fc7paddRnLYTw7DjEQ4mEAiMaxTB0UcfzZ49fctFq6urmTmz/4VvAp1Oh9Vq7fNPQUFBQUFhInl9ZzvPbWoBwKJT87vzliKKA5+MXbCqLOnA/ayuh/equyZsnAoKCgoKCplIooR/sHiE0QoFkiwN6rQVEFJ2rB8pCTF0MuMRUjX3EgVx3B2cwyXVWARBmNJi3WAMJVd4vHJEZVmm0d1Ii7cFu8GOVqXFEXSwq3sXmzo3sbtnN6IokmfMS4rKVq2VYDRIg7thXGISUkUHTIdGWKFYKOVEyjN7niEYi5tAji09lmNLjx2XcazMX5n8e1Pn0HJtE07bTJnoma4MW7T1+Xxcf/315OfnYzabsdlsff6NJTfddBNr167ll7/8JXv37uWJJ57gwQcf5Dvf+c6Y7kdBQUFBQWGs6PaG+PFz25O37/jyIoqzDYMur1GJ3PKlA11af/vaHiRJOflRUFBQUDh0SSUEjIVQIMsD57omtj9umbYDjHkihY9kc69BnnwmCraDOU4zTWAeK4aSaTte79E2XxsN7gZsehtalRajxkiuIZdCcyEWrYUcQw5WbX9TXI4xhzZfGx3+jjEf02DZ1nDAiT9V3wcRKUKrtxWdSjfg482eZt5ufBsAg9rAxZUXj9tYSi2l5BnyAKjqrhpSgzmlEdnEMGzR9tZbb+Wdd97h/vvvR6fT8dBDD3HnnXdSXFycskHYSDj88MN5/vnnefLJJ1m8eDE///nPueeee7j44vF7syooKCgoKIyUNleAix9aR7cvDMBJlQV8dUVJ2vVOX1LIYaVZAFS1uXlpe9u4jlNBQUFBQSGTSeWcSwo1oxCtUrkZkyXXY0ziOQ0YjzBRTchkeVB3aqZl2qZznI5XjEUmkDbTdhyEsu5AN/t692HQGAbMS9WqtKjFgdM11aIak8ZEvaseb9g7puOS5IEzmGHqO227A930BHvI0mUN+PjjVY8nn9vZ884edLmxQBAEVhbE3bYxOcbWrq1DWkcW5HGZQFA4wLBF2//+97/cf//9nHfeeajVar7whS9w22238ctf/pLHH398zAd45plnsn37doLBIFVVVVx99dVjvg8FBQUFBYXRUtXm5it/+oTd7R4Acs1afnXukiHl0wqCwK2nLEjefuSjunEbp4KCgoKCQqaTqjFPovnQqEWrwX6ex6nBVaJhz0DxCMgTkyWb6FQ/kIsz00qdU2UAw/7xTsPKpFR5zgkSx2qs8Ia97HXuRUbGorWMaBtWnZVANDCmMQmyvF8QHMwVP4WdnpFYhGZPM3q1HpXYv4na9q7tbO7cDIBdb+f02ekzZkdLQrSFeK7tkJig765DmWGLtj09PcyePRsAq9VKT08PAMcccwwffPDB2I5OQUFBQUFhCvBhTRfn//nTZOOxMruBf127hjzLwOVOA3H0vBwWFMZPlLc09bK92TUuY1VQUFBQUMh00ommoxVVUzUvGq9MWxh43BPqFpQHf+3GwsE8liTEuFSl8VGiEzyq8UdGRhYGf94JYvLYCKPhWJi9vXvxRX3Y9fZRbSvHkEOHv4OeYM+YjC3hthYHka2mciOsrkAXvaHeAd2zkizxz6p/Jm9ftOAitCrtuI+pMqcSgzoe6ba5c/PQxPdxmuRSOMCwRds5c+ZQX18PwMKFC3n66aeBuAM3Ozt7LMemoKCgoKCQ8fx7QxNXPLoebyh+4bC0NIvnv300c/OG10FXEAQuXXOg0eZjn9aP5TAVFBQUFBSmDKncc6Iggjy6HMtEo7OBEBinTNv90QQDOignSPhI5MAO2OBtrBzMY0RStE3htM0QfXlMSTWhkCBxrEaLLMs0uBtwBBzkGfOGVB2WCo1Kg0pU0eZrG7PxpXotEo7jqZZpG4qFaPY0Y9QYB3S9f9D8AQ3uBgBmZ83mmJJjJmRcalHN0rylAPgiPqqd1WnXEeTxiZNROMCwRdsrrriCrVvj+RY/+tGPktm2N910E9///vfHfIAKCgoKCgqZykvbWvn+M9uI7i/PO6mygCevWU2ueegO24M5Z1kJFl08L+zFra0492fjKigoKCgoHEqkddoKo3PXpcx2TThfx1i8TJb7DySMjVKEHs4YYPDS+0xyzKV8vRg/cT0jSKOdCghj4rTt9HfS7GnGprelbHw2HLK0WXQHuukN9Y56W6niPCDzcpiHSqevE3fYPWAURSgW4l+7/5W8fUnlJWN2bIbCioIVyb+HEpEwHt+VCn0ZOEk6BTfddFPy7+OPP57du3ezYcMG5s6dy9KlS8d0cAoKCgoKCplKa2+AHz+3PXn7sjUz+elZi1CJI3cpmHRqvrqylL99Uk8oKvHvjU1cc+zcsRjuIYU/4iciRTCqjWhUmskejoKCgoLCMElVFh9fYHQ5iukaXMly6jzVkSDJKbI5R+lwHCqJ5z2g03YCs3WHQqrXC/YLl2OUnZpJSLKEIKd+P4yF09Yf8VPvqker0g7YeGykJM672n3t2HS20b23909mqOif+QqZ5w4fCsFokBZvCyatqZ8YK8kST+1+CmfICcQzZhflLprQ8S3PX56MndjUsYlLFl6Sdp2pJppPNYYt2n6eGTNmMGPGjLEYi4KCgoKCwpQgJsl87+ktuIPxSISzlhbzv19eNCYXXZeumcnfPqkH4J9rG7nqmDmIoxCCDyU8YQ+dvk7a/e2EY2H0aj0WjYVsfTZGjRGr1jpo52MFBQUFhcxBJnVZdHKZ0Ww/1U9rQrwcw5/fdM9pQhqRydKgYnTCYZwp7tV0r5cgTE+nbWLCIBUCoxNtY1KMOlcd3oiXAlPBiLczGFn6LLr8XZSYSwbMbB0qCadtKrf1VHN6dvg68Ea8FJoK+9zvDXt5YOsDSXerKIh8vfLrEz4+i9ZChb2CPT17aPW10uptpdhcnHKdqfT6T0VGdOXy2Wef8d5779HZ2dkvpP0Pf/jDmAxMQUFBQUEhU3now1rW1sabLBRn6fm/sxePmUtmbp6ZY+bl8tFeB409ft6v7uL4Bfljsu3piivkot3XTqe/k3AsjFVnxaK1EIwF6Qn10OnvRBAE8ox5zMmag1FjnOwhKygoKCikINGMaTASbtiREpNjKbNSE+LmWDPYucJENlNKJ/ZligCTTjQfrXCZqQzlfSAIo3MZt3pbafO1jUmO7UDoVDp6pV7afe2jEm2TzvAUn9WhiNyZgjPopNnbjEVr6fO617nquHvj3XT6O4H4872k8hJKzCWTMs6V+SvZ07MHiDckSyXaTuuYkgxh2KLtL3/5S2677Tbmz59PQUFBnzfbRJV1KCgoKCgoTBY7Wlzc9Ub8REYQ4PcXLCPLOLYl+JeumclHex1AvCGZItoOTruvnRpnDZFYBKveit1woPOxSTRh0pgAiEpROvwdBKIB5mXPw6a3Dbg9WZaJSBEiUoRwLEw4FkZGxqwxY9KYlHMdBQUFhQkgXYn4aDNtJWngZlwwfu69lNuboFiCdJm2By8z2aSNpxAmJgd4ohnK+2A0mba9wV4aPA1YtJYRVx8FogFe2vcSje5Gzik/h7nZ/aO8rHorXYEuSsIlmLXDa86bIF2uMTBl3gdd/i6qndVE5SjZ2uzk/e82vssjOx4hIkUAsGgsXL/i+mRDsMlgef5yntj9BAC7undxxpwzBl12qjmdpyLD/pTee++9PPLII1x++eXjMBwFBQUFBYXMJRiJ8d1/bSESi5+cXHPsHNbMzRnz/Zy4IJ/iLD2triDvVXfR2O1nRo7iDv08nrCH2t5aRFGkwJC6vE8tqikwFtAd7GZn907mZM2hyFSEIAhEpAiesAdXyEVPoIdQLERUjhKJRZLra1QarForOfocrDorZo0ZlThwxpqCgoKCwiiRSe2yHGXHcglpwt176dy0EyGWyrKcOi84g+YlE2MdDBFxWopFQ8lSTkRZDGu7skx3sJt6Vz1RKTro5HUqJFni/ab3eWrPU7hCLgC2O7bzoyN/xHz7/D7LGtQGXEEXnYHOkYu2ifdritdDkIf/Wkw07b52qp3ViIJIriE3ef9bDW/x0PaHkrfnZs/lppU39VlmMiixlGDRWPBEPOzu2Y0kS4M3gxNGV/WgkJ5hi7aiKHL00UePx1gUFBQUFBQyml+9UsXeTi8AC4us3Hzy/DRrjAy1SuTi1TP53et7kGX457oGfnx65bjsa6oSkSLUueoIxULkm4bmRBYEgVxDLu6wm909u/FFfAD0BHrwR/1IsoReo0er0mIUjahFdfLCNhwL44146Qn0IIoiVq2VIlMRdoMdnUo3bs9TQUFB4VAkXTxBquiEoW4/rdN2rEVbOUVG6wSJpYky5kEF61GK4WNJ2tdfgBjTsxFZuucuIhKKhWj3tWPVWjGoDSndqL6IjyZPE23eNkRRJMcwfMNBVXcVj+16jDpXXZ/7g7Egv1r3qwGFW4vOQpu3jUJj4YiiqZJO21TO8FF+F4wnsizT7Glmn2sfOpUOq86afCwYDfLvPf9O3v7SzC9x6cJLM6KBriiILLAvYH3H+uR7Z6Z15oDLysgZ850xXRm2aHvTTTfxpz/9iXvuuWcchqOgoKCgoJCZPPZpPX//tAEAnVrkvq8tQ6seeNZ5LLjw8DLufauGcEzi6Q1NfO/kCvQaxdmZoMnTRKevc8iC7cFYtVa0opYmdxOCIGDUGMkx5KR0zmpVWrQqLRCPWvCEPezs3olZY6bIVESOIWfEThIFBQUFhb7EGDxzFkh2lR8pQ3GGjXk8QsouZBMUjyDLKQXrTCp1TukI5kCucSoX4FRkKJMFRo0RZ9DJTsdOdGodZo2ZPGMeBrUBlaBCFERUQvycpjvQTaOnkWAsiE1vG/JEsyPgYHf3bnb3xP81e5v7PH54weEEY0G2O7YnhdsfHvlDFtgXHBin2kh7qB1HwMEMzfCb16d1hpNZEw0HI8syDe4G6lx1GDXGfueIbzW8hSscdyuvLlrNlUuunIxhDkplTiXrO9YDccF+MNFWFMRpOXmSSQxbtL3llls444wzmDt3LgsXLkSj6TsT8Nxzz43Z4BQUFBQUFDKBpz5r5Kf/2Zm8fduZC5mXbxnXfeaadZy+pJAXtrTS64/w90/qufa4/plhhyKOgIMmdxPZhuwRRxTo1Xr0Zv2I1lWLamx6G7Is4414qemtodnbTIWtgjxj3oi2qaCgoKBwAFlKI9SMoDz8YGLEEBlY6BMFcVzcY6kaa01kM590r1umlJrLpHAmc8AtnK5h2VRjKM9HLaqT5xvBaBBPxIOj24GAgCiIiIKIIAioBBXBaBCT1kShvnBI+9/Xu4/7t9xPi7dlwMdnWGbwjUXfYHHuYsKxML/f8Hu2dm0lGAvy63W/7iPcCoKASWuizdtGgalg2JVJ6ZzhkFkTDQfTHeym3l2PWWvu5zIORoO8uO9FIP7czqs4bzKGmJLKnAMVfru6d3Hq7FMHXVaSMk80n04Me0rqhhtu4N1336WiooKcnByysrL6/FNQUFBQUJhOPL+5mR89vz15+/rj53Hp6oFnm8eaa46dS+Ka9Y/v7KXLE5qQ/WYy/og/nmMriBjUhkkdiyAIWLQWisxFRKQI7b72jLxwUFBQUJhqpMqchfR5p+mQpdTC2Hh8l6cSIWVhYoSnRETDoK9tBomfkiylLH0fr+zhySYmp3GZfw69Wo9db6fIXEShuZBcYy7Z+mwsWgt6tZ58Uz4W7dCMBu6wm7vW39VPsBUFkblZc7l6ydX8+thfszh3MRCvQrp51c3JplkJx22DuyG5rkVjwRP24Ag4hvycEqRzhieXy7D3QDAapM5Vh0pQDRgL8WbDm7jDbgDWFK+h1FI60UNMy0zrTIzq+Nh39+we9PtJcdqOP8N22j722GM8++yznHHG4B3kFBQUFBQUpgMvb2vj5qe3kjhPufoLs7n5SxUTtv+FxVYuOryMJz9rwhuKctfre/jNeYdN2P4zjYgUod5VjyfsocDUv/GYLMts7NjIho4N6FV68o35FJgKyDfmk2/MH9fs2SxdFs6QE0/Eg1VrTb+CgoKCgsKgDEmoGYXImUoYG7dM2xTZnBPVTEkinpc66Gs7ytiJsWQoTttMdVmOBklK/95PRcJpO1xkWeYvW/+CM+QEoMRcwpFFR1Jpr6TcVo5ePXB1UkK4TThuQ7EQz1Q/w82rbgbi4rpRa6TF00K+IX9Yma1DEeUzrRFWIhbBFXJRaOrvbv68y/ar5V+d6CEOCVEQmW+fz+bOzbjDblq9rZRYSgZcVnHaji/DFm3tdjtz5yrlmQoKCgoK05s3d3XwP09tRtp/HviNNTP58emVozqRHgk3f2k+L21twxOK8vTGJi5dM5PFJYdeZUswGmRf7z7avG3kGnP7HAdJlljfvp7nap7r4+44GLWo5rRZp3HhggtRi0M7/XEGndS76ik0FVJkLkq5rFalJRKL0B3oVkRbBQUFhVEiyamdtgijc9elFFATDs6xzrRNUfYuC6kFyrEcQ8rzmFG+rmOKTErnryAIQ2raNdVIm+c8TrzZ8CYbOzYCYNFauG31bdj0tiGtmxBu/+ed/8EZcrKhfQPtvvakaGnRWuj0deIIONKeTx1MQrhPKULLGfSeBTr9nbR6W8kx5Az4WXu9/nU8YQ8Qd9kOJoRmAgtzFrK5czMAVT1VA45VRJywaJdDlWGLtv/7v//LHXfcwaOPPorROPwOgAoKCgoKQ8fhDeH0hfGGovhCMbyhKBqVwHEVeahV06fpQqbxYU0X33l8E9H9iu2Fq8r437MWTbhgC/Fs2/85qZz/e7kKWYY7/7uTp69dMyljmSw8YQ97nXvpCfaQZ8pLiq6yLLOubR3P1jxLk6cp5TaiUpT/1v6Xqp4qblxxI/nGzzUwi0UJt2xgh6eezZFedjqrafW1Jh9ekb+Cs+aexQL7gkFfe5PWRKevkxJzSbJpmYKCgoLC8EkXjyAgEJNHXpKbysmb2O9YCxHpnKMTITyldaZOUEO0oZDu+CaPU4Y4g8eKdHnO40GTu4l/7PpH8va3ln5ryIJtAq1Ky5dmfYl/7fkXMjKv1b3G5YsvB+Kiq16tp8XbQq4xF404NLetjJwyIgPiEx6Z8h7wR/zUu+r7NK89mGA0yEv7XgL2u2wrMtNlm+DgpnK7undx0syT+i0jCiLBSJCIFBnycVUYHsMWbe+77z727dtHQUEBs2bN6teIbNOmTWM2OAUFBYVDlV5/mB89t51Xd7QP+Pgpiwp44OKViOKhI9xNFOvre7jmsY2EY/ETwHOWFfPLc5dM6mv9jTWzeGJdI7UOH+vrnby0rY2zlhZP2ngmku5AN3ude/FFfeSb8pNuC1mW+dvOv/F6/et9lp+TNYdzy8/FrDXT6e+kw9dBu6+dtW1rickx9vbu5Ucf/ojrll7H6qxyhNoP2NjwFu/6W/hMp0Ya5EJpU+cmNnVuYqHKzNe0xVQuvohowcI+y5g0Jjp8HTiDzgHjGxQUFBQUhoYkSeldlqMoyU3l5E00ORtz8VJmUAFqtCL0sEhzOpMprkUJKaXDMiFsZsp4x4qJdtqGY2Hu23wfESkCwKmzTmVFwYoRbeukmSfxfM3zhKUw7za9y/nzz8ekMQFg1Vnp9HXSHegeMDZgICRZSu9AzxCnrSRLNLgb8Ea8g54Dvl7/Op5I3GV7dMnRlJgz12ULMDtrNjqVjlAslMy1/fyEQsJF3eJpYVbWrMkZ6DRn2KLtOeecMw7DUFBQUFBIsKG+hxuf3EyrKzjoMq/v7OCuN/Zw66kLBl1GYfjsaHFx5aPrCUTiF06nLCrgrvOXoppkcVyrFrntzEqu/NsGAH796m5OXliAXqOa1HGNN+2+dmqcNcjI5Bvz+5woPr3n6T6C7bzseZw358scKZrQu5qRRTcxrREpq5JY3uHU5CzjD7sfoyPiwRfx8fsNv2d5MMROrZawKIC+7yS0WpZZFAqzMBTmHZOBDnX8lGlXzMvtgWr06+6k3FZORf5S5tvnU24rx6A2oFFp6PR39huvgoKCgsLQSdeMSUAYlRNWIrUoPB7OvVTbnKhcTlmWEeQ08QiZ4rSV0jttJVnKmPGOFaPNtB0uj1c9nqxWmmGZwdcrvz7ibVm0Fo4tO5a3Gt4iFAvxTuM7nDX3LCDuyNSqtLR6W8k15A45qiqtfp0h79l0sQiBaKCPy/bc8nMneojDRi2qqbBVsN2xnZ5gDx3+jn6Cu0pUkaXPotHTiFVnxa63T9Jopy/DFm3vuOOO8RiHgoKCwiGPJMk88P4+/vBmNbH9Zfk2o4YTKwsw69SYdCpkGf78/j4kGe5/bx/lBWa+sjzzOo5ORWo6PFz68Do8oSgAx1bkcd/XlmdMDMXx8/M5riKP96u7aOkN8OAHtdx4YvlkD2vccIVc7O3di1pUY9UdlBErRXml6mmer3sxedcPhTy+WrcX3eabEAZxW5QDXxAE7sjL4U1TPN5ps75vY7IiNBynsbHG4+aIrgbM0RAA3+9x8prZyKNZVmq08XK3oCiw3bWX7a69AGhEDdcuvZYjCo/AGXLiDrvJ0h162cMKCgoKY0Uq4SqRZzoSEkKfyMC/70I82HXMhaB0kQ8TUeItyWkczIz8dR1r0jptx6lh3GSTbsJiLFnfvj45Aa4RNdy44sZRxzudPvt03mp4C4DX6l7j9NmnoxLjJoMsXRaOgIOeYE//mKoBkGQp9SQDmfOe7fR3olFpBm209tjOx5Iu22NKjqHYPDUq5hbmLGS7YzsAVd1VA7qkjRoj/qifut46THmmcW38eygybNEWoLe3l2eeeYZ9+/bx/e9/H7vdzqZNmygoKKCkJLMt3goKCgqZgDcUpaHbR5cnFP/nDfFBdRdra3uSyxwx2869Fy2jKMvQZ918i47//e8uAH7wzHZm2E2snDm83CmFvjR2+7n4oXU4/fHSsCNm2fnLJSvRqTPHySoIArefWclH9ziISTIPvLePiw4vI986cDffqUxMitHkaSIcCydLzNTeTkre+gWv9u7msdwD7/cfOXr4uqdxSNu1yDK/73TwtMXMb3NshAUBm6DhmNzDOKL8LObZ5idFgoZYFJ2zAX33XpAllqoN/F6jZ6OvmXVVz7BVFaVNfeA0KiJFeGr3UxxVfBSRWISeYI8i2iocksiyTFgKI8kSBrUh/QoKCgOQthEZIxc5ZVlO2eQqEY8w1pm2qRyUAgIxxj8eIV3sw2jE8DEnzTDGq2HcZCMzMZm2rd5W7t9yf/L2pQsvpdQyeiNIsbmY5fnL2dy5me5gN+va13FU8VFA3JWpElVxR6o+JynmDkaq5n0JMuE9G4lF8Ef86NUDn5N/1PwR7za9C4BOpcv4LNuDqcypTP5d1VPF8TOOH3A5u95Ou7edJncTc7PnKtVmY8iwRdtt27Zx0kknkZWVRX19PVdffTV2u53nn3+ehoYGHnvssfEYp8IE4A9H8YdjhKMSoahEOCqh14jMsBuVD52CwhjR2hvgL+/v48n1TYSjA59gCALccEI5N54wb0CX52VHzaK608sT6xoJxySu/ccGXvjO0ZTalOaQI6Gh28fXHlxLpyfuqlxSksVDl6/CoM0cwTbBvHwLl66eyd8+qScQifGnd/dy59mLJ3tYY04iizbXmAuAEAky46Uf8L6/iZ/n5SSXu97Zy9c9XgAklZZQzhyCufMI2WYBoAp7EUNexLAfQYoQziojZJ/FypzZ3K0z0RPxMDd77sBOHpWaUO5cQrlz+9xdCSwrXMnsp6+iUwqzWa/jryXz2BvpxRFwsLVzK/Ns85SGZAqHBLIs44v48EV8BKNBfFEf/oifUCz+fZpryCXfmE+WLit1928Fhc+RTrQdTcfyhDsz5XtyHEquU41XEIS0IuWYkeaybrIFsARRokNynE47p600/k7bYDTIHzb8gUA0AMBRxUdx8syTx2z7Z8w5g82dmwF4ufZl1hQdaKCbrcumJ9CDM+Qk15CbcjtDzZaesDzoQQjEAoRiIbI12f0ea/W28tD2h5K3v7nkm0PO9M0E5mbNRSNqiEgRqrqrBl1OFETsBjvNnmasOuuQnNQKQ2PYou33vvc9Lr/8cn77299isViS95922ml8/esjzz9RmByaevy8sr2NV7a3sbXZNeAyK2Zk892TKvhCea4i3ioojJCmHj8PvL+Pf29oIhIb/OSjwKrj7guXcdTcwU9iBEHgzi8voq7Lx6e13Ti8Ya76+wae/dZRmHQjKqA4ZKl3+PjaX9fStj8/uKLAzGNXHoFVn7ndT68/YR5Pb2jCH47xxGeNXPWFOZTZp49g74/4aXA3YNAY4nlnskzJ27/gE18jP8rPRd7/O3SBtoizFp9Do7WIkG0m4exSOCgfbaBmCQeTA+QwspPmkH02rSf+hLLXbqPI58fQso8b8+Ni8luNb7E0f6nSkExh2hKRInjDXjxhD46AA2/ESyQWr1JQq9RoRS06tQ5ZlmnxttDma8Omt1FoLCRbn62UTSqkRZbltJmzCIy4EZksx0XblMLYODQ3ShWPMNqM3qGSdBkPwkQ5foeCLKX+HRcFMe6IzhCReSyIStF4I7JxvOaWZZk/b/0zzd5mAEotpVxz2DVjus9FOYuYYZlBo6eRfb37qHZWM98+H4jnpAqCQIOrAZWgwqYfvFpwKE5bURj5BM5YEYgEiEmxfjm94ViYezfdSzAWv844tvRYji09djKGOGI0Kg3ltnJ2de+iK9CFI+AYVGzXq/UEogHqXfWYNWaMmulzfTKZDPvqfv369fzlL3/pd39JSQnt7QN3OVfILLyhKE+ua+Slba2DCrUHs6mxl2888hnL94u3xyrirYLCkHF4Q/z+jWr+vaGJqHTgLNmoVXHq4kJKsw3kWXTJf4tLsoZUkq9Ridx/8QrOuf9jGrr97G738Ps3qvnpWQvTrqsQp84Rd9i2uw8Ito9ftRqbKbOdkblmHd88ZjZ/fGcvkZjMPW/V8PsLlk72sMYEWZZp8jThi/iSYmfuhr/xSeun/Cg/l9j+357jy47nK4ddg/Nzv0WyLOONePGGvfGLuf3CrUbUoFapMaqNQ298kQZ3+Qk4Or5G7uYnOdbnozBmo10lsqljEz3BHjQqDR3+DqUhmcK0IhQLscuxC2fIiYyMXqXHorUM6ig3aoxEYhF6Q704/A4MagNGjRGbzoZRY8SoMaJVaVELauVzopAk4a4bLHMW4k7bkbrrEttP+Z4bj7ejNHhO70SJtulydSfU8ZuGoWS7TifBFqDL34U75CbPmDdu+3il7hXWtq0FwKA28L2V3xu0rH+kCILAGXPO4IGtDyT3mRBtAXIMOXQHutnu2E6ZpYxSc+mAWbAyaRrn7WekEzhjhT/qH/A745+7/kmDuwGIx0ZcufjKCR7Z2FBpr2RXdzyar6q7ii+UfmHQZbN12bT72mn2NlNhq5ioIU5rhn3lotfrcbvd/e7fs2cPeXnj9+WiMHpkWebVHe387L+7kiLFwSwotFBqM6BTq9CqRbQqkc1NTqo74qWnmxt7ueyRz1g108b9l6wg3zL9chQVFMaKSEzi75/Uc+9bNcnGVgBmnZrLjprJN4+Zg32U4qDNpOXhy1Zx5h8/IhiReOzTei5ZPYM5eebRDn/as6/L2ycSYUGhhX9edSS55qnhALvqC3N47NMGXIEIz29u5rrj5lBeYEm/YobTHeymzduGzWBDEAQs+95n7Y4n+GleDtL+C90vlHyBqw+7us+F78FirUljosJWgVlrJhwLJ0u2vWEvXf4usvXZQ87ZjEmxZDanXqXvl73WcdS3MHTuwdSyia+63fzJlo2MzDuN73DOvHNwBp04Q06lky5x95CAkDa/TiFzkWWZRncjjoCDAlPBkI+lRqUh15CLJEsEo0E8EQ89gR5kZDQqDVpRi1pUo1VpMagN6FV6TFoTFq0FjZi5VQ8K40e6zFmIPzZSJ2wiHiGleCkLE9uITIg7S8ebhMt4MDLKaZvuGO1vGDddhNtEpZFRM3YTzJ9nV/cuHq96PHn7O8u+M24NsY4qPoondz9Jb6iXz9o+o8PXkZyQFwWRPGMe/oifvc69uEIuZlpn9nPdysjIQurPxWiiUsYKV8jVb/Jybeta3mh4A4g3efvuiu+OuTg+UVTmVEJN/O+qntSirSAIZOmy6PJ3UWIuwaQxTdAopy/D/jY4++yz+dnPfsbTTz8NxA9KY2MjP/zhD/nqV6dOoPKhRmO3n5++uIP39nT1uX9hkZUzDivijCVFzMrt/4GSpLjQe9/bNezpiHc73NDg5IfPbufhy1YprggFhQH4sKaLO/+7i72d3uR9Fp2aK46ZzZVHzyLbOHZOznn5Fq45di73vV1DVJL55StVPHTZ4WO2/enI9mYXV/59PV0HCbaPX3UkOVNEsAXIMmi47ri5/Oa13Ugy/OHNah64ZOVkD2tUhGNhGlwNiKKITqVD59jLJ5/8ll8clGF74owT+eaSb/bJIQxGgziDTswaMxW2CvKMeQOKshEpQpOniQZXA6FoiGx9dr9lgtEgvogvWeqtElVoRA0qQUV3uJuYHEOj0mBQGzCoDYiimqZTf8bcp67gXE8Pf87OIiYIvNv4LueWn4uMTIOrAbPGfEhn20akCLscuwjGguhUOkwaEwa1AZ1KR7Y+e9jCXCgWIhgNJkuoExfsBrUBo1rJ4R8vugJdNHuasRvsgwq2sizjDrtxhVzkG/P7XKCKgph01yYIx8JEpSgROUIgHKAn2ENMiiWXzTPmka3Lxqq1jpuIoZB5DCVzVkAYVTxC2mWEsS+7TxePMBG5nOmEboHJb+qUQJIHb9wG+13BoxDvMwlZlmn2NvepNBprqrqruHvj3cnje868c1hVuGpc9gXxCbtTZp3Cv/b8CxmZ5/c+z3VLr+uzjFFjRK/W4wg4cDvcVNgq+uS9SrKUMs4DGFVUylgQjoXxR/x9on96g708uO3B5O3LF13ODOuMyRjemFBuK0clqIjJsZS5tgmMGiOukAuH34EpSxFtR8uwz37uuusuTj/9dPLz8wkEAhx33HG0t7ezZs0afvGLX4zHGBVGiC8UZXuLiw9runjowzpCBzU9On5+Hj85o5J5+amdWaIocMZhRZy2uJDXdrbz0//sxOEN8c7uTl7f2cGpi6dOiLaCwnhS5/Dxxs52Xt/ZzqbG3uT9ggAXrCzj+6fOHzcX53XHzeFf6xvpcId4q6qTj2ocHFOeOtj/UCQak3jgvX3cu1/ghvjE1T+vOnLUrufJ4PKjZvHIx3V0eUK8uqOdbc29HFaaPdnDGjHtvnacoXgGrMbVwkdv/ZDf2w78Rp0661QuW3RZnws4SZZwBp3MtM6kzFKW0sGgETXMts7GpDZR66qlw9eRLD/0hr34Ij50Kh12vZ0sbRY6tQ6dSoderUdExB/144v4cIacuENuOn2dqEQVVp2VptN/waxnv83x/gBvmYw4Q042dWxiVeEqOnwdNHubmZM1Z/xevAynO9CNI+DArDXjjXhxhpzJC7EsXRalllJyDblDEuV8ER+7u3fjDrvjjjEhXjopI6NVabFoLeQacrFoLZg1ZsXZO0b4I37qeuvQqDTJz5k/4me7YzvburbR6m2lO9hNT7CHqBSvLjGqjXyl/CucOuvUActeAbQq7YATGjEphi/qo763HkEQsOqszMueR5Yua/yepELGMJTMWUEYeZyAJEvpReH93ytjiSQNntMrMPb7G5AhOJgzSrRNE48gy0NrVJXpOENO2rxtZOuzx3ziUZZlXqp9iSd3P5k8tktyl3DB/AvGdD8D8aVZX+Kl2pfwRXy83/Q+X5775X7OXlEQyTfm0xvqpcHdgE1vOyCApnu/MrqolLEgEA0QlsKYtQcqHf9d/e94ZAKwpngNJ8w4YbKGNyboVDrmZs+l2llNm68NZ9CZMosYwKw10+Zro8BUMGUdxpnCsEVbq9XKRx99xDvvvMOmTZuQJIkVK1Zw0kknjcf4FIZBS2+Ad3d3srWpl23NLmo6PXy+yqYoS88dZy3klEWFw/pBEEWB05cUIcvwnSc2AXDnf3dyTHkuZqXxkcIhSr3Dx783NvHGzg5qDnLVJlg+I5v/PWsRS8uyx3UcRq2aW09ZwM3/3grAz1/axcs3HoNapXTqTlDv8HHT01vYfJCgvrQsm79dfnjGZ9gOhkGr4sYT5nH7f3YCcNcb1Tx25RGTPKqR0xPsQa/WY+iupeOVm/m9/YBb9uzZZ3DRwkv6/W45Ag5y9DnMtM4ckpNVEAQKTAUYNUb29e6jzduGSlBh0VqosFVg09swa8wD/j5mqbLI0mVRbC4mHAvjCrno9HfSHezGac5FOOo6zt/wEG+Z4i7Ct/e+yBFFR5Ctz6bJ3USWNoscQ06/7U53olKUVm8rWrW2zwUNxC/Ie0O97HTsxK63U2IpIUefM6jQ6ov42NOzB1fIRa4xF1EQ+xyrUCyEJxJvjqURNRg1RqxaK2atGb1KHy+9V+tTd4xX6EdMilHnqsMb9aIRNbyw9wW2dG6h2lmdUtzxR/08XvU4bzW8xcWVF3N44eFDPvdUiSqsWitWrZWYFKM72M2u7l2U28rTdhtXmPoMJXM24QhNm007COmEyXQl2SMhpQg5QY7RdOKWiJgRou2QmtEllp3iTtuoFKXR3RjPCR9jccsX8fHAlgfY0LEhed+inEX8z4r/mZDfQpPGxJlzzky6bZ+tfpYbVtww4LJZ2izafe10+jops5YB6d+vwKS7rQPReBOyxLlLs6eZdxrfAeIVQFcsumJaVAAtzFlItbMaiGcUX1x5ccrlzRoz7d52uoPdlJhLJmKI05YRq20nnHACJ5wwtWcMphubG53c9sKOAR9TiQJXHDWL755cMSqR9fQlhRxXkcf71V20uYLc82Y1t52pND5SOLTY1OjkwfdreX1XOwNN7pfnm7n2uLmcu7wEUZyYH+mvLC/h75/Ws63ZxZ4OD//a0MTFR86ckH1nMrIs88RnjfzfS1UEIvETP1GA7xw/jxtOKEerntrizYWHz+AvH9TS7AzwQXUXa2u7WT1n6gmDidKynM4aCl+/netzDzhsvzrzVM4bQLD1hr2oBTWzs2YPO3rAorWwMGchOfoc9Gr9sEv0tSotecY8cg25eCIeugPdtFcamN2+k7Lgbpo0Gra69tLp3Ee+bS6+iC/eSVdr7lM+dyjQHejGGXQO2FRFFETsejsxKUZvqJcdjh3kGHIoMZdg19v7XFD6I3729OyhN9hLnilvwItNnUqXfH0jsQiBaIA2XxsxTwwE0IrxzNQ8Qx5ZuiwsWkvyIkuWZQLRAL6ID0/Yg06tI1uXrWSxAW2+Ntp97UiyxA8/+iGeiGfA5Swy5KlN2EyFCIZsNnZsQkamw9/BHzb+gUp7JauLV2PX27HpbNj0NrJ12Wnd0CpRRb4xn55AD7t7djMve16f8lmF6ceQMmf3P5ZuucG2f/A2BllozB2cqcY6mriH4SAhpRbrMiRuYCjN6GC/Q3mKO207/B10B7rTNh/zRXxs7NjIurZ17HDswKQxsSx/Gcvzl7M4d3EyGioqRWnzttHgaeDpPU/T6e9MbuPc8nM5r+K8CZ28PG32abxS9wqesIdPWj/hnHnnJEXZgxEEAbPWTLO3mVxjLga1If37lYn77AyGP+rvc476RNUTyc/Ql+d+GavOOllDG1OOLzuel2tfJiJFeKX2FY4tPZYyS//jmEAQBAxaA63eVvKN+UpG/SgYknp33333DXmDN95444gHozA6lh5UFqsWBeYXWlhals2y0mzWzM2hzG4cfOUhIggCPz97MSff/T6hqMSjn9TzlRUlLCpWytUUpjeSJPNWVQcPflDLhgZnn8cEAVbOsPGlRQWcvLCQ2QPkQ483oijw0zMXct6fPwXgD29Uc9bSYqz6Q/cHsrbLy4+f387a2p7kfbNyjPz+gmWsnJm6pGeqoFWL3HRSRdJlfe9bNay+ZuqJtoFoAFP9J8x/97f8zmqgRRM/PanMLueri7/RT7CNSlG8YS8VtooBs2mHglalHfCiYTgIgpB0AxrVRnatuYZz3voxf9TEJwjWfvwbvnzGn7Hr7XT4OmhyNzE3e+60cFwMhaTLVqVNKcypRBU5hhyiUhRn0ElPoId8Yz4l5hKydFkEogH29OzBGXSSb8of0sWmRqXpU5IvyzIRKS7k7nXtRSWoMGvM5BnyUIkqHAEHvoiPYDQYL7uWJfRqPXa9nTxDPFd1sBL/6Ywr5KLB3YBJa+Lhjff1EWxnRiIc4w9ydCDAimAIU1I4qSKqz2aHvZh7NCE2ivGM6KqeKqp6+mbhCcTdVXZDDja9DZvORqGpkNXFq/s5au0GO66Qiz09e4jEIpRaSg+Zz9KhxlCcnoIgIEvx3NnhClBDcegKjDx+IdV+U8UjTEQzpXTNziZbAEswpGZ0xB3RmSAyjxR/xE+juxGj1jjg72RMivFp26d83PIx27q29XGehmIh3ml8h3ca30ElqJhnm4cv7ItPVn7OoWrWmPnO8u+wPH/5uD+nz6NX6zl77tn8s+qfyMj8u/rffG/V9wZc1qwx0+5rp8PXwaysWUN6L44mKmUscAVdyQnjnY6dbOqMVyXb9XZOn3P6pI1rrCkwFXD2vLN5pvoZYnKMR7Y/wk/X/DTl96hVa6XT10lPoGfcspoPBYYk2t599919bnd1deH3+8nOzgagt7cXo9FIfn6+ItpOIqU2Az8/exELi7NYVGxFrxmfHLcZOUZuPLGc372+h5gk85Pnd/Dct46aMEehgsJEEorGeGFzC3/5oJbaLl+fx/IsOi4/ahYXrCojzzL57rlVs+yceVgRL21ro9sX5k/v7OVHp1dO9rAmnHBU4sEP9nHfO3sJH5TlffGRM/jx6ZWYplmkyznLS7jvnRoauv2sq+vGHYxMObFe2vkCS976Jdu0Kh63xl22GlHDNcu+PeDFeLe/mwJTAUXmooke6qDY9XaMxhwqjr4ZzYbfEBEEXo05+fq2Z/AuvQCb3kaTp4lsffYhU97dE+zBGXIO+fmqRTV5xjzCsTCd/k4cAQeFpkIC0XiDqqEKtgMhCEIyPzVLl0VUiuKL+Njr2gsyaNVajGojeQEPWncLvrJVBKUoHf4O2rxtWHQWyrPL02a4TTfavW0Ymjfi3P0f1hJ3a9ljMR5r7WBmNJ5d68+vBKsAHbuS66mDvSxr7eVR4F2jgd/bs2nU9P9ekoHesIvesAtcB+5/vOpxDss7jOPLjmdV4apk3nGWLgtv2EtNbw1ROcos6yxFuJ2GJDJt0zUiixEbkWA3FCcvwjg4beUUTltBGFIsxGiJEUvrYJ5MASyBRPrcYQDkzMngHQntvnb8ET+F5r7VA7Iss7lzM0/sfoJmT3O/9bK0WfijfiJSfFIsJsfY07NnwH3My57Hd1d+d1LPPU6edTIv1b5Eb6iXz9o/o85Vx+ys2f2WEwQBi9ZCi7eFfGN+2vcrjD4qZTSEYiH8UT86tQ5Jlni86vHkYxfMv2DaVVd9ee6X+bD5Qzr8HVT1VPFhy4ccW3rsoMuLgohGpaHN10auIVfpMzBChnTlWldXl/z7iSee4P777+fhhx9m/vz5AOzZs4err76aa6+9dnxGqTAkBEHg0jWzJmRfV39hDs9vbmFvp5ctTb08ub5RKcVWmFa4gxGeWNfIIx/V0ekJ9XlsXr6Za74wh7OXF6NTZ9aPzw9PW8AbuzoIRyUe+biOUxcXsnzGoSEyyLLMZ3U9/PQ/O9nTccANVmoz8MuvLOHYitRlZ1MVlSjwxYo8/v5pA5IM6+t6OLFyCs1mSzGsb/yUCDF+mpuPvP+E+4L5FwwoyrpCLgwaA7OsszKqm7xGpaHIVER12MNR2fN531WNU6Vi7fZ/sLTiZPQGG/6In9reWgxqw7Qvu49JMVq9rahF9bBP0rUqLfmmfILRIC2eFoBRCbYDoRbVZOniOcVCLIKl9kNsO17A3BzP/fPMOJKmM3+D3piPJEv0BHuodlZTmVOJVTs9Sh3TIdW+T+kr30Pr2Mt5JUWgjYuu3+31YitbTcvsY/DOOoqoKS4EqPw9WBo+xVz3Meam9ajCPgTgBH+AL/gDbNLraFWr6VKp6FSr6FSp6Nr/v0OlQjroYltGZmvXVrZ2bcWitXB82fGcW34uerUeszbeYK7OVYdaUCuO22nKUATTpBtzBNtOJ/AkhOOxREIadJ+JMv+RxD0Mh7QO4wkSj9MiDzH6Ygp/9GVZxhl0YtAY+ty/r3cfj1c9zq7uXX3ut+vtHFl0JEcWHUmFrYKIFGGnYydbOrewuXMzXYEu1KKaYlMxpZZSyixlzLTOZGne0kkXy3QqHefMO4e/7fwbAE/veZofHPGDAZc1a820eeOxPEikfR8K8UwPYnIMtTCx54WBSIBwLIxVZ+WT1k+oddUCMMMyI6WYOVXRqrRcsfgKfv3ZrwF4fNfjrMhf0a9nwcFk6bLoCQxvEl+hL8N+V99+++0888wzScEWYP78+dx9992cd955XHxx6kBihemBVi3yf+cs5qIH1wLwm1d3c+KCAgqzlM6AClObaEzin2sb+MOb1biD0T6PHTHbzrXHzuH4+fkZ6ywvtRm5+guz+dO7+4jEZC575DOeuHo1i0umb4SJPxzlP1ta+cenDexqcyfvFwW46gtz+O5J5Ri1mSPujQdr5uby908bAPh0X/eUEm3lzl2ogr38yZZF3X5RaG7WXE6f3b+kLBANEIwGWZizMOUJ4mRh19vRqXQcveA83l/3SwD+YDXw14/vQ3XSHdj0Njp9nVT3VLMgZ0Eyf2460hPsSXYXfrn2ZepcdawqWNXHNZkOvVqP3pz+vCIqRQlGg5g0pmGJDCp/DzlbnyZ710to/D19HrM0rqP0tTtoOu3niKKaHH0OXf4uanpqqMypxKgZfeRURiPFEP59GaaAk8esFmr3fzYXaLKZf8GfaTJk91slZrTTW3kGvZVnHNiGFI3/i0XJjoWxh32oQp7kP1PzJrJ2v4YsRehWqehQqfjIqOc5Wx7txB1knrCHF/e9yGdtn/Ht5d+mwlYRzzqUJfa59qFRaZSM22nGUJywoiAiI4/IFTokcVRgRILwqPa5f7nxFCJTNkNj4sTjdMjEoy/SfqdPYadtKBYiGAsmm49JssQjOx7hrYa3+ixXnl3ORQsuojKnss/kpU6lY0XBClYUrECWZTwRDya1adIF2sE4ccaJ/Hfff+kOdrO5czM1zhrKbeUDLpuly6LN1xZvDpvmfahT6XCFXPgiPrJ0E3u9E4wFickxolKUp3Y/lbz/4sqLp23T02X5yziy6EjWta3DFXbx9J6nuXLJlYMurxbVIECHr4McfY4yyToChn0V29bWRiQS6Xd/LBajo6NjTAalMDVYPSeHr64o5dlNzbiDUW58cjNPXH2k0rFeYcqyrrabO17cye72Ay5NQYBTFhZyzXFzWDFFHKs3nFDO5sZePtnXjTsY5RuPfMZT16ymosCSfuUpRJsrwF/er+XZjc14Qn0F9kXFVn7z1cOmtVh9MKvn2BEEkGX4ZF/3ZA9nWESb1lKj1fBIVty9qBJUXLf0un4XHd6wF3/Ez+ys2eQb8ydjqGkxa83kGnKJylGOL1zNu+1r8Ysiv3Bt5c62HcSKFpNnyqPT10mNs4YF9gXDbqI2FUi4bEVRZEf3Dv6x6x8AfNTyETadjRNnnsiJM04cctSAEAmi9bShcbWicbWwz11PTdhJTdRDdcxLrRwkCqgRsWrNZOlzyNZnU2AsoNxWTrmtnDxD3oELBVkma8/rFH54D+qgu8++glkluAI92MMBrLXvU/LWL2k5+TYEQSTXmHvg2OUsmHZlj33o3osQcOIQRe63ZQNxMefSI7+PPIBgG5WihGPh+D8pnCxV1al1WLVW1NqEs7xvxYNr/il0rr4G+7Z/Y9/+PPkhD0vCYa7tdbPWZOFfZZV8GHEQkaK0+9u54+M7OHve2ZxXcR4mjYmYHKPGWYNaVCsOnmnEcEqdRxJhIMdtnGm3P6TO9cPYZ9p4BGn881klUou2wIEmbJOorSSE2LSl8fudwVORQDRAKBZKNqr6qOWjPoJtobGQr1V+jSMKj0jvNt2fsZ/JaFQazi0/l79u/ysAj+18jJtW3oTdYO+3rFFjxB1yo1fr0z53jUpDVIriDrknXLT1hr2oRBVv1L+BI+AAYEnuEpbmL53QcUw031j4DbZ0biEUC/Fmw5scV3Ycc7PnDrp8ti4bR8CBK+RK2YtCkqV4n4tpXo02XIYt2p544olcffXVPPzww6xcuRJBENiwYQPXXnstJ5100niMUSGDue2MSj7d56DVFeSz+h5+/2Y1Pzh1wWQPS0FhWHS4g/zylSr+s6W1z/3nLi/hOyfMY25e5jn6UqHXqPjrN1Zx2SOfsaHBSY8vzMUPrePpa9dMSpO08UCWZS5+aF2/nOGlZdlcunom5ywrPqQmkLKNWhYWWdnZ6qaq3Y3TF8ZmmhpioNT0Gb/IsRPbf1J+bvm5/RqE9YZ6icaiVNgrKDYVZ/QsfZ4xjzZfG5ccdjU1jh00R73s0ml5fu1v+PI5f0cURPKMeXT6O1ELasrt5dOuo64z5KQn2INNb+Pu6rv7PfZM9TM8X/M8RxYdyQXzLxjYJSnL2Lf9m5xNT6L1xk0BYeD6wjw+NQzsUI4i0RN20xN2w34t9rX61wCw6WyU28o5xr6Ir+58E2v9Jwd2JapwzzmWjXOO4rdt79DiVaGRZWaHI8zrWU/Jm7cw8/BvMc9WTr4pnw5fB2qnmgp7xbQ7dklaNwNwrz0b3/7Kki+WfbHPRVk4Fk42b9OIGrQqLSaNiUJNIUaNERmZdl87PYEeZGSsOuuA7vKoKYfONdfhWPkNbDtfJHf9o6hDHo7yeThq92fszSrkR8Wl7A52IiPzwt4X2NK5he8s/w5lljKcQSfVPdWoc9R9LgZlWSYshdGImmnreJquJES4lI7Q/WLdSFyWMjKyMISGXGOY7ZqusdZoMnqHgyQN3gwNSDZilJBQMXmOzaTbOs3vfSLqYioSjAaTjfTCsTD/2v2v5GOXVF7CqbNPzagYqLHguLLj+M++/9Dp76Smt4bvvvtdzph7Bl+e++V+vw9Z+iycQWfSiZwKvVpPV6CLEkvJhH3fy7KMK+RCLap5cd+LQPxzfMnCSyZk/5NJjiGH8yvOTzaX++u2v3LHUXcMWkGmVWmJyTHa/e3xWKpBPtdd/i7afe0syVui/G4fxLBfiUceeYSSkhKOOOII9Ho9Op2OI488kqKiIh566KHxGKNCBmMzafnj11eg3n9C/8B7+3h3d+ckj0pBYei8u6eTk//wfh/BdnGJlWe/dRR/uHDZlBNsE5h0ah654nAOK43POHd5Qlz817U09fgneWRjQ5srmBRs9RqRC1eV8d/rj+E/3zma81aWHlKCbYI1c3KAuNt2Xd3Ucdu62jaxTR93LBabijh73tnJx2RZjjsXZKjMqaTEXJLRgi3E3QRZ2iwiUoTvHPFDNPuvJZ/QRKjZGHeXqEQVecY8Wrwt1PbWEpPGzs2VCXQHukGAnd072de7D4BiUzGHFx6eFGFicoxPWj/h5vdu5h+7/oEvcmACRgx5KHvlxxR9cE9SsAW4257dT7AVZJk54QhrAgEWhMLkRmOoBriAd4acfNb+GX/Y9SjfC+6lQR2/EO6tOJk933iWRyqP45Z9T9HijWfoRgSBap2WV8wm/hpu5baPb+f1uteTonubr43a3lqiUrTfvqYFrZvZptPygiX+G2hUG7lowUVAfBKlzduGJ+zBrDGzwL6AZfnLWFWwimX5y5hrm0uRuYhiczFL85ayNH8pxeZiQtEQHd4O2r3tdAe68YQ9hGKhpOgmaY10L7+Ivd/4N91Lz0cW4oLRPFc7T1Zt4AprJar999W767nj4zvo9Hdi09sIS2GqndW0+9ppcjdR1V3Fxo6NbGjfwA7HDhwBx5QtoT4UkeX0pfGJMv6Rbj+dNppwvo4VQxEhR5rROxyGFI/A+I8jHcnc4SHYfafqZ9sb8Saril6te5XuYPzcbXn+cs6ce+a0E2whXip/3dLrMKrjEUNhKczzNc/z3Xe+yxv1b/Q5HzKoDRjUhiHFEZk0JjxhD56wJ+2yY0Ui3sIVdOEOx2eKl+UvY6b10Ojzc+rsUymzxE0W9e56fv3ZrwlEA4Mun63PptPfmXytPk9EitDibSEYDU7ZiZjxYtjfBHl5ebzyyivU1NRQVVWFLMtUVlZSUVExHuNTmAKsnGnjh6ct4P9ergLgpqe38PKNX6Ake/pm9SlMfSRJ5v739vL7N6tJ/C5kGzXcesoCLjy8DFWGZtYOB6tew2NXHsFFD65ld7uHVleQSx9ex3+uP4Ysw9R2h21rPtBm/Kpj5nDLKfNTLH1ocNS8HB76KN449NN93Zy6uH8Tr4wj6GavpxnM8biDpfnL+lykdPm7MKqNlNvLsev7l89lImpRTaG5kKruKmbZ5nJZyfE81PouAH9oeZvfVJyB1VoaL+c25tLkaUIURApMBVg0lowXpdMhyzKesAedqOOZ6meS91+44EKOLDoSR8DB2w1v83bj27jDbmJyjJdrX+aDpg84b/55nGkoY9ZrP0XrPjCR5i9cxDsWK/+MxgVVtaDiGyXHU26ZyUxrGTqtGY2vG2PbNgxt29G3bccd9rBHp2WrTscWvZbtOh1eMT6Zs86g59zSIi7IP5Jjl13FIzseYW3b2uT+ikxFqAQVbd4WYgcpF//c+Xfm2+czK2sWOYYcmjxNCILA3Ky5GZshOFLk1s3cZc9O3j6v4jyydFnxiy0ZFtgXkKXLwqQxpXTDqEQVdr0du96OL+JLOnM9EU8y8iQSixxwNO4vWe8+8grslWdS+umfsTR8ihr43tbXWXn0tfzas51mTzP+qJ8HtjzA7WtuJ9eQiyPgYJcj3rhHpVKhFbVoVVqcISfdgW7sBjsl5hLservi4MlwhtIELCEujsSZOhQHZ8L5Olaky+lNNgAbZ7U03XNPOG0nO3JgKLnGADq1ji5/F8Xm4iklcsqyjDvkRqfS4Q65eWHvC0D8fff1yq9P7uDGmYU5C7n3hHt5rua5uFArx3CFXTyy4xF29+zmxhU3JpdNVUp/MJMRkRCIBghFQzR5mpL3VdgOHU1MLaq5fvn1/OzTn+GL+NjTs4dff/ZrfnjEDwd03OpUOnqlXtp8bVi11n7fQ92BbhxBB1maQyPabjiM+JutvLyc8vKBg6MVDj2+ecxs1tX18OauDnr9Ea5/YhP/umYNWrVyUqyQeXiCEW5+eitv7Drg4PrSwgJ+89XDpkxJ+VDJNmr551VHcsFfPqW2y0d9t5/v/3srf7l05ZQWh7a39Cb/XlKq/LgDHD7LjkoUiEnylMm1lVs3UaU7MIEwO2t28m93OH4xsyBnwYRnlI0Wu96OQW0gEA1w4vJr2NW6jk/w41SJ/OXjn3PzqQ8gCiJalRa7wU6jp5E2Xxs5+hwKTAXYdLYpKwKGpTChWIhqZzV7e/cCUGYp4/DCwwHINeRy4YIL+fK8L/Pi3hd5qfYlIlIET8TDozse5aNwhF8FHMwHojoLLSf/lPrC+fzigx8m93Hpom/wpVmnJG9HgEhWKf7i/RlysozW2cCc5o0safyMa1s2QbiLDw0GfpVjo1WjJiwI/LPrM554a0Mfl9aps07l4sqLkxeAvs3/4I3qZ3nWYiaCxJ/W/or/O+k+dCodOYYcGt2NiIjMzpo94DELxUKIiGhUU2iiLBbF1bGdzSXxjNgiUxFfmvUlIrEIvrCPhTkLKTYXD3uzJo2pT05dTIoRioWISBGiUjTZzCUcC9Md6KbTZKPlhFso3/YcMzc+DsCxHz9I4cm3cV30ZRwBB1U9Vbxa9ypnzDmDPGPegPs1aoxEpSi9oV56Aj3kGHKYlz1v+jeTm8IknJ6pxHVBEJKO3GFvf6hO27F0eyVyYgfb335xctzjEYbgtJ2IcQyGJEvxiZ2wZ0i5xlatFUfAQW+od0rlWgdjwWQTsqd2P5V0KB5fdnzSvTidsWgtXLboMk6ZdQpP7X4qOXH6SesnHFd2HEvzhp8Jq1fr6fR3TlhEQjAaRBZk6tx1yfvmZM0Z9/1mEjOtM7lt9W3839r/G5Jwm6XLotPfSZGpqM+5fSQWodnTrDhsB0FR1BTGBEEQuOu8pZTa4h/OzY29/Oa13ZM8KgWF/tR2eTnnTx8nBVtBgFu+VMGfL1k57QTbBLlmHY9deUTSXfvGrg4e/qguzVqZzcFO2yWHSLOxdFj0muRrUdPppcsTmuQRpSfauJbd2gOfu1nWWUBczPGH/cywzphygi3ES/ryjfl4Qh4EQeCqNT8mLxZ3bG2Mufjw5e9gaNsOxJ0HhaZCTBoTnYFOtndtZ2vXVpxB52Q+hRET2/MK5W/cyWubHkje9/Wc5RgdNZga15G1+zVyNj/JrHWPcHPdDp71iJzmDyaXrdFq+FpJIQ8Vz2PvhY/gmrmaP276I96IF4DDCw/nSzO/lHoQgkDYPouew75K05m/YffVr9Jw3l9YsOKb3LPiFs6ac1bygi4h+Jg0Jr638ntcvvjypMCqFtVkrbyCb849l8pQGICmiIsn190FkBTd69311Lvq+4hH4ViYJncTmzs2U+uqHeWrOsE4qtktHnAYLs1bikpQ0R3spsRcQoGxYEx2oxJVGDVGsnRZ5BhyyDfmU2wuZlbWLJYXLGd5wXIW2BfgPOJqmhbHY1MEZCre/iXfKzg2uZ2ndj9Fi3Mvuq4ajM2bsNR+QNbuV7FvfQbb9ucQw75kozK7wU6Xv4u9vXuJxPo3VlbIDGTSN8ESBZGIFKHD10EoNrzfu6Fk2sLYlt1LSEPOaB1P0om2By83UUiyRJu3jT3de9jQsYFNnZvY1b2LqBxNK76pRBWCINDh65hSgk8wGiQUDdEd7ObNhjeB+PnA+fPPn+SRTSyFpkK+u/K7XHvYtcn7/rHrHyOKjTJpTHgj3gmLSPBEPIiCmIyBApiTfWiJthA3XNy2+rbkpGxCuB0oKkGv1hOTYrT52vp8XrsCXfSGeqfkOf9EMHVqCBQyniyjhvsvXsF5D3xKOCbx8Ed1zM0z8/UjZ0z20BQUgPiJ8Lcf38S+/VmoVr2ae7+2nOPnZ2Yn+rGk1Gbk7guXcuXfNgDw61d3s3xGNitnTo2S84ORZZntLXHRNtespSgrfYOCQ4U1c3PY0tQLwNrabs5aOnw33ITSvIEqXVy01YpqSswlAHQHu8kx5AzcoGqKkGeIZ9aGY2GMOfO4NXc1t/Z8hiwI/FXu4Zj/3sBc+wK6V3wdz+xj0Kv16NV6olIUR8CByqMiW5c9tRzxsQj6/9zIFjHMrsL49+q8cJivv/v/BnUJZAG/BS7Rabkzx061TktEELhXF+btqkcpNhWzx7kHiLt0rz3s2uG/JqKaQNESAkVLALiY1RxTegwPbXuImt4ayrPLuWHFDeQbB/4tcB9+BT/2tnKNdxsBUeS1nu0s2/0cyxaci06lw6a14q56AZezCUv5aThKltLkacIVdKFRa3AEHJRFyqaOs7N1c/JzCTAraxaukAuz2sxM68wJcYGLgohVa8WqtVJiLqHrtN/RHglSuOd1BCnGGR/cz6byFTwTaiUiRXjonVt5vLWdgfzMtl0vUX/OvUg6C2pRnWwEaFAbmJs9V4lKyECG0lxKFERsehv17npcIRezs2cPOUZHkqW0TlsRcewbkTF4czVREEfcWG04SKRuRJYYx0QJoLIs0+BuYF/vPjSqeENDo8Y4rN+/bF023cFu3OGJK40fLcFoEAmJf+3+FzE5LlCeOedMbHrbJI9scjiu7DjeanyLfb37aPY0807TO5w88+RhbWMiIxIkWcIdcqMRNNS76gHIN+Zj0VrGdb+ZSkK4Pdhxe//m+7n58Jv7LWvVWfu4bcOxMM2eZgwaw5AmlA5FlLMUhTHlsNJsbj9rYfL2bS9s5+2qjhRrKChMHG2uILvb47OvM+xGXrz+mENCsE1wwoICvvXFeOfvqCRz/ROb6fGFJ3lUw6fZGaDXH3dILSkZvAPpochRc3OSf2d8RIIs42/bTIMmLrOUWWagElUEo0FERGZaZ06pfLrPY9VZyTPk0RPoQZZlyo66iYss8ezlqCBwa14uUvt2Zrz8Q2a+8F2IxRtaqUU1dr0dV8iFJzJxDTXGhK7dCCE392cfuFi6tted8mRTFlSEskqYXbCc/5dzFF/JXZV8bIdjB280vAHEhYQbV9yIWTs2zSFnWmfys6N/xp9O/BM/O/pngwq2AAgCmi/+iBtVB5Z5oPopwrtepOjd37L07+ez/I2fYVv/KKonLqTrsz8TjoUpMBeQa8glEA3Em7NNFdq2UHWQA77EXEI4FmZW1qxJEZ4FQSDfVID27D/RPec4AMRYmB/sWceccPy3oEqn5cHsgS/SDZ27mfmf7yGG4xO2KlGF3WCnydNEi6dlYp6EwrAYamm+Xq2nwFSAN+ple9d26lx1Q3dQpzt1EBjzRmRA+kZk48hQm3tNpMu22dNMvauebH02uYZcrForOpVuWOd2WpWWSCyCw+8Yx5GOLd6IlwZ3A5+1fwbEheez5p41yaOaPERB5BsLv5G8/fSep/s0KB0qiYiE8X4PB6NBQrEQjqCDsBS/ljrUohE+z+cdt+s71lPjrOm33Ofdth2+DtwhN1atdaKHPGVQRFuFMefS1TO5+gvxXEJJhuuf2MzW/c4vBYXJZEfLgZL6Mw8rYlauKcXS05ObT67giNlxJ0qbK8h3/7UFaQwvSiaCPtEIpdmTN5AMZNVMOxpV/EJnbW2Gi0SuJvZGD4iSs7PmIMsyzqCTEnPJlHebiILIrKxZmDQmXGEXCCJnHPtTKrLnAdCiUfPzXDsyYG7eQO6WJ5PrJi5Ae4O9kzP4kdK6mU/1erbqdQDM0NlYsuIaHMu/hmPhmbStvISWY/6HplPupO4rf6Tmkqeo+tY77P3Gv2k8+256vngLF66+hdtW39bPMXfh/AuTDT5CsRCukAtnMN5gqtPfSYevg+5AN96wl3BsaJNRgiCQY8gZmjggqlj+pbs4PhafZHCJIn/c+QhZO15AfdBxEpBZ/OF9lLbtTDo4jRojbb62KVOOL7duToq2GlGNQWWg2FycWtieAOzGPDTnP4JrxpEA6GWZX3Y5UO8Xuv5qy+LBhV/kqWVf5rnDL+LF1ZexxWonDBg7djLzxQPCrU6lw6QxUeeuwxGYOkLPocJQskwTiIJIriEXo8bIPuc+dnXvSiv2DCnTFiHpgBwLEs3VBm1ENorGakMlIWRlSqZtu6+dWlctRo1xwPzL4WDRWWj3t6fsXp8pJJqQvVT7UvK+8yrOQ68+tCvH5tvns6Z4DQCesIfna54f9jYmKiIhGIuLtgc3IZubPXdc9zkVmJ01m4srL07ePrgh7cEk3LZdgS5ava2YtKmbmh7qDPuVmTVrFj/72c9obGwcj/EoTBN+dFolZxwW71weiMT45t/X09jtn+RRKRzq7Gh1J/9efIjmoKpVIv/va8vJNccvyD+o7uKB9/elWSuz2HZQE7LDDtHjOBgGrYrlZXGxs87ho82VuRcv8kHRCHCgBNuqtVJqKZ3EkY0dJo2JWVmzCEfDhGPheKfdFTdiVMfdiq+aTbxgiTtH8z57BI3rgOvPoDHQ4e8gKkUnZewjonULD2YfcEqcs+hSepddQPOa69hy5BXUrvg6NfNPoLp4CbXZJTRpdXhioX7ussW5i/ntsb/lqOKjUAkqji09NulA8kf8uIIudKIOi8ZCniGPUnMps7NmY9fZQY5f7LV522j3tdPl78IVcuGP+If8Wg7qdtPoufTE31Gw38Cz3qDn5zl2Ymodrnkn4Jp3IgCCFKPs1Z9gbN4ExBuuuMNunKEpkFMci+Lr2EGDJu5yLzaXkGPIYaZ1ZkZUNVgNuWi+/m8aTr6DrSfcivrCf/KV/RmQMeCPgVp+4drCHY5P+EnHu1yaY+bomWVcV5DHv331hF76LvJ+4Tbh2t7Xuw9v2DtZT0lhAEYiXho1RgrMBXQHutnVvSvlpJdE+lzXhIg6lqRy/yUan42n2zYRe5DqszyaBm/DwRFwUOOsQavSjkkFhVFtnDJVDcFYkJ5gT7JZZ6GpkOPLjp/kUWUGX1/wdTRifHL01bpXafe1D2v9gyMSxhNfxIcsy30y6w91p22CY0uPJc8Qbwy6tWvroG7baCxKm7cNd8R9yMZKDJVhi7Y333wz//nPf5gzZw4nn3wyTz31FKFQ5jc7UZhYRFHg9+cvTTr6HN4wlz362ZQsxVaYPuw8yGm7uPjQFfvyrXruu2g54v5z9r+8v49IbOJK4UbLwY7pJaWH7nEcjNUHRSR8msERCbGmtVRpDyRQlpnLCMVCzLDOmFZukwJjAcXmYroD3UiyRL4xn2sOuyb5+C9zc6nVqBGjIYrf+x3sv2A3a8x4w15cIddgm844PK0b2WiIH7siYwGri1YD0BvqpcBYwPL85awoWMHS/KUsyl3ELOssZFmm3ddOT6Cnj6hq1pq5ccWN/O3Uv/HtZd9GFEQC0QCesIe52XOT26nMqaTcVs7srNkszlvMysKVrMhfwbL8ZVTYKigyFqETdUnncpu3jU5/J96wt0+jk5gUwx120+HroMPfQbu3ne5Ad78GR0ZzId9Z8T/JphDPWs387Nhv0nza/9F8yv/iKj8JiJfvz3jpVgztuxAFEY1KQ7uvPfMb5XTtplqUkPeLOiWmEkotpRn1mTTqsyg48ttQeSbNGg1fnns25dnlgy4fFAU+Nhq4K8fGpVoPN79+NXWOKgDseju+sI+9vXtxBBwTWhauMDhDyZwdCFEQyTfl44v42Nm9ky5/14DLyXL6RmeCIPR7P0iyNOLv5GQ8Qpodj6fDNSGGT7bT1hl0Ut1TDULccTcWCIKAQWOIVzVImV3VEIwGkzmoEJ+onIis8KlAnjGPM+acAUBMjvFE1RPD3sZ4RyRIskSXvwudWkdtb1y0FRAOySZkA6EW1Xyl/CvJ24O5bbP0WbT52rBqrRkxKZzJDFu0veGGG9i4cSMbN25k4cKF3HjjjRQVFXH99dezadOm8RijwhRFr1Hx10tXMS8/Pnta5/Bxy7+3TvKoFA5ldrTGT7QtejVl9tGVYU11jpqXy+lL4m54dzDK+vqeSR7R0JBlORmPkG/RUWDNHCEhU5gyubYHOW1FQcSsMVNgLJj0EuyxRhAEZlpnkq3LxhmMOy1XF6/mhLITAAgi8c2iIh7ItuJp2Yi1Jt5FWiWqkJHpDmbwMTyYaJjag9wUlTmLEAWRqBRFlmWKzEUYNUasWiu5hlwKTYXMyZ7D8oLlLM5djEVrwRl00uHrIBgNJrejUcWF/WA0iCvoYnbWbMosZYOe4KtFNWatmVxDLmWWMubnzGdFwQpWFa5iRcEKDss7LN7wTo47vdp97bT72nEEHPEsZctMluYtZWHOQrJ0WfjCPtq8bfQEe5IXgBWla/j28huT4sZzdS/xSu0rIKpoPvmneGYdDYAq4mfGizeh696HVWvFGXRmvgjfupkq3YHJlBnWGaMuWx4P9Go95dnl2HQ2eoI9/PCIH3L98uu5cvGVXLrwUi6cfyHnlp/LF0q+gE3XN2qlVZD47ac/w9FTgyAI5Jny6A31sr1rO9sd2+nydxHbnzEtyVLSub3TsZNmT/NkPN1DkpFexAuCQJ4x7vKq6qmi2dPcb7JkKI22BIR+y3QHutnbu3dEJfjJPNlBntdExCMgp8/NTYjV4zXB5Iv4qHHWEJEjQ24cN1SsWivukDv5W5upBKNBWnwHKmtmWmdO4mgyj7PnnU22LhuAz9o/Y1f3rmGtP94RCd6IF2/Ei1bU0uBuAKDYXJyRv5WTxefdttXO6n7L6NV6is3FY9arYDoz4uCIpUuXcu+999LS0sIdd9zBQw89xOGHH87SpUt55JFHMt9JoDAhZBk1/O2Kw8k1x/Pt3tndSW2XUoKmMPF0eoJ0uOOOqcXFSvMqgJMXFiT/fruqcxJHMnQauv14gvGL6cMUl+2ALJ+RjU4d/3n/dF93Zv4exyJEO3ZQu78JWam5FJWoIt+YPy0zrfRqPbOy4q7SxMX+ZYsvi4uHgEMlcL8tm1PKivm/7Q/yWcM7SLKEWWvG4Xf0ETEzlq4q9h1kFEpEXPSGerEb7P2EswQ6lY5CUyGH5R3G0rylFJuL8UV8dHgPiLehWAhn0MnsrNkjKtMXBAGtSotFayHfmE+FrYIVhStYnr+c8uxyyrPLWZ6/nJUFK5lrm0uuIZcicxGH5R7G8vzlVOZUYlQZ6fQdcO4cVXIUly26LLmPx3Y9xkfNH4FKzc4Tb+WpsoXcmJ/LOXlmNrx7B1qVlpgUoyswsPMvY2jdzK6DmpDNzpqdsReiRo2RClsFFq0FX9THMSXH8KVZX+KMOWfwlfKvcMH8C/jO8u9w/0n38/vjfs/VZadSEY7/fjgFmd99eBtBRzWiIJJnzMNusON1NxN4+Sbk38zA99hZbGv8kM2dm9nZvZN2fzvNnuap8Xmc4ozF71a2PhudSke1s5rdPbupdlZT46yhxlkTL50eQiOyGH0zbbsCXTgCjhFNvkikdv1NRDyChISMnPZ3dryE43AszF7nXrxhLzn6nPQrDBNREFGr1LT72tO6LN1hN7t7dvepuJgoPBEPbb625O0ZlhkTPoZMxqA2cOH8C5O3X9z74rDWT0QkjNckqTvkJhwL0+5vT+ZeK9EIffm82/bZ6mcHXE5xmA+NEV8ZRSIRnn76ab785S9z8803s2rVKh566CEuuOACfvKTn3DxxRen34jCIUGpzci1xx74IntqfVOKpRUUxoedffJsle6UAF+syEe9PyPhraqOzBT3Pse2g6MRSrInbyAZjE6tYtWsuEDW0hugqScDc207drBXlIntF99mWGegUWkyVhwaC3INuZRZy+gN9hKVouhUOn5wxA9Ynr/8QDmqIPCJTs0ftj/IX7b+JZnR1xvqndzBD4XWzdQcHHdhLSMqRYlJMYpNxWlPzEVBxKa3Md82Py7eWg6It86Ak1nWWcy0zhwzUV8jarDpbcywzmCGdQY2vQ21qO6zjCAImLVmSswlVOZUkq3LpsvflfyuPHX2qZxbfm5y+Qe2PsAdH9/BNe/cyC/UXt41GWnQaPitLoKrbQsWnYVOfyf+SOZm/Mutm/s44Odlz0Or0qZZa/Iwa81U2CowqAx9sixjUoxANIA75CYUC1FiKeHEpZdz21E/ZVY0LuY0qATu++DHqNq2QyxK/vbnOPLf1zFj539Rh32Yaj+g/D83YouEKDIXUWAswBvx0umfGpOcIyUiRfCGvTgCDpo9zezp3sOOrh20eFtwhVwTkrMtk75R2FAwa81k6bLoDHTS6m2lxdtCs7eZzkBn2k7lCadt4vPui/joCfYgCiId/uGfM6VrRAbj3/xrKGMWEEBOnb87EmJSjFpXLV2BLvJMeeNmnhhKVYMsy7R4Wmj3tccbhU4gsizjCX1OtLUqou3nOa7sOHINuQBsc2wbdkatXhOPSBhrUV6WZRwBBzq1jn29B3qCKE3I+jMUt63C0Bj2me+mTZu44YYbKCoq4oYbbmDRokXs2LGDjz76iCuuuIKf/OQnvPjiizz//PC7/SlMX766shStKv52e2ZjM6HoxM9qKhza9MmzVZpXAXEnfCJ3uqHbz97OzHfBb2/uTf6tOG0HZ82cg3Jta1N3Rp8MsV5qXs+ug5qQlZpL0Ym6jMrNHA/KzGUUmgrj5ddSjHxjPj844gf88cQ/cuGsMyg66Lfx/eb3aXA3oFap+wiFGUvrFvYeLNpaynCH3Nj19mGVwAqCQJYuKynellhKKLOWMStr1qQ6MowaIxX2CswaM47Agc/U+RXnc9KMeI5tTI6xx7mnn/ASEQSe3/lPDGoDgWiAnmCGxtFEwwQ7dyYd8EWmInKNuZM8qPRk6bIot5WjElS0edvo8HbQE+whFA2hFtX4Ij7avfGu8trCw7j1qDuw79ejNmnVPPzh7cx54mKKPrgHdbCvMGDqqWf+c9ejdTYkRfwWb8uU6FA/EjxhD9s6t7GpcxNbOrewp2cPbf42ekI9VHVXsblzMxs7NrLXuZcOX/x19oa9hGPhMf2OkmQJWRib7enVenINueQZ88gz5pFvzCffmI9RY0y5XkJcTYiXzqCTYDRIriEXV8iFNzL8c6Z08QjjIZb22X8i0zZNI7L4UMbueMqyTKOnkWZPMzmGnHGtqNGqtMjE9zfYBENPsId2XzuRWARnYGKjFIKxIP6onxZvPB4h35g/rSesR4ooiKwpXgPEPxNr29YOa32zxown7BlzUd4b8eIOuzFrzH2bkCl5tv0YqttWIT3D/sY8/PDDqamp4YEHHqC5uZm77rqLBQsW9Flm4cKFXHTRRWM2SIWpj92k5dTFhQD0+MK8vrNjkkekcKixo+XAhdiiQ7gJ2ec5sfJARMKbVZn/uUzk2YIivqdizdwDQkuq71tPMMKZf/yIf29oQpImThSUmtax+yCBr8hUhElj6ud0nG5oVBrKbeUUmgr7OEByDbl8ZfGlPDr7Qm7o6U0u/+K+F+NZryHniASCCaV1M3s1cSHeojFjVpsJx8IUmYpGJLYmxVv7fCpsFRnx3rBoLcy3z0ev0ieFW0EQuHLJlcmmaxB/P5819yx+uvhaLPubPL4RaKbV15rZjXK6qvo44EvMJRjVqYWtTCHHkMMC+wIqbBUszlvM8vzlrCpcxaqCVSzNW0qZtYxAJEC7rx29bTbfX/1j9Pu/8l4z6vl/ooeEvNM7/xTqvvL/CFuLAdB62pn9zHUY2ndi1pjxRXyDNriaykRiEWpdtbjCLqxaK0XmIgrNheQZ88gx5FBkLsKmtyEh0ehpZEf3DrZ0bmFT5yY2tG9gfcd6dnTtYJ9zHy3eFjr9nfQGe4ftdJNlmWA0mLZh13gjCHERVUYmJsVo97Vj0BjQqrSEY2F6g73D2l46p60gjH+mbTJXN10ztDGOaejwd9DgaiBLlzUhzn273o7D76DN29bvsZgUo9nTjCAIWHVWugJdhGMT1yg7GA3S6mlN7nOmRcmzHYyji49O/v1J6yfDWlctquN9AQJj2xfAHY5HI2hV2qTTViWomGWdNab7mS4obtuxYdiibW1tLa+99hrnn38+Go1mwGVMJhOPPvroqAenML342hEHSj+eXNc4iSNROBRJNCEzalXMzjVN8mgyh5MqDzR9emtXZou2kiSzY79jujhLT55FN8kjylyWlmZRlBV3rb6zuzP5un2eB97bx85WN99/Zhu/eX33hI1PaNmYLMEGKDAWkKU7NER4nUpHua2cfGM+Xf6uPq6q3iVf4XxNPrZYXORY2/opvcHeIQsE3rCX3T27J6R8uQ/RED1du3Co4+JsmXUG7oibbF02OYaxzy2cTLJ0WVTYK1AL6qRjVhREblxxI7evvp3ff/H33H383VxceTELZx3PJdH4+1wS4JkdjyUb5aQ7nt6wlz09eyY2b/GgaASI5yxOJQdYjiGHGdYZ5BvzydJloVPpkuJ/ha2CZfnLmGmZSSAawGadwY0rb0peCD2WZeXLM2fx0Ak30njSbfhLV1B33l8I5JYD4Au7Cb50E/rGddPSbSvLMo3uRrr8XeQacpMNAD+PWlRj1VopMBVQaCok35iPVWtFq9YSk2M4Q04avY3s7tnNjq4dbO7cTFVP1bCyJZs9zTR6Gif9N0FASGbA9oZ68YQ9mDXxhjkGjYEOf8ewPp+JyId0LtfxrqpIF9Fw8HJjgTPoZF/vPnRqXVp381ihElWYtCYaPY39mlElcoltehtGjRFfxDehDSID0UCfJmRKNMLgzLTOpNgcnzzb3bO7T5XLUDBrzXT5u8Ysh1yWZbr8XehUOoLRYLIxZZmlLKNjhCaTz7ttn69RqvFHwrBF25kzldkghZGxeo6dOfvFsk9ru5WGZAoTRq8/TLMzfnG1sMiKSlSakCWYmWOioiB+EbK5qReHNzTJIxqcWocPXzh+gaS4bFOjVol864sH8rXufbum3zLNTj8PfVQHgFYlcvERE/T7HnAi9dRRvd+VWWgqRK/WY9BMHXFotOhUOipsFeQac+nwdRwQbkUVwcoz+Lo7fpEpIfNy7ctJgSCdGNsZ6KTD1zFuHZMH3/Eu9qkPfK+WmksJReM5opngkB1r7Ho78+3zERCSXcpFQWRR7qJkc7kEp5d9kZz9sRefOrZS76pHrVLT6mtNWQbd7mun3dc+sceydcuUaUI2EixaC/Ns85hvm09UirIgbwlXLr4y+XiTKHFv3Qvc8v4tfND8AR+5avjpwqM5d9ZsvjCjlAsL7dy34S5Mgg5fxEenb2jZtlEpOuZur7GmK9BFo6cRm97WxxkflaKsa1vHXufeAcVEQRCSeeRWrRW7wU6+MZ9CUyEF5gLsBjtd/i62dW2jzlVHKJb6HKPD10GtqxaTxpQRcTmyHBdau4PdyMjJ77NE6bU7PPScTRk5beRDwo07XiS+c9LmyQpjE53kCrmo7qkmKkex6ia2n4RFayEUDdHobkyK6+FYmCZPEzq1DrWoRhRERFEcthg4GrwRb58825lWRVsZDEEQ+rhtP239tN8ysiyzrm3dgA7Ose4L4Iv48IQ9mLQm6t31yc+q0oQsNceWHptsPLi5c3MyGuTzSLLE8zXPZ/zv5WQwJNHWZrNht9uH9E9BYTAEQejjtlUakilMFH2bkCli3+c5aX9EgizHXZmZysFuUSXPNj0XrCqj0Bq/6H1zVwc7W/s6SX73+h7C+xvyXH70LGbkTIwDRmreQJ1GQ3j/5MlM60w0Kg161eRfoE8kerWeClsFeYa8pNAqyRLueSdwkduHQYofm3eb3iUmxeLZbCncQMFokA5fB/6If+Ibl7VuZu9B1Ve5htxp6bI9mFxDLvNtceE2VUZtcM4Xuab3wHF7as9TZGmz6An0DHqc/BE/Hf4OAtEAztDE5S3Gm5DFj6OAQIWtYlq6hwpMBcyyzsIVcnFs2Rf536P+l0U5i5KPt/nauH/L/dyz6R5ea3qHGiGGvF/kelenZvP2x7BoLbR4W4bUVM4RcFDnqsvYBnS+iI+63rr49/BBQununt384IMfcPfGu7nt49v48Uc/5r2m94ZVSq4W1eSb8tGr9ezr3ce2rm10+jsHnIByBp3s7d2LWqXGrDWPxVMbFYm4An/UT5e/C4vWknxMLaqRZZmewNDzqRMCcOqdjnCwQx0DQ4s9EBBGLR67w2729OzBH/MnG0oNhUA0wBv1b3D7x7fzu/W/G1XjvxxjDu2+9uQ2OnwduIKuPi5us8ZMT7BnQj6fsizjDrlp97Un71Octqk5uuSAaPtxy8f9Hn+6+mnu3ng3d35yJw3uhj6PCYKAWqWm0985JpMQnrCHUCyETqWjtlfJsx0qalHNabNPS95+tfbVAZf7qOUjXq57mds+uY1nqp+ZqOFNCYZkf7jnnnvGeRgKhwpfXVkaFwpiEs9sbObmL1WgU09eYxGFQ4ODxb5FxRM70z8VOGlhAfe/F89lemtXBxesKpvkEQ3MwXm2S0qzJ28gUwS9RsW3vjiXO17cCcB9b9fwl0tXAbClqZf/bGkFwGbU8J3j503YuMKNn/YpwT5UmpANhEFtYL59PlaflU5fJ52+TjQqDcWFSzjPU88/sqxEpAhvNbzFCTNOoM3Xhk1vG7CJizPoxBf2ka3PptPfSamlFI04cInzmPO5JmSJ/MsJ2/8kkWeMd0Df07OH7kB3H5E6JsVLxSWDldPFbB6LRGnRqNnWtY1qZzX5xnzafe3YdLZ+rrcufxeBaIAsXRZd/i7KLGXj71iOhoh07KRmRrz/QL4xn3xTfpqVpi6lllICsQDNnmYqbBXcvuZ2dnXv4tnqZ9nZvbPPsgICZTo7jaG4++fhlnf47eKL8Ef9dPo7mZU1a9D9JLJQe0I9eMKeCSsPHypRKUqdqw5vxEuBKT6B6w17eaLqCd5peqfPsnWuOv689c88XvU4J8w4gaOLj6bUUjqkplJGjRG9Wk9vqJftju3xiAVjATa9LZkTXOOsISpHydVnRvO7RIRAT7CHQCTQL67BpDXRFeiizDq00uihiKCCPL7xCMlGZOOcaZuId/FFfOQZ84a0TpOniTfr3+SD5g8Ixg6Us+/q3sU3F3+TY0qPGfY41KIag8ZAg7sBjUpDs7cZk87U5z1rUBtwBV24Qq5x/3wGY0FCsVDSaahT6cg3Tt/v2bGg0FTInKw51LpqqXfX0+ptTUYmNLgbeHHvi0C8Eegb9W9w9WFX91nforXQG+rFG/H2mXgZLrIs0xnoTH7WE3m2AHOz5w62msJ+TphxAs/WPEsgGuD95ve5YP4Ffdz3wWiQJ6ueBCAiRfpVLR3qDOkM8LLLLgMgGo3y+OOPc8opp1BYWDiuA1OYnthNWk5ZXMh/t7YmG5J9eWnxZA9LYZqzQ3HapmRZaTa5Zi0Ob5gPaxwEIzH0msybTNne0pv8e4lyHIfEhYeXcf97e+lwh3h9Zwe7Wt1UFln4v5d2JZf57kkVZBn6imv+iB9/dHjumKEiN6/v14TMrDVPyzL6oaBX65mdNZsScwk9wR46/B20zT6Kb6zdzpNWC1FB4I2GNzht9ml0+bvoMfX0Oy4xKUabrw29Ro9JY6I70I0n7MGun6AKqNbNfUTbYmPxlGlgNVpyDbkIdoFqZzWOgAO73o477CYQCZCjzyHPmId79lF8e+/L/CQvLuo+uftJblt9G13+LkrMJX3EoGA0SJuvDbPWjFFtpDvQjTvsHv9j2bmLWjVE9gvIpebSaX0MVaKKOVlzCEVDOPwO8ox5LMxZyMI1C9np2MnatrUY1AYqcyqZb5uPUa3nz89fxHtakR5B4t/bH+H8JVfS6m0l35g/qNjTG+rFGXQiIuIMOpPCaKbQ7Gmm3deenID4pOUT/r7z7306ridKfxOd0j1hD//Z+x/+s/c/WLVWFuYsZHHuYhbmLKTIVDRo6b0oiNj1dmJSDG/ES7WzGq1Ki11vJxKL4I14M0rASjyPnmAPOrWu3/Myaox0+jpxhVxDEiZlWU7rpJWFCWpEliYeQUamJ9hDWAqjFbVoVfF/epU+bWNJX8THnp49eMIe8o35afcViAb4f5v/Hxs7Ng7++Jb/x6bOTXxzyTcxaYbXl8KqtdLh66DZ04wv4qPQ1FfDEAQBrVpLZ6CTQlNh+uiIURCMBnGFXHQF4o0MyyxlQ5r0ONQ5uuTo5PfPxy0fc/7884lJMf6y9S/E5AO50h+3fMwlCy/pE+ujU+lwxpw4g85Ribb+qD8ejbD//bfPFRdtNaKGMktmml0yCaPGyPFlx/NK3StEpAhvNrzJVyu+mnz8hb0vJCuLluUtY03xmskaakYyrCsktVrNt771LaqqqsZrPAqHAF8/Ygb/3Rp3eD25rlERbRXGnZ37nbZatci8/Mkvucs0RFHghAX5PL2hmUAkxif7HJywILMuLGOSzI6WuPheajNgN02/kt3xQK9R8a3j5vK//42LtPe9XcPZy4rZ0BA/MZqTZ+LrR/YvzfOEPbR4W8jSZg3akGYkSFIMTesWdtkPuGoLjYUTnnWXiWhV2mRjn86lGvI/+TOne328aIm70D5s+ZAji46kxduCTdc3d9IZcuIqXd++AAC3/UlEQVQKubAb7KhEFTIyzoBzYkTbSBC5s4qa0vh3hl1vJ9uQjU516DQKzDHksEBYwJ6ePbR528jWZTMnZw65xlw0oobaBWdwxpaneTTLwl6tlr29e9nh2EGZuYwOf0cf0dYRcOANu6msX0fUXIAjbx69od7xP5atm6k6KM+2zFI2rfJsB0Kr0jI3ey7B7iA9wZ6kU3pR7iIW5S7qt/w1JSeyruNtAqLIG22fcMyc0zBrzLR6W5mbPbef2CPLMh3+DhDiwpEz5EyW1mYCwWiQVm8rFq0FtajmncZ3eHDbg8nHDWoDFy24iJNnnoyAQE1vDa/Vvca6tnVJocQddrO2bS1r29YCYNKYmGWdxeys2czJmsN8+/x+MSkqUUWWLossXRahWIjuYDcxKZYUjjMFAQFJlvCEPWTrsvs9fnAe6pBEW2QEOZ1qOzZZsoNuXh6a09aqs9Lub0fySiDEHcBqlRqdSodNb8OqtSZzh2NSjIgUISJFCMfCtHpb40K2Kf3xjEkx7tl4D1u7tibv06l0HFNyDMeVHceb9W/yYcuHAHzS+gnVzmpWFazCFXbRG+zFFXIRlsKcM+8cTpp50oD7EAQBu8FOh7+DHEPOgGOyaC24Qi48EQ9W7fidj/ij/j55nkqe7dBYU7SGf+76JzIyH7d+zHkV5/Fq3atJITdBMBbko5aPOHnmyX3uT/QFKDYXj9gg4Al7CEVD2PQ2fBFfMuJipnVm5pgOZInsqldQBXoJ22YQss0inFVMUIrij/onbiJ/EE6dfSqv1r2KjMwb9W9w1tyz0Kq0dPg6eLn2ZQDUgprzK86f1HFmIsN+hx155JFs3rxZaUimMGJWz7EzO9dEncOXbEg2J08R0hTGB08wQq3DB0BloQWNSpnRHoiTKgt4ekO8C+qbuzozTrTd1+UlEIlfJCp5tsPjoiNmcP97++j0hHhtZzsbGw9kZP7otMpBPxOesAdnyDmmzid/x3aMITe7918U5ehzMGvN014cGg6iIJKTU467dAVXtm/hRUv89/Hl2pc5ccaJdAe66Q529zkuXf4uBMC35xW0liJM+ZU4Ag5mWGeMqeg+IJ076RQkPPvfR0Wmorgj6xCLu7DpbVTmVOIJe8gz5vUR5rJmHEPYXMD1ThffLYiLO89UP8Ptq2+n099JsakYs9ZMJBah1dtKyY4X+XvtSxRHY6w8/md0aU2UWcrGNW5CbtnMLl3fJmSHwjE0a83My55HtbOaNm/c4WzWmPsJOzEpRmT+qXxnzwvcZc9CBh7e9hC3r/kpLd4W7AZ7vwtiT8SDI+AgNxJGFLS0x4J4wh50hswQbV0hF/6on0JTIXWuOh7d8WjysSMKj+CK+V+j2OdEV/UKMZ2FhQWVVCy/AefCS/m09VN2du9kV/cuAtFAcj1fxMfO7p3JiAlRELmk8hJOn3P6gGPQqXQZ83oMhCRLxKTYoN+jFq0lmYearrReltM3IoOhxSiMlESmbTox1aQx9XG0yrJMRIoQjAVp8bbQJDWhUWnQiTqicpSoFCUqR0GOi/J5pry0DlJZlnl4x8NJwdakMXFexXkcV3pc8rWssFWwPH85D21/CH/UjyPg4LX61/pt6+87/87y/OWD5qhrVVpKLaWDjkWr0hKOhXEFXeMq2joCjj4ZvUqe7dCwG+xU5lSyq3sX7b521rWt4+k9TwPxyZUrl1zJw9sfBuDthrc5acZJfd7jZo2Z7kA3rpBrxFn7Xf4u1Kq4dHawWJxJTcjy1v+N/HUP9blPEjX4rYWEjHZU5nxiOisxvZWo0Y573vHEjBMn5OYb8zmy6EjWtq3FFXbxccvHHD/jeB6vepyIFAHgpJknUWDMrGvQTGDYou23v/1tbr75Zpqbm1m5ciUmU98ShcMOO2zMBqcwPYk3JCvjl6/sBuDxdY3cfubCAZfd1ermqr+vp9Rm5LFvHpGRJdsKmU1V24HO24uUkvpBOaY8F51aJBSVeLuqA0lajCgOfFJf5/Dx7cc3UWoz8P++vnxCcqkPzrNVIi6GRyLb9s79btsuT7x795o5OZxUObggG4wG6fR3kmcYO/dTpP5DmtRq/GL8Yu5QbUKWDo1KQ7jyLOY2beCLPj/vmYz0BHv4tPVTDss7jBZPC3a9HbWoxhOOC0PN2x7nd461ZEkSvz7iNoLWQlxh17hEXPThc9EIhaZCrBrrIVnymXAPfp5svQ3H3OM4YevTLAyF2KXTUe+up7q3miJjEV2BLsxaM93BbjwhF8+1vMPLWfEyzt/tfh5tzkzcIfe4NnaTWzb0cdqW28ozxhE63uQYcjhMfRgOv4M2Xxvt3nYMWgN6lZ5ANEAoGkIURIzGHE7OruS/oXr26LTUexp4r/k9jig8gkZ3IxatpY+w3uXrwtywltZP7yFHZUJ7+m/oDfaO/2dyiDgCDtSiGn/Uzz0b70leNJ8rZvOj3RvQrX0OQZb6rBMx2gnmL6AibwHh7CUEi05gD2G2+tvY3VtDvau+T/M8SZZ4bNdj+CI+zqs4L6OctOkQBRFfxJfyczecPNQhOW2F8XPaRqQIzpBzRKKwIAjJiISEqBmJRQjFQuhVetSiGpWgGtbx/c++//BOYzw3WS2quWXVLVTmVPZb7qiSo6iwV/CnzX+iqqdvta9aVBOVokSkCM/vfZ6rllw17OeW4GA3ZroIiJHgj/jxhr1x9/1+ZlgU0XaoHFV8FLu64+exf9z8x6Tb/5RZp3DyzJN5r+k99vXuo95dz77efcyzHejVkKhAcgQcI/od9Uf8uMNuzJr4JPrBTcgyJc9W37mHvPWP9rtflCKYe5sw9/ZvAG/f8Tz7LvobTKBT+Iw5ZyQrM16ue5lcYy6ftX8GQLYumzPnnDlhY5lKDPuM+sILL6Suro4bb7yRo48+mmXLlrF8+fLk/woKQ+G8lWVo1fG335OfNdLj69+JVpZl7nhxB62uIJ/V9/DqjraJHqbCNODgJmSLixWxbzCMWjXHzItfSHZ6QuxoHbxL/S9e3kVVm5s3d3Xw0taJ+Vxub+5N/n1YSfaE7HM68bUjZpBnOSDACAL85IzKtBdYPcEePBFPymWGiizLxJrW9mlCVmYpQ6c6NJuQpUO36KtIoppvug5kcj9b8ywmtYmeYA+OgAOICy++sJdHu9YhCQJOlYp1VU8jINATHHpn8xHTuoW9mgNCVYGxICM6v2cSKlGFetG5CMDVvQeO5wt7X8CsNdPmbcMb9tLqbSXY+DGv6A6cnr/TW4UYCeIMOgfY8hgR8iB17aZ6v/ieo8+h2HRoRVeZNCZmZs1kWf4yKnMr0Ypa/BE/Zo056fZbkrsE9+Kv8NPuHoT9wtrTe55GlmW6A920ew90hPdH/LT72/lo11PckpfD1TYdUs2bdAe6k+LoZOKP+OkN9WLSmPjzlj8nhaTFoRA/2bcNvbO+n2ALoPH3YKn/hPz1j1D65s+Y9/wNnPH8zfzo9d/z190beWzBN3ngpAe49fBb+5SrP1vzLI/tegxpgG2OBEmW2Nixkbca3qLZ0zwuQqdaVFNkLkr7faZVaenwdwxtDOk0W1kYF6etO+xmV/cuap21A04sjQSNSoNZa0avjou2wxFsP2r5iKd2P5W8/a2l3xpQsE2Qa8jl9jW386sv/Io7j7qTe4+/l7+d+jceOOmBZKXOu43v9nGxDhezxown7MEddqdfeAR4I96kUzmBEo8wdI4sOhKVEBfTE4JtriGXixZcBMBJMw5837zV+Fa/9S1aC46Ao09lwFBJrJc4Vz24Cdmc7Ml32gqxMCVv/gxBir8uzgWn03n4FfTM+QI+2wzkQURZfXctWXvenMihUm4rZ75tPhDPVL93473Jxy5acJFyPTAIwxZt6+rq+v2rra1N/q+gMBTsJi0XHR4P7faHYzzyUV2/ZT7e2836+gMXKe/s7pqw8SlMHw4WHxeXKLmZqThp4YFylNd3tg+4TG2Xl7eqDpwUv7StddzHBbClWTmOoyGRbZvg3OWlaR3LalFNNBalJzA2wp834sXQtp1dB7n5DvUmZKmwWEtwz1zNslCYNYH4RYYj4ODtprfRqXXJpiqdvk627XuFzoPO6N717MOgNtAd6CYc6z8pOqa0bqHmc05b5aS7P5Y5xxM25nCCP8CccFy029Ozh0Z3I/6on0Z3I72hXl6ofw35IPHjPb2W3MZ1OAKO8TuWLRtpVIsE9jvgS82lmLTDa/YzXdCr9ZSYS1iWv4yVBStZmreUMmsZ2fpsjBojlgVnME9r53yPF4BQLMTfd/0do8ZIk6cJXyQex9Qd6MbVuJbH1PHKhqgg8GnH+mQzm8km0Szv7ca3Wd+xHgBrLMbvOx1oAVlUE8itoHP+KdSuvoqGw75KT/FSIrqBBUwBGZ2rmdwNj2HT21hRsIKrllzFNxZ+I7nMq3Wv8uC2B0cl3EqyxCctn3DL+7fwu/W/46HtD3HL+7dwwzs38Ndtf2V9+3qC0eCIt/95Ep3iU2HWmpN5qKlINAFLiTC28QgxKUaLt4XtXdvpDnSTbxq8ad5Esat7F3/e+ufk7a8t+BpHlxyddj1REJmdNZv59vkUmArQq/VYtBZOm30aEBfynqt5bsTjUotqZFkes3Oez5OYeGt0NwJxwXGyj8VUwqK1cFhe34ruq5ZclTzfWFO8Jtk885OWT5LfxQkMagPBaHDYk9n+iJ9mT3OyiZkkS0nHr0FtoMRcMqLnM5bkrX0IfU9cSwnkltN2wg9oP+JKNh93E47LX8Lzg318fMkT7L7kKfZd8BCtx/8guW7+Zw9DLDqh4z1jzhnJv72R+G/pnKw5HFt67ISOYyoxbNF25syZKf8pKAyV646bi0YVvzD5+yf1uAIHnAeyLHPPW9V9ln9/TyfR2NjM0B/qSJLMazvaeXxdA/7wxH5RTzQ79zevUosCFQUj7xp6KHDignwSiQj/+LSBXn9/geDhz02wfFjjwDmAU34s8YWiScf0vHwz2UalCdlIuHTNTC4/ahZfWV7CTweJpPk8Bm28XDASG70zzONpwehs7JObWWguHNf8uKmMKIhIi74CwE09vUmD1nM1z6ESVbhCLhrdjTgCDl5s/bDPunUqcHXuwB/x4woN7pofNZEAdFUl4xEEBMosZUrcxQBo1XqC5ScjAlf1HjgmL+x7AZPGhCPooL5rB2vlvheaAVFkd+0b+KP+cXOAxRo+7TOZohzDeEdwo8bYzz2YZyzAsfAs/sfZS0407mra2LGRnd07CcQCNHmaCMfCtPnaeG3vc4TEA5dab+ND9DrG9zM5BGRZptPfSaO3kSd3P5m8/1dd3RRHY0RO+l+EH7dhuH49ORc+SeGJP///7J13eBzVvYbfKdt3tSqrXiwXuXcbY2yK6b2EDgk13IQECElISLsJJCEhISSXVAgJJKGFFhIgdLBNdy+4d9myrN5Wu9LWmfvHSCPJ6tKq2D4vjx5mZqec9czuznznO98Pz3kPErj6KTbd9Aqrrvorn512NzuPv4mS6RdTPfZE9JY4FKWppsOxzht3Hl+e+WWz8NXykuX835r/67dzXNM1Vhxawd0f3M3v1v+OQ4GOHcbVzdW8d+A9fr3m13z13a/y6p5XE/K71ResipWYFqM+VN/jen0pANYnYbePROIRdtbtZFvNNhRZIdOVOSTD/vtDebCcX6/5NTHNePY4o+AMLhp/0aD2ef6488383fdL3u90bfQHl9VFeVM5TdGmQbXpcKLxKHWhOpqjzYTiRqeCcNn2n5NyTzKnT8w9kdkZs815u2rnpDzj9YgW4cODHe+LJEnCaXFS3FDcr9/SiqYKmmJN5jW2p36PKTTO8M0Y8SgoR9kmfOufAUCTVUrP/BG6YqE2VIvP4SPHk4MsqcTtHsLeHEKZU6mbfjGB/OMAsPoPkbLttWFt8/ys+Z1ya2+cfuOI/1uOZvr9L/PEE0/0+DeU3H///UiSxNe//vUhPY5geMhJdnD5PCMUvjEc4+8fF5uvfbS72qxu3oo/FGPdgfphbOHRycaSei59+BNufWotP/j3Zk7/9fu8uvHQkFarHSmaI3F2VRrOh6JMj8hE7oWMJDuXzTU+k/5QjIeX7+nwem0wwotrD3ZYFtN03uzGlZso1h2oI64Z1+eCsSNb+fRIxqLI3HvRNP7vqtl4nX0raOS2uAlEAh0yCgeCrus0FX9Iowxr7UZMg8/hI8mSJIqQ9YB9yiXEVRtTIlHOa+lECUaDvLrnVewWO1XNVXxcspwGDPEoOR43t12z61VkWR7aiISKLWhajD0t8QjpznTcFjc29djIQu0vlhYR/txgEzmaId58VvUZlU2VaLrG6ztfNNc9z9JWjf7dSBWOxsohO5eHx5aM9Y7FYRGfy66wKlas827EjcIPatrOx982/w2LbKEsUEaxv5hNh1ayTOsoDJSrKv5971LdXE1cix++62EjGA1SEazgb5v/Zrpe/6e+gZObQzTPuwHLid8A1bgeFFnBaXGSak+lIKmAOZlzmTbhPDLmfRHLojsJnPo9DpzzU6KOFADkkJ+KYAXlwXLKg+VUNlWyOHcxd8690xzavLpiNV9b+jWe2PIE9eH6Htuq6zqry1fz3Q++y0PrHuJgY9s9yKTUSVwz+Rpm+GZ0GK3RFGvi6W1P8633v8WqslXDcn/bmofaKkZ2hc7wFSKLaTH21O+hNFCKz+EzXYIjSXOsmQfXPGg6IOdkzOGm6TcNOufYZXGZOZg6Oi+2+x7tL26rm+ZYsxk/lCgao400x5qpbBZFyAbDwpyFXFp0KWeNOYubp9/c6fX2EQnvHXiv02c/yZZEOB5mX/2+PnXqNEWbOBQ4hMfqMa/T9ZXrzdfnZIxsNKgUbSb3nZ+aUTZVx99C2DeeQCSAKqsUeguxyBZkSUZC6jDKoXLh/5jT6av/hhQLD1u7ZUk2HfJgCPATUyYO2/GPRPo9HvHOO+/sMB+NRmlqasJqteJ0Orn++uu72XJwrF69mkcffVQUOjvK+MopE3h+zUHims5jH+3l5hMLcdtUHnp3l7nO6ZMzeG+78SO3dHulEG0GSFVjmAfe3M4Lh4luZQ0h7vjnep5csZ97L5zGlGwP+2uaWLmvhpV7a9la5mdcuourjivgpAm+botTjUa2l/tp0fqYniPcfH3hG2dO5OWNh4jENP72STHXLyokN9l4eH96xX7CMeMHf+G4VFbsNR5YX914iGsWDN3N56p9bQ/Gx4vP/7Cwq24X22u3k+XOQlXUQRckC0aDqKXr+MjhINayj7kZc7GpNiHa9oDT5aNh3Cl4d77N12qqeMuVT0zXeHPfm5w55kyaY8bwZgBF1/ml7uOrei1xSeL9wD7OVF3UNNcQioWGJrLg0HpKVZVQi5sw25WN0+LsUIxJ0IZj/BmEkwuw1R/gi7U1/NRnfJ+9vOdlFucsZkfE6ByZEIlw84LbWLvml1RozXzqsHP77qXUpIwhnBRObIEwTUMpXcvm1LbPYVFKEVZZjGjojrS0SdSNO4kzdy/j9GAT77mc+CN+nt3+LNdMvoaKYAWv7XjOXH+eprJWNsS8tRXrWRINEogGEpYt2l8aIg0sLVlqCqYLmkN8ta6BprEn4zz//3rcVpIkXBaX6TprRXelQ1MN1mgTszNmE41HiWpR/GE/hwKHmJ81n28f921+u+63NMeaiWpRXt/3Ou8deI+zCs/ilLxTyHBmmHEEuq6zrnIdL+x4gWJ/cYdjFaUUceXEK5num44kSVw84WJCsRBba7by6aFP+aj0I3R0Kpoq+M3a3zAldQqzM2YTiUeIxCOE42EkSeK0gtMS5nbsS3V6TdfoVY+V6H2dXtB0jeKGYkobS0l3pY+K+CFN1/jT+j+ZonuuO5c75tyRMOfvuWPP5fV9r9MYaeTTQ59yyYRLBiyKuq1uDgUOkeHMSNjvZmO4EU3XKGlsKwY1xiOctv1FlmSunHRlt6/nJ+UzMWUiO+t2UtJYws66nUxKndRhHZ/DZ4w08B9gXPK4Hu9ry5vKaY41k+3ONpdtqNxgTs/KmDXwN5MAMj95GFuD8ZlqyppG9dxriWkxApEARSlF5m+MJEnG6K12om1z1nQaCxfjKf4YS6CSlC2vUDvrimFr+xljzuBQ4BCBaIAbpt0wbMc9Uun3t3hdXWe3za5du/jKV77Ct7/97YQ06nACgQCf//zn+ctf/sJ99903JMcQjAwFaU4umZ3Lv9YdxB+K8cSn+5mZ52Vti8u2KMPN/ZfN4Pifv4euw/IdlXz33Mkj3Oojj+dXl/DT/26lMdzmAJiQ4Sbba+fDXUZv8qp9tVzw+w9Jc9vM6vKtbC9v5PVN5eSlOLj6uHyumJ9PZtLoHzq5+VCby6W3/E6BQU6yg5sWFfLnD/YSiWn83zs7efCKWYSicf7x6X4AZAl+dfksrntsJcU1TXy6t4ZKf4iMIbomVu5tE21Fp83QEo6HuenNm9hUvQmfw8fJ+SfjsXrMgmQDjTLwR/y4K7axzNkmDM1Mn2kUITvGh2H3hjT9Mtj5NjmxOFfGrDyjhIhqUV7c+SJem5cm3XCLXNIYJOu0H3Lcip+zQtUok3XKKtbjShmPP+IfItG2Y55tpjNTxF30hKISPvcX2P55LZc0BngkJZkqRWZ1+Wr21Gw3V/tq3E00fRLH55zAKweXokkSaw5+SP6sK/CH/aQ703s4SP/QqncQjgT4zJYMGEXICjwFg3a/Hc1YFAvMuwl2L+MHNbWsdDgIyBIfln7ICTknUBEoZ1e0JdInEuGW+XeyeeNDhCWJZQQ4OViDP+wfEdFW0zVKG0v54OD7AEi6zv/W1BL1TcB65RMwQBFNanHaStFmUlU32A3xNeqKIkkSpYFSpvum89CpD/HKnld4u/htolqUcDzMq3te5dU9rwLgtXlJd6QTiUc40HigwzHGJ4/niolXMCt9Vqfr067amZs5l7mZczlv3Hk8ufVJM3dyW+02ttVu69TmFYdW8MtTfklyy7U/GFqr09eEarqvTt9HMXYwmb+6rlPiL6HYX0yqM3VUCLZgxPq0Zie7LC6+Nf9bCc1ztat2Lh5/MU9tewodnRd2vsBd8+8a0L7cFjcVwQpqQjUJySvVdI2q5irsFruZZwvCaTtUnDHmDHbWGRGL7+5/t5Noq8gKKfYUShpL8Ng8ZDgzutxPMBqkLFDW4Xu6PlzP3gajhlNhUiGp9pF7JnGVrCHtM8NVrqk2Ss/4IcgqNYEK0p3p5Ljbiom2jnI43MVfufAWPMUfA+Bb8w/qpl6Ibhmee3JVVrl5Rme3tKBrEhIcUVRUxC9+8YtOLtxEcdttt3H++edzxhln9L6y4IjjtlPHmzmaj320jwff2mG+ducZRWR47MzMSwYM8bC0vv9VH49l3tpSzt3/+swUbD12lR9dMJU37jyJJ25ewGM3zGdMmnHjpOl0Emzbc7CumQff3smiXyzlyRX7h6X9g2FLqSheNRC+umQCSXbjRv9f6w6yvdzPKxsOUR0wro1zpmeRn+rkwlnGDYGuw+ubyoakLaFonA0l9QAUpDrJ9gpX5lBiU2zmg1R1czVbarZgU2zE4jHqmgcWkaDrOpXBchxVO/mwRbR1WVzke/JxW90jnrE32nFMvoCw2xDpvnpwN27J+Gx+ePBD3tz7BgBWTedGkqhLG8+CpLaCc6t3v4Yqqwkf6mlSuobdlo5FyIRzumfcE8+lZvI5WIEb22Xb1rYUMZrfHGLaxIuoD9czKWue+fpbljipFVsTfi7D+z9ind1mOuAnpUw6ZouQ9YekonPx584hPa5xd7uYhMc2Pcaru9uKId1BCuHUsSywGEJeraJQs/cdqpqrBiXODZRANMDb+9/G31IM7YymZnItSWhX/xO1RXgdEPZ2AnS7bFeLbGFC8gSyXdlUNVXhtri5bup1/O6033FO4TmdXPkN4QZ21+/uINiO9Y7l7uPu5r7F9zE7Y3avHQpjvWP54cIfctf8u8hyZnW7XkOkgYc3PNzn8xDTYry+93VWlq3s8nW31U11UzXheNf30To6vUTaGkOYGfh1URYsY2/DXrw2b2Id+YNgdflqM7JAQuKOOXd0cC4mijMLzyTFlmIec2/9wAqkS5KEw+KgtLE0IbnIgWiAYDSIS3WZoq1VtpLl6v7aFAychdkLzZEAK8pWEIgEOq1jV+2oisq++n2dCpa1UhYoIxQLdehc2Fi50ZweSZetHA6Q897PzPmKRV8lklJAQ7gBh8XBOO+4Dh02rU7bw+MiQumTaBi/BABLUy2pmwZeyE8wtCQs7VdRFA4dSnwV8WeffZZ169Zx//3392n9cDiM3+/v8CcY3YxLd5viT20wwsaWKvFFGW7Om278qJ82qa0XbNn2ys47EXTJ7spGvvncBnP+srl5LPvWEm4+cSwWRUaSJE6fksnb3ziZ75wzmVSXFadV4aQiH98+exIv3noCW39yNo98YR5LJqXTep8c13R++upWKv2Jq9A7FLReS7IEU7KFaNtXvE4Lt506ATAE2V++sZ2/ftR283vLSeMAzM8twKufDY1ou7GknkhLAULhsh0ershuK/Tw3n5j6L3D6qC8qXxADzCBaIBo5TbWK3GCLcPo52XOQ0cXrsw+YLG6qTv3l+iSjFfT+FKNIdrp6ER1ozPuqsZG7FMuxB9pZOqkS7Bpxmfmg0AxDtVBXagu4YVVaKqFqu1mETIwhrwOiaP3KEKWZOJn/JiILYnLGwMdcogB7mwIUj/xDEKxENN80xhrNUSIzTYb0u73qG6upjHSc5X6/qAfWMEKR9s5m5Q6Sbjf+4BFtRK66PeEHclcEghyfLNxP1QTqqG2pUjNkmATBZMuxmlxcvKYM81t11SuN4Wc4aa2qZZ3i98y52+pb6Dxkj/i8g0yT9Ce3DbdXN/hJYtiYULKBDKcGVQ1GWJ1ij2FG6ffyEOnPsQVE6/gxNwTmZQyyRTdwCjU9K353+LnJ/6cuZlz++X+liSJ47KO48ElD/K9Bd/jm/O+yfcWfI97TriHe064x3TPbazayJv73uzTPp/e9jRPbH2C/1v7f2yp3tLpdafqpDne3G1BMk3Xei1EJkkSmjYw0baqqYrd9btxWBwJdbEOhpLGEv64/o/m/DWTr+lQPCqR2BQblxRdYs4/tO6hAXdyeaweGiONCekkC0QCRLUocT1ORVMFYBR7FEWXhgarYuXkvJMBiGpRPjj4QZfrJduSCUQD7GvYR1O0qYOg2RhppLypnCRbx3vU0ZJnm/XR77A2GtdSIG8utTMvIxwPE4qFGOsdi9vq7rC+3PJfVx1UVcffgt7yveRb+yRyZPh/lwS90+9vi1deeaXD38svv8wjjzzCddddx+LFixPauJKSEu68806eeuop7Pa+3UDef//9eL1e8y8/Pz+hbRIMDbefOoHD78XuPKPIzE89bbIQbfuLPxTlS0+sJRgxHggvnJXDg1fMxOfu3PNuUxW+smQ86354JpvvPZsnv3g8t506gfmFqTitKudMz+LvNy3gw7tP5fwZhpAeiWs83q543GijMRRlR7nRaTMpKwmndXQMETtSuGFRIdle43t32Y4qdlYYD6FzC5KZW2A8VE3M9DAp0yhusXZ/3ZC44EWe7TATCXLaq98ntUVIWl2+ivpwPW6Lm8ZIY78LIem6TmmgFFfFZpa2i0aYnzkfdIQrs484i85k/5yrAbjW7ycr3vZw4dQ0bvY3UT3REIWSsuewKGb8dtZJOsXl62iONfda8KffHFgBYMYjKJIiRNs+kpI6noOLv4pT1/mCv02APTPYxNi8hTTIMh6rhzR7GrNyF5mvf1SziXhzfULdtnLpWj5tuceWkJiWNk2cwz6Skj6Z4jPvAUnmnuoa7O3ENlXX+XpQ40DeHDJdmZw664u4WkL2P9QDhJpqEiq+94W4FufVva9S0/JdsLipGd/Y00iacGbPG/YFR3LbdKih08s2xcbElImkOdLMwnsAaY40Lpt4GbfPuZ0fL/4xD5/5ME+e+yR/PvPP/OKkXzA/a/6gojpUWWVWxiwWZC9gVsYspqRNYUraFL4666vmOs9sf4b9/p5Hjx0KHOLt4rfN+f/s/k+ndSRJQpVVqpqruix+pqH16b0MxGlbG6plZ91OFEkZFUXHAOpCdTyw6gFCcaNDY1HOIi4cf+GQHvO0/NPIcxsFdSubKvnJpz/p8vuysqmSZ7c/y9IDS7s8V7IkY1NtHAocIqoNzm1b01yDVbFS0lhiDk8X0QhDS/uCZO/uf7fLcyxJEj6nj7JAGesr17OuYh2763ZTHiznUOAQ4Xi4Q+dHXIuzqXoTYIwWK0ouGvo30gXufR+TsvW/RpssTg6d/gM0oLa5llx3LpnOzE7byJKMJEtdFjkMp42jYZLxG6CGGkjdOPBCfoKho9+i7SWXXNLh79JLL+Xee+9l5syZPP744wlt3Nq1a6msrGTevHmoqoqqqrz//vv87ne/Q1VV4vHOlVe/973v0dDQYP6VlJR0sWfBaKMo08O509uGiUzMbHPZAkzLSSLdY4iNH++pJhQduaq7RwKapvONZzewt9roLZuc5eGXl83o081iT4XG8lKc3HPhVKyK8dXx9Ir9NIb6fjOzpypAxTC5czeWNJhFyOaNSR6WYx5N2C0K3zizs/Pmf1pctq1cOKvtc/raZ4kfbbGquL1o201OnCBxWF1YZl3N5xoNkT6ua7xf8j6yJGNVrJQG+jdcsCZUQ3mgHF/1PpY5jZtfq6QyJXUKFsUiRNs+4rF48B//JeqzpmPT4Wu1NeZr1zc0YhmziDpFxmvzkuXK4vjktgy3VXtex6baOgglCeHAp0SB4pZ4hExXJm6LWxSw6gMW2YJjzg3UZM/guoZGTgs2Mb85xN01ddRNOZ9gNEiOKwefw8eC3MWmN+81p438A2soD5YTig3+tzTUWE6gfj87bcY5y/fk43P4Rs2w6tGORbaQMvkC9s77AvmxOF+raxMrr/U34io6C9XqxOfwkeZK5wSL0fHYKMtU7Hm7W3FvqGgIN/DazrYH8hsDYZpPuTsxhQPbO227cZraVTsTUyaSbE3usePBoljw2rxDmqs8K2MW5487HzBiD36/7vfdxhoAPLPtGeJ627PHpupN7GvY12k9t8VNXaiuSxe1rvV+rmVJ7rdo2xBuYGftTmJ6jOT252EEaYo28YtVv6CquQow8j+/POvLQ56VbVEsfH/h983ogcOF20g8wos7X+Su5Xfxn93/4dHPHmVF2You95VkTaI+XE9tc/86q9vTFG3CH/Hjsrg6dAwkqgCeoGtyPblMSZ0CwKHgoS4zrcHo1MlyZ2FX7YS1MCWBErZUb+GA/0CnrOtd9bvMz/UM34wRifZSmhvIWfoLc778pDuJJmVT01xDqj2VQm9hl58xSZJQULr9vala8EXTbevd9c7QNF4wKPot2mqa1uEvHo9TXl7OM888Q3Z2YvNpTj/9dDZt2sSGDRvMv/nz5/P5z3+eDRs2oCidPyw2m42kpKQOf4Ijg6+fMRGPTUWRJb533pQO4qEsSyyZaGT6haIaK/bWdLcbAfDQe7t4r8WRnOy08Jfr5yfMaZqRZOfSuUYwf2M4xj9XHehlC4N3tlZwxm/e55yHPuBgXYKH6XZBazE7gHljBpHTdgxz2dw8Jma2DbHJT3Vw1rSOGVwXzGwXkbAxsREJ0bhmnsesJDv5qULgGxYWfpXLGtseOJfuf88czlrbXEtZsG/nORKPsL9hP7Isc6BmG9Wq8Zs9wzcdJEQRsn4gSRI5SflsPeWbRO1eLgg08eOqGu6qqePL9Q3UTb2AcDRMtiubZHsykyZdgrvF9fdxYD822UZDuCGxzr4DK9hvsZhZqFnOLJJsSaKAVR9Jc/ooOeWb2GULv62s5m/llaQ6fFRmTcWhOkhzpOGyuBiTNIapbmPU2EGLhao9bxGMBPvteu+K8IGPWdUuGqEopYhkW7I4h/3A5/ARWXgrVfnz+by/kbtr6ri1roGv1TZQXHQq6Y50PBYPqqyyZOzZ5nZrK9dTH6rHHxm+GLe3i9+iNGxcN7NDYVKnXU5S6vhetuoj7Z22h8UjtMdpcTI+eTxW2UpDuLMjdzi5etLVFCYVAnAwcJCntj7V5XpbqrewpmINQId4g1f2vNJpXbtqJxwPdzmyIU6813gEgP5otoFIgJ11OwnFQ/gcvr5vOIRE41F+s/Y3pkjpc/i4e8Hdw9YZlGpP5Ucn/KiTcLu8ZDl3Lb+LF3e+2ME9+8/t/yQSj3TajyIrWBQLZcEy4trAzEKBaIDmWDM2xSaKkA0zZ4zp6LbtDlmSsat2vDYvGc4MstxZZLuzO404GQ3RCNnvP4ilydBAGgsXUT/1AoLRIIqkMNY7tsfPmCIr3XbcR5Lzac40RG57zV7UQFXiGy8YFIMKU9F1fUh7iD0eD9OnT+/w53K5SEtLY/r06UN2XMHIMDHTwzvfPIWld53CqZM6V3IUEQl94/VNZfzuvV2AkeX6h2vmkp+a2Gyr/zl5nBln8dhH+wjHer+ZeeT9Peg61DVFeXpl34TewbD2QJtoO3+MGFY/EFo7UFq59ZTxKIc5sQt9LmbkGtlwm0ob2FeduCykzaUNNLXEeywYmyqEhOEibTzphUtY1GTEXVQ0V7KpehOyJOOxeShpLOmysMPhHAocoi5cR5qk8lGs3ecx+3jC8TAeq0cUIesHqfZUvOmT2XbSHUjApYEgN/ob0d0ZVGRNw2lxkmJPwSJbSC1YxCkR4/4sKOlsK1tJTIt1m7XYb6LNcGh9hzzbLFeWWfxD0DtWxUpaznHsnXW5uax+ynk0RpvIdmfjtDiRJIkMZwYzc04013kvXE5KUx2HAoeIabFBtSG+/9MOebYTUyZ2yvAT9IwsyRR4C9m35G4inkyu8zdyW30D4YIFNHsyyXBmmL9dC2feiLe1M0UL0NxclxDxvS+EY2Fe2PCIOf+FmI3GWVclLle8D07bVpLtyYxPHk84Fk6IY3ygWBQLd8y9wxwd8M7+d1hesrzDOpqu8eTWJ835m6bfhNdq3POsOLSC8mB5p/06VAeVTZWdhD5d13u9j+lPIbKmaBM763bSGGkcNYKtpms8vPFhNldvBoxRIt8//vuk2of3Prwr4faRjY+Yzl9FUsx/s8qmSt5ql/PcHq/NS22odsCf0/pwPYqsIEkSexvaakMUeIRoO9QsyFpgRoWsLFuJP9z3DrKuPqcjXYQsaee7eHcZdSZiNg+HTvsuUS2GP+xnTNIYUuw9G5R6c/EHCo43p90lqxLTaEHCGJBo+8QTTzBjxgwcDgcOh4OZM2fy5JNP9r6hQNALWV47Y9K6fug7sciH2iIYLd1ROaxDyo4U/v7xPm5/Zp05/91zJ3NiUeJv5ManuzlrqpGZU+EP8/KGnofFF1cHOzhfX1hzkGh86Koma5rO+pbjpXts5KUIh+ZAOXVSBk9+cQF/vHYu1y7o+iazfUTCfzcmLiKhQ57tOCG8DydNx93EFY1twmxrQTK31U0oFqKksaTHofb+iJ+DgYMk2ZJwVWxjaUs0goxRhCwaiw77Q9yRjizJ5LpzqStYQMWsK8zldVPOJxBrJtOZaTpDku0pHJ882VxnxZ43cFqcVDRVDDqfD4DSdaBF2WVpE21zXDkiC7Wf+Jw+qudey8G5n6dm5uUcnH0lVsVKuiPdXCfJmsSCnAVYW27Z33Q5Sd/3If6In7pQXXe77pWYFkMuXc2nLaKtRVaZmjq1UwETQe+4LC5yMqbx2ZK7iNncaLLKvpmXkmJP6TDENtWZwUmqEfMTkiVK971DZVNlQirU98byPf9lZ8T4TZ0YjuCb+0XSPTmJ6zize9ume3DatpLpzGSMdwx1obpBdz4Mhlx3LtdPu96cf2TjIzy19SlTcP3g4AcU+4sBY4j/GWPO4Jyx5wBGMcj/7v1vp326rW78EX+nkQ19iaeRkDrEMHRHOB5mV/0u6sJ1pDvTR0Wntq7rPLX1KT459AkAVtnK3QvuJsed08uWQ8Phwm0r033T+eXJv+Tbx33bdD7/e9e/u3S9q7KKKqvs9+/vdwdDNB6lrrkOp+qkMdLInvo9AOS588T37DBgUSyckncKAHE9zvsH3x/wvmpDteb3wDjvuE7RCUONGqwh+/0HzfmyJd8i4kylurmaXHcuue7cXvehyN3HIwAECxaY0+79KwfXYEHC6fd46d/85jf88Ic/5Pbbb2fx4sXous7HH3/MrbfeSnV1Nd/4xjeGop0my5cvH9L9C0YvHruF4wpT+XRvDSW1zeypCjIhQ/zoAcTiGj/571ae+LQtL+nSubmd8kcTyZdPGc9bW4zKlX9+fw+Xz83rNg/3pfWlHearA2He21bBOdMTG6nSys7KRhrDxkPAvIKUUXEzeyRzUlF6j6+fPzOHn7++HYBXNh7i9tMmJOTfXBQhGzmiufOZ48zBFwtRrSqsKV9NXaiOFHsKqY5UyoPl+Bw+0p2dr424FqfEX0IkHiHFnkL1wZXsa3FkTrVnYlWsxC3xxDm8jiFaM2u3zLocWXWgRBo5NOdq1HgYn7Otgy7JmsT4SReRuu4BahWFVcESbkAiEA3gD/tJcwwyH/qA8VC+q53TNt+TL+Iu+olDdZDpyWP7rEvJdmdTH6wgx53ToZCQ0+Ik25XN7LSprKrZTL2i8PH+ZeTMvNT8HA7k+9YfqqOudjflWcZ36zjveHxOn8iZHiBZrixqChay4vKHSbN5qdZjTHNldRBFLYqFkwrP4L97XwBgdcU6CiOXUh+u7/K7NFHous6zq39jzl9tzyOQN4cJNm8PW/WTDoXI6ntdXZIkCjwFNEebKQ+Wk+HKQJYGNQB0wJxecDrF/mJzCPV/9/6X/f79fHnWl3lu+3PmetdNvQ5ZkjlzzJm8vPtlQvEQ75e8z+UTL+8g4qiyiqZpHGg8gMPiMIcsa2i9xyNI9GpKicaj7K7bTVVTFZmuzH7/u2m6RigWoinWRFO0ieZYM1EtSkyLEdNixPV4l9M6OoqkoMgKiqSgyip1oTpKA6WUNpZSGig1i47Jksyd8+6kKGVkijW10irc/nH9HwlGg3yu6HMsyFpgfmcuyV/CspJlNMWa+NfOf3HT9Js67SPFnkJFsIL9/v0UpRT1+d+7NlRLIBYgw5nBp4c+NYtAzc6YnbD3J+iZ0wtONztW3tv/HuePO39A3zMj6rLVdXKW/gI1ZHQqNEw4DX/RGVQ3VZFmT2Osd2yfOt9USe2yEFkrTZnTiFmcqNEmXCWrQddghL6TBZ3pt2j7+9//nocffpjrr2/rlbz44ouZNm0a995775CLtoJjm9MmZ/BpS57tsu2VR5xoWx0Is+WQnx3lfqoaw9QEIlQFjP9rus5Fs3O4cVFhv/Jn/aEotz29jg93tRV1uO3U8dx15qQhFSvnFqSwoDCVVcW17KkK8t72Ss6c2rlipabpvLTuYKflz6wqGTLRVuTZDi+5yQ7mj0lhzf46dlUG+Gh3da9Cb2/ENd0sQpbmsjI+/cj6rB/xSBJl0y7mc589xl+SvWjoLC9ZzueKPodVsaLKKgf8B/DavFiVjoWnqpqrqGiqMIXB1TWbzHE983KOJxgNkmJLEeLQAJAkiRx3DlXNVRQfdz1Oi5OG5mpS7CkdRHBVVkkqPIUzP/0FzzkVIhKsKXmfadkLqAnVJEC0XYEGbLAbYoRDdZDtzhYFrAZAhjODQ4FDxjBaSSHLmdVpHZ/Dx4ljzmJVjTHk+GlrlAfqSqlKkWgIN/S7+JCma9Qe+IS17W41ilKKhPt9EKiySoGngE3hBsr1OG7V3eW/59yZN5C+61mqFIWVWiMXhhuobKocUtF2e/FS1sSNB/68WJy8xV/BZvV06BwYNP2IR2hFlVXGJY8jFAtR01wzpP8GPSFJEl+c/kXyPfk8seUJ4rpRJf4by75hjkw4LvM4pvmmAYaT9vQxp/Pa3teIalHe3PcmV0++usM+fU4flU2VyJJMUUoRNsWGrum9irYSUo+O3KgWZXf9bsqCZaQ70/slQK2vWM8TW5+gPFjeo3iTCG6ZcQvzMucN6TH6Sqo9lR+e8MMuX7ty0pV8cugTwvEw7+x/h7MKz+rkWpQlmTRHGqWBUrPjtDeC0SD7GvZhV+zIksyGyg3ma0K0HT6y3dlM901nc/VmypvK2VKzhRm+Gf3ez0jm2SZvew1P8ccARJ2plC35FvXhBmyKjXHJ4/o8wkmRus+0NVZQqc2ZQcb+laihBuyVOwhlTul+fcGw0m/5vKysjEWLFnVavmjRIsrKEluERiA4nFPb5douPQJybZsiMR79YA+3/GMNJ9z/HvPve5cbHl/Fz1/fzl8+3MdL60v5cFc1W8v8bC9v5IE3d3DyA8v5+8e958TG4hpLt1dw2Z8+MQVbiyLx6ytm8e2zJ3frek0kty5pc/I+8v6eLtdZXVzLwTojG/PECT6zmNSHu6ooqR2agmTtRdu5QrQdFm5cXGhO/+69XYOOL9le7qcxZLilRZ7tyFBZeAIXa3aklnO5tPgt84Yv2Z5MXajOePjTdYLRIJVNleyp28Oe+j3YFBuqrIKu8VG07fM4p+BUIvHIgN2BAvBYPWQ7s2kIN6DpGrF4jExnZqd/z2R7CotS2x5OPtr7Bi6ri+qm6sHlSGpx9JJVbLdaqGspCDsxZSJem1dkFA8Al8VFpjOT+lA9aY40vF24H5NsSUxMnchMmyFq7bdY2L79X8T1OBVNFf0+Zl2ojviBjnm2Ihph8KTYU8h15RKNR8l2Z3fq0AJIcWdxaktEQkyS2LnjFerCdWZV8qHgrRUPmNPnJE0k6Egmw5lgZ2sfC5F12kx1MDZ5LBJSl8WghgtJkji78Gz+d+H/mpm1rYKtIilcO+XaDuufP/Z8FMn4vnu7+G2aoh3vZxVZIcOZQXmwnF11u4jEI4bTtg+Ztt0JqnEtzt76vZQ2luJz+Izf2D6y9MBSfrXmV5QFy4ZEsJWQyHRmMjdjLt+c901OKzgt4ccYClLsKVw0/iLA6Mx6ZtszXa5nVazYVTt76/f2mukf1+IUNxQTjAbx2rxousbGKsOpaVfsTE6d3OP2gsRyRkHfCpJ1R0yLsal6EwBui5sJyRMS1rbesPjLyPrgIXO+7NTv0KhaiWpRJqRM6PJ+oTtUWe3x2UzTNWpzZ5vz7gMiImE00W+n7YQJE3j++ef5/ve/32H5c889R1HRyA6BEBz9jE93UZDq5EBtE6uLa6kNRkh1db4pHg34Q1FufHwV6w7U97quKkvEdR1dN9y49766lb98uI8vnTyOSVkesr12MpPs2C0KOysaeXHtQf69vpSqxrC5jxSnhT9fN58FwziMfMnEDCZmutlZEWDt/jrWFNcyv7Dj8f/VzmV7xfw8DtY186u3dqDr8PyaEu46a1LC27WuRbS1qjLTc8UQ7OHg3OnZTMjYxe7KAKuL61ixt5YTxg/czdc+GmE4r2lBG7qsYJtxJYt3PctHTgdV4Xo2Vm1kTsYcsyjZgcYDVDVV0RRrIhwPI0syDosDj8VwcAXLN7KpZeTAOCykOFIJRoKi2NEgyXZnU9FUQUWwAo/V06Wjz2P1kDHtciZ8+iN2W61sidTQEG5ARqY+XE+W2rtbqEsqtyKF/XzibTuHRclFiXXtHWNkuDKMc+LK6lLUcagO0uxpnDz+fD7b+ncAnvdv55uKg+rmavKieX0uAqfpGqWBUtIqt7PSboi2bsXOpNRJuC1CtB0seUl5RLVot4WhbIqNJZOv5PltjwKwrHoD06PN1IXqhqSQX3PNHt4JHgCLiqTrzJx1I4pi7dfDfp/o4LRt6NemybZkku3J1IfrR7yg1pS0KfzspJ/xmzW/MQtHnVV4FtnujiPDUh2pnJR3EstLltMUa+K9A+9x4fgLO6yjyArpznTKg+VGVq0W71M8QlduOE3XKG4opqSxhDRnGhbF0sXGndF1nRd3vsi/dv3LXJbrziXVnorT4sSlunBYHFhki5nf2hp9cPh063uI6THiWpy4HsdpcZLnziPHndNlJ8WRwPnjzufd/e9SF65jbcVaNldvZrqvc8Fzr81LRaCCfQ37mJI2pVvR/FDgkOmEliSJvfV7zbzcGb4Z/RLbBYNnftZ8vFYvDZEG1pSvoT5U36/RKTvrdtIcM8xHM9NnDl+Mi66R897PUVo6hOqmnEftmIU0NFdTlFzUIfu+L7R2MnV7OF2nPq/NHe8+sIrq427sd7MFQ0O/vzV+/OMfc9VVV/HBBx+wePFiJEnio48+4r333uP5558fijYKBCaSJHHO9Cwe/WAvMU3nP+tLufnEsSPdrE40NEW57vGVfHaw7cbVY1OZmpPE9FwvU7OTyEtxkOa24XNbSbJb2Fsd4Dfv7OT1TUYl2tL6Zu55ZUuH/SbZVfyhzgUbijLc/PWG+d0WcRsqZFniyyeP564XjB7ke1/dwou3LsJuMX4YmiNx8/24bSpnTc2iMRTlN+/sJK7pPL+mhDtPL0JVEvcDWB0IU1xj/MDNzPViU4XzazhQZInbT53A15/bABhuWyHaHrm4LC6sipWyiWdy2cYn+chpOOTf3P0Ks9NnI0kSbqub+lA9IS2Ey+oiVel8ntbuexe9RYQ6wV1oCrZO1Tms7+dow2lxkufJY3vddrKcWV0+wCuyQlLBCZz9sZ3dLRWDP931KqdMvszIQuzCndsn9n9q7KudS3NS6iSRZzsIkqxJFKX0LHynOlIZnz6LCVjZTYQtFoWKve/gKFhEeaCc8Snj+3SsmuYaqpuridbuotFr/PZOSJnY76HWgq6xKTYmp/XspJs49QpmffZnNlok9slx6io+o9yeQrYrO+Fu9c/e/zEHLMbj3mxLCqozDY/Vk3iB3uIAxQrxSL+ctmAMP89yZlHVVIWmayN+HfocPu5ddK+ZW3vlpCu7XO/CcReyvGQ5AK/vfZ1zx57bSZBTZZV0ZzqHAoeMCJtevnJl5E5uOF3X2e/fT7G/mBR7Sp/F0bgW56+b/sqykmXmsvPGnscXpn5hxP+NRxN21c5Vk6/ikY2PAPDU1qe4/6T7u/x9THOmUdFUQZI1iTHeMZ1erw/Vs79xPx6rx7wW2kcjDHseqgBVVlmSv4SX97xMXI+z/OByLplwSZ+3b/2Mw/BGI6Ru+jfug2sBiLgzKT/p69SGasl155Lnyev3/Zskde/iB6OwYiQpm1jyGNT6/TjKNyFHgmjW4dUWBF3T72/syy67jJUrV+Lz+fjPf/7DSy+9hM/nY9WqVXzuc58bijYKBB24Yl6eOf38mpJBD8NONLXBCNf8ZYUp2Ka6rPzrK4vYeM9ZPPflE/jhBVO5bF4ex49LY0KGm2SnFVmWmJDh4U+fn8d/7ziRUyd13XvWXrC1KBLnTMvir9fP5/U7Txp2wbaVC2flMC7dOPbmUj8//e9W87W3t5YTaCkIdt6MLBxWhYwkO6e3xFxU+MMs21GV0PaIPNuR44KZ2Yz1GdfCp3trWF1c28sWXaPruinaJtlVJmcJV+Zw47a6SXOkUS9pzBh3Nlkx43O8sXabWRkajJiEZFtyl1mmDU3VPF291pyfl38S4XiYdMfoqHR9pJPpyiTHldOhANnheG1e5uadjNLyO/lB2Se4LK7BDcc+8ClNksS6ljzbDGcG2a7sPueqCbrGa/P2KKR4bV5cVhcXZC40l72y7w2S7ckcDBykNtT7921Mi1EaKMUWCbIu3lYpfVLaFFEYcBjx2JI4Na0tumT1ntdojDTSEOmfQ7U3tMYy3jn0kTl/QtFFhONhMpwZif8OliSwt7h3+5hp255Ueyoeq4dAtOeh58OFVbFyxaQruG7qdd1mded6cs3c1rpwXYfcy/aoskqGKwN/1I9V7kVwlYyCZa3xQ3sb9rKxaiP7GvaRZEvq8/dsJB7hwTUPdhBsr5t6HddPu14Itl1wct7JFCYVAlDsLzaHwx+OKqt4bV6K/cXsrd9LTXMN0bgRoxGJRyj2FxPTYh2iZtpfFyLPdmQ4reA00+X+2p7XqA/X92m77bXb+eDgB4Ax4mW4RFtrfQmZH//RnD90+vfRbG50dFLtqQPq3JMlmZ6SUTTdKJSojzeiTWQtjuvgun4fRzA0DOhbe968eTz11FOsXbuWdevW8dRTTzFnzvCGMguOXYoyPcwpSAZge3kjm0v9PW8wjFQHwlz7lxVsLTPa5HPbePZLC5k3JqXPGbPTc7387aYFvHzbYv73/CnccuJYzp+ZzfwxKeQmO5iVn8w9F05l5ffP4JHr5nHG1EwsCXSq9herKvPHa+diU402PL3yAC9vKAXgX+tKzfUundsmtl+zoMCcfnbVgYS2Z53Isx0xVEXmtlPbsp5+996uAe1nT1WAmqCRbXdcYSrKMOQzCzqT4cxA13XqZl3B3bVtYsLfN/21Tze8T398Hw0tX02nx1R8Y07GqljFMPoEYVNsTEub1uOQao/Vg23apSxqNqJ0KrQwe2p3EolHqGmu6f9BdR39wCessduItYg+U9OmGnl/wmk7pNgUG2mONMYWnU92S+b9Cj1Alf8gOjoH/AdM8aA7apprqGmuIae2pEOe7UzfTPG5HEacFidz5/wPbs1wwC8PVxIKNVDdXN3Llv0j+MGveNNpnGcbMjNyT8Cu2hMfjdBK65DjfjptASyKhSxnFsHI0GX7DgVnjGnLy1x6YGm366mySo47B6el51EmMjKN4UbWV6xnS/UWihuKCUQDeG3eXrdtRdd1Hv3sUVMsVGWVr835GuePO79P2x+LyJLcwX35xr43ul3XaXFiV+0UNxSzsWoj6yrWsbNuJ8UNxVQ1V3Uo9OmP+NlTb9T8yPfkj3j8x7FKpiuTE3JOAKAx2sjjmx7v1fQV02I8tukxc/7qyVcPT+57PEbu2z9Bbqk9UDPjMoIFxwFGdvRAR2P01lnTOspBH7/EXCZybUcPfVZ6/H5/n/4EguHgyvn55vTza0pGsCVt1AYjXPPoCraXNwKQ4TEE24mZA3sQmpWfzC0njeN/L5jKH6+dy4tfWcTH3z2Nl29bzE2Lx46qLN8p2Un89OK2/KfvvbSJj3dX89Euw0Wbl+JgQbus25MnppPjNR4klu2opKyhOWFt6VCErECItsPNxbNz2hWbq2b9gbpetujMp3tFNMJowGvz4rF6qLW7mDv+XM4OGA/TjbFmHt/4aI83vGv2vM4HYSMaJTke57oF3yQQayLJmiRyMxNIb245WZJJSyviVFtbfu3HO/9Nki2JkkAJDeF+OvvqDyA1lneIRpiYMhGH6uhzxqJg4KTaU5EsDq6wt3WCvrHp76Q50qhuqqY0UNrttlEtSmmgFKtixVqyivWtTmnVxcSUiUdsHuWRSpZvCktkw90ckiX27XqVqqYqMztx0ASrWbn9BRpaigUelzGXuB7Ha/UOXTxNazGySCPEO0d59UaaIw2bYhtcocRhZlb6LDNTfEPlBmqbBzbCqBWXxUWKI4V0VzpZ7iwyXZmk2FP6NZLhlT2v8FGp4bC2KTa+t+B7LMrtXERc0JHjso4jzW4Irusr11MW6L7AutvqJtOdSbozHSQobSxlf+N+Uu2pHcSxz6o+M4ekz06fPaTtF/TMDdNuMDsnV5Wv4tNDn/a4/hv73qCk0dAYxnnHceaYM4e8jQAZKx7FWWHEI4a9uVQs/qr5mo4+YKe8LMk9xrO0Om2lwpPRW6I9nEK0HTX0+awnJyeTkpLS7V/r6wLBcHDBzGzsFuPyfXlDKaFofETbE9d0vvbP9eyqNIZ1ZXvtPPflE5iQceyIE1cel8/lLdEVTZE4N/5tFVqLpnPpnNwOTmNFlrjyOEN413R4fvXBTvsbCOFYnM9KDRFiTJqTdE/XQ9oEQ4dFkbltSZvb9vdLd/dr+1A0zqMf7DHnF44beC6uYHBYZMP51BRtouLE2/mGlE5K3PiuXVW5jhXd3PA2hv08tvUpc/5292TsOXMJx8JmYQ7B8JFsS2ZC0Xl44i25tvU7kCWZSDxCSWMJca0fv58HVhj7aBFtJSQKPYWk2MT933CQYkvBa/Mya8J5JLd8Fj/w76GmuQav3UtJY/dCfE1zDbWhWtJ02Lf3PaItn8Oi1Mn9KsoiSAxem5dF484z55dXriUUC1EX6n9HZ1dEP36I1xxtQvziMacTi8eG9ju4/XUU7r+Rx21143P4aIw0Jq5NQ4wsyZyafypgCCrvH3x/UPuTJAmrYh2wMLOmfA3Pbn/WnL9t9m1M800bVJuOFRRZ4azCs8z5t4rf6nUbWZJxW91kuDLIced0Etfb59keDdEIUjyCpeEgzoNr8W57A9/qv5G+8q8oA3DXDzdem5cvTv+iOf/45sep7ybKpbq5mhd3vggY9zlfnPHFYYkVce1fQfo64/5Zk1UOnv0TdIthhNF1HQkJeWAD5Xttv46OIilI9iTIXwCAvaEUS0P3ncGC4aPPZ33ZsmUsXbqUpUuX8t5772Gz2XjyySfNZa2vCwTDgcdu4bwZRiVXfyjGW1vKR7Q9D769g492G8PafG4bz33pBDPb81jipxdPZ1KLszgab3PhtY9GaOXK+fm06rjPrT5ANN65Wm5/2XLITyRm7GeecNmOGJfOzSM32bjJWLq9kk0H++7m+9Oy3ZTUGk6jheNSmZk3RMM4BX0i1ZGKXbXTrGsEz/0Z32vniv/7xkfwd/Fg/syKB6iTjM/hKeE4M078LpF4REQjjBAuiwu16BzODhlD50PorDqwnDRHGuXBciqbKvu8L23/x5QrCnushhg0IXkCbqsbj02c1+FAkRVy3Dk0Zk7jypDxGxuX4PWdL+G0OInrcYr9xUS1tpiEqBalsqmSA/4DOFQHvs/+xavONlf05IzZ4nM5AjgtToqmXs6kmHEet8lx/FVbOOA/QFNLtfCBEmgoIbDmcd5vKSCZbPEwzjsOp8VJin0I741anbYAzQMTn9Od6ei6Tkzrv1N3pDgl/xQzL3NZyTI0ffD3swPhgP8Af1j/B9PZeeWkK1mQvWBE2nKkcnrB6Wbu8PKS5YP6LGq6xsYqo1CzQ3UwKXVSIpo4IsjhANlLf8GUR05n4hNXMvbfd5D37k/JXPEXMlY9TsErdyH1Es8zGliYs5CF2UYufCAa4K+b/trlqLF/bPkH4bgRK3XmmDMZn9y3Qp+DQQ1Wk/fOT8z5ykVfIZQ5xZxvjS+Q5YGJthJSr5m2SBjC7fjTzeXuA6sGdDxBYunzWT/llFPMvyVLlqAoCgsXLuyw/JRTThnKtgoEHWgfkfDCmsQ4NQfCm5vLeHi54QxUZIk/XjuHgrRjszK6w6rwpy/MxWVty9uZNyaFwi4E7JxkB6dOMgqSHWoI8dK6wZ/D9nm28wqFaDtSWFWZW5e03eDc99rWPony+6qDPPL+XgBUWeK+S6YLV+YI47K4SLOn4Y/4iSZlM/Xk73Nm0HiIadAiPLH6Nx3WX3/wI5Y1Gu5qT1zji9NuQre5CEaDuC1uEY0wAkiSRLonmxNSp5rLPt7zOqqs4rQ42e/f3+cHU+3AJx2iESanTsapOvFYhOg3XKTaU3Hbkjg94zjsLZmobx9czsHGg6TaU6lqqqI8UE5zrJnSQCkbKjewqWoTzfFmkiWFvdv/w+tu4zfZpThYkLVg6IbLC3rE50znFG+bkLN216sEo0EONB4YkPAX1aKU+EuoW/4z3rFJppv6hLwTaY43k+nK7LaoVkJo77QdQDEyMNzkSdakI8ptm+HMYEa6UViusqmSLTVbhr0N/rCfX63+FaG4ES2xKGcRn5sgCoT3F7fVzUl5JwEQiodYXrJ8wPvaU7/HvI6n+6ajtgw5P9JwF3/KhGe+QOqWV5C6GZnjrNxGxopHh7llA+PmGTfjtRqGkDUVa/i49OMOr6+rWMfq8tWA4c69avJVQ98oLU7u2z9BbXEsNxYuomb21R1XaRVtB+G0lXrIR9B1HVVSjeeulmJkAM79PcdICIYHUT5ScMRy/NhUxrSIox/vqaakdnDOhIGwuzLAXc9vNOe/f94Ujj/Gh3OPT3dz/2UzzfnrTxjT7bpfPbVN2Pv90t2mS3agrCluJ9qKImQjypXz88huyS1eua+W//335h4zUHVd50cvbybSIu7ectI4JmQIIWg00Op8imtxAoWLuDX/LHNo9kf127nuv9fwxdeu4ytv3MhDG9qq3d6h+LBONob/hmNh0h3pomr1CJFsSyZ9yqWMjRhOmM/CVVQ2VZJkTSIYDVLSWNJrUY5IYzlq9a4Oou345PGkOFJEnu0wYlWsZLuy8Y8/mRsaDEEgjs5jG/+MLMl4rB6K/cWsr1jPtpptRLQIGa4MfA4f3k0v8YukNtHugvEXkZ+ULzrHRohkWzJzZ91oiu/vhcrwKFbKAmX9LkpWG6plS/UW9h/4gNzP/sV/3W1C/AnZJ6BKqpnXOWTY242MGeBwaUVWyHZnE46Fe/1OGk2clt8mciw7sGxYjx2NR/nN2t9Q1WzUkRjnHcets24Vn+sBcs7Yc8zpN4vfHLBzukM0whGYZyuHG8l592eMefUuLAFjRE7c4qRhwmlUz7mGspPu5NCp30FrEaN9657GdQRkoCZZk/jijLaYhL9t+RsPb3iYh9Y+xK9W/4pHNj5ivnbdlOt6LPaaKLyr/4b74BoAoi4fpWf8AA77/Lbm2Q4m01aSpG6vZ03X2oqcZc9GdxhZ3e6D6waUUS5ILOLpSXDEIkkSV7RkqOo6/CsBTs3+EAjH+PKTawhGDPHiwlk53Ly4cFjbMFq5aFYOL9x6Ao/fOJ+LZ+d2u968MamcPDEdgIN1zby4duDnUNd11rYUvfLYVIqE4Dei2FSF310zB6ti/Mw8t6aEPy7rPt/2jc3lfLjLeEjN8dr52ukTul1XMLwk25IN51PUEIiiC7/CN2krEBdFJ6hHqYuHCLeMvVoUijD35B8ar8ejqLJKki1p+BsvAMCu2nEVLOTseFvG5ce7XkGSJFIcKZQFyqgJ1XS7va7rVO98DY22PFuH6iDPnUeyLXmIWy84HJ/DRzxjCldbs8iPGkL8tvpdfHBgGW6rG0VWUGSFLFcWybZk42EtFubNXf9pi7Zw53FCzgnCJT2CqLJKQeZsTpKNcxCQJbZueQ6LYqG4objPBbkqghVsrt5MfaiOmaufpFTSWG83Pqd5njxS7Cmk2A0H65DSPh4h1M8ih+1IsafgUB00xYbfjDFQ5mXO61DkaLicwpqu8aeNf2J77XbAcCp/a/63RGHBQZDvyWeGr805va5i3YD2s6Fqgzl9ROXZ6jpJO99lwtNfIGXba+biQP5x7Ln2SQ6eex8VJ95Bzawr2TluEcXH3WCuk/vOT1GaBleMbzhYkL2ARTlGcb5gNMj7B99nRdkK1lasxR8xYr+mpU1jce7iIW+L49Bn5Kz+OwC6JHPwrHuJOzobj1oLhZnCaj+RJAlZkrsXbdFQpJZ9yzLSeCOrW402mYXRBCPHoERb0YMnGGkum5dn5qK+sOYgmjY8vfKhaJxvPreBPVVGNfVJmR5+edkM8Zlox3GFqZw2ObPX9b5xRpE5/YeluwjHBlZU7mBdM1WNRv7Q7IJkFFmci5HmuMJUHrxyljn/4Ns7+c/6zoH2gXCMn7y61Zz/0YXTcFqPzGFkRyOqrJLpzKQ52pJnKytMPvN+bg/GmRUKMzkcYWwkSm40RlrMWHbHmPOJJRm54w2RBrw2r8jNHGHS7GnMy12M3OJee/3gcmqba7EpNmRZZn/D/m5FosqmSpTN/2ab1UJ9SzX6KalTcFtF5MVI4LQ4yXRmsuPkO/muP2Iuf2bT4wTCjXhtXtxWd4d7kujGZ3nUZTiiZeDKadcb588qzt9IkmJP4YQxZ5jzL5d+gEe24Y/4++SAb4w0srdhL4qsMK58G0n7P+UlT5sz7MTcE4lpMTKdmUN/j5qAeAQwOoQynBkEIoFBN2m4sCgWTs47GYCYFuPDgx8Oy3Gf3vY0n7YUBbXKVu6afxepjtRethL0Rnu37Rv73uj39v6wn731RtxXgaeANMeRMQrTcegzxr7wJfLf+hGWoOHcjlucHDr1O+y/+CGiLfd1MS1GRbACt8VN0infxV9wPACWplpy37kPRijXuT/cNP0mMpwZXb7mc/j4n5n/M+TfmUrIT+5bP0Ju+feqnH8DTXlzu1x3sPEIiqQgSVK3vymarnWM8GiXa+vc+8GAjilIHH1+Kr700ks7zIdCIW699VZcro6W8ZdeeikxLRMI+kC218HJE9NZvqOK0vpmPtlTw4lFviE95vZyP3f+cwM7KoxedI9d5ZHr5gmRaYDMKUjhtMkZLN1eyaGGEM+vLuG6Ewr7vZ+1+0U0wmjkolk5HKxr4oE3dwBw94ufkeW1s7AlRiQUjfObt3dS7jfEolMnpXP2tN7FfsHwkupIxdHooDnWjEN1oDlTOemypzizeg9KqAEl7EcJ+VFCDUQ9WdRPOR+ASDyCrunkefJENMII47V5sU37HOe88Tavu5006jH+vPrXfOekn5JiT6EiWMGW6i0Uegs7PGAGo0Fqtr7E1H0f8qq3zalXlFKE2+rGaRF5qCNBujOdUm8e2affw1kf/5S3XQ4aiPGvT37GDaf+osO6UjzK48X/pdlmCO5nZywg3Z5Orjv3iM1ZPFpwW91MmXoVRXteZpcKuxWdV5f/gHNPu5/SQCkp9hR8jq7va6PxKHsb9tIcaybb4iHrg4f4R5KHvyYbMQUSEnMy5uCxeIa2AFkrHQqR1Q9qV06Ls8eiOaORU/NP5bW9hjNxWckyzh177pCKPq/vfd08noTEnfPuZEKKGKWUCOZkzCHLmUV5UzlbaowCgQVJBX3efkPVBrMg3KyMWb2sPfJYGg6S+fGf8O5Z3mF5Y8FCyk67m6gny1zWHGumPlRPtiubccnjDFf8pX8l+ueTsTTX4TmwgrQNz1Ez55phfhf9w2P18MDJD1AWLMMqW7EoFmyKzfwb8k4uXSfnvZ9ja4meqM+aRvn867oV5zRdQ2bg8QhSy396N1+suq53dPFOPBtdUpD0OMm73qV68W0g7uNHjD7/y3u93g5/X/jCF8jJyem0XCAYbtoXJHt65f4hO46u6/zt431c9IePTcHWpsr87po5jO2i0Jag73zjjInm9B+X7SEU7b/btn08hhBtRxdfOWU81ywwbnYjcY2b/76aU361jGk/epPJP3yTxz/eBxgFzO69aJpwrI9CXBYXaY40/CG/uUxXbTRnTSVQeAINk86mdtYVVB1/C/VTLzCzuGqba8l0ZQ59lqKgV1RZJc03mRtSZ5ERM/LJNvr38Paul5ElmUxXJoFYgM3Vm9lTt4dIPEJMi7G/ZgeFH/4WoGOerXd8t2KSYOhJsibhc/goTS3g+qnX42rJRX0jWEzJ1n91WHfH+sdZ2iLYpuoyZ069miRbUrcuI8HwkunO4ppJV2NpcUA9FynjwNYXkSSpWwe8ruvs9++nqqkKn8NH2qrH+Z01zINpbfc/5487H7tsJ9OVOTzD5RPktAUjf7E7cWG0kufJY2KKcT9b0ljC7vruI6EGy4pDK3hy65Pm/C0zbmFe5rwhO96xhizJnD32bHP+xZ0v9jnbNhAJ8Nz258z5ORlzEt6+ROI6sIoJT3+hg2AbSh3Lvgt+xeaz76HKYqe2uZbq5moqghU0hhsZlzyOSamTcKgOAJzJBYQu+p25fcYnD2Ov2jHcb6Xf2FU7Y71jyfXkkuHMwGvzYlftw/IckvrZv0hqcbBqjmR2nPpttB5EUQ0NSZYGlWnbUzyCjt6xE9flMwuS2QJVOA9t7HI7wfDQ5+71v/3tb0PZDoFgwJw+JYNUl5XaYIQ3NpezbHslp05O7INIdSDMt1/YyLIdVeayyVkefnfNHCZmiiG/g2VGnpczp2byztYKyv0hnl11gBsXj+3z9h/tqjbzUHOTHRw/VghEowlJkvjpxdM4VN/M+zuraIrE2V/TOavuq0vGMyZNdICMVnLcOdQ01xCIBPo0pDoYDWJX7eR58oQQP0pItiez/4Sv8oM3v8OdqjGs/pkdzzEtcw753kJ8Dh+hWIh9/n3Uh+tJtiVjW/kITn8ZTZLEupaczHRHOtmubBF5MYJIkkSmK5PKpkqYdjE3Vq3nj02GSPTo9n9y7e53KFdkKmT4OFIDivEZvL7wAiQk8jx5IvdylJBsS6Zg/BlcX/UZj/mNqKDf7nuFX2bPo84R5bOqz8j35ONz+rDIRsRFRVMFJY0lpNhTsNTs5jcH3+a1dk74KyZewfnjzicYDQ7fcPkEOm17q3Q+Wjmt4DR21u0E4LW9r3Hn3DsT/vu3rWYbf9jwB1PUvrToUk4fc3ovWwn6y5L8JTy/43maY82sKl/Fb9f9lttn395j4U1d13n0s0fNjPgpqVOYnDp5uJrcb2w1e8l/4wfIceN+IOpMper4W6iZch6VzbUkKRYssgVVVo3/Syoem4d0R3qn69oz5SICC/4H96q/IGsxMj95mP0XPzQC72r0Y6/aSeZHvzfnYxf9npg7DbWHjgFdN0TVgX6ftIq93XWGabqGRep4bUszr4Td7wDg3fE2TbmjuwPiaEZ4nAVHPDZV4e6zJ5nz333pMxqaognbf3UgzGUPf9JBsL1pcSH/uW2xEGwTyNfbZdv+cXnf3baapnP/G9vM+W+fPQmrKr7aRhuqIvPHz89l4Tjj4THZaWF8uosFY1M5b0YW3zprIrefKob1jWaSrEkUeAoIRALEtJ4ryWq6hj/sJ8edI4S9UYTH4iHJlY5ryff4fMDIAI9I8PAn9xGNG7+bdtVuum7LSj5i7GdG7NUah4NYy7NCa56tSxWdLCNJii0Fr82LP+xn0Sn3MBFDhN1lVfmxXM+f9Vr+E6+lqkWwnaOpTJpwLl6bl3RH+kg2XdAOi2Ihy5nF9Nm3sBAjbqRGkXns4/vItKUQ1aNsqdnC5qrNVDVV0RBuYG/9XqyqFXSdX3/yU15zG9vJGK7LyyZeRmOkkTR72vAVm7O3G3GZAKftkcjC7IWm+3BF2Qoe2/RYnx2afWF33W4eWP2A+Ru8JH8JV0y8ImH7F7ThUB18eeaXzeJMK8tW8svVv6Q51tztNksPLGVV+SrAGKF025zbRu21rDTVUvDfb6NEjPos/rEnsvu656ibfgnVLZ22033TmZ0xm+m+6UxKncT4lPFkODO6FQ5dZ/2MaJJRgNp9YJVwZ3aBHGki780fIWvGPVdo/s1IE8/t0QULbZm2A0WSJBRJ6TbTVkLqfF4nnYemGp31SbuXIsUTp68I+sfo/BYRCPrJVcflc/JE4wGkwh/mx/9NTJXDpkiML/59tekK9Llt/P2m47jnwmnYLQOr3ijommk5Xs6ZZmQmVTWGefLTvkVdvPrZIbYcaqn0mZPERbNyhqyNgsHhtqk8+6UT2Pvz89jwo7N4764lPP/lE/jT5+dx+2lFqIr4SRrtZLuzSXemU9vcc3Vgf9hPkjWJHLf4PI4mJEkiw5FBoz2Zz53wHcZHjBvwvfEA/1n1a3M9WZLx2dOYtfoJ5HiUMkXh59l55usTkieQ5kgbcBVjQWJQZIU8Tx4xLUYcnRuO/y5SNyPKM2Jxbpl+M6F4iHxPfo9uMcHwk+JIwWqxcd2J95DWUlT3UzXOx8vvIdmWTIYzg8ZoI5urN7O9djuheAhHQzkPvHUrKxVDwLPpOt+ccydnjDkDTdeIa/EeBZaE0yEeoWFQuzKrmB9h2FU7X5zxRdMl/O6Bd3lk4yMJEW731O/h5yt/boqGs9Nnc8uMW8RIliFkYc5Cvn3ct7EpNgA2V2/mp5/+FH/Y32nd0sZS/rHlH+b8l2d+edRGCEmxMAWvfRervwyA5vRJHDz7x2hWFw3hBqyylfHJ480OiD7vV7WhLPmeOZ++8q8JbffRQPb7v8ZWfwCApvRJWM/+ea/RBWA4bQfzuy0jI0lS9/EIut5ZFLa5aRxnFFhUw42493864OMLBod4QhYcFUiSxC8vm4HHbiR+vLSulHe3Vgxqn7G4xm1Pr2PjQePGM9tr55XbF7NkksiAGyq+fmab2/aBt7bzwc6qHtaGcCzOr95qy0z67rmTkWVx8zraEefoyEWVVcYkjcGiWLqt7B3TYoRiIQqSCswHHcHowWvz4rA48OfM5Nu5Z5g5mi9Vr2fl0h/h3vE2ttp9eHe8iadkDSWqyvW5OZRqRq5mpjOTKWlTSLIm9XQYwTCR7kgnx51DbXMt43xT+M7x3+XCsRdwfdEV3DXlBn4x/cs8PuMOfn/675FyZpNiSzliKpkfS3gsHpJtyWhWJ7dPud5c/udQMQdevQ3flpfJikaMgmKBCvJW/IX7PvoBWzEc80nxOD8ZfxXzc08AoD5cj8fqIbm9kDrU2DzQKrYmIh6hB4FhNHNi7oncPud2UwD54OAH/H7973sdodIT+xr28fOVP6cpZphIpqVN4xvzvyEKCQ4DszNm84OFP8BlMUaW7G3Yyz2f3MPykuXUhYwiyJF4hN+t/x0RzYgZOKPgDBZkLxipJveMrpP77s9wlm8GIOpK58AFD6BbjGKz4ViY8cnjB/zdIc+6hnhKIQDug2txHlyXoIYf+SRv/S/J298AIGZxEPrcn5AtDhRZMURbenDaoqFKA/+8m9+pPRyjKydvYPJ55rR3x9sDPr5gcAjRVnDUkO118KMLpprz3/v3JuqbIgPal67r/O9/NpuRCB67yt9vWkBOcv96HAX9Y3JWEl9YaBSsisZ1vvTkGtYUd+/oe/LT/RysMxwHJxX5OKlIDPcUCIYar81rxiTEtc4xJnWhOtKd6WL49SjFaXEaReXCfpIX3MqXFOM86ZLE/zXt5LubH6bpxZvIe+en7LWo3JCdQXnL8PpsVzZ3zb+LZFuyiL0YJUiSREFSAR6rh/pwPbMzZvP5aV/gvEmXcdz4cyksPBXnmMXEXOlEYhFyPblmLqpg9CBJErnuXCQkxo49ncuSjIJWMUnibqmGn2z7B+ozVzH5nzeQ8+KX+Lp/PbusxnlMi2v8YswljJl6KZF4hPJAOQqK0cE2nOdaktoiEgYZj6BISq/Os9HM4tzFfH3u103H8KeHPuW3a39LJN7/55LihmLuW3EfwagxjH1K6pQO7k/B0DMxZSL3LrqXVLsR8VUWLOORjY/wlXe/wt3v380vV/2S/X5jhGCeO4/rpl03ks3tkfSVj+Hd9S4AmmrnwIUPEHOnE9Ni1IfqGeMdQ6Yzc+AHUFTkdm7bjJV/gW6G5B9L2Kt2kL38QXP+4Cl34c1qy4hVZbXXeITBiLaSJKHQczxCV6JttPBEInajk96z7yPkljgNwfAiRFvBUcXl8/I4raUIWVVjmHtfGVhMwm/f28Wzq0sAsCoyj143n0lZ4gF1OLj3wmlmTEIoqnHT31azubTzMLuG5ih/WGYUXZEkw2UrEAiGh2x3NmmONGpDtWi6RlO0iermasoCZVhkC/mefDF0fhST487BrtoJRIOceMYvuUBr65DcbLPx+ZwsvpOexk3ZmVSpxkNCniePe064B7tqJ8WeIopYjSIcqoNCbyHReJRwPNzlOg3hBlLtqaN2uK4AUu2p5LpzqQvVcfGiHzDf1tbx9bHTwaW52TwgN3BTRgolFkOMzZTt3HPKL0mZdS31oXrqQnXkuHOYlTGLTNcghJeB0lqMLAFO2yNZtAVYkL2Ab83/limcr65Yzd3v383Gyr7lfGq6xtqKtfxsxc9MwXZy6mS+s+A72FtyJgXDR74nnx8v+jH5nvwOyw80HmBLjfG8aZEt3DH3jlErqCdv/S8Zqx8HQEfi4Nk/JpQ+CU3XqG6qJtuVTYGnYNCRG9KMK4injgfAdWgjrpI1g277kYwc8pP/elvBt4OTz8Yx5/oO98mq1AfRdpDOekXuXrTV6SIeAVAsdioKFwMgxyMk7Vk+qDYIBoYQbQVHFZIkcf+lM0hqiUn4z4ZD/PXDvd1+QR2Orus88v4eHnp3l7nswStnccJ4MZRwuFAVmd9eM5uTiowHy8ZwjOsfX8Xuyo5DsR9evof6loJzl8zOZVqOt9O+BALB0GCRLRQmFaJIClVNVUTiEXx2H1PTpjIjfYYxhFcwamktKtcYaURT7Xzhwse5d+ZtjLG1/da97nZRqxgPFGO9Y/nRCT/Ca/OiaRrJtuQRarmgO9rHJLS/52ktChjVouR6csVw6lGMJEnkJ+WTbEumMRrgrjN+x9fm3I7PatzfxCWJF5I8VLR0pOQ6s/jRqb8mxZNHebAci2xhWto0JqVOModyDzutQ6pDDaANXHA9GkRbgDmZc7j7uLtNEa+8qZz7V93P/639P2qaa7rcpiHcwMu7X+ZrS7/Gr1b/isZoIwCTUiYJwXaESXem84uTfsG9i+7lc0WfY5x3nJlfDPD5KZ9nTNKYEWxh97iLPyVn6S/N+YrFt9E47iQA6kP1JNmSGJc8LjG/EbKCcur3zdlj2m2ra+S9/WOs/kMANPiKqF3ybVIdqR1W681piw6yPDjpTpGUfscjKJJCxfiTzXkRkTAyiDs3wVFHZpKdH188jW88Z/Rk3/faNj7eXc0Dl88i3dN9z2dNIMy3XthoRiIA/OC8KaKw1QhgUxX+fN08rn9sFWv211EbjHD1o58yJs1FXVOEhqYotS3RF1ZF5q6zJo5wiwWCY49kezKTUycjSRJui1s8SB5hZLmzqAnVUBuqJd2ZzuSCk/h53iLe2f8OL+x8wXR2FaUU8d0F38VlcRGKhbCpNhGNMAppjUloCDdQF64j2ZZMQ7iBUCyEx+qhKLmINLvogB7t2BQbhd5CNldvJhwPsyj3ROZlHcdre1/j5d0vm07qwqRCvn/89/FYPVQEK8h151LoLex34aCE0xqPgA5hf5vztp8c6fEI7ZmRPoP7TryPxzY9xvba7QCsLFvJhsoNLMlfgiIpRLQI0XiUQDTAhsoNxPWO0UOTUydz93F3j/z5FaDICpNTJzM5dTJXTboKf9jP1pqtOFQHszJmjXTzusResZX8N36A1HJd1cy6gpo51wBGHm9Mi1GYlODvj2mfQ/vgAeSqHTjLN+M+sJLAmIWJ2/8RQvrqv+NpKeAVtXkoPvteitKmdYquUWW1R5OZhDToAo3dHUPTNSM+oYv9y5JMQ/pEIkk5WP2HcB1cixqsJuYSo3aGEyHaCo5KLpmdy/ayRv78wV4Alu2o4pyHPuCBy2dy+pTOw8U+2V3N15/bQGVj27DCr502gVtOGjtsbRZ0xGlVeezG47jm0RVsLfNTHYhQHeicBXbDojHkpThHoIUCgSDdKXJrj1Ra3dKfVX9GU7QJp8WJIiucM/YcFuUs4o19b6DpGpcUXYJDdaDrOvWhenLcOUI4GKW0xiRsqd5CZbASr83LeO940hxpIs7iCCLNnkaeO49ifzGZrkxsio1Liy5lSd4SXtv3GpquccXEK3BanFQ3V+O1eUeHYAsdRdpQw4BF26PFadtKviefe064hw9LP+TprU/TEGkgHA/zVvFb3W4jITErfRZnFp7JnIw5XbrgBCNPki2JhTmjV4y01h9kzKvfQo4ZxUQbJpxK+YlfM7LlgNpQLbnu3MRH58gK8pLvwws3AJC+4lECefNBOXbkJ/f+FaSvfAww4ih2nPYdCvIX47R0fm61yJYev++6iy/oD7Iso9NZtNV1HUVSuozFMAqYyTRMOov01X9H0jW8O9+lZs7Vg2qLoH8cO58awTGFJEl877wpLByXxrdf3Eh1IEJNMMIX/7GG82ZkkeN14LAq2C0KFf4QT67Yb47aSHNZefDKWZw6KWNk34QAr8PCE19cwHWPrWJbmR8At00l2Wkh2WlhanYSXz9DuGwFAoFgICTbk8nz5LG3fi921W4+ECTZkrhq8lUd1q0P1+O2uBmTNGbQeXeCoSPdkc6E5AlYFSupjlRRdOwIRJIk8jx51IXrqA/Vm8NoUx2pXDe1rcBRMBo0Cpd5x44OwRba4hGgpRjZwIaKt1ZTj+mxRLRqVCBJEifnncy8zHk8t/053tn/TpcCisfiYUn+Ek4fczpZrqwRaKngaEFpqqXglW+gtmRMB3NmU3rmj6AlS9Uf9uNUnQnJse2SKRehZUxFrtyKs3I745+9gfKTv0Ewf37ijzXKSNr5NjnLfoXU8hnfN/dafNOv6DY+rKe8Wei+UFh/UOk6gqE3p62OTv1EQ7QF8O58W4i2w4wQbQVHNadOzuDNr5/Md178jPe2VwLw+qbybtc/cYKP31w1iwyPGOY7WvC5bbx2x4nUN0fx2FUsinAaCAQCQaLIc+dR11xHbai2W6dNOB4mGo9SlFbUpUNEMHpozUUVHNnYVTuFSYZrOhAJ4La6O7we02L4w34mpkwkzTGKYi/aO2sHWYxMlVUisc4jrI50XBYXN8+4mYsmXERZsAyrbMWqWLHKViyKhVR7qsieFgwaW/Ue8t76EbaGUgBCqWM5cP4v0FUjKjCmxQhGg0xNmzp0v+uyjHzO/ehPXoqkx7HX7qPwP1+jYcJplJ94BzHPCBRLHGKUkJ/s5b/Cu+s9c1lV/nykk+8i09n9+1UkpUM+8uEkwmmryEqXom3rvrs6fmu7wiljaE6fhKNqB47K7aStf5aa2VeCGAEwLIh/ZcFRj89t4683zOe+S6bjsHSdBaPKEt85ZzJP3LxACLajEFmWSHVZhWArEAgECcaqWBnjHYOu62aObXs0XaO2uZZcTy4ZTjECRSAYLnwOH4XeQmJajPJgOc2xZsAYylrdbFR6z3GPsroLnZy2A6e3aupHOj6Hjxm+GUxKncRY71jzO1YItoJBoWukrf8n4567GXvtPgCi7gz2X/x/aPYkc7Xa5loynBlD/7s+bgnxm98gkDHZXOTdvZSip64hZdNLQ3vsYcZ1YCXjn/lCB8G2fNyJ1J3/AAVJhT26mVsdrT0xaNFWUujqEJquGZm5ctdO29ZYhbppF5rLsz76HWNe/gZqoKrTNoLEI34VBMcEkiTxhYVjuGROLvuqgoRicULROM2ROJG4xqy8ZPJThXtIIBAIBMceafY0CpMKKWksIRgJkupoc3rVhYyiVkM2fFIgEHSJJEkUegvxOXwcCh6iIliBP+RHlmU8Fg9jvWNHn8CXYKft0SzaCgSJRm2sIPfd+3AfXGsuC6WNp+Tc+4i528TZpmgTqqwyJmnMsHyHqPnHE7j+3xz49A9MXPc0anM9cixEzvIHCaVNoDln5pC3YahJW/9Psj76vTkft3nYvfirNBSdwaz06V0Kou3pzukKhqgqS/KgC5HJskxXh2jdv9yFn7PVaavpGnXTL8FafxDfhmcBcJesZvw/r+fQad+lcfwpg2qboGdG2S+9QDC0uG0qM/K8va8oEAgEAsExQqs4lGJPYb9/P5VNlbgsLiyKBV3XKfQWYlfFKBSBYCRwW91MtE4k05lJWbCM+lA945LHjc6oEnu7e+zBOm2FaCsQ9Ioc8uM6tAHXwXUkb38DJdwIGIWvauZcQ+UJX0JvV4gyGo/SEG5gfPJ4vLbheyb2OTMon3UlK8YtZtraf5Ky9VUAst//NXuvegxGWwdUP5BiYTI+ecScD+Qfx4HTvsMhWWJ6UgE2xdbrPnpy2pqi6mALkSF367SVkbstRGYWhZRVKk76GoExC8l99z4swWrUUAMFr3+P6tlXUXHSnYNqn6B7jtxPh0AgEAgEAoEgYXhtXqamTcXn8LHfv5/a5lompExIfFVpgUDQb7w2L0nWJJpjzaNTsIXD4hEaBrUri2zpsTCPQHDMoMWwBKpQA1VYglVYApVY/GU4yzZhr9ppFrtqJeLOpPTM/6Upb16H5c2xZhpCDeS6c8lz5w3nO0CVVfI8eWwK1XHglG9ir9qBo2onjupdpG56idpZVw5rexKJvWonshYFwD9+CSXn3kdVcw3p9pQ+3z/15KLVdR1JGnwhsu5GS+no3RYiay0K2b4DLViwgD3XPEnOsl+StGc5AL4Nz9GUO5fGcScNqo2CrhGirUAgEAgEAoEAMB6sctw5JNuSqQ3V9lg4QyAQDC+SJI1ewRYSGo+gyF3nLwoExxKevR+SvfxXWILVva6rI9Ew6WzKTvkGms3T4TV/2E8oFmJs8lgKPAUjEq2Sak/F5/BRHaqmbMm3GPfClwDIWPEX/BNOI+Y6MjuIHRVbzelA/nGEWwTcfE9+n/+d2ztaDxdnNRLktJW6dtNquoZNtvXutG1H3OGl5NyfkbrxebI//C0AWR/8hkD+fHSLY1DtFHRGiLYCgUAgEAgEgg44Lc7RLQ4JBILRRwILkQ02v1EgOJKRYmEyP/4jaZ+92ON6zb4imvLmEsydSzBnVodiY2C4NGtCNaiSypS0KWQ6M0csn16WZHI9udSEamhIn0jd1AtJ2foqSiRI5sd/oPSse0ekXYPFUbnNnG7OnEJdqI5cdy4ptpQ+76Mn0VbXdSQG77SVJbnL0Qu6rnebuatInZ22JpJE7awr8RR/grtkNdbGCtJX/Y3KxV8dVDsFnRGirUAgEAgEAoFAIBAIBkcCnbZ9qaYuEByNWGuLyXvrHhzVu8xlwZxZhNInEnVnEHWnE3OlE04dR9zRdS5tXIsTjAYJRoN4bV4mJE8gxd53EXGoSLGlmPncjkVfwbNnOWq4keQdb1M37WKacueMdBP7jaNiOwCabKHGk4ldkshz5/VLHO9JHNV0zXx9MEhIXRciQ0OVupYFW8XkmBbrZqcSZafcxfhnrkPWovg2/JOGKecSTh07qLYKOjK4My8QCAQCgUAgEAgEAoHNi6kKJMBp2101dYHgqESLkbL5P4x/7mZTsNUUK4dOuYviS/9E+cnfoGbu5/FPPIum3DmdBFtd1wlGg1QGK6lurkaRFCYkT2Ba2rRRIdiCEfGS485BlVWCFjuVi75ivpa9/EGIdyMOjlLkcCO2+gMAhNKLaIg3k+fOw211928/3cQQgCHadpc5299jSLrUyW2r6VqPMQ4W2YJG90UhIykFVM+7DgBJixvnUeSRJxThtBUIBAKBQCAQCAQCweCQZbAlQbhBOG0Fgr6iayTtXk7Gyr9gq9tvLg6lFHLwnJ8S9o3vcjNN1wjFQoRiISJaBAkJu2In15NLqj0Vr82LRbYM17voM16bl0xnJiWNJaiTzyV5y6s4K7dhr91H2sbnqZl77Ug3sc+0umwB6tPG47V6yXJn9Xs/rZ1UXYq2LU7YRGXa6ugdOsR6ikcAUCW163iEdlTPv47kHW9i9R/CVboe7/Y3aZhy7qDaK2hDiLYCgUAgEAgEAoFAIBg8Dq8h2oYaBrUbWZKRkMzK6QLBUYeu4y7+hIwVj3aIQgAon3wOtUvuRrfYu9y0KdqEP+zHoTpIsiWRYkvBaXHitrixq11vM5rI9+QT1aJUBCuQFn+FGf++EwmdtPXPUDPnahikQDlctM+zrUsrJM+Th02x9Xs/siSjSEq3TltVGbxs111urqZrPYq2FtnSq2irqzbKlnyLMa98E4Csj39PYOxi4odlLAsGhhBtBQKBQCAQCAQCgUAweOzJwAEjHkHXYYCCqyzJyLLcyRUmEBwt5Lz3c1K2vdZhWX3mFCoXfoWK9HGkdiPYAkTiEdIcaUxNm4pVsQ51UxOO0+JkSuoU0uxpFFucVOfPI71kDZamWhwV22jOmjbSTewTjoo20TaQPpFcdWAFXGVJRpIl4nq802u6riekMKPptO0iusAide/IVuSuxeTDCYxZSMOEU/HuXobaXE/GikcpW/KtQbVZYHBkdGEIBAKBQCAQCAQCgWB001qMTItBJDjg3fRYtVwgOMKRI0GSt71uzjdnTGb9Wf9L+ZV/Qy9c2KV41564Hsem2I5IwbYVRVbIdmczK30WsYlnm8s9+z4awVb1j1anbdziJJY6FofFMaD9SJKEKqldCqqariUk5kJC6jJ2Rqfn0Qx9cdq2Un7SncQthnCdvO11pFh44A0WmAjRViAQCAQCgUAgEAgEg8ee3DY9iGJkrfEIQrQVHI3Yq3YitYhn9ZPOYuMlD9E8ZjH5SQXYZFufRFurfOQKtu1xWpxkzb7enHfv+3AEW9N31GA1lkAlAI2+CTgsrkGdE1XuOjtW0zVkefCyXbffqTo9OnkVWaGv8eIxdwb+otOM48VCuA+sGmhzBe0Qoq1AIBAIBAKBQCAQCAaPvV1F+0EUI1Nk4bQVHL04KneY04Gc2QSiQfI9+bgsLlS59wRLXdePaJft4UieLPScuQA4avZi8ZeNcIt6p300QoPPKEI2mPzt7kRbHT0hTtueOsJ6and/C6D5x51iTnv2vt+vbQVdI0RbgUAgEAgEAoFAIBAMntZ4BBhUMbL2RXMEgqMNe1WbaFvhzSbNnkaWOwswxLtec5x1eiwedSQiTT7PnHbu/WAEW9I32hch8/vG47K6BrW/npy2PWXO9pXW79TD4xEkpB6dtr2JtpquEdNi5nwwf74ZkeDZ97ERlSMYFEK0FQgEAoFAIBAIBALB4ElQPILItBUczdhbnLaarNLozSM/Kd90U/al6JROYopTjSomnmtOOo8Ah6a9ndO2MX0SDnVgebatdCfaSkiDcvC20l0hst4ybRVJoac+BH/YT3mwnEg8YuxPtREYsxAANdSA89DGQbf9WEeItgKBQCAQCAQCgUAgGDztnbaDiEcQTlvB0YoUbcZWtx+AYEoBWd4C0uxp5uuKrHRyQ3bF0ea0JXMaePMBSC7bTLS5doQb1AO6bjpto45kSMrFrtoHtcvu3LS6nhiBvlW01ej8ndqb07Yn53dMj5HhyKCmuYa4ZmQx+8efbL6etGf0C/CjHSHaCgQCgUAgEAgEAoFg8CTIaQstVcu7EBgEgiMZe9UuswiZP20cPoevg9NRkRRkuu+w0HW91yHtRySSBJMMt62sxVBGsdhn8ZeihvwANPqKcFpd2BTboPYpy52jC1pJhNMWQEHp4LTVdM3sIOt2G8koRHa4Q7eVuBbHY/WQ4cygqqkKXdcJjFmE1pLN7Nn7IXSzraBvCNFWIBAIBAKBQCAQCASDJ0FOWwBV6nq4sEBwJONol2fblD4Ru9LRoanKKoqsmK7Fw4nrcRRZOfqctgATzzEnMw6upTnWPIKN6Z72RcjqfeNJsiUNep/difCJFOgVuf+irSzLyHIPnQjo2FU745PH47F6qAnVoNncBPPmA2ANVHTIcBb0HyHaCgQCgUAgEAgEAoFg8CTaaStEW8FRRmueLUA4fXKnYfWyJKNICnG9a9G2VWhTJXVI2zkiFJ4IVjcAvpK1NDSNzoiE9qKt3zcBp+oc9D67E057y5ztD6qkdhi9oOvGvntz2vYYVaMbHQ0ui4sJKROQkWmMNNIoIhIShhBtBQKBQCAQCAQCgUAweDqItg2D2pUiK0K0FRx1tDptdUlGypyOKncUX1VJ7VEkaxVtj0qnrWqDCacDoIQaSK8tpinaNMKN6kx70TaYMfgiZNAi2naRIpBop23760pD6zWz1swX7yaqRpIk8xpOtacyPnk8TdEmqgoWoLfs17P3g4S0/1hFiLYCgUAgEAgEAoFAIBg8CYxHEE5bwdGGFAtjqy0GIJBcgNvp67SOIvfsbIzrcVRJPfoybVuZeK45mX1wA6FYqPt1dQ3vttdxlawZhoa1oMVM4T3syUJy+hIn2h5+KF3r1Qnbr2PIcod4BDMfuYcOAEVSkJC6vB51XQe9Y7RDtiubAk8BVbJEU9Z0AOy1+7DWHeixbUpTLdNeuh3fjrcgFu7vWzuqEaKtQCAQCAQCgUAgEAgGj93bNj3IeARZ7tp5JhAcqdirdyO1xB40+sbisHQW+2RJxiJbuo1HiOtxZFk+ekXborOgRaRM2v8JMT3W7aq+NU+S9+59jHn5G9irdg5L82y1+5FbhGR/ehFOixOrYh30fhVJQZI6iqN9yZztDxbJ0qHYmbn/HmRBSTKcvl2Jtq35yu3d4pIkkevJxWVxUV14vLk8aW/PEQmpm17CXbWT8e//GumDB/vzto56hGgrEAgEAoFAIBAIBILBo1jMTMrBOm2PWlFKcMzSviBT0FfUKc+2lZ5EW03TsMiWhOWcjjpcaZBviH222n3YGw51uZoUDZG24VljWo/j3f7GsDTPUdmuCFnauIQUIYO2LOPDRdtW0TQRdLV/mZ5F4VYxub1Dt/32iqx0ap9DdZDnzuNg7hxzmWdP9xEJUjRE6mf/AozYEH3eDX1+T8cCQrQVCAQCgUAgEAgEAkFiaM21HazTtpesRcHRgXvfx6Rs/g9yJDjSTRlyHO2KkIXSJ+NQuh5Wb1WsPcYj2BTbkLRv1DDxHHMybf+qLldJ2fYaarvcbO+u92AY4lQcFVvNab9vAi7VlZD9due0bV2eCA4fvaCjI8k9xy+0Fr3r6nqMaTFk5E65zACZrkwsvokEUwsBcFZsQQ1UdXmM5O2vm+eyZvwp4M3rx7s6+hGirUAgEAgEAoFAIBAIEkNrREIinLZCsz160eJkffAQY/77bXKWPUDRE1eQuvF5pHhkpFs2ZNjbFSHTM6diUSxdrmdRLMS17uMRLHLX2x01TDrPnCzY8ipy5LBiZFqMtPX/7LDIEqzGeWjjkDettQiZjkSTb0K3bun+0lUMgY6e0EJksiR3+E7VdA2F3kXh7opCtjptuxJtrYqVfE8+FQULzGVdFiTT4qStf9acLZt5eR/eybGFEG0FAoFAIBAIBAKBQJAYWouRxcMQ7aGIUC/IktzlkFzBkY8UDZH/xv+StvF5c5naXE/2Bw8x4alr8G5/EzVYjXv/CnxrniDvzR8y7p83kPnhb49YUVeKR7DV7AUg6M3F487qdl2LbOn22td1/eh32qZPNLJtAXtTDb5Vj3d4OWnP+1j9RmxCvDWOBfDuendImyVFQ9irdwPQnFqI6khJSBEyaCv41WXmbKIKkdFx9IKu6z0WIWtFlbt22rZ2IHTXPp/TR2jCGeZ8+tonkUP+Dut49n2EreEgAA05s2nyFfXpvRxLCNFWIBAIBAKBQCAQCASJoTUeAQYVkWCKGEK4PapQmuso/PcdZmEiXVJoLGgrWGT1l5H3zk+Y9PhFjHnlm2R++gjeXe/hqN6Fb8NzFP77DpSm2pFq/oCx1exF1oyiWo1p43Cqzm7XVSW1e5e5Tp+EtiOec3+J3iJO+zY+h61mj7Fc1/Gte9pcrfSMH6CpxnpJu5aB1n3hssHiqNxuFpLzp0/EoToSUoQMus601XUdSeo5vqA/HJ5Nq+maca31gkW2oNG1aGuVu3//FtlCWuEp1OTMMuYDlWS//5sO6/jWP2NOl80SLtuuEKKtQCAQCAQCgUAgEAgSQ6vTFgYVkSDLMrIsd5vtKTjysNYdYOwLX8JZsQWAuMXJvgt+ycrT72btxf9Hfd68XvfhLNvE+Oduxl65faibm1Ac7dob7GVYvSIr3eY56+gJE/FGNanjiC7+GgCSFid72YOg6zhL15v/ls2+IhrHnUxj4WIA1FA9roNrh6xJjpbrFqAufQJemzdh+26Ng+kq0zZhTtvDcsI1tD5FbVhkS9dOWy2ORe15e58znYozfkjUamT/Ju98m6Sd7wDgKNuEs2wTAKHUsdTlzUOSpaO3yN4AOQY+7QKBQCAQCAQCgUAgGBYS6LSVJSHaHi0YDtvbsTWUAhB1+Si+7E8Up48nw5nBhKmX4br5Lbj+FbRJ59KQO5vyWVdw8MwfsfvaJ9l7xaNEXemA4dgb++KtpvhzJGCv2mlON6dP6lm0lZQeHeZdZYgejUiL76Q5KQcAV9lGvNvf6OCyrZn7eZAkGorahuB7dw5dRIKjvE20bfAV9eiW7i+SJHUq+KXpWkLPtSzJneMX5N4lwZ7iEexyz5m+iqyQkT2HnSd82VyWs/xB1EAVvna5xNVzriEYayLLmXVsdEr0g2Pj0y4QCAQCgUAgEAgEgqEnQU5bCQklHiNz1eN4D64BJDTVii5b0RUL0aQsamdcRiSlYLAtFgw1uk7uuz/HEqwGIJQ2jv0X/ppamwObplHoLSTJmmSsO+4UpLEns6tiLVEtSpItydzN3qseI//17+Ms34wcj5D/1j2UB2uomXP1SLyrfmGvbClChkQ8c1qPubSqrCJJkplp2oquJ7Yw1WhHtbrZc+LtTH79+wBkf/AQSiQAQMSTScOE0wjFQhxKKyDX4kSJNpG0533KTv02eoJiC9rjbBFt41YX4dRCHJbE5Nm2osgKWrydaEvf4gv6yuGFyHRd75PTtjsRVUJCVXpvX6o9lYoZl1NxYBWZ+z5CCTeS/9p3Tcd01OXj0NhFOCWFdGd6397MMYSQsAUCgUAgEAgEAoFAkBjs7YYMD8Jpqx5az5x/f43sNf/AWb4FZ/lm3AfX4TmwgqR9H5K28QUmPH0tue/8FGv9wcG3WzBkpG56CU/xxwDEHMnsv/i3NDtTCUaCHQXbFiRJwq7Yiekd80ljLh/Fl/6Buinnm8syVvwZpblh6N/EYIjHsLdksjZ5c/C4s3tcXZGUTvmm0DJcXlaOjUxbjOsgVLiY6rEnApiCLUDN7KtBUQnHw1hsHmoLTzDXce9fmfC2qIFKLMEqAALpE7GqdhxKYkVbi2LplDmbyHMtIyPpbbm2uq73qQOgu3X6ur0syeR58ti7+DYiLh8AzsptSC2u39qZl+OPh8lx5ySssNvRhHDaCgQCgUAgEAgEAoEgMXSIRxiAmBZthmU/w/LpH7H2Eo0g6RrJ29/Au+Nt6iefQzD/OKwNpVjrS7A2lKA21RLMm0f54tvR7Ek97kswNNhq9pD50e/N+dIz/peoM5WaYAXZrmyynFldb6faiIU6F5XSFSuHTv8+uqyQuuUV5FiYlC0vUz3/+iF7D4PFVrsPOR4BjCJkrpZ8z+6QJRlFVojrcdR2kk1cjyNLckLdl6Mdm2Jj98JbSDm4DiXaBEDM5qF+6oXGtBYjyZpEaeEJpO96D4CkXe/SOO6khLbDWb7ZnG5oKUJmUXp3qfYHVVY7FPzSdT2h8QiSZOTF6hiObST6FI8gSzJ0k9bR1/Z5bV4y0yezefFXmfv2T8zlcYuDg5PPwqHayXBm9Glfxxqj2ml7//33c9xxx+HxeMjIyOCSSy5hx44dI90sgUAgEAgEAoFAIBB0Rft4hJ1vQvXuvm2n67DrHXjkJPjk90gtgm0gfRK7r32KLbd9yNZbl7Ltf95kx43/pmLhl4nZPABIepyUba+R9/a9ZKz8C8k73sRZvgWrv4yUrf9l/HM3dcij7C/W2mKK/nE5Y/59O1K0ecD7OdaQYmHy3rrHFCxrZl1BoHARDZEGXBYXhd7Cbp2EdtWOrnWjFEkS1fO+gN4y1jv1sxeR4tE+t8t5cB22mr39ezODwFHVpmE0po3DrvScA6rKKjIycS3eYXlrXMKx4rQFQ7RtdiZTdfwXzWV1My5Fsxp5sjEthlWxUp87m1hLlEbS3g8T/jl1lG81p2t940m1pyZ0/wAWyTLkmbaH54TLfZAEFVnh8Lp4uq6bObx9JceVQ2zsSRyafpG5rH7qBdQD2e5snJbEZQQfTYxq0fb999/ntttuY8WKFbzzzjvEYjHOOussgsHgSDdNIBAIBAKBQCAQCASH01I4CIA9S+EP8+Cpy2H3u4YwezjxKHz2PDxyIjx9OdTsMpYrVg4efwubLv4N4bRxICvoFjuaPYmYJ5Pq425g1w3/ovL4/yFudXfZFK1F8LD6yxj7r1tJW/dM123ohYyVf8HqP4T74DqSd7zV7+2PVTI/+RP2FnE0lDaeikVfJRKPEI6FKfQW4rJ07zi1yJYORZMOJ+rNo2bM8ca6wWqSdi/rU5u8215n7L9vZ+wL/4O17kA/3s3Aac2zBQhnTMamdp9nCy3xCHLneIS4HkeV1GMm0xbAqljRNI2aWVdSccKtVM/9PFULbjJf13Udr9WLx55K9djFAMixEJ59Hye0He07fRrTJ+KxehK6f+hc8EvX9YRn2kpSWzwCevd5tYdvdzhxPW5ep33FrtopSCpg+5yrqZ5+CfUTz2L/3M9jV+1kOjP7vJ9jjVHtq3/zzTc7zP/tb38jIyODtWvXcvLJJ49QqwQCgUAgEAgEAoFA0CWZ0+Gkb8GKP0HLcGZ2v2P8OX2QMga8eZCUB1YXbPwnNJR03EfuPLj4T1QTQot2b9jRbG6qFtxEzazL8e54GyXaRNibTyQ5j4g3DzVUT95b9+As24Skxcn6+A+4StdTesb/End4u91ve+SQH8/ej8x57/Y3qZt+SX//VY453MWfkLbxBQA0xcrBs+8lrlioCVaQ58nrdSh0XxyG+6eej2//CgDSNjxLw8QzQZK630DX8a17GgAl2kzK5n9TcdKdfXxHA8dRuc2cjmZM7dVpq8gKqqQS0SIdlsf1OKp8bIm25nUgK11HYEhGlEaGM4PSwuPJ2v4GAN5d7+KfeEZiGhGPmecwlJSD1ZODu5uOosHQSRztY3xBX5GQDKdtSwSDJEl9Em0VSUGiY2G8uB43rtN+OoHTHen4kvLZfPxNpDvTqQuUM8aZ2WMHzrHOqHbaHk5Dg5GJlJravRU9HA7j9/s7/AkEAoFAIBAIBAKBYBiQJDj9h/DNrXDmT8Fb0PZaUzWUroWtL8OKP8IHD3QUbHPnwZVPwhffgYzJqJLayW3YSigWIhQLEdfiaDYPdTMvo3redTROWELYNwHdYifszmDXxb+lfM615nae4o/Jf/VbcNjQ8+7w7l6KrLUNvXeVfYalQRQ+6wlbzV7y3rrXnC8/8Q7CaeOpaa4h1Z5KYVJhr2KRRbZ0Gsp9OPWZU2n2FQHgqNyOs+yzHvfpqNiCvXafOZ+8/S2keKSHLQaPFG024xGCyXk4k7KRehKWW7AoFuL6YfEImoZFtvRp+6OFnkRBXddBN66VZHsywZzZRJ2GVuQu/jRhBersNbvNiI/69AmkOFKwyInNs4U2cbQ9fYkv6PP+ZWP/7QuR9dVpe3hhvLhmOG376wRWZIV8Tz4SEnWhOmyqjUyXcNn2xBEj2uq6zje/+U1OPPFEpk+f3u16999/P16v1/zLz88fxlYKBAKBQCAQCAQCgQBHCiz+GnxtvSHEjj8N3Fl0CkcEKDobbnwdbnkPpl4ELUNuLbKlQ2GeVmJajPpQPaFYiNpQLWWBMioCFZQHy6kIVlAeKKc8UE5VUxX10QC75l3LlnN+QrSlGJmrYgspW1/t09vwbn+z07LkbZ2XCQzUQBUFr9yFEgkA4B93MnUzLqU+XI9FtjA+eTx2tWenKRjnXpVVYlrnYmTQku8qK4SPu8VclrbhuR73mbKl4zlXQ/UdXNRDgaNiK1JLB0F9xhQ8lr4Nq7fK1k6ibVyPY1WsCW/jaEaV1A5CY3tanccW2YLb4sbrSKVinDEiW9aiJG9/PSFtcLaLRmjwFZFsS07Ifg9HluQO77PVGZuw/SObjtn+7L81VqGDaNtSFG8g+cop9hRy3bk0RhrJcGYMSdTE0cSojkdoz+23385nn33GRx/1/KX6ve99j29+85vmvN/vF8KtQCAQCAQCgUAgEIwEimoIsVNbis/EItB4CBpKIVABmdMgfVKXmx6e8dhKVItiV+3MSJ+BrutEtSjheJhoPIoqq+afRbagSIohOmTNh4xZ8MTFAGR88jD+8acQd6R023RLw0FcLe7NiCcTS6AKSddI3vEGVcffDAkUVI4G5EiQMa/chTVQAUBzxmRKz/wRzfEQ0ViUyWmT8dr6FkvRGgPQWmjqcKJaFItiwTrramIfPIgarMKz9wMsDYeIenM6rS9HgiTtehcAXZLNQnfJW1/FX3TaQN9yr7gOtbl/GzKnkNUHwRpaOiy6yLS1yseYaCurKLJi5vm2J6bFjM+5YriPMxwZ7Cs6jbzN/wEgZfPL1My+uufIjD7QPs82lD0DtyXx0QjQueCXjp7QonOtcQg6Opqu9TkeoUunrR7HoTgGLCrnuHNojjeT7coe0PbHEkfEr8wdd9zBK6+8wrJly8jLy+txXZvNRlJSUoc/gUAgEAgEAoFAIBCMAlQrpBRC4WKYfmm3gi0YQ8S7E20tSou7zubF5/CR686l0FtIniePLFcWPocPr82L2+rGaXHiUB1Yxi0hPO1zRjPCjWR+/Kcem5q8va3oWN2MSwnkzQeMwmbOQz0PxT/WkOJR8l//Pvaa3QBEknLYf+GDRFQrDaEGxnjH9KvYkEW2oCpqJ7dpKzEthiqpWK1uaHHbSrpG6mcvdLl+0q73UKLNAByaeAYht9EW94FVWBrL+9yu/uI8tMGcDubM6jXPtpXWAlzt0XX92HPatnTAdOW4bhVtW8Vcr82L7puAP2cWALb6AzhL1w26DY4KQ7TVFAty1kycFueg99kVXQmgh8clDHb/rYXIWvNp+5Np274woKZpvRbU6wmnxcm0tGnCZdsHRrVoq+s6t99+Oy+99BJLly5l7NixI90kgUAgEAgEAoFAIBAMA7IkQ+dR0cTiMZyKc0Aur+jpPyJmNYrepGx7DUd34quuk9xS1EhH4tC4k9hbuMB8ufU1AaDr5Cz9Be6S1QDE7Ensv+jXRB3JVDdVk+3KNnIs++F4lCQJq2ztNh4hqkWxKlZDtDvuf9BbBKSULa8iRzoXr2sfjVA++Vz80wznt4RO8rbEDKPvhBYzXZoRZxp6cn6foiGArh2WejfLj2IUWUGRlC7F+5gWw6bYzH8Tp8VJqj2VkklnmuuktrhuB3z85gZs9Ubutj91HKnurEHtryda4wt0XUfXdSRdSmjRuVaRVkNLiNN2sK7vREY/HM2M6n+l2267jaeeeopnnnkGj8dDeXk55eXlNDc3j3TTBAKBQCAQCAQCgUAwhHQnWES1KE7rwNxuVm8e+9tVoc9Z/iB0IQw6yjZh9R8CIJg/n3qrE23SucRaRLek3UuRYuEBteFoQg1Ukf/690wRW1OsHDj/ASIpY6hpriHZlsy45HH9rjIP4LA4enTautSWivOuNPSZVwGgRJtIW/9sh3Vt1XtwtrglG9PG4Sw4Aeu8m9BbXIzJW/8LPRQ8Gyj26t0o0SYA6jOnkGT19lmoUiSlk8itoydUxDsSUKW2eITDiekxHIqjwzKfw0dF/nHEHMkAePa8j9JUO+Djt7psAQIZk4csGgFAltvEUSOzWU540TlFUgxRGL1fTlskOoi2mq5hUwbutBX0nVEt2j788MM0NDSwZMkSsrOzzb/nnus5YFwgEAgEAoFAIBAIBEc23QkKuq73eZj54VhkCzXTLybgmwAYleFTP/tXp/WSd7QVG6ubdDaaruH15FA9djEASiSIZ++HA2rDUYGukbLp30x4+lqS9n5gLELi4Fn30Jwzk9rmWuyKnQkpE/rsLj0cu2wnrnUt2sa1eIf9ygtvM6czVj1GSjuHZcrWV8zp8olnke3KJil9Co0FxwFgbSzHVbJmQG3sCeehjeZ0bcakPuf5QoujtguX+UDE7yOZVsd1V9fB4dcAGBEJTnsylZPOBkDWYqRse23Ax3eUbzWnhzLPFtqEek3XTFE10SK9LMnG/nW9z4XIJElClQ7LF9ePvWtxpBjVom2rLfzwvxtvvHGkmyYQCAQCgUAgEAgEgiFElmS6i3QcaLanJEm4rEnsXvQV02mZseIvqI0VbevEwnh3vQeAptqpGLMAh+og151LfOaV5nrHakSCtbaYwn/dRs7yX6G0RBHEHMmUnHsfjRNOxR/xAzAhZUK/hMpOx1Gs6HoXyiWGVtAhUzNjMvop3zFnc5Y9QPLW/xrncrshwMcVK9qMy/DaDMdrfPbnzfVTtrbFJySK9qJtIHsmrpZYjr7QmtPa+v5bRbZjzWkLYFftXTptdTpn/FoVKz6Hj/0TTjGXpWx+ecBOamfFZnPaMmbRkMZTdIov6KOo2h9USe13pi10URRS6n4khCCxjGrRViAQCAQCgUAgEAgExyZdCQpxLY4iK1hky4D367A4qPWNo64l11SJNjHhmS/gW/135EgTnuKPUcKNAPjHL6ERjTRHGjbFRsqkCwi7MwBwH1iJGqwZcDuOKHQd58F15L3+fSY8cx2usjZBsm7K+ez+wrM0TjiVpmgT4WiYCckT8Dl8gzqkKqvdivbm6+2QlnyP+Am3m/M5791P7rs/Q205l1VjF5ORWmQOObdP+xwRuyEqe/Z8gNLcMKj2dkDXcZYZeckxixM9Y3JbnEMfUGSlQyyApmvmsmMNm2LrNtvYonT+HkhzpBHx5uHPb3FS+w/hOrCq/wfWNdNpG3ak4Eqb2P999IPWgl9mPEI/RNW+osqqKQrLkozcR0lQVTqKtrquH5PX4kgg/MwCgUAgEAgEAoFAIBh1mFXLdd0U2qJaFItsGbDTFsChONB1ncoTbsVT/AmWYBVKJEjmikdJ2/gCMXubO7Ru8jnEtTgp9hQAnFY3DdMuwbbyUSRdw7vzbWrmXDO4NzrS6DpKqB5LYwVKcz3IKppqQ1cs6IoF56GNpH72L+y1+zpsFvbmUnbqdwjmzzfm42Eawg0UJReR5Rp8wSaLbEHSO55/MPJsVVntfA1IEspZ9xGLh1FX/QUJHe+ud82Xm2dcTqYt2Zx32LzUTjmf1PXPIGtRvDvfonbWlSQCa0MplpYs1YaMSaQ60vslcimSYg5lB6PwkyzJpgP3WMKiGNdBe1qdqF113iTbkkl3pLO/6DRmtBTHS938MsExC/t1XGvdAZRIADDybD22pAG+g74hSZKZaSu1/JfweARZNpy2aOY11hfaO21bi5gNpuNM0HeOvU+8QCAQCAQCgUAgEAhGPe0L87SKF6ZgN4jK5RbFgo5O3OFl75V/JWPlX0ne9jqSHkdtrkNtrgMg6kqnOnMKDj2Ox+oxt3fMvQlWPgpA8rY3jgjR1uIvw1p/AEtjJZbGCiyBcmM6UIGlsQI5HunzvqLOVOpmXEb13GvRVRtxLW4ItqEGCrwF5CflJ6SAkiqrqIpKTIt1cFS2XgNdikaShHrur4jGIljW/cNc3JSUQ/LE8zq1S553I6x/BoCULa9SO/MKSEDbnYc2mNN1mVPw9lPwU2RDUGvvtJUl+Zh0N6qSin5YwG9P14AkSWS7s9k05niizjQsTTV49n2EGqgi5k7v83Gd5W3RCLHcOUNeeKs107a1UFhXxegGiyob/5a6rqPKap/3b5EtHURbVVZFPMIwIURbgUAgEAgEAoFAIBCMOtoX5lEwBIKoFsVtcQ9KvLIqViyyxShu5E7n0Onfo3ru58lY8Re8u98z12uYdBZNWph0RzoOta1KvTVzKpHsWVjLNmKv2Y29cgehjEkDf6NDha7hLv6UtA3P4j64dtC7C2bPpHbmZdSPPQm/FiIcrkcPGcOkrbKVvKQ8xnrHJmxIt0WxoEhGRICFzqJtt8K9JGG54CEi8TDWjc8C4J9xKZktbun2eHLmE8icirtiK/aaPThL19GUN2/QbW+NRgBozplFjqXv0QhgXPvtiz/F9TiKpByTQllXBa9iWgxV6ka4p8Vt687h4MTTGLvhBSQ9TsrWV6lacHPfDqprpGx5ua0NBScMqO39odVJHdEiSJJkdlolktZrqrUToK9YpI6fP0U6NqM6RgIh2goEAoFAIBAIBAKBYNTRvjBPK1EtiqufAtjhWGUrqqwS1aKm8BBJKeDguT+luvIL+NY+iRwLUTXn88TiIVLtqZ32oc69AV77JgApW16hLOPbg2pTIpFiYZK3v0nahmex1e3vcd24xUnUk0nUnUnUk0nMmYqkxZHiEeR4BCkeJW5z0zDpbJp8E6gP1xMO15FiSyE7KRunxYlDdeBQHYOKrOgKVVJRZbVTnmlMi+GyuHoWjWQZ68V/IpAxmfqGEpyL7+zSVajICpHjbob/fgsA39qnOZAI0balCJkmq//f3r2HR1Xf+QN/f7/nNvdJQq6TQFBEqCho0SreUCoI60O9dItr3S70ce1l1ee3bXe7D61Pod2LaJX++pPaVre6bH9qrZZqt2CpFxD9oZXaqIG24AUVCwlyS0Jucznf3x+TM2RIMpnJnEkO5P16Hp6SyZnvfM/kfIh9z/d8vrBj52SF/vnQhAYpJZIqfe4ple7lPF5DW6dVhBM0JlUSuqYPGugCfattg3X487SFmPzGLyCUjcrX/i/aT7kEvVVTh33N6J9/g0DLDgBAV7QB1uRL3TuhHDSpwU71tX4QhusrbYUQmZYjg/UDHoqUMrPa2fk5DPXek7v4LhMREREREZHn9N+Yx2Eru+AA7HiGNAYNAwGgp3oaPlz0b+m/J3vgSwIRc+Ct7XLmEqjf3g6R6EJ012/RcvGtUEZx83KDeeg9TH7qH2Ec3Z/1eKr8FLRMOg/xcA1k2SQkwrVIhKthm6Fh2wHYykZ7vB09nftRZpVhatlUVPorSx7aaFKDqZnoSnZlPZ6wE/Dn815LDaGLvgIt2Z3zmvGddT16Nn8XvqOtCH/wCqwD76C3csrI5911CNaRPQCA9sopqAjFCg7fnJ6h8US6bYVt2/AbftdDvBOBLnVoUkPSTmY+GEjaSQTNYM73I2pFEa2egb1TP4n6Xc9AJnswaf3X8e6SnyAVGPhBjEP2HkXt//tB5ut9c7+KU63okMe7yVkJK1GaVhhO6G3Dhl5AHNj/wwLnA4Tx2F95LLi7FR0RERERERGRC6Q41tPWIZQoekWnJjX4dT8SdiLncZ2JTkTMCAJGYOA3rTDUjGvT48U7EXl7U1FzcoOMd2LShuVZgW1P/Wykrv8ptNv+gIMX34YPp1+Jo6dchN7KKbCtcM7ANmkncajnEFqPtsIQBs6YcAZmVs9EbbB21FbZ+TTfgHDdtm0EtEF+JkMYLuQPWBG0ffxvM19X9vW4HSlnlS0AtNWcgZAZGtE4pmZmetqmVKqglZEnE6d/av9/B5J2ctifq7Pa9p05X0Rn9XQAgNnRiolPfxMiNXTtV736k0xf6/2T5yAy/VOjFpYb0oANO9M31m2a0KCESrecKSAU7t9KIWWnYEpzXH6AMBYY2hIREREREZHnSCEzG/MA6RWfEChqEzJHUA8OG9rGU3FU+iuHnt/sZZm/l+/4VdFzKopSiD3775l2CF3ljTjyd7+E7+bnoX3sU4CUiJpRxPPYcKwr0YXWzlYc6j6EoB7EjMoZOLv6bNSF6kZ9x3if7kv3Hu5HobBbu/OhzV6GhJXebC6667fQO1pHPFYwq5/t2QgZIwttDWlkhbZuXPcnIl3o0EX2ynjbtuHTfcM+N2pFURVpQNO8f0YimK7l4N43UPvCakCpAcdbB9/FhDeeAACkNBMfXfKPg7ZHKRVd9vWchZ3VR9YtAgJQKDgUlkLC2QsupVKut0KhoTG0JSIiIiIiIs9xNuZxVtgl7SQMabgSGPgMH5Q9MLRx9KZ6YWkWItbA1ggZDechVXk6gHRQZx56r+h5jdSEPzyM6DubAQBJM4jezzyEslPnZR2TT0uB/Z37EU/FUR+qx6zqWZhVNQt1oboxC2lMzcyERQDSAb6A6/OpiDTg0FnpldPCTmHCGz8f8Vj9V9rKSXNGHDBbmpW59m1lw9KsEc/pRKZJDbrUMwG2I58PEIQQiIViSAWr8M7C78Duu24qdjyF8uZ12Qcrhdot34Poe533Z16HqrqPj+qGW4ZmZDYKK8VKWylkJrgt5LyOb49gyfF5LY4FhrZERERERETkSZo8dlt0wk7AkIYrqyxNaUKJoUPbzkQnwmYYAT3HbfhCZK+2/eP/FD2vkQju+T1qXv5R5uu2q76L8tjAzbR8um/IXr5AOhhUSmFaxTRMq5iGCl/FmO8Qb0gj6+eUUinoQnd9xa8UEtr5/4BU37VVvv0pyN6OwseJd8L30S4AwNGySYiUNY54TprUIFT6FnShxJj/LMaSpVlZK66dnr/5iJgR1ARr8JdILfZe/i+Zx+u2/G80/OZbKG9eB+vgO4i8vQmhD18DAHSHa9B+7udHdZUtcGxFa6HtC/IlhICCghAiq+VBPvNyNoNTSnGl7ShiaEtERERERESe5NwuDKRDW1MzXQnsTM2EhMzqk9lfb7IXVYGqYfs2ipl/A9UX9JX96emcvTJLwehoQcNvvgXRdx6Hz/8CKmZ+dtBj/ZofpmaiN9U76Pd7kj3w6T6EzXDJ5lsoXeoQONYiI2knoUu9JKFR+YTTcXjaIgCAluhCxfYnCx7D3/LHzM+ivXYGgkZwxPPRhAbVt8xYQWWtdhxvLN061irCTkEKmfeHN0IITApPQtSK4u3Gc3HgnHR9CJVC9K1nEdt8N0575HOY+JvbM8/Zdd7nUVd2yqj1bnZkgtQCV8IWMr4T3BZyPR0f2o7nDxBGG0NbIiIiIiIi8iRnYx6gb8d4feQh2PHjGpoxaF/beCoOQxr5hZfBCUhNuwoAoPccQfjdF12ZXz607sOYuH459J4jAICOxgsRXvDvQwbNhmYgYARyhrZhM+yp2/ANaUATWiawS9gJ6NL9lbZAX0h24a1QSL9/FW88DpFHD+D+Iu++kPl7T/05RYW2utSzfpajHSB6iU/zHWuTopIFXwMBI4DTyk6DIQ3smv1ZHJy1BPYQPXEPNcxG6vQFmOCf4MrcC+GEtgKiJCG9xLFQWCD/jcQ0oWVCW4jxfS2ONoa2RERERERE5EmGNI6FNakkAkaOdgUFMDUThjCQGGRlbHeyG0EjmPcGUvq5n8/8vfyPo7MhmfXRWzj1sZvg/2gnAKA3EoPx1w9BH2YFatSMDnrOQDqsLvOVuT3VouhSz2rpkLST8Ov+gm7tLkRZbDaOnHIRAMDoPIDozt/m/Vxf6x9R3rc6N6UZ0KbMK2qemtCglIJSqmQh3olCl3r2ausRtMgo85VhStkU9NpJ7L7wi/jTF36Ld5b8J1ouvg3tp16KpL8cPeWT8acL/h6xcP2YBJOa0NJBvUBJrnGnLYINe8TtEcb7tTjaGI8TERERERGRJ/VvjwDAlX62zriWbqEz0Tngez2JHtSV1eUfaky+FKnoRGhtexD8YBuM9n1IROpcmedgwm9vQsMz/wqZ7AEAxP0VSCz5L4TCtcM+16/7M+FXf7ZKhzjFrAwthcFW2vr14TdUGyld6rAvvA3Y/RIAoGbrfeiqm4l4+aTcT0wlUf/cqkxrhHfPXoLy8lOKmosmNWhSQ8JOQEo5rm9J16SWWRmatJOwpDWiULUmUIOuZBd2H9kNI1CJnpoz0FNzBg6ecwMA4HDPYfg1/5issgXS4aiz2V4pQltNpN9HgcJ62jrPS9pJCAiutB1FXGlLREREREREnuQEC0qlN89xs5ep3/APaI/g3P4bMSMFTFICH/87AICAQtkff+3aHLMoG1Wv/CcmPf3NTGDbUXU6Oj//a4Qazs9riKE2I+tJ9sDUTM+Fts5t8M58bdsuaWgLAJFTP4kjE89Lv373ETQ+9b+gH92f8zmVTY/Ad/BtAEDXhCn46Ozri34vdaFDCpkObYWELsZvUKZLHc7d/Ek7CZ8xeGuD4Tj9beuCdTjQdQBdiS60x9txuOcwDnQfQE+yB/Xh+pK038iHE45ClSa0dXraSsiCV9pqQkPSTkKTGkPbUcTQloiIiIiIiDyp/wovXeowpXuhbUAPZO1ID/RtxqX5EDLza42Qmec5n4PqC0EmvPFz6B2trs0TAGAn0bBxBaq3PZh5qGXKZej82ydQXj0j72ECegCmZiJ+XK/WnlQPQmbIU/1sHZZuHfs5CZQ8UDM0A91Xr0FHeSMAwOxoReNTX4HW3Tbo8ebhD1D1avrnooTE9gu/iPJAddHhshOUJVIJaEIb1yttdZkOsFN2KtMio5ixTi07FRW+CqTsFDShIWSEUOuvxdTyqWO2yhYApJSQMv3vSCnbIzj/m/e8hISQAgm771pke4RRw9CWiIiIiIiIPMm5XThhJ6AL3dWVtqY0cfxePF3JLpT5ygoPLyN1SMz8DABAi3ei/vk7gEHaEIyIslH/7H8g+tZz6S+FxK7zlqJ38fdRExnmtv3jGJoBv+4fsBlZPBVHhVXhznxd5tf8SKpkerW1cne19VAmlJ+Kt6+6E919LSd8h3Zj0v/8E2S8K/tAZSO26U7IvhC85azrkKg9ExPDE4uegybT4RiDsvSq40ybDIWiP1zw6T7MrJqJ2bWzcV7NeTi7+mxMmzANjZHGMVtlCyCzAlaIEm1EJmSmNUIhoa0QArrQj12L4/gDhNHG0JaIiIiIiIg8SZMaINK3RJua6WqgYmomhBJZPV4TqQQqfCMLL42FdyIRrAQAhD54FeU7XNiUTCnUvXAPynb+BgBgSwNvzPsX4MJbMTE6Kb1pUYEiViRrpa2zuZBbm7y5zdIt2LadXm2tFb4B1UiYmom6unPwxpXfRjyQvh4CrTswccNyhN57Gb79f4be0Yry7U8i+JcmAEA8Uoc/nXUtJoYnFrxSezC60CGlPNbTdhyHtk5vY6dNhhvXgCY1GNIYUQ2VihOmZjYkc3v8IkJhp62KlOO7Vcdo4ztNREREREREnuSsBkvYCUSsiKtBhqmZ0LX06jGnZUAxfV2Fvxy9f3U3jMeXAQBqXvo/ODrpEyPflEwp1Gy9DxXNv0x/KTRsv+wrMD62GKdETxnx7dMBPQCFY0F1b6oXlmZ5rp+tw+mfWYrV1rnUBGqQbLwYry/4Fj6+4ZvQ450I7dmG0J5tgx6/88J/QDRch9rg8BvC5UMIAVOa6etTmp4KF0ebsylbUrkX2nqRs+FaqVbaOv1sRxIKO72lx/u1ONq40paIiIiIiIg8yelpG0/FEdTdDRVNaWZtctWV7EJQDxYVXoZmXIv4rL8BAGiJbtQ99x8jbpNQ+fu1qPzDw5mvd1/+z+iYclnRt3D7dX/6VvO+PrHdyW4EjSB8+sg2dyo151yTavRW2gLpgKs+VI/60xbgzfm3I6UPfUv+wWlX4kDsLEyKTHI1VDY0Ayk7BUM7OUPKfEkhYWkW4qn4qF4Do81Zaeu0MSjF+M6wI1lpCxTfmoIKw5W2RERERERE5En9dy13OyxwNjbrtdP9XXsSPWgoayh6AyBz0V2wd78I2f4XhD98DeE3n0DHrM/kP4BSqPz9WtS8cn/mob2X/RN2N34CpwTrir713q/70+ed6kVABhBPxlER9mY/WyD9c9KkhngqjgnWhFFd5SeEQCwYAz52NV4NV6P2wyYE413Quw9D6zoMveswEuEa7Jj9OdQGa13fxMoJJ93cgO9EZWrpazaoB0/aEFsTGqSQsJVdkr6xxbRfcK5Fhraji6EtEREREREReVL/nc7dDmqESPdxPdp9NN3XVQhXepHCF4W8eg3w02sBALGtP8TO2Eygatrwc0p0pzcde/u5zGMtF92CPdMWwGcnXbn13tRMBIwAOhOd6dW1Ap7tZwsc62faleyCP+gf9dfPBLcNc/B2sAo2bPh0H/yaH4ZmoDPRCS2VQEO4+MD/eE5Yy9AW8Gk+xFNxRM3oSdtTtf9KW7evJYcu9XQoXOBKW01qJfl3mHJjewQiIiIiIiLyJGdVmCGNkvQyDegBpOwUupPd8Ot+hM2wOwNPmQfM/jwAQE/2YPrjX0RZ0yNAX0uCwRhtf8Gpj38hK7BtnfMlHDjns+iIdyAWirkWroaNMHpTvYin4vBpPs/2swXSoa0udaTsFHzG2LRwcFolzKicgYZQAyQk2nrb0HK0BUd6jqAh1ODetdOPLtOtAHTt5AwpC2FoBoQSsDSrJKtQvcKQRjq0LVFc52xGVmgo7Pw7fLIG5l7Fd5uIiIiIiIg8yVkN5rQycJulW1BKoTvZjSp/lbvB8IJ/BT54Gfjoz9BScdS/tAZlb7+AvZ9cjnjF5GPHKYXgB79Dw8YV0Hs7AAApI4C/LFiBjlMvQWf8KAJ6ADWBGtemFjSDmfMOGSH4NG/2swWOtUdwAsyxVOmvRKW/Eik7hc5kJ7oSXehOdqMuNMLN5obhnHMpNqU60RjSgCY1+LXRX209mnShIy7iJQumNalBU4WPLYWELvVMb1saHXy3iYiIiIiIyJP6918sxUpbQxqAAJKpJCp8Lvd1tcLA3z8HPPcd4NUfAwCCLc2Y8rNlaJt6BfSeIzDa98Fs3wuZ7M08rbdsEj64ahXiFZOhlEJHvANTy6a62sLAr/shhUR3ohsTQxM9vRu8EAI+zVey4H4kNKkhYkYQMSOlfR2hwdAMBmU4Ft57dcM8t+hSh26X7udtCAMpMfSK/6FIITPBOY0eVj4RERERERF5khPYOiGj20zNhC50CCkQMlzoZ3s8KwT81V3AjGuQevJL0A6/D5mKo/zPGwY9vGPyRfhwwQrYVnouHYkOBI0gqoPVrk7Lp/tgaiZSKoWg6d3WCA5Lt9KhbQmCey/TpMaVtn10ocPSrJP+GtCkVtKft5QS+giiQCkkTGnyWhxlDG2JiIiIiIjIk4QQ0IWOoF6aYNHUTOhSh0/zlXYzrsYLoX35FbRvXI7wH/4bQtkAAFszkYjUIR6J4ejkC3HorGuBvnBaKYXOeCemlU+DX3f3lnBTmpkxA7p3NyFzBLQATGmOeXuE0aYJDaZmcnUj0mGmpVkn/UZYpe5hrEkNuip8fE1o0DW2RxhtfLeJiIiIiIjIsyzdKtkGVKY04dN9qPRVlmy39mMvFkDwqtV4Z9Zf43Drm7Cj9YgHyjMhLQConsNQULCVjaSdRMSIoCpQ5fpUhBCImlFIIV0PhEtBlzr8un/cBUZOD1Fu/oTMhysne3DvrK4uFV3oUEIV/Dz2tB0bfLeJiIiIiIjIsyp8FQgapVlpK4RATaAGEau0vUkdmtQwsf4TSIWqEU/F0X+Nq4CAJrXM7cua1BA2wyXr4VnuL0fQDHq6n60jaAZRicoTYq5usjQLETNy0rcEyIelWYhYkRPiQ4ZihIwQLM0q2fhBIwhbtwt+nl/3I2pGT/rQ3GuEUqrwiP0E0t7ejmg0ira2NkQio/OLmIiIiIiIiIiIiOh4+WaVJb7/g4iIiIiIiIiIiIgKwdCWiIiIiIiIiIiIyEMY2hIRERERERERERF5CENbIiIiIiIiIiIiIg9haEtERERERERERETkIQxtiYiIiIiIiIiIiDyEoS0RERERERERERGRhzC0JSIiIiIiIiIiIvIQhrZEREREREREREREHsLQloiIiIiIiIiIiMhDGNoSEREREREREREReQhDWyIiIiIiIiIiIiIP0cd6AqWmlAIAtLe3j/FMiIiIiIiIiIiIaDxzMkonsxzKSR/adnR0AAAmTpw4xjMhIiIiIiIiIiIiSmeW0Wh0yO8LNVyse4KzbRt79+5FOBxGR0cHJk6ciD179iASiYz11IgI6U+YWJdE3sK6JPIe1iWR97AuibyHdUknAqUUOjo6EIvFIOXQnWtP+pW2Uko0NDQAAIQQAIBIJMLiJfIY1iWR97AuibyHdUnkPaxLIu9hXZLX5Vph6+BGZEREREREREREREQewtCWiIiIiIiIiIiIyEPGVWhrWRZWrFgBy7LGeipE1Id1SeQ9rEsi72FdEnkP65LIe1iXdDI56TciIyIiIiIiIiIiIjqRjKuVtkRERERERERERERex9CWiIiIiIiIiIiIyEMY2hIRERERERERERF5CENbIiIiIiIiIiIiIg9xPbS94447cN555yEcDqO6uhrXXHMNdu7cmXWMUgorV65ELBaD3+/HZZddhh07dmQdc//99+Oyyy5DJBKBEAJHjhwZ8jV7e3tx9tlnQwiB119/fdg5Njc3Y+7cufD7/aivr8d3vvMd9N+Pbd26dZg/fz6qqqoQiUQwZ84cbNy4cdhxt2zZgsWLFyMWi0EIgSeffHLAMcuWLYMQIuvPBRdcMOzYRMVgXeauy+Nr0vnz3e9+d9jxiUaKdZm7LltbW7Fs2TLEYjEEAgEsXLgQb7311rBjExVjPNdlPue+bt06XHnllaisrMx7vkTFYl3mPveVK1di+vTpCAaDKC8vxxVXXIHf/e53w45NVAzWZe5zZ+5DbnE9tH3hhRdwyy234JVXXsEzzzyDZDKJBQsWoLOzM3PMXXfdhdWrV2PNmjXYtm0bamtrMX/+fHR0dGSO6erqwsKFC/GNb3xj2Nf8+te/jlgsltf82tvbMX/+fMRiMWzbtg333nsv7r77bqxevTpzzJYtWzB//nxs2LABr732Gi6//HIsXrwYTU1NOcfu7OzErFmzsGbNmpzHLVy4EPv27cv82bBhQ15zJxop1mXuuuxfj/v27cODDz4IIQQ+/elP5zV/opFgXQ5dl0opXHPNNXj33Xfx1FNPoampCY2Njbjiiiuy3h8it43nuszn3Ds7O3HRRRdh1apVec2XyA2sy9znfvrpp2PNmjVobm7GSy+9hMmTJ2PBggX46KOP8po/0UiwLnOfO8Dch1yiSmz//v0KgHrhhReUUkrZtq1qa2vVqlWrMsf09PSoaDSqfvSjHw14/qZNmxQAdfjw4UHH37Bhg5o+fbrasWOHAqCamppyzue+++5T0WhU9fT0ZB674447VCwWU7ZtD/m8M844Q33729/OOXZ/ANQvf/nLAY8vXbpUXX311XmPQ1QKrMvcrr76ajVv3ry8xyVyA+vymJ07dyoAavv27ZnHksmkqqioUA888EDeYxMVa7zWpVIDz72/3bt35zVfolJgXQ5el462tjYFQD377LMFjU1UDNZldl0y9yG3lLynbVtbGwCgoqICALB79260tLRgwYIFmWMsy8LcuXOxdevWgsZubW3FzTffjJ/+9KcIBAJ5Pefll1/G3LlzYVlW5rErr7wSe/fuxXvvvTfoc2zbRkdHR+YcirV582ZUV1fj9NNPx80334z9+/e7Mi5RvliXQ2ttbcX69etx0003uTou0XBYl8f09vYCAHw+X+YxTdNgmiZeeumlosYmKsR4rsvjz53IK1iXQ9dlPB7H/fffj2g0ilmzZhU0NlExWJcD65K5D7mhpKGtUgpf/epXcfHFF+PMM88EALS0tAAAampqso6tqanJfC/fsZctW4YvfelLOPfcc/N+XktLy6Cv3X9ux7vnnnvQ2dmJJUuW5P06Q1m0aBEefvhhPP/887jnnnuwbds2zJs3L/N/UIlKjXWZ29q1axEOh3Hddde5Oi5RLqzLbNOnT0djYyOWL1+Ow4cPIx6PY9WqVWhpacG+ffuKGpsoX+O5Lgc7dyIvYF0OXpe//vWvEQqF4PP58L3vfQ/PPPMMKisr8x6bqBisy4F1ydyH3FLS0PbWW2/Fm2++iUcffXTA94QQWV8rpQY8lsu9996L9vZ2LF++fMhjZsyYgVAohFAohEWLFuV87cEeB4BHH30UK1euxGOPPYbq6moAwIsvvpgZNxQK4eGHH8573tdffz2uuuoqnHnmmVi8eDGefvpp7Nq1C+vXr897DKJisC5ze/DBB3HjjTdmrfAjKjXWZTbDMPCLX/wCu3btQkVFBQKBADZv3oxFixZB07S8xiAq1niuy1znTjSWWJeDn/vll1+O119/HVu3bsXChQuxZMkSruqjUcO6HHjuzH3ILXqpBr7tttvwq1/9Clu2bEFDQ0Pm8draWgDpTzfq6uoyj+/fv3/AJyG5PP/883jllVeylrsDwLnnnosbb7wRa9euxYYNG5BIJAAAfr8/8/rHf7Li/EI7/vUfe+wx3HTTTXj88cdxxRVXZL1G/90KC5n38erq6tDY2MgdsWlUsC5ze/HFF7Fz50489thjBT+XaKRYl4ObPXs2Xn/9dbS1tSEej6Oqqgrnn39+QassiEZqPNflUOdONNZYl0PXZTAYxGmnnYbTTjsNF1xwAaZOnYqf/OQnOYMuIjewLvP7fcnch0bM7Sa5tm2rW265RcViMbVr165Bv19bW6vuvPPOzGO9vb0FN6R+//33VXNzc+bPxo0bFQD1xBNPqD179gw5v/vuu0+VlZWp3t7ezGOrVq0a0JD6kUceUT6fL69NiwaDPDc8OnDggLIsS61du3ZEr0OUD9Zl2nB1uXTpUjV79uwRjU1UKNZlWr6/L3ft2qWklGrjxo0jeh2ifIznuhzu3PvjRmQ0mliX+dVlf1OmTFErVqzI+3iiQrEuC6tL5j40Uq6Htl/+8pdVNBpVmzdvVvv27cv86erqyhyzatUqFY1G1bp161Rzc7O64YYbVF1dnWpvb88cs2/fPtXU1KQeeOABBUBt2bJFNTU1qYMHDw76uvn+x+ORI0dUTU2NuuGGG1Rzc7Nat26dikQi6u67784c88gjjyhd19UPfvCDrHM4cuRIzrE7OjpUU1OTampqUgDU6tWrVVNTk3r//fcz3//a176mtm7dqnbv3q02bdqk5syZo+rr67POnchtrMuh69LR1tamAoGA+uEPf5hzPCK3sC5z1+XPf/5ztWnTJvXOO++oJ598UjU2Nqrrrrsu57hExRrPdZnPuR88eFA1NTWp9evXKwDqZz/7mWpqalL79u3LOTZRMViXQ5/70aNH1fLly9XLL7+s3nvvPfXaa6+pm266SVmWpbZv3z7cW0s0YqzLoc+duQ+5yfXQFsCgfx566KHMMbZtqxUrVqja2lplWZa69NJLVXNzc9Y4K1asGHac/gr5xP/NN99Ul1xyibIsS9XW1qqVK1dmfdoyd+7cQV976dKlOcd1Ph0a6nldXV1qwYIFqqqqShmGoSZNmqSWLl2qPvjgg2HnTFQM1uXwz/vxj3+s/H7/sL+kidzCusz9vO9///uqoaEh8/vy9ttvz1otQVQK47ku8zn3hx56aNBjuKKPSol1OfScu7u71bXXXqtisZgyTVPV1dWpT33qU+rVV18dds5ExWBdDj1n5j7kJqFUXzdmIiIiIiIiIiIiIhpzcqwnQERERERERERERETHMLQlIiIiIiIiIiIi8hCGtkREREREREREREQewtCWiIiIiIiIiIiIyEMY2hIRERERERERERF5CENbIiIiIiIiIiIiIg9haEtERERERERERETkIQxtiYiIiIiIiIiIiDyEoS0RERERERERERGRhzC0JSIiIiIiIiIiIvIQhrZEREREREREREREHsLQloiIiIiIiIiIiMhD/j+ebHafldyOawAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1400x800 with 2 Axes>"
       ]
@@ -1005,12 +993,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 619,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -1101,16 +1089,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 620,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x184853790>]"
+       "[<matplotlib.lines.Line2D at 0x18348a710>]"
       ]
      },
-     "execution_count": 487,
+     "execution_count": 620,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1141,22 +1129,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 621,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1859b8ee0>]"
+       "[<matplotlib.lines.Line2D at 0x186624550>]"
       ]
      },
-     "execution_count": 489,
+     "execution_count": 621,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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",
+      "image/png": "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",
       "text/plain": [
        "<Figure size 1000x500 with 1 Axes>"
       ]
@@ -1188,16 +1176,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 622,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1859902b0>"
+       "<matplotlib.legend.Legend at 0x1866e1420>"
       ]
      },
-     "execution_count": 490,
+     "execution_count": 622,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1230,16 +1218,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 623,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x185b14370>"
+       "<matplotlib.legend.Legend at 0x186902b60>"
       ]
      },
-     "execution_count": 491,
+     "execution_count": 623,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1279,11 +1267,6 @@
     "This is because enough new residuals are needed to change the quantiles\n",
     "obtained from the residuals distribution."
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

From c7c209a14ff7c991ede2655d283e51b934c12f0f Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 25 Jul 2024 11:40:55 +0200
Subject: [PATCH 243/424] ENH: add docstring to class

---
 mapie/mondrian.py | 61 +++++++++++++++++++++++++++++++++++++++++++----
 1 file changed, 56 insertions(+), 5 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 7d7fd6cf5..ebe15c616 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -21,6 +21,57 @@
 
 
 class Mondrian:
+    """Mondrian is a method that allows to make  perform conformal predictions
+    for disjoints groups of individuals.
+    The Mondrian method is implemented in the Mondrian class. It takes as
+    input a MapieClassifier, MapieRegressor or MapieMultiLabelClassifier
+    estimator and fits a model for each group of individuals. The Mondrian
+    class can then be used to run a conformal prediction procedure for each
+    of these groups and hence achieve marginal coverage on each of them.
+
+    Parameters
+    ----------
+    mapie_estimator : Union[MapieClassifier, MapieRegressor or MapieMultiLabelClassifier]
+        The estimator for which the Mondrian method will be applied. The estimator must 
+        be used with cv='prefit' and the conformity score must be one of the following:
+        - For MapieClassifier: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
+        - For MapieRegressor: 'gamma', 'absolute' or 'aps'
+    
+    Attributes
+    ----------
+    unique_groups : NDArray
+        The unique groups of individuals for which the estimator was fitted
+    mapie_estimators : Dict
+        A dictionary containing the fitted conformal estimator for each group of individuals
+
+    References
+    ----------
+    Vladimir Vovk, David Lindsay, Ilia Nouretdinov, and Alex Gammerman.
+    Mondrian confidence machine.
+    Technical report, Royal Holloway University of London, 2003
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> from sklearn.naive_bayes import GaussianNB
+    >>> from mapie.classification import MapieClassifier
+    >>> X_toy = np.arange(9).reshape(-1, 1)
+    >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
+    >>> groups = [0, 0, 0, 0, 1, 1, 1, 1, 1]
+    >>> clf = GaussianNB().fit(X_toy, y_toy)
+    >>> mapie = Mondrian(MapieClassifier(estimator=clf, cv="prefit")).fit(X_toy, y_toy, groups=groups)
+    >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups)
+    >>> print(y_pi_mapie[:, :, 0])
+    [[ True False False]
+    [ True False False]
+    [ True  True False]
+    [ True  True False]
+    [False  True False]
+    [False  True  True]
+    [False False  True]
+    [False False  True]
+    [False False  True]]
+    """
 
     allowed_estimators = (
         MapieClassifier, MapieRegressor, MapieMultiLabelClassifier
@@ -52,7 +103,7 @@ def _check_mapie_classifier(self):
                 "uses cv='prefit'"
             )
 
-    def _check_groups_fit(X, groups: NDArray):
+    def _check_groups_fit(self, X: NDArray, groups: NDArray):
         """Check that each group is defined by an integer and check that there
         are at least 2 individuals per group"""
         if not np.issubdtype(groups.dtype, np.integer):
@@ -84,8 +135,8 @@ def _check_confomity_score(self):
                         "The conformity score for the MapieClassifier must be one of "+
                         f"{self.allowed_classification_ncs_class}"
                     )
-            else:
-                if self.mapie_estimator.ncs_str not in self.allowed_classification_ncs_str:
+            if self.mapie_estimator.method is not None:
+                if self.mapie_estimator.method not in self.allowed_classification_ncs_str:
                     raise ValueError(
                         "The conformity score for the MapieClassifier must be one of "+
                         f"{self.allowed_classification_ncs_str}"
@@ -102,12 +153,12 @@ def _check_fit_parameters(self, X, y, groups):
         self._check_estimator()
         self._check_mapie_classifier()
         self._check_confomity_score()
-        self._check_groups_fit(X, groups)
         X, y = indexable(X, y)
         y = _check_y(y)
         X = cast(NDArray, X)
         y = cast(NDArray, y)
-        groups = cast(NDArray, groups)
+        groups = cast(NDArray, np.array(groups))
+        self._check_groups_fit(X, groups)
 
         return X, y, groups
 

From b2d03b10f64a21638d21c9dd3104f067d0c9961b Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Fri, 26 Jul 2024 14:57:35 +0200
Subject: [PATCH 244/424] Add : new raise value error and linked unit test

---
 mapie/tests/test_regression.py | 21 +++++++++++++++++++++
 mapie/utils.py                 | 24 +++++++++++++++---------
 2 files changed, 36 insertions(+), 9 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 9bc5bfa36..9aae449f2 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -985,6 +985,27 @@ def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
         mapie_fitted.predict(X_test, **predict_params)
 
 
+def test_using_one_predict_parameter_into_fit_but_not_in_predict() -> None:
+    """Test that using predict parameters in the fit method
+    without using one predict_parameter in
+    the predict method raises an error"""
+    custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    mapie = MapieRegressor(estimator=custom_gbr)
+    predict_params = {'check_predict_params': True}
+    mapie_fitted = mapie.fit(X_train, y_train, predict_params=predict_params)
+
+    with pytest.raises(ValueError, match=(
+        r"Using one 'predict_param' in the fit method "
+        r"without using one 'predict_param' in the predict method. "
+        r"Please ensure one 'predict_param' "
+        r"is used in the predict method before calling it."
+    )):
+        mapie_fitted.predict(X_test)
+
+
 def test_predict_infinite_intervals() -> None:
     """Test that MapieRegressor produces infinite bounds with alpha=0"""
     mapie_reg = MapieRegressor().fit(X, y)
diff --git a/mapie/utils.py b/mapie/utils.py
index 34d077695..224e5b05d 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1398,12 +1398,18 @@ def check_predict_params(
         If any predict_params are used in the predict method but none
         are used in the fit method.
     """
-    if (len(predict_params) > 0 and
-            predict_params_used_in_fit is False and
-            cv != "prefit"):
-        raise ValueError(
-            f"Using 'predict_param' '{predict_params}' "
-            f"without using one 'predict_param' in the fit method. "
-            f"Please ensure one 'predict_param' "
-            f"is used in the fit method before calling predict."
-        )
+    if cv != "prefit":
+        if len(predict_params) > 0 and predict_params_used_in_fit is False:
+            raise ValueError(
+                f"Using 'predict_param' '{predict_params}' "
+                f"without using one 'predict_param' in the fit method. "
+                f"Please ensure one 'predict_param' "
+                f"is used in the fit method before calling predict."
+            )
+        if len(predict_params) == 0 and predict_params_used_in_fit is True:
+            raise ValueError(
+                "Using one 'predict_param' in the fit method "
+                "without using one 'predict_param' in the predict method. "
+                "Please ensure one 'predict_param' "
+                "is used in the predict method before calling it."
+            )

From ede2113a251ebc5d91423d734f411e53bee015da Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Fri, 26 Jul 2024 15:56:42 +0200
Subject: [PATCH 245/424] Update : import related to conformity scores

---
 notebooks/regression/ts-changepoint.ipynb | 93 ++++++++++++-----------
 1 file changed, 47 insertions(+), 46 deletions(-)

diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index 0f9f17867..9aa3c46cb 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 425,
+   "execution_count": 629,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 426,
+   "execution_count": 630,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,7 +66,8 @@
     "from mapie.metrics import regression_coverage_score, regression_mean_width_score, coverage_width_based\n",
     "from mapie.subsample import BlockBootstrap\n",
     "from mapie.time_series_regression import MapieTimeSeriesRegressor\n",
-    "from mapie.conformity_scores import ConformityScore\n",
+    "from mapie.conformity_scores.regression import BaseRegressionScore\n",
+    "from mapie.conformity_scores.regression import BaseConformityScore\n",
     "\n",
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
@@ -83,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 427,
+   "execution_count": 631,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 428,
+   "execution_count": 632,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -132,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 429,
+   "execution_count": 633,
    "metadata": {},
    "outputs": [
     {
@@ -141,7 +142,7 @@
        "Text(0, 0.5, 'Hourly demand (GW)')"
       ]
      },
-     "execution_count": 429,
+     "execution_count": 633,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -173,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 430,
+   "execution_count": 634,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 431,
+   "execution_count": 635,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -241,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 432,
+   "execution_count": 636,
    "metadata": {},
    "outputs": [
     {
@@ -271,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 433,
+   "execution_count": 637,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 434,
+   "execution_count": 638,
    "metadata": {},
    "outputs": [
     {
@@ -357,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 435,
+   "execution_count": 639,
    "metadata": {},
    "outputs": [
     {
@@ -411,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 436,
+   "execution_count": 640,
    "metadata": {},
    "outputs": [
     {
@@ -436,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 437,
+   "execution_count": 641,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -448,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 438,
+   "execution_count": 642,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -460,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 439,
+   "execution_count": 643,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -495,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 440,
+   "execution_count": 644,
    "metadata": {},
    "outputs": [
     {
@@ -515,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 441,
+   "execution_count": 645,
    "metadata": {},
    "outputs": [
     {
@@ -559,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 442,
+   "execution_count": 646,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -569,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 443,
+   "execution_count": 647,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -589,16 +590,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 444,
+   "execution_count": 648,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x184c64760>]"
+       "[<matplotlib.lines.Line2D at 0x186d0b730>]"
       ]
      },
-     "execution_count": 444,
+     "execution_count": 648,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -630,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 445,
+   "execution_count": 649,
    "metadata": {},
    "outputs": [
     {
@@ -660,7 +661,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 446,
+   "execution_count": 650,
    "metadata": {},
    "outputs": [
     {
@@ -699,26 +700,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 447,
+   "execution_count": 651,
    "metadata": {},
    "outputs": [],
    "source": [
     "def compute_quantiles(conformity_scores, alpha_np):\n",
     "\n",
-    "    beta_np = ConformityScore._beta_optimize(\n",
+    "    beta_np = BaseRegressionScore._beta_optimize(\n",
     "                    alpha_np,\n",
     "                    conformity_scores.reshape(1, -1),\n",
     "                    conformity_scores.reshape(1, -1),\n",
     "                )\n",
     "    alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np\n",
     "\n",
-    "    lower_quantiles = ConformityScore.get_quantile(\n",
+    "    lower_quantiles = BaseConformityScore.get_quantile(\n",
     "    conformity_scores[..., np.newaxis],\n",
     "    alpha_low, axis=0, reversed=True,\n",
     "    unbounded=False\n",
     "    )\n",
     "\n",
-    "    higher_quantiles = ConformityScore.get_quantile(\n",
+    "    higher_quantiles = BaseConformityScore.get_quantile(\n",
     "        conformity_scores[..., np.newaxis],\n",
     "        alpha_up, axis=0,\n",
     "        unbounded=False\n",
@@ -729,7 +730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 448,
+   "execution_count": 652,
    "metadata": {},
    "outputs": [
     {
@@ -786,7 +787,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 449,
+   "execution_count": 653,
    "metadata": {},
    "outputs": [
     {
@@ -864,7 +865,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 458,
+   "execution_count": 654,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -876,7 +877,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 459,
+   "execution_count": 655,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -890,7 +891,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 460,
+   "execution_count": 656,
    "metadata": {},
    "outputs": [
     {
@@ -910,7 +911,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 461,
+   "execution_count": 657,
    "metadata": {},
    "outputs": [
     {
@@ -930,7 +931,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 462,
+   "execution_count": 658,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -965,16 +966,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 463,
+   "execution_count": 659,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1854c75e0>]"
+       "[<matplotlib.lines.Line2D at 0x18672cd30>]"
       ]
      },
-     "execution_count": 463,
+     "execution_count": 659,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1012,16 +1013,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 464,
+   "execution_count": 660,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x18551cdf0>"
+       "<matplotlib.legend.Legend at 0x186665900>"
       ]
      },
-     "execution_count": 464,
+     "execution_count": 660,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1054,16 +1055,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 465,
+   "execution_count": 661,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x185621690>"
+       "<matplotlib.legend.Legend at 0x1854386a0>"
       ]
      },
-     "execution_count": 465,
+     "execution_count": 661,
      "metadata": {},
      "output_type": "execute_result"
     },

From ae15a19b9463b91b2cf56cacb363ca221da50923 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 29 Jul 2024 10:49:03 +0200
Subject: [PATCH 246/424] Delete : remove some rolling coverage plots

---
 notebooks/regression/ts-changepoint.ipynb | 172 ++++++----------------
 1 file changed, 46 insertions(+), 126 deletions(-)

diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index baa8a083e..4e9a285dc 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 590,
+   "execution_count": 694,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 591,
+   "execution_count": 695,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -84,7 +84,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 592,
+   "execution_count": 696,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 593,
+   "execution_count": 697,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +133,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 594,
+   "execution_count": 698,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +142,7 @@
        "Text(0, 0.5, 'Hourly demand (GW)')"
       ]
      },
-     "execution_count": 594,
+     "execution_count": 698,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -174,7 +174,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 595,
+   "execution_count": 699,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +215,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 596,
+   "execution_count": 700,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 597,
+   "execution_count": 701,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +272,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 598,
+   "execution_count": 702,
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 599,
+   "execution_count": 703,
    "metadata": {},
    "outputs": [
     {
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 600,
+   "execution_count": 704,
    "metadata": {},
    "outputs": [
     {
@@ -412,7 +412,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 601,
+   "execution_count": 705,
    "metadata": {},
    "outputs": [
     {
@@ -437,7 +437,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 602,
+   "execution_count": 706,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +449,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 603,
+   "execution_count": 707,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +461,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 604,
+   "execution_count": 708,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 605,
+   "execution_count": 709,
    "metadata": {},
    "outputs": [
     {
@@ -516,7 +516,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 606,
+   "execution_count": 710,
    "metadata": {},
    "outputs": [
     {
@@ -560,7 +560,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 607,
+   "execution_count": 711,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +570,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 608,
+   "execution_count": 712,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,16 +590,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 609,
+   "execution_count": 713,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1865b08b0>]"
+       "[<matplotlib.lines.Line2D at 0x1872b3430>]"
       ]
      },
-     "execution_count": 609,
+     "execution_count": 713,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -631,7 +631,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 610,
+   "execution_count": 714,
    "metadata": {},
    "outputs": [
     {
@@ -663,7 +663,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 611,
+   "execution_count": 715,
    "metadata": {},
    "outputs": [
     {
@@ -738,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 612,
+   "execution_count": 716,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +768,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 613,
+   "execution_count": 717,
    "metadata": {},
    "outputs": [
     {
@@ -838,7 +838,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 614,
+   "execution_count": 718,
    "metadata": {},
    "outputs": [
     {
@@ -921,7 +921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 615,
+   "execution_count": 719,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -933,7 +933,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 616,
+   "execution_count": 720,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -945,7 +945,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 617,
+   "execution_count": 721,
    "metadata": {},
    "outputs": [
     {
@@ -965,7 +965,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 618,
+   "execution_count": 722,
    "metadata": {},
    "outputs": [
     {
@@ -983,6 +983,13 @@
     "plot_forecast(\"ACI\", y_train, y_test, y_aci_preds, y_aci_pis, coverages_aci, widths_aci, plot_coverage=True)\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Marginal coverage on a 24-hour rolling window of prediction intervals"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -993,7 +1000,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 619,
+   "execution_count": 723,
    "metadata": {},
    "outputs": [
     {
@@ -1073,93 +1080,6 @@
     "plt.show()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Marginal coverage on a 24-hour rolling window of prediction intervals\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### ENBPI"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 620,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x18348a710>]"
-      ]
-     },
-     "execution_count": 620,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_enbpi_npfit, label=\"Without update of residuals\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_enbpi_pfit, label=\"With update of residuals\")\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### aci"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 621,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x186624550>]"
-      ]
-     },
-     "execution_count": 621,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1000x500 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.figure(figsize=(10, 5))\n",
-    "plt.ylabel(f\"Rolling coverage [{window} hours]\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_aci_npfit, label=\"Without update of residuals\")\n",
-    "plt.plot(y_test[window:].index, rolling_coverage_aci_pfit, label=\"With update of residuals\")\n"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -1176,16 +1096,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 622,
+   "execution_count": 724,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1866e1420>"
+       "<matplotlib.legend.Legend at 0x18624a1d0>"
       ]
      },
-     "execution_count": 622,
+     "execution_count": 724,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1218,16 +1138,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 623,
+   "execution_count": 725,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x186902b60>"
+       "<matplotlib.legend.Legend at 0x184fe21a0>"
       ]
      },
-     "execution_count": 623,
+     "execution_count": 725,
      "metadata": {},
      "output_type": "execute_result"
     },

From 2be47bc6a835081dd6c82f7ea8493a3d2b6b7370 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Wed, 31 Jul 2024 18:13:25 +0200
Subject: [PATCH 247/424] Add : **predict_params in fit and predict method for
 Mapie Classifier

---
 HISTORY.rst                        |   1 +
 mapie/classification.py            |  35 ++++--
 mapie/estimator/classifier.py      |  33 ++++--
 mapie/tests/test_classification.py | 182 +++++++++++++++++++++++++++--
 mapie/utils.py                     |  37 ++++++
 5 files changed, 259 insertions(+), 29 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 213e8b1bb..221254a63 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Add `** predict_params` in fit and predict method for Mapie Classifier
 * Replace `assert np.array_equal` by `np.testing.assert_array_equal` in Mapie unit tests
 * Replace `github.com/simai-ml/MAPIE` by `github.com/scikit-learn-contrib/MAPIE`in all Mapie files
 * Extend `ConformityScore` to support regression (with `BaseRegressionScore`) and to support classification (with `BaseClassificationScore`)
diff --git a/mapie/classification.py b/mapie/classification.py
index f4e19ba45..6e2573d0c 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, Optional, Tuple, Union, cast
+from typing import Any, Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import BaseEstimator, ClassifierMixin
@@ -20,7 +20,8 @@
 from mapie.estimator.classifier import EnsembleClassifier
 from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
                          check_estimator_classification, check_n_features_in,
-                         check_n_jobs, check_null_weight, check_verbose)
+                         check_n_jobs, check_null_weight, check_predict_params,
+                         check_verbose)
 
 
 class MapieClassifier(BaseEstimator, ClassifierMixin):
@@ -419,7 +420,7 @@ def fit(
         sample_weight: Optional[ArrayLike] = None,
         size_raps: Optional[float] = None,
         groups: Optional[ArrayLike] = None,
-        **fit_params,
+        **kwargs: Any
     ) -> MapieClassifier:
         """
         Fit the base estimator or use the fitted base estimator.
@@ -453,14 +454,22 @@ def fit(
 
             By default ``None``.
 
-        **fit_params : dict
-            Additional fit parameters.
+        kwargs : dict
+            Additional fit and predict parameters.
 
         Returns
         -------
         MapieClassifier
             The model itself.
         """
+        fit_params = kwargs.pop('fit_params', {})
+        predict_params = kwargs.pop('predict_params', {})
+
+        if len(predict_params) > 0:
+            self._predict_params = True
+        else:
+            self._predict_params = False
+
         # Checks
         (estimator,
          self.conformity_score_function_,
@@ -496,7 +505,7 @@ def fit(
 
         # Predict on calibration data
         y_pred_proba, y, y_enc = self.estimator_.predict_proba_calib(
-            X, y, y_enc, groups
+            X, y, y_enc, groups, **predict_params
         )
 
         # Compute the conformity scores
@@ -506,7 +515,6 @@ def fit(
                 y, y_pred_proba, y_enc=y_enc, X=X,
                 sample_weight=sample_weight, groups=groups
             )
-
         return self
 
     def predict(
@@ -514,7 +522,8 @@ def predict(
         X: ArrayLike,
         alpha: Optional[Union[float, Iterable[float]]] = None,
         include_last_label: Optional[Union[bool, str]] = True,
-        agg_scores: Optional[str] = "mean"
+        agg_scores: Optional[str] = "mean",
+        **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Prediction and prediction sets on new samples based on target
@@ -571,6 +580,9 @@ def predict(
 
             By default "mean".
 
+        predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         Union[NDArray, Tuple[NDArray, NDArray]]
@@ -581,11 +593,16 @@ def predict(
         (n_samples,) and (n_samples, n_classes, n_alpha) if alpha is not None.
         """
         # Checks
+
+        if hasattr(self, '_predict_params'):
+            check_predict_params(self._predict_params,
+                                 predict_params, self.cv)
+
         check_is_fitted(self, self.fit_attributes)
         alpha = cast(Optional[NDArray], check_alpha(alpha))
 
         # Estimate predictions
-        y_pred = self.estimator_.single_estimator_.predict(X)
+        y_pred = self.estimator_.single_estimator_.predict(X, **predict_params)
         if alpha is None:
             return y_pred
 
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 0c7fa16c1..ce99e86d1 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -222,6 +222,7 @@ def _predict_proba_oof_estimator(
         self,
         estimator: ClassifierMixin,
         X: ArrayLike,
+        **predict_params
     ) -> NDArray:
         """
         Predict probabilities of a test set from a fitted estimator.
@@ -239,7 +240,7 @@ def _predict_proba_oof_estimator(
         ArrayLike
             Predicted probabilities.
         """
-        y_pred_proba = estimator.predict_proba(X)
+        y_pred_proba = estimator.predict_proba(X, **predict_params)
         # we enforce y_pred_proba to contain all labels included in y
         if len(estimator.classes_) != self.n_classes:
             y_pred_proba = fix_number_of_classes(
@@ -252,7 +253,8 @@ def _predict_proba_calib_oof_estimator(
         estimator: ClassifierMixin,
         X: ArrayLike,
         val_index: ArrayLike,
-        k: int
+        k: int,
+        **predict_params
     ) -> Tuple[NDArray, ArrayLike, ArrayLike]:
         """
         Perform predictions on a single out-of-fold model on a validation set.
@@ -276,7 +278,8 @@ def _predict_proba_calib_oof_estimator(
 
         X_val = _safe_indexing(X, val_index)
         if _num_samples(X_val) > 0:
-            y_pred_proba = self._predict_proba_oof_estimator(estimator, X_val)
+            y_pred_proba = self._predict_proba_oof_estimator(estimator, X_val,
+                                                             **predict_params)
         else:
             y_pred_proba = np.array([])
         val_id = np.full(len(X_val), k, dtype=int)
@@ -401,6 +404,9 @@ def predict_proba_calib(
 
             By default ``None``.
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         NDArray of shape (n_samples_test, 1)
@@ -409,7 +415,8 @@ def predict_proba_calib(
         check_is_fitted(self, self.fit_attributes)
 
         if self.cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
+            y_pred_proba =\
+                self.single_estimator_.predict_proba(X)
             y_pred_proba = self._check_proba_normalized(y_pred_proba)
         else:
             X = cast(NDArray, X)
@@ -417,7 +424,7 @@ def predict_proba_calib(
             cv = cast(BaseCrossValidator, self.cv)
             outputs = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
                 delayed(self._predict_proba_calib_oof_estimator)(
-                    estimator, X, calib_index, k
+                    estimator, X, calib_index, k, **predict_params
                 )
                 for k, ((_, calib_index), estimator) in enumerate(
                     zip(cv.split(X, y, groups), self.estimators_)
@@ -462,6 +469,9 @@ def predict(
             How to aggregate the scores output by the estimators on test data
             if a cross-validation strategy is used
 
+        **predict_params : dict
+            Additional predict parameters.
+
         Returns
         -------
         NDArray
@@ -471,14 +481,15 @@ def predict(
         check_is_fitted(self, self.fit_attributes)
 
         if self.cv == "prefit":
-            y_pred_proba = self.single_estimator_.predict_proba(X)
+            y_pred_proba = self.single_estimator_.predict_proba(
+                X, **predict_params
+            )
         else:
             y_pred_proba_k = np.asarray(
-                Parallel(
-                    n_jobs=self.n_jobs, verbose=self.verbose
-                )(
-                    delayed(self._predict_proba_oof_estimator)(estimator, X)
-                    for estimator in self.estimators_
+                Parallel(n_jobs=self.n_jobs, verbose=self.verbose)(
+                    delayed(self._predict_proba_oof_estimator)(
+                        estimator, X, **predict_params
+                    ) for estimator in self.estimators_
                 )
             )
             if agg_scores == "crossval":
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index 85e5d5791..f6f02a714 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -14,7 +14,8 @@
 from sklearn.impute import SimpleImputer
 from sklearn.linear_model import LogisticRegression
 from sklearn.model_selection import (GroupKFold, KFold, LeaveOneOut,
-                                     ShuffleSplit, StratifiedShuffleSplit)
+                                     ShuffleSplit, StratifiedShuffleSplit,
+                                     train_test_split)
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.utils.estimator_checks import check_estimator
@@ -23,12 +24,14 @@
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
+from mapie.conformity_scores import LACConformityScore
 from mapie.conformity_scores.utils import METHOD_SCORE_MAP
 from mapie.conformity_scores.sets.utils import check_proba_normalized
 from mapie.metrics import classification_coverage_score
 
 random_state = 42
 
+
 METHODS = ["lac", "aps", "raps"]
 WRONG_METHODS = ["scores", "cumulated", "test", "", 1, 2.5, (1, 2)]
 WRONG_INCLUDE_LABELS = ["randomised", "True", "False", "other", 1, 2.5, (1, 2)]
@@ -422,6 +425,36 @@
     ),
 }
 
+
+class CustomGradientBoostingClassifier(GradientBoostingClassifier):
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+
+    def fit(self, X, y, **kwargs):
+        return super().fit(X, y, **kwargs)
+
+    def predict_proba(self, X, check_predict_params=False):
+        if check_predict_params:
+            n_samples = X.shape[0]
+            n_classes = len(self.classes_)
+            return np.zeros((n_samples, n_classes))
+        else:
+            return super().predict_proba(X)
+
+    def predict(self, X, check_predict_params=False):
+        if check_predict_params:
+            return np.zeros(X.shape[0])
+        return super().predict(X)
+
+
+def early_stopping_monitor(i, est, locals):
+    """Returns True on the 3rd iteration."""
+    if i == 2:
+        return True
+    else:
+        return False
+
+
 # Here, we only list the strategies we want to test on a small data set,
 # for multi-class classification.
 COVERAGES = {
@@ -1939,16 +1972,147 @@ def test_fit_parameters_passing() -> None:
         estimator=gb, method="aps", random_state=random_state
     )
 
-    def early_stopping_monitor(i, est, locals):
-        """Returns True on the 3rd iteration."""
-        if i == 2:
-            return True
-        else:
-            return False
-
-    mapie.fit(X, y, monitor=early_stopping_monitor)
+    mapie.fit(X, y, fit_params={'monitor': early_stopping_monitor})
 
     assert mapie.estimator_.single_estimator_.estimators_.shape[0] == 3
 
     for estimator in mapie.estimator_.estimators_:
         assert estimator.estimators_.shape[0] == 3
+
+
+def test_predict_parameters_passing() -> None:
+    """
+    Test passing predict parameters.
+    Checks that conformity_scores from train are 0, y_pred from test are 0.
+    """
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    custom_gbc = CustomGradientBoostingClassifier(random_state=random_state)
+    score = LACConformityScore()
+    mapie_model = MapieClassifier(estimator=custom_gbc, conformity_score=score)
+
+    predict_params = {'check_predict_params': True}
+    mapie_model = mapie_model.fit(
+        X_train, y_train, predict_params=predict_params
+    )
+
+    expected_conformity_scores = np.ones((X_train.shape[0], 1))
+    y_pred = mapie_model.predict(X_test, agg_scores="mean", **predict_params)
+    np.testing.assert_allclose(mapie_model.conformity_scores_,
+                               expected_conformity_scores)
+    np.testing.assert_allclose(y_pred, 0)
+
+
+def test_with_no_predict_parameters_passing() -> None:
+    """
+    Test passing with no predict parameters from the
+    CustomGradientBoostingClassifier class.
+    Checks that y_pred from test are what we want
+    """
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    custom_gbc = CustomGradientBoostingClassifier(random_state=random_state)
+    mapie_model = MapieClassifier(estimator=custom_gbc)
+    mapie_model = mapie_model.fit(X_train, y_train)
+    y_pred = mapie_model.predict(X_test, agg_scores="mean")
+
+    assert np.any(y_pred != 0)
+
+
+def test_fit_params_expected_behavior_unaffected_by_predict_params() -> None:
+    """
+    We want to verify that there are no interferences
+    with predict_params on the expected behavior of fit_params
+    Checks that underlying GradientBoosting
+    estimators have used 3 iterations only during boosting,
+    instead of default value for n_estimators (=100).
+    """
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    custom_gbc = CustomGradientBoostingClassifier(random_state=random_state)
+    mapie_model = MapieClassifier(estimator=custom_gbc)
+    fit_params = {'monitor': early_stopping_monitor}
+    predict_params = {'check_predict_params': True}
+    mapie_model = mapie_model.fit(
+        X_train, y_train,
+        fit_params=fit_params, predict_params=predict_params
+    )
+
+    assert mapie_model.estimator_.single_estimator_.estimators_.shape[0] == 3
+    for estimator in mapie_model.estimator_.estimators_:
+        assert estimator.estimators_.shape[0] == 3
+
+
+def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
+    """
+    We want to verify that there are no interferences
+    with fit_params on the expected behavior of predict_params
+    Checks that conformity_scores from train and y_pred from test are 0.
+    """
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    custom_gbc = CustomGradientBoostingClassifier(random_state=random_state)
+    score = LACConformityScore()
+    mapie_model = MapieClassifier(estimator=custom_gbc, conformity_score=score)
+    fit_params = {'monitor': early_stopping_monitor}
+    predict_params = {'check_predict_params': True}
+    mapie_model = mapie_model.fit(
+        X_train, y_train,
+        fit_params=fit_params,
+        predict_params=predict_params
+    )
+    y_pred = mapie_model.predict(X_test, agg_scores="mean", **predict_params)
+
+    expected_conformity_scores = np.ones((X_train.shape[0], 1))
+
+    np.testing.assert_allclose(mapie_model.conformity_scores_,
+                               expected_conformity_scores)
+    np.testing.assert_allclose(y_pred, 0)
+
+
+def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
+    """
+    Test that using predict parameters in the predict method
+    without using predict_parameter in the fit method raises an error.
+    """
+    custom_gbc = CustomGradientBoostingClassifier(random_state=random_state)
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    mapie = MapieClassifier(estimator=custom_gbc)
+    predict_params = {'check_predict_params': True}
+    mapie_fitted = mapie.fit(X_train, y_train)
+
+    with pytest.raises(ValueError, match=(
+        fr".*Using 'predict_param' '{predict_params}' "
+        r"without using one 'predict_param' in the fit method\..*"
+        r"Please ensure a similar configuration of 'predict_param' "
+        r"is used in the fit method before calling it in predict\..*"
+    )):
+        mapie_fitted.predict(X_test, agg_scores="mean", **predict_params)
+
+
+def test_using_one_predict_parameter_into_fit_but_not_in_predict() -> None:
+    """
+    Test that using predict parameters in the fit method without using
+    predict_parameter in the predict method raises an error.
+    """
+    custom_gbc = CustomGradientBoostingClassifier(random_state=random_state)
+    X_train, X_test, y_train, y_test = (
+        train_test_split(X, y, test_size=0.2, random_state=random_state)
+    )
+    mapie = MapieClassifier(estimator=custom_gbc)
+    predict_params = {'check_predict_params': True}
+    mapie_fitted = mapie.fit(X_train, y_train, predict_params=predict_params)
+
+    with pytest.raises(ValueError, match=(
+        r"Using one 'predict_param' in the fit method "
+        r"without using one 'predict_param' in the predict method. "
+        r"Please ensure a similar configuration of 'predict_param' "
+        r"is used in the predict method as called in the fit."
+    )):
+        mapie_fitted.predict(X_test)
diff --git a/mapie/utils.py b/mapie/utils.py
index 13641b154..9f5b6a06a 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1373,3 +1373,40 @@ def check_n_samples(
              " int in the range [1, inf)"
              )
     return int(n_samples)
+
+
+def check_predict_params(
+    predict_params_used_in_fit: bool,
+    predict_params: dict,
+    cv: Optional[Union[int, str, BaseCrossValidator]] = None
+) -> None:
+    """
+    Check that if predict_params is used in the predict method,
+    it is also used in the fit method. Otherwise, raise an error.
+    Parameters
+    ----------
+    predict_params_used_in_fit: bool
+        True if one or more predict_params are used in the fit method
+    predict_param: dict
+        Contains all predict params used in predict method
+    Raises
+    ------
+    ValueError
+        If any predict_params are used in the predict method but none
+        are used in the fit method.
+    """
+    if cv != "prefit":
+        if len(predict_params) > 0 and predict_params_used_in_fit is False:
+            raise ValueError(
+                f"Using 'predict_param' '{predict_params}' "
+                f"without using one 'predict_param' in the fit method. "
+                f"Please ensure a similar configuration of 'predict_param' "
+                f"is used in the fit method before calling it in predict."
+                )
+        if len(predict_params) == 0 and predict_params_used_in_fit is True:
+            raise ValueError(
+                "Using one 'predict_param' in the fit method "
+                "without using one 'predict_param' in the predict method. "
+                "Please ensure a similar configuration of 'predict_param' "
+                "is used in the predict method as called in the fit."
+                )

From 52608779248041f850d3a01df401466f38667098 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 1 Aug 2024 17:52:51 +0200
Subject: [PATCH 248/424] Add : mention the clipping when it is used

---
 HISTORY.rst                               |   2 +
 notebooks/regression/ts-changepoint.ipynb | 126 ++++++++++++++--------
 2 files changed, 86 insertions(+), 42 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 213e8b1bb..caf3d5577 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,8 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Update the ts-changepoint notebook with the tutorial
+* Change import related to conformity scores into ts-changepoint notebook
 * Replace `assert np.array_equal` by `np.testing.assert_array_equal` in Mapie unit tests
 * Replace `github.com/simai-ml/MAPIE` by `github.com/scikit-learn-contrib/MAPIE`in all Mapie files
 * Extend `ConformityScore` to support regression (with `BaseRegressionScore`) and to support classification (with `BaseClassificationScore`)
diff --git a/notebooks/regression/ts-changepoint.ipynb b/notebooks/regression/ts-changepoint.ipynb
index 4e9a285dc..e3a5cc745 100644
--- a/notebooks/regression/ts-changepoint.ipynb
+++ b/notebooks/regression/ts-changepoint.ipynb
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 694,
+   "execution_count": 38,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -50,7 +50,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 695,
+   "execution_count": 39,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -71,7 +71,9 @@
     "\n",
     "%reload_ext autoreload\n",
     "%autoreload 2\n",
-    "warnings.simplefilter(\"ignore\")"
+    "warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "\n"
    ]
   },
   {
@@ -84,7 +86,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 696,
+   "execution_count": 40,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -113,7 +115,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 697,
+   "execution_count": 41,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,7 +135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 698,
+   "execution_count": 42,
    "metadata": {},
    "outputs": [
     {
@@ -142,7 +144,7 @@
        "Text(0, 0.5, 'Hourly demand (GW)')"
       ]
      },
-     "execution_count": 698,
+     "execution_count": 42,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -174,7 +176,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 699,
+   "execution_count": 43,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -215,7 +217,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 700,
+   "execution_count": 44,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -242,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 701,
+   "execution_count": 45,
    "metadata": {},
    "outputs": [
     {
@@ -272,7 +274,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 702,
+   "execution_count": 46,
    "metadata": {},
    "outputs": [
     {
@@ -311,7 +313,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 703,
+   "execution_count": 47,
    "metadata": {},
    "outputs": [
     {
@@ -358,7 +360,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 704,
+   "execution_count": 48,
    "metadata": {},
    "outputs": [
     {
@@ -412,7 +414,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 705,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [
     {
@@ -437,7 +439,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 706,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -449,7 +451,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 707,
+   "execution_count": 51,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -461,7 +463,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 708,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -496,7 +498,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 709,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -516,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 710,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -560,7 +562,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 711,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -570,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 712,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -590,16 +592,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 713,
+   "execution_count": 57,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x1872b3430>]"
+       "[<matplotlib.lines.Line2D at 0x1855fb670>]"
       ]
      },
-     "execution_count": 713,
+     "execution_count": 57,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -631,7 +633,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 714,
+   "execution_count": 58,
    "metadata": {},
    "outputs": [
     {
@@ -649,7 +651,13 @@
     "    X_test, alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
     "\n",
+    "y_pis_enbpi_npfit_before_clip = y_pis_enbpi_npfit.copy()\n",
+    "\n",
     "y_pis_enbpi_npfit = np.clip(y_pis_enbpi_npfit, 1, 10)\n",
+    "\n",
+    "if np.any(y_pis_enbpi_npfit_before_clip != y_pis_enbpi_npfit):\n",
+    "    print(\"An approximation was used. All values have been clipped to the range [1, 10].\")\n",
+    "\n",
     "coverage_enbpi_npfit = regression_coverage_score(\n",
     "    y_test, y_pis_enbpi_npfit[:, 0, 0], y_pis_enbpi_npfit[:, 1, 0]\n",
     ")\n",
@@ -663,14 +671,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 715,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ACI, with no partial_fit\n"
+      "ACI, with no partial_fit\n",
+      "An approximation was used. All values have been clipped to the range [1, 10].\n"
      ]
     }
    ],
@@ -684,6 +693,8 @@
     "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True,\n",
     "    allow_infinite_bounds=True\n",
     ")\n",
+    "clip_used = False\n",
+    "\n",
     "for step in range(gap, len(X_test), gap):\n",
     "    mapie_aci.adapt_conformal_inference(\n",
     "        X_test.iloc[(step - gap):step, :].to_numpy(),\n",
@@ -700,10 +711,19 @@
     "        optimize_beta=True,\n",
     "        allow_infinite_bounds=True\n",
     "    )\n",
+    "\n",
+    "    y_pis_aci_npfit_before_clip = y_pis_aci_npfit[step:step + gap, :, :].copy()\n",
+    "\n",
     "    y_pis_aci_npfit[step:step + gap, :, :] = np.clip(\n",
     "        y_pis_aci_npfit[step:step + gap, :, :], 1, 10\n",
     "    )\n",
     "\n",
+    "    if not np.allclose(y_pis_aci_npfit_before_clip, y_pis_aci_npfit[step:step + gap, :, :]):\n",
+    "        clip_used = True\n",
+    "\n",
+    "if clip_used:\n",
+    "    print(\"An approximation was used. All values have been clipped to the range [1, 10].\")\n",
+    "\n",
     "coverage_aci_npfit = regression_coverage_score(\n",
     "    y_test, y_pis_aci_npfit[:, 0, 0], y_pis_aci_npfit[:, 1, 0]\n",
     ")\n",
@@ -738,7 +758,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 716,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -768,7 +788,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 717,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [
     {
@@ -792,6 +812,8 @@
     "y_pred_enbpi_pfit[:gap], y_pis_enbpi_pfit[:gap, :, :] = mapie_enbpi.predict(\n",
     "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
+    "clip_used = False\n",
+    "\n",
     "for step in range(gap, len(X_test), gap):\n",
     "    mapie_enbpi.partial_fit(\n",
     "        X_test.iloc[(step - gap):step, :],\n",
@@ -807,10 +829,16 @@
     "        optimize_beta=True, allow_infinite_bounds=True\n",
     "    )\n",
     "\n",
+    "    y_pis_enbpi_pfit_before_clip = y_pis_enbpi_pfit[step:step + gap, :, :].copy()\n",
+    "\n",
+    "\n",
     "    y_pis_enbpi_pfit[step:step + gap, :, :] = np.clip(\n",
     "        y_pis_enbpi_pfit[step:step + gap, :, :], 1, 10\n",
     "    )\n",
     "\n",
+    "    if not np.allclose(y_pis_enbpi_pfit_before_clip, y_pis_enbpi_pfit[step:step + gap, :, :]):\n",
+    "        clip_used = True\n",
+    "\n",
     "    conformity_scores = mapie_enbpi.conformity_scores_\n",
     "\n",
     "    conformity_scores_enbpi_pfit.append(conformity_scores)\n",
@@ -822,6 +850,9 @@
     "    lower_quantiles_enbpi_pfit.append(lower_quantiles)\n",
     "    higher_quantiles_enbpi_pfit.append(higher_quantiles)\n",
     "\n",
+    "if clip_used:\n",
+    "    print(\"An approximation was used. All values have been clipped to the range [1, 10].\")\n",
+    "\n",
     "coverage_enbpi_pfit = regression_coverage_score(\n",
     "    y_test, y_pis_enbpi_pfit[:, 0, 0], y_pis_enbpi_pfit[:, 1, 0]\n",
     ")\n",
@@ -838,14 +869,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 718,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "ACI with partial_fit and adapt_conformal_inference\n"
+      "ACI with partial_fit and adapt_conformal_inference\n",
+      "An approximation was used. All values have been clipped to the range [1, 10].\n"
      ]
     }
    ],
@@ -863,6 +895,7 @@
     "y_pred_aci_pfit[:gap], y_pis_aci_pfit[:gap, :, :] = mapie_aci.predict(\n",
     "    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True, allow_infinite_bounds=True\n",
     ")\n",
+    "clip_used = False\n",
     "\n",
     "for step in range(gap, len(X_test), gap):\n",
     "\n",
@@ -885,10 +918,16 @@
     "        optimize_beta=True, allow_infinite_bounds=True\n",
     "    )\n",
     "\n",
+    "    y_pis_aci_pfit_before_clip = y_pis_aci_pfit[step:step + gap, :, :].copy()\n",
+    "\n",
+    "\n",
     "    y_pis_aci_pfit[step:step + gap, :, :] = np.clip(\n",
     "    y_pis_aci_pfit[step:step + gap, :, :], 1, 10\n",
     "    )\n",
     "\n",
+    "    if not np.allclose(y_pis_aci_pfit_before_clip, y_pis_aci_pfit[step:step + gap, :, :]):\n",
+    "        clip_used = True\n",
+    "\n",
     "    conformity_scores = mapie_aci.conformity_scores_\n",
     "\n",
     "    conformity_scores_aci_pfit.append(conformity_scores)\n",
@@ -901,6 +940,9 @@
     "    \n",
     "    higher_quantiles_aci_pfit.append(higher_quantiles)\n",
     "\n",
+    "if clip_used:\n",
+    "    print(\"An approximation was used. All values have been clipped to the range [1, 10].\")\n",
+    "\n",
     "coverage_aci_pfit = regression_coverage_score(\n",
     "    y_test, y_pis_aci_pfit[:, 0, 0], y_pis_aci_pfit[:, 1, 0]\n",
     ")\n",
@@ -921,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 719,
+   "execution_count": 63,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -933,7 +975,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 720,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -945,7 +987,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 721,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -965,7 +1007,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 722,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [
     {
@@ -1000,7 +1042,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 723,
+   "execution_count": 67,
    "metadata": {},
    "outputs": [
     {
@@ -1096,16 +1138,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 724,
+   "execution_count": 68,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x18624a1d0>"
+       "<matplotlib.legend.Legend at 0x1849c1690>"
       ]
      },
-     "execution_count": 724,
+     "execution_count": 68,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1138,16 +1180,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 725,
+   "execution_count": 69,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x184fe21a0>"
+       "<matplotlib.legend.Legend at 0x185315fc0>"
       ]
      },
-     "execution_count": 725,
+     "execution_count": 69,
      "metadata": {},
      "output_type": "execute_result"
     },

From d2bc12ff2b75983a5fabfdd1cad591904b9cc3f6 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Thu, 1 Aug 2024 18:17:17 +0200
Subject: [PATCH 249/424] Update : Taking into account PR comments

---
 mapie/tests/test_regression.py | 30 +++++++++++++++++++-----------
 mapie/utils.py                 |  8 ++++----
 2 files changed, 23 insertions(+), 15 deletions(-)

diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 9aae449f2..80e578556 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -966,8 +966,10 @@ def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
 
 
 def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
-    """Test that using predict parameters in the predict method
-    without using one predict_parameter in the fit method raises an error"""
+    """
+    Test that using predict parameters in the predict method
+    without using predict_parameter in the fit method raises an error.
+    """
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state)
@@ -979,16 +981,18 @@ def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
     with pytest.raises(ValueError, match=(
         fr".*Using 'predict_param' '{predict_params}' "
         r"without using one 'predict_param' in the fit method\..*"
-        r"Please ensure one 'predict_param' "
-        r"is used in the fit method before calling predict\..*"
+        r"Please ensure a similar configuration of 'predict_param' "
+        r"is used in the fit method before calling it in predict\..*"
     )):
         mapie_fitted.predict(X_test, **predict_params)
 
 
 def test_using_one_predict_parameter_into_fit_but_not_in_predict() -> None:
-    """Test that using predict parameters in the fit method
-    without using one predict_parameter in
-    the predict method raises an error"""
+    """
+    Test that using predict parameters in the fit method
+    without using predict_parameter in
+    the predict method raises an error.
+    """
     custom_gbr = CustomGradientBoostingRegressor(random_state=random_state)
     X_train, X_test, y_train, y_test = (
         train_test_split(X, y, test_size=0.2, random_state=random_state)
@@ -1000,14 +1004,16 @@ def test_using_one_predict_parameter_into_fit_but_not_in_predict() -> None:
     with pytest.raises(ValueError, match=(
         r"Using one 'predict_param' in the fit method "
         r"without using one 'predict_param' in the predict method. "
-        r"Please ensure one 'predict_param' "
-        r"is used in the predict method before calling it."
+        r"Please ensure a similar configuration of 'predict_param' "
+        r"is used in the predict method as called in the fit."
     )):
         mapie_fitted.predict(X_test)
 
 
 def test_predict_infinite_intervals() -> None:
-    """Test that MapieRegressor produces infinite bounds with alpha=0"""
+    """
+    Test that MapieRegressor produces infinite bounds with alpha=0
+    """
     mapie_reg = MapieRegressor().fit(X, y)
     _, y_pis = mapie_reg.predict(X, alpha=0., allow_infinite_bounds=True)
     np.testing.assert_allclose(y_pis[:, 0, 0], -np.inf)
@@ -1017,7 +1023,9 @@ def test_predict_infinite_intervals() -> None:
 @pytest.mark.parametrize("method", ["minmax", "naive", "plus", "base"])
 @pytest.mark.parametrize("cv", ["split", "prefit"])
 def test_check_change_method_to_base(method: str, cv: str) -> None:
-    """Test of the overloading of method attribute to `base` method in fit"""
+    """
+    Test of the overloading of method attribute to `base` method in fit
+    """
 
     X_train, X_val, y_train, y_val = train_test_split(
         X, y, test_size=0.5, random_state=random_state
diff --git a/mapie/utils.py b/mapie/utils.py
index 224e5b05d..fa781edb5 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1403,13 +1403,13 @@ def check_predict_params(
             raise ValueError(
                 f"Using 'predict_param' '{predict_params}' "
                 f"without using one 'predict_param' in the fit method. "
-                f"Please ensure one 'predict_param' "
-                f"is used in the fit method before calling predict."
+                f"Please ensure a similar configuration of 'predict_param' "
+                f"is used in the fit method before calling it in predict."
             )
         if len(predict_params) == 0 and predict_params_used_in_fit is True:
             raise ValueError(
                 "Using one 'predict_param' in the fit method "
                 "without using one 'predict_param' in the predict method. "
-                "Please ensure one 'predict_param' "
-                "is used in the predict method before calling it."
+                "Please ensure a similar configuration of 'predict_param' "
+                "is used in the predict method as called in the fit."
             )

From 0ead65c7d71bbb0a965852092d07bd6b7a3d79ad Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 1 Aug 2024 19:28:03 +0200
Subject: [PATCH 250/424] ADD: typing docstring and linting

---
 mapie/mondrian.py | 312 ++++++++++++++++++++++++++++++++++++++++------
 1 file changed, 271 insertions(+), 41 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index ebe15c616..d0f0a3075 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -1,9 +1,10 @@
 from copy import deepcopy
-from typing import Union, cast
+from typing import Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.utils.validation import _check_y, check_is_fitted, indexable
 
+from mapie.calibration import MapieCalibrator
 from mapie.classification import MapieClassifier
 from mapie.conformity_scores import (
     AbsoluteConformityScore,
@@ -19,7 +20,6 @@
 from mapie._typing import ArrayLike, NDArray
 
 
-
 class Mondrian:
     """Mondrian is a method that allows to make  perform conformal predictions
     for disjoints groups of individuals.
@@ -31,18 +31,22 @@ class can then be used to run a conformal prediction procedure for each
 
     Parameters
     ----------
-    mapie_estimator : Union[MapieClassifier, MapieRegressor or MapieMultiLabelClassifier]
-        The estimator for which the Mondrian method will be applied. The estimator must 
-        be used with cv='prefit' and the conformity score must be one of the following:
-        - For MapieClassifier: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
+    mapie_estimator : Union[MapieClassifier, MapieRegressor,
+                            MapieMultiLabelClassifier]
+        The estimator for which the Mondrian method will be applied. The
+        estimator must be used with cv='prefit' and the conformity score must
+        be one of the following:
+        - For MapieClassifier: 'lac', 'score', 'cumulated_score',
+        'aps' or 'topk'
         - For MapieRegressor: 'gamma', 'absolute' or 'aps'
-    
+
     Attributes
     ----------
     unique_groups : NDArray
         The unique groups of individuals for which the estimator was fitted
     mapie_estimators : Dict
-        A dictionary containing the fitted conformal estimator for each group of individuals
+        A dictionary containing the fitted conformal estimator for each group
+        of individuals
 
     References
     ----------
@@ -59,7 +63,8 @@ class can then be used to run a conformal prediction procedure for each
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
     >>> groups = [0, 0, 0, 0, 1, 1, 1, 1, 1]
     >>> clf = GaussianNB().fit(X_toy, y_toy)
-    >>> mapie = Mondrian(MapieClassifier(estimator=clf, cv="prefit")).fit(X_toy, y_toy, groups=groups)
+    >>> mapie = Mondrian(MapieClassifier(estimator=clf, cv="prefit")).fit(
+    ...     X_toy, y_toy, groups)
     >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups)
     >>> print(y_pi_mapie[:, :, 0])
     [[ True False False]
@@ -74,7 +79,10 @@ class can then be used to run a conformal prediction procedure for each
     """
 
     allowed_estimators = (
-        MapieClassifier, MapieRegressor, MapieMultiLabelClassifier
+        MapieClassifier,
+        MapieRegressor,
+        MapieMultiLabelClassifier,
+        MapieCalibrator
     )
     allowed_classification_ncs_str = [
         "lac", "score", "cumulated_score", "aps", "topk"
@@ -92,69 +100,181 @@ class can then be used to run a conformal prediction procedure for each
     ]
 
     def __init__(
-        self, mapie_estimator: Union[MapieClassifier, MapieRegressor, MapieMultiLabelClassifier]
+        self, mapie_estimator: Union[
+            MapieCalibrator,
+            MapieClassifier,
+            MapieRegressor,
+            MapieMultiLabelClassifier
+        ]
     ):
         self.mapie_estimator = mapie_estimator
 
     def _check_mapie_classifier(self):
-        if not self.mapie_estimator.cv == "prefit":
-            raise ValueError(
-                "Mondrian can only be used if the underlying Mapie estimator "+
-                "uses cv='prefit'"
-            )
+        """
+        Check that the underlying Mapie estimator uses cv='prefit'
+
+        Raises
+        ------
+        ValueError
+            If the underlying Mapie estimator does not use cv='prefit'
+            if the Mondrian method is not used with a MapieMultiLabelClassifier
+        NotFittedError
+            If the underlying Mapie estimator is not fitted and is the Mondrian
+            method is used with a MapieMultiLabelClassifier
+        """
+        if not isinstance(self.mapie_estimator, MapieMultiLabelClassifier):
+            if not self.mapie_estimator.cv == "prefit":
+                raise ValueError(
+                    "Mondrian can only be used if the underlying Mapie" +
+                    "estimator uses cv='prefit'."
+                )
+        else:
+            check_is_fitted(self.mapie_estimator.estimator)
 
     def _check_groups_fit(self, X: NDArray, groups: NDArray):
         """Check that each group is defined by an integer and check that there
-        are at least 2 individuals per group"""
+        are at least 2 individuals per group
+
+        Parameters
+        ----------
+        X : NDArray of shape (n_samples, n_features)
+            The input data
+        groups : NDArray of shape (n_samples,)
+
+        Raises
+        ------
+        ValueError
+            If the groups are not defined by integers
+            If there is less than 2 individuals per group
+            If the number of individuals in the groups is not equal to the
+            number of rows in X
+        """
         if not np.issubdtype(groups.dtype, np.integer):
             raise ValueError("The groups must be defined by integers")
         _, counts = np.unique(groups, return_counts=True)
         if np.min(counts) < 2:
             raise ValueError("There must be at least 2 individuals per group")
         if len(groups) != X.shape[0]:
-            raise ValueError("The number of individuals in the groups must be equal to the number of rows in X")
+            raise ValueError(
+                "The number of individuals in the groups must be equal" +
+                " to the number of rows in X"
+            )
+
+    def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
+        """Check that there is no new group in the prediction and that
+        the number of individuals in the groups is equal to the number of
+        rows in X
 
-    def _check_groups_predict(self, X, groups):
-        """Check that there is no new group in the prediction"""
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        groups : ArrayLike of shape (n_samples,)
+
+        returns
+        -------
+        groups : NDArray of shape (n_samples,)
+            Groups of individuals
+
+        Raises
+        ------
+        ValueError
+            If there is a new group in the prediction
+            If the number of individuals in the groups is not equal to the
+            number of rows in X
+        """
+        groups = cast(NDArray, np.array(groups))
         if not np.all(np.isin(groups, self.unique_groups)):
             raise ValueError("There is a new group in the prediction")
         if len(groups) != X.shape[0]:
-            raise ValueError("The number of individuals in the groups must be equal to the number of rows in X")
-        
+            raise ValueError("The number of individuals in the groups must " +
+                             "be equal to the number of rows in X")
+        return groups
+
     def _check_estimator(self):
+        """
+        Check that the estimator is in the allowed_estimators
+
+        Raises
+        ------
+        ValueError
+            If the estimator is not in the allowed_estimators
+        """
         if not isinstance(self.mapie_estimator, self.allowed_estimators):
             raise ValueError(
-                "The estimator must be a MapieClassifier, MapieRegressor or MapieMultiLabelClassifier"
+                "The estimator must be a MapieClassifier, MapieRegressor or" +
+                " MapieMultiLabelClassifier"
             )
-    
+
     def _check_confomity_score(self):
+        """
+        Check that the conformity score is in allowed_classification_ncs_str
+        or allowed_classification_ncs_class if the estimator is MapieClassifier
+        or in the allowed_regression_ncs if the estimator is a MapieRegressor
+
+        Raises
+        ------
+        ValueError
+            If conformity score is not in the allowed_classification_ncs_str
+            or allowed_classification_ncs_class if the estimator is a
+            MapieClassifier or in the allowed_regression_ncs if the estimator
+            is a MapieRegressor
+        """
         if isinstance(self.mapie_estimator, MapieClassifier):
             if self.mapie_estimator.conformity_score is not None:
-                if self.mapie_estimator.conformity_score not in self.allowed_classification_ncs_class:
+                if self.mapie_estimator.conformity_score not in \
+                   self.allowed_classification_ncs_class:
                     raise ValueError(
-                        "The conformity score for the MapieClassifier must be one of "+
-                        f"{self.allowed_classification_ncs_class}"
+                        "The conformity score for the MapieClassifier must" +
+                        f" be one of {self.allowed_classification_ncs_class}"
                     )
             if self.mapie_estimator.method is not None:
-                if self.mapie_estimator.method not in self.allowed_classification_ncs_str:
+                if self.mapie_estimator.method not in \
+                   self.allowed_classification_ncs_str:
                     raise ValueError(
-                        "The conformity score for the MapieClassifier must be one of "+
-                        f"{self.allowed_classification_ncs_str}"
+                        "The conformity score for the MapieClassifier must " +
+                        f"be one of {self.allowed_classification_ncs_str}"
                     )
         elif isinstance(self.mapie_estimator, MapieRegressor):
             if self.mapie_estimator.conformity_score is not None:
-                if self.mapie_estimator.conformity_score not in self.allowed_regression_ncs:
+                if self.mapie_estimator.conformity_score not in\
+                      self.allowed_regression_ncs:
                     raise ValueError(
-                        "The conformity score for the MapieRegressor must be one of "+
-                        f"{self.allowed_regression_ncs}"
+                        "The conformity score for the MapieRegressor must " +
+                        f"be one of {self.allowed_regression_ncs}"
                     )
 
-    def _check_fit_parameters(self, X, y, groups):
+    def _check_fit_parameters(
+            self, X: ArrayLike, y: ArrayLike, groups: ArrayLike
+    ) -> Tuple[NDArray, NDArray, NDArray]:
+        """
+        Perform checks on the input data, groups and the estimator
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
+            The target values
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+
+        Returns
+        -------
+        X : NDArray of shape (n_samples, n_features)
+            The input data
+        y : NDArray of shape (n_samples,) or (n_samples, n_outputs)
+            The target values
+        groups : NDArray of shape (n_samples,)
+        """
         self._check_estimator()
         self._check_mapie_classifier()
         self._check_confomity_score()
         X, y = indexable(X, y)
-        y = _check_y(y)
+        if isinstance(self.mapie_estimator, MapieMultiLabelClassifier):
+            y = _check_y(y, multi_output=True)
+        else:
+            y = _check_y(y)
         X = cast(NDArray, X)
         y = cast(NDArray, y)
         groups = cast(NDArray, np.array(groups))
@@ -162,9 +282,46 @@ def _check_fit_parameters(self, X, y, groups):
 
         return X, y, groups
 
+    def _check_is_topk_calibrator(self):
+        """
+        Check that the predict_proba method can only be used with a
+        MapieCalibrator estimator
+        """
+        if not isinstance(self.mapie_estimator, MapieCalibrator):
+            raise ValueError(
+                "The predict_proba method can only be used with a " +
+                "MapieCalibrator estimator"
+            )
+
+    def _check_not_topk_calibrator(self):
+        """
+        Check that the predict method can only be used with a MapieCalibrator
+        estimator
+        """
+        if isinstance(self.mapie_estimator, MapieCalibrator):
+            raise ValueError(
+                "The predict method can only be used with a MapieClassifier," +
+                "MapieRegressor or MapieMultiLabelClassifier estimator"
+            )
+
     def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
-        
-        self._check_fit_parameters(X, y, groups)
+        """
+        Fit the Mondrian method
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
+            The target values
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+        **kwargs
+            Additional keyword arguments to pass to the estimator's fit method
+            that may be specific to the Mapie estimator used
+        """
+
+        X, y, groups = self._check_fit_parameters(X, y, groups)
         self.unique_groups = np.unique(groups)
         self.mapie_estimators = {}
 
@@ -176,10 +333,40 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
             self.mapie_estimators[group] = mapie_group_estimator
         return self
 
-    def predict(self, X: ArrayLike, alpha, groups, **kwargs):
+    def predict(
+            self, X: ArrayLike, groups: ArrayLike,
+            alpha: Optional[Union[float, Iterable[float]]] = None,
+            **kwargs
+    ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
+        """
+        Perform conformal prediction for each group of individuals
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+        alpha : float or Iterable[float], optional
+            The desired coverage level(s) for each group.
+
+            By default None.
+        **kwargs
+            Additional keyword arguments to pass to the estimator's predict
+            method that may be specific to the Mapie estimator used
+
+        Returns
+        -------
+        y_pred : NDArray of shape (n_samples,) or (n_samples, n_outputs)
+            The predicted values
+        y_pss : NDArray of shape (n_samples, n_outputs, n_alpha)
+        """
 
         check_is_fitted(self, self.fit_attributes)
-        self._check_groups_predict(X, groups)
+        self._check_not_topk_calibrator()
+        X = indexable(X)
+        X = cast(NDArray, X)
+        groups = self._check_groups_predict(X, groups)
         if alpha is None:
             return self.mapie_estimator.predict(X, **kwargs)
         else:
@@ -188,14 +375,57 @@ def predict(self, X: ArrayLike, alpha, groups, **kwargs):
             for i, group in enumerate(unique_groups):
                 indices_groups = np.argwhere(groups == group)[:, 0]
                 X_g = X[indices_groups]
-                y_pred_g, y_pss_g = self.mapie_estimators[group].predict(X_g, alpha=alpha_np, **kwargs)
+                y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
+                    X_g, alpha=alpha_np, **kwargs
+                )
                 if i == 0:
                     if len(y_pred_g.shape) == 1:
                         y_pred = np.empty((X.shape[0],))
                     else:
                         y_pred = np.empty((X.shape[0], y_pred_g.shape[1]))
-                    y_pss = np.empty((X.shape[0], y_pss_g.shape[1], len(alpha_np)))
+                    y_pss = np.empty(
+                        (X.shape[0], y_pss_g.shape[1], len(alpha_np))
+                    )
                 y_pred[indices_groups] = y_pred_g
                 y_pss[indices_groups] = y_pss_g
 
             return y_pred, y_pss
+
+    def predict_proba(
+            self, X: ArrayLike, groups: ArrayLike, **kwargs
+    ) -> NDArray:
+        """
+        Perform top-label calibration for each group of individuals
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+        **kwargs
+            Additional keyword arguments to pass to the estimator's
+            predict_proba method that may be specific to the Mapie estimator
+            used
+
+        Returns
+        -------
+        y_pred_proba : NDArray of shape (n_samples, n_classes)
+            The calibrated predicted probabilities
+        """
+        self._check_is_topk_calibrator()
+        X = indexable(X)
+        X = cast(NDArray, X)
+        unique_groups = np.unique(groups)
+        y_pred_proba = np.empty(
+            (X.shape[0], len(self.mapie_estimator.estimator.classes_))
+        )
+        for group in unique_groups:
+            indices_groups = np.argwhere(groups == group)[:, 0]
+            X_g = X[indices_groups]
+            y_pred_proba_g = self.mapie_estimators[group].predict_proba(
+                X_g, **kwargs
+            )
+            y_pred_proba[indices_groups] = y_pred_proba_g
+
+        return y_pred_proba

From 3a6fa2d738b44eeebffb5bd49e69a6c46c26d77a Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 1 Aug 2024 19:28:13 +0200
Subject: [PATCH 251/424] TST: first test for mondrian

---
 mapie/tests/test_mondrian.py | 182 +++++++++++++++++++++++++++++++++++
 1 file changed, 182 insertions(+)
 create mode 100644 mapie/tests/test_mondrian.py

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
new file mode 100644
index 000000000..e0d4ff392
--- /dev/null
+++ b/mapie/tests/test_mondrian.py
@@ -0,0 +1,182 @@
+from copy import deepcopy
+
+import numpy as np
+import pytest
+from sklearn.base import clone
+from sklearn.linear_model import LinearRegression, LogisticRegression
+from sklearn.datasets import (
+    make_classification,
+    make_multilabel_classification,
+    make_regression
+)
+from sklearn.multioutput import MultiOutputClassifier
+
+from mapie.calibration import MapieCalibrator
+from mapie.classification import MapieClassifier
+from mapie.conformity_scores import (
+    AbsoluteConformityScore,
+    APSConformityScore,
+    GammaConformityScore,
+    LACConformityScore,
+    TopKConformityScore
+)
+from mapie.mondrian import Mondrian
+from mapie.multi_label_classification import MapieMultiLabelClassifier
+from mapie.regression import MapieRegressor
+
+VALID_MAPIE_ESTIMATORS_NAMES = [
+    "calibration",
+    "classif_score",
+    "classif_lac",
+    "classif_aps",
+    "classif_cumulated_score",
+    "classif_topk",
+    "classif_lac_conformity",
+    "classif_aps_conformity",
+    "classif_topk_conformity",
+    "multi_label_recall_crc",
+    "multi_label_recall_rcps",
+    "multi_label_precision_ltt",
+    "regression_absolute_conformity",
+    "regression_gamma_conformity",
+]
+
+VALID_MAPIE_ESTIMATORS = {
+    "calibration": {
+        "estimator": MapieCalibrator,
+        "task": "calibration",
+        "kwargs": {"method": "top_label"}
+    },
+    "classif_score": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"method": "score"}
+    },
+    "classif_lac": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"method": "lac"}
+    },
+    "classif_aps": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"method": "aps"}
+    },
+    "classif_cumulated_score": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"method": "cumulated_score"}
+    },
+    "classif_topk": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"method": "topk"}
+    },
+    "classif_lac_conformity": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"conformity_score": LACConformityScore()}
+    },
+    "classif_aps_conformity": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"conformity_score": APSConformityScore()}
+    },
+    "classif_topk_conformity": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"conformity_score": TopKConformityScore()}
+    },
+    "multi_label_recall_crc": {
+        "estimator": MapieMultiLabelClassifier,
+        "task": "multilabel_classification",
+        "kwargs": {"metric_control": "recall", "method": "crc"}
+    },
+    "multi_label_recall_rcps": {
+        "estimator": MapieMultiLabelClassifier,
+        "task": "multilabel_classification",
+        "kwargs": {"metric_control": "recall", "method": "rcps"},
+        "predict_kargs": {"delta": 0.01}
+    },
+    "multi_label_precision_ltt": {
+        "estimator": MapieMultiLabelClassifier,
+        "task": "multilabel_classification",
+        "kwargs": {"metric_control": "precision", "method": "ltt"},
+        "predict_kargs": {"delta": 0.01}
+    },
+    "regression_absolute_conformity": {
+        "estimator": MapieRegressor,
+        "task": "regression",
+        "kwargs": {"conformity_score": AbsoluteConformityScore()}
+    },
+    "regression_gamma_conformity": {
+        "estimator": MapieRegressor,
+        "task": "regression",
+        "kwargs": {"conformity_score": GammaConformityScore()}
+    },
+}
+
+TOY_DATASETS = {
+    "calibration": make_classification(
+        n_samples=1000, n_features=5, n_informative=5,
+        n_redundant=0, n_classes=10
+    ),
+    "classification": make_classification(
+        n_samples=1000, n_features=5, n_informative=5,
+        n_redundant=0, n_classes=10
+    ),
+    "multilabel_classification": make_multilabel_classification(
+        n_samples=1000, n_features=5, n_classes=5, allow_unlabeled=False
+    ),
+    "regression": make_regression(
+        n_samples=1000, n_features=5, n_informative=5
+    )
+
+}
+
+ML_MODELS = {
+    "calibration": LogisticRegression(),
+    "classification": LogisticRegression(),
+    "multilabel_classification": MultiOutputClassifier(
+                LogisticRegression(multi_class="multinomial")
+            ),
+    "regression": LinearRegression(),
+}
+
+
+@pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
+def test_valid_estimators_dont_fail(mapie_estimator_name):
+    task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
+    mapie_estimator = task_dict["estimator"]
+    mapie_kwargs = task_dict["kwargs"]
+    task = task_dict["task"]
+    x, y = TOY_DATASETS[task]
+    ml_model = ML_MODELS[task]
+    groups = np.random.choice(10, len(x))
+    model = clone(ml_model)
+    model.fit(x, y)
+    mapie_inst = deepcopy(mapie_estimator)
+    if not isinstance(mapie_inst(), MapieMultiLabelClassifier):
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, cv="prefit")
+        )
+    else:
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
+        )
+    if task == "multilabel_classification":
+        mondrian_cp.fit(x, y, groups=groups)
+        if mapie_estimator_name in [
+            "multi_label_recall_rcps", "multi_label_precision_ltt"
+        ]:
+            mondrian_cp.predict(
+                x, groups=groups, alpha=.2, **task_dict["predict_kargs"]
+            )
+        else:
+            mondrian_cp.predict(x, groups=groups, alpha=.2)
+    elif task == "calibration":
+        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+        mondrian_cp.predict_proba(x, groups=groups)
+    else:
+        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+        mondrian_cp.predict(x, groups=groups, alpha=.2)

From 103ace56229638c301682c2bd263384d20580fa3 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 1 Aug 2024 20:03:02 +0200
Subject: [PATCH 252/424] FIX: define not allowed method insteand of allowed

---
 mapie/mondrian.py | 22 +++++++++++-----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index d0f0a3075..25f501c32 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -15,7 +15,11 @@
     TopKConformityScore
 )
 from mapie.multi_label_classification import MapieMultiLabelClassifier
-from mapie.regression import MapieRegressor
+from mapie.regression import (
+    MapieQuantileRegressor,
+    MapieRegressor,
+    MapieTimeSeriesRegressor
+)
 from mapie.utils import check_alpha
 from mapie._typing import ArrayLike, NDArray
 
@@ -78,11 +82,9 @@ class can then be used to run a conformal prediction procedure for each
     [False False  True]]
     """
 
-    allowed_estimators = (
-        MapieClassifier,
-        MapieRegressor,
-        MapieMultiLabelClassifier,
-        MapieCalibrator
+    not_allowed_estimators = (
+        MapieQuantileRegressor,
+        MapieTimeSeriesRegressor
     )
     allowed_classification_ncs_str = [
         "lac", "score", "cumulated_score", "aps", "topk"
@@ -193,14 +195,14 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
 
     def _check_estimator(self):
         """
-        Check that the estimator is in the allowed_estimators
+        Check that the estimator is not in the not_allowed_estimators
 
         Raises
         ------
         ValueError
-            If the estimator is not in the allowed_estimators
+            If the estimator is in the not_allowed_estimators
         """
-        if not isinstance(self.mapie_estimator, self.allowed_estimators):
+        if isinstance(self.mapie_estimator, self.not_allowed_estimators):
             raise ValueError(
                 "The estimator must be a MapieClassifier, MapieRegressor or" +
                 " MapieMultiLabelClassifier"
@@ -364,7 +366,6 @@ def predict(
 
         check_is_fitted(self, self.fit_attributes)
         self._check_not_topk_calibrator()
-        X = indexable(X)
         X = cast(NDArray, X)
         groups = self._check_groups_predict(X, groups)
         if alpha is None:
@@ -414,7 +415,6 @@ def predict_proba(
             The calibrated predicted probabilities
         """
         self._check_is_topk_calibrator()
-        X = indexable(X)
         X = cast(NDArray, X)
         unique_groups = np.unique(groups)
         y_pred_proba = np.empty(

From 258b2d180d78b919dbe9ac86f2f0fcc921f7530e Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 1 Aug 2024 20:03:18 +0200
Subject: [PATCH 253/424] TST: test for bad cv and mapie estimator

---
 mapie/tests/test_mondrian.py | 55 +++++++++++++++++++++++++++++++++++-
 1 file changed, 54 insertions(+), 1 deletion(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index e0d4ff392..e89928ee0 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -10,6 +10,7 @@
     make_regression
 )
 from sklearn.multioutput import MultiOutputClassifier
+from sklearn.model_selection import ShuffleSplit
 
 from mapie.calibration import MapieCalibrator
 from mapie.classification import MapieClassifier
@@ -22,7 +23,11 @@
 )
 from mapie.mondrian import Mondrian
 from mapie.multi_label_classification import MapieMultiLabelClassifier
-from mapie.regression import MapieRegressor
+from mapie.regression import (
+    MapieQuantileRegressor,
+    MapieRegressor,
+    MapieTimeSeriesRegressor
+)
 
 VALID_MAPIE_ESTIMATORS_NAMES = [
     "calibration",
@@ -116,6 +121,8 @@
     },
 }
 
+NON_VALID_MAPIE_ESTIMATORS = [MapieQuantileRegressor, MapieTimeSeriesRegressor]
+
 TOY_DATASETS = {
     "calibration": make_classification(
         n_samples=1000, n_features=5, n_informative=5,
@@ -180,3 +187,49 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
     else:
         mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
         mondrian_cp.predict(x, groups=groups, alpha=.2)
+
+
+@pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
+@pytest.mark.parametrize("non_valid_cv", ["split", -1, 5, ShuffleSplit(1)])
+def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
+    task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
+    mapie_estimator = task_dict["estimator"]
+    mapie_kwargs = task_dict["kwargs"]
+    task = task_dict["task"]
+    x, y = TOY_DATASETS[task]
+    ml_model = ML_MODELS[task]
+    groups = np.random.choice(10, len(x))
+    model = clone(ml_model)
+    mapie_inst = deepcopy(mapie_estimator)
+    if not isinstance(mapie_inst(), MapieMultiLabelClassifier):
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, cv=non_valid_cv)
+        )
+    else:
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
+        )
+    if task == "multilabel_classification":
+        with pytest.raises(
+            ValueError, match=r".*MultiOutputClassifier instance is not*"
+        ):
+            mondrian_cp.fit(x, y, groups=groups)
+    elif task == "calibration":
+        with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
+            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+    else:
+        with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
+            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+
+
+@pytest.mark.parametrize("mapie_estimator", NON_VALID_MAPIE_ESTIMATORS)
+def test_non_valid_estimators_fails(mapie_estimator):
+    x, y = TOY_DATASETS["regression"]
+    ml_model = ML_MODELS["regression"]
+    groups = np.random.choice(10, len(x))
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=mapie_estimator(estimator=model,  cv="prefit"))
+    with pytest.raises(ValueError, match=r".*The estimator must be a*"):
+        mondrian.fit(x, y, groups=groups)
+        
\ No newline at end of file

From ecd452bc2ec9221a9a52db16fda05f310f5976e7 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 2 Aug 2024 09:20:09 +0200
Subject: [PATCH 254/424] FIX: use model predict instead of mapie prediciton in
 predict

---
 mapie/mondrian.py | 19 ++++++++++---------
 1 file changed, 10 insertions(+), 9 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 25f501c32..790ea769d 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -187,7 +187,7 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         """
         groups = cast(NDArray, np.array(groups))
         if not np.all(np.isin(groups, self.unique_groups)):
-            raise ValueError("There is a new group in the prediction")
+            raise ValueError("There is at least one new group in the prediction")
         if len(groups) != X.shape[0]:
             raise ValueError("The number of individuals in the groups must " +
                              "be equal to the number of rows in X")
@@ -223,13 +223,6 @@ def _check_confomity_score(self):
             is a MapieRegressor
         """
         if isinstance(self.mapie_estimator, MapieClassifier):
-            if self.mapie_estimator.conformity_score is not None:
-                if self.mapie_estimator.conformity_score not in \
-                   self.allowed_classification_ncs_class:
-                    raise ValueError(
-                        "The conformity score for the MapieClassifier must" +
-                        f" be one of {self.allowed_classification_ncs_class}"
-                    )
             if self.mapie_estimator.method is not None:
                 if self.mapie_estimator.method not in \
                    self.allowed_classification_ncs_str:
@@ -237,6 +230,13 @@ def _check_confomity_score(self):
                         "The conformity score for the MapieClassifier must " +
                         f"be one of {self.allowed_classification_ncs_str}"
                     )
+            if self.mapie_estimator.conformity_score is not None:
+                if self.mapie_estimator.conformity_score not in \
+                   self.allowed_classification_ncs_class:
+                    raise ValueError(
+                        "The conformity score for the MapieClassifier must" +
+                        f" be one of {self.allowed_classification_ncs_class}"
+                    )
         elif isinstance(self.mapie_estimator, MapieRegressor):
             if self.mapie_estimator.conformity_score is not None:
                 if self.mapie_estimator.conformity_score not in\
@@ -369,7 +369,7 @@ def predict(
         X = cast(NDArray, X)
         groups = self._check_groups_predict(X, groups)
         if alpha is None:
-            return self.mapie_estimator.predict(X, **kwargs)
+            return self.mapie_estimator.estimator.predict(X, **kwargs)
         else:
             alpha_np = cast(NDArray, check_alpha(alpha))
             unique_groups = np.unique(groups)
@@ -415,6 +415,7 @@ def predict_proba(
             The calibrated predicted probabilities
         """
         self._check_is_topk_calibrator()
+        groups = self._check_groups_predict(X, groups)
         X = cast(NDArray, X)
         unique_groups = np.unique(groups)
         y_pred_proba = np.empty(

From 5e06b3175199bf9a40ee41b843b99a6f029b51c2 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 2 Aug 2024 09:21:18 +0200
Subject: [PATCH 255/424] TST: bad groups, predict_proba, alpha none

---
 mapie/tests/test_mondrian.py | 165 +++++++++++++++++++++++++++++++----
 1 file changed, 146 insertions(+), 19 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index e89928ee0..f231e92bd 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -19,7 +19,9 @@
     APSConformityScore,
     GammaConformityScore,
     LACConformityScore,
-    TopKConformityScore
+    TopKConformityScore,
+    RAPSConformityScore,
+    ResidualNormalisedScore
 )
 from mapie.mondrian import Mondrian
 from mapie.multi_label_classification import MapieMultiLabelClassifier
@@ -29,23 +31,6 @@
     MapieTimeSeriesRegressor
 )
 
-VALID_MAPIE_ESTIMATORS_NAMES = [
-    "calibration",
-    "classif_score",
-    "classif_lac",
-    "classif_aps",
-    "classif_cumulated_score",
-    "classif_topk",
-    "classif_lac_conformity",
-    "classif_aps_conformity",
-    "classif_topk_conformity",
-    "multi_label_recall_crc",
-    "multi_label_recall_rcps",
-    "multi_label_precision_ltt",
-    "regression_absolute_conformity",
-    "regression_gamma_conformity",
-]
-
 VALID_MAPIE_ESTIMATORS = {
     "calibration": {
         "estimator": MapieCalibrator,
@@ -121,6 +106,28 @@
     },
 }
 
+VALID_MAPIE_ESTIMATORS_NAMES = list(VALID_MAPIE_ESTIMATORS.keys())
+
+NON_VALID_CS = {
+    "classif_raps": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"method": "raps"}
+    },
+    "classif_raps_conformity": {
+        "estimator": MapieClassifier,
+        "task": "classification",
+        "kwargs": {"conformity_score": RAPSConformityScore()}
+    },
+    "regression_residual_conformity": {
+        "estimator": MapieRegressor,
+        "task": "regression",
+        "kwargs": {"conformity_score": ResidualNormalisedScore()}
+    },
+}
+
+NON_VALID_MAPIE_ESTIMATORS_NAMES = list(NON_VALID_CS.keys())
+
 NON_VALID_MAPIE_ESTIMATORS = [MapieQuantileRegressor, MapieTimeSeriesRegressor]
 
 TOY_DATASETS = {
@@ -189,6 +196,24 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
         mondrian_cp.predict(x, groups=groups, alpha=.2)
 
 
+@pytest.mark.parametrize("mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES)
+def test_non_cs_fails(mapie_estimator_name):
+    task_dict = NON_VALID_CS[mapie_estimator_name]
+    mapie_estimator = task_dict["estimator"]
+    mapie_kwargs = task_dict["kwargs"]
+    task = task_dict["task"]
+    x, y = TOY_DATASETS[task]
+    ml_model = ML_MODELS[task]
+    groups = np.random.choice(10, len(x))
+    model = clone(ml_model)
+    model.fit(x, y)
+    mapie_inst = deepcopy(mapie_estimator)
+    mondrian_cp = Mondrian(
+        mapie_estimator=mapie_inst(estimator=model, cv="prefit", **mapie_kwargs)
+    )
+    with pytest.raises(ValueError, match=r".*The conformity score for*"):
+        mondrian_cp.fit(x, y, groups=groups)
+
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 @pytest.mark.parametrize("non_valid_cv", ["split", -1, 5, ShuffleSplit(1)])
 def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
@@ -232,4 +257,106 @@ def test_non_valid_estimators_fails(mapie_estimator):
     mondrian = Mondrian(mapie_estimator=mapie_estimator(estimator=model,  cv="prefit"))
     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
         mondrian.fit(x, y, groups=groups)
-        
\ No newline at end of file
+        
+
+def test_groups_not_defined_by_integers_fails():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x)).astype(str)
+    with pytest.raises(ValueError, match=r".*The groups must be defined by integers*"):
+        mondrian.fit(x, y, groups=groups)
+
+def test_groups_with_less_than_2_fails():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.array([1] + [2] * (len(x) - 1))
+    with pytest.raises(ValueError, match=r".*There must be at least 2 individuals*"):
+        mondrian.fit(x, y, groups=groups)
+
+def test_groups_and_x_have_same_length_in_fit():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x) - 1)
+    with pytest.raises(ValueError, match=r".*he number of individuals in*"):
+        mondrian.fit(x, y, groups=groups)
+
+def test_all_groups_in_predict_are_in_fit():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x))
+    mondrian.fit(x, y, groups=groups)
+    groups = np.array([99] * len(x))
+    with pytest.raises(ValueError, match=r".*There is at least one new*"):
+        mondrian.predict(x, groups=groups, alpha=.2)
+
+
+def test_all_groups_in_predict_proba_are_in_fit():
+    x, y = TOY_DATASETS["calibration"]
+    ml_model = ML_MODELS["calibration"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieCalibrator(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x))
+    mondrian.fit(x, y, groups=groups)
+    groups = np.array([99] * len(x))
+    with pytest.raises(ValueError, match=r".*There is at least one new*"):
+        mondrian.predict_proba(x, groups=groups, alpha=.2)
+
+
+def test_groups_and_x_have_same_length_in_predict():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x))
+    mondrian.fit(x, y, groups=groups)
+    groups = np.random.choice(10, len(x) - 1)
+    with pytest.raises(ValueError, match=r".*The number of individuals in*"):
+        mondrian.predict(x, groups=groups, alpha=.2)
+
+
+def test_predict_proba_only_with_calibrator():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x))
+    mondrian.fit(x, y, groups=groups)
+    with pytest.raises(ValueError, match=r".*The predict_proba method*"):
+        mondrian.predict_proba(x, groups=groups, alpha=.2)
+
+def test_predict_fails_with_calibrator():
+    x, y = TOY_DATASETS["calibration"]
+    ml_model = ML_MODELS["calibration"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieCalibrator(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x))
+    mondrian.fit(x, y, groups=groups)
+    with pytest.raises(ValueError, match=r".*The predict method*"):
+        mondrian.predict(x, groups=groups, alpha=.2)
+
+def test_alpha_none_return_one_element():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x))
+    mondrian.fit(x, y, groups=groups)
+    preds = mondrian.predict(x, groups=groups)
+    assert len(preds) == len(x)
\ No newline at end of file

From f9687cfe014ce64d3d37dac982b640efd18bb8a1 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 2 Aug 2024 11:14:25 +0200
Subject: [PATCH 256/424] TST: check groups can be lists

---
 mapie/tests/test_mondrian.py | 13 ++++++++++++-
 1 file changed, 12 insertions(+), 1 deletion(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index f231e92bd..9ed87568b 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -359,4 +359,15 @@ def test_alpha_none_return_one_element():
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     preds = mondrian.predict(x, groups=groups)
-    assert len(preds) == len(x)
\ No newline at end of file
+    assert len(preds) == len(x)
+
+
+def test_groups_is_list_ok():
+    x, y = TOY_DATASETS["classification"]
+    ml_model = ML_MODELS["classification"]
+    model = clone(ml_model)
+    model.fit(x, y)
+    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    groups = np.random.choice(10, len(x)).tolist()
+    mondrian.fit(x, y, groups=groups)
+    preds = mondrian.predict(x, groups=groups, alpha=.2)

From 7763f5ba157d0ec9105c8fab85d3de1126981704 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 2 Aug 2024 11:27:47 +0200
Subject: [PATCH 257/424] FIX: linting

---
 mapie/mondrian.py            |  6 ++--
 mapie/tests/test_mondrian.py | 70 +++++++++++++++++++++++++++---------
 2 files changed, 57 insertions(+), 19 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 790ea769d..2ead9a903 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -169,7 +169,7 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
 
         Parameters
         ----------
-        X : ArrayLike of shape (n_samples, n_features)
+        X : NDArray of shape (n_samples, n_features)
             The input data
         groups : ArrayLike of shape (n_samples,)
 
@@ -187,7 +187,9 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         """
         groups = cast(NDArray, np.array(groups))
         if not np.all(np.isin(groups, self.unique_groups)):
-            raise ValueError("There is at least one new group in the prediction")
+            raise ValueError(
+                "There is at least one new group in the prediction"
+            )
         if len(groups) != X.shape[0]:
             raise ValueError("The number of individuals in the groups must " +
                              "be equal to the number of rows in X")
diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 9ed87568b..021aa2df0 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -196,7 +196,9 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
         mondrian_cp.predict(x, groups=groups, alpha=.2)
 
 
-@pytest.mark.parametrize("mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES)
+@pytest.mark.parametrize(
+        "mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES
+)
 def test_non_cs_fails(mapie_estimator_name):
     task_dict = NON_VALID_CS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
@@ -209,11 +211,14 @@ def test_non_cs_fails(mapie_estimator_name):
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
     mondrian_cp = Mondrian(
-        mapie_estimator=mapie_inst(estimator=model, cv="prefit", **mapie_kwargs)
+        mapie_estimator=mapie_inst(
+            estimator=model, cv="prefit", **mapie_kwargs
+        )
     )
     with pytest.raises(ValueError, match=r".*The conformity score for*"):
         mondrian_cp.fit(x, y, groups=groups)
 
+
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 @pytest.mark.parametrize("non_valid_cv", ["split", -1, 5, ShuffleSplit(1)])
 def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
@@ -254,47 +259,64 @@ def test_non_valid_estimators_fails(mapie_estimator):
     groups = np.random.choice(10, len(x))
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=mapie_estimator(estimator=model,  cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=mapie_estimator(estimator=model,  cv="prefit")
+    )
     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
         mondrian.fit(x, y, groups=groups)
-        
+
 
 def test_groups_not_defined_by_integers_fails():
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x)).astype(str)
-    with pytest.raises(ValueError, match=r".*The groups must be defined by integers*"):
+    with pytest.raises(
+        ValueError, match=r".*The groups must be defined by integers*"
+    ):
         mondrian.fit(x, y, groups=groups)
 
+
 def test_groups_with_less_than_2_fails():
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.array([1] + [2] * (len(x) - 1))
-    with pytest.raises(ValueError, match=r".*There must be at least 2 individuals*"):
+    with pytest.raises(
+        ValueError, match=r".*There must be at least 2 individuals*"
+    ):
         mondrian.fit(x, y, groups=groups)
 
+
 def test_groups_and_x_have_same_length_in_fit():
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x) - 1)
     with pytest.raises(ValueError, match=r".*he number of individuals in*"):
         mondrian.fit(x, y, groups=groups)
 
+
 def test_all_groups_in_predict_are_in_fit():
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     groups = np.array([99] * len(x))
@@ -307,7 +329,9 @@ def test_all_groups_in_predict_proba_are_in_fit():
     ml_model = ML_MODELS["calibration"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieCalibrator(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieCalibrator(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     groups = np.array([99] * len(x))
@@ -320,7 +344,9 @@ def test_groups_and_x_have_same_length_in_predict():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     groups = np.random.choice(10, len(x) - 1)
@@ -333,29 +359,37 @@ def test_predict_proba_only_with_calibrator():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     with pytest.raises(ValueError, match=r".*The predict_proba method*"):
         mondrian.predict_proba(x, groups=groups, alpha=.2)
 
+
 def test_predict_fails_with_calibrator():
     x, y = TOY_DATASETS["calibration"]
     ml_model = ML_MODELS["calibration"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieCalibrator(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieCalibrator(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     with pytest.raises(ValueError, match=r".*The predict method*"):
         mondrian.predict(x, groups=groups, alpha=.2)
 
+
 def test_alpha_none_return_one_element():
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x))
     mondrian.fit(x, y, groups=groups)
     preds = mondrian.predict(x, groups=groups)
@@ -367,7 +401,9 @@ def test_groups_is_list_ok():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(mapie_estimator=MapieClassifier(estimator=model, cv="prefit"))
+    mondrian = Mondrian(
+        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
+    )
     groups = np.random.choice(10, len(x)).tolist()
     mondrian.fit(x, y, groups=groups)
-    preds = mondrian.predict(x, groups=groups, alpha=.2)
+    mondrian.predict(x, groups=groups, alpha=.2)

From b76460573b5c4490c273e8d364c2936793ae0702 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 2 Aug 2024 18:21:44 +0200
Subject: [PATCH 258/424] TST: same reuslts as classical if only one group

---
 mapie/tests/test_mondrian.py | 56 +++++++++++++++++++++++++++++++++++-
 1 file changed, 55 insertions(+), 1 deletion(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 021aa2df0..0d1849945 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -35,7 +35,7 @@
     "calibration": {
         "estimator": MapieCalibrator,
         "task": "calibration",
-        "kwargs": {"method": "top_label"}
+        "kwargs": {"method": "top_label", "random_state": 0}
     },
     "classif_score": {
         "estimator": MapieClassifier,
@@ -407,3 +407,57 @@ def test_groups_is_list_ok():
     groups = np.random.choice(10, len(x)).tolist()
     mondrian.fit(x, y, groups=groups)
     mondrian.predict(x, groups=groups, alpha=.2)
+
+
+@pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
+def test_same_results_if_only_one_group(mapie_estimator_name):
+    task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
+    mapie_estimator = task_dict["estimator"]
+    mapie_kwargs = task_dict["kwargs"]
+    task = task_dict["task"]
+    x, y = TOY_DATASETS[task]
+    ml_model = ML_MODELS[task]
+    groups = [0] * len(x)
+    model = clone(ml_model)
+    model.fit(x, y)
+    mapie_inst_mondrian = deepcopy(mapie_estimator)
+    mapie_classic_inst = deepcopy(mapie_estimator)
+    if not isinstance(mapie_inst_mondrian(), MapieMultiLabelClassifier):
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst_mondrian(estimator=model, cv="prefit")
+        )
+        mapie_classic = mapie_classic_inst(estimator=model, cv="prefit")
+    else:
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst_mondrian(estimator=model, **mapie_kwargs),
+        )
+        mapie_classic = mapie_classic_inst(estimator=model, **mapie_kwargs)
+    if task == "multilabel_classification":
+        mondrian_cp.fit(x, y, groups=groups)
+        mapie_classic.fit(x, y)
+        if mapie_estimator_name in [
+            "multi_label_recall_rcps", "multi_label_precision_ltt"
+        ]:
+            mondrian_pred = mondrian_cp.predict(
+                x, groups=groups, alpha=.2, **task_dict["predict_kargs"]
+            )
+            classic_pred = mapie_classic.predict(
+                x, alpha=.2, **task_dict["predict_kargs"]
+            )
+        else:
+            mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
+            classic_pred = mapie_classic.predict(x, alpha=.2)
+            
+    elif task == "calibration":
+        mondrian_cp.fit(X=x, y=y, groups=groups, **mapie_kwargs)
+        mapie_classic.fit(x, y, **mapie_kwargs)
+        mondrian_pred = mondrian_cp.predict_proba(x, groups=groups)
+        classic_pred = mapie_classic.predict_proba(x)
+        assert np.allclose(mondrian_pred, classic_pred, equal_nan=True)
+    else:
+        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+        mapie_classic.fit(x, y, **mapie_kwargs)
+        mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
+        classic_pred = mapie_classic.predict(x, alpha=.2)
+        assert np.allclose(mondrian_pred[0], classic_pred[0])
+        assert np.allclose(mondrian_pred[1], classic_pred[1])
\ No newline at end of file

From d5015adbcd18123f9a18ffc063b14db3593ee383 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 2 Aug 2024 18:22:08 +0200
Subject: [PATCH 259/424] FIX: typing

---
 mapie/mondrian.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 2ead9a903..dd345b7a2 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -370,7 +370,7 @@ def predict(
         self._check_not_topk_calibrator()
         X = cast(NDArray, X)
         groups = self._check_groups_predict(X, groups)
-        if alpha is None:
+        if alpha is None and self.mapie_estimator.estimator is not None:
             return self.mapie_estimator.estimator.predict(X, **kwargs)
         else:
             alpha_np = cast(NDArray, check_alpha(alpha))
@@ -416,9 +416,10 @@ def predict_proba(
         y_pred_proba : NDArray of shape (n_samples, n_classes)
             The calibrated predicted probabilities
         """
+        check_is_fitted(self, self.fit_attributes)
         self._check_is_topk_calibrator()
-        groups = self._check_groups_predict(X, groups)
         X = cast(NDArray, X)
+        groups = self._check_groups_predict(X, groups)
         unique_groups = np.unique(groups)
         y_pred_proba = np.empty(
             (X.shape[0], len(self.mapie_estimator.estimator.classes_))

From 7577ffca6fa65d8fb72838bfed9d15a891defb06 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 14:20:36 +0200
Subject: [PATCH 260/424] ADD: docstring to tests

---
 mapie/tests/test_mondrian.py | 38 +++++++++++++++++++++++++++++++++---
 1 file changed, 35 insertions(+), 3 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 0d1849945..3f8df151f 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -160,6 +160,8 @@
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 def test_valid_estimators_dont_fail(mapie_estimator_name):
+    """
+    Test that valid estimators don't fail"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -200,6 +202,8 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
         "mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES
 )
 def test_non_cs_fails(mapie_estimator_name):
+    """
+    Test that non valid conformity scores fail"""
     task_dict = NON_VALID_CS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -222,6 +226,8 @@ def test_non_cs_fails(mapie_estimator_name):
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 @pytest.mark.parametrize("non_valid_cv", ["split", -1, 5, ShuffleSplit(1)])
 def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
+    """
+    Test that invalid cv fails"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -254,6 +260,8 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
 
 @pytest.mark.parametrize("mapie_estimator", NON_VALID_MAPIE_ESTIMATORS)
 def test_non_valid_estimators_fails(mapie_estimator):
+    """
+    Test that non valid estimators fail"""
     x, y = TOY_DATASETS["regression"]
     ml_model = ML_MODELS["regression"]
     groups = np.random.choice(10, len(x))
@@ -267,6 +275,8 @@ def test_non_valid_estimators_fails(mapie_estimator):
 
 
 def test_groups_not_defined_by_integers_fails():
+    """
+    Test that groups not defined by integers fails"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -282,6 +292,8 @@ def test_groups_not_defined_by_integers_fails():
 
 
 def test_groups_with_less_than_2_fails():
+    """
+    Test that groups with less than 2 elements fails"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -297,6 +309,8 @@ def test_groups_with_less_than_2_fails():
 
 
 def test_groups_and_x_have_same_length_in_fit():
+    """
+    Test that groups and x have the same length in fit"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -310,6 +324,8 @@ def test_groups_and_x_have_same_length_in_fit():
 
 
 def test_all_groups_in_predict_are_in_fit():
+    """
+    Test that all groups in predict are in fit"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -325,6 +341,8 @@ def test_all_groups_in_predict_are_in_fit():
 
 
 def test_all_groups_in_predict_proba_are_in_fit():
+    """
+    Test that all groups in predict_proba are in fit"""
     x, y = TOY_DATASETS["calibration"]
     ml_model = ML_MODELS["calibration"]
     model = clone(ml_model)
@@ -340,6 +358,8 @@ def test_all_groups_in_predict_proba_are_in_fit():
 
 
 def test_groups_and_x_have_same_length_in_predict():
+    """
+    Test that groups and x have the same length in predict"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -355,6 +375,8 @@ def test_groups_and_x_have_same_length_in_predict():
 
 
 def test_predict_proba_only_with_calibrator():
+    """
+    Test that predict_proba only works with calibrator"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -369,6 +391,8 @@ def test_predict_proba_only_with_calibrator():
 
 
 def test_predict_fails_with_calibrator():
+    """
+    Test that predict fails with calibrator"""
     x, y = TOY_DATASETS["calibration"]
     ml_model = ML_MODELS["calibration"]
     model = clone(ml_model)
@@ -383,6 +407,8 @@ def test_predict_fails_with_calibrator():
 
 
 def test_alpha_none_return_one_element():
+    """
+    Test that if alpha is None, the output is a single element"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -397,6 +423,8 @@ def test_alpha_none_return_one_element():
 
 
 def test_groups_is_list_ok():
+    """
+    Test that the groups can be a list"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -411,6 +439,8 @@ def test_groups_is_list_ok():
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 def test_same_results_if_only_one_group(mapie_estimator_name):
+    """
+    Test that the results are the same if there is only one group"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -429,7 +459,9 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
         mapie_classic = mapie_classic_inst(estimator=model, cv="prefit")
     else:
         mondrian_cp = Mondrian(
-            mapie_estimator=mapie_inst_mondrian(estimator=model, **mapie_kwargs),
+            mapie_estimator=mapie_inst_mondrian(
+                estimator=model, **mapie_kwargs
+            ),
         )
         mapie_classic = mapie_classic_inst(estimator=model, **mapie_kwargs)
     if task == "multilabel_classification":
@@ -447,7 +479,7 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
         else:
             mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
             classic_pred = mapie_classic.predict(x, alpha=.2)
-            
+
     elif task == "calibration":
         mondrian_cp.fit(X=x, y=y, groups=groups, **mapie_kwargs)
         mapie_classic.fit(x, y, **mapie_kwargs)
@@ -460,4 +492,4 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
         mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
         classic_pred = mapie_classic.predict(x, alpha=.2)
         assert np.allclose(mondrian_pred[0], classic_pred[0])
-        assert np.allclose(mondrian_pred[1], classic_pred[1])
\ No newline at end of file
+        assert np.allclose(mondrian_pred[1], classic_pred[1])

From ec47e49d285f9ff9dff4486102e70f361168b10d Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 14:24:46 +0200
Subject: [PATCH 261/424] FIX: linting

---
 mapie/tests/test_mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 3f8df151f..c47564dfc 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -268,7 +268,7 @@ def test_non_valid_estimators_fails(mapie_estimator):
     model = clone(ml_model)
     model.fit(x, y)
     mondrian = Mondrian(
-        mapie_estimator=mapie_estimator(estimator=model,  cv="prefit")
+        mapie_estimator=mapie_estimator(estimator=model, cv="prefit")
     )
     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
         mondrian.fit(x, y, groups=groups)

From 2dbb7c080865af37ea4e4640eb33ffdaf92fff8a Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 14:46:54 +0200
Subject: [PATCH 262/424] FIX: checks for NCS were not working

---
 mapie/mondrian.py | 12 +++++++-----
 1 file changed, 7 insertions(+), 5 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index dd345b7a2..4b3213329 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -87,7 +87,7 @@ class can then be used to run a conformal prediction procedure for each
         MapieTimeSeriesRegressor
     )
     allowed_classification_ncs_str = [
-        "lac", "score", "cumulated_score", "aps", "topk"
+        "lac", "score", "cumulated_score", "aps", "top_k"
     ]
     allowed_classification_ncs_class = (
         LACConformityScore, NaiveConformityScore, APSConformityScore,
@@ -232,17 +232,19 @@ def _check_confomity_score(self):
                         "The conformity score for the MapieClassifier must " +
                         f"be one of {self.allowed_classification_ncs_str}"
                     )
+                else:
+                    return
             if self.mapie_estimator.conformity_score is not None:
-                if self.mapie_estimator.conformity_score not in \
-                   self.allowed_classification_ncs_class:
+                if not isinstance(self.mapie_estimator.conformity_score,
+                   self.allowed_classification_ncs_class):
                     raise ValueError(
                         "The conformity score for the MapieClassifier must" +
                         f" be one of {self.allowed_classification_ncs_class}"
                     )
         elif isinstance(self.mapie_estimator, MapieRegressor):
             if self.mapie_estimator.conformity_score is not None:
-                if self.mapie_estimator.conformity_score not in\
-                      self.allowed_regression_ncs:
+                if not isinstance(self.mapie_estimator.conformity_score,
+                   self.allowed_regression_ncs):
                     raise ValueError(
                         "The conformity score for the MapieRegressor must " +
                         f"be one of {self.allowed_regression_ncs}"

From 06fb35e13eb24d7df005089dcb2a10d100f20366 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 14:47:21 +0200
Subject: [PATCH 263/424] FIX: topk name anddistinction between task for valid
 estimators

---
 mapie/tests/test_mondrian.py | 15 +++++++++++----
 1 file changed, 11 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index c47564dfc..02b754f48 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -60,7 +60,7 @@
     "classif_topk": {
         "estimator": MapieClassifier,
         "task": "classification",
-        "kwargs": {"method": "topk"}
+        "kwargs": {"method": "top_k"}
     },
     "classif_lac_conformity": {
         "estimator": MapieClassifier,
@@ -167,19 +167,26 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
     mapie_kwargs = task_dict["kwargs"]
     task = task_dict["task"]
     x, y = TOY_DATASETS[task]
+    y = np.abs(y)  # to avoid negative values with Gamma NCS
     ml_model = ML_MODELS[task]
     groups = np.random.choice(10, len(x))
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
-    if not isinstance(mapie_inst(), MapieMultiLabelClassifier):
+    if task not in ["multilabel_classification", "calibration"]:
         mondrian_cp = Mondrian(
-            mapie_estimator=mapie_inst(estimator=model, cv="prefit")
+            mapie_estimator=mapie_inst(
+                estimator=model, cv="prefit", **mapie_kwargs
+            )
         )
-    else:
+    elif task == "multilabel_classification":
         mondrian_cp = Mondrian(
             mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
         )
+    else:
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, cv="prefit")
+        )
     if task == "multilabel_classification":
         mondrian_cp.fit(x, y, groups=groups)
         if mapie_estimator_name in [

From 9c414791360832d7d5df1c75edcf1fd7346eeaa8 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 15:16:12 +0200
Subject: [PATCH 264/424] FIX: replace isinstance by type to avoid confusion
 with child class

---
 mapie/mondrian.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 4b3213329..b0fafe74f 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -235,8 +235,8 @@ def _check_confomity_score(self):
                 else:
                     return
             if self.mapie_estimator.conformity_score is not None:
-                if not isinstance(self.mapie_estimator.conformity_score,
-                   self.allowed_classification_ncs_class):
+                if type(self.mapie_estimator.conformity_score) not in \
+                   self.allowed_classification_ncs_class:
                     raise ValueError(
                         "The conformity score for the MapieClassifier must" +
                         f" be one of {self.allowed_classification_ncs_class}"

From 986e2c1024c057d1836e5805f9116a4168c81caa Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 15:18:31 +0200
Subject: [PATCH 265/424] FIX: indent in test in docstring

---
 mapie/mondrian.py | 24 ++++++++++++------------
 1 file changed, 12 insertions(+), 12 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index b0fafe74f..32e3b6fac 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -60,26 +60,26 @@ class can then be used to run a conformal prediction procedure for each
 
     Examples
     --------
-    >>> import numpy as np
-    >>> from sklearn.naive_bayes import GaussianNB
+        >>> import numpy as np
+    >>> from sklearn.linear_model import LogisticRegression
     >>> from mapie.classification import MapieClassifier
     >>> X_toy = np.arange(9).reshape(-1, 1)
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
     >>> groups = [0, 0, 0, 0, 1, 1, 1, 1, 1]
-    >>> clf = GaussianNB().fit(X_toy, y_toy)
+    >>> clf = LogisticRegression(random_state=42).fit(X_toy, y_toy)
     >>> mapie = Mondrian(MapieClassifier(estimator=clf, cv="prefit")).fit(
     ...     X_toy, y_toy, groups)
     >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups)
-    >>> print(y_pi_mapie[:, :, 0])
+    >>> print(y_pi_mapie[:, :, 0].astype(bool))
     [[ True False False]
-    [ True False False]
-    [ True  True False]
-    [ True  True False]
-    [False  True False]
-    [False  True  True]
-    [False False  True]
-    [False False  True]
-    [False False  True]]
+     [ True False False]
+     [ True  True False]
+     [ True  True False]
+     [False  True False]
+     [False  True  True]
+     [False False  True]
+     [False False  True]
+     [False False  True]]
     """
 
     not_allowed_estimators = (

From d39af294a153e0532de0c1ede431d8a6f0e868eb Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 15:37:54 +0200
Subject: [PATCH 266/424] FIX: typing

---
 mapie/mondrian.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 32e3b6fac..7209ad486 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -382,7 +382,7 @@ def predict(
                 X_g = X[indices_groups]
                 y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
                     X_g, alpha=alpha_np, **kwargs
-                )
+                )  # type: ignore
                 if i == 0:
                     if len(y_pred_g.shape) == 1:
                         y_pred = np.empty((X.shape[0],))
@@ -424,7 +424,8 @@ def predict_proba(
         groups = self._check_groups_predict(X, groups)
         unique_groups = np.unique(groups)
         y_pred_proba = np.empty(
-            (X.shape[0], len(self.mapie_estimator.estimator.classes_))
+            (X.shape[0],
+             len(self.mapie_estimator.estimator.classes_))  # type: ignore
         )
         for group in unique_groups:
             indices_groups = np.argwhere(groups == group)[:, 0]

From 098230e3c33c70ea53c8828b1abe4937d3aa15d5 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 15:43:09 +0200
Subject: [PATCH 267/424] UPD: update history.rst

---
 HISTORY.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 213e8b1bb..d250c8633 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -4,7 +4,7 @@ History
 
 0.8.x (2024-xx-xx)
 ------------------
-
+* Add Mondrian Conformal Prediction for regression, classification, calibration and multilabel-classification
 * Replace `assert np.array_equal` by `np.testing.assert_array_equal` in Mapie unit tests
 * Replace `github.com/simai-ml/MAPIE` by `github.com/scikit-learn-contrib/MAPIE`in all Mapie files
 * Extend `ConformityScore` to support regression (with `BaseRegressionScore`) and to support classification (with `BaseClassificationScore`)

From ca74087bd1e728bbe64a2f14a84dd3f0e51fd927 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 15:53:51 +0200
Subject: [PATCH 268/424] FIX: typing

---
 mapie/mondrian.py | 5 ++---
 1 file changed, 2 insertions(+), 3 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 7209ad486..b9a84e1e5 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -380,9 +380,8 @@ def predict(
             for i, group in enumerate(unique_groups):
                 indices_groups = np.argwhere(groups == group)[:, 0]
                 X_g = X[indices_groups]
-                y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
-                    X_g, alpha=alpha_np, **kwargs
-                )  # type: ignore
+                y_pred_g, y_pss_g = self.mapie_estimators[group].\
+                    predict(X_g, alpha=alpha_np, **kwargs)  # type: ignore
                 if i == 0:
                     if len(y_pred_g.shape) == 1:
                         y_pred = np.empty((X.shape[0],))

From dd1fe5019edbe2038027c6fa9b3532f3bff57bbc Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 5 Aug 2024 16:01:13 +0200
Subject: [PATCH 269/424] FIX: typing

---
 mapie/mondrian.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index b9a84e1e5..d8ad47cbf 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -378,10 +378,11 @@ def predict(
             alpha_np = cast(NDArray, check_alpha(alpha))
             unique_groups = np.unique(groups)
             for i, group in enumerate(unique_groups):
+                m = self.mapie_estimators[group]
                 indices_groups = np.argwhere(groups == group)[:, 0]
                 X_g = X[indices_groups]
-                y_pred_g, y_pss_g = self.mapie_estimators[group].\
-                    predict(X_g, alpha=alpha_np, **kwargs)  # type: ignore
+                pred = m.predict(X_g, alpha=alpha_np, **kwargs)  # type: ignore
+                y_pred_g, y_pss_g = pred
                 if i == 0:
                     if len(y_pred_g.shape) == 1:
                         y_pred = np.empty((X.shape[0],))

From 44f7476c3a6f29e4121634fd9a49da54f54d0a95 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 6 Aug 2024 12:42:15 +0200
Subject: [PATCH 270/424] ADD: documentation

---
 doc/index.rst                            |  7 ++++
 doc/theoretical_description_mondrian.rst | 41 ++++++++++++++++++++++++
 2 files changed, 48 insertions(+)
 create mode 100644 doc/theoretical_description_mondrian.rst

diff --git a/doc/index.rst b/doc/index.rst
index b5450722b..226b496ca 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -49,6 +49,13 @@
    examples_multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification
    notebooks_multilabel_classification
 
+.. toctree::
+   :maxdepth: 2
+   :hidden:
+   :caption: MONDRIAN
+
+   theoretical_description_mondrian
+
 .. toctree::
    :maxdepth: 2
    :hidden:
diff --git a/doc/theoretical_description_mondrian.rst b/doc/theoretical_description_mondrian.rst
new file mode 100644
index 000000000..5a3d01145
--- /dev/null
+++ b/doc/theoretical_description_mondrian.rst
@@ -0,0 +1,41 @@
+.. title:: Theoretical Description Mondrian : contents
+
+.. _theoretical_description_mondrian:
+
+#######################
+Theoretical Description
+#######################
+
+Mondrian conformal prediction (MCP) [1] is a method that allows to build prediction sets with a group-conditional
+coverage guarantee.  The coverage guarantee is given by:
+
+.. math::
+    P \{Y_{n+1} \in \hat{C}_{n, \alpha}(X_{n+1}) | G_{n+1} = g\} \geq 1 - \alpha
+
+where :math:`G(X_{n+1})` is the group of the new test point :math:`X_{n+1}` and :math:`g`
+is a group in the set of groups :math:`\mathcal{G}`.
+
+MCP can be used with any split conformal predictor and can be particularly useful when one have a prior 
+knowledge about existing groups wheter the information is directly included in the features
+of the data or not.
+In a classifcation setting, the groups can be defined as the predicted classes of the data. Doing so,
+one can ensure that, for each predicted class, the coverage guarantee is satisfied.
+
+
+In order to achieve the group-conditional coverage guarantee, MCP simply this the data
+according to the groups and then applies the split conformal predictor to each group separately.
+
+The quantile of each group is defined as:
+
+.. math::
+   \widehat{q}^g = \text{quantiles}\left(s_1, ..., s_{n^g},\frac{\lceil (n^{(g) + 1)(1-\alpha)\rceil}{n^{(g) }  \right)
+
+Where :math:`s_1, ..., s_{n^g}` are the conformity scores of the training points in group :math:`g` and :math:`n^{(g)}`
+is the number of training points in group :math:`g`.
+
+References
+----------
+
+[1] Vladimir Vovk, David Lindsay, Ilia Nouretdinov, and Alex Gammerman.
+Mondrian confidence machine.
+Technical report, Royal Holloway University of London, 2003

From aaa7f32e52937895327b8f295c2befd9ecb326e4 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 6 Aug 2024 15:30:08 +0200
Subject: [PATCH 271/424] DOC: fix latex and add figure to mondrian

---
 doc/images/mondiran.png                  | Bin 0 -> 92631 bytes
 doc/theoretical_description_mondrian.rst |   9 +++++++--
 2 files changed, 7 insertions(+), 2 deletions(-)
 create mode 100644 doc/images/mondiran.png

diff --git a/doc/images/mondiran.png b/doc/images/mondiran.png
new file mode 100644
index 0000000000000000000000000000000000000000..baeca6b71aae3325dbed28e193dc200ec149c7b0
GIT binary patch
literal 92631
zcmeEuby!qu_ckdlC<4+TDpG=^#LxmN-5t^m(gQ<>h=d{{-5o>MFodW`Bi)@t4BheV
z(c?Mq^?Tnxzdz4h*UaqMv!DIcihJE_&4eo}$`avI;iI9U5j~faRz*X@mPA9t2*kYs
zoVnwgC4h!TC~hq!rTkn<ib46cqlLAdIU1T=c$^lFwps^ys@{hfaVvDJ?BE%!TP*0=
z!Tpj*Tz<*h3?E4_CB8+mv(}NTvl~lkJ-buvKXywv;uVsB6~D7VyNu0IReTh(?7a_h
zS#7>fKJ80zvD}7Z`cFKMlKU80h#AAwP0@~Z`m>$+!QgX{Kkfk<wl>Dgn*vH--@V1h
zEF-M;<3u`ONKY76#@k$0UN@5y(TPP9pxwrHX61}d!rYZW+hHx0OD0BB=}c1SqImwK
zSo|d+X0Ye`J9%Bsad+}McMTI1^a%+TG|@h)Kc#UNLMzl_P~x1+l_DDUeM1`}5<r5+
zC-ikjP_SO)jT%EuDF3}TIcM*N=v@b{a}$J$JLa>AO;7dBo^=?-Fn6wWpNI|aHosJ?
zG5ZnrpqQ0%VeamFvdN5t<#OnYa{RB5FGFwS-taMAG0b&rGRm+}ewg#D2idgrn6@UP
z)4rkgqPbRnL&R|Nj)iP6%gGS01OxS}I?4X^Kp%ISuMH%e3#-^)S?%~ETukpY^@s94
zi71%sbNR~MtZGF3KxbDz<Z6RZR4*gCfZXEi14;RSu}1M267+J`uQ$DP$}{kjoI@y!
z-!M{_-<P3k7X13e$0R0ApPo&Q6zBV+&6h5Fp(_#gy(DE!cRc-kW%SEL#I)JsnIh<W
zv0+~*xEI4*PrMe}LRY>lp5y#jjXK;3*)7LUA<pz^zI&>je6KS~b!;*BVv~Wz5x?BU
zP@?!1duj|ZMYcY@66vz+w~ocH1U!O_Dtn$g%M65N{$lBvc*O+_6lNF_-bSYMjj_jA
z7&l3RTw8FC@GY#QGmB+$q2=i$7kIbmmx!YGV-NQv(3hHTR_+yn<YEuWf5?~x<dLlU
zE5{HaA5^G(F>%D3!!yIW-sug9BUtPS)tI<Oo-!T@w*||84t!7Om0d)na~t!`)mP(J
z3_ov-w!@v3L<kKbCT?FS>(2W{nkGq8Zwui@;|k!W1PiNSJzGf2B*aNk7E^v7F+}eY
zpeLz7F-3eFh)|{+Z9NPCZ4+K{In%F=V^^?0n;IzY_ZzsHB=q&9!WXV!qI?j|aQ>mh
zkFLO`2*F)^ujerO^nH7xP_pBQQ(xbej+j{*&SZZYNBOfc^i3Lfe3(D&H!5^m;Q&4p
z`>`i1bcO~>W<|C?^xm<jvgb3Mgf(X{v-OyznwFSC7b(1qMEx2LXo<AzGbvw5fMrBe
z@S1Ubg!F#6-F@cq+V%Af@iWN#=B63xt|sa?ijy7=t@LP~DR*gTMD|h$1kqbW1?nz|
z{Z@-9cLp;ETTa{b!|TL*=LZPTn;s0!DGp-Fv=C~yojyg^n#1#vK*L3U&v5g8KnK?u
zVO{SxW2(>CD?zuFZ+r?GHU^Dh)dZ#(6OR#7wW>JbCu7IAkU3#a1r_B{H=<q6(Vo0x
zz{ShB$16?zM24M2_$!_X!;Bo-?a;3b&*ktxMB(1T<0jDY|H!!f18hd18nh{OlPNET
zKn~|M&blP{{pb(oJ5n0T97814(z02EAHzd4o!9R3(x$v6&-}Kg>dK)Txh+H8ueSE!
z;KoFFlQ<5SKZ~i*XFQAOCtT#@;SWr$Ki^FXihD(-&8Hd0*S6|IwDI+<Ec)rKym{9t
zy3#pg5xUg{wOzrJ+diNsQpXm(U0Y9znP@*IWxN|$!htve3U`&{nB|#1lCa`V;)UT_
z&KV*eHZt7~7m{Ynq@Id+{#AZKe&P1QjaQ)E`{h9=pP!1~dw(G3exFnIez;U8b6m{G
zd-qtyn9>go&*?vYjC&WC{1GGWLtOBSXD`TJz&=K?8EAlW4-$$MYZ$y^m^wuE2=}=6
z_+Rs!a@QwaBxFjIXL_nEJWBX@`my?>P$y+4q?6j*++5AvW7*0)U{7SZy3;$ZkIjR{
zPyMHQxrRxBy!!imc8&3j*Q(1=oz$#xHqQ@p9;)pZye!}w8XO86A}-|Bz|p8-RdOq}
z$*~DSXbi{OnDG(t<xZGYOIG7oW7@<IpAB^&J|WoMqRwazUmUg}YKL%mt*A_S@A19n
z{baQ{P?!EQ>-j*{5Mlq4HMy<But>@2=g^<sR_s*qHU2e`?x7<k&xR@nUfG)4#943s
zv>8Q=jtsevDE6n88Wcd}VQM&o<wGVLW*h8-<4_m1)j{00Tiv%VZbirrMb-%|z_;ZL
zj0~C_3LN0uMAJ%OS2!A+9Zm+01QQ=PL-a1KF0Id~5Bt`RBZsKg1wRN^xGr72_aIWn
z)C;;BlyF0jXoBztVI*M?;j_>ULM7@W%JsXaROVDA+(HgRw(>kj{N#6`+@dBI$)yUG
z1C{Hx>m>)iq?obBq~RUpvF@>bxth7VxzBPBbBVhjSrAt8R%%#qSX{1rUzuO2?6&Pe
z>c()~C8s2}W$)E#tgWh$uhO>~w;DIivHe!VT2)xx;HXf2RGw;MU=NuNwQ4Kts2DeP
z`9@ytR^C%qQU$jQo2Z|_+vFc}dw1{MYx)N?8X&|c_*>9mQ%loGQ<p?LV|T`njGyz%
zR{K9Yvb5fb3Y;ZnMVQts^0#}N^0o)RW1$)pS*eaKAssCkSU=N7jJ1@zwDU(omhD!%
zJ!aN?CPP+kZ&&o^d|NImN-7$w?yVl}nd10X(z!)06>;mE_LrwfMW<tzv<=^(%dD^>
z*`{{i=w^{EP^E~6*U`I5s&@KX`zcdSHO{C}a;TzXgU8!r3#1}FDSebubsv)vk5MYp
zeb!Q*+Tx*$z<y3zR@xXcX4d-?jxGO2w%7{&k`ZE-E}fEuzD#t^B;DE=Y;14r7xS{e
zJ3Y2Fc&>krthIJ5)b24XbeevFoLe}XPyGQezbj-di))h0S=;3)72WM7?hD`GXTe;V
zPi{+BFlot%zNvVdNac1v@h&Zy0p%sHg?xAMXP5G-F_v=JtJ!a--#8**6ppl<;f~Lh
zUw;0q9J?d>Q><L1+~s0zx3b;n#emZ7`+N^#KahT?WhR!#{agl?JeH0Re?aP}SdqvJ
z83^e(i>+j)Ps-#E;aL{PXMFI)T&t5p@qS(+PfVv`$&0PkiaOgmPS2)BjYh*Jiss~N
z_8ztpkrL)<Hi$XcF;q$BnM|7h18q*vILhRDmVJn(*+^7z>*m+ZGn$;_kI8M43MQY*
z_(mUjfE~hqD5~E>BqN@<9S-?A=X-k7eGp)QvKSFu{iuCj{GeH`0l$%el|WSf<!5)}
zhVQQP2Q`#6Jmh_zns>ENCgpt|dRTO^$Lj1`y7gG}&T!)-#0uq^myS;@)KBjRu52YM
zC8Y6PAKz=w+{>&bxJ|)JK~3Jr=6mt7pR@Ww;e&+_dITT}2i;)K+w3+*BJR5l-)3KU
z<xq6dbg_0evCluQKiT+fG<qde$7^qW&OK^Q9Zwoh5I?IwU-`<iUdGGhbTMw8jau`k
zR$M6<2JS=<koA%!jVO=EjkuN)=}lJk`b_V+8I4#LH><2`=@^+SSJxpfZ_I=iQ9}eq
z?764ZYt{!XT306Z0&95H^)$8Y9yoA}ow@3Lsc~t3;#)Lc+6=clAEo_5n<8xLUNcfR
zJhaI@R$WY95;t_TomzL^Np}rWa659-L%1QjjsoEy;C;tAo3MH@SHCXw?*R=#<Tyn)
zf8LDaXCUICI-};H5>EP%D0DN7#=-i?PSo}5{Ew)thq7$Z*7SPr$*&tvPUgbDkYX}F
zV?I~RNdr%D=Pb{x@8&^kWom2E@IWqCc5~x{tDjdDlaGb`4g%Xl+9)U~s5$EmZLD0Z
zg%4m|$gl~6N$STi1BZqM-<*0={r24=Q7t8d{bu(gq4TpG>RSRr4&P@RA+`%8y_1cT
zAdd=UV}qX4@jl$^81MKceZE+}r`1l`8F7*rLfAk!+U?Y%8uS+8ihOds8^P?Q)Z|Jb
zmLpo?X>n1U+g`A0<!FqITP02PXeNQ5Olj@qeNWTiEHR?H=sJehT<u;?L%%|A7;$=K
z9b2^Pt=4j2s$de&E;pWEG#v!Zgr(3^()U4LTpVofS8--@+BQ%4*k6&KZCx4n&ZWO$
zTKVCN&87XbiWkigiRPb(g+`Rx_lR_9RZK@>$x@RN;~KB4?@h%5zBuiLU-Wj6pFl0z
z7>tOnIlXB@8Z9#)?Q1=z>*m<JkgH~F3R_MHZXbG5Y*X$^cA%fyl4URb!c{%Cs)pr`
z%Q<J|ywBT#gpWNyNb%NO`?-aJ0vaoDjEjbaPKAaI9H9eGQFQ9Rk7dvwp<(_ykAa33
zW{rmR*ENd3JL(YwJW*|azhiy~MZ*C;-3FeX=@@^m#+FRS{Bw*E2z-Yop(geGIq<G#
z`r6#w!Ntna6`3jf7C3?DB&XwohDQDX^+bQJdVdGF|ERUPwyU;+ypXA*{bLg|$5-Z$
zJ?))P_dyf!6ao(I&0S3xJniiqT!cJ9_kLX=1RSGIbKYb4b&0Dj=$^KMGJ}-kYjXzv
z$6SxO?up_vFffR`HnR{?m6rLdIq(T|&&t)+Nr;ov!^7jT2hU^2*Or`51O){-xwtvG
zxjBF<I9$9OTunSV99)=w-{jAIq|IGSUt2r5T01&0pzdq(%F)dgbnhOjqrV@&$7$|q
z{ZCI0E`Lo6m>?(W8_p+>xj6s6H_%iBbyi5(+SA-lN7~vRSTmpxQGQ--kzd#U*Ej$4
z_#Z8`|7rPzhnN39oBqdF|F@}zi}`CQM|+@8SJ8j=>#xTD`Q={?ML1DY|Bt2kz0SYR
z0t+pQFT(lvritQ5$^2Lb_L0U~T1g#v2c!)3K$ies9{qkt9cOjScP#0lp^2kCmzGfX
zMBkjo@ln?}`@S20|6s-&`REN9!P`ganSt6PZ%Z@Bo^2Y8magH~G-DI+Yi}-Ku)e{_
zy!Rq@<`z~kiMUX}1=7W>BG$};o5N(K7rEKq({1m9JXVz4>lQ?^ykF$aV)RGDz-9RF
z7drAs5+sOuSXa*fzQUhPoTQT)9UY5A9PPheL^#Ds5T|<G*!TYX#;DFD9wGm$&F}l{
zG@?o5uR1=Vc=Nx9g_Y5W^S^o|5pUVYz{*H%{-)3HKj(u>61+$EKZc6BSswvOF!+kx
zCE$N81TF(GQSbkr<F8TtJJG*3_}?8R`B!xRQrUlX^j~B2$9n&3b^moQfBf;keiR++
zU%>D$VE6-l{{;;Hp8><8*8%pEH99gfp>dB58h#kDB~$$9?5rr%C<?7)nfxcn@s##w
z7-ACm(Dipba9hK8eIVy(x1C;Zwh2<AS)z;QoaoqmS));;*#kb>w0`j7qud*ph5P@+
z9&a`M8LF%xf<k}HTg(6k0XciwdnVZl^LPV`!;Mj;yAhk4cGUcKpEXML$9sh~*uV4#
z*YhOf{%NA@3dAhWEW6&{`fDTsAIY_weCpvx6SfZhW96pp!Ifi85E$ruvvQ<RgH3q9
zn}>A!;wj_bQCGkTK1uNR(b)fp<DOy&yKg#4eXp^f)P)+tZ;zNQcg7ucvO&khu6Btx
z`ZJ`yq%t#y{_)TZBrr4ytIbfFe_}a?v=(7xqiG=#xSiHRyFfK(uSB6ZFH1JM#(FS!
z+#eltDq}C)%{`gu&piCa8?kUD7aw#u|Fb^&0bctn<9d#8bLM2HKsV$J=rJqoXuI+>
z$z6M-)Q&Ad^NX%Jt7&!DH)iAM3iAli=~C=Sh51X^(ubD|ubXJF{-}vK!!B@hQe>)=
z%%Av7T$P}4_d9j&S$gfPFD#Bt>oLN%qobpo{(8&?*>m3Z2|8)hJ1A1(bNazuwTsGK
zaUY?XVFpfhdoSvBeR*tucChZ=#Zg;V*&*8?Hp%pNkb1ufSc-*Nre2aioy$=Lba%^p
zpRMO@m9^cBu*S$#fK_hQgm>BXgl>fxddqb1!fMo{Hb&B4WK7<eG~Sx1<~RF6vN2Iz
zbh!4DY0QFM_V3V9?iG4q|NSPf<iABmc?hG@qNn2Qa6|79G28|&cw+i}>}xpbR<cWP
zxztU;`^Atw$83^=@6dSdmE4q5Eo_KkX7D&sJ8u*VA>?E}i~|IkF0s{>#L50AO80yL
zNVi&TY=p$0o)?(8OurKj#_YZ&W1EQ6GPDMyn&V(D8g%rzU}Z%?VA_n&c7)ya{DRtU
z20nVj2mUmZWXOZiJRVa>poVlqc9iF!M_{%Jn}B>QC*IJeM7Mg6_7n8aW}sov-D1E!
z{1Lgt{b%<BJn_4e1qWrKXx2j$lKAak+d^0gOZ4ks_s5SnxD|o+elTg~%2UFwug-%U
zC~UqB@@P&9><a`>++S)?%^BR9Hry(f2kZXW!YH8;a7d1%7n@0$drk`a`KJSPti}fb
z#ZYqIG5oh$KOyK&1x?DlyF1^T>D!7;F&0WhEf0V2GXAms<@vFP_nRBw_s=58$2XwO
z4jj5wWxkhZ*g%IBV#k}+O=-Q=@bPm=2d%MAf}16dtHQC5o=vRur8%~R5>*D>6#CH%
zXoQ@HrT0IYheg4FtFS};b^Z6!&vB-kw41g^9U7T!P1e?~rum8Xai=nnl(ZxZy4aoU
z%)75;gy|w@;2a!V(9}2`9i80qxx<UMY^7y;%Uu+n;m>0qj<5a9+FHwst{GGkgSVW0
zA#a2^1x22iyO|`BH8j1*88~w9;}3C&_X~=o7MMtv2r5=g5#l#)@xLhYiuzljW0nA`
z^4u3w5P}+Pz|ifJRk7>!BklcWglUxE_lHWUo6T?9ait${d2H(oV1ahOl5U+WMsqs7
z0|pL5HiNv6>P)Q0E6n%p_5q7HIaHul0<5C@UI+844n=P)tjWlvQp*nCa!|6C(sv{P
zrCJ%2r;c;f6KU_?n-GHzVy;=~{?RH=aX=t31>V2>W#r^&0WBD+AN>Z&0f@#Uzw2Ya
zTw?>NwxDXjRtm5Wn@Oe!xph`1*t}It6P>=3?Y=R>(bwRVda)mY{V9R$N`5yZ>QwId
z(?|xmUODNGcCGE`WAC{DoT=qPH^c^!p0O19-y(r~KL=1~)fs+8DwIMqXkg<?svY)H
z^trJSa%2>Y5?Hs^ocH-%nI>8{$wt$SRNIb9MyWa-eluOs8(A^H){-t?3Lw=R%itrp
zIZN#J=+G68Y&xG(pz}R3d8rg#ENh)_T6y!2tq?c928g8XYws4y-y<NY0eT-(x2vPa
z5<Qv2JU?Ac%i6vL?ir2PsuipGw%nbzH9nE3$x8aO+<kM*gjn<>eUeliBx(JRYR%jR
zRBPmN$>x{ji#!H|ewR9VDgJml-e@8ik92d)G`cz!<SpaFr_`ryKifS^vg>>5A=f8Y
z8J$d(VQq5Ya5(GH^@$@Fuj57h;}l`(89z|;A9sU}^+C^{fwvw>ZyAjepO{yG<gmE6
z#csZ~>`k_2l6hC=zBQo(xI`w=b0dDc%JX)PD9`2#;VMV{Z*-tS=c{iaqDBp_1@JRO
z(MY-kCJb;~1k<TR|LEz5m#7Z5DJ&y?b+CZy;OfQ>>;iIr1c+GI)PtTxK7yi}yxDCD
zdK-%ql#u}xcZw<jpKoKRAlC96FjhK%4=M(nvezf5PUZJh$dQi6X-fd+elfvu2iYR|
z_yo~p+qB4>yHd(jr><6qa2BM6XrCjQ+x%@L6(1{tuH^J)yV`$fE-9e7Wz=8R|Jo+N
zVDf%cL=;cLy=&osU#amqeSMsnB;e=}Z{V&YUzu#HeF8XR1-Q`0NXbSo4YVW3_iUY6
zc+MYlvuaS$#mIL4OUTjDq~l}=g`t)GL{&#nDdjS<`Px_D<Kgf`ccsK#r6xVtH@fQ;
zsK3Cw9s;|828jpWg)V~_U2Qr$f9crPLIh_G;Iu<43H}4C+<y<q(Cuk<MY7*w7N|Uf
zdAq-RGcjYj*#NwrgMtOu&rHNpc`TzMX#@w3wx?|&X9wj{#2{V|qeBu}0DVx0^4pE|
z8~R<ksX40ml$Poyo=zq?Hmx5$qUACc4@k{EttC3T$jV7L`aY!5s)=JTldkD?e(c;+
zT&%{#n|L(vl5@N>j%^EIpQ|g(nUE?}J+>9Z|2Y3$Jd$AYG(KqXFB2r*4eap~Qt$#A
z3NZjyZMpuF!*oM^W%a7g-S4r$vdoBHU$}I2bU1H-Zy@mMUwy+@YbkKzVq@z<j6xkp
z4Ll55ve?uit0^9vfL*GMRTN^G<DX}L$4gjZRGADK%<&pxTubx2vW^zJsv9mfm~^O{
z4|P&}Z~q@+ClBmthr`{x-(oib45<Ke4bh^9GPrI})jL=fkW{&>_PQeq^3Du1TSnuJ
zd<8uo`R3aX>!}C40)rc-eb2YBqewdfS$ernYgdXZhc|*o2{|-`romN(CKR=$WNx^H
zE{RW_Gb1T^zV|*|Bm|^Y?S5WI%|FWf{GK>T;(dX)gVMh|u{bb@M&kP<(G0viJcFs;
z+k%tTw(%MTs^1)%PA$5A{J_O8&e$K!Q}P(4(3DcEmTA5`(%YDAZsxP6GJq>ylP1K~
zPdDkn$H^V19Q?LBYu+93+A7z-bIUiJQFOL=!?`FDT@3;tVvTA0P1sh=q@>W-Goj62
zn*9snlwAWr>NpJqefN8#z5)Zf73=jn%o8i4@LbUE%4bv-Br)8@qfX6h6=OeD_cH~z
zpo}VUvc|r$H3$b5``G$+p0wl3PweFxQeh2;D$!3D-bjlX^b-GV0vTQa*8S1^7}2jY
z0hf&8Bn@Iiq)0wfB1sAk-6chEHrpV{5#US*JL5U&4<t3j_J7LKyN%k)oI2T9J=QL3
zDPaST=va=Mgwc{8jGcRb1FW0fj~GVH`q$>Li@mPtbPvBYQs_UkQsxDw@oceE<+oLP
zbQ>7KJrjk7pzl<{O_xWL07|s^%io9aeg=8hz>jCVxB1&?T;8R)Pgpb79+zBfR`yNJ
zA7J9#ta=g0dP>ebG^&y%GY)OOvKck<gKOj~GiLNg#{LbcNZvpJt@Hs2QvFtfXRP8R
zy4j7-U3Rns`%Cm{8)7N9reUM-89ESHd$iO*$`JPD3yWHA-K4(j&u8u}=s3YsGqtMF
z3^WZlIZpV2YVC1>qrp9&6amLn6s*fd7TqACy8ng6`Q|^;^z0GPNpKy_FIR>Z;0gGe
z?|MUavPYtN&8E>gaa=`er60}LBm!%JLFiOkjPK01xj=US;9vDnA#q8R_+W0_GOZeb
zdUb8zk@c%|Y+9xI={*>a2if-kOYhXy-vl{-*exxMFChl#Cklt7Y%Y3VO2|Kh_-}ti
zl}6oYer!lx?g>}WE$`sBpNaGCzpaR?Tls=gxIo8EILlgtjVx^ZhYK-XJ5D)PhUxcr
z<>92v4VGCt3xF}WikZ)TC<<PBFU!u%+>RJ3FvYw<_y+L;1ygR6h7auTb#XRVF-41T
zn}lu3hIA|bGZ;p|olEVN-Tp0WUjbJcx#jz)n_9>*<<estJh^dzMKt^(zN5^~z1T93
zuYk&?^0MObCx)SB(R^@Vp{!Ltzsrhzt!!S6AR<4jSPN>H!EZ_Xw_QWSzyp$8iDk8+
zq*A{WI6WQMb%|J~_(xc@+_sUCX|<wL22)#SZZ>NNH%rKWiavuovrDW|J2sjSP&{^3
z+WpHphl&^s_b2n)C&GHLDfEp2lto+*V*AGllgOz9GAj+|Nv8PSIXa-HnCZdgk0t;d
zz9vl|Wr`;`3pqCM_~dGkB&i&dkugE4Bw~U>)&eS!egHp)O(eVhvoN^#i-D1TnW0i7
z{Jl1TfQn!%e9__d+9LN}NHU+dqjE~R-FxwzGiBsEB94>R3F6H8?g4HJmq}oOXzoOU
zWvX=;y{~8gpb;Eg4Z6PA-v%o@;&qy9NuR91_@nJ|&jKWZZs=%gegd7XWjQxr%=%qV
zr;P2E)Xr2ixNUsDJSp%L5A0WO+|cJiO-y_M2v%i0HcC&y<(Eh8HEZry-q79>o3X91
zq3HFyyAy(+7+e9seRMFW1j`$MdwU4a2a{r?j%-iEwgAAxe~D3=8y*5a-llcZBS+;H
zPqFzOrmX0Fb^@%w@QF%yK-*lfNOl7KWoQn!CPxp12jARcV&~u}k$e1-{*@8nF8OVS
z^7CKZ=>Z~ztNpa=vFFSwW+}oRHMh@##X_hhtng=j9@C=c$8ZYl0aLRjfxuQ-Qu~z#
zDbF7Kx>i;fj7L4QD$W+K!rQ!bg^a`ZyYrHyFpF(Y9|+rrp+>W%u<Zs6&ipyaYH6}S
zM7&S+exKN^kn5VVvEDCSDjI+SJ`_eiCV+KmfE<lmj+B{5!460C2vR=w)QTAaVi90=
zTlthhLGpAlT8!LlJsr6EXDA`%!iYUoyZn{3JOIY4ap*i-fcVOqYTjkLesyA4%g7!8
zQW0xU4aYw-46%89*!YdM@c_yC;kfS|!9TW>rX|~C>JQo$E3c>3td#WU`vzN^bZo8$
zc!m`(m7bMSo#{4fy&Ai63PaD<gF_%R`LQ2S_N8yWElh7PSHXUF;oG({C++?=3|WIu
zc641G<^r(`Lc{pRnVv>*CV+q;n<}aJY{p#rK&w~fDF{&X+iS(0x3~<-G#F<F^3C3t
zz{UcArAX39m7X0s4OE5oOgQ-2_3^LnyV>d&RMBdeBx|rI7_MDSh||B=utazx;S?iB
zrvkdozVP$;p)!-UV)+Da_euNOaRZNO^PHE=GV6I4t9t5vc#;t>J}v&vH!EWS;{HBM
zly^%Ysln59N%+RtqKKu{MMy8n>E$%<l6u?Z@c9c4CZ;xr`epJWATAxtt8w4bJzt7d
zl6Mm)OSidw|M2$kS2+;)pbUJ77zg%Yn+|HxljuIEAP@qv<Cp#wR$c7(P4~N;A8omJ
zvgCcf`Z3Z+gmMUT&jQ4*PTFn?tudUpi(Snh4G_9To+qQZULbmo(!Bh}8oFt*1>7)x
z9z_<8J+Qac*<dv%C_UDzu;`&W4nkO_K}ODwk;vMI6=7+<X9ap5uKO!JsOSp@Istqy
z<+#KLPuu@s62uVz{*v+SLtZ-P^k{z~JB>m=T60mRg%#Uxq1nFm`w0+P7PCJ4kvVyJ
zdVn{|dYlg3aryR^Y#TeDLJQ`;H8pbsg*w8Nt-sxpr7;2EBg<9964@<rhI#CRFjVgx
z0AQiTzXn2Q-JBP3x33!Eu7MR=TzgQz%dVI4->Rh<(|^(xUQPq>_0f?<yc70z-Qvyj
z<L2wFsATJ*e3kxbe&mcTX0y(#4S^fD;+UJmISNFu({wPSt*4>y>GILlITE%Ualq(a
zWxv*+v7OHV#K;*`D`~8e47?iKyrbbL#4`6=m=_nDiz|@i7BVPdKBF6Hx%!z2_UD_?
zJD3lnZ*a?ZH3yJm>g0Z_w^j!kqrZ4DMq#wa?l%fpn95}XwL=}_h*X6i76!fqnNNXb
z;)~Dys(QxVpP!6Cjl|SAcB5#8bNOX%8NeUP*z~7MI3SOWIkNx?1~0;1YNXdV2}|Es
ze}l^XW&51T+QsSqG;xWM_ZFWAMvgxYo@quXLcn_9t7fVGdeox<vqUPBkNCh0$hNsh
ze-$*$QT|fL<@T2X6B}xN5kBo`-XbGeIeJ|zEv$VUU|^G7HiBlnR#Ogk_NHi80!iLP
zw}6hPOru5i-guWeFMb~nXHLDFhUxNA5?lS@vl)X1*Kz=wQ8a+4vqSwYaGI~qHnsqV
zd7qGI{}Xbex{G(pfV7cpL_KZ)3jrrD#Moyg!ScqGkKU0X%)w`gF&}BnTvcL?nUb2(
zI*}1ZhZs3gS6I<be35Ws(_3Vwx-}901i|vj7T?k+u;1z9*p1)*1Pfq%A}?f$$7?1_
zFMLvY&b^k-&yXs)@}We?;a={6<X4Jxjwp(QLA?Y}AcsqpXd~|UFf<Gm{3!rf*NbuT
zx>UTd?tg12-m0+Ln=(zAu93y};d#N3i+Ze%b>K%JGHft<8OLhweYE+6Rtq%azI{<d
zk%W+o2gtrYY=uPL4n@$Rx|)K->{X)YvvcHFdE2E@yI!?TTux5T@_0P+H>PxMyoUDX
zi@k^tss;lQ%h)k!<}nPTXS~vK65!ZMfW>4~(xw;nxjY=rz5NV9NtF-hcT9nByV$2|
zm~eX6SlZg^7Ra}3K^qXP-lxm)_CVn9Bu=9L4L$a%O(J_G5Y(U>_1O~ONy6P_>*;>Q
zmr4LClkxCIUYUgS=r*x5q+HKz5ax+mHDeVJQw@d$m+x0u4^E}toH*ENBPO0$WPB%8
zj)#HksqfF=dOc;V5R;c~+nX#nlKCv+jiO|A24ACQtRDP)8+HVs<mGWA_yVuQ%zIh7
z^{aTxoPB9G6Tb0O8~olR0ZxzVhvTr#imoZ+6@Sc|>Dezz<#)xD(;)yg(m+bD_?e4w
z9mASvQd3-Ks4`)L55-2;9v36B+4{Ba0_OG7UqE3sC!B>%vv^IBpVBG|)GPgo!iT@_
zl#5ZgA5j)hJJOgq?dLg1S4c$;)y;r5?!9nG=_@1NtYwtQr@uk`7o1^u0)VnC7$?ea
z>NRQKb%XYQ$QiI7TemNH)p4+<$k1`5iu9-Cv8b~wX>h@z_i_4QfR2jNz5#@uuKvKa
zH!ZdvA}*I}Xw>`K&z8wA8}FF+G!<%%0e1r5x=AlO^|)>hqev|;3Uqa{=zev&ngk9U
zxGg2_COPb)mBY5(uu=N;X@O*~Wa|>(E~bvF9|ox#8>uTm-2h)ZxzrK61z7VM0NB>^
z<QEAX00i<z6Li1%Xdvh1bjLh^Ew+5`#`wWgqxGsgLglY)Z+D7sv>9lAp$s@^)xps%
zI0%FnTJ|I^`}R96MhZ;u^ofveGZNUzlIZ#eWB&!gVyplM^mF!oIh(+E#4Pm9Q^KA{
z2^Pd|_Nm;}5~C%0t4CbQN{rQP(8d9dTza^O_dS3AcwpTkj<;DGt*0BRg)e*nO2N5c
zwpcd|KiEe>%HbUF2;NjXMd0s;>x1pIiNd}YCuKHlH$+GXBGl*J@$@<|E9#aR2k-&(
zgPalDry$4ahGAMnNx~SPv>yBez2_WyZJ0BAJ5dd^^XVE0Wy%{w&r(&@94+Bc4Ni6X
zoB8qierifqCCva93G&6=?e3o4+VI-A^oTgyWt!I04MTXnR9iDX6HK3}KsiRvrjvV!
zMbcmyh4!ew=B=y`%)9)#ELCXk3*17<Qv`apAqndz<m^F2jg3r!W$42tfLF}9wgK2d
z@wb4gkRz$*0M4XrqZHOl6p2s3)&{DCdmlH@dluy8>4x2;wOi><VDuR6KLz5kdJ~m0
zldp`#EuF7!DMu<R6c5%q8hLM&)V^KjWAb{B;^~1c-U3)Q0fnpK8hgFLzB`A!nnV+s
zR`?Qqf~z!xIL&F0qlDpN9iG0fM`n#D3lR$Q<u(C;Fz|U?#xP3B!?}<!q^KJ0i1^kB
zGI@Zs+5Tn~>w@`Zw4GI+KmoavSkF$W&MXC|A&nE{DWLdmDzB*j;+4jrsJZX^)w)N(
z`E5%|X6u-iS`&v$tVU_nM|HJWs9y_n7OO*X6k@W)gbpIAuLi$TpP5SdF<4q!!i*rK
zE}rv=^*68pgO3U=Y1#ze`qC9?rGbm=$I4n+)$>Vu9Zc-^0jn_y#IH%P62yHv@6Gc1
zzDs(Mler*={9HfbKn3!}W>EAo)L*nkzs4?sL#HCUKivQAhCCnMsG1VU9NKhN2xQ25
zN@RTj7nukyli}Xg;$1CD-(Sy5s|mowY2cH<q?H1tR!7>*XciqcGpOb~I^aQ;x6@S$
zZ`U#Pm%4W?nb#N(fAZhBXW;3T7dir4zc!rp^MhP#0X#;^$8JBb`_XukOg~@g{aF`I
zU|n`iMmg)mN@gugwamspbZ;H49)Wr}XDl?>K2~V*bxu<Da;B81GN469Ka`a~3s5I#
zz~yLyL4`sSr_VmBDLRMkf8UQIE46uAA$gB)IP9qB;nuYf0xsaWV_X}WS(Epmsb$(@
z);?PFEPon6huc_M=lkz8dL>L20DQaEGzvr`D>vR2dL<Fs13tYpON4CdhE4QUV`r1m
zM@lP=uU*@SiSDREgC}#o=3SqvC#r3e9*RZO+zOUqYxn&l+;*smWL&xQ8J_oL67m1U
zldP=S<nB7%sAH8kDzc#zuGewf7|yn7!8i=)H3s5xHKjp8>e|Ur+VK^$Ld`xrjY(m*
z6rtflE6QGH6vyjNfzo+o>6v7NlZ~g0izbDE>wDpd#-m|e^f-AH>pMo?`#k~*857yh
zV~c@^zUiBff)o1eSD^+=1X+XF#KHRDwBg4>*`nOkH2mAd!kcB6&O$29xlPBVD3{ez
zU<3xTT{t4Wf58ocy2k?{NWs_#*h%?n4<aV2tW8~3y2B63>L<^AbFBhwD$o3yR~?pG
zaULYo-SWFeAX|$TKl~=uc0L1QX(yoGeAFm<O-#+VN<6s{9G{`P&Q0k)-g;PUxp+Ms
z+Sv>NQhVNx^I&SE&t9|T@ytch^y+d~eAS@dXcbS2Q?;E;Yl0Z&_MPvNmyCrI8aAw-
z{DxFyL&_<VP+aUT82~GwmStH5>ftt&4&xQyc#G>EVMNgTo-p<`KfnjyImw3{{FL3s
zdU56<%O`E!@gTiLo{y7Hupb?VM%j)TIzGzxPM`Gbk-pYgX=;H`nwecgDq!ztXt}Q^
zY@yhVSeFhbeSIqvP4Mz(_6i8~vehU@x*fieVjWW;16N6^__UQ*N2dzFY`TI+zoUM%
zfL8!t9GR-ixaLwhI;Q2ql+?ka4atr3+pf@SGTiK*N}?TkK3uFh68f~V59i!WtT^Zb
z{@w-x-|J-aa#txS(yg{x*7;5fC`5Wb_5xY3O(xxNvU(2;AT|qrJbb}o6a6I$>K6ep
z9(MTLyb>u7se}Ohj1M=&j-e9u*=fZ|df38J=nfD=lPL7i#%8Ah?Z+d1I99)!Y76k7
z^|UVCPo5nPut4;u)j~e@0BGR0se&l4&xw;az^iNmF)6F6Exi5EPzCMm@_1J7bZ>bJ
zm3RV3V8INV&O-i%z#f3}83g2hRgra>I4iXZ-wr@uaWL&_ufu>~o7@e|b>pokmYx?M
z?}oL{ZV^U^asMbg0qjZVC5gyTwnA%fkDs7Ke)Ek#xdjFkIg<tB_!*ksq}4?qDnk``
z7DWo>Ism|_>owOQt(K9J=&6~9rpL9phzvllywzo(lgPI)(u|fOil-HPoozqcG<``)
zVRKTA(6U*1eaPj#5YGInL0(}jGc`IfK*#NA#Ddg=;Td%U>EXM@hVTYz-%l)R-&cB5
zHg04!kYhh<od8JirCxOi01_rqk)&Pa=Xl$bb0CMA=uML{3b?@Srd*rYfd#ho@IY53
zK9&qgho01#0ySn2eiyh{AYYGuDKZWfeP1@=)ucGqH?<$oiEUL4+5sU=(>LeK!j8rR
z$SJ^4Pi~G^PLGcOAw3v2A4)xS?4xhQ9vW02TP-Rw@@^M!X}=H${g*W<Joxpp=&P@|
zgUG4E6g?oZAp*^9NVZ9I6V*P4Og@HfM?8~|CBUBSt8G--+B<3-q~;y_s?sO2|L%O>
zajMRF8|Qx0<-N-nBvb`=|D>+P%TcgXw)89|+OLwmAteRQ`+lG+ue!Qn`DFsYJ_))@
zjDaNfo2@wVEZ5ioU*2$&%A6Vmb`40yO>|<=c}V?cuVNhFY;P-z2Xq6DaEd2M%&)21
zVOl7&QoZN7!cj&H7Y9erB^VV(!6n<HXsHjhf!s>PtnYcx*0<%2*1N8YBy8%ioqj1|
zK8SJdNTsFHInwW%`c?D~Er9Uq15)m$YPs^x-kmIKdyXjP3X@VHnAf-k?OLNg8WIqK
zV71Bolxc&=DNLB-;kgzV?*ixqTZD>S1k`_q0zp)K?0tQ8IUQvCX-{CrlWD1V37|pT
zyrXPzLRJ$Y@Uq;Q^oB5mbA#vZ!WaH%tIJXY*p!Rr52(vvj6L?6mZ4XUwZ*=)4Zya3
z5au-k^&P^70&wWoQd74>d_S%0DvDjY3g~NcT>`0BvZt4Tluq%o8^XuPQg7JqSGbQ&
zlYWw>2^}fY=}lc9nCc99l$U+nfvS=?8&@gSuQ$$9UVD8wL@VT)51VfE+W$~vCXqpr
z=!j_(yn;E;@|)HRXBQ`-nx3wA|ME(&-a*e*_!>T1USd0Dk26|J;b&lK>U+tQYii)_
zYARf>5|<w>K|;kxD}2viJQ+Lc=>o0*`y+#fssfLshda2nEI-sar9$2p(&w*itY<5$
zyFEGN^V=s5bbudfdXOV7CWFuQL4^$-$W&N;XsA+(5Z|$v2;G!(JFNp$V|Y(Zq4`1q
zeUEQcfAPkbh8l4NH^Jd9MRS!qhIh>Ok;_G+@5A)1cFB~H&qFTpMIj1|$)R!d$e|4N
z=Yir1t#L?DdcWa3#6&>~Np`Yvt~Q%@<{MdEfRo>31W*>%dNB8rYP<0PB1*E&Gh*iG
zL$`(MYXFVQH_MLZtF&=&{scaVgpH7R9ZG6(b_1m{lN_~E3rBOkmGAezYQ5BIh1TE=
z-y@Q0!;?64Q8B3u-3p^hrAIXSY}_EG+dPUIb~)@E#o;gL2lBb|k6j{vx#-ke{hUr`
z&w85+4C4`zKQRc{CmP!vs4BU3I&nYc3a529`-1gg7tQ-jSFU2$T|-V5xqfijqI9X$
zJ;$_bK?y70Td7R`EmtHb0EBe0gxsvPOuY6ogd7gL13m{;8011><T)R*&9#XNPo<Vz
zv`#>89^&pzg8HOF@K=*vq98z_$aai25w#}qxo0(yq;DXlE?Cu0k5L100Wy!c@q9uC
z-n}P)tyox@KVwr!bhmH6>sl9l1^B25AgoJ(AwIl(>>Nh8`gl&F8v_yG`GYa6F6XZ6
z^>CH^e6j^{1PAM^R#bU^_*^}|Kq#J!O_Q)nC)Hf9)}d!wv6^$T)-lZz!mBjYHm=2^
z31=poV@-egHFrmKzM?4%m4g=ED6Ygv%>`GmPH7DGVZ`0W9a@Ti|9;;04XG9Ux`J=g
zeNvBLWb)&K1PNy4Guu}^y6*1#Hkv~4xiGP-_HzJ!ZUMQHn_je6lVRZE`O8F+m!OtI
zldxnA$jKeV-Hy5_23|@e)#|SdQ%nVbnBLEx%>(44w-_`E#daJZlqW@#$QmE2`x^v4
z244;FkJP<#1##m_234S#<+snpGg2i<4zbi6pM_Xv*ifyAabWu*6dGPHzYtLF2Op}V
zVn3EDm8~Ygf84)z*ml8=$LG0+7l=W(h77@%;%hX7ZV}{2+S7Lk5oHW3>mGj6t1Fue
zdyNPg$$n0%W?Y!+hW~0$;F_*1@l=nGD57v(gjZiL_WrtP{(2L_x1ffFLlgr+RCql^
z&^OAfgUa7b=dlW8OeCHry?B}*TJmr!yz00jG{|wLaeT(_8X_Rq<+o{-S~Obt&{!1+
zr<4x73oSyTkB?P&S63!w`6K{j#&v9|k)K*KVfX-Fok!awy@`TVt8`OXJ{3y!3QGLi
zqWiN64y~(ls#|L+ss^XY06xBAVP9LEGX81hK%~Sd<D*-?N^exDK?nE13#>`LzSkj@
zT`8~c;a4r*(nVaR3ss-BJOk-#Eb@Iwz4TIi{5+K;_+dxn0`KpzJO%)7OUR(G+)E2J
z3deV-jo>X%YyAYh+#qg4D`L`hU49?AS@lqZX`;RMiA26qAW+_;JcWTzlm?_J2bb6o
zJ^3a>i6KHCNYq!08)ETQd=A&Ex&>S{qNNVMt3r8t8tAM^vKPrc_7z@dk3s+*YuY3q
z@qA_Wj!nL$=G8<-lf4B{Q9vu~?wZE>v{x*9wAP^5h|H&O#5IDPV~Vi?KN+I1TFQ-8
zb%OhdCcYZuk<tETRG!HO*iWlOcgG`G&}C(5r21VId$5JDmVY6#9w^^AHROM6r14U%
z^6N$$L5Rz0s&{F@eQ6PJ>UVHp;>F8YhJp}a9}f|<1$f;<dH{(O#D_hQn5{}bBcJ^A
z$>RXCve7-G(ot2fNK`Ca>Nf9IQn+3Sm8Et2sOQH!Waf6Y`tcnQ&BX4OI`{rEF)VCz
z=MTo|{WEs2sIcj0&lg`Lm9rPky?yk_YUKEk+C#eBven>>M!o&9?fGbT7WQbUp(F{_
zL|8R01Bo9x5sL=rqaK?Qu4mOtcLHUE1V13Vd3hQ`G0v58GiRI-WO;Osh;MOXTtiLx
zL=bq-J)%G#s6nz3BzEY;P;EXiw8r+4b2J@jDlxdpt+k2OOcklahi!(ar$XkQdjqNL
z22d;SR~?g2;KH*|M3;W-rt~DHJwR<b+|4nJ2_HE~RjY$CwtpKw0I35-T>45O{S8q3
z5nemJ0E6@+AkQ|bp=GdKieD?5k9^7sPd7ORigT{xy$NR<JRutJN#<0~FPG^|!org!
zMum7yK2mFoheGpovQkZar}3gyDoDj14Ekc<0<eFfZ@#4BVYepnma>AI^dWkP@68(s
zIJM2J)yO(LW9B&VsRG<-ae9bJ9d0rlaS0?@-K4;RCl5cqJ>J;rTf7y#tpqWo{>?Sw
z0zi@p%Z1<XB3|QY`b+l@55h88TFa9Gpy~s&INax_4(-N-*yPY#A6ZM{!2bNPF+6mv
zS0vh1R`01TtgK3|*0ea3j5xpDy_p0ndZ=hKRaZ_esqc4rcm!ComC}8`^l)=t(+b^e
ziI{8Ww;SbO42imigOyK{9-0dtjhRmS*8?H*sn)9KF?75@6|34-_Lxz38+{+7jFvf*
zh7Uqfu2W8D0QX!2fJ^}oTkLH7ms!@=SI5l?q-yK4P-$EI6SBa<E5wyeBKnjT+c9A5
z-7PtYT_5AO-gzjiMlLyPgiyHv_}RKx7i=Nl#!u_#Iue=SG}e~EbNzsPn>2FE!C)Dx
zLTGMyz9>)%n$8I|T!VuTUyl}9lp&q_X_Lti83l_7Z}nITGIO<P1xPePB<oT<vuqz#
z(xSJaF?)_LWuDh=Xp5C$2+Pv{RlDJzhg#TXI6X&Cl|U|evd_IerUcsIiUvsYBhd^*
zWIKO3=>|?INMo%2RxtU)uV5^aNBHQ6R5SoKr;aKJ%?`vkB#CTSw!cnw0AOH=ULJQp
zpOp*(VALjw0*8W%RC8Vg@W0GA2OM98EInk1$iw_|a{<M~@vI;6=JUR0#Xbm+??!nb
z$>C;%mKst3yDRZThLqFhqzY<X=bI%HDL?;4Hy_W(=N#+2@c1nl_u(s&!ufSXM!i^&
zvQdjpslK2GMz%4W995Mw((o>qJs6t(6(H3<2ygh5os&VA5$_9#`3_tpXQ5|1(o!pY
z<4Vp$8Jkn}m5}3UkL~3<{9Q1xdm&b^q)bY!O=0NZ0naXw+j3{tw*`#;Q<!vP<ijjA
zN5eQ`yrY>nC+Vl|{bA?;_{=&NI^%kAC^?)o5)sKT6yXtPD~U=F@1U}-OSPJnZb#QX
zsXB_kIPnJg&}mOIDOA?=Dk97G;*8S1#3DgeWlM>Kii}zq$WJ6=)!2={I+HpD@}{r7
ziCK+zh(;ri9`Fts9%AzeH&}dmtW#muBE{mWaW~)a6BT!=yn;y*Mqhy%pznjc<KfT{
zs+(Iz6t6ozJTM{3JEpBIg94QduG7p4iFddTc6yXc7xQt7z&8W{hgH_Lq63{N!{1iN
zXZRV|50vQE$4<Mh512P&XCU~B(R3x|Q7KWNj<IKP)jQK<wJEjj=f@D`4m_e&O{mSK
zs7|ti)HPX4p=DX8VgAjM(s=-{JQ<eec>7QbqMQl*4Gp4to$bl3wL|q%$yj=pVxplS
z;eOF3a|Eb>t`Gk<pz{%0aDOs+gg4iW!ZL=stj25-n7+`XqDc!rg)0g7@u=tj#T@ma
zl3E@wkr%!pmkqDfeu3rn-gmo~$%&~HmmW<#vD-#)(@c<Y1UPOpVKGq#20QO6ys|oU
zEELQOr?3L-wYW3hBWY?8&$9FOdIY;o&8}4<;Gv;%*u{zd(uemk`9hqUC<n>#ANebc
z<feY6@-1SJ8!hR`1&Z-*?Y!o#Y=o{Bg)Z29b{0u|_<Eqs>C*eQ-K#r?xoS`$BelM&
zyi{=KaaBLG(z4IpmdXYr(F**Ll^4jLCY;j^_HTay=d)a>wiFxIacd|%JFL*Jb28cX
zZuc0#Cm*I(pO3#Hnl=JS`kl<<cmz_VXyrdIP@mjSv@La35*w%>IirgW^KngSn7RBp
z1k(mJxTQ2OBjyW*KAtgrYc_Ne=~Vzdg240K>>w?-^S8?kUz()bR3Xk)O;%8hbhMh>
zThq03)p=Nx(gB=w9f$V0^ZQf94-CBYxJthvft(qTidx(5Z7imBpQIqjnj9Zz%Q8zO
z5{9Q}u*NxD>om5`B-98YOs(5-6zxpHM%d{OpRC>bD<Yx69vUnJsIqM=(ZcAw8MmJR
zF_Bvjx>4<zKVO7U$rbXlDIY3wB~}i;&f{7JYMq=n6j`lcsDj_7#8NG{lOZ-m(grNs
zrf`Ql_Y8odm{xuQ@A^<yF@Vd=<;H3<3~hDSBo^$^hvwq>4l&i*jaRhtd&IL%4R_Bk
zwnqzuIN2b!HVASL^x?k4v5%DpeG#DiGWq$sM2y!&pb@SmpfGlMT)|CIqfZ@vHFT~X
zQ1Ea`RY8u(`3mrJ3d=Ni8d=_{5u3q7q3QvW=@*Pey&T$oOCp?fi=?yO;$c6FrxVLX
zlw<QkO=Q*R)j{FI1Zo^J%VZcSwkBP7e)s|b!4VMm!$$S1Z8T6-kM=G8=-XlULbm98
z!NV*X);QH!(V{{gZ+y}rK;_2t6tYN}PZx75sk_USc!=6SL5%pq7K3@;jm&);D<cH2
zobIp08mfrp%Ho}F(@(?G`Pw(cF{$q@j%jDRZx-xoIn~=DMPUwKKIAhiAjYZ+K|||M
zqi!bAgk}$*m1fi>P_{6Gk!aQ~&{iGl3zV~f7UGp1@6X<6#fSN{YF%NnsM9=oCH;N~
zM+3Kji<?eL>5|!XVE0ZV_C|PadZ`C;X8XQ0zDE$>;mN49Od`h_Kpf%M=xBGj`65(`
z7HM7kB|^5d60(6@L>g=wMW5Wv=`kRg3kZCESf{Rr6zY*KNi9YUuh~}MsjI2L)ra?0
zs$W*aIo}OQ8zg4iehJ2N$X^!IiSJb+eGUg?0XtNeO<yhL#o=R(6nX4nL`Gq*xHi-S
zU<B)VEY5osjNBYT;PiL)5W2&Y+!MKp35nJQ3axf!R56J1{DzXBLpa0m5pOwA#PVl3
z1&}(u_vVx1Cf1lHRGHYZWpQ=baI7xRw+-Jk=kJuc{yf_I!*nB;BYjV>uL}AP7~erH
zA^9XTw&efUDVoCI??W?ArIcrj9H%QsZ}`G^7r3gVVp)&UNdt4+wHLz~hCG;0-lAcg
z>xsKv`AFqxRH{`AGuaoGi~>a~9<|0&&16pt1Tt)B=)h|kxrW)hHVAluzY1eQ=t)t$
zExSV7w1$|5!f4LSo7Tk8#G=(j06DDQ9K2bR0`7tXX$c30g7BP7d!f)!0=a~VKmP@A
zg%BEk`8YW@HLhaR$)#HE5+Y&rRBBO!Kqa6S%vl)be1+#WR8$FQ;`WX~+zaKw7|Y>i
z^)au*VJ%qK#kJDLFppDfAoe!s$S9A_DUnP#F=H2hWay>ZYCh!vq;2Ffn!eZc&}rCA
z7vBd$s<u3<#K#5!J<BQ}F5GD_YW8)Als`;V_?o+~N~{w1t{(@7iW|CG&I@nXh}WVY
z%NMXuM^(-W@3de}>t?d%%xzrKSm{AI_91oI8>%emho2S0SF#s*|4IzV-9}%HW>Ywg
zU2O)+(jPwW6@8-U@&%zF2$AjU60|>g7jtdE=d04Y*=;YObk;XLE_m6_LFh$zJz94h
zMkY9ZeuND&yw8?AVJz57|H<phB`{d-W-F@vQd}P^neJKr^+Q1k8TEEo#QySA_><|y
zr=a_obZrUAEXQuiJ6v=8KiOSvj92KiuA78nSEOA&i=01|(1=pA<ZLf!O;|j5(N)=d
z9nPaK*uL)dX_0WS*Etmb9Ll;gwobX8Y6$=Yf?@*80JZN7?LbsO*S1c1xA(DEjk9Ed
zh@xWMM?kcgd+nySn-oc?Y<N6Uc;siNNASFZ9K0`f+hLPWL1R<)OZncT0JWbCR0d*A
z^{t!V-7$R1SO<fW2H%+C9Q(4lxk-~pkq?6!PU1arNjBymTVaeBjtyR>Yfn(mZ>iB;
z`H1mjTBaFOdLp@HblT`<_)PLlgJAD{1Ht)5BYYTnUTC@Zv|<eXj(7L^gXG2F^!gfs
zDJLnDu!A8m^N&8bJ2UR#P<2SdT{qI)zkWX={sH)-gzu{|>%_g`&5phG=jUXC6h5K?
zdB^9KuX|67HG3bJuC9ul40gt#=U-E9x<&0*lNKB9YV3#~tUQ0|k_+WL35&dTz6aSP
zWa=ReWWD1SiZ27cTZ8%;%q#LYvLuDEP8J;Q*9>BZLu&iUvA%|EU!B2kmakexfzm9I
z$*+4~*C`Xc^|5t(N!GP*u5QDCvKty~P9vY)XZ@yvJguuw-85X14SfaiSqN+up?t?8
zWOs4!yY`UKbRc11E~rR6NfYuzF{OKI!}Vcqr!%<V2H}l;dQ0H<U4n{^Uug1b?Y6I8
zs~Q)a&kZGYtHjV2hEN9!wbi;9wIx(NSYu3?fo;1>g%Wm5%R~urUZPS*G?w{3y#$<%
zF^lrSA7KjVI6rS>C=xHzSmJtb+alajOpXE!GyBbZ`@sTFN1r68=6KbN!ZqbKysw4f
zN-oJ~MP4;{tKpm??%?v!%6iKhXE4v|(;Td+^QEES9v1^e;&~d&4`sl!cIq4;`RP~2
zzi%k1^5L3WHSQ0F{{E$wcm*yOuc)YpO|)9J-_RaAb@RQh5woo-5bU@C=dl`p8<hqp
zP~%u+VSv!AsNxDMbL^Y3%t|r6tUFTbnrNB?X<l}k7r(shbZ_L5@~LabJ4W!nW4rE`
zBkG8#PSvb~_PGO^<FC|$S3gCWeyA^{2Nd_>&KACD5(<b$71JfpI6FkP0PN^=N{0^L
zJq-fYN};0A+)zeK3yX&8>wDk;2I~LE)>i;U{f6BNq5?{HNp}g-Asq(Y-O`<s(j_Gg
z($d`>f^^5yjf8Y}+;`Rg_uZL$XLfL$*_qwn8&90)oU^<_fxO?;1r*rx!_8=4g2{zY
z4YF7HN1aL3E9giKqpGf&i@ix_vxFxX6kB>n_{T5zEoNHYhAOHkzR5^3`&dw8oR{-9
zp6yD$EGJ{5pcGGf{HO3ULL0$0sSYCiQr8q|lbx4Y#>1&XJ5~kZIaJ$Ys9q2iC3|5V
z=gZ1%!VPPv^agN~pSoLjv9jH#I4`n5;uIB%m5lwuHSF0Qb^=c{qlJH~vqeQUISdsT
z6u6co$!UpfDmAhuU8mET%2s}qa;~di^eX5`d?!_HaCS##fK;tgtOTJ<<#8<FZmwDA
zmNwa+^i}#DX0lC4UAPITI>vMo8FBAK)&Nj&M%4f5kMQODchCy#*4_b9J%{_b`dmFZ
z@2?+pDs+}A#PY-8`3kgKP6W<IbG!g+;|zUm-!XAZu)}3slWz*f%x0A1W5=c8+x7Q0
zQ@>!7_Ee<Ux4tdD+&8#~@~mm3y<MJU@EG4&D5S{cQfF&CS6m5emxJUg-bp_Zl6d*)
zyT%D~>Qvs8(xYsWD*et7Ti&Yhol{9Rh#*9i-?ao)oSg$J$rSrcGU;R5)W8s8*DW;>
zqQi^9StjPe{gMZ^#0Vc%Da7mtOsayC9I9c2Nw%3U!W2?u>knx)7eo>|5XIWps?Ry$
zm2e#w9H;67N53px8QY>bxj(GCK5+3X?Ex;mZO6w26?VM5dy9Mg8o>GGDfz;#_I0rm
zaT0Swe9l3JExP&Ma>@YmRw<!GJ<oMGFF!TUEJ=J!(Mvh{1MLgNpwt4XrNDxz!UGYA
zhLXn=+vWwUR7f_U-ed3pMo`QuDZEtv++gQlzF*`w=;(c+w)We|@!jv%K#xdtQ_9_q
zp|Ilj&3#YK)f$X)7tYVn=c->jq;Joa+k&ymDG)a}xVpE?5~+B(|Bk<G|5$yN^7s$=
z(&hQj*YHSARxOE;YHj|8)1>_Ai5)o%Zd#Iwp3jD+KYV2U>k&2B?W>chZ0yX^8zLaD
zr!J<oP*)!&lBe;;b^`*gILAe~li>UF3r(rh+$(@UfNGfPhtCKYLUittUbH<%`8D%q
z8e{OfM7sd70JKCW5ZcGfFO~HQUH!$!+%Z;7GR34+#ct9*`*<$F2_dck5U&2A_=C3u
z5YN~|*NgtmpkK9s#^IV5*A#x+En85}<U3$<sfwWmT!#;;fOqJ6!T-efGjlltJn#j*
zANWH=c`1&QYEU9ky>};|u8ExMzo=@BkITnN{@AMAmb+NqI|Ar4On_F<J&!7__pS8q
zX6eDH1B@A3aw_1q#R?rkUwO*Nbg0xlifSDeFoJ5ATT?g}I-NhKrmJ!02eD-rou3k!
zsEsO(x4c(d!{Vc(82=PF{tcmYUhfXbK!v%I8|Su~?$1iS68mUqUI0Rsy|+hon5(FH
zj}sd|55z!4C@nl=G2GoIDPGlx4imEwS|&OHTF2%>+uAU^Fu$j7J>TEQZvw_V_zPOM
zMjafDUh}J4PEP0V>|Fy|CJT!pGzw$))8%s|SNd{)2VW-*SbLyJJnokC#04O~pgiJF
zbU(TQJ1PmgQjO+0cEagIOsWW9S0SEg#2G+0UU+l}G?=}WZy>2A@TJ=l1^aofQdA=L
zRF5h&tm-?9NAr0qT5|Lj0mRSo@JO@SYZxd%YB#F4!=KVAl>z+)C!FW+?D+Xdib8qY
z(LB*SiLZah9UOjYNd=OFjSt@x+6GjfDV?e0%A}<>M!=2%Qfz~xq&?2lPwA|1_;#gk
zS@<<89FQQT<-#1nsl?N=wvqzS{e7p)O5cG_AH6Mw8D%32Nz}d5gdNniy`fa1i5nFu
z#~`I}@>qN!6S@V<lE0E6ZwlyCeZLFoTipQmLklI8lUAtFn8cAT!tZ9*Wp?9qACM64
z_z*m}SC)jO&-0WQ8o`1|Pyw+y*w&pLi+$#cE9^BV7#gAQA|>ua3ge+;j{8D|^KvRx
z=tPiFEr16q%=$J{zwk=qM@>5)Qn1QpUPH=u@z&CWh-x(pJKKHrly<)Yy~9F-qt$eN
zus^1L{*}*&uA?9XL!Mt860IEl>WcHMO3_2X|9e5_Ow%X(+ITZS0Gd~y=Q(IkY8Tha
z5{5`7m2zaCj~-qar+%Uu-bdW13Zvq}=X0@{#!$IJCZms)AF1w9Bj)&7nNMA47PE7^
zx-em{q}OVegKcCErBwxg{kA!fs1U;(8`(_-FV!>G{`Ozt^SKomPqm7>M5x@>kC)sW
z_#AQ|mn#8zuiM!Khtt*OiDl{Q_wEc*&BD)|X>TJ6RkeLRKqKwFiW)=-4~aAZD0o69
zPxv;`){=WQCA+2){~Fs0%HaHnbo8bPZk;Pj7w<A1q5<TvX*Gm?zTzpJV>*I2rlFZy
zI)_morh<&Tl<;@$4n!IDA`d`$%1Mp6yFTk*km6^-`fkqE=razOwGBUVIb=~3pEv>V
zD$VrLH9e;{mZ@=<_2Mr3Wp^~SjFX$*HI)@&RfP3SR~SJt5Hwops?^*4a9wTLgY|$?
z^cjJ~is-mxHuL_#`Ye3%!hgsJ&|&u3pYe$28C59B$R_$@Yz6Qrr#m7Fo(Xkr3?@G=
z4*!(sV|Nz7SWp9WTIOTlgWX&fyqcZmi_!dYW{Eak|Dg%Oe~7KKlE&f}wYhif<`%!)
zHW$`@JvN(}=Jxufg}YvPF~l=M7x%&oUMq&V!wOoaM?o(k7@m$H6}<uKZ$6ARlX_Qv
zPcW{B@~me7V@ZrV%H3HGCeO@|PEz@x*U$uitVCBHMm<+L<@ogUh12~u<Ps3<lg}Xx
z7eLVS!FyRHBg56+Y|@&YU->duZ+m+s|7DavJ7jD^RCJrPpf{);&!@y*9=<?rzR}^g
z{PcTF)zV1$152spUjh_vW8dFeNfs?uS~s7?i76lpPn9N)vXjPo{q}~Xj?#agBbB^y
z&ZlBMr=sJ*?mWigzlqr5;b`mulRxzdKv$lD^>6-$CO&9rx{`7~-Bdci%e;IsXC_<N
zRD5lq@rxQwH=?nTqmtZQ<waS57<3hm;lV0w(_wwdFUj&Dd7_NiGNB=h(Ls|STlz{}
z7slTZ)a<?*JdbkQ#Kr0r&ptrjp8=F8_}U25u#mIL^>Q*_p*xO2O}`+lU=etJ?j(=$
zeZ?91E{m^R(vTH}JQeZF@vE)I8O1%QEQ<itx9_#B)a-nqUHhgS*6}X)WgT5-6rvRA
z4d7xfYZvtKZt*7|7W*kUQ&M9w#hCw<uC+b?Gs)|8^U^eZ9cLs4WnLF3Gg+!7J3mpy
zxXScQ!JoyGDimuWcZ2eH5Gc}7!8B&ycGv_k7MuWYL)_7+U@cGfZS<PSDuI-u2)*Vq
zkd!;zvHS5Txt3#<eAU!21Uxum_&h7KYBR_@-aeG+4iU>U-T`>zFX_DX!d3!^_cSdf
z+{BGqvmw?Cz;CQ>Gj!Az0|MFYl=X(c0$Ez<!dr$JM~rMM^{alI9O{uFYkL{bD`aBr
zGxCU)K^Q8oPz5|mUJ5n6@PJB;7R-kQs6zv9V)#SRW~Uc+lRg=Io-^PPo7qv~!)Abw
zQzhof$FTq`WhqKL`D}3P`%*S|{v3NgAa4U7cdPq02kD0P4ZARknLOkpQ-sXH*t5p!
zGGFtUh+CiO$DnGHd<BD~=e6RGGtZNX|M3$bVN*@xxjr~3mdaP=jLT~XX2iK(jH1F*
zmbqP6Iis_lt*U4#`llDrCjWmK@O=W#Gu00%#8=^0=QL_`3ng4tEzOsAeeULDUh2Rh
zpr>+9Bk{DV$NAn31u}Z#;abY0%WgIibgjAz<)Qy1OFtBA7GmG25KaIri#bJJ#j^`2
zVhZ9u`77fg!p6h)=L-m(Nnm7sKfe!Z_tkH@EZ-1>Ojt-x(Eq#-{wGw}%hQ`o&0OhU
zSR-0{vgDTXYXEntRm-wbcY+TTTf8|2juz^Wm)jFG+Xg0G3#1|e(?HBOZ;%%^P2@bk
z;#oj^CNzTvRwqbK7hpq9fRos8h734P22d&V+5SSZAe*>~)1TH6D*<TZpwI{h(9+am
zp8&zj-u-?;2!Ahvuz?3>xd7~2s`RVT$&V%FSmtqa0Yzu~Gb+WJgZNXX`e)|sHi2xE
zGnBRs@ZV!VVeU}t_Hl!wakH><bZ>VzHcq>JaSJk35Ia<sVLvsKTRAE2!LWBZKwDfa
zw$3U?Y_R$>m;;Px`ED)%_ZCnwg82Gfe84*HqIh?P;xxWS11N6bKJmN`m@3_KA!%bk
z!Mrv!Tz=Ooz-<_o=3d7HRFRYUw|isb;|h}jBy2|3clWJ_)pO2sUy$Z3XW~-g_WbOX
zQUsXhl<1B33S+CknvSAi%|EEbB^Z}kFTF{wHwg%PIC`&9?<Ce1U8Iqfnlhc=NHn(b
zs$X&#z7FX*8c?4@)l~-q?xRoL4mKfZ#3YQxehaRe7(1G|UmFi5`Q7$rk3ucia<-_X
zw&x217Ca^If`&W1p4|!W5MUo@ECS(Tp0W8kk?;Ry?Evqh2E_aQ&YVJ}d+|tfyD31v
zk_qt+q=}<V*30GcqPWizLn@SIbri@B+T5F~HjZyTUU`_wjD6nDW0A!Zwl`~UT~?9Z
z?_bSN07e~9rEu3rB2(NX0l2f~>zsudj^!Onw7?9B>LM#=9#RtoEQGKOG{3IT-6emI
z8T3rjLS=XrwJn#-nj#g?1}_(=7^hJuL*r?q^<+{<=982|JhyaJnSULHks%3r5c*}j
zL1-b1RE0JLOA&JYoU^<`Bj<Z_51^mi4T2QJl$(DWsZc0JgyEFK<*{i;&mE9-;TMT;
z5+31QMfKS>1me3z^gsHtf8POB9-MUw6#6wvRA{lS)h?SR>Y#J6zFUbS@y7VMiYTW>
zMf(T=bCbW`@#s+{4y+hZ{^Ao)?s{-@IIU9=)ROfp8}0R^+~xsClF2$(?j1-`5|LJ;
zeg;411rPDD$Jrd!O~mUlQdN@K5;>!txtutntr)Lh6>|8nc#7`-)tyRGf;6kxFn4pL
z{%rVmp=<cYTt8J(MnrF`60o`!j<d-Y*w<^*s!ei`?k=+v2${In^?57q<XDJ&Q-GYu
z6E>gFZ(F3=bf<QPi`qrA!nV=U@uSt$$k{Abzv>*mQE>!@_9O$wVk_3oHv_3lk5G@_
za2fQR`(>8W2eM(xrHUCb?Djq*i-QGor0jix9%~%C^9ICBy;X@jHM1K;Jjw`#Y8h3I
z1sbES?8P6~Sywe~l1SKgH97+RcQ@gGfpfYcf0&*Jr)<4oq;fa)J1PO=j^4pcZg8Kw
zNbmHS<S2jIC*w%Q8xPYDDmsm>T8$?aZVbhu?hio`zrRo8d1GQrLP#8rXBp1}UVpno
zp{{x{`Hm}ND?_Sq7V>PeM%~EwS>~FuWd$dQ28>?&$WAoQpHlGGcW1-TiVT7T9by5K
zj^N~>%=CSwMcQ~jn=Czuq2bnY3rtoD;{`Dx!=esw8Q$O3{?{buGx)s`pOKL_MPa^e
z|Kvwq4ismXpR$~J3pWNBWS+|`M-(}p9~Pqc)!LG3EKFQDnakI{CDkYgdfgBa5-eX?
z$f^5!;8OcW@$%`m`-b0!a<1l&SEp;)1bb)sE5!7S&3+4R?yq@^KK|gzIQd3;=FH13
ze6hPoFj=oupxXB|ZJKXayD$S8F#p~K{@-FxIKY-h+HD`>LNM7{Q6s)Hn{hkubpL6O
zl5dPtU4VCDE5H6oU@*nZ+Us6ilj&mDYID{$kdQduz!nvRUMUiY&xoXmY?@X2E4DG9
zH!b?Fl{`WeK@j%HbQk+0e*N=@#2u`{VevF8qPLNT(=1XJ&L74D4_m%|1mR5GGGZJY
zCN8g0Rahz?-ExB@PO6QIGjG;TV$^=uIUtn4!RBN74NaE~?i1B=nRU{|zMm_L*5J+N
ze>Y8-o3llv!xVC@`dd0|nA5G=F|ej|u4S&|W#g;e)buNzDm!3*l4GxpH)IlM0+gCn
zcDq1N^zT?j_JPZu?|u=wGD}@Nqc7vx$iP&pIF%1Me^(o4%dJdDNRcXJGS$(bmWDC(
z?g>W;nztX0WHyPz@=v3h>BJG5I46<KuE}O0tVWMMyW~bYdxpRYg}JGkLgYMy|BN-p
z%b9pHGkEJ2s-BVl8@K$oGV&lwvc(>FvS{9jQo|Er=8`BzD>s3iI@e0(*_3eGY_$$(
z_Q*pT-abd!v6GyVO)dkrAt7%`lzc|Ahk|@yY;ome8(r0IrsTr*3WihO-soFozssE~
z<e!^1%G#J&+?{^clBcqzk$Vf#U{4w+gAWrA^Y~x819>PE09k?&bH5AaJgD+ESbfcQ
z-`1tH%d#1vrtSi}!%<Q#5pYl66!v_^Y+0;UZQNDr`Ai7pk2EtM-4EIRqyB?#S5jSM
z>gGhk%cK4SuxJO!j=KuR=hrS(KcrD-o9q(vkj4JatRug|a!U{1*V}K-*4eHV0V3uk
z(2&MBENA}h1_0fN=D?Q>YsB!avFD}v0Kc)r*G)prVJ53V!?!DoukOZfPUmE%`>N^N
z7busV*b66X8kt*%^ULG0ZTLYdOLuu9PYK_A6NtrOew9<VTw^BNi(L|88Pt3rnlzEG
z+dpM>#vk_Eyj2=$?@JCOljo_BiK4}>pI{&SEl?72c;33)Y!{Z!KXMpMYJH=s4(N!$
zCW6<9uuk@Gfqo45vKu%npXQ&uOgc}N(rRMKS&OrwiotbtB`8#?QT)*TwmK!3`Lc8I
z@)VN5bkh{gT$RP^5dIr?7ckq7FeGD1P6MptrYpFfgrAwkRW40gNAyeO$yI-`?3u-j
zH%PISCEA(dXb|)vVPmTO>`StY=M5!T13v-{&QrzVVL)fXtg8V@X$}zk5gCkO{+{G~
z3XtjEIlBpH?ffnA!iqWOG6oA~A4{;!%BVy(F*kx@z|HEXu0{o2L*iG02{V(&%ngut
zA7egf!wnI_L?C>Bhw6Ir%Lrj6RN<^($|^`E&(*2B!Rc+Xx?D53&sq8g)Np*{ktr#b
z3c1pbzA|Umx1cfj&@Wt_Y}L@43XDzu9!}`t`e%$x-Sy<nQNLMo)2jwNCwA`6<$cCt
zJ^!&_Tb8#@*u2gMRq=~ni*Y5|=%a~KrI5A~-V1Shof>!1n{pOdc+6tK0=SIfq3eZ~
zq{cT)3H&HUmRVj}IPnJmxF)Zex*MwBWAK((t>=Za13^9km{FzzmGquDaMS9q{6Y<s
zpGYvVeClPoLi@58fcT!^>McHY3IkNP-_U!REdF?WeEz%%6x;mH?=en%6zNZ+(p1Fw
zM72q`478Fp3h9h6j@v6P*6TK7S6(d0{K7WHeFvY^;~Z`O-qBkTL72K^#=^Hn>*dMb
zWGi_@+Q>YVHo9SuZTx+P2=HJes2IYoR5xxbk0$Y+sY1r^<cQ2K_7|YWD2b)}I%{2F
z#b7+9TtUHqnFVV^4QSN50OzO|Szw>{fD|anrDc0^7#bO=T^+l(5wVj2N-v09F2bO)
z!l-58W)e;_q<?Y8GT@pKjO(+FPgQ(3F3z}MLjSsU?tITp(>=xfuE3~RW36Cv{E8XP
zcXYRT=Dhzl&GI%vXEq7R+gPSVE<4U~XJP?aARVJ)C3<Zxr1FCh^tmp01gvj(jFj%@
z8+daX-OX-hoggrdyA43h+Sy>qLbOvi$-WyGezyCy{5ga9NeRG#1heSQFGs@+c)Avw
z-7N2Kuj1n3K7!KFskO^N&`JD}_Z?lC<s3&cm#zN3%>@uscJH~fY20!yxntC>*^>Od
zUtg6#JW@N*{F++p^Yf1@+t3a9R#77&R%3OM-ezrqp3*z9DTN#w7f;oT+~%zpN6fns
zOMv1z>%X_7T1sOK%M@aNA&|5GqtB7vvNOpjE-}`Wd+4D+xxg%JhohXzG5I*KN&q_o
zO;Qm0oMHlYM^D|{X1DVWgrO4s$;Ol$>tN4-?y=}fzj5D__3>F~b!0Jd9CqG^(kkYm
zJ$n=!t*+i!u>KeD6101sm$Nx6@bHay-lH#uw#Il^hq$8qrVncM)ClyKYaXiMfAfvt
z7${<?78p;45K3*R<dW+3k{K5&#?QG#1`1<)%bXy6Ge6k-pzW7mgDldhyV)3G|CJk!
zI~TR{ea6`yEcM7AOZ6j6kbe}{C9Kja_!__vaQS$foS?q>)Z?CcK-<7z^LE7ze{`xa
z+{`eShyNbppj|7iZ58UZgg(mRPC&er6R<E}-f}<-AZrpaxKq8;)y`0R-(jFYnB}lu
z)MQX8?Tz7a+*V{L?F@z1(7@cO2js|LcW16zKhz*9xU&>4y}vw9RAz5QL}8;E<E1x^
z27Mtt?XuKx&3y<$9zU{M?@pv~uf_cI^1R@;dE<t5Cg1j3_9pdE(f`gwJxzC27S{Rz
zluXN?yrCTK%cC{>z{XO|`ikk&db{<$8oiFE{XLT3qLe$s+n)c$>JMu{Zm@m=Ck$^f
zUEh5jmSlW$e$P%TA0*{l;#%kSoHvih|F<t}IiLcQp-WbiC~_eZ5#O4|0%5ySo>H&;
zZzun8@`spKH|wXfdegT8yV2Y`3~s|Mf`#_+v5d)jcgQ8y)ZAx$kJ(eE_yvlp>ghXn
zMjiF%pm3*_@Q7dngGd`WEZ|Ak_C`~0o?m|hmV2J96>mJQrvXQ;YH8RP<F6MZ2^t6|
z7%NOR$O!bWTsA;bS-egdDwXQ!<Jd}|`dd&xf=Lskdz#T=uCoy%kH_KFJg8nhbH2N{
zqp|B*4EzjI#xfgHW{MhO9X~oV7g+)>!5hu5rb|UV6$4VdgLviJ91yHxh2o+!hw!YI
z*()DBFLt{2&f^qrTvP3}rDxXpq`G<A^YIpH9i_|_%N<f3`G7d?7XWvRfL?zX=?#|8
z{vy#Fh4qp0Cjc$%LSdl`J?@u(bIDtPg(St)hD9?Wd#AWv%v_}n&{lQJa%~>3npzg=
zJ-L5`+#LcelSrxr>7Hd23|@6wCtLWOEQJj0)tFCd=Q?MrVNvT^XjL|)EHpJ#7szw4
zEiaivlI@|>#{;;GR_pnwo643dh0+GOGdm~sN;4<-=67+}U8v{Wh{7q_Ird)93H_Yx
z!X3*o{5Y1Eyvz2H2uLHhU;(mY8`uy*z&WJ2X%Av2f=3^)OR0)u)Sx=$wzK<N(qYDf
zKHc{13C~;V!;@>v^R~-QTkZp|my2yfcK3q0P}H^@@w*x7b#Fx&$Ldrykb7?7#U=y2
z<zwU$<3YMugm=ZM>Es_vt5bL@eXX*vwvAjNq-RC;Dequv6%t&RB3frk={cTgrWt2o
zOgmX?^p{xc1ZEt4(sl6>Tzzf`hML}+0OwnOnhxC4-rq*w-@G7TH#6*yXWrbD9-C*8
z#)LNcK+pvweCi2n)WvO$&pD@qbd<Z-ZzHVK7WVn7gfzoDg}?$=KappF_vrX-c{Tcl
zU2o|UYH=8hp{^R*_oPD>^6ZYp#~@acUhgq4XHCf&-R7q|6XPm@ANd%r<c`88s>g8N
zqBt&q-Mc%fVJ&q!{=x-NpU*0xQ%=op;lJ@#UJk@ttOK4Y!nn4P`kmczHS><ynzArt
zQ)S4#qi9w;hXFr06|#AP1y3@f0@B~K{a55)b&Qw*qRSDR=~&p%z!4y4NWSz2Q>!s6
z;6C+>d!=>Q0qR`{ED0zVKH0bb+niXiIdZ*r^WSk?tVf>`+}pdm`n?=4Xjrg?=NFE?
zm-_L9B=Qlo*q7FB>`c9+x+cv7CZtu)GUhwdE|})^(QH77WZpch9>z+!XP;ZM)jwo$
zR9+=SU0uPU)KO@Rzw5~MHq`7P)@D1Q#%ducqx46*YB*p@er0)~Rcn<3k}AD4Ea5*l
zf?rT4#(TFOEX*juA}_X^w8?A<HXCEsbDb^&us$UC(D+u0-`b;Gy7UrBVgF4$(dfuE
zS2WgtBN69MY$y}a_McxD-KFU;RD}(FI4Q4sOrTQU9d1T=MhZOe){&jH04C`60s^xO
z9Jzp#hJ?olCtw58{^Tp|i@oXcEMPjfqSA@|cY(f#g9X~2H!oom_I4S<fAAVAIvT(x
zTtU(W{xtA3k#s^^r9&TfojvPm(uF9V7b-GYq7aS$EFo?t`MLmWl`O|PxX)FS&wqRN
z_45w7`qqckQ>V1YMep*z1Ua2p@_Af45C9hLLH>=>0n*tEkWU0h5h>o|x&l+vX<!Q(
zg6*62*MBk65Cr|VP90net5_~hlLzOJ`7@SgljML{%g7T6E~rgcP$0BGD;?4@Pa9On
zL1Eni)*&}8AKlH~m>)ONhO(~NY1cZLnsq~v_{cr<m|wY`nk2KC%BKzjX2eH83D-|G
zf`+9`G}$&6V9bIw_+!ms0ubukT~D{ra<go4{~pmMfR%2C-SjAdP1+dZcbdujRAX%|
zJyCl2&&)CJ!dL>lwM_@w6wT!_)Yj8;J_xvN!n{<9Z8_p>{<<iFPwNmv6#@?*6x7h*
zWl>h6g(D^_r}nerygHLC4r>&m<Y2i)2A?U3A^JN+^6g_-Z$a0C6|bQ>oUgB5C-Pnl
z8B(2S6Q}WBEZVo6q%0-+4e%~qwM;71UOPriTlMfOn>s#B5*d))%E^AWY;4g#D5x~B
zRbbw+yO4udqB;Hmn<CK`)hTOjZ5<OA*S?hI<?FP_>qj2_2qE$j0v8z!Vg_;r9V4}j
zzJwgR6VBAKiR0s+l$l~tfi1#<1o(I+G^fiXB@N#Q2*Q<g_XB*nW-_&3q_A?mj2<V~
z49BDX0863E3tK|ww^+^e>=|N#0}LL{?}#jX`!Kq2QNQcVJVgyp+0MyWGWoN(#nHa(
zg4h1CN_Eo-a2Dcia-Aa}k?8^rD@QUX(%)|s`MDB39d)*serjaihw|CGa>FGAMP+u}
zv##M`jQH~}Kb1^Z;13Pa;Ibku+{f`*V#xB=L&;9cl{q+m%q=|rvFIM5Asx=B74^9g
zKBLl$E65{|h?$Iyn+o-rBf5z-UfgGTh4vy>0tNVLL;~KZ&r<MQnYh=$;%5yA>&klF
zO=I9BKSN*N5+C`GNNYCtb$uMrO!jU6svg<|zr7I_-<?A?HZ_-dZt<PjJ1tygHI4UJ
zPF>r2vaB`eSF@+!3nrPG{f=U_HLb0c;Mu3{32J^sS6DQKT!RzcQy8>SFm>vpW5`&>
zoSbrUe$Nmh+i0Py*HYg2h~8-@np{y_n`x9*Vr%_zX8DKQ`Ro&>-@#S7OA6+N8&CXY
z*{i1G;4Ea?Mt_blYSicg*O{6$E(sDC^gl0ZazWCnXzUNg@C9|Wog<$N<k8k8SQPd2
zhfm%{Rq7fDQO0e=k;r^!$;&rodvUP0=Dn|Xj~kEa^B!F!v9E0)ldYy|`HD!QT<z5^
z&4Qp}0`bPT=+9qJ1zuxc1;J^CLYH(7vGDNHY!5Q?7`Ex~c1v+UwMFKHp!2<G5%afc
z46i#g;-qACwwdUMROY1V=ljZ@Ga{sl7@kg4Fv+MuhKBRX?c5@HTG@b&jSbV^-~aG%
znTddX9y}Fv)!W$NW#RYy9IepnsjTmq`d1}K&Y7Grh*K9ExP0e+&H4WJGp~<3K6Egi
z$fUf?hUeJ~6zM^iOOJagZ|J$vt{9RL2W!yCB-5q@q!69sIxT|}-y%*LglsPr#3}A;
zjvk#8m)|-^lyi>xwa6um3SiqiZ>-(iiFGBZx*u$0EU?hB13}*&fjqpWjNcT+GA<mU
z(2Eo{Q*>-*?Oj~KttZes1HCr5;M$~=k?0UHbS4X|o8o`^n#bl=85{eN9cf^QLYEST
z)@QNy2(==j0EdhwKpi1vp@9NM1x<K;)x#qX^Wk>%+Kbr;ZZptp>#=~0^DesFElP1;
z#WnxR{z`zefAK3~8A1#(q}<Z^h#c{qOyR~Su4_yB8cnr<jgWg;_-cG2-mjkr5gcl8
z8+3ob&I9uRjx_3L{~ZHk3PI9|p+YKhO{;q<3`(O}x}ob3*muNQi-A!=PTB&<#{Ax#
zUi57$t?Fq9GUCEs>3RhDXQ`Fc2wd1OD(J%Nt%<|ITm8hWp-Rj?m`o3E3Z4asKS|xb
zdFzDsW;)Jmdv*+GpKRM}sK@X{1Og9aU4^vh4H?~BA(qHvfB5UKL3R;HBXO$%!dv;-
z%g@nmBw<r%13nrXle4o!Nb5&v{~lKKEelvo;k_nUwe{+{i=7Wz;vTvV?~+lB8gR4G
z(92+8)UfVl-e06=w5Ua&HHuTLWbTtDS(Lp-;Nt!BCDq@uoT|y?St-P=>*ukcDXKYF
z|M|B(Tlod#JzVUX0=$y4w=w!FnRKyRWNs$AhN;k2F&{Be=e)}$Z3*L`3}C+9GdO5e
z{??6=wVU+M9kSv1^PLQJeqpaIFiJS!DJhpUSIc~1sxk3{vv{j(E6uVvS8(S+Ot2E+
zzcyz1o6a)&a|ebEJE<a55T?iys@(e0KK*u?q?}D8%06Dw=O0lpR8#Ufc~{F*26-j-
z&C4nI0V^3T32$D#+^h<rvs^mY6(hQ@zJT*Y<M2-wM?ooP_Yzgdy=Rc0^}BJ!v&=fD
z@|UgtIlUMQ-?M~AfJj%S`s<jVA+uIP!s_P6O;1o0)oKeb_$T<Ov2J~976)KOs9I-}
zF}T?Y^EZr1wy7b26LZqXh)Vn`&V2gi{Op4Lc>B>v<bJXB5jz4Gg?FGQkDu1FezqH`
zP<rN;g4UcS*QAtm81yYe*daPUWi~bg=eO*LqK0urF{!9qzHc%-18!5|$YMF(^wIsy
z=~$;>Eaj!-b9JpJXCZs!^#P?jZ<-{OEW@z8(HbRwrLl?CS5{tS%FsqcIZ+0!req5(
zKu`FQV|1$v1J`lkb~#NY>do^)IY!H1&mZzO!N@<Q__I(cj$_qbd*Ewy-kX(az;iue
zN3y|q=nQ2%yKBEi8u%`QDa@GN>r^7HD`Se3idKKw-?zj2?fUaCR6d3lt|LffX>Es)
zs4u|!R$FfNE5GM&aHP2(Bb3x{;14>P{E6>ic^c@zHFnZgEB=J8(dd?X*><t4i<nWI
zcvx+KUU#(PAzZa}`8b$zak!c5akHOGyDyw(?B~}A8LV{Xg{V&jrtJwq!W&X$&1X56
zLHr>_CeKR#eJ#zr9_#MTgSt$h6_(mRa_0K*?ChsM*|wdX>vu5?okIL%(%N1xue_qL
zJi1zlMDP+zJUr>?@h@4I`ebGk3I#9sC=H+Nc4h^%95k|u)7eG2IvP^AmmmEgPxW@*
zqp4l%=oMB}WSpH!HzPkk>Qrhb^Ij0pA4egOS%_x_6pBCqYSz?ugL_8_D-U;fGporK
z!YswW|26aR;cCyz!}2KHS+Wo@>0}NhC}SX-No_&e^R3<LPXtj>QB_M{qPKsSC%q6O
z9o1|TYcfl}Y4uns)2`|Eu*VmUFs1ER>3L>B-({d{b&CKC6VhX`Dr^*V8ztBwGQW4;
zHbY&$(4u5i9tlThe?fp|(FE?JrNZ-<^A7Mc67)P(l>P%d{wakD9bq-GtGkWDCYslC
z!N|2U{q26WFTQm6{glnB3E9TCy+|UAQBge?i{So|U6j$QTR7dW9}-rtF<Q#-T)X%u
z0b+)Dd@!e$-)DBRJfE)l;heFqq)L7<+22c`mMrtQaWdC5BK{X@Z1DbE>xoaXX9?x*
zX-rvfI5-9^klD1JW}xL#+5Gy6W^rTr9GQqWXsi#N1&7n`Ss*Db$CJ%df`EiW)y9E?
z;iX*(Inxv-gTcn{)MGv#j^$$H6w6=Wz6rs{l5G_o*he`xsbj6`k!7BcmXBwgIXTe!
zyweDj$*Xkih;-46p5&l=7m{AOhg~o34_~lRcQy})Mub0^6RM7;_}k)xjT}9Ad$*I7
zpuu8k0F8p!i)_>a*^L<pJX>hm9>LG0saX6Lhvx4(m^l~Y2M1(sq3L#mfD_~kwcUfn
zrJYdzs{oYXX#5J=vt`yy3o9daRI_?jWjU=GlBWA3jisVV37nZ2_`P?@^;^16<d#%X
z1gr&qN;pfn>hrYwe396ho~Dd{<#jkM#IRI`d4sag&b#E9|Jdetj{wOV)<p~6Vie*z
zJKVX>Nx9;{FQ?b;@?I_t8gZ2}J1)C2u;G$zqy8h}<w&RdXWL@3{*F6U%oTkn{Oxhz
z5z8U!FRGQ{lq{;TMb>MsO$#d+!omM&Hh(1uHnA223LVDUH|;etU>4q1<e4lXW8OCX
zhzvZ{M=Pg0DbfD49*Yae4y-SRDVAB`3|p2|ytuxbwrG<@9C(#%Wxa112^SqnPbX1S
zbRb+QaS|?6E@u7SBRdzd_wOjT#r`QPAlhD4gv{<<vg19RNPFRhA1rRh4OMf8>aVyj
ztxjsEQ=0qp#MCbJbW4BWzTPFH5U0ks>`0zO>*?$NNf4?dq7Z#$))V5WLn<xPdd@`A
zO+{bpAW(|gDXEP!r(XRszkK1a0u{w2@-sb#jRWcF$v6F5w;eR8_ooAw4kCK#VmHr^
zeh+0S&)H$<BsHA`E(W2-&T<ALjtv*_>NYbT-SsEOpNU(1=3Ja{BYe2aZZp%USlgBB
z#l<7|B2yV!Zj$V(jl_=CbG_{mMPH4#`SVGZiXkc>nm?f}J0yWoA-5^KWL;5>sv&vY
zyDNTlQJr6fAIG{U2^SU-(GA+3GC&>b7%s|o;x*e`Tk{<olfS%j{AOKGuHh~XOCbn-
zY5l2rRkiS9?G6HbvDbNCZ)iKz5#MuD4eHSwzlU-7ef@xMzp&NZ8?PC0a-C*<if3Iw
zo(^`lXioh_tj6R$#j#%!9YyBl=;+l7OFox`qV0wdY{`eVD1TYCz1sk+8(F=~k%;V_
zNnTQ1nY4Jr%Vs(9f&p{xuCw5u12DD;<s2Gk%6I2$;k$U=jl&_GF-C53DmQMiSLc=t
z&EXNJoVOaD%>jE&j_YDH^OZ;$3+_j~TG41&olPro$Ht8%B7)d>jb=xssI8uC#1$d8
z3Y%1FmfiAbY`_TQRo8pw=I_b4bP5JNL9#FZJ~H^b0TLwpaQ8=<bz;klU@~-WZ0smm
z!YHz>{wxKwt@Vs~ySz$ph;38#^Mu>4?ca*A+#j8<VN3Yb5cf`XqeLuT!@=HKkhAq|
z4-i}QTMHC*P66B1$+}aH`p1m>0|68x{_tECs0~-5SofqM&wkt9I;z4hF1o8$($+7W
zsU1=F6~!hdS$=Y9SVN9tcDNVcL)#xq<Q<k7Yf4c`oS?^WBclwuipF}$*NE&rP@%5n
z*+N-fhN{!VnUSk>I1!TGN8%cs!OEF8fY#^xuX~Yf;{&;E<7)aeA5&yA$Xz^6@kea<
zPti+*)M@P9JhH8>wASw2B~L^4%H+~Z`ln}v7LHyj2bWjd!)2Z~_)@q`+z;}acK1d}
z775!7<QZvmi4mY6lwxQ3)T)ursr@oJghs$R+Vb)<oTmS0^5+@Wd$et1THTInA=+eg
z4ofR`S{7RKoQO+#43UOr%Rl&q#fH}86(W=th!fC5Ql6cbLwEx7>8L>?`0ZO;$tpe{
zh28nKtpf)4<pcQTR7eg3nh;Q{gr1%{WYwIZiWpV9sIjMlcIi+39r^ha2<U?+<yQ){
z$d(LwKPB8`5*VJM;cW!&xJu|*T<E$4>BEP+u(5G0k3K=kXsGEkyJ#07J37=M_HArz
zgm7q98%Sxfzw}%aVheE`vFkAz>CWYUV$D1UA3hnBfhkm7M04XL1{==c_ojkdhEtE3
zV)~iU`dI@*27|OiS=+T&nmR9O)_4TYR85P~*^cVbliZh|kg^}gpG9<8dKeU)`xCtd
zl^0=AUPlW3`Oa#~ZSqaZ5nh3XDV9*|kE);yJv}`d28LftBZwbT{|i$uz?OYnp1v6!
z-FDqYCoVbeRNLSVIlpNCY~w}o^qT9|X&H%kYOPX<*j)U4lRigZSH?5B82eXAk<Q_{
z7f#;Z$Ev#l+RDh;yNh`xaRQSj&;(wH!NeHdAj;_&Q+20~+}=<JEbmtu;2{)5aW^_M
zLLcz{E|>4tPBsm`n*=Q}n|&&;;+U1#*fZl?m~Jc;^Bv-Q8b3OVPgLGRR3!Eo4@;Yk
zqw^D)GqLt#?T3evaG`r!ls<Hifte!;a40XeGd;QjL(Hz+G*2^IHx7z_c}ax|v?Q@A
zI^FfHw=2&hV^^zR-i;5pPY6hln{};UGcT3BSh}qfj{o#vJD^l&)1#_ok@%WSLByRF
zE;=n-X}Js)YkMbod1WO$h=lkw{6!mk{z?#z4lIa!=qB$$bQ$^7O4ymy>~Bd_b4({4
z6ffb3&g|xd(tb~lrR+LZqoJ-D*-2|Td(L-a7CHT~RV3H)d>-G6N@vlQ2)2^rrC<)S
zz}jmG3O&sW)X=uyj6QU}>f`}TnsumP-l+895TG1?0wjfQz$1Uz*EW|utgy!@KW0u2
zMw=Kl!m$Z)eW-H<3})Ia97EcSmppuscqdaDCXZKFPpDSMo-<A9`*?s|wvdE=)!0u)
zE*{Zu{QXV`cDRjf`0gtguJtX8<Nq|OF5RP(wMCwcVmQr+%*}Kml$4dKUm@l{!iS`E
zjpf*1UNLEmQy9{vqA=|}aeh7~+uP~^oN}EcVaBG>h>20`xrM^cOg&_Zag&mhLt|oM
zCaEry{wF5DfdfX>c386xS72Uv)K=(K*(CCeadY@Sw<8yJOBd0}tI2Q=N43&1mT67f
z=?50B*CL#j#V9;N<mY=hLN)keGm9=H(39oM&0#C4)nDbK_11Y@y%GE#GHOR5`eb=U
zFSzckusO-{>e5wz)Tr5Z{J1%<m_2&712!;Qr&KE;gg|+B{2Q#`2BXIfnd2uUMw=)p
z$+mp>4;m@iv`@MQMH&shPKS++>Y1Y{;w{Ouu(7ja&CJY->Kgs;Ex`iG+|ggD=7`XI
z+sx*HzS04&Q_B50v%l=iZFq~TJ!<!|TYkrs4^=IWV-Bb9_pDcGtpT>=zyTwHElf$k
zU(>zhWJSjG9=Y?ez{~c15u>EYVZ0O{G!~(T>FbWqCLi`Nd@s&Z+E&vdey>ige$#KN
zJqx1JaMp?RD?f^}+=Q^9@<xAtj6s7&>-4dtx2B+Hc1s(r?xLs;xizRZ9=<@gM)XB9
z_$32C*nIP8HP-eM)!$p-UGm|xu!)Mc7Vn@GcI&5Q$V_BxfA!0#j8PYtZ3FeeI?ad7
z3SqwTL$tvYCujR^fscHlf)7?nmZKo(eYS+>YCu(2o%(F5W1N}MGDG)>75`Kx)2uSL
zk$|<|jOAvIp{hz12OFQfpn{n&`6DXldF(rlL}dwo(3!0@_ur88%sqxk4JOpk_0#<j
z6OQ^MuG2s=vm8GV&u8ga19_5wp7r=}drL92=0v@kWLFecTC@$H#UTb&$e24FJk^2Z
z3#}#>7GTT&oozGmzcZEo7(`;;M?bsc&|KCkr^+tctmD`@#?2(t^y-c(k_W4n$ra$R
zJ@217ZSJz3O=sSS+@yvQFg5pP)>w$bQjBoJ5o*E5DpvHF+dd=_Li9}}%TP8V-ijnd
zY|_>dhzeEL6CkgCf5*bcH-U4){6VA)l_}QXDnp(DHFghTReN#h(7lFp-lnz!6GE#>
zUe_}OEyJ3o&kq<wI%p-bC23#TTZWslC|Z?znJS`O!jG!%=Pe7tQatJy-k{I4nG5$d
z`sihSdHrd%Z&5Dc>&wxV;fQ8(p}Ep5S{B9oc^oV(QU-=14;<}(@Z=+e1QHO-;BXg}
zC3H9MiCixFYbP98$F56yFUS~47kV%jw}S+Sq6h~bhMyC$zrMM>7kD7DjXL@`cSwp5
zDY%Jl^ORH(y^pE1gRoo*GS(w&K$a)0UW{w@0aV2KSR!G;=^>1?0gH?qs_NRM^Q>+h
zR6cY>6CoK0QTKICS0tX7p3Oo(@i?sr#l!Hk6V?*pG*jD-lAgL#$;<507IoPk-AIbZ
z-2^d<qqpDMrd|rK+C_PG^xvK0pyzyctbs4MJOg6#H9&NhV?x>l^j2NqL}9sq<n!N-
z{;M1g^Zns|5eGlG+}L8qrj(U+Ck}qqkG4HHyKuZLzD?x+Btcr{-ofgAvWB=W54q}l
zPOUdEVq0)s#sUuO9RgP<DqSxoJgNTZz3^?Mg3kuk+LIVVn9L)*m4t6EgRQi4vpDCX
z?~1=aJ=o<AA0WrtBTi{JM<?r^KDC*jrtd2%K8T22(`m&_|6a7OsAItVtLEgle!qnd
z>9+(P{LWUde&o2-P!E5a(pOADT_KFuQ^7rzqx$cg>O&@^g!9x$GnH{d3BTw3gyg*v
z(4@DZgkY~O9kh&h6(_znCZnRt8RXXn#e}L%o1Cbu(bK)o5nxXLNJikU>NvspU)x9m
z^BlyQ>PP6ws^ZJF^9wFFp-)V#<kE14`o{@gmoz`;UsYfv_)`1@J@QIj#p3o|HSRmK
z2^#4|3Yb6&T^Z6Uv0G_wRkeuQ?WY}9$1Yyh-{Gzjuui@R6`GiI^)<;`Fy)B`mr>T*
zcOmj_29t^7P{MWOLO5&<?lhL7KZyto(Fr@rv77m8(J}-)aWx1EI!q$_sVGn$w(wQt
zzU6ksh4RfXLs!&T(h|8!=f0f)>A-mqeA`(w*(miy)uUO@7qt4Gl%fN~oyzyC4}&by
z1Gb9pub41l^OA~p29KTV%c_5?QX&>^lX5n9KfPM@xg?Rpq@8}XY29-56dVfkwXRMa
zoiC$V61DZzce9kDzZN>#X$mYcJ-qz{iN8^9Bw+8(<uEb>Bt;s-h7C>esHhsZ!S&4S
z3*Kw-Pd{J(PqFp{bfxN#f~6R!%jj=#ni_vGD(UB83gv3CKmHwz7l-mQjtZd%<zmV5
z9-Cgw(8R#Nx7X>|yR?>=iQw$n7ecOC3*I-NlCU9!Jx3LefIr&vK<lkn66N7wuL=7V
zqtO`{!kEp8m?4X3Mnp8%Su=rho+@}g`kI`3!H>?_Nj`|wr;=Q*<lV2)Tee1c9b@c_
zlA7MFGFIaaLo`NivYceKmC-SpAzF{jS9Ft2L2A??M2x+RKjM8<lwyyT>Z8a)PVmvP
z>BMwUUf1&(t$fe+x$+VdH4)3U)mX_)m{4k;>|IDM`yCi&$<?!W1YAhi=L@mW>e4o`
z4HAPCi}Ot_#-;0n5H30#^}}(x?wCsGE<j39p&p5X$H&1jz9b{#Z?+%f26*O=XT+j!
z-y*nsT=@qSw(4+u{XK||(5`;1a}pc%RZxabOW4N8HDX1L<iqphAC}QM3UHQ4V!2b0
zYRGh%J%7!8%@hCZ?m5*k<cQAtyWx(*O7XVg?$W{6(Bsa<0i%JKaU2>GFB%o)coWJ%
zN5fWO40rG3^i6KGTsz41;=!gT?~&P;!=Nwl^PD4f#e8V7*PKzIm6LvtW)~OLg!4Sy
zReivF6*tZ>{eyXCu}i08uolyZqYT;Nb#1lCKZaz==G6S8=ZLYl`#_>yN0N*be)8xM
zxyM^kA;nG;IE(rqtCV^Ug3j!X+8z3uB)i&7Yc=d9b+K_e;k*xTZ6Xf)V_p?b%MSe}
z8#}E(2^*F(?Rbq)hX^BqBNXeX+Um)F0t=`O|57Rp7IbMocjm-l<Q0{)HCURLt!y61
zspz<gcRy+s{DSXJ%7n{>2;-Ssc$hjG$>~5UtD)mt(Z}jrJd*wj4WVxfMyqWM9>D<?
zMgt*39?ru_;thupR$&sP-gL#q#>Sn1MKL73vSqk4V+~SZkwSg!_U2VyF=I7Z9&0)a
ziz1k`OtVr26krKqX>|RR^@XSNTU<r9i=*TCbu~SNWWsYx-{pwHO&RcxwCdm!P9C}t
zRx+jt7CK)eD~vIkkk+R#F6V#2X5`&x;XGxZUF$eO0|oNq=O?zOKhY-GC-5>+*0Rt=
zjgGloTwRj(`UC$~qQ>ZD{-|c)^IfLY+hXR14z$xk`C`fP^v&xqMV>ixYGO~a>vq+W
zp-=prw91aiKZ%@p#mB?;<FHQL0;~0|ks%Z$xw(O@mwq@yG92(NcFFfuN@~!iwl<cG
zOk^`+TMtC0jwQO=3O01ba!UV^)jM9)-2;Dw;Ebmlo8b+p#GI6>ga`%G@<Yu(C-3U@
z!}%oJun;8C9Dr-43$OqVlWwi7?I3Ur;r!R%e1=m5kYWKo9`C;{PH+<qJVe8pC{`+}
zMt(J6#Fw|7BYA@EsS01btobuLF;nr<!aXzPZ=MTm8r;1pm(uAtTJ?Kj=y!={m+Dz_
zlK1p|n=eTIpGFrBOL|tWI)}U2W|H{no{)>(kNyXlgrL%;;8G#dWC!WLgXH_#kjQGZ
z>~(kL=-E&>o0+I<IynK%Uns?@ck9)^fTb!0b^@|B9rZ6G?#i@b3vYcu`;kab7h}%t
z8dR`%BA&Ahz>E*r*X0wQ)@X^BhujEtEB*W?*iqlqk?Y0oS@g{NRNRWg8I-zYLXg||
z(0aLzX0)T9B*)YcrL2+P9SySpyQr%-ib*sThC|}!q*ckKI9;GQ%l+)&cTQ+lq7gW3
zi2tY_x~NL<vT%;Eu^W8<)^XVxlw4QRBrrmP%2NyKs{Z_x>@>yZK7o0L-zEm1fq`^-
zZV<&PC~^d0!-WN{N!fH1gVN3+Z?tlp=jwx9PICD`bIwbHoVGiH{ezY$D)#pQw*Ti1
zORl_79R)G8oLUaz$<E1?_EHrwAzJhBN#VTRCC&l@#H?$wf~8mH1J%DPw;ry^ekT7m
zpA}pYi>DAot9s8wPc8PZOZo@}R6+w$8hO0Y<p8^IK)49IQwU$IQ`<n|LmhF+4u=2p
zHJ_FJjgLhL2T{=Cb^(5i(T_<$G31HBHo5jWmRxY_`ki)y6n&&3nqE>C%R8l`m2XwA
zEP9#bnAlTpDE)OWNIf>V@+4@Se#NP;%eaxUF|-k+()v1}32%Op{t=WwQh2LRjc$Vt
z-CLvVE$V-JyY(LIZ4002U@*L49AwqIn%g9glj(C!TFC9^2NbluU$mK_F3{sCXpA<Z
z0q=1)WuSUBhoF^%fxT+($dY-E4EO9xRqIP$bMd|$440j-){~oaj+mXER|1%E6Lj%O
zL$>;Ex1;U&g%(vf!gn5gs0nWraY#=CNRR5M7~APur-R|Xc|Q`9HCCm(mi)I~HKd@T
z<j^#Q{6NS9@WAgL$#m*Kxfe<<(7sdG3)b~{2J?d*X5j_)HfvO*E^$ok)Q<jp8B)cK
zwSiiB8w&wEe*ckacrQYFJ3i7=S5iOi_oP*KV<!!vsOj*tV%T5D{~Ha`LuWzggpJ(H
zFo`G!C4Vn`+V8d)Sw7OAe<86EJh)R}?zW@@UksVRLPj7vJ}#4viVx0wCAVnnk;xVK
zJwQ(*{Ef!i4aHLf8SQ#jBPnC6uqnTYl(&NHFd^v$QRR>SEw%$UIH-;DU+sj6&|K{1
z^akr!wG$$Lki8@F-mnK@FBugq@d6cMCw~i%1da~%d1zP2LbqeNW9MgF_xeJ?N=7w?
z-$N_GE=F07V!^Y#hd6TH_bH4FaTBS}OW(GQB7l2wik0d0UvEwfK5nlgF^db{d$mFQ
z*66o(Rn@nNU!%~mkfYUCDuUfVef%W!V=Yaren7!RohidG-rVa;(6Fvh^4s>SxQ$Ls
z+ikb7GaHtSs&s!}>B@E+ng2dGsn(m};$q_b0j-8%5W%v~xIXsoj3xi6wqy0IOYh$G
z1Ezfw^gb)IC*Y6$x|`${ZoG2UYw9t$QxN14c0*>L>Hhl6)K(~gleP0}j)e%Uw8&?&
zym1CBqW`e_Wazxb{wHBu6g-qP5aImG-07`2E@SEh3h+_*+XX&C$Rka!MWyTZbQ`uE
z9F1r7IVttXWRdwrsFlUCxn-I8YQadj&HZT8y=YG5`_o}OOC3zKTRc)v)w<kA{Z6K#
z%FUZThF^kFx*Bh!mqXJcQdr`W2r4DAbqy_0-wZN!Wm-y1IIve)GWsE7yvc^U%H(lG
zMTYyBR|9G2FfbT@#l@O_#@IpylS?ItHjy76G4vm6V;>~q?eLhVM2T(W$tPteg858L
zZye=#p8DPF=!c3!hmmdaur;W{^aROA#%PHqmWp|`d8+pZ{jOQb@y`%<@L%oid??If
zHe%aq?J{mta9>dbII^*OvVT&N`1_+OQ%=QSPdyAxd{(r@L|$9na9}7Yi|V|4{0=U+
zF`1wMuGExl%Hfm0qko3HJ}iruB1#>C1}oXc<Uxh3rutOZML1LV!K`09zXsa>B(Vs*
zK$M-eqEv%YKivU-f%>eBt8Z}M0zP9384o0QgCF(8J>zC#LI$O<2>5Zu(aol}|G_{b
zXV8g)t9c(@BB{+yk_F*teq{HTmA9)M#zBdrmqF0XaZ0WGoK(>@Q9(P9+uU9;6k1+L
zTO3J0Kj&wkDLPi4Z>5r`#-%G98pf$<cjuFt;UPnuA4IPKtzJdhf&p1tE+$iKR(cN1
z1-yn?=LlDMn8#wC;-y>a;u8|F*+zfHYJHQhG_YsTp`K}VR=Q?T)$7hynNFj)&<ys@
zZrR%oFP0KF_@;Z~op9d$&k`ozmF#0QRzax-$9yqvuUL2V0d`2T4IRw^FxGo|MzF>9
zzF2JiSGCu%{+W&5<!n$9@;HqgdWk7LneTiSo>xXg(Va~~#Wy*$Shr|1-uX<bnGS!6
z)Ndq|)~{hPIp{T&w=|V5J*lGf`(j)=u@U3$?_~Vb2QTQ%I-#wuzT*GW>JDk9goV88
zw{yqG0A-XBlGIZ@h&+qp^_c`=SI*ha3$OQIhy`AD1fp;zDDX-ExSQ^99-0&P=;(Vt
z{VOE^J_XL-@I+o`LjaG(wGsKGl~&ks0c)0}d}FTOZrRb+cvfkUuAXRsoFu;Wn;J@#
zIs3C2{D<93hf}1rNtdwq?fW2%&>>9A!^f8V3WXo=+T-`OBn#_#i{Za+nEFeIEO1lj
zMl{1MJ)##+QfU9~&VM>aLBC=v3R9umSXB3&_;)??W?BP)uv}_RU%i_yWG`2Gqh`VV
zpm0F*A{3F$0*lk=y_KcTH-#_j?Q!{;67mTt6ZDAjl>YcNzmqGJsB3Dxpp#!RYZJ%n
zBgR{_jFvj(OGi0jcs_Npyqa3t+FoX~`FMDZ97Gy_#MTn*8YZ;46{G7%3KC&7tdyH3
z;VOWzQ-Wd6rTh!ml8D>9kLn=$^J|a+wqF#A6920m(odir?c<x(W3r~>QN9@E7*)2}
z8UAN0IsFf?D9$LMvz1ZnaBq5<+Mmr;;k>uIu<6*0zD~YFHLF*#_;dW@{tsPW0afL?
zcDn_nq?Imdq`SM3mhO^}?hfe=K|&-I5b2giVu8}qol?@>aNo6$diH<ZcMM>B80&lU
ziTT8Qn9Acw-U9lEauJi0lRu__IT0Xex>3fTdC;H3>TrhM26B`gD>b&C(=v)Ew+VSn
zOlS${C4NAsd*_|aO9@Zo8}vB(-dNr~lW=JE!+Hl?&ua!joyE`}i<TV0o$nyhKKQPc
z_cPV0_UQ%T7aEY|`thdmZ9*XLg?Qi@{Q}@y1+03OC~WS(#!bamok6FxP$njd@?!1A
zSy%|siu=KKo=hBM2tjhKh097C;t4YBzpGkhvPCkuU8S>?HMpxXTco-^E_l_1yr`+2
z+f#PxyjF9B;Q=TomX^Mq+?D1c{t(!c^?LgAre{b3dm-j)5is!GX+!BKj|F=LY`Fa{
z2zAmRVGgL>7%DGUzTIIj4jF~&Lzh^DGXRjjEM4ujYR<(G11X@FB4TPvAt0!<KED%5
zNgL<}QD04mN<-*B_6G{<#KNn`#LV@+f!1+4<W=?uzS})WywLh$SMTlYEms*QmWLhi
zNz8FxR37$pX38M(>ElTxvVwdnpg@0lKj7rT!|3ZH<{|cQ^Zj<a^T3tRdD;`{W>|W~
ztYLpK9+~-}v9IC4j}k(e(bA@whV*Ehb1~MwN?r4@27F7l1>7znUYQ<983do_Mg?q=
z(mGx|Bo`zYeQog3(^T{C(&2u7`eY2i!3lFpOUfV=_5$HvDwMLljv8GevreleI`ZXZ
zm@roK81&Qwa_HT#LCbGc74EeI$?|dzYJc!Mp~=7~DrFZTgFs8hZOPZ)BmVoZF%KDA
zZMzV9?@Fc`-qJel&(1NoO}w2c=j^bt5Q)t3*L`bR*<;_h227hw#4@nxd_f>nZy<Ys
zRrYf|l8ZXoF`zx~v@UBlcX3=^UIt{)I?ta!$MYR{3#Rp-uToj)u8#X2xMwY25nH=f
z_^p5Uj?eroyzCM%H6Ww!{GjCvW81JVjXd%@{J9_p-!fX1Xhh42x4_i>+ad#nBkX`!
zSnvwH4-<?aJy}E(TMRJlL<3GBwEq5n+wo9xOG2WiUv?LkB{)Zx^mtw^dgl;A5NVdf
zP4rLmco=%ZuZgS6VUjL#^fmDdLs7?W0>SH1&i0Oe)F*918>DM-Aj-Cq;c@mTATiJ|
z@q9tm0mNXAz{i#o2LSG%V$g@e4R(kP*sCcN4&B7=pXcV9d;8@S3z`8G1&ng`-$r=@
zCh)xodul^zgEQAurY55gFR9Nx8pztai+YjF*o4QJwS`wEiVfp6<HLST(|Oc+Kc*(f
zdEzMOa63hTG0b?(5zln^L(3HwWc{?4b<?Zp8N?+r(k!j*hB`cXEbZR$IvTO_TvcM1
zytn_wFR9#trQA8Rv0-T!=02(Z&h+O7fV?`|;Au-@p}Sr7;)Mz++%0#77Tgy|n+=nd
zDX*G(ycnhp!wuuqtEZfHS9b!wfB8#~LZZ@GB|wom(r*5SLc{Ko0AdzG{q3oWi$j+4
ze%LF`$FwYy&-pDpm8pVv*4@9atOw;`h-Oox<Kua`InnMugyg|A3&_{5!2a~M!3qvN
zH3H3NH%307I|o|b|9bm#a6=I28T|&+XjPFrosT0>f5PG?s5VYE1x)<wn^}otM5h5U
zWqR(C&yg{`eCZ#CAG2`~fbkQy1(5YHe&CnGJkE6GrKK&cnLJBH^zS1a3o<EybWX<q
z!dL%u_y6<rGY)hm0t?Hvbq%AW7d#MMQIj7?b?Naou0OtBjr`a}!aLhcKo&UPD1>kk
z!ijUWwz3tm-kr;rfmaBk?k{<&@T@RIC)VF(e>pr+UsGUJBWwGm!GG?-e*&h!odMRR
z_@UecbDR6y)^8l`#e{p(&*Xtoe7a#115n{r8<UOLo0)?><^_#eO<%=FTfAGLyoxC}
zi{BVMCtv9ndbK<a6BQIYIOmP%$(tLv59yA7^NIAL`{>({7P>t%X{U5I1w{poNxSLJ
z8Uh0O&&c0@*BdPPh8_@jwqIH+0oy2p7fBvAL?ebRcaUY)W2#@{q{jV2il0zq&6WK6
z*X=J^`yj&Y?HJk-6;#Q0>AWWx^PVQwg@dYU(7yFj@mO)wv^Iu9P{3g1<;ULBw0@-N
zSFLOo8@efHQq>vAw5vp4y9%>f+A?S7sP(rphH-H-yemdl7dd&!gnnPje?G84MS?8H
zr2s7xb2~k0bFmHz@u)ST9R_&;L-oo=*esjxO^WvrLv5EeF9}{o;1a8-j{IcP-mUo6
z{w2*3XI$c|(Fg8&{Hj&N-uS6!y(-01*3CmD=e+sul)Jq2iuKh~o;Rm8=RPMNaAsOf
zj_-`Od2zL8Gf{sk=6{|FAW-6fc;zpCqr*D-MnXAa&sT-`wkkuKGjSVp2K7i%g;-iB
zx%dXgy0&z9`(5K({>)bI%*p(nlBr}tl<U<Q`A5l?z3%ikukTmR<7XMGri?{qN3#D7
zJpB%1{P%c>Oh6Mx6}NOIl*35fZ(i2uYQ8TX_Q-@(2fIWQbCLLVy6JP*$4$z{Y7?SA
zK9UJBw-;=lJ-E0#32KBLycvsIC!=RpJ<`hy@RrOR`abzwAFybI%Of~IELxYJ_saHn
z)YUgG^%h;tY~M74LjV1?!?WU3=fmJF$N85>CA)*MKd;^|@%*1(W1+5k#zg+ddZe&e
zVljg@Q8hJnJc-tVeM_hX%E!<(Fu%b*`*${4p(w8D1E#Hza}iST^7b#zcYeqh1V=%1
ze!Oy-L)2t1Zx2TFBWKbE4fRGy4R&|acB;7_``2b_ofn+H%gMc=h$;($2|U=aRch}Y
z>0K7G`Q@7bpOGyRf!oU0&Bt4vqUnD@_`~Y0%fZ2_oEJIgB#`2@ENpv0^*jz7nz$oC
z9!xxmJm!&;*ccboE#D||=Pc`gEob++{wB=2Rvh;KeC)4j$3krl(~>Y%FXxU(U)rLA
z3CNjy>2{2aVR!%Ai*G&Xl953%4SgqX_zgu~8op$zwXyyFGcj$=m|V>FU>73DIm2ph
z)V4g)uMy5Y-aRR3LI2#$2CYf@m*ev5UC;qIPy-X+f^CU736A7)6T^*|L3L`YZ%wEr
zQ_II}ft{3Rh`9M3nko76{-Pm?TlSkb?yGrOaV9%LJL)UU+t?A_gS84{=V1m}K|NMG
z^;S1NHwVQSA2XgPm0gUjsQ#ae|LdlKOGdkZkcPy-uN4>~OgwJ0z73!UUIvvaVqF4D
z8>`M0d!gvaA5ID#Ji02Gfobrw4K~aYbCgBs+OXD3tIe1zW&WM^y<c%=1>Hl(T59&r
zmz&F2lq|hzn|>j}|9meE4LI0eafg823~|X150tIpj7gjIbQFVU6T9Y^DwkeY7xoP@
zTVSs6qP1ZBa`a_QU|T@h>ZAz)Ip+$A5NVDrZnLj`q~x^3w>!nW8CeN^@br_X!oMx%
zVkh)$94zxDY>}GdEUyqtm$*G)WI_Z;q1N0&Cb5~?Mr$%dH63YQN(y(rp)U>aOQuH5
z^t%c{Onq+4$)TxEqo#SoLAXhP4?Dnsr0_~-dg&k}F1t?t>hvbwPOQB7=7n6C8UH`$
z_xqU!iXh42^n9Cpjp;eK{iQoj8&aqrgL0)$`QWfNLneRvBO0F;MJQhtV&P)B6iQ7P
zBHfL*{S^OXi+j;>;n;c=8~k!Iy?srCFn_{-G{*n^$pbwrvGq)fn@OA|1?<2HTb5xx
z9<dkhN}dy5&OINT#U#Os*rJ&)c!E`3T7Tb=gtqN;MV;rFMpRc9&a3WW;)5JNyhqsK
zp!#BwrqtFK{BKV(uREXeQJVqfHxG}uzbnsj)6VC=%I@*0I%GTe(S7t<u4wR6RQTNd
z6W`+huaJO>ZPU`I5icspkmMUlt=c^}a4+QtI_5f1*AbU?2kuok<jW|VKBQx!Z~sxU
zVC;U%^jbZ0AZL@zNuIhp-{|||Hy-*Mo4Q6_nrA&k#dp)*L<`8Js3-khdMvX?SeP{{
zZ0)QyN*qnaM7_C=6zH+)+>7sv$*;Ydupp}^JWsA&oZZDe%F*FFT{^xWBffnk#kL%|
zl<c$DT(`md9h-=4EVtvzQDu&GJs&}vwMG1y%|Gwpujc`2Fpr~>H>w`9lf!{#<R%Wz
zqEv@ZtyC6<P|(Tzq!2E$Tf+^S_!wb~h4jnt6oA7}XjOy~;?^G6v9wfb+t~U@loVXz
z)9=~_c|z~KF*1oWpX0Ksd9pvntdzapd(tj4_ng*}hPeKs-s5KEf?TIov<4wF(;(Hc
zUq9`gn>u<A+P?}VH&9bDYFEIEfmA?Y1nh+9A$*epHaCkb=h){fSy|16Pf<VyY;}?O
zV~PsU3v2M;U-g-d1ejjT=i%ZLxI!zs1LrkGB0ev~yd)}k@f=ZOnOM25Df}ReMO>q1
zN17kJdnxa=swOV!HrAtT56{ql>`JhY52`;2ZK*%cDzM(DYj6IPf^L`L!Y`?Ovp<6M
z%YFFAiRGaL$CXP5r=HWtUz<2qEn+s}U96(~OYY0?cU(dw<Es(Xuim1WX{>=(=oh-z
zbz2A=5n>WuXxg4P*Y|LZw#yw6(NI{&bLD1>Fh0WB=`|qqD==ydO0OCFo}v3TJV%C+
z%N`w{Fj^Tv)%xU%`n)IbR65(AJDNlQo&r8LxV6mxs{3Ch64r(ZkP|<evg@&3J(M0H
zUtSpD>qz)@kB7-;OoE(9?&C<YRu*LDlBslB-dIhcw6I94UMJ%o#BYt%3lzvl64hKI
zW(KXz>pU&9>e-$`4=<&RrS7!)-O{H-oaNYKad8*FL{N}x+7kOT`C>9hl0$;KEeDo<
z^yzKycpDp&wYRrRlp5K3I`8aocT@afqiyg(M+d4}$+wR#GA_=^swT;!ZW6@yzn4pR
zMAFTa`T>9%+dy9%C8L(_0kBAW)JlY;LoSvKSJ?l)quICt?(?1qprV^^Z7?eilR|34
zEZOa+zxz)c=d%eg1sSEx8h{U&HSGtdTj@V&@uI<OhDH!|tT|C1q?691WO-d7LE}_d
zaAHC22aAm89n#-Dx1V+=Mfs0aGa48#F$JG8m+qp+DHeY(J#VkFei$#d0Z6$_UOIp-
zuS<YwP~61iowfMV_MfM68yasztGlb%Vc{xzB${NLhj%%9@s){Qt>ki(km1TNBCw<I
zNPVRg@<sH0$&an@jNEcR2u-7l^G;&XWyeBd{R>yvr{WrzmW;tqVg3C4QrRug%m*`3
z{r&TiE0F2<p@EJ6`&B?LLZ*gY%1&s{T-;?^R5j?6y*vO=!_@dm*9Uj0V&7<y$puf>
z7GiKJS;x93Z^{=P1O!eFm=gC6avzBa&zaFyn%T5i?cJQE-D4tlEl$RrnF$r~CR4+w
zA|t!!ygmQ{)M&8WQcUz(zXPPdRRpx8204cyXa4Bs-g8pHD)JhFDu6M0NPl;9;lzAQ
z&-}2=Pe2KkF45Lv&O<oCBtc%ty%vGHWHv6LlCR$m!S$#sEai0ECJbIig#MnYSb>S=
z8fwyoI07m`?(FjKe4u~qu?v6@)L;jI7MIs=&<qM0(a*xq+I33$u4%6w!aU*YNDvsR
zIT4)|jcE}#YSdQIAVlFbVAoSwyr0ZFh;WA3VY*J4YTa%@X@m<yjFdBsbYT|XM{zl-
zszgRd*TdxJi1FXa{W35nfFe}u(D6^y44vR29yVHXnb?AdtSI4Qez33mTtsEv`Z=wn
z$mPvu@KTD&a(J&xMIJ=P)i#OtTFrPepSft-+tW+iY*$2*cvq)lp|GWdznO&$R#5y}
zXJOeIYvU12GGb1osi>%^2jHl50KMPZXJ&Nk`~S`7vV`t7Y)gnYa=FcfBPx!fJ8>pS
zECRf{uy4nG14i9exFRn9_r3Bov2{cbLzPA$$vPCP>SkJ>Y#H5l7h)3*M^w^Dc1bo!
zGF~1@=KhEm5?|U}`q*<^9D@+=*?C$0;0bGhhnY)ZA?p;Vrj!(k!6-bBfAuN=(4!bH
zcMe=kX<RS<xh>h&7yt(_CSI7-e~I0Af+-f37qYY`u4nz=Xbouc4(vT^t$-2zJ0FM|
z%Jz$e*+51b-Ex;Xtm1_h_<qLz4;ie7bUZ9@69xkJZ=PKddRkp4%Qrg=rV**BbK=<>
zt(CU6;u;VnMgmKmy@Y7%6$2I(x@K?EmR6AwzkM;L7`8oA{dcUU<6*fC(390E>c6=*
z|J_VwXaE+pkl^=8(~p*%iA`_GSsJ6ddB@UhSPcJQVB*9>_Fksfm7_k9zt6O4yzaLh
zR~gLX_hrJJYjbZINS;ZB->&xPeiOER3Gpq)6wi=^v&C#*=TouV-U`b5LSHJXpzuB*
z>R6a{mh_>j7%LqY;%+ge-N6;)6`>WV(Q6VA6hzfsT}{4-kH?VCr1Sk#PC6(9>BY&=
zmpM$(cR?)&J==F&Z20e&1C<sWUTK#3aG+0xv2*s<mW5X~*$%rXaE-cfg=f=)^0=Z^
z0D>>*upTdwqP@$m(_l3{Bm#NMG+{KnK9m~`?^ivp)=g>43b=W3FCO!JK~Noxb^V%!
zT4j{JpQAyh_Qpv<A~+{CfuZpG(o)Xs+MhcN05RatRf|jH6LLg_*KVckRatF0Sr-}1
zlbr9P`!23;&R^EbHO(hp5#liTDU9e@D8309-F=Z}Q?S`&yQk6Ax=qZfKV5wS(n}tG
z5=QkmyyH6KZ<sXDin-es4oDBA<KV7Ix7sC}=YFCRkUC3`B~AfL<Y#>WGMbuhO-c5z
z6wg(USWd*?g9mt+k#K3)kcS>oSk#rJU=;S96ju$jgEr2rR-?45ID`LUau_88*SE@p
z&+q&{<%SE6;_Qsn$1&~Ex1}|V25n?VVoW->B(*zJ#k0>`N&Lp|*STFkZ;bFCURYo7
zCb*!f&XcIy4y0$fZ9*zsE96Ml?THI-${7<wLB$}qJQK11FeOw9u#+`;0G2E@Su}?z
zhjfC*j4_~CjQq(H*?P#oU1mHe97>DRYd_BYX>qOWOqUq@2dJO@M13lj8rKlHob0`E
z&Zw)SvmZd4maBmxX#W{e<)vjM#=W_3qfS`#M?K}u<;b3SHI^7#%k&gf9oMY#YD%rj
zxx_;h6VC@;*??@psI(ln2Z-&7BU!BHrBkVicsy#_Vf-4~pLnpUZ>n?gE-&+%;}<3F
zXHO5dC6(Dy1wYMiX=zCn^yUJ+cY|KO{7bW|ul`T>K9y|$?ytZ8y1QHC`-}b;DaUpO
zg3lxMiLR_;DXfI}zg`3rc-g;uOe~4{;_1Uq7`i}mEoBiA6`RMWk%5mM$f&@*MjQ6t
z1A$7!ToD<i^R?2wL#lFFa|?5K3-_$8L3g*cOlQ#SyRl`;_Y^1P?X!DPbTAS4ym&Qh
zkA_E_`yy91S3}QcG{wh6?5nPczSU)C|0P_O9#(>In*_K}Vy53|b_>zgZ3fnNCzfw(
zVX?6vMV_3Tok<L)8E>dl;W05WL8g;5Dol<H&#gyD^WJ<dBt1Kp2)>_`Vzz}SmFV7S
z+Wqmlx_*6;7oveZ)<-({3|ny{uqqya(vNZ4<N0$+C|G;EHK8=-zmB7j(DtV}0Lsxr
z#0t2!`?1BJ1g5R|k!`|^+uR{E#%)XZiTYtuaC5tJd>lg|)^!mJiI4n3Ul-x1yW&90
zR1>Z*%k4ga*j5xn{wRu4nBfA=J9dZeBhr+sl7PS?RBMg|Tl>M#CsUqyP2XAUo|@+8
z*EQ4|xt;ML9=oVM3~k|hY=lZSI#~kv+?NuSghUgm6{6!jeg%d9D-<8!Q$`%ySBPhb
z5ELeEjCQ3dC1?Kh`T29Q@3TPi2AOs!e;XXhMJ0u)_pHZwXW3J8XO01n5c;@Dk4Iou
z5XIwlc(zojRp*es-yIf$(k^%CGgsg3HZn_%#`;3M)8(d1F*>1n55+{6?&^rP=B0qN
zP7{nR%<_B;Ixi}7KXiOHIKYG}8iI^nJfTtxGIxJ@E<Yc#b)ohg3&&>tg_f@nYFHwB
z{CVr@Az2Ga0r7TJtV#gw)}~#6Mh5-&FmGm~n{V`y{Bu>tk|?i|d*15$>2N*ZN3-Re
z=+C_=pYKrl@Kit1HTHBrDhNr-Z@-dy&a2$O6QxMpSngq}28xL-ST9b0e}8CLkU}~(
zI3*?JfVJ#@M<fEHC<FA!L$rowckR9=;>u$d<ijN9DjeO1eclCJO|yFa?G<k8Rt!2-
zpBb{2qZhn(_2H7A=<3yo3*H$G-nZe&*Q;YfdGA}I9BqYRaE@s>a}Sy&CdC07d2|5k
zsQtwtbh>$h4!kJMe{R!%p0A*onoxKD$EtBFw<S{rihY?HC-aJ?*XiL;78KZeyI(tc
zSi%_)vJI#ui35H-5f}}UVRI?ViH6k!8~p*qnor!3B_YZbWdj-frXZuLK)<o}0Q27_
zDF!B)h*C}A>inh0v@RhbuR(6C9hRk8!K`Y<r%HL6=J<7kuUN(lf8~4Ud*^CCmt|bu
z!7Y<~@WkYG(gjoYdW8HNl8D%Nv$L}-fP5RUOkx82hKssm?ay0M427s_WU7c25W~e2
zyqI{hFOMBxMSqIp_e)fK%>607#esly@aD7LC*9MwfK`^=kK7>5N5C6m(BA{WaI|VV
zoI^pm*3;A9H$9t1Jar;*`!vtvb}ObNR)A?HBZD~awZ$lKb3EeUzV6-gCFGBFbx;GR
zjM()#akb$`UQdthGx4-z#c(S*(vM}d<KwVzS?w>B%&#H^MWs&(Lxvt7p<A&vvS#qg
zPvV3LT0B!T8>_ZrnYY2o`&&YY#EBKWk^q#1ET&y3)z%XO=iz_6@bNta=tkZ{7<<*|
zLcvVxi%T?`8)dg!sx{k~xvaGond5~eYX4M~U8yVUb48W31CSe)&Xr3$n=#?#GM!zc
z5@TPi(nx8Y*%%+ZPbO9E)8+9S=EO;YZBmB}1s&B`Pwayn+YK`8qSbQebwvifx*@Bb
zCoc*BTgW-!tyQV7ywEkLeGw=0=jk4Ys-%XdsWf;v5^X2MH*Y)(DMH|{jLg>w3YUp^
z>PdUKm>`m}@(iVQpYNlZ626~lo>M8cv2--o@MMA;n(KBm9ve@pbGY!Su$4-Rlj=^^
zA1arkP`Ll?{)bYN@z~`VMlQ0C%MoiT8!A+Fln)y}M#aTRySP-(eczIg@bdID1|*#x
zJ2ex^`SSP{KlrYG#Y}$>6-^18mJala71#Z-ehWt)CI#2--H$40Wn8h!#M0qW(gQ?Y
znWl}HDIRDO_Ig1@QS|MBy%f|<Y&x8EGixvf8@XSc52hEuC8eA4-JLxY5*WH9dmEJ#
zOBslK{zwea|Jj=3k&wiK!XxyXtFy48qN4soqmJM2|NSrxjeQZzNmXJ6WXNu&R$spU
zF)oyptliD?na!H_aWG<o<&IeKOubI5Cnlj>c#eOjLff`4u3W~{*2-1@87kfK?bBG-
zO74&$a(mXfc@C>lM9>(K-D*^s+ga}u@pF^kZ~FPL_#BD`PQBz8a}OyvJ1%@!=fbJ#
z=x!wezxdue-GcP9e{SBszl~OLs^%b??@+C*H{>>-=m^<vdC>XE`IS5y6SW1oaM@?0
zwcVx2NKv7>GAEXXr!WPLE(J;Nv5|8wNxqervoswI@OuDerlqyD{JrOIi2v8$jDHDj
zv0(Q8T}~z<ky3xBwY4G{H_gI!wNIXQ=*YCDqy#>5mG90sGJ2W^4_kbQwWoSQR@ho@
zQ;+)|f%u;moM-^0o#Eh(65>fpu(Bp@_e0?aL5&VeFh3^B49{P6v<?gmfQoZ&mpLVL
zbo3_Bej5*zCBVSKx@OQP{%JI`WC6?W=*`qD1r0aC47x33Ni__^{|OqtHok+qyR=8W
zL&uASGY+eu%G=nQ*OQNq-#>Zd;alQqQh*^UoFFkILxU-L{}U&_hOBo`p>WC7H>Mj$
z_n`6o(7-{NY&95mi-As{K(z}qN>E5V7VpWw<%;QDwB`KECgp!aZ^a_uU}8VE9+PP6
zhuN&&Yt8Gh(^!!*_o^_S)0-R0bz>Kpt)=Od7m{WYL&>PyjOG|&_%vz<f8T2<V>~J2
zDIB(P{@~4D7EI|(NlGu^D_Cn-u3x>%dsC4KCyzB$gh4<^7_~X_VFk4H<bK0V`X{2w
zg9dzYH@0LkxEoK1fQ{gO*>*JjQ+5gIW8ZH}5ao)A?>*;g3<Al)lrPG@wo4Y5qcN{X
zwbZmb*hBtOP!eXIjWsaDee`H?HBOq0n_1d5vSr>cmG4k|%n|Q1FVcnrBDUNDjbeKE
zQb(9LLxjbDnp^zxk)aC8!3Ug-9i!-^@a!%--Y7h)?Y)>LA8<&-H2m)M)HUXKY1?}=
z))N$E?54fR2+*{M9xq-Xjk_Q!Egk4pWsg_HlVKJsh&tZ-GL$2J0D63pad3<xNeAlx
z0mH9D#S~hX!463rC*4@*QCy1=f}mNX&ciPQ{aw8yhNBGI(J8GC4cawnZO(@xUKnDa
zmwFIByLq;8N2sC@!M4!<9%~*Nuy}l=XR}Ru@|Ru$rE@Z>Nt>ynrkMI<I_fBVGc*AQ
zhS8EbUWGrtcLdTK&8pfL#&-NQAp1F4%rft3(mv=M`VR06K03@e+UfppUC*zWbpSY}
z8LgPL#Iu3*{{0#mW-Dn52j}9Kf)(1X2>97rFzhKlQO(_MxVp@r-g(Y7vXpN<u3xLB
z2q1^EAqxhXghtzw6?p9CD4o_gf7%7>pb}DTeMNXH?#-m@^ROQb5K->E*z=cFIckY&
zI3n9eUf=tS)1&zVqGWM%JEEMHR#t<)9I6zk=b@R<b<sXx=-85&b@aJBa=}lgdfRk7
zS%lMH^?h91dJnFcG5B<EffQ6MN~)>7Zhmk2E2RAoQ;h&B2{C2lLOib;W$V(@3uMn_
z2jxYTMarow@FYm?Ia*zf2feMZnh~x`aH`k(Q21)>+ePV>a*@-HiS28R*(T42q&LST
z%UFX?Py?(~I9VzeXd$;Ql=rNWaum`yShr!l0MW<8-$^L{0KgsrXr>ww?Pf0e>Y8)x
zDu}@Q<VU<?IARRT{GpXGj2K)-qYsxG(KK~qo&hmQTeHIS*V+_oycE2!LBCr(ZQeId
z`gT(hQ=Jr)n6LLTbY;%P?%k7nkh9vG(hcYUqS}HGI>yG-MMGWw<+Qti)9xzNp4jr{
zWzk33wCB9Ho3e<g$Rm_EL9gXSA`q>jEuXL+r*lvoA7xpjQiUR89$&ODld~}lr;?OW
zW{1Db3swdqE}DQ7*}mxj9tH;H(W(^9zw9ok4FkJ-J@>Ax;;z$ROxw%LW9bGr-!cES
zRl*?*gTWR5`1ZMUW(87FH>nqLj5Qal%KP0{7~-w^=Xu52T>)l2CR%$-k3`4FKAhbl
zYKbB4jF%7!U2agQl^JBD|7nmx1>YkvImzd`K_`vlCQCmI)oE0UoQMTx);)nR$aaAN
znzbXiY+l6A&tS6)q7{5!^YfNcY*<m$shkf)RKcYF_Qy_9{A;JpSd^YHHHoV=;-P8{
zXCVG(RR$3#vx8rPTLXBvSwQ<Xz{4RaOLzP)>-+5Q_1)Lz)lS`qY?(7!aE;}yli)|B
zvDV0|AH3;;OSaXEOwvs*ioH}Dv`}u<fgN*viX87Hcz>NaOIpzmtx2F6rAinguZ)8=
zl=c!w%o4ETvA!6l6adW87;?+n|ArtFftU8@m{H6Kk27}(&uGxu35rcG93-`sbwD7J
zjwpORG%TxEUxDfsZ4qZp?%%5I{9GCo_0cu6dwKLKaiY>;--^2Q`KnCSbA1f;t_LUL
zJ}PY!Bc39Yo=RhgIm`R+fdn!>{Qi|zvot*;(dhDjwY&&SEx`CjHNN*-YW9s^xqZ{*
zaXSsiH@JHPe}3QO`JD>l13LR!X5v1Y_>9CDO(Bwyx5L6#QCjbAyAmIchF0j_H3h!a
za$~yxAl5+2N~iX#pW4TbxPY4WA&${Fu~u2JOV0u|L_p-t2zVR<E>E_$KCt%%|F1HW
z`OE~(SV=TiB@YEm-?xj@AM_!eW!*1^%d?d5oA{Su6-=gd<G*hUMC*7XfBmRk`OF^U
zVX!RYyTvZ;!AcJhF4rUDpr-C}z<36D+G(9;Og>Li{~>fALp2sh!E*fDh1-k!Gw61r
zZT*2*>}6Og*f)(rU2n3fo(;D=+pgzb>Yd<3VhG;;6sDw(T?AbHCld*;-eyIyC6w!Q
zO&F>RSo1u<Jce-6yn5EhFtqGcK&pz9s4Jn0!iW|hlhunY=IiTwaJf~sl9yr-7#9~8
zQ)Pbi=gP-IBTB@v`53k1dpcp_AJB28qMi{ssd82lOmn;lqr+6+F1I+%s>CQTyf~-Y
z+$S56tMA`5t||C|H?>FQtcOxd9u`D{F0=QlhUcvVeEsk^a@VtQVMN!$aq%m2Vhn;u
z=03%5t-b7oGId?sZ7@eC1Ei9IP)rIAGBs~7;wggBh<KpI;gSH3^TV>B{&R$A;$Vas
z=w`K)b!HY8ZF6(5E%PmI^Mu_pcU6Ywi|6(43;9PrO5(aSo*7pyrz`M8@%EZ8IXRg`
z_%S(%j_~z=`_%iFhs+*v^Xt7hz4m2as&xaMQdK^6ZlYYZG~%rixok?ZYV9iXP+yTf
zz<{<z4H^IQnxsbpb@qY4&TX6k?v!{TY7f`Hr0@yui?UuMaT*Fr<y)jFV1z8ED8(lw
zK55)ZOR`?sBpKWgL+MXSJ~%V<g45RatUTg2(_*K?Ae|*PEsPulRqU@ZbaKS*cc2V)
zlX#;nKlk_-MTb=mjlm`7_D(9wifs2@oSr2*P_0%j-*N*OC8$x5Y{n4CU6fK>R+s(|
z4*3O-@zmtPt=UXh%*D-o;Gfk{DQh8~93OLR-=jN)IXRoSYpIz|Fyi=A=;}gKo9GX1
zCUTI{qlE*Q{jX5!J?~nDKg^;b+=p8Uxn(Bfc`T7E=O-#e?SGW-LOZOkeVQLlVtIqn
zFzed-k*-Ma>bXD6EpM@&08{$LWNp&7LNSpgX&+lt7%8%2U@mlpk@|%LwzFv-3NEET
zppQixsIfm2{&vdHG4QPIJBnWKKv+uS11mT$iiT+1&SBf+1f=wE{#a&QpU|eJ*#y)F
zW5}2;&s$Z@86y@vpCg#xu~sQRIx>tL`A#Fq4#}7MZn)rthA@zqmeqt5*PSM{_{!?=
z6E+KGU>pZ^xp_Z9_|z~lzuPDeTgRV4rudfvKrXI2I?)vY>2l65gxyNq%)q!e(C~iG
zV19Q)O%U#;FzkoZg=1U)TOM~oLW_|qGJ`NL?|0_F<ZICOM;9Xt9nd>uw0oYv&~|W&
zVBChiZ<DoT9+6GnB`w?>=8R+kkyAGlnE>5XC)~ouqLYG$f(VduV5!KCRgiNE=h0pM
zFap7*Nkaq4-=x&c{0HQgmx{KOR;EveQ(PMB8%;cVX<Z^2iYnD@XPGV5j0P&`CtdjP
z>v=CE10#`&D_#Y76q~1xDLonl7Go`}Zn@h!fu4c_T|!D-1X5WvT0j4LgPKcoQalQi
z*24qZ+1GG}HF=WF8h8$OrdA!ar&%p~<l{v3iE#-pnHLCyxUgZ9>W0Jq5ThV+^;r~1
zQrDpX^<hS`z*qxkWPJR?6sHFPj-7K!HcX+nwEYG*zW(HPPph=$=mE&GU;4W06RLT5
ze%^;=lVQp4GTvdAURS_uz!EZO$;;ET@tw`%@!{E;Gui6@7+$K3WD4JG9vhWkOTY5$
zU~0`dtuuY>Jz8M%EN2Ad@t+a|{7I}R5F)10?|&Fj&VFf&DpE?L3M3%JHrbGo)!Mdv
z|0ZXFv_2Seg%z{;r9mp?tV7zia8d##DJfh2{Rpn&(n>vPPc_q2u}Qr{w6BTJS1Fw0
z{x9^OKj($*rS@CfC9-1oERNI)gm>%NRnz<t4F;PAO`!QEtz~{UKk0>bHAp91Ahk`%
zSEkEYwAaC_iY1Es=1CyID@0b8G|Et~DI@a&R)m^~V0zqx-M&wwp{(bc5e1RB2+`jt
zlZPK?5zBn;5&xkW>;GOKyP9FIjN|_0znouCrAiAV2O3}bnu}Z?Jn`u|=8G&@=ce6i
z;!y@H-@?#YZQ~r8gW+el>&e9T)xOV_$--S~ep(oT96dNZsH~PQ;H4SC<t94gvWqhy
zzIvo-o3#FQ%G1Mb)lrf?Q8G}S?s8>?D`|;5AGAvOhkg3rXAv@RgC4XwjHl@IwLjbz
z3v4<tP0=bc_OY1owBdDBSNHbwBS}3HVXSMME^|2T-!oiZ{CZQ!gSc+tB^)=c{-YrF
zJ=e>GW-H0JNw=To0t5&5)q7+A#To}23;|U?Is$|rK6?7?$fh^Ci2M3eF&=!HKPj%<
zGk5d5M{;rV82M0L9^u9?PWn@X=#0aapz-zSg=#9R(wtBZcIR!jc-N*mbzZ#*t)%kF
z-31%W|Eg{<F&;j!qOn5lY?o&RE+R3!G?0`>hwrbfvZb`M6685P&bXhzcilKYOVrA+
z3NwCw6PeaosUDAiB-H-#sK*oK$0=OexRrU)u|lmkBMqr=mVA4Usb+j)hW#)#?a%{X
ztB|GJjmM3dW7d_<KL46Jm;D3`fX)#L0|o#A+26;G$f^$0q@t=rzPt3Jkgf7$_KJZw
znBj>(W6zboRklX_YRHh}heTFq?<Sll@6FX73)S&UC2G|oCuBMjXI)>jV&!!t_A#E-
z{D>?)b77z8w>mXB-K*{0<YBmRccWs*EginiQZZ{%X70pA&GoM*Ef@<8ME#fJ_EH~O
zlxlR2oU0l#ZXn+=7{*L-V6{e!sW+!LRQZ$z9ui6}hGlbXKw{{LqN!TRA?v)>QG@Yg
z=c8<KLg2OA4?6Qb9>au)?FOr*d#Oys<)(X*5qO2}n4|OE!54qVW58}~gaMt*-Yu4H
zCFnKxMDG;^(Nk~pQ!8c6xCs>YD(tN^rFqOa;tvVx+#tpjyU?J!UY;?A`|RML%el}Q
z=U$r3yOYi}6|hCF7-XSLbLDXSXm%kyDlK(bpbrg``&?Y(j|Kg7q^y5^dHhRpsJ`rS
z06q@X2unz|4Naj8<r@8jcWJ-ODX;I|RV?&|%}!kq&h)KBTH)~ZR9-ZZK+SPDwIc#o
z%;)!GJuk52m_>(&RVzFfsG*22k}U44lq>|{EVl4cj2OPLA2}*ErgilVn^gLs``3Tu
z8k*8$W~FJVuXFmiHw?72EH;cCJ7zAdMDqXbI}r)gXiqRBnw_WL2~w4$vgx@{OgpOF
zYRs8e!~E;yOW~G7o&;5OhPV{-R=*Pwg(AEA`<)BjuU`ACkHf8%C)K{~oIoE3v<kh<
z>)bTV)4yRC9<%^X>+wZEHh{<Dkj=#By4HtZtW^O6Fhgl0qiiN86RiIVUn5j-`OTi)
z{*A~tFpOL(vg2yv_W2ax^muyDYJUu(v1ZCOzU-=sWPf46h_~`q$KCUyr%uhMR(tO7
zJTxh2Un9yAa+6Q--)s0<0-9-F=dy{F>9LG7*N2FGu{+;P2>R5u1G>nN#l^*hhR=Ne
zHEbO4phG}0lQOsm&m<VF_d=r}ZTO=bt<*TW=EL@PEf4Zw!~N78{!+=<sPt$w<CvwY
zm`I~CO#6Bn9QmerS-I#%hIO`8_9xB}bKTz)Qxr1-Y~nmV=PXjuc=v>bh38JZE)G{G
zKiQLJ`krbJpp4CJu#o*8gd9i{pbPr3%>MP<PqzuBH$g$5dA59Ui7!;}&C}Xy{gQf<
zf)WX33bG3Otp`O)<R@vKSGB;ZDRz10(|dT^rQGy;`Qu-j!vSC-4iL>@i-(~}x>O5k
zW`#G4fnM#p9Tp>?Rg}&|mI@~!9{lg5aR;lvSKw}jf2Cq4_fmCCQZ-BN%2Mje`>W-c
z8+BuXozdx%98ZQr^CTq<Y|~JcIW0*fQthSu&`ctmCQ2Tn7r*HI@8fSN`xx{QVmI#-
z0l_tB)W!;5=Sb_tix=nM8Q@n40Q^@xI!F13<nn?hZ**A-&}2L!^}h&3=^a)uXyJyp
zrJL@~Kc*RGJ1C<JC6W8uiTYwKFRYQ?B-iYFjM&Z4(=^#lIz@tES=q!|##Gm1tntjc
z$fjAC5pCJc@d&yovYHMI-SO_~n4S+)^;2K^dR{iZZCrZoW}VeV#{<we33I=D*MhSs
zH^)g`F(JQ9l+Rvpe&<UF>3pup%&pgz)N%tyhez=sMi}umf|mA6SgZYC?|P(jqRY7G
zb#WN9UN(E55hU-YFdNQ$Wl#PiJx&X>_fy9Iob^gEsx|&KSx2QyV#;)9wtk&2Vou(f
zls^vl;UO8C3g7GW0Js<RJ^|HnmfECBo$y!Lrq3V0>#-CjJk#+a0RC1K><6^Pgk!4J
z{Np%UKOr+^-X>%n?iO55Q!8zodhOEcV%yC(j~I9}gI`zke?mk=KY_gG>=@z_5Yb$&
zxt$m6`@29AI4h$M>zr1Rq4-9J%@Ck&DMC(lV#K$0FKt`%eH%}4GwJ315jdE{x^AL7
zbgsFr2R;7hkHUB(@QeG?IK_dtYG>cYwCn8b400g`#btM39w+{4IR;IMRqGOIrOuG|
zlfjy(rrmQZjcsUa(Fry|A$<5H#r!!4{9)Yf&i!@Jkc&k^5O8~jF6MII_6%{xc@=*C
z;*6n^?dYl1ys{?O&;wOnO{w7for}*1KivCyuX&IH%zBfJNAeXs0Gn`*xm(`(E7xyn
z_lM&eAjK|EI;);R@B~J@>z1S^#p3$CE>_}%m$#>?JXT`3w-uH9zI+h@WIW#*T&?9(
zS=x&IMpA$8c0DN3OJbcDCTfmeIHUej&M?&}$*1mXyw|Yrpne<W`<1bLTnDZbt7gjo
z_9Xx!d+mm2dJ#HEeeWgFsU_^FCgfm<(v<MWTsIB3KE_`;zw|QFxhi}l9wYKAtQ>%a
z_<enSWwOE)gcaS8H0}(OQc}X8iF>8P7R`<;T{r$AZ_37Zbb=N%@63}mJ+vsLWU|;h
zBOg(LIWuB>n2pjdHnCUrej<hG0-~XzuW1z0Bv?lc;Nv`hUi+7*&{^CQ6cQvHyzx{E
zHTz1<`5xw=ZH|?SutMVu4uqq-6g|D4nphg1m~0rx!ev<IBU<%|hUbj%t4Y>nozB4F
z&CqUosZBe(skU>b$DgwG$DeYXTrbR50XmcHyyLQ(Gj&FQE&S+YSQP(&wtRe{TIaC}
z)0tu@Oe!q#)Vp@9L<b#n828yv=k$u|v7%ZQw>=3OF<)>Pi5H^#oU7)?3``oxV3sgx
zMMVWPlp<HdZB}l4#>b#_67{k0Sr!tj%ku@5{Sz+#6BQpHftK^-2c4arMw8{nd9`^6
zK{G&0DT0-y{I#<8u<9~EHQG$XBj>vxYgIf%DH`%6P2Iq2m%hO`5>D-jQlz)W)T%PY
z#wpV;sH?BM6-{Mvsaz7Fq7v<F`4E&$DxOx2P&oXIpATcNCQ{``=@<K%RhK@Y*Wq<?
z5K;jgO2&mf#vO}GBl&o{Gj?pTy2&DiCiH}nQk-6LjcMH4BmJ`+FSQsGTdn_S-fSg+
ztYQbQ0eEz2Z!Zq?Q#{_DdZ_T0i;;Dy_4e`sA-A1GcPugLhw>Cln|FtoG1jU3V}xH)
zZpUeV8&@;>eSW^6qX(+63^JKYpVgSRC&+%=pRv!k7<h&<cD2DO{8Mj~qXILJtv*WI
zrmU1NAn^JQt^L)eTW@iii*o-lSCakQQHU=V2Ki)eSvLiv{#<4SYy+B9R0AWnsFpJs
z>IiqH`gh&Q%_;<|cL}Yo<{`IEhsbN&P0Do_Sccpm$s-{B?_6j?BM-9b&vk)G(%O0b
zKr^!0Sa+P>7e~dIw1qk#0Z{;uGJ~WTH_SwkQDF>7M<g>3LP4!1e!$yd^jIm(V-S>)
z28S5+%p{^oi)+3><-G~)U^?$Z5HDfrgU7BC>t}>UPJ0-8`(5cx_9*({Gp_si_YXPl
z?5{z;-B3XLvIw9lF-uF!S9M-ZQ}xa?fU_ugRBa@ymp1f=<7n!`pCV!sn##m}ZeQ<0
ztje9vs*4hXd<6B-K1=Js^qDNcxe?DrXv=|0Q6xu?VQcZ%JH%Wjb5GJ0<TW!onmK!o
z<vS1oO!~m66yVbzxLG;4nQ=kvfu1o;*hpUGG|8mEMvddOs0hQS3{Gne%w8xdt^<$U
ztFR`3(6YRJi6IJ@T{SzLJXmMS?dBRn@s=+VsN|qL@5ehcxPWzm9bb1F-RtJkHJX?o
z=ia@0)V7h{w9_6dN1CrP9h-R`P7NZs5L4bPTHVGuJiuZ2T&my5;;<+(`cYNd&AXN(
z7tc!W(&p_?xwQ=~Sz_<*(}Pknym{$rcMCj+;mVB|fVR7Smp0#9qDqFG>3Iwl!zM(^
z)r#>N`pi^#IgX_X+yRUt>(*P*vDl4M{NP!K1LRhRZ{YSwdRw4L!SRdn@QW7L!t1jT
z9Y+klgqHyKqg{ZfG{T1w3xCvG97ie~D;fk(#>aOV8#)d;hK?0!ntpzHuRy&><JDzx
zF+(_h_`7hbwfXsZ$scl!^=V%B6I<{N)7}(cHWJphz0bJ%TFGd0+)e6lbhOsLF<Z|L
zh=Vojo=%iK3H{;akxKM)FE^k#+Wihka>j?kB=ym->(9u1&AGIFa$#SPM596|#>5tP
z00>G=Hnr5T=`8g5P1Fa@uh${B?^QS})3htF*q!!|hbhc29$9QJZH6v*>nNr^G^B+X
z<bR|)Wpj0PZR-oOBI}%aUuUPadXe{CLjGd6mHE?C!f1dRyeIV@vqKAdHm!82oe74~
z%WnQ)0Dx);muCz9gnl-NXlP70?SQ4Y9FUPklL!)!klfv!ZGwq-fM5)&A7~KC1+qIL
zV~h)zzr5)<Km4Smp#J8j*vo}M3IrT&T0soOv?CPNawH$Mv-Yo3HQLbW5lhxz$q?nl
zP~UCRu<2EdD{k3}r4#&c<sr%yzLZ>8^JpTPgcTtPqPuLTu_FV_-VM)vT&ApvT-Cu-
z`~aOK>$lkS|Iq-W&=i}w+!6wuGp^?9G9FUr%(B+hP;~Be&TdGq?T6_cJne<rDzSB@
zJER#j8?HB5yy()$gb?85-ILNy1g=3^XL}eW;GYLGAzwCk6jhjt@TFS-d0jzKDj19d
z3!_It!{hV|4A<>yNmGM`QX1V4ZVcIGln~oY#;rjQhi_nCt8Q#}L|8}1DL$Y6jHQU&
zfsg*-ADx;Li|E7V&!^Z$Izjnf1=dh>b-%|P@^r9YTKL6_CLGA%Agr)EyHYskhdHE~
z<??m;NM0stTi9zs*0X?jz32)AIvY8uQ<u{pOyl1q<urtN6Mm8|i}h9OHvS75^Bb-2
z&T5`uzE{I8wt}^?zqEa4{<?6-?ENG)-tf(nst!D*X8VPf<|PdBD%g$B0VV}#@BvSL
zLJ}`y0W39ku*kxBgL&@T4+G6qa5Vv0aUOS&&HY1`JBf)~f0DM7W1=zM(P-pO1o!TO
zSXI53qP=nCRE&*vR#lmIvyDvGv;DdzmKv~Bc-Hk6x~t)Ic$|x?SURTINUs$^b@FDk
zr6^48<M^zsIKWnzot-W6jU;DTxdj@xxDNCg+Z1-OM|ZLLHnq(IxHBPu`D!Rn4!7EN
zX57ZVnddcisA8r7{z|uX6={*dWuL%rDEd2aH|gRYmK4j2_(V+lJCUA}%*8lItUKIv
zan?07yz_DJR4dfNcjs<G!0y%dudmj)a#xQk40tun_JV4)qZzmSb_1KLRMIEY1~Vj#
z9=8=i2h;REcQX#LgktR_v-7*nLDGT<OaCKD)B7(#<KeYseEF=STjIea0a{Hil!u{(
zGuCRdjmO}4g4`i%fhj^)xI@s?2Jiuby6|jJt`CjxG`ys1{-E$SG&h$r8jlrbX=%-0
z@xt$bd(zz}u8=`*G0;&-Ny1V&b;{B2`aqh9-wg$*7IE<1e>#x}3sDw~fp`8D7`uU{
zSZ7rjQQuLNZ*3?t%$y^X=ISxMlFcYQ*>`BnOyJSUk>F>fhp=sa6mE(EaG83Dx^rM1
z56g=UQt3#+0y#w!A)(z=8pfzk--T*@aY)6ItKCa+R=4apF?THp$vAcss?{8mx#X~W
z!_+znl@y0ykW6`4!8KJBIN$cdgju#f#5ME@I9k7(Y1Ads<FJ`z0p0x{Rj{8?3kni{
zP|A{0R73-b8Y&?BiC-*T&>M%9mDOD!3zRTGt)mv-t9L-Cx{s!uibRnv5Oi^MJ)Fy8
zDtVH40u6cS=nxqA-?*M%UA?WY_z?C;eCI`VD*aDEsRac$wj9jq*^q1|0Y!nv4fzNG
z?aG!t5B6AEmAE^}RKS#f-|LK(ols2UR~}~jTZM|~S(QfuwV6#`-j-8muxS$6_O87H
zX`bdMNP05QTFdf_-ahb4L34FVtbJH6`L*XO8-x1jyPeB1!sU-79o2{sJW$#LGX2nn
z8V!O)e0-Wj5gD(`fdbeVFVS6+t~Bh1K#U=g*gWpLM0A>^(L+PZdQBeR_RkuWvV;(k
zkXSn}Z_c1COi~OQj#%FL-(DbCj~9yqv*;Ue4ySB!xvs6+)_Ht%ru-J|>~`@OcSK3F
zg$f;Hw9Bfhs#qPDCB%ihqwy(vyGf#f=<J|nmHuT9k$^oJH|<Y;N(R;9C>FM?YJDOX
zAc<A0-mISW{?tT#WsZla4wm7#uuN^-lMVpc<29}l#ddvoQ^<7ih9|^VqB*iKcoK{7
z+;H{S7q?<fCV^-K%eGzI_rNub-(uzUMy}&(qX~|~@cl8Bq}{3f24i>!TIfl);+ew7
z{@d}sI9kVzz#t@iaIg^0D{0ex1l4vxA|fIqw|@?RjbgAi{A5S$wl^QMp5;#h$l|ys
zIqm0(1ig;y-s6+S``w&O6zG0pf=9t~kZw*#nBtu}*WnYKpY6a8LFB7muPRX2o)Qfg
zL%aozMZZ<QP(A)JCZ|~FW-87)Y8M(A@yjFlyRe?egzItGXQWNaQ>DCzQ%N{v42pPs
z_`Aa?e_21GGAYm?FR1W;+No>}q7B`3TT^%~+XbxpkO^bP_XkGNd`nu6D|gPb)$Xu<
zH;o%9w$7#vjowCw?5ZeLsr@H;f=G{msI6u}ZE-V)(Lv0qudm1GodPC;UFtnPmks6D
zPgK{?ip3ruA0KyuE+we0aYC201Y9;AVHqBePm!>Ddlx)bP=n%R-#;TsqldQjljQ4~
zDvlF43^#5ad-kll(JC^>jr+O$FK3MbntUl)&Q?$lCv<o6kci*AQ|}Y`>CK)$3gcD{
z-C<t>w;0M?<(*;iWE0WHiNq+|r|B3Ab>^xr_A}xq0IeXzG})M4k=1fzKzj2Ave%9O
zXd^_ZrhVTi?{U;19Ao-Q(c8gghxCgojTqMJYXO$^03DpU-qA@7*@^U7BKd1CqG9&Z
zl;;{IbAmCu5Q>b;WXq1HSn<%%Bhk}pM|kKeKnsJQ=KtDS=z6Ai=@<E-b`;1xvE|2P
zruh7B3?P{y`jccSa0EI5LUX?+@ZI?gg<rUCqbVoA_@b(fn=rFj2_Rge9~yVXB?=Ck
zRUj5=!0;e7@w^6IHuAMTbFSr0&_k4)V^NNNAvjx5j8Smh<|7^qZXQ+l@?YdN5DkQH
z-&fa-2P@`y985@;nIVLoY^>}xVlwyDVhgNzmV_vWDz6-oMP1B%=uRo~R`jfJoQ{we
zS2jcGM1?H1+epu3?jo7x#+eQ_r#xPq$UaYcJdBf)7*+JZ;Kf6VRlCb5b4R~qua>*-
zLVbG2sK|CWsdEykF4-xQSZxbRq`s_;ur5Q`;6gSKpi!HO!&rq3f&9zmz1BOf<7`0P
zy8#T_ispK^9fEn^Gkx*qN&4zsK@R)bI^*(^XL-l~yNfU}?J2>?Vn@s5ds&4$@c6(A
z=J3HG=HnZ|2w<kU<^@rR_Y~emI|4G5KHyP+zDnZj0~xFN<|%GKNIFfXa{LsMmQrAY
z%xUpuo&<n=Hsgcokf(D|=L&wO#QhffI4@pun?D4hxd*1>>1=W+*yJ0j<E)mdDu(S#
zezM!Q#$P{$GUqsnB7zv=0xqU9wQ}i$9hqziaOvebJD$>zXG<XhxlSi^dv5xDbrs)S
zI8r-aNJt1W%<uFa5tOLe7Nk^#t!4S&MgS5qyi@EFtqPM0$CGDdwFnqjZ(~UW8zj)Y
zzG~W%h@oI3=tMB0$R~l@VtRdkkY9Kn{TrtkfTkTB-E#>(wz#{1bt9QCmDLPA^%`K|
z7fUokd53?GEVQLzm&0!M3nOQKV;H?uO<^uS=4fhNDn8%(p!S!ZuN*>jrv&&oAr@Zf
z9}smI$TVo7pbZk|CE%sw6x#sZc!NRPC&F@&aQUS?x|veS<YS}OAyZ^J)t;Eq&34rw
zirj7(Iw_i_=0rmKO!e-@xA&=>);J(tItb(?&T!ybb1AZprd3bj{IcSq%IPLLWW@Zi
zznwXc%J7PF^Uf3<O|)vd9~y(3#=_`5OX<!tYWqe>Dc5^u&pa=*Th}VZEAXs@sK=vj
z&Y+~|s&8A~X8se>mU{_%y|B&LmmeFGmF!lUrKX9DNG|uIzFGons$fk9e@GFD7F$g9
z*|UAn*HvUY>Ph6e{nK<V!)F)EQOp>mr1mVHCu5o_wKh`;eW<Px5fSItM}w%n{g7dA
z!DP_frK4{$jnn#_bR3D7{I_?!DkwFy2JpY4boziLudvtHu{76D?^4i>bnc_<hpcT@
z6x71ELJ;*tcsz-RFq)N4jyzPb6P0<*@`8HG6hjEF{4(8W2qKxkeVkBrIOvfK7o5Fr
zy1~H2K_V?%PSLc2i5E>z155OQ#Luk-<6fRv!GVhl=F`4WAK?DW2@uJoGYEsM8~`3s
z(9$B7H61?9VR-6S59qx?<}$`I$K{tVUlL+U^yqIx`vIu3xw#1~!9cCjEJ+V!N~2dm
z@!cx<#9}wgqyN2!^0$RJ3XQ;Il2H^;mOJ%2G0fDdnZ3T7r9sJvb}|$H6&lT!2Iu>P
zhpz7{4%}<x9Fwn?ArEj-o=8hb;GG^YJh=npvMV$8t(>WV6ci31piEz{WBFdK59i6T
zdh8oXNJ`GB*he9V!ReP9-K(su94a#uu4`<ZAxE97mSt7r7H^aIIiAnUz$4HhVkM-F
z`Mmky!@<EU`croLnwDaJ3~CUa-!;cho@OP0n&v;_go|<?sIw&yv8(t1gkZ@-Df_t*
z$l3kRu;{P9sH8J6E2iQ~hG&bRaDEC83a_<aF!*7RyVj*{#jF@1?E2$TgUe?7pu$~{
z46E8^V}##Nz~0CZjADAigZFu@iAH-h@7{`DySDm`cbz_Swf(pX$a93k+b`##p1~C&
ztFSeuGI=mH6_xRH^@~r>e6}mPl^fl5bkFh)!hMARz-3>6)w>Ki^uXz+1P%~ucT)3D
zQ610@UWAjMQ#HX`&l}o|P?Z~`Eg&eMQlVGZu`5`2c23f#cOA2gxu1OcJpFo`fB*DY
zgDB|lmK{du&@n>I9cv8mc7rAl%q&2~UUEDSv?x^9j^FGscN&X9{ZC|MWE2F-h@b>5
zqPH)b!*fi?1EQj$Owr-JfAyxof%a90Q639^L~4bW#Tz^}bKACgM5E$Rl`T^TQXnm7
zs$mHxY+&5E{~uRh0TgAphASm4NGY9)f`D{)gOqeDNS8>*E};T~gwox$bayBz-QC?G
z-S=CMo^$V=ahP$2VgLQVdf%tMj>&%6<Et}H%na4)ae~dF1X33l9IWjVG9$*(2xCRk
zFCxy0To00jyw5-J=qc10y}={H_1!p-%Tdi_MlorWml5dz55DF4J|<2b{n<k?%ziXN
z)F%qDY?-YII1~Z_zzIfTO~PrJ5udsH<Y}+mDDpA0UsguOn~@xq^TQ5G^|Q$T-mW$Y
zV9wFO0D8l^KszGnmmt|Yn+3zymZAthaOpFoqeGd0^%QPl8Esg~V$Cuc0%pQ;4maCO
zFS>QDVxrhgkgbgE72lQXMVi90hrM06GrUk9^1}hmsWm(iV0WU-1Ogw~wbfP81jMB9
za3o>jrq@bHce_>d#U5w2;vGOm`8i7=ehU+^tmH95Ltv^H@-9FzB%n(JMF$7(gIM3Z
z`CE*>ho2BmY%rkrc?Tq$+4_-*-*EH&&LBS)TL%c^?|yFW-ciwW*GUk;B_HPN&>RZ=
zbP0ZqTj_<Svx$x&kDVssluUc{2QU1;-57j|dEO<4NS6W+-8^9@>S?V+jzCMLSqApg
zMNm{892~qPOQ19Li(@eBOZee34>8jsV%Y*^^2adTTLq4`E|ug>1uUi)z8Gs|X8nE>
z>rwyZJ4A%QA^lrhyN7L6ht0Gn*)$5x@|9FTYX2GJp~}#AH)#gqA?r2;53V=+hqE#=
zwE>_#6+e^kGXBZ9yeCzlQe`PvT^n=zdr9U+`9@F;C@B>JQ>F0gzk5UBqt&nV5`P*`
zMn<L?Tp9eD?hC*PQ+2CsC6i{M4lA7xKYG(8!R=0*mNOS0VLa-Pz64&s*z}*wR8;ps
zxHln-cEVS0gVq6n{jy>KY}M!)=HJsL%p0z%UM(w)S;diWYys8=f{sU5evJa=`7O3F
zN%7HETJ21x*5?-y_7hMBC{n~yOl0`yT;W&W1o4kg--7@tSDQ|rJyLi6$8+J{!Qfdd
z9=dYn$s>ZgjI%FE(5kbyyBiZwXpt3au7FXTsC8o88Y>7wK)O$`8Lx%W1a5d5$<ypF
zzSdIXvYx;N)vftM{<)3#=G7JXq4n+A0Hv=jC#2(4LehTsBc)$Sd@Ub`s+I|jPv~HA
zKJmr4BxKa!coDjuDtifKVf&jj0)ba@%ZJt9<33e2y5VzYdW{gUGL}m@=Evb4J1HXq
zvZkG8lZt^eQ~GKS1WXLIfm!j!1H1?u;rl%z!pOgmj<AhPG&G__M<VJR^%D@A%m-;&
zZj%8e%Mp-7(|)=;{qXVQ0A~H#H{iyfiE@kZ)H2X|hyXb5WZ2&H^$VYK`9rPeHf7~^
zt#d{;^@5OrS{2fAyE)m*Z?i>Obw3IDop(gC6%)gvqBPDA3&xH1Cjo^qTRXlbX-|{G
z+Q>7Ir<lmsu50jL`K%mVicBC9RcKIu>XJWs7`kP;rD+$CozW8hLIuxu<qc4zktzI=
zkdq22C!0lCL@>x9-SGz|wgczo-;VO{$7xIrpcGH##XPo7-l;EkU827IIq>Vfi-RVi
ze;?Sux4^Ef-XHc*rdfuHmv@pX!=MRD4k|Ffws0948DWhYO*UvjS-L>IqEe{G+hTPa
z3PmU5cOfujIw!jrTBqdAge!K!p5k{3l9h6>9)s21PHI<Aoo%7bl?hs-PSC-9ePrM&
zsv$t~Urob3@XD{5V}#C-5^CP-I1$}K5nQioa^sjug&mkM*+cc(8_S+oYxjIoS7PCI
zgsD70vig+v)jxg<`qH}+Sus)gGZ1`?&~0GeNT*xA<5VzWg<t&g0gPm1Svoy>=M&&A
zN1(QxjS#wbadowPGHw{XEyqv#@TDd)XI8Ezt6B&e!zG+vT?!Sxed+A~4?m>xc@3sA
z6G~WSp5e{A^0#;LOjEJItZ?q$tvjcx<Ka;_)12Xy{ViEjIW9L<h^~{AeM((Cg}zN$
zyE*?|Mq8<?F-(ubj_wB|`X5An`nTD3pXdT0Zv9%mOW19X7lne$#d)PWcFUu`XyPB-
z=DU2O-(@VykcbRz-w-Y868bbe#4f%Xn37gx(iPzoGr0rk!i??z3YoLv(zJGQA=AsC
z&R@M_bV1Ks=0C;|!zyDKeMzR%A6wF;CT|a&1OZvRoC4m~Ks|eH)o!Zni~9iZQHB?L
zADoZr5NeQH-^%J%TC>Ku?9Y*d$~pr6c5q{k#$Q+qSF5Xci+c$A*Q#&-3VHh+ezDDH
z@uOUx6j|HJYJ1wfnso}ehv2US3#99U2Wq)n(_9YwunXII(Q5IHG9(TG$Hl4XeusX4
zduL>aNk4Lf;Nfp-q4P>(Cxz&Z`8mIWy|N?B_rPhUeKEwwi=(NAe&AP^7gR9xZFDc0
zG-_eLfbrdAl<CNSM5l{GJz7A__~tDbU?!}XMOhIVxX4?t(Q~rd2%L0in3%MW67ggj
zl6+->_6RE42m?W)YHdMC?J<PnR;+4Y&=~Nbpe}?1AY~u2!Mz`#A^}<>MzDKxQCt9}
z=|@P%Sxz5~zze)0!9u9RX_a%k@s+F->h@P#{EwB3KCr6{|9clhb8qsNhKm<m@tE~{
zpOtFDg6MoTFpk9Ubl#d1Ye+>e?Q>Z;x9V9$eIojg>hO%2NsRSAn41{j(bb#N8PPkb
z!jh6go~vd(e$iHH%!JeQZBRg=hpre1RIckk6vi54_0GH9LMBn^iD}KB_%VRSo&t#4
ze>UCSTD|)6zln+mpQs2SoUlsDsX*TD5lr>J0wdMP-=jbtT5=39lR#c#S<~adRnNm<
z4#ttW?!%E+uVx&H#)lo`cIC{Aapr31?-P0o%2!lm{rH3AgXn36#@iKSHMHEE|9{r-
zBDb<F10t^bjZTH7y1>bZ>W3L~V_L)`&7QWX8*pd&&7=@5uwU4e8H3AVMgvl4w7I#t
zTZB>(5Vop1+3JoQ|Ji!@|IA{fsOV(>U-PfLwZ_uRtL3{F>4n+bb_|Q{?Do%wH4j@(
zkE(<<9EJ0w$&o#37=Mp`496AiC=~phV13b@-xYD#TJEf_GN@!|wDHw#`2E&3y1G`I
z#o^M(ABpcYDAX;lSl`?ak)&c`!Uh5sF@Pg}|NcEJEbM*W$HKzG9E}2!>+5T*?2dV_
zyPK-5b1_QfM*U;L2MH6|vTR6{u4KL2<;n!|x$yi#&Zms06}w{!vl(c{FaGV$&EMc9
z?T-!HsXBzFTR}tnr@Qe^KU;R)8BV$R8q2l?Dif5O408j{em6u8W~D)H844tjIoXbn
zj+&28M;)K|QSd=<4Oz}-V$#y`YEaRhS$<LkTXHEF*~rgdr0A&Pn?d7HY(2@=lF#Sj
z;u7H1hCXv~aWV9K#m&zPNC_Si5PTAXTy}=Yi*Mt`)U4?42v+Kbx8D~&ri&Y~hVeuJ
z<qLqRWL#SV9-gm+D-o<K{)7AAar0tMFNe4@A<yonj_EaQFTjoCyw5ocX33E19{9zS
zq*T^l?`xAqp0<pnVOdaoNm3UpM>83HZ1f`OavhnO-i*Pv%Gr@>&WbR7!q7^3Fq<+O
z8Y<Y~{2XKUWhJYhz;OignFwdLII$ZFF3lORY@OHErAleroy%$x;T3a(kKeUvO<<e{
z-l&8oPYTV~;TZ+a7`R$baR&+07PoiJSaF{}f8M!8%hCET3!tY*0=T+4E(G3{NDxoZ
z0ISBe_@J4&>aTi+-6~4z-MXTzPD1et0#V7v3d_--z*07vp=kK8ri3p+C$=5{*)d5f
z`z-$Pc`A}pA8!(=xaNE)R@~@PGQL`Mtgl#$@bou&A?%g%H0`D#Ft_5?*#7%gzOZSW
zps`p$3J`=NaBE%>U}3ht)m$Gwm>%qwsdvw=))#6;Z7>!ed=7gtqo<cI{L=axG65W3
zzel3~LUG~4?<e_ljTlDzN1hhb$<y-Iub3Py2T?}Tt;{c5;<Sbu!EH3+4k{`tk2%b7
zTM?vR^ocM0Wx&*3CcrH|s?yM$UuH=|tplKBfMdiHH4fQZ^_ZwUUbg}u-WWp|#J>Dp
zVLsDf4mDs06xg}Qmz~~DmLfjNm?UV6TxkBVF;GvtXFH%X9E<ZGm1}emK#<q>9bqBv
z)QzSW8$Y!2v`fy$Uih<OpPY!Q1*AIM#RVt&S9OccW@xZudA=!rYKZoZGpA%Q)=K+v
z)ge})KFdupV@cEY${1@7#YVb45tcOK6ueOuRFzUw6uAzovtE9E-Qe^&pGpiDvbFg3
z;J4bHaq7gh!xq@!rvUcEs8eODzd~#W8m<_(>q^I$E0JnW$!{yBEW2dtI9Nx_vsX_*
z9nre*<6{NZFRj0g=fZn<Z}eWKEjke2zJnUNDCEUU5vxsnaC{&kVNL*(lt(nKxQxR{
zkwV^P$y#i|HVdu_EGo2RJ})rScdgAeEG*(d{~Q7*Ei}dw*|mGH=}k$X<GFq{wi+eE
z!0MY<brTHfI9<p(R$5#_e8dr8^XasgpE}Fv)gnWkJCnpfbY^VBr0Lhp>}b-g$|5<|
zkwU2{-tbJ^t%T*m4uhb6Mqr%vRiD;~i!^}SImWE2Em@C2%MrtEXYj2OymwjZ9JGh)
z)fX=FQrD%W8y-Irl7b>x>>hYvn%sQsv1c3|n=%3%8`9yhl5k|)BkX^Te=OK}oYn@L
z3ijPlxLMy%KAfrk=Aj$xR>cHEG1eP?c9h`w1|rB!dGm;3gbv>YNAty&x>C4S6vp?+
zaFZA}PtM)I)GrI(xDrvJzE$NMJxM`$z&BzDU@&@eXLY85M&id;EqTTOx!H<YwhV2!
z1On>SKIDc5uhBHig-N0QmX_J&Q-;a2tF!%%>kCPo_^-jipU>?+wbh`l$WF6*UR`Mp
z(fNu8^3Bx^r<)Jw6wu5MB>+Sr1cU^L_q?fee0a42x^R`@UHTV^?8|=zaNZT*dsT4R
zCsuKvofBTyU-`mboKy;D&GgZkt-n2>F`-PcvlH(L)E~;e-3@iKLqTjAPm-9)Bn{LJ
zL_2BIB!@AcX49f;!GR%pd{a+eWV}MX);jk2@5Va$y|jr}i%EVqfdwoDzGCJUpAzy6
z$VN_p0j`%w+`}b8VxZlI2}6Fzcp8v}7I!nAL6x+=L9s(l+hY~3{MP^CqIM#|ANL^V
z#avBHH1vK1E+6@vfhEc0SfESMKUR>^IX<RXGen`<0Oh}Dk(s=;1Z>%cl&}K62S_o}
z3(6nv8(dv|a#MVopDp7tGgj3iXsh7(Ig~66{83agHU6C;GK>DrUDT?aE`bc|XYInA
zDu>R(pmtbvkujt8yDi_5*7g^7ul7ijzA}Kdy7J-zUe!n{N9(CwFdL|e>d%*g#>@k4
z{_(MJ@Xa#1#dI=*{Wr!-%bVwV9I99iMT(F8#`DZ4S6+}o4g!s36nkIK$_01c^K?`)
z0baaj0Yp37(3ZX7=WD#N#kRnLRi!HxpR-7dS1brBrb<dAvwjil0=Cm|R$TvsT&Djp
z=#e+?WvJDdARQ;dEaZHa6F4`m1}m~l=8Jd>j{<;#A<EQe4ihMEKj(3H+1NZ(b6oI2
zrDz7&Zmyf*<A0gMWS_x{kS3N5zM^g&h~`d;s%o8(yc~GaiwwyMf%G9Wecc;ILO;I>
zt5ubjtpCwVGgxOSvpC>sgMx^ZA!xgrfM=+~utacifiuR?`}KtG4L;cQZG01hiD=%_
z-0}z|3O5Ykud?i0o!qOPJ4#6doe1Gu2L^iTF?s`q){0|}0Hkjqs1R2xD_{L|p!_o$
z!~OC8ew<&wejNZo60F43>>nG?4Bo^CS>Dt{9&Bg0?n&?iu^ImGJ{8sJ4{Mbtk3SlT
zKh$pD4srMJ1bhv7>B>utjF0INL@ahLJ=+?XNw$S@;sZ(uKAxL5mj^6YgsySU7dPMB
z58kgnj>~Gvu7qbfL>*67QvIt8pT3M6vGn^fo1{z-B$h*C?N|zKg8GuuUT8B>j8LFk
z#WnZ)zp)0&0YKR$_VT>c6>&+zspQV+wJ9?=lyLDEK9l$ey?2Ss`pBo{<^_^N1WxB=
zx8!QavQ?@@$uySaDOZtODT)NW1XBc>ZIXn~3NeLUs-}~EM<H9oBNBEynkU&Os?7wU
zSL<d9FF68je5q%XU;lfemk7E+DQO<d9ibg7zrvYjRQ>*ez<)zVg0*$SM3Oj2kAn-=
z-}5l{?r2Q4MzzHFEox|gFme9yV2{f5<(ZZ%?!{2$1IwV8-TE9C!D_iQVNCQ5c#L~P
zaKO=OnbMTYp~t49EmK!R@N_x|<Ovj0K~w(}tVl$cf(qu<eqr6-&$ZrorU_NQfBv2;
zxVIbH5fZB#=8Y>3H!Vpotp<TaKPDb3HgVyyOJwF4x6bI?0m80}rRk7S1@%K>^oa!U
znejM2!}M_fC&(Sp$A9qO!tn1P;36~xfUj(8Yg-M73_w&fjp+LQ%f$KLr^bL!T_;oe
zS!yiX+10~XS#i%x=@>@nM-lmv7r$IfZY=lwaKq%qOngdh$=(U$LhjV6I?!X_6(fUb
z7M0Q!vxbYyevsYkM!AmH7q{%}U*KOy|5TV?^6=Lzf!MxC7b+J`3jM>8fH#LrZ5wk9
zb%#;^>lN+*Fw#|ph#ny(vLjeS!%SEgZcD}d1i$72<>2>w@klDl@|=ax%0&b{F?|ei
zEA{!#?hcX7;=Fpj)$Zll8T~`mv#EpV_L$Ge$MBpQ3hB0s4?{+7p7HIw19i)^fs5ev
ze+>yC0eU&}Xl8x*mG;=_Fq{)W5-fv*OPw;{;lG3H2PjOOiV;j)WSS?z?Qzc@dQnto
zJZ^zF{d`$(Ao<6iEe|i<Eq;Dw@fQJ^2d}O+A+1l6!i}N+!J*MC2IRl_e38WVa>B2`
zljyxJFCzd*VoF*Qz-U^((MrD<b!z|j_UqtdZtEZN1)MH1G*nU6jdfRul?m0Bu-aBL
zg}nR5p2ISI2Tb4a7;WG&ZUxfbyQJtdo%KHbUa>w$(aXW1@ALs}T)&SQz)Z>1-N-|W
zk=pic(ZLFLpbX7&#|x+|X=rJKtNy1SmjMOIZyR7@2c<~_{BmaUJ)4DM@-*D7VsWcz
za&}FuQIN<`44~*$-FE8lm0-4d-vZYj)vamq-yQoN`n50^^1FWDBJj@00>Hd^lqlQ&
zlQ!CZ*IlthkWsZAU(nJo&1Do9?1oWzjK^?$X_C~(8Z^Y%$=Hc%4?(yZ_W0}e)70vk
znsmw8x`7f&`Upl+%c1ree}MZp_r$UXRdbpT+yhJ^)9<g`bSX~|JRXWlCZl3K_U3h1
zGoOUC|6@n%;I^tg|1fe;Sw8zB;zJius8sE@`(=|S{JYPNz-BSyrbxzPFjq`hyJh_K
zQGWKeWoF>!(<11DIS0#Ie90I)vi%(RR*7VPMN>ob(T`bzHZTTra9=-UesFLn4v&EH
ze?~6kgd1J>l;l#2iwX@$v*E6c^DC5?wmgpI>uXPVdI+Fvs!gT<6*5v}csC|!80H;?
zyd#TDc*AViD1e8DN49ilSWYJW_rc%Wf^9$1<9gELKPB;>j<fOpE3_o~{^Vv@f!A_p
ztoYOLqQ@lEy%9UsxR|q<vEip$%=Cr;TFxuJ8PEX45#gpUt{dry`KJf3DH+vs)G`!z
z6^x8%fSkxLA%P6Ag^!tZ%9TmK-_pJL?%e>kV#NR^x~<VXv-;J@ztm#_Tm+Zxg^BCU
zi58&EM!&4I0LYOH10~LDHcAqLKsiEVW!|*Fa=PhJ5{qJN@UW<GZUK6~1h|nMqVGPD
zf9H9Y{mEZ)=|Wm6?Q2mJnS=fWTB^(6tQqYK_zv7b;YoJNaNe~=7fcr74OW8@6@c0O
zfLw@oCZ<^YZ?&8f0UjlDWkVW~4J`mN%(Xk|4Se`ZQ^6eJ6aYoX^2h?cUxFc)0^X81
z9@RgtWq$nbOG??3hmP^uvVoJSv=opZ%&p4QzRX%TdF8?zd}WtfRXPxX!5fR)UiKi8
zU>n&5aHB{E;HT|>ugL0VrRMB{|5E~aXEco=-(LXGn-*SJ2BdchZTfmoVA{4{Z&t^}
z(J>~242Q#4`-=y^kH4EJ(So<J{wJHN9WwWucfx(aQ1TZBtK*0_E1cO`v17{qoCj^W
zoV6n0q;DOdr=-B(*5sDFb)-Si#pUrheF4yNr|tPX&7vt9?6RrjlM_ifISi1P*!w&E
zzX%WmSZg7Cr906I=x_)vyuyW*gO?wtyDw|u{fGO^hW+gdxyGW66am<$KOici0jJrW
zR%BKNvLbMonA|Vzeimld2b6tKP4$mBv(qibb~!LAB1a)G--n^@O6r&&YXyC>D&lej
zMZq<Qgb1wL9{Y0*_f?KT+4BPszuyuQZSoxDtX+US3uqb@zHsILa|M_I<ab0{D(<G7
z1fhiN_M^6{{gac@G_E}9jwLq>?F_+0D$nP%sK8bE)h0V!C5&Z7X*d%!<Vi49icuuU
zE7F7KfcMJ<J5p3&=i2?hQ6PC;w9PEI@CE|}cEXmlx-PET=HJX83c`L$%Xp+UvYk8C
zU3z50S{$9t5o2Rx&w+e`T0(`@%JIzB>5ekxNj~U47YnN9hS=zg{|)CXyvVE)?`S=d
zdox<uRN{C;qrQn99@ROX`&v^S%Zk@froUYtMEcOuYHEaoNUE5LN(_>yqwqh2?2`tb
zk!UrwivQ4f!irr=o_#6kPDRGHPwC8)=p^IGiaR4b8G<yq2_~zPe!tPTYccW<)tW+&
zs9C7@1FSf^-p7J2dkMWmR+FVq084FDb~^d*j>L^GVBW(uZzUdxOOEFNAY?gxLsvsi
z0KCf^ci;6K;wNLl$m3@M@D$*-G4L|yq9=dTo@S3n=*F$~se*IOHWZ%cq+n!0ywd#Z
z=KS=Gg%f=;9b#8*H6<FZg(ZgQ{)FlQ5k4x@m)JC;_6@*=heerQ;ws1={}>%51jQp7
z7FM~0Yx}>kt%HX#6#51I+DdF*9QHpL_3Z~{z29Z~DfwPuPfK1rq@!=T_wkl5kd%9+
z7l5az)P7JmuNMrL_D8qVlLtx2a^Me?)A3anP1BHvaTxMFlMNb@m=bm<E)UI`3nXHs
z{0gb9drK-XRg(-a1m0sHy1BVc01Zij$#Fclos?~1X}{_RAFzL+;Rs(B>p4vQ8_O2>
z(kvG54w>|A4(#<>EK=|?A36@l!4dyRg3bBG<O@|(Vr}3ge_tNP?9)mz16zVrV1buy
z{=+Vw1=!6sp$iwq<jH;qi6L#vID)ydFPJ`FH<+N@&Qwc;hq;V4q2BC~ru+dHJ)Xd^
zP;64JnK)MWg9XvvIyMmN=6M>+B|K)8M27TFjztLX3TdplR?m^w3SvGMT-}Xkqzm#N
zi%vh?<@}QA6ozQ=Wn9F9YvW{_7bX^@omxf-3%;kst!}>oE)IjAkB!n-f58+^*;4VN
zzKyv`|EDG8Sf#rQL_IfFl~1WpYOf8$`v8ydj1EAAZzc>!Tn~oYf*uh{U3ygkhIIj0
zQ10%$;fFH(U$ZQ{10V7+t6~y=)5w8o9>hT^6Eu|9LR(!W%g-ccL9zT!he(QPe68iU
z(}N*Z^__OF!#feYKe2Epf~DMS!>p&n(VVis5BHQ(?XB_QlZ$S#OR}C{H6A}_lmd<L
z(IfvS@=+oQ6SMVh4j{zuzLBs55#D<OL?f?3kR>=1$5d-O%NIbLAs5I~{G5+Z=L;j`
z-_yN?msv5X@1&1K7Lp4X4?FzY;xgFE#~pHXE0Vw$e^9#eimh>)s7Q7qq(b~q?B@L+
z4uuB^>}r>ktMFg~5P9}GGAFc(<*D!yJoH4Zt>lvfD9GxMZgk;sJPh?9+PoDyF_$zK
zxU{mevH+ML=F%#o`9{cKiYSug;f4g_sfftVlUfi9qI(q-I1Zr8($~qn4p05Ch-tc`
zr2fG;Ibi+sZFQC!a#6p(o?-Po?e2&x7c@EdI5@)`po<z4g$lS6W1c-V$E(yHee=qk
zDD%(kF@q=A+@<HY{W^3vWGie-RZi5M^GBU_)D%boN|d&I?Xtl0@jOj<;2I0O%vtC}
zZrwaGipn{ZxcnZ~bhbT?1DHF9cVs)He+hvIkhE={8(=Pi_#1uY(aLgx9X$>2ZlEVU
zepE%?NE>^De|*khM>=mu_(8Yr$(8z-xfx(SE#m$%BHa;#f<6Jyt-hqBD~ULno^Sg3
zL-mUA2`K^Y*J#uRKM+<HcwMj<5g#1{XjIYHT~8!?FD@c}Y%6`$Joz|(dvnF=b$d+%
z?t5()yXO8WhS(=ZB_wnk^^Bo)Fe4*F_nDZu__vst&RtsDhl`m1scOvNrz5d+BBb(}
z54OkH+aJPBIhMxnnxd7NV70KvQI5CVha_2r{OPjz5K@0*kznQjjq)jLm)Nsw+re>D
zmTx#1Xg@lkEHre*348o;QAvTsM_J2qTP9^6fS@Y?qzegf{Cv?g9sK;$oZ_c5I^_rv
zQBeetD)QcnxPd1Y3iO#dhD}fv@wtDS2tFwOwj@5<>=p(Rv%*7FViVDoaFkl2`OJ8l
z0dX&VX>|S&$m*Vp&HV=}CeYK<Be>9Q+0;=Ky3fpqW)Ywsotv1Ku2@{O#e*e+*aufH
z-h3?wfVLngv{YXHr;^@PMdJ)XETW?d717WjDlIDm^z5^KAB}%A%Nstk7E-2a<<0Q>
zjCN1edE8F}=zL9Q<T_er@zrVa()5QP<fzwF7JhEjm1Xn<H%*IAF*@GwGi2KrNxjcW
z^7efd{leZ_VX?_}P4GOCe*=hS<a|uD^KqX~-DTs<mq7$1NZ^A{iStfdmNx2+006l0
zNoAA$;%~Qbc(zWO&G2b;-J;;ZVQCYkA5cIV&-QV`<rJkEl)?&xKn+r!KaN2j?;`g8
zO~7;fvSbxBPEum{b!`8Z@+N$vxvcVbzSsIFl`;w%3b0N&Y#kMy2iz;E^eWUDM!A)z
zmQ<|+{azOmr#;FZc%p4F@C>~T!VY!p{(w5hR0lO}byoOR2-osKLaMc%*lY30LhlYC
z@Wi1w6)$|BTN9N!IdXVzEX8+a^Zh3vXHOT$y8YX*zdh$JL+j>mlWt@=-G&j7Z7CbI
zT~y0J?9a-oYX6hif#*%312wmNS_|JK@+Y)QAa$o*47^5iY^BDfy$b$d&dR|eyg&9T
z1*C<&`kk+m`;@P8<Ez#tY<=}0>-*i(8^kC|4m&#@<AmDCE+e^a^Xe^E%zMU#Knjrr
zHnY)T^N6G%Tmhv-;V<e#Uk6&UapD|tJxrX#ZXa-buaqkBCq&5Nd%Lros*3tbo%9rm
z5Mr%YhCZV=`)VrYDoiY=rq%ra$wQ%z*hd)s<L#456vZNh5E_2M&m<NB-8J=zd!Xn6
z7WM=2OpM5#B@wJ;*VtO9GZg|OPAt9O*caxYswWReX4RXnQsTB4lfQXWz@M+>S!cH>
znHrm2o6-dIeclWIgJKzhyG|MXWFjvQ>DK)I3&u60cn*9u#V@!J&tnweDjp@mNVP;C
zO8z(0e=k#7Cw1ucxE?Omp0JmhixyA~mX<eK*yu2*sGAJSx?IZdZv+-Q6I7O8d;+=l
zkyKypNF*PQXwSdcse)UP)oIJ+?l}On-)kf193B;Y%+!I$!>=_zqSfl*iQ;tOiw3)0
z17n)dB{Ajm+Sh+zlfSD4-nNdw7tF~)-hO?|EYD${y&t4`M1qu>>01%=E_J(66EbiI
zy*zWpyA@3I)!S6N_}Mnvi-Mh@o$}GrFI6YSWsw2gxQh2O9F$z_K9ilEpl%<6#@;bl
z>IbB<Ja~*`rv$mX%Zx#<HMx5#2_Z0oGx&L1w4aqFH#YyTq+UgMKdy??blG=T^A`Pf
zu2#$`#eI}?0p$JUfPlP8da6o<0Qvs*nq#t-sC$6HRN-unXBV*%=ICh_g^$LaNc)ra
z#g@r^nA~xyhsWd7Pz}OX-#(q=5|8Pm4XQ+%qH>^nkPG>zVS|77diZzWpRgJ_0^$NT
zGQ?D_7%jx}zN38yw|bqhV}NA6cLgu_M7R}1CoA$jvx|mFAYCJ}9>!KVdAJVsJefDz
zFPVj1KGw>5F-LY9wIl>y@8@K=%HrRC6aoK;)ncphO-*6q;GTRe1w=*STZ-!&M2^3)
zDhu$#aLeNzdl~!T)Yrl4=Qrt<0<%ZjBY7HzHjjI6*fW~0jD?BQWhs-lF0i&Vtx}#z
zW26Qa4}ST-W>H-8U4;wffr{ETX@{XZeugZnC~gD;xr)ehnm{VVKwp|Py&WUF0QcQ-
z1LZ+0O=Vm{Oe(b3YJqwZ$`NxO(tI0UNsGeNlRI>}Ps6OC>(Ki6p1y>{i@y6m%l3Kw
zsW^1!Z_in7E6waGkx<V%=D!WMe7tjS4Wu-r32WRyMnJ}uQA6+@=z*9$D%5(zg6+W^
z7H;k3j>_L8nJsdOQ5r5B)m!x=x&E<C^g0LsIA7%snW`ajDr<>7E+QJ0_dW0<tN;Z~
ztn6er^ll0DL1RbdT?Nx^NCHJt{RJ^&SkW6Q$RIlWw{j4XQ)93CZdYD}ag1m`dmb=$
zFw#DD<(ucb!7v~M?zw0aqPS9_f^hL<e$$`KwR;75_^G|MUG(p#c#BjLQK9vYRSDk)
zdEE^EIGtXqv3F5R+d${anQyT_6GsKVD3XSW`^j{V|2n-a@baY@=LwaL0QFnkqW$rX
z5yPj`^z#o+JosR{CsrKgLWxBoTaWG5fv{}SpE9dlXf;ALA4n|bm<4G5e69a{H!*K2
z4iwaR2}axzVLu%Xp6=SJ2D+(eGcROZ5vpl)_^0U#d2Q~WZXHA;^8OkNo}T<{W=J4o
zDC*4Ce{s(-;7HUQcXvn1ra*qXkktuP^~@eXMY#jy;fj2>04ymZV$z|5^gsIV*ObD1
z70cEJ?$+hW0|)g23nx&<G8#741LFzPL1USfKXU5iNgt}?>=n*Vm1xndS>5S1p+hL|
zhm~T~1j_-uj_9v7I1eQ5At09q-4iumYaI64owGP=^faTQcQeV(EBNso8)=iVQ_*>E
z>g`#>2TdydQ)j<6Zb!e4jwyH`04claS8@T@0hnfyEl^AqBcfomPLX#|dy6z9UY+hb
z2<I=Xv%S%(6~E{6iuRy~y?;W}5Mq)p6<qrBGbSq3fKjus9LTOFKH><OgFLCPxn2<0
z1bQWkZqUrU>Y$$zMNa+VEFJpe<ZC?FSYyJqTTjtUwFZ0LwWnW68&;<ed`2OoiLu}9
zLff=uHk8anQ(bj#JyCWRttiW`-Htkj75vDrhKkp176XNsw}ib6)=7$j?nuDk+4w&o
z|G)??L14f}Kn^JYWE|?wc2QGh)tr0xP0G@sG-U<<UiI=s5<yW2n7Z%0n*mFyAi4p{
z&wTU2G~xpc|2n-|$6QX^nF&7}N?Tyebsi}iZu0W-09U!rQk@LRvs3^plUAeWMcN*{
z$^$Aig$RB(tfJ+8uUqFe3{q2Jz94#uX;`vr;7Q$vPIvtJh8Npmzwq6}N(I05&=z^6
zW4G;e!+zLmPi#vU!7n>bd7|P%b$Ojb&zkfGx>IQ|Y;0;^&WXDrZC>}UdQC7=HsNX-
z?#5MCVe05V)}$nMdylJn@7<|YkBso86xBl%0&k$mUq0<(ESm7cCd~&C&(6aN+}a?M
z0`Krc%~NFD&NX<5ZMpOA)Fc21?6MP`b^NS6Z!gxJ!EH8^AUvP|BrvKpB}okm++81W
z8MUG@YUIbvdh9ni0~t<TWSWXgR2vRhdrRCe%Mze3-kO7)i78I~OUiIu$DY7~)5ZFe
zN}CQL!=DK?AGE?5+IFY-Owc4Ck*<hf5GwD9ZVVEI-kyLC@Qqp0fL5MZsBmG<?Bo>%
zqh5~8F$**zB;brBh=6ZyulD!cD@d^Pm+#S{RMg08eTi1lhR~YcSoRJEb=W(*#)~Xc
zT)Nga`-kwQ9?%THy?o#&a|UwvY2dV?{B6BuceXQaJiK;m?)4(kE{{9EwBhQkmRIKD
zHQ~-m;OOqxVI8eh#LurOAD2oNRe4@~Amw+?S8a`CUzzM|9L2o_g0D#sh*$uGeYPlG
zfVZdR=Mg=nK~o4LjU{Flf4dPe8NO4uxok7t$JCguMr9~!HJK7wVdcEXiY_{T<&ch@
z+|Yh@EtdSu-BZXT)rs@xfb!!;>3crLG$Od}uO`x=gAmly8cYl(y%H*zF`@6A>n4*1
zOzSxLSo%Z^VJF|p6Vm!dx16okN)63@*aWGxmGgeH#3MAi`es0q6o4_T1byVNa25dR
z#<9q0;to<+4HkeOZI+?vG0u81^1}tldq>+0C?zHDhYaArpy?Ife?Y-!o}LX|U7-t4
zJa7oz|Fvo_)yY-lk>zAP`Rn)UA!TVJ&`##5r2E~h3*VLK+BJ+;H614nB#&c=a@$P3
z%TD+Z2-4G)EDWUGZ%iuI(Z$N9$h#5O^C(XZiF%xcceN_sX6`x(-E@ZY7exxE)~gq2
z^^%NRy7#OJ1aTpsR0r|C-8lM`N`V@RM)e*GoED+f&<GZC8|9ctG38L2DJweUlNDD9
zDtlzOV$Eae3;hh{C2%w316$x12Yc-WIS}+>2dsxBq*B7aJ888SzVfzx#ha*eDFQJO
z-4xLCw=R}}U)>XT=Jo+lS0MHoW3Tvkc9MHnw0KSDboyL*4d(j!9{y$PuSiR+ro!<o
zVn65Yv0muyg_&;YJJ}xfRQFksQ_%q|9?MBTjY9=ZFUth#ID7|oK4jM@#uvNS338ig
z_u!ex6P1rotFaRq1;mCSJrtq32hPrWxS>XmP#;hfC$IOvmzKAlC}rMZeAHvuN&MVC
zieG1`+V@8h7s;t4(oBxwvDYocFF0AKZ>X_dW6#O7%?c|ls1R3%$A`UNV3^}9!Oz0d
zWa;{XeVxV*9d%zXOl$zy<7?L_y{_%`Qe|i^-*s4B9`y?sfYaa<I8~-hqV=Q5(b=x!
zl0ARR)?Mw@DPTTU2jb72?KV7Lck;|faR2ONLx*wu*AgI!_3)Rx$$<@&nsnE8Yzn5=
zD3;07qV+Pn@WmTuud!|O+osygEE$GSpbsJy<moKG?vN~Hyn-L|#9#u)QRiDY*V^3)
zO;<a8B>#}*{ao*Whf$;2b%Zj|;_`7X=1HC^VKMYbXZUO@+jLY%qsaY+L-+zmkhhmH
z6B<>dZyH}^^C6kpAmdY_)}BP3YC#fVkKxPXFswTRuYOB}O#9*X2?n#hJV+leY_%(L
zni$crD=yhNi+_btX-|L{%3KdNZ8CiX>^!yev^M@@9IL6phdizaDY<Ocz*foSe!P*R
z;SB}ubQ=)lqLCtkz%4PlH3)?Fm4H5}S%fiQo;ZPog4q<#<3PCKmYZYHeoH(Q)BsMQ
zlVxV1yK@cG1<Hb88x+S`y-KG_vHStn)^r%Pcs@WWq!$UxZslj7SG%@3Ic=dsmT)Z^
z7>M3u_OxW`rqi~w<VhFyMo?nR?)Gjayp49+AaoXn+<RU9{%zfYFXqhW(4Tt*i&8f`
zAZJ`g7z_zoXxC-k!%wC+9-V{|R0l`xO*^UkMAF4(Q*{YymBkGMTN&jpOWN>S0|_h}
zc3f`lao9@2#k6DmP7kEPxyACT;{q&GLY_Ws7F})&{6T?eEA9e&*psFT?WTT5B;_K?
z{gOC#S!kW<?k<7Z!yn~R<kuA**$N34i3%5Ov1VPd&QJBaw|f|!)-0F`oR+yXR;rg<
zcJa<D+wzDe4-P4(N$Bq><XIeMKQK$!PcqcGIwS)JCldk-8k1_)*8(6two3FQ5}F26
zuU{WQhq~NvQ)-yG;rO*~g=J)&Ru`xVRLNmm2A@-O?#VOC`*W$2TJj-dKYU<=(W)eH
zD6u_TS5^1ZY2(P#%MlqAhN@3`;yAk%1y4rOkJ7Y{)yqFz2C+15_+4Hqi;jMSDqyyZ
z7U+)ix$If%v<d1>H@H*@9&9yK&d16Njzuw=F?$r>(PmkM|Kvo8jtg}IKDj-A+{M!`
z@8X3zm2>2ANxl$WQH8Z3C&ibwKkZSvdE`_c{f*Y_0qJBx$f`4i1vpunOK`zq=Xz2T
z1g%Il7J7*ix>?a)5jUB+ep)`GxN2;!8kxa>__T-Y(WTnNK4E~RLC&?CDh}gpt#MET
z&yHI*B1dUccOVh7DYrgfvmB8i^^O%vg-VcU1K*Mf{|0i$x5^MEzG>d<WYW#O9shfX
zv3lNA#~brhOx8nbl9;3*ObnZZ<5?lTD{wXC1N*)#B!W@1PaiqUVmM27`X|JGp&5bu
z<xtIrxPZDB48~l5`&>`hy8AJL`h^8+f-ef@)bZx<GDbJs8rF@6dgdyqfZ&x(WunuV
z_AUSYgC1`Lq#0bbARGu-DOqpVb-p#gZ};-J16M;7fC%%`jr35zn=ld7;6DW#nGhZJ
zbZ&n;mqK4jpP3W-)sEA+zFAS@CG2_3sF70%m+yeqDhNY+LG`|+RkG6arck8sIQMjP
zcf<$<aWhB}SLB|LIheiagh94m*t}j>LEJk7^ZqnK#Tui0&g$z{37-){v92`GE+;=J
z?OtimOU{Hk3*>sLZ_nTO5xm`~Ow`@|oZ2r*=IhR)HA3v}Rfg{u^-O1Inqb5D1$B-K
z;-}Ok<WRJC5U>}gd*E>H=n-0A)T&{_sA%W1s8*}yp$>H_PMcuQOUPCDkU3o-)(<cG
zK|omCqK|qleanMfWP*)EkG+~b&nTcVcZIF9Pv0*HD7@f?=Rlm@+zg20Od{b3guTtq
zRUap;1_j`V{))n|0=W98A0iBlz;gxPu^@+ad$rx=BCmCh92z-xc!&384l93WGEC@e
z&ONFm-ADQga-S!kMC;)>C(Cf*eUeIR6+(Nb;v-~ySFWIo)2~Ui|76DHRCX-iaZI;|
zW^Q}U71VBIplc#_p)^7lZ79oidu_Ar@wO@BB}Y<CDuJJY%b`LPw~~RG)+p5DdRIE_
zfoQ`KgGNU6tixzC!4KG(o<JzcDd$GT)%P{T;LIXY=9)HGvKDQWczB2kZi=K10T!_E
z4{&fq``ts7)`U+&cMr@8&*MSqEb2W`4)v(qpFLIpz6Ry|Pt7#9EAwY1z6>Z*C)?v^
zTt_j#kolZ<Hs)v*e3VO!+Fm(3`zx)3dW{vOf>!G5OGsG0!<dl-R6SNyuhph%))cmP
zy_Hv#517v9we@$mS2hO=Ke&L#8*qLGf$NU8z>QL&Ycic78&1&gHa~On)27mF-gbXF
zhSFiJ(?V(fQfxv+nk1?ySw;PmFo;-VZMrXN-s4CXAhPyogBzM!FiUVpDOEe<Kc!K2
z{|TnaZO|GPIH9TG`{r8Zp8eX)h^OKljc`f5T=dxN4rs{|n(W3VmhEo%qnN*kY>w^W
zS!v(Bh~Mf#z5G=r9$wk;yX~`?i_;pG4QY33W5?GUi*ksdNyPrqHvyHFSo{-r`h(z#
zO-lJo|LQ89AXxP6Ns*Vyl-EX^dm@>?i0LCLG(mS4b6`H%V;GeTBC2@r;L+~>6wa<j
zf($i%S6K)k3fi&zuP;^GlKcolNxAz-ILsHSNT=C-ykSliIb#*$2HSofu?rF372kG6
zJh#0EbK|Yhie?}s^loP#;m<wdXdJjYjncKXM^b1~pFde_j<;*H&~atBxZaKXZUhvY
zd)EnZ?o@hp7rEQ7dzdI~lVz#|-HysMs0_DYcIp|<8v{RV>kp;QTvBDOFlR%lIMhDv
zKa`>J@i!|hj}59q{vb)|RABS@C_`9{F6>b{KKvw-0PP7fU5Z^r%wUdea@%OUUz+g1
zJ~b)VsW`YPM@J7rDBSpIcHAXQa19yutq*hS%s0;5YoO_tyfUqMKc>9WYRcQ^<Ng=(
z2czYegAOidHS=AG%0dmZKZTl%+Av(8`726XR%1Q-%~Tg$jZ*h~z7m4{SZjWz!6Pz0
zE=bjZ<o={>?dDUD&CKZUwF$yrjhl0RmHk3jJ>)|9R8o=W<vmQrX8jb5>aYll3Wu8p
z?KFum#?YUz95s|2DaV!m<oUwdEe03pB<L#0DjbFpMcsGHb*dq1nJ_o|8>rC8{BHj&
z-nf|Tul~(gUBg!-HW8k6YwUm;DbNSQcM9?d=)D+VMd)JbOGakMrBTf}nYuf{n~cjs
zn`s^EWoP^ux1Xu@LN-A&hZ~>_UYA(TGiOzk#gy^ADjWXgyXO4y3(cv4!F_4Yj-2^i
zgORMd;A`9J`C%2Cri;?)e9O<4D&)&mN0w$z=k)V#Bm0{<mD?o!mJ@t1<NCx39oX85
z+??e|&f81kRBG?$L45dB8^fTuJ8Qa&W!UT!IO-8G)H({bbh5um%yDkP=FUKqs!(WI
z@HpOhJMVcEe1VNQ1}*P-GEiowG^~a}vRBYGU_1!+wRfP5{33YRif_wl@{LAvX7jOH
ztTwQc8r#dUHe?am#c^1?jte3Xy!xS9Y27;%bEyPX&RsIuxy@@~@ylYE0^*N;?6Bnv
z<xf-`H9Us2VQ&zxaBgg(A-waayMm9$BlQQXeVvNjcZ4N_wrO}C1CXK|fD{K+cHJSp
zrz!ZK?hkIM7uh)w^*l&p9Ij7qVu^hOu#^X($%oS%R}0%NNW4W^74f?LN6ewQEWQIV
z6Fg`>&0vWm(PK|9c11D*OTC=)PjvcfRo@k$o!NmNS_`ZvILs{WZHbGH0{VHkdO5_Q
zE`iG?u8!>*=#+Of(%O#r$UGcTq=HFSL!!(<TuxnQ|DfjyC>bX~_|-P^%W~I4Bkp6h
z^IVbBa}c-}myOqHrElLxcnU(lvM=7Yf@Ls$zG=J+ZCE?e(F#3{{=6%lTAGD7aGerI
z&oL&2OOi*{K^-QE?04PR*Ldv&=`o9E@v^<z@Kt59D_ro}47kYP!3FOk@z1-s=BBp>
zeITQcW4_Wpx3xBS119oO4(p`YX=VR8gfklsBr}TGO-|`Zg>1Dl>auI*EjURg)Dw$j
z9VnaV(Zz1wO@uTLf^{RvHwXOGfx4NK*-DhdfrG?@_02uox<$E?5y$HVYuFHQ(^9_G
zc;Vczfaya7>`)f9oUqkj;T>F{@B55tcKx#{&P8KC8k7%XAO_2d<h1ZPIR}<d1NCNX
z?5J-3PSf2@kq6UapVM{-)9JuA(k=i(9-KM0PM1$)LLXs`6L?#eurbArfExULokV#s
z6$i~zXECa6j2$PKm|c8*t497j;fdr*Ph`uUt#+HDS;cwIE)Q5ZzTn}p$5b17wGV=B
z&W^^BHmfBD@t2Z@hxxxzeH?IBn4U%{U_P!5*|u-D8%_)ya9P@F;g1PaR`QibE-e(n
z72N<VumVC0uxigXOAsmoV~!lY<YIzf4#VSP7}2l*RWN-8RUnbbV1fs6jdC~W`*%M-
zGI;;U0%Wgc^JD2lF&;UQ>m2tM>A(MhvWGE_5ncXLPl}_L)@?|Ze)9Kf_v{`}g<Hho
ztDP;)-`!T}LrzAC4@)O&l@ugXYP9Fw*2$+!O@b2b+lfynj6)~C&?m)Y1!?fl*E;3t
zL*3q`Gn79I+spfD+YrqLvze=(goIkn$t5O3lgjUXhdrvGdTnaXrYV`Jkvrbs7zZ<<
zbX$4Is9l<I)v$&D0eO$FAsYv)(S|y}qqqZ9lpp*r>iGRHn$8mE!%g{`SlXsI7;?O-
zwjnq`#ZqTM{Q_kgP~>N`EDiK%V#D}LZpe5dR8cQE%=*ZjHM_gU>R)|B#$CVz8+;xB
zQSW=9J}T4$fG~tPEQB$VcTgjr{~D*$l^^Sa`LzjoVX>)`V(3}K%<{Oko{dudjSLmU
z$;P3i!qV0G6-4DB2|g(*HEy*CyxAh`sn|LPG52YEhM4y(8VylALC?fl&?Vrqr{^O2
zeOQs7$=VC!D*-|7)(%)uF5<Pt{8Gh0lJv_NzyWcvH4Zh+`SAi>ZeQB;Y033mUJ^o(
z$vi(V=!L?Dp_k;b$2;8!sUr=8S+eL27rSDIYMSR$?+}mH2XYtP8MOsYCc4aR`f1N`
zQGEQ<ERuTXiK$X2%zXsdLD`4Zt{Pz^6igzlD=~MR4Z*xDNe7)OMXy2|yD6m4`DZf@
zfFfF+%t^j6OLA++#&Ox5GSN|!DRKb#S+<swVA43+(!@OUs`ut%Sg#J_?CNCO3<F91
zCO^3!^z4H-N3RUxaL!J~RiVm-TVoi64ApN-VXQY(g@Rs_@{~<)tP<Qt(`}vvMU}?0
zkiOB`87i_6GDPXVEMrT`l3SdubJ6{HAKQ8?U;EH8GBc!i@p|z}DT>cnk;sP!g~0DY
zT;oruVTs^u((gx|Hb`+AbjS*tf#kR%3+4ctzR%JCDyX{c&(nyT)lSRKTyNK^MJ~67
zYU3eXZzZT8v(sk?jH+qYRFGVw4Ij<@3i7(4I}eVAN3X0VG~GM!K4sCj9-+Of_x2@I
zSAO2A>Ya<r8g~jLV$<Mqxt&6a-QGtNKz{F1osB5|IejsXp+@at<|#;}h#~s;rNUy=
z1ck~V$fZxPD0w9mgKxij`N3H&C>dj}pPyNwNW(i4UHjZOPs?-N)f}8*Oj^awib5AF
z1Nw+p280j{_j0339cN4##E)N7XdCvyT_uYeAKmM=*WZ9vf6pHYW9etb%Dy&A*d;Da
zgELqUwRu5#zQ+w`bMkg)0m<W6DvLGH2WR{9`}fN+M1X^m*q&0_O1eO!NZ63z)}kcU
zv^Or(brY4swfW{^DA^c8K=}GVyl$s-#KF<>8%=?05ypPG-$e(WpA??&mXlNeiCG5X
zjB6Z&GvR|gn40j_WG3j`qxpNCvhcL(Aw>;m>-Xd9Ed9wRyf!+IczfiCdhHSwg?=#S
z==FTyCrssl*G^?<fHCMvbiHlKw)wTyX72)>CUluzS9Gm$9Zjpg?24yP7WCXrKRwO4
zn@a9p>llsJ2pWeSidF(>WGXuRhLM`atw1$zq|~Zz9`{$_+cRo3?CzgZ$RV)i7*^wa
zVB+4h0%ZRHfW#Ja3=CUL2#qXra>whvVyw;!{SN)CxMv+zhXU8Y##K^kVtkh(E&iSJ
zg+7yX2w7HY5zGh^b_0?K_72{tA6W1>n5#PFF<gQhPmRG@SiwE-$?o_lUI<c4I<;|4
zpbP*>?olC8+c1B<k@9E5X_7%R&oO7PuMMdXbtG~O8r=8N_(9LFfS?)6W-?Pmm}I4r
z`o8yyI2Qnon>U2A!v`KWGw=)+)4l}aU(}m>>6Ws+)Ok2EgfAInj`NljtIDwEL@8^Z
zosalq=p4vXHh<zm@quxSqvBiYym&QQld3Q*spmK-$pwT+K-ZwVG5owy^YLY4S@S!(
zAj2fR+*<v?5~aAvl&$N{>||$QEtNNUI4MgOzr>xG0>(h`?ONN-2Bx`btZv&E(+Kat
zwDFxOJKa<AQUS#c?HU=O?tWhx;x>gQG%DO|a55)&3;Fl!ALli&{#H*mqn*|(FmkHf
z975o-JJcinz4>h`tusWiW?VaXYC=*`a>IAkYpNTh+W-FbtZ94vgfCCOLNP)*v!M=V
zj@sbdv^!fxze_&sdMeei^{(T^zbXOutG5`^4Xx+x^+nd=Vbk^RApTa4p}F<mcrzUz
zZE>}R%6Gr;TwLoLDPO+vt{VtE)B7!L$J0mwFsuF*rMBY=F&b=cjhxgbvyaxGy-(&}
zPdCiW)kul@80SYc@4j7-Yj6wHKhBR#zc^j`sF7!th>GU@+y#7;kOhl+nGmpw%FZ82
zUL(edJAm7gY;#smvT(2CtP1U$)KAhFaKz&oL;ydqVXK&D$#=}NFOJ!po%ix(0KAo}
z3e*;^a<|)R$O~L^UEjf8n)!Z~ibD#DMkAdH1E1Z_Z?xgUiAz>2@5TsFK;Y@C!-TV7
zhDCKw$mM2sox_Iv^su|$?kh$P`!(0IvsxSes;9d;)yfY+ds0SHRnld`NN=OG9Rw_`
zv03>8_3ejTb`+YB6tyNO<V^fg(<bi{q@c%9K-Ao{mNWtLKm6n-SaDfM1k1xZlSjEO
z6zsiT#8!2;-{T9~zhqbEeQG=yWUq8Q`5sft5YrSkJG(B#s8bzN7uJqCU%&pUT9_%T
zv7F&5GVxNUkBrxtPpIId+6kZQxxDj2^KH2^=G6>n5i0=e=LcaP3Pw)-CRaj<_yg12
zfbRy9fvZX=0izg66CUJH{|-Re$waRqe#N(`8e$s5{jQ08xx%f3!V?Xt%^3P(>jy`d
zT`xj0f(W(Df26J~rnx{dXdKB)l_zpw{R3HU<b*k}DcjSX$y6oO(*{HerkRWsYY?uv
z7pCoGkS2ldaywq`hk)vgH<>}(dzh0t9_QX{<sO>Tr9{{Vh6}B(I?S&EL_<#tN;qs^
zVke(n9#fo99K%pgk{ESHyl!RpV`M0B$oW<@v@Bigp{AUzmwu$S@AH&S8h>H!{={S{
zXK6hDDM-i^GE)4ur8qm2*MBw5YUt-1ofp4~EJn{kWY<`tCi_gXDzw9*4kmH7=fF^R
zv{JE*LG<8?ec$E!6FPoA$Oqm_ey*d%7?4#Xda_<4l<>N(crN-f?D;oRQFEGk{X#(7
zfn9PD`8cQxl-}`6wZ}oqxXBpV8yuw2bfpbLvdem#sY>dty#2w(Y|bJV(;<6UjOXg|
zqjUovS?Y~oLE91lxzL?tQkzkRXgnsF-Acz0^)P%Nx!M)Wm|K+66z~KKn+AE*37n1)
z?oAw@V-&UEtY&dcG7FzaD2_0>;e$={__e!{Fju>cTqz5Swh!vR;A~(bKdW9cZ0)sH
z_9<WyCqYs#iEY)ZGKJL7KQTE`RCHWryzKJQ7>Bsb2dG@8e5m|6{TnN<35%a&sk~v-
zMG-VmRfj=Qzf|PXsI)Y)FArES@nqanBD9yz+e~s#Z8A_D4TOoQ$>$+z3fqbMVcDMz
z3q;K+ySs>!+-29uG`#Hx8(bMEWwT3Mtd(*DPrxqivfe);kN~r7*sYwN+4?o%{`Kye
z0=aUTslwSu6DHrR=g+RE&JULXMC-ga3>(=S&5NEd+;i@&g57dv2!O!CEB$VMbSBFT
zs{syD*s}+v<UH@23uML-UthkI+E<!wN<loL>gNn-L=*d{oPh%d29)I?-@!)ME(T6T
zKfjbY;=Q@L>HA)IZjVWrkxwpPKjw4X@#li<h0bMhP?9E1O}Hnyd|vH~<nO)lb^Wjz
zmwCE*9y*K-skq#9W8$0Ibn?x81IqlRy+KsOZ<x<KRS2^YBkr!(h4*k(3o6OfhF|L1
zoos_diXR2fFvrh&x#z9HG)%ivt3Ueaw=KM3Ggo{%0Ofp?im2a<2}^le;Jn{(R*Mk6
z#xufjL_D|x<9$_OS-DpNTUK++`|L5Yg#DJ2KAq^397UmNt5fSxHneK!B=`HF7H4l!
zF0Nc}5s6N}g@H1YGC}Wk3ZJ5?rmN^aXe!w`5=Q)w09yU&`QuT~N(=&UF;=1RYm*pH
zp|_tsV0>p*zZi#TS%>t}+~@V2M%8UycgO9Nr=6JM=ADKzWQn<yilKA<*rcW)ucoT*
zJaSDH`$OMK%8R94AkZ)n4S}=1pk{l90g3~G)6=HP$#K=D(%7S{$<(HBrmRQ5hY01=
z%O99ImxEg{`~)0UrU9)vh12w^WZx_4%@(&_Hz3|P0ijo6JJcRX_?*HvI>RP!tf+k7
z^lRDO&0(4DY}oOqBgqHrUQT(*rZ#a&_=f_jzd}RTt<>?D^lF;3`jU1O(HHcsZtGG0
zPkY}P)?~K*YeStNiX#C9DYjwgDosGTh;)<$qz_oAp%>|j1{4%jic$hZq$DAP7J7n$
z*r)=bg(5Yy1Q0@y`rlE{oO}MydG5D+KaF1uPe^#*ckjLS+P|{a+&iBnqqpwasZX*G
z-VWR#4t9Im&I2F8yA)lV-{N__B&R*-#es9cs~@}*eqTH*6rj`t>JdDnm{%>hsmJ6G
ziMoG2=zx60O2c!kG1qdgF&4ImF(>u4mxw;qyU@);%d~9ls$8PEuq#rb<wG8ZqaJTe
zr?UxmYIeNZOsj7`G9l6fau+s%<1TZhVD$z?4?QcpO2Aj~-f&6nxpvRv_5m%ZxGS7_
z<Jbe&Azi7dY=`c(bt#+9HY96_7mVDDK|?0AA0voA#O)`D6|s1ko4LEC4FjFnjLNMM
z<@>trg5Af}#;Om6Zv_jNKL|-FM^IB&uHLE^_dDf+&GG;cfXZXFf%L*>%ZERy@#hk@
zmmQG9nNOZACv>X?+yD94g4+tvhJnS>n>E@L(xdMET1V({KB_-79KT5cs(0CgpjDZf
z4rxY8@b<2?%nq2se3eizGM1vAa}Q2{of-{71<ViL0<`RnSPL1yU0>)7_5HA&R+#2T
z-Q_MS5`kLZA4(f;KkTkIR&PylZR(aLsv1);seBg{@V?cQz^He+%E#^QO)OV+x_|G#
zRqjo_%&O(NFjLE)-v3cNS*=#&{`yImjm*iK)Q!4%)Y94s_}4T=6E#{ozw%CmwS?Kp
z%_J0DPX#~-q8b^l)Bq2?MQl*|_p%vO=!Ln#+JI7*@Un5E?U$6c{U9ixH`Z9vb(MGH
zc%>7=<1GONR^rU%6FLQi0*AK{VV>vCLUmpU;EC|qpJl4kpg=lNzirxw*|9@mOz)B=
zY-FXyoho<QLbIg)5?7v)Pz<2<se{QE9~g#ju_f&)FgP^q6W<GcvPpscY&ws#+iLh!
zk-yZXoGQr)d@gzkg2+bjD;FSd?L8!vJ{@T~$Y)!@+pKJ^nU6gLV7~DZ*O@)Y=#kW|
z)}>ub&XfD#_QAuLSJ#cyb#;BWT5}9uRKv%yaovtK{NaTfFlha;zS<C6Py4=u)Cy0Q
zZ98InYm?yz1#d!3!n_W<bovRUzS0I|9^^fpzV&Q@Y7B+}Ryk^?cwcp-=kvR&gKFhw
zNjM=m`gl{K=%+Rn*$5-<)Rg&yG=QW3kS2f_x&O-EFx**V$`iAQl(`4qy5_8|rU{>@
zG&ktV6>%BpUE_46w|}&4sfm>xP`!mr=5kXg_Dv;LTx;B<&2_jgpAvmsWMti$(NMO!
zcm}^@X*-}Hw@j>^ubAtd+gqx>t)|+7?U`vEHK8fN36O}*)Ixgfqs+d0_oro2yFjPm
zPS~z+?Ug*Gw@VKfGkdpouW#5%yM%lSZEbInuQ7W--h<Y=7<x}5iZgVj&ne*3nHLv0
za|ItNU)Zl&1+eNWXhwfV_r^m2<QFjpYigq1G!Mz}K<F~15d?@lhg7Pg20YvqGQ#s7
z>DFI3W5|_fG4(sFvt9-JU}J^qnJ^iDfNqUWnly%WI_hB$#Qkx}Y$3<%%1)2loLbOu
zvBzd-=86w!!Kd>$O1s|23AzD<E^gg_YTHHFb7v-d{M502u*U4k2v~2RBrmyR;5CrY
z)LMrXWbSOS#GU}VabVo){Rz{AGO`J|C^7QRpi^CPsO!O&#)w9!#VVaYU>y`29?(gK
z@Jb$C`F=-1JiYlN*3k2u7_tDkd8%rTnq8!&LF3NsZ-E3p@@0#qK>XciQw3Z2vkjK$
z%>>VA9iI1;i<olev>$PzDNTm*XcG{pB)PdD>lzm$EUg~#@r7cY%fxq(mgWTquVikQ
z23{tmz>*SNqt6Niqat$TF%&t9tN=;`PJ{9<<n}LaEB={Jr%u>=tO``Oz00gCZA<r3
zNCh-`Sm5Ej_Omc}VMBV5=_y27dQ5vuQ@N$8lkyJ2u4ECpYpN^SB1d;3kJ>T&L3O7=
zor{jsmfE8v*IFQoc@JvPa+jw8mT!owb9o$KoC#6CMGt;I>Y0B2R*Zm^xMcUmM<e|J
z-x`cDz>nh3YK{0*=6V@&zRvf7y>;Z~EOmw=q*h*`%RzOa1FRB;0>>%-jyd^3Pq<WW
z56nMb=Y`zy@R52XgAq&`hu=+-urOW>`=$n!bh$#re(icOnGN^KWj&gd=IW7)cooIO
zmRzT}`&>g7Z><BUJYh09sbbQycAmUGFfuiR0|F(nU0DDLIW3ibgh7lP04F!abs#U1
zvL}a@-)iDr;_NJYULna0L}6pHT_8im->ZQ@BS<n$ToxLG<F{1boqEc`_u<TaF4Bkl
zuBisseTT6*a2FhnY1cmL=?V%Bn_%1h6;Cg0lfF-;dH9p0uR`%w1L7)dJ%`tes>|$5
z=H7$DfK=$URzfgLR-Tn=g~;In>b^qo=uv6tToM)JMeLsu%s;za9qU;lK7qZHCYj6)
z+vnEWo<U<7AC3~=6XreTi-{tLl3up)jnuba*D@u9Akh{9Gif}3ZLQ*tM8-Y3xCgEd
za<6hFvk2nvIwn^j+Hn7_;v2g2x9(ipaqj@HY1Rl{yZ|eWyj?8EMYQ<yRj&Ry{1xFs
zv9l@a5#hO+!Z3J9YQt6S&rr1>c<4wW2Go^icrCH@IkrTl0X2}<d*L*h>E!Ovv_(0t
z`xcd$6BVh$XYsiXrKz%42{`i{eW)3SXK=%9s9bPEtS$h!4;q+1#hh((OZDvHd_zJ6
z!;siWJrcvWO_g?V+WaWC$S~=Ri7GQuMOFLTjWX^yEnA~LQ-Yl;OALwO|4hJIa7FZd
zo_6KO5=4xRc20whbD+uudN$a|_sb9vMhsOPTgzXfgr)*)Akf0*5X&*O?hEJLY?Lr;
z)1`9hRQ$!!@Q+!C1kP)l<rCI(b{LNoh-X0GO*{SZ5ZTMF{@qz7=(<*<6HEFoPvV2V
zc8bdCoj)$?xSryaF-@lNkZ0SLMD9;LzA&7%c6vXT{`2r3St<7g2%9I3z8E%SvNEFh
z1(`L}=RCDa9Q5>)(3YR4cFwCh;1mj$Gva2hI!p~(4AJ(4o<S_F7_H2oxIZ@^aZRS^
zuwEWSd!DqmGw>OD_r62Dr##heTB95mi{+5$z-96WRGZ&YxzWnP(|vQow20#J3>vdM
zx79SLrBN9WtR2T+&9nIHT3&im6+J8XliaMHh|3S4k)SZxA{9jklE$68AWl3T?@I4H
ztA`+YA?kAOiU6(bFS&K7O*Y-G@+f1qtQ@0!t)UJ&>ALDwYqT@N!aq-A9*8Ix=+`99
zOT4^xrKi$+tY}@#6n>%LL<v?7e&?_oTdS#(b8lDucv6N7lLgp`nk3Ya8svDkrJmK&
zV{GKP=jcUjINaYIRBjn`;}WdLe;1JVvy^y_(>FJ2V8EFem)$(MNCgf$KQEmF{V9{h
zI~;S%xpuOIvf##%4-vh=^~|r9cS|29etl<=xxGbfK)9)3HhKsE2IL%QrCZ&NFFfVA
z=N6RrR{QA>ju?AMD8XBfwWYsYvs}cY)IkrXK%q9Tp1h~_JL|$DI_LXR<7I2)jC|4J
zH^WTNhOErZ`Se}pPDOag^Tqs76Ypn_q%R-*?f1J!-V6HG*I%c8mr}M|?^NlP;k-??
z&OG#pD8Q<eovkQ|r82!}P3aqTkGz?b0>-g`zABPrGPbFm&jVJ9vHh~BzkYMztkXe?
z9@<2g8Rp_N@;ZdT?`&@zu<POUjcny^s^!R=_ws4<%8d_wW`U|V*Z_IP=wbFT=Z#oi
zx}z0LS6J)LlI`X74y%{iIA|X9L9j#~&XXe+a`e74c0X)K4)-PP1n9%}N91PGW1td~
z=EuZunk`ImRHA7(FN?RRNtv#*-^x-W)i}ZzZEEV}kRR9DJk>h9Xhq&*mjm;Q#8<oI
z`~lUDe6|;j{R$)E>`dUV`E0y2v7L*&cGuqIV9khZ<QYHIMLM7;t}&9z+tX2umg*Mq
z1Em@n;&c4B3A`q6dVz2xP1zqSU>JW~>HIR!V3nwR&fQ2=qBCtD<=nT9MXYi6X4_+<
zh&+j};cR`KxK2y#EIj6e4|y%6dtFbsi`^1i;jcG$VS`hud6Xge8GJ4`tj^8pMXtIp
z(-(ipp+3y?Y5qZ=-3@lClKvS&c|Zt#NmX8pCb%yjvj0G-GEa=i-_BmC*KO;K@oyTN
zG2lEeb~<#Fu6xSr!Jl|Ovoo}Y9<|$TO0=XQEdv*3Hq8f6`>bU_XA|<$bsKasOF8uj
zv(DJLHgH(T)2ul@H6tro)=7db=4CTF`B7FlapSj*pk3K>xw5{R-;qbKdzMpq=N6-W
z3!yrOb$RsxDYE?Z!am{9c3ZTni}nHgDI0@i$?1b5D=MLF8LEtpm;B%GP*SJ$-75_>
z#FA1Wd6t8m5#=c)QI5yXC{|8KcFuap3FV)x!pe>7iBy3w<Pue$^SW?j#o<QKED=k%
zYdBz%Ts@e1b8_<cC(TJyuVM!{?p6oTXUvUV^GjJHFNZ9G<Ie~u&oc$xxWx@xwrAv9
zcH_{EXlTLEl8b8aQlNNQXV4#+X67SVhA<9XZe{`)CAct1Qv-J24s$--dIG<s!rL}|
ze<wWU!@6(bq3g?<v1$VgkvHdq<+yYK-!rhoR6X7mJKnvpI@bZj_f4ot<-_dbIrqnU
zuzcPQU)<b1LbhO>ha=xptIMl;)ixOm>T+XroKb@BT+OlJ_tYmLp@DhTewAj8$1ZVx
ztF=<NKpn;3VHQrO-=<l8NJf!7gTN-0eVY3cH`WZUgPOeGm7WAiiQy4*#Fm>8Gou{c
zb{=V&$medh4i$Z90DG9*=*pAKrVfDu(+NDH7@V8bj%=)+r?+qm0=r;pbT2mQPdDZq
zAW(;Rw!{YX37BheiAoE+6@{y*gKEFC9jLGeTnF2~8fsS(&sS;L*Nv>s*mpwn&zqrP
zQ;8haLQJ$nGuD-|kq|D`Ap%RPO3l?k+g8q~5+YD9c1g7G;aHgrlzS~!&9jcp>##H_
zS=D?aRs~?<jNtSYgZ^3v6)vilQldgoAlRNXfo5W_!=3ZK8_Vy*MK!q<i;*&UF5()`
ztLI0q0Cu2}=T)x%kmZQdW?Y_^%@UuR+BHJISyI2%oYA~Mm}%DbvRZdoi!tCdRR$4-
zP<9Tv<DR6r1~>o4lg?dQ+u=7%MFcaga*|u@*Q`at;9~R5=PH8rM8aIWUn#Xd-4wB+
zKt!pd@Mw!2aTV$<`0AU?(K|KX@jdb@q$2yDzKKiH+%Rse1&qQLAr8b;0D8O1!L%-X
zbKq<fCnwa^B~yg@@bIZQfR_2<8aB)e;#Zh}0Qf51{z~NPJyao$P`Bk1JuGpw%Ri^W
z^y;@BSOi26?w^0xkmBPa&+!OiOWYq*e-K&Azqg>>XnWdC%FqlL4Te3+{(Pq8WOVH7
zW}d4~%XB`Ov<1`5gI8=?z1GaH*FYBb+wb(YGIA)KlwamWC<6_q%07&Fs)Yz<N3&bM
zu68yF`i?{oek{MAQk;4UM){n2#GDe9I@_IA0c+Fkyop(`nr{SYe6{b58P)8N*Pk0f
zpT3$w7wMMqr2J>KQ57fG1Oo@2-A?j39d@fR>?V#ICXCG&yU?=>4w+{T!8uC8$#U^n
zi=Z?pK{na<kNr<{p4|AE3ut`J2SCc+Hr1c7{0Qa77=81BV{3aUes+xhUe(Umxp25v
z;VId;5Z1t3xGMXUES`uxx};@_7WH!M`&=GMFKKT%7iM4MY721t{+DqRB0$GsEAmmK
zONA5pJ4AwqjKN%kL9<=4N4vbb3%Yndi(P=-c@f&{Th{Y+^I(>1hZoweY*P@wxfy9H
z^;dQyhG!Ln@iFnedD)6NgQ^>nz?w3#9?;XScBc4Ug*yx-N@kT>im?lFK}(!kp!B?l
zQ@VE)1}pbGw=o-Vpc$347Zf+K{O%B)ZOrs$;dc`zY0`w^x8sZ4JU{WM@f1_H)8ynF
z&J?D8e<)-E)GIey)6g>GIh`4K>TsbV$qm>POdisHMhK0sFFEgQ@7E7Mxu!EHa6Tzi
z#a{Q1vrZ#2E5mJe@$NLg6>-b5L^jHbBApCT{`ltViILjIy2~r|({pg5mVDkxzpKh2
zIxBupX_T@6-`Sv{iD#s<A;2E;*RI9AXyE>kYth7T+}}v8PXh%?u7fIo1CYk+lMc(X
zDiVmE4GfTv1x7E4zxgEIBC{XPk*{S7r(uGYJj7pLgWj}~h;pl}+y$fKvTT-+poL?$
zgI+Cs9)1S*xqZBa^EYjaCq1tWu&)hg!%}L6Tjnun`j=K0%p3Ead5`Ofz@A<ih8-n_
zuo!wvwNX^X0R<zmt9_W@uHyvIiS0mzLHSVbf>o?6M#`?X7td%h76ERWX%Aog59kKS
z@qmW#tLMNdJQl?fmvQj*m`L;!MNC+A%S)D-2#tb)^9jgLmjYvyPh5K;0;L51uunD*
zJE@7dS_c>DxgrXfqAHDxn)&7rK~KNf!SAA*<_}lu)lttTm%1)K@2he-<6niuc{CSD
zLp=S3iM^em+-;^`YHEd<_E!?OsWyZ7G~WDX4|gC*v7QLL&^03&2Y~YH21FT1-gd9k
zD;CT;^BrMky9a!lr1ToHN#Vr&=i<7=mx)97?c4^di8c9&lRJ$FE@a?T0cTb0vWuV!
zfGYy;lt(cl0L$VqOoe#JUnF~t)U%n~8sWXKDNa+qz!*{|z%6m1C-h}m;;vK`xNpAr
zN4sK2ozJ4BZv(+I`ud0OqhHU+pZ$#HzmhlC=UQp|devLEM-hUp7lz3wh3*t}`d_uS
zeH1=sMW8MT`VqT$XgQ=8RBhF6J$`+COpTBxQ`2I5$ED>x#haN!nG>Gden{GEO!IC#
zf{n@2+}uJm#>*V;QSv^!YyTw~ZWHeQ{Hvz|HHAsoC>^)(cjHFdpW*TsfFo*lpsKXy
ztKK(S=~xd|1coe#N{km&Q83_kW*AC&`F)>wK?MdDT1Kh3n*TPG5v2Ww`R?&C*#RX7
z`A|!Hi7W_WhH&Z8?|RTIyG9xi{7)V<IW%Y=t0U%4iM)V@W<b>8KtO7y&7_o_O{}-(
z!mx6k_S0p*vq0W`W(p8!8KJ(Z_MSks!Q~B~#wdVh05vQ9iJdk1{4n22Y`=W+G&j$j
zi-)b$L;0X@<8p1H$)fTs{4O`6TZjG3yzr3TvdN++EO~jP7AOK`#L5CHNXE(Aiz_LV
zIMMwru7Qu2bbg=`A_8nwxY<bf;>MZv8`JZEGH!$KMsyU+(?2Mgjm{YOJ{byR?pF9#
zbJ&}J9r~3850J^3Oj{dT&d3`3@DiS(-Z`{AL$6$OHUFSQI7o75yt1c1c}93ov1l+8
zu%72EjKLi`2()&+dq#iLlJ5!w!VQMw87n9W7;MChM*)ATAhqynqzSSK+tif}{8+A~
zj`t#H$dxMM-{ITMmSkO5YZ!Uh<MOBNv=g+`>aW7LJgDU@cy@ErJg7MABYvPWQqd}X
z3Sjt#%FUcwa3ib^vaF;!@*0N?<O>{$%`(FkM=OxFd4d`J#HOf&f-_N3Ru~f4@K%8S
z7Rkt%32EuWh{ecB+M%sp8*$nNao(k1%s#L!YZWt{Dt7RK^X&s!FKh!+1UiB^wS(4Q
zv+~E}@(Quuoz^xIByznfft4k0oFHQe^{BgEUM1|M%#s*Tjm@d^`oL~Pky~X;Dch08
z>)7N0w)C1o??S#`Tl$~C`*2PXaj*1gds9P3JZ{MfKHFPYZAX(#E_*8!H1@Vq+)&0p
zvZ)ib!}s5Z%B{HRf%KeHb9SPpQns~^5rf5zLcw8wHHS^_DV*~o%3sJq3$Cp{Ro&`7
zRqoJK{jyaeka019mBg~djOGk31+9@p-6?BH=a>L+@u=C1sljjjHgQ8a<33=>)b8Dh
zG{tumd{={dxJ(gxcy^IMyz#i+ooggeajopivy(%eeOp&|+JmAAgA^*!r%Efkd5Epx
zf|Ns#&V<4Rv2QvEVm4`K9U^ODSZwaT$VijMr8w|4D(O=^ka|GOHshB2-3V2bc%Yr|
zwn(&K7DV;B9VOeFP2{v1;aX-%dF`+fm*7GG3y|SxetMDbpf)n$klJdNfFE{Oha13F
zD?oMeFI?(S9ydnka%-bXcSvF>*1iFr8oa(|@z32U9D_V-lYn5E@<>?g9C!zkcVdt)
zT|CQz!!^JSL>+pZFX>m*(F9TFah1#3zMfd^Owg{}>S!nDc=NMP4@OW8T+E_THGIw_
zn?5U+?Km@=C@zm5*6FeG5dCVFwn^KbLtXWJ5A*&qyCH$s!=Kt-U0>|MD7ZIApF>%U
zS+x{6MK=vbT*uxAgtPMPHeJwmPOE)uu&`pLRe!~fTICi^T3+NV_vqyJG`kcJ(X1)2
z?G;bPj<=>IKihVWnR?Ls+dcO`L9oK2*)2K>t&?&V^q~F_QrVR!leVxouY-6JqA&G5
zy)bvCNpHzo{EC5E#jRdc-^&xbu;$%GUR>7kIbsh$!SPIi)mfT`(Gq+ph=yW(URYBy
zm3J>QN~!?0U%a;Mjw|`xEQ=PM+rqBFnS@ABu2>VHuj0<qz^!K;RG@LOUx!d4&adfg
zPV!Ax{K<azT><LTF^?4nsRpqCnwNu9S$6Q@7XQAyC3$n!cmr-IcwhY1h?~G>gT+wA
zVO8-xq+ElDrm+XePu9Tb=e&Q0$!BnspFc7<Oh+Ipic^zBe#NB?jWm}r?=+XZSX-$H
zGy-^lONn7W*OJ*MhQ~+RkB9Sr1};h$(A;ZH+#6o!GW7A>jrWxe3G%Nkm+sm5d^&Tu
zrR}A+Me_HM40*R?qUZ1syS2wGpf@MgNA{>_OU-~*=!<fA*jB5`eC5&$@yBL6^7LKc
zS|2)i7|f|J4V#u7qmHR=Ndeb2C^$&wt=IZ~E!)`dF2vR3`Wu^h^GsPNg2TgHZU}gK
z1U9fovp|1ghTQL#i^`5lwBxNhAL`C)ND`XQ{hV7O+(ZD_cip@y$c;EU!v|~z*w5`>
z%<5vCF9ZvUULk#pBx3-)M+l0gRWF5l^q0S-@~m9t-0QD4dlF}VYQiSuQ(%SyOH@F~
zp^~lY_&y1<$8|6bozASpS_K+l_izpB?VPN183vS{v7Ya_++fPAHNZ2jBRepB<b|~X
zB27j>JuKa5^HvF+TGvBOpD7NYRCkw|`wFZuig?<lg=N5z)I0_edLKBkB(mOIYtV-t
z2gjwTpo_A}D)4!J{$Y%nXdqg6Yr~*10k809e-)#8)^H~sw4q%UIbl7xIVEFG`2b8&
zzK6QjCzCAf9+49ygZF4U^OC&N6@HKwE8sN$WP?0=B%<XO=EUTe(2}|jzwHxk-DtIZ
zJrx3sHZSHmU5(p))BkD{I5=OCJvo_v!8a1~yYKeBFtGr$_WT`XMxDL|K7F6XGB{aJ
z3iV<+H<xD*fo%G`nO8ngoNKBS+uU-yR+A!dS3JvzK=+S)FHAG^-&#LGvB~@Zm2|hd
zJ2m$nkH7Qmqq*aYB;Ko5WsYb(wYC*s`KMJS=)8KTME(L1o#exNH>PY_Xk$J;6EX6Q
zqx}6bX~L}Zwfu)Do9UY>Ex+AqN|to8QtGoF>6x|QG?{WbZA0DaQE)oC{fA<rw0Y&r
zqzmMmu=!Z8uo3_)G?$&M;0^hb9t|W;M#T@g)O2+<pjB(KSJ84(>yZ4CMIf;ZXfTCe
z?BKp)kOk_?vV|^z(&orlAlWo@V?79t6OluEi1LA<0WjYbIDUh6k#GU?zMD>zl`}X#
zQDjKa(s*f1j)c;^8@6tC^M*~CiX{=pB?0eBPF+R{ybGc{f=I{mig`RSM%<(utM`0U
z3d`%MUAP91YwDIX0#wL#OEQeZ4TgJ!ViaU$$at=Wi8Xg(mwy;t=|d@_Ih?=&2D;eH
zM7?NC_cbKY`zok_(;eMYrrtY=5HOo7B+sbCo+P>qbnEj-rHod24bSzLHsv;FjDtRw
zfcq;pU^BmEX8hMdnWllpXV)$sn-rL@WxMjr-G|d14-Fc?lgknPtqO&G<An=pq0#Lk
zu*(@Q=AJ7eKLGBk97tfUe|jZ)IwFPuOc(n1*yc+HdEFyW`bLXeKBYXRR8jv@0GsHJ
zi+lqpB?M@ut8nupH>+(LM14wO)nsb+!sis)89>3bk-i%KJjz`GrWCo0&&*wg)wXW1
zv!9W`)ouLuC8$><q1^Zktt!`4*2bFTO?+~c<z3AwDkkaPQ$Ruu=0EYgH6q+JNfjN7
z>idJX(c#MRjn6Oo$vQJ<#N$TzMn8Ge<+1fds=0l2VYl(r9nGdzGA$>j<JpnRr;#P$
zn=X{^lX=7<20~_0ke_guRr)A1S4nbcw$24L*`2+4Yto`v89>bnjvJ!W886l(;8_K9
z8=^#G(3wM8qFWoNFs1LDdHVFbtZxp&{!jR~hKY#4Vb-PI-F**t>=N5^IbMh^-Jh)!
zT6T>EXd8y~WP+n8S(BabUAVmqyMuDlkCRuj2owfP)iRY5Ng4z}J)maHM&`#tO$K!k
zjKbD=B3&C<?J;#-Awqo}wRW5}tLZ|RNCL%JXWF14!^HQ>#xuKv5tQ6dO5GR=5~KBD
z_{GL;&(M`rL1n+Q%iP2BRG>6Q1>XL^6DGE2^Yd@Z?~R0KLu@KVt>m2%e8lPGS2Fax
zuipR>7NznoFfHnnefU<An3gX6;&j9jx|wL@<6IZ#5Rf_QL!-9jSo;K&*X}>8=`RZ?
zMOczsW>7Zj)3voCejCf4h_2UdCm9AR3g94R+l{usvM#|;MY6m4Y&5iefh=OO^_X(_
z#vPTh$FlD2S(*&3<9+E-O+V&J${RMg0jh2SVko&P_KS-q9=H@vZ*KWD8_yjjoz<K=
z7fHMI^I-c>2TYcn{y&_qRA`yv?Sz2SqAa<agfg36-A+P7H0`%mk*|Xkv!`X`4m>%t
zhiM1?-}LFAU5(DNvqw8P120*Rh4WE<1F-2q`J-I(w{^3JSs8ItS2d?1?Rl*}If|Y}
zq9Ixm=~mJ+++87>Eycl>(M<_MM}`7~z@!hp2OJ`OKy*F`CxW(z$HVp=zsHUV+z;=n
zz3dQQ1PHwBWW4;T{aoV+9dJ@)7nnFM2-2EUt=PoSZzx^F6WK)7?tn+KhU*9Nl$W#2
zh~F2UY8q#y`h+(*uh9oF1o?|O&yKuil<c#sQGA6S4d1k<ZeCU)>Kj>?2c)R3ZzY7+
z=#*{d8hHBiNp^&$9pXAR)jGoW<(@-8bv<U>eH<3;F+>I>xI_xc((}C^QaZ~XfvFC@
z4eWT9s8qfOGqYycE}zMJc;{P9FN$?)w%c=GQYp4;*0xSNCHUFXk9|jXHAaEF_>Upg
ziI^Yj^!Mw)JNB^!PML5!Wv5nhB8s4YA$I&g?V4uV_w@to3u%Ejrba%Vm`F#)GiJgF
zPuNZ>S!UBIlXnYnBCzG>8tm(hP#1OtwvRY;^r`4BHtjkeK$3kBRw<hpR{u=YaaBK7
zWpLVNEN_^r;!|tU>K&`TBcG{&$#JqbId*H<Sm21i#i#r2pjL&|Qnge6%5JKp=k$Va
z-Q~4i@Xx55yMqT0|1?|vx`n4mP-3FAy=4^kbMgSE#5bY#vnw1;W4J9d&#;<}rr)IN
zN{HUD<Y1>9{Z<w^SS5FaX(v3Cy%y}hYA{sctvUqs)9Y<DCcXFWuaC{>?z4~h)S@un
zX|a2HHcF_X(qTS>RFkbn7}v}{SZKW|XH7!nP@M1hbcG>>yrPe+VCxfq>IHsm9M{3d
z@yK7jivI_SxQFc?3%xI&xEV+D&<o^XUKu)Q^(nMkR`(XS)JJmJowfr`P^L(KRZE+%
zIcoclhlO*^omNVm(ngTi1amHH>TAnlI3{W@`j&ojDM=~7y5(}-yD*Qzemh4gfpln~
zy5G-%{PCx&*wr?AHov<63c6-sG|$HKDs*gQY07ap&nn=517;krc@c2~|MP4m64>4L
z`q<?u`#QJ&L^h=@8k6ys{8#KnlL)Q`<}z^@Lc7RvWh7E65N&gB4;m68qn4*@NNfDN
znhS8fvSa(wkU;B!pQkJscmUCQyaX$Enb2iF@yzQAJQCOH%Nf1Lo6wlJFX`E7sHD03
z&N&8&L}psg-Y-cg8%&gx&7zY?7{{^pjO*nkQuZ~Hxl0f(E%t8s?f46orvM}-siG)j
zY+LV-iMwOhqch+zcb%1h`w?!hc!BlTpm-<A?C=^)1B%w%8#e*S_dDjrCR;0%SE>d*
zl6qvZNrHf~1gR}mU3PLTLHPRSJF{IWY49-D6M4fgnhl1+-q}a*MeA_#$u@19h-f-f
zbLl{%&3Q_VFW{%L;0G|z5AM%$OwBv_=e1O_Z+Cb<yKaC=rZ6yiqK#Y?eJ4lLc|WQY
zSPsg%_Y>6jbXd&3r@PlTd*558EY(WPHFyqjz9uYL`}tS|A9__Pjj!o*I&%447u=cK
zs`1$EI{)oSjmUOT+NO9-p8uVn-SG#%?iyq!L2PxyJZYr)*C>PM5d2`g5#84JDv2}B
z{o$en(#}d=?V-ceX3gMV!VxNRPthwZ8~OIV5#OF8w%x2BTLi40<vJFzU?1O`Ft2ty
zt=!`wjsOlNy;<Sg<vnVG$62-5Dxb-cee3F@h5W)z>Dg#a=cQmN_lfce^Wxi8m4)X_
z7_}=4#Q4+!=l|=7oKn0qG+L$b?ziz#7XS!3>?Q<#yuG2&5*4>4l2K?FD?Qjr3r_>W
zCVJ<*+9MnR$m06s0p_&WSx3B@pcacG9CY#ng1K~0^(|#e)RXUL3(5QxcSj4qxv}7|
z1!fK43(Lx757B~NP}7lhAozr+&XAw)L#Ga9F+MRRo!%ZVmmTiTU)K-UXxi&!T~W>_
zI{dEZ-`D&byBvau;`64Tq>B(3OkKv(@b(5GzK(gtCMo^bO1s7Hx(!@Wh!2HyT|1G_
zgkQ3dt`+*Q6Nt{vpfu01TZeQ5gxhD0aiayTWT#=Fknxs%2wI__ZpPzLT;z97CC&N!
z9{$;dSHl2=a3)45ZVn4+rG^aOm_P-Y9uiWX>h~O|-uig(YUmH3VD@te{_&B&X4KS}
zjIr|fDJ=Qaaztmqde~&BY@R;RlHQ5;LZ39RF-BNd`jQcSLex<Uc7hs$(zoFlXS)=B
z;7&X4y#uyFquP+q&=c&nbGitekv^x@gf}UNxJ)gb<9L}Wuw3FRQ<1LZTa7BbUQuti
zwHid7eGvae0e(0D(K+~O+~+vMZ4(l5tC!RGX_ZQUVZnzgReaE(Y(ktht>8GpWydQn
zb5H}NeT)}piq*PiFMvWX&Peo+tUxr^ZSFjw_>03y<p`R2$lGbRrSd03xetY)kt<+h
zpm_1xeoKwCV-nw1Wj-s9*xLGVJ8U}^^ipav{?q)kY5_pg%yUCsDhLRbY|c<<q#f8N
zaaL&8*LiQk|2>M9h9CsR4xTms0rZH30N+zCdAs-l8VynbH*o&N?ZwkyDGWZ>YJW{m
zKf65P@Pz}%e$S%J!TH_>2mQU!?rRS~_V6VNJ;FXAIyba?{d_yW6=#5BlyAgCK_d+{
zx85Q&g<wBS_W&A#t4Q>NtRdU4fM))+6x~4(dPlCa5&W^YMuNRHstyg&Ru@u{8*EJA
zxr3rCxU0-Jb3PZ>)DE#3+4Hgi#SstcY4EPWV&<1<5`Q65%SwGyQEB3Pm;p8u2Uy+M
zDNc)xX#`~bI`+;nmAXa_mqJ*fmuEA8NNB^kRCL`TBwf@gF1`C}*ZTm*F~I^@afbuz
z%lj6pq7vt&Fhps1=Ey-K7Ikr4pJI!{Lb2cJjw)OI9%H#-ph;Tr*RSEjn{x9m>$0t-
z`Z*Hc8QIvL#m*0X>>^u0YZl7F0js*(@W#c!wH<XRo{l(>ZX2boXAAm7eT#Nel8mO{
zt`5@;w734xzzlSgslBWhIBoX)yK?p(upssV)%Z0VIH<N%;NDjvR(`9;GbT~e9X^xp
z+}OlZIu^t0bL^nd1!t-vz0BXBhx%>UydQmkMEl+NZQzH9hsszti}zq}Hh$(IYUx0#
zO@w<R-jpK-9Nb<MmL@G(P>1(z8;8G~?@=bi<iUzYb7C#uj&WALogNwtL99m!mfv1f
zW)0LjFv+Nm?IS^)<HbIrFNlt!Jj6(487H}|3{*(^Qon~AZy}LXv$6YKJ9!D(>;`p%
z6}`%duh9CycQMqiW~I8P^7a=i>PV$dzYnbiF@aN|!DkkNCP0x}CBAw7o_>blGZUq!
z5`oe*{-#PhWcqqOd4)dYM73RKuJk0=om7=-N|33Th1VwDuz!6wWzFYtdExM6FTsCq
z<??`q`Jgw@W+kfxG75s%{)PL1tn(#UuYV=_{oDaG-g5#e?dLyCGSU}i5_b@u%32tk
zWd(R`Ynl}uGci+#jPYQtLTwv;`H#nH81CA4@HhYi^x^>2_#+sId;uxKe@a|a+bdqg
zYKv&IbB6~>#=p2F5l%K*{Wk5r*eQ0<xM>e&_FTw(mCqhW;OBe-j~n~{_l;h_{z6SK
zQA=s>`Pc~PsUi@h-v)OG{K{f)+Jb8<2${d&`y=CW|FZ~a&)C&~6l#5>kL?5(><m`z
z1n{hMHz*zad#xS?pL$T0WV8S8WccqbmVL*oVDYYfDiZwH@Ba#>zkdAvG6*;h9%uL>
z|Kme{Cnw?H62s5CvyVRi3d8??^+U2|^wRnh^6%gOcPIm|?q=@L%+EYP{WTQ->pMgo
z*h4sJ?sMWlKJ@o>d==H;o)K*b_%DALT*8%qZu`GI^nXVCKj-(4N&C+v|F5CppSAr@
zNc&f6%@!a36UqN|B>&fj^v?$N@6G=2SO4tW|83(H`6pfZU-=#T$A7Z4e}$62)7t;{
cIr*yka)Y|_Rq-+A4)9M;$M{mA)?bnT14#_@cmMzZ

literal 0
HcmV?d00001

diff --git a/doc/theoretical_description_mondrian.rst b/doc/theoretical_description_mondrian.rst
index 5a3d01145..8e57fa188 100644
--- a/doc/theoretical_description_mondrian.rst
+++ b/doc/theoretical_description_mondrian.rst
@@ -21,18 +21,23 @@ of the data or not.
 In a classifcation setting, the groups can be defined as the predicted classes of the data. Doing so,
 one can ensure that, for each predicted class, the coverage guarantee is satisfied.
 
-
 In order to achieve the group-conditional coverage guarantee, MCP simply this the data
 according to the groups and then applies the split conformal predictor to each group separately.
 
 The quantile of each group is defined as:
 
 .. math::
-   \widehat{q}^g = \text{quantiles}\left(s_1, ..., s_{n^g},\frac{\lceil (n^{(g) + 1)(1-\alpha)\rceil}{n^{(g) }  \right)
+   \widehat{q}^g = \text{quantiles}\left(s_1, ..., s_{n^g} ,\frac{\lceil (n^{(g)} + 1)(1-\alpha)\rceil}{n^{(g)}} \right)
 
 Where :math:`s_1, ..., s_{n^g}` are the conformity scores of the training points in group :math:`g` and :math:`n^{(g)}`
 is the number of training points in group :math:`g`.
 
+The following figure (from [1]) explains the process of Mondrian conformal prediction:
+
+.. image:: images/mondrian.png
+   :width: 600
+   :align: center
+
 References
 ----------
 

From 56ea9222859afa260e14bfa7b71319cf0e926445 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 6 Aug 2024 15:51:04 +0200
Subject: [PATCH 272/424] FIX: change image name

---
 doc/images/{mondiran.png => mondrian.png} | Bin
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename doc/images/{mondiran.png => mondrian.png} (100%)

diff --git a/doc/images/mondiran.png b/doc/images/mondrian.png
similarity index 100%
rename from doc/images/mondiran.png
rename to doc/images/mondrian.png

From 1e2ccb5fdbb055b028c8a64e0f6d6cf19bd4341b Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 6 Aug 2024 16:19:13 +0200
Subject: [PATCH 273/424] ENH: rewrite quantile in italic

---
 doc/theoretical_description_mondrian.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_mondrian.rst b/doc/theoretical_description_mondrian.rst
index 8e57fa188..3f59fc47f 100644
--- a/doc/theoretical_description_mondrian.rst
+++ b/doc/theoretical_description_mondrian.rst
@@ -27,7 +27,7 @@ according to the groups and then applies the split conformal predictor to each g
 The quantile of each group is defined as:
 
 .. math::
-   \widehat{q}^g = \text{quantiles}\left(s_1, ..., s_{n^g} ,\frac{\lceil (n^{(g)} + 1)(1-\alpha)\rceil}{n^{(g)}} \right)
+   \widehat{q}^g =Quantile\left(s_1, ..., s_{n^g} ,\frac{\lceil (n^{(g)} + 1)(1-\alpha)\rceil}{n^{(g)}} \right)
 
 Where :math:`s_1, ..., s_{n^g}` are the conformity scores of the training points in group :math:`g` and :math:`n^{(g)}`
 is the number of training points in group :math:`g`.

From c0532e4350a3cc7f5262a5acd02a8e9b16feee5f Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 8 Aug 2024 14:56:57 +0200
Subject: [PATCH 274/424] FIX: typo in docstring

---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index d8ad47cbf..2d3a67448 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -121,7 +121,7 @@ def _check_mapie_classifier(self):
             If the underlying Mapie estimator does not use cv='prefit'
             if the Mondrian method is not used with a MapieMultiLabelClassifier
         NotFittedError
-            If the underlying Mapie estimator is not fitted and is the Mondrian
+            If the underlying Mapie estimator is not fitted and if the Mondrian
             method is used with a MapieMultiLabelClassifier
         """
         if not isinstance(self.mapie_estimator, MapieMultiLabelClassifier):

From 53fb8b232eeae134e3e71a205cc2754656970b34 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 8 Aug 2024 14:57:55 +0200
Subject: [PATCH 275/424] ENH: put public emthods at the begining of the file

---
 mapie/mondrian.py | 254 +++++++++++++++++++++++-----------------------
 1 file changed, 127 insertions(+), 127 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 2d3a67448..0e72bf299 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -111,6 +111,133 @@ def __init__(
     ):
         self.mapie_estimator = mapie_estimator
 
+    def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
+        """
+        Fit the Mondrian method
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
+            The target values
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+        **kwargs
+            Additional keyword arguments to pass to the estimator's fit method
+            that may be specific to the Mapie estimator used
+        """
+
+        X, y, groups = self._check_fit_parameters(X, y, groups)
+        self.unique_groups = np.unique(groups)
+        self.mapie_estimators = {}
+
+        for group in self.unique_groups:
+            mapie_group_estimator = deepcopy(self.mapie_estimator)
+            indices_groups = np.argwhere(groups == group)[:, 0]
+            X_g, y_g = X[indices_groups], y[indices_groups]
+            mapie_group_estimator.fit(X_g, y_g, **kwargs)
+            self.mapie_estimators[group] = mapie_group_estimator
+        return self
+
+    def predict(
+            self, X: ArrayLike, groups: ArrayLike,
+            alpha: Optional[Union[float, Iterable[float]]] = None,
+            **kwargs
+    ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
+        """
+        Perform conformal prediction for each group of individuals
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+        alpha : float or Iterable[float], optional
+            The desired coverage level(s) for each group.
+
+            By default None.
+        **kwargs
+            Additional keyword arguments to pass to the estimator's predict
+            method that may be specific to the Mapie estimator used
+
+        Returns
+        -------
+        y_pred : NDArray of shape (n_samples,) or (n_samples, n_outputs)
+            The predicted values
+        y_pss : NDArray of shape (n_samples, n_outputs, n_alpha)
+        """
+
+        check_is_fitted(self, self.fit_attributes)
+        self._check_not_topk_calibrator()
+        X = cast(NDArray, X)
+        groups = self._check_groups_predict(X, groups)
+        if alpha is None and self.mapie_estimator.estimator is not None:
+            return self.mapie_estimator.estimator.predict(X, **kwargs)
+        else:
+            alpha_np = cast(NDArray, check_alpha(alpha))
+            unique_groups = np.unique(groups)
+            for i, group in enumerate(unique_groups):
+                m = self.mapie_estimators[group]
+                indices_groups = np.argwhere(groups == group)[:, 0]
+                X_g = X[indices_groups]
+                pred = m.predict(X_g, alpha=alpha_np, **kwargs)  # type: ignore
+                y_pred_g, y_pss_g = pred
+                if i == 0:
+                    if len(y_pred_g.shape) == 1:
+                        y_pred = np.empty((X.shape[0],))
+                    else:
+                        y_pred = np.empty((X.shape[0], y_pred_g.shape[1]))
+                    y_pss = np.empty(
+                        (X.shape[0], y_pss_g.shape[1], len(alpha_np))
+                    )
+                y_pred[indices_groups] = y_pred_g
+                y_pss[indices_groups] = y_pss_g
+
+            return y_pred, y_pss
+
+    def predict_proba(
+            self, X: ArrayLike, groups: ArrayLike, **kwargs
+    ) -> NDArray:
+        """
+        Perform top-label calibration for each group of individuals
+
+        Parameters
+        ----------
+        X : ArrayLike of shape (n_samples, n_features)
+            The input data
+        groups : ArrayLike of shape (n_samples,)
+            The groups of individuals
+        **kwargs
+            Additional keyword arguments to pass to the estimator's
+            predict_proba method that may be specific to the Mapie estimator
+            used
+
+        Returns
+        -------
+        y_pred_proba : NDArray of shape (n_samples, n_classes)
+            The calibrated predicted probabilities
+        """
+        check_is_fitted(self, self.fit_attributes)
+        self._check_is_topk_calibrator()
+        X = cast(NDArray, X)
+        groups = self._check_groups_predict(X, groups)
+        unique_groups = np.unique(groups)
+        y_pred_proba = np.empty(
+            (X.shape[0],
+             len(self.mapie_estimator.estimator.classes_))  # type: ignore
+        )
+        for group in unique_groups:
+            indices_groups = np.argwhere(groups == group)[:, 0]
+            X_g = X[indices_groups]
+            y_pred_proba_g = self.mapie_estimators[group].predict_proba(
+                X_g, **kwargs
+            )
+            y_pred_proba[indices_groups] = y_pred_proba_g
+
+        return y_pred_proba
+
     def _check_mapie_classifier(self):
         """
         Check that the underlying Mapie estimator uses cv='prefit'
@@ -309,130 +436,3 @@ def _check_not_topk_calibrator(self):
                 "The predict method can only be used with a MapieClassifier," +
                 "MapieRegressor or MapieMultiLabelClassifier estimator"
             )
-
-    def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
-        """
-        Fit the Mondrian method
-
-        Parameters
-        ----------
-        X : ArrayLike of shape (n_samples, n_features)
-            The input data
-        y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
-            The target values
-        groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
-        **kwargs
-            Additional keyword arguments to pass to the estimator's fit method
-            that may be specific to the Mapie estimator used
-        """
-
-        X, y, groups = self._check_fit_parameters(X, y, groups)
-        self.unique_groups = np.unique(groups)
-        self.mapie_estimators = {}
-
-        for group in self.unique_groups:
-            mapie_group_estimator = deepcopy(self.mapie_estimator)
-            indices_groups = np.argwhere(groups == group)[:, 0]
-            X_g, y_g = X[indices_groups], y[indices_groups]
-            mapie_group_estimator.fit(X_g, y_g, **kwargs)
-            self.mapie_estimators[group] = mapie_group_estimator
-        return self
-
-    def predict(
-            self, X: ArrayLike, groups: ArrayLike,
-            alpha: Optional[Union[float, Iterable[float]]] = None,
-            **kwargs
-    ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
-        """
-        Perform conformal prediction for each group of individuals
-
-        Parameters
-        ----------
-        X : ArrayLike of shape (n_samples, n_features)
-            The input data
-        groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
-        alpha : float or Iterable[float], optional
-            The desired coverage level(s) for each group.
-
-            By default None.
-        **kwargs
-            Additional keyword arguments to pass to the estimator's predict
-            method that may be specific to the Mapie estimator used
-
-        Returns
-        -------
-        y_pred : NDArray of shape (n_samples,) or (n_samples, n_outputs)
-            The predicted values
-        y_pss : NDArray of shape (n_samples, n_outputs, n_alpha)
-        """
-
-        check_is_fitted(self, self.fit_attributes)
-        self._check_not_topk_calibrator()
-        X = cast(NDArray, X)
-        groups = self._check_groups_predict(X, groups)
-        if alpha is None and self.mapie_estimator.estimator is not None:
-            return self.mapie_estimator.estimator.predict(X, **kwargs)
-        else:
-            alpha_np = cast(NDArray, check_alpha(alpha))
-            unique_groups = np.unique(groups)
-            for i, group in enumerate(unique_groups):
-                m = self.mapie_estimators[group]
-                indices_groups = np.argwhere(groups == group)[:, 0]
-                X_g = X[indices_groups]
-                pred = m.predict(X_g, alpha=alpha_np, **kwargs)  # type: ignore
-                y_pred_g, y_pss_g = pred
-                if i == 0:
-                    if len(y_pred_g.shape) == 1:
-                        y_pred = np.empty((X.shape[0],))
-                    else:
-                        y_pred = np.empty((X.shape[0], y_pred_g.shape[1]))
-                    y_pss = np.empty(
-                        (X.shape[0], y_pss_g.shape[1], len(alpha_np))
-                    )
-                y_pred[indices_groups] = y_pred_g
-                y_pss[indices_groups] = y_pss_g
-
-            return y_pred, y_pss
-
-    def predict_proba(
-            self, X: ArrayLike, groups: ArrayLike, **kwargs
-    ) -> NDArray:
-        """
-        Perform top-label calibration for each group of individuals
-
-        Parameters
-        ----------
-        X : ArrayLike of shape (n_samples, n_features)
-            The input data
-        groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
-        **kwargs
-            Additional keyword arguments to pass to the estimator's
-            predict_proba method that may be specific to the Mapie estimator
-            used
-
-        Returns
-        -------
-        y_pred_proba : NDArray of shape (n_samples, n_classes)
-            The calibrated predicted probabilities
-        """
-        check_is_fitted(self, self.fit_attributes)
-        self._check_is_topk_calibrator()
-        X = cast(NDArray, X)
-        groups = self._check_groups_predict(X, groups)
-        unique_groups = np.unique(groups)
-        y_pred_proba = np.empty(
-            (X.shape[0],
-             len(self.mapie_estimator.estimator.classes_))  # type: ignore
-        )
-        for group in unique_groups:
-            indices_groups = np.argwhere(groups == group)[:, 0]
-            X_g = X[indices_groups]
-            y_pred_proba_g = self.mapie_estimators[group].predict_proba(
-                X_g, **kwargs
-            )
-            y_pred_proba[indices_groups] = y_pred_proba_g
-
-        return y_pred_proba

From 791d750e94012fd945eb94e88721ca9cbe6d350c Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Thu, 8 Aug 2024 15:00:15 +0200
Subject: [PATCH 276/424] ENH: add in docstring that groups must be integers

---
 mapie/mondrian.py | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 0e72bf299..629a8472d 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -122,7 +122,7 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
         groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
+            The groups of individuals. Must be defined by integers.
         **kwargs
             Additional keyword arguments to pass to the estimator's fit method
             that may be specific to the Mapie estimator used
@@ -153,7 +153,7 @@ def predict(
         X : ArrayLike of shape (n_samples, n_features)
             The input data
         groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
+            The groups of individuals. Must be defined by integers.
         alpha : float or Iterable[float], optional
             The desired coverage level(s) for each group.
 
@@ -208,7 +208,7 @@ def predict_proba(
         X : ArrayLike of shape (n_samples, n_features)
             The input data
         groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
+            The groups of individuals. Must be defined by integers.
         **kwargs
             Additional keyword arguments to pass to the estimator's
             predict_proba method that may be specific to the Mapie estimator
@@ -299,6 +299,7 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         X : NDArray of shape (n_samples, n_features)
             The input data
         groups : ArrayLike of shape (n_samples,)
+            The groups of individuals. Must be defined by integers
 
         returns
         -------
@@ -390,7 +391,7 @@ def _check_fit_parameters(
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
         groups : ArrayLike of shape (n_samples,)
-            The groups of individuals
+            The groups of individuals. Must be defined by integers
 
         Returns
         -------

From 325c2a99b2a70c5f176b4dd3d0faefee76f54d9c Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 12:10:04 +0200
Subject: [PATCH 277/424] ENH remove MapieCalibrator

---
 mapie/mondrian.py | 69 +++--------------------------------------------
 1 file changed, 3 insertions(+), 66 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 629a8472d..348356b54 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -83,6 +83,7 @@ class can then be used to run a conformal prediction procedure for each
     """
 
     not_allowed_estimators = (
+        MapieCalibrator,
         MapieQuantileRegressor,
         MapieTimeSeriesRegressor
     )
@@ -103,7 +104,6 @@ class can then be used to run a conformal prediction procedure for each
 
     def __init__(
         self, mapie_estimator: Union[
-            MapieCalibrator,
             MapieClassifier,
             MapieRegressor,
             MapieMultiLabelClassifier
@@ -122,7 +122,8 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
         groups : ArrayLike of shape (n_samples,)
-            The groups of individuals. Must be defined by integers.
+            The groups of individuals. Must be defined by integers. There must
+            be at least 2 individuals per group.
         **kwargs
             Additional keyword arguments to pass to the estimator's fit method
             that may be specific to the Mapie estimator used
@@ -170,7 +171,6 @@ def predict(
         """
 
         check_is_fitted(self, self.fit_attributes)
-        self._check_not_topk_calibrator()
         X = cast(NDArray, X)
         groups = self._check_groups_predict(X, groups)
         if alpha is None and self.mapie_estimator.estimator is not None:
@@ -197,47 +197,6 @@ def predict(
 
             return y_pred, y_pss
 
-    def predict_proba(
-            self, X: ArrayLike, groups: ArrayLike, **kwargs
-    ) -> NDArray:
-        """
-        Perform top-label calibration for each group of individuals
-
-        Parameters
-        ----------
-        X : ArrayLike of shape (n_samples, n_features)
-            The input data
-        groups : ArrayLike of shape (n_samples,)
-            The groups of individuals. Must be defined by integers.
-        **kwargs
-            Additional keyword arguments to pass to the estimator's
-            predict_proba method that may be specific to the Mapie estimator
-            used
-
-        Returns
-        -------
-        y_pred_proba : NDArray of shape (n_samples, n_classes)
-            The calibrated predicted probabilities
-        """
-        check_is_fitted(self, self.fit_attributes)
-        self._check_is_topk_calibrator()
-        X = cast(NDArray, X)
-        groups = self._check_groups_predict(X, groups)
-        unique_groups = np.unique(groups)
-        y_pred_proba = np.empty(
-            (X.shape[0],
-             len(self.mapie_estimator.estimator.classes_))  # type: ignore
-        )
-        for group in unique_groups:
-            indices_groups = np.argwhere(groups == group)[:, 0]
-            X_g = X[indices_groups]
-            y_pred_proba_g = self.mapie_estimators[group].predict_proba(
-                X_g, **kwargs
-            )
-            y_pred_proba[indices_groups] = y_pred_proba_g
-
-        return y_pred_proba
-
     def _check_mapie_classifier(self):
         """
         Check that the underlying Mapie estimator uses cv='prefit'
@@ -415,25 +374,3 @@ def _check_fit_parameters(
         self._check_groups_fit(X, groups)
 
         return X, y, groups
-
-    def _check_is_topk_calibrator(self):
-        """
-        Check that the predict_proba method can only be used with a
-        MapieCalibrator estimator
-        """
-        if not isinstance(self.mapie_estimator, MapieCalibrator):
-            raise ValueError(
-                "The predict_proba method can only be used with a " +
-                "MapieCalibrator estimator"
-            )
-
-    def _check_not_topk_calibrator(self):
-        """
-        Check that the predict method can only be used with a MapieCalibrator
-        estimator
-        """
-        if isinstance(self.mapie_estimator, MapieCalibrator):
-            raise ValueError(
-                "The predict method can only be used with a MapieClassifier," +
-                "MapieRegressor or MapieMultiLabelClassifier estimator"
-            )

From ad8faab1cfc4be09cc00320e1a1c19cb607258ce Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 12:14:03 +0200
Subject: [PATCH 278/424] ENH: remove MapieMultilabelClassifier

---
 mapie/mondrian.py | 38 +++++++++++++-------------------------
 1 file changed, 13 insertions(+), 25 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 348356b54..0c34ccf79 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -28,15 +28,14 @@ class Mondrian:
     """Mondrian is a method that allows to make  perform conformal predictions
     for disjoints groups of individuals.
     The Mondrian method is implemented in the Mondrian class. It takes as
-    input a MapieClassifier, MapieRegressor or MapieMultiLabelClassifier
-    estimator and fits a model for each group of individuals. The Mondrian
-    class can then be used to run a conformal prediction procedure for each
-    of these groups and hence achieve marginal coverage on each of them.
+    input a MapieClassifier or MapieRegressor estimator and fits a model for
+    each group of individuals. The Mondrian class can then be used to run a
+    conformal prediction procedure for each of these groups and hence achieve
+    marginal coverage on each of them.
 
     Parameters
     ----------
-    mapie_estimator : Union[MapieClassifier, MapieRegressor,
-                            MapieMultiLabelClassifier]
+    mapie_estimator : Union[MapieClassifier, MapieRegressor]
         The estimator for which the Mondrian method will be applied. The
         estimator must be used with cv='prefit' and the conformity score must
         be one of the following:
@@ -84,6 +83,7 @@ class can then be used to run a conformal prediction procedure for each
 
     not_allowed_estimators = (
         MapieCalibrator,
+        MapieMultiLabelClassifier,
         MapieQuantileRegressor,
         MapieTimeSeriesRegressor
     )
@@ -106,7 +106,6 @@ def __init__(
         self, mapie_estimator: Union[
             MapieClassifier,
             MapieRegressor,
-            MapieMultiLabelClassifier
         ]
     ):
         self.mapie_estimator = mapie_estimator
@@ -205,19 +204,12 @@ def _check_mapie_classifier(self):
         ------
         ValueError
             If the underlying Mapie estimator does not use cv='prefit'
-            if the Mondrian method is not used with a MapieMultiLabelClassifier
-        NotFittedError
-            If the underlying Mapie estimator is not fitted and if the Mondrian
-            method is used with a MapieMultiLabelClassifier
         """
-        if not isinstance(self.mapie_estimator, MapieMultiLabelClassifier):
-            if not self.mapie_estimator.cv == "prefit":
-                raise ValueError(
-                    "Mondrian can only be used if the underlying Mapie" +
-                    "estimator uses cv='prefit'."
-                )
-        else:
-            check_is_fitted(self.mapie_estimator.estimator)
+        if not self.mapie_estimator.cv == "prefit":
+            raise ValueError(
+                "Mondrian can only be used if the underlying Mapie" +
+                "estimator uses cv='prefit'."
+            )
 
     def _check_groups_fit(self, X: NDArray, groups: NDArray):
         """Check that each group is defined by an integer and check that there
@@ -293,8 +285,7 @@ def _check_estimator(self):
         """
         if isinstance(self.mapie_estimator, self.not_allowed_estimators):
             raise ValueError(
-                "The estimator must be a MapieClassifier, MapieRegressor or" +
-                " MapieMultiLabelClassifier"
+                "The estimator must be a MapieClassifier or MapieRegressor"
             )
 
     def _check_confomity_score(self):
@@ -364,10 +355,7 @@ def _check_fit_parameters(
         self._check_mapie_classifier()
         self._check_confomity_score()
         X, y = indexable(X, y)
-        if isinstance(self.mapie_estimator, MapieMultiLabelClassifier):
-            y = _check_y(y, multi_output=True)
-        else:
-            y = _check_y(y)
+        y = _check_y(y)
         X = cast(NDArray, X)
         y = cast(NDArray, y)
         groups = cast(NDArray, np.array(groups))

From f9b79e22971b09c1cffcc1e421a4fc0c7208de26 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:09:44 +0200
Subject: [PATCH 279/424] UPD: test with calibration and multilabel as wrong
 methods

---
 mapie/mondrian.py            |   5 +-
 mapie/tests/test_mondrian.py | 172 ++++++++++++++++++-----------------
 2 files changed, 89 insertions(+), 88 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 0c34ccf79..43d9868ad 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -103,10 +103,7 @@ class Mondrian:
     ]
 
     def __init__(
-        self, mapie_estimator: Union[
-            MapieClassifier,
-            MapieRegressor,
-        ]
+        self, mapie_estimator: Union[MapieClassifier, MapieRegressor]
     ):
         self.mapie_estimator = mapie_estimator
 
diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 02b754f48..f5d1822b1 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -32,11 +32,6 @@
 )
 
 VALID_MAPIE_ESTIMATORS = {
-    "calibration": {
-        "estimator": MapieCalibrator,
-        "task": "calibration",
-        "kwargs": {"method": "top_label", "random_state": 0}
-    },
     "classif_score": {
         "estimator": MapieClassifier,
         "task": "classification",
@@ -77,23 +72,6 @@
         "task": "classification",
         "kwargs": {"conformity_score": TopKConformityScore()}
     },
-    "multi_label_recall_crc": {
-        "estimator": MapieMultiLabelClassifier,
-        "task": "multilabel_classification",
-        "kwargs": {"metric_control": "recall", "method": "crc"}
-    },
-    "multi_label_recall_rcps": {
-        "estimator": MapieMultiLabelClassifier,
-        "task": "multilabel_classification",
-        "kwargs": {"metric_control": "recall", "method": "rcps"},
-        "predict_kargs": {"delta": 0.01}
-    },
-    "multi_label_precision_ltt": {
-        "estimator": MapieMultiLabelClassifier,
-        "task": "multilabel_classification",
-        "kwargs": {"metric_control": "precision", "method": "ltt"},
-        "predict_kargs": {"delta": 0.01}
-    },
     "regression_absolute_conformity": {
         "estimator": MapieRegressor,
         "task": "regression",
@@ -123,12 +101,47 @@
         "estimator": MapieRegressor,
         "task": "regression",
         "kwargs": {"conformity_score": ResidualNormalisedScore()}
-    },
+    }
 }
 
-NON_VALID_MAPIE_ESTIMATORS_NAMES = list(NON_VALID_CS.keys())
+NON_VALID_CS_NAMES = list(NON_VALID_CS.keys())
 
-NON_VALID_MAPIE_ESTIMATORS = [MapieQuantileRegressor, MapieTimeSeriesRegressor]
+NON_VALID_MAPIE_ESTIMATORS = {
+    "calibration": {
+        "estimator": MapieCalibrator,
+        "task": "calibration",
+        "kwargs": {"method": "top_label", "random_state": 0}
+    },
+    "multi_label_recall_crc": {
+        "estimator": MapieMultiLabelClassifier,
+        "task": "multilabel_classification",
+        "kwargs": {"metric_control": "recall", "method": "crc"}
+    },
+    "multi_label_recall_rcps": {
+        "estimator": MapieMultiLabelClassifier,
+        "task": "multilabel_classification",
+        "kwargs": {"metric_control": "recall", "method": "rcps"},
+        "predict_kargs": {"delta": 0.01}
+    },
+    "multi_label_precision_ltt": {
+        "estimator": MapieMultiLabelClassifier,
+        "task": "multilabel_classification",
+        "kwargs": {"metric_control": "precision", "method": "ltt"},
+        "predict_kargs": {"delta": 0.01}
+    },
+    "mapie_quantile": {
+        "estimator": MapieQuantileRegressor,
+        "task": "regression",
+        "kwargs": {"method": "quantile"}
+    },
+    "mapie_time_series": {
+        "estimator": MapieTimeSeriesRegressor,
+        "task": "regression",
+        "kwargs": {"method": "quantile"}
+    }
+}
+
+NON_VALID_MAPIE_ESTIMATORS_NAMES = list(NON_VALID_MAPIE_ESTIMATORS.keys())
 
 TOY_DATASETS = {
     "calibration": make_classification(
@@ -206,7 +219,7 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
 
 
 @pytest.mark.parametrize(
-        "mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES
+        "mapie_estimator_name", NON_VALID_CS_NAMES
 )
 def test_non_cs_fails(mapie_estimator_name):
     """
@@ -265,20 +278,60 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
             mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
 
 
-@pytest.mark.parametrize("mapie_estimator", NON_VALID_MAPIE_ESTIMATORS)
-def test_non_valid_estimators_fails(mapie_estimator):
+@pytest.mark.parametrize(
+        "mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES
+)
+def test_non_valid_estimators_fails(mapie_estimator_name):
     """
-    Test that non valid estimators fail"""
-    x, y = TOY_DATASETS["regression"]
-    ml_model = ML_MODELS["regression"]
+    Test that valid estimators don't fail"""
+    task_dict = NON_VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
+    mapie_estimator = task_dict["estimator"]
+    mapie_kwargs = task_dict["kwargs"]
+    task = task_dict["task"]
+    x, y = TOY_DATASETS[task]
+    y = np.abs(y)  # to avoid negative values with Gamma NCS
+    ml_model = ML_MODELS[task]
     groups = np.random.choice(10, len(x))
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
-        mapie_estimator=mapie_estimator(estimator=model, cv="prefit")
-    )
+    mapie_inst = deepcopy(mapie_estimator)
+    if task not in ["multilabel_classification", "calibration"]:
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(
+                estimator=model, cv="prefit", **mapie_kwargs
+            )
+        )
+    elif task == "multilabel_classification":
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
+        )
+    else:
+        mondrian_cp = Mondrian(
+            mapie_estimator=mapie_inst(estimator=model, cv="prefit")
+        )
     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
-        mondrian.fit(x, y, groups=groups)
+        if task == "multilabel_classification":
+            mondrian_cp.fit(x, y, groups=groups)
+        elif task == "calibration":
+            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+        else:
+            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+
+
+# @pytest.mark.parametrize("mapie_estimator", NON_VALID_MAPIE_ESTIMATORS)
+# def test_non_valid_estimators_fails(mapie_estimator):
+#     """
+#     Test that non valid estimators fail"""
+#     x, y = TOY_DATASETS["regression"]
+#     ml_model = ML_MODELS["regression"]
+#     groups = np.random.choice(10, len(x))
+#     model = clone(ml_model)
+#     model.fit(x, y)
+#     mondrian = Mondrian(
+#         mapie_estimator=mapie_estimator(estimator=model, cv="prefit")
+#     )
+#     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
+#         mondrian.fit(x, y, groups=groups)
 
 
 def test_groups_not_defined_by_integers_fails():
@@ -347,23 +400,6 @@ def test_all_groups_in_predict_are_in_fit():
         mondrian.predict(x, groups=groups, alpha=.2)
 
 
-def test_all_groups_in_predict_proba_are_in_fit():
-    """
-    Test that all groups in predict_proba are in fit"""
-    x, y = TOY_DATASETS["calibration"]
-    ml_model = ML_MODELS["calibration"]
-    model = clone(ml_model)
-    model.fit(x, y)
-    mondrian = Mondrian(
-        mapie_estimator=MapieCalibrator(estimator=model, cv="prefit")
-    )
-    groups = np.random.choice(10, len(x))
-    mondrian.fit(x, y, groups=groups)
-    groups = np.array([99] * len(x))
-    with pytest.raises(ValueError, match=r".*There is at least one new*"):
-        mondrian.predict_proba(x, groups=groups, alpha=.2)
-
-
 def test_groups_and_x_have_same_length_in_predict():
     """
     Test that groups and x have the same length in predict"""
@@ -381,38 +417,6 @@ def test_groups_and_x_have_same_length_in_predict():
         mondrian.predict(x, groups=groups, alpha=.2)
 
 
-def test_predict_proba_only_with_calibrator():
-    """
-    Test that predict_proba only works with calibrator"""
-    x, y = TOY_DATASETS["classification"]
-    ml_model = ML_MODELS["classification"]
-    model = clone(ml_model)
-    model.fit(x, y)
-    mondrian = Mondrian(
-        mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
-    )
-    groups = np.random.choice(10, len(x))
-    mondrian.fit(x, y, groups=groups)
-    with pytest.raises(ValueError, match=r".*The predict_proba method*"):
-        mondrian.predict_proba(x, groups=groups, alpha=.2)
-
-
-def test_predict_fails_with_calibrator():
-    """
-    Test that predict fails with calibrator"""
-    x, y = TOY_DATASETS["calibration"]
-    ml_model = ML_MODELS["calibration"]
-    model = clone(ml_model)
-    model.fit(x, y)
-    mondrian = Mondrian(
-        mapie_estimator=MapieCalibrator(estimator=model, cv="prefit")
-    )
-    groups = np.random.choice(10, len(x))
-    mondrian.fit(x, y, groups=groups)
-    with pytest.raises(ValueError, match=r".*The predict method*"):
-        mondrian.predict(x, groups=groups, alpha=.2)
-
-
 def test_alpha_none_return_one_element():
     """
     Test that if alpha is None, the output is a single element"""

From 065184136ea8cf4c786b4f7275a95c331ef3303f Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:12:08 +0200
Subject: [PATCH 280/424] NEH: change kwargs to predcit_params and fit_params

---
 mapie/mondrian.py | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 43d9868ad..114f4d43a 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -107,7 +107,7 @@ def __init__(
     ):
         self.mapie_estimator = mapie_estimator
 
-    def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
+    def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params):
         """
         Fit the Mondrian method
 
@@ -120,7 +120,7 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
         groups : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers. There must
             be at least 2 individuals per group.
-        **kwargs
+        **fit_params
             Additional keyword arguments to pass to the estimator's fit method
             that may be specific to the Mapie estimator used
         """
@@ -133,14 +133,14 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **kwargs):
             mapie_group_estimator = deepcopy(self.mapie_estimator)
             indices_groups = np.argwhere(groups == group)[:, 0]
             X_g, y_g = X[indices_groups], y[indices_groups]
-            mapie_group_estimator.fit(X_g, y_g, **kwargs)
+            mapie_group_estimator.fit(X_g, y_g, **fit_params)
             self.mapie_estimators[group] = mapie_group_estimator
         return self
 
     def predict(
             self, X: ArrayLike, groups: ArrayLike,
             alpha: Optional[Union[float, Iterable[float]]] = None,
-            **kwargs
+            **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Perform conformal prediction for each group of individuals
@@ -155,7 +155,7 @@ def predict(
             The desired coverage level(s) for each group.
 
             By default None.
-        **kwargs
+        **predict_params
             Additional keyword arguments to pass to the estimator's predict
             method that may be specific to the Mapie estimator used
 
@@ -170,16 +170,18 @@ def predict(
         X = cast(NDArray, X)
         groups = self._check_groups_predict(X, groups)
         if alpha is None and self.mapie_estimator.estimator is not None:
-            return self.mapie_estimator.estimator.predict(X, **kwargs)
+            return self.mapie_estimator.estimator.predict(
+                X, **predict_params
+            )
         else:
             alpha_np = cast(NDArray, check_alpha(alpha))
             unique_groups = np.unique(groups)
             for i, group in enumerate(unique_groups):
-                m = self.mapie_estimators[group]
                 indices_groups = np.argwhere(groups == group)[:, 0]
                 X_g = X[indices_groups]
-                pred = m.predict(X_g, alpha=alpha_np, **kwargs)  # type: ignore
-                y_pred_g, y_pss_g = pred
+                y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
+                    X_g, alpha=alpha_np, **predict_params
+                )
                 if i == 0:
                     if len(y_pred_g.shape) == 1:
                         y_pred = np.empty((X.shape[0],))

From 3b261429015b8b43fb73c8ae29083a59fb46792c Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:15:15 +0200
Subject: [PATCH 281/424] ENH: rename Mondrian to MondrianCP

---
 mapie/mondrian.py            |  2 +-
 mapie/tests/test_mondrian.py | 54 +++++++++++++-----------------------
 2 files changed, 20 insertions(+), 36 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 114f4d43a..970d012e5 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -24,7 +24,7 @@
 from mapie._typing import ArrayLike, NDArray
 
 
-class Mondrian:
+class MondrianCP:
     """Mondrian is a method that allows to make  perform conformal predictions
     for disjoints groups of individuals.
     The Mondrian method is implemented in the Mondrian class. It takes as
diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index f5d1822b1..dfaf9f34f 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -23,7 +23,7 @@
     RAPSConformityScore,
     ResidualNormalisedScore
 )
-from mapie.mondrian import Mondrian
+from mapie.mondrian import MondrianCP
 from mapie.multi_label_classification import MapieMultiLabelClassifier
 from mapie.regression import (
     MapieQuantileRegressor,
@@ -187,17 +187,17 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
     if task not in ["multilabel_classification", "calibration"]:
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(
                 estimator=model, cv="prefit", **mapie_kwargs
             )
         )
     elif task == "multilabel_classification":
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
         )
     else:
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(estimator=model, cv="prefit")
         )
     if task == "multilabel_classification":
@@ -234,7 +234,7 @@ def test_non_cs_fails(mapie_estimator_name):
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
-    mondrian_cp = Mondrian(
+    mondrian_cp = MondrianCP(
         mapie_estimator=mapie_inst(
             estimator=model, cv="prefit", **mapie_kwargs
         )
@@ -258,11 +258,11 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
     model = clone(ml_model)
     mapie_inst = deepcopy(mapie_estimator)
     if not isinstance(mapie_inst(), MapieMultiLabelClassifier):
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(estimator=model, cv=non_valid_cv)
         )
     else:
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
         )
     if task == "multilabel_classification":
@@ -296,17 +296,17 @@ def test_non_valid_estimators_fails(mapie_estimator_name):
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
     if task not in ["multilabel_classification", "calibration"]:
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(
                 estimator=model, cv="prefit", **mapie_kwargs
             )
         )
     elif task == "multilabel_classification":
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
         )
     else:
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst(estimator=model, cv="prefit")
         )
     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
@@ -318,22 +318,6 @@ def test_non_valid_estimators_fails(mapie_estimator_name):
             mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
 
 
-# @pytest.mark.parametrize("mapie_estimator", NON_VALID_MAPIE_ESTIMATORS)
-# def test_non_valid_estimators_fails(mapie_estimator):
-#     """
-#     Test that non valid estimators fail"""
-#     x, y = TOY_DATASETS["regression"]
-#     ml_model = ML_MODELS["regression"]
-#     groups = np.random.choice(10, len(x))
-#     model = clone(ml_model)
-#     model.fit(x, y)
-#     mondrian = Mondrian(
-#         mapie_estimator=mapie_estimator(estimator=model, cv="prefit")
-#     )
-#     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
-#         mondrian.fit(x, y, groups=groups)
-
-
 def test_groups_not_defined_by_integers_fails():
     """
     Test that groups not defined by integers fails"""
@@ -341,7 +325,7 @@ def test_groups_not_defined_by_integers_fails():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.random.choice(10, len(x)).astype(str)
@@ -358,7 +342,7 @@ def test_groups_with_less_than_2_fails():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.array([1] + [2] * (len(x) - 1))
@@ -375,7 +359,7 @@ def test_groups_and_x_have_same_length_in_fit():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.random.choice(10, len(x) - 1)
@@ -390,7 +374,7 @@ def test_all_groups_in_predict_are_in_fit():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.random.choice(10, len(x))
@@ -407,7 +391,7 @@ def test_groups_and_x_have_same_length_in_predict():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.random.choice(10, len(x))
@@ -424,7 +408,7 @@ def test_alpha_none_return_one_element():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.random.choice(10, len(x))
@@ -440,7 +424,7 @@ def test_groups_is_list_ok():
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
     model.fit(x, y)
-    mondrian = Mondrian(
+    mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
     groups = np.random.choice(10, len(x)).tolist()
@@ -464,12 +448,12 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
     mapie_inst_mondrian = deepcopy(mapie_estimator)
     mapie_classic_inst = deepcopy(mapie_estimator)
     if not isinstance(mapie_inst_mondrian(), MapieMultiLabelClassifier):
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst_mondrian(estimator=model, cv="prefit")
         )
         mapie_classic = mapie_classic_inst(estimator=model, cv="prefit")
     else:
-        mondrian_cp = Mondrian(
+        mondrian_cp = MondrianCP(
             mapie_estimator=mapie_inst_mondrian(
                 estimator=model, **mapie_kwargs
             ),

From 48ebe097db1f372137ce64a53ec439f55d1e35e1 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:17:07 +0200
Subject: [PATCH 282/424] UPD: class docstring with constraints

---
 mapie/mondrian.py | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 970d012e5..dca386eff 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -32,6 +32,10 @@ class MondrianCP:
     each group of individuals. The Mondrian class can then be used to run a
     conformal prediction procedure for each of these groups and hence achieve
     marginal coverage on each of them.
+    The underlying Mapie estimator must be used with cv='prefit' and the
+    conformity score must be one of the following:
+    - For MapieClassifier: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
+    - For MapieRegressor: 'gamma', 'absolute' or 'aps'
 
     Parameters
     ----------

From dc5a3713b115a99428544edee2fb2a838d3b446e Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:17:57 +0200
Subject: [PATCH 283/424] FIX: Call MondrianCP in docstring test

---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index dca386eff..a46a97acb 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -70,7 +70,7 @@ class MondrianCP:
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
     >>> groups = [0, 0, 0, 0, 1, 1, 1, 1, 1]
     >>> clf = LogisticRegression(random_state=42).fit(X_toy, y_toy)
-    >>> mapie = Mondrian(MapieClassifier(estimator=clf, cv="prefit")).fit(
+    >>> mapie = MondrianCP(MapieClassifier(estimator=clf, cv="prefit")).fit(
     ...     X_toy, y_toy, groups)
     >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups)
     >>> print(y_pi_mapie[:, :, 0].astype(bool))

From 6646a071796b05d7866cb209a0b9cfedc75f1962 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:26:04 +0200
Subject: [PATCH 284/424] ENH: add single method for cehck group length

---
 mapie/mondrian.py | 27 +++++++++++++++++++--------
 1 file changed, 19 insertions(+), 8 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index a46a97acb..3f76ebc61 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -130,6 +130,7 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params):
         """
 
         X, y, groups = self._check_fit_parameters(X, y, groups)
+        self._check_group_length(X, groups)
         self.unique_groups = np.unique(groups)
         self.mapie_estimators = {}
 
@@ -237,11 +238,7 @@ def _check_groups_fit(self, X: NDArray, groups: NDArray):
         _, counts = np.unique(groups, return_counts=True)
         if np.min(counts) < 2:
             raise ValueError("There must be at least 2 individuals per group")
-        if len(groups) != X.shape[0]:
-            raise ValueError(
-                "The number of individuals in the groups must be equal" +
-                " to the number of rows in X"
-            )
+        self._check_group_length(X, groups)
 
     def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         """Check that there is no new group in the prediction and that
@@ -264,18 +261,32 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         ------
         ValueError
             If there is a new group in the prediction
-            If the number of individuals in the groups is not equal to the
-            number of rows in X
         """
         groups = cast(NDArray, np.array(groups))
         if not np.all(np.isin(groups, self.unique_groups)):
             raise ValueError(
                 "There is at least one new group in the prediction"
             )
+        self._check_group_length(X, groups)
+        return groups
+
+    def _check_group_length(self, X: NDArray, groups: NDArray):
+        """Check that there is at least 2 individuals per group
+
+        Parameters
+        ----------
+        groups : NDArray of shape (n_samples,)
+            The groups of individuals. Must be defined by integers
+
+        Raises
+        ------
+        ValueError
+            If the number of individuals in the groups is not equal to the
+            number of rows in X
+        """
         if len(groups) != X.shape[0]:
             raise ValueError("The number of individuals in the groups must " +
                              "be equal to the number of rows in X")
-        return groups
 
     def _check_estimator(self):
         """

From 7ecd6a84651fe9daa322e3090bd9726a6473fdf2 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:36:21 +0200
Subject: [PATCH 285/424] ENH: define output shape outside of the loop

---
 mapie/mondrian.py | 22 ++++++++++++++--------
 1 file changed, 14 insertions(+), 8 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 3f76ebc61..ea9b269f0 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -180,6 +180,20 @@ def predict(
             )
         else:
             alpha_np = cast(NDArray, check_alpha(alpha))
+            if isinstance(self.mapie_estimator, MapieClassifier):
+                y_pred = np.empty(
+                    (X.shape[0], )
+                )
+                y_pss = np.empty(
+                    (
+                        X.shape[0],
+                        len(self.mapie_estimator.estimator.classes_),
+                        len(alpha_np)
+                    )
+                )
+            else:
+                y_pred = np.empty((X.shape[0],))
+                y_pss = np.empty((X.shape[0], 2, len(alpha_np)))
             unique_groups = np.unique(groups)
             for i, group in enumerate(unique_groups):
                 indices_groups = np.argwhere(groups == group)[:, 0]
@@ -187,14 +201,6 @@ def predict(
                 y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
                     X_g, alpha=alpha_np, **predict_params
                 )
-                if i == 0:
-                    if len(y_pred_g.shape) == 1:
-                        y_pred = np.empty((X.shape[0],))
-                    else:
-                        y_pred = np.empty((X.shape[0], y_pred_g.shape[1]))
-                    y_pss = np.empty(
-                        (X.shape[0], y_pss_g.shape[1], len(alpha_np))
-                    )
                 y_pred[indices_groups] = y_pred_g
                 y_pss[indices_groups] = y_pss_g
 

From 884c3418c289382e45bb7f68af3783b36846748e Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 15:59:43 +0200
Subject: [PATCH 286/424] FIX: typing for n classes

---
 mapie/mondrian.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index ea9b269f0..90c842063 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -133,6 +133,8 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params):
         self._check_group_length(X, groups)
         self.unique_groups = np.unique(groups)
         self.mapie_estimators = {}
+        if isinstance(self.mapie_estimator, MapieClassifier):
+            self.n_classes = len(np.unique(y.shape[1]))
 
         for group in self.unique_groups:
             mapie_group_estimator = deepcopy(self.mapie_estimator)
@@ -187,7 +189,7 @@ def predict(
                 y_pss = np.empty(
                     (
                         X.shape[0],
-                        len(self.mapie_estimator.estimator.classes_),
+                        self.n_classes,
                         len(alpha_np)
                     )
                 )

From e32c8a011d0f93c66a5cc52014ac207333381397 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 16:02:21 +0200
Subject: [PATCH 287/424] ENH rename _check_mapie_classifier in _check_cv

---
 mapie/mondrian.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 90c842063..5ce339c46 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -208,7 +208,7 @@ def predict(
 
             return y_pred, y_pss
 
-    def _check_mapie_classifier(self):
+    def _check_cv(self):
         """
         Check that the underlying Mapie estimator uses cv='prefit'
 
@@ -374,7 +374,7 @@ def _check_fit_parameters(
         groups : NDArray of shape (n_samples,)
         """
         self._check_estimator()
-        self._check_mapie_classifier()
+        self._check_cv()
         self._check_confomity_score()
         X, y = indexable(X, y)
         y = _check_y(y)

From 05d74a6fbd528becd6b3c99d15630a2e318c9370 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 16:03:42 +0200
Subject: [PATCH 288/424] ENH: move check_alpha at begninning of predict

---
 mapie/mondrian.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 5ce339c46..77791fa84 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -176,12 +176,12 @@ def predict(
         check_is_fitted(self, self.fit_attributes)
         X = cast(NDArray, X)
         groups = self._check_groups_predict(X, groups)
-        if alpha is None and self.mapie_estimator.estimator is not None:
+        alpha_np = cast(NDArray, check_alpha(alpha))
+        if alpha_np is None and self.mapie_estimator.estimator is not None:
             return self.mapie_estimator.estimator.predict(
                 X, **predict_params
             )
         else:
-            alpha_np = cast(NDArray, check_alpha(alpha))
             if isinstance(self.mapie_estimator, MapieClassifier):
                 y_pred = np.empty(
                     (X.shape[0], )

From 518f78b76932c1f76afd3f838e9750644301bd49 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 16:07:28 +0200
Subject: [PATCH 289/424] FIX: definiiton of n_classes

---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 77791fa84..d2e54750f 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -134,7 +134,7 @@ def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params):
         self.unique_groups = np.unique(groups)
         self.mapie_estimators = {}
         if isinstance(self.mapie_estimator, MapieClassifier):
-            self.n_classes = len(np.unique(y.shape[1]))
+            self.n_classes = len(np.unique(y))
 
         for group in self.unique_groups:
             mapie_group_estimator = deepcopy(self.mapie_estimator)

From cc48cb19be470f7fd94a5af9e77e4de776ecbe8e Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 16:28:49 +0200
Subject: [PATCH 290/424] ENH remove old tests

---
 mapie/tests/test_mondrian.py | 110 +++++++----------------------------
 1 file changed, 20 insertions(+), 90 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index dfaf9f34f..e0cb989c0 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -186,36 +186,11 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
-    if task not in ["multilabel_classification", "calibration"]:
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst(
-                estimator=model, cv="prefit", **mapie_kwargs
-            )
-        )
-    elif task == "multilabel_classification":
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
-        )
-    else:
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst(estimator=model, cv="prefit")
-        )
-    if task == "multilabel_classification":
-        mondrian_cp.fit(x, y, groups=groups)
-        if mapie_estimator_name in [
-            "multi_label_recall_rcps", "multi_label_precision_ltt"
-        ]:
-            mondrian_cp.predict(
-                x, groups=groups, alpha=.2, **task_dict["predict_kargs"]
-            )
-        else:
-            mondrian_cp.predict(x, groups=groups, alpha=.2)
-    elif task == "calibration":
-        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
-        mondrian_cp.predict_proba(x, groups=groups)
-    else:
-        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
-        mondrian_cp.predict(x, groups=groups, alpha=.2)
+    mondrian_cp = MondrianCP(
+        mapie_estimator=mapie_inst(estimator=model, cv="prefit")
+    )
+    mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+    mondrian_cp.predict(x, groups=groups, alpha=.2)
 
 
 @pytest.mark.parametrize(
@@ -257,25 +232,11 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
     groups = np.random.choice(10, len(x))
     model = clone(ml_model)
     mapie_inst = deepcopy(mapie_estimator)
-    if not isinstance(mapie_inst(), MapieMultiLabelClassifier):
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst(estimator=model, cv=non_valid_cv)
-        )
-    else:
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst(estimator=model, **mapie_kwargs),
-        )
-    if task == "multilabel_classification":
-        with pytest.raises(
-            ValueError, match=r".*MultiOutputClassifier instance is not*"
-        ):
-            mondrian_cp.fit(x, y, groups=groups)
-    elif task == "calibration":
-        with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
-            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
-    else:
-        with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
-            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+    mondrian_cp = MondrianCP(
+        mapie_estimator=mapie_inst(estimator=model, cv=non_valid_cv)
+    )
+    with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
+        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
 
 
 @pytest.mark.parametrize(
@@ -447,44 +408,13 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
     model.fit(x, y)
     mapie_inst_mondrian = deepcopy(mapie_estimator)
     mapie_classic_inst = deepcopy(mapie_estimator)
-    if not isinstance(mapie_inst_mondrian(), MapieMultiLabelClassifier):
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst_mondrian(estimator=model, cv="prefit")
-        )
-        mapie_classic = mapie_classic_inst(estimator=model, cv="prefit")
-    else:
-        mondrian_cp = MondrianCP(
-            mapie_estimator=mapie_inst_mondrian(
-                estimator=model, **mapie_kwargs
-            ),
-        )
-        mapie_classic = mapie_classic_inst(estimator=model, **mapie_kwargs)
-    if task == "multilabel_classification":
-        mondrian_cp.fit(x, y, groups=groups)
-        mapie_classic.fit(x, y)
-        if mapie_estimator_name in [
-            "multi_label_recall_rcps", "multi_label_precision_ltt"
-        ]:
-            mondrian_pred = mondrian_cp.predict(
-                x, groups=groups, alpha=.2, **task_dict["predict_kargs"]
-            )
-            classic_pred = mapie_classic.predict(
-                x, alpha=.2, **task_dict["predict_kargs"]
-            )
-        else:
-            mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
-            classic_pred = mapie_classic.predict(x, alpha=.2)
-
-    elif task == "calibration":
-        mondrian_cp.fit(X=x, y=y, groups=groups, **mapie_kwargs)
-        mapie_classic.fit(x, y, **mapie_kwargs)
-        mondrian_pred = mondrian_cp.predict_proba(x, groups=groups)
-        classic_pred = mapie_classic.predict_proba(x)
-        assert np.allclose(mondrian_pred, classic_pred, equal_nan=True)
-    else:
-        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
-        mapie_classic.fit(x, y, **mapie_kwargs)
-        mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
-        classic_pred = mapie_classic.predict(x, alpha=.2)
-        assert np.allclose(mondrian_pred[0], classic_pred[0])
-        assert np.allclose(mondrian_pred[1], classic_pred[1])
+    mondrian_cp = MondrianCP(
+        mapie_estimator=mapie_inst_mondrian(estimator=model, cv="prefit")
+    )
+    mapie_classic = mapie_classic_inst(estimator=model, cv="prefit")
+    mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+    mapie_classic.fit(x, y, **mapie_kwargs)
+    mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
+    classic_pred = mapie_classic.predict(x, alpha=.2)
+    assert np.allclose(mondrian_pred[0], classic_pred[0])
+    assert np.allclose(mondrian_pred[1], classic_pred[1])

From 96e33582e662b6235f0217a391aa9e77cbcf1ce3 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 9 Aug 2024 17:37:45 +0200
Subject: [PATCH 291/424] FIX: coveage with frong fit_params in fit_params in
 tests

---
 mapie/mondrian.py            | 14 +++++++++-----
 mapie/tests/test_mondrian.py | 30 ++++++++++++++++++++++--------
 2 files changed, 31 insertions(+), 13 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index d2e54750f..cfe69e8e4 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -1,7 +1,10 @@
+from __future__ import annotations
+
 from copy import deepcopy
 from typing import Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
+from sklearn.base import BaseEstimator
 from sklearn.utils.validation import _check_y, check_is_fitted, indexable
 
 from mapie.calibration import MapieCalibrator
@@ -24,7 +27,7 @@
 from mapie._typing import ArrayLike, NDArray
 
 
-class MondrianCP:
+class MondrianCP(BaseEstimator):
     """Mondrian is a method that allows to make  perform conformal predictions
     for disjoints groups of individuals.
     The Mondrian method is implemented in the Mondrian class. It takes as
@@ -111,7 +114,9 @@ def __init__(
     ):
         self.mapie_estimator = mapie_estimator
 
-    def fit(self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params):
+    def fit(
+            self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params
+    ) -> MondrianCP:
         """
         Fit the Mondrian method
 
@@ -332,8 +337,7 @@ def _check_confomity_score(self):
                         "The conformity score for the MapieClassifier must " +
                         f"be one of {self.allowed_classification_ncs_str}"
                     )
-                else:
-                    return
+
             if self.mapie_estimator.conformity_score is not None:
                 if type(self.mapie_estimator.conformity_score) not in \
                    self.allowed_classification_ncs_class:
@@ -341,7 +345,7 @@ def _check_confomity_score(self):
                         "The conformity score for the MapieClassifier must" +
                         f" be one of {self.allowed_classification_ncs_class}"
                     )
-        elif isinstance(self.mapie_estimator, MapieRegressor):
+        else:
             if self.mapie_estimator.conformity_score is not None:
                 if not isinstance(self.mapie_estimator.conformity_score,
                    self.allowed_regression_ncs):
diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index e0cb989c0..984ccb05e 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -77,6 +77,11 @@
         "task": "regression",
         "kwargs": {"conformity_score": AbsoluteConformityScore()}
     },
+    "regression_none": {
+        "estimator": MapieRegressor,
+        "task": "regression",
+        "kwargs": {"conformity_score": None}
+    },
     "regression_gamma_conformity": {
         "estimator": MapieRegressor,
         "task": "regression",
@@ -187,9 +192,11 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
     mondrian_cp = MondrianCP(
-        mapie_estimator=mapie_inst(estimator=model, cv="prefit")
+        mapie_estimator=mapie_inst(
+            estimator=model, cv="prefit", **mapie_kwargs
+        )
     )
-    mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+    mondrian_cp.fit(x, y, groups=groups)
     mondrian_cp.predict(x, groups=groups, alpha=.2)
 
 
@@ -233,10 +240,12 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
     model = clone(ml_model)
     mapie_inst = deepcopy(mapie_estimator)
     mondrian_cp = MondrianCP(
-        mapie_estimator=mapie_inst(estimator=model, cv=non_valid_cv)
+        mapie_estimator=mapie_inst(
+            estimator=model, cv=non_valid_cv, **mapie_kwargs
+        )
     )
     with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
-        mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+        mondrian_cp.fit(x, y, groups=groups)
 
 
 @pytest.mark.parametrize(
@@ -402,6 +411,7 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
     mapie_kwargs = task_dict["kwargs"]
     task = task_dict["task"]
     x, y = TOY_DATASETS[task]
+    y = np.abs(y)
     ml_model = ML_MODELS[task]
     groups = [0] * len(x)
     model = clone(ml_model)
@@ -409,11 +419,15 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
     mapie_inst_mondrian = deepcopy(mapie_estimator)
     mapie_classic_inst = deepcopy(mapie_estimator)
     mondrian_cp = MondrianCP(
-        mapie_estimator=mapie_inst_mondrian(estimator=model, cv="prefit")
+        mapie_estimator=mapie_inst_mondrian(
+            estimator=model, cv="prefit", random_state=0, **mapie_kwargs
+        )
+    )
+    mapie_classic = mapie_classic_inst(
+        estimator=model, cv="prefit", random_state=0, **mapie_kwargs,
     )
-    mapie_classic = mapie_classic_inst(estimator=model, cv="prefit")
-    mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
-    mapie_classic.fit(x, y, **mapie_kwargs)
+    mondrian_cp.fit(x, y, groups=groups)
+    mapie_classic.fit(x, y)
     mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
     classic_pred = mapie_classic.predict(x, alpha=.2)
     assert np.allclose(mondrian_pred[0], classic_pred[0])

From d0842bb3673d6acd6d108faf0a158d5c7412ff5b Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 12:29:31 +0200
Subject: [PATCH 292/424] Update mapie/tests/test_mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/tests/test_mondrian.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 984ccb05e..b64583701 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -404,8 +404,7 @@ def test_groups_is_list_ok():
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 def test_same_results_if_only_one_group(mapie_estimator_name):
-    """
-    Test that the results are the same if there is only one group"""
+    """Test that the results are the same if there is only one group"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]

From 85fe87591ef8adde233219b79eb7ab62d475084d Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 12:29:40 +0200
Subject: [PATCH 293/424] Update mapie/tests/test_mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/tests/test_mondrian.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index b64583701..fb17951d0 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -388,8 +388,7 @@ def test_alpha_none_return_one_element():
 
 
 def test_groups_is_list_ok():
-    """
-    Test that the groups can be a list"""
+    """Test that the groups can be a list"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)

From 6f4b06c3f4486878c9406fd048d7daf04a0ee127 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 12:29:49 +0200
Subject: [PATCH 294/424] Update mapie/tests/test_mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/tests/test_mondrian.py | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index fb17951d0..76e4da9a8 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -372,8 +372,7 @@ def test_groups_and_x_have_same_length_in_predict():
 
 
 def test_alpha_none_return_one_element():
-    """
-    Test that if alpha is None, the output is a single element"""
+    """Test that if alpha is None, the output is a single element"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)

From 8f44c33a155464e3f9f5f9e41188ce807d03972f Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 12:30:36 +0200
Subject: [PATCH 295/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 1 +
 1 file changed, 1 insertion(+)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index cfe69e8e4..8acf24404 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -54,6 +54,7 @@ class MondrianCP(BaseEstimator):
     ----------
     unique_groups : NDArray
         The unique groups of individuals for which the estimator was fitted
+
     mapie_estimators : Dict
         A dictionary containing the fitted conformal estimator for each group
         of individuals

From f4a0a45fde900ae405d647db81544b9147b00fb8 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 12:31:11 +0200
Subject: [PATCH 296/424] Update doc/theoretical_description_mondrian.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_mondrian.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_mondrian.rst b/doc/theoretical_description_mondrian.rst
index 3f59fc47f..e0a4a8ae8 100644
--- a/doc/theoretical_description_mondrian.rst
+++ b/doc/theoretical_description_mondrian.rst
@@ -12,7 +12,7 @@ coverage guarantee.  The coverage guarantee is given by:
 .. math::
     P \{Y_{n+1} \in \hat{C}_{n, \alpha}(X_{n+1}) | G_{n+1} = g\} \geq 1 - \alpha
 
-where :math:`G(X_{n+1})` is the group of the new test point :math:`X_{n+1}` and :math:`g`
+where :math:`G_{n+1}` is the group of the new test point :math:`X_{n+1}` and :math:`g`
 is a group in the set of groups :math:`\mathcal{G}`.
 
 MCP can be used with any split conformal predictor and can be particularly useful when one have a prior 

From 2ac857e0df2402dca9c3493a96a46ebc8f1b5318 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 16:29:48 +0200
Subject: [PATCH 297/424] Update doc/theoretical_description_mondrian.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 doc/theoretical_description_mondrian.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/theoretical_description_mondrian.rst b/doc/theoretical_description_mondrian.rst
index e0a4a8ae8..7b93b3164 100644
--- a/doc/theoretical_description_mondrian.rst
+++ b/doc/theoretical_description_mondrian.rst
@@ -21,7 +21,7 @@ of the data or not.
 In a classifcation setting, the groups can be defined as the predicted classes of the data. Doing so,
 one can ensure that, for each predicted class, the coverage guarantee is satisfied.
 
-In order to achieve the group-conditional coverage guarantee, MCP simply this the data
+In order to achieve the group-conditional coverage guarantee, MCP simply classifies the data
 according to the groups and then applies the split conformal predictor to each group separately.
 
 The quantile of each group is defined as:

From 94415c1d6dc764a110725e40083727e4f95b0697 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 16:30:07 +0200
Subject: [PATCH 298/424] Update HISTORY.rst

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 HISTORY.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 580e59a67..71cd12df9 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,7 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
-* Add Mondrian Conformal Prediction for regression, classification, calibration and multilabel-classification
+* Add Mondrian Conformal Prediction for regression and classification
 * Add `** predict_params` in fit and predict method for Mapie Regression
 * Update the ts-changepoint notebook with the tutorial
 * Change import related to conformity scores into ts-changepoint notebook

From 381a8ec26a90de627529da98c5c20338c5a54b59 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 16:30:31 +0200
Subject: [PATCH 299/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 16 +++++++++-------
 1 file changed, 9 insertions(+), 7 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 8acf24404..da90968e1 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -28,17 +28,19 @@
 
 
 class MondrianCP(BaseEstimator):
-    """Mondrian is a method that allows to make  perform conformal predictions
+    """Mondrian is a method for making conformal predictions
     for disjoints groups of individuals.
-    The Mondrian method is implemented in the Mondrian class. It takes as
-    input a MapieClassifier or MapieRegressor estimator and fits a model for
-    each group of individuals. The Mondrian class can then be used to run a
+
+    The Mondrian method is implemented in the `MondrianCP` class. It takes as
+    input a `MapieClassifier` or `MapieRegressor` estimator and fits a model for
+    each group of individuals. The `MondrianCP` class can then be used to run a
     conformal prediction procedure for each of these groups and hence achieve
     marginal coverage on each of them.
-    The underlying Mapie estimator must be used with cv='prefit' and the
+
+    The underlying estimator must be used with `cv='prefit'` and the
     conformity score must be one of the following:
-    - For MapieClassifier: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
-    - For MapieRegressor: 'gamma', 'absolute' or 'aps'
+    - For `MapieClassifier`: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
+    - For `MapieRegressor`: 'absolute' or 'gamma'
 
     Parameters
     ----------

From 2aa97289377e3cb51b2284c7b774afa5ae6079be Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:20:08 +0200
Subject: [PATCH 300/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index da90968e1..0d0a7ea7d 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -45,12 +45,11 @@ class MondrianCP(BaseEstimator):
     Parameters
     ----------
     mapie_estimator : Union[MapieClassifier, MapieRegressor]
-        The estimator for which the Mondrian method will be applied. The
-        estimator must be used with cv='prefit' and the conformity score must
-        be one of the following:
-        - For MapieClassifier: 'lac', 'score', 'cumulated_score',
-        'aps' or 'topk'
-        - For MapieRegressor: 'gamma', 'absolute' or 'aps'
+        The estimator for which the Mondrian method will be applied.
+        It must be used with `cv='prefit'` and the
+        conformity score must be one of the following:
+        - For `MapieClassifier`: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
+        - For `MapieRegressor`: 'absolute' or 'gamma'
 
     Attributes
     ----------

From e58300ba82156e645f14883a5c2c6660ae5a9b4e Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:20:31 +0200
Subject: [PATCH 301/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 0d0a7ea7d..873386487 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -68,7 +68,7 @@ class MondrianCP(BaseEstimator):
 
     Examples
     --------
-        >>> import numpy as np
+    >>> import numpy as np
     >>> from sklearn.linear_model import LogisticRegression
     >>> from mapie.classification import MapieClassifier
     >>> X_toy = np.arange(9).reshape(-1, 1)

From 791abba627925cd54604e1cb15350b97c5fb8830 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:21:50 +0200
Subject: [PATCH 302/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 873386487..390d8bf1f 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -104,7 +104,7 @@ class MondrianCP(BaseEstimator):
         TopKConformityScore
     )
     allowed_regression_ncs = (
-        GammaConformityScore, AbsoluteConformityScore, APSConformityScore
+        AbsoluteConformityScore, GammaConformityScore
     )
     fit_attributes = [
         "unique_groups",

From 999eb2572cb8848370066e41674bc3f470e24a51 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:22:18 +0200
Subject: [PATCH 303/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 390d8bf1f..83430f68b 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -117,7 +117,7 @@ def __init__(
         self.mapie_estimator = mapie_estimator
 
     def fit(
-            self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params
+        self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params
     ) -> MondrianCP:
         """
         Fit the Mondrian method

From 9c85ecb3a29925ce08a47e583d25e6cdf3b9a0e5 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:22:36 +0200
Subject: [PATCH 304/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 83430f68b..76d87a858 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -126,11 +126,14 @@ def fit(
         ----------
         X : ArrayLike of shape (n_samples, n_features)
             The input data
+
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
+
         groups : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers. There must
             be at least 2 individuals per group.
+
         **fit_params
             Additional keyword arguments to pass to the estimator's fit method
             that may be specific to the Mapie estimator used

From c0646db028e4b22ec3321479d7d5dc826e7ecef6 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:22:58 +0200
Subject: [PATCH 305/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 76d87a858..ba15ecb19 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -166,12 +166,15 @@ def predict(
         ----------
         X : ArrayLike of shape (n_samples, n_features)
             The input data
+
         groups : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers.
+
         alpha : float or Iterable[float], optional
             The desired coverage level(s) for each group.
 
             By default None.
+
         **predict_params
             Additional keyword arguments to pass to the estimator's predict
             method that may be specific to the Mapie estimator used

From b4d5dd828083405f0b7f31ae0b86c6fdb3f5a875 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:23:13 +0200
Subject: [PATCH 306/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 8 +++++---
 1 file changed, 5 insertions(+), 3 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index ba15ecb19..123a1a4e5 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -155,9 +155,11 @@ def fit(
         return self
 
     def predict(
-            self, X: ArrayLike, groups: ArrayLike,
-            alpha: Optional[Union[float, Iterable[float]]] = None,
-            **predict_params
+        self,
+        X: ArrayLike,
+        groups: ArrayLike,
+        alpha: Optional[Union[float, Iterable[float]]] = None,
+        **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
         """
         Perform conformal prediction for each group of individuals

From b9a9ca79700c774db74a0bb2f6c72fd36fb3ee29 Mon Sep 17 00:00:00 2001
From: Vincent Blot <52573624+vincentblot28@users.noreply.github.com>
Date: Mon, 19 Aug 2024 18:23:39 +0200
Subject: [PATCH 307/424] Update mapie/mondrian.py

Co-authored-by: Thibault Cordier <124613154+thibaultcordier@users.noreply.github.com>
---
 mapie/mondrian.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 123a1a4e5..2fe584f9d 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -185,7 +185,9 @@ def predict(
         -------
         y_pred : NDArray of shape (n_samples,) or (n_samples, n_outputs)
             The predicted values
+
         y_pss : NDArray of shape (n_samples, n_outputs, n_alpha)
+            The predicted sets for the desired levels of coverage
         """
 
         check_is_fitted(self, self.fit_attributes)

From 63308724193eb0aa810ff94317dcd58a47917aeb Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 19 Aug 2024 18:33:40 +0200
Subject: [PATCH 308/424] FIX: linting and docstring

---
 mapie/mondrian.py | 58 +++++++++++++++++++++++++++--------------------
 1 file changed, 34 insertions(+), 24 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 2fe584f9d..1f7056576 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -32,10 +32,10 @@ class MondrianCP(BaseEstimator):
     for disjoints groups of individuals.
 
     The Mondrian method is implemented in the `MondrianCP` class. It takes as
-    input a `MapieClassifier` or `MapieRegressor` estimator and fits a model for
-    each group of individuals. The `MondrianCP` class can then be used to run a
-    conformal prediction procedure for each of these groups and hence achieve
-    marginal coverage on each of them.
+    input a `MapieClassifier` or `MapieRegressor` estimator and fits a model
+    for each group of individuals. The `MondrianCP` class can then be used to
+    run a conformal prediction procedure for each of these groups and hence
+    achieve marginal coverage on each of them.
 
     The underlying estimator must be used with `cv='prefit'` and the
     conformity score must be one of the following:
@@ -48,7 +48,8 @@ class MondrianCP(BaseEstimator):
         The estimator for which the Mondrian method will be applied.
         It must be used with `cv='prefit'` and the
         conformity score must be one of the following:
-        - For `MapieClassifier`: 'lac', 'score', 'cumulated_score', 'aps' or 'topk'
+        - For `MapieClassifier`: 'lac', 'score', 'cumulated_score', 'aps' or
+        'topk'
         - For `MapieRegressor`: 'absolute' or 'gamma'
 
     Attributes
@@ -73,11 +74,11 @@ class MondrianCP(BaseEstimator):
     >>> from mapie.classification import MapieClassifier
     >>> X_toy = np.arange(9).reshape(-1, 1)
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
-    >>> groups = [0, 0, 0, 0, 1, 1, 1, 1, 1]
+    >>> groups_toy = [0, 0, 0, 0, 1, 1, 1, 1, 1]
     >>> clf = LogisticRegression(random_state=42).fit(X_toy, y_toy)
     >>> mapie = MondrianCP(MapieClassifier(estimator=clf, cv="prefit")).fit(
-    ...     X_toy, y_toy, groups)
-    >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups)
+    ...     X_toy, y_toy, groups_toy)
+    >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups_toy)
     >>> print(y_pi_mapie[:, :, 0].astype(bool))
     [[ True False False]
      [ True False False]
@@ -204,11 +205,7 @@ def predict(
                     (X.shape[0], )
                 )
                 y_pss = np.empty(
-                    (
-                        X.shape[0],
-                        self.n_classes,
-                        len(alpha_np)
-                    )
+                    (X.shape[0], self.n_classes, len(alpha_np))
                 )
             else:
                 y_pred = np.empty((X.shape[0],))
@@ -248,6 +245,7 @@ def _check_groups_fit(self, X: NDArray, groups: NDArray):
         ----------
         X : NDArray of shape (n_samples, n_features)
             The input data
+
         groups : NDArray of shape (n_samples,)
 
         Raises
@@ -296,10 +294,15 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         return groups
 
     def _check_group_length(self, X: NDArray, groups: NDArray):
-        """Check that there is at least 2 individuals per group
+        """
+        Check that the number of rows in the groups array is equal to
+        the number of rows in the attributes array.
 
         Parameters
         ----------
+        X : NDArray of shape (n_samples, n_features)
+            The individual data.
+
         groups : NDArray of shape (n_samples,)
             The groups of individuals. Must be defined by integers
 
@@ -315,12 +318,12 @@ def _check_group_length(self, X: NDArray, groups: NDArray):
 
     def _check_estimator(self):
         """
-        Check that the estimator is not in the not_allowed_estimators
+        Check that the estimator is not in the `not_allowed_estimators`.
 
         Raises
         ------
         ValueError
-            If the estimator is in the not_allowed_estimators
+            If the estimator is in the `not_allowed_estimators`.
         """
         if isinstance(self.mapie_estimator, self.not_allowed_estimators):
             raise ValueError(
@@ -329,17 +332,18 @@ def _check_estimator(self):
 
     def _check_confomity_score(self):
         """
-        Check that the conformity score is in allowed_classification_ncs_str
-        or allowed_classification_ncs_class if the estimator is MapieClassifier
-        or in the allowed_regression_ncs if the estimator is a MapieRegressor
+        Check that the conformity score is in `allowed_classification_ncs_str`
+        or `allowed_classification_ncs_class` if the estimator is a
+        `MapieClassifier` or in the `allowed_regression_ncs` if the estimator
+        is a `MapieRegressor`
 
         Raises
         ------
         ValueError
-            If conformity score is not in the allowed_classification_ncs_str
-            or allowed_classification_ncs_class if the estimator is a
-            MapieClassifier or in the allowed_regression_ncs if the estimator
-            is a MapieRegressor
+            If conformity score is not in the `allowed_classification_ncs_str`
+            or `allowed_classification_ncs_class` if the estimator is a
+            `MapieClassifier` or in the `allowed_regression_ncs` if the
+            estimator is a `MapieRegressor`.
         """
         if isinstance(self.mapie_estimator, MapieClassifier):
             if self.mapie_estimator.method is not None:
@@ -367,7 +371,7 @@ def _check_confomity_score(self):
                     )
 
     def _check_fit_parameters(
-            self, X: ArrayLike, y: ArrayLike, groups: ArrayLike
+        self, X: ArrayLike, y: ArrayLike, groups: ArrayLike
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
         Perform checks on the input data, groups and the estimator
@@ -376,8 +380,10 @@ def _check_fit_parameters(
         ----------
         X : ArrayLike of shape (n_samples, n_features)
             The input data
+
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
+
         groups : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers
 
@@ -385,10 +391,14 @@ def _check_fit_parameters(
         -------
         X : NDArray of shape (n_samples, n_features)
             The input data
+
         y : NDArray of shape (n_samples,) or (n_samples, n_outputs)
             The target values
+
         groups : NDArray of shape (n_samples,)
+            The group values
         """
+
         self._check_estimator()
         self._check_cv()
         self._check_confomity_score()

From b4b9934b62c3c1fd8c981e80907044752f3cf470 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 19 Aug 2024 18:35:27 +0200
Subject: [PATCH 309/424] STY: skip lines in fit definition

---
 mapie/mondrian.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 1f7056576..959c6e3a4 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -118,7 +118,10 @@ def __init__(
         self.mapie_estimator = mapie_estimator
 
     def fit(
-        self, X: ArrayLike, y: ArrayLike, groups: ArrayLike, **fit_params
+        self, X: ArrayLike,
+        y: ArrayLike,
+        groups: ArrayLike,
+        **fit_params
     ) -> MondrianCP:
         """
         Fit the Mondrian method

From 0e65abc3fcafe86c82b13803dc6187892073137a Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 19 Aug 2024 18:45:11 +0200
Subject: [PATCH 310/424] STY: docstring style

---
 mapie/tests/test_mondrian.py | 33 ++++++++++++---------------------
 1 file changed, 12 insertions(+), 21 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 76e4da9a8..f98a49141 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -170,16 +170,15 @@
     "calibration": LogisticRegression(),
     "classification": LogisticRegression(),
     "multilabel_classification": MultiOutputClassifier(
-                LogisticRegression(multi_class="multinomial")
-            ),
+        LogisticRegression(multi_class="multinomial")
+    ),
     "regression": LinearRegression(),
 }
 
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 def test_valid_estimators_dont_fail(mapie_estimator_name):
-    """
-    Test that valid estimators don't fail"""
+    """Test that valid estimators don't fail"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -204,8 +203,7 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
         "mapie_estimator_name", NON_VALID_CS_NAMES
 )
 def test_non_cs_fails(mapie_estimator_name):
-    """
-    Test that non valid conformity scores fail"""
+    """Test that non valid conformity scores fail"""
     task_dict = NON_VALID_CS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -228,8 +226,7 @@ def test_non_cs_fails(mapie_estimator_name):
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
 @pytest.mark.parametrize("non_valid_cv", ["split", -1, 5, ShuffleSplit(1)])
 def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
-    """
-    Test that invalid cv fails"""
+    """Test that invalid cv fails"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -249,11 +246,10 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
 
 
 @pytest.mark.parametrize(
-        "mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES
+    "mapie_estimator_name", NON_VALID_MAPIE_ESTIMATORS_NAMES
 )
 def test_non_valid_estimators_fails(mapie_estimator_name):
-    """
-    Test that valid estimators don't fail"""
+    """Test that valid estimators don't fail"""
     task_dict = NON_VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
     mapie_kwargs = task_dict["kwargs"]
@@ -289,8 +285,7 @@ def test_non_valid_estimators_fails(mapie_estimator_name):
 
 
 def test_groups_not_defined_by_integers_fails():
-    """
-    Test that groups not defined by integers fails"""
+    """Test that groups not defined by integers fails"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -306,8 +301,7 @@ def test_groups_not_defined_by_integers_fails():
 
 
 def test_groups_with_less_than_2_fails():
-    """
-    Test that groups with less than 2 elements fails"""
+    """Test that groups with less than 2 elements fails"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -323,8 +317,7 @@ def test_groups_with_less_than_2_fails():
 
 
 def test_groups_and_x_have_same_length_in_fit():
-    """
-    Test that groups and x have the same length in fit"""
+    """Test that groups and x have the same length in fit"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -338,8 +331,7 @@ def test_groups_and_x_have_same_length_in_fit():
 
 
 def test_all_groups_in_predict_are_in_fit():
-    """
-    Test that all groups in predict are in fit"""
+    """Test that all groups in predict are in fit"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -355,8 +347,7 @@ def test_all_groups_in_predict_are_in_fit():
 
 
 def test_groups_and_x_have_same_length_in_predict():
-    """
-    Test that groups and x have the same length in predict"""
+    """Test that groups and x have the same length in predict"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)

From 5844fbee5136246fa6562fb5ada6809af0389cdb Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 20 Aug 2024 09:45:47 +0200
Subject: [PATCH 311/424] ENH: test test_same_results_if_only_one_group for
 multiple values of alpha

---
 mapie/tests/test_mondrian.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index f98a49141..2b881528d 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -392,7 +392,8 @@ def test_groups_is_list_ok():
 
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
-def test_same_results_if_only_one_group(mapie_estimator_name):
+@pytest.mark.parametrize("alpha", np.linspace(0.1, 0.9, 10))
+def test_same_results_if_only_one_group(mapie_estimator_name, alpha):
     """Test that the results are the same if there is only one group"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
@@ -416,7 +417,7 @@ def test_same_results_if_only_one_group(mapie_estimator_name):
     )
     mondrian_cp.fit(x, y, groups=groups)
     mapie_classic.fit(x, y)
-    mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=.2)
-    classic_pred = mapie_classic.predict(x, alpha=.2)
+    mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=alpha)
+    classic_pred = mapie_classic.predict(x, alpha=alpha)
     assert np.allclose(mondrian_pred[0], classic_pred[0])
     assert np.allclose(mondrian_pred[1], classic_pred[1])

From ccc1e2dd2fc785c2ed3366da0c7cf71b96d69958 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 20 Aug 2024 14:07:17 +0200
Subject: [PATCH 312/424] FIX: minor typo

---
 mapie/mondrian.py            | 12 ++++++++----
 mapie/tests/test_mondrian.py |  2 +-
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 959c6e3a4..c70b88a85 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -113,7 +113,8 @@ class MondrianCP(BaseEstimator):
     ]
 
     def __init__(
-        self, mapie_estimator: Union[MapieClassifier, MapieRegressor]
+        self,
+        mapie_estimator: Union[MapieClassifier, MapieRegressor]
     ):
         self.mapie_estimator = mapie_estimator
 
@@ -241,7 +242,8 @@ def _check_cv(self):
             )
 
     def _check_groups_fit(self, X: NDArray, groups: NDArray):
-        """Check that each group is defined by an integer and check that there
+        """
+        Check that each group is defined by an integer and check that there
         are at least 2 individuals per group
 
         Parameters
@@ -267,7 +269,8 @@ def _check_groups_fit(self, X: NDArray, groups: NDArray):
         self._check_group_length(X, groups)
 
     def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
-        """Check that there is no new group in the prediction and that
+        """
+        Check that there is no new group in the prediction and that
         the number of individuals in the groups is equal to the number of
         rows in X
 
@@ -275,10 +278,11 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         ----------
         X : NDArray of shape (n_samples, n_features)
             The input data
+
         groups : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers
 
-        returns
+        Returns
         -------
         groups : NDArray of shape (n_samples,)
             Groups of individuals
diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 2b881528d..7b98ed1d1 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -200,7 +200,7 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
 
 
 @pytest.mark.parametrize(
-        "mapie_estimator_name", NON_VALID_CS_NAMES
+    "mapie_estimator_name", NON_VALID_CS_NAMES
 )
 def test_non_cs_fails(mapie_estimator_name):
     """Test that non valid conformity scores fail"""

From 4b51a0aa3cef8cdb5055e0fc569f09f78e0f6a23 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 20 Aug 2024 14:08:54 +0200
Subject: [PATCH 313/424] ADD: mondrian to API.rst

---
 doc/api.rst | 10 ++++++++++
 1 file changed, 10 insertions(+)

diff --git a/doc/api.rst b/doc/api.rst
index 411221efd..ce411d3e4 100644
--- a/doc/api.rst
+++ b/doc/api.rst
@@ -108,3 +108,13 @@ Resampling
 
    subsample.BlockBootstrap
    subsample.Subsample
+
+
+Mondrian
+==========
+
+.. autosummary::
+   :toctree: generated/
+   :template: class.rst
+
+   mondrian.MondrianCP

From 70f6f34edd12c72f8e2fd356ba8568eb2ce4c388 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 20 Aug 2024 14:22:16 +0200
Subject: [PATCH 314/424] DOC: add tutorial notebook

---
 doc/tutorial_mondrian_regression.rst          | 1068 ++++++++++++++
 .../tutorial_mondrian_regression_13_0.png     |  Bin 0 -> 80486 bytes
 .../tutorial_mondrian_regression_15_2.png     |  Bin 0 -> 19209 bytes
 .../tutorial_mondrian_regression_2_0.png      |  Bin 0 -> 37214 bytes
 .../tutorial_mondrian_regression_5_0.png      |  Bin 0 -> 113234 bytes
 .../tutorial_mondrian_regression_8_1.png      |  Bin 0 -> 21550 bytes
 .../tutorial_mondrian_regression.ipynb        | 1229 +++++++++++++++++
 7 files changed, 2297 insertions(+)
 create mode 100644 doc/tutorial_mondrian_regression.rst
 create mode 100644 doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_13_0.png
 create mode 100644 doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_15_2.png
 create mode 100644 doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_2_0.png
 create mode 100644 doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_5_0.png
 create mode 100644 doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_8_1.png
 create mode 100644 notebooks/mondrian/tutorial_mondrian_regression.ipynb

diff --git a/doc/tutorial_mondrian_regression.rst b/doc/tutorial_mondrian_regression.rst
new file mode 100644
index 000000000..6704ff313
--- /dev/null
+++ b/doc/tutorial_mondrian_regression.rst
@@ -0,0 +1,1068 @@
+.. code:: ipython3
+
+    import matplotlib.pyplot as plt
+    import numpy as np
+    from sklearn.base import clone
+    from sklearn.model_selection import train_test_split
+    from sklearn.ensemble import RandomForestRegressor
+    
+    from mapie.metrics import regression_coverage_score_v2
+    from mapie.mondrian import MondrianCP
+    from mapie.regression import MapieRegressor
+    
+    %load_ext autoreload
+    %autoreload 2
+
+.. code:: ipython3
+
+    # Create 1D regression dataset with sinusoidual function between 0 and 10 
+    n_points = 100000
+    np.random.seed(0)
+    X = np.linspace(0, 10, n_points).reshape(-1, 1)
+    group_size = n_points // 10
+    groups_list = []
+    for i in range(10):
+        groups_list.append(np.array([i] * group_size))
+    groups = np.concatenate(groups_list)
+    
+    noise_0_1 = np.random.normal(0, 0.1, group_size)
+    noise_1_2 = np.random.normal(0, 0.5, group_size)
+    noise_2_3 = np.random.normal(0, 1, group_size)
+    noise_3_4 = np.random.normal(0, .4, group_size)
+    noise_4_5 = np.random.normal(0, .2, group_size)
+    noise_5_6 = np.random.normal(0, .3, group_size)
+    noise_6_7 = np.random.normal(0, .6, group_size)
+    noise_7_8 = np.random.normal(0, .7, group_size)
+    noise_8_9 = np.random.normal(0, .8, group_size)
+    noise_9_10 = np.random.normal(0, .9, group_size)
+    
+    y = np.concatenate(
+        [
+            np.sin(X[groups == 0, 0] * 2) + noise_0_1,
+            np.sin(X[groups == 1, 0] * 2) + noise_1_2,
+            np.sin(X[groups == 2, 0] * 2) + noise_2_3,
+            np.sin(X[groups == 3, 0] * 2) + noise_3_4,
+            np.sin(X[groups == 4, 0] * 2) + noise_4_5,
+            np.sin(X[groups == 5, 0] * 2) + noise_5_6,
+            np.sin(X[groups == 6, 0] * 2) + noise_6_7,
+            np.sin(X[groups == 7, 0] * 2) + noise_7_8,
+            np.sin(X[groups == 8, 0] * 2) + noise_8_9,
+            np.sin(X[groups == 9, 0] * 2) + noise_9_10,
+        ], axis=0
+    )
+    
+
+
+.. code:: ipython3
+
+    plt.scatter(X, y, c=groups)
+    plt.show()
+
+
+
+.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_2_0.png
+
+
+.. code:: ipython3
+
+    X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+    groups_train_temp, groups_test, _, _ = train_test_split(groups, y, test_size=0.2, random_state=0)
+    X_cal, X_train, y_cal, y_train = train_test_split(X_train_temp, y_train_temp, test_size=0.5, random_state=0)
+    groups_cal, groups_train, _, _ = train_test_split(groups_train_temp, y_train_temp, test_size=0.5, random_state=0)
+
+.. code:: ipython3
+
+    X_train.shape, y_train.shape, groups_train.shape
+
+
+
+
+.. parsed-literal::
+
+    ((40000, 1), (40000,), (40000,))
+
+
+
+.. code:: ipython3
+
+    f, ax = plt.subplots(1, 3, figsize=(15, 5))
+    ax[0].scatter(X_train, y_train, c=groups_train)
+    ax[0].set_title("Train set")
+    ax[1].scatter(X_cal, y_cal, c=groups_cal)
+    ax[1].set_title("Calibration set")
+    ax[2].scatter(X_test, y_test, c=groups_test)
+    ax[2].set_title("Test set")
+    plt.show()
+
+
+
+.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_5_0.png
+
+
+.. code:: ipython3
+
+    print("Training set size: ", X_train.shape[0])
+    print("Calibration set size: ", X_cal.shape[0])
+    print("Test set size: ", X_test.shape[0])
+
+
+.. parsed-literal::
+
+    Training set size:  40000
+    Calibration set size:  40000
+    Test set size:  20000
+
+
+.. code:: ipython3
+
+    # Fit a random forest regressor
+    
+    rf = RandomForestRegressor(n_estimators=100)
+    rf.fit(X_train, y_train)
+
+
+
+
+
+.. raw:: html
+
+    <style>#sk-container-id-1 {
+      /* Definition of color scheme common for light and dark mode */
+      --sklearn-color-text: black;
+      --sklearn-color-line: gray;
+      /* Definition of color scheme for unfitted estimators */
+      --sklearn-color-unfitted-level-0: #fff5e6;
+      --sklearn-color-unfitted-level-1: #f6e4d2;
+      --sklearn-color-unfitted-level-2: #ffe0b3;
+      --sklearn-color-unfitted-level-3: chocolate;
+      /* Definition of color scheme for fitted estimators */
+      --sklearn-color-fitted-level-0: #f0f8ff;
+      --sklearn-color-fitted-level-1: #d4ebff;
+      --sklearn-color-fitted-level-2: #b3dbfd;
+      --sklearn-color-fitted-level-3: cornflowerblue;
+    
+      /* Specific color for light theme */
+      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
+      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+      --sklearn-color-icon: #696969;
+    
+      @media (prefers-color-scheme: dark) {
+        /* Redefinition of color scheme for dark theme */
+        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
+        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+        --sklearn-color-icon: #878787;
+      }
+    }
+    
+    #sk-container-id-1 {
+      color: var(--sklearn-color-text);
+    }
+    
+    #sk-container-id-1 pre {
+      padding: 0;
+    }
+    
+    #sk-container-id-1 input.sk-hidden--visually {
+      border: 0;
+      clip: rect(1px 1px 1px 1px);
+      clip: rect(1px, 1px, 1px, 1px);
+      height: 1px;
+      margin: -1px;
+      overflow: hidden;
+      padding: 0;
+      position: absolute;
+      width: 1px;
+    }
+    
+    #sk-container-id-1 div.sk-dashed-wrapped {
+      border: 1px dashed var(--sklearn-color-line);
+      margin: 0 0.4em 0.5em 0.4em;
+      box-sizing: border-box;
+      padding-bottom: 0.4em;
+      background-color: var(--sklearn-color-background);
+    }
+    
+    #sk-container-id-1 div.sk-container {
+      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
+         but bootstrap.min.css set `[hidden] { display: none !important; }`
+         so we also need the `!important` here to be able to override the
+         default hidden behavior on the sphinx rendered scikit-learn.org.
+         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
+      display: inline-block !important;
+      position: relative;
+    }
+    
+    #sk-container-id-1 div.sk-text-repr-fallback {
+      display: none;
+    }
+    
+    div.sk-parallel-item,
+    div.sk-serial,
+    div.sk-item {
+      /* draw centered vertical line to link estimators */
+      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
+      background-size: 2px 100%;
+      background-repeat: no-repeat;
+      background-position: center center;
+    }
+    
+    /* Parallel-specific style estimator block */
+    
+    #sk-container-id-1 div.sk-parallel-item::after {
+      content: "";
+      width: 100%;
+      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
+      flex-grow: 1;
+    }
+    
+    #sk-container-id-1 div.sk-parallel {
+      display: flex;
+      align-items: stretch;
+      justify-content: center;
+      background-color: var(--sklearn-color-background);
+      position: relative;
+    }
+    
+    #sk-container-id-1 div.sk-parallel-item {
+      display: flex;
+      flex-direction: column;
+    }
+    
+    #sk-container-id-1 div.sk-parallel-item:first-child::after {
+      align-self: flex-end;
+      width: 50%;
+    }
+    
+    #sk-container-id-1 div.sk-parallel-item:last-child::after {
+      align-self: flex-start;
+      width: 50%;
+    }
+    
+    #sk-container-id-1 div.sk-parallel-item:only-child::after {
+      width: 0;
+    }
+    
+    /* Serial-specific style estimator block */
+    
+    #sk-container-id-1 div.sk-serial {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      background-color: var(--sklearn-color-background);
+      padding-right: 1em;
+      padding-left: 1em;
+    }
+    
+    
+    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
+    clickable and can be expanded/collapsed.
+    - Pipeline and ColumnTransformer use this feature and define the default style
+    - Estimators will overwrite some part of the style using the `sk-estimator` class
+    */
+    
+    /* Pipeline and ColumnTransformer style (default) */
+    
+    #sk-container-id-1 div.sk-toggleable {
+      /* Default theme specific background. It is overwritten whether we have a
+      specific estimator or a Pipeline/ColumnTransformer */
+      background-color: var(--sklearn-color-background);
+    }
+    
+    /* Toggleable label */
+    #sk-container-id-1 label.sk-toggleable__label {
+      cursor: pointer;
+      display: block;
+      width: 100%;
+      margin-bottom: 0;
+      padding: 0.5em;
+      box-sizing: border-box;
+      text-align: center;
+    }
+    
+    #sk-container-id-1 label.sk-toggleable__label-arrow:before {
+      /* Arrow on the left of the label */
+      content: "▸";
+      float: left;
+      margin-right: 0.25em;
+      color: var(--sklearn-color-icon);
+    }
+    
+    #sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {
+      color: var(--sklearn-color-text);
+    }
+    
+    /* Toggleable content - dropdown */
+    
+    #sk-container-id-1 div.sk-toggleable__content {
+      max-height: 0;
+      max-width: 0;
+      overflow: hidden;
+      text-align: left;
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+    
+    #sk-container-id-1 div.sk-toggleable__content.fitted {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+    
+    #sk-container-id-1 div.sk-toggleable__content pre {
+      margin: 0.2em;
+      border-radius: 0.25em;
+      color: var(--sklearn-color-text);
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+    
+    #sk-container-id-1 div.sk-toggleable__content.fitted pre {
+      /* unfitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+    
+    #sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+      /* Expand drop-down */
+      max-height: 200px;
+      max-width: 100%;
+      overflow: auto;
+    }
+    
+    #sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+      content: "▾";
+    }
+    
+    /* Pipeline/ColumnTransformer-specific style */
+    
+    #sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    #sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    /* Estimator-specific style */
+    
+    /* Colorize estimator box */
+    #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    #sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    #sk-container-id-1 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-1 div.sk-label label {
+      /* The background is the default theme color */
+      color: var(--sklearn-color-text-on-default-background);
+    }
+    
+    /* On hover, darken the color of the background */
+    #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    /* Label box, darken color on hover, fitted */
+    #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    /* Estimator label */
+    
+    #sk-container-id-1 div.sk-label label {
+      font-family: monospace;
+      font-weight: bold;
+      display: inline-block;
+      line-height: 1.2em;
+    }
+    
+    #sk-container-id-1 div.sk-label-container {
+      text-align: center;
+    }
+    
+    /* Estimator-specific */
+    #sk-container-id-1 div.sk-estimator {
+      font-family: monospace;
+      border: 1px dotted var(--sklearn-color-border-box);
+      border-radius: 0.25em;
+      box-sizing: border-box;
+      margin-bottom: 0.5em;
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+    
+    #sk-container-id-1 div.sk-estimator.fitted {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+    
+    /* on hover */
+    #sk-container-id-1 div.sk-estimator:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    #sk-container-id-1 div.sk-estimator.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    /* Specification for estimator info (e.g. "i" and "?") */
+    
+    /* Common style for "i" and "?" */
+    
+    .sk-estimator-doc-link,
+    a:link.sk-estimator-doc-link,
+    a:visited.sk-estimator-doc-link {
+      float: right;
+      font-size: smaller;
+      line-height: 1em;
+      font-family: monospace;
+      background-color: var(--sklearn-color-background);
+      border-radius: 1em;
+      height: 1em;
+      width: 1em;
+      text-decoration: none !important;
+      margin-left: 1ex;
+      /* unfitted */
+      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+      color: var(--sklearn-color-unfitted-level-1);
+    }
+    
+    .sk-estimator-doc-link.fitted,
+    a:link.sk-estimator-doc-link.fitted,
+    a:visited.sk-estimator-doc-link.fitted {
+      /* fitted */
+      border: var(--sklearn-color-fitted-level-1) 1pt solid;
+      color: var(--sklearn-color-fitted-level-1);
+    }
+    
+    /* On hover */
+    div.sk-estimator:hover .sk-estimator-doc-link:hover,
+    .sk-estimator-doc-link:hover,
+    div.sk-label-container:hover .sk-estimator-doc-link:hover,
+    .sk-estimator-doc-link:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+    
+    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+    .sk-estimator-doc-link.fitted:hover,
+    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+    .sk-estimator-doc-link.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+    
+    /* Span, style for the box shown on hovering the info icon */
+    .sk-estimator-doc-link span {
+      display: none;
+      z-index: 9999;
+      position: relative;
+      font-weight: normal;
+      right: .2ex;
+      padding: .5ex;
+      margin: .5ex;
+      width: min-content;
+      min-width: 20ex;
+      max-width: 50ex;
+      color: var(--sklearn-color-text);
+      box-shadow: 2pt 2pt 4pt #999;
+      /* unfitted */
+      background: var(--sklearn-color-unfitted-level-0);
+      border: .5pt solid var(--sklearn-color-unfitted-level-3);
+    }
+    
+    .sk-estimator-doc-link.fitted span {
+      /* fitted */
+      background: var(--sklearn-color-fitted-level-0);
+      border: var(--sklearn-color-fitted-level-3);
+    }
+    
+    .sk-estimator-doc-link:hover span {
+      display: block;
+    }
+    
+    /* "?"-specific style due to the `<a>` HTML tag */
+    
+    #sk-container-id-1 a.estimator_doc_link {
+      float: right;
+      font-size: 1rem;
+      line-height: 1em;
+      font-family: monospace;
+      background-color: var(--sklearn-color-background);
+      border-radius: 1rem;
+      height: 1rem;
+      width: 1rem;
+      text-decoration: none;
+      /* unfitted */
+      color: var(--sklearn-color-unfitted-level-1);
+      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+    }
+    
+    #sk-container-id-1 a.estimator_doc_link.fitted {
+      /* fitted */
+      border: var(--sklearn-color-fitted-level-1) 1pt solid;
+      color: var(--sklearn-color-fitted-level-1);
+    }
+    
+    /* On hover */
+    #sk-container-id-1 a.estimator_doc_link:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+    
+    #sk-container-id-1 a.estimator_doc_link.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-3);
+    }
+    </style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>
+
+
+
+.. code:: ipython3
+
+    # Plot the prediction of the random forest regressor as a line
+    
+    y_pred = rf.predict(X_test)
+    # plt.scatter(X_test, y_test, label="True")
+    
+    #Sort the test set and the prediction to plot them as a line
+    sort_idx = np.argsort(X_test[:, 0])
+    plt.plot(X_test[sort_idx], y_pred[sort_idx], label="Prediction")
+    
+    plt.legend()
+
+
+
+
+.. parsed-literal::
+
+    <matplotlib.legend.Legend at 0x1380f0760>
+
+
+
+
+.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_8_1.png
+
+
+.. code:: ipython3
+
+    mapie_regressor = MapieRegressor(rf, cv="prefit")
+    mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
+
+.. code:: ipython3
+
+    mapie_regressor.fit(X_cal, y_cal)
+    mondrian_regressor.fit(X_cal, y_cal, groups=groups_cal)
+
+
+
+
+.. raw:: html
+
+    <style>#sk-container-id-2 {
+      /* Definition of color scheme common for light and dark mode */
+      --sklearn-color-text: black;
+      --sklearn-color-line: gray;
+      /* Definition of color scheme for unfitted estimators */
+      --sklearn-color-unfitted-level-0: #fff5e6;
+      --sklearn-color-unfitted-level-1: #f6e4d2;
+      --sklearn-color-unfitted-level-2: #ffe0b3;
+      --sklearn-color-unfitted-level-3: chocolate;
+      /* Definition of color scheme for fitted estimators */
+      --sklearn-color-fitted-level-0: #f0f8ff;
+      --sklearn-color-fitted-level-1: #d4ebff;
+      --sklearn-color-fitted-level-2: #b3dbfd;
+      --sklearn-color-fitted-level-3: cornflowerblue;
+    
+      /* Specific color for light theme */
+      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
+      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
+      --sklearn-color-icon: #696969;
+    
+      @media (prefers-color-scheme: dark) {
+        /* Redefinition of color scheme for dark theme */
+        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
+        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
+        --sklearn-color-icon: #878787;
+      }
+    }
+    
+    #sk-container-id-2 {
+      color: var(--sklearn-color-text);
+    }
+    
+    #sk-container-id-2 pre {
+      padding: 0;
+    }
+    
+    #sk-container-id-2 input.sk-hidden--visually {
+      border: 0;
+      clip: rect(1px 1px 1px 1px);
+      clip: rect(1px, 1px, 1px, 1px);
+      height: 1px;
+      margin: -1px;
+      overflow: hidden;
+      padding: 0;
+      position: absolute;
+      width: 1px;
+    }
+    
+    #sk-container-id-2 div.sk-dashed-wrapped {
+      border: 1px dashed var(--sklearn-color-line);
+      margin: 0 0.4em 0.5em 0.4em;
+      box-sizing: border-box;
+      padding-bottom: 0.4em;
+      background-color: var(--sklearn-color-background);
+    }
+    
+    #sk-container-id-2 div.sk-container {
+      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
+         but bootstrap.min.css set `[hidden] { display: none !important; }`
+         so we also need the `!important` here to be able to override the
+         default hidden behavior on the sphinx rendered scikit-learn.org.
+         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
+      display: inline-block !important;
+      position: relative;
+    }
+    
+    #sk-container-id-2 div.sk-text-repr-fallback {
+      display: none;
+    }
+    
+    div.sk-parallel-item,
+    div.sk-serial,
+    div.sk-item {
+      /* draw centered vertical line to link estimators */
+      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
+      background-size: 2px 100%;
+      background-repeat: no-repeat;
+      background-position: center center;
+    }
+    
+    /* Parallel-specific style estimator block */
+    
+    #sk-container-id-2 div.sk-parallel-item::after {
+      content: "";
+      width: 100%;
+      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
+      flex-grow: 1;
+    }
+    
+    #sk-container-id-2 div.sk-parallel {
+      display: flex;
+      align-items: stretch;
+      justify-content: center;
+      background-color: var(--sklearn-color-background);
+      position: relative;
+    }
+    
+    #sk-container-id-2 div.sk-parallel-item {
+      display: flex;
+      flex-direction: column;
+    }
+    
+    #sk-container-id-2 div.sk-parallel-item:first-child::after {
+      align-self: flex-end;
+      width: 50%;
+    }
+    
+    #sk-container-id-2 div.sk-parallel-item:last-child::after {
+      align-self: flex-start;
+      width: 50%;
+    }
+    
+    #sk-container-id-2 div.sk-parallel-item:only-child::after {
+      width: 0;
+    }
+    
+    /* Serial-specific style estimator block */
+    
+    #sk-container-id-2 div.sk-serial {
+      display: flex;
+      flex-direction: column;
+      align-items: center;
+      background-color: var(--sklearn-color-background);
+      padding-right: 1em;
+      padding-left: 1em;
+    }
+    
+    
+    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
+    clickable and can be expanded/collapsed.
+    - Pipeline and ColumnTransformer use this feature and define the default style
+    - Estimators will overwrite some part of the style using the `sk-estimator` class
+    */
+    
+    /* Pipeline and ColumnTransformer style (default) */
+    
+    #sk-container-id-2 div.sk-toggleable {
+      /* Default theme specific background. It is overwritten whether we have a
+      specific estimator or a Pipeline/ColumnTransformer */
+      background-color: var(--sklearn-color-background);
+    }
+    
+    /* Toggleable label */
+    #sk-container-id-2 label.sk-toggleable__label {
+      cursor: pointer;
+      display: block;
+      width: 100%;
+      margin-bottom: 0;
+      padding: 0.5em;
+      box-sizing: border-box;
+      text-align: center;
+    }
+    
+    #sk-container-id-2 label.sk-toggleable__label-arrow:before {
+      /* Arrow on the left of the label */
+      content: "▸";
+      float: left;
+      margin-right: 0.25em;
+      color: var(--sklearn-color-icon);
+    }
+    
+    #sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
+      color: var(--sklearn-color-text);
+    }
+    
+    /* Toggleable content - dropdown */
+    
+    #sk-container-id-2 div.sk-toggleable__content {
+      max-height: 0;
+      max-width: 0;
+      overflow: hidden;
+      text-align: left;
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+    
+    #sk-container-id-2 div.sk-toggleable__content.fitted {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+    
+    #sk-container-id-2 div.sk-toggleable__content pre {
+      margin: 0.2em;
+      border-radius: 0.25em;
+      color: var(--sklearn-color-text);
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+    
+    #sk-container-id-2 div.sk-toggleable__content.fitted pre {
+      /* unfitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+    
+    #sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
+      /* Expand drop-down */
+      max-height: 200px;
+      max-width: 100%;
+      overflow: auto;
+    }
+    
+    #sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
+      content: "▾";
+    }
+    
+    /* Pipeline/ColumnTransformer-specific style */
+    
+    #sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    #sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    /* Estimator-specific style */
+    
+    /* Colorize estimator box */
+    #sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    #sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    #sk-container-id-2 div.sk-label label.sk-toggleable__label,
+    #sk-container-id-2 div.sk-label label {
+      /* The background is the default theme color */
+      color: var(--sklearn-color-text-on-default-background);
+    }
+    
+    /* On hover, darken the color of the background */
+    #sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    /* Label box, darken color on hover, fitted */
+    #sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
+      color: var(--sklearn-color-text);
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    /* Estimator label */
+    
+    #sk-container-id-2 div.sk-label label {
+      font-family: monospace;
+      font-weight: bold;
+      display: inline-block;
+      line-height: 1.2em;
+    }
+    
+    #sk-container-id-2 div.sk-label-container {
+      text-align: center;
+    }
+    
+    /* Estimator-specific */
+    #sk-container-id-2 div.sk-estimator {
+      font-family: monospace;
+      border: 1px dotted var(--sklearn-color-border-box);
+      border-radius: 0.25em;
+      box-sizing: border-box;
+      margin-bottom: 0.5em;
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-0);
+    }
+    
+    #sk-container-id-2 div.sk-estimator.fitted {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-0);
+    }
+    
+    /* on hover */
+    #sk-container-id-2 div.sk-estimator:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-2);
+    }
+    
+    #sk-container-id-2 div.sk-estimator.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-2);
+    }
+    
+    /* Specification for estimator info (e.g. "i" and "?") */
+    
+    /* Common style for "i" and "?" */
+    
+    .sk-estimator-doc-link,
+    a:link.sk-estimator-doc-link,
+    a:visited.sk-estimator-doc-link {
+      float: right;
+      font-size: smaller;
+      line-height: 1em;
+      font-family: monospace;
+      background-color: var(--sklearn-color-background);
+      border-radius: 1em;
+      height: 1em;
+      width: 1em;
+      text-decoration: none !important;
+      margin-left: 1ex;
+      /* unfitted */
+      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+      color: var(--sklearn-color-unfitted-level-1);
+    }
+    
+    .sk-estimator-doc-link.fitted,
+    a:link.sk-estimator-doc-link.fitted,
+    a:visited.sk-estimator-doc-link.fitted {
+      /* fitted */
+      border: var(--sklearn-color-fitted-level-1) 1pt solid;
+      color: var(--sklearn-color-fitted-level-1);
+    }
+    
+    /* On hover */
+    div.sk-estimator:hover .sk-estimator-doc-link:hover,
+    .sk-estimator-doc-link:hover,
+    div.sk-label-container:hover .sk-estimator-doc-link:hover,
+    .sk-estimator-doc-link:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+    
+    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
+    .sk-estimator-doc-link.fitted:hover,
+    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
+    .sk-estimator-doc-link.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+    
+    /* Span, style for the box shown on hovering the info icon */
+    .sk-estimator-doc-link span {
+      display: none;
+      z-index: 9999;
+      position: relative;
+      font-weight: normal;
+      right: .2ex;
+      padding: .5ex;
+      margin: .5ex;
+      width: min-content;
+      min-width: 20ex;
+      max-width: 50ex;
+      color: var(--sklearn-color-text);
+      box-shadow: 2pt 2pt 4pt #999;
+      /* unfitted */
+      background: var(--sklearn-color-unfitted-level-0);
+      border: .5pt solid var(--sklearn-color-unfitted-level-3);
+    }
+    
+    .sk-estimator-doc-link.fitted span {
+      /* fitted */
+      background: var(--sklearn-color-fitted-level-0);
+      border: var(--sklearn-color-fitted-level-3);
+    }
+    
+    .sk-estimator-doc-link:hover span {
+      display: block;
+    }
+    
+    /* "?"-specific style due to the `<a>` HTML tag */
+    
+    #sk-container-id-2 a.estimator_doc_link {
+      float: right;
+      font-size: 1rem;
+      line-height: 1em;
+      font-family: monospace;
+      background-color: var(--sklearn-color-background);
+      border-radius: 1rem;
+      height: 1rem;
+      width: 1rem;
+      text-decoration: none;
+      /* unfitted */
+      color: var(--sklearn-color-unfitted-level-1);
+      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
+    }
+    
+    #sk-container-id-2 a.estimator_doc_link.fitted {
+      /* fitted */
+      border: var(--sklearn-color-fitted-level-1) 1pt solid;
+      color: var(--sklearn-color-fitted-level-1);
+    }
+    
+    /* On hover */
+    #sk-container-id-2 a.estimator_doc_link:hover {
+      /* unfitted */
+      background-color: var(--sklearn-color-unfitted-level-3);
+      color: var(--sklearn-color-background);
+      text-decoration: none;
+    }
+    
+    #sk-container-id-2 a.estimator_doc_link.fitted:hover {
+      /* fitted */
+      background-color: var(--sklearn-color-fitted-level-3);
+    }
+    </style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,
+                                              estimator=RandomForestRegressor()))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;MondrianCP<span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></label><div class="sk-toggleable__content "><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,
+                                              estimator=RandomForestRegressor()))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label  sk-toggleable__label-arrow ">mapie_estimator: MapieRegressor</label><div class="sk-toggleable__content "><pre>MapieRegressor(cv=&#x27;prefit&#x27;, estimator=RandomForestRegressor())</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label  sk-toggleable__label-arrow ">estimator: RandomForestRegressor</label><div class="sk-toggleable__content "><pre>RandomForestRegressor()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></label><div class="sk-toggleable__content "><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>
+
+
+
+.. code:: ipython3
+
+    _, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
+    _, y_pss_mondrian = mondrian_regressor.predict(X_test, groups=groups_test, alpha=.1)
+
+.. code:: ipython3
+
+    rf = RandomForestRegressor(
+        n_estimators=100
+    )
+    rf.fit(X_train, y_train)
+    mondrian_regressor = MondrianCP(
+        MapieRegressor(rf, cv="prefit")
+    )
+    mondrian_regressor.fit(
+        X_cal, y_cal,
+        groups=groups_cal
+    )
+    _, y_pss_mondrian = mondrian_regressor.predict(
+        X_test, groups=groups_test, alpha=.1
+    )
+
+.. code:: ipython3
+
+    # Plot the prediction of the random forest regressor as a line with the prediction intervals
+    
+    # plt.scatter(X_test, y_test, label="True")
+    sort_idx = np.argsort(X_test[:, 0])
+    # plt.plot(X_test[sort_idx], y_pred[sort_idx], label="Prediction")
+    plt.fill_between(X_test[sort_idx].flatten(), y_pss_split[sort_idx, 0].flatten(), y_pss_split[sort_idx, 1].flatten(), alpha=0.3, label="Split")
+    plt.fill_between(X_test[sort_idx].flatten(), y_pss_mondrian[sort_idx, 0].flatten(), y_pss_mondrian[sort_idx, 1].flatten(), alpha=0.3, label="Mondrian")
+    plt.legend()
+    plt.show()
+
+
+
+.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_13_0.png
+
+
+.. code:: ipython3
+
+    # plot coverage by groups with both methods
+    coverages = {}
+    for group in np.unique(groups_test):
+        coverages[group] = {}
+        coverages[group]["split"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_split[groups_test == group])
+        coverages[group]["mondrian"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_mondrian[groups_test == group])
+
+.. code:: ipython3
+
+    # Plot the coverage by groups, plot both methods side by side
+    plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group]["split"]) for group in coverages], label="Split")
+    plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group]["mondrian"]) for group in coverages], label="Mondrian")
+    plt.xticks(np.arange(len(coverages)) * 2 + .5, [f"Group {group}" for group in coverages], rotation=45)
+    plt.hlines(0.9, -1, 21, label="90% coverage", color="black", linestyle="--")
+    plt.ylabel("Coverage")
+    
+    #put legend outside of the plot
+    plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
+
+
+.. parsed-literal::
+
+    /var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+      plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group]["split"]) for group in coverages], label="Split")
+    /var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
+      plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group]["mondrian"]) for group in coverages], label="Mondrian")
+
+
+
+
+.. parsed-literal::
+
+    <matplotlib.legend.Legend at 0x13847b970>
+
+
+
+
+.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_15_2.png
+
+
diff --git a/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_13_0.png b/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_13_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..71dbe253d40d2f6a8e52b685d2f1afde7f8b47f1
GIT binary patch
literal 80486
zcmbrlWmFv77B$+92iL~k-7R>61qc@0gA?4{A-H>j1$PbZ?hqV;yGsYBkq5cw-22`b
zZ+w5g9)oJScGZ@(=bUTp+N;78<)u&%2@nAQ0Llkxab*AiiUI%tW+1@5oMG+6!+rVU
zcaqR>`ebYB<oemc1n}{*lbw~Vla+-bnTv^oqlK*vCkr177ZaJelarm}TUJ)<|6agi
z>tM!8N=@nfQpp=TX-!7}0PXYZAFxQI&;kGi06vI|sko&dKYCVKFR9`pK5LyBRXp-l
z`jJz4@%sZP!ZygoxyUJsT1OvcN|dop-ryo=yaRktLV!c;g1-SE7Od-a!^Y)dyX%V4
z%jW;tp(HC%j?d2Vp?}JD>y;rxALZxwJgeK?Z>u|tx!(7V<lobBJZaN|98bo3&v`}i
zXSOvwS$14gU|=xS3{ph=J?I4FjR^ZofHaNcUJlz`NwA^+Eli<7LjTYCmqM10AhQ46
z^^HmN?+veqCN53Cf2(71u&4pN*7Tq1ri_9AwDVGF{*c`wtz9}B=juh~&3c>^2@pf~
z`ryB<d4`6r2a-OJ32#ygL(vmYxa(Mu{mHy(g^v+=x;4q_|F<Fs+&`zT9fT9UPZu#O
zyE4!(6}iLs=?&(_2zv~f)&AR)V@d^~D{K<q2e8IKOadc{_%iXuer^oQfBSvPxky`P
z_(o0l-+}y}M%XmP{!jaYoD$-SGGgUdH3}wiEs3*Vg3~XA&dmNDr%qAXyk+Z6+gabq
zQUfbQ1=IvjP9gYwH!;wICgP<4`LC&PX+|U0?#tKSjpLJoF-ZLb0|WC7cD(5*0z_^D
zMLM4!vYxSgA7Y-to`#bDwS4f9M;jdH7I=VkVC*|DE5zxg`nxJL2;0N8?K4lmeJd?l
z4NNJ^-+6M_a8O=6lc;D<$4RJ*4Eq0@69?d*rH;oXGKz<k=Rf)YfUWnDwwU;BC%mv-
z@t*TyP47QgaG>6PzIcW_A_>gtdw@f-?s?BV*C6NCYuDDduFt1y8#lhsH(+woKjgQ0
z3iniA1Fl~1de=2686Li@zIP+w)th$Q7@=!Cz<QLe7o{HL_q?!O=l=^DA+3k1mEC;N
zZqwgagJPccWBSjYpU$?JZQYSxemw10T)W!Y`RxhcG?{rN-()dWr2R7*elg*5f3$UV
zpB?q{m9@u}+p>v!Ca-NNA1dLKX<?K~&y}O|VfA)QKRDa=4HVnXJ>RFjt^O!Nw+$ry
z(5$<(ti`5@Rql(qhISnNryc!h+AJ?jaTtnj*Owva{F>FR_#e#WV?#)N@X$ma*q&xP
z?$!C;zWv>_(%`++u%TM%f%A0a`&fCq$@*mY@LCG4aBVy4ee8JHVI4TqfA$c(-_Lb>
zv021T`%T+zzwJYRZEFxC<NxS|WsC>c3XZbv{l0ohBY31a(Ou|!U$~{+zN3A9)bVtb
zaMFH`)Pb&lm!uyS18I!uS$l@~lCpXq$bz9o&Y@{fpV!v`uO<#fnD9Pz1TViBE0Zz#
z-S(=Fuh{<>x%gi1sziN5=*%6>=V{NEBn#4%717XsReqbOaEJZcfk^9Ju~Jsg#gS`6
zaT4SIqBY`}e!fzFz|8mBZ0qbHqa(1P?Zo=Fh84o$1!?ewRCj2~EReGPZ#*rDxE;5B
zjm}4Et-g?@HQ~Fb6I)*a;ioMTpGt3HA|k=qmC%&SmH$rT>hsgh7E;GK68KQPgCOfR
zEGzVt)@N=50_Q?&7ecp;zB`q@E+QA4B7rIGm-%jejC%g0&tTF4-+P7UHzN1BBF*#c
z!iTxShzcT)T&sVQ8`cwq4-)JycE{jzNGG7g|CJ;d_uGvSu-VgSR+q@rz%yRQ-S>_#
zdm`}dR?JrA^HoQ*?On2M*vZrB+GES@P}akT)?Mz(F2^j0&ckiYGoRkWChnPYWXj9b
z-j-C}Gk8tndSm?U{VS75L$UQRo0>n@{nZ}NV|Ky2?F6^WNvT(={?)`LDSko0Ufahx
z+l^pWFY*fg4xhzB$AWj_w@q0Pey=WEuP&3U@NfjXXfXYN(8zSznr!61k|t>G5X=|)
z{}Jf_6{h>^Y)FSNG9yv`->CE-xg-dgpUb~$_E)w4XFK7zv)kyiPFmkTv{{WSQr<IC
z8&{RSk4;$<nuE$x|LABbc?bB6<y6CW^FrY<ffZ82+VcwPho}xP@A-R?%l8RqdKcg3
z*HLWUpxZBHJFsoNwg@Nosm}C#uGD>T&`ze=&eBIZ&nR*GLj}RTSY#Bf*L9U`MeqM3
zYc+#u!&@;T(OC~eS-lwouA8+zJ?|fS6&_&p&I53DHq(k<1Q^g6;=K^E!kgjA3KRNp
zSoy@@a~tA|_lhewj1cXAdg>UbaF?jig`{^5^>94#WaV{TD*~x?djJoB3!i4P!Y87m
zqtAJtch`CyGcSmAI(&#LUvPI`+HqeBe$-yOS3}463$%PzZ+e^6*08^zh+4ai8UnW;
zwX`FVK5dXjzrt7_jHB=0)6+9_qknCp|CRMNfVC4#LC`<uHZ-Plz<YTBxZpJAwRYo8
zD!hRu3`7&UM1OHy_J5H#tsp|Y*6}J0wV78;58bQ}P<od+xEmWGr2Tu|t9u)5XWnON
z%MDxG%l?Be7e{qm&p#3Pyl5cIiyo6Ytp0DfNvzL*SO;Pr7h?J#ue71pdpuNEsJ|<)
zy{CB?@x5aO7x1>d>A1G+`1<#vJ)hIP%~v_5S@J$=@L_Df@$A5Tr6e12kFkiz<FrVW
z?en$mfUD1*>n8D;8~ls6-rIopJ%{HV-+LAB)vF5EjWJsv&Zn`CJ6`Px{`{a<VIS~)
zK6@s%^_c#C{`}ad-^=Sg&HJ&w$s5A+G%0c~b!+x~oVAhSdzS)k>43ESmCH|SS<}N!
z&~cS_-zvKp_3vaJ@;fe5<d?7a_Yq#WHnY9<Z#+YN7teZ!`7m7hfavogO@tj60v&<R
zx1`UM?Y9vfeC-|1N1)a7ue|G|Pd&b&6VDG50}=WUh7W@=4+u8PjsB#MlcX8PM*=VR
zf%l#tvi?(g((%{;u5V~#)`*$!c)D`CD%Fpwd}*XN>lVuw>Uo{?0lM|Jk9CzVD}=-Y
z;dwjj0qbE%<c<~`pVDf4#NY9P;65|&IkSym(krD~B;O6z^|R-;j*zUUgRD#|&S*-X
z{SY67zj{ue^@slUcjcp2>khMR)BKWiPC6s{-K!EkyqqP-blVJTtM}auqdi|Ris|bZ
zAoYC^RIvRg=8|Wg@VTx9zxuqt_B9f@Hgelx)DJTgfh>X_ZJ%~+4T+?@?hAcAyy~^e
z&n>d9E!^f?R=F3|No&qq_k_>@=Pxem<@n;kkY4=&&C<jgbSJG>H|_e%XTkBQPya!}
z>)Onh=ShFUSD3_o8{^?j|32UC8L}3m-C)zl!)v?RJK!}sAU8fRu_L&%>^Io5_Gshv
zIPWX;bSLt>*XZ+juh-$ea-<jX;%_;g+8`@u!j;qiva%&vz2kn?ZHsU%l(+5RBcToc
zS+{Jh6*k7=tyP^?pXGCOnu0US3rBucuRV{SJ)l1Q(SI6o8<N!v@V$F}5OLcRSPOeT
zdif7Xhg5T-^WepjlY8C-jB{<j$Z%VOtcEB&&M5S;A>(}ocD(4%e;~<*TvTWCu=~5t
z@>Q;BpZ|3iB}1<y``o5y)TX!PD01USyXJj7;KPK9ih9o2@rMz-_!71Z2(0?!w(U!J
zl5R<`dSI+RT{kEQfA3h6S_MbDW+w)mJ+7YhWj)sVf=F-dNkd*(B2>6K;Xc$fi*Xm>
z3ufJXLDw_S-SJ4r!w4As;?{0=ZJ$iH(<hF8{U=Iia;S)RMCBdv67t;t5pq7O|2TWA
z?R#Xq@v5ib0Nhr<i;r9jTxzi0Vr<`JycX`C_kEn-l6@H;cq*kG`>$?oi9Bt+@L<b#
zJ@>_5DmYh=Ua*q}DkJ)=!}|bU7<uB`<LkAwbpHPN=GnW%JH0TS5Z!${PKqbMl*D77
z{GnRpdcXy4`ac1?1F8dTxS9~w4&U5U%Zu7|j^4iV&g(pov{zRDUinq1x{GV1I`$*l
z5&rtvSM2p~Y>@wrK>ljztG_RNJE8Ejt<Znty>_$lN_TMcd>d*O7(dIO_9^BW`Wceq
zb5MNksQ1nR1C=ac;$d{6_tpQL>AGAO7iM-F#Rz?UL6L*~fBLmtekr;gFE;ejj?ar^
zA+dUGAl*ch>TI8ii;H$%x(~#&0*)_Qh|vDyTXD5EAIo2&UI`MLe-|xHn>Ac*|6<WB
zUXF;kW~a84`k&CoU2zAIRX*aFywKZc<y!q<i1k0~iEXiXAF!6+U?92r1OLD0(pc2J
z$hEeSw}pNB|9k&7BC{H9YSrEPAne+7t;g4Y3$nKzfPN_$9o~@Z{LFzs@&UmkPCU4y
zD}qi_v4-j4;DWP%!ulqPZnFLEE(%<msNHmMnVAsdx8jvncgNi4VG7UTR$=7qvy-~c
z!%%~ZC;9u;<-SKV(}yn72jHuR`rOD%SN+fW3|?G(Tc*)06SBMq)fw#M;%#7zL>Crs
zqK){?d1v&rIa^ca4Xaxh(kZ_5KZ~YqV0=-%d)Pa1x0e-M$e!RHf5K_ygmlxAOg-eG
zV(qJ@J{GWUUy4E+oeX6-S`m?~+XRI`jSM~ZG#Xcck59CDWbOLjSQK7Teke5Ek^eUR
zg0lm7m=B1X6vm`ti&gLU`}qh)_;pAC@SEBZ4y4v_ew~?ZAZC+tei^6<edBi<h8%@z
zrC+JdKT*nr-9c9foBn1RQ%%D$G<v-?Cqym?$nt#&LH`Xp)ISmYb{9q*J>@3S<b$o3
zw=<|z!p@^X%d-}I!+_(d|IhN3Q&;fQW*)a0kQkS7&$%PNNuNey;Mzcu<^4Ykus_Ht
zKJ3j<N8=PMt`}@BPrG<7mQPyZixrF%|4yU9b{_0L6K=o6_(zu&#{*q9PDx`ZNsZ^e
z`jmgeefO`!^?GF5^Hmw_6rPHZh~<p*W7?JmZzNa9!k_sSUiI~~e+{0-!0A{0My?*F
z@VX<z))y93F5$snk%GLG^)?J2XgYC+$VMS6R{J-MhUi{1`#8G_h=h2vIFubT^sU__
zEIz%5*k)x~_APHUq!wK0`I|Me{l`(>q@~wCdZ7PFSn%`lZEaeEkS!U;5l=y<Y4#_k
zE}0&2c2NBr{6Df3hVr7H(%(-v=~XBf^aaujWTrsJtisN7H&}L+>go(0^QO@MHGp14
zXP%C6+(o>PMK=kKTFRnbpE2mj{*Mc1HilT){<Y(sg8R#?=@4s*c2Y#77YB8Xw&wfv
zn(lmDyp=OWmgzV%Y1gZ_;!tUm{aV;*(w)f`#J|jy!u*eQn-nCBu|%a6CLm@RS0f1v
z{wzMC#%i}#+U_^Z75=cXI@v_Hz0G%4y&XsRn1F=06zc1D%&!FXXo5;`h?9_KYzaBp
z#r-VtT~+YW2IR<gHq$(Kup{U+oM)HjI|HrIc@)aE+k;=tqHz@t82TG&doys<8sNJ7
z<?9FC-m;5nm)P2@<sZW_fFTm&vc^k~LOhLb*E*h(j2y-UNEGS_=C5-HFPj|f8D|!7
zTolyo=y<x!c99CSa^161S=HGc*Bw$5wcm`n_Nu5(6X{5fCnV!wDC6<VVSn$8sK%o7
zinT{Zh<vtpeG60q*k)T;=tN>%0|szW@As+Ki~>Z8nVoCX8+7T^mc8bJq^P-#X*_N)
z%4vA-yZcRSB9iZuvKU5Z>+53x>4a&9CP$_4h#cMRC+y6zkm87fYC<4`h8E8l2xIp{
z?9t^JRKSF;{3mgnr{j~3?y{{;K*2!x&u$m>E%MiaM5IUrW`r>{*kj<vp>kN^<7s1>
zbY@U5gNknRCLdbD&6#WH&D?CG*Jd9^+()Hwt(b6#ka6Lsam32h0g<oVE2T~;-)4c_
zMld<3bHtpa#@$~0)M;ca824E-Qy<mD;rcCd-P9YC9&_BBMQhwN`l<fJefGD>U@v9q
zK5)-u_+|CszB_D@xJ9LFA186DZx3hGqe^*g%|;PbGJn=GuQ!UU8(yhB`xplTINa3G
zW~cfH!un`WmlI1nJZJPpxE->5w={iu%(Ja$q6FnRh%=PXfy;l&zqY1cKd2dI@OG4&
z&Dr!oUbLS}JR40Cr?h}UxlmQL!St6C=*nAof<;SyxFF4|#oqRjf3#$~-|;7<h9H9H
z*GTA50A&<;i`*+s(2~>hm@3)qH2wA{I-~8$piG@>`P^brGcJ=Ik<?BCad-#X3Lk73
zAtaFH<d2TQd%qqj2O3RxPZQsKKk_dE@^(?ID>K{GF+x^rsZy9;;f?{o3iiqxd~_^`
zNKYbaF~968EbBC0y2eJrv1KE16lK3_8i<nH{CJt-%1uz&3FWrP;hGgZ>YwqP@w_Cx
zcJk%xErdmJb^u`>YUB3{hh8deLfc^=Tlh5c=r)c^w!}$Pe|H@N@o8#63nzjU34F_x
z=p5ChK?w;K{IC{0kNVjye>MAxvNVTSgGX7Iw`+Kaws~;KfvEAQ{d@0MuKxE1$Z1?(
zdq<i%)rkprE1F6JEX`gUQP^qM^%~!5Ju}JrUiy&FIQbZvSD&4(IQc?|P&qL%Qlxc4
zr$sjO8RdIcV8o-rNMhXhmpLsg?~FH57~nM+ERMzc2$VF91D{rjQ!V3&kv#Z7ZKL<S
zt1c{@m9Uh~o9!D~K+{3gx#JuYAjM5}x-q@TxkJ`!%$??hDujrUdE)UY**j~Er@q~H
zt>q)bh;f_}MGw<f#U-ymqn)WS=JRBW&D(5_+QF1bSG1Zyv3LOL#hIyJCA5#XWMpXJ
zF-Irn#9YLJ1T)5G=ULqvCedx-_>XJHGaRQPo{~c!<{d%vp218_x?{%-H%x=3V<Qro
zO%$F?x7PMRvZ-*5Mc3|OyW4j(6n_3V3VVs;Q)*<nWcixQ#EN2_$+su<!aNb9+Z8M7
z<MMT8<h6U76WwB8p$M#<@Fv!Phj0t6f-ivbrm_K7aoRP;>_Q$La@5xa_sp`Ne6%%s
zS6#Cm%{rGTZj#c?MK8Y&9w&*u#m&e+o#Wt^_i#mQuBJ4!Oyhf5GDyvQV{3?(E4A;}
z&+LnGs>rq8^V!+XnXq00^H|7Rtxk_wn|zPxU*^0oyh01V#rW)38jS(}?JrCw@(T51
zmUg@1Ya0!kst6mH7o1jAtcKlyv-GaDk<f`%f_@Vae1-u*>5Lm@Hz&^N&xq1#__F~x
z1Im{IEC`|-qTQdInBA0~?*ml7x6pJBTzY$rKSCZWEHDzRg&R;@!+;-*xJ)Gsn)fC<
zjjCvsFEw-gV?!@Khna?@(D#^Rby7eg&~QOyVxj<1W22it@Y7)q1o>1@=qJy2X+t2(
z({vM`4-|l+w%sa?S)qhb!%1=k@$m~ECI?mV?Ba%iyxf@;Zd4gY!leqMMwJNQXQ2CN
zXp{B>FNsvnDX#L)CO%gkR2Xv$T$dM&9mt|K7&#Ih;J9;4lw`&pGNFr>Ug%_l{R_2?
zPP+lu)K5xL>ws2>kXlh4xdxve^sF=&n@C0hLfgdW-j&xj6bJE_+ui!OjwA(BK>%@_
zb=mUY)|XIy!LACpN&P-np3P59&n6ZfVC9vG#)WKnx#&s^4iiFg7$a+@Z_sL6v}C#&
z2)kd5`ipKu;p1*~x@BxY&L;JpbNxYVwQ+QCR)R>m+UT?v9Rqr2x=emI8+cjif8>AJ
zBPU?9aAMiu*cN&RGASmtg+wmY!y3RX`e3&@nXFcJhQ9=Q5V>Vc7j=V&mWS$}9qHG`
ztk|78f`m2YHlf<BC~hthQPAkS{i#;RH$DXDX!Xb3t?b?f6oPN`t|f}DVk{Q1;^@1|
z0DU@aY+r+fqNaUG0biL1>xg}o>uP0pt~9WO>RV8+0qSTuTpcf-WZPMTz(eFw06HoZ
za?%05VLlCnR&ih>52Y|YjGwf(znl2{!q+%C<}4mJx18*X5)-cvd)o<4Ssz&sh#C(3
z`=hg~NzEhiWN$oj^9NPEo+6GZTM(D8MseSoj^eVMG52&inO}`&qHKK8WW$))$kO}@
z8^iobQfv0(*{=~Dltia$xOKSjmegPu;!h(h9U^0`t+pn6g3DO${f}0o!E&p)>;&y?
z?3CZAd%#vbe$`~E2T!`NqK&V1!VZ;VK44^vzE0%io3zGlIf6^y?YF9xAJ=dZVoejv
z=HD+`=A?XjSlY9UL5YPk@MuEpGLImlq>bX&{<#A9tl!%tzv9s!dndT1mB?R=LxEF=
zq#V?u8sw~7${d=MvBOpn)YPA|%ak67Q&(2V0@O!WXdRy(-GOM<bC4vUG@rdK=n*+`
z`s6own-Lh(9{_9q#?NA6c*SWvR9s?(T5{Vzd0MODGtO01#4pW1?Y~yG>{%38)&&)^
z!wF~I?L$u4V;5Tp{iauL@_Fo()7aIZDe754*-mL(keMuv?ZZK*u|pkJKW38tuk5+W
z80>~YT{_<g`^)q{)1eouE~2@PT~-hlUoVy1=a&h5l^}DDgq`d{(m`_`xGBp-@vbdT
z8Ct=6a}#*%SRnvh=p{P9?)k}3BD8#sOjGe~{A$X2Adc7sN0r#mB~9Zs8f+2|lQ6Z7
zsMGALHvh=9&iHNEy}+u6z4VR*vZj&ze)2}G|86kxY5|o1D7Va#RQE0?@Rzcxe-$vP
z+F=H7a_!I-hPkdmoeN38!P%r88)z;Tw%Qy0Jwh@^MW9o)R)_23v}@kdEC+*PF^+0;
z@E|Zud)kl1^a|mye$slz`xA{=(Rh3##T0CX$xgbliO9maTY;s=<34dB@#RN9c!kUq
z4${ZHmym5NAk2*vNI2aGvO6{~7HM-|eOaT(CXy%~Gg_ZsjDx4Odjw*<;{sVlyYYz}
z*Dg+(L%n^LCUf@2Mn`W}y`E3nT6ATu<~ub?YKYA%zBzwRL=6oE9Cc?9ntA*jQRj~s
zHGxtTxX$)<I_}CPmZj;BWaIq8#P(%DrGkucX}oS0K2EC+gu*!p3>utyRF-sXNKC%g
z*8W}D0`-Mi3>-mBD)?2_z#(c0{Cv&IYx9fKV&=3Nr2acoYo*Oq2t*Ch22E2<zlma2
z$JHK*KJ+h!O6<2`u1JH|NbfGA_#v2Dh&07hwO-!lY*puv(=kcvvZU|-K@d%4>4=x}
zn*&4=a#r}<V!5`5{O&%-#F`xg8aLIea0?1AZETi;kBDFNO#}zElu_OB8<=xGrdb_c
zOxRz$u+yL>kE!_syGe;vz4;s#?CO%#F1JO&N`eWX#)g2AQe(u$`dcU+CCh^v6U5ej
zgjDj`P8jXNIR}^-=aKHJb!nbJX@$-k@~EARs=5Tm*=L*2GJE&O`1HcK5MkjHS%__A
zWpk&B5x<t%3aVUV%h)GJ@_VbOyYo>xZ?#O5KJ^@q3*}$OQ2Fq%l%5{npGx!A${nCM
zDYdIfC?0iXv>MB*49MULkujjm9nmd@Rs}UZ&q98SP(Zznz*;f&Ir&|FIlTU$tL;51
z>4%{Phzko)!v*s3Q+68+(T$Q(selr5WGu=eXBM%HGB~K10ut%_J5@XLBS!P}L-8pz
z_ez=f=|^U$d!AOO8&KG!qq3*9g!tls#p}vlX_2Z*>)8g9X)(i*qpL6qS}w4-(y9f}
z8XwD!47BJDu9IS?44fssz(4)X_Esm(9z!-cy?!+Vw;Kv3jKT8>q6T5}O4;N1%%O8g
z{)F65a*POS+mX@(;b%99*y1{VgZvQ+?ZW{*)rLt}{lgl#k`(X8jVplxYNJd{jn%_s
zbmJQ8{oFEs2r|ssFjPbJzN9H%9;c%FX3AqYHTTbC<bTLTOF%ERFI7K&KseTDzkLvg
zG5Be(lwpb+gQR34R+d~oA3;~f&7A=(9;H)_9sAh85-DILQ9PVZMsKHU!iX`Ut;1~w
z`vl@wi*A_^_~i0A5+U9nsubn(oZV;1&ysKwJVBhKzui@F2`cX0Y2Z2F*t$6?it=-I
zO91)d_kP}SrDaw~CgN;)FA>rXj;XX^C^lQooX-jFCvW>rXOXQwv0gX`LnuS3iwM0&
zG-3|d2j$p`+zJs|Gkya=lGQktI)b!6WpAfhgg^@-pDmDLkB&q<H9@mPRk3k%PH7UB
z3QH}_&QsUkI;j&%XD?{!2(g_7&!M|c%zuP|7M4CZ4qlxGMqYl4y0&DpNW_X3D*dUW
z@v5-U^!l-nojOP1!QR(i`?S0N=49(4HK`o|Ur%KRC7^I!5|CYMG&;y1(o&HsP>>k#
zx$a#tPOv4CGPmq?5nU%SaVUU~0Dm6d<P#P3N(#Hx_%CL@?GkBHoia3P4wL05Wmq|q
z#C>3>DT-rlEOe@dH@e9?nI(M_LS94;Y&W_jeMw1Sy6Oo;$#Pifz^8%=f38l#BXW~m
z$fsVCqPUY}{-rC$8{sR?v!EphSheBW_(8ukuZ3cIXbSTr#U+uL=>z4;jS50|woEZ-
z=&0gqbLBuya9L6Q{3tY8-@~PNP;Q*P21EaFqjR2xLutC5ER4b`K6u~xgt&?X6Llb*
z9LwDJ*A?*PBv3SWwW&mmtH@cF$zY#i0GaByv3SAJ5j9G)qdLu!f%?s2FD$1(;i&*S
zDI_g@^7vcNBgHh0i_-6|1yobGeB(}4Be6vKQZf>XPyHe{KS!310&=I6Jt_-{#43$2
z5{+St)lTHA5T;h%w@ZwR@-QBSN9mY|Ua*M+N<N(sn189j#V*J1rY2aaZnBlxKoG>4
zS|E3B+qWeu`q`Ke9v+}#S;e*qQ7|Q%ffD-ytV&lbXZ=_Vn~0_LgK1jIo0Zz=$Q0K)
z)U6%t(q^A5mx&Nam(>?7|0Eos0GYsN>t9-v&0CjcQ~ad3sTR?DE%?SN*hI>ml{{?I
z`OUcb-9^)b!7OZOdfX`OSFM`Jp1A6Y$ijJ8DWkrO>P<W2#NkH!SS1a+qaw095BCL7
zsH!{}RA%$1*ifxlB^s5_wIy%zL4&c160xx7_2Y&K+E$+}_dM|J^5CI3?L@I{8xE7b
z#YRCU1rs}|-u#}uE|peOS(1Gx%HfYY?hGeC#XDe1H~g|Wv}4fNK_AzU{Iu}tRaKPr
zeZdlMv~-=zFr6i%CHrGB5jms7m2c}19C8d~1HP?Xc!2mt!WPK&xnKr=X`q>ib4AS!
z$b9V!c9YW0{mrOOwQy}%O`-QYL&dC0MVg+OZ`LpDnyQ%2xp$rWg8@D#i0bS(lBefW
z?EA%jR&~2E=^><YdkwJ-26Noy-OP!}j0-Otw&jp1LLuGj$!&<RWZsh6F-e1gs;w3+
zh{?)gX&Devfr8UL<Rl&@x=H6`x^+m>xW}3BV#w;My9eO9ow1;^S>$?GHHWa9M)$`p
z6`=@I@k?&9{%$@zOa}@<XFkKEH@%dhvrzQGU4g%Aew)-L`Rw6cfH(u-puN&g57r{k
zG2k+;z}MBHKPBvMCcS<S6~yzsPu*pbW*n10cNo!2A5KFt;k^_xA!apF8c8g@zn5Z=
z9u!R(|DcRwr-E%6R9_JpINVrU(bzsrr;48tQi(!vDT64Nrj72*!8Hoy3}$v<sX^Hf
zqldoQSW2hv(qOFcWGlg;hDLZ-?jvq8k!LVdEs6=;LGa_Ps0}bF1_oeSNx~i$F@;P%
z)y*N8?v!Y9<zxR^w`l&|r<zx!9Rf!jQ%8}H7#~U3x}W?+C9S%*&O>8<QDF7GwmF57
zRhyZST{rZ)^}WIec$G%)d0=d^&k>l+C2Pz5(9zF0JK8`Ax3VAJ{fiwpNUf~<6u(OB
zh7JjxpVEFusD|OIz)|?pQFvhtBC1dyX6ME}2=ztyVn0FE!Vxs9fUq%VsyFYOH;dSW
z-BhZew%&%Djv9ZRsW-f(071`!cmn#r`Tz88l{WL%jkk(=Uic9-euTVvw#_#M8?>^M
z*&-pCecF^h>bz?ZmsD#grXpRg+T{;VMiYTj%%lbRQKr<C6D=xF;HQTCS#5HQFO?Wm
zLi7?o29cz#I$U&4RlZSeX3WuA8l7t$S&c0RbQ2&RolxtH@{^C;gdLwL2nKQPN1v}s
zRcwuw9{p^9Y8p<g49>@{y1wG%fNhHmsGkcMrI}iib&*kb24~Alz;tECuPj0{&?IR{
z@nukCakZ?{8E<BuduHx$8kTmK)5XG~Do+=d@t5W@8qRllC^Ao`blAB*$1S<8DoCfH
z&)+Q{_+&Qe^7SJ$QaHomItHf)Yaf!lJVr31GMWW2q2YNQj)yv%yx<TDiU?GhI=>qS
zr3p($AUoAwI=r$~+=DS1fJOpZmd4?a!o!UHB9Rn1`nHqHI>8==KJgnNm61taUU*j^
z4Xcjn@LuM1?a5iPQ5d|x7}HpqiyyKDVsBCxy@Evr;Uhjah)9}IS+Z)r{aSV)2*XJC
zOD~i;Q)mEoB}7q-uLyZFoPoegrrx@GX65ILkMEDg3$Y|7q_jL9RGOUEfr00T#e;Gx
zqx$7N=C`IW6_8r!b1ywR_C|-huKo&9={y#b3!#ogG!$8s3;l<x629_$Ech3QsPmI=
zE(ar0_$=u2S--X=_QVvFSzmZQH!dKKE}}bXQ(?vr3n0y$z0sM9Au5`Ed`!2V^ttdR
zFH81&*@+Nrwf?uuNK0rO4j{p!3+Rd#MNY8_e_WDVl1bS$N^`8+FAsso6y&X!3|8p_
zqF~$U38V6_1(+%bf?=L6v!15iUUrkt)At17_0M7T{a!Z8*Z3yh>xG+F7ZX&Z6rv&7
z5UYBvUx((#vDe`3@Aq%cJzgG__WHmERI{N*C|#CCm3^5N>st<2cXyV+C~^)`1B{>@
zt(`R|dkYJ+jnZzhFQt3TU#tyTP1*8?5c{^p&9{Y`H3LEolrWrssKz0@V=hZlvMk3)
z^e`(^n<DPdIjGf*8AB{F4O1I&p)2dqRF8r$u3zh^?gu~A8q#+tkQPT&&GonPapoXx
z1xT{U5U_=!Y2|-1qCy`H1;Q#7$Z?u71rDc1ysaSrwFVXhiN8h6I^mmzA|t+Z5G&6;
zh*rzMnZ>O~JpcXe@Q&Y&8S87}<U)$t&UCrEzFc1g;Xrk5HM&vDYGWCD^H$?`N7Il=
ztcj*+fyYhsiJ{Y^!lkzcNl%$>1#;X4!-%tC18+DC)f^|jEmWsAWbML^G(p9^?7ZrC
z&wGu{ufHbPtUm1W<|YPA+@()+m1{SQUsT~zFz-uoV!!yNbE$?ow?EgRH)~p3*UkGm
zhBJ`MhJllYN9XfR5%u#Qap2hiyxHbmyq-){21S})cq^C>-9QWl8hHh@tftVQDSjJ5
zp^k*{AKzOa>xK6DuHV+vM;v%Jrlc*3bX3uuObsw#yzgC3vNa1sEJkIZo^s)@$^XfZ
z4{we!w@RLm4a+loQ@V6};mZ5*a}>TNZojqT9X(t1%%9&C5JclkU5lorVvY>Q`*sfk
z=C_KNVtH!ty`c0jWl~3xjMiRuV$5pfb@%$tlWq!_F+xRfL`0WW-0h`x`~1O3P0&m_
zvpZWTmtI~{vvZi88eWkeaG`qXy0^!yzwmy%Sx;?i|5LL=MBD6ZL-etd^Dsa<0}AU5
zw`V-SX=%we9p9c;Pf!~izEbtb^Q%<0XQ%l<cYK!n;_UgcBdhhfniRtRve`A!C9rlU
z@6~VS)$dMnzid(#ph`m&O+a-!vk_|fzWa0Wi&Lpy0Mu4D79JZjwMIIq!qJ%AT-EzN
z2`gV21hNCk#E(Ns%+yX4>znY$hnQO|2bMZJi@7>}`a+opIIh;5@yk+*`L9N#62qr{
z?+FZT<4bEXmAS%vj&v?pTQ9>QC`n2(-3jOpJRo8ghP^n0f=OhrF@ZshbW~>=n|2oh
zS?JCtIjZ25imM^Sh|&kgmw=XP2HQR~>N`=_PyDWm;W$ou@eGkQ>alTLKJT?U@Tv+1
zO-mW`=o|fJ!j&1O?$Jw)EgDDhmixK`>Y5a|uA!?_RiU}Q)gZy*=$&<^rD_4_s~7Uc
zfo^=?M+!W=pkkFRB1}5~3douKzs&m)KH4PyoQI5$)hcTZob0I#n9nc5r<FvMjIbDH
zHt7*W%;Hfv&$@q%2T<v<5`-J!l?Kn;!)l^XcFrX*`iZk}Np26(X2AfwUUDJ!=gwlH
zqwSoWu+OqQ4vUfaD_bx?UCkv&+kqhju^xPLreW9E%bFw&kp{v5<*2eZ&I5ot?3vbg
z;!^@_Zwi9QLF%y=a^#s1gXJwiAsnHA52<f?ta4Va$eMibJE^=o6NNPON~VOQjSx<9
z#VkQBuh9D&Vm}Q<hgoddcl`{6rJq4|#a6{lL|m;5#a44D019;6x_5!@lba@KIHCNM
z$`Y|K-*k<Dvo$jI0$*9`8WfJMEVZ{usPH=6!yJvO6=SD|BjKFqcM=twa<Cm^5OTxe
ziX6ts=#Z4eeq~a?c2l-$WXf&I!XWDhjpkdj;Is4E5-dL&Rp2##pRf7z1vp<v62Xnq
zh-hRGMy95~XfmDwTd7GzkYim<kM+0@H+`JEk=i*C5#IvKMcj$Vi|{KoYt@^{rM$wk
zdu6JrreuYamqmz~FpB4feG@LM#bJhfO@r<Swnw-`C!y=p=1%vw)i8wi4Z?Qd-|T?D
zi7Gs=QcdD{Tzz}<k`Jdpb8y1!jlKzwR_B*!+RtXAX<$&78e1#EvyM`-pv*0_mGf3Y
zyM-ma?#Igh96}M);k7&Y=Fi-Ae$$q*vuJ9xDB;ZKoD<7=5wd_b56eEf(FEhuxo@Ox
zE3WKn;=o){UD`Sx+!2p5J{{aratg_W?;H-pZ5)a*qEqG$cEBXcSQW>saKdVohtPbW
zCk7X^h|%Zew+#20uu>A5g0|g!UDmX#Q4-e}9fjp)a*iD;j2+C!swy~iGsAeYmhTGj
zRVZbcuLZ9-U0e)_D5LXCmKX5MN|lTZI+;(DWg0*C>TqIq&H=a>0aJJ|j#z5C=t9xG
zD{nJ1F#3LKtg};6jOiXTi7tP?TShydd-7Dz1yfPGCd=5_oDUwiqBwDVHP<RDYcj@*
zHEglC8TWq!xwtUf!v9F|E!ujC$7t3Ez84HrB~zNhiVG*+{*qM%dP(H$h<;5%TWA_P
z(z|*4njHu}TRHQ&k;qtG?dIVh$eFn=cb*I{UakR+e;PDb*$Alq(wQ?u=?FV(AUxN{
zMBY#}s=#NRT-W56St@7#JBsxN;yqw0{e3CauW`tQ<8iEC_{)-dtpcxKUs&PJv)qSE
zU<sBa)dIcs(kb}8o`QI3p@SbX`&yf}lra*}s~S_RB*d@ERCv@zHEOGFtJeE;bme7<
zQ>0QR8=;ABnKBjeF}rSXW!N~eKec!iPqv!>;OcX0%>e?7<@*#w8KnVG?nYPGv5^r>
zj?9-yo0h3%Z<3#f)f5zmt6|yd4e`*STsNX8%kNR;DO`Giaj;X(OM{-%K_<3L8T~Yu
zj@F30RXZy?qMOVE%Y+M~PRbo32S6#umVNa;h6N91jMLK}hrVvhQhxZd(>rq?Qv?7x
zEqm7_AN6&E54_1M(kda%l|8RXTw8zh#UA!VeD_@DD|CD6EazD^6Hm3m$>l219F_u}
za~yX0p)e5p*cnW0=akBE>iDp7oMGlbRy6gEaX7OSe*m148$qh4Tkp491H`I?5LBu(
z1Bj<@I7<z2bs3k{&5nTzykPo1pMv$?z*~dxKgMrMJ5*N<Seze?Df9;L!n^073rZB)
z5&Rds0w9N~;97RwsJV*WT2e7&X$}Jls7-EZwRL6kDb<PKPL6(i6)l>im@ozcUI#9Q
zk0Mmo?R+3&*4Uqo#?e>@{inq~yGb8v17M*^bR<P>zCtRll9g^`DH_}~_k3qJzmoY6
znv0qQ*}?JQ^A%OIQCqhW*hr&kQEk}mh$nCh(8eQTAQ~uH0@eFFVVMFs_(!GVuC>_^
zI+f#kHSm#AX&iw~5WsFcW{l%h3ujZI)=4AM_%X=&Gcf;RO1%*K6C4HGO+{1C`wzFy
zi{56u8NGP&r#jmNl-+d{JM8`NVK{|OlvFJ?=LYN1pN}G|0N>T5mG-#0rJ+0|Z~tuT
z_`js`AGMzzoxdhbxgBY*h2yrL2k*HzI^rt$8>@*xGL#4x%7p}AX#;mWG1zH>WW_MW
zh#9_+CzcEYl0Ux3Feo^aHQ7#_MU$Je8iPBJb&Bte_fA2)zQxMo#Nzx)L36}Y#jzxn
zZq%XMux8Vuh>WE;b!JA=#=0WJcj{2ErhXdH_wiFc?3A(#Qf>uIokszdCYhl>8N6eB
zuO$?o>IiHvqc)c$dId0tK260E``ytSc@Z=b5fPL83vV$MzwIck`8q@0+I$qtwz%0m
z{zZyHoX^0W3YVDMfVi=8a=LsilTW&#t#+Cw0W(UR`tjA&Nf<i|D4mjH=&n6>u!jVq
zN6kkz=%l&EV977M7Je3&$@a}z_~OhL!Hn8SSU{P*w`I(pv@%hIrG*1dxGG}T!PDc!
zI%HgAy9K3Ck0wZ3&hOvleNym@9(ClbiWVSYgH^lMVyd8YGF-!O^}BpKwD?PUf}1TD
zP1c{1lObW_jjzk^sobU>qV&eEmURI%U+yq>bo>YZMuz$i5x)MukCo5df3x*g&mKtt
z>(v7!JyC?b;}^fo7)b6aqWZXgoec?4bD#-!p1tcWM|(frCpvI%9@XVlgAT-<`|6gg
zHUOnULnU9r#Gu%@p1w&y4z)PPxs$2jfrPNIkoO%QvX7j`(%RzxX320}P1>~;r5QW5
zKa$ANjZ1WsB`Lk8xjT>E3y<u)48+4@_%4YvaA?bGVcB*!hV;z5AOIm0Nff_GRofE@
znu8*&!GBFC1F|Hb1&2Z#HcCvkVQ0L|SB1Hn$fgAnuR%_yjwnc(9z$$U_GUlms_A#t
z_~6WwB59(c;qE(E^OR|N^z!x%GM!tqne4=s1Y%j=02Rnh8|H{0>e~(YH`A4A9i=F=
zc0V$t8?PD(hax}1+EAJv@$0g+qEr;SKfKiMr#?+$OMzcwzMtphL`HWHSX@6;wnRgd
z3iE#&h|N5;I9+A^ZBGk47Y+GfdjHEmK-$qUtZ+-Knoo&mDGONS&;OYl)L1WYlwDhM
z!~g&de#`T%F#EA~raR6px&4~?oay!FgV(N^56x@NY+py#eR0;?iJr`+TRwu)IsIGx
zhPlQa*dHvIL4ZT(-5fEKx@-`NXz31ua-E7}`8Nrrl7gon(dgASojmJ|j+dQpeztBY
z*K<n>)HQZ5!Ajnn|2gITRJD%i@aH|3Z>ikcfKP)Jbar2874dk(ig(+I9(NVFdG-6R
z%<C-dkMb0HMrB{RN^nY#gRVW%BSBmX0drpL)ha?2rbB87zb)f))UGaFTwTpQgmPgS
zA@1y!)pe5|(<*ZX?gVB**{&%XaV;r4!h&v&&7GFb)=`%lrp#lgNSIgg$8Q^Dv}c%I
zdW<-U+>QVS=rMMMlE${70Ksk~eRhG`&4cWf^fSW`-Q}BC4871g1O2ITq7kYd<*;-i
zQ5z8w>c5wvyFJiiL9z_ZcS{o|TYHD?4(ctbkLE@;mj>@esyr<nhqr{+!F&~j89)qZ
zRGf;AhdcQ1R4b03BS`JlvvSeEy>CdNryae?48=3)!)2F>I5S&0zl_BS<SK38vi9b}
zaVkNj1jNTT88c`|x;Zj^h_v#zB2iRh^iN>*lfjr9j!kb4GibA1kR5MWesmv;Yoi7P
zDD>kWV5}KUm-s_!%m5}3{{jd!y1$<E%j3ko_f;R{$@HnN!!W9S?;U}KrtX)W?==(+
zj6#u6zjP0(R<2Evq>W|lwh5<Jv%soIiAu)qze{Wk$Zq#^9>I=fLm*yh8Mrv`&xbx>
z&>}R`#fyI<ZtQJ~z$jw?Q17Gm6+uJ_Nhr;)BtWOjXT;py$_7+To#@0-^{lX|@%1j#
zWBZSdyNLHEg*FKTK745DsOk%Adcv&E({pTs!TZ(6WN-O#V^Q$;&iAe5&SK`W{!kSP
zMmsep@@+~LhxtHG02}W+GqRxXAmBo$>i38It^%vk)?)eJ{7<g?NNZ0TJw|GQ`$2E2
zsy|dhT7Q=XVqPCHO;WF1#c)y>n7d%|>f+oW7l<%)CN6O{Y`w*cJ5|wIFImMwP4#H1
zx%{^HzMtVXcS(!;s>)nqc%+LwXRZo1gFS|hI#32!gHmjcQvrr1If+)`;!{<j$5b}{
zBB%4FdAnCdq?U6J@y86eL$}i$BJ^N}^1c{sv-N$(y|}63`1FZUx3ijoLR7Oej@5^E
zXvqa3i!cUC*yDyaK|(xvs`5BcC3Z828^d^|bHR$dA*q%6_;H3v==0&ZSE0toxM|cM
zlmSD;oXT=ys_00uH=bXr;krDD4!FyZjEFApur$BfP6ab2%;8V2@y2@0uV3QR46KBF
zNDgPtF2BuY+3lr190)>!@u>WSjZ*8wifWjuXiy;~5@MHT@D`T|VGj3fAHyh13{U{4
zOib5%>tfdpMQ?d-mz|}VZJmc0h{Bb71WM<MO&(om@9#f~rfRqiV<MZDswRYmIxDQ%
zIURTEC5bqsN8U!seS<2$4};D*&5z{*6PkGV#NX$5bxifLp<+)>LA>tBo34V;S{Z3F
zS|w`kp#W1=J$ad^zoxp51R`??NGwVE^n4yD)mW$I<V#Z#-hy*2X3<roGOKL*H9JQm
zZAS*jIMKocs2Wyt-W~bPAbjk17d#|8<bc!sG-FX&B7lq3#!+#KDFa2c2HXJ2@Ff8g
zKbraIBIj7?kBQ1lMez(fxqPYJCc#Ku(-omx?o2lmb`uJzhBBH&4$RG=zAqnSHc-p8
zQ$-5KhmI*?v-}xNK!{mE;OAy-ATC0QPPXMzzPOeX#cJTqe^TdGhUBkFxV@8yG#QKa
zwM@)jXn8Rq$i084UYo$6*G8sS2O(I35g_PD0rk#(Mvmc#nCiN9HtI)Rm?B;g4qjxu
znQminFVn^pv*HTWDiP;!I)1KVYeyvWR}6Jb16ZI6L2Qi%42!gL&`kpdlKB<KWivX5
zOf}b*F^&QK_xv@yRyvP<5aqj*QyE@`{5g&CAjb9MaI@|^vNg@(yR!W6ur1fD!Y%RD
zZw+r|vf?<5dU4{9J$j?C)an`i!h%-KKu6h@$Sh0tkz<DDyhf;rKa75r@|hi6HtYQH
zFn!r%X5K`U5dTDETh3@xjub7qy3%<S5R^C(6rByV8^AQHfp#&3$fPEYgIcT6Fee88
zr8ryl+fL%rlef(eW{v?RS5M8}DeN4E7VBZd;ohI3mBk8fTXMTL$=S5j?XEk7S&kPi
z_0}!EkVaE0C{Iv4p=ERGQMDz`%hnSMN1Y)y;)q1KNI3+6!Nw?|pcaOy9GlJ_2ek=B
z0aaZf70)b;XfCO^`jqE#_+j6D&lov|4w52mr2UhE@rPF5tmS(s>La@Jgs3QqXskMM
z=SgyzT|~}bmWwQP(ZgS>OnR=ew9BN2-;xBW*QX-*A(}#I;_3)x)6v;m68;Jnq5e)&
z(!&5TT##3J@Lf6D?u3EMPCsF^tm_|>z&06?h_F`5Lytk1FER-#LuCq{hvrD4AJu_Q
z?e*eHp1i>)HWwo_&`!?~Jf)XIW`q_pqni2&`nGCuFIDT!AUae+=Q_V{z{F2?aZ5N>
z7GO6J?#94P`Jps>7@3VbnaR&w%GK>7Mu*S*nL02CfP}utAZ&_+lqt;A=&ds2irTdO
z&Plk_iTq=$<W67}Ln?j|Y(7*FcPQg2k;U@Mu4K~AX-w=}zoA25Xp1F`C?Xt{(~MAz
zsyY)7e<RPcC~p`c!a>p(k?IyKjHTaGc<&dpALqIv`EJ3|j%GlEB|bNBCYht8@x_;)
zA`yzM6#;X7^>cV4%1CqE^dGH3mL`%inTrM%(fy%R&_n^iUM(p<@vV0V0llzcQnB3Z
zl%E)el&Q~754d;=Rm9})eXJv;P2Z7sk69kZ^D{&S3NIZBTaEhA)C8j*1~PB8^$EEp
z$@Y!Z$2LN`Yd;zw!+ScmAWD)-A4atGc@84%V*d0Ll+J{@oD0U`!7Ys=MG5PD`04&R
z>-T+0g@1hptS(2HQ)|=@E##5!p2cbatutw5+%m*9>(X|aywk<@%J3MAjS_pqpxwSY
zx0Gw-`)WQ|(pq@lrRzp1!D;?3H|sUVV3ti&xhqNZpYrGnSube{30m1D98$x1O6s0L
zLg<TYgpJ0fHxz6Qt-pHxj*$iqgbPw$-adUH2!-O>-G+v}r7n0oV=}x;=Q@oXL`^)c
z^+Aa_G9Vl=2O|LJ?2YiQZ_-?i`@m2mpAjObCovhWrNo#*qBe<-tZZVjoYU3+juhTW
zeLy_Tb-K7A^|<90H`>Gk%iRXwN<&BWqF>z7qeySPASj>RDB+zf1vss9q@brpE)io$
zvGmh$sFwP$+$<so^$Bv+jMAqIjS-_g2kk6I`d=!bAx=~5AP*FT_yDT=)}il-A5Zs&
z{GAgzVv9_=Dfk$I(h*AKUfu_mlBP?dRah5G#-n7)W@*#ZWw%yRQ4ja`GM&jw^xWBv
zF3yKaGsM8X&YsHw5`jeCjm8>-3FIebBCs$x845&m*+K1i5`N9r^WEa!X{Y#DZ|3sR
zzT!a7SxMAkD_V*W!mou%GY+5*AG^yXtzu<!yE)GHGL82|Q&ySU(D&@Mt@;XVt*UZ*
zOVbMVjh)<F@!A}FwzEc)eS$?t0L@(e2vQ=qCk^V;z?7ZGkxT+*kBBx}E`Bg&=gkfU
zrE#hb_*H9q5agSEs(?c5f)cL}XQfdf#aY(FUDlds1in)dm-S9C@#k~akI<x%@UE9G
zQatin4;N1s+bYiefFsGpUrLll>X8`DYw$+vtYQ;5`qokZ3XTY^ql)!5<1JUpSpQkK
zvZb1#SGzmx*xNJ!NLw)<YA|+)Up|`4o!R9>I?T}NhDM$7Ti_(Csji>OPq9=eqS`|@
zm_RP7{^^TD0%^?^R<P!qoy0?esv~lejj>ZQ(M`(7m0q|*1&<cP6#zv*QM-mjpnBaO
zn{EyK5TmGyb|v1OFZ<Q9xIjMu<(K07B5R|cCV2>!!7Bus)`#Kb+ZvsHqVR)I?<C5N
z%IQB@BbxWLxT81rQt)~Xp0h6lcnxiw<fP4zLeB<PdExSaLHlHuezLlV1XbaQc6@3&
zEvCrXO$0#pf`O8O0-_5XP-xhyIl8|Y^7VX)Z}a$KEfazL4K)p^?Xl5BVOd9tUWe6^
z)9#m=HP7da)qJM9FAv|~i{a=l+8F7!SdvFS<w2L?|Dw{cW50YVjaEuYp0E|UdHTkp
z4I<;fLgRYqDU5l|cQ`82$}Jkf#Z)kICe0z6g&IVV>3jQ!CNB>RCk|7LqluV@VC)P?
zjakI4CTMy_$Rpkc{wB+EfLbz9Zw;mNl5rGav-Gd1|7;{dgE*sq;iT<d4Q?<Kqp8Ev
zv8bk}X$cue<zMlNHw=f>MhMf=37y|7N{ahn8HC=_HyGReRl43l2w;eWQo{4Ihuerp
zu2_S+j|zu--&L|k+WpU;QSTG~Mg^>o5eZp(Ei9F+pPDueey#DpZybI@$tbWtIz%Dr
z8{<5_@7X5w7PT}=07TbV^_x%&s~3s~%U|<5M!1O_Fq3`rB2z2}7)#){(!&2;wsTx`
z!k=JkotD3tcH=qe`h&<9Y4Jd#6KCv+Lu4S3DftI;;&_#Zqr828k1iM7Z~!cN8!P^9
zbk*Hj^W+~G9d74o^1||o0^g;BZ0q6<S`Lb(XikxKIpLe(hA4b`pkyM7ZWkeJzqfq`
z6KMS~O@34>u}lk{QDX2e&j3H-XQDkH`sqNBYOwIG466)?Y$_~t{lHm_raQq^G7{t1
zebH=Aw${aoEJkvrQVhy&>UzBh;863%ioPwmnmsvOTn3z?T=%wA$xAO&2L>n-eUw!@
z@vs7bBQd9hTcpj-8Ts}Gonqyzy4cRR9Zk-6o|8R;8dK)uTWVXbgWXfY!9O!2%J^wD
z&eA4{Wl7`%!-K^+i1bl5&N@Y)s`;+~HfjG*(>_JaP-HSRnP9RHz*0^@4uVTtAe)>h
zf%6>YhhVp_zu1LOth<p_FtyoWLnuQ$zmy?Lwz91TqP{aEzaf)G=nk**JoOfb(W<hk
z`jSaN$RX+O5{d_?HZWD&oi^(3o<fQJVxv;{_|bfT)569N^+<sQZW`|o^Lt4z0nzDD
zYvGz&#;LyW-bVGOJT3MLt>sw*i;?<s8Rl4>Eswq~4?GZD74qOO<MxKTvW8V1;<mWj
zyUXZ1)E}cX+`%qUuJ$&@U(Rpa0GB>w&PM$xWES{RVcO}@oN`|}@%)Z%)()gV)iO)>
zc8O72Zo-c^ElLNZ3l_S~OrpKw;{Oi-ra)Q0(-jZb6{7{I3NYzHdNrgqs-o~S3aG9j
zDMKr6Y6b*qf(({Ahm2f{#cK%4STT@RUmeGlT5L9<SOn`*a4-xLt4C$`ilzvB2P0zV
zts9ojN$rc}98`3REznJPSE>E?!jU%FE99OP@vH$qWc&_ov2Vw1N0r_`Yxk;F!NHJF
z*lKRrqB7Qk1A;LkRfEnvC{4t&UIZ`@lIq(0<<?KekIi~L%6#wyY$(!f*GXNKSWO2^
zf$1hLn-i@)yI;1AUmFp^RU?3bu$Bh+pw$u)W+3M_Y~xw^7-1wNt&@?m1&JH*HE_sv
z77Jp{<X|bVVLe+f7}(fLVhs+t0D0B2!1T-zfW3RKW1jj8m|o9>4MAJ@d>7rE5Z1Ae
z?pgz&SX!}L=4=^WZ0^cPO|GpEjhD733tVm2-o>>TF!A8*2x^y3<4AuW&|XhgW4ne9
zsEB_~y*}wvTzAcxU2B6(fs?z2#Lk%;xI)}NbK7vSOJOZ6=u8H4+ygcc!(ou>7>T1Z
zM;(y>9^%)cm*!`FjE#ArvH%ei5#VstMs^>#Jx0gUD`)ljL?P%R2D0j!$!c<0g++!c
zN~b|dQ+m0opRuTU4YB~%ux60r5MKV>r>2MoVeWLnpdtQAwZi}bt{7Zl^hy%lgzvtC
z|IXh}gGPH_tb|q2wr{^>ps$eVohz$mtx#IovCx25EVNBE6?5Hj)$dt!QD)JS`DIkP
zbSw<!g!QOCa&25!AZdjp#m=LI&zzV+!}S&;5>}nEEDksUw{EF{5Hv=Fz({-qNf{98
zfScd6nLBL95c4jb)zXiLOgJhP*H)T>T3Ncz!4VDd-~p0duUQ~T8Kmn66mdgUl1*Rg
zO0!oRk`<DzTB$XXg9CVSFfhx;nEbP2i)X<3drZ>sH=%tS=WVo17jc~fZQ<jHQR$|z
zSjO0p17Ot3*6me-C5e*eo+;#jg?qPz&wY;LB|*fB=;6EuPRDY_$u_;RExk9o_quV?
z-z;HFI(5&VaDX(+REd{@`F#uBW54OMm%Ltx1}h7I5<slQ6mffPy3lx82$$F&YjsMn
zShSnFVH%cfx_pAQt_Iz|01*=b@PXSSo<1sg>QGC#Uo{NUs}B!!BwLWwJ(B=Ze=~u!
zZg<v&mMLorol&BN1u1Eb0(C#LQL{SWD>IU)hf%uTBs<S+Vufux?>=6mQK-{={%(d$
zb~XhUZ6B$d{0(r3-JhnYM5FT!l!;}^gwMH_P_o+!n#J{ju=apdJ3o!3P-Rdn<FKg3
zH%dXN*@9C&NYAVzOT{HtK%~r;`TUKl4z^1eEhEMitjB<Kz|78>gpukIIA5PuvfYCT
zY1caaTbPk3VH9OWG+0$ISi`)+ts!2*gT#(>)O-J_FCG9&t*ET-J7I43N*Lo<kie0O
zDYalMzDU<tWQa%YP|FNZOXG#dRm?5(ZtnCrf6jQs`x#c<eyP$JA834(FRXqLU+iG%
zrc^cHw6JZ<?uKYxWxYR^REv{<OsdQJxUQ9v*s_7bjCZym#d)>h^v+GS!H8_RXWQ=I
zOrg!0){WoDT*uAc&y2F>ZUMX|i^l;dJK`O^t*pC4L_-tonJ^A9SyQY%SastR{XgCG
zyefwF0z^#4%9bTlQL4n4xpE=`1q(@2w1CK<5K7wqSt<Yml7Px>o!Brc*eWWz4>l$|
zSF@Uw5h=AI=T>8cj{#;a(!OII=I0!&1zO#X4wOtZX0I%Ig}GsIm;z^CoMqxO7wL)z
zg(9qgqyPn8t+2Uunm>JV`nDI3mr?(G(xPPs!p6!dMT3KxOewH{QU&n{k`W{ah~QFq
z8g2s-?qX>Si2j?T=T0nQ01&M$A_7>9iGoxBQX$6ns2$FbZO>@yb8UaMZi-%`X9x{R
z8KG*an~4bN0RbVl76i)x=)#b)fp_t`J|mb{sMY$(fRHTSgUcZ(54z}ppgp6k(r&~B
zd0d1oL_96@N1h$Aadulyc*|Q|oMiL!EsOBR_tIWBuwwwp7!gRvDqH7Z572eyM|=*k
z7(w98_1c17nX!V+yWF`U`d6KvnS6GVY;5D_%UgdR%qw-<ZtSaz_pskYN^*CfTAZ4J
zPi`5JVm+k1BXddyGK96Ot_Z-R>TK^bBtUld#euS`(k;P`S7nWR;DLzdzXMO+8LgwP
zTzp5}(~3aj27HjE1dqYc!KoQD-~_^2qumA2%8YJln1RKXHB%TVlOc29Xsj3pMjv;*
zH9o|cg5!hCJtLr36y0R^{rBKvzTL!}Gq*|~BVqsz$Y^~WOc6Lxnb57SFkPuC!veSR
z;_}q$PqyGjleo@xp$ddh2v!%OV8|MDsC{Apu-0?or~zWvAfg};9SZ_z(5m#A4qKFE
z2i~F+FBdI}7S^-^5zoN!&rc&P8mKy?6h+%g6;!L_T9}c9Fl0dFgt71pl710)FzO#w
z&ukd)Kn@PnqOWUI$skGEfxZ?9k&Qi_d$krMvA7X`{ypf7UNZ_vyh3k3H^~A2<V<G|
zWd|_Xah20gCJe)Lt8o9Q|5E3NG?yov)$ffs!5T;ppGO`v&d}G@Qk%KhG~jkjmR8~2
z*^>TD+%B12gZMoX*<KxZ5__@bb4;e*WX~Ft%dG^vgN!l)Y1IJ&2OiC3)nf4^bhH9b
z4{B{AA^OUQSegG4C)STknVj9#l?NJ#_#!h_#{as##+|WV8LH}du^0h(76pMcO(sxw
z;tI|OF>y(QL?B$Xa1sC^3uI+$H(j&3Fd%06Rxl67B%F+%oif~9iIggARR?=l1JD-L
z4peNgANDfJ6!+U3MNMt;dq0nG-EdWxOw>8><ET<#9zl6!$KbdOSD;!bsJUc3KEea-
zxD?cMV7^+0BrO7#9Y|Cw$1dDzeuxGh0%Aaf_1<*xABl@T=T41Gl&ft=QjzMBW!<cU
zSxG#C#nOVw@4bOCzc(k8yY}0!nL@hwtt(^2+9u%Ipfzi9%JFQT`R*Bi{@ub0A1j`w
zYW<yR#ky1+SWE;XgM=V;w9@PPG?}Xb)ou>-=tkNYpIFi&vnki&KH4YKh0~!5ZJn>a
zQCrOV;7*^nvTa*I00LcLE%VFfsdfqXSTzG0Zv9)Vgs1W9wOOr<s^BFX7zy`76l_s8
zh3cpU&pM<qWddfpWpamn``UCBq`S6`otiY+3`JpgwtGevixP(7yao%`xL5Fdmh^l)
zMo8_bsywFu4nEcp`>q}EVGr)zd_>)t!ZtK@De^!Ak!rOcp1d>Sf883DV!rY?M7jZF
z1K-#Hw}?HEiD*EQwJJMgozz{`!f00IL1fnKOgnXQ)EKFuklf`l)m?2o%MKjUO@2|j
zx>+4PBjWq$wv{cjrDs9f><TN<!9{=+XC54QSTia3!rvV)N+{c5G+^0dL6jNDFt7pw
zvj+rM6r-NCi5-E&%T+^r!$5+uaCV|)$rwNpu~;3M{*#$2Uu5Hbu6F>@GVsI&NTk37
z>ben&FQCLxbc&=x^1;cYE^fC0k-|90s{C%RD{>-SUuBIQu||0*T@fvJPBP6VgJm(;
z;!qi!Im)zqU?Epc=S|iK4Q9!b$0|`{^jZvo#{`-Cu3H?*&g1TAs5x^oxj6g24-WSR
zi*4Km7$R}R(thhP57d5NtJl;~cDB|Lt>%a=%2mtO$Nd$+gEe7Bh8-O3h5h4#sEpx^
zK@U25P|;mQe^27QbnC%f*T5^;I+N<^s{<A0g4G%!#lG`%LSsXM`80#jQ@BOlp_LiL
ztl3xLc5cV9(y4GmFr2S8U*}hcW8PnuJg`8-1MZ$aEO`IZYaEXC;z3AncAqkkuuZVG
zP(d`X4GCr`6aWdtgC2$@%DZbCtC%vVnmq!Ffh(eZ*}^R-s-ORJA2M)KFe}HzL!_EY
zDB`wfr>cA9(Nf_;k+8rza&?NjSs`)4ek|X=R7iB6v{Y9IK%qdo6hZicL&^rQ*AOWf
zD4T0W15}xmTGmAcalTQKokvhQ&=?u&GEEG8MCOa2-GjKNeowZK6J<mW<NoD(h-$Gz
z#Fi!uhL9;UVbJ6rpF9Qyo+$&YE93UM#!rSsc=+0&wGwO`ZA;~Trigdb?H9JnK6ij>
zV~d5s#cGDYYG+iSV9>&X;=X*FXS5FRp>ZN@3@Yo{y87Cggb{GM3&8d$)}5cg0LS*l
zjJnrh=m5G9lY(ycv{lxsvceT8M^Q19-Dv14j5-+5-&vMX_Ib{rYzw>Th`vdmgLedL
zM6<KrevO;%;{G>V++JKJW#|2O;zj8IwAK+4paT|1qu1xbssg6kTz%5kCDm)Dj|g4y
zNin8PZ0>EjtnNQO{JJVL^nnE;EP|tv@zkNlhFp$IM!FHQ|B#plhFKLwQ;JIQJ8h|j
z%Q%ts!zhTB;YMk48Bw@`YDLc3=wa&<!PQtI4?gq5wna<2_s8BszfPZorhKC;vndnG
z4n)vd4(3WS<<st6H?fTn)-uWC)Phot2tm@{_Z41QGiHRfJF4oTI=LohK~2h@70a68
zTEUrdG)e<_<Uys3THUu>tyFteHwlkGxT#uDs;A(RFoX!X+_7ifZ`YoEyZde2BpzW!
zo{@MB<`r^11lQ{z8IkJ|>Tna<wYOQ=+E$&~zT9;)e}snZ2%74CkkvI)S+L3}YOCZ#
zp&*}$E}F(vh%F=%Y)5>?8n`Ux_T(}GnIUD33PNEU*IGp|<`{Jgu+JHH<AHh>%*Hhx
zH|h4hpnbk_Bf%HrpgH2}2ACSFsbfn3csE`R7}wa38bPUCDpK}9dd;TMc{tdd^lUz>
z!4q@4LaewSvw$)-+)Zw1^nhhrX9!InKo|UJd`<iP{9mgO>n=7wPOFgIs;Ua4t^wCG
z-S~kssnSxTTbJd=jztx0^rvC^(d;quVrJOpj%&~AD#-&2L@H|))u#?MK=o4C5V6H7
zh6vp?#Y*B?7wR&gZc@Ok2_$ctRbs8@vPK=(AnQqkh-%lWtIQl_?*kvqjvY2xScfy$
z4T~w2YF1vihdwBetl5n;mZC8OesFIDCT9G%-}Xg<Vw+H$h~r52nRYO3!D9fTOf|t>
zGmOJg<07=&zXOzt3jM1E#?;z_7$`Bts~Iio7D>F0X9Q?as$L)2HWh>%ZkAoo+oe~S
zsU9h{PYJ{$5|0?l(<rM)km?%4_(3oYNNJ@DXF7nUdaJuiZ2T!0+73iq!5x)pEfHtV
z#;Vyf>YjP#2Cn*}aaSIT##`xUI?ocdS|L(L6kEu-R_$SDF#`;@bh~uiU`pnh8#4WS
z8pSo=_Dpo1%`uw2hv|dfNPxK_48&^%SzoN(g=LGbxPc+9ynw}DO|HFT4p}@OOeTx%
zX27@ayT3II^3K0uFm(WNcHjHz)(-wd-*_=Go6ovgZ2_|At}k?y6?PN7UWT@aNDe&2
z1ESk?Fn9XQw<CdYf9*5Cbw%ZY1tKhh|8;9^Y{&&3*awn>5b?MMUA+Y!Xk`n5N7ZjE
z3=;FdA>BGvF0oh(fC5UEGAC@U<`an5YJF6<-q;lBh{{9sNomZ;aW`wrcO7fd6qC;^
zi)Dep8_)Gh0LECjWvKJDRS<0^K@WmFQ>KQs(fadXdVD({?=>Uj%CcGXdYX(it81pP
zRupBf0B3*<xYVjOTcZAlM3zi4*N7GyGf)zK>~^cXzKB!vC*Irsb8#0?1*Zc8B?-CQ
zfp+OFNcBh=l6+lbhYnUzt0a^ut=hfa)hLe;;G@qXT31G?f?Nm~5^{QGyJy0B7eJfV
zdFF#vO!dXJR!!rjtXVN?v)XtTWkk@*mrJ4;1Fb5qOdiQ}9?orF|E$T*`WL$|Zn|56
zEwJ0-wKm`p0INvU_eGMV?2rMqYQTfTZChIQOvfT9JJ7Ps!fgYKx#;eXzGORQ2Z*-Z
z6a9|k4#(w)Z!7%=Va)W`9Xd9c4>6awpSv?=P2Y>@40j{59V7esNE;Ihcsz#0nH1Og
z4Yb)E05Msxt3R{ZGwnvyj$3jm^1uR-%3}Rwcq796T*Rzlp=?2=BLIR>w$Cl#iL~EH
z9d6oSPfF>hKA>>h#L~r9b%ihJs=2gPe1b@66T~P}sUvEw$gqGhi}EKC)LN_%lPU&p
zVAan-|Le+lKKjL#QO86~C%SjlRF}yl7DOztm4^1-6oGKnbT~Mpp*1^@+%lOej28a^
zv$|TezE-Ym0LUp&0!514y2zwvhL)*jRD$&lStF5_=O!*7f@QF<u@h@uSj5gZ0S+TI
zM#S%n-kV7vYJanj5wFTBlt4m2)o#84gbb)8UL(~*)ZvB&H^bPdWHm?p`E1S$y)Sw?
z#cGOV3r<}ZZj4AKbJ<LP|4eb$czlP?31&vE>f)6u8jP8|Z%k3JZvd7kLtUo@EC*^i
z6TwqnVT<V0YM(v-+2c9hK)5@ZY8L^V;gXHzEW}fX^_+azsy<;!NX!5nj5HUp$AEa<
zjK-8g=jx;;2TW%O$QS)uV&NudmM1L{5NwC^C)dUKw5wK7MACzs2227}gwdeqh<0|3
zrRV?&N`coga(8Yr7NoHx?uOAKZmZB&4eSKV<o7z>qq`oSUQ++^ihz$^9#|kEKm@3!
zbWTskT_tJI#i$;E(+auXL8?ap2%HbJ*NFM(IUOL^L;X1okd#4nt&8!-{-=mQ|0vDg
zu98e~SnwP(64Y7;4ImT?bg~F<7OZQ57|mZQV=P78Gm*y25Xn020+D;L*0%fLCnwF7
z66f9rrFlj`qdg~z#I2qQv*N$+Y)7{?!n$A(uNy#fg~F@>uL4AwZSk}@M8=Q<kSdzK
znn^)gN&_g7_RT0GK%*5CQx=D;dujx$x)ax|!v>--eQo#ZGyqv+OjLQ2u|||MgDTav
z6;!jrYCK{nw^7R-ZH{MkXE;CzPH@lY>%xMO6$Apn;i$~nkO-wF<Sp?FJBXJ1O8g!~
z9S2(gnKc!@ih}th3Bo1XD){1?sA#ofi+c$0oz(i(V{|v#586-b__zVX)7;kWFmqFS
zV4=U8#~w%{OZ*cL78kF#)9#U{=Q>;IYzWs1Nv3B+1Lz@oFX1s;HaAmO=q!$pju{BU
zgfVfow3}wPw&_ig?pnoDZEc&56^XyWyfVle8M1$_jV)CN8O!PA+{I(~{Mb4!a`t{y
z&gPn1hS{ql!Qllv5EQ>w2Zrq-_!;jJo_i(a!384SS~y8FKSPP&LrH3dnkAacZFq;&
zT<;*4JE-}Zo=d)lTn^ROp9WN1$55Vzq(QCf%`1roYwDvEv%XzXa7+$FKNA9k44haX
zSuI*;q!ut0l-m5ZEF4)AlQZb7hV2S2G<Q=B0U}J91l7th$=o9H>+wcet`3#T_IBFY
z;xoL&F@ov_7*Yx_q&Qg*diCpaZR!?=V;v2~WEA!~8XBoTmWow&>qgx#;1OQglqf|y
zt_QGsAga4&B>L+3XUB}CW52a+qAIKX4IngzODJ3UcV}rh9e_mBMhVu}W^Rq|Ko9F;
zY=GU<%7UX(krQxjm5?fe0XYwg{W3+wYJ`Lp3)G@umaSW=Rt{Ch(W_>v>m@;WY%W>d
zBRr$TGwC+tLpI)S`C!NS;w0zI?%y5z{g&fP_m3T9!Bs%VBpJxpN}-IEXgaf$0$CYj
zQfkccD0U|9>SY7njY(j!+Rhy^Y6TAG49EP8VdumVo6#G4jkV=)$EbS+4YYSg(ZoFi
zfxy9M)2R@xlA5Twn#0{GV%O~Ff=FF#Kt|%>x~`l;lKHc2-(Dqq;K2nVXN?c%%37>u
zgi-Rj+{RFD1v3)9d>%239|Wx)(VqoKfm-kfk6~Cpr7jyDPdtsND`0@cGfF)Dv#;2g
zOz~;`f*RkARWwzutXqtTS$Tcc**?{`*(R8LuP@yC5d#aZ6cn70Xng@nu8b|cHJB2c
zY+bP@1y2C^`KO@)h|Y>(kFw_i;2<nhzXzdUh}E3|Jyf;Ns|8`!IBiSQ<ceBGAS<;_
zJSxC5*9_9i-V5Bq(m>P{t{Ad=(~67jHPCi{ZdP%shmdq&?2N{xglouNlf{gf14_pT
zsSh7OXA$}_cerV&rF9o(+_9P>V^#aDvLJVtx4Dkqf3BK-A*{6oc&%k^^>+%G3lgcz
zhN`<nJ+)lNtd3=KO_IlPsNn*1A)c}e+@H;T+d85)9u(Y@IQ@9bbDac8Lg!L$)d6*(
z=N3B=MF6d?N_6QJjaj4dj?UGH!D;i`%f`E&V-_{4x}t^B*9waB(q}<d46UDkVrl<^
z+<^^$&K;X#dXGhr`r?YwLc*Kc6`(2;1lzuP=VkHn)bvX<TNMenjQ);8w}}NihpHph
zdo(%6lb`ohF%KSCAaeHNqrs>$(dJ{r_(6MLL3NFxJZ(UPEtRsGrFsW$HG@b~E%S8<
zWvs@hwCI%Z4ir1r4xKg26-X_{!Yg~iEie*jzXG4s#pu#C1OvQVCRd5RDn0>y5;k;h
z3rn|f>0!<7Vv4ur)B6IgU?+#0#eAOZK3)x6leo4pYivL2TaN2C`qx2*1EAEgE^37d
zC|e14w-kh-HA56MR8cFRnKZvd#QfZh!}S`g11)Hl#TK9FvYR{bp$km^Y;>V-0Tlgn
z-Ao<a@{G5L!ze8}rQJ%Sh+19SSSt}XlFRJ?a3mEq-{|gtcf9Q@VN;M$FmY5E>&iGP
zjIk&^zOIY|BoI*p$ydr2+h2TvWdp(*<KV&F3gm0mB3)c-#DpZPn3}m*p{}G0uP>_c
z%z6goxW%M>aZz_tpISGscW&1g3WLtYs4wR1qca<1nIM)?R{_te(FUEWZ&ENG=Gs96
zaIm?ru{ajY88vqtB}wQdkG|@`!~~!i_z@QC7sZ`U>lt0dTw!av*Ni4DWQRUITW^Mr
z9jIX^lYe@4wHp(Dj?JL6d(fY{ZRa6o5o)&O0kurZeV+Km$^t~rN=#XM`ED{rKa3w#
z+TMywsawVX!T2;d4F+1)h!JA33t6hgkSL&uih(A(KncZU^F^=5_GCet4#7z@y^xam
zX@%7_!wi;U$i1RUwN9c<G5BTo1;nP)x}6jHfV9pfqhGV-%$qBQE0Dchz1Ux?S_(n0
zuv-MZDKY<_@5_%FilTuX2SUuEJ$FDx6<bXLYJS+t$qKR-4Tv=+h6gK9W{Y`ZWxHVY
zLiD++5||p4&Q#g`Ia52ufKSv4nRoTIo-}+E>>MEQ{x)+r(XPNmAchfA65Ltq_!x_V
z5hCEJI|a|Wxx$d30hwEVPeyZJ1!iQQ^N7ris=<fMma<z5YSDjOwRxGaFG|M1)ip#8
z04Z%@6`!-+xR&PTY)k*{^V+<#Th4pWdvH%OTLl?FVu3j3wlrL!-o5IvT%#I7Ubqa>
z7V^m2b5_*jZF<(UxiZX30w%i3b%sPRte@*-g!}$`lb{b6WkAeD!`$YvUV~ONR^!R%
zl>(4t=jo;Fow*QZD^S+Etfu3@gT(ze<k@-x(|1#V*k8~5vnz;079es~LRPUe@fySh
ztMMriuQ7~IN1!Nm&B$Ew=h_y5s4ZcLpQP&VrC?Y;g_^IQ?wsIyVT#~e%4V`fv>1-i
zIwFNFwuK9%uGOzy8KqXE;f+$Y<9Po2y!Eu}1D~+j8Hf0?`EI?2fR;uyMk96GM%r#?
z#h5kK+&zq*%7g%=RIFS{xx$LVN(?DST7N)n@vduNbr3;zN~5yXQ6Wuph8Bl|FCuoo
z60ecVZFs7+e(uG>JgCf0OpL9-`6z}~Djvax=%G2lMlxXRE=3)?qn4m|YWo!6z}<F*
zwfLL6Z62x_Wc#}+-Ffd8_&6|iR>af9CmA4hM6DVpq?J@h1@@LA?_;kuHH5`9P+0zP
zuwzg0cyO{y`Z4DolfAG1v<DM+Bj|#g>|RmQv|cf=p$vpTYSTS>KQuOk+2d}w88kpJ
zOR87M7!!BJcyMfm2L$)A712{h8c0E~HXVpDJ;gkK9==UuG${0t>DW3#40HygAqn2p
zPP1(cY*9Op26jxJ*?84&Ee>T)ZrE@L7tKj>Udrb#WC0>)A!4by5afEO^%};fbz!Q9
z5F%wo;>mB1WuA_4#pAi$HcNKg(!71czC7_fTYGsgET)rQ3!)t;Wmp-5Fu0BwOSQsW
zg7sLyVP)7f>|#dT=ZS7Y*<lgj!oWhCz|C#r)d<xJsD_)Y<_25pn{$$`D45|sV0F=S
z-*sujah#4dQH#p09m^6i=B2P;AVDc@(Wzq9X+d!v!WGGil6i`(`uTGJ6hYTuZrk%Z
z%}Q|m1D?D~(Z^(r@4{Uaf4xpiLaJ+|Y@o%`$mO_fsF_q#)MM4w04VDI^0*NZVAveQ
z?d-1zD{`FZnqZ5k6mYF5)xbp^)m7sy6Ot%vmIeUYLxkKl$rU~5(8Xg*o|No(eHn5J
z+|yg(+Hg}WX3gCT2K?Ds3=Q<HmU%?X#wb+-{O;yxnJ`jEtC#H4%iT8>G{{8HIB3xS
zyUy@H*Y;c2KXBJ+2Ot1ve5ZzwUso7AePD8J`i?u$1a!x#6*45N)#Qw>T~x%k*!=3o
z0JC3D`=_q=-IHuSgs#eLTY$*Dh*^vz6|41=;B<gtd<wbTHtYBf`f}Q_80_9*yb6DR
zM<4DJta`jKCVgJ~I9wrFEf#P^Ed|+J7V6^`2#<iJ@Df?v1-Xy6(Uv|Ld%AKG>CWwS
zTyt249Pb@)C`DPICn%jO#*XF1j|ApUYR75dr!DwS@4~K6Xfu^ipaH2wTBJ+;&sq~g
z${M^n&_GmUMp|kv6hMlA(kc`kwpb|UJiEu|d7O0no&X&|<|FvnifX)+nyUt)Z2`D|
zhz)oh&yF}n?8Umqs#YS9a5yrqt&9bl#B*Qv%DO4rF0*C+v@z~3Ja{m-#~q2b7~oov
zL>t>x%bu#-SP(HoBU1L}Ky~)ajydB~UEjV7&%HevnZ6PMM<up4SA{ew)jQBu7OTlq
zLAipgTeP^r0d8}Z8B|7aPDo-#&?G3|fyWFa*!cj^EWEhy_8B@I<F$oC*C=ZPXI9wG
z2h<(M*a~M{!4`b$07cq0JCG4pw$+*Ch=cKCd1hLtYuz;y#tZ@|o&qmk8CihHT?%or
zQrb0QI5@2^lv~K-)9{X`z&8KwKGR%gfBp3_Lu`j)pHQ(LD*zkFHra`jEu0$l4;D}z
zQN|I2`)x(j6-hjZ%(F`n41*Wv@v~K6-^G4(ldY#Po6<li0iUoD1nf*mP^ZxE#jPr3
zb5s#^nF`XP?LGb@QtQn6P{XFVFN6ifuc>OVYNQ|YXn=#vE!13I8Vh1^7OM5oT8i)Y
zyiO;l=U1c7h<L|l%~DErjhya4Xs}u{*VLM)!Jbh%8H`w5%~%*TB(N}!N=2zz?{P}g
z?SZJ<0$i<lk!`|a^M*%gR+Uu<twsNpf?AYKBn$SY0K;wkYP^<N`j`cH_C~^a$URW<
z^SUBiMcQ`iBBTK5st<gD&vrY+ToYFQUKq&>E>1E_B;2qqUPKS*wL?7Sgxu&czz~7&
ze(~(_YQ6CUGdk@2xG}0)zCY<{qcJaF26Epqxv^s&+mal(bjN{RQw^4+S`)jbJNt%*
z5(v2i9sy!)-nH9*vPbWV$pS>~QoJ@qsz(^gEu?ycR1YmxQVU6){KU7rXwFTfEx@h_
zL6*T`0DxI1ol~J|HCQ)Do45=ONX(fGgfw5KF6z?O*Z>{Y;7YvPu7p3hfeZ%`u7uR5
zzR@)^TrmbX0>D`I44{fY1WJi}0GI{JLcs*vWN!UJbpI?CCj;PU)QktIN%zeov{8r$
zgLVbWAT1_Dk(d5Gp)uLTFS(74@;VG2<Dyl5&9#(^HgfiYO_RoWTc`sxY*kn=h*pO@
zvfxz!$ca!YW5}r~S^KR&=M%`63m56eg{NL>c3Gq1OD*;}TS2a3jVAk`NFRF~mTnJH
zCm9*~3C?bGv_*`L4L`m&*>=V)GYHn@+u4b3@fJaK7mZ~gX=<&rAT8TQb%25f@tP|v
z0CDIH*ggO>gT&6y4JS*}iTl#)A!e<u!@;a*Y*;WB#+DewfXE1VuNtdH?d>!?K7<_@
z`94JTs{^el5#aZX>>6Qn!hdVWB-eF{MB@JUf9KEUs>%XHPAjG*i3_p?rVir=k?Wzc
zBqtZAid_Mrarf^9v3tACw9n20aQ}11CisL_e=O{^HR%$u^s^BjH&MBQYXK!-WCn{>
z1M~tsY^BkKVluIT9~MFOi20t`fKBLNIuNhL_D|N(E?hd&6qx&s;TDQO*_@<G&Hb_>
z46>{s7PDTV$E#eSwHqj`uA{<HrrZ}3u8g4;6iUckL6QIuHij2hVn7n2$|<l@>7T_C
zb5~1M|NOg7z*=x`4RtYV05w=ZR*_a*T}aCm@c9sip8aL9rg#>(y0$EuArT4-vX$aj
zH@60{iU@M&(mM-QWY>Onft{TSTohdCv@i?aVgc&7Zh@;_kgJZ6ln?>Xllk5BL(BFs
z{OTun>>1sKoKaxne!JLp7xka`${GfYpry^4v18VL=og!pVYslyc!*fvC!5QVtf|t{
z8tMh`iOFL3+>8}xrJCN+nn}n87%in3sa0lXHrCby?7h4IGeF)xLcv00+}JS`209z8
z_kk^dmHYYR=ZdK&k2~6plh!?6VM=fNOn!Go(Z~WsPA#^mOEnhc2)W(?87LvzC%Dgu
zSh8M`l%UE<IzTc75qCR+qK*9ap=@Kl0J1`}*v9a&N-bd60$zP`LoEVhEs`~qt87i-
za;t8tN0Eqbk1PS{g1}56L={E{9qvN0dJK{F8yPgs+R$SdJK`Y4Y0hvB6?kx2utSNO
zp6l++x~NNtof%?h!3t0_TCB~wxi~zqj4*wUTOVByA?{W??6**F#;3I7HFuO+RSKYn
zU`T`p4H~pczuL#f0(-XxU)%1Win1`{sA!FmoCrC!K-QcnSO%d645|nQI$J<;H_~E5
z1Xa4MAnR-5V5E*~aMk)dwG?PZmG?(cfa(F*edkqjm&NJMYRBiN&=HeOkN@3iHBX<d
z)r)lhRhC2JL$v&PbDgrgOFZU-TgHq)Sxtzswp>()8k6g_gxn?Lz=k`W0wlA27~rE0
z)O3ssvrg~SI);Z%>fOlp%#*usAnJyrmX+r*eyxa`-UoGWo3SK_eE<m10+<ejr|&k_
z!@=A{$9B(PuV;12XM3Kq0FhIQw`$MjmipAqE#vh=<QO(YY|^M=Z?|4jhDfpjq@~}<
z%DI#Mj>4sJoSZzYS_^txAicXUEZ~a;L?gr&R^|dhMXhWWNVV({vDzbM8Hji8?P4zA
zuN#FelGQ~6K+GCfg<S!xRLQVr9E5wuC~g*L!k?%5hH|Ky4a3$W!mS@yDn7y07Xt)i
z<3VCAZy(*cR<=fZrgW0s6A>#8hSVuB09JXRw|x8=-tF@`R}q;&Aq5`=lA7C*GqhAY
zWlbM9(A7#>%hn4FwnKnYwJ6*wCk%;{9U1iZ&YYx7;qun;LgC@M$heVdpfw@|xv&DL
zL{PteFd&R|DZ!2~k2-#Ngz#W~ep|c@$dJv6-KFn4#%X$XXLOa_5_BP}k@(te3$X_>
zw+<<4C{(M>Vii>_=(9RI!VFKDkAU>lVmV-XqSYm_xG{Es1Iw(Zi1P`9$Oo&rTi8?Y
z!`<fJIXjX%wpD<t)s&elKrI-a*g%JY*66?spjN>OFr9-Aa7aw602mU`q`Ry0`x<u}
zlZSf3GrkI5&;^K`Moc*auryum8{?S<z|`7oJcOvf(RzGvIsoSb0A$Mk8XP4PkKlZO
zVboy<FKSlmY~S~G9eQ?lmJyr5&`z0_chA+>2d{iM0#%zZ2~{K^RAD6J5<HdBnGJHQ
z#H|#Y^nC|gw?rHOC`03l1_m}1z__wfg;cu9)s$0EsfplKT!VTA%))S25aRr_09A86
zc#jz|*H0>I5Q-`skhv_Xr9E$p2a+AH&r>F0$>>$mHf2NIx32WG^6F@y#5+V+s}x%S
zhGuVBgS=hQ25!}PJ(jVub>mGKtEEj_fxgOKA{z}vZrR^O0FrG_t#>mCY1iRsvSx8q
zVkxImG^JPB5_SI(JFwvh5|4`%pXc`$x3TLPZ{S42#Q?f(Z)`Ew#Z%zXn5uX5bFQNm
z6J!h;B%*C5#CK%Nv9JO^S?PVQUIekVs!$yvh_2&tE~(Q!&T;_3rf`jt`{4>^2FL-W
z)iCkiF@FBcW<;?88D&(1DGe92M4+rQ9(^Ev@_Hc-a@;uTtOoJ<694PJ+W{`*(oLV8
zvH+2N#Izs@BbPe>H~`3Cy<xWgjFM&66a^n?1*8F-Z=mJ_kT5tOfYUWq?vz@4b^|4e
z*Qoir^$n?e$Df?$(wc%$WlB&Nhb}zTg21#iyl}-Rz$!5cNb}?qv;wQB^-@I(gsEj=
zfCDW89cXbE^9dUCs>LCwHEF#Y7y!4n;a;*G5fR^jhBl*XN~ERkX*`Ul>&88>YOZRH
zX-+>`Mnr~y_$Y!=sU}-g)T!^4N!tqAyV%Z8b{(OSxclzGz=$bGA_cwLWKqCr1*#=n
zHHp`#a%1Ad-q>?@J6Hr`RW_k8N6g60gN)UXEPkZ15G<W?-tSiIzCzg@`08wgGZWOs
zy0q^pSe;VYf^7?iYJrMU!N!Pqi-b1T^aO-Dmm@l3MPQ5D#+mJ4N=`4HXfX{{J!hF%
z=Q4xDcG-fKfn$1rA#JX*HwSc<)eFe!zgnORfAv6AMpAOt0ENW1s3q>bsuNGIwI$p@
zjh274bLs$ww~){vdO!p3vwdeVR;5C+u_UNw<Oon1L&vxl1_lyPJqqT>$BU<ga{!L1
zPSH8q-bwd*ilCkKR@q62Tp{1g0z~!_8w6aeTkdTP>nCkt+U+tC8X#%V$cua(Ww;4R
z8JurGC|mc_70T)n4CB*)FjBsz9Z`+O8^)(m4j$8(6e}BlZ`ZNiqu-D>gLrK~#9|+y
z>1a+rj|IrtDkCzZLa;7XSp)+Nm9!2=4TWGz!qf5WxXcx~i!OJCjb2HyK*(GwmVFa`
zTXO|@_KOFHDgWIVm%fHT{LhUOw?wjAr<%f?XlTlei`F&Z3Q7d(6kE77y0LP$Gkm}O
z*(lZiF;?e9S0QRz>)2nT#kv4m20p$PW$%(#lGUbQ$L+Tn!h%xu-riZOi-uV+WI`gs
z+Oud(VMoxj2M=E`dCV+Gsl}*Nv4GpkSRtX-iUGbb3Ybd-O^Y!h@B&<DK6}qnb1)7E
z*%vLRVMMy2cUL1?Vrh@L2hnVoNb?sCH{Mt^09E4imMP<b?%HwzF<v}XUq~Qq21u|b
zV`EG0+SS!!dcoK%uj>ITpvI)lG6VRSJ4Q%#5JaYWNGeFINnR9a>*|R_Y>By@{C>J&
zCg=imr?=lfa~9Wu+<e>VZNDN2V^3Lt$R6U6+S;)05X1VZaM5&I!Iy$iz-d4k9s<dA
zaJ~i}ZlLBH`Z^65##`XD0_STeKB7!XXlLFdKm=v=2&ih6)_VgYE>n~(ohwB|`=d}&
z#GmRBkgYLVJya!71zbl6Ys^a(&xfzzs;melj|uH2Apj4~f{3N>idvGLTZP;r$|*od
z5Z6Yz2HWA_NqW}~ha!VG;7}`pooQjObZA{Z!~)G&V;d@~C8G<9x@43oF3>JsR%}jS
zV6<B>!wf%bWpMI7g-AO!leyPGm@ece?V}aZ%k>T_u7hA~U_G?&+gLgFTskdTOv#Ql
z;m%rt&M-2DlrUDox-gPHZqIH-5djgR;$$**VpqYj(+G=ogx&JF#v)Nzsl`%|tHl`g
zW!hnkoK&r>94+g`GO4DfDmLzaZ}E0fo`zY@PC91fI8J2S=s1&+?8Zm8fO=*ub&GUm
zHUkG%!_W;R1fXbt?KxCsZbfs=8$hU|F)FGn7^DHC1bA_>>=jnSo#P;)!V4dUJD3W9
zLDO?BRoH=+yR*HWh4^)tPV<Zy@tYu1)=Osh%7=;@Q1dM(EQnzC?TL@#%E<ym_7HDY
zH>{sjR-)eN{OgEU8d#blqo52oQ4Su}AXNaI29(vqmVJ;>Z6FF|NIp<Hjl5>V0G0!!
zx<(luLRuZ_cSrBPr)$|%aE&%@R`CcCAc-%wMm<M>9<-kZ5i%;Ol{ofdM1?D;k`_E;
zRn#pb2Ce6YG$y3EW0WOf(l`Rt5uC`}FbUo;$^$~<Z&13fpvWjy1wUf`eP#h=nVJ%z
z6dEH!5G(9vMasN*k~25#P0d`9Zr<0klAW9Yb-*Vif%7$u^#B`u>lV0O`|5Wq76&7Y
z1cF=bsjgu>MP!H_+YrzH3bYhAX$_Ie357L1`sP7Ksf>~d1ClZ#(rR|Ba*Aw~js4#R
z0GXeg??*b#9u&FV*Q3JGKL<#it3Yhw(gufQW5->EZkf-`)>fzrI|6<0>XP;rIp-LW
zjnj|o^?omAuDSjTprP{<)MCi`*fM7^!5Yt1D}$|0kd_B%P2+U&cTlJ$_k}F-nmT6H
z-MV7&Bs?~dBdx2la8=h`@^p7+OUUAcDumQ62QRW1E@!V&-RW3M;dYk8V`O4n_^s=}
z{(Pow#E0y90QZ4!W&t862(bq5T|0cAR!o(ak~Q5>1En~vAT*#3H&KV{D65CTq=9{s
z1`SLV0Vr842m_Qfegx3(EMHwDp$<18X@&9NLt??Zr|X%$cV!q8zyX7PizFi;2MsK6
z0ZYO-j#}<G>tHTMUl!}FFiOFYGivqRf18lvK3@YN+<^z6%R+I0Q9!(cYbv%38xKNl
zH3(c$HI`%&Js&i93lMW~<X795QPu?oYAs1MH=Km^lXz6cMw?@SfQhwpcTef0-)~t=
zh%6}j8gjW41~$ctC;Pi;Y^)_7A!!vQ;3Z`nBV7>Q<fk2F(OJI$G|<yhyvHgy8kJ?J
zqOKdQQaTs|0?SenzqEdrlS6G|Nw!|q47kXa+aaRq!x{@B$O(A*NEeuOQE*&qwHEQM
zL7Hb>cuJ;%R*&cEeD(sg=8!To4BCn8ADF)FxgIlq&e_X#+*zB$Wdiygpv3Q;0SH~8
z*)D1W*xY9UCu12>bF(lrB&&<qs~kt25-yNLYM?~Cnz?3#wH}b|1Ma#h1}Z&xziQtr
zu^u@sYi3Y(mqz`4(kC2+65Ungt|bRl4pb0%RU3L^7xQt_O}`86ajCKZk!{jxHsXSz
z+|r_D<&Iesn~#~(N;ApQfx21_9tEcr>hO>bM$3}vJFz0z6MfxlB|!obDWd`c=WD3>
z#(7;ZylgvSF`q@1d)_`Xq$<tHL}2TnSw<~{HWm#gWe|u6g&8aN@!GMfSvxxM-JdS-
zf<?75B(X9w_B=H6#bO4W*LA=U3a5b&af?N`oA?q|hxepmNul&o2X?5A`b^EwOm=*&
zp`o;Us^h6UBJcM!wkwMEdE`6UeRY33MXe95_W}DmcE7bETgRS&7hTfpC9BtvaAbg)
z&}!yj`-;A~O8~HV3uO~LXth+qNbb5(aI!Ay@*9#qyCHjLS^+bV(w4>vTNn}rn;wv=
zr|Bu?VpDm#d|Sgq(ZXv)8mOw(b_MGqNMTL8Rj<L=Fe_gfj|S$By(f3MBAnKi?W?IC
z%OY*;`0T7^0fH6nuRGG~;|w{Tb?p{xFZZsl63s}FF22kITca?VGgHQ*2xU}|+79gQ
z+X_@MHp2r`Ee^#~cRhwA+qtmnr^dciRAwX#ie>f~5-TSF4wlWcUxEcfV}k;)$NiGY
zUoy2fzo-4*y&i%|JlQLNN){k;j1Z5=^>)+@LG(75ihz{4;5EwX5sd{IZlVk~HRHfo
zk8aO94oIC=)V?0B_=V_P7QOx|v{9FA#9KhBrK9NrQY)iS2Fr*<%2pf|bKBHvn1WGB
z7nQ*j2(vbi^Ik8$J?gr#8&;cvxI*$^z(WJUAT6KYEvu%)bh6WUXbPoOWA#}sVU1FJ
zR-@pN`>YMD2!p%=t0@>QK;`EGY-TZ?jl$l!s8fcQ$T!u5-WnDfQ1f*Nt->9sfvaN#
zu<lcnW?KJ5dnR!Li1oFyA*0;~e;z$Jg8gz+YYG&A5J+P~40s$B*1%N*Afv^UREyam
z0=)fnwZ)!gT8Z^IW~9%+>CehtL5Z~Yt;U21YL;*bdGKsyMj>FuR=Jn8tUD!wSMv|I
zjBo>Lkm+(hr(ovyUpDUCspNQkGmjZ+pYC(a)54PJxlFGsV22w!XttO%ivfUw3yxS4
zL9~BJEoP^&6uvJGU89vyHNc*-H<;t7TcUQCH>dL&AZ5y$SfytUmZTbMs&3qr98^gg
zZ)P+_srK%s2palm01Uxo3J?L$?M^ndvI~UF-_M`+6~G-EvH+2-q5~w=HHPtNtkzG4
z@@{rmH@(sU#)HR@ctlw}jIw$J0D;prWj#*w=;xZW2QV92_N=UxMi-KJT!Bx3E*roA
ziMO||*3_#QOHykEana&uL>No4Al6PHiDlA=DiY7CapfJ+>pe>ofnE|85VIh4{$QQ#
zzKsDXw@4$v4UH1D>ur__(5VBhp^>Z+pm*41#JPD)0PSEG?rpNq$H0_!S1YCmw#ssF
zK8SykGKfWkp6L+cfHq|ayM0Xk9*+^v4ol&xRM*rB4phW|vjQO9FVAj`2kVVc&;e-b
zZZTI`BEhY7#gGW2rOGl}rB%;jfdYeUouB=)`6cw3FD!^{GpK7t&@52_P+`o6$FW#U
zNM!{^l>vY;NNbE%YQ@Te68##33E9dJwZ&)G?A>QZ%LxpW%ht`hu`};GGe4)}2$FrZ
zk904b_2|>v=c(@cy#j+tdaFG*1ps6L=GI@zj0~^L+Gnfif?hQfCIOz!QsHi1ZhE1M
zM+v5-j-gi=rf)>Fbb-ZE^~`KV$J|K=jtp=GKylsEqYD3Se($#3YuWbU{V59&*(Rbs
z@Lca8l{*@oI*SWYro-xkWMx2D*fL@cR0D+DWy`{Hl3YSr>jpw%#a4qGZ0Vrpw(v<#
z0B4{SLFNMH0<Hz67UYzb(P0ln?ftbLAEJtb#nWU9z}c)4oA@Gjyw1|_g#cX&ojn_g
z27RuKJm$lHuM2Mrc-3o<#(f5WV9X5ah{0GP4@PwVvmNkW?>eU1WNxvx_v*&u?lnL{
z*53#^2GaqU22C9&wm24zK_c&->&%J)2eK~FJH@V9lGYH>j=cqxwBVVRg@=v%V|HVg
z$wm-XQAR`;e0B95jukgnR=jQi3V9(k3eeyzhBFpD+mXzDx2}w9)Eetm*0>L#fs&r_
zK(Z$x7ROQrBMU%mAjDXa0$k$?vHl`DM;dr`wyp<v>Pp&vPwsh^2PxLrR5}i%5FM{t
zy5IU{`+}a6M4%BC+diK#k+Ic0{>lxa*1<a%5wX|~H+;->riP4Fz9j<zQ1^JLo`PMq
z^KTbaN}_d&xQcbs)q6wE4p(bxmolV|UE#)R4e=ygfL<6e;)%MLJi|YsiwveUFxC`Q
z{I1=%<C(J)#}*}k&bBVq-O2()<|1n4uGUXt7@xvWo(5Gd>@?Yo5+q$m&DYf(ldpv}
zD9LW?Rcax+Ln5%!Mt*b4_>}eGt_$^8)k5GzU`qO$lesXo9eQPTW2jX#jSra?8FROY
z!0Uh{*2=BkI-Qx3sgIhsMbYV8|Lgu^w>pCL(8Zy$=z=U_Q7z7pG?Uns7}??i>lxzK
z4$uYO`u#Z}4&W3|A>Jvl2!guxbs>VM4!2kjf@ihVd`+1TBGlmqP(%Zy(}8AT4F_89
zgpz7C^#6eQ7>{d=w@Oz7UV+ux0HgyDSc6(man)MA)<8uO99V{o0}ZNrjlo!m1gqjP
z8xxm{1089v`@arAyhe$G86qGF#W^Vm(KL0yTs594V`G}3U3O7NfL3jU_v67&e5VNZ
zST62Cx_#4**ZpVW=QBO&vx^dGqI{gY)wawb@ys`$!)V4s+<#U8Y(>qypNpp7TdbPJ
zc~lETB|uZ#nXw)#9;!v>JvCk7j0&-KQRbQqJQE<<iX7_xl3xv1HKOc)p7llS8FkY1
zle<y@WpewuzvWK#_pJZuIBeKIGwR+6Fe!3(Rb&AoGf7p`WLHOjgv29~F#%0%fuI3A
zT#tZM&etcsWUj948oe?d**rzco?*gVLA(ZI(56rY5Xl;q?%haBK_aa~;;FlC0tkE(
zIsg!OHAT0GB065wXYUQ6g2CVl_1E_976l3bW*|ksQ;XR4W2UeEX>OZ#CsZLNF43Wr
zjK<YPc;C2?{nWRr{{}oZg|x~flx6d#b0h+!sf&=)fj+NvfJAH5d;_T-X`tS46I7ly
zH;sWPqEL(tj?iq(`)dwYRIY0Xtt^AcfVu^?DiEh*fw*6(XUMP!3VF9){Vr3-STU?L
zJXz&l(t0-b{2fp@xbgjKPZuA*dOn2(LxQ<rG(IGgUca(56$dq8UA0=Lwk^=}sLrfK
z-3e{~-qSt4csfZDV1MI&o|E*kX7>Uk2$=hJ?iG}5`|W%|ZKd$Lhi@CUx<qWQkG|*`
z;Kk9L9pPFn`K<tSpzMhHXS8&D7)DTaC2E9JW+Zb%Gpk$DYZ`GW$g9?<4;I9)?2FbX
zahGz_djfaih@=RV=5W<?H>jUB^Lw-JP2LA(Tp=W~01+3_1!gF>006`IK>*kY>~k92
zT`+aHseXAHH0_qplj=IZk>kctQ<$?MZ_I|fD^zWl&`j)2@oGga1;a2j_CqAHQd*r4
zi}i4T(N9!6p-6iRkYo&jsPEo?&my|-j^T(DUE8n53}m%p5(RA=7%6UoAF7&lRV+=F
z<Mu~M_uY5%gu6Xc=M!sGzmIoB_THEave(qPY|JiW10p09l2+h+fU<fR!}yeDa;+ZL
zJ~C+pC?nT9`dh09;y_C?xVDUAvlxMdRPP`g;2z2?b5&ORtvxqOTi+(E7)U!h!{R=Y
zJ`*o)mYF~X5RPgErHml~V`+fI$IrSjuB}=ly*|4@1d#zFBB)it$E|fmB4Cx1b_6y+
zb5t1D2A%*P-;Y<}g)cVJT#m6(jDqc}i=3M^Iv-cv%jobuJ@UM!2zBni^O5fU_<ilZ
z(dx6*=4CLDh*VyZ3gH1B+rhOlq#^_nkK2$iY@}95HWno^VMIS?21o<fn{F~t;VvE$
z?WyJ2IAX!EU<D^0J154zh=s+=G|nx?3)=XYXASiU5$#DdW3GL20wuEh?Uj=Sh=d4I
zJwz&ZFqB)!^$2SH_CtYG!GjEdTtgWi(rgxIK~mnn`!khY!|Gn_6^VbFdl5Kg=`^SU
zlM-<Yt?-Y<eVB_T&(v|e?jqVnR3Tun&bwwJumKk&jaC<P(YRINQSx3lKxRs=u`CW?
zM1f*T@G{M+*cww4QVWohHvW^^s)?S85!vsqy92B})oF}~IOw1hi<ls3$@y61yoShu
zm0~{t<*H0w8p1`x`5L4iLQ+O%WlCD%GX>satjqk`2+(^^gk0``ts8F^2C})Wc+`f2
z`Vfw1MO4>oTL8Nm0w^Pa9MxD8X@<=zNxU9XD+SN2m0J%)V+$Od7LO5t1v(~O;2dDA
zi(1}^2+4rOSk=<@CU+XZ+AS8Mh5Nkgt4<A$1;=BI=A&Xa_&6hWga~8+o5*TiYylNy
z*+D>a%1QUPe_r!QrMo%P$-NLvUN;JEbI(mr*y0VqQ<InlHvJLxMrs8^kdcgJZ9S<X
zGm0xfoiQY?w-}r#>f*4ua&x$8tlMrCP^vM$gbN7SQhDvXEWkEujM()F5ScS!SD^)P
zZ<Ekj(%#SS&ko!<-48Xkl}_6Dl4JoQE+QD#8WXZQ`T$6rlJA5FHQxjvsKX6#x@K;f
zgA0iVnOiSYJL~R67Z~X)Oiz`Ac$Pt<A_Z9&p0q-A=7j)K%&)2`uGK9Xn}Oa(!uZnw
z4DOdj(rEbX3I{EAqqjUgN8ew24G=v>|JQ!!BudAeG=S9kZ*BWL-Ue3Jl^KXdxgBHc
zeY&?NFn!yzBxD8dELMbIE)`hgEqJPKwmwCkrmF?^G)5%1e`$p}+%N#OKuW(iBpEwl
z0lC&)c>CCD*X{+)h%1Duo`Ep3HOAAsI&9rbFKkr0r_MS6NW>N+qQ||l(zuPpEu*G5
zSTJ{v`{p$htKSz{%n3tQ86yh@k!UYpSr_cWg7sLVF`vh;xNEVG17S}$$JDw-x7ZME
zw^`3aQxFK+J$fCV&6^*#MTlk}=w};^8n>NjFkbTwPo4*xV`^$ROh4DHg*)`Z9N;60
z4jNE!{lVmO-`q9T1LGB#Boas}YVg3SIJ2lbxm3-JVnK7+`Z!^DIkPD7@7X%sF)|*%
z!g@{WVru8>W24nEnX)0fZoj}molHL4cIyRRy3v8Bn>%e&1)*05jVwUKBuUYtW#cVP
z-4yB6vB!mE3kW<wSv?XK=*7i^oX5cNYt+6nv?IKq!sX-(&|29a$=b2ESYVdr;%hw-
zK{n?riy#@r*-u(m;o|0!g9sL|cP15$JZDgV2vs&U7?Ie|41_er)zTD$X+aN6^w|D8
z5-qgm=SAk4X>k;1C+_ak1Cz2H(1j#g1~_X&1obf|i`PhC(jZYL4OI1DX%>HPU?gRG
zy+RpoBIl<KOe*6vdGcM&lF7#+mKC7Pn0JjPsYv3fy+;9r<b=7Q7NL$m0bwlqTohO^
zR!KvP9c*M$kg&=LwF<`4F&W~n69gcsVLc0qgOe)Y%mHMs4SMs}g>h%BEh|T?S)cS^
zmBZro?249^)kgEBNO<<_ManI+r~4{!2yHG#lbF^{1Ma?}yVFTiP|`21EtZGjY)Auu
z3OsziTRN@llMXP3>(zsu1*)6+W|*~Vz_WD}SL3I3#C2AKS&Urr0C;mDTg-^`oyCO#
z)Nl><u5Nevs6z)~*Tfahn2#Q)Z0+W~=P+}-Cf@X)@3)I{mj#H}1J31^rt5l4K-(ay
zR8~vVEeDUH4mb6;7ao}EaRJl!(@}X^BGndKo78iAxTyu5;7JO=W*x{%D+&V29#l=?
z6(;MrI>rLgPb`E2=@<%ADC&ZjuuK?9gI+<wPBeBztbKUzirIFBzAtq3BbrOX|BjWh
zN~8;{9CTwr-9cOC^BIWM;P4Z-8XF)FCRNZOX)qspHFiTmOT=Qtdt)Th06~h_5l}|W
z*OBTWgjUvAajpC1VsSM#z9xmw`tfAtv!UhRlZ;@jG=qjm?MFicI3FN76!#)BS?&7o
zeVh=0lmwNvwup$jS*S9O0PTWXweZ<tsakJDoDFH&HCj$yu!6Ea3`-YgMz!%-Z5u#=
zNM(u7qizKnSS6U72aWm{?ozsYDqVIA#(S3IM_=O;D4u5oB?BOXoGk`v*I|<GL+aKP
zUYP{N?kRKP1KW1tz{3p`UyOCWa@=d*NS}{>&hc5TX6zWa$jkt-=_f5nrnQeMBvWH9
zCJd|^K{<@348Oz76-3GEhIlCg9rN3ZB(^8hg74c+IPO+9J+=K->J8d_D>mO!S9fRE
z_LnISJP`3f#Dl{>!Z6-aOMmu=g)%q|sH=x9K=mPOpmg!sF#>e7{oJy^&d>HgfKB?@
z9{YxLSu9S1%}>f}%O<UWtYcD^v0zBvk7NcwxcSl}%LPrhs6UuO8EJmLx**ntAy(D1
z1)Mq%G3y9qiomTSGlXngmTA)|0lIh~y*`$@Ve!tM#3}D{@2j=Neak|iU?MAEGIt69
zNIC$dK*Z8{q4hyT$N;ha<$Tcf!buh%va*!(A=nYl*IRVTZ~z)P_9HW}FgZ&b3&f)Z
zzba75<&M_Y;T0%ZeUg0b_*>H5+MdN?0|$vTMcLD5kBXLsKgbGLqY<3|wfcgj+htO)
zk;oX1s>RC2stk%VBBg5W3T0KKm7(`@bJoTzUXg0_yM(fDZP_jT;@DYu9^eFVJc5CQ
zlr5_V>Ush6Sts9a%lT0TFmAY{;~|~W1-;Le&Rq_Z-#g17ZG4D815QnUMA0+bHY&Ax
z_8k~p87Zkd#ben30~yk?XT%Dm37cC8Zm>WBCndBRM;ZgiP=>WsGl+bsNOLRuwTPfq
zPIa&|zhS-SY>V2F?)6DnkcgtaLF{wm#pNTC2N#Igpe3ou<I@<{A82Wa{bo>VdwuQM
z7!Mu`Km6s0E;P!}lv!roy$z<5ejS}fz0sM05vtZJMrUeFepCf5t<Qpf#kT$i8xgds
zDRGN<7ps>M%7+=S^jq(VgRp)Ym}y{SnibQ7A+d!{1W`)7pWq5gtZJh%M2!=;`$Z&l
zjIkf&N?<}@QLrHTn*{Ge#A!fcK(IEKO9Br!fU@p-7U@2P5@0n&n1-lxBWV@Dqz*SV
z777Ybq}*<<pn2Vi4v4wWIq}-YfiAx4mRTdG6@&*QSjNfZ_p=4sACN250E)_7t&3ol
z)unQ$D!A~{@D4O6(tbHQ_>P59htySQ6&#g{bxjrn(kiTuRWo)Rm_UHTu?EO+FcPss
z0%Zxf*kWN;x^2I$QtjEO`rS-tjH}N3PbtUNQ_$go^=azPq3N<j_MeSKtUsGm`YMmL
z==;xQ%T}VGIhX}+i03gP(#5iIQKxYApma56m^7w}J;hth1!G25bm|r}R_uE(A;1oF
zxLT^No?~Nk-Cfkqy)(h=G9>P4KP$G~kW*`$cvhrtD1EcRh*13JYRCf&M6~8XF1K)S
z^uI8a+t~KlC9P2Nb*!&_sJdf-Q(FaRD^A-t4X|N*olzykiMSFrph0E@3M&{9&zca{
z;$|rui-7<EI@u{+hXu^-wO^0ExAZ({sbrv|Isv#FrzsTMgY=5PTsQW!R@$56XnHOU
z5QO@WzJz-NGIuSL093z5j|uTbMi&zbH;bm%3W-MUOGct(YzL_uhLjbUvKSt&28|hU
zrUPUwi@HykZ$uDm3uc|Gh3(^A0SLikNl2<ThD<A@bf8fmQqkya==JwF#3>Oo?F6FL
zEVEg=8p1NkVi8gI31u=yM4#(uRw|(By38$erm}*Uqfy7yXepoT!nm>0Z4SqZ!2(P@
z$d$VlBHVNi#*JbV2$FESzO8ca^HE0!?OZ@w?1vT`OwNc|aC2;AuM_nR3fv{vnd4oC
z&DP`LEUxMV>F%chky7h!uvU`Y-74xf3l`Jnb7b(DdnSw@V?tO&Gxt0-*8q<O!dI*d
zV<4*m(xvK(l@W;EHGl=LQWOqT1`SANFvS<}@sG(hZjA@Q=Dps~`!nfDkF7QApefx~
zq3-5`3q&ADyvFM20~ppHn8k(oVo8=^a1+&vmtCs3pZ8b|a8}I7Hh3;kCxt9x98inc
zTXBP}iCsF-xDG?&zKloIfK#a*@E~JC#J{&dSFH_@I_fnAkm=bJoxfcj5(6<F`_L%T
zjxy~nlZlz)*Lu=)Kdmb6Y)+?WDR(Ke(8WVEJy(NE2P9F~6j9sW15+ijEg*egqL#Yp
zSu@cK#(<1A$-4pQ-O<`O^}u3LyynD7*XK-u#f%s$LOdb?NcBjoyaER7rc|^+BlE;j
zC~09;*y<`#=Bh8vE~0-&ae7`^kP(QOkz#I_!pf4AD&c;)v-S#{22zUEFUg2N!*HY-
zVC-FYfLI)nwID!LdbtN1V2}G9i`aBq=|F=JfhYxSFTQVbAk#Pxn)IESj&yQ2j{6z_
z-LLk~j?=kUo2O4v?WdbA%FWT$A0}KY)pZXP5TF5+=%(xJC=04E5;dU0Totm1OGg5r
zXc0lxAC(z7No2@)Agj85GzKZN1n3YO!@XXKOBA3~TVcBE-gX=3wN_k?8!4O2RR<`I
z#!B5VxC+@b4=xZ9#xOpOVf>)EWV)|l)K)scc<`vXWL80YU#_Hf9v$TMYJ}Tm&jn@j
zcmB0og(WNv{oH7IciUKB8j-;)$Idcm)*2ITsfacbV~gQP=ED)@Sd>^)7qT5I6os=O
z0WQ4n4B$*gUq=Ljma-=vgV0<z{Q?qHJT@23vyhf0L*3%*P2&jeegnkDU2aT9qF_YG
znUQ3T=H}_$2Amy)=mShzd@sOA3zX%w3fB!7;0YkvEgol*>!G>p)<}5-Lkk0PTA>AX
zZ`yU9Zp@XDhHOEJIRHjwEwk=Gq*OsMfBjKuHhoEqbrIY+$ObMfpmnT~io5pMkXlh$
zohXIXHB(u@31F+*OQayE^%$}mE44O|3_%ov6KQn#c?i+*3xEIolZ^`-U!B9ITa-4T
zNB;-uct)f@>{%1AE-kMP1~ifuCk)VmC>A!E^8pkbT(lUH-o5Kh3Ox=D(pWW!kH6CG
z$7_oe8zTmc(TpK?3A;||3z@;Lrw`*k(_7c6`luH(uMK0an)vJr6as&6fe06@)*rx7
zo{mK)#C)xFxNey;4<UT@mn>qD>^;4=4FkvaXN7mYPlHX?*7VKeG}tO0s>=()NY;nL
z%^6=<^t@VNVc%gh0>g~@?u!BuI?IJ*;v05#75|ku?RE*a-((sh>O(kPDpSjgY;kEm
z|9e##4r{2Z+2Sk(6tqz?D>Na}cawq+qyZw;7MT<vh0OIbsd}@EZzY0Q-5UuPPXb85
z`8sMo0sv&H=JrYUY>x-CIyMy0C#hPa1c0aZ$}#5@V8;Rh_4ye{6pi6X0CG-Uc1$bN
zJ|xWwI4T;5dSJCihUA%B+%jaU{VR=r*JE%uCLP!c+{HznkpTc)K-A)gyo@}6^IMV4
zUDs9_9j^)k8p9EPiL{b16y)|Umspsnm8K`sG3npA6)nb^Ou9^O8473WrkM~VZLh6j
zI=8tCH#DZkku#po)Vixzd&ac!6>LSgR~nPyRaITTff-C)`hSd3vfrjyOWe3Y$Ba-1
zKfFd#cgfpt#g_53uRfo;-ljh!2bs>2U9vpDK!h=@pR&%QbAUnMd>v(Y*b0_iyP%4x
z$BT4}Rx4A}11O(YbH#La(p*^59njmvH$W2pbL%xDf~a8X$|FFD^lU`Wco@#fK%-j`
zAVR!A831pZ7u#7Tg`g|<?aWA6SRNlToA5#%Ln1qUw%uL9ZdEb^jA4bU5(uUP3I?=d
zYSwxy-bFVlBceb=|B?X|4NA?&*Xx)wC0$^vq-+b4Hm~F128c&gN)~)NTK3NNYO9mO
zSnLFGQI^qSPjb24rMu^;0%rBWD=0_+Qlx1H%?>SIMoFSRMD6~1Wmc}RY?!2O7IW>C
ziUvaRG-Ve+WaNmz(suQhHNY#{bILO8NGb>xCqjOX{!~xTp!o=Ci`PCs>gTwaE2e=6
z5P^&p5x*e+g=fuBY#n>*rL_`h8%&1MnW-1q{Z86lsYWySd=0mmbg?4hE?xr!_ME-*
zEptVZJYh@NQu95kzIX5FxYV(&)tz$`M~idt^~2c~2w;U9)%bBHxA~kJ%X7?V*?b_!
zN@yAiB22;R#PC5dWd1Oh7?DdAMy|JY&~^_(l|SL=`m>D<xnXq~E?+c0K&=z<%nKqg
z(lo~9OwAJ)OWE{5J$NRGsZe(Iz<sj}O*OS&8>3+;1YRg8s7z?w22v|Z<r5^*BfABN
zsA{Z8&xW+q=vu^2mAsQ5-I>RBalFnRi-63#6nDc2d@(m=>U}#q_lUD2Vuh$QLu&;h
zb!|`@^xj#?c7)5N-^U*36u_I{ZXZPO>Pl^4QAUJGlpXO(x*<Nu+A`Yw-6IkY5Gu-K
z)H~LpS6v_NL==`L9>JtzS<$7XYeoSnLa7x)-<{V0$jE{fi;f%1oEeJ(jkVR0@FIO4
zkm~#J=Tp0sOQq#=oF9ueR?{MWqk*;ve{Nva?#VU__QdD0k8dL^*9K~Pw$ojDj&Tza
zkO$jB!L73*jX9%+iOCnS_5s;yCjCjomHH=P_uswR>B)@Eui9&$>%HZIW{f2JPGgR`
zm=D_#b91d|tQj?sKn{MJvC~Kv-5+Qg_Yp1}@@yRg9sM*EFWk6!t&`U~Z_U2wW6M`P
zYX*OMUL|T;uJf|xLVOKB_=7))7ryX?xPJXQKJ`;S760Ks{D+G;?6J~Qt<B;kl+{Dv
zbPeU;vDQ)dDnLdPO19wi7SP7|ox}M$h1=gVpY-;*F7#qCBE@=-birb-5dfeIB4eMB
zoq}oUc<k%a^mC{1yU%nVYWdXK8shadzv-aZm+!cB`iu((&~1EI(oORiM+A5CIp}ak
zL|q}&;zCH%Y>^TuXHYf^H?KhXAV5q?Uc_wA=iad=0bnR~6<CLe6_K<uaK3FJn~MVy
zWkpB|QeA5gC$X9%?#hdw__kHM9eX8Ev96~+9#|#-VqG~3j+H6~LKMLIcN&~3(Feo<
zO!0t2d;Xqb10b5AO%@uku{@)u&2KP60ZTS-$0_7QHA7v2GPDZ1`i>fF>mVWx3VJ6o
z0t7E;hLoqe!)wvZPN-*0h<YM_^Mvn4^BjAddcZOH!t@ZPY)G5qecEo<o<drnx_)Oy
z6Kp(qJ&|Br+&wIjnv2jS5~;pTZ*}fq>5QETcA6SN``#o4ikLp4oj*t&>)|@66|y60
zjk`D3kBgUc2O>Z9Q$K~*zV@~F)^Gh*{OAAtpYiAZ+@HhefBxs=cYpVH&->8Fi_8@X
zf%5@szJc}i4>dOAI<7)oh>IGG8I)41_kh`ewt%*c&o}pWcP9~@XWPWr3Fixlu?Wnd
zx>mpew2803oG=v)NZ9{28b<k|;I0(&=k~>dd@*o#C4i4TYL7h$@X}pUd^){v-stY6
zJGuF-0}ixjM149gH2{zX`<FU5OV+aMX;9#4+X!VKUk6AFh9R$%C9%@&y8*)N^A1pW
z@ieg*6G;PvQtMHZR;c-!WsJE?rvoxO?g&{iIIp&v>N>J&diS0PBe8x*T-46@XI)AI
zy69Mp+1RPoYt=~CkyjhxF*jF?XUT}5TbK->^n)d+eHc)x$n}mDaH}9}f+;1s_Aqhp
zN-Ui__;xh#*xeyY8t+54eX{JCoCga|oKo?L$k-wZfDv06n4yipcwnpUIH$GDpK-h~
z-O)1^4hZ5m#+)#;qiz5KH+(w%B5-C_6uj7HdaNGcc;U1*Hrnz1AII^-#*udZy}PkH
z*{x${LaN-&jU$>YT@(D$tr6(Q)FsNfud?6wecy+#_=>N<SAOMJ;zcid5nliL*W>ZW
zAIBTs@P_j~^r=Ju?W8;0#QNH^HI*@4yZn^hUPvi)@&H5oY&w=U$u^14&)^igX+gk@
zu7@gas8vByI`#tb+UK6L6;XK1EtYOyBg<s6YabDWB_drKu5^l`fsPJjI5?4}Yy!ae
zF$`eMF6vbGXaZd*wWZXnq>=;49E^v*uKBtG4`?AWqy(abl(l{c<pGjcNO_1D07?Tm
z4X0Nq<W_XEnDqQ|6g}g<Yp(&8{pN%MiX@iOTMsR*w?r_r0+kw!h&Gz!C^zqHnx|36
z^RZ`NYs*q_X3hJ~_*fa4ThAHscoGM+dZv+z8Ry;C2~iiWgQX-bgJSckpNBNi=&80k
z-`#!UpAk~9rTUwkb?hjZ^|%1t!31o;sd}1#Ox<5XxP^QN)b=xP#Mys+CNg0}8uQuD
z)nHWPPAS;XX5A7&>RgNUlF{u{0d%otuA8`HSzyKW;KkE=-PHk=?tEqA)VDAodk&ZR
zX7hVHZ)@0t$#B5hkZ>j>p14o@{Z8dv<y?WtdcDRw-}z2_&gXp2<nzz@oX^4A-uAZh
zJoM>>GRA{vp{%rzj9aHy<1wi?{YrgBMEeE+ycGB}z>GGw1MX36_VpCzRJ(s>l?UAg
zv$+eWN1erJfB-hh!`kVTPoI7_u}s3cZLcSmb9dF!*^#~FvgJbW{vO%0@E$KPzk4g$
zp5iq*%}xn2TsQVZ89KFe2Q8IOgp`M1NGPv#A<C=c$LdaiG94QVjtqcUFB(qQ6qM&{
z@x8Jy{<B>wL=03^Tg2B9#7d%W4nWjpBL+seHYTNKRm82@_OKL?dNDJOg$bhRy+Ngm
z)`0MA2y|npjoXfW^S&{#!hK-mdeqLHN06uOZrsIt1tYtR2!E}?yfDy^J6DV|Bt0dc
z4DhCuK}a*V3fgaMm6`(_O4Kb}dVbV{pZ=T874qz4raz`{Wx_-_(1<zDZT8m)8ui*E
zV!)yS8m$Hm^x#6;E+AG#x@U1+*KlW)HB$)907B;i){Q3PXpc*8vWfpTKA*bX+KT`x
zl%ka4Wy`rnVgBJC{vm3u`0x+^@X6;N{^1{v-}}Aa+t~i-=m<wgNAdotr=B{`(=#Ce
zTFj{qH^KS9e6Jk^dX*#%y2z&$s8;ondhw#@nA%h5{SqAH?5PiX(l0#DCYn)ModpB~
zVhjW!f*N(9pr$x%f_uOz;&TD90NJb&)^y1UHEQSKASIb46IjVEBHTi~73E5%vyvy`
zAMVr&TYf?<{ZY)SRJK9T*9B4bAp!XssA_*0?cL%o8Kd0A0#=E%bbA6oy1*c797hBk
zo>n4Q(p)oA^mRS}6Cp7{#2VRg1*a7<CE&OkBhHLj>Gxa@t*}}`;<aYw02Vh=kXSRX
zD&JJ#QHwEZ0}_jZHUkmb0XSKDH(8)*ivxkga;z+fnzXi1cy@%HCNw6*+9ATTE|?P%
zSG~vHQ-=#E*<hMnwsfCue0i$Jz@V*JTAC%5pxGEn7m=i0?!$%RuDl?)avOK41)uh@
z0VEPG90E`2e99oy$%580b70I0LBL4TmpMCI**+x|d`1IH>Wmp@UZKy|0j6iyQbQ?v
zB}R2<U8Bv6n^r7QYs)yug-q*#1hAN$utO3N4-h>Gew-lGZpt*JyZhciB|)~J#5QI&
z!09E)g#<v%gJC?|V=hlT@dO@w?6LUw_~VbC`?(2pqOPxfhysyx(5!?jC%P!9LNUKS
zSzQRAi$9?D-c6luA9pticKj;L1^?b+McB(QPp!vEEJyr<kt{s<9BAL`Z=5BWQlep%
z*!jpzA#4DS9=L2#3Y{&q->uo8r@M(CnFrR-C7zpQ$5;%A$7U!vF*l1p>ttXh4M-li
zn(QAb1wYT4*=zXDnX_PQ%vEIU2d9;>BG(&p6J_DIAIRe+v=x9d9;qH#EQrR9C_p(f
z*2Jr_9$Db(QHwRQLU|&--&RT+(6Lk^BLZQu)~*D*2cWinNc(Hd2Oz<~s9ULoD=7k1
zjmc??nO|>*Q|ANjR5GI#r~!)<tcSZvJVuNnXqke2OUTK=NRHq{WmOFrgX}j3E@lPH
zQZOwP?^B9Zt~*O#&^njT9?RR^XjnpXt2O4LJzK!b%((^9xK^6z7Z{7tyXvZqAJMJ}
z&bUHBY}5|{5Ig7}@dT61+%>%(>wj{ZAtE^OzD`*xXSZ4<)yY3|*Ik9B?p%S$v!DHJ
z<eWFykN3UreH$zZ0KD#Xufvm1J{kYs{qA?4=jj0eBn_y;O^gT6Ld`d#8s(J~GQ|=B
zjs&5emhY7Wq4N^X_A>`3r#_8N8nB2@0$H?0Bepb<&^M%uz*1@T9opw!s$Dvli?0H6
zw-AD+_2c+4{a@RBw+5Jhv)`SXnInw=MFfNz)27)d8A8frAYTIqNO9L2Y5FB)<eZTx
zw}8VmAW_mnUMXXhjXi<km)Wd7Rg-8U%aTb3%va&AsrfoMUyn+ua^l_5n@$$@L9v)u
z<Z?T}NVsOCDlqX>S?Pg6TjkwV(|UV!!KS`dE4d3|24oBf-WHRt=1sI_mT(_M=8yQf
zD>$(ce2Sa8zidv+252cYkU`|}8(z-cdds8+R>)j6DeJh?;8fD>fY=8CNKxR5I$+Y=
zIpjeGaCc32zMZ>f$JyVt)xC9=2KZ?2`;CcVso$=1{JHnwr5P};h`7e50Ztsqc#x~>
z9}j-sI9E4<Fg?9sQTDyYy>A#z{!G5<;(y}54r<Xa{+EwL&J~Cp930?NKlM}b<~P52
z^7)(J{ARrT<uBjZ{@S%`c=XXnC;!e%JZ+NGHH-(3p{yQlC6O;kPJ1YEYbIIRr&Uax
zx&Zt=%vzno;I??2(RI!zmst9yTbQP>0}Hs-!VX5%U)#WmG$7M7iCF`t6t>g|aUj!N
zI?`&1bjqc>nG>M0OX2M&?Oyu*ZLCyd8De}H=wJgRNCPC_&;=w7(7F#=H^dhi$^$58
z3^@lYf|N1jgado6Rxcr^)B@4m3O<blvzCnou>f0VN78|%8lMzH)xbj%Wj_*+$b5uc
z@3hzvU+B3Yl{?0WjL_BrEx5H=y*@s?37u${RcA<`ePAM>+1xJ<mLmoQ5{!$FSu$5r
z03a^2_v4NNcXz{n%=a)`wiO|?&kRU7Q1!xO;^o|(5iu4`V~RA;nUVt81elIM10Cd`
zMB_>txX4D|>+vbTjRBSJG-HwL_I-ZRn2yaw--_=C<zY2xq|Nm(lDc1dUz(_4(?4S`
zh9&^I7!habAkN0M>b$+~nCO`4VeeU{x4D7H9#HBxZF?dB$%C7(1|xFrmAby+8@>Tw
z@+Dt_m%j9+c-hNdh9CanAI9(g?(gEOzUr&a`_Lx{fzt}*;1P`1KGXsOFZc|)#e(-R
zum}L96~M(}1QKAKHJ#em1q9B$Mf^z58%JTSGZGPiCOtMh=ua1nC@5$yxu!bI5}D-P
z(*zhu|F2UJ{p;Qe>2@`-n)KLlb~~?Qn~!(9)}YDf0)#~=ZmpV%)-vt7a7Wp8`17&P
zZP8IiBqJrwlF@}EL0B!?RVJ`xq@+ddG7)kjAgo+qNCaj=O43f%B&Czbnx30>%w-Iq
zbO4g789la$a~iBi%v_7L`_kF~7?AWnH0e+a38s~0&8TaHg^;8`q*__{6#JpBpNj{R
zQXPSGO+T+iTe7DNcej9s4z0FlMb@Z{CTtyR_)f4l8k{Ob`dUW4fA#F<O`H!|Y)UvD
z+9UI#WfH)&#BbUdGoWf&E}kjlpdqKM&ms||ixr8An;zt9&(b18ka7YQTd?$??%xT3
z?B(sX0<B%a9t)dZ6P*I=1Ctxme{7%YLf?AObWWG<erjA1`k^V9fCt}Ri<40YV8r2T
z7(D8uGeFq=9{yERQK!<m6#t$6h6qt0v28cKEfl{_0M!79wh*YR26QC=h|9SHkuUn9
zFT(rZ|9<?vzxVg>-uJ#2FL}vJ@NfU^zr{y><VT+Op^q0L)ZrnF2OnY`NH6}W=)x7M
zW{)3u28CM9ErmB(0jd?iYmk-E77I4xv_j%LlOwmrYtu8@530{52MmnF9y;5ur!C07
za|^|G6Tzv_9fiH(?i*(9FpE|pGuaONj2ktSP4Nh!UkA5e#*`tM4}txoX{KsmBYuLq
z3>iwgK&AviR;x1)dL8Dzp_Jm^kkmC(tFj+CCs1J|qv;O@`z)(Vkw`xqD?=lL=V!Xd
zGj^pV(!dfl11Qmvu{=XpP|No@pF<MgI(w1%2*L-txTgav5T-zcBnJxt2ohy2XlYFq
zb-M;eqys03*P1;f{`}Qi^$t8D+`yVt012rc={1NbyI^1`@d&m8Yzd?#=$%c0AJeXH
z>#BQNu+mwslpwa!5E=Wy7R!+=Wm{~fg)UI`A{kqnER)J($x=!naggG{(1@%U)hhtl
zz)R}b4|mV_zmBbm1x-V2t)xB-jGUlP?^50RZSUJQh$|prkVUjd?c1P8xjiy*u&2+@
z!cUva#{T1Vgrxfo>^-?I1JEfi-)rc>cGA;}*nz1g_oufX=evkmyfMFh-^s#@3~;6l
zSmz<NdGysidg;$hE~Iw(YhLpjyyi8pxrjsUFO*SM52LIeHf!}_pGLU*Z6vwT5$GIk
zE$Ah!Rv;N6Qj8rLqqtcs7j7QFly%WJE1i4>P0;W@;A5X{rr)cDx9xMYRC-<RfndEA
zW6i025B*$s#n6QTbk>M(*4~HJHaXiJGyE9oM@Um76z;Jw&76tyj=CPGng^gHNY?aS
zK!Z?I&Y)_2EZnNiD~kgGa?W5PTt65L8zf_(6m%dFkTnfB6XE)hqm;S#TcPgsPQK7d
z>O_>BN7Bq02O%sTdyt?HY@YGaUTevC-Og)=B=6QMEl|`!g;3IM$SP<iFR`AGq=4Ca
z-`E%-E>?HNY5NvbsJ&v=>SpTBCGi?!rR*V$<mbg}B)$gXf`LaM&T;eUpB{8<z2R}M
z^$)fA<F1xIb_mXR*yreKhIrkNtU>oFnLCH=_eo<(JPo(~%{t#AfRVzTBksNlaIM*R
znSj!-hOr8|3+MQ@6Wg@P#%CL2)|U7!!%RdCA2(uj>q%K=xK$)(IXQcx!%U*-;>OOy
z@M&*#EVc8K?u%{sw6n+BrR<Ph@HPyI`Hh<Ea~(Xf&S((xsLvX{3{2CpFH^2Y6P3%5
zlrbKBh&Hsls6%$oJEt}ve6awu1_GsNWl*Qni3lmL)W0Vd@W=(p`-K=COCWGh-MX8`
z=;!S-p(pwFSFW*#Ei*LeO|(hqo6ZBb!nMMtOga}%G>c-d8{uHaCcUAPxm)G9tJq~}
zoL2Pr$*i){0HG;uZDNNAOeJ^G2wC=m)p<x9?i(_2k`iGU2INF2DI+IUggL1@hEl>R
zTW3{cErzV$Wg;K}nF#CJfJma#q**$YOh7sSr0R88u`^{ZV61{I@Tl9zg#9Hebmz=N
ziv>|XJ0M7Pt*#%+AhZ5i3KAD{2`THxbp#DO1v8lTLyA)1Tn(i7_Q{Sts>_K1vXtS-
z@{x+ICEd@^tvSEfZROC#zPVeB9f5*S@5W5#ib|y8C1o9dIcIpBk>nc~=(fcvC9{J4
zuO2(41g;dZBC0YRxY)UMF(@PyERyJ1A(6Uale>q{NVebY_NlU6DKwXZH~}nH-gHZ_
z8{J+e?w0nY8%yEN4Q9nNmA0Ro^e+OS^cE8)YIm2LSijq@i+fhnFrvNI>=<?vDZmKX
z80tS$Fq^11N-s|yTp(hMh;$0|Tn=LkKu&~fL((Q)iEwSD-C;`=91JO_zmXU@5$=o?
zLK>L4$_Z<;$kaMk>qTk&5{w90EP@8H^(mcX$^!ssV}NhB^zZSy!y=hVC<zvGqfDIz
zea{z}z2;B`dQ%;s1ke#r-J4&N*%CeY5Sb_wLQ@LIsfMke5&;-Mu=*R$NO{bTBn$Yo
z?~=L@2o6Fh(s(sHMggP-r4ATU!bleTA;6F{H8Z7z)sRtyfRu5N6Rxi^hHPn;tm6*V
zQZbVt6JhM1(-zik@+Dv(Rsi9aG1Iwbm<$}(HTvd@^JlMJbxD~BP{d+1;4nm)_rySS
z>LqHi8z3Ye71VJnc;;Y4&(mBzT;M&B)V)(%04ilfH{G-{Aac~IznW{t&wI1y(CNU)
zvGAT~HfEEW+5~mKIDo)3ULy}H)X|y|CJNUd=K}>IDQV_a&PbBLS(#r-Nkh!+H6?ZL
zRnD<MDeICO@E~VkOrNB#_DC=}8ndQc$jMsdv>_r>+5DkEkW5ER;=`c&BZYpD$zH?^
zACE7Y#CpuYgMUwycTpm3^EeFkU!OQ~ocZwWdK0k`9-BJcA5HtdbElC9uNuIm)V8^p
zDjvtm0}MncQgwIt=(smgCY8V}7&2isBn$#PbWKw(OVtKkHxDunM{Qevm?|DR7?2M~
zj8$+{YHXVc7;?gxvJO+Xh&)}-16F-8BuyQ)vw`NdL4POX#`*4=ASZ*9=@vQ{x<J4%
z2s&#64j{V8-a&!>To^#MVpNSKQ9#mI7Ec$RPWt|}K+`;X&~bM{&@QYMPMhn;Uh|>3
z$^e2SeGWujRa7?jKyECSxRqkoY)T0^B~9N=2}91PRe@?sEjwng1?Ac*<JypLkTtcp
z0JyfwI4rF1S?sswY3ba7`(3m9nk_h*HSQ&)BfXZo?pBvw)*Q4TpD$ispbQFLlP&%_
zToXJq$4kJI=y8OGmR4(Nw!ye?1yKc|SU|0I%uPIM7howM86q1e@d$<n$|g@A_S9Y*
zr9Qg#%FYU|_cvy?zogZ-$N(&*9093hi>9V_Csb(x&aM-3QdW-g3fab#<e>qIr12fe
z+|mI+h>-JOZoLMQn;Xa25^?rK?@vmEDrD?mcRAs%^H5yB*8R1vj}JE>$o{H$OwiPb
zknW#1=&W$}Upum&k2u)uc0j?ZH4z`%-I(pS*~X9@H@#Fp?~$osJDIQ{n2y2?rX*qr
z5gAiDJt=C$5P7`F<;nvLM7jb{UMl&FC8wkos4%XtGOiD5tv-BBt6+{*kZHhUH&%G^
z&Ilslpb)OFGRDfdz5?V>@YoFnObI~F49*D!0?7js=b-ex3o8+#@Kh|6iDLm!Up?Al
ziZ&t7jCP((P+N4+)1cfYb+_d-ZO;D)DqC~*ZVhPlN46b*`j*Tm9A9lQPj)Xan~SPM
z(1|l3euZ6QyR@?VtpJho0i+(8?_Gb9$>~u7Wvva9Q-b6b4zhxf!4`v6P7q<NvN9q=
z*57&`lA8w#M21XQ<%G%=gSlxAhJ=B2!JyF>0#!tOtg1wtC*lB1bU{zWQhcx3*BbYt
zZ+0y-o&l2qw1qy#h-fudk!;`swkWtO$K5B|^Cj0q2$B^X(*V|BK9bh`U}IF^HA^h@
zm*O`EjVbBUb^&d8*DoihPK7>EeAS-D<#?P(P8uIXHcl#KASa+!OXbZ-j7ZU?;Q%ne
zoAQ91mBC7sz|>NCl?`bYv+p0Iu01x_p>Lb0KUW?93W~HkscyrdoGf6KCYN}(aw6ml
z9l>q8h855Uvy<tIww(h2WeL<Cn$<n(Oe28@$xhE`LzVdP{q4R~$SEYqN^<_G3p@?I
zuTLSFe)8w76N;js*;Vk(Jh(vQoP`MCf;r%XhYki@TP56B<)F{+taYKdc`b)a;*sn6
zND`6u%n;zw8>`5RC(@wCM52ECw8k)G6sxq!c>qv#p|C|tYmP9o82U~b5KS5FsdQB|
zI3*Zq=zRLwuEA{+n_CpSG8z80DFpJ@-GbKVol4810cMkZKJf*R$WHQ)WO|)3KaVBR
zyX)#wbcyVoX92^M;hk}{8tTwuA6!Y~0oi=<niVmCassYBf?-Hl4Hj?+{i^}2Z1qB{
zp9mE6uCo41q~p#Rkb@zGYp@s?@k>ctu6Gw6jbKv(>Toj@c^MuucS&yX6a+waxjc<!
z=ul+OqB)SzL7-*ii7S$58TeF>B9n)>7&vkOLGa3~34;O-(eJYbtfsmK)6n8o!c|k?
zopl}9(0rU6H#Y8Kp%K_(lJs3`Vwb@rNLfK_GFHRKiFg2mv7^dpWTdQJn3ZWG4Ps3R
z#J)Be!xZDq7OiM-m#`WJjKaDP+cqV)pmTr{Wnr|p3nl2i2l=wNQv)=1>7==K(w)=4
z0_93omGJcIE&KPN`2OhA;PuA9q?utfiG8v6Q-)^SQBQx38vCJfeUlR<z&k5%IKZd@
zKnECP?5U2k20SM0*5%`n1&ExLxboIUm-=L{tuh|FF$6O*WWpoYhgdKGD?mlcf)J@s
zp8y;T3G2cr%y{(rfKs(fB-IMa2N;Mkjz>UN%ZKtx=}|Ayopc~mq>bvr9Z}N04R;r?
zOEKI9Q*zSSR#v3Ni1=qMEpNJ$IlgFTvn3c7UXx+Et4TcVDK2LYs|02dhF0>-5_gGN
zY(pLbh#(zkAlY!kEYEC)umu?!BSJZ2Ai|gvhLkjAmy{(L5&=Vs)W{($+7^T4<LV$M
zj9JX>r|!M$tBl+0f>k1{?FP83252}h5p7f|_3H#FD^v4zq<RN{TH0=faXvt*M}WCg
zb}M0f2%Q!TU?QG=tH47uz6w&+tQoQ(QXpv@NAdtwM#2~XL)|V#W2%e+iKcrVFk2Oz
z==ZIrN#eDwSZ7l~wJ~V@{ctwWT^wlaNFqR#5vv}+Y^k}FGQh>=aYoK!uA3F|umZD0
ze31f}0f{wjoU-l53!e2EG*&<_=K&~ofBm@0o-;HyiJ9Y>yXK&jAl2g!Fqt%4;I3T<
z$9Fq*#_Y#Cx19q8^mAUkv&>{a6ZWFZsxbxC7@&=Vw-cIn-`#Z)r<3EEH=14uIdCyo
zi!&Z>>?EuA0t3Cm1|p4(J#&`t^s)evGZI(cgDn`>4>E2ZWITLrh3hM~YJuykj7*AB
zA3hkuLOmFob)S>&>-s9=#zDrlA>-j|y6yUKH06{`nXD{8brm$KE`FvM%#vaNlnONS
z!8@(m2%b!yZG-X*2WqE9)H9<W%;#?(a;lq2XUR0^(kmKBE2gw>49H$vbwDh(_xp#|
zQCFLb4ej$ZApif{`}<hiwzDoA|2)q#=Gy1n+uqB!^aq!wEp4$$6;!ZQ15|;iwMs~g
z38o4PK}njl+S){+V$#NhR<ZI=t6=?um>437F9?5-v{BPajG<JjhzhA9ZD_DIF}=0)
zp0n2+<M+q&GM+itTKnbPv-jF(pYgdlcds?qe4k@J<MX^rL8ZyuE(Hui69JnkSu<b+
zg|`~_APf<$R^hQ8M1V-QfOu(hVfXa{lc=SQ379Rwc~cr*a8mH>m$ujxb=}Oh;a<@=
zlY850rdzvd+$=lm@O@~_Jki!cxbg&oAS%dOp);TvS=)byopiK?1UE|w{5dNTshE<L
zqz9ZfE^3J}>q^9z06?94$}g++gX?|Jd|y9XV6A7xc$Z+EgtTsnrpDH*71O4*wyF&)
zz2F~{pmEb+W{d4$Can)58?-tbffcQl!UbFkxNK0M@gr;&k|GSAMKiIi3_P>-rtw%K
z%iJQfw0T_;=I$OmO~D@?$Vw(ZnAxl}X2GH!u@L5e4`3%+PTq-VT%^cZ^62KJJBCpG
zoE(qgrfGdYX;-;d(RqkkBxf<GrI95*^F<C}Om4AzTgH@5oVqlmzKSjAM8o?E#j{C$
zg+}`BEkY5qf)vK4FrK=rKK>Wp(U^@prB}Z67l4_tF*CWQW_H0WHo-h~XM!-{g?A==
z>2yZjvPHyOmC`b)5Slh>x+UvD!)Agv4W~BO+O^&Uh*4P}_!63#zYk9w7ZJs~RHA=c
zP2$NSv54K+vx{AuN&0!ACAGcF^Vux+(#{YDJ3p+hpDZocMT*7dRGzGLK+$JIN<;u9
ziY(?1V~H0}EMCNWeN08k$7yXSqMD_WTXnB6G#wYVT*Su@V=}Vx+-VIr%-xN;0Sl9n
z6WzZQ#_6nMsIVHgJ=O6{_Y{k_eAR1Zt-;a6ufKrg#K_1N7Yf9S`yO?1`@h!I*zegj
zfLbls;yhZ_&biHq#G+zrPZL%v$Eo&;X)}0!4&KQZ76>cbFKLJ2Af3CtjM5OebU0NX
zgLZ#o*06VTPE}Kxfy!o8n~eMw2wUN{?R~S3=on;Xl(K=8`w(-hfW<Omq&K&-W$I$_
zGVmTZqU45X1JHA(F$=ipINr`=zl*+a?3sF1_u0s*t8&bWI#Kta$%Tty-+jdy?`7P@
zzD`^Hw6m=bC30DkmaVr<@g!}%dD?fc$Fw}Bv(I=JE2GBV_4eDT3B8iFk0@_Q=o_hw
zk1D&>>_KKZ@y42*V<nG>K|~$`h=9A3;5k&h@Xmxy(SXz^Hmar0X7%V@hYtz2{+ny5
zcPAw<ZZWxcPtEmnZQwGY&IB$KxSW8wh3{9p$P?<R7RRF6;2nF_CoO2zN{WZkGB`CB
z&W-b>!vmn9yHHkkS7d963xnl4S<A@8=DZ^=KlksUy<O;?>=q?07$<5O;1d<PqC}+f
zM&mu203s+w(6)e-0&XhMregI%tU~79Ni;RLws5@&6Q*JW61u!KwURI)O250y?{wDt
zdUxu-74F$IlQ8aX1iyWM*7RP##|@fUfO95Vi;bz|)qGFQ8ZucB>kMwyVn)_+B7UcL
zcfJtJ9v{>!7&7X4AjNVpsNS~+T0!+bxNb4KE2rM?L8#SIb!&IscwnpsvbwuxgQz_M
z#4-1cfs4Hqd)AA=ra|MHA}gTk@1MC}*j+AKiC08$YOa>by+E19f{42HN>R6sb!wI%
z3uG}N1cJw!XwyWof@bWMd}#vneR$e1w-Q7n>W?(t_w3ieteHq$wMb*zKY{uZcOyI$
zEVPVlhDfWVG~69|L-|Yose$(IZvW;7&+owVKVuoKf6L1VQ3$h(ZF7;4+drI$<lpRb
zYhex3)rLjnk%bFtB484=fZW|wJaxC=?j)EB+oY0AXBT+$dQSfC`h^L_w3mVk5v#N*
z6Cn4^y{vHtjVeUcWDfK5s;}8nerNUZs{ftBov`bwS)3i7KO@OZ1B=hOD<*+<-rrj5
z!ktr-1_QW&q1b+L_bwzc3_F*ZHjMk^sij-JkGw(SQ!Urdq5_W@EEA||eUPaX)CM(~
z*Q)LoVK4zFn+d1e*<3XMQL`R0YAu`C{l7{~8W1F8BWf-2VrE73+C80e62???`Q44+
zOQ)J5{%kpo+oC;wDr{OmBTfW8D@Zn|RJ8gjv30MO=(1Z|1=oAaZC8(h&iqwJLvMm-
zXKJpL#!gVQOuNkY(X(O95>mHXhTNB>S|NZEo**U7OUY}%t&-hCaV{5KwuX(JmQt97
z0a({>QxXtG%)DV;Ck|xsAD#{4@gTz56cN<Wcv35drTltoIC<f-aLp9eq>ZqMFu*66
z<T)cU1ct_-p=ppbTUJdp-y<VA4InO>57n9#&pYd->)QNTuGBN7&Wb3ks|Y3jk&Q8j
zk*z8XT5dld;H-_?fzJu&-Hogog~2SZW3D?`!AMHm`tLok*cBPJMxh>@x~vT|>WDnT
zD8ksznwEP~)CF^YTXA<1JZb5<u5GUKv@b(Q*3kG8*4i3Ubg~gq^DWI5Szkchz@?Z8
z&eC_e0P}qnOihC;!D4;$dJ3wYKqm{&(}#GLHsQ7xpc9exI_|#7B*lMh%blg|4p^Pg
zwf}vq(2mGzSoh`ec!)|B(kz9Q3Ydy!bMOY#PStH80x^Q8F+B}Ts@)2yW+G-TttPyc
zU;stLmRS(9k<$qr<=d5Ks>Os&^Vx_7r+MnGcIZ`q@x}sLix5tVU|V}*uUZ>CY>1hn
zd-3i~((zEe4vz`8#@ek{tCDV0F!E(sV1I&<f#bx(3ur}b2b-J5GQTYSws~z4viOhr
zo^I!HBOY(EIF7}Fq~c}gO`{h=8csQN(pA~$2jfaX5D+bJCl-WC+=6t76wDT!PDUu$
zIx(XZY)|Lzl99=tsb*kl;IwFfy9F!~Ydw-STV|HmE3qstCI*)cu+=&-#G)2fD*^A>
zHfqJGn(I*;dhsk;q~((ZJf{T*V~H+;Of+a=Dm@?v_8hA%|99rWmMxt2?`ut`Gidc;
zP0ZWk!)krISA7{RJ~Nqp+hm2#LyVbbB`J}WMMh+C`B(D_Fo?(lLWn@ZxHB<ccxS@h
zNx}j$318W>b!~<Kl&Rpfk_jl$j2#hm4HOMvtJ7VGn|etre2NH8t5#zS`{)k+UVpyG
z6`f--Ny^1yt+p#T7U^_sgdiJJnyu58+xaH@&UUG=Tt;Q_>o?(=>V&j<3Xp(`pjGWu
z>j9hLo~ZzdXkjV0Tu?!w+2TJMDhXt6_8WmnEhb(SRm3YHDwsTlPvL>9M3ymAtNHWQ
zHRCC~wOMuzt9$0T(>?@d-v%il4u}G1ySIfGe;XCdatAcuN2PmOD#U8l#NN&guIRe0
z_nuLLa0=UD)cOD#xSgWa3B611j9RrEd<*-I9ophZydrBHr*7>gsP!dA@kJvSF99_;
zYV8Lkg4(#~a<c3e7Bjk;y9KjNEMlZXUA9Fi2!#_87?CNWgo9DlQpE+8b@Cz0)@rN;
z%tSQxpKU$1bY2a370b%oMy!c<e%=xTH0w5KTJr=57Y>?>7Jcr!u9`hxA0z<h(EZ+H
z5{b;3l@_yp+WS6t3q;$$;^ofnf@@J&X4K`9xNKSmIN3FOus1>^w~%l=3on@1TL&B$
zgNR%b{*DL}o}9Eo>XRqMG8R;;6~5R2dyr%M1Vk=miceHxnzNvCv9bV+R!*RetV)!C
z*1?&ofx^l|FH8(+@}<jpHg<5>Y_Yr3bKdWL+C~n$d0sN19ll00Y+4@~Ptk2k?yMVf
zBS9^9H48$XKx+)hCQ>cc(xHK;Oj;>dL|rRj!M$w_LgAK?Iaw&!o`aaRA{))z1?XB3
zH=QWhh{+_Kh-j5x!jmV0?W~y+g|)7YZ<EWBEv-a*?hOe5Epk?QgDNLz^K+;bnB@*+
z{%!PB$P135*3fgyQ{_zWpj)?^<xywN27_22YVT#^MbC7MH_Mi}2lW2Vy013FYtlp<
z%Eiv|63C>#*L47SnyqHSspp2m6*Eb}T(l{o6Alr=EZSL{o0u$6qT7mqw;qsc1Xfe#
z)H-LCalHa7k)TW)c&h=ib76}oX`<Va6eGG;1|H2w3Ts{pGX&=Tg|?oju=pTeRBFBN
z(ZcAv=Z2p->h_sV4=>(bn%0j)Tjt#bcCZv_ml-Uy=Y2}IDC7{Sb;pb#Jy_O>9(p=1
znG(6K3*z-gauw!o^6W_qBWvangqpq@HbqU_cP7Ri&qQFt{BX(+9E46vI&dao++#tJ
zhWlKUh}fi7Hfkn~w%;_HxmN?@wDCf-v{N0BOX2@K$;Wv(sPS~|#N~zeD7n<G&oPPY
z^Bp@(H(o8V?W_uKv&qfthJZw4Cdvkd0BV*tY9?z<kz_DA*#HXZxQU9Eq(P;G9{<7A
z!sS9H!8IdidG1Wh3-*F%6oRM+wu=4cy=ee*?^rOa2SZ;Nc3yM26PBVX8!&+F<|*)V
z>Ka+!%bka^#?GYBY(H)-@d~M1G$!;Ij~UHsh`@qo6;%T+J^QA)+ii=8(Fncx9P8fM
zH)Lnq78Zg&IqP$D*N9+dFG^<dAEGmou%KD(2e0cPEZ_z->Z!VLSBpP;n}8ZYV?bnM
z6;VA)MvvEux`x!UT0murOqy<Owe(*Lp5?Yx0>li=&DL#Nr=&Lh%%s1;n&8=OjkHlb
zxO`a`Lt9!{cdZ%O2txZFt6Nwvc=Ux#ICqT)981~afgTeQBoE#C;jh11ySk-PWdMg|
zghkM*#`=JJ9LcdUh{z=&OejQn@<fAD?`|Z_-YNjD_V{e6oYiZV2*6xX3WFekOqMAz
zf$DuUt0PdW3LuThUov7XfnzCQ%_mwL=;xY9*Du+{U%DN>kOfHFx0k-;;5>N|Gs+$u
z{@K!+t%9jn<kZQnTE7_89&5AixuWTE+FOPpTv0@u(%loXB4y1;hK`*^=v{NE6N|8b
z*>|tc;67k~K3;DmUym0zJ1K1Lo=$9plz5y>`rBN!`Qj8~Ik{vmB&SmECK6OWv2&}D
zSp(q0u>g7E$jfrIh@M5G6;P>pcVDlFx?L)ssbE^{J#6l2*+WMX_5>SCYF&WEfUwxR
zY4H{;1+$fIXBNzLwu!$7o9b!L>WcMv4T~-DD!d|$d2XhB6tiTwMWh(9@t#%Mk48i^
z?c7L+TWpFjSS&D=s%G180kf8(7wCA_>dzhwX)fSu8H3ppVl!)>VpsISECCl1DF@|&
zD>nN~Aj@sNPSLU~z||ocisC?VYls&$?x<Nnugiu-%#9Ni?^`uT3H}Bpm@tc}5MyRU
zaB}n>Ix+?kxdd{#>^ntcLSAs9l~VIWaaD-mqr#xtFxQ;2Syd2}jh`);3q-0KL8zH8
z+EN8s&GGZ8XVw&;omxO(aBn{{fCsmbWLZMC;Gp)gCt!84qgZXFappb7<kAm%(Q~JL
zYf)}sr)9&ztO_}aO(IrAjx;qAjpJSt3rgWAO6G)v(M*Hs&(@V0ueRzX87zS&ou_@b
z@#b}G;`c;mikoHOl~gG#CX>aj5VaUfg$egjwe<WY3VONap|Td;qBf&K%fZX(7xknf
zP4+^2Ts@EU6uqQ7V1#3WTQjtth~0;#ZkzRNw!W7w&qi^qhlsE^qMOJ)d&MUD2>$dq
z4|BI{!<yz65w-$igjTH6u320>pp{u{+-L>mnFZYZMR2RA1Q9{FWCX;M6;Xo)t&085
zQQ6f?$9rZCF+s?(eVD;9!<x>X3m1m0>{?sUkTl6YkR4&i8nR!VxsW{Avr?8}J2>Nn
zeY<_fG(*{9AC!NLkFHFlWu}nO%983<ZXyDNAk{LQm_Tzc;oxyc$HpKc7eMxDKCuxL
z)-0J#VVo4XvY3#)=K_m-=4zc;8<-nT0E|F$zcv%<ee2c<oqSLQkPS$-YR(nGESkcn
zg|HYkH^*yM`rR(NLCa}hG;8MI%N-`!<)XuTLAYktJM>ATwq17w&;`!}8Aj5NnLK%E
z{LtLI?gPODZ$Tl!Z1>NFHN&zRtWlk6Bt`^~-7k9oy=Yl$J&su1@0$(1<U*-mXyLOp
zn@p59V&tP*#a8?6&cyiAeO4aTG}C6Q%B=QTJIh^T+AaX=%v+-0(JXhhKMa;ZuxC#-
zhl#3h+1KeVo1FEcKh?N*YRTG5HN#T#u;1jER~OI5*@zMjkxmLc`-E6@VmDGzd9uP}
z<}R_!D9lich6gj7DISrcX@_D0HI3!*3CT%Ivl=AubX<!GF=+#qDG?mY+G8&TPyjkr
z0^{2@fUPGHd{VSAU4brdkVb%8Kd`(_XMBAi9FhV0p6?lRS6~v^_nKx!EobF0+<nqw
z9O?F*<hhc<>|MT_y=zuK21ZI?a)7aL&*2*9uR|C_<N|PEvJu7;n}R1#1TVN#B53ux
z&qWW(mZ77<eQLm|5V*2&3TuXCWump$U5PT>z-(q$P3LP~zrciKqhi`5nJ>F*Dh~sZ
zkZ|7Z%Sv!s)LPOa&`<4opW=#)!2I~lNJf>7*0f-+s_Maif+jG{m0}`MK_Ov*5VJ&i
z)V&9#HmFrYyXhJMfm=#wM|iDo+pa}~&RsXhix43Soz1m^C&K-iQBRwd?p`RX2ku>e
zP>l;(+oCl_l@m+#HS_ZPK)UV<J%H7F!bDnQbAQre#ONuW6Pt9o^IBg={@y<~sAWRi
zEA-k8T2M-?;;vZwE1NrpEyhU478j1OrIQK=m(ddJ9CbU$EELg01_1f~0iDdiN?wRT
zWutXSJSdg*UkIBBsO^~+8Zhr#we-v#56WhB2Qg_d^lTF(Xi16}w`ymM_nJTtI$qj=
z&j;6Cxn(XPo-TzYEp<}Sc2?-ONSRu=yZkrmRuPa{QX9-QLlJ<yovV=;4(k*JOC3by
zkpK~4QwUGpop4eZPu-a?9Vz8Wm76~3q-nN331hAWJa18$P)&pPNfB0mpbAH{JTn1h
zqb0F(<1J{UJlb|)N`)6<I(TyD%v9&dlZ795?|Lmr$d1hS?QBv%vj;>zR_sEBUeBwi
zejrL+b*-d1EW^RmBHgM%Bw%UaR#1#YOjfkatD1UcRDeLzbN_Rk$4TiOdI3cAdo`<s
z3!nA?n!H;;fK=;-+|B3xY!Ct19@twiF&TI#Vh|#m20+Bukd3Y9Gs$@N3!w*~E)4I@
z8>%fk{q8y2=Wz~>eoy<bOk|J<jXgu57ZB87;-c?r#BgYghm!&kyH}*+#ZB>1!BmP(
zR6Q_}gMb*HU;!GN*<OsOSdp`$rTMi*gb@@`%S$!l!(=TX!a^fpo*;4`Teq%|Rd#N$
zpj$T5sVCw6w&LEHJE@Q0-Dg0nzYhg%@n^AoXW2!D=VjOkr7@qZi1{}wR|vp+|EN{c
zUFJ+`u>?{YKbsp^mj=I3i;Df`DZ3W<tT8~#G62WIAR-sQMi^iA#Dp(<VuL$HGY!^7
z%B~Y+tXD`U4noB|x9~Ru)q>en@I+y(vVoU*W>TR?veCE^mY8^RCWu0X=RP8`d<VTg
z!oHL4>n}eXFp2K$J8jc|M*FwgSxSo^cfTJv&rmg-h;XsVm3vKwb_(K6*J4|@aEJ?V
zW=!nO5E(`4A}hr+W~eQj*quyXcnG#$I>k&ts4u%HeC!esM1x3!TJKp8n%1<Y$yBxY
z6g4fzo6|1+^A7hUGIN#2)Ks1{t+{Sd+pOv3-mfMXXWEN3VN9P>Jcl;KOBRxp`g_(y
z^040G!*h|l)?n%LiPW20l*HV4Vomm>2dt8J*KQTw<W9+pcEc@BCY;0)TMBkJ;S&?2
zXbhMiIxH<L@NTR*NY%-8OBokhZe9sSYaU0UrQVycd4nw0rB*G*=I2*yoS(;rWQ)!E
znKVDqXgbjL-{A9z!X*q{m!YCNa>luDdLj0bnmwO)-k}gZWGTGaf@<$Sc^P+mmu*ah
z#e+N;+T;s+jT&{m>ZbmD1`)aO5YYlq|GyWW;K@yiG{|Fth^*&>4*H1+t!dLTnyu#q
zRQqVgj9CPz4N^+<o*=P~rZ&McGpP2Cyp0M#TK}TCusAr`ncis(f4!Cq>9x#Qzo=&!
zQ`+j)Kd9TTteb1&$ywV|`oI5e)T@X_cU@Rl)LPN3`5*~LjVdv1mF%UTL>tdBYx6@f
zUyNH|{I+@!>)cvoysXXGYvK`q58RKu&F*s7&GZ^7HB786-kCK$-MjH_=Z4d{0;~eC
zCL*-&xi!=J8<}fn-l*WTrgRNW`@c{-+sbw(`hl~{B|rCl>mJ(2#jqYt>ASLxPGBSL
z+<Qz}9FJwEc{;sYCq+aHAbY^4)drf%q!Um0^ckoO;R1qHQ$`Go9akQKO}TK3hz2Qp
zh7Qb1<K4S8z}m=7l$7@%cdhn1v$+Ap(tA_!EZ>f$7e9-dDL{|NPC7if>*~;*rseY0
zqQLXAYwdLpJ@`Hk@uqlGY2Q99hyZSG`>_AqiV{i*M}af8XBs_#EcVNnhY}e<&V<@}
zA+ua<^_Un$WDi`p5&<V37qZc!Wxa9TanBF1K?gPKyNSJ6uow{oVsQr%gjPbpRW9bz
zDJFQj^`n8%WKs=kOJPoaX!;{96_p3s>$+Bm6P3ODx`Z_G?H9dwm{4g0Q13SDQRmh?
z#^OGD{SE+8VttyFh!ANZF(K`a9Ii9CTgcom^RAWPynFN@Lz1AA$y^cVUWBx50Mrt=
zEfrj~`s=+dp;cgF#{GG=B#;9)*My+Dv<j-6zzbp3SxK-~0QV}X4<ho{)buhpVRgJ9
zGH0L1p`+|Jvm_fnjOI-xNlX0uxblEd7SWg>8(Us}U1IV8W{Z1?j2ZSgq-Hfg5;3J^
zd=UuIPb#b?P7;`aX6c<&j68chNuR8=4~@o<kQ7igGHztbt+AQFxb+4^5b=Gvn*f$I
zWUT~<(Aw(Gt~uTr&*x#$%!j@G8eS7fVI*~}0_lsX9dpU8rd?^FkWIR>TTyjeqc9dD
z<Qp+NQM8&;$XPRkh}=jx5fLW5;Es0D-4x4uxavY77laR5j$#ijYDG*en46m1pEx-&
z1;l!hq!Q7r9rLuUxVvWDhs^~<S~{C8<^+^qnf{7);k<t$=o(DluI*(aXTSDgdAI+a
z8}NE)j$9u%TTx<0XZuOBj2chjvLc>TQ*0Ft#lCB*bYVhaL9JT8otdKRt&kE9uW;Jn
zg2icGpG*!}8Xg4a5oaePt!luk*ACf~hHYh3)-0tj-qS*1lMwFDY2&>+^~?zGR4m#A
zu%0S0;S=qUJKsC7gA3r2HAx-{;=!m7|LpZ(1=FZo3T(M%=&I#Ie6knsoNK|K;z7<E
z5Z}`n3-;Iz&79#zNh{Wra3>J``jx=U=q??$>^X9Blw3USnu*EH;t9YLnrz_xX`F0`
ze~<n(geG41M6IlzN!HKEM!b<VvU}D5Ei{*&T#&hk#R?9{1wHnKR(<e8Q?~-e&XXL<
z5;niL%zc@Y5x0C-|4u$Wwi>i-Rw$r9LmvxdDc_)8ljfKhL}U*L8X$CM5<Iaf__8Oa
zNV_~X+%B;$=gZ7mxC{(pU?O1NHk_PHz-%SPO`C!>%tAHo%E(Er>MPc|A`_am0&h5M
z5m`={l>RP#B)eO-J)c$=dbWFiFLuHe)(g*4;M<Z;d2_p#Vnoy`V&*b)Ull2srVTdM
zP*Al^Z93<57Th&cYkzKTg;<;e`tx1aN_p7mZk~Kx6?)zvZ(AALxkU|~hK8vy=)P-n
zhY@ulh&5}bHB>5ST(IQ{GCv2N@1L=U{d<k`Kj?7tfphWucRp3~8Dm92>?eH>RCA4N
zgx0Mfnk^$BYMq?O6a<9PAZ0?GPs1W1DJ51|GiBH^A-XGP-m!))NJzMe5JX6e=t++i
zk=~!CPflKOw`YY>DIl_#mF;ZAg;7ee7zFDQOj`3q>~kYQDGk-hspYOO2(sNTe+{Xn
zbl>!RTK;koaeTF}&;l1GE$ipC=XbLB7|*UHCl;1{M4g#LCKISzuax9<!dNVBkeE?m
z>q>X09UFs)>;tL!`2{Cx4S8ZyZUhNY<QKTR0~)EdhV6A=ODEm5h8ab*Fk`~%<7Ml0
z;uRLP?0c~2_{&x|fK-rRj}an#Sr@;GeFl-X`nz8LvQHocg9)~MMMBrY!|(0FHA9R_
z`l=8YpjK@X)_?{L;47k8CotCxgE18i7W3dieNJk*nu!+2Uz%+FT=su{*vRoG?pg{W
zGV8><u?COP)t9~B&H3V2<3$P@Gg`fex!%!nW$CO=XRhuNFFqR`9%pXZq0pL_>sr=X
zbgb5~Q_EfO>Hh<frhdbUxhao5fwXOqQ6{bFp>B>|^-{TlB$tN;OxuLQE8^izG4nDa
zLWNII7QYlRB6j~hcr}~$eS3Qsw3%K3(H6Z!uUTZoTruo1E+s0)o|J;;<}{f?$AwyS
z)$LG2sacr{jJTcGQTx#ITz`5fxXAAIoY;PK&VF-|<6lf}=`-uywzY;)@8=U)Gj6dZ
zB`M6FctFsaHJTBHR`od>Iljs_@1ht)WChm-zx)LoJh>@&@}!{L7!uN<W7f@aFtZ-O
z!kFjTvLP(>va!atum-7$YcHs(<%oCtoRjd=sD7<E;scykXRv<D4vBE5OlRZFcYDta
zu{7ZJ^_C>*oMWvnBiSd+8X`f+bUVv-C>u~c1)8=^pDZiJE7oqC{#DmjD&Ou(A!)#;
zmudI<A%}3Td*uO`%6c0jf)G%-S6)5cTACme?o5KyxkceMZ?t#D0phO<#F<Y(VikPr
zeUx->wf=`!j<`luRu6veFKr>T^WQ{1BGkl5tXDBrOBvb(wjzPdqSbXp-J&c7b!(07
z+@}wyCB$Yaj|ZV@@g1|Sk?+^KVrhI1*NX>~s{Dv31&<-%u8dey`5w5cgI6sxUIM^1
zZ_CoqR9^-aOKS%~BKt<--Z+sWn(c4fV+GtDVR^|#B6}|LX6)?aqGk?d4+mGz1f*La
zmun}dO^`o=zo*G1la2*Sf62==u+<0Ba_&z0yog<G%`D*zF4Et>BVrJd1R~(cO~Kq6
zp1wQvx(YXph{RycdHFU-*@V7NcB*ab?_%`@%EUkuP@ORea8Innv$d|D-Jffzd|(Z#
z3q!nHK;mqC`GEnw@Ah4?p@W8l>=ROLXT5Wu7VZC<=jCPvotpd0nJs|XE87<EreMB*
z&l<&Ptr?Jhu8r(vM@psaH9UBa;M#W{JEwldW>l?B<KsrjM>V44RuM0r<#X*BXB&UL
zyL017o8nPHHA13wMQVR`F`-{K8Nr^h9e$VSGVIr-^Mjo$rDy4?EhyX<fLao^wZ}S;
zOm3slcUc5nipGA3S6a3E9Q9t*MT3+9Lvg?S5@_*$^;1+bp-M?7Ro(30|LSLt8}n-^
z=0Oo7-3)Ki=Ygx?4rpxkP(<VH3P4+JbD)LSjPS6Q8wht6De=-~e!RE_fOdM%oHg-Z
zc=W99*OlN5?TDZXN~<YwHLl$VL^WbUMkt%DL)k5Z=rKcZh0-hRnrjf~%PeL4O3keo
z#2_LDA}u0y*Gjm*;7(Bs!wq;y`#`Ig5$MHjDj`gE4FGY2Sfo6gma-{=?WqceGHKOI
z*La!*vu@jD+8miFILC^t11}y%^fmSQ=fG`WYC{La&5o5Ln|0S(h<{TTglnNKm2hvY
z!MxO(23$h#Cr<^Zr}rZUq*x5jBouKKE+AZpIGNF9X3<GR#lPpj*Nw2&z?wV?0}a>|
z#{F5%>PohHt&>A3ya7+ojKEb5SZ%QWJ$0+vcLahyXiD$HLm!5$ZY9z>ACb<E6%$L^
zsuEXse@GAuBGD2j&9Yru3q^;CQALXjs^1?`D38rjK~1uuDTqk6Bsf1>B9ROtZT(;q
zl#?gVs>l|T*4&jtMyT{JmtG@;O9@h<u_pv>6RO3cRhZjG+W;@LdiZs6?_9oikH-7c
zdS=`rR^$OV#HEPJ17hDX)Cy#uJ5TTGT<CNZ<Egu3$mBZEdkZC!Y0p-Pif^yaj6L-`
z?x`)m0exnGK|}&X+H2$PBzW2?sCo(Pn}+o%^&&~>rA>p~s~!qj?Ex~gx(H@V!|U4{
zi)IF-SoVyY@6wyI_}jl7Dh2<%m5y^v9T&BphH%)kztBcNfN72``pPgWn#R;LjmPnO
zGg+UHQn*)96?0z*?>vuuE}5KMb#UrML8QHFYSnm<jWD*gYQeKp4p-AYtH`o26Zahg
z7R)IcuxbIUjVH9)0IHy-KE_6zDy8}pTmOA1SGs3kOJbpx5RVVBj4~=vHbL+_m2L&m
zeGmy6&6uWwc@{0BP6Xmf324(eBcA7&oLCstHcC*4y4%Ai7z;$#ans#4YeyEXE{iDr
zZW9*C1lOjusGM8y+$F%Vt;>b*D-vs&0A`aNwl8aj*T7maSSBmR2GDdz$tEQ?6Hyd-
z^J^lS;csGoJv|Ls(d;_*M~xg)V(*M7T77o;oEAruI@EY}k$UZ$g~dqUAz0~rfK!Z)
zB_GVGz@29_!QWRW#NHTh^#UIWgNPUigfDwy!V69&Jau=nf<MPTt6eM<u+?uIFZ6O<
zHdb9qFYP=PL94TN=QSciqQ$yk0deG(5V7BxLlf}{M<d%Q=7@c_EW~qqOV8-B`#?CT
zee8>4S!j<0&2&JR*a2v-9c(S5$O}RdrZNF@MK1KjR^+ZQ<4#fY{YDV66hODEWh=+J
z7}>Gw(>T_})7;fWq5-VT8q7UQ>p{4-ZACjwI4woI$wvQ1wOY?CY4DI@9Zs~5FJ9~#
z9$=f@EjzK$VA~03b1ZI)0Z+YiHTlG$rtc~Ok&XK9S%9YQicOK&hr4j3bnAl?kWdh~
z#|%(y3k`a41qVrO7Yw-wnrqiy4ObUh;29^4IgjDcz|{p)om?<u27^(fGOW}Fk`pjh
z<1*acPVUaJ7!jZc-!Icw8C2)RxbQ35YwKM9u<uq}Km;8eM=Ca!mYECem-2@s82wp#
z!8?!Dv!BzwP!dV=eTxU_nhgP95>z&FYo!NZZsu?krlNac1v@eZ5it}YJb6-ZcVimv
z>P5IQSWRSkat{+K`8YRs0273orT|I_Vj|4YYSLmag0pRP<IDtXy=F#4*e|238!~h6
zFU4-OxUaLKAPtM=b!W{y7oC(2%7VBr%Y6kM@YGAdv|m|RL>jc_0~%XVGP8WxeJ=t`
zg)p02CR}Ca=GX}iy}-2`6Mnu-y<nbrT@)cm)$5zAWPSCfG<^qq_p7lM6@bPdRn}Mu
ztqweQcLNZl@j(g6q0z9rzr!<#m-TNvLCaY%$l@AeQY6wzdnR~r^P<CPQA<cE8YjY4
z-^Xf`GMU9=Q#}1u_tTY%WTCL*xmK$#oAd9^GXBG3SF&c)-K%Ui{|E@TIx{*EZa{S#
z+q0DDqr=RoATTs`1f=zKnoI!QGmYG}bAs0$A<b>?G7J>%3BWtdT2<={7N2|A$a3S5
zd^xlz?tJ_Ypwv$Wt%cmyAGwoG7I3TDZ-z<Z`D6~uQe;7~*J|;Y5E~nAjY{{^U2u_}
zePlfLL`2iE2~Xdh@Z^bRKU{Z5-OD0`k!4*p8^nOKVjJitOq_HOw&tGEL9TU0G;mF*
zqE!v515emYg6%0Zb0#9)IRatrM37xJs#MW_AIt%D<x&&OWtoVd%b|0^!MQR56Prwy
zmMAFJZk-|8h|07KC3f2The^v`YfTpO@q%`a)8~`x6{ZgYCato1S~ZPVM7utp&N^Xu
z_ulQSok7k75gH^OQ3Jut=2tY$L<$#(fUrdr+I!$q@cYkW-<Z(xyP7^MPN?z(wuXld
zHc|Wdr#=aQ)Kcaoow%S(8(==wjF=*7Scw!$oiudpXh5jf_wk^^c&D*2+@*=@oEO8d
zD=aLY%4>Vc!!Rp5q0%tG*7cPOP`x`Z17h)GrpTz9hd@=Lj}FLPIYzR~GTY0OCjvtK
zydoANGJNcs?IvjOYM5CsWSqF%8@F%m!+X}-+j_rDUvnWdDL(d1S)0+R5bZ<_K-3Pg
z_$f*iYr9*^WO0g13&j%I!X|ONOh}H6$C`-f`=A=$|M$cOcPF`#F(Kz63ndp~0Qw|s
z@Dk#sNPHF|4PdROrqyVr)ky?XWt@6LJQK8y)~`DC1Ml3s{=#1Lbv~r)9ONLTDeZIh
zxaiJxDqQaF_t+!>!HkLxv=zHeGR#_}iAxj$VxL4(%By#w_w-g5qC4mJ2#qy=S44Eo
zY(#L6D+)92ZS~I1skqje;VwiGFQOKaqQRTiY{S!JHCDu21ZOhP2O)EZTFkqtx)bHf
zCz$0fDr;%_oIU35N`l9Z_<NwPO%`nqS4zR$W(XHR8ctzF1UXH@79{Qp2648CEE05;
zpa5uR-j{T>gkBp5Y3vbMOoD3XeSPi|yc2_#vUtiy&%@5Uuc|$+RNJ<GIpBy%@nUUE
z+DC`=e@3`G0LB!ecyD#g^|QYm&d@{lua7<=4hF<uaQn4;as8J)DAm?gONd*B0Iv(f
zt?BJIqQPu6qe$<uSHKQg<zqXPMJs}4LH9~dXfY;^iQ_wmjsy?h*$CsQyA!_biODKq
z9{-^{2-XalZ4h@>?K09$cxe(PCnl!JSr8f(b>#@VMPz9m(aFMJ9)L*p2u9lb@j+|=
zF7Q{o+Swm1|Cc`ok;;N13kQAvUhHF|W>79cGA2*4G_Av%ATnv)5ck{mOo;QFB;IsF
z;^WA#g-BgcCq*-a?rsDpMa(+l8tjF3Y%wAl6y41F7S(L2r0TZ$x%ubw@7r>m<tM!)
z)UjXE_#hqII@V`7iNM;*QxI;yFAY!)-(#)JA_8V`nY3OAYvDl8p5bYOs4i-gPAOK%
zOne!qcQ?C0{S;`i{8I4jn79|NR4n&JY5m|X)_q#fQTo1!bfWVKm2Bd(4&P)!u5fn@
zk=_@uWzQIaqZ9}o$M0Vb$$&${;rYB>i+h`f&zex|JK%DJCE^FS{8)2LUzcD-XZ>9>
zH<?&PUHjdw9jXiIGxLhJ5xeGDR_;=d=i9AzKE{Sc<cJ{Fz8WvMQ}CiEH+W)GZv2D*
zEMJT>bwr}xku(Hn4_tM{O-R!-nF_X6UK%`^nRtRou~b4qlPR>FMsw<T+Fdpj>cD|O
zL&8o!hutvqL)MM67W6G$%iYn0C<%nLkI)>#^a)gX62J_QrY1IGb4#f35Mi{`=fmSD
zeDChwJ2a#og|W7V5?np+jWFt_L{Q|mHt;<FRns-~Mw}J3=K?S_#^YQ>P6MUUv=&Q#
zkj=-oJ+t#YcFQr<J%oMx{+p42X2-7;RZh@I)FM((&E?`*G@X1v#5@N2z9=bG^5NyR
zHDgUPhuB@6rWCO$0NV7>-cfdvzr3A`33}i4$M{-Khy5i@#3SUyGRr9@{A}fkJ!ax!
zYnseJ+nOBFQtvu&tja1&OtjHpFD6j4s4+t`!#VcbW8aKCwOw0<8F!K|<Zvv>V#yW1
zi%KZx?($m^N(~^jXU+sRw-d8gV9xPDkpk}7Mr0N#QrEB(z-BH)P$qcjgp$%29>J=|
zgAn0lV!ZIqgcsZ?=W4At8RylV@TKh49bvU1psd=>e36c4H3o#(0?~@PDA<DbsDi3C
zqGE8@R@cFASibDJRr|g<>VY4@4&y)<n662+zU<&}2=Q`PnXG!H*uBu8OeGlR)~bZf
zlmu(qRVF2mBI(!%A}^BHqRJ{;h1Kn>rRzn2ZPjb8bVde^!7U9l3Ak<bI|(Xpx^)B^
z-xMbi{a%*tgyr;dMt~|&^m}P&rX@BY;y)OTW+MwVim<umCMNQj7@z{)fAgxSUdfb;
z27k}R-ap1knKZ-1>UH?Upyk_hy&k`8n_TStBf#<CvpR<eb1G2SH#$gcmQy*!gclYW
zBUHU#js4@kzi<t(X4-gaX=Cl|t1)Vb`(P2OHevVo&Tm6o-2-)MG!2D;&HFi4?7%hA
z&V9%iyYB`PB4y@`SKG;Ci}UXr_^r+sYp@g8Ijlm}#}hGTWHLg~xEn8g$AA!`)H_>%
zyqM+6Twl%HU(1f_$Cpo!FA-tFlbeF4?i6gKr%F9aIIL~=zd1~Vn)@VsWz0!fO~SJn
z`zG*o2P6!k2`DEz`EmiKyHV(?vQ}dCfvtkbwL{GmiSzLn@{AynU5ySq!Dr{+5Zh*t
z0HU-?SWMQ($qI|bEqLVl$+WSeWrW&P`>50~357zD0}2<~<Sl_tI;y#y6v7wTENa9k
z+@kndZNOX`=Kb>DcLo5cvIz@Ftw5C%pfyz90DzGKjUliNtrF|jVn`6rdA|kDQ&}mE
zhit0mp2gFu9*;%aB5(oPY*k>57*(q_Y;6w~q(s!R1JQ2G)G7)KwxX^XmIlZjNc(LI
zN~N%d5FxB4(NmFP+S<K!>xuFrT5Td4OOmtxEp5VSeLo*3AL(;XWN|IstsPnfPOpW-
zO~od2wZkx{?aUfxPK%xi*ew8ayuWAHPg&fd9pCM8*^(wa81#AWABX-NBhx99K8w$x
z*3^mwl{?H%AdFx%bJrQkbN7~4O4Z_IO5kYj<1%7IY(O>luO4{j$G~GtM2LVFJ-NY?
zC*{UgPdyJ8o#e81<iWVg9SChUlb-ZQZCFf1VTQ;AmTCb}R(Ta}LhnU&bb`>qA4XGJ
zzVYsnZdl5NiwXeR^Em|LLa3OaN`ic!=HuR3uV3K00TWrRi&;(U#3)n2L{(!TS_84s
zuGX8{`8{4-`Yi+7#2(`#rS(ph);kx*{cQzT)lknuV|Vwd@4Ctz(0m`2p@E@fgkaV}
zS9zQ*Z*nFYd7AR|`YhE8lllIf(CCC%`9y10P(kInTSLgY2oqc7N{NY+h}A8@dM#{~
zL`gJIQ;U7g+S_CHo-w|y#|&7e)to1&L=@KK;rSpmal+o0&&W}HWd>?0GM9;u?~5qj
zv8cMr++8JX6+W$@pZM=YAf7<Hwa>l0y<aPvrVw#CtM|=+TiI>wr@a@5!>r}NEz4Up
za}lP1XIifegc6o+)#5OV3?Z1?ym_36kqW<V?>R)>@)Wf}$gOYgx@lO%hWNoFDA{D+
z4XGn?{4+QbgfwM$BR4-?_o6uODq^6Aw$(E|e6WkKX=_>~k&5+^Fjn5YrUn&DyClo1
z0aUxpEIbp2{Q{3xANF6vo*(V=Qqf84t#*`uTL^|YH(l6FsCt7gH@$BPTiunOuF9mP
z-Gyn<81Xh)LDtAIMnyDWRY`~vk&_};l^A>9tM#q%8XfA2DVX^LAVK4e{hcCsweN%@
znAH~8#MIYmW>M+nrMnHY6;Cr=^t)G2)tVt<!LvDtmi0V+ym;LPn><Adm_*Ittc9c^
zON8~kDjqxGrO+*A0zjm;<^a}1_ijDLE;8hn>s&-yEdTa^$V>$DKXhUhF)@}g1L(c7
zm=N#GD=B`)16{qX{lcmk0rLlD^|g38a6|lkzsS1&)B4vO;F;xDOZz21??g!YIhy6!
z;x(d#yOT2er(fA3ziB}{IIZUmO36F#`g8D9UMF>KdEy>md=p3r@EBV}w4s;91Jnn}
z;kbFYh%oZHDY3qlA;+wrnHW<s7mf&~Qc#G@+-#-an*iKUn}D0^o+d2TEP!C_oDlUO
z&>k#pX3sV52#K`Jg~7f<QQp@=d$CCXY`%W|S46<Ip%g9NRhY3w1!@n{jXpAG`0gKd
zbPW#HakyWp_Gu!$Kf;(VQ0jk~CF7(-vsQo#wj|qTB)3HjhoF*JMG~^q+8S-syVWDR
zbky?ENL?=4S=vv;(qS!dR;?lGUa1}%0*!;}H5p73b&b{D50VL73hG=zvO(cfbwOB%
zT_z5fL}bP=0almAC-~J=w|EAAGikl-L9u81=tGmv!Y96Fu{kl}hL6>=YfQ;0qJ_9D
zFx4$VWCTns{vqm%5cIzIz4XbFd%z|$BO247yF-FCq?4wWl#qlR>bg&c0GAh0v^w_U
zWa6WdgS473V>>sKq86veWTgVkY9s}y?wav#)@J<(0%P}y7{m>cf=%6xsF{sGSWDLA
z?nroSiHP2R*F=v0ARY$J6Gm<Y*=HV%rbrA;v7=@JLV7B2R9FR2XHa9yZO@jFqP}($
z0<I@eI$(ZZ<J|Ixb!-UjZn1plDMCWl`y)G#y7-Oayj)mi>fo2cB9@M7eTPIl&!Saq
z3ZtDiwCvK#X5ky~a$hwz#U}^7?!sCEep?&LDWM7DOEj;`*(}hrWXuIqPy3|b%oPGc
ztEXr**)~1ezd@~7jkWCcqC>QP&}~?|P5btQhFR`_>lW(9tC5Oc<pf;sqwxka{5ZBh
z?%6V~$$Q6BDJ5AuCJ5#<`SxOWy^t4~g`kh$>_+wU%`<VqakdC{g&pUNK+gT<m=(<@
zcS%xW_C4(s=6;D7TewTXO3Ygcc&)dh6^wyilgCry)pgfnYWy5DAew-d37Nj1`!2<0
zkQbV(rZmJZTfYn+vkMZlxL3P^YKPF!;%?dTHN&pjHCE{SLybXI_ZP7ttg%RH>6jOU
zgFOA&$pte=yizX#vj+_Og)qp{()C_vE6MS#B9Ac<x&0HxdU6C*@oJ0Twe|x{(;zUW
z!T?nZKGXnIP9|kE?FG>oAWstm05_qVkn=`Pp?{F3v3ZJ}$q=CRK+oNa*tcLAsJ0Lo
z>9YzP&D?OkDa+HYSf(=FMw^Hk46UT<-F3N96k%f0^6ss*N3sas!vLFH_Gc%oHAU1S
zLV)?SD|#w*j}lS@5rI7;VvW&gvl0)|NyY1oge9ZRy`~MkUkwm$8+4Ka0F^h8_9f_p
z7c{h<ty51?Nyl-ef@<|b8VPFIq~pYuTNW8vjUBxPB^(9f@gD$S86ix-meh?mTXQ(L
zC50^Rh&)xdTSnIQyoWl}ob>Fqy67)qH6V(De?dHeUOdssfotT{i<F4~v%gcOwMQUp
zr0-e^FRkK0i1c=|2V|1Q52a(eWyn^=e1^gk*Vnh+^P9_<L${5P`3r~<Rey<9CZ_${
zu+(Y*M!Mbl1GA8L!#fUz&)r+@s`C3v-6H1?O8UKyJ0re+ijxZBn~8@X|G0Q;iOA!M
z{GxkXZ6(mkteLHPsP(2{W)#xW(^3lVGpTFFD_*jwu(ak%O$w-;0x41+s46V><o-L@
zvQf?x9%n6#NiLBQ25I{bz*x`H_uK%uXDTUmm`oEBVr-_;gM6tMD7!le?rnQD(R0uB
zmcfaxK(nMyiq={=Df*AluqllDb2aUIHw{WFld`#AW`GElCU7IPHmlLSsdY{>wUktl
z5t2q0&k<?9uI0J#wNm|7Y_Ad7ermXYe6rUx-Ho|s%thpGBa1&_YBo{K`o4(tU{n%y
zHHd)A2AJ=GnNel3dNXMvHsS9REC3K%%2L5TL*Vm$Vh0wVKU$Y$egE%zgfu}y$pyED
zxuM66u%Jq}9{K&~R#!`Zw#m05#WgUYxp_0*^Ond6^=5SW`mzBdn<@_7B3D#gY01O`
zKyPKC!RoVDWsc342Z$Isc0#s{5n*Q3xvG-m?ilN91S`2fai4P9u4c>3R$5<;90Kbh
zfZof77DOhLt`zTbOdOvI4Lo#YYtOFbCz~<#FIzw+Ta1VlPqS0^1WSK8h#AeQuY$Y|
zfcogW+1i5tU@t7WmbW^m1&LUl)Z*vEn%mB``#(P>(H;|P$OOH9$^n9^m9n_?!|#RF
zRf%oYY%HmK`{Q6`Tf^@!6XA)CW?M~#u@S)}8W3Kh9>new^!N`KjFmTNT+A|&f<@t#
zR$0kH1hMvNtS~J))}yJD2vYYAU@jovpYMkYX4!-VM3;L;PbW5hzR^I87+_7;RZ^ls
ztEChaE+|q!(-iLy=ZSzQOzdu*)O*@Z?>m#kneX-kJ-{=6+i^|wFX!yi?~V6GL68X#
z;EnFpPC#4^SrBW#2Pree2#%spUyT0l#)KPkV|SWcj>P4+o`$~~ujZC^9uuU-?^R+d
z{yE5jt><0Mb7^-a1UeQt-7{ZC;J%NzC(x?2l*oj;S&WoGdKF-=-_ozvF2pT&N5-&-
zJZ{)SO!WV~p21|LfrWc&DzWw#(YDePFyB`Ti1nG!m!sG}3%l}SM&X7r7QXMac2#9S
zYjo~C-^I7J_L78w5TVW<%x34At(E9P!aoyfyu;kcv^MK?VZ@`>m`4t}lpZHeqNeKR
zLH{UBm<j<<fjWs-dc>l>44_&bn!3<*+;|D<W-%ce!x0%YZMF)hZne;KS|=++jOqcf
ztVD&dMT3%MrhEN2v#1ak@LY|UY>dPtH=~|uC%0Tcw6*vKBOh!5s61(qvrMRU1`DH9
zoiMyqw`;=<z1yr8=Av}G#XWjR&*>6t+e@2cBGQYMpW_Z$^803$Vw>8k8r81-TM#%b
zYe}_z%OFa@tknpGHlB2n*7{)y;U>}J)*kak6vBrP22mp{t8mr+W&eGm^h8$5QlgN3
zkHJK8+!*X!*ocG^8(r6%kp@AH6kUn!$i^jQGMwF8<Zc{~14%I;v?SGVvUme9h{!++
zwp5`fO}~3=gfun(O_P-!mJPr&(5k%{)dae-`9=jA{Aa%Jq?_pR`my8Hbs4wtz4qmO
zgEbM+|4h{N?flmW)@nhBVE$Sr!rU5!fNkshauU`WW0Deqb5TK+y#zPyB^-|v3CSp|
zF*9C)mWrkzKCuzaOe5{kyW?-L8>W+oHCXgm5X)#&H%h}?wD}x+42RqSwb`sEj0SV3
z@J0h%txP*C%f?f3;ntL}y3~}tlPxTXkoaW8olp~7yL;2_sc3H|5do?alq#M!sp8gs
z|72ZO7t9&UiEuYf_aP0gi|%LV_^b2S^SA$|_XJh5pK3~F)+)3NnohJ3m$|IeEi-8|
zKriNHTE9-V1zrck<46eRDGo(vSyXKAm1Y@4t3~;e^7Yg1zIGL;{)b3KO*KW0LuQx|
zaz%(?Rh->IW-cq=j~AijL>#EsqR}ANbzKp`&K+>;l)q=@TE7VxMC37o$A#=JM>ho$
zX|01=wX8WaP+MZ>mSMxJRVEqQA=QLTvj}cfdyy<eM75jNU+$$6vR_yL-faTe(sAhA
zbB0d#Sw{?jE%A`9a2HWu&yS%*gfJEF5(;qZZiujWKCY}|517=3#Q;u3a7tQ+{mvwK
z?$leHSKPPtRk6;Ku4=blFwZR^8iT}`%Uu9kY_8T*0L}W*xL_9zjV3nhN9kqSZCRZ)
z5Vf{bT}E2_!`(A()~T|IxR5ZSC5=|cox1GQ5|SF*S&a}eS4=UBMv?9^@KM<(oIcCl
z$Pr~)`o-4eey>PCGJ|VFb2-8S;)-x7nESJcW`1yC@aYtE!n67S(s&#?4JANRiRb7<
zYbRjuXOB<QrHhm711?*iup)v<h4N>#cz)#1Ez6s<O{}k9yaY=#IpSGInn~5b-1`sp
z{$19o*{_*(ytBJtBJj7nue7`MisWXCNP1Q`0fUG<hFEGf{bl!wS(vc3Y=|P(cqxLc
zynCja!kL>@r7Xe7)l@Dj#>q<yCqa9bJfr;Lo2;NczuT!V+$tb1HxS!~hDNXw$z524
zaPaE~#rk^f*5ppKk|@J_%3Lgh@NtS62}0PE%uR*RwtBx#idI}@TGs5*XTgZ7nb1H)
zVteRK14zBhx>mbw@M^1(S%YoVAn{i9VclSD!sc-wl~00n<QNeUCrb(U67wrT-3v+i
zB%v8JuAQ<lq^KoiPL>a{2^VmaJef=C0K49&Fg%EhW-;kpd_0~xb{FTjYa>{^=wxZ5
zP9EUdF>}ZZW(or5_m@RbYmJzQMd<)nAiCPoCt8@LGbte9!pNC0JJ;9MBDWfZJU~AL
z=j%<E$}3H&XSdW&ObGB84h{9U0A?n&);1-so(}GWBZ`<AVIeQ$p3`~5dgQ6?ZZ*o}
z%xj6H3m8OX;8>uQaEjEy=?<8ch*X=j-7?_*TV@7HL76s~=NamMpHK@-6VwWSjhpuP
zAiz@@*(dwO$spXQVN{JbU(a=2FT_7nCIwDR(sf}aG+d7P|9t{vCN+&xuNUE|%$uT`
zim18|it9ky(|{vel!yo$(b^&#i~OI3AwrmEujA6vcoYrpx932haL+Vzy>ybH7LzI)
zP}PU)HAkq`NcZgltx7JcY;;|@6K)vaw#o_En&~xj55amX>9wgwOH#tELO$lpMt%G|
zsdirkn6+3}v;I6zY9c)~-!SUdN{_j@k_U6y-!8J>)%HvIVq^@0xy?o_yk=3i22jM<
zxb|;Fx!^(ZPC_j0*^c8WzTVI+tXJvZ;{W~(f-LnvUg#^7m~<^ce1i8_e;xnb2r^lr
zHxFuec|uIk3~>wOx3Q$WS+f+2fA$8W=!%^-y>k~IMGQ;?!E&#|LS*kvjt>EBpDVd|
zLnPQBB9A!;P~WUoD|GU)t*-gm#4DmEW)r?#CVjc)3Xr;Jz|teN$tD$MVEaXj;DGZB
zJ9gAW>=uq>sp#<{*@dz``&3h8MMhjp*L@{{?*avVa>-z_Huumt8TX?4<XR*Ns69gb
zN+yyUj(k777mb&`pM-H|Vzj2gsuMG6odH5vqpB7p0p?We1wC%WNd@3lRvECybPPn`
zYNG<4@AW`dDX1+4v>J(-Q|WlGV=|Y!kop|F;F+ftv-$H2r<Mp+CsHQu$5gB~2or%z
z(WZzZeJtAm_U}L^wdpSI+|Q$7ef%&@uE+K2jbw_F8oHSgFcHvvF9P3PqwldF3@In^
zowfJ~V9JjZ1h`{W1s9lR)WxJvU6!OV>2f4$xUH12w$O;UXU-Ta$ubM*Zb)D59e+c%
zxWIxF4T4)oqSH%|9wS|(>D$d?Vc19)vlf4==;XqQ2dQmE?`e+#S;X)xKC|DP{m=PG
z7(`?sP28Tw%Mg1hnF!4z+C|%U3u~F+83b-tk3nWj^_{GHsS6?LT0|UYX;E+zVIJKi
zboz@=l-6CGdw<bX0Qt*SL#tjK!&N4`UcbiPgGW#HWMbT(`)|ITPagUF2bOS46&tDD
z6zxH?tqoIcv23n=oo$5*)_IO>iRRj06;$e}h@P@bRMmxIMfKFm;Vv8PU*q*{l%!a%
z7#4H$==o|uD=bBrVq(fch2Ccf(DYI?H>)#b+E~eULMg7*bGT9>-IDVC0laCNF3XOC
zG<n=~>?7j)zWan|6Dj*7OXl)rB`aP>qY<MCEV#A1vos(+P4-Uk*q>XHFD`cuC<|de
zS)6Nk%>r#9t3n9DT^}k#G$U;3W^;K`y}n3XzwMhfi>eaGwnPmY{7+qWMmz|kv1b5T
z3{?~-YuzFeRuCr}OQK704Zdgus}9^SM0o>r`Jz5ct*b#G4J;EuqlStJ!c8+3aNinO
ziAKkREv1v;0qwru9l_uv!<3Tb)r{YqQnV}>(zd#PLi2Pzf8W-BVP?}rJXA}wBYG_Z
z;?RJ#+<kY8Uu23nx<-3sWCG_!&S<WfD`8{tBc!85C!d9qMm9%ya9y)r8x_%Sh6Igf
zBfTn28=asLYeo$hw5r0n@(DZ!MB_xPp`IR0_vK};UC0BfZ8x1HCrjaO017*~R!w)B
z$|82ei*rTuwg!+*YCeeA)4=zPCMPKm16MW<&)V;L0(6T20p5=)+;2fH1X?VvfVpVa
zOx0jz@$^)GZ~Z@e2A$wgCj@4d3m0yXzD_c;t9O0=%0oCW(!YqPx_IU-Szo$!)d*It
zsLkJpuIN+<3JbO|R@9}~2^pIlwR_2|t{o>Xxy}q(Ft59vn9-`)U6SP^_YQ)O#StSi
zaI@episUMRnIH+KwP<8^Va7~FCy`pwdl+cYUbPfEjXmimSg(~Jp{cB5@{;KylOaE!
z`0m@UuOJZ_T5OlfQ<Kx0egGvNK~wA;2~{YOmcH)5`SANBOqi>>XP616bF;c4z$vr@
zW?_o@BBm^2hR0UG(sI4atqSqRMJumbfGK=EFVRi~6g5WD@9yGpB9e%Qo^;X>sc42H
zDRC&mS`$MA*sAspCP=knWtKrBrDvDHNp9<8HoIeT0O}(-wk~kekuvH8!UHW^f44N)
za*CpGR3^}Ai|nILV7l;7a^mIGBYD41(ESYJ=uRTmDbf-10mS6o649acXIGM3zMy~B
zxFS0jX3*S{{IGS|T~r*`<Zi1d=Pus1fXp&&*J5P1laQ3=Y_hgdTit`j6S>s$yABK@
z@|YtLW`B(VAbLo2GrMUdJVA<}%__2m2wS+@Mo`M6bw=9OUZN-&v`0X=hzRd4!v8_g
zY=gAk?zzT$nyf8(eNG$bzL*$jb7(Yfx@EG-(1a4R)=v?eJ%S_obqt(|(14*6x%cYv
zn2}9k{Pz7Br>8SEg}q#ePCP`|x)x|w3h;y$#mzj(RD<$r^)9(aJgh!Q#U?CmXi7w8
zxr1hDx2<}ru-B#lz{NTwhW`#sgh}+@wgQu=d!SLVz)G8NU=7<$ll3z0IyNn>GWqAd
zu$JGih%2Ifc9I)_9nIn3ihbgRM{yyDq)()?*BJ7ER$}d3%nWhMs{h8`dP47gVSk?y
z<VIg+F^<s8z?I2MPt^^J=t7d%p9p&G*Fa%iF!@Ln<rZ^X26MN@a=1uZtRpO#Q^HY@
z4glT^F~+?G^^yo^D~MF&AYnaTB#2VFHvN(;9&s=FIJ;m1;FV(#kpTdS5OAg=_FGKO
z`ch0qux&u028MteV=5&qCL{%1XK1gMx+vNdt`Go#MvJ>}%ai@$B!X$^3LFAL{i^!K
zg0?K+E0qs0J*@9PWLCPip1!M^MzMlsp=lV?86@Q5pd_d#%bK~n5uDE2P;omqv|2F<
z<J5vwLzvtBkB3I)GuO^x@ZK_>ic4O+3>+CYjfxV6s*`~?r;7>2#$jUtd%OrSn1Na|
zdDZiuM2Q1Ki=%lnGKmQf@QR>&IJe#l%g{Ra-rba8wz3?wcHW7lcha_ByvA-VAO^9;
zR+uZ7MX;HQ)g&q)-opE`WJ`*V5lw^j_zaDw&@%p~I!64xH+O5$2s{3P%Y?hwD8K)#
z#DjyNSe2PX&YCMX-BhCC&PG%hV~#B4Hb^Uhxfu!a5(sAPb23)lRjKhu-Yq($TRL{s
zC$VRO{P)HTu8u)O9y9C}j;^W9%gJN8j+rZYve*gr-7^FQjM-XxD_NPq^A?o^gsts>
zqv>6KAG*tgBkwM(ip@F5GHo}`&)2tqS}8RX51$ZPOejHSY|?XWgk!?et+kd)Pvy(*
zne*=FfT9yYM4;y+jJq2ZzNb^u09Ij~lxD%J-oJ+-1hl54r4O7mcr1Iy<3&8}wwV>B
za?$5%X&kQMdhZ#4fL6^l(@p%OOeB#I!buVDJ*Ex$d~%Tq)MnFuZ34^Z2_LI_>Vl;I
z$9gv$7dpqqIuwg^;`NCg;Od3XyjF&QOR<cRE&P5)aBIA;J(!KdnvfJ73wzJX1Fg<Y
z<%Jo)lJ$n|7z|*xXS%G2YGlY-7AP@*N+=geu56LY%{Hq$+U1C!00%j-%pp|Gj*0pp
z+Tl4ABviA!A?^(skyFj9r#3H$Nj!kq@<Px0Ix&dIzz)`O(_c{4M65$EtGh;h-kNSE
z7DvKTKq6=;i6r#GWa2LudcOVg1ndR2!X3sPk_DsBODnD)8jnYZM-Z83JTSxih`VK`
zkp@f7$s!Wxxj4CfMC9hZ)Hen3Ob8HYIsHkLXiUsFof%)cuM_>L^=|jxMojwo{@kD4
z>^p;z36HN}L95+o?zKSxSa9n~6Qty}Yn>459HbLZB^@i;Rai9BM;O2isf@}Tl~k$L
zbgf<cW}7tDJ*OisgfhiW_w5lMQtOFsF#(&Jptgckbc?)zqFO|na9BgwNgp|pQF<Un
zyC{3C1zQ!EY~c(^6LQ1jIjo82Jg?4Kw{4^qf?Ym!%c)r+vXaE2nOjzz&k2xrTXsty
z_gIeR=VANI*;P=BG7%*gjgu9%>iIZVS70vN&Q>I^$Ik43K)Xco*ce3QF~hE<qiX|+
zH17=0sB9?SVYPs$VV<?Vh%loFqZEyLry{6LA`Mt4_!`<m9K+wCI2X5Gbl-9oY(4CQ
zBJH1vrm$wA00241Nkl<ZQ9ZLINQjdNyQpZ>t*j`73z7gJ?VTe|UP5pspX$J+5q{~0
zAR$FGMOw2l)fHBlqM#lVL4+Yq$IRST!FS|DghGRCP{)Z^Mb-7BSLS6=t5=*?A1^jP
z*yN$<!$k;l1)e-ncZ<fdG>|Zmic3-JolPU)kQr2G#Kabs0a_gYkal{j2yR#s2&k5x
z+pOxSTSH>9WvgqXwOG7MEy!J77Hea~1uR`ggdRawgT$?!sf@mz4|j@U%80FD$6)S7
z!<rMRAOPrOmKmskP_qiueh9Df>;yssnRUCSB@ryPLmP)Gk#WN13XxjgtlyDaJv?s8
z?pYV264Gj4IsP7yd-KNDAR+?*?8TwOLsYwls-PAo6tDUrDs-nw-7=FXlNTxAR+hCB
z$bIg8PRYMBfVpYShL+47i}@#Ow+*391*y>=jbK>$<T(+pM6_H3UJw#;KQt0yx_K||
zqkx^v1T=_E@NQx7^6wi<m7GM=rZ<yLJoC%|19kR^TeVL8uHq}|#Spr{_P8Tvb-y&%
zbYTI76tRoCt;Bf>6HeKB#59dzAwo2P<e4>9UECd$#>jytV*7N^LoS;~8F5W}h!n*{
zZ=}P>oLUM@v$sqm5y8yeB}3_RbmjGU8?zn=EWO_>0+baFeMr^?7*UHP$B2bcfXR+1
zN1tVcUJ0z2nk!fuysG1~yN^?_vbj%kkgQuyv;_Wa?jFAf%gVA&xV!x0eqI(6rxuZ#
znUz13_)FOTY>tg9OCtVX|LcF@FaE{9h*!SymAHHNF23e#z6S4l*Sm24{{8be9ykiz
zkH2d*>B5_(sHVz<O;I;Zu?a=k1T?E_My8Dt5`V{SU*hIOayN~_Eg-q6TnO#hKAT(p
zH#;}eX1hhCYca8dBn-&}6Niv74TqPn#b|8zU|8QZw}wqtfwTx?BkJ}zDH7wzEhLdP
z%Sujsr%|1=&*&D|mqv>(i;Zl8@EDGmaI~;gA1c;kh`swXFV10O*<rBEkE!?s>M6T1
zQZIIAZsXY6cTH)6_jl7XU3y;?vI$$YdpP-k5)%*X)IxXCE3PJMicsH&_QmS&d+st0
zP*_{CRoKUtx}fcxC_PWJdUUsulPPUH*N2v0m(@`ULc_LRI(J(qVr4u)m6C<T@1dWI
z?JM6?1o2tPzWxyU_BRYyW)b=C|NXzC){1}l5C0*4$M5(Z_%HwEzu^17|NHU9FMbj4
zd*A!6>aYV32!aaz*<6cjD}+zRUO`estFMw(oUBEAh%gbP2>4XMvH`bjPBhmH_m`e2
zYY^s}B`XqSXXcs%h*CFRvU(vs09LISu*$IAjN1u}Ye0Ts0ukyHi3TA=lzzjK6b)Ps
z9@`P$ZKB^mQ#S!%Q>-ox8n^*ZYzk%zV9mj+TOXJ3UP#S?5=|hBx<S+fL=aZ%n)}w-
zvkc~@spjNmxpl0})Z-B%jz#qRyW5B`fwARYyP=5SbekqKS{wa$aqGv^Qg{UM^B_;M
zXSQ@#`8r<O+?!<r-kZj2wGfOfSiA<oo=0oRB4U{-iEzSXfExuO8z;`ePo3kuL-BC}
z1?jSZ^YIB}UB*4@i-R;#cMlP(C}fEk5Dh>Tvm{9b^u{^(y=m@-aXIGY@BRFisk#?>
zJ~sqcMnvBD#y8@PZ+v5Xf7PpAg<t*EU&W98*pCe&a)WVbh`ItQCQ!Ipn{zJM6v4bz
zfzH;Griu05s6DDZQty&*@M!XG+SLEK>5|!&+YdpA*czv~wIg*h?L?ys5VS;S;ATzp
z-zH(ChbeZ=IswhM%zS<<<js9`SX5u|H@Sc`NcU1IsYpr-f`lm2UDDmXbccw52!a9v
zO6SrbjewMNFWoE+3%l=KzrR1<=l%2dyyp*|yAS7{J7>;(X6D>GGpA!!0@|<aR-)(B
z@g)|%@Wb1>wY`jN72jHWc0WR{?EdA`MyxkWP2MHFj^7>4rV_<1;?7*)4J0+Tx|D}1
z@*i32bP{78_@#7yR$r|mN`bG=34OS5S<fU8J~wUL4r(ISbETN3=3Oh-IFyS2(WfkC
zp7Ow&S_f-<!vxtrO=c(cqPr~LnYhMg&rSA~$<u^<Lj_BBs4uuKom2H5=k)9I!T^4y
zA$7-l)bT%Gnq2Xh-k<$IIYr2#+S*xxx7s<f$ou#Pk+4M9lxv8BF3*k~jEE9W)3cvk
zk3g6O{Kw)+kikbwQVY#K0&Oyg{hDbv18lKe4u_5Bz{#liJtoTEK04bO#KoB9JewF<
z@5GJct8QPM8ymt}{Zaq9a|fqK8E<2Tg8lJd$Vjj$4eJ<p#M3o7?gvu|cwDY^L6wBK
z5owViQorrdPhY1N`PJT-M#Q|vw}lnja1a`dTkEg<m`$v!yGfu{;H<UJ@_hPQdrj*n
z?jL&}J7Q)W4D;l-KOW>2Od1QTmBa0a<3H8PyBL3lyHI|^mppQ)z9bZ`&XiP~RBe66
z=H|eu?vps`){j{?WRIc}_@UuE{1mFL)$9euC-mQt>>S{@8kh1Mj5B2G$F?u=(mMQ2
zV>YWbiSN}<w}m0Ks9WZ|MA}Qm5}){(Vs9&Y`)r>>L6a!E+1lTlmR>Q?kaw`ZN`+8l
zrf;juF9%58p=Ph{MP0F=u#X@Bw1LBDu2&?Ef{p^<XA>HI?HR56@20){v3d5pV`mhY
z4GJ8f<EFkHpTlFH1XN+!%sv@wRG6YSjz2aHtDYh1emRPuJO2n}R-1}_Wov5v;_{TF
zB$_F9SRP9yu|ad;w>$&AuPwfXxxNrkK_bSBg75xjIYa#hPTc+FA7`If?}ncT`rl>5
zXyi#-f6-DK$F_;}-#zXvWlI%&#Z}jTRtvL=<5tzUe0jg}DgxQ&sd%$Vfa3Yp=Ab%-
zV*ug0M~5kEEOaAR#w_x%nX`i+N(0t<2#O|DJUJEJDE4VOuNu8vqmNd>LJYR(i5gld
zEOfFhetvFERij1=ZMdkKS(Ez6o94W@I$*?WY!XIMrLIFl`ZLODI@aZbeRQvG)1@eJ
z1{e4!__EpO>7Vm<)7u_XBgfOskwB13;2Hz`%kuS?D^<vj0Mo+dzhmZ*r@6_HT;y4i
z07Macgm5d-eDf9kZ6x-`5Z|xUR_g^Z-UnV82@V9}@?V0+eJEh-OQs6R_uP^~k+4Wa
zZzQdF<BHki{kCl}JPz~s<G2G6k%XLWB1uTSPE~W&8wT~0IK23ubp<gWu(`<=<;P+V
z3SHHZoeKn&iI_fRXO<K>GaqMM%zLd0Y~IqoeFGhPA(rSBFIRnLOyNOSnQG>Vl@Phq
z=v&2XYSFCeEKj#{{-ieVX9}K4^CK}Vo%~)94Zh_=(}j1NM>TllZw(HqDNpoL_<hX!
z;ItwlG%x)-?-)BZd*wbq{Uq~lejT#vymKylhNb!SXG+eig%4qh!6ZAPyO#U}qf$Po
z&7@}4X6vP!DJV?c#eX7muLVX`5i^o|J!Et2w0z-o)$4NmBJenzMbe`=HI*_uJKN~u
zBZwL64`q_{aVxF9Z5USpd+T%^)dc7#M<Rxhdy`YWh=QmlC5f{Hci4IMK%bYnPzk83
z06Dkra1s0GW%S{)=8d&NvrnrSNYM{b&g8{pdF`{Z+Cfm(7lZSItT#w&@Z(s=#!K3H
zgEv{$Q-7zPf@1CUG&>E<BYwpaeyt8Jm*5gnjJI=&^Nw*QE-p0=S(Jylu??$uM&rJJ
z5R+tIhp$Q4F|>iGICsN)9BK$v^PW~Css0=Cg&FSyAI@TviWaYMF6?fzx415qe$zS4
z%)<8XOMyPdR%5kSynJ+6jx`D6cXnL{Qyp3!!&fc~e}};zHM^g6?VR+#qYfzA4F2;<
z8JrX~U7&E3FD!PFm5yX9uRzuN-XlFtk$!6QIi}^RN1!*}^cF@~hRABa&bq31L5Q?m
z)K&Co+;(e(M5kZ(cj7iVL@#mtF$TlJXgjoMi_+&*AKY<*b7?8Qg|zQuZPICJ>yb&m
z>gMBW@wv6_;4$a&1g}~V{1xUSRbZDV#^hk-sisc+(Lh)%WKT&V$wBr*wOi=pUt;Vb
zbv}p5xyHdCND;&RUM+E$>Zdy8Igd{?1v8LSk$pvs>bq=B!aNU1xjh|0#(Tqa47QPu
z-J1fNp%^uZ^6ue%=U!;+g`VhnVfce4$9XISYJBr+iF-?G&Y!HeL5<hzd{YNmb$WVw
zFEB<F*kr6v9VtVLR;jrhDgD@XtGIbBCNl^UKhN3z`1UL7B(^7Wva+et)kul-uaEBg
z6xaHXRz73>A1$604+_chagK2hlmmsPccu=lMtKhr!;ERV)TK9Q;4a^kEyrcF-){IW
zBbK63rx2vZ?ZuGLh|#1uUOD!F1=-Y)VB%+%$=jQ{Mu%?o7%8&98)QAwbm`Aa_FR3^
zJbQ}!K4VC6VGKl4Zn{ltSq2#<r*GwT=jVOMFh1<GSh24vt|l~lbn}v7NZYYYI<?aK
zcu|LwV7~Do+e3*RM-}eRo%HyFrB+tuuiVLWNS7|mCV`5+Q@k7+jvO$#+N;la^1z9}
z!mT_47g{C}FKrS&-?4J=;QPh;@YV(G9`VOxz>ezr-ahcNO_Ob@l9u5L_{zaiucT3v
z$lK^gBERDFup>f~_Ecn=)NMS!+mj0uh;R^p$SEiqLRY7qO2^kv`c@~z%3KQ6h|ufQ
zE>kG@+6VQ!S7WHOFj?%QsrztS-qVTLulB`i8hHw7(Dw7Ib#9qhjo_11-yK!crmM2W
z*E%Auz9+*!s(zE#Ci>%i;xa#wzDg8)^=&7qGc41=R0HZR{Z+uIWOP4U|7Gkn6Xp=h
zAiV8-C*H#E)0-O39&F>-&RoOD;lQ5)J|0f@BJ3y}HGJw4ZULzN>Qi&bp~_eMx%HDy
z_|dxXp{3<_YLQMcsxoW8B77jx_gSHDDS_4?)zpBp-YJy&F`M&wW$ac#ain5Fv%lmF
zl^~DOTXp?H;<yH`g}RY<u0l+Kxy&r1ySRD2D{rDCtL4-8&f`k#z$7JHm|)qu0?2q9
zzcB9?qKAuh`TByhPfF%#$h~G2tW_mI&l>9}_O#@Naj82NMu#jm&w-s6^X|Fz`*q@>
zpqUv>Aak*LPYb2dP@EmZyPjo3(S)n03Nk$EHC7!^?@PN@X}b()pT4bVMuC{6t}4?f
zF-)z#iOpZiKHs^!AEPSL5JiO}>r$kyD!;nU22<YGd;8Mvi=1PVAUtDHnDUZ*3#0d#
zy*WMdet-S=IL+K#$8$x<)GwAYAM!}WF7>UfPT$=>5vk+G4-`LYk)2<Wa4A5IDqy>E
zmCPY?mU6s2QO`oD-KW*)0~Y#1dgb5nt|`1{@4276VC2fDx0JdOFqLt=&NEPLri0@#
z5)w2nK+Zmn_{-r#x8T28PDFx>TQEiXWUs8*!)%J;)3b=#k7<<!4T|z=Q$K{Cmp0jx
zk-f6)C+K4yf4*rg|KUxh@9S*crnk)RkDdtJD=h0@oF!f9B^n=duwK!8ov6J!$BVJ<
zsTej%89M}05hyy#AbJ=w=&@rikO~|&`-{9Mc1V7FKu&;$rO@z1?;|6A)FBp(LK>(w
zJw@{d9&G{(<%vw<j@vSRX)HY77?$Y@o;%Q2?Q*#7=b)F}c4MlP0B6ZJh`w#`s7<(i
z0#az0{^gdDjHkSuIRn$V$mYH@+oydSz&#m?>*rJ0Sd*k)s8_y=oT<dB3>kHBvITqu
zx~JV3Bg`!3>6$`mj|uUqth^N5oye`r$mJh8E0lcmfBgjeZ!>RAqr22$R~p@$8k6oS
zt63a=)_C|aFC$s#I|DsQ3G#d`rHD>>i$@uYG^_+7?H(oV@y+)ZM*F|ZDS-HK4H7q4
z=?8pNtfPj4Oam?J>DisDj)Qz%ZU2fBCx-ChtKSVegNW6C8Cqu*!rpyhkkqDi3*>$<
zT$ekOOpvv^r|Lh3S59$FjYnXPWqqeU*m%iH-C-msKl4ZM<c)AnI3FUmGzTaOs#F~5
zwQe=;)|kR<sQHS{(7r*)PTx<yqF6Nmr|HB}m00xD&!t6aMuvch*NoCB(B$^-u%#8;
zv}s|n#`wpM-IWMD9<OhHz1%*fH%m*lL2&`UkW!)2j!HX9Ey_=ZVFW`m^WJ7akw2sP
zF_A2nQ^NEIPGKu0sOxsas}rJE1q$K&6W3wG!l{fi{%~p`ic20Iwc6T{Z0-i<uVxIu
zcVSIenQMFVP5NC>)eDOu=5OT!uY9^*SLc+br}Skt1c0cSmW?GA{a5GD&(x=*W;A)v
zuZR~pcIj0`v+Df_h3YVJFg>wNXXfv5*6;-#p*~u@Zon08du=S5sv&~o7$=Qza{3IE
zM1UzQcnQ&AHrBui)GtM;%2<E`;|Dl&(N^^gx8#%x+*_7I*^#(a9=N|`5Hc)y-L`+T
zMjGFReEO9qWhY<77SyM$r<b14?3&OJ!qZYjNIq(XeT2PsNX-R}y07IiQHihD{pbnL
zfSJ7+zwKy_5^tD&Xw8yoAtr{4?AiQcljZbdDrne@y9ZxgAX5mUY&gA2YnS>mCE!~P
zgZ-HpWq1sc5q|XMI^!oAl&FJw?l!kC23@mYl~?Omea}uDY$D=&9^<1e6<FVctmJ=j
zG0s@beenLw%QD1Qf3(x=EWhZZjy+9KcAlsCm)n0hEc9K)_NOu04|B#sA{U!K6$jcp
zAs`{J;Q60!YdU>UiNAVwXM#Zql~hSeUOD|4bG&jKQ|y?=?jsdYLuk-UfJb_qnuBqn
ztDeT9mpqxp5K%~6>uIXKeo4xEilEBBVXKD*Ah#}~l6T9>P!&Bh$A;JgQF1xUB(H{F
z^-glU-S?UE`10=Y7Wb8OT#oqjSLPTWTDZkW-LL6AhArV>IxHo|r?(%xU#<bI3^Qi`
zo4lX9iJlA@XBhR~c@q<|y!c0rn<%@tPzj-TqM8lZwX1U+)4rH|XcwjRz{?@-klpZQ
z3kc_fS78$d8EE)^Sonug1~HP88FyG!)ePBi(vDoIN8P5k&PPYf$E-!AUUHjJ+cazH
z`&@Pprj$pL*UFi>N9c8Ai~mYI6hF;=J4~jz=(WN~mRVhOS!BhutrG%smoGhX&sm7Y
z_#{tGecz+`_2Uu(*}p?z*x`Ln{-?{LJU=7m`v|kNeNF|#={q!Kt$OJAdZwTU&gSxU
z?)&6MgvDuiPCtBS-Al`%gSDQP-pIo9X;Jc$-0HfWD08xu(5*yX|HYx*I4h{hx`p+)
zkiKSWwTdzRPN`tHUWqA1CP@C3_k$&U$h4k+dC;FG_<_7w=JHG#7r#QD+$1Y_Ix0N9
z{>nJ_OJCfT9{hJ>Dp`Gvmn~44eMCP>T^Py|`+<f$Y^b#;anQ=T(`xNU!LnH5_YH9$
zwxcwWH(RZ64s6nphIaAZ(q`Y;Mf3i4eZfasnPww1mX8M&1Wc6Ez^s$TLcG=kf{G7+
zHiac9=q;w`l&!M;6^;OD`Ba<*MSY*Lm}y!0@;f)q2)~T-!>g}1QtumHb^g|L-#%r3
z&<-M}Yj}F0Pc+r4?~Ve2Lj^4zs7TmaN92<x`vrJ~?3`Ot@u4A~O8XTH!0wA!gDXGM
zaL!mC%R6WXl^4dQcsXjzxW<aWyTI;&+d>X<I9y{C2h1$6$Lt2ib(wVv6Yj0L!yMML
zg1v0(&h*ggUq(_9vl8d-{ui%&FLIXuxPM<G4DY8^YM#ko6>yo*EPFE;Q@%2#7Iet~
zp6JG&V$=CFT1xmm;$2s4Sg-e)mQga>*qOl{ja|o~3BmY^9r8PmCJx_EF3LH{t_g^t
zRi|E>F4<?%CWpnlnQ*n|gsLae7YRb`N;^k%)G121fDB7&mGVtE1La%t*=-#hCoMkg
zafw7#L5OcoI_1lnR;r%-cKxvW(Es7_paE3iw{w=DnFWu(6-ogkrz6mGjWS`Bw=N-c
zPd=%&U)TX$`4|6?BLU$UIYdkL`f1lho>f8^h|BB{SMsf+vWDV_8>{{y%L;4o3!1j{
zK6;u4yhaW`E2b2Eu20W%-uETwJAj~!uJ7?dtHF9klbmZ2vOpx^vOxyH6T8z+k<jg&
zo~Z3A213@|rqITa*U(3OpV+>C9j^IJR1&wF&H#S-WB}W?sB-0Oh&5H%E$f18!HR7z
z0ODiBf8FZ$RE3-f{N%W$G8oTYM;$UPsh6P1ZN!bgC|py_w(9)RQ9razPu?ojx{i=P
zz&yadLa`;aFQk@PQYTep!&nQ-T!!3kX@N4$24S(5QvHmSs5;AeYGOP&;agYm><ulP
zFaI0g#Tq{?sV=@xhqxQ_VHCmP5YgwYXy5bXhdNl1z-e9-imkj~UQV2s9YzZptZ@iE
zO;sE+9#=?+cwcVA)Y<xQiT99e^_9Ky+i3O~sveM<`_U5&+SP||p*b_N0nn9VJNX~X
z%48{?bq>|!IEiI{#^>@ppY<OCFWN~HaCh|X(gR}Fd1*Z6u$kA$AG$H#3)59_EHF5%
ze?mX$I=eoTyj1YhflfuFY30OBclh!$#z#cGh?4o@Wjr2vBR^v`{dT@lVO&j6a>)4o
z`7Mr!ek=`wHy)rVAMLG5<^eg89RfSEN0p3yHu_KdXW@^oTwMGS9kXJC_7+ob5EiHz
zUK;y4GIrw6OONC<{TE3vv{>EkIi{*NWRK5~25;a0?2C&qeBlsjD`NVS4veE!K?wVf
z)wB@Xo+o7vt{=X%u~eoB05Dk|<R)}skjygIDh)=Xk>bjCi<KBlufR{$59#c#Yg*hk
zx<9rvsd*@`TF3b1!Bd=$yW-M2)|U65w-zotbeyWw96sXEJqeAdU(Ekb#7L>{tZC4p
z7Q64=-sC-7=Y0_S^~wctv4@y}-pX@}9J^Nez7ltxz>mVWtv6sOm?B>0f3hOE6yDtP
zymT>wUbyr)v#-Pr(<4*?M51L~Wjp*~_C8mRIuOJ|Kp%jj(-0OxWPQg_G8e86OX!?p
zDdI751;eIu27El%)GrHbQd%$f-!NvOk>*lL)phvd4W-Y=w_M|gpGplPFr-!-CaGxn
zjvm+*t%nxBdfM`VEt2|xK5?3L^dKPMz(jU9sE<o*(~nA4#FclSIA%%oPs~${F?_r%
z&Vs_bE6E;0{Z<Z6wdTPQ$#z2v3YkYI5&qk4Hu1_04{_8C1?`@zZ{|f{;`q{ekA%V3
zDls%W06+8k&IS`YYo!Veb1Vbl4;DOZFT>nE%#L|kV*S!?5^G-ys6B3}L``fqnJXp2
zQO45gyexGx;s6SYb@L4;`2D^CTQ@8RrW{YBOqi~2PRs_6@|6Bj@Kj95ziDLB)S6gt
zzdJIuUAWrCj7wVp+X-nR)XvKL(Aj$U^R(;mf^vf}P(z$aeC6*;yQ;5RDtm_oupGBi
zkC6=!;e}zS8VA<>Bd_~Brv!^rHasTKobCLj=w?HF+a*Q;m3y9web~lqJu!(X1obnd
zHNO?V+BZb<cBFY_9_ijn0y!+s1M*1TSUSF=U+m7;ViD%#biO98xmPE5Ul%WX1~9M<
zB1fk^^1ce>#-xTvSsyZ#4?pqyK}fdi#`!2@!DpTjdxu}DWB7?vOJfe<PwkBtmrSG!
zMA-~Q=8(wJ%A8Df>d>+h?xqU<{&(;E>qm(tHM0FkDZlBF5ytrlhsm02za$6|o2>-C
z>brJmdhvBd@{Ud}iub2$F5^Ad8-(Ug@yTLUjh~e1$QJSkv=|map!{t(+D(C|MXG$n
zPDEF@{=wX(K76Y1mmyGy1yqjt5hwK8sq4fX)8rKhELIx%aH0gfTkhXrj#G3aB3Ra6
zOa2n~z2ucJVMsc*&NuQa6J4tnYYREo2{AH|eSsg>NDjflRbVfYxpP)(=nFBcp;k(N
z{}VAOdx$I_W5Q22+nhJg@#Vh(r@B9Wx?3j=CR<9Qu^9Yqr$R!n==Ss_X7S{J!espy
zN4UM*CouD?vIk{1E(7!>G!0ExLGN&ME-g^T0l}ho__P#0D}HN|m`83j9~Z&v+5)Xu
zc@AParM%U43(()+q|UquR6nc*fyMNV9(+f=Z0Y#MBpzGUkQTYZ&@iTFxJoj4$I4h=
zDyep<0xe92pB|4My*qPysWbRWIl=bpAZDEA5ss^zM@CY!Pk*?!c$m)f7v$%iHgEce
zO1Y+@3!A;QGs$Z@LpU!8eF&O2UTFA#BRe2VN)yj~a;<mk9Z+~Q@4<;JN<Bs(gcIe!
zRPLrX6_=@m7<^?tSHKaWFnla0IBNQO{Nf15=lYW@0nGehE-1^9%WJp?cfKRBPcBGs
z{h}v#%@-zmU;k9&VR^0Il5xpR-5^6;d95=!`5sYDj1ri!)fr#yo16THQh}lLZ@NI$
zxvdR2+xJwaO;4u#)T`g)qi)(ah?P;Ms9KwwV1;+nXw8j_xINs-`ap6POfL6Aa(f83
zQzAr51fNAZB`q3HG9-<U*3r)Xd_{{!Mc>2ifSoXQz1jGx0MGdeNn)cMhYM-bIEHTt
zVtdSjXH>q>Q`gCR^?fRZM#(!u+vox>;Wi-aA&9JF12aX>wUlKfnf*>LbB04vNgy0C
z;|c5h&a$>be4HG33>C_d!lHc)#@0<<f3Y(c??CoWj=8f(Ue;LedA5=7xfVMsn}Qiu
z-tJ)k;;wwREl_C8iV?97=lU4ff-OpyY1PkWN|bJK*Yxg`Ri2c*d;6UWoe3+~lI8Xm
zH9vg-w8UnjpyTfYy~1B5d`3^H&zK>6qpf~MbWMSAB4qdXL=WSLFj+JGx6J-L>78<!
zrO%?u4O+9ln^*ZYA27n)3Ks=p9E3Xvl(_++el0&vL7=avCT7>LF2(6CNauCXHMKRT
z{?(c7jjGC+Bm~Tto-RyDBOm*KSklCIR7tw`w|4k=O_fj0Br^)L8;3>zDUvy07_9qZ
z$iK?9%lDCUnR(B#nIG-IfDRF<uTvX@^!<gSWQUPZtMi*9jGQ%jl17VBOZgCf?1}RW
z7MGD3hVsl%9;Mm|@3LRK^yWEgZ;m>pirOM^yWg%#+NKR{n^RN8zxT<t)A*j>mcWfM
zCn8OmSDw<~ibY~ikDbiHFHg;{`T&I4_#$FZosWbNw<|3@@_P+%ilQ3)i(gouXG0oC
z*z4I+R`A`Y0nTim0{-<+c*UAvhTz~AF-A#z_X`*iay!s3d%=>uvw#rkCUc18k&%yq
z%~VazsvaHvP=_|#cYEBv4U8oupcIkvb+G&GNldWg8>2ZYb?o#viq_YJc5U;Q>r~KS
zD%`)7F(bv8kzZ&?lVZNNiP`EitWT21<&8{1b3|TF%=*XqK<9xxdaM4+>9TUcFr3Wv
zd6p)cSDhq_;f7&oCB?}16#v022ATG~MLw8%;po6ULzft`N7;cj<UAJ6S;w>fiy<6V
zd4Ca4UQekP8i#Nu+L*0;pw;<Mf^l%I7JpeQ+UmJy6FQBT*YnJ2xwW0>{X$jc&2tK?
zZ#>T)9!p)M4&r-esA{RGceBl@TIo~5U2Mn_S{&7tNt`^UP8bsY(O}{%RZB&K|IkLp
zfe8+N_m(@uc^1P?@KLeuOQTE&FAo!ifoF8Fj1aQog+xnCLG^cLW-DA~rp7G4DgCP-
zMG(&Lkx?Ign`ecMEU2h3x36~hZje&QjWXe3vi>=j3p6Y^m3yEN?-6qzv?w(----jn
z>RzKZ1EN5UNU5u6r7rS0)q8~Fmc(^SZ|vTJ$EyiBmCAhv<Cuu_U%x@V!ZIT{c-SAW
zR9zDyxV)PYY_>9n$p)4Wtqh*pf8iCc2&NWi?=fco+2E;I)EqTHKj!yd65F<^Vy?-E
z0Jq4{fPlDc@;-dbBDYqIl=q^FgUiMkDPEp6=nQ%Om=_R$r0=fEas}lFuKsRb*a~$M
zjML^QNnw73eV4?C1>U=wH!1gr;?g2tknUkQp(oBRvAj#uun{VGNciQcf{2=d3;eJ-
zp?|%{A)TY-1xdQGp6P9NLu$@>u#d{*7UWJAQGUj@RF@Tx<~6f8wL)A<7%zUdnWrlL
z`$fK-dO}JpX5h#fEqbr#qjjItnz=q(zOi3p+EOO(xGA<J$Hq>kg0&hoiR2m955`0e
za%N!n-y0eK@|=tE#;pm#W%X)Tb<*-B**cE7-|(zYz0-}9k+>V*$i%K2A&{WcF&SR{
zdOOr~#c1bmxYl%7eqTyNaz@J~b{;OYZT@fxla)Kj(2C7meu#&ji?5h4vQL9-K}0gD
z`qtZGIozl_6!W^3*hd*mZ{DNaZRJF)Z=gL2HP%zER%CDVPURFuZh9nm=o@R)$1t|s
z-XgGxc9qr@|7pV{H44vP_TwWdyR!EV!}R%nUb|v)iJ%0Pf>Q9^Hz?ZXobQzBQ`KI`
zV;tAAzVHuBy80LzT0>@Ozw)^%_mh<_QONs;ZR$`145|3ursKm0Y%k5(4zLc>4Cfh@
zR1mUrE_0AU1)H?P=F9PmyB8(xP|7tY`Ud}o?_sUn*p<fv{$0;B8ofNWJ89<M?&KZo
z{jQF+kFDL>7@?Dxl~mzbozS?tIUMfj=*XPOsGeIJ@Si(;vI#EwyGz|qoicgzh(wb&
zT3=sR%X)yyeP|%_B%ep|4uMP1YngYSIKICl&GL;?r8aM5XVW;F{-(;5%FjD%ec?9-
zjm^$^BRD(t#M?Tbcv~*)rGO*eLPJBdqU#LVQR|^U9pbIKqtCA@^!k1`;K5l*ZE?NV
zL&<aMT^A{0J|Ff%;dj=Voyy(Y)!i^SEBN^?iIEhwT)xQqhdmA>q4?r+uU<7S5=X0^
zyGGRs8=SlsY~S92h7DB5mvy99fafs8bDHbTviE;vfd&?`EGSYse~cVYrJ^c0+QAp^
zb!tVz#8f-SH;9Vf(N_2VlMl-;DtlI4!f9T&F@3?j$}tDwxU6q5VQ-Ebr=N3)ne&l9
zyZ!K&S97=8aO?4yytB}Yv<btox(&;|z13=*faT=}vq|3<Fe`u8@7T3xKjNPV6SM-8
zE9vP!dvGAs)41WA#`(;DCBQ99&`1aX8&f;4QT}nQam@?C@XhZs!lD)I&a8vA<Q8AK
zRxAC-0&ZWbe933de(?;8)qzhpKn4T?@rO!L9-GtbN;~xW>r(sD^QTS4cF89dBnBG8
z?&qphf~ZR@k370K#OsPj>QR!U*;i#fmB8yYDR&7bieJhcme+oC1x-WFRdG>`wa+xi
zSWUT`9bnM!h$6~l4kCAnB|rv<yUTev)(QEh^*@H28p=&<*@~waKMLTbL6k8%<WyGw
z)N~g*auig?N3+bUV?c50Ua@Gd=5(x0kC<C!p=OS|Dt+nhXVqkQ$BE)1O>&+L2b<?g
zPg}9~6G1^&$?P<VRxsV($^_D{ioA)_>mW`)B|S)A6o_3BCpl-m4a~{-#h0#uelI5O
zA3dNTPu=tTLmD`^hUT->pL_@KP^<Q(K@4w`6TgLGx)rCyQF$4Az+DG6Hn!+JJidE-
zL(uc}FQu$JgFV$Vvwtv28C^!MOE=U@J*JQ>E<~}<T+j6yo~hw;fXC<ED;A#W@yDA8
z#wXBo@NuNMZMHc+tewi$rT<NKA5b>5B<&T0Ni94+!xoBfEp!IGH=+-A<%`ZGOIecX
z(3?v4*JmcO{^C+uk(fCNwy*C4AJ(CIlFF*oNs*K6jmExB@wko3)TFRmrV_78Js&`2
zj>$nkl@q-1N!_pW9evc}!>wwaoLBvU^ehm+&u?Cmi^CdtIabowIk(a+_t&OLPiM3w
zpZ~3Yei~F?a%RHE<MnPQ?`I?4_`<jNU`qacIY|(RnPA_#*<4sZ_Yy%Du#{iS`?LOV
zcI(eIZ_FR&o-H}IL4tvQ#$C~yuqi82Q~#nr44#lt=fE|I37i1EqD#Zv$`z7I+0lG)
zaX0GDboW8yFLL1!ZCp}%Bt)#ZUk7bO=^KhoO^x=n=}xRl@9lh>m}3C)tB!D~a{FE*
zf%hS<LCWcI>B{olw6*s^e+N4RBKkYK?dK0;!l>XgkNcR(%-RkB$r|oSn#8<Av`td5
zi92fR(=cFr5B$FI?f8ZDZtK8d<XXC%7Izrftpr4Xc2F_>5=(`1EX;T?!$^lH7?iQa
zo`>zWRXnOYcq0GUVitA;jF=yfo#`A9+ruVG=vFQq#q`1T{V}7a{nQid{?E^8ssFLE
z+m2)HwRGDwzJBW{{aB8rd}XcYx>+4l>LY=q)~mfiJP^p4>*66HO|=My>c&=rlXq!e
zfU?@Y8`FwE$qVrikH?3j9p|As>#Si~x@p>8k;n|4;h)*rycRM#g_BGNJ#7`(x|ao8
z#5@BX9qe(>j}GYu9}a|lIc&|4UfM{OLBpiZEIFN=Kb?wAPvJ*Jf4{`>nc2X4{FPS~
z4^Gr;TH1zL-v>wAbO^n{xOj?~nm-OMWc#@APyGe|>|}sRVKG~EwLEXwp!c|s?hnuS
zw#Uj;MkAEWwLFY~QgCMB7v7Ok%dF(qv07n`ddl3uyf18Cz9xIYr!f`J<n&Jax1U?~
zcZVq8D`CCkvPg0)2tZ;bDUB~`Bwo02s2&>wEzI7uJI6OH5izKl#A#)E+>A&88dt&?
z$gBC%n4zVb6{)=hwa=lUN|boZi(5M&y6P1J!EqnFa!lvh8DdsW*$JPZozCo?0xT7S
zyn!#Avj+p83Eamj@4p&sku*|~B_<+TP#)F);!Eo6Iedg<722}ULpuQoUeajIEfSnB
z;}U{ieV6_@hVS$MO~?GqNaXk|?8Hoo+4zi+-z}jJsBB5Zh#N4m-qMC&3FdN$|NYy^
za&KZzm=dvQ<IeOPVz#0K`$T#7+LamqIX$TnHc9KphqPW*j;0+CD{Uh(L^&i(pU=Kr
zHpZ(U0cg?Lk4lnU_koZlZ$+ZhMS_&66PK=a+WSm3CY|))5!x+yc6iJ6Z=*T-GWN4O
zb|o*eP3H-?=zzsiZ?JWGzg1Il?otcw8V)f>&x4#Mc1JHYQk&J`*1P}C{YCH4tIB;q
zK+Afy^&at8kC?MH-bXXpKl}N;i;Q<l4d%8ktz<l;DAqfRC>FG4kTi-8q<w<l0bq4H
z5nuj%=}GK+j2R#kuiu`+7*O`_;~T91JZlH+nqj066Z_g183|xOv3nPUQ68lX&mt^Q
zIRdqT__;ubP=-(kHt^-)V%Id}ZaO^XI94MNCP5;8Tx=0-TRP!URqb!|<j}G^mI!DI
z4~z)OulFE#6>FRVegj^N_A75N#xRoy#Shcs?~}|uOz^fv$jJjs!azj0o!Fa3nUafW
z%<YPnK4WVEqNSR5pA#>5O3LDPEqcA}9Wt?iWBe)sv@tfeQdim(`Smgi0a^POJ2@e9
zH{lD5<^E%gT51hw@HsLGgj~$pRGgfFk`li#3l7|?z7$3E%a%l6UJLHwpouGmS2`p~
zZZ<rLS9Ty?Mzk)+13_BDzKeS%KHr>{5KEV^oub?%h(N&XVEHaE42A@Ej%88kWT@xT
zUAyrKCyTtf8Nk~YE8`05i$}qt{Xb2aZ!Y88ui|}Ch~;FP+#u?sz^!KjEk{0Wr(dpa
zw`5pQ(sw%=rWa=*XE49i4$JQ(eZFTUtaHFx0_euHKX4Z=leughS@p<8Ec_SB(~-4w
z#&x?tg5-7IyY$Op17DRN-SFS8L2i>rq^4>{q~nWngXwcow_EVPZ#9fJpW-=#?Qusj
zsB0$#LVq)T{j+QRkil$e*S{O12D#}>YpNk~GandN%ne(9=^0?nH<=TwQr0BdHqvw#
zUQ5nGOY2)(nT`iI5Q9U@Kui`*Pm%Qb55<H&8|hQJTeu9e4?cpNmFZ%+4Tq2k1nsDf
zEcUU2p+u7~iBHeS-9D@;bPHLNkf*rk?TL|_?<|ji!Oyy_0J8(Ml1?DviHA@p=z7q!
z?Ft!2(}IC<ENprzOao@ahBB2b32v2P?>E`y=t>-pCIPw!<ct+1A?AAOS_$f>+i54I
zXpvB>`?vUgX@y7D=t!pyD!)(=v!di{jJ;jN>C)CePFA7!8+~0lPYw|fjocBBHKHeg
zP7*v;Mt>Y)U&UG!9EroE^Lqa85xD;Z-8Q$*5*4>|gq<EE<Iw(}g!Q1&5*@wh>HIzx
zO}`Q|T1(J8vw|f^KMwc(C*Kli1J&A~eB1ZUsT>9vOrRyj0h%=_-jX6r?!~{~Kqd*O
zoM!LiVo$Y)Xj?}xU=#p}=S1HcAlTpkZ}FoYN=hXJUC9uo92s*Y;SuQ3CG*f$=D(JY
zrhTug(g6JrqEfWpAMRx3K~tJc1x&%$Q&9OgdZe;0fNW91a7T1MNgZ^C@TTSaRP-o@
z=&g}6$scyc@b~EdF8rgOkx26~z#T#K%RFZakT{@)Xnpt55BRT*{V!d*3I1**MU!Rr
zpSS=7qidS~H~r9m^Z)Pq|BK6Sj0l`R@(#op$NM!S%-3q@B?TJ)y<BJzvkal==a|4%
zLQ@Qkmi>1E&rUMY^8%U?wdqwS%`p%Hn%hWH7eAWD{%tC5Bydy67wC)+_Af>R14;{C
zozY5!4sS$5TIz2Qsb>NrP>S~Q+F$VS^xS-5Z||4(;?@*>+i|483ZPdBK8++{HI7k}
zTlcD(-i-4|^#ZpEgRX_m7Pj_&HH=J$yX97pu9=vUmcyiTZ3x^F%qIUYY+bG51aot9
z`%X76MW&{vTIT*P>;0db@z$hGS$bRBm=_`tf%)Ou|0Ec|rzo=_utoc?!O=-R|4)%Q
z)}u&)9Frn3O|F0I!v1`D=6m2^8V|WVLM~Qm>`bk;9t8;mqksr`G&f=@uHq^>a&O*3
z?zP{PwEOQ*G)?*f(eZ@f&iQ5J1)92U7v!c3thpW04Bs$CZlKlw@wqAL{OYPtCK%|i
zJ>v|$%7UD_z|txLIS-dw<8@niwXX~zdoJIm{P*7m6u`%lgYPfHnU+H{TGlyP!MA4`
zs598*6#dfOp%2<*5uopKxtnt7skr@JVKjd&<*l$@6uet>d;x7|k~w=W6JdJu-L#YC
z<~al(0@Ju92;6;}D{=8-@loJ9JIgP_r4#DgXw)&w@hT~VcK(3c#kY&$7zp%-&lGv{
zz^jsjh*)k{@ZbT#BS+#FKNeJO(IyQ(ynGAP1t*ZX^pJ^0-I$_ykW0&mfU9Z&M73q^
zTv_Mx^)m>b4025-i8OEp`^p7%(7vh^ysmWYgF0O9K`f(?KsWY8;4$%6t?ieWFl?5q
zj-_SDx>7KX%uT*b+`rpq`lO?5a{NG${p7o<?Mv?s+hjUq2g?=q!LUp4sl}{=$?4~d
zaG9%cC5ik#?A*J38-|dJl;ptmnIiW+K=uN^wcbotII|#+qvxT4gV0)N>w#0B^moH(
zma9+}+X~3tUUCpUlzAC>u)NfEglKcA+Sfqs$Sk%YfJIZb@kC&Jy&HsF3#<i{)k&QW
z2_2v(!Zer@aM*YOU<As7g0r-zd-#{~f-jGj`}$;%ZY`_CE_%F>E{3HL2nrCDls~)h
zjx8CKA4W&K$ypwI?!ZA8n(2WEDC8M(o8>OQPdd#oKKD8^_t&1ur9|MCPcU~IjHeB2
z`IvY)1ag*)9IMjUn_4}ufJh<F+mX{(b*2bmj1I_+28uuibt;oAbIpxfPZ`<2fT2Ae
zxMBObVZ0xFeH~}{gm2m1=@gde?0;DaZyX6CIh9=FX+Pni83g{cQve;?y$WiY?rh6e
znT~w%Oi(w|YkO^ndr3hO(0iz}T;$u^KGYQpS`799o|eF6P+O>ObiTsB{>a|tD~78`
z(<|KDwDuESS9kZY_G21kXO+g$)Cykk3f?g~M!&*u*nt%c)OwJ)?UG55LD-=7t27R#
zI=9+_PGK&$y)H(B;*vTF-Es=?M`d+i-eHrHx&-YR!gEolklqp0?Fb7(%b5!8n5;Ru
z1TaXSeIs23u-Pa5#W2x;$K<kUdR6Mkj}&8tp$#aphMwM46$_#Y$c_X9I0(LnTF<?u
zyj|Boa<pAIv{UC^b#wm%2jK6i9!o8sO7%&%8YWu)iBF~?_k+iq5|PhV1qWW#<Sv?r
zW0T?m%a3|@J2Y}jBz-fM4Eqi^6;&ErI;(3eC^2co><Fx`BFKq1Ym1KMZd(Z+N*ag(
z_@3c0jBp|s(ze}_wtMY+K6m9k)o4g?Bd-At0@6Ak+kP>Pqz^vEgOCAH(ODQ6?Fb)~
zGr){n5ZbdN0lO>uZf4qG6u(<90Y<U7_5$eBPUyF;`Q>1WFbKTrVlm-G9Y@O~xZD)F
zNQ~<*zN(!%o`SYfp<t%Bgtus|a{L=ef0`-s+&fl6_OtvFK%EGzQRZGL+y($HWc1NZ
z`B5jgG(1*Lo*CSBoz?EgeRn$@d@y}H31p@GqY;e@(5brtMM_FrK&0;mk7+CbN9)G|
zOYIOAmg|0{awn5OK=v_?lYCmRA;=MwU(2CaMVDysnkX!s<(3t2%`{fgSOpebXO+*z
zk1+Uz8$yBFHNE3$0RT$G^LIi+Cb8M)i1~Wo8P5c;J%`;3lGp30S0@0u<ngEP;krQY
z9?_r)(SNIq9Ll{G`X_SenDK5rnX6y_6pl||pU+A(<SH6|lF`m+il{S<|EKqok=w~Y
zZG$63QjmS}Q{*xV0YOKy(k%l>fiV4(g*=*8d;!SyARMhRkNidgj(2r|(o@0bCGecf
z7Ca3}I1X|l7vV7tRBiu|<<WI~i9#;Bpfh(CMY7ZpLG4GEi66&#FhW{U!7%P%7@#;y
z9*_G}kjNlvWaa@hwUvL#&aRzq*%Uqin6mQEl+4=X0>V5$KR0NKJn`Q8^X1~FZtKsx
zQPR8D05_HZex>xb-$7B{6_UuDqwAch&xOZrd&WohBa)|T&&0*;AXlWQ)r-OQ+req?
z;J*eA)6f0>v+y8Lx^Jn4!vFkmrUra3z+nQK82$h5B@)FX>Bi>J5pIbAw3mvn6w2kz
G!u}6NVcg09

literal 0
HcmV?d00001

diff --git a/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_15_2.png b/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_15_2.png
new file mode 100644
index 0000000000000000000000000000000000000000..38d8c9538bbd78588ad6076ae6e7ac13c1eaf7f6
GIT binary patch
literal 19209
zcmdVC2UJzrwk?XOEJfv5ijh#IfFKG*l#FFS2_iv2kf4$zBT=%Jg%}VN34#QXoU`Ph
zC^<+5$vK0PGrv!&PQ7#PyYIgCe*3@O_HMhUVZq*OuQlf!WAxrfANz@n^rhW9kL;wN
zpx7;b`GOn;#pZenicJMS{(#^7{`-0te(+mdylx?HqGMrw%S@Z%$}J01LlX-_z1u%q
zX`7krnHY1i@v(8A`uUE9g{ip!JG;@}FJLn<(`Bb;JZ_1L>@dBoY)(Nzb&LFOQ>18w
z9tDMjmH36<uHEw=Z?{psHruoQeLnHw&Vffqk7aIC_I{hGrOgulzCG5^a(-0KSH*AO
z^l6?O#yWnIYU$jS{Ub&k@}+HQQiT$X<*L746z4vBv9WrR%BjLqYX|=puA$Mzdllyg
z{Htj?xV99was}}h>$q}k#*N6FqKn^ypA??j`}OgoVDnBM{J6Qv`3Qa_-aoe+Kdzq*
zCJS)tt;`nuIB+jh;H+MGX=zQ8X7RP)bK6=Bd6kMcQBYjG=_;$QAFbaQ%R)spxBX+T
zs-^Q%bCTxH9Xsw??!ss7y5vS9JN`9IjdqKvX#@4hPU@4Fv%XOtK6GfPhns@pEwh}Q
z-2R__y1g#AC*xI2yv(5V^pCrCh2m3XihfnwH}P2fGCu$Qwtu_R4+>{oaIdPF$tfvb
zzP`gFhnRbFa&!BKhn4%jlUK?N9M-g0S-878+oTci_B-o^&rhipa&2diIsC($JYHE&
z8>d>F3eUE!TTQwDvHuN+*kDzF{O#NC7^1|UA91ofN>+rq4woO-&NAySi+6j-dOtru
zpMiygL$)DWVYzjf-ky0cmQXL#JbcUM&8q%el|0&lBHmS$M&RD>vu>^v>XP!Ap1$yJ
z%b1ZWW73u(JKtfKK$h%HW%yOm)fp3$lv{;AToe;4tFFG<q;40dQ}J4M>NhN}#91uv
z4^(uqU%tpJE-%L@#3(MUEl<)G9n$31sW{XSt1Q2^wwBfFCYY*QP3OXK-er$Mn1t80
z2x;2WDyb#6+C3O0Ehcm4y%!uZ{W4Q!f`si9ulRA-EKlZZgnr*nMTb`g)Q4X=`njlR
zB+N&>_U&&EQw>{^f09N#blE#B&B#hgxeinZB?LLGDc980=r_d&xX}sg(@!_vFwyJz
z^u%_3b$)hrzB9&lc0|83FIP@po>5%G{=4%5Hq}TTgT|rCGo9C}1BJajJsHH8MicV6
z_3N3h1PC-R&R0jfy6#fC#C)WeiNSY$Z9d=G?XZY_M&?A;D2?~*Xj66Sotk$gohSJD
zTP|~!mX>~gvIBemHZi|bPft&0vLl!2^y$iNrB5f@GR=$X>gr;zCw;tS^NQ(8MWk$q
zD@RdZU*GGIhL|^JI_?$bt*tChGchxlRJ`UWJfvCi#w1TK%W7O-Lj$KE#bMb>)0M+v
zd5&k_3F%X2;ngC`6&J+Bj%a9T$Y+{qPrEy<6y{CTN68OFMFetEvnq09)dwcBCwcSN
zmSQ-HUgJmk!s4QDLf%r@ExU5>(_EW2ZDQi#X~0L(JFVQJv+3I_pLF}8e3Trcd8fm?
z@5Aj>UyH3LJKEcv)>j8`O3(1<*Z&$E9E_dCa+@4Ge*DVfRR0xI(?pxGq~eMv`%X-0
zC1~X5eV?A@vTnQU;wxw!|58gjNW_RfMZ5e!9rmzya8MWfV-)Bxr))k@;cHL-tb8%^
zq`Y}#x>0NR(9j(hZCU|~FK)DgJB32UZEdp<dNgd<=MmGnv6jv3gVjOoQ_ZQVsmGq@
z(kLq{msM4H2L%NcnmIW+8Fl0+*E(Ady3>nJd~vdOaIjn+Pd6BCjPnR(y5e`I*o{`e
zVJX#WqAe;lmAfH9L*vaElSV4h6`ZFZs9CCgG#sP0)7#4g2wE!|8pbxptHne{GA}RD
z3EPtQF0D*grx~^K9@fMfpU~FPImyg?8Lw^peH$gs`%KgBbM08BlNxzBRRMw#?~Pic
z?H8xMX50<oHR*_*>MtL~R#-Lf%e5L;`TY4a3lozVt77a6i(6M`k&0$}-9<<0u8KB%
zc(}bG<Pz1g(H>fXP!~Ge%Tz+&?puw24Rc&w(yNb@-J|Tdwy0}4^yN1yk%hO8OT#Bl
ztLI!N55iR~Bp+HF@vHX8C$?gDdL`^iVRBd3O>J%M;l9#Wi-Dy&AOF~4JKLZn$=ahG
zBiJKZ-pV&4rh9^6(}K-b(|#6>Jo^Wu0+u78?D;F5qv~n;4Hgq^nfApUj?3dJ`Hs1(
zw(VJ#mQ#JDLcOjW7poMJVbcs-<TQ)j6z<*2`2FF-+0qk1L)nvg@(+L5&7d|U;xRQY
z>Ow{DZE<U`=han_D4j~b(#}o|dWV^pWnQcio3{LL2Or=k;*b?C<@@fWLiA7}m1sTA
zS&&KK_uk^snoIPSLsyUrUNWrCwVG^f0FDuEOu{4e=7-JBfBZ2-)(r28JrQ{C9B!{J
z(|mCMKmPI7({sPWV!zK^ROYh-r#)O;Ty(1Z^+Xxk{rSzW;fTj+<aewEJwnpd!x^0V
z{yk}Ms3FE@b<`jsSMk+}tBTVDm6ih)hrQS})VXvjWJ1KA&)^OuJ&y3`hqh;18>}pR
zpKHy<UD>XDFXp-T?WVXK9-7-`N~;~Kg-5atTyIu&<5Y+Y%PHPkvHIck(Det$FS*r!
z2|hpGB)?Hk<jgj0@?@3}z0X>TxNnG2Vn@=J3=9m^|C$;_=d^Nn^;;p;p5CcZ(Sz(q
zxpZ8MTo3n}=Bi~|P0h_UX58(anXVQ!K+PCwjC+Ga?Q7zVzeSmJ<}JoQd-m*=kB^UW
zX8(cZWUO_JY>3!X{EoqLqD@&{U7alD>`;iieoL};v`p~txy;B<^2_s+O3pi}Y2G`m
zt=Oz0h0l$iX)}Fz-q$;{x%v9@!(3w7R^zo%@)4e|UL6;I>h7*|;ldwt<E;_cSpK$f
zo9Tf)P3HOQpY3X%bCjVB<1ACt(FIOAus_8@#3&^guLr5bfBJO6#>R&FUbHMS*W%KW
zx0n#Z8~ejA-<2hK^+dqy*VntQQ&ANwC%f#SP4;9}LQPCX?Fm3#U}0g2mJNA?tQeh`
z_^bHAEO!z>pC><^U}73*F$fq%5Z53e_a8kP(~_b)`o7gz9;-TAa@;*qDM4LATwM0%
z&6|O({ei}UqT9x8@-v#37Z!$sUFd2*I#X^eN%u$Rk*{enIXP_gmHz%TsCWau2abuO
zs--!p#jEnWSn5^>Dp**gz6%MN{a#ETqu&rs)&e&>CV!D_l}gOf*WApk9*NLCq5!!f
zqF`BlKbAQi!9t2oVKPb%^U0GE9UUEP65A*!+8V<8E2uS}W#!^P4$lnNspQ(Gb=Z!c
z7MHtz{dKfbLj3F3huZC@D?QjMPIM&Rdf0(~3$=0-4Xv+DuFq5nPDUctAQQfKsya2_
z*_NB_ll8smXc{uj>{ts|b=Kp@Kawnf&`i0ycv(WCHX(m)BvE(%$%#N2JO~nNh~;Rb
z-dJ<eoaL1_ybV2{pDDztq}fwC{N}jWe=tDEM$RPX`y&VM56H_jMXJ{AmMWQMVTH+(
z#oR_;ui4Fw$*wNVK3mCsRu<;GRN>3@HQy<JbqV{QX3)g8e0M+aRaD3&cm0kW8;{Vn
z)#XtX<0gbcL%NBEQS19FDLR$4CxYyIqO&RXAC{-4pzt9};L4$2ANg`_vNPdU;iD)V
z;W;^jlooD8TkhJ@sQ#C?4~}r(dE2Dnq=rI90&&`KvU7bcJz60qe0d`Ksz-g6r2#p%
zosP?Kc}pX#7cX3hy#4v<qRwGq+f-C=HU!;Ocaf#Y<mBX;1|<!>g>T*DvJ?61j<e&f
zO7!;M9-O=u`F8Jdu}H-@m8|u(<?+@GL_IYP%|^z-swp7BjEx4~>)Aol0eQ*nSA&Eo
zq--3DCggn{!{%Qb;?)E@RV_#AU%I=ylVpcAFqofc-*ZvTV`+9Y0+n;*^FfVkzzbx>
zDgy+Y)(%P3K4Z^|JaAfNXgDHBu`|y>0jF=-g+4;cS1$ELECriz8OoBN#dPJF*7af@
ziy;|uMr`}Nlo-T!?AU>iPj{!Hq^v{yr#T{dThv`SB4`!MbTu%RO)V>fK@^ac8j!_y
zX*j~N-Lfg_M!Ip<Y~zhIoysFF)*Z;)@z_Cwu7U?opFXXv3Auzn_z9W6yz0*vOcot^
z@;$pjM^0v-)0z!QERx=*YLH-eR+tsjTHhJ24I^pBvhK>6VI0wV>rMw<4N=`H5>X?8
zb`!EdAlj}qxI5b9uZFzBbIqDd2&dUk`>ygCivIlb*`sHSuLTHL&U8JbIw>TiiYy?f
zqM{OP7aOA(7mYlxk572|`0<ycCON@QtD_owG(Ud)I1{wKa;e;hz3KUxj>zG<h*tNh
zmx8nNMMq8QAMHLm(`u3*g%h5&IPhk6s+@h$PUcy;HkZ%z-Fnuu5)w~;|NVDQe}8*r
zlQW+C=Ty{%%RZW45u=!h2Al)%Xd(&izyY1%%eym@UFk(hY54N;_@$?o%YR`QpyB%~
zQg3*#RQmDA_m_F4p<Iz;A<bW8AuAJ!Kg$f@KTVFDG4_`Z5C}uH9a<a=YAShoT(aLz
z{?8ZW;_TYaw_B@C<(Ah+5duSDeg02`ezR=*+JQ?7_zm+RJMwA@N@ftjf2SU24_gg=
z`32+$j7HKU6<d#g6OzGJY01-l{t2?}(|%W~Mh&+f`yWNY6;SV!Nq7YC-96W&YX01h
zTkjo*W>KVexwlVlE)G7+FTebT|MLhXtNyL)!~U_KHYkH>Fb)zk7neLK>G8R9D&z_V
zP4Nv#dQa1Y>FMbe64cvES!54|+v6@XN;I5S!z$mL86|kT^}P}Gkt5Lqm2V9CN?wk%
zEq?4U2G_uz#Q;)F&Ce?#`FXRcsdVwO2Tmp7F_6sUabA;K4DtU*xOAjg+OuP}6GB*U
zvCM}(fq*RF0R>xI+Xubsj*Hrs!?hPh*A_wo?We@a`9cX?ZXHhtegFWMyLt2NPe1+S
z`QpXfgX;G__*!pzM`&b*D+=d8RX_~#!eyZ7Yrv1QwUX?JK}WMg^C-zNSA#_Kn-hbJ
z+-M0vyyDB*Z<0RQ+Z*!s?ax?gJhB?{iV~2q<j90-+QtU4)rUzQq!qN10!7Ibuo!v^
z>JXz>7w!jy>`)O#FAYAU{c=tw#A4%=s8)bvjkl&Zu_olai@T8?eaW5fh_J0iaC%aU
zVVLJ0Cl^8xyL65nJ7zIAro23BlE2z`Yc;*oF=jMAJN|MjAdRe*)qDBr4|`oI@l>WS
zLqi#aSH2a3Yxo%Fd>2!Ql=)52YV6AmliV;6txgeR5>1Hnxb%+bZIlP@EYFSm@faw+
z%)3NTAFxoIS~j6&vS-iU-@JLV#0Z<3*Po9jmuGL&ZdQJpoY`4iixmgrmtTGvX-!X1
zw;g6&&QQ{DAX&1u;K3H~Tg%he6VLA5yZ7P}9cql+`r2wknt>7{BctBTPz{Yhbt$ge
zrB%hpb>nW;`yYO0w_aD9nw@R<W5+)3TOa>8S4{Y%L1S#GwHj_r+cileAIy>_Ua}U2
zlgDAnV)VgF_JKbIfkc-h%;iN}m2(`D&CMu&5_PADSJ~j`XRL;2c4yEN0zL7E@akFL
zLYhBChB+C*me1R7<$Fd%M69f=&=d`5`Byt7Rx(kUXe>8uE*6)Nlzat_y}E=@*lBN9
zqLac;V>e}n*M&<u=ZyDt_bPQYOVazGlDFiGIIY<SZ}-plPeOs3<GK_U5y8sJTF#=m
zy1LrmeYbl(=<wmgWfc`UV@Zp`F1+j}EkZ(!;!J|_;V+hTrl;OJ1^D_#fq$|bUmq-N
zlAM~#Fzw__dCs}>>sU()7RAe_$=PS~t5>IADSUHCv9T>3P+n+m8~j>ae5s@3Ag8Xb
z?&!+m?@o_$Av^T;%7-T5(!AA_tvpPZn7Be-<OD9dRZh;=mzPWC<>jqA#nzCAYMbbM
zp1Y9w-sWD(K>w@BEFl~A!#A2op8!M`POkR5o{nmIoGKOKf3CxqZBUcde(d6Wx4!-J
z#O$H@uho)ME9?2?hg-yao&DeR_9V?UJ>IOkV&}xKnUb9B85pSE)%?t9@<Ghg%rb@(
zCyt6U@$+Y1c+VbHAPJ67Q}n~ut^4^J&x9uJaJcKd+S5&cy{j1^<}&{1(W3)=yvL4x
zyk&Ud!i5K#=g*&KH=JD8{T9{nwjhaa?ZF-!hx11c^6~!sbHOj?ajl&WZESO2&Qnnf
z%mI(gB>}XvTNpw-aTvofq@=a`mN`}Ax`$iG;;l_^*wtW7Ybs{zTd^EEIE2n_aUDkQ
zRyktsU8+7;{DbIRmhdDIBZiu(-4|P@t1>$iz9ga8C7Gm`tsdV=dn2>%rd>xg6}4k$
zhtoQEdQ;pDrJ?5Z4%>1OE5wnr+iv~U*TUzYtIJc;9&KLvZMj)xRhL@83Z->!*OMnt
zoZZGda<d)Ae7SUY(jNU1nN~O{c1v?L;d!$Aa(2^W6s`7d>ktXAliS=*4>vtlmE{rM
zNppj-Kxf^TJ7i1ccyQo0Q625?JUv~wG?H2Hm!xd#$@lK7c=NbJSqpti`q#w+0#<7C
z6w3zHoq1xh*Kbw-s$1oMu2_BLNRn2m$GPGvU#?<L&yQ=45n*Azxpwn@;MhMcuVpN$
zR#j7@)|~ZtzkcJ$^mpfk>Vf?tR!!&hDxBEu)e5q=QrwSmseBzX8WI9@zH{eJIWx25
z&0Dqv<gJzmOzmBpieA~SQRM0F&@1RQ)S8)jA}W!y|G4p3`yS2mPpz4~2F>|1^WH*+
z7wuXbM69He3tKZ6g`0c?`+L9Lm>16)I{oz-r+RFOyoeYhUrxYLffe&|c@eXqQ4hbV
zy&OzMQ=aaUPINqZk8*?<G}v3$=?yI6yIg*mZ0_E%XO!JB{uwUx<!At{##J_(cws}2
z{Q3288UNFv&34TU>)%=CMj3hgHLtx*W1$aOo9FX>()9QlpJiF`#cIo>aC=wrykime
z`{(AD!O#TmeR~UpL9<r1S@F+B2^i}+IQPyqH{*{3;fZ$-`sj)8aq3#D>c?-oG$xvb
z+{-Tp-Ol&-5m|0NX8*Lksi{e%I_<h9`%>>@Vsjl6ef2RTMG+B^xpxcJ)oGP=t?FxQ
zZt3as&Y~*Yv@8yd>b<i5&4%4Z-H<LYnfiMbH8*em;8%G$StqV3*WA9$yakTr79lOx
zp2;ttHK(<5`tGK-7@iyqIffcx^IoahT&=#-X4u2uX<F5X^<(w9cn{m>85vy%4GcFW
z1o_CnVAGB5|1&%_Vw0rrjVI-c@!7<p`sJ$jmT^G2a{w^>irgIB3yK*-upnxGm`BX1
z?sQ1Bs+qBLkG>`6clOi1PegrM0-prw5v_!Hb*mjgMv}4RWQUshV3p<VlAc8nN_K<d
z?uI|kIWYDd3WqLv7-d2w_0G!<hk3Q->?#7WzyjU@6=m?XJ4cCQZK74uqGLibPP?7o
zO_5>ix+B?Ylf+pFNeV<g)W~bgGen(*kXr*tVW{C97#I&RCrTwve`<QVj?}Wr{Pm7J
zh$pka3@X`Haqr%}BLV?XjUFKNp3X@CMHy%%1m}0;+6^wYNx0Jq&lGH-x&yf+S~(^B
z2(KaY@JCN~@mEYO#aI0KWC<xI0>+CM|JY5#CxbXjN(e$BKdBfSiOSCku-ael)6{O=
z8TZH5;_3j^9e)<e#uVMFl?XFO2QV&DhznnL7rGdr@{_fjfs7G}8y)!){1Di%|5szk
zJ+^}Z)~TAsd%&3a4RuRB7=Z#(i`?lGfF&ZK0{ihAsRRlEaw({)`rp2NI~DZCp3Cmu
zy*topzUJDci!2QtcK|~;0@1SYHMU|{V|~eoh^5buo;fKZqRyW4?W4A+$b{PlqPTrk
zc-E5$RuE~2zjYUWP1b%!(9`l#if*<3(#)_eB(;Vl%}bzCLxgLstgeF1o^mmI4m=F}
zkqt<~ef!goM(tS*c?WH4L&eF1;*F3{j_9>z+y!DUc@?3WY39do9v+{+X3JyT#@AT^
ztx=I6!UG4w1t)XlfdCsI3)2*7>FDS~_y97I!>-R(a8=&{tW~6EOW%ORrb&>EBJo&c
z1-U^k2oF(+zLt~#$+r`-dsJiGjaDBUJG&Uj6TpAw&5hXbJL_3u&naDQNl8h0GC+-s
z0@U5p%bv?7j=e4Hq>Ix5wjfF|xP2J90g<3a5)u+-e0{orKX{_S7bnh?d;LMhxVb=q
zd46F^k;XP8WuFH{36XJxrz^NTLV<w=V#|f;>?e|H*wr0}lb=yNe4Wj9V$ekTy5*C6
zLBC>bK9KT>*~v}~BBDVPPqmrWnVo2F?QS0Nq4TKGNuj=HxSZ@NOfbH27X(B_F|kLS
zoSgN+=eN690z>+MPhz`p^6{uR6U;(FIVGLPaRLeF1wl_Z_!%70)5|N&y2IuY^mduT
zN4qV-^vgS{yng@l!UfQ!MH~w2>b{>60cokbziA6MNGq069F(<&Kt!x?6qMkLYjoAZ
z^Zr@Zlkq6a?Y5(FDv38A5d8qc*<5mRBlt8#SwBwgrxUsJ>P9WejMaBj6PU&beKFnl
z;K2hM>A1jA5^_FlYBTlnSEB%eiCUnTpdM!spBY}#ItlSb-KL*9ccJImQ&-n|tP;=7
zg8SuFHeiw)YCev0L&jZAf+P1aNo9<UtFkmBJy67}8apN@=L>G>0an*v{|XEZj@k(E
zbDGu;l(f$`$R5||SDqty9-RcU>vIEdOxBFTaoYJ+={dC?KiYM;Cvgk{da_eHWZ2oE
zn!;PFTd0HzHt->Z+eRS&hepr8wxr^J^s(bT`}S8fRX*LCrl2_MJJ8#EoQjHyV1GzR
zUheK!1MBW?xv1?bi>+K+nZ9as?_No9@t}#x#(~!M1I|K~0WC3A2wFQ*MU7;IgjiD8
zJ9%P*9GvwX1lPc?m|0mRTUuJs_Tj9mtCNSUchNE8AqnI7A9>$gfw}~t(8_YZs`i4+
zF&v=(vUj#YJN{;qZ0z-jHhB;*fZ+Xy4-?KzE#Ua!_ig&Mp-%w+uZ4*1CUi_(Ts+M4
z04{FYS9>n^-uK(P>4dH$)a4+ce@)d3!P!HS5$XI6g<3J)xV=8x+GM;X<&vZiTf=;(
zQ-X-YlBby&-ru#^=fDVLkZ7E;k-7-?kA;OidbJFYc<&&2EV`|OH7leWtL;9*efid{
zTSTXTf-gGWoD|~uigf=DW{lPM0#1L;G>;H-J=C2jBp{%Iu&tcu1<j~Mw<XoAUjipz
z7JzH1+G#BWGX22?huXS2ufRa#{6G(n9)Cd)Ygx-n_Ifr)T{@T-Cbv-VJu9D+34#EI
zmEO5$kJWWLNI$*B?t^x3ND0A;5V6qS-u@bxjY8~oS^01&mT${auYV<sc~0{3<;U`q
z+i`#K#%${{?<#f7GmKqJQ%*IZ*6`f<@^kJ~87sMETr3PZFxqK-ZJV(Z2(g5;^m`3A
z8vdU86}&qmE7P#-lGjOvzTshCT@wqW`oP@AOeU$<Z;{{(niFq%ghB_AMfl3U;Wgq$
zPHl*kJzi~uE=d3A=;#WbjDn)X*)Gz--kxkI6h)VQ4h{|^IEB1)e(MQ;At9RMcDA+>
zS&6zw@mgjhwty)Ph)R_ZW|90AQD6`q?-myBE$?@O>Jg2_o%>F@gx>5L@u2OasQIK`
z{IL5-C|G?A)S~z-a;c1`xOo=_%ki%YI4`#2X-$N57d<~}fZN@<d$$(@BLf2;&8sY+
z2#G6K8i2JE27{c`!M^JQtUIF4blTS`SQE8FiQngdzW5P7)3-nDp?N)-1->$%JU5n7
zJ<_5Zbh`T*UUIf3V}+TX{6ksn-`bl0e!~B1)%wu$Qc>Jbj-GhTe%gfK#DEuD@3SV6
z#_j*2$^4&Q=6~Ot{y%-&H#19$g2N+j3$;_cdva@9c8EjeXn3@du>Xtv^B-H{|EC}O
zS10|y<!z@9j!X|$3mzxYBEv)-BQx07cL$XUa;`U#ceZTbbJ>H)IuO@Jli%|gedTs`
zb}mJhfBEtyl8t<DFA5o2l+4bL9+fpUDb0>F)PX2JGe3hXQj|Ych%A17Gy-R41i;V(
zmD6Ey_nEf0U|k1n1fLybZ|wT;kh~pz=4d3m5#*Mh-d@U_%H!@r@1V<~8?O8{-6Tk4
zv2ULmH1N-v<aFQHR{xw?lVdYI)9pe(5}!RmB#rTM2<Bp|kQgs^RDF7~uK{^)cDU}6
z2RD*hoO-TGUBuNUNE%bF8fp4oyoSwh@pO@LVG<spS(c**2GQ}Pppcf06|P52C;+`q
zpQ<h`mDSPFdHEhs8;ri=NdbYcw`?Hazc+68n=}$xUopRU@!~KZO%4gKKJnK1+;1N#
zp;xeeTMmWrz{vQyn>Z`@B$MW3AjHjL$zGj1f1cKxT`lVxV$A&I`@2(p;@Y~ptbBaU
zw`>54-RMOFCyn;d@V$j<SB{llU!8P93s9WkUa;(wrgXD-ikXp^gaa)5aB<U2tTJvn
zE+WuYg50{av>QneXj|(+{8vf3{aM?n9HNQpnW8>rZ`KnhE}>LW9ylc<Bl9AZvQCW7
zcIX#$-WAZhwlU^1>$|+0R-oQdn33@!xPp%t+zb@Vm%+^1>?U(na_%K5#;X#Cf|9z;
z#k+U!F7-TPug`N>K4wJN^3=>sMamfgWVvefQB~}$3>IcgTg3y#1<Ese-Y+Lff~1De
z!<3{K|FGub;0fSwvLRf$RYPdnf6cKm4_y4>{2hv#mR2YdFO7Hcr%#b6=8GNSXaERU
zjlD+mN>gJF#(}cJ!t+GI@1P~uJ7qfT`xE*?(KurIKuN%#buWH7zn`AoMAWPVqHjao
zjWf#&#q>_9UaU%ra3G{vPpTWid;yRqpP-&Q6)-`r<C%K9GB|I*&tNKG&@iQ?Z*VXI
zU4s~`?+BV)!L7q6K^`6+GiW>zsTRn-5&UGF-b(87Wm7&TOHd^0efvUmgB%oPWo4sb
zNg=&{yeyv2n$*t3*z3+qUP|gU3*UPQxaTqHP%S#-00<2t<rV0-q(>`sPiD&xyJQp<
zy_e?3Zw$Rh{*8RFW&1$3F<Q7b-Xt7s!XYiA(8<M~%LR_b<*T|w**s=GY^T42kX3na
z=u4ExD}h3bCX`U#C}g~;4fY^NW6&PS7Al`aLxzNg2kEcV@|hN>2;fBpmB<82fTC!f
zT@36e4YXCyF4BTSt1>o>+<u!X4n)q%RQZu#Z{9~aj6n6l8PkLGLfTX?s{Ativ3t@u
zf7L4D5&CTuZC8?ZT7vyi@7)^=6rzECfe(jRlbUq^HvXpQBhp!&tbdDF-WNQn80^jF
zuN8)!uBssM<H`S-tv<81w$>A-o<!F=JKRE-zHBTNW1yh5bhXg5E9pNGE!xi@{%!`|
zL80{QHuVU4Si^91)THXw^^K0k;7E+1<)na>!Ey`f>Tbmg+Z6F6_TN7r^gCk`IFZpy
zR!7q4Ybt545wRS?>=2rErdHB;alvV71FBijjQs}=Fb#jSAC`}h7WXjDomIpMs<9a+
zpL|9s_M8I>E=c8hk5KdhPb(*1sC5=@LT$BB<Rw=b$NJ6#$`PTQK@Z4dF0=mpqg^$L
zw+j7_`~$Br)&1;uURbN-AInYX)it17@Q$!g^;}y52J1eO6FM=GJTIfGqZ5r+ETPpf
zUOrME6#_TK3r^tC`!QA82lY)%OdxaJ;C&OvFom~JXiFpw$XJgylJ1|G6#<cND_9z7
zMj&_+%z>~atS{57pQaHlry7=S@+NCM5Y#DSWdCa(Gy`&yw@?0vU&o*Pe6k)&y}1=J
zjlGpRiR-AMpWTGh<>l?oB+gmH$G9JVl2P0rBXfe0ahN1(L>VDqKp&Sq2t@_x=!n`x
zp2u1-_8fU+5Jy&!WFp`8qq`2U6ytw@e1xer79|&*GIC-O@mAM6-u||QR>Z!U<pchF
zKl$x-yGR7|Xrit_Q|YS{0AU9Ff8Ri-RjU!%;?=B59eJckSS*a-Eos#I=QFa<L(zXv
zP5=<@dB9r2-wDsc9_yiTR#iowIwS7(c(5B<U@&NHKrW<N0lT?aI{R<Gks^jI`j;1a
zhhi~Capo*_{<+w6Bj}>%B63JsO95Zm*p;2LDb*sx#>E02iE4v>kjH8tDM<wlq1T;l
zhrR?8gV$_1d;Sm_c{LE;i5zqB(j`HfRT7XHwWNBJ9*MfctOEKNSAj`u(fWuA6t)v!
zB~`O)?&)gqC)L(;lO{CY)4&`UPn;M<AKpxie9&7dX2sw&9Hcb(JWi`-$RLZ#K_8e!
zBX0-^<r;$DKY&yN(;L`%61os}W@hTH64!_V!X_Yq5`SghhkE3M9yYqLUs{y$oBNy*
z*PR8@rHJ7H6$7f!PAV!Tu$_P-f9>nZdN2grnVL(aYf2z}(IMPh3{E!Ds-hL+M#lWf
zg>Iajd!hz=5^=SmPbm>`HK=i-J=@E};~g|8e!=5-QP=yYX)d-nHcPOT!3<%naR507
zF-xlV-@7eb&(F^fvR0MS*4lal|Mosl4x#qNq@A+_01pP*Nu$-9!Y*h#6G=dL_C!|s
z-|s$0tQxF5JPM>6jV|f6d`AZWBNqGyWaO@u7%o*{gj8z@GUB+<z5Vm2PebrYyyOr=
z8-|$WQl`nr8ILE=$vA;rL!yyEGn63;$H{iCMVIgth&)ng`sVT??v4@HABDArvh#+%
zgFX-B-sxCMNWb}xtK4^cE<`wQ<mGm4>Jd~~S+stwA3z@tNi!dmE)T|=?<e;p@0=xu
zF=178Hg<OQRkT6>dg{-37wXP1qLIyXl^kujZm#bkiQALfM<G%W79~>eAs1Gsu$?}A
z1<ry-;rX`SR8w;0?CjXWfz|1t^?Kys2EZU9#}FfdA@7@>o*r(?k!yfB1`yX6z2G#U
zFcri#A3l7TW;v?3`_Qjv;nMNs5cAMVX(1{LaNv~>A3iYtL}J(Ul8?h}AJ`n@R5Rts
zcB73CA`j<}MI}#@=LHeGrXY@bRO*Qb`9-91iq1c4hR<y91^o3tR}dbQQ_i9+kUTCb
zDvJKNKF;ICtak6yH~RYf!`>KYMQLpzwa3|1FIXL@hqx`_hhX95RV38`XM>uaUSnw7
z<JBtyDbUPU3-cs*ToNQnIr}g8v%9;SMDw|G=eXgUJZI7b4lTU4&`XR!ER1A{rv{VH
z1^mwlQ>AMhPN|Sir*$<fH(Twz_|>aQI3$JSJ*EeIs7D~RoD>uktd1(fVp_r=Wp6T#
z&V}Bm$9suLl#}z+O4@J7KKL7%OB~@(Nb;v8H`qT##fbL5%V^PSp>hTmLBSjDqH7r>
zZy;n7OIqpq%|h{5<38Az>s4^;QuLjiiz<$bmJ53-BrH5V_FP&@sv@OH#RIO+G)Sey
zCF-ji_>!T&udn3Lah$C<<rFcfU%G*<up5CGq-#x(VjFME)_Q&%T1rXDWrPIZDfcH&
zWZ^BCa;4g}tG-%vJr)_ig?)8-UI~guDo`Eia)wcp+fFoZp#u>Wu!sz_9bp0PCJN#b
zn#0(K1e^-VtY6i7hiXDdk{1#Zs)t<V(yA1rc*W7t(Z-tuN7&~u=PU@fkQHkoo`wJM
z$5tqz*IAX~uid#5$p9SJZQ?GD;z^O=AxW9WKoB=LLWOlZKq+PJ_-l?0ckQ4HITue$
z`Y$cwCq)_O|Myh%zmpUG@AS6+OZaZr)H&h*iLO4-^Z_%1e~viFcJA7B4Rt*Y4rMth
zQn~od{)<o#np^<9jz)ea0GBvg;Wr)Wu$xpcG&E#6K^Qa}Qbi!&-FLS06LC=4APlP#
z(*sIC_|>4m$pv&XHtbG~vA+LwQ9)b#a!|uwwyYHS{XV6tVk8$QXDU)QsqClR-P{@h
zuAikohbt~DJRAWivCZv&kp^xcXYMKgD-j$s>O~c+0aYPBy#0K{hl)O7vIf%7sea<%
zXB+s}EHE_<Bk3KOpb5bP;!^C&fZlWx7Hda-v%bgu6?n&4Hg+Y4UWorCb#+|T9Ydc!
zeG;;ZV<$iY0{RW;iwZC+?xcnG#R{Kcxea-wl?+3wH1K{Hm%)d66NO;H1tmQB%__)#
z3MGFi*qQQRG_-C;T0&--J)!+c)&e@uLaAnfWAF5h_w_FHj*$?-h-zQ!JbVNstO4Yo
zL5;bN_)kGgBVgHTo;O}zUS>tA)1D{MuEY*`6D;Q>3yV0Eaz7YPNKPPi1b%9dP(;6r
zrkvBa58KPi%4%U^iGzgwH800*egYN|W4&UsrK&d~8THUJhW>RkZ<OtH>1a~|`{G&r
zr9j(sQI=Gn`^m0E0RF!uEdG6b<G)38`u{t@nA!vf@*4R;(I>1Ol?L@X>dW_|yoNGR
z6QHhJfSr*J*I)IzJj~hld(Y=1LiZBUFbjk4HchWi41DVvH0HcsDmtM^GzW<u|MdCu
z21)kZP+(0xRPQi<8ti*U0p5>^hmrQ|bi!!9eKUn;M;zJ-@g(Q<>Q=lCM(f}X`f>#J
z28lS-qhST}-1^D>K5~$@smCuB{pT_|;I};Frov=c)9*kEgK@<(J>5F6m8eU?!tIX2
z{QSxuo*o{zXW95JN-y!3x0aY|)IM+6EuhM%nRI5z)XVjA$J;^$&I5K2_b8QKAHyF`
zQyxy(pXvUJ;{ZMVTZTgi`GIg>YP~mX=}EK)h6o31hRlLz)WLu4DaRjfGx(xg)^GO)
zj>Mzlsu`v*>{9x$cA52+>>b>{x8RBDdlvzpe+unmlSPB+Ld0N443f*U$Xx`hLvCT4
zS@z^1js-N2kzV#g1RCL6JQia9qK3a@h(^o8E%`-FgDVFVX+qM~Xt$LVYCC8BsVmjP
zpHAxy)A30@KEBS@YYFPYo!`-cfwp3nm;N2roP>mg!sMsatOrCh-cat^WwCrvlT%>(
z;edy(RlkW1GE*K_V?lOk*n8hTbL{*R^hnU_x+W#%S~vx19v1hLovj#h;jCWx(=2?l
zbFmKlb5`7ZSXSiGe<msY6Ly4b=<+}<O{8;%hem%>57ZHEE^qaF-P@Gf31n04R~2yi
zukpu+?$`BbwU7!!I0r~eG$egLA)DkZi(y!GyLh3U@gQpo)!k^@@y{pwImF!4*_C3a
z4%egJYJ3pI=l&^qcaj6O5C0dlTQZRew-3^HIX}$Zuc2&50|T`xZLkz8W%XT1PBF(<
z;_yJDaPq{v$%cESHPLlRhy~|84G?iqfg_N!2tKS#0qF;AxfJ1HVJtA9fkc7Y8T+1r
zzme#WxQdWa+jNs7PBCl^L8`PNGL#3|>Y>?9j2v<%%r4Mo;4nPqjSaFpxuM9AxWhZW
z`U17T5mz~M+5HC(MqqSAskl@L9+xVx`?-Yff`$U&_2hS-ppb4@_D-j_y+hcM|Ag)N
zuH~uiM6*JT#_$<c`pQFAan$*`eOHBoTaB}?z%v&F;M&eMiBoKesheS9StocCtWxPx
zSuZ3bSz<v%$4Va6z|zv+-T`I8cB=FQH8pjAVp~AqC@O1=M!w@tN=kX$mqVtYHk^J`
zq!k^&Z!Xqb^gLz#Qhfkc?-I_w98AWR<)_o{K#z@g^DHK_2bt!&?T{q0x*r`SgB1`&
zG6W8o&T?jq*kiGZs;jG|{J2X`!EZ5xktd{#>Xpv*)u0v_3}Si@Yq~0DSwz8gQdC@A
zY>y|xe2^J7(7%k>fu%szD=^|x3Aw<KOeECBDs#Oe195k(FagAY9uOCJDhH4+H(L8#
zuua9RCRz9tTOjJfVTcHamo6L$Z)2JY_PK_nF_D#T=X-m5HN8(M-*)9No~0RtPsInM
zf`d3wG3$gH?u$W#SDv1Iyzc!d*>}JdDr>_eD^Z|zjWfD`Fkkp~3-eTH>#<2&e@NCY
zmx2PlxX|lf37tX@1j`WUL<$2)F(EAw%|2a=l=&cqk+4{mw6dAW&dz2-UTFCADG+Rx
zinqi|>>r7_Y-|IC#Rsi0_rk2hTsp6zL%xCH;fJ%EeJrpY|CGX3Rzh7gp|cy~kcV+{
zdOW@Jh-O;^g)s~h+;_edoVuP^kY@<~M^fJa?%ecN^13pNE17d&4JvU+#>)|f+Gw(A
z?V%I810y_pI?E#r=)D?-hfG{Tf)mMuGrisF4L^kG(yA(6@;Av8hqcAQBApap=$)My
z_<~4Y0V!GvBZXJ+ze+4{1v(?Del>?Qp>bD0(A9S3(1AyN07G;<K%2bq18|^4$(xjv
z#8x>EGs2OpSFcXP1k&`^02%x*cSz^nXQ0gM)hkVORx8g_32UiCxLuyg$l$AkmuTO<
zeVTczb2pZqkb=;h2}dp`qh;3DaU9L%G3Z8C)f@)qgJE0Z)uk3Qp7!O%`MrF3%Sn(A
zaqU4YM#P&>%&Si8%NpYGc5nC~bwH=un8g9TZ^s@JStD<GLNMJoB*&yv1RWuwxa6Gz
zh{Ir>F_+~42wpvz9Kws6@(gR<T_k98B4=9K*~MivcfP~?S>}7tWpc1^cG93|6X&)q
z2Hh-RrZ#}S$yPZ>?5^D0*P^0`<;V+_H54-HdFVazaNnXc5E&-vBVjgP{}k+a1V#h_
z)2{nOdnfWG4t>qzy~l~~hYTR5Rx7g^`TF{P&2z{;aO(Qo?R59f!b?HAUu0O$0je;0
zx1^a`g-%K-`V0^t6p_|e)4n?&RR02TCXVPt#K1%30dRxNQ@YR=(L;WR+%Ms=I)4Tv
zfLK6KW_XR;m`iiRmnf8k4j#14C;(R_bA0?ZQ=teS^x;~Yh=#(ALdlB{vXafBbX;Gx
zUT(r@S^8vD$9tnU4mK((erV|4y(qK~9SwA_wevB(WX#FMbp-=q%1O7KB{};CQF`YR
zAjO)HvA?{vB?I%GPdm75XYTCVzh4W2jOyzzuVZ6Ry}}mbHg%wR=(uKnTUc6Hcnw1}
znH%&O;3|3z0$&X4=W9Uz*N6;X<l*HkFzq)8x$G5d9b`EYF**Py;h@z67;#BXxPi2e
zv4;4TEX$bkseWm}%muY@{N*dAimL1(ZP_$tmSOx)3Q4FE`(S9CJ<hui7JtW;@0XCH
z^c3NgB-w(?ZqCq0$JNb^7Ze~>V;wDLk}n8aIgF;m4k{|^7?zU-4foGEP?xT1dU<(i
z8)pqO)xEn&xt!G|=P**wBA=$OkYYQl@1v7b58-Pp%ohEL0c76+`t=1D<<vW}EGH^P
zdn$m}lCCC|l$LUU?sIS7Lu0DPr0FUyEzJulEol*hf@9^F`Gvlusu^THst@&v6U|HB
zBGr2|c-~u>7cwTZEk0M4Gw!KRV(7;JejVfWs}d7g2tqP|2^u%}^zj~I(a}t4kpge2
z#2wKz5|K1#x=N5uLNO6dskS!ElMJ96!&Z6s`{c$rDoH&m>BdGP3td}W?xLAAkU1F0
z+HY~NZ><6XX(bsAA``4_s3t|Nt*!P8-7ZD1DdlqkfGaSTt?IEiLxrQi_vG!A7A@D8
za9M-2qPxc!MLKCtYdL@vj5t%`(btpSK~+@q<`6|RZO|%W!G)HrbnTkwdy~$1j2?KJ
zeavC2@(&0&4V6c{78+p4=Vu3585ll=`7*Hl`l}3)l0dGtH4dP1la?7yNEj`<2Dv02
zFcx^67~z2l>OjtB5QSs}(L$UFrFwaP2(dh(Arh^Y{XTSExCr%y=!g!PtbIBwm`Wji
zDwIlQut;c;J{H~I^bszKkZ;O4)yr1ahkrz}%_p)Hv?K9&L8)-4{m2Upgm{xmK}>|e
zlP$%ep`j(DVLjSpEnd`iULBS$RggOZTN^MqfPRiF;@Qgrn~Rg$KRG$+;abp8rnBo+
zY`hqf#8(W3L5b%lBXXp5h|vZz+Ki;5k7q+4)WWht$ZE194z4Yt<aXLmKgTS(S3tn{
zRBYyn+Q%^dBLk#qmHtHJGbpcCZ(Il7X*TF+X_4VLIe3-OX&tWBlph>GM>K`*Ue_kl
zCB&R0nRC%jX|X_8Sk1hWhs|L{YE$uIWl0IKc=e5s$E~jniW=W_okY)B0cvil)mU>d
zY@USp5><Q_pQevcU|Z*}xQbTLHH;slsq(c!mPQD!z9n?kxveKu;hi^tr%}LuArZT8
zyzZWo{c903)Zg`mVo~xQbP2wU@457&Dh3FYI5b^}w`%uMUMZ9-EC3PJFibMS%lPd!
zvh_$?W(u0y@n|KL!TydVG@f;dBVIJ5`_=PSFi#nKeMfLEp{2kB8$T_-*#$xC2@sxi
z0&UcPzzk<9P<p$~U;vRK8TeOrWz|*pE=~_(D3-G_NK^x=ta(f^M-!|^8#x}j9S~Yw
z{`#s(`f_enB_~Q1)CUPN5^}?{pl$sut*E1_yYSp=bo8TfT1jOIa+vdN6?Q{iSVEH>
zqOAqF6Y@-Ra|3^{pJjO`qw!$pa)|mD$}!Sr!g?1JoNY0D{}cOz&!rh;m@H8E#w5yG
z)c%GZEkk%3r7^!n@3;_rZRdKw4koljiIaj@75qe<@>;blX6e^@gxoM>ehrqNH57r2
z%HDg!O(2Cp)c9C$hV;^VQ9xG;QF5O}K8G``%ggz@;+&C_pn&EB9Bmo>y$i79kXDr~
zs%5*G?iSOq5E+eIY>vOE11w@D{4~&m@VA03(b&AeDUVJ{YrVDKSS5;9JO}7g-)U8>
z+M+`njcIaJ-*z_5_%{COOmi_wGxj%JfukOZZLWYV7{zdkTHiin=19k`SnHm`6=+~x
zeqB+6zFlvU-?V`_m`d>DHnug-B?7tN@V$v9krW)9dNuEy3D#3{Jm%h`bE>jXkH7cs
zso|77{^OeBqTxzY)tbMxnl`yIK6kB>J)lm%sjpg?Y54<-+5B}HVcTn9?_WR%h+C8R
z`bai$J<LV?Wr*@Fr@LI<19VclLT-%0?u~~JOD=KbdN|Qdw>}coJ*B5*VI(#nz9qic
z)19?~jIJT^MG-qJ79Yksv>hy#X7pBj>DOO?LY9k)Ulrr@c<HKjD*N!JzCs?sqV7{Z
z(x+czXnM!4UEJVjb6n~DfgGdA^ITw&I!*CvS`5bzmtGd*+-&+&xV`7yOnFM!@R&A?
zxPW;?Y0d?f^kBezmiWOvao3=E%ZYZ0ZoJ1MX-sFU%+`40nM7j{>!QxiPK+jfK4jiq
z--F=fL}2I?+~1^whBWyo0^i8sFXP$+{*BvY==Jj2d$t=Pcpp^T9IngPSSdfuFdflk
z%X!X+I!a(K%FYJ-M2e_D$c&GY`z4RL^(L)zmTTtBMecc1agexhXz<|}rkp$2S5YA~
zjRC<o@Z-LQOj+ajrfu=w-_M%pc1}$UedD^Z&iu|pz4vz2k|r5abaD4K{`t5<P&i-L
zzg|rtEf&o7keD_I#s_k!M5cevv2OR9^pz_-kU@V1_25DRK7*qA8`}EuAU8U&BK`eo
zrpI<#9)8iOoCqPW;>wwJEmG~xy9zcfPXU-<3SF1b6OtvDr_j&dxNqbd@`Pk_ek^#x
zTyH$lvuK4<$Hc-?>NwJZfO`!#+Obh|Q8?1sCs86=@|~Qb9PT*(`M&E`T$>Z!xEQmL
z-&Q@7iIPbC4!APWf{AmSv?Caqn8<$$;L8q(=K)rVv+$_P!jg$rgeR&cBJa=5I(~SQ
z=Rc&!dWy_69)s(~!OeG7*mmX%oZtQ3-N8QUw&JKEieMBo<O>Q=V$d{1Qmw~<>cb2C
z{r!J(`i1Gw<L2G&O5q>MZS4|rhb_>==f#Vg1kXVX)zYv6*&>h+o)+P0bfEjQpSPv&
z`Fqc0lAF(u>maX=+3}F0itlnDHw|%Sjsp&4S+EcVpeG0!;#uDsZ?kE-=H%p*{PV=f
z?8I6Y7509V=Y;V*ccxYnJry|&oDm8QXwg!PZfF!>02wc3T%C;jESdk;Eqm-eTl!8W
z`^mF;X+w-5;|m$S5bc$G3jmmMEp*)tI?Ti*;epc<f*CrHYWKg75!)wOk+up%UJL|b
zZ7r=kAO&;%I8Sw+Eb<t)JwrZK0FpJC^=xi#mUA+A@jo`wRAf#j+c(H@NgQ8(5=g2d
z`G$_X6-=;3K%<Wb)?yJB#y7yECFlP`S6ln#--ky*V$Iy0xdaZ%N|@y}J8TANUtzg5
zMDYA1vf#V833sc~5U<42tFAu}ZBg=@y=RBt%4C<|siXiBDuH_|khZ2`Cp+`=$yas&
z^`8Mbn(JQ-_~`xjL&l#Ewt9dNKa5@Bf-dsP&re@t4fM~CQPR6PVIG3O)~#D#18Oap
z%$vRV`zCpq>%$D8HQ<|qjDIDQ)P+-Ev(4bm=8y>9U_zqRhw9L;1^7h@3hCbmGUJz>
zmMk|g{!M`UlV{JkK+z#6z;JYDq$$B9Z)HjnU`>$3Sc=84+_KTyY=V>5h>+l)SI}&!
z{PFFKY@}ohz9NC(dRlMf=n5KsvrmW2Z%tkT0l$KTR{`@9O_5h4CFj4Sy(K<FbrvWo
zd2sja2T|2fgv}Y|B+`<HT-xL?-yQ);4>KwHd(uzGX?pz?&*jEq6k+b?)XH$+nE*wJ
zK!fpX>+6Gvk^1^;(0s+8AK@WhmcLJJ*Mw^BvlabRd~ZlEydh+OeBaTd1{#i_zIAyH
zc6FiR`<8D!j2HOy)4$wd7T;_Jr54)PIF7^yi*MZ^R(MSJlXkxcn#fGVvVtxunArcw
zR+{_u4-#a9Z&`>UFpLBhBnIN3wz0K62~p+rK<i)UYvW-ao*REu_ECNJze|>z_zpUM
z{p^adBSYwL%>Ut!o<e{19ycaV|Nd5{|Ch4<iYWEy7R^b~7UC465WgsWA^zOW-~S(D
C0V(hR

literal 0
HcmV?d00001

diff --git a/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_2_0.png b/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_2_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..328c06193339a67190e3854fce92ed957adf7acf
GIT binary patch
literal 37214
zcmbTdb8sZ>_XnD6Y}?vwyz$1)#<uNbVq;_5-q^Nn+qP}p+4uXaTleo<HC@xyGky9z
z$LI5$4waMnjR1=a3jzXyATB1X00Q!j6a)k`6B-hDg`pc83;4z1D5B=52rzMU(YH4S
zk=A##wE{R=nHvy08{0dW18kV-f6=qh5}P?X+B$GDFj)V;f1n50n=%klkU0T~z}SkZ
zJAi;7>Hj-Gi}{MoK|o^0#DxWvTr(~<Ts-jyR=RJt&UF;48V@!-h6wPg%?qD4J&5^#
zz<_S@|3SxxCIN>YK$alBH{rytmS(-|c(PIN&|S%VLR#sqT@`O#>)@GSC-U;Ud~_zv
zymHRY%ybhKqoN4Q{Q>;2O`jw3%cG0PCp1b@kp6dVS@i$c$)K?dNF<|lymp&&Y+Rfl
za_^-rkSw|gjDJGh?5qN((`i5<>*f*4T<CD6cH8l4L)nJ@KMulXzpXU^iX@ZiBW=*;
zLd7H`VC6o7^9u`C@gx6ZapwmWRgUFSCF{}6V80`&{kF4|g+-}^@IUGU`n@<R`YU=1
z=5vK40o#k3fQC}>e@~S+$e;)vet)@3<@Mt13qq*N<@oP1KIv(wn7TR+maZq`6webr
z4i3(Yjm*Cr(4j)zN$vL{+3jyfC>_pghS*vvwyuiu^Ya&@|KsC^rqo|~%XJR{lTM=_
zNV#v-d$r!2`??!!244I>5n`h@5XN4tH93sYHC>;URqfur^tw-MxNJ$=+1X7@JzlKk
zVPySBi;-W9%9zFZy$6cO_Au2n_so65Fi4*`fPaqV-P@<)=u}ZbK~O^jcVJ+^;<fVM
zEx&{vCb#6<SRKY0hpIhaZZ)E{-3}7rv48&Ix@xZ*As7CSgJ-M;VR8zJKoX&#d3RbW
zs?hTCa?WU)|HS-@3Jm!fW&GPTr>`lk;4hzx)s)Ke(tj#|9S&=XDTw|<`+6WyL6*N6
zeJx!>S1DG_f|APP(U>?v_3!?@kudfc3ZL?~fEFlqM=&Gq=xdo;-fCz&@9e054E9q|
zmBkbwvES1Y#}>?m%(kOR77PE!l|iBp+i<NYTMV)WQci=42)Z3L%75Id8J~lt>GWVS
zGo0oKhyPEb6e=eK#hnmy;1xpiLZO(6A^#YZD8H3~oKX|_iJ%3Ba@qO+A%RU102TY{
zgTMm^EqqE;4o|hKM1TUcW;O%Bf{e+L_h4>rHgp;&rz@A8aN8bEGAoXDvBgr21bGI8
zE09~?mZxOO`>c@pH<YXzvASG$xw51v<Sh3mvdGLgs4f*>OAy!Z{lWtE#TBe*FcuX!
zK+?l%s;z#e?7R_F2vXx8(XjkXKb^pr9$)EN<(3L-q$I%n!b@q&P6>Kg(FJK@jlUqj
z*p;Fw#Q*fyp@?^h9<RLVs)o{bVwLiicL-Gw<bU8zaFOoTupPZy`%vctS1YXCeSg<`
z=wmQEEg%k!j}6{ZXhc<K&(}cti^59q7<vjj06^W#1OLq-_T@<R$$lQ*tRf(6DfY#b
zEK(~!VK`Sa=QOXCi{(jR2O`C{VDSUxK`t}|D8vj+rDwE6QHWaK6)e<|rZ?W91e1;_
znwsCE$hFT+e`>yt^dM7O7Mkq+BzyS6gG@J5<Qxr^46VaXgB8(5LLJtq*>ZCX7diAl
za(}6bsTT(*xwJa@QcZj>fbqSGO(bHI=j#7cIu-Mrwpo%Ij-%##SYs-n@t+3!IaE^&
z`R1`>m%sau#Rjtbw}e-RWuW+#;_O_ke?crKgcNHs0~Kv&qj!2O>LCQ~M-RgzZcm|!
zoj8lnRF9x;OO@h*$c=hP1GZyrqEb&@9`b){f=d(7>fPABLmjChkg5AgTF_$j;=}*y
z`y+BMmSScFfUU%r0bD!&8{?Jb=K7~+-de``ji;zdMAHw_0+CYQ{L8=d(5NsoYc6O?
z(f>(_8ZPmAG71ZC!$fK@7C+k|h!vEIj<f(=o$x>uS96EGRf!a6@2EwGSe{$^3G2oK
zchIRM;iL*0g3zEEL&t0i4-~W2rVzUSTX>1r=SIkP#5?c0F#EpF8&C;X;oU&trvw7O
z(Wczyub`5DK0+b$Qr}<QcxRzMpC+dvg6r{9l4xL^9fSEvY`hmQ=9388BQddGvS$G+
zWHF8Z`|Ouc!sEuT|2-h}HYVGp>0`Y8ogh+XZDATah6z4fhfALdDX})k5hHzU35Cgj
z29f=8q?^=e6Go1gAYx)c(e|S*Hc*vR@)K2siL{<v_|P)OmUsK(0EP}K_V)KQr!cXc
z2lZL0`hQB<Ut3>TZ@ZVS_4GJN(+Wr+#x3@u@GapmU3k|)q2Bw*tg5cQhJshm>?S`I
zmooQ(9diDKKnJN};e<;?6C2o7s&mu0IKTD*V{kyTjROf&wB%Kx4xKYW?74j!J*|Lj
z{zRte4K;|_4OY1cokRAa%TJtqeHK37w;sttgRK9UFB0}&aM<lH-6dFWdG;Z183i%|
z&XN(}%EFNB)D@~T@&Q`RME$b#3N>7@s%)v>4x5)D>)a*Y>BE5K{H2`vabCJUnRM%9
zCYT4f)vIm4Ap3OT&^Q<(4#!*JCmz$~$4>|mBSTVuWiNh3U*?NZ8}r@Q73zI~ro*XU
zRhA39LO0qzS{$&r6i9l<iRG<udNcq2Klz7#*`$H-%NW2Ef>*>+ZxeWFpCsRojhwf6
z>DD$`nEJ!*P0*!G?5qq;^OPWgi1>PbwjGGGB~_I)Y*g1XYAs)=lDEcbye#!Y;iSku
zG)D#5#EL^#h>lVRT$4<EGwN~EJFon}bwH}!kKWD>pGOr?A<+UO-mwbEf`BO!cyZ7>
zx5}?5Op#*+Gk>pi&&HJhu=?jn-3i|v>{SiL;?bMBX2`+yEC(PzTP~xO0!q(*zo&dT
z`HK&O@?E|`7Iy~TEx*34XmwUjveATL-QEvEG3KYCYSCq|>U*!qB=M-S!*Ha0DHeMY
zmGX)t@r13rkk{Hn3X&5u=|U`Zf8aIV-xK<6`(buA8voBTpy167&WWtBFMs&*D08{m
z-5Fvt3Ck0Aja*=1e1uR*dk?>*<??%1X#RpqNwF$h?yt>#nSkN5ftmV+dzCZY%?eJ^
zzCd%P(z($8c<!^g{)Hxt`9+A65<Tred`Bnt$z!IiL0BJQ0I}r$UxuMTY-YtG@HzoW
zfZ_>YrcokQS8a^*NZy)0)?k4G&(Fr;`WkBY<A{9Bf@V3y@Wit@zM<P8N5*0CY-L3e
zS5^HgqJ>gl^%G?=T5%komKo-Q7$t;s(1ZiS+{W4LU22T@KjUlJpd<##-fX98IuOMH
zJwdWu13Y;Nm>1uCGeeD!)03|=2vHS$Moyjb7t4L5a`EAb$v^9Mnf;9Ps<%2W1oh>J
zVTXv2t_V?qx<U`wY{7Ul<NZGqx@(oyGcsWdy_EB>Gu9aECzc0z5|;*v78YqcezPUs
zAq*S-M2+=?YligQg!7~%Vbc9A`J(Lv<H8Tk{wzc8E;5$(*F`9w$IPwnztkFvMmRAL
z`B5m`k*Op8f#<c^ZzQ-lBOl!-9*`9%)(J}Y23GH!omcP$wu7VW(w2W#a*0v1bAOK8
z{KXd60MueVm9Jh1z(;L}i-=b5uv}S53Gy6KT%MGAES=ziEy}`$R2$X(?}7s?3N&Ov
zlo9`Fb@32NbU+&e>D_@pb%Dk**X|d(pJcfufKU0iusL(*y9vdY`ytKf=sPq?jKc0+
zYx8R@*{`RF8P<)wuw5gd*Boa|XKE*|rDa5|p^mu2YR9|VD{gtX0fxqCe>~?DMzm|*
z{bW2-leuv8E$v-}?RCJee}nwS$8SckEg}IniinOI{@!KDN}&G2*2~N!@m7@`qhHl*
z*bx_pIy*c2gP#67E9(NVCWRBHB1^*_|MaGN;`x==a|GzTG@IlwbnIVMMCYnt&>rbx
z=ME`1b+@+EUf5F!GL0yE>7Z5WeGHp06D;yD0C>6!_OB_Gj&yq(^|10A-{am$@sD}7
zEVP+VzGyy#{9q(wuS#gzOrJW^t@vAB9#&p%B*SC(%QTxUEMY&X3eh4h&h(SHZN~cZ
z?(`|czSq{Xv#{tVwM6+@I#bG-$JU4g9ubM~K4t|Pw8_O=f|3g)Py8Eb%(;ZIUTMP|
zPo`+v0kI^%<nNFqN{e1*NGh$FGh|R!QDO78Mys*CQ#{@M4J0fD4V6If+uJeMmj<Gi
z&qdWA)Rks8olE6w6PF#46%Qa77@*!d(`n{_FjP&*e8j#ykMzxI9P+q{gELpF53lyd
z2OOz%6-y^roYfF@R~jZ47W*yv;f}nY;oiaU*48VZ`7lhQd#kiM*NDKZi3*mV@P3jL
z{e$MoFs(t+YcAn@Q?b7*>oe@_lCwhOje#Df3a^fJBr3O`l<xd?cjC#R2R{jO0_<;Q
z6IuK!!BEP$_;K$*RM>=7#d64Z$5#p@lCibN!rRepYNH+z;*WO)DpqjiY~qhP;xp-h
zGTFOF^%p{9osMM8J0GGO%{^)P&=7+*WlNVjySq-vY<8)UjH@LDH6<BQ_dBfF(`nxJ
z8xanI*|ZLTNR5PVR|EmZ3wbm5z09SY<<H`TxT~j%or(&+npPu9px-1Q`ina?<&B$(
z7ffC&r7nnnMvYFicV4I;9!?F$2sA3sUuxs0aR&96lP&AC;QZ+v@yAYMgG|QEE+VF)
zsC|}+QI<2!hytzsrv45kyqLaoRduc__o>uxSkbo*R*M?o#6x!TLM4Z9yz$BdjoWo$
zo)Ramd5g`O`w}WvMR;+%R0M{{c4)x%lZ}gpIaHp^gT-Sj4At6$Cj9v*O*W;&1>^7&
zAYK#zUwP(0OX)-ynh+PPGO^upaW@^<z5Ipa19;<pExIC|K>NT(8lMD)c1(F$hVk3D
z7g~nY=tdvvQzSRt*u%+f)sCxt2f?RfD6@uXSm+9CBQRj?8k>V4{TL=j6)V!@_f;1X
zwEPoPKjAH!9B5)SkqL3imd-Ucdz;mRIxSjsU(K8LeKlsSLC(W*`GQJc>yKx9b|<H2
zfBTcXlqs}0p3D#TD=WnXL7bH_?Z0lD+^ol*JZn6tuB@v%MGWv0%<yK(M=%a--rZT)
zjF<b1Q%^NfX%9ypd~(V|?Qf#cSFu|zN_p#k!hmxBR#A0rh&e@(x2mz9peOV`XKu8z
zbRUrYvv0%z_U2+`=OF~?e0USgus?F5TW2wGnKsnsGO-&}R`k<bstWT;Q$F|L1wmI4
z`$v_!@{^ow`A0^U_ytM`Ck+2lzo3@JxQZ#ifr5DgGrJGlkh9s!b5#n)<#@_Z8=v8@
zWDP7y=Ft5!T5BX=Y+Tmc2;y0HKROE#+=*`H`x<9-U8jfdGg?T~PDoOoDKX^aX${mB
zvA_N!!~JnwV6`RBZ=*wXVjg;b8e0LIO2t?*3Vl0LIe_q;1f-B<MXs@Eau238yK+QC
zK7yW!JZ>P6Z$OuHHx>H%PiasS3e`5w+ObW3&DyF&(WV1(7UN<=Q#LRojY<2QXbJ|c
zY`aE+Icn=+$Hhkz4N@G*Zr@d@Uaw1T#U<;i)J^>HCTRH>+&kW4r#ydba~{dJOdiJ^
zs`{bw12zHer%<F#9?8$s7K~X+C*66>j_~?F<{uY%W@s8rOeYpUzFhj&$hD)9or+W?
zas0ZYho|X(_<T>(Up%<9u=aQZmaa8Q`tlriSk~tDZG;=O*u3>Lltg?HpI#2n+wlYM
zcU#P7R!ug2gf^camhW$|;U{ZtLOdR?f%+-5I*pO-eoVDYaX1|9>9|<A{pCi<RU-oH
z&;q{!xE^I;mV+_Ptp4OK`(Y^}@j;?RuK4A!A^YjEwdmA3Iqts-|GGm)2IO0o`XK5j
zb-^2deN$IC;*bnrTbnJH5XK*5k;eRL_PyfOm2p$Y$n-FB=;bgRwj0Lq)|a1a;M8gz
z?7#79xeOx%EiS&kyT7YD-B@Iy==s~j%rJgP<ipd|<2CtegCNWOhVTN><=Ml`9M^o(
zn#XZi>^t#JgHG`px50}DSjuj3JF`F#<VhismbvuAo&vlV9x_H_+DidKLPzq?_s$W?
zr~{^vOP+|)7=lZYVh?!(7C}*EejjIASkpv$4fmF%rI;J~%Jmte=ZaI1A}VMo^$=D0
zgtAID<(=UcQla3sb9k|-Zn7obRc2bbgh-E$o4LPM=%*Q(QY^jm9tAroz2IHuvPqBl
z4QQ?;t4g$;CvZ+py#RBY>|6EZ9z@{5zqkyd#FX2gZ~2DbH|2I_Uaq^(hu5X=Xu^Pr
zuC#OsohoyDP<DC7!Q?^h6}R`+MyaJu@-hf1IVN5QpXx)BvPK(Orjz5AgvAWT>a`^Q
z`JS$--sA}-=anuX5o#_LbG@6Vdp+?x+&Ze-9BAbcL~~`6EUW$}t%Eba5sP2EbaklQ
zv76)&3Ayb5=%;XVe5AWKehzeHzXiqQNGQ|Pn96Bj5#o74gu3?)@RQGzlYn^7MdAn%
z9g%lj4hTR2DCy51X(mtYTX1Q0UTmy@zk>stO|?xR1cXn#jdc6OwPp13KH((sm*_l}
zYwNItZ;IGOHP)%8q1#CnKXk2vJtacR?nZca&O3E6DJv@kMKm`iOXnjkZdO9a<IS|P
zc?V*$`FLypkX1*Wsq1eB#hAeX|KA@Ckt65QX+>^<?*=F9Iy$n(OZw)9*x9V=6eNGj
zJ(n(#Eogi;Z@(*(qV5R__<K}&gaUZAX7?KvBzWESOl`Q&>>6#B@jkXSie082xj&}^
z?=RIoK0*_Wh}geQvFSMgo)trsvZq^SjLan9{!q@jhQTb(m-PFicsfR}l3TTImV^`q
zI~B}|a%!9Je8Tdy)eW9RZ8UHfh-A4Yhu+<XwNB2#`ZVU4hL|6if|loq1#E^qOsjBb
z1cH$$p>}n=I=!`SJ5{ijX>nmpd`Me2-(@>p9L9C*;E$JLkFuNn9`K%O4OK}*1~9Bv
zYokRZn`+G+=!Xb+Qc#52NCSvjDSXH1e`R3ld~{BF2Y^U-S5<PuQc*m;zktDpkH$(%
zA@I)k7%HGss3_rp+UPz@gzL~4TRlYJpWg9Y*$ZX20U0&OSBD*BL)Mx0V0S-f1q;vF
zR_I<u^Db8i6^nS=e`tXwNpmt_MTnQSbgr{`L3~B3Gu3fzc7HG%dLv>ujAuy7Il~Ro
z&pq>#?bMoVp5b|!`^xm4Iy$U{quh2rYkz-@R#N_8Vy)43rta!8rtS-NG(HKtuv(pc
zm*2XmRmA2cZ``6RuzvMiQgBuZQuTV)3qohqR>sg1S~*So+Ei*xy1Fx|?Jz*2-!V~p
z#r|$WBw5O}(FxK=4s5+f$mAYKSVIFBaZYexdlL1|A?OHDCk4rMR63+-DM>0xp!Ivc
zi9oTNn_EiMU2N)1pO?LXu~lwj+=UL)06zOEb!owhai9#a7ksYXMKoRSw{&lPx_EF%
zz_Bv9qL^g~MyBA{R8>_b#7+0BZCDMZlPcDI3qwVRX6BFBmx%l;ErDhCv;HMySQw32
za%OKpO~;M9!5NJ~S{zScK}w;xj7e#!Kk5<hRzv>g?S*s+(>i%5ztmGmfYAfQ0A<_e
z{#Xam-yHo*-4f@gtc&r<j|9BecOsrRTwU}@nOR!VLtT(T=jE;-M36(7;#Ifls}{LW
z{MuUr+h|IYcw~?j$Iw8r@QS>0h2QoT-zfCwH;bdW24!-CZ{PgeA2#2Cg_iWlI6%d2
z1LJpGrZUehZ_knbZT{zjjqX-ycBHthq~5qbkDL7~`})&%L>l-ZKa!GtZE|`QR}fkh
z!EE`5C=eQM>e3$cF{Rg2^?o4LPo)ag`ejU>Ouz@*RJn$qgNiWF6J|n50^+7DPEIj&
zJz~>Ebk}(ZTowp0PRMD$ZU9tn&Ih~fzP2fEPI=QwtuknRDqo%9ccc`Vx@keaC}$AM
zoFb%$x7`EUs*I&e$4anW#!txbGEY~LrO_hN%r>A(C{!voq}@dJpd=L~I$zk@N%_cv
zT`W}qV)Awr%%&9v02}d&C4ysLYMMLdiy7G$lgZN{XMGqd3LlR>ywAr$c2;p{egSg`
zS+9GruIx`nQQC<mCDKAgLD6uX<YKP8+S8>LB6e3$l2|3|16tbmp55YJvH}<k0ICkE
zefQ^RY0c#5gSA|!*f`gTpIyEQIZ0Y<1hlm6_(^c=3NLF|Y=h1ZBbE*nU{ZHbYcWxc
zUqd+EiUsD_X-!~?JWRzpVtI3xC8yi0jt2@;<;p>!H^VqRrKQ?>ir=>OFV5~!`@{Hp
zM}+bKm&Hbyv~9z_P6s(x4s9V|3_s>*OA!zq4d-u@#XU#~Anv*+JiK_p@EGkX<$W1~
zRY$B{zWk9Zq_)<FyXN%-dy+cx{*idryf=G-zMxoL#+w&?JQV4}%`*yQQp3Vk(ilnz
zvg>Pat^MN`f&4p*ryi!@b+X6gHoHsb<Rwom7ytPLo&LhAHayNUhrydwluMUpR3%IV
zWE=@7_tS`06yXe1w5C@<=6;7Y-`)6YDmsKu%f~7OV|<yrbdHMzl=rZ;_Xt2+j5H%$
zbF>HoJG~wzTV<n+>`(h>%f9j_e_H_ANzAXnKk9!}0l#Ht?V1}}A?9dFJpag4)R63S
zrKDWq+(=c|b1abUf@r%p8otl$Bno`)#V6l(&n53Yw17fG3Z^hFTkEPRO%i>+d#kNU
zsC%XFm#LSftv7nLDd{{vC1%*3Z!ZR^N!p3KK8WS&&t1Ml#v>Q=W>{}(4%@lN9Q9?Z
zXkewxQUk1Be7h>r<Y{0)m*wTVvsK#14)}OPT4vr8&84Z#cAlLjTLRL7I44pID;Z8|
zGe#AraEN~qk!tq&ld|_%*gKS(?lbz>-RJPq+V{GWXX7#<C}6+uTBMK}!4jW@apOul
zw(1bn>S_N61Fq?MZyZD4T2o7H62RacRB`r*3LO9qfxsgD(`J-^vg1_@GAbf6kzUuw
zRHuVt1uxg!Z(yv^ZdIJ@l$)}^Xz$PqsG09{tg!-MoOZJ}eb4V@!<h*Vq>ky9Gp_{g
ztfo|amFt2neEh-p=#w1w*hjL20&!K&09iXXjH|Ee4C|I1j5_LydxtNduKohc3>LP|
zr7PRkS~-j_ohK2cM>-UxAT9D%Je_PR%m^u>slWBH?d=CQX+yIQunOAB(w!=ovQknf
zrqVrIGl9vMC0N3Ai$Nj#XR)7E#hv)jNBb4NSHfbQ5Vtbl4DPpvX+btx3H1^~`kN^n
z*zKnp81w>qpJU*jmpZN%+?H7=_@;Cw*44{e&SK$y-S+Fthp>18)AEafdflM5n0Uur
zN^;eDkD;v3K#+6`Yjgu3dGtY*Q2gyY;rm!yaGq?{<Nf7Ir}A4$lL;PD7i+k58=M7C
zXHj(mrRjUOrbE*8XeQPSeMf8c>S9Nr*4?otdV&g@$LpivmDkYx%*PDt!VXTE$%T68
zR>Y?G5Auc-1~WEZOW2NhsOpLjQIi92zaY_|GJM6^b4|%J*zCWSqbp{;1etnx#Oq+h
zcMwK1)_RwDuL%~{W7f%FO*W5TT^{J<TZqQ8sCDAde-?XY50yw!`|=aG?Y7=_va9h{
z##zXrUH2=Suo*kqOZ3bA@aPhi1x82HmkwwPjFw46hE&QoM=D&p)J+M}*1ag+Pj;g1
z+Q^u<PvhVQ**oOPI<-4P*Rs-PjtO?wbM0l`_QKokj{@&cQwAzzr~Vd~zW_s=jvYt%
z;+2uq*K#GvT++dd<V;`?N<mMu%|yfrIrw%cf={lcplqQtcX&?;*^A8<7Px=i<5sEp
z<`J(MX1p#(v~wt09-0+dfGKT#I@qT=vIbOd@V%74u-~bZQ3L^TElOw+*#z-^rkK<c
z3JF9`(`9d2%RGf5yk>_HUR&b<85yggkl+t9raOO;*cDy4mv$TO2{F_FWb)_r<_%6F
z@74fgnle*NV1<b%HD`5gL;&AtgsiU3(0MBqZaNVFAJ5mt#9ZE2%7?wH8r{+7GR9ux
z1klgh6HHT3Gx3rqrSMeW+%Kje0WqPSpf~T28l~gzIw3lLxY{k%X>zc6f<n$U&VKuA
zz6Fm-q6NKW7qX8Bgb(vtJV?0nW@Za>j6403Ze*u9g>C>L7v$i7(arbOS94EMLiIfw
zu~<6fnPCvvDs0@Ttys*{wwv>y?pDT)rRi0@^}02P&8~;{y4Dq^PGSbLIu`L=8*Qdc
zXJ#uR2v_n}sy^%%FmEOMWMN^)sNLTETP;snRAof1WH)TiqAq_11gm<95lb|ga;Gt)
z#)2ujY;Am^_RS8)>=1~Tnt~MgKCtfb_a+{nqDCT<i6(UkkC`m1$p*2J+*^&UD)Nvr
z(i(r&KE{$GyS6WG{)Q+nhQHn^l6=1FBf2RH<|QriqISn#;AftVk5O1``KV=IYAqwu
zUV7d+LZEZMnDiQ$&~dfB%UY!POTeUny~<pq{3e%6vvRQ))Hx(#yanU_2YMs1J|c8P
z_N5M4&{pluXJ=teU^y`kBD9h)-z`{_x!)vqP-Z-?OYf4vzr_^fu#-xTwePaJ*Yu{I
zW`PW~FDUv-S|Kd?cJ@$q$1+4eB_f@P*x(IrC~SeN<*w7^2hH7*QYfo5&LYpbU+=9u
zQPZslX#qL1*-E0NL|oH=B}BtK4;BGhT^T)BDwTBSYF!3<(`Wzd>_&57_y_@{oqlxa
zy|}seu8VxMI1owx()fVsftE>@x>Fi_=U;dopGY5=&tMjJbU6&6zpBf-2XjvH5}T&&
z$upKiRqN=2I+%WvKxL9#(RpRlR-A13sAKRii-kx&*R3=5K?<p<n_23|-8nHe_bB~M
z9^}SNzv?6veasv9{djY#wd|n%UBUY)xT*L@RVRZ*TCGWYAesdMztgyv#xKjm;+ziK
zw(>S^g2gg2wBY;t@{Y#dgzr8YpY%_(Uh#<{Yb8v62|?5^j!OpgZ#UwdW7>1)Ua%y;
zs@}j&q*~%>0sCS0ZVxegamauCwt00{ZUGX0BFzox?uFW;`I7+Vf-@|s^LI!z&Q8JB
zqE8$jQ08om7as%dovyS66Js;>m&7|NnDWSB2>%3B_51Wy@enYW3S&vQ`h<UG4behj
zrJcQYA2dPyCt5dstf7mKD)-HLI6^jg8!6ytUW$`&n`lm@CBBQ}7;Jt4`D?SqDR$O}
zkxf8esqP!gn~BgsU9>a(?wocw=uf2AdYoBdkgukFR^BX6Ec2{uo1!Fe8aIzMS<hxK
z&=+4Oru9%GH-%xOdhb>@k>OQ}bux2-ohM*L<x94f*?p49P)J?x{}4D;U3KdrXhKyR
znw1whFg3^Vq7=u|fwxgfTOF(Xf)uOzU^rnIT6uXt)t(q7UeRofnF2SsnAFu78D<eZ
zTyH*y&1?csMMYH)*vev_{N>+ZnXX&gT(R1bN4&f7T1fTc9^_PK()niRRkmnuemW{M
z2VVK(>DRjB26|k4JDvAO17+k<8rqPECql-n)e4@@4Re9p=JKtOKN%)D@=qbp+OnG~
z<zW^z`T9(NbJg}P5#dqGgKk}0X~-WOgQ_laQed$4<r`t_h<u0@J3N1jKx*0;$sj1I
zE_)~QGa-pXmc~FpJGNq|(3CvUGX??@&1VL;W!{w!t|DeM;SK;pw@t8=+Lv%hi8^fc
z*JzV2`bRZgCce_p;S3Ik=9@vhR4x~KX46S>bXdFhn;}IVoi8t+g2sN8jCWi`dCV3@
zO6i*>TdP21KYgJzLWnDSb9rqy=kwyM45!m#l?$Oc@Gjm9+rp?zqKCI7Jm-VEU97v@
zRZ&ee79lIzwKcP<bpGm6v)>(4;#6)9XVDo<ZzPpzZ!9i!UXdNj0}bvfq$4r7JM(-I
z##HVUgnJHYYfDs&Kai{~9n)DMzPe))5hh3fIa6Izwy;R`r_Yr88tYgL1eO4Bdt*0J
zymBXr9cMv!|89!!D2%33?|Qojo`^RCIMESnz1CP`yVGmNhR5!HKSsi_fOYd>;!<s6
zVcNw@U;$aY_!~sK&1mt|PE6|Fc8A08Wk`2wZ}tvP*OLnQXKo|YWr+U9^BK`p#PX*J
z-HO>r_W~vFv+siDlW0d~qaaJOoL5C8Zn@Fh{cGyG;lmI*c(3m4-v%v2qK~DGwOUTh
zZxu5l*YBc)Er||k8Ms=;=Z8l;O95NV3O7svY^ZZ^D}glitGD0j2*?z)w0I7fn3?M~
zk3PKlyuTP!&nPG;lKL^qfLOxl?J!o?2|LRx275t5`_l~RA>H!2SuSx&R+z`_O<#&<
zKS;X{7Ehb;-ddO0mU6NTDIKftSLaRq0o+t1-U69H*-BbrKGXF3<z~|BnWgT{=?0eO
zq7Ju%IcFY|M_sX=qJIZ=VS?0fCD)wZZxLPwKpMdTwB?)#d@>@EVq;mo2$&7UcY=5s
ztLvB2X`NnaOll#nWlhHLDDffo4V!^8wGKJ0`+d#LTw}Mogt#?8SEhLUcSfV(826@B
zHfvmSb8}Wc?Y71X)CFdpj~bbe2(jg6cPv%u53VNNXO`^F7iMLQSXpNsFH8DpzB^Wo
zMCmIo<ew=wC#YF&`fMGoUvUBa4|Hh`lh_kJS?#$qS&3%D3?b`u!qRB_MrtlsP7960
zi1s#T^k+^+{^B9@;1}&w_?e1N&5t*eB1y88(ss%m_KV{^8o1tJZG0D~xb%0Ii?its
zUF%^S(l%tq(Bsdqo#KW~1sCc`A?uEF(zKe55lgy09X-~Uoo_Z_;o+9vjesp1Wmm?N
z_@zANIfJ7`Zf5|E-o&gmx7`=iy5}Ssx~QIAGZ?TncfDC4N_)VaODI=wN{{B%Rk^z3
zo2WZ%9PD}j9DdZhAFpwi&=N^>;`w=F<LRvH&9#jV{x|%z@6b2iIE^cma!@PqciEap
zV_R(qrV1^9BN*842Ii2bc?>CIiAP*Le-mW!)R1&^`97ie6=>@vg)=FVhA8}r*;?WE
zKop!e5GTBUm<~AXL*g@9ELV3tnY{)Pu3D0`u&}7eZY*#9!Fd)qEOhF6zN*sQ=hBW-
z^Wno|%4z}!6*Kv4WY-a$oTToP_%w4g)_l9fz~bTC>0hyxIs{hbIkLaSJ0M$zM<k48
zKGvk>p^SDld1dztKw!sp<ieNH$K=7p+eYMq$eH;R^-k9jccxpxGLdQKW?YlY@0Vvf
z0b=!F%IW~2({qd{yS+G<wqEOVSfPK#y1-n0k`(wSK(RzdY}bDwJG(dvC04=yZi4;p
z^X-b+<I&3fQ7JGft?y#C#y}~+JcTy3Ew<w9X|@A!mh3Y*#UQlADma1P%0O|xtUTP3
z%qQ*U_Qyt>nPYE17N5%)Ig_)Hy@g(y#cDNfsLJMr;mhmM!m-26?QJQ1$zka(8>HR>
zv@PitDw9uzc+pXI^l%Y$XJPvWPhfT(MqYcp(tDDR1QU0pyzHF6E1(cc+vC9*e!}}t
zc|eW+1s9h-_lClxsiyfJmhQLAN~#?M6)f>3;xb;y6H5BQuwhck^l*#+#>j40Evpy}
z2jLYI6qf5ukwAb>K~7FCTzAqOGs7Dg_v0Z9UjkZ<*GEwpHFi9L96ryOQfDq<|FW!8
zy53}iu(0AIq3Wpo@HQL~;G{*@00ipzCf0`YVZ8dhCpveTE<dM=QVQ#>@x891)Y%%!
z$5-3p_!xYWYvmFTtSxVBNx|9P*7jySnAxO@sx3eZ;df`_hVLWE`nce=?_!Tn1)Y1r
z1I%=8S&8qOfSt3Hh?%7S8vMj38X-^nT1oE$8q;SuZ7amMwi2cP_oz$}Lp825dSD8f
zljTF`-XDp#<oVnUXRsf@Q2lR7aR%D8-;MWM_o8?$h`+oq(^Y&RkRo7GyUfBV2D)+Y
z7NOwM5<@(%`(QQWk^kh@N^qL8Zzxv?FZRAmuXj8*6feM-OOzJP-uNpcsp-l~OeHY?
zY8jeC*{G6~G!iUIR<C`Mx&3Gu+I6d!3Tk+RyFiP!TI3Bh{Z;>l`)y0e7Gw5{4m?u>
zxHHViAm7EBSFh!k+Muc^G$?$ysBR*Px;ONAIF!vUYUHCJfk|7_WM%IyaA(0!yU|Qj
zYJAam)5PAnlh{UI%No>pyn>*+vf63#a=d)}s=tMIFN1F6(H6IZ7f4U4*t=O~%2+vf
zmjJ;gFV_G~CC%iOCw9Z8L-;9|*rRPl=jG3HpsO#zSF$PKD9f8(zI`9kZw}{>Trg46
zU}CVmnYoyBcBwvP;3_)IKi6O)D=<DO5v8OC0`C7vPn;Nwxrs{(bxzaR<$g`AJU?##
z@~M!7Zd1b=Bmq^$>QHS|5tkFXChqjIqV!D325Li>$a}IhNA@rqA{{RCcu%@AS0++L
zSFF|j-mzpjswW-G;v+(+<RzW7)MHo=4;(glQ*Jb96qU46Yl-Le=TcOX8-!x`>NW*T
z<mDIp^&2q^WpaeoRdL2SbL==hoVDITjT4;D!r@k3yH{VSTA6%ICMgF+<UhC4I|tRq
zG;FQBD&Z35*L&8=2s=IP&+_R5GP_93lB!#`_5Dm>KA3E3DVsk8O*r5lHv>q(@VWVw
z4Yo06SH2+KktEX<Lqik{H^K<=O(`AT0;giSnwkqqEkshYqjl29%A=F6qn9d+B&VFE
zFD<XTW!k@Dox;yV;@2veZ5-!Rr4%HRtkwsF0G@^dN)ogdN76kNC*aa=?$?g+M_C{x
z^jEyZ`hYycMK*MvzQSEB@h8MQ)6;@_*$mW~UONTc^B?Iwi*ZXo5Xyzn2v;cyrK{>`
z#P3vOPd3ya8A;Tik}CM)Nb~c)5T-fRS2HAp4N}(jNVl6RJ+4NeOt#&Q(Y`6?aPE0X
z_2rk>hXwn5eUI+ixK9*ww|E~9n`IFg{%Oa`<mK|YdhveXqg&&p8w(tiW2BrpI0%R$
zdv-k%TyxJy-B_d#L>9L}$<3~6{ghUuv{!tkD%KDaSnzbu*BR{|-}+6C6FaMCcX_p}
z$QXZmYKtnQQq%UX=$z~$dR1?EoNR{{s2pBQKj3stOECg18}gXe=OHXeLu);`BS|*E
zP6LFcI&+^?A@T5ytZ=`dsh%74jb=(;jva($YchNLgj{)REp6Y0v!CxTMRyHNwj03Z
zS)?gO`^va@ZS7qzDjF|Po7<cf!T*-<*}63GwkeDMLq|~+qshoc%4kL7<>7<+%?V&D
z!G_5<bfhU-)8_d-coF_2H@$wT)I$hAJpe?^GTIn8kdkJ-cUlO=bl@0LWuCV+u0FTz
z5Z&=aSIA<WO+f});+x~U1KL+$Qn`|o$$+sjnQIW4@GIK*n}1YRE@r2OspLo*N~od3
zjZgZzD5{`B)4K;Cxx@dIOmx2Mr*%DWa8RPIg-6=QikkYIs2)rFjD3+0%y_7iGzqY2
zXvflkLl0f}XJ?OJ)?V6WIN*2oLGxz^&SDE$UYk>)X;Q=4M!g8%h34-T^R1(<-kds<
zR*&0s^~Dc8$QNa}>tNWMR*g>_hR3R0()ro~Yy}bM>^ToXMC3(6O5k2FOZYT+u(VZc
zwQ6bLz%uPjcMrwXp?PZ%R4F3597gavpPZWfh-SFz_2TZSN9XVR%O0U3SqhpGx4GEF
z>yDa--<oTD)r%2xG9ec{6cy|KJxgoRyVZ};vGI<Mb-DyhG$~z2L6}Xv^M;(4Jb)NS
z_6})qjyfdSnn;k(w`VB{SUf&ZpWA|lGImkTCi({#<-=qRMZYdy)M8HC9n?e6>$XVE
zUuP{}@+i=z%JKyiD(LEM%{w{ky{UC3^&#+Atm+AGcx)IQ!oyatZR+Q`BSE>Q>Z;G=
zmT-h*dWTKL%1GZw5Iv{xjOd&Gfu_LqnoD%Ly6}!2TJL59-qRB5TX~2u5<zu*OtTPX
zL}afBFDySg&0Tn>yqS_y;qVCiaji*+=rp#hWX*9XxgVGS+D@PxB~&QUG&&a*)Q5N4
zlFO>ff=TRwdz^>`9SjK3hrKc1`7SlbWZllL(;qQwZ%+j@*8nA`WWGu~Ol4M8RGt9{
z15-?;AA3d=5hZu+TR(sfa)GWy$NyK6=cMuA%ZA|L*C_(%&+?9qa#WOp6)B-w4-Z}d
z4|{r<kBdR((GkO{pmHKJQV_yQvaZJ6R$1faPTKFS@7T32dK$$TE~<}7Np`o)UkCn{
z4271W?)?T~zZE31bok=`4f-j$@ZyMN>3mUuaCEY~n}qc8bF@xdsaZF%?M#bYIfFBu
zoJMM~p2Vn6zIU+O3Lia**}IgvttSW^UbH0Dc0>M23oEYJadro-6H<Q<{%5=4DM9;n
zO^5o}PDt)V-nGj?A-OaRU9GmU32`2}uYUZ~-9InVV@W~l5)Dzu-a+YKuDEE|T7FMd
zn)jD!cJ38O*D=PXh(!q5keruL7ygv<Dx19|2k*{XBNCEKzz!9Dq%kHnj;WxiT5xv+
z3|L8kS(3&5!Iz`hJ6AfzwK8|Gd%UDlp=PMaZO?bU$p?aijSO;F2)_JIO&7J&Z7c0X
z-H#=$g~1`79#07g_Lp!~4i{%ca+QOT^YX^vZu)z)ZGC2#(TD4s!(v5psQw!Vx{K~s
zvM)!%pYuoK`&9y@BmdA!1F|{C$6(bt@Xr3#<b%3SO|;~|dQ{ZXuSw}PO|}%eS)+ih
z{VO(`ox>*HuydKc?p?o$grV+N#tD5f(@i_srLmcunb}J^{oz%J&2OSG6O#D2eA0z5
z@jW)d$&P$Va8MVG3Cw&oh$=c-lRj5I>9SvU7|O=p$kUY$n0bYWM<I2CjJBS93QVMJ
z_SP(Bt%J=YVArmevUk)}6@q5p%=fIr&4wpSI|aqAHMlA&NHov~g+8m@@c69Od;2$e
zNV^<@OIb3OB$aTlR|o{d?{AcsPDAfuct#dF9<MGh(Um4F?-wIPzr}%|#tW-bGIba(
z%YS-ztUZP#lbS%GEiUE)XOsfN?j)Kmw&@b~iR$Iz0=&3xU*7q)u3vmQK|$ju@rRs{
zbS-rMN+S#Yg=xMHYIS(vauzF7r)9{(&N-ziM(N;M!xv}Omn*YX&wuwG#Co@Vt0HYv
zggVQbzyB6p^L^x^oDd%*8anPEN=7;>BY)5GW0URxzcI0M=Gc<F=OZ*sRKyG_F=rjJ
z$+~Dr9RU5O`q4mbBdz9-d79MM2TjJ@=jp1rm>0R{*XER#&oaRx_|i8jbC~t7@q>E;
zt*3nMgJDoMVRWyuB~@8d%`S)RiC87(c41@2)b?B0+tG8nSxiayB1b)24J;O@4cq&c
zjHU2B%$ZYVWJhV<yhXB-P0)L(j`p4lU;o0}v^O8XGf(7Le6eW?uskA+{XxS(f22z0
zGqg$PHsP)<B2H16v7Clf*v4Yh{=^c+W2oeBycv($0gdwV*6tkpO)%82qKApl&4Z0s
z)@nTs<~c+n8oGOzppC<ZKYRFUIp-3`9&>5jD@mg7Dug7IWX=sv1~Q)pc1{<9Bb0KR
zqj<lK%Kx19E#-YQ@R$@u@zv61=G~sFkV8CHy`xmnil9_eR&DJLFL^|Z_NR1wmF`=V
zgF~gYGpap*dFEJT%t$eCwo=>DWG(8wSzJ(1M?l>aOL>K!+VgtxyrBKs%?jNj-X^!P
z{c?lIyN68IXDY3uX6DVXX_Jbh-SIgRf6Xq1%w06>W0GczD1rXgKQ>C!RB!QuwF}3D
zg72y`=)mns0aYuMgyxZw;9FCY_*R52yN3jTD0$~S(fLgAAqgloVRBeD`nAzO?aDns
zGPlh1)q06zFzUC%37w5&Hs&sMV|kVt<JP~m9iL<D!u&syOgKbtx>Zk+uGgdQIJ{oV
zwC-oq(N&b#fFu1&JXGmj@LhiZQ6FSigg2U(gpQC@mCIVPRL9e4m}jN-r0qk<V~-m`
z#>KCVr>ng_^2dB~d&k>l6yn3QCYB>#&`04Eh}5PTc|#-0OF84OST7NnPL|($lg2fA
z2!^=zT(IRy0{)g#52qEZJzx9q)mY4^II2FJrz+B?@fX>sqoWTDJ^S&Os(49-El3iC
zPqM-_T^a21Wgo2e4cXqwquH8Ia*v2e<c>+^j*T&&b>-8dr^9HpQnKw?gvknj=F)Xp
z@H#YR3)Z$_QS>aZ%umrW7Q@z+zvRnY)m9l0T80ri@_(tpscZcfrXWR$YqKU|{``Od
ze0T+I<OoNaOEg-KAMp8n!D}yBl$K7qK2Y+yThl-B+QDRr+N}K&|0y57K@Og~N@o9~
zFM<2*gS!y}^X52}lEmP}tERiN6cq?9lOEb+62|P7Sh(k5&r{~?q*BqD8YqZ}|IEnJ
z@TK>BJBynCsyag*@$5R&B3}?!45bpAHF$`hhJ`WJT-93ZY!8j8`g$TrerTbp1U5?Z
z5yB>&O~`*$%w6Vw!ayKgwtX72(KWgp%!tK&dzknY0M4u35*4`SvPuwk0YrZTBUV@f
z+>^CKoN&L7v~J8rQCy%#<=4!(k*Mp$2Y^_?51jb+aseaq?!9*X<mwBa>v1im#i^9A
zQUt-71Z=vFipNCz1g0=5kFQZ=Ej|6N#i?w-aoTp~0L=wrpUQx*J)!zX&4xb9=$ZXy
zbG`6HG$t5<wa6z0%jSmKTZ-IwC%?(Dw_d%Aahk-C`rb6Rvlio7o*B)bw2+-C6BMO_
z+ubxw#WI5L>YS{<`_(!j^o7iVs8-~ati1XdcZ0nwId0OSU<SH-og+~YxowXbnHXC~
zmn9u4LDtM6>5n=#q)~HNlNyUqV=-4dohiFgpP?f3(8u05ufx_$w!~qc=N6W77{3k6
zIB(8Ri#?lfaoT7;Gv%nA6_)=t9(LQSr#7~lKQgZbwIM64Qq_=vOww4*;=FC-|4ZG?
z+~tL1-v22uf>|>?v??X)2eRR2rz!qz%=z+wL3i9>uX9=LeF|1V;<No-yR8p8eF_7w
z18xFkU3Yq195sNPN@wbCj&Ty(QTzPL_Z+>Drmq_k-S%JUnP)DVbH!ka7h=F!9E{*3
zo$B?;T~wX)t_0O1x}C2=nZw!|vI|72Qx$Y;N(MO#7i@h8^GQizmW(Ey@HNm5L%O2E
ztHBK8t;*f#B0ga^yE%W>HBPdTRb@Of3JXc?eiFm4f-~K&6zQvTsqc@coBcEy7ycJD
z^3v>D0(}s|`fi6mn5KW`*Ktym<%e|5UP77SNCVJD4r#3?d>boQC$$y7%Hv{wrXse}
zf&En#7Z&3*)sle^D1qy_<D;+UiYV69w2((K3;5+&-n=Ix3JR{wc+C?5JM~L4-C?z>
z<6zU%6*A{fT_ycFxziDSJFy@lXK8=8rj^*pI^7y$W?XxNT-aqDSAYp4zN^|(SPU5w
zo(IBCie<8i-BVpX$B~x-9eIJ#A|7A=5fq(7fY26gALUOkMjdHZ$W#SUz^i*i%Bp$u
z1_?_z8egyAuRG=h%hM>&u5^<NTHrevEWOY;rR@1F8S`bfF?w-_8EZ_8hyKQq3<1ZE
zl#Ya<l4_dUC9J{`^ZRB|e@i%S&s6nb;h{EW5HuI0{xzXuWUx<-8@tkU7!vj-JRm2%
z>dA48j@usBV=GD!d+hp8CpKAx9+4aR0TYhf=+%gqsK32F5N$zCxDnM5-hl<w<XgW+
zG5HpU;-f*(QYu<afB?SEqFUZK(e@bY8E^)wG|1HPsH%?vD9LJJ!-i0^IRZ{=>te0|
zF2bqY*;VEFy&JlSl=I0{=iqH_M4sffvGHH+<u#L*aOD;KX2A#rJ9KCOc1lbZ8b;J}
z#GDoI?-x$Ngx6`L3bfWT6r_=AcMhJq2O@zd?f%5)*_~%Da_5Xm%yb(q!R<_zRk`I#
zn5Da3!SiS+i0!yP9P~)zs4`Ro=&Ur5EYs`iS)r)L>2|Z`zD!)OCk;8Th@tgb1>EPu
z+USfWM#>*1b=+D=4oLV^-|x{y1We~qC8jf*UMqcN5_d8{d$xxYX?rOp|6?P#n6_#+
zw(M)djbmxIyXWZ9;kaooyYq_}yD9nC%u{wY=Jj4-g>x{99fNhoMB^>@KMy*b&U;1d
zYFOabEE_L5QxE7<Gd6cX{1_GuMqz3`N-+1l*|lmDlBXqa_z2(Rpn?hQiAt}45Yc++
zdUEyBe~-X|Nrk+!O1E^uo>0KGRRL`Tt6evC4xW}m<kVqF*1vH5kr;x%KZJx;Z57p!
zs@IN!h+oUVnvod5FGUE3q@?DxT?bg!!6s7N3ircE!1D%{vPpEt>OcxsHYlWh+QvY_
zxdXck{QYVkf8;}l1x&bKQa4EYp^75we%k4b*WgzMG+{9`afOB08Z4+Oa$1%{{8{sz
zbD`O-iCF=4H~nW|!`=gK6ttOjvzI^dWySE9+VtEBoHZd0I8kD|%bn7JGoqwyNf!-3
z|3DMuyaX>1?{i}uGH5dDccrtIrXYRWusa81^s;q94<?ZJt90f4yKHEv<4fj&hKRwE
z=~9q&T4}4LletIbtTjH;>!>xZ4|h>t-@=w1vTrlBH(29R<F>-qCKL*n#<$<R331YI
zP|fHa#h$+}o9$)jf?cu5&_LF!ui9yjh-V49jXH^^c{n~m+e^Nnr#yDPUY&mSd%dR>
z0`CHm!sgf*bm$@E*EzT11Sr;N6`j_g3trPQfoPfIbpaz=SrafXNDlj2PV7U*UjPkt
z6yG_;l8{wxf$6LLQe6Trlg*g6Q9SvunGmHfl!p`%C;It}osV4a7qO)U(9kJJC(kth
zz|OfPDt|<^`@zJUSW73frH7oWHH8<{T@YlX#l{c*vGd~CJ4AESe=uyX|AdkGTmVi?
zQIg?1Gw~6}Tz2C^wt{V024`4*3+nOl)&iCs6}Ja6rVAoBW{5Ag_`;mm`Sd7JXYUB<
zn;Wx-8Lo8LD3aF2ShI#^?5_li@hHEMjQbNv>dzEhI=?Dpb%%flRCg%YRU>Erq662y
z!UwV7;CoCsXTV8D%9dJl1YQ*O(!TRS4+t|`#cr83UNIAVkcrcF?`PjhtcC8tgr^VH
z*;r=H@&;%c*pTLP0h}Ud)z7+6Lr%(cDkF1CYc`aZQy&LfOUYwDkFDlRvPnXKdU~Us
z9#J~9SpIL$)1<ih;z?nA<lC%SCSJ{!Bs8cefNlDNh?~~?OZvV_5?S%*ocCs3<O$8;
zP4}~5i`BM%(tt;`bU{mF{TeRloSSYh`NUN-x$sbGW{Ezdv)XFL9d8YaUfOq>PHjVR
z%?}_f+83v~DvJ7TvPFt7#K!uT2&GqlYkh0#8Q-V)lf9K1eRq~&leDYUUA_g3h(t(&
z{CcQG_hw^~E~($Gn8*dtRIsslZ87=A|5pD87^Bm{keqhn7e76Io!c(DD@a^&e@-RU
zvwGeVXJx#xd%`SY2G=BmF`!7z$#;-|^y(j_{n5W&8EtsreOg&;I4}9pXSN;GMh`xx
zIv#G~ituWGhdC4p_EXx_D+!e8L7`2^tg*Q>lUR|~x^jYo^p00^JPx3uHoIuv#oH6-
zY#=f1#!L>MeRp}=N@J@v`D6zM-1Q_#pTQFcN1%G~Uc!W*d#yoA54)oS)HRGEME~RX
zx5K7g9~pj94qLE_k+nloBPN*6$K6^|{alF)6e6Ej1cmhg`kb$>43y65v9i(<s@?Qj
z2R(b$=8({Ax%$s464am8&xU9lt#h1XVh&taY`=lASjVtWV5`F<e)0Q<H9d;@-Ca;|
z(~O~*>L<?^iIxpi){e$l1xMxI)Vo7$C6}OQ*vGHkm?jT^Imdx-E9D3+z#!g7FgU=7
z4Fij%MtPrLt6>MUv1T;11I`1CN`}j7yWO!0N4T_LqM<~1zbE_)<AH6mZI2~O^YO>X
ziMTv~#)RVnbv8%4+#MP9ai=&dmK}4xVx%Uoym@5lqJq}&6pF$A_4k*taVIO>j5yUv
zM#5<`nvvbKTL;`pCG`*iTZY;jxm9Il65EmqDvD1*wtAHh(Sx%DEU9WqgI7^~$i*0w
z3|HD8X2VTcINvb=$#CxXhbO7eB?r$26R@gs_5P4Ap2;PyUR?uyw@;nPk9*cdj76Vx
z$~Z#pcb;h4&mKGAd-%g{p<-g3S6n~rw2lDJ_mlrUE%t57yFboZftiebwOO(X5oIme
zI70;<1V1wbq?y`ozI#ENqCkuJjOX&>geL6U#8|Er)eRoWXXo{r%79+Vcf7D$b?q2Q
z;~#2AFHV7O+%@-<{J9+GKT`i6uFiowuP0jLQJbVmW7}zLtFhhKXl&cIt;V+PG`4Nq
zzNi1S?pk-<5Ab5n%-J)u_p^V`enmYHWXl}3Fk(pvGrf*XQjE@5_$-YO`urx7Rc9uO
zcY}m2bj*`1LYF!dMA+YSb0JGQdGZi%y~Bl&{=)H7DTC4H@0Fcn|0pgcm`z@4e<;|S
zXOVO<d)9PHR*}V&c4%oS{<N{e=Li*YzK@7sT%-{OXHM=r5LbN1<BAfrjvk~j1y07n
z`!6(#O508OnG>E|j5|k!h^sonso=qa9JUKm+_2v0{T@hQx@4&p5(02BKec}g*XcYi
zKG!unUyP*zvLYgsy}(46mZxyGk`)~XOGR5yvaNeH`?HYzo0!|e%dfc2`qg}JaM<6!
z(bfZ}tM}6;pDxA`atF{<j^nreytb)~g=lP3nK!$^^T>@QqSychA|_FBkkrnDiltT}
zZ@S5<a&J&x<l!C9Hdo7YB#<`|M}eIic59i$+tMAM$(;siq8?CJ?@?%T6+tWHCL>CT
zH1B<ssQx(!&ije&qwuJ$fU?4vIK6YvhLpYj0dyRUn?Lm+Dcbt4*3+R&h=<h=nZq(B
z8)NmRx5jKO<C|z>dM{zFXC=jW4tPxlAo@~;Wos7*X*7F(%PQ$zMO0Tb)5V0iJO6&B
z9<JMBE%G{;_CLB;M>q|mT!gPt<4Ce!cjH11Av<$C5!;ORQ7oWd5I5yUJcLS<f9hCh
zO?D-{b(i`TqF2MDh)5%WA$<GB4c9g+f80G%Y6V#t9Z$eq3TZq*xY+_MKC%nqd?6b$
z7$2^lf%#e5s3YD}CaW8t;-)`aW7-*l&2L-0^k;-3$W*iRkCUzz@0}xlpdU@2tP%#=
zY8@#$p0{4A;$rU^&LPy_U2vV7YI+@XZ%`(|EoiirX5$vJ+)gOm9?n814-Mvu<vkwP
zot2q2@<`}vVJNj67S)m>zF@k#Z2tFS6hrq{34Ub}Dd&ldiog56&P!~i?(;FZ4;6lG
zT2@h;c<1w;FLjE@xkZX)uDT8LKZoJ0GEyj@z)8Le9OoOIImq=gKNx4*+yjUh`z1wL
z`aAuh+^=VK87@1a<?0P#R_m?8ZLS2h=pn5dyF2a^p<8&^2e&OWrsTO9P7o1HSxEHj
zx^5vY0sT@5^E!i21N&^Sl^M@)@Uon-_OEciHB1pdHR=MgAFYN+ncG`8PLPEOw!UVt
z*o&Y~VC!m-mbu$zI6Peqt)NrK`Q7?5QJd4Ag#OKo<{g<=(HUo1)CBJ~h~^mtG#foY
zL2(=q-Q=!wBP_!|#)N_+qIqiYdM~a}{0h5;?oNj9`4vOqH}%0}tL_(Fv(Cw6Z~3Go
zm4~Dm(8Y=L^!}J#)A=L%^DG<DK<J8be$9LSG#}Y8aeM5KH{zH)4N}!Qp^v=2slEJr
z|5q?jG+NESTF&cAr`NmDd|CM3ZwzVDi#t^zYj^9C7Y&7M5W4SS=SdCog&e0xizNJx
zlYdXUT0(NjrV;K;vjasJ)f(;LC*Rq#=@x~&uZK8u9oCY%?@({^YA*wXy7B;()lAN$
z?JzR?wfRwdNT1O~U42XO2FUp6XdgW+r$ez;X}e%B+}Uzn(ZqvSS9V-noH}3ZmUP(I
z&*B>LNW-VS9>ir8cFNJrdD8O!q$tc1*yAN%e%#gF>9R6O*wc)@t_hAnL#{$U_A9WN
z-7<|2o(Jfmc~l3O!|}9h_G@rl+VK?zuB3>5&O7CxgxZZ&?AbzTVj7x?gCg_AzvUoD
z*9{*zT+eTPEti>_YQQ1%Cp*I~9OvbSL;FgmjyF*ibPoq{aE^xO@L6|iR2sM}E3F>8
z)mwusd(|NYVb)^Wbp&v<EG8pbUS2+G)h>D@W@BrTp@Z)JKa7*u7M9qw>DJ@6G}`9V
z#7!ZRKc-Q4XMbh58vU-guD>zzvLrF<u-U?%E5w!a=yA{^+UnnXN8{P8K{N9I+lYPf
z53fj7*&sl3GeTUr@kAoA2F6mE%z1e252WDu-mW-T-8-}u6cx?ih5Wio9SwwJ+O8oS
zlJ5iKxgN)A+s7B;w8_y@qWHvl;d$&Cu|0UHC}Nu`+hZs^-8Ik?z@yh>PSrqcU=BN`
zWipNx$#z{{DpRe2_0h#*|9akZ`*u5d+Y3Wi?RIZw)ppL>Xt_Fgzo>(MUT7WwM$`4V
zRO1|6tRiPA_DKi~^fyH^ljzw{dU!MktTXbkeej%N7Uw7P!P^0rN9z~pd}QN1uBr&A
zAaL}UTXuTau+yH>4{I7r+LP=hK)sYbgy$Z?_kJuJfx+Ns-SwU%%lDpevEC-yc8nED
zL8H-ad<RYJqBn2g(*A(MKO}=`BH31fG_)yQWOIt8lFiO~c5XkTkm5C+Dj#9OcbJKf
z>l_YFpxfB#6*fPDtE#5NZu2I$G$tUKUblC1zu5J`()Dp)Rbf)9O@pbM7PDipQsf;X
zz`T9RlS4dQ!aE{SY7r@Dn}mnUXv0<)Y@iuk8F8`_3M7|=DJXF`%tg}JtvFVNRHl5P
z6L!JK<k^|zzXwkdF;Np~!nKRk3^vD$EaQTKn3@Ue6r{d5nJ(roz8~(?{gsi4uwjO@
z-QiyiW#Ebt4cDw7FSyS)FD^JQ5y}Tutjf@LhJtwGcd{C<<ANQ~L#x;fUG@_|g0^B~
zvThF}aZa!j=CpZ=*>1l<gMbZo+y)Wgq}wfvVZT6AU3$~e_*b1hiOq=Z2e$8J+^>PI
zzoo77e*`Zns*cP^)3go59ogEu(47ui547q|xsp4QKkV!UXQ-6^<h+iAOKw@6IarY*
z%5CZWRJ6|a>L>^t#fnQg1F{td;fqud!NW9Wu1M|&SNw<(djz03kIzuQI4-$N*lm=S
zD35^~SL-bA*!UlfI)ZahOEN@EM%$mND&JJzp24En-97HWzX@K@nv2p>T@!P$lnhj5
zt~HV8wK|1O+-JQ%d)7hm907-}(jiw#K@#%`?3iqn$wVdOi3Z^I79Wz~xk@3e9l8EC
z$D;WbTvq}81D0=0)6^Mi9KA|M3Q}DBiZNHV2(3$dk-1REh0LJUIaK$T&#jqqYzC6e
z+VEfjhqbO`RNG(M%esSpc!!jL0dI|TaUnQ9ODXBNKgSJx*J+iuL*Z<8EBA`$P!;^Y
zNl}4&FDv_bTod$fr4QxxG*Gbzw-m2!OcEixt<pXjW{@=S=$PJ6s>^SOo{;{wPHV-a
zS<0J{EdPd!m+HueJc%LNMyuv<w(e_h#g~}8z&mlBEZfM{&RM&n(FUf!x?i5>q)3kG
zyXB9sT;G}r(UeX#h`Vs|K4-JlEPLD?u088(fG*)TL1l8#3@STL(D0Ddc-Rf?Si$sG
z+yl#JRt+R!_LpWwZ)W(qfNaXsVGMWwq0{w{Wb`gPo!7#VQ(;2+Dq<>CT6z@LWaSQ$
z5w*_xOD4ya2{J6Srj>D~O#pZm9kSygjazcQDf^9=0Y-Tx)@i$}1vU~CMihtmLdfBT
zMEbKJ;R9+in;MG}GFUVhqp@dl;q}z9B5@sB<Sg&#*566{mhE<7&(i!`Hz6DsHVTiK
z6+KW&Kn{!F^`IyvMQnw){nx(<SqwV+qdhGz=uyPlP)$`)A_{APO8@*DA;w~#FATlN
z&w8^qVMXC7b6`Rnxl*>9`$SyVvUtBV&)sSL?!xa<547M9l+qA~j7qSdLac6X4;zif
z4$S|6`>KWPFe0(P6zfU6a=}9`lrvLpzj2_$ewGKTyVK4|M1bSGR2(DYk=;j7i=bXu
zAYCd%67&UpB0VVhxZk~Sf*s6EV5&lp)4%obYHFT**XbBa{{6`pFD@ejC98A)EMz^6
zBS*uCb5Zu2xA3ML$5qaGKhh<1>n&QxY)lkaNjem4@Bj{4#cy+^h9L0Z=muQRV245z
z8O)&VocOlcC?7a|iQ(n=(qugLgX+$Rlf}U3gG&ATg$_WYFA94+EZ^tO=Dxpfv(EaP
z$aCfQP1_@%^)ejJ(=^mqhj!k+3;G@H#<Hc@;3$!c7|vwsrgm|l&7*pjmN%niQ%JYb
zFEPV~ITUdy;>=%9;(&TxwdnRIR0?x5xoGs6;;pKi9au7+`8nlu5gcZS<k8y826<AW
zIl7kf2C231-_vHUd<yk~b;9}(|BaoiVg1qcU!h{K*sd=46D=oV8?M_L%+mEH!b+nY
z$4AaS<AmfcYio|%(;19b1cbAD1CxihFXaO$$tuYmGU8WuJqd`K_B-r%7{%3&y*Zs%
zS?ltueW9TtYkbc$;+sNM2kqMA=(g%RTIxDhI;2Dl7p0q-KfCm_TrLYP)!H01&Nf9O
zhv<w@c--DiaTw~2rTw-S94on8xm9Fv`wn=%h*2D;#$#SH;8y^36GioJZDEq>&%H<e
z$kH=^?3u&gd1pG5COG6dtL-$ACM2_Z^{;;_{p%w`7O?4``-8*a_@mHNp}HzymChrn
zy+c^$?Se8mg*o52qXWk(S*DA20Uirx5qAiu$MVt>NIOe_vnCqatG3|+S-Qp?a9LsP
z@l<iM_<GpW^!j$;f&pe};x<9f=q`I=V=Y?<t7vd@qamX7DDrhK!>yUjXH@Q5+316=
zzZ5EmkhZ#iK_K$6yZ#J#!Jl}69=H3^5FXFTiX!LV^YBb?e`~Y_TFEl(?_)^{o`A+x
z+9I51{hymM%RmIu7m*yK;<(Bq#Pz79fPcoZH!mD<(2f>UYR}?raQN@9kP>aYPdcNZ
ztMiSQio<a)%l(#}qH4UTDOeX1yLTOyk+9eZ$9Y<Y`jY7$B4x{EOctiXUZoqpy+zU>
zkCyBVnHa(zmk>9(k%gXaAe3PT?SwF1SSr*K_`9(l8(IKr0+*xzf<BiP!)62P2V^r2
za}Yl#z2rStt)C@^09A~LA=)@>GSGbdV+&qh+w2{steTw;zDKJ1&^#t%Yb_q9+nNkt
zMnr(g3ZC<-ZrhIQQsNziykngZ5>DBXgWmO9G=xEN0Zpk;zc05ox_0*MS7(Z@DV83J
zUC3o1mu4bxHB)f7WzTU*g2YOmIS#*_KWms8s|&=5B$A!_C@7yKqkl<?LZJWzKLZ*3
z(2FTdbcUfIbwp@wcyxUGl>Kv&6(6{Bm(P`O%mm~%#Yhc7i9^pJH_`_3^-MA0zvn3$
zkY2|bs_-`9s_krephU>qCwivw6R$}WAQ|rC@=r)SCN)VB?B^z6NzvxwvNBuGtCIx=
zMx_PkB9c{SQrhi%INZ_|w!7WCPCt&yC+j3lEGQ=nn12mK&A<+6))bwfMupz{G@nBz
zkcx#v5)3Y>NkOXs{S<hv_bLvj>l+txX7(%7dm!OR#r*4BSI_n4`mL&JlKJFMPeZBX
ztX|}O)=JE=yVsoN5g{#~_7-Buv;3RF$K5a~&6=+0J_rz<b@jr)93~{hh1v0SKsx1%
zcHy`^Om6SN3|`NuIQMJQiI>-z1kzWn4um^rM3c)5{lej^ru$?+%@1O;(G|8x9#vas
zKj0@8<RnGoD)ygo9V(X~hbJ&g`1!9Or0;=;xSL^`#{Dt$_`I|J<)}4CFU2HH_jZM+
zT)jP!iIykhQTt^hYb&GijGO?Bz;inKwOm}#md)|CcUz(i>-fQDqh*l6Un$;!E>Q%*
z{_~x#3XNoZH>1HQUvcBeKS6xEzfTJTLO?Ygi~V|P>Y(Fs=h5T(0t^H;@1@&b_xDBx
zw%u~I8Tm2?Yb2k|80r$;8tOFs%I-|d9neW=O<w4F4wv%<=f;&aw!zHSL7cXpA?<*Q
z6$xj~T~V4~2f$^}_W(scG$o=P`ad~6ZFC;1TepRJwH6yjYk29LZ8%TkC_<05WW%Qi
z&zo7IGDZ?}D<P<W9?I%T{LqsbCvOC5frJQ)Y`N1;)Dtz{@$hZ~hhVoM2h&Hav7W=x
zd{7Ullok}{T+dtxbYED0K@)Z`*?nMM#rgw5liQR4F-1rm=)2BkbN06r$6Zxtt@5dp
z)c$9-lbY1gbYga+MH3gmdo$MCOL4HV4v}P<#(Ch8a^@()@k~cp0jbnZVktsG4Cb(*
zDwG#kVvAhxWs@!t5i&^Xt}m0-ce#{7TrG-!dmlsQI6h9MFtun85oEviV>1MDkXVCz
zZm5<iCI_4f6?-urIvhFXfPPR}DY39W*+lXA$_A+bgIItuH*{6L&yAs6Whmr)(sYHC
z{CZ{oaMw=4S%4VFk#TcUh<G!5uXyz<>m152EW&j`8q3LN(J<D*<fH8X`=K0`smIb|
zKU$tTj{Dr}?^eiK6I^zZT0(abOxEL0NtP?PHSZkqn-6N-?md`Knza4Cj|;9GltKO@
zAtq4VX;D9`^QdtZIgMcj<?16fl4S@ZI{sBO5g}W57E1207f@~gJ}b^b3{ya_-Bw99
z9|aj4F*P&wo)`)_YZK|W5@Xi^4QvZV#&W1OQ)<-T$nGyD$~QH~t)sL4B6hUy_TR96
zP#$IwNRzPLbAf6>QGSP~g5grYv>RN1^pkso-*ZBusNiwpxSGlV-gNG}8J4xH;Mt>%
zBJfTjcr{!Fj>oh-7qdYchf9t3B|$o!QPd_pStpr$Fp)G;t$m;_=@YV%#!!$CoJgwB
zD#Dj|Gbx!UV$I?4`)<**g%=}W+xkpdv*{k%P~bdvCr(*?iC5tJ%TzX7Y2aPe)TaXD
zeAo`$MPMQCH|n8V8=8_bV`-@zUD~Z=I$Xetw7LKESb9L?O|&^N9%^x=>JkK8%%nKI
zo_c%^gdKe$lkVnFiT&wVaRBwWca%057ukl>)oVr4lEIn?K7}Kv#<CvYfA1N+8$oOE
zbQ|UjbVot*P%X)=Mg2+TvNf$~Q!hs@x+K%*q&=t(`4y04Wg1^9njdN7u4$4snCac+
z15=glNB8F%w2J1ySsT}yM}qJaj{90fadJ+oO)x~V>)4sd*a=rs4(lJqW{{9c?yWHW
z_ZO|dWntT(<uzC;#2(k2y{yMCI)jdCfX2WwVzk13nP%vAF#3`4Ibl22y4x2VR0u`6
zicu=Q=DkhWqf5p~gzoSj?hCk+=oGt_iImAAyic4iDSzEZIj3&xlH5#IMS+@f5PG8k
zF^5xp{#RAGm}R#tVnp#|jg9i#2`mXA7f&mh78{Q}=d<pfQgswJp_L+X`?;%)*v%UM
z$nj${VB;Vw;Bl@})PmNtbaqRgHgqd1(=BZFILh4?Yz-1mHcbD9%gWDr^=mJz=#|k;
zMd-vcB%%nBNTOpu(q^Np6Ny2bf;YYJX%I~%5cAp((UZ$~*G~6Rj@gnnbUa?yAm4QI
zF;7JzSS0=%NY%kk6S(a2zVrIHEr%eshgNG7QZia}^0h?S@Ha8`)g%&#UqmxTmZ}Zm
zs}tU>Mm6mrwp+F`@O}r;XF2^XWlajI>WI^xlncnL8O@G=Y#lEt90%QSq(D^evLZCf
z)(z|(vkk7ytm@s2s$A=rvScpm1w~tC!K@-u;S;l07TP6*Rsagq=UX2a#9L1xn-y)*
zLv=vN5sG$Sg2l#eJD!gTR?74QkY60%ZTQ&pRGTEmOkPiBYK^FTbk#C(kQ_O9vkG2x
zt#nslogLB=y-dhZk9KE4M_vtNBa><}Wh~fqSg_y<J!rP2iH!B-Xl$q`ylh1LO`7Pz
zV}K~>wIU{N`z3v${(K}mIF6Rc%YD#z@!MYp%7N298fHH@b(Q`TJrH9@+)-eaf^Sv!
z7{|oN^q8l26_Nbv_>n=&95sqLat<y_xO`sbjQ_5{b<-8fR@dr6o<3@KeQY&o^w?8o
zy4JR1Od0v&egfeq1HIndv8m(YeMsez{rKDA<L}pm03@-gQEJON7R_r`Pn8umMh#qw
zRiF9rCcDcj?cCh*($-g|G_`icDg>#t2HotOYrY08b)@&U#%Li!TS1A?SzZF=Y~w-0
z`+rQV>+4Ekl&>Sl88JOH;4%O$ii|97g97*ORX~%_Yj^zwtkkLIX$=#nPVebz;o`FL
z+2^KOCS6}a3wzUPBF^l`udLNOsJ7C^+&_By3vb@cPxTntxeg%~+Zh3zIK4b$jg0_5
z<uL5qNb_MUdPymi>TKlE6cvPXBFPpEor^TG!-Rx^u{MJdxb?|Ad6ZzZzwjo}ur&q*
z?m{vyRI`|knzQ>swmGknj*_@^YhzVu%0Qq7J9Qp;bYv%3_C{+@z;0b(4h%YzQ>Tb&
zaq+>mg*DuvQ%4vtMVC~~#GTRYE9mUfug;|BFPrjcl!Sfp<4<G5doRwn`&sZyN(~K4
z*KY!GCw9t20-E37P-ASOEGx={NG3d=FTg0N!2XKCOIU^ozEZC5llk6VAwkY2)XU1|
zIu}tJ(XFeN*`%6ERPx&59O9X7&I^~{u+uZ#s<?`Y)srD!n_1nIN|@hc;yzsp2c|zC
zo<z5}|4ps1GL||(#7VMcPaj!0lcimm=BaV+5H1fuxRb&AdASGLfOna(lo^Y;tEN#K
zQN$`^?kFYm%fiIx_8a?x<Y~mzId)$GQY`!9?HS7gDPgsDchPMe;y8$Ni8#HmIb$lI
zOm|XeoUP;yP1Ja}T7F{zt}@dUREe`>u!Z(cX~Obc;q&uY#DrVUi+3a<Odg<5<AadV
z-qi-Yiw`_W7S^ZeA+v!pn+ZJ?D?=G;lTW&bpX#_UJ~m4<T-`P3TK6*wmX_M2M7Fd?
zO)hg?eXSet2Q=lKhuBif-uoNfzJtu9<dGGf0yt~L!96^7C+jjL(op=LcNPn1`^&fz
zOi(dXLgLZN#|be#0cT$AZSRL7P4V5~*v7U)IkUOwQW#REjGnujYFe!%`mZSLm+g25
znVQB^T!1PXg`=wKmuV6xaqAy7HC3vkHBe2kE7UZSvu{fH_ow2e%!C-zZOFm!7kZ8^
zw%dc--|1#B!UJODPq!p(pIywx=G&e3tdBEi2+J2e(%U#Wdzkx-+cu+2kih17h-BJ=
zpgE~G2B9X!G*(qY#WJ{_{cKZ=kcat6;Ol`Z*qmVe76l^$Bxihnc_-!9!ib~sUAC-0
zUvnF$N_Ze`^!N@VvaenI<=YhKzAMS&-Q+<>#$xjHxk>BgaC7zql#CSbrLV~nb#4mr
zY&GM>GE(ep+dwuGZc}Rz%ZbjiFLua6T0QlZfHb&?)W4heX4uciYP#Lp#+pwZuC;j<
zenmZZ7~yJzGHi{p0#(TULSl;Q2mk4T>fLqmD-OBcDfmQH01A)WgL0SCp@u7=I^;)X
zbtz0_18EybY0-Xbe42-J7*{)sN$zjKWl90Yn?MB5UOgrwkl-i_ju1GLkosVRv<@nR
zGNJtM?B_duu1EXkn4Us>Yk26i$rD?BZZ?<5$u{$sGFusCQ3MPe0^}-1S_^07Uw-X1
z(<2F;ckx3>MH8eHPZQ?d`$Ht27#f;5!Bv{TwTHyW{cxM-l{RJM^Ckvl9FpSIaoDf2
zmJaUUdxkU5&IRDpWTY?>S|MDD0M@7|Nyg0Gf&9VRs7iraP~`bQbxp^K=ftj~{km4s
z)e_I8I*!sA6q)&V6%|y!Z?OM}26>`)HQbFZ4kA_j++B9QfINW$WsX5QWH6H*y8Oq+
zL~U-xoBcTX+QKKv*L){^%VDcM!o`&|g#2bC_AdAJ+6(2Alqnj@S4aIxzn`2A!r@XT
ze2~)NnOW;gT_p0<4EJZ#Zx_~wam$s7D(}iNs8Kn(cDp)Xq@j4<Obdjk`@0C2IWp$*
zvcbvRU|@;e1hZjHiF|WRudYs_*_&1&0Dzda_h_!sbt|6uc?$0Z=gRY0rcvtdsZ>=y
zXb9BjC@Q9S6t?vr4jUgDkm1FFUr1o3qCy;h8H(@+_u_-=^taI@zEMFv`E%PXu8yyY
zyMid3>VOj`rE*4YC^Ht2G)-}w>azFnMRN!KnpA18u}WS4!z{34r)EDS5AwPGMq@4A
znrON2IySB&`<EFrV<T|;c-^27ZZvSca<|6uQC=@${<y`oB*tO8{+kdlA&x7#x(hj=
ze#QlQI?SfEIQ>PO4t2aeva5`#%^ps90uvz=+M}?kaxrzyzE`p2LGuSkTrE7w_3qAV
zy;<%+OI^&wpLV6{IkPv$cQ4#{TF`8YM(;g1$VQFNhpoPH+70=bdUQMrdeNo`cHfB_
zb`h3r+fKdEOoZMMRC;Zlm^-65{!`53PI5oVhsPm+Fb(fE15$Ubg(tm%gt%F<`bwk%
zg|3qImuV&dAV7~r0QI$8>!O1jB^BA7`L|0gd(mkkH}lyStIJ1Yhv^so4w<%5c?w+B
zif*Rq3#gL<pj{y8Z4HUn(_A}T2VWwx!aaK1E;%)X;2vKspG5l6X0Q4M@|CiJIAH4@
znXbEN$OzI>*wycMOG8kOqJBBqZ%0#sJa+~B$_rO6+UeFW&l!t-1jTm0h=_7_gbjz^
zQwl?2sedMQMU=aI$6V1fH~jY;pN~(KD%zNBIc|wbNk}ff|53rQOC=_)WDM=hlR#w%
z&&}vZ;pN+vU~GR_7%q-BReik7x)3Mo+ilnI@t=j;^(B<GQ!ffPxjdiC_56ha=y#O2
zi_c6yZ7XgZ|EBv<H$$m$epkBzVZ6i`>(z)U<oR+t7pqiKP`kF-%5NLhDo`xVM945n
zU}_At*@@7(h9J%*K!So10A^b|Vg!2{o7N+UEDK~?IoCFFersKQ@nyVuD_CGg)5GF?
z_SO9M)<Wa<URde`*EUZm9;%(1dc^3A{{;U~Weoa37bDC@P;kGjWbqJ4*?7EChFi-b
z3a+S(er8WU4pPx8FWTR1E48?BB%s?rg()t_qspPcB$TFr@gw~2x3&r;L`s8+&dFzn
zOKb^2VE`>1F`72E_dp(ZP^lGK2L#RQd-&=mGjbys>sk_y@-cTmkZAuM%QR@;7KA70
z_Jt@?c^)DO8iS}aS#_h?f<o*a%JK+Z=9<2JCl{xEgx11oK4K@Ma7`{3*l20)(OMEA
z5H-Bl_?S3d#7|Xsc%3uX=r}n-B=Hvc!Lv14SC{eiJ>~PthW8B5?3yAo(QrVYyV0pB
zdLMEGhciFJjH`AZyI)Z%Bvc~oe7tsj<;r3{Jy%pSvX)D^*ZpTt8hfrQ56C=XCVz*m
zig?Z$dyvQ9a=Z3H#91_@KX0#~B0-x7tP9c=aN=X^c!)-HHGYLbfq0k~@0OCH6!_g<
zd%xL$_vy7crc7#1tG9(1l;<WSFvH&yG!BwCb)%e@6k@Sl8(3D=nU=-l9v2hy^Kzr3
z<+Ae?3Sv(EOO4fBzZdt-$vrG@xqHPBixxnaB6fW~ZToAI17`k%swZ~VIo<B!I1kWm
zjkD)15`1NyrXup`EtSybyvQgXAoOvlf+vJnN2*hA&$h7s)`jgt>5b{dyLiu0xM9f*
zVKqL#=f<S`0}ida+=bu21DlKtpvBkb=`(=@!OA01nkl7gBX%f7<&oY<ehz@)YG%?{
zvzR3#n=h32FiU57KB&Fi9ruNyQZ+xUm{ZWv9ma~{g(}xc1i<1<M~Z$p^Arf9qM#+j
z#%vMRX?<%gdqEpOzI;oQB<K58bSv1pbBiV-Ba`;;>GCG04@GeYmN02s*kD7SRsJX-
zLNT;4a$f+{0!PhAT2N7EYw>sQbuP-uXBO9;Zz3`FZL%OgCB#d)Vh>q8q;0EnjnX?Q
zaDn3(=ACp+*P*5HKx4HVTceUiuB5&!v%^}ibTw+(&DVUGWgDKJ6vG@2+Ji#Okboo7
z$M2!)M0UN<<Tky~T@gN)^JVCOZN>g^QMPhuh<|=Qd0bqU>fv?y!*Pb^K4SZAuX95-
z{mx}0>`yh<GxXFj@<89hAuLmY>XLk)!zhk$7(Q?RiUQ$rGXp~>(WJfeO8b#{1QUVX
z#@KoAhX<uZIlcOJ@+RhA!ridj)0$BKQ9mZmID-bhX#9XtnkHok_bPcHCxARscPaXk
zT)ZIz)uV@roT_tW89o#OQ4p@sCs-<ZpCXzaee#jvW&}p28Daxp%Sm896oP_|9-o@J
zymkA(RceVdN~#J0zIrP$Vgt#Hasx<!GEhL0Tr_McMImnIdX$hca%ikMEc`AS(Bb{P
zXE$DU_oOG!_Ph$-ek(pbKB1RNaq_pSlcOCyo3*EXUsWEps?w=*(}BK)6MobLCXyn+
zyqL`IbZHngcY|VnDS|6|eouib+L_S|`6@$!h1EJW9az@$<yIn6Nc20MB*Khd2O-!L
z!F%==SD^Yk0ugTP8y9eIZ4@$EzFUvPX1nP}mc5Ghe#`5zER;?k1c&F=RT7h+`fXS=
z$79ofd=`h37@}%sqYTMOR#4s6zMY2Eih#3P0S^LA%k~(R*$auSqJU`jhUQBKQS^W#
zwwS<tf$7f72AAHwxXPx02RlVY6Caw{nuqHUbX^bhgU~X^txr%a>`%Aj%wGs~uDyD7
zOu|4IFp|d}d=!9LK<bcNo!P_FS$IY4=z?N#A|Zzw%AJVOp4uWqyE#stTqbGC$Ss^d
z7spkUjhV3|&xVnOk_fFZ2V7!Y;rcUQ22FkK2EX8kns&Isry0LMZ2A0nMHFCusnhN8
zQ?FWXFh?iPumL;Fa;II?w3qgO?R>pxqoAd&d|vMKbie&18ekmC000#1`BV$?Do_&T
zGXYx<vEQqZD19WqM&xVt(kYH3Yg%34IR1)%#asOY1CQtBF@A~J2{CcZ6)*~kJBXO+
zJ|`gnLT1=t6<dwW8P$z^a2};<D=<`t8E*?Ax1Mf$_4z1^Intt4ReGC-C+^weeTA&K
zdI~3V<bqb@)Ed(U`If13|KTZ(R6E&Kv;l0})~&eneY)(H6{-2yh@CoL1z~8Aw=u)7
zBC1)SebMgz<1%R4^n8i5n#~i92561IC>0B(^m+rJrLuWDrWaj1wA;0fE0t!@94}(W
zDbOPicNY&nZgOT9Zr~L8^*B;7X{xGnnKxTJgB4Vjh6P~`2}n)SeoBNAk{;gU4)Pcj
z&AE>6`@;z?LU`*u;STufSOvQ<XCj_++Vxdm<IJ}hXBy@);9sat-HCh`b9!!Wm0f2y
z$fR|kz3>7Du+;!)wT=SzAbv-}K<a?xg$%w@$#Xe@tV}Ygy4?lTYgSfN2Yt|3x@6wK
zu3Of%4q;9Qhjvxzq_`)0I4$049IDs@GOntTE5;3axs)+2a<#>t{NMNhZBYBmN!gLx
zZR@$DgoK!+WL|S~%G}(-_^wKBAH7tT_md*;(<MB}F9+#Bb^z%z{Jh)pa09du<VlZ|
zQ6KtCmo3Sw3l!4%&boci7ey|+e~CocJYN&=?TVunFNwHZs!$sq0xe_)dM!**zNUA5
z6-^>f4HnVvt&Xr7_o`4#fCzUn+TC1IYjONMPdTyM2I~Q)b?54AWj67%?l_y8s?uA2
ze7K|xkbSC25Q@Su^q%JD8sXJvpe8jeWZXK>y}&r1uM7Z`fa8S<>}c*AsMm+{GFMu1
za&q-?O0-$WNb?K!Sm4x{Ti=XS!5DD7^aTT}5r7^5>dv}a<hW*|s}!S-!Ll~9!RY$I
zi;-M30LldbfG$+uzR5<m1OW*MbzI5vScRLj!Qf>rHBXpzZWFX1p__w;!{IAK2EdP{
zkeLD+Ef?nd_lk{W7OU+HKsP64#%?yQtLOcvJxGWK@%d(yZn;_yAZ=UZ0Nz6lS>gKR
z5&Gck1@<Gt$S$|bY9>njV+l`f9fd>376b*hMpV&C1HRv^)UJ^5aqhV^)jdd$PDhk$
z^X%;XCt;X7m)=3$OPv_G9+|@Yo+B?NO^pL7MU|B23ASO|{7vNM3NN*72T;W*PT%Es
zCS4ctgf|JbVt9=sJ>ZiUcguT%Bn%y-#K`+*Z)*6-;xxS(>K-a1bbr<8{k|5c7g26b
zNU^9?9fjo5v1Eh>{lV5Ah4qFhpv(C~8%d^#%kL`y7f1-XB6WFMm0XNj;8?$q%LSM)
zJr@Wl*v-tD2Ar^_^Slm!Y6!dy0*JzJ`I%~kCz`(061VbEp>Q5ez!pTW;PU$e{meo_
zYT~3i?M&G}g*}u-sA)qdl>!qBS$_GHalj6(yO^jK1z0r<2G$3Nx^U2sTd5HAi4)U;
zsA4;eP|O8%;~1KRulvr8Qe5UD^|4m1kI$2H_twqw7E)G?>BXP+&9*4+)-fkMHRR|j
zmT6my#RK<^^o=P!5o4L&^^q6k<1Ff+eC4XfjLKxLN_zXa*@+zA7HMk(@S=`Li3sQ?
z|F`Wl^dD4}LJ~HKgT~Q+b3QW9;P`9!)vCiP_FvOBszI^*emR2;IpSdT6zdE6W&JJS
zu@cCcN8#f7NnjIRI22BLBA&5S;}+X+vEgAH*#hErDs$QwI_{zbSRRZtWz!VVSHaqU
zA_~Ez${|LSX(L998ow=RI|ct)(tX%SOrdG7$R)fwvBKjxQ}Af*NTka9?ec<Qb^Zt<
zI3qGoLVSEd4fqp)iZ<|-^fVZkLW>wl=}Ha+0RYgEX`BV(xTAWe;#c}M;^O4AZ#nfN
zx%i8D$6&WPO3r~~F%uLVOZke0aDBVVI*F=}w^`@Fot2mGY$Xh0(DA5O$zRmo@Gy&J
z!<at@hu=j}a>(%htFGrTo=0oCfqjsjOw+Z4M8xH)8&BMejbqg+L%uKJ_qE<g1iu|!
zLcTrEz|G=vMK&Qp;^oAV=JGsM1(z)+-`gn6iBMI@N2maIA7J7nakUba8NSyo$gj+O
znL*dVFT>>maKrqjz2RkOH{Fvuc`&{;U2GR^S{f-G_Do&49HHr=I2*a|jgJLE>-e?#
zx-i)?E$Pg%`r#{ftta!2-l-Gm0B63C_mSRncC!q<7n$?#Nw0BgmeX75V<yQ_=$&Lq
zR$@I}iWIqQtbv<wd%s=iqyMZis5%@GkCX9u|01Sf&006&`qM+*Z<emtI;6}xRiTu|
z^B>h*5Cw>3-|Sqs4y!=T;$r+iU-84cj6H^|B95mDHWt~8?1K48PP8duz2jHE7WAyl
zXhn>hc?vJOOq13?K7Ri|2fR*dN-StV-W9$w0RbbexU(vD-*WP*zQ%&~c6M2DFXi57
zjR<W><0$3^<X0!?Ang<*VyLlTZwnNSQ?j`23N9__AddZuo{L9Uc^ZXhWiu8&SkCc@
zInt=MRvku1EK@Qqv$lFeweai1F`gB+i`~+yIowHkQy8r#tStU$suu137;Q2m`+0R5
z$(|2P+h>@?CIM^!F`y!(EE!n(BLg{!QJ;T1Rl6y8N{?Q#UKEmFX21yz3Sc}c5;D@d
zJEC>`E@q}3D_#@^DMaJw?=O~tymo>J^-^1%vh3eKu%@;2&c@-7-ayJfV#36&x>nYn
ziN4~wgLdQtlYM<+fK>lvY@-;#5240k{Wk$pRXtY-8qlOXW0psDzk7_?WHa+@Fl}(C
zfysc)c3GB+n%|Gtv~Z_4wjSoF-I#P(lT&@HPGszeEgDDlv7lBPhD@j96rjJ2()wAK
z>44nJHn1V5{sV18m=w<8csWXE)15O}n|1g?j>u~9f(VUv`9H}))Wr&R#k2;KpFwM*
z$J*c!7g}!kAbeu$RP&!pTN*1tUldt<=IuJJ2%}|$8HSox6(R7rpO>KO##b`JhMhr4
zH`rAEAwZkY4@AcoabOQ5q~v|KQ=7%6XlyS21j=pN))Y>lCIU9g-AUfG&Pr&^EviL?
z(uP2ewJA<gGS_@Wjs3XZkU$ls+HNAa7|$o|omIV!ZhB=>VAKV68`Dw`Huf;X+janv
zktyz~dDpzf-iN1!)G0WJImbZZ8yybNe4JdS%$N)9t1;{9HiCI4F=}!Bp|~CzLN>_L
z!h#!JNJiMG{28GE5J>)wVwjD^t|IMcybU4ID@*`Yr;$;Kbrh1kk`lk)p9Pu#)bLtH
z^-h#_B+p>ylL`34k1)1Hn!}-WjaUUn)*>qOJ8Y5He&8DFZdSDYFG2b>dogej8dr2K
z6*`wW8(`%Ky^5xEyu0W(;5`3X6Dx21L&DvMpdKb%p(aX7Bq2nj*~$_m5-U0ZJ=ADL
z;X?Yt=oz|3Z-K)Gi;X`04kvp#@pRTXHo7WTwAL2Xo!8P_z9^4qSw}+!Z`Dg79s)D$
zVjUw?CbD97PGk00of5E+G6VW<oq`eM1)zdpPsW~VI?myQYeEtHf<-jP7&zvKi+|3|
z!3>Lw9Uj*#PVrEzP9W@t9ps$uX3_c!l~JOZEN@|2^9wH6Pjof!Kyf(_%AzU|>W(13
zKd)OC?kEV4uhc^%2X{p^$LOd??E@X6&?$s3g?sd5WlgQ5$a=Kf2^#Qmm9(?9wc<qk
z@KA`^MP~rJ7G-r0J!utJpr8BtF&u+a47FInYnp>09x&PgQW?#S6=yVUf>7aKUCLWY
z;~#zy5x#xvlF3tWCC!aP-ZgHywLU1X;err0wFg)$R?bj*44{3v*EYS*ek|XYfx4bV
znK(u2bB}-(_pPn+M>S!R8D7%}x=s}km4MGk)g6ENYT&Qigo+Gt?`7*QdEoQ(ahgyZ
zuW<9^d3pQ1dW)FWT>vd$I#C+66mt^>w9WjGNRQt;7B|(c03>d|75@qw^PsHT64erg
zaYcUzl76O%fFf+*(0UT`XBtxdlltAoVW4ys17K{ncPG{{b%D^UOvvKLIFi|Y0B1<w
zX^mVa1bil1`xRRHmGl~l)OF`z0k-=N{cs7_4>0%1jGxTW>P7sg^Qj2>g03wxsuJx4
z_Ajv1+TK^HQ_ZHiz1;<YSS%-5J41!d6q@u7_(W{R7e7`5>0=Zu^*b)#hPxkZx^#dk
zh$(7Q+k>bW6R8mnpd4c#27vG{4+-9VxoaBTgyB$MPt`0Ka_ezyqC>2(SpnRrM41$~
z75xhTKxh5U6V_I0QW79-37av3h_pl7-w0C#!k+@gRg*)8*iJ>vjd!FKk;28#kS7hX
zJ6#}MroMkC2`{=RJbsd&#@C6~&UHD)0KAMpEC&H{iOmQnQy0RoG#bpjOW}OYNU?UD
zSUd7L>ddCkdg?gD5^rIW46GJ8g9Hho1m)wII^0JdM@;Q(Y{H-Rr?yP04faK_6P)|>
zZGCbHtOh#!k50jIqDjtR7b1fu&9V87ds1m1%mOZm0t9iDBK4n$u@5$m@M0@@^Ihaw
zt2@n^LXp7#vjh???VN{JooO{8%bS&Y(^1@Qf@Te|eXZBY?vwq8_N%yIx%GrK^Vo6c
zTw!tD-~5c^{a5o%kSf0P2?dn7pKmr8im~TCbA^Ee^Bx+KkAh#MW5<rJ#W!#vr!Kw7
zLKbn3E4wd;0z@QF10Y=!B!lFtWDk`RIhkQ@dxE05G@~+z`!61{ILM(R;cSHKVvxyx
z8lx?~A9U@ZCoJPe<<?`qGkJkaj|m{UOkaucp!r{+T~V_N!(57#QDuVvI|e2IjQzK|
z48CqfiKULBkYK#mu?snrf^nNu3fR#PN&m@HqF9+>a)$bKZ~*AA(EAr_q4xSa7<V2q
zPN;ne1Cd0(mecn39%{n%_2DQW@KS<Ryt(xoKU>ohJB-3~3eNjsDUh5nEB<h2<oF?o
z@|;WzEx+zgz)+B_^&7vo?#z=}-$1$yR+{yewGwV457F(=Ejw`r;uFVkzfJe-YX=X}
z_Snf{|M8CV^|J~^<BVhE<~nyG;G(12V3fSRUB5Ci-v*512b#nqoSs0DO7XYz-xE=@
z^oFDav2O}^-T5P(AR2x1=&=urZrO3Q-_)MWp*-M^d_W1mAAe%4<q__31SA=nHY>|D
z86^}yIUL+HiwXFsn;-uO)6cXlus)X+&^Kot@9D)O-oXkJd&ed^wG;xM_5D38i11|l
zX$=`y`R?s*gIK%wp*(<9_V1Cd)h2R1y>{_7aYck6fQi7K1KVHN4B0vDr=O~f0LUBE
z1BPFaivLu%JG1&jTS$K$Lhgre;|w+66KL{u$@1rfI6rpvCC318BTEQbN!RGbeeA@g
zV;MhMBFsE1NBM8j4%27azW!F45`O+*ymW-~B>K``L_lRsK*cA3<$kg$u8W9ZkxPmU
zcB_tW6CrN^e39Gdt}p}`VOw7aMqkKz8G~@BaY+{F6V>B97B*72AJoCiy2FC>S2quv
zt2rGtSkfl^gd7vG%U?n(ItVL0=p5T0ku~qy^#Lb{@<e76@TWUw11lY`5=$*@Qy26o
z1=1&3ZhB+ngpe2}^|uZ`<(B)C$ey}>qG<;vK++(c;7O<!?Xhcys{GrUp5L!$vF(0}
zmoT*kqmphIlH<rUCENlH)YQBVwabDo2I^8!w1K5`=6Hy1a4yk#ZjDl&4p&m8idejO
zfDc?~v6m<360U#{G$`&$!3u6rZoRkw)K}y6zNs_5_^F@kk*d?fnE@sX-Rj@fp0z7P
zg;v0G9&=i)!ipPc+{lGiHr4S>U=>0`kmQZC7-udJh)S(5;S7!XH`_ghZNCvh0BqlU
zSrL26k_EPX?Y!-cpan_7s#O0wY5K<nHzAYM`B`_x3$PBA`=2Tu<V4R+A?1&VZm%JO
zI8<}GN~F1iQ7565=C(|KXD_YIP}*{TC{GFNGc{IDKta%wAkm9W*^50!8ah843mHh*
znAK60o8>r`-WE^A5gtnj%_Jg*(=3$WA(G|>Ai(0OK927H(r7}L)0~;xViK0mt<dTf
zV(!(Evlm+cCuwc9BRDQ*c9P%Gk*+_9YfjgCoDw3*LXMQf`F95B&$M6p0jODu<^E#k
zJ)n|iwcIne7HK0bG-`M7oPNX4BdU+|r*cmSX2q{pGL-fHcy|=)&GR+JafuE!ft3NO
zB&0i08B5Xf#I1l{Q3$Z2iI1%u4EZ5JeU$AAgNrO{rBCH6DoJjVG{<zW0tWV-?s(%5
zVx(me;>8m(I!Q+O-xR#2@Ff;NLYWYPEO7N#XTso4%vhEQ8Ek^rGC|#3L|#9DOf%x_
z3ZY}R(%b}QhFg&0yOl})M@WCaY*9XP>WTrPV%sG7@_NyHjU~Kkf+W_Dd)l9u-;ZSp
zS3Z4`9*hITOdQj%1%Sc+0)#!p-W<`A6ZqE7KgOMOyO*1%JJ>JBrXRlM{gA+YXY%eV
zeu$1gz*M+R(HPB<eW}7ocy{l_gsd&yB2)&v)PAAMnA3pwaCfD1E8-{|Xn}8<hLk%`
zc?|if1(m;msoj5{p|*5vyZ(6(Lm^-HTZ7lS9Xr(#(Lu|gzq*oIvz>+L)jVXhU^-8O
z)i~1)LopiHPg13&HMc((PF!@B@Yn=QgrV?mcbaKBAUtgAf&P(T89>m1=K1uk@J+P#
zl0&D_ROM1nLF>kxazsBIFA@lh7URJ%e*M!V3iyP=SowB4zcE@3giEzU#olNHT-7&Q
z76E8IB$mrDRi6mC7H3dIX@MZPld%z&Hu~`s3g_LSyMt0m^64sP4^%_N1ee3<S&$zv
z7W&4^qEC^YF7?!4Mxgi9{LWDEo{5`^XF7v1XMDBnkBw*pTMIKL*)}Lzij#zWTu6iv
z&E736G=Ndc$CuaBARL@{d`fKrtTw`To%t&B|FQ|=k9?NhA#y}WVY*szdn9!Gs9rKW
zdKmF!cLf^B)FV`HPaanJ36>AKIX`4b3VOocx@tS_X!<|=e$O`u_HsQyIRL2b{+P4Z
zqZk)vcPgX6_Z4tIBL<#XI`aA1t`Ce+9niWzH<w6n*@89<E^y|`ZU0UB9E5)j$9Y5f
znygw&HjTzD0G6Fm|FY!fFuD^KPR_U=0?H~{@<W1s0reyOyucc?-o%$Xl)sx=2r4r6
z{Iln!w`f`Y28&Rw(HkbRa4-Y-7m1z%W=8L!P>I$oHSmDNAT6-A!jRP7UmSD+zZr%$
ztV`c&xJs}#M|WPpc`2qh_uB+#gC!n7w4ng-hv||Zt0NsIPnsO_pQLMdlW*TWXR7j;
ztKmZX%Z!1hzB#f-B>FHcubDCHo%ydA?&(WpUL~<E5_1`jD|!s+0nB?p`E$vOyruQz
zTRM6G$LwOj?johOq0AuNL7cO0fw=X6c%3Z*cGJeA^5IBYsv%JfNh^|ir06#@qCcE%
zYm^lGza3~C5&6Y#CA1IJEV7MVE_t`TZg8r_pcVHI%P{kHc$Lb(nE4};-q0{f2Z$cb
zPZ?xu4&oejMsMQpB$<jb=1<1~_9kx*<^)I+7JJW6g+L%M3qV-&n!R4L9~!<pUB}~h
zM6z27x!}G@zHPovqj<Y~Hg7pz0g6fuaMH+UYx6fH9)Q2GPFRt3iUunyh!z0#D~s%G
zm0fXkxhww~AKQ7{Mb<N4v%P>^1wxblqVkEl#e=nG*<=)pMVu>?h~T$d2z$^>;SWf{
z@z%JA0=CX(MHvo*8>n>}W(UPiB_wWEfb;E-NU{4n@^Pksxb71-$kJ~P4zWUFNzAqA
z@kF0-BCFS}wW&+Tj>`L=kvyn6_PSdoD%P@_cc;f`c2;>+=x%6+oxaqaFn*AW%69y{
zt|4=u=CgJ8zynl0m{K?<?EB{i^0cX8zucJDU08f#j6o)fUt%-?m{emgTvWZ)zaqib
z<pQp{Q?{n!`EFn1bXcMX3-?&PRtwT5=Y9J)-;ldvMO|&ETCZYHFntiVD;S71wGh;*
zhy<|-n~?&rxpxtzqwqt$fzz%<joZ4_KRP_%tf;Y4Sr|$NIc1in`<9}8cN%XX{|h)t
z{t7E%i|6bZ2+BQNQhHz@17+U+V?R@JC({+RHe&rqPIQlaWJt=~ffATN=9<V?Q}(9l
zCxQrlG2tM+Ab!t!3E<$}{L7r{w2?0Z61M3yEgJrxVP*o<az|6jsP{eon{Q0PxmyP<
zne55r&hOP2q*lja!|~Sj1QiIIW30l2l)}B@(D&xoCi2)V^ET3C)PYbbOcKMS?U}gg
z0BoNewZZc9A!No;lj5YI<_z?|5*VvnUV{J8L4^S5B?Ko@-oa;EfoAts`KKTh%ifLd
zIEQx`9^!!A<%FuPD-xK^E{h`j`(A7V9t?z_$K$RW;CM;gbGRJ>?RmUHI(xH(B5cYd
z-F$u2VC~r4U^sZdJ=(s$h!nSv(=4o*DV72|(%SaV3wOEG9RO`Yy(uuCcYj(_dHMsu
zHn##D)a|aqbHG+wzhtBo1mClBY=(7q7OCZ8gqY*`NzXCswpk0ue(tB_F5o+_4(w@C
z7cU%K5}O79RZj|&0C)i}C=TP#Oy5|=U2U-4DUD{9FybQz=w^t|XVSDW3S9p!A)|>$
z^XFAQzPwUOjKp^szzdbpRe^7U#{jm)wIIKMjNU^GU9<A)YXU_WQZ~06XU9yHnbYJz
z=Lmk<8kq^20+Om=tFwt@pZP4cxX_yy-|k?f!SPH1wRU?7BC&|-bUBh>67}Yw=PYl2
zOSpI-z-osE`XB-Sy8{Yhj+ei_jz&i~<++UAfH&3d?qqVDj+~5<5z;@?K1Ep!5&hh>
z+Oi)bbosCfTEY3(2FVoQN&U#tz)yL7zHZKzD3T}9YMBY!OTfBad-D+pIS$N-*&kzB
zcivGc#Cb|vY5dacvG)4pTy@&>FXydKNL0GM^ESGkHkNeRxkQhR%|dx5Koz#lXPzWT
z)4rP!uGJV1FfA#DzkNTen|uN?@~(TaqPgn{NO=+xRBE-<ZsD6>{Jt7%zgcU$?vm#x
zprEQ;JugzFTo3Cf0R}ET$bEROZ!nFQ)IUdbDy>Q8&BxAGwEFu#U$`k5D8qK7FgxW*
zTHI%*jumodDUM0F-C7H52jYt#LeAFPlFh3+lHhrteER$Q$(kNt-=E&Q<`REUn3iK@
z?VuzkPGch5RqIY|J%4%)$=<To8og(!Q=K?nrVa2oXGpoJ6Kzgv$qFJbd{?~Loy1-S
zZb?xb$MVr?@(D}i0lJ1j8m8@40=Dig=36C}_{9b6*h`1y^?-lzeTW!E%T5?o^T*36
zCJ$#j*319t(XE{RCR-yXB0|KQ&5}2)Qn{@3&poKK&S&%O!?E+-RtvCg?g9L&a0`x5
zgl%(w$$Cc}-NCIZ(sdUUfB=F=U$0{}#y_*>01zoLEi+`z`|}-&5bIs7S{GE7`x*j3
z%&&PquIpU_<-yCs=H})DW1uoK{#`bdA2^c5N8zs=fy|gNN{bU9?5yH11~+tQ*;0lf
zwIQu#Vk9`S4LN^?BD+)X9$D$klJBYjuUMO)eY+9hU^Y5ROh;EWZRvJ*)bj;hvs)D3
z^OrdxejojmV`eyAXG*e;SIcnYuix`e^1u@L-qba+(J_D_k3}_uqJ{2LJZqp}jxBvP
zLDfaZB=}ub33!i74l=s(ly^K=8!a0K0eE>sc!^@s%>>saz113fD?mBl;{`l$bpG+z
zx92aPk7f%}SSzS)&i42{ZqI?RKVb%VtWfwrb!}!@2pR&8*w@5l679~X3l$5O!^89I
zDjn{Ra8%IpJF>u3Hf_3a11^?&Kwffm70>~%IqlW}cjX$F<;JuxS8#8iG5nb==YzY{
z(US5g1vH9@N~ohPnhSJr1?GOp(by7paX_j0p4%&}DOT2)mfTqf8Ze(@zi$46!&?bY
zt3{((0Nfi%N0DOjc-#>BVBRUw(&!7>xSY^f)m8R>wmgU-fGH><H$SzX6m&Xj8O#K9
zN~KM)5xuIQ2+OO>Xnjz~|4(n%9o1yA#lf!Zf|ON2iXe4al&S^<U8;m2i-<^bWt9?y
zVCV^*MJZAQf=EzN`hpMz1d-yhbd(@nAS7!bAYkaB3wuBJo%7Dw^ZtDA<*&(CrrfzR
zbLY<een0u#XQ@69@eAiQ%=9(XP)^sj6J%~%;B-`<PGaki-SHbu550=8KR#!I@GPEX
z<2n>9%G_DJ^Z<VezzvVaqD#w?MZB-@mDFyY-`b4oSsVRTUFDlHIg>7p@DeOWcpwAl
zo(KL6*~^)2jKtRpt36R~i1K4`{Kw~MZy8zBwjFW8W;<nQ>(isi6nuHJ3S^4rG4SZe
z$Q0P9Q#otaN~<uj8FEF@!})hmPqS30XK!j`cG}lx<qmTRqiT*I)vTyyxZZlB_q$aZ
zhFQwPN8zl(wm4w6;r0)9Re}zOfjly!%v?JT$V88Q$ybCT%%Wo^tCOAx@Vm5kD6t}x
zGV4`Dw_P6v&<@VLX6M)@>TouHps!u~G5e(vw2-ip3W0)`4K0LZ7s%dQmys>*#@J%6
zW9r3i_wm>%D+qA#_P0pc-3jE#qRQwzh1jgqQOHKbuF}!-iV}N@jL==blrS|}W>dn3
zRKUGjk~jE$Y*yoLUkz(%D!YHb%gaMjy9}FBUrJ~L9cVlqT=4aHV?LZyBqEV#uz(2Q
z1)G=U9)=s|k;cj?!L!dTTYQ4(>+;@MGJG?IueZ4_eK0pmAy$Crt78CYGLLF2#eJtJ
zi_sZRqsK2CI9oZw*%N-QpgwJrMOUBfMyU%pLLC%T&msFaLM=`rcrHk-Tky!p3>dV@
zk2t1DFFKt|j-I0AP<<FXF_xtvqxAE43ZZo$Fvu>*2d1rz6Aqo<4q)WekNwu~oi1Vm
zN{+Yp`xt4P7^->GDy7VWSDc2taXO>$S)oDvz$i1Ktl0}H+cskTM}_!q8JPf}k<F(!
zvC!>>WSR?Bco?2mpgtAgZxUb+=XTdQj2e0%Ho?l{tUdW#ZsaFLqLN56t$T|_WSt-S
zG3Sh?6TwO-Pcgc3_#Se{7mS==npah8)6e`CT3fVmfLpMIgRJMpd>9KM!x&NDn?^Ar
zWVu%>RLjXlzd-udHSQ+az9#BL%OGGQakMAa@3OM6g6^gk7|xO&2pte{AGQc1^<6in
z;YMce!B`-vXijVv6AefY#=It%rXcoT1-YtV`Gp3x2LTy@DgS>@G&}io#r_hd6L{CA
z&P|?6f3L4xzCO8wA5^~F41_X70(MehIrgs0td>wLk`&yL@Zf!-HZC3QhK{J^24yH$
zMIQU+&Dqvs|JwTTVgk?*hLPsJ&^X{_Ywg*t5uz9eD<hLZ7HrNvnyt&MfgM{HW7$uj
zYRJNg&S=qKk$W_VIhPGyk8bCnb(UA8>ncHNS7t~R?R6NuDg=H+4`To}Ew)pOO1=n`
z!p;uo5MjZHsmKoYJCK!hoZoDpR=Md56=`_f5OggAztHR0YV_Iwqlh9*-QqcR5i$Zj
zFBdVE)C>#%qMe2H4KxH^y62(Bd18Z%JFRek=r~f+ofb9|$afP*<@of@Du{Gx*5gz5
z&VTy36V5~}{l9~z$lSOd+;>t*QBg`+IiAV<1#lqPyv8xdfavzw)oPwFV6Ou!<A%2Z
zRB}EWHR0*ImS1iH%8n6Z41DeQp);UTP*V$`VQ?D#R}zgrPJKK3CLmVBJ>hGPq+8;r
zJ)_EEF&KI2Tv(WW*h78e=aDiEu_fKz7lDsnbVM%iaILyoibJ(uQgU*{LEki-XFQnR
zul?BV%k1I42%<#}-Q{M%%AhEOHQga*77G@y*rH9aSj9(LzIr{PlDh?Zbfn37UAd-d
z1r<qTa-r_0LqE-yY4&cof6LLe)35F9k7*7Zxn$nT?e@G7(Qt(dINtj^fh~)P9++l9
zoKV9wXGk1vZ~2cmZ?9<O?JYtkomc_JkbC0a?=6F8;U78uJIj=qR!ego!_LP#^J_tI
zqMES~eMp_T$E5xzPNjh%@yqrZKo+C74_C{%3;=be+Ws?R$)?Apl$7o^HX0bk3D^H|
z8;+lqN+R{7W~wG606Pa@c6ZbgIZX4=b-_IBkT^Lz7(hE75~qIind;pywM#N+?#JSZ
zToPjVz?GQD$X#;gkKeVYDk<Nk0A849-I`n_ov0ML+8}wQCP39#48KeMaey>f8D-k#
zuhd+qVz`=lqjBn^w*We_|F)i3=;k~rEp1rb)}DMeRJSMJ+~vb_N53e6v;#am>8J71
zxXO%9-HkY_qRaDO1N^$Q<WsGGSBXz&>0sxX#c#}Y*JO&UDdQibB*$k%x3$3tiyV21
zvLz5u4N-g(ZStAVR@R~<Lmq&^M3T=8N?u!>NJ&X)z_*B7m(lHd+fF)v{_tX9eVRhy
zd<f&-IR@Z0PL>89`Z!$4t}%%H`OGUjL%M&zy)Jm8Jzd?UJv%`QP4{{WCvrmmGc}7B
zP}pu6aP9^|-la8Dg+*>Ue*HEsZKlVo8uOx=d`B4|C^EyWhlE0*7Cb{Y>^iPnSy`zm
zE1T&qzEi*7*qcW**b&afbNKk!>S!V?*Yhosor!0+TrDqo=;QIKT<PiQCyfO{5vg6!
zUAvK>vwQ{m(oQmDRSKfE#%bQ2xxpLDabl=sh)$!<JZDmKOj1^%K{k~*aW06wVQtL5
znaD}GP>L#DeMwk!Uj962IbHLKLqts#WdERjqIoIHKk0s2T9l)(&!_iK`$xsKe6E3x
zoizMzJBLII*<fSH8ZlD(cRq+;uqTYhpSn`+%D*_#2_Kohs=)ThJq}sN;@*7m+nU|p
zN+c|f&u1_;_fQ7jI;A-{`v>4h!KBQA_$uz5d!9^d-Eo`)oRn0a6E*K=-ZT5dy?+j#
zXezd=v=z|LKpXHL)azY$K-)~H%6j2mPs-g_wrgHtgo0xc?*w)<4dfoQ#!6ca(yeZY
zxtWJmBQCj0fJSl|C5W|w4ieTU^Wk%+9}z92)UAI{sa+QRbuXWDTe2*TI9-e=?AsG>
zXftz6@}r5Slc~`+TwJoJiI3=sG#X87@QHxPDMpYwB3eeshO)zJ_?qM4tI}H^^}M{j
zMKs+mbzFz~vnv@3V=3m$m5nank>-7)#w`irA{wqg6}Rl+6>F`Yt-EPyFh7}~J@aQt
z^$atD=Q!6j5Ucyh>r`w{9^gr%y}Z1_rXD9Hh->$s!LbN0%;Ndy1I{4h85FFa7&QJa
zT`XkvBgj7!<PYRYH7oSMAfWGA`aIZ!40qKQv4#cKTbt4wCLNuhy0WWQnRnq&3|ddx
z6>zA0^D-%Rl?ecJGc05gR9H}OuE#C&zK?@-kAHT7OXuUUv9VsQ69Co(1zjJ6@;T95
zNH^OPCv)Q@bjbS!<y3;#eg1{a<LU2;XEh)39D6x%^?GxhNKQ^p?~)$Ox=Hu$UGw&t
zmy(sur9Q~W$&o27E$zMXa9=gMV)*ptTmxaQelKdaX7Rx(PYGmLLjba0)FJB&I-E;e
z>jQ7Pi|tPB;gfjBY`RNW>N2i^vGbX?qpQ8W9mc<Y$85$aPo&>&I6EUxB2yUB)9cG@
zQ$}4dlD7#4GnRGtc&Xeu)A8}f2cfWmE)86n=^mYLZQ#|$m?_5ZctXzv1gtL5G%~sF
zh>m3NQj(b3jp!v}t=I2i++JU6B;=1qqsNphY0}lc({jPOnV<WL(l#3;w~l@Zc>`0)
z&@-E|DIJNQL)KUq+ou$3097pWU0YkyRf@F)?54fe7FhS2VC&pXCK-QS?Q$zl!l^Bi
nBz?#d;71lE3-|2*Sz>Yf-K9#!k*>KzT<|k7ykJnGcP-*C*X^!r

literal 0
HcmV?d00001

diff --git a/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_5_0.png b/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_5_0.png
new file mode 100644
index 0000000000000000000000000000000000000000..c8f7f5174e32e239f4efd100822f26fa8047c9ee
GIT binary patch
literal 113234
zcmc$_1ydYR*DZ_$3&Gt9?gV!Y1PvM>xVyW%yE_8}3GVJ1+}+*XU2gNda_<lLs=lfj
zl4-i9cW+sH?R5^Jax!8FpK(8ffq@}Nh>IwIfqiNP0|W1Zg$9o3tLOLu|8O~o{&rBb
zHga&*wKD|!sq0{4VeMdHsz>Z(XlHL~ZN<XC$-qiSY~tWxW6#aVX!*Y<Fj(6eGZK-L
zIs#t;XCtm^4+e&$`|$=Z6eutS0|x_>5E1(2l5w)??D0!!sq=EZX?lvscLF6(N*-KD
z{>1aN-0{@BtNpw@r#$R<XsP9QR}cYRp^rGmVcR@1ao#=mZRUM+jG#Dks>InyUdFlC
zLt~Tuc@t&K%2kGarkFT2c~}nB|9wsUF!^)l^Zy>n!Ipq(MHiL--&X>==>KmA5|n?!
zZcb+f!eg-?^U4amvI@iMb6Zx1g?-NSdO9@njs~8jY_=~FuW2_){b05POIKG{L{!xF
z<+A^}8=g^4PHw~5{Z{k3KN7FM(RSOcT}DdkJ8YK}W82Ki*|Wj%Tp91{&2(OV{_Nw)
zUb^*P<MB+PL$T~a_tYFJ$wZyDbwQJgHXIzBkF!d(JNOUwm6wq03wg4sMyo6qtJSpM
z_XXZ{yZoR=vIIKj?tCF(06QDjy?FLP&RR7~unB;r6fzmGa>s_!^62?|4;%hSjr*DI
z2lEv~?=SlTIGi@b49VfMB`VivE$7u1i_GosZ_h?qz~yU9*$qD%9FLWO>jwbak3KcL
zy`FntcS0fs{MCn+zh@SePoU{?>+qe>a%W*?PT{mA;d8$;Sg6z$;%k`lD)dR7Dh4@B
zNz}EUP5gmO`#ZbnRSTFyLPFxfFFRkZdAL+(9{*GbB*Uk^YQy2i=d%{B=aZVp&0wO+
zMGLkpkmudv%Qn%w&fUpkP{wcwRygUSJJHo&=kv9o+FanW04>FJYjn{fDSFdiORcus
zeM~MFI+%=FBPmS!At50fbL!Sm8^;wLdl~j)U&+Y>FD`7LzhHOfM)OO$y1JIJAE~cR
z)fme%G#`){jc1Nt_7lmvxNyEc-y07lQ1joKU+oNjA>d9ZETqcG%{^T25Qyfx`(5T)
zU0vOHJ;no=Ppe##sMY4iWV<D@V#Og>m-qL3Fd~?A24|H~*!3vK`bt6v4d5i@-KPCa
zAnMQO9on=DN~30VSIHeO`FrNt6_2-EZW8)zn)8q6-tSWPXRC1V@Y}s;vIc9du61`s
z?ZKs*ElIQ$t#J<z4{5}+N3$hOduiqei#3#+o0~T$i}B_aZAqTbceD8Kfcf^bN-E$s
zPlpBb8pi@}5Bk|}<dIG-AUHINi4qmYQe|4rW(VDuhf8z}jM*jG)nTB-)QAeE2W!Up
zrQY9Os}?OB4kmont(pQ?R#w!WN3#U70I$ZTq=*B4S#5FN52G#5&dJG<nAg&rcfDIQ
zJ_OPTFr@e2KPH0#WG(O4TeauYrMmFr@|LKb9Rmw?JV@v-8>bEH<&Pel-GLF<Ob|Z6
z*XQr80JT)h<Si6t7m~J_Ukjg_nz~}sgYa^!{l4|#tm$ICwic(;EkAgMjV>4>GG^wu
z)8&Tim+SEwyWJt~>7e5p-5%(-%V_V5gWTw;Dt$5jmy7RKEvM{HH-|k%1({15w%VSz
z822moNcXGGM2u}WsA+$z-0!XGtya})WR;aMNyVZ>rdcyRt|Z*<&qjd+bO9bnLP|>i
zXr?GHFYlZCoy8c>Iq%1JT6MfS`J|;M12w6>d+CxQ;LT5&VFR(+?09{cvZr-@d<~@9
zWUd&JmisB2gXVr9mevMfs_|rwXapf&^IUtfwinmDhCPDws-w!!7<zj8NUzr?+flap
zn3xzrBO{7<N?D`c5EP5m=ES@_GKSU*;Y{~4E<*PcrZul;%P4_2m+Z|{O$AAYwi|`L
z(e(ZRI5cb?CptV1s~zB3^%koQ>D22>m3`#?E2c=A^G!Q(QV-kF0uS>YZ~uULSZJ^&
zlI6Qa-|malzdf1_2o8=<O6mg)9ayN+Ph~dzY-?+a&+8JiSYy2Hz@6@J>bV(+<pQLV
z>sBZw=k1K5ZIgz@dVAZP&d^+$Iu47`mydkdP1gRaSRgZ!$`TADcUEgFZ>HN3^@qKG
zWk5sp4J+Mfvb<fuU0}17XQ=w(!={2(9HxIVGc#Au)lbXt{DiOr5?w2L)={%rgQ3Y}
zk_2u50grvg<8DS#b}-jl)9vQqYfpFgPP^x`S-q07@=!p)CszQR-1z3(?$>Cyx3*f(
z?19fig@uJ`r&83r0J4&SHyI9NQs*Z$H8r8e01uroe+OjxQe0^%9o&F<dDCBs{%7}5
zfz#UQ9n%7k^7ZXnn>#r%kMkKFEiJ8ieR4v=z{hRoRE=j%&j%^9ENZo_8q|{#69)$d
z#2=rIE9NcU@0P8}iLvN@%k8J6TQy7E9Zcr#9Rtpj5^TTdhOfJO22LX<UiW@~O|v}G
z`b!lyRZZ~fbqzR7Qcxy~zirN|M7b=^#{;lEFG0Zz$Whdo<M|5fN=xsGHY`ntSbTPi
zlzGoFzI%<uI&&3O4Gj(R=Z{AQykQ_DBvke$1LB@7u&0$&EF_PVY(XX>8pkotkT-TW
zGozW;bS40CJ9Zl|>sI~d1fCOYq25wG_Y@J2J>qP&W!{4T6Z6M<yQew5XwmpUEGab&
z&DN56#cn^5HyI;i#TdW9`wL4!mKQgO$GPv+Y5s-l{pA|`cddzh?yDl;N<{NBwhsP}
zqz2j@o3@yknEmBHtQwa~JzhfDGS7;}tuXHX<iy0w0n+G+0$HLFBNn5YQ~*bZhKJKv
z3XT@d#2BTTj*3e{A|gs_b`<_AYooKg-%=YJIT_j?w$$clb$fy)ixdme!oquk5PAU2
zyzh7i$?`u2%*@OfWx4MR#9Ay=`nsPr1#{W&$tfr(9NV|7j<vd8H=a}v1Mpus+X2te
z2nHOx+8s_`MH0PC{Om$~Dl91}39>XaG>jnNNy4C2zBs8FueR9~RxVTf7lFe9R#;kU
zQM=mP+snOd!LFX$zvi+XDV@q32!O2ZKMa+ajEs>&`P>v9XRA^E#l^*u3@)_re+tLr
z*+TwcN1j!3=%S{KadB~g0XpYvZTzR!y@0ELU=%y2Pao!$mX^-lJ)MbuM98}B7W1Q3
z+wYC&?hYlEs?~)d5^%FN$v!?lri|`!4=V&9I~>h~zP!AsEzh=n^zYk&v9Z5}g|H>F
zYWquRiHS<~ny0HR93Ag(JcEOS5kvx+Kq^nSIG=Mr*1s_6c7M`xJ0wqH)E;}>&yv;C
zBQef&r6^IUfG$20uxd!>Sof%rS!Zb6fDqKYPux)8YE5w3JP5d!V}r<yjqU0Vgg5{3
z;L8o?2?>xJt4-kjr?oZXUpUq^P@T4uqF7b-`!Ya<3MnX{eh>Px+425r-PZ4Xw!%gA
z`U~(ws5AxI#~o{oMoIYi+9q3xe4F=-4Gm{ofEI{~fuUsnJb9n9wWa6v_GtV89E6@X
zlWC`j!Tx@JeiZlAOiU;Z%RjBGtje;J6M*V1O%v(*A@?8Nu>nMpgo&xDi>7ySa+2-c
z`(&XC2q<QBe-F?jU4iE7deH@QxKKq3z;5LFK70Bo@5@Ec#~WyVmjGB@FX%_xo;B^;
zZFGWfZErKBO0NKs23**_#_o1|w0rN0kze$yQ_8fVSerMh{N#4c{T!-}C^j}W!~5-#
z6D0G2#q}18!qJ3$jR!g554ZE$hd?YYFE0@YwO8N&j(z&bg)$21l+KEdw;=-Om9A)k
zw~$%0vfHNNT|I!*Xn0)y18><IPhs-M()Oq_Qq?y#rDic6GXNeYMVg|b|MS-vg}KA&
zU?Q*I#$0&<^G__}yzy9CF23Ue?-{{_ZiPnAH;2>IjEwsuEaNwa?$^Q1$<zRy>KDb*
z>{ZeB4D8?5ohg*(C(vh3#bz@@|DZNYwWc4O;sXX%=huL8D{sA$L?q-L@JHeoS5w1<
zghAZ7nU*yMn%fr~=Dab_NAaWPickI@wKMlYtfHg$%UjO2fWFhMSM|+uiIv@A0r|py
z-L{_q=qmPCgOu@cal$}@ji>lY!p>fAwDX#3oT+=Z(gc)O&_|m9dY^8?GSF(b=e50e
zSDn}M&F9NS#Kfk74(j!CDK^G?EsctbDlREmteB$(q<t9<pBpRCBlm!=xEX*ZvjK2b
zqls*xMhym_8TA9-Ilj^5-}JB*4(T+?kt&@5FvpKjA2g3$j|<33NWh^{%2?biq@-ZW
z@ZJ8>0{otKe3v5{{s54Nknr%5x^v)qaF_Sj`_QN;3Oc$7fYr^m;9mmRu(x7Az8fn>
z_$MzfAYPWg>96GW#b!^_WglMpEuV_we`R9}K+00sEpf3lY`~FtFTen9(W_S#lbJc@
z2L;~<wBip;asfa-6`&440KWpwv)`(L#c&86Y>fMu4y*$JeQggWe;A}afc@nGH1cG*
zA$nfRJ(7^m&E}-4AL0UNDE33t1>c-bexJ2JTe;nxNC4>q)Vk5$NGh8vh5P;4Pk^RP
z|NW`o=>rZ7A(Kt^#%$LrOv;5dd$eJ(xW!YPCKK6i_u9=4MfiMfwGkm9F$CNWF#ra&
zXtX$4g2KKcD4Dhb?d!PK)q%X?$peVx6_5+b1Uya&$|XupXDg~&p0~4h0Ewu(8y+1^
zykGb3IGHU;uKi8N{Pn9)rcF286~IH20eEG5etmmBw|ltQoHDAB5L^S=zSejJ&<B%%
zIY#T_V>Mv$lv&*n&{VlR9&8?W;$??2GBVO$)9gpB3njOK&@ocVWKaP-kfuzz1fSod
zF@wcercA$N8Mt@Z^!?3^gDU{Z0}l@_I0OXr;{aQ0a=kWM%oU5~p1%T|_w(*(Rw_;U
zubL-+vt(|9QX(di>kbO=dnD7U-v*MBVXeT-VF>iFve%ja;HzQD`D@><-C(kon*@Nx
zzk&h-2Z6S=*x~K{!8(RCTbv}Q^EK)%Qd@z58UUrAH)<3g8X9_4J1rA0@Oq<IR{zp;
z*Zy)fYzIss%+%BpYGulcZSKyl%a#p;wY9YXKVEH7S#vustCmV;n9{2n0PZuuq~AAS
zoaI?R=cVoSn18a~(E)h0SfK(HSy+<#JG%Kt)BV4%o+6sa|2shgq%=JTLhJwg1A2&#
zFs+l0pooYs!2E+E9MH`3G3{n1#7#{pa66wx{4HWalDo$ROd~CNmexGt2Zk~|I_+~d
zdhU(H*jS+kt5xAu1YiJS-|zH=bU0mNn^`qMnQ8kWkN(f)h~GY?dHiGe`=NiIhDmS{
z78M0u+#Z^el7f5>oRNP^OHKVpy}^oYUPW}(9C-SOyfYTZqUQ)eEU5Bmo3B0aQMpZ6
z2Wr~U9za^A7&Qgkl;(Nh8?j|qjS&^^aezB_nw)6mV$RY2zuvJ71F#G@VA+=^5sxf<
z0gS{!&HsBwK+J#tw!2uV<O30Km<_}WTmDC(kPIpE0p@_PsiVZiMBCTf`D<W20mv88
z(HU^(|GZH3Xg2bJ|JAx}_ebvoD!@)@&6t^kc5si7goH$y!2j4%OHU7E<;OLwdnFa^
zdcfcWaDbtLSIVDhy}hAmkSnzR6Ln#U(F+B@1!`pgJYN74_yy3=(J(RhrkT(vq_Z0v
z8~4V6*&kv>>VK~A`}sdtu$j-r#K-r*QD(^1jBa060xS(kKYDyb7>f9!IVSdWg!EC&
zlX9B>v0miAtylYK-RbO>#dW72o!|TYjrVGR)X`7#f9*&5&zV!g5^eYsLLy%SU>%*+
z@(vD6ppghQ%+(DH$biCdtOq<>%c0;VnB*5C2y0&00wl@QR+9rI!>U9IHN?-tFLGs|
z|G2#N+Y~>Z>K%9^(x?XWt<Qiu@e6K1^W}$Okuh%I)hC{XFVF@!I>Jk1q9Uu2l{fH?
z_a8pVsJ&W!NxJ1zmzw(;E0UJO$#rNkcH+XM`}ac5S=N*U<HL2PLI#WK1JxXbN{YQT
zM5<S;ob!Sw;WP)cA%M0M)C>>Y<SQwk>wsTLS3H~xqUEiRKH}6R51B_qWhV9z17Jig
zReJdRh5QjMm^c~OZYJ}d(fh#O78~!xJ$1J<%AXUxbYji)&MH`HP1s@LN{}zIFM>o-
z0|k(Tn8MBVgrA+j55&9_Zi$|yE5{L?8UV`^ltq&Gy?<4fq`}ybh7%U69es!)8y5?Y
ztf%xh-`{!SL@1{-$4*Ldz8+~V6W>DIyd1|qst^ya1zjP<h~AUf%YNschq_t$?>G#E
zRe)Jb%&{5#+wVGx`bg3t@iyd>4mpkmrJpc-c=+M|i}WA%m}^ej;WxK+jgxarUGHtr
zX$qYvEq~3&Q?;ROoFiOPV0(b@$&99J#5t6KUwBmNlmf~z-^MzBGZ`I>V1>tj77s|X
z{5sq7+&$48Aqt!G&p!YiMqaITWC$`P;J$bbu`NzdMK{K6rjHZT&9#3LoU+<OvQwtw
zKQKifXmLUgg<Rix{~Z7|Q;<js63-$3Spi`0ohlox!qAWV8k6EFOGWW#y4GM2uac=c
zgvfssKsB&KnS0${P5(-GzbT>*8!(vfp~~#HpBKkqJ+wbe@ZH{$DUEMA7KadOxX=%&
z>V>LMv<xkPZGCx#ARhMo%*`J7U&#hmt%n=A_GJPYg#$YG1K1>K$pV5xaL)~q5V{$J
zQ(B<QWPzjfs`nvlH<7pjf-}!eB6e0ohC*jbcw35&k2V2?@8=y&07V4sAh#Lma(`p!
zoPpFmBTV=Y6qNmXNDL{20T-IhZ4XWJqpG(JSup-S#|8u-sjsZ%0ClZo%_mvVMrmH1
zi{Y*p`KGk&f_!HOcHs-7y`Y*Bdj9Be@rbcf7xPJ`2MImOmvB7MeH)AjQ|3h*#}wR1
zvKfG4%cFn&B(e&*nJm2fCGQ!`qzc*TtMJmFE10|xuw_$NdPHizjg;up4Dd|4Z;rwN
z+KBZm$VctDRFZgmu~uu#*AOj3^~6PZHaLmjq7=C^2>deZzVh!9%@yH3aPafye}wB{
zcYKAQwdz&uE(r@f8x9n;Y3LC7;;U=h^PoQdT$%cdS)(Hr#_w7dH*+^1+{j?jpfdAQ
zk}i1qQ^^MgdUem><eZ6uO*LVvz98ipzoWSSj@g{|b;oN|R$zl4fjm1E@8_+I2za<h
zQ4=v~2M62(rk>i70y0d;d+~7Xg6Jay2}KX^f21f0-D_;rx(TOaIGCbr0Ial$B%u7E
z|1%cIuh{#;mQ~sCVxy|1X=b1-Qqx2nh#-phKz2Hcd{=20Uk)0$ijFaikBt(Dy}Ah0
z7SV{lCy#WSl@edX8}REOxvmg&V}@Ka{>UlE`0_H~m@nmPp{$17<KQ11Ec|vDf~N{|
zw(6Z!;#&oIbn=I!ol(ng&R9M{eN(NjLMQTg=w9S5k37(!AusCxo#+pF1FBuu>R!Az
zvy)i#Lizo|ZmwU{CFkO_Vlj8t>MRr(g&7+$QoT{CZ~hf$D{aybwA#m%TPpD^_6a@I
z1xJ}2{FUQd#j>&fj;RqnGYq-x`eB_?yrH#%<GD%cx1mXg%J+bq9hhttAW{D$Zx56@
zQ|q%4fGos*AiK}p!74HbfR-;1lTeV~=6yigA0>1-SQ_EjnBNd=DCA_wDr{EDsD@JO
zRp+4491GRpYrpu1UhGpDFH9^H2QeQG>{nzaSyH`s6(!|%dBO|)PtVbj=fxjUm1?$!
z=iHk#kxzhb>Z)5BU;z4QEw-*&<NP?3pG~~l+$#Q56@WbK7e!$AR-G7?*CiialV5^%
zT-2P*I6*AUMhXqFt=cGE-MQwE3AufZmx08YMwn)xmYRt_K740e=#w@(FEMS*E)k?s
zXprc`u89N-3IT_oNhz$4i1M3(O)v(KDRVNCp;}L<=o&_X`e%C$e*g%90HxS`PD_pn
z5wY`lNg=)~JdNEYnzUJv@S_VD(uQ#IN~{G&iY$>dkD|)2;7Eh@V_+5xTEke+YxygR
zVL?GrW6p;}_I6XM8f&-KDN91Pl&0^2X(FAsSAx6I$kz*-`Zu2q2B(SdIc!E^gBxnV
zaz22zF>E~h#S;=2RfoVx@B>Zg$BPKY^i9`zGnye^3k}zCaYO5)fEvcgS*!C>aO-SB
zZKcD5LO9e23u$B~_LO7!PjGNR(A$^IdGe^{WxYmIoL}DzoRTfKduEcU?-L+qRz%3S
zrLOjvUpxDK&mE+)u_g}`P*vR?-X#fCT?)>nFn-$pJOvgG!ls}l4u2$dm<vi#RDz<u
zIWlE`z<v_tTG~Kc|64k?6fYR=-!vR;&2D!a>nzGjlQ}u3s>FB~Bqycw=^4ZONX7p$
zCwrkhLi%L1_+@o~TVl3^!9bHVT)f_qq8opZ5odkk0!rq}`0YGn@sg2jiThLa@*m?P
z@Y3u}_$D7xZHkQWu3a%iR{AC!&<A~8!oO#qxK3M-3JEOi$6c5G0PkbW$?yW`wyvbO
zx)a)Ue+t$oF{D2#2HGN|hZ<3NQUtyi$0_wPuq;!&l}GJ6<QL?0{V=6S;Gj)5*DHgd
zFF3-|(qEXEm;jA6O|`}d>M8+XlOf1{&(D$MeQKaADH24{Q?{>%c<J9GW`%-01zMyU
zV%6GL&1cbk`<`+P5xmxvLqbYS9A<=%Lo(e_+{SPQx?x^<e2eTATL8%}r&e^zN{$Cg
z&V_W!voqyGL{y{tX@ZaCPJ-q5MvIlF@sCOV)#J0qp@OmNn{ej(8!S>;M`rN+`T$H3
zV^x8LJF?p<cgR4xDdz&Y&o|f(R-JvMgD(~3nk`p=Ruawc`2=<W=!-Uk<jIv*tK85C
zxab3lv|*@Vf#uTBea4Ds)p6$`g2}j>6qFLWyD7CjSk)ZP0@EhyX~+pJ!;p)sm_omy
zNqejVhQ^Pc(PM&XbRN|i-jK6sxIakKX;2f#n!VVQ(6|uhDncmBY$+6w1*8WG`!$}{
zHT5>sTN6iE1W4-P^HupS^z)7#@v)%=fsM(3C@uH>cg=0exQ@`!bcB050w?q!o!)$)
zTmPfXX}<$PoFT9_LZ{uX0bmNygTsrZe&qd=$oscgTF*N*eV5EaNTQbsX3t^p2$Q`}
zxMT)d{EnE8eF8z1751eA>}zYXlJZAN!2BU6?iqu5rb{Ebx$<$R<W%uFB_nPAU;;FW
zO;QV5)o{sY+4e5Z)il$p>W%2xbHzzVt^c&oytCRT{X=Ugg7P}(eG{ChTI(`(uza9x
zD@ZK#A(w))cQ3A7cs-s^8xoR}SpbDf2c#^I4tX2SkBETQ44&JEscXt%=oN^J?8+jt
z0(Q3Q1Fy;iNz;+D$Vi$Al6!;{(O7<o`tTurQUW(;@>g2#PkcnuAe2oNA43z+q6*0q
zQ=U1W3$v-$#BhW|xgfCC1^8PqiZ8y5{Mbf{L$kYOn$nC|fZ!!g;Cg>6Hn^g|NcVCn
z>QmA#<Hk!u=Px1)1cSx&4YlRY0`3I<;97t#{`I;2x&dDCGo78u?Z)K9iAP>u-UFCF
zzTk6Ku6Yt5!XO3%5;Gtz3OWPDBOs!xhYfQ0;BT&@<GbO*Eo&l;)ZN*cb%??#T~kD0
zhaR8wY2siF{&B#55a4`*YjF<ZZ%`M1xDxV8MKISO#{{@qH6{Z}UvRq_0v9Cew6FuH
z;ZO@mhXn${tOYS4m611zrA%7QltF~pw`CvIAdVOfGJU3~`Qu`!bmjVqn;TQNhn&Fi
z&RHsX{*H_rhDHH$b`?`r66TkOL=UYh?*<p_m_R2xgU&0g$&{N9Ap2B#y}EtKk$~ni
z`jca|7f^4GSDmNwBzpV$=3WkgkJ&9i^s^YwuQ;N@`Kku{oh$Qg?8jlS;~oC`wwXmn
zxVgq;8{^C*0etQFYrcWu8%gH+9V}8sM=Vfwo#`!^x>B~^a|g?k>AX-gW&^c&>)y@Z
zs{rYXN;q6B{Ed>tm7K(tAZHs|BsdV6i%=wj$k^Gx$(zM(y}5`)17M&M)Q{h84cRd(
z%AasmYgnt=b&?rBLDcMdV*XCwTZ5@CjRlQ}@AvsQk#2x9mdr|K@q2P!_M)Y-nxX=#
z%`Tt<RyeN%D{gXtVt>8g39f|kLtYKH#%Oa+3+WN#<_L`~1}4fgP3*a&a~o32$fZ>c
z)7KJRDfb3?Vo8jF6{t`%1<7{q3-IZ?4ynJd7uR}bi->Eop0TcrEZ-M+^d)357mTMa
zsG0)8WMitU*JJ~cVd+u7N&`RWpS(n_Xkc5q?`0BXMThRVhD(}Z=K(!bwatyqIKvh?
zS;G!az1{-OaUPhL2hOT3Pw18-EM&A+bH&O0^`kgS+m!nYaOSJcprpZ|=z~?*s13^u
z!^Nr>Efy^5iSNbb(Y-&)QlWKh|J6C!ayQ)b+)ir<!=W;NhOEPST%;FaAdE56bi^r&
z>GUIE1j)-uly?|OmTi&KG5abT<pI=E&Flax6i~g8#nJvoY=jN!S?)9g?^nR3w;~zt
zY9h3#yWWv(D=kK?bntZr>t4B?pd_?mxN1W>bKFpanu~qAY5gfD+K8Tisty1GLfIbS
zlm;&DWYX`Te+^kHyHg<OJMCCErtTR894BxS5Uy(I7WgtX_^FEEIyVupo;D`%?$PXc
z`~#5AKGp}}u^0>hCDxa>EQbobKG&gUJ@nWfr0P+BZs|4n5_IWFNI5viB-mz7V6e-G
zU0*txYt%y%HpNaqm_B?1NkkgUX1JcbGfFNEFGh?zLP?EGH~I{sZCOPfIfD+VP>My`
zkB^;-q;p3Y5`a1huK%3pNJnzFSPVhqmPzFKjr7zW`Sja{Bbicwl>E})&$6mD*JzNi
ztYqAiNt|bg*VxLVW`gHx?9`xvEpPu<_hRJ=STaJYSE2xrYoVTvr?ir&3f*^zr49g_
zt7~dLM9IOC5i)l6q_5wD{<DC||9m2tsSHr+5=q1rR?#t!wZ&I$LcEL5MrEF3H*sUP
za&Psv2ut2ShK!MgM`M7g<8(ozHOU%Dy6Ev~AQ`1a>;0;Y^nO*bo=?XUo@Q8w$Z5*#
z%0hxV_FuHmtFakUskqHs=*T;*$l8%HCE37dfoM~n7wT@PV&vqUTK3R6g9?8tgvkl)
zSQ{FNdV@QaJUvWI=zoaDzgMW}PKg(wh@~>YpOJ#wx$T63g@;O7XsS({E(~z-Z$$nf
z?z9`P@CVC#Vnm+k1A7>vaIqrh)Am0O4|aI$7k{;eZJwH>oyQvdV@1@yiQIr_1CyqP
zt^?o$)vd$Ob3G3$#!R#v!rvVox0@N;l<>=$<&5|GH{Vl>0P3bAsrkO|TpdT-;Cy(J
z+SfH$sJdsT`%IQ%jwt0hIV68&v&KH~36O|!zqtKOV+{>Oql9is2Bex<iO>QT#{Q0#
zXk6UbJrf4;rZ7%(VIQ}-Y6)42UtZz&O0=`!-twO4Atsd|cr^nZ9&zM2K!BQhvY?mQ
zPxl;tuzI(<OP<%fs;tm+u34I&co_np+iPUk*QkUS{I$yY(HKLRRp6d!oH)!wB?1c~
zP*q}WL7%^(+<!KynxS{f@hzey*(*w;ufBSAyW@#NOn6>|MCfL`zfP&|$s*Cn{%lJv
zX&#TC)U7(`U3AalOFV`CGX+!2+<pC^i+AQ;;FNs<%Ez;uh=-_3SUgGik!RoqFUC#B
z7^z|WRwOKE=2%A*9Bs~f@Gj0j*e?H9n_0%x9|2jUrZ8-&*+4Jj!#N|67>TWv$~I5E
z63NKIU1<SnEtUQ<Fpt80HAWlmBr5An6^kGri>ViuNO2e5k{t+>&DP@{Aeo3mLGca@
z!9!6S*#zBML=gM}M`1zvQ@5Smoa;jSKS^^8uTq(#W80RMAK<v%M|dA&aNK*&B~bOp
zhWv5N;x6gj!0qW5!<h#?wdEf{NyP!XV(8y#ac4hZ?lBD>-9rw7znm@Ten=vp!2-va
z&wgJL296cWuak<+ChwhYv@0n-9OSipGpEpKu7GpEn`6tt0#m*7|0rBY4EHKCcA)XZ
zqM^xr9YJ3chc8Qp<$+PHkNi<$^|+*U<N2SRCsPW&Dcf)yqH>3(YXjB+)EC3#exfOH
zD3k922i|V$@>0E(hSz`fk(tnt>!;J!m1*<CY^Wud>)TDB6sNV!FRrDZ!L=42$=Y(C
zYT6vOH<9wf>B)~56sn`Hxiz~Usa2rcYv>~%vC}Y5ivNE9hc!^>j?%67tHMkkoKaLB
z49K9*pYaV3Zs~|7^9|Gog)nz5c58RQ4V2lf&Dtqge~@SNy_{q{GU=<8F?TLYaWM0k
zbKl>&`pIl+;+YoR#ww7EdV<3%B{rm~qxAVz7FHhsU`=l{I+D^`lah7KQVr)12_dw9
zaYhzg*Rc{r@}gj{%hAVbbTnJ;ubKVvEb4!yK<Co}x6RQaTXADcUFEqWV`(z|l??dn
zYgcH?GB7CGY{!j7=d9J(Hy+;{nX8T2{v(utdGR;auj#0;A#_#1%*X1`<_a@H3MMNZ
z!UURJROT*C#E4rR*djgCDtuV4#3R-=lF%aMYCKMsiT{9WME`B_O)jRKU?3;BbGjAZ
zM6AwCM?Gwc867?8dN?DJPPH9AdAO(Z4b*hgy?Mh$!8N0)=yz}um0Ve@!$f{Y(h;2~
z&LG*;_aIXTorKougb56>C+-3Egl@a%CY#1MQ+0FV7@G<S_2#mRPCcJ9q-C>)E*j&J
z+uO10;2=oK1Fr^Wera1Mu=uJ&lz*rrU~(rn?G_?p3I}w?-zRCRX>$jPL;iGILXt5l
z^JtQUh1R$-qE+OwP#JMT>IGfu9!bI0x`~}RI)rILSx~7B!og8;>@S-!<?Rz!Z#;cB
zUyaN&i>9(1-m`>x+(%niFm?WFIP0&t^u}v6w$#bj5WQWI9R0->DN#o?j-x@MtK3HM
zllQ3;52SuD&RW5oAS}3T#aF3J{~>FWH9NFy1Blw~4{B<uVv2Uggg|YdT-`yENEoYx
zVaYeuGxQ=#HwOBi#79tLWGH?DJ0{*9!+^r~g~z&ZU`RVA|GlUfeO&{bu04M(0bLY4
zThS$Be*C8}DLHS{I`e07dWoEFOTL@X>|UI{`WzVpWmd1MCFN>g4ksA{=SB94;a{w*
z6^g;?c&hwR1~@+`QK7LE7)?DmMB%DyUmr_VJEGg3kCfdk#Z{a9#WG(ITefW~;XAjB
zp5D(r8e^2qrz6garyv&jFh&N`&gPkLae)yf{QXPj$i-cK=?<0f?@7I^A7qTa2XLe2
z(wMpKc9DlE04;<^)eq&UZ94)>6LU+6d!o@Mc<}l14DR5N(G02!^uD?p>t9r8otwCZ
zPOp6rqVpO^(NR(I@2gcrV(XE*R2S0aGUqtyIh{LnwxD#o760s*j1H->W=Y0qoxZps
z@+*4g5I1&Ug^p55Q91h8t)rSLiS3+Z_b5U%#qoi`!ac_lk;Lq1&S>ESZf@MnJcZD>
zFDb19wIpoWdTmz2TGn_u9~ay56Mp>21CPx9!=E)VR`2wV!0M|Y+N@O8u6Y(^=B<a!
zAC4-ff7mf9x^|7(v<X0}sq3~DH^iu=mdA@Cl5pj98-t(d!AnU|axVK_fFX*+TBsrg
zI<xP*4Z+ST;DROHt2GC^^K??RL07=_F~7H8)Nbp7FlF?woU@eaKh(0mzmg$!zm#fT
zN$)w>lS{d3Z!eY;qYzQ~W!(LZp*y|<lf=X|jDG@m^^G^HIh;PdJTb)meKwHs<M@bJ
z6&*a$i}oCwlSuD2NH{Zz{cpD5)SxZld9M7$x2anZpvHqh<f*rNSGefQiHVOblgL{3
zo`xzvrdgURC$Hf|$|PgN;>F#5++CJk(`TIh{;A~m1cpMhkYD@7eExj<J@WzDb*ADj
zv#sf6`_kHFGqt1@x@693AsBgvt36FDxpcXah}w{6ICk<4KnJ`w*_ncg#Qx!BqW#pa
zbDb$^PF^P(zFm=&+$yuVK0FiOylo_d!QLfsT6(w~BB227Ep<{RMxjk@c~_#uIkU+)
zg4VaQ^BpXF83Yt8yfMCptga^G;w`?gci!gU<OTt&-|m1+<bZG<nh+=2RMUEPJVQ{*
z@Vk2faUa|1E!)--LbloVG&Nc@q?4Za7dYcTN(ISGk25jl^0jcUne>4qOBkGY@9Bqw
zvh>hsbDrIf<@9gyfzChH#%j`yz6rS7gT@=s3pj93Abbb=gH6vFnIKAx3`M$&`0Hl3
zlsqse_Q+8j_g1JSD<!C1AIsJ!@=MVwi?uQzuGH-xu5vp#uuJ3IR`rj0qNnOz^JD_3
z$If+B>X>EW(Ad{x?g!RR7?#6C^e~O0qKUo(O6tx63kvL$2$GK6$fXIm$AlHb;RBkg
z3ydR#k{e<5Ll!Y#NXYQ9z??m@>LmuJivKsEg;^bKScB(v6j_>VBNysFm;F<kypJo<
zj`dg=DpxDR9mKI2&U+ikH`1nMc?ljdPbB%7dRce#69;H)J|IJN+=cHphV$tt!aDcA
z<Th^Fzi52d_x?vH;Jzx1T^CW6W-KNM!>oQrpk*pQXSpzbrBnngGs&@SY5o4y<t$KM
zv@f2|7Wz$fQ7LV4BXIM;EKDq%b@>oiY39Vs#AIdZxJ#L9v_Y$U*4MJ*B(Sr-f{K5k
znKb?;q8l1gSwYxHnJK-V(4sQ33$n$f?5Wt)Pw2I!a8c9pm$D(s`|=g!_MXg6yAq4-
z@lS7QM{yoN6QB7sx-vC7<oD0RUFUPS{eC5<Ga}L7_K_NOBC_GNi*W-gfC1;f=hG}G
zy)!1!fdUd7jgf}|%xp3idW6|fgB(lLc~7H&Y5`k8XxpKd)H!V4DMQ+GT{XT6hPP>1
zmc#u*zA-iATvg`aKmMEr2xu>D_Z{NZPNnzP-{><q%}K!0kcLNvbgoQ0Fb5L!EZnO}
zz(m^+6~QaOs0OGvmyxh8?^pz`4)N@{V$!A`gy>f?9@W34;(B#WI1Vz}FUj~_-kBM`
z5hDKD;Xs`;bLnlf53tT9i{xtACjSzGV<fIl|J~<WYGJ=vl~HoQ(D?c8ISVv>;kBRT
ziH{lH)RmoFE>+%q()-sz)^u}f$gWZMF#RQ^9XI$)m;?ZRk)fdEM)pJzF@ax#c@wGQ
z>Md{I16xKG;<3Bh5LQ|)o4?3o`s>S|{wzNQCKov+vVS2fu@o~o$mGB3t;UIIm)&hM
zbG9UvE0(eH2&pZ*s$ZaBiRQ&%Kk4&ijFyfZ?(iIYS2!Oz@k@F_QIji=4l%{PqG$=z
za}z@U`(mxI{(Wo(Y2=lOHGvEw*-j9u^~>ET41Ck&%{$ZWEZ4}B6S;OrM@)zI0}Z3>
z17<_XZ4~bgR>JJBK{Ih%3Bwq-+)qP&-JT{|jBkF;aK|MQTRk*wV`CSAL7|^z;XN)*
z5QmX*-`rVzMqn0Q)>uX1@)mxt8yWQVv9@Y!SbTbSeNg~*Hc^~joJ-%nciaudMi6*f
zTyq!KijP~Q$)pZPi4h`B?C|6)$R|Yfpf&1*N6DnN1Ykgg<F0cpI1tg264qtWy`$@&
za}frd8bB^9qOW71KeAuvW`DC`5lN`0Tu<G;u70eN*hjVs)00?RfsUS>9CX6}=&E32
zM-R!+C6neRWQ*L)-|8KqTLX|oQoI#0B$=r)c2caRd2Z!;*r3y1Pig+PGZ*X~lcKeZ
zKid-2e}lj0cQl1{oAVfym`q`nKX&I<6K)+^pIyS>9>yD-+yxoQF>6TC{-pC@rCuG_
z>hHW%N0z}1`5Y6r)$*s`k(8gouRA9U`aU!g4(MB}ZT0q3A^HynSM(z(O;G|#r`@M_
z-?FVgCu@4N7LP6bIAs{sv%mDst(W8uN}eLGhD|SW>zpVIlM~C5sIIrcDWKFgaakVC
zaWq--8VmH1_b>fhTpXi|g*}rwl_pO8Xn%%jB%|J!X^Hc@%&0GeFGT9yiCNSA@!@(=
zngBJ|bD5EvrjrlY)=6dm1){7gdnWd6;5iasi~@1>c?S_8vTc`#i`|XbUoY6+H97s^
zZKDA86Sv#XuSr04Ip1gcNYt)<&h6%gK;;+0e23v(PG8NWb3;aL7x^8D1X1jhEW}F)
zGPUMtZP$<-EVWj#UF9>5o9JqQcb2kSTkO9awob==(U|^(mBqmt!lw|SeCWi?QNlH>
z07XdsEqt#n;=o0`vNqE0s`^H>a{gzv87B&on%>5EE|pfwPh96!?kI_hm!%E<1`TiK
zWhVoYfb4rF0$8LHfyUK3DTw=ZM~eIP{5XRKm=>v;Ba34cCBglHxZETbXsg5e)oBRh
zqI4La!`~M!s&iC&T~2DJmOl`ELa;?%&;u%Q=KoI7h*P_sh917K>m#I5OgKt#DM7w;
zjQ$=~Jb#HqlR3k?@VYr)4lM_5bEPcWL=E%?hzB8^qFl67l`XGT<JV&l_2#4=;CVtE
zhbKKdWQbJf)>;WwG(XFmr|y^z?lXNe!uH-9B^{29QmSDlbNbY?^+R`#?&mAJ+812{
z@X761(R-YUG?654pUQT9LG?Od(}d8S#V~P2><f9bnDIY9%M9<^0pi;s&VGcG?#8Ou
z-?au}p!7%yG&1Lq&8rT4qO>vUpp^I~UsRQA!iV8iWxcW}qkF@rF4+U*B;ogzucKDO
z;tJf^TjmD;k~g@(NKi*Sg~Gv50xP3}F5po}@Qbj%|J(nR|H<QFmL{}eiX_8%PG`jm
z380ofSDz_`Ve=9Kg4MAnI06S1F}G|Q!C{eVYz{?xnr{0PE7fMU#odjj&EB8IQV1NP
zN{1HFBBauTe(YaZ`3l5l1t23|V^?3)e^)<RlNGti4f)<An@0(Axa9Z8m2}h}ImxT9
zuAmmzzC*jmnx*)&!l78{#f6K5gXYDF=k$Ze<R_Bc9*8BRub+@sJTaqr9|=O9Xdl<S
z=T%dF(kMnNbc_rMQ&>Gk)j_$N*QW;j7$49-K(0diikpRUJS_b!RA&94*zC_^6#m}s
zh=F4lDt<EvC;>`J(7(<UT}zS$r5=#vEMGxWO&B@n9%(*|?!^elZGb7VyXfj?F0pCW
z&1o<CSY$u%_iGYv^8TyWkvh(dv+v8Bc_?j<{hs^CXas~?S~R1V#%FhFMfE@PrS#XN
z$>JFAJH{3L*G!mpxAL`o=~{6#W-&C$#3>t<@t@SbFBuCjN+AnCt+<1$*l_PFhpHte
zZRcDE+1(vNsy5ppJw6)t_WiiHK0zp5cga@HUzcnNulQSGaNf?F>3(G=ePo!wiW|EC
zQ)1Ltq{6387M%P?YQhh_e}0mS>#%eqV??&wiMh8UnlAM>+l0bTDG`x(coZ)j1x7(k
zzRzQ#*!%-qYJOT*G-@N?`|3+-UDmy;^Nc38OcW&XohYxp)P}XoiO??EBY6K{jSJY5
zSIEABZX$ZPq<9=7bYBRJZH`j<jMKO@tuW~q2dfj^ZJK!Tmw8g*#|eb>{M0@#CIJxH
z==tc5&S}06b0-dJGxTG2R&o1x!eU^PukOO=CUUo@v&+A>z=cw9R#;@Cy~nWGP)hV$
z@qrSFkSEqe%HmoUTW_ik?eKSqt<-^IXXNC+_FtXD#F}mtY%Yq0e~;tNJmdTqXt%|w
zRa<lg4q@O!qQk+!f#Mj_W+dvWYCC2eFRo=^Se=g0N$#a{&?|H5c~aaNGFCaneENrP
z{5yU0>#!E&{105OR8H2bT&%C~R(^#relskcTrh`&KT>ASbEcRVEHGt=mWh0t;Oncs
zrY9d$i^buFRr0U)!&dp*Dyb|L6UZY<+#`&Z)&yb%QwA|=b+9^9dRe&G&Ezg*4k&eb
zFyi+7%;{a2OYF>4#l8mMtk%JNE-$1xCZxh3BF-iH#S*%VQZW5<Y0RSH8TRW!y_dHf
zgq9q}&C`qj@?t${eCrGI);0+?Yc_lu5?1(4gfOAkiFviX<V0=peI5C*^W`O$l<Uo(
zYp(r>r03@f*75o0q}X$+G7hn-D6-}9SZvH6rDc!j-+H#hzUJ?2w^`48c2bHzK<Bhe
zwY^ZZIx_8L?wp8CWQqOv&RQ>zf)%rSZN@oE96HDpfjdt$It=Tm4_=idIZw{m&J^tZ
z_N<;%w(?zB22OR6{H5In@7?7E@9yNbDlBFy2O;EiN<6SH=vUeAa}-UT2lycs82%j{
z!vkYRi8ctDj=qMgGwuOY8%sEchA83^tpN0Vs5CC~ws|bnpJ*c)b$`0X6Yn-N^~X2;
z#*_xl_!P{)smM;laW6KbnoATe#u=OrD6LxU9$7WiJ?KOs<954|ki6&td?4T(Dc_@<
zjgpSPCRk(a*$i0LcT)*gZ7-rvLRqP_0XjPGV;OFC+HOv=Bz(C(aSG>$oEZ!pNCJ0k
zuCDolv+j`zO7FKW4w>$cSnQS(NI&iqksqi%9Z8{@$eQl<#;>L)Vic+iJb1XGBDlNC
z&A7Rb;gc1Qt};Itk;k941^a1ox3}RvQoWdMFLC>QcELF)?`Nc-m|L#qAMCx%sxnsp
zG4bS3E7=l{!!mDo{7|G}cbdM!?M1_4RGvSzcA+qwWmLAG%%wCq5?wk)m7iBTGL|dr
z9lV@N(_811>_jk`x1B@p6eYzeZs(%9&GTH;nc5?@Or)<a5uzQ$XG;7!Xid@8L?+i(
z^d8atMDR=PZ$#;q+M{+9P$Ur}IUA1Rrm-Vwq07%1p<q`hDBuID<N!D7E#kq(W{HS}
zE3os7ZsFp(EX5=`F$<ydCd#|KUvuMn`!eR5fZ1ii#PracsRcvp5N+^q@Uf{R5zr!d
zFHi1YcGEbTF3*WBlg-x*x_b{#WJZge-$kXI;y{X1I{B_YVcXN%nPW7Sg$pNC*9Hh7
z3f5l89k{uHWqtfo6OU{yG0GZd5QH%y*PXP6+p(l88Etz2#m~448Ig5smvmru4mw&O
z&s-)lZOp4nAa)#VAfiXqt3afsDc=}_;opvQ_BI}0(+CTrU990q=Ad{V(?s(6VJoqU
z<wi}eJ^p)kBf7R+%Fa$owOP+uho|C`NdB@!R2z0nzi9sj=bPsW2JEN5Kdy}=y4heT
zoy7HRgfZolIyDO&9r)Ltxe=pnhsj^(17_FViY`|#gcoFBj~)|}4A;voDBkZW-)<Z3
zU60OWA1wRK@b8FsW6VV|=6$n^HDanFPjYLKkjfuF!J%`LP}{2_WbKm2{&E{yRIBz?
zg7Ut2Dlc%gyXJezyhXz}BZi9lLXwY|xDI_D5Czr6lRQPD;d)$zfT(OjAtW4Z(wB*~
zQj6B2Xy16;C9B%(;rvr(;3)snS>T0By<r~otI}hZu)>!onnAUmrcSB6TOK#r#1fEv
zdyClwV3blY$84)8b>=+WjUV*h4JfRdqTj9DQ~Ehp{~^H$AYHaZ5oHWBF<8%k%@O+v
zj+qOPjxP2x>09o$`Rz$nW@9urSR?(`hdz8ne7eGGFlyh|L#Wo9z6|bubWEj~Sk)az
zlETHjDlh%lM^!&j$s(&?UJL3;J=@0}I}xtqHj~}^T-W!;DUFxvLI3V|{N=rkMTD&@
z`r$Vg8Z|~pIKB6s+T!X&hjCZObC+%1!b-G0MHrkRqzOFUOJ|GXF2h*YmcuF*Y=C4<
z<=fq?!JS`s*}UHDpwg934QF!*TAv;q-n=$x4G5`V9q&2sN4m6(kHz8kL0p_J1PW?-
zn)#_Gv=uV3#0$y`p?bzjw;a@6!(?3)F>GA4QY)e(BQJ539MQ&#mu950sB#L72*BK5
z2>PaNiuD7;@3Z`N?y(V)AD&|tL&DAOXDrZ)Q9|E1yQk%$kBC^%GG;-qncTQanCWe)
z47vwT9-cnn5mwrjxFwv#1g*Prb{|`i=?>5&nn>Y8M9S!DdMtHJBx+y36S$GX+Z$c>
zl^d%`Owc0N<UlsC0vnCKD65_oDg_qv6E;LG6Rh~CILW|yGpx^U{L`2x_oiExkg*C~
zw+ueIBhEKiThd&)nwpQme&;4V_XM?QWsPO2PYKWD&gr^e3R!90Azs%sY;d7t+esjG
z7?f@g321HR^1*YmFS%ckbt%i_{liGGy9C3fn4-O7e{l8nq^GSp(;I6?d1-$)*gF-7
z1O-}e&qX%5eAX2fS=;PKA*=++>R7?vhPcyn?gQIL1@<hq;^(~}1qEtrTc5i48CwFp
z-#uwEkr}x<HZryk67a)|GEto3DGuhyGi%1NvHdcs=?7zXx4>7Xdh2u0+4l-`?o&{Z
zeOk4GoATnRiz*&#@yTZ#RuQ4*q3`oZ8hsJ+4I8iO6;offAvG@MV{eF-BL9#^Z;tgd
z?By=Li&eCD5tvB*95#F&ldd>tHhsQcY$mOlyDQ#OLlB6D1Ltd@ESBuDlvYzkd{=ws
z$~w{9siRWyE6XsEy7rfDijtngqAw^Ke?tZNCz{d?fo4zIKV_U6M3m=++z3E#(%z<9
zRx-44vo_0mD4=t}z*nh2TeqDho#I6DQ%OpI(nUNpK7kasTHlV`c)5F`j^|!vo(=`3
z0WpB+Wh96X=Rjd=lDx#3aHGAUeSdph8LCa7%r_w}G`+oN({}a~@TZ=!+{0H+SE@@y
zTFVljY2gpT`zOgz?IDqtQ+GS6c(0AFXp`j$yc~}LPZ->yF;W|_F>gq=tdYHX{xIS;
zJ1ARX$-f7Ow8MOBpAz>BFcg#<%dO>IZ|y*y>{LKl&9(nVA3YoH{HaN$PV~ou%XXmo
z;W{H!p(M8k0UZ%<C47Z|H~prrZ)#t8ElKs;e8BG#(6@LzTN5J*otA7Cf&Jvp%)HdR
zgcxrw7rzWi;a-?~56SS!8f(h4$9wYDr&+$2^KG8SoggNWAHh&<L;(5nTsrUUT4G6O
z<lN`1kiyLmpk<IZ{kjylny<RjYQY?C-{8I|6^F0fT~);uw4(mW&+zd&l)0a^2_bYR
zdZlUfT-qn}!n~+iAJ0|M3A;mhKZb(ruRN}T{F%;mTHC;nzrC1yiMIVv6*>qtHe-gA
zHrt-YC}>0ryRz*GCxJams#ows5B85%G&G<L&Q?kR!*c7&)!qQAzKofdyhQR{r@0f!
z{@u%O+3sS^jv*z^0@UR1Ztuo>WfjkrDtt%RefLtFrh-UM&ez5`YRe7zoFG2KwPES!
z?$Se{^i?P&#;?GF!TjRcl+<F(GqzGgNG@X>_rEdA6Y8fJFSt=zKdJ!1UjERC#xJUu
z2#xp3<kj=xf&yT$l^G!+tq3SiZnJi1D2U7#it+hpf+;fx?(T}p3WH~5YvI``Be>>C
zCk8Wze-OebfLRWdY^}3@M#pX?1M?M|KM$|Xy~w;)!O%r`+^uheoEpU!@3zdHeXuVl
zv6D}Tr=TP6wO|9L1x(~vvbeOCZ*#L9Awnc*p3p*CI%I1ycx=BZ*;@!L$A~KrS*fdB
zvHxbb+dc?;O`F`|tzj4Dgs@=#C_;Wu(yf1w<Dsd8EDmVM1U=l-Y^{_qwD+zc)wvi{
z=dV_)fvV<B<G0-_2wPg;G5N%24Ip!`Tn|6#@h5#S9&aS?!K7JJ{`2$NmWSK)#L`!L
ztO_qjXF-20fRX$Cg{wH8{+XsD*dfz;H>TnB;>g#`U>;?9uyA2RJ1*NAmy=`X^=kRi
z=PG|oN8ro`19tw?GxccBo!;G>Jq%B+GC(UQO<L;}4PvVHb}r|3r0OSH&i@-WRbW3q
zgLlFmN3oGEisy0_RD;Lo?Oxqb`u3Uqt=fG=L`rPJPb>B>)zc4z;u^l(6D)_r?9ISB
z?q{ivt%V2oi2$Z#9$hX(6m=enk_4;bA6yPLBVvqhU>eIpPwcph7H6#AitxoxBP*mZ
zzPIIFihDjg<XC6*C3m^-axXg@%Bz_bNJ-*zxsty;EmN>t^NDK-;ZVf)4WPN%j_#7;
z@J=*%;Jne$lo;$?m03s(cN<J$vs6+V6L{0Hhr2m^4)Di-G6_**q%!)W&xVtM-GCx<
z2oQ}I<vV$kv16vaM<G*94H=G)$#`6wVSl6Dqr0apqQ!(NdDY=FLCYuSx%G+dW>g&0
z+}763^~sBG@RWq>xnp<sW7r~+{l5VQ7H3$ZaOvXU<{-Nh){)T}xc^0d7-u=Awi=%u
z^VHg*gV~ug9iDrNuQ&wT{JP>9La3A+(Wk?qDRZzTEB9!%vm-lMB4r|=YZwZFWc4`~
zBg@ZIdDf@h70e`bLj(LlakQP^2%_#mj;{{K;C}9lGMnyV$l2r|_wP=u_<j3%eJq%4
zV<l9y98UKYK`-^n{Iz_OGz!k=2q9@ueS)_4^6wg1$Cwge(-888t^4mPHzXpjCov@q
zNoo|~i|203Jco$g#QitP{e(v7XY%}=SR`-V%fD@osFIk8%a4|$GG6L6@ruzrL0D&n
z#XekycVRy|fNh$4ZK{!H^J@WY?wB#3J|!~1lZ1%Q0-Gg^8Z11D?6DxwP@Vnzn{QAv
zGKITGA{jZXXugh!%ko!W7}=a>ANdjV68j&^Ru=iFxTB$A2?Zx`+OdS=uJmuu9pd?s
z?~gJWuVM1P7%*|UBB*dXwm80Nw5JOsoo#6&Wg0&myyPIbyX^F6+qm+w4O$tOt&F7$
z&~1XA8TV5XM81#Due~NU`BJibc=jfJpPlzhgt??LEJw|u`>CCTDwo-(y#pa^eKs61
zwZuulklA{z&t58CmWr@j+miD5&u&jzd-R>Z9poj{+F@XTIlohEF>tGrHM4>Ge^@%p
zuqeA|4GYrU2uPQ7cL^vd;750-bazTf4(ZS!A>G{|-Q6&NbaxCfoY!;Cueta&!^GZu
zt#v>5hHfLd?>0*sB&g;ct0K0n6u%I5PORW1x6&-BzLN}HmOM{wx75f2%gNBkyv;Sb
zF+LOA%kgvFl#bGm059o=5`yg>-jkB{{#BO3XFIo26V{?ZcX+!tH+JCS2<OI$4R=3a
zncagq(Qiz)IOcKm%{wroVlJo4-r*`t?OqOeU9qFf7mB{F_NxiQoj1=wt#tJ6PcB?m
z%TX;p)M7#=$CXE3hfn0Vqa^y$tJHx)l>#encjxEDg_+T|3!kWPo28a;G(W!~+5>3Z
z*ZXkwvz@|d-rV4tm1f=JrTEH0L8rDqD_BoLvZ>gw^)n&KJbez(nb$n`NV;H)2$e_R
z2megQ)8VC9o9Ds)E}AbhY41l_IF2jjbht6VD>$oa9Wr#lsoa)(%R2wC1$Gy`zv3|b
zZZKbZ&d1Rq9)ZCjDG_-3Wpeiw@=5|;buAXoEoYKRGE$w$s`8f&GhjRCj#Biz{=1Oo
z-YA=zl^UuXHi|cwnefJU1RLA9^@jV3MJrLO1s)7}n6&6dbgeJ>*{w09s{6YNV$eOH
zq0vtl_e?$~Cal~R{krxf<N(f?J596GPg78q`LFvi?>G?^zi9j7s*?Al*<tNsF^SY(
z5#?8ifxUL}@~agLtto{Kktw+?{ilk$G%bBO*>^LL6##&D3SHoeC<?ergD%(Bec}I2
zwwvyJeprEg!4$auQ%9&hC<y6Of^}w_4Bfmbb1d;;o^RQ|aTtl-bZ$dXXiC$pxTv#O
zGL~gt<}f(Et7{6Tr!<WlZWR>gW@2TT#vad6>or+9z0w*jw3UBWXa-vMYXP$*e$dk>
z8L$|I(*2d;`80jna{qIU9kEsBN*Bc65}2LI<GYWbFP;|dL=<KS)#RMh%I72B-gs}^
z(Tv~?xQnUmZO?sHa>$>;l?VU8D=5$S*xYF^b3p8>-?1tplRXScRB3--g6l{Y*H5sS
z;Ik;>RDpRS)IKd7aki!)XW-KNj=y4M^a{F)BQ=LXVa<S<B)`4k2>F-_@>Y?m$otbH
zLdkyd`VmfITzAr-DUPRra(1sG42j#(%S}E&9<ff-em|<Ff-!z6k!^JKbItLAaa8vq
ze*%uCWb<-!RJWx`51T>k8RPWjfrke{=Ll@(9E&XrS!wBj$X)0KpJJdO?85~?0GzrX
zQ3!t9)H;c^rF)kRHn2?dbZJA34NVQeJNwBOO+48}oK@wPPnV4MTN9|nvmH$}P!g@m
z<<*1k0oV<^XltL{w?{<jMfySu4_3=qz`sI3p&G+SSBZSNAcvN<8}OcfD%juR5+mkb
zV(~I4)dVA!Y?w!n5~$p#urxUJQ=eGGXhFMiX0R%Tw3n<7N|Pgq*0M$;Atoj@0>vTq
z8o}^*2Su);*E)R*wN(ykZ2}zS@|q+rNww-2bGv=K!yui2h>C*OCu2he!;EJU3J_$o
zwomA~@y%C#)k9YoUn5j%?M4@s?>YMfDSV_SrF(Y=1mGvws9T{ANy%c?VD@-?^O=%(
z=NREYR3^m<n^M7yy$o1GQI6V)us(bJkyFL4Jp**#*#oJ~;#=Le?~Sa(l;zbX)tX^E
zI}bioza<g%2KYym!`(SyVYhuvsS$Jj%RVxJiwjq+87pHeI}@V3Z5qMz5#09c-WLq=
zd67ATo8>?BBF=)(Ifv!Pi506nWE<16==>*lK1O)(MD}0!PW(<KBg+!c?>P2jPwO7M
zCZ(`6-y8l-Fwjvy=St9I9PE1s{`&5t+=hed`XAp~VP8=jdZPt|wl{8v#zu#W<zubr
z>k-{LR6l-@1>?~3R6f)2?rVgwGv@!OO&kE%P>`I!Yj^o!V;k_K-E<60h;B5~cTDT(
zAcpOlTM!GB_yaTz@kZrlX$HeJ&==WxW-+pQmJqi(jKU%K@io1_Jgqr@GEGTP_3KFt
zRU6GnRkLDV!&_Q><=xSj^R2lu%fMr=DeUpk6{F6G`@8n9JX9n$aepCZuj9S`IlCvy
zVD8FZ#OI2^j%TY2k(+3Tv_ts<+qb+Al^PW-WqNL$h?XBek6&;PHAjbE^;~ytIyFC!
zD<OARPj^H{aaq=XU8GmkI@x>5S*#J^i^%&M{{54x<~o&Tl~TUipmuK(nIe;O3u!>G
zd|dREO}JJAN=EEfM_aYSq-51oEXC6IxXbB)+lCHK)L1R$=14{24!M?9NHV1s9qDq)
zv^l^lFy*}~+oOhPN`?w?j=BKFE*7(nQGKTS=h5bZ>)XufOPWxe%%w{uO)F5*P|em?
zaSnO+6i{#^Xqx)(PSB0Kk4pmmE8?JCNN4D!btRuMrahrJz1tUt*g6i8;5rW5b&Gjd
z7e&srdWQ7~Kc;w>f|zI1JM$fU%LdMyRDyC@x;)R8jvONeC$E+&rUw6}K%pv!SHz~X
z9y(q(CU2`nNBx!CPcXg<ygdv1%KPTZgwH`WZ<*LhjiS=6l?D!VWji~5`TY_Oi{mr@
z`6>f0Mvpzt1ckORa^HD>2oOBjALtNx*jJbOGuKy7)hcUR<#si#Sm$+g+C{QJX;k%A
zsl~8apMt{QN7#0uXdcbYZarZ*G?J(JwUVdhFGs}_Yz3k}ZBMZ4fP8+I9xeNL|0O|p
z1umFQ=uZ=gDRe?#eD&VrJ0Xd?+nv^liPiE?UAPL0gpNbpWnCot@4UrE0B8@f_Nx09
z4wj_J|KoMBJeomp0^fYFGO(EW^k;k8`>rs@`&nY6&ZAens;83HVwb=b7$|V1#Z;j=
z+nvW@26$W^1d9XZ0+O4`URP5#XnxH}qQVmYwfTy->v0Lvr~Sv<x6i&$n<jla>J5Sp
zVa=!XEI4!z@{&i(E3*OKyJUOh?r%|MZX^5eH?}nXj4{2(5<fio;1zj_SVbz1$8jrV
z-ec6m6%h|S$A~zlJ9aV-Gh3<g>f=j+!=&ARgd{J7WMsi-_F3)3`Lge~X9Gx}{V{>A
zNW>CcVaO0@f{4*mTpgu%4rsqzN@2LR;)yitu-qmDwjN_pIji~a;6&UA3st<Qf@_+y
zE#-uzcBWeiOd8vptW!mOzQ!@bG^F38#r#1s0Ib><upi~lxi_oE?j`K^6jE0xRX-=2
z$cD4F59Zi@$sg*vfhM95SD8jQZ2hb#F-0o%^6#4p`aTHLKjn~G?ww!)Oofx6%1*}l
zIBi`d&tUQtj+E0ZSJCD1A;`r5`xUwKK85~~Z?vqY@QRdyp9qmbLW2`!b$D)KosjBG
zN$y^}H$wxC<87>9g#?l9EWAv%j7+jWpV^-79@`qyoRLlqbq&{6-$1DAz5O=E;a~#0
zU=i$D@eSkq)fC`F`x}DhcMG-X=NBH3<=QUE5M-eLHL*{=oe#aB)G9;M6@Q1b8a6qw
z7QJx7tPS5({@8-8X=;KQ3jjCYIz5gH?Ku1p+uH7G89?<(x}ss2E%>BtF`L*rFkCNg
z)RF!TV(w2GZOQS>BOKd>vYlJ4HLtr{wv0TmT7l->a^i>EW5RG)JM!qE+0!o@n8Z(D
zraU25c4_fO=(EMz&9hedT4&e7lDbXdcYy%yKW`mhyE_~wy3a0Po2AEzt|}+{o)W8M
zO!VhxnX4E3-Bl#VM=xJOov2KV$DEkgkuSwlf2nOLU~7VpT-fOezO&IhujS^bp<*h{
zHb36Yri;dN6w-0D^;N?%Im;Q>TN#Y6xT&0PEojq|V*&<LY}*6aLpQ<8;}swGvt$=_
z>R7V8k3DMKjWo;JBbq*4E_|Rk2!zYWdwOus)cdxxn!&@gX^j`*AW+>wRhLCr4~MM-
zjoq>@rH@6@y*ZZApZMMJX5W%8aOdM9{l=K~?!EJt36Tt_I9=6ym!1)a41Yj2hrNVk
z90wklBAC7g`*m5G!C}0gN_%J?LXsesy7)s*e06lw|HY^l)sr?~KyAM~&vXA1`#Yha
z^X&G>u@Jnys*Tu6#Ygwk?nwVOlI)1`is{0Mk!P{zgt}O^&xd^Zr%H@W?kjR=EZ8g7
z?Qs)lGo{^?vub_eYlFp3MQX1}X>@gd{9<O6jN*1)<C+nSIsWuUPPsR}|69QfyG3^<
zQgAIAPm>yAu@r_zRoQU$u;&h|6eyHwsA^C2E-EvK<QHqE-K~|ILi`?)(OeW;wjgy<
zOVY3X{W4B38u<@5WflSC#3Uy4!ljo{6>64M1;y>PJx{<P$woZqIvQ&0efRU)AJQeZ
zqV`kC$3k*jWTCet!}%D#j_BETb*`gohJz-1V$Utq`rsKpgDLc_Ep;go(`Ws<h@s?#
z?e;PmCnqC{*`jA)oRM!9%W{%y6Y?tNE_5$(MGx$rd~eI=ZT_1OGNaNm@X6aaJ!;+Q
z?)f8gzuYEK#k77mT%NM({qiI+#3U?AdzPAvz@YESz<6^?T7BI}%yQ*XQ+N=RBa7+*
zcBA65%b`B!$CEFrUU+h33PmKjCneA)e@JTaAc2^FwDS4~r{mJf;E<Cmxwvox;tD1a
zj}z<XZcSz}zM|n2QZ2R>$jS9SVKP*G<i)ya%?ZuKBmzG-TeK7#Y#^GMtXvbi%<6XZ
zZdAw$BhPlRm8X={^>u?xh19ws)q5wMC-vz$OW*aeTIN1*@498}Ze~&R@{+aFs5a6t
z3$Ga#vrV$x)rRK5N3&@72UBt5FKk{73hP#A{P~VYiag2!n6%#!1rD^oY!6lF$28@F
zy8D7>&TTs@oeJxAHd9X6AKyeUB1c9m+)H{8bq?nsSM_XN-X-q6IaCbNed8=1ATVyR
z^C5YL)bDB9v=*Ca0nLNlW@YtyNWoFJni}WoN)dY1M%WI8h>E}Z2rUcUf}S^gwwcy^
zdYP8WJ3)gQ>b%+D;nQiT%`#CJvOOuc@4N?^Lb_f;v3;472<b+NWC;1^VaW_WKRU2m
zZHs>GEW}9|DX4N9j94Pua9CLNo$*s-{pp54CuqhJg*u%#`!}$BOSeteD7OE4Ib5L+
zR)KWH`Z`Et!Td$08x%$JW$*UFtSWbE0`->)Jl{}vugGD^DdC4M!}G}Q6j|tb`5l$(
z_OJ(a=pe4WJi0BMp>2Jc!KwDmJYgL+SFvf_XlXjC6ZB=duY#s>tEipH4s@)c07!Q|
z#7$%-3HkFq;-UEf_5p%h_#%)_cqb7cN-Go7ed=)y*xp<xg!ZceV<QbMS@!L$t_8&h
z>#I-Em@KMHY^7Afm4_UaFqm%m^4OY7FO$Ci@)mBw-$aG8r+wlRmC347EHraV!fV)I
zG-_SiG%t`Xm8bql<+bzcnq-Qje;?K-6HZN^s+7K%W~dB~aQEfbZeq;0%$T>MVQ0>y
zAm46z-q{wselX(_Fwu3@+glJ@Yw|&R8Yz=0SwmWrD@FPEH(@G9VJcvH>VSf1vN2@e
zf5qr;&)GyN)W`n}Au}8Ph#hsh#I2}~{#+;S{6XPug$^9q=INdd{IUGz^K21}FJtfN
zJZ<Ljuyx|ZQH$S)E&FlAj-lPfAl^p<Xn?8}y$o9!vA>lZ7u)GoA$FZFWOTW=FIx1G
z2WsY7ceCcJ8GPXUkG=6yolf3X*AY+!$hHh~3)as1&Uo1;bJ<N+gWoGt%*|HwuclAn
zqFVo8n51EM4y$X(rG43U<qR|at(8t2Nzo|vr+#J8Cpgl{Rjc*W3;ltA;vM2wn8(zl
zUav-AFkGSiN)t=;q1sEvx&}FI8}wH<YhY{~+V;_?j1#cpW7w#m^el1t>ol8-P^i3>
zA9NoE{=yX6NO;dKz~SFk44EuIARivxU^86_J;s#qR8%|kxPqK*{QFfgi)nn<mT$Cn
zIB{oQym`0ndXxdA#Tto<u7_G}lui=p7IbZvf?)IO4)iOkDMk|&ZNv4HAHl*|e_D-s
z_{62fk_#9q(>$4mGB-1)=4LM#-jZIVi}`v~R23;_DFEw-?Lzq$Aos%DDUtsB@!Pe+
zg=VryI^l(aLQI~k!uzze&xVv79HRSz04y{yod1Y}Yx1^~9Ye3t>S9f?%ZanDc!CQ+
zF0hDWG2>Rt8Q41xyx2RO1cBkgGmOH`z)^K->hLaJO<l&&S*Xn8y>Va8y4Op#BI>o-
z&yRZdR%ckMqwvl(i95^r2Y5O$;S|ZshQh+~zcIznIXF#twAdt=oX>KH{tJY>D@Kd6
z(0Gnp&yr4UaWMTx&1?QZ|5Yd}9L(p_ouur}$+>wmHgNynN{oRXma68A7yAzhOGgIw
zOU45BLys@qMMk1y)!({1ANQW-{jU0FN;%9=*Ah4Uy3&6*u>gytLe@47tz9Oc0~P9J
zf++9SG14kAJj?|)CG&)9QfJI%(~R-u$WfCgszagnGY@}@grbWluXBOn*S}a+YLaNu
z_&g!vN!q#p6=3HjH8w8XJyherTiEg*)4i}-7>QCTX2zt<qrd(08oH@)e7n;1k~2-5
zfiwL5wI$?NzOLe}(7)CMtA6cUYV$0eLI$O&7FW@t%pD`F4?+Jn-^yiq=a#IANUls@
zQwadV=gSFvI7S;`CZiCYFEn3;y;tSAUUhRYjTr3rFDt5UIrvxsmnbUB1QZWpH&K2t
zp{aYga6eflP<h6{K;GVN(<#xee@R&tUd3>XvhZ7jqp@*VaF|eC@rC;M0S_wvOCD(Z
zk>Vc5!olx?eW?Jze_AEHdf1E8IgP7xW!^?bm;HpJr+et)tt@nH6Mg^1_VLldHZ`od
zuh{Zd5~YU^4BAX-I#$86ok1W^GJ60ZqXmT{>!voZ$3Tf1yIqQ^)2CXzzO5ThH>bsa
z&}{ho*)&ID0Wq$EfCJ?QODkbVzgOhf$KyjKhq4Nt)DFU=DU384{R|+HfNF`G2}FNM
z1h)R%-#*}}j9dHFm_=jD9wZAkEs|Zp<0J&it^^|LNUrOcl4{Ea&vuD^8qfc!*+6{g
z>*{^70P<VWQWzfN_^CBsdUfFU@V83zVr)0_61){lY4@gRblfU-XZz9RQ32)yMLEF|
zxF)iv?aGb$H;aiqjYyvAu!S$mUIPQ&((-GXJe!a+1N`QP_JOyCM0JtZGB^8+DXSPd
z3)cKL7-xl+n*4jeV8k^w))MrUBMitL%wqf=1oYD!>>S0C9KkaB4{4d{&jirB`8RZ-
zS#tG!p&VBe^P#+f=1W4qM4Ir_?-Ur@_@6p6l~Dw@IeNV>B(#hj%p;8zU4dFi^r^4#
zi(dc(RvY^1lt|)W{%nOFJcqa|p(V%s76|Bdv_GFpb?UUk1%I4Hpa4xMZm{ocv8d1r
zj}1g`uoqO9&lKIcM31bvbsrbq%-b=qY;0a!G(CY`CHJ>5qK@|($PJE8n8x=KZjIt~
z4jro%RK?`%i73JuDEhvAgFPjF<P_XP2#|Zty!|}N<hHx`xJD~x$EGuhpK<ng<t)%j
z-E>sv?t>U~DCD?XttruEGZn|giVX5?3f@pvfg<3+L|JH;013OYD?PDyt9wiE{UJLO
zS7obgt`KR-(M~L2LpV;{t|3p5En`wpJhfX*i^R!;98uKN;EKMErMNSv0)u@!&2nY?
zg_bg#bmNJ3woJd78YN*^`8H^G*O6FNb*mWFkS~m#e&e+z(ig`4d%;@4*K#on4#h)5
z9*YD0DEbE(z6U6cGl@ennK$u|G7Xm<!B@W3QDP}xKW~D)v-k_4+5^dpg8x#m{zFCx
zH9&FD;QJ3X24t8S)=_(zsWD%`ZVNA}9Fajx`4aw6U>y#+46I)<BI7Sq)7|rDNblX+
zgDzTK59WwW(_52zi6#PR5TiPkCictszF%ZjXI*T6wq@}405GKYMRwgcOJ;$+#%o#b
zz!wMREcUL_cZ>T`YH_sOV=LwSE8iz@Fa1OxDS)Au?9bg(I~S3#32Q7(!9!A#_I;^H
z<#PJq54MhZaVRd{1xcoZ3~q-sD=WwuOib4P+}uT9l9nb=PNCA~K=K;Ulzj)(y7&|n
z6M)^%3kKZ>q`s$`w&QC#pg|BxO}c!(bWyV=@kuGar8;Hj>odh3`_f3<`m#0~gAC|o
z{KvL0G>h!ymLwW#IgLc8F+6fBtw>MKj-Aw*dH%BLLpM<0!TV7*J-|3@u>ptS*QMFx
z5z<@WGw+nBP6VkDCs=TNGvz1^G5F6?<U3w1=QsE!`Ba@`fwJirI$@)eKro6L-C#e|
z$nRG=yTOhId;i(Qz&C|l=?CpEy0eh)7MES!bydUuHryu93Zc1(?PneLhWrj|Ly7c?
zq-$2dKM3fUxwH+q=MsgKMs2BhB~4v01lCr)Ts$3t>FU~j6~~e%RTso~F(`nk{UN99
zNWFGsjD7DuY&G5ejP|g#Ah)=EcyN`~(y-$R@67L1B=c-9)>K%ZqLFfFtFIFxi?9-H
zm~cC(`K^*O2|t?X8F#;7SL_{mM^VDZj9kUJehg(lc<NI44*4xcXTgse5=khu5dN`a
zXqW{R-8kGLvy(lt$!A@au$kB3)%)F_QGd(wp1Czy*C6FCyXiQ7T;2fyLzm*Q0q=_b
zDyTm;pf*{!GlZs_j?{|P;lscMi<^y%ynE>>_H_}ZeA)h%ohlr?P6CakoC-1%roXsI
zVY4D=EL_#f9vo@k(=t%th%Qq6B>xpzOZkf+Nm0>{n;DWFb@pE~S-@TzQSfM`M@xUD
zkHjEOT3s3ooHuI`>UX4DsKGd;I)+m9lT?nrqw*LJ<jq&j?ya|HIJpo6*-xRditKg}
zjDzPymrmVg$dozJD)!D)ks{~Y;~w|wKfb}_pt)ru$OUuha9hCk_S~cEFQ_bzg9i!G
z3A~fyD?{Jd*h5rL4dX*r9R^jMAC}$~ELu(C`QC=;zZ*rbc%Tq`nE&Zk6kKOBuahzO
zwz+r&Y?qm9e%pE8`SfsDl%rj33}?{Bub=4pClr-{m>BTYI(2X0<^8I&u-8p0{q80g
zx!X{6PAy&nRMmkG-sAvbLH51@0sx6OJ|k!BM$6jW-4YB+@iR5vLKKf4$3gPDGF<va
z7lOU3F}x!0662>I;==Khk8lZmNez+gh^8|k?(EVc&%{X{4~2qbYY7tx#8Vf$DpRz3
z!Z}xUfH!9$;m1&y<b%;KKW0Ho0iVpv6~~l;J9SJ!j<y0FVCN)FUTXK@pbt}`Ouf*!
zXHdVhj|upo+#$dQieT`I9o^lC4&10De|U@}gFfUeUYs9Knrfc|Ufwk{I4g|f-atno
zBf`&@c}qHy02K0h0xgq*_*MT&D4U@ZX5%G?%8}=2$KPRg<)=_#oV>j~5U<f05{5is
z;k#8vu%xAWI3Y;0gqWM80V5mbcIex^H0)U1W%|`ulZCb@nKQwWRW@~9CA$|VxtA3&
zMuO@i^h<BX`bzk^g)KwOmmko^@_a|aG(DL5^(9L<1(1pYg4gt~o@~a#9+87~cQ3+L
z+%}mm$}cQlx{0+e$Yf0gb>6Fz?~#bOOB$Vevn?m7#8;A+Su*_39fxVgAxVw4D5k8p
z*baq1Ey!?Agr7ahY#Sm9gq<4f1|_*qVOo1*%sGRtnXhGp!1ra8yKzTiv@<c>_1D5^
z%gVX<J84>+7SLOt{X|ul4gdSgm-~%6z~}qV;p!fkF&;j?APmky?fYe29{4KftwZ0(
zz#HgxP^5F*VThrWz6svoUnogGaB3yfhhHY3FmTywkxri<m|JEk;oW2~=d!suCrZ^(
zs+j(pX^(<1Q;i;3;An@H9xtiFpwHI39PH&vU%P-F6#jAhA+8F?;ZXhD`Fm1!-_|hw
zMkjnQYGlz~R~F60Z%03Tg>zxB^e&^)lT13xbI4R?6QZ>W?K#)@hi@*tzJ<~6%vc5%
zr|W$2z9YF=uEve|7`;h%J-<9i^hdtZX?J6^%I5E`G7H1O5Ml_U9xO1fN!9KeY|8CO
z<^*;KpdX{ugyp>EdiidGeA)xd3$<0T;XglnjFoU{%%{agNwk|5y!rjxFa97k9_4QZ
zp$1{@cdx2t?e-tqQ1LyPs2Vv@_2$`Sy(=OA?ZHK8`%G4UT*%@OYffyz(K5rpN{-p~
zd7)Wf^W$p$q%;xv`cJa8Up}aCV=vO4XGFi*_UVwSwFJF)_OPAW{EcP~E)UELiDx=I
zH?!N&(MHp{tjWUq3EkwP_QuqZamY{g!rG$Hkdum7P4Cu}1hFxbKYd<lzDmSzzFgr7
z$23jW5H54^AbEGAFv?C>mm4|}X<ujE2hEDOobN2e{B1CukU84|)6W9DUtClYms{w5
zADH1OEB+W9Ey7ff>{z^}kHtmMjPh;g3IkXjG3NX5$xrI2pYf#iFb{BcA1=3{fSn!s
zH=5%IV0&d@i3Y^r%q;$jGmGgM<nA#>w(Dhw6zf$5@tDKIx3JMIU?iyHE=%cqe`Ne1
z*SCXBoS1YRm>sJS5skb74aT;r;4PpuL%LOmw#CKk=vW(9mwh9k8#}?$0phIRNMsat
zpg+==I;faV=wyog6jUD$kU%`PxG^U;aJ0gmvI;xibfK{Yu5<mF$ycboNk%gGu>IL2
z!W{ep`8xM7Bf@lGgmr4OBi7>j{w2cQ(L;o&EUmd<k5K+{UgBH=RL!ZFrPG$qz?qgF
zc$wN}Y?~Cf3N{!jn<c(pJtEhBPVN7C`q2Rw|M&AqQ}o=GMG!eX$y%!)H&C(6sV^tW
zxsZ<!w`km6Cuoo;*06{k+^0`0{E_)?5OH|I@zOY<*(Q=vNV1ryBVNX^gs3qevS_}c
z=yM2ik4u<|WDQ6zg`4Z%S+`5y)bz410is118s3qL3JTv|Y}g=Kyh&L~UII{{`3jd7
zNmsb@U4_VXH}?Eu0U7<_s4e9mJ;UC%W#5@yQ#!p(jzx?uc84i33HgeD5>mQsnB!NI
zyl?o}Umf77634>}+?;T@Z@0IwlTKlVBA_#Ugj$;#DwA`d9HU5~eO_}&2o2w3Y;4N_
ztE{4+^1uE>A{OCSJ5(NAEg)+$RB*89wJAKR<`)BLyUIu4j;h(B11hHBB*wR#4r|T2
zt3Uxf0$kbfen<`3peo>YlJ4@eWL#jv+T*9s%OBVs1mlZsLYkLau~`N&kD_qi3H!S~
zRkBvL9vWmnbfF6Y0Ejr{{llCN>vE>0wgg4TZ+`|Yu|#(F1%eX`EgJ?016k=gX6WjB
zTuVk(B+tGVz)u)c__jCk+^QPp_&(c@RTo23Ez@xjJr)a>aTb^!S>AtEU=&=yDFx$G
zcQ7J3s`!z3JB}l&I(Q%GQ?wj@w`{=8Aqbl%%`BAf@S(ee*#?>&)l29+40?#Re!>q?
zlc5}KR(re}yOt0rdwGl8u@5VrMDAj@x*D@c#dsGMc)1#uDUe4wjoUfSZC#eV`YeV8
z8m)d(v`;LLdiU49hn!W3`{~|p?`DU(&@hPu&scVgOi&C_c3dj_h&X8+`ua<4M<=y`
z)-r!35c$yG+gz@Bk*ct6eebe#Pf^*_y2Cp%8hsS{649Wft8ue!rI>a*u2615B+8D>
zw2t3$v63YCG_zg9yPbV~2p)ZSKHlXsYQv@v_G}StzutnUp(!^kyM+mR@`!j?EId`x
z275<0I!}8`3kFz&818FUm<DfY*tui!T(+}EY3zwq@ysx6s|pX_Q4F@xo^Ki*i!<ie
z;v@mAdmb$b-eQ(~Z)I=!e2yCX$a~9!HYE=Wf-G8T_ZLDy?f5BVzAFeUeSa!6ba-hm
zaVQ>C{``yCu^?NK>gPM3!Y9MjVJ*0<II6<(dlfx&4>;Brf5jxL*N(d#jJvb3Zwt^#
z5!dR)_ol7vLjHDv3-)W1e+xwbh#NU5rR*NbuHveW<K@p?R+{)}+mo^3e5>bMk?Ym;
z-&&#H(1Fg4>+WBzcU?!ckR|cxj$NVf&X@!4dhZ3g#8Y~*)D$Q=g$>Qj%=*4D*u*~`
z4@lCdi6LFQ_ODh42sVYDtg%H_(VqXmXnRkk$)TdCx<5W)&<OyQ1j=G}k~i6-4;J`>
z87W&*bGlk`I3ylLc!0-O(Eui^OyM9i9}jC)*yy*E0QGW&2VgkEFsYAZJoKS`HUIkB
zWgD%lUZzcfXIayQ^J5SD8_6`5hq?wt80JB0!vWC41RKi8ELYtkv|-H_ejmHaXZG@*
zK`~Wgl8~#O#B^tr8d=QjoASBFbJT0X22m=KfcD&)nOG_dYX1yF$kW=PbN8YFPXzDp
zezVzE7CUjCuI?J%r(G>4rlWP<LJt>j4MB^%4|_k-fOmGe8u|tuwlwjZ?J%GsblTm<
zOQRq6rOm9azRxzq`Ej6tb9)Y8(NP6leb0HCkC*D8ZZ}>KvRh9)JIqNr2@#7QzwRm8
z21kNx1-qAqsbX<a?9yXLTVdnpX1z+CO0B3oz8fn4T!O;f=d2$BiEl(hC{ExxJ$XCj
zd-d#!qR>Y(o_YQ=)E(Y#Qrg<yc#WK2s4q#@GW4OT+jGb(U;A(W4=aM(2~Kac<Jhm_
z<~+-%nvNu)0Bh`CD=+fH*!#W9iBSRG){W=ew&_q#-p$({j?tE*xnH4(HVo2=wN;{^
zHm!gky=u2XxIc&!q+|s<(zR@VEi>Pu?U|1o15Eds#2uiS?J;lxVm{$l_uqXcd^G$*
zV_F;JH4IkpjAS8<fM>oJTSEWZ9gqyOp!;}FMjXcCIIaCK!#Iv!&98Io{}*nO{2Z3#
z0JJC$+Ns^nb(>5LfW!NbAHBio)CjLW7orqiJBs@3DoH;VBO;&ylgX;w7IzBIlZ}V#
zwf0ZVcQJb!ci+a_H72>E7(!mB3{E1cdi2lKrWB3yY}HRQX|X){@y<D*@c6rx`S8><
ziBt5y@0Er3sQ++axC?|#Z2aHXfh=vW;4vX!G@bW%hZsoj)j4~^zFfPGIHK3ra3kc8
zOQW60&{U86`L<9CJ<yT%@t>S_*KTA^>DqWj9lSdm%9HR^)%EZS*93^bv##SW12Ny)
zTQ3*U7n9QGZ<xDpnM$lF>mK-iakWYt$#@<dUvn|ilPfanvFc!LTUr!?2a}9h82_xz
zJtx7A$;47eTi4_inqMdLrWXP}>BSY!HAIAKrfWBRswy#jQ~ZoJvLSx+8RcAZ=HcE$
z!*Yl!WmhRDpvadl%)9WJ%U3OFRZcl-Z=AR)-*&%M=SN{^d1m6j{=S&6YZ`1eJxHdy
z$!@gR74m2&5pQN)jh;JpX5N42AKtjE>zCMyQ*?&4`9nm|9o+*YR51S~_uFqRnO!*G
zLq|FDUVr-4?yWeAL3`cZz%|!_!W1Y2vF9fPjE4=ddIl5RaXr=KIVPeeIOs>T=n?4C
zOU}i)RiH8K?<uQ6OT>AA_IPaI>kpMq?ISg^w~xO$p($iPVr_J*S5}K*2_vV<zbAD1
zA?5JBRZ++;(HIUj?vdXL{%Bsy<JkpQG5gfNjYFV|#YRE<lG-5W(+^R%ZPT&!`y`iV
zwh))zf#cDbBZf(%;!*62gLlfr=)8aCj?9v-xJcjS*YLim_w|jGPD}n}R?t=conDP_
zN|(e1lh*nTJ5b1?nVc?VjZeDr1G5}B^Id}LB_1AaAk>m?@7Lkfu{GJhdbj^>)tF56
z&?E7j)m8@m_Qgy-)5nQH@jazTdJ%Hxu$VuEt!g!TDH1j^y?ETmd<SdUHzAVppnUI=
zT9!g&ojc{E2}i-Ady*`rU+{)VKtz7Ee48)HcztGoqX2Sq@bky`-V;Z0rQ(e=R2{yg
zIC#x;*Ml+VxiLNZRV*xw><(dPt&p&D{lh9AXf7?OrDuvR4s*B!<C36Cnobx9zV@!~
zd^1)OwA#h4FIFrra%Tl|rY9%*o_BzOn{hY;VSYI>5WTjh?2?Rw6)n|287i;c8yr3<
zAu>@}UTTJ0o7oW(jC>UU-z3J8>yx&bJPE$oah15*^SyrX8DK_3>p)~h2>Tq19W%$s
z(iR%|`ZRnj9%!eqyC%D_UqSh5HzPz!`!+tenU5rUD*BykuH~Wv!W2^;v_<*F0<6ie
zrH(k+@q#a_8@E-Etb!`ASQ$cYsmw6Jk8ysFnw31{YHN@Cdm;iYq5hntjcs^mIEjg!
z-Eytj8Myao?)l#hH`413n)!KSJw5)gNx!3o{w9|{2668$a@vepUVW}aJkIzTKI)No
z`)sd!8(MV(!AWlPm@Z8nj8dv)qL=Ij)Fxh2{=|aTuX61TAs^~!KL|xF5^<RMJ6}gP
z(1+#>6)RKX*&iC%`i8c!Fw%7?E9?kC$ehmKbcA78A9NN<U&%h{9TUD$Rq(L4u`K+I
zO3?v(6PAn@u^u*WDKksjdBCPy``y6znGBJ$Rcd>hS1JCTktdrk&=g-af`!kOKL<_V
z94%0@JH<AA^#GxRDKblng;J*7qM7Y*LA!vRnnnvwo_~8WU{TT05F(Z@kGo0(BM-3$
z?#2S{h9G_hW@&u1=Wl9iYC<ELI!aCZ5iQCMYRi^S&Ce$HC!&pZ>kvVBL}wc@H4*@S
zbSN+J*UhJEf>pQnZIXQM@dYF~Z0)Y8MnCXY-u+JRc^`{+ORRWv{JDR%<!A<<WT^`<
zet5he+S>B8$`f!3<O^X5vTs7t93MONpV&+}?RvNLZ65o3c!8rn)|RBcu1moeEzw;I
z6St<_1Uq*6H3H@O3JI=-&QH2NxVGdUGwrRvj}AW#Y>bYSi{wJW>O<K7BgVHW(eu{m
z6OBx!sMea)cc7NksIdC?TMaH2T~h65c$X1)B=?&E!66*uNJgPZyS7&G=*4r2w<oBR
z>E=L_kEbGUW?0d20xssoGYOBoE_%84lxh&OG?1ue*__*NxSavP48+5ZDXEvFq4VP-
zZ$4T_G1M`2csTdrOZo3j$ZX&wEI~~q^ZW292CESr@R>6iSbjZPiCoVLn@|2&?q2P9
zsI04c$-*}Az<}vk)A4O@UTKk@<moEyW{TMF1tx`dt-!=qxwE~v=QN&b)pGJs)a>?<
z)GoTEhgGmZIhQV6ahSijXebm_EVu272`Cs?r>EV)BJDS)Qg@cb2!29qxfGIU0rgZ{
z42n2EQ`ManW5=ROca7P$yr=RIYkZRTR*TK1e*E})Per$~I`l-Q@?xy5XVIS`>UwS!
zKwf8W#qbK7ypKg{EA<xD=>I0#y7K}eUX-U`Ei`}m{Z<LfL*wc1D&eie>rT`BRE(^N
zQR<5KnP^bg-Md}@CJd9eMnTw<ezlLA!0pFS5yrVWijifx46SR)BMVH%+Re-!CCR$t
z`i@7LZ#CA?7lC>G$elnCfCm(S1FWSXDDXTQAPDeD2AVj{%-&Kkh7z(l;L+Hy(;P`w
zXPZ9U&(rBXVVyN1U&>E^LfG`DsxiX=!o*Pj%-^UK{s(?v82&5jj>Iu;NcDPv^wwz+
zJ=c%}@b}IWO;zsJu4|YDAzaI%{$iZ#C!g&FG1jr=Z*q@Bbr+l;#PJnTDxEFP@UYZR
z_zKvl5x5ot=wvfxGEW6_LJU-W;(up<d`aQ_u1NKVgQs1CuXpr~6w2SEyrD@O2=9VH
z#_V##E>+-jW7;GnQ_;;1fgXS%>Z<jXvC%Cn_d!A#4;yo<hY~jz#kuQerW?V(8RbQH
z3m`GVHN>_Hg_p~V0~J~1-csxTMgEyVv#eUdy;!z*W5lu6eQQNdd2wA&mp;{8zl!qv
z^o4fQ><=kkQv89BMoPw4T$I+(d#VMzcB*8QHiTtW7caPG&yds5ulF<uE3-o)-6Nj|
zG!gWh+yqFTuKg!gxlc3p)+r%RUbym+(-=S?>w=TTF|a!5HY2_3z328qYh8I^@|aQA
zYW%kth&eH0V2gnwZRlnr#L7u$F~JWY@g#m%F)aQ!lSwpO{-Zz7`{2EfDn8CU7U44L
z=a^}FgB=kUxqA91cYR=Oql?H<(CpVAF?iy%O>m16j6hKY%YX>?&TxQZQCot=InAhH
z{;z^Q$t|=mkEn&aL*m+UVIc3iXXgd_-VEMebmJxGb?@W;W9Vj5f4NSepg>i!2G4gZ
zKndoq6`N<~f`N=pWXf+NzVCUXB_&p-S!%y*^_*D!3h@zt+uGFC<7@OqqB>-b+w3ZX
zJ_2kyeG|W?_eS4m)-_llESUl;IY32sct5$tgO)SF{H%{lm+12MS0bh9_^m)bzy7H7
zEsOZ2&~U}yj|wBD`c$f5uQlP%Pz(1EDGE^B=$7S7uhD{T-hZl=j#n6DbvkCxul_yX
zK>-%PX=n33Oz+rk)twVCS!Kv%bQbtCT*36J)M~7QL378}0A%~u__KcZ<ZkRIZdL|3
z10=`g=;|B9(dOK&DlRGXHEx8$U8lB~E)N)V)pBD3;5b~pui@n^EU*~9WKLKtc8r%f
zz78}=lsmZS$zIJ}yU@}Lt_KG?xwg1t?U86vmo_A-Ug_;2{r=5vXIXvx)feS1g}#=*
z37v$n#$2;+bLR7qq1+oHix?~4o-*}T?q}hNg0N}&BhyD<IpwNpdLTF{rM)7m&OCW_
zmbp5?K82<Pv9O?y#f-d+kGYHH8liziy~I&3aT_dsgt8ngCv+$6?*qQ9!2D=v?Zb(C
zN*B0<!ncw5^$DyJdZ0fdC}z_Dn9RyL!+sd-8#_9hMzE0CcRx-$b?|Nh5o{Q>o<(-y
zNx~#nJ_=>bxX$@~GDQ{MC!O6El%k9&E5~@{g9A4tUk;pUoLl_L-k2f)Y_>VUyn+iQ
zxrHt8<~NBrNh}}ppH*Gc%*^3t{~lt(T1R9}{#%})&8Iju0XC$F=*4d@sm^0)F`v(Y
zZILv;&#7g2g5@H0aQapuz56n=BG+Q_+2*4~m?Lep19-4HttG{SSmL=B65sfywO}r5
zy#$zX4m*cu^=8zUhva4!6#{SY+Rt-uizkk7;xEnzL<(8_rsW>2y4F^1t)e9bL@E|0
zk6}CNtY}9q7Spf6$DrKox=O0wm|vx6TC%^L%V_gEvd|kwgT0E56Ty&7X=i7P^<m(-
z0Px+F<MoeC(GB_%TB7f3fJ~M}%KVIL^n*!><9FH4mH70Y@|1yzBL|lE6vQNVQoSoY
z5l$XjuLlsjao-;;Pcl5O-G@L3RK1b7JxURKOvKJ-`QBXP+2f$Bw~GZ2+kc#*{MW?H
zroO3H67!}-{PW2k$nsH|2UWF+veFcX!g9A>?Qyo-U9bQqS{ienOOaKr6H=GHu!Uks
zRZGw0y3FK?EC6Sc-2tt8PiEHNkD5^8RznnT%9N*~&0o}SR}{fU8NZ{+ZW4Id1b2=c
zl$`RFB-0gIkfmv3rvjhi)!}=NC4OpEmnP3^%<oF2?=Jr`>fOeOY?bEf{25*0ffPP)
z?`(6>&CK$6o^j?c-3s0sJ!OMD4WGz=>2IeJnsqW>a8ew$%jD;U>gJ|dI*|u6gL2CL
zGXaOUA}a)a9t=0DLE0oS`0Hu088b#>==~%x(e|vN+;#9-6QK4tG0Z^h%QY;~Q3{M!
zIpR}Q*StgrDA-LGIfER#b6oypYgUhmN^m7UuRDycOVxv2B!#EJ^6$w06g%ljB}Qvg
z9el7<H8xVK8?OK-0zf7(UNG~Ypal|yC=rZR@~2wOShaN*Yzw>3lo?kHK$Ik=<8zLh
z5uq`DqQl-4BCJ$?QMq?jU8o&nW^Jb&mME-k-|z}`=sYtAoDAQ4x4z+`_LaSGTbWg4
zd`g}Fk~$wsi&FO&qm!bMlq97m$39+<M6&3@6kaH}s;naZldF*Y&v04PdqNdpJ9St3
z*0ofVQlH52p}tDeI8VAy+ow-ouPjQl#8tq<lPDpM5~cS+lvrs`2&KNt&;4{fq0T3E
z#}>v;jqneO1Bn~V<hc}~!u5vFRk(rG)CY0^H4VaQDuBF&xMcr4YI_;wLLI+YHkd~{
zNpOq&FhGz<VyLlyLYD2`7ajCza2%HCcnEe1+?+Vkk7k8QT=3T6HUG!jA+`RN9%alD
zQ|ry&Q7*$PBXaxwY`cF&H-OAgfX=(_C7#>kGAhY>*AU&+N_jsi2%eWqnp=S9Mwm#5
zEVnXek*~VftxWhA4f^8^v`4hi7q5?_?03NG2<A+Mvf$_Y=nW_^Gx9HMTvUEXelb!R
z!vG<A4rU47|Ef8Vf1ZU0HvA+5t@<h+4TPdOKU|`=4(ji^uKGs(uFk*EKp@47{OHs@
zS`hS3$<zT=!qkEA;#&<8=wKM&iobh^N1E?6!!<PCo@5VYaOjT~XjvwtFSIvY$tX+~
zi|xAgJgyTUQ&C&;l>`%LNcLQO;9;4;;w-mC<9B_6IyWIY3(<aT9{$|btp7#V7#}Ud
zCiZ1bZe;yQj^hQcjgc&xNVaIGt|pB%kVJ_EmRW)LDs3#KnsQ&1xAW#np>yY9N2@0N
z?^2>fB;LLzP1ufk2D-d;BIBZBeRXL<)D|qYgNf=?-04>2hGZ%2DW@gV4B2~<&F!|3
zvDOXh(0D$Arm%&(f$}eyDPSP*dto7M)Gu($rcylXrAmC^rya*IiZ)Q5&zPAgnlK{u
zTOql9E##N4Z3*q%(plmqwsgLkY*RMeRed!1(hXeQHInJ;G^!AFLMEjBi0N@JZYRY(
zhc?Y-@<wHx&Vd*{;bPs6D`ARyrO3Z9Syb#$_QV6cai3<iZ)d*71l#SvlZsFOIf(4I
z*4Hy8b$2?+$U64N`E8qN&0J2UnUxQ_XZs`Du7_3kc~~3I)pDamMDS_#m7jDdjy4~@
z@_MRhR#=?e)yXd13Nu!Nqg-9=46YsP`9Sf(9MWB}!a7axI20e1hcW5Bvu^&WW%A2N
z+#fBhhRqV25{-)!Nd*C^clQtoo<okkl#7D?)I_08NX<TltU^;O$0ZO6{rj@$xRadp
zh)^K`0gR$GxDAaRFS9d2nWOe$BXq?F2r1+}5Y#IggXhz_b3tgzy|?e>RS<oxosDn=
zGRK3-^csf35Z#zMpBYEIK%|sbfeP_SthY*anG?6(RgMJ=#<TR_1=7susP->^ay@hy
z^7~FM_1D122tFa~TG4AFg-i{}(=$u#5F#KB5Z{C8!rapYjB>kIl-IL|B^<q~7F3~+
zfXv1)%MDXN-ye@_+63+xBvFveYS>yg(TG{nqlaW#Vs+Cc7O%?f|LTI489WANdK0WD
zfGA}DoobL=vBjZ`aY>^BI!Vy#RtLPS>pPH{{6GrpWP8GC7>DW+Btk>3HBD?uccepR
z>3@H<`fzf{GY#PL@9@X`C;P@S|0Ti8OW}_h)yJ|2w1efDmRm$Jo<q1>3;K8fP9lzS
zCBMfQkm$iKA!E4+ZKWlDKDhVm#C)ltTGGfIgVlQJ`*8NshR~?c6;c>gR*<$uLu}nR
znCOAW!cL25Nb&)^1cO>5^S_O$;`olQGbu;dy#mb|wZcOqZzz>hCToFG<?VbC1ZFIm
z)voi4hCb7GAUD0rHF{KTd`ti)s@`#aEZccadFiQn<a9BRXCeA>EZRG{j;Hg*(sXPK
zP6G6ChDDgN4rL<!{EMe(+ewz6ZY_(m8XxmtOO?x~6K0>H9)GoH@%*jPU!l^$RYy#N
z<VwGcwhbe$!C&t`qH+fQbbkQaJ?!9&lBzKWw~cB}^rjbk){Q`Cjtzewfm!j^x2`zR
z33K~Hez8xkQO!pZIko23kla}7pPKc#yW^6)7L)MV8;@#VC+4|C2|ioXmE1kLuAzRR
z3k6vl9;;gY+fA5#kimveVf_6&TLc-=4h&*9@}Xs@eAP1xY(_Y#bcgwq@8=$rBTf{?
z9XN&j%-jrWzZsA&>I==Bo1J29L{YG9bb`Kn_e*B&+M$0kY|vBy3$t8%BsqTM+gw2)
zA#^SuKAvc8uPmQD4$;G&=x>@LpMFq2AQ0mJd*h|D;OJ?kx~I^;0Y`!z;s#>6u}P>m
zm$vc@1~TFd+d-E?Nea=U-e>uGk%~XcZ+o>2P4^2?H?9uYC`}|vTQvhDNhDyc;x`iI
z>f@h|SA_n#BYps#Sf^eJ*!}!-ch;12e$PqOqJycPrt;?FREYlKadMF5&*&O;7paIB
zx^N8yt_NB<S2ttH%QsO5uyJ;Iwnr53{snp%yFo#UUr*j!;zR=|nfu}-<>>{7Ms&NA
zPYgC43!9?pU}{ifl`mZx_@sby(${ng*yAN$TC-n$|B_L>PGT7P_$z<>+5M0oxS{-p
z0KD4rlH#}ow%5O9Fp7CSL1xaBEXP@eH(!m08hc(KCD4rhLSaUW5=#)gZRHW86OJ@t
z<SV;f8=*JX7~47^PC}t3F)bmTvy<=t5o(tcynbhXOF6JV3HQ$R=XvA~8<A_>)h7i5
zmrHNymcL)Q@QqxI!oqZ_b;b``>G;^d8^jK~pu)BfRaTs4YTp<b*qT*BW?}lW|5-e-
z$H7-Ou||}=dd2!`sr2A?Q?r!~E0s6kfiNZzn}<$)14KYKFxSn?Ct<cXn85}51Jn(I
z5+H$8-hkU6G-5J3oJfRGMe^tK*Ur51g4%_<BiyhG+gUHqnJ*O2lZ0>7)de3m%DNg;
z+nJ?Mvi){wRO`?l27P&knb1xS%7^9WdGf)5XiD8Tn0j7$YAzq2@a$UWmJQNl*S8Dw
z%xQpJM&%(__&Iht-KxVg?*U3(65OSZB>TKmDmo9#h%XN_bBeaB#WwOAVfI=Uq8YwN
zJhwwr_q&cA4SsuX`$%@VIrGu<U5l%Vnd})K`p4AQ0v$Sny%gLW_16||?r5)z>8ha#
zHpw5|dnea}OI*MupuIU6!yF|jKB_Mo$UM#W895IJkE5$frdvkXmFVA;aUv*MWi>k>
zZCU2-ck37mewhF~!&|ccws2#9giNQRt>s9gk{%!|r_`~2HL|mgJ?Qv#Ji6!I2Th2d
zOC0qXDp52Gq#;Oyj>Ca#bk0SSc$7~ku_da^AbIwN8=w;pVy!j7mI;rH`LPsm_K>|m
zcF5t>m%Y+gr1K-j;6jmm*O9Vw=h0$(uZtTlN$lnZ1jIP*GhHYgePvWo7A;_FsXFj0
zXE1a54wX?oxbxRgmRaVwiE9I{qU#-JO8taDvHY-+q@hShed1-;EckOi)gnNH=HzSQ
zfR=<qU>6%49&xVfDSa5>0Vb>2wEJ*7<$|idwlbi{u57vW*Q}IBmy<~U7aW70wyZ%{
zJa>qY`IqS5C?QuQlybL*mcO~DUkXq*>Me{m{2N~A=<*bGFBk};qtJV$CqutaFca`;
zbxfJS{Y*V+LO;Fqb-DqPaQ|r2d{f#5s!y~5+*pafMe7S%8i+0vjTCnbew_v58S5PO
zlys7UzrM=lS1M9?ABkLDm01R9bj8<)lJo=dL^$Jx5qn8YkCs2V+_-K$&n=!3T1wDF
zdfylo=P5i34Wc~6OR45A-e4N5P4E%rIL(^&XKA5Wx_>+6a8K92{m_G;UAb`de$CmU
zvR)XNEqqdP^zLE*n64`mpiS>I@mlt(2uH?azQYxOiQpz5xj&cm#BZaMKS*n9=>dm9
zF%`xL^TwToJ#l|Yzi3m6m>nnHVBg;r2rOIXw=~8UmCV+sa&z)_gBZ0&5%0g?&bc-b
z@L-kaF6jg_e!PD`&=c}EbFH@S!SH}XIhhHsGD#y3t4dI-Dejq6(tWp{S9MWRA*ZMk
z&L}bYrUtjJJYWFhRyyl4@b%y3HL)(OmbiAhuw3uM@z1XorRbM&i1$Ur!*Z+K32`?}
z&c*tt7A<#FwEQ^wM6_A1e|l@G6~7AOsG)kDl58!NFZ!##0NY2NYPv|1U25zHZD(gy
zb!N-0<FNmV{<z~X(cARTT2CN~sVvI5HkCKwQfo-fHga4@xXP28E#;)^IZj#Mw({N;
z5hJ`vZV)T})L`pnLN#S)v*H!R|JbB@tx_jMo3YxN+LLTy8?pfUI9kg3_r;*=x-Yyc
zwI1rLxpyi97Oq(s%0(h}M_7#(PSiVmngQ)4xi#$xkoqw@JN)YIj1udotl@m|!}|Gh
zqIRsw7H9lyNH)O5I4AZMzTEZxQ}62YWnE#wzHs)%6cmGGjd1fvnnvtvWlYz$8xnT0
zp8dnlfBs&_g~viReOv(&T?(jO&y5KkPgif=E#>1&gAUhrM9q|_L8+U<?oeHE-Nx2+
z)_e}v<>5LoCySYwPj?7cV)nE~iKNtRz%18Q6<t`?NpChOaIE)Q-(GsS)NU9+|Lhjm
z`bI|x|Av*)cUkzKk25?Lb-j4;H#y19Zqu5MVlB5<g3GvMzH<zlPD`c)6#wk?%!=8d
z;@&dBieoD)>sG?gryx$+6Y*exN}eN{zKJ_gwTSH#p%~p@=&td+Xh3YpIm=7t$(8q#
z*mxE@%hDNl9mFQDu>DC<tXU{Jd6Udf3KW!+PgRwS+T=F`WVn7eSGO-%{fm~id)4z2
zrf4i)0XdTI+{Z&^h+4O$leEfO{z6```+j>Gqn-K=#s2~CKoGyUWYLRE*|UJ(GdJ<m
z!8P2wdJ1=}c#o5l`UUXiqdrxC(Wxa#c!H8hxGG12$>%{8s2Xzeup##`iXB@U-Me$t
zh1B=ZlCNtmV&-HWuE6AXQd*9kn+_P>rltvg`1DV|j){n{@!F(oc;&gja_*UfIOU{1
z^gFhb_hZ5>bnlu#X=zoCqx^z0KK$eluD<zQF1+e>UYq<or;I+9U%t7YTW>hU@9*P_
zZ<hmARq*<DO$u5H83DMC%4z<>?|(4%wfFfmwe8L*UYWO>yH<R_8S|duf`$Jhy&`bA
zYGW*RY$i_#2lEBfGPa}HZX_wZv7nbup<@lYY`B`LMT=r|=g%;S`txB4OoW>|(<SO+
zsF>D_eg}f_&zUXr$+Z>Qv?4Vm=haLaS;ZAW9cA!WgVnr^J`bR|z+8q;lXvEB-_64_
zehqZD0_K;Dbs;*l;9x~OyGxSUT-ce_xjood8W;43^G?>HCLE=aTKut3dx}O=915~L
zChRTcrKEwIn$mV8b&3u6IjWb?@7#QlXU2b4YX*<UBU|iTl<Irnm1BpX2l!&7YhX{n
z>wQKOf@!Na1hgN4_IuVQUf=O65{Z83k4IJ&C)iqikau^_Wn@C{Aooh3BgnG{CXK|=
z=K|Pi?@Wo?zw4_&kXHOzqx*G<x|j%aHw3kp=I?i#sutY0l>19Hsfwetb(sf@A3W1_
z$%f^7{60<y!3o29@W_MbGx4!YIG9o7b^nS=7mqwOoy%@`o2Q=pk(=)Nh-u$0Cp)Kv
zvT_GIc4hIxYqPoS-cR^r*-pQM8_QQ!z3Y9wa6R8H-st!D<FD#pe^+ExCHkY4JDlA0
z)sO7T$qRU2g6-FX>$rFI6xJ7})ocsg<xmoDD{p+a0P{#UpYMBze3!4w8@>OHL|VEr
zrt{Z?o4Os^qSdr3FcV?wMsCGoq{sJV_(7o=Kg(1Fyqdu6a#NU7jHB}K(uqJ-HStE%
z1gD)p1hd5y^aQ>BaEArw6-hUNy#u&?d=A#7Wd6=nwq~}@QTU7?=TA9IaO9K*lhDzn
zMv2oFl!tSHq>Hr>t7uA9IGrNrdg5)vY3%YBVeaPja{vw5g387FQqovy&Hk9PIcVq7
zJvv+0?W?s6Wzc}mA+N^Rkj|ar02nu{H{*u%4%jHI1l(a%T|T^=Lzj{b2aQj>8Z1&u
zKH5DW(ZDz9kveM%QrS@){0C(LRC<D$z+SS~k>uBGUWtdC%EgWDXVxMZl`@qWYwZel
z5u$|#mm8@LrnG+DdIhEc&i}J_-eHne)!u&h36-mJn4X*i3}MJQ3!;K3AcEJ739mVa
zPp^@C^{RLch!MpEDn<|_XAlO4G%zy^lSAiJ`Go!bQQb4$)7{fu-RGQrVEX0hhw197
zy>?Zdv%}hJ{nk{Mhi&cs6P9kVEQh=AUq^qYIBFEuuGU^QY;3D|&Dz&rVEx8cetzqt
zmA!D1NDYcrP#U8^v!py0|Kj1t1L;pt^p<jb?b#av0*WFQ?8;U1aO*rCX`9FTo<{nu
zXaIIc>xtmGEbRY|5v0u}Xkz>V6Y0nzPMdQRM>hX}q%oIrMS2aFBAvxYd7}N}Y_+oC
zI$Vig?0J*Z4zCL6ZtdX*AN)D5t9u)7m~lDhSG|QVU;Hgzd}<3$fSZ6p;l~BbTX_E$
zUdz(O&EdLGhoDC_F#W&GB;^x#sk5&?W?X)|`lWdaUv0*f$_nbjVM$dhM>cF@K~)#k
z(O|T5TFh<^j_;V#hIVfHV#<U5NCY}ba{*C(|JA4jk`~>?2+6KQi0PucFL(O7gH2u6
zb&Usadv?$7Cm+9r`nqH&BZ`k)RZjQRgy3~2uVh|Rt>-od$6GVUgm!OMNS%Wdp{>}*
z#!M@6oHyF*1f?A+nGx@0aa9|Os@kbCgVSmQ!EURD9i_^yDJdtG3K=(MaBRy@hrW_p
z&E*+O=UGc<r>!ZH4j2U)Uipk^ykX&r7R<;*ivdlXs+y@4oCr%g{p#no(b4OFG*BrH
z`R0K`{QXS1jw`w4xtCX}@jrJyf$au6f0kSWiH0jRI*Uo3>1v@pA4DYaa9mur<lBK5
zV}J3I`PFZkaGOGCbQe~WwFeI{-C_TK(Hd{2517QeJa2M(hcrPtc69RIBR)xQ``)T|
zuH$n5pC94jf2>9}H6arIRu3{GI_c^J4b9cO^5Ua8^R=spL`@dV^XD|i3+$p>KBDkB
z63K~<9_~949^V?WP|gYRik3sU_QkQQbohye%fYp~NEp_zuh!qMS+Xj*g+`-)N_Gs9
zWJc<hII_SqonPJ4cG>^$kTB+O%#0tizWWDI6pX&Jm6*UHBj`#l`#}ruI55q`aU6$x
zu78lfUHvb%KDQmi)LDM)QeOYzS5eH5t=HfS0*ZybI){d#^Yt&ho{xO?dI|+AF#VE=
zu=<HDNGbX1Z*J$$4?N>bbj)Cz63}ztB-rmpWKn~fVm4k-bi$=?X$Y73>2k3d2}&v$
z&a5DfUZ7JGJ#5UsOX<>6e3FvWP0VN^qBjnCEo`t2#K7FM-4R4A$&pc;YEAJy=T1Ak
zUhW7XIOLdxJbwQMPup$X*+Z#h6NyZG>nWw=_Pf?{)4!gkt2;v^Vo)jtJP+=JAI`wh
z&>YIe>V|$?Wm&-y<VqH6w(sKb*)4(e&2Khk+R%jXts3L#h<*Mod7-a?sz`yT9{e>I
zHx^T{pJz}PvWy0}%uc<I*{KUA+?MU;*wXhq#yY7h0?$}FhnOlg%=RLDnBb=!UK1eG
zo8fIsKS(h@vIL-XC05abU9IQ~=T%g8w^FQ<!&iR%b`~xlKA@RqFoW-8F?t>B8h<Y}
zC(4);!FX)#>G8NX!LTIV!dWv8;m+O9j0pJ^h;dKmr$SRaGs4*w1U359szVBN=A<t|
z7ZK)Gy@AHWDQxTi6aA$P=pssU@+_JYr=baxS*h2sBlDLrTZ-UdhG1gYg9x&6*))D&
zTQ>i7;s5f;KZaNMd+2YE^3dNN#aK8O%t&DUaU7TVONWaDFJIciFMsq-uKV4C-232q
zdV0fA9e8(ZFMqo4DgOLGIqlQWWwHRw?0~~-KNM{nDW$LL0>5aERg=`CWSt-*sm4N*
zFiLyIeiff)LMx(IAV?~4m|y*-QUA6pa|_RPeQQ|pl%%m>$ZH4xzxSP%fJci4C&w(J
zN-wAIIJu|sk5|9(SkE0^cFCc;y`Sb;6N{95=D8hw?#nlZW{v@<tx0gk@yqz;-|qE2
zf>$p=yd4Tn1KBJ4K?0Xvdp+O0;FY{)<#J#96$CLoxOL9hl$~C!`80D=!54+vkln!{
zRfmv{EM<4@He6YvDzcn;RhKa%aaKj^ir(TgICB5@(N0IFS#xPN!Z5~rpH4dM@EV65
zFYM%P%RV&z9B<pmYz~p~chkA;#r6>nuLbiO&@~atQt<*X^Afv8bx+2|a@?2frsTg4
zy_lG8a?_3{0Es3v9O+UQNzz&J?<BS<-tSx6V<YIb6EsF9|Gtvu0v6Z47e|)Zo&6Ub
z`G*0Q#`uZMOuZUI+q;9Uj~z=x>;yUr<>w5gL321f;)Euh5kQvyjfftnx*O9>f!|&E
zXCAp}^_YKmas~A2;1+_>sLAQ)9X{-_$DiE7kFUL#=hlTwpQ>az{PJIqF;+~bmsp7b
zO_g!(|NAy>;nb^s!nJR{h=nh)Xs@Vla>1OV`Q7FR0<Oe38iE;#zVV;tBWb-d?W%><
z@1QAh%818zWd6>R?VlO(c-G!L<hRi-EP;(=yREUvRG2C&{4bm_O|UqQ!@Ylah`WCO
z03F-AsA;aIe%V}KY<=>rKPFuj=d{<X;x%tPmRMr+`RwUuxAVbI{Vr5d!~pMn%gNNG
z6P$a@a{lq~T3@Sl3jhhxj<VBhJX|UH^j~k~xcPHv@OdxlH>b}ynBQ%wtg5F!2>MGZ
zw7evni}lPdKF*5zPq4D#^HXeXVsl236lK?t+N$rea5EzQYERn?M?!Q+acqYxj{5Y3
zSI=&5ukYgV@lhs+n(73npRyv9x+)M%l}5e9q>p_QrV()1tan5-gD)I%5q~@N6MTHd
zdAx7Q*?jAWxA2!!KjE)k-BuFz`c!T<SvxXG%{sx+Gk!$g+QHr1&gRMXPqQ=gcXnoO
z;+f7bap%@E=*;gGA{9b#Wb=2Km3j?(he}D6a5*C45W2WZ>!qnhH%%Gavbpi9f9!X@
zk=_2!F+=&-l^2st?X6Dt@S_{~*ynCw-TK{u^`R1Ww)Oh%nz7?hs$s(OO=~908-D&f
zTKivmsbH5cK9eO?Eol3^SzQFVbd=ObGt@<M0ooi%$(^`nLR4#DS?z~MJnqO6YdimY
zj8{$U%H8cKUbdSjsI@<hXE?UQ@BjPHoS%L(U%%|TJoLB6*zov^Ja)?({&mA6zRWdk
zJ9^mo+%A6h&6|1SiC^Wh`__$kj+BybeD{{nIlDga?lU;=mE|<$k6d^v)hT~vY+#`J
zyaPs-3F#9%QgY*y&+h+8F98m#naAO^^GD1*!6VUv*u;)Nkac4?(qLESmMOPZ6<I!k
z%JWd8Wl-(x0wDJX&zqcfc#VPUx?K6@|06fSiXZ@F^WaKfHzX@ojehF#f%lxjtd^Qk
zI=zzh{!a?d;97JNAAQcUIsLmo<(>_jJnd7EG{<YWc;4~6W#K8D+_apiZW7lgDQY$W
zELT@jD8a_${>(D&>|Mc&g@y|IASu{wJhJ<8iq4=GjI$><-j+EYZG9g(Yx|y8=vth^
z8o$mNb8cg0w1$IChgA`~yzU6yt@hGB(L0clQgZ9HcaiBCXH#D+;&yfg;`Ev|ubz`%
zdB~pMZO7#s-@OGXC2j~02qa~>NWX2As;D@9zQ0)F$M*+Q)yqqgMwDxgc^B_md?xjg
zvUsm9G!CtqOGE_Y@}HaRW=VCs^6|FYNsx0UcGyyMJLt$iIO6g4+<jPX?-Y6=P_7y&
z{1@w<#(xU=A|E*Zv;6F{zok?duGc4}1jFz>>!Db%_`-Xx<(bEZuf3mNzniVwyL`1h
z8D9B{z4M^}`g6ggdkY}i2l@oIqpp-Z`x31@KNKMZUp(X@v_KD6MyDpKGNo2vd!U;b
z(QBAF?C^eHE0RXxP^DTUauC_+d6Vj|q!1#d<WpyVg?p|axnLRRg*e-`<EJNJ_x8~}
zuQk;PuKv!OIrgZ9zS*8EKm6h)Xg*GyP6X`qz%P?D)0WBd?%)4~U;X<D&-$er)5ofP
zZTa;0Px28PoehN+9?2}M=yvPK-#@V77%jZlMM`Tx*l{>9qnWf`#dcc|6Jd0A0p<$T
zJ(y+^Y+t{VH_Ut&SH0)Q6MDYv&-$N!E#S-xjvV&TV~=m9vnyn!tA}96@cqVg4JMs0
zdHoYldD@qtNElJxx%dojKK(QN;iQi+H&M?M-J2;*uma&!Nd;PsaeoN56w7I;6S)U=
zWd1(lv4YdeR20Yrk}4(9219VRskJ}Nl*niD{Pfem=7Q?CuwnI!V?NK_wgV~sJ-~<Z
zz4YH7`#tWEihMhEg&@LY6mGranLYph=cCVK2Uvwy0+Bu@H2)NB<!*6LxVv>Nc0lvY
z=tvCV(il@A;UcNhbLhgtc1x`7{06sgIgML4AH}`fU(4pc-{H8G&5Z}ue~PNeQig{L
zN;F&!F>Q{BhUmHx^`195t>HBaKmE*aSaaty<3DjUiY46kPV{DfDyF&n8DMSO;qfQ8
zjQMv}n7Yo~rW!W4`ZI}HNhU2=zb{|E^=?jEynwlNHB)aaU~Jdnsh%zLTLqr&*+!r3
z->F1fKFy4HA5lfAje}oyuajUyp_!%e)(O38W6w|7--<!vuq*p7R@4tq3GFMb<KMZR
zXozq+y8w)3s(Ua^h4i%d^4=pqNikc&!X=2(>=I6U^`Sid=q8?7y%`~(+p-5rVT9`j
z7`&Scwf;vO={6+Vz%jt80w*^9YX-3#msS7$eUi}#=dE1Mn~yo1nblSPb=G5S>F}|~
zuVZ`8`;*uGu{&Qyb+j`5EPs$JjI~X8{NI_qiH82;nVr61&tp-&o~bCzNd$8Yh5B6p
z_!l`ojlblx1wMZIf3e|-O~~=m1*ODUzY#PIec?RNG~W(dI@|hq`q51scI+Ym(y0mt
z)&P0z@y%R#{*ja{oA3VlzCdjYKsX@m@hj4A?GC5Iuu^W@9rPj5;6}21Zy3U5MeS}3
zB~ed;`OyvlcIAd2o@A_ztnK_dJ2QXhm>EAErainrOfAjvE!VLv^B1=D-9X;jKtxE6
zjMyZ?c95^tc;4i+!)qV<y85}{8-JZzBk?HGbrG8P8<VQqvD21r{QKh+i)zdDj>fhf
z-K<(X%QuIYW8#^^OA~_YAAg!poOSZl8w-#dw?4v;U$}!VABF0N;z*5Wx?5OW)lQAl
z&4^%gv4O?0wh86gyNs-~I^sEU@B8V<Kf>m|U(=qu4~dO~z><WkIwJtpXnIO@C#I=@
zQqkhO@B49O&Mp8em@)p7Z~ieIt$k<%4mQ_ui9~ct%_*c2p5So7K)hxoxISq%M7y9^
z3r+<Y`#H-ZXIcF6k;l2=@u#`=%@=aW%o*dqFa+G)x|XfEj==496%(}OQ#8dhNU3U3
zE@?FSiE5QOo1X6YFB%gk4>-D@DSisNNaDy!(*ny2oO!xKo$8bmW2IXA(@dJ{|K~5P
z-%nK6sJJd}N0)Dh_=9ZU)Un6mwPJY-nkGW0;WZf1^^X6&o<d2@UQ4*+yUaIsbhE3s
zpO$L1nHW<gV+Fsri0eh7x<hR=M?)+Vkh5#hhS+ncxG>sAOJslP?~<P4Gpz6aKbF^h
zjMm(pv}W(bkp<F`CCo`*NZe={@hV-5Gr#Im=2u;cyZca>z@A76wzm~xVcOv}f}5`S
z*Oc3kyV`I>JEAI$)=-ZyygPZ;hn()W?d;)SH$KLW%^k#(QEq+o1r)9b$0S=S(b?yp
z;B0arUEMLjPL)N4C+qxjq-0%NyJvk1g5PYqkMFMgXTVk^mTT}_PYW^KB4rlPkjzZ>
z5;uZf2uL*g>=-rX)Favi8dZ^UYTye!*Ra0(dkFMBI$t2zW@{`k+;H7y`b%TeJWV-e
z{?FgNnd`pt2QuB0(8IB9oW6dvbjsJ%#*Wr<RlK25#ahWBx~reUtSShRUSzqNRd93P
z;2ChV2XZkgo^Ko}$ypXx-0(N<`rzdlJ{*pD%{{Ga1F3*H62Zp42C}xvyi~V>_(BOt
z7RM$QMLhhKF5CP6%A%Tg0Oifb(%KL4T-Vq4`?`AJ(kb_*ouq|izTq%a55c^nd7o-B
zjsJAF_3)do{b^FKmA37BCG&W4Z$Ylx?|LqD&MtsM4w}WTu0GaoRilw50Xe~4`}UsR
zm%PJkVXBE8`7Rt8R8*^=;6!MOchMa04`i&Z*ZZh5v$R=NI8viZ&od|5MU63${fx`j
z{y)-|y@#B$ZD6nB(we)A7kYlo!S$bGUezUIzdU+86z@jGtUfN%`{0dfhu1z_|INR7
z(~y+N-aedM4t?$%&zxTW{KLOVRmJJ=$#KI^@7cTf69U(9iAHsb&8cA0UM7I6!KZ(7
zOCTE&%`R_1v=Wr6nUvE@05S8W6SCh^%<{c;Q?KArIeS0cf@4w`2(v4?1_#wvzA>08
zjz@BY5|?>Zm(rPkl=a=;8+dGZUi*u-#w^_>LWq3JQf&pZX(rBZzWOJA`Niv})P~&E
ziqq8%#S)^r3az=(e_b!icfB1mJc*|D5;X}2Wy5+NoQY)=48xU@w*D-4uG`4jE0%cO
zJ<rKIC56jkL2IrWq+njUD_~!1*_b6KvxXD&m(~w^#GSxceOSas7trNEtqw-0?zfOe
z1WdDEK7ICACqGbTvS8cb)7sdK=p22@@;$%*{hw=mu`?AkP4Mb-4&?_oJ*33Gq^wsw
z(P1bZi~8E1Ah=-8(cIm(Hc*=q!In%Ny`?BCYW)p<5V)*LZAC+}FgCg9X;&7=IpsoI
zd-p2k8~xeNuaGci)0jAMbo+_7Ba3%U%C7HdP2M-@d5bSmN=1v`eD#lH$E%W6DVB}f
z>0Pyv-~Z??e)^4@x&Fs@BVCC*_%m<{uN57lZQ-e+Q>xJd^$JJ<Mh^5z>fB1;tfh-Q
z?USE;;i*61hQ>QD=q<%5I>A)$+KVKmd81c0Cv_1`iPPBJ_iF^BzmM65BpU2xJwEOZ
z)jgPI0(7_aa@|)vDz{uLZ#-pRKMb6}`;BX8SPhf#?)XS4CCIh}J?4~=luO1>7DLlm
zvtyTM{qd89sb;12C4$}gDmL~tD6zk>Q27E}P>$*vr08_9Zr~tgY$RJ94P8K^5jJNR
zOgp?@dff7hJ9zP_sp<R5UjKx<U-#CNNLTN@Pukj5h=lK-Pk;1W>g!T$X$xK-Yj9>S
z)lBU9hNjV&4A(qe2^p)v57ynv|9f(32ZNJG&{Im%mh<;Bln8{B9_%H?TKfq$_WpF_
zv!$e`@Dv;KPtnV)a26t(pe|V@Oh<SDx&AyKKjXjlTd`y+$lg9=W4$k$Ui%^dy<npi
zZQQ83uU7(gLSUu*f6uM~rPP3y%7CEDWF21B@fhbGywuC?`NqQ?8+f>L{rFD|6hYB3
zi0VOA`;f#$iKsrckmso0%+lKTGduM<gb;KW9v>0e1z;dqVuYxY$sWoTCWUzEL?<4I
z-1e)xacz&o^QFXT??i9$-yvaD51bl@#M>(=YnouHQ(To(nBt_AFNt(UfY;1Af?saB
zM{zF%9fdSA6TKu&D^R=LP7+s&328+RB5|0JICIbMyD~S!DD%p`Y_oM1g)MD<5PP@k
z9!ygKfBg2}x#r`)npz`5_?9)I>4G<Z@D;-ztFBIvsZ`t2M|54VU~bvbUY8Eukt8Ws
zA)aVDqwTtU{kD7f*6Uv7Yrp)U-^z3OBiFDc)2{R|KdM>O#R_3m390}|SK(0{w}7sd
z3qcg@HXd*LFg?Ym5a_fDm*c}&BSMa?S3X%@8Y?ZI`Qe9u;hVDyC~J#2!EKTypyz^I
z4316^gOwA3eB%&j7l2k-^{k<M>w<HM8sYZ?_gi^B@%VMByD=(8g~Gm*qpYhE6&6P%
zH&3~>`q+`^B0*Q-F?MI~z>%XbwwiS}w1cL!eH_E4@r3KY@fUv@?$03m{V!PIBqE_&
z{RzOxNwku>h4EzP8YpXdov>i8cb)M8DJ8$zbRX|}<j1PJGJvF`prSyBz^x#cSD=%Q
zETbWQ!k)(pP8;Lr0}F}>ieWr;2Vca5r}2k>{P;GHoLxF@CX3tGkL!B7zH=Q(S9{;E
z$IgAl%0N=|CyL`puK3h%`TIj_X{ifN7t4-8z8>-|VAWRk+JCNHPgnM(Cw1|Y7w%B*
z>;i~r4!ZF7&><?UK$cS_w|sxuPM!yMUP@2#X`p-{(<>z*l*<EsI>O6y`nxmS_NxF+
zErUMARUD%z0J8v=Es;Kzw3C#pWbHAyZwD&G6mat5`BUpuK-{$Baf)u(DsoC<x<y>~
z+u15fXhr5mx|kX1!l>ZHeJpln|H;nm&Eq~V?SU=da9<v!s9&x;|L6<!b@=C$a(jBw
zs(fCI>*3ji3^G`*J?NuY3E0VrYpRUJmH)bj&%g3?j-EH$lYV*4RnOnd@3uUkx*Nl=
z+~CvZqgd!ne6ufcaNQCcdw<59^o2zAIwE>K6BSYlQB8>9$mx0JrNHm5{4+oQ+;6Ae
zkd$TpF5AY<<sq3s%xdw}A$9F{{>3N1u~%ICwXZsyzu){M?VWvi>|dJ{5-8<uzVf4g
zVkd&x%azh#rI~aEddHQ#(9y*)FQL@NqGR#b?T;yUb^-L;F)UXn;$yZ-LJKB6{H|?h
zk~9`zx!siZ*(Yd7GA>l9QH2<r$Vr|z>Fb?)@4feO-g)OSbLLEh5d7sYfAP~?#gvK`
z*Z<F7hgG5Sg?KzrhnE1{i2AEnxR7Xdd~?1z(Sgl|NAs>F;k;|Eqb-0)iK+5%=L@R4
zun*-~d2XuP=RK$7<n%gDN^f9ZtP@l7BLBVI?s#ZOW*H$^eT{{vQ{97U_CZR?qqjW4
z7cTf8K6=Xk^Ve7@fT&*e>(Eir^TBjX4*9W@P^y}Go9o&-c*}Kv<c@V4r{0*yY|6BS
z&e;W^PenLwlFW;BRy@~a?M<xj{y*;7b}rq8Cz+W%m+==Yl9X@>ZF0H@WB$uDL;3eV
zANR)DH5%C*Zijce!~pmH^C|j<R4lGa$NAnjFXQ0lvplskSqAUeNVk9cnV~q50cY1x
z<C7p;d}-<Jt?S)M-U&;e1sTg<;s0JIjwN-9j!w=oaim|HCrQ?Raa3p5(Gi=praVSw
zBwC~QO?uwq5VF}UhaGkp-~ayi{WLQvGCf&7eB$T*OEDRO)=(Q*c&-GbCd#QNiV>6r
zeGQ*BBW}4cpr@>F>)@>K|C}eb@AS4|kMTOJ+zS>}wNoGSH-vhAw4JyXOc?OK2tW^W
zKEDrTe5aW_uIuvccmIgbzUu2d^!LZGiatkV26CtpwpCG?NY<r-;G0P!DLc9H&6BSL
zJX)JlN<Q<qTPfM$irW4A_SNVjY)RaL?m`mF4X$4iZ04AqlYF(rmRTO!^%lB{PcT3I
z)^VOMn5nxwXX&gdsz10Kfga9C#A(X7uFI8g`afT7$ev!Lhig#BcG<CI`2E$~RK<JV
zajIuFriuV`8?*q<l}brGnQGsA{a^XnLyt|pp#bPC_Va<qexbS>!|^$z!$s%rzUAEA
ze-QWeFXztQgLo!8i=1Nw;(%IYIMfUT4TNo9BZT*JpRX&1bI(1OuYUEby#DpC_tVUz
z_|Ch3#KtE#`)^1HL_9wJ^E_bi{wOvkLlX}#0VC(rPQ<4Hp=dk2@fX+g#hdTI4xwWS
z;E>ud>Apy+%^XefjIV>Vfz8rbtFnRFLD1{l3Cz?H+5w-T9x877@$KC5vpdVb1mP#r
zgs+tvr67E(qCq4?yMu}Pu~Sn|W84=B^cITT9<nH}o%wFu&>j6k(KXnfsR_(p34<+G
zS+sicABV%5wvVwh`;T#+Cs}XnY;`o+Tn#&HAHR8o>K-T~>{%-St8aUXOpni%+Fch)
z-bUg?W5Z(_ySlS{@oRsbbbE=oKQWTR@&AUDCZ1)keB<`}xcS+2-ZbVTUwZnF<lV4D
zcY#2XHvKQ0AcweNTr_rAwLI9rlz(Ry_`07GNalpKFbaVF5{vMvlwz?+Utiy_A3tc@
z*1>&$dc^-!Mkue2EdNh!CEz4X3NzEViI4@u`*hO^2hS#j(YSu~Q+(t0d;K#o1umF<
zwCXMlLC~DY_%5Miqw}xcL%5@71rPQu;_lvM+}*pJC$n?twu8|r2+wr9F9M9^syi{w
z08&c+@U6cEYqVIxZR_x@c_(Is*5M@pCl*W<WWVM!L(_P%GhF&eHIW2bD8F+-TR!b$
z3dl&d7VB|6nBz+Bc=xE-^Z&ssI#fQ#$ST!6P)3-uRtCBE4-cvCLdDcKrJ1*6c$MK_
z{Q7>nrr@|M_!Lns4S&ZR$DDXI#)<Nozx|7?UE!KrhEj5E9_a{IF^s5gVFprwYhMHo
zIz3K`Zrl4xg@eQdM8Y_=4<PYAP$*OmuPd*-lIrT}JwJ2i%<;v}Gxt5`>q+<s0=TH0
zm`<rVg^Y$T%ER&Lt`a_vMH2YUqfdsi(n({iika~W%eMG}B%)bVnYjS=!es+C2K{cF
zqKr_KCaqRA|IRF8Q=!4{4zSbKc&?<=;fQc;LZ%57J?(w$+SnSnUD?x%3|U&7-$=@u
zCqekU{d9QQu1mrQ*J0HwXC96t!x`o5qG35frThs>(j@2ltLZY-9)OLY-3ghWSzoBS
z2g(R@)(VslTJHbzBmNuGG>CX7o09D>u=Rz#^JSr6am($`K+%DsJt>c`>qF@|3AjEL
z;Tpt^;Q1U=ux&2-+3(oZ+wX0|9?_cb4T)HS-1Gl}W6)nxDGem)@^MiU%F2vG$$ZQ6
zCPU@$`pjoOLvL^Io}X>ow)tWw(2c5Tg5kdtN-H>MnPSL}0?XJbe=f5ibZc$b<&O0)
z`fq3wym`SXiYyPxHbYG$&!P1@F$0$x*}LeF1OHxMXrar;6A_TKx*A&@jmJtR4-}0M
zO&SvHa9*beB7sB?kAcki<Dl2_liPR%fn$P^Cy@RLALpG79sWh=O@_m2=5cuKeAV3;
z6FuM)_~5U8$tF8whnR(1RQKQ|Lr_^OgFJQ5S~A@}r!xYgrW)a)W@NEo^QpJ~gx;Pk
zQcC`O-2-SFJBjb;BeAm|vo%v5+ZE0~WoMT%^HZk3Smf&a18{~;f`p-3b6OJBEMmGv
zO(e^LR0qd4Z6O^|nbRR~e00$ifs)!=YX4a4J#R8p4zFl5N>x?Wupd8|y|l%bc1J_2
z4@R(F>Y^Oo6{4d+2^1Q1q)(;ng#)65ud7f^h|pF#dG)L#SXMPNaJvoB4D(Z+9A3YT
z6}7F3!hT00kZdW``)Z+VrF=bSr3lHX)F9#N&4El$Tdz9fKIsl>fG7o1rK%DHU}izj
zy1vhU63~nIcB}p6spmpfC0Gc-R}OuH>TZmJByAQj{0{RTg1{viP#O85R{NwzAxyVi
z@ov>Uc*zh{*2*Av{_cKX+Xo<-Kx^=}BD9o}{@y(Qamtl^;y<q8SKq#Mm{yiHm`kj9
ze^aXR|72Gy;Y(JKE8)*it_@vb6oC3@nuM;->6J2z9MQO)RSi2>QQO9hWF{0c*!6~=
zYXL#l6{>Hku@T;TmqX?73W~p9eQO|gL|qM}Qvm*o2}3!Z(Fo7iDS=#*j-B+cPXdT{
zfS$*<4Tvix^XqH;X?%*rbd#%(z8o!xoY^7RQl}-^PuvLC#3>PUIetcX?Q5@m4Tfg_
zZ!A*XgJ}k^OA7Ceh=0e>k=^}+Wd(h+Q7IBYK(W^U&4>V`hHo+j63J~L6RvNL*AUgh
z6xvX}_GTx$16{0}IA}gs?HY^lL=pq;F4aAF$q-c5${_ds>0$pGk44dD&On<v!#5MQ
z<?!s|TY%w1tQ$2#5#41c{U2~NYryH{!`m%64n6sT_YHf%8|R&<%y!N)6{j!mi^RaD
z-taeC^Lb0B6pna>k!aorFZk*q$z(FDS+j;UYt{g;Y11awtXad>ty}#xyI-=sS#JMz
z07B%N28lRAlq+ZX8Rg~1jL<}PxjGc81o=h{Hy(_LaI^=EK8B`WdxkCqz4>CWnr{`b
zu5Tx9z>z&Yr8vFCSeW*_dkFFlNQ!bX)DYZufIx^aP1pxEvzN9664^TfLq{Zi)O|=s
z(E5wuxS?@+*%8RhGQdY|mBDxFYT(3tGlMQP`V0P7hN_fhRl!BYkB&qPq|$3h7FV@X
z6Ag0XCW!|g-Bm_M5(~u~Gf2o%EJI9CSt|gyUV9h)T|PyGG&Z1BS0SPiG)+@8w<$sb
zc0!PA4rY%ffN0l1>ik~y5YI66!1YHa#Cw*U#lcmxmDm<S;>z#@jDX<i)Qi4fZoN|Q
zc*zLMQf@f*vz|BU>o@$^V~??F)hbr4Dpz&+=tn=ws#UA_!WX{ar`i3odviOE9e_*I
zb&=b5Av0OuCkH2@E8l!bz>b0w6=*I<O$6Gf$WYlD4?k{4N<R75Te$MJdqU^*l2Y=&
zYyYmmvf%mNW;zN<)tB8qNS{N1htn8@h}?vcp}Rg0WRlfMYG>30ZdaSp?CX3Zbq%*F
z3WgpEPp_jxVTJ)drOgkrr*58GK49?++jW^+TkW5LNzxScZ=*i0?N}c(6FoF1`dE<e
z<jBUYG{*hcDH@6?Y!<}Y1GQO3GD~*@j^Q2$-A~zoX(oe|l3#rOcm5e@@4#(s$Jw+M
zd)tn{3!~@-TgiMQ0V@uLM*ruSp=d6|+rjJuVd1Ic>+x+ny=ll}gb*Cluvmd*$?}>u
zsv=>Ce3J-Vj!0~x$_V1%OVA@V+MQ6%FlAittBU%1RCC&Cr-g14mhBC;;-0&6H)8Pu
zgpXczGgHE@i3K_!?~7nS+hkx=HnR?yIl-I+4pz?6i~gOy|MHP1IBoF)PFOh49|MzM
zW2Tjj+2AX|0T@DEddL2l7Y#=AIW1h4nJ&_UGWFgum}EJPYM#0OIl6ZFSO(lQz#6jY
zNW{?_>Jf1t!;b(F#9>hd_*D>PECiqzK-eI4JipzlVo}ao7QWQvoTE<1($HMyrg{{P
zvt`xB1|#?iX98GYgy~=f(9}6URg=kNc;ST?_WVAWwNh7C$K1JdmG!HAdncVceVlKG
zlFQ?4+krlBj;F%)W8p*%tm-H#7Fz-^^H6jpL5n&G36U)5B`7p{*KS`S433@AdA=`r
zXIzz8pehoEsA|cgXe)CfokaB@*IANXu0|6fn`eB;TnbYRUm#rlk!u6DDSa*lD_n`5
zvwXF&KL#&LG33Wi1fT2Wr+%$21iyLgN&gH?f?Id4QDQfu*&I^0i@IoT*jF3|-*%Or
z55&$q!zsI$LgZhP*7K^nFb(C?_HSK&wXZfrB96XfA^L)O=!+L%%$eo8vkO4amM}YV
z;kelWE2Syf1jRZ4AA%e-;KvU>MprJFacrXG6*|1Sib=)ete_-Kx}8+umKP1^WQJ*;
zL0_P{2g(Rz)(SwrKj87AWFh<fcK}=ID8-p++=Qv@CJ=yTshjWI2%hf!BStt=8UZ%;
z?+)B%eJmRajl>2c!{T^*z|Jlx&u?LxU<UF2Iz6BqUQ$XnuGt*C-J-wVV+k0UlCn+6
zXt#<Ra7rT#hza|IBPGx7YV*%Pg|rv?xNg&ZN^H+bbz^GIi1mm1MY+I|enu7Uy`CF&
z8Hy?{!>~4}?!h#`#x<MSzTRJ9XPGM?Gz}3keT^R87a>6_3>>b6!09Cwt=)wSu`a;H
z)6Gavp~w&JeVEt&_&T<AE3EVxA_|U4M<Jp7ax1#Q3-`ZZo7_`0SzR>9geJK1z}4^?
z%v#y^=h|zpRn{jdC11Ye+kxA4eLl!{O%1C)Uj9Y#;?l^4=H{C+AHDIPp*y@Eq@x&o
z6s<18kuo0FN=oK9fg};aY#oI6#W4;jhu4k`yVRsu`aD<*Kr7mzs-7hXF8o*mQh*cn
z*G|+3&qD9Np87-JV)XzTE)B7a>h;GMNOWJz)QeI66(UMcsP4fuz)e5B&3_}7g`3F+
zKA#CF7f1o+n~**4T)~_t0L=j-hv$QVD<z$o93Q>$W^Wsw0z2~orAv*$#@<G{i{;I)
zswIvof{dac`X&V(uHd1fNxvI{nWyxa>K;rpgui_MpKN`0d+>%G|8%^h06XpTg1!PG
zeG;K4%1bZe`5g1*f8NPm>tFP?Q4dHO!GGs@Cma=sEvYM6Qb`1}jSxC?^;*xHJfIw2
zxBcoKC3gL+nzhG<gxHQgM8OV)(@P65C0YQbDtz}soV{FypyEi{3cWnqrKWVfsX5_`
zY#N|i&tdrL@ESzY<BCv(K@Y>3j_^{=gMWLJe_S1a!wYbCw?e73C(0lwB;hDcDmBXe
zP}mnUi!bHl9VvNg=Wd?erADK1UpfkXiY%Aanq4$D_BE2V3}s)p7RcN($nZBQ5jenF
zD+K9Cays%bEV!=AFTZfT8snyajsmMXR(5!WV4fKyqA0{tvj_H59?mnG5L|u#Bi=RW
z5r@~#58P&ZJ{`Et(P+2QIDw}YN@i;g)mqp#Gl0Fx^Ck}%hu6BtHuBrA-x$1M(?q0_
zftb>47qh3}%L2So^pYefkAP!<9l%F?`~r2Nq(j-*uWl1$ODzok)KQQuiwBkeFW72p
z;dA_H;j8X;AhC1fZoc@sZv<!1adEb6$8GCC7K=#N4aV^mg6m!aR#I(82BW<Enr{WL
zW=HUvCi~K7<#7}(Dyqx^&GCK~SG93u<5rSpu+`!ABFQ-ZA6)JqLAR@sRecz*&sW@m
zX#jR@YUPTfJ{fo+u0gbF|B4p~M}}ga*@FhIB44uvq8<m>x>EAwj@@*JOe<1FByq_#
z*xX+SjEofta<Ai{^FpCHID>$J<RCL_V)cV~|KW!xN6Tk!yy_pp8#N8|*)x4D6F(FQ
zlo}GsCJM)dVy%wU1;Uo-ITt7Czo@WogWeC0&yM=5?|Xz37OHo8MKpD{ts`}MN^xo;
zdBulsdj~@umqU}=s5OFF_-ROH=wYkS2&6xzx(5d|SAXQ%U=JH~9j&n*QI!sCjscK{
zrlexlzC`*ZcGAU-Dn7UK>0^+95dyFJwoJPcyD{D3;5zjVzd=MubWU<okW_i989PIr
z?P&mL+t$H<9{U;kI)c|c0w5kor2Q-6VD=Z0x+qtlS3J*HaY3O$4|HEO?Cus&s=*ig
zB|ALMj@6wnvN5CPFn@Qxnv!cUJK00hEEj08U64}43{XUVbFqQAR$@-13-JHauht-d
zLaUQQpW}IxVSTHf^T1#J9gHDOL!?t^EzRf)<{=`%<OfT@i5Xx7U$trw-2w!-5rGpI
zIH}-{L}LAv1%!R%-&?nH#r1!sV5vG~F0-;`4npDa*Y-?p2;}0*^+0tTW=A?`GJ{$8
z)oU)N$1G;);n@5PMQFi9l1?*{9WU(U;eV_S&Y-5D&z%*xvkO2JZRMMgLF<t|b>S`&
za3c7-5emUD^WAl~D6!iR%Y-PJSkPdoO6wessoDoxdR%b_rup?({z!kP8fTZNsY07I
z!}oc|><dsAWel7M6dDJdT>;H`wh49|oOoHPCD#m&4+WTO;xXz|s-srR^jmp8v*x!-
z`_NTP@>JI>9&4Y&;~lekqGK*vz^Q%$Xfk^Ps{kLG>2*}Rcg7wt4zFT9n6ld1+*xQX
z&1ecXBBjQJ(v64&jQ)WQ$k2WSv}!f-_<jf9x&B4|-%YpsYh)avy2&9m>LSfDc9iFP
zW?&7(cuTqWCukfDwv?J^xBPeZq=d_n5gP;L(m|slOW~}%^Kz%RqmTC;@u@&<YW20{
zboqcULJ2q_5fc`GkyAAA4A{5)3vvM9YyY~NF2!jlPxfq9V%N}Alw}zWNu$wAQV&wJ
ze)N9CkW{OVG_#*lYySYJR48%#ukTgo40a_#(|k{#7jAH}C}UBo8aT8MaGuqjazt0L
z7VH$h?a~iz@xBV)Wcl0n)$~|uQgipkat(@($?Q}Qdcb4t{Ae4o0CkdkU4b2rNp>X8
zPS2Y>ARJ!Ztv!LbGDTetS~ME?CIrB$j)JcK>$DU5^dUgcfSDcmhmYU21pfZ?bL{9*
zd)oWKWv^6fzqb@;b;lf@?`~#mrk0(#DrF8JLpV6n|7(ANQ9f}9o1<$Fh5J<Z;DF@+
zz4m{|X9~XC(T1#oM0J&tdB%+yxCyiDWOCIxn+8#JD@vf0^1m{0$#J=H&9nX**$+j>
zQs#6jIl5vQpYl7e(Q_P<R8R$P1cD`|qkjHzGm1NKVAI!?!78Y8zRJFSob9^;u_Y8a
zyd>p>5jzq5$)<2Y_o*1lQ0{$kGpBy{Cp@>i&0j+k;9tAKa9CUZz%^Xvr#hJt4|b22
z(26XMwFRsjCb+L)venXXLg)DMKEm^5ui@3*+QYjJ{iy$j(kZltdjIW=MF<jcOH~?9
zLfvXiv&-U+#ahZ^myg;icWro)|2X<^e+*23Lu%$yA4$<!Qn~mPbe9rzmlA-)5H8J$
zVEZ#K+QE|8ZZxFNvO<y>x(uIC{ZQO3s(Wz2vF@>rJonIg&)cnOs^XKs{7+)Z7_RNI
z;E=h*QZX)F`APaV?L_FoE9jO}_;k$fJVt*B;mY!3NfWCkj*JA`CkaR$B_8N<bQ!p2
zIrYRhq~xg`!E7!^qrIp|BM4|Oq?nruUc|VJq)N-PINnNwkx}Sm8r+1zGSgvJ7|O?s
zCe=MSph;G%PeapZH2Hqu;+h)LRHGEX=bb6K@uPiE6yPNBbuZJA$?=x!{>V-5eG5%K
zYO;+-#`gcfa6FPmk(NXsb<rHUf~tk{q8&i+ms}B8YwK)xG>(ecMAW+y2Jk+m@n!Gu
z`uXR6OK-dXi#8Ec6WKM0R<NMPheYoQA}Kq(R3ASY1<+cdSOcz)A4hAy+EY#;1Pzh2
zVu#lt<;fwU2diy1JJQMWcyOCRJ<QH70Msvp4>TUR$$Q-{p$UGo<wr!KBlRC6GaAsH
zaym)i+FgK=DLcBvkjM0_g`Tr0&Pe0Nf?ZOSs?@E{w19jM`hE_x+Hx6Ma~W!5ab~By
zuhRN_ue!)4%Qe`Rsby}uTS<)sLvv{~)y}VJA(^YY%rIQ_+FgU_>I?TA$o%`}CxS7Q
zh{L|=)P{~WdnUs4f1J0gqDqUS2te2pX{b3+iS?F)DXm1gLZlnRmz=ErLXqFEeu@vC
zc7pefPml$vCbs1|mDvZFpY9@U7L{#bo6J5%2kK>`eG;CtbPkP#Ac`39Bg8Q@zC1t3
zXAAu6SNHhVemosx{>pMC0b!`~9ZH~78&jHI;zUGfJOY{v@h;GPls@^*zt_-_$&UX-
zpC~vb)@9V4{O?Otvq_o3=;Q$iBuipJR&)_$UF8{E`$w-+-Gc*;^^a}zyv+~&@Li)i
zyMVHON;HBH!gmdoeUUKC*)@n5_+z9k6X*@%nqZ~XEny`7r!-37#6|N)Jiei)gSX%I
zM~=Pzhn)BKUvbhOuHkk6xQ>T*ZS~at!yPYp+FqsX$W^nkuYQkSWN-)yuAzLq&r89q
zFgd#boOP;ua6t3RFJ2#r9g&EmEtrSa*np_7MVmbnec^nxXfPU%PSj9-fUY`iUvnVZ
z50NasW~}_<S+)1{g>#NoY9An`+e$AtO}S#FV7IHGTB&j`5zkwESv$NscXZ*}-XFtO
zHKe)tW9RYHXTHNvp87U_X#FXg!f;SGrbE<JD_E~4@_0-n;OF6N*0T7~{Q*_}9EJZ~
z`*&rwM<OV?20OFfM;MGnt&t_71zS>is~xh7d|&jD8{=vGWv$2QjESl^uYUI{#{9b+
zvM5p!Q9PDK0cegy%Lf<bS*!^`<%j@=pu=Xi4EuducRLsS<Ja7~W7DuD@{{emdDE@e
zbJsR+oUl?#?%2M;leQ+0pe<j;>W(?|mSTbKJ#%i9ouwM3PA?n5COc%ybSFFqZ3i0N
ztvzgeeusYsh76}75<!>-T3ro#Lp@qD5$v3!suI%BE`+2yQ7P?j`~3yA6)_L5osTZm
zj&qi)DZSkKoVe;54M+sNuKMd80O9?9GGsHUw><N}^Pac)t-HU(pW1%TyTADsn&vbB
zP}5kgbWn6P4P15F4z7Sgou+(p8j1jPi?Z^hPyBJsvsg~BP8TR|L~h;@P+0qf5wzyh
zfn5To2>x^5>S%PkV#Iq4&1ElM_8ID-qrb~ruX<J8{#Uk%l)AL_@n$#)Xr*B8ODVvK
zsg3Ru1XCIKFHfx<_WKtezMX<?;p{W;xJaaweCmN)usv80tm)WAM?M^tvp~>U@V^r8
zzBZr9&*9#_C6ruU!8k7g$p%}e-3ie=6QUS^1C6$A?Y^~L(^$o-GY;Vc-+w17&pb@I
zF0dZp;mKHNA#l3EpjM@S?PrQ5p4}B>#>8-T<hyXBT7AOyg3n&XaoAd{4~0f>2)a<l
zhyBdU*5Q@w&vVs#uJN?Z(=IuILr*!#(>A@p%vr>C_Ny!5D=4QkhNN=WFnqjw^Oi+#
zK6tUkC%QLd`{~J>B#zVrsg?-PW~Et|pF!RY&dDPYJXSK;Xls;)T;d@(04y_o@*XIz
z4~Gngp0@w_rKV0@B=Lj@XhmVi1bPu=bBzS@^<by*eT8_-+PzVPTl;%>WcRia&aR>T
zYRhN2Z^x!7+r4$m23$vVRO{aN*<MKD$dLyQep8S!p3k=gKF-I0Lk)|wVipbR<xz()
zeXyd6UwroWp0_z?`Aoj|@Ynf6>(BVcZC~L0D_+SvzJ6Ko5nV%&fSyzDSdH&?(6YfF
zuI`Iw<LkkS<+{A(x<9h3S51);Q>dS3cIVPO-_y*7o<=$f$-uYM?RJ{2#j-4ksx7pJ
zq(*^Y|ENgs4|iX-4zF9Uy&K2FbjDY{<8(gts}GF&x1hCDMnhOGX22RMgV=G1bU7q<
zI;6MRq_*0`TW$1$I;V`JEcEC%Di#D=vmJrkoEQ>K1oNFsG;(ePDTDp|T?8-K26q)q
z?#i3tD&;HGo=_cVJaNa<p11ktFI>Fezb`tdWxs#-oAEApCq9Pun8)_byd>V{py$;b
zu(&aBBLs3Bx~s3BEnPi8+2J)F0@yNv=Z#0fwy=x-exKvB_XBpN#&bQ*xC)$JyR2HC
z&(8?#JWn8*tvS@_pr{b(*eKte9+>pB_p<u-r#x+Q&a#>O@Ud^O{Fo)f9$#^|T6qKk
z=p_eAYNNb_1Px7jdZp>3h8L9gu=$e3b&sma9GR1>XGW|>iS051iJ+$xr=yr;LvIsn
zI%iXGf^_Z+UMMtD3|y~r5Xo%aML~^*(b1+gye7;;e|^NWw&ou)myiAQ3XA|MlS~!?
zXs()yCT0#I8OdOlnE>>HB;M*^WYsyntR%i~NdD{QJNz{;3Mo@vTGQA_>Z94fLUNnU
z9#$o{64%t60!jo$pcJNnMs3>R6%<lRzI4(5dfMjuzVkL_%x~K7b7szK@YH6djCFfU
zz)70EmysW|?;4Psq2nYJg}wxM&pT4`>Z^arV_UaV9T|V@mq677o;My#Ya1w98nzX@
zgJ~=>R-9+Mn{fg@=m6MKY~-H46?8js-#-tD;00Ib-lEB~C0)6;%P>SRqUF;J=eJ+K
z(bM*p9J!DmKK>12iILx@;qy2?5$uEs5w&|RCoZs419OZ*ud^tD?sJVSM@s(wjQ27?
z6G0Oi=gvG_iS50JAwN)b4Ayqf2yE;%usJ2Yo`_IWB~b?!8|tGO_u%gIyvdiP!>gDt
zvgYn*J!|jEn?JkXzY8I#sS8?PVkEArNlFeKCdv%{JJz8_d_oGa1Nch*_52I$?h7^y
z`1ppU7@<04Emv-vmFx|~nm}?;e22=4)c`Cr9Ae?nG%aH+SKWgHhINm=K)&C*bmsSe
z?;X7UL$4n9d5f0M^vvdDkfZznIB|ngLojoot3j^Wz>O%}fVc!Ic44_L@A>^-SzKMu
z>{Qi$pBvS6&YZJo$~MnmxPpWcp;R(MGsfw{Wm!!-x(ISjQNGd}jb7@E43B5#2V_@Z
zBAI14q0n~OgBx5m=4tkF`*rtv*4F8lp2YVa{yMSv=+4XjULQ-GPXJdJl$zA<sY)re
z+Qfr%tPtg@#D0@4s!Jp7EUHPT92-H=G3hQQe79s^b8^)>bRm_^wGPl77QuP|>HQe=
zvUGTT=RH5hEB(87edEnE%&H&vd8sP(Q8z--G2s3xcd3fbbfW~eA-VZkZ!5KpM@%<~
zXkl`A1sd=`v)MyLbCqtwOeFIRwS5?YK1+2E4j6v+x!-!)-ivECPw08`mo$57w^9V)
zMs)1zC{8khT@|I!lBU$0BB))tmDUDCeAV52ngzmVU6p;w6ieK?b_1V0^3?r4?>&bc
zOLcVW>k$n>DykDtgvY_r81E;d1$_cuHr8pUIXJO{Q>)hnW`Bv{Q1h}}rB71{yIySd
zv_Q&de*Hmoee5-4v7q*zx2pymaf%mMuBrY+(~d)~8Ds=s=NkdcudngXz&PC7_M9@?
zV<Q7`crA<X!Vqfr69TB!WoV+g{n)_2?55I8PnJ7=<IPEkZfLyW<L6K4xox|GiEh~+
zTHXp#epMMOt>_X9@MGAwy2<lh?OgNpBmCg0hq-^}W?YZIlXZPN$vNTKgbWT$+q2%6
zksXZ|vsd|L-lV(Xsb(^Wy-{@!4hRalB5Uq=#?v-?+b4FqnXHO?YIibdB?nP*C^aUq
z>JzvzBhXDjDWzV)TL95+hKIQYz&G!_k4(PEc?(t$9oVoMLMvB{6dG3?dK@1<{KP5S
zv0axp-tv30TKAOO@{-1ww^dOmj=)8*tyqf@V8NKU`u+AuwEagIPUAgZ7T~IzQ8TXg
ztm@(@PBIdT3tuQM!X*GR5Aoe#`aA$pL9V_OOW@KY5A)Z^81z|rdaSTi1RjnXK>B=x
znNc3aTdzqXp_*^JpJ&4>N;OID`rZAWEkNJ+nb%GDxt3yaMM+s;5m%NgCWZuCfyPR}
zdl0$@Dm8W&@_gX#zj6PrEoed@DCfn`PuFnu85gjkZvXl`6D4nl$=NlCpsSd~l^tk4
zr<<gWyz)WW3^b)?VhEwWs?mYK-+p+D=WWhCXkyWMci*(ebK8|703&bXWG$SO_rjv%
z5|ZFn-GW#flxoT%_Ce5*$??U%-ih68BQ;1G5l)-Efa7M&<=pv8X^4B126X$@7kH{;
zH`R4&^hCzOP#oF&LSY6CW`?w0^xc*~QXQ6xSwo2&qq+wN6t-ov=5Fr}SU&KB%g23=
zVd})<CWU<PYYGWSJshzZW=>L6m^wempySI*0zOW;t7fYyit~>htCiTD2uaib{gw!H
z$2vOE(Culw_o;+qRPm6V8(YVJ(geBSKhK@;b5%#20F>(E5R!>v4*|~=a=@k);2~sG
z*RWleH~;Gf9@@PX09VRArQ^3{db#AEzhh^n&l7vI5_O)m<2@n}F=5rqk0_EDVLAyJ
zz&J>C4-N?a`BU!;Rh)6z$rF0szy9=?=e8$<1hiuChdi!9sBS#X1ubBmSK)w`DbMp+
zyTr|#*U^$pd*<u{xM9On<#9GlMLB?iZ7A$dIm&BisV0y<%+g&VAzRLsQCEoQK;(%#
zpTYI09P}BNoy1A6J9a|Poi)GF6Ppzy2rNB*Jj{$+)@xKe2o7o_M|=)&{rN*`98XVm
zZ&hM<|CF_fo8ta!i*?&c<%LO#A)4n0ksjZ7p`1>qLQlIlsfG2kYN=@qCPm8&N>vfu
zsG)pd9*P8n73_-HjX=HuWPsJw*SB?Y`?eQ&rn7aAlWQoBlw_?E*FNXXWRf)0>&1<R
zE?f-Z|6;h`i7CCzOTao?=YfL3CQHK#$t@?0xvINxK(KxNE>GJ#;ew-CuySJGUE9tc
zPi;>Y$q=2D04XKaiU^N}9q@205a^lVOSdZ@yW7*n;jU~y60k~onEZ?MlwwNzQ?2J&
z8uRW@b$=|-LsT(qDAtpzdvHLp>&4xkv@>&YGoShOhbH{|BTiiGiQS5kgl3EvE+lr_
zdxK3B_e(G0Yx>HcpL`bE4c?*c$gqud<qvsrbvvf!|L3u_*r3?QE9r8Da;MwAjF);{
zDK9&R*Os;0J!$9Ei%+QN`LTq8)Q%wt1TvzB>h#++$_}WY`Xqs34ak5Kb-VL<{`TT?
zX#07ej+ET^f;W{z6Gj9XvXgg^G{yS^`Kq@S>y=*S<&;@LzZ9&qb?z^i<fae3r<nvN
zU3hFon-JRx$HGuXb)<r-z%pbJUdnLUv+<F@#@;S|yKW8tx8^~9@Z>|RX`h-?wk4S^
zw=G3T<{8OBwL6)Y>QUN@dC`tNRnz>WTDT-blDa#MI;50b|382Aq^+K|$}$Y1u>env
zhadnw8y-Dk&4$!Yn@E>h*%|=NXT25|ShIcSn13(WHh+C_E#G?lKCXW15nk-=^2F}S
znmL}d;|-F!XpWV&yJ(1I1F_icB-xNJOWvq*Ivs3=tQxQnysR|53b`U1R=?;;TgRNc
zs-owge9lU4`}5<T*sc@_=tbLyjMvKhApkonDAZ}lfaCE6U}m8du%>FpD)GRUE#r#Q
z_gfz6en@aE4I>gBFN=oDtYl9hw%YA9J4!XoG|Tpa-~W~knw0b29pH(Q!3j}Y>0aD*
z^JtoSQS)iW=aNspmRG#xROTI2*+Q?WrIwCe-lRrP76~}9foS4@FL<GPk{f~qoG5{7
zib*7H)E$xA{key3Lm&7)zWMlltZJOekIr~4&588r?MO+^wlFoF$UYU@E?m5lJGXnY
zmhys_ZqZ##;J6xfG4IQjPn1TpKafqqJj212p~>tTjy_Ly4-Oda{li1FZSC-+E!U}R
zy=a)~wufeK0VipWJ;(Sw_YAJ&1$*~30jPCM44?b)tvepzcW=LB#N&5tdx4MMdlS8-
zB8H~nO3DAMzK2)OU&jBQ{3_zc<Q=3ZHZJD}>;E;CZhOYOWG5}jU=Htx@?x=>IwMQH
z8H|%{U%Fh4nKDe8o!+NZzpNZyR?$0gxx>#`S<&<V;{&fy=I|1rMm%^3c1)0O8nS3s
zbk`VJa3bKu**jDDh(uCqvMIR(JFzkB31D{0n-tmGw!J`KUyR!NTxbR-Q?mmJ{VgMS
zCO4bDNSuYScA^3Ag5yH6)UavR<(>^mk0a=Jp-SbceDMK@pQW$^(@caf{^{cr)=}A)
z*IafSYaid@agQp;Nkx^6@n+V=s#ag65eDYnfU`Q~z*fwzAAPJHno|E%$1dLZ&)@UU
z*S?*Yv3IFgbZmaHb~V3T`#8ID{RrT+*$cVi(Bn9|c{Tu#x0O%%OC=)=A2kKXWOuHb
zc9Etorc&=EnB(`lA#+RUh#aT72L}w-edQ0Hw4>=7D~?-Q(Q6JpW}zpxCxcdUh;8qq
z*qA`Z%)QZGLLdqj($K;1=LQ)($PjM%$2A3|w1yLclBv2T2C9?!$CEooMhCBM-NC!=
z`178$`Jri#|J<@3+jY6-%!wCy+n2v@3x_r-K*UfLMK4*MozF?>^(6FQi$w@R=nP7|
z)$=A_Rw<45|M^i*+G&_okFHm?b8Xw9u6(x?U@O{y3{tEfxRiw2Nnx;De6$-h3*XnL
z(vanmVo+6bKU}u#a8GRh;K_%uEfdEH&x*L{2JLVaY%SHZF5etz2yk3Tj*nWyoLvCb
znk21yG<ChzmYT+Uj(Ei(lXi9ioOss39`<H(Sk=*T`n&>k8-yK>R2P6qAK(Cz{id{g
zg&3G*wA}cEjUy$S`nvhYrswxOR&;FM@~=PejmPd~SFS9^D}j4=Y~l?!|CV1oyP93u
zey)4|iE<lCMdF7dkO)#{QK`;Iubl|y^9aRfK03l&q`C_S1X4=RBwAd@<#iu?RYkA4
z{V!?`@CVUsm-wy>u`N9q-FeKO0?{3PL^|_e_`BFD)Enexnq+62<eLo~1=Z$9LkqwQ
zKAH~kRcjvD6YaJyu9V!mbpy|JwN2XQt-HdZ3#bW~`lzZv0)a&1k^Th)Eu$tWi`IuH
zX9$Q)ylCJ=QN2a;l;3^*Mo-##-3KfGpgY_BujDWmQWHvJ-z5;~bJ0rSSfvTT$V057
z{C{6<+2eoi0yw0825(w+SVe8SQnIbTmu>w$I0GY~<vKjm*$N0;SN#OM7l9pke+0X%
zI_~aU!4sLeY%0|IZgH7uV=(e|3&C0lTy;+OFz~}4{*VO=77&ZYIP%CNx$nOFRCj3v
z7k%vfDZM@tRimpihy>ikkP~+W_QT<T*^8M0vmbtO@#{E#elS`jT47=Xj)vf`FRtD5
z`_G<zj7N8G$K7Y^;>dFB*q8o&2gl!VH6?fNDYsP$$1<_X%(J|vRcV#pHY**-Sl<ys
z0pT(N%*y0GrkNbiJ-psC$LGZ#JCEaDbL51dpU)P#^^e{J5KIM0SuYYzLkfX3G)lGp
z$~Fi<vqLA6iv)7fU|bT<?{44o@4ixz`*&_0)e$y`F2t~dm3@&?a>uq8c<aCZz_Gvo
zA*cQECwz1Dz3k4G_su<4&b!V}0aK_?=_?WC!T!Y*oM2<FcDow)7ECf>@p=`m^SsH>
zMX^nVU7My}J!k&PIh_CgS62MGxr>^nY<to;Qi-0*AY})W#EnQw5q13-4ZpM91OhE5
za1(OO2(NkR8h&@~8;MOY$K92ZUq1IZKY8X+c4o_ZJx%c>?>P7<W+kcy)<BYCK7wf$
zLi2IUG0%Yf#gYbXb~OcQvLM#!n;jELvnI#<yp?|s#zBfZppNUWzn%|&_{040hd<=R
z6HnxtYp&tkbI)b%+O^D`8=mTwt4^;hReoPOT6?_d$P|d}%3#M0N=>Q24_b<K8knkY
zw7m!*G9bAzv$mR_|Hp+K{I&0ntLHdnCfK}-q^DRu^^pVf%D&F7{b3k3k$^T?<1sf?
zcIc}aXC-N^z>=6Z74ybpi>)(Dw^c6d#z2Cz6}085JMyyU-akI%Njs;$;dtKt&9_wa
zn&+R~ic_KHm)A%Eg_bmOX!Us!OiA2GuS-6rE8SNOWpKo{lEK)!@Mpi}hnJkssf!no
zDHX?^F@zBGmqynpcBSOg58T2H8=giNVvpnOM^8V(FQ0pyU!QYHMRSs$SW*wgSDuw|
zR%hpPWO5U+K&thY-*5@ZqeYWbVivmU%f5KO=S_y==*|NQx#E=f+L|W#zX!iYEK#{I
zM_;!Zp*a{>bhl^Km3J8Eg9yjEcn^ZIo|oU4-3F1J23ltH+v%{znIz4Lbwo<ZXCAnf
zFFkU{9%mOoTRzMGJa!LX`u80Jj|eO)JSF3G;b17e03=c2Z{B-cxS{#HM<r5O1mVkx
zZ-4vSyz`y!<nqfeXT^#YTy@n|%$YNXtFOLVb=US_MD!^O0k400hey4c9D|p?p0|l~
z274;&=wPM7ky^4Z^O|d!(^!or1aoV>(P|tEsY~qIi7$c|R@VXLlls<NrXpLe8;&ZC
z5zQhUDJuNx1^sS}T~=Kn*7~K!(^ffcIBbz#vT&>FE*uba?(!yL`wgFXZN;z2W(uCz
zo)QxD+`yVHV5t<ND2cV%<(n@ACD8@ts`oM=B>~%Y`M@9kPPSB{KAs?M?0?<jxGwXm
zYDfLs@7F)c4I7>=KX1tA=nNE1dFSnaQvLDn!Ic`Fg=8rDlXYV}l?(Q;Rw5`O*zJVC
zHRO6ztYau4cvs$#>8WfXHyVqNSV%lIxp3LP{l=Tqn8`3$#y8Tg&glkV<U;200)mp3
z5>jCH=;-|;Z>f`8=I#-D9FGTfZQ=S2PmTIF33L>440?7W(Xf;(lgv!^D&B_*W<`2@
zwP1&QxD*5{bQu;(r4p-GujcHt&mQ*p*=L{4gAYEas8=JYX|9=a8<~D}73zi}06kLz
zJGe35PKNjXd-to3LktXnZ#(t~-|V=Wi`C>*R2lNP8Ray~Xob3@08$FtcURNVT19_f
zOqp(Ea0OBJ&(>lv4ljV6j?R5WldX<MpR0V&y%#GSdOI(779T!;%55~yYh>w>m20jY
zv`T#?PC>a^qbiTDS%B0Ihe%I2#va`s&?OFtqU~_ePk&3va(Kg%L-$kw9t}g&c>SW0
z`l3=we)7ztW2T|IQqoxnM>=B}NpC3{iZeBdpxa5&X9pA2IMi}`802z%h~^CM3bI1H
zSPZN@Y4Ty(Dn;vIAgwTGA&TmpAesY4M*V5XFao}B^*(Kc^54fFxP>!s{2AA;e`;LE
z*KeL*GXe4Aa_V3-YAODxB_OFX3(QRPD(y#&o~6<38~>?OASWRorj5tGh?MFcD50aH
z1IKY_Zf+j-cyn_zyLXSCf>bOP>Feto_TxA8Eu)`+Rzk-vmG{|j1fb^wT!Dvlwet3J
zj^^BB_O2aXd+0&b#Qj;MI%&83IaDyO{dMDWJhFQmrlu3oDsy^CS+2(7x*GXxl&<zF
zEGyW81rg1rDb^Q)aaWLagWY`Oq~LkW;K5>q*02<*k>PLvyxdu|YQdD-IQ@+$PU>}a
zja9VF3C_7Y7D9ria8Dy-S)VxG>L_3MNx%r$H?*m<oA=-FH$HIK@w6n<Bb;7?zkTu8
zGpUV@3>N7t71`9+z2D?3Luh-7v4nxy$sW~T^$;YTcJS+ZSBBvGh)izm+8-fZLRL;~
z)6~-ljz9mX$=h#8Puc!dk${mcDeXh7%R$eDW3;GBMFV$u2`NSu6yDU=#it&)g|Gbk
z&i%gr->uswtpC#8nZ^!y^D&gDZZkjK#fsY9%4#O0^*jg1w{uMD#Q^#sD1{)ue{5b>
zl8hB2_W5F@lp`J+<jO0rq`JC#&(EAW{)>$-J9_by+fc2mG89R$ihX|TF;14Zo;`fs
zkcgQ4<kHEPXx|_E(~2C6hg-LC$v=O`s^5NZm>B)U5X1g0CG>U$Q>oApHimFmT-8Q%
zA`^;nS65iaYK^6bBfU`sQ<&jE=e`>snQ|K^U3lE2U;DYME}OD_Pf&c#3qUVQOvP&6
z0`OV2V+0a-WXpE8b@uY-*Swhv7q1+4XtK1XfghcI0spalYK1J~TIDiA{vcS_+pPMM
z+e10DA^3@}Dhv_T=?{9|<jcn4HC1gCfZ0oDPWrVczCwMucmUz3&oz&CIK*~a%1=ZL
zO%HUkltN-B9HcSQ=-?wiU%Q&0KJzH{D0OEy_IA=)m}tsUc@XUDiIdAj$!4OIiounT
z*Kj$keg`dyK4s3XS&=S|PrbmbNKas!Av;7TQTrlGkE!m05*ivB&~<%8G*??&+lZ*I
zK|b@D&(Pc3yXR-ywr#%m^TgLq{y=4rrk0wicf=zE;6{UUvYJ^}`Q~RdZhds!h{ybh
z@RC&S>>5g+HM*#9We8kXr&RR+d$*;IEj8Bm%;dSAW(8G%ha-?Qn<^tL1=}Z3720=<
zWvY8{K(P6lt&@JO5U}#(6)Zcd@^r>Q79BK;rH9O#^y|Gq0y3iRP<JS1zhV)8U^G93
zU(v*a7@Ec{Yd6psPx6hE&Smwb|H(gJ`!?>o=-u3M!P|M|{H0^Qwki^3Sxv)!r!8B$
z99%Wu^L|g3tR1BzA5e9GagYeA^&rFg%I>w<x?+cy){LgNc;Bq&EsnxVp<KLfYBbE8
zd<bu{%HLAC6U8;u`!EGy7Qx7>-JNvx<v4Bryg*zOPRyY+)5fWFk>eC&{OY53ar_Ne
z^Y<5@8}|6`*F9BXUj>e1km-++?T^vZmBO~QK%8DK8g2QsGM{Er<T*ILBWNvC`a)YS
z9<ZKK-2){=A`y-}@<?vG?Y3c$-*($=oN&SkqrN5@jZ#%rHSEVvs_Ig#I`hyex3lVm
zB_8x<>PWybH3)DsA|yn+2Nq)C$vNQ8H7|^KtU4a^eBKcVgxUb^0VI|cgrie1=;7-v
z#aZ90uoCt_JmdVB_d>dr(CVsR-y!`!90)T(?Pn5`)Ds3+c-VZt_{Wb;xexOfkA7ej
z1QKu(CNd;@njrPF#_EG`*mLM3fQt@4nD?G|jIVYr*WuS|pWu?4f6EQ)pTf{IR@5~y
zCtWjU1pS^q3&Fb&IcCgrDqRR>BvKsLxR`v-pr<QNJ{Q?Dy{nq$YoqG5QU#KPR-n%C
zQ=40k__svRFBKQ7aXWo!*7uQQuw4A-KKD7Ec;X38KKW$MJ@;I;Zk_z;#b{Wisf|Vr
zGwY}H`sdep`+g>ilMY8{sAS+oUNP@E;TT?d(AXysUf@Jr?D~QCx_6DfU)u9oK5)<9
zxoOk$dmekCw{wyK410|h@&T#z16%uRv6T#rlDKXuNpacc=pl~r3UAN+=A$3|C_nw_
zPx<-Jf6jByJ;z5r@)5Rf-O794`(D*uLkRfdjh`5IaX1ldD?E99!j%wkie)6@5^J}I
zr#C8gezkV@k9e#)9%I#<$w!Qjh2F1)BFaq!-U&f(DM7{xZhqUBBa)j*>0U(1O`iTR
zIJ{858B|43`x$_I|KzT<lU{!;Klt~5Q`0y#nU{4>Z{xPVJU*p1CW{250INQs#Qtc=
z5Zro#0bMg8xa7z~`Qo`}aLaS+eYacCY#wRd&L<zZg;W3VV>b16jrrUinLh5_v5Ci8
zw_~{uuU~X9?>hKsfDsyCX){W5JjEC8yPr&7f<&rFEMD5PwpW$L-dR*#sZnAGhgHd~
zK7IyrpZ}Nlkr5;fnp7+yU?ZK;^>?R&@5gjQ%f$m+b=6hedh4xRef8B`dF7R!+39Vc
zn&|Lr-t&qny*`-^x{|^e43^MUEO=2qjZnWbU({01Rf}KEo$EJJv@6vw86SznsTyc^
z5+C`O|9vMl(HPe~zlMi)O?}a%Yc4_ra|yU;6l{}pB<QdML5H0t=a?k)V2|B$Qt+&$
zbFk^CzQsnOO<l+ErOG9jTta7OCtv>Zm)W&z7b{n;<mQ`iX5KvY)u!I`|1RRtQx5W^
zt*}HA3jjxA_7<_~g6)%;lN8gkym|>b#B&tvF`RzyI^ihZcm3ZywWSp`D7j#wP>@0>
zsSqrXbQY4NOch~nf|zD2URnuCKv6<e<t1-~Bg#|-LG5P%+PAk)>UD4W+=b}I)FW?y
z`ad@-+w=>-(L^YsyP)h62t$=%UDx>RIVW?@K}%__O7ZBH?X211y<=csoW$NauC0*e
zvRi-8y%)U)Q`1@3-Okqj9*T~`AJ#v`y*oGWaiP@55`6Hm6Zq`Wr_m5kaLv>IrmMKu
zVct_J(9>D&zm$TW&NR`OMY=kNrf`1S)Zai?A<oj8HiEkZ%b!|A*vyJ_GcVdnRP%G5
z6$3W+aY8D0c9oI2Un9~V_q0u49bSXw;y?ZAPY-*1ta9<iVv%BT@2N&5m9x_IlNXJz
zYpLZ`?>cMJuLnris6UAiK%_HAu~~h!HVL?<`nzUFM=#aMIEJQC#0Q6$kjN;L4z6j<
zWq8xAH=qlFQ_<>T??7c3HnF&cVcJBZ<@XrWfK_lzlCq>k$E(v$k<f#$j=ICqSZX?`
zi!~Sn>HYQnQsRm$uHcF*uJGNCVd~h{#6Rl`Km2OXY**JNIqcYlJpJe<T*bMy0x)_D
z=(!SBbu6&xVkZJJrhfA=NX;0jM`tWu#De-7Hg)!RW=kN5wwp*HaZ>Kys*QmmF-!-;
zR5R8%3d1b673)|Y-=)m{AeL%}SL{^^jjN;5{mfM9YOP!^Zs}2rSnNS{$KBhzcxLtH
zDYr8fbSE5#_Yy!rDXo6adEE&sdCPG}?D_r1C!a+VV#0;4DnTNV5s964VITkLu55;%
zJo6|wZ(heUovmZG(OJmx#Yb-E?(Hvf*Nzv*nX1a)bFpCJI4NrDRTe6d^jZm??3m4=
z4LdOd5OEj4QOOsnHF5#I?~#Vohp7yZ|0{Ux@bdKzuC!eIR4QjhW0Tu{)-+Y|?R&mN
z)f60N0J%&!!o2mojnS1?p5`F{E2Vydcz#>?l<cHM^U;0q$_ODm$t0`H*#(2%`Kp>M
zb&VO~$s%UN3H)ns&8L-#)~l#q0+LypLqxqVcNEm85FZFM&2OCWb9F-_S?#R=>VJG-
zYSGAEQr0$c(euGYS{AFrAz}>W@&DlYGsgV;=g&FAAG=bZ_iLE#y3e=$@U#bAcd4$;
zD%prgARcf;?@)G>YO!T78ku7ukZPS>0B(>X%Hg5X{S1^3@%#GvhW+@-=YIELPul5d
z^|zlBK>&JIP4TOt6^Nw<xn>=y_y9i@i}22KhwF9i?Ctl(*#+gp6|JO!HhTO2`}lpV
z?P{A)TX*ev5t!&yC9o|ckisbY>qEIJyTQi3hCnRNFng#ogLHO{PLC_F!r=9iBc&py
zN+2GIiI$63Q90|}%g<ug;;B~yc>Mm6!nG=pfM&bw$!iP1-e`|m34s$+y)uK=p7Kre
z_T!H5CnCZ{fuEr~9jB`DL}TR*z+h%i(1T^a6{9VmQm`fox_U{O#fHtnk#IVeggNz-
z-D$?BlEamQPh9SacGpl=9yy;ceE-d!w>uTIQZNxejtNde{XxngQc7Nb(jjC1{iH?n
zxajbezStHBgeB1X{QNwIUSpD)x_)$B#cH^GywMQPD6n0o&d6d8NCv1tAeo`LC=Z;^
z{z!2L)H2ci3{341U!TMmJ#*36&i6{FtyiBV0$}v22_$e0D5VAYMxE?z12?LE&bj6z
z7tvfhJTRj%mGte{S0ZsW7m**`f|SxXXB$Wg`Eja$1%jZvn4swRf5<i`+NC;91ppVp
zW?NU^7j3rpO?uwq5GpMfA9Up`p$V$$ycyUt{o#o40&tRMprOFS>O2T2RcjO)bmgJQ
z05fXJ6U3_7Gx^GE&hn*lmYxk(NH`L;5Sk>OD23o77HsTmU~8rp%L!^xAgR)GR2zkW
z?3Jk%E{n}@<hPH8G>=l<g#!Xv!AjsizyD5O`f&8A%SfccQJ@6MQC`@N?}sc_T2mgq
z413)`m!EqqOJ?o=1o+dhIEA?B&uMjV^N&CXiKZmDuAIKVu&N`F)RM876YW%3p;ux6
zOBAJ&4#Rm-bq|zK>3#;Lc8G79m1V8OJ=77NHNSx+D`zRMgX6~42uEl(=oT1Rmsp2`
z+3%vdJa^Y9d&~<CJ;;}#-^InMbFf+*+?vrX`1xsQnk1PHc6~oKf~@8LTFaJNX!aHx
zZH=PpWtKViLE>cbb%#2ca`BU;G?^-m5>C1Jcu(vb0hVVUk4R@eFbB-=^Is@c2};%4
zUT2r$&B@`5XYKj-#j6hCmMh*yQ_9;EBfVDxo4`JEU3W>Q@}U_VL9jDd!|IN?JlQ#u
zyzOr&vWDc)MDSHn^Ag%@L7OAUhcL)X)70w-9tgx!6Wdrc&aG#`!E=4-!*70Y2ig9x
zEC>f8iwph>nL82nBJ}`GwJE;*wsW}R{PEW@=~$E>zTtdKpHz5-6!;)dIRe8}6WwHL
z4oj<BX;Kp1CHMR!G&Ne4AZgSj$44zf5!@)k5&aW1liANq<q$trTiH5w>CuZSdj5D^
z{)tzisn*Xj)Yun*UXU2M@=vtOA-Tf_g@w3mXXGG%!os<%Y-#e`mWD>8%fQ@ipyh)8
z4kR`88AXxAqa%d>eR;9aq<XtNfFv#?%4Jdw?+Z@({+5C&7oY8|JiY1sE6$;5jyL(a
zjWcRJX=kbk09T@CgR5jDhWtO;D~a^Ed#gn%LKASzvN<f6Q8(=I`SrCNHfx5b6S<oh
z5Oo|Zdf_vUqv5)0wM0fkBFNcMY}a4suLL5(4gA2ZfZ%b<<nfZpJw=n%MT0_Uz5^X8
zRa7rK@R;bF@vS?*<cs}6zQiAYcCRmOPZj~lh~d98N}~J_1>k+JIiA-XJMqe!CoG)D
zP49avuUNj=pAM<_0s2BhFETIHMO{>tZj@k8aTuW@x=K<lBu7N-a5%dFq8F&{fii;H
z&j8F>URg@doF%iye^Lb;ddxz;e9hZvoS~-rvqW8`_ra1-Lw{Ty&=oo01T3F3(y^rp
z!F6xB*t7kN+-CzL95B0rb$Z3(C3Ia~>iE70Bx$oa{u8H2kCUM2^WJG<2xcmbq~6mD
zZQ@$F6UFx_^pZ<1;i{{y;>%zDGKU|2IQQOruP^1|lj+JzqbI83yze`2^UVIj<=*7Z
zdxajrY5TrN%9Dc8?_%^g<x^)xqt>j38ov7Wb4UH#`G>6VEcaX@k%GN<(xB7J$9cL`
zG$|F0&`b!!k;Dvtg@q*=Ph{sJ5yPBb{_lguuzz>Bf`<zxCH4Dnz=7gjsylH&aK@WX
z9``xtUVa8G3rA0LnJUk&-b|?&{P;c|qJqkmlf}q~;%4K&uDgx5fAjY|xpC)&Uo)?+
zhO?G>8-*c}xRH@nwEVy-={QcXlg^6TRvP1KMDq7#OR+(TR%Nf$Xm>;K0SppZuDS=x
z2x>nAFndX5dx|5^In47u9C^}GuD#=vEIxRq=WR~}Ex?2Op^SF7IY~9?^tvWE?XX27
z^t%R0L?SF|s2lZfl^~Ibecri1;Nl{RBCrgWSUgMzx02>~A4VV!AVI$q3&DAwRG-d>
z2{G@BGX-*Hbj1}{uzB-lip3(USFh%jQ%>>RMx??jjkA`vpc~$;5)VJlr<2=BAOJm6
z3M}Vb0#;IxZ86BsHp$I4v8#1Z5RROPntbBo)A;RY-$cV0)zQvawur-KdFP-U@h$^+
z+qR`+Tj7iH3M$l~+ez_Y{}TF~_@2)o=uMGCPyn{rVK|imoL#DWa6oYEtB>HI<Cmi8
zBd1SI*Qjnt^VToD(Kq{r04pL^gya9B(9F1@iSl}S({A4N?LY9?`iblEJm1#t*$63-
zI4KvaKB&keL3c+AH!$amBxjpp_^JWH)?z)5R1sz+*)|{|9E#6Ec&8QhQY47|48Xca
zH%|DuLK7VSnj?Mbho)(?ZtwP`?foGD-EzTFA8i8VYK=jo3Bjy}YCd=AS^It7xd$zs
z^1QOIvkO4#606z4sSWNs&@>e~xpLJ1*F^HnOLqm<hYfk}(^D%)(4l0W8$$9O2+ey9
z_pk&>2hNgJ3q5Ub`C)UIyEqINYZ(DVS~G#Hkcu@Lg$4sTw5UeF1C{_xL+7Ixp28)k
z9ZqsT4p2kW_}OKz=cu{foLaf&D4((K?_Z<0JBe*+jFQ@~6n{4bdq3-fYm(H9ickLq
z+w71GqguV{9vl$py3W^c{w&A7`Uqg4I_iM9&%A?X^PT&?LVb&WDeeoF`V%=eRkWi2
z1Ci!JqSYx^#vPp76nOz$DRHFBSATQ+gbNu&bluZ~a;+!=iS8Q2bv1ICpo%33cIT@?
z5#0sgpz}cgVk|f4@xB~Qyw28Xbp$2VjxRT)4)>QCxe5wlo%)93Jw3o5%3rQ~fPO{Q
z5&@#E8MM5m-rF01&!;*Wqq#Ok#L$`5P{sRSdji*e`ZDUO_OH)+$q|Q;h<IDoP$IGF
zZ5Wi~oy0giD_ObI!nT7y$X}T5U|IEU>Y`cFW`X)>mK8O-Szgl`sBU9JM13^#49#Jg
zp?Fg%{n3H;S6w<_bW_KfU|PkD1>WX|3L(f$LPyL`^ny)fcZO1P3cv1JCnB(_2PWE~
zcdCNB)^%OJ`rAAB)$ObKzxQ9j+@`TNo#|MV-@Nq_-t)UZbN36ICuL6}L4cc3GZ8zE
zPIqS&Ac@8-lIa45q53q;#nk*8EDaoN3<Q!|BZDbazh5X3Xi?NL&>(t-$_^Y*B&(Br
z`7fVh`-WXSe%n*nmd&!G7jw|@OUEox?-euWd80e&C3eEVt`DYYf?kqDdR<C&y7C)O
zO4!=g!&94faoED~4^U2AG>>Z@c+?Zy0#PoO5-5UvE<!S02;^h$E+%Npr)Z3o1?yFB
z1`0C5Bbh~<nBEX*UnGLfwnjbBq&sRq2j?kZzQPV17#x23!EAndV#RsRd+#~E^x@&V
z*K*C*|KUs96F~x+?GoLcK?-maCU!cC8~4_^v#-rjZ?6FS-0y$wiClE@p%uTbA(i9@
z7oW%5esiN|eUTA~oMMs8o!P#MUsK3OiN%AP*mU927|);ysEes`?3VYCn~Kd8U6X?o
zI}{X1k*qKr<_zekDP3AwkNMJUNJ105Vbz%vFJoC<?`^nTcSk>6?JBAWjZL}Ih)uN3
zzkZcc>dM3$nzl!*Q3B3D3g?cFUf%WXKhU2aUuf`0Z@hqXwDM7XP~6e3a}?Y~`>Nv$
ziUpJIjx@H?Xd_8eEQ9W2MUxeYogABdfrZgFW|);vrheex(yJ0&%C5I*f@JoR7S6xo
z9A5vSS99<Q%Y1is0qoe^;k%7WDAvX(HKzlM>UB)Y&Mr{z0U2ONNAHB5cf@RuwQwYX
zL>>$?DK*@{Yzj8^HL$6_9!G}ZyB1{K7)75>pvObkgrZ83L$Ed|?!bY;+rN70gr1kE
zigU=xD?GPfDq8&aM}O~myOl6hG+vWnWG!O5GiAvZA8igr0#;mLB{g5>o54NID~?>s
z>rPxb>2?bC)DD{W6|8q-a>}}0lX-cmXi&%pm&zX1E%jsSkm4vity<RSTLQK(Ng-)A
zT(p79T<Oxves;o9^Of+ja(KP+9TSXj5OB;uI*M03eAjcHw&xXYRHsx^nV^3~?MF~{
zXE?4qs`$L)N;-P8{Ntgu<F+A$;G$KNn`G=dyF4i<Vc*YOASLwm1QTU35{W5n=A``9
zdWdTlHD;d0@it~hgV&W9iKIz)gVOWdKZQG0_h1^Iu`P$sUH<dXRONGZfipP21T+s=
zX?2GzV^N)$n68nj>(nQ`8>0mREgO8<cs)?rLP2Z3ijG1u@XEX+DOaRX<9rjexf;Dn
z1?)v=#-5iuiBz29UwhQJ&->i(KjO<l%l-d)hGGcyA|+sU=Y5|825V0h>U9cDhA-E>
zCIrjoH1g%Qp2L;zcon)f`7OCK#Zw4@81D2^cApFSdJ<$a!3em!s`aWyAlO=}qZCNR
z#=#F*kU?;N!3f9FN@k$+u;<N&?VFww%Z{2@1*F4IUrEEvsaLfaiW`7nZ>2afgJMe>
z8TIeR5ks|90%km`x2|3{p?31c$|rS!K+EePTl@)Z%fJad%1hE1%Wz2DE+V?W_d#o^
zDg>LJ;bff<oVo+Ze$_pg#%CV7b1khqx&y12G#;+02R{BgQPszf!>LZjIC|Nf2|xe%
z`4fNcM9_0Vj`xySLQN!TUnw4fakkun9i>_&%N~Jbm!md{TbSNg1Ds}bzWB$FkD0k*
zL=3)u^Jh8cm8*QQ{o?bx(A3{>BM_9+97V}L>5NjWe3);gHSFZTXW+w}v#_O(-+bmx
zyym!rru<nS)Y9bbcBF_>r&m5Z#eB*MQg96A#Zl6Fo|TDhoL;q-Gpe3tRdNgcP7qp|
z!z<4V!m_l|z#czfWfHBve3&u6iNjA{xko^FGz?Sczi#}5=eFlnSVGuuBv2aERPpf&
zHge%}^g;RkXY%7O%(JBue|dUx(Rq4a!|2xb9t92lD5h8px@w?60?`134!4!+L*!#D
zf7#0-A*mXmdtVxTnsBlDzV+y%REJc71l**lqC9G-c;9&^5HTm7;`Y9ikM-2P2sq*=
znH*m#zz@2LNh~J}m5Bvgir#9#R)VGS{fh)dPgB@|0}EZ(`NnNu;Of=i;M6yqz`{f4
zanK3N_`)AP#?ATPa<os*E`Ufh9AXV)A$&Udlz>&Elbc~sto3)UwLJ`!m|0!L=}Q-)
zPhmbDjPkOR4o;ogy`7XIAc+>i_Dqd3N6!qihvQQ(&|>xw(Hu<8rQXO;Zw9XkZX>Xj
z&#wS3(g(4aaIBm?<bB_H8<ALK)ROT4pZw(qNRCtSVltR!&`yStNWhL8<Y%VI&#ys7
zbl*24W>z8`0zc)~HL>3l5nU53o;iNix{V!O6zs_hB<fk6$SwoDS3}Ech$6~2WKu!8
zg(2Kv!jI9|Qmj|J=_z|QHQ?}wO|2e=8A3qxWYwLR#%FBX8KJeI5<3-C#lF25dGE{A
z853|K4n9X8n0KP4!N)E-l{cJPS;WiY#(Lgy+>w=caug&I8Q}PnNafLdj^Z5)iJ+^L
z2*nAsi$<R_u{2gMkwC&njm&WoRS5C2s&(~}VDYL2eE#<z;hHu7%XjbpDkoofobP&(
z$Der+ZrH-<2atgDY0o8qNbkTH=d+m6?1pMj+xzmX&tg_h)gE22$siEuSq-C8S5OpX
zIE8#LQC^+J6r1|$kONDLp!OFrQR0xqHeh&NITeAGCX%Iw6CS6KNPFI7Xx^JwEIf1`
z-+kb#tUPJ?u*c`Fn8la=@<~pA<B7i5z3tDdeQCE+jJ!>x%m4iLL4<@@Tkxae%K0<)
zI~hB!<f2o?uXbXHsow&^6&M*Evq#75Fc69-2^EzZ3yvAsMr3_q2D_~)B~Gs>lDWFe
zL59N_F^d_YaCX5!pug%)Oye_451AdBO^5&`>Fj349oX@BV>@E#yy4Wt_IrR5H4Xmp
z)pzpcx1Pfl=bylr-f}j#{m;93)0vag0{!G!C-d4vmXH6$3E(9B9`E;l&eByCVO2h4
z*xX;QIHg^p;~<c@ipJYnDl0LIa7X=2;~h&5p2cCuF2aK)vZqMEjT=bAzm6GsNphP_
zq}M?>{tY<yKf9Tq-|}zI+G|WD`P1c>@uAaBAZAS7At(@NMM0#^81D26bnF#arQn?F
zyYtno?wHHgOl@E(j<cg(AxR^Y%+?*|8Eyz<0|qGG=6RDZ%cGh_ht20(cYTRnn_6kx
z(m_>yiuo(&`g$U=eN(${ZBLZJcPENA*b;(qj^jpypArA_f|K~eJx_Am>h=57D=d%6
z7oB`4M=qH?ZaYhw>Zwh{>CSmqj@Ibbv6@t+xw%B5!F$|#N--=~!w|0TgSSNEsoY#*
z$&J()`9LR&gCaJKnhejRum=}p9z;@gN2c+SGtWPQUwrFUiiMK$)GpT$pdh_cI1uS@
zC^hPy)XuI>#(4WVNAuv?&1`M!8Rk&bL^;Ld3;*#7nrc$K=D35V+I^s0EM;c()K2#$
z5@~>&@cTm8`}yW`F$(!8@kEhix)9*lTQ=lM=qMy<jAa#U5+tT@IIZgWfgS~U14=-!
z)NqKYoO>e2D>Ob$(0uN@Z{myZyO!sk*s8iKz<|F+6MN-ad_oKui}biedRz((I(E{Z
zx$gUaeSn7QB<CN0uqW+R#bUhoq+^H~I#=Fy@1)xi2&5}9`t))&`tpMTA`t;cvs$js
z&Rh*$#RP}f@9^DTt)2^kv#X3lNVJFtv7Yg~$&mT(y`^PAGl!jaFbfWu=X?0C<)eUN
zrPwtwFoXMQWx&~-2hVHfh{dz`+B?qW!xx@HQ%!2m-)7cV@tHTC!DlW#V?y7#sq3Dl
zm%zZuNJ&!~3S24$on9*ejhqz;Z1Aqu^VFKbI=euI)Y%0P0pVXY)@i~)G9BZyS6zl~
z=*k6!?PMhI2rm~X)ahksmq+O)?Y&tZTfd!aKmJB8J$)64A=)*EFPg~@KX@VM9^*|k
z)=&}=b4vRMHzu(f?Z8)YQVMd}D81cD1x{DNu3Ys%KZ3VyNrT;1HJXqC%sqSGU|hf<
zrp-cAv1Y0{x`6sLFHh3dalZAN_j2VgF6Y?OydMKAK?1I+QD{l|8r?OBV6T5C*5RNP
z{k!*meAC08M{b|JV#$=-5D2uKC{Mf!h{n`Z2`xAgwq$C3x8q@;`jm;OOqCxE;k{hO
z%QC`C1<MYd&DtlnsO|<*fL#?0G)7VjSmo|J?>iqM1iBEs`K%+k?2J{k^<)qTnrc!N
zZv-zt;VAB0zmX@l@A9mVT2bH{L3}Rdliy+gmKqS816CI<^V3~aM+<@3Y%=?BW$?M~
z;dFKZu~#9&mU}RbFC2Z!GQRi6|K!GN@8kBrJg#iP6zMIZSq`PfR3JBo1PTp0PQqJV
z6-@~4cwz(R9JP#(U34lRc<qUF_UDL4Olng8Ig!s^zL;;_dH;l;FOf)7;>28JLL!xB
zmkCNmlTy(j8VmMxCuc|5lBs2Wx;s$2P>$;Awd2f;^aNl{a=2-e6q1w{hO`$T_j^cO
zqPiE;1c(rV!;f3Up~o!XvXj3~Z&y}LKLFiwiSEu&Y)&Dg!E9d#vqmDlE`=t4)>u0`
z``Fyt#ln{Hb%!U)%<3x6T)L1u*1tHVb_4?suRxcV<>;<xOie{#fX+hF_c^A+PE%*7
zxu5oBrpi?L(TJVxd6UySB#$`jb>2k@O$BM{pauK2YPo?Om(M#dO~9##E~KG4HSBR+
z(`c!yVn$thQc(|@5Y#2S4UrCR4=P6dySF<LP<0c~a5$)T7mcw@fE#a#E>uKR3OXIw
zVrgu!bhg`CNGh5Dy1J@i(*)1_r8D^G*DvN9*S!ZbqVC*E&)GzG_62fG=|%)j()*pi
zD<#>&-i39<G-#=-^3~Y|Fu%T*`E@lDe!f5;O>nDJJG%yv<a5CnEdb~#`kT_UKZ0{S
zX>71{6evcFjr}ZA26wPEO?Y^A^%iB$uAvA=Vq}7Kgb4g8mNJw=$@^%g|9<T`q@q*P
zwvotyJO#tWX>{F1S5^y7QWx{TO5%=EEe`4{4=*%awW;!>5x>;)Cc`w9zNA^WtcB)T
z{)a%0i-J2xfbZ2zpgbJD_3Wd3X*)6nfyaFrJc(CB`8&vGgV=~jTy&o8YGG4<J$WYx
zp~rF*TgHh6D<?3(qa~B)Y@HWvjb|*KI}0XT19C3zm(m*5J(xxTE03JdcW=0YbhVmv
z*1=QvNW1?VAuB0-ncp-aJUv;jgr0n5&I2tclvmE$i@;Gb_DND^F>tFIE&@q|(I0@d
z@^zk-8uu4WHrg6_7pwp)h>hJfHEBlEsHL;5S9KTmAQgo>3ZG6m4P9e)LyhO{R>fj`
z@YKoc?nxwWEGUQ9@bA>Nb@Dkg(5i`LBm$Tb@AKV~B@I?*=I?QOsVWFFPY;hIQyC4-
z7+-_C@>ZwujJJO16{@>{uw1Z%4Na5*T}5Ad%Q+mrXqGQ+zwrofwJG$R%3DiND*7Gu
zA&F`h38P3lQlu(UAf{UZoPL)L>?+liYh@|WaVaOZl>hGnYb~9fs-0fa|H*lpaI#=o
z3$IWdVQgeAw1U4IVX6BwxVTdCiX)c#()M`dt;#+#sY$eKSe9)Pp{ZD*3uYvO8t)`6
zW;e&DHt>p?XL&`<v(y`z0POC))=LOpunq1hMtGoTDE4;>5UKH>HqFZpC*Xa>SO~yL
z1{>l!ILbx(y&vy%P4LR2mQj_AdEPd_g@+$ZRjl#@a5?a<owOQfh^9+4t$fY-yRVSL
zel#S@YjzVif*EOhoFw=5En`Dr2Hkcdz$(Op?^)0rj$3;#hCl1Eu&kV37M${`!>F!P
zTO~~ZW?vytS8-736|X$nKhezN7R;qF<;`~|5NHLpx0e53zhHT78;8~J<e=JC8e-W%
z^{pIqp2^Jt)SQYmX)eb^EY68poE@`>3#s_zUG=g5G%7j%OmC_OP8JCmUEWo?9SuLV
zBA<G3r*CcVk9l>q6N*%ox};QRW3@QAHQ`yE#uGuN7L~vMxv4HvW)OBI%j3H^G`XF$
zzBdw2m6n!3a#X}tE4(XeeJ=ntO*kp!)#)h>A^|6=BO}4*x)tkm&!db5Fr%)D55N8t
z&)VFVR5Z#@F1-M468EhemFPJQvrUHpzkW?~sjcrP5_NnZZ)>AD>Y`cdqB-WLx;V0N
z8#Mvy5kooBYg4h2N3)Aqmv0HkYWag*YS;`GybcZNPx{jAw8P6obY0_<-?&71^(>>e
zh(4I(K6n-TdNl|0ejV4}h5ilixYWCMqS*OiM*KXLku*dzBn-8w4c&H<q8t3eXNh5R
zMAW8MD_82$1XOE5RPEXiPVfTG(}ahEkDQMlK;^2j5P)8Ez{fG#$hiCWHcWDk6TlN2
z)i}H^JL=GJrwZ4Ql$vd1Tn1bgemwja71psSr<h2pBY9TT>}FOnnAEB|Bg?#KC!kg^
zUPlrak{Oz-Uc6qa(QKPWJw=}*cqfWg6ASDn7>t3j;{q!sa3kJIx16+c0oQ%%QtGPw
z)g<i5<ZvsT9|Z!vsG(<dL`ejewW1piRW(_(fYuV6n6Ur=xwA<`K~&TQ+hkcyJIiX?
zXi4^AXh9z+4gX%Ymg-rbZ^jKE#c{sjve0ycP$Zf-iMOFePx7VdX@{4`9C6|jzH!~V
zS$c@~`KJ>_0FjOy(Ov!M*%G4YZ)m1ol=vBkDS)|6HAGE+&o*aPr+Me`Q%?<&NMux{
z$4ih%`lWFqsg2}VRNd}V`x9qzY!?yD{}Z60)M_q^%#mT%>Z><~V=+378iWwkH>Op0
z11$d*i@FVF-x!YksjLizIzQ5dhp*(g`E&Mr`fgV{gPGJJLr^SCO%QqiNE+i=j&I(~
zam|}KsJ4}wsJ~H!Lzx@t3^2W9BuODT%(OTwW^roF;?Rhr{uExFiu5#!X>6%c-30)Q
zKL7Rm25&RhgkqgWp;6zfQ|!@A*ie<=f8TSS@5NC7u6^Y3DNi55_ICo(G?$vXe(D?h
zF(bjO>rJ6^W=r^-_?wDNJdv4;zpgjQLL+3_UFMN!RiE{(`Durj4;*~Ne7<|b`)Qe5
zr>qYG&<i$^_8igHjKA(%3Btiwck~-iU*%idLwVJ~%ck6hK;WcQ4kWH`5nehD%WK<|
zI(TAQiG$<YnH}i~ydU#)N9iVLOoY-zmuW)4iX-MLvn>H>YT$FKNwnJ~((Qn=&rLX$
zBT5NJFPk&<&P{@duJhwdFW`MA9Yb`K!YNXh$XM7SyY`avwK$3h)WNkcRYqRfJeL+O
z$44!i^}SIJp*VpzR;cd5G>SO#q@_fnYS$S8(DSyh2ka8a%+~3jYml31Vx{%rH=hSL
zVs#?Q4?cJyriaC{_Q#Vuc27O&#K*1*A*rp;?Af;jHP%_u;lVc8o~fbasCMQ{G#si#
zmctWUSe4p}PqL)|#DydhhB;*@)@oeGzoIbH4lf^Ro>i+ny%E5!^4EiHr8K00Z==*H
zhc4uzlMnT!?R`0DMiZtsx!Yn;<k7BnkbWL~kJF1t)W3*62c4p&u8eL{D{w+;12ZGt
zN;Ux1nhZfS*C@EXNEse&pJ{^UHE%ptnQcM2V2bcr*jND3K9|H!n@C6bXDVnykcvln
z^|9(U@=?R!gQuOqpWgW<^nC_3sfX9uRj$&g`#2X!s!eqcFDIyKJfpBO;-E1ya#*?3
zOZs1pb((OJNJhEj-KVMUMtQFB(c$IlfbLE1@T-nlPF*^IY3Rfv26G#0_|WT4;xAu*
z2TNu(Oue%cWUBj6fxt=lH@C+UMKs-2GQM?{B-^qzJl-*z?xKon*L&Zzi$<SJ(P~xs
zKJ6EzilSc=pzwF!nw|~|@R4&aJc>0BZB%AoQm$-er~P$!mD2t!45Oyb$1XmN3r<*x
zp$D`06hcs&h|`vt+`>)@iIY<4co5jOj_blC{dVKFzrL@LL+V@oXnY(bhbFe63s;%b
zDeHobAR(l$r!SRbfy{$YTQp-Db*wyU0k41i$=vvh`;_&;=r3Z``Kw0)pu3=F%d0i3
z%0)&hAM6AWix~Xhe_lW;u0E|{NmD)lcky}r=M8_`dm1l8HOP#DzuKS$*B~xzB^#ku
ztBR!79=PqaaH&>O{c-}79>R4x(e!%NJ()%UZ+!3Rl!`Vt{N!$Bn`J3L`m_KX)a#lm
z0$4n&fv>#nm7yr|aPp%0-1htnlX|U0;zlJh;@=k@i>oe(AdtYio@NfK-yT?n#(n!R
z$S$jvRH4Aac!zJ+B!Z%ZnEK0eIMy2AGGCgWj_~r4lg>Vb6^GANVp~#<>?+Kv@>O7{
zoXUbPM~_9z;nl~i2+Y|v$We1=O{$`aKp><*E2teoTt_39^{2!!K*o+^xxo(|ReGK(
zqo6dx>M<+A14WZN3nuF<eMDxJI$|JHoK8&>CNBTPxt#pUV4`97MF3hp*oC62fix>0
z5HumU{M=*t>zCioVGC#a(6h;L*0M$X<kAcF{4OlY@_eBgXNDq)MEr9`=|Uq@T&ygr
zX<M-#hv1``lbWO`VT-Ntf~B#;*6^`FX@yA0|4nr#rcs2ZX}s+t=WywJ&ro7tg6|Qp
zrMiKVbyK+UV-b@tz4e?>IJ*Geaopszi6s(gO6+>a*UvpDf%^SZc6znut5kOcfESBR
zxIQ0vb-N;b5-q~N1Jh}TmyZ~x&ilXcS|zsKh*qv<;!$YsSonRf_+6QCG%h>pu!<|1
z3?h+;lIoFLkk3VYU8rx()cV)>7z`~9uNnuzCR^jtq7e?KSL{55m>$j#bc7Im@XHsd
z?nYo(smiZ;O$gp}<`KN_{1d3J3SUIz#?HMJR|Nv2PcLsa!Z6MZLy$=MCm=mDUdy+R
z_#p3I_;Czj`etV=iqhn<%zP}VPj&xW5xtJa-3629ES-(E&eN98T?LbEc1Xte(*3G?
zFpWYS$K~#upHyO70Jvaw`;+P{0IfK1qfc_*w6LX)>pp!c%jYzC*Rd&b#GILY<n$9N
zdW}Sa23DivyQe^gCX!juRrEhXeIhvM^f>+=Vz0N2a?nbc))vNc-<l4|4Epk=v$ao&
zU9IF2ZOi(8B|CBd>Y3K1<0NCji(wr#cNT9y?ubdhUdw6tTwY^bq^tS3K@zm*Q#b)n
zQm#TxmC+IONWpd|1pU(0RfU}<T%^)*>J(ILlz<x#)-mBkD<6YaEuO{u&Z}(kA0*H1
zY8&>up4TwC^?`8eeSVZsHlD7^`JxwjXw5vXJp59AaP&L5Ve8HO=!L7WrJ5?Vy-t#Q
z`j)dc-x8S3Jmqwga>POT{|@l1Ww0v@PA?grgQsbN=NI3;g`HbEmDv}RQ|J8(O^Nio
zQ`*y<dgvk+wuCR`H&?PI^tJ}Ih@}CgCOZ%>L68Ru<KcHyRVDbn=gCXK?lA0MhElxS
zx28k(PG5cqMM6J=RT{OTztsZ$ydN(+;|L`o!9oZ=b=E0-=+xsYe=P#d5eQ2tK9ToV
z>{y~vbb^%t?66be3%>=nhG7%ptW({CX+p$DzJ5`l_6BcDzYC=7i>Pos`1B>G^TQ9m
zo>(Nj0O~*$zw)Kqdqy6T=~<1)E(4=mNAK6rdo@s42v`V7Jn4__%6T)7pe0_*+aCHR
zFXV1Qh|!&DA|N$?7lBTYDL11TDu?*3_q9mC1kr<Y{!J4ux;y%nYD5kqBnZc!Q)MG3
zG5Q7u*Gb%LMN5VL)BY(r4!>Ld)VOa)Qq~|YHP~3qc3>*sLWCiuv5e&!Jkc?W=X;uX
zuB!#<)}SeIl}M7he-YFmi6D92(s`t4u+!GUvA-(aiR-lc(sT%RYA<gNUNujdZ3*Z(
zf5iYbpAKGYQ*qvQ_R;<{J^_RfTye^AL?)s9E|KUtp|rYIBs#IvoT;FTV8hrfX|TOi
zr+&dd3_(u$iI7GJ^PQ$q$WbRP<)U|<5{RwAE3ed)L>j>^DU5t&r}49!Y62?-KUp?*
zbdCDBkODoYVf5+fS<Tln@%u;knu5|}W%<bJUs4loFUL5IVM+!v=1Jy9J6IOq9e}Oz
z5agwxU-f(wIZ>_sX_WExkNh??>o4ik0#^XBZkKqQjb5lc^k`mF@NysHvAegQzQP2Y
zCjvA}pl38hP6U>zBHjI03=I03D>xCli%EK{7-u&92Tie{o&1tIJwx2D4K@IR5?~Ev
zsyt;GJXQ*cL`(NOk<xp|@wCIsXA-FxGv}!ZZxsNRzsJCONrK~jJnF)h+9{XP^#U>3
zKDa9TE=i{RO*_zpLsSnw#NhdS3%jjqAUt8vrgCSKM3_e@?!z=;;~gJ+C2#%k*@4({
z5(Z8(`1ErlD>1Upga-o4=lReJdh`3&r;$jUq^tBm1;8(P&AemLUCL4)%PK8U0+Q3y
z&#^4NJ2cS;0G8_M#@VX41JkHr%L}bM^Vnw9-6$92is<0u;fer^yd>7?P^rjKd*9?L
zy!(l%kAFHMksv`UYG?(4*{u`VZIlbA_)M`7{MunWbfK{%-NL!Ej^z`}uH;Q~-$zsn
zsxXvbc4YK}FR$$k{ePD$*kFg?K+t|33jgxF*=dJY0KD(>=P9%8cS@iDVjYtY{~e3x
zxA_y&SSgczOVUuaDwRZICCrGwM>A3iYz0m|iN;g8xjfXrh&C&DouZ)_NR}Bkr^Zx<
zu#0k4llTP+`!G!y5khcK01?LJ^m#Ror1JdZVr1NLJ>UXZI;SzPzD<^NeDn&ta_~P>
z;#4UXiA0d?_db0!p-1Q_^`i?7E#L^^kqFgNoTC!2G>_L)1h{SEKW&<q9e4coiBKFu
z1C*Lmf!MK9T6yBAU>|kE-D|Mya2>47s7kT8p>DrtBx2ydVgGi)Y&U$5^vdTVxMM_l
zNjZK5*i_L6Tp@cu0XU|95kENk9Yiz(Lo+$1{uDlc(6^Z#UjTrH?rU0oR8w&c3ATlz
zDzJvk-{3>5)A-I&CoNM}t<j13pM-4|B=J_q)2ymO$w7u7d?yj9T!dL5a1&~l%DV1Q
zRpU<uo1qAF3U;&acE+O5N$_NDp0Wxf29jf=7V`}kMKXN@=!n{D5Sm)&-!$Q5-jZhY
z0MZQoN-voR0*H0FL_6&<=O9hMNh=r7QWuVr<NM=+Lsy^=e4c|&E&z0Js@y=TW=#aE
z<b8pHkFD5yI9PB@r4hUdQNTsRazpT810bo>ToOvVBS8zFAon!>)7{bUYsL4m*tbqc
zu{r5$bk|VqMCC=f`f^2{dU2OeJ(?Us2;O_ru@!I82Ay7hM|nx9(bJj284yq(dUoYH
z8r@wfY^(Cfqt74wdQPZcI_~paT&_O)az47^)qLWh^SR-q5A)rl-cC(qcopX=vxbi?
z`zlu~`Yb0joWU`5r(g>2qti4Ts*IfH?M?&-K}L1jJT5rfJ#RI<%B?RmUUTVjq^sln
z_s4!8m`w?RrI{E-Nxao2KV#sMn&QOhxGw#<B2~%JPrHLc>}QIQ$YCD-!Qbt)gABfk
zIv~*v*Y`C^bF7*#9&!<PwEdW!3JW$#1kdE=a7bcX;I<bT4oUwClvaR?^4z9rLQ8dR
zlG9#w7<b+DB(6^)zmfs5dMTCN5CSl>P#i-(#ng2^|E9A7?bBp=`>{vzmnYYfDVDIM
zq|{(TgituShT^r3*U@zgIdArnXhQI}?T@iLSH*&~x@vHF8R5SEa*6bMqn|~wHfj}Z
zLIe^GSYd`BazqP%l;kwtqoVR}p(%-8uo14rH8t#1G%#mZnaPcDg_4@8visw_Lzc6>
zr<belez4-#ijszm`g@4)I67VJX(CY@Gg8i)uu2A{qCr_Vvhp^+xBeD-t?|cf3DieZ
z99chp^=3_IEUP+%WmShza!WkceShU08>crZy@;VSl2<hXv<A;x4cQXv<;^MQ9?GlV
zcx?F>pZ5h4u+tIHgZql@<bW-YHa&WPeIJXMIkv-4AchO)>R{D43e!ylw&jlxdvR(8
zA761E-#hB<{PomNa8&&w9I1Zsz00bm$B70S2_TTnF<iy^GK09t{%@)7#55u1-JgCn
zv*y+Nmfl{jAEy=xxCV?BZdtS){`u(hf%a*#v{a}0^;_S-yt<n515rY_Qj-YH+i(53
zzMT{ui?e1NLTw~TN4|=_Qm|D&_aX;eOl3Ol6pv;W(rqV{^rb<Q93Qnvd;FPKjsUk?
zaUZ5p!C4m^;oG6fShz6*t1eEdF^OFh57^lSTB&lOSOF|(sq?2pQ{;UoAIq=ba&g74
z6Mhy89BzqH(Ind+C)*#VR5Te}VdzHX*WN^&U4z8+%7R0RPJ!=l_=*qvAc3whnchH>
zR_<(*i1_P0Z#8twsFyQB2;TpN*K)<@&nKDoK5}U=<QKq6nppM0F3!sN?%r&w4w~CU
zGNxY0@U$fh_UqUX2<)UysmY==!^Tai7Zdh_w=X)CH_SVpV;h#BiSn>f8}Z%)Upaz)
zI}vDLNlbtdrtjE#9O>*(-HT~LN>y!=@BID>&VK!ozS+=1pjl!k_J31eb>n@k-r#+h
zydNxWYT)Mgy_Nrb#j&BCPhxRzD?n%L0vj`}M0Jzz9Qjt_MwE3uEtDKhL2Bc^A9^{~
z`-$wlfcqjjDQa<K)Fu^@)w&RBqwc3s!<>apy!z5(1GDGqqI_VlFpNIZ?^I-86L8!?
z^O#v*?PIT|#?kX;bJD_jd(!47gwm~;BoejIb$<umN?ZTOuIt&Fd(oGk=*sjfC3BU_
zv;de8Ki~6KLo$UPI0zwl)g{MZm%QB|NWe*$*i}*TGt-osQh`ns67VwQ<*jEQ?O9t>
z;mt=Mju0cI2?ry)Boe1eA|r%gV<L&h{0XH_=;f1k+jY2W_cMI`nLo3(w=EP^5-ozQ
zDxzM{aTMGjD!NSbQYD#=@!rp!M=areuC)`<%7-gH&o|@Uc2?E?#{pdv{O-;tyz7w9
z2q8$NRYf=rGTNsu(KOd{hu2UJs-DdaCx4W8E<THbJ>NGwl_6*}`U1DP)7Hs{Mm=9b
z>`c|2m_`xr``r0lcEuUQ;{JO;<X|+H!bPTDf@t@|igIZJs*`a(^Tsm*=-Jfx+&QOB
zymBLys{$)QB7`Je?Y}N@(wO*MTXYIM)Ojb;r%0?Ov)`9?$6`>rr%976Hq;+pBI<lO
zSee%FQo!0Lwo)v3>zI%cJ+R6pqagqsIc7>pCCqE8;T1=YFEkhgiyG_s&g);zM{c|c
z$K5;O4Z6T&R1&biWxqt@MWkEarf43zvrWxlTeg!AKK2WC<h#&>U|uRiQ(U#5Pts|n
zSrqFC%-$Ts#g(4=cc+e~u7JQaFAZYx2(NwfiCllp-JZ5<)%qVvEZLhnIr8uUtdu~G
zGxs>Ie91(34bqy+DDJ`FXDavnt{W~jwOLQ+7l5Q5Wnrpm&+iS<G?y<vlXomSou6&`
zHqZ2~9`m&t8m`Z2AoWHcD-*#-(!S{Eyy$4uYm$RZo20U1Y7L@iAdE$-Ixvl5bY0`k
zA9w{9z2g*~`uB^JiZ;n~jL*I6=bq=aYnDTNOAojbsSE6Mw5$~wu+&MU&qXUqN@)!z
zKJef^hdWVI=QYP2#5-SkEHmoDUqC2sn%M5h@p7mx@u0KIu5*;`aikQuu7<Ar<A~7^
z9MiCvv>6}&$pf?&cVM~x9QYW*p(WDeOWR|i1G9CT1%|6!o&q$89PC%i(*#X_uMfhf
zAqcgJ43>tUBC{K7rrsG3IcLQZ-hA|`QU4}Gb_ex#rO5P0J>QHt(&hb+{*>K?9spb^
z$yibKL2U>u8I<2%a)@cO#Bjpp?24R-(3{48-ZTe1|MC8_Xq;K&S$h%?0i-j!5y;o;
zAVya0veJTL-NaVpVY+Q6!qS>Bq>Yy&x)P}ZHFf>eHS|wPPi21y1Q*Udme?pdDw@!E
z=Yo%Ne#>P<#PCyi4V{x4&gP7!^G3YRYgWW}miwyYA`*1Df`^JGIaR?Hk>e57-%w;+
zJzx9U*Er#X6G$Wy)YR0d?v7I8$tcI3zJilqc?d_GxP)U*TY=`aBgzHgN(7)e60@g3
zY-c|NtRp4?;ewfSiFS-kas0pqCvoq$-_PeRJL@Ik>;ky{@uzY3*{j(}2Wbw}1r`!F
zF0q;%+=R-#W%;wtW&DeD7D90OqWwp$?#ON7{*K#tu;UIoimkY=U!RHOh{R@$fKwW6
z1e+b5`-*BSBbRXziOYOx+1H)o*S_{OZo26v)~s1WBod*gr^ioop{0;3v1?l=X2f9T
zyn1HL_Fm9XST5LZIl@X&&b!&?VkI@CKDBCH$1b1iVXwTQCLTi<0%xDmK-gj0LJ6dl
zq^i7En7m=@L+mc}4tuP#kYs+k6CJ5gW<e6yf>G40(<HMD<+H&)n27YJRd-^Vmk5Tb
z^Z)+uO?>F$A53atGg^dIv@?&}Tcj|%%D1`4H3fD`Ba;vqIf-UVqy~0ELrzdoPuB#;
zu9)Xb|NP>_nPKR!z;$&Z5r;_BTj!>Rz(sOw{bJs|Wb7)Mn$S3N#(A7M<2-iex3N99
z2~#s!nqEoLNaIL{ZmWaU-4A%$zd-~p3C&Vkl~*EgkgT_K4)^$b*e{|QQF{%Tt9&bb
z@lv2vDsl0}7jyja$MdtF{Y-UtLd8eFb|Ig7+fUiNez#|Bj#SYDY?nw!F3@ZiCjx~=
zeeXOoq#Cg%1l|2POd~{T9uwfvt=mU9yfj;2HHRRbZ3JRKyxx1N%s5{@^bH(ZJNllp
ztJJ}-Hh+&z+4aL7PwM_SHfyvjHHQD`o1-HD7aP#!-nCxfLw@XHh@9+8)4n>q4)h5k
z+h5?9SKZ33H?F2MkiytJyOvAeb2>HkNqRc7p4k$Bp0{umYJO-gBzD^r>hyBx?<DRN
zT@zez;!5v3H$_fdIB(Q)zQO_=%CVBl*Mbtmr?TabbZi(=jm<@4T~9MBYIiGq>w-Da
zV5&yV)}3&rGKxm1{RzTcrn)E7yo6YC@N7Qx<=6AwFZ>x<f!E8`%47Uc25$vnyXe^x
zPBP-p3Td#Ln!rv?u6@UqTz1Cjk-A}JMlwy>jL>iSr<XM`B*lVBBx-xsVp5N?ta>J|
zn|%yt&RB_|O>EORGd_oz@j1gD(}m8P=e?hm)kpH1EkDGT-t`j(olOEmNy3MuUXwIv
zE<!+$E3hP(LCr()*C8TD1Y|6{eCbPH0^r(fuT|TT5K>i}<f`kh;O?8A<l3ulrKiK6
zRBQq0IjelQA5g_cy;R=U<XVh!+We6DO(IDrRO>8O&S*o2C<@2{Dhkn#gOtPkjGl1r
z%)|No%GYCRqq{+}c82e+|1bKik^76R<B!Hx#*I*t22tVd{54T*G#)4#oDj8%soLjg
z{#6wB)!}s@PZ0TRkx#t&8n(R9itFrMB(?77=Bod9qc1k45Z;dp6{0ziXm@ZmaHE1^
zwT2s=_{8+px1CEW9=-|WkeSUKKC6W_J9Ytk^V<btzzwdjb3?E#gHq8T8uNC*wB_g_
zwtZHd7kV36TGQ^Sy@?@dH2P>Tf)RL3h!8ou&_K3B@pgzDq`EWHyu>)?qN7=U*j)bl
z>j!!8_Gjts8vpP`$k8Lb7<kO=DU=;vK49dhzT%BXO%~4zLyF+U=_=$x;Or{ny50_c
zA3NwgUNg%(M~V=FBWjOlUScsf?YiFMXfHv|iIUa}fiAD&LUKgJBCWw-7R`KP@4o|K
z+SxT}ip3(u;@$}KzP_M~9t1};W^&GjNAdK(U*xWv)(qSCc!>bCl7kzN!>g=M3}(Me
zsb;uSUP^ey5#i7qu7nd7&SUkqox`HM1OlT=!;ZP3+S3eHY4nm$9dtey&OL6w&v~%p
zc6zO@J$t<%u!+!P$7zlPD_TF4Ln96`b%|n-`w4F918AD}QGFp(AbR=mmtQ@=<_)bQ
z2Yny!oiIh4YcA+{NwjBTxyXkuY~kl0e*@<n9nNSKAq3yM_%*}^LYM7?GoU9ULva@g
zA<+#NO>^n%Nd%gQ-C0ahaC9Z5<D1OBz@{UMP)OwsFmPXGzmLrSLv2^4c}X&FNi!e(
z((CyB1D{8qz~({7{jY)lKYQmLCs$Se;pf~teP(;F+4SBMl8^ui5C}bV1VpNWfJ(7|
zD5xM7_@SVph=M3c5tI&*DxHKhN_tN=J=@Ead(ZEW*<G^PHnTf3cXswXepa$G=brQK
z?%aFMdC&WPUlBCX>*M2`5i*=I-<KP($sgp^C2L}6Rs0DU$&+GY>!f;9Z~q)Np)n_K
zR39zsoE&E!7gbzCzOyK%{ca1yUW%eSG$hj<LA)(Dop#WHOX8M#0)k&8Y_wG4&O7fU
zBO{~jtE{Z7=ZpF|6(bAj;(RoABzUhQ0DFDMA8JTZHWh`5eJ;d;vnP+kuIn9sjT#zS
zeG+VF+9B|G12Nm*j!rM>(b)y?(Sf%U>g)oELj=i7aD~Q$@d!ztZly#GfC0@<$ED|_
z`egh4erjuL+kN#{UU>XNWI~VOK=WG@2H6$ilbG&*2S2^|cs{!CTRi;T^O!WWBr%5d
zlKeCuU!Q##0H#|KC=P`yn6#=VTY#pSq-8XaTTnwzULCmwb!28Y5b!5bQ$Vn_F58la
z<l5#U8cjz+98r~qmQ+sVFap`|r>IiX6pDjc{J+&0q1fzD;yDL7s!jBU-ha+DA^3RJ
z))-n8e{$Vkt4(Ob<>~0m?CtaL+(Czv>4<OcOoev@S`3%CI|7+BBxtSyDAG;bT|^Zp
zZXVLWv?6*tG2C|BZ3rQ{{aUhQNw53A>86{gsi|rE+O}<546RR2=AS+l5yzdS6ks|N
zRjBAo#3BBE0gNv1pVhJRd|vK9_f#A<9qo{ZiRrYeDn{qnY@wK@((gV*i?}Q5Ly2=d
z2_zbliQa9NW|3qas##$bfluSoZPbsYcuo+havBl}8XVY@crUH3F3{q9NutefH#e^k
zP~Yh9SAvh{O`QOMXaWS`3MLkX|7w~^R&E_GcY85in?3g8!d(;g_DMviA3G7}UrbQi
z_^56eO=f&VEe~dwAqdIjy1hU`Xbs53=$I+`FaG8rYd+n^+RwJ*Zw|75Pc1*Z{4f0Y
z8-I(qkM2JD0^+(8Z>`qV7Y;2tlI++tjVx#6<<jGYFRd9}kInCHdiGGR8Mz=PeRanX
zdGj%e-2z^sv$%dRCW)B?3iTw}d}w)ATM##{x#k*Hty<OX*SK-L*OYZSousFyxBE(U
zGP1nn$FLIc!AKyK;j%<qLLRzJ5m21dfB&AdhYVuT)tB(~+0z+akVkoT2By~x3mqlV
z!EPv|W5)1N=(D*ZYL{itiQS;xATTMkS2HVZJ=wO{>3a9hmLx&M$Dti{uh(324d<PA
zUbk&3Dk^$q%uP4l#P!!--}ZfNZEe4Fdg(R|yVFK6!4X%&lsI?R5y<x<ZJOmdjuHrU
zqkJbN<fJFh33eppcs-0SF5r^`+q)LDNf5rYCN!;M_-U(@TOufSXE7$jjzix>Rm;ac
z)1J*B&oIh8!+3t@V{x~^Kubs&-%Jf6EC`o)Wb&jwDUypoAjG4;dyN+!`;f+lu%3*|
z<3O4w{^scBsO~=g5)g4L8E-fsnAZCnP)f<9A;mGYC^1|<ZW_P-<Q>b#cYB)g_+nQH
z_nMIl(Bl~b(n7gsC?^-4%L_Z7h*_TjX+uxY9ALxpB^+RY^BAj*h#R@Nx#Z^dFI~U>
zfZZP34QuOC+weIEW=7_)i1qkEU;Ag4lyLIQ{;i){n4ZRU^Ja71yx9Z{gQ@?#FKIL*
zzeHdf8d{8^k1`z|HrDTEh_CzTyu)*k;a}SyipiK=G0C%uJX<|lQsml6gjK0ZhvI)7
zDQ`xI*y?#l{ZxwQ1mSc#aXOPvusm9X5X?PsB5ypm3?q&kfX>hZ2CnV3gxofQ1wPAH
zOu>w<qqn>+2S<z_l0a)>!?%t)oJ;@nG$E{WG6-Sfba!1QJ>GwI4Z&;2{s^z`Y^$hY
z$PiLWY%OAg_pJGGe2pL+3S4Nf#-YXkZ&9S1R@a2+9IXJf#GFPc`WJ>_a{IOaV97gc
z+oioWH^zQZD+nLD#EYP>_lGz_fXiX$<XQc1gVGeRb_3#c$4)TVrQ50SCN5%Ve&Iq2
zTqXQ<(>*cKW6?-lU7&p?WN1lHD5d6m$egDTTK|n^i3MA?Zsov%18m*86~i!CzI-_V
z!-fsR=kr->T#_+tY%$w5#x@nUxyXm<ihm^kp-95fU4gXrUWuW_Ih5sPCfuHQ;m~y!
z3>v{x>p!#Fln#V~IyQT3_R^nKZ{xgof6G?}&E`9!PVBNG;EbHPe7OHDDx0=+8YwK$
zkbR6GwhJ=}oLymea*ie$X-kr*Vsj5PzXI;lV%lw7f7M&JZe{uM<?SYj<;#~-Umu^6
zwaLWUmmi7EuA^D7S`marey!e_8#XYzsh$1p)92uFBt93vuN*OS5TzD}rt2083u*|)
zWtQW!x!O$@y0Eo5yDFQuvbk|xoGp>KwE&~sm2n;?Ow#1HAA;~)gh)WdgrfiP{(nDb
z@&DE&MB_~YW*pPg?Tu!SbDGx`t`eyqH1Fz~z^?1u{f$#dOTr4kJ@M$;6_$-Hgo)iA
zo5KVR!5L*UaNFWj$+IgaWKL&(K`a)|k}>Wb37L0oNHUYb*#%&1K{h;KwH220;~)Q+
z$&)8@>#es^UtiDU$&;Bpd2*jLzEYLbzjj2-`jQZq53xYE3&3<hW1)?<(9V`aoe6_u
zTNEQ17?+cbnq-pNT2IWX1e+}fAOGC)9{=3ZbtPYi=HQy)w=gYhP6ufghvvj*OY5^O
zb*4M96?Z!!LrA9DgG_UTIJ9<=bqWv~viX%b^cqJ7@&r*fB%h!E^)fPZ!Y4nvO>1+0
zjh$LSeo1Cr$2WWqOT~sHAiHpOWq4iu=z?Q8>&S_TH7Z63A(%U1u%$Lw`H(b`oKrrN
zFKV{4^uQ*n0v#RLAMSrM&Q?f<I(IQUZ7q(3IHDwOEcYRIL@NaEC01LZNK#&U;-iEe
zx(FbY7C&c(-VDO%UPSMf^N-=T-#m@OMwOH4b5oG%<D8==^5l=c%FNM&;$~Yssk1P`
zOG-gwgA>{EYnR>s`nzR(W;s_5KQ^`oMu6XFCf%NeSMU1<WrIII0m~}MG)IUWElJW#
zFro2r<N!~bfB4~tky3W}nmc!{)utsK!^f6z$^|`F!i$8~1AGy%=JTfqz_UAqYflK*
z?hu5+_vOFe`;4XQwp(ITyjZbsr`0BOf&dB4jj>-_{B7gggv_o#S1!#>h){><;DG6;
zE)<#geRpj0XIpxxVM8**86wZtc>slV`yCTx>;t3X>N0=$;fHzn;fLcnL{c+m@*p00
z<NJL4_8K;<s>I>6G5x4f3?5O45i<Dbt<^ko?|<34es@e3QIeJ%_jD)Hp<B8;52v?u
zeEsqh4&#awXZ9<h`p(F9c`ViMG7X(zK*MG?<N9aT5N#(St^b~!Rl)zQdVvGMu)v}&
zG>$G9$91DmBG(z-RO}DLzp|5oEuoXeCPQO*H_o-hr#lL8pNfc+z@wu7Q@M3NW&*t9
zt#glz<0-rV+`B_G7TBQG8PF<NC<K=rKb_;IjYJ61FH_~aBHiiaKy#BtqYE19+ynwT
zX=%;qw&>@ZVs{o_8*&sUl}y2*CH7fW71+;x>we1qz#jTOVGEi}Csl@rY+GY2%<Ggq
zO_CX-0s%2Y)`F}9k!iIhihja3Z#j+P@?0Ld_rEkX^j!ncRar+c+Z`6IPcR|`x*=&S
z&=9&nN{Oy({NwedOc`F9aC_oKWkceNtwl;8pcCu5zEyz+*4FK0Oh)H_!!K%=v9#*_
z7?1iT4W^4VO@)kf?`pSykOwVq;z_zDlOu|_13K7-$GlgS2NP#>o6f9b#&F>^$8ydU
za~V9Mu<iB3j~&Y$4_-=Ujt@=m+n+)L{sJF?T#x0>u2y@b*6}!a%kHXv@9Y97@%H_+
zxUV$SyW=iU@6_zLbq8LXlc_mFIJ#gg%c?fFIlBN1Y4XPI&$#Tv`>76uU-#&~I9F&C
zR0gu**nh|IHiUEnNwp7=2=`f58=**I(zD`I6y)IBKx%?uri(yste2x5(SzWv4B@CY
z(E<`JAh9(_Ji9`iJ9|QxdK~?kuZ@~w>G%$3kj$*cUOBr2u(PR}0#`=Don2B&9^800
zRe^o2uf(L^MkBsyu|>LRd74W{G!WKT3sv+l7G5-)XO`T?$O(OS3JJh*pb5P$b_Fyd
z#n7QSPs7mw-W?%?Au&wJd!KJkxGnL-mO|1Pkxo^9qic=I_x8Pp)(ba@hK5J?k)~(%
z*mko&kEQj4s5ZV_KM9eTEqu~VGi?|M_qj3eHFZAS{gj;ibnbcln@l-;SfBbaJa$Z%
zp13=u2L$1bklxh(miq6~b!^^sz#5}sNHCUla(hX@Z>P4}1ARy{>4_#YK$8*R!ipof
zY3xGY*!_8@?F?zMyQzjpHvJEPsac1|L?pO70v+l^$DbXct{<5RzwJC1A#4>19#qkv
zIOf#Jars=DE}iB=AI*h6n#wW=Ww{e}1862VYfZen47@u-xc3|Ae#uk+NlmGQi$+f&
z!;$!?1^|R4Ju`fW(VIW7Z2A9tR==1K9l3+q+^~-AO`AFu9PSG?5#Q7*yGXWEcO9I^
z#=K&dV&HK8Rk5k`DFJ&`Q^Mt1YzBDtm^kW8gbeR(0L&!V2gH)mIf*MM5-kpA<j-9V
zL7^+N$9B5V80^dM^8H=SJ9?ZyY$B{%EL#2t;MTn~8uio})e+gdH<ZrD^+Wk@^;kZs
zD~srwSWs<>)(qVwp=ILpUW2fYjCt?M;gtjwlw|R(pPtd{>m^`o4C2~eixy>>`FP@J
zkhu36h~SrZl&%S$|L_Y7t%?l|Nm9DExyix4JsB8=i0c~Qf%UKP=%#nNEhldxd1}jh
zJh<U*R#q>@sl{`XCUJ?_RS%1lpiGjTi1S25+7GR^LXqUmKVvF+MVZ}35No;?c4kEJ
z(GF&~Fx_@cw>>G8wI%T4t1GQGBQ9jQ+`M?&*BF+Oo$#?;u0Ws9&?K;>VIQk%cf`!D
zNLX9{S+7p#yF<ey*B;-ZOHEQFS%jBB_^!0t2t_|;+L0qTY;NB-umaEwiRQOvpCbWN
z12lmq1Y-szqsV*jNpuu$WQ6slT<*yBT)ECPt{8R<|CoI(Wu6?gE+#KsXv{AhPo|^m
zD0q+FbA)`pvxx7H`W2^_T*ShX3pugyY#NN{Q$w?D^-S?@W@_5DsIToU62a#I-AcLd
z_UL7ZjJwd%z7?15lkw9%Akq7<X=uGt7!v2MI+{z;k@n<C8?-l?1Tw<*NGaJ@)6l~e
z$xo5T`jp02q!cvNyGYN7pU#UW1WONW?(vk~Z<;Kt{sg;5+#|e7or#YW40iJ)B}~Ih
zD@V)vO#%i|^mn{I7r*}dH@N-V|6tu`JK6;7O;a**LMdPS_HitEcOCn8*N~ax<JrIW
ztXCl+Tpm90qIF%`W7wh2A3#c(l4*^lz6@SF<8oFX*uyRFFJj5w#8jrW#i&9fz<z)7
z>cjLzhTce-b3eJZI&7VZlg5zkA&v56DG!eqk#!%!KF(@G6#W=Y)41ig7jo;Be`DDP
z>-*fFu*f4U0DpSeiEc{C`SYwzH;*-jl;P|gOGcN_HoExoH31n}^=RD`@z4-FbL5Q_
zxHH-v%Kv=QSGe+{KhtamF{Ih9eX%=>>qei{twmF^4rhDQ<}UX#0>QNG*%Z6WD0YV<
zv+iDVOC&#RJBsa9jPq=_<oCYQ)W{E+6xk)FwD|nedo{v+YTUY44zDC3D=!UA7nnxx
zKk|a`9>iAbCzum|J(Y+#XzeB?<YvT2S0<5o9EmNh-V=dlJ2E}&@QO<clZ2oQg*TJk
zIK2cCUBuhfCGlth${qXTI!b5kGBrj^qr;?-&=V4K8)o2BG{^V^4yx#{<QHf1+b6GP
z?Wf!Mbjc<F#!ny2$noI?+T_E-;-ITnZjH#{Wjghwi0V2RX%jT(*k}%ha69Z)n-e!G
z(z95e$Vyr<jA+eurpK(WNEqxL(Z_T~i9j;MwTEG@y$SR0)=G^nA&o(nJH0%R{~<zC
zsf>!gV|UoN{lQDP;E117eV}iL-lj!5z=jUN7Khi_M^50lX(KGLBVG(i&$3L{Nw=B!
z(wb;)au5pYAi(Jg;`aE_yD8!!2s^flJsmZDN2V9^_u1F*_f7Bc@{VORn?bT1UQRDN
zjPonzkZ$i@mvCmzT;AOCBK4u#4s(fyhS!$H%$%d!UXxO?wQ)mKKet_k80!f~KU=e`
z608W>*k(#fbpwx(c(t%|DB)*^b+;oO(-5}!q;)EXSCSxvVCbk~)_vZ$uzvyA>H|sT
z@H%+SEP<0|jJC{{SmCoLm%~d+jpjxN9$)mSmIAN|onSX*$2B1s?9FF;V|P(il3Z7P
z+#iy;wpzw}c3|(OVq$y{1d(hDX$-P|DJ-mot0pIKvXXG%Y-Hwd5w7DCHK?K=BZOe&
zgfd1>=z1aX%wOM$s4tmh&MpAcAux1!b;%kQ9-g|<C`<P2Ae2Dwy5DcZ<LjG9v_LS(
zo6D&5;uu*K8N+>J$#)jB&%dizjxW0iG1R$-L9Xzem&nEcs-RAzDHv)ua9DM_`M!gu
zoL!0-qG?*6ZX`+gz(qO|@9>g9$g83IB|p3BM2??6GUcL)PZ~Um+dg`o#!zDALL5#%
z>6uMv8aQ3irK^5r&}@z_?0U{G_2h8V*oFLH%xQ$Afjx?}O^?mTwZnhRpEv%RJ^r0&
zgg<vC206}rt{C!Nyf$CEYXl;y?3NNo6{00=(>eqPOu+$D10?fYL2K-hjmY}nBQk#x
zm+qB@R}%4^JI;(vM@f^A8|L8fW1ri>kfIz5t%(g|v%3~LOe_+IGl<LW$M5f*|9V_T
z8S{(AcdO53NRzY5XL3aTXtYknek2-_5^qhfY=|7mWban&aTX5l8o{cN&Rb13HU>5J
z8Unv*#mS6`tiBEz+M4Kr6)A|6k`LZk)pI*Q1Pqc)WQd?Q{Onq^c#XxD#Fyq!Vzq06
z0gYfl>-CbNRT}QPQ70vQb0~!1iXk`RwWal{VsKE}$gnk8^idaVF?7}kd;h!dJsN0*
z2v3aGqYA}A_?sd-ls)ugF98GuS`YIRd}9d0#ZpclIh+NDjY>IZ7l7C9;Fq%(Bz#=c
z)Ozh;d2GvjJiYa!9@_~aqH=b%lJ6?wrqOqC)zF(brr;EgDL92IhyH+@N8e3>tNkI4
z5Q0%@6Qb(hr#B~6wZt)Y1E*ylZ3CL0N6PqAVpR^WB%!{xiQnGxOrQIafV3sopH(|(
zjlN*|DC-P~C6-Ti5|6i;+=3c1vl~dyY$7YCu~$8PTC<IjX~pP5>(rU03BmZxGG^os
z<Bo~vv7mTTTRoE2)=oXdnCzXnZSg%B%CXg<Szd@#(2T}<Lua3%;k4{_Gm(vtS~a*L
zg^*Gb3P$Ec)=Wt<6||NDq&@sVsBcb=Vj}TkWL9oM$Ftcbnr32};oq-}vLbN0-NXM)
zxrkYL)};RAyNdY1=wESS(K!@2i*aj_%P9y<GHp$kX79>gIO=*&OVo^Q7$W;02rYSZ
zN)@TWrnS3hYK-g|eefq=0;bzWb8#Bd+2XXYU=iqY+W67;PEPo^iR6?aqZypuF+DJz
z7$Ln+f6m{nd4*;petQp1XpBmqz{$nua&qyxj8313)<uB8yn^UHABIep{kp`Cq)fLu
zl|Be&bzFK*mf!S%=bl@hX8o$zR51#rTU5flD_TBfLI}>AJIPX0VueG~ExmcXzGi&s
z&1i=h(VIgr0sLXzB7QmP0x}%o(=(gU&|7ApY1u=$XX?dhLc^uoxoyJPJbTo4`QDh*
zxOC`j#$@hfa?W<r?D5}hIz{qSAqBxMQ&4AGwIebi@Sa6uD^dk1B@f@TDB|}10-x0$
z=XMAQT0o)+!H}ZFS29c_WoemYIubX(Ynq8}qfJ94+KP=e!A3r-NnF*b&hXjNIj(Ra
zH;=iS%ZFSa;Z+9<CtJsXK14o5h2vQ8UWLf|J0j<KgsnUQgDM6Bp`gL5PcPxxGw<Wt
zGw+M~`qm|_Ki`LRSgXQgv*{$mb0OZGGGug@)eYl;l!8znqFEaZKmXg)bA);#815Ux
zjO@7)x8D;?PZCK~K@dTW<(24dpv6b;Q)zf50lRlp@&0R{_xaE~Ts_N1C@p#Pr34W2
zguOD~J!fu8q%#8Onnqb#CR^&N6F#=k%r13RdNW5!AYg{5Y&yV;bAQ19_N-z}Z6yxf
z#u0g=8IfMp;hI8sCTEt<0KhMou&H5fr#dMDfm`>|XvC(Jdx+$zJ_-;-@PXgPbVrD+
zZiF@ChM7zbKt+G#i3i@|sR!SVsIBR?VY-t~6yCi%L_>*<GmnTaouEJR)0xNet^d7{
zkg-g&@5efS-S8$0%chg>%7}qQeWTnv6sP9GZ#MVPQXmo3hMc77e#`v34N|bz5aehw
zzMCQ~3*oyqCDXJN))5SZ_{r6e@X5RD5Sl>tB0spbZl7~!9f3TrCHh^h)HnF4t8XSF
zy?e3q{?7R$Ci1|iAJc3kYJDlFKHwuOx2{`(`tEpk+hUF@vUdItngRa2@h;ZXeTqOs
zMxY(JKjfn(<R(qGHkuf~PLv_sF-Hg22?!CN`%6W5B>^ix-WZb%5oy=(7bef?duzG)
zkUMP8=bLv~Yfubn4u#m=7#k-}w76Wc58c&-#`?Nl*fgD^3&(NQh~v0y=+T`zyAI|2
zL096nrJ)`2IML8Zvt@9@$lE!w=xjW?uZtccrzYe{GKo5Y39Ja%tg%a-#4Gwhb7O!f
z9(XI__93q$`7|N{VZzL!V#W{d`FYjfIc3Ob=9dji$XKDE-lvoIR$i`L9us@|ibHd9
zRNew2jnb$wJeDq}Tu5}{?Cb()ehO52yduW@>o5Of>3iz|dgbhDMF3jBz*Sj`)@<z|
zpTk0jTcRlRrt#phXS8K`Bm##s)N5xKz|yKs)CQYkXi@Y$U-?hg)_n#Lk+Um|U}-}I
zbw=39W6J2I1Nw@NWUM{NJXerR%Rb&Z2q(gKb=*c!5nf4vX~ZUHR@(Yj0VUQ5z@MeJ
z+4Ry!tN6xAGbzo>wA8Lx@Y()d2|WF1j~0g<YbhnmtG4jN<^N=`FP~FOr$u}spX)5(
zyQA*p%{?#j;r_R1G8*vcUS{UZV_yDAq}ww&zVHl=$v>6K<}H}gB*T`)@|us?+_09$
zP(5qwKaXfEzqGR}m_dQP#`4pUFA+2Y`%TDA^kmUZWle>mCzgM-f#wJ`RRE-(Tysn-
z0-$bSL^}#0xc}&d{C4?!JoLpU)Ca?VKbxje;!S5;eGO*JR?C9AS~r>54M^F$KfNXd
zdz#~2%p`Ia6rIIh|8AC5f6!xFy%*ITW65B<L1u!ErDel|;5i@BP4OCqB2tWy!E=wk
zkBrf?NDy`cIQBNsT$*mlJTz_cAl$Cx$)JfRbBZfCrfe9CwysOSXcBK)Q?KV32p86R
zfA1O=6i-4*$%^VN{CDR{>O+3YJvp3QI+cPrMj1C7jl93_^{6T|wi1vwKCB-~wylAD
zdo3Q#Pp+-LPrr{y5lE&wLS$))t`w;Q2kl>M!w?z2MA(MJZA9hpN&-et9u$*)M9?51
zcSwXtz}6^faa<#W;Em<$xoG}0OYMpU0n<81gK5NmL24q|(y*5;4SNB2aQz$HIOY`2
zDxVqA^%?do7M5Jd!jcOyq=7CvdVXx0jdIV>w(sZVp9q}D(yI3(c6b4N(Nuz0^OL5B
z(^8V*<1UcYni{!@7JQVc6E&$wJWUPJRm0IjX4sjM3^&~&?Ao0?PL+6L(==|Fas*dT
zn8}KLyD&_XQQ3J^*4J{%^N+-ItYE-S%>gfkIR?%2lnU4pQ%bU(eUI*q1=|`o^7`)Q
zSyBCQ4=uwQUS8dA>5@t#ffazwN-!$HR+^vz6{*a=-L+KjZ-{B10uUh+tvO^#YQ1T-
zo_7K<Op~CIn0=1TZuBX-tCc1rKtssS_m=&G#rrq3Ri2kp^5^xhbIr&FTsZiMK9AEG
zTN*bKl)cHmku7Pvf&(ER2SPpsk^*}T6Fl+Pzbw#AvJ)=H;()w25y4MDHiB&!*v7{_
zrCuH7Bmw0^^O<ni5Olq7e~#w-<XBX;A^_e0<qV-|f`$~SqBSZj&$6+bo8mLko5*%w
zzxv`i-rci0>V7(Nc6G-G``(PIeMstjR6mq2no4Le9Z8X4B>~-vO_7Ma>X<@tD5Zm=
zdyeUK{-ktv0W>wow?b?(;<DM9QCP;|#e>Lpc^H>l$nCR^>$9x#!7Gt;yOVQ<PbA&h
zyT}HKWI@rSK3ddwKCAhJ|6B7TR#bo7sorFJ!dnnsn3jIrv$RC-um{kNUJ+OHI8}?6
zgwq?XVCjl2y#4MPOU6zlOZQZA_>=eZ=8laC8Bfz<vxpz$&ErQap5T-HVY>}!Vo0-X
zPjl~@mwB~vMNG!(Krc5UMfNI+>{Vzj&t46aQpW*Cx_2gI?kUmD1n;4AaG#E*=OJ7R
z5biS&_6Z5mk5my}Ny2x3bq+UP_$T)4sP0t=u&DtKJ*hS$l5olRRzikJML}v*(aLst
zDEDQswLbAt1yX7>)Vc7bM}OMhiXbc~Z*~)Yt08!J<6F$hAJyw|BH=(_Z`9YAXlxH;
zvptZ_?6fubbn6|dc?sW~2!!jT1dpjmC5DYDX7I>DwyfKY8HvV<DZ^iaq?&7hn5EA2
zN2;2e_~+UcJhyQbwSi_P<rcBJYA;Q_NW0gB;A^8NbNrwY*n~!g+s@@5-H*SQW#7*(
zpGAqs+Ihd(Xyk93?~B0E^(7fvBLUNf)B+ZK)Fs)P#Fxk;X^{hZaRLWaq%t}A>68x6
zWyj`yG2KuLKw5IT80LY8-{z<}BP|<0-fXNnzy+^9PE#mx5&=dGvQ`A(((Jsux9dZC
z0X(?j4UQ{{y@RFHGZ<aiFl2PUH@6lfOK)VPdnX{U2_vC$EpCy>R7MT-LWJ)-i5l?G
z(G|%+Zb1gWf96|Uc+D}rx)BYRJ?S<eVMN>ir@5URJvlLAyNTeJvlm!4c4LE+x*Aun
zL#&J3Sq%2&qjeJtU?N#ry_LE+2toRdX5QQP8oyh2J9VMzKJ+9pr1g)nOwGiV==^sr
z7u;tga!^HzA%x)iJI=!Hw4ogmVSMQK0Kr^OQaZb!Z=%TknGH4jng8#<aMvgAu)1n5
zl?`>Q+`p?=&MpY2Af7s83^NPMnO0EBQ(NCBV1~Lq9O(?FX5*3}bGdG`^-lT42i_%M
zMwh;uW@~Oso410|_5^FvIVK{6Is;e41|bAzUw%}~qemoQxUBn}Zm;ZJP(pv<PoI8_
zKY`9J03#GGj?xz-uxRIsZuc}2*wV0<9Zd)NG*)MLZ9WdmJ`xdSeDy|u0iQGu=Iz?i
zZ1N{QbEsJg60>v?;64|T{V#-`lZXL3VEs-}(S|q8g~#WjHzgaj7zy9U5&`Iea72I*
zf?F>-4yQeJ7F4r}2Qe+L#G=t94rd4<dKXE%-(Sz9tO~RUHm-g%wv*pJ|89Q1>PDX2
z_7~RGe}O;BjYv!EARy6@mTpP_ay65~97dnhh$5pUT~secWd8%9r6pw4WM%W_&0Kcb
zWegrX7`NNa(4j-Q_10Sn1gwwv>n{u&Tf%RjxSCl<k4Cf{Eoz!z_WZF-n)RjTo@vv<
zx+zKM>;iB)6R+uFHKvqY{@OEC`J37~q>Nr}$aEo?QCP;L+;Fu^DJ3sgF2j(~tuN-~
zkLBfgH*?j9<JxpotYk;i7Br$uwfCEL?3z_6je>nf->YLrg;AHl!4w05`KM3i?8}b=
z_)@Z^5BHM<437;o>z^s9%5$5(uq3g$q2Af&K0?xrY){k6Xyu+bz2vKux(D~_S`&@U
z{#;fzmL>GLzr)ZH6H%6N7h1+oEOT~&N@+|wK3lS>SJw$6IZqq}kQUUr`-U@^HloaG
zb7Ia<51-G8&pw>+aW(x*hgWY1njwby3JCSEk;!s+$#TR_C(4v2f86j(YJ*iRuSEN~
zm85G;*ff(udlkhFYpXR1BoiE=#09-*`G}04fa6G2O$%7PdNrnL@~1!jiDAQr@!4me
zam5u^(9qDpU3cAOwR!!9LBk8U<=zWvsB5OCs)39wFWxj4&p-A7%RkrvfSKgw*vE`2
zvf6}%^G@YvHq}=3+%Q5W=N0p(qZhVCnTMp&s{@adN+#p8yJBWnU$ONfa;e(zV9ErF
z#vj7wkVdi1#E~e`{Lo6IG!Rj0a4HmCA%x)48y0Zn2@`nv$&cBxZWnH!i&e|E&`=lI
zL1pU-Q2>U|P9WD>-BPE+mJq!mD=?)@Xe#5u1pIcOciAKYsPN`9A+ww(w{~BB*Qwbk
za%c5woSty%F1|D3HlEz}7d|@hb}xDm><(s99ok2hJ@%P3ow3Uh3^l<k;x*4W5$U%e
z+^58&^Qce`uVh1n)s>=k1O(HaNmZfoV2Y$GC7oRWW3u!4`QgX$!}k}V3DN75^=Q#V
z-^Qu%<+Gq<GWV{1g=Qnr>8DLYaAw(zn4ZwDs$Ice|IS`tKgh9{QJ&o~v!}1*YZ6By
zZ#i=(*e57wR|^XkEMUQcFH?($3>m_jHEX#4{`-}~s~33FTzJ#IbW|*Re|_7(lk-UB
zOTKaGQC6Ffa2D^{j!kGi9$fUq^!!q8m^z0U1!e6vbCr$JJJSU4!j7dJSumzgHpP;$
z8B=&?-=c0>bX;=<rH#r!CMAw)OO{oBG(Pm(m}n1?nGhe8AN+5F>*z!brWimB8&k}+
zw=D#~-yG!ZnLmx_Iw>IJb$~7ir8$vy>z%RL*{NG7NE3o0PZ~QL6Cdr_m-U4NE*UzP
zd4*%iSmmKM*o5q)M_mX`Dw%@E*7s<#uCQr#&L4Co1+G$_t9&%->jjb>fvki$yc|fT
zIF0yfMW)|`@SGdR0U}53nv#t1(+3mntuFy1-I<imE&#W?ch#Nx3+IoV#Pg?I#KIwo
zTHs4Gy>F=&=akRHYjg6miRYpTt%FlbAQ+cf&P79FyNO&|_gU1bB?mLqwKplMGPdH?
z%mn4XAB36sLJ9qenwlE2v%43`_WS+R*4DQBQgmef7dzViEt-S9u0)gwKKr85Y7-Jp
zq;tSAr9-hFk_OcJ%)fl>G@d;1E6gk`?-050)YcE9wn+l3YIpS5E|H8(8&8p|46Vy0
zuk2KNWpz^#0U4gJtoZ0RNsSK!b^-|+TQ<L#n1K`nmi>Eb3HrN^B0Bh^?$7rU%=Hk=
z_9TU~3!t<(KG8wR!iA$IB}ks5J?ZI9y?(}yPA}p9b&ELroqHIYQHqY}u&2=wlzDQw
zYWQ(6u(oH8DL9#%M&HHZIY;9Vkw-gA1Px~V9C8PbG|d?#y^p~Y-6Np!u{ic09m?UA
zWE^|i6kHx>o0du>7#>^FI=cX}GTm025O>Dq7IOEICz0<?#OaT~?diQn(d@jDoLV{!
zfFtuq^Ou=da#a54w%qpuR|Z#)IG*26zZ91qTV0%Js$?CCQ}`{^ROt+fi#|zG^a2gE
z#0y*Y7dC9zz<u}K$CX!J*=@T!@4S<YjEuIgva-~SK<z7zgLJX<<dcbP$M)^97otse
zW)zn77~NIi@$tYhr?cjw8~FQi=Wy1raU5GVjEhE3=GBFlaLZvwcbTHoWb|&HacIev
zJfI1UD~JAoJZCuVHSG8j2s8wOvq~>vX3o40*A+NwacTx&%@WHcBt@D@T=ygg<9{1z
zD(Al<et3M{Z|0@=QkdTKIFfE}Gj>#JS2DeL%v45Y=O=Vbo6W%G3HJI=y{5L3olVtL
zHdXP_{`DA`j7TeL6R=OWySZq{k^E)mm1H<PF|@pU3SA|fTYd$<p7ap^H|g(uW61Y$
z>pkreB`z)gX{C9ZBwdSl%D#lqTBKPY&p{$4xmG0#ylF0ea{t%((Kj9<=nq9!Dqben
zgm5TxW{t4ggoJU<@Cp3k(+@G@d>kx#xdJ}EXd<_cKMR}o<!RZNj8c9v`2tL75;P5*
zF?Mnt%wX?u{<rs)UR~?clSPPJGE6rpwI{kVBY+2?`4T;7!ny6X+xW>(e$s85#fuj+
zZQ8W9?<*@SS+HOMXP<pGmtTH)x7Xfu(@k7|{q=3%*Vfi5r&lM;o<Ej<{pEj{rVK9~
zLnhd?q}zl5)Ye;Dyuu0&FB-(Kj2t%A9_TWi{<1OC&|9Lrjwq?%h?0tk`=6RUgy(mB
z5|vFFf@2EDMRiRg$aG}${n5YVvzn!RTD_Qn^i$|6WmeA7WI1vOnL&0oZDVuex|S;>
zgPr><QHs!!jJAg;(GyuR9*}j&n(rdA{*=H$6$6RPY#-yMRq(~qEuHRfG#Pey1v6Yp
zDbsTDf{9j}5Ldi*2Tz>v6@I<sZT`7-WuMdKBO%Re^y&Kc1zW=QuBol$Z--w^i8~vQ
z&4FgI42UjljLDe9nDj|}QuSUpEd|An>bToepqmNQs7#J~SUJ3sknz(8^XHdt;N>Si
z;)B<}V0UE|O$}Xd_O<39ma41vz}NvKCVcUjDLlDuCHtBh;#%pXvB8DI*%0-0rJkI2
z&aQ*egoaa(@1!sx<1o@}8PtdBkWv1ILNZwbQUx0fonl*fnoaC_tR5+w5F)jsCa<~X
z8qPcKyl&f6R8+M6yRx#9dGqEmYt}3tc;JB^`*%8>IGu?I8}Hwoblyy!`^N|P{XwKD
zvF~ppSeTp+*GQ@}r5c(LJb3IGocr=W+0$6xw(Qqi^duJ!8N+2`r*+#$cN||lk)MD5
zZvqht*J}uzx}7u2X7qZ@c%loPiJ8-xm^r<}dp6BZk-MzT;bp_br$<{sI5rpvJ`dWc
zF-=C>jRY?3B>3M%hBhH=y{k5+Q1rwFS0BsGm;R;OHl{oIGFQfoEFv#A^($kiJDmLN
z@Z<Qw^tr4)u$MK}`^a*-cxm$*p5L@O=HtyME@64~hCXG09EuQv|7=^#_s5<Vvp)Mu
z$PBT%c11TWBw2a`xwiOgsOcog4P_gN(2_&<E?IVvibKdN%;2JLAIJUAeTUyXaW%TV
z>pzvWYhaj3tFN+p-7c$5NEo?pFF&0*Kkj9H1AaSA4GyG??$7?--q=r8(O}bTTruQE
z99s0cZZ(EGX+Gi-!52ZBMeDKT(6Wdpxw*NF8a1lhFPAH<i?CzI4(86C%akcoc<7;r
z&{WCtn2}$c$<H3R7>_6H9JDnB(W(+tJt$hXY#VqfjQ|ztSu9$3DYqPUG!t?PsYuUa
zUg=OCntvAe9C=b(bXRZCg~m@OoE>p{kIl(%r(H~eJG0kgTFLslFAn<7+Q~*D*k))f
z57-hs?+Ng~lgI%T1B^*ChjQ($3#~RIYSwSuLt|62WE#XDuie4af)dUjIgt}8MzXgl
z`a|}f5I|GFB-VM`hBR5ce?v_B>?@zwE~UZfD!orqXs>39Z%ab<6%7fRQ-t@j#0(Sx
zYG=?7C?A^7PyTRG*9~+H?0V9jhP2YqO>|l+^7Mw!5;9I*t%v%IeN%i~UAT9fCTVQj
zXsn6R*h!N%wr#tyZ8mPK#<p$SHYWJ?d%l}<c`o)`&TnS#z1Di3zn+$(yq_wIDoWV@
zTzYwicq54;-#H9tsKuPKgec}EymCse-Gyu&c?Nuy^L~4O6E1K;4K@2)UT$=;)|{A>
zWWT8GTH|`D<8r>zU#b0NaG1z;Kn{P5-1g1S(ndS<*ijEYUCO<y)}@!xD1JGShBcFs
z5uZ2eP`*91aiY!tYYcyYd!b0n+(CFS094+#ny19?9`V#pmhnDM457R0Up%HI{+d2y
zsrVzHHrmmZg6Sud`Ag~w75j{Z!vgJ_CaIqu#Go>Q=t9*=OLfpE_RcsbM_CfHtDC$e
zl?;7c^M@sget5mmR(3{K=HY+3pO)6!@<p~2Av8G**H^i|d2^t*uhxY1g%;XyqS@Km
zWWZGJg;gs9<jWuoF5ffYf$z-7xu}hT!7-B?*8E;Z!gMd<r^!;d*QvQkZPiu;%?Wk^
zGv`bGe#Bp3GN~hM*5Y~MS@YO~h)4S#uq{~{^9&L(tr6t2@AGxwK;59*;g{?*n9dU`
z*X_*a+;9t+nNbQ44`1oPsOjBxX1$z~db(|cylLMSNv6=0dwC=E&+Rb(O~YgAF`?+g
zzh5>+hqF3yy+5C&rN|O;c;FLr`*kwRviZ@iuGFUgd*K-+=EPg_%51oh60Y=~(mEvg
z2{O7EC)Dd{pwwowaUMjyPVnXG1Yh@VjYf>y{Awum(HsJPWPlkE(f?d*zP~{Bf%3=Y
z?gi_qpP9kT*Oa<I{rdfda0WE!@r~A5g<ACQyIy*s-Y@0}`Kqy2Lx&?Qx<r8bGsK~C
z^*Uq`@Y;>A;e1XfwLdSFu<#d3#wX`*#>$fOIVGeIGjJ8jEBhf?Fvo&`$l%XvaqPmG
z7QHuA++c~+!E<B*px~7OAiVXu`J7&w#|bV?EW$L{QUcnUju1OS!jIoCsMxw*EWlMW
zZ-M}`V6$de54&zqXTD@zl5>$^hV*p$yix00UF$*DGPU9)$a+q6k$Hz%bbwGl*P({W
z-We(CYWZ19!=YT0VoO0SFa_EJYKhk(lNO_fVy<^Pa05lO%6veQ_4<WjHNF?s28#M;
z<X+?Grm2keFIgi^NM;`1oa1N8SA+WtqSZzNY@T8)h5ayVVy9(svb0R?X;*TvP~G$p
z+bXfX)O;&WZQW#RUnQ>ObD?;R97QGCn~I18q~1Srq_066bW>LQHJ6RZe_^U94zg4Q
z_5&7MU0v!$qzkeM5_-Dn)m!?3B6XmZmy_CwPmXPPsB+Nc(*0!`B~u-gFEi~yhm*k)
zbQ$>syZq1=x^jtFL^;}0jF|Rk@b8^(AcD30kQ!WnF1=3eumyUH*kXMFkDM%&a8Y-?
z4AIVQ`nixAc457Rs&#ePsepSR10uMboy7#B^=thbZ<>G@gk8VdW|5_=-I&s29N7f|
zvF6M<7{ZRgtf!`oXd9VQ4qC?1F}Cz%$x{9PYi3K7FC$kB3pW(Yan)49K)_&Tcgu_R
znOSA{Q%fwno5SkYb%A0i|HsG5^VYa_3M1&}-WT5Ir!HP?ASeh@Gri8e(+0<OE_y#|
zrKU}j)8G2NJkWcs5eHAM=I39Ow9tRVBwm~Ye|wj533Q~!;6Gkb7{YJ(ep?|I4H8L2
zcno4&eyaeH>az!-d497>$9p%yH$i@9{2VFEbYLkGEgh;ot+-7%5i{@}?CwsT8<Bk1
zm0U7Lb%wzsGo=~0k_C$djtYDWlWGp>Y8`qrH2?_&-E~H>e=U-1?7=(1Pp<&wY9f&T
z;N0oj&T21HS8c4Tm;-mGO8U=i)r?<i080dS4_6v!4-liMW`A$WMnl4c{h3Sdnsk}X
zAKL1YTUDY6cC1WYbtIPyjBudrAaH5)s`c6;*ddnA<%~L1Xbz=Tc!E=n%IV$?+Bf3O
zFT=||cCz2y<8JdAFvbASl)!?-o!j}2%1%M?la>u)^S1QrTK9~ac#Dr}Q9jO&u=hn}
z=V0;K?v?}L29PQB&@rXjx{_bn;;wmN>^qz2Lw@M1E9!<!!p#%~w~3QjvDIqz*V)K3
zbQ+G|aR-v6Qn^X#D|_W9n8?AV2UKpNVZHi)9#Q^r(78go%JUF6GH^ppa^%)j-w7XJ
zGwxkiAXnF#ut0p5gkIj=Tuy!@iXDC&Dow(xFZ!My`gm^YS01oJ+#q`gDmeWq`oaKH
zVfo!Whz!fflv3iGld2p4@UzwFWLw9nGCOGt1_r2LIBx`Vjon4x05Rf=Fbrd9Q}AYG
zdz9a?X@p?NcI7%2QwfD}i&T2@&=D5l@{@w-z$6-1k-;C9?P}7@o1`?pk4a=+Z^6BW
zkbANOTNx1=_G)C+KokNMwE_JgW9zE7=h^<Xn!WLf)h`msKkZgxS;iQNM1L2zS2~9A
z@-n*cu%AK*2`oKAL!jnr$&7v$geG6-rFHq4Pf1^j?UI2<_k}0_9J>7_Ds46QK|&I2
zBsxH-hmU26Zs;F=C2g>_F;w4#(8xfWr7hGa-}+O}L?71!;sc4u_ET}$;ooBQX7c!j
zWoPD`1$uqsaRa}GupPa)LGf8B>!Bq2>2n4c73gxizMwzPf<s@h$@jZT14||OB*w$q
z^{nY}`DkkYnThSqea-MJ&z?6bn5ZjW(w=aYSRane%wHm+F>z%kJQ9c}(OHT`sKbq{
zp>9D5`24L$Chk+^%XEpB@1Z&%A8PPi|91)rr;({mc0nEXgUvwAO6f%@jBY^TF!$M%
zg@&Q7*AvaH(E*;b1I!xX>RLXbzB6oM@u2q)$AgmtQUWtFTHSYA33G|K2E^hQJj}i+
zE=}+Y*4d?A$^58o*~&C+Q@nn9v{S@ROa^PjQ8LYInG0w}`^Naq4f<%KW|G@{rHjd*
z^Bi`9P#`4y*=vJM_n2uGiKaF5yprp8eh0IwDL>-MAhiv)v@kLAO2x$~)tW7Kww@1C
zAz1a8nG<6xdx`~MSjn4FR;b9rR0I|sGT>;k#Qvq3lslm*t<v=>n5QZr2xxOZ$~!$w
zCuvicqIAfD%2a)K_Tjj;&D41Rq$gt*D-~_RJWgbkO*h2sxdR#5De8x%9Ud<{jSpBZ
zbh(l`+uyyjwcnNhyNJBORGIr}6^Qipmx@l>%r>&HBH!k=rA-T#3C{p=-W|V)v@GrO
z!|G&Rdq+_(m@`rUsuk-^=ZDjc(*g=0!G)KppzDY;nG7xUF4_Hb>`_-p?2g`TqkLuG
zC4RZ{Q#Mvk(UaWa5_M?6w|}KiAg)aZ4T?HP5WR5`H*S`0Pi`Y>Xy~&G9xE9g=MI#E
zZnkxXMcLBon&Yn_+13yi+(K`h5?lI;7r#}3)?mT%&VWwaXTna+4>q5h=f<ahCQ_{~
zo^P#7#77-yYc#HuHVmneAwH1w5$)JRMbz|oz`aX#R&!9<qoW%(I6Jx!i^ZI-tG4ne
ze$oEwX!0Bhsh*(5o?Dauo)Zc==vrtQTOm_kE=Z9%sLttzRtt=VVr)HQg+?ZdTs=2A
zn#%oe>MA5e3HrJj2U0O%$gI4J506i4wi7%bDGB*{FS0Gyu!V9w8Z9Mf+pUu3kAw?1
zr~4bEDnuE&y?7dqU}_Vjp2F4M-+l}qU#^xi*LdI195T(<T~Q=VjEKe9dE1msYt3%p
zWFcE41zJMJ-iB5(;t+jfqSRT0fc7nQ%^$(%dTQTjKgd>Ny(ssT@WZG`TZAWp6BxJ7
z2CWiyS{0o>vy1kee0=Nn(g*(pzCOJ3$c$xYA#<Z4%r@2jHUlt$&#MQXC5XQoJ==Mj
zfY`~6^@(H5Gg5|0Mq!NQvIJ{F$iSeWSq2yp8$v?EVnDY6jple53811#M#dgbv$h=;
z^|-Xddb$EBh*^uro_PM(Y5SS#$>~T;w3NK3y5jLmi9J-XKjyxLv+$TLZwah_`w|~N
zU#4o9PUZVDoAdN~JK21g?fC@wG=|1GH?8W;X+!i=*}>?Vue4|dfwsEt2>oJdDjD&K
zUTvPdfQZ7=%2aY#qLl|bfjARO+pg~*xV??}5v;{|LUDi@xqsAC<7GQm!YB9$j-31l
zDl-<J__4<gKh0xbXJ%Hg%DO66Tu-z|{RKiQ3K!qdXkA;9jk`2`V0^n!Ueutsx`y)g
zy#CUDv>n@FMb*^tW$_?3Ze53O=^r&!P$ef9A*e_J*Qy>D)_8l0WsHt+rn_<T<qgxY
z*zvjPSeAiCU)~<7ae@o_Qw6p{D``iYyod*~iONOjUvLyuFuR)xn0&p#9r(09j%tpE
zILosf?sIB~{q9i68XU(Ji*cj^n+7+9rcM8y1;zdCd~fDqnxk9S*B4f&s_bweT<7{;
zfAD74-c;#e*xYm6)X{!>i~MQ*PR9OZSQVECD{#z17Ohif-OvounCy`IyXG4(6Ef}|
zM!1fVl??YkL<E`t4rP$gl|UpIVS7$lVSfPMb}!8|yJqEIqT%qz0?BKxk=Bv@<0Td<
z3{?*32Wxc+=mw^+@_{FSZ@R+J(m%@0N`9g&b)1PRXEG~c;;{TC&CNRQRCf?Q<>EjS
zkkDQIt0COLPY?(z3weVne%NoJG^1J+5OA4jq`G1X|7Ge;W~2Z8YfdNvJ}7ch031nI
z+2kJ_41LjZTIq(E5q|{ATD~1*_4Iif;J&RTe_EODq}T#t@Ws&N5h|X(kUpGM6jqb8
zcpbb)%FViMrOvu<&BnMNMJOyS5->Jv#%1>>?V8$6TwR~9=(3u8Q@)LOUd5JqHhToI
z=dy5g2Kw4QwT-ff%J^>LxZ^aIp9y50U;LV6BWn-s?sygWu$r!}yl^}bCUm?<J`mDl
z(!^U}TjGQ$zSybG%zx6`#6J%_SzQTkqc6pciT)m!pWZ#;?dD`jcmxsliHchoD|;ls
zF2{U*EBteJ5d+xmHj6_gV;V_W+6LV3`1*i-NNJ3F_EQL^d$>@UyTk}CN$yvmYyg=8
zR<zDh#U1s+&vnD$CMtg>e;#x~Xr%$(I+;M35nfXgf{=hyu0Tm0_ei6>qN3olvy1!F
zS06#2&dzi2%F4uj6o&52T0XH})jj%<Oh;)~z4Lh6?K@92_XrQmol!r&{tx=k1V5Cf
zk3Fj{v{2_$%|WV-cnK;AHj{f9y*4JUj+FTL3nYH}9V|;6yuNC_)Z-@TJkZcQN!_x+
zW}XRTBQj(|^9Ig-RRJ};;@<aBO%-Zb^lDxHQ1B}q#H>azmJ8+E@8C;78!Ql?cD_q&
zyE#T|<D2ZN^Nz8+=71NE;3`cGRDJN&go51`Y-r)yv&|km^c^fd(5-l<Q$gangNyq`
zR>C%@uEfha_#{SnZB6cS#lLDPuCj*fhMjnZ9E<7}H}8c@8Rz=$bNG5XquT^>6GW`5
zzva1b<T9;hX*m($pQzTgx=Vz>QP)zE%nO-HkYr^2mNhA?7_^L=l@U=Y$UtboeWK5B
zbe<ah1s6_s(RTgt%}Gv=;>Wx;t$u&;>XVl<<u1_3X`u=iWD0q!LPmaTm{-ScubZ7>
zdG(KbK-p+wJf$Ez*MxaAzb2sAU#iys^0@9w?7Hdg+IW7^b}R35laNC(R63`(TPl6`
z@+o~a`q(;*NG`rTN^Wtf+CIj?&AI1Cp@`iH=vDj?)eg{msfx><-L+dsfl)(4OYu*H
zcjsoz+iN+dF`B|@?J&RSNhj!5ynpsgr~5oKhfm|IqD<(38V?wlXnr~{qs7GTnbl|<
z;{KtOhzkj{nI+>!{UIu@5u9=P@xqs_ea-g%@bVaMB7797NHc9*pYRn>&YX<?8Op$N
zhU}?2$n1Kp)fal&wm}CFwIkuH{N8A)b-L$;osgx)s5<x_?pxk;q^tLh`u2Bj50Msr
zx;z)$ul2V}ZCac=)C9V2&^GI{`y6P$e-F$A!;O+{4z3~&@RkF@hUn$cvq2P%o2=k(
zN)an92L(}i9uNKdDj9zyNs-u@z8e^y#2dwwTt{yXc#iXQH;vhAxRLMhkP>A*!rQc-
zg91_3eHhedI-2B-jVS^0XzQ$Q^5ABa>9XM?!=?owIMN%ctXFv!8I=NA<PuK4Ai{Jt
zH-2tk*xcm^>q|r%{8Y+AjChPhy@|8fu2TnW6IZEhLyu29ZPbNEBIg&OS=vC`eW*Hi
zT;HxzQ)ENQo6%TrTyw@bGgLdzHd2S*Y7$gewtLws1!rMYjxZhD^E+{&7=rq2B5F@m
zH0VP-rqYt{evMj+aGS$IG;W^iPG$|n>&tWCB(T|;amZOw1ezNA^G|jH2NaD^oPfUB
zhR)Ulrv%EaIqRZ|9#Y6Wx&t=Jf@RhcUeW4zJqLp4pKig8p5uj{T)9W|(L?;%#6A!A
zHsUDk>GBqc*tiV`OE`p3>MZcjQIMaPNZ*hkp-H+~F!jqM{~(6{mbL$3q9o=@IL1p{
z-p?$5^zUzkFe-zRhPNa&jIAg;USYuo`_P<0eV^sA=%}y}Kl>kLoc|LFyKoA7PaTLN
ziJKg5M`YGhjy$Ec1;uZ|DOANGH@wXjU#Dcx3M@}YGX$vT(L;Ue8b6SIyt>%_>t-f8
z?M<Pp$N45_dvDReMcKbbR#1N8DpjUjcG+!5eA=W%a$c@s1EvTndm0i!-Y}V|szkkc
zm{IfQz>i8bm}8m0&7YT_V2t=fr^<m+W@X!X)w=N>C$H7*+Mq}d%sR(BgN};%j?drP
zK)jV^njA<SKtaJa+dH%!1njm|xEab_OPLSzGCHgGwDsLo5%l2*+DDn{9;nE+9gVY(
zQ2uPvoeRcuL+?V5?wb$^`jzVi$6*3ciEgo6=-k}R^1@FYQPqgjTYupCQwV&=)ZXu=
zR&m5{xZBgy{k>j?sZ0oqK;+>a3EKptDe}~1Fbd4j{{5eMC3pjtJa`Y6sQm;8BBMyw
zU=C}xbV8?i#5E8o92^~o$D?FvJO`r*$1*r$N|cLlrbLi)&4-&0E+);=2d=IQ%hj7J
ztnn+M5Hefwuds{NeX0eGmAJ`+OWtHv&4mA>ha8Vn&G{P5F~J#lv^PceD^o0cO)V2k
zFgV}en<`E99TB^MBiU3(UxM`MU3dz?;i`>ws5wPeGeIovoA$blzlmNB?a(aPLT&WE
zdoq?|Q_SezOA&=B&&93(R&=i@!)E8@w8|FT(a?}wTU%=;N1*un+A`SVvqKSeWnp!P
z4u`vBHT>&$UT0mqzF^A~vysscaZ+<u?pCr6ap#`Ba&^D0O<tnh0}?>JE<t3P>=R{=
ztK8D2;#9^-mA1Doon0~EAJa}E!-noU%B>(e`q~MJP@w1}XA&G@s{={_6S?(TDD(U=
z_n%b#^(`cYjLPlIc(2*CuIl&;g^Gk{P|(@t!z$MdX3s_jH+2(Q+_g-g+&Xc4F2Eu*
zJ02?O=u7~_e*Z?h=jsL>{ZFJ=HHS5TA!x1jJ}D+*w!0`D%sGV0s5a_UR!SUJu+>PN
zVgb|jcQ{oN2$JtSOj>^gDnfduxkDL(>jB2Hk3Td<GgHU~)gX}0t2)PP)m6$`mO!Th
zXUi8JcO|qMG3-aqG-`b>T{bn@w^vS{1)qj2=bUrDn@FQJ%}+3p2q*ZjMkY?@>#CwL
zlNUXaQ_F?$-+$93^xldC5vLPrnH&jWX(2VaJ{-=u7OZE>2oKRhr{9G>{CsUwF85b0
zhZ`6BSLR^i&X}Dao;e1%g;_FDTDok;H|27<5_3h+G><uy0zYXhOmHhs3*zR0*)}Tp
zGW=>LEtEgOkg1{1;T|4EeUX;{3H+Aq<UVjggv4Ud=zF<8RqyoSFDxuP-$;<sqHGs*
zSTk3Ce)IkiyC|_uu`A>Vv9qqqZKfbR3lkLT1V=kc$~aPbt^Mxe^;DIZW1KYB=Fuuo
z&E^j|r2CLkgr==kC8v)fyU1P|tcJVlgva7;+rdg$^&nGHQCV;ytF>HY1R!zDD$=h6
zWy!XP;O2Pd{yn`1!oQ6xfr*AT-AhD-;wbX?vW=cio+Dz($(Mj$_v#<udgRH>?f5}x
zRfVXcjJXtJAL}x*Q|fy3ZLeX@{?qARU=Qf|w*my{0pEVZjQaOdzVK(e%^V%zOXXP*
zhUbPcRWfoVNwpgISP2Y|c+SbEVZI?&q?9m02ZH)m{(SE3$JUKZtes^Nv#MEj#?s;Y
z>MO+)ycli12-9o@>A=sPVyw1v2BFqja{5J0Y!gQ8ZyFbA8Q(;5A2DDQ3BueIC9yNl
zrEqbDy)md%ruWl}1V?5zg)Nk!$35j+ZU?1qVD#Bh*p6&1SbxO<adDG>d7%TGaTByT
z{kxhQrw_l=PGxrwYT$9xRi@<VPSteoQ6pSFG6>}~KV6-e6U4a^3}R(U%WZwqHvjAP
z?^F6dIJSCVbmh+kJ%6-3EjaM$zCuxSXhM75O0q|+vl2EKY1s5N6|GF}M*$Fc@W9a?
zQ_rJw&Z4>VIWOmh9cRl!JN&&@OqV;;ILBr`VxG4GR2W5_{hUf}f_T}SOJ))LbfaGD
zYuJd1=bX#J=_?CS5l)=IYI>E0+IZc4!HYt%nGV+#(1(AE13NqOCc7pqFc&8In#OL4
zqvYS-!ksyrUC^m%ipIP9!nPQ#)wR4|H`Sv4esRC(us$Qv1dcc)UjR#ER9*UHhk!nU
zM~W(}t2l6++geU>FLZsWk00p&F%tdwUP3)QsI5rOfw%Ke=1f8`>tR!**q7#vPhR#X
z(F_ibwP|<I)JgqSIQNK<mG-W+Lv%;<;pWevXcJc73P}(dW0RJP&)s^0eKX+yni2Q*
z&Wj*NB*020TBxXl|IfWzZC+BBQhtSGs{hOs|0^m~NwfS=fbdRVuhB*@e<bqGQ2Og3
z%lQ!M(x-M1H}QZl43*r`hyLH;P`O#|{8#3p!#jFWaNh!Qll!uv)b8EN;pVtwQp{(k
z)FX;`L`2K06|Yn$8(U)%x(krFh)}R;#S+gpq+4h3R9ns2&)g$SXXjy6%!TbK_b23(
z*qjWLdfIRd5tt};FmpV0A?k={J?!*%Vz>!ILz@_3R3;){6PDjXH$&VQm=C^he~paI
zG(M*w^S6vXQX3=g;P)zDKJPbR?lfR(T8mRlYLVfYU#Sc_sWMU$isF%->c}8d0TP>>
z+jZT{-mN$>v)ny9BZBb6_ydzu2N&fuXJ_8#y#TO!n;@%oH0PsNcU~6Vj@2(Cuj-E|
z#{9+}NdbYYPR7l3To@*Sc=8c=E#cq*dhndgspynL_`a6km3OfM|2K>pGEQpPENL`<
zDV|5#7eP#1>{tTYD72hdgj%s%b=x!vt9;aDUy;zgu;d>!ExTRs9y%WP%Ce_{8oXCg
z5itOWka!TTKWOLjEStHKA}!;R>nJ_^(}(k|HZlByClxhqvth~q;W1Tc2eOuY;nG3)
zAtCfkqT8Jy)C(nzWacxz8TVE@MjV)+2Sh*6AQ9r=cJDY-*S2>1^ozh}ZK<GQPE9+(
zEADei7n7hbRwrs-q}_K$zv2cj9Nl~}BUeY`lxa<VQWT8lo?n9Kk8zhLuJpqTvTF<q
zO1?P@rf6r%-QGN8;GEF0&A)QtWag>Nl^DfrHnlnJWGL)U;N442>tHf5+WRc|+{32i
z%W`#HbIB2>1l$Rf1~Y&?5H}JGA9Babr9=EBb~CHEIcDo~$F9qNkOC<(oFLC<x?uMW
zdSAm@e1xoUukzRAEugxAP1O~@$DP@GCd|3&c~AfrMhC`oG<h#K5gmD$CY{&`gX1{8
zDvBw^K@%3)jgEw4q!?FGzVUj5$`zwV_n;Y+%oJRw(CEh}hVxdKcIFO2QAnB)RX9D@
zCA)&w3V=CDw9!0U*6IvNn%5+EarwSvTvy}c`WhJy5pr-w+EYdj@jbULP;=ll7)orB
z%FPD`ey&j>?=$(ixMo3GtAfH$L;~e$R-b4)*<b0R;RV8r?9r(Pa@@xkPml1UFqG2=
zl5spRxoq4Yi$FYO1!X%bTC-KQ8M4Dax*@fY-~21bBv*n)w`CL(f$752{6Mmh;L}RZ
z^Cer39;C1odWUb%tNq$_{wzQP7rJ{+e#>6L6NnUis@iDjeBQ^Ntf|+}flBTtmRsU3
zzxoW<K6t;zg|y_yI<6N?-lb(4%YyWUcM50t+%#vY3Q0Nsv`J;!Qb4128sLD8u4m*B
z@MnB&!r!|Z2Hx6RCQ7I!`lkBEaQ_oW{9KfzXno~v@*GFLGH*@YgH?!Dc@AO?G>Ysc
zk?XV2HxXv$R+7gI4{+Zv>3`j?OewK83$BeU2&Z}gi~L+<r!pNW%-bXnlviI-Pb|y|
zN`!93-!M7eu<dvMDb*5-vHZf-$Swe(LG;vtieHBj&UJk<8=>A=ie~iW_QMhmh_Xzc
z<j80^qc%!ia>67=W6EB;5x~nE{o)n27d8$I^r|VQTB_~STbxOa_vMt!#pj;-mm0eq
zh=G=2cm4dHV>mR>6nTnH6~ZlI4W}jSN82mG<3oG(;G{NlC2^&Lg1x%)yCpz5u!v%Q
zc!vGEvz(XB!tcn%gYXwTZ8HK*E&-$k(aEMrT8!qAyyzPU<}nK@kDeq#^j0i53HN5^
zVN~cs5PB8N;x*b2SUwjC2QCWCa1|Y&8$%!PuIb+@YqCV5H%DsJz{tH&XvG4M@X^z_
zXkr{T0L!z$IskyB_=wOZuM*A9kc@)Y+B+0Ecb<!C)%q})xT0|-GVvfHosi$8jIYrU
zmgD!_hD?jKSYkoe$e&2Z5=p_BnSVjDV{hEn9+51fBCPAUJ>uZEh=rR=D}Mjs!L<N(
zd;=TJHbBmZ(S7KwVJ%!N3G05c$A2I@L{B}6>V7ZqyNil_g3!s0pdy?=`^d|2X*{v%
z>*qhY<YZn1m`kUhPLbZzQO{EFa=tomSTxKMu#H>&wws9hWeO*j1N#*4dVi6!+JXE`
zket)xIuLK1Q4adfB4l?IR)W&T`uA2{@e=$>W=UF2PX(gIxHeEQcGeWgfRt5m7~>G`
z^4)yuv0zieYhAcq*|ZII(|faot)}8OL$EP@&L*z5x<nK@%jy(s2~!Rv)XG9#){hW{
z76>g=w-@7XQwk&vxGe|eDznSkowc#3WW+fDHYU1|$;+Co1v01odhX}(xd_@vs+cR2
z7XAc$@r~~KUZ1|TSM<~*He?3>gh+?O4o2dcYl(kgxX<{)HC*1P+oe<&K`ciyxuJ+T
zZLQHSD+f%v)fWS&$;CO^7WJ^<7!uOxw3N>m<kewA$Qd3oFj0iT6JQ@r1hV$m8fK~7
zQ2gVx>;~Vjby`wvi;}RsE-LEQ;?j<Qaa9yDuWi!yBd|#EM4}7?*?6V1-+X4}hKu{v
z6k8~o>bMfdqOarN#FDwJN&>GvVOfeox|dEYmDiIZhkn(d9+yzfZj6hSjgO!6r|9^x
zamX~vjpN6P7+}w3WFJdT0;~dtL$#hPagn3+yG?npO->>GSgxWl-PQkC5M<za#nxL=
z79aT*&pAM-b+M&5B!5|<lpgMxXQGGR>4arslJtS6S+b(LdEtr59*e|JE5CtkvY3rm
zCZ||HqLhC2*Ef5-{^Jj*8t*sBB7X+jW{Fh^*i_hRXu+7+mh*GNb`7gUZYcb%Le7Yx
zuN$Grh~0@7+BnNS;)p(h!UR5h2Da;+m)X1(^zyvcxSp<srLb?uC6cg-_gbd*)qail
zCQI(Kv5_6r-Ya}AB^5*qC$KArU-kT#&ycI^a`_8C?qcbO?kN5$=N57hPEyw3wphSa
zUg{AOffr<PqU?TWtIB_Y8&<_J&tm99&JUC9GYR37x!r$vd*5suW*=FfXNG?f-o%<#
z$s~UfdNn&^fr5(LYjX|zv^tc!T%g$JzxMioq~@8GBP4%As7Lc%U#uKpnjAJ`mVSKD
zZyUjciPlyfds9FMW$!M}Kk1VAd}W48AC}bA1@ZH6#fTn>vD5*N|0}h(F*FnZH7fWt
zJ)R1%;;gh#0ZJru@d9j?PCJMQm;1IDXG*E<3b$#DawFYausw0HI2@eeLdIut%GzzF
z(0}-b3zp?SfYqwEd(d94-Z*N(#$$XOUIB=H6WK%jU$5T8`0BPDNeGUOq@~iB0z411
zd-mFPLct?;Zd_$vCT+5EG%Zul($9-&DZXeC$geRgdWdNc^hKt`!=tV5{k5f!%XmYD
zT|Z3gOU~8s=V+%yypOtL*dNHPEDZA`iHgRE<i#6O(!c2IE44HF9XRW?lviatGb>Xe
z)viE_h1b+=?_-%_6P(Ri@H=nLH-+lss@8NIAlu{m#@aBiphnrX%X?N}6aFI*eX(X~
zl*<j}NLv=T@VJxqdCl^zZYXI!gh!K9R*p?yp<<l@<frkqT6|76jYv$t_5L#D2&<ax
zMArJfOdSM9){z&m)D|*JTO~U{vkUybTlbU9tKaVaYdDReCt+L<#2<y3OA7<Dy&B?W
zwZIRzub#Wuk{Xaankx0Ycoy6rNrURTS+CDi*9B9*dT9G~xBb7XIw)N?1I}Zv<|D1O
zmXOlelyt?LpAqPiZ8dHkA@OX|$}Dy@%g&WA+bM-zP+M!-F*>sZC8g%VO?y4d|KW2i
zphW?3R~e(<;v$(KY9tC$y|B%J!|z$&M4TL3kGE2xF4%r2?}+ulXIGIqyjSG4V!Q?C
z)mpt@J<W5S)l^9@s&>4+`J%DLOUaYh<0PvL0YY2rmfjGuI4#iGQGs(&U%2Rz2m~am
z6^uEM<D1AK<iK5qEDr2;pK_SE>DU`)mJ(mc0;a3IF6G}_oxL*>PW5DIVR4{+)t>eH
z-kzgK@K0Ro>h+0>WY=N22h%1I$$ckaHqFvyC1%bVDgAE*3bX!gedun26=@8{eo)JA
zg><(2`Nvm-0%EtyEyM+53CxMGy^X0b_lK1uMdI-0pjD=|Tk`t4h*v^ZvE11oQk**P
zkVyWrvp=Na@ghXUK)9$t-pPvwb4*2`;=1M<uSL_SMGxtQ4}Cx~yqN@MXZA;8gjZIt
zI*Uh33Q(T|sQ;7MHFVjyq`431TB~Ru;j<7nUdz-JiW!Uzjx*~ZMs(v+&r`wy&TlF-
z$3L9{1<JO4H)k{7S94|1@|5tD{jLqAr!<sy;t@%^_NC7=PtDw2cCi7I(qH()L{iF0
zTaA7Iy7jBbaA=py{p73j!Sk{4z=9~Q_Oos&tG{35z(M2qUm_puurt=x_;zEhRg95o
z@9e+%ng&Q%80ceYZVlaY6q1DA-+vSdMlyE??4N23)!Gg4=fS7LbR&Dbjr?NX0rerc
zhuJJyqa7Xvg&-OwbVOd0#(6}jmIEWLg9`eMx2V4_F6R2v#@ddvC@aFI(HzTY$5Stx
z+C{;1kojj{2`k;Igx#3L&2aT20+D*KlNjK{YJcdY_ZR2b0tY@)+xvrs$>dKclAWN*
zgPjiZlpFJV(0^zhVy?+ZdvoSb*22vbG>OsDx6ZD_u<&>^FPv(&S=>JABSVjTq)KH}
zGWXrTiM@1COaF{@0YB2G=u{4Z5N+=<q@tD0z_acj=<ITMBHw2Svph#LqDWMk-r;rU
ztOoi1R%OZkdVRh1t!uXzT#VF0QEp8L%f&qe7QJrrN-PycfHkyO3vNKQ-tTI}h#+-1
zF|8@~D3*j?4fV2xi+^j&g{S^4FMEhNs&}?<e7AqC+)6T})#D*1`EK~?Xs-xHF9sIL
zE?SqZrnREJk!V(P6tIk$=%Ih&>2Z}?8ZJ~UKoNrin9M2Kdqj(lFixPuG)iJ4EnBlt
zMw3@Iqh3A9kvd8xg}i(@-?NB+P44xyofDu#3`E~<B_pC5h}?7TrzJCaE?xzy0vQKK
z=wMX=JZW!S{syT!-m7Io?T(MJhM1>U6jN+3+{?S6qK}$fMs9fAK7Xe}8fouhP3Bso
zSS*7Mx-Ki#Py<c+Ozls3VgX{gl0G`Vr|@NRUCb0^ix>Y^5X8b+xpplXK2FX)S8Y$#
z(mrG%bp0*IM>9wW+wxmBi1AK$aG_O--;KewtcN9>3IxRcs}^h+v||&&VgLr-hu<xr
zyc_W+OY!G!V^i!Mn&`tOYD2eu4Wr6`y$DGHdkg$l+F?qq{wxu&PQFbL33RN6@@d0N
z;Ip7s;46&GY6|AHItgf-y_$k|I#Sc_fL<aB;;{ynxN*#8;Hn$<LaJahd<x!Rp-dQ6
zp@CW|7o%9DaFlLGq{R^<;r3EkkNh<g>z_bXmIsI}{M}#fwz~D2L168EK5+TIFgoQM
zbb+MY&Fu{Bx3gzGuDDbshnZ{ia*#+|Pv96ZJH!Sz3_l^#Y)^gdkffGKeD57wU%|(l
z190Yk9*AM6UkPLcIN3TT2H;ID$m3&3D5D-bx#QP{f#WyV;?@^$^gc7=o3d6aAeCzX
z6}S{ikUCLeuLVVo1x6i1dTEr8JV882g_eIs$-Xt1%N0iTwE~@{APK`9j46413xE1?
zlr=fMU&Y3Hx{BcuT{z^)hwgBxXLC$fz#aS_5Gt_z#_%ehcc3AaZkF~OP+D3=<_rrd
z47^p*RhiB>B45kMP{N<K>ya%=CNtF7DJWZpnOmo&^b;#lE74IVdJ{AnPxTU;l;D^>
zL{VKb<e3w_sC|NI)mwcU=S?UGIc3vCItE8CRlK^gi=aseSiO7x#J#!=Oz$=Et?OLY
zaF-xES5W_f{hrRik|a<riVg}k)$T4W)z!3?zBBrOAJ3IXz<i65+8uQ}d~;5YsxpUY
z9PO4Gr2Z($%k$lxtHVuqS1-P-5U}IimOK>|U6%0@-mxAvknOn77Dk1CIIqetZJD-U
z^SjD@Ypmi)R#wO~`VS~6o?^?H>^~1Q66qOm(syX06TPdsVLbZ!L;Gt!fex)OD^IJ2
zJ@@?JnIbURi`@h6KSK@nSC?4=I2H=>46R1aywez_^da8{rh%ipnS5HxK`cZB)9}G;
zE_)flDf-?x9q;fPG@?Ln(a3To8Z-$SqpOXO1+G?hIlXkWoW<cP9~Y2%S{y$UX1JO%
z@sg;up0mF={GvtXo*Dn!VwV8(fop%)uB)#PI$OG80+&JKX{O7JQQ5d6!Y8Nk{-#V#
z^^QwVSiBz&O>Adp;WmQgU5-S;r@ENGlc>qWt~?0X*)=pd%IKoGw5-osdy33uhZw?N
zTHv84w+ly%pQ7}>ZhL5(A>3mE8dan6F<Fg<`L~G4hxNEFR47eh9&#&hduGe+HP^To
zRJZWC-RBh5YAfR%_(GV^fL6okrqAJE5<g&d-uVY|5ZMOgSSeQwO7<dDY`)aMib&Hc
ztAuRe;|^C$l_Ev<NxJy^;{{&XxEZ+w=7tgl44~q`(~(?q3vl|<8al9C_ywBlfMmud
zGT-<**Ox-)n}^YR+g_OkL~PRW_LJcN&=8FLI2qo4oMj>(#C@n<4Gi9hm*59nYv3YX
ze>s_RIQdq*_t9dojR>0TQB#Hpo@d@SYF`=`yBtC~M~v{mn@j9u1F<X&QpiTHM<aQD
z{di!vp&!J2BqQI}mTYEBJFo)OSM<y4x=@Gy|Nl1=@|Lm0Q?$sybkoBBq$ng~u`2P7
zY?u3xS#s+=L;abCtz$O^v1(^W6gKoKqUNf0*U6Cz$k$LO_zihJ-0jomP5zNHjQ4-M
zy=r;s6wenGQ0%gfx;^~8yx!R0C@)?At$jq0%^5$9OR189<yw&F)o^LL5E=D{wIG2C
zfls51e>yWBGWX?lC=(l1<59yJPK=eb(CGBxOLC-jdi>@45$#3WJwm4ZudE;X3`Cx=
z%a!H?W;$?p$B+g1kV!>Z)2R%G|02<!P#}l{>cBf7I5FSeB+*HinAM3;eJDv<RF<-z
z08b(+9T3HVOzAWhN+84<nEcl0mJyHgGf*{LQ7bX55=h3m<#f#+jfML)Pm%p!;sxA*
z-Tu|{e}&g)VETlb^VTBdm?UVUrjt5&Sy<KuuL)V>L$nc#wR`FF_+<YIdk|SA3~BXh
zMFfF%g9BaX$883%eiaGE{+3_nin-fl+7QP`RE0LRlLk1U^m8tdmobXJ2u5011(7WS
zV!f0+e}uB}Pk&0dhweEAhpR5VIn3GnF-oLF%<#ttDnnPobtK1J#f=8pscN_N3)ImQ
zSSl!*`6$8#w(qG_L-p<Ti{`0llwx1H#DO9dP5*Q$yUi3PLI1%n$D8*GeW<8!|AY>s
z=1)g=j9lZywsCqqwvP62NCfB9)KTLYQh%4Y^zcK=N43G_BXgJ*ud~4gQ=0ON!oa}>
z#x;k!hUqU7N<A`5-|&BAooczE=;>5VPyRs7`+4eM&*OSNiSPYx5KA*((zqZ}3)}7I
z17cr~w=&`M15IdoD|)jjQtk7+K#VyhpzRYLO9k~=ps%E|cOb+_6taFZm|SR~X24&i
zq!?$p&XtON`{)y8sRj@+8FPz<2Ci`ZRIiVPV)dt_&1>IIT~R}22Q14U1&o%ZKD{H}
zjZ-KqXxAJftc>{-=M<kni|+xF$(zmT-TxX!B16c!)sS<U$Qr)q9wCqMSp5Z>g01Uf
zK`M+BnHsX7JWIX@Rx6E3C^Ts=c-%~T(!!1)Zz4x;ni&#YSjgD<O>t<|2Y0Qq|Im@|
z(oz~44nMtTox2+=`4;{=uchlZTfSZpDd&%1u%MhTbc5)zsAw@@cY{(?)yUK~aex2m
zzGbS~ddbM?B0d_S{iE2IjQ4?URsJk9>KeC(08yMhdR1PwsuL#qC_|EV7OEFueHPgT
z9hCQ>s0!$~U<v=0SR3>fwf{%P{BYX-!*@>5;xY=O<?$Ka!56}Bk|lMahpN51BFSED
znR=_0yN8Z~B=#Qp!9%v}zYcM`!gcX#%u%KS^u<s2?anF~No7Sl{0XJ(^?ri{)mzrs
zs$ZySGD=JWPON9CwQ~b_B>L&`zbsvDZhQsnS1|fN>07QV!;s0HVlLG?6=A=kzWpgx
zA@h$4S9Q!o{eIGxjGhY=rXM7{qY*`vIg(yOp{n+9K@h>FJ_%NfiOH88udnb<R_QV3
zP@NK?S8EBU*>Xl-840hKLs-i8^aH10R*F%Z`LaI0FM_S{+Dm6r$zHFkY&ETg@eRcR
zSl*v32&f6gl0MK!2dY@v;r<B(H?&Sbgp^hn9_bntv~nxX>(%3)qd;VPnsdV|q+0>!
z+-D(eJ^uLY><@p0K`yxVTUr_3FCp2>7wZI%vJdPX%W{JLWS#=TqFZ_m3Xy$QL3x{7
z_PoH0m{~|irhpsGi4+nQ?y#?=(iiixCC-|K{y8dm%fw%t&}CgB?9vFjFeheJWo`kq
z&f-t7C<|yYE*yw()9s-I2AN$JIs-SCVg_}@-B+5MtozH$-pP4Sa(WYe*v~MIsQ?vl
z1JT>q_nztD1@q<ytpBQ^hD)xl%JqfQ&#<I80_?;wyv=>Lm?dK7E4bwqFJGt@0^2W{
z*@IURop4L++R@EWmlohqHe|YQIer~zOkZ+sFWJA|9>*k<8YCrFTe?=vo6tbRxQxp@
z-L;>3cIBF~weIC;cj(6X2(;qsp1NeBNeBdj9*hu$|B51to!Fm*p(hM%7n=K{AH?*Q
zIfY@`lNf<&ttMTUSMOY8?y7|h{=o8**L8@Pub5})Z~~CDSKH|+(C?wjFR`t@YaZ}s
zpsFvD1&W7V7BpC%1Z22Z-4F<yXlLY)i7J|#xdj<jkDr<7k_%OtMX;XQ{!L?ra@$VY
zLy1dJx=E`0ITudW!F4BVf*}z4iyDCxaufn28!9v@`E~I+bWIhdIReGqmb5Je;0^M0
z9S+Quhb3L}snuJgJUkqZRKF?Z2wz@19iE{bVR$N>H6VA^RE7MY@L>qK;Lu>V8#~D*
z7AxnvpHrq~VjA3c`0?{+pK9mJuWxh!UPNcS+>t+GfsPW0%n^b(e2AYyU|i1~j$!<q
zeOc3ZO!$ccIv$0D#n>+iY@=_x+nS|wI912h_QXPUxGD1c(5yC7nf9K+NM$-da<a94
z)_b7X)7JVB$R8qG!aH%-CF9eLdjBemHdjdprPgU4Gf(yDkEOr6L?qA{^nW55<qyjr
z)g3IsRAYVjQLd&y(f2D)4ys1{zVvcA94h=dHsO5t(N(uAbUTRUm<j7gbUKxq-B}Zk
zTE60)Y|SI&T0=aeQyV+`b^+O&zYTav<&k}zsgq-Y^Ad8q$g(&k>`DhMZ8}_l&31z?
z+v`ROm}t~Hs~|WYjEK*5T3%NU{Cyx<N_??`Tdii5-3k;n6Hnja{>*m)o`3$<B7=%;
z@x^j!_6|!Mn+3JJtfO<*^>x6fVNO8)*DWh+)^2K|D>eF!k92a`E&JmOkf8mV<CF%m
z964~<T`6{JPXL?!UdCVkUmI?Os>CiUC1;JEhD!txs|cU)0yM8OS{d*=6)zd@8Zp0Y
z^Khdc&|tvk7SKcpZruJ702_Q&-Fz~u0s{<WX00AXdeL<sTZHF)ND(XmY)W^^#4!V<
z;8;(HS{pcH?&~7CM)Gt#3KBy^3Do0GO$u3IcXpBM(&`hV67;ZLbm5XK8faWb`EUG1
z8GHI%&;zN%duaI!4+_`cb|jq8f0v}0b-OQh-#YdH<)kI}@z3{wr*34QuAU%xSm=PI
z^99TqsbAp{tSktYN{0cY3GsQd?>Ar>JDxd^I;x-xM_aJarTFA@!Ad8DuNkqtrp;;@
ze(6WgN+~!NhhZ_JyyS%;V5XxdHS^Ndqe%x_Yw;KcLaM}ub;x9EwkM75`~jrF(gWtW
z(h5a+dsHvJ*ux-Uq8<nKU^j3(N2W{OiWkk!Wb(@3GpjBCk3gAQEVFor_F{VuJ0hqD
z*f3_<TuGYUfkb8?Ybm*M39wHr-UBAf>%qv~cI3s|$zs8atM_Ws=RG6OXS3lV8!)a?
z+@OMufE-~h4O;lB&KN4?WOe5=`2(muBoSJDA`HVV<~zCzUI4FJ+bKSKAgGb6j{@kY
zP3C{MrpABxa^3UciL4&;a42ofIigOa#Xf$ky|%yV{LK34VR-8qZpg#v?adD{(-Gxi
zIt&bjV24D&)s-J3FtQy&T$!nDzmu9Tew!iq$_cR?`c!Rb%JK0siN?rfTEqcO0}UCx
zEcsY+sxMp{w}8`IQLj)_bdufh*h3qu-!R7?T-l79<V_fo7ZE*!3^8mZ5z2QyihnL+
zn&Nk-OR@&9EbItaah3dD+xG0;+TRx}_OFyE0`WdfDSDRo>k)HV{^5p&5#pG`|Cp($
zCinZa2nz&U6!(edDmo279gZ0d*A4{*)z!7V9<@@l>1)w)zFtWs`?gffx}0)e<Q9RV
zNHLqO*4mJ@7nu>z<<L`$jLgJ{Hs23wWN|c0FPMTTVRGY6v&AY~+oO|7zW{qAYq~J+
z?}rRj5m1fMV?Dy?E6=a@!u2~wS~*^RzG$65$yTJ99(){Jt$^|lXmcx4f^*8;_eDvt
ztmuL*iiQiluWbK!uMhC%p=&3YFe)l)ER#C{TgNST^}(l;xO4Nrxf&JUDp6_81+ZHx
zQQ_26x^M34a%cd(PvsLq!0I?^RxUg?BR}yc<zO|<H?YmBZKZo)E^dm!BHK!P_HKGK
zBOgv}&3V1UI)y@l_NA$XFG8g|39IL;dVE%sg&S?$V4q@aj!iwR_0L7n!<uAn=;szY
zYNO1sXIHzPt)xuFYuZ7Cmq4Y-zzea%dzRRH5{(1?P{`%KifY}7VF69g!3ou?PJt*#
z=|YRWkK#VrG4}Z~I4tFU$_lAN`N%rmoy6_wu)EUD2dy#A$7dGCmmNGH>tzKgd#XV~
z{^Vu05c`{J^ryFV?5mVb-LXD<<B{b;Ngfvt`aG6@?GYH5u&L~jjqxVL(<Ml$LW8En
z!eN(*(W>~Zv0oWK8G4S=L0g9iboUQ^Ng*+2RuPmQaRlUVq=82dmc#Ls*5{qz^~+kT
zWmZ7W6Igkw<uoleWFs8=pZt`Sm35_KWMl;G+K|>j^jFl%B+ZUY++$yIyX>JQ?r(YE
zU<QuAI;ThHwvKyA2!0HH7TDTiF-SqPx$2IW$+r4)3Y|@tF;XzqFo~vWN#gb+=_k35
zumcHcjUBvg4q!*N>3B8)jG9X|hJa7NjodWL8SAq1!-p?~(9X!z9pEyeX7XeKP6Yu4
zu5D0JwLRNS-2M4XWY{hcs-`!?l9%;%QqdVTP{r9Nt_H8PQrzSNaa(aZI(+T`=p0!M
zSM+qe-`GGl8zH0SrW57ckwicGMytF>WFk~<Iv*Er`qQ&Ay<Q<@m1`}=CqiG6m1-ST
z5of7XuM#rk4R05`w<*;~0%uE<!Z3yhFM=138RvBj0O-g0Ac?_Xe?t>E7eHT&K?g0!
zrotP9OaG$HTWYUSV-crH*v4TdrQR32>q)TwyWis+i*_6Cht8`-d3kx~oBuN(<$%?o
z4@NwONKRcHm)Pftw9dp?hvD{rYvxqA+w({`R|hNkzkQRBYguAPM!&H!UCzemcG^HW
zYO*ebDY@>RfX_eSG+LO&SQ-ABggtF_Ch&KYiO`Y<FPo@0o89Q@7ABrCA*YF_Epf=u
zKr;BIy+X?^`&&YVB*MP=VRiEXs&FkS<C|sAa6y|cM0MReCTQV33h$#Bp%E9M26w%H
zJzeONx;Noo`?NQEs&E{Hk%=4EV`T42hoItoSJEPX&%?;K*zI#K;9%=)|KPBCIQo{9
zUbGjas&X^1S_T}g&|SOsb}v#1%}+hcTxZg0-{bfKjmY^4ds12bL?(?iwl4wpa#!e9
zNhp3l-G5PM<3334BiGSXl@71=*gep}O-!`xOBv@saTmhOS~nW`V)SC=Uyr8@rkOlJ
zf_zWO#=F><CK{jygDu59ej*8dtkcrVLm|f|zZgb740i<*pfqJdTElhD5dfnkUp@=?
z7$Tn^<}dVOBF0$LT)9tX!Ai{SQj^1K(u$Ma{JmNCl<;ovlw0kSri!F99vd={u~tMN
z0F+-ybgyj0m+#P|b&N&cmObN{@x@ombU+i#2%6_ZFk6cK>2!K#(GOXN25}kSC@*WR
zOcS4_yLQW(#9Ousi`4kei*shvyTKi--vCSzz{CH-_fQq1UgyKv`FgCr<h1`&h2raR
zH0qh{OLa!6=JvQG9g!z4k!Ro!LH-Fxs#K1+`@+!F7{T}dxwst<Vv~~k2cqzwuVZ}2
zfT*X|$P1;rEHh*YgK@D#+BZfqgCToj@n|q}x_|5PPcSp@Nw%ry+id%h)K&wg;p~j(
zR2X{+vNhROb~O1NtTyos4$NVj{AS$DCr2h2iO#9$Nbt97@aBhrPE|@~CP9PE`bgcR
zz^buk(&YwkcsMJ)t4kDtP8T<P@$L2w;@kp<x<!P+R_t+l*R*jJ8|YA?V0o~wQg@Kc
z>jCb8!+QykzcCNb%iV<<dUC*2gr&&=n1tTo#5X`rfl5B%7@rtVISRTx_-K)2P@$p!
zLw{awlcl{4I=riNY>&L4^ElSSpKI;F)ZTGvbw5Zm<vK_;#Nl?JNYU}2-5rS9J#XAp
zLDjO~tLLonY?w^rh}?}PGF^TO{~e<LqTccN9|9n)sPb5gJv<dTT5??#`Nuev2dvfN
z;&S{))z8red}{Pb{p0K226?P!jOayLShFo@?eo6QN+!;KYF6dFYhYypR*9l>%hiSp
zF>6fygK^xjQFA$s5GuVjASAnDbDW{^Bja-Og}qq!%JbrVy^Nic?)s<LYmGS(&?k+e
z7AmkZe7vXLsqgD{?`W&mJapNv!oo|qrbrzXAXE#-jd<1Y*aw-j%U!L>(4m2{g{RSp
zG;xs_%2q+!><LGx*%$XkT|!Ns$Fj;!!%x?{?z>T(OP#)g<KyF2EC0g4mz_EB@qlJe
z#vF;@ufkL6bF;Y?ILh$pcpQHE-%mwGr915Z?n06;xA!b1)_;iBAKbLL2s>G!f{(;%
zUP*GVKQlXcGcqXZzG9(Ft^!KXvA<MFPJQ7IzVAHe{u(;5B|rU``?|U-Gp7^p7S31d
z^|-Np&Gr27>-Kry7VT1-gVM`o2$~v$)qOiD$!b-nU)wb^e?|@iwztR?<K-MN{*!0M
z#_m*D@ptj&PbXv;YPpiX(A2r7*^Skxf$|OQfzwyC>+GGax&Fc)0D*(3^5$yqGvUW4
zMXr>2)-jqVMYyQM7mJ1C3ib{Q>pX3>8<Uuk2M(QY?jvF-s8<JxKVGm_$)-33?gL@9
z9J&bz<avYj<FgNx=6x|?C8URe;*qP#Pm0MikFhD5<5y?YKldk+es_%8>k>Paa68h>
zLBCVAzT6k>C<WoIUimWwU#8Pb=tX;&)h&T{f*NXz5Y;v@BFy&PzTJU3PX$C|!DIqV
zW;x|rt9_LXupXhrIIcO+IDHm2wDX*dA}iVr&KtovL;M~Mt<mVCg{W0b=2U22jHA|f
z^8b2!%cv@#rfnDjkwzLx2}u!=ZUN~A0cimxq`OP$6r{VmyHh$2DIId??v8gK@8@3M
z^W*#bzO2Ps`>eCi?wQ#$bM2Xr+S5<{$4*yShVwG%@%u%AE`jJ;XBsA473;r3FizT)
zxub<l_vIc#!wZXD0Hsn)P-IZdndKRz+oN1NL-LJ0uO~c{P=vdj`tpi#M+MZHcYns5
zce(9cPeAu{VUWq?x(33d|G*7XWY+hTKYeFFU?*uZm@%aP&iS1T1M+J(!Dlq&x#{xG
zG3xA<2^O>c7wE6RvqOn5U#Wt$$dhqgWclN?xt|4e^&nt90tB%4QO{<#7*k9a&^h*8
z#giqTyW`le;L=i?BVE_N;anSW0e8rrSpq+*hMBsyaK1y&0?55=!l#kZj%()@Omffa
zT~Sg>#9j)vS_l+G2cWJDbQ~KLITSg4IqnsBrt_?awzE;EYc3~+9<QK%Od@P`HZHcp
zb-y&wAsFEaLf*tk`bnQy^$+16o4T4E^M@ttzYTkBKp30oSeiEEIx2@zUko4rJr!QV
zYRMgh-OQ%nGqRUTCP==2E<19su<NwhUY*1{*IF(^CLkUF9by4)t<&VHrYe!#wmMbi
zrltT_po<!dWU+@?W2uQ~@@j<_A@Y=^y7(3w=vl1Z&d_~TZTodSl(S4!XopGSN_oML
zhK*ebyQPExC15OFlA4S!MX{}HPHujmW6cm1KzgY!5d4R)nw0<IV)Y~!y|U@A9MBp^
zfJckS$c|7w06X!SK1E1*g!0H6G3nLv`TE-nJMM$63KQ%Px?jJ#9=Uei4hr3$@8|ax
z6_~Cb8xz=d;A5S%i&{w}OBX1f;Zxe52uRdTpv$-C&#+rX$DOUUjlB;EtClSentwgZ
zc#<!P?yrB+Lh%?<xFcR9XDVS?3;BkNhk&eO_m1k6xRu-5c-lg;D?RVzKmc3U-NR_@
zr`{1bY!n#<kVb>gFq79h;pJ~}QDtNwq34*rPEDi>w}WLzP`-yQLNmwO9GS%2FN!X2
zgqprmzSus94=n<ikk^}E#>uh@(2DoRk2!$L&*T|T0pqUT>gl}`4!`8>AZ>H#P;0pG
zwgAc$6J9|`FLW(;S7GhDossQvj92$tz6fvZ_H8-^;R!?_G+j<pmQ?#J&us*5CMDK3
za&3ATR9sCPM6k(3*;tGfph><$!#X@#PkBnaW|r@i(8Z(iTnIln1)U<AJYd<o<Qq~Y
z_g;>bUZqd|2BBMoG$A889M}Q(w81=8nG^Yn^fv?PXQwY>?sckTk&FJ|;_B4DNU}q-
zlTPiV9XEWZHd_4E%Q^|T3XooVdi`{J1QCC3*wBCJ|2Um*g$&9;AKY38C=Ra~<Miwi
z<2=rh5ALVbuXYbva;xiazd56dWnzoGty$64uI>DzuK6IukA?51U?kZyJvVn~e#J6)
z=~<Rih4zw$<jVbVA24HnN=GKVepwe&PNDh-L|^Zb?=AL8{?DHy6W?7&&h$0jL4&Z~
zhJMJ6XFz(FFk&OiaK-FeH%l3HKTM&tC*b#IAQ+xRPwi7E9!8w5WX0iHzByj&AY4mG
zh5hKWA7-8(J!nz!pQTXopIkkIIgGO;=*KL@48Ry^pk@GJ*blC^$0Tkx+t)zx?ap-p
zC?0;w7eybngbnj6H;qumf=><tc1_LEe`mAEA-`i27~(fK3AhxGcx`ajml+?LrT81S
zt|X9J`a$mNJsN;NMW$=~fC$D{o>lcb04=^JcKpk^kKV*$&_iP3vhofenU0F`%V3{j
z8;OWlT)n+4hb|32w5934oW!-Y4cf6V8y`O0#D~^BzDEFBr~cYeXDSI39&36#TbvC~
zU4?<(bmk#EMq5Rk;lR>oiLQ_<Q8(*3+kWd%XqHag2lnu3wUM*V+X}G+PkQsNm%C5C
zoY%bEy&4b018$oSe-Hvqim>RAVKzqsmc`$5=P)R%%J_J6cw{H>ULt@9PRzB?=6o?c
z*V`8S!c9zr$^1H+MRdMn0Y#HwOH{RnqTBm3WJ!}<?u@7L(==jZsm(l=e0Ofqik;YF
zPp%8Sf>6N8nO&o~%Kl??E;**Gh!Gi(eL16Uvc+;A>2Ju$>$0>gRzC4GX`&!f6*%T8
z{tibNrf|~QRyU|@yB0Nfb!{@y_ZETJ?0s_&8z~$@sIM03*?9Qr4pBlLYjTLSCda%!
z6h4*dTQT+~`^%3?S{3EG4D(yIPe7Q(kRzkvLuBHtluYS@vnfsfC=>2AFb1{zho%>o
z`TyW|d$RF>MtOA{XEoz5I_HEv(%@09hK=FbmiB_psNA3%HI`YcCtK=QF{kPKp9z11
zq!{l3N8Y%rGJcy)RkpYb$N`JMYVEx$0*{8S5jiJtmNV<%-KVuvnuhAsvQe4kDQ5FR
zTAJZ?2#Lh+F8LZumDCCgIT&$5q+Vu1of$-5lJ4s!#XCLQSo$17@JV7^C#&B9vR6}8
zgiooxB8c!;FiYW{2FD>Z5#GI`@g0Z-0T&WySv!a5ml>8?Xj~Jun@XnC6kPplwCIiB
zO4NmwFK`{T`j~sZ(!|AYS#w*yCGYNL^y3{PwYfqj^{{`>L&j-&eb7&qtOIGQ{64{Q
zB~bTpr*Uu(HM_gogxK~xZG#E7TlQ3uNc8&iKINPR&X|EBML1yP;G!IPBZoW0XF!wi
zqF=)XsA1WB;uEUAYD7NsYTw`R66}p}aJ^~;kCJQ^p!3Buw_U9yvsf^1L%XHSmlqRT
zir*uGzjS7N)t`PxZNkuRE5Bd+p&TL@`qv!p&OSMr=4B*@kodsN770V+#(JEd5eQ<m
z;4rPDmh^7r$R$PF{o$|2PS^TQ{`JvnX3T=aVG(=v){mOhZ4eRvddf=o@6@!=;Y2*D
z-&8h_I4}XoPs`Z&s6eQD1*jv981SS*7RetiigHm<PbXmVs@LPS->RatdtL?#-(t|H
zdbZw-=}mfcl!I#9;fCffov@I6EX#1ef_qdLZD6MHpzFxAHfvBK8cGxeqKTcn^ldLa
z*UqT;(-tdr-PT^PIX-JRUJ_^h=>YOP<CVP|4s}t-qhr~=>e<l2VpGFvFw+&#A;Noc
zXHAQj5A$kkYZv1(FO$74&`TT5E%U;QQb*Z4^i@F2xVHO<!BUs_GM#I6&-;1xtF9j@
z0c&+9y=|>xl4SYxH>@JaJsWq=$CNF0e>y!ON;(x~&ro*8M@Smhy0n{5fmEJUk=c;-
z?pMM@@^Rj>?gzD8u2Mq=yp+GAZ1K^TGVe)kx5!g*PZlKJ(%(1Bmsy}&WC_;$9B>44
zICA|it#tGf9?oQFPso#B2g>Uw3-M>b7>pZ1Vpa13RgBMm?3Ah}8wId9o>z|sGW$Za
zYM1Dqh7>O&iP&H8eQG(>lHEE+DfGc!emeIaI1a&spdk>;vA^1MVy4-}?!)w=%Vx^m
z8l6y3nGFImCE{j^3sGJwcjRMpeX?BAMY)p~LQGR33X&+;R}>@+-oa)rxqcF>GLhOl
zlFz#r9`OQKm6#tn%1<UNWE0rwFNA)IM82$CoQPLCBW#^{MEizNG^cwxhs4UbDUQl-
zSHdQrAqoG-a%Q+@+2i#A{D}xMQq1O2m`r%^E;p)}0-o!po%3;)d9i2wVwlxSl+_w_
z#YoR5Urmi2T#ya=a>;KAOo%F3|IpG200$-f$LLM#-+#MYZWn9RlHk2d(cW7Fey2)D
z6P!)G$eP@^LfjZWG=4N20KYO?MHK6TFPD*GUiQfKEC6NN@8ZJ|A_rwAo^*EyEW5D)
zZ#Dso?hsH{6f7*5xfbnJKHe|??_i3smtIU-`1*p{&5=OCr`4+6w{o+X`RU&3u+GX&
z=2+T%5c#Hz0~5RDuzz+++F`g|tC~SM!hHJUMl`>e&S$dWj79D<b8r#))wmiX5{aB0
zz2I^wg9x7yk0-HhT0CXuRAHyiA?BB0Lnf246(qaqoQt=xm^sEwjg#m!FFjgrh_hE2
zMl)I4`$7^1tNpoQr}56xF*dH?7gB|r0y7Ecez)ax0-G6;aVl@k54Z}zHlp*=2E+X~
z8%_0<ffWK6z%c~^0ARq(#<8PAztaI{NA3u9%T;d`F^`dsg84rgf~Q^j9GUR(@fSRA
z_uWBSN$!*p$3wX!&M>q$_1<mw`&KEsyY{?%+F4ryhaUCe6c2mLWV*}srYzHoXNY;J
zZ!0FYN?W7SmhD_CMaJ|b?a=9lSr>pGHaJ`Vd@cpXyb8?*WU6@XqZweYOc|Zj>iB$v
zd;yRYW)a5t`y-+cQwT^qgJlLFuxmr8bl{s}#eFVk;^$^4U@l%trmm3Op!zg#QfXk`
z5o$iBalk2%{FWWPXXct#;-D}_wD4VprQK@ppt_`egz(wHS_pzuEi&pZ@|ip>R>xd=
z7{kc(sF6JCzfGtaIo(%zQXc=_tq$*3EVf)O4FaWo>B0lN$?m1_;#c55Mr1Rh&)EF%
znTYoBvgr}t-uhdDGL@|JdJV*YH~0DX@m1NLaAo31R6mi(U|$iW%e%jAN_t%8l7hy`
z@BG;06X4r1gxq0v1PS|+=Rec(FqQG{<05C5jl51J*aIAE=Ig3=eB_3jaCNwg-$C91
zz@Hem;}X%s!|!J(6w_~IWmRgkSsC&A0~kaBCr5DPY_|OgWxR92pZV#l`CuV|BSMac
zs!gb^;ItayCL4zTB!$&6NGIOcFF>x{=Sb)2Qqk&#9YcC^bMxY*U>cTphG>AYrsg+4
zDTMGGrrOHG_a1SFG7P5Q4tSddL2hE?C(G9x?hd1wiu8C413w~~yAmBzC}bUbC;Rsu
z$jaJW1vm8`Hp$B-*gJ-{Mf*mSm0t%xgs4FD9gm^KWe>RXJE^8)yEtpW{P+n(S8brx
zw8(q~JFtpMsa-+6VVmoZp2TsJkt5IH83*jf98~vIBAd<gJ%6TPl);nQ&r!}lB>g3l
zY2Bp%qq?jhv8e{(nvfUGQ=$OzapMA>?uZf8hl_ns#8A%II^3Hj(eRU@<Q4Zm_oZ!L
zw;i3qGjX%nTH!BkH6T}^Wvp=Z6UmSiBTj<9yY>6p(&)JJJJ@kK4$s(TNGZz&>Q&U3
z@<aO#pSmt@ow^}dVqZ~+f$RttQH%$|lPuz-jgDLlbBR)0=bYzU+HU-UWxwbiTWEI7
zEI7jy=^Jqll{*vi<icEE`%x*yzw6tWYnzCHN*gHRUXixhq+?|vqqfjdX`|hY22c^e
z;(#K%GvbID`SWEuiUj6Aa_0KZTWLrd<;EeXp09nmjpoc2$xzJZF8Z^V-4GYqk)bHD
zpGi_G5I&$YRjgTsE8L6!_bB5!*=2{lQZ<8F3>65=H%XdoK45rwoI21QrtqHa>eI{K
zEE%g}qUgaJ47bciqXTSe9-D`&{8A-)94X**7p}?;Q2TCVXk2zfxCQV61LGrGQ|G|y
zWqdQ)apf+*vwN>o%h0E=xl2gVPv+DT_#D0qnWD6MM;?`O^mp78v4hTMq9_oLiAIG^
z9qw=68?eBN*@7i@AF8(!Ul#p}{!rH2@6pDaR=1=xj8<0q1(K@StV;(R_83VQm6l!P
zo;DDfnSI_~3W3<7viN}BEYx|RTz?0XcnOm}Ru5Jjd`_xP^p{I7^mtC+K7a@W*q3RM
z2OF>fp`7*>*^7%sO`K**r9aW++b<o_^y6rS4~*^2rSVmP7`pzw=RO;X|5T)S#zpE#
zqW3r#dAEGnL_pYjGTuPyu|_{IXnQs76?@dtOY#=|*H~6C&+cRa9^u!MG{U&!4ha(E
z7qrmcXltO<5ecbnmowm@cE2~g8g;jq`%oQVDmIM>%qc;T`G%|kz-xu9UY~A;iAncI
z99&@?dX|^!BpSXoIU4s8`nuR7uy9#awqiebdy_?v7fb^J87V&p2)n}r-n7TtMK71l
z9}X6#Gy&i8xk)+Y<AuVFyviq@uZ)a3gi#wutgJmnP&tzyYXj0N9Zey`#Bfs*{bZqP
zcKS@0@T+@QN$1ELvWSvs^l4{QehHP8gaFz}!;h?QXRGRByJffx2abe+y26rI!8*!0
zot@WEe?ih~eHNJoN5op~on*ybFwxrTv0mYRJK&?vsFeXoeZPO;?A>8s`GFeE)Fy}A
z$_01*UM6*;g)tGWjJo;yeZ-&Bk=tEf3&X^ynq8;k0kbrlIjLW6U)unIy_u9}76ZRO
zsnQ~-G}a^A<=pGZH8WpU>h{2D#`NW)?)V&Ntfx(`+NLZWjK*eYRW5@4%zk({{mgFm
z(<v8k$OX>U)t)F(f`h<ql90I#rV|-HEvpH-Mb~nD8gPj*8OIsCAD<*$DRt9ZKd<Ye
zYA?YorNnnh=Jw*}OcK|%BxcLS=&+nF;Z%oB<v!F(&jAG9+1Cfol6}nGu5OokGZFbz
z#Sy1gm)Ebv5)i*r?s2w=;xB`xIxbpn?o9P?zz%x^CaUNznG8wnNlWJLeux~k5LXze
zVRM+KYjvq>Rg)4SWMq#1S_Qb903{`vQG8YCnn$*$p{@SKG-bUt3)6me-FPO@9{R=w
zrm@-0o&yfeyRUBWIX=w2@lb+P_wG}sC#fG5!%t<~LCC}KIKI~W#?L>Gma~l!5~)|-
zZFdI-Cw3FXY|8(pk$A?=<#pAD=JK$P&;NL0dvQ=i$JR9rh`??K1$VX~FSbWqD5qOv
ztFcEY?IAvn&_i0q4tB<*dKFfh*|4bj1_y&7wa51qk7#V2VG#g+qM&e7hU%BumQJdR
z;OoVIte^DWTCe_OJgcw&qPvgLk4kW_*vs#$TU)?;(&$_wV2ADu8&rEAbog;#Vx%X-
z_{mSGU})Ch(r5LqqCE@Ohyj1Xrae6a#eF@%T*yqr{~ka3fWxj2p1Qo&sLh$UL|0MP
zTOLw*)E$~qCr$Z0hzAps(#{K*Fi;`@--95<v&?RhCG(f3=ZAMG{x=((!(}%}LJIfB
zC}nLm!OP1YPq6sUIF-enMgbQH>W8E&(iclGgk!-aCL+9kwAV^Y)^TYty*ujp1@izI
zm?Vf`yTKgft(VWdT>@#<uTHJ{XnWQd2Fi!ZK&E8PF%G&{rg>3^JdBwYJMg=PUus0g
zgg%}1Y;HAfop?+{H=oQIqf|@6b^swJZTTMO{2BNatDKao1A^1SJylvsVJLSa+Iw6W
zA}6G#0M)OP>Z9*8+)KPY9hYS_d5yP`fNPhE3O0*GhMJeSRlf45utVfcGJ6ybZ-NHt
zGc{UEu*a5){Q-Aj{6aHg*3bhkOb3TyNrtIpSf|1D6`oLXwUXJ{^;-Z~zMj3vznSC^
zcC3v6j@e0xT|Dr}Af2ii2~IGrO5HS2FzweVB(>vwn%+1r^U*r}8rKykRcN$1dtT6X
zfz8$4b-PnETt<wa5sBdG_4bzQrA$CFX<8y1Y$LGM|5(l8o(f8!v#ho1r74Iv@{WF_
zSAM#FL=1;a$$dJ>pk(!)m>8b_Vp2leakvXxokNMfSp37;r`^^txzo4Y&{fNM^!|Fp
zq+E7#JcJP3&4}K&jgItYi7U~#w6vpCFyVmyfwZ=-5H5l!F(TKY<yqw{J5aLTc(q<q
zJvc+u85sKlV}elqs8GPw?n&oto`T`{)w|4NmzvnS#YzW?>E$!-j`XoEd{n>lwMOId
z#Von>Uz>u>y`KKYy4~s3Xmh35GtalY)2Z%7{J(lqixV*6-3KU9gSV&k$0@COrr%dP
zWk>iOzI`uIrvJyxch*qNYY*xwE#p#lOH(7dN}aaP8e!}aXlSekpY-{MKGNe60ILN8
zV!&b#07MfkH@lPjU2iMtn8im~@D*My-<m<cq=;bi0pTfv>)5IKf{%)|ps&8X*VA{D
zD9H_1BBu5C=nxaVoL1lHP?_Dw?U2y^>n9@ly?F+oZJ{;2cH4e-N3JOrWu}Rr50WGA
z9uCkXPa9rW%l|ucKB4wp;2vEt-*s0-C~r8FkK^ey{S-KfCl6Yh7uYRZ|I!eovh$3@
zGUA~!VJnNPWOG!26bMm~k-~Cv7~9)pJf?VyrzuIm(-d5ST?bMC1<q0fe@kBX7$9)Y
zJqz~;g$s8y6(rNCQ=BbH*`CcMagDizKg1@KI}LN#=X~2_5AOq~%p7r$iP6cDB^+a?
zzx@KI4ktaefJ~|F*VS`$;swuXwC3{>Vtbt~_E(9tK$HTV6cPp!E6Y3C?q1w~JSgti
zw0r0JK`A0J8tkr1&{;!aAv@+vfP6QcKG1X*B$Si$$uH%|$*vfqpc}*so{8E_7*@(*
zsVq5QxZrFJVG#Y&l=qH)U)w!ALoEr|ZV=@I!h)cK-~q$GRl;l1*`(u2=_zD$I@#Gx
zvr8}ZX(pwh8=`;!JPp}g97qYOn`r2h1IUvxy2}i%+~W3$=jA5XmGyCt?q!DA_u!KS
zBF!Ad--LA~KLJAS9o4QT06>8=;wLf&IljFgif8?S&!kokFbK4AiC=1@f(gVU=y!fr
z0wXP6I(xHn>OLQ)_4>KjCRBm*ynv!BI@vX|usH{O3!2Cva&|v-Th6Mlp8>iLjMknq
zb~1H0JhE7g%@DP-yU|V2`o5R$U)SRk@q=Uf_Y*~WBvO{PyLTZ3%&XgZX)wb}?Hs8X
z+AY>(m7NoYWnM~{)<vb60`s;Yp~MKeuv+eT3^=Pm6(0Yn%60T0LGt!iG43<C{sPDj
ztpo)Rqkk;8z`%%*9<LwrU(*M~>)}T)G-!*sZ8>83G3UYp{521e{4}4fO#_;((X$2u
zIW&sN$BTz)?#<9`_xshX(Q3;9ZbrP|XbEtOi5GC&&2)4`d7|9O^+q5V5Ir3mb!Cta
ztQS~klCQF(wcU@qwO!U@WP#o|oWvzzznJ2*1d<n={JJ9MdOurfA*8NziItehN3im3
z*n~BEE$#7!NDjHxvtKEQQz=7{1{?Nnb{05omLIRf0_VuxPgt|5BId1lQcfX>8Ob{T
zGppceXzjeEDDv3;bkr-E`oq@S{F0J&;1mrm{NF*eYP+}TUO3-9K&(b5NfWdOp^(=`
zhY!3<?#3KmE^A~kRSTw=K2Za6{6a~Ity@?qWdKkD8;K+ohLEMyt~@{Qr@i6Ou5J<(
zMA7VP?f6{qXb~U2_`g%;DgECO|E+KL>rMfrYSo^1zJ<$Tj{Qyj3jD)GU7Lez$;9@a
zby()%-n?F~gbWqS@2^C(dM&HMiHSdE>@ifN$?xX*hLQz+DZC!>n#s?(l<AdIK#~>B
zqQv4)6;#+=7|y94HatxilT_4_#*^$}>bkeZ`_W(4H|I4TwmHL7`1~oBoTF@A|GV$X
z!3zu8K)rP{cRFmjeDHF+Bqvo($g{cQS(M^Mp$QB8>5GUSykLc7-GDqoU;aHmq_VSb
zAY6s5=zEmDx_-=y*J$XglEVZ_q@;rd*xIA3?^c{cyb*hZ-_mqG$Hxy0^?soyNkv(A
z;Pe<M44@p@O$u%!mnt!gcH{N}R_&)BA8*cFYD~s5?UpI{9A$B7Wk=^~%p9gv)&1D0
z5^XZ8_b)~Gv{ad-3egZDWb-=KpRfcgb=!s$Xa`qoFjv0lQ#e-?Nv*Y<^t7sBG_4x6
zfO;f4I15M1R;{YVRmNs#dY5vFy&*H}h;VRl2jeALb>ho)R?E6k_OoB(7z6|s!zD{&
zlkq5`Hc^nmf-lIXS`oaa(;r??y2!(gF%(}OeVdtDyWUK*nkuE8nwnx{VX->%tG!*F
zOgI&!tqIZNsPQ2XdVGj;68ux*R6J^C^}rl`47!3#mwVUf_|fQ69<^IcL$^#qtIo^%
zuL9!BLO+X@^L-sTO=>zi3D3621$A*G+{?o(!`=(#hXrWG7mahen^P3T?*kea!56LS
zIEBujwRWtLoH$)ZL-b4P+Mur*HxX&!sw7+)R<W$&#*9TQcR_F;YB;f)I~$5n_!514
zFmRD-uUyVPkLGLjbVq4U6Oc#bOtG}!X4=DsHtI<2ua~PQVEtpRuBh#=yWJ&8>+4gT
zSF}ATmkr|+Z%|zX;l^@?FON_+OKJB^aEQ6&NsEQrS`~T~lGRqUC^V{H%XF1Cf28(5
zA1xIK&^&kVNLT7uqAn~bQPDR4aKQuX#GAIXDYvYY+{MrF;hdU`&qnhh8}a;bUe&nh
zTg-x>?u%`*^F0bDlc|uV*3%2-9wIqXp_iTgPd;cS<q__E!?~ZUJ2kzA3XS0o>P_n{
zU0*GHZDVC6Uwo^desFyqQFG^{HFuUQuB>C>?L!FbQ0sctz<z&5aQXUl-ttB1T^-iW
zM4Q)`$<|kmDAsY72ajT2y(rS4d^mHw+U=)Ji8Dn|vHj|+X_Ye<(*{9qXKk=a2sb%{
zX~{1QLr-c6>b9?WourjTI<HwUkYF9~)mUw+96;-KFTEBr)K4#p8gu29@52rc;{`>a
zo|BLbUpOkCK?l-OnawW_+Y48VAnKoYVWFAUJ!$MKxrXBbf<v@_4UJJTFzB`=bI3d0
zn-_<t(f@)FW>rUHHs?OkrZGS$^VgC4+%`j07B$yfv^rRLD)^@|n-ZaE`%jW)-2^Rc
zBp*V2uei_a&v2Ry;mU=RoJ@aXrG3>%biYqGa^kqo4dJVuSQNUAj1_#RT2ZltoPz-S
z5nTOS_w2D8%cOU*TD5J!R>73Gw?;4ZtBfN1F<Bs0#`6iRQacu=x^qLc5RY(MvvKlx
zlkVh}^X-M;z552ya7uSM8mtc7-CG;KIE1G^#EnNjGQAmLN@J<*hqQ4!h@%l$jQU(g
z^n(N&$DdG<!rq+FeH$x#6uZB*uc73CgOf`sDOA$YQQV)ucBV#x8x4dVUo%iw`iq1M
z+;V68pm1T~w;H2+OAGe~#XNcSfhhLZOe&9@7QRRqGI2Che~{QOJ8EvuwjQOFvm)4H
zRg<Ug+gzm}0@o@z_Q&%TfXZp4nl9WkRn44Cy^f8ecAUss&vRePCAjAp7}S^1z}0KK
zd_I|`%P=lOvOHA*&3h%~;g+*Qr}8MJ9<gU?#SN$QBZ8gx_iQ2Ya6+57*Qfhh`IORV
znio=s1mt5LSa)T$8(uG^=8i9f$=>kH?s+tQ7tSgy)GV3*_`H7$4vxLjQpIP3SvwDb
zf|So`?{wT&;QZD?v)m+W2VR(D;tAXy9y+l=rG3?dad#5$!&JFUl;G8tFTv}%^eL8u
zN)}S@e6R|Xy3X5VWOrA-p{D$OU>(1N+QznI2D21Zb3Fbaa56?m3R{}dBt57H9q!H?
zPiQ_gF-NuTlv}+Ozu7%dI2+i#-3vs53xtPp!8%8G`ho|H2PBM?iW97B-ji)9lBg$v
zoaoJ@FP|fSgQkTfHorCb3>NnfgOyeVV{7rvNH0e|zZR~4G#p&=@?crZ^lD4vYZ=Wp
z7b`^ECvchl{=;tTrWth2<T75aIrO$Za37eFbTJjxJk{3MjkVlEd!b{0sUH>V&caVz
zwLB@uIBG-#apB=cdDBR*B|@x*h^S-S9Vn#Xyl+W)ZIy*j7X`49i9Kxd*}nru#sr#L
zIjylge3Hf@&UJVC@+myC-d~4_Y|4zlr*Q9+x^E4%?zUQJy1Q*BT;Z&XPFpT2UNZ=`
zZp)v;Ed=<)AymtpnI3tE66n1|gcsHkSVcYGNyyZ)8u)g>N<OIk*s8-}+UadA_7pB%
zEKB0d{1}y`41|<XYN3iphI=m86MitYN~abY*%R7K_22QZLTj;?ui-nO6_a)9g-90)
zaWumZ-4QV!5N?N7n(GC_xmP`=b0uBpzcam5was@+i4fpkPWFViikLEZx+J;a_Eypi
zR@%=;>vPf|z<u~pz-8ROd(7_ECK1s){wgKPexJj`ToUQ!+^3EC`5UpwlM~zPd(W7d
z7zQ4m>pQRZb=~3lvgOxaIzHZ!z0^{%@IL7`XVOzq0ai-SybQ8>pZyT;4Np_nd6_>M
zjd{||x&@yce)>)2PCgTI3>mb4{7&d_R;6;{DiFnriWs))QQ>;kW;Gf3H*S^;yWMFe
zc@D4b*Qi_FQJ`myNfsmI&*Y`=j?h@tTsP=6vGA;v%t9XdWwNH5?$?#|w|Rq%=W|J3
z6Mv(ANe4zu#Yd`}^rc%eA2>g47vL`A>^`L7eF`tU?|S*uasS~%jf3!cV{=H4-n_y-
zj)$WJ!YKSmf!r}4O2j}FhpU}d#gfoJ-rH66j^m_o5FffOD(gK_bJ0BTN&s`BBQ4*w
z4!w>KFiyhXyXPj8s&QuEf1d0q+;cx1+*Nz%?~X>B)z3{?aDmF9ZU1-}+zLH<^FK!Q
zf1$+c_{=1TRXpmWuCApW4xF{W|EX!$HNHItoG*^R1fBo_T*lN~Y0!?3x|o<B4FffG
z*9HyT%4^^;Ik~-e><vVP)g%`<<#0uU>x8=N7$(qr-o7G2-vMx9X=&-GuNoE)r|awM
z1ATp?V_6b00LJL=I~L?nWyxi|+n9r<)x;_e8k3fh`PJK%H<yF9OAJt&UltEE>(-WP
zsw`b;@wO5UdrQoW7mMdH-q<ge>?h{8b`C<1=>-Jtzo*gh@XV?{Iun;$I_WPSoO(=;
z@EwGKH*^a(hd&*fBDAJQ8t45}O+!-~-dgJL?K%?vS#&JfCSG#<&(eBly*+~ex3=C{
z+^#F@r`Ofie?_j{1)H)y;O!bMTCV(YkU7wGtG3x)e!A;8-T3OVG{vFGY;mk_*ozR>
zBB=Acz(L|%Zufs$thGJE*Zt3SSyLeWw}kiqSL(%VPIcX3_-bUtC@83_p`kHXVHmI&
z*~tj*QeFNSD$2@HyiNzjNmi{I0G*t%SV8p$d=vYXfsb$gfF2u`{L-{1^hM*<YDi3c
ze16A|XGQ7h?FXOqRn^t=o}v-Zv$3h5prGuGWd)GCtYJU>A>5SZ_tDd{l~y6OxVoBM
zBQ__Dgp2d0fmIw!=&pPmfG>XjOgAfS5PybAT25Ci(@<he)0pd%)aajTV+oP8AI9>T
z2jv0@2zQe)&82<Vq5!ndXBymbV^UH|0?1t@{rvnIZ+40t?$4)p!4sbvKFt~P<yM_4
zqv7#Vou=rxxV*+j-Y^OwUE`7D3O>!>d$Uy%OzNeR?LG+m;W*fN@+sr_N?CX$Bt`0a
z9;)5l-L9)abdwpP&%ns13?l?`^U^#u0e0K<@&2;Lep^}K;R>qR>fW$8CbBb>z(&k&
z@)O+ulkTs_b7X3OGP}O9@ePtHAT1>|R&N9SsHBAbH~V0%CpB`5xL-#{^xr%hY3CX$
z%(h;*_FxE5H{_X_AHIOT?+U=;a$0my&oXRryW~9Ggw!!8t;NK~5=$A~UhF$c1O)~M
zqfS&AEBuO!Gwul|Gus$SFm%c;PvWvleIejly~s*sIi4-G0TQE`+nWzWGZ=xL;(UcF
zBxMA)cC!@==?CqrSKY5QD%Tb&3JV#bm$$=U!7M~YE;EdG#yITu!tvD3hNhF3*m_1e
z#sW(-jJIBF+8R#JjIo6`br$93ia&k&lvv2qJqrr$jhLRdYO_!&>;pOSe$hDW&5Ui(
zM8Yz#$tAK&fizYri%L{R6S;E4y!QFI@+pNQDST#qKPg=Sigzy@-!V%f;tv3v*dMJ3
zOO3l7w&bPoIelCG9mGi&k_;AN_jeF(*od8Anu1|(xWkmo$Qv-QEn?#=<Ar%P934Zh
z#$0j%sJZ3-i=fg-stGn$N|#uA+mkh^r|?f4w#ReEj^F4sZh=NPbAn$RhJw6gMSvF+
zc|ATj_M$X#nEf$1TxyxCGL8VF+yOXh%?blQSfyta?ut!Lhkrm)M!Gae9^XNI*MYM>
z>=hXhB?V50ODC=^sEF{I&CV6M3Tc*3)q$qmaU4#mObiSa)KNvFZ0B3VoB!V+!68Pd
zy9oU-Eg8A}q8Fo?_*3lEmYbpMFGG0_w+r+A`8owYr-N!f=@~Ks*N=7^Lt`XX%^6o$
z&P~t{AKwy9f{~qXWkpCw`J=zcM0kd2(jAN+{wA=-Z2H?&p{na&Kf=i}Jt4r&0DWqi
znwlo^*yS!BQw<DnHkhnjEDPyC-OtbksBKECl9Ie0&q7ZdY5;j{Bz3x^+AXvfspQFn
z`N%C@(=#($yyLOY1RJ5A#A%@n2zGp8Vm}<$5$rf4GqWmi&NmqPynlaveLXNdtm@!U
z)2v$-hSEjH!J+DLdtqT;tdJ(CS!bCd7eoaGA>60$u1`6DVlFlA{RH8ovT`Qi!qd^B
z($c}%H%?;8Dk=~%r$LtyOUpyp1}=<ooQ1l%iD#PaZCw02c6nK5U{}MN0@Ss&lR*$+
zK*rNs&bqp(;^CyGDuqXEJth}>vzl$5EdZikI5Z^NL@oBaW?HxC{`RscKYwds8Z4ay
zFcW|eeUaUxpC|K`<Z4X?osZHRoenKkGW$T%wdmf7tI73qSF$=kE17p}Y%x($<6Hdd
zDk@oEWB6|P37Hq>z+r>df+4kT)WRsZsiEe-WIG1p=DO0-)U`&u199tFD=I2B0C(g7
z41^Xi%WAXf5mFx8-v=i8b|AVtN!zm3lIt$B-QCr(ogO$#juIi<Tze)4p{(Gzak8?q
z8Xd28Z-A#_?~2sRID>+MjKBsvN}v@buo));K9&gbPtKAGy3c@IXG``wll}Zh?Fiue
z0>BegK7BGlMMDdtsRHZ=_5p~&P}Uwm=)bS-J*zzLU8}qvJ=r{N?0+RDej6AZw2(i*
zwA_1PxRq#SD>^;OHgW(fn{4*LCMpi$PSR@ZIY_lxYab*sguR}eXmNAGB_uR1Z9b9i
zwff3bFk1#vZHye4U~l@{nv{cVCO}d2f}UQ>0;(Dwh+*0f$8!WC*k~k~Cs(V^l2%Y~
z>1=DPi~np?G`F#F?!bhy#$vwK=rJu799Y&|Pm*OnG{VH_XgtscunQUvJ>1{!W9+}P
z-2KlWWnhEchm|Sd>l`p(!oO#BVVuM9GW2UGYes__`hUb<c^dTK@6-R3oeWq0rzD~O
c|Dbg3W$88Xdt%p47S_F#nEZ!Qk<Z`%4>1kz<p2Nx

literal 0
HcmV?d00001

diff --git a/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_8_1.png b/doc/tutorial_mondrian_regression_files/tutorial_mondrian_regression_8_1.png
new file mode 100644
index 0000000000000000000000000000000000000000..8557a3dc13770007daf6923cb0a9b1601d4391d0
GIT binary patch
literal 21550
zcmZU51yCGs@Z}~X1PQXZ%VNPLxF)bTfdqGVcb5cRBxrE^VZq(qEd<Eo?(Xh#!~g28
zuIj3wSeu=h{`%{F{rWYbU*#pyP>4`KAP}0g6jTuedJO@AUZo=;0<W-k5a0uU_?*Sn
zot5lNo!tx^O+a4^ob9dcoUJVkDO^n)oh<BZKeF($a57PtJ3HGu@w2kp{NE3-*g2Z9
zlG9MR0AGS^FQwrG0$~`u{Jts>$+rN3lv1UkpOxLy4j0|raW?S~Pu#}2g(w3#6_EmU
z5s+SIF^V(Iv?AfZy~x&YR0pAo=gkm@8NMQ}Lw(KQFD1#3<r(sc;uDq(!>-peQsA$}
z;e_N{Q`6<;^9}ad^mM+G=8tA=j=}+h#vDIjFqjmG2mT5Z43611gor|+&@MJGB?JO7
zNP@k*$R-DU`3wOq1bB=3JvJD43oAGj2@D1!uT%1atp5D@6P=I{{QWzK;5{OI_2n2e
zIa+K-M@LLt+_yYD`!-}n^3budF-&Z1D*pGt;f|jyEg7<Na!PG;_Mp@h6rfMHBGhbb
zU+p=8Qy7OI99Z}C^eAihmwt1BIF<^~vav<KoG+$thNHwkKqd75yBvv0BdE0jNAg!i
zMgOreMR-;;R8UB0_2`J@<$Q|Ye`CjFXG7A2yzp*sZ?nZHkqO@;LM0`;mzVXNKLB_2
zahMijVq(JUu=ZwQaS;q$HY+PDp3wNKxWT}Pi1%s2K14r>c}$0pz%e5h++cbJ22l+S
zLPJBtmHmB2ev;U)t)7?Bv9bPZYlbgo;?KYBEUJx<q&YMg!UN2@qLK!fFwo%tSQlPN
zuk`yron}{DKaq=nVg!Qj6-xKN49S7JpJoGtSF%FL{5$o(13&rsbVS_NFtbb_JP!|#
zDzfXx>*f9yk8|reZW@}mhykw#2M5uQ17NTM?v!+{92W${_Z<t3PT#b(BQE!+jV21d
z4Gax+AIw%4TdQenzgJLDkd7q8!O0Et!34*&aFY!7G#<QO7^l>9{!lxn5)K<Q7WMrW
z8ciNIXslKukprwT7#14Z8BHm6vJ!w{2+tPBU8L9Zy`=%J3MmJU1lXsfgh}vjCkF`?
zRZL%>;_hNM0Jt%g+3ml{AHif~k$_u7knmx>efw4=qkO4UQB^f~TF;LZ85x<4=qK#w
z&)yn~aS-VA^c3{Hrw0mn(8b>5(19=tD(dM*oC1W2>HX5ul9Y^0SDno~Lxx_pDcXBP
z1l2O_&`45&uIXuf;AFj7Vj%~f?J(GYt62MtznR}sQ1`YIG(?e6rxvD7&mtZ!JrqRZ
z_00IVy1#idzq`I304x-!1D+#!I<6pM(CPmc*M$=ijVLfW8k0$<3Fq_Y&o>w23h;V6
zJrZGW0!m8Co8A0OHjgu7?+du(&QhyD+wB&y_etkF^O=gQUL*N5UT~(*72%T4wGK56
z%_?ATUiT9UkpBKu>CQwU_S4-yYmN0RmaeX@>GkQu`KS?a%A397a!h>u5S}G>gva~q
ze&A}rSXhdci{Y$ou=-_R;>*3s?j3jzyI~K~1F-Dq?RrqCa`XA@TXQV3ubY*J^!TTb
zQ_6BJd4!^mKm8%i@@k<lRhz{o-1F^G$>lbmSHJMB?zd9yzLx6>fjWv*iYv@U$#V1a
zyUm%N|Fv9BX{pzkgP*S3pI70M<h@?Uts!7434Oq2K;Nfzz0mv~FV%jk##jOS_~hyS
z*l4OmgFx7u7xdq^90_1OUVjfWnl9DC2Ry{|+M$%uVyVT0fY+V^v{F4P+#QC?c+!in
zx6-(sQ9R>1S7Wg=TTKVj?+QdF;55fXK|xtLJUqN1mV`oGo44b>fW9;K(%js1qWkB-
zH6Uy2yOEFO7x=E$UJqCEb|=@@lG4)1Vd3Ep{iJRy-54T0{p5ZT4Gj%pP!P!VVCKid
zg7!qIR{bC0GVMlxqyETOAaoMm?|?zoJlbRvS=3uSYiHNVdF??#K|#2KQB;8I1Op!6
z3@pzYJFq95_a>0vp_3F;*`#vYF!)AAM7#k3hN9x;o?2bU2FI)`a!gjrr<P%X13Fif
zuVJyC*GE5RtIfK{$FaYC`=)}Jdv|-QGFw+t@;)XeM!nkf%g@I*@7@VGdy|ushv=%!
z);h3HGG?X7{U(Z1dAio|Dk~}ZyGsCsv3X}Mbrso%hll9s=<mQCX`1!c*e#Q7kE92L
zguKJT!YX<Q3=Vz`O5w4K98Tp?^U%3$XS7;qVE6X+u5jFxsUFoM##YqSv~(Z`cGSt)
zSywO`F#{PR3rpYC;XJ&>!>Rb07KqnFZS;6&aEbFD;my0hQ~%}3;=d;oBq(ox;!okW
z39G5$d~y4LcNh}=k))>+N(u%yC#yR%l~l2@u_ude%Lafu;k_&N^zt%#xH)xyy4_yg
z+zhf^Y@(#1Lb$&=>>h}r0^{K!0=LVgRrekaho4+sm9e`_kB(wQ30)CncpdQt1_n}a
zbK}{yoFbl`pR+q|NYN}2K_ErFb-%);gk1=8n)-_!nOSKeMEYz570$bgEjMf71nh=y
zI5;?fobrQA$df`)kOXA^{CK@SQ)xsX;wuO^lKu6f+j?a`S@+pyf*H8=;cV#S<fJ24
zI_@PW4Gau~Ina`kk=+2%>*Nn(x&BzLG_Zf0XUnpZKbmC&3)UTsCMGYB&hL5v+Z;;F
zkx%EZYQYDxP;;yz_R(Xux4ort1&^%_HVtof%6nQbRQ(Sh1O$kI2r=;IkEp)Be*E^!
zMXdE8_w@HWZ3T^-JTrr1oQULZTKkMfudMtleTdWqrv*7M{RAg<64tevebxHeUs5TH
z6!RhGOK#Qf`B6Pfa5?EpBACd`kCTl;LL~~_fE*>af<1u5p5O>>roF}{MF)fGfP`!@
zGF!s<;X|EZZCh2ihj`Ly|0|L&qvE>XZmp`yaE1uUECMx6|CzUmLz#WFsY#ziXsQlQ
zZb>e@+(->Yy(^w>g==j^d^>GRX8g$XY<aUjIp@+?@=aUTiEn6vD;~UEeihK{;^5-G
z`qb<12X$VoYTX8b3h~QlYJ42`OhBI;EGEO{Es~Ls%XlN&qfBLp$YWYL=OJrHN0BQl
z`oOM10D1fPsEq%GMlv!oib+Ty0#8?0vu2~{V)h=}wwadnJl=q!Ih>+b%%uVYwjJ3F
zm$<zj^u65Y8+B8Dar*pjuVXl<H4eP!93DoitgMt{3N?1-_^9BFl>53E{rCF(FdchQ
z^DV=I%s*#{AsYcEAz^4m1se{&(@DIpyN5^T@R#b=U?{Z6*g{Im)*!U%hjPv?bc4vv
z%`K9S0+L9fB9O%&Opj&ZUcmqaFAMS)M1!w9|95z8j6-{bRgsp!(c)!Y3WA@GOU0Vi
zR9CDMY{mnnA1C;qG65VC6a2x2oiF2?aIxv+=Mfod4|V`9jj<;<(bbOkmJk9iT~zWn
za90-Nzk=Mqg^who0|3x6)FDTchO7T1cv-{Ue`h}1Cyj6RhrBCS-w12@Y5T*Z=;>J0
z<)IvoC{}{Bo`ZxhID*K~Y6vP}JB%uCLMy-!+S0rR8=jACiccsk_y6BO44<NDvm5Jw
zpc#wY#R>WUJmSx;HR%2SQ5$JdsfTVU0{)=Kl&LD|(0~dX*8D&U>~9h~0?&dy!t2+s
zG4b$%I|JUW|H~3{B#VVfO>YpXyKkN0dN<!vhsmZhewWyb*ZCUpPGlLvA3<dN1v*ux
z6V7hbr+>aRtX^sORz^nVU%8&h#->H_9x*s(YGP{T+vBb6N43>FaWA;d7DJ;bq;re~
zKgmbic|WOyW+{<bOLg<O(P{f7&kML7e2O9y+7RRfgZU^H<u<>JMfMJwolIa1W_MSF
zXIl*$o0z-;iHeH;71mQ%hjn%B>-hs%QG5+Rlw9#%?z*})YU(5#NmU%!s^l_~`#ThY
zZ{5`d%WA3Le6_DA8Z_=|a9EH2^(#=dR5N+ev@e1X2?eF-CNwnk8-pMO@`X)GnI_xO
z>VYR!L8eMUab#3Lk^QXraOZwXGgZ$>LBo%RJ^>a%DuCDQcHDQp>}ThY8j4K^VPq^`
zqvPUIDLjF}oW&#AOC`shqT}_Lx-%||p}#xZ88LDZK#nM~#<T6QIMUDz_cdfDjjA`r
zYUL8n&NaP<5fW6d5fF+W07PbETFw`ox}*_~TFd0V4Fgb&{kXvXzoSVJn&o<38$RDx
z!@9b`<ve*G99yuHBfq?XyM(#{7_8E$-*B}nNWIn)cWr%rQ;-B4bEMI1-qLnJ5&3(2
zZJn#%ayceliH4hO*m=W*<#3lg?wg`%S8ngUv52avEv09BZQmZZrM3rtv`58W61x$b
z-;+0!ZVSPySFd>8jz~ZhA3h)n3ky4vrN9PEwCNdwq7uD&mmQto6`W&9Op|;mZ~Zz8
z_%tr8$G55u_BTU_SVKc&XR;U{LPf>s>-%sj$%i2e-Qb{QUJBoeOQ7VtbnG<>qA^Lb
zku8S34Wh~8A}e0NGUP~RvpCg7cchUXn1dJ^5|e>rO4^o`Gu{KPZ{KSqVqNU>-}4gZ
z&?Z<|$7^BFsy?kj-C@Cir*9UEcGlpw&B@(p_H)bgCy3@GM(m?|HtVFeRj^cmBI}k>
z-xvaKrcIk7WLIsI5|UY7pTVYHy{F&QyIq&jpuS%DXS!%8jgPAM*}EE{pX2N#)6P|W
zfwp|-&N^AvL)=||#FR!fE-3r$FieW*+OG-OLgVd>_ZkE*YL}Lu1Z$&q8AJJjW%F;y
z+!c*yLQwIC4`s9BuESYQ)BS<oEn5Xxs{_Hz5Gc{F1eGPMD#pvIF98#<G?m8UbPt$&
zd|YSucC8P(K14Lv!Km$C5{Pvlv-{si`^fA3#V7Sw0xB_!g`gG)oHE*fhB+G}vs@&S
zygnU^G?>9Y0*FWwOv`TQBAQ8HDidEtp~hB4?LVqm$mwmr^w`_BpNI{aZOvGAAFQ4|
zta(%DwRYXXFJDM<9IX>ltASIDVIt+4hG1qTfB5M1+!(fu7BB4lwl^=S#hu*N5*(ST
z6)1}rkg>^MDg3vfyk3?dl)u6%7zVR6u^^3O<xcB}npZl7p|eJh#8%<%P6T<+HaS9<
z|5k`YbHZeYzJApX1KBA6C7Hf8CMAt}Q$fa%Au}<}g}YO2?{*q98AH?jPi6?w<n|$#
zxzMJ9_Sz@$M7lfMVA#eQk)AC82jV?XQNEuk4nd)g>jzA`Qj4z*{@pAh0b%=xu%-`A
zc-$xqJz&6qs2%Z-OyRY}{xd4CM4(jkVm|Bo@MZ`I<W^IBeB%2)MFcHo>hHt4r^T(_
zlL6$*h(S0~48;|v%QkMpO0`RWOwlPyST06CN7?9qgARra3>w=ErC;QroIBMn@oEdk
z@sw(8HoLFnv^+rwOIm8{d}@3e;27abD+!|)|C$>^sI`+SGLwC>&nv(&+Im*1H1^W{
zHay9j9D->PUK*fc^JpvJC9o&=gNq)pjgceD%=<l=wxwT+F@fr_2`Vejq^}@~A!8hy
z?L*l^pV+`~0_<i(=k5f0Hx0Ego4M)WoaB*UF(^vq)ns`~N?qw<*+^YjSX&WLtz-4T
z<3bt*evc=X@#4QP)HRSEw&fdZs|_hG1J_tD^Py_}k#r7czy_o7C8~T^m?A6p5gV)R
zB!6V)f1@}P_P7l{*fh!RrVyBQ^dW!RRNcndFpfjgf)1F92`@+Jx#g*NFx6M!xQ#BU
z+t8v}{Ae_t_LJ8U4M1*R_Y;suVDs~zaiT5mGs4C{is9u~jFW6zV(}KjjE$Uo(hG|W
z355ldj21NBZMOWRt61Nl8OeoXS_Qz5?%b(BE3=ty^tkBY!e5HLDB|1VfWePs@^;fi
zY-GjB!chz7+_dDgZ*xs%ydV6(%!jurQC5X$4^70iobmZ2=AJ$HFDA7ZXYQ~KgyH+m
zhG)le2%bL5r`DAVNB`6w-Ibjy{4L<yNhZkY#t4`i!jCh(wt+VEM^om_z|T<F$V26J
z$qwd#L@igdZ`~0udfKq+2O=zk&dBkd>m$vUlOeJT4=c2nq-?FEMM_w!;q(C^4(~h|
zX59~ku1Bg6^C((!tZp9`N)49qWdzm)t@~aaCuh2?p4@8xU3Nt2|9F0~T11sC20_*+
zLNK%v`jDDv_s`wKB*A?eHgsF3(#H6KrQ7Lw9t|Xw%qKMxJs>@)z@ep}jf0I;DB(c=
z?rqGz0VEro+{l$`EiGc3S^KK9O&3@xMW}-XAyB_V?;H4$&Q5ViBeyJCJ_m*(#URlt
zO~&;fjcc5o=_?c#&pV89Y4*Nsl;cO2H>G>;*6Qh+!qFvbWvsQ`etY7#PM!RfRF<CG
zWc)zSjO}=kjm6$ibII#1T>oeJ1s~4-$b5-5vsRJGKDO|YkD%|ZX1oORL{0&Tm#|PI
zxJqiI&Zp_*Vz!NQ<}ebwhpxrgBfIO}p#fGyQo_=CpF79i<MM|(J#7kk&%<j3cc0nM
z+WPezlm0Kqp4TVf8!1};Gv5=pz%Od{nOZjI@@UMxr`{A2bW7dAq*mGKV)kK@GAE{m
z@Lx^#;e1imWM=$m00eXOI<?u950lfUa0qepFX>)KuAB5))7^|}f|DhFlA(c8A0tO;
zIjFPsS*(xyq1z$XA<MiQdS;nGBz*ZPXNLV!nfCyQRVBi?r4aR8z4Aw2(7VyimE_E_
z^2`mXwuk<V>Kpq_@T%-o+y$?d#P$^F2o9<L;#f5*j>uN8;QU^V`R6DeH3ZO>$QYpx
zaS%#m@Il`6P)T)Zb(D{quhAI2@SAkvsdxyrL6WRWFCN&@W9TZQHvk3gtIF7jQlfas
zC+fz(+K&sIU-^I_*ZF?V<5ryJ#`Rn}t$b=N8;Tzda50r#<Nj)-a{VEUQTl^roW`5@
zjGopI=7A=~2xL@Oqwt@_262KjTw*Ni@p{+mAvT3cty=_78UG~VMi}>ojIJsv358IE
zhse0Zmu(07bU&ld<lT~b&`)jgg6W<X{B)fOVvAaGe`wm4EUCL<idf6`V$3VjXke`m
z+>!wBiE1Em((teo3Z=5veHe(aLS5lPx{UikWYh3VE%5q@Kf+6(??08t|HAUhMzs@M
z=SrBlNlN)uC3m+r{bs)85Q@{7xLd$=;RX$XarkF6^V4lLOz-dX5n2Qn>~Cs+#2(^<
z%5dUknnY?1rXwgcZhO_6wwX|5Hy*#^#wTP=Ygzr<FXgHtm$ji~oee4HPVXrtx5Xqy
z0q-u<hQ4xCmh#=&HXgh#q_qZvjb{?ID(;=}G@*zP%D?0}UfiPL1)m0^;*{)ttUuX)
zMvizoiX|5-MNc0Bllt%dicenI`h7`L1*_>yPB{3JaKuM99H|_}4Dmk@PJauU@#7|z
zb<JCij4D~Km4)Y9Hr$ndVEjng{5=3)=gTOMTFxq}Y^6{y7oIAW3%j!B*6D=!ZWC1M
zJ_%L;btrVqsV8cyz_I!L5Q?2Vr&|fvwIrmV`&0XL;w3yw&5Pw|lIW~l9g_QES`wSJ
z>M1=w#|+P@8n$G)87F?!#vmG8W*%s+h$o|g-Ou9XhncWYm_LfF3_DORJy?yHR*!Du
zB`7;5ztnNpzE$8td2RzuEw2shgfm<@W{tLELEF^YSM`1kKWNfIod*)`!%68_QIaQr
zn~vxr)O-(6!Mzv~F{{}7Z<Fg&S!r66m7<8?*`+I}0|-Vz<Yb4C!PDZ*cXA92w5`U|
zgrE)w<mT|A=X)q;4G=S#&>jDk49Skte@|>C_V;OphGc7cjT)~W&2BCRD^6Ve3qP_e
z!6WrQn1N(dXr)rmx1E{w9xG0z5<DKF<KQ$;;poE2?vKb@+RtD+Y*PQF`5QW?)L|X!
zYEbgyWT9sab#gNz<hlLUt+NoJO|oaAN@ULG?}9>K<(bfu@FxaL`-<paTSfie&h89c
zb^su9TBOlXXn0cF#1ny3X2O9?_khQIv`f&(f(>dDI8$l!b=(*Es8sjka;lyCD@{44
z6>m+_keosUY;bb29PPrxk>-Wp=q*$fMbD03<FvVW@$10p@i6^|KA`;NoF1#$Tk#OQ
zeUvY0X(B@#?4N8it8Mh|^=2$D<?DoBXoEVoiRGnmVmOl~c;%;{#*0lZ4Tp8}FZ6dn
z=XddNyg?6M6?ztCF(6@5`K~FFjM|r4RFxTD)C;iP&66hG?LQHXmhu-Y)rv~|o!33R
zr(uCfqBH^F(3IRQ50x)&ya*_^&d$#M)!AtMP2rLq=`I+HnpYGL`O4Vpho5#^<yUi8
z@MZqdr^b77B(r|{;aTU_u-w#AKI!7qmw>Lxzxqz8NV`8vs!b8mNnk$^@^l^~8H-rC
z$|YpbuI@aQPyjehvix(eBsu7MN!u5xyQzor-+Y}4XK{PnyAe}a@?UZ@5J)*{JYrg^
z9PQK~Ws!u65ftEPW75-gHYFKHwA&O-(mtWQD^N+QaVH0!`VY!pX)OG%QS6L+jg0q`
z;R8!=OtH`4^o-XNt4sFWp`V|e>rHJgD?Wn?mW~GnDFCSlX)MKNiqEdk8#CQh9x{}^
zM#L)a;>Tj=2@>Lx1~nBhfRovc5dHoA8GHfe9U5XB=VPI<!|jBN*w#*#+!!=4K;Ki|
zJO&L>Va!Vvl`U0PV=VdQt^Mo2Hy5@1O!4%ytXd^-Y%^&KLcAv@qI}j<QTmW?dUpK@
z*!b)KAp*}T=<{ovjl}nz4Va9#WO{5_=LAuFtj8?Pe<)1pd`kG0kZe!iesw-|M@4A<
zyod78sIVM9Gb^j|c9>Q^4d$@kr}B`Vo?aSiyivbs_6kJDasKbLw*8HykpS3ddOyne
zzrSxTtQ8fu){Xx(J<pk!-adwu=3}Z&W89Bboy;A-TQLPij_QA-r%&2{9S@nE3g5hE
ziu$JM#)|;5m)k^mB2TJDo>yd<bZ@z{(4WEAdIcgcsj_*WQv6i~V#rBkz)2(?Vo)E4
zKk-QOExapsER1z+sqk^tj`R6c$?`zvvjctGyrWFjqm67M>{62B6q@;F@T%|^GU_!5
zbjOK`pzmV=s)}l?KU--eS@`IcvsM``_?LzfWY#|;12kP!nHO`0O}X>hso(ZaH#450
z$`(;WAafW?_xnh~8eVc$kBeR|D)WruG%2Ldqn7a;6(<R{A{I|;;?s?P`cES@h$~hq
z%TDaoCe4w12ky#8hKI!+9XVp-;s9I8r+oq*%csX4vHT)&I~1_xNPbCKvueF1W4Oiz
zPibc*@u{wTX8V&W=7VHbW!yn=CP1Biy0V>Gxq0&zC@<s>_|t<*={EStEq;`Q4gmCL
zOi~gI=qZ3YUdn#pv2+~mpoe<Zc$eSE%F!&b!?xrWDq0~NVBY{#(it`xW~HQV=(nk4
z7VM#%S($@d=J;+eLb81}vOP+-;}K64UKZ&B9l$GFAZ4(cmMhn7otY>$oPJtnl3~%i
zy^``|k&ziPwjwLxknJCg>g~9mMgXyF;H~}inwx5Re8-W<KE1x}?Qll8055CXVN>4r
zPPnsrvm5l(rjpR;*tM5Js)#k`b62&4Nn(0rw0x5%QgQe1(!zPW3Z>$#!$X>-#!-%X
z)~l~h^J6ha)ljFd!ecZO{~Miu29ZTbrSd=M?%BZ(v3V}omggfrHm7qdss!@_ulj#4
zLmZUr*pt5_V*5%;$Q#7>la(q5<P80dC%Bz-eeiS%tHP?`zKkCr+M<ly<ve7?k^Z{y
zm9b<}V@zNq#i3*AqefDNJ<k~msh-2+=+2MkIZ3sL#sA96D=bR@#H4)Nd`7xZyUar}
z*l}o+V;ug&?Po5o+^={C`gVeMqZav1b=71k&1gErlenb8nATv24j_dC-#W`1W8w9P
zNv_~lJlg*JDZAQ0`nTMVl8!krQ~DNam~5qf|1JYF?-*%3P;t+`?N#^UMWBh-<fGLs
zoirNmGb-c54iANiBGOIuGrHB(+<4C?>3a)h$MtS&O=A3_VyfO~(iU(e*YI%tyFY}!
z_5#W?6msd`sLVIud&#Wq*Q#8ad$MHQd*Fv(@tI$x7{yK%QQEk#j1Ln46_D|kZo9>u
zFXND^I*@lL)7QmLj#<zco@`Y_3i`N(QYN}%EVk4beH*M=w6iAlkM5YHsX`IH%pol6
zMFCj(1)<)YThi~uX@C97e(lqQEON~IGi1van>1_-AQ`)WOFBE>^S`n(zqH|6Api?(
z;K#4DDe%lbHmMz%hic73UxVse-Lb1s!@h~k^dHn^9NF&RkSpdj4Ci<AH%$D;?8G<P
z#yO;Ske<(CFxlxGv6~LEE7s9Jn6IC%<@$E88$2PPB_dvSksd#^dd^pC2TkdR_MnT{
ziP)Sw!~Y!Z6DWO4&C&f1zI<I$o_{_(ATKq@)E++PMT`2V8MuBe^k-NT+zCJRyB{@O
zABh@jwiH}kJTYGG!;&z`0I0O-RQYW2Uyh3=xn%r@K}vaPuRV%|;`rIXl>t<fe6Q|V
zt0XYoIDaC6zK1=B(e^@r{ArFh5-d@il$$2`)3HHwkHd#&fsc*1tzm^s^Y~(chYIG&
zvO_PP(vZUX`apR1T?C=l%tw1gbb$3W+AF_LW8m1Z=5PY|$Zc1O&OA!Sb^?sib$4X7
zK6K%t2p#(oBZy(hI^or2+~}>F<on~fPq9YfEIQH2T7ivz`7`Mee4>$lMasv^&3>c_
zrjohDgnU6a(`m!%Rg7PpKTv0tJQ$J;6|}}*>V}%0HgeEHI5h6HS>0tCOv5#gx<*X+
z<{1n!7V>2O9xlgpDHAqEPO(w8rU3lAHNWpG1>g4SKkH0m`8J{DPoZsg<-Ks{eFRgt
z^by4mkViv^Va|A>b*8@>YX_{M$(KVS=gXA*UzH5S5Bp>5qAs~DCEoL@-)U_cRf)~V
zPs68psPoyum7PGH@b7rhh1>4=k%~u#EMTXMke)hUS9ODTtFMw|cp#{naHKLq`=M+l
zMZl8k8l^AAV7*@oV_BnUFG|Ra-DFTWQ5RnVT@Gp)qo(dJAhHB|$euI%+v4Cxe2_7Q
zfo$-^0nI+pDE+Sir~I)_4{pT2KPO;(MEy<pzrT%f{fFeX_IsBQXIL@SY}=BC1A&R)
ztN92t+cU4T`}7%mYNg6`p{qraq<Z$Z01NZMRZgt~nv18QW^L!%dK^sf#?$K$mSp}7
zrpA@kV7^t<`Z*dMj?nM9&DrRPqQQT}M(}(M_1<MxG|Qt4;p0przt19&MyA>L;4D4s
zZx0QT<!<6zY~UD}U@67)<Y_!zXC<}xR;{?4W<k||oxd+*q^A>K7x+pF7kP>0U$--F
z#*W`p#Lm?$U$18>ZDtOSC!Vn%UO!^VUDM^UfeV95nIY{$G1OKhGOR*RL-IeqL=g>q
zo7mgb(i-rM0NSbq&+UsdUcuFGi{=v(R8HH>ffO7%I+fX>qsGYed_PorWBrKy{@Jm(
zq+c*4!)Tfsu__`|iWxv3ZBsT^@`rjuM09t*06V|ivhG@FPe1M|WFC%%pjFq8`gVBj
zR~V}i0c{~tjN$m}+}||m`Q<$TaptfeLL9xd=9Z2zx)|m!gPZuyr{Yz;Z^J8~7<6~K
zrTh`oSY~yZ5h<f=al0w*WhLkegjJr_YIN!H@`Me7vdB;v?%UqFpU5XXO{RGd9>hu&
zw>RQ)kCV5bOKZ7qq#W!#R`*`p*72vReEmjjIGpt!8S*c)44~x0`uawsEH1O&KlogT
z{_OXgcfmt_Ui~gnt988cX?h~u8aW0F8_dYdG1v3}>BrBln&SVqwc(7QrB^l`d`|Xd
z?-hvbfFyt5lx3rFszXRib9>AQ#W*V9ATdS>_R@n-&*z<z{*nYsI^8ehv=;V#wT^#K
z=rZ5$#id9Aafx;GG~D#@sndO$<&R|1FL--S*7T~G_VQyh|4H{Ek(94sS+pj0{!!6*
zPf+t|J83?n>H)2b{jl&`Mu5^@3AM~i84g80?Ic&Yw4b#lEKP0S{I!E=yuM}nDLP0)
zwRHcTv~SCg<8sMAe?P|_s|_WE6vVae2gPvAKoo~}W$Vz|?hS9ySTY06Z;icnw9#EN
znwa?C_g-2N{=gJrg&UZsFn^p`(&+33PwLMd-?O_uiQo?Lnct@6naM$mDlY?w@brIi
zp|=)42~S_O&408qUF%w`waw{a=u(VsicW8b1D4uh+!fYAoBp6!zV~bg^m$&l9}0&*
znpIl~itGf;nL%SYl3>ba>wL5`(_F_4Xs+_Pk0#p|$q;td`ieK<v6s~(&3SHKYaOQO
z$KzsMDp{cu#)k&y)$=kkRv9zbc_sqB6liz?${#agiVVPzeTH_Cx&?x#>wX<9(&@fK
zXV-emLVSIxZFLg_$}v!H*um~TXTZs)O6CVSD9OpTpBD`M^Z7<Y)`t%)lk>NohN#Nl
z>*;<k&k~IvS({v5N?`c*=KXe#H-a=?THvu<+z865D9xi?*5TWU#+`sn{J&+jbSWOl
zv})&Uu9|zIAKmQOl?eZ`Rq_GzVFim^XZGK9|0(UEpNk%;terw39OJ_at?YC!^HZuG
z_3}Ey`BWxm$HU;+S^br(wzNoHVTD2^Pi=+EY^e`t;S|1n<8nRA5GC@oC^8f8NeJwZ
zV7$15^>r@E(xYvFxnY{~K+IE1j8EJnQ?e7^No&2mx;*P(jdJTwT0%C>%yiU}-A1>c
z^e4?{2YL#>;!l+o)+%acQUQyfb^f7Bs`%#!{s<5Kp3-I*?v_!=p}TQ%TeQ#LQu+6e
zpZNLC`eViuJE4dTYi28CvV>5r<-Nr8=_?SY%YS(+>e(vknYSMJZA;Eren%p7gHIC?
z)BJ}X8~cY+@~up>PrM0VHRYgX3=H(eE=SMTQ40lMIm?XpL&IZ_6zryvv{*csMI5Ol
zXnWU(LhLF$+OORrK8G~$HxzpCwh=6W?Uu&-LO+x@|FWC6oYYLPSr1#tCEMi-B6xr=
zXLo-sO~NG4>UTQ~(~kFUTXip;Z)n~<<k+UhUBcNpw4F@Y9b>0#bHAO!b3MGilnIqw
z-r7W%G<|aTcNFxhSgY=u#FM))q(Hxtj+?LYTDqAeUAg`wb9~14?Cy?J_Ijsy(+*ni
zp4D#_j`UqR{Ca9G{^Y9F1~*KCIJaqKo`3P`uL%o;w!Z$z>FHzJl|5q9h&Ih<PUs%{
zQNB)-E?(FCxX#XLG7NO%IX<Xa-`Ffx^ExJ)Cb_Y!wrihDrtajTsmYQR921)ai|(G7
z5!IP5p<`~!`b;Vw*-iK^*X7*^|2O9N7qL{6qPik2{~PIB&&Kt*EYg#+Qs#fWfAySs
zp+#3(wN<Yt*{4<M&{b<c&ztWi%DV-ov_btyrQV_E%GQY#%2XHc3OP>52Bf^$o!99W
zg?4cY%jNMWK{8I%=KTfNr^wvxRnt$%lH;*`4bBip`f?;X)~)F#9ph+UGf?zh#NHBS
zP7@CfDWb){Ju|PYd*Wj8kk7{WiAO2B{7&&-oQ4*a&sajqW|`g<9xwjkT=@7g*NMqh
zVb|>RHW88Q9Y_$~?;Wq4orI|er^K8c&eMXH;Uv!0q-!FcHWpK(bIq5pc3O@fTiaaT
za>F}$Icz8d7dM9@{76#+$?MWZbq$d5Xz~YLNg!Z(-GbJ)X}|Jgy?i!j`DwEN)P{}7
z8OKL7zr&6ZV{Q%`ivEcF>iZP@bCRo>)x;%!GZSwN5`D4<!QCyMwcs9qxLMJUAzBDM
zw`m^`l!mtC37LU3d+<8puPU*b)6z@H`?KGjgU*KV{!wJco)a6O{Q<5#ajhzJ;O6<T
z?(Sjx`3R;mdZGNKJ|e@XaW!xUvmiy%lN0*AeqE^kygscfdi3RZzRc2geyHY3Y^ap)
zH{`nC>U!3PzwuVh)fH@9h%b_Ha1#?6mAkIBpMPhI6{xuX#}i9;Db}2%4;0^jSdi81
zRdT4t5yQ|w7sM)}MY&G1CCWkZ4tN0z(eW|<FrH{PY1qNEUOO`V<Xw=dbpN5k;ZuXE
z^}pMhSB?D?h3SkXWTg@;w}p(Qc`KviJY3&CVBMRS!dJTEC9RiKi~LP3uP0&hCtruW
z?TK5wtxy-@)t6VN{aWe8oAd{YVx&h(|Kop-8Nm`4H6}ky1Ji8S((L=4XK>p)jnClf
z<t^zt%=|tCsPy;yQ9JkY*dLB#cQtGb8t*#ySQ7Bpwu9V`>_U!yijjIgUgW)_iG(qo
zo;#L}e4Unvd=!}qe`$*Fze&uKf}EZ9lT!%`lL5-Moqt&fQ{{T)JqFBGzf1+4oUj)b
zbz6%MIE2Q^B)vF$l9o1geB-t1Y;C_SeZj`vV1_WIHcAEHHdf%?kA|annhfgxI{@TR
zJ%EtyClD+N0Pd+fKhrnt2f{nU=ch+NVHbaYVydyX){t++sU;?b_oWMg!MEJ|o8NCe
z?3yz2z@sl0UObi@$=a4|9N<~WE4&!DC69Mhl94?H-*Wx|3Zj<lC9f9|AfRg76s}o(
zZ|y`uSo0IPFCDK>gFtA(!7ao*@NGe}cu(@?+lz^m>^*Op$xg5atrbL3SvinY!1X`9
z_M&~Wi~VT~VjkOH=l@hSWO(=Pyy!_neHb#$x)3yO6;bg7r{0G5I|q2>C2%wAChE1-
zHg=b{UL6a?B!?v8AND2>spTSv!APzK|Dgk}Lje@vrlzzqva(<@U6&?9cX!^A^y0=`
z4+8^A6x4G3UxOp6u8Wwp3OdDUpK8NUx^54gf54-e7p#&gbK+BH8k!;%)|oTGCLOJr
z)=*Pha9y5uPgTB`6g~-<?~_nHG)&v0_!>iN?_<XQ!#Np;NhU1*Ir?3UKpfyBq_XTO
zw#j+-EpDQATnG3H&<entICNsh>;cVdEFvNlH@9n|Qu5v1T`o;&kp5avC;^YHcJK1+
z>}(Y^)zH<H^-s8eGuA#8QwI$1+YdI-3xTyKMws0V5eT}`Re&tkq~OjQ44sCjuv(h}
zt#PZ#ZzX~Qs#1``!8ynpAizQ=<=5{G!&M>E1H>0iu7{dNUO8}BM@I*Ph$Dfgk@nC2
zChj7}p~~D102M{iUSjUqL3RA7wuBduhas;m{V0)*SVob~E_*PdmS90qZlxTq&_j~+
zIpK?NW&)EeiEcn@BsI#?K^b2kTf&$2qh5_fqAE<b<sE@Z<{N4L?BAa^s{9=Le-ein
zbO&bv!n>WZJk+hf$pK3F@&*?>a7XgF?1m;Twedp&HOWuZBll}r#3#LX*f(c(EF}3f
zJlx8DYg&u**R{PLA5-)JZ2Ho0%fGG{+Jqe48Y}fK`~BSi5y(~<dI_{B!5izL@~4z4
zWC}{BAT3r59%mcn`ZdI%#o@13m*x<z7@vp%KqeUlcq>ME%NfdCtyPH2{*=M)SRO3A
zc$OjSSL|4d-dNjkZRL}s%VmHM+io2m&`CqR;HD8|n0Y70FRS#t4fIe{o|Y0ErDGrY
z_b&3hUpVoF>)qdTJgPC0P5nko-RUb%wN-m(`Ccohx<Bn@_mMhdVp0kV)zo7;i+~Nt
ztEi~>6;P+V_`M`XCDj(=GBXp=I2v#(p>;%hCKu%nX+fVhUJ4GpOeLj{t}Z;B1#_Np
zm|IcX?>NOOSl`venONS*6??T6$q@zW9;X0Razr4T-w(JH6(tj>KY!UDQzXNC0{X>`
z)Hppw{_j;mn=hQ}VJAklVPoev&}+B$I;ABLx{MF06cmcFISU8K4wcIbGiHo$PLYO%
z-o7@{_Sbz`GnolLQ9Z=bN$btxJOKpQ3-F+%o<eZ0yC$spmlX43DFZMMB*;cEA@l`#
zO)g)lc`Rs#!=#IcX*l-axn#Z%3ixn?=zxifP}f;#AdKYt;rs8D!?Cp6{?DKeeBz=J
z&gx&*0JAYbh8@Fb1X+8%^<HT-<#m}vxe=7?IR1Nk-{mjo%<N?bCyRl|IDEL_n4_F-
zmBOieNn>&AtvJqM-tT5ZM;)MzcQDd(cjF~Weql9e;Y}Tt8~8OuH&IL7--xhLg)Ftg
z9^(vUa`Nuvu1X9v!4u8Z6HLhNnaVFerZ{ev5(<lK_3{&#z_<dF2Ftp)s8Ll%?LQix
z>{g&Bu~j55mRoUo@B#Q=d;PTWHR^e?0Q<2(T%I=t_l?4+1nm7;-^>y+AR2c}ksV2a
zS0lRkIKVr<ooqCi-E;Lamr}iyH{MN04LkNY>t+}B4JtipX|ElaA+#S!aXrNMw5jp|
zV-nL{RPq{0AWugw<ZBS9I-rp`zSe>|Z*@~1I#{JjJ)F4@*Lq6{+u9=56p<?%9TK!g
zf(zyT(CT7;C$G!sDsDfNbg)QPy4M8SV@>UqD9s9bb&o3y0D|eb5YR%X5VW}_l2v$a
zmi18Ce_mR6gP<AjHqTwOy=^Z?{=f*X4yNXR@Wjf|620ZXwj^*K!2_okk{<+Uh|Ptg
z2WZ0@6W&M;7fz($s7>QPAB}aa`UnBI8}$7}4U<ZHRqC$&#b^C0#nDSPMCRa`pBuS;
zcn$<|-;7jE;;rEGZdrf}LNE`F_Wx5;Iv0mqui9O7l-gvoxjxB<Tz@`0J9jLHH{l=b
z2l*)7ckfWP35j~Yf$}Rp65+3uK5Ofhq24!j6TZ-H*1WO16TT?2)>ayDQC~xah<IXX
zS3rbHMspp@&!|L!qg~9OU!RLc<J#0Mh0Z^Ec694_6Hy4ihLDDrDLLhtwZ`nfewzN_
zTq2bA17Hzic9B%#EgB1R@eE!q-8{t*pYG34r|MyYchFWmv3+ek1>i-ZWq=SMd`&ta
zH#@N?hyT`0xxg2HA@7?`O(c5N?WG&F#A>;N5ZiT@ykdw1${9~lyNc=~IF>1b>?m7|
zBHjG!^y!btt0qNdO?W9q(1E|<jhDxSdj9UeMiTFeGnjEdJe$8wR|*NW;>H_j)~h^C
zX^`bJTDEKG1NxJ|HPMS@TqPY+5!Mo)vc!^ppcf<@k^YR4qM#W}N|&c)Tsr(q#Nj#Z
z3#dp}>iE%^%6HjGwQAjb(CfOs`@g9e_W~<~@cD<EouB6T*d^+6xps2UmrT~2BP%w4
z=Q-$b?HU1eJ24$x9#0MjMyd||j+W_fY}`ByPqxm)VvoGE{?N#2RAGq6Bi5RtmY6~2
zR)CGR(|L7r#kqWjLuL*&%aG+1L&bfQ1Z#kqf`9A`I#5<QxqWP~?n<0cZ|X8ttlESM
z+8LoN={Trgsi$qqwPHAap`FlmlHiO4gpsc+o~`S4Fq!Vu@(F69N(0J%_Rx7HBrt!y
z&WoIgV1}?*YPZt@hJ+Ab+Thu`)n{t+bSmOiP`w7uS3<j*AlV(~q`ZW}P|R~|L3Pi5
z(H5<5QcZ3h)$hqAy1ZrJ+CCM(L^s9_2p_2lX%P2S+o<%?yd|c`t>4u}_T0Q0R$lJM
zu)ixyakyiNk=M}@j41NlU#bfu-7h4MJsk<_p7Gd=9WEba9qWroDxk0fpgKJuO^6BT
zLh#69+s;6TR~;>DCwAvOK!)bk<{#80GA1bbhBb%LA+10_6P;LYkTp)N8B(*~@Q(a4
zZ@Ps?+5DgS7A(z4?fx$ISn1DF+77?)*kJ@t-jyxkcR)UB@fA=@*q=A=Gd!^roBUX0
z8CQD^0u)3;gbCdf!W9?!&(2`d*V5)&I;B8=?=2v6dS|gJ->njH=P9uCbdKpemtlEz
z+mfy5T}}Ts%BNmzb>M;$$X{+W3H5UkhvVw@oEs(Y_<O_9vrzCjsT?~Ba@`jug^BDQ
zJqUzSD-26xWtgeIlb7+MfEq@-{-!75AICA%n$^Nkp!;{9UvxZc>}KQIh4bo5P4&zf
zJM#pmStUnBT$Y(Gz0jVZiAx>;pY!OFOppNMC--{0804r{j?sd38PT?Ifm(aFkNSf%
zw(2*dgo5YiEb!H&%`MZ4l)#D~(ImZPg!mi{Iyk}3k-hJpWpGHte%lZ%H?N_0PY>nv
zElO;=PitFAFazWUAzQYh;&N@#18ow{MEMKoFEIp+Ex54A_Bb-t&}#E*=PM_3Q)Kz%
zw&b-XY#On?t7+Sl5htceXf7u!yMLe4ho-8mz$+nw#?wMs-z~4{ee<_Pt|&nECjED3
zH8P8*kFu`pGO$;r#a`A)Jj#u|9LXmCsc?sVQfk-XRXoN^o#3w3+2v}SjJ&DiXW0kb
z`QkD=;-X5Evp~#J(4E($yWC<TD#U{g>|jv7JdU^=aRBf~ki?58gitnu?*N4j+#eIK
zM38kaG_b@{IZEkrz@Z$?_ASWzHxCepKSZ{0V|FvfyOj${^bB=3j(<D{jiJy&j3>ML
zn?lilaPHL8#j;&qB@j5)+K(-gW>;PYwIQ7r&yueG5nN!-^cNu-)9sW>>)&nO%e(s1
zDhC}XqR>+S6oQDgMdX32I>a5NV-$@QpX{ttW+e5M=f;YBqjxZ+a&+6ZbZ8c=x`BL+
zV|1Z^%3~$CnAhxNgOANL+a}e@-#6K*@VxulZ~gKvml;wU-&0k^0FLR}Q#ZFD`N!}Y
zU~qOOD0+5{-kan8xIO~fr?}}>MBwl8rwJmgnmZguz>rfen=F?w5Fc+g9W44xfLW{e
zZB1WE8x!`(S(Wk({>|$(Nw1n)_LmU?6~g(zCt%O*N<=Go>}zgLc9hoo2Nu~6vjZDC
zx8OEMy}2(p=*p3<G7`o?wm&(rcdmK!+yM<_vK;EPr4Hct)m7)1y8Io4(aNssj8uhk
zvHD&B{JN#~xy>m8DY@2;A+etBwk8xE5F=!Be>n(%eX;|TFM#_PpO5{wCDrWx_W3CL
z=<U?SpSd|v%W|qNXyD(ok7qfbspAJIG8z0BTPf19x)53`f!1Yqx4nku(UH;g`z+rI
zg+pR6c=q$5+iIC8>Mb%hxa$Q(zd*!4Hq6ivQ7BaY>~w4@Pe%(L%LFLzPuKp#GsoAG
z1m?|h#T;CG#6wmTvASMEL-g@!A<9`ga%{S-IW%oVaB1KZP3&gsAz{n&&_p%9R>0c}
z>;u&=<ye8m4Re4(RM<;o5#W>sb3ptDA}5*$kOMBP2)~_P^HA{{b;IQ@Y{+QY7&aT5
z9a@lpIxKtun6dg-Yo&H`cL(9+RUZ)p4;FoK#xxa~A+uYWmvH`&Yw)=(gJ6OdYl%gW
zt4&K(2Ts|LcH`-rEmk2jh4(T$w*brsy`cNHn>AEE3~}mIfa2(f*`BH-)X+itdqVD3
zk8|*$Az7$(b)2`vLKWwmsjItLBXEK$S_n`LyaoMD<xv>v#vHU%z7Bo~_xNKF4osl8
zG~KiawC_q>Qms&zCV_gHpQW*YL5P8g3F_+V8cz;^l#V&Z(3|a6n5G&c)`b=LP=Vks
zWup?FRdz<8j>W?qlTc~-BBBz0qrQkRRU8PU)_uFJZfEWT4$p`>JwMK{QPxKf!MPkt
zPaagU9M7k0vC7m%W{48dYwXmoxU2TLcMb7R=5>Hn0e<eQsH8+XhY_P}ZE;84|8V^q
z$V?R)%Ui23q%%BH#BaD?y2|NDfw%`0(VTo@gGYEVfb@Fp$2+o$rY`}AXe20o=A2#T
zoctG>5WX6-7@paQ)A6UGtXhY~NWxfI(Ik;J!dc{5H5tnigIpC?I!IPl77`j-JQbc|
zA#1Q(&p<he)#g~$+b9?D;=9iP`sUJ?_{@MsxDg;lnx0}zO69mWL9gB74$hZP7qzgU
z|DWFZ%iz!Tw5~`(u_abnsbi<&E@uA}CIr$r$Zur|%)3a*$@Odu#>J$jh6CMjYFb);
z!Qj9i81iXvae&rpw;xRydXO_z30sEbz;$|}{hR6PkIls+1nV@g=-9t5v#)>yc${xl
z2E*jvH?yOIPI|5d-b#uBB+shqrSLU?jbZ?St|s&?N|=-YXS9HnjEV~sHo)~L*o3NC
zbn_Z_N5^+`YG6chmNv0DSKG}g+`c9Xt#2UL9rOvHo8vH%cPhEjfHa4deTIA)j7V9O
z?&|J+$IPZ(I&yiwQwEHLp##+(pj#u>ZL1C1`KoLOPqH;9C_1{--{=9N*noc>d#B12
z0ZL`HTphZdt~BBiB0|RQw6Zh7|4g!0{by6-<V5|hF6`F_tg+_LHrm(Kz&3{k{_`^G
z9HVQdDrC1}Nggl!4BNrv9Qv<Y+#6sfbNAlu=ZukqSG1>5-kEA%z0?Y>W{l2}?HSQ6
z{0;FPu%1|>ujf{3%cD``)~vT}d4148>doD<cI)(C@M4Rk9ZX9CPnS7<>Eg&te4tSh
zLXo*|@)WX2usybj9WTvZXTI~(;f3_O<Hm6FHG7Ghv13Nnl!G~UgRa)cZ(5x6wAee<
z|8aJ46{CbJHU{;?OHYcf?UwzNZQ;(Qo}A&$!tnLu7+gD-6B|&SH2crpqhYn|3htjx
z3HfGRS|W3Yje8JKpijOqM~elt9e^>_v5NUQ34@_f)E$EUW_^fSA0SE`2zizXtfvzZ
z-r#lPK`>pIM09g2XvN(S5h7y11cz=N?gE?@)^{~5M_8wRL1n_57;W06G3_g(_m-rg
z0J7Pc&CNLIhnydo;|BpfA%m_C(`>PCzl!nO*5d<V$|l`6OYYA<T&u{|^FDK7r_$YX
z_XVH&e4Soy+bg6d%?S}`BZDIYQavovu&-qaNiUPfDxqcT;R17$_@szndO*?2_gDMp
zTQyzQAGt22eb7x!Y!IeS|82HMI`0={+?hKl$ZR+oJ7XvAMb}(>rv99;$C@EFcQWqR
zw6cgBa=M(Fs_e0ltjIMOXDb2BIuK3Nwu}`fN?5Y)be=z^u6nyt9#tB2aO~n^gB_C^
zP3DE%vFJj9&UFV}H~)nD!V0uDMPHBg&3(dhP`Pg%K1!G@`^n=as0=@b$ju4c`b#5>
zGTo3=7=89Pu+nYbJBV-tq-`_j!&%W5%V^_=mOt%sJlw|BJoNd^KT(I6y_&q@+t<Y&
z0V=z_&(+|^(o1BI*t7NdbApDpH$z4pgNtlXr*<-F1ec<PY*g%h*?6_I>2n*v_b=L5
z`O)cmcwoe|{=8tyaGFVUPJBoG++T`F4l3oyV^2YW?C3RL5Vq{+SJfQPJmg?Qtw@ER
z+ByUcBmpYZ{{N=5nd5$$u(;e<X)CliO3Jo4C=iES?_1Uq!zKXK%eR^HZeH!d_i(E!
zk~M!LrV6X;;S`WPSUUGl1RqjD*MLc<!Jr!tgQISqm3v8B98#3s=uZ6(I|b@2ul27F
zSAm=H|Ak5s^ZF#F;gRo!U?+R*EOTV9RW5z$ej@?(#9KN1p?Vp>gsxZ{@=***G<n{C
zN0lt!|4BI@{1Tf3Oqu@@G^WV@bo|Fn!YX=pYi#ZX)KDLm8q3p}?(44?j_oc!o#g_V
z<tR8IE^!D8U=@Iw33=2@p1GP|0x=&EYr=ZIt4_t~c$szb3Rux5t!Kf~`han}T&<`}
z2EE?P#wTrr(3<XAe$yEMr|@5SKmR5eqgRa$Ix8|l#)4S9_}?N2!FwP&#+xj&aA!nI
zaUY~mLK3H#AfT)#3F;&bw?5US48eFTK0ql7&6TAk_L@r+o+Ztr#THCg=eq+?+sW|n
zxB*6n-e=b|S9cL;0zf=;0{o@Do#f+}_xeN%(5BUa3N#*`05NH)O?C*f!K)-sNyfl;
zM5M6q+QaFgSFG>v06oUdDx8>|WALu%pJor<%hdf_F7);#w41#ZRqob4K#4=V&$r}L
zU}_Df<PeM0z{_O{_#SU;xHs^TE#acLGPzQ^j2knB8L~0<x>bnH%lw_-&20ih6svbh
z@qaKW#=kmDFV(aX{y7{db~h0qV;=x_8(Ua>zmtHexqb>D$5-&^DUXKc)!SFY(2&mz
zblJ7?!+_fO4aLl~5-_5|&SWm3xY7@(z#AK^O80$rtw|<`wepW+`40hc!Li6cd1r#E
z>Pw}f?Z6<Q@YzMwqQ9W}H;;Y!z(+cV3z#~wZy%5E&rat!T@2367YL-P8?~PEr5*4h
z-f;fVw2hK(6>k^ZQieCRztpe=@)R=a=WXOv<Aul6kR8I2qOQ!T`QIyM>GjjD)@cx1
zE{=@d<PEc4i0}1!0}OQnFNX^dR#^@;nUNxy|B=hbP}AJHH|4lEM=RYUot}%rG83U-
zi+9bYFsVddt6=C$YZ6Xrjt(TFF*JnKI;jf&w%U60irO*;0FFYTSTPLvPs=+j1ji5+
z&tzD%?8nBrJw+(d7zYC;Xk~TFlNpJWI8a|IUmb4n0oYHCoWCidd$<7aLCU%qxTti8
z{Bymwvy?<PW<HtvPqAVtQBHny_x(447sCTHrYh@|K~YYdI*{6eeY?Q&6&ixzC4zT;
zIY#Vq?Zvg-{g<bX{qxs#y~NiwpQEFEr@LdB!P3&woiEe3<m4E@^ew051X3KGg2cCP
zG1?*2Ra(oY2B5E-vP;5skp=d!0Ysaefd8EBeoi1{c>z5@fXa$dAG+N-LWeh$8%5;y
zs}j$S{RPH=Uj}0{eedXj0ptp|V_m_s0jih(BcNs1N;7Q`N^%C&I0V;992q;~>VR@4
zrU6p~fTr(tNf%JW1p2d2!~On~>o#iH67oinqwN@e+uyECrv>5A?*P3gVEb?F7?@FB
z17-}9guJ-1!HVesxyeMB3an&o$CKJ!PVV0~<gAZZh0PoYJ#yo|w^LgLXR`^K_0?P)
z0i7M9VQTbqrwQ@#LBOzn_$WReUUo%A#o9422uRZffslfQGQLlA4ugJaSg=_T?ESAs
z&O9FKc5UF(>4=_4W6$1@WFj%hlERougwohEB-@D8Sd)>&%%ExNA^Vn8vSr_rWJ{5;
zCOnQc`__mo^<MLw^S+;RKIeVU=lyeLe!sc5>%On$zOHZNSVExsVu1;%@S7gPn#O}a
z;WsCuyR~ExEqUV0sJg1l$5UozW{sxC(Y2e6P>?NoBB{H0V=Kq?R)6Wq)1pV^hI{Mh
z#<p!Dv<o%1i?opmDpy;3<PLr~IZ*Chu#yOs(=a3i#GH@fVnna{4D%m4kHzEhjiw$R
zIorQbC~dd$$b=TP{x2y9a$3D6-sui1);4{ET40HonIC8o#zy~8gr?t|mCjP#PJFq0
zN4j3)qPx5M)KIlX6=^z5^X8=0?G&b4IMb~}L-xWexcSYxT!Yf$j<V{Aru;4-R7bS)
z{U1BB)O|qJwLRT`@y~0e$@Y{z8X9qmK58;mjqC&}xW2?H;kr$57KN+zu6DdH<oa>G
zPoF-`uq?RDCNaSB-iwZorhSF8W?bvY3+^{Ju;WG#`Sg!D5>zo!T>B(wE-i7Ctsh_0
zjt8;i?@OMbO-)THe0rq`cnekwL771MgcBtD-!Ip>_<QU7UdxpZ1KzjJDhb;MF0}eT
z(Xu>}%z-HQgL#2~!H#>3&TNd3pHx%!8uT1@yX!1Cdeo1AFAq0kRoXO3$U86}Qdp)Y
zSB{B>7qu3s<!%`iiFRx84V<@YfbvYRSScjw?EBs^&0Zdo5fGN*`o7xG-u)q<@a7R+
z2eN+WSdM%FQ$h|Uiw3g>1k<RL%BB4qwZ!qKOp4FnJoi><D5aHJYaUw<UguFOE03d)
z&OJv529F>2YkP2R#GZ$t?t4vu#yGpI?q50BQyUV~9CvbPC$I0d&^4(uCF@#JrupVs
z7&%my&4<*5h3?rH$x?ESreETjv|Xk58(#XCVAI!DBYtHgcf>=`G~8x?^<;a`b-`wA
zxW!9rEP?GjCO9gKD1#?g5|wwQZ}v#r947t+jWxjEXF0cbsZ6IcV?{~xW{DiMVY^16
z<lmM-p*Y#DeIV~}s0(t(`btLPm_>`ac&<?u3vR`xdP;&<P1H;H7*9NAQ42j7?$?PC
z)<fAUY9?s>o-)dVg{_~?e4rsi%M<imClf8HEv2iqL0wLJt0@!EFU;44@4eM_HaHIJ
z;QYxpu1zA<&VB+<Bl}+?IEvj1^U(t%<0f39*XPfp)0{=S^K3?x)x!mOsjQE2TL)U2
zi$~X7y}VGjxs9E2M@5h~4)>UHtrD$^k|?YK@`vjIRbm~wWqWl71IhX9iMtj1i1&~9
zeU(DCS`I_pyTBB&eK`<tOpn1cY~3ljJJPpeS?tt!(T4gS18llRl}4G4)vBn5#!kqz
zqVG_Y5uki5Ei??rG|QUHsXRB4{Y%XW;_w3sY5LoUgt<%6>%<($z`3Ja<7pJLDDQqg
z0jr3*!EGdinpYO#zV1rSyY3k;myw(IgFsazwpa*VbLqbx%I{AtF}5&~?nOP6w1vUW
zrTpga>U2BHc{1Z-FxN{_KE_4yY0=3Qm0mD}GJbE_1IjX>jC>Js>a2Uh7N;z#D70K(
z?G-Tsb2{g0RiM5!ijPj^+C6bbg}{1V84)e>*~SPfD+zYyVW{yRw=ot^&j^B9m7nCm
zW~noMTOWtG@m~wvVe*izf>4=8wd8T!)-zXwkNbOHHQl7I8X*we5a^gLBt;dwoblQ9
zZgtu-`H?BsgP(siin=o?>9&$?H*cha7}rtl+5L>G!H3SuAFgtV@TV3B4z%)nK_DaL
z<Z)!BxZHux(dx7MjS=w$K_2w)KE8$9Pm>Ucyjk1DE;SME&zM)dy(jv_e&W=*_>fd^
zZ3HYUk>e?w10!+0-wRwc9SOw><j4)NSKc0ExZTR@?ZfT*AzhaLZ8xAAG}(i`bn-K&
z)XC2~eEj5gfqkE|9G}KCC~|VeVzanm^^f<iNTXbPR@aK$wKW8jy~Bca&>?GA9$)8y
z`S2Mv-Gj~Yb<id4)N1H?yLDR2cF1exn&LhoLc@phGBf$2M5rGsTy3oV{C(6!e9B|j
zMO754GkKD6$7;!{&!(Jc^-0S1@)Ww}p!$)}V%8By4_uDWlzl21(vF@OVecbT!XNnR
zR6iP>(>&yv{8^ZoI)5*FWXB2zDEWFQZnb#ZS!MAP$J@Ip4VC?{cSdb+J_in?Pztb7
z1cYVu)Gk4LoG=a}hF%XYK54%d7~b-1L$6J-UXVtkP_mlZ{V$|I$^;g>ifxGI{TWMv
zy7WOS6G&$+1_u)rtTW{Ev_jz2JV%>o@3->6H^%xDN=b~pY8GWtaylBfy0rvBNVs=G
zp#&irhUO+^%ZW~#zL#n;laMD<A7p^V=2NL$huiJim*4KBJ|CN<Cis`)btD1z5lQ8G
zp%3}cTi;gN0@5$$_J4J#e0^|`yRLXUQC@sM68WBb_tIIl+&oUEx||4GUU>Z{+p*Z7
z`eLoa?M#m6@!>!z;QcfQ^T9?wT@XLivJc5rgS83#2xx>U1#5oBfcF~-;4=W}1Ly24
z3pzalw4o;NS$IbjMMQO0`+IKzMdvtaYM;;%t5CQe9sv#r<J$vxAnQQU`QhzF@-N%v
zTXuHha>ntY03LW!Q6Z9=nhI6s79Z<sfuvAn^hygQeDrdI|M2a|eEVt}V|^$(F}Jd^
zTK4zzE4wfP00#!5h!-ybFc6@qn3a3kCu{ls7A1VNtYSWTzv?~#0gGjR;7<7m$e!A)
zAV8=Q$uJTzmV5L*RoFN|g#%(!rB!Xf7V3C)<uu|H<P<1fuAzVl<uud%0`LI!-fy;D
z%AUto3PCl22mlBUC@XiQDxYmM`e3~7eoMj2ZC@DJFKp};qoJs{7%3p&+_gHe08A-W
z{)B{tKXv+!n%A$1Iw2b@ci>tP#+qUvl6Eeyx#+7DgbA@Tgir9ov46yZeB}#?nx+$Y
zUQog}?2CBtkkElH^BSUMWyyBGxX9T|mud#ONyl?@nIsznHks>}vsms7Fpci*5zh1;
zu66rTau^paat9y>j`LqifwFW%A`YIzV9SIUy?M4*dU7t^m`{aoNTI|wHm&PF5x&@A
z;(tb0{WBP>=n_7*zSM99hRqHLVX<XZenYi>66Lra>KS0FAf)BwaMxww2j_z1Wbwap
za99sD5sD7lBeTH11S^M&iwhuE4@m5k2*@ze11cDh5DpG$*f+0P?+;FkeY<9@8C(#1
zpl{PtCE>xNM|Yn;m#?U_mg2~q5khKcXaK|OLr`>G-Kmfk!McBarFqlN_FRh^Xh&%O
zzlmmc>d0fFG4VQqD$%$nQ)Z-?#bT9pUG&|>;v4Ii(FQz=#W|cW`Cv+FD?OvwgflAa
z_kSP1oeJ{D`WT&Ub9;MxlcqX$%=Q%sOFUfo-&q=&?>bBOtGd(?l>Y`8DIp1(Qi|ry
zT?UbR4Pk9ZW@n@P=D#=scn6+~efW^ChShnFLZ?U4>AY<Am*wR^4q%gI8g!LFI>SiW
zfR}-Vg#bXXgcNNoCIW%}05CCW1%=;$&%h=wK;g?Xt6-(alr89Dz{d2m<<Y34PCyi5
zbF0|pUm)_-y(X6b7rSEuetZlYA_8pBbdx0YOataqfGB0ymU}I+SPfF-tvi6L8k;X4
z#$CT&IMqR#SnYFYK9m+5W?k$uQg>%(z*vmN5^$$Rfz%`*k(fO(F_Cd?pe$aE7}x)G
zIADyY=3Bcqd=g?eLZNUC27hZ4oPGViecVaQcg9)tL=Y*qIZ=)1lBnz$mQJxV2nwp&
z9qJCU3|&b9LAnS#2yB{M<<+_VE*8*=sC&=)H_@fMfj<IBgasZ^Obf=TfucYk^6j(F
z209poCdr8SkRKC|X>UHXwi?6AW&{5RuY)%;BLKfg<@U<^L$rqR3yLU1LqmWIac->6
zT23s7Y;QP1V_}&$cz|bt2nCnpEo15v8<64FAw5XyGJ!7T9XuD84yRiMcmV)Gihv+%
z@b`XKSj`mJv$=Mae386TyAX3<O81DVd)6tI52B8%Y-<5)u02KDa-zx&Bg!mxHEqm~
z%DK!s?*WH~yrQCEKtMoj^SgKNd=>^Z^~|T!{)Jvb-Fw<HWPOAaP+})Pmt9K&QJ!C4
z9%DOHO^}Tvd^unTELY<rQ{CN=5Jdj932CEUG0S7kGHCRkwnVkN)z#XNJ9X^Z7tGeI
z!!JIGL@)d5?+MjPsXBod7$SBG$jT;yvc-=9XO*6S=qy!mQ0D%4Ee72OA0R&~JA3rS
zv()-D^3UBM|ICpo8=&E_2};vlfdAp+=YQortP2p3Bq}AG&Hf19oG*_*N!GO~^Q4ZC
zJL_z-x+pYS{nE%g!^meuR6&$dECn!E^+Q9iakHm`f`Vc^A7m{z@S76eKRjqv?j-@N
zK*Pu<Xf`1xC`e0-G-bAI1>tWv%_2eQG71a#Ei5ejMW&nH(sHK6jhWec1z=gtVH|t@
zYM5pE>BQQf3WifM0D<VJ@+Btg1oDC7M?j$-f%qXN6lH+gB}hjH+LK-!Ljk0Xos@^q
zwKAVr7DVIIiGBHDnVpCJ4YOhxnWcSu^Lu0JiCYhnlkJwfK!fN2XbO%#7d})5(6M^r
zLqgV<92*)Nchk<K0rhHR62DqBzOc|fU9Cc+(*b{mjh%HeH%G!|eEHdz`P2rBAPiRc
zh^Tvtu;)|BzKYPEta;pbMgT{bRMf)Q!#Tn|`6-Rw7$qztEuEezsPK0(OW2dH$k5PG
z(JNm6P=ogy8J}U{9Z2(E1iPM@Hs4+wiUdZQ6Z~Dgs_T9BzJ;UY9o!7<6BCPyQ?z9t
z5dmLQ{FQ+>$INiI@$ymv56@<K4H!2tzG#sBhNh-bIKi4&(_PF8pDP%zK@s@t$by2S
z5#0MTjYDZ4+sqT_x*IJ5!osdluBuY%J)C;c=qjRa?h{Z64I{IO$+hS-2l94Lcg`jB
zBc>ZhjoAAU<JvCHJTQG8H2O5il}ck%QzB?g6CN>j%xi#vW!Km1Lyx}9^t^0KrNd@|
z;=tBtOXpK{`UQ|kM-|bSY=DEsf1m4rVimj#(1jP|0PzAGrq-uT6t;q^Vu$tqUqH+M
hr0)JsREqz#6Q7N?uVKA40Cp$htl@cVzCI!BzW}D-#!vtN

literal 0
HcmV?d00001

diff --git a/notebooks/mondrian/tutorial_mondrian_regression.ipynb b/notebooks/mondrian/tutorial_mondrian_regression.ipynb
new file mode 100644
index 000000000..54cc551a2
--- /dev/null
+++ b/notebooks/mondrian/tutorial_mondrian_regression.ipynb
@@ -0,0 +1,1229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "from sklearn.base import clone\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "from mapie.metrics import regression_coverage_score_v2\n",
+    "from mapie.mondrian import MondrianCP\n",
+    "from mapie.regression import MapieRegressor\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create 1D regression dataset with sinusoidual function between 0 and 10 \n",
+    "n_points = 100000\n",
+    "np.random.seed(0)\n",
+    "X = np.linspace(0, 10, n_points).reshape(-1, 1)\n",
+    "group_size = n_points // 10\n",
+    "groups_list = []\n",
+    "for i in range(10):\n",
+    "    groups_list.append(np.array([i] * group_size))\n",
+    "groups = np.concatenate(groups_list)\n",
+    "\n",
+    "noise_0_1 = np.random.normal(0, 0.1, group_size)\n",
+    "noise_1_2 = np.random.normal(0, 0.5, group_size)\n",
+    "noise_2_3 = np.random.normal(0, 1, group_size)\n",
+    "noise_3_4 = np.random.normal(0, .4, group_size)\n",
+    "noise_4_5 = np.random.normal(0, .2, group_size)\n",
+    "noise_5_6 = np.random.normal(0, .3, group_size)\n",
+    "noise_6_7 = np.random.normal(0, .6, group_size)\n",
+    "noise_7_8 = np.random.normal(0, .7, group_size)\n",
+    "noise_8_9 = np.random.normal(0, .8, group_size)\n",
+    "noise_9_10 = np.random.normal(0, .9, group_size)\n",
+    "\n",
+    "y = np.concatenate(\n",
+    "    [\n",
+    "        np.sin(X[groups == 0, 0] * 2) + noise_0_1,\n",
+    "        np.sin(X[groups == 1, 0] * 2) + noise_1_2,\n",
+    "        np.sin(X[groups == 2, 0] * 2) + noise_2_3,\n",
+    "        np.sin(X[groups == 3, 0] * 2) + noise_3_4,\n",
+    "        np.sin(X[groups == 4, 0] * 2) + noise_4_5,\n",
+    "        np.sin(X[groups == 5, 0] * 2) + noise_5_6,\n",
+    "        np.sin(X[groups == 6, 0] * 2) + noise_6_7,\n",
+    "        np.sin(X[groups == 7, 0] * 2) + noise_7_8,\n",
+    "        np.sin(X[groups == 8, 0] * 2) + noise_8_9,\n",
+    "        np.sin(X[groups == 9, 0] * 2) + noise_9_10,\n",
+    "    ], axis=0\n",
+    ")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(X, y, c=groups)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
+    "groups_train_temp, groups_test, _, _ = train_test_split(groups, y, test_size=0.2, random_state=0)\n",
+    "X_cal, X_train, y_cal, y_train = train_test_split(X_train_temp, y_train_temp, test_size=0.5, random_state=0)\n",
+    "groups_cal, groups_train, _, _ = train_test_split(groups_train_temp, y_train_temp, test_size=0.5, random_state=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "((40000, 1), (40000,), (40000,))"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "X_train.shape, y_train.shape, groups_train.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAABLwAAAHBCAYAAABjW6KCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3xUVdrA8d+509I7gdARpEtRAUFR7H3trnXta8G+766irorrytq7WMGGHSyogEgV6b3XECCk9zr1nvePkEBIMplJ7syknO9++Li5c++5J5Dc8pxznkdIKSWKoiiKoiiKoiiKoiiK0kZooe6AoiiKoiiKoiiKoiiKohhJBbwURVEURVEURVEURVGUNkUFvBRFURRFURRFURRFUZQ2RQW8FEVRFEVRFEVRFEVRlDZFBbwURVEURVEURVEURVGUNkUFvBRFURRFURRFURRFUZQ2RQW8FEVRFEVRFEVRFEVRlDZFBbwURVEURVEURVEURVGUNkUFvBRFURRFURRFURRFUZQ2RQW8lFZNCOHTn4ULFzbrPE8//TRCCGM6bbCKigqefvrpZn+PiqIordXGjRu55ZZb6NWrF2FhYURFRXH88cfzwgsvUFBQ4Hd79V3zx40bx7hx42q+TktLQwjBSy+91Nzu+2Tr1q08/fTTpKWl1fns5ptvpmfPnkHpR6Coe5miKEqVYL3fQGivvRkZGTz99NOsX78+6OdW2g9zqDugKM2xbNmyWl//5z//YcGCBcyfP7/W9oEDBzbrPLfffjvnnXdes9oIlIqKCiZOnAhQ62VMURSlPfjggw+455576NevH//85z8ZOHAgLpeL1atX8+6777Js2TK+//77Zp/nnXfeMaC3Tbd161YmTpzIuHHj6gS3/v3vf/PAAw+EpmMGUfcyRVGUKsF6v4HQXnszMjKYOHEiPXv2ZNiwYUE9t9J+qICX0qqddNJJtb7u0KEDmqbV2X60iooKIiIifD5P165d6dq1a5P6qCiKogTGsmXLuPvuuzn77LP54YcfsNlsNZ+dffbZ/OMf/2D27NmGnMuIF4sj+Xsf8qZ3796GtKMoiqKEXlPfbxRFqUstaVTavHHjxjF48GAWL17MmDFjiIiI4NZbbwXg66+/5pxzziElJYXw8HAGDBjAo48+Snl5ea026lve0rNnTy666CJmz57N8ccfT3h4OP3792fKlCk+9Wvy5MkMHTqUqKgooqOj6d+/P4899litfbKysrjzzjvp2rUrVquVXr16MXHiRNxuN1C1pKZDhw4ATJw4sWaK880339yUvypFUZRW5bnnnkMIwfvvv18r2FXNarXyl7/8peZrX6/59Tl6SWM1Xdf573//S/fu3QkLC+PEE09k3rx5tfapvoesXbuWK6+8kvj4+Jog1erVq7nmmmvo2bMn4eHh9OzZk2uvvZZ9+/bVHP/xxx9z1VVXAXD66afXXOs//vhjoP4ljXa7nQkTJtCrVy+sVitdunRh/PjxFBUV1dpP3csURVFaH6fTybPPPkv//v2x2Wx06NCBW265hdzc3Fr7zZ8/n3HjxpGYmEh4eDjdu3fniiuuoKKioknXXl3XefbZZ+nXrx/h4eHExcUxZMgQXn/99Vr77dq1i+uuu47k5GRsNhsDBgzg7bffrvl84cKFjBgxAoBbbrml5txPP/20MX9BinKImuGltAuZmZnccMMN/Otf/+K5555D06pivbt27eKCCy7gwQcfJDIyku3bt/P888+zcuXKOtOG67Nhwwb+8Y9/8Oijj9KxY0c+/PBDbrvtNvr06cOpp57a4HFfffUV99xzD/fddx8vvfQSmqaxe/dutm7dWrNPVlYWI0eORNM0nnzySXr37s2yZct49tlnSUtLY+rUqaSkpDB79mzOO+88brvtNm6//XaAmpuXoihKW+XxeJg/fz4nnHAC3bp18+mY5l7z6/PWW2/Ro0cPXnvtNXRd54UXXuD8889n0aJFjB49uta+l19+Oddccw133XVXTZAtLS2Nfv36cc0115CQkEBmZiaTJ09mxIgRbN26laSkJC688EKee+45HnvsMd5++22OP/54oOGZXVJKLr30UubNm8eECRMYO3YsGzdu5KmnnmLZsmUsW7asVoBQ3csURVFaD13XueSSS/jjjz/417/+xZgxY9i3bx9PPfUU48aNY/Xq1YSHh5OWlsaFF17I2LFjmTJlCnFxcRw8eJDZs2fjdDqbdO194YUXePrpp3niiSc49dRTcblcbN++vdZgytatWxkzZgzdu3fn5ZdfplOnTsyZM4f777+fvLw8nnrqKY4//nimTp3KLbfcwhNPPMGFF14IoFbUKMaTitKG3HTTTTIyMrLWttNOO00Cct68eV6P1XVdulwuuWjRIgnIDRs21Hz21FNPyaN/XXr06CHDwsLkvn37arZVVlbKhIQEeeedd3o917333ivj4uK87nPnnXfKqKioWu1LKeVLL70kAbllyxYppZS5ubkSkE899ZTX9hRFUdqSrKwsCchrrrmmScf7e80/7bTT5GmnnVbz9d69eyUgO3fuLCsrK2u2l5SUyISEBHnWWWfVae/JJ59stF9ut1uWlZXJyMhI+frrr9ds//bbbyUgFyxYUOeYm266Sfbo0aPm69mzZ0tAvvDCC7X2+/rrryUg33///Zpt6l6mKIrSsh39fvPll19KQE6fPr3WfqtWrZKAfOedd6SUUn733XcSkOvXr2+wbX+vvRdddJEcNmyY133OPfdc2bVrV1lcXFxr+7333ivDwsJkQUFBrf5OnTrVp3MrSlOoJY1KuxAfH88ZZ5xRZ3tqairXXXcdnTp1wmQyYbFYOO200wDYtm1bo+0OGzaM7t2713wdFhZG3759ay1Fqc/IkSMpKiri2muv5ccffyQvL6/OPj///DOnn346nTt3xu121/w5//zzAVi0aFGj/VMURVEOa+41vz6XX345YWFhNV9HR0dz8cUXs3jxYjweT619r7jiijrHl5WV8cgjj9CnTx/MZjNms5moqCjKy8ub3Kfq2WpHL0u56qqriIyMrLPkUt3LFEVRWo+ff/6ZuLg4Lr744lrX1WHDhtGpU6eaiovDhg3DarXy97//nU8++YTU1NRmn3vkyJFs2LCBe+65hzlz5lBSUlLrc7vdzrx587jsssuIiIio1b8LLrgAu93O8uXLm90PRfGVCngp7UJKSkqdbWVlZYwdO5YVK1bw7LPPsnDhQlatWsWMGTMAqKysbLTdxMTEOttsNlujx954441MmTKFffv2ccUVV5CcnMyoUaOYO3duzT7Z2dnMnDkTi8VS68+gQYMA6n2xUBRFaS+SkpKIiIhg7969Pu1vxDW/Pp06dap3m9PppKysrNb2+u5F1113HW+99Ra33347c+bMYeXKlaxatYoOHTo0uU/5+fmYzeY6y1KEEHTq1In8/Pxa29W9TFEUpfXIzs6mqKgIq9Va59qalZVVc13t3bs3v//+O8nJyYwfP57evXvTu3fvOvm2/DFhwgReeuklli9fzvnnn09iYiJnnnkmq1evBqruP263mzfffLNO3y644AJAXfeV4FI5vJR24eiE81A1Ap6RkcHChQtrRviBOgl9A+WWW27hlltuoby8nMWLF/PUU09x0UUXsXPnTnr06EFSUhJDhgzhv//9b73Hd+7cOSj9VBRFaYlMJhNnnnkms2bNIj09vdG8H4G65mdlZdW7zWq1EhUVVWv70fei4uJifv75Z5566ikeffTRmu0Oh4OCgoIm9ykxMRG3201ubm6toJeUkqysrJpEwUZQ9zJFUZTgSkpKIjExscEqxNHR0TX/f+zYsYwdOxaPx8Pq1at58803efDBB+nYsSPXXHON3+c2m808/PDDPPzwwxQVFfH777/z2GOPce6553LgwAHi4+MxmUzceOONjB8/vt42evXq5fd5FaWpVMBLabeqXzyOruz13nvvBbUfkZGRnH/++TidTi699FK2bNlCjx49uOiii/j111/p3bs38fHxDR5f3f+mzgRQFEVprSZMmMCvv/7KHXfcwY8//ojVaq31ucvlYvbs2Vx88cUBu+bPmDGDF198sWZZY2lpKTNnzmTs2LGYTCavxwohkFLW6dOHH35YZzmkP9f6M888kxdeeIHPP/+chx56qGb79OnTKS8v58wzz/Tpe/OHupcpiqIEx0UXXcRXX32Fx+Nh1KhRPh1jMpkYNWoU/fv3Z9q0aaxdu5ZrrrmmWdfeuLg4rrzySg4ePMiDDz5IWloaAwcO5PTTT2fdunUMGTKkzn35SOq6rwSDCngp7daYMWOIj4/nrrvu4qmnnsJisTBt2jQ2bNgQ8HPfcccdhIeHc/LJJ5OSkkJWVhaTJk0iNja2ZuT9mWeeYe7cuYwZM4b777+ffv36YbfbSUtL49dff+Xdd9+la9euREdH06NHD3788UfOPPNMEhISSEpKqlOiXlEUpa0ZPXo0kydP5p577uGEE07g7rvvZtCgQbhcLtatW8f777/P4MGDufjiiwN2zTeZTJx99tk8/PDD6LrO888/T0lJCRMnTmz02JiYGE499VRefPHFmuv2okWL+Oijj4iLi6u17+DBgwF4//33iY6OJiwsjF69etW7HPHss8/m3HPP5ZFHHqGkpISTTz65pkrj8OHDufHGG5v1PVdT9zJFUZTgu+aaa5g2bRoXXHABDzzwACNHjsRisZCens6CBQu45JJLuOyyy3j33XeZP38+F154Id27d8dutzNlyhQAzjrrLAC/r70XX3wxgwcP5sQTT6RDhw7s27eP1157jR49enDssccC8Prrr3PKKacwduxY7r77bnr27ElpaSm7d+9m5syZNXkme/fuTXh4ONOmTWPAgAFERUXRuXNnNfNXMVaos+YripEaqtI4aNCgevdfunSpHD16tIyIiJAdOnSQt99+u1y7dm2diiENVWm88MIL67R5dCWv+nzyySfy9NNPlx07dpRWq1V27txZXn311XLjxo219svNzZX333+/7NWrl7RYLDIhIUGecMIJ8vHHH5dlZWU1+/3+++9y+PDh0mazSUDedNNNXs+vKIrSlqxfv17edNNNsnv37tJqtcrIyEg5fPhw+eSTT8qcnJya/ZpzzW+oSuPzzz8vJ06cKLt27SqtVqscPny4nDNnTq1jq9vLzc2t0/f09HR5xRVXyPj4eBkdHS3PO+88uXnzZtmjR4861/LXXntN9urVS5pMplp9PrpKo5RVlRYfeeQR2aNHD2mxWGRKSoq8++67ZWFhYa391L1MURSlZavv/cblcsmXXnpJDh06VIaFhcmoqCjZv39/eeedd8pdu3ZJKaVctmyZvOyyy2SPHj2kzWaTiYmJ8rTTTpM//fRTrbb8ufa+/PLLcsyYMTIpKUlarVbZvXt3edttt8m0tLRa++3du1feeuutskuXLtJiscgOHTrIMWPGyGeffbbWfl9++aXs37+/tFgsqlKvEhBCSilDFGtTFEVRFEVRFEVRFEVRFMOpKo2KoiiKoiiKoiiKoihKm6ICXoqiKIqiKIqiKIqiKEqbogJeiqIoiqIoiqIoiqIoSpuiAl6KoiiKoiiKoiiKoihKm6ICXoqiKIqiKIqiKIqiKEqbogJeiqIoiqIoiqIoiqIoSptiDnUHvNF1nYyMDKKjoxFChLo7iqIorZ6UktLSUjp37oymqTEPUPcaRVEUI6n7TF3qPqMoimIsX+81LTrglZGRQbdu3ULdDUVRlDbnwIEDdO3aNdTdaBHUvUZRFMV46j5zmLrPKIqiBEZj95oWHfCKjo4Gqr6JmJiYEPdGURSl9SspKaFbt24111dF3WsURVGMpO4zdan7jKIoirF8vde06IBX9ZTfmJgYdXNQFEUxkFpScZi61yiKohhP3WcOU/cZRVGUwGjsXqMW1iuKoiiKoiiKoiiKoihtigp4KYqiKG3GpEmTEELw4IMPhroriqIoiqIoiqKEkAp4KYqiKG3CqlWreP/99xkyZEiou6IoiqIoiqIoSoipgJeiKIrS6pWVlXH99dfzwQcfEB8fH+ruKIqiKIqiKIoSYirgpSiKorR648eP58ILL+Sss85qdF+Hw0FJSUmtP4qiKIqiKIqitC0tukqjoiiKojTmq6++Yu3ataxatcqn/SdNmsTEiRMD3CtFURRFURRFUUJJzfBSFEVRWq0DBw7wwAMP8PnnnxMWFubTMRMmTKC4uLjmz4EDBwLcS0VRFEVRFEVRgk3N8FIURVFarTVr1pCTk8MJJ5xQs83j8bB48WLeeustHA4HJpOp1jE2mw2bzRbsriqKoiiKoiiKEkQq4KUoiqK0WmeeeSabNm2qte2WW26hf//+PPLII3WCXYqiKIqiKIqitA8q4KUoitJCSb0c0EFEIYQIdXdapOjoaAYPHlxrW2RkJImJiXW2K4qitDdSekCWgohACGuou6MoitLiuXQnLt1JmCkCTagMUK2dCngpiqK0MNI+G1n2AbgPzVwy9YDIWyD8GoS68SqKoiiNkHoBsux9qPwGZBlgQtrORUTdjbD0C3X3FEVRWpy08t38lvU9W0vWIZFEmqI4Oelszux4MWGm8FB3T2kiFfBSFEVpQWTZW8iyN6hVU8SzH1nyNDjXQOyLKujViIULF4a6C4qiKCEjPXnIgqvBkwl4Dm31gGMO0vE7JHyMsJ4Yyi4qiqK0KJuL1/BR6isASCQA5Z4y5mb/yObiNTzQ9ynCTBGh7GKbIF2bwLEIKV0IyyCwnYEQgQ1JqbcmRVGUFkK6th4KdgHoR35S9R/7TLDPDna3FEVRlFZElj5/VLCrmgdwI4seqlrqqCiKouDw2Pk07S30Q/87kkQn057O7KwZIepd2yD1AvT865H5VyDL3obyD5BF9yJzT0M61wX03CrgpSiK0kLIiq8Ab0nWNWTF58HqjqIoitLKSL0Q7L9QN9hVTQc9GxyLg9ktRVGUFmtd0XIcur3BzyU6S/Pm49ZdQexV2yGlG1lwK7jWHtpSNfgCgJ6PLLgZ6U4L2PlVwEtRFKWlcG+l4ZcUAB3cO4PVG0VRFKW1ce+j5kWiQSZw7wpGbxRFUVq8zMoDmIT3qt4OvZJiV2GQetTGOBZ6ecfRASeyfErATq8CXoqiKC2FbOwlBRC2wPdDURRFaZ1EmA876T7upyiK0vZZNStSykb3s2iq0m1TSPssvIedPFVpWwJEBbwURVFaAOlO92HEXYOwC4LSH0VRFKUVMvcFrXPj+9nOCHxfFEVRWoHj4k6sk7vrSAJBt/BjiLHEBa9TbYmnCLz8/QIgy30KOjaFCngpiqK0ALJiGo3eDNAQETcGozuKoihKKySEhoga72UPDcIuQpi7Bq1PiqIoLVn3iN70jRqEQNT7uURybsplQe5VG+JTdflwhKj/77+5VMBLURSlJXDMwXv+LsDUA2HuHpTuKIqiKK1U+JWIqAcBQdWjvomagii2cYjYZ0PWNUVRlJbGrbvoEt6r3oCXQHBV11s5LvbEEPSsrfAl5OQI2Awvc0BaVRSlzcuqzGdVwTLsnnwiLREMixtF53AVjGky2XB1mBqNJNRUFEVRFCEERN0D4ZdC5Qyk+wBosRB2IULaofIHpIgA22kILQ4pPeCYj6ycAZ4sMHVChF8OtjMQ6r6jKEob5pFu3tvzArvKtiCpG3AZlTCOUzqcHYKetSE+5R/WqRr4Nz48pQJeiqL45c/cHbyx/Uf2lhcBoAmdjuFldI38gWFxw/hbz/uwmVQyXL+ZB4LzTxqe5aUBZmTZZLCeBJZhAZv6qyiKorR+wtQZou5FANK5Fln8D6Rn/xF7WJDh14F7G7hWUjULzAPu7UjHPLCMhPj3EVpEaL4BRVGUAFuZv5idZZsb/Hx5wQLGJJ1Bj8g+QexV2yIs/ZGO32g4dYsAU0+ECExoSi1pVBTFZz+lr+ahNZ+wt/xwWV5damRWRLO5IIUNRRv4JO3NEPawZZOuncjyj5Blk5GOP5Hy8IVfRFyP9yWNetVLSNkbyIK/IvOvQHoyA95nRVEUpXWTrq3Igr+BJ/2oT1xQ+cmhYBccvgcd+q9rNbLkmSD1UlEUJfiW5M1tMHcXgIbG0rx5QexRGxR+JXj5OwaJiPxbwE6vAl6KovikyFnB81t+PPTV0RctQaXHQnp5DFtK1pJekRbk3rVsUi9CL7gVmX8RsvTFqqBV4S3IvHOQrm1VO9nGQfi1h45o6KZQPd0XcG9DFlyP1MsD23lFURSlVZNlb1J172isMMrRdLD/hPTkB6BXiqIooZfjyKx3KWM1HZ1sR0YQe9T2CFMyhJ3b8A6WERD+14CdXwW8FEXxyayMdbilt4dlQXZlNEJqrC9aEbR+tXRSupEFt4Fz2aEtRwStPAeRBTcgPRkIIRAxTyNi/1dVVr5RHvAcBPuPje+qKIqitEu6cws45tFoUZQGucG12sguKYqitBhhWrjXzwWCcJNa1t0c0rEQ7L828KkA927AFbDzq4CXoig+SSvPRWskZ5RHmnBLEw69smabW3ezoWglc7N+ZEnuXIpdhV5aaIMcC8C9ifpfNjwgK5DlHwNViYZF+OVoSTMRHTeCaKwIgERW/mRwhxVFUZTWTkqJXvoSFFxmQGPu5rehKIrSAp2YcAqal5CIRHJ8/Jgg9qjtkeUf0XDYSYIshMpfAnZ+lbReURSfRJp8qbAhAQ/Jts4AbC5eyxf73qXcU4qGho7Od+kfc3LSWVze9W+Y2kH1J2n/hZpEwPXyQOWPEPPY4WP0CmTJ0yD3N3DMkYe3swCioiiK0riKz6D8fQMaEmAdZkA7iqIoLc+pHc5jad48nLoD/ahl3xoaibaODIsbFaLetX5SesC5ErwsGwUN6VyKiLgyIH1QM7wURfHJGZ0G4/G6pFESa63EZtI4If5kdpdt48PUlyn3lALU3EQkOkvy5jL9wMeB73RLoBfR6FISWXr4/0odWXin70sVTXFN7ZmiKIrSBknpRpa/a0BLJrCdjjB1MaAtRVGUlifemsi9x/6bGEs8ABqmmhlfXcJ7cG+fJ7Bo1lB2sZWTeA92Ve/T1GX3jVMzvBRF8cmg2K6MTOzNqvzdyDpJ1asuZF0ji0i2dSbCHMkvGd/Q8AVO8mf+75zZ8S8k2joEstuhZ+oBrKDhC7mAI18mnIvB5UcONC25GZ1TFEVR2hz3NtDzmt+OiIWoxxrfT1EUpRXrFtGLpwa9wZaStewr34MmNPpHD6FXZF9EI+lcjqRLHYHw65i2Tggz0jyw6r7kJfAlLMcHrA8BneE1efJkhgwZQkxMDDExMYwePZpZs2YF8pSKogSIEIL7+40lxlqdn0siDkXtNSHpG5tDjNVBhn0/u0u3kVq+3WvVE4HG+qJlDX7eVoiIq2h01ELriJ57EXrOGcjCB/07galzU7umKIqitEXSblA7BVB4I9J9wJj2FEVRWihNaBwXeyIXdf4rF6RcxTFR/XwKXOlSZ3n+Qp7f/ggPrb+eh9ffyAepL7GnbHsQet06iMibaTjYJYAwCL80YOcP6Ayvrl278r///Y8+ffoA8Mknn3DJJZewbt06Bg0aFMhTK4oSAIWuTAbGZ1PuspLviECXGuEmJ0lh5Zi0wxeyN3c/02hbmhCUu8sD2d0WQVgGI8NvhMrP6vsUkIcqYPlbLv4Qnyo6KoqiKO2GuTfec0f6Qc9BFt0NiTPVrAVFUZQj6FLns31vs7ZwKeLQ6hcdD1uL17GleC3Xdb+TkYmnhbiXoSdt54Pla3CtOeoTE2BCxL+F0GIDdv6ABrwuvvjiWl//97//ZfLkySxfvlwFvBSlFTKLqktGpMVJpMXZrLY80tP2lzMeImKeAHM3ZPkHoOce2moDqv8OmxjsAiifigy/ACG8l1VWFEVR2gehJSDDzgf7LJof9PKAeyc4V4DtJCO6pyiK0iqVuUvYXrIJl+6gc3h3sirTWVu4FKDWqpbqvMVf7n+fvtGDibMmhqS/LYGUdii8tZ5gF4AGca8ibGMD2oeg5fDyeDx8++23lJeXM3r06GCdVlEUA/WLPg4NE7oBo8YWYWk3ZX6FEBB5M0TcAO7dgAtZMQMqv6LZLyOenVD5A0Rc2/yOKoqiKC2WlDo4lyErfwQ9H0ydEeFXgGVondlXIvoxpGs9eDJpftDLjHSuQKiAl6Io7UCOPZOtJetwSzddwrvTJ2ogPx38kj/z5+KRh6+nZmHx2o4EluUv4PyUwFQfbA1k2ZvgWtvApzqUPoe0nYEQpoD1IeABr02bNjF69GjsdjtRUVF8//33DBw4sN59HQ4HDoej5uuSkpJAd09RFD9EWWIYnXQ6S/Pmec3P5YvLu95EuCnCoJ61DkKYwdIfAOl8BGMqkghkxTcIFfBSFKUd80g3IDAF8KE5lKSsRBbeA84/Obxc0YSs/BrCLoXYSbVeGIQpCRKnI8s/hIpvQBYDVhDhIEtovGpWnR4Y9a0oiqK0SA6PnWn7JrOheGVV8nkEOjoWYcMlHXX2d0uX1/YkOgcqUgPV3RZPSgdUfEnDK1k84DkIzj/ANi5g/Qho0nqAfv36sX79epYvX87dd9/NTTfdxNatW+vdd9KkScTGxtb86datW6C7pyiKny7v8jeOiz2xycd3sKVwc88HGJN0poG9ao2MenmQoGcZ1JaiKErrIaVkZf4int/2CA+vv5GH19/Am7ueYUtxQ6PJrZcsfhKc1YVePLX/a/+xahT9KEKLR4v+JyJ5JSJ5HaLjBkSHhRA5HkSMH2d3g7l/M3rfuqkiXIrS9kkp+TD1ZTYWr676GlmzNLG+YJcvBAKz5n0WWJvm3geyrJGdzEjnxoB2I+ABL6vVSp8+fTjxxBOZNGkSQ4cO5fXXX6933wkTJlBcXFzz58ABVRVGUVoas2bh1l4P+RX06hM1kAeOfZpH+7/A4wNeZni8WhaBbRRVo/QG0NpHLjRFUZRqUkq+OvAB0/a/S6b98PNiatkO3k99kXnZM0PYO2NJTxbYZ9LwKLmEik+QsrLeT4UQCC0SIUwILRIt+n5E8ioIu8L3TpRPRcr2OcurugjX6tWrWb16NWeccQaXXHIJW7ZsCXXXFEUxyO6ybews24xsTl7do0gkg2OON6y9VsenGdeyagVMAAUth1c1KWWtZYtHstls2Gy2IPdIURR/lblL2FK8zuf9d5dt5Yt973JeyhV0sKXUJL9vz0TEDciKL41pzHaOMe0oiqK0QCWuIlbkLyLbcRCbFsawuFGUu8tZnr8AqD9Z8E8ZXzAgZhidw9vAagHHUhotbiLLwbkebLXz5Eq9rKoSsHSBeQDC3PXQJx5wLvK9D+514FoF1pH+9LxNUEW4FKXtW1u4FA2t5h7SXBoa0ZZYhsW3n9zl0p0G9l+RejHC1A0ZdgFonRpZieIB2ykB7VdA3zofe+wxzj//fLp160ZpaSlfffUVCxcuZPbs2YE8raIoAbajdLPfietznVl8tu9tfsn8hvF9HifJ1jFAvWsdZOUvGLas0aNmwyqK0jYtyZvL9AMfI5E1Zd+X5M0lTAtHIBrMJykQLM37nSu73RLM7gaIr1WRD+eTkdKNLHsNyj8F7Ie2CqR1LCL22aoCKnqeH30wI+1zEO0w4HUkVYRLUdqmCk9Zs/MTAwg0JDrRljju6fMYVs1qQO9aNimdyOJ/g/17qlavCCQeKP0fhJ15qGJwfUxVRVcsQwLav4AGvLKzs7nxxhvJzMwkNjaWIUOGMHv2bM4+++xAnlZRlACrSg7cNEXOfN7Z/V8eG/AKZq19zvSSzvVQ/rZxDdp/RspnEELNkFUUpe3YXLyWbw9Mqfn6yJcRu17/8r0j991Ttj1gfQsqy2AfdtJq8mxJKZHFj4D9Z2oPrEhw/onM/ytE3upnJyTICj+PaTv8KcIFqhCXorQ2idZkr4MoDdHQGJFwKkm2ZPZXpGIWFgbFDmdY3Cgs7SDYBSBLngH7D4e+OnJChPNQsCsGKOFwwRUBSDAfg4h7K+D9C+jb5kcffRTI5hVFCZFEa9NzRuno5Dtz2VS8iuHtaJrvkWTFNA5f9I3gAr0ATCkGtacoihJ6v2V936QXkGpFrgKDexQawjIYaR4M7m3Uf98wge1shCm56kvXxkM5v+rjAT0bSl/zsxcSYe7t5zFtR3URrqKiIqZPn85NN93EokWLGgx6TZo0iYkTJwa5l4qiNNVJiaczL8e/3I8aGpHmaC5IuYo4a0KAetaySU8mVH6L91Ur1QF/UX0UiGiwnQtaZGA7SBCS1iuK0vpsLU7nm33L+G7/cvaX113ysK9id7Pa19D8ygHW5rg2Y1ywC0BU3TgURVHaiDJXCfsqdjdriUmFp4wSV5FxnQohEfcKaLHULXaigakrIuapmi2y8vt69juSDpT72QMNwi/385i2w58iXKAKcSlKa5MclsJZyX+p9zOBINIUTZgWXmt73+jBPNT3P+022AWAfa4fOx+xQkiWQvk7yPwbkHpgZw+3z/VEiqLU62BFARPWf8n2koOHcqVUvWqc0qE/Tw+5ihhL1YV+Z+nWZp1HInFLV+M7tlUizNj2rKcjtChj21QURQkhZxPLwB+twJlHjCXOkLZCSZh7QuJPyIpPoGI6yCLQkhERf4WIGxFazOGd9WyMHVQBETMRobXjl7qjeCvCBaoQl6K0NusLV7CiYHG9n6WEdePGHveQZOvE7zk/saFwJfnObPaW72R6+lQu7nwtKW2hQEpTyHKqBliaku5GB/cWZPl7iOiHDO7YYSrgpSgKAIXOMu5Y8R6FzqpR3yNH1Zfm7eC+VVP46KS7MGsmXLqvCXQb1jWiFwBZ9oMszfudAxV7sWo2jos9kRMTTiHMFN5IC62XCDsHWbaNRqtu+dpe9H2GtKMoitJSxJjjCTdFUOlp3shvhCnwyyWCRZiSEdH/hOh/et9RS8SwZfOmYxAxExC205rfViulinApStu2vmgFU9Nea/DzTPsBXtn5JL2j+rO9dOPhD6SLLSXr2FKyjvM6XcH5KVcGvrMtjfkYmhbsqqZDxRfIqHsRwmJUr2pRAS9FUQD4bv8KChxl6PUsH9GlZFvJQRblbOW05AFkVjZvar4mNEYlnMb87J/5MWNarTLA20s3MjtrOvf2eYJO4V0baan1kVIizX0BC1WVt5pbEcaCsKiy6IqitC1mzcyYxLOYn/MzsgmDAwJB5/DuJIe1v9yGIvxSZOU3xjRm6tWug12ginApSlumS53v0z/1uo9E4pLO2sGuo8zOmk7HsC4c397yE9tOBxFfNeu4qe80shj0fDB1MrJnNVTAS1EUAH4+uLbeYFc1DcGvB9eRYCujQi8DQJdV6QeFaPCwo9rQkEhu6HEP+ytS+TFjWlU7R73MlLtLeWfPJJ4c+BpmLTDR/lCQrh3IoofA07wcaEe1amBbitIyFBSW88ucjSxZvguXy8PA/p259MLh9DkmOdRdU4Lo7I6XsKpgMSXuIr+PlUguSrnG+E61BpYTwHYWOObR7HuEcx565e9o4WcZ0rXWSBXhUpS2a0/ZdsMKnExPn8rwuJMQvr4YtQkCIq6F8snUVF9sUjOBWwKuAl6KogBQ7PS+bERHUugsZ0PRKrIro8iqiKHCbQMkMVY7XSKKibN5LxOvozMoZjgDYobxYepLCLR6R+51dIpdBWwoWskJCSc359tqMaTnILLg+kNr3Y2kao8obcvmbQf55xPfUml3IWXVg1Pa/jxmztrAPbeP46+XjwxxD5VgWZI3t0nBrnBTBH/tdjsDY4cZ3qfWQAgBca8hS56Dym9o3nIToGQCtOOAl6IobVdpE+4xDSlzl7K/IpUekVUVbT3Sw8r8RSzOnUOWPR2zZmVI7AjOSL6QLhE9DDtvqMjKX5Cl/6mqFN9cASy+pd6UFEUBoFN4nNfPTUIjJTyO3zIKSC1JosJtPfSJoMQZxraiTmRWNH6x2laygTd2TmR32Tavy1Q0NK9Th1sbWT4FZBlGJxIGF1IvaXw3RQmytP35rFm/j7376lZ6bUhZuYNHnvwOu+NwsAvA46n6/+98uJBVa9OM7qrSAtk9FczJmu7XMRoa13W/i/8Mnszw9ras5ChCWNFin0YkL4HoCc1rTBaje3KN6ZiiKEoLEmOJN7S96gCaR3qYkvoqXx34gAz7AXR0nLqdtYV/8vLOx9lSvNbQ8wabtP+GLH7ImGBXVYPGtFMPNcNL8VtBYTlrN+zD7dbpd2wnevVICnWXFANc3m0kL2+b2eBEVI/U6RqRyNwsJ1VTVo9U9XVaaSJx1krCzQ2PJuvoZNj3N9ofSdXNos2o+BqjktTXJkHPgSMrdClKCK3ftJ+33p/Prj05Ndv6HJPM+DtO5/ih3kc0f5u/hbLyhiufmTTB1zNWMuL4nkZ1V2mhNhWvweVnNV8dnVmZ33Fs9CASrG3r2URKCa614MkELQGsIxGi8cd4oSUgIm9Br5gGnsbvvQ1yboTwM5t+vKIYwO3RWbdhP3n5pcTHRXLi8B6YzaZQd0tpxRKtxqZKiLVUVbP9I/c3NpesObT18NuVjg4SPk57g2cGv0O4KcLQ8weDlDqy9H80awljLVEgAvf3oAJeis8cDhevvzuP2XM34dEP/3APGdSVx/5xASmd4kLXOaXZ/tL1RH4+uIadJZn15vIaFNuVlfm70RBec31lV0bTM7qw0fMJRK1KkEeTSHpG9vGt8y2c7lhNVYL6ABEq2KW0DGvX7+P/nvgGedSv9p69ufzj8W944ZmrvAar1m7YhxDUOb6aR5es27gfKWU7y5HR/pS7Sxu9T9Sn0JXHy9sf55nB72DSTDg8dio95USYo7Fq1sYbaIGk4w9kycTaASstCaIfQYRf4lsjMS9B4V9ReR+V1mrRkh28/u7v5BccTg0RFxvOPbefwblnquI9iv88uoeXtz9uWHvJts50De+JlJJFubO87uvUHawuWMLYDucYdv6gcW0CT7px7ZmPRYjALTxUAS/FJ1JKnnzuR1au3ot+1JvIlm0HGf9/0/jorZuJj2s75b/bmzCThXdG3s6zm2YwP3tznc+3FKc3GuwCgcnHh2nvLzECq2ZlRMKpPrXV4lV8FtDmJRF15twpSrBJKXn5rd/QpawTsKpenvjyW3P48qO/NxisknrdY+s7j9K2eaROhCnO72BXtTJPCRM23k73yN7sLtuKRGIWZo6PH0OPyD7k2DMRCPpED2BQzPFoAXzQbi7p+BNZeAd1AlV6HrL4nyA9iIjL6z/WcxBZ8RU4V4MwQdjVYF8IZPvfEU8qoGZ4KaHxx9JdPPncj3W2FxVX8tzLvyCRnHfm4BD0TGmtpJR8kPoipZ5in/bX0OgTNYCdZVsa2ENwZdebEUJg91RQ4PS+DFxDY3/FHj973UIYtYyxmmdHQAcyW+4dXmlR1m3Yz/JVqXWCXVA14l5YVMF3P66p50ilNYkwWdlVmtngheHoaopHizTb6RpV1Ox+aGjc2uvBVjnNt16u1YFtv+yFwLavKD7YuiOT9IzCBgNWUkJmVjGbth5ssI3BA7t4rfqqaYJB/buo2V1tVFZlEf/b8gPj5k7kvlUzcetNf0x1SDu7yrbUBM3c0s3KgsV8e2AKi3PnsDh3Dh+mvsyzWx8ky97wz2QoSSmRpf+lKthV/y+WLJ2ElHVnEMvKX5C5Z0H5B+BaA86VYP+aJgW7ANwNveQpSmDpuuStD+Z53Wfyhwtwu9tQGgwl4NLKd7GtdIPP+5/Z8S/8vfe/SLJ2rPfzMC2cOGsiAJoPy81BYPJpvxbIlGJse7IiAEW9DlMBrwAoyCnh2w8X8fYzP/LpG3M5kJrT+EEt3Jz5WzCZGn7B0HXJL3PaToLx9mpl/h4OVOQ3EtZqeMS9c4RvoySNObvjJQyIGWZIWy1DgPNL2OeqWS9KyGVn+/b7n53T8H7nn30cFou5waCXrkuuuuzEpnRPaeHSynK4Yemb/Ji+GofuQiJILUlEyoaXuDaVREc/VECk0JnPW7v+Q4W7zNiTGMG9Ddy78boMURaDY1HtTa7tyOJ/UFUkxYjckSbUohAlVLZuzyAr23txnqLiSlav2xekHiltwbL8BQgf10cIBGM7nM2szOnkO+t/r3fqdj5KfQUpJVbNyjGR/RFeQi06HgbFDG9S30PO3A/M/TE0lCQCl3JABbwM9s0HC7nx9P8x9dU5zPp2JV+/v5C/X/gqL0/4Frer9Y485BeU1VTJakhxSWWQeqMEysbCfWheL/6Cugnrq0niwyq9zs7whYaJCk8LfPFoDssQGv57M4DMB9l43jRFCaTYWN9mZMbGNLxfXGwE/3n8UkwmEybt8O9M9f+/9sqRjB19bPM6qrRIz2yaTpnLjkceDtDkO6LYUZSMwxO4YIuOTpm7hBUFixrfOdh0HysjHrWfLP8UY+85HoTtZAPbUxTfFRT5NvOjoDBwM0SUtiffmePzsvmxSecQbopiSd7cBo/R0cl2HGR32TYAzur4lwar0WtoJNk6MjC2dQa8hBCImH9TFUoyKJzkmG9MO/VQwzUGmv3tKqa+Mqfm6yMTu8/7cR22MAv3PnVpCHrWfMlJ0Zg0Uet7OlpCvMrf1doVOssbydHlnWZQMlxLK00sfDQp3cjix8DxWxBOFogKkIriu6HHdSM+LoLCoooG94mNCef4od29tnPSiGP4ZPItzJi5jj+W7cLt9jCgbwqXXXy8qs7YRu0uzWJz8YGjtko6hpfSJbIIm6l6wNCoilBHn0mytnAZpydfaHjbzaJ1aNp+zoWAUYOspqqqkGEXGNSe0ppIKflz7hZmTlvGnm0ZWK1mxpw9iEv/djJde/n489lMHRKjfdsvKSrAPVHakihzDAKtwaDUkf7I+40cRyYO3fvkDg2NveU7OTZ6IINih3N5l5v4/uCnCAQ6ek0hljhrInf3noBJtN4Ko8I6AhI+QRb/Bzzbm92eLHoQEr5EWI0PAqqAl0E8Hp1pbze8vlxKyaxvV3HdPWeS0MG3C3dLct7Zx/HLb5sa/FzTBBefNzSIPVKMVul2MjerOctSBR4pMIvmvYzoeEiwtI1y8rL0RbDXTbJqOBEPWmLgz6MoXphNGnfdOo5Jr/za4D63XH8y23ZkIoSg9zEdCA+rP7jdtUsC9991JvffpZJktwe7S7PqbOsRVUDnyJKjljMGbum23dMCZ6mbB4DpWPB4WdYoYsF2Wu1tRg6AiBhE/BSEsBnXptIqSCl57Ynp/DZjDZom0HVJOVUD/L/NWM1T7/yNE07uG/B+9O/bia6d4zmY2XCOyMSESIYP7RHwviit296yHLYVH8SimegbfQLri5b7dJxEsqO0bkGvuvtRa5nkacnnMTBmGH/mzSPLkY5VszIkdgTD4kZh1ixN/TZaDGEdAdHjkUX3GdCaRJZ/gLC+Y0BbtamAl0H2bMsgr5H8JbpHZ8WCbZx/9cgg9co4xw3swrix/Vi0ZEedm41JEyR3iOHyvxwfms4phpiTuYESV/Me+MvdVmKtjmb3ZfrBT+gQlkL/mCHNbstIUjqg4mtkxZfgOQAiEsx9QYsAEYWwjobwCxEiHKkXQ8XnBKUEfPjVKom30iKcd9Zg3G4P73y4gPIKJ0IIpJSEh1sY0DeFyVMW4nC4AQgPs3DpRcO59cZTsFrU40h7ZtNq//tHmB10jqzK2ROMS5uGRufwboE/kZ+EEBDzGLLwtkNb6t5PRPSjiEO5T6QnB+nagqF5I82DEJZ+xrWntBpzv1/DbzOqClLpR6zw8Hh0dF3w7H3T+HzRBCKjwwLaDyEED9x9Fo88+R2I+iv53n/XWZhNKlOPUr/MykImbvqOtQV7a7aZhEbPyGPoELEPIRqfEevLTDCJTt/oqmqh+Y4c5uXMZHXBEhy6nShzDCcnncXAmGFtIthVw51K1T2nubOKJTjmI6UHYfDMN/WEaRB7Rd0KOUfTNOHTfi2REIJ///MiOiXH8v3MtTic7kPb4aQRvfnHfecQEx0e4l4qzfFn7vaaqbZNpUtj3kwkki/3v89Tg95oMeXipV6BLLwZXNUVXSRIJ7hWHPpaQ9pnQtlLED8F3HsBV3A651aJWpWW46LzhnL26QNZunIP+fllxMZG8PPsDazfeKBWpd9Ku4uvpq9k7748nnvyckzqZaXdGpHYB6tmxqlXPVskh5ehS9CCFMfX0Tk56azgnMxPwnYyxL+PLJlYNdBSTUtERP8LEX4ZUi9AFj0KzkUYPsjiWoJe8ipazEPGtqu0eN9/sqRm0OJoUkocdifzflrLX64fE/C+jDyhF88/cyWvT/6d9IzDOUs7dYxh/B1ncOqYxmeaOV1uFi3ZybKVe3C5PBzbO5kLzx1CYoJaCtmWFTrLuH35exQ4a+cH9kidvWWCMndXesXsM2RwpVt4L3pE9uZg5T7e2DkRp+6oqXBf5i7ht6wfWFP4Jw8eO5FoS2zzTxhi0rUT6ViIcUvodcCN0cW+VMDLIF16JjV4U6im65LufZKD2CvjSClZu2E/6QcLiI+LAAHHDezKXy8fwbG96y/PqrQuLt3TjGCXxCx0Yq12w/pT5MpnV+kW+sUcZ1ibzSHLXgPXRhp+mTg08qMXIQtugqgHgtQzwDkH6clGmNTvotIy2GwWTh/bH4C5C7ayftPR+ZmqSAnLV6Xy54rdPr2wKG1TlCWMq3uMZtreP5BAmMkVtGAXwJjEMzk2alDwTugnYTsVkuaCay14MqtyallHIYQZ3bUHCq6oKuseKBUfIKMfVDOJ2xGn003azmyv+wgh2LZ+f1ACXlAV9Pr8g9vZtiOTvPwy4uMiGDSgC5oPF4v0jEIefuxrsnNK0LSq97Uly3fx8RdLefSh8znnjJb7+680z9f7lpHvKK03R7GOJNuukRgWRqyt+e8ww+JHIaXk471v1Ap2VZPoFDhymXHwU27qacQywNCRlTORxf80tlERF5Dl82o41SCJyTGMGtcfzcsIdWMBsZZK1yUvvjGHf/77W5at3ENWTglZ2SX8vnAb/3j8G/bs9bGKkNKiDYjt0kiFxoZUrVjvEV1g+AvKVwc+5GBl6GcvSb0CKr/Gt/LuOsgScG0JdLeOIKtehBSlBZo5a73XFxJNE8yctaHBz5X24Z5jz+GCzlXJaj1SazBXT9MJOtm6EmtJqNmSZO3IVd1u5eput7X4YI4QGsJ6IiL8YoTtZIQwIz35UHBVYINdALiR7tQAn0NpSTRffh8EQZ+ZK4RgYP/OnHpyX3p2TyI9o4CSUu/pOJwuNw8/9jV5eaVA1XuNlFX/9Xh0nnv5FzZtSQ9G95UQ+Cl9dSMFuSS59ubP8tPQkFKyp3w7OY6MOsGuajo66wuXU+ryngqpJZPuvYeCXTq+vRv5KPxq49o6ggp4Geiuxy8mJtbbsj7JxPGfsWdbRtD6dLRKu5MZM9dy6/ipXHLtm9w6fiozZq6l0t7wUsufZq3nlzlVycyPrNIopaS0zM6jT3+H26MqxLV2l3Yd0aTjoi12jks4SHJ4WeM7+6nAmcNL2x9jW0mIX4Y9aSD9yW8mwbkkUL2p/4y6+h1UWqaMrOJa+V+OpuuSjMyi4HVIaZHMmonb+5yJAPLsEQHI3SXJdhzEqTsYGDOMkQmncn2Puzk58awWH+xqiCx/H6Tx9956Of4MznmUFsFsMTH4xJ5eByt0j2T46D5B7FWVtP35PPHs9/zlmje58e8f8Zdr3mTCxOnsTs2pd/9FS3aSnVPSYKV5TQi+mr4ykF1WQqjQWd7IHgKn3vwldDo6ibaOHKxofKBeRyezMp0CZx459kxceutKeSQrvoQmTZLwTkTfb3iboAJehurYJZ7bH2m4pLWUIHXJtx8tDmKvDisqruDOBz7jjXd/Z8/eXIqKK9mzN5c3Jv/OXQ9+RnFJ3Rd6KSVfz1jV4I+0rktycktZumJ3YDuvBESl20mOvRi7x0XH8Dj+fdwVCASmRvNmScI1Byd2SGNwQhZRlsBdqHV0Pt77Og6Pccsl/deE1d96qfHd8ELYTgjq+ZSWZd+ubCb/9yf+9bf3eeruT/htxmoc9iDlkGtEnNeBoKpckLGN7KO0Dw+u+QQJFDoiKXNZDZ/lJZFUesrZWrKBVQV/8Pqup3ly8z38nvUTFe4gBY4MIqUOld8G74SOBcE7l9IiXHnrqQ0OVmgmQUKHaMaeF9y0E7tTc7jrwU9Zunx3TU7I6qXxdz/8OVu3151UsHzVHq+BO48uWbYqtVWuwlEal2htbPaWxKo1PwdVmBbOcbEnYNZ8e2f4Yv9kJm65j/9ue5jHN93FDwc/D/G7jh+cyzEub9chokdNARajqYCXwTatTPU6vdfj0VkyZxN6CGZjvPjGHA4cLKjzACmB/ekFvPTGnDrHFBZVkJFZ5HUiqNmksX5j/flZgkVKqW5UfthblsPj67/kjHnPcNHC5znz92eYuPFbhsX35INRf2dEQm+vxwvg2LhczCI4f+d2vZK1Rcsa3c+j23F6iqteBIxk7g2aP/n3TKBFGtsHb7SuCFOn4J1PaVG+em8Bd/3lNX7+cgWbVu1l1aIdvPr4dP5+4StkHigIdfc498zBXmfrSAnnnTk4eB1SWqRdpZnsK69OkSDYVtiJYmdV9TcpwcskwSaQNTkrS9xFzMz8kic3j2dT8WojTxJQUi8N3uwuqJ0wX2kXRp0+gNv+73zgiKWLomqQIjo2gmc/vBWrLbjV5l58Yw4Op7vObC1dl7jdHp5/bVad9wGXy9PoO0JV5Un1HtEWnd95OMLrbCRhyCqVaEscAo1OYV182r/QlV/z/x16JQtzZvHGrmdaSdArELOiA7c0XwW8DFZZ7qhVhao+HreO2x3cgFd2Tgl/Lt/V4MVc1yV/LNtJTm6J322H6vYgpWTe2l3c8tLXjBj/OiPvfZ27Xp/On1vSQtSj1mF78UFuXvYO87O34DkUGHJJD79krOPKP17hoz0LODbae/AkwVZOpMUVlHLxABom0iv2Nvh5gX0tK7PuYs6+Efy+/2Tm7T+NnYVv49aNuXgKYUJE3uHHER6wnU9gbgj1iHstOOdRWpw/5mzik9d+A0A/tLS8+sE+L7uEf/99Kp4QLzm/4Jzj6JQci6meEXaTJujWJZ6zzxgYgp4pLcmR5eIB3NLEtqIUNuanYPeYERCAvF6HuaSTKamv+bQcJdR03Q4F1wb3pCK4gQ2lZbjytlOZ/OMDXPDXkfQf2o2hI4/hzscu5qPZ/0evvsEdaEtNy2X7zkyv7zJp+/PZuiOz1vaq4loNP48JAT27J6pKwW1MWlkO/1r7OZ/sXeS1KFeCrZxoS/ODTLmOTNYXLaepz/4SnYOVaSzKndXsvgSc7WSMrqSI9D8G4Sv1m22wLr06NLpPfFIUOQcLsVcGb73ulu0ZjT4oSkmdm0R8XASdU+K8/up6PDrDjuvW/E766T/TfuefH/zMhj0Z6FLi0SWrdx7gvre+5+PfVgW9P62BlJKJm77D4XHVBLuO5JE6y/J28lnaH17bSQorC+iLx9EkEnMDD9uZ5b+xPPNm8iuXUR1+deqF7C56jxWZtxgW9CLibxB+w6EvGrp0Vm0XUf+HiL4HML7SSH2EZUBQzqO0PN+8vxDRwFIN3aNzMC2P1Yt3BLlXtUVG2HjjhWvp3y8FqEo6XB0sHzywC68/fy3hYYGZxq60IrL+14RYq50wkxtxaGZJYLugMz/nl8CepJmklJB/OXiCnEpCxAf3fEqL0bNvJ+759yW8+tU9/O/jO7jkhjFERocFvR/pBwt92u/Awdozmy845zivSxqlhCsuUWkh2pLdpVncvGwyi3O3e9lLkhxWwrGxOYbcWwSCZfnziTLHNLkNieSP3LnN70yAifBrqbpjG3lTdhi/QueQJiSmURpSUe5g85q9yEamxBbmlXHHBa9gDbNwzmUncON9ZxETH9jlT76U7IXDVVnyC8r4c/luKiqdnDi8Jz9lrm+w3cSEKMac1PyklSXldtbsSsfl8TCge0e6dYirdz8pJf96/2fmra/7sFc96vPG90s4aUAP+nfzZxla27elOJ09Zd7LTPvCrOlBm90FVS8hA2OH19nu0kvZkPsYVbeIoy+SOsXObewp+pB+Cc1PgiiEQMQ+iYy4DFnxDbj3ViWy14tAPwAIsJyIiLwNEXZ61XITEQ4y8FOTpe5AmNToe3tTVlLJ7q3ei6CYzBprluxk1OmhDYomd4jhnZdvYOfuLDZuTgchGD6kG717qWu0UmV4Qs96xuAlKRGBG/WtezbJxqKVwD1BO6e/pP2n4Ae7AETrSqqstD3h4b4NjESG1x5sTEyI4tGHzue5l39BE6JmOaQQVcGuU8f05cJzhhjeXyV0/rflB+wep9fqjFHmSnrH5jf4ub8kkgJnHh3DOtM1vCcHK/d5nVnWkBJ3IW7dhVlruc/1wtwN4l5DFj1I1WQDo/J56QRiPpYKeBnofw9/yZbVaT7v77S7+PWblaz9cxevfnV3QINexw3sgqYJr+vTTZpgQL9OvPbOXH78dT26LmuOaejY8HArz0+8kqKicn79bRO7U3OwWEyMGdWHU8f0xWJpfLqj0+Xm1emLmbFkE64jlt7ERYYxsn93rjp1KMcf26WmitKnc9fUG+w6kibgm0UbePKGsxs9f3uyt6z+Cjb+snvMREtH0IJekaYo+kYNqrM9o+wXdOmg4YW1OvtLv+LY+HvQhDGXO2E5DhFbO0mrlG6qRjl0cMxHL3oAXNtB+jYa2WzuNDCpHEjtja9LFUO9pPFIfft0om+fxpfBeDw623dlUVpqp0tKHDl5Jfz62yY2bz1IXkEZJpPGyBN6cc3lIxk80Ld8Gf4qKqtk5vKtbEnLwmTSGDOwJ2cdfyw2i3p0CoSDFQVoiFovKGahYzUZnBi3EU7pCOr5/Fb2UWjO6zHm+UFpXVxON3O/X8PPXy7n4L58wsOtjLtoKJfddAoduwR31t/Q47oSGWmjvLzh39HwMAsnHt+jzvZzzhhESsdYvpq+kmWrUvF4dHp0T+SKv5zAhecMUcsZ25C0shw2Fu1vdL8ydzibCzrSPaqIGGvzr/sCQawlDoC/dLmeybufo+rdwL+gl0mY0ITBywUNIt37kRWfgn1W1YC+eQBoieBcAfhTzb4+ZgK1+FA9tRlk99YMVjVh2Yju0ck6WMAXk+dz12MXB6BnVRITojh73EB+W7Cl3qVomiY4+4xBTJ22lF9/21izT3WQq75glxDgcXtYsXoPH376R1UVSikRQvD7wm2kdIzllef+SueUuAb7JaXkkQ9/YfGmvXUSShaV2/ltzU5+W7OTU487hhfuqKqAOWX2ika/X13C5r1Zje7X3oQ1exaQBASVLgsiiEXV+kcPqbdsfKlzFwITEneDx7r0EpyeQsLMjS83biohzEhPPrLwZnDvoOqCHcQgg+tPsKmAV3sTExdBh5Q4cjOLGtzH49bpG4Il580xZ94WPvxkMTl5DVc6dbt1/ly+mz+W7uJfD55n+Oj8og17eOTDX3B5PDXJbmet3M6bPyzhnfsv55iUREPP1979lrmBJzZ8fcSWQ9XXgpUH8ShlrhKiLE1flhJQnsZf5AJCb+7LjNLaOB0unrzzYzasSK2ZDeW0u5j5xXLmTF/N/z6+nX5BvL9kZBbX5KpsyPVXn9TgEvnjBnXluEFdkVKi61IFudqo/RW+ztoSlLoi2FIYQYTZQe+YvGZVnZdIRiWMA6Bf9GDuOOaffHPgQ4pch5fYWoQVl2z4HBoaw+JOQhMt72dTOlciC24D3NTM6HJvAXQwDwH3xmaewQ3OxWAb18x26mp5f5ut1J+/bW7yhVP3SOZMX43L2fBLe3Nt25HJvvT8OsGu6hjCoAGdueqyE/llzkafczNJWTU7672pi/F4qm4eUh4OjuXklvDwY1/jcjU8OrtmVzqLNjZeCviPzXt56dtFbErLotTH3GdWs/rxPtropL5YfSyXWz9Bt6h8ukUVGdUlnwyIGVbvdpPwLYeESQQ2l5aUElk0HtzVMw+DPKNGz218H6XNEUJw6d/GNDjTUmiCyOgwTrug9SzV+OGXdTz38i9eg13Vqu81L70+hwwvQT9/7UzP5f/e/xmX23OoOqCsKUaTX1LOna99R4VdLe8yikt38+LWmXW2R5idmIROhcsS1JyRAHvKDw9guvQy8itXkle5Apfe+M9lwAVztKmWwFXQUlqmr95dwMZVVcUkjvwd1D06TruL/9z7OW6Xm9TtmWxcmUpORlHA+qLrkgkTp+Pw8q407Lhu3PDXkxptSwihgl1tWJTZ/2f+CreVLQUplLualk9UQyMlrBuDY49nSe5cvjnwETtLN3NDj/Hc3XsCf+12O7f2eohnj3uXHhF90OoJwQgEmtA4s2PgJsA0ldQrkIV3Ay5qL1889L7j3ggiqplnMSGdK5vZRv3UDC+DVFY4mpW3zV7hpKignA6dYo3r1CFbt2dw/yNf1rusRUq49sqR3H7TqXz57YpGlz0eTffyXu/RJZnZxfyxbBdnnNq/3n1mLtuKSRN1ygvX7afkh6WbObGf7yNJowf19Hnf9iLKEsZ1PU/m49RFTW6j0BFJl4gSpKybQLi+bUboF3Ncvds7Rp7J3pJPvBypEW8bjsUU4JF610ZwrQ3sObzROofu3EpIXXLDGDat2svy+dsQQtQMHmgmDZNJ499v3kCYj3lPQq2iwsE7Hy7w/0ABP81az123jjOkH9PmrYUGMm94dEl+SQWzVm3nirGtJ5DYki3L20Wx6+hgisDpNpEQVkG42biKwGZhwS1dje4n0fHodnYUvsr+0u8OLZ0HTdjoFnU5/RMexqSFKPAUfgFUfBaCE7vQ3RloZnW/aQ9cTjczv1jeYF5iXZfk55TwtzOepzCvrGb78Sf34c4JF9O9t7H5GVeu2dvowMb+9AI8usRsavoFQ9clpWV2LGaNiIjgFB5SjDUkrgfx1kgKneV+HCXQgX1l8QyM9z/X8YCYYQyNG8kzWx/EqTvQDlUwXJj7Kz0jj+WOY/6vJpn9nb0fYereV9lVthUNDSEEHukh3BTJzb0eoEt43SW5IWefCbKRAR/Z3EERGbBSzCq8bZBuxyQ3O09KeERgXkpem/w7Ho/e4LLEOfO2AFBcUulzcntfCSFYtnJPg5/nFJU1Guyq5vboFJf7/st0pXoZqdedx57NkLjuTT6+zBXGipwe7C1NpNJtRpdVS0ghcBW0nt36ME9svJuP977B3rKdNdvjbcOJsw1HNFgaV9In7u+B6VT1GTx5yIovCOnl1Do6dOdWQspkNvHEGzfw8HNX0mdgZ2xhFqLjIjjvyhG888P9DB3VO9Rd9NnCJTtxOPyf6azrkq3bMxvf0UcLNuz2el8SAhZtTDXsfO1BiauS7/Yv543ts/h4z0IOVhxe4pFdWVTvMZFWJ8fEGJdQGEBI325S3cJ7sjp7PGklX9YEuwB06WBf6deszLoL3YfAWSCIiBuBECUzrvwiNOdVgi4no4iyksaXsR4Z7AJYv3wPD10zmfS9xs4837glvdFZWQWF5WRlFzepfZfLwxffruCqmybzl2ve5PwrX2f8Pz5n6YoQFIhQmsWsmbijz5lNOFJQ7IwguyLKr7hLorUDIxNO5cv97+HUq2Z/63jQD82E2l++h/f2vFAzIBlpjuLeY//Nw32f5exOlzKuwwX8rce9/GfwO/SLbpnpSaRrAzT4rlWtuatbdDAFZkBFzfAyyLiLhvLB87/gcLj8zU0HwLDRvYmKMWa0UNclS1fs5vuf17E7NYei4oaDRFJW3SBWr02jU8dYw5MbSylxuRp+gUmOi/Jphle1CJuN4b27sG7PQa/7jR7Yg47x0X71tb0wCY1XTriJSxe9SLnb3pQfVyQa2ZXRZFdG0ymiGKvmId5WQYQ5MMtyHXolDipZX7ScdUXLuKTz9ZzR8SKEEJzY8U1WZ4+nyLHhUD4vSXWVj4GJj9Eh4uSA9El6cpEl/wXHbIK+hPForjVgDW0VPiV0TCaNsy87gbMva91l1XNySzCZtCbdh3wpkHKkjPxi9mTkE2a1MPSYFKxHJKJ3ub2fX0pweLmvKbV9t385r27/BbfuwSQ0dCl5Z9dvXNr1RP418BLmZW2u97iukUWAsQMpLhpfippoTcbpWke+vaF8oTqFjjVklv9Gl6gLjeucj4S5JzL2eSh+OOjnxr03+OdUQsLUxLQgukdir3Ty0UuzeOrtvxnWH1+vA025XrhcHh59ejpr1qfVCnRs3ZHJhIkzuP+uM7niL637/treXNFtFOVuB+/umotH+vdMkVraAYEkOcK3GWL5zlw+2/d2gxUZdXT2V+xhZ9mWWgGtHpG96RHZWgYlgzSoX/kDRN5oeLNqhpdBIqPCePDZy5sU7AK46NrG15z7wuPRefaln3n8P9+zdsM+r8GuI2XlFHP26QMxmYyvCnFs744Nfnbx6IE+B7sABnRP5r+3no/V3HA/rWYT/7jyNL/62N7EWMJ5e8RtxFgimtGKAARZFbFEmp0BC3Ydqfpm8mPGNPaUbQfAaopjdMrnHN/hVcLMKRwOPunsKHiJ7QWvGj4SL/VCZMFfwTGHkAe7AMo+DXUPlBAqLapg2/r9pG7PbFEVGf0VGxuB7m2dvBejRxzj034Z+cWMf3MGFz8xhQfe+ZE7X/uOsx95n4/nrEJKybKt+xqd6axpgv7djF2u01b9lrmBF7b+hEv3IAG31GuqMP6YvppnN89gbWHdIIpF8xBjDV4l4CMNiBnG/tLv8P6IrHGg9Ntgdaku9x5C8givls+3G8md4+jULaFJ6Vp0j86KBdspyi9rfGcfDTuuW6P3t6TEKDol+58aZubsDXWCXXA4T+Sb780js4kzx5TQEEJw0zGn8evpj3JdD/8HvveVJfo1y6uxpfIaJjYWBSY/VTAI28nUzt0VIO5NSPc+w5tVAS8DjTitPxZr0wJGnkZGlH01Y+Za5i3cBtRfWbEhsTHhxMaEc/dt4wzpx5EuOKf+/EsAJxzblVOPO6beCnz1+WXFNjrGRzHjqZs4JiWhzucd46P58B9XqwpaPhgQ24XPxtyL1uwqWAK3rPq5D1ZiYQ2NRbmzar5266VsL3wFu7v2siaPtJNaPIUNuRMaLYzgD1n+EXgyCcrF3xcyRFW7lJAqyi/jpUe+4bqx/+Xhaycz/rI3uOWsF/j16xWG/rwHy+lj+6Fp/j+WWK1mzj2r8WUAOUVl3PT8V6zcvr/W2FRppYM3fljCA2//wL1vzsDu8P7gquuSc0/s53c/2xspJe/unNvw58CvGevq/UyEcCBhYMxwKt0H8T6YoVPhSg9Wl+pyLiUUgy0i/Nygn1MJDU3TuPr205o8kC+lJC+7xLD+nDCsJ926JmDyMiBx9WUjmpSMfsZPa7x+LoTgl9nNrUDnnUfXWbMrnXnrdrF1X1arvIe3RPHWKB7ofwGjEvv4dZxbauTbmzMp4GgSh243sL0gs50VtAEPWfmN4W2qJY0GWvTrRlzOpr0A52Y1f+RA1yXfzFjl93HhYRZGj6iaUnnlJScQGxPGR58uqRnNEAJ6dk9i/4F8v2ZjAQwc0Jn4uMgGPxdC8MIdF/Lq9MVMX7IJdyOjN1PnrKJ350QuGDmA7568icyCEuav342uS/p17cCJfbsZnoesrdGlzsr8PfyRs40tRek1o+1NJyl12kgKKw/aaLyOzu7SqsCu01PEhtzHqHCnU/+TmSSzfDY9Y64nPmx4zVaHJ599JV9woPR7nJ5CwszJdIu+kh4x12DRGl4OK6WEiq9pMcEuoMlPpEqrVVJYzsPXTiY7o6hWmfbcrGLefPoHCnJLueHes0LYQ//FxUZw/VWj+PSrZX4dFxlhJTqq8YqtU2avpKi8ssH72JItaT6f84v5a3n2lvN93r892lOWTXplQeM71sOpm3HrArMW/Gvb1L2v0i8ins7mdIRo6PwCmymEA2shqhYpRVyzh8iU1uO8q0ZwcF8e06f80aTjo2ONK+ygaYL/PXU59z/yJQWF5TUDrNVpUc45YyBXXXqi3+3quuTAwcJG99m7P68p3fbJrJXbef37P8gpOjwj7piUBCZccyYn9O0asPO2FyvydrGl6ICfRwl2l3QgMWyfIe82EkmnsNb7bymEBRn3GhRcHfiTuXYZ3mRAZ3hNmjSJESNGEB0dTXJyMpdeeik7duxo/MBWKj01p8mVGlct2t7s8+cVlPlUyv1ot9xwCmFhhxOgnjyqD5ddPJyUTrGE2Sx06hjLOWcMomvXujOqGnP/3xtPGmi1mHnkmjOY+/ydXHP6sEb3f+bzudz20tc88fEsvl+yiV6dErj+jOMZ2b+7CnY1Itdewg1L3+L+1VP5/sBKtpYYM0IdimUnQgj2Fn/GvP3jyK1cjLegj8DEgdLva76ucKWz5OCV7C76AIcnB4mLSvdBdha+yZ8Hr8Hh8faS5gDpS4BaAHE+fjfNZeQolNIafPvRYrIPFtYKdh1p2tvzyEpvWrAhEPLyS/nyuxW8+d48Pv96Odk59Y/833rjKdx6wynYrL6Px1VUNJ6Xye3R+WnZFr8HbRoye9V2CkqaW5GobatwOxrfqUGCHUXJQZs1fCSXdLK5XLLT3nA6BoCu0ZcFqUe16aWvgydEibTt3ze+j9JmCCGaNJ6maYL+Q7vRsUu8of3p2iWBT969jXtuP53+fTvRtUs8o0f25oVnruSxf1zYpHcAIcDaSA5ITROE2QIzR+SnZVt4fOqsWsEugL1ZBdz9+nTW7grhTNIQsHuc7CzJZE9ptt+5t+qzOHsr96/5mDJPU+5Hxr3cCDRGJbTedDvSkwdF9wXpZMYP6AR0hteiRYsYP348I0aMwO128/jjj3POOeewdetWIiMbnvXTWmUeKGjyRIsNK1IpLaogOq7pL67+/lqG2czccsMpXH3Z4RGR4pJK7vvnF+xPz6+qDgpkZhXz/seLCbOZ0TTR6FJJIUAgeOSh8xnQL8Xn/sRGhiElmE2a15leTpeHdXsyWLcno2ab1WwiLiqcHh3jufzk4zjz+GMxN2Fac1vm1j3ct3oK+8qrRqncBtxIqghA4pHQjErQftHQ6B4Wz7aC533aX+LB7s6q+Xp97gScngLqLgnRqXSnsyXvWY7v+EoDrVkBG+Dt5mkC60ng/NOn/jWb5vvvmdL66brO7G9XNnotnvrKbCa8cl2QelU/KSVTp/3JZ18uA0HNPeTDTxdz1aUncvdtp9d6SRFCcNN1Y7jy0hNYtnIP3/2wmh27s71+rx06NF6gpNzuxO40Ls+gLmHbgRxOHtTTsDbbms4RCWiIJs8iLnGFs7WgI/3jczCFYKZXmiOJHrZCwrTa13qBiXBzV7pE/SXofZL2BVD+dtDPW8NVf4EBpW3atyuL6VP9m91VHSO7+aHALH+Njgrj6stGcPVlIwxpTwjB2DF9WbhkOx5PA0nHdcnYMX0NOd+RHC43L327qN7PpAQdycvfLWLahOsNP3dLU+l28t7u3/n+wEoqPVWDWEm2aG7sdSrX9Bjjc+qbalJKthan88i6aU3skSTWWmnYgP7V3W4l2lKVX86tu8m0H0CXOp3CumAzNT5DPZSkJx+ZdynInOCc0NTT8CYDGvCaPXt2ra+nTp1KcnIya9as4dRTTw3kqUPiQGrzSvBWlDuaFfBKSoyic0ocmZlFXh8vLzznOIYP7c7Jo/oQEWEDqi4MW7Zn8NxLv3Aws6jOMVJKHA4XjQ2O26wmLr3oeC69cDidU+L8/h50XW9S0NDp9pBTVEZecTmrdhxgxJ/deP2eSwnzY5ZAW/dn7g5SywJxsZLk2aPoElmMhh6U2V46OkmmDT7vLzBhM1ctPylx7qDIUX/eGKgKjmVV/I7dnUOYuW5iaiE0ZPglUDmdhpc1esCTTnUwMOD0zMb3UdqMXVsOUlbSeC6IP2ZvJi7xJ6Qu6XZMMqdfPMywasC++ub71XzyxdKqL2Tt3JLffL+aiHArt9xwSp3jIiNsnDVuIAnxkTw04esG2xcC/nL+0Eb7EWGzYDFpuAxM6m9WM4q9SrJFMza5P0tydzRxpF5Q4g5nU0EKQxMzgj6TWKBRpA+ik7a21vb4sBMY1uF5zFrwZ9bKik+oKg0foiX1wv+E4Err9dq//Z/RF5sQyYP/uYKho1pL9Tm49sqRLFyyAyFknVmlJk3QuXM8p5zkXw4ogMyCEuau2UlxuZ3OiTGce2I/osJtNZ9/+OsKyiobHjzVpWTb/hxSM/PbdG5ih8fFfaunsLnoQK0BkjxHKa9u/4X0inz+OdD3AYZ8RymPrJvGxqLm5bftFuV9qauvEq3JjE46A13qzM3+gYU5s6jwVM3oMwkT/aKP4/oe9xBlbnzwLtikXoEsuj94wS6omjBgsKBGA4qLq5YBJST4vzSupXO7PBxMa/r6bmuYhbjEqGb1QQjBNZeP4JW3608Sa9IEHZNj+Md959ZK6mi3u3j6fz+xbOUer+37shLE4fQwdHDXJgW7AIYc05lvFzc9MaR+6E61Zmc6b3z/B//66+lNbqutWZi9FZPQDJkiXJugU3gJZhGcYBeAGTfhwvf1+BJPzWh8sWOLD0folDi31xvwAhCRf0fafwVZQd1ZYhpYR4HTvzxEzaOWVrUX6XtzeezWj3zaV0rJzC+WV82q8uh8+OKvPPDM5Zzxl+GNH2wAp8vNZ43k4/py+kr+evkI7A438xZtI7+gjMSEKMaN7YeU0L1rAqee3Jc/lu6s8yKiaYLuXRO4+LzGA14Ws4nzRvTn15XbDFnWaDZpDO6lZlY25qH+F7GxaD8lrsomB70qPTZ2FCXRLy4vqEEvgUZc+JmcmvwU+fbVACSEnUi0NYQv8s7VhDR/ZNg5oTu3ElT2Sie7Nvu+nE4IOOMvw3nwP1dgbmSJYEtzbO+OPPvEpUx8fiYOuwvt0DuSx6PTrVsCLz5zFWYv1eGP5vbovPztQr5ZvAGBQNMEHo/OS98u5OErTyM6zMaL3y6ksKzSp/Zyi8radMDrp/TVbCra3+Dw8Lf7l3NB5+EMiuvmtR1d6uwpzeaR9V+QWelvsEpSPUhtFh6GJh7Eamr++5JAMDjmBH5I/5z1RcspdOXX+twjPWwtWc8Tm+7i2u5/Z1Riy1n2KD35yILrwFO3knLgmBBhZxveatACXlJKHn74YU455RQGD66/mpLD4cDhOBzpLikxrrpHoDkaqerkjWbSOOuS47EdkUerKUpL7WzbmYkQot7qHrGxETz/zJV1Kpi8+MZsVqxObda5q2maYM68LZx80rFNOv6s44/l5W8XUlLhqAleNYUuJd//uYl7/jKm1mhKe2bXnQEIdlVJDi8L6ouIGxN2j5kwky9LlDSSwk4iMWwUUDXbyxdCNHx5FObukPA5suhB8KRRlQ5RBwSEXQhuVTVRCYyPXpyFvbLxnFXVpC5rAjxOh5sXH/2G+KRoho/xf7TaXxs2pVNa5n0mmsPh5vnXZ/PHnzvRJWgaeDySt96fX7NPty7xHHtMR1LTcmuWu5s0wbix/XnwnrNqZio35rbzRzJ//W4qnS6/qhjX54pTjiMyzNqsNtqDzhHxfDz6Hv635UeW5e1scjuFzmgOVjjpGhm850KJTqwlgShrb6KOCHJ5pJNixyZ06SLa2gebKSlofTIyp0yT2M4I7flDZNKkScyYMYPt27cTHh7OmDFjeP755+nXr+1Wa83cn4/HjxmxmkmjQ6e4VhfsqjZmVB++//we5i7Yyo7d2VgtJkaP7M2I43v5nRvs9RmL+WbRBiRVycr1Q0slHS4Pk76c7/3geiTGtL00QEeafmCF17UQJqHxY/rqBgNeUkq+P7CSj1MXkWUvamIvqoJdUWY7gxKyMGoCt0mYWZQ3q9H9JDpf7H+XCFMkx8X5X3yhmi5d5FUuxe7OxmpKoEP4WExa096DZfG/wLOvyX1pMmH8SoSgBbzuvfdeNm7cyJIlSxrcZ9KkSUycODFYXTLUkjlNy2sgNEFicgw33Nt4cndvKiqd3PevL9h3IL/eYNeAfim8+J+r6lSyyswu5veF25p17iPpuqSgqLzJx9ssZl69+xLufO07nO7mjWI6XB62H8jhxL7eRwTai16R9c9Waq4wkxOLFvwR57XlPRgdvaeRQJugS9TFDE58AiGqAr1J4SdxOEBVP5MII942zOv5hWUgJM0B1ypwbQdhRVpPhdL/gdv35ZaK4qui/DJWLNzerHLlmhB8MXl+UAJelXbfAnML/zhczMZTz6XkyApamga6Dr16JjH+jtOJifb9wah7cjwf/eNqnpg6i90Z+Y0f0IBjOiXw8JUtZxS2pdtXnsfKvOZXXUovi6dLRElQB1dOTDgZp6eI9NIZ5FQuodKdjt2dh6T6Z1sjJfJcBiY+GrCqjdKTB7IEtORDuSGXEKpZXkK0zyBve8tJDPgduPK4dbr17hCg3gRHRISNSy5s3gzo/JJyvlq43tBkFtsP5NCnSzAD68F1sMJ7gR2P1Nlf3vAqqsm7fuPj1PpzoflHEG52GRbsAnBL/ybEzMz4isGxJ/idswwgs3wOW/L+i1M//PdpFlH0S3iQHjHX+NWWdKeBs2nVWZvHA7ioypdsnKBk9b7vvvv46aefWLBgAV27NlySc8KECRQXF9f8OXDA3xKiobNlbVqTqoN0SInlta/vIT6peet2f/h5HWn78xsctd62I5P0esruLl/ZWMDAPyZNkNKxeTke+nbrgEnlRjFcp/A4w9uMNDsYkpDR+I6GE5TqERR7Gl4GbBbRnN51LkM7/BeTdvilOMzckc6RF9Dw5U/QI+Y6n/KzCCEQ1pGIyL9B+F+hfCo4Zjd6nPHab5XG9lQNOC+7pFnBLqgalNi8ei8lhU0fmPBV9yZU9m2MfihOvTctj4cmfI3L5d+Lf9+uHfj6iRv5+J/X8O/rz2JY784+P9wKAWcM68N/bzlfFUXxothZQVpZDkXOCpweF/9c+zmeJr7+aXhICS+ib2w2vWPyfEqtYJTj48aQVb6ChQfOZXvhqxTYV1Lpzjgi2AWgk1X+G8sybsTl8V691+UpocSxnQrXQZ/OL52r0POvR+aOQeadh8wZCXo5oVvSaPI687ktmz17NjfffDODBg1i6NChTJ06lf3797NmzZpQdy1gOnaJx2z2/TpnMmmcck79K3hakrJyBxu3pLN520EcBhYyqbZww55mzyA+2pTZK5t972/JIs3ek7ZrCGIs9Q9upZZlGxDsOvx3G2u1h6Q6cLVsx0Ey7f5X5swqn8e6nP+rFewCcMsytuQ/y76ShnOh1su52u8+GEMjELmPA3rnklJy33338f3337Nw4UJ69erldX+bzYbN1jqXnwnEoWisf/9IeVklxCY0f3Top1nrvV4MTZrgl9821qma6HC6G1wC2RQeXXLhuUOa1cbU2SupNOAmZDFp9O8WmFlNrdHinG0IBNKwC4mkb2wOmpBBTyZcLcPZgXhzJbLWC4CGQOP4jq8QbulU73GDk57E4ckl374CgQmJp+a/nSLOpm98E0rvOpdD5SdN+0aaK/yC0Jy3BWhPI+8xzShqcjR7pYsYYyvG19GzexKDBnRm247Mel8AmnPv8eiSfQfyWbx0J2eeNsCvY4UQDDkmhSHHpGCxmFi/x7egvZQwf/1u5q/fTbcOcdxz8RjOHdF2lzT5a09pNu/u+o0/crajIxEIkmzROPSmpnyQ6JiQCOKtFVUVoIN4r9lYvIhY+T7mRoqxSDxUuNPZW/I5fePH1/m80p3FjoLXyCyfjaTq2SbG2p9j4++lY8S4+tu0z0MWHd2WG9yhegkBzP1Dd+4Wpi3nJAawVzh55OYPcLt9X9Lo8ehkpRfSvXfgnrsLCsuZv2gbhcUVdEiK5sxTBxAd7VuFu4oKB5OnLGL23E04Dw2UREXauPKSE7jx2jGGDWKUVTqrcnYZGPRKyy4ks6CEzolts2jE+Z2H8dW+pQ2mXdGRnNO5/lydP6WvMSA/8eELfCgqAh+twl3q1/5S6mwreNHrPjsKX6Nr1KV+LG8M1cQTHeyzIfwSQ1sN6BDl+PHj+fzzz/niiy+Ijo4mKyuLrKwsKit9S9LXmgwddYxfa92r6R7dr6SQDcnJ9f7L4dElmVl1Rx979+pg+EjE5A8XkpPrf56NSoeLB9/5gQ9nrTSkH2MG9VT5u46QWVloYLCrahQkzOwOWbALwGoZRkrk+Yia2L0gKewkRqd8SlL46AaPM2sRjOz0ASM6vkdK5HkkhI2kc9TFnJTyCcOTX0YT/ufTkxWfE6RJs3WFXxWa87YA7WnkPblzHP2HdW92Oxarmbik5hVJ8dU/7z+PsDBLnVm7miYwmZp/8Zi7YGuTjtuXXcir0xfz2+odWPyYxVDtQG4RE6b8yreL1PJlgB0lGdy6fDJLcnfUVNmSSHIdzcm5VfXzkVUZS7ErPOj3ms7WwkaDXYfp7C/9ts5WuzubpRnXklk+qybYBVXVgtdk30t66Y91jpHSiSx+lKoB1MDk3Wya4FZ4bal8yUkMVXmJS0pKav1pLd6dNJOdTXg3+fXrFQGZiSSl5INPFnPlje/w1gcL+Oq7lbz2zlwuu+Ftvp6xqtHjHQ4XDz32DT/P3lAT7IKq2V6ffLGU5176xbB+d0+OMzTYVc3p52zm1uSaHmMIN1nR6gmymIRGn+hOjEseWO+x6RX5huYntrubl0/bCAk2/5YGFzs2U+lOx9ukG7deSm5lw2ml6rA2PY9Yc0mHEctTawvoDK/JkycDMG7cuFrbp06dys033xzIUwddr371zyTxRUGOf5Hc+kRF2SgubjiQqGmCuNi6DyvHDeqKEBg6fXP7riyuuuldnvi/Czn7jEE+H/f41Fks2ZxmWD8evaZ9Jlc9ki511hTsZVdpJm5dR0PUKvnbHBFmJ1IGd8T9SAJBjKUDw5LvZ7D+bxyefCxaDFZTnG/HC40OESfTIeLkZvdFSic41hCqlxMhvScGb098GXlvzQVSbnnoXB656YNmtRGfFIXVGpylSb16JPH+a39j6rQ/WfjHdjy6RBOCU0Yfy7hT+vLM8z83q/0DB73n/qjPB78uZ/LMZZgMGIV/8duFnDeiH9ERvs0yaKue2/w9Do/LsPtLbZKsihjibcEdLE0y+/ds5vTkIaVeky8SYEfhGzg9BUfNQobqF5PN+f+hU+RZmLUjZqPafwfpfXlkSLjXIKWz3ebxquZLTmJovXmJiwvL+f2HtU1aVfTjZ0uZ9c1KrGEWPC4PKd0TufDakzj70uOxNOOe8+mXy/j86+U1X7sPJYF3uTy88+ECIiKsXqv1/jp3Mzt2Zdb7riOBeYu2cdF5Qzh+aI8m97HaKcf1Ii4yjKJy457LIm0WUhJjDGuvpekYHse7I+/gX+s+J6OyEJPQkFKiIxkS151Jw67DrNWfUy7KHGbou012ZRSdI0N1/RX0jRpEgtW/gJdD9+05yOnxbT/pPoCs/KEqb6SeSyCWGHql+16YyVcBnY4gpaz3T1sLdgEs/X1Lk2f/vfbkDPbuzGrW+c87c7DXHGK6Ljl9bH/WbdzP4qU72bM3B4CVq/cGbK3ysy/9wvadmT7tu/tgXtW6dwM789uapleEagu2Fqdz5eJXGL/qI97YPou95TmGvozoMrR51iSS7SUbmJX5HR5pItLS3edgl2F9kBJZPgWZPQbw/8XbsH44t4Ts3C2JryPvkyZNIjY2tuZPt26tp7DFkJHHcNdjFzerjeSU4C6L6NY1gScfuZifv7mfLz/6OzO/uZ//PH4pZ5w6gGN6dmhS/stqlX5UrAT4ZcU2Js9cBmDIKLzbo/PzcuMKv7RGu0oz2VZyMEDBLgBBmSv4s7UF/g3omLXoWsEul15GRtmv9QS7DtOlg8zyoyp4edIIYk0pP0iwzw11J0LK15zE0HrzEm9btw+PH0sZj+Z0uCkrrqSywsneHZm8+dT3PH77FBz2pi1trqhwMO3b5V73mfLZkpoKvvX56df1Xo/XNPjmiJliui7ZsPkAC/7YzqYt6X6thNF1aXhVxRH9u2OztMRrgnH6xqQw49R/8PoJN3N77zO469iz+XT0eN4b9XcSbA3PSD87ZYih9x67x8qBsjjA2MkgjREIbJqNy7r+ze9jw02+TboJM3vfT0qJXvo6Mu8sKH8X9DyCHuwCMHc0vknDW2ynigvLm/wzUV5ayeO3TeHj3/+J1da0qZRXXnois+ZuoqzcUefCLAR07BDDi2/Moai4omb7sb2TOX5oD8NneB3pjffm8c7LNzS63/z1u9GEMDTg9cEvy7nq1KGEBWkmQ0uyrzyPu1d+iMNT9YARiBeRQkc4vZpXa6HZKvUK5mTNYFPxau4/9inCTMFdciHLXoby94N6znpVfISMurVJVV3aEl9H3idMmMDDDz9c83VJSUkrC3p5z4fpjRBw3IhjDOuL26OzdMVuFi3ZQUWFk25dE7jovCF071q3Yl1EhI2IiMOBCyEE/3rwPB545EtcLk+TlteHh/l+z5RSMmX2SvzPtundsm37uPaM5lX2as28Vc8yiiaC/9Bd7IkgwVzuU9BLYKJrVO2cIw53NhLvL/kCE+WuowIhIpLQJab3TjrmIcIvDHU3gs7fnMTQevMSG5nmpPqRfsuaNKa9PY9b/3Ge322sWLMXh8N7Xt+CwnK2bDvI0MF17+M5uSVkZhV6fc/RdVi2KpW/3/8Jx/buyKI/d1JadniGVkrHWO6/60zGjGq8uvHXC9eTmtX0KsD1KaloH7P4NaExukNfRnfo6/MxJyUdSydbLFkO42ZlpZfHk2ArJ8Lc1PyT/usbPZjLutxISrj/z6LR1n5EW46l1LWHhlaaWLVEr6leAKj8CsrfPvRFCO9B0vj3dlVmyCCV5U2ffqd7JIV5pfwxe1OT20hOiubNF6+ja+e6WYilhKycklrBLoA9e3OZ/tOagEawt2zLqHPe+mw/kGNosAugzO5kxfZ9hrbZWnySuhCn7g7giDs4dQt2jzmk1UygaqZXRuUB5mTNCO553fuhvHlLywwj88C1MdS9CCl/Rt5tNhsxMTG1/rQmYRHNW1KU1CkWtwH5QAqLyvn7/Z/w72d/YP7i7SxduYfvfljNjX//iE++WOpTGwP6pjD5lRuIjWlasNrbqP6Ryu1Ops5Zxd6sAsOvigdyiwxusXWJaqTCVvNJEmyBryp6tAOOBCSNDwgKTJi1SHrF3lRru1lrfERIomPWjpq9EHa2nz0NIk92qHsQEu0pJ3Hn7nUHK5pL1yW/fLUcp8P/AEJZuaPxnYDyo/ZbsTqVvz/wKVfd9C6Vdt8KYe3Ync3PczbWCnYBZOUU89gzM1i6YnejbXy9cL3hz8Xb9ucY22AbUul2ku8sa0YL9f1jSZx6cCZLHB87mqcHvsk9fR5rUrALqgYPByU+jkCjbmhHAIJBSY+jeamyK6UHWfZOk85vOKf3GZ1NoQJeBjmQmtus4zWTYN3Sxi+k3vTolojN5vsvqK5L9EP5VAKpqIHcYuV2J5/8tpqL/z2FhRv2BOTcpRW+3SjbEo/UmZO5wdAkjg3JqQzxFK9DJDp/5s3D3eRqYE04Z+UPtKhLqKd1LJcwmpSSe++9lxkzZjB//nyfRt5bu7eeqZvo2ldSwptP/8DfL3qVgkaKnXhTXuHgnoc/JzWt6t5XPSugepnglM+X8ORzP/Lme/P4esYqCgq9BSwEhUWND4zUJyu7hIn/+8nrPn9sSuXcR9/nrR//bNI5GiMDkKC4NRme0IsYc6Bm10oE0Cmi+blO/WWXVjZXVAXPvf0TR1qO4aSUTwk3166CHWZOJs42DO/3CZ2UyHNrbRGmzocKkbTvGbstyeTJkykuLmbcuHGkpKTU/Pn6669D3bVWo6LMwcF9/s986tbFt3LCXY4Y8J+7YCv/evI7du42JkBbHcB64915XmfAOV1uMguMv1ZZDKog2Rb9nrUJtzRiNpKkulBIvLWCKLMjKDmK1xYvY31x84u1JYSfyKhOHxJt6V1re4S5Kyckv05K5DneG3DvAL2FDGjovqVD8kf7W+sVAFJKdm1pZqVFCR69/gCFlBKH3YXVZkbTGr7oLVqyg117/BsFMLpC49E0TZAYX3cte3G5ndtf/obUrPyAzhDq1iEucI23UHaPC5cenKmoOZXRdIsqbBGP5Q69kiJXAUk249d+10tvXt49o0kR2yL+HYJt/PjxfPHFF/z44481I+8AsbGxhIe3vapixQVlrF2yq9ntZO7P586LX+WzBY8SFu7fjLE/V+zm6Uk/4nR6v84sWrIDk0mg6/DelIXc9rexXH/1SXX2W/znDjThPajgzfzF27nq0hMZ2L9znc92HMjh4XdnojdwfzWCK4BttwZWzcztfc7gle2/GNhq1Q+DhqRvXA7hQVxacqRMVzzlZWF0t+WRbC4l3BROtK0/Mda+RFp6EWsdQJxtaIPLyfvG38vKrDug3oW0gi5RFxNpqVt5VcQ8iZQesFfPXG4hQVWtZ6h7EBKBqDzYUsUmGJt/6kimJgRuhgzqRkqnWLKyS+r9d9A0wYC+KfToVjUzraLSyUtvzgGM/XeTEjKzi9my7SDHDap/FrnZZDJ8ybwmBGOPMy4NQVuTVVmIQDSjCr2o+W+Yycmg+EyspuDe03/P/pFTO5yDycsMLF8khJ/IKV1mUOraid2dhdWUQKx1sG/pTlpS8SstzvgmDW+xvWrm1U3XJRn78pn00Bd8/f5CCvNKKS2u5OPX5nDNmGe57PinuHT4k7w84VsOpNYf1Jo6remj19deOYqoSONzDYwdfSzR0XWXO7z07UL2ZhcEfDncoB5BCn60IOEmSxCWmFRxS43MiqrlYC3hedCiBbF6lObbqGPQiIhQ9yAk2tvI+xtP/WBYW2XFlTxz72d+HbNtZyb//s/3jQa7qnk8VcVqPLrk/Y8X10keLKVk/aYDTQ52Vfvi2xX1bv/kt9XQjEdhX5jaee48gL/2GMOdx56FSWgIBOZD/22OzhHFDE86EPTqjEcr8YSzuaIbf5QO5Zyeyxmd8jGDEh+jZ8y1xIcN8/oykRR+EsOTX8YsqoIIAjNVj96CLlF/YXDS0/UeJ4QVLW4SImkuIupBsJ5i+PfVJFr7Du62B/FJ0UQE4H0gsWMMXXom+X2cpgkefeh8TCZRp8CJEGC1mHj43sOzVxb8sR17ExPk+yKvoOHlc4VlFQG511zXjnNEeuPWPSzM2WpI+haz8DAoPhNLCK5xZe4SDlTsNaQtIQQx1n4kR5xGnO04n4JdUnqQpp5A/ZUwg0sgwi8zvFU1w8sAQgjik6LIz2neNNZdm9PZteUgf/y2mc/enEt0bAQlheU1s7BcTg8LZq7njzmb+N/U2+k/9PCooN3uYt+BpidJPOWk3ow6sRdr1qXx2dfGrZ0deULdUYnC0gpmr9oR8NllAF8v3MD1Zx0f8PO0JJrQuLTbCKbt/SPgY8IaEpOQ6HpVlZtQEQi6hPcg1hK8IJQIuwTZUnJ4Abh3gu2EUPci6NrTyDvAykXGVgRct3Q3Gfvzfc7bMu2b5t0f3vt4EUmJ0Qwf2o3wMCtvf7CA9Zuavxx349a6s6yllMxfv9uQaozeZBcFf7ldSyOE4LbeZ3BZ15F8t385P6avJtdR0pwWyayIxaJ56BxZUhMQ1Q4V2QlFjNElXeQ7cki0Jft1XErkOSSHn0pWxVzKXfswa5F0ijiHCEuXRo8V5u4QdTeCu5Fl7yLLXqOhpMRB4QpM+gml5cg+WEiFj3mz/HHWJcObNMMLYNhx3Xnrxev54JPFrFl/ODevlFUzg2fO2sB1V42iY3IMBw8WYjZpPud39FdCPatWqm3ZZ/ySsP/eej4Dure/wXtfTD+wgtQyY/KbJYeXYtH0kNxbAFx603OBN4WUHg6Ufk9ayeeUuXYjMJFk7sQxWhbxplAWTdGQ4X81fMWKmuFlkNiEhkum+krKqlwgUpd43DpF+WV1gkIej47L4WbSw1/WWqJRUmZv8gwbs1lj/P99wYOPfmVosAvg94Vb62zbdTCvweWbRpu+pH0m8v5br1OxaIGP1Oto7C1N4mBFXMDP5Y1EMiwmAV0Gb9mLsPQFy7igna8xQgvcMgSl5XC7jL92/vnbZt/O7dH5c3nzAkhlZQ4mTJzOZde9zatv/8a3P6xucltH8tTzciMlON2Bf3BzuXUO5hlXIaq1klLyW+YGpqYubGaw61B7CPaVJbI2twsZFbFkVsTg0kXIXkgAFmWMZ3vBq1S6Mvw6zqSF0SXqYvrG38sxsbf4FOyqI/IWsJzs/3FG8uwP7fmVgCsrCcyMyv3NzHU8oF8Kjzx4PgnxEbVmejmdbmbOWs9t935M2v58IiNthhfBqtYxOYbjBjZcFGeRwfmIhx7TmXNP7Gdom23Jl3uNy8uZFNacxPfNI9DoGNYFl16K3Z0b8HcZKT2sy/knm/OfpuzQIIbEQ567gJVODUdIJ/J6EAHISawCXgZxuXyrAGIEXZfkZBTVSnIfHWVr8gwbtztwP9nrNu6v8yLS1BGepsgpDt0FLJTirJFYNUvQzpdeHofDE7ypsKImuWTVn/7hGbgcn7Im+/6gBr0we68GGEzS2ki5YUVpQGWFbyOLbpfHsJm5lXYXP/yy3pC2ALqk1J3dqWmC7slxhp3Dm7SsgqCcp6WSUvLclu95ZfsvhhdMcegWSp02Yqx2LFpoZ3S6PKmkFk9lYfqF5FQsCuq5ZdH/geuPoJ6zriCmDVBCIrmeau9GWDZvKyVei5d4p+uSl96cQ3FxZd3JALqkosLBcy//wmmn9AvYCpJ7/35GnWWV1dJzi/hhqW+DR77akJrBrJXGzupuKxweFxn2QsPaM4dwdlf/6N5szXuIuftGM//A6fy+/1S2F7yKSw/MO2x62fdkVfx26KvDvysSD33NHqyhztLgXGJ4kyrgZZD87OaPZvpDMwlStx+uYhAeZuW0U/rTwHU4pP48qozvoB6dsFmCs5rW4QheILIlKXPZKXMHNwFhnr35sxwbY8XFCZGpdLEWkGIpok9YNqfFbKeHLR+Q5FYuYX/ptwHvB4CUlVAZnHP5pJ1WaWxPApV4vXvvDj7tZ7OZSTRgNnMgXHVJ/ct5uycHZ5lzcUULSvgaAn/m7uDHdGNm69Un0uIk2hLcJR+1SSI1O1GaA9CRuFmb/RCVbuOrSdU5s9TRK74Hx5yAn6tRYZeGugdKgEXHhmOxBuAZXeJ3ZeCMzCJeefs3zrv8VU6/6EVWrtnb4Axjjy7ZsSuLikonZ58+0K/zNBboCA+38J/HL+XUMX0b3Gf6kk0BqXr/5Cdz2LDHvxml7YFJGBvCqHBbQpKLWCDoKGZSYF9Ts82tl5JaPIWFB85jfc4j7Cn6CIcnr0ntV7gOsrPwLdbl/B+b8iaSV7mMvUWfU18VYBOSbubQBf6qSddOw9tUObwM0tzkrP6SOlhttf/5br7uZJat3IPD4WoRCcSr/TJnY62bRJjVTJjVhCMIs+IMvh62GhmVwZ9tkFsZQafwYgI5ga+rrZBEczlJloZHCfcVT6NnzHVNar/YsZW0ki8otK9BYKJDxFh6xFxbq4pWqXM3dncWkY4fCMP4PBdN5jkItK98de3NkbN6jeRr/i4hBJddPJyPPl3SonKnCQHDhtStdJdTVMafm41JBNuYXp0SgnKeluq7/csxCc3w2V3VCuyRdIkoxhSSGV5V5+wbnnXEi4BEx83+km/pl3B/4M7sXIss/j/wNLMSuFEsvULdAyUIzGYNVwDiyzFe8l8dbeeebB545EscDhcej++/9z/8vI6brhvD3AV1U6o0REo476xBZGWX1MopGRZm4ZzTB/LA3WdhNtddxZBdWMpXC9fz64pt5JdUBGQppUeX/PeL3/n6iRt9q7bXThgd8MqujCbeFvyBK4HnUFbGo++dEpdeREb5r1A+i52FbzAo8Qm6x1zlc9t7iqawo/BVBBoSiUBwwMukgBhNYm4JP2JuFfBqsQaP6MmKBduDdj6JZOS4AbW29eyeyJsvXMukV2aRmta8tfJGSs+oPeV0wfrdFAcgIWY1qYEUVUHB8CgzK9IOMKxrCjZz+/lxtwVxOSNAuMlFv7hcTFpgEwqHaY393EjK3ftIL/0BuycbixZHp8izsZkafxlNK/6crQX/Q2BCUpX3p6LkAPtKvuT45FexmuLYkj+JEudWIoXkZJsLSWiSJ9dLtLCqkYrh/pi9KSDtPnD1O/QZ2Jn7n7mcYwd5zy109aUnsnT5brbvygpK4RHfCH79bRM3XTem1taZy3x/4WmuPp39rz7WlmwvyQhYsAug0mNhS2EnBsVnBnRQpTZJsrmEvuFZ2DQXZnH0z7tOXuVS+mFMwEuXLsqce3DLSorsGzlY+g129z5sQjLUCtEtYQBPzSRuFwKR8N1sMZHQIdqnfaWUPD3pR+x2l9/3mV/nbGTcKf7lvRJCkJqWxwdv3MTBzEL27c8nLMzCcQO7YrHUn65jZ3oud7z6LeV2Z8Dvhbsz8tm2P4eB7bDyfEOEEFg1E07dmDydhY5I8uzlJNoqDrVvSLON0hFsLO/GqOjUBvaoSt0igc35EwkzJ5MccVqj7WaU/cqOwlcOteCpaalV8OxBSjdCGPfe3hJun21Cckpc0M6laYKx5x5HSre6L/F9+3Ti/ddv5JST+gStP94IAXGxEbW2vf3T0oCcy22FyiSo6ASVHcHeCfIiXfzts+84+ZX3eXvx8oAlsmxpOocHL/hh0dwMSsgkzFQ1Yy+QN4lCV6RPF+yNeU+wq3AyW/KfZf7+M9hR8AbSy8tYoX0dWwv+Bxy+MVT/f4mHNTkPsjzzFkqcVUHt7mYPghYU7AKQwQ1yKsG3ZW1awNpO3Z7JP294r9ZS+frYbBZenXQNN1x9EjHRYQHrjz+klCxeurPOtg2pwVsGYqln9L89kFIyff9yCpyBzJcpAIFLNwU5bYMgxx1LuW6rJ9hVrfnPFLp0s6twMvP2j2NJxpUsz7yR7YUvUureh1VIupk92FrKvUbYQt0DJQhcAUgH0rWX74MC6zbs52BGUZMCSUITzF+0jS5+vJdJKdm5O5v8gjK6pMQzZlQfjh/ao8Fgl65L/vHezKAEu6qpwih1xVmMTLEg2FWczP6yeFx6MMMjgiJPJKUeX66tGruL3mt0Lyklu4vepb5li96U6AI/JlO2KirgZZCt6/Y1vpOfOnWtCmgJrXZVosiYMEad1h9PA9Wn3v5gQZ28WaEiJZx7xqCar3OLy0jNzDf+PFS97+tHvvMf8XdW6nDwxqJlPP3rPMPP3RJZTGZizOEBP49A0is6H7MIzprvTHc8u+ydfFqyK3FTNSriZk/x++wqeqfBfVOLPqPhy6GEQ4Gv6inHHU2hX+Neh/OXUPdACTB/lnX4S9clLpebqa/MbnTfsDALt/1tLN9/cS/ffXo30z66A3MQi5HU58h8jftzirjqP5+yZPPeoI1olla0oOXNQTRlzwKe3/pTUM7VMTwURWgkqfb6c9wJTCSEj2he61Jnfc4j7Cp6B5de+4U2xeThFJuLbiY99EmEqwk1sNLWLV8QmCTpo88c1PhOh+xKzW4wOXxjdF2yal0a11w50u9j7Q7fih4t376Pg3nFQZ3lbAtEXrVWSpc6K/N2k29ANeDaBBkVcazJ6866vK6sz+uCWw/OxbfQHdH4TugUOTbi9HhP1m93Z1LmSsXfARkPgnSPKfRpkbRkQ2d3gQp4GSbjgPE5k0qLy3nuo1uJS4is9cNXXmLnpQnf8ugtH2E/qrpWfkEZP/66PvQ/rFTNROvaOZ6zzxiI26OzcMMe/vfV/MCd0EOjweyv125iW1ZO4PrQglzX65SAZpaLsVRyQof9JIZVBDH4I0hzdGBB8QCKXf7NLkkt+giX5/ALhS5dpJV8waIDF5Jd+Rt1188f7fAvVYu8cHqMDyQrLUtix5iAtq97JKuX7KQwz3ti4YpKJz/9up7/e+IbHv/PDF56fU5AlsD4o3/fTgAUl9u5/ZVv2JdtXPUmX4z/7AcqnEGsENsCZNuL+WB38AaRIsyhCCoKij2ROPW6Mz0kOolhJzWr9dzKJWRVzOHoF5MIIRliqRrUbEnFiIRsn4Hd9mT1H8bnzwEYc5bvieQtFnOz8kRKXXLxeUO54lAxE19yX4WFWbBZzei6JDUtl98XbuWPpbsor2cwY1NqJiYtuE+C0/5cR1Fl+y6OArC3LIer/3iNe1dPwRPAIS2ryU2stRKPDM4FuMjte347j/T+c+Dx+zotAA2BpFzGIbU4P483mDR+9YAKFxskEIkEy0sd/O//vqKspLLW9uoRha1r03h30kwe/M8VAFRUOPjfq7+2mLwqQgiefOQiUrMK+Md7M8kpCuzorMlJ1TOjl38KTcD0DVt4olNyQPsSbLrU2V2ajd3jpFtkIvHWKK7pMYbvD6wk2278NOgIs5MB8VlBLtVwmAszK8p7MyoqlVhzZeMHADousisW0jX6EnTpYnX2veRVNm15bYkuSNRky5rlZfKt0p7SeiX6mP+kWSQU5pURn1T/uTIyi3jw0a/Izi1BiMDm7PPH2DHHAvDDn5vJL6kIelL9VQczuPebn/jo+svbTWLhXw+uC2pOEIceukdWvd67nWR19t0MSnycHjHXNKndA6Xf1cobWa27uepVriUFuwDQVA6hti4szPhZfL36daL3gM4+7z/qxF5NHrg3mQRDj+uGEIL77zyTM07tzw8/r2Peom1e34/sdhdX3Di5znab1cyVl57IbTeegunQTOaq/wbv6ieBpTv3c8qr73HLqBN48PQxQQ+4tQQFjjLuWvkBJa6KgJ0jwVZOr+h8rCZjcoP5RlLo9n1VTkbZL/SOu73Bz8PNKZhEBB7Z+N+TVUskIex4bLg4ltWYZX5VIuxQksZXP25/vy0BEhegUu0lhRXoDSxj0XXJvB/WUlxYTtr+PK659X1WrkkLSD+awuPRefntudz52nfkFgc22CUATQetkaoyuoSsAPcl2H5OX8Oli17ihqVvcvuK9zh//iQeW/8lGwv3kWM3erpvlc4RRSHPYSXR2FDRtd6R9/ppuPSqv4+0kmmHgl2Spjy0pLm1FvGSX0vkXaHugRJgWenBqb4al1D/SKOuSx556jvy8qtmgFW/kLSEGcVLV+wBYNbKbUENdrnCoDIRdDP8uXc/V035kiV7jE9x0BJlVRaiBfFCuL80gSJn4JfqH80i3FhFQzmNJFvyn2XJwb+yPPMWdhS8TqXL99xxZa60OsEugERNb3nBLkCaWkZ+WCVwzv+r/0sBGzNk5DF+DQR0SYnn1JP7NmlZo8cja2Z2AQwe0IUn/nkRz/77Mkya8LtNh9PNF98u5+U3f6vZNmZgDzxBnlwgdHB5dN5fuooX5/0R1HO3FDMOrKDYWYEnQPf4BFs5fWNzsGjBDHYBCOzShq9hmR2Fr5FTsajBz01aGN2ir/CprUhLT4YnP88AsRGzrH7GDPVDnfF//yrgZZDO3UNTktzt1tm0ei//ePwbikt8m+kSTDt2ZuGscAXlhUhyaJZXIzKKi0nNC86LY6B9mrqYZzZPJ8teVLNNR7Igewv/XDctQDOwJIlh5SEP+FhxcVJUKmbh64VRp8J1ECklacXTaM4FPU/XSHO3rLcRIVVC07Yv8D9zSR1j2J+aW2/QaPW6NPanFwT9Qd8XS5ZVLcMpCWIuLWc0OBNAt1LzT7M5I5vbvpjBlGVrgtaPQMuqLOL9Xb/zxIavmLT5e1bm7UaXOrHWyKAGF3UE24s6klvp+9KP5pN0t+Y3GnwqcW6hwL6KPcUfsTD9fA6W/exT61YtjmD8XhvGMSfUPVACrHP3RMPb/PGzpX7nOp7w0PkMGdwV4IiZVY3/roy/43QGD6hbbfjkUX1466XrOWnEMX4/v0oJv/y2kb378gAY2KMTQ4/p3OQ8Y00hj5jgOnX5Wj5atrrdLKMvc9nJs5cwK2M9esCCMZKe0VWpQUL1ftMx8q8khZ3sw54ae4qmeN3j2PjxWLXGC0V0jroAWTkT9Cwfe9k6qYCXQYaNCd2o16YdGeTlt9xZS7IyeJFyzUGjcYwtWbmcP/kT7v/uZypdrfdmkWcv4Z2d9T98eqSOQ3cF6MYgSS+LJbcyklKnLUQVPSTHhOVg1Tx+jYLvK/2c7PJ52D3NnS4r2O4ys95poqW8+0s9uDmLlOAbfEKPgJ+jIK+UCbd8yMPXTqa4sLzWZ2vWp9W8eLQ0FZVV1/IeHeODMuvIYwVX9arPI05XfTl4/vfFbSJf5LS9S7hk0YtM2bOA3zM38dPBNdy7egp3rHiPk5P6BfDloz5Vf9GpJUl4gpJIuCpHgsnnQRWoGnLysCH3MYodWxvdu3PUhdT30JKvay1i5mQdrsDkd1Jajp2bDhjepqYJfvj0T7+OiYiw8dqka3jlub9y7pmDOOWkPvQ7tpP3Y8ItdO+aQGlpVY6j/IIyPvzkD66++V0uvOp1Xpv8O6eMPpZfvrmfW284xe/v44tvlwNVVfD6dk0KWgoZAbiOymn+wu9/MPa19/kzte3OKF6Zt5u7VnzAGfOe4YKF/yO9InC5amMsdmwmTwgH8wXTMraj2W4kKbyxoJdOoWMNumy4mqpFi2J0588wifrzYQlMhJs70yXqYqiY1ox+B4Lxz5kt88m1Fdq0IjUk59VMGlnFFUEdZfBXg9W8jT4PoLnAXIZPk3fmbt/NwzN+DXS3AuaXjHUhOrPgYEU8u0uS2VzYmdW5PdhXGhf0h/NO1qImHKWRWvyxT3uKRlMcCrI8Juwt5aWkgZua0nYkdYoN+Dmql9Dv3HyQZ8Z/VmsGj8cjW+xclOp74JVjh6AH+GKka2BPwOt9RhOCaas3BLQfgfZ71iZe3/ErEol+6I9HVhUn2Fp8kHd3z2VkQu8g90qgI8izByaNxNHnAthp70yJ27/rq0CQVvxZo/t1ibqYCHM3BLWX5u93m6oW3LeU+0s1qYqjtHWZB4wfPNN1yYble/w+TgjBCcN68MiD5zPxsUs4mFHkdf+KShePPDWdy254m6ee+5Gb7vqIad8uJzunhLJyBzt2ZfHCa7O57d5P+GPZTr+DG8tXVb3rfb1wPd8u3uj39+Ov6l9/V2RVJfqjlTuc3PXVj+zKyQt4X4Lt14PruHf1FNYXptVsC9zlUBJvK298twDzSDef73uHfJevyxu9/41EWroxpvPXhJu6Hdqi1bQrMBNh6kZuxWKkx/dl+EGhGT/LVAW8msDtclOYU4yj8vDSiUBVNfFG0wSnnT8Es8UU9AS9/nCHBecVSTeBvQO4fXwO1qVk/s5Utma2zlH4jCDnTzlMcOSUBpPQ0aVGidMWlIdzgaSLtRCraEpVOJ0i53ribcNp7PIXYe7m9XMADdmCysX7UtJYac3+mLM5aOfSPTpb1+2rtQxl0IDOIa/G2JDOneIAGDesN+OG9A7YKK0E3GFUXT68nEOXkg0HjU+8GixSSj7aPR/RwDfpkTprClLZURz8B2UBVHqCmcBesteR5NdsXomHnMrFje5n1iIYlTKVGGvtCnYRooUGlxupDqa0ft16BaYAzv+zd95hklXV3n73CZWrc5ienAMMYcg5C6KCKCCC14wJrzmHT6+Re/WqeA2IEcUEKkYQJGcQBoY0MDMMk0PnVPmE/f1R3TPd093TXd3nnH1g+uXph+nqqr12VZ2zw9pr/dZUl4dt7f0Tlm6xLIe7719Hf6Y4ahTWrtZeNmxsq3jN2tdfYP3GVn5+66OVvXCSSA2KVVAao0CzpDwW/+LhxwPpT1D0lnJ87dkbAQKKIhbsylXzcOs8Hmmbx/qeRvqtaAB2R+uJ4Lks7P+OEaTNJWhi/AIT6cgiTptzE0c2/4CkMZdyRXoNlyKdxUd5ov3jbLWy4TpciV/seZPTDq8K6G7t4Qcf/Dmvq3sbb5hxOedXvYUvX/wtXnxqC06Am4BB4ccFy1t4/xdey9LFzeG6UIegRzUw/b/MJOUTd6mzrz9mXH67+qV5Cl9txpXKCurCYUlVK0c2bGNBVRfV0WC0c1rMbg6O75jShnZu+mLKg/5IBDpJcwFRff/VqASSFt3FCMmuRIjporsvd/q6g01d13WNB297ds/vJx+/hPq6ZCgjio9cVU731ITgdSevZF5Trec2BsdbM8eEdm9WSJ2DE6Gt2MfGTCtynDfa6wSlHSqZEe9lcVUbi6raqTKD02oDwW6rhh2lyq4pKSeWChk3ZnBsy0+HvpJazeUZS+dZS2enrSmSDhgFGV75jGm8YeGKFszIRIsBTZyqmqkVnAhLOv3q57bR0et/NFApDvnmgUP8/Uy5jiu55bmXV6rxP3c+ge0GO3+WXAOJhis1OotJnulqoT0fRCTxcFxctuTbgRhjf/GS+dVvmXCbQmh0F1aTtQcPMN1h/3/RGm+mD5jEuzxvcnqHNEE6dnTy3iM+SW/73qp3ruNy/58f4eF/rEY2NSES/lYPMiM66eoEDc3VvPLioznztauIRE0WzPPnNMYL7KJbPqLwORLJiY4e7jsRHt8aslDOCfKKlkO59sWxq3T4iYbLwbW7SBjWsK/W369Zsiq5hUajf0p2DJFkRuqVOLLAM51f3dN22f/vkDDmcMyMH7O++wdQFIy+s5WYwCIj6Eou+0EL7zgwzcRxHId7rn+Qv139L7Y+tx3D1Fl+7FJe/+FXUcyPrdfgCwKKhb06h4ah8/UvvJ6PfPZ6CgUrMP2SibD6iS1IKfnqb27nzw8840t0zGCbEtBz4CTY70ZkUYOaYjZeUHTCpW9pCJf56XKxGQlKKhjOrCCNXqBTEz1szL9L6dJTfJKS003MaCGiNw55LWywDcTA3LPdEUQsyZFRm2pN8T3nTju8Xu4IITjkmIU8fv8GT9tt3dlDqWQTiUx86yml5Pa71nLTv56mqztDJGJQKgU8D+6DH87A0dAcysXqhn5cZVnBEeRKFmu27+Lw2S2B9M1vNmXa0ITwrRrj6Ih9/i15oa+BqkiBqB78NRc3FlOwn2dQF7KMBrjMTr2O2akLJtyW7ebY0vc7xjqpKwLPWDqHmCo1zPYifJjgpx1eEyDTm+FtSz9IMT+yBKB0JVbRgl2t6AvnVVR2t1KsksNXfvx2Fi4fPqCZZjCD72Qxeh3sGn8vNSfKmBPBeGzt6fG4N8GwJN3CWTMO4c7dz4wI+RWIcU/mp0JTPDPC2RUENfrUqkMKdOakL0IXEeZWvYHGxCls6/8T/aUN6CJKc/IsmhOnoQmTWanXsCPzlzFbiuBghmBi2IMIsnLZNH5gWzb/deH/8sg/hlf4e+hvj/LQ3x7FWDgXzEhg/XEcl/lLh4sEL1/awi9/9E6u+92D3Pvgenp6w1EdeHdrL3+67yn+/EA57dPPZbIAzOyAw2s/FG0bKaWv6wK/aI5Vk9SjZJ0gI6nGQpIwSnvGfhWfZkIrolcgSCpxmF/9plH/titzC891fWtY8ZSksWDIawf/v/edloBHiwYnxyyiSi+n8Di5p/GPV7/xWM8dXnbJobu9n+ZZE4uUfOa5HXzuyzeGZo4BqK1JcMbxy/jm3+71PYJXL0G8HQpNA9kr+0ECl157Pd+84JW8ZuVyX/sVBDE9uHXO2JSdXq35NHNTwReF2pDdwSFVh5COLGV37jZcWaIqspx5VZcxI3FWReuK3uIzOHL/99FOR6dZc2k2wjDGe59OOu3wGocdL+zkbcs+NP4cb9vIbA6R8nfTue3F9hEOrwXzGtA1EcpS8QIw8xK7RnVPxsZy3JfspuS/Dr2YmG5y044nAIkmNBzpUhdJ0lny7yS2Kd7vW9tjI+i1EzRGJv++kuYCFte+F0eW2J39Fx35hwGXxviJzEy9GkPbu4Otjx1LQ+wEOgrl5wylQXM4POKEJp0RQJb+jYgep7ob00yB3135Zx65afWYf3dyBfTq4BaC0ajJ6ecdPuyxx5/cwo+vvZfn1oVLn6pkOfzyX48xVkyml1iJAU2Vce7/+zZu4dSrfsK1b76IhS+xaK+obnLBnKP53eYHAq7EOBqCGYm+8Z/mI2m9Mu2qhdXvoClxyojHd2T+zpPtnxnxeNbePOS30S4sgY1ku62xyFSYKiv8L5wxTbA4jsO9f3iIu37/AB3bu6hprqZ+2VxfbH3yrT8mXRXntNes4jVvPJZYYvT57Olnt/PBT/0uVFHEAO98y8nUpROce8xy/vHwWl+rdAsACdGOstNrPFwp+fRfb+W4+XNo8Hkv6jenzziY322prKqnPwj6Syq0vCSbi/XMLj7OivpPsLLh81NsbWJzRpEUZdeQ2qrvsnAzIv5qT9sMR0J0SHFshyuO+vSEV89ub6/v4vHxUSaHmuoEp5+yIpS6KhBMlUa9xKSPfROmEXDYrHdENIMvHHIRfzv1E3x8xXm8b8nZfOuIt/CXUz/hr13dVhL2+lRuNlk7MknNOsHRM35E3trB3dvO5sn2T7Mz83d2Zm7imc4vcefWM+jM7xUiFUJwZPN3mZV6DXuHSkmVcDki4hC6uErrpalFN00Z27K58ap/7He+kd3+zzFD+diVF5NM7a1O9/CjG/nY525g3frdgfWhEna29frumrHjUKphwvNNaybLG37+O1r7X3qpYJcvPpMlVS1o+7xZXQS5dJTURzPURXMB2hxJyZ24ZkJUa2BZ7YdHPO7IEms7/3uMV03syt3tKF62myeotT+NZzi2w+//+8+cn34zX7/suzz0t8fY8PiLPHbLGv51wyO+zDVtO3rY+NwufvbNm3njiV/lsfvWjXiOlJJvfPeW0Dm7PvDuMzjvleU05Y9ceCrVKX9lbGCg+rwDRpYJ6RM7UnLjk8/u/0kvAQ6rmcfhtfNHzD3BI9GC2MSOQJB1YzjSYFfmlim3VhVZPoHK81CjZVDt7AIg9wfPm5x2eI2B67pc+eb/IzfBiiAAZPPgSl83JCtWjX7q8oH3nMGslppQOr2S9f5PCs7gWnQSH33Osjn9uz/lng2bPO1TkDTHa7h43vG8ZeEpnNy0nKhuEtcnKWo2ASxXV1Iowcbk/swy7uxdwYuFhgr7ICnYbTyy+3KKzqAWjLMnN96WOR5tfR85a/ueV+hanMMav84Zc25jZbSGg02HVRG7vO4I3a320j7RO5CRUnLrtXeR6RlnU18s4nZ2BeL0mjWvnpNfecie323H5RvfvQUpJW4IDwgkZblIv22Uqqg4fb6/WOLH9wdT1ctLkkaUa455F+9afCb1kbJ4ry40Dq2ZS0QE4/Kfm+xmSXW78vG220mQcyZ22FJ0O+jIPzji8fbcfVhu7xR6IVCuGin8F+uexn8K+SIfOP6z/Oyzv6VUGK7XJ6UEw/A966FYsPjCe67lmceGr72fX7+brdu7fLU9GV75ir3zoQD6c8Gkewsg2gf6BLajrpQ8+xKtPD8UIQRfPOQitEAPV0anJqoypVZge6CbGNFrmDns8H5fK5JG4VClWiNyENnpeZPqr6QQIqXkU2d/hXuuH7lgGeeFONu246cnYPf20T2vNdUJrv7Omzn2qIW+2Z4sP/ziJWg+TpxOBOz0wC+TNNOWyfLu3/+FX/37Cc/6pRrTx6p9bfn0+E/yERudDYUWHsksrOh268jfP7DhGC2810VKa0DYcTgxo5m0XsV2WxATYXR2AeYK1T2YZhJsX7+TN8x8F1e958cTer7sz/qffi3gzAuOHPbQY49vprMrZKWrh2DH8P3GdA0mnS95/RNPedqXoEgYUd65+AxuPv0z/OGkjzA3Uc8T3ZspTbAC4VRJBVqNcX8IthTLaakTuQd2ZP4+4rGC08pUFMgEkiol0QZDcLaptT/NlMhn8vz007/mguq3sGH1i2M+z922Ayn9PcCH8r303x/7Pe6Qinw7doUgwmQUbr3jmT3//uejz2MHWIVXApE+JjT/FG3lbnFPuLt1LXZA88zoSAzh0hhTIeEiqdJz6MLCdkus7fwGt285jVs3H8W921/L5r7f4biVzY0H1X+KqshShocKSjRcDjFtjoiG6LoR9Z43Oa3hNQo//cxvWHPnM+M/cTSKJdz2TvRmfyqmufsZYG3b4fEnt4z5d1WYus7cpho2t/oziVlJJi1Yvy9fv/VuTlu8gLl1NVNvLGBs1+Gpni30WwWe7dlGn+3fqURbPs2MeB9RRamNg192r5NgQ6GZpfHWCb1qY89P2d+KQeKwK/svVtQPTwntK63j37mdaIhwOrsAjGWqezBNhfS09/LeIz9JMVvBwiWI0uwSTnv1oTz96CaeWb0ZIaDbsgdKYYTrBhi8mwu1/kcc6TYk2sAxwKoCJzb+awaxHJdV//19ktEI561czpuPOZyZ1VX+ddZjbOnwscevY3vO+5PX/bE7X0V1tDL9LH+QLIy1TXj835m9iYb+45mdvmDPY1G9nqkozEkEcwyF+l0A+Bc5Po2/5LMFPn76f7HhiU3I8dIFpcRZvxE0DX3hPNA03w5aOtv6ePrRTRx27CIA0qkKBtYAeezxzVx4/pGULJvv/vm+QG0LQDgg7PEr0m/tDqfDsFJu2x30IdHgRrJ8bxjCZUXtbgwlUU+C+dEOAHblhh+eZKyNrO38Ojszf+eYGT8dpj28P0wtzfEt17Et8xe29d1A3tpAREiOjNgkwnaQr9V43uS0w2sf+rr6ueEbf51aI/Eo0nURmvcbk6rasdOWbvz74xSLasv1jsa2Hd2sWjzLN4eXY+KZw0sTgusff5pPnHXy1BsLkBu3PsKPX7iDLh+F6ofiSI1nultYXNVOjdLNiGBzsYHFsbYJ5dm7jKy0OuI5cu/76cg/wua+X9Oeuw+JjYPAkoSrOiMA1Wh6SnUnpqmAUtHiu+/7cWXOLgArmDH+k2/+MR2tfWgDDjbXcYlEdErNKaQRnuBwwUC8ZiS4Pmk2RLugWAtOBRn7OcsiZ1n88pHHueHxp7n2zRdyyMwZ478wBNzdupatuY7A7XYVE7TlUzTFM0ipalEuaTT6iWqVOZue7vgvGhMnDzi6oCl+CrpI4shK0wLLC5x5ukOd6pQTY6Va+9NMmuv/5y+s309U16i4Lm5Hl2+H+INs39Sxx+G1YnkLmiZCp+E1GNH1jv+9nkJJzV5LuOO7zDd2dLOrt5+WarWZGFMlYwW/t4jrRaK6Q200R0MsE5Cza9CG2HOouDDaRktkf+nvkp7iM6zv/j+W132M1tydtGbvxJEF0pElzElfSNxoGfEqXYszv+pS5lddirv7UJjAnkgJtrcVYmHa4TWCm67515TbkLvacEQ7oiqN1lCPMLw7edaHnO637ezhXzc+xo7NHcSTUW599AUUrgjHJFsq0dnnn+BsciBd3THATpVFhSfr/HKkZF1bu2d9C4LfbLqP7677Z8BWJZZr8FxPCzHdImUW0JDMTPYSN4JdCEg0+uwYNebUI9oEGlWRcmrgCz3XsL77ewj0PTpfINhmayww3HDdZvpLY9M8TTll/s/fvZnrvvIHMt2T0MOxbZy+frR0ytfUxo7WclW8oVHFWskhuqufwqwqCJFeZLmaVXBz3+AZcLQHcjEqnm8cKclZFlfc8Dfu+uDlGD4cjnnNPa1rA6mAORLBxr4GsrbJvFS3gvhCCUgWxyYWRTz8lS7b+//Kopp3AOXNxtLaD/Fc19f3+7p64dIp96adpIVkvuEwUw/BvKN5n2oyjf90t/Xwm6/9aVKvlbbteyXzeHJvQa7b73oudM4ugEMOmsWurj7WblWjkSUBOcFd+/ae3pe8w2thqokd+S4cGVRUq6DgRGiKd9Ec7w9krK3X+6gzs7RZVThSo0rPMyfaRY0xkf2My9b+P9KWvYecs21gr+LSmruLF3p+zMH1n2Ne1SVjvzx6PBTvRsWsPi7S++CNaYfXPlz/zb9505CUyN4+nFwOfe4cz5xe//zDv0lVxenvyXHDT+/Zu/gTAtdxiUZ1is2pYNJeJoCrwad/c0sgGxHNLm9AtBKUqpmU00sAMfOlE7LfU8zyvXVTr+BROXs/3IJjUhgIsyu6BgfVVr4xmCqOR3KEEpd5VZfSmX+U9d3fG3hseF77i7bGLN0lQoh8y1JtBbNpJs5vvvonfvnF66fUhtzVitPVDcUSoroKrbnRf10vyne9HTM8iab1EjUJB2XDeh6ciWUUDMOVkrb+LHeu28jZK5Z43T3PyTslhctiQWuuGqRgXrorYF9r2ZglJ7OGE/SX1g97xJ3AWH1YxEYX0OVArQ5GqO63kEYETLNfPn32Vyc/UGayYNvg09rYjOgcfcryPb///Z9rfLEzFTRN8OpzDuUHNz2kxL6krFc80WEoHYv62p8guGDO0dzdtjZQmxLBlkwdvaUoK2r9DX4QuKxKbUVDsjA2uehpVxbIOeVCW3v3KuUb/dnOr5AwZtOYOHF0+4m3I4t3Tcqu72hN3jfpeYsvUUpFi9989Y9kx6uSVSmWjdvpXbWR3//oLn76jZu5/sd3I12JO/gzcAqvFR2ibeGpolOq0QL1CkgBRg70SercSuCspYs87ZOffOGpP+CGxjsv6C3FKTrBVO/aiySleaNXNid1EU2J09jc92sEo7+P2YYkGuxlPT7uDtU9mGYCdLf1ct1XPCq3XCxvPGVfPwycwPuNa2hYDZPw7vhIKSXItgQ95pSRlEvGTxZD01izY5dn/fGThalmpfYlgt35Kjb2NSixv7FQ+QJcUI7qGqTodLFu4CBlbCRbHQ1dQKMRNmcXTDu8Xnrc8os7ePGpqen7yl7/hLtf99aTSFfvvU/a2lWIhO+fT3/0XOrrUmxpC14fa3BmL9VM7PmGptGYDNc8XQkZq8AP1t3K/3tyageDk0fQU0rSlvP3M5QIhJQe7CXGWvsJNvb+dOSzpaSn8BQb80/Ry6xwFiKaoC5ZJUxHeAG5/jwfP+O/9luxZCrI3j5kY70vml77IgC9YKMVbdyouq9XAnZCYKX8f8+uAaXUgJbKgKDLZB1e1fEY5x681Mvu+cbDHRt4uHP9+E8MFEHRMYjqQVX7kMREiag+tZDnqN7M0tr3Mzv1OoQQdBceHxHZBWAgWWKEqJLJHsoVJoV46UQnHojc9qu7cW2Pw/OlxGnvRG/x3yFhVQ+cGofE2yspa5pgqlNclVOY4qSUL4l0RoDXzDqCX266R3EvBB2FNDMTvSRNK1C73U4SV4oJaUUOInGYkThzz++7MjfDKPPKvmyzdRaFLW1+EHu36h5MUwF//Pbfuebjv5paI0L4ljWi6YI3f/AVwx6rrk6QqVTb0kd0XbBgbtnRXpcO3pEkKVchnmg6o+26fP22e/jW617la7/8oN/Kc/kj17Al0670MD+qWdTFCmFUCaoASVfhURy3gK6VC0EU7DYeb/swPcWnEOi0Cpfjw1gjQo6tVz5ZXhorLZ/54Yd/4ZuzCyhrizjBbZIloOeCXAyOThCDlROBfOMQZxeAVln1rKF864JziRrh9wP3W3k+8fh1qrsxKkaFwr6To6yrEhUWRyQ3T7m1+tiR6CJBV+ExdmfvwJGjL7aadTe0g6a0fRzDpvGE67/xF38a7s8g+/0vWOEkIqFa/QnAyEm0krqF8WTnGihreZ2wYK53nfEBKSW/2XQf73rkx6q7MoCko6CiQIfArSCXV6CTjiyjIX7CnscKThvj5wMLigiKYTx1B1/EhKfxh3/8+DaPnF06biaL29eP63q7vnMdSVd737DHzn3FykBS9CeK40iu+NRv+ftDa2mpC04Xy45Bvh7yLWDVVvbaf65dT1f2pSd18ZMX7lDu7AKIaA668PfQISqsQJZTJbd8f9lOngd3XkpP8WmgfCDTKyUhlMsDfTrCy3P6uzPc9qsATi2DPsVVHKMoALMAli2RPpW0k5SrZO0xuG8HKsQUcPLi+VPrVEDctONxim7YKnJKYrpFXPfb2SpJaEXmRrqYGenCqODEfSx2Zv/JzuzN4z4v7YEt33BfHqWoX648/+gL9HX455Rye3rRqnxejOvh2YQMMliuPeijWAnYyYlrqozFlf+6h8+/8nSOnjfbk355zVXP38zvtjyguhvDsNzg0+ajWOhMfLMvcXDdEht7f8ac9IVE9Toiej0TFVIKUU2I4cjpeealwO+u/DM//9xvp96QlGX9LtvGzeUhmUDMavHUIXXtd/7FJ7+xV1z7ta86nL/f/CTtnX147F+bFBIo5W2+8qN/YieC2cuV0mClmXQFeseVvNDRxTEvodTGomPx1+2PKXd2AfTbMSxXJ+JjtkpCCyY9/K5tZ5I0FmC53ZT22SdoSDIuVKlRhRibyEmeN+nrnXvvvfdy3nnnMXPmTIQQ/OUvf/HTXMWUihbX/89fh1Wh8oV4PFCHlwDciPqrV7iQaHV8c7450YHNhkfzriXhvGuuoy2ASImp8nDnC6q7sA/lWXluqtvnPafk5PQ6TkpvYF6sE1PzIv+93O54aEhmGSFYfY2FqFPdg2n2QUrJo7c8wRcu+B8+esr/89dYKYCoXvXr0FHR88F0TA75sZNQqpp6m+vaOnjLdX/kjnUbp96Yx2zKtIXO2QWgT0U4bZLMi3VWPNdk7U2s7/4/7tl2Lt2FJ5mZPJfxFyySlHAJbXK6CGP+yzRD+fc/H/fG2TUa2Rxuh3e6xAD3/vMp+oZULK5Kx/neNy/j4OWzPLUzWQbv2Ei3f/uZoTiRAWfXUOOTIFt8aentrevdSd4JS58FGctf4f9eJ44ldYqOTs4xcaRfmydJ1n5xVGfX0RGbtKY8RmYkWqP3TXre4hCy2SyHHXYY3//+9/00MykyPVk+fOLn/UsxGYLWUBtYeK4EpCbKqSeKEZQFfQ2fNiNuuTCgp6xv6+Cs7/+cJ7bv9LZhj/E6rNwLFlW1Ux/zN4R6frSduBZMGPC+CEAPcz6/D2V8p5k8ruvy7Xf/iM++6us89PfHsIo+R2QGcqgStlVRmUjWpYLgm0kjKTu58s2TrwQ8WptSSj7zt1sp2eGK2v379tXoImxJ3ILuQhLLDa5fEWzmmJ2TfLXEllke2vUWuopPMD/91nGeL5ipuzxn6awpqj+4HIEezkjEacq4rsuVb/o/X23Inl6kh2tQx3bZsWX4/dXcVMV73nGqZzamigB0G7QAzpVKSTyZat97/V/52UOPTb2hAOgs9vOBx36huhvD8Tmjw0Xn7r7l3N1/EPf1L+fO3oN4JjuLnGME4oBaZDjUDAQNhG1fI4T3c5+vK4Zzzz2Xr371q7z+9a/308yk+M67f8TGJzf7b0gIRDw+/vM8YPD+KDYmQxMPLwEj59NOxKcBoWg7vPEX1/PFm+/ADqFjCeCQ2jDpvkiqzRxN8Yyvg7SBzdJYq7KB2UHQ69sJzNQR2gGfoR4qbvrx7dzyszvLvwThJ9KEr5UapSB8q6JBJES7g4n6sVNTT2PcFwn0ForcHrIor535btzQHf1C0TXYnqkJzF4Jgy1WWbR68h+Hw5q2j7Ez+49xnidZb+tsdTTaXA07bB+/eYTqHigj7FkrAKtve4pMj8+V2l0XSt5G4kRjw2Mau7qzfPFrf510e4m4PzGSwvH/hpQmnmWufOP2+7j52XXeNOYjX3r6j+TdsER3gUCSNv0vnCCHuGFcNHZYtTzYv5R/Zxb4up8SSOaEtTAKgP4Si/CqlGKxSF9f37AfP2jb1sF9f3rE/1RGKK+OLP9PbSXgJEwKM9O4iZAFw/t00woHzyaF0bh+9VN84/Z7/TMwBS6YfbTqLgxB0JzI+C6hY6Pzr95DuKNnBQ/2LeLu3mXc3bucJ7Nz6LaD0SkIz3S8LwKM5ao7Mc0AUkr+8K2/BWu0WMJt6/CteSdmhNbhJQAnFkwVZL/mM0PTeLEzXPpINZEEWii/c0FbPu1jCshIthbrcT2Y40ruePeo2PP/wSs6VD7HyCrVPVBGmLNWBrn3Dw+p7kLF1DelmbdkeJXhX/z6frqm4Lgr+BRRLQ1/xxzHnHg1xonymb/9i61d4ZpbhlJybR7pCFcxjKhuYQZSgGtfBA46PU6Kbtv7SoWDxAREwji1DyDxPlAoVA6vK6+8kurq6j0/c+bM8cXOM/c95+tJ+AgCuKicmE6pOYWMhi/Kw/HprhI+j0US+M2jT4ay0kljrIqZ8QrLtviCJKJZVJm5APbCZQM2Bv1unKKMUJQmrVY1/84sYlOhwe8OsNPWeK6k0+GIcG1CpgkVPe197NrYGrhd2dOLDFlanN9IyponQfi8/XR42a5LwgjXYdUrZx6OI8MZ5eyiYTlBpfwJStIk5/qr6TLUHoCNoN0JmZ9ZX6i6B8oIc9YKlA9a7v/zI/4b0jSIeCebku0vsPWFvfNloWBxy+3PQNGBoj0pj6/rQ+k5Cbg+FeEaxI3g+RxTsG0u+tnv2NXb723DHtGa7w2JWILc8/+CY9BfUikNJOnycVETysqMQ8l8y/MmQ+Xw+sxnPkNvb++en23btvliJ9CNqmmC4b8TSis4aJliyI4Cy9gpfy6zIHRrbdfljvUv+m9oErx5wSmKLA/KNkNNJM+h9TsxA5ca2bvokAP/Xl9oocvHExGQtLoaWx2Nx0omDxSNEE0aEuxwXqcHIoFED4+BzPjjoNfyk9t4BIFjgpEnkP4JH+edDR3+RehNhsNq5nF47XzV3RgTPeAT+OCvfskmO2SHmIVbVPfgJUNQWSuDPHXPWjLdPqczAqKmGuGhZmSxYPOpt/6E3p4sUkr+fN0DaBs7ie/oI7GzH7M1Ux7bFc4/ZWeXMvNTpr9Y5Or7A3CGToKEoVpzWhLVLGK6RU0ky4JUB8c2bSEdUZnTIXB8dNEUgZLaW2r/WOs9bzJUDq9oNEpVVdWwHz+YvbTFl3ZHQ6sPRrBe6qIc3RWio0AJFBp0pE+l7IUDooSvq1BNiNBWOnn1rCNYVjUzcLsN0QzzU50cXr+NFbWtisJ+RyKQbCnU+2qhbKV8PWelCMmp1CDh6s2BTE9brzrjPukOaoAoBVOlqhIEYGZBz7q+z38S0Iv4dqv95annQlUlWAjBVw59g+pujIIkbRYw/A7zHoLAJRlQGfmhVvukRiFMt1zxbtU9eMkQVNbKIHf+7n5f2wcgmUBr8LYitJSS/t48/3HKlXziP67hV9++dZhWljaYS6x4f1Oq8v9kVyvhS1aQK+HPT64NXWEUgPpomoSuzumVMosc0bidVQ07WFHbxoxkJhQy2PWGn2sBQdYVqm+psRHee5dD5fAKir9ffWsgdkRdDaRTAP6KCUM5ndEIz9cpgVyTjp30r08CiHeAnse3DYgrJfPra/xpfIrEdJOrj74cLYic2SHYUqcl2U/cCNfEKRF0OX5GeO1rr3wNhmb/byxS3YNpBrBLCu+NiH/H0JHd/YNlBX2zUSkSQECx1v/NiACkhm8yBRLJbSETrm+O1xDVwhTaUL72Zie7A12smzhoPlftGovwRBID7i7VPXjJEFTWyiD3/elh/xqPRdFmzkCbOcM3E7bl8OzjW4DhQ6wbNUIR3WUn/R9wNGvA6eXD2y05Dj35gvcNe8Ab552ozHbOMhWnL+6LxMCm3vA3WvN5S6cUjniFkWjeBy/46iHJZDKsWbOGNWvWALBp0ybWrFnD1q1b/TS7X7K9WW7/dQBC5JoGiThClCtn+Rnl5caM0ER3uToUqgT5Rg03HoyIcLQHjD6Gpl97hi4EvflisJpvFbCubyduwJE9PaU4vcVYmPa8ewjyDkiKcjHUENx2gECIME3YBzazl7YEEtk7AkNHJH3UfUhG0PqL4LihcXrZEcjMMsBHbZXBd+pq4MR8M4MuNPoL/leGqpRlVcFFxY+ORAyk0mtIFle1UxMNcuMmiQce3VVGRxILxRwzSMhSLENMUFkrALdeexf9XT5FhDQ2oFVXIaIRNE1TM7cpQgJ2QpBrDmaPJYBo10Dq/F71EM8w9cD1RybEe5acxcFVs5XYFkLiyvAEjIDg6NSLvl9uvVLweMkIy1JuOPaznjfp6zf82GOPsWrVKlatKld1+ehHP8qqVav4whe+4KfZ/bJ7czuOHYBL03WR23chM1nfJwcnoivffEjAjoITFWguSE0E2qfIUNkaDz9uR0o++ddbuPK2e0Ln9JJS8r9r/67AsuD53mY6CknVl90+SJ9DgIfTGISI3ISRobs+D2SS1UlqZ9QEbleb0ezrfCN1DbcqCoYeCk+vo0NhhgE+pc0Psqdunls+YPEL23WZW1vtn4FJ0hT1b6M+ru1YHzMTPTQn+liQ7uTIxq00xv3XKRqKKWwWxNoDtVlGMkt3A47hHgfdT9mAaSaDlJJffP53/hlo78BtbcfZtBV7247AC6PoBVvJfCMBKw6FRv/nmKFoLsTbwewDYeGp4+vxbTu9achjhBAc07BYgWXJ8po2qiJhiHyTCFyOSGyiygji4EvQIzXanVDNMGVc76uK+npUc9ppp4VuE5btDbbinrtzN2LxAoSPXnUzU8KuiQU6IO+LYEDbZGBUNrMOmTnBnAQOVs7Si/6dvv/ykSc4bfECTlg4zx8Dk2BjppWN2eArwQEYQl16x/6YFw1G9DktXJaZLtKDMvXeMZhkOY1qnrn/Obp2+VgG3DTAGrLpSMTRG+oRcR/DjwARxGFRBfhVEGUsBKAXQNggdTy/3apiUc5aFr7U5JyjTsfS0FzmpnoUjrNl1cYGI/gKZwJYYITpYAXQ1Dk/VZPJZHjhhRf2/D6YtVJXV8fcuXOV9at9eyedO32cb4aSy+Ns24E+b46nwvVj4UQNrOooKhZbAjDzUHQlQYs6CQmRLOglKDR6125fIQyOndF5vi94Z1yVWaAqEoaoasmiaCtzIp1E9SDXWZJdrkYTIZtnfMhcClMMXyBc/ZFrA7cpfS4FK1yJnrN8tbE/Bi9LMeTHTgSf6+Wnfq0uBL957En/DEyCW3euUWI3otkcUreLumhu2FccBt92lZ4PxM48wym7l0LkX5LW86q7MA3l0/ZvXX61rzZEIo6+ZCH6gnnoixdgzJnlu7MLQM+WMDty4bjZASulKdkExToAH4IcNODHDz5GVy6YcWyi5BU6vLqKScXjrKAkTbqdVOCWFw3oZIZpnkFfoLoHyghj1gpAvj/g8aJkIfv8dwBb6SjFmWnchKnsJhCAZqmb79wInu79I0Y4UxoBNvbvDtxmfSwbCo3EBiPD4nh7wM4uKM9vYZpgBvH+Oj2gHF5SSjau2Ry83aK/3mMJGJmSsk3IaLeKG3BKIwyICfuEIyXP7mrzz0CFtBf6uG7TfUpsz0l1Y2rOiPWH+kW5wJJ+RhXuvZ4bdRmKKi7DsDao7sE0wMY1m9m+3l9hZ5krIDQNETF9jR7eFyFBKwwcroTA6SUVrd2FC7EuwMXTzUhPocgP7n2Y1/74OrZ1K6z0GSIKjklHIaH8ciu6wQv3v2Cb3FM0WV00yIQmuPLA1fAazFrZ9+faa69V1icpJV++5NuB23V9Psh3TQ2rPl7+RfHi0siF5uabMh+98Z+hO7wfpKMYfBStLsKRMh7XSkrmOIEkEcJsHYT3xTEOKIfXjg271KRY+jxYC0Ar2qGqniUILvxYAlKAE/XXTtQIz0Lvv576Q+Bi9QAaLg2xjOr1xxhIdF/L1ItR/hUiSk+o7sE0wE0/vc1/I5aFm8kGPp8JKBdIAeWbkD3vXMGcJwDNASOH54OBKyWdmRwfvfFmbxueAjvzAaVLjcHG3ka6i+XNr6oljimCjKIf+iYFHa7goaJJvxuCmaf0mOoeTDOEv3z/n2x9dnvwhh3/dLykADvt84K+AiJ9EqEoyksv4vkc8+V/3sld61/0tlEPSBrBf+d5JxwViC2pK1lSSQSzjRA6dPVaz5s8oBxet/3qHiV2/ayatceGhNiOPkTBDoXTSwswJVoApSp890KcvVyFoOJI2gq9PNqlpnS9qTnhi2waoFrPYfjq8BpE0uOKUIRBD8OZjvBSzYN/e5R/XB2Awwtwd7dCqbQnyiAoJOEoTSo1yv1Q2Bcziy/l4x0peWrnbp7dpUajcV86Cn2KLJfVml00NvY1krUMZV+3FsjcMsjwNykROMBaKwTpSHI68jAsuK7LL79wvRrjpn+OAqs6VnZ4hWCeGSSiKMRSswf2Ux7OM5oQXH3/I9416BGH184P3GZ7Ph24zdFot9I4gacWSmZrDtVa2DYzgL3J8yYPGIfXusc28tuv3xi8YdNEpJKBmNJsF7On4MsCvFL0kiQoj4BjgB3AR3z8AnWipEPZ2K9uE2RLLQz+1FGQLIsHlf8v2Gpr4XP8aeGr7nYgsXPjbr74um8EZ9BxcbZsx23rQBaLSNcNxvEVguteAlZSbUcGo7z8mm8FsGZH8Jom+9Jn5XEULSqqzALN8X4WV7VxZONWkmaw1eH2Isk4/h9c7h9Bt6uRVX0Yr89X3IFpAHZtauWKoz4ZeCGuQbRq/4oXCEeGYp7ZgwauIl+zBKIeB9i6UvLkjt10h0wrUkUkcck12NxfB4yMFQlyr+Og80KhOTiDgIHkIDNsYvWDeP/hhydHy2duvOofwRs1DfTZM30tEz8UCZQaEsomCseEYq2O1AXClug5Fyfpv6iwboPeD47Pjvqr73uEExaqd3pFNHW3rSN1ekpxaiJ5xYdvewdDHZdDk9uoNYJZ+GlIZuhhq9AIpD+mugcHNFd/5NpgDxt0Ha2xHlGVDmyOAZCGpqRi1r5E+iV20sWNKj638+ljkIARAq/6tky7Ersx3eLgOvUOv0FESHROclKQVHmqGTtDne1pAGjb1sF/HvsZ+jqC1zwCIB5HpP0r4mD2F5G6KFefVzzPWHFBoUEPdF817O4WUKrBF/slOzzOjtWdL7Ixo+Ywf3e+mpJrMDvZQ9IsF2ixXZBSwwxQRH5zsYG4VmJOpAvw/9Kv1iCAQquTQ5/jeZNhfaue88Bf/h2MIU1DpFNoLc3oC+YhIsHkB0vAiRlgBF+1ahDdAgS4EYETFzip4I5EYv2UBYR95N9bt3PF9X+lYKk6ZS6zsmYOQuHx1/ZMbQjk4sr1QHUcZkc6aDKDWfjVay6nxyxm6eHY/AxFaMFEkk4zkh0v7OThf6wOzqCmoc+dFbizC0AUbeWbkEHrkT514S4ScCL4uxEKwTDz3XW3KLHbEMuEKJpY0GCoSuscjqnaB1p6XHEHpvn1V/5If6caZ5eoqUKbNcP3ecfsC1AXZQycCBQaB5xdAc15tgl2HJx4Waol1wyODwWYa+Ix6lOqo1bL9Ft5PrPmt0r70FVM8lTXLB5rn8PjHbN5omMuWsCpfgJoMnsDqwAfmul1VLx3xh4wDq9iPpiy2trcWWgtzWgKNiKlxoTSjYgEjP6BDcjQfgS0ajXy+H4H37n+Rf7fTbf7a2QcorrJITXqIs0ydpTne2ZgDcR4S7n3J2gcDLaUmmi3/Hf2pITLkREbA+XSQaMiS+GsvPNyp7ejj/cd8alAbWr1teV0+YAvQgk4IRETFoCRUzTwDNi3/AtyAOBr/7qbLsVpJ0/2bFZi19BUlGUZDUmj0UtSD1K0fvR+xJBUq440s59Sa/8Ap1Qocfuv71XnDHYlQtN8T58XbnCyKGNRqho4tA9wntWdckRXsRbsFL7t0g9t8b4K3mT52/bV9FhqUnP3xXINio7J7FQPesBj7fxoG7EAdZJ7XYEdjkl2JM5Oz5s8IBxe3a09gbgyRV0NWjQa+CYEwKqOgq7+67QT+/QhQM+AmR34h4/ftQT+/vRz7OhRe9r7wWWvVGq/txRndcccnu9uZmumlp25KmUOIEM4ONL/a3+BUT5xCJujaw+F+1T34IDk/732f8hnCr7b0YasgkR1lZJ5xo0ZuAmz7GRy1TmbBlF1K0qglPK/MnDJcblxzbP+GtkPRcdS5nQqObpiGZ/yO6/RcxySVFAFbwSCJaajfv5xM4o7cGDT15XBKqhzvsq+fpztuwKxJRQ5vCTlhBE7EfzJpnBB96kYylDufXEzp373Jzy3u81fQxPgX7vCcFhb/sA1XOanOpkRD3aPl9CKLI61BawbJrivYLLV0nBC5/jyPsLzZa/h1d3aw7sO/aj/htIptIZ6/+2MRQgScUtVGk5C3WpMs8vijsVaymOXj9oqd214kf84+nB/DEyAQ2rmUh9J0VlSufgUdJcSdJcSgMQQLs2JYPszL9rBkthuNKTPskKSGboMn1D9UNwtqntwwLHusY0899B6z9utaarik7/8T2LJGK7tsuCQucRSMS5d+Un6Nu1E6MEr6JZT+HTM7jxGfwnhyvJjSROrJo6MBNsnCUgdJR7ocnQZuGY59cQ3pOSpHcFsLkejvahIIwhoL6SYmwpexLiMpErLszjeSoORUehkGtyEwTLTYVYYyseLJtU9OKBJVqtPQ9NnNPl+4OKaGtJUoxS/550puvGjfVAU4CTxdS/Tkcnxluv+yE3vfQtNPmqyjUfG9v/AcDySRpGWRB910Ry6gqqF86IdQPCXXBFYa+tsdzSOithE1LsSBvA+pfFl7/D6+ed+S2+7/4s2EVMT2TWIVrCgxodE7wkiKTu8VB8/GgXQWqFYA66PH0drv9pTTiEEDZG0YofXIOXJIaIHq202O9LJ8nhwm0E9zM4uANQvhA80HrnJe90uoQl+s+VHRKIj9R8jMxrQnXI6SeDaXYCRKTu6xJDH9KyFnrMozkjjxoJdUpSq1K3OtIB8D/p+DrPachlacxlqY3Fmp7yv0vp8r7rIJss12JapZW66W0mNBENzaTTVzq9pIZmru8wwXPXaXYPo6gv3HMj8++YngjGUiMO+6dSGjjZ7JsL0f5y3q6JKi6MIQNgSaag5UIn1loMprUS5QqQbw5ecrP5Ckd+vfooPnnaC941PkAWpJrblOpXZB5if7iJtFpVtYZvMPkUH6mWjfRKes3QOjYQgihjwI8QxNL48P8hnC/zrV/f41n48HWP+ynIlAWGo8x1KoFQbU5pe4kREaDwCmgvxLtB9lD75zb/X0JHJjv9Enyg4JdZl1J3870tUs0mZwejkAQgkS2JBVnQRZF3lGVz7R69T3YMDjvWPbfS8zQ/98F2jOruAsn5KNIIQwncNlX2RMMzZNYgY+GOkLRPoDeKaYKXVLWEk/qc0SuDERfNGPL6hp4O33/ZHjr3+h5z3919x0h+u4XX/uI6Hdm311P7DHS942l6l7MhVs7Gvfo9eZHAICq6hfLzPS8GsMDm7AJx1qntwQPPLL14fiB2RiKMtmIfW2ICor0WbPRN94Xy0aDAajk7UUH6ArjptX3Mg2g+RLL5mrPzjWbX39IVzjlVqHyCm20ovN025YqVgt6uhWqlyLxHPW3xZO7w6d3Th2t4fw1Y1pLn4Y+fx02e+w0+e+jZ/aP0pmsJwUDemQ9RUK1ivJvJ4TCTg5+Fs1rL40f0BVf4chf9d+3dltkciKLoGO7PeRxiMRa2RJaIFW1J5qx2yi3xf9JEb42n8Za3H6YyJqjivfvcrxvy7HLIoUhFRPJZFAWiORMsHF+VZrAlB5YgAhqDDZrYM+31ddzsX/OPX3Ltj07Al8pPtu3nTrddz1zZvnLBSSu7YvsGTtiaPoC1fxeqOOfSWYoHuPXNulJ2lGqVOLxvBJjtky3QnPAdtBxrdrT1se36HfwaGDKciEkGYBlpdDXpDPVoyEeyco6oS0tAuhKQ6USntb/v9BbUVMQ+vnY8u1I5zlqspvdz6nLjqGg1IBL2u+usdAOH9fjJkM6m3PPPA8563+a27v8Sf2n7Ou7/5FprmNABQ01itdFB0NQ1cddoOroBCnaZ8chqKAHSLsvKkT/xxzbNYTrBOFyhvRG7eEVBY+4QRtObTgV0Cpgj+c9/qaHS7YsR7DM1lL172Geqho7/LW696Vd3+V7aJlLq09fFmOAlopWDuSynAiamN7hIEUxn4L0+tHfb7Fx6+nYJt4ewz8LhIpJR86oFbsD1YD6zp2EVHPhyVs0DQWfC/Eu++Np/Jz+HeviWUXF3BOC8ByQZbp90J0TwTohiAA422be2+tV3TWMWqMw7hA99/JyvPPEy5LrAe4OHJWGghcLrBoFalf+1niiWe3RVkxsRwduS7cKRafcL2gs9exXHYWqwPhUZwCLpQxof9zMvW4ZXrz3PVe67xtM3TLjmBQ085yNM2vcDMWcR29qsr4SsopzOGpHLXMHy8e/OWRW8+eLHFJ7o3Y/vpyZskjtQDqZYI0GalWZefQd4NzskjETxWMthoa5SGXOIhOAAsM33yHiiuD4cMh562//mlpk5dJPGECGhFYccEehFlc40E8vVgp/F9hXjL2r1RhFv7e3hk97YRzq6h/WrLZ7lvx6Yp2711ywZcR3VU69732V5I4ciRBw5+U5Axns+3BD7Ox4BDDYuzYhYN6uVRhzB9sKKKX3z29563OWf5TL738Nf5Q+vP+MZtX+D8K17JO75+WfARXUOQgJ5T71iN73bQssEfru6LZuPrwYrlurzluj+yq1dNkRJTqJ9ndufSPN/TzIaeRjoKycC30+12mq3FsiyJqi20hqRGgWD/qEjvD3dftg6vW6+9G8fDdMZXvuN0PvWrD4z6t6C1VEZDWC5mwBoqg9hxiHQ5JHc6pLfZpLbaxFtt9II6p4ykLPToNyoWBFuzHYHbnBgSTQTznUs0cm4EKYP9/F0EL9gGdxVM7sqb4SrlK9SeUB1oPHHH0563+eYvXLzfvy86qGW/f/eb8S53Jz6G9pjH9s28JN7moOfVHLC4MXAjBHIcuquvny1dPUDZ4TUeAtgygeeNR8G2sIv+fp/js/cDdqXGcz3NuAqcXrusWp7PzwjsPE8gma051OigqBDp2CjfnB6Y9LT3svr2pzxtc86ymfzwsW+w/Jglwx5fcbhaeQQB6CUHSmqjvIQEDU15eqXpo4YXlPew+ZLFdY+qyRyZk6xnRqxGie0yAomgpxSno5hkQ28TazpnU3CCdO4LXig0Ugxcr3IQyWzdRUGNhtHRpjW8JsxDf33Es7bmHzKXj/30CowAKpNMFgForpqKJmYOIhnJYJaZAPSCJN7qYGTVOb2sJL5vSP7r5jv8NTAKKVNdWtPYSOqj2cBCcpvNHlYltxLX1JwCSgRFoBSmgEZt2uEVFFJKvnTR/3ra5vu+8zZmzG/a73Nmzmvw1GaljHV7S8BJmr6XkR+07xqQm6HjJBSFvgR4zztS8uqrf8ljW7dTHRl/7JdA1QSeNx5LaxsoWhq2FbyDaSwyVpxnu5txAj7oANhSbOSevhV0O3HfPw+J4AVX596iybMlXbm2yzBEMKLl0wznt1+/0dP2lhy5kB8+/g1iiZHfp+HzOD4R7IQJEbV7LicusFMDc4xCr7Moge5zjSxHSv7+jPcyQBNBExrnzjxcie29iCE/UHQM1nbPCGzsNbA5Jb2eiFBTJVEHFhvqoxn34O70vMmXpcOrmC+y5u5nPWvvyps/u9+/CyGoqQtaX2IkrqpJSg4dJsoM/jvWoUZ8QgqwA/hK/vX8C+zuCzYM+PiGpRihOmUtV26blewNxJpAclB8h8qK1QAsMxxi4dA0LWMsV92DA4Z1j75Avt+7dOb3f+8dvP5Drx73eYuWz/TM5mSQo/wAOHGDUkMwc6CrQa7ZwI0o3IC4BCp2Ybku7/n9X1lcXc/sVNV+nxvVdc6cs2jKNs9fuIKEEaFUMkMzxunCZVlNO7oIck1RvtIbjV7qjAyPZ+YHZFdgIpllOKHQdtmDG1w15mn2cvt193rW1umXnsT3Hv46sfjozsv+Xh9LnE8AVxeUmtTvqUoptdrEg3OsEOWqxH6TLapLI+0sqkmnHBtB0THpLiZ8t2Rgc3LVOnQhlc21DtAdFsF6AOn9GPSydHh95z0/xiv9u3d94800zKof93mpKvVRN0LRMeD+Knepis50owS2Ibn5WW8rtY1HTDeJaGFyeAUa8ECz2UNEc5Vuwkwk8wy1fRiBPkt1Dw4Yfva533ra3otrNk/oefOXNHtqt1IE4EZ07OooTjKCXRWl0JKm1JwiqF25ldYGRHzV3XyaBcJnXZV9yRRL3Lp2A5888tT9Pu89hxxLdXTq6xENQSqqE0+Ex8HRGO8nogV9Al421mmnOTixg1Or1wVmf4nhUPOyXKVPUwm7Xmz1tEDKZ379QXR97DVksaD2nrerBhxxihdYrqn+RNOOQ64ZZARf5xsBzK+v8c/AONzb9pwy22MjA3F4LYvvwhBq9xMCqNVlaKK5/djAv+ym0kxPljt+481JiG5ovOHj50/ouW27golu2R9a3gpRflUZK63oEgvwY9i3usnzXe1c8/Qj/OCph7l/52Zcj7+TdX07yTnh2YQMDkzbs96XkR2NFrNX+WXepLvhqWYyiL1RdQ8OCAq5ImvueMbTNnvbJ3a6mayKe2p3MmglB1FyKDUmsOoTuDEj0E1BKaVewVsA0W6Gh7kFwDfvvI9Mvsh5C1YQHdiwGkIrV4wUGlccehwfPvzEKduxXZc33vJ7slr3lNvyksaYt1VRJ47ARbCtWIsRYMGYuPr99ki0/UcYTuM9XhbgqptRM672rOriKE4yEooLXyguwiUAMw/x3WD6PBRL4NIjD/PXyBj0WXl6rLBUBB6OG0D6fIOZUR7FW69JIqGab7yfZ8MrSjVJ7vrd/Z4tQN/4mddN+LmWYnFFSVlgEVeWKyaGBFuBxkrAexC6Byo1dhfyfODuv3H/ri1oQiAo58XPT9dw9RkXsKJu//o8EyVvh8nZNYigq5jElR1oPqebpI288kHZDDSlZmIIob6q0YHA3354i+dtzj1oYtF5d9+0xnPblSIAI2/jZC2clPfCouMSkuBW3YJYG5RqyiL2QdCRyfHFv96J3WwjNUgaEU6bvYDjZszl3PlLaYh7kwZ0+9YXeKpjN9V1avRExsJQHNn7QmEGDWaGtF703ZZAUh2WillDiRyjugcHFLZls+Zu7w5YPvCDy8d9jmHqxOImhXzwawoJSD0csRhmTlJUmDo/yKDjy0oCPk65q2arKYrzjx2rldidCEnT//2WCHTHOjrR0O1pvPephGNU8ZCdG1vHf9IEueA/z53wc1VHnACU6uMQkoliDwHPFXLAphXgIWRU17Bdlzf/6wYe2r0VAFfKPaXjt2V6ueSfv2Nnps8Te7MSdZ604z0iEDFh1cuPKJIYYToJGcA4SHUPDgj++VPvC1WccdnJE3rev/70mOe2J4MEtGLwhzyuQahuPM0FLWCdV+GAlinP81m7xM2b19GSTHvm7AK4YcNT5XE2TEU5gIJtKOxPOcprZ6kmkD7M0l0iIVvOlVFXiOhA5Ik7n8H1qBz0GZedxEmvO3ZCz501v9ETmxUjCCw9fjzMjFvWa1Q8CA5qJEd8DnD9+UNqHE9PdW9VYnf/lLWJm+L+a4t120nlhUmKIZrnB5Ee33ehnE6nQk+7N6mF9bNqqWkMJkXLC0pVEdyooXxgHooEhBXsitmNQL4BZIDV1Ne3dXL7thd4prN1j5NrKI6UZK0S1z7nzWSSNNTrxY2GLlwM4f9iuN+JKb3MS8A6W+eBgsEmS8MKyS0njLmqu3BAsH39Lk/bO+GCo1mwcmLfXdvOHk9tTxbJEJ2VACkl1YoIj4Yd8HAsEOiZ4Uu3Kx+729PF4a5sPxKwSnqY/Iv0lGJK+1Ot51ka8+5QdX8s0ENUMWso1pOqe3BAsevF3Z60M3NxM5/59Ycm/PwjTlzsid2KkYRmjBcuJHbbe4NNFPdLL+Jr+sqfn3wW2w3eoe3IsI115Q95cXU7pub/57G12KDYxyvpcoVyp9u+jJd6XSkvO4fXlrXbPWnn1ItPqOj5sYSC1I4hCAQyoofq9Ns1yg6ooPokgWINyIATdbvzef724nNo+3mfjpTc+II3lUMTRgQZspN3kDTF+wP5qrcV65Ve5uWUWUG/FKyzde4vmOSmD72nmQQnXXhsRZsQ2wnHwlBGdaSCqsAyZBFeynDZs/GRwMbeLn67bg3/t+ZBfvDkQ6xpn5pTtiWZLrctRUjmGclBtTuZl+pR2ot50Y49VdP8pFq4JEOSujuNOqSUXP8/f/WkrZ62yjIMorEAT42HIAA9Gx49Ys2G1E6bSJda2RoAzWe9FhdYs33nmH8v2DZ/fXEt33r8Pq5+6hFe7O3yxO7KmjmetOMNkrpojpW1u2iIZQOx2O0k2ZAvS96ocjodbIasGrAPvKw0vAq5IhtWv+hJW8ecu6qi5598zkpu+/PjntiuFAnoRduHjNfK+yEAOyoo1GnIgOPxBWBmoRSwpmrGKvHAli3jitP3Wx7pfkhwLA0jEh4vi4bLrGRPILba7TQ7SjXMNMv2gt//lg2alPPeHeCJksEJUVvpXtx182iaelHzacbnkk++lle85VTmHVTZQq+ppZaOXd6kRk8WCTixgWjigC94oXqSG4IEnCiBa4pJJG7UHZHb/bmHbkMf+D6++fh9rGqcyY/OeC3NifTo7UhJzrYwNZ3IPhXbLly8kju3e7OW8oKYbtFdTOC4GnUx78uVT5RGsz+QTcE8w8GVocnsGo4+T3UPDhju/cNDtG3t8KQt16lsvWiY6raHZm8BJ2mOPcf4PPcMruQFe/c1Aah1TAyf+/GtOx7gd2+/ZMTjd2x7gY/eexO9pSKGpuFKyf+svodXz1/G/578KuLG5B2kJzYu5wfr/zWVbnvGvGQnLclgDu+H8mKxmTojS70ZjJNtX7pcjZkyTJqd3qdUv6wcXlbRO3G5w89YWdHz/+MDr1Dm8BKgRjx4CHZMkG8asmhWdNcYWXAi4MTZO1P5iERSarIpuQM7sTHsCWB2ypsU2du2vUBfX4za+lxoBidTswMJ/S0jeCY3m75InIWxdqIB74LjQrLUsGnW5Z4NSY8j6HehSuWpfPEBiJ+lsAPTTIQv/unjE9ZR2ZfXXHYcax/f4nGPKsdJRZWM8WbWxapWX6URBubdgLM65cBWzK4ffawdmlL/VMcu3vjP3/PP176dmLF3qVd0bH6xdjW/XPs4u3L9COC02Qu54tDjOLp5NgCPt5VP+UslIwwfNQXHZHeumnaRpi6mUu8lmOP3uiFzS+iIHKe6BwcM133lD560o+kaK45bWtFrDj1moSe2J4NWcoht76PYmEDGzL3RXkIgChbS0MHw5wYZPMgQbvnHNQWltIYTgpKpuiYGqpP5x5M7RkYHr27dwbvu+POetPmhaY//3LIe23W55syJF3nbl79vC4c2KUDEUBVFL1mdnc9J6fUk9GCLRQhC6AxKvdXzJl9WKY33//lRT9ppmFWPrle2c23boa58txRgK9qADFKsHriUhNpJYbBcfLQbNAvf16fSkOWRYlBVcj9ctuxwT2z+4MmHsC0TxwnPirjoBu1wFWwt1bMu3xxoCHBcSI6PWsOcXQDVmiStejR1vdWWmsZ7jnjFIZN2dgGceNbBHvZmcljV0XL6vAJ0G4QdjlQXgEgf4ACS/aa0e4UYmGS03PiDjSMlm/q6+cem5/c8VnRs3vKvP/CNx+5lV64sxiuBe3ds4g03/5ZfPLuaXdl+rnv+iXIbSkXihyKQCFJmUanOSK+dCObzCMVnPjrCXKS6CwcEUkrPJFpcx+V1H3xVRa+5/S9qDvAH0WyX+K4MsS09GN15zJ4C0R19RDrzYPi72CrUG+RmmmRnm+SbDRwF1eZHQwvACz6aDvFVax7YE+22L66U3Lp1A891tU3a5q27wqELqAmX+qiqQAKBROOR/gUELaMmKYvWhydnCIhMvGjgRFG9RfOU3339Rk/aed0HK/+gO9v9r+QwFlITSmPfJRDpc9FK4VilCcDIQ6yj/H9fbdnjf+4COKR+Bm9adlhFbbtSsq2/l239PTgDI6DlOjzT2QoIXFf9BLwXgR14fwS7rDru7lvBE5m5bCrU+25xmWljMPJ2C8FaCFx1Tvcw8MMf/pAFCxYQi8U48sgjue+++1R3aQQXfez8Kb0+ElWjqzKIBNzEkBN3BfbD4YDZi5kBsx9+cfHrOH5+MFokWmFiA46G4K8vrt3z+8+efYxHd2/H3Wfr4shy7NiX/n0Hx99wNSV37ym3VQrHMrHKzLO8plVpld42Kx3IWN8RQgHhMgLMyuQ+ppkczz283jPH58UfO4/jXnPkhJ+fzxa59Y/eBBBMBUlZLxJNQwqBNDRKtf7KNghAs0MnkosEctFgXBLP727f8+++UpH7d24e1RE2iC7EsIOVfZFScse2F3jrv/7Asb//Iaf98Sd8c/W97M6W9809Vs67zk8BU6hP6SsR5cH+xRScYGOudrsa9xZMMmHxehWu87zJ0EWxTYVdm6ZePWf+yjmc//5XVvy6uYuapmx70jhSiZ7KIGUHk8SNSEoRdf0YDb+z3QQCLSdwE3LMCK956Rp+d+4biU0wx92Vkl8+9zg/eeZRdmbLej1N8STvOPgoZiWqBtZAkmI+gmkWQvNxyzHPgPzFkgZtdjUFabIg1umbHRNJsybH/LyVfw/GQYo7oI7rr7+eD3/4w/zwhz/kxBNP5JprruHcc89l7dq1zJ0bnuqVq06vLFV+Xzpae7zpyBSI7cpgxw1KTanAD1qKdRqYqm+0vQggMiC5MbO2mp//x4Uc/LXvjqvnOGUm2LyLpKdYPvWRA/PKvs6u/WEYNpGANlnjMTdVduirGWclaa3Akpg3FfPGY4utM0u3VS7rxkCCsxW05ao78rLn3j8+5Ek7p77heN79zbdU9Jrnn9yKU6Hml9dIAYWWNHJo9XkRTEncSK9LPqYr3VcNZXDEtpLB2LvwZ7/lh5ecz6mLF5ApFcedMYQQ9JdG1yh2peTTD9zCDRueRhdij+PsR08/wi+fe5xfn3NJRXOSn1hSD8VXnpVxVmfnc2RyE7FAqvWW33AJyaNFk1NiFrrqy754v+dNhuPozgMKueKU99qaJvi/B79GLFG5MEdftxqhORhYdLdn1ZV3GOiDCMe6eA+Csp6X35jdBpGdBlpm9NvpsMaZJM2JdURKyWceuJUvPXLHHmcXQFs+y/88dg9f+vcdA48ILCs8JeN14WAovQAkUeFv3ntcuKH5vEclUlkE4cuJb3/727zzne/k8ssvZ8WKFVx11VXMmTOHq6++WnXX9mBE9CkLAa9/eodHvZkcg5e/nreJdAZ7KuvoYKXCkVqyL/Oaa5nTWI0mBHNrvdFqHAupS+zaiS+CF1bXAeWT+tZcpiJbrhuOKo0RzSIdKSr86gX9boxdVl0g1vqlxtNWOW04bJFesnCr6i4cEHTu8iZi+w2feG3Fr8lmCp7YnhIS9NzAmi5gqRSjKIm12eWbLwQl0QXlqvcyICUB23X5wB/+TrZUoj6eIG7sf93iuC7zqmpH/dvv1z/JDRueLj9vyOfoDBRNeeftf1L98e7BlRqdhWQo+pN1YzyVC/awViIoItjthMA15HpTrGMoIXhX3pDpqWwhNxqLj1hIPDW5cNm//cab05jJMFjGN9KuzukGYORC5vECgpKWElJg9ujo/SNvqZNb5u4RexyPB3Zt4foNT436Nwl0FHIMenYj0bCULJPMTPYq3ocKZkV6fLVgh3y4FAQrdBkWSqUSq1ev5uyzzx72+Nlnn82DDz6oqFcjqWmumXIbmX51FeqGIgA9UwI7uDHfTob3/nv3q45DDAyAbzv2CN/suJqL1eRUVBnyuBnlNMtohbqkAFIK2ndX09FaRX9fTJluZHAFUfbP1mIwDi+AnY7OfUWTrY5Gn6t8z70XKxx6Oy93Fh8+f8ptNM9rZOmRlWuuHbRKfSVOQblaoyqPr1mA1A6HSE84xh7dHtAlDoii7XDRT39LyXJ4w5JD91QAHg1D03j9opH6olJKfvLMo2OmobtS0lkIRzpjGUlvKRqKMzWJoNtJ0WcHE9U41HJ7GORyfIjmDO8KskK8WAysPGnyYdoqRethIK0wZyGK6pwgmgNGRv1pyDACvm/1Pm2E8t/HH7iFM2/8Gdevf2pcx9dvnl8zAfHjcupgNGop/qglIKkyC8xM9CrtR42epdHsG/+pUyAnIR+mjce+iJTqHiiho6MDx3Fobm4e9nhzczO7d4+eglQsFunr6xv24zf1LaOfgFZCxy6V99lwBKDnA1yBh3S18vZzjubcY/auHV576EHUxP1ZpDppt/w5VDCv/XXjczyyexumphPRKnN6SVmuxuK6GvlslK72Kiwr2IIFEc2mKdYXgnFXkHWD3XzkpOB5y+DBYoTWsBSpkeFwALzcWbRqwZTbOP2NJ07qdXWNVbTMCc65OxZCgqZwTyNkWZ843uooX/hJwAjYN7Sps5v33/A3PnDY8bQk0yOcXoO/ffHYM6mNjQwW6SsV2dTXvd/kK0Noe4qxhIHZyR7VX/UQJJ128Ot6V4bg+4hMbuzaHyFdQlZObdPU0wiOP/+oydtvSE/Z/lSRgJEtKe1DrMtBz4VjtKhKRQN3eAkpRhUU3tTXxaceuIWv/PvO/b7+363bJqz/0t2Zxg548zGUqG4zP93FitrdymompPU8B8e3c0zqxQD6ICiFIL9/bF5WkowVI/b5YqSUIx4b5Morr6S6unrPz5w5/ouNLzt68ZTbUK2rMhRJsIp9fusxTpa3nj183ZCImPzk0smXaN8f+9OKHIuHW7dxyT9/x6rffm+YGP3EEMP+LSX0dgVTqdAQNoZwWFzVRnMiE4pxV1dYx2qNpYcjvVEEU5jhQOe+Gx+ZchsnXXjcpF/7uasum7J9T1B8zQtAL0pMxWreAvBZtWMEEnhky3a2dfby59e8mQsXrxx2aLK0poEfnX4B/7F8aoUs4lrlMkL+IFjX26z6kttDObQh+ImvKgwR1ekPet7ky8bhNVVtFIDZS2dO+rUnvkJ9uXgA4ajPNY93OMR2W8pPRAxND3SylEJiJxykMdLo4CM/X7uanz7zKPYodWc39nbSWZhoylJ5EOzriZPLRMlmopSKwZWQ13A5rH4HLYk+Zc6uWZEujk+9wKxIT2CbITtEJ1H7IkuPqe6CEhoaGtB1fUQ0V1tb24ior0E+85nP0Nvbu+dn27ZtvvfzxNdO/kBlkKaWmql3xCME4EaDc7KaWVf55mc0bn103YjH/IjwklTu7BpKnzW6qHBlCFxXp1T0/3u3pY4tNdb2zOS5nmYFVYCHI5A0myojLAXbJ1AV2neMZap7cECw+Zmtk36tELDy5BUsO6rydMZBFh00i6WHzJr0671AAjKi7lB3KGa/YhF/YDQ/hF/RxIMYmsYtazfQGE/yjZPOZfWl/8k/X/s27rvo3dxywdt55fylY762KhJlWU3DfqctW7ocXDXf835PlqwdpSMfUHWAcZAIqvUgw/rK7rXZumKHl9aMprd436znLY7CS6FcvG5qNMycfAhvssrfUrkTxTXU+zAFYBbByKrdnXT15Tht/rxAXBROzKE008apdZHjFGP86qN3cfwNV/P3F58b9vjv1z1VYV8FjmOQ6Y+R7Y/R05XCtrVAnF4uGm35tDKfZlLkOTi+I2gtU/pCWy4esJ5R3QMlRCIRjjzySG677bZhj992222ccMIJo74mGo1SVVU17MdPqupSrDrz0Cm3s+qEJR70ZupIwDE1CHAzIiREuwcWYuHJOeDONS+MeKw6HvN83hEIhNoA7gEklhWEo1Mw6OHrLcVZ36uwEvZAPOP8mPdCupXQ6qpf3yH9PxwIM0HtZxKT1BMGWHjYfL74x49NuQ/zF49+YBQEEnASJjIke5og9bPG6oNw2HPoI4CLDj+Y9554jK92Ldfh12uf4JKbf8sN65/C0DRW1DUxJ10zZgT9nj4LwbsPOWbMcypdCGYkUhzXuJh8zqCnK04+F9kzvaua5muj6rVSBZKEVqDO8FObW474t0TQLxUXq3E7fWnW95FksFz85z73OZ544glOPvlkzj33XLZunfzpxWhYpamNRg0z66f0+ufXePt+JouTDkdoqGTgRF4x7znhGF550NgnEF7gRFzs+oH3uneNvl/a81k+cM/fuWnT83see6G3c5IBDHuNFvIBqfQDW/trsZUMjJK4bilJcdnm6CGO8Qpvz/zmox/9KD/96U/5+c9/znPPPcdHPvIRtm7dynvf+17VXUMI+OKNnxh3cTgRbCs8eX3ClRBwimUk4xJrt4enNyp2frV29494rDoeY9WcyUeMj4Xer4Uiyk0E3glBbylBxgpuftuXI5KbSeleRMlNnv4wOLxQvxlURVD7GYB8/+SutZNefyw/+Pd/U9M4NZmXdU9v47a/PD6lNiaLBKQAqz6hxP5oyBDceoIBXTMhuOTIQ/niuWdwy3Prfbdb0Gwead3OJx+4hVP/8GM29XaN+dwdmT7u3bGJx1q3Y7kOr190MO86+GiAERpgaTNKczzFF+67n0xfgkSqSCxe2rO2V5XG3llUXalRYgiHVcmtPn4Gcp//l/eRESQNmlQsIWAjndH1d6eC78d0Q8vFA1x11VXceuutXH311Vx55ZWe2eltn5ro8Gve84opvT6ZDrqSwhgMltBVLHhRPo1QvzJf1FLPVYtfzZ3rN1K0K9UvGR+JxKkaaHcSH/mX/30nNdEY1z2/hod2TX3RpGsuQgRzCbhoFGyTdCTosANBh50m55gk9GCP3XJS8Kylc7DpqL7FRqJNXcfwpcoll1xCZ2cnX/7yl9m1axcrV67k5ptvZt489dWm3vT/LuLQUw7ypK2wRBILAEdi9hax6oLtk5mTFNOyvHoJOsRzFOwxnH7vOuEo3nf93zy1pRU1KLlgolCQQiipECyQdBaSpEwVYW6CmGYrXloJSkBRQlTlJW+EI8pUBUHtZwDat7dP6nVv+tyF6MbUI29/+j83K9v0C8DRRCiiu6DsEghDlWBd1/jsOSdzzkFLaa5K8eiW7azZ4b1jYF+cxN45rq2Q5bJbf8+9F70HU9NZ193O1v4eio7DH9Y/xb07N+9xodTHEnzwsBP47NGn0RBPcM3T/6aruNdh3lMq0NNZ7r+Ugp7ONNGYRbo6h6ZwftvU30BXIc7SmjaCuwTLn1pE2MyKdDMv2klU83OeFQM293FCKnd2lZG5XyPSH/e0TV8dXoPl4j/96U8Pe3yscvHFYpFice+pRiWVs9L1kxeNr2up5VXvOmvSrwdI16g/iRBApDNHsVl9tTYJuKb6u2ZHRy/VyTiGplHEB4eXkMgokw6uac1leNOtN6ALgVPR6mLvQGUYNrG4hWHaRKLl9xjUgFVwTJKypETHq9NOkdCDr466w9FYajqoizUYA+n99f1S4oorruCKK67w3Y5h6tjWxD/rCz/8as9sV9cmOezYhTz96CZcxbm1AjB6C+i5ElZ1LLDoYicCMqZ+8zFIzRipR2csXcSC+ho2dfZ4ZksgiLabSCFxUu7eqo2BITFMB8NUM9Y4CsMsthXrWB7fpcz+IJZqh1fsVQqNq6PS/cxU6WqtXC9u6VGLWOxBdcfd27t4ZvXmKbczFXQnHIf3EkBAKa1+znndCSt5y3FH7Pn9xiefRRNiwoWuKkEikRGJnXZhH//prmyGnzzzKDdvXsczna1jttFZyPHFR27n7u0vcteOF8eZqsrfc7Fg4jgpautVFiqR9FoJnuyYzaENOzEDEHEXwFHJF6k1cgG+75GG1IepDJD/K3js8PL1Dq60XPxUKmdFY5PbftY0VfHte75E1RQcZgC5TDjCvPW8jdGVV57mIQArpX6CeH5b+ZTspEXz0X3wygghPMkkq8zZBYPe+aqaLHWNGeLJImbECfxrb82nlYnWq6heApJZmkNEvS93FNSnEB8INM1vnPBzdUMjWe2tAOp/fGBq0cheUtY2cdHzwUVaFqvDIWIM5fH/4Pkzxvz75885wx+7UqD3a5jtus+3/fC0B113qa7NKtmISCAecETvUHaUarFlMBqZYyGQap1dAG6QIsrhodL9DJQP8fv6+ob9+MnnfvdhT9rpaPW3nxNBAHpWTfGroRWIpQa5Zh2p+AA/FYvwznOH63Xt7O33xdkF5cMVraSBNnr7//v4faztaptQW3fteBGY6FQlsC0jkMIo++tDuSq7wca+hkAszjB7qDODdHaNTo8rsMLg9ZIjpSKmSiAeiYmWi1dROevcy89i1uKpVwN4/P6RwrUqkIBmu+zJawvA3miPWQmBE1O9MoPWnvJN8/bjjvAnIsJFgZ+hPB2nqvJEY+UNwGBmT9CDZb8Voy2fUrAmCbp6SZnFhs3BkXA6loQ59ZPdacanksOVg09c7ol211BWHjmfL3z/zZ63OxW0UjARP44JTkL9QcogUkpef9IhY/79pEXzuOjwlb7YFgiEJdD7/Pw8hl5jkpq6fnRdxWq4fLzREM8osF2mzshiDkgGqEAgmaG5KA+cz/5IcQfUMtH9DEztEF+fxBc9c9HYzvdKqK1XnyUCYPSqCSRwDSilBflGnexsAzeqfs655iMX0Vw7PDCjMZX07dhXInE1F5Ef3YKU0jdnG0gK+XGqfwWCoLuYoOj4f8g2J9qlOk4FABfBZltX3xdttvdNet7iECotFx905SyhCarqvBnYM/3hiPACcJJmoKHAw+o8CChVaxQadOWhyAAzasoTxKrZM/nKa87ypXKWng1WSDgStRCaJJ4oheAjFmzsa2BrphbLCWpRIKnSc1QbhYDslakVLovNcOS3j4Y0j1LdhQMCWcFK4D+//05f+nD0qctIpMJRoATKUV7Yju+HLFZaUx69DHvdQJefeyxLZu3/BPjTrziFuOnPaXVw849ACImmJLiunL6/sKojkNSSsZgT7VRYoVdiAEsUpZIOw3lRdQ+UUOl+BqZ2iG+alWWtxD3UEZ41v4ElK2cpr4PjxtUIR2g26BYYWZdYh4PZ76K6PHfXKHvMCw49yJeh38XFqnOwWhzcmtEt+PlpaJokmQ52fT82gqzl/1rLllpo9hYbbY2i6mWWOfZB4mTxdYc6mXLxk6U0mSqNEk65+HhP7BsBlmcfj0hnPjAHzPDzXyhVCUrV2l5nl+LNyeGLZ+3598WrDuGWK95GU8rbFCO9VwObwD5zXXdJpvKhGRxBsDNXw+qOuTzZOZO87W8osobk0ETwVVHzEtpsQZsjyIcwyEvT1OsIHgjEUxPbWDTOrWfBwXN96cNvfnAH2f6wLAgHIosL/guZOxH1IvUATbUpvvy2c7ji/PHXMelYlB++4XxMn1R4hRT4IE85AsNUE91kag5Lq1tpUhLdJff8JDQ1WpUgadQkx0ctQhHcKMIR/RM0k9nPTOUQP1Vb2Tp16dGLKnr+eFz+iVehqdbPMtTYF4BekBi58k+0yyG5wwaFhbhsZ+Qgf8LCuaxsafLcloZGpMvA3G2g5YL+DiQ19RlFkcSjI4T/fXmx0KR6uzyAJC4kymVSDe8LTvn+loIqF5/trTy96fwrzqFpjjf5uY0zaj1pZ6oIAFeiZ4OrZCSG/D/aK4n0uXurRSpCE3DMsjksmFE37PH59bUcNmuGpxO5mxgQdRwseuEzjqPhumq1REZDIsjZUWzXX+evBCLCDfzArYDgccvk8ZLJPUWT1UWDQmi+Ax0pVVQvO/CY6JzxrTu/5Iv9Qr7EH392ry9tTxYByIjhvzNKovwQBeB9rzme1xw78cqbJyycx03veyuvWL7Ynw6p9wH6QkIvMi/VRV1UlW6UYE9xGKEiukpSLSRHROxwOLsAoheo7oEygtrPAMxZXllKz5wls8Z/UgUcesxCvnzN22icoab6s0RdpffBklBDf1wd0BU54AQsnzPSsaUJwXVvudiz/YzcZwMjHDC7gnV6xeIldF1d6vi+CCRpszj+E6dIj5Og6PPeaWIIZiqMpN7TC23iWrkTxfcp9JJLLuGqq67iy1/+Mocffjj33nuvL+Xik+nKSqMnquK87ztv88z+wuXe5M57hR7Aafu+SMqTgtHvYvQ45VNnRaPWrIYavvr2c0f92/mHrPAs91wikYK9d9Kg08vHebpUNCkVjdBMCMORxAx/hYUlGk9k5yIRw5xe/u+DxbB/d7iChwsmJfX7b8CB4l2qO3FAcMabTh73OedefgYtC0dPc5kqz67eTKkY/Pg+HqKCypWTxciH4mbjv667jbd/8/c8uXHnhF8zr66Gs5Z6G4UhkbiRkVW0/MAq6ThOsJPOitpWGuNqRPL3ZUuxQYlWZa/UaHdD8AEMElmuugfKCGo/AzB7SWV7iiPPOczzPhx50lKuveOTLD54pudtTwQtwIP7oYx2tzlJNen0uiY47bBFI/S7BklEIixqqBv1b5UgkYh93rlA4OIiiiKwDJZYPEwHt5LmeB9GIA4gQb8TC8F5nlSfzogJMe+LMwVyZnTFFVewefNmisUiq1ev5pRTTvHcRqTCKo2zlragG96tEg8/zqeT20kgAD1TCiznXAJWUpBtMcjONsnNNrGrdVAQjpyImnzi4tP47WffRMMY1dHOWLaIw2Z556Ac8S4DeNu2pWNb4YvyCirUoNtJ8WD/Evqc+J7PIOhNkURQALbYYTiVAZxdqntwQHDsq4/goOOXoukjp09N16huSPPWL73RN/uFACsiVoTj/6LQzLihifJ6etNu3vXtP/DY+onr8jzf1u7ZCCmROCkXqz6oyCNBd0cKNzDniyRnm2H4qgHB1mI9jlQRZiXZNiAiHIrPQninFfVSJIj9DMC6RzdO+LlCExz/miN96YemabRu7/al7f0hAN1ywVIfbQLlao1BownBrIZqPnfpWft93rkHLZ3yvLKvswtA6hKr2cGpDe470DQVOrly1P/XRPLMS3cF1ocNee/TUytH0KFknhtC8h0IzXsN97AESQfOklULPW1v+WFzmLs4DBdrGQFEWjOBrJCKNRqFBgM5tKhGwAOWAExD56MXnsIlpx1Ocj8OUEPT+Nllr+eoud6cWrnmKJ+x7+9fo7c7iW1pjJLarxR/NwV7P+uCa5LUi4pP/wXb7JAMo1q96h4cEOi6ztdv/izHvvoIYKA66oC4z7yDZvOd+75CfYt/Ke4LlvoTOTZVtKL/A5HmQqLVKVfGHZzbFHkBXClxpMtXf3P7hAsZRHTd04NyrSgCie4axHU1cpmgiiUIduerQhHdBeCg80D/Yiw36PG+HE28ztZpdQQl1Y4vN6QO95cZuQoKYZ36huM9PcAfyl+ue4B+RdUSAaKtGbS8pd7bG4DPpzoZo7k2RcTQmdVQxftfewLXffoy6qr2r896yRGHkIh4W9VQIrHqbRiU5J3EODwrWbnTwraDP8hfmO5gWfVu6qJZ0maB+miWFTW7WV7TGqhuY0NEfTTzDM1hmaF4Uyn90YH2V106YHRTx5lgWsWMBd7mhwohyExCR8xP9IJNZHeGUnMKv+5aJyKwqgcm2qF3qoK71nVdvvrbO/jV7av5v/dfwNymsTed6ViU37z1Et7zuz9z9wubJ2VPIkGAm1AzETuOTndnFSCJxiyS6QKGofo0zMXU/BssDRxsDASSWiODKVS/XygRaFHUMdAgeobKDhxQJKuTfPkvn2L7hl08fttT2JbNsqMXc9DxS8csUe8VM+d5ozvpJXbCwK4PpmiCXpJEel1KdbryG09K2NrWw1ObdnHYwvEPUE5cNI9rHnjUE9sCUS6WEiiCfC5CMl0I5GPvLiZoy6dojJUF61VvBgoywiOZRZyY3hBoXySCzbZGPdCkO2o/B+cpYL7CDhwYHH76wWx+emLFec577zm+9OHhu9dyzdf/4UvbE0ECmu0S3Z1BCnATJnY6ihv31rmzr81Rb68A/Nzvf+2JXHTyoRW/riGV5AcXn8fbfnOjZ32REYmcYpHMndm+il9TyEeJxYOb2HTh0hjPoAmoi6ly7Jb3kJuKzRRdk4MTOxQUSJGsMBzmDOinKV1aWY/50mxIQhO8YdWZEy9jWV3vbbhcd0c/Xe0qqgiNTbnSiE20zb9+lVLqysQbmrZHrFECzkAK546OXt717T/Qnx9faPBHb7yAdLTyUX1Q3NGucxTcRfuq4wuKBZPujjS20mgjSVMsg+5jRZMViZ0cm3yBhdE2ZpiVT6Z+4N/SqxJcyq63aYJk9pIWzr/iHF7/oVdz8AnLfHd2AezY3OG7jUoo1sYoNacDi+p1TMrOLlDvARlgR0fvhJ53xGyPtXAkgUQeDDMpNdzAbAo29jWwqb8eR4bhuxZk3RhFn06gx0PXhKJKkUOwNijuwIHB27966YSel6iKc8jJKzy3Xypa/PdHf+95u5UwVDRek2BkLWK7M2XJFh9tDkUCVlxgJTVf5pvBPcwrjlzK605cOel2CvbUDpr3Fax3Y3LKul2TeXmpaFDIB5fKHtUt9WPqkOIoO61a1udbArYvSSFpNly0gctc6dLKfnbCUfOV8LJyeL3yHROMcBBwzEA6ilc8fNdznrbnFW5Up1RTmaB/Re1HUHZnOK47qvi840o6erP846G147YhhOCiVZObZOw6uzwpKGHfz1wgJfT1BBNlMRKJKWzmpPzVeohrFjVmnsXxNmZHg9eVGImkSXeD0vPcP4U7VfdgGp+xLYfPXf7z0FTlc3WBMzi/BDQPWGl1hyxjUZ2YmK6RqevUJ70bowUCLRt82Xg9UNlCQWu+io5CMvDKvCMpV6RR43wTtLkavUoF7EVoxp6XO4lUnONfe9S4z/vc7z/iy0HL/f96hmII9SIlYHbnfZ8DBmtPFep1Ck2GL9e9JgTL5jTy5beew5XveBW6NvktuTPFUwiBGO70UnafC/p6EmQzwQi4u6r1qkYg2FqqoxRIxcbBD1iQQXB/wQjH0kpmoPSw582G7ZueEsuOmlgFpMNPX0nDzKlXtRiKG4Bgb6U4MYNiSxoZ9e/GEUO1VAJmPKu3rl43oXbyxcondYFAy+shW/wJbMvACTzKS9IYy3BI3S4iur/3QWupKgSbnuFknRCcugPIftU9mMZnHrpzLa07ugOrmDQerqkHPv47UdXHj8OpTsY4etmcCT//+PkTf+5E0HNBjveSWEJNJGlfKR6CcbbcgR2lWiXLHoFkl6M2iluY3lcDnGZ0vvjHj4+ZuWJEDL5+82c55pWrfLH94vO70NTfcCMQlNMcRck/6YxSrHyYb6U17OTAZ+DDnLOwpY7ffOZNvOa4g6b8WR/U0uTtdqSIUqdXPjvFfMoJUnAM5cVR0nqelYltnF61ljOqnmVVcgv9ThBamcMrzwvCsrTSwB4/YGUSrb58mDG/icNPX7lHQHg0DFPnv278hOe251VYQthvJFAa1FTx8Qo2cuFz9EH5/Wfy4y/Ms6USf3l6ctF5el7D6Nb2HgUN/iimWAy6aqBAAtEAhA63lepD5GMsf9nLIiGpGqB7Xxp9mnCx+r716KNUh1SBBNykqaI8aqh433knEDEnnuL2vpOP9dS+CDjiJ5kcXyrAD7qKSSwnDJWJBVuK3mrAVoKl8v2LBERPU9iBAwtd1/nGbV/g6tXf4NQ3nMDCQ+dx0AnL+MIfPsbN+d9ytE/OLoBINBxiDWMhfDr5lIBVo5ObYVCs9XeutT0MlJhZXcXpSxeiT2E+HozyspMOTp3avZ2UIiCJFsH2TI0yR88Ms4fjUy/QYvYQ0RxMzaXeyFBvBq8JbiOwlc+vAC7SB7GYcKycPeR933kbkVhk1JLxAJ/85QdIjlPxYjKYZtBOhpFIwI4b5Gemyc+vQUZ03zcjZlYiHAI/5dc1sd8TEV0TLJ45ftW6jR1dFOzJCyTqWZ3ILgO9V0PLaogiyjdkUkG6hSHcYMKP0dhSrFe86Sl7NjXg8IhNrR6GGUJA9GTVnZjGZ2zbGaG1oRI7FVTFvr0Yee/L1BkTcCIOPkcfmHeipsHHLjqVN5xaWcTL4sZ63nHckZV3chQkEhng+JNI5dF8juIdC4mgq5QIxQm0i0afM7E0Vi+RQMJHjcxxEVUI8bKqdfWSYPGqBXz+9x/hmjX/y3fv/yonX3ic73qRx595EG7YwukHkID0ac8lgEjvwBjns5jR7IZqT9v78qvPYlZN1R5dsMkgEOhZDaNTV1AUZTiFfDBRXp3FFJv66pADS4ug9hcxUeKQxDZgeF05FYGVJpKlhhMep5BIe95kaN6bVyw8dB7ffeCrrDxp+bDH566YxZf+8klOf+OJvti97c+rfWm3Uoy8TaTL//z2QYSEWFvwo+LK+TP2Oxk7ruSiU8avdmKIqd8CwhUYGR2zR8fsMBCKJwkVlRoLjhnYRmR9YQa7rSolTq8okrm6y0GGwxkxixmhcHbBQKC/6k5M4zNLD5kdmk2IACXp7GbGHdjxeGM7Yui84dTD9nuAEjUN/vHVd/Llt57DFeefwJfecja3/c+7edOZk9MC/eRZJ/PaQ5aP/8QJ4CT8Hu/3fs7SVRVhVT5k2NxfrzilXaLjIJC4isbbWSorMYtwR/1M4x1LDp7FYccuwoMlsqdIwI0bSMO/jhl5SXKbTaTXQTj+DTj3P7uZn9/yb8/aa0wl+dPll/Gh004gZkzeISgQaEVBpM0Y7vQKdOwVWKXgnOu789Ws7pjL1kxtYCmOs6NdIUkjlMzWHeYZbghkAwYo3uZ5ky/Lo5pFh83nW3d9iV2bWmnb0kFVfYr5K+f6eiKyfVO7b21PlMF3J4o22BKCcY7jRoOfEf/zgpP4wz1Pctvq9fvUKyyPyRedfChHLpk9bjtLmuqpTcTpznlXjtbo1rGaVKW5uUSiwXvcekoxLFfDEK7vg7dE46ncXGLJjdQY+UAni4MiNo2aDM+ksAcXZB5EUnVHpvGRM88/gl9861aKhZLy1C7XEKAgvVI4EOlwKDXqntTOrk7GePs5R/Pg2s1sbesZ5lDURDnF4wv/8QqaalK85riDptp9oFwsZWb11E73JRJpgJvw+0LY+/nmczEK+SjxRJFkuhDg2Fs25EroKCRpjGUVbBIkpnA4rer5gR4FeQNKQLDUcIipnHsixys0Pk3QfO67b+KL77uW557YqrorwIC/RRN75Vp8QgwYi/S4RHpcCg06dtKfue77f32Ahuok5x9/sCftVcVivPekY4gZOlfedu+k2xEIpCsx+vRyNfrygwEiEQFHs1quzs5cDV3FBIfX7/DdXq2eC4Gzq4xDSLSIB7Ge8bzJkPnuvaVlQTOHnXYwCw6Z53v4r+WjgGKl2DUxMIP7amXA2ZwHz2vmiMWz+No7zuXDF55CU01qz99m1lfxmTeewWcuPWNC37mp656ll8DAyUhJUxoKXCoFnV4rAY2cFQls8K7W89SawTq7QIbU2QUQAeFfNdZpwkEyHeOzV12GbujKBYXtZCTwCC8XKNTpZWeXR+km7b1ZHlu/nWs//kYuPX0ViSHaNYcvmskPP3gh5x7jTTTWUBIRc0r7BzcmsRrtwFdxUgpy2Si93UkFTlfBrlz1QD+Ct21Jg91WNZqQAcw9e99gXMAhps1CU7Fmqjm6gPo0L0/S1XG+9Zv3cuUvLufY07wfAytBAq6pUWhJ+RrdNYhgr38n1uGgFf27935808OeR26fvWLJlNsQCLScKE+8CojG1FQJLTgRtmRqfbfjYaD6lImpTJUfDeG9XMbLMsJLBcsOnc3aJ7ao7gZSgF0VDTRGUgtY5e477zsfIQS6ELz5rCO57IxVtPVkEAiaalIVbwQvP+Eo7ly/kSe27/Ksj1pR4BpBfC7lk99h/5YaEJwDVhcu89NdVEcLgdlsMvtwZfC57uF0dgGxVyHClnswjS8cfcoyvvfH/+TGa+/j9r88rmzBFOktolkOpcZUIDeGBAoNOk7CW10VTQjueGID5xy1jI9ddCofeO2JdPbniEdMalL+OZHPXr6Yb915/4SfL5FIU+LEXGRCIpVmlwlKRZNS0SAaC/Z0J2dH2ZKpZX66O1C7UI7q6rXjzIz0BGBNcrhpkxSSlBaGtBcg93tIXKK6F9MEiBCCw49bxKHHLOCyk79Ob1dWTT8A3XKJ7s5QbE4ho8FsXwezRiK9LoUmf9ZYOzv72NzaxcKW8XWHJ8rM6irOXLqQO9a/OKV2BAMOr0CXl+XormhMTUVggM5C0vc5psNOU2eouZ/2ZabKVPnRiJ3teZPTOySPOOt13kUJTQVp6qAF+7VquYEdl887L03AqYcupKE6NexxXdNoqatiRl16UlEPmhDUJbwNkQ5uDypG/Lu3OxFQdRPQcDiodheNsUyAG2+JLlREVAo6HdhiaWywdLbbWkgqmgC6uqph0wTP/KUz+OjXL+Yb170HM6Lu3ErP2UR39gVyTOlGBE7S+52/KyXZwt6FdcQ0aKmr8tXZBTC/vpb5dTUTfr5AIE1wq1Q7uwaR5HPBFy0A6Cn6m9K0P4JzPGlssHUUKEaMjf0sUgZ3sDVNeHh29RZlzq6hCEdiZIJ1hAj8KZYylELJ+2im/37tOaSiU9O2kciAPAXDxWl03UVXVgtOUhfzv0rijlItDuorDy80XLWp8qMRv9TzJsM0lb6kWbisheZZ/odAjouCO0eA79VMoDwcvvXso3xpOzIFgcd9GTyJV0P5O8hng9iISFx0nu1qYVN/PXknCKHHchRbxokFKycwwKMlk+dsnRdtjWcsnTsLJlsDci7ul9Ia1T2YRgErj5zPd2+4gnS1mnTWwVP3SLv/GyErKXyZ3zQhPD1Zr4SefGXOAy0nlFfO2ovAUTL2SVJmMXCrGi4CSYPRH4C18nWelRr3F0w22hp5F6wAK4iN3TU1aUbTqKWzrVd1F/YgFYU7Cp9OOA1dY3ZDjeftVsVifPDUyevuSSRuLCiH1/Dv1LZ18jkz0KqJZSQCmBHv892SJQ1WZ+bvcXqpKggzTw+PJFMZDc0YX4O78lan8Yy3fugVqruAsFywgw1NlAFdRV96yzkcvmiWL22/8QhvtSn0nMpbS1Aq+h31IYlqFgmjSDpSpCpSIKZbAZx+lw3sKtWUa2Up0HEpJ7aU/+8Cay2dnaqdXtL/yXmacLJgWQuvUBxhbGQttIK/G2Gp+zO4uFLyiiOW+tL2eOStyj4zqUm0bFiWbRKhqVihC2YkghrvJC1mN8enNvCKmmd5Rc2z1BuZAOzuvdZLCF6wDe4pRnigaCj2dwoQqfGfNs3LDifgfcVYCMBJBhviOjjKRfq9H+90TfDKo5ZRlYx53jbAhYcfTFWs8gNwOfCunbQaZ4imSzStfM35u6+Qw340JMtrdhM3ghlpe5wk9/YtY0NhBt12IsAqxIPvWdAbjlt7L4Y/67GwrJxeFpzyqsOorlNbJU0AZl8hUFdxUOkVrzzaP9HM4xbMpTHlzXcnEGgFtbeW/1+/YHltG4fV7+Sg2lYaYtlA9a0cdJ7JzQHUnIo0aS7HRCzOiVmcHbOIC6n45F31sf80Kjn+TG+qB04WCRhd3lW6HQ0/tSKfenGnb23vj3l1tROKVJW6pFRnY7U4uDXhWZ3GE0GmFpW//7mpLlJmEHYly+O7ODS5nbS+NxJPlZaWQHJUxFYrvCtqfS8ANU04KeTV6SkNIgEnqiMjwee6Fet0z6OMBTCjrooPv/4Uz9rcl1Q0yk8uvQBtEvetG5XoOQ2zTYcAv35Nc6mt7ycSdXwfb2ujOeqiWeqiOeanujiycRs1AeoRQznSa1OxkUezi1iXbwnIavmDXWrYDBa8Dg3pT/rS7LTDy0N0XeOT37hEefUso7eIPpjjPngV+3o1B/N+Hdffhf4N77jUu3ei2PlhRvw8lZE0RDMkDLWpDd12kqIb/MJniWFzRNSmVitX6tIEVGtBVO3aD07wAs7ThIeDj5yPHkDlqrEQgF50wMcx2si4+/c2THKOE8ATG9U4vC476tBxpwrXkJSabWRcBlwWfjz8FxXWsSmnmEiqI3lW1OxmVjKY1Ko6I8u8aCegVjBeRxIXkhmaq168XqrRbJtGPUWfI3gnilUTC+wmGIyBseMCKyVwI95Kt5y0cgHXfepS6qr81STsyORwK5wfBQK9qKFndURJBJa1ohsOVTVZtIDWEpyYsQAAubNJREFU1AXbZFlNO8tq2mhJ9mFoQR8oDf9etpbqWZ9vxg0gxXG+4bLQdINQJJo4ibehRU/ypelph5fHHHHiEr5x3btZeoj3+acTRQCRjhzRXf3o2RKi6G9opl6U+BmHKYC5TTVETX/PNmdWp3nn8VNPDZJIZFSlx0uQSPqlcSKpjeRYVN3uU/sTZ3FsFxHN/xOgodRqLovMkWHW6qs3hmMxOo0ahBDUN6ZVdwMs/xaLug1m3xiO/KmsDIU6P9KFh6/kuPlz9nv6blc7g5nUIUNQLExNEHn/SBrjWY5v3sxxzZs5qLaVmqi/UYRDmRvpDDC9ZCRJITnMtDgrZnFqzOLQiKP+FF6divQ0imlsqVHdBQCMPv/1+wZvM9eEYp1GoVHfq1Ps0U3YUJXgf99znu/FUQCufeTxKU8fwvZ3AtI0l5q6fuob+zEjwazrTaFzZkv4nPibik08kZmLv643yUIjZNpd5pFoVZ/1rflph5cPHHzEfL57w/tpmVunrA8C0As2kfYc0daMv+5bCcLBV3f0G09f5VvbQ3nVwcum3IZA4KRUDiSubxFeOg7La9uUOnhiWomD49uZHekJvB/zdEfpJmhMhNJEl2lCwIoj5iuzLQFpauBzqkm02yXS7YCzz004lSNKCUcvmzP1zk2CiK7z40sv4H0nHUN0lMIpUpfIWNgiu/aS87U4igjUwbUvVUZe2TyXFi7HRy2a9b1RDqE4gTdWqO7BNIo48qSlRKJq1xkC0PO2rwfsEsjM1umfa5CbaWKl9eE3nwc3YnUiyg8+eCGmh8Wy9seTO3ZPPenEx64KIamtz+zZtwQ11lnS4cE22J6poeSoduZLNByqRJbDE1tYldqKT7KlAMSFJBKGOWUo1mpcx7+iMNMOLx9pnlWrPL0RwK7y14NtpTSkgW+j1CmHLuSikw/1pe19OWhGE6Y2udtiUOTRrnaQfh58j9OLSNS/iD4HnaKlroxuUitwQmoDMyPdSjYA1ZoMQTTXKLi9SBkebZ9pguc1bzxWmW1BuWCK8FnYWADRPpfETptIR/lnKmhCkIpHefWx6jbyUcPgg6edwJ/e+Sb0fQY1qYfX2VWu0qj7OBdIqk11Di8nqGo8o7Ay4qAxMnJYudNL+l+NdZpwkkhGueTdp6nuRnmu8dHh5eqA7k3u8KuOWU5dOoFp6MQiBnMba/jQ607iL19+B0tmNUy9sxNkqvtQgcBJ+De3xxJFNN1VMr51lSTbsjU83jGb7qKKatd737SLRp9M0WpV+T7tF6SKol8ToPB335qeDgvwkXMuPIo1D21UZl9Srmxlp/11eJXS/iwMo6bOxy4+jQtOWImhB7P4FEJQm4jTlpncwk4KiZNS6XgQ2LaO68Ik/Xbjtr++r5FD6lv9aHwcJIcmtqELV5nTKeuCjSAhpK+nL5VjgcyDUFs0Yxp1rDxqAa9/+8nc+Iv7lPXB6C9i1fmrRwKgu6Bny6s1q8pFRiof7AQQixj8339eQCquPq1hSVM9/3fxefznDX/bexovQzXIjIK/2qAF1yAubCUboVariqTWHrjttHCpVlL9cgKUHsJ1OtH0etU9mUYBb3zv6ZSKNjf85G5lm2UpQPq0ABzU65oqAlg8q4GvvO2VoSjycPKi+dz2/AuTeq1E4kYlMuLfFx6Lqy6IIJDAup5mDqvfHliFxn37MMguq5ZUocDCeIdv1iSCooSY+stzONYa4DJfmp6O8PKRk84+hBWHz1ViW1KO7CrMqvJVYKicyoIvR49Fy+HVx6wIzNk1yFFzZ02qoolAIKRAK6gdQVxHI5f1p8QxQMaOY7nBDx1Vep4qo6A0wuoxK8IDRZM7CybrLH1EZpU6YiBUnE5NEyYu/8S5nPzKYKJh90UAWj7YhaKrgTQrHxA0Ibj83GP565ffzmELZ/rQs8lx1rJF/OTSC/b8LizK8nyhGWeGIolE/HNG6UIjU1ysLKppW7Eeh+CjmZOapChhi63xgqWxw9bwsUBphUjI/011J6ZRhKZpvO0j53Dpe89QYl+C7xGvpbqBknVTuPFjEZMvvfWcUDi7AN5x3OS0iSUSaUrsesfXzz0ogfr9U3Z6tearVHcEgBeLTbg+H3h1uyJ8Ei3CvwPTaYeXjximzvu/8Foltt2IVq5m4lFo7lj4fa/4XZlxNC476rCKK5oMIpGIkuqRW5DPRXxdqG/N1PrX+BgMLQ2vGgfBJlvj0aIRDqdX7DyEmB7OD3SEEHzwyxeo7kZgWKnKr3lNCN505hG87/wTqK8KX0Tkxs69FVcFAqNPL282wjDODEOQSPknID0zXsu3jvgg7174cQxh+mZnLIrS5LHMfCypT3X/WxG9jsbdBZPnLJ2Nts7Tls5dBZNtdkjG99L9qnswjUIcx+Xum59UYlsAuGB2+5PqLACtOHCjT3LftLCljus+fSnL5zR517EpcsScmXzk9BMqeo1EggZWo+O7p0CGJpJZ0FX0P0J9Ijjo9Dj+HmKvt41y0EqY1hbGct+aDskM+vKls7VPiV2t5BJty/p+JTtx/+qZ1qXjJGPBi2EdNXcWbz/uiEm/Xstp+FxeY1ykq/m4QRK051M4AU9SrkJNldER9EjBdicE/dLVVYWdJlyk0nHqFFRslIAbD9Yx4UQrH4NWLZ7F+86rbPEfJM/tahv2u57XMLr1gXDqfX6UUDaersr5ohd5aM08rjz8Un5w9Du5YevN/OiFq7Clmiq0vU6Se/qWszbfEpjNPOV0k3LEQfn/DvCsZbArDE4vGZ6Dp2mC5xffvoWdWzqV2ReUU+f9Ck2J9LtT2tOceugiFraEL+U3V7Im9LYGtYilAaUmOxAvQZAHCuPhd1RVJfi958lLwe4w7F+GUrzbt6ZD9k5ffghFQj+DVRq1or/VAv28Hy874wglIcFCCD511il86NTjK38tAs0RCEsoPpH3V+xYolF0gpUA7LBT4Qu/BbaGYRPirFPdg2lCxEe+flGg9gZvS78LpOyLqHA8+PQlp/PDD76eWCS88qXbe0YekulZjcguA6NbR+/X0Hs1UFgIWAhJPOmP7sp5M4+gJV7L2x76Ji/kbkKI0Z1qIiA1fxeBTpApN6MZKof4rfe1SMAE0YNz/k0TLro7+pVqRA4iJAjHn1NlIycx+gYG10ncbEVLhf7T/ik5Dr9b/dS4b0dS1iAuNdhYzXZgKt9ChCGlsYzlGjzRMYvne5p4rqsF6dagKXGVSJK6fxHUIIkged7SeaRosNPWwrG/Kt2J67T70nQIdmovbw5eNU+ZbQnoOX/FADWfDl5rU3HedvbR/jQ+AYQQXHHKcUQmofwukeg5obS6ViQ6sdOcqaCLYMPYLGmwvVSnfsE/DEFO+YmQRgjznaZRyFEnLeWid54SiK3BK6/UlEQawS4pjHxlp/Gzm2oCKwU/WTZ2dI36uJACPadh9OnoWc3XMvH7R/iagtJjZfnI6l9SE20vxziNYUoGMuZJ5kc7WBbfHYCt8RDkpaBf9XwTOVGt/WmU8fWP/DY06y/p0wJXALFul3ibjV6oPPRoXlPwch/jsbu3n77CBJwnAjRLoGc0RE4Elqki3f1XDAz6mis4EbqLSXqsGGc2/QdNsaCd/JI6PUPcrw02AIISYA1kqjxlGTxWColES/c7kdJ7x/G0w8tnEqkYM+epCW8VAJa/x8B6SYLjfTzqwpbaKZfS9QIx2T64KvsuSab8TTvQhENUDz7E4Pl8C7utaiA8YdCmcmeTRJhHKe7DNGHjHR97JW/98NmB2HISBk4y+PRzIysxss6EBwLL5/nQC3rz+9enkUicpKv0QAXAL3nNvGPRXcpSH8sqLVACktPTz7Esvjs00QcAlurpxtmquAPTqGDLC60889hm1d0oF+SK6ODj4YoAjLwk1ulQqchRe9/kKrz7iamPfzoikSBBK2poBYHZrRPZbZQLp/hMsRD82mE8dKGxJN3CubOO5RPLruRNc99HVAuqMJRgdmT0gy+v7Qz9f5cr2GCH4EDQfh4K//K82WmHVwC87/PnKbPtd0qgAGIdkw//HYvnt/kT0lgpk31L0lC1KpVEYyXMiL9HM1IKbAVOPYnGU7m5PNS/iDYreJ2ioRhIlhk2p8ZUhrCLclWT+AUK+zBNGBFCcFAAEcbl9Hk1jqTy/ONiZNxxPeACWDqnMbC+TZZ0fPQKu3LwP1PiVCkWiUSQ7fc+fTWhR+go9aMLDS3gCOKRCNVSnKMSrzSP11MEuG3jP22alx13/PWJUBxCA5SaU4HYsVJaeeKoYB91w91rsOxwHazMqEqxuKFuv2ckYuC/Yf92wWw3fI/0yuciuGNEeUk5fgTYVNEHXCF7PwE4pn4RPzj6HUQ0A0MzOab+FD5/0LeZHV8AgIbua1p9VFNxDQm2haIqsIbM3+BDq9P4zlEnLVNWrdGJ+Z+EbRYk8VbH06yqQslBhiB8JxGpXIRZIHATapbKuu5QVeNPBZuhCHQKdrPvdsaiz0nwZG6esgojBpJjoxbzDRdFMn0D6IjaaxCaWuffNOFk3pJm9ADSDP1KL5kIAoh1uUQ77THnIE3ASSsX0FIXjpLj++OCQ1egD3yeg04uAHRwqtxAqmZNBKvkfYGCj63YezhYcEylEbwJUcBFw3JD8GEDIKnTXBJKuyNBC58g9zT+093R72vF97HYt05HsTnpa3TXUOxE5UW5+nJFnnpxl089mhxCCN570rEVb9EGnV5+R3lJqdHTmcIZEFAfenblOhrdXSlsq/LIo88f/HpOqF867vNcXK4++nI+uuLVfOKg87nhpA/z3aPeTk1keBXnKrOGjy37Klcs+iwnNJzBMXWnYArvo9M0HKqMnOftTgQHQUZ12jwuONs9bzW8yq0vM15z6XGkq+P898d+H5hNCYGlmRhFSbzVJj9j4JKa4sTouC79uSJVydFPu4NC7GeKkMhhHv7B3+20gwy+ijog0Q3X9zVJc6yaLx5yEXe0/ZidCgs2SQTP5OZwSGJb4LYXGQ5J/wqUVoCL8tymaUJLdW2S0151GHfd9CSuTyK/kgGxeimV3RACMLNQHEM+xdR1Pv+mswLt02R5+7FH8pcn15IplrClpNRgQ4RQOLmG4rWO19F1Czlv9pFoQvC37Y+xO1fFgrS6anB5GeG+/nKJ9Hqjn0WxNmoVbUIEEg1YYYYgckQLf5TkNN6jovJvoSmJXrARrsQ1NexUNDBnV5nJjXGFkpqqsvvjvEOWs72nl6vufrDi14qShoz6e4jvODpd7WkiURszUs6asEoGpaJBWTey8ja/t/4Weq3xx2wJ1EdTHFk/fvVmTWgsqzqEZVWHUHJLPNZ9f+UdG6c3Kb2IoTSSVzUCtAbPWw3ZEurlzSnnHsqr33hs+Ref9wUSsNMR0IP7iu3kgC2PNj16gH0fi5g5tsNQIIYL5+pg1TgK000EtuWPD3txagYfX3Ee3zvq7fz11E9wVP0iHKl+8b3LqqbHiQYaCSCQzDFcxfoyg7jI/m+p7sQ0Iebdn341s+bV+5beXq4fpxYJuAaMFW5ZtB3++ejzgfZpsrRUp/n1W9/AvLqa8gNRQrhSkwjN22/9ub6dFB2Ls2YcQm0kSUe+ir5SsFU/9yIHXExlOu0Uj2YW0m0nlPSlQZMcH7VIe/yZV44At1txH6ZRwVkXHOHboclYuDEDqy5OqTGJXRMP2NkFWqlyoVgBLGip86dDU+R9Jx/Lr99yccWvE34WCxxuiVLRJNsfJ9sfp1Q0GVxh6Ebl195EnF2DxPXKgkOKToHvb/iKD/sgga4wmd5AklbubJOI+Os9bzV0y6iXM0II3v+F13LF588nmfY+cmkw5BfASZpY9cEuzsr57t5sqg6a20Qypl7I8LQlC/br2BAI7KRNqcmiNMPGTakXE/aDty86lTfMO55jG5agifKwURvx3gNfOYJ+OxFoYElMgBGm79h6HOnsUN2LaUJKVW2S7/z+Cs5+/ZG+2dBCoFliJ/Z/U151432s3uB9mLwfLG1q4Ob3vZX/fMWxoZ1PYnFvK0Bn7AIbM61EdZPvHPlW4kaUdT0tlBxNQWrjvh96Odb7iey8gPsiOSlqcWTUJhWS1boQaqPup1HDnIVNvPrSYwOz5+qifGCvMIw+0u9UZF8TcNxB85hZX+1jr6bG0fNmc9LCeXvS5ieE0q2YJBK10HV/B95KW//HruvZmnvRl57oyvQrJXOVy7QAmBA/3/NWQzKFHjgIIXjVJcdgl7zdIEjASZjYVVHyM9OUmlKBThQS8DLk5a1nH+1ZW1PhzUcfjhAjpQkHI7skEqdKIiOEYGMiicb8CaVO6OWTdtt1uGv3M3z9mT+ztrctFFUSLYKtKqJe0HEU3CAqukzzUiWZjvHez51HLOF9rrUT0cqHKz7PN+PddlbV/scBXRP87s4nvOuQzwghSKVVRTgNMtqnLtE013OH11AOqp7NDSd/lHcuPouCPd83O5UhaDT6Are509bJh0Y9X0LsTNWdmEYR7/vc+Vx2xRnE4v57QORgNJfCRaZegmj3xIpyaQKqk3E+88YzAujZ1PjcOadNvHIjlKsCK0EihCRd7b8ucdKY+FxbdAo81Hkn0qdIrCaz35d2x2JQuqdJkyw21B9eIup8OViZ1vBSwNontlIseOuYkBE9sMoloyEAHDlmSkklzGuq4RVHji80GASLGuv5zutfzUduvAnH3TvhDbrAZFQSsL9lv0TjRaRb3qZoHrqzTc1gW7aTDz72C3bku9CEYF4qQ1NctZ9P0mmlWRTrCMzeHMPFlZ76d6eOpq6AwDQvDWLxCK9984lcf83dnrZr1fofSSzZmzo58vABrJRAjjP3OK58yUR4DdKayyjuweBnKvf8rhsuNbVZT+cXgKQeZWGqac/vDdE0ly8+k3csOp2fbfo2z/SuHvO1Ghquz2kg9UYfKxPBR9K+6Oi86GjUa5KDTJuksmNqDaJnIIxFqjowjWJ0XePNH3gFF73jFJ7694uUijbJdIzPvevnnue1a5ZDfEsPuBKpC+x0tKwVGbDUSaTPRStJSmkNJz7w4D6HO1FT5zXHHcQ7X3ksM+rCX0AobprY7sjxcqgu8aCzy65zlO5xpBS4roau++OI0RAc07CYtBkf/8kDtBZ3YLn+HPgYwqEl0uNL22OhA7N1mwWGq1yeAjSIHOxXy9METX+vD8KnIQi1iQyWhp8isxtrpt4ZDzlnxRLefPSqUf8mQ3YH9XalyPRHPd+MONLl/Y/+lN2FHgBcKclYUfVBbQgsGZTfXnKIabPEcMLl7IqciNCbxn/eNAc8//H+s2icWeNZexIQAaQzuhHI1+sgRlbtstKCYt3EVuRhum0nwtKaMKSNlyO6EqkCNXX91DX0T0pPZX8IBBfOPZbYKDoqmtB454KPcn7LZUTE8FN4DZ2TG87mjKbzRrzOa1bEd5b7quQiEnS5goeKJjlVwRaR4xHV31BkXD1f+9rXOOGEE0gkEtTU1KjujlLiySjHnr6Ck195CEecuIRjT1uB5rUjygXhSgSgORKzp0BsZz8ErCMmAeGCmXOJdoxMcfzABSdx77ffz+cuO+sl4ewCuP7xp3H32avZSQdpDtQFFhI3LrGaHNyEyr1l+bPOZfyLdBZCcPmi/Uet7sht4f6O23iw4w46iq24rl+fieSIxGaMgFMabQSbHZ37imorI5dxEYnLfGnZ153i1772NW666SbWrFlDJBKhp6fHT3OhpZArsWtbJ2bEYOa8embM9l7QUFgu2G45wkpRzrvZ52IlNaQ+tWpdxy6f62GvvKEmHkMXAmef0UDYYdpCCaSUpKq8V5i8YetD7C70Dnuss5BkfroTHalQZkES14JR1KzTJLMM5bPBPpiI9KdVd2KalwiGqfP//u9NfOjiH0xqYTNahFW0M4/bW6Qwu8q3ucc1BXZKI5MQGDmJZkukBnZCQ05QUE/XBMeumOdL//zixJlh6K/AdXUiEYdI1FvnpobARXJs/WLevWTsKpqa0DhzxnmcOeM8dua3sT23CVMzWZpeSdJI40iHbquD1d0PeNq/QQQuCc1SWpVXInCQrLd0Dvf4e9gvohpRew2Yq3wrfPFSoFQqcfHFF3P88cfzs5/9THV3QsVHv3Yhn3rbT9m8frdnbY5U0QNsl0hHLtBsFicChUZ97zwzpBqxJgTd/TlMI0RpHhPg0a3bRzi8NFtgNYcgnW0EYkC83nuqzDhfPvQNHFo7+p6zq9TBrzZ/j03Z9cMeT+r+ODZrtCw1hv/pm6MjABcb0OXetV7gQ37sYoic7EvTvjq8DvQJIttf4JdX3cqtN66mNJDC2Dy7lkvfczo19Ul6OrOe2RKAnrdwFGp+aC4kdtsU6vRy6O8k7hRdE1xw4krvOzdFTlw4b9RyvsICHMqxksrXghLDp7Llj3RsGFGNzUVjXU8zK2p3K3zrgjnRYKpGzdadcKUyikao/QnCXKa6J9O8hFhy8Gw+9OXX890v/BlZgdfL1cpj/FAGbwVhu+jZEk4y4s8KabCbmsBOTa59x5VcdsbokbphZUYyTdqM0G/5p5c1MSTFgkkkanvWYlKPsqx6Jq+bfQxnzliJoU1s0zgzPoeZ8TnDHtOFzpvnvZ+V1Ufymy0/xJbe9RMGnU0CQ0nCh6RekzRoLkJAjyuwJJhBzUPx1yEiRwRkLLx86UtfAuDaa69V25EQUlWb5Krrr+Dum57k2qtupafDn1RsAeg5q3y4H1DFRr1U3tfkWoxy2vyQ+U0iSSdU6yxWzmhDhygKyAEqCtGOg5TD/IyecO7Mw/n8ytdjaqO7QbJ2hu+u/y/6rJH7i6zjj8ZWxo1QkgZR4e38NVFsNB4oGpwQtYmp2udo/lUU99XhdSBPEPlskU+8+Rq2vNCK6+xdJLXu6Oaq/3ejLzalEN6PChWiOZBod3A1cKOCYrWGG534xPTVt51LKh6+CeSQmc0cPquFNTt2AXvz2wViwA2usHNDsC2DUtEgGvN2wLTl6CG2fVacF3obWVrT7qm9iSFpNPpoMIIReEyIEDm70EC2Q/F2iBykujPTvMQ456KjWX7YXP766wd58PZn6e0a//BFyNEjvBh4zOwplB1ePmDkp77ijUdNFs8KQ4rgxOkq5ELg7Crjut4Ofh876DxeM8s7R4oQgiNqj2deYjE37bre42gvQc6JktYLgS6v4kJyRMQircFgFs18Y++/A2Ha2TVpisUixeLeCPS+vqCLHgRHNGZyzoVH8fS/X+Sum9YM2/d4iQC0koMbkMOrrE8MZr9LqWa4U15KeMUR4dAbroQTFs5j9badw6K8BAKjS8OOuaHa0wxWafSSTx10PhfOPW6/z3mw43Z6ra49e70gsInQY8dpjgQrWj+UQyMOMXWJYpD7CW7qA2ia91F9IVMgevnwl189wOYNrSMHfR/vnTBFm2su6HlJtGdiuciHLmzh5x9/A+ccHc5oFSEE37v4NcQGQpfthDOgJyNDcRcJIdENF01z6etJBJqH3VlMUnB0JaXjm8y+wKYji1BI5Q0wcF9lv4/M/0NtV6Z5STJvSTMf/NLr+P0Dn0efwOZByP2vgf8/e+cdJ1dV/uHn3Dt9Znuv2fTeE5KQAAkQCD10BESqgoBKsYAoqCAW+KGgooiCKEgRlA6RKp2ENEp6TzY928uUe8/vj9ldstk2szszd3b2PH5WsjPnnvPu7tx7znnP+35fETTR6+PjnBEynDLflxuwyR/k1SVrYmhV/AkYyZNiYuukelNv/xw2oTEnb1QfLeqcHGceF1VcQ76zKKb9bgvEXoqiOzQk0x1BvC03nXbQgUvilnoCgl8kbLRU48477yQjI6Ptq6ysrOeL+jkVIwvp4nw0diR4ryMAe33HH2pIUTYVhYl9LsSCsyePw67rHX6NGhpafRJsaNoh8HgDMdvfjk4v6dHZBfDRgbcT6uwCiUMEyUtwhcaD8QhJnm6lRA2AAc1Px6XnpPpk+/1+amtr2331V154/ENkQo/hQPNbEwbZFeGTmO5/Bz+9+Hg+uu9bPPzd85g0tCQxhvWS/DQfX599GACmWxIoDoUrmLSqJ1uAppmkZTSQW1BDTl4duQV1ZGY3EPAnUlNAsLUu25KH5NrmIhpNR0IcUZWGllRO5TAC2fCnqFLTFIqDWfy/1Rih2OxQ4jkHOapN9Ibef841Ifh41dYYWhR/ct1eq01ow+Xp6Mzs7fPw3EGHk+mIb+5MhXcEWgyXuDsCmTQkaK4BKNJN3F1EFSduHtLAtCJ6OzHcdtttCCG6/VqyZEmv+7/pppuoqalp+9q2bVsMrU9O5p8+FRHHnaUUYDoTVajoS8Qh/n4BjC7vn9Wx83xe7j/3VBw2He2Qh4nptKoqRufYbKGYptLvPkSLuCvqQ4lzPAkkGpKJ3m2WZpFkHapZYRWBT+PSbdSPpXhOEKlyGhIKGhzYk3gvra0u0OJ8SaLNbzc3rxBQ3+jvV4KP502dAIBoAgThCiYWhf9qmklWbh0ud3sxXZs99uLCXRP29klEpx87EedfTFDa+KhuGE1mfEQtD2aXIQjJ5Lq9QEJoDcjE6Jgp+i/+5iBrPt3G2s+2428Osn9PLT+8/C/8+Bt/i7iP7j76AuK6E2+t1tUXDi06kuzYNI0sZ+Tl0uOFL60ZXY/N72569lCuGbkgJn11xxF58zGJ3QJeorO4fjAmiZkDCvUk2XxoeVZbEDeuueYaVq1a1e3XuHG915R1Op2kp6e3+0p1MrK85MewEvDBSCCU4bJEW0Ieuk0RUJzTf/+es4cMYtHVl/D12dMZXZBHeXYGptcMCx0l0cFuKKRjxvBR6LO5ImqXZc+J3aDdIDAptFczK2092bbY6Xr3zpYkQcTHoR11r9dccw3nnXdet20qKip6ZcxNN93E9ddf3/Z9bW1tv3R66TYNu9NGMMERV8KUOHfWEcj3Im2a5TmOEgh1o3wnJeSkJ88JdiTkeD2MLy7gkwPbuxa0SRC+tCY0rWP4aauUWyIodmcxJsOOQ99EVcvHPdOezSDPMMo9w3h+52Nxt8FAS0gZ30wNIiwIl3hk8qQ+KZKLQCDEo797jRce+5DGhrCejMvjQJom/ubo5qjuPv4SEHEsGW84IJjW+xtQSsmkIcUxtCgxXD5uGr/+5B3Lxnd7mvH4YlcJ95qRC9DjGQLSQrlnKCcXncsLO5+IWZ8BaUNP0Bxgw/IlHGCA8xirjYgbubm55Ob2L12//oDdHtsNa+tS2/A5CGZG5rCI9fhBX/tnlpRw8sz+rZ9amJ7GdfNmc9282QQMg6n/vC9pNCO/RGAaGlqMoo8yHR421u9miK/76LzDc4/hX9sfismYXTHJs4k8Wz1aUuTaSYRVqUqH4n8dKW9GiNjqeUf9VIrnBOF0OnE6k0+wPFqEEMw9cSJvPLcMI44bgM7QAwau7bWE3DaCBT7LV0zBjK6jt7wuB0eMH5JAa2LDt446nEv/8zQhrHMyCGHidHddJj3ef3YNwWvH/giv7myrqGHIEFJKbC1ig/es+XF8jSAcClxor8ahxf9vUaSbVteE6BytALTEnEYp+heGYXL7tf9gyTtr26W9NjfGflErCB+6xIugr/eHOEKAy2Hn5Fn9b4PSFLB2A+J0x04w2KXbqfAmLmJofuFCClwl/HXTPTHRY9FIXF2gfSZkaElQKKXme8icpxBa/41miQVbt27lwIEDbN26FcMwWL58OQDDhg3D5/NZa1ySMWxsMds2xjYVtrnIh+mKfyT/oUjC0V2BtPZeiaHFOZTlZSbcnnjh0HUWDBrBU+s/s9qUDoSCOjZ7bPbSn1Vv47x3f8tXBs3mO6NO7LIi4IycuXy4/y0qm7Z0Giks0JB9jCC2CzMJnF1hd3KOJhlpS5KoYnMPNL8C7tNi2m1cf9Vbt25l+fLl7SaI5cuXU18fn5K1ycTZlx+Jza6jWbBaEYRFfq3emRs2MLupnf3t0+fgciQ+F7+vHDF0EKUWh6brFgoLagjGZZbjs7naTRa6sLU5uyqbtrK5cV2cLZG4tQCj3JVxHidM0uS3H4LwXIRIQMSEov/x/n8/Z/H/1iRE400CZhzT0+2NZo9zmtcVrhKpHzTv6prAruvc/Y1TSEvCCsA98ejaFZaNLYSJ3R6bwwQNwRllM3Db4lPJsysmZE5nfkEsFs6SMe4dCZl3bUgyk8HZBWBsQTY8YLUVlvPjH/+YyZMnc+utt1JfX8/kyZOZPHlynzS+UpXTv3ZEzPqSEJYPsUC3C8B0QGOhjbbQzpZDnQmDY1sUIxkYl5OMmmSS2lpPzKoEmy0HH//c8h7/3NJ1JV+H5uCa4bcwLWtOB2kWh+bEZ0vrsy11pjuxFXc7IUNIptqDTHOEOLR2kXUKEBqy+ZU49BpHBvIEUTYkn5//9TIyc8InP7pNa3N+jZ1aQX5JZlzHN3wOS8WGJCC7cHZleF386IJjOevIiYk1KkYIIZg7YbClNlj5kDSRnFM+ky9qtrP0wCaqAh0d2B/ufzPOVkiGOncxO20tjoQ4oiRuLPchd0QrBO8lVluhSFJeeuKjhB26CCCUFj9nht4MwuheRO+W84/ht988jcNGlZPpc1OQ6eOcoyby5I++yszRg+JmWzypszDCS0pBY4MT2fJrl93/+rtlfGY5Vw4/NrYGRoDfaCbdnoVX7/sGZYM/PwHLKsk0R4gcLUnSSzCh8XHkAE+bf/jhh5FSdviaO3eu1aYlHcPHlnDKBT1XwouE1gN8EbDm86cHwLM7hGtPCNfuEITC9+W/3/uMpeu2W2JTvFixb6fVJnSCAAnNjbFfW/xt49uEzK4/Vy7NTYhQh+jggOmnLhSZ+H13bPNnW3qoYUMywxkiz9Z5AIV1+x0TZOwDo+LqMn/44Yd5+OGH4zlEUjNm8iAeeeP7fPz2GjasqsThtDH9qFEMHlGIETL46K3VPHLvf9m6fndMFlGSlhz3dCfSoVu6Ow+nt3R8/fITDuOKE2f2K6H6zsh2eSzV7zINnWBAw2Y34/pn1hBtJyKt/56cVcH/rXqBA8GwwKIuNI4uGMd1o08i1xneVOwPxL+yk4EtoZPFJkNjeDfOtcSnOwpwn4qIk8Cjov9TuXU/ZgK8461zj4zjKbwA3LtDNBbYoNUZ0CpYKASXHD+d46aNRAjRL1PluyLd6eRAc5NFowsa6twEAjppac3otujnm2J3Fl8bchQnlUzBoSX2WVUV2M99637K/sAe+j5hC5pMJ1WGh2xbYyzM65Q8TZIZowIBMUPWgqwDkWm1JYp+wjdvOY3i8lweufe/NDX0XQPQVVlHMNsdFq1PMCIEuiFpLNAQukBKia5r/OP1pUwZXppwe+JFZUPii61FSiCgE+u6vlWBBtbW7WRMRud/wy9ql7G06v0Yj/olDaaLNU2FjHTvard/kActb+JJuiaTI4q4AzrYhse8V5UHE2d0m86sY8Zw4TXHcs4Vcxk8orDt9cOPHcsdD15KZo4PTe/bn0IC/gIfgVyP5c6uVnuk1vE4eMvuqn7v7AIo8fb9tLivNNS74y5Q79TsuHUHXpuTqTlDOLpgLMuqNrc5uwAMafLG7s+47MP7qQ6EX/fovjhXaBRUBrLi2H/H8TaEdAJJtQ+RCPdZVhuhSGLSs+JTFOTg20BqgmCmi0BurJejHdGD4K0M4ag20QISLSCxNUg8u0KcO3tCl3oc/ZmvjZpi6fgOZ5DMrMZeObsEsLOpmgJXRsKdXVJKHtx4N1WBfa2vxKTfRiO+abFFuml5mktHNBDWVwtV9C8WXjSbZ5bcxsgJfXcKCcBxoAm9LnYFNCLF0KE5T8PRAN4dIYQhMUzJe59vTrgt8cSWQtIYkU5VfqNrjcr39r2G1gs3iYaGR/eRpmf22HazP4+l9YOoNr5cPwmRmC180k0zbRgI97kx7zV1Pt39lJz8dO55/JtMOXxYn/oxPHZMl564O6UHBOGNkN7U/pb679J1bN51wBqjYkgwlnVye0nAb6e22gN9SDPpiSYzQJMR4MKKI7hp7ELe2P15p+0MabK7qYZHNv0PgKlZh8dEJLg7Ah3qRMefHaGuH5kJv+08FyFsFQkeVNGfOObUyXH5XJouneZCH82FPprKMghluRN2A2gmOGtNvLsMvDtDuPcb6H7JU/9JTamEi0ZPIcOR+KgGAIQkPTN8iNGbP69s+f97Vr+YEB25g9ncsI7tTZs6FRzuCzYR39Qqu0i2U3cdnMfGvGKWYuBw/lWxqfQpAXtVU8LlWmwGePaYOOpMhAzvbQDMJNgHxJLROYkrKBItDmfkz10NQaErs8d2utAY7Mvv8v1dzTt6nD9sIqxbLFr+B+DWvTQa9dQZ1RHZuzeUzsf1Q/lv9Vg+qSuL6JpYUG2K1gzdJKFl4vNeg7CPjHnvyuGVBBSUZPGtn57R6+slYGsM4tpeixZlmfl44miQuPcaiOCXd5SuCZ7/8AsLrYoNDUlSure5ycG+PRkYhojrGuCB9a/x6KZ3ui0nbyJ5ast7PLjhblZWL8apxXOTJnGJ2FUPi5SgTJKdiPNERNoPrbZCkeTMP2MqeUWZ3UYQa7oWdmZE8dEOZrgw3XZMtz0plLUl8OKrK602Iy5kudy8eNrXGJaRnfCxXa5An8/QJLClYR+ranfEzK5IWFv/Wa9O57tDwyTXHt+iS01SJFmEl0D4rrTaCEU/ZtqRIzhsbt83sALQDInmt2afIwFTAzSBJgRjBiWjyHvvSXQUbmSE9aVc7sj2XLnONK4Ydgz/mH0tZZ4ctC4mL11ozC8cT6aj6yh4j95zhHy+s4jvj/oVp5dcxMKSCzmx8GwajOhTQ7Nt9UzxbmZq2raor+0tEsHnAd1Kue/26GWIjLvQ0r4Vl+6T8dM9INH7sGlovVIYEueuOppL0pH25EkbtDeaBDLC9kgp2VfT0MMVyU+Rx/qUxlakFDTUu0jPiJ/OiyYESw9s7vGU3m+aLKv+BLtG3CO8Sp2JjxQMdvMjJUzDSytHy/pNAgZS9He8Phe//vs3uP3b/2DdZzvaBOxNUzJyQilf+/ZxLHl3Hft315CZ4+PZv/esVyEhXB4+8aJ1Xdpj2KHOH0BKmZJpjaW+DF4743JWH9jDLe8vYsnexFSmtcWoQiPAfn/y6sP0RIbeSJlzP7m2OmydiZPGkO0hjfJkKQ+PF5F1H8I+zmpDFP0YTdO45bcXcuuVf2PZB+v73J+wyCMsAH962IluSsn586xNN481hpTh7BxLRpfQNrr48jUBGVn1aN0U8dAQPHPkjRS4M9odyv980le48qM/02wGMaTZrn2xO4vrRp/UrUVTsg5ne9PmLvcyAsG07NkUu8sodocjs+5ec0skP2w7iu1VjPNst+T3vtPUyA+ZFNklprT4/NJ7NcJ9aty6Vw6vJCErLw1NF5hG7z/ygvAexFbrJ5gTfz2ViDlo7SYl5GbER1cmkXgciS2t3hOBZjsyPezwiksKk4SgDIU3k904vQQSXcTb1SXxaAHKHfvjOkpn7DI1Rkujw6SQ0BMScxtm4FM0x/gEDqror+QXZ/LbJ69m7afb+XTJJgDGTx/MyPHhBdrkw8PioKtXbo3I4WU69aSI6joYaRM059hoDoZwO+xWmxM3KtKzWJ7ISloxjGjNdabHrK9IGOwd0cd0RolAMsS1h2GuvQnz79ZKjS0hjUGWO708kP8hQlOpjIq+Y3fYuP3BS7jgiJ9TfaBvh96mRTrAhg7BFofX2UdO4LhpIyyxI14MSc+yTNdJt5n40ppobnIQCuogwOkK4vb40bsp4iEIO7aKPR01fUemF/P32dfw903v8PKOZTSbQbIcXk4vO4zzK+aQbu9el3Bmzlze3PMi9aHaDnOJhobXlsbMnKPbXpNSsrVxQ1Q/t12EGOsJRz9bs6wSrAjZ2GZKSnUDj5B4tbBzKOFnh3W3Ix3TELb4pHUqh1eSIIRg6Khi1n3et7B/AdjqA0nl8Dp4zSyBk2aMtsyWWOGzJ5fDS0qN5kYHHl/0qZYCsAmdYDdlxzUhGOzNZ3tjd1FVklxXfdwfkrm2WsZ7dmDvpmJifBAEkWwLaQyyfyksrIWrJidwspJw4Gxk5m8RruMTNaiiHyOEYOSEMkZO6Hoh8cxf34moL5lkzi4A0yEwdHh1yRoWHp660ShL9uwglEDvut9vw+Prm0i0QDDIm8uo9OIYWRUZw31jyXcWs8+/q1eOL7sIMdS5h0Gu8JyXyMX/qqBOoykYajdwWHW7+b6DppxdihiiaVqfbiRJ+MBFOhLv8JJAyKuh+yU/u+pEjp8+MuWiiU8aPIrbPnqdxlBi5UIcTj/pmU1oGjhd0aWrfmXQbI4u7HrOL/XkcNPYhfxgzGmEpIE9irRNj83HtcN/zJ83/po9/p1ohD93JgY5znyuGPJdvDZfu2sEIqrslhJHFaIlbdM6BAdMwQFTAyTzXEFr7JGNyPrfIzJ/EZfulYZXEvGdO86MTUdJk5Ab5uDwY5/bwZCiHAutiQ1js7sWOkwsEocziMvtx+/vnf96XEYZ84vGd6vPZUiTs8pnckT+aLROxX4kGpISb02vbIgcyWTvVuxxFg7uGsGqkM5bTTY2BjX2GgK/acXJjImsvg4Z2progRUpiJSSD95Y1XM7QAQMkkxkiKA3/OxatHiNxZbEl+ZQYrVrggGNYEDr9ZKiVRruulEnJXxzKITgiiE34rWl9apicFDayLA1WfRRF2wxdNYHe/+77z0aaIUIT4zWowrFQfibIjuUPfRjLwEEBFoP8xN8YwjCxVJOHjmcBYeNSjlnF4DX7uCXsxckfFzT1HrtYHlsy3s8vfWjHtsJIaJydrWS7yriptF3cdXQmzim4BSOKTiFK4f+gJtH302Bq/0hjhCCUWkTourfpzcnVbXEMXYDp2UfbQOan0fK+FRiVQ6vJKG+tonf3vJMn/uRgLQl159VO2iNvmD6KOsMiSWWT3YSj7eZ3IIaMrMbSM9sIiunsVc9fVqzjZFpxehC69SZpQnB+MwyZuQO446J57GgeFJbPZLW1k49xJjsnbht8T0ZyrHVoVleiFTQjMZ6Q2efoeG07HYzkY2PWjW4IoWQUhIKRuZE1kxJMhW0lgKkPfxAqG9OjmIi8WJkVm5Cx7PZJZpmYprh36+MsiJwut3D/025iFl51qT+5LuK+MHoX3Fi0TkUOItxa5HLKQhMMm1Nlmbvyl446npPS9SMbRgi+x8ILXl0ShWpwaa1u2hqjOwZffAnXwKm20ZzcRrS2eK0sGAR6HLa+NH3Tk74uInklCGjGZzeMT0wnhghvU9/zt+teYVmI357D01ojEqfwMnF53Jy8bmMTp+I1kWAwLyC6D4fhkyW/brEjqRMtzqVPghmdVx6Tpbf9IDnnlueZv2q2FQxCqVbVMK8K1o+ZUIIzps7yVJTYoVD0y29eby+ZnzpzWiHGNGbQy8B/GHdIn456QIyHOHTM5vQ0FtmoGnZQ7ln6sVoQsOl27ltwtk8e9R3+d6Y07h06GzGZO5kcs520uzx32w6RfJUIQXBLlNQZ1q1IzIh8J5FYytSBSklD9+zKOL2/gKf1R7ndggJWkCiCcGQosRXMkwkZWmZZDoSN79rmoluo01DJdqKjTXBRj6rSVzVqc7w2dI5rnAhN4+5m8uHXB/hVQcLJ1vHgT5EPkSF41jwXoXI+jsi53mErTwBgyoGAqGgwatPL+HaM+/j2jPvi/r61iWtP9+HdFirwmOz6STDcyHenDJkdJfVDeOBlIJQH6JZGww//9vTc4R6IhjuG8OM7LkRt98TTE8SSVRBEEGN5ZXobaDFR+9TaXglAbt3VPH+a5/3+dA8XD3LRigtefSlBBDyhL0y3z1nbkqkMwLomkZZWiZb6qoTPrbQzC51VXozR0kgYIaobDrAi3N/wP/2rGJNbSUO3cYReaMZkV7U4ZpCdyZnls8AwC+XsKlhbac6KQKBW/fSaMSmlHuI5Kk+ChBE8IHfxnRHiKxuhDXjhplMDkBFf+SJB97iqQffjqit6bZhupNPFN69O0RDiY0zj4gunaA/8tOZx/Kt/72QkLFMs+/HOn/d8CYLy6aT78qIgUV9o9w7FKfmwm8299BSIBHUhpyk6X7L/LsNUrDXEORoMr6bIi0L4bs2JdO0FNYRDIT4ydWP8Mm76xCaQPYiP7i1bp9zZx2BPK8l+l2t1Df4qatvJiO9e7Hz/s75IybywKcf02wkan0pqD7gIyu3rluB+u54dttijiuydv4PmgH+vPEu1tR9ikBDRqAduT/koybkJk23NpoYwE6UIdwxRwfXCQgRn/tLRXglASs/3hiTDBFp1/AXJs/pe2u5+JA7bM/nm3dZa1AMaQ6F2N0YGydOtLhcsQ/d1YTG+rpd2DSdowvHcdWI47hs6NGdOrsO5fxBV+K1paEd8jjR0EizZXDDyNs5sehsPLqvix4ipybkSTKJOoEJrAjYrLFLncQr+kBzY4AnHngr4vYhryP5NCIBYcLMomLGD+75edXfOWXIaOaXDUvIWKGg3qeT91ZerlweE3v6ikNzclTeAiKN0qgxPJYup1xIdOLs7AJofgpUerwixvzrL/9j6XvrAXrl7GpFAFrAwFVZi9Zs7SGfw0KHW6Io9Kbx4LFntGV5JAKHM9RrZxfA0qpN1AabYmhR9Dxf+U/W1n0GEJGzK4xgaUMFdUbYyWPK6KUD+oqOZKw9yDxXkEzLPt46CCfCd3XcRlARXkmAacQmZ1ba+lYBJdYIwJ+lt9n00ker+MZJMynNy7TUrljw2f5dCTz9aI8WhxxrKSXLq7bww+X/ZLAvn1NLp0V8Ip/rLOC7o+7kzT0v8uH+t2gyGvDoXmbmzGNe/kmk2zM5vvAMjsk/lcrmLZjSJMOWxfKaj9ncsJbGUD1r6z+PaCy/tLMrmEGBvcby05AvETQD+03ITfRk0U1lTYWiJ5Z/tIHmCDVVAKSeXHNMKwKgbmBEOwohuP/ohfx2+Xs89MUn1AfDfz+bplHiTWdbfQ1mzFbLgrpaN5nZDUjZuz+9JgR7muNdzCRyFhSdxV7/bpZVf9BjW70X1R1jyWC7QWaCjqVlw4PgOR/RTfEahSJSjJDBc4++j4zRs0gQdgI49jbQXJpuyTw0bfIg3K7kyaCJJ3OKKzimbBiLtq5LwGgSb1pTr+cYCBfWen/vGhYUT0JKyYrqLfx350rqQ37KPTmcXDqVgjhGGTcbjby/7/WoKjS2EpA2PqwfSratgXx7LR7NT66tvk+/j0gRSKY6QmRpia4UqQMH7V9swxAZv0TYhsRtROXwSgJGTx4Uk35E0CAhd0iESMDWKGlxXCOE4I3l67lo/jRL7YoFAdM6R4NpxH5BaiLZ0rCXLQ17AXhw/RvcOOYUziqfGdH1GfYsFpZcyMKSCzGkgS46en5smo1yz9C27+flnwicyNKq9yN2eAF80VSCV/OTpofTUpLh4y6Q1JoauYkWfJSpEzWpSDxNDdFVw9FCZtiZkgw33SHs2p08TpV4Y9M0bphyBFdPmMmKfbswTJNR2XlU1tdyyvOPxHSsYMBOzQEvhTkGzfSUCtgRU0qynX2P7o0F2xo38vru51lR/XEPLcMaXl7db+mSSiOBFYDNSjAqwVaaoAEVqcy+3bVU72+IaZ8CECETrTlkSWq9abWedwIxTJOgkZh9js1u9Cm6q5UmI0BDyM/3lv2Dxfs3tFWel1Ly5/Wvc83IBVw4+Ig+j9MZWxo3EpS9yb6RLWm7ggMhHwdCPgSSAnsNEzzx178s0E2yEy7H4oC8NxHBT0A2gm0Y2MbHPaVeHeUkAeVD85lw2BB0vW9/jub85FhUtiIA3f/lDKFpgoYUqaI1KivPsrGbm+Iz0ZvIdl+/+uI53t2zOup+OnN2dccH+96Mqn1I6nxUP5QvmkpoMB1JkWElAd2KTZGIvOqYQnEopYOje47pdf6kdHYBNDXHt0JsMuKy2ZlRWMbhxYPIdnkYl1vI7bOO67Os8sErkXS7k3OGTyTd2bvIBolkQdGkPlrUdz6r+YT/W/MjVlR/3KneZHvCWxBdmJZ+3LO0RO+wB9COXhFX9DhVi5eE0xutYOmKLbz97hpLxk4kS/dUcviT9/Pmjo0JGU/TYrOIH+TN40crnuCT/WG7DWliSLNtT3Pvmpd5tXJFTMbqQC83IkX2KorsVWgtz14Nk1LHAUa6dyZk7inRzQTvoXRwn46m5yFcCxDuMxD2CQnRj1QRXknCd391Djde+Cf27KjuVQiwqQEOPak2I5JwyfhWQoZJRUFqVNHKdnko92WwtT7xUQVSajTUufClR3/a3oogLChvdhN+qyF4eONbzMkf1etxIuFAYG/U15hobA9ksz2Qzey0Nfh06x2peQnfnAiE66QEj6lIJYaNKWbwyEK2rNuNGYnGiinDX4KkmmuAmKXO9Eeqmpv459oVPLdxFbWBZiblFrG6ai9NvUi714Tga6Mnc/ygEdiERobbzuUf3Y+/l2Xfzy6fRYnH2nm/2Wjkb5vvi8DR9SUZWiMB04bUAhZ81CXZmsSXyCNpkQN6cQIHVKQyOfnplA7OZcfmfTHdUAtAWnK6GOaBh9/mqDkjLRs/3mysOcAFrz5OUyhxEgFGDLJWBODS7Ly7t+tDegE8uOENjiuKvYOl1DMYXdgwZHS/N7cWJEtvZKR7JyCwCSOhci1ukchURg30QoTvO4ka8NDRFclAbkEGv3/mW1x4zTG9uhENlz3pNiBhRNv/p7mdHD05MWK7ieCXsxdYNnZjg5O6GlevQ6yL3FkM9uWjdRMLYCJZWb2VujgIQfqNZt7b9xq/W3c7daHqPvQkMaT1j7FMzcSV6Px3LRfcZyRyUEWKIYTg+jvOwu60oR0SYXzo9wDBXE9SOrugb6LI/ZkNNfuZ/++/cNcn77C6ai+VDXWs3L+rV84uCKcgnjpkDDMLy5lWUMpTW98nYIa6PRzpDKdm45Ihc7lutPVO+cUH3iVgRpe+m2Zrbkubt4LR9gRr0jkmIoQ6A1fEBiEEZ19+VMyjRyRguKz7nG6v7F1QQn/hT59+jD+Bzi4AIxQujtIXJLBo54pu9zQS2NKwlx1NB/o0Vmd4bT4Oyz4SEWV89UZ/AZ80Dubt2tFs8ic+c6hZChKzdBLgPheR/S+EnpOIATtg/U5R0YY3zYXH56I3JRu1GAnfxxIB6IFwRIAQglsvOg6nPXUWVLOKBzEtr8Si0SUOZwghehdJu99fz6SsCrQINq5+M7aT3z7/bn6+6kae3PYX1tV/gT/KjUhL7CAg0ZCWbkoAcjWTwxxGn9OIIqMlXVQvRWT/A6GlJWTUZGXz5s1cdtllDB48GLfbzdChQ7n11lsJBKyP+OsvDBtbwm+fuJpZR49GtBwtappg9vyxTJr1peaeadPCuilJ6OwCcDoTr+liNaaUXP7aM1T5m9o5pIxebsh0IZhRWMak3HC1SyklL1cuw5DRrS/m5Y/l5Xk3c9WI49p0VKxkR9MWNKJLtfebduyaVSmNgn1G36tjRoVRl8DBFAOB+adP5ezLjwL4UrKlD/eTBAyPDTTrnympynObvrAksbmh3tnnPkLSjGxP08to5Z44veSrlLoH9+paE43N/lxWNpYl9Lm/w9ASE1GWcRdaxk8sc3aBSmlMOj5bsqlX1wkjOU8cBJAW1Ln7hjOYNqLManNizk8Pn8+Jzz6c8HE1/UuHV28ImiGG+QoI9bCRyXJ4yXLETifKlCZ/3PBLaoNVLa9E+7mVpGtN2DSTQnsVxQ5rqzUKJBMcocQFvWj5iIyfgONIVU0LWL16NaZp8qc//Ylhw4bx2WefccUVV9DQ0MBdd91ltXn9hkHDC7jl3gtpqGumpqqBjCwv3jQXzz/2Acs/2ACAmeTl2MeMKrLahITzTuVmNtVW9dwwQibnFfOnoxe2RZmHpNGrA4+TSibjs7tiZldfsfUicqna8GDKBIrGH8LmkEaxzcSRKNH80EqkNBBRanAqFF0hhODSGxZw5AkTePnJj9mybhdun4tVy7bQUBfZQWXrClEAhs9OINda3VKnw5YQvSErMKVMaCrjwQT8dqB32SQCqPDmMyGznCe3dl9916nZKHJn9WqcnnDqLr4z4jae3PYXPjrwdi96EOwOZlId2keWPfaZNZ2xyxAMNcEr4jXPOBHptyDcp8Sj86hQDq9kQ4TVlaJ1BATTnUlVofFgiqQrJZ1dAGOy8/n54cfxw/cXoQnR65P1aNFE38ap8OWzoGQy9659hWYj0OmnTUNwZtmMmJ7Qr6pdwV7/zl5fP8ixl1Ge3TGzp6/kaxJHIm85cyfYJyhnVwsLFixgwYIvU4uHDBnCmjVruP/++5XDqxd401x40750VCx7f33bv5PzSOVLvnHpUVabkHAW79qOTWg9Hlz0xPT8Um6YOocZBWXtNnOVjdE70wTwwo6lHFkwpk82xZKxGVN4Z9+iblp0XHMFpY3KQCYljmpLllV+ND7025hoD5GZEB+UH/xvg+voRAymGEAMG1PMtbctBMDfHOTsmT+N6DopIOR1gC4IpTmRduudsdnZqVsoSBOCIk8aOxsTH+3Zl/WFBC4ZOpd5hWPJWOWhNtiE7KRHXQhOKpmCx9b3aLKusGl2zh90JQWuUp6rfDTq6wWSHYGshDi8HEimOI34akWm34TwnBvHASJH7ZqSjPHTBxPtrS8BI92ZlM4uALe7d9Wd+gvnj5zE86dcxMIhiVrgSzS9b6kW55TPxGtz8rOJ56IJrYNTSyAYl1nORUNiu4lcW/dZ1KklBzPUtTcpqjK24tXMBOW/H4SZ+EIJ/Ymamhqys7sXyfb7/dTW1rb7UnRk/Rc72v4tmoO9rkSUCD77otJqExJOpHPAMaVDsR+SBtT6zL987DSePPErzCws7xC58NTWD6O2SQKranf02C6RjEwbT5GrDK3LJa/sVHtldVMRARmer6z46DdJjQ8DdgIJGls2v5iYgRQDlv17agn6u44iOvij7s/3Esz1EMz2JIWzC6C0OD7RQcnChaMmJUieoz3p6U29fsaOTi/h/nWLOPudexibUYqO6LCn0RCUeXK5avhxMbC2Z44pOJmbRt/F4TlH4xCRRztLBA1m/BxyB480zRkkvY/BE90joPGxOPYfHcrhlWQce9oUXB5nm5ZKJBgeW9I6uwCyMt1WmxB3xuUWcveRJ5HvTsTpj8Dt6b1G0ey8kZxaOg2AI/NH85eZV3JU/ui23PcCVwZXjzie30+/FJceW12caKpkHYpT+C3UVOkMiYs+SVL0Ag20xAtb9hc2bNjAfffdx5VXXtltuzvvvJOMjIy2r7Ky1IxA7SuOg3Sx9OSTiWzH9u2xF6JNdmYUlvUY3VXg8fHAMaez5qIb+Ptx53DWsHEcWzaMi0ZP5tWFl3DLYUd3maLTZPRunnFoyZU8oAmNK4f+gDxnOO017PgSCDQEgrNKL2FmzrwO1xU4anFqBmDNEsslJEWaSVz3JAfjfxtpKue/In54fN1v5qX4Upje9DiSbm8zdlRqVzK9eMxURmXnJ3RMm83A5Qn2+k+9praSnU3VVDZV8dH+9RiYjEgrahOwT7e7uWjIkfxl5pVkODwxtLx7Cl0lnFt+Bb+e9BD/N+nvfGPI93HrPe0RJS4t/mmleZokXYt3yr6E0FqkWR/PQSImuVYlCrxpLn5y/9f40TceIugPRVYqXibXhHAoc2aNsNqEhHHO8An84dMPMeN8HCx6uQI+vmgit44/C5v25WnZmIxSfjH5AkxpEjQNnDF2ch3MYO9w/rf3lV5d69OTRYhcMkw3KLUnujIjgAeh+RI9aMK57bbb+MlPftJtm8WLFzNt2rS27ysrK1mwYAFnn302l19+ebfX3nTTTVx//fVt39fW1iqnVyfMnj+Wf/3lf5imDC8dQybYtKTbhADU1ltbvMIKDi8axLCMHDbVHugynf6yMdPQW6K7jiip4IiSioj7n5o9hOd3fBKVTRqCYwrHRXVNIsh0ZPP90b/ks5qlfFq9mID0U+gqZVbO0WQ5cgiZQXY2bWNz47qWKySDnfssUYrQkYy2hyjRE1kyHpB1yJrvIrL+lMBBFQOJzGwf46ZV8MXSLZ3ubzQZdngFM1xJKdNy8oKJVpsQV7x2B0+c8BV+veRt/rl2ZZ/T5SMhzWv06fr2BVvC9q6preRvh19NiTsbj82BZrEMiC5sjMmYxJj0SXxS9V43LQXFjuq421OgmwnUqEyOe1g5vJKQ8dMH8+eXbuDlJz7ig9e/YHdlFU0NXW/2teYglqqr9sDYASQmfPGYKTy5biX7mhriWumkscGFw9kQ9XWlnpx2zq6D0YSGU4/vpDAh4zDSbBnUh+qQUf6GQjJZAlIFGwydYlt4mk3seqweKZsQIrWjJq+55hrOO++8bttUVFS0/buyspJ58+Yxa9YsHnjggR77dzqdOJ2JCBvv35x03kye+8cH+P1BpCmx728iWJicDtdIqjOlGpoQPHjsGZz78j/Z01jftuzXW/QkFw4Zw2Vjp3XbR3ccXzyRn3/+bwIRCtdrCJy6nTPLZvR6zHiiC52JmdOZmDm9w3s2zU6Ru6zN4WXDxKdHW0E4NhgIPg/aqDFNRtuNBC7tJPjfRIY2IWy9qzamUPTEBVcfy82X/qXbNqY7+TJXxowsIi839Stjpzuc/Ozw4/jB9LmsPrCXNdX7yHK6eGnzGp7ftDrm480oLmF59YY+ZYAcihCC/2xbzPfHnhazPvtKyAyxunZFNy0kWXoDubb4a6jpJMgNZRuJ0JJD9y5ZdpCKQ8grzOCibx/H/c99h988cTW6ves/lbRrYJhJq6+yet0uq01IGLluLz+dOT/uZX0DfjtNDQ6kjO7P/t7e2E9W0WDTbFwx5EYcWvS6bjWGO2k+4hLBF0GbReux5NCyiCe5ubmMGjWq2y+XK6yLsGPHDubOncuUKVN46KGH0FTJ8piRX5zJzx64GI8n7By0NQXRqhJTPShatmwbeCmNABXpWSxaeCm3HHY0E3IKqUjPYl7pUB6afxb3HHlSW3RXb9CFxk8nnB1xe4/NyW+nXUyBO7PXY1rJ+rovDvrO2slGIthmaCwP2BI87wnwv5PIARUDjEkzh3LtrQutNiNq1m/cQ2NTsmQaxB+v3cHUghLOHzmREypGsrcp+kP2nhBAU8Ak1nnbhjRZvH9DTPvsK5/XLqXB6Cq9T1Jor2aKb0tC9hUNCZpThPeyxAwUAWpn0A8oH5rPT/5wUdcNdA3setKdhrTS0E10Wiry9IbPEjJOXa2H+rroolRW11ZSF7R2wzrIO4wfjP41Y9InRXWdQCTVR3yfqfF5QG9zOsZ/U6KBfSpCpHYRiGiorKxk7ty5lJWVcdddd7F371527drFrl0Dx8keb8ZNG8wjb/2Aa25diOlzYAuaSXnAsnbDLmSS2ZQoMpwuLhs7jedOvYi3zryCB489g3mlQ7rU5oqGowvH87tpl5Lj6Dqyz6s7+c7IE3lh7veZlFXR5zGtoD5Yy77AlxWAS5xVSfARF+wxNWoSKlshgGACx1MMRNav6rqwhQA0v5F0c0wgaPDyopVWm2EZafbYR8ULYIi7NC4yMMm0XwDY3byjQ+GUAls1Y93bOCp9NRO927GJxIilbjcScHBumwiu5ImwUw6vJMcIGTz557f5v5uf7rKNCBhoTclbQasgL/VDgFsxpeS/W9cnbLymBjf7dvswo0iB9xvWL2azHblcOvg63HrkApISQYPhSKqP+TZDZ7E/PHHEf3I1Ed4r4j1Iv2LRokWsX7+eN954g9LSUoqKitq+FLHD43Uy79RJ+PO8BPK94UOWJFtN+v0hgqG+aYEoOmd6zlB+PeWrzCsY287x5dWdXFAxh+fmfo/zB8+Ja7n3eLOqbsVBpewlFc79ltrTikBSGUrkUt0E+/gEjqcYiHz4xqpu37fVNifdHAPw6htf9NwoRTlp8MiY92kCC8rGMCdvVFTXufXuD34Fghk5w/tgWexoCNWxqnYFVYEDHdI2x3oqKXVWJ0So/mCapWBVMM5Or8y7Y3LoFiuUhlcSYxgmd3znMT5844tuN/maIXHtqkdqgkCWGyM9uRadTX7rHSyJYndDoqtRSKTUMQwNLYIyaul2N5mO5MintmsOvjroav688a6DNhrds9Wfwyj3zjhbFh2ZCaocKdK+h3AdHf+B+hEXX3wxF198sdVmDAjWbtxjtQndYrNp2G2pn+6baA746/nesn+wsnorutDadD8y7R7unPQVpuYMtdS+WBE0v4xEd2sBXFpyrFsk4E9YhJcGegXYO2qcKRSxJNTD4YTeEESvacZIMvH6LduSwxFuBSdUjOS3y99nS21VTGRbBDA6O5/p+SX8YvXuHtu3Yhc6pxZP5YltH3TZRiKZnF3RdyP7QLPRyDPbH2FJ1bsYsrPPu8SuGUj5pUaxTSSuEv1WQ0cEYLQjDgeF3ivQbOWx77cPqAivJOadVz7lg9e7d3a1Q0qknhyTwsEsWbrFahMSxrs7Nyd0PE0zyciux2bvefrREJxZNqNL0XorGJsxhe+M+AnZ9ryI2u8NpiXLuqcNmaDHqPR/hAxtTshYCsWhLF6yyWoTukXXtaQ6TUwFQqbBtUse4vOa7UBYF6W1aldtsInrPnmELQ37rDQxZhS7B7X9O5k+RQJwxVjfpmtMcMxS95Ei7gwfW9Lt+wJwHGjCsase4Q8lTQZLc3OQqurYa1n1B5y6jX8uOI8xOQUx6a/Qk8aDx5zBuvpd7Giuivi6kDR4ctsH3T6nBYL3967tu5G9JGQG+cP6O/n4wDtdOLsABFuas3m/bhhv1I7ljdqxvFs3gm3+rIR93LcYGk2xVKgQmYi0HyB8N8aow9ihHF5JzAv//BAtivI8gWwPpsceR4t6xxdrKq02IWE0hRJ7KmyaGv5mG2YP/i4BjEwv5mtDjkqIXdFQ4R3Oj8b+hnl5J6GLlvTAlqnMJuwUOkvQW4TanQkO+42E/YlJuYfAu8j9Z2I2PoX0v400B6ZIt8Ia6huarTahWwwjUTfiwOGdvatZV7ezrdT7wZhIgtLgsc3vWmBZ7BnkGUqRqwwNjSbTgd9MjgQIiaDUlsDPdtOjyODATdtSJIZzrpjbcyNNhItyJb4cdrd8snzgHOIfSqE3jedPuYgnT/gKV42fweGF5WQ7o6sanul0cePkObx+xmUU+9KpCTZGdb086KvrNpLPa7ZF1W8sWVz1Llsa1/dYjX51cwn1pqvt+0bTwbrmQrY2ZyfI6SVY3VKAq9fjaaWQ/jNE9qOI/HcR3kuT8tAkOWZ0Rads27AH04zsEyg1gZHmSKpJoZWa2uSs6hUPhmfmJnhEicNhoPcQtDUrdwR3Tjofty05Bc81obGw9ELmFy7k8a0PsLJmMQJBSAbZ49/ZlvcekMn3yKqVGrWmQZqI9+1ngKyD2h+2TPQ2pOtURPotCK1rQWmFIhZkZyVHKnRXeD3J+Wzrz7y+61M0IboUFDakyaLKFdw0dmFiDYsDQgguqriG3669jYDpZ4s/h+Gu3RYvqSRlukmalsjoFh3Z+E9Exs8SOKZioDHhsCGc8pWZPP/PDzt9P+S2h/Uik29LQyg0sA9XhBAcVljGYYVlba/9+dPF3LHkzc7bA0eWVHDakDHMLh5Egae9rnOJOzsudjo06wJAPtj3BgIRoVxLywE/BiPdOyl2VKMlLKoXdpsaKwM6o+0GvfqNmduh9kdI+2RExh1gGxZrE2OCivBKYtzeyLW4DLctKZ1dQMROu1RgZmEZegL/Dh5vAKer56inWbkjktbZdTCf1ixhZc1igLaJ4mCRx0bTSW3IlSzR7S0IVgascMSFoPk/yAMXI+XAqoSqSDxHzYm9YG0sGTwosrRoReTUB5t7rJ7VZKTOs6fYXc53R93JjJyj2B4oZncwHeg+kiBe2JAMsxmMsSe6EIMBKsJLkQCu+tGpXH/nWeQXZ7Z73bTrBApanF0i7ieJUTNiWGxS+lKJK8ZP56H5ZzEoLbPDexJ4t3ILN777Mot3d6zOWeLJZmr2YLQYejc1BEcVjI5Zf9FSFdgfsTYxgIbJNN9GShxVCXV2tVJp6LzZbGeZ30a10cu/Q3BFSxbKc0iZfNk4yuGVxMw9aWLEKY3Slrx/Sqcj+aJy4oUQAkeCNLKGZ+QwKNce0VpgfJa14oGGNFhT9xlLDrzHurovMDtJkZFSsmjXv7vtxyYMbMJoaR8XU3tFgxRsTmglrVZMCK2EpuctGFsxkKgoz03qKKqvX3yk1SakHIO8eeii6+eaILxZSSVynQWcV/51fjXxb5xa/gBgS2iQSY5mMM0eZJ4ryDB74gSMv0SAFl2KkkLRG4QQzF84lb+9/n2eXf5Tnl3+Ux59+yac5ZmtDSy171B0XTBuTAlDKtThSmek2R3sqK/t9D1DSkwp+fbbz7O9vqbD+zeMPgW7Fv1eUXTydNYQuHUHC0utK76RZs+Iqn2p4wDperOlH3kTwW5T8HHARu/iVEyQTVB7I3LvEcjGf8XaxD6RvF4SBSefPxO31xmR0yvkTd6NyMgBdhoyIis34gVyX55tm2qr2Nvcc1XITLuHMRmlfRipbyyt+oDbPruGP6y/g79v+R2/W/8zfvL5t/i0Zkm7dnv9u9gf6L4S3Ch3JW4tmIyHfgQsi70XyKYnLBpbMZCYdVhyVuQrK8li7OjuRZAV0bOwbHqn+l0Hc2b5jARZk1g0obGj/p+IBMd3pWuQrUusrD8knMdZN3gSsnnzZi677DIGDx6M2+1m6NCh3HrrrQQCqRPdaDUOpx2H0052fjp+u550CzwhICPdww9vOMlqU5ISKSU3vPNSW1GT7nh09fIOrw1LK+SSKDWGJ2YOwqXbw4GALf8D8Nic3Dv9EnKcad13EEdmZB9FNDu8UmeyaPIKTAS7jD66h8z9yNqbkQ1/j41ZMUA5vJKY3IIMfvHQ5WTlhW9a3aah6x3/ZKZdQzqTN4rqnDMGVonrr46a0uMS+Zbp83j6xAv4+/Hn9HqckDQxIngoHZ5nXSrS0qr3+dvme6kNVbd7vTq4nwc33t3O6RXsIS3PLkIU2auTbR0EhIWF0y0IQ24dHWOnRWMrBhJz54yy2oQOZGV6+NNvvmq1GSnJYF8+lw2d1+l7GoLxmeWcWZaaDi8pJTvrX0aS2JTCJglR1CqKMTpo2eA+3SoDkpLVq1djmiZ/+tOf+Pzzz7nnnnv44x//yM0332y1aSmHYZg0+xNb/CkSpIRvX3kMxUWZVpuSlHy8eztb6qp7bGdIyUe7OheTH5wWXXDE5oa9vDD3+1w36iTmFYzl6MKxfG/MqTw/9/uMz7Q2q2VGzlHkOQvROnGzaGik2zLbRae1HuQnAwJJjRkbY2TdXUgzOaqaxs3hpU5EYsOwsSX87bXvccu9F3LaV2dz+tfmkJHdXjzYtCcmha43OB02pk8ZbLUZCWXh0DHMKx3Swbff+v0VY6dz+bjpTC0oYU5xBbfOOAYArRdPuyG+vE5Deg9mVu6IqPuNBYY0eGb7I922eWb7I23pjbmOAuyia8nEdL0JQXKlMoaROJAU6FYJmQrQEl0sQTEQef3t5NL20TXBX39/CV6vq+fGil7x9WHH8uNxZ1LmyWl7zWdzceHgI/jd9Etx6slXGToWhMx6TBK/Xt1jaARlIuc5QVv9Ki0XkfUIQrMuMiIZWbBgAQ899BDHHXccQ4YM4dRTT+XGG2/kmWeesdq0lGPJ/9Yg/KFkXOhx9+8WEQgmnzZRMrCpNvIIpa72OoM80a1ja4KNeGxOzquYzS8mn8+dk87nrPKZeG2R61/HC5fu5lvDf8zwtLEd3huTPomrh/0Ip/bluiVoJtc+PnbOtybwL4pVZ30ibmFBB5+IDBs2jM8++4wrrriChoYG7rrrrngNm5LoNp3Z88cye374xjnzsiP42tG/JOAPP3hFEk4MrfgDIfbsraWwILp85v6MTdN44JjTeeDTj3noi0/Y1xwuuVuRnsWV42dwzvDx7dpfMmYqU/OL+dsXS/lo9zY0oTE1v4R/b/i8yzFES3/fGHkYt6zsPp3t7lXPMyK9iMG+/D7/bNGwru5z6kIdc/UP5kBgL1sa1jPYNwKn7uKwnKP4YN8b7YTq29qGvCyqGY+OQZGjmsHOfXh0ax3oAokAJjlCFp7Kg3Cfad3gigHBpi37ePOdNVab0Q5TSm77xXP89hfnJWUZ7FRACMHJpVM5qWQKlU1VhEyDIk8Wjl7orfQn1lTdY8m4JoIvgjoTHQZSxjuzS4RLyjtnIByHg+s4hEheeYxkoqamhuzs7vXr/H4/fr+/7fva2s71jRRf8tyjH2Cv9RPIS76qwLV1zbzz/jqOOco6MfRkxRthUSwBHFFS0el7r+5cEdWYaTZXtxqTVpNuz+Sbw25md3MlGxvWIIBhvjHYNTt/WH8nzWYTOgYGOpXBTIZoe5MiyksiyNVidYCvg7E7Rn31jbitWBYsWMCCBQvavh8yZAhr1qzh/vvvVw6vPpKZ7eMfb9/MH25/lnde/hTZ3HIakgx3Sid8snwLJx0/wWozEopd07l64iy+MX4Guxrr0IVGocfX5aZsQm4Rdx/ZXhsgZBq8uHlNp1WyJPCdybPJdDrIc6az19/1Qqou1MT1nzzCv468PqGTQ22wKqJ2NaEv251cdB4b6lezu3lHhwonsiUg1UBnRyCbnYFMZqWtx2uR00tDUqSbDLYZ+Cybc3XQy1UKiiLuvPH2KnRNYCRR1V0pYcWn2/h8dSXjlIZXXBFCpJxAfVcEjCq21VkXvbPT0DH8gpH2EN64LuskIu1bCPdp8Rwk5diwYQP33Xcfd999d7ft7rzzTn7yk58kyKrUYOOqSvT6AJrbjultiR5Nkr2NTdfYsHGPcnh1wlGlQ3DqNvxG9xFwDt3GV0ZM7PS9bY37EYiIqhvqQuPU0mm9sjXRFLiKKXAVA2BKk1+t/gF7misByNQbKHVWUx1yEZIaNqwoVHIwEo+Q5GixWucZoCVHkYeEbtMiORFRREZahpvv//o8nl3xM/72+veSZkLojFAo0WW1kwebplHqy6DImxZ1BMKv5pzA/LJhQPjhbhMamhDoQvCjw44m3Sv51uKH2NeNswvCOfM7mg7w/t61vf45ekO6PSuidhkHtfPYvFw34iccX3gGbt3T5TUSgYHG0oYKyyLfx9sNxjusdHYBjtmI7McQWvKdhipSi9q65qSNonr2xeVWm6BIIQ40L0FiZeqSZI+p8Y7fxnvNOsG4zHFaeCPiOiEenfcLbrvtNoQQ3X4tWdK+uE5lZSULFizg7LPP5vLLL++2/5tuuomampq2r23bOtcuUoQxTZPqAw0IwLm3Ab3W3+M1icSUEscAqjofDekOJ98Y171es03TePCYM8j3+Dq8J6VkQ+3uiJ1d6XY351fM6bW9VrGmbiU7m7e1ZbHsN9LIstUzyrMbu2aFs6vj77tJCmpkrAzRkMKBlNanAifszo3kRESF/0aPrms89dwnSR3hNWpEkdUm9EvcNjt/OuZ0Pt+/m+c3raY20MygtEzOGDaODKeTE9+8EzPCClI2obHswCaOyE+c6PTwtLGk2TKpO0Sw/mByHPlUeIa3e82lezih6CyOyD2eH3729W5GEDSaTmoNFxm25tgYHQVZMQv5jQQB2CH9DgQGIMExFWGrSKANioFMcVFmUkV3Hcw7H6zFME7otKiLQhEtUlp9SCcIb0Q0GqWk3jTI1GK1xGvpRMtCZD00oFMYr7nmGs4777xu21RUVLT9u7Kyknnz5jFr1iweeOCBHvt3Op04ndbrCfUnhBBIGZaKcB5owqwP0FzgBT1mN0CvMU3J4TOSs1JxMvCdyXNoDAX5y+dhJ7GANnGSWUXl/N+cEynypXd67d83vcOmxu6rtLcyNqOU2yacTZ6r876Smc9qlqKhY7YVQxE0mQ4coskiZ1dHtWmJZLnfxlGuWAjpS6i5Adn4CGQ9iNCskzeK2uF122239Riiu3jxYqZN+zLUMNITERX+Gz27th/gmWeWhCeDJCQz08PI4YVWm9GvGZtTwNic9tVLXtv1KbXBJossigxd6JxZehEPb7630/cFgjNKv9Zl1Mj+QPd53wKTYa7dpOmJPgWUeJA4Ezk5uU5C+L6NsA1K4KAKxZccf8xY/vTQ2xhG8jm9mpqCfPzJJmYdpjYjir6T4RzLl04nqwhPMAaCxQE7RzuD2Ho759gngD4cjI0g3AjnseBeiNA6RloMJHJzc8nNjUwoe8eOHcybN4+pU6fy0EMPoWnJuebuz2iaxvDxpaxZua1tG64FDJy7G/AXp1l+sD9lUjkjhqn9TFdoQnDLYUdz6ZhpPLdpFfubGinypnHa0DHkuLrO2AgYQf64rmdhcw3BnZPOZ15hRyH4/kLQDHLwvDLIuZdMW5NFmSpd3UuCZmCfKcjT+2pYy/XBz5A130Vk9XxQEC+idnjF80Tkpptu4vrrr2/7vra2lrKysmhNHFD8/LrHwn7iJI3wamz0EwoZ2GzJVYGiv7O9YT+60DBkZFFGIWkyObsivkZ1wuSsWYDg3zseoeYgTa9sRx5nln6NcRlTurxWdKs3Jpnk3Uqerc6Cj70gSzMTd8s5T0TL/L8EDKRQdE1mhodvXjaX+x54w2pTOqBpgs9XVSqHlyImeOxl5Llns6/pAyRWR3uFhezf9ts40hXC3ps5J7gSkfFLhE3dH72hsrKSuXPnUl5ezl133cXevXvb3issVA6QWHLc2dNYu7J96qceMHDuqsef5wWbsGS/U1KUyU9vWpjQMfsrxb50rhw/I+L2z23/hFAEe5l5BWP7tbMLoMQ9iI9bnEB2EWKEK3ywn3zbd0ltTBxerRjgfwsZ2oiwDYlRn9ERtcMrniciKvw3OtZ+tp11n+2A8oxkvFsACAQM6hv8ZGZ07d1XRI/P7sKM0NmlC0GBK5PD80bG2arOmZw1k4mZh7GhfjV1oWoy7NkM9o5A60FA/4B/b5fv5dtrybfXxdrUCJHsMHX2+TXGOUIxnBC6wG7N302hOJShQxJb6TVShABdT845UNE/GZ/7M96tPJuAsc9qUwAIIlgT1Bnn6I0DTkc2PoFIvznmdg0EFi1axPr161m/fj2lpaXt3pNJXCW9P3LcaVP5/X3/Re6pb5dwpTWHcG2vobkkHeyJP0C/+45zSEtzJXzcgcA7e1dH1C7PZV06XKyYnj2H5yv/SVAGKbJXIyyNIu6e2MewCvC/CxY5vOIWk9t6IlJWVtZ2IrJr1y527doVryEHHKuWbw37uZIwxeRg9h2ot9qElGNuwdgeHUYQThtMt3u4e8pFlpbv1YTG8LQxTMk6nKG+UR1srw1WUxXYj3GQdsriqnfoKuS21HEA6+SEwjb5gU8CNvYbcd5oJ4HYo0IBcN+fXrfahE4xDMnUyRVWm6FIIVy2PDIcY+g67SPRCCoNrZepLwaENsTaoAHDxRdfjJSy0y9FbLHbdRZePAd/STqG145p1wi5bARyPTSXZVji7AJYv6nrA1hF3whFqJk4Ias8zpbEH4/Nx4UV30QgcGmhJHZ3CfJirlMsgGCM+4ycuInWqxOR+KNr4YWY3hTEcCRvyuDWbQcYNjg5IwP6K7nONM4pn8XjW97r9oGZ60zj5rELGZpW0E0r61he9RGLdv+bHU1bAPDqaRyRdxzHFpxKTeAAXWmoeLQAmuX7kLDGy9qgziw9jk4p+4T49a1QRMjOXdVsSMJFv64Jhg7JZ/yYEqtNUaQQUkr2Nb2PtTpe7TFb4gGin/o0GOB6XYr+w0VfOZxln27li9U7rTaljSee+ZgjZg3vuaEiaob48lmyf2O3FRp1oTE3f0wCrYofkzJn8J0Rt/HB7jsQJN+aCsCDxBvzGAnT0v1M3EI+1IlI/Jk0axhSgr26OZzTnqS/W2cSO+P6M9eOXMDZ5bPQuln+7vXXct3SR7j7i+cj1vtKFK/vfp6HNv+Gyqatba81GHW8uusZ7l9/J+n2LLQuHlFBqSfJx11QIzUa4vardSAc03puplDEmXUbI6uglGgKCjK448dndFn8QqHoHTIp9Lu+ROJE9vKgx0S4FsTaIIUiLrhcdn703ZNxuexWm9JGfX2iiyMNHBaWHtatswvgwoojsGmps5es8A5nYfkvklWNiFJbrOc+HfRhYLduP6PKjPRjSgfnMe3IEdgAvTY5H8ZCCCZN6P9hqMmITdM5o/wwpuf0LET7xNYPuHnZY0nj9Nrn381zlY8BdJjoJJINDWvw2nyYdG5vZSAr7jZGQ0CGZ63YOuEEeL+G0Lyx7FSh6BV1dclXFdbh0Hn4D5eQn5tmtSmKFEMIjTT7MJInpRHKe7UJ0UEfCs5jY26PQhEvHn9mMYFA8sg5FBX2f/2oZGVoWgGXDz26y/cnZQ3iG8NT7/nlthXithVbbUYnSFwiGmnwVsd0Vy4lHUQaIuteSw8mlcOrn/PdX5zD4JFFaKHkcGQcipSS5mbrcnZTmXV1O7nkg/tZvD8ybY4393zBop0r4mxVZHyw/40uo7fCSD4+8L8u390ZCC8+kiPKC1xChoMs+9RL60TQcorlXIDwfadPPSoUsSI3JwmdShKCSTr3Kfo/FRkXkBwpjRKfkAyyRfNZb5lHbKMR2X9DiOSJllEouuPA/npeevVTTOuEWjvwjYuPstqElOaKYcdwdvlMnNqXSkt2oXNKyVR+N/2ylIruOpgRmd+22IL295hA4gSyI9bvckL2k4jsR8F5HIh8EJkg0gEbaDngvRSR+xzCNizGtkeHcnj1c9KzvNzz+FUcftzYpK3UWF2TfJEBqcAvP38WvxHEjHBBLoCntnwYX6MiZHdzZZfRWz0hEBS5wlU/E/eR7+p3LMnWTNwtT1J/n9ZnEuxTwX06IvufiMzfqE2KImmYMK4UpzNusp+9IhA0+Pvj71tthiJFKfWdTqHnuJbvrFlfaUjKdJMZzhC2aExwzEZkP4rIeRqhKw1VRfLTUN/Mb370NF895pcEQ8mTTjxn1nAqBuVabUbKIqXkrlXP89TWDwmYX0b1GdLk+R2f8ML2Tyy0Lr4U+Y4jwzkeKyOJWytF6i1zzSxXEGck5rgWIvJeR3OMRTimo2Xdi1bwLlrBx2gFS9AKv0DL/wAt7bsIvTC+P0QEKIdXCmB32HCku602o0uUblvs2Vy/h5XVWyN2dkHYZbOxfnf8jIoCh+bsIcKrIwJBobOEW8b8hgVF1yZ4fggL1LdHogEj7UZbpJm7T09UDRxT0TJ+jnBMVZpEiqRC1zSOmGJNOenu+PcLywgl0eZIkToIoTM5/9eMz72tJb0RErlsnmgPcrQryFiHgT3a6SDwP2TzqyRHhJpC0T3+5iA/+Nqf+e8zn2AEQkkTvn/C/HHc8aPTrTYjpXl7zyqe2ho+jD/4r966v/nlF8+xtWGfBZbFH03YOazwAQq9x/XcOA7YgDLNYIYjwFxnkDEOAzvQGMntZ5/Yrw5Tkuu4VtFrPlqy0WoTumTx0k0MG9J/bor+wLbG/b26zqknR8TQxMwZfFL1XlTXSCS7/Du4e80P8Gm7mOQJT45W+YUyhGSMwyBNxGphZkIwOVJOFYqDeeWpxfzlrpepbWiGsoykiib2+0NU1zQmZ8qlot8jhE5Z2lmUpZ2FlAa7G95m6d5vxXlUSbqQFNn6OLc0PgJaHvi+ERuzFIo48erTi1m/qhJk+HhRawphum2WzzWbtuwnZJjYdBUfEi+e3PI+GqLLA3yJ5InN7/Pdsacm2LLEYNfSmJJ/N02hGznQtJiQ2YQUEptw8fn+n2PIxriM6xMmhzlDbQpcQoApw0c6finw9nRY0vBH8F4QF9vigbqDU4SmJNXJ0jRBdU18btaBjNfmivoaXWgcWzg+DtZEz7iMKRS5yqKO8gIwzGomesKVHRO7FgpHebkwyREGpTYTrwjX8RJRCTx2R3I4JBWKVl5+8mN+++NnqK9tIpQe/XMnEYSM5IgGUKQ2Quj4zUSUkRcMsxkxCXKRDX9GykDfO1Io4shLT3zc/gUjOaJ2V6/dyfsfrbfajJTmi5odPWarPLdjCTWB1N5Lum1FlKSdyqCMc6lIP4/StIVMzP05bXqMMURDMt0Zwkb7/YvW8u9sPYLJx9yDNBtiblu8UA6vFMHndVptQqdICXnq5D3mTMgsJ8sRefU+gcAmNM4ddHgcrYocXeh8c9jNlLgHAaChIyJ8HBXY6wDrDv6aEeyXGp8Hdd5qttMcM81sgXDOjVVnCkWfCfiD/PXul9u+D/kclp+4d8aSZZusNkExQHDoiakQvDKo81lQ7/v8ImshsCwmNikU8WJPZXVbPpsETE9yzDVCwH/f+MJqM1Iau9bz2t9vhnhg/WsJsCY5aAxuZ/meH7Bs7w1A7J2/RbqJU4QdXJ0R2WGLRAY+iKVZcUU5vFKEIYPzrDahUzRNcOy8MVabkXLYNJ0rh8/vsZ3WInTltTn5zdSLKfcmj/Bmuj2TG0bewbXDfsTc/BOYlTMPLYKTjHLnvi4f0vFHtPsKARtCsTh9EeGqJu6FMehLoYgNn7y7jvra5i9fsO7G65ann11qtQmKFEdKEykl+e4jsInID5t6SwiNHYbO+35bDCK9/LEwSaGIG+mZnrZ/B7NcSTPXSAk7dlZZbUZKc0T+aEQEorzPb/+EplDqR6s2BLfwXuW57Gx4GRkHZxdAjibprghqxL7m6mswq69HGr2T2UkkSsMrRZg7eySfLNtitRkdWHDMODIzPD03VETN6WWH0RDyc//aRYSkgS40zJaV8VEFY8iyezGRjMss47iiCbh0h8UWd0QIwbC0MQxLCztF3bqX1/c81+01NhGzkKoYIKgyBVL29TDSjsj+K0JLj5VhCkWfqdpf3+57ETSQDj0pTt4PZteeGqtNUKQgUprsqH+ezbX/oDawGoEg1z2bEt+pbKn7Z0JsyNZlH283AbbhsTJHoYgL80+fymN/eB1DynDqfBLNMfUNymEcT84bNJsXd/R8aNVsBtnVXM1gX2prQn++7w5CZn3cnF3w5bF93zGh+WVkcAXk/AuhJSYCujcoh1eK8MKrK602oVN271Ubke7YUV/LG9s34A+FGJWdx+FFg9CimOgvHHwEp5RM5b+7VrK7qYZsp4/5RRPIdfbPNNKTis+hwajjw/1vttP3MvnSyVUd8pBnr0uWA0D8aOwxBQWR5Lx3iQDbyJjZpFDEgtz89g5Ye52fQE7yHWAoQWFFrJFSsnLfj9hR/yzhZAiJRLKv6X0kBoWe49nV+F8gfgcwBZrJSFtfNj06OI9E6EUxs0mhiAcnfWUGLz3xEfvrmpImuquV2romq01IaUakFzG/cAKLdvW8j3UlSeGteNEUrGRf8/txH6dGCgpj1psBRiWy4S+ItBtj1musUQ6vFGDj5r2sWbfLajM6ZemKrVabkJQ0h4L88P1FPLPhcyAc6WRKSZkvg3vnnsLkvOKI+8pweDirfGa8TE0outD5SvnXmZd/EosP/I/aYA2Z9iymZx/Jy7v+xbKq99kayKXAUWe1qe1YG9Qp0EN96MGPNPYjbGpjokgepsweTka2l5oDYWFSvS6A5nVguqyvnnUwUyYOstoERYqxs+HlFmcXHOzUaj1139W4iGEZV7G+5g9xGX+QbjDaEY1wvU57rRcdtBxE+m0xt02hiDWZ2T7uevRKfvydf7C2qbnnCxJIU1OQmtomMtLdVpuSslw8dG63Di+BYKgvn0JXZuKMsoD60OaEjLMjpDGypTBKbJZyBjQ+gfTdgEiiteHBqGPRFGBHZbXVJnSJaUpCoeSotpJMXPv28/x74xdIWgQ6W1a1OxpqOf+Vx1lfnfz50PGk0FXCKcVf4YJBV3JS8bnku4o4uehc3LqHqpCXmlDyVIsTSLyxeL6HkjNKUzFwsdl1rvrhl6XABeDcXY9e0xypqmlCuOTC2VaboEgxNtc+RndLZIFGQ5w2Jw4kIWCJ38bnQZ0qQ3Rzu+ngmAXuM4GW4kXCC56vInL+raK7FP2GorJs/vD41Tjssa9K11e+/f1/EjKSSU4jtRiWVsjsvJFtusOHIpFcNuzopHWmxIpE6EMCBBFsCGmxPbeUNUByOasPRjm8UgCfLzkrNLbS2JT6IoPRsHzvTv67dX2bk+tgTCkJGAZ/WPlhxP35jSAb6naztWEfpkzdCTnHmc8NI3/OCN9omkxH0uy3JYLyPqWdtOD/X9/7UChizFEnTuCHv72AgpKwNoOQ4KxqRj+QHGkeF31lJhXlyVOMQ5Ea1AZW0V26osTAH9qH2xZ5NPaXdL/0DiCoNDT2mRo7DI2PAnZWBPRORIY10IsRGXehZdyOKFiOyF+KyP8ELf1mhJ6cxYwUiq6w23VGjkg+J+2mLfv4aPEGq81IaX428Vym5QwBQBcautDQEGgIbhh1MscUjrfYwviT5hiBJhJzoL8+pFNvxPLs0kHboUsSolIaU4DxY0rQWlLikg0hwOVK7ZzraHl24xfYhEaoC+eUISXPb1rFr+acgK2bcr1NoQB/Xv86/972MQ1GWFSz2J3FxUPmclrptJQ8Ccl1FvDN4beybI9kZ8PTtNWxtgQJCAbpBjlaX+0QIJPDgaBQHMqc48Zx+LFjWL1iG9X768kryuQ7tz1FfYO1hxnnnjGdy756pKU2KFITDTtmt9UNBbrmZFzmrSze/Y2o+nZpYdFlXXPTENpCZ4412RLp0PrfXaaGKwSj7AcdrnivQngvRmgZYYuEDsIXlS0KRbJRXprFp59vt9qMDrz+9mpmz1QFIOKFz+bivmmX8mn1Nl7b9SkNoWbKvbmcVDKl3+oSR8uq/b/AlImKkhJ8GLAz0xnEJ+hjeqMOrlMRInnjqJTDKwXYtGVfUjq7AI6YNRyHXX3MDqba34TswVETNE0aQ0HSHZ17y5uNIFcv/gtf1GzHPKivnU1V/Pzzf7O9cT/XjFwQU7uTiaEZ57Gz4V+W2pAmJINtBkW6GYOwYIGwDYuFWQpFXNA0jTGTw1pZq9butNzZpeuCuvrkDZ9X9G8KPPOobHipm0pZkgLPPPI8synwHMvuxtci7rvZbNFcjSogW7A1pDHMZmATADrCd03YyaVQpBBFBZlWm9Ap9Q1qvok3QggmZJUzIavcalMSTlNoJ9vqn0nomCEE7/rt5GqS/JbDe2/UPisNhBPhuyIeJsaM5HXFKSJm+afbrDahS3JzBoZXPhpKfRk9tkmzO/DZHV2+/8y2j/j8EGcXfBnv9Mim/7GhLjkLGcSCdOcoSn2nWzCyZJAWYr4rwGxXiGJbLJxdAKJFg0WhSH5ef+sLq03AMCSr1+602gxFijI442st/+rsAa/h0LIp9p0MwKisGxJik4nggNlij32acnYpUpLjjxlrtQkd0ISgtDjbajMUKczOhkV0Pt/EG8E+U+OLoJ1QVOO3uJD0EkT23xG2wXGxLlYoh1cKIJM0ugvgmeeXsmHTHqvNSCrOHj6+24g8XQjOGzERrRtPytNbP6K7dD5daPxn++K+mJn0jM+9jQL3sQkft8xuxvDBGe5JpN+K0Ati1qtCEU+2J0mhFKdDpcsr4kO6cxST8+9CYCP8nBa0Pq+dejYziv6CTfOwu/EtPtz11YTZ9aWOlyoGpEhN8vPSKSrs+WA4kZhSctLxE6w2Q5HChMw6hGVuGYkbk4xo5Fnc5yCyHkbk/hdhT359NeXwSgHGjy212oQu0TXBsy8tt9qMpKI8LZOrJ8zq9D1dCAo9aVw5YUa3fexoPNBtUqQhTbY2pHalRyF0JhfchY3MhI3pBHxarMr4AvZJiKwHEJ7zYtShQhF/bDbrlw5CCObMUnoqivhR6J3P0eWvMzLr2xR6jqXIezwTcu9gbukrpDmGs6/pAz7Z/S38RqLmWkmaaJn5g6qqryJ1CQRCVpvQjq+cdRhDB6siEIr44bUPaqnPm1g0JCW6yTC70UlhlK4RrhMQzsOTWrfrYJS4UgowekQRdptOMJR8J36GKVm7frfVZiQdN0yZQ77Hy+9WfMCepgYgHDJ9wqCR/HjG0eS4PN1e77E5qQ91rSegCUGaLTGVPqxEEzaGZ13BqqpfW21K9GT/C82hTgwV/Y9J48t45/11lo2vaQK3y8FJxyf/qaKif+PUsxmaeVmn760+cDfhSOtERNlLfELibctiTL71nkIRC/btr6OqutFqM4Dwof23rzqWU0+cZLUpihSn0DOfz8XthGRDwsZ0YXK4K0T0sfIu6AdRXQejHF4pQjI6u1pxOdXH7FCEEFw0egrnj5zEFwd20xwKMSQjm1y3N6Lrjy+ayH+2L8bootKjKSXHFg0MZ4rHnjhxSz/QLMORXn2K8hIehH1EjKxSKBJHKGTw3ofWOLs0TWCaEp/Xya9+djZZmZE9LxWKWFMf2ERtYHWCRgvXapzkaD3918A+MOZ3xcDjd39+EzOaUJM4YrfrnHbSZKvNUAwAdM3FuJxbWb7vewkaUXK4M+zsim4/I8BzHkLrXxWBlSciRfB6HDQ0Wls1qyuOOFxt7LvCpmlMyC2K+roLBs/hpcpl+I1gB+F6XWgM9RUwJ29krMxMWrbV/YvP9t2RwBEFW0I6I2x9cTBr4dx3kfoReIrU4/mXV7BsZeILpQgBc+eMZPLEcubPG4Pb1XVRD4Ui3gTMqjiPEJ7XBZCjScbaQ7jbMkdMhPfiOI+vUCSe6ppG/vfuGqvNaCNHFd5SJAgpJTsaXkjYeIN0A7uIxtmlAwY45iDSboyjZfGhfyReKnpk/rzkq2oCkJXp4fhjxlltRspR6snh99MvI8cZ9rDbhIbekkc9IbOc+6Zfgk1L7QpOuxr+y6f7bkMSTOi4m0Mae6IqJ38odnCfHStzFIqE8u8XlmJFnRQhBLd892ROPWGScnYpLMcV9yIj4V1IuS6Z5mx1drUs2d1fBeeCOI+vUCSeHTurMZIkuksIwSkLVCSlIjFsqX2MvU3/S9h4w+wRVJkXXnDMAttwcB6JyPxDWHdY9L81mIrwShHOPWM6/3lxmdVmtCMtzcX//fxcfF6n1aakJOMyy3j2qO/x3t41rKrdgV3oHJ43ktEZJVabFneklHy272fWjI2gMqSTJgw8Wqs90ZySBKH668icFxFa91ptCkUyIaVk6/YDloxtmpLv/ugp7rz1DJxOVZ1RYS0eewnZrmkcaF4K9OkEpBsElaad0ZgtY7SME/gQAh+Ds/viNgpFf8OVRM92j9vOiGEFSCkRMatUpFB0REqDjTUPJXTMiBxAMoSW/bd4m5IQVIRXilBclImuJdef8zd3nsuQClXVJJ7YNJ2jCsZw5fD5XDbs6AHh7AJYdeDXBMxINt6xvyeG2UJMdhq4D1r/RLcWMsHYAc3Pxdo0hSKuCCGw262LHF22ciu///Oblo2vUBzMqKzvoUV5bpxuH49Hr4i4vSGDdBDFN9Yjqy5G+j+MamyFItkZPCiXooIMq80AoKk5yPU3P8mv7301aTTFFKlJQ2gbzcauiNqKXkjMd0ZkxzQmsuFhpFkTkzGtJLk8JIo+4fUmV4hhcWGm1SYoUpD6wEY21z4SYevYnrxnCJNh9nCffTvwE8imxOXqKxSxoL7Bj023zuFlmpIXXl3Jh4s30NDot8wOxcBGSsn2uv+wYu/3MIlcO3Vi7i+ZU/pPxubeFPE1WVpnVSAlYCJrf4q0Ir9YoYgTmia4+ILZVpsB0ObkevHVlTz+9McWW6NIZaSMXBd4VPYNxMJ9Ux3R9iiIrLsTuWcOsvnVPo9pJcrhlULMmDbEahPamDS+DI9HpTIqYs/WuictG9slJM0x8aFJkP3/xEQxsPj3C0tpara2OIphmHz/1qdZ+JXf8X+/X0RjU3IWa1GkLmuqfsPKfbfQENoS8TVDMi6jJO0kADbX/oNIlt+5msG0tsqMhyLBWA+hLyK2QaHoD8w6bKjVJnTg8ac/JhTqS7EihaJrPPYybFrPBRJKfAsZnHEhpb6FfR5zfSjSIBkJBJDV30YGV/Z5XKtQDq8UQUpJWlpyVH3TdcG3rjzWajMUKUq137oH7h5To8oUMRDt1sE2LBYmKRQJ44WXV1giWN8ZgaDB8y+v4Pqbn8Af6MopoFDElhr/F2ys+UvLd5HcDAKnnsvwrKvbXolE90sgmeyIYINt7IzABoWi/1BX32y1CR2oqW1iw6a9VpuhSFF04aA87Ry6dssIXHoBE3J/AoDPMZjWwia9wa2VcVjZJ+C7OcIrJCCQ9Q/2ekyrUQ6vFOGtd9fwzHNLrTYDgF/99GyGDlbaXYr4EKv89d4gEX1MZWzFQLjPjUVHCkXC2Lu/3moT2mGaklVrdvLyfz+12hTFAGFr3VMIIk3rFdi1dKYX/BH9oKpWIoKNildzohFB6ryWE6EtCkX/IDvLi6Yln0h8yIhXcQqFAoZnfpNs15SW7778/At0bJqPaQW/R4jw3JPrnk1kBy4d8dmHMavkYYTQCNqnEbk8nQH+1/ttGr2q0pgi/PGvb1ttQhtjRhVbbYIihSnwHEWV/xPLxq8yNAq0Poa2u84Ch6qwpehfZKS7OVDVYLUZ7RACnntpOQtPmmy1KYoBQF1gPZKenv+CDMc4Cr3zKUs7A4ee2e7dHPdM9jS+2U0/giJ7PkJE4GC2T4zEbIWi3+BxO5g0vpylKyJPGY43DoeNinLlXFbEj/rgRjKdEwGNhuBmgkYddt1Hie9kBqWfj9tW1NY23TGCHNdMDjQv7nIeGZpxBT7HMHbWv4Tf2I/HXkZ52rm4bcWsq/4jO+qfw5R+NOwU6Saj7Qa2Hv3MQcLRydZpufYW5fBKAbbvOMCu3dbrAQkhGD40H487ucTzFalFadoZrK36AybWhL3vMDSG2w10GalwvRNoEdjWChDeS8HzNVXmWtHvOOHYcfzz6Y+TqmKVlLBrd63VZigGCHbNS/j0vet7wKFnM7vkn12+X5F+PrsbX+t2nBJHPoQ29mQNQqhEDUXqceqJE5PG4aVpghPmj8OrdIkVcSBkNrJsz3fZ2/R2S/SwQGKgCTsjs75DadppnV43Of/XfLzr69QGVhFO2DPb/lvmO5sRWdcihEaJ76S2a+qDm3mv8hxCZl2bo8xEUGloeIVksM3sZl8jQK9oizLrb6iZMgV44ZXkEJGTUvKVs1TUiiK+OPRMZhQ+iFUnDCEEywI2TIhAz0gH1/GI3FcQuYsQeW8hvJeoTYqiX3LmaVPJSHejJ1m6SXp6cuhXKlKfQu/xdOfsEugUe0/stg+/sa+HUSR+LYfuNVo0cMzqoR9FrDj11FMpLy/H5XJRVFTEV7/6VSorK602K2XZvqPKahMQhA81hw3J5xuXHGW1OYoUZfme77G36R0AJAaSECAxZYCV+25hb+O7nV7n0LM4vPgxJuffQ4HnaLKcUyn1LWRW0aOMz7u1033Gp3tvbefsakUi2BrqeV8iPF+N/gdMEtSuKwVYv2mP1SYAoGuCeUeMtNoMxQAgyz2Jo8tfx6kVWDL+flOwJBBhgKyWhbANQdj678mIQgGQk+3j93dfwIjhhTHpLxZRjpomWHDsuBhYo1D0TLH3BNy2ki50vDQ04aQi/YJu+9hW9zTdL7811jfvBOGma6eXifBeEpnRij4zb948nnzySdasWcPTTz/Nhg0bOOuss6w2K2Wpq29G1605WLHbNGw2jdKSLL55+Tzu+9X5KrpLERdqA2vY0/QWXRcxEayrvr/Td/zGfrbUPk5V8zIyneOYlHcnE/J+Spar8zT3+sAmqvyfdJkC2YzG58GuRfNxHAme/qs9rFIaU4BkEVI0TElNbROZGR6rTVEMABqDW/Cbuy0bv1DvLvS3FQPhOjkR5igUCaGkKIs/3vNVPvtiB1ff+Giv+3G57JyyYAKapvHEM4t71YemCTIzPEq/S5EwdM3FzMKHWLz7m9QH1yNaltGSEA4ti6kF9+KxlyKlwZ6md9jb+C5SBslwjqXYdxI2zUtTqJLuqzSa1IX2IXL+iKz6OhA4qL0OGIi07yGcs+P6syq+5Lrrrmv796BBg/jBD37AwoULCQaD2O3WFdJJVYqLMjEMa1LngyGTkqJM/vL7S3A61DZZET92NbyGQO9Gz9Gk2r8Cf2gfTltu26sbqv/C2qr7kBgt15usqfotg9K+wpic73d6uF4XXNfpCAJJqW4yyGbg08KZK+32Nloxwvs18FyIEP33Wafu5BQgLyfNahPaePu9NZx2otp8KOLPltrH+TJvPVGES/MWaJJyvadxNXAeBfYJiTBMoUgo48aUMG3yIJau2Bq1ptf1Vx/L8ceM470P1/PPpz/u8P6wIfnkZHv5aMmmbvsZUpHHT24+TR2yKBKK217MESXPsL/5I/Y2vYeUBlnOiRR4j0ETdhqDO1i86xs0hDa3OMQk2+qfZtWBu5iS/3849TwaQ9vpOjVS4NRzEc6ZkPcqsvFx8L8BMgD2yeD5Csh6ZP39gA2ccxD20Yn7BQxwDhw4wKOPPsrhhx+unF1x4pijRvObP/w3AtmI+LBjZzX/+s8SLjhnpjUGKAYEhtlI96nrYUKykdYYw621T7Gm6p6298IpkGG21D2GrrkZlX0dh6KLjtIPAslkR4g87csb7Utnl4D0XyDcC1NCc1g5vFKA/LzkcXi9/tZq5fBSJIQa/2ckwtnltQ2hMbQZiYkdGGkLUdKtsCOAANcJiIyfp8REoVB0xgXnzGTJssiFhbOzvPzouyczYVwpN/zwSZZ/uq3Tdus37qGmNo0hFbls3LwPXdcwDBNNE5imZGhFHt/+5rFMGFuq7i+FJQihkeueRa67vY6WIQN8vOsymkI7gfabEUM2sWT3tQzL/DpV/qXd9C4pTTs9PI5ehEi7DtLCGxgZXIOsvgZpbCEc7SWh/tdI+0xE1m8QWnYsf0zFQXz/+9/nd7/7HY2NjcycOZMXXnih2/Z+vx+/39/2fW2tKq4RKWk+F7quEQpZl8Hy9LOfKIeXIq74HIPbzRGdoQsXLj0fAFOGWFv9+27bb655hKEZl2LXM9q9nuOaji48GLKx7bVy3SRPk13vZ2p/DK55IDJ7/FmSnbhqeCmRx8RQkJ9utQltVNc29txIoYgBunAnZJyJ+XdwwuCVnFDxKccM+oRSz4TuRef1MZD1IFrmPYgE2ahQWMGUiYO4+YaTsNt0hBDomkDXw/dGfp6PcaNLGDGsgFNPmMijD17Bvx+9mgnjSrnmu4916exqZe++OjZt2U9JcSbHHT2W2TOHcfLxE/j93Rfwl99fzMRxZcrZpUg6djUsojG0vYsUFQmYNAS3kmYf0akOmEDHZx9Cie+Ujlcbu5AHLgBje8srBm2HPsHFyAOXIGUwVj9KynPbbbchhOj2a8mSJW3tv/vd77Js2TIWLVqErutcdNFFyG5CkO68804yMjLavsrKyhLxYyliRFWN2s8o4kuR98SWvUznaxmBTmnaGdQGVrN093W8vvVIAj0UPTEJsqdFBD9o1LC++s+8te0kXt92zCH7JskgW1eplOH3IQBN/4nmR0pa4hrhNW/ePG6++WaKiorYsWMHN954I2eddRbvv/9+PIcdcGRnea02oY3SokyrTVAMEAq8x1BXvZ54Rnn57EPJcIQFscObazsy889QdRGEVnV+kbEaqq5GZv8N4VDRjorU5vhjxjJj2mBeff0zNm7eh8tpZ86s4YweUcTLr33KS4tW8r/31/LFmp2cfPwETFOyas3OiPqWUrJzVw3TJlfwk5tOY/PWfeElWCCE06lSiRTJx+6G1+ku1V5isLvxdeaVLWLl3lvY0/Q2B6c25roPZ0LeHdi0jmm6svERkA3QqTPNCM9J/tfBtSAWP0rKc80113Deeed126aioqLt37m5ueTm5jJixAhGjx5NWVkZH374IbNmdV4t86abbuL6669v+762tlY5vaIgN9vHrj0qKk6Rutg0DxPybmfZnhs5dN4Q6LhtpQip8cHOC6PqN2TW0xTayQeVF9Fs7D6o3y8dazoanh7DnjRk8PMIki6Tn7g6vJTIY2JIhvK9rVx4niqTrUgM5WnnsLnmEUKyIS79a8LJpLxfArCz/hU21v6NGv+nDLcZDLEZ3aQ0mkAAWX0d5L3RfTSYQpECZGZ4OPeMw9q+P1DVwJXXPcL2yiqQ4e18TW0Tv73/NWz26CqVmqbkhVdW8sbbq6mrbwbA43Fw+kmTufjC2TjsSplBkTyEZCM9HcKYMoBDz2Ra4e9oDG7jQPNSQJLlmoLXXt71hU3/oXNnVysasul5hHJ4RUSrA6s3tEZ2HZyyeChOpxOnU1X36y1nnDqFPzz4lmXjFxVmWja2YuBQ5D0eR2EW66rv50BzuICPLjwUeU8g3TGCLw7cGXWfPvsQlu/5Pn5jD+3no9bDFQ2PbSjwec+dCUfU4ycjCVspRiLyqPLde0d9vb9N28RKJowrZczIYkttUAwcXLY8Div8Mx/v/gYhsy6mfWvCzZySp/DZK1h14C421TwMaNiQVHTr7GrFBLMSAu+Dc05MbVMokp1f3PMylTur2wkOt/47GOxuw945hmG2ObsAGhsDPPavj1mzfje//OlZ2HTlVFYkB+mOEexv+rCbqlvhzUgrHnsZHnuEUT89znMmmDWR9aWImI8//piPP/6YOXPmkJWVxcaNG/nxj3/M0KFDu4zuUvSdaA9HYs1F5yn9LkViyHEfRo77MJqCu9lU+zA76p5ne/3TvehJ4LYVY9PSe9CJNKkPbSLgno4j9AldH9IYCOe8Tt+R0gBzLwhHv9COjPsq8fvf/z5er5ecnBy2bt3Ks88+22Vble/eO4qKMix3do0cXsA5C6fz4eIN1NY1WWqLYuCQ6ZrA0WWvMzbnR6TZx8SuX8c4fPYK9jV92OLsAjDJ1U30iGN7NQitiZlNCkV/YMfOKj5ashEjznOSlJIlyzbz5v+6SC1WKCygLO2sbp1d4Tbn9K5zvYzuK3rpYBvUu74VXeJ2u3nmmWc45phjGDlyJJdeeinjxo3j7bffVhFcceS1N617ts+ZNYzjjh5n2fiKgYdh+lm+90Y21z5KUFb3shfJ0IzLqfGvjKBliDr7XLp2dumgV4BzbvvrZABZ/wfk3iOQe49E7pmJuW8hsvnlXtqcGKJ2eMVT5PGmm26ipqam7Wvbtu5FbRVh5s0ZictpTVqHJmDcqGLWrd/NLbf/m+/f+jSnX/B77v7dqzQ3K/FURfyxaR4GpZ/LEaVPMi3//pj0WeVfweJdV7H2wG/bCQtHd5dJ6KQMsEKRynyxOnGFaTRN8NxLKxI2nkLREx5bGQ4tq9s24TST6BGe7vWmwEC4e+lMU3TJ+PHjeeONN9i/fz/Nzc1s2rSJ+++/n5KSEqtNS2kOHKhP+Jj5uWlc+41j+OnNC9G0VFAuUvQXNtc+SpV/OX3TJRZsqX2MoBmZ1ItwTECk/5SwO0gjfKDS4hrSSxBZf0WIL3c+UgaQVVcg638L5kHi+aFVyOpvI+v/1Afb40vUXpJ4ijyqfPfe4fE4+fZV8/nlbxLvXTUlfHbIBicUMnnhlZVs217FXXeco9JNFAnDpncU+u0NkgB7m97j0ImnQUa5AOoiFFihSFUSWTnRNGVYJ0yhSBL2N39EwOz+M7m17nGGZ12FJqLUsvWcC83PQ/BTOt0Uuc9DOCZG16dCkaTk56Wze29dt5UwD8bndfL1i48kJ9vHH//6Ftsi0Dc+9YQJXHLBHBxOO4Zhkp7mUtV/FQlHSsmW2sc4uIBJL3uiLriONVW/jbR5+CDFeRSy8alwVopwIZzHgOtYxKH6XY1PQuDDTuwMfy/r7wbXcQjb4D7+HLEnaodXvEUeFb3jxOPGs3tPDQ8/lhwVME1TsmzlVt77YB1HzRlptTmKAUJjcHvPjSKm44aiyhTsDUGOBlq3flwNXKcidKVppxhYxOpUXNcFhtHz4i/Np6IoFclDVfMyBHq3aY1Bs5aG4BbSHMOi6lsIJ2T9DVl/DzQ9CbJFPkLLRXgvB8/FfbBcoUguTjp+Ais/j3xNV9/gZ8Gx43A67dhsOt+/9V89XvPcyyt5+bXPOWH+OL55+Tzl7FJYgikDNBu7YthjKII2gh0Nz5PrmYHQixBp3+rxCtn4aA8tdGTjk4j070dkZSKJWx6cEnlMPJdcOAeHw8YDD//PalOA8MbnxUUrlcNLkTDsekacRxB8EnTgEyYjbAb5ti425M55iIyfxtkWhSK52Le/jjvvfqnP/UyZWM7Qwfl4vQ4efrTrQxwhBMcfM7bP4ykUsSOyiHbRy0LvQvMg0n+I9F0HxibCul3D2qWdKBSpwNFHjeLZl5axeu2uiHWKAwEDp9POoPLIRbSDQYPnXlrB2++t5cRjx3HygomUliS/CLciddCEDYGG7FM6Y7RI6gProrvE2Ez3UWgGhNb3wab4EbdcMyXymHi2bt/P0899YrUZbZimZNceVWlTkThyXbOwaWlxH6deCpYGbewOtW5aBGgl4DoDkfMvtKz7EUq/SzGAeOPtVVx05V8J9KIK48FomuCwqYO55utHc96Zh1FclIneSdSYrglysrycvEClcCmShxz3YT2K1ju0HDz29uLypgyyu+FNttQ+zs6GRRhmcxdXhxGaB2Efi7CPUs4uRUrisNu4+/ZzOO7osWgRRF5lZXrwesP7yz/99e2ox6upaeLxZxZzwRUP8sBDb0ecSqlQ9BUhdPI9R7fTDE7AqNg0b5SX9OS/0UCLjbRMrInbLNkq8qhIDIZh8r0f/4uq6karTWlHbrbPahMUAwhdczIi8xq+OHBn121Iw6Cn8u49EV58fWbkkF/wDzQ9H6Gpz7piYPLyfz/lF/fERkNSCIE/EA7Hd7sc3PvLr/DjO/7DF2t2tqVLmqZkUHkuP7tlIRnp7piMq1DEgiznZNIdo6kLrO3S8TU442toBzmpKutf4vP9dxI8SPvLJryMzL6OQek9CdUrFKmLx+PkputP5IJzZnLxlX/FMDuPgBHA6SdPQdMElTurefOd3lXIbvVxPfrUR+TmpnHGKVN6ablCER1DMy9nT+ObhD/NXTlbBTmuw9jf/FEMRpQUeRdEd4nzBGj+D3R5qGMinMf30a74oNTEU4T3P1rPzl01EYf9Joo5s6LTqFAo+sqg9PMZlf1dtJYIq9YTE124GZtzC3NKnsIm0mMyVtCsZX9wm3J2KQYsfn+Qe//0esz6MwyTYYPz277Py03j/nu+yh/v+SqXX3QEl331CO791Vf46+8vprS4+2p4CkWiEUIwteBeXLbC1lda/j88DxV7T2ZIxtfa2u9s+C/L936vnbMLICQb+Hz/7WypfTwhdisUyUx5aTY/uP4EhIDOgr10m4YQEAga/PmR2Mi6/OOJDzCMRKaYKQYymc5xTCn4LXq32SGS/c0fUew9hdbKigIbX1ZYjBQNl15Ise/kqGwUvssAvYuxdNAHg2t+VH0mChUHnSI88vgHVpvQKaNHKNFuRWIRQjAk42uUpZ3J7obX8Rt7cen5FHiPxaZ52FH/PCEZq1RbQWOosudmCkWK8u6H62lsDMSkL00IMjM9zDxsaIf3Ro8sYvTIopiMo1DEE7etiCNK/k1l/QvsqH+RoFmDzz6YsvSzyXXNahPGltJk9YFf092J/pqq31LqOx1dU1IgioHNcUePJTfHxy/vebmDXEooZPKXv7/Lcy8tZ+/++piMt/9AA+s37mHk8MKeGysUMaDAM5dxObexYl/Xou8CDb+xl2PK3mBHw4v4Q3tw2nLJcIzjo12XRDSO11bO9ML7sUWZfihswyDrAWT1tSDr+NKNFALbcETWA4hoqw8nCOXwSgEWL93E2vW7rTajU9LSlI6Rwhrsmo/StNPavSalZGP1X+k+ZDgaJA4tLJQvQ9vB3AlaFuhDVbUfxYBgz95aNE30ObpY1wS6rnHbD07Fpqvgc0X/xqZ5KE8/h/L0c7psU+3/lKYeDkxCZh17m96l0HtMrE1UKPodHrejW23gWDm7WgkEIql2p1DEjgPNH3db6Vdisr/5Q2x6ertoYYBCz3HsanyNzqrMA7i0Qsbm/pB8z1EI0bt1lnAeDvnvQfNLyOBngB3hPAocs5J636McXinA088tRQiRlAKLS5dvUWkniqQhaNZSF+y5KonAhoygrK+Ggzx7Aeb+CyH48Zdv2IZD2ncRzrl9sFYRDX6/nxkzZrBixQqWLVvGpEmTrDZpQJCZ4YlJKv3RR43m/LNnMKQir93rfn+QDxZv5EBVAzlZXmYeNhSnQy1dFP2fwCFpjF22MyJrp1CkOs+9vAJdFxhG/Pc7uq5RVqqqNSoSi0kgouN4KUMgHO1em5h3B+beYIsWWEfSXaPJdc/stbOrFSFc4D4D4T6jT/0kEnWMmgJ8sboyKZ1dAIuXbrbaBIWijZ6qZ7WS654dUbuKtJPQqy6F4JL2b4TWI6u+gWx+JVoTFb3ke9/7HsXFKoU60Rxx+AgcfXRAOR02bvnuyR2cXc+/soLTL/g9t/78We7942v8+OfPcsYFv+fFRSv7NJ5CkQy49chSpdw2lcqrUABs3Lw3Ic4uTRPMO2IkmRnJWXFOkbqkO0bTVYRWGIHbVoIuOhbs0TU3Jb5TurxyT+PbrNj7w74b2Q9RDq8UwG5LZBnT6FCCj4pkwqFlYdcyI2gpyXJO7bZEsM8+jOFsAPx0nJwkIJE1P0bK2OgbKbrm5ZdfZtGiRdx1111WmzLg8HmdXHz+4X3qw+XqqPnw4qKV3HXvqzS06IO1nunUN/j51W9e4dXXP+/TmAqF1aQ5RpJmH07XS3GBU88jxz0jkWYpFEmL3R7/baumCQrz07nm60fHfSyF4lBKfKehCTvdidBXpF/YafqglJJ1Vb/v5lqTXY2LqA9ujoWp/Qrl8EoBDp85DF1LvrxZIQRjR6uIC0XyIITArvVcobHWv5ZJ+b/AqefS2WPSrZdwWO7PEKEldHsSI6vB/3av7VX0zO7du7niiiv4+9//jscT2Wms3++ntra23Zei95x/9gyuumxup46rntA0wWFTB/Pnv/2Py655mK9d+Vd+/dtX+MODnYfkt/LHv75FSB2oKPoxQgjG5vwQ0VJt65B3ARib80M0oVJ4FQObzVv3c9udz7Lysx1xHcftsnPemYfxp99eRFamN65jKRSd4dAzmJT3S0AccugenhPy3EcwKP28Tq9tDG2jPriB7jWKNXY3xK6ydn9BzaIpwJmnTuWFV1ZYbUYHbDaNE48bb7UZCkU7dNHzIsakCbetiDkl/2JL7T/ZVvcMfmMfIJEYNBk7WL3vW0zo8QmqgRHfBdpARkrJxRdfzJVXXsm0adPYvHlzRNfdeeed/OQnP4mvcQMIIQTnnXkYp544kf+8uJxNm/awd38DlbuqCQZD1Df4CQYNOsu8N03JG2+vQrb8G2Dr9n2YPfiyDlQ1sPLTbUyZNCj2P5BCkSCy3dOYUfQXvtj/C2oDq9pe99oGMSrnRgo8c60zTqFIAtZu2M21330s7gLyQggGlefwjUuOius4CkVPFHrnc3jxo2ysfpjdjW8gCeK1V1CRfgFlaWd1eQhiyKYe+xZoGLIx1iYnPcrhlQJUlOdwxOHDefvdtVabAoCuC0Bw2w9OVSckiqQj3TGcuuAauo7M0vDZhwLg0LMYlnkVTaGdbK//NweHCdeF9kTwBDUhohRKxcHcdtttPTqkFi9ezPvvv09tbS033XRTVP3fdNNNXH/99W3f19bWUlZW1itbFWE+XLyRPzz4Jlu27W97bdiQfL515TE4HXauvO6RLq81DhG978nZ1Up1zcBbtClSj2zXVOaUPEVdYB3Nod049GzSHaOTuuKVQpEIpJTcefdLBAKhmBRH6Wms1Wt3sXNXNUWFmXEdS6HoiUzneKYU3N2i0S0jEpp320rQsGMS7LKNJNS2xxlIKIdXipDmc1kybnaWl6mTBrF7Ty0bNu3FbteZM2sYZ546tYMAsUKRDJSnn8uOhue6aWFS5D0ef2gfDj2HvU3/a3F2wcFhwnUS6k3wCuh6X+IEpyonHy3XXHMN553Xech2KxUVFdx+++18+OGHOJ3Odu9NmzaNCy64gL/97W+dXut0Ojtco+g97324jh/+7N8dXt+4aS/X3fQEp54wsdPorr6Sn99zerJC0V9IcwwnzTHcajMUiqRh9dqdbNy8N6prykuzOf2UKXz8ySYqd1a3O4SJhJraJuXwUiQN4YOPyA4/7JqPYt8p7Kh/tosiXQK7lkaB99iY2tgfUA6vFCErw4umibifgBxKTW0TP7zxJHUSqUhqpJTsa3qfrXVP0RjcglMvwG/s7rStwM4XB+7kiwN3tggK6wj0TiYPwdqgjSnOEJLOpyPhuwqhpcX4p0l9cnNzyc3N7bHdvffey+233972fWVlJccffzxPPPEEM2YooedEYBgm//e7/wJ0cGqZUiJMeOW1z2I6phCC0uJMxo5SGpEKhUKRioRCBvf84b9RXzdsSD5nnDKFM06ZAsANP3ySJcs2R3StEJCfpw5SFP2XkdnfZn/zRzSHdrXbt7TqgU3MuxNdOKwyzzKUwytFOHbeGP7+xAcJH9cwTExTtqQxKhTJhylDrNj7A3Y2vHKQ4yr8eRXYkIRa/m1HEkIeFApcF1xPd+KPe0yNFQGdMXaJXZiEhYdNwIHwXQXeq+L2cymgvLy83fc+nw+AoUOHUlpaaoVJA46lK7ay70B9l+9LKWlq7jq8PlqEEAgB3/nmfHXQolAoFCnKg4+8w5p1nR9MdsfUye11Ha+/ej7nX/7nHq/TNMHM6UPIzlJSLIr+i1PPYXbx46yv/hPb6p5p0esS5LoPZ1jmlWS5JlptoiUoh1eKUFGew4nHjefl/34al9SRzhACKspz0XVV7FORvGyofpCdDa8CHHTaIdv+m+EYS77nKNZV309H51bPN9NOQ6dRH8XhOZeHBepFFriORURQDVKh6O/s2RtZhUshOkaA9YQmwOVy0NgUaHtt6OA8rr5iHlMmKrF6hUKhSEUamwL8+4Vlvbp26sSKdt+XFGdxzFGjeeN/q7qdg1wuO1deOrdXYyoUyYRDz2JMzg8YlX0DAaMam+bBpg1sR65yeKUQN1x7PD6vkyf/vSTqa202jVAouhLvUsKZp02NeiyFIlGYMsjm2r/TleNKYlAT+By/caAPowgKvSciXAv60IciFlRUVLQIfCoSRWaGO6J2Qoio/zamhNtvWUhmppcDVfXkZPuUNqRCoVCkOKvW7KS5l5HBoVBH7aIbrj2Oyl3VrFqzE0HHFeHwofmceeo0slWhLUUKoQk7LptaM4FyeKUUNl3j6iuOxum08/fHo0tvrBiUy/oNe6K6Zu4RIzlx/viorlEoEkldYD1Bs6aHVoJmY2ev+hfo2LV0ytLO6NX1CkV/Z/rUwfh8Turr/V22KchP5+sXH8kdd72IEALDCB+utOpOCiHQxJfVGnVdYBiSr198JFMnVwDhyC6FQqFQpD6tc0S06Jogq5OURK/HyX2/Op+33l3NC6+uZM+eWrxeJw6HjQ2b9rJuwx5+cc9L2O06xx89lquvmIfHowrbKBSpgnJ4pSDnnXkYb/5vNTt3VXco994ZXznzMP713CcR95+R7ubrFx/JCfPHq3RGRZLT86JJoAFaO+2uzlqFNb4CiJbHpiSEU89jeuH9OPTMmFirUPQ3HHYbX//akfzf77sWF77y0rkcfeQoysty+Nd/lvDBxxswDJPRo4o569SpFBdl8p8Xl/HBRxsIGSbjx5RwxilTGD9W6bApFArFQGP40Hx0TUS0h2lF1wRHzRmJz9u5o8pu15k/byzz541FSsmP7vgP736wrl2aYzBo8NJ/P2Xdxj3c96uv4HTa+/qjKBSKJEA5vFIQn9fJ7+46n//73X9554N1bWkkh2qoOBw2Tpg/jmZ/sNMQ4M7QdY0jZ4/g5AUDU/RO0b/w2YegC0+LaGPnSAzy3LPZ1/ReF2V8w63Czi4dj62UDNd4CjxHU+CZhybUY1QxMNmzt5bFSzdjyvBBy3MvL6exMdA216Snubj2G8dw9JGjABgxtICbbzip076+feWxfPvKgVcqW6FQKBTtycr0Mu/IsO5WJNXnNU3g9ji47KIjum1nGCZLV2zl/Y/W88776zptY5qStet38cprn3HaSZN7Zb9CoUgu1E4tRcnK9PKzWxayZ18d6zbsxqZrbN9RxX0PvA6ENyPBYIhnX1weVb9SSooLM2NvsEIRB3TNzaD0c9lY8zc6i/YS6PjsQxiZdT37mz9scQ53HRUmMWgMbcPfuJ+hGZcrZ5diQNLUHOCue1/l9bfDIsCtDq4hFXmcOH88NptGXm4aM6YNwW7XrTZXoVAoFP2Mb191DBs27WHz1n3tDuvDxXnba0JOnlDOd755LCVFmQSCIew2vUMV38VLN/Gr37zCnn11EY3/3MsrlMNLoUgR1G4txcnPTSM/N43FSzdx759eb/deb7Wdjz9mbAwsUyjiS11gPZtrH2VPw9sIdCQmtJMr1XDo2Uwp+C1eeznTC/7I0j3XETRrENiQhDrtV2JgmI18uu9HHF78WKJ+HIUiKZBScsvP/s3SFVvb5pDW/27Zuo9HnviAv/zuYvJz06wzUqFQKBT9mvQ0N3/4vwt5/uUVPP/ycvbuqyczw80J88dz6gkT2bmnlsZGP6XFWei6xj+f/piXF31KU3MQn9fJScdP4LwzDyM7y8uKz7bx/R//CzPCjY+UsGtPT/qvCoWiv6AcXgOEfzzxYZtAcF+48tKjyMn2xcgqhSI+7Kx/hWV7v4dAHJSmGHZ26cKNy1ZAie9UytPOxqFnAZDjPoyjy95gV8Or7G16j8qGF7vsX2JQ7V9JXWAdaY7h8f+BFIokYemKrSxZtqXT9wxTUl/fzFP/XszVVxydYMsUCoVCkUp43A7OPWM6554xvcN72S17ka3b9/P17/ydhvrmNs2v+gY/T/1nCa+/tYrf330BDzz0NlJGd9Cfke6Jyc+gUCisRymODwCamgMs/3Rbn5xdeblp/PDGkzj3jMNiaJlCEXsagztYvvcHgHmIJlf482/IJsbl3MqwzK+3Obta0TUnJWmnku85KqKx6gLrY2S1QtE/WPTG5+i66PJ905S88tpnCbRIoVAoFAOVn9/9EvUHObtaMU1JVXUDd9z9Ip+tqow4ugtAE4IT5o+LtakKhcIiVITXACAU7F1531ZOWTCRG649rkM+vEKRjGyte7IlfbFzBDqba/9BjrvjiWErunBHNJauRdZOoUgVqmsaMYzuNw51dc1IKdWcoVAoFIq4sX7jHlat2dnl+4YpWfnZ9qj61DVBdraPU0+Y1EfrFApFsqAivAYAPp+zV2mIPp+T88+ewfXXKGeXov9woPkTehKeP9C8pNs+ctwzenR66cJDjktFPCoGFgX56eh690uHnByfmjMUCoVCEVfWb9oT8z5HDC/kvl+fT0a6OtBUKFIF5fAaAAghOPPUKVFvQBoaAjz21Edcds3DEVc1USisRkT0WOu+jU3zMDjj4m7bDMm4BJumNB4UA4sT54/HMLp2KGua4JQTJibQIoVCoVAMRBy2yKoAjxiaj6Z1vQey2TSuumwuf773Iv54z1cpKsiIlYkKhSIJUA6vAcLZp09j0vgyovF5tZb83bJ1H9ff9DjBoNHDFQqF9eS6Z9Ldo02gk+c+vMd+hmdexaC0C9quEdgQ6ICgIv1ChmV+I0YWKxT9h1EjijjxuPGdvqdpguLCTM48dWqCrVIoFArFQGPq5Apstu63sm6Xneuuno/Drnfp9Lr2G8dw3pmHMWJYYTzMVCgUFqMcXgMEh93Gr352Ft+8fB6F+elRXWuYkm07qvjf+2vjZJ1CETvK0s5CE3bCVRk7IjGpyLiwx36E0BibexNHlb7E0MwrKE1byNDMr3NU6YuMyfkBQqjHp2JgcuO1x3PphXPwep1tr2maYO6ckfz+7gtI87kstE6hUCgUA4GMdDennjCp28P8sxdOY8yoEn531wWMHlHU7r283DRuvuEkFp40Oc6WKhQKKxFSRlOkNbHU1taSkZFBTU0N6enROWkU3fOdmx5n2YqtEbfXhOCI2SP46c2nxdEqhSI27G18j0/2fAtTBmnV8xLoSEzG595GWdqZ1hpoIeq52hH1O+kd/kCI1Wt2EggaDB2cR3aW12qTFApFEqCeqR1Rv5P4EAwa3H7XC7z1zhp0XcM0JZomMAyTE48bz43XHt9Od3Lr9v1U7qzB53MyekRRj5qUCoUieYn0uaqqNA4g9u2vY9fuWnw+F01NgaiuNaWksdEfJ8sUitiS55nNUaUvsq3uKfY2vospQ2S7pzIo7Vx8jqFWm6dQpAROh42J48usNkOhUCgUAxS7XecnN53GqjN38urrn3OgqoG8HB8nzB/PsCH5HdqXl+ZQXppjgaUKhcIqlMNrALBt+wF+9+c3+HDxxl73oWuCivLcGFqlUMQXt62QEVnXMiLrWqtNUSgUCoVC0c/x+/3MmDGDFStWsGzZMiZNmmS1SYoWRo8o6pCyqFAoFKA0vFKe7TsOcOX1f+fjTzb1qR/DlJx6oqq8pVAoFAqFQqEYeHzve9+juLjYajMUCoVCEQUqwivFuf8vb9HYGMA0eyfVJoRASsnlXztChQArFAqFQqFQKAYcL7/8MosWLeLpp5/m5ZdfttocRQv7D9Szc3cNbqedjVv2smtPLek+F0fOHkFWptKVVCgUyuGV0lRVN/D+Rxsw+1CXYOjgPC44ZyZHHzkqhpYpFAqFQqFQKBTJz+7du7niiiv4z3/+g8fjiegav9+P3/+l9m1tbW28zBuQbNm2nz88+GYHuRZNCx/U//b+1zjnjOl8/eKj0LRuyjgqFIqURzm8Upg9e+uidnYJAVLCNy49ihPnjyczI7KJXaFQKBQKhUKhSCWklFx88cVceeWVTJs2jc2bN0d03Z133slPfvKT+Bo3QNmybT9XXfcPmpo7FuBqzWgxpOSf//oYTQi+fslRiTZRoVAkEUrDK0XZt7+O3//5jYjaHnzyMaQij9tvOZ3zz5qhnF0KhUKhUCgUipTjtttuQwjR7deSJUu47777qK2t5aabboqq/5tuuomampq2r23btsXpJxl4/P7Pb9DUHJlcy+PPLGbv/jrefGc1f3nkHf7+xAds3Lw3AVYqFIpkISERXqqqSWKpq2/mmhsfY8/ensOnhYAFx47jw8UbCQRDpPlcmKaJaUoVAqxQKBQKhUKhSDmuueYazjvvvG7bVFRUcPvtt/Phhx/idDrbvTdt2jQuuOAC/va3v3V6rdPp7HCNou/s21/HR0siL8RlGCZfveJBmpqD2HQNU0oe/Ns7zJw+hB9//xS8HvU3UihSnYQ4vFqrmqxYsSIRww14nn1xObv21CIjSGfUNI1XXvus7ZRk5efbWf7pNo6dO4Yf3niScnopFAqFQqFQKFKK3NxccnNze2x37733/n979x4cVZmncfw53Uk6CSSNiJCEBAyK3G8GcLkIziiwgqiLssLIyo5TM6uCkmFXYZUZLAsM4kjNFlEcHMupEVzQ4jLgeIuABMZBMCTCEBUViIyAGVZIIiG37nf/iEFjYkiku097zvdTlSpz0nQ9b8DzVP/69Hm1aNGic98fO3ZMEyZM0Nq1a3XVVVeFMyKacaK07fdCO1tVK0mqCwTPHdtdcFgPPrJev82ZJsvitQ7gZGEfeLGrSeS98sa+Vg274mK9qgsEG10S3PDfb75VrLNna/Rf901Qx4vY5QQAAADu0q1bt0bft2/fXpJ02WWXKT093Y5IrpacFB+S5wkGjYr2HdW+A3/XoP4ZIXlOANEprPfwatjV5Pnnn2/VribV1dUqLy9v9IW2++LUmfM+JrWLXzW1gRY///6Xdz7WrXes0Gtv/i2U8QAAAACgTTK6dlRm904KxUVZXq9HW7d/cOFPBCCqhW3g9e1dTVojJydHfr//3FdGBhP376Njx/Yt/tzjsWRZ9Sf68wkEgspZ9or2vlcSqngAAADAD86ll14qYwz3I7aJZVn6j5+OVRs3oW+WMUZnKqsv/IkARLU2D7zCuasJO5qExg0TBrb4efRg0KhbxsWSWtcWHo+l1S+9E6J0AAAAANB2I4Zfpl89cIPat6u/4Xxz9xu+LPMSJSbGnfe50tMuCnk+ANGlzffwCueuJuxoEho3ThysP7++T8dPnFbgWx9ZtCxLw668VCOG9dCuPYda9XzBoFHB3iOqqa1TXGxE9jkAAAAAgCauu6avrh55hXb+9SMdO3Fa/qQE9e/bVWerapXcPl4Z6R317B93aNWLu77z9i3GGE0cPyDCyQFEWpunF+xqEv3at/Np+eM/0RPLX9df3vn43GW/MTEe3TBhoO75+Y+1/Hdb2vScRlJdbYCBFwAAAABb+eJidO3YPt/589tuGa78tw/q079/0WjoZVmSMdIv/n2sOl+SHImoAGwUtukFu5rYq+NF7bT411P0eWm5Pjh4XF6vRwP6pcufnCBjjPK2Frfp+Tp3SlJCwvkvDQYAAAAAO7Vv51Pu47fr93/coVfz9qu6pk5S/Y3v75g+QuN+1M/mhAAigct1HK5L52R16dz43YvauoCqqmtb/RyWJU258coW7wsGAAAAANEiKSlev5w1Tnf9bKyOnyiTzxejtJQOvKYBXCRiA6+GXU1gv9gYr5Lax6viy6rzPtaypCEDu+nWm1q30yYAAAAARIuE+Dj1uPQSu2MAsEGbd2nED59lWZp8/aBmdzX5pos7ttPdP/uRlj4yVbGx3gilAwAAAAAAuDB8pNGlbpsyTFu2v6+TJyua7OQoSZOvH6T/nD2eS34BAAAAAMAPDld4uZQ/OUG/vGec0rte1Oh4+/Y+/XzmGM2dxbALAAAAAAD8MHGFlwMFg0ZnzlQrLs4rny+2yc+/OHVGCxZt0IH3j8nrseTxWAoGjS7yJ2rRr/9F/ft0tSE1AABNna2qUV1dUO3b+XgjBgDQiDFGZypr5PFYSmRHeQDfwsDLQaqqarVm/W5tfLlQp05XypI0LCtT/zZthAb2S5ck1dYGNPfBtfr06P9JUqOPM5ZVnNX9v3pJzz35U6V08duxBAAAJEm79nyiVWt3aX/xZ5Lqdx2+9aYsTZl8pWJiuK8kALhZIBDUn14p0ksb39Wx46clSX17peonU/9JV4/saW84AFGDjzQ6xNmqGs2Zv0Z/WP22Tp2ulCQZSe8WHtF9D/yvtuZ/IEna8fZBHS452ex9u4JBo6qqWq3bVBDJ6AAANLJuU4HmLVynAx8cO3fs89JyPfX7bVqwaKPqAkEb0wEA7BQMGj2ydLP+Z8WbOv7VsEuSPvjohBYs2qDVL+6yLxyAqMLAyyFeeOkdHfz4hIxpPMgKBo1kjHKWvaKKL6u0Nf+DFndnDAaN3thaHO64AAA069jx01r+uy2SvuqwbzBG+uvuT/TK6/vsiAYAiAJvvlWst3Z8KKn+Df4GDZ2x8g/5OvLpSRuSAYg2DLwcIBAIauOfi5q8MGhgJNXW1ilvW7Eqvqz6zsc1qDxbE4aUAACc38uvvdfivbosS1q/eW8EEwEAosmGzXvlaaEnvB5Lf3qlKHKBAEQtBl4OUFZ+VuXlZ1t8jMfj0eEj/1C39I7yelt+IdE1tUOIEwIA0DqfHPlHi2/MGCOVfHUfSgCA+xw6clJB8909EQgafXKoNIKJAEQrBl4O4PO1bu8Bny9GN/zzIAUCLV/hddOkIaGIBQBAm8X7Ylv86L0kxcVy03oAcKvzvfaxLCk+vulO9QDch4GXA7RL9GnwgIwWXyAEAkFdPeIK9eqZoltvymr2MR7LUv++XTVpwoBwRQUAoEWjR/Rs8Qovr9fSmFG9IpgIABBNxo7u1eInVoyRxoy8IoKJAEQrBl4OMeO2Ed/5AsHjsdS3V6oG9k+XJM3+xY+Vfc84db4k+dxj2iXGadqtw/XEon9VXGzrrhgDACDUxo6+QmkpfnmbeRPHsiTLsnTblGE2JAMARIOpN2fJ6/E0e79Hj8fSJZ2SdO01fWxIBiDaWObb2/pFkfLycvn9fpWVlSk5Ofn8f8DlXs3br98sf12BgJHHI0mWAoGg+vRK1ZKHb1EHf2KjxweDRseOn1JdIKjUlA7yxTHoApyO82pT/E6iz/HPy3T/ghd19LNT8no9kjEKBI3ifbF6+L9v1Ijhl9kdEcB34JzaFL+T0Hu38Ih+tWijKs/W1PeE6j/RktrFr98smqr0rh1tTgggnFp7XmXg5TCnyyr12pt/0+GSk0qIj9WYkVdoyKBuLe54BcA9OK82xe8kOgUCQe1695B27f5EtXVB9bq8i8Zf20/tEn12RwPQAs6pTfE7CY/KszV6c1ux3j94XF6vR8OzMjXyqssV4+VDTIDTtfa8yiU9DtPBn6hptwy3OwYAABfE6/Vo1FWXa9RVl9sdBQAQhRIT4nTjxMG6ceJgu6MAiFKMvwEAAAAAAOAoDLwAAAAAAADgKAy8AAAAAAAA4CgMvAAAAAAAAOAoDLwAAAAAAADgKAy8AAAAAAAA4CgMvAAAAAAAAOAoDLwAAAAAAADgKAy8AAAAAAAA4CgMvAAAAAAAAOAoMXYHaIkxRpJUXl5ucxIAcIaG82nD+RV0DQCEEj3TFD0DAKHV2q6J6oFXRUWFJCkjI8PmJADgLBUVFfL7/XbHiAp0DQCEHj3zNXoGAMLjfF1jmSh++yUYDOrYsWNKSkqSZVlt/vPl5eXKyMjQ0aNHlZycHIaE0Yl1s243cOu6pQtbuzFGFRUVSktLk8fDp9qlC+sat/47ZN3uWrfk3rWzbnomFOiZ78eta2fdrNsNLnTdre2aqL7Cy+PxKD09/YKfJzk52VX/eBqwbndh3e7zfdfOO+6NhaJr3PrvkHW7j1vXzrrbhp5pjJ65MG5dO+t2F9bddq3pGt52AQAAAAAAgKMw8AIAAAAAAICjOHrg5fP5tHDhQvl8PrujRBTrZt1u4NZ1S+5ee7Rx698F63bXuiX3rp11u2vd0cjNfxduXTvrZt1uEKl1R/VN6wEAAAAAAIC2cvQVXgAAAAAAAHAfBl4AAAAAAABwFAZeAAAAAAAAcBQGXgAAAAAAAHAUxw68nnrqKWVmZio+Pl5ZWVnasWOH3ZHCKicnR8OGDVNSUpI6d+6sm2++WR9++KHdsSIuJydHlmUpOzvb7igR8dlnn2nGjBm6+OKLlZiYqMGDB6ugoMDuWGFVV1enBQsWKDMzUwkJCerRo4ceeeQRBYNBu6OFVH5+viZPnqy0tDRZlqWNGzc2+rkxRg8//LDS0tKUkJCga665RgcOHLAnrEu5rWckuqaBm7qGnnFuz0h0zQ+B27qGnqlHzzi7ZyT3dI3dPePIgdfatWuVnZ2thx56SIWFhbr66qt1/fXX69NPP7U7Wths375ds2bN0q5du5SXl6e6ujqNHz9eZ86csTtaxOzZs0crV67UwIED7Y4SEadOndKoUaMUGxurV199VcXFxXriiSfUoUMHu6OF1WOPPaann35aubm5ev/997V06VI9/vjjWr58ud3RQurMmTMaNGiQcnNzm/350qVLtWzZMuXm5mrPnj1KSUnRuHHjVFFREeGk7uTGnpHoGsldXUPPOLtnJLom2rmxa+gZesYNPSO5p2ts7xnjQMOHDzd33XVXo2O9e/c28+fPtylR5JWWlhpJZvv27XZHiYiKigrTs2dPk5eXZ8aOHWvmzJljd6Swmzdvnhk9erTdMSJu0qRJ5s4772x0bMqUKWbGjBk2JQo/SWbDhg3nvg8GgyYlJcUsWbLk3LGqqirj9/vN008/bUNC96Fn6tE1c+yOFFb0zNec3jPG0DXRiK6hZ+gZ53Jj19jRM467wqumpkYFBQUaP358o+Pjx4/X22+/bVOqyCsrK5MkdezY0eYkkTFr1ixNmjRJ1113nd1RImbTpk0aOnSopk6dqs6dO2vIkCF65pln7I4VdqNHj9aWLVt08OBBSdJ7772nnTt3auLEiTYni5zDhw/rxIkTjc5zPp9PY8eOddV5zi70zNfoGmejZ9zbMxJdYze6ph4942xu7RmJrpEi0zMxIXmWKHLy5EkFAgF16dKl0fEuXbroxIkTNqWKLGOM5s6dq9GjR6t///52xwm7NWvWaO/evdqzZ4/dUSLq0KFDWrFihebOnasHH3xQu3fv1n333Sefz6c77rjD7nhhM2/ePJWVlal3797yer0KBAJavHixpk+fbne0iGk4lzV3nispKbEjkqvQM/XoGuejZ9zbMxJdYze6hp5xA7f2jETXSJHpGccNvBpYltXoe2NMk2NONXv2bO3bt087d+60O0rYHT16VHPmzNEbb7yh+Ph4u+NEVDAY1NChQ/Xoo49KkoYMGaIDBw5oxYoVji6ItWvXatWqVXrhhRfUr18/FRUVKTs7W2lpaZo5c6bd8SLKzee5aOD23z9d43z0DD0jca6zm5t///SM87m1ZyS65pvCeZ5z3MCrU6dO8nq9Td75KC0tbTI5dKJ7771XmzZtUn5+vtLT0+2OE3YFBQUqLS1VVlbWuWOBQED5+fnKzc1VdXW1vF6vjQnDJzU1VX379m10rE+fPlq3bp1NiSLj/vvv1/z58zVt2jRJ0oABA1RSUqKcnBzXlENKSoqk+ndFUlNTzx13y3nObm7vGYmukdzRNfSMe3tGomvs5vauoWfoGaejayLTM467h1dcXJyysrKUl5fX6HheXp5GjhxpU6rwM8Zo9uzZWr9+vbZu3arMzEy7I0XEtddeq/3796uoqOjc19ChQ3X77berqKjIkcXQYNSoUU22aT548KC6d+9uU6LIqKyslMfT+NTl9Xodt4VvSzIzM5WSktLoPFdTU6Pt27c7+jwXLdzaMxJd47auoWe+5raekegau7m1a+gZesYNPSPRNVKEeiYkt76PMmvWrDGxsbHm2WefNcXFxSY7O9u0a9fOHDlyxO5oYXP33Xcbv99v3nrrLXP8+PFzX5WVlXZHizg37GhijDG7d+82MTExZvHixeajjz4yq1evNomJiWbVqlV2RwurmTNnmq5du5qXX37ZHD582Kxfv9506tTJPPDAA3ZHC6mKigpTWFhoCgsLjSSzbNkyU1hYaEpKSowxxixZssT4/X6zfv16s3//fjN9+nSTmppqysvLbU7uDm7sGWPomm9yQ9fQM87uGWPommjnxq6hZ75GzzibW7rG7p5x5MDLGGOefPJJ0717dxMXF2euvPJKx29lK6nZr+eee87uaBHnhnJosHnzZtO/f3/j8/lM7969zcqVK+2OFHbl5eVmzpw5plu3biY+Pt706NHDPPTQQ6a6utruaCG1bdu2Zv+fnjlzpjGmfhvfhQsXmpSUFOPz+cyYMWPM/v377Q3tMm7rGWPomm9yS9fQM87tGWPomh8Ct3UNPfM1esbZ3NI1dveMZYwxoblWDAAAAAAAALCf4+7hBQAAAAAAAHdj4AUAAAAAAABHYeAFAAAAAAAAR2HgBQAAAAAAAEdh4AUAAAAAAABHYeAFAAAAAAAAR2HgBQAAAAAAAEdh4AUAAAAAAABHYeAFAAAAAAAAR2HgBQAAAAAAAEdh4AUAAAAAAABHYeAFAAAAAAAAR/l/bn/TxNPrc0wAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 1500x500 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
+    "ax[0].scatter(X_train, y_train, c=groups_train)\n",
+    "ax[0].set_title(\"Train set\")\n",
+    "ax[1].scatter(X_cal, y_cal, c=groups_cal)\n",
+    "ax[1].set_title(\"Calibration set\")\n",
+    "ax[2].scatter(X_test, y_test, c=groups_test)\n",
+    "ax[2].set_title(\"Test set\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training set size:  40000\n",
+      "Calibration set size:  40000\n",
+      "Test set size:  20000\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Training set size: \", X_train.shape[0])\n",
+    "print(\"Calibration set size: \", X_cal.shape[0])\n",
+    "print(\"Test set size: \", X_test.shape[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestRegressor()"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Fit a random forest regressor\n",
+    "\n",
+    "rf = RandomForestRegressor(n_estimators=100)\n",
+    "rf.fit(X_train, y_train)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1380f0760>"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the prediction of the random forest regressor as a line\n",
+    "\n",
+    "y_pred = rf.predict(X_test)\n",
+    "# plt.scatter(X_test, y_test, label=\"True\")\n",
+    "\n",
+    "#Sort the test set and the prediction to plot them as a line\n",
+    "sort_idx = np.argsort(X_test[:, 0])\n",
+    "plt.plot(X_test[sort_idx], y_pred[sort_idx], label=\"Prediction\")\n",
+    "\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "mapie_regressor = MapieRegressor(rf, cv=\"prefit\")\n",
+    "mondrian_regressor = MondrianCP(MapieRegressor(rf, cv=\"prefit\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,\n",
+       "                                          estimator=RandomForestRegressor()))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;MondrianCP<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,\n",
+       "                                          estimator=RandomForestRegressor()))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">mapie_estimator: MapieRegressor</label><div class=\"sk-toggleable__content \"><pre>MapieRegressor(cv=&#x27;prefit&#x27;, estimator=RandomForestRegressor())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
+      ],
+      "text/plain": [
+       "MondrianCP(mapie_estimator=MapieRegressor(cv='prefit',\n",
+       "                                          estimator=RandomForestRegressor()))"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mapie_regressor.fit(X_cal, y_cal)\n",
+    "mondrian_regressor.fit(X_cal, y_cal, groups=groups_cal)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "_, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)\n",
+    "_, y_pss_mondrian = mondrian_regressor.predict(X_test, groups=groups_test, alpha=.1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rf = RandomForestRegressor(\n",
+    "    n_estimators=100\n",
+    ")\n",
+    "rf.fit(X_train, y_train)\n",
+    "mondrian_regressor = MondrianCP(\n",
+    "    MapieRegressor(rf, cv=\"prefit\")\n",
+    ")\n",
+    "mondrian_regressor.fit(\n",
+    "    X_cal, y_cal,\n",
+    "    groups=groups_cal\n",
+    ")\n",
+    "_, y_pss_mondrian = mondrian_regressor.predict(\n",
+    "    X_test, groups=groups_test, alpha=.1\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the prediction of the random forest regressor as a line with the prediction intervals\n",
+    "\n",
+    "# plt.scatter(X_test, y_test, label=\"True\")\n",
+    "sort_idx = np.argsort(X_test[:, 0])\n",
+    "# plt.plot(X_test[sort_idx], y_pred[sort_idx], label=\"Prediction\")\n",
+    "plt.fill_between(X_test[sort_idx].flatten(), y_pss_split[sort_idx, 0].flatten(), y_pss_split[sort_idx, 1].flatten(), alpha=0.3, label=\"Split\")\n",
+    "plt.fill_between(X_test[sort_idx].flatten(), y_pss_mondrian[sort_idx, 0].flatten(), y_pss_mondrian[sort_idx, 1].flatten(), alpha=0.3, label=\"Mondrian\")\n",
+    "plt.legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# plot coverage by groups with both methods\n",
+    "coverages = {}\n",
+    "for group in np.unique(groups_test):\n",
+    "    coverages[group] = {}\n",
+    "    coverages[group][\"split\"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_split[groups_test == group])\n",
+    "    coverages[group][\"mondrian\"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_mondrian[groups_test == group])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group][\"split\"]) for group in coverages], label=\"Split\")\n",
+      "/var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
+      "  plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group][\"mondrian\"]) for group in coverages], label=\"Mondrian\")\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x13847b970>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAtQAAAHACAYAAACCgv/gAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAABKdklEQVR4nO3deVxU9eL/8fewgwrmhhsiuQVZlqC5ZGUpprbdrGx19+tSmVqa5i0tNVtuZppLXbc0zeXqbTFTydy1TMNKJctcMEVJNEBF1s/vD3/MdWQJOQMz6Ov5eMxD5pzPHN4zA8PbM585x2aMMQIAAABQLB6uDgAAAACUZRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWeLk6QGnLycnRsWPHVKFCBdlsNlfHAQAARWCMUWpqqmrWrCkPD/YHwr1cdYX62LFjCgkJcXUMAABQDEeOHFHt2rVdHQNwcNUV6goVKki68AsZGBjo4jQAAKAoUlJSFBISYv87DriTq65Q507zCAwMpFADAFDGMF0T7ohJSAAAAIAFFGoAAADAAgo1AAAAYMFVN4caAABcubKzs5WZmenqGCjjvL295enpWeTxFGoAAFDmGWN0/Phx/fXXX66OgitExYoVVb169SJ9EJZCDQAAyrzcMl2tWjUFBARwNBAUmzFG586dU2JioiSpRo0af3sbCjUAACjTsrOz7WW6cuXKro6DK4C/v78kKTExUdWqVfvb6R98KBEAAJRpuXOmAwICXJwEV5Lcn6eizMmnUAMAgCsC0zzgTJfz80ShBgAAACygUAMAAAAWuLRQb9y4Uffee69q1qwpm82mTz/99G9vs2HDBkVGRsrPz0/XXnutZsyYUfJBAQAASkBqaqoGDx6s0NBQ+fv7q1WrVvr+++8dxhhjNGbMGNWsWVP+/v664447tGfPHocxQ4cOVaVKlVSnTh0tWrTIYd2SJUt07733lvh9uZq5tFCfPXtWTZo00fvvv1+k8QcPHlSnTp3Upk0bxcbG6qWXXtKgQYO0bNmyEk4KAADgfH369FFMTIzmz5+vn3/+WdHR0WrXrp2OHj1qH/PWW29p4sSJev/99/X999+revXqat++vVJTUyVJX3zxhRYuXKg1a9bozTffVM+ePZWUlCRJ+uuvvzRq1ChNnTrVJffPqoyMDFdHKBrjJiSZ//73v4WOGT58uLnuuusclvXr18+0aNGiyN8nOTnZSDLJycnFiQkAAFygsL/faWlpZu/evSYtLc0FyYrv3LlzxtPT06xYscJheZMmTcyoUaOMMcbk5OSY6tWrmzfeeMO+/vz58yYoKMjMmDHDGGPMm2++abp27WpfX61aNbN9+3ZjjDF9+/Y1EydOLHKmzz77zERGRhpfX19TuXJl849//MO+7tSpU+app54yFStWNP7+/ubuu+82v/76qzHGmL/++sv4+fmZr776ymF7y5YtMwEBASY1NdUYY8wff/xhHnnkEVOxYkVTqVIlc99995mDBw/ax3fv3t3cf//95vXXXzc1atQwoaGhxhhj5s+fbyIjI0358uVNcHCweeyxx8yJEyfyZK9fv77x8/Mzd9xxh5k7d66RZE6fPm0fs2XLFtOmTRvj5+dnateubZ599llz5syZfB+Ly/m5KlNzqLdt26bo6GiHZR06dNCOHTsKPKRJenq6UlJSHC4AAODqcPbs2QIv58+fL/LYtLS0Io29HFlZWcrOzpafn5/Dcn9/f23evFnShXfnjx8/7tB/fH19dfvtt2vr1q2SpCZNmmjHjh06ffq0du7cqbS0NNWvX1+bN2/WDz/8oEGDBhUpz5dffqkHH3xQnTt3VmxsrNauXauoqCj7+h49emjHjh36/PPPtW3bNhlj1KlTJ2VmZiooKEidO3fWggULHLa5cOFC3X///SpfvrzOnTuntm3bqnz58tq4caM2b96s8uXL6+6773bYE7127VrFxcUpJiZGK1askHRhT/XYsWP1448/6tNPP9XBgwfVo0cP+20OHTqkhx56SA888IB27dqlfv36adSoUQ5Zfv75Z3Xo0EEPPvigfvrpJy1evFibN2/WM888U6THpzBl6sQux48fV3BwsMOy4OBgZWVl6eTJk/meyWbChAl69dVXSyviZas74kvL2zj0RmcnJAH4eQRw5SlfvnyB6zp16qQvv/zf6161atV07ty5fMfefvvtWr9+vf163bp1dfLkyTzjjDFFzlahQgW1bNlSY8eOVXh4uIKDg/XJJ5/ou+++U4MGDSRd6D6S8u0/hw8flnRh5+KTTz6pZs2ayd/fXx999JHKlSunAQMGaO7cuZo+fbqmTJmiKlWq6MMPP9T111+fb57x48fr0UcfdehNTZo0kST99ttv+vzzz7Vlyxa1atVKkrRgwQKFhITo008/1cMPP6wnnnhC3bp107lz5xQQEKCUlBR9+eWX9qm5ixYtkoeHh2bOnGk/JN2cOXNUsWJFrV+/3v6fhnLlymnmzJny8fGx5+jVq5f962uvvVaTJ09W8+bNdebMGZUvX14zZsxQo0aN9Pbbb0uSGjVqpN27d2v8+PH227399tt6/PHHNXjwYElSgwYNNHnyZN1+++2aPn16nv/YXI4yVailvMcEzP3BLehYgSNHjtTQoUPt11NSUhQSElJyAQGUKEo/gCvJ/Pnz1atXL9WqVUuenp5q2rSpHn/8cf3www8O4/LrPxcvGzNmjMaMGeNwvV27dvL29ta4ceP0888/a8WKFerWrZt27tyZb5Zdu3apb9+++a6Li4uTl5eXbrnlFvuyypUrq1GjRoqLi5Mkde7cWV5eXvr888/16KOPatmyZapQoYK9KO/cuVP79+9XhQoVHLZ9/vx5/f777/brN9xwg0OZlqTY2FiNGTNGu3bt0qlTp5STkyNJio+PV0REhPbt26dmzZo53KZ58+YO13O//8V70Y0xysnJ0cGDBxUeHp7vfS+KMlWoq1evbv+fWq7ExER5eXkVeKpRX19f+fr6lkY8AADgZs6cOVPguktPJ52YmFjgWA8Px1myhw4dspQrV7169bRhwwadPXtWKSkpqlGjhrp27aqwsDBJF7qPdGFP9cXvxCcmJubZa53rl19+0YIFCxQbG6vZs2frtttuU9WqVfXII4+oV69eSklJUWBgYJ7b5Z5uOz8F7Xm/uNj7+PjooYce0sKFC/Xoo49q4cKF6tq1q7y8LtTNnJwcRUZG5pkWIklVq1a1f12uXDmHdWfPnlV0dLSio6P18ccfq2rVqoqPj1eHDh3sU0Uu/Q9GfplzcnLUr1+/fKfA1KlTp8D7XhRlqlC3bNlSX3zxhcOyNWvWKCoqSt7e3i5KBQAA3NWl5cwVY4u6vXLlyun06dNavXq13nrrLUlSWFiYqlevrpiYGN18882SLswn3rBhg95888082zHG6P/+7//0zjvvqHz58srOzrZ/ziz339y9u5e68cYbtXbtWvXs2TPPuoiICGVlZem7776zT/lISkrSr7/+6rBn94knnlB0dLT27NmjdevWaezYsfZ1TZs21eLFi1WtWrV8C31BfvnlF508eVJvvPGGfZbBjh07HMZcd911WrlypcOyS8c0bdpUe/bsUf369Yv8vYvKpYX6zJkz2r9/v/36wYMHtWvXLvtxFEeOHKmjR49q3rx5kqT+/fvr/fff19ChQ9W3b19t27ZNs2bN0ieffOKquwAUGVMVAACXWr16tYwxatSokfbv369hw4apUaNG9lJrs9k0ePBgvf7662rQoIEaNGig119/XQEBAXr88cfzbO/f//63qlWrpvvuu0+S1Lp1a40ZM0bffvutvvrqK0VERKhixYr5Zhk9erTuuusu1atXT48++qiysrL01Vdfafjw4WrQoIHuv/9+9e3bVx988IEqVKigESNGqFatWrr//vvt27j99tsVHBysJ554QnXr1lWLFi3s65544gm9/fbbuv/++/Xaa6+pdu3aio+P1/LlyzVs2DDVrl0731x16tSRj4+PpkyZov79+2v37t0ORV2S+vXrp4kTJ+rFF19U7969tWvXLs2dO9f+GErSiy++qBYtWujpp59W3759Va5cOfuHH6dMmVK0J6wALj3Kx44dO3TzzTfb/8c1dOhQ3XzzzXrllVckSQkJCYqPj7ePDwsL08qVK7V+/XrddNNNGjt2rCZPnqwuXbq4JD8AAIAVycnJevrpp3XdddepW7duuvXWW7VmzRqHd96HDx+uwYMHa+DAgYqKitLRo0e1Zs2aPHORT5w4oddff12TJ0+2L2vevLmef/55de7cWUuWLNGcOXMKzHLHHXdo6dKl+vzzz3XTTTfpzjvv1HfffWdfP2fOHEVGRuqee+5Ry5YtZYzRypUrHbLabDY99thj+vHHH/XEE084bD8gIEAbN25UnTp19OCDDyo8PFy9evVSWlpaoXusq1atqrlz52rp0qWKiIjQG2+8oX/9618OY8LCwvSf//xHy5cv14033qjp06fbj/KRO/X3xhtv1IYNG/Tbb7+pTZs2uvnmm/Xyyy/ne1CLy2Uzl/Nx1CtASkqKgoKClJycfFlvN5QU9lpePcrCc01GAO6qsL/f58+f18GDBxUWFmbpSA24sowfP14zZszQkSNHinX7y/m5KlNzqAEAAID8TJs2Tc2aNVPlypW1ZcsWvf322045xnRRUKgBAABQ5v32228aN26cTp06pTp16uj555/XyJEjS+V7U6gBAABQ5r377rt69913XfK9y9SpxwEAAAB3Q6EGAAAALGDKB/4WR1UAAAAoGHuoAQAAAAso1AAAAIAFFGoAAADAAgo1AABAGXbo0CHZbDbt2rVLkrR+/XrZbDb99ddfLs11NeFDiQAA4IrljA/WX47ifAg/MTFRL7/8sr766iudOHFC11xzjZo0aaIxY8aoZcuWl729Vq1aKSEhQUFBQZKkuXPnavDgwRTsEkShBgAAcKEuXbooMzNTH330ka699lqdOHFCa9eu1alTp4q1PR8fH1WvXt3JKVEYpnwAAAC4yF9//aXNmzfrzTffVNu2bRUaGqrmzZtr5MiR6tz5wt5um82m6dOnq2PHjvL391dYWJiWLl1a4DYvnvKxfv169ezZU8nJybLZbLLZbBozZkwp3burB4UaAADARcqXL6/y5cvr008/VXp6eoHjXn75ZXXp0kU//vijnnzyST322GOKi4v72+23atVKkyZNUmBgoBISEpSQkKAXXnjBmXcBolADAAC4jJeXl+bOnauPPvpIFStWVOvWrfXSSy/pp59+chj38MMPq0+fPmrYsKHGjh2rqKgoTZky5W+37+Pjo6CgINlsNlWvXl3Vq1dX+fLlS+ruXLUo1AAAAC7UpUsXHTt2TJ9//rk6dOig9evXq2nTppo7d659zKUfTmzZsmWR9lCjdFCoAQAAXMzPz0/t27fXK6+8oq1bt6pHjx4aPXp0obex2WyllA5/h0INAADgZiIiInT27Fn79W+//dZh/bfffqvrrruuSNvy8fFRdna2U/PBEYfNAwAAcJGkpCQ9/PDD6tWrl2688UZVqFBBO3bs0FtvvaX777/fPm7p0qWKiorSrbfeqgULFmj79u2aNWtWkb5H3bp1debMGa1du1ZNmjRRQECAAgICSuouXZUo1AAAAC5Svnx53XLLLXr33Xf1+++/KzMzUyEhIerbt69eeukl+7hXX31VixYt0sCBA1W9enUtWLBAERERRfoerVq1Uv/+/dW1a1clJSVp9OjRHDrPySjUAOBkzjgzW3HOtgYgL3f/XfL19dWECRM0YcKEQsfVrFlTa9asyXdd3bp1ZYyxX7/jjjscrkvS9OnTNX36dOuBkS/mUAMAAAAWsIcaKMvGBFm8fbJzcgASP48ArloUagAAADd26fQNuB8KNQAA7oQ9/UCZwxxqAAAAwAL2UDvZxQdhv5Snp6f8/PwcxuZknM9/sM0mD29f+9UCx0lKS0uTv7+//fq5c+cKfHvIZrM5HHvycsbmZKZLhbzt5OHjV+DYSx+XcuXK2b8+f/58oQecv5yxAQEB9jNHpaenKysryylj/f395eFx4f+fGRkZyszMvOyx+T2HNi9v2Tw8JUkmO1OmkPtm8/K2f52ZmamMjAwpI//nw9dL8vK4cN8ys40yCtrs2bPy9fWVl9eFlwKTky2TVfB9s3l6yeZZ+Njc59rHx0fe3hcyZ2dn6/z5gn+Gvb295ePjU6SxJjtTNs8L2zUmRyYzo5C8nvmOze/31MvLS76+vv9/rNG5c+cK3O7fjb34ubZ5eMjm5ZPvujx5Lxlb2OuJh4eHw+/95YwtqdeItEyjnELemS7nYyt87EX34Wp8jbC76Pfaz0vy/P+/yxnZRpmFnJvj4rH214gCXPx7fzljs7KylJ6eXuDYi3/vgauKucokJycbSSY5OblEti+pwEunTp0cxgYEBBQ41jeksQl9cYX94uEfWODYqKgoh+2GhoYWODYiIsJhbERERIFjQ0NDL2zv/2fwqd6gwLEe/oEOeX1DGhc4NiAgwCFDp06dCn3cLvbQQw8VOvbMmTP2sd27dy90bGJion3swIEDCx178LnyxowONGZ0oHmhpU+hY3cPKHdhrDFm9OjRhY6t3m2i/TGreEfPQscGP/a6Pe/7779f6NgVj/nb886536/QsUuWLLFvt8r9IwodW7nTYHveqg8Vft/ef/99+3bXrVtX6Ni33nrLPnb79u2Fjg1q/Zg9Q41eUwsdG9j8QfvYWv1nFTp24MCB9gyJiYmFju3evbt97JkzZwodG9CotcPvRmFj/a+Nso/7u9eI22+/3eF3o0qVKgWOLenXiFxRNT0KHFslwGb/mTSjA83toZ4FP2Zl8TXi4EH72BdeeKHQsbt377aP/bvXiO19ytkfs7fa+RY6dl33APtrz9++RqxYYc8wZ86cQsde/BqxZMmSQsfOmTPHlJTC/n6npaWZvXv3mrS0tBL7/rj6XM7PFXuoAUCStv9bGvPxha/P5hQ+dtdCacx/L3xdwLsEAICrh82Yq+ujoykpKQoKClJycrICAwOdvv3LnfIR/vKq/AdfxpSPfeM7lejbubknqbAy5SNu7N0OY8vE27nja9i/9PeWPGxFe9vVPnZMssPbufk915c75ePwW/dJuugt2osyXqzIUz5GJTi8nRs6/HPLUz5yn+uSmvIRMSbG8pSPS38eNb6GvDwkXy/b/x9rdK7ghyHv2OEJDusvfq6LO+Xj0BudHV9PLnmuPWySv/f/plCcLaTYe9gk//Ep9uslNuVjVKC1KR+j/vc4lonXiIs4dcrHRc91saZ8jEm+Iqd8FPb3+/z58zp48KDCwsIc/s4CVlzOzxV7qJ3s4hf2ooy9uIQWprBxF5dpSQ5/4P7O5Yy9uOBf7tg8j8tFn2K/nJc++9gifIrd19fXPs+1WGMv+uPvsNjTJh/PIm1WPj4+9qL4d8+1zdPbXv7+jre394U/WgVkdBjraZN3QXkveV5sHp6yFfHOFTQ2v+faU1JRfzPyjL3kub74MbLZPGQr4u/QxWPzZLzkcbTZbCrnoyKx2Wx5tlfYc13U33npkpx/81yXK8LPQq6Seo24uOAXa2wBr5+XU5AuZ6zl14gCXPx7X6yxTnjtsb9GOHmsl5eXvVzjyjFmzBh9+umn2rVrV6Hj6tatq8GDB2vw4MGlkqss4bcCAABcuawehvCyv9/lHbawR48e+uijj9SvXz/NmDHDYd3AgQM1ffp0de/eXXPnznViyOL5/vvvL2vH4dWEQn0l4himAACUGSEhIVq0aJHeffdd+7vO58+f1yeffKI6deq4ON2FqUk+Pj6qWrWqq6O4LY5DDQC4eowJsnYBSkDTpk1Vp04dLV++3L5s+fLlCgkJ0c0332xflp6erkGDBqlatWry8/PTrbfequ+//96+fv369bLZbFq7dq2ioqIUEBCgVq1aad++fQ7f74033lBwcLAqVKig3r175/ncSo8ePfTAAw9owoQJqlmzpho2bCjpwpSPSZMm2cdNnDhRN9xwg8qVK6eQkBANHDhQZ86csa+fO3euKlasqNWrVys8PFzly5fX3XffrYQEx8+dXAko1AAAAC7Ws2dPzZkzx3599uzZ6tWrl8OY4cOHa9myZfroo4/0ww8/qH79+urQoYNOnTrlMG7UqFF65513tGPHDnl5eTlsZ8mSJRo9erTGjx+vHTt2qEaNGpo2bVqePGvXrlVcXJxiYmK0YsWKfDN7eHho8uTJ2r17tz766CN98803Gj58uMOYc+fO6V//+pfmz5+vjRs3Kj4+Xi+88MJlPz7ujkINAADgYk899ZQ2b96sQ4cO6fDhw9qyZYuefPJJ+/qzZ89q+vTpevvtt9WxY0dFRETo3//+t/z9/TVr1iyHbY0fP1633367IiIiNGLECG3dutW+F3rSpEnq1auX+vTpo0aNGmncuHGKiIjIk6dcuXKaOXOmrr/+ejVu3DjfzIMHD1bbtm0VFhamO++8U2PHjtWSJUscxmRmZmrGjBmKiopS06ZN9cwzz2jt2rVWHy63Q6EGAABwsSpVqqhz58766KOPNGfOHHXu3FlVqlSxr//999+VmZmp1q1b25d5e3urefPmiouLc9jWjTfeaP+6Ro0Lh2FMTEyUJMXFxally5YO4y+9Lkk33HDD3x6tZt26dWrfvr1q1aqlChUqqFu3bkpKSnI45GdAQIDq1avnkCc3y5WEDyUCwFUo9/jyVhx6o7MTkgDI1atXLz3zzDOSpKlTpzqsyz0efO6x0S9efumyiw+DmLsuJ+dvTlh1ib87msfhw4fVqVMn9e/fX2PHjlWlSpW0efNm9e7d2+HY6pcektFmsxV4bPuyjD3UAAAAbuDuu+9WRkaGMjIy1KFDB4d19evXl4+PjzZv3mxflpmZqR07dig8PLzI3yM8PFzffvutw7JLrxfFjh07lJWVpXfeeUctWrRQw4YNdezYscvezpWCPdQAAABuwNPT0z59w9PT8Sw+5cqV04ABAzRs2DBVqlRJderU0VtvvaVz586pd+/eRf4ezz33nLp3766oqCjdeuutWrBggfbs2aNrr732srLWq1dPWVlZmjJliu69915t2bIlz3G0rybsoQYAAHATgYGBeU6tnuuNN95Qly5d9NRTT6lp06bav3+/Vq9erWuuuabI2+/atateeeUVvfjii4qMjNThw4c1YMCAy8550003aeLEiXrzzTfVuHFjLViwQBMmTLjs7Vwp2EMNAACuXG5+srK/OwPip59+av/az89PkydP1uTJk/Mde8cdd+SZn3zTTTflWfbSSy/ppZdeclj25ptv/m2mQ4cOOVwfMmSIhgwZ4rDsqaeesn/do0cP9ejRw2H9Aw88wBxqAAAAAI4o1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAK4Il3s2QKAwl/PzxGHzAABAmebj4yMPDw8dO3ZMVatWlY+PT57TcQNFZYxRRkaG/vzzT3l4eMjHx+dvb0OhBgAAZZqHh4fCwsKUkJBwVZ/+Gs4VEBCgOnXqyMPj7yd0UKgBAECZ5+Pjozp16igrK0vZ2dmujoMyztPTU15eXkV+p4NCDQAArgg2m03e3t7y9vZ2dRRcZfhQIgAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUuL9TTpk1TWFiY/Pz8FBkZqU2bNhU6fsGCBWrSpIkCAgJUo0YN9ezZU0lJSaWUFgAAAHDk0kK9ePFiDR48WKNGjVJsbKzatGmjjh07Kj4+Pt/xmzdvVrdu3dS7d2/t2bNHS5cu1ffff68+ffqUcnIAAADgApcW6okTJ6p3797q06ePwsPDNWnSJIWEhGj69On5jv/2229Vt25dDRo0SGFhYbr11lvVr18/7dixo5STAwAAABe47NTjGRkZ2rlzp0aMGOGwPDo6Wlu3bs33Nq1atdKoUaO0cuVKdezYUYmJifrPf/6jzp07F/h90tPTlZ6ebr+ekpLinDsAAChRdUd8aXkbh94o+O8DADiLy/ZQnzx5UtnZ2QoODnZYHhwcrOPHj+d7m1atWmnBggXq2rWrfHx8VL16dVWsWFFTpkwp8PtMmDBBQUFB9ktISIhT7wcAAACubi7/UKLNZnO4bozJsyzX3r17NWjQIL3yyivauXOnVq1apYMHD6p///4Fbn/kyJFKTk62X44cOeLU/AAAALi6uWzKR5UqVeTp6Zlnb3RiYmKevda5JkyYoNatW2vYsGGSpBtvvFHlypVTmzZtNG7cONWoUSPPbXx9feXr6+v8OwAAAADIhXuofXx8FBkZqZiYGIflMTExatWqVb63OXfunDw8HCN7enpKurBnGwAAAChtLp3yMXToUM2cOVOzZ89WXFychgwZovj4ePsUjpEjR6pbt2728ffee6+WL1+u6dOn68CBA9qyZYsGDRqk5s2bq2bNmq66GwAAALiKuWzKhyR17dpVSUlJeu2115SQkKDGjRtr5cqVCg0NlSQlJCQ4HJO6R48eSk1N1fvvv6/nn39eFStW1J133qk333zTVXcBAAAAVzmXFmpJGjhwoAYOHJjvurlz5+ZZ9uyzz+rZZ58t4VQAAABA0bj8KB8AAABAWUahBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMAClx82DwCAsqruiC8tb+PQG52dkASAK7GHGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALKNQAAACABRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAs8HJ1AMAZ6o740vI2Dr3R2QlJAADA1YY91AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALKNQAAACABRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAsoFADAAAAFlCoAQAAAAso1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALLBfq8+fPOyMHAAAAUCYVq1Dn5ORo7NixqlWrlsqXL68DBw5Ikl5++WXNmjXrsrY1bdo0hYWFyc/PT5GRkdq0aVOh49PT0zVq1CiFhobK19dX9erV0+zZs4tzNwAAAADLilWox40bp7lz5+qtt96Sj4+PffkNN9ygmTNnFnk7ixcv1uDBgzVq1CjFxsaqTZs26tixo+Lj4wu8zSOPPKK1a9dq1qxZ2rdvnz755BNdd911xbkbAAAAgGVexbnRvHnz9OGHH+quu+5S//797ctvvPFG/fLLL0XezsSJE9W7d2/16dNHkjRp0iStXr1a06dP14QJE/KMX7VqlTZs2KADBw6oUqVKkqS6desW5y4AAAAATlGsPdRHjx5V/fr18yzPyclRZmZmkbaRkZGhnTt3Kjo62mF5dHS0tm7dmu9tPv/8c0VFRemtt95SrVq11LBhQ73wwgtKS0sr8Pukp6crJSXF4QIAAAA4S7H2UF9//fXatGmTQkNDHZYvXbpUN998c5G2cfLkSWVnZys4ONhheXBwsI4fP57vbQ4cOKDNmzfLz89P//3vf3Xy5EkNHDhQp06dKnAe9YQJE/Tqq68WKRMAAABwuYpVqEePHq2nnnpKR48eVU5OjpYvX659+/Zp3rx5WrFixWVty2azOVw3xuRZlisnJ0c2m00LFixQUFCQpAvTRh566CFNnTpV/v7+eW4zcuRIDR061H49JSVFISEhl5URAAAAKEixpnzce++9Wrx4sVauXCmbzaZXXnlFcXFx+uKLL9S+ffsibaNKlSry9PTMszc6MTExz17rXDVq1FCtWrXsZVqSwsPDZYzRH3/8ke9tfH19FRgY6HABAAAAnKXYx6Hu0KGDNmzYoDNnzujcuXPavHlznvnQhfHx8VFkZKRiYmIclsfExKhVq1b53qZ169Y6duyYzpw5Y1/266+/ysPDQ7Vr1y7eHQEAAAAscOmZEocOHaqZM2dq9uzZiouL05AhQxQfH28/csjIkSPVrVs3+/jHH39clStXVs+ePbV3715t3LhRw4YNU69evfKd7gEAAACUtGLNob7mmmvynedss9nk5+en+vXrq0ePHurZs2eh2+natauSkpL02muvKSEhQY0bN9bKlSvtH3ZMSEhwOCZ1+fLlFRMTo2effVZRUVGqXLmyHnnkEY0bN644dwMAAACwrFiF+pVXXtH48ePVsWNHNW/eXMYYff/991q1apWefvppHTx4UAMGDFBWVpb69u1b6LYGDhyogQMH5rtu7ty5eZZdd911eaaJAAAAAK5SrEK9efNmjRs3zuGkLpL0wQcfaM2aNVq2bJluvPFGTZ48+W8LNQAAAFCWFWsO9erVq9WuXbs8y++66y6tXr1aktSpUycdOHDAWjoAAADAzRWrUFeqVElffPFFnuVffPGF/ZTgZ8+eVYUKFaylAwAAANxcsaZ8vPzyyxowYIDWrVun5s2by2azafv27Vq5cqVmzJgh6cLh726//XanhgUAAADcTbEKdd++fRUREaH3339fy5cvlzFG1113nTZs2GA/hvTzzz/v1KAAAACAOypWoZYunGSldevWzswCAAAAlDnFLtS50tLSlJmZ6bCM03sDAADgalGsDyWeO3dOzzzzjKpVq6by5cvrmmuucbgAAAAAV4tiFephw4bpm2++0bRp0+Tr66uZM2fq1VdfVc2aNTVv3jxnZwQAAADcVrGmfHzxxReaN2+e7rjjDvXq1Utt2rRR/fr1FRoaqgULFuiJJ55wdk4AAADALRVrD/WpU6cUFhYm6cJ86VOnTkmSbr31Vm3cuNF56QAAAAA3V6xCfe211+rQoUOSpIiICC1ZskTShT3XFStWdFY2AAAAwO0Vq1D37NlTP/74oyRp5MiR9rnUQ4YM0bBhw5waEAAAAHBnxZpDPWTIEPvXbdu21S+//KIdO3aoXr16atKkidPCAQAAAO7usvdQZ2Zmqm3btvr111/ty+rUqaMHH3yQMg0AAICrzmUXam9vb+3evVs2m60k8gAAAABlSrHmUHfr1k2zZs1ydhYAAACgzCnWHOqMjAzNnDlTMTExioqKUrly5RzWT5w40SnhAAAAAHdXrEK9e/duNW3aVJIc5lJLYioIAAAArirFKtTr1q1zdg4AAACgTCrWHOpc+/fv1+rVq5WWliZJMsY4JRQAAABQVhSrUCclJemuu+5Sw4YN1alTJyUkJEiS+vTpo+eff96pAQEAAAB3VqxCPWTIEHl7eys+Pl4BAQH25V27dtWqVaucFg4AAABwd8WaQ71mzRqtXr1atWvXdljeoEEDHT582CnBAAAAgLKgWHuoz54967BnOtfJkyfl6+trORQAAABQVhSrUN92222aN2+e/brNZlNOTo7efvtttW3b1mnhAAAAAHdXrCkfb7/9tu644w7t2LFDGRkZGj58uPbs2aNTp05py5Ytzs4IAAAAuK1i7aGOiIjQTz/9pObNm6t9+/Y6e/asHnzwQcXGxqpevXrOzggAAAC4rWLtoZak6tWr69VXX3VmFgAAAKDMKdYe6rCwML388svat2+fs/MAAAAAZUqxCvWzzz6rVatWKTw8XJGRkZo0aZL95C4AAADA1aRYhXro0KH6/vvv9csvv+iee+7R9OnTVadOHUVHRzsc/QMAAAC40hWrUOdq2LChXn31Ve3bt0+bNm3Sn3/+qZ49ezorGwAAAOD2iv2hxFzbt2/XwoULtXjxYiUnJ+uhhx5yRi4AAACgTChWof7111+1YMECLVy4UIcOHVLbtm31xhtv6MEHH1SFChWcnREAAABwW8Uq1Nddd52ioqL09NNP69FHH1X16tWdnQsAAAAoE4pVqH/55Rc1bNjQ2VkAAACAMqdYhTq3TO/cuVNxcXGy2WwKDw9X06ZNnRoOAAAAcHfFKtSJiYl69NFHtX79elWsWFHGGCUnJ6tt27ZatGiRqlat6uycAAAAgFsq9oldUlJStGfPHp06dUqnT5/W7t27lZKSokGDBjk7IwAAAOC2irWHetWqVfr6668VHh5uXxYREaGpU6cqOjraaeEAAAAAd1esPdQ5OTny9vbOs9zb21s5OTmWQwEAAABlRbEK9Z133qnnnntOx44dsy87evSohgwZorvuustp4QAAAAB3V6xC/f777ys1NVV169ZVvXr1VL9+fYWFhSk1NVVTpkxxdkYAAADAbRVrDnVISIh++OEHxcTE6JdffpExRhEREWrXrp2z8wEAAABu7bL2UH/zzTeKiIhQSkqKJKl9+/Z69tlnNWjQIDVr1kzXX3+9Nm3aVCJBAQAAAHd0WYV60qRJ6tu3rwIDA/OsCwoKUr9+/TRx4kSnhQMAAADc3WUV6h9//FF33313geujo6O1c+dOy6EAAACAsuKyCvWJEyfyPVxeLi8vL/3555+WQwEAAABlxWUV6lq1aunnn38ucP1PP/2kGjVqWA4FAAAAlBWXVag7deqkV155RefPn8+zLi0tTaNHj9Y999zjtHAAAACAu7usw+b985//1PLly9WwYUM988wzatSokWw2m+Li4jR16lRlZ2dr1KhRJZUVAAAAcDuXVaiDg4O1detWDRgwQCNHjpQxRpJks9nUoUMHTZs2TcHBwSUSFAAAAHBHl31il9DQUK1cuVKnT5/W/v37ZYxRgwYNdM0115REPgAAAMCtFetMiZJ0zTXXqFmzZs7MAgAAAJQ5l/WhRAAAAACOKNQAAACABRRqAAAAwAIKNQAAAGCBywv1tGnTFBYWJj8/P0VGRmrTpk1Fut2WLVvk5eWlm266qWQDAgAAAIVwaaFevHixBg8erFGjRik2NlZt2rRRx44dFR8fX+jtkpOT1a1bN911112llBQAAADIn0sL9cSJE9W7d2/16dNH4eHhmjRpkkJCQjR9+vRCb9evXz89/vjjatmyZSklBQAAAPLnskKdkZGhnTt3Kjo62mF5dHS0tm7dWuDt5syZo99//12jR48u0vdJT09XSkqKwwUAAABwFpcV6pMnTyo7OzvPqcqDg4N1/PjxfG/z22+/acSIEVqwYIG8vIp2TpoJEyYoKCjIfgkJCbGcHQAAAMjl8g8l2mw2h+vGmDzLJCk7O1uPP/64Xn31VTVs2LDI2x85cqSSk5PtlyNHjljODAAAAOQq9qnHrapSpYo8PT3z7I1OTEzMs9daklJTU7Vjxw7FxsbqmWeekSTl5OTIGCMvLy+tWbNGd955Z57b+fr6ytfXt2TuBAAAAK56LttD7ePjo8jISMXExDgsj4mJUatWrfKMDwwM1M8//6xdu3bZL/3791ejRo20a9cu3XLLLaUVHQAAALBz2R5qSRo6dKieeuopRUVFqWXLlvrwww8VHx+v/v37S7owXePo0aOaN2+ePDw81LhxY4fbV6tWTX5+fnmWAwAAAKXFpYW6a9euSkpK0muvvaaEhAQ1btxYK1euVGhoqCQpISHhb49JDQAAALiSSwu1JA0cOFADBw7Md93cuXMLve2YMWM0ZswY54cCAAAAisjlR/kAAAAAyjIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAsoFADAAAAFlCoAQAAAAso1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALKNQAAACABRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAsoFADAAAAFlCoAQAAAAso1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALKNQAAACABRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFjg8kI9bdo0hYWFyc/PT5GRkdq0aVOBY5cvX6727duratWqCgwMVMuWLbV69epSTAsAAAA4cmmhXrx4sQYPHqxRo0YpNjZWbdq0UceOHRUfH5/v+I0bN6p9+/ZauXKldu7cqbZt2+ree+9VbGxsKScHAAAALnBpoZ44caJ69+6tPn36KDw8XJMmTVJISIimT5+e7/hJkyZp+PDhatasmRo0aKDXX39dDRo00BdffFHKyQEAAIALXFaoMzIytHPnTkVHRzssj46O1tatW4u0jZycHKWmpqpSpUoFjklPT1dKSorDBQAAAHAWlxXqkydPKjs7W8HBwQ7Lg4ODdfz48SJt45133tHZs2f1yCOPFDhmwoQJCgoKsl9CQkIs5QYAAAAu5vIPJdpsNofrxpg8y/LzySefaMyYMVq8eLGqVatW4LiRI0cqOTnZfjly5IjlzAAAAEAuL1d94ypVqsjT0zPP3ujExMQ8e60vtXjxYvXu3VtLly5Vu3btCh3r6+srX19fy3kBAACA/LhsD7WPj48iIyMVExPjsDwmJkatWrUq8HaffPKJevTooYULF6pz584lHRMAAAAolMv2UEvS0KFD9dRTTykqKkotW7bUhx9+qPj4ePXv31/ShekaR48e1bx58yRdKNPdunXTe++9pxYtWtj3bvv7+ysoKMhl9wMAAABXL5cW6q5duyopKUmvvfaaEhIS1LhxY61cuVKhoaGSpISEBIdjUn/wwQfKysrS008/raefftq+vHv37po7d25pxwcAAABcW6glaeDAgRo4cGC+6y4tyevXry/5QAAAAMBlcPlRPgAAAICyjEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAsoFADAAAAFlCoAQAAAAso1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALKNQAAACABRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAsoFADAAAAFlCoAQAAAAso1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABZQqAEAAAALKNQAAACABRRqAAAAwAIKNQAAAGABhRoAAACwgEINAAAAWEChBgAAACygUAMAAAAWUKgBAAAACyjUAAAAgAUUagAAAMACCjUAAABgAYUaAAAAsIBCDQAAAFhAoQYAAAAscHmhnjZtmsLCwuTn56fIyEht2rSp0PEbNmxQZGSk/Pz8dO2112rGjBmllBQAAADIy6WFevHixRo8eLBGjRql2NhYtWnTRh07dlR8fHy+4w8ePKhOnTqpTZs2io2N1UsvvaRBgwZp2bJlpZwcAAAAuMClhXrixInq3bu3+vTpo/DwcE2aNEkhISGaPn16vuNnzJihOnXqaNKkSQoPD1efPn3Uq1cv/etf/yrl5AAAAMAFXq76xhkZGdq5c6dGjBjhsDw6Olpbt27N9zbbtm1TdHS0w7IOHTpo1qxZyszMlLe3d57bpKenKz093X49OTlZkpSSkmL1LjhFTvo5y9vIc1/SjdUNOlwlY7E36HCVjMXeoMNVMhZ7gw5XyVjsDTpcLQsZrxS5j5MxFh8foCQYFzl69KiRZLZs2eKwfPz48aZhw4b53qZBgwZm/PjxDsu2bNliJJljx47le5vRo0cbSVy4cOHChQuXK+By5MgR5xQRwIlctoc6l81mc7hujMmz7O/G57c818iRIzV06FD79ZycHJ06dUqVK1cu9PuUNSkpKQoJCdGRI0cUGBjo6jj5IqNzkNE5yOgcZHSOspDR1YwxSk1NVc2aNV0dBcjDZYW6SpUq8vT01PHjxx2WJyYmKjg4ON/bVK9ePd/xXl5eqly5cr638fX1la+vr8OyihUrFj+4mwsMDHT7F2MyOgcZnYOMzkFG5ygLGV0pKCjI1RGAfLnsQ4k+Pj6KjIxUTEyMw/KYmBi1atUq39u0bNkyz/g1a9YoKioq3/nTAAAAQElz6VE+hg4dqpkzZ2r27NmKi4vTkCFDFB8fr/79+0u6MF2jW7du9vH9+/fX4cOHNXToUMXFxWn27NmaNWuWXnjhBVfdBQAAAFzlXDqHumvXrkpKStJrr72mhIQENW7cWCtXrlRoaKgkKSEhweGY1GFhYVq5cqWGDBmiqVOnqmbNmpo8ebK6dOniqrvgNnx9fTV69Og801vcCRmdg4zOQUbnIKNzlIWMAApmM4bjzwAAAADF5fJTjwMAAABlGYUaAAAAsIBCDQAAAFhAoQYAAAAsoFADAAAAFlCor2IXH+CFg70UX1l7HHNyclwdoUwrC89xVlaWqyP8rf379+vMmTOujlGmlbXXHuBKRqG+ip0+fVrJyckyxshms7k6ToHcvQCmpaUpMzNTOTk5stlsbpk3MTFRR48eVVZWljw83PPXftu2bfrpp59cHaNQCxcu1Ouvv+7qGIVatGiRnnnmGZ09e9bVUQo0b948tWnTRkePHpXknmXwl19+0bp16xQXF6e0tDRXx8lXcnKyzp496/av4cDVwD3/sqLEzZs3T+3bt9ctt9yi8PBwzZs3T0eOHHF1LAdffPGFdu7cKQ8PD7f8gytJy5Yt0xNPPKHbbrtN9913n1JTU90u78KFC3XffffptttuU6NGjbRnzx5XR8rjm2++UevWrfXPf/5Te/fudXWcfH3wwQd68sknFRUV5bDcnZ7r2bNnq1+/fgoLC1NycrJ9uTtl/PDDD9WrVy+dOXNG48aNs/9H1J3MmTNHnTp1Uu/evXXLLbdo3rx5ro6Ux/z589W5c2fddtttuuGGG7RgwQIlJCS4OhZw9TK46nz66afGz8/PTJo0ySxfvtz069fP1KtXz/zf//2f2b17t6vjGWOMWbp0qbHZbMbHx8d89913xhhjcnJyXJzK0Zw5c0yFChXM2LFjzcsvv2xuueUW06xZM5OZmenqaHZz5swx5cqVM1OmTDErV6407dq1M7feeqv9sXSXx3TNmjWmfv36pnLlyqZNmzZm7969Jisry9Wx7D788EPj4+NjlixZYowxbvUc59q1a5cJCQkxH330kTHGmHPnzpnExESTlJTkNo/lhx9+aDw9Pc3SpUvNe++9Z2644QZz+PBhY4wx2dnZLk53wbJly0xQUJD5+OOPzZ9//mkGDx5sQkJCTHp6uquj2S1ZssRUqFDBTJ061axatcr079/f2Gw206dPH7N3715XxwOuShTqq0hueerVq5fp06ePw7oZM2aY5s2bm549e5r9+/e7Ip7dnj17TOvWrc3zzz9vHnvsMVO+fHnz7bffGmPcpwBu3brVNGjQwCxYsMC+bNOmTaZhw4bmhx9+cGGy/1m7dq259tprzcKFC+3LZs2aZQYNGmQOHjxo/vrrL7cphj/99JPp3r27SUpKMrVq1TK33XabOXbsmMnOzjY//fSTS7N9/vnnxmazmffff98YY8y+ffvMkCFDTOfOnU3nzp3Npk2bzLlz51ya0RhjYmJizF133WWMMWb37t3m1ltvNY0bNzZVqlQxgwcPNrGxsS7N9+677xpPT0/z3//+1xhjzB9//GEqVqxoXnnlFZfmypWTk2PS09PNQw89ZF588UX78ri4ONO1a1cTExNjvv32W/t/AFzl7NmzpmPHjub11193WN68eXNTrVo106dPH3Po0CEXpQOuXkz5uIrkvq2akZFhfzs4MzNTktSvXz/17dtXO3bs0H//+19JrnubODU1VZGRkXr88cc1depU3XvvvWrXrp2+++47t5mjvG/fPoWGhio6Otq+LCoqSmlpaW4zdebPP//Uww8/rHvvvde+bNGiRVq8eLHuvPNOhYeH67333nOLubbh4eHatWuXPD09tXnzZv3222967LHHdP3112vq1Kkuzfb7778rNDRUqampWrVqlTp16qQ//vhD1apVU3Z2tjp27KilS5dKcu3UipMnT+rkyZM6cOCAHnnkETVt2lRTpkzRiBEj9MMPP+jNN9+0z1kubZmZmdq+fbsWLFigBx54QFlZWapVq5aeffZZffHFFzpw4IBLcl3MZrPJw8NDp0+fVlZWlv218YUXXtDXX3+t5557Tk899ZQGDx6sffv2uSxnTk6Ojhw5opo1a0qS/fc3PDxcLVq0UExMjDZu3CjJvab6AFc8Fxd6uMDo0aNNcHCwOXnypDHGmPPnz9vX/fOf/zSVK1c2p0+fdlG6C37//Xf718ePH7fvqd62bZt9eVpamklJSXFFPBMfH29WrVplv56enm4yMzNNeHi4WbFihUsy5efIkSP2r/v3729q165tvv76a3PixAkzevRoExQUZOLi4lyY0JisrCyTkZFhmjVrZpYtW2aMufD4+vj4mKCgILNlyxaX5jPGmHfeecdERESYKlWqmGHDhpm0tDT7uv79+5vq1au7/Hdm48aNpmHDhmbWrFmma9eu5q+//rKvW7Rokalbt67ZunWry/LlTju5+F2mr7/+2pQrV8589tlneda5yqBBg0zlypXNww8/bG677TYTFhZmfvzxR5OZmWlWrFhhbrjhBjN79myXZoyOjjYtW7a0X//ss89MUFCQOXr0qBkwYIAJDw93qykqwNWAQn0Vyf1jlZWVZW666SbTokUL+7rcUn3q1ClTtWpVs3r1apdmvNSJEyfspXr79u3m/Pnz5h//+IdZtGhRKSfM6+LMTZo0sU+xyMnJMQMHDjQHDhxwaSZjjElOTjYff/yx+eOPP+zLsrOzzTXXXGOfc+tqgwYNMosXLzbGXHgcW7RoYapXr27uuusul037uHhe77vvvmu6d+9u/09K7rq4uDjj5+dn1q1b54qIDu69915js9lM3bp1zYkTJxzWNWzY0LzzzjsuSlawxx9/3Nx8883m1KlTLs1x8e/Mm2++aWbNmmVat25tPvjgA4dxUVFR5rnnnivldBfk/sxt27bNXHfddSYoKMjcdtttxtvb23z44YfGmAtTvcLCwsyxY8dckhG4WjHl4ypis9lkjJGnp6fee+89JSUlqXXr1jp//rx8fX0lSUlJSapYsaICAwNdljE/1apV03vvvaf7779f7du3V1RUlLZv364uXbqUcsK8Ls6cnZ0tLy8vSdI999yjxYsXKyQkxKWZJCkwMFCPP/64atWqZV8WFxen+vXrq2HDhqUdL1/VqlXTxx9/rMjISFWoUEHbtm3Trl279M033+jDDz90SSYPDw/7FKPBgwdr5MiRql27tqT/PcYJCQlq1KiRfbkr5Gb84IMP1KlTJyUkJCgmJkbnz5+XdOHwapUrV1ZoaKjLMl7K/P/pCPfff79SU1O1a9cuSa47TKbNZlN2drYkafjw4erVq5fKlSunSpUq2cekpKTI19dX9evXd0nG3ENeNm/eXOvXr9fIkSPVpUsXbdy4UX379pV0YapX5cqV5e/v75KMwNXKZgyTrK5GWVlZ2rBhg5577jmdPXtWw4YNU2BgoBYuXKiTJ09q27Zt8vT0dHXMPHbv3q2oqCg1a9ZM33zzjby9vZWdne3yrLkn0oiKitLo0aO1ZMkSxcbG6ueff3abjOaiY9VmZGTooYceUkZGhlauXOnSY1Pn5tq8ebMeeOABNW/eXPPmzVPlypVls9l08uRJXXPNNS59/EwBx/nNyMhQly5d5O3trf/85z9ucYzvX3/9VU8//bR27NihJ598UqGhofr666/1559/avv27S7/OcxPs2bNVKlSJa1evdrVURw8+uij+uGHHzR37lylp6dr8uTJOnz4sLZv327/j7O7MMYoLS1N//jHP1SpUiUtXLjQ7Q5HCFzJKNRXMWOM/vzzTw0bNkyxsbHy9PRUaGioli5d6jYl8GKpqanq3Lmzjh8/rr1798rLy0tZWVlu9YetadOm2rVrl/1Ddt7e3m6V8fz58/r00081f/58HTlyRDt37pS3t7dycnJcXgZTUlK0atUq3XnnnapSpYokOfwMutPP4/nz57Vs2TLNnz9fx44dc6vHMdeoUaP0ww8/6Ny5cwoLC9O///1vt/u9zs0ydepUrVq1Sp9//rlblMDc/0AdP35cjz32mLZu3ar69esrNDRUn332mds9jpmZmfrqq680ffp0HTlyRLGxsfL29uaEL0ApolBfgQr7o17QC+zJkyfl7e2twMBA2Wy2Ei+Bxcm4a9cuzZ49W++8806pFNXiZGzfvr1OnDihH374oVQK/+Vm/PPPPzVt2jT98ssvmj9/vttkdPUf/uI8jhMnTlRcXJz+85//uM3jeOnXGRkZstls8vb2liS3yXipxMREValSxT7FpiT/U1KcjNu2bVPlypVVv359eXh4uOXj+Nlnn2nLli16/fXX3XJnA3Clo1BfYS5+IV66dKlOnz4tT09PPfDAA6pUqVKe8pLfC3dp/kErSsb8lOYftKJkzP163759ql+/vjw9Pd0uY67cOfO5c0ZLci+bM57rklbcjOfOnZO/v3+p/wf0cn4eL1bSj7Mznmt3y1hWXx/dae85cNUooQ87wgUu/pT6888/bypUqGCioqJMQECAadGihfn4449dfiKP4mYszcNpOeNxLOmzvjkjY0k/plfyz2NB23DXjCWNjM5RFl4fAeTPPSb7wSly91gcPXpUGzZs0Lp167R582YdO3ZM1apV04wZM7Ry5UpJrjvgf3EzluYeTGc8jiU9j9YZGUv6Mb2Sfx7z24Y7ZyxpZHRtRuZJA67HlI8rzBtvvKF169apXLlymj9/vgICAmSz2XT69Gn94x//kK+vr8s/SU9GMpKRjGQsuxkB5MM1O8bhLJdOLZg+fboJDAw0YWFh9jO35Z4x67vvvjM+Pj5m165dZCQjGclIRjICcBKmfJRxuVMLjh07Jknq1auXpk2bpoSEBL322muSJB8fH0kXDq1Uq1atUj/gPxnJSEYykrHsZgRQBK5u9LBu4cKFxt/f32zZssUYc+HU4rNmzTLe3t5mwIABZv369WbXrl2mY8eOpnnz5iX+gTkykpGMZCTjlZURQOEo1FeAnJwcc8cdd5i6deuarVu3GmMuvI04e/ZsU758eWOz2cyQIUNMly5dzPnz5+3ryUhGMpKRjGQEYB2Fuoy59EU093BJOTk5pl27dqZ27dr2F+TMzEzz8ccfmwoVKpgXX3zRfpvc+XhkJCMZyUhGMgKwjkJdRs2cOdPs37/fGON4DNK77rrL1KlTx+EFOfetw5dffpmMZCQjGclIRgBORqEug86cOWNCQkJM48aNzcGDB40x/3tBTk1NNQ0aNDDNmjUz69evN8ZceEGeO3eusdlsZuzYsWQkIxnJSEYyAnAiCnUZkN9ZsBISEsxNN91kbrrpJnPgwAH78rS0NNO5c2fj4+NjHnjgAfvyjIwM8/HHH5u9e/eSkYxkJCMZyQjAiSjUbu7i+XZJSUkmKSnJfurZEydOmOuvv97cfPPN5vfff7cv79Wrl9m/f3+pfWiFjGQkIxnJWHYzArCOMyW6MWOM/ZSyr732mtatW6e9e/eqffv26tChg5566iklJiaqffv2On36tG6//XYdOHBAZ86cUWxsrDw8PJSdnS1PT08ykpGMZCQjGQGUEE7s4sZyX4hHjx6t9957Tz169NDw4cPl4eGhl156SZMnT1a1atX0448/6p577pExRg0bNtSOHTvk4eGhnJycEn8hJiMZyUhGMpbdjACcpDR3h6NoLp5vl5CQYJo1a2YWLVpkX3b48GEzevRoU69ePfPVV1/lu43ctw7JSEYykpGMZARQsthD7WZycnLsezWOHTsmf39/HThwQCkpKfYxderUUffu3VW1alXFxcVJuvDWYi5jjLy8vMhIRjKSkYxkBFAKKNRuxsPjwlMyYsQIvfTSS0pKSlKLFi20Z88eJSUl2ceFhYWpatWq+vHHHyX9763FS78mIxnJSEYykhFAyaJQu4mL905s27ZNK1as0NNPP61rr71WnTp10rx587RgwQIlJiZKks6cOaOkpCTVq1ePjGQkIxnJSEYALsRRPtzMu+++q/j4eGVkZGjq1Kn25ePGjdOUKVN00003qWrVqjpy5IhOnTql2NjYUn9rkIxkJCMZyVh2MwJwPvZQu5mffvpJ7733nnbs2KG//vrLvvyf//ynpk+frmbNmik9PV2tWrWyvxBnZWWRkYxkJCMZyQjAVUr+c48oyMaNG80777xj3nnnHZOQkGBfPmLECGOz2cwHH3xgzp07V+g2SvqT4GQkIxnJSMaymxFA6aBQu8i8efNMw4YNzaBBg8yMGTPyrO/fv7/x8/Mz8+fPN2lpafbl+Z3CloxkJCMZyUhGAK5DoXaBefPmGX9/fzNv3jyTmppqX/7uu++aJUuW2K/369fPBAQEmI8//vhv93KQkYxkJCMZyQjANSjUpWzv3r3mhhtuMB988IHD8ocfftjYbDZz9913m+XLl9uXDxgwwNhsNrNq1SoykpGMZCQjGQG4IQp1KVu9erWpW7euiYuLM9nZ2cYYYwYOHGjq169vvvzyS9OuXTvTuXNns3TpUvtt/vWvf5XqPDsykpGMZCRj2c0IoPRRqEvZuHHjTOXKlR2WHTt2zBw5csQYc2HvR+vWrU2rVq3MgQMHHMaV1gsyGclIRjKSsexmBFD6OGxeKatfv77S0tIUExNjX1ajRg3Vrl1bOTk5Cg8P13333aeKFSuqWrVqDrctrWOVkpGMZCQjGctuRgClj0Jdypo1ayYvLy998MEHio+Pd1jn4eGh1NRUbdq0SY0aNVK5cuXISEYykpGMZATg7ly9i/xqtHDhQuPr62ueeOIJExsba19+6NAh0759e9OkSRP7W4OuOsQSGclIRjKSsexmBFC6OPW4C2RnZ2vOnDkaOHCggoOD1bhxY2VlZSk1NVWStGnTJnl7eys7O1uenp5kJCMZyUhGMgJwYxRqF9q1a5dmzpypX3/9VXXq1FHTpk3Vr18/eXp6Kisryy3m25GRjGQkIxnLbkYApYNC7YbKwl4NMjoHGZ2DjM5BRucoCxkBOBeF2sWMMbLZbK6OUSgyOgcZnYOMzkFG5ygLGQGUPAo1AAAAYAGHzQMAAAAsoFADAAAAFlCoAQAAAAso1AAAAIAFFGoAAADAAgo1AAAAYAGFGgAAALCAQg0AAABYQKEGAAAALKBQAwAAABb8PxXrA2PvP9k1AAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the coverage by groups, plot both methods side by side\n",
+    "plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group][\"split\"]) for group in coverages], label=\"Split\")\n",
+    "plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group][\"mondrian\"]) for group in coverages], label=\"Mondrian\")\n",
+    "plt.xticks(np.arange(len(coverages)) * 2 + .5, [f\"Group {group}\" for group in coverages], rotation=45)\n",
+    "plt.hlines(0.9, -1, 21, label=\"90% coverage\", color=\"black\", linestyle=\"--\")\n",
+    "plt.ylabel(\"Coverage\")\n",
+    "\n",
+    "#put legend outside of the plot\n",
+    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mapie-dev",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From 147142df697a1f91bc3ee3e36e42b0a1b278927a Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 20 Aug 2024 14:38:04 +0200
Subject: [PATCH 315/424] ADD: mondrian tutorial to index.rst

---
 doc/index.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/doc/index.rst b/doc/index.rst
index 226b496ca..42fe01304 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -55,6 +55,7 @@
    :caption: MONDRIAN
 
    theoretical_description_mondrian
+   tutorial_mondrian_regression
 
 .. toctree::
    :maxdepth: 2

From 8773dfc880d792f8eda74ee52551c1a2218c914c Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 21 Aug 2024 10:03:39 +0200
Subject: [PATCH 316/424] UPD: odc

---
 doc/index.rst                                 |   2 +-
 .../plot_main-tutorial-mondrian-regression.py | 181 ++++++++++++++++++
 2 files changed, 182 insertions(+), 1 deletion(-)
 create mode 100644 examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py

diff --git a/doc/index.rst b/doc/index.rst
index 42fe01304..73926b81c 100644
--- a/doc/index.rst
+++ b/doc/index.rst
@@ -55,7 +55,7 @@
    :caption: MONDRIAN
 
    theoretical_description_mondrian
-   tutorial_mondrian_regression
+   examples_mondrian/1-quickstart/plot_main-tutorial-mondrian-regression
 
 .. toctree::
    :maxdepth: 2
diff --git a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
new file mode 100644
index 000000000..955db7830
--- /dev/null
+++ b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
@@ -0,0 +1,181 @@
+r"""
+=============================================
+Tutorial for tabular regression with Mondrian
+=============================================
+
+In this tutorial, we compare the prediction intervals estimated by MAPIE on a
+simple, one-dimensional, ground truth function with classical conformal
+prediction intervals versus Mondrian conformal prediction intervals.
+The function is a sinusoidal function with added noise, and the data is
+grouped in 10 groups. The goal is to estimate the prediction intervals
+for new data points, and to compare the coverage of the prediction intervals
+by groups.
+Throughout this tutorial, we will answer the following questions:
+
+
+- How to use MAPIE to estimate prediction intervals for a regression problem?
+- How to use Mondrian conformal prediction intervals for regression?
+- How to compare the coverage of the prediction intervals by groups?
+"""
+
+import os
+import warnings
+
+import matplotlib.pyplot as plt
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+
+from mapie.metrics import regression_coverage_score_v2
+from mapie.mondrian import MondrianCP
+from mapie.regression import MapieRegressor
+
+os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
+warnings.filterwarnings("ignore")
+
+
+##############################################################################
+# 1. Create the noisy dataset with 10 groups, each of those groups having
+#    a different level of noise.
+# -------------------------------------------------------------------
+
+
+n_points = 100000
+np.random.seed(0)
+X = np.linspace(0, 10, n_points).reshape(-1, 1)
+group_size = n_points // 10
+groups_list = []
+for i in range(10):
+    groups_list.append(np.array([i] * group_size))
+groups = np.concatenate(groups_list)
+
+noise_0_1 = np.random.normal(0, 0.1, group_size)
+noise_1_2 = np.random.normal(0, 0.5, group_size)
+noise_2_3 = np.random.normal(0, 1, group_size)
+noise_3_4 = np.random.normal(0, .4, group_size)
+noise_4_5 = np.random.normal(0, .2, group_size)
+noise_5_6 = np.random.normal(0, .3, group_size)
+noise_6_7 = np.random.normal(0, .6, group_size)
+noise_7_8 = np.random.normal(0, .7, group_size)
+noise_8_9 = np.random.normal(0, .8, group_size)
+noise_9_10 = np.random.normal(0, .9, group_size)
+
+y = np.concatenate(
+    [
+        np.sin(X[groups == 0, 0] * 2) + noise_0_1,
+        np.sin(X[groups == 1, 0] * 2) + noise_1_2,
+        np.sin(X[groups == 2, 0] * 2) + noise_2_3,
+        np.sin(X[groups == 3, 0] * 2) + noise_3_4,
+        np.sin(X[groups == 4, 0] * 2) + noise_4_5,
+        np.sin(X[groups == 5, 0] * 2) + noise_5_6,
+        np.sin(X[groups == 6, 0] * 2) + noise_6_7,
+        np.sin(X[groups == 7, 0] * 2) + noise_7_8,
+        np.sin(X[groups == 8, 0] * 2) + noise_8_9,
+        np.sin(X[groups == 9, 0] * 2) + noise_9_10,
+    ], axis=0
+)
+
+
+##############################################################################
+# We plot the dataset with the groups as colors.
+
+
+plt.scatter(X, y, c=groups)
+plt.show()
+
+
+##############################################################################
+# 2. Split the dataset into a training set, a calibration set, and a test set.
+
+
+X_train_temp, X_test, y_train_temp, y_test = train_test_split(
+    X, y, test_size=0.2, random_state=0
+)
+groups_train_temp, groups_test, _, _ = train_test_split(
+    groups, y, test_size=0.2, random_state=0
+)
+X_cal, X_train, y_cal, y_train = train_test_split(
+    X_train_temp, y_train_temp, test_size=0.5, random_state=0
+)
+groups_cal, groups_train, _, _ = train_test_split(
+    groups_train_temp, y_train_temp, test_size=0.5, random_state=0
+)
+
+
+##############################################################################
+# We plot the training set, the calibration set, and the test set.
+
+
+f, ax = plt.subplots(1, 3, figsize=(15, 5))
+ax[0].scatter(X_train, y_train, c=groups_train)
+ax[0].set_title("Train set")
+ax[1].scatter(X_cal, y_cal, c=groups_cal)
+ax[1].set_title("Calibration set")
+ax[2].scatter(X_test, y_test, c=groups_test)
+ax[2].set_title("Test set")
+plt.show()
+
+
+##############################################################################
+# 3. Fit a random forest regressor on the training set.
+
+
+rf = RandomForestRegressor(n_estimators=100)
+rf.fit(X_train, y_train)
+
+
+##############################################################################
+# 4. Fit a MapieRegressor and a MondrianCP on the calibration set.
+
+
+mapie_regressor = MapieRegressor(rf, cv="prefit")
+mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
+mapie_regressor.fit(X_cal, y_cal)
+mondrian_regressor.fit(X_cal, y_cal, groups=groups_cal)
+
+
+##############################################################################
+# 5. Predict the prediction intervals on the test set with both methods.
+
+
+_, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
+_, y_pss_mondrian = mondrian_regressor.predict(
+    X_test, groups=groups_test, alpha=.1
+)
+
+
+##############################################################################
+# 6. Compare the coverage by groups, plot both methods side by side.
+
+
+coverages = {}
+for group in np.unique(groups_test):
+    coverages[group] = {}
+    coverages[group]["split"] = regression_coverage_score_v2(
+        y_test[groups_test == group], y_pss_split[groups_test == group]
+    )
+    coverages[group]["mondrian"] = regression_coverage_score_v2(
+        y_test[groups_test == group], y_pss_mondrian[groups_test == group]
+    )
+
+
+# Plot the coverage by groups, plot both methods side by side
+plt.bar(
+    np.arange(len(coverages)) * 2,
+    [float(coverages[group]["split"]) for group in coverages],
+    label="Split"
+)
+plt.bar(
+    np.arange(len(coverages)) * 2 + 1,
+    [float(coverages[group]["mondrian"]) for group in coverages],
+    label="Mondrian"
+)
+plt.xticks(
+    np.arange(len(coverages)) * 2 + .5,
+    [f"Group {group}" for group in coverages],
+    rotation=45
+)
+plt.hlines(0.9, -1, 21, label="90% coverage", color="black", linestyle="--")
+plt.ylabel("Coverage")
+plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
+plt.show()

From aa47daeba9207d826b6605e1e680f1cd89303a5e Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 21 Aug 2024 10:22:24 +0200
Subject: [PATCH 317/424] UPD: doc

---
 doc/Makefile | 1 +
 doc/conf.py  | 6 ++++--
 2 files changed, 5 insertions(+), 2 deletions(-)

diff --git a/doc/Makefile b/doc/Makefile
index e8dfac770..ea4d9f315 100644
--- a/doc/Makefile
+++ b/doc/Makefile
@@ -52,6 +52,7 @@ clean:
 	-rm -rf examples_classification/
 	-rm -rf examples_multilabel_classification/
 	-rm -rf examples_calibration/
+	-rm -rf examples_mondrian/
 	-rm -rf generated/*
 	-rm -rf modules/generated/*
 
diff --git a/doc/conf.py b/doc/conf.py
index f7f3c5e86..400d9c96c 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -316,13 +316,15 @@
         "../examples/regression",
         "../examples/classification",
         "../examples/multilabel_classification",
-        "../examples/calibration"
+        "../examples/calibration",
+        "../examples/mondrian",
     ],
     "gallery_dirs": [
         "examples_regression",
         "examples_classification",
         "examples_multilabel_classification",
-        "examples_calibration"
+        "examples_calibration",
+        "examples_mondrian",
     ],
     "doc_module": "mapie",
     "backreferences_dir": os.path.join("generated"),

From cc6e39cc9a33b411a0b5b9ecc5ed64b8ff2036bf Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 21 Aug 2024 10:37:29 +0200
Subject: [PATCH 318/424] ADD: readme file for mondrian

---
 examples/mondrian/README.rst | 4 ++++
 1 file changed, 4 insertions(+)
 create mode 100644 examples/mondrian/README.rst

diff --git a/examples/mondrian/README.rst b/examples/mondrian/README.rst
new file mode 100644
index 000000000..8be82d866
--- /dev/null
+++ b/examples/mondrian/README.rst
@@ -0,0 +1,4 @@
+.. _mondrian_examples:
+
+Mondrian examples
+=======================
\ No newline at end of file

From 2acb98d899349922c49ac5ef987f018c8d56598a Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 21 Aug 2024 11:03:47 +0200
Subject: [PATCH 319/424] ADD: readme

---
 doc/tutorial_mondrian_regression.rst      | 1068 ---------------------
 examples/mondrian/1-quickstart/README.rst |    6 +
 2 files changed, 6 insertions(+), 1068 deletions(-)
 delete mode 100644 doc/tutorial_mondrian_regression.rst
 create mode 100644 examples/mondrian/1-quickstart/README.rst

diff --git a/doc/tutorial_mondrian_regression.rst b/doc/tutorial_mondrian_regression.rst
deleted file mode 100644
index 6704ff313..000000000
--- a/doc/tutorial_mondrian_regression.rst
+++ /dev/null
@@ -1,1068 +0,0 @@
-.. code:: ipython3
-
-    import matplotlib.pyplot as plt
-    import numpy as np
-    from sklearn.base import clone
-    from sklearn.model_selection import train_test_split
-    from sklearn.ensemble import RandomForestRegressor
-    
-    from mapie.metrics import regression_coverage_score_v2
-    from mapie.mondrian import MondrianCP
-    from mapie.regression import MapieRegressor
-    
-    %load_ext autoreload
-    %autoreload 2
-
-.. code:: ipython3
-
-    # Create 1D regression dataset with sinusoidual function between 0 and 10 
-    n_points = 100000
-    np.random.seed(0)
-    X = np.linspace(0, 10, n_points).reshape(-1, 1)
-    group_size = n_points // 10
-    groups_list = []
-    for i in range(10):
-        groups_list.append(np.array([i] * group_size))
-    groups = np.concatenate(groups_list)
-    
-    noise_0_1 = np.random.normal(0, 0.1, group_size)
-    noise_1_2 = np.random.normal(0, 0.5, group_size)
-    noise_2_3 = np.random.normal(0, 1, group_size)
-    noise_3_4 = np.random.normal(0, .4, group_size)
-    noise_4_5 = np.random.normal(0, .2, group_size)
-    noise_5_6 = np.random.normal(0, .3, group_size)
-    noise_6_7 = np.random.normal(0, .6, group_size)
-    noise_7_8 = np.random.normal(0, .7, group_size)
-    noise_8_9 = np.random.normal(0, .8, group_size)
-    noise_9_10 = np.random.normal(0, .9, group_size)
-    
-    y = np.concatenate(
-        [
-            np.sin(X[groups == 0, 0] * 2) + noise_0_1,
-            np.sin(X[groups == 1, 0] * 2) + noise_1_2,
-            np.sin(X[groups == 2, 0] * 2) + noise_2_3,
-            np.sin(X[groups == 3, 0] * 2) + noise_3_4,
-            np.sin(X[groups == 4, 0] * 2) + noise_4_5,
-            np.sin(X[groups == 5, 0] * 2) + noise_5_6,
-            np.sin(X[groups == 6, 0] * 2) + noise_6_7,
-            np.sin(X[groups == 7, 0] * 2) + noise_7_8,
-            np.sin(X[groups == 8, 0] * 2) + noise_8_9,
-            np.sin(X[groups == 9, 0] * 2) + noise_9_10,
-        ], axis=0
-    )
-    
-
-
-.. code:: ipython3
-
-    plt.scatter(X, y, c=groups)
-    plt.show()
-
-
-
-.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_2_0.png
-
-
-.. code:: ipython3
-
-    X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
-    groups_train_temp, groups_test, _, _ = train_test_split(groups, y, test_size=0.2, random_state=0)
-    X_cal, X_train, y_cal, y_train = train_test_split(X_train_temp, y_train_temp, test_size=0.5, random_state=0)
-    groups_cal, groups_train, _, _ = train_test_split(groups_train_temp, y_train_temp, test_size=0.5, random_state=0)
-
-.. code:: ipython3
-
-    X_train.shape, y_train.shape, groups_train.shape
-
-
-
-
-.. parsed-literal::
-
-    ((40000, 1), (40000,), (40000,))
-
-
-
-.. code:: ipython3
-
-    f, ax = plt.subplots(1, 3, figsize=(15, 5))
-    ax[0].scatter(X_train, y_train, c=groups_train)
-    ax[0].set_title("Train set")
-    ax[1].scatter(X_cal, y_cal, c=groups_cal)
-    ax[1].set_title("Calibration set")
-    ax[2].scatter(X_test, y_test, c=groups_test)
-    ax[2].set_title("Test set")
-    plt.show()
-
-
-
-.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_5_0.png
-
-
-.. code:: ipython3
-
-    print("Training set size: ", X_train.shape[0])
-    print("Calibration set size: ", X_cal.shape[0])
-    print("Test set size: ", X_test.shape[0])
-
-
-.. parsed-literal::
-
-    Training set size:  40000
-    Calibration set size:  40000
-    Test set size:  20000
-
-
-.. code:: ipython3
-
-    # Fit a random forest regressor
-    
-    rf = RandomForestRegressor(n_estimators=100)
-    rf.fit(X_train, y_train)
-
-
-
-
-
-.. raw:: html
-
-    <style>#sk-container-id-1 {
-      /* Definition of color scheme common for light and dark mode */
-      --sklearn-color-text: black;
-      --sklearn-color-line: gray;
-      /* Definition of color scheme for unfitted estimators */
-      --sklearn-color-unfitted-level-0: #fff5e6;
-      --sklearn-color-unfitted-level-1: #f6e4d2;
-      --sklearn-color-unfitted-level-2: #ffe0b3;
-      --sklearn-color-unfitted-level-3: chocolate;
-      /* Definition of color scheme for fitted estimators */
-      --sklearn-color-fitted-level-0: #f0f8ff;
-      --sklearn-color-fitted-level-1: #d4ebff;
-      --sklearn-color-fitted-level-2: #b3dbfd;
-      --sklearn-color-fitted-level-3: cornflowerblue;
-    
-      /* Specific color for light theme */
-      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
-      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
-      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
-      --sklearn-color-icon: #696969;
-    
-      @media (prefers-color-scheme: dark) {
-        /* Redefinition of color scheme for dark theme */
-        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
-        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
-        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
-        --sklearn-color-icon: #878787;
-      }
-    }
-    
-    #sk-container-id-1 {
-      color: var(--sklearn-color-text);
-    }
-    
-    #sk-container-id-1 pre {
-      padding: 0;
-    }
-    
-    #sk-container-id-1 input.sk-hidden--visually {
-      border: 0;
-      clip: rect(1px 1px 1px 1px);
-      clip: rect(1px, 1px, 1px, 1px);
-      height: 1px;
-      margin: -1px;
-      overflow: hidden;
-      padding: 0;
-      position: absolute;
-      width: 1px;
-    }
-    
-    #sk-container-id-1 div.sk-dashed-wrapped {
-      border: 1px dashed var(--sklearn-color-line);
-      margin: 0 0.4em 0.5em 0.4em;
-      box-sizing: border-box;
-      padding-bottom: 0.4em;
-      background-color: var(--sklearn-color-background);
-    }
-    
-    #sk-container-id-1 div.sk-container {
-      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-         but bootstrap.min.css set `[hidden] { display: none !important; }`
-         so we also need the `!important` here to be able to override the
-         default hidden behavior on the sphinx rendered scikit-learn.org.
-         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-      display: inline-block !important;
-      position: relative;
-    }
-    
-    #sk-container-id-1 div.sk-text-repr-fallback {
-      display: none;
-    }
-    
-    div.sk-parallel-item,
-    div.sk-serial,
-    div.sk-item {
-      /* draw centered vertical line to link estimators */
-      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-      background-size: 2px 100%;
-      background-repeat: no-repeat;
-      background-position: center center;
-    }
-    
-    /* Parallel-specific style estimator block */
-    
-    #sk-container-id-1 div.sk-parallel-item::after {
-      content: "";
-      width: 100%;
-      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-      flex-grow: 1;
-    }
-    
-    #sk-container-id-1 div.sk-parallel {
-      display: flex;
-      align-items: stretch;
-      justify-content: center;
-      background-color: var(--sklearn-color-background);
-      position: relative;
-    }
-    
-    #sk-container-id-1 div.sk-parallel-item {
-      display: flex;
-      flex-direction: column;
-    }
-    
-    #sk-container-id-1 div.sk-parallel-item:first-child::after {
-      align-self: flex-end;
-      width: 50%;
-    }
-    
-    #sk-container-id-1 div.sk-parallel-item:last-child::after {
-      align-self: flex-start;
-      width: 50%;
-    }
-    
-    #sk-container-id-1 div.sk-parallel-item:only-child::after {
-      width: 0;
-    }
-    
-    /* Serial-specific style estimator block */
-    
-    #sk-container-id-1 div.sk-serial {
-      display: flex;
-      flex-direction: column;
-      align-items: center;
-      background-color: var(--sklearn-color-background);
-      padding-right: 1em;
-      padding-left: 1em;
-    }
-    
-    
-    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-    clickable and can be expanded/collapsed.
-    - Pipeline and ColumnTransformer use this feature and define the default style
-    - Estimators will overwrite some part of the style using the `sk-estimator` class
-    */
-    
-    /* Pipeline and ColumnTransformer style (default) */
-    
-    #sk-container-id-1 div.sk-toggleable {
-      /* Default theme specific background. It is overwritten whether we have a
-      specific estimator or a Pipeline/ColumnTransformer */
-      background-color: var(--sklearn-color-background);
-    }
-    
-    /* Toggleable label */
-    #sk-container-id-1 label.sk-toggleable__label {
-      cursor: pointer;
-      display: block;
-      width: 100%;
-      margin-bottom: 0;
-      padding: 0.5em;
-      box-sizing: border-box;
-      text-align: center;
-    }
-    
-    #sk-container-id-1 label.sk-toggleable__label-arrow:before {
-      /* Arrow on the left of the label */
-      content: "▸";
-      float: left;
-      margin-right: 0.25em;
-      color: var(--sklearn-color-icon);
-    }
-    
-    #sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {
-      color: var(--sklearn-color-text);
-    }
-    
-    /* Toggleable content - dropdown */
-    
-    #sk-container-id-1 div.sk-toggleable__content {
-      max-height: 0;
-      max-width: 0;
-      overflow: hidden;
-      text-align: left;
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-0);
-    }
-    
-    #sk-container-id-1 div.sk-toggleable__content.fitted {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-0);
-    }
-    
-    #sk-container-id-1 div.sk-toggleable__content pre {
-      margin: 0.2em;
-      border-radius: 0.25em;
-      color: var(--sklearn-color-text);
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-0);
-    }
-    
-    #sk-container-id-1 div.sk-toggleable__content.fitted pre {
-      /* unfitted */
-      background-color: var(--sklearn-color-fitted-level-0);
-    }
-    
-    #sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-      /* Expand drop-down */
-      max-height: 200px;
-      max-width: 100%;
-      overflow: auto;
-    }
-    
-    #sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-      content: "▾";
-    }
-    
-    /* Pipeline/ColumnTransformer-specific style */
-    
-    #sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      color: var(--sklearn-color-text);
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    #sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    /* Estimator-specific style */
-    
-    /* Colorize estimator box */
-    #sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    #sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    #sk-container-id-1 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-1 div.sk-label label {
-      /* The background is the default theme color */
-      color: var(--sklearn-color-text-on-default-background);
-    }
-    
-    /* On hover, darken the color of the background */
-    #sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {
-      color: var(--sklearn-color-text);
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    /* Label box, darken color on hover, fitted */
-    #sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
-      color: var(--sklearn-color-text);
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    /* Estimator label */
-    
-    #sk-container-id-1 div.sk-label label {
-      font-family: monospace;
-      font-weight: bold;
-      display: inline-block;
-      line-height: 1.2em;
-    }
-    
-    #sk-container-id-1 div.sk-label-container {
-      text-align: center;
-    }
-    
-    /* Estimator-specific */
-    #sk-container-id-1 div.sk-estimator {
-      font-family: monospace;
-      border: 1px dotted var(--sklearn-color-border-box);
-      border-radius: 0.25em;
-      box-sizing: border-box;
-      margin-bottom: 0.5em;
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-0);
-    }
-    
-    #sk-container-id-1 div.sk-estimator.fitted {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-0);
-    }
-    
-    /* on hover */
-    #sk-container-id-1 div.sk-estimator:hover {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    #sk-container-id-1 div.sk-estimator.fitted:hover {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    /* Specification for estimator info (e.g. "i" and "?") */
-    
-    /* Common style for "i" and "?" */
-    
-    .sk-estimator-doc-link,
-    a:link.sk-estimator-doc-link,
-    a:visited.sk-estimator-doc-link {
-      float: right;
-      font-size: smaller;
-      line-height: 1em;
-      font-family: monospace;
-      background-color: var(--sklearn-color-background);
-      border-radius: 1em;
-      height: 1em;
-      width: 1em;
-      text-decoration: none !important;
-      margin-left: 1ex;
-      /* unfitted */
-      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-      color: var(--sklearn-color-unfitted-level-1);
-    }
-    
-    .sk-estimator-doc-link.fitted,
-    a:link.sk-estimator-doc-link.fitted,
-    a:visited.sk-estimator-doc-link.fitted {
-      /* fitted */
-      border: var(--sklearn-color-fitted-level-1) 1pt solid;
-      color: var(--sklearn-color-fitted-level-1);
-    }
-    
-    /* On hover */
-    div.sk-estimator:hover .sk-estimator-doc-link:hover,
-    .sk-estimator-doc-link:hover,
-    div.sk-label-container:hover .sk-estimator-doc-link:hover,
-    .sk-estimator-doc-link:hover {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-3);
-      color: var(--sklearn-color-background);
-      text-decoration: none;
-    }
-    
-    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
-    .sk-estimator-doc-link.fitted:hover,
-    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
-    .sk-estimator-doc-link.fitted:hover {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-3);
-      color: var(--sklearn-color-background);
-      text-decoration: none;
-    }
-    
-    /* Span, style for the box shown on hovering the info icon */
-    .sk-estimator-doc-link span {
-      display: none;
-      z-index: 9999;
-      position: relative;
-      font-weight: normal;
-      right: .2ex;
-      padding: .5ex;
-      margin: .5ex;
-      width: min-content;
-      min-width: 20ex;
-      max-width: 50ex;
-      color: var(--sklearn-color-text);
-      box-shadow: 2pt 2pt 4pt #999;
-      /* unfitted */
-      background: var(--sklearn-color-unfitted-level-0);
-      border: .5pt solid var(--sklearn-color-unfitted-level-3);
-    }
-    
-    .sk-estimator-doc-link.fitted span {
-      /* fitted */
-      background: var(--sklearn-color-fitted-level-0);
-      border: var(--sklearn-color-fitted-level-3);
-    }
-    
-    .sk-estimator-doc-link:hover span {
-      display: block;
-    }
-    
-    /* "?"-specific style due to the `<a>` HTML tag */
-    
-    #sk-container-id-1 a.estimator_doc_link {
-      float: right;
-      font-size: 1rem;
-      line-height: 1em;
-      font-family: monospace;
-      background-color: var(--sklearn-color-background);
-      border-radius: 1rem;
-      height: 1rem;
-      width: 1rem;
-      text-decoration: none;
-      /* unfitted */
-      color: var(--sklearn-color-unfitted-level-1);
-      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-    }
-    
-    #sk-container-id-1 a.estimator_doc_link.fitted {
-      /* fitted */
-      border: var(--sklearn-color-fitted-level-1) 1pt solid;
-      color: var(--sklearn-color-fitted-level-1);
-    }
-    
-    /* On hover */
-    #sk-container-id-1 a.estimator_doc_link:hover {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-3);
-      color: var(--sklearn-color-background);
-      text-decoration: none;
-    }
-    
-    #sk-container-id-1 a.estimator_doc_link.fitted:hover {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-3);
-    }
-    </style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator fitted sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label fitted sk-toggleable__label-arrow fitted">&nbsp;&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link fitted" rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a><span class="sk-estimator-doc-link fitted">i<span>Fitted</span></span></label><div class="sk-toggleable__content fitted"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>
-
-
-
-.. code:: ipython3
-
-    # Plot the prediction of the random forest regressor as a line
-    
-    y_pred = rf.predict(X_test)
-    # plt.scatter(X_test, y_test, label="True")
-    
-    #Sort the test set and the prediction to plot them as a line
-    sort_idx = np.argsort(X_test[:, 0])
-    plt.plot(X_test[sort_idx], y_pred[sort_idx], label="Prediction")
-    
-    plt.legend()
-
-
-
-
-.. parsed-literal::
-
-    <matplotlib.legend.Legend at 0x1380f0760>
-
-
-
-
-.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_8_1.png
-
-
-.. code:: ipython3
-
-    mapie_regressor = MapieRegressor(rf, cv="prefit")
-    mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
-
-.. code:: ipython3
-
-    mapie_regressor.fit(X_cal, y_cal)
-    mondrian_regressor.fit(X_cal, y_cal, groups=groups_cal)
-
-
-
-
-.. raw:: html
-
-    <style>#sk-container-id-2 {
-      /* Definition of color scheme common for light and dark mode */
-      --sklearn-color-text: black;
-      --sklearn-color-line: gray;
-      /* Definition of color scheme for unfitted estimators */
-      --sklearn-color-unfitted-level-0: #fff5e6;
-      --sklearn-color-unfitted-level-1: #f6e4d2;
-      --sklearn-color-unfitted-level-2: #ffe0b3;
-      --sklearn-color-unfitted-level-3: chocolate;
-      /* Definition of color scheme for fitted estimators */
-      --sklearn-color-fitted-level-0: #f0f8ff;
-      --sklearn-color-fitted-level-1: #d4ebff;
-      --sklearn-color-fitted-level-2: #b3dbfd;
-      --sklearn-color-fitted-level-3: cornflowerblue;
-    
-      /* Specific color for light theme */
-      --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
-      --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));
-      --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));
-      --sklearn-color-icon: #696969;
-    
-      @media (prefers-color-scheme: dark) {
-        /* Redefinition of color scheme for dark theme */
-        --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
-        --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));
-        --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));
-        --sklearn-color-icon: #878787;
-      }
-    }
-    
-    #sk-container-id-2 {
-      color: var(--sklearn-color-text);
-    }
-    
-    #sk-container-id-2 pre {
-      padding: 0;
-    }
-    
-    #sk-container-id-2 input.sk-hidden--visually {
-      border: 0;
-      clip: rect(1px 1px 1px 1px);
-      clip: rect(1px, 1px, 1px, 1px);
-      height: 1px;
-      margin: -1px;
-      overflow: hidden;
-      padding: 0;
-      position: absolute;
-      width: 1px;
-    }
-    
-    #sk-container-id-2 div.sk-dashed-wrapped {
-      border: 1px dashed var(--sklearn-color-line);
-      margin: 0 0.4em 0.5em 0.4em;
-      box-sizing: border-box;
-      padding-bottom: 0.4em;
-      background-color: var(--sklearn-color-background);
-    }
-    
-    #sk-container-id-2 div.sk-container {
-      /* jupyter's `normalize.less` sets `[hidden] { display: none; }`
-         but bootstrap.min.css set `[hidden] { display: none !important; }`
-         so we also need the `!important` here to be able to override the
-         default hidden behavior on the sphinx rendered scikit-learn.org.
-         See: https://github.com/scikit-learn/scikit-learn/issues/21755 */
-      display: inline-block !important;
-      position: relative;
-    }
-    
-    #sk-container-id-2 div.sk-text-repr-fallback {
-      display: none;
-    }
-    
-    div.sk-parallel-item,
-    div.sk-serial,
-    div.sk-item {
-      /* draw centered vertical line to link estimators */
-      background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));
-      background-size: 2px 100%;
-      background-repeat: no-repeat;
-      background-position: center center;
-    }
-    
-    /* Parallel-specific style estimator block */
-    
-    #sk-container-id-2 div.sk-parallel-item::after {
-      content: "";
-      width: 100%;
-      border-bottom: 2px solid var(--sklearn-color-text-on-default-background);
-      flex-grow: 1;
-    }
-    
-    #sk-container-id-2 div.sk-parallel {
-      display: flex;
-      align-items: stretch;
-      justify-content: center;
-      background-color: var(--sklearn-color-background);
-      position: relative;
-    }
-    
-    #sk-container-id-2 div.sk-parallel-item {
-      display: flex;
-      flex-direction: column;
-    }
-    
-    #sk-container-id-2 div.sk-parallel-item:first-child::after {
-      align-self: flex-end;
-      width: 50%;
-    }
-    
-    #sk-container-id-2 div.sk-parallel-item:last-child::after {
-      align-self: flex-start;
-      width: 50%;
-    }
-    
-    #sk-container-id-2 div.sk-parallel-item:only-child::after {
-      width: 0;
-    }
-    
-    /* Serial-specific style estimator block */
-    
-    #sk-container-id-2 div.sk-serial {
-      display: flex;
-      flex-direction: column;
-      align-items: center;
-      background-color: var(--sklearn-color-background);
-      padding-right: 1em;
-      padding-left: 1em;
-    }
-    
-    
-    /* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is
-    clickable and can be expanded/collapsed.
-    - Pipeline and ColumnTransformer use this feature and define the default style
-    - Estimators will overwrite some part of the style using the `sk-estimator` class
-    */
-    
-    /* Pipeline and ColumnTransformer style (default) */
-    
-    #sk-container-id-2 div.sk-toggleable {
-      /* Default theme specific background. It is overwritten whether we have a
-      specific estimator or a Pipeline/ColumnTransformer */
-      background-color: var(--sklearn-color-background);
-    }
-    
-    /* Toggleable label */
-    #sk-container-id-2 label.sk-toggleable__label {
-      cursor: pointer;
-      display: block;
-      width: 100%;
-      margin-bottom: 0;
-      padding: 0.5em;
-      box-sizing: border-box;
-      text-align: center;
-    }
-    
-    #sk-container-id-2 label.sk-toggleable__label-arrow:before {
-      /* Arrow on the left of the label */
-      content: "▸";
-      float: left;
-      margin-right: 0.25em;
-      color: var(--sklearn-color-icon);
-    }
-    
-    #sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {
-      color: var(--sklearn-color-text);
-    }
-    
-    /* Toggleable content - dropdown */
-    
-    #sk-container-id-2 div.sk-toggleable__content {
-      max-height: 0;
-      max-width: 0;
-      overflow: hidden;
-      text-align: left;
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-0);
-    }
-    
-    #sk-container-id-2 div.sk-toggleable__content.fitted {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-0);
-    }
-    
-    #sk-container-id-2 div.sk-toggleable__content pre {
-      margin: 0.2em;
-      border-radius: 0.25em;
-      color: var(--sklearn-color-text);
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-0);
-    }
-    
-    #sk-container-id-2 div.sk-toggleable__content.fitted pre {
-      /* unfitted */
-      background-color: var(--sklearn-color-fitted-level-0);
-    }
-    
-    #sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {
-      /* Expand drop-down */
-      max-height: 200px;
-      max-width: 100%;
-      overflow: auto;
-    }
-    
-    #sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {
-      content: "▾";
-    }
-    
-    /* Pipeline/ColumnTransformer-specific style */
-    
-    #sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      color: var(--sklearn-color-text);
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    #sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    /* Estimator-specific style */
-    
-    /* Colorize estimator box */
-    #sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    #sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    #sk-container-id-2 div.sk-label label.sk-toggleable__label,
-    #sk-container-id-2 div.sk-label label {
-      /* The background is the default theme color */
-      color: var(--sklearn-color-text-on-default-background);
-    }
-    
-    /* On hover, darken the color of the background */
-    #sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {
-      color: var(--sklearn-color-text);
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    /* Label box, darken color on hover, fitted */
-    #sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {
-      color: var(--sklearn-color-text);
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    /* Estimator label */
-    
-    #sk-container-id-2 div.sk-label label {
-      font-family: monospace;
-      font-weight: bold;
-      display: inline-block;
-      line-height: 1.2em;
-    }
-    
-    #sk-container-id-2 div.sk-label-container {
-      text-align: center;
-    }
-    
-    /* Estimator-specific */
-    #sk-container-id-2 div.sk-estimator {
-      font-family: monospace;
-      border: 1px dotted var(--sklearn-color-border-box);
-      border-radius: 0.25em;
-      box-sizing: border-box;
-      margin-bottom: 0.5em;
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-0);
-    }
-    
-    #sk-container-id-2 div.sk-estimator.fitted {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-0);
-    }
-    
-    /* on hover */
-    #sk-container-id-2 div.sk-estimator:hover {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-2);
-    }
-    
-    #sk-container-id-2 div.sk-estimator.fitted:hover {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-2);
-    }
-    
-    /* Specification for estimator info (e.g. "i" and "?") */
-    
-    /* Common style for "i" and "?" */
-    
-    .sk-estimator-doc-link,
-    a:link.sk-estimator-doc-link,
-    a:visited.sk-estimator-doc-link {
-      float: right;
-      font-size: smaller;
-      line-height: 1em;
-      font-family: monospace;
-      background-color: var(--sklearn-color-background);
-      border-radius: 1em;
-      height: 1em;
-      width: 1em;
-      text-decoration: none !important;
-      margin-left: 1ex;
-      /* unfitted */
-      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-      color: var(--sklearn-color-unfitted-level-1);
-    }
-    
-    .sk-estimator-doc-link.fitted,
-    a:link.sk-estimator-doc-link.fitted,
-    a:visited.sk-estimator-doc-link.fitted {
-      /* fitted */
-      border: var(--sklearn-color-fitted-level-1) 1pt solid;
-      color: var(--sklearn-color-fitted-level-1);
-    }
-    
-    /* On hover */
-    div.sk-estimator:hover .sk-estimator-doc-link:hover,
-    .sk-estimator-doc-link:hover,
-    div.sk-label-container:hover .sk-estimator-doc-link:hover,
-    .sk-estimator-doc-link:hover {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-3);
-      color: var(--sklearn-color-background);
-      text-decoration: none;
-    }
-    
-    div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,
-    .sk-estimator-doc-link.fitted:hover,
-    div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,
-    .sk-estimator-doc-link.fitted:hover {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-3);
-      color: var(--sklearn-color-background);
-      text-decoration: none;
-    }
-    
-    /* Span, style for the box shown on hovering the info icon */
-    .sk-estimator-doc-link span {
-      display: none;
-      z-index: 9999;
-      position: relative;
-      font-weight: normal;
-      right: .2ex;
-      padding: .5ex;
-      margin: .5ex;
-      width: min-content;
-      min-width: 20ex;
-      max-width: 50ex;
-      color: var(--sklearn-color-text);
-      box-shadow: 2pt 2pt 4pt #999;
-      /* unfitted */
-      background: var(--sklearn-color-unfitted-level-0);
-      border: .5pt solid var(--sklearn-color-unfitted-level-3);
-    }
-    
-    .sk-estimator-doc-link.fitted span {
-      /* fitted */
-      background: var(--sklearn-color-fitted-level-0);
-      border: var(--sklearn-color-fitted-level-3);
-    }
-    
-    .sk-estimator-doc-link:hover span {
-      display: block;
-    }
-    
-    /* "?"-specific style due to the `<a>` HTML tag */
-    
-    #sk-container-id-2 a.estimator_doc_link {
-      float: right;
-      font-size: 1rem;
-      line-height: 1em;
-      font-family: monospace;
-      background-color: var(--sklearn-color-background);
-      border-radius: 1rem;
-      height: 1rem;
-      width: 1rem;
-      text-decoration: none;
-      /* unfitted */
-      color: var(--sklearn-color-unfitted-level-1);
-      border: var(--sklearn-color-unfitted-level-1) 1pt solid;
-    }
-    
-    #sk-container-id-2 a.estimator_doc_link.fitted {
-      /* fitted */
-      border: var(--sklearn-color-fitted-level-1) 1pt solid;
-      color: var(--sklearn-color-fitted-level-1);
-    }
-    
-    /* On hover */
-    #sk-container-id-2 a.estimator_doc_link:hover {
-      /* unfitted */
-      background-color: var(--sklearn-color-unfitted-level-3);
-      color: var(--sklearn-color-background);
-      text-decoration: none;
-    }
-    
-    #sk-container-id-2 a.estimator_doc_link.fitted:hover {
-      /* fitted */
-      background-color: var(--sklearn-color-fitted-level-3);
-    }
-    </style><div id="sk-container-id-2" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,
-                                              estimator=RandomForestRegressor()))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-2" type="checkbox" ><label for="sk-estimator-id-2" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;MondrianCP<span class="sk-estimator-doc-link ">i<span>Not fitted</span></span></label><div class="sk-toggleable__content "><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,
-                                              estimator=RandomForestRegressor()))</pre></div> </div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-3" type="checkbox" ><label for="sk-estimator-id-3" class="sk-toggleable__label  sk-toggleable__label-arrow ">mapie_estimator: MapieRegressor</label><div class="sk-toggleable__content "><pre>MapieRegressor(cv=&#x27;prefit&#x27;, estimator=RandomForestRegressor())</pre></div> </div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-4" type="checkbox" ><label for="sk-estimator-id-4" class="sk-toggleable__label  sk-toggleable__label-arrow ">estimator: RandomForestRegressor</label><div class="sk-toggleable__content "><pre>RandomForestRegressor()</pre></div> </div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator  sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-5" type="checkbox" ><label for="sk-estimator-id-5" class="sk-toggleable__label  sk-toggleable__label-arrow ">&nbsp;RandomForestRegressor<a class="sk-estimator-doc-link " rel="noreferrer" target="_blank" href="https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html">?<span>Documentation for RandomForestRegressor</span></a></label><div class="sk-toggleable__content "><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>
-
-
-
-.. code:: ipython3
-
-    _, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
-    _, y_pss_mondrian = mondrian_regressor.predict(X_test, groups=groups_test, alpha=.1)
-
-.. code:: ipython3
-
-    rf = RandomForestRegressor(
-        n_estimators=100
-    )
-    rf.fit(X_train, y_train)
-    mondrian_regressor = MondrianCP(
-        MapieRegressor(rf, cv="prefit")
-    )
-    mondrian_regressor.fit(
-        X_cal, y_cal,
-        groups=groups_cal
-    )
-    _, y_pss_mondrian = mondrian_regressor.predict(
-        X_test, groups=groups_test, alpha=.1
-    )
-
-.. code:: ipython3
-
-    # Plot the prediction of the random forest regressor as a line with the prediction intervals
-    
-    # plt.scatter(X_test, y_test, label="True")
-    sort_idx = np.argsort(X_test[:, 0])
-    # plt.plot(X_test[sort_idx], y_pred[sort_idx], label="Prediction")
-    plt.fill_between(X_test[sort_idx].flatten(), y_pss_split[sort_idx, 0].flatten(), y_pss_split[sort_idx, 1].flatten(), alpha=0.3, label="Split")
-    plt.fill_between(X_test[sort_idx].flatten(), y_pss_mondrian[sort_idx, 0].flatten(), y_pss_mondrian[sort_idx, 1].flatten(), alpha=0.3, label="Mondrian")
-    plt.legend()
-    plt.show()
-
-
-
-.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_13_0.png
-
-
-.. code:: ipython3
-
-    # plot coverage by groups with both methods
-    coverages = {}
-    for group in np.unique(groups_test):
-        coverages[group] = {}
-        coverages[group]["split"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_split[groups_test == group])
-        coverages[group]["mondrian"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_mondrian[groups_test == group])
-
-.. code:: ipython3
-
-    # Plot the coverage by groups, plot both methods side by side
-    plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group]["split"]) for group in coverages], label="Split")
-    plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group]["mondrian"]) for group in coverages], label="Mondrian")
-    plt.xticks(np.arange(len(coverages)) * 2 + .5, [f"Group {group}" for group in coverages], rotation=45)
-    plt.hlines(0.9, -1, 21, label="90% coverage", color="black", linestyle="--")
-    plt.ylabel("Coverage")
-    
-    #put legend outside of the plot
-    plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
-
-
-.. parsed-literal::
-
-    /var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-      plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group]["split"]) for group in coverages], label="Split")
-    /var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)
-      plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group]["mondrian"]) for group in coverages], label="Mondrian")
-
-
-
-
-.. parsed-literal::
-
-    <matplotlib.legend.Legend at 0x13847b970>
-
-
-
-
-.. image:: tutorial_mondrian_regression_files/tutorial_mondrian_regression_15_2.png
-
-
diff --git a/examples/mondrian/1-quickstart/README.rst b/examples/mondrian/1-quickstart/README.rst
new file mode 100644
index 000000000..cda071b2f
--- /dev/null
+++ b/examples/mondrian/1-quickstart/README.rst
@@ -0,0 +1,6 @@
+.. _mondrian_examples_1:
+
+1. Quickstart examples
+----------------------
+
+The following examples present the main functionalities of MAPIE through basic quickstart regression problems.
\ No newline at end of file

From 5ba97d6e7800ad5f39f47186326dbcc2d69064e9 Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 2 Sep 2024 09:44:46 +0200
Subject: [PATCH 320/424] UPD: use copy model to prefit

---
 mapie/mondrian.py | 75 ++++++++++++++++++++++++++---------------------
 1 file changed, 42 insertions(+), 33 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index c70b88a85..657b1c6cd 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -1,6 +1,6 @@
 from __future__ import annotations
 
-from copy import deepcopy
+from copy import copy
 from typing import Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
@@ -148,21 +148,24 @@ def fit(
         self._check_group_length(X, groups)
         self.unique_groups = np.unique(groups)
         self.mapie_estimators = {}
+
         if isinstance(self.mapie_estimator, MapieClassifier):
             self.n_classes = len(np.unique(y))
 
         for group in self.unique_groups:
-            mapie_group_estimator = deepcopy(self.mapie_estimator)
+            mapie_group_estimator = copy(self.mapie_estimator)
             indices_groups = np.argwhere(groups == group)[:, 0]
-            X_g, y_g = X[indices_groups], y[indices_groups]
+            X_g = [X[index] for index in indices_groups]
+            y_g = [y[index] for index in indices_groups]
             mapie_group_estimator.fit(X_g, y_g, **fit_params)
             self.mapie_estimators[group] = mapie_group_estimator
+
         return self
 
     def predict(
         self,
         X: ArrayLike,
-        groups: ArrayLike,
+        groups: Optional[ArrayLike] = None,
         alpha: Optional[Union[float, Iterable[float]]] = None,
         **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
@@ -174,9 +177,11 @@ def predict(
         X : ArrayLike of shape (n_samples, n_features)
             The input data
 
-        groups : ArrayLike of shape (n_samples,)
+        groups : ArrayLike of shape (n_samples,), optional
             The groups of individuals. Must be defined by integers.
 
+            By default None.
+
         alpha : float or Iterable[float], optional
             The desired coverage level(s) for each group.
 
@@ -194,37 +199,35 @@ def predict(
         y_pss : NDArray of shape (n_samples, n_outputs, n_alpha)
             The predicted sets for the desired levels of coverage
         """
-
         check_is_fitted(self, self.fit_attributes)
         X = cast(NDArray, X)
-        groups = self._check_groups_predict(X, groups)
         alpha_np = cast(NDArray, check_alpha(alpha))
+
         if alpha_np is None and self.mapie_estimator.estimator is not None:
             return self.mapie_estimator.estimator.predict(
                 X, **predict_params
             )
+
+        if isinstance(self.mapie_estimator, MapieClassifier):
+            y_pred = np.empty((len(X), ))
+            y_pss = np.empty((len(X), self.n_classes, len(alpha_np)))
         else:
-            if isinstance(self.mapie_estimator, MapieClassifier):
-                y_pred = np.empty(
-                    (X.shape[0], )
-                )
-                y_pss = np.empty(
-                    (X.shape[0], self.n_classes, len(alpha_np))
-                )
-            else:
-                y_pred = np.empty((X.shape[0],))
-                y_pss = np.empty((X.shape[0], 2, len(alpha_np)))
-            unique_groups = np.unique(groups)
-            for i, group in enumerate(unique_groups):
-                indices_groups = np.argwhere(groups == group)[:, 0]
-                X_g = X[indices_groups]
-                y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
-                    X_g, alpha=alpha_np, **predict_params
-                )
-                y_pred[indices_groups] = y_pred_g
-                y_pss[indices_groups] = y_pss_g
-
-            return y_pred, y_pss
+            y_pred = np.empty((len(X),))
+            y_pss = np.empty((len(X), 2, len(alpha_np)))
+
+        groups = self._check_groups_predict(X, groups)
+        unique_groups = np.unique(groups)
+
+        for _, group in enumerate(unique_groups):
+            indices_groups = np.argwhere(groups == group)[:, 0]
+            X_g = [X[index] for index in indices_groups]
+            y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
+                X_g, alpha=alpha_np, **predict_params
+            )
+            y_pred[indices_groups] = y_pred_g
+            y_pss[indices_groups] = y_pss_g
+
+        return y_pred, y_pss
 
     def _check_cv(self):
         """
@@ -263,9 +266,11 @@ def _check_groups_fit(self, X: NDArray, groups: NDArray):
         """
         if not np.issubdtype(groups.dtype, np.integer):
             raise ValueError("The groups must be defined by integers")
+
         _, counts = np.unique(groups, return_counts=True)
         if np.min(counts) < 2:
             raise ValueError("There must be at least 2 individuals per group")
+
         self._check_group_length(X, groups)
 
     def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
@@ -295,9 +300,10 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         groups = cast(NDArray, np.array(groups))
         if not np.all(np.isin(groups, self.unique_groups)):
             raise ValueError(
-                "There is at least one new group in the prediction"
+                "There is at least one new group in the prediction."
             )
         self._check_group_length(X, groups)
+
         return groups
 
     def _check_group_length(self, X: NDArray, groups: NDArray):
@@ -319,9 +325,11 @@ def _check_group_length(self, X: NDArray, groups: NDArray):
             If the number of individuals in the groups is not equal to the
             number of rows in X
         """
-        if len(groups) != X.shape[0]:
-            raise ValueError("The number of individuals in the groups must " +
-                             "be equal to the number of rows in X")
+        if len(groups) != len(X):
+            raise ValueError(
+                "The number of individuals in the groups must "
+                "be equal to the number of rows in X"
+            )
 
     def _check_estimator(self):
         """
@@ -405,15 +413,16 @@ def _check_fit_parameters(
         groups : NDArray of shape (n_samples,)
             The group values
         """
-
         self._check_estimator()
         self._check_cv()
         self._check_confomity_score()
+
         X, y = indexable(X, y)
         y = _check_y(y)
         X = cast(NDArray, X)
         y = cast(NDArray, y)
         groups = cast(NDArray, np.array(groups))
+
         self._check_groups_fit(X, groups)
 
         return X, y, groups

From d7e88c58055b504731546f3bda22c01c8d3311cc Mon Sep 17 00:00:00 2001
From: Thibault Cordier <tcordier@quantmetry.com>
Date: Mon, 2 Sep 2024 09:45:48 +0200
Subject: [PATCH 321/424] FIX: lint problem with group at None

---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 657b1c6cd..43cb40a26 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -165,7 +165,7 @@ def fit(
     def predict(
         self,
         X: ArrayLike,
-        groups: Optional[ArrayLike] = None,
+        groups: ArrayLike,
         alpha: Optional[Union[float, Iterable[float]]] = None,
         **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:

From f8cd3664b6dddd265b9bc56b578137b6db3463e9 Mon Sep 17 00:00:00 2001
From: Baptiste Calot <baptiste.calot@capgemini.com>
Date: Mon, 2 Sep 2024 14:34:20 +0200
Subject: [PATCH 322/424] Update : Change "assert_all_close"

---
 mapie/estimator/classifier.py      |  3 +--
 mapie/tests/test_classification.py | 13 ++++++-------
 2 files changed, 7 insertions(+), 9 deletions(-)

diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index aecfda437..ac882996a 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -415,8 +415,7 @@ def predict_proba_calib(
         check_is_fitted(self, self.fit_attributes)
 
         if self.cv == "prefit":
-            y_pred_proba =\
-                self.single_estimator_.predict_proba(X)
+            y_pred_proba = self.single_estimator_.predict_proba(X)
             y_pred_proba = self._check_proba_normalized(y_pred_proba)
         else:
             X = cast(NDArray, X)
diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index f6f02a714..a1ff9c8d9 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -31,7 +31,6 @@
 
 random_state = 42
 
-
 METHODS = ["lac", "aps", "raps"]
 WRONG_METHODS = ["scores", "cumulated", "test", "", 1, 2.5, (1, 2)]
 WRONG_INCLUDE_LABELS = ["randomised", "True", "False", "other", 1, 2.5, (1, 2)]
@@ -1999,9 +1998,9 @@ def test_predict_parameters_passing() -> None:
 
     expected_conformity_scores = np.ones((X_train.shape[0], 1))
     y_pred = mapie_model.predict(X_test, agg_scores="mean", **predict_params)
-    np.testing.assert_allclose(mapie_model.conformity_scores_,
-                               expected_conformity_scores)
-    np.testing.assert_allclose(y_pred, 0)
+    np.testing.assert_equal(mapie_model.conformity_scores_,
+                            expected_conformity_scores)
+    np.testing.assert_equal(y_pred, 0)
 
 
 def test_with_no_predict_parameters_passing() -> None:
@@ -2069,9 +2068,9 @@ def test_predict_params_expected_behavior_unaffected_by_fit_params() -> None:
 
     expected_conformity_scores = np.ones((X_train.shape[0], 1))
 
-    np.testing.assert_allclose(mapie_model.conformity_scores_,
-                               expected_conformity_scores)
-    np.testing.assert_allclose(y_pred, 0)
+    np.testing.assert_equal(mapie_model.conformity_scores_,
+                            expected_conformity_scores)
+    np.testing.assert_equal(y_pred, 0)
 
 
 def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:

From 5979dcb1d995e18070d3c374dd6086225b71abea Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 17:24:41 +0200
Subject: [PATCH 323/424] ENH: check group lenght in check fit params

---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 43cb40a26..6dd1aceac 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -145,7 +145,6 @@ def fit(
         """
 
         X, y, groups = self._check_fit_parameters(X, y, groups)
-        self._check_group_length(X, groups)
         self.unique_groups = np.unique(groups)
         self.mapie_estimators = {}
 
@@ -424,5 +423,6 @@ def _check_fit_parameters(
         groups = cast(NDArray, np.array(groups))
 
         self._check_groups_fit(X, groups)
+        self._check_group_length(X, groups)
 
         return X, y, groups

From d6aa5464aef918acd7eee52cf849d678ef0b0535 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 17:38:00 +0200
Subject: [PATCH 324/424] ENH: rename groups into partition

---
 mapie/mondrian.py | 101 ++++++++++++++++++++++++----------------------
 1 file changed, 53 insertions(+), 48 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 6dd1aceac..5aa274c20 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -29,7 +29,7 @@
 
 class MondrianCP(BaseEstimator):
     """Mondrian is a method for making conformal predictions
-    for disjoints groups of individuals.
+    for partition of individuals.
 
     The Mondrian method is implemented in the `MondrianCP` class. It takes as
     input a `MapieClassifier` or `MapieRegressor` estimator and fits a model
@@ -54,7 +54,7 @@ class MondrianCP(BaseEstimator):
 
     Attributes
     ----------
-    unique_groups : NDArray
+    partition_groups : NDArray
         The unique groups of individuals for which the estimator was fitted
 
     mapie_estimators : Dict
@@ -74,11 +74,12 @@ class MondrianCP(BaseEstimator):
     >>> from mapie.classification import MapieClassifier
     >>> X_toy = np.arange(9).reshape(-1, 1)
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
-    >>> groups_toy = [0, 0, 0, 0, 1, 1, 1, 1, 1]
+    >>> partition_toy = [0, 0, 0, 0, 1, 1, 1, 1, 1]
     >>> clf = LogisticRegression(random_state=42).fit(X_toy, y_toy)
     >>> mapie = MondrianCP(MapieClassifier(estimator=clf, cv="prefit")).fit(
-    ...     X_toy, y_toy, groups_toy)
-    >>> _, y_pi_mapie = mapie.predict(X_toy, alpha=0.4, groups=groups_toy)
+    ...     X_toy, y_toy, partition_toy)
+    >>> _, y_pi_mapie = mapie.predict(
+    ...     X_toy, partition_toy, alpha=[0.1, 0.9])
     >>> print(y_pi_mapie[:, :, 0].astype(bool))
     [[ True False False]
      [ True False False]
@@ -108,7 +109,7 @@ class MondrianCP(BaseEstimator):
         AbsoluteConformityScore, GammaConformityScore
     )
     fit_attributes = [
-        "unique_groups",
+        "partition_groups",
         "mapie_estimators"
     ]
 
@@ -121,7 +122,7 @@ def __init__(
     def fit(
         self, X: ArrayLike,
         y: ArrayLike,
-        groups: ArrayLike,
+        partition: ArrayLike,
         **fit_params
     ) -> MondrianCP:
         """
@@ -135,7 +136,7 @@ def fit(
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
 
-        groups : ArrayLike of shape (n_samples,)
+        partition : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers. There must
             be at least 2 individuals per group.
 
@@ -144,16 +145,16 @@ def fit(
             that may be specific to the Mapie estimator used
         """
 
-        X, y, groups = self._check_fit_parameters(X, y, groups)
-        self.unique_groups = np.unique(groups)
+        X, y, partition = self._check_fit_parameters(X, y, partition)
+        self.partition_groups = np.unique(partition)
         self.mapie_estimators = {}
 
         if isinstance(self.mapie_estimator, MapieClassifier):
             self.n_classes = len(np.unique(y))
 
-        for group in self.unique_groups:
+        for group in self.partition_groups:
             mapie_group_estimator = copy(self.mapie_estimator)
-            indices_groups = np.argwhere(groups == group)[:, 0]
+            indices_groups = np.argwhere(partition == group)[:, 0]
             X_g = [X[index] for index in indices_groups]
             y_g = [y[index] for index in indices_groups]
             mapie_group_estimator.fit(X_g, y_g, **fit_params)
@@ -164,7 +165,7 @@ def fit(
     def predict(
         self,
         X: ArrayLike,
-        groups: ArrayLike,
+        partition: ArrayLike,
         alpha: Optional[Union[float, Iterable[float]]] = None,
         **predict_params
     ) -> Union[NDArray, Tuple[NDArray, NDArray]]:
@@ -176,7 +177,7 @@ def predict(
         X : ArrayLike of shape (n_samples, n_features)
             The input data
 
-        groups : ArrayLike of shape (n_samples,), optional
+        partition : ArrayLike of shape (n_samples,), optional
             The groups of individuals. Must be defined by integers.
 
             By default None.
@@ -214,11 +215,11 @@ def predict(
             y_pred = np.empty((len(X),))
             y_pss = np.empty((len(X), 2, len(alpha_np)))
 
-        groups = self._check_groups_predict(X, groups)
-        unique_groups = np.unique(groups)
+        partition = self._check_partition_predict(X, partition)
+        partition_groups = np.unique(partition)
 
-        for _, group in enumerate(unique_groups):
-            indices_groups = np.argwhere(groups == group)[:, 0]
+        for _, group in enumerate(partition_groups):
+            indices_groups = np.argwhere(partition == group)[:, 0]
             X_g = [X[index] for index in indices_groups]
             y_pred_g, y_pss_g = self.mapie_estimators[group].predict(
                 X_g, alpha=alpha_np, **predict_params
@@ -243,7 +244,7 @@ def _check_cv(self):
                 "estimator uses cv='prefit'."
             )
 
-    def _check_groups_fit(self, X: NDArray, groups: NDArray):
+    def _check_partition_fit(self, X: NDArray, partition: NDArray):
         """
         Check that each group is defined by an integer and check that there
         are at least 2 individuals per group
@@ -253,29 +254,33 @@ def _check_groups_fit(self, X: NDArray, groups: NDArray):
         X : NDArray of shape (n_samples, n_features)
             The input data
 
-        groups : NDArray of shape (n_samples,)
+        partition : NDArray of shape (n_samples,)
 
         Raises
         ------
         ValueError
-            If the groups are not defined by integers
+            If the partition is not defined by integers
             If there is less than 2 individuals per group
-            If the number of individuals in the groups is not equal to the
+            If the number of individuals in the partition is not equal to the
             number of rows in X
         """
-        if not np.issubdtype(groups.dtype, np.integer):
-            raise ValueError("The groups must be defined by integers")
+        if not np.issubdtype(partition.dtype, np.integer):
+            raise ValueError("The partition must be defined by integers")
 
-        _, counts = np.unique(groups, return_counts=True)
+        _, counts = np.unique(partition, return_counts=True)
         if np.min(counts) < 2:
             raise ValueError("There must be at least 2 individuals per group")
 
-        self._check_group_length(X, groups)
+        self._check_partition_length(X, partition)
 
-    def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
+    def _check_partition_predict(
+            self,
+            X: NDArray,
+            partition: ArrayLike
+    ) -> NDArray:
         """
         Check that there is no new group in the prediction and that
-        the number of individuals in the groups is equal to the number of
+        the number of individuals in the partition is equal to the number of
         rows in X
 
         Parameters
@@ -283,29 +288,29 @@ def _check_groups_predict(self, X: NDArray, groups: ArrayLike) -> NDArray:
         X : NDArray of shape (n_samples, n_features)
             The input data
 
-        groups : ArrayLike of shape (n_samples,)
+        partition : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers
 
         Returns
         -------
-        groups : NDArray of shape (n_samples,)
-            Groups of individuals
+        partition : NDArray of shape (n_samples,)
+            Partition of the dataset
 
         Raises
         ------
         ValueError
             If there is a new group in the prediction
         """
-        groups = cast(NDArray, np.array(groups))
-        if not np.all(np.isin(groups, self.unique_groups)):
+        partition = cast(NDArray, np.array(partition))
+        if not np.all(np.isin(partition, self.partition_groups)):
             raise ValueError(
                 "There is at least one new group in the prediction."
             )
-        self._check_group_length(X, groups)
+        self._check_partition_length(X, partition)
 
-        return groups
+        return partition
 
-    def _check_group_length(self, X: NDArray, groups: NDArray):
+    def _check_partition_length(self, X: NDArray, partition: NDArray):
         """
         Check that the number of rows in the groups array is equal to
         the number of rows in the attributes array.
@@ -315,18 +320,18 @@ def _check_group_length(self, X: NDArray, groups: NDArray):
         X : NDArray of shape (n_samples, n_features)
             The individual data.
 
-        groups : NDArray of shape (n_samples,)
+        partition : NDArray of shape (n_samples,)
             The groups of individuals. Must be defined by integers
 
         Raises
         ------
         ValueError
-            If the number of individuals in the groups is not equal to the
+            If the number of individuals in the partition is not equal to the
             number of rows in X
         """
-        if len(groups) != len(X):
+        if len(partition) != len(X):
             raise ValueError(
-                "The number of individuals in the groups must "
+                "The number of individuals in the partition must "
                 "be equal to the number of rows in X"
             )
 
@@ -385,10 +390,10 @@ def _check_confomity_score(self):
                     )
 
     def _check_fit_parameters(
-        self, X: ArrayLike, y: ArrayLike, groups: ArrayLike
+        self, X: ArrayLike, y: ArrayLike, partition: ArrayLike
     ) -> Tuple[NDArray, NDArray, NDArray]:
         """
-        Perform checks on the input data, groups and the estimator
+        Perform checks on the input data, partition and the estimator
 
         Parameters
         ----------
@@ -398,7 +403,7 @@ def _check_fit_parameters(
         y : ArrayLike of shape (n_samples,) or (n_samples, n_outputs)
             The target values
 
-        groups : ArrayLike of shape (n_samples,)
+        partition : ArrayLike of shape (n_samples,)
             The groups of individuals. Must be defined by integers
 
         Returns
@@ -409,7 +414,7 @@ def _check_fit_parameters(
         y : NDArray of shape (n_samples,) or (n_samples, n_outputs)
             The target values
 
-        groups : NDArray of shape (n_samples,)
+        partition : NDArray of shape (n_samples,)
             The group values
         """
         self._check_estimator()
@@ -420,9 +425,9 @@ def _check_fit_parameters(
         y = _check_y(y)
         X = cast(NDArray, X)
         y = cast(NDArray, y)
-        groups = cast(NDArray, np.array(groups))
+        partition = cast(NDArray, np.array(partition))
 
-        self._check_groups_fit(X, groups)
-        self._check_group_length(X, groups)
+        self._check_partition_fit(X, partition)
+        self._check_partition_length(X, partition)
 
-        return X, y, groups
+        return X, y, partition

From 2fa047d746f19fc056318f2e6529730d0f7ca518 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 17:39:58 +0200
Subject: [PATCH 325/424] ENH: rename groups into partition in tests

---
 mapie/tests/test_mondrian.py | 94 ++++++++++++++++++------------------
 1 file changed, 47 insertions(+), 47 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index 7b98ed1d1..ada5e6ec7 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -186,7 +186,7 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
     x, y = TOY_DATASETS[task]
     y = np.abs(y)  # to avoid negative values with Gamma NCS
     ml_model = ML_MODELS[task]
-    groups = np.random.choice(10, len(x))
+    partition = np.random.choice(10, len(x))
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
@@ -195,8 +195,8 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
             estimator=model, cv="prefit", **mapie_kwargs
         )
     )
-    mondrian_cp.fit(x, y, groups=groups)
-    mondrian_cp.predict(x, groups=groups, alpha=.2)
+    mondrian_cp.fit(x, y, partition=partition)
+    mondrian_cp.predict(x, partition=partition, alpha=.2)
 
 
 @pytest.mark.parametrize(
@@ -210,7 +210,7 @@ def test_non_cs_fails(mapie_estimator_name):
     task = task_dict["task"]
     x, y = TOY_DATASETS[task]
     ml_model = ML_MODELS[task]
-    groups = np.random.choice(10, len(x))
+    partition = np.random.choice(10, len(x))
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
@@ -220,7 +220,7 @@ def test_non_cs_fails(mapie_estimator_name):
         )
     )
     with pytest.raises(ValueError, match=r".*The conformity score for*"):
-        mondrian_cp.fit(x, y, groups=groups)
+        mondrian_cp.fit(x, y, partition=partition)
 
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
@@ -233,7 +233,7 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
     task = task_dict["task"]
     x, y = TOY_DATASETS[task]
     ml_model = ML_MODELS[task]
-    groups = np.random.choice(10, len(x))
+    partition = np.random.choice(10, len(x))
     model = clone(ml_model)
     mapie_inst = deepcopy(mapie_estimator)
     mondrian_cp = MondrianCP(
@@ -242,7 +242,7 @@ def test_invalid_cv_fails(mapie_estimator_name, non_valid_cv):
         )
     )
     with pytest.raises(ValueError, match=r".*estimator uses cv='prefit'*"):
-        mondrian_cp.fit(x, y, groups=groups)
+        mondrian_cp.fit(x, y, partition=partition)
 
 
 @pytest.mark.parametrize(
@@ -257,7 +257,7 @@ def test_non_valid_estimators_fails(mapie_estimator_name):
     x, y = TOY_DATASETS[task]
     y = np.abs(y)  # to avoid negative values with Gamma NCS
     ml_model = ML_MODELS[task]
-    groups = np.random.choice(10, len(x))
+    partition = np.random.choice(10, len(x))
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst = deepcopy(mapie_estimator)
@@ -277,15 +277,15 @@ def test_non_valid_estimators_fails(mapie_estimator_name):
         )
     with pytest.raises(ValueError, match=r".*The estimator must be a*"):
         if task == "multilabel_classification":
-            mondrian_cp.fit(x, y, groups=groups)
+            mondrian_cp.fit(x, y, partition=partition)
         elif task == "calibration":
-            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+            mondrian_cp.fit(x, y, partition=partition, **mapie_kwargs)
         else:
-            mondrian_cp.fit(x, y, groups=groups, **mapie_kwargs)
+            mondrian_cp.fit(x, y, partition=partition, **mapie_kwargs)
 
 
-def test_groups_not_defined_by_integers_fails():
-    """Test that groups not defined by integers fails"""
+def test_partition_not_defined_by_integers_fails():
+    """Test that partition not defined by integers fails"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -293,15 +293,15 @@ def test_groups_not_defined_by_integers_fails():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.random.choice(10, len(x)).astype(str)
+    partition = np.random.choice(10, len(x)).astype(str)
     with pytest.raises(
-        ValueError, match=r".*The groups must be defined by integers*"
+        ValueError, match=r".*The partition must be defined by integers*"
     ):
-        mondrian.fit(x, y, groups=groups)
+        mondrian.fit(x, y, partition=partition)
 
 
-def test_groups_with_less_than_2_fails():
-    """Test that groups with less than 2 elements fails"""
+def test_partition_with_less_than_2_fails():
+    """Test that partition with less than 2 elements fails"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -309,15 +309,15 @@ def test_groups_with_less_than_2_fails():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.array([1] + [2] * (len(x) - 1))
+    partition = np.array([1] + [2] * (len(x) - 1))
     with pytest.raises(
         ValueError, match=r".*There must be at least 2 individuals*"
     ):
-        mondrian.fit(x, y, groups=groups)
+        mondrian.fit(x, y, partition=partition)
 
 
-def test_groups_and_x_have_same_length_in_fit():
-    """Test that groups and x have the same length in fit"""
+def test_partition_and_x_have_same_length_in_fit():
+    """Test that partition and x have the same length in fit"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -325,13 +325,13 @@ def test_groups_and_x_have_same_length_in_fit():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.random.choice(10, len(x) - 1)
+    partition = np.random.choice(10, len(x) - 1)
     with pytest.raises(ValueError, match=r".*he number of individuals in*"):
-        mondrian.fit(x, y, groups=groups)
+        mondrian.fit(x, y, partition=partition)
 
 
-def test_all_groups_in_predict_are_in_fit():
-    """Test that all groups in predict are in fit"""
+def test_all_partition_in_predict_are_in_fit():
+    """Test that all partition in predict are in fit"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -339,15 +339,15 @@ def test_all_groups_in_predict_are_in_fit():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.random.choice(10, len(x))
-    mondrian.fit(x, y, groups=groups)
-    groups = np.array([99] * len(x))
+    partition = np.random.choice(10, len(x))
+    mondrian.fit(x, y, partition=partition)
+    partition = np.array([99] * len(x))
     with pytest.raises(ValueError, match=r".*There is at least one new*"):
-        mondrian.predict(x, groups=groups, alpha=.2)
+        mondrian.predict(x, partition=partition, alpha=.2)
 
 
-def test_groups_and_x_have_same_length_in_predict():
-    """Test that groups and x have the same length in predict"""
+def test_partition_and_x_have_same_length_in_predict():
+    """Test that partition and x have the same length in predict"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -355,11 +355,11 @@ def test_groups_and_x_have_same_length_in_predict():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.random.choice(10, len(x))
-    mondrian.fit(x, y, groups=groups)
-    groups = np.random.choice(10, len(x) - 1)
+    partition = np.random.choice(10, len(x))
+    mondrian.fit(x, y, partition=partition)
+    partition = np.random.choice(10, len(x) - 1)
     with pytest.raises(ValueError, match=r".*The number of individuals in*"):
-        mondrian.predict(x, groups=groups, alpha=.2)
+        mondrian.predict(x, partition=partition, alpha=.2)
 
 
 def test_alpha_none_return_one_element():
@@ -371,14 +371,14 @@ def test_alpha_none_return_one_element():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.random.choice(10, len(x))
-    mondrian.fit(x, y, groups=groups)
-    preds = mondrian.predict(x, groups=groups)
+    partition = np.random.choice(10, len(x))
+    mondrian.fit(x, y, partition=partition)
+    preds = mondrian.predict(x, partition=partition)
     assert len(preds) == len(x)
 
 
-def test_groups_is_list_ok():
-    """Test that the groups can be a list"""
+def test_partition_is_list_ok():
+    """Test that the partition can be a list"""
     x, y = TOY_DATASETS["classification"]
     ml_model = ML_MODELS["classification"]
     model = clone(ml_model)
@@ -386,9 +386,9 @@ def test_groups_is_list_ok():
     mondrian = MondrianCP(
         mapie_estimator=MapieClassifier(estimator=model, cv="prefit")
     )
-    groups = np.random.choice(10, len(x)).tolist()
-    mondrian.fit(x, y, groups=groups)
-    mondrian.predict(x, groups=groups, alpha=.2)
+    partition = np.random.choice(10, len(x)).tolist()
+    mondrian.fit(x, y, partition=partition)
+    mondrian.predict(x, partition=partition, alpha=.2)
 
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
@@ -402,7 +402,7 @@ def test_same_results_if_only_one_group(mapie_estimator_name, alpha):
     x, y = TOY_DATASETS[task]
     y = np.abs(y)
     ml_model = ML_MODELS[task]
-    groups = [0] * len(x)
+    partition = [0] * len(x)
     model = clone(ml_model)
     model.fit(x, y)
     mapie_inst_mondrian = deepcopy(mapie_estimator)
@@ -415,9 +415,9 @@ def test_same_results_if_only_one_group(mapie_estimator_name, alpha):
     mapie_classic = mapie_classic_inst(
         estimator=model, cv="prefit", random_state=0, **mapie_kwargs,
     )
-    mondrian_cp.fit(x, y, groups=groups)
+    mondrian_cp.fit(x, y, partition=partition)
     mapie_classic.fit(x, y)
-    mondrian_pred = mondrian_cp.predict(x, groups=groups, alpha=alpha)
+    mondrian_pred = mondrian_cp.predict(x, partition=partition, alpha=alpha)
     classic_pred = mapie_classic.predict(x, alpha=alpha)
     assert np.allclose(mondrian_pred[0], classic_pred[0])
     assert np.allclose(mondrian_pred[1], classic_pred[1])

From 9b85022ac3e75cc7a5a624b90d8fc8b671f70115 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 17:47:52 +0200
Subject: [PATCH 326/424] FIX: test in Mondrian docstring

---
 mapie/mondrian.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 5aa274c20..135358a61 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -77,9 +77,10 @@ class MondrianCP(BaseEstimator):
     >>> partition_toy = [0, 0, 0, 0, 1, 1, 1, 1, 1]
     >>> clf = LogisticRegression(random_state=42).fit(X_toy, y_toy)
     >>> mapie = MondrianCP(MapieClassifier(estimator=clf, cv="prefit")).fit(
-    ...     X_toy, y_toy, partition_toy)
+    ...     X_toy, y_toy, partition=partition_toy
+    ... )
     >>> _, y_pi_mapie = mapie.predict(
-    ...     X_toy, partition_toy, alpha=[0.1, 0.9])
+    ...     X_toy, partition=partition_toy, alpha=[0.1, 0.9])
     >>> print(y_pi_mapie[:, :, 0].astype(bool))
     [[ True False False]
      [ True False False]

From 36b03c93d587e35e707b21c08d9825bc2fc74310 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 18:10:23 +0200
Subject: [PATCH 327/424] FIX: alpha value in docstring test

---
 mapie/mondrian.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 135358a61..003cf59f5 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -80,7 +80,7 @@ class MondrianCP(BaseEstimator):
     ...     X_toy, y_toy, partition=partition_toy
     ... )
     >>> _, y_pi_mapie = mapie.predict(
-    ...     X_toy, partition=partition_toy, alpha=[0.1, 0.9])
+    ...     X_toy, partition=partition_toy, alpha=0.4)
     >>> print(y_pi_mapie[:, :, 0].astype(bool))
     [[ True False False]
      [ True False False]

From ed374e65b5e73f3207548d477753f777cb6f2a8d Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 18:17:24 +0200
Subject: [PATCH 328/424] DEL: delete unused notebook

---
 .../tutorial_mondrian_regression.ipynb        | 1229 -----------------
 1 file changed, 1229 deletions(-)
 delete mode 100644 notebooks/mondrian/tutorial_mondrian_regression.ipynb

diff --git a/notebooks/mondrian/tutorial_mondrian_regression.ipynb b/notebooks/mondrian/tutorial_mondrian_regression.ipynb
deleted file mode 100644
index 54cc551a2..000000000
--- a/notebooks/mondrian/tutorial_mondrian_regression.ipynb
+++ /dev/null
@@ -1,1229 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
-    "from sklearn.base import clone\n",
-    "from sklearn.model_selection import train_test_split\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "from mapie.metrics import regression_coverage_score_v2\n",
-    "from mapie.mondrian import MondrianCP\n",
-    "from mapie.regression import MapieRegressor\n",
-    "\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Create 1D regression dataset with sinusoidual function between 0 and 10 \n",
-    "n_points = 100000\n",
-    "np.random.seed(0)\n",
-    "X = np.linspace(0, 10, n_points).reshape(-1, 1)\n",
-    "group_size = n_points // 10\n",
-    "groups_list = []\n",
-    "for i in range(10):\n",
-    "    groups_list.append(np.array([i] * group_size))\n",
-    "groups = np.concatenate(groups_list)\n",
-    "\n",
-    "noise_0_1 = np.random.normal(0, 0.1, group_size)\n",
-    "noise_1_2 = np.random.normal(0, 0.5, group_size)\n",
-    "noise_2_3 = np.random.normal(0, 1, group_size)\n",
-    "noise_3_4 = np.random.normal(0, .4, group_size)\n",
-    "noise_4_5 = np.random.normal(0, .2, group_size)\n",
-    "noise_5_6 = np.random.normal(0, .3, group_size)\n",
-    "noise_6_7 = np.random.normal(0, .6, group_size)\n",
-    "noise_7_8 = np.random.normal(0, .7, group_size)\n",
-    "noise_8_9 = np.random.normal(0, .8, group_size)\n",
-    "noise_9_10 = np.random.normal(0, .9, group_size)\n",
-    "\n",
-    "y = np.concatenate(\n",
-    "    [\n",
-    "        np.sin(X[groups == 0, 0] * 2) + noise_0_1,\n",
-    "        np.sin(X[groups == 1, 0] * 2) + noise_1_2,\n",
-    "        np.sin(X[groups == 2, 0] * 2) + noise_2_3,\n",
-    "        np.sin(X[groups == 3, 0] * 2) + noise_3_4,\n",
-    "        np.sin(X[groups == 4, 0] * 2) + noise_4_5,\n",
-    "        np.sin(X[groups == 5, 0] * 2) + noise_5_6,\n",
-    "        np.sin(X[groups == 6, 0] * 2) + noise_6_7,\n",
-    "        np.sin(X[groups == 7, 0] * 2) + noise_7_8,\n",
-    "        np.sin(X[groups == 8, 0] * 2) + noise_8_9,\n",
-    "        np.sin(X[groups == 9, 0] * 2) + noise_9_10,\n",
-    "    ], axis=0\n",
-    ")\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plt.scatter(X, y, c=groups)\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "X_train_temp, X_test, y_train_temp, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
-    "groups_train_temp, groups_test, _, _ = train_test_split(groups, y, test_size=0.2, random_state=0)\n",
-    "X_cal, X_train, y_cal, y_train = train_test_split(X_train_temp, y_train_temp, test_size=0.5, random_state=0)\n",
-    "groups_cal, groups_train, _, _ = train_test_split(groups_train_temp, y_train_temp, test_size=0.5, random_state=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "((40000, 1), (40000,), (40000,))"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "X_train.shape, y_train.shape, groups_train.shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 1500x500 with 3 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "f, ax = plt.subplots(1, 3, figsize=(15, 5))\n",
-    "ax[0].scatter(X_train, y_train, c=groups_train)\n",
-    "ax[0].set_title(\"Train set\")\n",
-    "ax[1].scatter(X_cal, y_cal, c=groups_cal)\n",
-    "ax[1].set_title(\"Calibration set\")\n",
-    "ax[2].scatter(X_test, y_test, c=groups_test)\n",
-    "ax[2].set_title(\"Test set\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Training set size:  40000\n",
-      "Calibration set size:  40000\n",
-      "Test set size:  20000\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Training set size: \", X_train.shape[0])\n",
-    "print(\"Calibration set size: \", X_cal.shape[0])\n",
-    "print(\"Test set size: \", X_test.shape[0])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-1 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-1 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-1 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-1 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestRegressor()</pre></div> </div></div></div></div>"
-      ],
-      "text/plain": [
-       "RandomForestRegressor()"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Fit a random forest regressor\n",
-    "\n",
-    "rf = RandomForestRegressor(n_estimators=100)\n",
-    "rf.fit(X_train, y_train)\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x1380f0760>"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the prediction of the random forest regressor as a line\n",
-    "\n",
-    "y_pred = rf.predict(X_test)\n",
-    "# plt.scatter(X_test, y_test, label=\"True\")\n",
-    "\n",
-    "#Sort the test set and the prediction to plot them as a line\n",
-    "sort_idx = np.argsort(X_test[:, 0])\n",
-    "plt.plot(X_test[sort_idx], y_pred[sort_idx], label=\"Prediction\")\n",
-    "\n",
-    "plt.legend()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mapie_regressor = MapieRegressor(rf, cv=\"prefit\")\n",
-    "mondrian_regressor = MondrianCP(MapieRegressor(rf, cv=\"prefit\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<style>#sk-container-id-2 {\n",
-       "  /* Definition of color scheme common for light and dark mode */\n",
-       "  --sklearn-color-text: black;\n",
-       "  --sklearn-color-line: gray;\n",
-       "  /* Definition of color scheme for unfitted estimators */\n",
-       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
-       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
-       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
-       "  --sklearn-color-unfitted-level-3: chocolate;\n",
-       "  /* Definition of color scheme for fitted estimators */\n",
-       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
-       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
-       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
-       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
-       "\n",
-       "  /* Specific color for light theme */\n",
-       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
-       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
-       "  --sklearn-color-icon: #696969;\n",
-       "\n",
-       "  @media (prefers-color-scheme: dark) {\n",
-       "    /* Redefinition of color scheme for dark theme */\n",
-       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
-       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
-       "    --sklearn-color-icon: #878787;\n",
-       "  }\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 pre {\n",
-       "  padding: 0;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-hidden--visually {\n",
-       "  border: 0;\n",
-       "  clip: rect(1px 1px 1px 1px);\n",
-       "  clip: rect(1px, 1px, 1px, 1px);\n",
-       "  height: 1px;\n",
-       "  margin: -1px;\n",
-       "  overflow: hidden;\n",
-       "  padding: 0;\n",
-       "  position: absolute;\n",
-       "  width: 1px;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
-       "  border: 1px dashed var(--sklearn-color-line);\n",
-       "  margin: 0 0.4em 0.5em 0.4em;\n",
-       "  box-sizing: border-box;\n",
-       "  padding-bottom: 0.4em;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-container {\n",
-       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
-       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
-       "     so we also need the `!important` here to be able to override the\n",
-       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
-       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
-       "  display: inline-block !important;\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
-       "  display: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-parallel-item,\n",
-       "div.sk-serial,\n",
-       "div.sk-item {\n",
-       "  /* draw centered vertical line to link estimators */\n",
-       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
-       "  background-size: 2px 100%;\n",
-       "  background-repeat: no-repeat;\n",
-       "  background-position: center center;\n",
-       "}\n",
-       "\n",
-       "/* Parallel-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item::after {\n",
-       "  content: \"\";\n",
-       "  width: 100%;\n",
-       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
-       "  flex-grow: 1;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel {\n",
-       "  display: flex;\n",
-       "  align-items: stretch;\n",
-       "  justify-content: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  position: relative;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
-       "  align-self: flex-end;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
-       "  align-self: flex-start;\n",
-       "  width: 50%;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
-       "  width: 0;\n",
-       "}\n",
-       "\n",
-       "/* Serial-specific style estimator block */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-serial {\n",
-       "  display: flex;\n",
-       "  flex-direction: column;\n",
-       "  align-items: center;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  padding-right: 1em;\n",
-       "  padding-left: 1em;\n",
-       "}\n",
-       "\n",
-       "\n",
-       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
-       "clickable and can be expanded/collapsed.\n",
-       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
-       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
-       "*/\n",
-       "\n",
-       "/* Pipeline and ColumnTransformer style (default) */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable {\n",
-       "  /* Default theme specific background. It is overwritten whether we have a\n",
-       "  specific estimator or a Pipeline/ColumnTransformer */\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable label */\n",
-       "#sk-container-id-2 label.sk-toggleable__label {\n",
-       "  cursor: pointer;\n",
-       "  display: block;\n",
-       "  width: 100%;\n",
-       "  margin-bottom: 0;\n",
-       "  padding: 0.5em;\n",
-       "  box-sizing: border-box;\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
-       "  /* Arrow on the left of the label */\n",
-       "  content: \"▸\";\n",
-       "  float: left;\n",
-       "  margin-right: 0.25em;\n",
-       "  color: var(--sklearn-color-icon);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "}\n",
-       "\n",
-       "/* Toggleable content - dropdown */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content {\n",
-       "  max-height: 0;\n",
-       "  max-width: 0;\n",
-       "  overflow: hidden;\n",
-       "  text-align: left;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
-       "  margin: 0.2em;\n",
-       "  border-radius: 0.25em;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
-       "  /* Expand drop-down */\n",
-       "  max-height: 200px;\n",
-       "  max-width: 100%;\n",
-       "  overflow: auto;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
-       "  content: \"▾\";\n",
-       "}\n",
-       "\n",
-       "/* Pipeline/ColumnTransformer-specific style */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific style */\n",
-       "\n",
-       "/* Colorize estimator box */\n",
-       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  /* The background is the default theme color */\n",
-       "  color: var(--sklearn-color-text-on-default-background);\n",
-       "}\n",
-       "\n",
-       "/* On hover, darken the color of the background */\n",
-       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Label box, darken color on hover, fitted */\n",
-       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Estimator label */\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label label {\n",
-       "  font-family: monospace;\n",
-       "  font-weight: bold;\n",
-       "  display: inline-block;\n",
-       "  line-height: 1.2em;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-label-container {\n",
-       "  text-align: center;\n",
-       "}\n",
-       "\n",
-       "/* Estimator-specific */\n",
-       "#sk-container-id-2 div.sk-estimator {\n",
-       "  font-family: monospace;\n",
-       "  border: 1px dotted var(--sklearn-color-border-box);\n",
-       "  border-radius: 0.25em;\n",
-       "  box-sizing: border-box;\n",
-       "  margin-bottom: 0.5em;\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-0);\n",
-       "}\n",
-       "\n",
-       "/* on hover */\n",
-       "#sk-container-id-2 div.sk-estimator:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-2);\n",
-       "}\n",
-       "\n",
-       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
-       "\n",
-       "/* Common style for \"i\" and \"?\" */\n",
-       "\n",
-       ".sk-estimator-doc-link,\n",
-       "a:link.sk-estimator-doc-link,\n",
-       "a:visited.sk-estimator-doc-link {\n",
-       "  float: right;\n",
-       "  font-size: smaller;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1em;\n",
-       "  height: 1em;\n",
-       "  width: 1em;\n",
-       "  text-decoration: none !important;\n",
-       "  margin-left: 1ex;\n",
-       "  /* unfitted */\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted,\n",
-       "a:link.sk-estimator-doc-link.fitted,\n",
-       "a:visited.sk-estimator-doc-link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
-       ".sk-estimator-doc-link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover,\n",
-       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
-       ".sk-estimator-doc-link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "/* Span, style for the box shown on hovering the info icon */\n",
-       ".sk-estimator-doc-link span {\n",
-       "  display: none;\n",
-       "  z-index: 9999;\n",
-       "  position: relative;\n",
-       "  font-weight: normal;\n",
-       "  right: .2ex;\n",
-       "  padding: .5ex;\n",
-       "  margin: .5ex;\n",
-       "  width: min-content;\n",
-       "  min-width: 20ex;\n",
-       "  max-width: 50ex;\n",
-       "  color: var(--sklearn-color-text);\n",
-       "  box-shadow: 2pt 2pt 4pt #999;\n",
-       "  /* unfitted */\n",
-       "  background: var(--sklearn-color-unfitted-level-0);\n",
-       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link.fitted span {\n",
-       "  /* fitted */\n",
-       "  background: var(--sklearn-color-fitted-level-0);\n",
-       "  border: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "\n",
-       ".sk-estimator-doc-link:hover span {\n",
-       "  display: block;\n",
-       "}\n",
-       "\n",
-       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link {\n",
-       "  float: right;\n",
-       "  font-size: 1rem;\n",
-       "  line-height: 1em;\n",
-       "  font-family: monospace;\n",
-       "  background-color: var(--sklearn-color-background);\n",
-       "  border-radius: 1rem;\n",
-       "  height: 1rem;\n",
-       "  width: 1rem;\n",
-       "  text-decoration: none;\n",
-       "  /* unfitted */\n",
-       "  color: var(--sklearn-color-unfitted-level-1);\n",
-       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
-       "  /* fitted */\n",
-       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
-       "  color: var(--sklearn-color-fitted-level-1);\n",
-       "}\n",
-       "\n",
-       "/* On hover */\n",
-       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
-       "  /* unfitted */\n",
-       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
-       "  color: var(--sklearn-color-background);\n",
-       "  text-decoration: none;\n",
-       "}\n",
-       "\n",
-       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
-       "  /* fitted */\n",
-       "  background-color: var(--sklearn-color-fitted-level-3);\n",
-       "}\n",
-       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,\n",
-       "                                          estimator=RandomForestRegressor()))</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;MondrianCP<span class=\"sk-estimator-doc-link \">i<span>Not fitted</span></span></label><div class=\"sk-toggleable__content \"><pre>MondrianCP(mapie_estimator=MapieRegressor(cv=&#x27;prefit&#x27;,\n",
-       "                                          estimator=RandomForestRegressor()))</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">mapie_estimator: MapieRegressor</label><div class=\"sk-toggleable__content \"><pre>MapieRegressor(cv=&#x27;prefit&#x27;, estimator=RandomForestRegressor())</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">estimator: RandomForestRegressor</label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor()</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator  sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label  sk-toggleable__label-arrow \">&nbsp;RandomForestRegressor<a class=\"sk-estimator-doc-link \" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestRegressor.html\">?<span>Documentation for RandomForestRegressor</span></a></label><div class=\"sk-toggleable__content \"><pre>RandomForestRegressor()</pre></div> </div></div></div></div></div></div></div></div></div></div></div></div></div></div>"
-      ],
-      "text/plain": [
-       "MondrianCP(mapie_estimator=MapieRegressor(cv='prefit',\n",
-       "                                          estimator=RandomForestRegressor()))"
-      ]
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "mapie_regressor.fit(X_cal, y_cal)\n",
-    "mondrian_regressor.fit(X_cal, y_cal, groups=groups_cal)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "_, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)\n",
-    "_, y_pss_mondrian = mondrian_regressor.predict(X_test, groups=groups_test, alpha=.1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rf = RandomForestRegressor(\n",
-    "    n_estimators=100\n",
-    ")\n",
-    "rf.fit(X_train, y_train)\n",
-    "mondrian_regressor = MondrianCP(\n",
-    "    MapieRegressor(rf, cv=\"prefit\")\n",
-    ")\n",
-    "mondrian_regressor.fit(\n",
-    "    X_cal, y_cal,\n",
-    "    groups=groups_cal\n",
-    ")\n",
-    "_, y_pss_mondrian = mondrian_regressor.predict(\n",
-    "    X_test, groups=groups_test, alpha=.1\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the prediction of the random forest regressor as a line with the prediction intervals\n",
-    "\n",
-    "# plt.scatter(X_test, y_test, label=\"True\")\n",
-    "sort_idx = np.argsort(X_test[:, 0])\n",
-    "# plt.plot(X_test[sort_idx], y_pred[sort_idx], label=\"Prediction\")\n",
-    "plt.fill_between(X_test[sort_idx].flatten(), y_pss_split[sort_idx, 0].flatten(), y_pss_split[sort_idx, 1].flatten(), alpha=0.3, label=\"Split\")\n",
-    "plt.fill_between(X_test[sort_idx].flatten(), y_pss_mondrian[sort_idx, 0].flatten(), y_pss_mondrian[sort_idx, 1].flatten(), alpha=0.3, label=\"Mondrian\")\n",
-    "plt.legend()\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# plot coverage by groups with both methods\n",
-    "coverages = {}\n",
-    "for group in np.unique(groups_test):\n",
-    "    coverages[group] = {}\n",
-    "    coverages[group][\"split\"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_split[groups_test == group])\n",
-    "    coverages[group][\"mondrian\"] = regression_coverage_score_v2(y_test[groups_test == group], y_pss_mondrian[groups_test == group])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:2: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group][\"split\"]) for group in coverages], label=\"Split\")\n",
-      "/var/folders/7d/cdjx7c6d3xx42wdw5bnrmmb80000gn/T/ipykernel_90633/2054907134.py:3: DeprecationWarning: Conversion of an array with ndim > 0 to a scalar is deprecated, and will error in future. Ensure you extract a single element from your array before performing this operation. (Deprecated NumPy 1.25.)\n",
-      "  plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group][\"mondrian\"]) for group in coverages], label=\"Mondrian\")\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.legend.Legend at 0x13847b970>"
-      ]
-     },
-     "execution_count": 17,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Plot the coverage by groups, plot both methods side by side\n",
-    "plt.bar(np.arange(len(coverages)) * 2, [float(coverages[group][\"split\"]) for group in coverages], label=\"Split\")\n",
-    "plt.bar(np.arange(len(coverages)) * 2 + 1, [float(coverages[group][\"mondrian\"]) for group in coverages], label=\"Mondrian\")\n",
-    "plt.xticks(np.arange(len(coverages)) * 2 + .5, [f\"Group {group}\" for group in coverages], rotation=45)\n",
-    "plt.hlines(0.9, -1, 21, label=\"90% coverage\", color=\"black\", linestyle=\"--\")\n",
-    "plt.ylabel(\"Coverage\")\n",
-    "\n",
-    "#put legend outside of the plot\n",
-    "plt.legend(loc='upper left', bbox_to_anchor=(1, 1))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "mapie-dev",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.10.14"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

From 7672b2e61ddad02269d7eefd6b59a2f949a1640d Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 18:22:25 +0200
Subject: [PATCH 329/424] TST: test that estimator don't fail if given many
 alphas

---
 mapie/tests/test_mondrian.py | 5 +++--
 1 file changed, 3 insertions(+), 2 deletions(-)

diff --git a/mapie/tests/test_mondrian.py b/mapie/tests/test_mondrian.py
index ada5e6ec7..a1b3d3bd6 100644
--- a/mapie/tests/test_mondrian.py
+++ b/mapie/tests/test_mondrian.py
@@ -177,7 +177,8 @@
 
 
 @pytest.mark.parametrize("mapie_estimator_name", VALID_MAPIE_ESTIMATORS_NAMES)
-def test_valid_estimators_dont_fail(mapie_estimator_name):
+@pytest.mark.parametrize("alpha", [.2, [.2, .4]])
+def test_valid_estimators_dont_fail(mapie_estimator_name, alpha):
     """Test that valid estimators don't fail"""
     task_dict = VALID_MAPIE_ESTIMATORS[mapie_estimator_name]
     mapie_estimator = task_dict["estimator"]
@@ -196,7 +197,7 @@ def test_valid_estimators_dont_fail(mapie_estimator_name):
         )
     )
     mondrian_cp.fit(x, y, partition=partition)
-    mondrian_cp.predict(x, partition=partition, alpha=.2)
+    mondrian_cp.predict(x, partition=partition, alpha=alpha)
 
 
 @pytest.mark.parametrize(

From 39c5c06f537359ed6eb0e2d6f7ced7ec58daa441 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 18:29:20 +0200
Subject: [PATCH 330/424] FIX: legend inside plot in tuto + rename group into
 partition in tuto

---
 .../plot_main-tutorial-mondrian-regression.py | 58 ++++++++++---------
 1 file changed, 30 insertions(+), 28 deletions(-)

diff --git a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
index 955db7830..b133ebde3 100644
--- a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
+++ b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
@@ -44,10 +44,10 @@
 np.random.seed(0)
 X = np.linspace(0, 10, n_points).reshape(-1, 1)
 group_size = n_points // 10
-groups_list = []
+partition_list = []
 for i in range(10):
-    groups_list.append(np.array([i] * group_size))
-groups = np.concatenate(groups_list)
+    partition_list.append(np.array([i] * group_size))
+partition = np.concatenate(partition_list)
 
 noise_0_1 = np.random.normal(0, 0.1, group_size)
 noise_1_2 = np.random.normal(0, 0.5, group_size)
@@ -62,25 +62,25 @@
 
 y = np.concatenate(
     [
-        np.sin(X[groups == 0, 0] * 2) + noise_0_1,
-        np.sin(X[groups == 1, 0] * 2) + noise_1_2,
-        np.sin(X[groups == 2, 0] * 2) + noise_2_3,
-        np.sin(X[groups == 3, 0] * 2) + noise_3_4,
-        np.sin(X[groups == 4, 0] * 2) + noise_4_5,
-        np.sin(X[groups == 5, 0] * 2) + noise_5_6,
-        np.sin(X[groups == 6, 0] * 2) + noise_6_7,
-        np.sin(X[groups == 7, 0] * 2) + noise_7_8,
-        np.sin(X[groups == 8, 0] * 2) + noise_8_9,
-        np.sin(X[groups == 9, 0] * 2) + noise_9_10,
+        np.sin(X[partition == 0, 0] * 2) + noise_0_1,
+        np.sin(X[partition == 1, 0] * 2) + noise_1_2,
+        np.sin(X[partition == 2, 0] * 2) + noise_2_3,
+        np.sin(X[partition == 3, 0] * 2) + noise_3_4,
+        np.sin(X[partition == 4, 0] * 2) + noise_4_5,
+        np.sin(X[partition == 5, 0] * 2) + noise_5_6,
+        np.sin(X[partition == 6, 0] * 2) + noise_6_7,
+        np.sin(X[partition == 7, 0] * 2) + noise_7_8,
+        np.sin(X[partition == 8, 0] * 2) + noise_8_9,
+        np.sin(X[partition == 9, 0] * 2) + noise_9_10,
     ], axis=0
 )
 
 
 ##############################################################################
-# We plot the dataset with the groups as colors.
+# We plot the dataset with the partition as colors.
 
 
-plt.scatter(X, y, c=groups)
+plt.scatter(X, y, c=partition)
 plt.show()
 
 
@@ -91,14 +91,14 @@
 X_train_temp, X_test, y_train_temp, y_test = train_test_split(
     X, y, test_size=0.2, random_state=0
 )
-groups_train_temp, groups_test, _, _ = train_test_split(
-    groups, y, test_size=0.2, random_state=0
+partition_train_temp, partition_test, _, _ = train_test_split(
+    partition, y, test_size=0.2, random_state=0
 )
 X_cal, X_train, y_cal, y_train = train_test_split(
     X_train_temp, y_train_temp, test_size=0.5, random_state=0
 )
-groups_cal, groups_train, _, _ = train_test_split(
-    groups_train_temp, y_train_temp, test_size=0.5, random_state=0
+partition_cal, partition_train, _, _ = train_test_split(
+    partition_train_temp, y_train_temp, test_size=0.5, random_state=0
 )
 
 
@@ -107,11 +107,11 @@
 
 
 f, ax = plt.subplots(1, 3, figsize=(15, 5))
-ax[0].scatter(X_train, y_train, c=groups_train)
+ax[0].scatter(X_train, y_train, c=partition_train)
 ax[0].set_title("Train set")
-ax[1].scatter(X_cal, y_cal, c=groups_cal)
+ax[1].scatter(X_cal, y_cal, c=partition_cal)
 ax[1].set_title("Calibration set")
-ax[2].scatter(X_test, y_test, c=groups_test)
+ax[2].scatter(X_test, y_test, c=partition_test)
 ax[2].set_title("Test set")
 plt.show()
 
@@ -131,7 +131,7 @@
 mapie_regressor = MapieRegressor(rf, cv="prefit")
 mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
 mapie_regressor.fit(X_cal, y_cal)
-mondrian_regressor.fit(X_cal, y_cal, groups=groups_cal)
+mondrian_regressor.fit(X_cal, y_cal, partition=partition_cal)
 
 
 ##############################################################################
@@ -140,22 +140,23 @@
 
 _, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
 _, y_pss_mondrian = mondrian_regressor.predict(
-    X_test, groups=groups_test, alpha=.1
+    X_test, partition=partition_test, alpha=.1
 )
 
 
 ##############################################################################
-# 6. Compare the coverage by groups, plot both methods side by side.
+# 6. Compare the coverage by partition, plot both methods side by side.
 
 
 coverages = {}
-for group in np.unique(groups_test):
+for group in np.unique(partition_test):
     coverages[group] = {}
     coverages[group]["split"] = regression_coverage_score_v2(
-        y_test[groups_test == group], y_pss_split[groups_test == group]
+        y_test[partition_test == group], y_pss_split[partition_test == group]
     )
     coverages[group]["mondrian"] = regression_coverage_score_v2(
-        y_test[groups_test == group], y_pss_mondrian[groups_test == group]
+        y_test[partition_test == group],
+        y_pss_mondrian[partition_test == group]
     )
 
 
@@ -178,4 +179,5 @@
 plt.hlines(0.9, -1, 21, label="90% coverage", color="black", linestyle="--")
 plt.ylabel("Coverage")
 plt.legend(loc='upper left', bbox_to_anchor=(1, 1))
+plt.tight_layout()
 plt.show()

From f937bc85074fd0a86f0da849bd0c4e1d488d7e24 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Mon, 2 Sep 2024 18:46:45 +0200
Subject: [PATCH 331/424] ENH: increase figure size for tuto

---
 .../1-quickstart/plot_main-tutorial-mondrian-regression.py       | 1 +
 1 file changed, 1 insertion(+)

diff --git a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
index b133ebde3..ce47bb36e 100644
--- a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
+++ b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
@@ -161,6 +161,7 @@
 
 
 # Plot the coverage by groups, plot both methods side by side
+plt.figure(figsize=(10, 5))
 plt.bar(
     np.arange(len(coverages)) * 2,
     [float(coverages[group]["split"]) for group in coverages],

From 12e71f3e4ab1cbaa7821fd37143bffd8d1a32d3f Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 3 Sep 2024 11:52:33 +0200
Subject: [PATCH 332/424] FIX: sections titles in tutorial

---
 .../plot_main-tutorial-mondrian-regression.py   | 17 +++++++++--------
 1 file changed, 9 insertions(+), 8 deletions(-)

diff --git a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
index ce47bb36e..903b23702 100644
--- a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
+++ b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
@@ -35,9 +35,10 @@
 
 
 ##############################################################################
-# 1. Create the noisy dataset with 10 groups, each of those groups having
-#    a different level of noise.
-# -------------------------------------------------------------------
+# 1. Create the noisy dataset
+# -----------------------------
+# We create a dataset with 10 groups, each of those groups having a different
+# level of noise.
 
 
 n_points = 100000
@@ -86,7 +87,7 @@
 
 ##############################################################################
 # 2. Split the dataset into a training set, a calibration set, and a test set.
-
+# -----------------------------
 
 X_train_temp, X_test, y_train_temp, y_test = train_test_split(
     X, y, test_size=0.2, random_state=0
@@ -118,7 +119,7 @@
 
 ##############################################################################
 # 3. Fit a random forest regressor on the training set.
-
+# -----------------------------
 
 rf = RandomForestRegressor(n_estimators=100)
 rf.fit(X_train, y_train)
@@ -126,7 +127,7 @@
 
 ##############################################################################
 # 4. Fit a MapieRegressor and a MondrianCP on the calibration set.
-
+# -----------------------------
 
 mapie_regressor = MapieRegressor(rf, cv="prefit")
 mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
@@ -136,7 +137,7 @@
 
 ##############################################################################
 # 5. Predict the prediction intervals on the test set with both methods.
-
+# -----------------------------
 
 _, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
 _, y_pss_mondrian = mondrian_regressor.predict(
@@ -146,7 +147,7 @@
 
 ##############################################################################
 # 6. Compare the coverage by partition, plot both methods side by side.
-
+# -----------------------------
 
 coverages = {}
 for group in np.unique(partition_test):

From 2774aa3da3a1fec3b43697de7639fc0a2b72cb37 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 15:14:17 +0200
Subject: [PATCH 333/424] chore: Update error message from `predict_param` to
 `predict_params`

---
 mapie/tests/test_classification.py | 12 ++++++------
 mapie/tests/test_regression.py     | 12 ++++++------
 mapie/utils.py                     | 12 ++++++------
 3 files changed, 18 insertions(+), 18 deletions(-)

diff --git a/mapie/tests/test_classification.py b/mapie/tests/test_classification.py
index a1ff9c8d9..6b9cb502c 100644
--- a/mapie/tests/test_classification.py
+++ b/mapie/tests/test_classification.py
@@ -2087,9 +2087,9 @@ def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
     mapie_fitted = mapie.fit(X_train, y_train)
 
     with pytest.raises(ValueError, match=(
-        fr".*Using 'predict_param' '{predict_params}' "
-        r"without using one 'predict_param' in the fit method\..*"
-        r"Please ensure a similar configuration of 'predict_param' "
+        fr".*Using 'predict_params' '{predict_params}' "
+        r"without using one 'predict_params' in the fit method\..*"
+        r"Please ensure a similar configuration of 'predict_params' "
         r"is used in the fit method before calling it in predict\..*"
     )):
         mapie_fitted.predict(X_test, agg_scores="mean", **predict_params)
@@ -2109,9 +2109,9 @@ def test_using_one_predict_parameter_into_fit_but_not_in_predict() -> None:
     mapie_fitted = mapie.fit(X_train, y_train, predict_params=predict_params)
 
     with pytest.raises(ValueError, match=(
-        r"Using one 'predict_param' in the fit method "
-        r"without using one 'predict_param' in the predict method. "
-        r"Please ensure a similar configuration of 'predict_param' "
+        r"Using one 'predict_params' in the fit method "
+        r"without using one 'predict_params' in the predict method. "
+        r"Please ensure a similar configuration of 'predict_params' "
         r"is used in the predict method as called in the fit."
     )):
         mapie_fitted.predict(X_test)
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 80e578556..9fe6f9c5c 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -979,9 +979,9 @@ def test_using_one_predict_parameter_into_predict_but_not_in_fit() -> None:
     mapie_fitted = mapie.fit(X_train, y_train)
 
     with pytest.raises(ValueError, match=(
-        fr".*Using 'predict_param' '{predict_params}' "
-        r"without using one 'predict_param' in the fit method\..*"
-        r"Please ensure a similar configuration of 'predict_param' "
+        fr".*Using 'predict_params' '{predict_params}' "
+        r"without using one 'predict_params' in the fit method\..*"
+        r"Please ensure a similar configuration of 'predict_params' "
         r"is used in the fit method before calling it in predict\..*"
     )):
         mapie_fitted.predict(X_test, **predict_params)
@@ -1002,9 +1002,9 @@ def test_using_one_predict_parameter_into_fit_but_not_in_predict() -> None:
     mapie_fitted = mapie.fit(X_train, y_train, predict_params=predict_params)
 
     with pytest.raises(ValueError, match=(
-        r"Using one 'predict_param' in the fit method "
-        r"without using one 'predict_param' in the predict method. "
-        r"Please ensure a similar configuration of 'predict_param' "
+        r"Using one 'predict_params' in the fit method "
+        r"without using one 'predict_params' in the predict method. "
+        r"Please ensure a similar configuration of 'predict_params' "
         r"is used in the predict method as called in the fit."
     )):
         mapie_fitted.predict(X_test)
diff --git a/mapie/utils.py b/mapie/utils.py
index 4ac6b9251..23d69c438 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1400,15 +1400,15 @@ def check_predict_params(
     if cv != "prefit":
         if len(predict_params) > 0 and predict_params_used_in_fit is False:
             raise ValueError(
-                f"Using 'predict_param' '{predict_params}' "
-                f"without using one 'predict_param' in the fit method. "
-                f"Please ensure a similar configuration of 'predict_param' "
+                f"Using 'predict_params' '{predict_params}' "
+                f"without using one 'predict_params' in the fit method. "
+                f"Please ensure a similar configuration of 'predict_params' "
                 f"is used in the fit method before calling it in predict."
             )
         if len(predict_params) == 0 and predict_params_used_in_fit is True:
             raise ValueError(
-                "Using one 'predict_param' in the fit method "
-                "without using one 'predict_param' in the predict method. "
-                "Please ensure a similar configuration of 'predict_param' "
+                "Using one 'predict_params' in the fit method "
+                "without using one 'predict_params' in the predict method. "
+                "Please ensure a similar configuration of 'predict_params' "
                 "is used in the predict method as called in the fit."
             )

From 3f2113462ada69059a8f47ed922b21e1c35efc4f Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 15:38:07 +0200
Subject: [PATCH 334/424] ADD: history

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 71cd12df9..e2feb19b9 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Fix the CQR tutorial to have same data in both methods
 * Add Mondrian Conformal Prediction for regression and classification
 * Add `** predict_params` in fit and predict method for Mapie Regression
 * Update the ts-changepoint notebook with the tutorial

From 8b7d706494a9bf62dbfec9f7fc11a8d53c6d6020 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 15:43:39 +0200
Subject: [PATCH 335/424] Refactor citation link in README.rst

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 03a0a663d..19a435237 100644
--- a/README.rst
+++ b/README.rst
@@ -234,4 +234,4 @@ MAPIE is free and open-source software licensed under the `3-clause BSD license
 📚 Citation
 ===========
 
-If you use MAPIE in your research, please cite using `citations file <CITATION.cff>`_ on our repository.
+If you use MAPIE in your research, please cite using `<https://github.com/scikit-learn-contrib/MAPIE/blob/master/CITATION.cff>`_ on our repository.

From 2493ef0e3cbf4cafd7ca9bca6f42b9f09f725cd3 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:19:33 +0200
Subject: [PATCH 336/424] FIX: link to citation and license

---
 README.rst | 13 +++++++++++--
 1 file changed, 11 insertions(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index 19a435237..496fa98c0 100644
--- a/README.rst
+++ b/README.rst
@@ -228,10 +228,19 @@ and with the financial support from Région Ile de France and Confiance.ai.
 📝 License
 ==========
 
-MAPIE is free and open-source software licensed under the `3-clause BSD license <LICENSE>`_.
+MAPIE is free and open-source software licensed under the `<https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE>`_.
 
 
 📚 Citation
 ===========
 
-If you use MAPIE in your research, please cite using `<https://github.com/scikit-learn-contrib/MAPIE/blob/master/CITATION.cff>`_ on our repository.
+If you use MAPIE in your research, please cite using:
+
+```bibtex
+@inproceedings{Cordier_Flexible_and_Systematic_2023,
+author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
+booktitle = {Conformal and Probabilistic Prediction with Applications},
+title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
+year = {2023}
+}
+```

From cbb5a60532519e6713ac07485b401e7de388beac Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:23:24 +0200
Subject: [PATCH 337/424] Refactor citation link in README.rst

---
 README.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.rst b/README.rst
index 496fa98c0..c1949f141 100644
--- a/README.rst
+++ b/README.rst
@@ -228,7 +228,7 @@ and with the financial support from Région Ile de France and Confiance.ai.
 📝 License
 ==========
 
-MAPIE is free and open-source software licensed under the `<https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE>`_.
+MAPIE is free and open-source software licensed under the `license <https://github.com/scikit-learn-contrib/MAPIE/blob/master/LICENSE>`_.
 
 
 📚 Citation
@@ -236,7 +236,7 @@ MAPIE is free and open-source software licensed under the `<https://github.com/s
 
 If you use MAPIE in your research, please cite using:
 
-```bibtex
+```tex
 @inproceedings{Cordier_Flexible_and_Systematic_2023,
 author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
 booktitle = {Conformal and Probabilistic Prediction with Applications},

From 450b1f304bfe37581dd37bd45223760f3aa72966 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:24:25 +0200
Subject: [PATCH 338/424] Refactor citation link in README.rst

---
 README.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.rst b/README.rst
index c1949f141..a1826bc2c 100644
--- a/README.rst
+++ b/README.rst
@@ -236,11 +236,11 @@ MAPIE is free and open-source software licensed under the `license <https://gith
 
 If you use MAPIE in your research, please cite using:
 
-```tex
-@inproceedings{Cordier_Flexible_and_Systematic_2023,
-author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
-booktitle = {Conformal and Probabilistic Prediction with Applications},
-title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
-year = {2023}
-}
-```
+.. code:: tex
+    @inproceedings{Cordier_Flexible_and_Systematic_2023,
+    author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
+    booktitle = {Conformal and Probabilistic Prediction with Applications},
+    title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
+    year = {2023}
+    }
+

From e47e710f338d93d9f30ddff16a2b7384764a05b0 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:26:18 +0200
Subject: [PATCH 339/424] Refactor citation link in README.rst

---
 README.rst | 16 ++++++++--------
 1 file changed, 8 insertions(+), 8 deletions(-)

diff --git a/README.rst b/README.rst
index a1826bc2c..4c5707579 100644
--- a/README.rst
+++ b/README.rst
@@ -236,11 +236,11 @@ MAPIE is free and open-source software licensed under the `license <https://gith
 
 If you use MAPIE in your research, please cite using:
 
-.. code:: tex
-    @inproceedings{Cordier_Flexible_and_Systematic_2023,
-    author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
-    booktitle = {Conformal and Probabilistic Prediction with Applications},
-    title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
-    year = {2023}
-    }
-
+```
+@inproceedings{Cordier_Flexible_and_Systematic_2023,
+author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
+booktitle = {Conformal and Probabilistic Prediction with Applications},
+title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
+year = {2023}
+}
+```

From ef1cf71cdd1457f5989f23d896411d17cff9e394 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:29:11 +0200
Subject: [PATCH 340/424] FIX viewing of citations

---
 README.rst | 18 ++++++++----------
 1 file changed, 8 insertions(+), 10 deletions(-)

diff --git a/README.rst b/README.rst
index 4c5707579..26db490a7 100644
--- a/README.rst
+++ b/README.rst
@@ -234,13 +234,11 @@ MAPIE is free and open-source software licensed under the `license <https://gith
 📚 Citation
 ===========
 
-If you use MAPIE in your research, please cite using:
-
-```
-@inproceedings{Cordier_Flexible_and_Systematic_2023,
-author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
-booktitle = {Conformal and Probabilistic Prediction with Applications},
-title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
-year = {2023}
-}
-```
+If you use MAPIE in your research, please cite using::
+
+    @inproceedings{Cordier_Flexible_and_Systematic_2023,
+    author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},
+    booktitle = {Conformal and Probabilistic Prediction with Applications},
+    title = {{Flexible and Systematic Uncertainty Estimation with Conformal Prediction via the MAPIE library}},
+    year = {2023}
+    }

From 1c9658b08e85d8e62c550449d94c7d5f1403bb79 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 14:35:15 +0000
Subject: [PATCH 341/424] ADD: history

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 71cd12df9..112f9c58f 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.8.x (2024-xx-xx)
 ------------------
 
+* Fix citations and license links
 * Add Mondrian Conformal Prediction for regression and classification
 * Add `** predict_params` in fit and predict method for Mapie Regression
 * Update the ts-changepoint notebook with the tutorial

From cb89ce4f4d2958eca089c14db88acc6aee7f1b57 Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:40:49 +0200
Subject: [PATCH 342/424] Fix citations in readme

---
 README.rst | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 26db490a7..45683afc7 100644
--- a/README.rst
+++ b/README.rst
@@ -234,7 +234,9 @@ MAPIE is free and open-source software licensed under the `license <https://gith
 📚 Citation
 ===========
 
-If you use MAPIE in your research, please cite using::
+If you use MAPIE in your research, please cite using:
+
+.. code:: latex
 
     @inproceedings{Cordier_Flexible_and_Systematic_2023,
     author = {Cordier, Thibault and Blot, Vincent and Lacombe, Louis and Morzadec, Thomas and Capitaine, Arnaud and Brunel, Nicolas},

From 08bf18631a8e07c1c7182a1f8e1a33b4ce09b58d Mon Sep 17 00:00:00 2001
From: Louis Lacombe <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 16:46:25 +0200
Subject: [PATCH 343/424] Add Capgemini logo

---
 README.rst | 11 ++++++++---
 1 file changed, 8 insertions(+), 3 deletions(-)

diff --git a/README.rst b/README.rst
index 45683afc7..97aa824f1 100644
--- a/README.rst
+++ b/README.rst
@@ -166,10 +166,15 @@ For more information on the contribution process, please go `here <CONTRIBUTING.
 🤝  Affiliations
 ================
 
-MAPIE has been developed through a collaboration between Quantmetry, Michelin, ENS Paris-Saclay,
+MAPIE has been developed through a collaboration between Capgemini, Quantmetry, Michelin, ENS Paris-Saclay,
 and with the financial support from Région Ile de France and Confiance.ai.
 
-|Quantmetry| |Michelin| |ENS| |Confiance.ai| |IledeFrance|
+|Capgemini| |Quantmetry| |Michelin| |ENS| |Confiance.ai| |IledeFrance|
+
+.. |Capgemini| image:: https://www.capgemini.com/wp-content/themes/capgemini2020/assets/images/logo.svg
+    :height: 35px
+    :width: 140px
+    :target: https://www.capgemini.com/
 
 .. |Quantmetry| image:: https://www.quantmetry.com/wp-content/uploads/2020/08/08-Logo-quant-Texte-noir.svg
     :height: 35px
@@ -184,7 +189,7 @@ and with the financial support from Région Ile de France and Confiance.ai.
 .. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg
     :height: 35px
     :width: 140px
-    :target: https://ens-paris-saclay.fr/en
+    :target: https://ens-paris-saclay.fr/en/
 
 .. |Confiance.ai| image:: https://pbs.twimg.com/profile_images/1443838558549258264/EvWlv1Vq_400x400.jpg
     :height: 45px

From 37dc0189957dbd9d771d6602886d44a8710a8f5d Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 17:16:39 +0200
Subject: [PATCH 344/424] =?UTF-8?q?Bump=20version:=200.8.6=20=E2=86=92=200?=
 =?UTF-8?q?.9.0?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 CITATION.cff      | 2 +-
 doc/conf.py       | 2 +-
 mapie/_version.py | 2 +-
 setup.py          | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 19a4fa709..0936e1d34 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.8.6
+current_version = 0.9.0
 commit = True
 tag = True
 
diff --git a/CITATION.cff b/CITATION.cff
index 8c89d0e5c..4eab6cd4e 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Thibault"
   orcid: "https://orcid.org/0000-0000-0000-0000"
 title: "MAPIE - Model Agnostic Prediction Interval Estimator"
-version: 0.8.6
+version: 0.9.0
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
diff --git a/doc/conf.py b/doc/conf.py
index 400d9c96c..58989f4c8 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -88,7 +88,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.8.6"
+version = "0.9.0"
 # The full version, including alpha/beta/rc tags.
 release = version
 
diff --git a/mapie/_version.py b/mapie/_version.py
index de77196f4..3e2f46a3a 100644
--- a/mapie/_version.py
+++ b/mapie/_version.py
@@ -1 +1 @@
-__version__ = "0.8.6"
+__version__ = "0.9.0"
diff --git a/setup.py b/setup.py
index 4eb3bbb98..94571a0a6 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "MAPIE"
-VERSION = "0.8.6"
+VERSION = "0.9.0"
 DESCRIPTION = (
     "A scikit-learn-compatible module "
     "for estimating prediction intervals."

From 9a0f359e34bd8042992a2173e9063946c6facb04 Mon Sep 17 00:00:00 2001
From: LacombeLouis <lfmm.lacombe@gmail.com>
Date: Tue, 3 Sep 2024 17:34:52 +0200
Subject: [PATCH 345/424] FIX: history for version

---
 HISTORY.rst | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index f054b3a50..cafd62cbb 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,7 +2,12 @@
 History
 =======
 
-0.8.x (2024-xx-xx)
+0.9.x (2024-xx-xx)
+------------------
+
+
+
+0.9.0 (2024-09-03)
 ------------------
 
 * Fix citations and license links

From 7d67506151f5faad52af78f0626d8efe6b1167e7 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 10 Sep 2024 11:14:05 +0200
Subject: [PATCH 346/424] FIX: allow to import from residual_conformity_scores

---
 .../conformity_scores/residual_conformity_scores.py | 10 ++++++++++
 mapie/tests/test_common.py                          | 13 +++++++++++++
 2 files changed, 23 insertions(+)
 create mode 100644 mapie/conformity_scores/residual_conformity_scores.py

diff --git a/mapie/conformity_scores/residual_conformity_scores.py b/mapie/conformity_scores/residual_conformity_scores.py
new file mode 100644
index 000000000..7f3db4bab
--- /dev/null
+++ b/mapie/conformity_scores/residual_conformity_scores.py
@@ -0,0 +1,10 @@
+from .bounds import (  # noqa: F401
+    AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
+)
+
+import warnings
+warnings.warn(
+    "Imports from mapie.conformity_scores.residual_conformity_scores " +
+    "are deprecated. Please use from mapie.conformity_scores",
+    DeprecationWarning
+)
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 367871827..7ecf6f7d7 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -195,3 +195,16 @@ def test_sklearn_compatible_estimator(
 ) -> None:
     """Check compatibility with sklearn, using sklearn estimator checks API."""
     check(estimator)
+
+
+def test_warning_when_import_from_residual_conformity_score():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        DeprecationWarning, match=r".*Imports from mapie.conformity_scores.*"
+    ):
+        from mapie.conformity_scores.residual_conformity_scores import (
+            GammaConformityScore
+        )
+        GammaConformityScore()

From 79ba0f8372d086e72434667127e1c0e79d5e6674 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Tue, 10 Sep 2024 11:39:43 +0200
Subject: [PATCH 347/424] FIX: add ConformityScore to have no breakning changes

---
 mapie/conformity_scores/conformity_scores.py |  8 ++++++++
 mapie/tests/test_common.py                   | 14 +++++++++++++-
 2 files changed, 21 insertions(+), 1 deletion(-)
 create mode 100644 mapie/conformity_scores/conformity_scores.py

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
new file mode 100644
index 000000000..1eb1d56dc
--- /dev/null
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -0,0 +1,8 @@
+from .regression import BaseRegressionScore as ConformityScore  # noqa: F401
+
+import warnings
+warnings.warn(
+    "Conformity score class is depreciated. Prefer import " +
+    "BaseRegressionScore from mapie.conformity_scores",
+    DeprecationWarning
+)
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 7ecf6f7d7..e8578c4e7 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -17,7 +17,7 @@
 X_toy = np.arange(18).reshape(-1, 1)
 y_toy = np.array(
     [0, 0, 1, 0, 1, 2, 1, 2, 2, 0, 0, 1, 0, 1, 2, 1, 2, 2]
-    )
+)
 
 
 def MapieSimpleEstimators() -> List[BaseEstimator]:
@@ -208,3 +208,15 @@ def test_warning_when_import_from_residual_conformity_score():
             GammaConformityScore
         )
         GammaConformityScore()
+
+
+def test_warning_when_import_from_conformity_scores():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.conformity_score"""
+
+    with pytest.warns(
+        DeprecationWarning, match=r".*Conformity score class is depreciated.*"
+    ):
+        from mapie.conformity_scores.conformity_scores import (  # noqa: F401
+            ConformityScore
+        )

From 72e28ecc56f33b2d96feab2c2692a56c5d66be67 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 10 Sep 2024 11:53:34 +0200
Subject: [PATCH 348/424] fix: Update pip requirements.txt to match conda
 environments.yml. Fix requirements.doc.txt. Update contribution guidelines.

---
 .gitignore           |  3 +++
 CONTRIBUTING.rst     | 19 +++++++++++++++++--
 requirements.ci.txt  |  1 +
 requirements.dev.txt |  4 ++--
 requirements.doc.txt |  6 ++++--
 5 files changed, 27 insertions(+), 6 deletions(-)

diff --git a/.gitignore b/.gitignore
index b2d6f9ace..ad104822b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -85,3 +85,6 @@ target/
 
 # ZIP files
 *.zip
+
+# Pyenv
+.python-version
\ No newline at end of file
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index eb2a0bdef..8492a3385 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -50,11 +50,26 @@ Documenting your change
 
 If you're adding a class or a function, then you'll need to add a docstring with a doctest. We follow the `numpy docstring convention <https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_, so please do too.
 Any estimator should follow the [scikit-learn API](https://scikit-learn.org/stable/developers/develop.html), so please follow these guidelines.
-In order to build the documentation locally, run :
+In order to build the documentation locally, you first need to install some dependencies :
+
+Create a dedicated virtual environment via `conda`:
+
+.. code:: sh
+
+    $ conda env create -f environment.doc.yml
+    $ conda activate mapie-doc
+
+Alternatively, using `pip`, create a different virtual environment than the one used for development, and install the dependencies:
+
+.. code:: sh
+
+    $ pip install -r requirements.doc.txt
+    $ pip install -e .
+
+Finally, once dependencies are installed, you can build the documentation locally by running:
 
 .. code:: sh
 
-    $ cd doc
     $ make clean-doc
     $ make doc
 
diff --git a/requirements.ci.txt b/requirements.ci.txt
index 587a04f87..ab1896a2f 100644
--- a/requirements.ci.txt
+++ b/requirements.ci.txt
@@ -4,4 +4,5 @@ mypy
 pandas
 pytest
 pytest-cov
+scikit-learn
 typed-ast
diff --git a/requirements.dev.txt b/requirements.dev.txt
index 9f73c46f2..ed23426e9 100644
--- a/requirements.dev.txt
+++ b/requirements.dev.txt
@@ -3,8 +3,8 @@ flake8==4.0.1
 ipykernel==6.9.0
 jupyter==1.0.0
 mypy==1.7.1
-numpy==1.22.3
 numpydoc==1.1.0
+numpy==1.22.3
 pandas==1.3.5
 pytest==6.2.5
 pytest-cov==3.0.0
@@ -13,4 +13,4 @@ sphinx==4.3.2
 sphinx-gallery==0.10.1
 sphinx_rtd_theme==1.0.0
 twine==3.7.1
-wheel==0.38.1
\ No newline at end of file
+wheel==0.37.0
\ No newline at end of file
diff --git a/requirements.doc.txt b/requirements.doc.txt
index acff47a80..b81b9c823 100644
--- a/requirements.doc.txt
+++ b/requirements.doc.txt
@@ -1,8 +1,10 @@
-lightgbm==3.2.1
+lightgbm==4.4.0
 matplotlib==3.5.1
+numpy==1.22.3
 numpydoc==1.1.0
 pandas==1.3.5
-sphinx==4.3.2
+scikit-learn
+sphinx==5.3.0
 sphinx-gallery==0.10.1
 sphinx_rtd_theme==1.0.0
 typing_extensions==4.0.1
\ No newline at end of file

From 2ed041051347c11c6df02403dc551ec816853c8f Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 10 Sep 2024 11:59:58 +0200
Subject: [PATCH 349/424] fix: Update gitignore with generated folder for
 Mondrian documentation

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index b2d6f9ace..77dd29547 100644
--- a/.gitignore
+++ b/.gitignore
@@ -14,6 +14,7 @@ doc/examples_classification/
 doc/examples_regression/
 doc/examples_calibration/
 doc/examples_multilabel_classification/
+doc/examples_mondrian/
 doc/auto_examples/
 doc/modules/generated/
 doc/datasets/generated/

From 71a31781eaccd5f0e2bf4f1b4b2f5882d22c3c02 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 10 Sep 2024 12:10:04 +0200
Subject: [PATCH 350/424] doc: update authors.rst and history.rst

---
 AUTHORS.rst | 1 +
 HISTORY.rst | 2 +-
 2 files changed, 2 insertions(+), 1 deletion(-)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index a79a0da5b..8fcded38b 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -8,6 +8,7 @@ Development Lead
 * Thibault Cordier <thibault.a.cordier@capgemini.com>
 * Vincent Blot <vincent.blot@capgemini.com>
 * Louis Lacombe <louis.lacombe@capgemini.com>
+* Valentin Laurent <valentin.laurent@capgemini.com>
 
 Emeritus Core Developers
 ------------------------
diff --git a/HISTORY.rst b/HISTORY.rst
index cafd62cbb..41d5c3d28 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -4,7 +4,7 @@ History
 
 0.9.x (2024-xx-xx)
 ------------------
-
+* Update gitignore by including the documentation folder generated for Mondrian
 
 
 0.9.0 (2024-09-03)

From 00e96b5575b6ac9b083415811bbb27ab958143ab Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 10 Sep 2024 12:51:41 +0200
Subject: [PATCH 351/424] doc: update history.rst

---
 HISTORY.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index cafd62cbb..1314cc4a1 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -4,7 +4,7 @@ History
 
 0.9.x (2024-xx-xx)
 ------------------
-
+* Fix (partially) the set-up with pip instead of conda for new contributors
 
 
 0.9.0 (2024-09-03)

From bcf283e16f4d2bd365d2b8d47283f3d3a93f0193 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 10:53:25 +0200
Subject: [PATCH 352/424] FIX: change warn place in file

---
 mapie/conformity_scores/conformity_scores.py          | 6 ++++--
 mapie/conformity_scores/residual_conformity_scores.py | 8 ++++----
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 1eb1d56dc..27ce5aba0 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -1,8 +1,10 @@
-from .regression import BaseRegressionScore as ConformityScore  # noqa: F401
-
 import warnings
 warnings.warn(
     "Conformity score class is depreciated. Prefer import " +
     "BaseRegressionScore from mapie.conformity_scores",
     DeprecationWarning
 )
+
+from .regression import (  # noqa: F401, E402
+    BaseRegressionScore as ConformityScore
+)
diff --git a/mapie/conformity_scores/residual_conformity_scores.py b/mapie/conformity_scores/residual_conformity_scores.py
index 7f3db4bab..4b4a260e8 100644
--- a/mapie/conformity_scores/residual_conformity_scores.py
+++ b/mapie/conformity_scores/residual_conformity_scores.py
@@ -1,10 +1,10 @@
-from .bounds import (  # noqa: F401
-    AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
-)
-
 import warnings
 warnings.warn(
     "Imports from mapie.conformity_scores.residual_conformity_scores " +
     "are deprecated. Please use from mapie.conformity_scores",
     DeprecationWarning
 )
+
+from .bounds import (  # noqa: F401, E402
+    AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
+)

From 3a1bc0e76d8ae70d96503fff3876aeb97d0131b8 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 13:57:35 +0200
Subject: [PATCH 353/424] FIX: warning raised

---
 mapie/conformity_scores/conformity_scores.py  | 420 +++++++++++++++++-
 .../residual_conformity_scores.py             |  38 +-
 mapie/tests/test_common.py                    |  56 ++-
 3 files changed, 495 insertions(+), 19 deletions(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 27ce5aba0..904d77db6 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -1,10 +1,414 @@
-import warnings
-warnings.warn(
-    "Conformity score class is depreciated. Prefer import " +
-    "BaseRegressionScore from mapie.conformity_scores",
-    DeprecationWarning
-)
+from abc import ABCMeta, abstractmethod
+from typing import Tuple
+
+import numpy as np
+from sklearn.utils import deprecated
+
+from mapie.conformity_scores.interface import BaseConformityScore
+from mapie.estimator.regressor import EnsembleRegressor
+from mapie._compatibility import np_nanquantile
+from mapie._machine_precision import EPSILON
+from mapie._typing import NDArray
 
-from .regression import (  # noqa: F401, E402
-    BaseRegressionScore as ConformityScore
+
+@deprecated(
+    "WARNING: Deprecated path to import ConformityScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores import ConformityScore]."
 )
+class ConformityScore(BaseConformityScore, metaclass=ABCMeta):
+    """
+    Base conformity score class for regression task.
+
+    This class should not be used directly. Use derived classes instead.
+
+    Parameters
+    ----------
+    sym: bool
+        Whether to consider the conformity score as symmetrical or not.
+
+    consistency_check: bool, optional
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        By default ``True``.
+
+    eps: float, optional
+        Threshold to consider when checking the consistency between
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        It should be specified if ``consistency_check==True``.
+
+        By default, it is defined by the default precision.
+    """
+
+    def __init__(
+        self,
+        sym: bool,
+        consistency_check: bool = True,
+        eps: float = float(EPSILON),
+    ):
+        super().__init__()
+        self.sym = sym
+        self.consistency_check = consistency_check
+        self.eps = eps
+
+    @abstractmethod
+    def get_signed_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_conformity_scores``.
+        Subclasses should implement this method!
+
+        Compute the sample conformity scores given the predicted and
+        observed targets.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Signed conformity scores.
+        """
+
+    def get_conformity_scores(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Get the conformity score considering the symmetrical property if so.
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Conformity scores.
+        """
+        conformity_scores = \
+            self.get_signed_conformity_scores(y, y_pred, **kwargs)
+        if self.consistency_check:
+            self.check_consistency(y, y_pred, conformity_scores, **kwargs)
+        if self.sym:
+            conformity_scores = np.abs(conformity_scores)
+        return conformity_scores
+
+    def check_consistency(
+        self,
+        y: NDArray,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> None:
+        """
+        Check consistency between the following methods:
+        ``get_estimation_distribution`` and ``get_signed_conformity_scores``
+
+        The following equality should be verified:
+        ``self.get_estimation_distribution(
+            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
+        ) == y``
+
+        Parameters
+        ----------
+        y: NDArray of shape (n_samples,)
+            Observed target values.
+
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        Raises
+        ------
+        ValueError
+            If the two methods are not consistent.
+        """
+        score_distribution = self.get_estimation_distribution(
+            y_pred, conformity_scores, **kwargs
+        )
+        abs_conformity_scores = np.abs(np.subtract(score_distribution, y))
+        max_conf_score = np.max(abs_conformity_scores)
+        if max_conf_score > self.eps:
+            raise ValueError(
+                "The two functions get_conformity_scores and "
+                "get_estimation_distribution of the BaseRegressionScore class "
+                "are not consistent. "
+                "The following equation must be verified: "
+                "self.get_estimation_distribution(y_pred, "
+                "self.get_conformity_scores(y, y_pred)) == y. "
+                f"The maximum conformity score is {max_conf_score}. "
+                "The eps attribute may need to be increased if you are "
+                "sure that the two methods are consistent."
+            )
+
+    @abstractmethod
+    def get_estimation_distribution(
+        self,
+        y_pred: NDArray,
+        conformity_scores: NDArray,
+        **kwargs
+    ) -> NDArray:
+        """
+        Placeholder for ``get_estimation_distribution``.
+        Subclasses should implement this method!
+
+        Compute samples of the estimation distribution given the predicted
+        targets and the conformity scores.
+
+        Parameters
+        ----------
+        y_pred: NDArray of shape (n_samples,)
+            Predicted target values.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Observed values.
+        """
+
+    @staticmethod
+    def _beta_optimize(
+        alpha_np: NDArray,
+        upper_bounds: NDArray,
+        lower_bounds: NDArray,
+    ) -> NDArray:
+        """
+        Minimize the width of the PIs, for a given difference of quantiles.
+
+        Parameters
+        ----------
+        alpha_np: NDArray
+            The quantiles to compute.
+
+        upper_bounds: NDArray of shape (n_samples,)
+            The array of upper values.
+
+        lower_bounds: NDArray of shape (n_samples,)
+            The array of lower values.
+
+        Returns
+        -------
+        NDArray of shape (n_samples,)
+            Array of betas minimizing the differences
+            ``(1-alpha+beta)-quantile - beta-quantile``.
+        """
+        beta_np = np.full(
+            shape=(len(lower_bounds), len(alpha_np)),
+            fill_value=np.nan,
+            dtype=float,
+        )
+
+        for ind_alpha, _alpha in enumerate(alpha_np):
+            betas = np.linspace(
+                _alpha / (len(lower_bounds) + 1),
+                _alpha,
+                num=len(lower_bounds),
+                endpoint=True,
+            )
+            one_alpha_beta = np_nanquantile(
+                upper_bounds.astype(float),
+                1 - _alpha + betas,
+                axis=1,
+                method="higher",
+            )
+            beta = np_nanquantile(
+                lower_bounds.astype(float),
+                betas,
+                axis=1,
+                method="lower",
+            )
+            beta_np[:, ind_alpha] = betas[
+                np.argmin(one_alpha_beta - beta, axis=0)
+            ]
+
+        return beta_np
+
+    def get_bounds(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        estimator: EnsembleRegressor,
+        conformity_scores: NDArray,
+        ensemble: bool = False,
+        method: str = 'base',
+        optimize_beta: bool = False,
+        allow_infinite_bounds: bool = False
+    ) -> Tuple[NDArray, NDArray, NDArray]:
+        """
+        Compute bounds of the prediction intervals from the observed values,
+        the estimator of type ``EnsembleRegressor`` and the conformity scores.
+
+        Parameters
+        ----------
+        X: NDArray of shape (n_samples, n_features)
+            Observed feature values.
+
+        alpha_np: NDArray of shape (n_alpha,)
+            NDArray of floats between ``0`` and ``1``, represents the
+            uncertainty of the confidence interval.
+
+        estimator: EnsembleRegressor
+            Estimator that is fitted to predict y from X.
+
+        conformity_scores: NDArray of shape (n_samples,)
+            Conformity scores.
+
+        ensemble: bool
+            Boolean determining whether the predictions are ensembled or not.
+
+            By default ``False``.
+
+        method: str
+            Method to choose for prediction interval estimates.
+            The ``"plus"`` method implies that the quantile is calculated
+            after estimating the bounds, whereas the other methods
+            (among the ``"naive"``, ``"base"`` or ``"minmax"`` methods,
+            for example) do the opposite.
+
+            By default ``base``.
+
+        optimize_beta: bool
+            Whether to optimize the PIs' width or not.
+
+            By default ``False``.
+
+        allow_infinite_bounds: bool
+            Allow infinite prediction intervals to be produced.
+
+            By default ``False``.
+
+        Returns
+        -------
+        Tuple[NDArray, NDArray, NDArray]
+            - The predictions itself. (y_pred) of shape (n_samples,).
+            - The lower bounds of the prediction intervals of shape
+            (n_samples, n_alpha).
+            - The upper bounds of the prediction intervals of shape
+            (n_samples, n_alpha).
+
+        Raises
+        ------
+        ValueError
+            If beta optimisation with symmetrical conformity score function.
+        """
+        if self.sym and optimize_beta:
+            raise ValueError(
+                "Beta optimisation cannot be used with " +
+                "symmetrical conformity score function."
+            )
+
+        y_pred, y_pred_low, y_pred_up = estimator.predict(X, ensemble)
+        signed = -1 if self.sym else 1
+
+        if optimize_beta:
+            beta_np = self._beta_optimize(
+                alpha_np,
+                conformity_scores.reshape(1, -1),
+                conformity_scores.reshape(1, -1),
+            )
+        else:
+            beta_np = alpha_np / 2
+
+        if method == "plus":
+            alpha_low = alpha_np if self.sym else beta_np
+            alpha_up = 1 - alpha_np if self.sym else 1 - alpha_np + beta_np
+
+            conformity_scores_low = self.get_estimation_distribution(
+                y_pred_low, signed * conformity_scores, X=X
+            )
+            conformity_scores_up = self.get_estimation_distribution(
+                y_pred_up, conformity_scores, X=X
+            )
+            bound_low = self.get_quantile(
+                conformity_scores_low, alpha_low, axis=1, reversed=True,
+                unbounded=allow_infinite_bounds
+            )
+            bound_up = self.get_quantile(
+                conformity_scores_up, alpha_up, axis=1,
+                unbounded=allow_infinite_bounds
+            )
+
+        else:
+            if self.sym:
+                alpha_ref = 1 - alpha_np
+                quantile_ref = self.get_quantile(
+                    conformity_scores[..., np.newaxis], alpha_ref, axis=0
+                )
+                quantile_low, quantile_up = -quantile_ref, quantile_ref
+
+            else:
+                alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np
+
+                quantile_low = self.get_quantile(
+                    conformity_scores[..., np.newaxis],
+                    alpha_low, axis=0, reversed=True,
+                    unbounded=allow_infinite_bounds
+                )
+                quantile_up = self.get_quantile(
+                    conformity_scores[..., np.newaxis],
+                    alpha_up, axis=0,
+                    unbounded=allow_infinite_bounds
+                )
+
+            bound_low = self.get_estimation_distribution(
+                y_pred_low, quantile_low, X=X
+            )
+            bound_up = self.get_estimation_distribution(
+                y_pred_up, quantile_up, X=X
+            )
+
+        return y_pred, bound_low, bound_up
+
+    def predict_set(
+        self,
+        X: NDArray,
+        alpha_np: NDArray,
+        **kwargs
+    ):
+        """
+        Compute the prediction sets on new samples based on the uncertainty of
+        the target confidence set.
+
+        Parameters:
+        -----------
+        X: NDArray of shape (n_samples,)
+            The input data or samples for prediction.
+
+        alpha_np: NDArray of shape (n_alpha, )
+            Represents the uncertainty of the confidence set to produce.
+
+        **kwargs: dict
+            Additional keyword arguments.
+
+        Returns:
+        --------
+        The output structure depend on the ``get_bounds`` method.
+            The prediction sets for each sample and each alpha level.
+        """
+        return self.get_bounds(X=X, alpha_np=alpha_np, **kwargs)
diff --git a/mapie/conformity_scores/residual_conformity_scores.py b/mapie/conformity_scores/residual_conformity_scores.py
index 4b4a260e8..9ed1e3d9f 100644
--- a/mapie/conformity_scores/residual_conformity_scores.py
+++ b/mapie/conformity_scores/residual_conformity_scores.py
@@ -1,10 +1,34 @@
-import warnings
-warnings.warn(
-    "Imports from mapie.conformity_scores.residual_conformity_scores " +
-    "are deprecated. Please use from mapie.conformity_scores",
-    DeprecationWarning
+from sklearn.utils import deprecated
+
+from .bounds import (
+    AbsoluteConformityScore as OldAbsoluteConformityScore,
+    GammaConformityScore as OldGammaConformityScore,
+    ResidualNormalisedScore as OldResidualNormalisedScore
+)
+
+
+@deprecated(
+    "WARNING: Deprecated path to import AbsoluteConformityScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.bounds import AbsoluteConformityScore]."
 )
+class AbsoluteConformityScore(OldAbsoluteConformityScore):
+    pass
+
+
+@deprecated(
+    "WARNING: Deprecated path to import GammaConformityScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.bounds import GammaConformityScore]."
+)
+class GammaConformityScore(OldGammaConformityScore):
+    pass
+
 
-from .bounds import (  # noqa: F401, E402
-    AbsoluteConformityScore, GammaConformityScore, ResidualNormalisedScore
+@deprecated(
+    "WARNING: Deprecated path to import ResidualNormalisedScore. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.bounds import ResidualNormalisedScore]."
 )
+class ResidualNormalisedScore(OldResidualNormalisedScore):
+    pass
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index e8578c4e7..2f4b0ad72 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -11,6 +11,7 @@
 from sklearn.utils.estimator_checks import parametrize_with_checks
 from sklearn.utils.validation import check_is_fitted
 
+from mapie._typing import ArrayLike, NDArray
 from mapie.classification import MapieClassifier
 from mapie.regression import MapieQuantileRegressor, MapieRegressor
 
@@ -197,12 +198,12 @@ def test_sklearn_compatible_estimator(
     check(estimator)
 
 
-def test_warning_when_import_from_residual_conformity_score():
+def test_warning_when_import_from_gamma_conformity_score():
     """Check that a DepreciationWarning is raised when importing from
     mapie.conformity_scores.residual_conformity_scores"""
 
     with pytest.warns(
-        DeprecationWarning, match=r".*Imports from mapie.conformity_scores.*"
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
     ):
         from mapie.conformity_scores.residual_conformity_scores import (
             GammaConformityScore
@@ -210,13 +211,60 @@ def test_warning_when_import_from_residual_conformity_score():
         GammaConformityScore()
 
 
+def test_warning_when_import_from_absolute_conformity_score():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.residual_conformity_scores import (
+            AbsoluteConformityScore
+        )
+        AbsoluteConformityScore()
+
+
+def test_warning_when_import_from_residual_conformity_score():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.residual_conformity_scores import (
+            ResidualNormalisedScore
+        )
+        ResidualNormalisedScore()
+
+
 def test_warning_when_import_from_conformity_scores():
     """Check that a DepreciationWarning is raised when importing from
     mapie.conformity_scores.conformity_score"""
 
     with pytest.warns(
-        DeprecationWarning, match=r".*Conformity score class is depreciated.*"
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
     ):
-        from mapie.conformity_scores.conformity_scores import (  # noqa: F401
+        from mapie.conformity_scores.conformity_scores import (
             ConformityScore
         )
+
+        class DummyConformityScore(ConformityScore):
+            def __init__(self) -> None:
+                super().__init__(sym=True, consistency_check=True)
+
+            def get_signed_conformity_scores(
+                self, y: ArrayLike, y_pred: ArrayLike, **kwargs
+            ) -> NDArray:
+                return np.subtract(y, y_pred)
+
+            def get_estimation_distribution(
+                self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
+            ) -> NDArray:
+                """
+                A positive constant is added to the sum between predictions and
+                conformity scores to make the estimated distribution
+                inconsistent with the conformity score.
+                """
+                return np.add(y_pred, conformity_scores) + 1
+
+        DummyConformityScore()

From 0fd0fbcd9e45ff1c97a2f74511777b0bb18f1174 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 14:16:23 +0200
Subject: [PATCH 354/424] ENH: rename old import into new

---
 .../conformity_scores/residual_conformity_scores.py  | 12 ++++++------
 1 file changed, 6 insertions(+), 6 deletions(-)

diff --git a/mapie/conformity_scores/residual_conformity_scores.py b/mapie/conformity_scores/residual_conformity_scores.py
index 9ed1e3d9f..2a5aa5227 100644
--- a/mapie/conformity_scores/residual_conformity_scores.py
+++ b/mapie/conformity_scores/residual_conformity_scores.py
@@ -1,9 +1,9 @@
 from sklearn.utils import deprecated
 
 from .bounds import (
-    AbsoluteConformityScore as OldAbsoluteConformityScore,
-    GammaConformityScore as OldGammaConformityScore,
-    ResidualNormalisedScore as OldResidualNormalisedScore
+    AbsoluteConformityScore as NewAbsoluteConformityScore,
+    GammaConformityScore as NewGammaConformityScore,
+    ResidualNormalisedScore as NewResidualNormalisedScore
 )
 
 
@@ -12,7 +12,7 @@
     "Please prefer the new path: "
     "[from mapie.conformity_scores.bounds import AbsoluteConformityScore]."
 )
-class AbsoluteConformityScore(OldAbsoluteConformityScore):
+class AbsoluteConformityScore(NewAbsoluteConformityScore):
     pass
 
 
@@ -21,7 +21,7 @@ class AbsoluteConformityScore(OldAbsoluteConformityScore):
     "Please prefer the new path: "
     "[from mapie.conformity_scores.bounds import GammaConformityScore]."
 )
-class GammaConformityScore(OldGammaConformityScore):
+class GammaConformityScore(NewGammaConformityScore):
     pass
 
 
@@ -30,5 +30,5 @@ class GammaConformityScore(OldGammaConformityScore):
     "Please prefer the new path: "
     "[from mapie.conformity_scores.bounds import ResidualNormalisedScore]."
 )
-class ResidualNormalisedScore(OldResidualNormalisedScore):
+class ResidualNormalisedScore(NewResidualNormalisedScore):
     pass

From afe1b6053fcf2f47b051181d849c3ca716355883 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 14:16:36 +0200
Subject: [PATCH 355/424] ADD: wrapper for get_true_label_position

---
 .../utils_classification_conformity_scores.py      | 14 ++++++++++++++
 mapie/tests/test_common.py                         | 12 ++++++++++++
 2 files changed, 26 insertions(+)
 create mode 100644 mapie/conformity_scores/utils_classification_conformity_scores.py

diff --git a/mapie/conformity_scores/utils_classification_conformity_scores.py b/mapie/conformity_scores/utils_classification_conformity_scores.py
new file mode 100644
index 000000000..c145b002c
--- /dev/null
+++ b/mapie/conformity_scores/utils_classification_conformity_scores.py
@@ -0,0 +1,14 @@
+from sklearn.utils import deprecated
+
+from mapie.conformity_scores.sets.utils import (
+    get_true_label_position as get_true_label_position_new_path,
+)
+
+
+@deprecated(
+    "WARNING: Deprecated path to import get_true_label_position. "
+    "Please prefer the new path: "
+    "[from mapie.conformity_scores.sets.utils import get_true_label_position]."
+)
+def get_true_label_position(*args, **kwargs):
+    return get_true_label_position_new_path(*args, **kwargs)
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 2f4b0ad72..812cc5abe 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -268,3 +268,15 @@ def get_estimation_distribution(
                 return np.add(y_pred, conformity_scores) + 1
 
         DummyConformityScore()
+
+
+def test_warning_when_import_from_old_get_true_label_position():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.conformity_scores.residual_conformity_scores"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.conformity_scores.utils_classification_conformity_scores\
+              import get_true_label_position
+        get_true_label_position(np.array([[0.1, 0.2, 0.7]]), np.array([2]))

From 0aa8b3222d071eb6be58a03012fd2969a63cd359 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 14:41:43 +0200
Subject: [PATCH 356/424] ADD: re-add old import path for EnsembleRegressor

---
 mapie/estimator/estimator.py | 12 ++++++++++++
 mapie/tests/test_common.py   | 20 ++++++++++++++++++++
 2 files changed, 32 insertions(+)
 create mode 100644 mapie/estimator/estimator.py

diff --git a/mapie/estimator/estimator.py b/mapie/estimator/estimator.py
new file mode 100644
index 000000000..c4ee7c973
--- /dev/null
+++ b/mapie/estimator/estimator.py
@@ -0,0 +1,12 @@
+from sklearn.utils import deprecated
+
+from mapie.estimator.regressor import EnsembleRegressor as NewEnsembleRegressor
+
+
+@deprecated(
+    "WARNING: Deprecated path to import EnsembleRegressor. "
+    "Please prefer the new path: "
+    "[from mapie.estimator.regressor import EnsembleRegressor]."
+)
+class EnsembleRegressor(NewEnsembleRegressor):
+    pass
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 812cc5abe..353cab7ed 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -280,3 +280,23 @@ def test_warning_when_import_from_old_get_true_label_position():
         from mapie.conformity_scores.utils_classification_conformity_scores\
               import get_true_label_position
         get_true_label_position(np.array([[0.1, 0.2, 0.7]]), np.array([2]))
+
+
+def test_warning_when_import_from_estimator():
+    """Check that a DepreciationWarning is raised when importing from
+    mapie.estimator.estimator"""
+
+    with pytest.warns(
+        FutureWarning, match=r".*WARNING: Deprecated path to import.*"
+    ):
+        from mapie.estimator.estimator import EnsembleRegressor
+        EnsembleRegressor(
+            estimator=LinearRegression(),
+            method="naive",
+            cv=3,
+            agg_function="mean",
+            n_jobs=1,
+            random_state=0,
+            test_size=0.2,
+            verbose=0,
+        )

From d870bb8d27ff6940e91290837e1f195eb4cbb652 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 15:22:43 +0200
Subject: [PATCH 357/424] FIX: coverare issue by changing inheritance

---
 mapie/conformity_scores/conformity_scores.py | 267 +------------------
 mapie/tests/test_common.py                   |  20 +-
 2 files changed, 26 insertions(+), 261 deletions(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index 904d77db6..dbf426750 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -1,12 +1,8 @@
 from abc import ABCMeta, abstractmethod
-from typing import Tuple
 
-import numpy as np
 from sklearn.utils import deprecated
 
-from mapie.conformity_scores.interface import BaseConformityScore
-from mapie.estimator.regressor import EnsembleRegressor
-from mapie._compatibility import np_nanquantile
+from mapie.conformity_scores.regression import BaseConformityScore
 from mapie._machine_precision import EPSILON
 from mapie._typing import NDArray
 
@@ -84,6 +80,7 @@ def get_signed_conformity_scores(
             Signed conformity scores.
         """
 
+    @abstractmethod
     def get_conformity_scores(
         self,
         y: NDArray,
@@ -91,7 +88,11 @@ def get_conformity_scores(
         **kwargs
     ) -> NDArray:
         """
-        Get the conformity score considering the symmetrical property if so.
+        Placeholder for ``get_conformity_scores``.
+        Subclasses should implement this method!
+
+        Compute the sample conformity scores given the predicted and
+        observed targets.
 
         Parameters
         ----------
@@ -106,63 +107,6 @@ def get_conformity_scores(
         NDArray of shape (n_samples,)
             Conformity scores.
         """
-        conformity_scores = \
-            self.get_signed_conformity_scores(y, y_pred, **kwargs)
-        if self.consistency_check:
-            self.check_consistency(y, y_pred, conformity_scores, **kwargs)
-        if self.sym:
-            conformity_scores = np.abs(conformity_scores)
-        return conformity_scores
-
-    def check_consistency(
-        self,
-        y: NDArray,
-        y_pred: NDArray,
-        conformity_scores: NDArray,
-        **kwargs
-    ) -> None:
-        """
-        Check consistency between the following methods:
-        ``get_estimation_distribution`` and ``get_signed_conformity_scores``
-
-        The following equality should be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
-
-        Parameters
-        ----------
-        y: NDArray of shape (n_samples,)
-            Observed target values.
-
-        y_pred: NDArray of shape (n_samples,)
-            Predicted target values.
-
-        conformity_scores: NDArray of shape (n_samples,)
-            Conformity scores.
-
-        Raises
-        ------
-        ValueError
-            If the two methods are not consistent.
-        """
-        score_distribution = self.get_estimation_distribution(
-            y_pred, conformity_scores, **kwargs
-        )
-        abs_conformity_scores = np.abs(np.subtract(score_distribution, y))
-        max_conf_score = np.max(abs_conformity_scores)
-        if max_conf_score > self.eps:
-            raise ValueError(
-                "The two functions get_conformity_scores and "
-                "get_estimation_distribution of the BaseRegressionScore class "
-                "are not consistent. "
-                "The following equation must be verified: "
-                "self.get_estimation_distribution(y_pred, "
-                "self.get_conformity_scores(y, y_pred)) == y. "
-                f"The maximum conformity score is {max_conf_score}. "
-                "The eps attribute may need to be increased if you are "
-                "sure that the two methods are consistent."
-            )
 
     @abstractmethod
     def get_estimation_distribution(
@@ -192,199 +136,7 @@ def get_estimation_distribution(
             Observed values.
         """
 
-    @staticmethod
-    def _beta_optimize(
-        alpha_np: NDArray,
-        upper_bounds: NDArray,
-        lower_bounds: NDArray,
-    ) -> NDArray:
-        """
-        Minimize the width of the PIs, for a given difference of quantiles.
-
-        Parameters
-        ----------
-        alpha_np: NDArray
-            The quantiles to compute.
-
-        upper_bounds: NDArray of shape (n_samples,)
-            The array of upper values.
-
-        lower_bounds: NDArray of shape (n_samples,)
-            The array of lower values.
-
-        Returns
-        -------
-        NDArray of shape (n_samples,)
-            Array of betas minimizing the differences
-            ``(1-alpha+beta)-quantile - beta-quantile``.
-        """
-        beta_np = np.full(
-            shape=(len(lower_bounds), len(alpha_np)),
-            fill_value=np.nan,
-            dtype=float,
-        )
-
-        for ind_alpha, _alpha in enumerate(alpha_np):
-            betas = np.linspace(
-                _alpha / (len(lower_bounds) + 1),
-                _alpha,
-                num=len(lower_bounds),
-                endpoint=True,
-            )
-            one_alpha_beta = np_nanquantile(
-                upper_bounds.astype(float),
-                1 - _alpha + betas,
-                axis=1,
-                method="higher",
-            )
-            beta = np_nanquantile(
-                lower_bounds.astype(float),
-                betas,
-                axis=1,
-                method="lower",
-            )
-            beta_np[:, ind_alpha] = betas[
-                np.argmin(one_alpha_beta - beta, axis=0)
-            ]
-
-        return beta_np
-
-    def get_bounds(
-        self,
-        X: NDArray,
-        alpha_np: NDArray,
-        estimator: EnsembleRegressor,
-        conformity_scores: NDArray,
-        ensemble: bool = False,
-        method: str = 'base',
-        optimize_beta: bool = False,
-        allow_infinite_bounds: bool = False
-    ) -> Tuple[NDArray, NDArray, NDArray]:
-        """
-        Compute bounds of the prediction intervals from the observed values,
-        the estimator of type ``EnsembleRegressor`` and the conformity scores.
-
-        Parameters
-        ----------
-        X: NDArray of shape (n_samples, n_features)
-            Observed feature values.
-
-        alpha_np: NDArray of shape (n_alpha,)
-            NDArray of floats between ``0`` and ``1``, represents the
-            uncertainty of the confidence interval.
-
-        estimator: EnsembleRegressor
-            Estimator that is fitted to predict y from X.
-
-        conformity_scores: NDArray of shape (n_samples,)
-            Conformity scores.
-
-        ensemble: bool
-            Boolean determining whether the predictions are ensembled or not.
-
-            By default ``False``.
-
-        method: str
-            Method to choose for prediction interval estimates.
-            The ``"plus"`` method implies that the quantile is calculated
-            after estimating the bounds, whereas the other methods
-            (among the ``"naive"``, ``"base"`` or ``"minmax"`` methods,
-            for example) do the opposite.
-
-            By default ``base``.
-
-        optimize_beta: bool
-            Whether to optimize the PIs' width or not.
-
-            By default ``False``.
-
-        allow_infinite_bounds: bool
-            Allow infinite prediction intervals to be produced.
-
-            By default ``False``.
-
-        Returns
-        -------
-        Tuple[NDArray, NDArray, NDArray]
-            - The predictions itself. (y_pred) of shape (n_samples,).
-            - The lower bounds of the prediction intervals of shape
-            (n_samples, n_alpha).
-            - The upper bounds of the prediction intervals of shape
-            (n_samples, n_alpha).
-
-        Raises
-        ------
-        ValueError
-            If beta optimisation with symmetrical conformity score function.
-        """
-        if self.sym and optimize_beta:
-            raise ValueError(
-                "Beta optimisation cannot be used with " +
-                "symmetrical conformity score function."
-            )
-
-        y_pred, y_pred_low, y_pred_up = estimator.predict(X, ensemble)
-        signed = -1 if self.sym else 1
-
-        if optimize_beta:
-            beta_np = self._beta_optimize(
-                alpha_np,
-                conformity_scores.reshape(1, -1),
-                conformity_scores.reshape(1, -1),
-            )
-        else:
-            beta_np = alpha_np / 2
-
-        if method == "plus":
-            alpha_low = alpha_np if self.sym else beta_np
-            alpha_up = 1 - alpha_np if self.sym else 1 - alpha_np + beta_np
-
-            conformity_scores_low = self.get_estimation_distribution(
-                y_pred_low, signed * conformity_scores, X=X
-            )
-            conformity_scores_up = self.get_estimation_distribution(
-                y_pred_up, conformity_scores, X=X
-            )
-            bound_low = self.get_quantile(
-                conformity_scores_low, alpha_low, axis=1, reversed=True,
-                unbounded=allow_infinite_bounds
-            )
-            bound_up = self.get_quantile(
-                conformity_scores_up, alpha_up, axis=1,
-                unbounded=allow_infinite_bounds
-            )
-
-        else:
-            if self.sym:
-                alpha_ref = 1 - alpha_np
-                quantile_ref = self.get_quantile(
-                    conformity_scores[..., np.newaxis], alpha_ref, axis=0
-                )
-                quantile_low, quantile_up = -quantile_ref, quantile_ref
-
-            else:
-                alpha_low, alpha_up = beta_np, 1 - alpha_np + beta_np
-
-                quantile_low = self.get_quantile(
-                    conformity_scores[..., np.newaxis],
-                    alpha_low, axis=0, reversed=True,
-                    unbounded=allow_infinite_bounds
-                )
-                quantile_up = self.get_quantile(
-                    conformity_scores[..., np.newaxis],
-                    alpha_up, axis=0,
-                    unbounded=allow_infinite_bounds
-                )
-
-            bound_low = self.get_estimation_distribution(
-                y_pred_low, quantile_low, X=X
-            )
-            bound_up = self.get_estimation_distribution(
-                y_pred_up, quantile_up, X=X
-            )
-
-        return y_pred, bound_low, bound_up
-
+    @abstractmethod
     def predict_set(
         self,
         X: NDArray,
@@ -408,7 +160,6 @@ def predict_set(
 
         Returns:
         --------
-        The output structure depend on the ``get_bounds`` method.
+        The output structure depend on the subclass.
             The prediction sets for each sample and each alpha level.
         """
-        return self.get_bounds(X=X, alpha_np=alpha_np, **kwargs)
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 353cab7ed..8707eb59c 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -255,7 +255,7 @@ def __init__(self) -> None:
             def get_signed_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                return np.subtract(y, y_pred)
+                pass
 
             def get_estimation_distribution(
                 self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
@@ -265,9 +265,23 @@ def get_estimation_distribution(
                 conformity scores to make the estimated distribution
                 inconsistent with the conformity score.
                 """
-                return np.add(y_pred, conformity_scores) + 1
+                pass
 
-        DummyConformityScore()
+            def get_conformity_scores(
+                self, y: ArrayLike, y_pred: ArrayLike, **kwargs
+            ) -> NDArray:
+                pass
+
+            def predict_set(
+                self, y_pred: ArrayLike, alpha: float, **kwargs
+            ) -> Tuple[NDArray, NDArray]:
+                pass
+
+        dcs = DummyConformityScore()
+        dcs.get_signed_conformity_scores(y_toy, y_toy)
+        dcs.get_estimation_distribution(y_toy, y_toy)
+        dcs.get_conformity_scores(y_toy, y_toy)
+        dcs.predict_set(y_toy, 0.5)
 
 
 def test_warning_when_import_from_old_get_true_label_position():

From 27515212b78fc2eb1683a7a905903aa13af8c08b Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 15:29:10 +0200
Subject: [PATCH 358/424] FIX: typing

---
 mapie/tests/test_common.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 8707eb59c..450b1fda4 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -254,12 +254,12 @@ def __init__(self) -> None:
 
             def get_signed_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
-            ) -> NDArray:
+            ) -> None:
                 pass
 
             def get_estimation_distribution(
                 self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
-            ) -> NDArray:
+            ) -> None:
                 """
                 A positive constant is added to the sum between predictions and
                 conformity scores to make the estimated distribution
@@ -269,12 +269,12 @@ def get_estimation_distribution(
 
             def get_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
-            ) -> NDArray:
+            ) -> None:
                 pass
 
             def predict_set(
-                self, y_pred: ArrayLike, alpha: float, **kwargs
-            ) -> Tuple[NDArray, NDArray]:
+                self, X: NDArray, alpha_np: NDArray, **kwargs
+            ) -> None:
                 pass
 
         dcs = DummyConformityScore()

From 3ca10b21cf3acca0a8d1274d869994a6bcf22009 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 15:33:34 +0200
Subject: [PATCH 359/424] FIX: typing

---
 mapie/tests/test_common.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 450b1fda4..ef139b1fe 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -254,12 +254,12 @@ def __init__(self) -> None:
 
             def get_signed_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
-            ) -> None:
+            ) -> NDArray:
                 pass
 
             def get_estimation_distribution(
                 self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
-            ) -> None:
+            ) -> NDArray:
                 """
                 A positive constant is added to the sum between predictions and
                 conformity scores to make the estimated distribution
@@ -269,12 +269,12 @@ def get_estimation_distribution(
 
             def get_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
-            ) -> None:
+            ) -> NDArray:
                 pass
 
             def predict_set(
                 self, X: NDArray, alpha_np: NDArray, **kwargs
-            ) -> None:
+            ) -> NDArray:
                 pass
 
         dcs = DummyConformityScore()

From 195dae35143339b22304943219271550027df354 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 15:37:11 +0200
Subject: [PATCH 360/424] FIX: linting

---
 mapie/tests/test_common.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index ef139b1fe..1253f239a 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -255,7 +255,7 @@ def __init__(self) -> None:
             def get_signed_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                pass
+                return np.zeros(y.shape[0])
 
             def get_estimation_distribution(
                 self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
@@ -265,17 +265,17 @@ def get_estimation_distribution(
                 conformity scores to make the estimated distribution
                 inconsistent with the conformity score.
                 """
-                pass
+                return np.zeros(y_pred.shape[0])
 
             def get_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                pass
+                return np.zeros(y.shape[0])
 
             def predict_set(
                 self, X: NDArray, alpha_np: NDArray, **kwargs
             ) -> NDArray:
-                pass
+                return np.zeros(X.shape[0])
 
         dcs = DummyConformityScore()
         dcs.get_signed_conformity_scores(y_toy, y_toy)

From e333152d1d8af52aaf488944d03ca1f1e20742b0 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 15:39:50 +0200
Subject: [PATCH 361/424] FIX: linting again and again

---
 mapie/tests/test_common.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 1253f239a..5a3bfc25e 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -255,7 +255,7 @@ def __init__(self) -> None:
             def get_signed_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                return np.zeros(y.shape[0])
+                return np.zeros(len(y))
 
             def get_estimation_distribution(
                 self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
@@ -265,17 +265,17 @@ def get_estimation_distribution(
                 conformity scores to make the estimated distribution
                 inconsistent with the conformity score.
                 """
-                return np.zeros(y_pred.shape[0])
+                return np.zeros(len(y_pred))
 
             def get_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                return np.zeros(y.shape[0])
+                return np.zeros(len(y_pred))
 
             def predict_set(
                 self, X: NDArray, alpha_np: NDArray, **kwargs
             ) -> NDArray:
-                return np.zeros(X.shape[0])
+                return np.zeros(len(X))
 
         dcs = DummyConformityScore()
         dcs.get_signed_conformity_scores(y_toy, y_toy)

From b6b8392f26fb02781b35cf62ffb9d8460ed00700 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 15:43:30 +0200
Subject: [PATCH 362/424] FIX: linting one more time

---
 mapie/tests/test_common.py | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 5a3bfc25e..16a701c15 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -255,7 +255,7 @@ def __init__(self) -> None:
             def get_signed_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                return np.zeros(len(y))
+                return np.array([])
 
             def get_estimation_distribution(
                 self, y_pred: ArrayLike, conformity_scores: ArrayLike, **kwargs
@@ -265,17 +265,17 @@ def get_estimation_distribution(
                 conformity scores to make the estimated distribution
                 inconsistent with the conformity score.
                 """
-                return np.zeros(len(y_pred))
+                return np.array([])
 
             def get_conformity_scores(
                 self, y: ArrayLike, y_pred: ArrayLike, **kwargs
             ) -> NDArray:
-                return np.zeros(len(y_pred))
+                return np.array([])
 
             def predict_set(
                 self, X: NDArray, alpha_np: NDArray, **kwargs
             ) -> NDArray:
-                return np.zeros(len(X))
+                return np.array([])
 
         dcs = DummyConformityScore()
         dcs.get_signed_conformity_scores(y_toy, y_toy)

From ae03b4f1a94ee94b8465029f81de3e5f4abdc229 Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Wed, 11 Sep 2024 18:25:05 +0200
Subject: [PATCH 363/424] FIX: typoe replace ConformityScore by
 BaseRegressionScore

---
 mapie/conformity_scores/conformity_scores.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/mapie/conformity_scores/conformity_scores.py b/mapie/conformity_scores/conformity_scores.py
index dbf426750..e6953a81d 100644
--- a/mapie/conformity_scores/conformity_scores.py
+++ b/mapie/conformity_scores/conformity_scores.py
@@ -10,7 +10,7 @@
 @deprecated(
     "WARNING: Deprecated path to import ConformityScore. "
     "Please prefer the new path: "
-    "[from mapie.conformity_scores import ConformityScore]."
+    "[from mapie.conformity_scores import BaseRegressionScore]."
 )
 class ConformityScore(BaseConformityScore, metaclass=ABCMeta):
     """

From ba2546539d9a43371f11a46e905170888e2a2b5d Mon Sep 17 00:00:00 2001
From: vincentblot28 <vincentblot28@gmail.com>
Date: Fri, 13 Sep 2024 09:55:29 +0200
Subject: [PATCH 364/424] =?UTF-8?q?Bump=20version:=200.9.0=20=E2=86=92=200?=
 =?UTF-8?q?.9.1?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 CITATION.cff      | 2 +-
 doc/conf.py       | 2 +-
 mapie/_version.py | 2 +-
 setup.py          | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 0936e1d34..dcd13cc35 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.0
+current_version = 0.9.1
 commit = True
 tag = True
 
diff --git a/CITATION.cff b/CITATION.cff
index 4eab6cd4e..191caacd4 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Thibault"
   orcid: "https://orcid.org/0000-0000-0000-0000"
 title: "MAPIE - Model Agnostic Prediction Interval Estimator"
-version: 0.9.0
+version: 0.9.1
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
diff --git a/doc/conf.py b/doc/conf.py
index 58989f4c8..6a8e2d9b6 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -88,7 +88,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.9.0"
+version = "0.9.1"
 # The full version, including alpha/beta/rc tags.
 release = version
 
diff --git a/mapie/_version.py b/mapie/_version.py
index 3e2f46a3a..d69d16e98 100644
--- a/mapie/_version.py
+++ b/mapie/_version.py
@@ -1 +1 @@
-__version__ = "0.9.0"
+__version__ = "0.9.1"
diff --git a/setup.py b/setup.py
index 94571a0a6..c200663c5 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "MAPIE"
-VERSION = "0.9.0"
+VERSION = "0.9.1"
 DESCRIPTION = (
     "A scikit-learn-compatible module "
     "for estimating prediction intervals."

From 64b547c974de48f58a2109253f57a41af8b5b5c4 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 17 Sep 2024 14:48:49 +0200
Subject: [PATCH 365/424] chore: bump wheel version to avoid known
 vulnerabilities

---
 HISTORY.rst          | 7 +++++--
 environment.dev.yml  | 2 +-
 requirements.dev.txt | 2 +-
 3 files changed, 7 insertions(+), 4 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 26f88d79d..f57c47dec 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,6 +2,11 @@
 History
 =======
 
+0.9.x (2024-xx-xx)
+------------------
+
+* Bump wheel version to avoid known security vulnerabilities
+
 0.9.1 (2024-09-13)
 ------------------
 
@@ -9,8 +14,6 @@ History
 * Update gitignore by including the documentation folder generated for Mondrian
 * Fix (partially) the set-up with pip instead of conda for new contributors
 
-
-
 0.9.0 (2024-09-03)
 ------------------
 
diff --git a/environment.dev.yml b/environment.dev.yml
index 3548e9b53..033ba24c2 100644
--- a/environment.dev.yml
+++ b/environment.dev.yml
@@ -19,4 +19,4 @@ dependencies:
     - sphinx-gallery=0.10.1
     - sphinx_rtd_theme=1.0.0
     - twine=3.7.1
-    - wheel=0.37.0
+    - wheel=0.38.1
diff --git a/requirements.dev.txt b/requirements.dev.txt
index ed23426e9..4174c5608 100644
--- a/requirements.dev.txt
+++ b/requirements.dev.txt
@@ -13,4 +13,4 @@ sphinx==4.3.2
 sphinx-gallery==0.10.1
 sphinx_rtd_theme==1.0.0
 twine==3.7.1
-wheel==0.37.0
\ No newline at end of file
+wheel==0.38.1
\ No newline at end of file

From 6e620bc90b257d7970989ce18b469a6acb71c58f Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 8 Oct 2024 08:45:20 +0200
Subject: [PATCH 366/424] feat: fully detailed NaiveConformalRegressor class

---
 public_api_v1_regression.py | 51 +++++++++++++++++++++++++++++++++++++
 1 file changed, 51 insertions(+)
 create mode 100644 public_api_v1_regression.py

diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
new file mode 100644
index 000000000..0e90eefd2
--- /dev/null
+++ b/public_api_v1_regression.py
@@ -0,0 +1,51 @@
+from typing import Optional, Union, Self, Iterable, Tuple, Any, List
+
+import numpy as np
+from sklearn.linear_model import LinearRegression
+
+from mapie.regression import MapieRegressor
+from numpy.typing import ArrayLike, NDArray
+from sklearn.base import RegressorMixin
+from sklearn.model_selection import BaseCrossValidator
+
+from mapie.conformity_scores import BaseRegressionScore, AbsoluteConformityScore
+
+
+class NaiveConformalRegressor:
+    def __init__(
+        self,
+        estimator: RegressorMixin = LinearRegression,  # None doesn't exist anymore
+        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
+        alpha: Union[float, List[float]] = 0.9,  # Should we set this default? Actually an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
+        n_jobs: Optional[int] = None,
+        verbose: int = 0,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+    ) -> None:
+        pass
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        fit_params: dict,  # -> In __init__ ?
+        predict_params: dict,  # -> In __init__ ?
+    ) -> Self:
+        pass
+
+    def predict(
+        self,
+        X: ArrayLike,
+        optimize_beta: bool = False,  # Don't understand that one
+        allow_infinite_bounds: bool = False,
+        # **predict_params  -> To remove: redundant with predict_params in .fit()
+    ) -> Tuple[NDArray, NDArray]:
+        """
+        Returns
+        -------
+        Tuple[NDArray, NDArray]:
+          - the first element contains the point predictions, with shape (n_samples,)
+          - the second element contains the prediction intervals,
+            with shape (n_samples, 2) if alpha is a float, or (n_samples, 2, n_alpha) if alpha is an array of floats
+        """
+        pass

From c6a4c4b46233bf81bf1dfca69b745fde3e6681e5 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 8 Oct 2024 17:12:20 +0200
Subject: [PATCH 367/424] feat: add fully detailed SplitConformalRegressor,
 CrossConformalRegressor and ConformalizedQuantileRegressor classes, few fixes
 in NaiveConformalRegressor

---
 public_api_v1_regression.py | 121 +++++++++++++++++++++++++++++++++---
 1 file changed, 113 insertions(+), 8 deletions(-)

diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
index 0e90eefd2..0f475ef95 100644
--- a/public_api_v1_regression.py
+++ b/public_api_v1_regression.py
@@ -1,9 +1,9 @@
-from typing import Optional, Union, Self, Iterable, Tuple, Any, List
+from typing import Optional, Union, Self, Iterable, Tuple, List
 
 import numpy as np
-from sklearn.linear_model import LinearRegression
+from sklearn.linear_model import LinearRegression, QuantileRegressor
 
-from mapie.regression import MapieRegressor
+from mapie.regression import MapieQuantileRegressor
 from numpy.typing import ArrayLike, NDArray
 from sklearn.base import RegressorMixin
 from sklearn.model_selection import BaseCrossValidator
@@ -14,9 +14,9 @@
 class NaiveConformalRegressor:
     def __init__(
         self,
-        estimator: RegressorMixin = LinearRegression,  # None doesn't exist anymore
+        estimator: RegressorMixin = LinearRegression,  # Improved 'None' default
         conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
-        alpha: Union[float, List[float]] = 0.9,  # Should we set this default? Actually an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
+        alpha: Union[float, List[float]] = 0.1,  # Should we set this default? I think an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
         n_jobs: Optional[int] = None,
         verbose: int = 0,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
@@ -28,8 +28,8 @@ def fit(
         X: ArrayLike,
         y: ArrayLike,
         # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        fit_params: dict,  # -> In __init__ ?
-        predict_params: dict,  # -> In __init__ ?
+        fit_params: Optional[dict] = None,  # -> In __init__ ?
+        predict_params: Optional[dict] = None,  # -> In __init__ ?
     ) -> Self:
         pass
 
@@ -38,7 +38,7 @@ def predict(
         X: ArrayLike,
         optimize_beta: bool = False,  # Don't understand that one
         allow_infinite_bounds: bool = False,
-        # **predict_params  -> To remove: redundant with predict_params in .fit()
+        # **predict_params  -> Is this redundant with predict_params in .fit() ?
     ) -> Tuple[NDArray, NDArray]:
         """
         Returns
@@ -49,3 +49,108 @@ def predict(
             with shape (n_samples, 2) if alpha is a float, or (n_samples, 2, n_alpha) if alpha is an array of floats
         """
         pass
+
+
+class SplitConformalRegressor:
+    def __init__(
+        self,
+        estimator: RegressorMixin = LinearRegression,  # Improved 'None' default
+        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
+        alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
+        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in predict) and BaseCrossValidator (restricted to splitters only)
+        n_jobs: Optional[int] = None,
+        verbose: int = 0,
+        random_state: Optional[Union[int, np.random.RandomState]] = None
+    ) -> None:
+        pass
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        test_size: Union[int, float] = 0.1,  # Moved from __init__, improved 'None' default. Invalid if split_method != 'simple'
+        # Future API: X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
+        # Future API: y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
+        fit_params: Optional[dict] = None,  # -> In __init__ ?
+        predict_params: Optional[dict] = None,  # -> In __init__ ?
+    ) -> Self:
+        pass
+
+    # predict signature are the same as NaiveConformalRegressor
+
+
+class CrossConformalRegressor:
+    def __init__(
+        self,
+        estimator: RegressorMixin = LinearRegression,  # Improved 'None' default
+        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
+        alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
+        method: str = "plus",  # 'base' | 'plus' | 'minmax'
+        cross_val: Union[int, BaseCrossValidator] = None,  # Improved 'None' default, removed str option, update name. Note that we lose the prefit option, that was I think useless in a cross-validation context
+        # agg_function -> moved to predict method
+        n_jobs: Optional[int] = None,
+        verbose: int = 0,
+        random_state: Optional[Union[int, np.random.RandomState]] = None
+    ) -> None:
+        pass
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        # groups: Optional[ArrayLike] = None,  ->  To specify directly in the cross_val parameter
+        fit_params: Optional[dict] = None,  # -> In __init__ ?
+        predict_params: Optional[dict] = None,  # -> In __init__ ?
+    ) -> Self:
+        pass
+
+    def predict(
+        self,
+        X: ArrayLike,
+        # ensemble: bool = False, -> replaced by aggregation_strategy
+        aggregation_strategy: Optional[str] = None,  # If None, the paper implementation is used
+        optimize_beta: bool = False,  # Don't understand that one
+        allow_infinite_bounds: bool = False,
+        # **predict_params  -> To remove: redundant with predict_params in .fit()
+    ) -> Tuple[NDArray, NDArray]:  # See docstring in NaiveConformalRegressor for the return type details
+        pass
+
+
+class ConformalizedQuantileRegressor:
+    def __init__(
+        self,
+        estimator: RegressorMixin = QuantileRegressor,  # Improved 'None' default
+        alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
+        split_method: str = "simple",  # 'simple' (provide test_size in .fit), 'prefit' or 'manual'. Future API: BaseCrossValidator (restricted to splitters only)
+        random_state: Optional[Union[int, np.random.RandomState]] = None,  # Moved from .fit
+        # Future API : n_jobs: Optional[int] = None,
+        # Future API : verbose: int = 0,
+    ) -> None:
+        pass
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        # groups: Optional[ArrayLike] = None,  ->  To specify directly in the cross_val parameter
+        # shuffle: Optional[bool] = True, -> To implement in a future version (using the BaseCrossValidator split_method). In that case we would lose that feature in the v1.0.0
+        # stratify: Optional[ArrayLike] = None, -> same comment as shuffle
+        test_size: Union[int, float] = 0.1,  # Renamed from 'calib_size'
+        X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
+        y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
+        fit_params: Optional[dict] = None,  # -> In __init__ ?
+        predict_params: Optional[dict] = None,  # -> In __init__ ?
+    ) -> Self:
+        pass
+
+    def predict(
+        self,
+        X: ArrayLike,
+        optimize_beta: bool = False,
+        allow_infinite_bounds: bool = False,
+        symmetry: bool = True,  # Corrected typing
+    ) -> Tuple[NDArray, NDArray]:  # See docstring in NaiveConformalRegressor for the return type details
+        pass

From 70e017b82257368cf3d97178895dbf1d8e1a9a83 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 9 Oct 2024 11:26:56 +0200
Subject: [PATCH 368/424] feat: split .predict method in .predict and
 .predict_set, add "QUESTION" tag in comment to identify important points to
 figure out, add few corrections and comments

---
 public_api_v1_regression.py | 94 +++++++++++++++++++++++--------------
 1 file changed, 58 insertions(+), 36 deletions(-)

diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
index 0f475ef95..febf53540 100644
--- a/public_api_v1_regression.py
+++ b/public_api_v1_regression.py
@@ -1,9 +1,8 @@
-from typing import Optional, Union, Self, Iterable, Tuple, List
+from typing import Optional, Union, Self, Tuple, List
 
 import numpy as np
 from sklearn.linear_model import LinearRegression, QuantileRegressor
 
-from mapie.regression import MapieQuantileRegressor
 from numpy.typing import ArrayLike, NDArray
 from sklearn.base import RegressorMixin
 from sklearn.model_selection import BaseCrossValidator
@@ -14,9 +13,9 @@
 class NaiveConformalRegressor:
     def __init__(
         self,
-        estimator: RegressorMixin = LinearRegression,  # Improved 'None' default
-        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
-        alpha: Union[float, List[float]] = 0.1,  # Should we set this default? I think an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
+        estimator: RegressorMixin = LinearRegression(),  # Improved 'None' default
+        conformity_score: Union[str, BaseRegressionScore] = "absolute",  # Add string option
+        alpha: Union[float, List[float]] = 0.1,  # QUESTION: Should we set this default, or should we keep None? I think an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
         n_jobs: Optional[int] = None,
         verbose: int = 0,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
@@ -28,25 +27,36 @@ def fit(
         X: ArrayLike,
         y: ArrayLike,
         # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        fit_params: Optional[dict] = None,  # -> In __init__ ?
-        predict_params: Optional[dict] = None,  # -> In __init__ ?
+        fit_params: Optional[dict] = None,  # Ex for LGBMClassifier: {'categorical_feature': 'auto'}
+        predict_params: Optional[dict] = None,
     ) -> Self:
         pass
 
-    def predict(
+    def predict_set(
         self,
         X: ArrayLike,
-        optimize_beta: bool = False,  # Don't understand that one
+        optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
+        # **predict_params  -> QUESTION: Is this redundant with predict_params in .fit() ?
+    ) -> NDArray:
+        """
+        Returns
+        -------
+        An array containing the prediction intervals,
+        of shape (n_samples, 2) if alpha is a float,
+        or (n_samples, 2, n_alpha) if alpha is an array of floats
+        """
+        pass
+
+    def predict(
+        self,
+        X: ArrayLike,
         # **predict_params  -> Is this redundant with predict_params in .fit() ?
-    ) -> Tuple[NDArray, NDArray]:
+    ) -> NDArray:
         """
         Returns
         -------
-        Tuple[NDArray, NDArray]:
-          - the first element contains the point predictions, with shape (n_samples,)
-          - the second element contains the prediction intervals,
-            with shape (n_samples, 2) if alpha is a float, or (n_samples, 2, n_alpha) if alpha is an array of floats
+        An array containing the point predictions, with shape (n_samples,)
         """
         pass
 
@@ -54,13 +64,14 @@ def predict(
 class SplitConformalRegressor:
     def __init__(
         self,
-        estimator: RegressorMixin = LinearRegression,  # Improved 'None' default
-        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
+        estimator: RegressorMixin = LinearRegression(),  # Improved 'None' default
+        conformity_score: Union[str, BaseRegressionScore] = "absolute",  # Add string option
         alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
-        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in predict) and BaseCrossValidator (restricted to splitters only)
+        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in fit) and BaseCrossValidator (restricted to splitters only)
         n_jobs: Optional[int] = None,
         verbose: int = 0,
         random_state: Optional[Union[int, np.random.RandomState]] = None
+        # groups -> not used in the current implementation (that is using ShuffleSplit)
     ) -> None:
         pass
 
@@ -72,22 +83,22 @@ def fit(
         test_size: Union[int, float] = 0.1,  # Moved from __init__, improved 'None' default. Invalid if split_method != 'simple'
         # Future API: X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
         # Future API: y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        fit_params: Optional[dict] = None,  # -> In __init__ ?
-        predict_params: Optional[dict] = None,  # -> In __init__ ?
+        fit_params: Optional[dict] = None,
+        predict_params: Optional[dict] = None,
     ) -> Self:
         pass
 
-    # predict signature are the same as NaiveConformalRegressor
+    # predict and predict_set signatures are the same as NaiveConformalRegressor
 
 
 class CrossConformalRegressor:
     def __init__(
         self,
-        estimator: RegressorMixin = LinearRegression,  # Improved 'None' default
-        conformity_score: BaseRegressionScore = AbsoluteConformityScore,  # Should we set this default?
+        estimator: RegressorMixin = LinearRegression(),  # Improved 'None' default
+        conformity_score: Union[str, BaseRegressionScore] = "absolute",  # Add string option
         alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
         method: str = "plus",  # 'base' | 'plus' | 'minmax'
-        cross_val: Union[int, BaseCrossValidator] = None,  # Improved 'None' default, removed str option, update name. Note that we lose the prefit option, that was I think useless in a cross-validation context
+        cross_val: Union[int, BaseCrossValidator] = 5,  # Improved 'None' default, removed str option, update name. Note that we lose the prefit option, that was I think useless in a cross-validation context QUESTION
         # agg_function -> moved to predict method
         n_jobs: Optional[int] = None,
         verbose: int = 0,
@@ -101,27 +112,36 @@ def fit(
         y: ArrayLike,
         # sample_weight: Optional[ArrayLike] = None, -> in fit_params
         # groups: Optional[ArrayLike] = None,  ->  To specify directly in the cross_val parameter
-        fit_params: Optional[dict] = None,  # -> In __init__ ?
-        predict_params: Optional[dict] = None,  # -> In __init__ ?
+        fit_params: Optional[dict] = None,
+        predict_params: Optional[dict] = None,
     ) -> Self:
         pass
 
-    def predict(
+    def predict_set(
         self,
         X: ArrayLike,
-        # ensemble: bool = False, -> replaced by aggregation_strategy
-        aggregation_strategy: Optional[str] = None,  # If None, the paper implementation is used
-        optimize_beta: bool = False,  # Don't understand that one
+        optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
         # **predict_params  -> To remove: redundant with predict_params in .fit()
-    ) -> Tuple[NDArray, NDArray]:  # See docstring in NaiveConformalRegressor for the return type details
+    ) -> NDArray:  # See docstring in NaiveConformalRegressor for the return type details
+        pass
+
+    def predict(
+        self,
+        # ensemble: bool = False, -> removed, see aggregation_method
+        aggregation_method: Optional[str] = None,  # None: no aggregation, 'mean', 'median'
+    ) -> NDArray:
         pass
 
 
+class JackknifeAfterBootstrapRegressor:
+    pass  # TODO
+
+
 class ConformalizedQuantileRegressor:
     def __init__(
         self,
-        estimator: RegressorMixin = QuantileRegressor,  # Improved 'None' default
+        estimator: RegressorMixin = QuantileRegressor(),  # Improved 'None' default
         alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
         split_method: str = "simple",  # 'simple' (provide test_size in .fit), 'prefit' or 'manual'. Future API: BaseCrossValidator (restricted to splitters only)
         random_state: Optional[Union[int, np.random.RandomState]] = None,  # Moved from .fit
@@ -136,21 +156,23 @@ def fit(
         y: ArrayLike,
         # sample_weight: Optional[ArrayLike] = None, -> in fit_params
         # groups: Optional[ArrayLike] = None,  ->  To specify directly in the cross_val parameter
-        # shuffle: Optional[bool] = True, -> To implement in a future version (using the BaseCrossValidator split_method). In that case we would lose that feature in the v1.0.0
+        # shuffle: Optional[bool] = True, -> To implement in a future version (using the BaseCrossValidator split_method). In that case we would lose that feature in the v1.0.0 QUESTION
         # stratify: Optional[ArrayLike] = None, -> same comment as shuffle
         test_size: Union[int, float] = 0.1,  # Renamed from 'calib_size'
         X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
         y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        fit_params: Optional[dict] = None,  # -> In __init__ ?
-        predict_params: Optional[dict] = None,  # -> In __init__ ?
+        fit_params: Optional[dict] = None,
+        predict_params: Optional[dict] = None,
     ) -> Self:
         pass
 
-    def predict(
+    def predict_set(
         self,
         X: ArrayLike,
         optimize_beta: bool = False,
         allow_infinite_bounds: bool = False,
         symmetry: bool = True,  # Corrected typing
-    ) -> Tuple[NDArray, NDArray]:  # See docstring in NaiveConformalRegressor for the return type details
+    ) -> NDArray:
         pass
+
+    # predict signature is the same as NaiveConformalRegressor

From c9fcdc318ba96103894b38563ab9007faee6b41a Mon Sep 17 00:00:00 2001
From: sd29206 <hussein.jawad@external.stellantis.com>
Date: Wed, 9 Oct 2024 11:44:45 +0200
Subject: [PATCH 369/424] add classifier

---
 public_api_v1_classifier.py | 135 ++++++++++++++++++++++++++++++++++++
 1 file changed, 135 insertions(+)
 create mode 100644 public_api_v1_classifier.py

diff --git a/public_api_v1_classifier.py b/public_api_v1_classifier.py
new file mode 100644
index 000000000..495dc09c2
--- /dev/null
+++ b/public_api_v1_classifier.py
@@ -0,0 +1,135 @@
+from __future__ import annotations
+
+import warnings
+from typing import Any, Iterable, Optional, Tuple, Union, cast, List
+
+import numpy as np
+from sklearn.base import BaseEstimator, ClassifierMixin
+from sklearn.model_selection import BaseCrossValidator, BaseShuffleSplit
+from sklearn.preprocessing import LabelEncoder
+from sklearn.utils import check_random_state
+from sklearn.utils.validation import (_check_y, check_is_fitted, indexable)
+from sklearn.linear_model import LogisticRegression
+
+from mapie._typing import ArrayLike, NDArray
+from mapie.conformity_scores import BaseClassificationScore
+from mapie.conformity_scores.sets.raps import RAPSConformityScore
+from mapie.conformity_scores.sets.lac import LACConformityScore
+
+from mapie.conformity_scores.utils import (
+    check_depreciated_size_raps, check_classification_conformity_score,
+    check_target
+)
+from mapie.estimator.classifier import EnsembleClassifier
+from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
+                         check_estimator_classification, check_n_features_in,
+                         check_n_jobs, check_null_weight, check_predict_params,
+                         check_verbose)
+
+
+class SplitConformalClassifier:
+    
+    def __init__(   
+        self,
+        estimator: ClassifierMixin = LogisticRegression(),
+        conformity_score: Union[str, BaseClassificationScore] = "lac", # Can be a string or a BaseClassificationScore object
+        alpha: Union[float, List[float]] = 0.1,
+        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in .fit) and BaseCrossValidator (restricted to splitters only)
+        n_jobs: Optional[int] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        verbose: int = 0,
+    ) -> None:
+
+        pass
+    
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        # groups: Optional[ArrayLike] = None,  # Removed, because it is not used in split conformal classifier
+        test_size: Union[int, float] = 0.1,  #  -> In __init__ ?
+        # Future API: X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
+        # Future API:   y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
+        fit_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'categorical_feature': 'auto'}
+        predict_params: Optional[dict] = None, # For example, LBGMClassifier :  {'pred_leaf': False}
+    ) -> SplitConformalClassifier:
+        
+        return self
+
+    def predict(self, 
+                X: ArrayLike) -> NDArray:
+
+        """
+        Return
+        ----- 
+        Return ponctual prediction similar to predict method of scikit-learn classifiers
+        Shape (n_samples,)
+        """
+
+    def predict_sets(self, 
+                     X: ArrayLike,
+                     conformoty_score_params: Optional[dict] = None, # Parameters specific to conformal method, For example: include_last_label
+                     ) -> NDArray:
+        
+        """
+        Return
+        -----
+        An array containing the prediction sets 
+        Shape (n_samples, n_classes) if alpha is float,
+        Shape (n_samples, n_classes, alpha) if alpha is a list of floats
+        """
+
+        pass
+
+class CrossConformalClassifier:
+    
+    def __init__(
+        self,
+        estimator: ClassifierMixin = LogisticRegression(),
+        conformity_score: Union[str, BaseClassificationScore] = 'lac',
+        cross_val : Union[BaseCrossValidator, str] = 5,
+        alpha: Union[float, List[float]] = 0.1,
+        n_jobs: Optional[int] = None,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        verbose: int = 0,
+
+    ) -> None:
+
+        pass 
+
+    def fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
+        # groups: Optional[ArrayLike] = None,  
+        fit_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'categorical_feature': 'auto'}
+        predict_params: Optional[dict] = None,  
+    ) -> CrossConformalClassifier:
+
+        pass
+
+    def predict(self, 
+                X: ArrayLike): # Parameters specific to conformal method, For example: include_last_label) -> ArrayLike:
+
+        """
+        Return 
+        ----- 
+
+        """
+        pass
+
+    def predict_sets(self,
+                     X: ArrayLike,
+                     agg_scores: Optional[str] = "mean", # how to aggregate the scores by the estimators on test data
+                     conformoty_score_params: Optional[dict] = None,): # Parameters specific to conformal method, For example: include_last_label) -> NDArray
+        
+        """
+        Return
+        -----
+        An array containing the prediction sets 
+        Shape (n_samples, n_classes) if alpha is float,
+        Shape (n_samples, n_classes, alpha) if alpha is a list of floats
+        """
+        
\ No newline at end of file

From 8e677a7443b3e054f8e9237bd40ed6c005327ba3 Mon Sep 17 00:00:00 2001
From: sd29206 <hussein.jawad@external.stellantis.com>
Date: Wed, 9 Oct 2024 11:45:45 +0200
Subject: [PATCH 370/424] update regression

---
 public_api_v1_regression.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
index febf53540..b8ad38f2e 100644
--- a/public_api_v1_regression.py
+++ b/public_api_v1_regression.py
@@ -175,4 +175,4 @@ def predict_set(
     ) -> NDArray:
         pass
 
-    # predict signature is the same as NaiveConformalRegressor
+    # predict signature is the same as NaiveConformalRegressor
\ No newline at end of file

From 0561556a0354c8b494c8ceff879f6df49aef938e Mon Sep 17 00:00:00 2001
From: sd29206 <hussein.jawad@external.stellantis.com>
Date: Wed, 9 Oct 2024 11:46:10 +0200
Subject: [PATCH 371/424] update reg

---
 public_api_v1_regression.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
index b8ad38f2e..febf53540 100644
--- a/public_api_v1_regression.py
+++ b/public_api_v1_regression.py
@@ -175,4 +175,4 @@ def predict_set(
     ) -> NDArray:
         pass
 
-    # predict signature is the same as NaiveConformalRegressor
\ No newline at end of file
+    # predict signature is the same as NaiveConformalRegressor

From 77b616e28a79b91d8030fe13c531610e9481b971 Mon Sep 17 00:00:00 2001
From: Leo-GG <leonardo.garma@gmail.com>
Date: Wed, 9 Oct 2024 20:00:50 +0200
Subject: [PATCH 372/424] Update quick_start.rst

---
 doc/quick_start.rst | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index 3754f5ff5..380995301 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -62,9 +62,9 @@ and two standard deviations from the mean.
     alpha = [0.05, 0.32]
     y_pred, y_pis = mapie_regressor.predict(X_test, alpha=alpha)
 
-MAPIE returns a ``np.ndarray`` of shape ``(n_samples, 3, len(alpha))`` giving the predictions,
-as well as the lower and upper bounds of the prediction intervals for the target quantile
-for each desired alpha value.
+MAPIE returns a tuple, the first element is a ``np.ndarray`` of shape ``(n_samples)`` giving the 
+predictions, and the second element a ``np.ndarray`` of shape ``(n_samples, 2, len(alpha))`` giving 
+the lower and upper bounds of the prediction intervals for the target quantile for each desired alpha value.
 
 You can compute the coverage of your prediction intervals.
 

From 4577f2f27c631a5749883b8f772036737fbcc88c Mon Sep 17 00:00:00 2001
From: Leo-GG <leonardo.garma@gmail.com>
Date: Wed, 9 Oct 2024 20:04:56 +0200
Subject: [PATCH 373/424] Updated quickstart

Updated documentation on MAPIE regression output
---
 doc/quick_start.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index 380995301..4f8866cd1 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -64,7 +64,7 @@ and two standard deviations from the mean.
 
 MAPIE returns a tuple, the first element is a ``np.ndarray`` of shape ``(n_samples)`` giving the 
 predictions, and the second element a ``np.ndarray`` of shape ``(n_samples, 2, len(alpha))`` giving 
-the lower and upper bounds of the prediction intervals for the target quantile for each desired alpha value.
+the lower and upper bounds of the **P**rediction **I**nterval**S** for the target quantile for each desired alpha value.
 
 You can compute the coverage of your prediction intervals.
 

From 2ebd4878838cda58604d0d8de89c326fbd93be49 Mon Sep 17 00:00:00 2001
From: Leo-GG <leonardo.garma@gmail.com>
Date: Wed, 9 Oct 2024 20:09:53 +0200
Subject: [PATCH 374/424] Update quick_start.rst

---
 doc/quick_start.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index 4f8866cd1..bdae44981 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -64,7 +64,7 @@ and two standard deviations from the mean.
 
 MAPIE returns a tuple, the first element is a ``np.ndarray`` of shape ``(n_samples)`` giving the 
 predictions, and the second element a ``np.ndarray`` of shape ``(n_samples, 2, len(alpha))`` giving 
-the lower and upper bounds of the **P**rediction **I**nterval**S** for the target quantile for each desired alpha value.
+the lower and upper bounds of the Prediction IntervalS for the target quantile for each desired alpha value.
 
 You can compute the coverage of your prediction intervals.
 

From 60b8de51cc4025402b1d0bde79e7daca491e675d Mon Sep 17 00:00:00 2001
From: Leo-GG <leonardo.garma@gmail.com>
Date: Wed, 9 Oct 2024 20:13:37 +0200
Subject: [PATCH 375/424] Updated AUTHORS.rst

---
 AUTHORS.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index 8fcded38b..ce5e1016f 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -42,4 +42,5 @@ Contributors
 * Ambros Marzetta <ambrosm>
 * Carl McBride Ellis <Carl-McBride-Ellis>
 * Baptiste Calot <baptiste.calot@capgemini.com>
+* Leonardo Garma <leonardo.garma@gmail.com>
 To be continued ...

From c763b11e5a646a9e0791611b20297c51b50c233f Mon Sep 17 00:00:00 2001
From: Mohammed Jawhar <mohammedjawhar365@gmail.com>
Date: Mon, 14 Oct 2024 15:55:41 +0200
Subject: [PATCH 376/424] Fix: Correct EnbPI interval centering

---
 mapie/estimator/regressor.py | 6 +++++-
 1 file changed, 5 insertions(+), 1 deletion(-)

diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index a200586c6..3ec9a66ef 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -565,13 +565,17 @@ def predict(
             elif self.method == "plus":
                 y_pred_multi_low = y_pred_multi
                 y_pred_multi_up = y_pred_multi
-            else:
+            elif self.method != "enbpi":
                 y_pred_multi_low = y_pred[:, np.newaxis]
                 y_pred_multi_up = y_pred[:, np.newaxis]
 
             if ensemble:
                 y_pred = aggregate_all(self.agg_function, y_pred_multi)
 
+            if self.method == "enbpi":
+                y_pred_multi_low = y_pred[:, np.newaxis]
+                y_pred_multi_up = y_pred[:, np.newaxis]
+
         if return_multi_pred:
             return y_pred, y_pred_multi_low, y_pred_multi_up
         else:

From fa3cdcdd2a0f793a87a960e69ade6364cb7a770c Mon Sep 17 00:00:00 2001
From: Leo-GG <leonardo.garma@gmail.com>
Date: Thu, 31 Oct 2024 13:49:11 +0100
Subject: [PATCH 377/424] Update doc/quick_start.rst

Co-authored-by: Valentin Laurent <valentin.laurent.fr@gmail.com>
---
 doc/quick_start.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index bdae44981..380995301 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -64,7 +64,7 @@ and two standard deviations from the mean.
 
 MAPIE returns a tuple, the first element is a ``np.ndarray`` of shape ``(n_samples)`` giving the 
 predictions, and the second element a ``np.ndarray`` of shape ``(n_samples, 2, len(alpha))`` giving 
-the lower and upper bounds of the Prediction IntervalS for the target quantile for each desired alpha value.
+the lower and upper bounds of the prediction intervals for the target quantile for each desired alpha value.
 
 You can compute the coverage of your prediction intervals.
 

From f483bb0225717ddf68c90f7da4d4696ca25dd613 Mon Sep 17 00:00:00 2001
From: Leo-GG <leonardo.garma@gmail.com>
Date: Thu, 31 Oct 2024 13:49:17 +0100
Subject: [PATCH 378/424] Update doc/quick_start.rst

Co-authored-by: Valentin Laurent <valentin.laurent.fr@gmail.com>
---
 doc/quick_start.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/doc/quick_start.rst b/doc/quick_start.rst
index 380995301..d7f86b2da 100644
--- a/doc/quick_start.rst
+++ b/doc/quick_start.rst
@@ -60,7 +60,7 @@ and two standard deviations from the mean.
     mapie_regressor.fit(X_train, y_train)
 
     alpha = [0.05, 0.32]
-    y_pred, y_pis = mapie_regressor.predict(X_test, alpha=alpha)
+    y_pred, y_pred_intervals = mapie_regressor.predict(X_test, alpha=alpha)
 
 MAPIE returns a tuple, the first element is a ``np.ndarray`` of shape ``(n_samples)`` giving the 
 predictions, and the second element a ``np.ndarray`` of shape ``(n_samples, 2, len(alpha))`` giving 

From 6ddcea1f2a750c6b34a0c3540efa268988e0daba Mon Sep 17 00:00:00 2001
From: sd29206 <hussein.jawad@external.stellantis.com>
Date: Tue, 5 Nov 2024 12:13:00 +0100
Subject: [PATCH 379/424] fix contributing.rst

---
 CONTRIBUTING.rst            |  49 +++++-----
 public_api_v1_classifier.py | 135 ---------------------------
 public_api_v1_regression.py | 178 ------------------------------------
 3 files changed, 25 insertions(+), 337 deletions(-)
 delete mode 100644 public_api_v1_classifier.py
 delete mode 100644 public_api_v1_regression.py

diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 8492a3385..7cacefb5e 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -6,7 +6,7 @@ What to work on?
 ----------------
 
 You are welcome to propose and contribute new ideas.
-We encourage you to `open an issue <https://github.com/scikit-learn-contrib/MAPIE/issues>`so that we can align on the work to be done.
+We encourage you to `open an issue <https://github.com/scikit-learn-contrib/MAPIE/issues>`_ so that we can align on the work to be done.
 It is generally a good idea to have a quick discussion before opening a pull request that is potentially out-of-scope.
 
 Fork/clone/pull
@@ -14,61 +14,62 @@ Fork/clone/pull
 
 The typical workflow for contributing to `mapie` is:
 
-1. Fork the `master` branch from the `GitHub repository <https://github.com/scikit-learn-contrib/MAPIE>`_.
+1. Fork the ``master`` branch from the `GitHub repository <https://github.com/scikit-learn-contrib/MAPIE>`_.
 2. Clone your fork locally.
 3. Commit changes.
 4. Push the changes to your fork.
-5. Send a pull request from your fork back to the original `master` branch.
+5. Send a pull request from your fork back to the original ``master`` branch.
 
 Local setup
 -----------
 
 We encourage you to use a virtual environment. You'll want to activate it every time you want to work on `mapie`.
 
-You can create a virtual environment via `conda`:
+You can create a virtual environment via ``conda``:
 
-.. code:: sh
+.. code-block:: sh
 
     $ conda env create -f environment.dev.yml
     $ conda activate mapie
 
-Alternatively, you can install dependencies with `pip`:
+Alternatively, you can install dependencies with ``pip``:
 
-.. code:: sh
+.. code-block:: sh
 
     $ pip install -r requirements.dev.txt
 
-Finally install `mapie` in development mode:
+Finally, install `mapie` in development mode:
 
-.. code:: sh
+.. code-block:: sh
 
-    pip install -e .
+    $ pip install -e .
 
 
 Documenting your change
 -----------------------
 
 If you're adding a class or a function, then you'll need to add a docstring with a doctest. We follow the `numpy docstring convention <https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_, so please do too.
-Any estimator should follow the [scikit-learn API](https://scikit-learn.org/stable/developers/develop.html), so please follow these guidelines.
-In order to build the documentation locally, you first need to install some dependencies :
+Any estimator should follow the `scikit-learn API <https://scikit-learn.org/stable/developers/develop.html>`_, so please follow these guidelines.
+
+In order to build the documentation locally, you first need to install some dependencies:
 
-Create a dedicated virtual environment via `conda`:
+Create a dedicated virtual environment via ``conda``:
 
-.. code:: sh
+.. code-block:: sh
 
     $ conda env create -f environment.doc.yml
     $ conda activate mapie-doc
 
-Alternatively, using `pip`, create a different virtual environment than the one used for development, and install the dependencies:
+Alternatively, using ``pip``, create a different virtual environment than the one used for development, and install the dependencies:
 
-.. code:: sh
+.. code-block:: sh
 
     $ pip install -r requirements.doc.txt
     $ pip install -e .
 
 Finally, once dependencies are installed, you can build the documentation locally by running:
 
-.. code:: sh
+.. code-block:: sh
 
     $ make clean-doc
     $ make doc
@@ -77,10 +78,10 @@ Finally, once dependencies are installed, you can build the documentation locall
 Updating changelog
 ------------------
 
-You can make your contribution visible by :
+You can make your contribution visible by:
 
-1. adding your name to the Contributors sections of `AUTHORS.rst <https://github.com/scikit-learn-contrib/MAPIE/blob/master/AUTHORS.rst>`_
-2. adding a line describing your change into `HISTORY.rst <https://github.com/scikit-learn-contrib/MAPIE/blob/master/HISTORY.rst>`_
+1. Adding your name to the Contributors section of `AUTHORS.rst <https://github.com/scikit-learn-contrib/MAPIE/blob/master/AUTHORS.rst>`_
+2. Adding a line describing your change into `HISTORY.rst <https://github.com/scikit-learn-contrib/MAPIE/blob/master/HISTORY.rst>`_
 
 Testing
 -------
@@ -90,7 +91,7 @@ Linting
 
 These tests absolutely have to pass.
 
-.. code:: sh
+.. code-block:: sh
 
     $ make lint
 
@@ -100,7 +101,7 @@ Static typing
 
 These tests absolutely have to pass.
 
-.. code:: sh
+.. code-block:: sh
 
     $ make type-check
 
@@ -110,7 +111,7 @@ Unit tests
 
 These tests absolutely have to pass.
 
-.. code:: sh
+.. code-block:: sh
 
     $ make tests
 
@@ -119,6 +120,6 @@ Coverage
 
 The coverage should absolutely be 100%.
 
-.. code:: sh
+.. code-block:: sh
 
     $ make coverage
diff --git a/public_api_v1_classifier.py b/public_api_v1_classifier.py
deleted file mode 100644
index 495dc09c2..000000000
--- a/public_api_v1_classifier.py
+++ /dev/null
@@ -1,135 +0,0 @@
-from __future__ import annotations
-
-import warnings
-from typing import Any, Iterable, Optional, Tuple, Union, cast, List
-
-import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.model_selection import BaseCrossValidator, BaseShuffleSplit
-from sklearn.preprocessing import LabelEncoder
-from sklearn.utils import check_random_state
-from sklearn.utils.validation import (_check_y, check_is_fitted, indexable)
-from sklearn.linear_model import LogisticRegression
-
-from mapie._typing import ArrayLike, NDArray
-from mapie.conformity_scores import BaseClassificationScore
-from mapie.conformity_scores.sets.raps import RAPSConformityScore
-from mapie.conformity_scores.sets.lac import LACConformityScore
-
-from mapie.conformity_scores.utils import (
-    check_depreciated_size_raps, check_classification_conformity_score,
-    check_target
-)
-from mapie.estimator.classifier import EnsembleClassifier
-from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
-                         check_estimator_classification, check_n_features_in,
-                         check_n_jobs, check_null_weight, check_predict_params,
-                         check_verbose)
-
-
-class SplitConformalClassifier:
-    
-    def __init__(   
-        self,
-        estimator: ClassifierMixin = LogisticRegression(),
-        conformity_score: Union[str, BaseClassificationScore] = "lac", # Can be a string or a BaseClassificationScore object
-        alpha: Union[float, List[float]] = 0.1,
-        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in .fit) and BaseCrossValidator (restricted to splitters only)
-        n_jobs: Optional[int] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        verbose: int = 0,
-    ) -> None:
-
-        pass
-    
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        # groups: Optional[ArrayLike] = None,  # Removed, because it is not used in split conformal classifier
-        test_size: Union[int, float] = 0.1,  #  -> In __init__ ?
-        # Future API: X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        # Future API:   y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        fit_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'categorical_feature': 'auto'}
-        predict_params: Optional[dict] = None, # For example, LBGMClassifier :  {'pred_leaf': False}
-    ) -> SplitConformalClassifier:
-        
-        return self
-
-    def predict(self, 
-                X: ArrayLike) -> NDArray:
-
-        """
-        Return
-        ----- 
-        Return ponctual prediction similar to predict method of scikit-learn classifiers
-        Shape (n_samples,)
-        """
-
-    def predict_sets(self, 
-                     X: ArrayLike,
-                     conformoty_score_params: Optional[dict] = None, # Parameters specific to conformal method, For example: include_last_label
-                     ) -> NDArray:
-        
-        """
-        Return
-        -----
-        An array containing the prediction sets 
-        Shape (n_samples, n_classes) if alpha is float,
-        Shape (n_samples, n_classes, alpha) if alpha is a list of floats
-        """
-
-        pass
-
-class CrossConformalClassifier:
-    
-    def __init__(
-        self,
-        estimator: ClassifierMixin = LogisticRegression(),
-        conformity_score: Union[str, BaseClassificationScore] = 'lac',
-        cross_val : Union[BaseCrossValidator, str] = 5,
-        alpha: Union[float, List[float]] = 0.1,
-        n_jobs: Optional[int] = None,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-        verbose: int = 0,
-
-    ) -> None:
-
-        pass 
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        # groups: Optional[ArrayLike] = None,  
-        fit_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'categorical_feature': 'auto'}
-        predict_params: Optional[dict] = None,  
-    ) -> CrossConformalClassifier:
-
-        pass
-
-    def predict(self, 
-                X: ArrayLike): # Parameters specific to conformal method, For example: include_last_label) -> ArrayLike:
-
-        """
-        Return 
-        ----- 
-
-        """
-        pass
-
-    def predict_sets(self,
-                     X: ArrayLike,
-                     agg_scores: Optional[str] = "mean", # how to aggregate the scores by the estimators on test data
-                     conformoty_score_params: Optional[dict] = None,): # Parameters specific to conformal method, For example: include_last_label) -> NDArray
-        
-        """
-        Return
-        -----
-        An array containing the prediction sets 
-        Shape (n_samples, n_classes) if alpha is float,
-        Shape (n_samples, n_classes, alpha) if alpha is a list of floats
-        """
-        
\ No newline at end of file
diff --git a/public_api_v1_regression.py b/public_api_v1_regression.py
deleted file mode 100644
index febf53540..000000000
--- a/public_api_v1_regression.py
+++ /dev/null
@@ -1,178 +0,0 @@
-from typing import Optional, Union, Self, Tuple, List
-
-import numpy as np
-from sklearn.linear_model import LinearRegression, QuantileRegressor
-
-from numpy.typing import ArrayLike, NDArray
-from sklearn.base import RegressorMixin
-from sklearn.model_selection import BaseCrossValidator
-
-from mapie.conformity_scores import BaseRegressionScore, AbsoluteConformityScore
-
-
-class NaiveConformalRegressor:
-    def __init__(
-        self,
-        estimator: RegressorMixin = LinearRegression(),  # Improved 'None' default
-        conformity_score: Union[str, BaseRegressionScore] = "absolute",  # Add string option
-        alpha: Union[float, List[float]] = 0.1,  # QUESTION: Should we set this default, or should we keep None? I think an array is OK (already implemented, and avoid developing a less user-friendly reset_alpha method)
-        n_jobs: Optional[int] = None,
-        verbose: int = 0,
-        random_state: Optional[Union[int, np.random.RandomState]] = None,
-    ) -> None:
-        pass
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        fit_params: Optional[dict] = None,  # Ex for LGBMClassifier: {'categorical_feature': 'auto'}
-        predict_params: Optional[dict] = None,
-    ) -> Self:
-        pass
-
-    def predict_set(
-        self,
-        X: ArrayLike,
-        optimize_beta: bool = False,
-        allow_infinite_bounds: bool = False,
-        # **predict_params  -> QUESTION: Is this redundant with predict_params in .fit() ?
-    ) -> NDArray:
-        """
-        Returns
-        -------
-        An array containing the prediction intervals,
-        of shape (n_samples, 2) if alpha is a float,
-        or (n_samples, 2, n_alpha) if alpha is an array of floats
-        """
-        pass
-
-    def predict(
-        self,
-        X: ArrayLike,
-        # **predict_params  -> Is this redundant with predict_params in .fit() ?
-    ) -> NDArray:
-        """
-        Returns
-        -------
-        An array containing the point predictions, with shape (n_samples,)
-        """
-        pass
-
-
-class SplitConformalRegressor:
-    def __init__(
-        self,
-        estimator: RegressorMixin = LinearRegression(),  # Improved 'None' default
-        conformity_score: Union[str, BaseRegressionScore] = "absolute",  # Add string option
-        alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
-        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in fit) and BaseCrossValidator (restricted to splitters only)
-        n_jobs: Optional[int] = None,
-        verbose: int = 0,
-        random_state: Optional[Union[int, np.random.RandomState]] = None
-        # groups -> not used in the current implementation (that is using ShuffleSplit)
-    ) -> None:
-        pass
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        test_size: Union[int, float] = 0.1,  # Moved from __init__, improved 'None' default. Invalid if split_method != 'simple'
-        # Future API: X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        # Future API: y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        fit_params: Optional[dict] = None,
-        predict_params: Optional[dict] = None,
-    ) -> Self:
-        pass
-
-    # predict and predict_set signatures are the same as NaiveConformalRegressor
-
-
-class CrossConformalRegressor:
-    def __init__(
-        self,
-        estimator: RegressorMixin = LinearRegression(),  # Improved 'None' default
-        conformity_score: Union[str, BaseRegressionScore] = "absolute",  # Add string option
-        alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
-        method: str = "plus",  # 'base' | 'plus' | 'minmax'
-        cross_val: Union[int, BaseCrossValidator] = 5,  # Improved 'None' default, removed str option, update name. Note that we lose the prefit option, that was I think useless in a cross-validation context QUESTION
-        # agg_function -> moved to predict method
-        n_jobs: Optional[int] = None,
-        verbose: int = 0,
-        random_state: Optional[Union[int, np.random.RandomState]] = None
-    ) -> None:
-        pass
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        # groups: Optional[ArrayLike] = None,  ->  To specify directly in the cross_val parameter
-        fit_params: Optional[dict] = None,
-        predict_params: Optional[dict] = None,
-    ) -> Self:
-        pass
-
-    def predict_set(
-        self,
-        X: ArrayLike,
-        optimize_beta: bool = False,
-        allow_infinite_bounds: bool = False,
-        # **predict_params  -> To remove: redundant with predict_params in .fit()
-    ) -> NDArray:  # See docstring in NaiveConformalRegressor for the return type details
-        pass
-
-    def predict(
-        self,
-        # ensemble: bool = False, -> removed, see aggregation_method
-        aggregation_method: Optional[str] = None,  # None: no aggregation, 'mean', 'median'
-    ) -> NDArray:
-        pass
-
-
-class JackknifeAfterBootstrapRegressor:
-    pass  # TODO
-
-
-class ConformalizedQuantileRegressor:
-    def __init__(
-        self,
-        estimator: RegressorMixin = QuantileRegressor(),  # Improved 'None' default
-        alpha: Union[float, List[float]] = 0.1,  # See comment in NaiveConformalRegressor
-        split_method: str = "simple",  # 'simple' (provide test_size in .fit), 'prefit' or 'manual'. Future API: BaseCrossValidator (restricted to splitters only)
-        random_state: Optional[Union[int, np.random.RandomState]] = None,  # Moved from .fit
-        # Future API : n_jobs: Optional[int] = None,
-        # Future API : verbose: int = 0,
-    ) -> None:
-        pass
-
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        # sample_weight: Optional[ArrayLike] = None, -> in fit_params
-        # groups: Optional[ArrayLike] = None,  ->  To specify directly in the cross_val parameter
-        # shuffle: Optional[bool] = True, -> To implement in a future version (using the BaseCrossValidator split_method). In that case we would lose that feature in the v1.0.0 QUESTION
-        # stratify: Optional[ArrayLike] = None, -> same comment as shuffle
-        test_size: Union[int, float] = 0.1,  # Renamed from 'calib_size'
-        X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
-        fit_params: Optional[dict] = None,
-        predict_params: Optional[dict] = None,
-    ) -> Self:
-        pass
-
-    def predict_set(
-        self,
-        X: ArrayLike,
-        optimize_beta: bool = False,
-        allow_infinite_bounds: bool = False,
-        symmetry: bool = True,  # Corrected typing
-    ) -> NDArray:
-        pass
-
-    # predict signature is the same as NaiveConformalRegressor

From 739af29de761f81c3f14be880216c7e74fe9b8c0 Mon Sep 17 00:00:00 2001
From: sd29206 <hussein.jawad@external.stellantis.com>
Date: Tue, 5 Nov 2024 12:21:35 +0100
Subject: [PATCH 380/424] Fix issue 525 in contribution guidelines

---
 AUTHORS.rst | 1 +
 HISTORY.rst | 1 +
 2 files changed, 2 insertions(+)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index 8fcded38b..deda955dd 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -9,6 +9,7 @@ Development Lead
 * Vincent Blot <vincent.blot@capgemini.com>
 * Louis Lacombe <louis.lacombe@capgemini.com>
 * Valentin Laurent <valentin.laurent@capgemini.com>
+* Hussein Jawad <hussein.jawad@capgemini.com>
 
 Emeritus Core Developers
 ------------------------
diff --git a/HISTORY.rst b/HISTORY.rst
index f57c47dec..6912804a6 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,7 @@ History
 0.9.x (2024-xx-xx)
 ------------------
 
+* Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues.
 * Bump wheel version to avoid known security vulnerabilities
 
 0.9.1 (2024-09-13)

From 6c6d5a56c163d5fe951c88b26ea50f820d18c2b4 Mon Sep 17 00:00:00 2001
From: sd29206 <hussein.jawad@external.stellantis.com>
Date: Tue, 5 Nov 2024 14:22:54 +0100
Subject: [PATCH 381/424] update contribution guideline

---
 CONTRIBUTING.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 7cacefb5e..1a1083857 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -32,7 +32,7 @@ You can create a virtual environment via ``conda``:
     $ conda env create -f environment.dev.yml
     $ conda activate mapie
 
-Alternatively, you can install dependencies with ``pip``:
+Alternatively, using ``pip``, create a virtual environment and install dependencies with the following command:
 
 .. code-block:: sh
 

From aad830dc41a923cb8c9099318ec374798546bd17 Mon Sep 17 00:00:00 2001
From: Mohammed Jawhar <mohammedjawhar365@gmail.com>
Date: Mon, 14 Oct 2024 19:46:34 +0200
Subject: [PATCH 382/424] Fix : Corrected EnbPI Prediction Intervals centering

---
 AUTHORS.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index bd1ded4e0..f3fb6f468 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -44,4 +44,5 @@ Contributors
 * Carl McBride Ellis <Carl-McBride-Ellis>
 * Baptiste Calot <baptiste.calot@capgemini.com>
 * Leonardo Garma <leonardo.garma@gmail.com>
+* Mohammed Jawhar <mohammedjawhar365@gmail.com>
 To be continued ...

From 6c101f0d334040c2e9c5dc631dd959204bb832a5 Mon Sep 17 00:00:00 2001
From: Mohammed Jawhar <mohammedjawhar365@gmail.com>
Date: Mon, 14 Oct 2024 19:51:12 +0200
Subject: [PATCH 383/424] Fix : Corrected EnbPI Prediction Intervals centering

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 6912804a6..916c81546 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -7,6 +7,7 @@ History
 
 * Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues.
 * Bump wheel version to avoid known security vulnerabilities
+* Fix issue 495 to center correctly the prediction intervals
 
 0.9.1 (2024-09-13)
 ------------------

From f4a98dd54941b2207441031f0caad0acc723b0ec Mon Sep 17 00:00:00 2001
From: Mohammed Jawhar <mohammedjawhar365@gmail.com>
Date: Thu, 7 Nov 2024 17:26:25 +0100
Subject: [PATCH 384/424] Improved readability for the EnbPI interval centring
 fix

---
 mapie/estimator/regressor.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index 3ec9a66ef..733e5f59c 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -565,17 +565,17 @@ def predict(
             elif self.method == "plus":
                 y_pred_multi_low = y_pred_multi
                 y_pred_multi_up = y_pred_multi
-            elif self.method != "enbpi":
+            elif self.method == "enbpi":
+                y_pred_aggregate = aggregate_all(self.agg_function, y_pred_multi)
+                y_pred_multi_low = y_pred_aggregate[:, np.newaxis]
+                y_pred_multi_up = y_pred_aggregate[:, np.newaxis]
+            else:
                 y_pred_multi_low = y_pred[:, np.newaxis]
                 y_pred_multi_up = y_pred[:, np.newaxis]
 
             if ensemble:
                 y_pred = aggregate_all(self.agg_function, y_pred_multi)
 
-            if self.method == "enbpi":
-                y_pred_multi_low = y_pred[:, np.newaxis]
-                y_pred_multi_up = y_pred[:, np.newaxis]
-
         if return_multi_pred:
             return y_pred, y_pred_multi_low, y_pred_multi_up
         else:

From 70f60c76fe2ce13ba4dc51f1e4ab166572a3e876 Mon Sep 17 00:00:00 2001
From: Mohammed Jawhar <mohammedjawhar365@gmail.com>
Date: Sun, 17 Nov 2024 13:31:38 +0100
Subject: [PATCH 385/424] corrected tests

---
 mapie/estimator/regressor.py               |  3 ++-
 mapie/tests/test_time_series_regression.py | 12 ++++++------
 2 files changed, 8 insertions(+), 7 deletions(-)

diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index 733e5f59c..d300863a9 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -566,7 +566,8 @@ def predict(
                 y_pred_multi_low = y_pred_multi
                 y_pred_multi_up = y_pred_multi
             elif self.method == "enbpi":
-                y_pred_aggregate = aggregate_all(self.agg_function, y_pred_multi)
+                y_pred_aggregate = aggregate_all(
+                    self.agg_function, y_pred_multi)
                 y_pred_multi_low = y_pred_aggregate[:, np.newaxis]
                 y_pred_multi_up = y_pred_aggregate[:, np.newaxis]
             else:
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index d3b9ba293..785cb9088 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -94,9 +94,9 @@
 }
 
 WIDTHS = {
-    "blockbootstrap_enbpi_mean_wopt": 3.86,
+    "blockbootstrap_enbpi_mean_wopt": 3.89,
     "blockbootstrap_enbpi_median_wopt": 3.85,
-    "blockbootstrap_enbpi_mean": 3.86,
+    "blockbootstrap_enbpi_mean": 3.89,
     "blockbootstrap_enbpi_median": 3.85,
     "blockbootstrap_aci_mean": 3.96,
     "blockbootstrap_aci_median": 3.95,
@@ -104,10 +104,10 @@
 }
 
 COVERAGES = {
-    "blockbootstrap_enbpi_mean_wopt": 0.952,
-    "blockbootstrap_enbpi_median_wopt": 0.946,
-    "blockbootstrap_enbpi_mean": 0.952,
-    "blockbootstrap_enbpi_median": 0.946,
+    "blockbootstrap_enbpi_mean_wopt": 0.956,
+    "blockbootstrap_enbpi_median_wopt": 0.956,
+    "blockbootstrap_enbpi_mean": 0.956,
+    "blockbootstrap_enbpi_median": 0.956,
     "blockbootstrap_aci_mean": 0.96,
     "blockbootstrap_aci_median": 0.96,
     "prefit": 0.97,

From 58e839cb5264fe2a07ee95daf2a8dfa81a5189b9 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 20 Nov 2024 16:43:16 +0100
Subject: [PATCH 386/424] DOC - Fix most documentation build warnings (#539)

* DOC - Fixing a good 90% of existing warnings

* DOC - Update contributing guidelines regarding documentation
---
 .github/PULL_REQUEST_TEMPLATE.md              |  2 +-
 CONTRIBUTING.rst                              |  2 +-
 HISTORY.rst                                   |  1 +
 doc/conf.py                                   |  3 +
 .../plot_main-tutorial-mondrian-regression.py | 12 ++--
 ...plot_tutorial_multilabel_classification.py | 56 +++++++++----------
 .../plot_conditional_coverage.py              | 16 +++---
 .../plot_conformal_predictive_distribution.py |  2 +-
 .../plot_main-tutorial-regression.py          |  3 +
 mapie/calibration.py                          |  9 ++-
 mapie/conformity_scores/bounds/residuals.py   |  4 +-
 mapie/conformity_scores/classification.py     | 16 +++---
 mapie/conformity_scores/regression.py         | 19 +++----
 mapie/conformity_scores/sets/aps.py           | 16 +++---
 mapie/conformity_scores/sets/lac.py           | 12 ++--
 mapie/conformity_scores/sets/naive.py         | 12 ++--
 mapie/conformity_scores/sets/raps.py          |  8 +--
 mapie/conformity_scores/sets/topk.py          | 12 ++--
 mapie/metrics.py                              | 34 +++++------
 mapie/regression/quantile_regression.py       |  4 +-
 mapie/regression/regression.py                | 10 ++--
 mapie/regression/time_series_regression.py    |  4 +-
 mapie/subsample.py                            |  1 +
 23 files changed, 132 insertions(+), 126 deletions(-)

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 1d3951238..9567edaf6 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -28,4 +28,4 @@ Please describe the tests that you ran to verify your changes. Provide instructi
 - [ ] Typing passes successfully : `make type-check`
 - [ ] Unit tests pass successfully : `make tests`
 - [ ] Coverage is 100% : `make coverage`
-- [ ] Documentation builds successfully : `make doc`
\ No newline at end of file
+- [ ] Documentation builds successfully and without warnings : `make doc`
\ No newline at end of file
diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 1a1083857..81b04b707 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -48,7 +48,7 @@ Finally, install `mapie` in development mode:
 Documenting your change
 -----------------------
 
-If you're adding a class or a function, then you'll need to add a docstring with a doctest. We follow the `numpy docstring convention <https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_, so please do too.
+If you're adding a public class or function, then you'll need to add a docstring with a doctest. We follow the `numpy docstring convention <https://sphinxcontrib-napoleon.readthedocs.io/en/latest/example_numpy.html>`_, so please do too.
 Any estimator should follow the `scikit-learn API <https://scikit-learn.org/stable/developers/develop.html>`_, so please follow these guidelines.
 
 In order to build the documentation locally, you first need to install some dependencies:
diff --git a/HISTORY.rst b/HISTORY.rst
index 916c81546..5a896877a 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -8,6 +8,7 @@ History
 * Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues.
 * Bump wheel version to avoid known security vulnerabilities
 * Fix issue 495 to center correctly the prediction intervals
+* Fix most documentation build warnings
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/doc/conf.py b/doc/conf.py
index b56a02a87..0b1af45f2 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -67,6 +67,9 @@
 # generate autosummary even if no references
 autosummary_generate = True
 
+
+autosectionlabel_prefix_document = True
+
 # The suffix of source filenames.
 source_suffix = ".rst"
 
diff --git a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
index 903b23702..6a58fe0fe 100644
--- a/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
+++ b/examples/mondrian/1-quickstart/plot_main-tutorial-mondrian-regression.py
@@ -36,7 +36,7 @@
 
 ##############################################################################
 # 1. Create the noisy dataset
-# -----------------------------
+# ----------------------------------------------------------------------------
 # We create a dataset with 10 groups, each of those groups having a different
 # level of noise.
 
@@ -87,7 +87,7 @@
 
 ##############################################################################
 # 2. Split the dataset into a training set, a calibration set, and a test set.
-# -----------------------------
+# ----------------------------------------------------------------------------
 
 X_train_temp, X_test, y_train_temp, y_test = train_test_split(
     X, y, test_size=0.2, random_state=0
@@ -119,7 +119,7 @@
 
 ##############################################################################
 # 3. Fit a random forest regressor on the training set.
-# -----------------------------
+# ----------------------------------------------------------------------------
 
 rf = RandomForestRegressor(n_estimators=100)
 rf.fit(X_train, y_train)
@@ -127,7 +127,7 @@
 
 ##############################################################################
 # 4. Fit a MapieRegressor and a MondrianCP on the calibration set.
-# -----------------------------
+# ----------------------------------------------------------------------------
 
 mapie_regressor = MapieRegressor(rf, cv="prefit")
 mondrian_regressor = MondrianCP(MapieRegressor(rf, cv="prefit"))
@@ -137,7 +137,7 @@
 
 ##############################################################################
 # 5. Predict the prediction intervals on the test set with both methods.
-# -----------------------------
+# ----------------------------------------------------------------------------
 
 _, y_pss_split = mapie_regressor.predict(X_test, alpha=.1)
 _, y_pss_mondrian = mondrian_regressor.predict(
@@ -147,7 +147,7 @@
 
 ##############################################################################
 # 6. Compare the coverage by partition, plot both methods side by side.
-# -----------------------------
+# ----------------------------------------------------------------------------
 
 coverages = {}
 for group in np.unique(partition_test):
diff --git a/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py b/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
index af4d572e9..096c184c9 100644
--- a/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
+++ b/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
@@ -27,7 +27,7 @@
 
 ##############################################################################
 # 1. Construction of the dataset
-# -----------------------------
+# ----------------------------------------------------------------------------
 # We use a two-dimensional toy dataset with three possible labels. The idea
 # is to create a triangle where the observations on the edges have only one
 # label, those on the vertices have two labels (those of the two edges) and the
@@ -94,21 +94,21 @@
 
 ##############################################################################
 # 2 Recall control risk with CRC and RCPS
-# ---------------------------------------
+# ----------------------------------------------------------------------------
 # 2.1 Fitting MapieMultiLabelClassifier
-# ------------------------------------
+# ----------------------------------------------------------------------------
 # MapieMultiLabelClassifier will be fitted with RCPS and CRC methods. For the
 # RCPS method, we will test all three Upper Confidence Bounds (Hoeffding,
 # Bernstein and Waudby-Smith–Ramdas).
 # The two methods give two different guarantees on the risk:
 #
 # * RCPS: :math:`P(R(\mathcal{T}_{\hat{\lambda}})\leq\alpha)\geq 1-\delta`
-# where :math:`R(\mathcal{T}_{\hat{\lambda}})`
-# is the risk we want to control and :math:`\alpha` is the desired risk
+#   where :math:`R(\mathcal{T}_{\hat{\lambda}})`
+#   is the risk we want to control and :math:`\alpha` is the desired risk
 #
 # * CRC: :math:`\mathbb{E}\left[L_{n+1}(\hat{\lambda})\right] \leq \alpha`
-# where :math:`L_{n+1}(\hat{\lambda})` is the risk of a new observation and
-# :math:`\alpha` is the desired risk
+#   where :math:`L_{n+1}(\hat{\lambda})` is the risk of a new observation and
+#   :math:`\alpha` is the desired risk
 #
 # In both cases, the objective of the method is to find the optimal value of
 # :math:`\lambda` (threshold above which we consider a label as being present)
@@ -148,17 +148,17 @@
 
 ##############################################################################
 # 2.2. Results
-# ----------
+# ----------------------------------------------------------------------------
 # To check the results of the methods, we propose two types of plots:
 #
-# * Plots where the confidence level varies. Here two metrics are plotted
-#   for each method and for each UCB
-#     * The actual recall (which should be always near to the required one):
-#       we can see that they are close to each other.
-#     * The value of the threshold: we see that the threshold is decreasing as
-#       :math:`1 - \alpha` increases, which is what is expected because a
-#       smaller threshold will give larger prediction sets, hence a larger
-#       recall.
+# 1 - Plots where the confidence level varies. Here two metrics are plotted
+# for each method and for each UCB
+# * The actual recall (which should be always near to the required one):
+# we can see that they are close to each other.
+# * The value of the threshold: we see that the threshold is decreasing as
+# :math:`1 - \alpha` increases, which is what is expected because a
+# smaller threshold will give larger prediction sets, hence a larger
+# recall.
 #
 
 vars_y = [recalls, thresholds]
@@ -177,15 +177,15 @@
 plt.show()
 
 ##############################################################################
-# * Plots where we choose a specific risk value (0.1 in our case) and look at
-#   the average risk, the UCB of the risk (for RCPS methods) and the choice of
-#   the threshold :math:`\lambda`
-#     * We can see that among the RCPS methods, the Bernstein method
-#       gives the best results as for a given value of :math:`\alpha`
-#       as we are above the required recall but with a larger value of
-#       :math:`\lambda` than the two others bounds.
-#     * The CRC method gives the best results since it guarantees the coverage
-#       with a larger threshold.
+# 2 - Plots where we choose a specific risk value (0.1 in our case) and look at
+# the average risk, the UCB of the risk (for RCPS methods) and the choice of
+# the threshold :math:`\lambda`
+# * We can see that among the RCPS methods, the Bernstein method
+# gives the best results as for a given value of :math:`\alpha`
+# as we are above the required recall but with a larger value of
+# :math:`\lambda` than the two others bounds.
+# * The CRC method gives the best results since it guarantees the coverage
+# with a larger threshold.
 
 fig, axs = plt.subplots(
     1,
@@ -216,9 +216,9 @@
 
 ##############################################################################
 # 3. Precision control risk with LTT
-# ------------------
+# ----------------------------------------------------------------------------
 # 3.1 Fitting MapieMultilabelClassifier
-# -------------------------------------
+# ----------------------------------------------------------------------------
 #
 # In this part, we will use LTT to control precision.
 # At the opposite of the 2 previous method, LTT can handle non-monotonous loss.
@@ -266,7 +266,7 @@
 
 ##############################################################################
 # 3.2 Valid parameters for precision control
-# ------------------------------------------
+# ----------------------------------------------------------------------------
 # We can see that not all :math:`\lambda` such that risk is below the orange
 # line are choosen by the procedure. Otherwise, all the lambdas that are
 # in the red rectangle verify family wise error rate control and allow to
diff --git a/examples/regression/2-advanced-analysis/plot_conditional_coverage.py b/examples/regression/2-advanced-analysis/plot_conditional_coverage.py
index df08059f4..655df767f 100644
--- a/examples/regression/2-advanced-analysis/plot_conditional_coverage.py
+++ b/examples/regression/2-advanced-analysis/plot_conditional_coverage.py
@@ -171,15 +171,15 @@ def sin_with_controlled_noise(
 # adaptive conformal methods ?". For this we have the two metrics
 # :func:`~mapie.metrics.regression_ssc_score` and :func:`~mapie.metrics.hsic`.
 # - SSC (Size Stratified Coverage) is the maximum violation of the coverage :
-#   the intervals are grouped by width and the coverage is computed for each
-#   group. The lower coverage is the maximum coverage violation. An adaptive
-#   method is one where this maximum violation is as close as possible to the
-#   global coverage. If we interpret the result for the four methods here :
-#   CV+ seems to be the better one.
+# the intervals are grouped by width and the coverage is computed for each
+# group. The lower coverage is the maximum coverage violation. An adaptive
+# method is one where this maximum violation is as close as possible to the
+# global coverage. If we interpret the result for the four methods here :
+# CV+ seems to be the better one.
 # - And with the hsic correlation coefficient, we have the
-#   same interpretation : :func:`~mapie.metrics.hsic` computes the correlation
-#   between the coverage indicator and the interval size, a value of 0
-#   translates an independence between the two.
+# same interpretation : :func:`~mapie.metrics.hsic` computes the correlation
+# between the coverage indicator and the interval size, a value of 0
+# translates an independence between the two.
 #
 # We would like to highlight here the misinterpretation that can be made
 # with these metrics. In fact, here CV+ with the absolute residual score
diff --git a/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py b/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py
index c0737c7ae..e8f368a56 100644
--- a/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py
+++ b/examples/regression/2-advanced-analysis/plot_conformal_predictive_distribution.py
@@ -54,7 +54,7 @@
 
 ##############################################################################
 # 2. Defining a Conformal Predictive Distribution class with MAPIE
-# ----------------------------------------------------------
+# ------------------------------------------------------------------
 #
 # To be able to obtain the cumulative distribution function of
 # a prediction with MAPIE, we propose here to wrap the
diff --git a/examples/regression/4-tutorials/plot_main-tutorial-regression.py b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
index 51d97c8f4..a1e331fb8 100644
--- a/examples/regression/4-tutorials/plot_main-tutorial-regression.py
+++ b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
@@ -9,9 +9,12 @@
 
 - How well do the MAPIE strategies capture the aleatoric uncertainty
   existing in the data?
+
 - How do the prediction intervals estimated by the resampling strategies
   evolve for new *out-of-distribution* data ?
+
 - How do the prediction intervals vary between regressor models ?
+
 Throughout this tutorial, we estimate the prediction intervals first using
 a polynomial function, and then using a boosting model, and a simple neural
 network.
diff --git a/mapie/calibration.py b/mapie/calibration.py
index d15c83872..ea3834a38 100644
--- a/mapie/calibration.py
+++ b/mapie/calibration.py
@@ -34,10 +34,8 @@ class MapieCalibrator(BaseEstimator, ClassifierMixin):
         If ``None``, estimator defaults to a ``LogisticRegression`` instance.
 
     method: Optional[str]
-        Method to choose for calibration method.
-        Choose among:
-
-        - "top_label", performs a calibration on the class with highest score
+        The only valid method is "top_label".
+        Performs a calibration on the class with highest score
         given both score and class, see section 2 of [1].
 
         By default "top_label".
@@ -54,7 +52,8 @@ class MapieCalibrator(BaseEstimator, ClassifierMixin):
         The cross-validation strategy to compute scores :
 
         - "split", performs a standard splitting into a calibration and a
-        test set.
+          test set.
+
         - "prefit", assumes that ``estimator`` has been fitted already.
           All the data that are provided in the ``fit`` method are then used
           to calibrate the predictions through the score computation.
diff --git a/mapie/conformity_scores/bounds/residuals.py b/mapie/conformity_scores/bounds/residuals.py
index f59084455..5ce0d799a 100644
--- a/mapie/conformity_scores/bounds/residuals.py
+++ b/mapie/conformity_scores/bounds/residuals.py
@@ -17,8 +17,8 @@ class ResidualNormalisedScore(BaseRegressionScore):
     """
     Residual Normalised score.
 
-    The signed conformity score = (|y - y_pred|) / r_pred. r_pred being the
-    predicted residual (|y - y_pred|) of the base estimator.
+    The signed conformity score = abs(y - y_pred) / r_pred. r_pred being the
+    predicted residual abs(y - y_pred) of the base estimator.
     It is calculated by a model that learns to predict these residuals.
     The learning is done with the log of the residual and we use the
     exponential of the prediction to avoid negative values.
diff --git a/mapie/conformity_scores/classification.py b/mapie/conformity_scores/classification.py
index 00e397128..5dda679cf 100644
--- a/mapie/conformity_scores/classification.py
+++ b/mapie/conformity_scores/classification.py
@@ -61,7 +61,7 @@ def get_predictions(
 
         This method should be implemented by any subclass of the current class.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples, n_features)
             Observed feature values.
@@ -73,7 +73,7 @@ def get_predictions(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of predictions.
@@ -92,7 +92,7 @@ def get_conformity_score_quantiles(
 
         This method should be implemented by any subclass of the current class.
 
-        Parameters:
+        Parameters
         -----------
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores for each sample.
@@ -104,7 +104,7 @@ def get_conformity_score_quantiles(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -125,7 +125,7 @@ def get_prediction_sets(
 
         This method should be implemented by any subclass of the current class.
 
-        Parameters:
+        Parameters
         -----------
         y_pred_proba: NDArray of shape (n_samples, n_classes)
             Target prediction.
@@ -140,7 +140,7 @@ def get_prediction_sets(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -205,7 +205,7 @@ def predict_set(
         Compute the prediction sets on new samples based on the uncertainty of
         the target confidence set.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples,)
             The input data or samples for prediction.
@@ -216,7 +216,7 @@ def predict_set(
         **kwargs: dict
             Additional keyword arguments.
 
-        Returns:
+        Returns
         --------
         The output structure depend on the ``get_sets`` method.
             The prediction sets for each sample and each alpha level.
diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index e6e098464..1803ff54c 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -23,18 +23,17 @@ class BaseRegressionScore(BaseConformityScore, metaclass=ABCMeta):
         Whether to consider the conformity score as symmetrical or not.
 
     consistency_check: bool, optional
-        Whether to check the consistency between the methods
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, the following equality must be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
+        Whether to check the consistency between the
+        methods ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, ``self.get_estimation_distribution`` called with params
+        ``y_pred`` and ``self.get_conformity_scores(y, y_pred, **kwargs)`` must
+        be equal to ``y``.
 
         By default ``True``.
 
     eps: float, optional
-        Threshold to consider when checking the consistency between
-        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        Threshold to consider when checking the consistency
+        between ``get_estimation_distribution`` and ``get_conformity_scores``.
         It should be specified if ``consistency_check==True``.
 
         By default, it is defined by the default precision.
@@ -390,7 +389,7 @@ def predict_set(
         Compute the prediction sets on new samples based on the uncertainty of
         the target confidence set.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples,)
             The input data or samples for prediction.
@@ -401,7 +400,7 @@ def predict_set(
         **kwargs: dict
             Additional keyword arguments.
 
-        Returns:
+        Returns
         --------
         The output structure depend on the ``get_bounds`` method.
             The prediction sets for each sample and each alpha level.
diff --git a/mapie/conformity_scores/sets/aps.py b/mapie/conformity_scores/sets/aps.py
index 8e5cb7d27..9847f8b7d 100644
--- a/mapie/conformity_scores/sets/aps.py
+++ b/mapie/conformity_scores/sets/aps.py
@@ -53,7 +53,7 @@ def get_predictions(
         """
         Get predictions from an EnsembleClassifier.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples, n_features)
             Observed feature values.
@@ -72,7 +72,7 @@ def get_predictions(
 
             By default ``"mean"``.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of predictions.
@@ -178,7 +178,7 @@ def get_conformity_score_quantiles(
         """
         Get the quantiles of the conformity scores for each uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores for each sample.
@@ -197,7 +197,7 @@ def get_conformity_score_quantiles(
 
             By default ``"mean"``.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -222,7 +222,7 @@ def _compute_v_parameter(
         """
         Compute the V parameters from Romano+(2020).
 
-        Parameters:
+        Parameters
         -----------
         y_proba_last_cumsumed: NDArray of shape (n_samples, n_alpha)
             Cumulated score of the last included label.
@@ -236,7 +236,7 @@ def _compute_v_parameter(
         predicition_sets: NDArray of shape (n_samples, n_alpha)
             Prediction sets.
 
-        Returns:
+        Returns
         --------
         NDArray of shape (n_samples, n_alpha)
             Vs parameters.
@@ -337,7 +337,7 @@ def get_prediction_sets(
         Generate prediction sets based on the probability predictions,
         the conformity scores and the uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         y_pred_proba: NDArray of shape (n_samples, n_classes)
             Target prediction.
@@ -365,7 +365,7 @@ def get_prediction_sets(
 
             By default, ``True``.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
diff --git a/mapie/conformity_scores/sets/lac.py b/mapie/conformity_scores/sets/lac.py
index bf5bcbd01..e5f088158 100644
--- a/mapie/conformity_scores/sets/lac.py
+++ b/mapie/conformity_scores/sets/lac.py
@@ -87,7 +87,7 @@ def get_predictions(
         """
         Get predictions from an EnsembleClassifier.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples, n_features)
             Observed feature values.
@@ -106,7 +106,7 @@ def get_predictions(
 
             By default ``"mean"``.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of predictions.
@@ -131,7 +131,7 @@ def get_conformity_score_quantiles(
         """
         Get the quantiles of the conformity scores for each uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores for each sample.
@@ -150,7 +150,7 @@ def get_conformity_score_quantiles(
 
             By default ``"mean"``.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -180,7 +180,7 @@ def get_prediction_sets(
         Generate prediction sets based on the probability predictions,
         the conformity scores and the uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         y_pred_proba: NDArray of shape (n_samples, n_classes)
             Target prediction.
@@ -202,7 +202,7 @@ def get_prediction_sets(
 
             By default ``"mean"``.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
diff --git a/mapie/conformity_scores/sets/naive.py b/mapie/conformity_scores/sets/naive.py
index 19b0e42c9..09bafa181 100644
--- a/mapie/conformity_scores/sets/naive.py
+++ b/mapie/conformity_scores/sets/naive.py
@@ -67,7 +67,7 @@ def get_predictions(
         """
         Get predictions from an EnsembleClassifier.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples, n_features)
             Observed feature values.
@@ -79,7 +79,7 @@ def get_predictions(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of predictions.
@@ -101,7 +101,7 @@ def get_conformity_score_quantiles(
         """
         Get the quantiles of the conformity scores for each uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores for each sample.
@@ -113,7 +113,7 @@ def get_conformity_score_quantiles(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -241,7 +241,7 @@ def get_prediction_sets(
         Generate prediction sets based on the probability predictions,
         the conformity scores and the uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         y_pred_proba: NDArray of shape (n_samples, n_classes)
             Target prediction.
@@ -256,7 +256,7 @@ def get_prediction_sets(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
diff --git a/mapie/conformity_scores/sets/raps.py b/mapie/conformity_scores/sets/raps.py
index 1c39aed8f..435c135ba 100644
--- a/mapie/conformity_scores/sets/raps.py
+++ b/mapie/conformity_scores/sets/raps.py
@@ -388,7 +388,7 @@ def get_conformity_score_quantiles(
         """
         Get the quantiles of the conformity scores for each uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores for each sample.
@@ -435,7 +435,7 @@ def get_conformity_score_quantiles(
 
             By default, "None" but must be set to work.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -549,7 +549,7 @@ def _compute_v_parameter(
         """
         Compute the V parameters from Angelopoulos+(2020).
 
-        Parameters:
+        Parameters
         -----------
         y_proba_last_cumsumed: NDArray of shape (n_samples, n_alpha)
             Cumulated score of the last included label.
@@ -563,7 +563,7 @@ def _compute_v_parameter(
         predicition_sets: NDArray of shape (n_samples, n_alpha)
             Prediction sets.
 
-        Returns:
+        Returns
         --------
         NDArray of shape (n_samples, n_alpha)
             Vs parameters.
diff --git a/mapie/conformity_scores/sets/topk.py b/mapie/conformity_scores/sets/topk.py
index 4e86a2671..cfad29a0a 100644
--- a/mapie/conformity_scores/sets/topk.py
+++ b/mapie/conformity_scores/sets/topk.py
@@ -92,7 +92,7 @@ def get_predictions(
 
         This method should be implemented by any subclass of the current class.
 
-        Parameters:
+        Parameters
         -----------
         X: NDArray of shape (n_samples, n_features)
             Observed feature values.
@@ -104,7 +104,7 @@ def get_predictions(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of predictions.
@@ -126,7 +126,7 @@ def get_conformity_score_quantiles(
         """
         Get the quantiles of the conformity scores for each uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         conformity_scores: NDArray of shape (n_samples,)
             Conformity scores for each sample.
@@ -138,7 +138,7 @@ def get_conformity_score_quantiles(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
@@ -157,7 +157,7 @@ def get_prediction_sets(
         Generate prediction sets based on the probability predictions,
         the conformity scores and the uncertainty level.
 
-        Parameters:
+        Parameters
         -----------
         y_pred_proba: NDArray of shape (n_samples, n_classes)
             Target prediction.
@@ -172,7 +172,7 @@ def get_prediction_sets(
         estimator: EnsembleClassifier
             Estimator that is fitted to predict y from X.
 
-        Returns:
+        Returns
         --------
         NDArray
             Array of quantiles with respect to alpha_np.
diff --git a/mapie/metrics.py b/mapie/metrics.py
index 20c5065f0..9fb2b0938 100644
--- a/mapie/metrics.py
+++ b/mapie/metrics.py
@@ -41,7 +41,7 @@ def regression_coverage_score(
         Effective coverage obtained by the prediction intervals.
 
     Examples
-    --------
+    ---------
     >>> from mapie.metrics import regression_coverage_score
     >>> import numpy as np
     >>> y_true = np.array([5, 7.5, 9.5, 10.5, 12.5])
@@ -1175,8 +1175,8 @@ def kolmogorov_smirnov_statistic(y_true: NDArray, y_score: NDArray) -> float:
     The Journal of Machine Learning Research.
     2022 Jan 1;23(1):15886-940.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import numpy as np
     >>> from mapie.metrics import kolmogorov_smirnov_statistic
     >>> y_true = np.array([0, 1, 0, 1, 0])
@@ -1231,8 +1231,8 @@ def kolmogorov_smirnov_cdf(x: float) -> float:
     Ann. Math. Statist. 24 (4) 624 - 639, December,
     1953.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import numpy as np
     >>> from mapie.metrics import kolmogorov_smirnov_cdf
     >>> print(np.round(kolmogorov_smirnov_cdf(1), 4))
@@ -1282,8 +1282,8 @@ def kolmogorov_smirnov_p_value(y_true: NDArray, y_score: NDArray) -> float:
     Ann. Math. Statist. 24 (4) 624 - 639, December,
     1953.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import pandas as pd
     >>> from mapie.metrics import kolmogorov_smirnov_p_value
     >>> y_true = np.array([1, 0, 1, 0, 1, 0])
@@ -1333,8 +1333,8 @@ def kuiper_statistic(y_true: NDArray, y_score: NDArray) -> float:
     The Journal of Machine Learning Research.
     2022 Jan 1;23(1):15886-940.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import numpy as np
     >>> from mapie.metrics import kuiper_statistic
     >>> y_true = np.array([0, 1, 0, 1, 0])
@@ -1388,8 +1388,8 @@ def kuiper_cdf(x: float) -> float:
     Ann. Math. Statist. 22 (3) 427 - 432
     September, 1951.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import numpy as np
     >>> from mapie.metrics import kuiper_cdf
     >>> print(np.round(kuiper_cdf(1), 4))
@@ -1449,8 +1449,8 @@ def kuiper_p_value(y_true: NDArray, y_score: NDArray) -> float:
     Ann. Math. Statist. 22 (3) 427 - 432
     September, 1951.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import pandas as pd
     >>> from mapie.metrics import kuiper_p_value
     >>> y_true = np.array([1, 0, 1, 0, 1, 0])
@@ -1499,8 +1499,8 @@ def spiegelhalter_statistic(y_true: NDArray, y_score: NDArray) -> float:
     Statistics in medicine.
     1986 Sep;5(5):421-33.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import numpy as np
     >>> from mapie.metrics import spiegelhalter_statistic
     >>> y_true = np.array([0, 1, 0, 1, 0])
@@ -1556,8 +1556,8 @@ def spiegelhalter_p_value(y_true: NDArray, y_score: NDArray) -> float:
     Statistics in medicine.
     1986 Sep;5(5):421-33.
 
-    Example
-    -------
+    Examples
+    --------
     >>> import numpy as np
     >>> from mapie.metrics import spiegelhalter_p_value
     >>> y_true = np.array([1, 0, 1, 0, 1, 0])
diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index e30646ab3..df04a41d1 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -686,8 +686,8 @@ def predict(
             - NDArray of shape (n_samples,) if ``alpha`` is ``None``.
             - Tuple[NDArray, NDArray] of shapes (n_samples,) and
               (n_samples, 2, n_alpha) if ``alpha`` is not ``None``.
-                - [:, 0, :]: Lower bound of the prediction interval.
-                - [:, 1, :]: Upper bound of the prediction interval.
+              - [:, 0, :]: Lower bound of the prediction interval.
+              - [:, 1, :]: Upper bound of the prediction interval.
         """
         check_is_fitted(self, self.fit_attributes)
         check_defined_variables_predict_cqr(ensemble, alpha)
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index aa6656e81..8d6e10ffc 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -67,9 +67,9 @@ class MapieRegressor(BaseEstimator, RegressorMixin):
           ``sklearn.model_selection.LeaveOneOut()``.
         - CV splitter: any ``sklearn.model_selection.BaseCrossValidator``
           Main variants are:
-            - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
-            - ``sklearn.model_selection.KFold`` (cross-validation),
-            - ``subsample.Subsample`` object (bootstrap).
+          - ``sklearn.model_selection.LeaveOneOut`` (jackknife),
+          - ``sklearn.model_selection.KFold`` (cross-validation),
+          - ``subsample.Subsample`` object (bootstrap).
         - ``"split"``, does not involve cross-validation but a division
           of the data into training and calibration subsets. The splitter
           used is the following: ``sklearn.model_selection.ShuffleSplit``.
@@ -624,8 +624,8 @@ def predict(
             - NDArray of shape (n_samples,) if ``alpha`` is ``None``.
             - Tuple[NDArray, NDArray] of shapes (n_samples,) and
               (n_samples, 2, n_alpha) if ``alpha`` is not ``None``.
-                - [:, 0, :]: Lower bound of the prediction interval.
-                - [:, 1, :]: Upper bound of the prediction interval.
+              - [:, 0, :]: Lower bound of the prediction interval.
+              - [:, 1, :]: Upper bound of the prediction interval.
         """
         # Checks
         if hasattr(self, '_predict_params'):
diff --git a/mapie/regression/time_series_regression.py b/mapie/regression/time_series_regression.py
index a2c76ce95..e4e6f5520 100644
--- a/mapie/regression/time_series_regression.py
+++ b/mapie/regression/time_series_regression.py
@@ -451,8 +451,8 @@ def predict(
             - NDArray of shape (n_samples,) if ``alpha`` is ``None``.
             - Tuple[NDArray, NDArray] of shapes (n_samples,) and
               (n_samples, 2, n_alpha) if ``alpha`` is not ``None``.
-                - [:, 0, :]: Lower bound of the prediction interval.
-                - [:, 1, :]: Upper bound of the prediction interval.
+              - [:, 0, :]: Lower bound of the prediction interval.
+              - [:, 1, :]: Upper bound of the prediction interval.
         """
         if alpha is None:
             super().predict(
diff --git a/mapie/subsample.py b/mapie/subsample.py
index ed3c3ba4e..88293bc5e 100644
--- a/mapie/subsample.py
+++ b/mapie/subsample.py
@@ -170,6 +170,7 @@ def split(
             The training set indices for that split.
         test : NDArray of shape (n_indices_test,)
             The testing set indices for that split.
+
         Raises
         ------
         ValueError

From 87c6f0eb8e700b4f2333cae59fbf34d237e5adeb Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 27 Nov 2024 17:38:20 +0100
Subject: [PATCH 387/424] DOC: fix remaining doc building warnings (#541)

DOC: fix remaining warnings when building doc
---
 HISTORY.rst                           |  2 +-
 mapie/conformity_scores/regression.py | 27 ++++++++++++++++-----------
 2 files changed, 17 insertions(+), 12 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 5a896877a..e78de6b1b 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -8,7 +8,7 @@ History
 * Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues.
 * Bump wheel version to avoid known security vulnerabilities
 * Fix issue 495 to center correctly the prediction intervals
-* Fix most documentation build warnings
+* Fix documentation build warnings
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index 1803ff54c..e8ad3d44d 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -23,11 +23,14 @@ class BaseRegressionScore(BaseConformityScore, metaclass=ABCMeta):
         Whether to consider the conformity score as symmetrical or not.
 
     consistency_check: bool, optional
-        Whether to check the consistency between the
-        methods ``get_estimation_distribution`` and ``get_conformity_scores``.
-        If ``True``, ``self.get_estimation_distribution`` called with params
-        ``y_pred`` and ``self.get_conformity_scores(y, y_pred, **kwargs)`` must
-        be equal to ``y``.
+        Whether to check the consistency between the methods
+        ``get_estimation_distribution`` and ``get_conformity_scores``.
+        If ``True``, the following equality must be verified::
+
+            y == self.get_estimation_distribution(
+                y_pred,
+                self.get_conformity_scores(y, y_pred, **kwargs),
+                **kwargs)
 
         By default ``True``.
 
@@ -119,10 +122,12 @@ def check_consistency(
         Check consistency between the following methods:
         ``get_estimation_distribution`` and ``get_signed_conformity_scores``
 
-        The following equality should be verified:
-        ``self.get_estimation_distribution(
-            y_pred, self.get_conformity_scores(y, y_pred, **kwargs), **kwargs
-        ) == y``
+        The following equality should be verified::
+
+            y == self.get_estimation_distribution(
+                y_pred,
+                self.get_conformity_scores(y, y_pred, **kwargs),
+                **kwargs)
 
         Parameters
         ----------
@@ -302,9 +307,9 @@ def get_bounds(
         Tuple[NDArray, NDArray, NDArray]
             - The predictions itself. (y_pred) of shape (n_samples,).
             - The lower bounds of the prediction intervals of shape
-            (n_samples, n_alpha).
+              (n_samples, n_alpha).
             - The upper bounds of the prediction intervals of shape
-            (n_samples, n_alpha).
+              (n_samples, n_alpha).
 
         Raises
         ------

From e99dd30d75bafdc9b1cf0f007931c8206846a9ce Mon Sep 17 00:00:00 2001
From: jawadhussein462 <jawadhussein462@gmail.com>
Date: Thu, 5 Dec 2024 14:57:08 +0100
Subject: [PATCH 388/424] FIX: fix broken ENS logo

---
 README.rst | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.rst b/README.rst
index 97aa824f1..f117b4036 100644
--- a/README.rst
+++ b/README.rst
@@ -186,7 +186,7 @@ and with the financial support from Région Ile de France and Confiance.ai.
     :width: 45px
     :target: https://www.michelin.com/en/
 
-.. |ENS| image:: https://file.diplomeo-static.com/file/00/00/01/34/13434.svg
+.. |ENS| image:: https://www.ens.psl.eu/sites/default/files/logo_ens_psl_en_png.png
     :height: 35px
     :width: 140px
     :target: https://ens-paris-saclay.fr/en/

From 3007a9fa5cf24f927211319f74fd55c0a5c98238 Mon Sep 17 00:00:00 2001
From: "Syed Affan D." <73064995+sulphatet@users.noreply.github.com>
Date: Thu, 5 Dec 2024 20:01:38 +0530
Subject: [PATCH 389/424] Fix #548 by changing 'label' (#549)

DOC: fix plot labels in main tutorial notebook for regression
---
 AUTHORS.rst                                     |  1 +
 HISTORY.rst                                     |  1 +
 .../plot_main-tutorial-regression.py            | 17 ++++++++++++-----
 3 files changed, 14 insertions(+), 5 deletions(-)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index f3fb6f468..236746766 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -45,4 +45,5 @@ Contributors
 * Baptiste Calot <baptiste.calot@capgemini.com>
 * Leonardo Garma <leonardo.garma@gmail.com>
 * Mohammed Jawhar <mohammedjawhar365@gmail.com>
+* Syed Affan <saffand03@gmail.com>
 To be continued ...
diff --git a/HISTORY.rst b/HISTORY.rst
index e78de6b1b..79acada2c 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -9,6 +9,7 @@ History
 * Bump wheel version to avoid known security vulnerabilities
 * Fix issue 495 to center correctly the prediction intervals
 * Fix documentation build warnings
+* Fix issue 548 to correct labels generated in tutorial
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/examples/regression/4-tutorials/plot_main-tutorial-regression.py b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
index a1e331fb8..8c3dd89c8 100644
--- a/examples/regression/4-tutorials/plot_main-tutorial-regression.py
+++ b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
@@ -176,13 +176,20 @@ def plot_1d_data(
 ):
     ax.set_xlabel("x")
     ax.set_ylabel("y")
-    ax.fill_between(X_test, y_pred_low, y_pred_up, alpha=0.3)
-    ax.scatter(X_train, y_train, color="red", alpha=0.3, label="Training data")
-    ax.plot(X_test, y_test, color="gray", label="True confidence intervals")
-    ax.plot(X_test, y_test - y_sigma, color="gray", ls="--")
+    ax.fill_between(
+      X_test, y_pred_low, y_pred_up, alpha=0.3, label="Prediction intervals"
+    )
+    ax.scatter(
+      X_train, y_train, color="red", alpha=0.3, label="Training data"
+    )
+    ax.plot(X_test, y_test, color="gray")
+    ax.plot(
+        X_test, y_test - y_sigma, color="gray", ls="--",
+        label="True confidence intervals"
+    )
     ax.plot(X_test, y_test + y_sigma, color="gray", ls="--")
     ax.plot(
-        X_test, y_pred, color="blue", alpha=0.5, label="Prediction intervals"
+        X_test, y_pred, color="blue", alpha=0.5, label="y_pred"
     )
     if title is not None:
         ax.set_title(title)

From 476d44880925e646f4187fbeed3d43075d0b2cde Mon Sep 17 00:00:00 2001
From: jawadhussein462 <jawadhussein462@gmail.com>
Date: Thu, 5 Dec 2024 18:15:04 +0100
Subject: [PATCH 390/424] ADD: add comment in history.rst

---
 HISTORY.rst | 1 +
 1 file changed, 1 insertion(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index e78de6b1b..02c1152f9 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -9,6 +9,7 @@ History
 * Bump wheel version to avoid known security vulnerabilities
 * Fix issue 495 to center correctly the prediction intervals
 * Fix documentation build warnings
+* Fix issue 528 to correct broken ENS image in the documentation
 
 0.9.1 (2024-09-13)
 ------------------

From 0179598ff76eeca491bc00cc6d4f2913db20b8ec Mon Sep 17 00:00:00 2001
From: jawadhussein462 <41950044+jawadhussein462@users.noreply.github.com>
Date: Mon, 9 Dec 2024 09:53:57 +0100
Subject: [PATCH 391/424] Fix: 547 wrong warning (when using
 regression_coverage_score) (#555)

---
 HISTORY.rst      | 1 +
 mapie/metrics.py | 8 +++-----
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index fafdfecb7..18a4fe855 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -11,6 +11,7 @@ History
 * Fix documentation build warnings
 * Fix issue 528 to correct broken ENS image in the documentation
 * Fix issue 548 to correct labels generated in tutorial
+* Fix issue 547 to fix wrong warning
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/mapie/metrics.py b/mapie/metrics.py
index 9fb2b0938..4926ab782 100644
--- a/mapie/metrics.py
+++ b/mapie/metrics.py
@@ -8,11 +8,10 @@
 from ._machine_precision import EPSILON
 from ._typing import ArrayLike, NDArray
 from .utils import (calc_bins, check_alpha, check_array_inf, check_array_nan,
-                    check_array_shape_classification,
+                    check_array_shape_classification, check_split_strategy,
                     check_array_shape_regression, check_arrays_length,
-                    check_binary_zero_one, check_lower_upper_bounds,
-                    check_nb_intervals_sizes, check_nb_sets_sizes,
-                    check_number_bins, check_split_strategy)
+                    check_binary_zero_one, check_nb_intervals_sizes,
+                    check_nb_sets_sizes, check_number_bins)
 
 
 def regression_coverage_score(
@@ -55,7 +54,6 @@ def regression_coverage_score(
     y_pred_up = cast(NDArray, column_or_1d(y_pred_up))
 
     check_arrays_length(y_true, y_pred_low, y_pred_up)
-    check_lower_upper_bounds(y_true, y_pred_low, y_pred_up)
     check_array_nan(y_true)
     check_array_inf(y_true)
     check_array_nan(y_pred_low)

From 651231215ccae27d727a3b5f633e02758a1dc5b4 Mon Sep 17 00:00:00 2001
From: jawadhussein462 <41950044+jawadhussein462@users.noreply.github.com>
Date: Fri, 13 Dec 2024 13:11:36 +0100
Subject: [PATCH 392/424] DOC: fix display of mathematical equations in
 generated notebooks (#562)

DOC: fix display of mathematical equations in generated notebooks (#562)
---
 .../plot_comp_methods_on_2d_dataset.py        | 22 ++++-----
 .../4-tutorials/plot_crossconformal.py        |  4 +-
 ...lot_main-tutorial-binary-classification.py | 22 ++++-----
 .../plot_main-tutorial-classification.py      | 22 ++++-----
 ...plot_tutorial_multilabel_classification.py | 46 +++++++++----------
 .../plot_cqr_symmetry_difference.py           |  2 +-
 .../regression/1-quickstart/plot_prefit.py    |  2 +-
 .../plot-coverage-width-based-criterion.py    |  6 +--
 .../2-advanced-analysis/plot_nested-cv.py     |  4 +-
 .../4-tutorials/plot_cqr_tutorial.py          |  6 +--
 .../plot_main-tutorial-regression.py          | 34 +++++++-------
 11 files changed, 85 insertions(+), 85 deletions(-)

diff --git a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py b/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py
index f156233a4..014ed943a 100644
--- a/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py
+++ b/examples/classification/1-quickstart/plot_comp_methods_on_2d_dataset.py
@@ -13,7 +13,7 @@
 # We will use MAPIE to estimate a prediction set of several classes such that
 # the probability that the true label of a new test point is included in the
 # prediction set is always higher than the target confidence level :
-# :math:`1 - \alpha`.
+# ``1 - α``.
 # Throughout this tutorial, we compare two conformity scores :
 # softmax score or cumulated softmax score.
 # We start by using the softmax score or cumulated score output by the base
@@ -23,18 +23,18 @@
 # * First we generate a dataset with train, calibration and test, the model
 #   is fitted in the training set.
 #
-# * We set the conformal score :math:`S_i = \hat{f}(X_{i})_{y_i}`
+# * We set the conformal score ``Sᵢ = 𝑓̂(Xᵢ)ᵧᵢ``
 #   from the softmax output of the true class or the cumulated score
 #   (by decreasing order) for each sample in the calibration set.
 #
-# * Then we define :math:`\hat{q}` as being the
-#   :math:`(n + 1) (1 - \alpha) / n`
-#   previous quantile of :math:`S_{1}, ..., S_{n}` (this is essentially the
-#   quantile :math:`\alpha`, but with a small sample correction).
+# * Then we define q̂ as being the
+#   ``(n + 1)(1 - α) / n``
+#   previous quantile of ``S₁, ..., Sₙ`` (this is essentially the
+#   quantile α, but with a small sample correction).
 #
-# * Finally, for a new test data point (where :math:`X_{n + 1}` is known but
-#   :math:`Y_{n + 1}` is not), create a prediction set
-#   :math:`C(X_{n+1}) = \{y: \hat{f}(X_{n+1})_{y} > \hat{q}\}` which includes
+# * Finally, for a new test data point (where ``Xₙ₊₁`` is known but
+#   ``Yₙ₊₁`` is not), create a prediction set
+#   ``C(Xₙ₊₁) = {y: 𝑓̂(Xₙ₊₁)ᵧ > q̂}`` which includes
 #   all the classes with a sufficiently high conformity score.
 #
 # We use a two-dimensional dataset with three labels.
@@ -241,7 +241,7 @@ def plot_results(
 # in ambiguous regions.
 #
 # Let's now compare the effective coverage and the average of prediction set
-# widths as function of the :math:`1-\alpha` target coverage.
+# widths as function of the ``1 - α`` target coverage.
 
 alpha_ = np.arange(0.02, 0.98, 0.02)
 coverage, mean_width = {}, {}
@@ -288,6 +288,6 @@ def plot_results(
 
 ##############################################################################
 # It is seen that both methods give coverages close to the target coverages,
-# regardless of the :math:`\alpha` value. However, the "aps"
+# regardless of the ``α`` value. However, the "aps"
 # produces slightly bigger prediction sets, but without empty regions
 # (if the selection of the last label is not randomized).
diff --git a/examples/classification/4-tutorials/plot_crossconformal.py b/examples/classification/4-tutorials/plot_crossconformal.py
index 7fe8bbac5..f9469300b 100644
--- a/examples/classification/4-tutorials/plot_crossconformal.py
+++ b/examples/classification/4-tutorials/plot_crossconformal.py
@@ -18,8 +18,8 @@
 of this documentation.
 
 We start the tutorial by splitting our training dataset
-in :math:`K` folds and sequentially use each fold as a
-calibration set, the :math:`K-1` folds remaining folds are
+in ``K`` folds and sequentially use each fold as a
+calibration set, the ``K-1`` folds remaining folds are
 used for training the base model using
 the ``cv="prefit"`` option of
 :class:`~mapie.classification.MapieClassifier`.
diff --git a/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py b/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py
index 24d20369a..f83d24011 100644
--- a/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py
+++ b/examples/classification/4-tutorials/plot_main-tutorial-binary-classification.py
@@ -45,7 +45,7 @@
 # We will use MAPIE to estimate a prediction set such that
 # the probability that the true label of a new test point is included in the
 # prediction set is always higher than the target confidence level :
-# :math:`1 - \alpha`.
+# ``1 - α``.
 # We start by using the softmax score output by the base
 # classifier as the conformity score on a toy two-dimensional dataset.
 # We estimate the prediction sets as follows :
@@ -53,18 +53,18 @@
 # * First we generate a dataset with train, calibration and test, the model
 #   is fitted in the training set.
 #
-# * We set the conformal score :math:`S_i = \hat{f}(X_{i})_{y_i}`
+# * We set the conformal score ``Sᵢ = 𝑓̂(Xᵢ)ᵧᵢ``
 #   from the softmax output of the true class for each sample
 #   in the calibration set.
 #
-# * Then we define :math:`\hat{q}` as being the
-#   :math:`(n + 1) (1 - \alpha) / n`
-#   previous quantile of :math:`S_{1}, ..., S_{n}` (this is essentially the
-#   quantile :math:`\alpha`, but with a small sample correction).
+# * Then we define ``q̂`` as being the
+#   ``(n + 1) (1 - α) / n``
+#   previous quantile of ``S₁, ...,  Sₙ`` (this is essentially the
+#   quantile ``α``, but with a small sample correction).
 #
-# * Finally, for a new test data point (where :math:`X_{n + 1}` is known but
-#   :math:`Y_{n + 1}` is not), create a prediction set
-#   :math:`C(X_{n+1}) = \{y: \hat{f}(X_{n+1})_{y} > \hat{q}\}` which includes
+# * Finally, for a new test data point (where ``Xₙ₊₁`` is known but
+#   ``Yₙ₊₁`` is not), create a prediction set
+#   ``C(Xₙ₊₁) = {y: 𝑓̂(Xₙ₊₁)ᵧ > q̂}`` which includes
 #   all the classes with a sufficiently high conformity score.
 #
 # We use a two-dimensional dataset with two classes (i.e. YES or NO).
@@ -281,7 +281,7 @@ def plot_results(
 
 ##############################################################################
 # Let's now compare the effective coverage and the average of prediction set
-# widths as function of the :math:`1-\alpha` target coverage.
+# widths as function of the ``1 - α`` target coverage.
 
 alpha_ = np.arange(0.02, 0.98, 0.02)
 
@@ -332,7 +332,7 @@ def plot_coverages_widths(alpha, coverage, width, method):
 
 ##############################################################################
 # It is seen that the method gives coverages close to the target coverages,
-# regardless of the :math:`\alpha` value.
+# regardless of the ``α`` value.
 
 alpha_ = np.arange(0.02, 0.16, 0.01)
 
diff --git a/examples/classification/4-tutorials/plot_main-tutorial-classification.py b/examples/classification/4-tutorials/plot_main-tutorial-classification.py
index 1003141d2..cd57da03a 100644
--- a/examples/classification/4-tutorials/plot_main-tutorial-classification.py
+++ b/examples/classification/4-tutorials/plot_main-tutorial-classification.py
@@ -33,7 +33,7 @@
 # We will use MAPIE to estimate a prediction set of several classes such
 # that the probability that the true label of a new test point is included
 # in the prediction set is always higher than the target confidence level :
-# :math:`P(Y_{n+1} \in \hat{C}_{n, \alpha}(X_{n+1}) \geq 1 - \alpha`.
+# ``P(Yₙ₊₁ ∈ Ĉₙ,α(Xₙ₊₁)) ≥ 1 - α``
 # We start by using the softmax score output by the base classifier as the
 # conformity score on a toy two-dimensional dataset.
 #
@@ -42,17 +42,17 @@
 # * Generate a dataset with train, calibration and test, the model is
 #   fitted on the training set.
 #
-# * Set the conformal score :math:`S_i = \hat{f}(X_{i})_{y_i}` the softmax
+# * Set the conformal score ``Sᵢ = 𝑓̂(Xᵢ)ᵧᵢ``, the softmax
 #   output of the true class for each sample in the calibration set.
 #
-# * Define :math:`\hat{q}` as being the :math:`(n + 1) (\alpha) / n`
-#   previous quantile of :math:`S_{1}, ..., S_{n}`
-#   (this is essentially the quantile :math:`\alpha`, but with a small sample
+# * Define ``q̂`` as being the ``(n + 1)(α) / n``
+#   previous quantile of ``S₁, ..., Sₙ``
+#   (this is essentially the quantile ``α``, but with a small sample
 #   correction).
 #
-# * Finally, for a new test data point (where :math:`X_{n + 1}` is known but
-#   :math:`Y_{n + 1}` is not), create a prediction set
-#   :math:`C(X_{n+1}) = \{y: \hat{f}(X_{n+1})_{y} > \hat{q}\}` which includes
+# * Finally, for a new test data point (where ``Xₙ₊₁`` is known but
+#   ``Yₙ₊₁`` is not), create a prediction set
+#   ``C(Xₙ₊₁) = {y: 𝑓̂(Xₙ₊₁)ᵧ > q̂}`` which includes
 #   all the classes with a sufficiently high softmax output.
 
 # We use a two-dimensional toy dataset with three labels. The distribution of
@@ -205,9 +205,9 @@ def plot_results(alphas, X, y_pred, y_ps):
 # classifier.
 #
 # Let’s now study the effective coverage and the mean prediction set widths
-# as function of the :math:`1-\alpha` target coverage. To this aim, we use once
+# as function of the ``1 - α`` target coverage. To this aim, we use once
 # again the ``predict`` method of MAPIE to estimate predictions sets on a
-# large number of :math:`\alpha` values.
+# large number of ``α`` values.
 
 alpha2 = np.arange(0.02, 0.98, 0.02)
 _, y_ps_score2 = mapie_score.predict(X_test, alpha=alpha2)
@@ -243,7 +243,7 @@ def plot_coverages_widths(alpha, coverage, width, method):
 #
 # We saw in the previous section that the "lac" method is well calibrated by
 # providing accurate coverage levels. However, it tends to give null
-# prediction sets for uncertain regions, especially when the :math:`\alpha`
+# prediction sets for uncertain regions, especially when the ``α``
 # value is high.
 # MAPIE includes another method, called Adaptive Prediction Set (APS),
 # whose conformity score is the cumulated score of the softmax output until
diff --git a/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py b/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
index 096c184c9..a31794bea 100644
--- a/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
+++ b/examples/multilabel_classification/1-quickstart/plot_tutorial_multilabel_classification.py
@@ -102,16 +102,16 @@
 # Bernstein and Waudby-Smith–Ramdas).
 # The two methods give two different guarantees on the risk:
 #
-# * RCPS: :math:`P(R(\mathcal{T}_{\hat{\lambda}})\leq\alpha)\geq 1-\delta`
-#   where :math:`R(\mathcal{T}_{\hat{\lambda}})`
-#   is the risk we want to control and :math:`\alpha` is the desired risk
+# * RCPS: ``𝒫(R(𝒯̂λ̂) ≤ α) ≥ 1 − δ``
+#   where ``R(𝒯̂λ̂)``
+#   is the risk we want to control and α is the desired risk
 #
-# * CRC: :math:`\mathbb{E}\left[L_{n+1}(\hat{\lambda})\right] \leq \alpha`
-#   where :math:`L_{n+1}(\hat{\lambda})` is the risk of a new observation and
-#   :math:`\alpha` is the desired risk
+# * CRC: ``𝐸[Lₙ₊₁(λ̂)] ≤ α``
+#   where ``Lₙ₊₁(λ̂)`` is the risk of a new observation and
+#   ``α`` is the desired risk
 #
 # In both cases, the objective of the method is to find the optimal value of
-# :math:`\lambda` (threshold above which we consider a label as being present)
+# ``λ`` (threshold above which we consider a label as being present)
 # such that the recall on the test points is at least equal to the required
 # recall.
 
@@ -156,7 +156,7 @@
 # * The actual recall (which should be always near to the required one):
 # we can see that they are close to each other.
 # * The value of the threshold: we see that the threshold is decreasing as
-# :math:`1 - \alpha` increases, which is what is expected because a
+# ``1 - α`` increases, which is what is expected because a
 # smaller threshold will give larger prediction sets, hence a larger
 # recall.
 #
@@ -179,11 +179,11 @@
 ##############################################################################
 # 2 - Plots where we choose a specific risk value (0.1 in our case) and look at
 # the average risk, the UCB of the risk (for RCPS methods) and the choice of
-# the threshold :math:`\lambda`
+# the threshold ``λ``.
 # * We can see that among the RCPS methods, the Bernstein method
-# gives the best results as for a given value of :math:`\alpha`
+# gives the best results as for a given value of ``α``
 # as we are above the required recall but with a larger value of
-# :math:`\lambda` than the two others bounds.
+# ``λ`` than the two others bounds.
 # * The CRC method gives the best results since it guarantees the coverage
 # with a larger threshold.
 
@@ -223,20 +223,20 @@
 # In this part, we will use LTT to control precision.
 # At the opposite of the 2 previous method, LTT can handle non-monotonous loss.
 # The procedure consist in multiple hypothesis testing. This is why the output
-# of this procedure isn't reduce to one value of :math:`\lambda`.
+# of this procedure isn't reduce to one value of ``λ``.
 #
-# More precisely, we look after all the :math:`\lambda` that sastisfy the
+# More precisely, we look after all the ``λ`` that sastisfy the
 # following:
-# :math:`\mathbb{P}(R(\mathcal{T}_{\lambda}) \leq \alpha ) \geq 1 - \delta`,
-# where :math:`R(\mathcal{T}_{\lambda})` is the risk we want to control and
-# each :math:`\lambda`` should satisfy FWER control.
-# :math:`\alpha` is the desired risk.
+# ``𝒫(R(𝒯̂λ̂) ≤ α) ≥ 1 − δ``,
+# where ``R(𝒯̂λ̂)`` is the risk we want to control and
+# each ``λ`` should satisfy FWER control.
+# ``α`` is the desired risk.
 #
-# Notice that the procedure will diligently examine each :math:`\lambda`
-# such that the risk remains below level :math:`\alpha`, meaning not
-# every :math:`\lambda` will be considered.
-# This means that a for a :math:`\lambda` such that risk is below
-# :math:`\alpha`
+# Notice that the procedure will diligently examine each ``λ``
+# such that the risk remains below level ``α``, meaning not
+# every ``λ`` will be considered.
+# This means that a for a ``λ`` such that risk is below
+# ``α``
 # doesn't necessarly pass the FWER control! This is what we are going to
 # explore.
 
@@ -267,7 +267,7 @@
 ##############################################################################
 # 3.2 Valid parameters for precision control
 # ----------------------------------------------------------------------------
-# We can see that not all :math:`\lambda` such that risk is below the orange
+# We can see that not all ``λ`` such that risk is below the orange
 # line are choosen by the procedure. Otherwise, all the lambdas that are
 # in the red rectangle verify family wise error rate control and allow to
 # control precision at the desired level with a high probability.
diff --git a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
index aab634638..9fec3d91d 100644
--- a/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
+++ b/examples/regression/1-quickstart/plot_cqr_symmetry_difference.py
@@ -111,4 +111,4 @@
 # each bound, allowing for more flexible and accurate intervals that reflect
 # the heteroscedastic nature of the data. The resulting effective coverages
 # demonstrate the theoretical guarantee of the target coverage level
-# :math:`1 - \alpha`.
+# ``1 - α``.
diff --git a/examples/regression/1-quickstart/plot_prefit.py b/examples/regression/1-quickstart/plot_prefit.py
index d982398b5..91498c3ee 100644
--- a/examples/regression/1-quickstart/plot_prefit.py
+++ b/examples/regression/1-quickstart/plot_prefit.py
@@ -74,7 +74,7 @@ def f(x: NDArray) -> NDArray:
 # quantile regression using
 # :class:`~mapie.quanitle_regression.MapieQuantileRegressor`. Note that the
 # three estimators need to be trained at quantile values of
-# :math:`(\alpha/2, 1-(\alpha/2), 0.5)`.
+# ``(α/2, 1-(α/2), 0.5)``.
 
 
 # Train a MLPRegressor for MapieRegressor
diff --git a/examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py b/examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py
index c606d6095..f9c7bdfb2 100644
--- a/examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py
+++ b/examples/regression/2-advanced-analysis/plot-coverage-width-based-criterion.py
@@ -33,7 +33,7 @@
 # Estimating the aleatoric uncertainty of heteroscedastic noisy data
 # ---------------------------------------------------------------------
 #
-# Let's define again the :math:`x \times \sin(x)` function and another simple
+# Let's define again the ``x * sin(x)`` function and another simple
 # function that generates one-dimensional data with normal noise uniformely
 # in a given interval.
 
@@ -70,7 +70,7 @@ def get_1d_data_with_heteroscedastic_noise(
 ##############################################################################
 # We first generate noisy one-dimensional data uniformely on an interval.
 # Here, the noise is considered as *heteroscedastic*, since it will increase
-# linearly with :math:`x`.
+# linearly with `x`.
 
 min_x, max_x, n_samples, noise = 0, 5, 300, 0.5
 (
@@ -92,7 +92,7 @@ def get_1d_data_with_heteroscedastic_noise(
 ##############################################################################
 # As mentioned previously, we fit our training data with a simple
 # polynomial function. Here, we choose a degree equal to 10 so the function
-# is able to perfectly fit :math:`x \times \sin(x)`.
+# is able to perfectly fit ``x * sin(x)``.
 
 degree_polyn = 10
 polyn_model = Pipeline(
diff --git a/examples/regression/2-advanced-analysis/plot_nested-cv.py b/examples/regression/2-advanced-analysis/plot_nested-cv.py
index c3aaeadd0..1613dff08 100644
--- a/examples/regression/2-advanced-analysis/plot_nested-cv.py
+++ b/examples/regression/2-advanced-analysis/plot_nested-cv.py
@@ -22,9 +22,9 @@
 cross-validation occurs on the training fold, optimizing hyperparameters.
 This ensures that residuals seen by MAPIE are never seen by the algorithm
 beforehand. However, this method is much heavier computationally since
-it results in :math:`N * P` calculations, where *N* is the number of
+it results in ``N * P`` calculations, where *N* is the number of
 *out-of-fold* models and *P* the number of parameter search cross-validations,
-versus :math:`N + P` for the non-nested approach.
+versus ``N + P`` for the non-nested approach.
 
 Here, we compare the two strategies on a toy dataset. We use the Random
 Forest Regressor as a base regressor for the CV+ strategy. For the sake of
diff --git a/examples/regression/4-tutorials/plot_cqr_tutorial.py b/examples/regression/4-tutorials/plot_cqr_tutorial.py
index 444ef37de..e5dc76c7c 100644
--- a/examples/regression/4-tutorials/plot_cqr_tutorial.py
+++ b/examples/regression/4-tutorials/plot_cqr_tutorial.py
@@ -230,7 +230,7 @@ def plot_prediction_intervals(
 
 ##############################################################################
 # We proceed to using MAPIE to return the predictions and prediction intervals.
-# We will use an :math:`\alpha=0.2`, this means a target coverage of 0.8
+# We will use an ``α=0.2``, this means a target coverage of 0.8
 # (recall that this parameter needs to be initialized directly when setting
 # :class:`~mapie.quantile_regression.MapieQuantileRegressor` and when using
 # :class:`~mapie.regression.MapieRegressor`, it needs to be set in the
@@ -241,7 +241,7 @@ def plot_prediction_intervals(
 #   model on a training set and then calibrates on the calibration set.
 # * ``cv="prefit"`` meaning that you can train your models with the correct
 #   quantile values (must be given in the following order:
-#   :math:`(\alpha, 1-(\alpha/2), 0.5)` and given to MAPIE as an iterable
+#   ``(α, 1-(α/2), 0.5)`` and given to MAPIE as an iterable
 #   object. (Check the examples for how to use prefit in MAPIE)
 #
 # Additionally, note that there is a list of accepted models by
@@ -413,7 +413,7 @@ def get_coverages_widths_by_bins(
 
 ##############################################################################
 # What we observe from these results is that none of the methods seems to
-# have conditional coverage at the target :math:`1 - \alpha`. However, we can
+# have conditional coverage at the target ``1 - α``. However, we can
 # clearly notice that the CQR seems to better adapt to large prices. Its
 # conditional coverage is closer to the target coverage not only for higher
 # prices, but also for lower prices where the other methods have a higher
diff --git a/examples/regression/4-tutorials/plot_main-tutorial-regression.py b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
index 8c3dd89c8..3c5cdb8e0 100644
--- a/examples/regression/4-tutorials/plot_main-tutorial-regression.py
+++ b/examples/regression/4-tutorials/plot_main-tutorial-regression.py
@@ -4,7 +4,7 @@
 ===============================
 
 In this tutorial, we compare the prediction intervals estimated by MAPIE on a
-simple, one-dimensional, ground truth function :math:`f(x) = x \times \sin(x)`.
+simple, one-dimensional, ground truth function ``f(x) = x * sin(x)``.
 Throughout this tutorial, we will answer the following questions:
 
 - How well do the MAPIE strategies capture the aleatoric uncertainty
@@ -47,7 +47,7 @@
 # 1. Estimating the aleatoric uncertainty of homoscedastic noisy data
 # -------------------------------------------------------------------
 #
-# Let's start by defining the :math:`x \times \sin(x)` function and another
+# Let's start by defining the ``x * sin(x)`` function and another
 # simple function that generates one-dimensional data with normal noise
 # uniformely in a given interval.
 
@@ -77,7 +77,7 @@ def get_1d_data_with_constant_noise(funct, min_x, max_x, n_samples, noise):
 ##############################################################################
 # We first generate noisy one-dimensional data uniformely on an interval.
 # Here, the noise is considered as *homoscedastic*, since it remains constant
-# over :math:`x`.
+# over `x`.
 
 
 min_x, max_x, n_samples, noise = -5, 5, 600, 0.5
@@ -97,7 +97,7 @@ def get_1d_data_with_constant_noise(funct, min_x, max_x, n_samples, noise):
 ##############################################################################
 # As mentioned previously, we fit our training data with a simple
 # polynomial function. Here, we choose a degree equal to 10 so the function
-# is able to perfectly fit :math:`x \times \sin(x)`.
+# is able to perfectly fit ``x * sin(x)``.
 
 degree_polyn = 10
 polyn_model = Pipeline(
@@ -226,7 +226,7 @@ def plot_1d_data(
 # At first glance, the four strategies give similar results and the
 # prediction intervals are very close to the true confidence intervals.
 # Let’s confirm this by comparing the prediction interval widths over
-# :math:`x` between all strategies.
+# `x` between all strategies.
 
 
 fig, ax = plt.subplots(1, 1, figsize=(9, 5))
@@ -285,7 +285,7 @@ def plot_1d_data(
 # 2. Estimating the aleatoric uncertainty of heteroscedastic noisy data
 # ---------------------------------------------------------------------
 #
-# Let's define again the :math:`x \times \sin(x)` function and another simple
+# Let's define again the ``x * sin(x)`` function and another simple
 # function that generates one-dimensional data with normal noise uniformely
 # in a given interval.
 
@@ -317,7 +317,7 @@ def get_1d_data_with_heteroscedastic_noise(
 ##############################################################################
 # We first generate noisy one-dimensional data uniformely on an interval.
 # Here, the noise is considered as *heteroscedastic*, since it will increase
-# linearly with :math:`x`.
+# linearly with `x`.
 
 
 min_x, max_x, n_samples, noise = 0, 5, 300, 0.5
@@ -341,7 +341,7 @@ def get_1d_data_with_heteroscedastic_noise(
 ##############################################################################
 # As mentioned previously, we fit our training data with a simple
 # polynomial function. Here, we choose a degree equal to 10 so the function
-# is able to perfectly fit :math:`x \times \sin(x)`.
+# is able to perfectly fit ``x * sin(x)``.
 
 degree_polyn = 10
 polyn_model = Pipeline(
@@ -447,12 +447,12 @@ def get_1d_data_with_heteroscedastic_noise(
 # One can observe that all the strategies behave in a similar way as in the
 # first example shown previously. One exception is the CQR method which takes
 # into account the heteroscedasticity of the data. In this method we observe
-# very low interval widths at low values of :math:`x`.
+# very low interval widths at low values of ``x``.
 # This is the only method that
 # even slightly follows the true width, and therefore is the preferred method
 # for heteroscedastic data. Notice also that the true width is greater (lower)
-# than the predicted width from the other methods at :math:`x \gtrapprox 3``
-# (:math:`x \leq 3`). This means that while the marginal coverage correct for
+# than the predicted width from the other methods at ``x ≳ 3``
+# (``x ≤ 3``). This means that while the marginal coverage correct for
 # these methods, the conditional coverage is likely not guaranteed as we will
 # observe in the next figure.
 
@@ -625,10 +625,10 @@ def get_1d_data_with_normal_distrib(funct, mu, sigma, n_samples, noise):
 
 ##############################################################################
 # At first glance, our polynomial function does not give accurate
-# predictions with respect to the true function when :math:`|x| > 6`.
+# predictions with respect to the true function when ``|x| > 6``.
 # The prediction intervals estimated with the Jackknife+ do not seem to
 # increase. On the other hand, the CV and other related methods seem to capture
-# some uncertainty when :math:`x > 6`.
+# some uncertainty when ``x > 6``.
 #
 # Let's now compare the prediction interval widths between all strategies.
 
@@ -647,16 +647,16 @@ def get_1d_data_with_normal_distrib(funct, mu, sigma, n_samples, noise):
 
 ##############################################################################
 # The prediction interval widths start to increase exponentially
-# for :math:`|x| > 4` for the CV+, CV-minmax, Jackknife-minmax, and quantile
+# for ``|x| > 4`` for the CV+, CV-minmax, Jackknife-minmax, and quantile
 # strategies. On the other hand, the prediction intervals estimated by
-# Jackknife+ remain roughly constant until :math:`|x| \approx 5` before
+# Jackknife+ remain roughly constant until ``|x| ≈ 5`` before
 # increasing.
 # The CQR strategy seems to perform well, however, on the extreme values
 # of the data the quantile regression fails to give reliable results as it
 # outputs
 # negative value for the prediction intervals. This occurs because the quantile
-# regressor with quantile :math:`1 - \alpha/2` gives higher values than the
-# quantile regressor with quantile :math:`\alpha/2`. Note that a warning will
+# regressor with quantile `1 - α/2` gives higher values than the
+# quantile regressor with quantile ``α/2``. Note that a warning will
 # be issued when this occurs.
 
 pd.DataFrame([

From e47171c202bf5397b24576fd45c97e2e6266b072 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Mon, 16 Dec 2024 16:43:20 +0100
Subject: [PATCH 393/424] REFACTO: split MapieRegressor.fit into .init_fit,
 .fit_estimator, and .conformalize, split EnsembleRegressor.fit into
 .fit_single_estimator and .fit_multi_estimators, remove EnsembleEstimator
 useless interface (#564)

REFACTO: split MapieRegressor.fit into .init_fit, .fit_estimator, and .conformalize, split EnsembleRegressor .fit into .fit_single_estimator and .fit_multi_estimators, remove EnsembleEstimator useless interface
---
 HISTORY.rst                    |   2 +
 mapie/estimator/__init__.py    |   2 -
 mapie/estimator/classifier.py  |   3 +-
 mapie/estimator/interface.py   |  40 -----------
 mapie/estimator/regressor.py   | 120 ++++++++++++++++++++++++---------
 mapie/regression/regression.py |  70 ++++++++++++++++---
 mapie/tests/test_regression.py |  18 +++++
 7 files changed, 170 insertions(+), 85 deletions(-)
 delete mode 100644 mapie/estimator/interface.py

diff --git a/HISTORY.rst b/HISTORY.rst
index 18a4fe855..af40fbb2b 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -12,6 +12,8 @@ History
 * Fix issue 528 to correct broken ENS image in the documentation
 * Fix issue 548 to correct labels generated in tutorial
 * Fix issue 547 to fix wrong warning
+* Fix issue 480 (correct display of mathematical equations in generated notebooks)
+* Refactor MapieRegressor and EnsembleRegressor, deprecate EnsembleRegressor.fit
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/mapie/estimator/__init__.py b/mapie/estimator/__init__.py
index 5758db9e6..f4b325fed 100644
--- a/mapie/estimator/__init__.py
+++ b/mapie/estimator/__init__.py
@@ -1,9 +1,7 @@
-from .interface import EnsembleEstimator
 from .regressor import EnsembleRegressor
 from .classifier import EnsembleClassifier
 
 __all__ = [
-    "EnsembleEstimator",
     "EnsembleRegressor",
     "EnsembleClassifier",
 ]
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index ac882996a..0777b9673 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -10,11 +10,10 @@
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
 from mapie._typing import ArrayLike, NDArray
-from mapie.estimator.interface import EnsembleEstimator
 from mapie.utils import check_no_agg_cv, fit_estimator, fix_number_of_classes
 
 
-class EnsembleClassifier(EnsembleEstimator):
+class EnsembleClassifier:
     """
     This class implements methods to handle the training and usage of the
     estimator. This estimator can be unique or composed by cross validated
diff --git a/mapie/estimator/interface.py b/mapie/estimator/interface.py
deleted file mode 100644
index e015d4d7c..000000000
--- a/mapie/estimator/interface.py
+++ /dev/null
@@ -1,40 +0,0 @@
-from __future__ import annotations
-
-from abc import ABCMeta, abstractmethod
-from typing import Tuple, Union
-
-from mapie._typing import ArrayLike, NDArray
-
-
-class EnsembleEstimator(metaclass=ABCMeta):
-    """
-    This class implements methods to handle the training and usage of the
-    estimator. This estimator can be unique or composed by cross validated
-    estimators.
-    """
-
-    @abstractmethod
-    def fit(
-        self,
-        X: ArrayLike,
-        y: ArrayLike,
-        **kwargs
-    ) -> EnsembleEstimator:
-        """
-        Fit the base estimator under the ``single_estimator_`` attribute.
-        Fit all cross-validated estimator clones
-        and rearrange them into a list, the ``estimators_`` attribute.
-        Out-of-fold conformity scores are stored under
-        the ``conformity_scores_`` attribute.
-        """
-
-    @abstractmethod
-    def predict(
-        self,
-        X: ArrayLike,
-        **kwargs
-    ) -> Union[NDArray, Tuple[NDArray, NDArray, NDArray]]:
-        """
-        Predict target from X. It also computes the prediction per train sample
-        for each test sample according to ``self.method``.
-        """
diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index d300863a9..bad8988ca 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -6,17 +6,16 @@
 from joblib import Parallel, delayed
 from sklearn.base import RegressorMixin, clone
 from sklearn.model_selection import BaseCrossValidator
-from sklearn.utils import _safe_indexing
+from sklearn.utils import _safe_indexing, deprecated
 from sklearn.utils.validation import _num_samples, check_is_fitted
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.aggregation_functions import aggregate_all, phi2D
-from mapie.estimator.interface import EnsembleEstimator
 from mapie.utils import (check_nan_in_aposteriori_prediction, check_no_agg_cv,
                          fit_estimator)
 
 
-class EnsembleRegressor(EnsembleEstimator):
+class EnsembleRegressor:
     """
     This class implements methods to handle the training and usage of the
     estimator. This estimator can be unique or composed by cross validated
@@ -409,6 +408,11 @@ def predict_calib(
 
         return y_pred
 
+    @deprecated(
+        "WARNING: EnsembleRegressor.fit is deprecated."
+        "Instead use EnsembleRegressor.fit_single_estimator"
+        "then EnsembleRegressor.fit_multi_estimators"
+    )
     def fit(
         self,
         X: ArrayLike,
@@ -451,42 +455,60 @@ def fit(
         EnsembleRegressor
             The estimator fitted.
         """
-        # Initialization
-        single_estimator_: RegressorMixin
-        estimators_: List[RegressorMixin] = []
-        full_indexes = np.arange(_num_samples(X))
-        cv = self.cv
-        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
-        estimator = self.estimator
+        self.fit_single_estimator(
+            X,
+            y,
+            sample_weight,
+            groups,
+            **fit_params
+        )
+
+        self.fit_multi_estimators(
+            X,
+            y,
+            sample_weight,
+            groups,
+            **fit_params
+        )
+
+        return self
+
+    def fit_multi_estimators(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **fit_params
+    ) -> EnsembleRegressor:
+
         n_samples = _num_samples(y)
+        estimators: List[RegressorMixin] = []
 
-        # Computation
-        if cv == "prefit":
-            single_estimator_ = estimator
+        if self.cv == "prefit":
+
+            # Create a placeholder attribute 'k_' filled with NaN values
+            # This attribute is defined for consistency but
+            # is not used in prefit mode
             self.k_ = np.full(
                 shape=(n_samples, 1), fill_value=np.nan, dtype=float
             )
+
         else:
-            single_estimator_ = self._fit_oof_estimator(
-                clone(estimator),
-                X,
-                y,
-                full_indexes,
-                sample_weight,
-                **fit_params
-            )
-            cv = cast(BaseCrossValidator, cv)
+            cv = cast(BaseCrossValidator, self.cv)
             self.k_ = np.full(
                 shape=(n_samples, cv.get_n_splits(X, y, groups)),
                 fill_value=np.nan,
                 dtype=float,
             )
-            if self.method == "naive":
-                estimators_ = [single_estimator_]
-            else:
-                estimators_ = Parallel(self.n_jobs, verbose=self.verbose)(
+
+            if self.method != "naive":
+                estimators = Parallel(
+                    self.n_jobs,
+                    verbose=self.verbose
+                )(
                     delayed(self._fit_oof_estimator)(
-                        clone(estimator),
+                        clone(self.estimator),
                         X,
                         y,
                         train_index,
@@ -495,13 +517,47 @@ def fit(
                     )
                     for train_index, _ in cv.split(X, y, groups)
                 )
-                # In split-CP, we keep only the model fitted on train dataset
-                if self.use_split_method_:
-                    single_estimator_ = estimators_[0]
 
-        self.single_estimator_ = single_estimator_
-        self.estimators_ = estimators_
+        self.estimators_ = estimators
+
+        return self
+
+    def fit_single_estimator(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **fit_params
+    ) -> EnsembleRegressor:
+
+        self.use_split_method_ = check_no_agg_cv(X, self.cv, self.no_agg_cv_)
+        single_estimator_: RegressorMixin
+
+        if self.cv == "prefit":
+            single_estimator_ = self.estimator
+        else:
+            cv = cast(BaseCrossValidator, self.cv)
+            if self.use_split_method_:
+                train_indexes = [
+                    train_index for train_index, test_index in cv.split(
+                        X, y, groups)
+                ][0]
+                indexes = train_indexes
+            else:
+                full_indexes = np.arange(_num_samples(X))
+                indexes = full_indexes
+
+            single_estimator_ = self._fit_oof_estimator(
+                    clone(self.estimator),
+                    X,
+                    y,
+                    indexes,
+                    sample_weight,
+                    **fit_params
+                )
 
+        self.single_estimator_ = single_estimator_
         return self
 
     def predict(
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 8d6e10ffc..950a9f6af 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -513,12 +513,26 @@ def fit(
         MapieRegressor
             The model itself.
         """
-        fit_params = kwargs.pop('fit_params', {})
-        predict_params = kwargs.pop('predict_params', {})
-        if len(predict_params) > 0:
-            self._predict_params = True
-        else:
-            self._predict_params = False
+
+        X, y, sample_weight, groups = self.init_fit(
+            X, y, sample_weight, groups, **kwargs
+        )
+
+        self.fit_estimator(X, y, sample_weight, groups)
+        self.conformalize(X, y, sample_weight, groups, **kwargs)
+
+        return self
+
+    def init_fit(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **kwargs: Any
+    ):
+
+        self._fit_params = kwargs.pop('fit_params', {})
 
         # Checks
         (estimator,
@@ -540,9 +554,47 @@ def fit(
             self.test_size,
             self.verbose
         )
-        # Fit the prediction function
-        self.estimator_ = self.estimator_.fit(
-            X, y, sample_weight=sample_weight, groups=groups, **fit_params
+
+        return (
+            X, y, sample_weight, groups
+        )
+
+    def fit_estimator(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+    ) -> MapieRegressor:
+
+        self.estimator_.fit_single_estimator(
+            X,
+            y,
+            sample_weight=sample_weight,
+            groups=groups,
+            **self._fit_params
+        )
+
+        return self
+
+    def conformalize(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        **kwargs: Any
+    ) -> MapieRegressor:
+
+        predict_params = kwargs.pop('predict_params', {})
+        self._predict_params = len(predict_params) > 0
+
+        self.estimator_.fit_multi_estimators(
+            X,
+            y,
+            sample_weight,
+            groups,
+            **self._fit_params
         )
 
         # Predict on calibration data
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 9fe6f9c5c..1840ddf91 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -1036,3 +1036,21 @@ def test_check_change_method_to_base(method: str, cv: str) -> None:
     )
     mapie_reg.fit(X_val, y_val)
     assert mapie_reg.method == "base"
+
+
+def test_deprecated_ensemble_regressor_fit_warning() -> None:
+    ens_reg = EnsembleRegressor(
+        LinearRegression(),
+        "plus",
+        KFold(n_splits=5, random_state=None, shuffle=True),
+        "nonsense",
+        None,
+        random_state,
+        0.20,
+        False
+    )
+    with pytest.warns(
+        FutureWarning,
+        match=r".WARNING: EnsembleRegressor.fit is deprecated.*"
+    ):
+        ens_reg.fit(X, y)

From c39946fc6b846c8efd9c1e53a252bce2fda6eb96 Mon Sep 17 00:00:00 2001
From: jawadhussein462 <41950044+jawadhussein462@users.noreply.github.com>
Date: Mon, 16 Dec 2024 16:52:36 +0100
Subject: [PATCH 394/424] REFACTOR: restucture the MapieQuantileRegressor Fit -
 Split the fit into prefit_estimators, fit_estimators and conformalize (#566)

* REFACTOR: restucture the MapieQuantileRegressor Fit - Split the fit into prefit_estimators, fit_estimators and conformalize

Co-authored-by: qroa <qroa405@gmail.com>
Co-authored-by: Valentin Laurent <valentin.laurent.fr@gmail.com>
---
 HISTORY.rst                             |   2 +-
 mapie/regression/quantile_regression.py | 201 +++++++++++++++---------
 2 files changed, 130 insertions(+), 73 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index af40fbb2b..86fcf7873 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -13,7 +13,7 @@ History
 * Fix issue 548 to correct labels generated in tutorial
 * Fix issue 547 to fix wrong warning
 * Fix issue 480 (correct display of mathematical equations in generated notebooks)
-* Refactor MapieRegressor and EnsembleRegressor, deprecate EnsembleRegressor.fit
+* Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index df04a41d1..8c5ef3103 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, List, Optional, Tuple, Union, cast
+from typing import Iterable, Dict, List, Optional, Tuple, Union, cast
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -546,93 +546,150 @@ def fit(
         MapieQuantileRegressor
              The model itself.
         """
-        self.cv = self._check_cv(cast(str, self.cv))
 
-        # Initialization
-        self.estimators_: List[RegressorMixin] = []
-        if self.cv == "prefit":
-            estimator = cast(List, self.estimator)
-            alpha = self._check_alpha(self.alpha)
-            self._check_prefit_params(estimator)
-            X_calib, y_calib = indexable(X, y)
+        self.init_fit()
 
-            self.n_calib_samples = _num_samples(y_calib)
-            y_calib_preds = np.full(
-                shape=(3, self.n_calib_samples),
-                fill_value=np.nan
-            )
-            for i, est in enumerate(estimator):
-                self.estimators_.append(est)
-                y_calib_preds[i] = est.predict(X_calib).ravel()
-            self.single_estimator_ = self.estimators_[2]
+        if self.cv == "prefit":
+            X_calib, y_calib = self.prefit_estimators(X, y)
         else:
-            # Checks
-            self._check_parameters()
-            checked_estimator = self._check_estimator(self.estimator)
-            alpha = self._check_alpha(self.alpha)
-            X, y = indexable(X, y)
-            random_state = check_random_state(random_state)
-            results = self._check_calib_set(
-                X,
-                y,
-                sample_weight,
-                X_calib,
-                y_calib,
-                calib_size,
-                random_state,
-                shuffle,
-                stratify,
+            X_calib, y_calib = self.fit_estimators(
+                X=X,
+                y=y,
+                sample_weight=sample_weight,
+                groups=groups,
+                X_calib=X_calib,
+                y_calib=y_calib,
+                calib_size=calib_size,
+                random_state=random_state,
+                shuffle=shuffle,
+                stratify=stratify,
+                **fit_params,
             )
-            X_train, y_train, X_calib, y_calib, sample_weight_train = results
-            X_train, y_train = indexable(X_train, y_train)
-            X_calib, y_calib = indexable(X_calib, y_calib)
-            y_train, y_calib = _check_y(y_train), _check_y(y_calib)
-            self.n_calib_samples = _num_samples(y_calib)
-            check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
-            sample_weight_train, X_train, y_train = check_null_weight(
-                sample_weight_train,
+
+        self.conformalize(X_calib, y_calib)
+
+        return self
+
+    def init_fit(self):
+
+        self.cv = self._check_cv(cast(str, self.cv))
+        self.alpha_np = self._check_alpha(self.alpha)
+        self.estimators_: List[RegressorMixin] = []
+
+    def prefit_estimators(
+        self,
+        X: ArrayLike,
+        y: ArrayLike
+    ) -> Tuple[ArrayLike, ArrayLike]:
+
+        estimator = cast(List, self.estimator)
+        self._check_prefit_params(estimator)
+        self.estimators_ = list(estimator)
+        self.single_estimator_ = self.estimators_[2]
+
+        X_calib, y_calib = indexable(X, y)
+        return X_calib, y_calib
+
+    def fit_estimators(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        groups: Optional[ArrayLike] = None,
+        X_calib: Optional[ArrayLike] = None,
+        y_calib: Optional[ArrayLike] = None,
+        calib_size: Optional[float] = 0.3,
+        random_state: Optional[Union[int, np.random.RandomState]] = None,
+        shuffle: Optional[bool] = True,
+        stratify: Optional[ArrayLike] = None,
+        **fit_params,
+    ) -> Tuple[ArrayLike, ArrayLike]:
+
+        self._check_parameters()
+        checked_estimator = self._check_estimator(self.estimator)
+        random_state = check_random_state(random_state)
+        X, y = indexable(X, y)
+
+        results = self._check_calib_set(
+            X,
+            y,
+            sample_weight,
+            X_calib,
+            y_calib,
+            calib_size,
+            random_state,
+            shuffle,
+            stratify,
+        )
+
+        X_train, y_train, X_calib, y_calib, sample_weight_train = results
+        X_train, y_train = indexable(X_train, y_train)
+        X_calib, y_calib = indexable(X_calib, y_calib)
+        y_train, y_calib = _check_y(y_train), _check_y(y_calib)
+        self.n_calib_samples = _num_samples(y_calib)
+        check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
+        sample_weight_train, X_train, y_train = check_null_weight(
+            sample_weight_train,
+            X_train,
+            y_train
+        )
+        y_train = cast(NDArray, y_train)
+
+        if isinstance(checked_estimator, Pipeline):
+            estimator = checked_estimator[-1]
+        else:
+            estimator = checked_estimator
+        name_estimator = estimator.__class__.__name__
+        alpha_name = self.quantile_estimator_params[
+            name_estimator
+        ]["alpha_name"]
+        for i, alpha_ in enumerate(self.alpha_np):
+            cloned_estimator_ = clone(checked_estimator)
+            params = {alpha_name: alpha_}
+            if isinstance(checked_estimator, Pipeline):
+                cloned_estimator_[-1].set_params(**params)
+            else:
+                cloned_estimator_.set_params(**params)
+            self.estimators_.append(fit_estimator(
+                cloned_estimator_,
                 X_train,
-                y_train
+                y_train,
+                sample_weight_train,
+                **fit_params,
+                )
             )
-            y_train = cast(NDArray, y_train)
+        self.single_estimator_ = self.estimators_[2]
 
-            y_calib_preds = np.full(
+        X_calib = cast(ArrayLike, X_calib)
+        y_calib = cast(ArrayLike, y_calib)
+
+        return X_calib, y_calib
+
+    def conformalize(
+        self,
+        X_conf: ArrayLike,
+        y_conf: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        predict_params: Dict = {},
+    ):
+
+        self.n_calib_samples = _num_samples(y_conf)
+
+        y_calib_preds = np.full(
                 shape=(3, self.n_calib_samples),
                 fill_value=np.nan
             )
 
-            if isinstance(checked_estimator, Pipeline):
-                estimator = checked_estimator[-1]
-            else:
-                estimator = checked_estimator
-            name_estimator = estimator.__class__.__name__
-            alpha_name = self.quantile_estimator_params[
-                name_estimator
-            ]["alpha_name"]
-            for i, alpha_ in enumerate(alpha):
-                cloned_estimator_ = clone(checked_estimator)
-                params = {alpha_name: alpha_}
-                if isinstance(checked_estimator, Pipeline):
-                    cloned_estimator_[-1].set_params(**params)
-                else:
-                    cloned_estimator_.set_params(**params)
-                self.estimators_.append(fit_estimator(
-                    cloned_estimator_,
-                    X_train,
-                    y_train,
-                    sample_weight_train,
-                    **fit_params,
-                    )
-                )
-                y_calib_preds[i] = self.estimators_[-1].predict(X_calib)
-            self.single_estimator_ = self.estimators_[2]
+        for i, est in enumerate(self.estimators_):
+            y_calib_preds[i] = est.predict(X_conf, **predict_params).ravel()
 
         self.conformity_scores_ = np.full(
                 shape=(3, self.n_calib_samples),
                 fill_value=np.nan
             )
-        self.conformity_scores_[0] = y_calib_preds[0] - y_calib
-        self.conformity_scores_[1] = y_calib - y_calib_preds[1]
+
+        self.conformity_scores_[0] = y_calib_preds[0] - y_conf
+        self.conformity_scores_[1] = y_conf - y_calib_preds[1]
         self.conformity_scores_[2] = np.max(
             [
                 self.conformity_scores_[0],

From 662adad6851f8786e5e57b9403ae9e46deb9b659 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Mon, 16 Dec 2024 19:13:40 +0100
Subject: [PATCH 395/424] FIX: type checking from PR #566 (#567)

---
 mapie/regression/quantile_regression.py | 25 +++++++++++++------------
 1 file changed, 13 insertions(+), 12 deletions(-)

diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index 8c5ef3103..4b5c4564d 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
 import warnings
-from typing import Iterable, Dict, List, Optional, Tuple, Union, cast
+from typing import Iterable, List, Optional, Tuple, Union, cast, Any
 
 import numpy as np
 from sklearn.base import RegressorMixin, clone
@@ -547,7 +547,7 @@ def fit(
              The model itself.
         """
 
-        self.init_fit()
+        self.initialize_fit()
 
         if self.cv == "prefit":
             X_calib, y_calib = self.prefit_estimators(X, y)
@@ -570,8 +570,7 @@ def fit(
 
         return self
 
-    def init_fit(self):
-
+    def initialize_fit(self) -> None:
         self.cv = self._check_cv(cast(str, self.cv))
         self.alpha_np = self._check_alpha(self.alpha)
         self.estimators_: List[RegressorMixin] = []
@@ -667,13 +666,15 @@ def fit_estimators(
 
     def conformalize(
         self,
-        X_conf: ArrayLike,
-        y_conf: ArrayLike,
+        X: ArrayLike,
+        y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
-        predict_params: Dict = {},
-    ):
+        # Parameter groups kept for compliance with superclass MapieRegressor
+        groups: Optional[ArrayLike] = None,
+        **kwargs: Any,
+    ) -> MapieRegressor:
 
-        self.n_calib_samples = _num_samples(y_conf)
+        self.n_calib_samples = _num_samples(y)
 
         y_calib_preds = np.full(
                 shape=(3, self.n_calib_samples),
@@ -681,15 +682,15 @@ def conformalize(
             )
 
         for i, est in enumerate(self.estimators_):
-            y_calib_preds[i] = est.predict(X_conf, **predict_params).ravel()
+            y_calib_preds[i] = est.predict(X, **kwargs).ravel()
 
         self.conformity_scores_ = np.full(
                 shape=(3, self.n_calib_samples),
                 fill_value=np.nan
             )
 
-        self.conformity_scores_[0] = y_calib_preds[0] - y_conf
-        self.conformity_scores_[1] = y_conf - y_calib_preds[1]
+        self.conformity_scores_[0] = y_calib_preds[0] - y
+        self.conformity_scores_[1] = y - y_calib_preds[1]
         self.conformity_scores_[2] = np.max(
             [
                 self.conformity_scores_[0],

From 4242e83984361595c2c4ae5b1b792c00a2fa1fc2 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 17 Dec 2024 14:02:37 +0100
Subject: [PATCH 396/424] CHORE: fix sklearn version in documentation
 requirements to avoid a bug with latest sklearn version (#568)

---
 environment.doc.yml  | 2 +-
 requirements.doc.txt | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/environment.doc.yml b/environment.doc.yml
index 7aea1de43..0013d1fc1 100644
--- a/environment.doc.yml
+++ b/environment.doc.yml
@@ -7,7 +7,7 @@ dependencies:
     - numpydoc=1.1.0
     - pandas=1.3.5
     - python=3.10
-    - scikit-learn
+    - scikit-learn=1.5.2
     - sphinx=5.3.0
     - sphinx-gallery=0.10.1
     - sphinx_rtd_theme=1.0.0
diff --git a/requirements.doc.txt b/requirements.doc.txt
index b81b9c823..8d81a37d0 100644
--- a/requirements.doc.txt
+++ b/requirements.doc.txt
@@ -3,7 +3,7 @@ matplotlib==3.5.1
 numpy==1.22.3
 numpydoc==1.1.0
 pandas==1.3.5
-scikit-learn
+scikit-learn==1.5.2
 sphinx==5.3.0
 sphinx-gallery==0.10.1
 sphinx_rtd_theme==1.0.0

From 89f55a35bc6d5a7492c0f379a0beb97842ae5982 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 17 Dec 2024 14:23:45 +0100
Subject: [PATCH 397/424] Remove enbpi warning (#570)

ENH: remove optimize_beta warning in back-end (optimize_beta has been introduced only for ENBPI in the scientific literature, but it works for all regression methods, so we can support it)
---
 HISTORY.rst                    |  1 +
 mapie/regression/regression.py |  8 --------
 mapie/tests/test_regression.py | 13 -------------
 3 files changed, 1 insertion(+), 21 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 86fcf7873..7f20e9e97 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -14,6 +14,7 @@ History
 * Fix issue 547 to fix wrong warning
 * Fix issue 480 (correct display of mathematical equations in generated notebooks)
 * Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0
+* Remove optimize_beta usage warning when using for methods other than EnbPI
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index 950a9f6af..f0191d4ab 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import warnings
 from typing import Any, Iterable, Optional, Tuple, Union, cast
 
 import numpy as np
@@ -694,13 +693,6 @@ def predict(
             return np.array(y_pred)
 
         else:
-            if optimize_beta and self.method != 'enbpi':
-                warnings.warn(
-                    "WARNING: Beta optimisation should only be used for "
-                    "method='enbpi'.",
-                    UserWarning
-                )
-
             # Check alpha and the number of effective calibration samples
             alpha_np = cast(NDArray, alpha)
             if not allow_infinite_bounds:
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index 1840ddf91..e062a3704 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -864,19 +864,6 @@ def test_return_multi_pred(ensemble: bool) -> None:
     assert len(output) == 3
 
 
-def test_beta_optimize_user_warning() -> None:
-    """
-    Test that a UserWarning is displayed when optimize_beta is used.
-    """
-    mapie_reg = MapieRegressor(
-        conformity_score=AbsoluteConformityScore(sym=False)
-    ).fit(X, y)
-    with pytest.warns(
-        UserWarning, match=r"Beta optimisation should only be used for*",
-    ):
-        mapie_reg.predict(X, alpha=0.05, optimize_beta=True)
-
-
 def test_fit_parameters_passing() -> None:
     """
     Test passing fit parameters, here early stopping at iteration 3.

From dab4d4793bef2f583bb701322cc004334bf98607 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 18 Dec 2024 11:50:12 +0100
Subject: [PATCH 398/424] DOC: make an image generation deterministic, to avoid
 it changing each time we build the doc (#573)

---
 examples/regression/1-quickstart/plot_toy_model.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_toy_model.py b/examples/regression/1-quickstart/plot_toy_model.py
index 5cf8c9bb8..0435801d5 100644
--- a/examples/regression/1-quickstart/plot_toy_model.py
+++ b/examples/regression/1-quickstart/plot_toy_model.py
@@ -13,11 +13,14 @@
 from mapie.metrics import regression_coverage_score
 from mapie.regression import MapieRegressor
 
+RANDOM_STATE = 42
 regressor = LinearRegression()
-X, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=59)
+X, y = make_regression(
+    n_samples=500, n_features=1, noise=20, random_state=RANDOM_STATE
+)
 
 alpha = [0.05, 0.32]
-mapie = MapieRegressor(regressor, method="plus")
+mapie = MapieRegressor(regressor, method="plus", random_state=RANDOM_STATE)
 mapie.fit(X, y)
 y_pred, y_pis = mapie.predict(X, alpha=alpha)
 

From 77df567bd632f3280ab36786397893667da49141 Mon Sep 17 00:00:00 2001
From: jawadhussein462 <41950044+jawadhussein462@users.noreply.github.com>
Date: Fri, 20 Dec 2024 14:51:16 +0100
Subject: [PATCH 399/424] =?UTF-8?q?REFACTOR:=20Break=20down=20the=20fit=20?=
 =?UTF-8?q?method=20in=20MapieQuantileRegressor=20into=20mu=E2=80=A6=20(#5?=
 =?UTF-8?q?78)?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

* REFACTOR: Break down the fit method in MapieQuantileRegressor into multiple sub-methods.
---
 mapie/regression/quantile_regression.py | 221 +++++++++++-------------
 mapie/tests/test_quantile_regression.py |  10 +-
 2 files changed, 110 insertions(+), 121 deletions(-)

diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index 4b5c4564d..638bdac87 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -346,13 +346,11 @@ def _check_cv(
                 "Invalid cv method, only valid method is ``split``."
             )
 
-    def _check_calib_set(
+    def _train_calib_split(
         self,
         X: ArrayLike,
         y: ArrayLike,
         sample_weight: Optional[ArrayLike] = None,
-        X_calib: Optional[ArrayLike] = None,
-        y_calib: Optional[ArrayLike] = None,
         calib_size: Optional[float] = 0.3,
         random_state: Optional[Union[int, np.random.RandomState, None]] = None,
         shuffle: Optional[bool] = True,
@@ -360,61 +358,33 @@ def _check_calib_set(
     ) -> Tuple[
         ArrayLike, ArrayLike, ArrayLike, ArrayLike, Optional[ArrayLike]
     ]:
-        """
-        Check if a calibration set has already been defined, if not, then
-        we define one using the ``train_test_split`` method.
-
-        Parameters
-        ----------
-        Same definition of parameters as for the ``fit`` method.
-
-        Returns
-        -------
-        Tuple[ArrayLike, ArrayLike, ArrayLike, ArrayLike, ArrayLike]
-            - [0]: ArrayLike of shape (n_samples_*(1-calib_size), n_features)
-                X_train
-            - [1]: ArrayLike of shape (n_samples_*(1-calib_size),)
-                y_train
-            - [2]: ArrayLike of shape (n_samples_*calib_size, n_features)
-                X_calib
-            - [3]: ArrayLike of shape (n_samples_*calib_size,)
-                y_calib
-            - [4]: ArrayLike of shape (n_samples_,)
-                sample_weight_train
-        """
-        if X_calib is None or y_calib is None:
-            if sample_weight is None:
-                X_train, X_calib, y_train, y_calib = train_test_split(
-                        X,
-                        y,
-                        test_size=calib_size,
-                        random_state=random_state,
-                        shuffle=shuffle,
-                        stratify=stratify
-                )
-                sample_weight_train = sample_weight
-            else:
-                (
-                        X_train,
-                        X_calib,
-                        y_train,
-                        y_calib,
-                        sample_weight_train,
-                        _,
-                ) = train_test_split(
-                        X,
-                        y,
-                        sample_weight,
-                        test_size=calib_size,
-                        random_state=random_state,
-                        shuffle=shuffle,
-                        stratify=stratify
-                )
+        if sample_weight is None:
+            X_train, X_calib, y_train, y_calib = train_test_split(
+                X,
+                y,
+                test_size=calib_size,
+                random_state=random_state,
+                shuffle=shuffle,
+                stratify=stratify
+            )
+            sample_weight_train = sample_weight
         else:
-            X_train, y_train, sample_weight_train = X, y, sample_weight
-        X_train, X_calib = cast(ArrayLike, X_train), cast(ArrayLike, X_calib)
-        y_train, y_calib = cast(ArrayLike, y_train), cast(ArrayLike, y_calib)
-        sample_weight_train = cast(ArrayLike, sample_weight_train)
+            (
+                X_train,
+                X_calib,
+                y_train,
+                y_calib,
+                sample_weight_train,
+                _,
+            ) = train_test_split(
+                X,
+                y,
+                sample_weight,
+                test_size=calib_size,
+                random_state=random_state,
+                shuffle=shuffle,
+                stratify=stratify
+            )
         return X_train, y_train, X_calib, y_calib, sample_weight_train
 
     def _check_prefit_params(
@@ -546,13 +516,12 @@ def fit(
         MapieQuantileRegressor
              The model itself.
         """
-
-        self.initialize_fit()
+        self._initialize_fit_conformalize()
 
         if self.cv == "prefit":
-            X_calib, y_calib = self.prefit_estimators(X, y)
+            X_calib, y_calib = X, y
         else:
-            X_calib, y_calib = self.fit_estimators(
+            result = self._prepare_train_calib(
                 X=X,
                 y=y,
                 sample_weight=sample_weight,
@@ -563,33 +532,31 @@ def fit(
                 random_state=random_state,
                 shuffle=shuffle,
                 stratify=stratify,
-                **fit_params,
+            )
+            X_train, y_train, X_calib, y_calib, sample_weight = result
+            self._fit_estimators(
+                X=X_train,
+                y=y_train,
+                sample_weight=sample_weight,
+                **fit_params
             )
 
         self.conformalize(X_calib, y_calib)
 
         return self
 
-    def initialize_fit(self) -> None:
+    def _initialize_fit_conformalize(self) -> None:
         self.cv = self._check_cv(cast(str, self.cv))
         self.alpha_np = self._check_alpha(self.alpha)
         self.estimators_: List[RegressorMixin] = []
 
-    def prefit_estimators(
-        self,
-        X: ArrayLike,
-        y: ArrayLike
-    ) -> Tuple[ArrayLike, ArrayLike]:
-
+    def _initialize_and_check_prefit_estimators(self) -> None:
         estimator = cast(List, self.estimator)
         self._check_prefit_params(estimator)
         self.estimators_ = list(estimator)
         self.single_estimator_ = self.estimators_[2]
 
-        X_calib, y_calib = indexable(X, y)
-        return X_calib, y_calib
-
-    def fit_estimators(
+    def _prepare_train_calib(
         self,
         X: ArrayLike,
         y: ArrayLike,
@@ -601,47 +568,62 @@ def fit_estimators(
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         shuffle: Optional[bool] = True,
         stratify: Optional[ArrayLike] = None,
-        **fit_params,
-    ) -> Tuple[ArrayLike, ArrayLike]:
-
+    ) -> Tuple[
+        ArrayLike, ArrayLike, ArrayLike, ArrayLike, Optional[ArrayLike]
+    ]:
+        """
+        Handles the preparation of training and calibration datasets,
+        including validation and splitting.
+        Returns: X_train, y_train, X_calib, y_calib, sample_weight_train
+        """
         self._check_parameters()
-        checked_estimator = self._check_estimator(self.estimator)
         random_state = check_random_state(random_state)
         X, y = indexable(X, y)
 
-        results = self._check_calib_set(
-            X,
-            y,
-            sample_weight,
-            X_calib,
-            y_calib,
-            calib_size,
-            random_state,
-            shuffle,
-            stratify,
-        )
+        if X_calib is None or y_calib is None:
+            return self._train_calib_split(
+                X,
+                y,
+                sample_weight,
+                calib_size,
+                random_state,
+                shuffle,
+                stratify
+            )
+        else:
+            return X, y, X_calib, y_calib, sample_weight
 
-        X_train, y_train, X_calib, y_calib, sample_weight_train = results
-        X_train, y_train = indexable(X_train, y_train)
-        X_calib, y_calib = indexable(X_calib, y_calib)
-        y_train, y_calib = _check_y(y_train), _check_y(y_calib)
-        self.n_calib_samples = _num_samples(y_calib)
-        check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
-        sample_weight_train, X_train, y_train = check_null_weight(
-            sample_weight_train,
-            X_train,
-            y_train
+    # Second function: Handles estimator fitting
+    def _fit_estimators(
+        self,
+        X: ArrayLike,
+        y: ArrayLike,
+        sample_weight: Optional[ArrayLike] = None,
+        **fit_params
+    ) -> None:
+        """
+        Fits the estimators with provided training data
+        and stores them in self.estimators_.
+        """
+        checked_estimator = self._check_estimator(self.estimator)
+
+        X, y = indexable(X, y)
+        y = _check_y(y)
+
+        sample_weight, X, y = check_null_weight(
+            sample_weight, X, y
         )
-        y_train = cast(NDArray, y_train)
 
         if isinstance(checked_estimator, Pipeline):
             estimator = checked_estimator[-1]
         else:
             estimator = checked_estimator
+
         name_estimator = estimator.__class__.__name__
-        alpha_name = self.quantile_estimator_params[
-            name_estimator
-        ]["alpha_name"]
+        alpha_name = self.quantile_estimator_params[name_estimator][
+            "alpha_name"
+        ]
+
         for i, alpha_ in enumerate(self.alpha_np):
             cloned_estimator_ = clone(checked_estimator)
             params = {alpha_name: alpha_}
@@ -649,20 +631,18 @@ def fit_estimators(
                 cloned_estimator_[-1].set_params(**params)
             else:
                 cloned_estimator_.set_params(**params)
-            self.estimators_.append(fit_estimator(
-                cloned_estimator_,
-                X_train,
-                y_train,
-                sample_weight_train,
-                **fit_params,
+
+            self.estimators_.append(
+                fit_estimator(
+                    cloned_estimator_,
+                    X,
+                    y,
+                    sample_weight,
+                    **fit_params,
                 )
             )
-        self.single_estimator_ = self.estimators_[2]
-
-        X_calib = cast(ArrayLike, X_calib)
-        y_calib = cast(ArrayLike, y_calib)
 
-        return X_calib, y_calib
+        self.single_estimator_ = self.estimators_[2]
 
     def conformalize(
         self,
@@ -673,8 +653,15 @@ def conformalize(
         groups: Optional[ArrayLike] = None,
         **kwargs: Any,
     ) -> MapieRegressor:
+        if self.cv == "prefit":
+            self._initialize_and_check_prefit_estimators()
 
-        self.n_calib_samples = _num_samples(y)
+        X_calib, y_calib = cast(ArrayLike, X), cast(ArrayLike, y)
+        X_calib, y_calib = indexable(X_calib, y_calib)
+        y_calib = _check_y(y_calib)
+
+        self.n_calib_samples = _num_samples(y_calib)
+        check_alpha_and_n_samples(self.alpha, self.n_calib_samples)
 
         y_calib_preds = np.full(
                 shape=(3, self.n_calib_samples),
@@ -682,15 +669,15 @@ def conformalize(
             )
 
         for i, est in enumerate(self.estimators_):
-            y_calib_preds[i] = est.predict(X, **kwargs).ravel()
+            y_calib_preds[i] = est.predict(X_calib, **kwargs).ravel()
 
         self.conformity_scores_ = np.full(
                 shape=(3, self.n_calib_samples),
                 fill_value=np.nan
             )
 
-        self.conformity_scores_[0] = y_calib_preds[0] - y
-        self.conformity_scores_[1] = y - y_calib_preds[1]
+        self.conformity_scores_[0] = y_calib_preds[0] - y_calib
+        self.conformity_scores_[1] = y_calib - y_calib_preds[1]
         self.conformity_scores_[2] = np.max(
             [
                 self.conformity_scores_[0],
diff --git a/mapie/tests/test_quantile_regression.py b/mapie/tests/test_quantile_regression.py
index 60a42ace5..1feba0c30 100644
--- a/mapie/tests/test_quantile_regression.py
+++ b/mapie/tests/test_quantile_regression.py
@@ -470,11 +470,13 @@ def test_for_small_dataset() -> None:
             estimator=qt,
             alpha=0.1
         )
+        X_calib_toy_small = X_calib_toy[:2]
+        y_calib_toy_small = y_calib_toy[:2]
         mapie_reg.fit(
-            np.array([1, 2, 3]),
-            np.array([2, 2, 3]),
-            X_calib=np.array([3, 5]),
-            y_calib=np.array([2, 3])
+            X_train_toy,
+            y_train_toy,
+            X_calib=X_calib_toy_small,
+            y_calib=y_calib_toy_small
         )
 
 

From 4561bcc3e511114913691585a8c8419bc67d9cda Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Fri, 27 Dec 2024 19:49:47 +0100
Subject: [PATCH 400/424] CHORE: limit maximum sklearn version, update
 maintainers list (#574)

* CHORE: limit maximum sklearn version, update maintainers list
---
 setup.py | 10 ++++++++--
 1 file changed, 8 insertions(+), 2 deletions(-)

diff --git a/setup.py b/setup.py
index c200663c5..b77fcc515 100644
--- a/setup.py
+++ b/setup.py
@@ -19,15 +19,21 @@
     "Source Code": "https://github.com/scikit-learn-contrib/MAPIE"
 }
 LICENSE = "new BSD"
-MAINTAINER = "T. Cordier, V. Blot, L. Lacombe"
+MAINTAINER = "V. Laurent, T. Cordier, V. Blot, L. Lacombe"
 MAINTAINER_EMAIL = (
+    "valentin.laurent@capgemini.com, "
     "thibault.a.cordier@capgemini.com, "
     "vincent.blot@capgemini.com, "
     "louis.lacombe@capgemini.com"
 )
 PYTHON_REQUIRES = ">=3.7"
 PACKAGES = find_packages()
-INSTALL_REQUIRES = ["scikit-learn", "scipy", "numpy>=1.21", "packaging"]
+INSTALL_REQUIRES = [
+    "scikit-learn<1.6.0",
+    "scipy",
+    "numpy>=1.21",
+    "packaging"
+]
 CLASSIFIERS = [
     "Intended Audience :: Science/Research",
     "Intended Audience :: Developers",

From 364df9a516854d4a51ebaaeb1b68d732e1b5c5ae Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Sun, 29 Dec 2024 23:10:52 +0100
Subject: [PATCH 401/424] ENH: change warning into infolog (#584)

ENH: change warning into infolog
---
 mapie/tests/test_utils.py | 24 +++++++----------------
 mapie/utils.py            | 41 ++++++---------------------------------
 2 files changed, 13 insertions(+), 52 deletions(-)

diff --git a/mapie/tests/test_utils.py b/mapie/tests/test_utils.py
index d4ea8df2f..6249e2335 100644
--- a/mapie/tests/test_utils.py
+++ b/mapie/tests/test_utils.py
@@ -1,5 +1,6 @@
 from __future__ import annotations
 
+import logging
 import re
 from typing import Any, Optional, Tuple
 
@@ -228,22 +229,24 @@ def test_valid_verbose(verbose: Any) -> None:
     check_verbose(verbose)
 
 
-def test_initial_low_high_pred() -> None:
+def test_initial_low_high_pred(caplog) -> None:
     """Test lower/upper predictions of the quantiles regression crossing"""
     y_preds = np.array([[4, 5, 2], [4, 4, 4], [2, 3, 4]])
-    with pytest.warns(UserWarning, match=r"WARNING: The predictions are*"):
+    with caplog.at_level(logging.INFO):
         check_lower_upper_bounds(y_preds[0], y_preds[1], y_preds[2])
+    assert "The predictions are ill-sorted" in caplog.text
 
 
-def test_final_low_high_pred() -> None:
+def test_final_low_high_pred(caplog) -> None:
     """Test lower/upper predictions crossing"""
     y_preds = np.array(
         [[4, 3, 2], [3, 3, 3], [2, 3, 4]]
     )
     y_pred_low = np.array([4, 7, 2])
     y_pred_up = np.array([3, 3, 3])
-    with pytest.warns(UserWarning, match=r"WARNING: The predictions are*"):
+    with caplog.at_level(logging.INFO):
         check_lower_upper_bounds(y_pred_low, y_pred_up, y_preds[2])
+    assert "The predictions are ill-sorted" in caplog.text
 
 
 def test_ensemble_in_predict() -> None:
@@ -331,19 +334,6 @@ def test_quantile_prefit_non_iterable(estimator: Any) -> None:
         mapie_reg.fit([1, 2, 3], [4, 5, 6])
 
 
-# def test_calib_set_no_Xy_but_sample_weight() -> None:
-#     """Test warning message if sample weight provided but no X y in calib."""
-#     X = np.array([4, 5, 6])
-#     y = np.array([4, 3, 2])
-#     sample_weight = np.array([4, 4, 4])
-#     sample_weight_calib = np.array([4, 3, 4])
-#     with pytest.warns(UserWarning, match=r"WARNING: sample weight*"):
-#         check_calib_set(
-#             X=X, y=y, sample_weight=sample_weight,
-#             sample_weight_calib=sample_weight_calib
-#         )
-
-
 @pytest.mark.parametrize("strategy", ["quantile", "uniform", "array split"])
 def test_binning_group_strategies(strategy: str) -> None:
     """Test that different strategies have the correct outputs."""
diff --git a/mapie/utils.py b/mapie/utils.py
index 23d69c438..037707fae 100644
--- a/mapie/utils.py
+++ b/mapie/utils.py
@@ -1,3 +1,4 @@
+import logging
 import warnings
 from inspect import signature
 from typing import Any, Iterable, Optional, Tuple, Union, cast
@@ -573,39 +574,6 @@ def check_lower_upper_bounds(
     y_pred_up: NDArray,
     y_preds: NDArray
 ) -> None:
-    """
-    Check if lower or upper bounds and prediction are consistent.
-
-    Parameters
-    ----------
-    y_pred_low: NDArray of shape (n_samples,)
-        Lower bound prediction.
-
-    y_pred_up: NDArray of shape (n_samples,)
-        Upper bound prediction.
-
-    y_preds: NDArray of shape (n_samples,)
-        Prediction.
-
-    Raises
-    ------
-    Warning
-        If any of the predictions are ill-sorted.
-
-    Examples
-    --------
-    >>> import warnings
-    >>> warnings.filterwarnings("error")
-    >>> import numpy as np
-    >>> from mapie.utils import check_lower_upper_bounds
-    >>> y_preds = np.array([[4, 3, 2], [4, 4, 4], [2, 3, 4]])
-    >>> try:
-    ...     check_lower_upper_bounds(y_preds[0], y_preds[1], y_preds[2])
-    ... except Exception as exception:
-    ...     print(exception)
-    ...
-    WARNING: The predictions are ill-sorted.
-    """
     y_pred_low = column_or_1d(y_pred_low)
     y_pred_up = column_or_1d(y_pred_up)
     y_preds = column_or_1d(y_preds)
@@ -617,9 +585,12 @@ def check_lower_upper_bounds(
     )
 
     if any_inversion:
-        warnings.warn(
-            "WARNING: The predictions are ill-sorted."
+        initial_logger_level = logging.root.level
+        logging.basicConfig(level=logging.INFO)
+        logging.info(
+            "The predictions are ill-sorted."
         )
+        logging.basicConfig(level=initial_logger_level)
 
 
 def check_defined_variables_predict_cqr(

From abfc3097f723894113c6efcb0c6f08f2c6b4ae6f Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Thu, 2 Jan 2025 12:16:19 +0100
Subject: [PATCH 402/424] ENH: MapieQuantileRegressor - remove warning about
 alpha and correct docstring (#585)

---
 mapie/regression/quantile_regression.py |  9 ---------
 mapie/tests/test_quantile_regression.py | 24 ------------------------
 2 files changed, 33 deletions(-)

diff --git a/mapie/regression/quantile_regression.py b/mapie/regression/quantile_regression.py
index 638bdac87..90654864f 100644
--- a/mapie/regression/quantile_regression.py
+++ b/mapie/regression/quantile_regression.py
@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import warnings
 from typing import Iterable, List, Optional, Tuple, Union, cast, Any
 
 import numpy as np
@@ -26,8 +25,6 @@ class MapieQuantileRegressor(MapieRegressor):
     """
     This class implements the conformalized quantile regression strategy
     as proposed by Romano et al. (2019) to make conformal predictions.
-    The only valid ``method`` is ``"quantile"`` and the only valid
-    ``cv`` is ``"split"``.
 
     Parameters
     ----------
@@ -197,12 +194,6 @@ def _check_alpha(
         ValueError
             If the value of ``alpha`` is not between ``0.0`` and ``1.0``.
         """
-        if self.cv == "prefit":
-            warnings.warn(
-                "WARNING: The alpha that is set needs to be the same"
-                + " as the alpha of your prefitted model in the following"
-                " order [alpha/2, 1 - alpha/2, 0.5]"
-            )
         if isinstance(alpha, float):
             if np.any(np.logical_or(alpha <= 0, alpha >= 1.0)):
                 raise ValueError(
diff --git a/mapie/tests/test_quantile_regression.py b/mapie/tests/test_quantile_regression.py
index 1feba0c30..a1791f482 100644
--- a/mapie/tests/test_quantile_regression.py
+++ b/mapie/tests/test_quantile_regression.py
@@ -589,30 +589,6 @@ def test_non_trained_estimator() -> None:
         )
 
 
-def test_warning_alpha_prefit() -> None:
-    """
-    Check that the user is warned that the alphas need to be correctly set.
-    """
-    with pytest.warns(
-        UserWarning,
-        match=r".*WARNING: The alpha that is set needs to be the same*"
-    ):
-        gb_trained1, gb_trained2, gb_trained3 = clone(gb), clone(gb), clone(gb)
-        gb_trained1.fit(X_train, y_train)
-        gb_trained2.fit(X_train, y_train)
-        gb_trained3.fit(X_train, y_train)
-        list_estimators = [gb_trained1, gb_trained2, gb_trained3]
-        mapie_reg = MapieQuantileRegressor(
-            estimator=list_estimators,
-            cv="prefit",
-            alpha=0.3
-        )
-        mapie_reg.fit(
-            X_calib,
-            y_calib
-        )
-
-
 @pytest.mark.parametrize("alpha", [0.05, 0.1, 0.2, 0.3])
 def test_prefit_and_non_prefit_equal(alpha: float) -> None:
     """

From d8665e46788839111310f7b7aed31b359eb3a8c7 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Mon, 6 Jan 2025 15:43:19 +0100
Subject: [PATCH 403/424] REFACTO: in split setting, remove checking NaNs and
 irrelevant aggregation to avoid triggering unwanted warnings (#586)

* REFACTO: in split setting, remove checking NaNs and irrelevant aggregation to avoid triggering unwanted warnings
---
 mapie/estimator/regressor.py               | 7 +++++--
 mapie/tests/test_regression.py             | 2 +-
 mapie/tests/test_time_series_regression.py | 3 ++-
 3 files changed, 8 insertions(+), 4 deletions(-)

diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index bad8988ca..ddf778e02 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -402,9 +402,12 @@ def predict_calib(
                         predictions[i], dtype=float
                     )
                     self.k_[ind, i] = 1
-                check_nan_in_aposteriori_prediction(pred_matrix)
 
-                y_pred = aggregate_all(self.agg_function, pred_matrix)
+                if self.use_split_method_:
+                    y_pred = pred_matrix.flatten()
+                else:
+                    check_nan_in_aposteriori_prediction(pred_matrix)
+                    y_pred = aggregate_all(self.agg_function, pred_matrix)
 
         return y_pred
 
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index e062a3704..f06fff2e3 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -701,7 +701,7 @@ def test_not_enough_resamplings() -> None:
     """
     with pytest.warns(UserWarning, match=r"WARNING: at least one point of*"):
         mapie_reg = MapieRegressor(
-            cv=Subsample(n_resamplings=1), agg_function="mean"
+            cv=Subsample(n_resamplings=2, random_state=0), agg_function="mean"
         )
         mapie_reg.fit(X, y)
 
diff --git a/mapie/tests/test_time_series_regression.py b/mapie/tests/test_time_series_regression.py
index 785cb9088..77e4607b4 100644
--- a/mapie/tests/test_time_series_regression.py
+++ b/mapie/tests/test_time_series_regression.py
@@ -318,7 +318,8 @@ def test_not_enough_resamplings() -> None:
         match=r"WARNING: at least one point of*"
     ):
         mapie_ts_reg = MapieTimeSeriesRegressor(
-            cv=BlockBootstrap(n_resamplings=1, n_blocks=1), agg_function="mean"
+            cv=BlockBootstrap(n_resamplings=2, n_blocks=1, random_state=0),
+            agg_function="mean"
         )
         mapie_ts_reg.fit(X, y)
 

From c134fc41badc7a8feca4bc7656a6c8da6223325a Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 7 Jan 2025 19:02:28 +0100
Subject: [PATCH 404/424] CHORE: increase max line length from 79 to 88, update
 HISTORY.rst according to last few commits (#587)

---
 HISTORY.rst | 8 +++++---
 Makefile    | 2 +-
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 7f20e9e97..71eed8034 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -6,15 +6,17 @@ History
 ------------------
 
 * Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues.
-* Bump wheel version to avoid known security vulnerabilities
 * Fix issue 495 to center correctly the prediction intervals
-* Fix documentation build warnings
 * Fix issue 528 to correct broken ENS image in the documentation
 * Fix issue 548 to correct labels generated in tutorial
 * Fix issue 547 to fix wrong warning
 * Fix issue 480 (correct display of mathematical equations in generated notebooks)
+* Remove several irrelevant user warnings
+* Limit max sklearn version allowed at MAPIE installation
 * Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0
-* Remove optimize_beta usage warning when using for methods other than EnbPI
+* Documentation build: fix warnings, fix image generation, update sklearn version requirement
+* Increase max line length from 79 to 88 characters
+* Bump wheel version
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/Makefile b/Makefile
index 2f761ce38..e6142c895 100644
--- a/Makefile
+++ b/Makefile
@@ -1,7 +1,7 @@
 .PHONY: tests doc build
 
 lint:
-	flake8 . --exclude=doc
+	flake8 . --max-line-length=88 --exclude=doc
 
 type-check:
 	mypy mapie

From 423a87b3845cf51511c1a596c3fe6e03c066f028 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 8 Jan 2025 19:02:36 +0100
Subject: [PATCH 405/424] DOC: push generated image fixed by PR #573, update
 conf in preparation for v1 (#590)

---
 doc/conf.py                 |   1 +
 doc/images/quickstart_1.png | Bin 84288 -> 72875 bytes
 2 files changed, 1 insertion(+)

diff --git a/doc/conf.py b/doc/conf.py
index 0b1af45f2..2c5a729a9 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -69,6 +69,7 @@
 
 
 autosectionlabel_prefix_document = True
+autosectionlabel_maxdepth = 2
 
 # The suffix of source filenames.
 source_suffix = ".rst"
diff --git a/doc/images/quickstart_1.png b/doc/images/quickstart_1.png
index 9c6a54bc2a2a40b35c77cb1849b3962ebbaa2a1f..10d3716486bcf3f8b54111e9d88ac95b56ffd3ab 100644
GIT binary patch
literal 72875
zcmcG$cRZK<|33USGm}vyqZA6!FiKWg5tW&ckqDI$*{fkS5Q!o)D;Z^FkA_NCNV1cV
zJ<8@j&g<&)y}$SU`2O*`@5k@=cywJ?mvO$&^YwbZp3mcX9LMtvK7CS^k&cs&L?SV&
z9aqvOk;wf>B(gplYJ5eax$6i1bJ$tgz*)!M%GuTYswGL&+}Yu>z4K+8^V?l4uR7V-
z+Z_~@5Zy1b-P+mN!AV+7?8<-rfT;b|i(+5RXUcIAT8HCjok%1mbK)Pebon$J5*dl4
zrgT)-Eq1Ki)tOCidBfD?<;jtIq#d_-9y^B#>?P-;2@2BIRj|2Db$xy_StOtGX{FmM
zly7e&1Px~z9M#&yMrM;pNxLg7u3NKkvSD`F;#2qsyZ3f141+!m9}ahv9(0}jRCsan
z$+B@s5DmlYefaa~ilTN4qZvnlIsUAj6Cqvr=dZ=}xuh`SOB_EXUz02R^WEr9271!g
zfBtHB@c+mUu5D%)H73<Me$BVhDQG)uY<zcmpmwLE<QA@4^QE~FlHb_aWd>e3E@o!t
zzC#;@&tyL@tbP7IRWqi_kxSJ0)l`nG!0hA^&p#qdzefYZ!ZtZ@1#%y8X?2buk#L2c
zGWW<H*I%s}ZJi07U;W4^_L<k*dTd9{dMiTk)G>jG{)OoQiS7r!RP>~}YDZ%QU(Y{d
z86&B;$2yCcW`EY-Y_@(rl4|Tq&o24~AGy!w^RCN1Wm}U~L#atJt8*<2KV$6odH%Uf
ze9^Vnd;XgH*tWqF@r(2KxQ}e94dH%$gNi=aeby@fQhRo4jPnV{LtN2^odj&#o>3JT
z-V5PA^1XDjg5GWY(<hC)9Q(-=6BGAwFmTAY?Q4japUgYw9zS}AT`XIB*?0B#8L#E>
zBK&e{dhC$9$k~^a!NI|&Gjy#xUh>N=4JZ4Ju1=IrHWgetV=c~k%irI8VXEK9_wtv_
zGmVpuveR!^<vKa<sc6I=V%E~qYO*bOHJV*IH5Bhl?fof5@AqQoOH2B1`8E`h>i+5J
zyJh}-wRm4wH`^f^JtP*^u9Rn0zt5php={y%ooe1W&ExtljIXq=^pu@-UM`>Sd5tF@
z(&@>!Y4OcFyZKv@J@3!P_)y1zcRR$z^HXD-&aEyCgbuf6u>Nd#GO8ozHf1I=-{sUk
z{sJ2x)c-or;(geTq?zVqRf`|*@1GWm#CIM&w#@6u^<DQ_nXg>8l(lOA@XB#u>Vsgs
z8A8JPYwixoE8lLBQBa%BPjnA|D;SlS-zzHm?wo5kRm6u|>nlU@VReHBFV0hW%ne`P
zsT#7KkMCH%cW;IFeydL$S{>Z3U0NxB#Lktlm3z*s+@xky8eCtU^nKwreO@DaAK7?k
zQ7u*`L@Qb4`oN*PT!&{KU-MZUj3V|0YjpGN9ro9kB?o@Do2s^FpL1N8GFMVo4lcCs
zE|G}y{&QCAY_iHdr6TvZ+N$dmj&s9WpQ86u+`oU{Z8WQ>DevMbE@@{P#%+?%Tc^(q
zE*j-oF$$cDyW3OlIZ)7UqNt)mZQ{FnZDqQa_mD#$NmnS+dRZZ^(#(YQl*}|GH3NrV
ze!loFetx}Q3-+9EC@*|`ut)b?+MOZymxftw+Goz5h!W)|&CU1vnno=Nk4{#u3y|`$
z6SkQL_wT<?y^(8Jr@@g+^U;BnZ#=cUS^6g|YD3DiLmh>kij99b9I`Fh8W|oJcamg1
z@;RkV|DvO#V?is0ANI{?#5-qxSzyzKja*ZQD<xy@g@=cyI}J5#o>>2tnZrVocAH8l
z8f(qa*%hg7^JlCh=}J!R{)-ncMra5m<6(n79z1a1xM<WJp3oupUthAewPl}+>9U)b
z7`^i4`Bn+*CWX0*!&iUMsO@6$`1$CfQm4SKT??z1MMeig<?#epIwkGTo;<nHKOo@q
zO(r>m{<SsT!9}M-Tx^`2>fNQTf^kFTgQi~7RAdQKJ#LEm-U*GS`*LX0TGY(V&BZS^
z#L|tHcf26&Q&=Ofa_w;soG2L;y?y&OpZ~F=nlX|oMLiyM7kk5{2C~nUreEnSe5xyC
z^Y->;B_-$n8Y)h4>rFP|ocQsik(RU;{0I$cyr<kqx6!_GRb|hfJs0+I9M96%K_reu
znfgrk$h%G$llRTKW|<M^D-{8*^F;dE@x;@o$G;WWAb#b@etmsmp_bXEjooJCk%`VX
z+^ejj@;R?jZgjR<&%od~c7^|DwZe`aJH!#N(q0P=Rb*!7<~wA6x9aGYx?IuT&Gz8I
z1101JfzVn-C8bi=$v>X4+obH~jCice`DJ*RvW$zvA4}PvR8I-#+qrWOKfk}v`l>4*
zUuCEk3&X?w#2>LtT!+F~SXozjE~cG)LOgZS%la58ofye0$5XVm3=BAAr>ZENr@z0Y
zYutK&ifVQ^IaJ%&m}?Ik-Lo?}JVZuG61$6F<dpL|<nQl)-!Ll``Rp^6V5D4LSoqFU
z?LD|&W7=A4t1h`hrhXRPuh@r_>j==4&BFSpSC@XP@!fC2>U`*TXo@}Tl#ZPsmNL2d
zG)~n>EwAsK`$}1vjK@NMpjuA5r|B8h5U%Ov66tHd*bq^7YieqaXXt8o7G4>dZIoxf
zGT<z2gKXcLaFjw@D3Xnb=OlJo&>Kl}FwVkv^2M!Nl#{EzYm0*2-t*m0+eeA@&oL>t
zUSC^Hl2$j_iNv3%79pUetILKkd$=539h>};%uLPm)DvlC94Psb-yU>KOlgAgUg4J|
zV;U=TQZ*7A<(BV;M?|P$bCDy5M{_E-TyaIvJQlZMk+5wI(iPh59~js&)&DwkTy^N~
z)`Q1|BEvsiV2KnneicU2UGTB)t&lCS@9jw|Gp+Q&p6(J7K5UiQ_RC35J<mYI`B&?n
zkM;FgxvP_9vm410P=<m&p2;!MBn`g_W=)!5l3xiUH6eG@WgV24k0&+Z6TBu)oIIIo
zSYR9cQN6;6Yh$g1uW^^}`YIbM>%i>RLyq5-K3j`lDC(^1E|Sr6U)#tntwgf^{i{uG
ze#BY&*XNWw=^{--V$toC%*|LR)xAcAB1IKOsE-ss#7LKMyZ5$9h3G#wIEpPz@>yMC
zCV9-(ODT8q@7i@5sdTr9$X%=Y$I2*pDL9!rj*k1=a!lh}Z6)d-OWe`VHdf=7c23aG
zGF+telV6#lKs^2GDn7W^v?7+cC_Zi%P9@UJcD^K(S>m`KCt2e(=A(>$D|4TfkSC!M
zt58rgB>2;_e=fN;I{c!RXJ>}4`s+X@_A62@V+Pt8x({MwHA3Z<c4wW<3kYD`rb1FW
zD%LpLTJGr@eteg>`^*I_&}KaK9mB%QTThd$Ul^oY);(lnW<I*U*ywAGlS-Vt?}tL-
zJbvxrW4ZBwI2#u(>?*M9{^l858_Ju8FR+q+br$6m$uxPKxQj(0oklF#41SEvzua+v
zQ^qY<YO15m{otNRb!5Dl`Tc*!J5`k`$of1d%ZFy`B91Sx>|GXKj67qqBV=Is`Uai^
z<e@t#ou^PNbaPB(g+xTke!ACv`t%exdtW6vk;p&a3NG0+#2#)1TAHjld-g20Rnen7
zZf&`D{qv=qa+NH@0={6SW1U6z^IzIB&xqN6dH$%;;#0hQJPxF#r6tw1lPAclutQon
zfi^ha`RCu#sh>KP*41Tv`_7%@i*fD&U!I>;Jn>K@@!h>`5yqlvSXa-{DL0wcVyBDY
z>M7#ae(4fho?+ix(KD|;(%uHZwrAI_TaP6!spM2F36(GYj3HL~`=OK0vBYu)4IFyB
z&z!t`^-pKM*Wy)=sp<{HClAIA5j%W)voH-wcA@VkvBOJad5vYEnQgXvL`6ePd=@PY
z%iQ+!3kZa3XFTooS*|xy{c!2&**vS(WK|}tN}B}l!6qjqT_4idBxQOcRTCdRH}#r{
zmBcCQk>6N>^_VI2#ir<1`gkMqA4EkBb=x6pv_8`l_LzRlZi980i(g*}L_*oUW5;!o
zEq8_w*VWfE4mXZCd(4lvq%lcc`PMol<F&pzPwD3}(RFsBc*aeJC=#Eas>#ilPu0*<
z`hBv>8;&k8-0R4tN=mnBNu8_Sz`HPJG?RQVtcO@29VMk(y;rmJg@1MM@kzT*#P;@N
zeHJyX&>#)JWjCEG&hcLUL+O`oT>O3SiQ~t&7Z_@4XS6Qww8WjuElXNA`7O;)PU=*c
zzosTv02*wU2yKghCgZ#OXM87VuHne!LKpky&C0d~hHTHNXI7||A8%)oyTi{Cd}@<t
zra76J-wKuAT7aXDwstzsY|=|)Lvn>umvLkwoIFw!Qg@jp$~qDkA05d(E)9o9TV4J3
z@axyM1={D&^Zg$0Y&Y66wkfyL!XV$88IL(KGSc(%g+(n3$zvoXN~yEQv6ef@<T=3i
zCX(UH3&BO7#$9xZ?7GS6NT%4x&$b25KR<2NT6}&s4_WZGkL*LKPyO}=+nYRmSEg0T
z6;8=5K0Q^Q(&+83SY=6}5b;3?#ftz`%`ON=<|hNleqp*oDOB`Zf(nngjB$Lpa3Fr>
z<-5CE6<LDcbe<T=>266oH9umrTSzGB%^M|@skr%@*SbrtQTpNW1fHc4af2f<Ij+!X
zqeimc$FW;bP&GfRbNtU%I#Cgkr_s?I9&^pA<(Bb2OMA#Rc?Y}8@ce4ajA|Tx<~rHa
ziU`x1q(T-+Il*rGH8-*7xn8C|8Dm7GUWsa6{tm@}uw#h{sEGC*gLP3TSp*~~x8%>c
z56qx{XiSxD;V_?&JgIEH$5XjeGfw6asR^K0d0B{!jtGdNvEk-qx$=rNxr2Wudry&u
z0j*~rM#iQotf{Ym)jWA}OL>EO>><Y+q$VVRy2Coha>pi}ZV%fs8Qa=ED|(K~ntydr
zeQ9O3(FVD;wancqp0_}$n#%87smt$?4nY<bijO{0n`q^qlA5pyAG2BprcYa2@BTf}
zZKN9)6{TvJ^^nxW>$}pwmzOw6OTRMnEowI|Rjx0~PYz9@%8Gq6&1_pL3kV8Y11?`&
zZJ<-qi<d|CLI!4yjJhjF+O*0+`(u&*kHk^uf-{9rod)&aT9KW(ZkY9F&Hlinu&Ag&
z9$AmRmBrb8k2I35u(Gn=4-e-P>MXd#{(WG;ak6~gqYjw&)Gt4v_P()>JPrw4wwc-4
z`}jp*0RxdOa}(X$<kwdYy2a(p?8pEA7#vK*lW*talJoj{artH5dsGC0$7;XZ&X^t*
zh%m<y?KzO4dmBfRsx=NUC()?Lj)tComgl002@lX1MQH8Kc;B_dUlkdi^_-GlW#g({
z)DR)Nwe@240S<=Y-b(pGyKXwZnTLqdTB3_VGN&hft?)kL7FQd@yi;lX&b@oeuLBrg
zc+O8;<-IxNKKAtm1uEgpk!=M}yG^}+Z<cl*-d;jul-6SY{Y~&Ihko*taWb?d4r%Az
z7Y=NdTU)T0bU{U_{SYBIT}8p<R(HcY8^`abQ(}p(kpHp7DIV3MN6EVEq_FLRkAq)b
zY8QP!c(vJ6k?+14@PE=09Ua{ifb#h_LClE#nZiBt^1J}1htL!sICzkZgtiiOpRyrN
z)@Y)b@j>lCby2U+9RzFwazW3`t5=8aa!SxuuFTLQi&@9hvI-~&Md};4CJYT5evC^C
zpC345>dh}S*^(AITr>Z0uThQJzM!6g6#0Y3qU_k6W?-&c0Rgwav2NRTXR=~hG@%j&
z-hDo=<Fb#SR;2NYeO|{$*@p0C^6vF^Q{T5br)2$61lfs}2KeLF&-$1F`Srz6>(V$`
z&l^GH>MtVV<3n?E_f=lW9CJiZAhWhG02s(iwA!(<o?8Mpa@Sz#`<$h{|MV6+3C8)Z
zxij#{a1a&WXK6(6>C>lff_l2DF&}jzrFZV!x&QndnzH4IQiBSw!|1F-(e`~u!Syp1
zo$rtj8xs54PDz@a9kTy9opr08Jo|>J@9JhWH*eQgm(2?dmFFL62;91L%gtU;KwxPs
zJ}xfg>C;{DRY_x?UWALKBLzO}+!FEO$Gl=1AG?^zn>%6^K~IjjCgtWfr8Rt3PyG?K
zWy=<UZ(_2t+-RtpMx2wzIi>8lPJ3cQ?o_TV)eSkS|M>BPQumbQsOE_iiYQr|cz8@Q
zYNv;AlU2xFL?`S#@|nuU#^#XoaM*77{*JthN0kN}N`x1uW(B3B*sb!m0+3W%9r@hS
z()_|gHGU@i(HJtcbx+x0Gyu(|uJ)I^OE!yDt{fokFpK<f0nwM9USBP9X03;URL1)!
zBO}A1*x08?%>5j?o#qP5Ug4pJClt))02|Q<FP<zOza1J{dqS-IR`tyd$!Lv<D^FA`
z+uh}n<pgS*EA1`#@$+YmZNZZF6R9iT*kl?9KBt^Ge)8lWPp{3iKe*1F=xEo|)@Ip-
znnZU$oOf4Bq<3iNWl0gkd>V8vN>)~>!-1M3jSQe5<h++CTPr>TG;(bWJXPFnH{au%
z<xQsR;p>0wdWKG#$Ial|R?i=%%^lv;UHGw(o@C-N!jDW~{vn*dM_dpU{k27?Y(T-K
z_9d^M58pY)O38jfXiWhZoH-G*l41K~^h?&wdaKt?jj}`aKZvjI+_Q%kpsDhw<C5o^
z(Cp*g?)Z1|x>XH%mAX1MWwNKd`KcOf*`IHh(sj}{ve8jhEd3IGZt$WeO*Q*~{ZkhH
zYkNT`WEmCxDXlN)VH=!wlNs(T62sZt=k!xU+~zZ-Vb+#Mj~;PI*~K>Qr~GWBr6<#v
z3=HMAwm7ufV=8&?sAl`Nprbdbk0v*p;r@m{r_d9rCs^sm@?@oHt6a#y6gY=v*~UnB
zpUm^%O5A?jS9SRDo;lyHn-f`08TZJ%b?ZpCz*nbVld@B5%aayeuN*lJT%fV~6n%Yo
z*czQ3vD^rwqeZ1ZB$m6J8W-nYMeU~LL<bpy(&dGFS@_dw{{lDz&qfXu{}v3{O`kC{
zs-;J#yN76Yuiwckn|B-WvjP?J^_An!)Kl>x4Ns1EMwo|k9oC+E)IaSO78A4Wv}euh
z*X8B0i1;3pZ=V5d$VusXnO`JEwS*#juH5YAC4rTrP#D(Cz8e%o6VKb?=U|Mxpk@wk
z@J$#;hx;|xoMMZJf#1Q44ci3-sPAz~)TC=AZ@+)s%ZDLyaPrmn@86#}XRl;@oZUb|
zr@Vl==Q#ac+4z+M87(dC@#Du8qD0Sa+PqoM+#~O0?b%1|N*`v=EX_{nzrmI+eZM2t
z=Wr<3Y3O8g&d*DXZIO{-VSeBQm%O&e9&tUHf9^xr-D<P+DfVmoDh+T118^IC-LAcx
z2es$~%rBnBM-Un5w6U=Wo#DxNIqs%4H8r)}qN3TljYqGw#yog%%f4cfnu?05eT<6X
zr_7}SL#(#!*TO5?OgzUWyoh3gu3%VE5P_$3>&=@t2{)TTy>T!E(B-uYg8uO4(bmx^
zpN}H;kH>e#adO|(qk|WT6xi(3Hf`)Z^Fc7(v{J6nzE@N_%`&i$m4zkObKcG%OJ7UO
ztYcpN!&IiRC{y~m{{DU<P^RAR3{80TiUUnqE!wCX8%fDYNeRu?A7v{6pO)XUJy&RK
zE<kqGZ#8n8=sG+l6YBB%m)<j3kP&ZZ2J6Z#T}rzJ7gthFM3GM<c9nZddHflpRd{b&
zgb$6bx8h)+o|Gi$p&nBngzT2RhJB&(Yg5IOV8Mv{oby<f=+r*Yb<U*GMHw*j25N0<
zTG_SHFT_Tld?M|SrX={A-c{f2=g*(t#L7ycK=X2btm7sYUw>g|bFU+MFHY20Ah59G
z*(S^`eN|-E;*Zr=_ho;aJ!1%*SKgbYlFUpLhg|d5LR~pyj`}|(rqR2jQX031T;E1k
zN}U_7?z1O~dkvv$QFRT4V*9~^tVdiY<{oy8yOg0rNgDrFc;yZfyogaD!`ruS<$ga{
zi_B`XCEK15sj$LL!Q78S$h5%mk#nCJ2}K#r(!kzXHyMKhTRny4P_Vem^OLnwy<P)O
z@y*t^I>)nhOUbib@sQj3(1!J1Ed4wL?t!}C+eZo}8Na$HF$;i)LmqSK!-moduO?0k
zMV?<<S(JONs&s2*W##JV!{fV%5U5!APG8A;mH#&*$3D^qCZ0DaPUZ7wMYcRQt>i%`
z%j&%cyagXWKNV8JTNLSp)4RRGP0rLh4Ngk9VXd^bk@c5<zgny`$rv~E!sEAXvrBVx
z^KeI=;Fm0;p?P-Sk$LqFr)MoAO(^G;iptlyR4aW}rxu6e?*!=O<>gHy+>?+HlEhw{
zjZzEAskUqXk%=th#p9Z0{CVJgFWazyjwe)ASHI&DDUgYGBY@TK(RR-|u8kWc?Ye@n
zkGIK3z*%|tRz2uh$F{JSv*YC41LD+b9i7N=eVLw41A<=(eKz)BbZ?Pi)^SZu#^J}g
zNHEDLrxzoGQGFCeqXx2ydS)InGk?iGcV}V24Yl#Dk<Z}A&GF#8@#q1#N)U@2I~&`A
z47+F)Jw7zxSYj3U;laJU;HzHd<-PD;cCkMT2v~TfGXP0fPbkqFSk~97F^;jQ^IlrK
z&o`qjpJv&IFD1Q6R16G@jTI@mJNj^LvR<mVIPwWq?BmDkbpAIC^R18KoD1wVDT@Xr
zT4u9Hy|HGmeW9(E7BkxK#u)0K2$wXVoYP3@wCg`;g@F_8z)<KG*`0fQ#8ouQv@#9^
z>j|98G|!3R+R|=3A}tBK?APNtDGO>p^rI#P*exKsV3cy3>Kjz~wrxVcI`YzbTRHjN
zNp(VxEDdS~rkkF)_Kav!%tDi(enJhBL~>We2kVuE>3pktPVgL$=BG~#_0w5xaoG1e
zgh%$<-+Kca*VL4#sC;n81_llV^Z*aVOi%K9&)t`*SM4}k#eZ3H8>p|(qrXAz?m2R#
zGp+m1mc4u=q6<QsM@uj)1pY!tWbUsP2%dMhq~w<vYAYN_ar+(_bszvW3M8jvPCq{-
z64W0S!uxab12^>3v?N>_ZF^Yb>h-vm8LN7hbM=p0&;}5^jx~-JLGXU~@F8i%!>%ZX
zz|rayEjgz0lb)6>shT}kPQ@NN3W}HDZNR@JA>3N(#40sF4s9Czb2}s?m3gO1Q)W(u
zI=EU%S)j-_V83G|#~%-slnv~`ZSnG+qTeEt0DK$yt3Aimj+4XpVBsq592c`*EVUJm
zRgd^S(D^eR?WWq@Xqubv&hNK)N3@}7aUQpEa8w80-Ae{AM}MzWz^_`lzP79;5YY@G
z&Ke6qEE4#>xt3G%!G)vE$$_U_sPj5BP$}5Bxitxng;)^K!|4FbNoR)vI#cksW_qzQ
zh0*q$w8hyWonyzy(QSjuuSjn&vm(1_X?Y){I`ZaXmz2dj266>f!IMt`=wm?dA18Tf
zM?JW3Zujcpmta#kkvGbLSn_>VJvjHDrzA@N6LIHHH_A3YKY#P5M+a@dbmcqrADbNe
z3O4O_;kD9kkobb1N1Vld*SrkNJtX)A1tUyy<{xbx&Cwi0u`RN0=JOa%3>>~E)k82=
zbulL6;INplu>W&fZX91QVE|3aZsL|$Gf?&~>;AcFtgAQzA*hDNA04$SXrgEHucgCC
zVL`)a2E`#RX=k>vfx!Ty`dn&Ic|?qnFA2m`3nFt5AK#4!>ii%Wn*jQU19^QmU74S$
z6VvhXl2O|gPS=|&cq;BGFk{%swB+~ikArKvA@y6)l3amrhs_9@>hj<O)leR8UB2O@
zB)-S8o{|t8{D9y<x@!X?Kti)j@-nZc^s8<ibt$!h_R$Kc2M9vk(CYGhnquHa_aZe-
z&6J7m(g?#WfrJ&MrWDz^CPhj=bdWj|FMO9q(psUtf#i7FW}5*XS={m?o3!_mv;WXu
zS=lE9NdesroKXf^F5-KY!3m0rifW+RQLmJO6HXkAsqYyOUcPR~7v|)VMG6~oH+<!=
z0}mYr>g&kFtD6-oGbcEYxI91Q5D^n|f(S~18$9SER+mO|DE$gAckq3a+`IQ4@lh{c
zyx0|0w?|GcZlZXIXX!`8sj|+pnUBJ)iHaNO7#aD&E@?g%Pb8QW6Z$pdQMa{~*;DFj
zYCE2sROP%EK*%HZA@5nLsr^8L&yZIE7kvhYO>i0z1~i{Y7j!8#Sw=FF=;`T4?yK@7
zV$~5c55mKbp^uR3)bp>mN*;>y3^{LMp$3v3EY)i2u4#6KP0Z&?oPz_Hcu!K_yzVT`
z-3>_O{AcWJvn0s9y#&X!%t_10n1V%G^onDlVkfaMccWF5+hY`Ti}yW$V+tsiA3rWs
zPVV)s{&JmSLplHf+m<aVh%3;61nd0!gYW{x0~E2FyRiV+W`fuLRv>((yQIzDh@Os)
zU@MY=Il%@JG-72=z4_|^=oi`h1ovpQ#Z{t73P-9@0Ie#{$$0Ho>j(SJo9=5t<er+F
zn7zThQC3ZQ7jcHXZ*=BQRe{pN0w^PX;J|@Ryu79@_2$vrzM$4QKo0JQ8d8m{e72>&
zxc^g(6w~ZbqpF?V#9rT{%l9q-t`pT4C{MUNZsMK*K(58RyVdBLE|u(QZwuR@2#H&6
zc`Wa--N$RS*<{bnEoYg4Ql1+XZG-}Hp_=#g1MNTM+E=e+RGTS4xUo&wV;IOp`5$i2
z;e9A_R=Y(rV~gO)SU+?+IP3ph72VU+V3esxi)r_-uOfY%lFq}YTeFPV(YOSBxKM*m
zBNT#-7bseZZt;NPM2#3pM#&gP^s@o4`>Q}<zp(F>6ES|3t8Vj<<EO_0g6~oZZJWU1
z!ySV67qbZJGcwNF?cjMbGsY8YYSkbza4p`df$0R$Z(m<p-f0xk3MlSIYNZGEsl^J>
zmUf3wGjf`>J=2SpzGkZ_bq@}N)YsW$|7A<K^gmk=7S9l1A1Xg;!9Qx{{&%6gp<llm
zzykoToMMx{DF>=OA0HnU64JxjE1_raBmG0wZMNO?DQgG>6P7C8mAmV&Wq{@cTSJUg
z&gbQqqkA$N!}(7%ArZt&v_0Bp1eNULx!x81*W^`K9k|$=deKnYmSpfDwqFx?+Awv@
zssNev^0$222m#F+kQ)0v=B!Co$o=<-Rv_<U<2!uOalYHtG^(~jt77hvZ!d&E$FZ;b
zMGSIDF;ey_U}SAe)QZ>wUtb5-PeLg;o!K^wAF8_a!1qtb#p7yfM?nuH=n82;281o5
z>tfc}%8Hi=T2BB7Rft!|6cwA1!6%p6^~@z>rlHQB|M}@rl7tJH85(-)mQ+TFQsA)e
z6gv%3OU#Pz-OGqva?IIT9PKuz<mI4saC>r{I4zq|myUrS#QHVioUBaxuFrOa{7>eC
zKBkhp>&unvir9!m?TjYCZ@(ytz0=S?zXQZ5ySW|0NrE030(tgz+BlA>GEhKb%Ls7(
zO#oR7X!A+qghZ+FM0zs{J{$_^Mn$43cR8-3@mi3XK>QMz^#T+O-zKS;$MuTd1CWFA
zhI7zDQ&y$P2n%PX#t6Lx6!eQ4U6hJ^9=GYe`u6LPT6cE44?U3^+`oT6GB%T5=fFa8
zo9$}0N1sdS2dlir3qu#}xb-t5Nlm`%%e{~7ZZlE7F!ZnVUd^J^W>pE{QakYIZk*@D
zVZ$Oj?rRfWkHFcDcb68`cn?9UVUn355!g@i@>hkNvKhL+S{L`>t8YrX*-gEUUM-HL
zX5!HRAbpCSAc9D(<O*UH3+E%hRYG|I#VD7V<ps79$fWyP`#GXiKbE}0Nhsmpg9XW-
z%_H%$0X>1zO@{NJrPIDrlR#2X^@9sN2kuGHkQgE55}J*${_`o(>BW(>_}hg~Bra_s
zO8}ME1Yb;fU5ht}?MS&bP$DcO>@o*nXHRL_&+l*VP<&gDv9H(x)eVPWPSjfpbj9QS
z76I0!gcd|<dd(!Sfz6r~t=t{)VHn+9&p4r|p~i?``6liGfpr84B^Bg3LGdG)wVv>V
z1|stlIu$-L<{$0Qe$T6q-`(S$>t8Bp)%!BeMnbw<I!6~W@M?5TS>J^8p7Pz`)!>Ke
zrH`JbrA3vp8CymcnYiZK`IDybk#a-rR(V=rkFWv7+SeXFZ(N3_#Ip`$-YK|9HeIQm
zl|e?i^FZA)GA;rgz-Qd`m`>00vj^d|*<5TaYTtp~HHDyuPV-v5v@8#kgndt}G}OZW
zc~vxE8PH#nc#{Mof{C((lGF3@?p=oPy1}HOC;#2D;Obv%_^{13h2Sh2e+Ke+1VO#u
z%*JNy<O{_DG3JcCf4_EMkL!`BI_NQvQS3y8<bBs<$PyqOe>VC9k$B0g2a0#V1ND^N
zh8xJp+Q*L7>|IxkV<bCM4biO`B88Hws$zz25^|<Np4GiZ7d+i-+4-)UL?!`dyJ?v9
z4iK3{0>}3Xf<j`OEi*UwICsUIRGAl=y)J0CG_<tWRg#Z}^2ko%Gd(8brR;e?1d)-B
zYiP`E)G;0P$R~(U&_t?$s)@)cX(zb}SCd?(DD=#Y(oZ%Ld=)SxmLw@DX$k~J^}uvc
zp#<kWuvlEjjvd>?$$1<6C$+?`E8?8{0e1i%H>jtuFmj4l0e1f?e~5lc7QQVA&{BkE
z;2xUyW9bNA4M1S|;Po#?H-_BH)|N)Co0F)Jmhd4CNRQyM*GSuqdCXoIOu^LT!AsCs
zB(EH@GGL0+(D5p3JD^3zC%#4#$qP>3-!MxBIR=L{7+L_8a}Q|m>8}={?tvAsIP8aO
z>(G;vlae^FwX5^Jlid0vtr-#42D^_O*>>O&1B&u|#`@ey3+@xkYjt`k|Ho1z<Y04%
zZ^zZuZ)j({0XN_8EIkd@C<&iOtgL>fGQ=%{afz>OY;3Fr&NSc0QI?YlO>?QdP+US{
zGw`b_ZVL~F1|bk7^md_Rv2nD#uN+Pw4R}rjk@NVs-;WjV!E2@npieRbe8X*Ew`Y5N
z;Vhb)?B%7tNrtV!R=q<PBk(Nwhr|qOiuufs4?!UzDhP%;?@wk8_bdNg<qAaYCX8hG
z75L@cer2SY6?Lvo<Uz(kFUEzPL2mt8a4BkdZEnt4BTi;3)cgRfo{|#f1bP?S(uv;@
zr{u2d3cZEFpxja@k}g#C4+r!p-l^rpd{BsRmo*QaK7T%GzF4H%kt={<D>;f4mhjHR
z2~Ewn$WX#w&vB(~5GmOKj&};lyU*-`;1L|mDtJtw^zjW>-1v4rFVI)*&*zcN-l8Yj
z56?_`n=QDj_{JU<klA1+k{T<++b@hf9U;^i2o11FiAZliX*~wp4DQIZfBInomjB1l
z(4AkDetUFMtIoez;0_QS$JtkMaghXhND5XWzw`F{ytH~$9aT(y0FsoJJ4zi$=;1@9
z&Liah$98<n2K)n0{SF7w>(>N0TX5)(pjU5ioKJnna`a3!gl+WycL~V?;X?R~a4!sT
zL;Hc_n+wxU#lM3~Aqb$8i&a2F0S%$~3xg({VdLVfRHtNrvmqA}<jK;i**bh(&6U2s
za5QZ}ujjACH%*-Y2&;@HU>}eevH~?)Z)R8_`p+#N6cT#YWkFDe&-H}`G#}qWyl~05
zZG!WHSOYK@0^hz2Om7G0gVg=zrDfd|!rNkTW)M+Z^HdEMNF{=<A0S{T3}n~A34Djk
z=rsTZg>;&J=4M%sIRz^#0fJs6Kp=r-fQcNzq&x?#1yp(q#4?TcHQ8nSn;QTK8OgQF
zF>v0ns=|BuJdABe+l;;gPQS*LoA6wBdF6TRinig2-Xc~5@Jr^1Yz}!J835|O)o^un
z^%+kYqG%${Z}$4GbHi*m|1d2rZ2%oCYgT5lSv5II{f3<?cdI}=-;as81Cccm7d+&(
zun*X#PS!q3%v1&IWrmCcHw(e6BTX%!$uTQ1Ogz0i?8%dRqUTDgYzw}_Fo?FCvd4S=
zTo-na_!$D0?*P)qlUJdACjzh8f?#9A)KgxedA})_<~McogI$pYv^tm(X_%M-J1)N?
zU~Jz_rt>woXd$uHtgrU2@4&Kyhf_jrB%?XHh<?Lis_&@AV{uyaR@Hz$6Z6-ueSbsS
z1@sXF?F5o*LYwW2Yoq!MTMtkY5;Jm}9O;-tK04}2CmVn@giv3j{JIj*&}!TeXeT~#
zaW<m4;PIL|$^hO^`bop`D<tci)n--DW0{JLLqG&1UTln8K-F)8@S}AO&6)~upo!PC
zvVq9MtMQ!#uS+BaqJ$(V-<iUh>qqmh07DtUFg3dn${%Rt5F|OpoOVEpo+=<X2axGl
zJ<_J^G{VzKBv+)dff|2$R3t`_zrlosHo4g?KJUhj8+4u21Pc$}CF$1o66ixPvL;=@
zu9HZIVC)0euVelM>@3(gxK<2`ukwGgI_NP!ajxHozuhPD+>v5*4#=UZ{_WmT#=iP4
z?lnlC6n>4N6=>`d5lKDEb+ZU>3KF;gBA=ouKbm;DuVQ)PwUK?f|J0PjWTC9*pEszo
zHxM)C=m2v&$3e?()(~JNY&=>qDAmALLLSe<oxG7;|H-}MLYZX)mpcph?qZ?c$SZdj
z^uog9S{tPDCya0oBG13YZM^_iOV<@5O_^*fv1t)-p6J?2Y(um48}LncAyqar(f9_@
zbaZqOidS(CiUi4zkP&fQR8QCX>ST0md><27xx>u2dkRb*;YQIqi;Pc}0Chh2BVrn^
zOE`vm4#d=}Ab@s|@{!y4SW13ule~N!zmX(rli8*Y({F2$y{Y}#T*a!j5cB3FW#Gn?
zv^3AUcOl#-NW&>nCM;JxYq?!d!{Sn2<U02CD9M^o#SRp8YT}ORt!Wm3)&yYv#KSR1
zHo}wzW6-%Ww`QY<VPSo)G#owwFUhm!5sGtL<#uZ=bwx>0mN^cnG%rd$(g1-m(s40<
z^Od3tA6S|XS3t`Kg<+U$cHO!ZIsu5O**#*HkQa8{KR%4&ELKE#G6YFUqv<eE?uG{y
zg@1_K?kuo0*;KJ)>VLOne3+`NHcN<&<!UapE9$&xotm2)1}R{;S(W$qLvJuFlzwR9
zMhLQ<uyMq<>!E|rP`<MTZZ_qi1XTXkTysVHvq+CC6+S}RS7j^%{Q?=b9s^JN`Sa&8
zODwWLxydOsO7jmR%T!8S#yOid86^w_y?3R7<WWqiAU>w5M7YDq6+jqm9?v|zd$_CE
zsplnXItvML7+LhG_u@M=EVLxJGdz1<hR?FSH~nytn_27X)tAH9^pP}JDg8i88#O#)
zxzzRQMUf9U6|u6Z8i_!*;2?<Qd!(fw1G)Wlf&fN@d+2fvz1Sv_kdP2dWR#a=)Ea|u
zbA`7j4or*0DDfe*BpgFDF0xATUNf}yj}8W+1GzTxS$S^p!ILMNzy({L41j>aA&pOO
z04K8KWy;J9VSro#oanPDx4Op%Ea|XKmGjEV%FXZZ@60Lot$VmvEx9=irZOD6X0YAr
zXx(;_<d)IZK7_KjQ@R0tgO07Ot^arzOl)KcM1MEySqc6H?3SKEWBpyA)uSV#R&ZRy
znlU%rOaXiq2y^3=nCR%B2M^e^I$(SOClZR(+isMqodNGXWwQ%xoYj!8_uIDaM(N%u
zCdTCM?w)HPGTdD%<pcduJ&kj>H^Y`YRQNKXfd;*w6#Fa~=XT7w6x2piVmQb{x&x0s
zb?&4gX=kwEzUk9t5$%eAW1F^Y`6=5#+|EeL2A<FjaEdtP26E_@6Y2I)$n3}}`F75e
zv=6|Sz^g~(;?o(*u7m{xR!dr>QYymY$Su=o0WU?;pSSPcB>^0?b4?7m^T2Xcg(Sby
zpt9Fy9)z(!Nc|w6=22&9!c~thqzd_yjHcZ9z#|RuZ8NA|humkLS_jvd#b@Foi*qU`
zQnV+VG}J=xca9UCK4A~`C?dQsKdu%Jm+yeG>G2Kx)c-#(M^M1Z!1wR_u&OKIM&&ML
zoP?EKJ0riQghKW|QcWc!Yc2P9Id7%%4runbs?9c)*PigTdSxt%tw@FpLc_?oxj+!p
z7buhG6nqKy+{DGa4iFCBA&F0i(#8h&40eD%Mn3ij`AZLo0IpBi^*!6BhH=CwWpeYd
z>pLN4st3P!z=c9=(?R2;r`?0xM#{B%#>NaJ!tvVUyY9`fO_D*Ze2#@M`4Q6=FoWK7
zHM*yA6Ah3BEHnh{>2V+8e)ION`EXO>AvmPcHQOAs>v%49^nWo6ZLuvN0EipP6@i?}
zrr&LILm7WzS+}KmSudx3=i$R!VbQGyWi|c9$i8~qg_twIRD<yReo$@%c0(2~?rP2m
zZ!9p{y6u16_JL(e>f{$&xWt(J7{(BPe=2W=|H1c>9Von4L_{Os)Wbs>TD6zl%KyXl
zk=bQF8XFty_bcOaIRtrV;}5M5r99(t>mwr!L{QaK!A*xfd>DX27aVXLeH1<4F?6b{
zU$hR${5)l6CyELRPQ|C*1ugS3gChCV#&!HH*?snB(A~RANRC8+mmwTTxBT?rg~Bo>
zNp6@mUi4%lRblYt4t#1m(Wi(;)lL54&&dq$f6MZ}$@h`epE}bJD1SXR`O=={62QJ2
zfV#zYIX^b2*;w`tfKh~qRD-R)|LD=4C3BsngNiBIi^dz+X$lM(17G*+m|U$tKSG)_
zgfnhz)fZ^Ir+pCscg0Q~q{G;^0(yu<al5x?iSmz&PJR`V&JJJZ1xV6bghznT$PrSF
zo)u6{d?qc`_(8#pX>Qe-8OSbaxYptFuwvrX<#N5l4LI@UNCd=038Y-KD>sq<Zj=9k
zn6Mr3gOc)=Xk&GJ_4T(vUJ1f{(d6&cwM|+nK6AYq1|$PDY#ouf2tm8u#FJo)uS01h
z(gT#8g}L{7Kkw-#J<Wz{{?N1hREMANCZr{B4a%@|SvAB41J)~Kw(&uPM&B@dI6%Qe
z_}T3t_c`F=E~QwQ9!e5_F}^|GfmEN1?IdhKNP|S@U~=Nj=l>;Bh;q9!mFLOT@(skf
zBRYG<0ETKPRGYZD8DWh>1>LcC@7`gTs4}Z~5MA*+^DO*FE#O|fkLUv)<s!&=FkF6k
zBs27z!1G>Bq1?|E<XN9BbCC_sTbYj5<-WlHP%eOi2FwuXb^i#w<~e-z>F~kV=uo7j
zq!Rwz3Jmo0yoaMuR-T`74!x0|L%ne>Y_~+OL@>@i6jczs2yM3pyj1z3lCUrzA36F7
z)UBq`-~Tw(NHvZR$^)i9Mhf?gPJGn4+O;MiOPZn{A$oh1n_SRm%`UJ!U@9on`nwq>
z7F;HK##zU72aRjrz6E`;T~M&LQ&J)N(}VbUQ}ZYA5Z9nH6_RN{o4J*+;lV{M@J~-@
z{VhLZ_|S=H4FRGH2X;jlQf_5*S0%aeT0#-$7LxvFFnnZae~H%KM`FW)gE>lobZB-*
zW`7EID>E5CF*Do8A+(~*{YmU0@KHZn8#fRf%B%w9ny-GH!Vmi_^lxBeXTNjfA%N0`
zd|z-}uwe_gIi2SIEx^y;4{Gk{(WBK8o@6%<H^M=L$tfm6vL=EJ!ufu}?YK0hrmjxT
zyaVjhu$j1!5FPc+YQg3!gh$0Eo}dIKSNYMX-vFlT>eO+73-=SOfcd7^WX=$f0>Wn~
z!tG9jO~;M}Xq;m`<=gZ#^;+o-ATAtO5sIp#uss7FgU9^`v&_u<oiMqmAUO8h_mm}2
zRKc?X7sl1b93?(l>+d(@ug4`{I_&x9d3w<lpi`f_iTKUK(SPn@9u9Q>JmIp4f3$|o
zM)xZ4Dy~8(AfyDG!>@3Z5cWg9_kb{E^JfC;t!B5jk^G>dTVSpuTFzS<=jCih$%#v!
zpVE@dB^^e3euTRa9d(k~Bn&F=-@i|H_<RgVU>XjlWy)H>J!*$*FHJ!OkQTTnT#8YM
zTA*_f&71=!E5t8-`DA1g>2z-C5R~t}z3bqes%E|XuVH<O`o|3xNm=%n-r<u9Zud5F
ziqilwR}-LCy5}Xn1wP1Iptj5QtAt;q?#zkev8h_*Gm30Tt5j%6ljhi5O1aSm7h0F%
zf_$*~0CdfoY|u6wG3~%klE~FdzfW=m(d1OFc~sS&{(|X5P}0gH4l+Eas?PSQg8PQ}
zIDiP4E^f3Ir;glTdEoa(lue}{QjF>&vrrmus$ZFfWwZ~EPq=8H>I;=T+_)*bG(tTE
zJX6K6qdJe2ynJO>2|o)h5dvVIYhs?gGcCmv``z5YbX)rfOtk-HA=w`A|F)3qyYPOK
zRyq7ei=K=;ha9Q|SC!P&>5N6AhRdsGW%P4Q_I?wUmgXcHFN9)QIY&yQPtF6`G%!{m
z0jG}a+Vb9UZnT{Vr>of7Kmv>#`l}s)-oFlJN<^1jzm4Yff$S;%$y%W5GtXo#O_PD&
zXBPRg$0YpF0S49%F#k3?%(d}y*DGeQ9BfpdQETVc{Py%u9=H!ncpn<JsbZBs*t~b3
zu7W|yzE2a(c&^Y=^?*S(%3Dl8P%s&g)u7U+eApOn1!@RJ7>?{Y0N{$Vp;d+1HM8B3
z>i7Vy)|L`D-$+vp#`aAKWatP9XxKda;QsyVz}dI03O2%JdbOC#zWn#YqPs!4F5`x8
zYHO2GCpPogpDp+Jeay}6)hYFSiA!G;;Nt76y592=X!L1n>TS_PLV+il^WXoBR7!fJ
z@pCot^o*iAf+A_0&|hQaybYaWQ5^_+1pHPgddOf=rl}!F;H1+T1C<#op0k>Ec9s{(
z(o$2y0mR|4YC?3V!QPSz!;*dB@6UM-bun3ny1Ghe_IE<&C0r4(+&&@nOoTpW=`J?%
zOcuguClINwjANGq8R#>hz74PtF|<(vinXkhTgDB8MF>d3gq{prZF<+O)RSouu(<3#
za3CDa8Zpt8gktFtnmG6ABRJr2{mkDP$rm1)(_$oz7`=jm@mR+Fz(3O<Nbt5$*%k8q
z_HLmsuxSZE3v-WGK8{c=h*KfwV`pdQKeXA$ifn_9z1o*01Val*>ywZ)62ODpwmP;E
zh8Xnk#Gnx7U3V^x7xkiV+u}frei=&boQEu0oR19+e{AMKa?p^_A^AfCJ$C8K^F#pc
zI8d$z=AZjP>fgS1kN=*EBJyn-7N8m8-R{GOAJNg%2OmEkunqGx#N6G*1};Mkkiv8J
z_)thP(&KXLBA9Yk5{xk7)=k^vmnme^2J0X5^LY$CaV^W!ZHE_xSnZ~!)3CgxB9BhG
z{Oeg!(9U>}dh&@nxMcx2R2r8}I`VBITsz_0I1M?S=<dJ`Src<8K<+JAbz&B&XFMr6
z*#>RM93;HYgz5uU{1BWVXn0D07K={0h49Lsgk1;5dBPLaqelxpVgC0~=id_F?cWt5
z5${Zzxkv`T#to>eN||SJQgKwGNlidVUVnZ^(Ma#uaa4T3o9Naf)kiYY;*yBvz@Tk9
z&^;_QT30?a695VZ7I7F&{?Wg%sMtDa1gDb5F<jdp@ni@3S%RMOfQCiP(hxt2IXOXu
z7rf1xuB8+kHWGsos1w9WdFi_g$;QO&o;q8lqy)=y+6G>)rnD7!rniR-q+_;?5WqY@
z4inRXw`f_FI?<_r^f9TJ`+Nd!i$CKhhzCK%W~p_Q3zhI$aU)l7?Y_p2PUZF%QRCwf
zxCAnEe>rWu_$eCH2m0|jR0Gd5<cpy1$ZaF!@#_L4ag0<F%9RZsp0Y|X=IDpfyg3mE
zz9dy%?*}BpW<rd_Im4mEN`heoDLj002|?Exg8C}Q7&PHIFwD{yXI;R$@sa+Ch#>&)
zdb#m<>1#r5Sw`~@Pf^yBSCJM^P~^d7ity*p&=n$PCyBv|9;Q(R!k=<!5$cv2#z(=}
z$wKPydBC1hDIRpo(l1~mw>mg{FfMyAl>{O5DA6HVmx9ZEge}MN=ti^r{P7{8HSH8H
z%ol81x2l1Do=Wve*B~9=^;DZf)<b+N12r8jZ6bJv7ED@FtW6QKF=)PZFrPq7q{3<U
z&pFv^q;fK?rS#lBGP8ZZOS<h0OPu#e*nEDoOhvHONFHIXG02s_ddiPvwAqsPt$r64
zBkf{2&`*&7sQkcljOeegjem>83AHIOv_*x+kg-49edqe!Phh$Xrm53-M9jiHgBKLv
zQ+{x47u+g@6$Il6Pqj1B(dNv7np(<gye#P%Iti+8zEpGj(3~OJ_QqMlqoj_xs1$8A
zFlrmTF^(+`?JmAgKuRh~<sPRpQULTQYY0Vi$QvxAZ*Uq|cM|id2dx;;$A@E(4NgHK
zy$z#GOxw+V6wWyf8hN+4_yZ{4c-mV?Yui2cE;wZkEP`AmhGrpr!3|%6S$QvQd&+Gj
zYNwEo^rTa9GHNJIg5Z*f`C`n&#an(P|Nr8K!6X{<A`=OM{0ZP`SR41@km+XX@8vvv
zbr&2arb*-Kae*^yAAR(xSV<MIa6N4qSxNo0i1D~|oKnI_ghM(9>S&?t=STWNx41NL
zo}gXtVal4AH(r^TyjpAxOGRtRwMz&O{Qhuu=|mDRV{@7;PiXD(5-~*_8#^|?S^+!Z
zjd*9P!LyY<vJ!S((J;*r{xD4Dd67SxAy>d4H9|WXISqm+F=30;=-+4oxrP}1N(YJe
zR6mm#0VM237+224d1+U3_qA+P1pp@o<jA;(>hJCpYKdxWeHAHu<~!JG6-XEe<Vz)o
z8Ju2!dttGGI9~*J@oKi=2)-&6i}Pd-EA0FF`tm_B>&p8;;;lKRys*txTZGE$x725B
zA#?&5*TB426E2wo2wlW13%atN;3GvsE{tZ3!ZEA_oe%PpX!GdTI8q<HoQw>=bb^uk
zX5%w@2Av25&J^6^7BGHAHZ*(eO!U3Fy#jVAyclA10&_%fL0;Q0_A!~|`CaNlHP(W4
zlknUNM9?BX%7IOohQ+1TxrO6rzyY+UUoT4%1o6f|3QQE#LMh30=&un<IeR{O)Er)n
zwgZ_9DpiigDNeaPmxc1yrt5E0`ALWXHiGJ{LOvwHISVtiIVz)Ep3>HzF72qFBBm8w
zn4{w#b2$RTq#wa>gCitNI_A+a8%wGG>&+ei%{TKuz0M<sQkx3189<C^sg7nb))8+6
z(Wu|S2eVru3@2g7?HVigp}DCBJ0M(Mb>yE{hDeOUmXr>vT2m@nrRxD%A2hLC*h(jp
zMxiLWVUDt#KBjZrMFu$xCLeluezmo=r$D?wY<Xu?3tf<qVTslP$dIs<qeyR$pz={i
z;U@J{>2~<{{PUWTMb}65R#@-aWm%siC3`5UK=oT-Sd`eiAR1!02)+DWoIZlzA*2yP
zRW=9chbCK%-`fu^Xa%Mu(yh&uM8^&{5Wy~;$u_Qz-fyuzs_q5$!mm^7z}TgI90sKK
z#M4VX>Rg=3G7?}iLznYV^xSRa%AIJV2&9SxwUGoDvNFo<3t|ihJRx{}2_n*sE4(^7
zg<k;#eu_QJT3lRwdN&)HY--14mE*^0AUgc}=9HfT)fJWY*Uwe~+8GsIzW(<7QJj0n
z;ies+-M~}UmTE%OJdK!|WDz51A21_IiZ!78_r=s>ZtG2@55vFjZCOu^xk|%9BBl{w
zh<<HXq?p#y4?Ry2xPzDj+08~rjA0dq1_vJnVL{q1EPR4(C9=!(s;N6R1hX{rg&mi(
zQ<MLB$xFcEd{@4#9|Yic_jjr~z%)Q;SH#wU$IKbE5XGbjkWi1jBt<mw3(+ynn%hVi
zRz$HQ0h<y#*n}ZkLcb5XchA3N<W{pa+m(38Z&-Aa!sW{%@L79Lh!y9^!21Mu*~}W;
ze;FQ3xBq>~jHyElwV&m*3==wNbV$cw>n5;#kL%K$jd7V9GrDSO5^<0qizuEv8R8m)
z?cWG#8&(*QctAU@@5Evj{|Vqv^}kZfs2%X;31VW8us6Z#>4gc6uXz`lDi(ik!~9ks
zAygi0l(`A12-6CLvkf1i0%I461mHrf;yyUGh<B?%qH^o^XWs{saChWKPC4m+UxqU}
zn#w?Ox_WgVX2S__7t2Gue}%X{2pe`%E>xo9yI4SNYCNDRxdjy+)jAl>M<T>tSeiJF
zxQJj>Ddn;Bu75A+TfaAAL#{xs%rRnzoo3B@dyASR{Qli;4YR-T$>0=YPPYB;t9t^d
z{(ie?>lV_1e|~LX`W0DPN~)1#-TA*iCbi`fS(qo%N_(^BqYeM0hG?U(x{c8XNEfv<
zi+((;{ri3&+bzteV;R{j$;?KA*BSr5;3xIxq1$9F{8d+SMjm$Q|GkM%m5l?`w?!$w
ztUR{+a3%QfU96Nor}8_@+Se`79p1U+@2i3qU0-q6>|zQmPBpsPY4P`#+S&0*6l@pC
z-n2Vk;`sZfq3F>MS|W2x#aAS{SyI{lK9A0-8<SZDgTbtA3IAQ*|J;*D2OXJ?KvgNe
zsB!qKw!=Sbq{~#q`u-FJotW<4h1o;b<$EbUoq3jJbC+ZEU-Mw9Z4l{%=xOJaUlB6d
zD^z3Ws5!*Q{YR=^$CGoNksZHK_V?j51`qG`o7VoVF|*H-WGX}DN4mz*blIV8^uo%2
zmbAK*Cqm|!ErljEsn0JYltN3>V6yjG&EIvrVR4YI{F>gAA6l^#D}~g<hnSmcX)U|s
zs18Xg_j~+j4X!&LA#<WOlhVuL>7j^S;aXU|Qf0|1;Qdr&(}YEJZ{>}DSJjDr2Ome4
z=%Fr|2M1`6NnCZ%<5uA(7jai;9(;Cc2G8qbLj5C-#jaL-gS5t!`zyZEZ7yGT48^uN
zG2Es3n{>9^w<sKNOWpcNML3p+hc1J1I+0U6RBHRTglEPnCxy*QC|^C-yA<2G<EhKx
z5~}S?+uP|BD>XL+d#uuloP0WH5T<h>P;289XT`7Dss*!u_ppon>L-`6!%yfV>VvJz
z>4b{eT%tDE6f3_Mc1gH6klshWJC5CBMZ}$y{)<s&daJ_^ivD$v?(0Y5Df90qY2vzE
z>S9VSy8ZWMv2d1iT4j{Y)K)#X^Y@t}cJgsGiod?#lw@-2%Ms&edLK`pN_^|@MIUnU
zv163U)~~0Zb??%cnL3o2bN|CUS=2`I`bmmk*Jhm<6bwVC-ny`VKi1Y)RlTEM$d}=h
z#0%lQL^=5>a529m%qE}DFlUA~<WPuWzKOp^@@ADwRCzA4q6vLvQ)eRkLTL`o%TE2)
z@N=cNJizbJc**Ccyuz!WVrlO~aHm|)92hhd44D2oe_cPg?08NxF-02xn)it`zAbZy
zV&lAofP=xztumh<8HN_0&iI*{P#o}Kw~Jq-wmy(s(~Z>^HHm)p{ggs&73JOkJgm;g
zU8j%p=a$Me33*q38y96_(+*zm`)J9kKjU!r*%z7~My_?X1&f`1g%sL{9cnoe%q#s|
z&K&0tJF&~*m3#W2wvwgKhDVt+zEUxCQ9>)<aP52lxi)><duHBeM-A<mf9=UnGB@Kp
zcJe;AnwG~c+aG6Kzq^}{o174RCi7wE<lV4u{F8Sbj${{vH66}BS*16ucR*=W?|uCT
zrN(5P^1-(|?%hA@du!doOz6KZK5%Gb{Qx6&yzf|rvN-)AEYhZX<oMDV3dPdJcr~7o
zXYz-bbL}HHt?c=&P^+deuy$0e|DB`49s2bRZ5?9DgF_a}1L_rT=Q*N<{${l<@+3x~
zxyxLG^%T(;9Tj#&Zr`KsD-jr1%TMX!V$sGM=C;F^x0dW&Javc{jq_s$>zbQ6!jmGo
z-sI|nZijh~ke0W}QS|Ip)n==sFz7zS_blLtT#;a2$6e6%z>FBFdu+FnNS_ZXS%Ua^
z_g&QY4iC)Q^tF<+Ns0VPT)N@8vJDGfbYU?<LaBkS2|+E@*&)|ub`^d*Lym;}{NMa(
zw}-2CheqZr!@ifa<vtDHpXq5(#&%HQuscMP1#?-u%O?b<8+)_=A@6Nqj=JW{V|{?c
zMWP_xJWBOIZcFO7zZuU?S%wKo@a^IBnO#ge)VcE#eKYYj<3ZF4^Fu=HD~GeHQm=oP
z969f|L6Smy%h;;X-waj6sd_)`TgtNt88<11Ig7qVSN}In+Wbuu%n5HD6tsgkeV^J;
zHEDmRj=Fr~n}TPCM;;}-_!s`!ZRU|OYuJ?ClDV<)nE9$nC~J`Y24_#|eXVpAaY@2e
zs!X@*SV}3pe{PU9iq7EqH{V7ZT`y3G{-O19&d}05_DtaU0xg9o8<HRu=O0EngPr6)
z(M++uJ4JaH%?HWV{_lsg5?z*$kh%TL<jj&>UsR+*=vIaCKgZ4vtVL!Dk8<QXJ%07P
zJ6R!Szn(3hj_ltjliJCbu$z8&gG!{JyTPpgg$91vYpO-YjHH``j$}r)G_NaC?k%&N
z9)8xUk-GGsRg?_jOKfkvl4B_Qh9YB~V&ikF*eyF=IMr?NE3^<k)BEULRovU}FXd*h
zFUj6OtUM396jm(ypAV!DHG0+1sDJu*$=#8v#=7gzD{pmbY2IoTWoEpQQ)frnvyD&m
z&Sgplcj1MO8d2e?T9v+qw3^?6-llXFvKs#Xxy;R9kAG+-_1)OC@<CT&d0Jt{+fRwp
zOSUqLY;DQFSkk$C6N$RcYwN<yh;Kl_j=8HWFCNvjll{%lDnDIlu7|xg{$0YQ?58cZ
z691LkTAs|hX<Jh*wdomi>D;VOryV1%E6i_=w2v31J{@Rs&h(J`Zu+%s)_=?7r+Y!i
zhiQcywq&<u(y1~#OkL@ljK97tb&m1ngj+hx`1kAUvh>DsBBH*-tP0zHNO&q$6;Jw`
z>87%IY+wGgL3f9rF}GJ|wBLWCL_Fktm2(ECfP2`cxp7a$=n;An`}`TXg1Quns`i6#
zEziYKK4xO95FJo9o4nfhQ7cjE#cQ|3vIFJ!HJbjjB6K}Fjfc*#Jl9ip+&DEls<4z(
zGae?bP;?P5fVi1ccV&Yl6NT}^OiT6hrcps1>N8QbR9vex3r+{CN^f1-Bw=1%6r92G
zpU0~*+^%t>@0?!F=$nmweIDj2SxVITJXHBUyBEwDH(3o#81aj4%gw*)PM#Nn6Y0->
ztMi&PP5aWwz>S<c|GA8Y=G7zq{XM@B%1PTbY1Yn%U$<){>kqDOH~&B(#~~_u$6k=q
zH}X-m<5j__x=S_dzxE}}>Tb_qkNKgc`1i3St{>+}-P`bNX%|zG;_)BPY1!>uqiK{R
zsUI_J3O}-8G>yM%B<GFg&K{~`tU|r-q`7bP_dGRGs1+ndCeB>julMd>jIdZlU<{VN
zLcQ|ry1?u4QM3_))FP}Ntq(jBMr5noBRx46#vC2bW^TA161v>GW_a7_R)K#+AWhNl
z7gg_>NSaDh*Y>xC{F~)x?fYeGuK9c^d)96#MOK+b)!?<a%jF2bPQsmRrP8ireNpiO
zqP(-kVd4DgB{EeX8n5?C(Y$Ulr&Y*0a%<UO%Juc%qQ0{2vcKP+JOHF1zkFW*;H@q@
zNgJ&t4UQ)#HBp)NF3MKT&%UvgU8fNL#qM>EYRJFmeQZR{_^_3qkobZ}^k_B<J9TRr
zG{i)xnrJEENqvXW6$gO$W=SX@(BO~4g9j!BvP%G7=GZJrh~=QY5}U1|K;GMW(5h-Y
zJC)ysc=IInCIR;!X|xATGdWi}?p0-<tqK*RUk_}fm^eq}`~*DXkO=jc|BJ4-jEeFL
z`-Nxd?(S}ekQlm=l9CWa1W74}PU)dbBn70TL`pzPlo06@1nCAr8U&=?jsBnaIUmkh
z%eDC8S~GLsdtdvyezi}@FoT%uLk<=&884gY+uHHlN=U;Obt<8|*#vljwQi)k8QZ(X
zzo3@@Ww>qO>z*y#suFLbh(>^UFk9M}<d%Qg2PozWP=zys;UfYZ6}Q#{077+Dea1OY
z0vT%m&++k<wDfInVI<Fn*GuuJ><?Qai5tFd3cA{=eAZ~kiLbuz{t2BQCA?10%xf=8
z-sxy@n+4^QpSIco3go02#akBLz7VX@^GSrVvglmYTM+T30z|2|$rT=Z^Yj2#0A8^(
z5K15v&?tzM0Sx`E5)(k;x5kbD01q_+6bo^FXaij15Xh|u7MMj?Knes7vY?||G;8oy
zpWJI5+TLb+M+o%EHwaVYI1J`EvCRV~4F67kcn+1Hr?wPxwx3(v7Pdoq*vEI1Hcq>F
zqBt>DlYId_bB-rEWA`c#kSS6pS0pS^d?<60+5v{xG3LehV6KHQNt<O{2mW6s*k22u
z86mf66+qM2L3;_62V4<QDQIWImrh;52zr~6lz2P+ee_`vw82s^H4KwWzlZf!mY(pk
ze#gPrx^OW}j%6|H<_|^?p$pV+7#nuRYx4PYuhqCe*$+zi(fD3FBc^>}NNMpT`<WG*
z_!<%7DNySG=KdBn8vWQb;+d10iVk8T01iL;0WhLHeSL>6_a)DGn?A3~1_8<>9B|CW
zN5r>-e*;>U-lpXdyxe@UD6CC@H+;zoB3Nwvsj~lWus}&{zoZtK5hixpc}&uP#ELg_
zrn?z0b1q{ycIP)k_DL+w@7!<mVe7A4{y-2QhXo#Z3BQer+`fKrYxMzx=1QxxSQZe~
z=WJg$f=?U3A6OxY@ZTv0d-e{>#R8e%6hxkqRcpU=rio<74dtOKHm_ewQctjuR2X5k
zPeV1^EF&$_Go;9k$(M`C7;^y@1dpz!%NC2Nz&jLZDno4pisfaXKl}nX`Z$1-`1e~`
zSpizoQiMjOH5h0S!B~Fc;to`)de7Nw!!<u}YnB-(mc9ET8FTw}?uA8J)HN?6j&%Fj
zuqJxiQz=va>tp|*K9+=BHp)LBNqC6`HM+RVJ6W6drzk0*s4fsU*v}iBP`gj_D=%w1
zb{fCmmiG~dEr=dX_>b;Xlah?Gx-4M`8_Vr{o%{SZHJHi6z9=7%9sTc&e=9GAKyHQ8
z02dl<s@>;(3kKu|)fP}UH#Z`Bezw6&00si;{FWFAIt{)>A7~X5ek819+FH|8ApCr|
z-jIBWbQqDI54I26+KPZ>RVPRG>pv%~j3wWSS3_wYgcM|<J-%)70&*MqIH;+PF_>Op
zJkCAGskTZMj5R64l)Q&QGxtcQ)q{lEBI<K<0R2%_Gq)WSV7t_}xQ{GQj)}O;PCaBG
zAwj7(-As55BK>Y7KW_b90D}N<5Gv#rSqe_VC<cfu(C<(Hx+(PCJHFdQl$Gv8LO4EN
zmdY0})^GO+&D3moz_i3T3r=`SSM{eJF|3mPATBThV-uJ1Hd%+KZ<-mz@bb^<Tw7|P
ze85b(jY)U#X0fr|C5F`kk7dS(I!oH%C=5f#4IRyJ>7Yl&IFna~n4XP9m+Fqij4{uH
z7T!QDd><WvAeAqGz=2>z=YdCW5&%p;2sj;ZjkN=bEIZBoJsq8HpbM`CB%po0=`AUL
zYmL{Un8HnNy?x3q_{!mPWld`R{EDqu6`TGMQ3&RRP*rC6Q!3iVw;b*m!P)masvVXV
z+7=slCTENb7Nz%l3zy&9Dup{@hS~5#2r5yh6G$t2QN!iyg`s6~`bC1X<!^uNY5l%>
z{t_ct9OpO66RL<NQ8Lr)v%{)>!P3<cp00L+?v^&%JBxfU`GyER!P0UfJkSR--eA%#
z5Cn&BFZd0ikTjz`raWjH;tct<vV0iXfGPJ*11<YnLrrRh4`a&<x?4G<LJYTzfAWZr
zsq7rj<JZLnN9#XK^zF;e!Oovc`N^?WFJI2!=GEDR^n^K?PZf`iokBmqL*tX^*on9b
zcibA!Pc7fhx{2y<mPHk55kG5=OAl`C4_~r}MA>82(bERVti5nVVzW+&T4j_`{Ogll
z{53Msz;q1;VbdZ!k_LL7Z5y>YMPa*^tVlB!r~BM;0;IO<C><jX4R*oPJ7HDbHmLKo
zd_$t81f(RAm{PIdS5OiH(dg#+E(PvGcNn0r2+LnHKh{Rsbc9NNV4IDP`g`6FI13oC
z-VG?lcq+jm^4&DwV->fv0D={+7>iopSvOM7b6!vVLk%m+G!8NyPa0ZbadlE7sQhrc
zZ8G>wEY<N_^l-4;Jn?naocX4L$QQ%P%J(T4%=xwNmTe3(U$y`3Pu4$lyV98|yCKY*
z!Y}!7JL)vJY)AkEfx}O*Y2e1D;)psN_(Tiymo3u=B%<Y~OvhVlOKeE^)cc;@-^H&+
z%dOWYA7!Hkz!lAm8c37gmpc4>@CqN}`kGqplg@*_3RC@-o{*%ott|clGx7hP-Lu=l
zL_VdQh<?ldMH(vD5|(w?ou9xjQj7V{Vn!?fEDbb>=S1+!PlCiN*YS!hcG}DM*Z&Jp
z#8NV_g311ds)Z88?lXD1%>F0Amtqfs_RxoZq5S#8wu+X*9pZ0nxX?%Y3X8d3)8-Ig
z_(t+ygV`XUDXZ&YqUMRhWL7T!Kt^S;)xF4u9`!d26Gs56p>qogYq4ifM2RJh9K+Rf
z@{abSzJtRrIGt^uMZ|#?BEcC#|FY|3L5Rlw<^g+x5arp4=VFJPX7TH59D7N%v(J*D
za+w-fYN>zTPIg^7{%a*?1I@hJQlRkIfZb&{TKZO(2_D@8!t818S))kkqhjdE9trvb
z<^KcEV45vS)6B;)T!|TtI5Ob~r}rbs7J76^W%U_LwvB}W-b4y-WI&iewQ=eKbu*ru
z@xv`Eo;>G!`1zFM%fGk!TLBHAdtE<D4>cHqtB<EkR-lfb5G@CgycN<wQhLfCb3NSe
zS+&ZKF@o6P2z#e<`oU|TM<4H3NyvxIw>Ok8ejK6Ae6H9?KZmz@O-E!dch-EVw;8U^
zaHyeWYw3dZ->W42Zyx(3Ibqph+Je3T+H92iT`uqYC6SS_8~G{G_j}bIb=jYOqdNF%
zc@l0j%afI`mY%A(&p8R9=fF9KHaV!Z4X8tv_~mI$PMR}ly8eCt@9VCp>soEM!<tWG
z(r}e|LwUKghrf1)uATyeEYbj_Nx5-d=SrvJ=k$iG@+DG7d4~Knh_qR9Fzgxa=tTZh
z<+l)}J)G@RhjMPhTps1klb3zvC9=h;|K^@1PZQj5FgU3*80sU;)i$%;^&f~9p2jNr
z25(Pq{ewfe>X;17qrP91MDKp48YTbX{92`DTA^6CCM$U1I}UG*M}Qd@ZWZBi4x<^U
z5IsnBhE$W9@FvUuKH&s_p(J*J9KHr`D?777SQ(+K#1V#852O5NQQBPP^;0mSg9h$e
zFM+((7Sm4zWy}=LVZEYiXm73{zx+w7X^~6I>q_NS-GmU47ad6gnmca&|8NucZ6*>9
zA2K`KT8r|nLrY6xB)3vy4uTW0aX%O)dDbjU$b1<z921i;@tlg%5uZ<v(8BkB@u|oO
z(n^JQm5VyX6yNs@?ZGoykLdjGnJ;o-Xr;e>mfYR67sB|M_<B+qE9CJLZ-_$W>XH|P
zQI_d=>?+KfhYWuq0v=6*-M&f#4z3!VV5O|^m6V<kvBD4|LZff>zS&9NRVX|4TV|L4
zTmQ2@iHMxX>O5&%`VDvT;xR}hQRz9b%5;cUH5fSMFau4PVJ-ebjm<ekQ>kG#Q#b(8
zB@g}FXrx&6*nUof{xu=eyzzTtkyjJ{KOmsV_diq^Wxi;3zNLWAGU8cFclf*jlA<w)
zG_UlF2E+G&#VgBY%mhdD*;<w}4?c#;iFp}kvJqbz2#Fhk(Om(0PVzLwH&ugH*n07Q
zrEq=43#YS{z+JcHe5dtW-{`x2Df^p0C3g27<Jt?$PvslzQ}$>uJe8mbw|R%*z2$y@
zN*Gk)rk)|d0114={N`2JoHiHl(}$kANSxL{zWRRW$bVIQFbs=p2pUrx`g(P#>QviB
zOKI#{bj0+ALM+A^`wPd#+~<N+>8%l)x{F=k1MR^`+@`ZoaH#&SqOd(jX?sCfi4i=Z
z@RkTg&QR@SqIcHdzo$wEu#fuK8H(dd8~?pG44liu&*K_nrP_%JOANvWRIT*(hA!IG
zF9}D}8j>-4!d4gTl<Z_VVT`S;XS2S_V0aBdbqr6{gkz5cP>t-qSlr^K<b1cBUxl9H
zo;|P=^6$IH$Dcw}j3JsXMde&G{*#MSZsBWJP)JYW&(LqtE4o-REOgR%4mTk$KBr1|
zxDn)yKCX)SQ0o~;u|*LU`s~d?a+2G6AMuixGOFd(d?`o!<XWN`>$_DwhX+|r&;Q#h
zu7*#NBjpGhFjWXBt@R0%<!zS*?PI>V<{$V_6;6k-hL-mbl$2aru8r~f+Iz@f$XK;4
zZT&V<LDTkn81~_W_XR5v1ZND4f^W(EE=}>OE=}8tTuJ09c~@c9Wsfp~{#!H$25#VE
z(7Om$8Lv@@e>9!(zM(X5<Hp-E?Qy9kk&LH-EFy4870Jd9BUK~_ZqK6Qfo}V=Zi><8
za^iu|gi;E7T3Q;jxHT@66u+gLFAVZ6cSA6qhfj#V4I5)J+cs{O>>Akl@14hosZnk5
zH|z8B`cl=xf&<MHIKEohxAh{_XXrliFq~6xz9S$^W)t2t5GFIFE2S?+ntR4qbrtYp
z-E1m^r}H$^eJ6;dl?k&CqZte{>FoqZ#w_IWiWG69YAFWmhB|*WSVL4L_W=0n)qYHa
zjf>`gB^gh<d}Mp$y-_P{9{-Oc?QxJ(c#3yuIu{~;7vV!DR1KdS;kM>-Yjmw?i_wjz
z^Gl^uU#sZUM7!K4K?F9GHrn_2>)JmV<<Cf!hAeiNFQ!jI9p~j5g3w>#y&GGKDeXiq
z>hyhD&OEF!#suK%{aPOeu4cRkhY;O~?S6Ii-!Sy_Tnz5uju8sAXTQ<tUctw`8xdWr
z!M*EfI^nZVFp~?8?e%6BKcB+3;nlTTH0fT6D9&Hy3FR5JTKJmUs-9s%nV8U?0uE()
z#$XrTpUiWkzN23zs`@{7Qe?aWq)UIJZ}!Cf+&R1e33|j#JMS@^P7Y`2%crEvmxszF
zDTNAkcw*@^+6|aU3d&a#V;2}f43yQL?}#$e#CA3Jk`W3L=Mo5uoROAZb!5H&Ok8EL
z;}P64G!m9p<YkHS54u@j*l@+}Z1uW-^1WObQgIjhRkM2b0RA4<ly^d;Vdu5frofsW
zikfFkoFZJ-m~dwS;!hFX|7QSo>-1ef%i$1q`(uy$c^7eg6*$X@X1L+sp{rY4Wr<DB
z|JLKVH+ecBHb4Q=WT<{dKH`Q(bTo{^;2nnnCy+!bh)4Et)yW$={1k&QCGCA7Y{B+<
zR^50V718$GF;u5}*$XSWT$97nBO5?cWR)i`)v8*bF~Osg223ao!9hAh@)vQ;2#W0I
zV3YDPd20^(=rVy|)NSOSf%K64ZDeNM&A?YK^_eGi60TkLN3ff9#1U5=JdaoAV5LoR
zar5qScjGg9q6zrfhext}Y#!gcp;zfx_<CC*wx>o{jUhq$E#pvY=n&kNO734pGJ7oj
z*nvIK0ibRObFAw3I#>;buTbW4PqJ3wpxK%U7yOT58s%8MD5o!0(XXRe8scrkwCa))
zrRKu>hSBeW68I1|``ol~=RJ1!yK?I%oA24&nAjL}BMo()4><qhBL+^a#BnG87<L0>
z4)x5vcI5tDhNb3)-=x8|VO0|EXetev-n~Xq@e=<>f%z4o%}x$OGlOEp12;)6D`qW-
zpikv+lt#S@b-z0aj#w(Lam1Y!MibfM8+Y#ubq4>dd?$;p(BimqsGetIH6{G;(|%BE
zrF=RkfTUL7ZF$!lynAB`i`yGrv>v`B_Oc>pVn5>AULzHAAVja2-YwA1uz;57MH$$i
zeR(89g8zdb8whB=M%**^)H-BpmaNMfr&U`J82Ebc7e&A$7peVgK}$DP>>>sq&~4fq
zMku0YRAF`2I;)~qsG^K*HHsyodI$x}rd2HTn^FGT?{C5TJ^mvRTW(HCjQIU<aZ1c$
zgjO-H%T<0)Lc#dHwYQR+o0Lc7JjsHD1-EX?^L=ODzhICph9<du6=nF+=14Or%7mIb
z9hbQOUZVm2Flv{Hyeq+5o8(At)U&nv^2v>yDT3Jp#K;8MgX~710}|7{=UN84an7jz
zxyDPY*f?2I`!=&?Vv8dj%v?KydYe0_JmUAK7`1Vz9MFUCSvn0+{w>G8+AL)7;7Y0A
zBL>R-$?mq?J?xjNZ*cN<9^|c|&cBx4V@tpZ{Q$b}4||mPo*#U!<_%xCTdXeYo~m&u
zin#T#lK`fwBhDg7vpu!ZLl;XU_I>ya@~5HP{Fni=q61~wN55^G47Yzf4EuFAe#RZh
z!~BJoF#M?`Pr9iVJg$#SvkA9E2#w$u*3VNRE19a+S<Z_JBiuGoqvieh(_9-O9p2I~
z9w{0j$Y&LNCG8OWb6Z+7>BLHWmi#337kc&~qwC6GEie3WLbhE`+`{WK%~W~Y-{j}C
zp|4AE)K&or?rDEA-Yp3I$0C!`X@06!g@MZ&yhFNv4z_G8pSSt_&<@~03wleGcSM$_
z@Hlt{A=^N;klUx=QiIBqiKdZBW)z93THY^izcSZmml)QsLuz>C`cix;W!pmJM>{F=
zP+sWh`qv^9+T<1dDBQoJLLD7$#};=b##>54NDC!|ptkx@^~e&OQjn%%zo9}7eX2tn
za30dM{q6TKl+g+&&?B=V^*IzuRS7Dc(sTM@tH&*0+5V5|$7p{4rwuDfgs+W;W%tm1
zND+bkX(<sJN{@io;>CT5#wT^m8YO6)OY+KsKOX7!7X|+a6t%zbC)~f1)_eFVJ`nF|
zehxDd&83BwB9hjj2~Qsz6FGodIEbC~GE$XUCBrB>n?)+-*KYaGC&xk^e_Dz0!G_Vh
z_jbd&-=G6g_5PI`ny?o8-`BY=mi|B7cDZ+tn5;qA`T>SAt-(#)hAo(M8o#p<vM}*?
zhdyb+buIcnZ`fa?o4RWgD-t?$+Kgy@nCsgy^a^2VUvKcJTL9NNS(B{?_P$doI0bdw
zjXK*UKj$2IzWAv{XPh8WmVt3RGjg0x%9=yU2Sr-CeHp>3*dM|aV#Wi7B95dX+<>6h
zqTu9O@y@ff%P*+*Ym?yxxJ`Q%`Y%e(H=d(i)#EU&_`mf3fHPmWQ}^p{O@cqNqrR8t
zC2LxG`zED54FC89b63NFHD;LHZPPHXXIB-lwfQvYPTCAEuOqcsLQI*Vm;{zI)FO7W
zs9w)dXT>bPmxFHV@Qco)pbZn<U;Fax?4_x~rEmfU7BY$1B(>nm;wnVR(-NO<|Alb9
zNuuJgbc^>bL}mBodDGi>y$ahpSQnPVO_`DA?2#HANG%p9gzHFWsq)))UV|M<zGLOk
zuZqg>A)FjQ(o|W0Qiv$C{aa$1?hz9MGT!pNgr0!nHvQoQkBwRi_Y42?XJ&|IA`=0=
zJOW-Y?e*>|D+pP5H7+h;gO?m;nh=r9S0{BN=CSVH@l|LL42FO3uOV$qznMNyL5URD
z(-k?0X_!{v<7pI6^o=qDg4J7t&E4e}5!Z^$9U^XDHdDCjnwY++<t3}<-`9BY%vG@i
zx<rExiohnD*SPu>M2LDJ7&XLzc}?4?j7*z{Z>c!G^phlYG0lIg#pZt`03?t0IK9af
zpZ14pEh%fipta4yrRz3?7fvoCz<^Cqk`)P_h$##iM`!QnN;^&UISftOOdo%CheLkO
zty-|AfKFSSup!byx7X6!pDA4fLKLxHrNTO+H~K-u4HvG&2g&Q<I9B{l@U1N`d8vn;
zG)|0?Oqc*C6HT%RbRh^j{_O4%j|C}RNn8jd;m=jxFUc-{{3oepib(2DeD%2QsDI1E
zfOFr{`qJ^fZZFcLG`kzSUX)Znz1rZFK1aJvA*uiHdi$2g<2&fFh^xoMIL@#sI+q98
zE^6#_iq<Y7uxok}Adzs6T^4&e^467Pz>6@Op+P(k?aNauGJOJvA~K*}XbjOk!j`r)
zXLq=ne5GJNvgaxyVwR%?J`!=zp`1n)Mw9c>y77Spa&!v~8a2Xg@xIwL(e(}u%_$1x
z*VY4gi!{EHe)pC{jXsj-%+Q#rSMWbt)8+nuXjloYr2$rv3?b9Je~@ZEdbjYsj{PzX
zuR$_CO5AtwfL^R31C-P+7`68SQXgT~f|k3bs=&YW!qOi3>5m$nhIowr7ke^&BB)q;
z_d^T_MGK};IsR~#xhfTP>&~HksEVGZE++?x_g2^BIQJWp9`=p_t9mn>FpY#&Q;a>)
zXcxlv&Z8)gdKCJ3OuIuYu9?IzjmmegU%rh;Ij)DOpSNb=xwHP|zXqIn0I>FmpILl`
zjSK@jxO}DAwuLn44eiqJ{Zhs<q#_5hUrA7ER@axUfQ%1<fVEJm#**gDVj|LArM!4B
zmhFr=d*IjvnzT&X<%%h$A)xd^4vV9>tHJe60Z?#SsE$f1Zy8og&cR<iRqX7@6`DPK
zQ94LlpV&?^NgYiiLDNO`u{DzYn1>o*!`5V`d4KFUgfR)0hVXoxRN(3-x}0Y5N$aN}
zjw1|xBjH;TnZor5nZjF2MrgqEZ-1IGL%avI8&bygBp~4SF%y#fsE|3g);_AKI`v4J
zMtM6g8TT={1~n$qAOR(dR5|>}w<`*l*l*}ic=(A^yOjMRak8LyhhFyBN$^~k(jT4c
zPb`tNG(}|5FH8{~DY#~0(|W6j_T6RI%lFiy7@R#uQ6`yplm`cmCc5oBZS&jKL(;HD
zG1!v}>SZTc+~{=WG{Sf}XouDw5EVgbtOFwasHODS@Y{7<-Sbmi;U1%LZ#LoK#wXHl
zU^2tqEHH7PTrd8sqso|xuZIXe#_*x+sK=?hhjmmrYgu=&zmJB^sSu}tt(3_i7MXRo
zW-hE>j21@z;Dw=uqW?E>D%(pOVV}id6uF<6%Lp~bQVDJYH&90vi$^2QbVU^>jH@R5
z`Ms6{UZbPQmn4Tojfifr&RSk89B1TC<R<oy`S|#Rs=|<F9r=23?R#%o&?z{FHcXcg
z{&_<;xI5Y(Be8S{AZ>3dW2GFPLh84s`#+;=<Wu@xSx<-r3<%z^{KXSzVY37P+Iyd+
zLJI2;!Ko`R*-s;%d4y>4U8Rs(`b|V8oNw2^d$<VdOymrHdZcc@E3^<tPC12>7J|^9
zCS&Z0ENCN5;Nn!M2u=zb-U^>siU1q_fR`iO(V&#JQZa25BXTB;D~IGoWq(G})%((>
z$U2#7aa*#jyIZ>HD-e`%9PSu8n0@M}$B)^|li)G9qoR(y%kKlxAQ|i!zv5}oRl*nq
zOOY1$4CfD@g(UqQ7<o<^g|*`U!bt=7xbli-DqZl%HXvOlTwEWF8?~zh?-&!5zl{8`
zs~m0sMaYLtU`KYN0^QTEC!FtA6~Z|&Z8!JD<cKiK*gMEHESto#aKZ52ML?MCVp&`h
z8EjUIYQiA}19?B`8hJ0&_jQMa$30^v_a4#I6ZyLETdQajnJ}dyk~H8z(FDXtfYr%W
z((12iGfasMEV;%-enW%68OBh9R2LFa%%h`NIFB8Wr_J)8`#&2h(_>IMpz8lV>7AXO
z;spxTb&mT>W6n2BEL8ETjkp4e>e}!*mtUH^!Qeo8V}!KNA<-s0$BRtUdLuY(rrT81
z(;%6jqHa;sLZl=Ce?*4U(jQ-ICN0*(*zr`I)mqXT(Tvs9rCqw#(Z{Hfnbe}gzQUBQ
zPW(2lb8RU<T|MdkFZuf$*jiqfxtmkJCuw4HZE{{bw!ZtiD}<|+#xIqKuX$El7{@sZ
zU$1@bAn(}tK`#&6^avGMqzH2QRfv^3>3&u{g<^`^rZigZ;M$`LN+MeYT4zO`Y@C{L
z*Z}`OnWy-Rm^7E|A8?#e405+PifkEgimxC05eL%Q+Jr-E6opeuA?EB#2|q$~<54_2
zMWZh4;KycOLNwi5OUp=g3ZxMxM1Co`<LRf+3tU<G`={J|26DKCoOBG_mW-zDYsbMG
z*d)=_$64PxJMG;&ejdnIrG#Nc;p(xP70Rde5+nkjX^gD`LHcQ93|vN5%?zbp6+eD1
z{zyz*TAHXlT?v-jw!=&&H5Vnum2cRGke!S{0L5HW$jjVL@|lUOo4tVA-o+w~2ht>J
z&K!+0XiX_$6YBHzLC_^X)h6ZTZ&{mRV9a(=5ML_?InzO3fzMeNxFG)B;TW~c$#Q$;
zU{q`@h&xjSu?0Zi3!*|nrn(OZpat~cZTwg_ga2(z_sy~%V06(+evLT;A_g=b75Wi=
z#HJZe+CWvv%6Wl9z&M?l{Z4a)$A;-Of~_;0)OeuLz`p%ldTFl;kMwuR8f&*xq!~vS
z<4^Q6Sy9^0+5KGyTBNJ`*vrQ_9@V?;R_Pms$O||e677Ng1?m<}h87wLdA-qoL?<mB
zvgwcoG<=HVZsCsPy1Z{T`)|U1n}%aTKMy;|gz?8O1mEQWeNpJl4(R5Tp1h%O%f#bo
z!S!mud?86rqq<GR`1ZEzOY1KQrhggoyxii<JxBZ|_iiPs`l@a}JC+`=?kZ8`(-aN(
z-t=cQ#r&=i`_?|r)VOpld}T2bzv~D4-Lp?z`WWP6yF!74e+eX}bOE2j^WDcFpn2YB
z1^@Q)3y}Ku1>{R5B`1g82KN+^MBHWY+ho4gG2fb9LBiIp!S^=H0`Q+ppP$14M-1hD
z24DveNG4>8J&;)jPhAV1u)S+mN(1zk2{sv)brQ^Lkk=aTuZW{IUKsysYyXT(oc|Im
zONAUPe3p6*=A=HE$PelB3idDdQJZxqv2q<4+w?T43_j9==JwmYeIK8s2JKMV;K+0c
z=wmSp@HCW@h&vNxSRIZ`Y}^Bg{=k!2j_rvmp~I*~>9i6Yn9GXT-s9LvGY;fTrxHi1
zPh{HC2j=UJ$PtD!Q#G@S5e1_X{DM>sYpRNZ%OF=~4YHX>jZW_iy)tOP37FL%1{gER
z)Y)e|)M?YmCVJpxumJx5Ta(l0Q(|HeFB1)di=n_6i}ClKo~Y>Pb|AMGbX!%+mh{8{
zp&cE-@sR+mH&6(8(8${>D1oo>_F{w2UVPwzh48d^WKj~6B?Q>N`_cHoEY68w-Vyo@
z_m|+0oFvVnML3~>8@3OMfiQQr3j+>z<m?lvY!|{URr~z=eauU6wnx(mXP+As6&c{{
zJlXH=kaQo*ASAz`5&E{dl7Mpd;E~HE>6JnU^oy&mM$rg&5g0<~U<|EQPV(<hf=hd|
zwm#ySKgR6k;Vw9FAd@niSLenrI1wc41p{k-nr4W8H*Ind6?VyY1!qeAmb~jn^66!Z
zBE)L%;g)qR2mt=9i==3<jg+q?slhEbTc)5*%AwPirnzgsAXsIOfcn|(0EZRNy}St+
zGaS!BSnP|`)Z5!SeENZh<~DxnRsaI7?zs)8C#Ip<0*!v2POfZ{8r$t{mVnd$^vYfm
zNDoAP(_P$sKR(rEbD=f(uBA5TH8sni#zyBuQi-f>@}a6AP=gbE@dqtGsAJfK(omdC
zGOq?&7xM=Z_bQSU`6(kL>7|*N35VW3wA5MU8n4QV(k-DCjz;^^V(H$vwB(B*T9(T%
zj6)@6dg%r_Sl!{53BARWknIU8IGqrd91<^71BNt*DYRnIPjiIh)FN*(M+)w1xN^;L
znGT{tSOoS|$J8xlEFyVGcD27*mQO%&v{7W*pJ^+BC!#v)H=Z~q4@>o9RH{mjmO6!(
zB~7c)x<1cUj%ZStb%R>`bzg9Bs2VVUTlZQ8czJPe1MMzsgsE?D#h5u;LBPT76CWD}
zZQVUZvn;8K74vB%mX$<riFcVM8XkWFvILai9{+m};}oJ<T9O#P?kp)VsTGm<m@$CH
zm`nl-Mo9P?Qig`Os(lnS7J^nrOQ$H$L^pEURWrqv!8+Y$>zzNfq-g#^b93Ut?)y8f
zju1}lC{DFEbUx;4-34hxwiy)6eXQ5g&ufPqnTntoNIZ=uv{64xQNrl(BlXT?vI^wr
zI@!tkBLc@rxC$C9(z9(1gRr0n7P)O!sl$_1tuKLph2d{grWl+g8Ejxar$gO2TkX;W
z+4lpP_{88EI}n9z0d5lRa;1CKO>4n3fc&{^(lbG_FKoH2)GO*d=tzBgUc@QlDvIKi
zS__w2yO8-|;b7+_x#_AEL5Q8S3C&y>WZrb|%O7mWzk+fWn1@ZUhKvq%GI&KI?&U^y
zcLmT&OA}V;6BG{Ow`!m;1ov-+(n>E9!*W_Qz3}eOJyhPgo)RSR9etF4zZWc})~B|Y
z1nt+XT7~j=;;AcfA1~?-TqS9+C25kwr(DqEVB9?lSl{~cE;r8li{E2&Rplzf@J`<<
zm0zy{vCMiJE(FMHppaW#O>FE@8o1r`HidoO8E96NFTf==WMpJMr>Ay6@8YwbSxzaG
z_1DN$Li2DKXh858C=2G&M_J!eJ$*xj@1FLlIDQ3A<*ZF+nlr*>t=soTx0j8pPSR4h
zpSUtL43Z>)tD_;SWYvug2~Ncg>Cl6aQW6jnztPWU5Z3`!1k_e=NIJcwcVPhoauP&v
z1}bCM>F1|DKFgc@;PUZ6P`)8Iaru4u@}ztd{xI)#XWv#gbQPlqK{pWBrw^IgDG#k{
z1SfXG-q`cI#n5r_)v;8Lk5rq&@B?iK@3C2wqN}|jcj^&rD1HcoXlZFF12@lYRK>G4
zJFzU+|GYww5s<<Ox~<zAZGj;O9dZdmSr>g7fK8vT!KFwayC}Khyhl(ApKAy`rV_GR
zhRCQru17G;ht59HuJd>`xTI(|qqDhphNI!u(b<dOa1k8(G9N~iNkH;?aVZ3F1fWh@
zlYn6wl@2prAEVV2qx>5xN}h4QVA}yX#<#?R^-5vsc$9mnKvSdq)3B7XOd)MFK`|Cr
zU9Z^<RpkC3y`EE;lJhn@Bv|Mp`Rpja<p-$);|N*;@kFlHG1|*iEFT-fObx1C!>!AO
zVZnxIAY9@D7YPGT{Kp0m2KbCl!VL|CiIjpU;@f*}jLm(~LGt!-aYz}E``l(<LO@6e
z_w6-Ew-*P4SYu@WTF0#m4ICJ^wy6x-*l#$%bd#=PFyPv_E@;<i6f%M);edRbL{+5l
z`UiI5U;>D5#_RQ?Kd6j_1Q(3h?t47P5MuV!GkdR`9T}<Lr_iUaD`kMxRHe1nXI|7-
zLmJ8`zR|B6%Mx6^3{pwFz@4OcSVzcgR1&Ij3*Ut_60x%-nMujJo62K~w;|jx;Y4O`
zsAIG;p_fy;;3Rit${yqn9o<q5$he_yo;3dvMmpBKx$?k)GNSz+4dfc+Y24lo{jota
z!Uh<xVuAE<3G`->T~FFUwi%F?je?we5Wv%)g-QgGJzGS*l}&<hga~Dpb|CB8|4RGp
za=W`j5O0NY7*{4R&CKWM9UAj3z?weTBPP{$gqEWb)2Pco&7sWnBnW=i5dfdo4zE0C
zLfo)Pz~;*BT^n5``{M#)Nf{Ur&Atda&Uat)4J{DgX-m`n&5kpkec@|+8cS2d%oF~W
ze11`<gp9-3?2i0JAVXu^0u*8*B$M>xjJS~;V{eWQuIymg=p@o%?apep;=g0j#OvQv
zFK<sTUrF#sT6mx$hP7Z(&V&lmG2-^mv4@AD)FpM>M|QZ7_+3d4;fS+`4(skO_;#IL
z8xS1E&;zMl>7I1hljkICJoE)88g+>t%myVGB9q#_`WCLsX4*p6jk>|$%UjZ*)A$eN
z?Ja03&+NoEGX6NhP~v{c)a`V)P60hfBT&@!NX`jH$VVXFEE;#X109eg7~WNxdhCez
zrhZ0Yxg>q4^9b%yLu5(APNVFo;z^2S949tT@U7rct6=`i#Fto_jXw)`q!R~rkhkhD
znOjp>16!gd*5$9S!D0e4{Uye|d1;ymFte+c<3^iON^Ur-psrM%X)d5%EPsVShTfjo
zQ<6s<Vkl~cYP=5qD&+TNGZpT}jL97&gPq#a(~|SB;LGn-@qB&9_5*1%2ErIR7cw)L
z;!9J%(lz6%Oqlxp`%+rCt@<e0muB#JicTc{ldMj+Jt@H=a{29bB-)4#TqgWM7>-7!
zK?+0hF`xZjAA?31q9S+ThR+Hl!2jiZ*HtZgQ3ukYhiG-@=Vq<~xd3qK`l<S%H^k{B
zIch!U$kkqju5XO|VqQ|erFRo(V^k)WQg;+Qe5K*Jj^vf-mv%M@)+-En+K)K>E*EHr
z<@G%LxqiWXd>8<7j_lHgn@J1DiCJej+0&IPb=uRf>X(VjJHF{bKkcGpx@b)D7Fg15
zW3qM{aZCZIYygQosxfMhAF(7j;C*H`@8FtN5tB$dc9icmS!O&W`paE7B>!VtP^Yu8
z)P@@t`#)C#C^KZ*(t*obzfmR}B^K3ssBPtgv%OIO4Mex}UHpQBT&S5RNeY4ZVTG3(
zdvNEDLJ0D-^3~F^ZzE7xRe-5o_)Fy5{G*JK-SmTkZtuGd!8ND)pcg}|CKH2sT5(($
zKtq<^N1&G>E=XQd%qQP)=jVFgq9>;xOI3k3=q?*(xxfk8qp0k5lvBvaI3P?o%7rvL
z5k3YL2T?KRO;N{zCpeDr=2v;VB~QmZJ6JBFAsZpfe0gw8@_UC|Ka2W-4ydp#qLUIA
z0UJiiQH<fMMFp!Ed^B5&PQK3QL)|uXN^3ZYD3x6SX4~?TuEqP#Essto@AlnQ*Zn;d
z_?$FEF~bNOnWE%iMEqXMOL;rhKIur-;qkfAcmluYMqywfMO0mjYgt<YY7nJ74|Hf9
zv}z&EOAbJ32L6dejpz65hJqFFVG%x^9Y82~K@O3(H1J1w%OXcMr)9fM=iOXvCU?mP
zL(_P|m&_-2eEBIm4KV(`m1}3OBj7!uBX=>iaAg7TKkhljn>C8gWd!h)b^Kh``*PG%
z+nA(hukua-y``AJEE^@r9eb)c5Tdtq((Vg46PjK;CbHI-?{keB=SIL9sJu!rok=>t
zCbIPOvvBtvG0P0gw_A^%Mo52T{CzhuylW|9?LD@N7-gM?gf$5Pz<7~eFrTQqX`>;H
zx&*)-=az})!ou|nxtVo90}!+_tj<h~#N~I*q8`3(brX_Tm1rd6ZZ6YYmIaZ@%NyDe
zmVJK(I2jIrL@`B*`lSAI|JkroB+?4nK|XW@Z}C;bUAbC`RJ<tNVt;YHm$Gy}1Vwf6
zfC$fxvRNZx<!)Uku*_+Bv42O(#ERU-0<wk_F-kgc8@4zwt%ahL<APV}5cxFe^)sY$
zA9KGGfV{(IR=!rp;u3A2JsgjyxHliH#DIcM{7&1Fqw>dYgf@GmJ`DLyX_1Xikqn1S
zYL=^7Qjb>|Jw5VnO((v2e)paws@~nF3SKKh`#tplf%v*xZ?sd&HOy@708}Z8v?EQ{
zRI2}sZ-y!VKXKF0&GSFR&2_JwLoa3w40rN#>XJYzE(DOnx=!8-u++sT`S_4UDAQVS
zni&trp%k%->VqN&x^>fAc<&V-+G4o5NREEQtTZo{^g9?~9S=1?KJt?_+rx<7CU-iy
zEBK#-$Xv1#uO9^r(X(f=SgE)Ymu3toI>B8_3MuB$mVVTF#Tb-wR1$)HqP(<zriQgV
z|Mrrp@)lZodMX29IwQRBLL)Abn}J{WJ`)6=;qr{Qn@=7n+C`M1{6+l?6o4pMro<g^
zBE%KOfXnFEJ!Il<!1C=PIMy0teKmZ$wdnUv=v=I`c6#{TeC*~_w+*Aju%P<jsCJj=
zLNv)>>GeZ3Ws8^Zc)Yk~mzG5qjBtnD-wjcKg^jK&nnls@J4Uu`?Jw%*<D+2%2%lMl
z8k|F1UX4p$M@jD5(q3LPY>lrnnv!t4SUPcg8c<K(!Ah^2imRr6Mi_C24gL(#fGNG`
zL}FE-z{Cj)C-BSXLRx11M>~(v$q<Tb!pm;Qd;lkAIngL3UuK?mteVy%Kx#LB*VzqX
zCyKB{`Rf#Rc?iXyF{-{f(7FRe%v&n<trrfd!s9O;lib<qDYbtP848Tgm>9a<dkkO<
zjuukmIIo^J`XW(dOsZ9(orA1}BcXAMyiP9S25zD;^e&~=By5ydF)hxy3b?e=Uo1Hn
z&>0*3JF(H>4YXe3gqbN};iN`gN$TuL{ctcEDE~}N(qu>YV}WIFx*ilSt0JY`eWY+)
ztER&8ON`S%Sg~d<TGNFPVCZ-b=*x-*x7@|tT13hUQ30_Zbzu(WDDw*axEJpBskxqb
z$K&Y4c4rI`fb8J$<ktK5KdSHi%TGh6dxCRmBP=WbiVJ4bP-s3>1dnb39683mRf7z!
z@u*AJq)HxE%hJ#j&(cDGQ9s~+?=kSR0`LvJ3qlyYAp?{eN`TNRNl_wy`*p#Wf{Guu
zN$v&)TUApzW!&v&kYJIw$cbHMH6?!LYBo!T8USLHBrz*j1ncv3oqv2Xl_Oo}Mop%V
z4XmnEV8j`c<CILm{Udh9_k`t`2i?PnbCrYuJfW5u)P`^PIT{gv^h^P63e3WnId%#F
zSTc<!tru`one7s2BPNt8`6{r~sb^3nK;|YvvZ_r4l}tS=S4|-^HEf?u`lp3N_0y(`
zz{a~Rp-npmaHknKPaf0A5=Dd{usD1;aN%+`Ns@|#eCf<qHm>>^i)+}qzG~^!NK|Yj
z&%KuP%yT)WHw0r;7_mr1TL6HmXz5r|{e(`F=XXWwuA?h9V~1yNhUfhwpZZOl+*G;m
z{u%du{j;{Um@^!Wgw%nFgxvsLfrv;Cs?t@;bj@{Gzh5tY980dN_bPPU0?QB&B#w}b
zdDl97{TcYS=QnhI<vOhG^`2H={r&34O2?|ff!X2bxpwQ*gZ^@BSF(g*VVvw1x~7W~
z7-90PYHo&Sf1WD!rhmzN;+wQT+ZAMOx3T59w{&>tCgoW}e<K!2(T@=>Arr!=+74F1
zKC;)WFBFOF{G26}G!&fIdBlF*2tWu#hc^=D<OX*KAC{r*zA8IzAgJ4wsbBF?Ba9GJ
zC_8`z^-;vG`CFprbv^ObIsiGefyn2Y&t0;25qkWMlU|5L3jP83eUVKP<yx%g#>w5I
z&O@xSf%$u5@$So7FC3L7h?&sp+Y0H6lue@FZwP*Gp_j|Cb=g#GcKv#9x7W-#Nb2H(
zL2^(Y@|B4`x@}P{I=>XH^7y_>4zNsx!<~A6?>GODd+7apyE`Z@OH4YK(}_Oc(<0OM
zN4Ix&y@`ym!-x1Zlg(njV-{N}q4?zP-W5^UtVD;tfYq}Nl((#tLQ;OrPEAR*35P#<
z5qmF77KBpn#)uI5I?-i=Uu0j6GV-KGx)^Rg^W9>TGe+wvPe}i9U+{OPLSG#en^e^w
zR`&-jmqsek44=<#zGrnFlEIbv4f{R$sW>H{+@?`qmr%y+Zk{lzdb+cBKvMM28xD7k
zft+h45e=n)l_7SDLz09M15`>IJPpd&YQ@lPtU8P${5~qh$^6|;+O+xJC{r`<-72#)
z*_4O2T!sb#k*15pjRfq&`LuQe9#16ojAk`y`#$wk-xt;1x)fOQWK@zq75E`L{f2f@
z&HGc>%g>QI3s;B7?wti&l;gV#gcDC{W}m%-)IH!!<ldeC;Cz?+BAq4e4|dacHD*B!
zhR^i}t>}3|QS)?(xOPq7?`1x<Ih|TJqkc2%(Wt~ZL<RFtd7j8O$@iP|w*dZ>sCl~r
zLVwQ`CcUo^T8A$0xUB9(=ltH)DxThoT=pu5+66xQbppj0tB(7Nbqe`+;RIGh(Hp%(
zxKz`t*!3IBxfh>5uHmC`J_;ussACZm<<-JR(z%eIx0r0!c<1Y+@V@;N%ZYx}njuvC
zrMUb$RR5x-EB@1k(3h3Rfw?;mcPG9P>M3^^a#rNmb{z`-w+3B3X2d36(GqxWmQcqh
zCvNy1FUK{xbcpYnnbccJ4Dnfvh5c6Ov5M$lXc_#$cVSlh3~A>0L8PA#U!Mn}C}@rk
zua?del^aY{VYD$ec+lbzrtUI#b@a<;_9ZjdTf2#om9aaVdaCZ-0gS`MaVat+HW_>D
zZ%iypgqOQ6ua}>_liIcPCe59?7IRyvuFWO3JAVaF)qfW=9-d_<$)<6~2w%5vgaOZI
zYf<BM^)WVGPX3$3fR1uvyVkS(ix8jGj*%a{G9IVk4V}i22ZPNeq*C~+rbcf)0STMq
zD#^9g8doGjvBJ}<lz944wRpUyHrQ0bjs`<>d-uF`Iy<K{<BGNtZCq(kUv9nl2Z09d
zn{au;v##$gIF6J{)!v;)PjfFxytG82jkXIoM{_=dtcVMH&j}t5+sbsk6LzbK;G)33
z#NRiu8*d()dw&R8I5zUDr?<{5CNJ_>iQ6fh@Vw*GNuK+Xb9+{3L5W(Ob7(4Qwl|Ug
z5_hn%+RMb{byq{fHQvVS01VG_qRT2JwbFa!sdBMhht*d9iUf1`VqCoEZ-JAh=b!q0
zfZScpT}5iB)CczPZl6zrHMlg8Z_%QOD5zWgkZaffQq1nHNG2=*LrSmjiGWqn<EcU{
zW<*X>mH&o&v(!De_A23;12!)tj5n->&uVwxbY9fRW(I)_<0Woovr-&B2=cqJn{)sC
zFbHST`9h*N0_6iH_`h*3*+*}3Se@)s5;EJ%(O|SR><;;nViC12SHgp5Oqs@ZH|kZk
zmdo6A{=HXsTt0Dz`dRKQ2kud8f?smgi36C1!wjpB42NHKI%g#(nT@N*brAHu)-u-H
zh)mTVcb|4Xkz<Z#jUrSW^OYYyq>tugkG%@tCajApU=W}*KFm2J&2$$dDvIH~+Iau0
zpxtCh@S)N3gWZhnnkh}Hia+H~h&5g-O>qf*i<y!%U5eD9<viOH*E2o)-E0^5``zcz
zJ}N0*7)f+HQHjLNU7l|8pu0mqapPIh4To+1q~t<B_vPovsqapz3wtiaXYFDCq?l=4
zTfz(^1m8Q!;@^vSU!%8NS0-7R;^V?R#uHNeeK-DFw%;eMrdGTq)CSfvBn&Ku;RR{U
z=RapY%H9xu9U#eCneo-RGEUNGnS~?`c(OLT#-}RuiC(0uyALXgx4#JW>$Q5zppPr_
zDiz|s??7XGx!^hcXL(=q{Mk9%tVFw7Q>Pkxp2<nC+ez#5Qw+`VProTIN0>JHvw}jZ
z^;mFU{gOJVZ@Ur!4_xD3=`)n>>u23hd_^p7*i8A-y4m92cNg0tWpE#TnBdu#zES?p
znKRS*p;?LFe!k@5sdL8DZaiU?AMP16I4|^>&<uXM9?Z(Qng`<RuI>cN`e5+Zuplf8
z7)Wel%#-g-#y8n62uDf@vDz>Wa}T^u|AQT-T;o5rR<;m8?;`ua-)qaj=jOLuL8_F&
zoqNS1>XknlX6-&v9-R%|8=4=3fA=1?Z#;jAI9XsGlfB1Gy>Rx#*3{~Bq4E4R;<8|l
z_WRxjE<Z*8DJd@m{J|bX9Xyf|#%-L^oN5>sAW<+Q%IQ2dwtX7TAl1Ens=oj0(WdLf
zJUc;!vZE$b)+=_{l<dIo?}ooFw;q#}GE8H^HX_fGKB1*X;H9&%FEAToC~2oiUJg6s
z&en`r)nrwVL}QypIxOlR(rf>G)PBe1s;Aj*!Q=PCCZoOL1)a=NKP-!<N#MnZiEHwm
z+<%*|SsNgJ(D0t{(_y!ux7ESH6n9j~wPZ=e!~MM~*CQD{>ab}H#~pC?;ndt7f!Wp4
zQ_>3uem&oG7*@XiB*4Y3Exoox$)Ch#usVCq@ztNwFJSpkr^j-3%`2VFrY|N#ug_)U
zJ8wD@ke@jr1cF_1EpkIUPwrGEt!j*FlBbRo(%N7_Nfe}pM!qh#B%4tun=P^TUk3h>
z+8zqDL+~_6Upt4t*0lSeG~qoN{)~S=>JD;4QVT!z!6t{t%Et%#dfaBG(8g=jF2z{d
z4qrLUl2*u@PQh94m399Pe)8|Iz_q}SJ>ctSKY-fTJkVBSh>?D5g~uRamqO92>UTzp
zk1}aOD&2qOF}Sc7g%9eG8EK>`ZRK=}GUw0_F@1AVi-|TRma~pu+}Ug&T;nB>0)9H2
zq9k`IMxT*)v-gqpx1w=dzM;{_rlauVZsHk}O}pThqYg?caUX1U|J-?lPcf(NHQ-em
zDI3OCLNj|Z*|d#Spf*bLW?U&z>u1XcZ$pBHzz;-04`IQapNZd+Ztn)|eH}av51zJm
z{d_UU`{4l}A*T@pEPK*~i{uUe&U~@N1s1hb6Gwbh$DQRNCL5P^rX3{eszwE=_uVfG
z2Tl(NDvqD3aBsLclN7}$@e*5!GBTsFhRI}q+NYM4J9i)OBx`c@$-4G^n}2sx;*G2J
zE6P-fLVOe@-l2?lNUd?yWvx4t(Y>j*wURvxhr6D87cZsb3Hun=#%m-k<%%eI>v|v1
zSf>P}=j-d8XYFx#EaM5n$MRK`gN}#_4H++mrFX=cb~=BT$ap9YR#DUj&bJzygQd@!
z`1cqKU-9w%beBkxsFC3DN6*(%6n>2fZQg9;pG&t+lC!l-l<d3&$TQztz3WrvH{;wL
z`Y~p)*AZ-<Ei}PJ!c$=I((zv5uUcU)o-iewM3G4}j`d$kjH_{}yu?)6B=>*e!}96W
z;)u{*D?5g5U?tNTyhX69aqrW&??&^qD!;In@O^y{H2%2e-oQKm$5jJ?v0uuC=$hk<
zo}-0=0v4gF#>;c)FN0}2gI&R8RYxOe`kUJP?D6ZSOUvKU>D3<BvHNt9bq9?F^)0a0
zGbjsP>9gRz&9K&&phu+ow?PoIGQ+3#reP^k>;3~?PrP&tGT)_VY<~$@N|O+bD0$W+
zIA<bhqRfWo4Lim*Q;<2Hl>Y4^J)7TRdcNHyaVIB3J0-skRM6y&JdZ@@;xAu+H=NaE
zCXNa6Lq6S?%&me`5;YTqDK9Y*Vf}fkT^yrAZlyK)e#kw0;@1+qU&3aee?I>vy3p{)
z<DZlVhOPwEGz-BgBzM(R%b_`NE8ir*U}x+o&&n=v4&o2S^m8<H-v52YD}^Br5tKbr
zfqgzu7wLm$j&WJprv!M#8~+eC3$#GCAxcaCNX(=9)PNt#_TLYs%h`A+;qZHqs`Gjc
zx&ycRNFiu&_yUE(5Br{7o37;WtmR;UIEOI?GulyWxI&uyP#VXHVPb2OcBt5^HrJ_^
zDb7A}!-Xz)v`pc;!nmTQr$f!>a%Y*Fk`Rh7Sd7?@2P)=!t!r#&jgLu?d+KQy*i<3H
z5h4P~%5GM=rlfwb+Su4j^9%m%`IUFn>**7;dL8ICh@vm4DA-JG<e|}~ZMr|~l6dHt
zd*W=lh@|<ig>^D7&|a&LbXYDFQy!Y!WY(p#q71WZki~|jWGxd+GMY8nWo7s)`U%$6
z?EkKPz{kn<-^OG0{p3&4>~ysE3i^vPrpO%Ut6+1pfe@8)bpYqXu3%*c(CYWrTh?u@
zXhg4ItxI7y40uU+zE@>Oj7~EJhw-8om<gFR_nUGc&|jeHn4b%BZtvy2FoWfOjXYLa
zjZS>Yu}br&tYB3l&JdNLfknRp99#4weM!9WtZV`#j~69&`X^gIFZ2byp6L=3!Ke_e
zBs;78;@RdM>*XfbPhFJ`T5wyNBuXQJ11Rj(b4I1sR>-??o%Ihd&HJ5u1)mCZrM?1{
zz@ikBVeoyoBFG`?bat!o?f9_dPZ_fIf@M%i7UAv$T7sUxy_v)kyfQL(V<bU;y+sIg
zL?04T_E&7Q{AmQX7b6&OU9~Gk*@NnZc$qxt-E{7##ekzlHQID@8LOQ>mRHLl%+o#M
zD-}Wglk0D-Da8!?P1CVcjvUxlD%l%vnm8s^JfA5QMx|{lQ@4uTeHu2FC+dVSI>Wuj
z8f%%KG|cW|+>p}?$P<lX`8r4ywxJOYy(`Z-RGJ<DS~hvm<w&XW!ee#!=G1xm1Dub$
zoAUoyk0<AmpV+5J|9P4~Bxr&=nI`v&Jr{nTufj!Iyy3k28K#ti<qpaCry#ip;5SzP
z9S|g#C8j%{HH%u*<R<YBTFb1a5ce8*5pgDtC>DAU+kB7F=lP^5fruW<es$ba-|mTa
zlC0^Uw4~8qSwj;a)8A0?(BmcE<Z5>7<<rZL{UzIS3YB;%oM*9@l-!z;qshbe6GV2e
zl({inhYp$82Sw-D?xGqlu-}g`;0`po#@#rW^W57=_r0Sj0!0c(ByT`aif}86Q#ig=
z)7fZ=_Bk<fuc)az?lMi}a}Q1FZp>_v$=<otC@wEy<+Z|-2zAN+79aCCe`Cby&$d{Q
z%w0h>{E0T3PqYhbl#LO=TX2Javk+?dzmjY7k-<%m|3R(SLD5q2jEhyEmz*2dn{*K3
z)m2xJk{YNXpa)9e7CU7H-W*&B8b8vR8ro&v@zpjuXN8=Oc}mcrpgDk5N9uSO&(0lU
zX6%a-W|1A3{DP;edZS{CTcOt*D_-d}7(g|%d4Kc&eoCmHB9;w1JqTJ|sPZSg$-WU}
z)YIuCW>QHI%frLdum6BY(o8b>!a$RRn+-D!)5j!hgFtp@{riN+>#G~8w^~eSv${>e
zZNzlg=I%H%3U2r~VcYL0T{C$o9)iyh@7+DbMceNinL;uDd2x6Ic4{Ksznx0Mj5ZiU
z2sF!)@CpXfipF0io3*n#y{QFyhd)&%P*e)=`dd@{Hj@cNc<x(IR}1%)qQk&~@>t16
zcA*dbxFO7*Kj(ow(%Yz260p?~`>HROpUxF0gqHAys>gHmt)VID8nr&8IpYWIA>N*T
zP!HEmy*n@KdD&&fv4@bm9ang+koQ%CAQO7|On34lE6<32SmB+=OPP^!iYBK*STq%T
zV^3YIl6Zz@AK2L>eM`_cuuY0$afBNlf6M0TEsN;-UMUn|{_2iCcbbi~3Ew0W+jwyd
zm$>7mQ_ZFPliPM(qDGIpj!<D1#iDT;R8)-gAn!EoY`v35W_J;N2JubWijGO63BqG$
zcMJ}}k9%5~@zicy6SnZtJvzFmywUz=r-3JC@UXk=O#zJA9NSeT{r{utEyJSf-ndaf
zq(MMHx&|br8ziNM4(Sk(k{Y^8O1g$FX#r^vhVD)gkZy*O?mV04|6cDo=L4U{>vFHX
z*1qEx_tGS*Lt(;lXFV?oq3k10FnDb#m<@&c%0sQQZQBM_N?%n7pB|aBZ;;*ch@j)E
z5`1Nki+i#BMOv-mahcUSY8Hw4gjKBOSfc2bAF&GG-eUVFKV+{2o_MLjRi5@EBueu|
z-p>yeJQW}{u=AZyitx5`G76LV3jI_FFpC5y@gi=9+FYYn((Z?@=ELG$+MNwhrZN5p
zO)#)MNbiuN<j?rdJClckDdD#E>q1D{U~pRj8GMmz!Ms{~qzQX%tr<rFsKSujpyb)c
zoAkp}1>jgIi>P+BaroQXa@;RPe+RJNqwY#Tt+j5^A9xrb5uWdvUOHAlruh8Fk1B*`
z?Yy`h#{6-jd)$#>oYdkKS`S><70CVZR@kuLa@IdkC{+~Nd!!?CIzXT|lq*)EDM?xE
z;&QP{jxk4aBDw`hP?4}2cn4n2hjEi(NSeS@Puh0JP=MYlmpZzYmamti42-<SZkFlW
zB=)QE@iCwn&HG<5DpG@{hf1?gbv0bV%;zZkg;E_2E0mGNV&PXbbhnq}%vv&?MMy35
zsB7CI#6ItOCTjcWPoeFn_}J3oIFdJrjA~Jvf-wc81UXZ08Pr30Gz1!X%0zQnJT3M?
zA{mwC!c**p?E_gB7_tPWuaNPB@Ce!?RA>vSwMh%LE4iyCRCb2ZVxy$CrmpAzl<7c0
znfReL&fXxxoD;o)+$2$x{5rOc?oV>gqls?OTt=9GxmSt_iwCB!csQ#A4VzUgeyaJ(
z`6~j*#AM`uVWvmIW@l8=AsfWC=Rt0Wt)a&~r5uqaV7AjQh?;yCKs|^BIuZ5by5X3O
zq}t*R(vWi+)&7&#sQx`z*E@=@7ysvsG=@AqB%Y};M%+3OvG2FFU-K2i{@AImtLV`d
ztRPRIMz=Xf1|x6-tqu>3CymQSi<2HdJ#Y9Yu{09ZcutRmprnu+4I3MX-eGAm8@I~2
zMUn-aW-Yyc8mWDYK0RSJ>yfk;F+LM-I>y=CYi*x9NiO<UecZJA{?q^R9;aNB^t1~7
zm6!E97m~`5MZT3nGV>l-9+8JjVpP!Z3nFh4Y<9Tm1ygpq4|=!+t6yg9x8WJ&99(JL
zF$na}pE$FLvSCWc3eG;&6MBaYhq<g}*dckP;<92SX^aT?8WE>gtAYT9=B2v2=7A=s
zzDIUT$Sf>}ns&A39=j*4V6)jrDrf9iy~mntZy$cywO)A&JTFW?;~?#bdnMXhnp6c0
zz+(sGl|R!&Flm0o+SSdl5e=IW|JPgQ<Cu-4*DFx-M*C3Wl`q0)F%gCPh78;xAvgA2
zp}BE9+6RWj6Dovao6CJ==lvu~5#B}{nWOCy@PxGWIg|mYOvZ()%u9i?967G&nd1*v
ze1013{!3?1%Pj%py9^1ciwgUa=jB9*vtvK78OtY|Qc-{IvsJo^^(JO@fgm$j+)Vn4
zWojrT7C#KGiDnciTLtn-y`CRd{|`mQHBZBhbcl+zkE}Z8Qgg*g8AsiqUNaE79sdVI
zGH(l?q{FyRFh*Un!%G_~zPgG;nPlxa78dBT-9Mm|m^Sftglk_Dp2&|)t{{EV<+&L@
zjcjBV4mYrY0w;m>mXb<y<j*ea`pV)8h+6+nJ(9Unqa7zEmeVWCgJ+Q*;bq8Fgi*a0
zCnFo)>@o9Gd&xIqb2=&wm37cI$!Z9+#Wb&Sueksm&)hA)k>q`!jn=s|l8Lw`?)ySh
z=I9{|xP;el^}otj<V)&NkI(MR%cciW{VaNB`ww4NC%`#cP^ZJ0?etYaz2l3-Oqkn<
z4|`F{=D<gdZTrmXT(!4gOyidFHCnh!dx*?2;Zw<dm<LgkdY1U-53QLz?7zZe35H|t
z{pyG{J9_6*JPWT6nrffe#D=2}=w>kTXNF}hFD#HC=97A&PV&=A9%W~h2Yk|Z*L<z<
z?r`>11Py_hj!BfD4%d!&4qco^NIx2b5<$Fe79+zTp>@THq5P`QK8qiMN&a&kA9TG7
zT!~3V_rEuf5yiRa@k41od+od`Os704NhGXF&9sQH+<dwM>vz3igK{QkD;M=3VGM*r
zJRM$_mno4@3u}`sF(^#gba~LYR{sY(V9DvsG6gxs(Qtay{MaL*aSWm1N3nEe1LSb=
zhH+|oE$r7DuJE?O_tEaV+FS!X*)OsQ=wQ0W5+XHQEaH_oFE9`R0IE<(#<K@D9yU^Y
z9V`^<GyPWPn<)%5#w%|z;5s5_{w+SD;qpBIoLK&P%`N9V<hQolU{|20iMn6*ZyB<)
zc0=>rC!_rDYxt_wtmJ;%Z0qEVJl0f+jywwPFyLR`CJEEGZ0>=X)=+Y^COAXrO-oX~
z^B~)+ePL1PuB~A|`FT@$+NtEQ1u>%42vrdpFJLoRS5(6^%+mzI+<bsObO#swW|y+>
zyY^jU`?eo6GIM`6YyU~9CFmFbpa;7NEGK~}T0-2YMb1gPH0(34W_O3R6IECbLAZv=
zk4<OTlg5EAIfSunP3XIJLm)4PHqk$0=`|7^7TEnbg#z)5AjR^+tUppI0#hSy_z8;~
zWnVJI+Okfn?jIgHb%E94|F^mz)1|IZ7Heqhoh4HquZyMX*;J~nIBsZvE9)??_nW&#
zzJ>gpZJuFKg;wZ-g)pluQy)QF|9l5deQXZz)8N~P`{hwg<LzI+V<(JS?MqVD<s>S2
zkpiJ#tM$G~)GiJW#0ovildr4XU;PvwPFkyNH0h4SMIq9|>-?hSz&<$XRqtg5G+8ag
zq&;}5N$8c(6zA&ew>jMKv!laU4RmrnCulZ*M*+HA;1T+13vMm|KgS_gd?#uIDqG;r
zLnN$j`d5X#`AG=D-`B}3UZYajn}1?n^l0ptzDTI|sMcZaKmLxqT4r>?Y;=}Bd;F(Z
z^deU%7ASuL1-wi)22OkXWYW0#W=JZjj0eg=hvIkCKL}Rv_7hlRipVS%xn@n;GH{y`
z)@ovpFzmB;xfa4X&_ra{OH$utjGwYP^5RQnU_5m>!<c=^J;@2bs)&Qm<?G*q3EM!{
z$fC`C%pcMam&|@qnANQ>JvPs=63t|7ZOuJ}?a!p?gr^c?bPx!3sg>dez=g^(T6=3n
zK#m688krf_Ob_U!E{Ml;wqHUS?T*RO4An+ZX(7Slm{N`DrnJabhMJ(OchWu9G0>{N
z{C}ig0l$R#aezs2CY1?_UwJDwpwyx0#wXbV>z6(Gj{K>4X&bm{+XA>*x8-)aa{FVY
z7a=g&%IC+JMYRrPFS+htD_N!uU}&xLH}2l-5VpwH;Nlz+w{cq4tqDP7S>0yxlf!23
zgpf`M+%(5G($`0}CSkJxCPQo`t6Qx|vnq08Xk`5uX`;4yWmGc0^{^+qqegqo_*<Bc
zuB7i1F`ZyG2(L_mM9ty{GR}0uc%rxVS=n;fEX9q=>6^2UX9DCj=t(pm(?jefGnqto
zRna@#>E$croPs`l!ipN^t8^C~)NL9#dOsrYwAcR4_CPU|T!Mcre5yqU9}j5-Z{Xu3
z92|%nV)=;)Lc;7_heDDiP)Ov3Y-I9*qM5hpkxsAadVV6ByMW+B3GLvKW6m5<IfZ(v
zA{}o~Jqci9V%`t8-(wyDg9|<Tg_k>f-x!bB7@d)3Ki$JOJl!@%Om8H5h`XbwDCX6M
ze#H;zMONcC+Mcl-=<hjixP(Q1>8$?O-^t}tBmv(S^;MB4nkxTBR=m&{^QnchhD#2Q
z*g{wur4l5;)XnD`U?N051G<m)qFga&@cKw@0p1Wo8ppHxXgh8C9pB@RWQqZao?3*Q
z+2f~OrQVKZX4G>52hDc0F8Xuw4qSMI7L@bw`aFSL8|+kwr@ksL(d*8EGRDzxTILg=
zTD&RXL(`Z#{6Mw+CtB%Ov{u@`9)YaXEs0Q0)l&*`rjpx)Bng=ctO^j5cfyw)E<J4G
zifdLJ{9ActVohBLKY@N*Spo1s_~qDbAanzpbKv;IY)OnG#6(9s&Y7n?|DBXO_(oo}
zUA4BX6IOoLD?il`yU@7rV!uZMZb0&59Qy*CCwgE9rpggF>W_&78G=8x5K%7pjU>_z
zg8nUU_#S0{uI5rD*V)^U>zs+_3WQIPcKGnqjByQ>Dk!83^4=?)x@ctvsFY&L!S<<g
zaN@Q@dyS$%6dWkkq@BskAssIf#qfR8BT;hn-Lv`OmF79MgfuY)S6@N&eZ&R09x*|1
zA*=-QLTrH2+!U5$+U*ydvUm-1f=w;;5P~Yjr}=B?XQ3}%jmIC5mviW$>m2k4AL5$v
z=8zRNv$~nxT34s4W0E6{=aPD0rT8Mr;y2+aG$|KWCn}ewqG6*2a)!EPa_VC4(vvjz
z?eh#>Qmq4Zs}5xieOy`^Nk!v2#`7O}|JvNRA0PiB<VA6o8>#<wnEycK<6fnxW^4^m
zsBp*|ZLWp7XG{xA?g+A8Cgwd@DaJ1?WW01f`Zr)P2QNVH)EO<#VI*p1q8Br-$ciJA
zvWeqLP&~7_&0i(<lX?(Ak^kE7|HBL(|FK#`A^C+eX9`>{T%6+@A+LL=d!K*RA+NEf
z<r(}YGEV)IRthJsctt#Z0WMTz@WcD>Da@w^8cf39MAm#<U(5=?xeYFWQPyJL{KL&H
zp-V|9Z|o^7>b7hD3`hLnGyG7W#l=}?vFEehhob#Y_w7opT+5z<4*N~iv`;YZNH4yi
z7yesEaj=1P>h1?kpOnr$#eCd}AIO>2)%I`1S&4=nf$xlCm`n5|T;+KAsx9T|@#ty$
zXYJT}w1W?b-Ft5M$Lv0W&w&}$hNa)O<#Qe37l39Eek2FvO7xfq8@e$J8vGqJqDzyS
zo$UO<7*m*kO1p6pZ@EMt1*YKyQh~$|DAh1fOFu}H^SpRn74Lb;iN0vw?Bs$J_SqLX
zgH!sC>?+3c=jCz1lk)ws<FlI#tMIom`IGaXBEButw!ev1#r>&u3vN^Bb+^LCQYkxt
zulM?!>9DUSmmZXhWZP%Pdu5~^)4$0Q>C??1MMGSWh4mlx%ki!<HOtCFX|>ah^1g9N
zXOi4@qILd7F8(9QM^BQGZVvD+<T{j9A_G5Aa56#_GPwj@R^B)QFT^YXl^y}OZ)`&E
zEK%$x(ZKg&I@9-ZuHV3UQu@uL6%XZz8St<2qW~p<S^fwRj2G^QnwS$gLxIbGG|KRz
zJ28fXF7n0_6HfFz6r<*x#J18OhFLGv0S0E;9q3Wc@uhhoe<@dnFj1*2x>%7Qyv1iv
zrwY1xF)Wem>w5#njxiv9qTak<UEcF46lFNha>XMcl@7crq1tLcPlQm&_oke`4k8Lc
zulqDTJPr&sF4-@}DCY%@1;F=SId|UIBe-O~t-jRRE55GiPL>Hj@#?so+;fyZI{A?z
z@{5OsTyNKTUNbGbWCDl5aVemNeXd<(6*@?h?RU!fkC9=Fz_AGJeb|KaVLKs^hdoDt
z{Ir_$Fqd<({-!<kn9`>ifixNgxbF-e<QWD04*eg!OXQ7ymwdbbD?nR|=<f)A@l4rI
ztkwAkmh*L6yAsggMUz#7#a!`gbDxV#Y5SZb;IqYSMK07@XboJK)q0G^yx>J()quB(
z#5y>ka0<rY24~ZO3AewkoyNDKYM`}sWvHyCXI?e>%SoNjE-OUz@Aoe32(z_;&@yR*
zK9D(UPaprdIuyd!#>f*WcH4<;p?wrEU+Sm69-H94);qhQquqpdn3qx}SdRidww}(t
ztM?T%`~>+VJ|uNZ$5~%U(##`se(%ct=`<j>W}Jj<UyiA~HxgK%xCkTSb%b`ydae*4
z3HO9TjH9#q_i0qbJZ(o&Atej>Y40F<aU7hQ*9%K7jm_!(-YMn7lauzdR90%(hJb7Z
zGWtlQ-epDqsctV(EfX|sqCTFB=aWsfzH)8S#N0#J`>Sj+4$x=IYkU)vCBU<nRTtqo
zX06!1IP@>~esxs-ATqm?IG9MDXzlbdmCw7(*{>@VluIdBJ%8&)Z*^@g+BJn5!uU_?
z*YOgC*m%|$5#JpSnnWh^wihQr=TexN2Q&7pw4$$rXdxc9FJh_4w)@gVf%qT!fV=_(
z*|NFI3SeCmBHM}LkGGXi>0-C}Vy13x$^GvS9q*@&9|(a<%$7mEtdU!IYmcPDv9nTa
zr7!E=SPtq(o||<VpxS5JPenUlvR{#uq5P42HOur<-9T3T6d9v&Z;YM~>m`tF^vD2E
zCy0kZSFfWo7GYu>%YQg_<Hxs!UM(n>ob?2OouM6;g{#cCqZYS2lizFy$NR^*yY~Gt
z-`cr8?@|YGa0=b9Bl0FVkt}G4=0!mq!LJeNm#Pt63aViF3a5&p@m0wR_|R!=%?VTG
zdO^HU(2I9%zE&aK<sM4n?qq`X9UG1rVSk%BzQ3_lTneII1^^CNbBvWOVtbbWWK#8}
zlfE3d6mr<nLlLv%mG6#}f!myU3;g}j8gNMxeEjKt9JZOD(0DPUPeMiIUl40qE%H-@
z+6>MrQ}^okfER0iqg_R_){+xNr;7r|ijqJbz8bcHfp#1`RMA>bZODnea9U-jM-aw^
zmgpS*NaZJRi?tHPzK+61Gsf#ZO`OzW{?}QnH&3Rl`KK;cK%77f%`m2f6;{jg16_*H
zijMmx|CtPqaAK4<;662v+oq;hCn9NUVnj;F7p$p0kQ30QG-W<IedkFuk7j?R0{?fY
zpDWUJ6N#S0VO~L23t*qffYbsBNqZu~xeex!2D6W9CKY7RVPB2-xe@29n`O8~!Sk?M
zQHgG{&;n#j8aU@Wgr{?rCuq(cEN5f7(vKJ6$aCEL`n*_dRP13&>?EW8CL{KEmo+&o
z_Rj9W7VCWbyD%ofSDSGke?!+Yh%(-JVw90OF<<PpXr!8CS<a*iCTNFossLue5azM9
zns3V)hDaR!Doz-qmh@sxAfX11>m4CsIziy<8xtp|+k$F`JWu>9visiicotxNEIsj`
z4n-VBD=Xi?0lr^&2&lAw4sl7T35u-WtK*?po>y$jj(q$u52skh!S_$~8l)c+2@NvU
z-TDP%!(2i($Eu$w%-Ke+82`Zg$urIg;wio(&&LPu_jz)z1nXtwO^wP!1-JK!0MbD9
zER`_8I^F4C{p@LSLyP%8XLT@J+qG#$(_X3U@n@pp?(qjMH@7L;cK+DeirqfJv!zrE
zdV$h<rh$3@1;9#q|J$JU@3`u`DmGo%0!a*k=7%ALQ=k_)<E0B_Gk=V>JWIMaPz5Bc
zlI!vkMc6UGcE3HQOXCZX!!)S-jrBW9>~y(Xetn|p>R8V&@^ASRR%_m988vr0-IyAw
zdQ#;mCgswl@7I<PV~E%wqa7|JT9ELaK`59_t;kARI(R{y-J~!_o&D}Vs#6&n4-Ds%
zk>fqqQA5wtnT$ED7Zd{-uK%X<oS+6f`N%G8;yg1Y$H1Z^BO@6LQ&UrQu5QA^(d}Vd
z$s28N<?$2fbnhCcc72S;t*U**1ij|+-w((+N$*hjS5u#pCwsl9*?O@`p0OFvtW-1`
znZh`2g&jli^8?AJW2cZwNFznO&8R)j>HZ)pNiKFr%EiXD_6<2vEf@IB+W|DjyH$a&
z;2nVH)YZnbBt^V7GTHON;XhG0eb^tnIJ|#pV3Hm1VU<}rv)@B~U9gwkq{AIKo;g_t
ztCw}{$HD2B8|&<YMYN8e)~A#gKK70(MG*>Ia=Q!>UOQ^94Cd98i+BNO%E+nYnvHjN
zQi&@Wl_~K8R|O2=jeJP8=motB4Od8{rikJkaW`{|nYML$j_^7jBN!d1UCqlEY|A%e
z?xMf@t8^%N+>I{>lGavF5mp@kLNbX8_t?3KD1edAszR5Q*eV!MKGsY8(!j9a5gC?V
zK<AN2CY25d(HqA~)oR(2spg9$LcV!UZ9bL2Pu|s%X}?IF{$=m@#52X}f-j^dGw3Xz
z#DtX<LOsFcpK(uw<6aoWno47o^T15P7Q=X$VxtO^sN7@ekGHvGQzs(p84?#C$$|Mx
z8)B)-oH{<xJ!*&~YF-l;T4SpZwROw!em3L&T#^#c6peUmJ+_wgZBkF17sBM(8@2YS
ze(Aj~yV^VLM!~F4ja-V~=#(S&cJXZ9G_RTftpN^YEfU1Vg`nRtH6ug+>IMcA1a6a!
zy-ROs*?RXJ6>X^8g(&zOmhXMv7Xu%QbBlQ_<M3SV1bk08#baK6!$tvBvJg3lqzTj6
zR%mGW&<apVxQ|?=+Vle=6y~<io^eyrTy>n#_u)rqT;@By=*3P9&Pf`(^yynIgU#30
zhXiE1q^LZbdB>fYBE`oG3Ivh_&Y{*7@tanKgY|0+EtRKS&NoeGE%d(FE4&MWT%{g8
zeOuo|6uF|0z)0G-#imM`gVW0nrjiwZV&PcTvF&Q?t8tl<XyG5eV*{<aL6{xLTzrD^
zT;5rF+FjmRDS@R)O(xwYoj%rp3^*`@?sLEF7#RL74!T=<&CK^cWB4K#FTWq%WqKdN
z3_Q1zT2^1OBZ%9KCTSZDk}V&H0v`z)(pPGM!&%m3S<IE;{-f;V_oadTo(rTtCo|F&
zbL}H9g=L6;+wF{2O+@|(1>`?0{dBtI|7;pu3yzzFt3*EKrmR+MZ^@(;kvC@IvmXDv
zev#QF`pDz^ZY8OiBWBgDK1-9NZjzC4zNW@%v<Zo;IQ8kwP`)(s%hosD?f1($0CH2m
zj`jGrC8}c5#@Q!^HR}85(j~J2)1b4|D!4}TMDm2bg2=LJMUggm&A2vFqN%YWh^P0$
ztUkaV`g#i^%A`#rD(FL69+4MTl86=x6H5?5A+~&SEpUG&o?rCIyJDx)L%XP1slG7q
z|NBm;JIqrI`<{`T?Biet2v887!2KWL=XVeF<)0$AKA2TdoJ6;>kQ?mgb7F;1W~3+8
z@BO~gyR?G7@UxOld>@t~XW>w!^p$}g_{q$4Az;J)@e6X87A-k)8ltS!Qjo&$VHo4B
z2%BK_33`orO9C~-5O_8~xawG^n%JY8yXd36?unDjG63q`A89|Ky9#x+ya~DIvLNYm
z)*44~5C5q4O~U%H6p0eAsCd}-9(t$7$1y&4i?jUJk8;J4nDNy(USo~i1JD5k9^u|5
z*#8755OAhRorTBrY0*KmjcyOVmL-dGshPgV(1yweQ}a>HWHo@pM{dP$11Zw@G$Ey`
zs;XmQ<<ZT;(ozt(2@nQc=NPMt1rS@v_Ckx>pjq;Dh;ZJ4AXFs9KDG9*!mQ7I9TO2>
z7YMv-6=hMokV`b?cv(bKYC^FR|3}ijJZnDsj5lZp6p1+sRwJbP9ARJ$Zj;JiUlYX9
zPgwArT$RGItVQsV%^ng<hgZ{urlCdLv}o1oWo@$U6LMJD4QOjSmjw=A`cOG=9T}yp
z+#T(u`6wDhxpZ=d0{gI{OmnSO`3Aqh$XDj~M$#>}NMaeHmD*O~t>kk@?Ioc8U6Y2E
zLUz3gXEZ^j_Mmz~LFb@Q@sH1TGn2lo!q0OqFUL@>bfswgi-|39@&v9SmYiQ1nC{W@
z7&$=X)I3Qg3P5o1=q5X?-m<CAWE`mni8_ta;IO{>E2`e-PN80?8_qQZLqS;-SXw(6
zmAa0h8zV?stq^-uo&k}QzYPb8$Z{B<|EOg1CVp|3f&vIm^9c(DBv%C!CS0v#Nnm`7
zqHm@YoSe#acP$HG>(aMVB6i0s2p{aTo+$iDjK@?M_KOuOFv<bF5KfPHsWyx2sT|JH
z-iTrL$=FV88o}`X^4V}r!*7Oyy*Kot{b+eDaq(Z9OMOjwDV2ZvL2|FzDnI5FF-6#I
zvk#x$G`ce_RgJ{4FnQvS<DF|nGnmwoa!oUG5;PHaIMa2TSIS}F1>pTrpsdXz&hHdJ
zj9hygn$G(x`MvT2$LHSXH}dBs^-TcLaCi)7sKX*`L{ZrEPA+YN{ZS|EHw}ayP|I{A
zJ-1_*+cbQ9LjZ?eGg?U<P5aH_B!LK}8!V>Va8SLd0(%sX+{dZlkRt?`t-1pM5BS@#
z2v1JKfiWR#55D_g$O}mg=eGa2A%fiqJK<@oU866kQG`)wj7Bh<K(_!<jSO{24_*k8
zh&noyID6NipKR$#=gM#8o$=VLVB|OQaGEJnS-Cn+<m{k942O0Q(uAt!BQ7MjC7i#R
z3i(qg=kor&fb&)&y6@njllVK<`v))im>oh>_c|hVOhbJ0okGFrgz?P}9j5FiZDh5U
zxhktf+!0Np^2>`ozvA%$AtkR-!>{M>MlBp55Qou~ZQ>ju^m&`Y*X}m|FldM|-g{N&
zQCJoXLHhcH0B|#HuG`j43qmdhwb8mAY%-7C@&h!d$uRR@ktlqxQq+d3c;s{_3#`g_
zKagN_$HZeK*@l4KlZ9KBpuS=3?kbkK=QdGFnGq8ST4HZUdCe{YwA$rR<I9pbX9<J5
z==r7JI`m?BwBI%FFAs|rT?eg3pF7RiY9kX&I>hu52k96Ra}vk+Fw8IqcJPldE%sR)
zo~vvmo|5#;oBVw(M+5xVnlDmn`LQ{t<OSW!hQVT69pGT{fPFL>W>JlcxGy`um7Iz~
z!B`1c|Flm+8@^}70U`?n{<X|^*f#s7K%T9C=7lp?>0mN8Qagg5VQ(8V(9R22Ay9R8
z!4KABzl!>~`<oAx^hexZ&+Adu9yosPG%LxW4f(En>KNSN__8}@y!^}%8tSz(8T)$7
z)^M;EC5Ntiqx>;rCxR=f!>m1x_KUQpNJ@vRl_ohLs%N1HEff96n9n`%jVlD1oXHc`
znE6J?Soez3FOr|*pKTX!g21>VGIE7f1p$mxI)(#j7mdJl8icESvF*ubynW<>7Y_{J
zIF@}Rn;@uml8Wi3{%Dmi$33dfUPw`x*<|t!(>=yCYR$%-buvanhJ{9|uq)^o!eSc7
z+($+j+SlMDEA|N>Oc(@{I;5<XY7e-zYAX<d12Or34#X;$ls?uzvzALP71CW#_}4Fi
zdnrPok&7Dga(2;!1ydr-(9Ar$svKrtvN2GDx=8p>3)_IG2|e3cn~+7VSEyVoJ=!9j
zYFu<+`x*fOwMV9utG7|gmIU4UmD8^%O#yd>pgFe@Q?Ww4Ce{o5+Y)A5RS{G?OU3k;
zk2z;=_Lmplcp&-P_+@sy2B;LhO0B0Tnkf`T6UldRRv33F2@H-dBPs*zr#@%A<hg=9
z#5tFK#4V;X6YekdGt9N}GU?d);-pl5D-itCDK7`bYQD&B@rJ%X^95v8XRW5(UInBR
zDZHU?e`&CVWKMIOzn1>RT+}(CbV@5UZhr?*S+X?GvK9sbK=0gVk=E-6PJoTLnvv4y
zD3V;Vx&Sz_ZF%Rb*aoIV&eqHcO1jMEde5l(VLbIh`J^sqa;`#>0>O_fFKGa2ZTw01
zWU(js9zfgT8Ba=%VT-E1Ty6WRqrB>9d**<Z!)G6mvfBg36QX9w55hHYAP6}SbZvO#
zo7qVL;BR~9N-mqYF+6DGNc|EDQnmO1FuyJ!Ylf@0DpZ7Pgrq#va8|qApZZ;g6eg41
z%e(wiQR~I}p9#_e#TlfU>E+`-8Mz7?jR^3M)E@xDXEE-Rv(1;Y7NO#t(f&61y%-&{
zl4JkBR$gOO8szDG#hN5Oz=x4wU<NQDu`<u_qB0~H*T&fwL1J$rJi;X=$*+s#LUjBu
zHOYxo2aPQ>)mqvH300s7|F4RZg&wDTkrg%<PJ?OR7ev6LAl`rf`}BxA?+s$3jA+a&
z&vDCDthKiorqZQ6{}@3AqbOrMfPtfLpD>SyVT=b%#<tCDl>XEB^m^eyU9|gUqx#oL
zX^b#jx}gcyH|3uLP2!jhcGrcz6JRytu@8!MYBb^mn1^1+J((@4dy(srA-!}g67Nuj
z{YXvrL8U>i2o?ylVCrzBo1Op^S^y@pSD448$L^_05w=GsG<1^H$+_@H7SJ^x4Y<w?
zJU5$GU4XA(3pL>Xm0358{7N$lS9J`l{c6tl5@)I41MeW9OF@#T#phUw76gjLHVxZa
zN^9d}kuW!Cg7rYO$%g8H3xh=k8Mhn|b0qEE-KEmc5U)afrZOe7iHG{AKgb9Vb&9th
zm?%~kveMO=bwUz0niIk~;7>nWJeRz8(&Dr&9cX)HF(4FiU7hX}KH3wk&2qXrb%0j_
z%{Ocfu+*RxEpS%u_<!qnq#*6oUSv2KT%B7@;)DUQHFqDcF~MmSA0GOF1nCWz`8Q{2
zYRokTWa#@8`-FJP6T)+V|3`13Wq_$&&e&;CweC3fP&vYNM(q*wfph=v`P_?91GS|x
zf9MNLJ7tVFbD9pEy6m~wte2*@@?#Yz7}MWzCAt4aIs7w5jQI6smFO{mU$KFtAVPzo
zY{X4Hk7vVrThAWT9pbH-&%0or^F)qvs;(}%7xSqk=Qk<c<~(2w@JWe)e@85ouzJzO
zF%^EZdu=?obaM;v?(&zWZFy8-`)G?FXRT>4s%IH*h<O%d%K*;}(Jd!g7+A5v6i_h{
zj_2Z=`!KgdbA8gr`He>EPY5cYQ7MtE%ZzH!7h!rkHRg?7lI%sVNt)zCkHC9?+J2LJ
zz)7ZzMjfbc+xELAEPg~)2p>&;ob^?Bs8`$)&;VM_(4n>^1VHy4`{jQJKj!mOU)uVu
z{+0T_L(BcDZl&-@uHJ-!ZyU8da%t8K2c+)d&b?r<Lfw-&Q9X&Dy*32Nj4{FXd4l=#
zCjI69{hCK_-IA2y-T4lm=)oivQ4^8QM0ZR#U$X7enMS@`+Sz0M69Ao)8!+K^nUv|Q
zr@;N;sb^0F?J88!pvc)Ke1rZ@VnFu%h1$9|J-&4Obp!v+OdwH~U(f6L9MJyA87%ir
zBBRnQ$(BRt@;3q;&~)BII+GI15H4Da!3GRp7UN5xLi!smu8n#GCgfJS;>~8o8DG5z
zXm~i&7yU8ht83>3S#85&{TD=c=xv}mK`s5rWkA!HEX(p=^y4_oN+l~69${^m7)BLn
zsp`<-{lv(q#_kEUjz0ZTXcF&^0UA_|6d-vKQ-6k`VXU+;P6M(=fpP25uZB}p8{xw_
zU7RG~+k)4zX5RTd_m5C}DECqfqExSQ#OUfr<gX(9=ck!G{8-w)%e)@K^+xVXb58?L
zFYW*6JK{BhX@cL*#_i`l><b}rkRQ?kP>x%aCUFIFrgm0;^P>vm)g>ZC;h$vYp2fTx
zm}TsQ)0Pa~kz&x6fyRZfeuDno;H?b}VC2AQcv-@Ao{$MsuK5C?;4rwbDnzpU%+Rd7
z9N36@;vNiq?9%msp^$|y-B3RNU~cHYLh)Ib<n)zmCH&_1yR(9SBz!Q61P)6hjCnPg
zU!PKcqGI9qIza*TA2}010lTmitqWr%y{`*QbL&xO2iysc6^!pzhR#r^Ohv@7#J#T_
z7d?k;bfKWdUk+)7_LAgHALL`;7|Fj)ccsALk$tM)B<!#(cQZT{jvn^M|2b^u>n}_E
ziJi-o5(ZE7;hLk+V1dc<G!ef?Nzeiwz(pTO%<YB$a9?){C`#sz_ZHrgd{#7O=_6>m
zqMp-Tj)=JgU4Y*K5<vqRVrEZGq?80W*EAZ_9h-1JGgKt}2pknSL*464A1~H*yvaBA
z_fO+Ep$JWIy%!Mn8UB-WGEnys+s}(d>NI5;)_0+fui|>)1cG+WBa{<r&3<;-G8DZg
ze79~u29$qR$?qTGm(n=zWLaO~JG~r@7#G}r>sNPXj~tr)<9MmOvg4qIICyp2cX*L=
zmi{9tg9b6R^0Xdv7|Y8TkSYdw{`IS)fBl(vIlmqehq2=6NvN6bCY0#R%0AX9Lro^G
zD}wksO5rPgV;3T4eNTD0+6X%H$2|885BMe88<#KMc{%p|ipOAx))rihnQNW^4f4Py
zc@#uJ(*Y%3a_?A^xsZ`oTm`EBShi5o-ec5`33QtTac7v@tO9)P|CB|7lN0Hei+;CL
z{M<xMXc%Sj2@E96H$cEEZBt+v`um5m*1H7^WyYyTv9;C`^er7~!1*PYWI_*i3@Ola
z`o`5e?IjEsG+X?_cP4Q|zW2bXoV`yI;6U}o3@b`2s7pDdi~V>Y50Kg2_B~fEPFF*&
zvX59QYwcZcAj!rIdzPQU8kh#mfeM*6!GQY@0}0#~xi42^5JYfYaR2iW&GnXb5bv?~
z!O<dtTi_}NG40R{>wvVfVwBU~K6&mh8qB(=-$`scDq5=7jorQ8yxFm@a|;pkA?nj-
zOctKhe|?LMRUNLVS{_4(oIoXX?l*oyk=9@L@6&V6>wnI9A875R?r=RDWQ#6@as&fE
zePR5bvk3^T?WjLu#1p<78}teXR3Cq9CoD@+elx;;j<BDdAF%k?U58w&J2jSh7T*nP
zmb#`_6y$6IeyIHEaXGHuB8zVzx+Q_`Pmn^oS?QOMZ<JKWPe+`xelSml3|TS}`^GN$
z*B^0Mu?7A{SpEF<-B<hfo+Yv2HE!~YH>gV4WIRY~Dc1pBY#O&5Iq~CNXW+&X)1s^i
z6(?Y_zfor8$18+_I9sb=V4G^DQyW3V5q6usBH#5Fy8b=j7J>jJdUfgen%fBfp#iN%
zdZaQ*`#A0KfXKFsP1SZuW3}2ZOq&1O0fh$)efLFMCqifgofOaw@)qXT_?HFt1IZ3>
z=Ms5wvIIf(R?D~U-8M2Sm)Zu@-8<4+D^IsKHK6i328?H(N3?Z(R+S@Gqn3=vsrzq{
zvp*M6<U?~`Z*9*13~y&0=jb@{INCaK8gBwUIX=F=j}?rpfuA-iNh$I&vtGQ0GHu`6
zr|qu^<G%p(j*R@!_hHyxF)4*qYZ|wb6-kzXL_YF4O6fWBOU{GpN~@$^WkhVS-y<&T
zW~DxW-M{9j=5MUER!e;)HrHU*4Y7ieNjwN@k>{uFvkb)SIA#m&mrK(1t43uWKuBj_
zY7!R)&+2wP_qTr)JZ=Zgw2+@`Uc%?7AM19jFp&mGe?>oH8ur=HglXLdNB?e8GP1+U
zWf%0n<0y6@riEZWH3P04Z3pA;{O-bHi9V?mUSbFhX`?>x=f=Rvs;A_2cmR^REe|-h
zCnHpNA>XDl-Jpq|F|ZpYAvMCoN6kk@?gr*`>>KVJ_~dp7-MLArlV8G<{Du@6au42w
zQ_5EiKqbI|>`q2jZ3DGaEGMd1gE+H}D6n{|e8&Qvasj}#@pg<L7mn|hpbW(;Ot0}z
zAy#(o{`{U*<dr_&C^&#I7V?b-=L1WWW<+YU%$)ISf7NgM6=Q%|p-4Z-d-ROA4+4l$
z+@Ljfn`|08;8md0uW~5y&j~4b#uyEr`#}-CK~pQL;5zq_l;8kBj}K5siY#5u6k1+P
z3J<~d2^yCaMSC<c1c1uBuOqvA*tYpBsD<a&!GM5M^l6OVpkf>}iIiniW3V9{F~O2I
zi5qG_R)9-PJr&Z?LoB-4dXeh<;B*^#z^&mCBaU%w5FYonJ1cc<OLU1*)~4HPiQ$+|
z-hQHwgIA#EHR_SM`sfQtkVweY4|ra14<W9N)T)p`O;z}0U)**CFmi-{EH2MVT8ah%
zI~1tVfq)1|P#4>v&QsrInqMPD0InR({GQGv#O0;+dG$g^3VGghE9t9j_yXx?K>idp
z+hvBFjr$cK$#a7Ldj<db_C)46P`sQcvR;n7H{vrm5p=gn1T3_ThpQMtnrhhZe_?B5
zh&4+L)!%6gxZBjq^<G~Z*VNy&BltvW+<2dfY=6vBB#DtD&zHae97%>3IIPTJ@)hxU
zKSZ+U+S834#cB>&0W$RlbTic>NrM1@0Tn%XWrWQF@XH&@*YyFTqxegjYcPBjY>)N(
zFv0Q*bA)-#8>?f<wvC)oCw|bZbh8cuniRjN`pb8v0reg9Lp)gmAnlM;MW<14`|;(1
zppmq8-!7a+cRoy0?UW8wAD|3fLz76h`8v(lq{~~C{WDD9>m3xHv6-%E-IcF*BKz74
zC-Py_=zquHfA1h&&MS2Wv8nZOo>sz+H|Np=Xi0&VLMp$<YP#Q*9Gq=ngFaqIdpUA7
z$EUYFzg=5WzSz~TFb<!}GPKe_E6aDVLokmJO6Lp}_=xp86Bx!IGAj0?fr_c7{G{~0
zVJDR<hN&hvn)5oQCkfviE096Xsg+qZlJQrDg%F^GHk0+iEOx~>xf(r264me%?H)DB
zEJTc>Vak&xM~5M;yu%^!VgoM9iver|-Gr~qd7PJs|EE^|E1+z&FpzS2X(j9XcHez;
z`=8+f;L4D9k`f!76VRlc4!JG^T;J*DL|%iwLuc54jD&#GD{1v_yZVOW5(AAjP!0Ug
z0~w1__jSx9`3+93W!pI5Et?AYPFfiPD2zFLGA~W|$8>kz_L475s*e%Nz}tpA9AYw-
z;MKW!zu6tuufDWPv+z-K8R(+bHqRE;g_1|(Cde^CIq!d-F=Y?m-Y@%Wjjx|Qa-&Oy
zAH4lM1RSk>IVbaPQ&==F=~Vz-LVZUedE;l@uK1zt<<-w^s*iY=UJQ<54H;fLr<whB
zpQ<xXOUQ5%%Ot<o=V29M4yV-|ap_$+27fY-Q%5&o6SM=eKj!2?)1STuadRm9at&m0
zu7#WL4<ewm!(2*TAoAi-pnQFSzX709&}F#D%KuMEvL6kQQ5$!3bnk#<vDWjPE92jO
zk+{PhUJ{)>G|qiv$-{yesBe2iskb{7p+2@WFCv3H=57WU(*jKoNAbR5D`ymsJ#d3q
zR{E$L=ftvH_H6lh!9EYl^6cS`^aW+_<Yfc+Lg-!xpm1S-CovDXLh*MRi8qdGdEM`L
zoFqXKvVq}ZA8{jsmCL&QWWc0452%~wUW?doo!MqQB8Ty}g(UVf$S1AE#OH{krGDco
z*r=LN0zT9rKzn|Hstr`9R`nbfY^Q-Q(|#l84kufLcnx;D8v>rAE*j!_teAeV*h6-$
z=d&3nFG@i<=kwF=fL^~SkTj>Ur`V}a2reK^8UujD6?Ok5qGKQawsFbd&ybn&bB{#H
zq^ZT~*%Q)v3b>711<by<mc9ADDF|c?J(dR8sId(y`;`WOyyw2Q1j={j?9I)Pr|ry;
z*tIkQdV@Am7p7TP8^skK*$OPMbwW#(<7CMADXHvyr^UsrvA6%AmSFqgKzmkOgw2RO
zIpWwC8!a2DL046nq*)viQl(X-jBfRoaN(=dQ&?=R5*5PgpDlJLg{?x57;5bhY(>HI
zzkkr?+IgtKyg`8BO$kbb1o=q`s8h8vH_$u#*6`~)SfWzugSXund1uDFj4jXjuu}xx
z_0J?o1OQb9puhQ>{%X}!tkZq-0YBjxAAmeNfllQn^aBo`1^@hGX-9n_AJN^*v}mlf
zkN|LLNx*nvuhq9=Se-qWs4=))xUufcA*v^&V}9dsU5rJnGeI~+Zfyp}=J=m7T>R%@
z`c+~T^Jc`}N}4JA(dm=*63Mn=&}>0_OaX2<J_o_O&t_VNn!%lpI!k|e8OtFqLoAy~
z_up<XDrP>{^lT-;Ps&ddOQe}9Sbzf$py9f&c8VkPErfx+q}U)Fa|^qXY5TxGD4)#x
z`XWGP2l4BTa#x_OxB`~iVa%D7?tsT`1@}tMTuT`3xI5rgoBUFcrG)g1{kZ+Ny~_VH
z%2eLDl%8`AQp&z=OwiKQtV6I*wLeKCv)rM_NRz#HyJG)=i#Nz+OnmEDp$eafCX{0Q
zk{d~Y15A54rOw~<-r?z&(;yb>F0GpKp%vh3zq**K<SLhiyiA>@tR@Q4u|=k=01<x{
z+?FHrboIX0^d)0g5*=#PMOs}r)Fl_#;ys9Ef!1aMI-uk=u4g+F_f!mkJxj3+{kX-M
zV!=st&Cz&kGvbB;+Usy0sDC@iYocQ^NK&L{!?B=r{kt4<A1j3_99X2JoE{)6=?;7b
zy}JWvLh2P9Vm5S}7d2}7@e*2e&`u}@mqRFXT1}Ee?7MW{QUAblEcC;9JxSp+;Z14H
z7u>Uok@_Y0d_7ZrbsgLZk&fGpFxaPkY4-88VWxkQws(%v)yQ~>^Ns#P*GIK}J^#P)
zy(6D?z|6Vb)Ki-gT`Uu-o7c>vm*-l2qxg25J;i%sovx=?*?Oera@#H`+yI+K7AlEc
z1pxNKU;t1V1lpH#orB)<#LSZ10EyCFIzU5@SDzsJ_iqO!?}x%o$KM~Ka}(U7)@rgw
zpBs1YzUTOKr}X<>sUI_}Um+&tz%dw9)R&P?$Q-yCVRLnwe|7|KI`+XC;EtDsXq=Jw
zG&thP3rjL7y>|h|P2|FVQ*H{($9)OH1?J`;&6>n*@BA2EIUCDE_8?#fFUu&ZrV1*A
zOXO?MhQ!)p!~3ch$5oq-S2Sca-u5NA0+8|e4%OQ@QGP7rF3D{*M|w%g%rAOsGW=r$
z&e(Z61datA?k@omREIN~!QgM|!q?3Q#AQ_ujvaQxp<3oF*lK1@1D5*`PuCae8>DOt
zb0UD?OHlN=EL~yO#98X*&}o`g2zk~};WVK5TtQO21`Bna2B9V;Ih>kKbDVm4&JPTY
z=iBWdo)>&2LH8y87!~KU%toULKqH_i_R*FSr<1tJF@~$@hf5mmbiUe&M|k5`s)!lw
zhey5EIQCSA)mwZ9ZANU^q#VYYdjYP_X~Z1N{wGIYRj<tn>CpyMu}AoHc1npnnC#Se
z9lta@3=yz*PqA=18fo50%H{>`CLt1zo9xRK9=~$goaZkj@~F#)Mt2jIzSf_tUSBjc
z14OdGq>l<Oca_j#H$K1ur4!Tp-==kbA`zM*PMV57^X7FmIxP0N`;^fd`5vcXJx|E;
z`+=|`PDs=yMa!^%?Cp>{q7GKW>CL3Jsi{1Ra*5ZNo|raMv=dz$0nkj;aT1$B3aN*9
zK~25F&F$VJBt~TSc&(ahuGAW<80hL|JfCF^8D4tCu;PoUii|&yvTuWtxVYA87Sguj
z<}qYmleAiBQZ%tW@R&6o)RLPYr2pX3JcR@!AYM$oWO|k{D$`6D$WBasBvM$=-C-^Y
z#Sa~(oIZXe`9C|R`#f)8SV}&dXogKbY|GCpM=>kqj>)*qHE?0ONlv!PykKNa>jT3O
zCo^3B-#FwkaC|j2)8N*p&4)=_J3D5GJi%a6#hK^~ndW65LHQrA)hd5a@j3S&VQkJz
z!CKH`y$6%*p@tcX8+K-g9>vT)rg_M)L99dq4Z;dxDi)NnOUy@G{gOU=1Lzg`KHqt^
zo;m&sCqcOMU^t=L%Arm+i^AA>E0_x*E`MoD%Emnvfo?DefH_VS07oRCu8wV35CEVY
z<^^5UYQ4M?atk@1I)kB25x@*{r80Diq>lQ~;pc2X{XCn>laT}ER`#bM&+{aUKQOhE
z$$68wEuaTBrw4m<&6tF;i1u|*IKRiKz(Nxlgv?nMy#|GR(x~kT9czG9U7#aq$&77B
z=Ci|XoGF*wWZv-r!@}Y=3x;qTzjnC(<uWp1bb+lwQVgus?f#}8!R>A<fpu>f^jLN)
zOk&6gp)bgSHIc#?HG<XS_Pba(;=a}#1q4h2Uc1m?k}0a~!9ndfVlU`T>XU)}4+A+~
z$eiZI<UGehxS+RlkR6Edl3f-+>?{T?3)Z>on|4e%0QNL;37ILfHz8}|<0TIGKqV~I
zrdV-?zSx0$eI!~6;n{!H{09)H0ALT`h)G6O5MnI#n?v}NRpR{41pv#fWv#0{%Yb_>
zXloXa^DPwt76w)SMIE1}RIDZmioDDq3$p=gY3xSEZpg^2a=y>dt0_Iw)d3vhZgsKV
zczmW@I{F7q#IOHA=6!oKsM@qb52-84k5HwNa^cg;6#uX!)`E5UJVox6@RPz0cZ$$1
zT5WB{px@~-a}2}S6)bV;_}4THwN&A2`={ih2k%q(KE7Qtm4y5voQkQ<3t^!LATZl6
z$(_Vsa%F4R%iqD!kO7Kt=DQAt{4u)hFV5xj@MKh5vdWXXC9{L<9~J1r|7W$7|E)G+
zM!L7My<>8QO;$0>SG&g2L>#)+sE$vcZl4LvRpHJ;L(JsS1&v{>ZOi|yI#tT9J?9h^
za3kU5H9)5-ZwW4iJA&B(hme3e*Bs-diqm!z`q!@XLbcOlgF$NUx?7TBoQk#LM1NiP
z&2}DkU=1f8)+ReCO!9Myfd31yLIT90lzB>$U#L39tm6+9Ho^v}4*|H87@hol?V*Ri
z$&D2nG}t|D^>_^q`_SjDYRLEK!!j&|0n9erb4UPy(gA||#lC*s^hBW=&T$5r4wD`A
zKN24X_(*j0MugD+Z?Te#ftR@(dVo1Ekx7YvD7+@K2F|q*BVYIS+>kl2+kjy$wA+r$
z@Nb#s8GUx!Q;_XH!&e<QS>uk<5B&f%+)4+Gx^QQ`)Y)bDJX8f!qpu`|hISAJm;GWI
z4vN}^3HrI+@XuyhD=QzD-KIzgh{w;7H9q(AUN{w?T?Ud!z%o8)R<}3B_%XaT6SJn$
zxpPSN#%p)b=d0xuX~9TAH)|Q`1DynhgzCx3Qdc|2kV(rSFsZ96>1+4DR2GLccv^AP
zRD_6y-el9y4OmZp-HA)K>kx|1EKDVDjLr2~V;b~4N^0xzyVDN&SF0^b_7jS5uFzfy
z@H>d^UNN1X#ziFds(}<Q<b>|03#}Wg(cq23ll&)qDCFAfAHL{@Xpo;+ZqD{~Ot;6o
z@7oHOY@emS$S}xQOF@~SY)&Hh<{mOf8Y05Yrc0iFvz{*+B1a2!l~*P0sf|Ym3Cq~V
zlcsOFE#|r%>42#*HNCo@_k{hsKRjt6xD_>k40<Wf-q(D5YKsSVc)a`6L`Vp0ZW|8u
z3STR2x3Fq8Vf@^d0#JsIfEnHU4GO?tXqigrWt-aWqb#)51Jytn$@7W58!D~G6ne@|
znU+kE8H5)#O7*4yUFd|XPW>7P{4rjvxh2K&T&#zvo+<zsav^Hu%f$0*(vWf@Mgtgv
zr*;a&Mnwjc2)j9-l5w4PKq4xD<&lq{#B@Fxy2m)YRM|9EU<X9%)?&THKnX{zN=mM)
ziQ|jfRFbC#VD2TIxxIWF4gTM1^>QTAGZn85)I4#FFS4V8q<A?7@ULT}@(<RU699H&
zh`LzKDeX(}Kt8j3w!Tj7h$}v2``jA(Lb}U`=pQ`tt9QSA0f#zkEW&d=a3zyXAWvz1
zC2Giq3?QLS51oYD&FIz@=tyA(08ma7;Ffs?UDlq<yLufNSIOfa#q#I1PB`zQDODl%
z#Y8cY5EN<9hrJi?>tv})0+V{-F1=tIK?~a>>X;^frEvEGivx%~y4H?yb@g`uy_ss!
z-#uOei!rBZHufPNbu?fWLVZKdEL&@s9uIcM!Nf+R#Rkiba&vG57neDsH_k;(KV*)<
zg}!zvm52Yj2Dh<<EBMgxE1NdTitEID+&Zm{8yCS0i5Hq550FSqJwH9eO^dJl#2qNT
zYCoN~8v7IIhYpMQIVak@Vx0<)C^cL~BW*MJj(oNhrIM1}9hdBrdz;TA!|zlCO0||f
z*9ZZv{#3&D(dJkJqqRiI>^ND`o0@?>7bRA)v}dghJi0A8uT=U&D?`Vr#n%fXZj>b%
z#s-r*7vF#VOUy%Rh8)Q9_b+30W<NAR+O$FW>ZG!`VQsMS%=fcpNW@VU7o!DAhItO8
zzITE3uI^T_giin_L-jyyw)2*xexkojWmPc~!^GoHO0?m2)iZyD@O<z#;x8jhjVS>G
z4JM+cgLNF!D0Z0sx5i;-)F=74U)LZwuXLtf;h2AFiaNU|av2dbpr$Fz`K?08uiW5B
z5oVOhJ&iVr1`SL{ZBKf_;dR!Q=IY&ev$-H|n6oyKzpk*Z=nPj^Q*tpkftfNg2Y|^m
zPpC5KiUdd8oXXQuq<qc_j0^Rb1_$-|oZtp2A%0PCa5`ZQ5C8d8)Eto_F9wz9cs$P#
z+q{*KyfXB22pUf)-K>B_h=ecZLh)N`&9C*`<L>Lc!YLqmCNeT};a9*EE7n&po)4dF
zzH9u-g0aU$18K#zh@*buXr!_ELg1C!j4#hud3P~)A$=W^0$`d?w>cFqkk4~7CKQ}&
z7J+6268!zOIq(3Z-SlITk700EvBpC||FuUfe(&O3F2j+Hr1z$UgRXc_p@GPOaH6?p
z;%EdJs}tu_4H31)k)v&~VLRty)mMgXGk^Ow`u9fa%>eSFNG44g3JUqIb-E)%BA;C=
zA~(lDkia4}=uVRI^@H`H&d!Da{)!{Vv^?o(bS96UvE^@(XI`ruvo7ByEBEaG8W=64
zfjJk|K2*<UYeC6Hqk1>_cDQ+8Rr1mPpyb5odGd^l1<{`4r+cREDe3e1%;rE9oYtsb
zmlw1wdX<nKiHRx^IZW=&melDf80|QA+Hbn7mwx4Ecl%?Gm<u;%Pp*wI`hPBwiS2fZ
z1lw4`xujQxu44iffypsCqy-=3$D9`(j@8cu!L;=KCS?YnV`u2fx*Xdwsuop9%0hy(
z3ye;I>XT=fQoiV$lM=%az&p*Wp`aNs2>=s+DzGCx2U>_hyg0lsENvUhN-d+-fH7(R
z7|j>t%#{eVkW%daKZ8-Lo~aMh8`0;9G<h|W*S+Q5t>-&FD{h$p1%bfBB6=~wQsXE4
zBJ|1!%lY3O9y9$LO5;+|j0_nz3G|eBf&OT9ByH0=z;n!{ezMCT*eYNdxBchEn{mh8
z{Y>p`DmZ>y+abAwvw!r9s9dL@VqSI0*3C7!KwRmlg?Lz#-eC6?0a~ZMdv?k;l|`{2
z0+Kzi{*CO3*~p+gxQvM!z}WLBwg6%S5XIYAQv`j$+lq+)BF;aivqMjA5N-uE2$js4
zNIA%~!cAeY&LB;jX;iP8h)%O($P_Zim7Ms0T6@c&x|S$h5J|8g!68@(esOmQ!Gc>L
zxCD21IS_&cOK>Ly3&Gvp^<cqWgF6RjntR`ysd+!%)JzRURf-(gy}Nt$THms-|Cf+%
zpNsWd>u*}6fCTctId7ILKnlv-*mw0=g$0(Uoyq0`)BN(<@H+Cy8vs0*ID~ob4kxY`
z#t8<hd9<Frdjt7u#!?VZ9YE)Z)Bc+AnJV4DaoXBLGZ*Vz4Fc_{_#I>i6uowWzJSca
zR+L<f>(`y%U+26<0fl5f)0FC8wg1MLq&ThobfJ8fDO49j5hfBAX!f?R^)JP0;i=F+
zS<nQpJ`dryB$a09!i%p)qnrS0t{yes)(lEkZ1U1?cc57T)rtE=#TD-5AqFJbqE)Cl
z;upU-<tV368%+KL9{1t&FQN*Ebo;%FS#Iqn0rM<ha0d|#A)fv<&>3*Bm_Haj0!t@F
zt2fS4`pJd+2M*rFi%i?MJt|B9*iN6kYqhve;*!e|Dmi?0XjZBTXpN{)VR}bhPdmsx
z7>GyttcQs0b;Q;*5@eVK6HFC)rc>n-X_dyQj^*%0f<&Cz<!=waOo;={d2C0%Lk=hq
zjp@#Kb4>axVjZ1pn&13XKchFMsN6QEV^TQ`MC(&X)^t||Vq}$PI+!lktM`|*|AOwM
zpC255aU0524yd_Bm!-Wg*}>ps=R!K2NjO-1uD2q4_vde&ti!&6m}A~?dWRrj1=M6(
zrg)lW&3|F~&A)d`-s|sM>??6zng=V!71Mg7@VJ$12TdGp3yKqB^BTO1u{?awt5RxX
zJ`+1sKt%Z*Q2(Zy6rpo~N;#Rl*-@08?yn%#(c&|YAb9&YXlmu_cjTQ;c;b2g?46S8
z)QU{N%NSL-1A61~eC4-k)_FSY$vl7*7sE(a4~m=M&dH5e@iDX4dmL@KpGS?cK5gF{
zWayriRGLkL_E}Kzj&uT2H0ZyM2o|TFDwO2X>Mc>UGCRI^hF&M%GW~sJ8XXzQ@h{~U
z<KA3b4-tr|3HDlX4J@(4Kqn~hDg*KT6M8XaN`KvKp4|Ic{^pvsVysTw2vVuEb6yI-
zDl5KMfKxow?^Zu~@i(8pgsQ(PZ|4?p=GmpGVd%<c489&FX1U#vno#1VQ~L8wt1A_=
z89t;@dn_FNWN(d^XaiH0p$Z-vz{kskfy09ykD?cHt^ckukpN!;9)Ef@`LNd04`xjC
z?1;pRF8dE9S8$yRPks-7<S5NVp5_Y5kHjr7RzcQJf%<W!86H)mUQJKthGKk6A%@w>
zqB7%kqD~U`fR8_ohVF4(-l2@lXquNNg1l3!-3yNv63!2o)3)e3Od!&n1+ub(SfHw8
zn+=_S;+WY-daeH_!WJ4|<*ho4?j1@%)aZLV(v~lUDNv(82bnW?8mQLrJvRul0&Y6%
zZ#XwGx|Uv!igf+NrKmOJcdWE_o{tUN_dKG#!~4`wt{>kEi`A6`HuCz84`_(hq0iN-
z)T^iVnOr@qABc320iV!cTf_I~M5VT?=aH@m)1ZK?`32%7^94{qLogiLJsKWvaaGeK
zz>e}1chNA*c?If(r)n~Mr*nB)o~w}Q2<{<byR2V)1AXx|TZnZtO2=gVBjqU<@PJo0
zAp{-(1ko9Z;!m9)&9QcQsz*(m2r4)#Lns8!l_qrx5f~SGrg>XZ1kdrfmwPQFS^H<X
zK6JpSIg5!DutNw;zfQfDL`;8Q-aNTUSNa&_3azj7!h~4qk2s}K#<#mhh0==s#aHjn
zijn>+>tw1qpb8F~T&MHm<EHdYW;dNUYV=WrXy#r@;;0OIydSm<-Iq3ZQV_ad+30@-
z`iFKjx_s!irs_e9T>ie`vl76A2uJx0)U$c@2RWbw^8`BESYHO9H2hmR(ncHbB$!nG
zKrkufKWJ2WgPH`d>oI+Z^xeH%)W<qc8CA<=DHg;Q_S1JI|A@x(1yeH$-C;73sH!u$
z+1SKcNb?5@<&%4T@Xef6e8)TDq4xkka-dgxm<(C)5xfYq-5<dNEgPD<ban3xs{pwN
zgXAh|k~LQ_hz|&;=FK0SrR2668hfGm@0>FDI8PMrAjR_ad_(^PQV_~>E8dbc&R}t!
zn>tEP{u!?5okk*#tQagQw<>@66CGrCf&1@V6U|;>L>KpJ`ON$B?X&ck9oo(R*PMiI
z!X*b`BASh)(b>%p9K{9Za+6ZLmEeI4`Dr7?W31&L?To~@`n0h*4~TLxOraq14e0c|
z97h0G+n18gvhA@*=LF8~1Q*uU@2%d8XO}jf>_?HAfcs4TQjG@_q>&1LRMCD6wu=Eh
z?k3C<#F30-F}hiiMozz;p_mDz5>E~4>U&elkz{Du+#@ton-`=iW9kC;J$wKA#tn;O
zZEpKg5WK9lTlDlxyidi{pek`j5arsfDImOq@=#811Wf=K1b%i9t~m`9C%`{gy0tiP
z5{kOKA$j5CMs~+bn<olQRP;qQusrxl?j<3<Jr?fVhp+M*L_|qK-hKH-gzi|vaNX_H
z^IGMqgc{1R#B+t%Gkn9}1r=GPKxK3dhL!$s?RduvXhu{o^>%aL5Y}xpat*S~OARt%
zaR{jN@Go?bb4!VqaB81i+iV8;Eco(NXR-5mbk`KK2X_`HEfBdyX-w?{{q!H>aGhg=
z9BaRY#%Jym<R9t&bxi@XG=>eanrdcG791Y9tb<|E>_NpKe0^(t;M6n^Xo@DnRTY(5
zhii`RsQelb+P2yie6o4PX5IOm*I$Bduf?hcAZrpKQ6nzaI+Eix1Gv~yUhVRk68rSY
z=Z71bXsjuNPTtLA8-0y5a;u&_$}rVF3HJB+C0u}){4JHsN2*y;o*Ocj^v91Qp{1K{
zk6bKJzj&$c-Yfi@dL<c>wW8yBoqd|~m<Z-!4z<ECa_N^SDe?aGieGpn?Ho^@i{V6l
z4>o@NEQ@C<4)956`oRH|>GUbqg}pe?#rxj%%nQpT`UlCj3qii;Kmd%r!36_O#S_sz
z<5asP-oDoo>)SiH&(yr=s*?Tjpo8`P1H1kU6i`=^;+K*g%L7Ti8)+~neM#bx_{bP%
z`o7z}ugVBe)u>|uAWm;P!>*l0S_hD0Iz6D)|KmPWer}q<c1wvM{Fcfhh~%#XJMP6d
zx}#ShE<>tIUN$2KhawcBFpOhvYo+`mT5ka~*!R5tSetsU8d+C?eeleB`hRRiJ4gr)
zhJ|U2CYyO0nJqRgjlIC^gjJd{*xjNl-Cr)R><}@Nn$<La+p%T+SxPn+GS4L|qLSC=
zs)8{=#9<02P8r?x`xvi@?H)bxILH<DSh;Ju@XhWmgCqeV^B=Q<m!7GwXaE=jd0l8c
zXb8e-03e8?T}UbQ!J0>^7QXDmTn`ytYr^KKn_Dlx(j0$BuoNoDahG$vhYw&}y%A?_
z@_uPAQ&ND$@`r8Z?ULc|n*J0HU||N0zu=x`UbMozTYG~D_21X)=8aj9!cq{CF&%bt
z6IEFfL2K<cuirXv-<i)Mx53SLBHwnJGs<Uwy+*eaz-MuE3-K5Xq|*+#PSw&+S_Pfu
z0640l>e5<K#{cMBZ+3e`8@xaFTbvSG-$~lswJYI=Ve!|U(XmG<m2eH<6`M+<=`w^H
z6sp*$qCG{diAKyk=U1nlW&9GaZohiGX;pnx!}QiEK*Fr&>9SP(i%O~`@srF7gQ)ac
z$a0(B+=ggvUqK<m@sx-VgKUfD7Lpn{W=l`(&>Qv9>GnA!lvp^7Z+KQj)kr-1%S$}Z
zHq&U^Y<zGP!R!nZrwU{FxDuPYbtDNo<NcS_@2HBxdqN#qsKCDMl&0vyV$;A43f2Qp
zY%rof(!SxhaVdD7gc}S!C)8~deOMPIKXc8+OVsybh>eZCJ9>Oz<>cgSI*l*wIur7H
zgh43$1&^9y<yf2Jrkf94eO3@DRt6YLH_RcI=E*53YcX;xo^{JHn=Rk+K>})AD{<KS
z+-DvLj`!9rpz4AxV2<Ga9eL|aGI$>+_*==$lw-@v5ouWCJ=Fn#S1STIVL$NA<)_4D
z{Z5YZtXAISytM*-QiBN(F?u`9d*mmKr_4+oU-t7sB>R*!#WY%;xGH$l?_CK7)#jUd
zSYwEwNkbosV!V_&$;=vh<$lyjv4W_A^QRXXI3U9HJF9o(WI(t@l+tk9BS<5T9Cy2h
z$TIDF`o4gt^y=#N&3~ttTpY!C2r)AS^2KxccBz+S`ASN=c&Y&kxvb3G<cE4<hG0>c
zefosp4+Cx6P1nQrW=2Nv%P`Ng)a9Xk(R)j~rjwrP>E`oX(28y4c)gjpoWBhk8H0m^
zEo$c{_bx*AeYFmQ*+&iNyHXhDO4I4L3u{@GQFUbt6X>LIO~3lZoW(5~2Zfz1CO;7P
zTqwQWW)i(Us=IWMXB`1Og@JOu+A6^u!P<YzjjKAAy5SvDo+ukj*1uVU@>syTkCS=>
zx>`?`OB(M*ceWk)3D;!9z6D`d;jJ`cnl5MQpL&c(83}~tX<a(x8h+b~K;n-@X5DsP
zl4Qcd1NDs~vK=<sW;G<hUPX<e%}f1;&25RSner`1g826iEkixeFNe6Te+w%$=?){<
z|6Md8+Oo$-5iv%8SdaQgz3yc5jC$G!IF|Yi9R0_e>9fTnzr<eAhZ9}bKkB+ZC*6Dc
z?)@~0$346)C*((|)TlBsMZOC=F65_`I)_sKhTIMB^P>m0gvkRKEhQ!K4=m&-X>XTX
z+n3Mk*Va$zL$LO<5~Df)(2s?OlP>eAxvYIE_|!t;yfj7IEa7-&Ls*RQ`kTNi*`R%v
z&7pt}lkA<@^#M~if>Hod4!s#7cQDs9kuj(@@Am82TE&U_2J%=F<Mx&Jf{*^0B@P}E
zm5|+8CM?wig~$n>gxL&@B)e`<H4Z?OW_XAoxjT&GJSRuDgrj_unkOQX?Sqr0G=JrR
zNBT^q2J2JuWM&0f6>el?qy!hzHrHEOS-E_c0ywDgB;#arjSp{2+?&JQTh7v#^VM{w
zvbfL9vUs5<MS4*qTf+L!+4^6MtqT?+j1hS)-;=iUJ|c<k8NS@lyfVT(@Z;x1?AI)2
zvtO!ICZhI^(rqry`lD2D+tTa6-}agfVdPM^UoxCR7dBLyd2Uwnr_rcIj$8SBf|Mf6
z<yjPcS7w}~H34D^-K?;_F*M!%bOZ6o+-4^guBr*uEB*k5^4D|n1A3YP**M=sh2eKO
zmpHC>1GIb1=&A8=%dR_ho%=F{Oe`BHqH7I^s1wEQb6e*<QnNMQPt_yrUD;A_4_o#R
zw2)(D$gyx~{q<HV|7M&Tc^)p=pw7}M0ZZe4MRl}bI%X=by<gYJ8KT?*XhT+aicTc$
z-x97Gr)x^>O>YPB5TXJk>iVA(a;U9Wn7=uL+IACp%VCq#l^=w|0NwCJv50t9uP$pA
zelB2eQVVuk0XZ!Jm4Wun&~E*KN_P*TgVa(3_k`qKOCO_N`<EUpXA`zx@FLX3U3h!q
z5vG~VP&ji#ImNr(J@2-VB1f}ow$+3#f?*}L<VW-8J|cVi^3|$R2qjd?$&?>o32l=z
zrdl7}+f{y_E5wQCT@w(Qk5~=f;o*h4fNZ&Zk=Ki!<WJCdiJ<2Skk?nfdGcGy5rwRt
zt(4x+m7Yuh3InkD;Lgs4d~XFsMW>5DA4_i+z}MU~;N{zJmNrl;osQPIM+DaAHA>Vi
z_n^4{Ko%#)*lDx=O)~*$p3c}D&)&f6Zfpms<ub{-{pC|*ru{qiZz42?##9?^PMN~t
z!cHI@Wio*Gg8XOFqVt!WjrKFa$C04rCaXpM7T<P*iZ!LN_or_Xc{P||xX(tFoY8-1
zRQixnq6&3xD7S$gpo7{6GODvo<R5yXVfXpX88i^wp{o8kN{8;<v*(9aG-pWzTZ?=g
zXVbqDWd0%P|Gd>u+ME0-kIZo@kMbX}lit?<I|o)k`u$VFp2C5g8^OWGCZfE&JY++k
zP}c$eUz2=yC$zr44yWtbjX$~Pswj9+Rc$SDmJBWOUc~>X+YSuT@wc>gCsAT1!ZoFB
znBT!az&mlS<(W2H)c#I8_~Jz@hE<9gTmRcB5bzjO->pYi&kUlg;iySsQzx8!7aeoN
zro~zgsh3`nMGEV1eOAv?hx3!koFwGBe$&0!`*O9Ib!Fx7Bf?7}amMivh|yLn4t2R&
z`!wj|q|oo6D~-Xy;Tb4{IMGIBSY6quoSAA#L7a!g?ERzk>9=;)Gmu1-yT@BT0rBDN
zKi^{Lri*j{d*)=v`s0{FeBSotad0iw;>D8hy#%24qiHSs$q)F00iW8=R9jDr))PAM
zmyU#M5+&tx@Za1t#U$nPSN*zI+DiU}K2(Y|Jf|u%x-Ik5dl>ak&>oJl9>2}bYAxMN
z)t-j8UWHrZH$55NrrX7%Ed_foT+uU5(N4ma<L4rGt5|0{N{}0DAM<Fj(;N#JEtXA|
zS>4*i!WL)`iqUl(Y?mAnxXVoAf)Q_NjBK|``9&$l8F$b!!^g{4PO@@%xksu00=?5A
z;^6uepz)}aY0s%8mqNC9*G0|6{VWzaTU{PRm49QD`ugPjgB%<$rjf*Vi<vyWar0<@
zG_s!k`{+Kpb!0!8nM4bPnknG(*Uz8n>jG4(tN+pSsj8`|!z(28UfeC!_O7ml)p+RG
zR@bOEW=&h^v5q>~GH!m=c0|l!BEWkY@a#Q;7G3iF-2>mtq;GJz!SWqVnbFeZu8OqF
z*QD^CFz=RS=yX>^cNk4fL=>d&!#0ae_s3DZuN+l2Ts>XeC{ngpqIUaf{Y+RM7FN;a
zrtX44B7~-5gsYk{J<1!S#RmI!`+>hh5AAZPj8^te^92*+$FalL9cLZgI0OGW&bjWV
zhQ5y?`UZcT;eEIoMt7N6?6q5vk8li-4x8;9*S?!zI05q-xQo?fAdcb6N5pJ6r5o1z
zct4U!%>L=w<8ABXU~%ozfH;<L7q}=MAOyc`a(~}$u3`*wTR}mc#`6TDw`@=`i!)h8
zZy3A}_THEuH31z?xblYc1bMhIT)6~7=7a)$f}yk=QERUlwcVQ2+=;+JmGpWNt^E6<
z)xFipGw;kZ14+GWE=8y9qt?jgN+)r&=E-V_V~`A-)}Aw-u|>MD#U?G+XnCpA#lPhg
zL&lSE$d3;hvtaDw*Tdsqt@@2HHC3f;e)dU~piJ?(<LtqgpQvd(u~|Va<l3!7D~thc
zhG^)4l+UM9T2@&M2C{WShJOer{GeWG-xl#$kBIWc#qK9VY``+CDTr2Py%?R#o$eSu
zo?=}^EUcEBQSaJaoj#VbL5HtJtnHMtl}k#vBnrm({ncj%J@CCyJL)F<)RcDPXC?Zx
zlM&?kYr9S|hR&lJtTkBtqoQ2b>*c+T4a#eeG`BY=_mxiBa`#0ub9L^v8DeVQaa))4
z@sWddKTi1X%usLxhiM2$ea8QwQ>e}nZ(!QYj=zdBt|X3ozL>_v=Rdg0cTU`^$hz_I
z^WeN^K?%*!IK~B+y99%<VRmmx0;XG)1d3N$L?H^f%l>@ABIR#61bGUbpWOa$oOCCa
zml0i%l##%NdfL{MlW*ON^}KTJgzq*LQFx6$mC-^3Agpz1HD##{Q<z`x?Y{c5pf$6+
z`_6YT_AJZ<Cx1eWFJUD+*zHpowecWgTG?5&?#yvW+$zOzYrJjA_YdC9=!}lo=X{pY
zv=*4X4s$l>%;zfm?iB?hUC4qu{mudo0_gwPMCwi+Q|AtY*7-Bc&B(8X#LfdB6qzsG
zkm^gAb-wzryg8xvo_o_&%iP~(6oh{2UP%ru6EiI1KAWZ~iboQbCsA`6?I+Kc4NMcB
z8=gxcsM*KN{nFVmRrc=&Qlj~f^L7k->on%LiyRr@e+XEV5rPmg2J7|eWTE{$;S2Iq
zOqn+Z3l`RcgLhp-wTRa2hJ#Gnu0e3F++oM!xktj#X@{w3=C@i+4G-xs(JYTvD@3C|
z*WSZ`4G(nVplaCJ{>)<j+`HMfU17|-u3L8^dP0*TL!@XWt>3=&75Xxo81MR$LScGu
zeSbmBdI~yrCKWmUBV-5Oy_U`!{j~AF)2Id)XUvFQ`Prb3fpeW^z3f{3m&kI;wUZ~i
zsIO?@<%Y^U>1NK4K!uBgDoJCHMYohW<NaL}wRzIK*HAI7QSpq4sClDpTvL%e8$Xok
z8eUbAwdP84nwXCrmSfbm--*T~Vq`ke`e6e64((qoQ*3_MA}kz>>Ep;)SiDf%cP+0u
zSN-SJvT1jjH3NzEaK_fLSfjg+z$Y79^i`*szo9h!&T|6dKLW(Epo`a<d`a7IsfeG&
z+jP3V|J^Z(77r4~#_q>J^LN_+;-*uqHzZ-!V}12f|CXK02kwL|qWPJ>2K1#{&@RJy
zJ$MkYx>Z>=e@Bh*0hhZbW$#f9XWeM{yq)sIWV@!wTGNH7BC+4zuhKH(t8I8Tf1$+4
zyi02JAd%0WBY{yL<){_hxI>7w42`dlp=+oaLxLkJ`*zV4JP?7xzzI}IT$g=2^KZ-P
z72fVj^aI`$yjszbi*9K=B-frSNZXAkjm4MD3<{ae-7kN^d7C}-?auIGe_zOhqfaJ$
zp;PHMHZ>GMh}HR(&hVmgD#|F4V*l2A!Bv=W=EzM|&x#I5Nt4b3Zg`bwOcS9NzLt9-
zP;_ea`Yg|AJ)YA~+Id0Dh-qwA`KGOgG4i0m`eFf-1j7~^&8@X}GCG5jXj#E%Nsue!
z64y$H42T@8Mo&h07v`{A_Bl?0f>FdSCyV(V*huJ3n;8*TRS>G=TEssWtL}nfU9mH1
z<oh()Su1^J_Yx8kQC|>#m_%8=n`g0Dl<DE|v@yUeee9YOLSS=xC4ZmCO;z*JeH$Sp
zn|4+6bNh2o|1i;g!-n!SR1!!$htI9&W0d=lr#zpbTWJB+Y<+un!=!Z9boVK9B!l$@
z>33RM`v+!I@1ovb+YvDv5>65Wee&#6d4W%!Q|s#<K#Yoedv71`4|HI>O*3*rmKB0{
zx%5z(^6BowOM1Z+st<_*GvrvR>?Y5iKQGAl3=It>Wq$s)Kf(efrADlY%GtQY13_b`
z@?rKPg84F9XP%qiJlBp5+yRVlW|1K4ll|N9BVUQVD&jBmmkxWqwfX3auSkbu*kKNr
z-#D6bc;XJ{+;Vd}1@wzYw|m&KaxU#Z_+CAW@(86yV*Jdh$!<Ywd@6VHsd#zNdCmgu
z0OGT7UB7y7Jc~{e7)U-xg?j1Ib=*jLrHffpJ8x;nnw|5@-%<Rmnny`Of<qBrIZBZO
zp1FE-$>9o?S9t28dA;ngw&`#)t>(4psuxWdP6x~A_-^uEjGU+Qbut&f<K7W}^MyS_
zX9@p0yqouL2dOm%^W8&h`o%v1(Mf@k)<oA(1B91o5hk`5HX1dF<D&#d@2P|HQ+s=n
zw`xnG3lYib{?LfHReiBUqqF?+MWAVjo@FC#EU=34BiLuu=WlnKqiC&*4LA~#Pqf`N
zeeZwg>y*kVePPT*f@lrQH(ZX)*P5~7DB*nVDzJ}KCR!X}rdUiqb#4sSA4({n*2#D0
zawR>LUqmFk3a!sQHhDELLWx&oX1+;dSgAHk(9Wgs^MtxlyOSY0x-nyJY;k?pzc);f
zFI2b&=ks@J{F7%By>ZGL?iAgv^4QqlGZ^4xZ*bW6sG}X6&3fc|j1C^#PM5U=gTtl8
z#Bwj1Syqpfo6n@;ZwhSD9UpoqR7dap2B6HTr;O<K?B6xlj?H(;Ew;50j_y*vve%Z=
z#I>n?>&=U`Uo^m?E%*2=)A^N1jr?^2m$yhYB8mrT9FmZYJ^Cs`UuRV#syE|%zIzXa
z*oMoO*>Zt%S&VPq^u{6l$f27w6h0LuDAt2RibmFfnvap!1=|^1&FB9xqA@SI5woh#
ze`Gu&l+Q2cmfvTqbY}QzdpuIQdLKm8h8jR<LEre5Oo5D{GU~Qt;}-{HTPj+d9lc1e
zn~a^u_3DHWtKHZgC#uW!RY+*)Iq~zi{2b+F+<Sz!gF_yR)o%P}NJM9rdr6h#E_&j~
zhH_u{52~%p={nM<V=@qI4EzW(JBM?cQzbDy^9}<<yfBtLQ0ClqN_%}?L5VAqla9~p
z)ULi6RfPZScteX9ZFErb%k`DCw|u^1Xz2S)Rk%2DPKCl^xp@|jsW}?rkZazz>jZnQ
zyueP6yJF~F_d-6+gq*1^olT*-JFn&JJR_U3Kg!^7C(S7yLGz<H5+<gG(5E>Ien>-m
zi~TfID3RM9HI@N;Zrk}gwSNcEpof!%zOkJuG7$0s65qMyzx!e(6C(7hdT@1LM|`@>
znsGv7IqkH$XH88@;EpGl;5ypExmWDrBm(~dT~PjtBh3q+PnGu*sbkXqKc%B+5M-{h
zDNE8fZ(^8%Jt#Bj5a_U7rW(yQO)0ZPea2Xn-VC?i`fObUnZ-a`oERysVkfbVmsPR;
z;c^E{#hQTPdMj#VF6NekU#S7<oh#dpHvOw)4L#wC#^PiNB{t6Wu+UHwf3WMj7GJhf
zhHBidZ<UPZ1(o?oMPSy}1UAbb1$G}$U)XK5@z-O844Eu`V=TJxfQNoB?G2Y+X{O6E
zqS6(bmQ6T>)4j^v`7=p{c$d4vD!P$J0HoVS84gcQ$V&JyvdYA)2~nr9t5aj9u*IJ$
zGi8rsD%@bX)y#qQ%o_4@XegU)@xr6}sC^GhQy3lI>SG7Eo^<*TZa&n?cztMaA?eMV
zH^ez`^rIT~>DAV>oSgaqT#Z~$JuLa{%$!5YHf{^1M;=)sQUc&}E)2OB(CvlMDDGyx
z4#=LFFQ(NWo6i*YF0_ax69cWZL8JmVWLl27cekWT?`%9(8uI)xM#DPMPEva{Oiry7
zA3c!uZfZ^<Cd$|PW#T<)hl0Nm87uGgo<?Ny3Pah{P42Cxj)VmD3p_Yy6%JHe!QvXa
zWUGS;=nr;fzpvGMuDc4-lXIJ|2O;L^@IF71TNhdbsP+2!ihRb=Ab|IAw(r!jnWT`h
z(fWus`fezK?#-KVWbiD7x^C^;gisTUCR#>ag@&oQF>9jZg4?#SaI>D_`PJD%)zZk8
zmw6Dn`|=paJ%}x)wZ`GXe3l!2L@S4Qr3=!P>}xj7R3QlolQu9x{II$|bGgP)6%6A;
zL3OH(w%QAPAx83c6keTT<B^tbFSUkMDA#JKd;3(aYANKJ-<r<WJrnXPlH!77rTRAO
z#wW=k*R}W#Z;uf-O~0@7St9Sh!g9|3yiIqsr56oyYu4>$2{1uk!^`3e9e%*z6f@oQ
z%HQL`fizomwo%M_IZ(1TPhap%if=Pq*w0VTI!SX};A`=D+Z1cLo8|*wk#D<pAdy{_
z7lxbV)`E5LV#>)`qKTOhkQ^ozIH8Pgswv|?=v8jBxL+%r<Pc3MSl6oN2+QfHs4?gs
zi<0`Ij5zs+4c@6VRNt=-OcKj9lC;$Pn7?H3ey5`0v+!PrPbv!<Id4RUT-(epp(Y*2
zNk)OA`Ea~MwO6J+BA*;vf4tovvZCzpcLNg}oA?S3E?PPdHOs3I)n^I*I~*J-x{QR_
z2WKSh#w1#s-!D|^cZhrT@2KFIgVwJs_Ay+pkS69P+rjLEI`yQdLXI-SS1xYVO7+e=
z$)mr_!>(;ti2`4ovXzNYLoIucB2{ZHx1U|^`}*v&23Fb!{^s=ZZY;1-w2qb57c=X@
z#m3$NLJ-Y6=2n>HTqJBPUK6}NSsfe}RTpP8e55RR=REmQQz0GW$LMxm3Aem8pWk0+
zr+HZ0k!Ye9*rmGjcJZ39S=e=uh`agKd@r%J@%wcrdGw!jKIE|EX>IFsjx6Wc5aIv!
z5EsqAH{4`A<>bYmSU?&!pV&E78=B!HTyBD+^CJ`@?|Jp(?S|Wo#qN6?1=3G7Cy^nB
zi+7XO23L#N>GdQE)L}HSbz~avt9rF%_z>Z~_=;a!?Az4L1v{Kx_1C3OQO!}!F^%Ke
zp1#EXzMh7#a`JkiskT~<OMIQXJ+<`<mH|?m4hMXGamsrknT)fQ*|PTwbKh1g+&RN*
zy>Xj7^meNqGeZiNh1Q&O-AEKuBQ3Z(`cgzy$~Pfb+c}S~@kpPy)r_B6Mt6mE;c!(+
z0$jImg{ihou;W+xUI{&wdwrX6XYso$&>4td5LVtMgo+Qk{k0ex^2kFaA?!?TEqh0U
z6md)Hsh+Q*O=9G2i~@zXK8BN*e<h9`OmKtZfq8XcLc1Eh{6%k1Rdl`ZteAGp5?Id&
zc^jj?-<i1R({Rd7>+0{?Rg#h>xm@9Bk8G`TxJmPC3J~)bd-`xhP2rUW%)W<pqBUX#
z9RA?m%U2)=`Wq1s4*B<}U14>2a^H{#v*5~+Xu!ddc4@d&NB#2Waa?x)T~%ZSG8H#2
z*PQ(hTXe}|@3aqzH<wB&ww{RkErPCh&PV>`i8%%zt1#Bn4LC3FML!whI6j-@+O<ky
z9HGIH1BhSDS1w5mo>!zcu&0JdW`f!>Ws=Kn#c>A3Bh3TtbY^VP3_lh3G$e;iR#HL&
ziQ{=&04;g>$WdgCnVmc*Dx@AaMWjZtpU;*c;!$-VxlvMt5H|$h?Q+$6w~V|PlleUN
za@BjSV$v_LMfGa@sF-SE{NjYe<DPDG$sb-qO24mRptOu??adTHyJaOjZ6Jajz%DzX
zG?rmq^rsDua~J?E&Wz#o961^qk~V2|3!;3+pWC2(^M+p<-r)zELSe7od`U!=n~c#+
z(biyLW{|U8?OcXScHj0-hq}VsVf;rAq$NMD3uKYA*B|8n`s!sNS=jrpyyV+<TM$=V
z|9~rXaDVrnsoi`2)k$MI5m6Ve3{GBHr=1}Yl3!wALKG3%7`1+4vvZ|vmqYsxm8r2O
zA>n@y@9;V_xJawjL)xq-+_mj%Z`Kc-9>1Oj4OiL^8ICi<Sixdeg^5$?$04k_EtXgO
z$Nfk7t=~K3wc&Z$X1shm%;9iF939QYw-H5KvzTRtEZq(<-DYfOFakX~x;EJy<}l{6
zX-`aW%5cL{yP7unWP3j|ievnI+Je{5+a8#b{S&mU37FJsTCIgOY!~4bDI)l=7BuAI
zs}NExJ09=pY_wy%?&XxUINdDKCtgLx&NPR$HDVbZ%|tqO3l-vr8wKLB9-WxO4ltK$
z1^J<tRH9Hxah;@3jp3RdU3Q!fGd5jhB0Sw^#3~x{?OARJ;h~`$aA4$rUVZLiPPRT6
zA2T^PT&$b%I$#7#zDV&fUT#gslosn{yT;AtsG;4tk19Fal>Pk<1FEcdD{W);`HOJ$
z=;g&T=`^>QOss#AB|~E@@^&LQj9g#dbScndTv@<0%Ggt+Tb8@)D8<mLJ%C;(&4-&Y
zkQ7fwh2)`9umtl$T43^qh1D<e4H6&1i2M13Ak}}IkKcY|MMxAa&bs{a(?<Uu$ymfA
z;;jiC97cEk+2OZB2bVZbc06Ghs7Mj&;I68{z@bU$VEgDhERwxgmz8`5KJ?U`+=i|}
zFsN)`M%6AJHa0&w104BLN=-*0OlxoNtCaSErmWI?NE((w@JGKSFWVlN;YL+e2nVhs
zjvS;bC#4nsr7M1g>AuY?IcUzy%3C*P;lFn76~jA5cUQQ-=$R>#w!03>R90NAwg8|o
zVSV1V{@wD>0Q!*+y~n_E_%ibGldN?j6^>oNV++%Twj;OCW%+sUTp>5wNfZH`8%F&%
z+w2zE4$FhSgBra-BbP(Ps4mn-ImfL;wH0TEV}Qxd-@+ezsI|5WxWk(4byl$qPxqH1
zFGflY|4bB2i`>%3MJPauY6J=i4fWwcEJ9t~z!-Kchl-!>GO82(*JhOk-QC|%i$*NO
z4fYi{j8Q-dznOw(+-grDEb48x7eyn49xQ4qP;@%$@wh}>bEUx^$0-<-{b7V1jjh4I
z864V$>-KqKGCsXpFx_xHqY4qysbfs-1hGzL*;byXoy>*v5SqD+a*fa_tWN&CduBAC
zuoG>Zay(9^Tq$&M^DzV2K;~xsU>3dfo@n>Mve~XM&?Cve*Ln1CaRkRpK&MX7Q&Quq
za<8@=U~_?U-tD(j{|cdnZC;xJlKHX~x2xjx<>v7?fF}y~v=O&HH^$vmWv4P%y%a^2
zU(@yZ)=3;Xat&uLGnn@UPb-5Ooji&rWW6Rjq<}{cbZr$Dq0TC_tX(EEaH3S9wOURH
zLCF{X4RRXu{%N~9tF{=UUL-~5p!<<MKIfec-`-ZB*~_P;3nbF$!-&g=3;<6%)I}AJ
z)FKA8Y_=Fe4%4!@YD>vwJm~I;a6K!=phCWxglI{3TqM}P?CM-$uKBzi>g?^aM+qf3
z9M}xGlXdtVof#V1HwK<TALFQbFJ$BQoaM2;I(ur%|LjN+Qgpd^e9IE;7LtaP*|MCX
zARl7+?m+ez-*8I2^YzNl`YEPGY@wgRPw%v3SUYun{dJ;H4ouD~2afOMq<tBW+WwM7
z^TV+{+vP+v$zC<G;eDoRsoqJ~LjwDbMPXp8D2h&Ed;Oz5L?KWLR#H=Pibl!z;Rzy2
zaL7X{FU`v_HAS$d4s8$CFx!$J5i;xe?#o|^e0kg%$2yK<ZBORS5uFMs*84;@C2x{L
zbMNT&$z3N^NT_`%k^$(=bUe7*!f5@#RzKu9H?7h_R{sKCt;$b3?`aL&Bd4_65q)hb
zJx(V|t)XTf2Y$c2m%a3OFh}aizDj||ilHATer_eTu@w3G<SJ0*!Oc)4(=t7#<3HS)
zI$YW+{|S#Irf5bAKay$YIbgZ!l58&AyAL|<fnL_SCHn}cd1&42AvLa_i;2<jHCkgZ
zgBME90S_TY@;SXd9x-M+**({3ln_6@qA8gEj6bC<+E@v5#gDEam&6hroLk*vJ-FAr
zC2}j|S^kkt@wBW_p$LW$b!F3HnB4RLozb;D0CSgYahYikn%^~Ms_h`gI~B1jXRS4#
z4QV~limLsjai6Rxwe1s{N+3HPiZ7U!QVe-<DAQ_LVcD^jTeffh(l!wKIN~Mr0;oK<
z=_Lj7X3ao)@T$_bZN*i~6{aty_1wv*q#|CkvPSRjS{*jS^nZ?74BTpz>JRg8rv>(|
zXH`_hP~88OMvlAyjPIOo`<g=JSag{_9IVafss@WvTS<A<Bc;|?ji*>_>r3aI20H20
z*Czd~;?h*lki?s)#JgXuFb2io;yr*VR#>zR!<87cW|(mF8%GQG{8Gu*Y;<F;G>6jS
zp*cR@!w^Tu$2&j~OkV^sr^or2v{t1V#<adS^YiD=bs_f`a7;Qi5salxG4;;dr^!&C
z;QUO#lw?lx3a3rko=B3!VI3hSL5<GL2kwixjajx;#+~Rtde(`k1_5fVpN411nDe12
z7tXo$87faQIKrj??w7)r-UGRQ_GqC~6p-pgTKO(bzIxbrWVSt$UZ<<!QGYffs8wbN
zzc*cqyb^A3_a;j@R5F~vHOhG_*_^|u;~BZ{`54vBLP7$;>Dig3ogFRFkOQ=#M!=$A
z=fOLSNXYxo0|C3?L05Y9FL(Gs`Hr9ShQ0snxz6(O!E}`Qgzn25IQFNQeztBLAK?QO
zj@WyCbOisYO&H-&(b<_(Qc^O#blK-jVMG|VFmjCS0B+hSH|j)sMM5ID%xT;c5$DL#
z@b}G<&)IMbLqjdk9kJhS|KzdzDx_`yaqoUV#VC*xw^*y8(mrU=FCqEK<%lUqg8&h@
zAM*Epw(timo9ESPh#<uy!u~>iEH;%Y32tJ1ysKYnS=qOAL3cNO!DC@T_rv{@$~WIp
zzQ4~obCr`rQflQ(bHKfW4NY3-(zTh2BBu)9pICj=5{Ak4XqNUuP<wp-;XKgXX8~Sr
z)93%>o1<iT2o6vorxOJ`_LcfZ+rtV93Rc10PFr6YOAIgfXN4ou_?@>$XhcO@&k8Fl
zPLm+CaP|+UE61>UgU%u8aV*GpjXm8}$=bHU$LIZZu8W65@>gf^!0G%~xNeI5I1xQ=
z4{Du3)|%o}m`XZo|89G3PCHL2b7ZnW?HaK4$Dg*Go;tZ5%*8v|F7KV5o{G1aFT)z0
z!7*^0x2<(;v9q+~uc35+N*{M{7fdUrp_(tpJZK`<xb2jY2dcTnRA-DP1S}&xWoWEo
zK@6!6Zqy#ZP(}uohDN$ZqbM&gcxNJ?({W8=YdAHec(A@)El>VQoM;KcS6#Fff(cpm
zhmzQh>6w`JeV{Qka&?;tIsy>8leKR7WRBcksB7b5-QpqFQ4yj1#N0;t!NKI-$7YnJ
zwewzUpim$2Y7X_5HI<)l`@BHPbULqo#bX^CQc8WFzzKFV2LFBw`}q6sf&!ztD$5V*
z>Q5(1&A<RUk}jw=b?AFNXwb9i<hbZMU2eB1kl}l&Meg-CD{i(i)xIUU^=^|@=(r8B
z2%OF%-y?4(gO;>Zy9Nf5+W6i3=LM@r`#P&Pxy>&iz-XOS79E>Aw^>#KZPYJ+<&0e1
z+x(y!BnlKMd=A)T@`CPg!b-<=DIU9pK9EvWYpE^GR`Bx^VQszRaNZg+TI&i!f5jCF
zXv6a>kzcog&GEe1$-h2yZyf}cCS+34<inqzzqpacYWr!0f*GXQ_-Az15w9}pxjNfl
zT#d|D`D(Xveo6?+20r<{CYbs7c2u~C|7=XPh<^x9bX&^yY7X@Cpt;_QTK{=_;Syo(
zC9#)9e-}rL!5jL$^@T%TLH|Me?!f!_2642BY!23@<*kHzVzR<pO9d6c@-X-XK-+hC
zIe>v)S_ajU2Vi(ZIW7sry9<(u=gR_Gb@%R`^feWlKVmXav>y#;J8yLw(;SG?9EI!d
zq(xf<Ez+00xvXCT^_;V;0!P}XA^O7pNVvA{$H3f%N=Cq9Gf#f?2vo=A>Iok8`DerN
zeT!@vMGK(QMauW<7WjN%;Tc|RyinCI4gyAXx!~jdS#)5Ia7%0Sy~8hS!cIu<XE38(
zV?qQGqobaNF;IgeMOnl4IFix4KxV^L7J3z&Y3h4q(V1z42uZ%keewa{@QcBhwF;J1
zzi2C~G#vdq6mfv2S*-W(DAf7%aF^nFmg5!q<gh~6>g+k;-p^X1kp^O^H1Bf?Lubvs
zyZ?TXjsfnH)0Nq1%Ff>GF0+)T)wS8xwl9b;f9b7}5fXxq{&)O`{hM4ukV8_Jg};BV
zCzq5<vTJ!58Y6o1>U8Qc0wcS%onZU@o!}>V7fFWG3#a_jPUDMnXJihYDu8*=`Oy6D
zK~k%z*fOi0YT)=21?3}u5d#q_k6<AC?AZT|c~~C)VJpHQ4{&<&^Rcnt!j~<B^tsB!
zq@rb8U^(lrgM)5!L*>8DYSXXsxJl`f+`qKiu(bY}>rM{fVFzpBw4e;PPY3dmV-h&g
zmA6Pkb|H~@n0vw@rp|AFAoz3>m#98%GV;^Q%o}x@)?!D|9M@w#n1b{TpY4=4CPV4V
z&*IfjJ0#)dlEg*no7=W$@;)H3VTvu!GS3IvmBWsg#kE4YvO_sDyQusDBd8Pq$p~(I
zi^ID??PMe3rG782^M$!l{DZ(cpF%8%Mi|q7US7{>n4BIK!TdLpiJW@c^ptVQKrj?l
zeRa-eE01bztv%CR80O$jlt0`v`i|mHZcCs}Tiu<8HKDn_8`=|^ftP_V%VfatN+Nfd
z&?Ti=!s`WZf6S+94nn-o7~6R1tzDnQt}PdC28%dYEMWTiiCv0_H(HSv04dNt9Po(s
zKoGewa@T5y#%3PKg10vDSA*M!p&!S;H7w(K$^yNY0s>>MNP+j^Uqu*d5(nVRCPUd=
zBls}XBq@X8@?(@8kN>|vfe`Q0pG2v^fs=*Z&yMB~cHj-a6udHcG<7dM9C)A54Wj37
z!KdFzv*DgFN{lfc9FS}Yr-=Vof4tu31`m#93S3oHl}SCA3pT&%?ZeU%4CcY23MYhC
zTwDzIK|{mr-;ZQIT3a(-UtiC%P%)x&Caxc;$kN76PEFyUJYz$rn%{J>vb20cK(_NW
z*yu==dWx9?o8HKa5SC@|Ex2)czkS*)ess9ZrJ${&Q}F9oo7v#-ABPlpAvB)U$Sm;?
zv&IPW=Yj2MchT@0m(78PqKP12c0}fM``~=NM0tM;{gd44>%@8a@ATRJ&*^)RIyEjX
zcpo(bTVRtozU_3qBnHbIeEP<$_kFDQIr?#X#B7TICI=QX1^$1(sBP;D;5q^+b#;95
z=#ch6<g+J3I5#(EllX_O#%8v|by}}C6q~AVa4@@}Aq9+@Xu*|oFn-g8<#&vjr<neA
zdN23KkJs~?q|U8Z-K<0+K78OooFjaJ;HoRSd76tb0MR0!ifq4rMMLj2Z2$axmZbnX
zlrES!p(L9A_Vrs!O9oE!5tGhfj9HdKsL*L2RhrWPLpsK{eEH;#OmJDDO=3JgY|)(}
z*vMg!xz?mH4qhDon2*=@#s$>0@blQrw1u;_^yMj}UUd+*d^@PP>AJbtlc9|jx|}gC
z(y5Kgd^pb+y7`;!vn{yX?`G-y=2fIqH~vS5^+;Z`#yI1{S@X=znt8kRCGR8C&4Ks`
z3ek*kmWI%Vh6bBNx1peSls(ZDP*%T_@Tb+*_q|Zx8cOcev2X5P>kbd={-o=%8uaB|
zmQCUmT`(rufG=#Z_bWc5W{VI1b|yq5(ZG)%Tu(U&#@$_XU*0V5C0~zIQ|xzRKn!Q#
zH(l)r$}=J|<9aph#-MeQO>y5x%Kk~BX7I1t+14=U+1Xj8UJpCB=c$SFNE&~7DQ?&o
zEfp0bV6%C{)h~qG(nKMCHG_RA4GjXgYzE-UHJij3?x3KcEqlMSRGY*xb0B%cW7*QF
z;e@O_P{AXiv#nJ75jPpcU^6)9z6@b+4^Rx0B;fjwVm{~mritW#pmLT)oP+*VWW}d$
zG_#?kG0$PMN_OKO^d}t2q(SRjTwhQ8n(i@z4hJ>EbBp+0)=Pk6`rY__&A>bAI`z{q
zKpr;x=;5~f?IE}Gpr+_+c=&9CE8`17mLWG8t#V-d$^EX3T23PEO&am~e*_03;e<Ui
zH8o|J9vm2$sdqL{_#M-dAWsXf($V4DE2<>owbQK`+~KsIDr!H1t-CeWuLhGOi9$r^
zGCV%j4DRAWh0f3!KbDvrF4WJmn7#!*t^Z-unId{8-^MhM1;J%VFPZx>*x!@WL0041
z`RlpqmSBE=EPol;1H6@$Ro&HEc>UT}#w1~Hw}gL0y_)OYzw5G@kC0B4=nZm0g<=m>
zZzcgU)2z0_w{N}Y;a&2esx%v3yN4}9-L!faYHa80K#iy;Sj=M&p;;F5x8o`fh|0dc
z!r=BukqvLDDAKUOO@Y5Rmk09&MMWmF6{d)n>|zZt109{gD$5DJvms8;yOZAXS){|q
zqn+W>`}Jti5$4*3cWP=A>?JIMKga0xOA`t8JpzD3<EEd<ZB3w{puo?)ZcBu8whdLf
zhJAZs*RVrNz@pn{UE16OPHcsqqQ-C%dm!-K$i4T=Z4#a2nKjG&p?;T@GY6L=)?pF|
zoor>ZHWF{X#~t;zlg_fd9sdaUpJilZO8t0}<q=+nNpNVXX3B0qSX)^oiTDcO(kYrO
zHF+Wm+_jJ0&ry`>vJgJ~SAPsmdOKaRe1%Cr4tR&WeFD1PILj0tP(yC=aCgn5*TA*i
zNuk*aLq3HpM^8Q#=cjO6tCr|F4|7M52_`%V2^vHi1eN$MFX!#g03dd{oHbX|)cp5M
z*FtW2e-{=a@9!H3fUQd6v3&(p#rnhTQGQEH%bEA)=B8Frk-g1gL%gu}Wtr=q1`wt~
zjba4D$zLj0_YeC7iAJ@HZlGs%_|tffjC_VrGBSnFH)Z-R?rGiFgYl^Pni?K}MQ5&W
zvZF;l{rRnqhmWtKt{&e#5Y^1@ymfp&p;YOx`VJxB?RE<E?#>4|1{J!C<?g^OI4Mx_
z{rda&Kj4%-VN|UK7Y4}vo<XJF96KS(GfeHhdi!E;mST9}e`XiIF!he_{wW*=E<h@9
z-16oRctG*J-I7Bm#ugBZN<Hn&BwqUrRF~WM7oZ65&p~tR=>X#_%R!O{aKVPbC=;7b
z$+2`wJj-5t#WsnPbo$^{9Xm+6IS9!-O(WyM2b#*uI-h$H21H<rl*cA%$EkiOCI;v1
z^9vR>1HbD{ph>x{5M#-U|A$dP^cIVIGQl|t|9{!qeRTr^B~MR&QBcAuSjwzZgPu6p
z4P2QD&E(McW;;xV*xwPG&d243()eRd2NT%MhKOk8liD^D4MvpwPGfBnY2U>B3jp+n
zk~u?K?+@(dQ@Fyot*4HGSgg4<R_Y~$zVCU01TJG^W9@a0>oYaBB#atGD;u#&K(T>T
zY%VBxd!uJFR}}`_yO@4zof;dUoWKY9#Bagq64@%WN*P06P;rSk&Csz#u3iJRl2uj3
zHJ=+6YVrgp`rr@i)-m-xR-ZqdL_=0!xBN#<XMIpmZSYfamPJ8=+V~trQ^KS)S1$2%
zB@pWrjEsYhR6c7XbiEPV^l-EHWW#1zl)x`?A^rze{^!5{pFqq11LAq4aAi~3t)C(M
R3J1PqBo!q}-Wh)WKLA|(KK=jz

literal 84288
zcmcG$2{e~&`!;%;r%b67LZXzSgpkY;4Wu$<rZQ*FJf<`uLsA(kMVX6CiBchDNJ8eJ
z2$^MOA9v65{@42U{(pPz?_2v@>wVYrJpBCb`@XL8I?v-gj^n(qY9Ci;X5?Zd2!dJT
zu&NG0PzDkNMK?Vyej?e>-j9FCxE<1WJ9*)p+eLGiv&1oTHz)fGZuT}7{2pgrTx~8m
z?h}<1-Mfq5+Re?$RaQ*Q;lDm0dcozqSc~~&DZYfk>9Bz-L9m*W|EEY(NVOp-2tq?u
z>6GXFk&cVLr%r#T8}BM@v=e`tqIg(&Oo!&z?!Z8LdVS$*-IN=QL`u>v-UQREyCraq
zQHomC%`dl?F<3ZESXnrTdhM6CY44=jh?*Nk@fzX`LNQYvtVxpR9c5;Z`}~X<a8gw@
z+j!X=|I6a?wbTnF|83*tqC$P5YR{UAJ!@QJ%_;ct&x$>tPG6=8B!5(~M{m;xLj3Q)
z8lC+=@<F3@t*x!y(kx@IrfR(oJZZ9hpK!7B>b7Hd1N)>uB`NR`+WPuoS2?x|$jWl@
zRGR<#@_69)eRr>cgp1|QJkMXe5c@6@e|@Ya*K(u0ue5-G0Dk(?zV%DXX-dM0=SnCO
zr<7+`$=K_~CEdZDQnp`(Ki!o~&rN^xRmH|;hnwu?*GrQ%AF@6kdP7;x<;27Jt&Goq
z?m=$)&Rn}@(OpJ`Z0pyrziAxHG}?MT>HUGRS0OU4Kg~Ld-S5b{?<Bfc7RM^}O#MP>
zm^P3<+_=}`9gWQF<H^T$M!t=yT9ruwD_0L)-(Zg44R)6LSnB<{*6UI<UbQuXfA3y)
zuA8)_-ZO9aclmPsS@H7nx+7t;R$%K^a|tfz3g-RY@2+wvwKYu_tt?H|C7oOjSopQw
zcX_7Sv;UU5;E!N_vx(mKhmRc#HOy2?J)SV$bA|7+U;BmE^R#}8e?oQBL#wUc;u};}
zTI@Stc~uQBR4S~n=Q$iYM02h#@n%bl{-B%e8ZHU?vgJQ#O0$cex%#chFAQ#V`~7{r
zZ9Z3SZtfi!*KL}bn!zubX6M^pdF|V#m3SjBPr^FiFwe1ri&bu1v3}Od%}rwT)zoph
zKP{Hb#iKRM>Z)H`Vhh)0nwFVN&iag%ULkL2_*-Ul3@f+Pnu&=C&KI`%-oGBljn}KN
z_ViarTjS;|^|tdLIKch%=~Mj@kKN}!-ElSv_xhdRtSlO5IZ&GrHHly_8?1kzRM#_~
z(mcG^x%XZ2hGR)}bP<(Qv6?jDeWES%@gMq}q?4O$C&mI+x$ESnj#}!e2lq+KO}yh^
zwx#KpW|{i=xnelG%zs5hR|_k?Ps*`9Y<_Cs(b<~Yo`|J!{LW(W&+oyuf(x08hMBtL
zLd3?#dUglVC2I@kSXHlOWA|TOzNo6MPWSSMG)u*0=88O<M#0L$pLgx*U;8c6HV3p9
zIy*`8%nhYy3;tl*u&aEkP9ehJ#kmLS5xjFFFY3m-R#!L?UR$0Vjn^68TQ=9r)b3Ql
zJor6d<jL^_#uu$O5KHTK#xgF>j;Y(*+ncrh{Cuy%O1vkCjyIC$(dl&E!PXb&yDuoQ
z)n}V3@JKrmtOuvrEG#U>KX6~@p8nO?QgDIK5W$~3tPnSF;r+GscMe<>y}V{q@I+tb
z*8TfAvtKXlG|aOOI_UF9F;kcBCG+h3^{ZEvEGz^*+~mG;?(^NAI)&ADA7e#s-M)Rf
z%YTuX>*ltc-#ryq8nTQJ_|A4*+$iVi6q2?2T<NGjZcTk)*n=Th(W7Mf6;FCGr>XBP
zqP{wMyI9o(_Yk(0I`CVTu}^1s{N1~n&87i<+A@{S5*IpOO?f10<KP`jl3xx^?9==?
z;aT;=Mbbl(OK|)4D@6TuvDcoR;Wqh(bv4dJJ%^TDL${yU^x`%ipFblE#bqp4LcTOM
zKD{{B={~ypmPw3_SojfVN_qAQE8n*GqiR_8NPC|xSw%THk^L?*nFcS^bQ15~JHi;M
z>>{tkmS$S!A9MfyQIqU~Z+$QAn!~XiTfDlKaFE|k$t_PMt;X2ji;7x1`69PCLSf|&
zQNKOOcdf9<&Ycgdsty;=|GYQct`OtuwPGa7c}Pu7XLV&+gYA;y{6JE`abhsP*(BPY
zZ*-~vM=<&M#V)@%qW<26*PEvGudr;k9{T#!op;uylCpeHC?kR8b>Nb)W_$nPLzK?b
zl$Q=|U$`&4V$aK7{aonKCN=aell$qZorce?7}(j*o<HAsXRqbq^d=p9`(2-t6q4|9
zH6qv~PhoXw-&rfG$Odb{n`$b{i?gW-2QJd`2J~LtPOn&B_C5FwU%=9>ZO88)K6Xz^
z=&_DQb#=8wz{*lG;l42aE2Z(5@c#XAj)gt6Uw*l*<KR&Fd#vg7yu4(vJqb6|wjNSd
ztw)$!<3Y{o0)PDY;nw9hd&0#<d`GONrj}MJ9z4-ko{#<Ng`e8ZMIOJuNB-#HZLS-b
zYWVW_$WxUoEGarqg|}_HEG{lSh#(LWVDw@M*fW?NvYKcbusjoU_b%6@Y<B4%Lp<=k
z6niAfulaU*I_XVT;_=p#{nc(*HQBLEx8`VED>q-%R#v9Og>5@u$t?Hrtw#EqTb(`I
zfHYTaC4Nio(F2oG?*_ynyXULq<UlL&O}mW>_YBU($H!9#-rjviH``~3b;Z<o;t;v4
zj~_o~ubavXSoL=)GMa9&j5yJpWn6775v7~{7{~d`Y*zrE+X`Z#`1i}UDziP*CVFM7
zD?U}tO*jxXZ!a<64i7)pIZ@!)G5DfRKH9#|qr|4?ZRppBhnRGpo?5ql{h?;#i<|wB
zvL!qFw8wH|Vq(hskZ!qeZrmHZhL-A=n=F@{=bqo4rDt5$tlITo;qy;Mb<xHaBecGG
z)-JR^8lp%pdyUY3a?i2&D*oosp+gO&vz<p+u57jL@|ybDkecML`Ma%PJGS=s_xwkz
zi)E|P_HhXb$6SAYdZ3$Wc*`*JNm`olU1{fDuf>LWWZhdww>|p!QBz|cH+_T{@>Q4~
zYzXn59%Mfy?>py&J&lW*&u(=rpzDZuW-rCPS>E@~wd>c1GgjL!lpQ044;(n7^V)ab
zg;EjQ7niaW!Ya>C<XKjRb43^!8fp@Q53cYsdS~DS#N4?fykjb0pwKAGFu<tYt?}48
zK0dzbP0~Jp?7!IN_q)m3wHIs~m|VjBM-cUsKWajF8;57UrfTtN7!=l-3ZHusPf*j;
zsj6Zt6tE<k!Sy2SOhm=Hoy17PBf8Ib_J-WvWuRPhd-o%32?ybgr50*?oRXq_eZ%f5
zEAfU$5%QWMdRZyBKy;JsV^ps%))Lc$k0QR1!Fj$e@oSOmKr%57c(4@<Sn4zTB+k@7
zp8V1TS$6^a2svkqz(XoJmio{8yN(jp)lovIezJxIj>egM#B94O>rF1nYl(h;3=H#a
z*MGh*6OE`lc3=9$^A6-=jZEE>s42g~4~!<S7tyW1#K1uxC^)#DI%r{bOiQk=vAxLE
zruF$*3D0pe#Q8~misaAd9vx4RJ%PHqJZ!r9FjhqG3yxn{pL8)GOxWQyI^QzSsPEmo
z_v_;$apms3a-Nao4+!g)9P^+_r-<SGmLJwoDsEK|Pr(Pi91$y3-QVt{!ar_M;J6w2
zWT-jYw40~I<F~-WZekpf#AJJue{Gp+`l_b?&-nAW&Rsb#@zRM8+|&KhS~^FMu93XZ
zdH-Wgja}&lAQWurVZ+SZKkJgT$aSsc^||r=r2}_M0ghX5UthJ^p8B%Y<qO_FV)fEo
z2WpX09dJ~gGTwyHoy0|hYbWrnQ3$}`;9&2LlL15v3XA5EBag^5LN2?EjLaz`qoWKA
z4AXT1E77_c`byV1#h&09qm>^dBfrsw_e{;FA^QtPR}YSsj6Fe8Nx00+y}>j4l%Mft
ztOp=qnsJH8G-^XViu!T<(f05@!RGnwfaUAb;g3*|8<3PW#LAY$JA9`;PXk^xdnU2+
zNGl$>y=x2MHuZUbW3l`2V561-^L@S4#uG?72bZTC$q$~iwcUwjTtLF7svIv<`<`#7
zPDQ&W87QL3yVhxp{lsSqv&!(zHlOb7on9EuZgeQ<K%zF3CbWTMsAf!y-F6`H+(M3t
zii#@tNb;Rza35`t=M7kLHZ1j)BsX0kx>|L|jvZDnS_SJ2oSmKZSKA#gnw33JxuVnd
z(jjEDqM*zF2w@FWCD|#GRHwc(_S(-+@f_1F0V`8{l#1tG7`S|vO$e6`-%5Oa>2St-
zb$K@A&&=xJnpF3h+5=-LgM)*SKV%a`h0~<{mprS!Wj?FDFQY;XPE_)-dS?KuPzO#<
zO;Mf2J)|^PAKn&C<Dat2xLCrvYr}5C`l?%7Lq0ZV7~l_3Ak$_HRb+rQkUFUYJN*}b
zCs$fnT53FMI2_#MlvJi2&U+9k0oa+@+kSarhB}Z0vpM1JMHUohca&)Y9~3+96BifW
zz5AHX?Wiba!WvH)HAx;qq8=HW@+`{z_dJ`e3<Ql*GJc?=rgpi%qQbF&+z4iFsa@K-
zU83FE&ExH^b?lYSG11W+!uRC;WGRwU6TLN`|CVev+G_Lu`}4C*6v^+ebJBf$Wg<2+
z+$@$sjQ>ha+Lmx!9nTvZqp&de2mvKn#%nk9lfG#;+o&^{>nRmyJAE$HJEwOp-YpJH
zrwariL_HfM@tA#q=Y&P|!P?qd5_TZrMA^?GildA~S$AeT;-244`6kDTpDG=wOXB<b
z#Vl7}SW+_f?*0qBb7sc*wx_FD%TO&Fy5EFQ1zhpbTBb;DuuggSP@wvyN%sCUk-}H6
z?&Cy}se?Yo;d|ay;_&NtD74JHa?*qDl2%o0LoK<FQf8FdsD~O;S@Z2%#kId7o8=T&
zKTH4q(4eW%x$p4PQ#!i3y2&G-;`dqY*_alvx)LP5YL{}M>$SVbB70~+M3^lLfoK(J
zbsTLkq7G!?k>+pJdwS{%@FLY&Iu;&!+ZpDW_*|b^M@q%9(pi(_PXS49=k;bP9g)_A
zviFshtt?z8>VX*T3Qt<(@C><L8ywqE`03LpMS~=HKWj8=)7bomYwJbQfCZEkY_AS+
z5`TWT?N2+Kba;e0ZKXp|a3zvbF+zU9`ex<yyzjbAn>17|uelWy7ZVeCJasFPhqJCY
z5A-t_%4w3k`^?MwGZ#wN&n@}Q^;{8J9Cj;CZf0X|2v}Y0itIAzk!~J{Kn{~}eUj4N
zwMZyVS_-xgh^-5-RG+C6dss%*fS4_;?K7%v+12f;?cQyD_N)+^oIPgc#C>UJO6+fx
z!^x8;Z{51JWd}l$8;5j{$4HA}ykRDb=H~Imv9dmNnuR}ZpV2lnyasSU7WH#4%&Q`|
z%;EfOp?@o58uIy^iiQRQ@D-K7+m+?TkeAu=*E0Y-qgvff{pW=MOUd&p99?ZyIFRV`
z_E{Y;X0ZSA!W%1bnyeQoDb!f1(7J&N+^c@qk&I^N{SM#PZ9Hshtg_90<4v_2JThy!
zrR<5*@fT)r&A#t;*@s@7`%Hwr_)4u*o}v*gIAixEg#nGgRTREvPZlM%oU=6>Zfx4O
zt>6AsKxx_1<dNx-)|2A&XR@0u|BSR&)z*GMb|Hwq%7`mQqLr0=0X!|Eh)ij>K{m8~
zJi_T?u9D_&sjC&;6@49qV?UO=$Hd0I8J>CQGdmg;A4aAn<p+_k_IoB)zVez9tP~Lu
zsX)<=(1_ml#v+$`=oA*~#*H;R2RzoGiV%Uo(n~YVrqUiG`Zs5#jO3G80R|<>CL}K}
zPtgkr*RklwhLQFn4rH_Ebr}XP_@k?b<U9&z)1;clqF0yo4|cq-f1q+JF_Cw!<Yemc
zJzO`VW<8tg)<jf>eEFi2XWc-FJ&anAu=zsW<vVpue^vJ4C{IgWkCWnK9U9}1fbp`5
zp0o+s<%rOdRV{_(ON*l=tN=0Z6;>u#N7@QlH_N(}m#xf*RePZLQ?rLk7Cp(!TemHG
zhgAocx3OGZUslQJTOj-^X#Kkp5@hRzHkG=dZFAl#%GJbp1#91BZpZS?7u$nYm&XFC
zs;bIGvR?(ax3^E}q~5(>v)9tXVvo%i!B@Dn=lqX2bhQj4ExA<D+qEt$KcN4v&_0k|
z%ql-eTqvE{z>7XKlEZkZ&NMB)Qg1C<=5CiiC{R6Dw<ng5c9fVGv<XF=*40Jv6Bxf}
zYinD-f>L@|ON*6p7un*>x2@7h`YlXz4eJ;fY?@R_6$NMTwl6{<Jo8M!!&k552Jf7n
zPwm$`D;_Vn_@lp{f6EpM?^2WJ#;7tC&+2k5E5{KP)CrZ@0FxPg64QkS1_p<Z9#uVc
z>QqQA$49h#xrnim&Qfk%MM+n80}lE2YAw%^mc48zZoU{@;`4v7j+3*(Ug{B|JOA6(
z?c3=<elYI5fZ*w#VA(9ozik^WPW~Gd^YQ6MEh;uD-6ErKWTGQOMo9xZzxoi0-4*it
z_Hpn=Z14ZG(j2Rs9y@YoxzuH8&Y6v!*JmV`>!b`&_m&+y7_<(~tRuUcJliHlawo&s
zx9)KtI)!FpN56mm4pGs|9geK?vbgqc9GPE@sY3fW?27pwTyl-DJ-O^2o0!OwI1r)}
zHWv_i>h$u#Q737#z?cJJSJl)+FXcR4h<~7)zSnKAK4(ZTR_#$x%@4+%7iANID(%`h
z6+>qAcE%QBTdCft8t>D!)0s&;m3c;Gn1&$l3+X1ubD~%AZZtrvB{G42k;~co^Ny!^
zDrvr0E+cLV(QworNl=z)w#+ijXJolTg)rdf^&M9t%7G^?Pfxpn(<%Bkyk=m&E!UFn
zT<!g<I9=Fi;eO|(x~|y7>L8jxW*pJbEh?9j(fN4-kfDt`DIBZmi%`>lZl$4p-AJYL
zu>Xo>>0IyiLEzyk2V0+$U*8Q(nFC7-;{b1skPDV__^z1RxJ68ib>F#9^u?E*3`J^V
z`RO-G*>B|apX-T?D6nlBijsAd{%~UxRi^HBl;mWsBzb%T6>`C`{T+f&`6`{~%FvfY
z&K7N{8!NgaewNX(Y}tcsxp7%0Df^Z{+$DB@Th1}RF`Km6@`ah<y@0Vdxc9S=1V?Xg
zFHS$X0b4{w(mQAVl%3=A9e<O&;i5u7eM?zD0D8b4G&2=|f;T1YzL^1}V@-uXOx1cP
zmArntd{nw?mOgF%dgc@4h`^tTKBI2{-1Y{n_)d>@9Ee=w^D$;eRNb1Sx+}O=&JYJU
zKkFYy4<Dw_`=T40l*CFP(lchHcgF6_aqyd^3w(0y9wpnEXl_uFvV)Zp{3gwab|uxQ
zMLx&U7)_yX83rL>{dxu)9)(3jsHf{@I@Rnh2ppJC=2K}3EzY`h>5^-)-h=x!Q*;D7
zJ3C1YIUzEw&Y#~ZBt!$)HHeB17{$J6lfm#x{=ooL;&SAeh6gHi_U(o2E?$FdRjW&<
zmtU2v%_VYz<X4wm3ynn4JfEISFM63(Jfx$e;{?Jine`G<`jMAY7pphk{2&`}qIrO`
zhU6q4sYmp7T<nVNKk-cKZ7uonas1~S+kDO7+vpuG<p+3s%Vy}>g&4BbRysE}Hcm9@
znLfzK$oQsPzR-R?;rNw?N9ujiTJmqml!iLB5#TP`m+eVvYDie*-MfZkW)f0T8`K`L
z)3NeqxR;<xM%JmEJ9n<zxy-rmLl2TCqZ<|9LGN`WUo&!1J~90!_kMxhyP5Cpzi_*~
zamaWa%h2-L@4$#hNy_@11)uj_%g?lh-#*hjb!r_MKJCDoIfj{oU1bUzM2+;vh&{NT
zVdl#Zi#1ce-PfPKep{-ZFXJ(CCnbM;ZlaHk`hilB@_ulBIP85Oj96l15^9rEic__c
zSV}@&0B1HzJMk0AN8&`u%YHhy?}nPC**!32)V$dYbyI^axm*8?c5qL(Bp&pMynUNC
z<c+qS@lr>JWWIg#V;r#-94~FJI8oyq@MwyOMbBm?6?ntv=H?Vf4G>tM{V0&{o=vG+
zqzw-t?tv;Ol-SCr0wU<!UYuvmGjs^?-<VW0rFR;&oU$tuG5EKp9KEAG;4gVF0Qna5
zGBpBp9Jl|?yX<(P97Fe5=Pi--+*B=!E2bZtN)$l2asSy1t|c-q?g}zH?Z^Aaw|Aoc
zReOM0QNgB~c>fa8&-|9$cWOB{v+)<fpAoC5Y)r-8gAHb0<a)*}X}b;>8I2da@YKAo
z{!4oU{Y9<25)365$5=Fz8N-_UXS}&jWjjS203cvvPxPIvPX7I@b9=&Zz3e7kk|f@D
zzSivXT}f%D?m(+uoB6{_0CYSjtG9;~M}*O%`H|$kH{eAcZbYJ2m6-hMgxXE6P#_~3
z@)~mIUbN<T&)9+~G@5)2I7p^8`<5IEKEG*p94>%_?J}eDo>|JJw6i0vTc2*_f2}9S
z8_{37g`V=Zn5k&1sHi9liuSu_r_+^R+Mn{vK2?_LUCO)LFps{~Z+)%%8a{cRB)>oL
zDQEI?a@decC>H+w7MT~%7|RVR9y{UitZVz~qH}-ME#_xhGtGm~&(?HL%?qAo-b@7&
zMRm?sKEtF`R{N(*QI5xN!|9=>BWn*QK@AwEdNQ;EJe=NN>eOGw?u)i7N_SQ}GkU{y
z8bsA0^wQ`mHwYKB{jU1-X^q`c{jtu{6DB6Sd6QMl``d|7<)HnYo?ErmRLSH8;8~B@
zRP^%q_t(A{AS_loy%t@W5JOVU$B)5@Ou#wlHh483uMMGPW{lCgFU=kr8rt&8%QZOz
z2$Jk8k&-!Fc9|O2&df8Ng=94DU0S%hvPgAy#%qO<k#P^$o^iPdaKg`OyZyUf`-O#t
zNvB$M$5DNwY*DPZ!g@vL={w+VC$Lz^480&gRE>;`s>BktM~jC=U#%<*7lIFU1|PTu
zXkn~u)!!^9W3^enZc6{fIcBwRo~y`;eSg|6sO#vgot&J^@mur&eDz(KA0XH~8-UnX
zKg8@f33h!CIDbVm{nPiP93m4F!|C{Z*#HxP>BvJ>HG19Y=B9nLB$>wRH+=$W@)7!b
z@=@zO-`<s*u2*R(b{A2L;KirTC7&s9%xSfs1NENk+^2>Q?@u$3Z|`G))B&9M-Y&cJ
zGH{;xyD)Y(YKj9MBjw3TA(VEHJ%+zAgOc5C;`L*_;I?f7l9H1BbFyV89;v-C+tdAq
zj_=aZZA=S7_i%gX6TN7WwiG=)q!5gBA#VZ!*?IxS-jB4tpz1SM_WIL$egg2;vOJLD
z&Osk3P$buvmV9wiwzm|yZimD(0S>CSf>q%ix@l)LRsuVBGJ@X`#hJc}T%ut}5PPjl
z0Ru>8pMUGtt!cwQeC70zwm*P(cka8nzZc{}CCK(%mmivWcFo5pEzd)JfIhGtP?3U1
z!Jh|B;~M}`st&o+XFlLygoD(4h)2I8X}96amoKEM)=_eilm$>;2m)V1o*Grqnb5pK
z!G&4$eYnYN!a5s&K9ONpljOxwBk=SY03@PuOdw?(>-P==F1b$jYuwm;V8d3m8zd0T
z^<VaGcd6MORh}+CLy}Gar1c<qNMwlh&9n+-PVTWJd}fA1L1{9O>v-Y>J1I&4OmjjB
z^c>DC5NtbsPl}lg5J8Gx;K(L^-n;Pkfk8IX<DBT3c+o&hic++hpZpP2eG=J5#ou2Z
z{DZ@NS$9TYRwg$Ms&X(q!Ac>tq4^&_VmDZUCNGpNZ+LOOjwNRM_U)D{OY@!+?>T!W
zJnJ8+v)Z>lry;`uEuPpff72W@rS8&S7akjz`5y!bx`aMXCA+x~9O!!hR3|7#n(|sN
z9Xqa((ZI3&xGMOFj~_pdBT~B2Ie8)k8qkWBzr^p}K^M4=zuXKJfH8x*cWOS^pSteO
z-mPiJIeX`Qa2}k;yOoehm|Ti@&b+jLLjoD{dk=a~Th~wRw|GZSHm9r#OM5&<+jkfo
z+$k^53;ikj>oAC{H#nnNb9)1KF(197{5Yr;XTg2w>(#Hek|cF!;@K0bxVHY8jlONo
zM}M>!#W=U%XGoU$;CMMzPz>j3*DLJozq`tkK1A?oH8eDkjJ=RhD7qO{ob3dw5^&6S
zk&nU#=4DU5<&kv@yUem#)4Mbg%9Ay)PS97@(<klbIF$4TwDz4i4O#VAf~;gDy*DV$
z8BJ=3TzzX~pTD&D&R9}pK!@-k&cs_IN-RU(#%##dD7*QH*y}lge4ED0$na1^zTjNg
zASDGki0|9?6ubq9DEfTC;H;r<XD$d45&+&@*?x^rj(sh-U<&C1LPi?S4Qhs*tV{pl
zgxTGYl7Q0GpktDcvwQbVoEuJYb~h_4!KY2OL#;1T1=-zBpL-$W<Tu=$&02I!dN%J#
z-VRj@@7w2-{B8pgf!l8YeWU`tfCgfSag<s7;iK%%KyuV;m9Tpu{t#p-6;YOMS+!+k
z?7A!Ez$Iz(<<dGv8X+N}27o^s9H1bFM6aKlaJ}syu%jSRAeibO9=a}=9ja|xl87U5
z6Xp7Fc5^f+K?Ey}i|WF>>DC82e=g}H`CQ`qQcf;7Dd5nC1RdSR(cw4SA-OF$OH@ec
zP}F1^SRxV)UAua9OR0(0rcIk-;^Q~vbreB{obC=`)rsDAjMOM1cn>N+5!|%LOtF4|
zmAwHN$foz*RVxWDjqK(|-03f%%P5D}D+_j9Qug}Yl54@jpaQTi$O%0&<=QCl5Dg&h
zWBV;@8;j<Wj*wadTIBi8$<&T#3o_5o*YOSkhs7V=2Hggf`guciG}<O_kVC#8IE8oY
zxQcE4HRC<hRv_lklC#Ci7)8t)t<Sp6n~#A{rd_j<Z8Bd5fCBuy`%gh3*@A&F=zz(<
zT2XJz_%n5nG@JN1R24T5%&Y9)5r)t6xBeOLsh|#gz5J(5HhapY@*CxA>fOf0>qxD@
z#ntszOw8AoCi6Q-zJWD#Lc+8F|5}K`#Z@93cOR~R-s<g1s?N?6>lKohb)H*RvaI2e
zW`k11Y@#)*!@<WFVbl1e=RK#X{m3&T?WuvfN}${@s2au-y94dlm~Bl@Ot1RoX3_NQ
zv~}xqA*Pj$Pp%D(tzT`?dDIY=Wn3bqleux5ewqI=%Ivu7&_Y=ZNqAseNco<tWGCk-
zoD;H9z&ZbUjdg{PqRnZz$)~_e<}#p2Z3Ba=AS$<_-z6)nrQS_*(H*4Tz{5i4Jv+)A
zR}=dA-hNB)u4JmI+wpmAg%SPgY`|)B%W3Hg-@!)2R4r*@eK-Fp{4PSlL~t9ZsnL=b
zBy}wiF6d<|)cO3@NgRnW&+-VNWBpJgR(1o&kn(sc2e48(a1WQ9O^Er%pSB-9{Feo!
zWuxL>2g#lEHB7{&f6OG}y9g`MYa=7}@LOE$^cr}zxe<5)RI@6=4|WX51+0oje&z_d
zu<&r&N@x9Iw_jcd&Ku*io1J*J8!!hKO_JOY8FCpRB_Pv3WUVFkZt*a_dd;pa?q;f7
zzy>js8#p@DnNMqtisg7&aoTnoWd6|n@ZZ}ua!hJsjI&5x22J3BJNwSFpcGztdNP%g
z7{zk>&NS)i=a^BDKV;vkW)$gay3=&w)BF8?3vNr`_Vx3u*~#CbpzZgF%@Rh%i;$b7
zU(3PqpuxI6T`xPQS$GvB1vsXlckfiwPVIztZHebovO61h`*ttlMiq)FJ2&@rP+dko
zzw?7;vg<-y?LRPyy$V$4^%ArWO(2;UyXGwI^d@A{O^KepSG{*T$D?UK<5)xk-oM^j
zf()${kVkY51?CmB<oAT#xS<Mh2qz$Lb!E0psf{5x5XAOYNlDVS0oCd)S`Gcx>sdcD
z3p$m-t$?R!z*gqOQOzv=F6aWAsjqtIkWzZnL+~)3a9IQoy#|JG`}KteAQ@G4ql}9n
znCa8QxtzNVsl)p~v(af3DL*JzSe@4b$3TBP^@Yhy=`Kk}-k~GSfV6_EsE80bJ~A;f
za+N$=ix{#<NDq*pT^(ZR$)H}FMXIvy&BmOkc=`CW3Oc=qy$)yzLxAXiMrV>8$HSto
z?cx9PBPc8eVi`5xapW1b(m~yH`uq!Nv5H(3z=(%M;~G)>52KZS2p>(fk!b4X10G+C
z#?XRl0DV0n^&<qg3Y;HnNPPu~wku%uU`_{qahx$Um?VP?p}mx%`bk*V^Mi3#SecV)
z3IJDtG#H|LW`43p>}sDhDamJ=l-?aJ8sP2qN?e(U2)N|l4h`v~sw#Cp3<HU1TD&0<
z{mQN6eFVUH_$79wnVv9)9GYkGo{^N6NkxaKNBt3OgQw;Zbs$>8Lx`Fu(4!KGdhAe3
z_pm8YK1DJE$F};RrgR(BO6ov-_k7NuRrp6p>OR|~bTC%W<Uv!qUL&{|WpE*pRwWR5
zlBfe6OMXj{ieu^cTc()Xx7A#V?t{qXm~27Munp-F`VW}{rahtLLooLSs2U3BiG!Ex
z@{X@MPRv5|l&!!2Ou1wXWj!JGkYHEGiKo(dT<rN>O2utxr_`UTcKkq4r+#?c&GpeX
zKQXS)h=HOT2|0K)yP_u<9MlltV@SAxwzeX!j?9{+*b18#DG)zT;HM`wO~~K_)Azx;
zUB&&rOrAtooj@9{?wg?gNQN~>rL)`Ic#mPkFKBuQ$FEo;*)jz%*BXF6kxOiu85x48
zYe22cF+YC(i~`9lHx|sVjl3!>BNPA1<U&HTVM2#vDm1O+x}Q)6V&N<Si{Dfn0A~Mv
zWo6^d{hf#9ld?bjM$)j6;DQkTFl7mj0qVd}$e)pm#}eh@X<2!XLdB!sM66}M$$RiF
z2q-<fgW1#>OmjRL#w8n&9o3ACcuwtH58CJolw4|uX0#tScn@-!`pv8_M%#K*Ed>4?
z3c|(3g*;*~SG{}xo~Bq##a(Qy^#vW!u_6K}&u}++a+CZQc8t%x4P}~u9#28&oIFX@
z@iqUZI!Wd^7XR8NVfSqfY(PLX@0Vx0`0|F{g1k>|9=?UH9`uP)cJo%S9V98}E1zW5
zcI_G?<VGq`NyrZGpgRvY2^pU_5k%UXP{p*Lp0aLuNJApQa{ap`5y4YQTCe6vt5o6f
z)wu%#Zy~-@k;V-zHtJUkL+K>j0RRaO3XV4Vk&Y71KxsMX96ex(nD1Mnvm}Bm35a`K
zHa0d}vR^=>Q8K;)Af=A>0YVnpLF_pBg!CktcNBnL{&ZhvU;Rw%-MiP|a47%nLHAO|
zxtAKx`b4qdBjB;fuMInQ?Yac<%#fa!2<MTZ4DTC9i_<)uxKJ7Jdiu)|)QlZ4{yflm
z3TA6dVTYsK%3R+dTry}dk{f@Kjwt)C*9QleaQsPg9_X8HclYk{ph(Lj`}XaFX-t*O
zdzreApjn_{Ot<1j4^6szpebE~i4Iv6Jir+{y*_Nrt*9tU!V_KwlrK`V^YleJCo>}~
zG1J}$Nlz8n3xmJD4QNk%KWh`N7Zyt6z%>8$ZD`NHA_g=;TKe!O4<9`u`{bsU_xWJS
zfnF)Fy}(eF@bL&n4D|P#L4ULED3+AqVkSS^cx-<{fpa$Zen+&O6=Yj^7TF!d$~#n~
z3V(%D>p3~;GId4fc{Gn5W0s5ahcS{gpA{K_qPWzb`{~nBG(yj6T@&QI)&?w1&;haU
z+@SzD2SocNn8;K_R}avPp|w&{Z7FoN%&c6-Vv}76X_!Od$!YtolvL_}?!_h)qA0k+
z?4H|xnXZ#xT*Dhd2W3CDoisx7qj>~plQrd?-UiYPLcUNXbs%ghZ_d349l4w}gq*Et
zZoUP&F?HgA`HNQN@JG|0azH`gMdM;B-@gwUn2)UP!5ylD*vzeU?jdb;wFw7KK(`$B
zh&ynbet{=LKbMkRD`3<uaWxYt&z4v%bnmeAMcey;YiWm!UpSmqvd8Y*F4CMRb&r`m
zvS<n65RfM|Ah0}2RjhvirFg+_l1=XTI}R;SvWi48upi_w4lt#aU^ORTq_jX)2+CKm
zw6wH)by-V(p4y{iGyF742oeAJADp|+yj)XMR5S-KgJ9c&ue_C&RkB{Ti6?1mM2YPM
zKc@z&4fvLd;1?h*eq`HEmXc(LwwH6j4SUf7<%~Y<aI6fZpdz`tlF?!&AioMA#~iaa
zl>Gw(me5Y2hVLd}2zpXJ7`I3-B0)48xgImy)tzHru?H=__vYpcxDaV!+=I@i9llub
zJXk6^|CRakZeyLBNd<>=)FO4$zD~grQZcE+>EG+{U1ABF4)m{{EC)3PR<ajX#-B;z
z8l|lQ%`4gEJ}fNU2!o9Z!ap}))nAn~CUwCt5Q49J`0ybmx@k3%w!t%i!F}skpz`$8
zL(pn?z#u#xQtb7?L2FXu+IYZY7iqdAU2Y`I*keYJ-2e7OPi2>Il0p`bU$ei%?PTfL
z4*nH07uf`oIzh_CRm`OB<(~V@MQ$_6UhLYy1Q4+s%iayAfD+I$5vD{+#N&XlX1F=)
z#wX13iz6>0v(rJyF_Fv>`qy!gF3PEm<_NKk`yF}6uAaP?z>Pcy0jz@tzSv7yf|8pf
zQ4$Ep$XTzYxd}3(A_qs1Hoiw{H-k;GS??D>%LO^*2?hEAtf96|JIUUGq$>ao#j113
zWB`LLnV>*LNy-=WbUyli6dWoLd!=jL!7E4|o8Wolg0E(x(`QWMWgFr{>O$uY2>5S7
zHz6Tbz$-+G0<fD>5&p|FdL!TSnNB`Ass!sDOwYMM8stU6C<N=KGs~Ut@efi{RaJbT
zO|XRw!uR#YT0-;XFSMORHvqpnTIFH6b1?s+3WEE~ar>on=kvKWo5WLUm+{{OzxD5a
z<RS|&R_@`8e<BppU(5A?^Pu1#R5yh`G%7lpDx$d`BnAxuv`fYnDDx07gs87!;4P_6
zpx`)wNeV+4SisY?ao@Q$AfM5FU54AN9qdk&EF5on4sE(FqN}ByWdRC*({1Uq^k1?i
z?!&Z+17v`;d$c*^o1`{auaS`Afj%Icz(Ue?Q9?(Ob<-*M_ef16mX)wg4M_M9AG=}W
zxLj=&wG>Fa8_V_?EuupQvRxJ>cv#X6oSy0>aE0cuxXl!=)M)leDVoSDyHi}Ty<AsS
zl?+3ehJmIgBL#UbH9mrZK1-<x0)`<*hKI!#K}3;^#KUo`MX>An*&`pTsv;$Rwf*)$
z&V7t1qWL(~M#D421vyEkS2`S(pYkkB-`k1luP3!MUY5caoCY>BsI0)g70wpewS&aL
zbU-O#N@tq;Z|o<7uz&?UC$$pea{7kY^?yw$i(DnF&;I&P(Cdj5t^TiBP~^zGF8R#|
zTo<*Xc=$()Tx}3lAco}XJDwwO;o@6??ki1t2Vv5pTVa_^Tqs$+skRmRgT~7l?@}e>
zH*kHzxu<op-B~@^9+3nyrKm9%DGvbTbtgABMQ%|^14e_RPiU5u|GuK(P&5G2Q+e$_
z)LWJ~WLyp^gtXW}#Rz(iIu`lHEE(ZN_i-uv7I?|aP47319u#04rSvDAtAQzP5n<N<
zy(I-^Lr`^!25EISfIngS1AR()c4cv0t-|X0&Fm-s8)J$NIxQ9+Q&1fwcs49qcQp*9
z3M#Ou)hr*u1y&?sU>SBZ6{5-__wdK%q|c`nDwbf0R3cf2%u2KxyC{;8#)@aYogqmb
z(k*%mJ&3&D0<a|`6O#@6A5lI$;I`m$i{hEWqO&f%77%#eYEK;q(z-!5yPz0#5#CFt
zd3{iC$Ku5we_KA_KBNlz1E{bAX#qaJZN)Hzz_|MwrkqEp1)5p=KwW_Y3khGuccKST
z?cSI6QnVXO$VPbggmS(u>aW=_Oes%y`raM5E>7s^1XEDalCuM885!_0193q=(P+it
zj*N-9MDjQvf$Y$b2(^wPGgRUyq&kVSunx2pxSF8P=PIzF<1hzENjE7r*&&DF$4c<?
zD5`D=xV1tWZ<ji7{515ck$)MAjCiftsfqZbmvQ{IXDpCB_h552-_dO+yJe5ST;n#V
z4b^06t}kMy^qu$SiwL1^Y0<P9CdH5qbo;q(lDG<`fOz(Nuof<Z8{p_SpcqmSP`Ni@
z(BVspM$`hRHEJNWjQkQ{RFbR(3T2StZZaJM&AS#lUCh$M=HVH#Xdpj210xa^B5_8d
zai1>Bd5-e~_Xrw1%z0hcgv1(b_~@+orWnn$-Ll|pyJx%`N_{RaL8%D(jdh|8Ro>!t
zVO$QbEfZbc%Oqw1j}iRdg5m2^tP)5z_*OPT&>#r(|8wZ+oZ#Ew8gnRwKa1qhplu!h
z@*^oSGLY=#4_q8wV|c_J-DzaCl{@c&cAr!ChGwf*{V{kVCiDYsPi)?~i$h--)E=Ff
z<jr1)2O&+GivXJV#l>}Wl0d|Eqx>)oo01m7MWm**Q_qs^#tW8C@g?d+qbapOpYI8d
zs~&bgX`XPdlA{z8ohXr+X=!QnGM2GnZ$B>JD;de1+Vp3tx#Wpm`f%}*-Pb>Z4OBqw
zl~|K;B&{AKr4I-@QVgbf*<_nfLeeK%N#P_00(UO8cJ10d@C-s;Fw#k#a?VaSO<kS&
zUO$}I_WKU#PZOWtk~hF%k+qG~h<v9T)T3t8-<T=lRPII6Avg5Gs|i{tjHKBIMY-Gq
zc-?bpqH+Rfs_FaIpXbHn2}PG1BJ5;PD=bfQfEZyQ)dP<b<4Juqb-??S0jW=((Ed(}
zjt-fgo=z?YpSmHcY*}pXd+`Yz0xGfZ3)1dG>qt{2MDg{=UrLo39~F4BcEaP=Dh99v
zgQ*gqKbaTO<#E<3;F3SlcQ5aMG`GaKrQT16#$pKo)KcoRAC9gN5WTPmX?;25O*&iH
z(W-8m+_*BDs|}B|xzxQ79j;SxFd=?rX5g=1j>ZlCe{!}Eoqn^%uMiMW1}|0iF%*@q
zy8jq6nND9Zk?~)W4x^7a_-Er^i_35<PBFZVjIi}mfdL4`_nSe?B3m9=JhMyLchY9#
zSa8KRp}?VSLoh$tgjs`2uNqE2B2D_IO!a>K`qk|vZ{41_N~dwUsjCkTle&YmJW!9X
zqgShDz4t6>lWo4NHII=r8WJN>J?)}^PZnvp#t%rp#CR8Q2*aydQ_S&1wsj$MwZJI{
z9CJ`Z(fjq>uyJF4le_UsB}RYFY0U2~Xb&dGqfotq7X8rJ&|DUp?*JwairKrucX43Y
zL@)b%lXGQLgpX)*5lRxd*JMl9JWM*g_BwSdYLv?(h2&l6;w4oA^szKVIq7vpKNs4;
z))R1Sf4}<bk4yh+mKVwJ*{8ftp%wo>`dwIWJf3QX{cx#kb=kCPu|0BN*63)wI1T)|
z2guPOl4FB$uN>J`u<zlJkCsX)-M4Jh2(U<I=-$Y7lMQ_x5-F+x3rgioR>7*|B6xh-
z{KCcgkPoPsTSY{eAYN}G^9*cpw6H9{f&J1MQ2pCt`^@^$^Ka*0t1EsswU;cdOsDlC
zDPwY9NiG0gdXQBMHt&}2IV5Dn$(7SdND+$ZusACx$G&_9m6GVaQ?wu+e-xuWdpZMF
zmm?{<L9$&XAs*U5U;ZR(l|$<46~i-cEpp2-74eZ{?OGzN`GnsZOGwpb?Z(HJnm5~i
zp$b%tB;_XiwwKIcb#B{pf)YTb*WAXkO;FGb`G8-b+OF4V)E#C&<jrf>u2H&Ey{kTo
zbl}dr57Tkr)ASOW=TU}wNkI(`Cc!q?T-O4sF&hn_Y>_Sj_>$M1uf5M;Kj0>dycwC0
z!2GB|c)npwL4C$KS&wT2Ayp3wkOg!(4!fqm<duE<>crr(A*DF)snZS*=~fcW%p|ym
zmkt>xneGA_+74Rd8sMs)xVb?4rSu0WDJk6;5D00qjiuhL9?rvHw+iwL9tzrgp8FAf
zu)Or8XTMg)c4cM;&J@)Fv64D1>FFeON@SM3;FGq`^B>jHqGR78DVg8-#u^X+1cmm)
zHqv{eEYAw6AgFSg%!%j(Me22&rUy^#($7(R-UX)v*);*GB6%9PXr*6#lbVu3E+BgJ
zDBW_14gs_2Fbt-f0D>1n4siz0TVb335VDzmp_6$jRXLgw>-_IrV`6nv=Escag~$>F
zyryBu6v3wed#K>PJHscF{s(7F{L25-8S}zQpbFg|WyJnV6+QGrO+?_2nm9{LzT8m=
zkmvR4zeT&b<Qcjvg6%w&p2M*}%|3wZQD$RsAy<9=%zQUb+nDUXq3P1nRIVg{=vr&4
zuTM{GDJeO~8zK11?H>;f7mvK}M!-_0n53H0Ebjy~UyLzhV4{Ngzj}Rg>?rO*O;j>5
zNlZCZ`k*;JE{;x#%?Vhr7m0}ZyA=3v%&R4(sQ*N-OR!1E{M4-YKuATd#<|RlMxbV1
z_PYUK)c@m$#+Q@Q3CCZ-$`JJ%Y&Pkxdj-BJ$eI82>Hm0S@<bkF1%-`+n7!=B+=P)2
zMw4v4D3;2Bsn7%(f8Z0cJiG>RmRz}W=gy(ny=7Y}6ZV)>h6!77#XOtUNNsEq$^VQy
zasK&f6M1aWC`I0QO!dzBBD!8w*OY#_m8sjeZ-4W&PaN3m*e*@FB!W&!J{UTPkyR2Y
zM15ret%0MY2HHc&l!P9nrId6_HmLI{Jw0WC6cW{!7y+xKol{-nL8V{Yp)7V!cnXd&
z!vTzuooWw|Ii$@A{o#7+_i4|Dd>$_xY#N>cJ$R@}G}kH15^OAKM-%LL1~<tZGEaTM
zg|1|S%o!Oe?b(CRj?FCg2PlA^d#*I|bcM?p?x1>3XO&9gIQ%E_Nm^fmfBZjfF(1Uf
z?zshZh#VkPva|y4ZrP5}&_aOe5W}cvO#7K{lnoax_~b%eBRhB9Y?IwZdLp_RM5Xf`
zX=jV%jOWkS;($a2&cIL`sQLIUSXiOmnReeYC<4nr%>XC`4tV(jLqrYi03$`J_@wV&
z<_YNHU4H+ApN7QY=ySGWmxP7B!<LSbQD=+|(S3w1GcrDY$0lU}O+lhpQ{Xga2Wef3
z<i=Irtg6goln4fY^uIljrh|q@>K{<aV<sdy01cu#99!f#fdz<|C`-~k%u}gxnce7?
zXs2&<&6yd4a@>8&shv-8=Kc<az?`Cm8XROleENLPH;FS%Xv?sxkmc*iktK3Wb7rJ9
z8Y-DGF$lGQ-o6Ch8B$!$Fxcmf4#|3EsA(DmmNsY^8#K2;ze!$-QW1ybAx0J=<d?=P
zxH#?>cQ}$WdUx(@gu#LxMfC<P3(v;F%g^?S;B+5{1T(mC#o)5Pd{P7)d9u(uKwTWk
zE?besm;n&Q(7JEC%<g?wLklfbWOSVDtTXj<gW(~2Tt0stLt(G7>Gv`f%2&>u&*TG$
zFU1MS!=x;ig8wV4ZT)E{9vx0?Yyc^9l$?59#^{IYIyolNg^m7?Kje*~c>E1?Pa#9g
z<oE_b4l?XEd3A@J`XS8@$o9``Qz}5UeNEBez{nUT7kx?917m%WkT<jd>{JD}Rosk-
zNDT|4_a4cuBnPxG8pVlz9DE9o+b3wK9EAABH4?BGC0ACIrb?po5%5Neq<V2mFVpZ4
zCJ%(kNlqlx9cX}Z5}Jn}BfF79O33>6OahkOx#WF4yGth_Xi~pi2HeMJ89AVJ5YkQ_
zYLNHBuT-0_X(!OVr<RjP41}W~*_#c*GL?dSPEH-6HA?;FW865ygApZY6r@D}u$3}>
zEx>&FaLC|T^Wn5jZ4WN_U??MzrajkEoeK=gZqwKINv(%cv7p`MNd4;vYn71w51)Ib
zbx-OhM$G`!{a|w5d3UcRt<d)EC*9npy^e(Y17?x^=@nk@Fr4*6Amg@R|Na_k+L$-)
z;TJxj0zKpJ^b<KI1p(?8#`Ycp{vyc0Pb$#7Bu$A>j;`Ij`Iy8n*0HJ2UKCw@n?!R|
z^(z!Xauhb?BRE4>EH$a0kn$Ezb)08nDkiKnUQU&%Jqs|ApZR){I*`-{YvROGK|Dk9
zQ5q<QCd)uDb4i4O9#R9VybJdpjo!HU3TgPINX7>nNybf|p&#TMYx?mAyRZ;MJ=)q|
z<f1~=U=K{Tr(J12-(=9OstRg?jv^V;1`QB0eqmTrfXD-lTJ5p2zS!~AgqmTl1<gUw
zapcHmYGdeq2BXu)SCg^jX9nC&M)iq}Pqb~!;O5aJ$NusSkAbL5<uvi!GH$;4IB{6;
z+-}f_|996+nMByTvw{Eno*8Ba;_ACoj9|BfFfIl0Mgk7?@31qs@bPZA9=c(dz9M9J
zevl^QxF3ch$+7t=QaVZ3BJ`8`V9)o4ed#Gwq9C!bTU*t?0A5kHS)DugcPjZ&16yD@
zr|B^Y{#$n#_z33BvUwluy7Fu|W`4KDAppU}kqj4}Gy?ftXTYzfy7~du^#q^^CfvxO
zJP;NdI$>~ElM}W(#KogP9FTeGH5}uSmHX~}4jMjF=I>YK@U|#5pC&Mvzn&Z^CCA1v
zBX!p<>jB_;Fxp4nb?v~KjpziFVPZl5W(@&}j52ltM<_zD?Hn!v_9G1&n4&FA`bFU8
zI|lV*js4W8eRbNXX5{73i;xx}<a<G{-IsLQX2_O3=GHAGc#*)NAfAMUXVm<tU{QW_
zm$Dq4_21E8vps0MAL1|{u#SK3L{&aH=~4`sv;$Mgka93Rtqo&xy~n`Y9;bRzWIlFR
zVoR*%At;danvV{xlO=qSanqn0k<lX>cM^0c=$lZ46DtC2;K7rCCWZ7N4&wFXM-jZ9
zK;_A&c|6=xcs@z%5oAh`POH?VKW!ObQY(>@f+rwjAAX>%4IV^gbZo*CD*stBX?+=<
z!Qb1|Co4@O>gk#QFKA3mH6KTjQtssPYs`2lhq;}TG{B%>zGKH-m}(xUrZ$2b4ZdBK
zv4e0c>Bw{HQP$MZ*b=I&gy2aQI&zyj0nK3JNv(s20H(HLNSur`_{zvgOPERDL1d(*
zkwJ!P$}lV|cAn=%{!8B{4gcF#8qAkm1z6o>@=7|*XTpW7rZ8dlfhe*eoitdL_r340
z--?ZOKOVhbjP4&R4M+9=#7eU<Z-}x&;t8_597-B#*W<qbXhui*yeq#vLW6uW$H%^j
zZph?sNN!I=evS&0U26M5Qjj95aj}~cC<C&|f+Y3|?$?{fl9!88P;L%Fg&Kst1B3h|
z^NteNgGsg0o$?v+H8qgiT};lcH)WI#w{v3oR&y2j+U{XpY%opW&0Sl!Qem;)K$KyH
zu^XCp4~jP9U7?SA^h$s&NO$=M453q`HeN-jnqhOvSrG0LTf9XjZnG3^<lpzM{M-pV
z`v3Z3m1C4LbZFlJoA+#7Gh@hooBX>wA5>Ix^mkz(fx6B#!NGhNAxDP>$Yz{!?C(oj
zB(gw3Z<UcL%1v(&)+an!Ff>j~(0r{A?fmQ3DJq1uowTaW@s@Gr65~8`C4OLg|MBwt
z6nB+m?JAZ%@cLh8j|H4P|GXsTV-E3O_f7fVTY&!0eDLR&zA3`-jAe80wfFyCSah>$
z;A1vn*>{xXOouyL*8lTT8wx^tCo#3De86_(uF=H5uc|rPHz`fw%G)iz-{V*o*S}Bm
z#F<{cJc_kNlhC$o{`u_ff4)}!%+-xc44ogcs!w12_w_eMa-!)i9mBcgpWFWX=Re<0
z6~8a9GiYd$G%x?p-wG-b8KHrh^!yKU?JDm7*PX6WpKr|*9sQ8SaFY37zm!Z_yGL$G
zvxCFP-93_K|Gsc%;7T2nel79m3;9X9f8V&{>KL|id|3JQhb(3e+JAr6=yd!AZpdBA
z<JcYVZtfCF<_UDj7SR96kf?NSd4BeK{{$EXygjG74Bn>0FLz*;2WB8f7}H=zf)dZ6
zE5e;#PQKj7e4;q;IftUb2BsBJmAJEn^^4Ye`1eq3Uwv>;yRk87X=$mv3|LHiat>c{
zja1VxPREQ2z(n%7U|au$X33H-3Nq@XPLh66Fu12X!+zxZ9%eTK4~hs(Dnf3}aOaNI
zB>kk9d`Do~BiSNS(IQ18A_qzB2Kp(|-wu0KolgQ8dOx3^7vl&~c+-d_u5CEmO1Uwd
z^eq5ek&Gl@HirDl&I{tz3EZRwtDBX8z|9B+2{Zj4(nSB>PjS0c@|>{tt%~3>k$3nC
z{Wz?K86-WtH0KZo)K+G)1I*#_CtEilDX3r|U$(cZi^iclV5Fg;k>qXu;qvB(3(bnu
zz0aH@Tx5JdX_%L@xSsTAq&KOgn^n=Fcy6Fn)94mOH%v;Ds~tdXaJw)7+d~`V+hbJ~
zNy|4|G>}*U3dKwQ+27YJ#fb`(8&c~tiLOjKgfn)hJ}$q?7uY#VY^pPApE4E7Y7y;z
z27fji2S*4bX)5BbgiSCx7c6Cx&3X3hS;=s2>Oc@`$<LpQkB@=d<IL+MPrhq|%+7ZV
zytnt=eBqI8M6+&%#!2psMn7e-z!@UF=JV%6@Ed}@B?!1O)gT1In|}peGzIY*gU<B9
zfiP(`dGg?OI<cDWCx$+e(ZLy8x#Q=Qt8stNF+oB`fnrDVRiz|lsx`jrwACq}E{i!D
zX9<u&lW$0s=|wof2u==lg<ZR*j3FHihG-nUvxfrJlpF`dw3s<Ll?nEo2qcw8lCy-C
ztD9xS03QabGdGAS^L>HX{yXW{yE*j6b7G0)>pr7z%L0wFsoq}+qKF_e>(0#`9`bM=
zc3xk+i>jP7siRs3m95U#1yB+sQw?)YH^k&}eBpKYVqoRBKu_xlh%S_Q5mdhX=<7vC
z=$_Pab(VOC2Wfy4UC1Y&_UNMtv94{NVxwM}hswp$gZjFz1&Bxl*7-RPJH_^e4GLUY
z)MLRrdQgi_>FU0<mXPvrCqrV}zbV4BeahRooR5#HEMR<gXiKIVaqv9F_rdPpH#Gv~
zMGKdBa>vGM@@W2M(L1!`|D+J<fv(r|(({kzx+XRU%3q|)b>K=B)sy+>uz!j@&PLd6
zF%v1C+vjtY2<KQWX|zh7qpx?Bij<!lbNTtu@}Kl!RL%3lLVV!*!4I`2v`fXb_3w#}
zF}9lCHmZ}Ibe<dhC8YkWAs_KmY*inzbBZYV(VN&jK)cdTJz}~&d~eK-AeO%Od7=ce
z0Q-mmnNIJBT<zvKvo!L!FwMp|?47T_LfMu?KEmS6^>nYf5QU#nZ~jSqMLH6?hMcav
zIelmO$M#QLaL7Xr_~Ep-XrF8Kzb_Lr8s!qnyv`>^$GAdi#hBb@)qVF25&F`3=<TJ@
zf3mD=(WKYA^12iL&UGT5LG7iAFYMlz%}kUh(<mObE5GC9Jp5LI{@)7aq9T~}#C&(*
zoQ<)|EM<u)-NzR}d$TDR65dVb1#uSeev<jmWsl#F?Hw^m8pfmE7dt*))wyLQGn_Dr
z<t$+N<W=$S?elmVE1nhY7AH54r#y)DeSmy~^wUSnHx&yu1%4#2e1HExY5&fVc>XZs
z(r*GrU-Lu*Tbh=-0w@n%d0!S?e9gxAp^n&+SAZ`&L3hKKBFx^~yzb(9i+}g#*~nRo
z;|Jd~N)HT3=jPX)U@<ztY27*YR;<gTm#(`lG+?#k+&ZVv4xE4P>uvdW;~#%;;5<KD
zoXI)+s#eo%tWD9EF8RkEHj3LRN=}u3F6%rFqTmtLpF3YmCtoa6_^Pp=$^An**FQ`2
z1ORX62yfvl%2Tfb4`0|Tt={@R^HMhrO8~`)ACr?o;7Ee-^|6~7I;_`r9rvWBVC=jm
z9m4g?SyuH|%)nT?JnML?TylR}p6}NqiY46d+JirNRsOSDHiC!FZD;uze@oAiYD`$^
zQo*_j?;rE5&uv}LDdgB~()LhZI<S-ZXO?TOSTk*UBUSWqn%MhSYDD-8+AlL+;qK5m
zI*{sLR=WMAv~JV<x#VW!0e|}^y8kY(nzofk@0G3ZbIxcs9Mz?``GtwIE@_oNol&5?
zclzybj%1hHimQwFx{up)@}1fgIJ!^CL9MT92UEsz?zIAvLB~yRD$}~B(9W-Cv0C41
zW}9U{=Tsi75STfuStD?llH(iUa=Jn+l7ai*I1<<@z$1Hl&CY}2Qr=yy{MQeZDd|d{
zmD>OOnthOHhjiQCX_?&3?<Tsxhtm{yv7~J^bM`!8<@`3~B+bpazVbIICm&SxUV8s4
z^u)=!J#P&dUhQX5dJ(_9lu7l;{l=NSGyjRNJH95THY5b^9;5F)E$$Sl!7D$&FLFuz
z9{1w&n!t}I>kp+~GcLaSXHLXs?Wf$1`r>8DFrDj}Hyx9R2!2JO0TtdOLIWpy*t-8T
zS<<W(a6UIyy{b_|CrTKel^B0^$Mrv_aAO#8hQX}*N^@^u<v@rguXnIYNN|SX@p~70
z_sH@Yg>Gzf<{4b(YQH9Jy-r;J`*Ag;P^!tDnKv~zvC@cz&o>HNDOI|9(Fl9;Y8ng@
zd#f6<1Njo8Z$D-sE{nbz-*NU1pyJ=eB`R_L@twN5VQMFe;EcP4yPU?nxPR|cpkk$<
z%nt1IU32!XH1XgJH?yrw?;6|w+il5N;<Ri8{Uw)sOx4>quNjh64!g!<R^eu|sgmlX
z!uq&dmR#%q-4W#p>G!_=O}8%`xS*tajGE7>|44VUq_)ia8Sf*OK0R*fo1AQVh3@HI
zaOjujKY08KA_|*by{jAF?VV5g%enY2_t+~@v%bx}?s}4cB$G9^I}2P>xgYj0W@1ko
z6u?O>UFWw9Gy`fWx)kC08xr;Yjl8L2;}o}jb8Gg$n{*i9v1+<Z-95D!_%?(fOjY+S
z-Sy<Y@7#Q;<NL<PCm)`<wquV?%G)iD(e_`O5+9zc+v)td`&n@pg<AqGzo)>p;DHbq
zCf$xF$`tg56dJb--42Tj)TTL>Gn*^_o0jg2tvLICqug8MvYQLn3hfJeXBRjbtys|Z
zrhuDnC1%t7d);?~v5_@a^}?P`-L?)6H@@sJx6N9Ua`Wlad=?_IgmZ3E$;RNQNcZI*
zL8`R~**`yGn(v-r3$)_SRHAxuG$_M@Ni#!qjs51D;PNS`Vt>(ry!2;_f`L|N|DH6x
z$kQCzOmlsf{7<>rx=eNryD-(8%O&r9(Ejss)35jOjCA3g1`qT^*dCo0(98~O6Z!i6
zdzEvV--hR<>+}`26#9Oo3*5VRd^>yOX~Au`vvw48PY8%8M%wrF$_$=zk<c58G`6jH
zBe#!s)2WODb8`Pe-1|(~OlhB(xdu41WJ5CUvQe}i2u$iCcF`!-YLyqRw_r+ErV_@>
zw+g-;yCGaqe8SdsOUb9p0_kgv@5R>_*;p-~;ilos`F(<aR?lX>K2-60dGhDowZ*PX
ziJ}Em#6uN_3g@tWX}9#u)k8a|8s8fV{b2m?Z`z_)Vk_~^JVY;9I~Kh20L6&+X6dPW
za|NQbgVGFXvhjkd>1S@3T(v6LVDje5+H`K&$2{8{4u(8cNuoFqceutdf^tFOl%-*u
zlM9pVK}zD8Gk3o7<+b;iM8}F!KbZg9Xv_exHWt$-UiUFcH846bJFe^e{K`=Zx4Qx!
z^E)M?j;Z7qJ9j6a{zz0MM^<awyN4#(7f(y){5ozEufy6txi|T1Q?Z@2&4sUw^5b0(
zUOIdGYJZ&LqEdb0|L+BQCZ*qvniS+a*9O?`ce1`d*Y$Kc$i$VfRIWN!*LIDU_rQ2V
zX5b?uL;pTa{+Gw}n^?C0K6WyC13!QETi#d9>`3-0m0WC3nG@vhJ>~nS&Kx!NHQAea
zy|n+~{r`)ruZoN6`{EtCyJcvQP6dXPjuDY=kdhRnC8fJdx<OF78|h93X&G8dT0j)<
z=I8(3hx_KohtadoK5MV_tv#8CF$Ie3g@IW_1hJ2<AwEgFpP>~*+1OrU&`VjU({*nR
zsr1FMm()<!zPrSFai=X&DF#GKRDz9%TedWb*ZKW+CEG3|sF92^<#OT7Oj!_@-2cst
zb~4P9ZjD>>8DF=3Pj`FjN%IsAvM;9><2IWQOcd?RV_^Cgh@hG{SJjdC_5R3Rcg+o4
z1&^Hmj6v3FrWsZmgjb2@ru~H|;JWMxwt-W}u>@LsQ!mKWqyIex8xM|J28JgM#Zk)r
z=a4t7sPVJ(yTSE~VN?Q4=t}98d0r_+TS<CvZtZfu<(IEAh~SYPjP^?sDGHy-c?#8~
ztg>NDH9I;?uUo<ycSeOba7)fWBgp^C!^aLs{=B?;#;v}jAB$mIN99OZA!jJd)3?K7
z)83;HNa}n1VOt53>@KQ`|9ke}-ujxcW|4Pv@1CQShoi=L;@bdnmbWS@zd`t?1|m&R
zYL`8LU4CU}$v+^OozNDJrZ<{~1Hgs*PZcG?*7%jYIVWXkE+x^Vh85n1d(YJXiV`m|
z3SX%xeyPEPl-DcX01~m+&qT?hxF%1mk8-uG%a;S=10*<0#N5*%7p<l1^Cb9r)Yq;|
zHU2rU)<Jdb7$Yu8ouLZzot6JpawwU(0YzT<;Iq+Jj)xQMvLEK~It}oH;YhjRmh6C_
z3Und!-M0zQT~|3jWm9TJVos*zJk)Pz2vHF-cEel{&^34;jrOpUB1L1TgOS+!(Xk0n
zQv<S;kzxMw5yg*e6~ccnN0&EV6IW(mZy*>G=G2B9eqNd2ELA&)Mm5ABQzS=HL`+^<
z`gXM*bRF}h8y~5D!5{(cL(t#Eek7KIo;H?v*^SirX=t8*-y>KK9(Li$mE<hf9%$sE
z9KQW?OeXhOU8=ghe{ljjcKn9l_}!aZARoH-EGML=W5#XXr{0Wg%7uWxgaW|_kTp|^
zy0OL4h&^0<Ur~VzoG6??n+@!^{H{AHdX=#$^O67M;etW7rwI0_f@MWDb@6ohgG)=N
z2{<?9%E;74ytZ_goKIrVBxUk_ntlWyp$mQE;aS%yInbt?Lmf<KPFXanY479{lc;I;
z%dh{-+1yHKttMNY)N^=J*uNjPxQ-Uuj)_H7^G6@C%KsTry%yYkNXO%Qf)O#Fd&}9)
z*}pZHJdaW-pm#El$umYSl{FCt<j|WS2C5sx7zES4=<wvfm)YDaYQZ2oU=c4726#-U
zs2iqTBP{ec@JGr6-E-H?4z;H^?R`qZy&_5yP<hdQBKIS}fYy|@ctpTvsLsNw<l{%S
zJ?GGDTDs9g#|0x3J!4hy|Bsj{pKgmpQizqj2;^`HSbYsCy^8R+SCMjw@9u+Q$Q_U^
z_%Frwp^=wui)*=1HNdG-Txd@f1Pd>*563=~T~W%3-bu6^=OvK~9pG7Vwbk^bAxK!g
zvK_YN!m-TZIdQ^BL?aYGI#hCCg*E<SMoF-^K43A80?q~`Lc_lR1vu?SA-MNkqe8%>
z#R$x<AR8DTgr)`ogV??I`x;p3?j>80ZZJvm0fKaEXNM7><Q1-26IMVY2WDepg`(3S
zBf9&+IJd>YygYA6r+Rf9pSMfIDBw$p$M-pGLZUOyY^NLFX|^qT!dI#UkKrO!wpKE0
z@AyBPjys_J?8Jbspe8&YFPb-FoOb_cStpw@5$h+i%~scfIK!-I?R4_-bMiv#|E~Rg
zMjV?(hoVCD`EUvk!w`MudB3*!$Hgb6+!{&AlMWkj<FmgIkIsi4Z=U;>o86$qD*I!^
z?-JeKb*UIa?C%ZKq@<+x0k?s5V7R>Z0RvI}y*0iD1aASwHG0q!$o<X*i7J4Zx;Hb1
zi8qaH2aR1`LCoq~e>kGtBNK@?;`FYxTrUSM;j0_K!)W8&dqhSEnwIH}puX+UiA~vJ
zh4E*K$UW;U%$X@QZQ?K!8~ONIHps?Z-P)6|A5(Z6(pi`2RXI3vLU^LOI*E8Xr5Jm)
z=4#(hFi(D=4twEmdkEui>&g@*Fx0{|tpC?<&uR8wuXOLUgC-{F{I~mr3@P<;(aMgD
zvg%FiGoufYjfB@;EKcU_*UcIjDLJU-2DxkJe7jpwI1peLgFt{1Jn0OiWOQ2q#3dp+
z`K~ZwY#Trb*sn-N@1@c}MsX%SpML*c(t{?Ke-vAlp?un?+VRWBJrgb-0??|gUt;lz
zb(Xn#<|m%hGY)&=TZL25<^j+HMVek~L1KE?*9qH&R6m<A1!ZRWni~FPh^fi=Q^IZo
ztdp${X7>35a!-y91Ii@N`pyM5Ovh5bv{-2*iWbrxnEXEC#-8S6xjo6jMU$10_YXNF
z03DO&aV}@IrV9J{ZMe0yrmNKQjMP;|z{4}~>;RFxsuswJi~+C~-T_Dg{T?~80a<X|
zKKJAIZjcggnUM(+hX~zW6rt)P(@9zV5~^F5(crdc_D@$gj-xJ*ix4879mw!kJT_Yb
zWu)TW&TNg|h|3%Lv~#7nS$cjMoYr#ak*a@4S+@D_=^bb03^%nsM(2~vP;*@eyh_La
z1_u)hHgQ(RzIi^Q=PbKsus=^I{on1J*V`nW7+z5n=qRVD;b1>FNYv$HHpBwMUhO3T
zVOs2gpRoO9dx)_!k||I-LLh+uR{{w*DHP=7g#fe<bN~t+=3B2$6A&we_LD%qL_@kn
zBj+#t5qL1^sBu;3_#-AT=fO)c#9@yG_og`~LarkooXvU}TkD>drMQ(p4Ef@Kje3Lv
zDIGhS{;-Yk%3t{r7(<fGsq5sZwdBWFwxtxOmM^;}<i@dtTtYut6Wc@^ELz10b8^r9
zvVGcf#G(Zg=d&e~uJlDl0)q>K9JW+R5$62wi}?Jrx*7XZi$vnn(djkA-6UPU2%!gj
z%n=l>M^OGwJj1r5B#!~K01z;31A@`IK<Sj~wg#R8U|a_1ItFCA{mpBjB{u~sxqC+u
z1Oc%9aC*$|z<Fn(odfosNcr$#KY?OYqM>8>m(Ae0z2Lsh0XnbL1^+gvu={g`_Wp=Z
zZwOIqFGmb8rLvbPKN`+ezbCKRzXdfmorYHKK#Q;eKKxQO`-4;d>`oXxA)`BoTd#V|
z)VCO=W>^TDZeozcGe`MAF0v!>poLV!K9N7h6QSf7*c13r-m}6F%F+Ewf)^ZUkN_#C
zmQE!hTf~W{G>n=)DZpgy8E%)Cu_KcAWZ24jXkjrWPc<ZVLwuLaO$TzjhEbJ)T^4Iz
zBYeFU0D8|k_aBpwyT$|{4F;9>;g5*p`{;^$g8)G6G6n6!fJ|%?<UL3O#UqNXDJaD#
zk3xaCH2NMo1q#;tWD?*YxW~|du#M%As{8&(36++!HoQ9oU!dvLHDQ4cy+(P!@<HE-
zhM44HR<u8^S`>HSPXYeMh$(InRpZr<1qR(8k2lFsuTg_!1_O6JnX?)YBKRhOroy_W
zVF$7e&VR5VH6ys!>~_fbHr|Yb`Jq$Wf%B}WgQwb0tS_r}giaMK$qk(9`T?qd;gI>g
z0Cd!ju08oW`A@$nS&}U$yjYite{=v;Zdn&yr`S%9KH)cXP|dpZQ_T)@H%l9rppi<^
zSDV%;V+6O+76brj#1H+@m~+zc;rcPlrd#bgUAV0q05)6R9GifMbheXbf1_^zX*~rB
z0K|RHI#5UhPu9KT0Z^{@;@d!I0OSN<pPRsMGzjE<-RI&DRnh~1eEg{J1r9ibn6%BV
z01Sd&I)u?a6b|d;;QR5sGKERdAbJHG<E_M7^!ZXu#7aT(HRnrlQt=nn_j_m9i&h{v
zct}xgMC95x1Tz--6kQPiSI2Z(N*@O8C+cv7=NGJLs+j3_OY0Y&44bgw^2Vy0sSgMd
zD0c(AEi*FU{X@LR*i_i#_?F~)iR4AHb;=a!HN+MpA<nzO((@W`H^cb`9x9E(@Rj1f
z6!fkuybWct+)uxN8DC2@w)zZci;vb1d@3@8eE8G1lF>SA^wSGMO)|#R56;v(GA+^5
zkuJ?)-Z#?aNz%sLo=iO`KDIndloFJSUCOZ}lo0`)i`!til?Nfn<~Vw9Ls0+#&?YRb
zJh-X7iUElu(+49S6P#O7>vXE7-(As;gd~p4z-(kpP9+4t_L-q<5$S<f#dH+OfDUs;
zgHq&IsmZZ?^{Bx7zuA==mkVAUsG_M*le_@mdJ?N}49UQ5SE!|UzWOMCg+oXB&jAax
zZ&WmHu|MzPrN4Mb6yK4T>L&eozjAjKv-68N9-nVF605@XE%9F+MTo=?R50URBf5We
zn0rBZ)5q%m18G}bK5T)wb0^P^h*=Kicw6ypwI!T_Fly#9yPWRn1o9fS+sFK647^VQ
zB}>;7qi$h!S}ZUc2{iGkKAJ@IX6aW2%*2M*pto8H3iz_^p(VN^UG7ypw1*(Vq?HaB
z;zY<r;^9{lc$Yx2GZvO?oV`swMAuKQyB#_t*z)KyFVPjmF^(3WMD}m*gF{Yz-AQ)P
zS#9_Z{{k!`8d{iH^Y1@*iIl&c#i#ES@|SU)E9hMH|KY)|%8|1i)3e2WphAHrMU8V?
z?fQ_f1-sxxL~Y-#uS};m|J}SuAOpNqu9;LbAFZO7p(#N7NT<>v@2t)o9E(V@(OoDQ
zL1~)3NNBHkcnt=qrp36iS;&P|bt96sQ3L4-83YXoayLy~eb&!@nR<8=7U?ob=!lmt
zu7_stI<>9fhY#W8Xy6B8($eK8IS0Vw-lM%J7<d;S%xkwZA8>f=owm4g{YMM!XC6=Z
z>QS&srBsYgBwJ_US2tlnMx>Jkr0qqPBkvU?80WxWVL^gp%3ZuHW#58-^JHr=$QdxP
zeR%l9I8Kwr%1uDeEP%{}u3&aM(12lQBl=NUUuHLGe0pb%sC01BKjCPf+)Nj^h-vv(
zp&d6x#FyZQY-Z})6voNdbF7xL==|Lx!7mY_fW^tSqe|e~c}?5Vmgo>@SoC8(Ku;z9
z^e#1$MB>o#8dYi#WrVG#A}F*cHS2_&Fo%boqrSCPxE(hGjT{q_sbIT)P6R*VS&)z{
z9<noY_U?UtZRAF|(5RBVD7N8Mt73s1_C)*k?JjjSBqlOIXn!O3isK*mxRXTkTFU}1
z#`1O{EB-S!tC0&WycJiW@$A(}>Bo6g^f=j^k+|}zyBQ828guL}REQ^&Cd080Z%e<u
zDa5p_uG63Vb%QEep45GBdWr>8e<kZ(fsfwE$cK}@2~)<V3|2so?!yTLJ9)G~6M}R0
z1OH=M<vGptWhS}Sk12gUJJ;Y{So&l5FbrQnB-|#0xWq#kd)$#ZW7eJ3w<!(zQwJ7Q
zLabh|3Y`mN_U^2-P@oP)*GFDHQ?vMLztiy#;&HWg)<I77vZ-`ucyG^$pUYpA{}~Gu
zyjY{KYfL;*W~#}92TgODb$Xu%yS1CBGoB6Rx9nlM#YRZaHI=C;zn@SguoO4_{84TF
zgnsQX#DtqbMe;)?au{X}%$LuGRBhNZhTV@kC+8D<-!eJtM7DYR`#i@5Gp7HfOeZ|f
zJQ@c(!;P;<x<sm^FE#{IP+SHNE6>jnfv`d}U-dh_fY@XpU1XzuH=o>o_cc#cBm08k
z$&@|GBZhwiaVvAe5R$8zkhq6yewL6ywM@92^E<18MKo!8J!9Hzwx;0CmUCj%q#?AS
zAB{F<>@sG%UWo3VqKKiuE>4CM7d@GZZ$um;4Mu~qgOMN1FW@5M_ykZ#q^0`$;PUr5
zwXn{bmX#J&XWYK$ym%kKw7sidZ;%Z<cpUYhz4ea>4jxA+vvzEr(O}Om!-B-OX{<a>
zRg`5~GV2>;saJ-GHlKhNk6jPhgQo>#?LLeBNzqaNp#>_N#J+4JQ$3M~P*Ou^k`~L1
zH(FiPz`G`xtf&1=SS;X{m~5`7mFgnx7Yw-P`y7?X=yu2u0STO8A#j08btkp4ZMIqw
zv(FreOQMb-h!lB>XKO_RWs!duOBsCa{cn=RrMMcHM8Y|863E2zaJ_XUKZBaivZ>j*
z%Cg`3R9aTYcL#%pNIqIm)`|W@Jjrq3L+tN@)HWhUhe4fso+747sBSY9$Jr{7|8U;)
zK?`14+dJjMC;HIuZ!a;tO2-_)IEh!O5k~I(tLEz&p5Y>vb6sD?9|Y1u>ddf%AdQFL
zdHyrmN=gk0vxd2C>9-{~srbSGwWz(-WeDyo36>fBo8$3N2gmVGY=lS3)5*k_ZTQRP
zj^WSghdU(YvK?-sAI)}D7|f+gW>U`8wEMETdiJ{sh=&Wb<G@>?{N5!nlg|!F_zq~I
z&&h*%Td{{d6`-$6XTN&pp2w36-B4v3U5f{)#QwwKjC>ub0odfS>{OVqA@B~LFjzcU
zCvqGaCzCQbI6w32S><hq4r0E!e|uaBVhL|qdOrA9OE!OIKOc)oIn02+*zROodn{3#
z>%D8_G+Vy**)Wf5_a2#3S$E8Z9!;j=!AzG8X9HaYp6K0FurQ<p_h<#Vqj&95Sk~8C
z6~npgP%xA!Kg1iYbWB+?s%hz%=6Lx%#ZqpU8KqFw5AAoG0`$d)*{GY}5PA;oj$4ne
zGLSn^N9ijN`)|LD4{V-CSWHef1%y6$y1Pq;$st0}zRXJXbeH1e%PL2x(*{NWs%Pe9
zaLE|ga12%iPt84i_%pNh4pF|c-uvQ42Vf!ndvhn4sHO4K;s(*hN_ZSJCdFrH-|r@5
zr3F7wo(sL@p6{{q=quKgu0*V#G1mNi4Xe*C-xZm)x19di7@@^E!)Ow3Rkexex}#9@
zHd<k*%eX!^Pc9$8qTMfdor`@6GXC#bV4UZK5JMtUZc@W<lh}gqCZF?=Xntzx=<3#M
z`zX^m9;%g%)!#r~BvgX`F84gq8m*4B62VTG&&DA_ZR8R-_awyyKNX)BUg;=jv!xV0
zhtA#nkT3!Iq30BtGh$bL??P(OHR3CKqN!G28GXzm0PxV}Coq=cWtjsU72b3vX@wID
zOcgYfuH;e#k}MCzriePD#s3tJTK%-kHk;(i5Uj0=PD$KPHE{W()S8F+AKbNzMpV%I
zBXEop-7Rsco`m@DWdO?y6%mbRm$h!%wxr^2pe(2oY;`>L#GDhLAWiWc>ps$FK$i}_
z!)t&(eD8DG7?^tAU7YmvUDzfJU!jqBfSdw*Ty<g9T=*k=63vPRC847H;3wt@F$1>_
zMyv+D$dqi+){OUG#&o!U^6X-TLm1P1CkOf)>#<&C4I0%NvXtGEv9y4d7f4Q0Su;}c
zU{Dn}+^rMRK4<yb)>I>;fO}`jHQPd3YaMz&=UD$ghEFlH4<&bZdIfS3<w38oUFrPk
zqs89GH=mFL;5|$_)iSNCcyXI{Mq|x(Y8&UGGi`{c-zR<1GZ5&3!9_6YT;}`9TsBVl
z;yDKrCY&l<sS?U<ei<=6y#ABOWz1o?ye1eE!p5gx()**Q)|}I26l#-ZD&=xBWplGT
zCOzN!XQ>gP<((3q-f3i*v|kg<%_wz(Tvu<;dX>$MzS4P&Zf+3vNBnDvcUONxeciJe
zDLLZ&>FnlnfnB<PyT?`IAD=i}?$o{hvuDRm>LssKNjb8YH(6LytgJzW<W`q9tH5>p
z3*eG*MrYYbj$vzxNT#-gVuY$JJ$rx&hdMU6L8YQej-`*dkRe<42J2HPwl(&e>tkGU
zW+f!-GfVkW$tdAUImF>p>1LnVX9Ols-UJMzd{h#AE258tA3{6v3^DcoDgPd+jn(JE
z7Cr*mxHT#_zo&w4iuC3mI8;E1w~&eV`F%txOrMsb!t48~Cip7@zPS$MSQsE%m{cBX
z>X)mHR0n=S`EFECfpMh%eV$Qb+F;bE?O!c=Oi2{U?N5fUWsrlQ8Bj{iDwu}QhGJWD
z<Xr!BB6?^;8u2we_eS53G1#BWs8|P!5#g4B;t1kwP1V@@NAT1)(mOs%D<Ag~Ofz5o
zg+NqaKK77%-tV{{VrddB<V^;505zunom`^~dMBLWLpVJo6`vqe0SzNUl|Y&vSrN4~
zJYQn@dXUKJphv7b`FY)+=DMzHH4l^&C`J)Ief@BhKs)sj2@`UC+5|oUEDmR}(NMQ0
z6%1#pj*=?!S=}$+GQ^d0iJ-gXc=r%<%)`vWH`jHBqzYo?;#R}sJP1^Fom0todjEA7
zUrfn72+l^2=zT<RzZ&)1UU!@Nr^<x&V+vwd@<7My^ZhxsW4TS{hI$u`OrY$1eLH7>
zt!02$;Wl8?ayNA6zhi=zrl7LCJoO`dvm=(ROchPjhD*AH6$uwPq^0AaF&AzyVIkN$
zv?pRsLly9dZ;92R`aSpk5(@!Cj=)h-vh{vNL+mPvx8(8QFSG+WT(443F?Sb?UWvNv
zAv)}ew{lEpIkblpJmFss?jLpe(8lDXKz88Drt7bTlVp}&x)A1f_jxqqXH2spH1Pt+
zJkP|H-iH{_r)1Nhj2iv=-#bkoGed*NOPhTlwEnCUeM0I&YbO#fQ(rXc>vvb{B-cG+
zPW_7Kb!_De{ZOm)?Dp<unv>yM8G!TfHxAu(m7DHdu!(761;g=qvDhAqt)E@fr#Y87
zwXB~Jdrk&2C!>R^^c)klTyuPLC>&E`TYy#$#0V5t3{5-i`Uf@XC!TQC8H>ubp}{&N
z2_bQt`3cSm=h#}JtHYHweZ|R}DxoNl1j2$q_+;PS>AGhvTI;rl+cSRY@|4hQn#x+s
zqJbWk=_OSZNJpY=!c`}Q(joB=&9A1~tcPrZqAicHPT1W7hjM7?QUoNoVjOoq|0BxZ
z-}#yVMV`Y;B(1HG<%3{;xzFa#eg;&iY6~~N?Ck$+bQiSlqJH-1M4QY-$x%U*E&b`_
zwOOD(jL=r-KDg*s+z@!>wT-_ZVZ!`iZu;X^f~&adyCuLwD%L4STd6NejhO?6&olS|
znTPOWc*fR>9U2B%E8o@Ds`TZb*B3`HyB2cnR$R$ayjq1?v}#bnC-wqmSetz9$Tnhj
z#*wLG>_jpZ?R#9sxl{?xCRDYSB-onU6JP8}waYPyn=sgTH0V+xrPfo@wjyObdo2Uc
z8{ymMQPp@EY!H`@DG|5Opv1@gbnu4g|Iu*Tvo54N8vpqFhrV(FomwgfjGks{V??jD
z);$x^XsaRGsivV3rtUswS|bIzBM0O2yU*X;qq-Ys-CEoUYNAi4FBW_v*jKP1grwe1
ztH%NA%c!lXlighAtuf)a&?xSWC`tesxN)?m@5mQPo!fBNJv|z(b^@D)B^eGeeWB_#
z9g?EzLy_Nn)KV;2I$K$*C5RD35aDt={g^PqnXin@$s3bQm0%VwGK)>+%iR6rtT#W2
ziCc#b^R6z+!O7EanseL6T&r*YC&MQVqxJsXx*{?B!pCNWknYN;{=6gFUiBA@8=L0S
zCa$%EOU{UkS|x{NY>}Z+@MdJQrSU*zDIE%Dj3yy6U;*>NB5J}I>c8rJ(o%7+%W+4I
zg5iHpu<1`k#+lR-O{6RclbP(>1k%99>~M`qrf>Oy(%jYjrN(n^tmLH>A}>7_j3pmO
zf9@?KOh~9Fi(JfNATgqMf{_VRs-x_5n?^~uO~hiSRl}zN?i2{119bzm3p<f)6D08=
zY4%M7Oa3zw6o8q4K8(;_je$W1@9x#w>p3iTqS7wK<K)-qt5E}y)xBe!J&P+5U)lP3
zY)UZz6q&Ugj@gI|T5otIYV#BZFIG*fF8~c@9>area)?o7x)!m*=fl}z(;unRJ98r_
zbC4%S-^!#tqLbaN42X@FQkTQX=5a0T&c+m(3e*&<<t;5VQ7)n`9zAn#hM+gS%gMR6
zP>h^FD~VC`u0WzmiiyCfeMRSL`%hv-B9}CqanAVJ5O-3a$YuF^{RfjS38N61avVDm
zf>a~)A~+Aazk;)iY^yQu88$b*p0UXZg7Eb${bKQ8B(Wj=YNT>$!0M5#V=IkgUO=2T
zXOik$<lGKE#ioI8{f1^rcJWq;4<QBCMD{5zfbv<Pm3do&f`BBz9`ed?htsF6Yh}<N
z0wS={;wab?Ej5@X^TU+O<*v#!oYq#LZk4xflJQVUDv1U-_=lmqbpU*jW!Y5sZaP@l
zIO*O$lj0#sHd8!Eaq>h3n75lPIhK^sKnNzEm^j#7fjW<>;}a003R6#>NQd08LAnd#
zKxY!Y#=(go!^Bb$%mjAA;TuE>->xR!Sn~kY9HpgNsOql?BcXrwROxrl>>_$b5v9Uw
z_KnMr28NlR%`emQ)e)w-XrWdZsDu|BA{%%Ql;`aV7O|Rj!8!zw9rVpG;wV9YtjPRo
zebM<1H=b%sCkJVUF^usMBmW;A80kakj7P9)kyN(2%E88K`3lMC761@uxQLDO@Jd1G
zcVbR4k>OlCExxFo8tB3BK*o9;jPcY2XJ`_WR?89MNP7Mam#z5Efmm79NSa!2=Q^bd
z=}_!&#ftE-aOsi&dWc<UI{nh&iBx%tEFq>r3GTBZ-0wN$u_OZ?_o5ozv2_wm><rHd
zyEh_jI}TDl2Rx8~=kFD)diDA*yYSdtm}*y|wv;L@eGA)Yz0mr|2hSX!L$zAGY5!9e
zBHa~F;=8hEj&kO!d#`W2G;4e;f1v$+4Nf*YfCKTVA3#j)pY`sM<?D`ul4R!UeX1Ks
zMJp`@Z$P)}2~0_*PB1zV)XK^2mG&Zzs#HCUq(uf#i5ZFu#ej~N<7P+>Jc+)ROC$rj
zoChW8$5V=^0GnuR0lEk2A7_l;@_RGj7~rELvJ>&%sgj@i(#|};_0INmji=o7bGg~s
zuLRv6V2Tgjlt(t`-%9`;`j4}M@%2De1{7YDq!>}QZPf*1ll})<e0wQ24?f68drB0#
z!JV>U)UEydLG*+x;Cl`rVtpY~Aw~gFTd{=134TJ`=3^wr)-x6Y<V4WBvCVF@GwiQ|
zV{MMm{8+28W@DCxNB5#v2w}S)K@JCcDy^b@=kY5}64qy*G!&m|mc5(+>>>DiS~`)l
zO&IvT;Xy22Xwg%lR#_yu!G<&Q9b^xBup!f1$I>oihCt{s%{_ox5;K%2^XL`+>q<@w
zy=)UwLt`+9FjcPvg2o6iRPAG|=*Fm+TVaa@K`N64p`0W|uEA-qeRy{Co$N^3Y*VJ$
zEa{O!>D3aT)oH$Z#UoAg*}{ETSWMl{xyM;(Rpq$Brk{FXgmo@i{I<O!o<dK}k!+qO
zZkFRj@euF3!KpuN<_y2ZVky6-H_|fs0bGPmfF&jmK3UYu5F#-q%3Ko2z2@=1OJ7ki
zC4o{cjQg1HUa1E2=Y9sbr1CMG`|0o)9U?wd&?<~m^eQi(DhCOYQl3=9WQC1CW+0Pj
zt?-2nZ{vakpw7KJ6)eJtMZ|1g4eVQf81TY4hSC5w4$6v;_Sk02$p<whHO)NHOIpqE
zlWJx-*Qy{JZ*321e)nOBG<UedNMD)yJw0N=u2|BC7p{mBY=D#lM;Do5P_0(D+IdJR
z7-i6IQ6zrz-Puy)vxSdoRu#n=C(X6&43+=j8`O-K2yQZ^NaFUX?cZxRS09@p$QzzA
zXjAhd;88P(`5q!a(EM}tlE?l(VMlAP@<X?f2Q{xb(YUM;g3IftH~C4?d%BpY;fewC
ztWrFi1xc=WcD5R$Amavc_6ym#_pKk!n&R^4Yb1z=YVeC9$8O%Gw>93FCTSSpko0BV
z*(>K3Hl4eF`j-Cv-b6;uLms+WY}z2HlMyda+J299VAjgiZo$Im<`G5188&vaNI{T1
zDL{sSV&~L?W|LYcl~z5l9V&&|Jdc47%TiPe<(U8%+Gc|fnVHZ{KVa<}3ce${qGUR}
zKHq+s1fHSyBmo!7Rx?gRzq1B_Pry&%6t`iCH`Avq8z~9x1^sZSgwak9p|rsQtRu6k
z75+VX5ZXgGtp$|Lvn0kw^HKa%3cg(=S~18ToIp}q>L0%1D+j98V{GRY6F<Yuextw#
zQ0}_!oeP<VyFIQ@Edl(2QOgGOaN{$RYYmA9{qg~CqL5gnBGfod-MwmW<*2#~22aDa
z+og-#&C`ymDu`3WR;J6z)W%+(U?Y1kcMW+SSWAN|K^kzagiW<Z!AhUBuq8a7W>qu2
z5e_y81W*W&UZ}rv2b#1%>1W_c2rG3Fa<w$_<klzGa<RCuGym>t`+)lT4<GdDpekKt
zTki@KT}iTOPsLqw!wq^0@;^sbFjBV(>oWc)6%=E8V%9o=)(L(Z&>JLDj~_aFRL8Kq
z>dO2N{o5hdu|6>7{!JpvLF1_W`CD@6OD9qq#bdb)=KQnf$28#C!CAF;*^{pUHU{Dp
z8xR{s${`PE^9bDi{IsU7(kwLseNnxBsIrlAWY2npz$ck7R%_-d38I_WeQA+Q@(3QZ
zLg|B~i==?~Fl`>AT`^#6Q$86}uptWJs(XyS4U4e&gBp~*Nq96eblA%p{L@G0oR4Ex
z8IgnB07olqfCuz%${(ZvZOJs-c7m=g%a`(P&|&~36lg(FfDA)ilJ2dTHj&axO?ThI
zY8c6?>$jke->(?=hOMNI+s)#-(1!$Y3R2FUys;jz@a}n6FMXuqn2QbCB`VQnVAntq
znF=l&RBmSsRXzNny=U+DBfT8uzg~<77{b2IB|4==IPp!zz{9C0z)PK0LROKX+Snc#
zuC8GyS7ObW?s1kfs35<6o>nxxA!8n5fP3eQS`MhLB5ZJcu|^$5h_-j=vuP)b`uHMq
zsq(g*C8wL55@m`!QvVUv|MZNU&6PbrX$+yp!lY0cVDb#8G7w(1bI%4{=>zFEJ((5C
z0N)oK1ZUN-aARnfPG)SRXWU9zQnPwfWL!ua?jqsI<?pCQ^GRSO;zewq+AEJh3QUd+
zTrk*s#))N3J&&8UVZo=+V<1GL$CL}yx!+`QBMLzBOppB2%Fxu^8+nri3zd;yuY%1*
zRwoxtW53%y3#s7~(Mx=!+xju*hHm6oI6B%C6HqP5gkXE80tJ+xi{hM?usk{I*=SA;
zY!&7?h==w%aXjoDg3KII8jF44K4e7mUmM~YS+=xf?crJT*A2kJ@x_<GRTM;Aj)P)2
z^z{L%&VmHMLqOjIqLCBYIScA20w}~G0RUJi9d(EhECXBy`rt=)OM&zTY#_t%XeI#l
z1_nudz>+C%UyyLxsEW!Cm<Zlds7|RO@Jy!^$xotRxE+SzB6nm%^%LhAKueaT*?mNo
zpK{JuHn24XsyUc(`i?@ML!c(k?7~!23>vH++d9YS-1V)9pV;7xac=9yFIDXRph4{@
z^eHxS=LO4UJ)8Nd<ESfJeJf!>N|CHR)0PXZA~qLWQ`G7JzX4g{h572zWBzW>CdX7&
z0@B}WV}my+mHYM-%ddp4vG4lTy*L5mOV2Dt>yKe@MH%%z5-ZS0%iLYMC_%b30T2)=
zdO0<%9X3Rz!6qe`BCWxjkZO8_WorMT;6xZYOp1;*?s7AjHJ(pb_&>1C2>D~v&(;n}
z%d`amDtQaVhp4i=)jIh9B&dQO80{VAV~OuvW$h)=GUjPL$M??=Dy<{{crFufCzkI}
z55`wUCxo$Wi6=1$!nD@|z8r)uKV^H<B2^+)VS-h@94(L~W)p;RuXja%s6d9XUCYbg
zvZZN*3X}r*b&t9PRxlk@1U{>Xg6_Bjxo7_(0P}uY-ehrtfbm^U@_UMl6C{q*V5YxJ
zW}cKJYP?EHxNPWYnOh5xOZKo5Uf7{6wchntdR3#XCeLSe1!)RVaky|IF0M^Ka};Q@
z^OZg05`8GY;h9KAhA(WHoqjh+_dTOwG+}K1=Ej<)+!Fe@Rc!jlwa>ISr&{hEE;JJr
zbSWO~Z9N+FQuc1^pl$-xc%rBg$aqNgviZtLr`flG0BE?{jX^?{+y!i7AESd;--T)s
zj7gAIY+MrT|GxYO`j@rdCem?Cv^Xo0NEqi_-_yq#WSE+SoT1^sGT=q#0pvA;RfG8(
zXz43xYAJ>c<w>IbV0HBp$!=2dVaAffp0qCoDB{Qror|Yq?7tL($_dlh0Zn%35G&Y@
zYIf)AdIfmm{Z&dpQfnT~Y57#ISQYEd4?0SX23m+3{nPHN_anm^O<_RQj<_iN{6qRx
zHojcI*n#XVDwE6TtKp_4q7R9|Qhau)5n!p3&WCv#0n8&+ryQ#ki(HG&!a+lu&DGSq
zb0)*EoA}<t3~uH0<~Z#coz%NJLBLS;9i0a4iu6;@Dm}J!djC0aiXgjZfhUr3<zdyJ
zRCQB>Fq|r$ZOFDkqz?c6=qCt7$cvN_^^uVgqi0;Oa(T#?gT9_eR(g|7D~m>wFbQOM
zCGD0jtRN8()uV{BMTX75^!oB@Ej*#}W+9z{K-rL<04@e}?%5H(kwrL-%EW)Q;Y)-}
z>>OuvI}qX}T|6LNHX3Gk&$p=CINtt5Dv+{Z<m4q=Y{7v1Y9OLvr|83I<cf<fy*p;0
zQ{h5b@_<gtl6Zo1fXZNBzB;{3rId6DNqapU{1o<t>abV$CW>W8i$Y}#)4~e!tRnzj
zc*@3Rj_UfDFbPqq;?5^##Ot2GwI)uuF9TExLb5jzXUGo*)<iB~5bWG=fN7oE(17iB
zu~7C-wmhZpfGK5h6|~$Zzc9t((|Y$wQNBDP!(Uk0$n3q!?V{u8;b|rNWLwqu##xhL
z7p8Y)!e%KR!yz)uA(EN-7!>q-wB6Wl=m$xO4Q(Y(1+%%cx4r7NMru8An4kU3@wAQ;
zA!3{Kn#r3vu0K93O&3Y7rbQg65oefXVrA-akg?Q8EMe;A!F>m^F2(5NX%sr8Efv{P
zAs)|S7<7%X8iaJ7Vn2ftq&=ji4aI7LD}J9x@6-AZVo6uf@~fbwN0)P<B50P1m}LXn
zgPxr;Y5yk9g}qZS`3LKUIq4wpJPMe{!pDTuXzZOJYyzpa@p&zw;UP+7mL0e{X%w{a
zkYtlc<a!ctut148Xe8xq$!cJJCz!~dxr!l~;gI~{Lj`aeV{_!dGG1V7-uz|QpSGTE
z_9yrFhDt~DR<#QS3ehfsS*daFGHecv+|&P{Gteoe0MZDcp~R+&zko?ZP4ZCfo_f)5
ziu8Y<n`ZX{jhD>e`Mu_c1d}I^Mq*5I2r}j${NrHR;hRk8muRrjOUDk`YNJSm3*F4i
z3xB;TXB%>k5M)a<`~jLdk_RP+$5>np?=q7KJGyrk5f!1303%J_S2{6a4>_>lapCiv
z-5Y08MJY@SGH8uP=s+yr@;q&M{p1&Nd7)g{w$q+A&>XSVx0}rln|(%aMrc2qD^eeZ
z#04AL1{soIY-~HVbrigy#{Hke^+BS4MQ$5hxKfqny)`?Z7*UaC?*TDFM2sXQLJi2n
zCB6nI9W9DG(v|`v$;)@pcJKX!qEIWnRY{vv0XaJ|wY+DgjJu#db(+UX<_6Clg{rcI
z!`Z|8w#itZ410dp68N4X027zgf~`2Up*1ue$?wx02fC&OqD&8VP>Wsk^#ljcE2@op
zfy3R&8?b#XM3tII^w+5Q{rmWCt_ImAK!w%4&2*BTj-+3dP@r6l&hL$epI6(>N|?kr
zw^8?sMbYVRBtlzU(xH)3M_b%dE@2GqG5<#fOjLkH5Eq34vEWDdoR4HKDGMv$q*QY}
z58-d!;4;B)puw^9Be-a#mejT%{tH7XU&hFo$Dm?GufEIZ#E(zMG#qq>nP|j$`la$`
zLorfuYlsUzC}U)eQMeR7n=77<N~dgTh$P(F&%9sGU``>3QHv8puCGSs`3LuduOJ)_
z6#}w~#KFBS*C03n;nJD_+^_f1TtG${t8N@-R;9tw3LE>fcZ&AV2dE@c*togyc>!`(
zkPsP}?7-vvTQ@`}5?@)ti5aqk+^vHfAVa&BC%Oet-IqcXU?LYPrO4!Hy@26^Rvpm3
zfOdzV5+Xf&_5h5po{{;wBcR<lxyHn%V)*KgZH&(qIKuVz{V%uEgv(qUhn2<%N$ohs
zXzG_96dM<ICs068q0@pXf)>q+qR<^)JhVT<s5-9vrnnpTcrj$G)|<>%vi9IYr4@zV
zIqY?iI!fhuw8@$(cH89hhGg{_E+#@HA3s!DXax_iMxlFIjZc575aCMN!G&kR7PyOg
z)#>*WpT9Jv`Udk}o5u&Dr;=}~jOLu%d|XV4ahasma4{KsIr14f+J3aPl#Bn^tSdKN
z*bOzy!*G8&cez^_ZEk={gn~?riXI4DoCG=T<(#^!H^rR4?_hI+#>T58rs!cA=eZQP
z#_r(<+3jt&eZszyHv>gGs&r3eeC~`R%0E$itm=v1v>k_9cVKfG88}bA{Nk?q#(XD}
zA@@M-bW7*<&!L85`M&e6Z}{6x3hE_QhIdmsI9D;DxR3mL`pFm&-73*v%%gnju{8sq
zvT-Yeyth~4cDmn_N;#kFBVjs$YbwqQ2>fgg=BsX}x9={V|9$L#+w4E~;%@r|h%5H$
zqyE;-X>y7!c{B3-Eq9!OhQ`_B5;CttJ9()7Uj16N<DY@mDBr4F`;%6Z1JAhPN24dk
zBRwPD>nPuG&2Cg?!hBIf0=hXhxUBZce$ctk?7Ui12;^kY$y}uGCz_6Q4U1%SVp-QG
zvcRdmIGbs2^}6OqCFl0TpYo%qpSopuaLM6-llELV;!$6xwRiRkr8<Gp&I2-oImS6A
zCl=lD$LSRqFqxBX^E|VUEiO$xGpP(Y6BAn=1J+&%)B3|sC5ZN&zb^)lJMEVnxzTUE
zj=S=97xois)z!@|icS4eHrjbe1oNnWHt12mhw9Uk8xHJ7Fxhi+{)v$|!fu_RFsgFW
z>Wv*cwz~Ro#q<jQ51Qw|F@^BgQz1VBVZ9%$Zpkr?(r{rNXl4xNYJl9HLnKXA3N*cJ
z<~gZz)pYBs4!2uzf3k=5#;BF=^7T0BRK|Ey)1YoRdzb6v)F6|m+`E)+O(RHK)fl+Y
zIIj9lyYU@@;4a`z=Yg^B7hyjV$g$^;SPMw~xVb2OGEtxR=dk*xItV<?eUHh|Vx=2A
zCp~)FxGpsHNj2Mfvr9<$5Ja4K$`eOqF?zE$Z7kE`eU-_y7jUqZr=94wa7gDTziEk+
zqaLH@=CI@pPKi25vY(KMmaw8mBCfTK*SJP~u*XnSq533k>X|>m<`<vV3Y}Feh(m?h
z8!_gQ=${_kZV818U1Z2{mH1vxtu_Zd?j%3sbZuV^R&ZYsdpl1K>xr|s`kwH1cy1GT
z7|o0E+|pg7D8Adts`o<*W-iU+U|d^oGserUEgjIC+x?2NS#hBeR~?&PaT}8RG>KE^
z*FeYWqn}M8t&%H}V%FT7(R9|w>u)wn4<MG0gb`1!)?UjyW*_)0RFUUK#ZK>j9}T#r
zxE*gKjh$ZJp|RmUq^owZvlwPk4-t?*G#cRi=2Jj&JpNOHx69!p(OIwe$;;CIloat2
zG(=k01<TIP4rX7%{e=$AU6+;M27)w@y_F+A<uwy+(r~%JS=AB*+}|$C4$Y~^HO0kS
z<)d0mTWD#|C4J=;Uk+ryXg*IjxvgL`+#lk)H2)P@XnZ~#Q$n(PUDa(crjVUnZZ7nC
zK7H(mM{20!_=ngK&bdF7hA+P@J{x%C%lykD>{0WygNd}DTZSDfRG-S}ID@v8U(Teq
zhq{fYh-=6dT9s*!5J|eFXydy0EJKQpA4gb$_b7xgBr9yXahhckSG8mO{K@UHhtZcc
z<Fgkq5?b?w4~4CEXqVGwg**H@=Qg2|;qUDaX`-8&-VmsK^<2DkUGZ>>{X)%Oo<-e-
zQTlPk+cZ1UU|5Frn?Y5Cx8lOz?!WQgS4fZzrzoGFcaSuRgMsU;b9ajKt*i>P7+s0`
z7xgi8rQ;Lf$?n6dY~$&@n7T@@KITtSP1U-gJ3J@lVn2cjHwuNMSXkowHFY50z$I<J
z*XiyUKr)L64i3)m4c<G(DW7lW_hBXaMn?Tteg>6an|48JVSP)B=ghpymz2lPn%}M-
zT=M){0LiUML%s5xqF-fRBZZBb(q%OcE$(YQVYeFdXykytpsC~6dd$i5B{m^eeNaPK
zftoVf7hhHTlm^tn`n97IgG&md)U^rqUE*&uo=gguVcj%0v^>cJ3j4<AG++bk(-h0)
zM(<)8DQ+l9Gr5g4Zd~stT$&A1Ou3D?#D1&r7L*~-?WNbw)}yX{(-Fjp%KLj)_oyY8
zx8YN4eA5eMiIWqk^n@$y%d>P_bl(H*N3$}=W)6n<Dqk0#n7k~=;u{rA+~r%wO{}wd
zoo8HyX-L-VnF!XmfkfcbU$T`MUn1AdqY0{!pz!mpWEp|w3O*GwnFyNQR=uWUost?8
z?#*R*u3By#^2q@2+taUqyFlFS{jK2lS9bcJQuqUH{7s$7dF5-rgSdhF#Y(Errr!R!
z-LU1M<)&~j(2+6O6;ofs&Z7}i%jHJW!2OOMSyw<wu*>jzL!=U0L+PPis6<q`h2vgl
zJg3f+fM|uy8WV|bR~}p95Qb4o)ykpqH;Run2&NKoiJw*BMmx)V7f}&LX$ZPaL$`#x
zPG#`e2a^sZs>~wW71Xz4WhZgO?vf6Eo=W#;6~4N&y=|nw{(k<K)B=rUHN`R8VPfLQ
zQE^r_t=8@YTwiLPs8LgHyx68_I5=g)<4&;`V!`ERp7fI&6~m-&@55_L<+&!^ZK2k5
z);0afsWXW@*LuI^@lsBnLLuY9=2Y=@@t0_b8<5Dv(|#y`PbWFv>^s(+@RBM=)!LBE
z$<}r+=uxn>*O#ns{nTsMgOx9?yd}WpHRp1)?chT`2yrc1nY?@1RZb@HzTh`E5*0yN
zfAnKZZ)cPZhbariAo(Zi&=;H^9hWHg1DhKimYxcT53yVr*#-9X<PX+1G63bOkYo>D
zw7+z58$|rO@kmblr#v)~iPX1JTkVv%ov_UXgW4mz%I*FoOm+q*=+|(nP8f=Mv8E^f
zD_pYHCQLKEz0#1}l@-S@aB9uIjm-P<seN<lZhy2n5q<OUO1zj5n&}dGjt!@H$`#0|
z_oI}8OrK(N#cQDUa=G>A7M<n{6VIQoe@EaYqb^U@&q;U(Wb4}f%;>sla(#E53%J(K
zi8iuR5^MCwZ&FVkSYDvz3L!i3QQlt~YI3_Y?*hWx*1c%y?oPtJwmDy50WXwzH%K9F
z1$l|QzaB^v5~6Ou9tS(>D4TBE!y|HY7@h}C8XTTHUpb<dm8UJcv!c{bhZ0AA$>T*#
zQTVieg&I@Ut~?)Z7}Nu0XHzgQ)G<aVEYhx~u>sK@6lvGJ&sRcWtreWW=Hy&#`ewIY
z-1_bc>ASb)Z};tXFZz~Deb!zg1YG&aNUMMCR40y~MoIK<!zuQUW*Jt@kLRXYCh)3A
zTkvBv`q(&=U^SsEQGy8XAj`*y_}QtW7CO50yZIIE)<_An5LUFv&)~C`A~kuN6~sAt
z$rM#_F0%Zf)HTRD??!WQ{un~<oW+$RIbKknH?sZcGTQ2I?zYkMu9IvkDbuW@v8I=C
zIO{r59<LWXXXn*4&Qi}($O4|6KYG&l=I!pAy4P!x;PQt)!*6Hg;D#H)YA?JAtK@lb
zi+jgu>}$(cuVNI6dY<2HJ$C`OA-%()2N8VnkvQ_LNLnaqf8M5U+ROgPJwEOAcAD`x
zaS`5WYX70=P7xs<cma(G)!d~b%rk5pjb+dci&a+@bh6bEN3XSVNOw7Y&u}rwX7OCi
zJlvZoXxdeeZb!*J5%~dGeEa345sC5O-r(iRqA!u+SB(@Qw~d%ZE3^{9EAC5?T2W?A
zc_Tv>r^rY0N%kR)p0rz|3HHzCu1<}WpG>MPX`D(c1#+UbHvZN+o?o=sX{K?g{DXUU
zGwp9#kX*PCb4$_wSSJ2PwRf&waDQFI{2H0@KxS^*m3PemCi!Xnj`t51M%rH@TS0{u
zt{;Bv=V={Dk5B@QsGQI5elqF2m@?jhWi`2c755$z7(#o|9r%k&i`C<;0t?G?KG1u5
z{<^R=InSK00UgoS7W0(f57+;?9PnqzYoF!i1dX#%-7c3bA~$7a<q?Z@8Ffc%A{jny
zf{f#s?=fdMj<1$l`f6`~nAOxp@xL2TjcXe4HDtPoaP3RnX7KoYc9WdL-A=SdwDM`e
zx;ASX(^v-si(H!f!bVZDRFuC@j(;sO(l>nA|9iZTJ-64pv!Ja$@y!$Kn-j86!zx9i
zY;(W&#;4S9#9b=)m_?jFA~}U1dP*dVw1{>yji?2V5SX-f{@8)F@>)5K<fzQ8J#UOd
z7Jdt#to+veoqtzObG2ct`&Y8juAkz$d-Mg7enZ7UpyHd9vsRKQ`!|A=<>(Zo`nxz=
zINIvBo%p(bXKO?|^3gsP8&;|&zhs%`L?BCZy=pfy>`;mgqBIenmJ&|-2fY1x=P7^e
zTeg%ZHGi@mPu(3((f+(OqdfcB{3)mT)1RXkB_trdTb!&_EoHeiB)4y$k41ZTqRe5i
z`BLm4+sI^%zg&2f)Vhs7H^q}!A>*5su)yeJ=c)_`bm$8@20|kt?PcPEP3`#0z^7H*
ztit9$<K?fbn03QOCU?CSmtIVdrXjU7+_KXq@S5tj_YTr;st8+kk)ZIc;+A>hQy0Z-
zn?xPME675+qCb1oBkvKm$x|=5GZ$&C!;l@g$fl&R$LPsazLR~+{JS6LbCEaj{Bz{b
zbM92^7qGdP7Aa)Cwwfw-T$fRb*&MWbP`-JM*{SodVr%io=KPKsBr~=qLtEc5S$S@W
z+DL>Q#hxPIqD+0i`270GPxN_O#+_?-uP=MQ4&0u-BYFy_<ppOa=-WW!))QK|GRZO`
z*u_M*L@M~;7r{Vt3A}#cshYtfbv&gMv)VL)QPL&74V8LPrB*is*AY1f`$-k5U?FlS
z7J5aE<q~nrz!lRP%L%kQs1`RV8>_Rwx$2LIkKz?1;l`oE@Gst@_O8Tr__)iH0&-DQ
z*^w%b&~mE-u>LfxVxVh7)-9}*SL(5x<2B#b2pu5)6(r7HxKh|%pnt0N-iCMHx99cQ
z!{O$&ECPkKp1gzCy${9;Jo?Rf3(`p6Pgaq-ZDfBuCKi?Vg-TW7wf_8oMvL*Vh}&Wt
z=jK#fJX`^0zR~q}!uHm$p}GxwEpesmZ#Ycn10lNDuE2aayS1pZdT8oKVSV~;y+g*P
z3{0ATSJ&3~H$Mx@3wbz_9tej%BXg={6J|509QzXzoa}Cph|wqb{dN57F|Atq8BAm1
zmwMlsiw!hSjbyG08X`CCnLL^cD?Q3VN_eMShKwI(bgewCQzA}Q5_>FK9oKIAmn$cW
zzZOc54O*Z%Ui@c+^8zA53Sw@ptq(pw*HVvcP`jitQY>sJ*bqB()YaQBX+hptF(qo?
z9I$fCX6Hgy#1vQDz?Oc`Rr}fZ_{3OT(cfcP;y`_S*Zz|VWy>KBRO96-RNA`OiAK5#
zcdQ6%!40Q==Z$3=>6@m*l23M1K|td8q@cz3ePH9eWNO(#M*pY3v2p}_HvGnE{&GT-
zH`cPIpPX=pzEtsv`9dvgh0^ZeT$E?IBDltMMl)F0OS~(i-9HqPpq3T5q$RSedsbXX
z8UQLe%L^m|itVPA=QawG^Qxi|sTfiNV!6ldNyBfHTx36ze)u|zLLgULWBRN%Kd>=-
ziPBlSBv4B2lqEK5$efpl7E_5q$z>u#RfTmjqYMo-4$<E>K+VlCv$FeQjP+p`hW{(x
ziB#xohnI$-_N{qN?@5Xpr1hUBk&_L>5ee+FPv&89^!-T>o|bQHz@uwJ(<fX)FN~`A
zug*6|j*nWtt$n*ZK)rN!#4_T@?bnNDV$s0ta{4HnP2HvsguXHJzDY(X<P7~f?lt6|
zY!)rA3x?LWktT+Bx0VuCD<c-Iy1#upI{fpQj_9F$bo+cq(Cv=NWaE;Nn=_MorVlwf
zCSr7P@awqcfGg-^$wm1U--S0^-Nu^?<xs5YXq;L8-!)>Oh;|WsOzz}h(Jj~HJ+q5m
z)u+O=HTx?f^(|b&t+r<F&K=sL$3lNbL+*{0rlR%X8sFKf41?z&j#=lyn!6Lr_!|vT
z=gG!NtQfJe;6DipTW15O$GcT`R6kPFNGN;s3e)X%PK;W$*F|Ca+x0{9U|8%;m6uyt
zVzW+X+aG;dN1k8#GKKwP?o$L#*oVR_8z@#4sg}dwYnH#ZL+)=)=lu~%Zrr`(J5%Ms
zDOLlUql=8y^F%=^wm&kD>&)=#FCj0{R$;6cMiq^?k7Aq1O9T~l6^obhD~t7=qci$1
znT)(HcoziQ=mNMXH@8ZvH!l_{V+1;1lDQTP;5l&Rh6J}~KJGmVdPZ-f8?5*Vx*;EW
zf@2qdgz*7XA|-YRnBL*E7)|o6+fjVRB_DK&7Q+2ZB-|FCm=rzI(E5#Q!QH2_jtN#o
zrdNx}w2gLJEY;PsHJ*v3NF|x$887?JR&j_$x$KH(==`q#nBeK|_3!WGv@u=ren%YF
z?s4Tpdu-cDk)nMoH4D*;t8rzaJe+?*Hp@|TFsK3#61Jk$KPtI~_S8vod{(jxd|LS+
zzk)^cUH6-G4;mMT>x^|n%6jVMs$=}W3X-)~Ysk~lu8u3Ky;o3$4D$qzvu7x~3Ii5(
zCP*_yRKIWqVwune$t#b{K9*t77I2ER(vomaSaRp5ZJ}26S#krr8^!%Xaf_4x^ryhN
zeG%w^EF^l0|D@}jLKr=ABTn-m=0)+qt<ncth!VxVs~U}ee_mQrQng?SgbQT6dSw&J
zOp~K1(fRo<FTUH75w>cj)5J0Io$oPt`VlX2S6~^_nPx<VshEQEYdDj*E^%9uL?tFe
zcdf{L<sX#!i@)S+R(hxgEr$-_nCyG~QR<nS%8}9t?4I>hnG+F>gq_SHds(Y}Bu)1`
zjCUH|k&GAMw*{0)!CZr6#ILCc@A<{HIV~6KcYeHiLoMcD{MLPbS^v4<{~_zGqPpI`
zsBc=NQBu0QyN`4u-QC??(w)*ENGc7|oj*E7K<VxdX?VBi{Ga!^c<#JlI0l;?Yt8wY
z>sttc{=}6hySLU_fRPk5YPRgqpy9Z3T31f=<_>KR2YbpGEU{R0{=4QQf`ktxXH<3?
zez7W?KM|^COd3I_zvcNsndrpIU*N%mq?au?F2GM&q|+6b$4_cQU?M&9fDDPH>)sB3
zRpY{4?0@?O{X6E>4jF;o*&@2-2SJT%?}>cpd+Ukw223uvDvFG6@MGMfHu<CAa;ZW^
zkSpA<zdzHS3#Zeh_B?yMei4(ahOJTQ$nj<ICVKfnAoevawI@*=pF12Md$Krh>*_6p
zR9Svb^dNt<j2q=Ggw`!7irkIJr0LtUG1(PmCI+ucjqpfjS%lt6<(a={m+@f3^JJ!>
z(Q-D~d^jaX1)56tn{8&A`L}-(kjsMPa<CiO(3x*Czul<`M!>#RzmKAxzzWh_A~j_K
z=iRxeh`>_IXmU^3f&X$)dT_fZ<OP*XkU7)Z)$`0Jjh`aeK)!rKB$#AX%<p$8adD;G
z@I2jr`{7P@ith^7sNQ2b!j6M>4#A$OGI}z<^qucuG}4L})7oNn?)s;Q`8{g<x3h^B
z5ULr8R7Umi?SSZA*#&G8d(_KAw)tz}-qB=F*eTZqzxpTH6I4f&E?skfwG5(p?!uM1
zh(ue+ejJ_@mNixWE-7>|ljuJ=)(BC(?nqrCw)SkRyV8PEHID7kEG_{TLkTC%6X*?H
z=?PwwUw2x!Wa}0t@hrk3-HN_owq9+dwC%c*ujc1%RiL~2$V_BTMEaXzw$N4BF=twR
ziKS8wzl*~xl+R~8O44`Gc~oU4!alcesl-H`#F+o1f-LBffLt)OUeaM2tNtW)btW)s
zs(9`6sGytv$MoJA9F6ZEkh=f&{4B3*ky*N)$+i5aBR<=H(CaUt(~!cyp%&_NvIzmQ
zW=nu$Ol{dDSe=%1MY|}-_WNiWsx}==<Ymm#AN3UIF~uBX(*rK`cXo4i2~M6@cAdP>
zCLRin#<i2nmeIVtK@gojR*p~y;}OEINb{-kbNaEhIrv44S0-PyTEVkdu5ydG(>k<3
zw1cE1YfyVHp!aUI<@lXuj&*?J4sJ#nJlt}qV$KhIdXwUzR7gf|7mE`SW$>p%F+IAo
z<&A5K>LEmf1Iy4VvkZESVEsy@hHeQHzRvodN`lkf%ntMot+4m(A`eWA`hWbbstx=o
zwfOsDJC5le1_}p{S}(q-2!)0oVErt4FMR*Fo}5fW@V}d*f>M8PB1DnokMZt1ta-6?
z3I>V|y=g?jg`~j|((MxteHZ&?lakIBX7XmY))Dg=E*H~tqTvD7ZB;2vd<b3JJ<0Zm
zXHv%-g9rvHSiOM!i#+L6GZVN6!U(Y$cVS<@&pK=07nHQ|&2FnmohbYIr<AZ8zGQF+
zw(^rsa9$4;;xrTL5+yT;6`)*yNZi7(jMnYfe@uD)L}W2R@)9nxWw`Tpj$fu0p0Kq7
zO%*{KEsflg*Ga~uZ+iB9?RrHFB;Eo-aYYC^ynV@TpPaWoX!$fjCI~fe2@dY~MLc$x
zZmp*`;%TaP<Cj+_lR4zFKXxxj<#jzE3fh%P{R$m$a3U$hHIf<rl&Aug2EO)|6VlN5
zYfC-6JGGwtl+1N^{FeL2N}*3>KmpLO>&t{M30^x_;UzpWiLH4+z7kmao%AE*@wdwE
z;UL3}M-O6ipjNFeisJX}DtX|YuJqlYz9iGRBYcFt`|LN}lBEF|y!4aQNh0X;v+Q>h
zqI5DRE8<S5N|k`dHe??$PNeUwUwQ?1-A(0$B+^SIBC+2aOg+Jgy11E;W4QikzOz_;
z=lydG6|Vc*m!9(O-4o|33UO^{Ro%Ap-bh`l1S$@FXWzg#)`Z+^y@jL%jNq)(A95~e
z@;|5MoSt%Vf_B$N>DoD5S&`P}^1ggOZP8;JIVV<qJ}I=6DvKb%X=FCuWbj!Ao8#?&
zo8z}xOS+x+-RH_@U}ETT=$gygbvvaJ$?n1A=-+A`&|J;rbTg&l9=u;?Nv98~P2=Pl
zbL^)zbtdfD5{JT)!^z|SE^C>>M#0m(9wcHG_~HR2NxtE~64S!bMF<T_1`1LqIA0yi
zD0ZM6P5~dFq|_^H(x@Q%w=jm9e^bWgr{GKrct?&A=3&lG3cTt&Mo+27+Do}m7vJI2
z5W(zEn$NuRW1LN$bBIC?zZQ4SO!5?yIkeIDAbgeIFifJ@$GG%0r1-!iRmvFLt3%~P
z1rzBS2bEGAS~i;d>6C2gax-?G+w!RMZd?L>0`=oyM%TkOd5%e46TfnaSBl^^ExZwE
zs9%TOKJ39VxNX|$W)1G&ecH0*_S#t)C@f?A+;#@+3z|8pia#71dh4>o2TmNjn!5bX
ziXy}K2{3|mS|`O@116P_Z?X5wr`wtPBJ256(hkVEeIoOQBT@Mw8qth1h$#6qnTW7{
zn&j|HUA+CDVC<PtS1IPxC^=~De@Ak-s0-4lLpH$a!baCXkdS-WzFWPlJc;noTTI|}
zlt3Of@5m_km`xJogyk`~-skksQqTKsG4v?ed(WDmheH6#1&<!i@m6=LTD!@6Q}>J{
z+PFlRQrFy10Xe(GFRuiNKCR4r+<g{5M`$P|+V$=SUy}>9YCpSj9qFQ$%gss=`3(ws
zCEtpMC85<q0?Q*3mJ7ene*aKtoAJkL?@<zJdl0iB93Ayai42$CGFJ|nnr?lcQot*T
zZZM9gfX`=OE2+^}9{8c)lEQYIZLcczkt=O<hGiq47vG@Ao}+p_cbd1nFC`#(Q{c`1
z`zj{v)gJ8NJH^;t>6FH!&#}C<+$IzgAIc)#>l!n<E(zan3AnwL5YgC!B7~kq#JNpM
zHC%Mo@8Y`~tr+DtV9DR%zcP`q{MPt-qgQ0Ic;o#Z-w{D|jj;SLyzN+tHQ878Oe%;l
zns4sH?&@o&Q1q)bZq9or4B3l$HWzJPeIxOC-h9>;Avb8KaX&9Ne7a$#yQ5e6_x<=P
z^jUcVoLV&@@vIzHgZ}Y}i94ZIyqY>>lF=tDQ%2<FqDUrH1gU>$hI{Benqc?~V-*Xe
z8=!6XV^#D9*IS*U)IH;Wwz{c~3wZG<uo-nYBd5<D$n)~ab%avR)7m<P^}E9*oo@KL
zaGZ>hQc-#v^UM%g6pfIZCt=$$dWouB3Z)>>!9NfLlL$Tj^)m9*4f<-&cMDO!$z$Fz
zSHYR1MEO`<-c=~A<Z8gdbk+-YXONTdYKB7$R1h`je$P~WKqkcT!C%m_IHF%&^%hCD
zyn$j(aTlKR<6EC)Sr41xNO-XHS1!{0)hcdk*m1*1clYl9{OYCbX0p?2`t*hFHH({_
z;AQ8R_BJXV)T`^Y;}9P?F76H};O*o#^UuhS%P00~j;F<;cL@<sv>uOYz*|lI>4*Dj
zFIMHiV+cJO{UW&M*ze258)2+=L}WqQn@xdajk0}8eB@;;bH;|L_oWdQd*rLeWtasi
z>q7?^t4W;n%zZyi8w@j8+eF{Mqn%siI?xQ`m%~3EPB=OZ4<y;oyJ#Kl@|3;g`s}T5
zL1K+5E;7!=aQG#3)5jwlU7H-y73TM1*@fMC^V4am4~2aPDKuWu{kB-VThLc1qWFzh
ziRr`4q98oBeL#$bW@zQd5pm<k)8G@M^;;^v?E#zO3;*luOm;}&mbJvcHD>OgQ1A58
zTRd9iUC2kKYZD+;*8WuD0}tATzDE<oU$Y4IQ$yw8{}oOe(-8bLS(Puf_i3b|lTKC6
z&CcyBy>lNatFcI^3o^QF58PeMm315&Y~pH{nx<&RV@9&M(-?WIqU{$Qa@G-p*b!`*
z>M9f87f8ELn;^&)F!3vEAtT-*T=N+;-CGKrKu(Fg1oV%mqOl(a=(YMS7B!5R7>83j
zOd%UXDk8rd{TJi?;u+C1zrBie+`00}4hY0vCA%Q>DU6N!uqiuSJi|3tE;7_>J>1dO
znXYUezdr>o%h8albyrUk^kB)_<-;(gqX!_Dy{7wnB@WM|kKZ<0@EesJ97RIw9zY&P
zr`@?XV)uQ9R?Z_S=Yc)^{fC@4@X>V1SyjW8ORPdq;^UO1Wc9SwU~l5ozl906-FseC
z#EF>aP+>Uz8atQTXyY~hQy5OCqpOn?v&(O#&*(HXK3_Ap(URQlUIX*SoNpF?T~20?
zkJes$|10Co+ae6!`0l-eAAAe~k7-X_yh^%H<5VI_!U24%osKBQsDZA#_&BX}pWI0%
zUGa;eQv(LX$9Dy)vrWGfpZw}PhJ+oDpLmGx$!v^a9dGE(;G5r;-<&_bdPxz?+WGY}
zq0X~CdYAil>LgH#Wn#LJEFKG#=stS}l%6-v-LIqhx@zu*5}4?`qyKkANcbaG@FS5;
z*O4I~z2nzT?uZGT2h@a&8r6GJlXXZ-@0kbZCI7mL{+pP6KUv3+bW<sL)chY3JxSLD
zU|L#I33$<XD!cLs(xPu0JHMR|?kwIWb`r;Q*S@2Jlg2X(gA8lU(p*p#jwQDu*~7c9
zLfqjxcR#$XnNk=EGxjX+)#E72Gs9E;RjI{*sPC12KPlh<c@=_t{yR!@%EiAbHhO#s
zRJWs5d7F)ewmB=2+C&z&@mp(Z?o!*A7267BoZudsD9PmbXG`E?ADq5}zBkR=m=~0f
z_*E{4C`&Wrctz3T_%@xu26Qp!?ZD)iK^bn?aelIumHsx4MF9EReaz(iTSE!@vm%6#
zvT7}`?Ro~F{5l{tFU@1uN6)lZ|CTfJ4o8$#`-ZQTeT(E&e*jNTWE|fdh{P$}y5#Kh
zY41y{q@ZO=fQoEbwmil)3e=2&%ezmH1llq}qSnK4#h5-ngUQM5OVg9K!=RS%=p-{2
zT*x>-VuLpQN4PvbA)QQpNuCX5a&4AU8DsVEbF`yI-hxL5KC=k+6VjwGYY)>gA{{W{
zAbvBQ+^?u@fe-%ovD(fWa`hM!&TVMt1J_tSK^^zlUc3vnjqt-j`H_P(8q>3o>)=Y9
zsS0DTsw&Hur^h^_>eaP6hD*j96~LFQ?6hx@s>*Z0lm(4Wr(&AUuANWYq<T!BxVI0k
zLu6$;pZ|AkLc9vU-R~b2p!3b^-3ApCOKHA3B1&*#Nx8EzF3xlD@$WC5aR-$*?JS`_
z(HqNmQWnke{g`Q%R4<|wx?3}ChQlf%>+ue$EI(I8<-<CstyJycUd2<+XE~TS7k$w_
z-=pDM7AJ$UCq+UU#Wg2YaA7yE)yhhWpn&Vp3IgV@6YAh?)3D<y@BO4^V34YIe_S0Z
zb9Sj?LpuXgM3~|jumg^tgjJ9{8W?cfH7%JO(;n!MKWD@#*M0%Iuau^1L63|9_q~6X
z4eCdOOC*Q9hX9+lYGb7~1rg<X3KC7(4LvrMcT$MdHe_|<P*NjOOYH$_Udm*T!3z{3
zjYV75;{V;C3tP@fi`myCvwf3U$dg<Q;+=BIwl+N;&{mb^EqO*k`jt-Q{LP+><TK&L
z4x$jx7Gc4@RH^60xidtZp8Y>eOFVY0(wq&fy`FG3DWCSqM=EfdBggr=qIK<#Efz?F
zrd;vOqdDh!J7c&JY{{H@T!k#M$aHXpeYJmV&)>h84>mk4k9DmFji9<|gvUxGm-<NO
zWSh{LU*-*(c+V)gNLsVq?8?XpT>-5gc{#Mu8n%!NwrM;{&Tuqjnxj<xu`*{xo<LC3
z31Szgrk%pw9AC77edCj1!|yzB&-wsfx1xs+(Nf^1MEh4bzWCX1wZ|fOaNBs9X&(->
zT;A`3MF(w*7WXQ}+QepKTnw?Z#Oz{K5yTPHwwwy-wu(=f^8<O%q@MIkA`dQ|MJ0IU
z@2G8p-xp{deC=-`yPyJc#5*reQCvFs5YDf-3`Mjnj_o_fl}aphIz5^6WuY_UiP1E}
z4aG#>kH0op*wQ}#_F(dCnLlf)a|D9ohFTJx7?+#&0S(aHv#>$I_z%+=qMi&5H+ox;
zFZX8i24BueY@!PLy5$n<E$fTQi$)0n*>cj>O%6%nu9Ax>=U`{;F<(a~#d#5>YTA%%
zV`KG$%!DY5ab@0{-K;5H%i=uY@L0pz%ndeOP)er=qHLeWgC_hd&RnlmB}Sm6|8EwG
z;<f*WYf*Hhgsa**_{|R!1$kqMQ255<vyaHC*L{Z<H-xmAqB$(E!k=cK@pYV0iE@uC
zT!>n}-Fj{Mw}Pkg`uDO?yNH5x`&4{E6w`CxmqxiIzCj$Gn|5VA8bn)bH+FiYyzUO#
zJoN@y6guKs&Z&8n)U-*!*Jr4y&M0TD6Ez@G)}sesr0MxM)geh?tiH{tOY#IRrrn}B
zX6@b2$Sit+BG?>el(tLS{>;?%UbA9eG83tw-^;SKE8mzEOE2|lw!du6+X9Tro89Oo
zJdY;wfimH*JmJ7-%>L6rVgPr4ZPGU1=Cs%ftQNBWZ49zw++zc55oQ)oqwgNm_F7Vi
zI<rRQCbKcFZcE$0FHZ;S=prD(&n5S=ibKnGZmvr`iCL#ApIy)Oap#k~sz%-V*41c?
z6X{^)$<(v-`iwZJ6_G+Hh!_*;!sZffLuzqM87tkAUYZ|~1ty*c+g9Wk+C%Y8tu_1*
zqD<Tq*)*<!@`<<e4*IsrZ0TH^z&vN4QVljj=$QWcY$H%8r*cJo{&eH({i%^63slX~
zc<s7kVJyTMF8?sGCER9BuO>`j7Lm7uZ3CcUnUdY2H2LW}&A@QYYw?iYY7ww_>)D>R
ziAzVE`X~KKJ3U2k`QM&ucwU4)WJQqjv()uj-5k~K!!P|vWc$7ePFUf<f>BsGA@RT<
zE;dOfC++-^yjB*P2^V1qDrZB)uUK(8c7<eHpsK&3u?-W+qh)s)Bu{u>ut&|8$*j|+
z74qX<g;*U7Owe5LV?|J&`eW@KOHK1rd)~W1DiXBL%=4k$ofc|<^mxSsJ4`}%RBNj+
zZhL4X@tSw_yWwlS;nFzF`4jqF*s$u=uoGn=!}YnsF37J7dngo%ewr+nP2`4MV<%~L
z2?|;ZoNVj_kNOT9PY++OmOKmw3CoPSp9*^ed~rjT-*N03ih^P$sgYjK(tT6t3&U{-
zVm7nvzt>-902_jUR#BHvxct5pmTQf^-&ToMXGU2Bx`3%izWuKd1%=xdPnYWfk#i;2
zmvGbPaW^26UAu2JN}ty>vT`Y=zDE{ICIDvz+$4xX5ksE8@k+cLjMt9%lZVo!Ux)l6
zFBV69pR><I@;+(}bt3OvZ+F6eSLV^#!S~OLQEj?Q+@TX-;Cu%QDt(izLqM0dOB%Me
zX69b*!Z#AVo#eCUvTX-z%S00Blv->M+oOW4S8|wI=}Rg6r#zM^5ISZp{{GTC;Z`F>
zm*zv7-ZrSjU>15)>rmgNMpS4{IH02^=Xh`#RUCZc<do73zfy$+MbXd18QF5&F@i18
zX8{UIgoG2z;-Vt6|8%IkGNa03juDwuGAF9ap~Xh6^9=mTx<;qi!kA?#rkx2~k{|||
zRPQSeiw}(HOD_RBc~7tdKZv1J`yTGveTfRyOk=68D05Zq&cY?v1cFhbC{UtQ82`N4
z$-*3;GA~VMm3IN6FE)oeyZs(jR@rVcEqHKZ;R~7Oe?Q!5KRXR3t#Dr+HaFm;n`MQ3
z;sTf5Y<F?qH>US~jrQ5}b(!_+%E-%=^=QRR3pm!)FK#dF019;Cus2Jg^$EGn_jMah
zFcrCcVAQ+zQS@B&`pS(*#h1yh<`)6%$a`i`lna_F*~*3HE)x?|^40B9R*83x1`X54
zJcTyAA8<4M`b|##{Lbe+{J<ZvUtaPe%%J67{I6zsJK@5z4$8^P&(j)m&YBDxS^B6Y
z?-JM|xCq|ga2|d}6@J0%UxM^Y{k5A?Bl6e;MteV>m97RiP)L8#{5@XE5D?lh(4d#O
zg~9<jAcbhPWd24*JS&3I#gi6L!MJ1YP-ByJ(aD#BxFh-xXBvo>pmgd+Hq^Z;<yiAP
z4qSH5X=)LXWq0M!9Q!$h3V=;EX)Mo4&*8Y9)L@@Apyab_BWHZO1N92K@}+aJ7$jX{
zjx~6*oPvCE-4wm5hHVVl)k5<J9^t0;*O$=ZJ@!tcPRlx<=Jf6I!1&aF=pLtGvpC9m
zY3TgZH$KTi$B|;H4fc6GzmL~F+%3bZHw1uQkK@ahE-x<rEEXuF&1g#j`MDJE&bNwo
zu>(LOu>VR`-lEzkRDM@M*sXu7Zpy11awX5=$pg}DKq0cvB9gR-dMWxXLi+XgWOR}y
z{b<&!?wBV9IBQe&&Yd<kmG4&lcW%~^163X!yCffD=g(hzGbrO>t+>xYJPBRw+2+>x
zU^Ig%;1;x83Z09)-J>}g$rCh+he1mL6jI<9$y8Z$>XWaUXV5EFqI$PIRoCO$35?iJ
zj6deT*oe-XCm<`Yedm^LxpaGFjsi6IsU*RgpO&TR8*Rx?o0tpTDk3}2@L~{8{AvVF
z1~lZ3SUv2^0OaMAeV>26_z3#$SM%1NPa-lNU!eq2bY;Fh6cctGBSR8IQ;?MB5!FdD
z_RrXm9aeWmEjd_+*FCpvih%X}`tdE|pLz-n1iH)51quNgKUvJMmxh5v>x8Bp-35rj
zu>Y!cV=dbj-%cr>2>|0Qzj2%GHt#arzFkOc+x6A6XS&JRH*6NwLazy-x@WjFlU)~Z
z8I8QtW8;x_1>MzhGlt6LXH2c0hBg8qzeiw`I0fO?U1qjHpFFBIZxo-6k&vP2$P=%w
z^=u0dXtg>|46wo@tqRA8?O}ga9WY{q%iWq%i*s!COFq{jO9osl4SIDl-MAjbQrZ3T
z!jo?R`J(mV#Z26bqyt=hn8qxXn?A4zVCDQNgEh8X-MqUq6;<jLmLo3Qv$1hkQ?<U#
zD*+**UkAXX{op=|uSEUE7fvAXGQ`952%E0})y&{W9r(601rGfafuNL0?9P=Wfc1XD
zB(#jv9}KYWu9+PNtN*-QoZ8)NNd}Hg$vflPh8*~<X@?R^>dtl>XHe2f{O@2&TBdNU
z;<KrvdyYN4CIi<uz-Iu*hG9Myk7Vp=ntJ#ScCIbN-Zdcqa7yl?^3Mk8IA6Qo?cgkI
z!jPvnNeMu-fkLA2u#iA2JTZ}R7vKL*?y);!Y9G<}PA=MJ?j(C%Jy7pg925**wRZ)f
z;vN;G(N<agQk%$()_$s&Ol(s&q8ol5Td%Dg@*IVXNU6-I7z?5C+F$26<z3AV$un1$
zUn7WW_<&9X{Ia&-2i$v<x8psf^rh^Cx-kHBnsqJpLGy?0prjecZipVN(g>5IhS`}n
zP4l4}z$1j_*p~_T_VP;pfC>tyqr=bY$hT$k2<Y`&eZYC$RL=t@F!zYoUI_s3NYt^f
zMOOkXYqhw;=^h4cm)wkkb9p2-8i@0}r$O0Al=3eZ{F*{tLV~IN<63aRfrpEKzgkCc
zL)$yuz~OoQ@zAc`^Dru5KNU9ROMC&#3CDZ_*OIq0i24Jz(BL5OYQ{<lBfc+-zFAt1
z-iwW{Tm>Z+v4&p-x+?eqg0Cl#rE`J$)|;f!#DvmOtN2N*7^>_I^ytjq*#OCo4NQ16
z+mIX}M9jkoYO>2><ua6pF;`JIg>}1A&5Ro@x=1=OR+dEwe0W@27OCGPe$v03eGF+S
zW{sG*F8P2B>5Xc<;$F2Z!~AeggvNqC+)#qqYQ+$FNe&N54IaZ9rPOwqS|Fz!tB3I^
zwa8D$xMR|>_#E`@GHGD+7WYP5iwQ`hBU&!@z{5}44aWEho8XXxxr<Fc>8(abzS#bf
zMV7B?C*z2sT85n`_vchf@wAYN<&i@bASbu~`ByZt-Awd0GF7Dr0x!+lBo-(t<L+%C
zqL26riHw_H54CKiUHgDx^P_Y@W43C8?}#J;kduT4_{!&mt<I7{XVLUo4Jhmoui38~
z0*K0L#R%5QWQYCu$e+CyH-`LZBvE;q?}vPU)t$3Z;!yc19^EU}>ux_om<KgpE!9+y
zU+wrU4qs#D3Dhc<poo5?Kvvx@&6*5~irh9B0K{Fz&L+}(2gW%ausB)=CAy0vofO{F
z=S^(0cfF0it_!~*fsRj%4c*3LT7pvFe@sHf9ZHL8zPU&1Q=7RU)T7&vce$5|r=e1j
zEo#^r_yXp&VR}PcMrh}^7B&tt4u1+Q?JV`vs=`fE26CM{@=^c!!2=z|&ieyHt4}g#
z21P$BNzQ>W2&*~%pIwP~mTV6mTsh2^#GBOt)<XSj>ri42)fqxTH~hDzl{9NKwV?d=
z`>*^CosJgtXdsWTxcYsn+0<}e(E$89n;=jYu2UVt4ZrENg4$SO9mC3JP`2<*tHw%}
zqb>tF`1VSe@DnAC4GxT1A|1U$tuj5vVwnnvM}SS)6e>-_{Y}vFrT0~{_jUE_F~pA)
z*Ouo&_oLe5x?YyyC>#nr6D<$&>40qmbjz5h$y_)-M;yj#s@cet0v2*dN=VRXZY@$i
zOQyYs7C})_Vb1a~lJ6pBW@F9@-kY39+IgX~>>Nj{i$70Y!;k+YTObvqrceG%LcA|6
zk*#GrYD1g*IQJ2Q))dbSp|2!~QzCm=C!|rLVbaqq3CaXz9!+?-Zf-?k{7T!t>DL<4
zfUU6Hj?Wft-y~Pm4@$l*{;x!LdQIi;MP@*6=r<^49YX<TSk^HuE#pdKwq)F_ny~a-
zGUx?ts}nA@mD^pWrTAh0??w#A2)6a$(jqD%G$e4yKG!{=iB*SvM-DN3d6Qo!)TC)G
zR>B#itJX9DtaP_<?^?U0ZWWf|Jgmx%VaXvoiXvj1oxWYoV$d}K72-)eX(#&Oq$^!F
z{RJk<0<zlo)iUs7*hq1On+f0(5x)7Lf&By?+^Pg{<&%$dB^hSW%PHeAp4x-<ntyFe
za_mGydQH~H`R_$Ni}*_bXI)x`LU$8B@1d6H=8(nWg<^RJyW&PgB^9$7Ev?`>YYRyU
z2ftT&jdd9@N)<A~+S8u~G-bJ{<)hA>1=6r?Ex9$E+)!Bpw5+#}hwBf&XN6vOf6wXv
z6g=e)8Y-}DMCaGH?h{)y;GwN-TLL^|Bq3kZwqjMr?`bLK!!v#0YXG`!lRwx~deC?`
z5aaU#FYMZ8>FuPozBRPFrun?70nR&~*8+0)UqbQmzWRk9eAruX6Mu58>*eNUqAvHk
zqWZcPgj(3;xkL?|?zYm<*A7@Ao|F*-FjbZ4ydFLvwIEh-xpSeXJ$BJRkr(}v2z5lm
zC#4a7^&{u+m(BD(_l=4vI`%imo0VYNMRmof7mF{5WhVOKKKS_v^;&m?=Bw`Jeb&uD
zCU5PFH7*<CW{7UwSeJ={_Hjo0hskYv;D;PLbs2PI@2aC?+lVZf<dCj{EF`Ieo7z<y
z>9clGf&Rn*w`jR_l6R}o>9!ZK=kW%_A+(OD{wm*7(SohO98-pD$js8HhU@79!OV=y
ztTvCdc&(&^2}sBg2bj6Uci+N@D~G6#884UjS1;l&kiU?ABFnRD4^7~mgzyr-@{8!k
zHq%i-C9GOr-YENxkW1lhR69tj?o}nm<@9^D%E`_ABNog&(+;L*Q#mf@T4&-@`~?AJ
zDt^T#$2RSb00n<x&L4_Qr*t4pI0#n4ujRem<y{W&bSAAnEw6&)fWtaZz$VXaqvHcN
zvDX@ONl8iP+#my;_Xz7OqPMXSt>3eYL`Kz*pXG>*7v!$q0OyR-`4|{)9|QS5B^T)$
z#$fbaBeG+(#1_%0CC|l1XeSy!T^@JCQCFRwq=zUlxa08Uth=GBpN=Dj0UrIt2fk2k
z+7RE#%sa9%O%0prgA&fMMvYmfafWqDE%;0SX#aJlPk_x4d16sgMFF3#IN##dIsl+F
za4S3{JhHjcth8;ew0j>k0m)`Q3comO>Mdt4IrT(pu%33Sb1>Ufm5-Gzeo@v0ZL#{f
zwWg4djH0J%hVVl&o3OW8T%58bDO`MLgF!FL{>yNVUJvQ5i5o+cQpX$2xZ3&PT5t%y
z3re3CnN50|R1vo>F6`u{2tFh`mt;$94E0HzeYWi~%}J!pdhgp%c^dH_N{eeq_?Jt1
z#GKQ47((~80Rfk$voTBIPpi+zs|ekAec<C6x5IglGS4%C_eu%3j|SCJb-#-ikAGOD
z)QW?;Ub<a5gYVF8F`XrCQo!Jn_dEMi7SYUr%ltlC3mYfBa0GSv&)|XuP=_j3Kq^q)
z{E3tL345Ba)jSl&;*ThN>C37EyVjGL&K~$1-Q`pi-A=hwXK%o+AHU^0)_bfs^O*JY
z#F>ra24rw;wHswE8#j-g=aB&g`?2hAia(TLaIO0H5gyxg>*u{tPPXha0X3cE$e%%*
zSB6JpYXPucE|pP!3A68`Q!scXgq;eoWO2Pav>l}D$rhFdh^l=^N$1nRUHH1)1b^%2
zLL$d<2B;EvSzJyx2z~_az*e5F*DsCQF_?WPX4S8DGGbqdu4>E3sLQ_DTPJoU^Qqm~
zjF@-Ma`n2FnF##Bs(zK6lWxGHsR1R|X_FEdC=$o;Q9I4~;kfCtgr~#$&{jp(;Hm)E
z86>du&-kpfl1ujIs(m|F-Udtn%?-{QW!Zb^9{2H2i>vo8a;(Nyo~r*yr0e*JM!%lG
z(wji2hVIR1?Fg&!^%B=ccgV=oS<h<lwl6g6HJ?MCKWe!t&t_J^ft#Ow<f(L4l2o#-
zq;+%(nyhWv4~iKCP1&-bn8xscvb}irh1rjid#cyt8W7eA*LV4&BR&!Sf!9xhyY045
zTe&2=bt?*LdJ!|9@ihM_Lj{I|yscg)w&W;MJu)?e#Y?jMCxVbhEbX=`QswBk1QIz*
z+Z<dVFA2!<_7k?P+NMhxZ&=Tt4pbMEPji<o<=6Hg<$11w>^P8p(_pREE=9E!y4L%e
z4*iAkV3R|9*de|m?iczZyVk@zACjDkndtVIc1cjczwMdPfU2shDrm6q@$p%l|N8Z7
z3-}r}%Wclzc-!CX)rRJ#7cjk1W5b_eP&9)g9JzBCSSPBvm+Pw`q_Y+19HNe&Ys(D1
zraw!SL~HE02{9TRHoic6K1rM_@PrZ=j!y5NuFc$3l50d*sUlVzmtla}ljwNm@NOG~
z#W9yVI+(3w*HeS8l-32Mub9(JdQ39Rb99vOiX@OdD&C!yaX6Yko+a5^>Q)&-j^{fw
z`$l0a8bL8<RAJtTy<w5%Hj@SMN%NtVNiol$_0uFx>~CTI9vVif`mPu?p7A-s7J##-
z)P#A4YZrH(b)MUO(kQEpxObvX$P}jDM&?7370o_XJ$7{}dUY5HvX7rf3#;G|P_2$c
zy~yT=LT8g&*T_F`T(s;Eq7Q-Uy&6qP7y5Sl3~aS1p3d_)-L8{sAMnkEU0bgL0E@5z
z209PAkoc~4*QwUy3^W)I+)poxjwq$|Impy9cDlD#V>mCJSBq=fA_M%WCr>}<pTwu*
z1!0<`Ya;z3^!jL3X;ter8FE8v8(Hf#vavh$po6D8o9$XB%*J=CIP^h%lyvIyboRF0
z>nXugZ)V}9Ghy>EdbU`2^sjf`-%^6(V~658CxtJGr9CjBv#|uYX;G<Un?BZATKiJC
z<dKrIargC48QFw}I=Ot0gsVO`tLx<jBR*}-;R#JI*t5|IjcL5<AjKu#**EKo%D=OY
znoV+OS=aEm9xkCH`)kQ(1%eZVrZ_hm*#y=D6RZa>8(YvOFC!C^{loo_A8$A~IB@+G
zG2r0dpUv_b$PCZ3>_honLV6*lTYg>5b;xA@zYYwh?zgi^{~}q1^bFvM3buLmFm$~w
zsR|w*MHWcjB%n3@H4_gaw$e(ZMMf1kp!URW(rS+sw5%J?<{06UanK^!2H`Lb$nN)j
z{>i1)ou58qq_eSy>H!BoS&o`>UxXg0)1LeVErm=otX%BzO7KBKx7*b!l(}>BBRn14
zf{y4snHv%HL660j{=oDsSuRbcZMd7ML>Y>TR#B+f4(CgFmx*B@=0_^ev>Uk9aiV)D
zu4A_MwaWU)Laj0r`xBANl_L_U*<DAI7B6t79KQLjt`q1Jx^OBbv0e-S8n~>-{@8iy
z3X)Uj>*#?Q>0OW8|J1@={QWmi`#P%OVqa>CpHRW2=q-f6aBhZW2Uqi)uva7OKmUp6
z)X|a0l^DNZT5wX}l9`&j^P>>gs|QCY9;p4^t?hq|S6y5RGp=nlx92W`xNrbQj0G-r
z;5N-m1N;!CQh^|qw`pqu10GFtoSV0m->g(%Yj@}6INr9!|IvI&;6^swdSW<CIHqY*
z^&F{guQ<S%?Gt?;?Myv6y6Kk<SxIf<1x6}FxCJbGdyLR3MjZb3g5xY&1<RtdB6E@$
zly7HA2XePGcXk5@*fxbZ@3Usgyu7N!BYP)nvbV-=Omb_>JNjzw)`hddp#`A9TP_m0
z{PT$b?!j<q&yu?KFZ_2k!HS5=%s!y_knvhT`sd@{Gu3|LUcI@K?YN^o>4VdCPSDlk
z#Jd#{cpK3*Cj}PuzDEa-vCk-p33r1f1o;l~S#GZdaC5BlNSU_k&E8#;rN!7syU&qA
z$bkv<?<fe<1{}%osAIWE%I2!kIZk+gulGQNu_qy}@SPOa>V62N1%K1$Q4H|CNI&$z
zZxHJeM?qD!IduUfE*QEN%ltvtoz<SEiU7Ao{I{6rqnll#6c^UJ(HzSK%jK~>Nz8|Z
zCXHuym;Cc-4FTZ{@Kwm23{J)31abwOLAE@&!j$=k$wkp=j=!?=)-La;tW3QUJK8^=
zu8X@7QIJcP<`ZiJVvlzQ0A%53&PD%di{;psb+&<ZGa<1DkwRUfv(i3WEk=eAEGb0b
zPnFhEpZpRoD4X0jFz~qp+lT199_M4V&uqDtw)XnCV*m%Jll20ht{?kE9{b`UiQv|X
zNpwTj#ZC2tu(($lF11{J$_X<N^-QV5v?PT~k5&KG^@pwG^qC^K9l3ktSKLy=?t}K5
zPqBINEx#*SHLja<(gE~7<ym@<CW?rV&F1<*V?(I@Zb~kiD*h9gy}||}D{7TmNay-d
zSTHr5b0=HAr;p=AyD#Iv4XHe`)#}YNaOS|bYiXR|eN?4efHN${2$#wJk*o>FoV2@`
zn7?R&T^aS45RU!8HkJIW7`YN_$FsIUYG+>S@g?=0Btm^puVs7oZ{2e81~O%Q+rE@c
zA8Ce*D42A;Lz7a<8Tp@no$KAVXni}Ed;+JQe)dEDF+w2Q!4cQXFG<m2`|F=yYz|e}
zyUZPrz<=)a(m@9M44gjm(WauU+5(lm-0-XOI?)TFD4Lj-@6o$Z=*0p3vTu2VH6UM!
zTl>EXPj10>!FSiN4}P^Y=zBUz-3yY4js<!x0~JfNpG$dVdmy<efff-ZkYxfOrlsjM
zOA4h3Z_J5mf=_6d%N`rZ^DK&MXwI(_HJ2aW3QRoxmJ7`CbR_(^q=JnjC7NT^T1$RG
z^g}PP7zqw|+*{~Tl=IF`VGwK>TjLDec>~Z=XirnG%tSYj8SZ?i2pr0xCq&Y){?RCu
zw#ADxs?~r|D%$HtVN{!4!!Dia5PD=2(XZ{CJLP&C4!e0oR4sgD>8!zd^NBo&5;zJ|
zg#sgd+|knXpQHzkf1Yy4V}qM>z;g@j+yHc3Tiezwf5QgEumf0?D?RMEb?$dAs3C{>
zqiRB1R|APP>d>?*A}JOX8`DH+*7Jw;6bZll5j^w!sFx3@mN@>5$EJ{CtTn%}WpcK;
zeH?Z_2-<oses3(0{LbQ?a0sPkZ61aXT8P>Vk-0oDde!|ZJ}Ree+Kkf9jCq=hDqS3y
zzGO-^`WxFBAK@sj+}3F^zWNMk5veU5juD_=4S8nRhXb{kMj1drRi=^ZNyCSZ)Ztd9
zm@t0x?`82~1WOK~b19mOjGL{mJ3>9XNQ_Zsz5K|1Fau9Jq1M$iG$7P9yNKbL^xyq#
zLGlbmN|x|zO=PXchjaZuaq7(hjt|Bn39goGi%FBBO&DYTZ(U9$`=^>?y;<LSd&Tek
zluU|w+_1HjV*#RZHAN>m^lj^WNBrL?r{LdDy|+*NRjJI(>kaCC6#}Ls1nILAV@Tl^
zx&6c@iv>oa#3%CaZZ2b=rCOd!45dDZ;g5>QcjZ1yucLBRrP#%?*XN%Nh}E=b4hoEM
z<sW`PCZe{1=&g~?0wdB6MHK~yJCB0WE56^8aX`o6$S+Z|@@YK2uJdp~g!IgRfIW;o
z4KSkSC?RT}=s`UjCU1Km)vLp*MM##YvSIY$pAr;h(ozKCc&Nc75P>&vhW_a{%HjBu
z0B4<~mwfEaoR{s*)x^|{B0ymiZ(cyWArO469M6rT`Ef2y?(2abzy4}{6*%i=N&*{G
zYR8x&5n!QQ?T4H`H0U#yB+H{A628x|X18|^V@$KE*xt{kQ&+jT9@eCUYFlPz@tKbK
zn?VOxlE~Ph!41F1@xry&=Sm<y4gbSU-{@P{(<gj1t<@};e2Z6puS?K5sBte}{erx@
z;ES%%fYklmkWlv_5Wb2c32|;Sr_9Akuf;l#Jdy)a>M%yDZiPHPMRi34eBAs`A5itt
z6xB-M(UzNk!Azu*T|_@F2bAGyB0tVQb{+Gsp2|NGay(QUIZ|w0(r`Bey`ResQ6HDC
z8!v`}vXfz`o^*nX!<}R`sUm^>yB_YWuoO-y@q!6ah~yi-WXZkYL{0|c{8JaVqr0P%
z05so20xQDyFkPmxWBiLA-*mnid}U{<T!t1Cevm2ytL2{P>{Ku?{;-Im@|3|)YiY^m
zKTm}=s8x;_n!M%HUuDrxs%jVO;sYeVn93E)6B5&mQkU0YT@E9wrJ*`+I!?{a&BHu9
zFF!v{lRRNMdXMUQfmsv%hlVdcori6m$de3<&>&{kWTjr@6{|sVo}Vz+Timf4D2~al
zac#|`=z9FQOQt!12FjXsVpz^_@czn`+fZ|(4d{)S-oaUh?NtqlTUqkfgs*e4)L;_;
zDk5qGU5xT1p3kk2sEeKczDoI>TJB}zdf<|C$;-IhZRAayJm#ALJ}J!zx6Ois5>vSy
zZIxnYjIl=1f^G@KVHKAA4eulT%MSxJ!b8%j1MYC@hA_!GYs#<nEX>n!D?1_*yeBs^
zT;Z`N&Haz4me}stnNyh@8YNQQUc^&1mv{$(^D(Ueq!qI9Xt@$(ilZo<?)?3CD$y(y
zt(6#Gxvgs79T%vSvd*14ySabl4wu~9^o3NcuGB^*{W!9h`a960mBXYZJ&iaJEDVhw
zgyZ9<PoMsVt+(PF{V4KY9(TP51y*`&4IgxD8s3SV!HRS%J@zX(?H8vD6I(LT(cRe?
zF~!bBdsNRR9MGX#u9IWbmqUY~V?Y71L5db~(-SxNND_ul2qv+oQfEe-+za;m;@0bh
zG?NKM(jiV-RkLgA77HO}ou5IcZ+63hLL)gvv;(OB`LtpKn6z<eo)5B@oA-g2jK1@Y
zn~ZD&)eS2s-R4|*V9rJ!U*p7y$57DlJ6Qs5N&Ep(CJ@99VNW}^=`K^Kh1t-1>l0>l
zK?LY0GEIbHXk#m{a7Wqapk0(_%)FdNoo$lxu8-U`Z+62H^$A?=<oyp<;^tv;efRq{
z+Wfr}H)MuSJg$`+7FnWkl}YjnGa-Km@`!}`Z(^rCytd?Ls~Q_lDaL7~wZ$)o`%8o^
zDe-mki)NeaeXaTRdp3F`CE;(Ild$+^sJ9n}<Z<oQ-P$XW$W30qK~KL|p3CmIn)>e8
zU4Lx529#c|ltc_1)xnJ@p7bYQ0G&SIr&28r2pEBG*JF##g-B3yO*SH$1rt!nKcYw^
zu73Sy&*iF6S{||gv^u){gmloK_&eM}Nq~GNtB-*K!CG-oMzVAuBXr7hdLWsh1G|Ab
zY;9{Ga>{)ug>0Cqr7_m!J(JUK9u0H5ZH~u`dqaWvi@u$4FXa&a52$+e*lL1bt+s8V
zTZHC~TZu2aT}aFsxhA*IE<S!bWXohnNhX|R%Jm!;51Vqgk|JIfPqK_AI9hIRznrfh
z<A<VY7xVL&N5?`Yd^13lH)}j%!U7-@*OwvU@_P_zG@R=lM=L_U=KB^;@zK`9y8`V;
z<<Sa=()_}+Ak-!Jfue5{G-y}X+gXcfyUsB<7YPGG8vj6CWn9LSQiVZzg{vp59Sy=c
zG-QTJP-B-l;N1JSZxWrZk-VIfw16odx21Xabe*RUoe7eK(A{7j3UR=eR6x<+kg1Y|
zn#R~2717$X<3$xHaRq1BCsg%`^-l5|0G<*zWm$H@@f$JIKD0R-hXps5Sitp_+QQTd
zdiOx;>LjK!*Vo=rYfV@OJ6pv{8pb~+I|6JV`v{vpYDoZ>+jmcDo`Id+02~{2MyK2O
zkX>09^Zj_0iiV#s>CrUbypqfC&+5KDR}&3-mwO2r)<>NA%RoE<BB`^NWUT=4bbccN
zQm)_Osz_&XicveX@*hnwi^$*_rpQ#YwD8k-;Pt@YOvHc+FK4afPGsHanH0*F(>rjx
zP5MSSFFJ5yFBT&-SBynDyEAAJGnl1(+;@TjYuf%AKXGDCb8_-zynY?uH!D`%pT2Wz
z$_z*mO3Ahk0<GZ^XBAIOHt(JP*sOhDt#$!b;qs-Bffo9RhZBgn#DcqZVq(H1_TO;~
zd3R%$&CmCTo`O4JEPiB_8ix$t*`RKwL9#WjQG2xHfNV}X{9Rh%=QX4omOs-AYl!bF
zIIFOM<yM1WkEFV#v*vQ=X6k#T6$mZ-B;a(k$?8ebzV!kj=Uqz8L1S6XqN?x$U6?mG
zQce%&Xwst`Hw~C7iDnSrpJII=kXI;AMtO^6LVabX{GlGl95qB-8JTcp;e}7Mtz+bl
z%^NvzuRU2FKV|N=iqYa#@s}Nd{)bUS>*?DgCA~JPFiv^LZl3riwgF&plkOQ6uP3fd
z!doO!nQ|#)8jNk+azgGxtk%!h#^k&${TN;gYepZ#{MM06CLmw!2}B|GO_1$=1vC$s
z)85$@Wb+$~zmr;oft!7~j8}{bB;`Lr2$fd8ime#Ir9MM`mTJrqe*x&bXt=e-l|S5_
zyS|+ftnFUjU9&dMN55*Tx9kDolZ&{a$hP}+rjkWyL1CrtcMZ*)Da@<;776?qcoqBZ
z-jVLLHZ*64M1`K9;cpmZ-YAA%xPIr8(T_%C`3<=8iXxwIbEvQoLsOb_#kJ%L7?Xer
zgn*(d!J~MdBYFg11^T3v?{1QO19-8uw8+(IBwJj5W`mWt^F7tdsQy`V-?`;$vFd`W
zU`_MoxWH%9-kN{AM;oYdZ&fbXqrx8n!F|<pA~1mo2gz^Jm=Q_qa8N#ri~k&@q1)X1
z{*LgHXYb^Jn>*+AfRde432vS67+y6t@yC`%6!x%P=@J6ypu;2Hn=mhN=+p42jF6hU
z$ZGfeqm@$0W-6q%!dN9j1M@P2mbFW<YI(%J(Yp)m*WL4-e;N|z`M~0I>TJnOp9rx)
zHf!xJ{=9{h#!Xvb%YXk#LLT--`Fyy6d;-PUBIA@=k7ND|(Vdq{?>c&4*CR8EW;D;|
zHt5cE8sj4<sLTmwyjQQj)W1hW*7y<&O$e@-{8IWGW~=Z_YE?j_DPI%$WYgiu>Y_z@
z2lTysCaS)>e9q>r()H57@P?Upwhkv|=u6?8H$oY}VOmimh-J%W(i~=GN#;*`MVI+q
z8ah|EUSxAAF*{ZRVj38b!Sn4!Oo-&nx6*J8kP5>--4|ErI5)U*Z3okG9Mm?@QS@U)
zM09uw(Z@San+?aaGch&7W4BA^?m)F_0F9oPG&GQY?E13^sycIqhlTvSLxSOVqkFqk
zZyjp=R8c*0S%%#+F~EaCSu76&7OmBY^9q*-GOswlGWdNs+gdCcSG%!8AxRyXh>-5J
zenPZklxmn8CH7>G`$r0r@Wp^)URiep=R+k@X780Wyjq+edY|azXeO+sRvKMFYo!y^
zy)hC2C&A@Z>cyPIlH?_?_S8G7h##AMrg+07P<0)6fh0_w7dC=|7k7$iV*Xq82EY8C
z^A#~T^Y|9gK;mx1;C*8cO{qCC2WHE`#t3OV;CC`0>b(k)Kn&Xbm|k&_-1E?)9SHqV
zO;Ud6@@KIAV%6wEfeSwld}yax6+Ssp5F5sBw9lTiQ`voF*0|1X?=Twp&yz%JekcKK
zS@|4~AaA|Me%(XVfEPAlp3{Kg;@50+bewTltNp~J&L|k%vu4)u?~Z!&FK+*pbq>dV
zs-jDl%AnxHu>IC`4(irqHp`XA+h6L9kBe)e2B0^Xw8`Llae|qESj_n~tJyE#U1(k%
z-KoU?EzOR>NT`*QQwj&@^B2<Oo-&FJ#@5Xtxf)>_Or&m9RTQuFgRo%8NUz`u^>AZI
z7BDRNuBXJ16dX89zduGCdA20?Emp0|dGNgae(txx%7!Gp*%uvW;nv(0faqmYP^n&j
z<n<g*HE!Qgz0^->gv%uzMt$P6Oe9!His)fb;YcBiituZ@!&}W45&b9ry_4m2%M#av
ztV&bQ0IgW035Sn;hO?VF7-3O4^i^O4c;h<i2^M>GyKKK&bM#1@2TM^=rmAke;*HE;
zPgFN=KfilLP4eO`NCr25WMj98hb9z3IS(QdSxJ*zZSS^Wr2kL6QBVR<QcJ!jNhcd@
zcEasz?1L?%FxFM)W^9+5=N=Q;#Upn17jt6ifWiXe6?<`MbFw&gk|`AZq*wku+@xYx
z_o=VQc<bf|RKiZD8Qs@{=_7ouVXtV)0rKiFPGrEgyf$%%X2`qz9%j5Ofq}A$A;T8u
zav+ayrAI3UBaM;^nCsL9AG1ztJde2pSf4hDb_3m?FY)k`2}0oOxx9LNT#4Q#C9okV
z80Q{ElczTw;ClB&lpKWJe4blso7$D<$tPiODbBRguu{{TyT^YDh~U|NAyLI@WJZsc
z^9npRtTp&O7<zs;@MKD>uQo8@#(~5Cx@yx2BLBJ8X8KwGh&r^YkE<;U%z7hMZ~Aj2
zze787gdg*d&(#y%n`)Uw9dHMXcBE*``Y-LCPpsk@5S9E5Ntkt9K{L{Mzft|01p7)l
z`WZT}<&yZ~*`;<2Gh85z@1y-F3>ZGLnWLEF>hzsezfo(W{a3U;kr`o5g}r?WaZZKH
zs^}2FCw%IP1;?;7w{9+l?hH9>!H5`(D}4U9tG!F`J4lmmCg2U1gt#_#f?3d<`ci<M
z+b_-<@^mWd){$%5pr}E#m2nlLnLsK}^NV|-d9DV*pGmWqtXIbdp>S?V+6-{n1Ov=L
zG{f~JI9`-3B?JtDgCEfK5Hd}^6SHgIXyF4i`DBK`9OZ|}PNn)eUI~(|QNanvm<^%g
zpADVywvKd{Hec8WT`p!v^+c6KhCXu+E@~U%8^$Y4g<m4t5-NzhL#{1m1tX#~7A@)D
zJsEw}@;pV90p6^$H~q=}yH%@JRsOdhTw)2u2J#&$zM?zl%=K(~)Is3gZ25}_46DAj
zt?k2(Uj^~{uE1&YcmXB%p$6lOGW*3&kgl^4fsT%>zkWY)Kx>+~B_3ex(=IFzTuZPQ
z;Xa`2YuwY#ScGZrcdg^^2t!GJpBP+Z*;RWY@EQ=m4t5R0p4^rjg{@j#UU-v(+z~2n
zEjpR6OxM5ASxX;;e8Y^Qbwn+=y9V@c#p=P>dA$Jh<JHq<>{E@&ZKC@{XeTpF=d{v-
zdm7q>O_gppp>C~6j=10T`oj(IjLK`%h(w+BoLik?2DM~%z?CgIfL6#n8!znIeY=j#
zn-t3?%k?t&;=r!=STi#FNxdr5R^^-sV1WRanJArz)YDe1K+mjzM|rCtl(Kj-TBxY_
zn{o!>n9ImS<ZF1={^dyn3p|1IggBR9?|*9so`bF8f6H9#Wa6ux{hL=6SCY8;J67uc
zCjv8oX=&LaG}bsLL{pfBic}pJ^%T#+POW(|RbSxB172g>q#J$_l0C@NZT=&$wLQG*
zbh(!2G>u0XKki51Dc^}g^Np>6ZihJ(gLMSNZt=X)wB$TfJ{^+N+qWNZjPVHFsj&s}
z%3|jU)dtbPul{%t5oEdBYn=ZnZBQ8)b(@1FU`kqN;TjdPu{uCr{00B-{DSR(5kORD
zg6y^S>x^&SjBcs`JtXL>7<!wAW6Q~d`jVM`82H5^BGfx#)j1Gq5;Nn=`(*3(@f)r=
zFiGMo2+^W){E3zN2{Y{X^;<KsHol9|_vl0Ezu^i>CrF{3BD50&@23apc^`SB{T+(5
z``4AxUDl!YAea3U7Qi$-qh)#^totu5RUN2?&@q$f79?e`!-@c~!`#}vU>Il3@?J0+
zRbEZZ;~1@pd>J9C0MPn|1V9!5aO>2&dzpb);gFaeX(#L^g|6^FOJsj$#Y)~mdyaeV
z#k%N8t;x1vqt7e3fRTuMu~_JgXjW=({Jz#W4yS+tJyxpXRpui#Q%vk`{!%JLpLcd8
zyIMCF0+yZ`DZZi(ka|fAYDe?&^@u%8GQ&tAfr%2v9F!MSL7MCMZP{M&AUio7hwN+<
zE1@d~NtI7PyR2(Zf3ku8=v{J$IN6&DV;WPZA(Re~#QFxHjFB28(k&!)rKVx}pap>p
zqxq6Ed*Oyakt_uUAdAJvSJQI7_bu^VV1njMajHJef@<gNBJt@hrfpyBwQ;ztD<K)8
zV7e}Ptn<Fx@#rHN11LPol>c6D)ta$%Zv?|fb?Jyd)bo=#WSXWhK#{-nc0I*~86?+v
zk{4h)Fvr+6TD;0E*x*0X7<8WL;5Ci`o%-N_Aw=0S#O+V!ZRyUv5Yvth_8h`%hK|_H
zLs*-9If%QpByn7~c|MSe<ua>Yh2}E$f5`dHJ%my4O&aZcFM@d@eSHJm)}!CWp{^W`
zrFXVBb{^|Ibnu7o#TP|T#ywCfICR7#S}D-~A5mYyRaetAi@Qs3hu|&&4o(OO7BoQM
z1b24`8r<D2xVyW%ySux)eml>**1dn=z|5ZM>8`G-mW?86saZ57DTmeM)7Z~**Mo4|
zk}ghN0y@v&XiauQklXx*Rm1EWVn-y>dDMCr{AqP1uMCWzj{T>>Boi0_%4)1DLE{Ol
zE6v4yROZ1m06GkuX_!D$Qd+rE)|@$s<7S$I^EQ&`zRpD1bUz8CVb9yQaKlnoHkbL=
z_TDXGnusdA49(ekup*zt#=6R%Dibu6Wa2NdKk-@p71+wf|15sAB6^5Y=t&pDt^N~-
z3>=10Z1Qn>$@j#U-d0o2G0rb^EGobf8>B!@x*kLX@N(X4rLx1h*}bziu)m_*UqM-}
zUw4zk6tx9B&k(CRv$RzLm1*THBw3SH(xco6xdIB71l0{bbujBLlAexJGav!f&rIPM
z;4TgBKO)!BQ|l>HfsO#|7pA^DA0D&{e0sd7t8QC<bbMp-^q6F*{jeUx8!dLLpghXd
zE948!8}5SxM~{@fyCPD=C8c4>$JTevnzFJK*=AbS8FWhlki!}`{f?$rOwW7BA8Ovs
z-xS@5h8cP=0hI|)@0@iWs+5L*66H;>ax|dIuVonFAu}Bw=Yk3^Ls`H#*coE84;lJP
zg#>+|OeEyyS^3q@zIGwH>{I^*Q8PY4M7J5c<ovk#uWSQVM%^qzTj$*Ipj%8Jju#se
zX;v4jSZ)LE3?~`?LZa699d`SDies%C3b78L+nmO$mx*n~?2>w$I$ZNn_2O{b?8HKK
z5kxwP5K@=Z%kdQ#FlONUHEbMb`?|D#IWo4sm6DaUt2VkA_j4ZeV1;7{H$4_5CKY|P
zenj_^&i7*PI*MYSP=FQlX%K)a0Fp%JAj9nh?NvyyHtU#)zYvjlFWa=t9JC|)`5vlt
zd*vx%L}iK=`dY_V=?c^0)?+03ri+&Nl-IRtX|f@x6rv(rEubOIl2o$U46smR9e1X~
zNq_1BjvtzHRiOo$xNgB#I``Us=nt&AGz&gIK8)|=G>THnr*QfMt}S#*K!gG$!21Y&
z#X_#jaiw>xUfW-b{`3%i+iDq5?E86y8&O#l0RzNCK(68UlUY}H-ryj^52C0;NO(*9
zzLdPe?HX6<D}5o{#mEu9>e#=gsJ0X17Ql=rH%RJHP*ki+`^N-#6V&FabMk+CqFme~
zUQq|dBLm`~MiVX9VhiUvJC@PWJu!csNID@;Qy+kTZn-d>2fhV#CxBg+At6PY&X-(z
zlX8!6Gcsp*g({eiZ%)FV8t<e<&1Gw*^m|JgaXO7Dw$?>MlW~wCX^D=d*6HB_gKL#Y
zN?~5TmSVzVg{qpw>NN$&nUc)*L7d%vbj%FDFZqS-w!%PVr^3%xl!Y=>HKMzGjqDP#
zvN$O%fnu6`OIabT+Xz7zA1IJS)zlguYO*09h}4@>CT+rkk&pWeJdfJ1Q$-^@TW&@}
zX9a%1L3#>a>iIFtm3@oN!)z$%*isDCmh94J?ywR25~SOf!lr=SU|J`~s-SlA16JO|
zr_xV;brlU@VOr-<Tf>M2cJ0gaudJ3^Pp=J6b~;KYD*Wh~K{O*U@c;P`c`7Y<!^@?f
zpMql-y6b2OuuLBRah`Vu$E?F{d$)<zPb^*TzymXnZvSI9w({<0qqD^9`CFz|^49>?
z3?It#n2WnZzmsWf_X)}xCaXBsiVH|yb?8P6`J8WGDZLYUMJ;bZ&z^5NKmXvGIQ|Ie
z;-PpeNn;qr4VerFt({#&gjG=NBsTZ~6svN8_J$acuf9?nMIMyOXob*>1z)GB9bWqo
zwU7R}d9#?m*0`!Ikr6`Xs&Q{j`cz_5CANb5jziQoMd-UtF2hblFTuWy&l}wl1K?0N
z4RqOu`N4-j192kz!42DB>v30%Ir%tJyC)PPTR`r)#awMBPIF-bLyD_awK9G{!xt7D
zaExGUF<4?SuIBf1gVHyLuxXj0Y6xfc#~<xFh0Nb;GJ7I3YGHe75u!tB)v1qsivx{|
zjm*Tq(TcloY(9Cg01uMse{VT0<03{=zWTX+!)s6955+$9@isfzG}ez$eqM5^*SWhw
zuUwK}2laQdKG_+%OWGP|{a8p1wJp-=TQyP?pa*;_&&sT*)!oQwKc<hjDzWa)h-Ad}
z;UCZdz3fEMEJXKC6;4?|a+`KdB4^p7EoveySd-Ie7|HPQ7&22Mk)Kv-b4ZuSIWu8`
zWcNhl1wvTxVc25cTJ^NiP5b1TuZklllPQx?{U|qdO1Eyzj;2lN#<i3gOB!aQ8EX6z
zLlICrs}t>bNfeGsgiz}NI*n#D6-ir%d%c?W2g|Rny+r4pEL)4hod#ntwk9G@v35<I
zT|cEa8fzFyW5NJYVm5YpbuodZg&L^J_<4*+OjD9W1{bLFs#c`c0ax`cv7U3XVjDMi
zFGezkT?T@Q>wI28+X59$ju@Sju@l4dRCE1vDCRZl`G;^fvCzLjoAZBa<iQx!G(2R|
zOa8eJCL})8?FEI`|EYi9V3#}Rx;AqmTh%tDg3kF7D6B~eq$50%r={%xvFb^<bJ-Ku
zbT@p%{?%W7y;ud*Vy5;Tl2Az%OBvi$wf&th0ACPMH6yg|=Wx1XPuY)weK|+9Q=^8e
z3h~2_y=KjdlsOrop+Z;|8%zB1CxFpB8H9T}iMVFHmr$SPPEGlCvr(?o%3ulbDxPit
z7?rxaq(VH%y9;C;X-W)<yDlN6Uh}2$pWv47AzYlQ24GZKgh*vFhSUU`7!8Dce_^OT
zvcK#*eYHL&(!L*s2!$MP(GNW|95|(&JM0a<{K;NV2av9@irR*6n>C!vd^iXJK~HCt
zAMYtXz#eUM@vIpgqEQM4k~b8iCdBH}K(6FG>6kG(+ADA}Ao#0Y@J=sTw9sgYVy&-=
zqVjAWsICi40r9GPcKvU6+y1L`Vqnd58lYY!D*QrhgcX?XV?vVog%x!_1Lab5=z!hf
zM}jE(ad$|ud7Z+))`fh1#ggX+zYlRBIIGvc^4dTHqU&6p4q0r{{vZAj0>{xGzF87b
z^N|<8qIGN9^%tUrtonAuG$@i!<Selbq2f#6G~{@3_%+_hP<^JQ^4K|emz_naOM50!
zdJpP8$%|?n@q-e+`c_3*NDYkC`)D@ewAJ;MpIf^V;UY~8qc=%ZLwW}p@TX_1!x)t!
zLeNB$1uS*oDKZkgwwrQzbT23FEAJc2zwj=f=VDY?31pErY1qZRa`neV3+mxQscu-c
zz3H;%;_U-^bVAjCJ_%bOEDGLpJ6o5@IHi{J&REbEKcrA*zKJC81$6*FRBk&|z&67M
z0I2_|wTPoNt5?1^xHb9LTI;&kY`X$xi^<@ysV6`nxa0l5IND(t49~E5w=2ovfTsCa
z5rmn<^A&Qt)+<i*=gvv`^-qV|vHI}0XbqW7iv$qnHN2Lr)k%{DlwD?uIif-k^;Iwp
zTtDVRnRpp<$Z8e`z6;9|ENadYd8veYC%HmsPM-Q@Vmd;@;S8Cr!G8Ag*z?+u({<9(
zCj}d@7*n=PlxcHgqee~melj{DzpBUvKNka<B2M1W)rlCKcZbsemyEKYoJEgwS$8IT
zgjw_ZKw6kp4?hL=Z6KB2J0v#OL1LgIT-cDGa9*`t-2RVCz##jSmLKTq>qRnP85BIS
zq$}yar>_M9S>(>o>g@>4@}WPBtoz*0u<y3!X-Y)weQG~(BfzJ|!)*pxAFP2K^l>b3
zRwwxUD?mzXG*pG&$XaJC)%vF~{qj#^+7@4c)09t^3<5;}8z!g1F70Tk+w(YnPZE%n
zr=NLTMQRi(A@pZrW$GEC{!ZBId$`0!ZYADC^IolSd+qH@HI!ZKF^SlW19dnCorOYQ
zG=uB`NDb*YrXvmof)RF#Ft!p#@ygtVO!)Vfrv+&L_ch^~;I-0<M^y+ZU>4tXAN(P|
z@`Je!J9YyapiMy3%)fvq&gm^lIo39hvH)!P25Wocpj46O;OH|MlgeOa%G{IB1zv?1
z4w0&HyMqMF^R~Xha96~){1woBSsG&?B~A=&*q(s{z?nwFz<r#1gGhRyGMnw#Js1kC
zG?lM8%7qW(3q+beG$CudCa?n_^20n8S;ninA}6J=NW3|p5RdoGHdVmG)%HK-^;K;L
zK0Z^Ic2YpspyQQw-q?rXJ~jiVW+}mz3#(nbzn-gQAG8Ekfhh&PQ`WNw)ek$wj%XK)
zjps(D@7yylr`Nhhu!cK)AYbENY~QlsBR0t4Jij3Ex}?|Q+Ri8_dKe^pJ)iaMdGa-n
zKH>J0_iU=4H_@SAdZ`Avhkj1+_D4nQBy$eI{MiO5@g>YCG3YWs2=bC=MWr;PIP~hz
zq&aUAE#baW0GacOyvBZ0%(;Gv>vtP)CvTGj;v`?zQ&i7Q_a-0O=|F#6G@y-&?Zfm2
zCKQ-+c0}1$Ta*~foOmyxq}Df3_&TdK#|J#WQ3=-nGx+^a_Hs3ASo4d?CGiE5>^Hm8
zzA|8MNDm+d8_IFA=$ohNl_^=)MVQvV^?cWbJHwitD%sdd&=uFLe)TpGS8_a}`u>r|
z;ROg|Q*iT9zUd2CiJYTeOI7`YVO^SPMBTcqfJsydDqs=`^?B2dwrB2R2@aGFPP?{e
z!f=|>a@lEGspqhyBs{*BO`^q~%)EHbpD-Q&O8@k_ou<TR5SNNL5+o)0814t4a<MUP
z8u|k0q>^=t01I2ei$qgjKY3YKH`n{}hPxd}OtsjH2G1i9KKQm3pO#w4wY3T(*p>8)
zj4*K$k8{~Z@FHPhmBxKV0m}!Kk=>TQ0vCd?FW2nt9{*gjw`KpkCqH*=CeKFUB3>5k
zc-LnhOZ*1T936(?tzCbVELPc6ewZ=#F$;g^gP#}bvcO&!cbb>RFP=5_8{eI<5Bs@s
zF|PG<m$RQ|z&5OI1K~n20B|nc=8hQuODBLZZjS|sp(^_n5s87T>$tQ1wX=^cpHp#0
z{2SFxg<3k9XW;xo`~J#n+;`RXAoMw{Z>*z*PyPzOMn(;q65WXmgGCk15$lUd7qV6b
z6p(oV57&VqYiX2vs=t8HKIk?PTq?DBnvaUBs$067=O?^<^hOJrXeAtj9q_B4JW<;~
zkZSa~m#1<nXXsyI>+$_KYi|j?fANQVp1FdGKz>!hYNUfv^vo?{R|pE;KkqxYxc{Cm
z?64mbT3(Zc=<{dpyeu_t);x#+I$?2*ctg@D#=3Xiuj;aQu<bT|aem=Y*$J|USG&`+
zT$wjKSF5>w(J4|p%@5^p;8_hDa0(I_Gz!PP_5=j4$4>PRD?zAZD7vOYkQzBZxWoXm
z2{3Fy#5&C>nN0KX)?&Wm<I;=bgD2;J;LH)kxI=-n-`COyw8AN4Jrh+xToY2SE_#y7
z7qxG_XrI=qhgmwXKX4TZ7{z~ZMNCuhuLb5U0cnpfp!!Bd=-j9M)t9Qia|SM_O!^J?
z>C_A`_#xH;8yZAXeqy#+ijv#`0hskv@Jq-O!>>@}+M`nYZ;VR%9w~mc6!5g!Q_=rk
zo^Stoil)<*Fn`wagbaNO);bd#hTngNLBj%iS1@)*b}z>FtV!G<5I5PasZCpwhhbpU
zw8Y(CLu>y!-pI5dz-Nb``1c5pxG#p9*<l~SU*Sa{ow|$oXBa0SNlBy0J+1AoR^vsY
zo+?g+Z>Qv8a`EA1^X~Z7<@{ZDF^Ognx6f3QA`<7#p}Pjd0|456(|h71o?QTbjje?*
zPP9T9Al)6dEe}ivu@O{GgqcYA<%Uq{=pEwBr$1>K6AN*@>cOP<6pmdK7HSz5DQq4U
zGMvg@;y<DRh8LEpI+eysn%%a-?rK`S0ng@Rbw}g&*hQy&1>a-n)P51g$20x2e&hcq
zmAm>Ji3U(Zf>|F<kw=dO2`#MOuEz-9>WU$KKHKOU45&zZj$ZS7qISqDKR#c8)Kc9s
zRX;Ny?YqGLGy^6T1lYrD&uU9&^fVea5Y!rm0s=7VA;9u4Ta$AcA;_-xi7g*YU-Yw_
zv@OP{Xt_S^{%@W~r10UdkSW4URH$#+TjX?#VzG5Hvu=Q|TCe5ETM*^BV<ZF6wFdyL
z)zd)0RVk?oNG7W=^Bcsn!ZxQ0`NPK&1>Z>b<XZ4d0G`9<%yGk3)v&z3#eF><V|Y30
zBL1$ip0SGC%4pbXtxhl%%smm{Pis^{C~!0m8_aMTmT<Zs(#nwkoLr*uvkJH!0xtjE
zj_sf!B;ocPyU74e<6q}dw(hKVEfWqv$#XieJ@ZAZgbTSeWdP(koL0MkIUbf!mHf=b
zF)YTKNyuxgci2O4+-+VxmB9Jy`de+Unh(qzJV6r2gJo3HzbtqFo&qo`1CXVGI5>(1
ziS^EiV<!vHaoX5y%c?s`OkI~j3~7Eduwop@P({%l<eEQYbp5L`2z=ABywgE|CECq)
zsDkCfY8ha>>P<zBiw=;g0smtFYE&JYA<%llaV2}9^0vqI`gLqd%KOk=GH8<%DKk^|
zj+wwPs>>>BGWwZ&qonB?EUHfo0M>wAw3w7=@s?oGI4ndx)F?O<-r<+Fg~{MQBlMj7
ze^5*Wd|!t)A~14>^^D5ttz|8H^tN^3`U5`|;Wb%yZHs966P9V4bcA4w#Z|&!%MVPK
zJ!3-Lgt%W~MJPVJv}4?v^}n2NX^<>}!!#^9lPP~PBT+A@h5j+!(5?(s^rND2DNEvD
ze5rdT^4q@Vdr>@o&(teMc^-UkZv{ofWS3#TzZVr3Kc$}W{r-55xQSrYfCU*;)#0J=
zskn-vIFJ}A<Ox<2R#{+<EGx^|3;DQcqv`@T0vBfz7PAE*9|j>>1S|U6$0XO^PiIF*
zMeAhToZpNLqm%T<MxGy?cXXPXnvSO9B3+&z-PX=ORoeRX>BnXiZq~Utmr0J5O~jOI
z8zW^z4m~eUq{AO(t(Vtx?!(tsn=w)Dow?f~Az@1AH#>m(>(`~S?yVqVOMWM#Hz%I$
zNNM8NrT;x`*s%ym%&p#^`IV`uqiPjZwY~Y9lv^~dqVT+Zy{bsu{)6ukTp2Bmx-Z6b
zC&e<2+h&&^U9jTqF{9R_)GQxs>kXDsstsJc0lU?j0MKGDmz^^1t@&~ACtUTl$>SN}
z?QX3ckBdvW*9v#<e4VknQ7~XX?8NTY@00WLTncadbpUbYHKeZJsXJ^UZi}|J_o+M+
z$QOzqX(FCwGLBxs5xmzYKI4y4=d^1gi)VZ`rgV5rxaG{EzDxnGpy$W6m+h3tt)hX?
z-%>Q|WL`Of_jG+owt=0WaYODe=`2%bQwh`6Y9s`!6d=PFAQBV=d+$F6VK?o)*B6=~
z#h<axex&?(jV+3m3DGJ6Z&lut6q%`{EDm!GvYCYdeP|Cz__6lIX`4Yl)_v|8`M|@`
zzVc-2R?v9-`e5vlGrcJfgTrB@4JK~$T@wdkR=hh3qio+xF~AQ)7mH+y)9d}t>b-9_
zK}+a@pD+yVMJP?qZj5i=`fkz4Za>ZG>S~zPZjdGcBclKDB0yMxpc{8OEbTtQRZ?wk
zSLFHW?3J%IR(EaB)nwt(wY@IQ^lE=#eOgma-s~Att7qnLXo=3*7n(n9k;Ad~RGJ!5
z9(!srieYL+iQK9=mLy<JH_59g%LGZTH&_13F5Zj^PJQ{j>N5PwiZB1>>+FJOr-t*y
zgR4ERp$30bR%AUGr?=ZtbM0VA0UJf$mf>b^SXlslTAmK{X0E<)!|8>qA~CPbAm>RU
z7H6L+)9HwKC_n#34GzoCF2X>Oe3ZVB!jiqFhbMyXt7_j`$Sa$~sWz5PIADnlg0UAE
zrvlV9om3fxJw9;1wJvyf{z{W0aocr7Mze?o9v(-cL1a(T$;!%#w>h1+-s>*&2)}Hi
zBf>xtyWdQfXw-)TQ@>Q8L@KS~s<Z|gCj^TkNWN-mjbA5tD#4~?>`m5OQ|y^P(*LGG
z7crVnS*+XnYcdG;HC}+f>TN2&tcZ9H9}>N)m!tF$iaQgc+SGdn#=-G6{$M2TU@!pz
zPB}F=WZFrK#M3kVxY0ORO1wu`=aHLb$fMY_W!Yrs=qj$j)GOm`%Y|V#SaQsUE{@Sx
zCe3-GgN<vbEZ|gmU=c0+xZVUA7D6@ipaF&}OijMuQBy{o#u9EI-dtINX6}_zvh9*e
ztnqY1Wr0AzkIJZ*PBmOlV~`?K)t@=MwZ0&?x`IV)&;~v})s7}Wu{W2+JgX=&6c&+?
z0hn7_6$1PX91^K?xFA0anb4zgFakwhmN-Ae3ove_E8*W{wDG^=et5qIxaT!}%AU1c
zPM2y^UG|b-pqlTGQeDp)-ENDua`W>8K8w9Y;knv?JtMU0EdENjb`w}=t-s|&?4=Wr
zf?cWvUDjr{;5`=?u<A%W2o@hWkam$SGrCjadz?)qSr4<s!PFwM^7%Ur<|gBl_{9|^
z>|z2q+OFUKxHZc3+Sq{ElyRr!5#j8Ecae&=;IoUebofn#=6}CtRC=s6rJu(AgnIa+
zH&`bBL$Ile#*=_MLyv%=Rww53LI$l?9v(Ul(L5|?1}4e@jyb&ay++Gnk(5<sXmD3a
z!?*deA95DWf2o3xAu4X^N==9avusaR2yrA9jciUbPt?n3l$eyr*k2d>+wS%LePJ0}
zngDPs&2MBL*#2ydZx~Xve9Q#Yjr~1;X%Wf90!@x0Q6YriE+g!=f@l>rG=^io3k{-v
zTInbQH#Y!7l^~5REDR>IBvk&In41$jTNOZ5C^IN9#KHZ_pFXsCfzy?uzj>(kz<u^&
zWlwHvd;LZx*A%zZ!vqAY8bh2nc~?kC*<N*9w8;$h+eTOi9l-LIoX&JSmQ6V>k1%9S
zEGd2sYgEvMpMxhpQ=#JZ)!n68Ok_S#-2=p%zZF%%wKo6!ml&+BbxYEg-yU(>8ABfr
z))h_jPn2>R`v|7Po_IC?3G3(I5Tpbtaq9~6sh5cfOrvB^N5oRS^+A?fl)st>0MO)B
zhRUQHw-k+Bh}WO&47uO2Mj5xWu53ru1u*%@W<w{9K_ym$Y_vKy&U(JxL{zb0tDar^
zyR(w5O-vq#Ar_`XM)B%GLCMUMi~!TaiS3!sT{VWm0+p3M)$!bsq3SjHAthGdrlW_@
zV|SCV(Z3DiQS{WXl|pqKm$#>vd)yX33+&ITYV8J6g>NjGm6Px~o>cZ@A;3HgND6h0
zep(MTNrtz7PqAuC0tTjqz~88|%c53}pNp71gw>R@b`XI;Y7bAXOeUVb^AXQDmRr16
zyGZ2)#nuAGN41k&VajV|Kd)Ar^}d$vROqM@f|cl0G$|p|SaN0Bls~E#WfW;AfK9#O
zlDC}JcxxZ1@eI$)37_|p<6aV6C<?`rQctmJ?M;0kl~o|;sWOU<gT(}P#_vXDeZPHy
z&XGnt6iaG3&NO4N+on*INJ-ZA(rpx@)Zb9n{C!0e94=`wu+cL2o<V;L1@Y*=?c8)J
z=2#F$Oj<XAvk=8_@wA6#ucBk)?(6692C$<gs_qR6_G>DcWt$Enhd?>RsJhoCHc-CI
zY28HGGcc%ij=O5>(R;?@+vw#EQyLZDzA!V1XRDP}G*XEh)p21^BT@7y=Q^*g@dYTU
z0#z#*41N|cy4>nj8E$(0<+6(#&#E=%k4mIgXHI|zw#PiQPlhLT*4{|%C-Y3LZ`ozG
zcXEnwhO-95**AB0SRuV=FCjsN`HE}V?#q_&KfA4Z<`E8Y=H3Z!8{d1Fo;gQK?cUQZ
zBsJyglnqs_g2K&%vQDj?m0};C)vj&?oGs1JLBDN3Gx;!+@J-RADA$FS(VrYKZ$Z{6
zb#idUAEdc4D5cSB^=;fOeo|7^_%1p3(eTjx5E$Wwl)iP3f(C1E%9K`e%GUgGAg_*z
zb|fMu6!z8KzAj|@=^3j!m!rJa;3e6cZpS=8rc%Z!&sWEoN!Fh)+9LI#FD)mcH8kN?
z&tyDKO%0DywFuZE$<9Iyb}o)w4sOvH=dZM;1l<Xy1es^4qtc-2mpt`eo-H6;FIjuR
zx}WYt0C8c>TUG!kgBh%^18y)KoWB<MxBXKX2Dg}~Rm4m^IB|0Mg@uJllfv!!vnbl)
z;?St5sPH$7z_i`WB8EYIEU!35)vKg&H-Fp4P@M;dK2rA^g?MHSo|8A>X)bi<J9!!G
zboj+LcaK@vGqf82^)>&~<K;Tiw|fu_NDDdxBYCv0TlllpD-r}{UguQKSP5asoP8AT
zH8xWL5&t4&QFVZie_ymeTl4SNCeI|XB(FUAGUt)<$%ee=kEX5ZMdO~I98n*8IFhqO
zkEkH)@{#ByBK*I4X^V&}sVMidg7iS7qR>D_bE^<j0?vd)hxL;efqvolE^HY_yvF*>
zlol?72uj&rNdKEjO{nnw1p;%JT-9`IH!s^nZn<S{N_OHRAva_f2{vEMVgjG;RGALM
zgyi>i_Y~T6vo1x<!j@(D$ke&9D#kz1FW1ahwv-{T8YOHmog+!nukmst1LC^^P!op4
zE2k^BG2KG=VJOYjAVEl)0<V(5>&<k@uhwK^uA!w`v&Ow-Gw1i`bzzJZu=A#$+hvH>
z&XhFkZqsp@-s{t?+w=1JUdsB*9(BYonPf5a94FCSu7lE6v&-A_b!0-q9|8kq$~wxB
z0~u{?<8EYgul1g+R6WJjZ&Bf}`+7;u37F5-iq{74!}8yJ{oCi$7rWy(;k6Wo$#oBV
z#6PBhIP^ns#}1y@t<IjrXN}0J*2cEyG`{sQZT;{9CdUNkatdSWN=tqio{(o`sfAoc
z_Y#PZDU<;(<j9xZ7#uWpv)GXd3(IvW(zia?K@q(p*QRzK*UFE%k*(tT%r61IZUEIJ
zH8F!g#9s>J<sU2~GNk8m7!1(+Z~d7wIVlZMbnQASDr|W!wY@SjF?ry%^3}>RvNoDX
z=P-f<HsFKO8nI}MZi2+b5ZD#a1|cT6WIbNC$*uvK^dH5-o-B!w24Me+?qKI;W-bP(
zBCKasR4a9Rk&cd!A6NE;Xlb+!DM9D7&I?I8-}xp8J*eiKaWXop+5=)(sOHNiR%7(y
z5681^S>N-ncm!QinykITN(th5zI^p9GQu8?L(<^Cf?n(<ImB{Mh*4(`DKLl(O&%F-
z89>x1N^q?wLVncQBCIm_`bEaD-v|0HWTJ{*6hrLc$Om>VpR&|2g94mkAUhXV!Oe>A
zw7K4b=r%gIYeD$F79O_*=M|$ScDNIPh0?Ro!px1Kk2mzTuPi}fcvy4y;bq_K@lU?e
z<pd5aSSf%hvs$YOwsKK>7KJ!C0A;U7-27~yf{o4DrCv};{}-i<RcD@B843|K+*2R5
zFNV@=%@Q>4>;S2^N!gZ&;eLj@hxp;h(u<1{e3$~VL!im0`D^2096omw=-a4Qre_yY
z^JmUjWa|aK=7?L%fCsmX-;I13#avE!1Mx!{bf2O4uT8~0Sbx3rgtUM!tBpw#(%q6O
z3OPZRmD$B2W_ezjg)pdT*}61i&UZti;!AFK=moyfK<aF6{GGG;<L8ANqXr~{_L-2f
z!UZ|MTV{GO$g~!4XQ49?k@6COH*~TcMTkzl?@-L;3RH2*3K1KIUq1FqTQ}2AAU++W
z#_u}mV+#TCHS^nNQRUM^N>qgq?e&t~ygTBwMM@ndBQqm30ks8;&MBOxE1p}-klaDG
z-m1R?Hhns%Dv%G6h@c|4$&U!1P!>D01y7Bh&(by~)9DqlkuLVSk6a9l`p~fq&@(Wm
zqhDTxJ{v*wX6k7km7MDRUAO+C(QT!+YSpo{oUvPu5!e9~75MkiQEeOOjtLBlGwHjH
zrK|Ra(w}90Dt6ByOCo?|$a8hh2X}(c(Go?8b73Mmv$p;#hv`<Ij3F%(@NWAl23^;A
zq`~IS7x_>BJ~CP))!3xX$(dHxjF9Qf#3+JT6JZ9tt~Yt#gj#}+70D_KP+<!Y;nb$^
zGucg1hx|!vM$*bU!i_F);`Y?{uWW5mqmq9TKF0^vJK-6B+l7zU#*O`x?ADm&V_<B*
zZysDHK6Z@&XKy(B;A$Ii^v14SC7b2(K{|nnl3;Na*Vt+>u~&VV5KgMaeoqR9D~<+r
z@(&hkaMnZ;b7d{#C;i9<H#r$^VTzvEkB}LOReiZ*KPhHa$E_}e+T96cQQIkSr!QW>
z7xnP3lvBx&lI4)@4>?)D8)Fcrz*CoJBS@#JiR9?FXDQs70bFWRq-3ZJd0PP>p9Tu^
zsyeL!XA_Q*qONV?X}9mQwOYj*vS&FF(@E8XcRO%*N4UdiR`z?&rqX!a@!^?;pP7BC
zj;EqnCgK<s$k<JS)f-6dcY+r{VABLbx}jg|tJfJ8f?Ooq?ct++pzW9vR_@9|O^`9`
zZfb*som2R!pi4MXpS=7NmR1+=N-Nl2HMqMH?a%rrshR<t^=3ss(N*C)xrND5Hu~%+
z)hi?rPcE-nQssENIIu}a1$-y(kNDpr>44jn1mmSEMQBNI=5<^Whdi-dHSX^5%rQmi
z;q%db1<BkQA^jCH_6aF~RRCJ9d1~FWj*Mgx3*;K&KAZseMQhK}Q?Vzt!7t>~L|^IF
zZ~QzdWp;#{mOc~KS5KAs;X(yczP%rst663m=)lqbCf;EB_n(^Wh<PH~yZ2T{Mm(3%
zD5jPqS^-;D#l=Q4II>Ww(D~n0jU7sLvd4CaYV)eogBEZhs%eEXdn1XKgp5qO|B$S8
zPL4STf0~N|$Ca!r>X?hnRD_O=^zbeqA>%{y4OP~U%CNTp@n}a1*VYkM{1(pPeSuRa
zpB^(8Cs|@LR1Xqa95|ZovkG>9tL=lcPB=rHJVmoG#AFPxAcE=puLb7na}JpoR9s8C
z^2WMEoO^^j{<OJ!03vWS%jV-X66!BA9wknom)+&J5-nYdk|+ETb@nVE^3>!Y0f*oC
zE7?~Dw%gTjn5~Z-m+}x`rA}yBm5#CNH`({#!@c6Nd*P6_0XcUxX)(;9IHb}-C_B^F
zF}y`ZmyBnLftf;7*nwp<Fkg!D8XWo&<wthud`=C@IM!;eLTpq57<)epu*+LjVF>4*
zDfO>MO!F7ijmMx5tspq44)<H}AM%N+W|L9ORa9|CzbG|v_2(sJelUGSp>B;$MXKAz
znozM<>Aijt5md4_9*#$t9ivg5-l?ZLZ12ZUFj>_4?@Yu2XJY8v?r=@c6?OL9pLz9b
zfdL1RJcpj=moK^tgdVPF(_^9?=Le#?6vX#J2(z6E=MWA%?}G-xXp33mN=#pyjGsl7
z*24h1;~DNITb}AI3U7a;^nO0@y|tQ?gL(ojk#I+zfBjTE%a#f;kCXmj0?F@SgVZwO
z!GdpAotg5E1<0^;yeyF7y%SI~5-3gA9u?gAeL2i}!0AwfbjU!{rpwso<*}=K4?fT$
zgUM&T(0p>TK9VHY6lRNmJ<a9f3hTYa1QHhWLILy0(*_m|(6#u7=`tA81B5S0Bq8WY
zI}`>#eKEwKMA%{Dh}%>9A?MXW9qt~kL}k0n89V+Sa+q_k!p@KRZ&gORnVv7P!{`TZ
zuhj<Erp$Hr4feYhS$TiT=)D56ytaSO_&Jy^r*B;(SQS3G*<Bc)mA4~JbA7$Xp^GKf
zrSQqfd^Aw9HTaE;M)Y$UKq6!(yxJVC!?&aB8R<j{x1t$h7gwV4w_FmNf&)H9qMj-X
ztTi5MjL<(IevE7Co|JoUpK!j(IOmFPjYU50DpA9r)hI$C&_`E|bTw=0a;){=4cW^e
z0xRZ8AW+<i0t?zrZ2Bt;IwITH^(K3`J+B2JWSc(jxm4`hI3LLk)IRtBde-v1J0`yw
zk0(gxiA0#xs3?qmeR8tf65Z4PseXOJ?IZN;<5xeS9#lo8B9iO7-K6F}_L3ndqIxM1
z5m~5Us~KDua>_q*8&_V~z>*a0qjXt;5+^T`tqro@mD&*HDp0~>XTM#Q$VfRJ%g8JZ
zyR~}{ddzPPX$q9cW3#^y0G3kp-x^dRmzB6<t)rdAy-QbJ>h5=vz}WiIMve<VJ){Y<
zdc^xgUmp=DdhGuNY}+r2q4h`b5BO)-J_>bi$8M|N9gW=wJxNS@K(|sH)oZ?KwE@2<
zNi*LTt4;;^fMhwW62f5<q6rR;rT_I7pu}1!yYC%RS(5)1__Nf4%9qk|I+0dgNs?fj
zJJL7D6hhcFqXy@nr-(f>$?d1ynF&%AQ&n-i`B1?XrY)a+(14#T#)Y4;rEEMJi%>|j
zb|fSd&>DEVciF?_^Kw${^1CxBhY|9~JmFK_9SV`aygzg*ukhGYn#s}>jpjU+>EZL5
z^kRMyd3}~HJk~-db$U@Y#9%@1nDQSbyxGaP>Rg!+3^5e(e;<%8xa*3q@^xMvdcg6a
za~=Qmnn-21KL6Y+!NdEnyH3|7-c;*Nn)#Wd@%l{Tds6qS5PU%heoMvb-s~Jo{Mq?4
zsPz6*>dfVJk)5>UD|XU0KxR|X(KGXpSN>CVjEQytgaVp;v55&F%$l1HbAT|ZNl_+d
zjHi-WrdHyN^fcl#G8JqYxIW7qWq*8B^aF}TZINstoQ;Mp&KBl-#x4D?^xOgo0;$ey
z;Q?Ys->68Z78a5}s@nRarbE`*?+oG$H>8IYJ?#Ga@X7b?IrJ%^mzz<FMSw`Pd<^D%
zofHF^@c$oIn$!BRpBXSDRu9i-eG>&g+x$!+(c@cS<yyThX@stlx-k#N7J&>AfG_M-
zXqKA)AlLn<x1!=9T64$Cv($w;^cHum3p{PWAxQvm7~lb+YX)MlS6*~2Now%$mOlp4
zzqQIPbWCw;j*GL3Hf~Ku(E~sBVaAW|V@1q%-D}`y8%_xDPiV84^IZB5R4NrNmMNoE
zeSv>nmHw@0o;%-KAVG5y9pkv`BTauUNOh-tH11W1ib`8CcqD(hh^pk=)m4O&IFf@I
z$m>xMA$nuE_0wuaU};vN7Jb(DKgGJo(FZw{Qk5L!+|YQ8FJ`YJ4rXVXGz!}_)Een?
z22ceG-=zi#2%OnYk+Fzo@*nQ*#;xl-Ug;^st|Bs8BvFFZQ#1?3s5kCMN-Kf<2j*}v
zVS+nOb5K+ThYN?tX>VaJj#l@`7n-opElIOc41<mjsaDG0X@}HMbpG$les-b&uJGT{
zu>Gj=0%hWejLhxdq>x}LEk|H=+nEsMXHqc;@M_9rq^rR6%RnwH<WCdw2ZWb*5cJcO
zz8K;$5b`o6fT)`hO<yCkKE@q5jx2UgIsObvx_cVx7fWDj{=YPM$>P^n<qyp3TqNZ-
zd`WGucB6uPrs98I$BEJ6L|YPMCNl-W8?wu7(p7pEv#u!-Pjt&fYcc@NlX0-oa3<0v
zm3J_)l16I<Vk$sIj}1qJ<yxBdh%be&QP?^x6+HR6TVWrjKSHT{)FTebKtjsbEH*P!
zVav=k0NfDdxxyFrw}=@UBfHNWpC|9EHSxeIopy^+SNAWls3hoC6L!{u2h6cas(6H}
zoJb$Z`H5~IkV4H}y+*DODSCvyLD1##jQeQjlf5*K34JF~**jyqV^YEj?pP^(7oV=v
z%KD8~mQ~4UixP&JR*<W@%Bn&)y%MKqDbQ2WAki~Lzi=kvrGJD(KhJ8#i8Yib$@_j-
zuHxCgW76U=LMy7kCK#dInJcKY6E?QeQDsMIJ`q!|-d_Dxx~QY2O>A6{k4RjJ-5xi$
z+;?P5IbC;gyFx!Qad$jX-M<fW6XpA!jFdru?EzbQsXHiTB>PdR>5s2o#lV!j0wOE~
z7e_Bgv!Vn5@^AiTIdn^ED)=UV(=nxDFH2)wuw(H)&N_ofe)j0*4e7baqq49~KtK^V
z3dyN>OPqt6f+0pM0G!tu6&dj+gU`V89@p569t<T-=nYdZG!%_@PNgRIIRBlvU&}}<
zFhOk~SRL{A)$Bch4cy5D&6!7?xdq9K^p^owmrI4h*!VN^>R-N<1ZB>b9m*_M5|6i$
zW*ex=Kl3#G=++v)gMH%D(51f^f5$T!#$o4%l~tnW#qFT7B*1Tya_^zmur-#(8;sXH
z5+h}Aj*D?zlN7gv9-aI?eb#3Uju7q{msC<F)Fcw0|BA;*t@nnam8WKCCmC*EC^N$m
zn8<&cL`l-jp)3&5R3S>2fnAL<R3<4qL9XSvu&6-}rb8F_t2RN7iKJb?0NL%8{D@IT
zbjSXUh=;<Lmlw$T3iCB@pxZI$fJnP{hOp(cw5D3%{<;o1kQCM;@cwE)TSiWRTt!Xr
z5fkZR<=6A8fB+%oN6_!9nw${)roOHv3`nKilZKN6`Uzz{g)(LQD`8MRjBxvi6-j-7
zFv~O39~W8_b+$$x-DvtHpSu+aW*_Xmeius(^yVoE0SQr99Fa_K6Jy{8<Id;u=*th>
z*=lY=#yh>E#UY^)N*0gi#OkT5k!=R%Av9$HuW|;r5_McXeXI`*Y8?2G(yz*=OhkN!
z+SJ7SbR@c(5rzvke=8FTs5k<X39Gk6-{zwGxRinJ;aVSf5sO(6HeACIfAX>4yYAgH
zS>N+7a140=9fQpuFVQTG?&sIb%LN=x0n48I{LmIlE$XyeBcL?J)6E-}NBH=}50ZyV
z9UfiwIv1}vILW#nHTbJ=hDrU8BGwC?^(_MQN2tw@%nt{Yh<J*jCym<RkI#xp$G;^^
zw4Sua*4<8U%XIGWU6LO=Es1|D;p+2FZByPJ98Cj?f-EzJ_{_)7A}G*rB%D(YPotKO
z#zrm7+bRfiRI>hYD64#NSzQJsAPwqa5*|nf&<SQfpkmdRHqcbfXH?2MY3lKY1pcAY
zL#)b3Q6}R^miWV=MK%AvtS7dODCQp*7uY;cWPhM=W-v<sz{UF|i;y%yhhm5YR#E{8
zhFkhRp3T_F(z#H3<_V)69)3%``FC8RUvGRZPvkG#_XlNaZ*6Uh?_!JBY>s>1zJTMY
zyP-vwt?#F~;sOuhgIfJK1YuLGMe>Do*^B!=E6pB>)a<%E+|m~HE7jx7tw1J=GFRw|
z>PEQx&$`<<sPxKncHhs=s%&*a;NqEj^8?+=`YlW@hnkEb;nOEHX(<niL(=Rh;ZH3I
zVSloLTcd!hrb(7Z(d6pSY)UXvjJoMUTDKj|0_2&DBVhrFX%+G+aR3nNzCfKpKjRO5
zvttp@^Vv8s>1JoBWhPqJghQ<I1VEzc`QX&6gT7{k$7I1RMspVQSMR%2%`p06=*a-d
zM`N%_yL*Zf?}juA5DW6(UTsyL&jrwv=ew59pN2<8-g33B+;-0Inj@?mp80U`z^cYI
zP>D@LEuRCO*+c|Nva`1CXe?u^|53T&!lqrv-SwfqNj)OfDUna8KDeB>HDD@HX<UVX
zB$?96%!3xiZ)Pn{fiI6#4ICubD>~91@jq=F-;DBWV;3LOUdmlqR*~2HUi=B;<6a4x
zg14)zBNUlm=b1pbazEvVtMcdsA35SDWi5C`1}47Y0sYPKbCnW0x?r(|nIJVdB&4Dv
z_;CekBe_xusZyF^S~mNzio(sN3aYsBqsx_a7Bp(t8_##X%SdWhAkh?!2A;Zbg|qAB
z1VK)xQVOWdbj_f~AU1{K^Kw^HA^{iBiSwK%Nl7ngx4T6*RR)Ns=R#eZXZdDt9_?v`
zSb1er=`53J)j_)`hP_PoxIZjj2pz^-)Lvo7-k?^)iWgSW^~_WmOAjesmML&1jDyvg
zN<u2A3G#%n4x}9iQS|u}{)3~$77gSC)M{l;nn?X@Ve_F5&C~4|JaZO>qSpqnlvQ@`
zJMA9RJU(*QhQ%VYWNHQl7TVb>g`Ty@8rAuhF{t_fsJ&^aVHcAfAU8cUKjv9n18d8z
zh^gXo^{(F=8AsNV9Cbl@LkeFwf`!#s@ojFBUGR&5gV_%>>Qw~Om!~Dt7ZyaVI?EXD
ztNv?O^Z>XDH3E)UAT%{_QPeD9IZ7jswV9so?&e>meNi26i%w*43rKX^SEBXtVbRl@
zy0xxu8Q#*{^;o>Hg+%hOGG1@PG0&+qqfufQ7EYG*Jb%?G=LsrNYS|_(;{ds_hqw5c
zp~v#Mv)!`btdH=Efb+G3{r<2(duhO3C>a~S9IW*Gzz0Tir>oc@Tt(Ov8dMz~ryaOa
z`FP?4&B076rLGBF6qVeM_!$Q{^dOeu(}YLsN7|W2u=(I?n3b}S6q7k-Wfx4gLU@{n
zxY3*;a2LvUBANM(i^RT<<<S~pqZ7@j{VDp@3Qc1@CGk_yCAry-tB(mqHWQ@PHKFY7
zKUM-jd(<(GsAAO-S>P~?A2&t$El!w5<JUl!C$C<+y28?_bHAdnnhnFyOf~$L+2n_$
ztUB5UV9UsXEyIUz9Jiu#oquHnkviszN#+G+7OOn(!bx>3zzRR4v?Kj^L!KGAMK?NC
zIHf8<g)h3>zujRxk-{yENW^DH?su?ZARW9yV_A07yX_6dc)9*gjzKV>Jq9G&W&86(
z*W_AEo5}+G)hxTg0sEq^d0!_cisD^?p2L*BZ(1RggHA}cp(LU)1JN>va+8zokE8a)
zxMv~=lUjc~z2YD`$gaFE1M((;u#;S{vVLiTunc9=l;(;=XuxYWxunrE#T`FhsN-RQ
zAW_KDIE_&o2oG(3K3L5N?m1;V);&V9!xHVvlUq|*`1MVjo9kk3ksUx#?lpQEX^ntZ
zVoc~yA1DC{g|A<je=0tTKEwK9h=qm%7{f~($HcO&{PW8lkLGIonI<Awh0oM6RDXst
z`H3i43Hjj<DXkV#h+Ke6{txiE$Q|aKKQ#3RIG}OfWXDsse;!~5jtD>dwbr(2+7-7}
zbgcjW=16%R5A7k-!l|+mRz~cPbeaU!7%kolEj?dh4+}1yNN3)3k#_{k{Zs?(pcq&z
zP-`&g9TCP(QEN5D#4LLRc!y*kyzo8z@F05ud3Umj_UF=CDJkJ)(tpUHHPTbn<Wea_
zViETr174J7McAcjn!R~{f2KxkE-nC&15#*1Nrvy>fgBdB3@TN_Vv<h8k4Y(kC@0Is
z5sA;tTbi~n+RKDn<4`Z&vuTZrvmT-pz8F-rkQw;Ga)aypswb=OjoB6DO`g@ycgF_J
zj+HyRWZC8(Bj|T;Z^dr`K9{1MWW*gpT|&W7l^H@9BI-?jSz+t>CjC}LW3Jm3=vTU1
z#B$%cFm&!uc8Su|dFl)%aQsMGUA&vVxa;3n<>i_wGmrw+P?MTrL+_ZN5#6YTr>@R2
z6@>k#)V^Y5mYrL3Ut6Pa^E%&LGZ=@oz5=qAqhzU$Ue@o`e|N342ckhpNSR(^EuRxL
z2Xz*^8i`5M#1Zx6_s+fs^QJVBnd1%PcI|C=<^}Ic{cmmAz{85~Y6)9q5ouw>boY2_
zq2=&C6FhpEhR$C8h(~9MC{Mlu5yxTNS23@Q<G(Wh$(U@D4O2}-DQUVnJmzm>?vXRU
z2TpnVg9H*To0La%cY1X~@}3d86CTO$Dg)bOANU?0NT4Uvdp)6ykVLrp$N{@dfl1i1
zqfVd0t{mc(x5X?N5idJF!M<M_8{MA`-QOdaye0Zm30BdCsQ-3?%QHv-lSi$++No!F
z{rM-u{3r%k3bpq@EZNlm`^174=DzcQwwyrEUN{Oe^k&h9_4%u}7y`wY@4!=8XZ!m6
zGPLa&LUDZILK+mFpY*=8s@CfWF@!qIKK8&nLnf4UeAZfSb>8IKT^BuEhuwVsm)E7d
zn>F=K(7c!wF9E#z=D{;>w>T3|qrrq-YtK`=Z!LhXa%seND&@B(MlkLJP*TdAKg*KH
zUYb1`SiqxOkzb(VEG4}^t->gkUmqQ=pQdEaELv~TF04f5wi#!e5sHe_5U>|U;G3V`
z^xIB(!&GL7<{g|=r*EtcuZuIsxfXpHG^Link+4DgZ78RJ6F(j_nZ$hR)Sdz4L6bnK
z7aFEGr*+1>-~akU{B*w9>=|2n-gB5tDkHvqv^|n09~h5BXB2q1GC!~7RMrkXKwbuo
z4u&peRVDe=e1jdkm+D_wY;P1|el)PH%;4-SP}iAPT0hXzgi&gelBJ|0O8MEYNTlf?
z!<E4xU#xDY{%bKR5gh}5(^xcI2q9xfnere0VZF<6_@vnv@??!Z@l9(d?BJ=-07*i_
zej$mWqEJw}hob-;wjd0DAza$MXUF%^#`GzD)HtENcuYn4L<9<y*rUR=)NHHA!<i7O
zo?2U?)BeAhlMdsxwZY4y?<kgO)Md$sQDJXXiA8~f(-gxv81No&wnP8I?Lk_7bQ}nW
z&V+EdK41Txb@e<Uc5!<0Jm>j=abnrd-Y6JIRI52vIwq9kxqAT$+CRq8&|)k&LaI0W
zqcO&W%3iNe>@W-1g=qQm;v&Ic95QW@TU1$P^Flwi_f}MqRk-UU;q0Ta6Lh2;Q@_R|
zyXhWF@y}ns=5VrU&8FiRD{DcLu0OVW9udP+@XC`%iBGSb-Wx~N<FsF)pnf!18M1uZ
z<Fun%WChTB-5Y8M9QZy5iutqi+v|@!*h-fS#Iw)5(psLe!c48<&Rn5|#{$h;X7>AO
zuV?chx>KT!Ka52Yen*>v_g<JeAHFi7M6$%XSQeAvu$CYz*b4x>JFr4v0LpJ<11W=q
zr9iSkKPdhXqOJlk5&#78A%+AWSJqH?7d=@l#Lt+{2)%tfdNL$t-25U%3l_bpsFqhY
z7}!aC`s&r_h|pg;k#wsujanOE%{9DUTZzz5*6fwgtlTax$u3;RY4^PR|JIPLdT_a1
zuW}O9eqWZbF?fz~w)7dFx9ow~6S>7?=lK>*nI8K77ld5p(?hc-LTyMf8htQX4Ne*o
zyR?y=Wcr%-DXo2md<7$PQmbDyS%r5yJR2zounZL0+^rw-%8B*+PE}hO8rCm<#{F#p
zk(OvHP6a#o!XQ;>E)>~pDvhPp5NFnB@Y@dn{t)B+=2KMFB>MXXplI0B009i}K|Q~&
zuEzE}HwOv0)0)4vzVl=Ji4p4QqI&<spYnP>50X!Z|Lz9+>wW!bJ6{V~vQ=*(Vi7mN
zzH$YRTqQ6Wu4%9S+wdY95PB=E20!6U&^MC;bVpK@iUy!bH<rhqQ(bjKTjF1zbpu~h
zEVTD-1&qrOlvEn|P1nuh36G7Dq7?UynbQt{m)rrY;;ww_;rZnXjO}SP|Ht9}u|1VC
zqXX0G8krw{n@)s@SMGl8g;(IN_ek$r&gZ)AWZeJMXL4SO*$BiX!Tgjx<yFSUXsA&3
zkkpc#HL(&yYkD-}CyNyYu^8Q5byZAU|3+GKgZ9<x&+MlMJW|SwV<+<7lKWwV$3K4w
z2p)|9*j&a1D4921($}j$O&`^NPeBQWWA?oDY0uNsp6$<ktGEQr5NKZ>x;p#U--R)X
zNLFicV9|@7*PtN9X)sZ)_6$&K?&81VS}4O(`hkhpZCNkJ_FrR4c}MsiKiBh5=+22N
zIs}gSTd#g?F9gDp$!8$i*65a#A)L1ETTt4!LvH?N%YGYKOc6RG)42N3M_Zl!Jr||D
z0jlYfDw*&*5t~tO0gH;G;Fb<FP5A^su0lM(Pu5zZF#`eZUv7%DzGQOA(K3pdGYQW_
zQg0$M5gQWW3-B9@noU&uiXarc#z@mS!&`O47?i^Sk2w(U^Mny0Bx-FZfj3&}`%9}d
zf%BWC^DdtCjqusp`HyC2`K;bV*EdO^e>mG8{%*v(dj;L3DWX(Z3Dr{q0d(R4cIDdL
zgt8ydk^fBRHRY6^C3156x4T&QT3Fy*S<MBvXFuYp4PJAEkQ~D=xl#M8yC48e%mClo
z&9e7=BkEXRV5$D}evQZjb=<?#X(Q?UYIyj>sC-p)`4;QtXnVGQ>-zHTQh1P;&~Z*E
zV<%cLK`(*Fmp)#QcwRzPg-IM}-43)Qcbx4m=)EI4micV2!(XeC&ZrWqu$w_JZ=7Ev
z_n{xP{aYzIB7`5=je+AMA|7_>EzQHQQ-)d0JAWm{4-cHZbgs60=DdyeITWF{@9z@z
zlRbMRZzw+(Z;d9R70i@(4em6<ElzKxt3S)6MQ=wREesYmdq_bG(uG(-<9H_BldKa|
zmw(FAopX(K5GC0c(Ey6BSPtg*+4jS{6t!AY+)VjEX91xdzR}UqRL{3ZUond4LF&IR
zJG3^bM-cl#wCNi?*x!4&WaC-DL#s~ff^R;5rKAWB77$vOX=xu^Zz&#j`(HO$F)kC_
z)^A^-(cT2mr(grdN@SZhHc+{v4jV59!hC$%XaD-63yT8({`K<jg~=Ey^CPY=%s{g*
zJe_#l>Npeh>1rw9b-2<FTL&N)g|BhRYIyYVmAeHml6ZC<IK%S)63jUNJ>iXL%!`#J
zi}eCvh$vjlMi3Uud)~+_+Pgz>fR&>#{*6bj;^xcsIKVv$qW$X@4Q_U{nUuiO!H3g&
zyGnp`-VXc3`~Gs>xE0LUaKGm20#NB1PO63zxy?T8r@Nkg_RJ01U3e2}*EO~6uhhmB
zAo<f<3_sKq8oz(a;<YlRM6Fb1qiJ=Z-W=BcN7$|Q2}*hm%Bpd4e2ye52JQhixzsrl
zTnLr2YzHLGUs{MYiO?d|wB-S-`^4k(&%<X|>&600=*~pp__t0%bYJjwt8+_$9>j=s
z@IETvcMj$i_xn{%_uJVV#loWP_Qz2x=jG+KzOghAK%j)#7z3T}0IZ(u>}($q@(zc2
zaDTbytJz$+mheU6Jz%t&qe$-ouzkW0-T>-aax&p>SJ$BbtL@9fp?v>;#}bK9ipWwC
z+Q^c9`9vkVLiT-&$XW<9mJnHrvM-gA>`V4#?2~npY>}~K$vR`0VT_q`KlS;Y>vz8A
zI_Ix*{PkRqdG6=Ax7T}nz2A42y#t}SJARc^GU>C<G2L<$9Z`aaN?DrSF7g);cOtuE
z=s-wj;*XV_a3$M}cg*2lzrFRjr*Dhs(#u3pQk?<~#iWi{27%o}rj67oFtw`9)7Qtp
zM;GUrh?;zP%{wSpY)9=YI*7dxu!bSum6@rHSKJXS-(JHF+@H>L+YQ6q?;8h;?(F>^
z2V{Q6VIKxB#AvBip9G3mik(aYF>5UY7hX4Q^<K*IYG*38YD}p@t2D0of(a2bu?g8J
zAG`8e>y_4ru(99ULXN%s1;MpQw5Q~c+DAy52cDCB^jCs8`jscB2iNHlU@4t7yqtVN
zfnGYY!64MH$4=WN0hq4N8pbKqg}kTh2`g;K`N#LoRhBaxhBO^s{Bz<+Gn_Hr0Yitf
zS>`gy5sh8h#66epy;}xQkS5v@HvZ#>`AISjU73Z21u;-hAv@ndl%0k3p}04GKo;L6
z4)^|wyHH{oI?Y)w1;mwdUqisg#+d?dFYGm*Ah8i)HJZmeoH1sQ#{7@TZ?RYD)w2&1
zHpsd=jQTCSdb>+@Ws1Vt(w2<YLG&ptC;f)YKKPIBurFIY$-aFrKKEy%2GSN-EVTIs
zuz-V|G5kmw{88>w6KSQ0*SnK`GyO*gM7XUUvsT0^Wa~+JPB&?uMynX}BD%#fUB6yE
z3IuF|wv9?LW_0LqDR1(b0tDIy*qNnufR=8BPc?LL8S@<)Ei=i+a9Hastk)U}OcMJi
zuls)+-=kFuY_z$pfH@=rZ&g*9&ekU6Grn~mq(PS+0Z6&6n-0Tr<nGdQ*#zTM&}K~m
zj$NUd85seK=`Mu{)z#HUo5rZf0~A3?g!B1P`F%#)4B>qFqt1rHRmIG^#1I@@b@z01
zr5G5aC!zVGr@HvXru+up_~t6?H2Phr&azuW2KUkUfBUi){&C}}EM*2MlgF;bOXlLc
zVoX#Fc)B9|81WX8R4%>rp@3T`EiE0_Q+==0vPW0;=Z&BsMTldlArSJ)^m6wd4Zdq$
zgB@(DmOJ&(t=E^>N)K<ozvy(ZHthqS+st;|v3T~W0r_h;Kk@n#F5Vqh>iAVsRe*+3
zd7b{q-I5L5pcTgP9^rMNFI;L>tGMG{a_?lnuBqTEqEA%O^%XOAjvB#*SlcWyGLqZk
z^ZFJ|UKnG_+r4h%$9dmyQPma9pyBvuvg^>)l#ai@EPz_0izi1*EErqAebA1%aV`sF
zH1Iz9ekXW_PGAO9_2nrkDG4S33$U=LlqapN5rV4&!sOofk1Ac~Q!U#!5!4{~R&p*D
zDx!QGIYHy&{$=f{8oCqHm=*nq*rrYFa5ZTgdO)5RTy`GH?Na;^-glr~-QCS7*RrQ!
zys7Na9K+bL>7*xg+i+S~;T@%R<kpnte9xq<1BM!t77}hV>eOU0x_*UyCp9SktxE~;
zA3S@j(<gZq%(jFoX(*9_4?)JE<X4NUVB<9&r%kK_&+9AU=x4ps*x=ZR;Dt*z*T?)(
zP0+nDOW&gifJ<qL{$jGz0rT+Gw?ygRuD<}i^YND&JvB2fE|~+?918z0#RG@u=<q7!
z)pv<R7o79+g|n&VCV)vHK>xUfm1_7iul7w;{gu7*k53u_oO&GJ<@$V8?xA&X=TF63
zo%qM%WrU4T%=8SA**c)RQ>;;IABr{DCR6Z1N77PgW{f00A;OMaxV`yQ%N6-Cp`KuJ
zFi}Q8?v>?L*)AYk!-q9a&<FA>)sAe|NIZDv_#7ZP5J!6EX$37=+0g<>w%BIlu|~v3
z{O5H1os*FuVtZOfR6$I8MKG(W;IaE$rCK7U=+vP>@|bc<KmGQl>EzFz9BR)ZglT4^
zt!Q(GdT%0fZCsPw*jsiT0!VsPV#6VkU|2#Ru|O++ExoJJ1<Uy6&5OgCpIu@dV-sw=
zCh#-p+V&!3p#t&sA%XWHZF@p@3LaU3nCvNoI|jf9?dPc7Smyoaq7yQ10IwSE!)ib_
zhaXJzb8wRnaZrkLopq*^!OZa{3Bj_&rebsNzg)F#?`v3d9Xz7CY)0~@d~>OTe@{JS
z+_q4H*a{+UxDsOgasoNZJb&j8eKk0F=xY?RJIoEvGdjIN)M6-5odTiNp&fGE3xQot
zXmHsbZqT;=K7&~oCpIj^G?y*4)gX7J5>PED)q99jO+=MvGJIGkjk{j*_cq>9TF%DG
zQ1R$9miljIc!P%)XMZWo=wm_`Ka$7V=(B>kdEq%W<AHHUGnq<wC|ghJ9`<iu02Y^X
zGR*5@6(0WXy1XooxG_!p0J%!sFqT#=l2ySVHqV5so@$w?jMk_yom)o2Q=-TzD{Kb`
zCU(=G_M2}}9}z4*2io}cVU`N+my@l^=gu9DL$6I5q<=j+bmlpi*c+94TT5C!&=)n@
z)=w8PM?ZORATahkIY?#-!S>O94V~!<-=M*v)V>65@=mVLy?u0Hv~MRU21nj~Rx0uC
z-GDmI5>ewx(W16HpSXwk!R9y5+`VkUCSxS*m%zTdE@$D-lXdb+avV@CL2uSHVRFX0
zeq3y0xa7+@jSmtD9xm;!CUJc^waUz5A2Y{r{-=wAE^`eq@XN1UqVG6%@7LN{?fSUF
z`*^P()?p$@I=<5f$7P<Qw-PrcCIhZUSxo<|%Ec82u<KWeGGv}%cgk4;<U158MY<?Y
zIM#269hflPz-Vo;7gxe7BO7n7+ap6Jvp7&YDb-J6Iyb3IAMQ5S;yw(w+&n9p>u95y
zHn1*#oS9C2a528_K7QJV4}P>mH<45p{{s0-C5m9YY;I!wL+a+-%QKJfbJo;1hGf1I
z70Tnh@<|EA!aGezB~AdP_65w#xzB&^N@pt-V_1eoAUgN<-7$~9DzWh+nPd)13lI0F
z2vQ>x<XzaB_$!6_*-u2iOnu_5h27GhjifSsJS<wiws|0wXs{lJ_}I*1viw9tKf$QY
z`A$e$F=t1C)$N-CmZM#g(h9!kwFc?Ab~!;W1#iRTI3@y3i5ToR{n@5WyA4eWE{|eI
z-faFYv3rnO$hxkR)mg()s?K+A)=sCle(krvP3U4ZW%G1uM`5B`$1w-rb(J9TO@c@P
z{n4YB_}|L|f=!iZ<h3IwBeCq%lbn4gX)_euKk<UqGG`0kmK-3~L@7Xi&)nQf^f<t8
zz`T@pHbMaxd)aF-JQvqakeoe2AN}kRYhhNOK?;(&!J6oznA-FRnXMglWvG&^<75ND
zDBHfANs9Mvqk%A>grr<3zsg?vV`(XnAB0!`P;%~Wn*sG{9PEPp`$30tn)no~cWh9L
zt-VF${xO_U#VkUFMcJY#5xJgub)WtmzmTEwyy4&n)jauchf&RAsxm~O0Ccn9ie+V)
za`3*^Rc@nY^E(&J9*y6l6TO(SI+T8?LXP|0veEdD*Mi*D<t*_aM+5k?bwyjZ<Rvs=
z$H2&n0RUmj+2b#6L{poOg>#69FKp6#WF<2Ls$!Fu{!Rsl>S2+P`!fq^DZam#6tFAm
z*eSMyIA_7-%?HwXlzl-K3ZXjMvn#x_{Q68zWNExtIOw!zqRUTYlcEU!Iqqu6r+@zS
zM=6tYZ$4dieO4K)7w6c|)0GP5_3SM@+Sc?p6tBl3ik1zEIESXSU>g30e3GgCu_nA~
z6pI4teO6x%5xymZeDR1HViMx+kCmm}pX{hb_WvsQV8~Twr>@eQ<>iyx*BJY*T){1=
zH}!%%1*omwU?T0CZo>i!a7su->1u0I?JkP5nS1&`y}$|>USjWOa;`-rBA1YFoi|ie
zpR_JmlAI<FTADq{LkN#sv+uQMzSJfv<OS9#2VV{EcQn?B8MOBn(!oE-7BrtWK5<j4
z`S97NX$Hq({8|k%``5n?a<vwQ)r;(hy?7VriCwD4B6A}bMZnxG+%hGN9<y;aTCgN6
zzOr4l`RC5;7ysS9{(A#7R&HPUTbfz2(~EbdiiXVI{1yrWsY743N`X+mph?@ReqCb0
z9~-b^60$x?>-C)n89x~?iDf6_R!2}Nlx&Qahf=D9GMj?TKU=q{kAnHLypIYcZZVq)
zJXI!Q55>2AaAj~3W_)KLg~B|j1Ht5^1uHNvhDr3H!|AKL%0an}%xlF6cbuEtjv!oM
z68F9N5d5*`j>OM!1m$-DVUq7fLOigUYD~x>kvq}*sKkKDG)on?-+;+TkxK5kj&M~x
zB|tE*YyuHa`-@31FNIZjhxmFp<0Ea`1(YoTHy?!dp@I9n1YfHEfbugp<g|Vyf54)u
z-Ml9Z%_bLAv%v~OeW6H#wvul?PkAG>6m~H(14uR<)#ETd*$@32+%=?*nQ`K7Ut%AH
zjRh$7I?92H#Z<GM3PTlI6w)(z)8azMC1mIlf_@<j;r@HVt2g>=5^t&nYijz`xLoE3
zZMKgp5`$G)V7Yb5flq8G_r;C{&5SV4W>f1GiwK`DbyKGY^gBGuid07Hc&8~P5}Vvg
z1^B2>Z<YLxel8*E1L-Kf`pvIDZQ5Tn$XPh`1c$OjYJk$&pk2s)69hi=Q4aHI1T&iQ
z5lMffe2)m1Si+^Oh6ahB3MRMjA8hbAxYrGy))=tSt@CHh@{#9-Pw%4rHm(o(+3VuZ
zDh*b1Kw21+5Kv@B8~Ag`tbQ$v$kO!HTF*@XAG_0IC9P$uG>xOi0;)^Grn~uIF-eE`
z?JIh|s=Sb}8SujWr;j-YaNCGzl+`Dl^h7Yn+?Z=c4i{Wte~3+X6}~zH!M1QXeW>Ax
zU~XjU1`*-;)UNBM*R~=G`MHmjyU$${q~LZm3%*kzCY7Pmki(d1G5lSYnzGOckGeBx
zum;cB?mbDDEi3b3=Kb#Oy%cWSZnAYx)b|w*wZq=`!I&1t4$z!HMz8mzs(EF(AQfcV
z#Z~TxTuuumPOlQ#_#=l1Ljy?M<-SpPv?;uis-TqU5sf&kf6c%alP%kw<V<3^#%TJF
zIkcl7Kq}O0=oI&{M2zoFqXyeTHYY%pT})#O+aqm_kor42Lj%Bapk!l5RcU6Hk3GH|
zDDL4qSYrN|uL0kE>aH|G-$6+Jx?vnN;lH4d`!;|(3Vauo0A}~#I?`@tX;j`AFHl}a
zG-~}7-48f&-c?=+lu=A+;u1U|;5Tp`eA3-=PWvj6{0Fg+jJ*j-ACEmc@&<kcwV1=Q
z6teJ!gP$;=G3Fb?<^N(UiQ+!pWN6#fRgTqB_{OC0#HasAV2A&7c{jGK=ole2h;Fu`
zF&}D47m_UR{o4|wMSqYfx$5`bd$U-^G-Q*0w1$ttLtjs7qP%O5e<k&PDXu$p$kcwK
z<MH$}QwHs%*$T64<D<azGg4&W0;7I5UxW$f60XI~-{j^mpAM%ktzIR*muMcD4)GhX
z=fcbUR1YaaqRWcS`-zW_RVNgaro6Ic7lqr^ommreUOfWPf0m>P&~blN82~!S7bdCz
z3{cNSyddo_=T~vtBN`K(N|0*49!e)_Msj;<y10D$?Vd+v4NO+yyKlQOCZzpsr4Yhc
zw-9dlUd4(-g*<jcfW#y`Uog5Yx4N+UwXzYm9Cvws<fcHUn?jNUK`Qg3Xq?q2t_!Px
z45!8dL7Z*{*T$X(G7=C7c8>{h>hz!T<M5cx#MV$^+L=;2rljQb#=4zlJcZsv4wb_d
z`T%k-0%c=gc6=*f2Uk@}++Rxg7|f-#PiIOQ{8>$>-$1|cs_*b`&b&*4Aus-Y$_DJx
z>p4q9nYG@PgfE1)W&#r1IWPi=gh5LC?qH~~d%)fjwq4m^P~DlUX+4t?6vlLPpZxHA
zg}m)8VFKjN>y!pN^u%zESY^w3T@#Dbw^|%>mHjlYb?A%n1iyUF54)rSkJwfkCvXL<
zV9quVtB!gB<#%HDr>|Tg>(B%{f2O1i)Qrwm){>Lm7+}qI78(~Fj#TTM$DKvrIpOrR
zOn|zJGG0=`b#i0{%*CYT_W&!het)A8g~0BA%@Nz`>}CZN?`Ihb@pqns0C}DW3OL;W
zr;`$+$FzZ<&dhA`VZzu{C0g2Sh^MhwWV>K4a3vdLBm0{cF>i6pxW>&hAx>6F8k$%U
zFadGjK4;N<_Q+%wtNW9+7a>5l9mj(sJ;VkTJ&BZB<%lj|*G1sbs=1d+>%xOJn^RHz
zL2-Rzjd5mG&fXjKO9@NZ=wE^Ss}|q3Io&x&BPFzNevg%`i+;HOQ&fD~<X|>gEQ}*M
zjj6an#lg_pX^HvkRudAnkw$D~;v)5xaa>^EsS1jp>6|tJn_upRP9F^W+=hIv74EV&
zhm-r8@eXPMRNfyt2hQK~gn9jXsj;=BN=^5K<1>?w!}7PB0K*{Z4)aa!DRuvf=9l19
z_`5sND=F<6iiAuo@PIKMxtwa{kLl|T^5y$dv$wwrOL)Tg-okdTk?i<9=KUdZ5CaEC
zkPW2xu}fV^4b@lyrun;;mK;Mp`A7i@HaZ|j7CQ~n@#Oc{s)`0xiBrC2jgQxFuyKSY
z&D)z;Ue0UKj=Zxn`yJDqJa7Hu(r(aZ2)1-HQFJ#5ZUH5NJ|$KcE4^Arx1z-&qg8*4
z1cxG*9*&@dWp)iggyee1OUUO53@%&qL0Cd3@?a8)OQ~&&G!X3+OjK$|lKYS^2AubH
zQL3!}Ja}*^K0f|W*0oT!o}uW8!wih#;wf6^wLW}nUG-B?cAmfX=yhoR-ODD2GZg~F
z`mA@4<4U;-2m5jZ1>XuzE4+K~gx~!k`sJ0B^pkAwX3&>e&F@*CDzIkN^B{64W$cIB
z6)`-ixqe{hfHT%W6^4yler2mUnFaRUSrX(HBCVD!71<oDsGtLPMoi@Ao(bOaYd-DS
z<UK<a$?~GDw@k2jSnqTOro=9b3C6c>;Z2|aS5}%=n#Hevn{4YHN=<{5tLm(qp1G0Y
z+ym!WR-g{Yl*_Y|cMJ_~Kh*^Wh~!PwW{aK6Y#H^?DivizGL}ZxU}Ct!xcQPd<?S05
z#1$<nfK3V^DxJ=MI;(IrHq`Hi`U~1Nq~jm%G7U?+y1BgrUYC`uni?7Vit!g!2$<}Y
z7u}e3&^k0`15JT=)&QNLyC)(SeC+`R?ta;U`%GQlp}heKOn2<}@^=!Fg`Gzm=k7kX
z(g|694L3B=JW2lGvSmf_@swCV%5O@W2A=N}DG0i)dNbmFa_;OUD<!bEo_R|+(Y8iK
zG4Sy4bgz?EM@s}>-nPwv3rs=zvDgkalWhEQUSg%sLa$w!UB+VT5=vdt*?Y8-!J5hX
z%kaftBkX(2$1`qalvkvN=GzxsJdZkGH#V(?CGznUBDZzQS$R_SK3GAKgj|JtHV=NO
zJ%a11di0X3A#u;DIlLv};p;2)^XE@#Sy{tRMy9n7`C!_qV&{*?V8Il|g3-`ZT$jGw
zH#ejqu+V2ik)%RLnR~@UJAKj<nXMnPVrc2pbDm5gB9}x|_n5$0a%CdFE%uTmo=4>^
zeaj@fWEu5cf9)P)t^mmQz}-K}9|WVJjg)crs7ozfNIhOnqVQxrioj^9XU`xxZ;gwu
z9zU$V76K_d+-~L!p}G50OS?mfaA_>*DTf|x28R+@Dy|xAzsl0G|8vu4;M&+sAV2uz
zg;5L71yBt{V7DKnb?)wzA;7;Zfe~=vu{fCBM?_K&G%wIp$=(FfR@xa}%ejbmtx1k)
zz^F7S{O6qeaU*x0S~45H21@Wxc8_^`dH)qP%oYkO@D$Fva75o~DZ2yUhn-O$M<|Xz
zck4XZTZV?}o}S_r<EKO}IZhrt337O&=u%aG<$24FM8f!Efi@pD*h2UGRXu8&jlP%K
z&LiA2nJ|R_S4f9Br9V%?2W6zCNQK(_x%pb$Zt^>=wSNg#_fq)PFy)ZL5I0BeeY<Y*
znsrCU_&#vCvfGE!hf1}rQdb!fe>_J!Y5ox^@Se-+-}+m`_;G*N`dNmTFsy-5#oJf7
zDmR*Y!H1_}ZP4PDsud1*0FIE5e<hv7d7xwH2Ikl-Pn6a*N=4#qCv)xA`6!eIJaU%N
z>K@Br-D4^j--CNoHXE<S_#t`p>Kt%meg6|H$Wu0azecJibBcOg4w~0ERT5G%e^Caw
z?*LRnKH^fFySJc`GE4v_gpU8<;+}^xNaQ_kmt3CeK**<}*0K}d%*=jb==I(Vn-`}S
zl&**{1mVZ6)EuXJ2;$Q!fqr*OiA|<<=?iCHe=H-DOO4Mje6EInCG4}1UOs+VTb}Sm
zP*{a>WMscB*AE9lPrlNgt8%>Kzeo`iu69-_(dSK6lCcJUHuWo~ax7rb+!;E{ne|5X
z$3LBzC9brYiCf?+)p)Nm!9E@nsm>OxozmVPN~Ph+DXf}(db(mj!Qh$>TF}_5;?l-0
z+pUHGs8ho!pi^Pl5nPna&OX(=(JS+}oQJCe;;Hwp%6pmj;5gKPk02r9&Y7gAw>b*t
z0|lU0#BqUFNTMUhC|tmdKJmmLbh=@Ff=4OiF#nN8SyEuY_Ajk|-KR0;^V=vlFz3@T
zV&|?v@E~y>sQV*ZCIQ>GmV*Zne3^8Y%HG|rr7e^10=$hI<DK7)wGy=TOK)^ankKO%
zhR{B9RvX|gckAA52Sf5SjRj~l#t%G+5vqbLB5)Y+K}biK+X6%UAHG(jHXA~A=HV*9
zhLll?x|w=%#AYR4=ubrIu=nAYKqppMWeB*)AY!E;I`mG4_Y9~WSjX|jwEKSBQ`OZ1
zZij$7P~UV8LGRd!ZcESQC3xJl3)X5rbygLd{CC-$)u#Pt7MffTTXsym0gd3i4s9#9
zewddH7n^bBS{roBo)##Ghw(~;X8o&wW&Qj3Kbwt1A1HrD{)7BqPKY(a^Svb9_R`xc
zo?*HK)6&-L{_Kk5(4#AZ6A*RLy~5J?l2=e+t5|K+aMJVJ3?Kae2mnJ(@iO(erM5)^
z?}UB28Scz=b#>ztabDr$O%C_*bH$kSE__oDe<8Gvgk$<){PvPJqX{}MZ;ao#-Zr8W
z`_{~e1zD^uSvUm9b-k&YaZW?`|MdMd&@=7VVznn8F`Z)gU~qi0xM*e0j++NcJ4a=A
z-ud%|l0b)k2DpHwuir??MZdTv*H~*>%Kp_z$Lbyxg1L09ccJPr$AlG#x96%@^RAe1
ze@3J6N<hYeRP9dnZ*#iPRDN<~z#6>aft#foTscx6oS^4Y(>V$y@I^Y^2WjHq*KTSw
zfL!!8&UGG&8L8=rfQfl<<mu@%Ka57Y1i7o^9+=Sq9SkHH{#U;49q{{?ac0SK&VL?|
z0ju<q6a!FZS@`<TJ8%LIBJk+}1!m<U?!{Rt1hk}pXYa?*AHX@tDHM%;^#rmKI?sSt
zam5~adF!yq_19B;P-_su{UhC_4RPX%WG19XK8p|mix~n4*0iA>{upSmS<ga?=iU?=
z`}+M+=&3jf@YF@KjgwHTaxdL!p_|XRoqrq(wWxi#04f_mC1vO%uBTi!YX>S`p!;O0
z!sdrm>$y0o!Pd&livyV`=$NYOW2E2uh~8rwykSmeyuSSsN@Q#Y$Idm-8kpjN&&)7c
z_wmrx1~LjkW-M#kIs!^0$kyWr75BD3sw#VdK;I&W%GkAqA5sDV<0+rME5WnOHZwt(
z{pnVDeU&|-O>kMa{Y^V?Hj>M<%K7Mn(PBGLZy_01{r44dbAdUs6A^NzwNpOK{!`wJ
z7B%iPGYN_tGg7&GEI`$ur=?goKt*0am66b+r)6N+9AZ<nr5H+`O<CG2dst5~em+q(
zM{%>s_Xbh-<M8+I-@i^yYG39&J@0uQ8Wh|aP+Vh{WySymdOwwv@C7tKUN;|9!uN;G
zu+2>QqMhcu<F~iBS+aaVtt=C(CFm9?AHgB@XX!rElw7B4)y<Ugp7f~2y-W;toH6}Z
zb6na%KJ@VTxg>wE$@anM;!C}cMQ}*@RMU|)6oxf{gXVx4l8YpzdhS<${_Hg!xa8e-
zh|6nlAvJOTlxNGHvzwb61AHJ;)^iH--~$D0G+&3WRX=!+!!j7x^|$H@9THRTD^g@q
z<~&kpY}~^K18V%|>S|-YRm9Kl-#?>$Nf?3N{~p}bW(WTB|25#ZJe88dd9TcRAok>?
zy4BJ~gOQb({{LQB<P=Paw48U@q9*RfISH>pheYLWP)kP{`)?S~gf+-!SXx5Y_Z3D5
zENymsw{%JER>6^P-`dSwvb)H8_&w4XQV$f10rO=XL=<bns?mnQQmvm{fR+pqgHXj#
z2(?Ado0LuJ0g_CpMJ}Ai>@u8|ai3wf*`-A*6L|#=mNi7?AN(D^v<p&na(5F|NTQiC
z9`?iey8YRTwJXh(Sea|rt|@JO5wv_XVq9!m5l$jKQ6bi&p;OlbW~Pjb?s;QEh`0$f
zJVhTt8s>dGZl{E8rMqm?60C+ygqC#Y4Mm|9m>%aRK~TW~IL5Z{K|EqK%zt)I1mJ}J
z`3;NIIsvA4{-9{-F8d!piV6ww5dF)_3!vCj9bvjN)u7M_7-G@n9s@%|sfmuX6G`+w
z?3QCrOM}@r^6s3tnZ7p5aw31aC1gMD=XVA3t^Qm3ITOo0Qi@l%rl89&0XZ7N$##Ub
zi3m3)^a)A20Cpo|KBHLYi_g03w*)AnJyNHY1D*3k&}*$<e4)#ISwj~JX`sIx68!A;
z41f^%=OPN|G3@pk!q91_A8c@<U;gt!`}aajf8>dM)JyJv{&bZ47VZD#qE-F7;xvvG
z)cHzuGV9N{_M8pGTi2%QeJi7{IeGkSc&#no{Oaa&&SNYLW|fXi(2FZPf-0kBKK%3>
z)-5)#jx`Xyp~RoNHmhTXIoR+wtMfXW;^e*Z>El+v6k3PyBL6c~4Xf#3-O~jO0LUq&
zV^g|1p`P*WA=ndoDtnIahD3WDlde4&E%%=9?%_{Kn-hX5-6#RITp+@Y`&8Xa=%v6<
zl<tq*|Cy{@eBnVy)X}=qJ_LX%Z11HqEI1!M86ZM3v<i}yKVKNA@er$<p)CdUi5~vm
zSC6O%MBnx5u`O_za}K15^8)07C>Eb<YxN0V^NRtzc%%4GQ1jsTpFzbua#&GmL+r<#
zC$Ugy+^4=@ekn^`EH!iOZOjFo-qwD0BzZv`*zre!nbdJrI7n`|{YvdrJD>kl8>rVK
z93*Nu4QN3FU|Z>4e4VlOR!1JRbrFznBx^Bn#Tg=duB<1&!2UQ2I-pV{(9baUohW&Z
z3u!x$aV=T-l`BMNag^)<HZ&uIT|N8>Y402vycm`@HWsIYHk|-E94E)C0W}+t3X+vs
zB;qG(27s>tJ>in8?4@IDJgBno4hSWRF@hY?mDAJKusx#oS~7iTbU8qT1TQ%qD=R(_
zaaY=%iAYv{@9F?{-PGerkKkW|0_kgSpSc!SRV8bEP;+o_7{PUS-!iwb5OFn{GgxeZ
z*eBOKpA2W3wgm4vvT5kp0a+zDW`j@$6%6oWpi~sYAoZ7sY7et`Q!B8-!}DD&{f2J#
ze?CbaQjrP;&;%GeO3?I(A<0{sJbS<7U$_WAP2(wTSh)yjJLp6NdA^4~Hf?UZhd=aG
z{eR+PSy|af;9&r~g)Go|QXqa22TaxqU<!Kk|NbuV!==V|Ne@Cl;2+wZCbq~9=4EEw
z+VAhgg6cJ(v{y|{P2s$UhlljFYr5YGI^VA32^@&BDQ~lb*9xR7`fose@`n2Kmp{S#
z)x~P;&>N$R@e42Vn}(FC{u5l&<*&o9BG5Og0sROdZUA6UF(jR`99yVB@y~<Cmw&P9
z@k%P8=N$z0Pa$`^#DRB8#Psw1AxAb%9Xfz9;W==UYfcEkOi%T|?|2M7kPwUAr#&5e
zlFm!H5j>^M&CTTirNFe@7MSOxvER$frvmhy-J-_+54?$F#kvb~peWh`a7mpJ$@V+`
zG0W!&hBR9HWx*$SSzQ1uM_scC_Y@%IjyAyo`3o{{!7}R4RYe@vkhx|B(00ln69TP`
z8+k)v2Oj_o(?_&v@@J=2DtUhi1P3ZNjTD&#foF1DGp5@knVW&(+df(0fO2H>=ml)7
zMkU)XHueF~Ny5>=&WWm&UGNXB`t_R0rak;pP}o1MiVLp{o4}rMtBjWZpiJ4FSEn?S
zfK@3sp`Mu(wO<R`*Z?02cH0UJQWw?wuS)qQC;tU(kca>G;(tQ;|BH)lFtTp|MNS;C
z*GGrS3Et8KQY}%LFfNn;<Y0HoHZ4e_QHC_8Vza6!h)Czum*NcwA9yw@mTl_EYDu!P
zG%#VMj)O1mw1(C*oY;u*{-!B{0~R=btqN_QvFOOA4H^S!C%_+@s`j>ZYoTwVB}5Ue
zN>xGujSMUb@b6b^G$6~V8N$45MgO@5F`(sTugwC1mX-{m`gnxLWR_3w1zx~6f#kSn
z$f`vXd8<z-xu=Ie1L%v~fIZ1>&jtbL9*~dS&&6AA3mu;YKq&>u)a|e8|9)#F{pF|Z
zAvpnaOf5Slp58z<vp??w348z@qg4-n>rVk-OjcHcx+O^msTzkRI=-;{{}_JIIhOx@
c8u3)RMwWej5>F^64E$-{)VootW*h!L08(tWDF6Tf


From 9cbaad868a2c4b304746dcd529812cbbefe39a0d Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Thu, 9 Jan 2025 11:28:47 +0100
Subject: [PATCH 406/424] DOC: add a doctest step to CI to allow writing
 testable code blocs, fix existing docstring code examples errors (#592)

* DOC: add a doctest step to CI to allow writing testable code blocs, fix existing docstring code examples errors
---
 .github/PULL_REQUEST_TEMPLATE.md | 11 ++++++-----
 .readthedocs.yml                 |  3 +++
 HISTORY.rst                      |  1 +
 Makefile                         |  3 +++
 mapie/metrics.py                 |  4 +++-
 mapie/mondrian.py                |  1 +
 6 files changed, 17 insertions(+), 6 deletions(-)

diff --git a/.github/PULL_REQUEST_TEMPLATE.md b/.github/PULL_REQUEST_TEMPLATE.md
index 9567edaf6..be4e7573b 100644
--- a/.github/PULL_REQUEST_TEMPLATE.md
+++ b/.github/PULL_REQUEST_TEMPLATE.md
@@ -24,8 +24,9 @@ Please describe the tests that you ran to verify your changes. Provide instructi
 
 - [ ] I have read the [contributing guidelines](https://github.com/scikit-learn-contrib/MAPIE/blob/master/CONTRIBUTING.rst)
 - [ ] I have updated the [HISTORY.rst](https://github.com/scikit-learn-contrib/MAPIE/blob/master/HISTORY.rst) and [AUTHORS.rst](https://github.com/scikit-learn-contrib/MAPIE/blob/master/AUTHORS.rst) files
-- [ ] Linting passes successfully : `make lint`
-- [ ] Typing passes successfully : `make type-check`
-- [ ] Unit tests pass successfully : `make tests`
-- [ ] Coverage is 100% : `make coverage`
-- [ ] Documentation builds successfully and without warnings : `make doc`
\ No newline at end of file
+- [ ] Linting passes successfully: `make lint`
+- [ ] Typing passes successfully: `make type-check`
+- [ ] Unit tests pass successfully: `make tests`
+- [ ] Coverage is 100%: `make coverage`
+- [ ] When updating documentation: doc builds successfully and without warnings: `make doc`
+- [ ] When updating documentation: code examples in doc run successfully: `make doctest`
\ No newline at end of file
diff --git a/.readthedocs.yml b/.readthedocs.yml
index b7ba60457..e084df9b1 100644
--- a/.readthedocs.yml
+++ b/.readthedocs.yml
@@ -4,6 +4,9 @@ build:
   os: ubuntu-22.04
   tools:
     python: "mambaforge-22.9"
+  jobs:
+    post_build:
+      - cd doc && make doctest
 
 python:
   install:
diff --git a/HISTORY.rst b/HISTORY.rst
index 71eed8034..7dd5d83cd 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -15,6 +15,7 @@ History
 * Limit max sklearn version allowed at MAPIE installation
 * Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0
 * Documentation build: fix warnings, fix image generation, update sklearn version requirement
+* Documentation test: add a doc testing step (in MAKEFILE and CI)
 * Increase max line length from 79 to 88 characters
 * Bump wheel version
 
diff --git a/Makefile b/Makefile
index e6142c895..10415d049 100644
--- a/Makefile
+++ b/Makefile
@@ -22,6 +22,9 @@ coverage:
 doc:
 	$(MAKE) html -C doc
 
+doctest:
+	$(MAKE) doctest -C doc
+
 clean-doc:
 	$(MAKE) clean -C doc
 
diff --git a/mapie/metrics.py b/mapie/metrics.py
index 4926ab782..9990f4cb9 100644
--- a/mapie/metrics.py
+++ b/mapie/metrics.py
@@ -560,7 +560,7 @@ def regression_ssc_score(
 
     Examples
     --------
-    >>> from mapie.metrics import regression_ssc
+    >>> from mapie.metrics import regression_ssc_score
     >>> import numpy as np
     >>> y_true = np.array([5, 7.5, 9.5])
     >>> y_intervals = np.array([
@@ -1283,6 +1283,7 @@ def kolmogorov_smirnov_p_value(y_true: NDArray, y_score: NDArray) -> float:
     Examples
     --------
     >>> import pandas as pd
+    >>> import numpy as np
     >>> from mapie.metrics import kolmogorov_smirnov_p_value
     >>> y_true = np.array([1, 0, 1, 0, 1, 0])
     >>> y_score = np.array([0.8, 0.3, 0.5, 0.5, 0.7, 0.1])
@@ -1450,6 +1451,7 @@ def kuiper_p_value(y_true: NDArray, y_score: NDArray) -> float:
     Examples
     --------
     >>> import pandas as pd
+    >>> import numpy as np
     >>> from mapie.metrics import kuiper_p_value
     >>> y_true = np.array([1, 0, 1, 0, 1, 0])
     >>> y_score = np.array([0.8, 0.3, 0.5, 0.5, 0.7, 0.1])
diff --git a/mapie/mondrian.py b/mapie/mondrian.py
index 003cf59f5..86c76549f 100644
--- a/mapie/mondrian.py
+++ b/mapie/mondrian.py
@@ -72,6 +72,7 @@ class MondrianCP(BaseEstimator):
     >>> import numpy as np
     >>> from sklearn.linear_model import LogisticRegression
     >>> from mapie.classification import MapieClassifier
+    >>> from mapie.mondrian import MondrianCP
     >>> X_toy = np.arange(9).reshape(-1, 1)
     >>> y_toy = np.stack([0, 0, 1, 0, 1, 2, 1, 2, 2])
     >>> partition_toy = [0, 0, 0, 0, 1, 1, 1, 1, 1]

From 7736558faffb0425e94570356f4d3da967674e0a Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Fri, 10 Jan 2025 14:38:03 +0100
Subject: [PATCH 407/424] FIX: temporary warning users that optimize_beta is
 not working (#596)

---
 HISTORY.rst                           | 1 +
 mapie/conformity_scores/regression.py | 8 ++++++++
 2 files changed, 9 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 7dd5d83cd..842bf2697 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -11,6 +11,7 @@ History
 * Fix issue 548 to correct labels generated in tutorial
 * Fix issue 547 to fix wrong warning
 * Fix issue 480 (correct display of mathematical equations in generated notebooks)
+* Temporary solution waiting for issue 588 to be fixed (optimize_beta not working)
 * Remove several irrelevant user warnings
 * Limit max sklearn version allowed at MAPIE installation
 * Refactor MapieRegressor, EnsembleRegressor, and MapieQuantileRegressor, to prepare for the release of v1.0.0
diff --git a/mapie/conformity_scores/regression.py b/mapie/conformity_scores/regression.py
index e8ad3d44d..b58f4a264 100644
--- a/mapie/conformity_scores/regression.py
+++ b/mapie/conformity_scores/regression.py
@@ -1,3 +1,4 @@
+import logging
 from abc import ABCMeta, abstractmethod
 from typing import Tuple
 
@@ -217,6 +218,13 @@ def _beta_optimize(
             Array of betas minimizing the differences
             ``(1-alpha+beta)-quantile - beta-quantile``.
         """
+        # Using logging.warning instead of warnings.warn to avoid warnings during tests
+        logging.warning(
+            "The option to optimize beta (minimize interval width) is not working and "
+            "needs to be fixed. See more details in "
+            "https://github.com/scikit-learn-contrib/MAPIE/issues/588"
+        )
+
         beta_np = np.full(
             shape=(len(lower_bounds), len(alpha_np)),
             fill_value=np.nan,

From 092ef05661ab580ce8bcd4538a811d78cd0a2629 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 14 Jan 2025 14:30:49 +0100
Subject: [PATCH 408/424] FIX: change dataset loading source from OpenML to
 Kaggle as OpenML is down as of 14/01/25 (#598)

---
 .../plot_compare_conformity_scores.py         | 31 ++++++++++++++-----
 1 file changed, 24 insertions(+), 7 deletions(-)

diff --git a/examples/regression/1-quickstart/plot_compare_conformity_scores.py b/examples/regression/1-quickstart/plot_compare_conformity_scores.py
index e4b79c701..1dd0fc79a 100644
--- a/examples/regression/1-quickstart/plot_compare_conformity_scores.py
+++ b/examples/regression/1-quickstart/plot_compare_conformity_scores.py
@@ -9,6 +9,8 @@
 We use here the OpenML house_prices dataset:
 https://www.openml.org/search?type=data&sort=runs&id=42165&status=active.
 
+Note : OpenML is down as of 14/01/25, so we'll load the data from Kaggle instead.
+
 The data is modelled by a Random Forest model
 :class:`~sklearn.ensemble.RandomForestRegressor` with a fixed parameter set.
 The prediction intervals are determined by means of the MAPIE regressor
@@ -31,7 +33,10 @@
 """
 import matplotlib.pyplot as plt
 import numpy as np
-from sklearn.datasets import fetch_openml
+import requests
+import zipfile
+import io
+import pandas as pd
 from sklearn.ensemble import RandomForestRegressor
 from sklearn.model_selection import train_test_split
 
@@ -43,12 +48,14 @@
 
 # Parameters
 features = [
-    "MSSubClass",
-    "LotArea",
-    "OverallQual",
-    "OverallCond",
-    "GarageArea",
+    "MS SubClass",
+    "Lot Area",
+    "Overall Qual",
+    "Overall Cond",
+    "Garage Area",
 ]
+target = "SalePrice"
+
 alpha = 0.05
 rf_kwargs = {"n_estimators": 10, "random_state": random_state}
 model = RandomForestRegressor(**rf_kwargs)
@@ -63,7 +70,17 @@
 # in such cases.
 # Two sub datasets are extracted: the training and test ones.
 
-X, y = fetch_openml(name="house_prices", return_X_y=True)
+dataset_url = (
+    "https://www.kaggle.com" +
+    "/api/v1/datasets/download/shashanknecrothapa/ames-housing-dataset"
+)
+r = requests.get(dataset_url, stream=True)
+with zipfile.ZipFile(io.BytesIO(r.content)) as z:
+    with z.open("AmesHousing.csv") as file:
+        data = pd.read_csv(file)
+
+X = data[features]
+y = data[target]
 
 X_train, X_test, y_train, y_test = train_test_split(
     X[features], y, test_size=0.2, random_state=random_state

From 0f511e619fc6f4ddea359c993295308553547039 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 15 Jan 2025 10:43:33 +0100
Subject: [PATCH 409/424] REFACTOR: remove random_state from EnsembleRegressor
 (not used) (#597)

---
 mapie/estimator/regressor.py   | 8 --------
 mapie/regression/regression.py | 1 -
 mapie/tests/test_common.py     | 1 -
 mapie/tests/test_regression.py | 2 --
 4 files changed, 12 deletions(-)

diff --git a/mapie/estimator/regressor.py b/mapie/estimator/regressor.py
index ddf778e02..3ec18bb16 100644
--- a/mapie/estimator/regressor.py
+++ b/mapie/estimator/regressor.py
@@ -125,12 +125,6 @@ class EnsembleRegressor:
 
         By default ``0``.
 
-    random_state: Optional[Union[int, RandomState]]
-        Pseudo random number generator state used for random sampling.
-        Pass an int for reproducible output across multiple function calls.
-
-        By default ``None``.
-
     Attributes
     ----------
     single_estimator_: sklearn.RegressorMixin
@@ -161,7 +155,6 @@ def __init__(
         cv: Optional[Union[int, str, BaseCrossValidator]],
         agg_function: Optional[str],
         n_jobs: Optional[int],
-        random_state: Optional[Union[int, np.random.RandomState]],
         test_size: Optional[Union[int, float]],
         verbose: int
     ):
@@ -170,7 +163,6 @@ def __init__(
         self.cv = cv
         self.agg_function = agg_function
         self.n_jobs = n_jobs
-        self.random_state = random_state
         self.test_size = test_size
         self.verbose = verbose
 
diff --git a/mapie/regression/regression.py b/mapie/regression/regression.py
index f0191d4ab..bfca560f6 100644
--- a/mapie/regression/regression.py
+++ b/mapie/regression/regression.py
@@ -549,7 +549,6 @@ def init_fit(
             cv,
             agg_function,
             self.n_jobs,
-            self.random_state,
             self.test_size,
             self.verbose
         )
diff --git a/mapie/tests/test_common.py b/mapie/tests/test_common.py
index 16a701c15..9e4901181 100644
--- a/mapie/tests/test_common.py
+++ b/mapie/tests/test_common.py
@@ -310,7 +310,6 @@ def test_warning_when_import_from_estimator():
             cv=3,
             agg_function="mean",
             n_jobs=1,
-            random_state=0,
             test_size=0.2,
             verbose=0,
         )
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index f06fff2e3..e2934bbf0 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -751,7 +751,6 @@ def test_aggregate_with_mask_with_invalid_agg_function() -> None:
         KFold(n_splits=5, random_state=None, shuffle=True),
         "nonsense",
         None,
-        random_state,
         0.20,
         False
     )
@@ -1032,7 +1031,6 @@ def test_deprecated_ensemble_regressor_fit_warning() -> None:
         KFold(n_splits=5, random_state=None, shuffle=True),
         "nonsense",
         None,
-        random_state,
         0.20,
         False
     )

From 3d61d5a7970eb18513544c351c7bc773d84006b8 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 15 Jan 2025 14:57:11 +0100
Subject: [PATCH 410/424] TEST: add sklearn check_estimator test for
 MapieRegressor (#600)

---
 HISTORY.rst                    | 1 +
 mapie/tests/test_regression.py | 6 ++++++
 2 files changed, 7 insertions(+)

diff --git a/HISTORY.rst b/HISTORY.rst
index 842bf2697..1fe44eaa2 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -19,6 +19,7 @@ History
 * Documentation test: add a doc testing step (in MAKEFILE and CI)
 * Increase max line length from 79 to 88 characters
 * Bump wheel version
+* Other minor evolutions
 
 0.9.1 (2024-09-13)
 ------------------
diff --git a/mapie/tests/test_regression.py b/mapie/tests/test_regression.py
index e2934bbf0..1a7abc7c5 100644
--- a/mapie/tests/test_regression.py
+++ b/mapie/tests/test_regression.py
@@ -20,6 +20,7 @@
 )
 from sklearn.pipeline import Pipeline, make_pipeline
 from sklearn.preprocessing import OneHotEncoder
+from sklearn.utils.estimator_checks import check_estimator
 from sklearn.utils.validation import check_is_fitted
 from typing_extensions import TypedDict
 
@@ -1039,3 +1040,8 @@ def test_deprecated_ensemble_regressor_fit_warning() -> None:
         match=r".WARNING: EnsembleRegressor.fit is deprecated.*"
     ):
         ens_reg.fit(X, y)
+
+
+def test_mapie_regressor_sklearn_estim() -> None:
+    """Test that MapieRegressor is an sklearn estimator"""
+    check_estimator(MapieRegressor())

From 73e070e252d1f58d5ae50e0e6855061cb9c61712 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 15 Jan 2025 15:08:09 +0100
Subject: [PATCH 411/424] DOC: prepare HISTORY.rst for upcomming patch release

---
 HISTORY.rst | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/HISTORY.rst b/HISTORY.rst
index 1fe44eaa2..4e2236210 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -2,7 +2,10 @@
 History
 =======
 
-0.9.x (2024-xx-xx)
+0.9.x (2025-xx-xx)
+------------------
+
+0.9.2 (2025-15-01)
 ------------------
 
 * Fix issue 525 in contribution guidelines with syntax errors in hyperlinks and other formatting issues.

From 8f809a598914904e6244bb93f39b21ea9faa4866 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 15 Jan 2025 15:12:14 +0100
Subject: [PATCH 412/424] =?UTF-8?q?Bump=20version:=200.9.1=20=E2=86=92=200?=
 =?UTF-8?q?.9.2?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

---
 .bumpversion.cfg  | 2 +-
 CITATION.cff      | 2 +-
 doc/conf.py       | 2 +-
 mapie/_version.py | 2 +-
 setup.py          | 2 +-
 5 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index dcd13cc35..9c156f5f0 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.9.1
+current_version = 0.9.2
 commit = True
 tag = True
 
diff --git a/CITATION.cff b/CITATION.cff
index 191caacd4..563ea7de5 100644
--- a/CITATION.cff
+++ b/CITATION.cff
@@ -5,7 +5,7 @@ authors:
   given-names: "Thibault"
   orcid: "https://orcid.org/0000-0000-0000-0000"
 title: "MAPIE - Model Agnostic Prediction Interval Estimator"
-version: 0.9.1
+version: 0.9.2
 date-released: 2019-04-30
 url: "https://github.com/scikit-learn-contrib/MAPIE"
 preferred-citation:
diff --git a/doc/conf.py b/doc/conf.py
index 2c5a729a9..b6ae40791 100644
--- a/doc/conf.py
+++ b/doc/conf.py
@@ -92,7 +92,7 @@
 # built documents.
 #
 # The short X.Y version.
-version = "0.9.1"
+version = "0.9.2"
 # The full version, including alpha/beta/rc tags.
 release = version
 
diff --git a/mapie/_version.py b/mapie/_version.py
index d69d16e98..a2fecb457 100644
--- a/mapie/_version.py
+++ b/mapie/_version.py
@@ -1 +1 @@
-__version__ = "0.9.1"
+__version__ = "0.9.2"
diff --git a/setup.py b/setup.py
index b77fcc515..1132c6a89 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "MAPIE"
-VERSION = "0.9.1"
+VERSION = "0.9.2"
 DESCRIPTION = (
     "A scikit-learn-compatible module "
     "for estimating prediction intervals."

From b6b315e8dda0847f95274b00ef10d0e118942f2b Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Sun, 19 Jan 2025 21:24:20 +0100
Subject: [PATCH 413/424] CHORE: update release checklist and max sklearn
 dependencies (see issue #574) (#601)

---
 RELEASE_CHECKLIST.md | 18 ++++++++----------
 environment.dev.yml  |  2 +-
 requirements.dev.txt |  2 +-
 3 files changed, 10 insertions(+), 12 deletions(-)

diff --git a/RELEASE_CHECKLIST.md b/RELEASE_CHECKLIST.md
index b9e5a897b..d06a306a2 100644
--- a/RELEASE_CHECKLIST.md
+++ b/RELEASE_CHECKLIST.md
@@ -1,7 +1,5 @@
 # Release checklist
 
-- [ ] Update the version number with `bump2version major|minor|patch`
-- [ ] Push new tag to your commit: `git push --tags`
 - [ ] Edit HISTORY.rst and AUTHORS.rst to make sure it’s up-to-date and add release date
 - [ ] Check whether any new files need to go in MANIFEST.in
 - [ ] Make sure tests run, pass and cover 100% of the package:
@@ -11,19 +9,19 @@
     * `make coverage`
 - [ ] Make sure documentation builds without warnings and shows nicely:
     * `make doc`
+- Commit every change from the steps below
+- [ ] Update the version number with `bump2version major|minor|patch`
+- [ ] Push new tag to your commit: `git push --tags`
 - [ ] Build source distribution:
     * `make clean-build`
     * `make build`
 - [ ] Check that your package is ready for publication: `twine check dist/*`
 - [ ] Make sure everything is committed and pushed: `git push origin master`
-- [ ] Upload it to TestPyPi: `twine upload --repository-url https://test.pypi.org/legacy/ dist/*`
+- [ ] Upload it to TestPyPi: `twine upload --repository-url https://test.pypi.org/legacy/ dist/*` (you need to create an account on test.pypi.org first,
+  then an API key, and ask one the existing MAPIE maintainer to add you as a maintainer)
 - [ ] Test upload on TestPyPi:
-    * `cd`
-    * `conda activate`
-    * `conda create -n test-mapie --yes python=3.9`
-    * `conda activate test-mapie`
+    * create a new empty virtual environment
     * `pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ mapie`
-    * `conda activate`
-    * `conda env remove -n test-mapie`
 - [ ] Create new release on GitHub for this tag.
-- [ ] Merge the automatically created pull request on https://github.com/conda-forge/mapie-feedstock
+- [ ] Merge the automatically created pull request on https://github.com/conda-forge/mapie-feedstock. You need to be added as a maintainer on this repo first. To create the pull request
+  manually to avoid waiting for automation, create an issue with the name `@conda-forge-admin, please update version`
diff --git a/environment.dev.yml b/environment.dev.yml
index 033ba24c2..0c231cc29 100644
--- a/environment.dev.yml
+++ b/environment.dev.yml
@@ -14,7 +14,7 @@ dependencies:
     - pytest=6.2.5
     - pytest-cov=3.0.0
     - python=3.10
-    - scikit-learn
+    - scikit-learn<1.6.0
     - sphinx=4.3.2
     - sphinx-gallery=0.10.1
     - sphinx_rtd_theme=1.0.0
diff --git a/requirements.dev.txt b/requirements.dev.txt
index 4174c5608..95f886f46 100644
--- a/requirements.dev.txt
+++ b/requirements.dev.txt
@@ -8,7 +8,7 @@ numpy==1.22.3
 pandas==1.3.5
 pytest==6.2.5
 pytest-cov==3.0.0
-scikit-learn
+scikit-learn<1.6.0
 sphinx==4.3.2
 sphinx-gallery==0.10.1
 sphinx_rtd_theme==1.0.0

From 4d4ee32601059a43cc2d75a5c2b8b03f6928a976 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Thu, 23 Jan 2025 15:41:36 +0100
Subject: [PATCH 414/424] CHORE: update release checklist, again (#604)

---
 RELEASE_CHECKLIST.md | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/RELEASE_CHECKLIST.md b/RELEASE_CHECKLIST.md
index d06a306a2..8c9771d04 100644
--- a/RELEASE_CHECKLIST.md
+++ b/RELEASE_CHECKLIST.md
@@ -9,17 +9,18 @@
     * `make coverage`
 - [ ] Make sure documentation builds without warnings and shows nicely:
     * `make doc`
-- Commit every change from the steps below
+- [ ] Commit every change from the steps below
 - [ ] Update the version number with `bump2version major|minor|patch`
-- [ ] Push new tag to your commit: `git push --tags`
 - [ ] Build source distribution:
     * `make clean-build`
     * `make build`
 - [ ] Check that your package is ready for publication: `twine check dist/*`
-- [ ] Make sure everything is committed and pushed: `git push origin master`
-- [ ] Upload it to TestPyPi: `twine upload --repository-url https://test.pypi.org/legacy/ dist/*` (you need to create an account on test.pypi.org first,
-  then an API key, and ask one the existing MAPIE maintainer to add you as a maintainer)
-- [ ] Test upload on TestPyPi:
+- [ ] Push the commit created by bump2version: `git push origin master`
+- [ ] Push the tag created by bump2version:: `git push --tags`
+- [ ] Upload it to TestPyPi:
+    * you need to create an account on test.pypi.org first if you don't have one, then an API key, and ask one the existing MAPIE maintainer to add you as a maintainer
+    * `twine upload --repository-url https://test.pypi.org/legacy/ dist/*` (use `__token__` as username and your api token as password)
+- [ ] Test upload on TestPyPi:
     * create a new empty virtual environment
     * `pip install -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ mapie`
 - [ ] Create new release on GitHub for this tag.

From d7e869df82a7a49f3f56ce9e50c3849a7ce17372 Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Fri, 24 Jan 2025 11:10:04 +0100
Subject: [PATCH 415/424] DOC: remove useless .md notebooks, move calibration
 notebook in its own directory, remove it from classification examples (#605)

---
 doc/notebooks_calibration.rst                 |   2 +-
 doc/notebooks_classification.rst              |   7 +-
 .../top_label_calibration.ipynb               |   0
 notebooks/classification/Cifar10.md           | 919 ------------------
 .../classification/tutorial_classification.md | 259 -----
 notebooks/regression/exoplanets.md            | 373 -------
 notebooks/regression/ts-changepoint.md        | 453 ---------
 notebooks/regression/tutorial_regression.md   | 764 ---------------
 8 files changed, 3 insertions(+), 2774 deletions(-)
 rename notebooks/{classification => calibration}/top_label_calibration.ipynb (100%)
 delete mode 100755 notebooks/classification/Cifar10.md
 delete mode 100644 notebooks/classification/tutorial_classification.md
 delete mode 100755 notebooks/regression/exoplanets.md
 delete mode 100644 notebooks/regression/ts-changepoint.md
 delete mode 100644 notebooks/regression/tutorial_regression.md

diff --git a/doc/notebooks_calibration.rst b/doc/notebooks_calibration.rst
index 236a67c78..e022fbeaa 100755
--- a/doc/notebooks_calibration.rst
+++ b/doc/notebooks_calibration.rst
@@ -4,5 +4,5 @@ Calibration notebooks
 The following examples present advanced analyses on multi-class calibration.
 
 
-1. Top-label calibration for outputs of ML models : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/top_label_calibration.ipynb>`_
+1. Top-label calibration for outputs of ML models : `notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/calibration/top_label_calibration.ipynb>`_
 --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
diff --git a/doc/notebooks_classification.rst b/doc/notebooks_classification.rst
index 35747de19..986dda103 100755
--- a/doc/notebooks_classification.rst
+++ b/doc/notebooks_classification.rst
@@ -1,13 +1,10 @@
 Classification notebooks
 ========================
 
-The following examples present advanced analyses on multi-class classification
-problems for computer vision settings that are too heavy to be included in the example
+The following example present an advanced analyse on multi-class classification
+problem for computer vision settings that is too heavy to be included in the example
 galleries.
 
 
 1. Estimating prediction sets on the Cifar10 dataset : `cifar_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/Cifar10.ipynb>`_
 ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------
-
-2. Top-label calibration for outputs of ML models : `top_label_notebook <https://github.com/scikit-learn-contrib/MAPIE/tree/master/notebooks/classification/top_label_calibration.ipynb>`_
-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
diff --git a/notebooks/classification/top_label_calibration.ipynb b/notebooks/calibration/top_label_calibration.ipynb
similarity index 100%
rename from notebooks/classification/top_label_calibration.ipynb
rename to notebooks/calibration/top_label_calibration.ipynb
diff --git a/notebooks/classification/Cifar10.md b/notebooks/classification/Cifar10.md
deleted file mode 100755
index 681012e36..000000000
--- a/notebooks/classification/Cifar10.md
+++ /dev/null
@@ -1,919 +0,0 @@
----
-jupyter:
-  jupytext:
-    formats: ipynb,md
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.13.6
-  kernelspec:
-    display_name: mapie-notebooks
-    language: python
-    name: mapie-notebooks
----
-
-# Estimating prediction sets on the Cifar10 dataset
-The goal of this notebook is to present how to use :class:`mapie.classification.MapieClassifier` on an object classification task. We will build prediction sets for images and study the marginal and conditional coverages.
-
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/classification/Cifar10.ipynb)
-
-
-
-### What is done in this tutorial ?
-
-> - **Cifar10 dataset** : 10 classes (horse, dog, cat, frog, deer, bird, airplane, truck, ship, automobile)
-
-> - Use :class:`mapie.classification.MapieClassifier` to compare the prediction sets estimated by several conformal methods on the Cifar10 dataset. 
-
-> - Train a small CNN to predict the image class
-
-> - Create a custom class `TensorflowToMapie` to resolve adherence problems between Tensorflow and Mapie
-
-
-
-
-## Tutorial preparation
-
-```python
-install_mapie = True
-if install_mapie:
-    !pip install mapie
-```
-
-```python
-import random
-import warnings
-from typing import Dict, List, Tuple, Union
-
-import cv2
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-import tensorflow.keras as tfk
-from tensorflow.keras.callbacks import EarlyStopping
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers import Conv2D, Dense, Dropout, Flatten, MaxPooling2D
-from tensorflow.keras.losses import CategoricalCrossentropy
-from tensorflow.keras.optimizers import Adam
-import tensorflow_datasets as tfds
-from sklearn.metrics import accuracy_score
-from sklearn.metrics._plot.confusion_matrix import ConfusionMatrixDisplay
-from sklearn.model_selection import train_test_split
-from sklearn.preprocessing import label_binarize
-
-from mapie.metrics import classification_coverage_score
-from mapie.classification import MapieClassifier
-
-warnings.filterwarnings('ignore')
-%load_ext autoreload
-%autoreload 2
-%matplotlib inline
-# %load_ext pycodestyle_magic
-```
-
-```python
-SPACE_BETWEEN_LABELS = 2.5
-SPACE_IN_SUBPLOTS = 4.0
-FONT_SIZE = 18
-
-```
-
-## 1. Data loading
-
-
-The Cifar10 dataset is downloaded from the `Tensorflow Datasets` library. The training set is then splitted into a training, validation and a calibration set which will be used as follow:
-
-> - **Training set**: used to train our neural network.
-> - **Validation set**: used to check that our model is not overfitting.
-> - **Calibration set**: used to calibrate the conformal scores in :class:`mapie.classification.MapieClassifier`
-
-```python
-def train_valid_calib_split(
-    X: np.ndarray,
-    y: np.ndarray,
-    calib_size: float = .1,
-    val_size: float = .33,
-    random_state: int = 42
-
-) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
-    """
-    Create calib and valid datasets from the train dataset.
-    
-    Parameters
-    ----------
-    X: np.ndarray of shape (n_samples, width, height, n_channels)
-        Images of the dataset.
-    
-    y: np.ndarray of shape (n_samples, 1):
-        Label of each image.
-    
-    calib_size: float
-        Percentage of the dataset X to use as calibration set.
-    
-    val_size: float
-        Percentage of the dataset X (minus the calibration set)
-        to use as validation set.
-    
-    random_state: int
-        Random state to use to split the dataset.
-        
-        By default 42.
-    
-    Returns
-    -------
-    Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
-    of shapes: 
-    (n_samples * (1 - calib_size) * (1 - val_size), width, height, n_channels),
-    (n_samples * calib_size, width, height, n_channels),
-    (n_samples * (1 - calib_size) * val_size, width, height, n_channels),
-    (n_samples * (1 - calib_size) * (1 - val_size), 1),
-    (n_samples * calib_size, 1),
-    (n_samples * (1 - calib_size) * val_size, 1).
-    
-    """
-    X_train, X_calib, y_train, y_calib = train_test_split(
-        X, y,
-        test_size=calib_size,
-        random_state=random_state
-    )
-    X_train, X_val, y_train, y_val = train_test_split(
-        X_train, y_train,
-        test_size=val_size,
-        random_state=random_state
-    )
-    return X_train, X_calib, X_val, y_train, y_calib, y_val
-
-```
-
-```python
-def load_data() -> Tuple[
-    Tuple[np.ndarray, np.ndarray, np.ndarray],
-    Tuple[np.ndarray, np.ndarray, np.ndarray],
-    Tuple[np.ndarray, np.ndarray, np.ndarray],
-    List
-]:
-    """
-    Load cifar10 Dataset and return train, valid, calib, test datasets
-    and the names of the labels
-    
-    
-    Returns
-    -------
-    Tuple[
-        Tuple[np.ndarray, np.ndarray, np.ndarray],
-        Tuple[np.ndarray, np.ndarray, np.ndarray],
-        Tuple[np.ndarray, np.ndarray, np.ndarray],
-        List
-    ]
-    """
-    dataset, info = tfds.load(
-        "cifar10",
-        batch_size=-1,
-        as_supervised=True,
-        with_info=True
-    )
-    label_names = info.features['lac'].names
-
-    dataset = tfds.as_numpy(dataset)
-    X_train, y_train = dataset['train']
-    X_test, y_test = dataset['test']
-    X_train, X_calib, X_val, y_train, y_calib, y_val = train_valid_calib_split(
-        X_train,
-        y_train
-    )
-
-    X_train = X_train/255.
-    X_val = X_val/255.
-
-    X_calib = X_calib/255.
-    X_test = X_test/255.
-
-    y_train_cat = tf.keras.utils.to_categorical(y_train)
-    y_val_cat = tf.keras.utils.to_categorical(y_val)
-    y_calib_cat = tf.keras.utils.to_categorical(y_calib)
-    y_test_cat = tf.keras.utils.to_categorical(y_test)
-
-    train_set = (X_train, y_train, y_train_cat)
-    val_set = (X_val, y_val, y_val_cat)
-    calib_set = (X_calib, y_calib, y_calib_cat)
-    test_set = (X_test, y_test, y_test_cat)
-
-    return train_set, val_set, calib_set, test_set, label_names
-
-```
-
-```python
-def inspect_images(
-    X: np.ndarray,
-    y: np.ndarray,
-    num_images: int, 
-    label_names: List
-) -> None:
-    """
-    Load a sample of the images to check that images
-    are well loaded.
-    
-    Parameters
-    ----------
-    X: np.ndarray of shape (n_samples, width, height, n_channels)
-        Set of images from which the sample will be taken.
-    
-    y: np.ndarray of shape (n_samples, 1)
-        Labels of the iamges of X.
-    
-    num_images: int
-        Number of images to plot.
-        
-    label_names: List
-        Names of the different labels
-    
-    """
-
-    _, ax = plt.subplots(
-        nrows=1,
-        ncols=num_images,
-        figsize=(2*num_images, 2)
-    )
-
-    indices = random.sample(range(len(X)), num_images)
-
-    for i, indice in enumerate(indices):
-        ax[i].imshow(X[indice])
-        ax[i].set_title(label_names[y[indice]])
-        ax[i].axis("off")
-    plt.show()
-
-```
-
-```python
-train_set, val_set, calib_set, test_set, label_names = load_data()
-(X_train, y_train, y_train_cat) = train_set 
-(X_val, y_val, y_val_cat) = val_set 
-(X_calib, y_calib, y_calib_cat) = calib_set 
-(X_test, y_test, y_test_cat) = test_set 
-inspect_images(X=X_train, y=y_train, num_images=8, label_names=label_names)
-```
-
-## 2. Definition and training of the the neural network
-
-
-We define a simple convolutional neural network with the following architecture : 
-
-> - 2 blocks of Convolution/Maxpooling
-> - Flatten the images
-> - 3 Dense layers
-> - The output layer with 10 neurons, corresponding to our 10 classes
-
-This simple architecture, based on the VGG16 architecture with its succession of convolutions and maxpooling aims at achieving a reasonable accuracy score and a fast training. The objective here is not to obtain a perfect classifier.
-
-
-```python
-def get_model(
-    input_shape: Tuple, loss: tfk.losses,
-    optimizer: tfk.optimizers, metrics: List[str]
-) -> Sequential:
-    """
-    Compile CNN model.
-    
-    Parameters
-    ----------
-    input_shape: Tuple
-        Size of th input images.
-    
-    loss: tfk.losses
-        Loss to use to train the model.
-    
-    optimizer: tfk.optimizer
-        Optimizer to use to train the model.
-    
-    metrics: List[str]
-        Metrics to use evaluate model training.
-    
-    Returns
-    -------
-    Sequential
-    """
-    model = Sequential([
-        Conv2D(input_shape=input_shape, filters=16, kernel_size=(3, 3), activation='relu', padding='same'),
-        MaxPooling2D(pool_size=(2, 2)),
-        Conv2D(input_shape=input_shape, filters=32, kernel_size=(3, 3), activation='relu', padding='same'),
-        MaxPooling2D(pool_size=(2, 2)),
-        Conv2D(input_shape=input_shape, filters=64, kernel_size=(3, 3), activation='relu', padding='same'),
-        MaxPooling2D(pool_size=(2, 2)),
-        Flatten(),
-        Dense(128, activation='relu'),
-        Dense(64, activation='relu'),
-        Dense(32, activation='relu'),
-        Dense(10, activation='softmax'),
-    ])
-    model.compile(loss=loss, optimizer=optimizer, metrics=metrics)
-    return model
-```
-
-## 3. Training the algorithm with a custom class called `TensorflowToMapie`
-
-As MAPIE asks for a model with `fit`, `predict_proba`, `predict` class attributes and the information about whether or not the model is fitted.
-
-```python
-class TensorflowToMapie():
-    """
-    Class that aimes to make compatible a tensorflow model
-    with MAPIE. To do so, this class create fit, predict,
-    predict_proba and _sklearn_is_fitted_ attributes to the model.
-    
-    """
-
-    def __init__(self) -> None:
-        self.pred_proba = None
-        self.trained_ = False
-        
-
-    def fit(
-        self, model: Sequential,
-        X_train: np.ndarray, y_train: np.ndarray,
-        X_val: np.ndarray, y_val: np.ndarray
-    ) -> None:
-        """
-        Train the keras model.
-        
-        Parameters
-        ----------
-        model: Sequential
-            Model to train.
-            
-        X_train: np.ndarray of shape (n_sample_train, width, height, n_channels)
-            Training images.
-        
-        y_train: np.ndarray of shape (n_samples_train, n_labels)
-            Training labels.
-        
-        X_val: np.ndarray of shape (n_sample_val, width, height, n_channels)
-            Validation images.
-        
-        y_val: np.ndarray of shape (n_samples_val, n_labels)
-            Validation labels.
-        
-        """
-        
-        early_stopping_monitor = EarlyStopping(
-                    monitor='val_loss',
-                    min_delta=0,
-                    patience=10,
-                    verbose=0,
-                    mode='auto',
-                    baseline=None,
-                    restore_best_weights=True
-                    )
-        model.fit(
-                    X_train, y_train, 
-                    batch_size=64, 
-                    validation_data=(X_val, y_val), 
-                    epochs=20, callbacks=[early_stopping_monitor]
-                )
-        
-        self.model = model
-        self.trained_ = True
-        self.classes_ = np.arange(model.layers[-1].units)
-
-    def predict_proba(self, X: np.ndarray) -> np.ndarray:
-        """
-        Returns the predicted probabilities of the images in X.
-        
-        Paramters:
-        X: np.ndarray of shape (n_sample, width, height, n_channels)
-            Images to predict.
-        
-        Returns:
-        np.ndarray of shape (n_samples, n_labels)
-        """
-        preds = self.model.predict(X)
-          
-        return preds
-
-    def predict(self, X: np.ndarray) -> np.ndarray:
-        """
-        Give the label with the maximum softmax for each image.
-        
-        Parameters
-        ---------
-        X: np.ndarray of shape (n_sample, width, height, n_channels)
-            Images to predict
-            
-        Returns:
-        --------
-        np.ndarray of shape (n_samples, 1)
-        """
-        pred_proba = self.predict_proba(X)
-        pred = (pred_proba == pred_proba.max(axis=1)[:, None]).astype(int)
-        return pred
-
-    def __sklearn_is_fitted__(self):
-        if self.trained_:
-            return True
-        else:
-            return False
-```
-
-```python tags=[]
-model = get_model(
-    input_shape=(32, 32, 3), 
-    loss=CategoricalCrossentropy(), 
-    optimizer=Adam(), 
-    metrics=['accuracy']
-)
-```
-
-```python tags=[]
-cirfar10_model = TensorflowToMapie()
-cirfar10_model.fit(model, X_train, y_train_cat, X_val, y_val_cat)
-```
-
-```python
-y_true = label_binarize(y=y_test, classes=np.arange(max(y_test)+1))
-y_pred_proba = cirfar10_model.predict_proba(X_test)
-y_pred = cirfar10_model.predict(X_test)
-
-```
-
-## 4. Prediction of the prediction sets
-
-
-We will now estimate the prediction sets with the five conformal methods implemented in :class:`mapie.classification.MapieClassifier` for a range of confidence levels between 0 and 1. 
-
-```python
-method_params = {
-    "naive": ("naive", False),
-    "lac": ("lac", False),
-    "aps": ("aps", True),
-    "random_aps": ("aps", "randomized"),
-    "top_k": ("top_k", False)
-}
-
-```
-
-```python tags=[]
-y_preds, y_pss = {}, {}
-alphas = np.arange(0.01, 1, 0.01)
-
-for name, (method, include_last_label) in method_params.items():
-    mapie = MapieClassifier(estimator=cirfar10_model, method=method, cv="prefit", random_state=42) 
-    mapie.fit(X_calib, y_calib)
-    y_preds[name], y_pss[name] = mapie.predict(X_test, alpha=alphas, include_last_label=include_last_label)
-```
-
-Let's now estimate the number of null prediction sets, marginal coverages, and averaged prediction set sizes obtained with the different methods for all confidence levels and for a confidence level of 90 \%.
-
-```python
-def count_null_set(y: np.ndarray) -> int:
-    """
-    Count the number of empty prediction sets.
-    
-    Parameters
-    ----------
-    y: np.ndarray of shape (n_sample, )
-    
-    Returns
-    -------
-    int
-    """
-    count = 0
-    for pred in y[:, :]:
-        if np.sum(pred) == 0:
-            count += 1
-    return count
-
-```
-
-```python
-nulls, coverages, accuracies, sizes = {}, {}, {}, {}
-for name, (method, include_last_label) in method_params.items():
-    accuracies[name] = accuracy_score(y_true, y_preds[name])
-    nulls[name] = [
-        count_null_set(y_pss[name][:, :, i])  for i, _ in enumerate(alphas)
-    ]
-    coverages[name] = [
-        classification_coverage_score(
-            y_test, y_pss[name][:, :, i]
-        ) for i, _ in enumerate(alphas)
-    ]
-    sizes[name] = [
-        y_pss[name][:, :, i].sum(axis=1).mean() for i, _ in enumerate(alphas)
-    ]
-
-```
-
-```python
-coverage_90 = {method: coverage[9] for method, coverage in coverages.items()}
-null_90 = {method: null[9] for method, null in nulls.items()}
-width_90 = {method: width[9] for method, width in sizes.items()}
-y_ps_90 = {method: y_ps[:, :, 9] for method, y_ps in y_pss.items()}
-```
-
-Let's now look at the marginal coverages, number of null prediction sets, and the averaged size of prediction sets for a confidence level of 90 \%. 
-
-```python
-summary_df = pd.concat(
-    [
-        pd.Series(coverage_90),
-        pd.Series(null_90),
-        pd.Series(width_90)
-    ],
-    axis=1,
-    keys=["Coverages", "Number of null sets", "Average prediction set sizes"]
-).round(3)
-```
-
-```python
-summary_df
-```
-
-As expected, the "naive" method, which directly uses the alpha value as a threshold for selecting the prediction sets, does not give guarantees on the marginal coverage since this method is not calibrated. Other methods give a marginal coverage close to the desired one, i.e. 90\%. Notice that the "aps" method, which always includes the last label whose cumulated score is above the given quantile, tends to give slightly higher marginal coverages since the prediction sets are slightly too big.
-
-
-## 5. Visualization of the prediction sets
-
-```python
-def prepare_plot(y_methods: Dict[str, Tuple], n_images: int) -> np.ndarray:
-    """
-    Prepare the number and the disposition of the plots according to
-    the number of images.
-    
-    Paramters:
-    y_methods: Dict[str, Tuple]
-        Methods we want to compare.
-    
-    n_images: int
-        Number of images to plot.
-        
-    Returns
-    -------
-    np.ndarray
-    """
-    plt.rcParams.update({'font.size': FONT_SIZE})
-    nrow = len(y_methods.keys())
-    ncol = n_images
-    s = 5
-    f, ax = plt.subplots(ncol, nrow, figsize=(s*nrow, s*ncol))
-    f.tight_layout(pad=SPACE_IN_SUBPLOTS)
-    rows = [i for i in y_methods.keys()]
-    
-    for x, row in zip(ax[:,0], rows):
-        x.set_ylabel(row, rotation=90, size='large')
-
-    return ax
-
-```
-
-```python
-def get_position(y_set: List, label: str, count: int, count_true: int) -> float:
-    """
-    Return the position of each label according to the number of labels to plot.
-    
-    Paramters
-    ---------
-    y_set: List
-        Set of predicted labels for one image.
-    
-    label: str
-        Indice of the true label.
-        
-    count: int
-        Index of the label.
-    
-    count_true: int
-        Total number of labels in the prediction set.
-        
-    Returns
-    -------
-    float
-    """
-    if y_set[label] :
-        position = - (count_true - count)*SPACE_BETWEEN_LABELS
-
-    else:
-        position = - (count_true + 2 - count)*SPACE_BETWEEN_LABELS
-
-    return position
-
-
-def add_text(
-    ax: np.ndarray, indices: Tuple, position: float,
-    label_name: str, proba: float, color: str, missing: bool = False
-) -> None:
-    """
-    Add the text to the corresponding image.
-    
-    Parameters
-    ----------
-    ax: np.ndarray
-        Matrix of the images to plot.
-    
-    indices: Tuple
-        Tuple indicating the indices of the image to put
-        the text on.
-    
-    position: float
-        Position of the text on the image.
-    
-    label_name: str
-        Name of the label to plot.
-    
-    proba: float
-        Proba associated to this label.
-    
-    color: str
-        Color of the text.
-    
-    missing: bool
-        Whether or not the true label is missing in the
-        prediction set.
-        
-        By default False.
-    
-    """
-    if not missing :
-        text = f"{label_name} : {proba:.4f}"
-    else:
-        text = f"True label : {label_name} ({proba:.4f})"
-    i, j = indices
-    ax[i, j].text(
-        15,
-        position,
-        text, 
-        ha="center", va="top", 
-        color=color,
-        font="courier new"
-    )
-
-
-```
-
-```python
-def plot_prediction_sets(
-    X: np.ndarray, y: np.ndarray,
-    y_pred_proba: np.ndarray,
-    y_methods: Dict[str, np.ndarray],
-    n_images: int, label_names: Dict,
-    random_state: Union[int, None] = None
-) -> None:
-    """
-    Plot random images with their associated prediction
-    set for all the required methods.
-    
-    Parameters
-    ----------
-    X: np.ndarray of shape (n_sample, width, height, n_channels)
-        Array containing images.
-    
-    y: np.ndarray of shape (n_samples, )
-        Labels of the images.
-        
-    y_pred_proba: np.ndarray of shape (n_samples, n_labels)
-        Softmax output of the model.
-    
-    y_methods: Dict[str, np.ndarray]
-        Outputs of the MapieClassifier with the different
-        choosen methods.
-    
-    n_images: int
-        Number of images to plot
-    
-    random_state: Union[int, None]
-        Random state to use to choose the images.
-        
-        By default None.
-    """
-    random.seed(random_state)
-    indices = random.sample(range(len(X)), n_images)
-
-    y_true = y[indices]
-    y_pred_proba = y_pred_proba[indices]
-    ax = prepare_plot(y_methods, n_images)
-
-    for i, method in enumerate(y_methods):
-        y_sets = y_methods[method][indices]
-
-        for j in range(n_images):
-            y_set = y_sets[j]
-            img, label= X[indices[j]], y_true[j]
-
-            ax[i, j].imshow(img)
-
-            count_true = np.sum(y_set)
-            index_sorted_proba = np.argsort(-y_pred_proba[j])
-
-            for count in range(count_true):
-                index_pred = index_sorted_proba[count]
-                proba = y_pred_proba[j][index_pred]
-                label_name = label_names[index_pred]
-                color = 'green' if index_pred == y_true[j] else 'red'
-                position = get_position(y_set, label, count, count_true)
-
-                add_text(ax, (i, j), position, label_name, proba, color)
-
-            if not y_set[label] :
-                label_name = label_names[label]
-                proba = y_pred_proba[j][label]
-                add_text(ax, (i, j), -3, label_name, proba, color= 'orange', missing=True)
-
-```
-
-```python
-plot_prediction_sets(X_test, y_test, y_pred_proba, y_ps_90, 5, label_names)
-```
-
-## 6. Calibration of the methods
-
-
-In this section, we plot the number of null sets, the marginal coverages, and the prediction set sizes as function of the target coverage level for all conformal methods. 
-
-```python
-vars_y = [nulls, coverages, sizes]
-labels_y = ["Empty prediction sets", "Marginal coverage", "Set sizes"]
-fig, axs = plt.subplots(1, len(vars_y), figsize=(8*len(vars_y), 8))
-for i, var in enumerate(vars_y):
-    for name, (method, include_last_label) in method_params.items():
-        axs[i].plot(1 - alphas, var[name], label=name)
-        if i == 1:
-            axs[i].plot([0, 1], [0, 1], ls="--", color="k")
-    axs[i].set_xlabel("Couverture cible : 1 - alpha")
-    axs[i].set_ylabel(labels_y[i])
-    if i == len(vars_y) - 1:
-        axs[i].legend(fontsize=10, loc=[1, 0])
-```
-
-The two only methods which are perfectly calibrated for the entire range of alpha values are the "lac" and "random_aps". However, these accurate marginal coverages can only be obtained thanks to the generation of null prediction sets. The compromise between estimating null prediction sets with calibrated coverages or non-empty prediction sets but with larger marginal coverages is entirely up to the user.
-
-
-## 7. Prediction set sizes
-
-```python
-s=5
-fig, axs = plt.subplots(1, len(y_preds), figsize=(s*len(y_preds), s))
-for i, (method, y_ps) in enumerate(y_ps_90.items()):
-    sizes = y_ps.sum(axis=1)
-    axs[i].hist(sizes)
-    axs[i].set_xlabel("Prediction set sizes")
-    axs[i].set_title(method)
-```
-
-## 8. Conditional coverages
-
-
-We just saw that all our methods (except the "naive" one) give marginal coverages always larger than the target coverages for alpha values ranging between 0 and 1. However, there is no mathematical guarantees on the *conditional* coverages, i.e. the coverage obtained for a specific class of images. Let's see what conditional coverages we obtain with the different conformal methods.
-
-```python
-def get_class_coverage(
-    y_test: np.ndarray,
-    y_method: Dict[str, np.ndarray],
-    label_names: List[str]
-) -> None:
-    """
-    Compute the coverage for each class. As MAPIE is looking for a
-    global coverage of 1-alpha, it is important to check that their
-    is not major coverage difference between classes.
-    
-    Parameters
-    ----------
-    y_test: np.ndarray of shape (n_samples,)
-        Labels of the predictions.
-    
-    y_method: Dict[str, np.ndarray]
-        Prediction sets for each method.
-    
-    label_names: List[str]
-        Names of the labels.
-    """
-    recap ={}
-    for method in y_method:
-        recap[method] = []
-        for label in sorted(np.unique(y_test)):
-            indices = np.where(y_test==label)
-            label_name = label_names[label]
-            y_test_trunc = y_test[indices]
-            y_set_trunc = y_method[method][indices]
-            score_coverage = classification_coverage_score(y_test_trunc, y_set_trunc)
-            recap[method].append(score_coverage)
-    recap_df = pd.DataFrame(recap, index = label_names)
-    return recap_df
-            
-```
-
-```python
-class_coverage = get_class_coverage(y_test, y_ps_90, label_names)
-```
-
-```python
-fig = plt.figure()
-class_coverage.plot.bar(figsize=(12, 4), alpha=0.7)
-plt.axhline(0.9, ls="--", color="k")
-plt.ylabel("Conditional coverage")
-plt.legend(loc=[1, 0])
-```
-
-We can notice that the conditional coverages slightly vary between classes. The only method whose conditional coverages remain valid for all classes is the "top_k" one. However, those variations are much smaller than that of the naive method.
-
-```python
-def create_confusion_matrix(y_ps: np.ndarray, y_true: np.ndarray) -> np.ndarray:
-    """
-    Create a confusion matrix to visualize, for each class, which
-    classes are which are the most present classes in the prediction
-    sets.
-    
-    Parameters
-    ----------
-    y_ps: np.ndarray of shape (n_samples, n_labels)
-        Prediction sets of a specific method.
-    
-    y_true: np.ndarray of shape (n_samples, )
-        Labels of the sample
-    
-    Returns
-    -------
-    np.ndarray of shape (n_labels, n_labels)
-    """
-    number_of_classes = len(np.unique(y_true))
-    confusion_matrix = np.zeros((number_of_classes, number_of_classes))
-    for i, ps in enumerate(y_ps):
-        confusion_matrix[y_true[i]] += ps
-    
-    return confusion_matrix
-    
-```
-
-```python
-def reorder_labels(ordered_labels: List, labels: List, cm: np.ndarray) -> np.ndarray:
-    """
-    Used to order the labels in the confusion matrix
-    
-    Parameters
-    ----------
-    ordered_labels: List
-        Order you want to have in your confusion matrix
-    
-    labels: List
-        Initial order of the confusion matrix
-    
-    cm: np.ndarray of shape (n_labels, n_labels)
-        Original confusion matrix
-    
-    Returns
-    -------
-    np.ndarray of shape (n_labels, n_labels)
-    """
-    cm_ordered = np.zeros(cm.shape)
-    index_order = [labels.index(label) for label in ordered_labels]
-    for i, label in enumerate(ordered_labels):
-        old_index = labels.index(label)
-        
-        cm_ordered[i] = cm[old_index, index_order]
-    return cm_ordered
-```
-
-```python
-def plot_confusion_matrix(method: str, y_ps: Dict[str, np.ndarray], label_names: List) -> None:
-    """
-    Plot the confusion matrix for a specific method.
-    
-    Parameters
-    ----------
-    method: str
-        Name of the method to plot.
-    
-    y_ps: Dict[str, np.ndarray]
-        Prediction sets for each of the fitted method
-    
-    label_names: List
-        Name of the labels
-    """
-
-    y_method = y_ps[method]
-    cm = create_confusion_matrix(y_method, y_test)
-    ordered_labels = ["frog", "cat", "dog", "deer", "horse", "bird", "airplane", "ship", "truck", "automobile"]
-    cm = reorder_labels(ordered_labels, label_names, cm)
-    disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=ordered_labels)
-    _, ax = plt.subplots(figsize=(10, 10))
-    disp.plot(
-        include_values=True,
-        cmap="viridis",
-        ax=ax,
-        xticks_rotation="vertical",
-        values_format='.0f',
-        colorbar=True,
-    )
-
-    ax.set_title(f'Confusion matrix for {method} method')
-```
-
-```python
-plot_confusion_matrix("aps", y_ps_90, label_names)
-```
-
-Thanks to this confusion matrix we can see that, for some labels (as cat, deer and dog) the distribution of the labels in the prediction set is not uniform. Indeed, when the image is a cat, there are almost as many predictions sets with the true label as with the "cat" label. In this case, the reverse is also true. However, for the deer, the cat label is often included within the prediction set while the deer is not.
-
-```python
-
-```
diff --git a/notebooks/classification/tutorial_classification.md b/notebooks/classification/tutorial_classification.md
deleted file mode 100644
index 2e9e099ca..000000000
--- a/notebooks/classification/tutorial_classification.md
+++ /dev/null
@@ -1,259 +0,0 @@
----
-jupyter:
-  jupytext:
-    formats: ipynb,md
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.13.6
-  kernelspec:
-    display_name: mapie_local
-    language: python
-    name: mapie_local
----
-
-# Tutorial for classification
-
-
-In this tutorial, we compare the prediction sets estimated by the conformal methods implemented in MAPIE on a toy two-dimensional dataset.
-
-Throughout this tutorial, we will answer the following questions:
-
-- How does the number of classes in the prediction sets vary according to the significance level ?
-
-- Is the chosen conformal method well calibrated ? 
-
-- What are the pros and cons of the conformal methods included in MAPIE ?
-
-
-## 1. Conformal Prediction method using the softmax score of the true label
-
-
-We will use MAPIE to estimate a prediction set of several classes such that the probability that the true label
-of a new test point is included in the prediction set is always higher than the target confidence level :
-$P(Y \in C) \geq 1 - \alpha$.
-We start by using the softmax score output by the base classifier as the conformity score on a toy two-dimensional dataset.
-We estimate the prediction sets as follows :
-
-* First we generate a dataset with train, calibration and test, the model is fitted on the training set.
-* We set the conformal score $S_i = \hat{f}(X_{i})_{y_i}$ the softmax output of the true class for each sample in the calibration set.
-* Then we define $\hat{q}$ as being the $(n + 1) (\alpha) / n$ previous quantile of $S_{1}, ..., S_{n}$
-(this is essentially the quantile $\alpha$, but with a small sample correction). 
-* Finally, for a new test data point (where $X_{n + 1}$ is known but $Y_{n + 1}$ is not), create a prediction set
-$C(X_{n+1}) = \{y: \hat{f}(X_{n+1})_{y} > \hat{q}\}$ which includes all the classes with a sufficiently high softmax output.
-
-We use a two-dimensional toy dataset with three labels. The distribution of the data is a bivariate normal with diagonal covariance matrices for each label. 
-
-```python
-import numpy as np
-from sklearn.model_selection import train_test_split
-centers = [(0, 3.5), (-2, 0), (2, 0)]
-covs = [np.eye(2), np.eye(2)*2, np.diag([5, 1])]
-x_min, x_max, y_min, y_max, step = -6, 8, -6, 8, 0.1
-n_samples = 1000
-n_classes = 3
-np.random.seed(42)
-X = np.vstack([
-    np.random.multivariate_normal(center, cov, n_samples)
-    for center, cov in zip(centers, covs)
-])
-y = np.hstack([np.full(n_samples, i) for i in range(n_classes)])
-X_train_cal, X_test, y_train_cal, y_test = train_test_split(X, y, test_size=0.2)
-X_train, X_cal, y_train, y_cal = train_test_split(X_train_cal, y_train_cal, test_size=0.25)
-
-xx, yy = np.meshgrid(
-    np.arange(x_min, x_max, step), np.arange(x_min, x_max, step)
-)
-X_test_mesh = np.stack([xx.ravel(), yy.ravel()], axis=1)
-```
-
-Let’s see our training data.
-
-```python
-import matplotlib.pyplot as plt
-colors = {0: "#1f77b4", 1: "#ff7f0e", 2:  "#2ca02c", 3: "#d62728"}
-y_train_col = list(map(colors.get, y_train))
-fig = plt.figure()
-plt.scatter(
-    X_train[:, 0],
-    X_train[:, 1],
-    color=y_train_col,
-    marker='o',
-    s=10,
-    edgecolor='k'
-)
-plt.xlabel("X")
-plt.ylabel("Y")
-plt.show()
-```
-
-We fit our training data with a Gaussian Naive Base estimator. And then we apply MAPIE in the calibration data with the method ``score`` to the estimator indicating that it has already been fitted with `cv="prefit"`.
-We then estimate the prediction sets with differents alpha values with a
-``fit`` and ``predict`` process. 
-
-```python
-from sklearn.naive_bayes import GaussianNB
-from mapie.classification import MapieClassifier
-from mapie.metrics import classification_coverage_score, classification_mean_width_score
-clf = GaussianNB().fit(X_train, y_train)
-y_pred = clf.predict(X_test)
-y_pred_proba = clf.predict_proba(X_test)
-y_pred_proba_max = np.max(y_pred_proba, axis=1)
-mapie_score = MapieClassifier(estimator=clf, cv="prefit", method="lac")
-mapie_score.fit(X_cal, y_cal)
-alpha = [0.2, 0.1, 0.05]
-y_pred_score, y_ps_score = mapie_score.predict(X_test_mesh, alpha=alpha)
-```
-
-* ``y_pred_score``: represents the prediction in the test set by the base estimator.
-* ``y_ps_score``: the prediction sets estimated by MAPIE with the "lac" method.
-
-```python
-def plot_scores(n, alphas, scores, quantiles):
-    colors = {0:"#1f77b4", 1:"#ff7f0e", 2:"#2ca02c"}
-    fig = plt.figure(figsize=(7, 5))
-    plt.hist(scores, bins="auto")
-    i=0
-    for i, quantile in enumerate(quantiles):
-         plt.vlines(
-             x = quantile,
-             ymin=0,
-             ymax=400,
-             color=colors[i],
-             ls= "dashed",
-             label=f"alpha = {alphas[i]}"
-         )
-    plt.title("Distribution of scores")
-    plt.legend()
-    plt.xlabel("Scores")
-    plt.ylabel("Count")
-    plt.show()
-```
-
-Let’s see the distribution of the scores with the calculated quantiles.
-
-```python
-scores = mapie_score.conformity_scores_
-n = len(mapie_score.conformity_scores_)
-quantiles = mapie_score.quantiles_
-plot_scores(n, alpha, scores, quantiles)
-```
-
-The estimated quantile increases with alpha. A high value of alpha can potentially lead to a high quantile which would not necessarily be reached by any class in uncertain areas, resulting in null regions.
-
-We will now visualize the differences between the prediction sets of the different values of alpha.
-
-```python
-def plot_results(alphas, X, y_pred, y_ps):
-    tab10 = plt.cm.get_cmap('Purples', 4)
-    colors = {0: "#1f77b4", 1: "#ff7f0e", 2:  "#2ca02c", 3: "#d62728"}
-    y_pred_col = list(map(colors.get, y_pred))
-    fig, [[ax1, ax2], [ax3, ax4]] = plt.subplots(2, 2, figsize=(10, 10))
-    axs = {0: ax1, 1: ax2, 2:  ax3, 3: ax4}
-    axs[0].scatter(
-        X[:, 0],
-        X[:, 1],
-        color=y_pred_col,
-        marker='.',
-        s=10,
-        alpha=0.4
-    )
-    axs[0].set_title("Predicted labels")
-    for i, alpha in enumerate(alphas):
-        y_pi_sums = y_ps[:, :, i].sum(axis=1)
-        num_labels = axs[i+1].scatter(
-            X[:, 0],
-            X[:, 1],
-            c=y_pi_sums,
-            marker='.',
-            s=10,
-            alpha=1,
-            cmap=tab10,
-            vmin=0,
-            vmax=3
-        )
-        cbar = plt.colorbar(num_labels, ax=axs[i+1])
-        axs[i+1].set_title(f"Number of labels for alpha={alpha}")
-    plt.show()
-```
-
-```python
-plot_results(alpha, X_test_mesh, y_pred_score, y_ps_score)
-```
-
-When the class coverage is not large enough, the prediction sets can be empty when the model is uncertain at the border between two classes. The null region disappears for larger class coverages but ambiguous classification regions arise with several labels included in the prediction sets highlighting the uncertain behaviour of the base classifier.
-
-
-Let’s now study the effective coverage and the mean prediction set widths as function of the $1-\alpha$ target coverage. To this aim, we use once again the `.predict()` method of MAPIE to estimate predictions sets on a large number of $\alpha$ values.
-
-```python
-alpha2 = np.arange(0.02, 0.98, 0.02)
-_, y_ps_score2 = mapie_score.predict(X_test, alpha=alpha2)
-coverages_score = [
-    classification_coverage_score(y_test, y_ps_score2[:, :, i])
-    for i, _ in enumerate(alpha2)
-]
-widths_score = [
-    classification_mean_width_score(y_ps_score2[:, :, i])
-    for i, _ in enumerate(alpha2)
-]
-```
-
-```python
-def plot_coverages_widths(alpha, coverage, width, method):
-    fig, axs = plt.subplots(1, 2, figsize=(12, 5))
-    axs[0].scatter(1 - alpha, coverage, label=method)
-    axs[0].set_xlabel("1 - alpha")
-    axs[0].set_ylabel("Coverage score")
-    axs[0].plot([0, 1], [0, 1], label="x=y", color="black")
-    axs[0].legend()
-    axs[1].scatter(1 - alpha, width, label=method)
-    axs[1].set_xlabel("1 - alpha")
-    axs[1].set_ylabel("Average size of prediction sets")
-    axs[1].legend()
-    plt.show()
-```
-
-```python
-plot_coverages_widths(alpha2, coverages_score, widths_score, "lac")
-```
-
-## 2. Conformal Prediction method using the cumulative softmax score
-
-
-We saw in the previous section that the "lac" method is well calibrated by providing accurate coverage levels. However, it tends to give null prediction sets for uncertain regions, especially when the $\alpha$ value is high. MAPIE includes another method, called Adaptive Prediction Set (APS), whose conformity score is the cumulated score of the softmax output until the true label is reached (see the theoretical description for more details). We will see in this Section that this method no longer estimates null prediction sets but by giving slightly bigger prediction sets. 
-
-
-Let's visualize the prediction sets obtained with the APS method on the test set after fitting MAPIE on the calibration set.
-
-```python
-mapie_aps = MapieClassifier(estimator=clf, cv="prefit", method="aps")
-mapie_aps.fit(X_cal, y_cal)
-alpha = [0.2, 0.1, 0.05]
-y_pred_aps, y_ps_aps = mapie_aps.predict(X_test_mesh, alpha=alpha, include_last_label=True)
-```
-
-```python
-plot_results(alpha, X_test_mesh, y_pred_aps, y_ps_aps)
-```
-
-One can notice that the uncertain regions are emphasized by wider boundaries, but without null prediction sets with respect to the first "lac" method. 
-
-```python
-_, y_ps_aps2 = mapie_aps.predict(X_test, alpha=alpha2, include_last_label="randomized")
-coverages_aps = [
-    classification_coverage_score(y_test, y_ps_aps2[:, :, i])
-    for i, _ in enumerate(alpha2)
-]
-widths_aps = [
-    classification_mean_width_score(y_ps_aps2[:, :, i])
-    for i, _ in enumerate(alpha2)
-]
-```
-
-```python
-plot_coverages_widths(alpha2, coverages_aps, widths_aps, "lac")
-```
-
-This method also gives accurate calibration plots, meaning that the effective coverage level is always very close to the target coverage, sometimes at the expense of slightly bigger prediction sets. 
diff --git a/notebooks/regression/exoplanets.md b/notebooks/regression/exoplanets.md
deleted file mode 100755
index f71758520..000000000
--- a/notebooks/regression/exoplanets.md
+++ /dev/null
@@ -1,373 +0,0 @@
----
-jupyter:
-  jupytext:
-    formats: ipynb,md
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.13.6
-  kernelspec:
-    display_name: mapie_local
-    language: python
-    name: mapie_local
----
-
-# Estimating the uncertainties in the exoplanet masses
-
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/master/notebooks/regression/exoplanets.ipynb)
-
-
-
-In this notebook, we quantify the uncertainty in exoplanet masses predicted by several machine learning models, based on the exoplanet properties. To this aim, we use the exoplanet dataset downloaded from the [NASA Exoplanet Archive](https://exoplanetarchive.ipac.caltech.edu/) and estimate the prediction intervals using the methods implemented in MAPIE.
-
-```python
-install_mapie = True
-if install_mapie:
-    !pip install mapie
-```
-
-```python
-from typing_extensions import TypedDict
-from typing import Union
-from sklearn.compose import ColumnTransformer
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.impute import SimpleImputer
-from sklearn.linear_model import LinearRegression
-from sklearn.model_selection import train_test_split, KFold
-from sklearn.pipeline import Pipeline
-from sklearn.preprocessing import (
-    OneHotEncoder,
-    OrdinalEncoder,
-    PolynomialFeatures,
-    RobustScaler
-)
-import warnings
-import matplotlib.pyplot as plt
-import numpy as np
-import pandas as pd
-import seaborn as sns
-
-from mapie.metrics import regression_coverage_score
-from mapie.regression import MapieRegressor
-from mapie.subsample import Subsample
-
-warnings.filterwarnings("ignore")
-```
-
-## 1. Data Loading
-
-
-Let's start by loading the `exoplanets` dataset and looking at the main information.
-
-```python
-url_file = "https://raw.githubusercontent.com/scikit-learn-contrib/MAPIE/master/notebooks/regression/exoplanets_mass.csv"
-exo_df = pd.read_csv(url_file, index_col=0)
-```
-
-```python
-exo_df.info()
-```
-
-The dataset contains 21 features giving complementary information about the properties of the discovered planet, the star around which the planet revolves, together with the type of discovery method. 7 features are categorical, and 14 are continuous.
-
-
-Some properties show high variance among exoplanets and stars due to the astronomical nature of such systems. We therefore decide to use a log transformation for the following features to approach a normal distribution.
-
-```python
-exo_df["Stellar_Mass_[Solar_mass]"] = exo_df["Stellar_Mass_[Solar_mass]"].replace(0, np.nan)
-vars2log = [
-    "Planet_Orbital_Period_[day]",
-    "Planet_Orbital_SemiMajorAxis_[day]",
-    "Planet_Radius_[Earth_radius]",
-    "Planet_Mass_[Earth_mass]",
-    "Stellar_Radius_[Solar_radius]",
-    "Stellar_Mass_[Solar_mass]",
-    "Stellar_Effective_Temperature_[K]"
-]
-for var in vars2log:
-    exo_df[var+"_log"] = np.log(exo_df[var])
-```
-
-```python
-vars2keep = list(set(exo_df.columns) - set(vars2log))
-exo_df = exo_df[vars2keep]
-```
-
-```python
-exo_df.head()
-```
-
-Throughout this tutorial, the target variable will be `Planet_Mass_[Earth_mass]_log`.
-
-```python
-target = "Planet_Mass_[Earth_mass]_log"
-```
-
-```python
-num_cols = list(exo_df.columns[exo_df.dtypes == "float64"])
-cat_cols = list(exo_df.columns[exo_df.dtypes != "float64"])
-exo_df[cat_cols] = exo_df[cat_cols].astype(str)
-```
-
-```python
-planet_cols = [col for col in num_cols if "Planet_" in col]
-star_cols = [col for col in num_cols if "Stellar_" in col]
-system_cols = [col for col in num_cols if "System_" in col]
-```
-
-## 2. Data visualization
-
-```python
-sns.pairplot(exo_df[planet_cols])
-```
-
-```python
-sns.pairplot(exo_df[star_cols])
-```
-
-## 3. Data preprocessing
-
-
-In this section, we perform a simple preprocessing of the dataset in order to impute the missing values and encode the categorical features.
-
-```python
-endos = list(set(exo_df.columns) - set([target]))
-X = exo_df[endos]
-y = exo_df[target]
-```
-
-```python
-num_cols = list(X.columns[X.dtypes == "float64"])
-cat_cols = list(X.columns[X.dtypes != "float64"])
-X[cat_cols] = X[cat_cols].astype(str)
-```
-
-```python
-imputer_num = SimpleImputer(strategy="mean")
-scaler_num = RobustScaler()
-imputer_cat = SimpleImputer(strategy="constant", fill_value=-1)
-encoder_cat = OneHotEncoder(
-    categories="auto",
-    drop=None,
-    sparse=False,
-    handle_unknown="ignore",
-)
-```
-
-```python
-numerical_transformer = Pipeline(
-    steps=[("imputer", imputer_num), ("scaler", scaler_num)]
-)
-categorical_transformer = Pipeline(
-    steps=[("ordinal", OrdinalEncoder()), ("imputer", imputer_cat), ("encoder", encoder_cat)]
-)
-preprocessor = ColumnTransformer(
-    transformers=[
-        ("numerical", numerical_transformer, num_cols),
-        ("categorical", categorical_transformer, cat_cols)
-    ],
-    remainder="drop",
-    sparse_threshold=0,
-)
-```
-
-```python
-X_train, X_test, y_train, y_test = train_test_split(
-    X, y, test_size=0.2, random_state=42, shuffle=True
-)
-```
-
-```python
-X_train = preprocessor.fit_transform(X_train)
-X_test = preprocessor.transform(X_test)
-```
-
-## 4. First estimation of the uncertainties with MAPIE
-
-
-### Uncertainty estimation
-
-
-Here, we build our first prediction intervals with MAPIE. To this aim, we adopt the CV+ strategy with 5 folders, using `method="plus"` and `cv=KFold(n_splits=5, shuffle=True)` as input arguments.
-
-```python
-def get_regressor(name):
-    if name == "linear":
-        mdl = LinearRegression()
-    elif name == "polynomial":
-        degree_polyn = 2
-        mdl = Pipeline(
-            [
-                ("poly", PolynomialFeatures(degree=degree_polyn)),
-                ("linear", LinearRegression())
-            ]
-        )
-    elif name == "random_forest":
-        mdl = RandomForestRegressor()
-    return mdl
-```
-
-```python
-mdl = get_regressor("random_forest")
-```
-
-```python
-mapie = MapieRegressor(mdl, method="plus", cv=KFold(n_splits=5, shuffle=True))
-```
-
-```python
-mapie.fit(X_train, y_train)
-```
-
-We build prediction intervals for a range of alpha values between 0 and 1.
-
-```python
-alpha = np.arange(0.05, 1, 0.05)
-y_train_pred, y_train_pis = mapie.predict(X_train, alpha=alpha)
-y_test_pred, y_test_pis = mapie.predict(X_test, alpha=alpha)
-```
-
-### Visualization
-
-
-The following function offers to visualize the error bars estimated by MAPIE for the selected method and the given confidence level.
-
-```python
-def plot_predictionintervals(
-    y_train,
-    y_train_pred,
-    y_train_pred_low,
-    y_train_pred_high,
-    y_test,
-    y_test_pred,
-    y_test_pred_low,
-    y_test_pred_high,
-    suptitle: str,
-) -> None:
-    fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(20, 6))
-    
-    ax1.errorbar(
-        x=y_train,
-        y=y_train_pred,
-        yerr=(y_train_pred - y_train_pred_low, y_train_pred_high - y_train_pred),
-        alpha=0.8,
-        label="train",
-        fmt=".",
-    )
-    ax1.errorbar(
-        x=y_test,
-        y=y_test_pred,
-        yerr=(y_test_pred - y_test_pred_low, y_test_pred_high - y_test_pred),
-        alpha=0.8,
-        label="test",
-        fmt=".",
-    )
-    ax1.plot(
-        [y_train.min(), y_train.max()],
-        [y_train.min(), y_train.max()],
-        color="gray",
-        alpha=0.5,
-    )
-    ax1.set_xlabel("True values", fontsize=12)
-    ax1.set_ylabel("Predicted values", fontsize=12)
-    ax1.legend()
-    
-    ax2.scatter(
-        x=y_train, y=y_train_pred_high - y_train_pred_low, alpha=0.8, label="train", marker="."
-    )
-    ax2.scatter(x=y_test, y=y_test_pred_high - y_test_pred_low, alpha=0.8, label="test", marker=".")
-    ax2.set_xlabel("True values", fontsize=12)
-    ax2.set_ylabel("Interval width", fontsize=12)
-    ax2.set_xscale("linear")
-    ax2.set_ylim([0, np.max(y_test_pred_high - y_test_pred_low)*1.1])
-    ax2.legend()
-    std_all = np.concatenate([
-        y_train_pred_high - y_train_pred_low, y_test_pred_high - y_test_pred_low
-    ])
-    type_all = np.array(["train"] * len(y_train) + ["test"] * len(y_test))
-    x_all = np.arange(len(std_all))
-    order_all = np.argsort(std_all)
-    std_order = std_all[order_all]
-    type_order = type_all[order_all]
-    ax3.scatter(
-        x=x_all[type_order == "train"],
-        y=std_order[type_order == "train"],
-        alpha=0.8,
-        label="train",
-        marker=".",
-    )
-    ax3.scatter(
-        x=x_all[type_order == "test"],
-        y=std_order[type_order == "test"],
-        alpha=0.8,
-        label="test",
-        marker=".",
-    )
-    ax3.set_xlabel("Order", fontsize=12)
-    ax3.set_ylabel("Interval width", fontsize=12)
-    ax3.legend()
-    ax1.set_title("True vs predicted values")
-    ax2.set_title("Prediction interval width vs true values")
-    ax3.set_title("Ordered prediction interval width")
-    plt.suptitle(suptitle, size=20)
-    plt.show()
-
-```
-
-```python
-alpha_plot = int(np.where(alpha == 0.1)[0])
-plot_predictionintervals(
-    y_train,
-    y_train_pred,
-    y_train_pis[:, 0, alpha_plot],
-    y_train_pis[:, 1, alpha_plot],
-    y_test,
-    y_test_pred,
-    y_test_pis[:, 0, alpha_plot],
-    y_test_pis[:, 1, alpha_plot],
-    "Prediction intervals for alpha=0.1",
-)
-```
-
-## 5. Comparison of the uncertainty quantification methods
-
-
-In the last section, we compare the calibration of several uncertainty-quantification methods provided by MAPIE using Random Forest as base model. To this aim, we build so-called "calibration plots" which compare the effective marginal coverage obtained on the test set with the target $1-\alpha$ coverage.
-
-```python
-Params = TypedDict("Params", {"method": str, "cv": Union[int, Subsample]})
-STRATEGIES = {
-    "naive": Params(method="naive"),
-    "cv": Params(method="base", cv=5),
-    "cv_plus": Params(method="plus", cv=5),
-    "cv_minmax": Params(method="minmax", cv=5),
-    "jackknife_plus_ab": Params(method="plus", cv=Subsample(n_resamplings=20)),
-}
-mdl = get_regressor("random_forest")
-```
-
-```python
-y_pred, y_pis, scores = {}, {}, {}
-for strategy, params in STRATEGIES.items():
-    mapie = MapieRegressor(mdl, **params)
-    mapie.fit(X_train, y_train)
-    y_pred[strategy], y_pis[strategy] = mapie.predict(X_test, alpha=alpha)
-    scores[strategy] = [
-        regression_coverage_score(y_test, y_pis[strategy][:, 0, i], y_pis[strategy][:, 1, i])
-        for i, _ in enumerate(alpha)
-    ]
-```
-
-```python
-plt.figure(figsize=(7, 6))
-plt.xlabel("Target coverage (1 - alpha)")
-plt.ylabel("Effective coverage")
-for strategy, params in STRATEGIES.items():
-    plt.plot(1 - alpha, scores[strategy], label=strategy)
-plt.plot([0, 1], [0, 1], ls="--", color="k")
-plt.legend(loc=[1, 0])
-```
-
-The calibration plot clearly demonstrates that the "naive" method underestimates the coverage by giving too narrow prediction intervals, due to the fact that they are built from training data. All other methods show much more robust calibration plots : the effective coverages follow almost linearly the expected coverage levels.
diff --git a/notebooks/regression/ts-changepoint.md b/notebooks/regression/ts-changepoint.md
deleted file mode 100644
index 3837c3d36..000000000
--- a/notebooks/regression/ts-changepoint.md
+++ /dev/null
@@ -1,453 +0,0 @@
-# Estimating prediction intervals of time series forecast with EnbPI
-
-[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/scikit-learn-contrib/MAPIE/blob/add-ts-notebooks/notebooks/regression/ts-changepoint.ipynb)
-
-This example uses `mapie.time_series_regression.MapieTimeSeriesRegressor` to estimate
-prediction intervals associated with time series forecast. It follows Xu \& Xie (2021).
-We use here the Victoria electricity demand dataset used in the book
-"Forecasting: Principles and Practice" by R. J. Hyndman and G. Athanasopoulos.
-The electricity demand features daily and weekly seasonalities and is impacted
-by the temperature, considered here as a exogeneous variable.
-A Random Forest model is already fitted on data. The hyper-parameters are
-optimized with a `sklearn.model_selection.RandomizedSearchCV` using a
-sequential `sklearn.model_selection.TimeSeriesSplit` cross validation,
-in which the training set is prior to the validation set.
-The best model is then feeded into
-`mapie.time_series_regression.MapieTimeSeriesRegressor` to estimate the
-associated prediction intervals. We compare four approaches: with or without
-``partial_fit`` called at every step. 
-
-
-```python
-install_mapie = False
-if install_mapie:
-    !pip install mapie
-```
-
-
-```python
-import warnings
-
-import numpy as np
-import pandas as pd
-from matplotlib import pylab as plt
-from scipy.stats import randint
-from sklearn.ensemble import RandomForestRegressor
-from sklearn.model_selection import RandomizedSearchCV, TimeSeriesSplit
-
-from mapie.metrics import regression_coverage_score, regression_mean_width_score
-from mapie.subsample import BlockBootstrap
-from mapie.time_series_regression import MapieTimeSeriesRegressor
-
-%reload_ext autoreload
-%autoreload 2
-warnings.simplefilter("ignore")
-```
-
-## 1. Load input data and feature engineering
-
-
-```python
-url_file = "https://raw.githubusercontent.com/scikit-learn-contrib/MAPIE/master/examples/data/demand_temperature.csv"
-demand_df = pd.read_csv(
-    url_file, parse_dates=True, index_col=0
-)
-
-demand_df["Date"] = pd.to_datetime(demand_df.index)
-demand_df["Weekofyear"] = demand_df.Date.dt.isocalendar().week.astype("int64")
-demand_df["Weekday"] = demand_df.Date.dt.isocalendar().day.astype("int64")
-demand_df["Hour"] = demand_df.index.hour
-n_lags = 5
-for hour in range(1, n_lags):
-    demand_df[f"Lag_{hour}"] = demand_df["Demand"].shift(hour)
-
-```
-
-## 2. Train/validation/test split
-
-
-```python
-num_test_steps = 24 * 7
-demand_train = demand_df.iloc[:-num_test_steps, :].copy()
-demand_test = demand_df.iloc[-num_test_steps:, :].copy()
-features = ["Weekofyear", "Weekday", "Hour", "Temperature"] 
-features += [f"Lag_{hour}" for hour in range(1, n_lags)]
-
-X_train = demand_train.loc[
-    ~np.any(demand_train[features].isnull(), axis=1), features
-]
-y_train = demand_train.loc[X_train.index, "Demand"]
-X_test = demand_test.loc[:, features]
-y_test = demand_test["Demand"]
-```
-
-
-```python
-plt.figure(figsize=(16, 5))
-plt.plot(y_train)
-plt.plot(y_test)
-plt.ylabel("Hourly demand (GW)")
-```
-
-
-
-
-    Text(0, 0.5, 'Hourly demand (GW)')
-
-
-
-
-    
-![png](output_9_1.png)
-    
-
-
-## 3. Optimize the base estimator
-
-
-```python
-model_params_fit_not_done = False
-if model_params_fit_not_done:
-    # CV parameter search
-    n_iter = 100
-    n_splits = 5
-    tscv = TimeSeriesSplit(n_splits=n_splits)
-    random_state = 59
-    rf_model = RandomForestRegressor(random_state=random_state)
-    rf_params = {"max_depth": randint(2, 30), "n_estimators": randint(10, 100)}
-    cv_obj = RandomizedSearchCV(
-        rf_model,
-        param_distributions=rf_params,
-        n_iter=n_iter,
-        cv=tscv,
-        scoring="neg_root_mean_squared_error",
-        random_state=random_state,
-        verbose=0,
-        n_jobs=-1,
-    )
-    cv_obj.fit(X_train, y_train)
-    model = cv_obj.best_estimator_
-else:
-    # Model: Random Forest previously optimized with a cross-validation
-    model = RandomForestRegressor(
-        max_depth=10, n_estimators=50, random_state=59)
-```
-
-## 4. Estimate prediction intervals on the test set
-
-
-```python
-alpha = 0.05
-gap = 1
-cv_mapiets = BlockBootstrap(
-    n_resamplings=100, length=48, overlapping=True, random_state=59
-)
-mapie_enbpi = MapieTimeSeriesRegressor(
-    model, method="enbpi", cv=cv_mapiets, agg_function="mean", n_jobs=-1
-)
-```
-
-### Without partial fit
-
-
-```python
-print("EnbPI, with no partial_fit, width optimization")
-mapie_enbpi = mapie_enbpi.fit(X_train, y_train)
-y_pred_npfit, y_pis_npfit = mapie_enbpi.predict(
-    X_test, alpha=alpha, ensemble=True, optimize_beta=True
-)
-coverage_npfit = regression_coverage_score(
-    y_test, y_pis_npfit[:, 0, 0], y_pis_npfit[:, 1, 0]
-)
-width_npfit = regression_mean_width_score(
-    y_pis_npfit[:, 0, 0], y_pis_npfit[:, 1, 0]
-)
-```
-
-    EnbPI, with no partial_fit, width optimization
-
-
-### With partial fit
-
-
-```python
-print("EnbPI with partial_fit, width optimization")
-mapie_enbpi = mapie_enbpi.fit(X_train, y_train)
-
-y_pred_pfit = np.zeros(y_pred_npfit.shape)
-y_pis_pfit = np.zeros(y_pis_npfit.shape)
-y_pred_pfit[:gap], y_pis_pfit[:gap, :, :] = mapie_enbpi.predict(
-    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True
-)
-for step in range(gap, len(X_test), gap):
-    mapie_enbpi.partial_fit(
-        X_test.iloc[(step - gap):step, :],
-        y_test.iloc[(step - gap):step],
-    )
-    (
-        y_pred_pfit[step:step + gap],
-        y_pis_pfit[step:step + gap, :, :],
-    ) = mapie_enbpi.predict(
-        X_test.iloc[step:(step + gap), :],
-        alpha=alpha,
-        ensemble=True,
-        optimize_beta=True
-    )
-coverage_pfit = regression_coverage_score(
-    y_test, y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]
-)
-width_pfit = regression_mean_width_score(
-    y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]
-)
-```
-
-    EnbPI with partial_fit, width optimization
-
-
-## V. Plot estimated prediction intervals on test set
-
-
-```python
-y_preds = [y_pred_npfit, y_pred_pfit]
-y_pis = [y_pis_npfit, y_pis_pfit]
-coverages = [coverage_npfit, coverage_pfit]
-widths = [width_npfit, width_pfit]
-```
-
-
-```python
-def plot_forecast(y_train, y_test, y_preds, y_pis, coverages, widths, plot_coverage=True):
-    fig, axs = plt.subplots(
-        nrows=2, ncols=1, figsize=(14, 8), sharey="row", sharex="col"
-    )
-    for i, (ax, w) in enumerate(zip(axs, ["without", "with"])):
-        ax.set_ylabel("Hourly demand (GW)")
-        ax.plot(y_train[int(-len(y_test)/2):], lw=2, label="Training data", c="C0")
-        ax.plot(y_test, lw=2, label="Test data", c="C1")
-
-        ax.plot(
-            y_test.index, y_preds[i], lw=2, c="C2", label="Predictions"
-        )
-        ax.fill_between(
-            y_test.index,
-            y_pis[i][:, 0, 0],
-            y_pis[i][:, 1, 0],
-            color="C2",
-            alpha=0.2,
-            label="Prediction intervals",
-        )
-        title = f"EnbPI, {w} update of residuals. "
-        if plot_coverage:
-            title += f"Coverage:{coverages[i]:.3f} and Width:{widths[i]:.3f}"
-        ax.set_title(title)
-        ax.legend()
-    fig.tight_layout()
-    plt.show()
-```
-
-
-```python
-plot_forecast(y_train, y_test, y_preds, y_pis, coverages, widths)
-```
-
-
-
-![png](output_21_0.png)
-    
-
-
-## VI. Forecast on test dataset with change point
-
-We will now see how MAPIE adapts its prediction intervals when a brutal changepoint arises in the test set. To simulate this, we will artificially decrease the electricity demand by 2 GW in the test set, aiming at simulating an effect, such as blackout or lockdown due to a pandemic, that was not taken into account by the model during its training. 
-
-### Corrupt the dataset
-
-
-```python
-demand_df_corrupted = demand_df.copy()
-demand_df_corrupted.Demand.iloc[-int(num_test_steps/2):] -= 2
-```
-
-
-```python
-n_lags = 5
-for hour in range(1, n_lags):
-    demand_df[f"Lag_{hour}"] = demand_df["Demand"].shift(hour)
-demand_train_corrupted = demand_df_corrupted.iloc[:-num_test_steps, :].copy()
-demand_test_corrupted = demand_df_corrupted.iloc[-num_test_steps:, :].copy()
-
-X_train = demand_train_corrupted.loc[
-    ~np.any(demand_train_corrupted[features].isnull(), axis=1), features
-]
-y_train = demand_train_corrupted.loc[X_train.index, "Demand"]
-X_test = demand_test_corrupted.loc[:, features]
-y_test = demand_test_corrupted["Demand"]
-```
-
-
-```python
-plt.figure(figsize=(16, 5))
-plt.ylabel("Hourly demand (GW)")
-plt.plot(y_train)
-plt.plot(y_test)
-```
-
-
-
-
-    [<matplotlib.lines.Line2D at 0x16a409930>]
-
-
-
-
-    
-![png](output_27_1.png)
-    
-
-
-### Prediction intervals without partial fit
-
-
-```python
-print("EnbPI, with no partial_fit, width optimization")
-mapie_enbpi = mapie_enbpi.fit(X_train, y_train)
-y_pred_npfit, y_pis_npfit = mapie_enbpi.predict(
-    X_test, alpha=alpha, ensemble=True, optimize_beta=True
-)
-coverage_npfit = regression_coverage_score(
-    y_test, y_pis_npfit[:, 0, 0], y_pis_npfit[:, 1, 0]
-)
-width_npfit = regression_mean_width_score(
-    y_pis_npfit[:, 0, 0], y_pis_npfit[:, 1, 0]
-)
-```
-
-    EnbPI, with no partial_fit, width optimization
-
-
-### Prediction intervals with partial fit
-
-
-```python
-print("EnbPI with partial_fit, width optimization")
-mapie_enbpi = mapie_enbpi.fit(X_train, y_train)
-
-y_pred_pfit = np.zeros(y_pred_npfit.shape)
-y_pis_pfit = np.zeros(y_pis_npfit.shape)
-conformity_scores_pfit, lower_quantiles_pfit, higher_quantiles_pfit = [], [], []
-y_pred_pfit[:gap], y_pis_pfit[:gap, :, :] = mapie_enbpi.predict(
-    X_test.iloc[:gap, :], alpha=alpha, ensemble=True, optimize_beta=True
-)
-for step in range(gap, len(X_test), gap):
-    mapie_enbpi.partial_fit(
-        X_test.iloc[(step - gap):step, :],
-        y_test.iloc[(step - gap):step],
-    )
-    (
-        y_pred_pfit[step:step + gap],
-        y_pis_pfit[step:step + gap, :, :],
-    ) = mapie_enbpi.predict(
-        X_test.iloc[step:(step + gap), :],
-        alpha=alpha,
-        ensemble=True, 
-        optimize_beta=True
-    )
-    conformity_scores_pfit.append(mapie_enbpi.conformity_scores_)
-    lower_quantiles_pfit.append(mapie_enbpi.lower_quantiles_)
-    higher_quantiles_pfit.append(mapie_enbpi.higher_quantiles_)
-coverage_pfit = regression_coverage_score(
-    y_test, y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]
-)
-width_pfit = regression_mean_width_score(
-    y_pis_pfit[:, 0, 0], y_pis_pfit[:, 1, 0]
-)
-```
-
-    EnbPI with partial_fit, width optimization
-
-
-### Plot estimated prediction intervals on test set
-
-
-```python
-y_preds = [y_pred_npfit, y_pred_pfit]
-y_pis = [y_pis_npfit, y_pis_pfit]
-coverages = [coverage_npfit, coverage_pfit]
-widths = [width_npfit, width_pfit]
-```
-
-
-```python
-plot_forecast(y_train, y_test, y_preds, y_pis, coverages, widths, plot_coverage=False)
-```
-
-
-
-![png](output_34_0.png)
-    
-
-
-
-```python
-window = 24
-rolling_coverage_pfit, rolling_coverage_npfit = [], []
-for i in range(window, len(y_test), 1):
-    rolling_coverage_pfit.append(
-        regression_coverage_score(
-            y_test[i-window:i], y_pis_pfit[i-window:i, 0, 0], y_pis_pfit[i-window:i, 1, 0]
-        )
-    )
-    rolling_coverage_npfit.append(
-        regression_coverage_score(
-            y_test[i-window:i], y_pis_npfit[i-window:i, 0, 0], y_pis_npfit[i-window:i, 1, 0]
-        )
-    )
-```
-
-### Marginal coverage on a 24-hour rolling window of prediction intervals
-
-
-```python
-plt.figure(figsize=(10, 5))
-plt.ylabel(f"Rolling coverage [{window} hours]")
-plt.plot(y_test[window:].index, rolling_coverage_npfit, label="Without update of residuals")
-plt.plot(y_test[window:].index, rolling_coverage_pfit, label="With update of residuals")
-```
-
-
-
-
-    [<matplotlib.lines.Line2D at 0x16b986710>]
-
-
-
-
-    
-![png](output_37_1.png)
-    
-
-
-### Temporal evolution of the distribution of residuals used for estimating prediction intervals
-
-
-```python
-plt.figure(figsize=(7, 5))
-for i, j in enumerate([0, -1]):
-    plt.hist(conformity_scores_pfit[j], range=[-2.5, 0.5], bins=30, color=f"C{i}", alpha=0.3, label=f"Conformity scores(step={j})")
-    plt.axvline(lower_quantiles_pfit[j], ls="--", color=f"C{i}")
-    plt.axvline(higher_quantiles_pfit[j], ls="--", color=f"C{i}")
-plt.legend(loc=[1, 0])
-```
-
-
-
-
-    <matplotlib.legend.Legend at 0x16b985390>
-
-
-
-
-    
-![png](output_39_1.png)
-    
-
diff --git a/notebooks/regression/tutorial_regression.md b/notebooks/regression/tutorial_regression.md
deleted file mode 100644
index 5a45f2ecb..000000000
--- a/notebooks/regression/tutorial_regression.md
+++ /dev/null
@@ -1,764 +0,0 @@
----
-jupyter:
-  jupytext:
-    formats: ipynb,md
-    text_representation:
-      extension: .md
-      format_name: markdown
-      format_version: '1.3'
-      jupytext_version: 1.13.6
-  kernelspec:
-    display_name: mapie-notebooks
-    language: python
-    name: mapie-notebooks
----
-
-# Tutorial for regression
-
-
-In this tutorial, we compare the prediction intervals estimated by MAPIE on a 
-simple, one-dimensional, ground truth function
-
-$$
-f(x) = x \sin(x)
-$$
-
-Throughout this tutorial, we will answer the following questions:
-
-- How well do the MAPIE strategies capture the aleatoric uncertainty existing in the data?
-
-- How do the prediction intervals estimated by the resampling strategies
-  evolve for new *out-of-distribution* data? 
-
-- How do the prediction intervals vary between regressor models?
-
-Throughout this tutorial, we estimate the prediction intervals first using 
-a polynomial function, and then using a boosting model, and a simple neural network. 
-
-**For practical problems, we advise using the faster CV+ strategies. 
-For conservative prediction interval estimates, you can alternatively 
-use the CV-minmax strategies.**
-
-
-
-## 1. Estimating the aleatoric uncertainty of homoscedastic noisy data
-
-
-Let's start by defining the $x \times \sin(x)$ function and another simple function
-that generates one-dimensional data with normal noise uniformely in a given interval.
-
-```python
-from typing import List, Dict, Union
-```
-
-```python
-import warnings
-warnings.filterwarnings("ignore")
-import numpy as np
-def x_sinx(x):
-    """One-dimensional x*sin(x) function."""
-    return x*np.sin(x)
-```
-
-```python
-def get_1d_data_with_constant_noise(funct, min_x, max_x, n_samples, noise):
-    """
-    Generate 1D noisy data uniformely from the given function 
-    and standard deviation for the noise.
-    """
-    np.random.seed(59)
-    X_train = np.linspace(min_x, max_x, n_samples)
-    np.random.shuffle(X_train)
-    X_test = np.linspace(min_x, max_x, n_samples*5)
-    y_train, y_mesh, y_test = funct(X_train), funct(X_test), funct(X_test)
-    y_train += np.random.normal(0, noise, y_train.shape[0])
-    y_test += np.random.normal(0, noise, y_test.shape[0])
-    return X_train.reshape(-1, 1), y_train, X_test.reshape(-1, 1), y_test, y_mesh
-```
-
-We first generate noisy one-dimensional data uniformely on an interval. 
-Here, the noise is considered as *homoscedastic*, since it remains constant 
-over $x$.
-
-```python
-min_x, max_x, n_samples, noise = -5, 5, 600, 0.5
-X_train, y_train, X_test, y_test, y_mesh = get_1d_data_with_constant_noise(
-    x_sinx, min_x, max_x, n_samples, noise
-)
-```
-
-Let's visualize our noisy function. 
-
-```python
-import matplotlib.pyplot as plt
-plt.xlabel("x") ; plt.ylabel("y")
-plt.scatter(X_train, y_train, color="C0")
-_ = plt.plot(X_test, y_mesh, color="C1")
-```
-
-As mentioned previously, we fit our training data with a simple
-polynomial function. Here, we choose a degree equal to 10 so the function 
-is able to perfectly fit $x \times \sin(x)$.
-
-```python
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression, QuantileRegressor
-from sklearn.pipeline import Pipeline
-
-degree_polyn = 10
-polyn_model = Pipeline(
-    [
-        ("poly", PolynomialFeatures(degree=degree_polyn)),
-        ("linear", LinearRegression())
-    ]
-)
-polyn_model_quant = Pipeline(
-    [
-        ("poly", PolynomialFeatures(degree=degree_polyn)),
-        ("linear", QuantileRegressor(
-                solver="highs",
-                alpha=0,
-        ))
-    ]
-)
-```
-
-We then estimate the prediction intervals for all the strategies very easily with a
-`fit` and `predict` process. The prediction interval's lower and upper bounds
-are then saved in a DataFrame. Here, we set an alpha value of 0.05
-in order to obtain a 95% confidence for our prediction intervals.
-
-```python
-from typing import Union, Optional
-from typing_extensions import TypedDict
-from mapie.regression import MapieRegressor
-from mapie.quantile_regression import MapieQuantileRegressor
-from mapie.subsample import Subsample 
-from sklearn.model_selection import train_test_split
-Params = TypedDict("Params", {"method": str, "cv": Union[int, str, Subsample], "alpha": Optional[float]})
-STRATEGIES = {
-    "naive": Params(method="naive"),
-    "jackknife": Params(method="base", cv=-1),
-    "jackknife_plus": Params(method="plus", cv=-1),
-    "jackknife_minmax": Params(method="minmax", cv=-1),
-    "cv": Params(method="base", cv=10),
-    "cv_plus": Params(method="plus", cv=10),
-    "cv_minmax": Params(method="minmax", cv=10),
-    "jackknife_plus_ab": Params(method="plus", cv=Subsample(n_resamplings=50)),
-    "jackknife_minmax_ab": Params(method="minmax", cv=Subsample(n_resamplings=50)),
-    "conformalized_quantile_regression": Params(method="quantile", cv="split", alpha=0.05)
-}
-y_pred, y_pis = {}, {}
-for strategy, params in STRATEGIES.items():
-    if strategy == "conformalized_quantile_regression":
-        mapie = MapieQuantileRegressor(polyn_model_quant, **params)
-        mapie.fit(X_train, y_train, random_state=1)
-        y_pred[strategy], y_pis[strategy] = mapie.predict(X_test)
-    else:  
-        mapie = MapieRegressor(polyn_model, **params)
-        mapie.fit(X_train, y_train)
-        y_pred[strategy], y_pis[strategy] = mapie.predict(X_test, alpha=0.05)
-```
-
-Let’s now compare the target confidence intervals with the predicted intervals obtained 
-with the Jackknife+, Jackknife-minmax, CV+, CV-minmax, Jackknife+-after-Boostrap, and conformalized quantile regression (CQR) strategies. Note that for the Jackknife-after-Bootstrap method, we call the :class:`mapie.subsample.Subsample` object that allows us to train bootstrapped models. Note also that the CQR method is called with :class:`MapieQuantileRegressor` with a "split" strategy.
-
-```python
-def plot_1d_data(
-    X_train,
-    y_train, 
-    X_test,
-    y_test,
-    y_sigma,
-    y_pred, 
-    y_pred_low, 
-    y_pred_up,
-    ax=None,
-    title=None
-):
-    ax.set_xlabel("x") ; ax.set_ylabel("y")
-    ax.fill_between(X_test, y_pred_low, y_pred_up, alpha=0.3)
-    ax.scatter(X_train, y_train, color="red", alpha=0.3, label="Training data")
-    ax.plot(X_test, y_test, color="gray", label="True confidence intervals")
-    ax.plot(X_test, y_test - y_sigma, color="gray", ls="--")
-    ax.plot(X_test, y_test + y_sigma, color="gray", ls="--")
-    ax.plot(X_test, y_pred, color="blue", alpha=0.5, label="Prediction intervals")
-    if title is not None:
-        ax.set_title(title)
-    ax.legend()
-```
-
-```python
-strategies = ["jackknife_plus", "jackknife_minmax", "cv_plus", "cv_minmax", "jackknife_plus_ab", "conformalized_quantile_regression"]
-n_figs = len(strategies)
-fig, axs = plt.subplots(3, 2, figsize=(9, 13))
-coords = [axs[0, 0], axs[0, 1], axs[1, 0], axs[1, 1], axs[2, 0], axs[2, 1]]
-for strategy, coord in zip(strategies, coords):
-    plot_1d_data(
-        X_train.ravel(),
-        y_train.ravel(),
-        X_test.ravel(),
-        y_mesh.ravel(),
-        np.full((X_test.shape[0]), 1.96*noise).ravel(),
-        y_pred[strategy].ravel(),
-        y_pis[strategy][:, 0, 0].ravel(),
-        y_pis[strategy][:, 1, 0].ravel(),
-        ax=coord,
-        title=strategy
-    )
-```
-
-At first glance, the four strategies give similar results and the
-prediction intervals are very close to the true confidence intervals.
-Let’s confirm this by comparing the prediction interval widths over
-$x$ between all strategies.
-
-```python
-fig, ax = plt.subplots(1, 1, figsize=(7, 5))
-ax.axhline(1.96*2*noise, ls="--", color="k", label="True width")
-for strategy in STRATEGIES:
-    ax.plot(X_test, y_pis[strategy][:, 1, 0] - y_pis[strategy][:, 0, 0], label=strategy)
-ax.set_xlabel("x")
-ax.set_ylabel("Prediction Interval Width")
-_ = ax.legend(fontsize=10, loc=[1, 0.4])
-```
-
-As expected, the prediction intervals estimated by the Naive method
-are slightly too narrow. The Jackknife, Jackknife+, CV, CV+, JaB, and J+aB give
-similar widths that are very close to the true width. On the other hand,
-the width estimated by Jackknife-minmax and CV-minmax are slightly too
-wide. Note that the widths given by the Naive, Jackknife, and CV strategies
-are constant because there is a single model used for prediction,
-perturbed models are ignored at prediction time.
-
-It's interesting to observe that CQR strategy offers more varying width,
-often giving much higher but also lower interval width than other methods, therefore,
-with homoscedastic noise, CQR would not be the preferred method.
-
-
-Let’s now compare the *effective* coverage, namely the fraction of test
-points whose true values lie within the prediction intervals, given by
-the different strategies. 
-
-```python
-import pandas as pd
-from mapie.metrics import regression_coverage_score
-pd.DataFrame([
-    [
-        regression_coverage_score(
-            y_test, y_pis[strategy][:, 0, 0], y_pis[strategy][:, 1, 0]
-        ),
-        (
-            y_pis[strategy][:, 1, 0] - y_pis[strategy][:, 0, 0]
-        ).mean()
-    ] for strategy in STRATEGIES
-], index=STRATEGIES, columns=["Coverage", "Width average"]).round(2)
-```
-
-All strategies except the Naive one give effective coverage close to the expected 
-0.95 value (recall that alpha = 0.05), confirming the theoretical garantees.
-
-
-## 2. Estimating the aleatoric uncertainty of heteroscedastic noisy data
-
-
-Let's define again the $x \times \sin(x)$ function and another simple function
-that generates one-dimensional data with normal noise uniformely in a given interval.
-
-```python
-def x_sinx(x):
-    """One-dimensional x*sin(x) function."""
-    return x*np.sin(x)
-```
-
-```python
-def get_1d_data_with_heteroscedastic_noise(funct, min_x, max_x, n_samples, noise):
-    """
-    Generate 1D noisy data uniformely from the given function 
-    and standard deviation for the noise.
-    """
-    np.random.seed(59)
-    X_train = np.linspace(min_x, max_x, n_samples)
-    np.random.shuffle(X_train)
-    X_test = np.linspace(min_x, max_x, n_samples*5)
-    y_train = funct(X_train) + (np.random.normal(0, noise, len(X_train)) * X_train)
-    y_test = funct(X_test) + (np.random.normal(0, noise, len(X_test)) * X_test)
-    y_mesh = funct(X_test)
-    return X_train.reshape(-1, 1), y_train, X_test.reshape(-1, 1), y_test, y_mesh
-```
-
-We first generate noisy one-dimensional data uniformely on an interval. 
-Here, the noise is considered as *heteroscedastic*, since it will increase linearly with $x$.
-
-```python
-min_x, max_x, n_samples, noise = 0, 5, 300, 0.5
-X_train, y_train, X_test, y_test, y_mesh = get_1d_data_with_heteroscedastic_noise(
-    x_sinx, min_x, max_x, n_samples, noise
-)
-```
-
-Let's visualize our noisy function. As x increases, the data becomes more noisy.
-
-```python
-import matplotlib.pyplot as plt
-plt.xlabel("x") ; plt.ylabel("y")
-plt.scatter(X_train, y_train, color="C0")
-_ = plt.plot(X_test, y_mesh, color="C1")
-```
-
-As mentioned previously, we fit our training data with a simple
-polynomial function. Here, we choose a degree equal to 10 so the function 
-is able to perfectly fit $x \times \sin(x)$.
-
-```python
-from sklearn.preprocessing import PolynomialFeatures
-from sklearn.linear_model import LinearRegression, QuantileRegressor
-from sklearn.pipeline import Pipeline
-
-degree_polyn = 10
-polyn_model = Pipeline(
-    [
-        ("poly", PolynomialFeatures(degree=degree_polyn)),
-        ("linear", LinearRegression())
-    ]
-)
-polyn_model_quant = Pipeline(
-    [
-        ("poly", PolynomialFeatures(degree=degree_polyn)),
-        ("linear", QuantileRegressor(
-                solver="highs",
-                alpha=0,
-        ))
-    ]
-)
-```
-
-We then estimate the prediction intervals for all the strategies very easily with a
-`fit` and `predict` process. The prediction interval's lower and upper bounds
-are then saved in a DataFrame. Here, we set an alpha value of 0.05
-in order to obtain a 95% confidence for our prediction intervals.
-
-```python
-Params = TypedDict("Params", {"method": str, "cv": Union[int, str, Subsample], "alpha": Optional[float]})
-STRATEGIES = {
-    "naive": Params(method="naive"),
-    "jackknife": Params(method="base", cv=-1),
-    "jackknife_plus": Params(method="plus", cv=-1),
-    "jackknife_minmax": Params(method="minmax", cv=-1),
-    "cv": Params(method="base", cv=10),
-    "cv_plus": Params(method="plus", cv=10),
-    "cv_minmax": Params(method="minmax", cv=10),
-    "jackknife_plus_ab": Params(method="plus", cv=Subsample(n_resamplings=50)),
-    "conformalized_quantile_regression": Params(method="quantile", cv="split", alpha=0.05)
-}
-y_pred, y_pis = {}, {}
-for strategy, params in STRATEGIES.items():
-    if strategy == "conformalized_quantile_regression":
-        mapie = MapieQuantileRegressor(polyn_model_quant, **params)
-        mapie.fit(X_train, y_train, random_state=1)
-        y_pred[strategy], y_pis[strategy] = mapie.predict(X_test)
-    else:  
-        mapie = MapieRegressor(polyn_model, **params)
-        mapie.fit(X_train, y_train)
-        y_pred[strategy], y_pis[strategy] = mapie.predict(X_test, alpha=0.05)
-```
-
-Once again, let’s compare the target confidence intervals with prediction intervals obtained with the Jackknife+, Jackknife-minmax, CV+, CV-minmax, Jackknife+-after-Boostrap, and CQR strategies.
-
-```python
-def plot_1d_data(
-    X_train,
-    y_train, 
-    X_test,
-    y_test,
-    y_sigma,
-    y_pred, 
-    y_pred_low, 
-    y_pred_up,
-    ax=None,
-    title=None
-):
-    ax.set_xlabel("x") ; ax.set_ylabel("y")
-    ax.fill_between(X_test, y_pred_low, y_pred_up, alpha=0.3)
-    ax.scatter(X_train, y_train, color="red", alpha=0.3, label="Training data")
-    ax.plot(X_test, y_test, color="gray", label="True confidence intervals")
-    ax.plot(X_test, y_test - y_sigma, color="gray", ls="--")
-    ax.plot(X_test, y_test + y_sigma, color="gray", ls="--")
-    ax.plot(X_test, y_pred, color="blue", alpha=0.5, label="Prediction intervals")
-    if title is not None:
-        ax.set_title(title)
-    ax.legend()
-```
-
-```python
-strategies = ["jackknife_plus", "jackknife_minmax", "cv_plus", "cv_minmax", "jackknife_plus_ab", "conformalized_quantile_regression"]
-n_figs = len(strategies)
-fig, axs = plt.subplots(3, 2, figsize=(9, 13))
-coords = [axs[0, 0], axs[0, 1], axs[1, 0], axs[1, 1], axs[2, 0], axs[2, 1]]
-for strategy, coord in zip(strategies, coords):
-    plot_1d_data(
-        X_train.ravel(),
-        y_train.ravel(),
-        X_test.ravel(),
-        y_mesh.ravel(),
-        (1.96*noise*X_test).ravel(),
-        y_pred[strategy].ravel(),
-        y_pis[strategy][:, 0, 0].ravel(),
-        y_pis[strategy][:, 1, 0].ravel(),
-        ax=coord,
-        title=strategy
-    )
-```
-
-We can observe that all of the strategies except CQR seem to have similar constant prediction intervals. 
-On the other hand, the CQR strategy offers a solution that adapts the prediction
-intervals to the local noise.
-
-```python
-fig, ax = plt.subplots(1, 1, figsize=(7, 5))
-ax.plot(X_test, 1.96*2*noise*X_test, ls="--", color="k", label="True width")
-for strategy in STRATEGIES:
-    ax.plot(X_test, y_pis[strategy][:, 1, 0] - y_pis[strategy][:, 0, 0], label=strategy)
-ax.set_xlabel("x")
-ax.set_ylabel("Prediction Interval Width")
-_ = ax.legend(fontsize=10, loc=[1, 0.4])
-```
-
-One can observe that all the strategies behave in a similar way as in the first example shown previously. One exception is the CQR method which takes into account the heteroscedasticity of the data. In this method we observe very low interval widths at low values of $x$. This is the only method that even slightly follows the true width, and therefore is the preferred method for heteroscedastic data. Notice also that the true width is greater (lower) than the predicted width from the other methods at $x \gtrapprox 3$ ($x \leq 3$). This means that while the marginal coverage correct for these methods, the conditional coverage is likely not guaranteed as we will observe in the next figure.
-
-```python
-def get_heteroscedastic_coverage(y_test, y_pis, STRATEGIES, bins):
-    recap ={}
-    for i in range(len(bins)-1):
-        bin1, bin2 = bins[i], bins[i+1]
-        name = f"[{bin1}, {bin2}]"
-        recap[name] = []
-        for strategy in STRATEGIES:
-            indices = np.where((X_test>=bins[i])*(X_test<=bins[i+1]))
-            y_test_trunc = np.take(y_test, indices)
-            y_low_ = np.take(y_pis[strategy][:, 0, 0], indices)
-            y_high_ = np.take(y_pis[strategy][:, 1, 0], indices)
-            score_coverage = regression_coverage_score(y_test_trunc[0], y_low_[0], y_high_[0])
-            recap[name].append(score_coverage)
-    recap_df = pd.DataFrame(recap, index=STRATEGIES)
-    return recap_df
-```
-
-```python
-bins = [0, 1, 2, 3, 4, 5]
-heteroscedastic_coverage = get_heteroscedastic_coverage(y_test, y_pis, STRATEGIES, bins)
-```
-
-```python
-fig = plt.figure()
-heteroscedastic_coverage.T.plot.bar(figsize=(12, 4), alpha=0.7)
-plt.axhline(0.95, ls="--", color="k")
-plt.ylabel("Conditional coverage")
-plt.xlabel("x bins")
-plt.xticks(rotation=0)
-plt.ylim(0.8, 1.0)
-plt.legend(loc=[1, 0])
-```
-
-Let’s now conclude by summarizing the *effective* coverage, namely the fraction of test
-points whose true values lie within the prediction intervals, given by
-the different strategies. 
-
-```python
-import pandas as pd
-from mapie.metrics import regression_coverage_score
-pd.DataFrame([
-    [
-        regression_coverage_score(
-            y_test, y_pis[strategy][:, 0, 0], y_pis[strategy][:, 1, 0]
-        ),
-        (
-            y_pis[strategy][:, 1, 0] - y_pis[strategy][:, 0, 0]
-        ).mean()
-    ] for strategy in STRATEGIES
-], index=STRATEGIES, columns=["Coverage", "Width average"]).round(2)
-```
-
-All the strategies have the wanted coverage, however, we notice that the CQR strategy has much lower interval width than all the other methods, therefore, with heteroscedastic noise, CQR would be the preferred method.
-
-
-## 3. Estimating the epistemic uncertainty of out-of-distribution data
-
-
-Let’s now consider one-dimensional data without noise, but normally distributed.
-The goal is to explore how the prediction intervals evolve for new data 
-that lie outside the distribution of the training data in order to see how the strategies
-can capture the *epistemic* uncertainty. 
-For a comparison of the epistemic and aleatoric uncertainties, please have a look at this
-[source](https://en.wikipedia.org/wiki/Uncertainty_quantification).
-
-
-Lets" start by generating and showing the data. 
-
-```python
-def get_1d_data_with_normal_distrib(funct, mu, sigma, n_samples, noise):
-    """
-    Generate noisy 1D data with normal distribution from given function 
-    and noise standard deviation.
-    """
-    np.random.seed(59)
-    X_train = np.random.normal(mu, sigma, n_samples)
-    X_test = np.arange(mu-4*sigma, mu+4*sigma, sigma/20.)
-    y_train, y_mesh, y_test = funct(X_train), funct(X_test), funct(X_test)
-    y_train += np.random.normal(0, noise, y_train.shape[0])
-    y_test += np.random.normal(0, noise, y_test.shape[0])
-    return X_train.reshape(-1, 1), y_train, X_test.reshape(-1, 1), y_test, y_mesh
-```
-
-```python
-mu = 0 ; sigma = 2 ; n_samples = 1000 ; noise = 0.
-X_train, y_train, X_test, y_test, y_mesh = get_1d_data_with_normal_distrib(
-    x_sinx, mu, sigma, n_samples, noise
-)
-```
-
-```python
-plt.xlabel("x") ; plt.ylabel("y")
-plt.scatter(X_train, y_train, color="C0")
-_ = plt.plot(X_test, y_test, color="C1")
-```
-
-As before, we estimate the prediction intervals using a polynomial
-function of degree 10 and show the results for the Jackknife+ and CV+
-strategies.
-
-```python
-polyn_model_quant = Pipeline(
-    [
-        ("poly", PolynomialFeatures(degree=degree_polyn)),
-        ("linear", QuantileRegressor(
-                solver="highs-ds",
-                alpha=0,
-        ))
-    ]
-)
-Params = TypedDict("Params", {"method": str, "cv": Union[int, str, Subsample], "alpha": Optional[float]})
-STRATEGIES = {
-    "naive": Params(method="naive"),
-    "jackknife": Params(method="base", cv=-1),
-    "jackknife_plus": Params(method="plus", cv=-1),
-    "jackknife_minmax": Params(method="minmax", cv=-1),
-    "cv": Params(method="base", cv=10),
-    "cv_plus": Params(method="plus", cv=10),
-    "cv_minmax": Params(method="minmax", cv=10),
-    "jackknife_plus_ab": Params(method="plus", cv=Subsample(n_resamplings=50)),
-    "jackknife_minmax_ab": Params(method="minmax", cv=Subsample(n_resamplings=50)),
-    "conformalized_quantile_regression": Params(method="quantile", cv="split", alpha=0.05)
-}
-y_pred, y_pis = {}, {}
-for strategy, params in STRATEGIES.items():
-    if strategy == "conformalized_quantile_regression":
-        mapie = MapieQuantileRegressor(polyn_model_quant, **params)
-        mapie.fit(X_train, y_train, random_state=1)
-        y_pred[strategy], y_pis[strategy] = mapie.predict(X_test)
-    else:  
-        mapie = MapieRegressor(polyn_model, **params)
-        mapie.fit(X_train, y_train)
-        y_pred[strategy], y_pis[strategy] = mapie.predict(X_test, alpha=0.05)
-```
-
-```python
-strategies = ["jackknife_plus", "jackknife_minmax", "cv_plus", "cv_minmax", "jackknife_plus_ab", "conformalized_quantile_regression"]
-n_figs = len(strategies)
-fig, axs = plt.subplots(3, 2, figsize=(9, 13))
-coords = [axs[0, 0], axs[0, 1], axs[1, 0], axs[1, 1], axs[2, 0], axs[2, 1]]
-for strategy, coord in zip(strategies, coords): 
-    plot_1d_data(
-        X_train.ravel(),
-        y_train.ravel(), 
-        X_test.ravel(),
-        y_mesh.ravel(),
-        1.96*noise, 
-        y_pred[strategy].ravel(),
-        y_pis[strategy][:, 0, :].ravel(),
-        y_pis[strategy][:, 1, :].ravel(), 
-        ax=coord,
-        title=strategy
-    )
-```
-
-At first glance, our polynomial function does not give accurate
-predictions with respect to the true function when $|x > 6|$. 
-The prediction intervals estimated with the Jackknife+ do not seem to 
-increase significantly, unlike the CV+ method whose prediction intervals
-capture a high uncertainty when $x > 6$.
-
-
-Let's now compare the prediction interval widths between all strategies. 
-
-
-```python
-fig, ax = plt.subplots(1, 1, figsize=(7, 5))
-ax.set_yscale("log")
-for strategy in STRATEGIES:
-    ax.plot(X_test, y_pis[strategy][:, 1, 0] - y_pis[strategy][:, 0, 0], label=strategy)
-ax.set_xlabel("x")
-ax.set_ylabel("Prediction Interval Width")
-ax.legend(fontsize=10, loc=[1, 0.4]);
-```
-
-The prediction interval widths start to increase exponentially
-for $|x| > 4$ for the CV+, CV-minmax, Jackknife-minmax, and quantile
-strategies. On the other hand, the prediction intervals estimated by
-Jackknife+ remain roughly constant until $|x| \sim 5$ before
-increasing.
-The CQR strategy seems to perform well, however, on the extreme values
-of the data the quantile regression fails to give reliable results as it outputs
-negative value for the prediction intervals. This occurs because the quantile 
-regressor with quantile $1 - \alpha/2$ gives higher values than the quantile 
-regressor with quantile $\alpha/2$. Note that a warning will be issued when
-this occurs.
-
-```python
-pd.DataFrame([
-    [
-        regression_coverage_score(
-            y_test, y_pis[strategy][:, 0, 0], y_pis[strategy][:, 1, 0]
-        ),
-        (
-            y_pis[strategy][:, 1, 0] - y_pis[strategy][:, 0, 0]
-        ).mean()
-    ] for strategy in STRATEGIES
-], index=STRATEGIES, columns=["Coverage", "Width average"]).round(3)
-```
-
-In conclusion, the Jackknife-minmax, CV+, CV-minmax, or Jackknife-minmax-ab strategies are more
-conservative than the Jackknife+ strategy, and tend to result in more
-reliable coverages for *out-of-distribution* data. It is therefore
-advised to use the three former strategies for predictions with new
-out-of-distribution data.
-Note however that there are no theoretical guarantees on the coverage level 
-for out-of-distribution data.
-Here it's important to note that the CQR strategy should not be taken into account for
-width prediction, and it is abundantly clear from the negative width coverage that
-is observed in these results.
-
-
-## 4. Estimating the uncertainty with different sklearn-compatible regressors
-
-
-MAPIE can be used with any kind of sklearn-compatible regressor. Here, we
-illustrate this by comparing the prediction intervals estimated by the CV+ method using
-different models:
-
-- the same polynomial function as before.
- 
-- a XGBoost model using the Scikit-learn API.
-
-- a simple neural network, a Multilayer Perceptron with three dense layers, using the KerasRegressor wrapper.
-
-Once again, let’s use our noisy one-dimensional data obtained from a
-uniform distribution.
-
-```python
-min_x, max_x, n_samples, noise = -5, 5, 100, 0.5
-X_train, y_train, X_test, y_test, y_mesh = get_1d_data_with_constant_noise(
-    x_sinx, min_x, max_x, n_samples, noise
-)
-```
-
-```python
-plt.xlabel("x") ; plt.ylabel("y")
-plt.plot(X_test, y_mesh, color="C1")
-_ = plt.scatter(X_train, y_train)
-```
-
-Let's then define the models. The boosing model considers 100 shallow trees with a max depth of 2 while
-the Multilayer Perceptron has two hidden dense layers with 20 neurons each followed by a relu activation.
-
-
-```python
-import os
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"  # disable debugging logs from Tensorflow
-from tensorflow.keras import Sequential
-from tensorflow.keras.layers import Dense
-from scikeras.wrappers import KerasRegressor
-def mlp():
-    """
-    Two-layer MLP model
-    """
-    model = Sequential([
-        Dense(units=20, input_shape=(1,), activation="relu"),
-        Dense(units=20, activation="relu"),
-        Dense(units=1)
-    ])
-    model.compile(loss="mean_squared_error", optimizer="adam")
-    return model
-```
-
-```python
-polyn_model = Pipeline(
-    [
-        ("poly", PolynomialFeatures(degree=degree_polyn)),
-        ("linear", LinearRegression())
-    ]
-)
-```
-
-```python
-from xgboost import XGBRegressor
-xgb_model = XGBRegressor(
-    max_depth=2,
-    n_estimators=100,
-    tree_method="hist",
-    random_state=59,
-    learning_rate=0.1,
-    verbosity=0,
-    nthread=-1
-)
-mlp_model = KerasRegressor(
-    build_fn=mlp, 
-    epochs=500, 
-    verbose=0
-)
-```
-
-Let's now use MAPIE to estimate the prediction intervals using the CV+ method 
-and compare their prediction interval.
-
-```python
-models = [polyn_model, xgb_model, mlp_model]
-model_names = ["polyn", "xgb", "mlp"]
-prediction_interval = {}
-for name, model in zip(model_names, models):
-    mapie = MapieRegressor(model, method="plus", cv=5)
-    mapie.fit(X_train, y_train)
-    y_pred[name], y_pis[name] = mapie.predict(X_test, alpha=0.05)
-```
-
-```python
-fig, axs = plt.subplots(1, 3, figsize=(20, 6))
-for name, ax in zip(model_names, axs):
-    plot_1d_data(
-        X_train.ravel(),
-        y_train.ravel(),
-        X_test.ravel(),
-        y_mesh.ravel(),
-        1.96*noise,
-        y_pred[name].ravel(),
-        y_pis[name][:, 0, 0].ravel(),
-        y_pis[name][:, 1, 0].ravel(),
-        ax=ax,
-        title=name
-    )
-```
-
-```python
-fig, ax = plt.subplots(1, 1, figsize=(7, 5))
-for name in model_names:
-    ax.plot(X_test, y_pis[name][:, 1, 0] - y_pis[name][:, 0, 0])
-ax.axhline(1.96*2*noise, ls="--", color="k")
-ax.set_xlabel("x")
-ax.set_ylabel("Prediction Interval Width")
-ax.legend(model_names + ["True width"], fontsize=8);
-```
-
-As expected with the CV+ method, the prediction intervals are a bit 
-conservative since they are slightly wider than the true intervals.
-However, the CV+ method on the three models gives very promising results 
-since the prediction intervals closely follow the true intervals with $x$. 

From 86854891e81c133284ede9d28fbe347d77c6380d Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 28 Jan 2025 17:48:07 +0100
Subject: [PATCH 416/424] CHORE: limit max sklearn version to avoid tests
 breaking in CI (see also #574) (#608)

---
 environment.ci.yml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/environment.ci.yml b/environment.ci.yml
index 07f31c0a3..1e9568a45 100644
--- a/environment.ci.yml
+++ b/environment.ci.yml
@@ -8,5 +8,5 @@ dependencies:
     - mypy
     - pandas
     - pytest-cov
-    - scikit-learn
+    - scikit-learn<1.6.0
     - typed-ast

From 14fbf6b749b5abfd525166a8890afa3246b4be1b Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 28 Jan 2025 17:49:13 +0100
Subject: [PATCH 417/424] CHORE: fix github yaml CI file formatting (#607)

---
 .github/workflows/test.yml | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml
index 4298a96f1..af866084c 100644
--- a/.github/workflows/test.yml
+++ b/.github/workflows/test.yml
@@ -3,9 +3,9 @@ name: Unit tests
 on:
   push:
     branches:
-      -dev
-      -main
-      -master
+      - dev
+      - main
+      - master
   pull_request:
 
 jobs:

From 9e18aee006188fdae0f68b4a8536a429ae3419bb Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Thu, 6 Feb 2025 18:41:57 +0100
Subject: [PATCH 418/424] CHORE: make test suite fail fast when checking
 coverage, to increase CI execution time (#610)

---
 Makefile | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 10415d049..ac35e4308 100644
--- a/Makefile
+++ b/Makefile
@@ -10,14 +10,14 @@ tests:
 	pytest -vs --doctest-modules mapie
 
 coverage:
-	pytest -vs \
-		--doctest-modules \
+	pytest -vsx \
 		--cov-branch \
 		--cov=mapie \
 		--cov-report term-missing \
 		--pyargs mapie \
 		--cov-fail-under=100 \
-		--cov-config=.coveragerc
+		--cov-config=.coveragerc \
+		--no-cov-on-fail
 
 doc:
 	$(MAKE) html -C doc

From 38c52e69b33bfe6b577c34891f60115fa0f1feae Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Wed, 12 Feb 2025 13:45:56 +0100
Subject: [PATCH 419/424] DOC: remove commented out old documentation (#611)

---
 ...theoretical_description_classification.rst | 50 -------------------
 1 file changed, 50 deletions(-)

diff --git a/doc/theoretical_description_classification.rst b/doc/theoretical_description_classification.rst
index 445fcfe42..8ad8c42b4 100644
--- a/doc/theoretical_description_classification.rst
+++ b/doc/theoretical_description_classification.rst
@@ -178,56 +178,6 @@ where :
 
 - :math:`E(X_{n+1}, y, U_{n+1}; \hat{\pi}^{k(i)})` is the conformity score of label :math:`y` from a new test point.
 
-
-
-
-.. The :class:`mapie.regression.MapieClassifier` class implements several conformal methods
-.. for estimating predictions sets, i.e. a set of possibilities that include the true label
-.. with a given confidence level.
-.. The full-conformal methods being computationally intractable, we will focus on the split-
-.. and cross-conformal methods. 
-
-.. Before describing the methods, let's briefly present the mathematical setting.
-.. For a classification problem in a standard independent and identically distributed
-.. (i.i.d) case, our training data :math:`(X, Y) = \{(x_1, y_1), \ldots, (x_n, y_n)\}`
-.. has an unknown distribution :math:`P_{X, Y}`. 
-
-.. Given some target quantile :math:`\alpha` or associated target coverage level :math:`1-\alpha`,
-.. we aim at constructing a set of possible labels :math:`\hat{T}_{n, \alpha} \in {1, ..., K}`
-.. for a new feature vector :math:`X_{n+1}` such that 
-
-.. .. math:: 
-..     P \{Y_{n+1} \in \hat{T}_{n, \alpha}(X_{n+1}) \} \geq 1 - \alpha
-
-
-.. 1. Split-conformal method
-.. -------------------------
-
-.. - In order to estimate prediction sets, one needs to "calibrate" so-called conformity scores
-..   on a given calibration set. The alpha-quantile of these conformity scores is then estimated
-..   and compared with the conformity scores of new test points output by the base model to assess
-..   whether a label must be included in the prediction set
-
-.. - The split-conformal methodology can be summarized in the scheme below : 
-..     - The training set is first split into a training set and a calibration set
-..     - The training set is used for training the model
-..     - The calibration set is only used for getting distribution of conformity scores output by
-..       the model trained only on the training set. 
-
-
-.. 2. The "score" method
-.. ---------------------
-
-.. 3. The "cumulated score" method
-.. -------------------------------
-
-.. 4. The cross-conformal method
-.. -----------------------------
-
-
-
-.. TO BE CONTINUED
-
 References
 ----------
 

From e3fe2db06418f3f23342a99e8e6a94aa5218ba6a Mon Sep 17 00:00:00 2001
From: Valentin Laurent <valentin.laurent.fr@gmail.com>
Date: Tue, 18 Feb 2025 14:41:44 +0100
Subject: [PATCH 420/424] MNT: remove random_state from EnsembleClassifier (not
 used) (#612)

---
 mapie/classification.py       | 1 -
 mapie/estimator/classifier.py | 9 ---------
 2 files changed, 10 deletions(-)

diff --git a/mapie/classification.py b/mapie/classification.py
index 6e2573d0c..5eab26e10 100644
--- a/mapie/classification.py
+++ b/mapie/classification.py
@@ -493,7 +493,6 @@ def fit(
             self.n_classes_,
             cv,
             self.n_jobs,
-            self.random_state,
             self.test_size,
             self.verbose,
         )
diff --git a/mapie/estimator/classifier.py b/mapie/estimator/classifier.py
index 0777b9673..9cd45e64e 100644
--- a/mapie/estimator/classifier.py
+++ b/mapie/estimator/classifier.py
@@ -74,13 +74,6 @@ class EnsembleClassifier:
 
         By default ``None``.
 
-    random_state: Optional[Union[int, RandomState]]
-        Pseudo random number generator state used for random uniform sampling
-        for evaluation quantiles and prediction sets.
-        Pass an int for reproducible output across multiple function calls.
-
-        By default ``None``.
-
     verbose: int, optional
         The verbosity level, used with joblib for multiprocessing.
         At this moment, parallel processing is disabled.
@@ -119,7 +112,6 @@ def __init__(
         n_classes: int,
         cv: Optional[Union[int, str, BaseCrossValidator]],
         n_jobs: Optional[int],
-        random_state: Optional[Union[int, np.random.RandomState]],
         test_size: Optional[Union[int, float]],
         verbose: int,
     ):
@@ -127,7 +119,6 @@ def __init__(
         self.n_classes = n_classes
         self.cv = cv
         self.n_jobs = n_jobs
-        self.random_state = random_state
         self.test_size = test_size
         self.verbose = verbose
 

From 8ac445f349164c8c1d8528473ff2eeced4e8777c Mon Sep 17 00:00:00 2001
From: C-BdB <137887330+C-BdB@users.noreply.github.com>
Date: Thu, 27 Feb 2025 14:49:42 +0100
Subject: [PATCH 421/424] DOC: fix typo (#617)

---
 doc/theoretical_description_classification.rst | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/doc/theoretical_description_classification.rst b/doc/theoretical_description_classification.rst
index 8ad8c42b4..5144c8487 100644
--- a/doc/theoretical_description_classification.rst
+++ b/doc/theoretical_description_classification.rst
@@ -159,14 +159,14 @@ By analogy with the CV+ method for regression, estimating the prediction sets is
 
 - We split the training set into *K* disjoint subsets :math:`S_1, S_2, ..., S_K` of equal size. 
   
-- *K* regression functions :math:`\hat{\mu}_{-S_k}` are fitted on the training set with the 
+- *K* classification functions :math:`\hat{\mu}_{-S_k}` are fitted on the training set with the 
   corresponding :math:`k^{th}` fold removed.
 
 - The corresponding *out-of-fold* conformity score is computed for each :math:`i^{th}` point 
 
 - Compare the conformity scores of training instances with the scores of each label for each new test point in order to
   decide whether or not the label should be included in the prediction set. 
-  For the APS method, the prediction set is constructed as follows (see equation 11 of [3]) : 
+  For the APS method, the prediction set is constructed as follows (see equation 11 of [2]) : 
 
 .. math:: 
     C_{n, \alpha}(X_{n+1}) = 

From af890df1d9d15b05fd86c7232df66aa3fa83ff12 Mon Sep 17 00:00:00 2001
From: C-BdB <137887330+C-BdB@users.noreply.github.com>
Date: Thu, 6 Mar 2025 10:03:59 +0100
Subject: [PATCH 422/424] DOC: fix readme URL and write precisions on
 contributing.rst (#619)

---
 CONTRIBUTING.rst | 10 ++++++++--
 README.rst       |  2 +-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/CONTRIBUTING.rst b/CONTRIBUTING.rst
index 81b04b707..b5f297392 100644
--- a/CONTRIBUTING.rst
+++ b/CONTRIBUTING.rst
@@ -23,7 +23,7 @@ The typical workflow for contributing to `mapie` is:
 Local setup
 -----------
 
-We encourage you to use a virtual environment. You'll want to activate it every time you want to work on `mapie`.
+We encourage you to use a virtual environment, with Python `3.9` or `3.10`. You'll want to activate it every time you want to work on `mapie`.
 
 You can create a virtual environment via ``conda``:
 
@@ -38,7 +38,13 @@ Alternatively, using ``pip``, create a virtual environment and install dependenc
 
     $ pip install -r requirements.dev.txt
 
-Finally, install `mapie` in development mode:
+If you work on Mac, you may have to install libomp manually in order to install LightGBM:
+
+.. code-block:: sh
+
+    $ brew install libomp
+
+Finally, install ``mapie`` in development mode:
 
 .. code-block:: sh
 
diff --git a/README.rst b/README.rst
index f117b4036..815d12b46 100644
--- a/README.rst
+++ b/README.rst
@@ -160,7 +160,7 @@ The full documentation can be found `on this link <https://mapie.readthedocs.io/
 You are welcome to propose and contribute new ideas.
 We encourage you to `open an issue <https://github.com/scikit-learn-contrib/MAPIE/issues>`_ so that we can align on the work to be done.
 It is generally a good idea to have a quick discussion before opening a pull request that is potentially out-of-scope.
-For more information on the contribution process, please go `here <CONTRIBUTING.rst>`_.
+For more information on the contribution process, please go `here <https://github.com/scikit-learn-contrib/MAPIE/blob/master/CONTRIBUTING.rst>`_.
 
 
 🤝  Affiliations

From 534002652bfe0e2da04ab481a9257d68b281c683 Mon Sep 17 00:00:00 2001
From: C-BdB <137887330+C-BdB@users.noreply.github.com>
Date: Mon, 10 Mar 2025 11:04:48 +0100
Subject: [PATCH 423/424] ENH: use pyproject.toml (#618)

* ENH: use pyproject.toml
* ENH: use twine and wheel with there latest version.
---
 .bumpversion.cfg              |  6 +--
 .github/workflows/publish.yml |  4 +-
 AUTHORS.rst                   |  1 +
 HISTORY.rst                   |  2 +
 MANIFEST.in                   |  4 --
 Makefile                      |  2 +-
 environment.dev.yml           |  5 ++-
 pyproject.toml                | 55 +++++++++++++++++++++++++++
 requirements.dev.txt          |  5 ++-
 setup.py                      | 71 -----------------------------------
 10 files changed, 70 insertions(+), 85 deletions(-)
 delete mode 100644 MANIFEST.in
 create mode 100644 pyproject.toml
 delete mode 100644 setup.py

diff --git a/.bumpversion.cfg b/.bumpversion.cfg
index 9c156f5f0..5b1a6d3a2 100644
--- a/.bumpversion.cfg
+++ b/.bumpversion.cfg
@@ -3,9 +3,9 @@ current_version = 0.9.2
 commit = True
 tag = True
 
-[bumpversion:file:setup.py]
-search = VERSION = "{current_version}"
-replace = VERSION = "{new_version}"
+[bumpversion:file:pyproject.toml]
+search = version = "{current_version}"
+search = version = "{new_version}"
 
 [bumpversion:file:mapie/_version.py]
 search = __version__ = "{current_version}"
diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml
index c36ccdeea..2fbb49099 100644
--- a/.github/workflows/publish.yml
+++ b/.github/workflows/publish.yml
@@ -16,9 +16,9 @@ jobs:
       - name: Install build dependencies
         run: |
           python -m pip install --upgrade pip
-          pip install setuptools wheel twine
+          pip install setuptools wheel twine build
       - name: Build package
-        run: python setup.py sdist bdist_wheel
+        run: python -m build
       - name: Publish package
         uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
         with:
diff --git a/AUTHORS.rst b/AUTHORS.rst
index 236746766..50d5a9fa5 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -46,4 +46,5 @@ Contributors
 * Leonardo Garma <leonardo.garma@gmail.com>
 * Mohammed Jawhar <mohammedjawhar365@gmail.com>
 * Syed Affan <saffand03@gmail.com>
+* Cyprien Bertran <cbertrandebalanda@yahoo.fr>
 To be continued ...
diff --git a/HISTORY.rst b/HISTORY.rst
index 4e2236210..b762760d5 100644
--- a/HISTORY.rst
+++ b/HISTORY.rst
@@ -5,6 +5,8 @@ History
 0.9.x (2025-xx-xx)
 ------------------
 
+* Fix issue  512 to replace setup.py by pyproject.toml, bump twine and wheel dependencies to latest
+
 0.9.2 (2025-15-01)
 ------------------
 
diff --git a/MANIFEST.in b/MANIFEST.in
deleted file mode 100644
index 6125ce68c..000000000
--- a/MANIFEST.in
+++ /dev/null
@@ -1,4 +0,0 @@
-include LICENSE
-include AUTHORS.rst
-recursive-exclude doc *
-recursive-include examples *.py
\ No newline at end of file
diff --git a/Makefile b/Makefile
index ac35e4308..c5e96d156 100644
--- a/Makefile
+++ b/Makefile
@@ -29,7 +29,7 @@ clean-doc:
 	$(MAKE) clean -C doc
 
 build:
-	python setup.py sdist bdist_wheel
+	python -m build
 
 clean-build:
 	rm -rf build dist MAPIE.egg-info
diff --git a/environment.dev.yml b/environment.dev.yml
index 0c231cc29..9b9d63310 100644
--- a/environment.dev.yml
+++ b/environment.dev.yml
@@ -4,6 +4,7 @@ channels:
     - conda-forge
 dependencies:
     - bump2version=1.0.1
+    - build
     - flake8=4.0.1
     - ipykernel=6.9.0
     - jupyter=1.0.0
@@ -18,5 +19,5 @@ dependencies:
     - sphinx=4.3.2
     - sphinx-gallery=0.10.1
     - sphinx_rtd_theme=1.0.0
-    - twine=3.7.1
-    - wheel=0.38.1
+    - twine
+    - wheel
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..184367675
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,55 @@
+[build-system]
+requires = [
+    "setuptools"
+]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "MAPIE"
+version = "0.9.2"
+description = "A scikit-learn-compatible module for estimating prediction intervals."
+readme = "README.rst"
+license = {file = "LICENSE"}
+maintainers = [
+    {name = "Valentin Laurent", email = "valentin.laurent@capgemini.com"},
+    {name = "Thibault Cordier", email = "thibault.a.cordier@capgemini.com"},
+    {name = "Louis Lacombe", email = "louis.lacombe@capgemini.com"},
+    {name = "Vincent Blot", email = "vincent.blot@capgemini.com"},
+]
+requires-python = ">=3.7"
+dependencies = [
+    "scikit-learn<1.6.0",
+    "scipy",
+    "numpy>=1.21",
+    "packaging"
+]
+classifiers = [
+    "Intended Audience :: Science/Research",
+    "Intended Audience :: Developers",
+    "License :: OSI Approved",
+    "Topic :: Software Development",
+    "Topic :: Scientific/Engineering",
+    "Operating System :: Microsoft :: Windows",
+    "Operating System :: POSIX",
+    "Operating System :: Unix",
+    "Operating System :: MacOS",
+    "Programming Language :: Python :: 3.7",
+    "Programming Language :: Python :: 3.8",
+    "Programming Language :: Python :: 3.9",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11"
+]
+
+[project.urls]
+Homepage = "https://github.com/scikit-learn-contrib/MAPIE"
+Documentation = "https://mapie.readthedocs.io/en/latest/"
+Repository = "https://github.com/scikit-learn-contrib/MAPIE"
+Issues = "https://github.com/scikit-learn-contrib/MAPIE/issues"
+Changelog = "https://github.com/scikit-learn-contrib/MAPIE/releases"
+DOWNLOAD = "https://pypi.org/project/MAPIE/#files"
+
+[tool.setuptools]
+include-package-data = true
+
+[tool.setuptools.packages.find]
+include = ["mapie", "mapie.*"]
diff --git a/requirements.dev.txt b/requirements.dev.txt
index 95f886f46..6ad47933a 100644
--- a/requirements.dev.txt
+++ b/requirements.dev.txt
@@ -1,4 +1,5 @@
 bump2version==1.0.1
+build
 flake8==4.0.1
 ipykernel==6.9.0
 jupyter==1.0.0
@@ -12,5 +13,5 @@ scikit-learn<1.6.0
 sphinx==4.3.2
 sphinx-gallery==0.10.1
 sphinx_rtd_theme==1.0.0
-twine==3.7.1
-wheel==0.38.1
\ No newline at end of file
+twine
+wheel
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 1132c6a89..000000000
--- a/setup.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import codecs
-
-from setuptools import find_packages, setup
-
-DISTNAME = "MAPIE"
-VERSION = "0.9.2"
-DESCRIPTION = (
-    "A scikit-learn-compatible module "
-    "for estimating prediction intervals."
-)
-with codecs.open("README.rst", encoding="utf-8-sig") as f:
-    LONG_DESCRIPTION = f.read()
-LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst"
-URL = "https://github.com/scikit-learn-contrib/MAPIE"
-DOWNLOAD_URL = "https://pypi.org/project/MAPIE/#files"
-PROJECT_URLS = {
-    "Bug Tracker": "https://github.com/scikit-learn-contrib/MAPIE/issues",
-    "Documentation": "https://mapie.readthedocs.io/en/latest/",
-    "Source Code": "https://github.com/scikit-learn-contrib/MAPIE"
-}
-LICENSE = "new BSD"
-MAINTAINER = "V. Laurent, T. Cordier, V. Blot, L. Lacombe"
-MAINTAINER_EMAIL = (
-    "valentin.laurent@capgemini.com, "
-    "thibault.a.cordier@capgemini.com, "
-    "vincent.blot@capgemini.com, "
-    "louis.lacombe@capgemini.com"
-)
-PYTHON_REQUIRES = ">=3.7"
-PACKAGES = find_packages()
-INSTALL_REQUIRES = [
-    "scikit-learn<1.6.0",
-    "scipy",
-    "numpy>=1.21",
-    "packaging"
-]
-CLASSIFIERS = [
-    "Intended Audience :: Science/Research",
-    "Intended Audience :: Developers",
-    "License :: OSI Approved",
-    "Topic :: Software Development",
-    "Topic :: Scientific/Engineering",
-    "Operating System :: Microsoft :: Windows",
-    "Operating System :: POSIX",
-    "Operating System :: Unix",
-    "Operating System :: MacOS",
-    "Programming Language :: Python :: 3.7",
-    "Programming Language :: Python :: 3.8",
-    "Programming Language :: Python :: 3.9",
-    "Programming Language :: Python :: 3.10",
-    "Programming Language :: Python :: 3.11"
-]
-
-setup(
-    name=DISTNAME,
-    version=VERSION,
-    description=DESCRIPTION,
-    long_description=LONG_DESCRIPTION,
-    long_description_content_type=LONG_DESCRIPTION_CONTENT_TYPE,
-    url=URL,
-    download_url=DOWNLOAD_URL,
-    project_urls=PROJECT_URLS,
-    license=LICENSE,
-    maintainer=MAINTAINER,
-    maintainer_email=MAINTAINER_EMAIL,
-    packages=PACKAGES,
-    python_requires=PYTHON_REQUIRES,
-    install_requires=INSTALL_REQUIRES,
-    classifiers=CLASSIFIERS,
-    zip_safe=False  # the package can run out of an .egg file
-)

From ef7d6f69e18b6d5fbf4d357d64735f0cd4ce6068 Mon Sep 17 00:00:00 2001
From: FaustinPulveric <faustin.pulveric@capgemini.com>
Date: Thu, 3 Apr 2025 10:15:18 +0200
Subject: [PATCH 424/424] DOC: fix typos in documentation and regenerate image
 (#636)

---
 AUTHORS.rst                                |   1 +
 doc/images/quickstart_1.png                | Bin 72875 -> 67720 bytes
 doc/theoretical_description_regression.rst |   4 ++--
 3 files changed, 3 insertions(+), 2 deletions(-)

diff --git a/AUTHORS.rst b/AUTHORS.rst
index 50d5a9fa5..402bddcea 100644
--- a/AUTHORS.rst
+++ b/AUTHORS.rst
@@ -47,4 +47,5 @@ Contributors
 * Mohammed Jawhar <mohammedjawhar365@gmail.com>
 * Syed Affan <saffand03@gmail.com>
 * Cyprien Bertran <cbertrandebalanda@yahoo.fr>
+* Faustin Pulvéric <faustin.pulveric@gmail.com>
 To be continued ...
diff --git a/doc/images/quickstart_1.png b/doc/images/quickstart_1.png
index 10d3716486bcf3f8b54111e9d88ac95b56ffd3ab..c218691f157908f7e15bb0ee820838d2e69bb594 100644
GIT binary patch
literal 67720
zcmce;cTiL97Y0bL0xC$6sv<=?NDocyfS@S7M}qX;yMTx^k=~mEO7Fc(4^1!u1BBiJ
zp#%sJ2z&AKx4W~mvw!XkOft#M&AsnA?<vpooJ457ctS&UlZt?VfaclLhpz|-h=K?R
z2<IutfLA2PXO@8nS+_@eZrY9zH&1gHO9BmZHzzwsH#=(!E)Po=S8GQH31LZLF+r|(
zZf;Jlav~!3|K|b1jxJUrQ|9}vz)2{bp6a_25YU-l{u34{6<QMz5)eFlsPft?V=I$9
zgU$V<H+y={p*;D(gi<os^ZTo>AvtavMHKJzg1StVuc?sS5YUi&D)PKMPPynA*VlTK
z`%Yc*NnjRo#t0(qJ5Za@?AzY3>fzyWBz>M*Z_`wUx=NyQ`S*dQu2g~G?^6=<q1!}S
ze_uCGx%L0|v#Hx9Mvd3g3Kr&kci1Z|J>1;peUUhhA3uIfR*5ukiA%PYmC^EMdEI`l
zruO>)d4b1Hy>dD~PB$_&U$1&QaGlL(J(o{V@HGy%QJCFZN6@!FWQwmEMYbONQX}_M
z?@qiEBSW;7={X1E_%KGzC}cnq-W^Oz&1fdWMdWmcsXMD>&JUwur`B?T$8D00{rU4L
zcSJ*a_A3ee5tDH!IfoZPVRk@M8a|)%oZ{kSG|=hrw{G!9IA_2y<Ye1^r9i~adjy10
z_uK1XYS`;#{VgVCHIh-P@b%Us5J#V8z&?Exo9uOya;D~;HhB~8l|+ufz}Y5unUm%C
zm#<%knu&e{$H<ZmC5s@<+R$aFcKl&EP7;ru^QF5u-wFIJr#J9cykRBDP}IDO#0$i+
zRnblBb-WPn+hXJp;(s(vu9!{N)6=u5B8}``J>FZwl)=N8Od2{{T68l4@h5yDBH_rL
zzF=~WkFiR4W<%Eth5i0JQ<llblTwQfBMMlf`%+Rzwik!XDJin9JTajCr$(N$?##3@
zbw>nsZ@gaCyIh=Oi-~1CPor2QDoQCkQ046$R+;Wea%17*<?VR6qbV@m*&<W>l$o3+
zn)BkgFMYe7pTK)h0eGXjKsz)5hd_QnY|q)X3)vaxjq|OJWJYFRoUK`iA=GzI4}a2Y
z@)hF~__ZfPID~8*TlPL&S5Du*qV=<;wsyfv3CEK0g4dz_++(Z#0)Kokp_ypaF?cJS
z?Y+c^%pKc5Bl0^l<$D)L_3b?><m{mX@w`?VYhtR-4~FK;n$e%Evb@O(Gd<F-3$-}d
zMXhBAni#|Rq8g&K_(J8;4XXCBnu;e6Denr%Z`aV5H7tHAvDPkpF{}Lf)vn*sv{l3Y
zpukHVoexUq8=5lnLByhgc)!~1`U5-;lYJ2tDbl8A-;Qu-rm=;Wy+NIfKjB;mp;Oe#
ztEk77OZWW*tWeb3WH&9Yo_3#7^``OqwPYig2t}=Qf%t*-xTg=1Y-hk_zgaS>v<Pbx
z>fCcO!p^vk1_`-#g;JizJ>h8Vbkmra#Q(`SJ`XsDN{r5ww2LJ?<B9hHalpy%6zi0}
z>JuNW4bIEY2P-FaJD&q4SjTuuW4$Nn6_4jbXzCpA-~7$1k@j_`aV;zT4DT3#VLd$e
z#;m$);P!_^+?!|1O#Kgkr~7B3HaFeEyK^tyZ8WXTW7}YM!wquIu|tYOH<v5=9K2IV
zd~Wa7C}A}+$*Y-_;Xdbgx>49P44DvVKie<y?u%wk#{Nke%8yn2QP;T?t28HB+SX<w
za_`<RO@+hP{%Si)Wvqd|?dM0@h~8!3Vzh_Ox;gP<h3D{e@a?##>wx>7*1}DS>D(U@
z9H|oEfz_R)1MMoewzL>|HX2<z-od2}SpDE27Sn(eOaG&-=_bTB%n|U5!|B09fkWWC
z4X`JEHnPda9;s=u?T5Xrj&Ft50xxjc0aNDT8=YoaSfAESZg1?YXS3p3ST=IV-(LMn
z+xZdVG=ev)D3(RyaVfFLLpIei<!@8D>kE&zQou1U1CtXIH*}4hd&ro|l9CuDmHPDc
z^op{bds$9-v;BU73Hx_0cEgokgBMb~7LE_g14|1htPf@D)!Gi1t1ucn8!j5hcx?Mc
ztD-GTu(rG*YX%L3joecZ(lNzij4h;bxDm<!8dKTF;ajxSIY>+g%Gs$J5^m~SddZZl
z{X+JUX{%4|9DRf1i0m4h|Id%tY|eXQIm=DG7Q*zUqb!G#zMl_UVwnH<V`mQm?`}h3
z{`@g;e|fed64=<e-+sC&Q0w41=WSJ9_njZ_tvc_l<h=OMePU6pvvTjjko)Rh7sf2x
z|7hGa@cfV_puMv4<ptWay_kuC!Hr$Fg>Y5A%*$D6A(Vd7$A!~HUI_!0U^QBA%xd}W
zlJ(`wb^o1KEbIb@yzpp_#$<v)?HY>sWAOQIH=QncS$03#(N8s7-qQgl4h1g{OCu*m
zn$~Z-&tVGS?5)#}8;Qo)MC$vV@n(MQ?KwQ_qzlM#7;HqYxTC)pX8o#1ILOy3@ynU`
zY<w#@@fu!iD14FmBSu>|wr^5d!R<1PZZg~1+76`!OlU}d7jxM3n{tO6{}xDArBis#
zo_zvKg*VS^o8M)lEw-N;8><-D!?bm*xXA0DEOA~G6^Ix$h}xb4$9^qn$Bf9&3Ri#s
z{%pA0KAsx;yFcy@X3o`X%WiAoecwY%%M~(s2WC{sl-;UQPaXLM=ZHT$vEgtiHX;fR
zYJ;2j9$H@<uBLGHv3q8;b<@hPtFm7UI=>nO${CSE3A$@9X~dD!uQj5|nx$k|yyF*!
z*9(=-zXt%0C~&QtK3-aN?xSpG9=LwiW5RRBxsQfq-nP88K)B!U{psvZKw&^UIbY#k
zMC4^a+Wa1kTv=nF_p{$3+68v2snX)dJCg$^$?a#4lz`~*dtivV=>`>>$(DCzZvQc`
zk4hXW&V-8BSF~Bf_#JndO#LeV2)9tWlqg&aY8i8x`}OP9IS^x0|LC-gPyBe2YyOF$
zj45pTh5YtpZ5B17pw`8{C#~%A=duk2<1$JtfAZe2V}SN{yrE<3n+Ox_W2aPxD}FB0
zflKFD<fs6_4iKYhvOtuMw2hhr?We!XC%yPi8#I6{;ZqyK;KE?Q3I;OYCQnDC6e$QE
zn-@%Xch$3T)~m9R;uuJ*Wk?T%{ifbh+bOVRW@`n}B6G*C3tnD!PO`1j1qB4l;@&OV
zT>Hmg??y|%rqVGG^-`a884^0Jv7Siy7D_Z8D$U1%J3ChsX?>jup6Od{v`{GXV$<T<
z#~v?_dV8z87{P!vC%fH(8JW0D8Q(FV*68}+?4Ujw4?NG>N?W0scwll~ED95Nfeo~g
zWw-$89PdIbIPS0^@wlDxnBwSjh-UqGFUkB7!m*s=exndyp6oV|l#uhyGg+(0-dnDL
zdntLxQn|GsF~&d|JvF^J)vaaswqJ6O=?itwcHUC(ei@a{=C|9q*)Y8FTi5gr_|mY=
zvpQ!Q4vtilMrDnwHxJLX_!4SIW~@N8$I^k=PIAtf`Q}wsE6tK(ByJ8nDI4b~7dT`y
zeOoPAc-VHf>Xor^>FR>iXc+cZfR48Igk&`CJ@!VH$f_z8)=IcVWKS-cx-|K04u}wC
z*d+XxlF1WUx<_upep-ui`aL$9N2ZRklo$gj%A)KeyUj;}t#AV9TJN`SADJ`+Kqtq>
z21g~kAG$X*mCoJZ_Z558vlSK82=?ELSq?JP+M#+v+DBQhIEMPW6RsJCr#rUkXKJ;m
z7?N@&`*zbvza4G{_Lr4@WrJ`=tTaEp0R?}O&us$lr3K3KL%6AxMxpX*5A^h4Dl`2~
zwqZ`;(6={X$igsgCRE+d2x*1@fj8koPk7QRff+@Zy}C-j^NiP0v_8fkD7FSO6XX?`
zs6<RE)CDZtoVA%(4Q~EHAbyKIjI%b;JK^d$IY#L4>G9F}Hfv}QK1JP_RnzgZTW)&K
zugCxTB*%PzIbQRJrJkb&n@C^0uMxZ3n9B2Ay;7zbvKo$Eka+ILjfiwXrs78vgFt*P
z=IDQCSv%Lj-@vcz8hg!PPio}|H7xZfh2RWU-<BxJ$vGmE`5ty^9FzV2XK41Cfj`Dr
z;@!%!pWpnz><a~NeNK;)&(;bf>hENiTKvY=X+I74!TF0xW0LIXIp{$J@7N0(3VthL
zbL;AfKI0_eRhnKUD|4(Ak0g?ZF$MBoB@@q~U<6f|?5Ik>1(5AF(_U1LhI5+4UhKsN
zx)}$SH*NkX*}*h7_FeT}2xHoxtM+*ih~+Ggy#H_+qquA7&%7~!M{SFl*AAxjCgy&6
zONb?gNT-T6B<MfCa?cWS4QXf&gPVu5CrNIvB%AgYz0BV&b@VT`y-953szxsQG|`fm
zS#V}@@@qhNxsyx@ktH$!XMY2VK)TQGH_Wsi{fT_8OUk(RCH+y|P8$wl`KtAJG4ft?
zR%#=UM>G3n)$V&gtB*6#NGP7gVby2feYcbRPUeeZ_ibtgV|2Wx;&-AdS9&#yLM$UB
z-5-;V{0WX#D5(2Ba9LJd*Xxa8FP^*1H9EM@`Zln=%AYhLMJ%9M_A?HNFR_ZJ>n+no
zs|}5gKm@5T;o4H>$`L0>eb#@SV)=L}Ru}gfeBWH3Bp_grzwp2(JuSnji;|V(2WHBt
z(a0DzJ~I>b`rbEKcUnZhYgjM>=GqOZUihLWJYc}lKFZDI(06onR3^69vT)Le`8XXE
z`s@`olkn?_Q)+m!ezrPQnvAmr{NUQ1$3>W<Sx@8}6`W3ku+fFa_=J6pm`i=Ux8i<E
zMVG%7jh|K0y;yUUtV1h=JN{X%ed<=nwlzCltTE5X;d1R#j6CG-^s-u>c*w<xw`WTB
z;Q<Z`J71s+EGp}4sT12Xhn48xd)Xn!8GqXgfAsYyZ9VyDgdZ4%^4ZgCpMOui`^UEY
z`z9F(BCEr(7V*#`WJd|NRo$@pqxki&e%ZB*koq|vHTDb0s~+X3@A9YB>O8qlnds&n
z#WNu3Yxm8{@3iEX$%8I%RTn<K>Q`MaR)#<v&z~|nDb062$?b^hc*_0dkcE`XHdLR~
zO!%?_WkML4$EC|c=5q9W^5l<wPlV;tEe<z--&@IKib_gKqDivuQ6H%9Le>PtqJE}x
zgoPzqPZS)!0~x_bl`i~3kYeA<%Xh|IPMeG%0hMkl&z@0|QJJ*xjd^8g8DXToa;spx
z%g0_J8^WlOmgAND++39iYLjCrGAzxI*s=wr3gapq!ep+fr&WVTUYuv2Mp=B$`@C3D
zj=P7HRfa~M>zew1BYD(zGQ9L@MMTP@OwhRLwS6CuV0BuXSeZCm92yqL+(X_%xZ&w5
z?^AjJlNmP9dC~NHX1L;BHI(XClL7u@Ja>p-P?&zqdI#IE=pLSTyFT>eiE>q?Ylid?
z5bs!k%o}@s0!Ge?k_>@UQw`2N2p$l>-T2d@vTRiP$oLn0NBf;bHG+XYeNb<k79~{~
zQ(C@s8)U-&RPS-Gplg>wf?%8rP!Bjd4cB)0M(J}zej94T&F;|lr|gd-E%87#_p*<<
zSSBTrO~!<y+D~h4%4Zc3=r7B2Fmeu04##(fQgkSGrOP;enJCoM)qDf)je1GWnO&4>
zl4To*8j5&pAsqRQH04irY;-0AYqf!BSQaBG%x<x%f|fyKQ+;XFI6O;F;FLNr9=teV
ztLQSsY`48~L}IYYXT(>;M{pwI*v6ab_6m0FRq4I)EqUKzt!)d5@A$%CmF-_wrHDyc
zN&zx6+mU<vsysQ+zXpB>M<+=n<rWncX`wWOJ6%iaQ~CrQ*%}$KM&k`Y;P{R-<EJ&)
z!2`8%v1_ulHX}1~=HMVrbPVwhEM12laW2luJ^Z{Fdsp!Y=!wcKra+Jq_!SdVz9?TJ
z&IWY>-mo_7Z?&(qwe#^S#D98|RMlc-9Ua>>y@YsMgpWv*7zX@Pq~MWBehzA=qn(Y0
z27j-ps-tDNX<+o=;B)tw@h&Lp8M<3b6BY^H9IYFC5_rV9u=msTOE0BjP86&3i&n4g
z<?hvj!St0fE7O>Wq>*#0);XF-Kz|aqmgW0G;pLbM#NTEvrC6AWW74g7#pLR{o(hYm
zx9gv#C+XcCv-I@(lbEg>FqOC`_-I#1Amzq2I-AsV#;d&c7CHR0QG+{2JuaeGhgSUj
zp0D<R!8#ptc2=J@^JC<mH>#ghDBIpNE`&wW1}66r3dyzJ$Zz4>pPG7o=z~u8!v*Wy
z#kkUcqG`%dmllnsZ^|O`F|E{LHZJh1s&|+$VgmK`x<=|q72goW8i~!8*Q6Z1HJa&T
zX!3J5=A+tJMU@gPE6z7GA=+A0(xnTAYon#eN^5m&Z`HH*+@vPMT7nK9kKMMe{kqJU
zUD#7y$oL#>*rZR%DRZ2qW6uHSDIv59m}8$AK{&Rz{$NR_ZXey&)6%LKuS`Ls(SnHd
z_jJFm+#Lv8DhRr4Gv9QYqZ**2zC>Z1rUm^4k7AR~H59m44Mp%g#w8%ehV2sY;@(U$
z`4Ui;2(*-^VRbkRYu3f0-fA!ZUHKaVIz>D7FS<MGf_@SlUBf(+g`aowE@eHSCe2RZ
zyIsoktNnc2Q6Dp$rC>N~=h%9v;~qcQbDE%0sp9jCk{c<v>&Q5}B_NnHaLmn!xxese
z6Mzi1#<46HD!TlS^n{Igg_}hV+!|XtoLDPTJcMD}KeuPn9R%Enb#--%SqUP`48R){
z#>F$7XR}7wYe<Q*B6+XeJhLU$XKj%gjUaS7&{2QiHjBocp-eA+ga#cJ{4BKN$gVl7
zyr)!3*Z8QM+`@E4p3M7YT}+&lpqszJD9v0A{<u#GGW-O^97Zzx^d#%JbnQ7>YK102
zC&8~QFRO%n*15(=Z}x|s+VPkw9lvhEA!G_+iG9mzA2qZ6HY+wtb9>0kCHAFo>{?@D
zTQ5OXhg@>c(yvflC|rk%z4&uMV!vNdWxz!@of1@zi4V%cYE#>^R{cWUAt9Hg=4`jg
zwu_t&|3cHE>)nlSr2O?mB;7Q}W?c`4cxGa1WF)wQ8HMRNnj6u0zul}3?^4q2lijpt
zkp{M=Xm5^ct}1uKJ?53}FiLnFUGag@!M7a$K&+|a@$LYMa(2ppI#5A@N1acKrOcdW
z>E;)^ZNY@jG*W2EEeKQ$@%h^gz24x%P-qncOmGHW^(HlVU`ws~3Y6V*CU>x-q9#J0
z7n&B<D=rA`i+Pj!PO!ZrFxA*=^5qlv;Xao;XToaDqIY%{!a31(SFWguI6(y^y9Bi%
zr|eE0gx+elDeeTWV%1klr)^5UL6U+O>Nf0Im|^nZRe>QM(dtM`Jt(pswy>$e=;W9*
zw9gD-VMfy@oy*{lm#W2YHTs_@KwMl|(=8QUs)I_WXQ5;(kt|h$jOik{G^8bjwK`Px
zF=SiJ)m{5hxIS?O08kcRxgz1MzZApb-Dqj1cRTE^MS}?l<}#F@a_J4PmKIV{-%P>%
ze;dO}Y9TkPU&}(W$e|v~4W);NDU?I5-3DYIER&(GD**C2JSg>6|9aBm{JvM2C^!`d
zH9!>S<vmaWV8S<b+(R8kPMxGSh86dJ^{zYkL&Zh&A4k7hqfZ_x8AT6vEDueMJkhLw
zsX5poiW(OrcKu^rcvw^<9N3+<HxlKFCQmwD@Y0itaOY}lCI2BKhnm=Hu+WgjlZ{fg
z1)@spt5(4*nKKAxHJ{z4-t!^M$39t9MwR*(N$O0*2e@1k(Slf)_!A>t4r0&|4lpJr
z3cS+bx^U_wTT<Nzj)9A`jS3lC=onX+JkfLcn7S&6{JFP3wE_ZROLmVJPcN2vm0q&c
z3+`D$`t}_KK;?R|36e(*c#xu((O$;raMUmmhbk|;*w02h+wun&MbqJe<-NZIb$qHX
ze0<bXu#aGM%P!$^w+V1B$vzxLd8y`#=6_xyMYiZ+9};|78A>i5<s;Hx`?&G|`h^LW
zPG&wVLUqmLowxRai}y9NXk~$x={=r@*Xq90dyMBj`r$w?Lcs98Bm$BYupUWpKQign
z!_=^x6HiAf6>a8fIeFXHFW<TvwFGGy>7l|79}^rw8i4@3I^qJ#h{c6je17y?!^`>W
z@oE4<@76nkmZCighaL2Mn-vSCgKe7(S=rEHPo#9?tlp~+(pE9aedIml?dj!L?~{_m
z8h*NGdux3rvY;`bk!f<yZX%jeImv24BLepY4AH+86ONG3ozc>IRg)5y!w&-7LZNo+
z35Pegtw|YgJ?SGc)XJXUe8?|)P0+X`og<%7paV7VhGba!p<aJqFDyJPN53p$jlM^Q
zx+h)oHtrsQZ*_DNeGq805!txS7sH>|EI`uAPuNfXsd`k=aND+w7oNPPiuOAX2IGK6
zqU4AZo?46EPvjop<?2t9w4FTNzxh+l1u3Fm#cj*Mcm|;q!Z0s5D_6$(Dr}b$a5JD9
z?;1kM-@2Oa`{hmzdOEDo8wx(_vZcjxjv9aBq-vRIdM`+>S~&{NYVSN;lUxB>sUdmT
z>MG=oM;P`7NA*m>NsI=}f5p4%d)n9Y*+5X8x5>WViWi?2Cc0G`S6<jhV!q%S!;iF5
zrHQ_%uPy!jK<d8v#N@D>e-jyrN<Uq7ajZOA<_*LW0HfyqbWg9Ef$nrutqZem5^f`|
zM-_wlh-TCMw+ov~I~!DcKS{7S^SN$S_WjU96lVEQsj^29&I~TYI4!PlEhT%6U)|%S
zn9UY|*zto=9@Cb|rBcEw{bA2cq9`o~l5z=9miuSm+K;)=WYd6%OTex8?6=mFs-SxD
zD|o8{dWrm$hh1Oj{A_^!p1l5OjKn<s{mK(BdW+8rA%iRpcmPCLDoA(O)J?ssGR0Q$
zbwVqc@>*jUH2k&%1iJ2;w_x^=WM1dPbTstJxB2`>A*m_QCg||>8=(s$(rtS*Z+t>E
z*J~0az{!lti~%<Q4#y|_Cjr%aE{gYDS0uQ}{$ClKsoNFuMB1p!)gd(GY0j&#WRj<K
zn%>fQt~Z^Ph@6g(pU~H_QtLciax`dGp-`o((*Y+HPLn6EysIOXVN43xKzZKHa+x<H
z6Ge2b9KB|$vj>3vSO;7>o#aCThh>YYcqW{vgPN$o7wMq68h+LiQC$3cR9>WT7XW7l
zO=v0^Ygo}ds^bOYxLzoo?RHBKIXVhR`<oASn>1{F^IwXV{sFcd$&fYleX2?Y@vdH`
zz*$Okrwje``IDb<h)6N&+o?*NULVi@O>FK^ZdXV2Rzv<M5Q5aJQ(7KIZUxSArikIv
zk!O!fLu&hk%~6_3Li=8zA4DX+a#$XW+jNzs%y!MhYlNx;Ytj`Q05i39`q?e@A1?Up
znR@};Mjy3EOGT$2LlHyq76K}^{f}Q6H^l=RqNv0`I3B!ZEw2@SNuMB3VfC-u9ggm1
zN!l{v0p!<C)T8bf_y$d#%iGcib<)fJV^oNay@e0cP`jmp?STCO-eQimMyKpbcME%a
zp${O#=Ie)bR0y=5X^G6paH4#sePG?)smz?hZJ#=B2sSj!jc9`(Ac~r8<ddlpnU$#C
z)lg%(eRl;jrfWK#qopGvSJ-dIeUwWI1W<Am;%4iHPTWrOiCnN9O&d@L6~oN8csg|~
ze9?`CJ{LfxW4GUIN9h4{y+ubE02q#Z)5qk|W6l%_U^z9mZI`92`)8Te6!2eK?MEw@
ze7%$WCI54NQ^_#8a_ON%eIfuFzO22sv{T{Ff}pJePXN5eRQ^g;{`Ka0XojqIDJNo}
zvh+^#qMkXcWEr{q-O5LI`oML^Z|iRf*a`J-Y!UT{rEYIbEmoCRs8S|(qHh@$Oq;}m
z(Ug~5+G{3ylciypr=5Kt%tc4I{miA-2v|mEi#pTg%B~T99yW6M4^F<W>6L~$mX_a|
zVgdo+ht<t?p=GA*FV6ao6t2rh>qS4!c7>Nv#=I-ErF7macy>m*3QdwTCh=w9txjZ9
zCy6229ZpqN?glUSm7!9eYjCX>31jeWd_{3YTCF64Q!qq0l^Hd<gxE^>CRh9G2Od!x
zhQ3Nzez5TPR__url<s;9#Q$U^nbnvljMDe?6@&dIz7xM-*ADT&YHw?+Ei^V&F!Dz=
z<Mvb&@+1>L<8==`SBdQ%7pA6kgt331;Vvf9(>@Mhrwj(GOPttX4LvXQwaCg>T41Ie
z6cDf*!s(^~9_pV#4Ff$&>5;1<n9%TUl`&E)@G}#WlTrg<sn_=6X^R^+W;E?ez{;^g
zr322q*xV;y4hlWfy*7Nbb)=$GYVZv@b6?F>wg4xd?R#>`MxoVv*~htG#N&N;TH%MX
z#_hT$-rr8sSN$e5S}VQo*9ppPe2q_dmVpw5K$#^!%#=}Ia(m*H<%abyY6lmPZs})0
zt*UcKT{?CSJ&}FU#JMhvidIX*h-bwDtcTuCG@3yo=>sARRcp~hwem|8J`vTv)EdR(
z=jx-GY}<teFm3WWWPtBV+q{oUbKt@FlTBA)2{ziLHXIstN>k?8#;lCSM$q%F8f!A%
zA8}`|x_Z1dUHx0^0lcri{oKApzq;TFA{w^}G=keM{(H<hzM#0LZo}1nCoZ+^8ue7h
z4|VFVF7dL{hTCu^U1r|jO|t+M(E6tUa<*1lKlfu%?2-jrHLiIVcM}1;QwU4#qaG#p
zcnKhh(o)C%O<U`$R%>*ahSRO;(f!_}sua1|03%Jbs)JS2T*)O+ljWxA%+WLe0JYiC
zO*g$`qJa+aeAqR*eHeoPKt#ot!hA7tYyFoD<RyuwKO`C(2VgDKmw3C}&PKsj%$!}m
z-_nYsv+vkSkF&Ev;BhzS4}(u9X77k+?Ps32rM8~!59w!P(t4aV)1@@n3ynBpzXyNv
z^<RwOHLFKB<4@K+HJ0!Tl?tBd8IEi^orY-d7Q=E?kP_72y34)?#cDhT(k*dfEi-BL
ziEr;g{Q{Cx3}Am5Te(=zHlQf&Q*RLR9$xfui~^9j_Q7b4WU+c*?(5U@e{9`VTr*4I
zN|=-yMvcJLRI5qv4l%uY!g5x-M}yn5hF(VdMzzB6MlJ()H=9yNY3x^W!#Q5TZ+*}m
z^B>t+j?7<f@XpRdz^UNr$m+5+6W?vp<e@}K<RzuMH;m?NWOM1iwJCD}z~o{HaSz7M
zgDhgnuadH9u3v^9_5+Zfcg5Nq=#nbDtZ`*_S}>D6@#IpZZ#6Wco46!WgUJKisBr-8
zT;?FyLb*%SK|I5d!{FS!U6;OjuPcSD*KsBQw{6_2#BbKLwAaU}%XB?!x8?I_y0m7G
z&F=D=m5x`XxgE`tvzJ65K;6GFH<=qd@$aIZ7VBfn_o+4k$h3Q8p<=Qv`dE^fu&yMF
z4~S1)343f&YfBz^LG$7)o{w?|S=Vmp8s)ZKg4v!uMEfKV<lvkjJXHGT+}Fm|)VE#7
zY!2YLetzy_){GH*hq<o}H2a<J=h(=>S{{HmmTj~&IC;a`(pu(?eYa~j9}llo2MS<G
z!3lhVd_GP2O-%;T;nFfO5uIyBx^u4^&@IP%d4trqkQejhN?N$|+ev*QYhX=d6ZOVS
zW~HA?P%3b~7kG8JtsarE>-BAY{+LggZ^SF#;+9($D@X)D!QX6Mjg{btjM}~hDvVJ)
zeJ2^+cM~2fUGCR+1EEtXB5KHwKPXgs^F9LA&5FB3UttprPo$B4uJE>HJ-&Sw-=9mo
zoS?o2POmLz=o2>Q(5CNc?P}(|tU@9EnJ)@`#=x3-pA@pBb-YUF8=pD%*{XORxUt$_
z;m~IIVPwkT8THM^+PV{<g|a0%M-4sn7N)4Es3!e*Go0RER|J_*#9E@Q6f<_u@^_bN
zKck)ntL9pwtAVocfP~7>L8J@pzd8BcM&4jS@pcP8CK#-}Yn1cUOpqz|k;4<JeGT*8
zfR&>ctM1`_xPryD;p6I=qfjG}FSxNjaw&%=7z}#;Fll#YL40*IyL`l!V>Iw|B75@W
zZI^$iYZ{y`OB%FuEZwZJX98xYAEhfZY_lI8J<*zEZ8FrHYxY7et|8CSp3+TOGuVR4
zVC?;vIJo;9{8&^OI;CY&d?GsR8jH)YpZOFSS<S(<pNnd0;@RiOqV9YC&TPTovcIZU
z<8#O~X~yW4K!AGw&P*b3|1PE(;L2TK2bU+dIR%QM%$3a)MSnn50ua?j*7sE>zK&WN
zYWzC$Z&G2&p3-G<YIAQmSxrw=dtW9@-z48S!|Kp*%XSV>vFK+j_<F#7%c$3y0aZv-
z!FaTC@&iqyj;P_B^q|#~8JQaZvtMq~Dreig>PoogYI*3^2rzJ`WN2dgzoZKEu-YN8
zNn#xW2p$BgR+o+{9l%X_GBK=<En>Ecy@g}$iSi1`fWAVt=ej?jG?|UT0i3ipxVrac
zZGC+)_}$s@o-vxS_m@-K%oKA|`8o$`TS{{FA}ZoXdHHBsa^K@J+k$W39yJC5taV12
zb(G<vICHUO*PaWgvF|q!B)<Z@{gIC#<Io>6U^67k#j3A=8uC1|qjL3kG424k@RCo4
z26cA&@S4p{o7S8hWhC$cDVfKY@t31<%BLxMzcJ`Ux=?>MV0kf8L}%g><}TTBIRp5x
z?X=!e+4D<T$hssLJ=18@hTCZm4B4KHPxdaoa|!0coFP?C3lAhkmWF(~0P3(QNfW*%
z2{)>KnS9&Y#^#s6y?c{~_n3sOfZ7nbytD5qBQh!^uv$CqLnD#f>=6s-TGm|}=}!Ec
zbT{h!fB^kE<7PP>sIC-V*wiHF&Y@TDtgoMXS?gN~#rFlG&p(BBn;)`g4Yh{BO|mX7
z6*;<lndvUsI6uWh3n<5^KYnO$Ilyz^%iUAq=B+QaSw`9AZKpQzOMdgg<dbKBqDE;y
zV8nF+D5-yii!TzP$#v)@vP*8Vvi_#OZA|Xx7=|WOtTfEE4b!1AFjGHv>n`*Y@LRN;
zri28H*Nh++B6Wxr$1kN0{=$-G#^l1WL*EiV!khgxzt(K(FK8$KezOlO<2~CV>ucOK
zH!EXpHFJK)4K34WG-mmYXFj0jhSy#~WHndENx#^h-`4SbuDK8|C=^knte=h^OM8qJ
zD20UTFrifqY)Y2$-=vg%if+UmD>Q8%f6W$Q{mmwgrODnZ)jX&%*?X3%!)D;9F2ID%
zm1O-=#v9;Z|8ff9|48jsnPghuhmABbp~p21E(-4=s<Q-YuIi7{7|^>2ZJ#NYt^m+f
zLMnLCyBia`c1?&nGzny|EmPX=<iC-Z-_y0Z&nadRD(7geKeYXA(0$?5@l%#Veso-O
zCKMCb7kUFNUo4r<k_-A!y4G-*rb5kHFJyc%lLgz`o%$x<%3BWLPn9TNH`MrGVx;xS
z0FCAbtwDzO%$VF`YNmF0=FsF!9r9$vGvNlmY45#JWY%l{`J?$0u)&8?)T1WDdW}-1
zA(Lx9*O~8iVKzRnI%qM97_gfH-d*Nx9ri6bg3%?b5YRj+{jThH_1*aOjkvAU6)pjv
z{ew9C>KU9xZ;&Gk!K;Z7Bfc7p>Kc5v?p)d%w&lZ0j?!d>gLaxj&}VAGjSux{n=vm^
z;kCI@4M8}<;cf=1sQ7t8+c2Wf4m8CC&@cyO6Fb*1Qjr23C~*MT;WeIvZffoUoYq(?
zDmG7z&ofmA>Lnv^rh&pXyl5F)HaYVW+oUwo?HzSnt1J5-5tF2sqt?6mVnc*MgW(<M
z1z*_-s~b|!_$wQyqbP&#iRdi&B&RrK@x8MlYY2`9PV-D1OQ*CZzJ9tGLi$L+Q8?7%
zv$Uj16t1a!Ka)7r;oXPpyLsv=ae(}&L3}oHHdCOb(OLdi;)t&2(qagcW7ZYjoaQ*x
zu#9F?DxZ%JHI}fP@Fd37!SoY#Z<+qA#;kp8+od6WjrN*U2{T#itrS1oZLzJ<WR$(7
z68vP13W+{U>qI?rU^CLp>3Dm^L>L0idlTevWo|}ey-X_B^+Wk`NM6eOI0xrqE_Obs
zvuC|Sx`7DB^#hUK0x?;)FdxzI_OK|8gy2AEUz5IguU`Xek$tB83)`7J<uYE?dtU4-
zK3eO}{u}5zDvuXS-o3jwd+)5d?s0lA$8F;6dec`!`rJ)9PRZ#3c#6VtvE`XK<3=6A
zL0gR9Z*zfOa*4r3$i-haP918w7lam`<JEOKBvGW1fJFctjN)RGDC|9xJCEdYnW|>V
zjq|Yf0Wo*okMYtdd+SDp;3QcpCaQZ>%J`x5auzkKm{_LtWD_6$9S`&new(hvJPS|&
z4xU26G2qobhv>-7ZK+{T+OaR4;AqV)^&e&g((r7ekTpF=u~>Z`-W7ux*GG520w=6d
zcfTISID3FR^>w41gVWQqOz6pcSmV$RcC5`T24UBGIO7BPs-XW*-lh23U*4sw;Z+xT
z&D?`)LtY!?oPMI^1aHN!mOi;uE;esQ9Np-B{wiaPNP{|&5<X-UJH!0=g~SrNsoQAB
z?-|`shK(B7tNu%pXx5B%;?z-tZ8>G#xc`Ry`83Rk2dltY1FgLF>4Cu$>00%G7ei+S
zuy_tKA0DrZ+6T^0D;<qBQN&aV;x+qiKqbb?PQ5k|P5f80aL2D|F`iz?3Rbw9^>#H?
zlsMX<b|!w*CXcs3?L%3LhQdqiYD8F=9gCS7>b<0SULmP&vmtNO?RUNPpBQ;M4@k-c
z+7V1coyvmA7%}SfJg<M8j17c%;zoDmjqJ7!xmpvy<7N=;yYY=u=5M4ieUBY-5q>xx
zWI)cFfwP%Vix6R^iB}y<p*kOe=XCvu3WpNVtSG5r_fA(kQ2jg0Y6C*o(Hj%z&m*lh
zdwcxVbBCDVVR@eiZnfSBnjvS?^+Y7UZ|9AmJ_o4A5ST%6?Po-^#c*2ZO#0p!t4ie8
z-&OJH8=8?;nnDRP^)<^^6`gh!O8>wfUe!?%RobDhF<SMJq!1u;%GMs+S7~V9Rrv!Y
zt22lnc9t0Lzju_<(et%g<F1gX*n(pq4nek<g5)i@n-xLBo!y)c1KW;U$3)O6LPITY
zL<Ud6V25wNBv2wTDTqmNK=4KGtWe5$h%YJhj@mcRfQ$2_L2F7Jh0pA%U^B}pxgY+Y
z+zj1)TW>0kub=@}O^P|YlVzLUV7*F3OKrQ)Sqa7i^|WW{cePHbG`+ICU@kHs6<-6=
ztV+ZCd)lEZUGAG2J!fr#$<C59WG90;diC`6u8z+UV>D1|W|2BUV~gl-cic){0(U)2
z+rfC@?2P7+#rN-{>Fz?lr}-_dwwc3<YHNs>6s`Glu2z^_Wf|>RiceDNogI_wMl!Ga
zC+;vW5A)Wak@R&%HXY8)4~I(c7zq%Dkr+QJxRKxNde<|3KR685=_gzX5HW_`<7w~Q
zi}!hYpaBLZF4YFU)mvG1|H~_|&uvk>Smu>|7ssFe(S&bLIE066Iu{ieY7=i=(YZBz
zl@UV-0h`~R;q?<8j*eR)rj=M^!6n;18~j#iX{HW$MK?aJ=kB=$A8IP~D*alq=3}^X
z_e`PK>>G9Rp3e-ej+#I@|4*pVni$lU2tM4D%_nYsSOuwdN%hmqJDdnb=HAFKzkZ;1
zz-VA_VwPxlJ$mQl^FwSgC3veL3wHIMi7ti2Y^jsN226^R0ZMLZl3gN7n@DE?$e@l^
zC6m{#aQ-Y^AUrYqNV_p~N_Vf$gK^Mom<W<|tvWQtzMpEp#A)QgH%P6!Eu(|y^bhED
z@X_5!eR0HQj68bG6Jamy4`^#&4S8XPrJGkRlQR02#`^%7hB!`-CG$eOdABG(W;4xk
zuEl$^`Lcr;><3F?(Nt+X*C{0-kZ`e~gwz0XHYa7coSuY0>fT_~s1a09l^!x(DNio(
zD%HB*zB@`0;&I4Loic3&-L!xpux(g{H$8Zmq2u=H`G*f56fFs{kVeSwQy{sd+hkXK
z!UOGJb|U4#_Rx-4d^+M7fV@oS1kG$cq^ng<EIzPRAM9J2Vw$1r;3UyLuitNUze9g(
z++c^O=<1YPt+G}7rw-r%@%Sst*W%zw!4E^wmmb&6j=12FeK)J)E?axY3TWUt%G3tL
zh5_`xvul^sj^N3f(#3S*w?Q!si0|ILS4*q-T1eOa%zPa<PKQG`oWtiGywL>!U?G-~
zj7&_$cFzFbj!Gh_any?~O%#Cv5ColB;wko^NADYjQk)yanhdi6d3w<yhont{TsE|f
z1WF_wONSEsA_A7@RG59tIz+VU{t34m8+ev~PYY@dSivBcN(Ccb!m<OESqnJ6l#KLt
z3o{7bgl;Qn^^9xEZE(9ARbq$(L)J);?T0W!eT<lZG*+tj+-LK9;g9W6|53|?goGj#
zFEx=N^$X0+5$J1&r5RyOm>qiVEg?Ol-72$-IrbU{xP`~Xu0*QC_HMJ2o>*+$4C-#N
zsFR$4l9Oo`pf<PhZfx(t#9=CzD9R0`XWioeJehH9M0`esV_ft>bkG-Ebc)AjvAqd&
zG0~W+rxM8n;14^l#Vjd(g_6`6qRp8T-T}aY9LM<0;_{KS(XFwbyzv%%MF{n%e^xk5
z-Hf9+BX&(09S{JN#!}2m<zg*x+QSE9e~F=0pgnja&0wO(x9rs^CISOAj*#Cj0gtcs
zQ(qejOSvmjB280Gb2W@Yq^9M&QzJ+PfGTPMacT9E#z&?kjl@uW03iwhko#hBN{06>
zS5v=np57)HenrLbQV^$7+1W08lw|_qi}KFX%mV2f=g11r5ddwsfpo2Lb8{TfWiha}
zG*+|4dWYmNXD9^*z(FkyT7ItWdsrx|sVC9r3O8b<r6V*g+Bb=Op>8!2Jl`Z}(&815
zh`wv-mAm5Q?Wg6p+YGyYyXE^{o;qRYQc~XL$y)YkbBh+=WNiRL$jSPE)NBgDGHH@D
zFIp`o)iO+m!(r6OSykoP_yd)}rB7WU(r&^yKs!E=mW&#(S9uA2K@Q?P&iVO^g~#q%
z@8Mb<fWaAJ$Sf5Y_bV$8PXKAun?{~2O@^fw!nLHi^-_YZVX4g*1pA=1fVH|MnlI7~
zGg6icH-h|fWPldyciXCui@&$x@(7$$Myf7&3BM#)t^VABr4&uV1@{Ipd>aM+n@?ic
zz7K}`0g#n9R@Jk7#`cXP2^S*S8L=#oikZ~b{7%kP#`K6~sCSvebv8nXgPOq&5Z%A&
zJ4_J+?+0WXi_35ZhA+)W_T3&bTImY+z#K2iLdhfj*5s3u%QN&nwdSiB=#rnQ5_8J9
zlD+}f71d3y7ZZURKvB16Q`d_;5>tD<$F}Moye7LNeG(IoHfcnFFQwDXwC?xuNLbwi
zs%cUAgfNJ8RUwPX)zCB1=Cm*;eMneOc9<PAqPK#msV&?`8COSQTpxvK0ebA$<|nxX
z#?@}$B%<CHnQ8;-*!ziQmOSVyZ@9^v1mX?pBgBHDOf7igKPsuL<Bi0?YAlAy2zQmM
zbv)20hzCeO`k?kPsxDNYcu%+e^u`Cd_LE^pCqPI;>%I4co7zjDdp;0yO+w<6X0+f*
zM~+^Sz*Q1L{go$tL@XV{UkM;RRY%jnK+0sfxZ3*)S+mD`%5QHtQj0$ORxYVW&gzOA
zyYQ;GP2?odz4aI({c?9auTxzeBDn&Ipz3l8pAHjL*gwOJ3aH6evs)7`Pc#8^(U|=I
zjV%%j`pY0ubYHpl|MKh^(FVEtj+L*7)EQdCQSa}(P#s58S$=D)@!?x5RLjCgijDx~
zJvf|f=kIy!0J2*@WIX=~SVo+Wlq>x?#!|}IDt;RfK$jI)%BnpC<n%XwWKf~PDkrJZ
zjdgW&JcL3tS}(=nEx`E956ZJttE`Ai9jRnGn!n~Om0Zh|wUzqv+!dCTVGjm_z;k`!
zcS2qfyehpog}3X>iofe^1vH0FP_e%GOElKvv;8E1>XCu~UjN5!c~ofS{5}AG7q*T1
z_+FB!0CE5N)^u9h%?v=u?<AM`qsUHEY_^Rd@HPP{aDV1bxim|=rMI-nE53exSUW4b
z_mlpmiAf9)$e*5TOz8vaDwZEzSPtK<V4xPKe8TRY;IfdW(+SWvhHvbxM%9ytelPJ-
zP^o+DUM1uMxH*cDkk2#i<bjN$rQU9bAG*zd-?jlX<WG6xWq?7A=G9aH2(qb%)Ojtm
z1EL!LB()R=3y!OR#Bk;w>el7w-&cHNP7pTR?_o0K>+JJV>fKd1oYeji{RbFK`XT7`
z=+Ps6jP}-V80<&;N%@<z)a>+Zck(656Y*R#hQGsJ%yw4{sOK+r(o?wk5W3gm<nc1V
zAXJThVEnV&_gT_K1_&504>T7Qe@Dty>Ssr$<a=ay7wR$N`~=XXv)`+{6ZbTywpP*>
z4`3XJEDP;^*|^3NeNQ7`g5;h_ho2Zu_rLM63bA*jW^H*;x6xNu#nZ9CSO@CVZf~*Y
z#}>75Z|KFZFe6h_q~xeTc}pIV%En_fZ9QT(aZez%{jcv_(IK<E!X@Nfa?<ycR%aj5
zy94+^#AV|=C<$;{Ym0!QNH?{U!TM59nz#-o6O}y}Q%xL`KR=jo9ulfAYG}7DYb3iI
zw7Ks6_ly*(sVe*g){Wp<Mc7XMJOWU-CLGHze^#Abt49Vj+ibgZQS!dM@m{JDvF!!K
ztT$U+VY$d1U9nK<8K-U?BZ0pTA1c)S`8#og<Y&1DmuD3{rWiQ)UH>2VN}-@ik^{WV
zMbRbt|Md*PvBB_}!g2fM75#_*(Y6)#*R08IwlNSku&^Y!|Mfl<=J(uSWrP9Ocw6lg
z@#Sm(c~BQD>~4hoX3&vA+w`A&7RHYRe+_Gny0$~`2S)IH3dS)|@!;Q881fF}x8D4C
z-+nvpirW9aX(b-?02QQeV;lQ+>ff+msF3PC2X615s)oMY-5mcmzg;kQtWQop<Cuh@
z@BQ}_%75=)D)3960oRS$KX<MP{_m^j2fhYhjR)PVKYAJp`rnIY5p@Li>ibU}w&N&f
zxk~5%^FHQRlsVQcV^vjwiz1l>ifx<}zppH>lTcd_{=JLbi)S1QWI3$`z@ua~@y-lY
zO&OVi4k6P2`dCfy&F^Co|1RV7#qBFKWh^P&bUcwDtZF`cMm=+iH&i)Ft3Tx5A$8H*
zv0dXG2X_eGb)cCDRwjB*>r?-2!v{Mb>u$oD@I2lXQu7!1D_a4ITB2PkbvrkLc=)N(
zS^urT0chY(4dch!L!?i`I?H@_JK;c4+=25{bg$KId>zHVAVHCfk~6sT?58$M1G^N%
zNygYI>hYUr9MQ_Y?icZ(l)Gm~rOMsOy|_XB4u>`SUDxeO28)lK-|vz^*h2;XGfLOh
zTt$ZN_wKh!Y@@m2Mmj9(&0ojSRX-+udF|l9J~x75td8vI<5MJg$~R+0Uy`wjpb5u@
z04ugk%R^gQ-ii{%sM8~6gG}Y#<c$kC3RbTF1~)%^Joa^UCjOI0`I^CN-ucfpwSvl$
zT=r;oXExE&7Ehvk@nV7jHD_W1_3eUwRL2J@OFMfwLf;F5-DJz*2Yb?iZ>Z%go-yEN
zcW-!ItCGjH(LSjE&%B|x=8wLu#4bAeeQzH!-t?3gom=S}ET}j=>mc$RHToX42mV41
z#*ls5jv<CYi6QLdQAp`gIc0r3AO10ecdm-+Ou?Z)dua6SxTL?^sNWpQIonoqw({vt
z+nQUz$cfZ*Im-JY(kAEE0$;nTnUb2EQht8MGQs9BUxe8Iq|}w7)Z}k{l7EcC$WbUf
z5d532MElLV$ievaqhazw=?_abZ4SA`$?oUO*>KLOw(VI&P%Y=popwB(=y|_JK=hI(
zB<g3PVtL3I_@5ED!t)|1IipVUhwd}ISX=Y%xiM5x5&uMdrd?(1SN;66`F!7?*H<d!
zgEL173fTVSm%IACNY|hQl~2U%yO(#w+4X%&O}@RsKYc7m(fatmEqKL6k0&g(N<GP}
zCF3^@^jNt`hc}U_=%gwhWD~{FcCNAi?(7fWc30rQ$IPhrBags7Egy+uRU5roH{lCG
zqX3FM_fm!i#gmm0$R4VA=Rd=e$ujr}#&@af^Qh@n2k~c6`3oO<CyPdf$;E>f&o7*N
zajk)cx9~Hhfa4QRv`wQJ9WwlJi5Edu0~q~@WL<xHNsR>>xXL=y|6I6ZpHDE}0!H|@
zh0VC2Ce8i#7+}CK(~{Z?rT+J0CCDc#b)&ICnw&&O4a~cV2LbxgYC(b7<hH@dD{r2d
zp%aGUp2>=RH`*kiy`T01Q8C8*AonU2HT5_Q_Q6cFp*H-^WlWs)B%+ii%ymE4-2Yfd
z;CexEJQ$=PycMX{U?og=vu)5Md`B_bLntVv{`yGQ55gUi>tAs7djDl&K9$_pTb`D@
z-ea4|>+8t()*`2;mh*vuWQ(^tR!KZ$D0dT&?7an$gm_>3qnh(Fq5{s`PaRrGq}F@Y
zWs#z0|NJ(WA|51X6C87j=k?Z7ZgXYTI<MSoa}%#X#^QMJQwHd{JJo#*MI<l02`VC|
z5U>9CvBxd5eA~Z}+;mAECtEM)mvB*CV;<6?Yj$QhdO|}^?w`DLM4Wgb-#_D?1a~~P
zkTPK=|7`VvYJ=gbf$h%?8WJN~9QM^&bQ<xNuk2N}Y1~T1479rtqM2U7>9l*;I>N|E
zM)qIi5oP#zpY<6B1XbIXC+MG9rWYt%0ml*!)2hpU#acu=hm;%B2?k2|p4M}2rhjH2
z`&X}trabEfT;LC`z2waf@eZD2>f*WCL0Rv8*a3>V)|6m0K*{${hHH@V$ohp$Sit|h
z=}t{qt;9fn84z^ic=A)Kt-s^8;tq)_>MLOLU@K+^QvoXlx(1?i*24dD9XnH&Rf;)%
z(MR<u7+nzp&KVl+uE*wU>@Nr*+0*qamFm#+_K~b!nf3@Awp8Z6Ee%yV>Azbm`T~Lx
zj<4cHirsJQ9_<AE8I8uSOho?@7To(~IHkXg2Uiy+YLLd9YwuKt#b3MJbflU$_rk+x
z#&CNS=@0+yt3qzN_|?P@$F5#M#_i;Go@{2-*Z^6<E9WQq(-Scl#kUB2)rxL<;h$tW
zY>%9VxVftp><0b2<2uV%&Z-oZxL)Okm|0TPP{o!Hhv*4cPR<YPkc~bmC~N6nRseSe
zGSQ!tllp;gBn-<mhxWP_m`?fMJyZxi7-AAK_Y>18Z|`!PLk8V&B<7uMHhH^!mZ=ye
zK-}U)s&)SC=wJs#cs%6O9;0R|`Ln3oUXP1KVx6BPV{R^1V_odu)-${_M_5zJ62wh6
zd)0sEqO<?tZt>btYt2v^T~Kns7X|xrPJ-%~o%1RKUE-ZH+M*L^1rPd20qkR168E6|
zUr~UdO0BuJX7#BS-@(zbGfP<FGwNk3LpFt_GFyJpYRVY4PRvU%uzPClc6Va)$&j(*
z&`-Z3(cL{_WghFFT&JIWm3VSZ|M|K-&p`g4tB=kt7bZGd+HbPdLc3-+?wb|v9GH|`
zgDh1&&PluzfT))X7NpfEHXVAtbe=LBrPu!cTB~W&n8?3S3gb=`ooOYGTQ>jI0=*iT
zh#<t_nfX%3aF(I0!Q`6ItloeQhCu&kP`v}eU9^2Ka?ZkRR4w=!@-ugAY3P3$e9Gn-
z$D#5Q`^zNC4}CDwc75sGVU5^wB530Yb5Ex4kYPPRq$xDZW8YtSyrC^N4yM@T%YW+8
zesz}w*Cv>Y{C7h_G(83a@u0kOQ^sm%WZUnjSu%?{-FdyoEi6wC1N3PkgA0ZGv+9<<
zcc5h_e2994e5oDb`dn#CxTBDu@*3>WbuzYpc7GHP`VssTe35*^mKg6Ty?dyJDh*m7
z<I)-rcm8%|0bV2Md&)A(d{^J6w&Z@r-;#mVVSUlLqdzlx^x>Jk#Q!cyrH1sKA^d`y
zt|=zD)qxvQLCSjHY*p+r%MINe?2sXzXLnPix{!TW!fZ3Q)l>)L(tT6Ez2D1JjDDBl
zFhI5oQb-p}XqbIDbJC1%5&#%=t(Px@fjKG0NG6eyG+xHY%c&(5YioX@V1gGi3nY`j
z-RqJz-U1UjGQiaT0_J4WrL1ND`PWy(lNL`d3w9_sodGQ6<d5mzlTPfuW-$C>^tjmz
zv|B14>@SRJJyvrg2vp!QJi8G?)`qp&>ESGm#o%c{-dVTs8H#2*LqTBlrXkU%ifNVv
z#*WMJq6qm<%2ajP=UY}XKxJO}Zu~YdzAiZ$8*p%^Ww(pk>uj&@rJmkLU`CSx7zP6r
zuA@ca2`>s0kBfi&=mMzP*#E07YuG4AuiL+Qsc#J7FxXuGVqd-y_$8b&?2WIGnJZCq
zhQrUb>C9HskuzM-3HGff4rZ+UyQHmjEmBG9dCTWL9D(n})!k%lfPNRF>V!MGu~S1;
zHKc0(B{Lc*{omQzs(O3Na`>#@%J9mI>%s%WnC3udmm=>eeEE|GEx@lw$ZQtnYZ)6~
z&WuQo0+U_Q*=NhVHr~R?w(;$(RAC7X%R8hYUxvAXH0So^Bl9<7W@5PMx{lCI+eyrw
zXj9hdnhcns-rLi-j3dW*TB0^*jf=rbL4TTKWH!x$w7KaO3Xf#-1vi!tnZexL*qzP|
zqLDE9&j|_48#|b_(I?`mKQCuLB}Wf7Cd53p^zIT+Ud#egm*$_GvcS(kOsZ5mEp)ZC
z)5rGxUV|lHHXv?s0|fTAG;uJ~z2vByd7A?}gxN}M0Y1}{bCF7aeDD|yO%L*WECIPO
zLVnNcXvcVO<kVVl+hy>am>Yk;!;~;#bV_hhlliBxnLw&K88A%Gax~}b2u-t0DYLb;
zO(ZQZ;*;M^u!zl0#pv~9pIo+pmEr#fSyvShRl7y$?i?EF?(PQZ?rx-|K{^y91Yrp2
z?k;KRlI{*gx;yVd|F`=*4|C!>JJwozZ9ua2WUY_=+CQpdJ90BQ>`A>OH&~ON9v?+K
zq|{gLKh&0r!e(|d`4Jw#)C$TuBhG^Q(&qzgaok92^3=xTpc0Pvp1ZB8tupK(z|<&?
zkb}Bi00O!JJo44UP7gq|vkf$UC%!7k8vsbe^KkNdCYIR_9TmJ-M`}^RKBfC5o0(At
z%TxZaIL!11=|Fk^;>yeWh`2gvBed)n+{eN0fOs=0EMsEYPvYbyv~g)JW?J*EG1Ouu
z+Z}F^0_7d#U~eoSnIe4^8py<vu5xEYl0-^DRa8{yX^UAyUU0f)0&^Z#%;Te2Jb#Az
zjy%g=pEsMkne7LAsz`4J=%$_siY#n>uiUV$D37O0_m@ELpYUA=yoAh~bf4&|J%x*$
z!3|ZJyu&rR{FgGOk8A#nXg{sEnc3Ik77&4mooyS0LVlbb@4u2P(${^86(q#p2>{O9
zz%YFGYY&WwU8^x_GoD#d>jTir+*;4!)ru%19f#kG&T#qy0ox3jnfduL7%afDqN><*
zB9+s|DZBv-_`(}`u5v(l<;Z46_Ars|a4fB;c~KuyxEVf0Wz!0Z#AOI2Yz9A^i6zyn
zOug;u(1F68e$ci*4c@LTsv$d_41{pF?)d&8t7CA`+}bN%m<6toRD?|%=zK!+=9;Ro
z8|gwhkbyx@R&9cgaiI&8LZxqW+!+MI##Lsq9lN=oPl2Dd02#YhZfr<=^{>x*aMl1?
z-3^S`|JSYqQW%ogp=Mu0IGNf{!1YFe|F3;fTPKywnGc69Ov<C!;Mh9XU4wmU^Pj5F
zzaU_HcMTli0(Eu^|9)p@Q#0EZyNdKvuWa}CFa1LuUF8=|^93?1>tY+s?7dn32Mak8
z?_WsMp{fNn9))jKLhHr^%E~Y<0clq-;OC-ib;8keqY`+p0rvBnE!C2zn~EB|4+cy`
zXaWGxzh`rI5t1JM3()3I1T}T@cD(DVjqr?Sy|P)gxm_GCWGlpjdeD-|xoS$`EP0u7
zDQhf;h`rNTFZZA0U{^{1D(tvZBXel#9LWWJ@eDO8GV*DNGyI(ijGf4#Q_t#1Z+wNE
z-0^y-280viAS4h|*tl#FX-^;j%p&-vpSITZp~0FIdN5;={9N#o=MOQKec6W&xI{Po
z(TR#ovAvFVOV8$1wZ{6{Y0)MSVupDz^LaJ!X01Y=zWP==?+`^WEP550?*sWa3R{^t
z^Oo<92Axnwum8vSPd5&SMe~7eCk`n4#&kJXCiQoH2|WlbGDYh4{Wn(Ep*MZEpAscw
zQUsjU*1=a<Syn2`f8Lw!H2HQi^>cK%m2Jk-6ler3+L(8_+{c8)2M>er;9&md&+h7v
ziyex$rlVxcL5-RynR48R^m!?6Oi97h)}zz3yT?}6EIIZb>m$LFt=9qkidf%YQV#V)
zqem>n|EyV0)_(YDgASb~Wcv8?n8Ixg&N_pQjm1Q3$%+c&3)p3u;%`9}W`?fN&bI8O
z=TB64N9i)~4W+60{mi~C;sATgLL8I|ne(^9HF5F!r=hO&I+U7O1ChWPmq~s8a~{(f
z7Js7Y?xLW$XS+uwc6ns;YR+S4G5OYLNaAl!Weg4^|7x2aQW}2;lfpM@b#O`zW<Ym5
z%pf<<)@RbS-J6d@^YPLp_;3Xi8B%MR=){KD%KaxDY0;XxLF0hBQGqobQ>&4utnlf?
z(BgFeF(k348;}M@h2V70c&b-jWf+?v>5sNxkYrH}8>_1q|A7@K&QtW{^?1LB@D6$U
z<E*Y!3rzI$B9@cEqEVe#f#~IW?gat&7^g#>(NfW0kJ*v<r|;+KpVN8}naa@1*5$0D
z*G(5w+d9wsmAVpMtW^Ymo5;{OI-8xWRhht{eg9F6D3^TJbWGelM%1C6SFd2QkBVgQ
zuioiCrCx!l*C8rUhM?|>8ff)(%Bbrm3;?Q7TRporR#J2-3%v{>y62s$cjB^4sa95o
z%UCk@T)~UmQK*=@oe19dTZg|-J_uAFeD~KKfZJ#n3`)vi)<``P+_v353C0L?x6$}g
zt6I-y``?|4(v2?7K}BApisaJlYn1?0%-u#+SMoUF$=>tI!9!OsbR7)dD@>sF7(;h1
zVPS_ec3j`1@k?6C9%saUSykv|U}9II@%@flJH9RxcHOR?>6%HfCCtQOmvRx|;sCVv
zQUMlSal&(ZD8|1Nc`6ROJ_|*1_#^^h<+A8|1f<D$n(e20%Ai%&QG?wSZRuB-fwM0Q
zaa{#D@KY+;YW!1yvmvsgNU?KYj$KT-fo)q=3gY37MYbgc9E?tv+_}s3n^frqn*VMI
zed{P<mKB$%*=lNOz_=C1@WB`|dgc4F-k!SkE99YlID|Ipa}a%)x+h{y)YJjCFl!f&
z5^c%y5?n7vALQ$OmS8k@q1s?Qc&QDmi27R@n}Mi&%As?AKAC9KIL=%Ub)MWi5j{LF
z$Q3V0k%;tTb8LpP2f9BbOe&Cd7bmG${+vJi*@^+;EOVYyn#LXBak)6*yuZ1JFGkBM
zny9{C%z&;FGAsKB1%g_to0$7dY!YNUvy<XrGg7mER1&}&*(jcvnHbP~Wm`^0B$FP)
zJLI}~(95b~S%vp#H4Ns@^V5@{o`ZWlamvdZJjMr2@9qo#@<l-?tsmL#@L<Ah{14`o
zjMH4Pbp->cq{@hSQ?v>h+-3cyww1qVSziUb%iP=FB8s=WzKIPV(ykUq1Xm#as*&pa
zH0)KalG@<f0`W2IxmWt?V#)YElbt0Iu$a&vas2x4Ig`*urEL7buQPp6xT2NefVF@h
zYHkm14@7`r@a-)3xKKOq#?|}U0eUbhC-t$s@qlQh)P!#HBdWgcxnd8E7AK}V`*TY8
z<Y)98te08+^Plw@h_F{MsF52w`B%-Q*2a}d1?zi7pjzu(^E%IlJ<HKRVKky`=?T5Y
zCL2KbMzrm#VgIG_kTnxxXWhP>cD1fG9qrJ2AEIx^PDT$O>=i%7B1!n~*lk2a5=38c
zD%7ChSlqxo;&wF)2d|EC%4c0RejUiQ3G#9t&-A>l%86Np7XC~;s^Ph_{eW@?3`W%7
z<Hqy_Z7{HzKjqhtREzy>hS|Jx$lqBc$kq;+?9KTO+y{-GX1G<FJ>@=J<oiMDkO}yx
zMn4~f9g-EA3hrO8C0m&J@lUtN;-6$Hl1|2?g+iMZXcQR9Kt4}BvnqhN{`DwuWF=vn
z$1xv$uNvCs1Us=g4UB_dE|f>~f5@5MWA@`SZB@}`2;KUeEs07zoTna=iJWEmGj_M#
zLvOS%$Eb7W{Q*sn)}X><QuOT878pg4{0q<G;9qL!?lac{LzG!pV~Z^)Uw57fo(GXE
z3QI|XN{`M10yQ#(`gdnjE{3%hP0~Da)jn6T{<hzFo*X=<&Jt-hhCqYyOn&pIyQ~5N
z|97SEk=1HT)VN1iI&KwS*X~exV38=yvaIkzn=jQ5)lU1Vpw64O6Aq2W9_OZZDMuo0
zguxS2hkHe$s}D;)#JgL`LFms8?kqX2-gAgRR$yndmBB~X=Wo?hG4uWhr7m0psEU1%
zi)0plynXw($G#KQmD~9H@{;$gOqnit`l>-w-f-eo6$2SUQ3s-3Gvk3@xlP>188=p+
zch}Gpw!qXj?*!~&z{))LH(-^4psxGy!(V2u_Q*Z>{|>|#`SU?pp(GhUA;gbH8ZwO@
z*vdgw7P#8V52X4dUg3oYa*YFtNv}`*1ElFZ?|4we)rG8Kfo}at7Q3H0**lno9jl1j
zx>nTB>({i`?fBKW`k2s6N9u11jKWy{<t<5?Axg5JB(7Zk=fv^{gr`26yswz>WvtXH
zOQ&qtAFYdtQrQF2iVs9bztlEmC+FkLhh^g|{jaa<?J-!_N$bj@e2Z#!cRcVxfhr4R
zDQ;+Nccys&hO&W1SQGGVSAz*f^xr=%qj8N-&MG9IK)9dVRu~~Z9j%-B9^j{x^H=wa
zA`^_$afM@l>YYxpbR*wz&88+E*fsC0J@-)XLzO2L!KDVjo$Yfw9hw;dz;APHX8|87
zfgqnic<naWboaRU7=_=>BLpA}TYgJB{49_9km`SYMR%h(g}LriLi{r;<JXYqyKTDL
zUD5?kL`w&ja$-M<sY>}UY<d8Ks~^zNT_qNeACH8J)6ly)*FT|?H)(~`eU_Q~b!~C-
zaYQHF-*0*-wEP3S_YE5P)WY4fu<3m~h^>tpc)%mf#=E5a0zh>dR-r%xU9SUehD6c+
zGE(+}<o%~<A`Ki|LdYz=Y%x2EjCm8&)$5JL?dV!BjXk(4Kq#=4qG=p-Bny9GaPkfx
zVtgmjHRqdWGN;&pW6?#U%|z|p8W{?t9NpByki`rO2m>ToEZ=%rGVqCQ^k0y35YDZT
zA!>3rHwEJ(LynsiA_>?S!rofW*vTpK!8DE~&_Xt0e`O^8MAI&pKLwPG>~5AnBDUgI
zc}WGQF$%TEd^^gh4XaEQn$e%lb$J(kLficod%VQVFSsawghrC8FG=sd`S+PJqr>8@
zw%0TU8?jWF2xb(@mxg(DKs4AVoECZLGPYKuM339)6r>{y39O*y<tyNuyuf>3>>S#`
zzhI?!i>(jLHqT%L;&KK(RUR7XeEGolBsK}^vZJa)kWl#tSvlwlj8et9za&dHojrI#
z38tfHdU~EN+_@*IVon4{&EaV^hF2_=uL`>F643;xMy~}bUY{!zK@2>7zxl1J?waS)
zkQ!F=a3{8iLpe7NrsR9jUkdL_hyML!0%l|S?>`6<)bbWwIaXe**d=dA{s5o(J0@05
zRN@yM%CC)_L_JK)Q-n&zBJI`>yx<Qxx5&EKqPv+-hnoO?d(0|!SHBilagUvs?e+s<
z<69F5+N>lwb-lYQ$&&z@&o{-D3Bl*T_YzEnUiWnXg31SAEMT-(CAR(fS4(w%F~1*O
z=m3f#Eh0WoD7zTKt7lt)v0!{V1A(5I`b4e2w_A9`Tu=Ud;<Ry)Jcn#t$+>+22lWk`
zju_VaGrYA=clAFKq595L9nRtjj$$a(X*Q0Gjmg8EjPuxW{vs%Kz+t9B9#dCntzhX#
z%{2UHVf<#t3FCYA>f`Oh)Xs~rNyeu6m<SVI!^s+L3&Mf=WgmY9(9p-KnL$EQgqs@j
ze-V_&Bw=9bD#VuD^e$25#p+QwfXuXt>~f(+X4`aP*=NLn6Wx2S^pK#rTyNlp`Sab+
zZ_!o>xvnxVYo$l*<a|OhcKCG$pU6=#{vDp+a3H%45q$y>rFq5q<NVMdEw>nl6~u?@
z0KF2>X%%+#FBEK~vJ)XEgo=)hbI2UR9^sK%7Qq0b>Q=bodIf4#Rq7|d@Sp!UM>A8?
zQKE_f2R9y!Zs(!EU{+H2*yqqU^>krqEsgV^a}MSkch?a{Uo@^i%0A+O+U?iPF+TSb
z0^G3QdUL;vtlN?pIS>VzgZ|b|uM8tegP8177nP5yREOX4{cT(L3!LY(Qx*O+v9jsa
zKKH_2hFe`BZK<dD;l?_7{N!d-q3>FM43yV%dj#F;(TAQT+4}_gbacE=Xt9U>D7oa9
zkbi@?gy>hHIjFtcu3prDXS&m~Cz&aP3j4ZofSo89NtjRStwEfWIm7qAvOd&IUhTXY
ztF@mjjp<rYna`Q{qe<`s7nYQglFx*Z54-<XEwOQ)@WepdOzaGYu)yeWEZC@$-2ET5
znRAn`A!f^r;W+f*>J0mB|NFyOh1e?&Tg>0pA*!BQr9|S&q&pwgqKZSXi_lC`%yjC>
zxO92nN9S`NQf`-(L>l~(lofQpLzgC@M#V{R$oU<G<)aa9ly!v|`17Kvk74KcKp1ex
zyEYrcH4#y${^Q_a#v=~GQ#{nsbC{fu|IUoBY%^e9`R%QTy+7x{v6}7G;Bez9z2xI0
zR^uP)u3wUxqwD7pXl?ga!a(3V<#Q-?G@#0qv8Vc;W4rC0*#e<IU0W_KD)7WYl@3|o
z*!N}9BO1YXf>6m!$qi>Y2FYA1UI#a7Cq<8?P@%Z;8Nz#8hMbf`z<E>vGR2K&;}Uh$
z^YUK_NZ|ncFYQct7)3I84w*JZzv3^ILo5WG@9rI`hC;WTpG)!FdlU3BczLR8C#B?g
zt9l$i#OIEaK<uPjd=mRhOVW)ELp#~>D8<w<gTv<S;3aCVavl4=b(M9F5{?8ij`h{%
z3Y&dIEzw2V^hPo+g7*9dmrPmFkF8|6;kG!dORsC!aL9nKp=x(|!kpod=t-X?5>#N+
z4?18(F!)Cs^rYXbq9D(OS66aR{jMd^`qfjeCawu1obhZ(<1^#PH6%GJS&7bg#@jvR
zmu&viqM<gaLBj6^jdJa1LwT}4^SgV6KDZ%!353o3$>WGU-=nM2)d!7Ay`S%!ep+@r
z;AzJ<1)F!ekz%q{b`K&R-iOcUY{3dD2tXBZa9MJEr=+fE8f=6(+voyyzl0u4??;Ht
zK#ET6A*jlpcheqpkxO7@v?~LiV(HfY8+W?d@B^J$oWu;ca#rd|i%4oL2}cN*CKoUV
zg0b7^#<@V;mpqUaHubr<cZS2YGOoX?7dJc>6G2SZ{2LGV^cyh)!J0FJzE;krV4<P9
za@-g*_ulE#1rP64G;c0cJ0n>?A=q!I3q?oLhpX2P*Z7?t`1;Tq6Fkl_wK!l{!&5<$
zPX+3>Xf_i#%3TMwd4QUFz)WYG^0<)Ibz`eDo4gT??*8I_bS;=+cQlr~{+|Cq*I5+<
z4)ph9?7?Z#c;$zQO!iOS-`%gGDY`UIs<uNhV!us{VLguj5jEG-s0;OzM2s#|Q=7NN
zAk>()aN!-h&D}Z%0yDRDWjgGkpS1Cr>{dzrd&p@VIko+20%#XoLYZX)7d1y^;jXvq
z5FM_}?zXxewXl66v<%Qr^-^D5ZXM(gVOKn<A-)%5Whq!e!aJ}>-|dTQRz9KI63y%d
z9MjYkoct@cgAEGFQ6T<?%m{jbb^e?L7PW87;#`CBs>egqfx0@P56gXfbyvvYyJucu
z7ejx;#O&byByh=RT^>N(THgUh#38rjFZYuleT^iR9gDFgctozi14dZ6SKaP+ZNbCc
zZDA`r_r2a+8^99=<CUuiHp!wg{@OJs3n7IUF`AB;v&CWo{B|4|Liy6T<}4n1@l(#8
zMK_hX%1-)eNs(?ulHL2ruZdLR<2M<B5bF4^Y3{+Hu~D6w?3Sr_hgusHSokVa#?{}a
z)kJeTmLMaART2X>W#4*PO6p@KRC$vwM}J_FLm6&ILdI~`)GJCv(rOq+Qlun72bQ78
z*vCrj)#vHAn&0qPp7qNQ%AJrf1hsJ5KeFD%_WzP(?MmFZw?rBn2gVxOId=OpzY@RJ
zZuV3;=FjrbEcMdre5F^Aigy08{<J$466RH8Z}{wPf)}gHP2@ItRVCChc%c7#zBq4j
z3~Hp1`h1aBYGlN0)WaY-y*hR0t(#SZV}T3K8N9btVqEbg5^AKAKtwlWfhRQQ5hCD2
zH8y~=4;9?{62dWRGMlDF5uw+N^JUVq#hwsxVCs`pZgc6;i)wk3XAC1MkrPWNtOdhJ
zwU@Z3BFX&6^`hni*aW=ZBN@0>b1XY8MH>z}g=2)LuQ%}v$Ck}r!pNjHHazbG&yt~G
znU1E~Jv1;H|AjIX4c&dKaueB}C21E}dfmAP56K!B5&hXHO@Yuzs{k!F{?s|lsG^Ox
z%eC^=UkW@(Y*A^eW%r?p7w(;f4g|2AX>Wog>o&0;ETd2TRF6GMP9~D<!Jq8V2F`w*
zTT?$4YQv~%Z>Y`nhaW7l75sKg2L<rikKWvlOMCZbrfj&Gzq6_y@wM%~aktZG<G9d0
zc&wtT!P~PH#M|3uFYNu?tv4Y4t&MR-dK=E#LSO;e8)f4h9mox4oPP;cGrokklE@W3
z*K59`URJ8pgeO>5qfpNdN{r@1C^R}S8?J;+NW9y-hss>C*bKpzJ{IsYIE3K3eh&o~
z>kCnKen%X$HR!C(k*?==1n#g08H=qVO(fXEkMfCo6Kbo3l72!ZI~I${QKnG7^{}Ew
zURHB12SV6qGbvfQ{Am#;^J<vxjJ)nnOcd)XOgA}&%uxkbW{D3$L-twSw+-XDtHz(2
zWF93d1|DbMD3*eP6P}%9``O`imkK20TM<H${soX2IZh07+PM7pn^Q^lp2xN^)|%Iq
zN){*?2Gr`hIra^dU>OgtDDIz_GdxXZ^NlYXVRQJKVuY^Er`kW_Z}U%0y}1R2l>4vQ
z7pbDuq>XWW<_8YA-KnbE*vri?cFharR*Ig@4vBr44|ucEyoF1T@@ASpC<~_ZDCxbN
zd*GVA+5e4-nIwJacu(wxw-z_$-hN;HQxH!u?h?{?ynng#3P&f{?27ZV9&$JNU%gki
z%Ntzyr8$YX@LDXED1Vkwf|LEUeu=RmO&LV4{!#W&@C3&o)4Df9!O9QHZAMr95lK3g
zIx(*5K@n(lfjC*W7MS0NB*ysB_kQs(6laOGEG)%#l9A(~HPoatjWS897Rhn7mfT}y
zfV^OH?B*oSi$SHQw-%y*s-;kJ_B5~WOToLA0|~4X+aDqgab{9S2k(3b@p=V+7yaP&
z3502n(7kY<f3BddrsGm*t@dspM^XQqxKU>391m>Z1onet^|`!yc@~T22S+_95(bNP
zyVR{4-l<BkWGa`HZ??q3Q26TW%}XF`IHyvwtE@xzu{2FqG7r}jXuYukyKj0e4CTGS
zk##gcVI(3ct^Yi3*5kGB+_xa*`LebSUJvU}B#ZTI`PMTWGm4Kb2>LU?zEept&$zf2
zB-#1MZy3qL?JvIheA~Q5#bvWX=Ligu9qdOTk1jtuYIuJRCRb=3JkkKI3}P|?i2RIx
zlb*D>J)=5v+2$O!T)w(@pk5uroZ-$*HpPYvMTPSQ8aFn*uITvJ+C2IMRSHgGg<SQj
zfvszb-UNE4Ntw-)u@lo8M~1mR5;-alEAC5ym@%?Pw4a`&AK{96VkxSyhuq;{(TMg1
zw^HJDKYiYQ8Psq3PC4GY94kCILn>*(7kds>MTp)<QITM8kXy`fNuH`~v)Vn%LiL<0
z?~XGT#9NDJq9}m=@;O>6XE_oATUR%?u<nL+^LF=KyTF<!Jkb*~0`xCBsva}~lnNuJ
z?mA(ALV?1>l5Pc)f20(l0+B$XbZkhe2#yZ(azFT1bKST_GIdC>q7-5%I^J>;E3$0e
zo%%>F?467HMQBR=Jr=J|m6&}Cj9qK&O$}v2Bo{|CQ9VJ$I^Z2hhrP#zw4!X4L8P{;
zwaRPzq)?RGT=7)oLb2Fje54Oan(l(!Cc^rm1_vywk<C)Z;KFbwtK%+9QP3}a!@B(_
z|LA1vi==yrlA!hAKKo)Ia$#<H=oB{1vykVw->TdiAt!ON|D<3mTmZ{8pVkZsG_#-(
zl;OO)qo(zKU_v7%o>G<}dEj;6`ieixx2pA6AR5FD1-S9Q4z7Wj^b)vL+}t=jE;3HY
zK`6P!#Rz~Y0gHfuK;vl|z^7iedk}?VtmZx@pYUg7&Ckzne7$p1GijCaFSK$0(9(#C
zY%ifZjinul|G~eb5!Ef3n_lfL@~UiE+S$I2hqsp<G+w#AMz_6GFmkK(TJQJSd5eb`
zC@nhckPf&OI6g<uq#=cQCz`L~G<hXbs7NB%QPUDt8hUsHonCS~%#XF3QA}qyGlL^%
zgY}~1j$WnD?esJ*&BP1_3wtibc8754c&DcF(qhzKCzkLlpRv5}%{QI|F&}SPjjmp%
z#vt5@1mhP}cRfl{#P!j?SB$Q85_>7|RZ6)#(WjPAu~=!Kj%0ljHT%R)#~ifj0s<`Y
zDPN7oi31<ch&}`WdfE;2z+327hfu{7Hb}tgni);-kc~;d9)UD&lQieHtfb@zpyUm)
z<cI|9q=*0lRRrMEb1JH30Djirc~$1NW6D|5c2(!LmM2z@tB}c0arf5TQBLpIA>o(F
zpYH+%E)`|;Q2$ieiraOSl!sc#`_o;tqQ04}#;Kcs#Fjqlhjv#mWyrkD3K{S2JGE3F
zP6NyB$rUY9_8JzB^^?fi(4~M_5fb57>1GXFyYZ84VtgX~@JN|jAil~YvwiP{C;US(
zgtNcA1yakm_`@zOpHhpL)8lmTo|zK2uQ&<)$IA}|nay`P5V7p7+1*F|OLKDCPom(G
zO`c*W3qQUp2FRX}X`DdFI8JAJ0{SBg6nMA-h{qk?&JyEQ;Ww{N8Uw)Ocqp*IK}M++
zSRbLLr&q!hzW#jObOxB{ZoL}m0=(iUez%=spX0U*Wn$M}%w}NxCdo<-Aj=J$SLebj
zdvKjYSk*tyl8_WnPv9aqm+}{yKc{;Na>ay<7D|z~`myo%SWQn%{CIA+i)Ox}-0<u!
zv`eYqSqC+2u`gV5Vez2j#oY09&@GUCo^N<lQIuoHgR3F;W3J@c)FQXC@P}g)<whf8
zFi6GPMfK-;jnS{r@;B>(J1<}8L7_)No`6bpQk>DqMt%5K6co&IM(HirR}oXa_3lCy
zw&Rh`E+S6(R@1HEF5_g0izIV(zTi1s_3w2*(;_^-p~4!|I`I5-@%QSTb&h7acAglk
zEUQOm4j4N`qXeQQC=trVqFlEIs-dA__{d%z5PkrZK8f3(F)gSsFH=YMfTZ)zk%tJ_
zR{%{67{wUaT>=M3*mf1H=#?a1Q;>UF9q7yuVarevrYys2zL`6OqEG2cy?d#sA1M;W
zB!MXhQ2Wd!(UTD|Ec*J3azfD5&tPw_-fvb#W?Ku0bn^L$$Xet1nw=(%ntA@w_v37d
zH5;58)%K--j&Cm+41vlIL$4DpE0Hv&2`NsSqOr@a=XMIhI*yT}He3)}pN@2qWo+qZ
zXrfkU@Y~PP0bQ;t1nPc<5xaw{8&eK+6Y))jZ~E3|qM)>h(<l}mQKNn^ZD(5Z%D3Js
zG%Y;igjEcE!4AR+48s#;{NNnW9|%|r)QWiV5!2Dr@1@a_op~F!D@x={K*Skiz~uPp
z?uWk;rJ$3`ZtSv;jBDjgLDq^ypLy#}IV!^4Gv2qXI!OwoYA;qDxf)q*sY7twyjkQo
z$q|foy~g3fDLwh1>VnWw%phCrjxS~L)+)!|L4{DeeXLyXBi1*ZFaZZorSC5E5bZo9
zI*qH*=kC{6mzC^I;$HBW?))2`wU5!0KSOi5n^2I8rX3v{cC_%ve<0}4mwxt=y!xdv
zN$N)Nz1}QTou~Z3royc`cwr<wwLRPdqSdYLkXL*GkKjF&i`#o(5^($eplW@o2RPm-
z-F%{8Upsp{vE_Yg7hlIgzUW?f&O*nz!R+BL1y|8}9i$(jNoA3knRXp4VniF=>KF^~
zl=71xR@R?kmG`{ibn$w-&zCXYE>94SkZZTqVNNHHnR;kw>ODR>(dy^X<9It{qbSNG
z$emTyV+wd>ZzCI~`BK@h#KDbASoDdMF2h%Ru})2GV_8tvP4_Ki4f{3xhpqFR>i4k6
zl2HopAJ2N4qlxW9+C$auF9%r$!-cXQPY%jGs|U=4#YR*Jp%HIrAplv@Os9v32Ltj#
z*ohZlt61r?e;~LmEAX~P-`~w`{^r8<KY7Y{EkrB<r!{AJI5~m@>C{`z{L-30A9(Oy
zB@=HEcVUb~ym>KoLQvcs&qor{k4o+6rO}2(@0a&;^dGyfaSHqJOZ#N&C9KpOrscoP
z+3|CMzYgy*1OU^)hMq<SDD+0MR$UeHZ^O*Rg7NxHu83+Q3@pmJMHvFoItOA(gOCG~
zp*dyYS~;9s>xQ|TWHIhe#iMnvL5IFrF|iEwF(SGZCjNfJ%*>cc8|Ez$1hfkWW27A%
z<bzWz{AY<FY>J@wP+#;qaa^m~ul18R@>DGx)`9gm`D?G+LrVGfAMY;MfsqjIBfzv>
z#|j0+xIp66WYGR9FySLX74t*QF5=zWsqgPD;56fM2Njqr=AKbqM|Xrf_nT<K=1!+U
zo5b<Kndp#&OUObrMpwQ&<#z8)>WjTf4^{j#%XPbghT~20iE2t->imRqi**aMYTIi4
z-sV)+_F751Cg3+LX*BCaP9t{&*ahqBF!U-?eWO;^kGru75Q$#iS`+y2lV%@UDdo@~
zdyU~G9pjB-tUwy!<HOBU(6RB&eP(Y68!<s5)R(J!FpE)I((M_rDyS6HQDZT%1$dkE
zR0UiyDyDK2lmUL8et^S=F82$tbi@TPKiLMB1@r(xt75pz5ZFb*ATGrb1S`H!C?}=)
zVocSe_mw`IXJ;oLfr;=>?;r&_P2$4@NIsjJP*Ot!JGP=7IxnGY_QR1hXZ+fq_nLUI
z(jONoSTcf_D}Pijvb79`a#yG&Er{<*SlfXmT1f=)>NkJ=79GD|^=er3?tLNAgcTaz
zm*eG9s97*wZWdDr%Nc(V4EdaC&48FNMy{<SOr)gsKwq|vu&b-S9y=$8loxejv+LAW
z_t84hgB}F%Krm+2NL_$GIs&lA$aLOsn%NI<czRXj^l9lB6}*G3tt4ai2WI=?7#_^$
zAgF-x%xo-wcoO$7!_UIN;t$OuU;re{5z1O~Vhi7CceL(^<P~QuM$2Kr(J>N$A{CUu
zxnuV;q?i8#CZC=PyFI+XO!45VAEOx1E;HxL?A}_!Nb5$lLbV(MA+gWK^qh3FHw1Rw
zM!@QI-hCV<s^L4-s#uC@t7JJFnBw?`V_Wr%wX}pjo7Ir?V6j?X7yll%j_HGgiO%X$
zRHLoD2kub8@))VIs45qni?3fh39MOh?jBP*?ImIhSkJt$qC&|72NOmdgkkOo5bzlb
zzslP}{<MVXaCvUbvL0x~+eB4HMpjL#u_5xPh*_;2>w~4#x{z;qjL-A=)4Qhb(LU7I
z>LH5LSSu<7#z<<fByg__7SQH6J+kjNEog;tBcy(r(rW&W4Nl9cUh$H@i@@tzf|cuZ
z<=lxCg1(+~)7(olObO!%^gxdCcT%*5v>B;Q{XPm30~#^{69>>;N>VqTQ}DTxldPFH
zX2QeGM*Zs`5X#%Bx01Jg`VW@B^KSEYx41k}Q#Vx1$NLA^zF!G{Fs>F|BXf<oU&C^S
z+)2d$BQ@zr$Dqext#1X%fV^_+ZY7NN@CbjJztp;;u()!2<LUKd`$}@e@{3~8B&kZy
z_ZE_AS-h?TLo!ZBa7m6BqsuaIb{jgDvusCIxsf-WpRgTA(l%ABqM;`Esip)<Lr9Zm
z%$d<Geuf<HA8x+#e6f#u{UK~Ce+iaqW(+47iCKoBJX$8_!N&8NVmH2c2_pky4@XSm
zJ(EvQ+Cp$DEhGY5&FRWO*TbE~jmA13B8NG^!NGu6UB@}#S>e2uQuUm0^+2ZFigkqB
z+KsEgFhVS?rywig@vOT{*tgV3SEvQ6d18!b$^*NksRLdArxJD_59D#ve}oDX6NL&Z
z(lm@Q#th+z`-O@4XYSYzh<7lA<&*$6!=D<uEfmJg_AixhW}gsEsNE?g8)sc)YA}n_
ze~~p$hfMyktUgmPC6LzRx|`F`On%R2?a8El(bO!v9hf&R6Y7Pva)y&e!L9`%kSU+X
zpT4)BCSEg;7Q`H%KMW$(`Xx=i8o_@Oz<O|_fxDb{mA!5rwHl!^Utfww8g2|f*C>Ud
zV5Lgvk~gv4H4H`o^M)lgr|a(M_($2yiiU4Is6eD9V#YNc_{f;c(AKZ~W`xMF-?Ru(
zh+J<s!>g|XA;iKRzPHB?Q+i_)2}sT6<j;4DVFh%*x^L$!6eh(=I6AuFWR*MhPxZ4w
zu?W7CbjAH9i`i(juRy%=OxyfL3JdLnegH~GB_GsgeZ)>9h$0e4qK3*m5@n~tRy&de
zuxwzqz6sD;j$LX@iNa1dd|{2QymOx8BPH+?rcqucwW`6H;~F^q5oJwHlRQ($Ow88U
zTLwd9@^u8NloGM8q|*n|Q_o!#s)n#39ei$-bMz#MqZPeoYSe1~2fW<M%ZR*m%(2p*
zxk)fWjo>f-s#WL))8OqZp6jmbzqg14;Yc!m>(-r{$Vs3BhLorh2<mM(Y*A>Fu3~Cj
z+1I_Pxbd}Zw@;gIa1xUh!@zTi+%acvXo9BmT#2xTx=Rzmx)d=+{kUb`f^%0E3H?l7
z!m%&+$$A#Ls_*G`v_BX;c{aD89;xXgW|ZO@b{&Z+$YR+Ehc3<W$>7+PUT004Y4qh9
z)!-4=Ui0w9OLc2SSJ7sLQm<uT*t(lHipDo#%T+CXzZ;2>iW^h31}s)1UKyk?$RJqU
z6mM}aYG%{J<&j!?z5m#ly-5!GS1FVlxE>C-DOrpwUhOT}UVUsfXQaOa@~OZMUGM^)
z+cs;Tj;UUoY9yN%SIPbL)YKo>r3vD<4ECD<EUV2et@*OHq^cNMrYR7Pt-b!K^fs!T
zU5Mui{gWW_=9GsNmJE#(9a#NXw`saEtiXC&b@zQvC?a`0j0~QyCbM|Qcr((e*;zbj
z8!gXii9k;9skCstu*K7%bnFeWabErBk@Y$fDg+6<zkUBbqCysvE`E2*oLq9>JI#2N
zt0SwoIMU6CwpBb^>%OYP;i}{wy$Jw!pu38tHJw7yoi-|HYqI$`T1Pi;fLY^r7TMU2
z6*WQ}Ub@3xVOu}O%_Wmf87jwt+Bpzxrv)odY@MAqxGnW*u5BN^dLZ|3iY3mwGbTa}
zK5x)&=2W53BMFx{wn)u*LxL$aV%|#DjSB4>T)s!6rCS$4#X!JMm931fO^AN-UDpll
z`S?$9Od(aAbxi|Cg6FxAuv%i->PP~}5~6=e*a?-ho1HDV-(V{^#y;G>NkEU8l;hWx
z52a~ISeqbJ9uns?+oFnsdURG<ZopDRVT(Y8r(w<9VHq`qr%X-7+NZHF^8Eem!532>
z3ymt>4kiPwUgj%EZMP3C4;eGEsHTt_iG(S(JZIua)gsr5r7w3#AL<Xworr_3Ny>L=
z#_pd#>?d%b`(+bKzri!7F+u$N^~II~HEufi+8e7NAX%Q+%qv!oDHdAe#Pp@{&xQn4
zrX^!3V?dNHM`$e+ZySpNYm29QD5GibJ^zIl6edj0pI4sEY+;JB>4>I7WrVD6{Z>72
z-ito5U(mm}N-sRgL`F@dqj*Hus{c}LrJ*n7h%mIrY2ep;G6rO!cT7y7-OU}g46t$2
zz$Y8N;CXbidAh4{N*PsOxw$xM?9JwZf#o8uP@sv&V$e8AG4Px3kcW?iPzdegewKm)
zMP0ZZ{YuUrH4sYN=kA4cU12-AVR~~By@>?`%n{4q79>e?>{{udqigfZYVs5GW8vMS
zFj`2Pb9)**s6;ChJo(wTX`J1Kxy2(vinO9qhGe4p-gquIP?DvQAjHBvS_!Z5hLAh_
z6&j)_`2K2Kg~5q?6k+z%-n3Qm?7}Vyp2ifH^g}wT33~6A#_sQaFjYIh0%_$pVmYO|
zjHNLr*{EcJ+6ItH^$}m`T=>oE>~1}Oy>TGB%hG@5@TYoC5x^T^o5BFM#LK43+hDlx
z1Lz0P09*J_VheKs+|s)zzWyym$}ywo?eZ&Ky=vr424ON-Qhca*7yyEe9MWZo(F{2Y
zKvQJ8VSF>_v9myvc(>U^&F1^t0#tL)Z|e)k{`0e!g0j@Zt)%Aa57)7<TF>(b?b;}b
zKOY()GbMWV2hxIpg#H&|19>`3ISN*^$x?jLW{Q#x$g)kv0hG0T+Z9C3{Pba?DIJ*7
z#z~`?QM791y=f7Ja!`P5M6uF^&Ys|5uQ3c82#Sdfj&FY)r(oTI;x;jal9o^f4u?Zz
z_>;zLU6$y}xGb|v2m?ziO=NT`bt;K0u40`Y;c3EmtSWj&fTfG$OCL6>ILs_9<MFk`
zo8GU!ZyQA^{Qae3k?*JCST-GVaq8#zvP`uRJ%ctuurfcgMt`&ho@!n3<v<x=-T}Nc
z9$HmCfB?So3q%g7{;LrIX*<j~9-Nms=m!Lt2K#+IHk}RbaY87ksAoJYvYPT)O}8cy
zjT^~%-tkf=Ac3@Yw<QGNKkh=?sj>v>VL@<LlJ3Wr?)JnH&v*+$5k%vUT4uByk@>^n
z*`@W9wX*!Sb@YtnCOEO5?NQlB&`2!4j<}c|9qC!T)Ngq+5&EF)L^Dbs>{}<19M&$k
znqLt&qVy-<O-y8pNy=(kA>;dGq-d)JwbLZ!mQY6i&Jo6>dxB}O@`JRv=|%$!_`}7L
z&R#A<T+><eqAy1r0n^DK&?^|tRzCQrvEV3rv4Y7+;!SfUb?y%1v2wP1cgl}RSWm8U
zBNQ+4uT37Qdpnk>oxfu?E5~*F!x4$f3-B-bv<{60N}$g?qfPD+80Mew-?rj=EFT3^
zIKL6moeYae#P?**+d7^SU=Ea_D7Oy17qM3;;%vDe-L9tGg<^4RAuZVJFL&*1pKq5F
z{6MFezv;D@bepegDJ&NCGfh>|3ck$B%Kg?5ZIn<gs3hlR^X?bDt?UaV_HfiBDWjPA
z6f2t}rN_Om*7W!$dwIZdNXL;OkhG#-lU4-ocP9h=RUp4i;>1!W4bkb)t~Ln9F)lSj
z%y`(6cq~k6e7-}YaiePWaBGyp($yqUH{z53pnPknoFu#qiY@c~@RjkgTC!&*_HM!c
ziqeG^1^>PM?=_H?cC=a^k|xR;a@PFD=Y1YN4npFz!r@v~MVYTMb_A_Pp*_|b;FgT7
z3m4O;^}2}1xdnyIOkeV5ZTAuNbdH+5mf79TZ@{yAXaukwb3_7U-E~aK?`vU`ZYM!a
zjyW~i;VR<GcY?w!>$O8^0XSn0i1@_`A!8xc;jdx8ySjV7v0&rD+!0|@3QO7;KOL<o
zYO>5eZo3DUIpeqPc`sD|tAbOEAp&c?e>L5`BA~uj`e|;#JS*x-qu?_)DlTNxaH(U3
z$;a(H71Q@Kz>w#0W%m~SAB(Nn2MDJgUm13vU)Nft+x=7bZ0Vk;1tPRtb|hCMuCoMT
zi2@eQC1=Cso7#dh+lz?v6a~xd^y!*<ZH0VROj&Q#w{L4{^0y#Hpq;F896n1`oBZM)
zwe!WhR>ZRVhTO<+Be}epAH4iVxl?T-wLKVUCC&dvd|mM-<~sjmp87oDH1(~x-#T=I
zATp4W6<b5D7&V&%2J+x-$Akd+)QknM4c)um=cL<J1YZtYq=D*Uo_xbHF3<0rn1)ln
zb!tn}bryGn785*2IEut-<zS_=!IN*?P_&7#0t$V-s`K$uT~-<)74+#{yglO^#k~f4
z7JlBB+`DuspDZ~-=WHdudw)l<M<IR5(8krgf}3tp(qoYA*V(v@fLA33pg=DClc4eb
zjL^kt>o>MUK?d+obFh>sBU$7K|McT1jC4BykEx{rQrKTsQ`^w4oRMOPY1CCuQY6sX
z*GR33#yyOJjpicSVpXAyi}}t^k3hd>>w_YVZF?wJN`=~z%8&PPHS~qwYrr$s<I~9<
zrDkke)DY>%_e9^QE#4cAd>w%XnQZAuX9{k;)wJGsksLtP>uhCfW)&d_B$dHwVGn-t
zrF=`RdXuSH%Hi*3aSD1E+=C^h<!Ff|!RsvOHJKvlEiJLh%Xx27KRLDVt^4OH(Bl*s
z5fjDL0IB!lwQ~qFgI{fFRi^u~)q<gJS9~jqr&NjL%U0Q2^zL#}VgS_c&4e_8gol2$
z*JO*D|2uZ%-ra*ScM|P`tRHIDgQs+cfBt^?S=xbZc{x)z+BH`6QTgYl`btTwGeF{y
zVIE&GcUUSzzJ1f5bmuxaiq!n#huemlW%ab9Ah?2fJHkG65`;R6HixN@&z3WjrSq7&
z?QTb~;7ACi3_qtND(kVPE4ZhbZz$fYhgMh={;f3>ENOjnFMr>GB98HI>;gqW2G@t(
zO-hwoiH{^g?xOX`=-3#k(6p4GqtUu+`sO{AJM6Ud!+vrD0*`;hXLXg*{3!Ms>Sn=P
z$pr@<TQuHT%>AW2hC1+EXPh=SW&Ch4o-2-(c$J37Om=uIp1A){A)NN}gYtFXykE<=
zRgTVl`@ngXC|Qr$f>>@S;5GJQoeB%NIqo8j$5|tEtHDZr>&31;)|`C<TyBp63XCkY
z=l3Io?#7_>NFZM-Xd)(?cQQN+b(s1Ki{(=H$jvbK+>$HggKE6LQGA_Lo5qu*O4asw
zOfedPL1y6zLL)Z3Q8@(P7niLcmmNE2Qv)94keYD9r5hXu9kUGsr<23hnjg*SuD&NS
zreQ^E%U@q-sBd)zkQpP$GE7}9Bes&=DBUvaYk0yW5NM4>9B(E4G)wAhy+|=X<Z86-
zY4P)gyFKRzCJAz=yXoc}ia{P&@AOCfOq#z79<l%!A>#G<XP}f({+mCF_v}SYIPtzX
zY2yWIlZX=s;g!lX7vM?DpThW2d4qY~O^?rM${iy;se=KS+<Y97I%Prt|LfWv^vWKZ
zr8_FC1-eigKgwPhJhd@V8;c;8-O_^kCz?a=n*%r|$6YZCKVsCJl+y8mmcVpp-vP$k
z{;h$!0KZA)dD8%Er0z`^254oi$S<k=^*FKg*QLKO8qF<u-s3Sd1~)msHqIv_MUu-W
z11XyX$U4r*yj~wWiRnP}ujmlb-^uEp#w^x9X3M-Rz=k!vJs0j3eHw*Rwr3{`>1g`r
z%%^(799^W}uq@u`Xj<0cR%TJ9>wUcuoxx~pv!LJy5?a$p9yqk05t;%qXn5w=e3PU~
zrB<~?@Yrf8VVmz=NR1ji-1@({YHYpK_IGp(Lz^6ZsJL%eb(EVu;tXd+7OWeMnHE>b
z*ZCg*9gX2FHQ*59g8}$bQ09d9C3^{?r}&ibxBb{{BgJ<*M*BEVceJGr>kK2l0QU+<
zbXOn!Q8>uKrXM}$q0n1`)Uk@MZB7j-;!oc#J+T3sZ91oJeQ`3;2P`Q1lkZz#bi0l;
z&5y5^_EJh?24wHu+ct$D5LXMg?wVaXS94Z60I9SUIU}mKCW^VFEL2JT<WXp6;JZUi
zVu&6OhjZllBwKe)Ga&JZ$05gi%l~xlt|XtdC9H@K+vku-cG0<Z5`bk27{Uky1pe3G
zQ4ln*YhB#DY{kTrYTAq+qJVIp@HgDcU0*zzI}&-4meJpo?h(=yw({Y;&91aYT_V(G
zv`^PsN!?;KA;9w-H5C@d3$%t~eVu=CMJb54TBzKVZqxT5-IuV_^1fRdy<-T7h2j=3
zBV_hP6TFPB^u2BDPbg+M&F0vZVM-M@qAP^iNVz0@g!QtjxN%z`i(BZ3B={}v-Hqtp
zJ2klbmP<O}w1t@Nk`UZ^f8AXAdtgDl^=B-U6eg)gF-O$qIpl*0B2{-+*b}^R@pmYh
z@+`M$unvIwio7<HAgrekO2A&QHeS5>GF<`vp56XSMtpkBTxmKSiDFOw$3_?$Fudj)
z+p^|mD+f$4uT<hKk(#A@@h2J%pkq?#Wm8B7|B%Siu-b3A0(t5`AXqoF6-upYI>LZk
z(L=`_L&whe9EbG>k4esO+N<sO98hAm7q}ElP_ijAWrR{C7?OHX_XOFE%ZBu;I1@xA
zSM-9g;v^CIuSs_JWbk}6Rm`qWth`#68k>Dh@Lcjn|46*;dEhI)V=tdk7N0(*?Y3*8
zRIAV1T>80hydujbh(Qd5-}_c7;0DS6-r(iahl+`#p;)|zoD?i=E%B<_()MSqjQw@L
zu0g8HYyGeUH7RaXM#63vL9XBO-1dje#b_n5z>+PGUUhlH@Z7grL|<48=e8AW1lWvk
z%r%z>_&RnFarOWe?Rb0Zw&O$QDc>Q3eX?-Coe5vOdZn&Dmfa6cmIF<s>#?<-Xrgq|
zJ5cA~qlS;V((!2eC$D;=FH&$U8M%{it=1Y)aoQa_a#&UYyzeB{m`czo<EPXPy-e#V
z!~Augk+CUFihpa)v;`;QAI4B*8N^Ul9F#t-{)&R=2T%B~ek9qX=PD2I>}g>@=-&8?
z(}cEU0SAo+N4qSIk|92XL<Fgwh3!{m4rQ#lA53_p?~Im>rXm?OlP$9)*{U2zxKTe|
znKyq_M~f;kfiQH_YV7YAGbugU@!zU=ZB%~0WWL&#8hj+__<;3CN>r_LH9Kj`b^NXF
zVS0omQz#PtNK5|adjJNBg>EU7(xI?&$9lUbFhvlh3;&LY$kk$bR_{Q!EO`TdZ{*8d
zN^?$`Z#aMxB&n1^qA5vI&rkl5po-U);3%<C-JPorJY+sJxM527B_{Y8o0J#Vqfi+-
zF-IJ}yeGz$DPutuEM0>EXy2y%Q&Wzs`GdRFWE@3-R7w5!9oIj7&_WfRzXx@>2*nh}
zhSgpsdC+J{QK)76!1mi=_Bs+!@2V379INLJJI$+-MQxWYd5#t{sD>mQ>$A8jDM&?j
zVx8Pian$3<9Ce8>$C>HxxLKO^j5I!-+3twtZ?P<3yXJF<@x^O{CGdhPodExvA-d$M
zpJU+emZ*z7>JzCW58~PD_jC78OdcX&+wJ(^SVfw=FtWN363^m5{tg$8@ThG3uZ$mT
zBlDxxS!-tbs;`Z4JehPx>=2cE<~3Nm=FhOG1h8Bdc6VY+7rL^SBh9lkBDY~@9f#Y6
zZ9WVYN@rKi?C$))#e^}Ncpdo)6PH~h3{Y;BN6PVtI~wax^4*N$mUK3Nx8Z>KI1m}3
zE00JrH=_}&hdQkF?Y*@o4YN^uo|dTL$J%0MM+b4Mt_)loImYd!IneVFA68!~!n1}8
z1^7Vgw0CN_U?fEhNm2g>lm(0~xs!QOsSk&bTCOO<f$W@rklZqV(d{&LtATb>%5Z4g
zR7{)fSF&OhbeM4Osuceo5Ne#ff1%(;-U?P^y69O$fDzOtH@nzuuf;x<HYLmd##JvP
zsQSx;ftO1s{4H(ac+<jxI#_cK+0u|wq3f3i{PHRwOZWO=y$%?PIRyMo&U7_%rxp?j
zen(TK{Q(`*3XY(7LufySsA#ZS_vclnl#NX<{tY)l45fTkQ#Xd1ekn|_u-#{>qz1?L
zK9Et5XdfCYagWZZh<%X+xjP|rN=aB@RJ4yRZW&x<_pxprJQID4=#Y0AKJ#WOQ(`IP
z@F%F6R|{uCAy-d?E8e(LWb~supquhCjp}ESZP4068I{`oW+()<TLKf&Axs%#<f)Kp
zd5Vf>W)dan0Ht2xVUIya9IQtfddnnC!U|3g_6R;Qy-3cRs4VdfI8xh|jyE(s93Flw
zFc3_b&>|p+`cvr}Y*d}}78c`gkHi-Cw@1px3x>yQZk2Csb`Zexf8yS^*ZHBnt47c{
zTXAR`FNQo)*gHLZr4TI1%Y-dF<xcOHE`K|j%U8NkCjVmsim!I!v)BdHVdr%~BGti+
zbHLU*KBgCJ_gOxc8?|!F$GgZvrq86Sv6}K9$A614CG`*Yvn^2|QcE<|RrI1Zb^-{*
zQ;FoMw{M`;Vx1kmr919<(boN_B}Ux)mnDqTCDUmKK+_q_xfqd0sbZmcAQTpR%Zc=q
z^p4o%_9pCGNTb`GAxqITa|T?WQ{$&RIxBPJ0fm}nE!Ho5fbEBFmz|{qGBAGs?@l9*
z7tQ-C`j&zk8&U|O-z;qbbJ^8ECL;_S^(=r@lq5W>kKIATlk+6U93sz9q<o8PMimLw
zB>7$44%Xh*p^K&LIrd26q<GzKKE%7^l!{3KT-MQs^VR)l%hBk5K^KQ%gZq<=#@nP1
z64PWB*GL%xPRi>c9Q7VCf)&fBt25lYgCKUVmbAid{MP5fk>nBf+BnA)LsF^8@!;CD
zCRf#~?Nh66(|U8Y1BtMx0mjMZr{e4=#C$=aj2SKQceD0DWMJ8#`z+%ZU2YVcT)C&-
z8PC+C|4Fej=YRl~GFsb7eL@c-g6eOuTFL(^fBVKd(aa1B`rx*`ty;Tpc=Adf|Kt)A
zmZ3FyPQv<ZN_eDu2*;mk%y7EDSpZ(da58MT@H=1i9y<)jn}$)H8bMMyvIyPJ@Mskz
zktkm|>Biid91+SoT5&Y$C2G@=!xP7?B)L+5h}?hV<9?Ex`{Jya8G+8hxoOEP_WP0>
zCTkgvO?F|?PEC^nD@}gRRJ%@8`*RNw>5osR6b<Qk<Di*C7kcZ_1(HY?2@ggyrJkuE
zqDi2<>$N+}d+>hiF9@D1(#$f|f1vZFe{Mr-egCcQ1De4GE)E`%wx}%Y!!v-$6#sp-
z8`LWZVWp56Snq_HJQZD=MpDeFpHq$w0KvIc++s%ziA=e{n{*fcLtAq}9;MI`Ye^4(
zN@cqM7)W>13gcMOg18?P{%vn&+$kxOYuwAjB!UMPB=qNYJqvCUQ#$}vHJ0NoQ9u%R
zWfiBz>DL(_2DcR0Y(1n1Cu4)6**Nw9CJe_W&S`x4u0@CFaX0gq-fw)>XL<l_An&#n
zx|KJ{v8QXxLn||&KKx#G^DXA^tvHzl!cT!n<<`=x0g!Zuc9T@%Df3mU8nH`B$h=4v
z{5Gx1M{S<>3diC=g@N_=g?^yQ6FVRzCm4HR-(J{FU}kn<FdI<RkK*pmY{wD0ifpcZ
zWS5-(4bhMD0I+%6+i57zX(CvZlb>j(9w<r#HR+7gYTTnsESNHvQ^1oNpEznLheF2D
zs0%?=UaAM%d{8G=F?$b@3e^&1Ip*hd*NNeAl-qtC(UJ0E>bGi&|6}SKxZ~iWwHv3g
zt;U$xY-6Xf?TI-_8rw-@+qP}nw$Y$X(x`Xx-u12fWv%>xnK@^ly`TM{T$*q8TO2SF
z%+p@x#{3n{gBjhdEcfW%zW;DKY{tdWkyZ;fejI^Y$-$ZFqDi3xgT&rCCZElW4t0}~
zC|EtlITCSUxHgzDp^Y~FBcukxr4RjQwl!Va_NrNB6g5@>j3i`rY>#mO1TRft#SrXE
z6BpGb@o>K52`zPp<5Davxos=h*8seMuzRlW-~*WoJ<;xkrCW(>Df>+eZvj6-E?1xq
zrRVZVZ&_1c=2wiT0|$nxb{4D8@Ew~>W<83LM2YT1X%JF_Y80*`mi1NX(s<&Z+q1=O
zO%n$3d@p96-EU<pY1l0zih|TNEPKIQa+Pn9z#`L4(hi?laiQwPFlV`52+vzhEbJ7U
zS>^9&L3z<102(aZEok68CC1)KefhS4^YY>u)F_!SPNlO8qPJd4KZYmZkEeAic5rz1
zxWx5U(b{huF1c+5d8F*B1W1)!O_Fn-C7U@OPn*VS(ia&2du1Z59x`B|sw1~1vKaIY
zP{;D~z}D-JRTl$6o<)6G)Fevxz-=5VieByCvN6~b8EFXXkDgFTAx|zu-0(+b+sG@V
zi%6pgTyyj!IZ#$`<o499tG}r$l(pt4fF?i5<Kc)DaNY_yDkyA>Bt!bo->!B_Sjj9I
z<ha2`E3+gT+5G$IWzi0E!u!co9`StR08i^w>3SQ?I9|##K^<A)VZY}X-ydfCTK_A^
z7br<l25<>EHR;jSeyapYND!>^(+noyQ!RsBy`po>uP2`0dd^1nzozM-YfNhlH~>}u
z==2E0C?$D~Iq*sd7_uzHAQH`(kiPU+gB;YF)hlIFzQm8_&RQ%oBK^n$M^IIe1ZOR|
zOU8{DPmlMfOw(SF!5FUyRDV+B(FQZJ`&lGEc842SfATan94!X56*Ei092hh{LTeQX
z#0<6HEVqGc4K7FZ0thJ<u}!<7>&~lG_jS(-4eC+8FA9U3hpJ0oDCKc&N;0?>u2At=
zUDHglP<icw2*;{pwb6Tn&q$#_xGq`IoAuqxRN#<nhAPER1p3DuC<{XUY1RMwYlc+A
zQ%mMO4;<eJ{T85V7Q#+VB-pJ{spt8M)lGH&R4QJY;W{6S{qNB5qfHVFCrA7@-DP0>
zC!z7=iE@0Pej-*H%qts&9d4fM0wR!9>>`e+lv+pTbyE~aeBI!SWNVncA0^K@f9}KU
zb<CEyoPTF!o4ska{wh#LjL^dk-;ylW7-+j0Wn;XcY5s1F9hiBrd#J-B>PcsP+0*}6
z`zZl!qA*V<lFqH&tBr*zHXt#O+u}3Ne(Uf%v)ml=<LyeCQY(WiK;0xL0=2QBn~Y-#
zF17lQK8Mg#9x${hQ$VEymV^l;sY{bo@}7cL8yI~CRZKPCE+yW;E%JXJ9yXY>f7%Ug
z2{CLrSEm;sD5^wMsN_?Z#s^^=G;C!l=de$PwS6zN$z`C|I6I!z$&*B^If|ZNVm>$b
zULevMtzM_ca3jVH%j^IuXC?Y@q|2YaE+41}x^WT`rB`p(m6Nc^N?!aZNkX9&Ww>_T
zN$0#4k4bCgbp~r6qsrU&8-ZaZzikln(~c@OyR&$E35If!73|S|tcPok2TT4Dy|wwl
zUC1{vXqg_j*{3N=U0KM+n)r;UbnXVSg?bRFMjk?wF5Gp-=X@JZfknD7tX<f;pxdnH
zR{=9y{*w{gU^X^4<#}(3M1g8eBrV?B6=(-jm$X+%hEg~|fNEQfaAGxK?&YS!+G475
ztG{#Q#~{37t{nN1yfJP^%Qt>^)8Cw>3La|7SI!TCXp2N)R14$E!zlQ~b&XCVcNKua
zlA^Rt2r6jaEZseKNVxT{Qv@qH48Hzc7TENB`{ft#2e7~@(eWpY1%}CLl2;wABNxMB
zTdHL%3bd9NrBl~EvlX|xO4fj;F-Wts2^VuHi=XN^qG}-_28(`>HBi5pY?TipDHrs%
z6js-QIN!mX0IF}+3oxr)#*p*^-Ha`m;7R>Ifxpo%1{cwXTWJ>qT|ZLz13}@%itu-d
z-3Rx(>0~)Guu37S#+qMs@5nd@QJ(m-mmZ;rGe;gYI->)Y4dw#Ft>D`Z9&&;2uhfv1
zo2EyHBiLiFSCW?<w(Hoth^EJJM1iPFQD-<r#+rXyWtx#YZP(KT6V}<Hw?m_tNoKWq
zoaM_I#N3u<lcmkJ5;i%a3#!X3|3;pDA#{i=R`rMb)<d6SyZK2#nV)(#V$^l;`7+T@
z!&}eaj-9Ne%8*P7kcnRYDX-SM-v=p^CUZ(8#RG2n)G(yecB&SOc8UtX(O<*tDCk?U
zFiXTdET>5r%wS}(@{_>*B)cQnJlGhVI(0E)48En1p9#(HhDymv&7%?W$x{tR&A`?z
zwt%a6MW*l8G>8mQM=#|}g={~xEZYW80w(hICelHRA^GFy$EH_3E=V+I%zKKq*-lr@
zeX(#39LLlkF?t+uTLg>cp(|BuVNYH)ta}JDa%v-0Du%G&Q{2D_%$7c2TG7KR;53as
z;vdTy*n`inr-~RCX}P<qk7Y>_Pdjp1<!ASd-f=qTS|#RtXkzYnW1q=~zp~<W;g4l{
zp!FYF`optYCsq{NTMZnw<P*r!lT#gLVGQ7_t*497Brhs&;RtbsUR%+avX@-d3kfPg
zv%TeLW^gQj6LGBmK?U~DMoRo-V{JVEwiJtUJQ&^shaIX1I4qVby=fJK9o7Iwf$E&g
zn<kzd?P@8UwM*JWaB>X4^r4GCRHue?SH{cN6IZ5vs~^kPN*g4Gx@8QDdI<@w)zt+J
zDizCKyLFMwysv>6Q>QyG+U8u>vZOI6?t7sA(`_TD{m<<{S8P(LxlukZEJrNg#$lLF
zrppCZ)UQrL$()BfMGF1=&oQ;uT<)uH6j|T}ux?euf;n8YgIvEMfLyOuPAm_mrhhpk
z@^Z!~G-|G=g#i00<wA^dqD3-KPbD0H<w7qVbai^!C`FL}z0LPb$=t}YMKsAjFBfqL
zqx>JsN@Q@UNepCTNBPe>-rez28J2}|X(bt9MzEwK7xD(o1(XmKg(7j^h1hi4@qQX&
z_Q`|(QWY>l<cyT5Fvf!!^d+OVj;U}Eyo+DXV;>~wFo|;et7Z)O^=oZ!SKbTjJW|jy
zM4IaI;WeKY*vr?rubqp^g_bt$wHF$FxP1;=&W&NBC2FQp;<HHUbmIQ^Gkc>i)O5et
zg0W5RpS)>0><XhRwcE|!>`E}Q%NnbFG-L;FGKwpr{JIp>E$AJzo-w{T)5c9Ov%S}H
zri%q{6>WAm&ewSp4pAn2du8gt@NM`#^n^|lM3e9h0n~XdgvZ>0(if#PglB-GI7axO
zh%^;fn4Xh10Uj?QQslL;d70Xiy%h){N#MpI?UG=&@WLx5En@loU`M1VCR#8NRD42l
zz*)R@q94SUSO@?!^9Y#AUae2+`^pNwNF<p)&u8$^kWbfcJ&}>j-FPa+a%aD0z&7Q`
z!VFkjk&UZI2m;+FzmYd%%Ub8gD2!Ua-N$W=<x|BEpBL9gtPpCP2W|l2CZOiAiDaqw
zWLpY24^(jK;r8Kjhpva6&jjOopF_XPG>gednn)snGW4`wcqEcFjpPNRQ-qOu1fC&L
zqF!mN1-?q_`6WF#&RW`C0uV+0B_Jlb>=Zim+Pr}Bg$yax))73~H>*x|SwaM5jZh3k
z1-CM1XHsw@hBow2IU8k_u?gd^^gcDLM1dnpVRw+Y-I^;g!w;$Sn(`_3V20*0Ke)?V
zY1yy~95Iqqp@#RJqYT0Ts{TkN6NxN{Y1YJ0gCTJlOi0y5S7l8D)7CQ>KNl7k!`5`(
zMf_O10)0^ZKa9~>oR8%;ohBHc=3;#~6u(lz_p684iEF1LMXrW8{4hyfE5$E(|Lpl#
zA6nB5bfZk*FIH!x8wds?QNs_=oz>9i>-+Q!DOWQx2Nw?F$Dfyy55|B0%^l!Gp~;rB
z$BUgr(nV;YM2-PbFvXI`Fda()q9G-bt7G4&FZk^X-THs2(9m<2Z>L4&;lz)ZuJT8G
ztyu^e&m|-dZ<JZRdAXcbcD&4fha_Edla%fab}7c{3HK(QdSH{g#WkvNq@raoypx%?
zTuNruZj!G7T+*L+zLk<s>Mcht)=QWHDTQlfZBY?c+EbC6>&s>jKug0@Mhi-q5&gfM
zN&{BR4R*<UbcT#&Aj8Pup_o1;j8TFEtF+Aiy!`V07fB3Sh4^q7{K!*)4ix5`QTZ+y
z>^#l;(e@#IIaM#5HGC!9>TYvrclv6c|7}3#bj<RxY79_Y7!G`sHD?<SXl=|R9<}t7
z4^39seOjF0;kk#s93{)|U`6<&8D3sl&#;U_RQ?)aMo?v{>M~j0CDVi2F^FRkz4yAP
zX1Nq$)WRMY*aU%$UiSQ0ALK$XPA(}+`f@+NwLvgf*W-(gV&?@<l_tT{2?2QgRcU;S
zF+Er6vgWYc=yb`_E>-my#OBf=C<A48$zOzY>!RJ3#J>*|O8^eW^YvdVw@xsycJ&6%
z-BL>6zSK5TMJ!FBA4#CJUg7EO5B)Ch&!*W2S0BJx$|oXlaNW2IKiJWq)qY#xvT;Fy
zaMBr^Z<1eX>FpH(F3qIv*lAZ2L8!y8T2*pbn#g%9=Fs`H5skUL@r>t|xA~DgEz$Tx
zJ7Cq3S~$*dN8J^^2?U)Vtqt`c&Oz&_N$Dct((8RK@p~p8hCF<|OAZ?oDC8CYfCYEb
z6rlsQt09;4-x`Xnquw(qSdiyn|D54Y-Ov(|bKhYDKjNAcF8iy-eEII6g1u0o&5|8I
zah-yP?;h`=GN-0E^l~W7EYlK!X4h$zwRto$B9{<z9MVJFUTF2g&8E(PYH*ThPc-ye
zGNS|mC6rJo1sRMi%N!E3R3X{UC1PxP&E1V_)X3g~zlqD!luw=Dj6%nGX_~$T`7I`q
z+ZN;CxqT_{Fa|LM!zefTVAdbXiP&0H1Yt58gx~i`o4of`jqx;$sw1B76sNfvR4~Df
zCBL6V6%8gsSMP5SXfE<@on5<@IJ!@Wt}@)mJztqaJEC?6qp~8h7OPtl2q<KXyB9fr
z*Lt1ogj09jlU+Jq*qv2r-Ec&VXmnU<XVJeQztRr6LP_$!DArUJi5nx=i#!W}j9Y7)
z(?o!=-swH7E7_X}QZpVq3Et`T92z03_F&n%>J7`44BuuQ@-aJ_usE8e37ZN^J=TOZ
zppj8hRrU{<zLLGwICU<&n$Z-cyCsM3$HDcJdjJb2+;Kp~o^eI`lYD;iSH!dmT!zK`
zG2<cy-COzR57R!K7ZdH8Ivw8a?P^*tI4dDHbLHx0dM3(ZTD`%PQI0I28hVwI=f~~{
z!5cIbVb67_P;=9c`MZ=;q~8U>G5fLkEVgI+!V|lBsiBcXGp1Dof-ols1(O_w`*f#c
zQRD4J>&~Fzzd*i?e*F7-?I<Vjh0Rom>HY&AGUKp$&_7K;rn`{(>WFdFL^YN!AWlM0
z66pwnn>zU=v9#c6Baw_t+)(<j)TOR|01T&5lQ+JKwA*dr9i$`Jt#j5s#GB^lS8$^G
z)0Mq%<odeyW$QsqD>%ZfZo{s__R8pUe=hCiBoOdtepxe8)taK871{MmX+2|<qs|hM
zI%F8<1?@84!mw|b0<xTaYDfNO3dAj$(>WQ9Q`hqB=G$V})_|gO?tW=v-@ebaB?X%K
z(JINp8PS6LNG;X%i>||`YtfVjF#n2f7=+=j-QM}EHx14Y9Nl)6S8Y%R6B@}yqNAm$
z0ZOT=0kPra7YA^3pR{Cus<(jom~CZRtB5)KoIBjCRy*vhO&npP=jvX~%`i(<bxaUj
z`iUa`8{xzwSjHZr;5`8P7QFG&g}V%zHW;Kll7tzsMDTvQXe5UZ_!(-%_=(i}w~|`-
zF<4PI4E~S#NU`7nJGs`n-x{l`-pn(=4}=O~gA|5yo2rDB>Y=8j4lddsm@aS*u9usv
z%1se5v1zl3P?~ub)bxb3JJb0wERjdhBZ?CL$S<hy0PdMk!n?yzP2lzsi9+yndW$C1
z>ZQZ!p+xIamHxaM%HPb|)NZ&^s)UdoUXYJC^US{sFaPunm{7E>5eZ(%OP21FUZ7z2
z0bUm@DgKgsVsTE^vUW(fWt1pfzpb-{1MjJ2bvO3-MmYUyusXPh_Z}$~OJr6kmz+kD
z=KD+XIFtKB88VhEVy2Uwi=QU0T`&8_P0<p(F_kWzYW~R`SnMBTLmL_5FGJpd;}Hiu
zPO?hcQ_&FmaP{3GJ|aGD7joWRtWm$CHE;D#mp951&+1$MKA+92)KI@jR^k;G<;hkZ
zil@t$(PQF}H~@>^)zB?4<jqwrVx=`B*~z~fR_a$3rwpl6n0*)hW%I{K119b&jCUc7
zMHos#mz3Xm)^*Y~m!&}5AwWHPr{iC0Qw+0_y@1U>b&am>7)^vYQ*BqwyouGP88qKl
z@rJudgUAD<6KfvboJh;zU;`c+@@snG*^6(0GqLauDfPT3GYho#Z2Vs%f1LSsOOaaD
z$S2c}!y?p()DE`{UrI6|C=n#c7PCb<RaU_ie;b96s!wv17^z=<g{r%s&#QqhSG5Ua
zn{Cc5=c~|08BCX#Qh0BkDs|N!U`I@4C`R84y^QqpE^w*Xcczb9!85<$B8F%|Ub2;@
z+I`iAEGg9Q&wKVP_qFHua)+1Py;`&>6|(2*&0A9qjiyZCO)O%DYFQ}WW{5imktIx%
z#Y^qCOn0!V>0~sa;O<(4@dnU7@E~*B6G)xo51IgWC-M4KH9B*buJ}hDu$e#tp=^z1
z;iPBmKj*-^VLnx0@^a~1e^nVpOtJ%{aE|N{vlAgpU9jy5IM+cH{Un)#F$*iRteWTR
z*73JVo#4a{_&Ik3rs@=zYj9mdWU+xUG%=x=KjWZhU}z}5f73AVPI~13U;W#cf|F7*
zqzBVju0PBZVTa!Vx%{nV6al99R=wRuG^oRfYz5$mnf%Yoa|JJ|qOyrfu6&tgjWX+{
zc9HasJTTTb2Tv5VMZJ(W>gJNK6Cf;t1ffE933PBcXins<H^D1O$2f*UbHnB5f=>`B
z3$*8*h&X#M%`f%Ct{Uf>o%u6L-X6qf9L8m-<Lzd+C`)jfq2sU7#nFZ65kkWl$!qJ5
z%~vp&CI-35Diyc~*S}ex##xaec+g@gRpZWcN5XSA^2CV2;HaWD>>Cptf{jAsNW%ED
z2KMjLo!~(7RFSpt#166WudAzE3L22<pGoH1MA3p6`)aZMy!VbX?1#a6s-C9MFYr*=
zmwiwC?X-HJj`7tgDq=ec4;My(aO2*NJ+r`NUhM}w1}SUYp^X$y;&E5GNFDMt^&I}x
zLSed8)_BtO-{eo|;$8YtUrTy|WB<@CO345S?DS0jhMU47=e5?Cdt{*`{i#6UvnJR~
zgMP>Omqw3dJD-cYpMMzpGf0%m-NC~~Cf+>CprkurcFm=^{F<a)>0%}t&(3W#JQqh;
zyJhcea{H+TgocqW+g6~JK73MNz_Hs`Y8-oh-j^!D6lu3}o=iKAut|R$XJQtW7r_p#
zi2a*<_x@l)j7sK}>ZUhDbF(%(y-1I;@Be*%Gc&VtGd=L%vP*YOIa@ozp@<u6f<z@w
zFRRJ0%|jhR|FObD7`2M#GTLw_w*0EEmAJ+{1iyVN1*fHb>42pyBR>Pcw!X0&%GH)t
z?KM?AF@u5*jL*`O%$%66pdpVn#>8k6vDZSnzT=;US<GhA4^QzRGBhe#u3XVSF%{vv
z-mKb+E^S1Xs4?rG3%1AE%FX0jPMz|s{Y6n&FqkHoSqgWqQ_RUt?)#N$rYq+GR?w=_
z;Zj=;VrZxEn4rMRM>uW;uCVc&BsXLD%8*7jHKWhsS-=nD&NEb1y!RZ)dY>cO#<5_O
zHPY)&-Ok02yPoU}>6Z}DFa})`u=%`F%4KnUMzrSpfNSA(l8hKW=`rD&&tyk~mDb1~
zG1%$DlOo+cU?cK-pOOD8r;(QVe72}OXRTT`p2$1vT9sT6R-KLM5ZG<MNg@;Q<-@yY
zma{lkJC19w*y)xjUBuXqX^yVVtud!alL)myEo>q4a6FI|n^4)w>iPvYkIh~YBp1@q
zM(Rt<l+Lq^Gg`CjHlQF&&Ffh&BwEUvB3YZ2ZAW!v^-K^<c-i9EEqxd5uxkzZYrG#m
zem*r;;ZU0>8*#O3BSk&Wqu_~o->9Woq(N8rx8|_VFIsB?WQKqEdIV$FD5(-w|CL#<
z`3nV6P@NyZc>sfpF)rwb5@mg-T!K+y&@uhDp;i1;F0)Py5A0xlu+SPjC7G=vd=s(W
z{s;s6&`+=X0-!KaLObdMV2o;&R&a*kCUn4XTxGo}qM(9V8+(;3q3i?gU0dDApfRAr
zPNefew((2#{_@k?X0t7&!7+0Yu59;e-j}g;FMP67XcXDbD5!#(et#_EcEoE__Dp?b
zyHL^3)WcBokS+S9bu+bjo3yW)l}RHQY;zn;i=_SAG0gSDPLAhJi}}zBus3P=oyX@&
zevaW;t`OmLavyt7H{$Pxl;?FS=5*Cd(;9X+QtO{-U`nS>pZnBZ>{5tHQjo9e8tbo{
z2IFVJZL~{r=WQF2URT)P8g!jBkz6mp!<Gm2LsorvR1Daq0our#y%J`Zt|T)cBm`F1
zZOgSBjl&ba=k;!akTL|m-_TIWm5}70WtB{=VH?(PPLroHm_X^W1m06befYrI`DORv
zNy5Wq^IdYZqd|OEY@<1Q;M^*keBeYB%LqrDe5C@E^&5}trx1(#Akgv;52x%7juy~L
z*XttPwAsSG=lLSaYk#KSlJL^EZd~94(slLsXyT<`tMt3Ke=bXEmFZ!q@clJ#7I>2g
zVu^OGZLkid$6sjT3)#KM%pn%BNt@mYbK8xYjO2m8U$m%#-))=JAHR~broq$z2Rr;R
zuOwsiAJWH-@K!L}I^?PXIOKH;`a7EGH@Z+Jp($M8M={-zpE?@4P6k4cI}E?8)UF(|
z)!lp!sFi1}gdxl8XyH$(Aw!}DOC(6QYA|KI!COF?Qr4H{kxMwS#0$3))?k^=x?XnK
zlGN1%;v2DY^jPtvqeyi|^9Brr9yNJ%?30oGsminwByDiKMF@%bdVG5-{iBGizHI8P
zJuoxX1L8&Uy&@Ui75dg^2<XnxL>#{dUCtMb!w9tF?+suJmn$h!XtPa=+rW2Dk=cJf
ztty>QZ&?1@c*qGq5a>6-a=Ye=tb~1Ikr={SM5Z^6<tyU@<a?OM*$?=QNV^<mbKGLn
zaR5AtvwVC~JT*+gFo(B>r&%q*rA2g43~rb`P~4BZn6Bq4R|zY}E%<Wkn$Lx$lCRRz
zF?Wg|-M<eRb<YcSddvf74x%;lhID<x3hF~)bR?~`l#tg^4F{9(d8!1tIwF`WeuY)|
zuGuF;{;b+s-3o-sw*5hHZONE<tDfvEA<L7UblHdZ_2J?Tyb&S~Z^W=Q6T+2&RH(E;
zIjBhgIeu5vdCP~SB<Ti}2ZRruAzaQhTZ_LO+{cxGX&HShIHF`g&ChA>^XT6hT3Ck?
z_1Lj;fuBudu7w}IJER?qRsQLkcp!d7<tK|sOb<QM&?NksABQ6#2wZ$%hso7WR+1&~
zT*KL;iIH6VO^|p%^76U#US6eoi%zJylKuQeY0`=~WR;QOrt}OjyBQ~yH+mtWR-~h)
z281xXL&B6fEuV(2_(@L^eOsh|1evXi4z{lW_gV?yEliLw%E9w;qgYa@(@`r3=wVD`
z2#1I^5>LMtaO;P~%-OVrXJW5(wU8eLL>y^|7o>d`(q4<IvD!#VB~aN{@dndQ9l;_w
zX%)akd`L`qk<U{C<4wzoahY=jeZrmk3&`?D-~VnLf0m7_F~nqiUxUP@LV*lRt65<J
zPiN4BBXoSVh;XGw(jn(SL+hd*gW`tp(d^W)W_!Ax;kDW7&3uOTOQkDyl;UK%PW}W2
z{4SI?U-vLxrNh*qbF)2Ja$UioG(>rsYko!A^{;eA$c7PKRmYPhMx|A9MMs|^A6yA&
zL3no}%h(c@{Mkq;f$%7XPZndErk5m5wioLMt7bNFiLT%(k|F*9r#M*xjXQ}Bp5tB0
zj>EpC$cwsi_~)*~Sds_d4k-|+&cz&st1p$bFea>#tqsWC7;520Pz?9X5tzGK($;m<
zwsz}jt!cvZ5=EN4EU!3N9`aJ|4RTgj%vS&1b>pv-A@#Sk#_}nFvV${$x+>R=7%&H;
zNkzJ&c~Z(+Td6!oEo(Q#S7t;OzWmn2J|VJo(?SF&TOpeCMaFP#ovM6QfS={DER5kG
zWMFfi3MZsL{`3!`=$0Fe%&<~BNxrJRmTK8PW45RSfN8%Zd302^dJ}909|pbXkKs{~
zOU^Pm#*?lhdCN#D#pB0P6f&&2r7ERAT@mD85g3MzfU8x|&h**J7g^J$ikHr1lSUtk
zW^}OK6U(TQnOLVnQZCrireJ>WOvWF1L`8FjU?@E~%t^(k(U6s3^NSqQqtW!!eq|vF
z``W<zdNdB}LyMVQX85_9EUq<Qpwcs6=1(tZ6XS{{i;5;C_*INq;$S?Tb0w`>6WO;^
z!Djd-TUL$`Oximh)r&?4a@#~<$Mw9S_|kd88;ciWt&0b~<6Y+G&kp>YRHdt0Vp{)P
z+I`ZfY_Rf{4Min*6QYp}JgGwxZ0&dP-UN@d^&_1+cLksw2<k$ZLPH#fgc`+EtdJ(Z
zndj)Y7j76!q~R(PIGE(nnaLnId(LJl)J>il=|CVb(Da`CF$^WwbA`?0*LHn1l<jaI
zx6Gnh>jvMJ!2i81CX*7_FNv)YG{mLWv1L4*IQr(5qzRbA*q=gG8~Z{jmpMn~aKcR<
zTKAD<I#Ly}sON!T_nN*T<FUp~r7vvD)8Ulh@zc-S#J&`TNNFnaL{Rb|vR8;6f+|-m
zDI|BMtTLXo#!lSCQ@2ftaKpc;N=Z#WSY5nDZ=fdg(HJM$JbLePPz`<!!Kh`f-6C`Y
zyv!-Cs62Sk#HiDhNot=1YfL~}X!-;!j1AB`&O*U5c+wQ4YoK1qX+b4y2a06F=t1am
zgiR^XbBH9ihib}CQ$FM#n&@#z8soAk4DX=>JuAXn0~G+j9*I~NMXigpq=ueiyT=(0
zml1Jqv6M)-?EjsMyeF1}=P-UFNm?tH6GPpdN<+LIJfLV<B<Jbn4VH(}0>Kpq59JDm
z)P#||HPWrAwpLxaj>&kIH0$B?;LY1{hGIHUb?0rgnS#WXBrvQCEykU|Fssu=qRmY(
ztIMCRS;kvI75Z%)c}l1oOB&9EshfC4;qFQHPO39ZPz_nAc&VHTtf&FR4han;P11gC
zHzWou+l11uDes5vCoVRu;^vedaTK7%w#STUY!fF%ZcgP<X>GvJ{>H~q8J2f?aZIhl
zQoMAnsAtA<ZhUeiF&AB68<#VHruX}X%{GUn`!EW-wn&{hZ411L1{+(|GJdEYLOual
zCi#2;f9}+5374D1;$nhp13T~wwm^*|UGRem<p*%Y#f|w&yR|k*{MtA63*KcveoJ7=
zG_rQvspi35h_ISzMU@W1p(gVb*D@25xDbVc6%tZIu1~2!c~oi}?Cs{jw*ne9DvMRW
z=G2gQcNwz@^&iTuc}Fx_7qfYu4xl@F%3Wn9Ed>4ya}6Bvb{o2P8?FK|#20K&T~MOC
z-uYX5GoAwK*9hX<a3|=Vt5w38w%AXZLZ@X>GD5dJnKD1?7^DG348qb^60$%*Qpkom
z{MQIUQfu1bG>!#epEHbK|D4}K$M(6^SaCD;kJLZtYuobw89JzckjX`*pzTU7G`D6$
z%CaS*P@41UPt}@!6Yv|4mvguE*NBk?E6VF&kb>2|w^mp|nGzFAxEJe*uC@|tpPKTZ
z^TR10R!$DAXT$*EIJr&D#0+|pUyS)NKD(neTGMM4a$;z3LX-u+GQqtn@P(eCz;K|5
zd|>5!m8(50RKI&F>;5r$U~Ercmg)}v+u2U*T~Sj6zINE}q+EK=N8?!LDbgw~LqgUU
zum#YPh4tqSP*b$}#T2yWQUoB_jkZGM`+-&&GA<dhv@K;Hi>1@wj;<#3j@_oGWbfx)
zq|=+FxBtFGo^|{>?tY19B0b6c#os6rlSrZVF14w|zu^}{XC|cjtamC`s6^;eKox(?
z#n2VQoisrH2LQc9B7obDCcChtD_nV#+Tjk&sT~0xotuk><7VA*<kO`uOw^GT?V7W-
zQ##Y1yer+t4b~2rLk0ZtYq1GcTZ!HZ_s<&t{YG&M7R(Jo0O0#uKfFfe;<tJzg_5iE
zQ4MS`31lmu$V{OAa!RcxX%hpI*Pq!^^@XunqA3(cb6JWeC)9*7*)f7ar^9S~<}dlF
z?awKtF}{9h*MYYVYq)b6<N1R+5$@VYZK6%SIksFSjzh5=sA?#5pb#<isb{(f>}VP8
z_kx3sjjewrw|cYtd_Ux$wa?SnE7iMkrpTT%am42NWV}&`c&R4A2FhXxlD`F`bsM3+
z!!e6VETA_e@168rv{`AMvv1UJ^x#P!J-w}|5rZ;<TfCmKxT+E2mkRoS9X`s{aoB|-
zt3kk#hXd#tD)#JCp;n1HD%<CObXALmY;>#sov7Ky1h1fLK8o&VIZ2Kw$igZb$Sw<Z
zZGL{GYiIIPm-|TqwPPZ6evSNB1^yP0iWV-RXjr*l<PiGNaZx>Gay?N428b?o`;Io|
zQVY5wF3{YOOvi(^IB4^9HBlT;<g}4g9;)hZV&U-BMNo|sO+(F8E$~49T522CcXP8d
z?j$tEcLbpeor|^>NPs17!4B)K_>xlY@A9^68NzL^xJ+hCoA&F%&wDL)H{Aa4qVkT}
zZ%Je37h}A-@Spx-Wn3F|#AXS0hc%As*S1S8)xUbBh#hn5B@UU0;oT3VX}+4rj!wO<
zloh?&e8>wH3c>YIw<)1w+af&)Fg*O}V{uokJv-oq*#Z8?8|*){gIK}$^U7K@i!mZf
zRy<VP=w7^f;hS1-xT%Ebclr?&{c<m*q@UU)`k_hDXp3n@$mEH>l3HG>_%4r?!);@s
zih0d<7r&E;B9I%p{g`Cwgf>?LuQg$T>#1={MlE!w)9J;i=`#&=%qv)dhd4rF2H`pf
zs94)A1thT?n=z{7(Lb<HWzC*8Rb$q>VKYRPwW4R1kf3mA0iQIuS}1BSg%+EIV1{P%
z_+O=n-eD$dgb=U=>FkD@CE`z`%<75**XtGRXyCR$TvoGohvz>LNkWkq#=D4cMXwCX
zIpWz<%LC~o>14{&Svr^+H?RdTJTd-&=UcIlq+q$(=3j6b%_3*|foDGKaIqi*#fc?g
z6Iu2G+Ei((_Vtk!sqH1%t>nGKA1b>bLC~wUJZ|&v!dcM<PXf(BB0Dd?SczYI>4v(u
z`)|WDy{RDjM{^Ck%f9soyhuTYCeN{}^d;D4Cx=Qz7*^}X6paCcs}@_gogUj)p$=!~
zG$Lltd!EuI8>3KGwYgIv;C5^jDWw9?@8`C)tqZ<_)-8_+-xL6Hi<73`@iOIS(@UMX
zGxV(sihPkik>Z}HwE+Rx3TgDvr8H!^!8>t7l2G84X(3T~hxJGF6i1JwnqnxfP8Bw-
zV=xQNW!xtVVKiz%Fy+xGKkwqydM8M1LMAc6I?2s46=GDlB@mfZ^_GHF4foDaA~=@8
zN{3vUBgmVlbv?8tgJKi1$U`W@*gv*8=#Znc?0cV=HLp71ANN@&F351FNt@XFpxq9g
z=T>cX36CFGCG$DBsodkriwId#5R)mZIJld4m^>u09n&LUVm>K{6Gcj-4-||OnO!T5
zB(5+0{=*R}d8IRsO37afrRAmY4-rx-cSAX1XG18adD3Q5Cqqo(`;DM{%?<J->vE`I
z=GCLH4v}CuhxrPuyxXtdOp2lH!`aOQF$;1y3n0`F^!{Z{0tP<*+v#I{qOS#+P`Q$P
zBPlr}iJ)5Wp(p-0^l?&;5Z*eu!}~-VY&;e*&<d`zV|E5kE%77;EEZH2%{*i10v*e-
zu34Y${)TLF{RaKsE!>oKFFJ(e0pK(ayLe4Jkls8)!fyb)s+zJKx3^@|{9KK2D<gto
zu$q310V~eUVOYX=-cO&lK`mb4w~UJ^!48(;xNxe^wQc<2$5IhP=vx#`MrY3<cE@PS
za)^IJfiXYH^{_$-eHW*No3<lp0405PZUf0ntFt1HCvFSXCa$^dlA2hERw9il+*7Q~
zf#9*5yDhFmmB#oTwo<w^_l&L@EBS>;SJ=x$lsj@UxPcy|1ATnLI))ZMqgq&s#~K9Q
z@{xQzvzQQ!BmJmn@Lwq%g;}Xof%%)=0$8^qO3WGUDnzdH8I$#YDSA#)0-_DH7J{!e
zF`W0A4;a$KZ^#0x6;QJqm=Ec*?H+R{e|?(;N_qmIO0vjJ=4Uye<`Tzk4?CYG$|#}z
z?ZIj{qhwc@7O#?L`==B~c#CslZCo+e(a3ADkdtT2X?r!#%pEvy!OzQyRyRJ|1YN{A
z<L@w$zhN}3c%f&pJ9B5m)w2TqwnB81gP8GoCgE|_7PMxOtM2Bu_0u0OWD3zA(*K5v
zv03!NHW6)s*vTd5TvX$~UDPbbg@+hBG(RBnv3XpUM~vL3R`9QdvGHJpI6>0Uhivwf
zLxR~@-#^O#twKH_I1)Ex4Ma7%BqL%XYahVvi3GZf&+9*`-7Te~;%TKG&0s(E!U$xp
zITP#jMy?(eh$?%g?p_X+5Y(TM6Sij>FjnTqa#7~tDD5xKo4oLw0gCfgIE}KaL6%*}
z!bYYwS3Ol<We035At4F{+Yi)!QYSfNuPx$^X3NPauvpK>$1!&?z)v!VN7gJ|swi#k
zbtkbf#~naH7M8{OWyqyE0J)W$idWRLZ%G!nq-7Y_kOF#A?#S5g7^#y-N`Ok!ozC>Y
zlXhIr+ov1Cfv)3Sid3#?J7y9K3=4!N37=2RK6o2fo!VFuGO&7ySERg^bPn^Vle)4+
z2&Z-)zebP-_uSsWV6H7*&Efnv1NNWM_K#R+@a3mUHi+Sb9a9n8?#T=`by4FYVRERH
z>6S_OQ)5-uSHcS+aE?mn_^Y{Jkm25=7H$)TNb&XjtIq}8PHN+8*)$52+}S@fjgL{#
zN!nUqq`LHlUK;FNcfV73GI9|t5$(!t2#jh~Xks-=EYO}wJ9r_f;ZH!KR0GUQr>yZx
z73})r#$J?)TyI>Hb<FqluZS!oV7OqU7U;P%k{Td^96!6ME39;n3P2o9US-(w71!_>
z1C6k;&})lMsLL)DPh@A328rbkjlWjH&wmn5bxC)R^eMh1o1@VYgmm|Gte?n2#s!go
zzRQD*C0V%LLXQVOhc6f(m0_FAAKM<zSVwF85`KMxzlJr+EQ7+mq%CD5nNmAlrqeuI
zk1et&<Huj3?TFj!?{j#bNj(p?L}3Q_C;6g?4lqp2jz|xBr>9|NFAY#r<6pwbFkDon
zGZWk~SJvK@9hhZ*r#<BzITd0@6IO9$^n3aN^?x#efY{c2`+&~#0lUF^0z~GH<iR*%
z)Pvm7NUtoRr<<^Q$j+10$ZXA+p5*l*Km`_5ol7@@l{&=PyD~5FoC^wl7wW*SC)KUw
zP9zXp=yV%_QWe)}4Z4h<Ya;PEOP8~e9wAcS!k2LC6t%W^{L2v9^|X+|4?&FR#Rp;(
zK7}uH9T@-j4P=iF8i{dH;Cx5I`z+;hRqBlQz~lZ94LV5vOVlYG2(8dfCAZY)O8l84
z$)lFTi~6;z;wJS);MKr|FOsVLa#z;*F2ZPeP6|nBQkJd4se{vW<<|PD*(=W0Z(^~A
z+{)dMb5gJWx2wwzB_D7b9B88XnhLq3JM<u|gGCA&WaaEafD@O$eN4W<6WHTLrP{;L
z>_||xKXf4caj*M#VU)#qo3hDAIyufLaFUctpPxhY#CymEmE)eLsTs1o2Zy;bwtzXw
zP$?y6N@pkO+xLK=sy+z}ZN`)(`P-AGjajsZT=wj>D(`Mew6nK)*Rm^qEWU1juxmNA
zk->*FYZ$5>R!nDZVQoAh`e97DBWVz+39W2P7P}|!XITMzGV94ItSs-qH?5b6(y$hj
zqAOj{Ahm$&6FB`itWa9@h^HksXl=F~%T-zJZigPHkRw`r^Q1D;0R`dl@x2B_knNla
znKA(>3m8Ze%P@9$Ni26fvx(X0p_J0&2t94A8{kfvKvLye;kVe&$!4$J_W88DZgHcq
z-GEvptO1A=pV0ZsM>%Vml~J4#$IkIp$~dLmO*M;h&h=S1sk0?TykQE~R~+isFK-{U
zliCK425ZRI3)7HpU!d12zT~Pcn8F<$;g_J()6p@L0!`lLJ$56^?i)Bm!z%Z6;{Uq|
zwLQ|}t9u)mvJMl;BGF^D->@k#EKPrt35!&1=8^v#gi&jNFMo=oEw6jn3wrn`4*b=T
zORys|(l@F;Bd#|Zy~3@DHdE-a!*tb8Rk|dtj~s7Gsds7_lQXXrqQCj;bq@S6`W)-j
zYF9Z}!g7cPE=jNlaWG36nDv^mp8S(CRYpt!L3v&xpj>&Q$TRv%T3=PAYAFqA1zF4f
zi)P47DkB1@GuYpyuHcDC0s_sZx^#sLE`@z!J4rQ-?n4sF!;P40Iw|HP8)6(_{0#*S
zn;P~^EgC#jCOS7^h2D1QZO)7Jx`EkghkwV>FYk-2=h_Y*s0Kz1aarEHKpIlumGPx4
zSiaWp6Ac0$+kE8$x=?Dq#_@h3)e@=Jb^3d()3!%F@{K=LJC(Fi!s9(!y~yJVXY$eu
zHq=cBp(8H1c=hJT4WJYAzThYjR9y^ApSJz}Ose*5S<U>to7m<V<e#(V8zB)193Q=(
zx-`=?o-~}Zyt1E)FB`CJ5K$Q;mJ30z?F(Gb#$u9B>a*&?Pig!bLha3NaMGK7QaA32
z9?5HUrHm9cVkW6KI%FJ0)V27dxSTXtGvN~emB*v{=fm4NHcS(hT}<oVuGto3P|2t3
z*)WUya~Xlu6|C)oPZk&vSbySYAPO($Ur57$Vu2OiDIc%mn{X#)SQD~Xhay8txj2!U
zGE0&LtiXcfkL>A*jvp(HBeTRa4%SQ9T9FO;z9PVNEX@m>g<I}lJ$W1I$W(}xcCoj^
z$m3Ja+7NfjhbM2AY*ScH=6~#C>Pg&qd~_U8uO#3#%zGwJ=C|f7%9-K~^F58K3h`>S
zr>xVCk&)E}BWE;m4v-G0R$YjBBMHBI%nWU0AMBPeQrciCDIPoDUhj*k50qCaRZq%S
z-56I_LED#ZUD{0lR@BeI{DR`8K8rqO=*$!zh7m-g?qA@{q}nRUoxzRs;w62kv>0gv
zHhD54|Hv16plZWH1Hw9)Z*ZZim510p;2{ym7EVUs)OAt?(5N?l$(}P*Qh74JWO9!a
z8Q<^w8b~_Qu)cShiY#J+87^MuRIT@&4X$MXMX;j3RuOyF<x@t#!pKh6#;;#p)NWAP
zzm;Q5Sf4!y`I@4E?^D3gsdBP36)kut!nPejuiW=9OydFp2n886_tjTuHrcLJTt!_s
zhpKkbT#4@P=0E8Zu(;&+V~^jwP#Cm-RFxv|uvCoC<V|9U$=m5gcz*Fea`Kq7W%Qr<
zZoSDyG$_F1;Nd%Y93A}M?}+>HJ9<MTt<bIq3x$M(*5=G(1za+-OYWE5hHnq=s%+kA
zANluw!`5kjB}^)7a-a2Ks&6Cfu#m1*3)StUp1Nwh70?e)DF+cm(ryRL)r2dk84O!o
z{nNNf|89NnkmDoVQ@XKCdTjND*U=WoT~D3TnXko>mtoegZA4>%m8v6s$RGz}Fu%8F
zk9fi+bC*eUHlKVs!30WDes(CO=#Pu(GFb^ro9oz`to7#TCy|S*crD^NIh3k6tQsHZ
zV5?dM#Quxs13#Is0d_AS{|UAX8Zb!qp{VKKrp9Ei@F8e32(@UPBJsG(8^*HTch$bR
zod5G=IRfQA;)=zkb6k7#Oy<S5jmjui%sjNj;j}Q%Nyk*gTy`gc62beW3e#bYS2fN2
z^0z8dPk+lK#x9vq++WyvhjgQ9tSiVYPC%R+azb4JjqtTDyKk(qrT|%>v|mN^_ft~;
zh5g>U_=UN&hJlH^*>8Lm&PlP5nQBhaw8ct6NO&k2{S~XF^O9;a*QPjgjmRn!U8}1}
zgQ0@3<l7QKSYOKnP&ks^pNVJjxE!?1??3W(Kj@a1j2zfuO_0(JFzxJ8sdK{-%TgG>
zT87=HnJ=5`{PX$a4x({YCN{&EUihcA|H`&TQJy!Nzpk<(az&ct?qh@ac?e_8&(39a
zZ8D15gfT31&)6obNWRn%=cr~6&6V|+@60qJ-h4lRUnRoPREsvzP!+ZCmg;9VfG@Pf
z#jsp7F9ul!3I;G-#rD}2I&}O%HpXYb%0P@|C>cnpbH<K~B4X?U7OFLjpVVtVr89O(
zyI-kuB>$Lt{YKCF;HxrvD4f){j=}7e6g|5@a4gR(89oA0v!JcmwV||IKY9EajB>|<
zo6J>0N>6v+n!GYVF9`cwE@s;;<E6Hn0c@PQUOA1Q=wTtQHXE$WiSVMbBb@u_Ez~oy
z6m~Ms6dXsbVS^)NMfhltO7EAx1Q{y6x8$IjMG+^#xSLpBpc=cYQ}=KCnxuR8^~*li
zvJH$A!PRrqC!DO7z(d%q5jA0kVEF|DGJ@%*t^mY3JcYxbDmJM`TM>-f6gAk=_|M-I
zo50dVa@iO&xF5>Aay7plFOucRTv*Gs=Z88lwRkFvzZLxLZ@m^x@kHP=At(>T2MH{2
zN@BDE8a%+AWAMjys}q|_pJPg+jcnig>{@)Uue%kFx?xhQiOrFyOvMsxDPsoVVY7qD
zV|9;NOF*w02{q#}-Jh|Ykb6`qFq1M`eyYq))Gj9y`}mKqYIQNe6hgYcTrgRfGN}*u
zcc>%eHdE_SN9W2wsQ`R~3slAWft0xu;JTF9`tVaUxxrH%3`r;qs3OVY-pVP%A^Hz1
z2c4}~mg@u5jmAvR7h5{XG?6(bdff<YGankssz&j7z_iq>l8v0}EOJFK=sI2+Cd^!w
zBk@Pvq~$TM0`4<W(kX|o!w3gXzv~+z-{=y9{{;Ah7JQu5D40VZslx(27aZ5WiYGr;
zAoOS&f+Cu-SbrC@@#NP9iIaJ+tv~RjdnRONNiNkjTc&R`x*Bm?<^M7kKK%8H(N*Mp
z>$OJZUbGdXX@KIB(x=9}ZZ7OUu8`5+;Ejx}53R=qu#XmAa>;(PR^{F!eBOd-dBwxo
zmtN883@YSE?@LtXZc(G_m@!l084o^w&%_B1rY!4ctAUFCLTKJ15Ou)DT(?Gvf=c3=
z8F;h}bHi&}dO!EVx!T6xA~dMO!}X_-XEI%Tu}B}T{3V)%V@FDOTCWZXfU}?}^5VHd
zv$dLtoZrP9S?&h|w*t#r!ZrFRZ)d>j3XUVV#Q%U#zK>B)l^Zbe=EB_PO)uURwQ;AL
zn4DCZ8_K#N@GkXAT4*>%$vRVx1&p24H~cI>$lS0~Z{>PH8PZvUIlUo;o~eu_o6ibb
z!Z^asQ>Rm#mEvh*#L0f4Na8A547~G0oa&+z!vuP<-<vG@Z;os{)t_vfPPOp}@FgwG
zq02i@tQs|tpRzrk(_;};UD(v;zBH|2X##L~h?yn_sd0R3bWlbH9-L?4NXth-NH97S
z!0oaLMqNaFrcT|q0#E8W+=q2IIi}xfgJe@DadebWaJohErSSYe&!zLr`eQLI5=|tE
z?7yvsmx+Q~;+YS?pdfhMMD?~{d?(j3#jBLPNp8iJI&QoS5TuNsC5|$+)+DY1@JA`V
zbB2j5{J90W_`5r}p@VcN@Q9ln`D=}h=irO<(<Nv!;-qy;Hou1c@R$qG1Uln8%pf~q
ztTK>2MiRr|B}&x|m|*W=ZavB&$Qisy9x$9q_xItQf{e{#gH_{rq*U5>COfUmOWEv7
zhs{_TR}-vZjKHGRA3{M!*e>nCsJjMr#%J|ocecOx9%-hVqqrWblI=DdA1%`&E~%KS
z8Oo%^>P>$$icHq-{Su2A<AD5Qi*s}snWvKecrhdd+tv6S4cr1bf4ni4Qj#9NzZ~Y6
zh|Vj|!oX&Yz9b#UIxk?m7Cv1R!WONK0(^k|)xp}g`8c5_LK@p2XwkQ3UeN{l-w)P$
zM_QfmN^okd3~e<s4-7}~bxH4p|0o5=h~#721>bq;v4ti-fV`?tk^?-gT2%L8-@bkd
zF8({2)#V~&yO~=<bz)1NAgUoS5l}sO@@@1=l&@qzb@PI!_z@kfEJ5)dNeiQFu$)NF
zmAt2j8*}JbKXY~X^HZvfa-KG5ZE6_PiHcc&sgIl`zW!djqpu@ogB{drjXDf$uzDis
zpg12~`~rSSEj}t%oJ5CM%crt!;%pH{=_!`kq{%x8q;kKwr+Z#GIF@kc@^Yo28t(M|
z#p)rR@)vst0({E|?3$PFBbdzzaZ;wO`zN*9N_$|5LoS+S`Q(`ZjntY4M-ABWSXX<Z
z7@vo?8{XnLo3S<LU$YOob>a_GXL?JDbSuC6N2yZ!H@cba_rC+RBPbP10Upst9GV@9
zZ`(6UX_`|jX~}v}L&Zk6l0<FquieTu3BRhS;@KXr`D{u*x3<NnqBa)Sp2WDr>X6eB
zZKL%+Pg810c`hK-XImw5FH60sgHJ8#M<S90-W8<Y))|oF5!Z?TMwkzwW}rWgiHXo`
zIqlxRtkV2C=SnL!0TVS(cw^9@MFQAw(jDoih-oY+{ky2))zyWOFGq}2*iNg7zfb4e
zXl9%mr~CX>tKi?RfEV`-56bu5?jW6pHL+xaj40a0D)3z07F_l$`5vRGE;?%4m)VZi
z?D8kicuJIq@(vmh5|O#z+pyhmTmQzRDa>dDg~R<>k3r+CbV={rIhVVP{IL1<0lggI
zIIo#w>#Ld|YDLR_STRd}1Sy>xb|y+_qdkl;+QUb#mXSs&zH@>kSF=t@LUNk^-a!4a
zzRMunsIu&WG`|fpjxVL_^E`b_Y)=%dH|Bf<B6IN95Q`>?*^K4AQyB+W=3S@WU-kW0
zjbI0#qfP;<DRZe=AYV(!SgWJ!^Ga$#6`(bAqjSgay!xBUZ20jtWxIz;=-K&EKIzfl
zKO;wRpM#(?MvMZPyFVM8cH_RQB7SY*bf-;T)Q@<4-keqDwfpLMsSYgws>X}AcC!Xd
zF<ONVVYQl?I@WzD{I^kpzqw9TAdACATeN`Y0%Q2&2l!(Jz4?DM2k3?7`^4Y87$iHQ
zf<iD*Q7TYP&W=gMRLqyAfof<@cv4mfe8#-5IWIQQu88kvFSH_NO{peov&$*Q+%;+t
zC}oI_I0mwmP$hz)KPN&MyXY1}bS)#zS@iS*Gn^l>JFlUhC2#95Ottr6J=H}eIRY)U
z&Cn(6Elyt+@&;IFg(UXUx=1*Y-HH)`I-lwWs)aj`c~<!_YQ;SFTs7x1I4ocXDBquT
zI)7hCi5#`!SIF>(z;&V7l8^l)T!-+?#aJGPf=H<#>-j!O^Z_S*C|g;G)`GPDsHhY?
zJI%8=6N=ctYHbtv$H(kt9`5W;O5BKEBpvf{+JExpWHMfW`OH#2p@b!8Pd-8JATCSV
z;cx&<+9>D4vV`4jfih6*LGG!mB2Nn@{%6;Qtd#*p#l+u|y}(OM*gj=N-WE>h6kLe!
zJ<HmT^CtjRQF}Imj!0#}=<|LRl-;*UZe(^stGYbFkZG~>jaJk4Tp3c^`&9yn9nA1=
z>j)TktE+FaBHT%1Y2>pknyXq_`jk+}smgl6KUa7iX0QYkVx7nAWy+8}Oi2_!uGh!>
z)Eo%{Ud4je*lADyjux|=cj!wj9PjiM&}a^-t#KKFg*{zkfRP+UhNMIMsdLw1wlE8|
zX|S|s!eJvNvz@a2Ty`@x=NWf8T2S+b&uy)`SLQ`{)~TI0_N0!Y>TJ`A8kdPq2qE$d
z`iQIC_!E?31blTppg(S0=C$RS*K1WMvL&Pw!>Zrt2%_<^h<Bov>2R-#a*_I><0`DE
z)VL&{Ce17mP7QYpg^(U#DmGWw%npWu($P7*)Zl}y9lvSik`<;6lEUyMOn5lt-nUQV
zweRCkKVzcPg6reshx>)cy!YE2(^^)x#8B0%j{*N@z$f0VRi7369h}71ixFBNt42-J
zhSg2%Zpmy?m47_DPK{UB`#&1tA*0c9-ine60@bBmdSmMK@QnN&#@1N1)Bt{BjREtf
zv?js%AMO5lyrH&OH*)5fY#w;u6oiv@!of2`SyV&=m~29R0BlVHsA|qxp7rzTSwKkk
z$C={-Go*PV*%3A9#-`+$jM9Y*v-&CJ&KwOQxL{OH8v>vz{-?FCjH;^p_NKd}I|b?P
z1`$+3P`X39>(E`&p>%`NDGkCQ1Vmb->(Jc|2YC14dH!SE@qW2?+;JIxf#F_jues-%
z`I~b>p%(^am{e~?zK0X}Bc>VDRhB8(8<jy5uM0WoywqOrEoM*}E}hDZewfOw4}l33
z99RpC!Srq}s@aXwkgK~S#Z-h;benh-eUR(4V>$uj9(7plIXts2ahYj9W(VJi%+Agj
zA;sGXR=ZO6(I1J%g|>gZ`7uC6Ag-2hL_z|y2D<$6W_3TT;<bz5yX)`+COdf%{>eat
z>De(E`q-@Y=NYd<H@0?5Y_@7_7gBRf_Bvnf_M}<ZS#&keS^aeU5}&{LzW2_{8uM3|
zfXw+fb+_ILagwy<DH$>E4`t!+b3cc%9;|<}XIO_~w^BNZu%~H~$<BQUlk6EKi+GM9
zW@c@7FbL_i2|0?jcqlngTrPhK@xp=^pDO^QUT8Zo%{9OIja%k8CyM>rj0kY%IeX1>
zMvdoI(GeyX6gIhkilRH@5MEUHV&HH@V~^-~c6!6VkFyEYz&9gay0IN*e!Zy_B$v#8
z`eE#%^nTD@54l9W31T}-L$uKIS(x}8s$>_RuMKZMg)||tpYe&j!+b@NGb8Wki52HL
z&uj|xuC=6SXZc;48d(K=PN<<2BA4D~&z?&j9Ypq3=Rlp$jj<KJ$#bOAWM#Gmt8Wyq
z#?!9K^M3CAT)3qzO1S~tB^;o8V5BjXac#HIc223gxb|2g_r_4CKf!?Nws56qA<V1%
z=bXX=--hqoXRm=0Oi!?*##pbb!(YzIa}lo`PV#vABvtMP-cFhRccD_)@#WZ#b6X6l
zlaM;SS!~5bZVkE#G#P{lgyhl-MT8{X!i50=w}mfIvLZ9rE(u?1qgQfQJqkq$fdrbp
z-NCU1z-X0hH32~%RmMc-i@DjnaiVF+^V*HdK!MUVsy)huc-L|J6IaYe(QWxLd$SOQ
zj4G3c&OLrJlUDqlpvCs*2-fx#uV(EcpO52Ru3VFS+aA!ad2Sf|+gPNu-2g)rzwj?-
zak`Yv9Zp!>#F<+TEpSIYfICWPQm-h6H?La*_Rw0ty8DAs=QaXiO>3EbDFY->Ijy|l
zj*v;{sUDtdElpYM;T@~UL@ow{IEFPu!4o6&2?gpU8;V<#q57YS1(eE<S9@{AcsNd2
z9sUBL+9k((q@0O(h>mzXPL7y5A?AY0F_a2jyv(RbgNh@Of0W_<N5xpS=)fiTR7;ey
z5Y7q!d$LqPa<s9#;xW%XVX`${EYjtxub4iH+_dP1r-i&NG6*W4w<6Nl=!_L{m(fO;
ztkHd&=T?8vm{(Xr;&PX?5@PXy(iHRtY2wODw<UKJz$rZ4(6CiH0-<vMvKBS7=g0ob
z51OpV+^(v1OA$AjkDrP<ny$0bYp0KOZ+#7y<u&+4rS_d_UZx;DuMBXVw-;Yi7%`e0
zOZ>H)rgIR%nA#oSa6@0gzM<5L_cJv)Gf};Aj32ud-hrrC195Qx%O==iiBQ&d>UOn$
zr-wh-(_S;n)Q4oNmpTj{8I0+VY2HE0_&|4%6OA~*YKf7g`VldLV8OfSt^y}x^39wG
zkQgZ4$O)RXPb5q?WG}(=;b~~3WNes#Z=;~=Ax+FH+!}bM<Y2_>iv@z8XpxC8?BoH^
zPqZ;WS~_eXz6yR^lXg~MS5Xx?`X{bNC-Zklm3?#e?OV(&Zwyg(Z8aBBL(JS*@-W@f
zH%lS5hxY=itF?8^Uw@O4F{;n4CTfcCRd-kC)!sO(S9#|_E;=I1Yw#!XHcRZ^GU0x}
zn&r?Wj;rqx${FagY;vai99mknm$n(VQg>TurR3ka<uPlEr@mTpEChkf!D-#OWb_p{
zn0aKyQa-0GEzlwXoe;-2#vwI@zJVqJU+Vs}$}-|9)J|x0)7c&;KbFXlR$zE468M+C
z=!ggX)c`y!q<RNf+Dt;FpLUKXm75N&=sqB$XD&6m3wsYbKKpcz$*x6USbfhvwL5t%
zM#=3K%NFV`BSZ<Z$t@D`iT{4eoDwESD-l=3ujJhCbn-ob%QG9hqvynI4gdVBYmF6(
zT|ItietWxx2G4fm8HQl1g$L)Ls}D|ekgHwwYRu2iPPC}XwQKEBXU8hnZgPAmKhRwn
zsUo?5Z)HxCb`P;iVb-U-j&i-xZ0c_jeRCJal9nMypZ$(0A#ffj=##FYu5nn-po+~a
zG$XvA&y<F-BkW7Pp*;9wXK@!B+@%w6<13Xhp*L$E)O7T+g!si0b30tMmx{C~_D2_s
z@a0YF5O8=56=A+C=iJva7rB_xl}-TUGTNql&#>9M%;GsL*3(3%IvYwRf^93dx~=lj
zn86iI#2-7PJTMFW_1=`RlR$Ot-w<v3Ahxz6S$hzsQE@LHrsJd^F^?rtMSUzox<x8y
zL**MF#`1a1hV{pfy0q_5!#A6rnf>Q3xz=doSRj6d_d1l4-zdKcOu%_q6))F~qltQ}
z6d3;JGy@6+gLXHE^7VsN-ycd=iGR8G7-^0MTTfMl)QVm(Ry6VFeL$xpd6QqsHN5)U
ztf~8`K7V0ro%pDY<5|u)TkX7#(&aio|HtVyi;JA#X}xJDy2E$5fu8a&HRz+~@}e2E
zm^;GCe1RP5jlYIJ34cg|S;hV}BdU|e*zjamqm{(lb87JoFx;X*aaxs;YNqxp5B45s
z?)6nh8TnN^=>>t%=06J60)^e94o2Pf@cMK@Q!flWg$zQ%s*E4`#1xRSfdL;srIGTu
zy?%Wg0v$QXx9zj;RWFp9dY|R0&Yn^l3Lk@h5T=hL|4?_5#1t<ff3K5W9`bzdSGsM&
zxa{N#C~Xr9<6TiDr446<<8yF4MxhhV$pC{tVyaMJl5+dl1y*%%AqM5E8r2%U17s#w
z`5Cj9AJS1J1}Mc6aQj&+CVnTrMpfN(hrBsL_8bB+LAzZ3&Tt^2#7w=;D36%<{R7dN
zlRx+MYTQCQo35$2;%m<3AJ+_S<pkF6uzwi7&@CWCHz!7tN&5)9Dv}G;i#C02F|>2$
z5jp{->^LJG(M4DOhzgCVx#jQt1BF(T#c70qpp5;5N0Xdq1G&aMJquA;)k{9OB%?3A
z3Tmv>ZNY1?9!~xRW9uizkhed~(?f2lu~FOH9C9uAu~TBgKc<{Qv2aw2JYL2bxH4uE
zHN-KG>|P^U6sMZTuF;Aox53j@BZl`xhwcXI30=FR^O`3#+oJiXE`rAyaPKqZjC}Ze
z4^1=9_;OVfoRb;$ShtMYtSU1@d4V1uN}@pgfFM%|Oa3cat!II;p(DQ}g3W|{o30ad
zb2-1l2c1UI%cjO;isZOa6nU!Sc{Fd*yZY+X(2RMDm0fN6^8D_j&_PoZy4O`k5)%=*
z799r1<eACj3oFey)X~v(x6D!Iw*13gBY<t)lvJGJ)ncwl|GCVM|8C1wo2TLK6=m%O
zo$$qTH|9^xHh9Y4)X1Dxns+v!8N(89i2acog7&%{DBqK;ULL8=h+t)K@GlPIG|^Z)
za%erCzGkiiIB6YdNX#8nXrMbbF{{a4Ei?n+R-K$v{(_s^iGK7z36j7zs4b}6L8#~V
zLqK1Q>X?!YUBCe_6P;^%fkBkX(ARS5TH7Q!7@WT(gC<T!gZCcekL1}$r<{t6IPDwN
zdmKKjPlhe{f*I@Edn{I_Pg?DMSXAOu3D<5|mMHuEn%)5knH&sp<@NI+^8s#bwq{fY
z-Y`N^TUu~YoiFa!*%bT8<BRYRIT~|<TYVKf&6g#Y({EsJE}O_r_gMEJwtRy_%&rX=
z-yfoC<q6ch4re+~=~$kK5D^hI-)v^)mu*H>h;#ioM!=DRItgd1DCJ60f$tWNF2}|2
zE<dSrjnyXg(hTn<<_8I?0(eYlI`xEe#uf#7JwxhGqj*P@l%YHLjA8?<o?Xa-WbAE^
zTU=OZaxZk-s8^my^j0aj^m!A}vQ;}uarykuWyRl#&78+(^>Y*}uDT)=+}Ro{-`Q$(
zhmx)8bt}7%I%_9>bGI>lYH>Ho60f7g45_?cFLL~TJ;AP0k?qYgMR|ruYL0p=Q1r!H
z2b$=x=GRFrmht<CIF~lXW!|5oO;yVbWlAiGqBX{E$->78H%`cGB0cfC)9PVgN6)oo
z(*YBo=>@@egtTD`=an#$)ikrXE=T{f%!8a_49rMySsCk(+ud?2pexO{R6~rm#keE?
zt2W<}aBPa}tn%s(@&M=>O)XFy?R)5&`_kQAKt<~Q{QkHq)S$p(*eX@2-dN}ea?{v_
zg6b`~us-7~racT-D@eQW)6}{0Ncddy+er~G#mXnle7j&FvoTFv%m#gHT&GUtrXJ`K
zQ(4Y<lu$Iz$jqJk)jJ-EwlWA~Ro%jG3JLZ|_GpP#DImMKk$@2?5s8J_{XV{B{_1{;
zS&P?NfxDspLLZyFf^oYxdF`Ne@L82zpuvYR3gxb?@2=lm6^`No6LI~Ls&%B2?OV@3
z5J&+&s2$FelwqbP47pNJRw^;|xWAZYz>g%P3^*Nl;e0u3S=i7(l$@OGyAPYrrqyvu
z<OTwDuQ(l7KOQ|k;Ie5}kY2Y&bGCk}20BH&JDA#2C{zR5W}IJK(BYxQoU{?3<SiFO
zs(h#*v$zw;cz<%5p;C37SVE4`pjsN`4vlf)`{~Kd;fTofh&HBio^_+hZm(RIRzF{1
zby)Q23#Hb*6)4W)bjpLVWmUeGMUYM7#7)w2pwHniqhx35pDQB$ho;+EPYlfF_a)jx
zv{t&CD>XW^uVrzT&5BcKGsboB=GZYJxKl+m)CHu%iVrZyKmTo379Y;+nyNt6GqbLe
z{&UZtd<&Z09I+&GnQ?U_uT@IvE!t##*<6NRw<n9d8u!4OpbAV}iV7-$-bwJV1-7j=
zOdIxO0e!%J(#UAE+g7!p9DyGtVKZnYw*VS&T#JfEAZ^6``p66AxfV*ON&P1>-qN%9
zqerIS+Jhroh?HS6Vxe3r!fbJ3)9f9Yu2A(${YuDN`rbCx-uSui<Mq~MwD1C8o*@xG
zwx+(k&-TOsL>F?IqqoA-xTvS=yhc1~zpcVnW6S>nO)xGYt{`2ej4)};MWkZ;?YgSb
z>gI1FBi87U??<M;eZUr5&bFm-%9EBZd2aD5{W1;IN(0{G_;2d03YDpEE}Ysvo4>A0
zq>jf;#zxI6`thaEg88ertx>D={+qu*38<_yHw!Xks!|X0Crx62(&)iHCBA#9I)+1&
zh`Ll7Fr621`F5)5a<?7xUNwMUWt@|$N4^bfX;@4jIRWODP@9o{0zdL+Io?yo=@X(5
z+Lb}k8i$&Z?B8JizPIo^YgOT+oplhHZ#bVzlp_YPE^4dJr_*E@+(NCSm~GVTp2O3)
z*BB|ge1UPKzACW>EjZf7wX8L)Mb~xOLsRVSjB~$bRtEP3?K+(^X{5cb^n9~Fwc|S<
z5fXp&FV5~YVcpualXkiI<CjEA)%acH`C!6aM?F}`hOMi$0@om$l~4iW$Zo``P3S1A
z3ft@JA{$z;s$2{OyeDkZ@Ey<$L1k2@#f8ygI3w)7x3Xy|io)@SGlVCy_xASo9whZS
z#qBSa+f<{5o=1IVDi%!@9ht{Rr@Yx#z$+KBkXzGf&H+<(2!1eT6nEDSfCy|~QUwB0
zxOzE!wAOvAcuQ^Qx9{Ym+iHZ-u=b|OuUy|@7@V+=WZCpbIvRX%;<E1uIUX$)>alLh
zFZ>q2g)r#Hxn0ZO1hN-&4d*Vle!Yd|NhL_BNhABqJS<?5KC1@$0Kaw8*LrsR^+5gl
z2Q|oQoYR=YvgR2wXb*=i6Y<FJm)%!~E4q!+u;E?l&nCuRiZq&yuhghqYOTY+BbUy#
zRPptl@YIiL$-!o%ty?ajb!IxWqNf`9->8hHi@NVR7(@@zICk7vbk$y7UIuNQ=AQMV
z<i4zf>(O{bbHSX5s7W1Dj|0h+S*8{r4nfB7-(WZEu;BNW!POC<kP}h}3ix7WPe`I@
zQ|9Hm*#8O}mO!uAiwgN3XFbgsDJAI1ya`l`SuKJfKelsgA(hh@hii*w{!xYs0<vV2
z3xk<c+P+DY-!Nz=Id>$jC{PXht&1=0RY2P+I`@iSK;r$=JD8M#%BTh*4hmFbQ~kg+
zLzesMY9~GKnctgur+fi)xu|5094;eHU1!fP=WgE%f93pj_8G~~!Nqg-LQx)XZi}rh
z0Ekt7&U9vg8yp<GF=MCo;C|KX=H}*vgmnURDEr<lww)J(GUBxw`2wROZX|!L+n;vU
zEJ~`KyUfxu0p8Tm%+q)(meovTGKRZ!To(_dNoS32Y;TEGZM{XiErt#H_H4s|OdXX_
z&~xl}V_6_YruQm4t+hLa#^Nt_nYFOetSF}X)v_Eh48yjD6JP$(<}U#U&(GwdcrW%T
z&;F2)w`9l{;n*;W*=sguYZ4dy?WjC%Yav?DcPG!dUHZAnLb676R9kZCrSm08=Brp@
zSdWbY`{$QWPB$;!;(L-y7i2f#5?<1#TNAEygy>4{Hm<~Rymz+HoSl2@a3mTH^->OY
z+hxETV3Jyshl@k{eZ9A{vocC}^WAQiS5igCUoscBI2%Nc**@yK$#>m_>)(0iZ}fOO
z?R?NMjiY|d{IHSZbJ!K|uobWx#a7iR^?3769D3Zdk41b*N7yTSur#!dCGZ@9WjBn~
z=<lJy<U-g>-N$gmxm;<VBQ?(-=d2(siYjh;49M`#wn~~9(1-DJ!uP_CpJB?2&Vt$>
zT}1UkHZw`XFZ(%ma`WUD-ydgiQC`?K-!me{pJ|BLNM13yT72TOzZr8Q)|FS;Fd9{z
zHV$g1O8JB7RaH~vg7U=+mm#5{v#Hc0FG**^qZVy`Ci^3uCYjT*w-A1SSge1os0oLy
zbfZ}Km!eLw^>;$)GOx#y;KM?9R7<G-Ax%^WIt!d*+kv3?acARV+4?SHPQZg#mfN2<
z0Deom-s)MKe`&$ig}3vpdFl%IVGoSbVe$J53;U%*^9ss>`?Ntxd3jHDz`gAN+=A9v
z4LO<kukCS~176+NSc!D>wGZ}`4^#1w;k3$oC6!l9sWOq%ksnpe)f8Mk4ciFNZNlUy
zfH;QjA|;4T9K@z|usyYDGkDVCw(_tP;g>6~F!@G!hHT+d@V4CL@1KKD7Q(AxG34VP
z;~4Zr*T`mEczo%b(E2G9^NQI$^S8LK-&&-}jvPuyQc-f%6);*HZiWTD1-B6WG-)`^
z5NYd${9ZmD=lW_l2p=FI8x=g4ATps~zt~l&AD1y;{X=VJZ(&K8;fZ6SBG9uDo56{c
z`L(eiGJzdQ$VDz94FChMRzP2}y=6BX7!F;wHj|kkSFU3&RO$%F=TfLs?LF>fmC~%w
zsDdD}wR@iRa9n#&4<n}@HKwfKUM$P<7akYIiZsYnrKo*lH%iV_Tj}{T+<sr@g+V{S
zew(E87w9nb!J8(0^!2fA1(y`EAF5GY(OeWfosjVNFxGU!qxWP^s}LJ0F;9=ywV6YB
zpa`gk&#8nVqYo26LQClL{MwdT{mzjg(`nwnpV56leLf904EO%}tXpS;7ZI4~@s)}7
z(f{2L#*-UhS}9#%J7blqHeQS2xl2q-WfIgN-i-H%ksaYV7a6A=mEHLb2)YY<3Ua4U
z=BaV!EcO*xz0^vYud6GA3OnsBJL^v8VsZvJ9;5J!6`5xAhxECiM!#s4rwNnSW7kh+
zyO<S`-0Z33X+98~vB$7sZx?RgyDHiCiLWNO`n!^xG01b4fGk|`mydQmT)r`Z`MJp~
z!6+o#BHNO3(6?u3EivgrD>E5PXBsQyGsM2^P}hGR1s9jOvcC5G5+(X2e(v{6Wnj9u
zClD>%rzm;bKhDW8KiZ}!iyb(LBQW#nTddaVg&g+xvZ$K*xQ>r;<;JsFefGvKf5Ig+
ztwJ<wG*I-J6@*5-?=g{64QSHh6?(Ls66)UZjcM=pnqborC#ioM)Y>Td2EE)w%ajDN
z!=n#6*hIyZ_l}+ol2%bvl^?i%-e%P0ZxRxFdie?Lx`RdnbuQ`-W=dq}6Bd5-*k3ZA
zrPHfUbpRL{LM|Y<(jJ|{7iQGzWUyR9mwJ3l5@h`%Gnp#_wJ+VnR41OI(octyvc|-~
z692>whq^c!S=EO%cB+G4)`0?35?_0LNFwh~wAs`$vcee~GS|sS?G^nRXa9;0)rnGE
z@6xka6Ng6}>a@H@7NG2`Qy|$(TN~6n!F(ZHQ?N=2%i9C=$ouX<?tS}bzQv=klv41D
z2~wkz@LMfUApM=vH-+3%`ZPyO*X_Dq6eJt?Q9;DtBQpTHEIShDRfv=?45P9H{-Q;F
zbbjpcMe7tC*FE0$EuQK`XGPt$(~w5=rQ8clCVY<xHp2P~bI({kqu+!ggro1NE<3F?
zgkLxj3qpQsNU=9VH+NF=DtFePwn?%M5y**O<HBoF3w6S)v<iK}mmT~?;p|qo$8^mI
zuE#}wqOG5GD$qE8`UHj9L0-GRXaRb7wkuA1TYmUyM<G_Zpn}s%Re~cbq7u%G=6Cr8
z;eotUR50M9`9UVk<^xj2q&_azb-Sstz$?Gr=kSB`JffT6**|uk_fYT&dLyk|PWw6B
z!eJ~gZ^JE4uS*5Fxpq(R+J!&D%9NQ{Jm9b6c<~%wk?SquH`M+r)x2|vj)Z8$Tu1()
zM~!h$2wKS^THCVR(TbG3ZOrAq#hsZJzxuVla0QRL@ok>%`1EJvi#dC{xB0tt!z?P*
z$EQAdJ~cB2H7TQTvTa%yupn^gq72}a_l<KNNZakrq>DGQ!y7)-T2s>%<sh+NC+R$!
zA25WtO&Pl)4o{7@U;hzgS5GlMoHHWUm-Jd~f~0%VZ{n04s!eh@9s|Wpt^taIm&8N6
zv%JrSj2<b}FVhazn%l94`Kp7b5Bl7iy4rkN{3>>Z3kK|HaI^1hSuc($>BA&bn-3O5
zi+1TWxQaDqYF=UI6@f5h^SkY-0RtkqrWgp`^o83YeBwv6vDvSBqXGVQl?i5Q+H)3y
zi^W9AVd6=~4J;A|A9|t)+#@I-IF8S8VEj3uUZ~kA3Rdh#;H&Z6v=^>w#LuA%`t#CE
zi}`g%D4oUwAtJM*9}c!Hb)d4cbVTsCpbxe6`@Tb1n#T>>7uGpkNi$QD)U9Q)Rpx5I
zQ0!*mFRd?<L*kX63);QW9Z#*0^Lm3;w-@{bAEb>D0j`(C%jrNXwuFVL_f&kl@kmP#
zMv%O-Kn5wT1a~1fYAyv1y`}E_Lc#UTl(H%cVXHG5r;kGmO_`QAYErUI<?xp=x-}n!
zXqY9wba8A-AGs(Q+_*~S9KY+0vesHT_=w!(YRf0m9Bxg6JLULtezHC?R4_@E+5LHC
zA?C(*AZl?+xHB|{*Leeg7o+(gKMU;Me)DQbM@m9Sa5}mny~TfmI3u172Z8f}08-8_
zY;OfJ^tXtYv<k1{OOX+7L!NeQ{s!5b+Z*ZZ)5#TT@{oHkwpc2b2E2=vCr&GCyaGKp
zw%*<-0=wqle!hLQB9NmGyTKShp$4;NkU*rgWgeD9iVG62sEs-{re#XuPs=I}AWDrF
zS_{;Ja{snUwcwj~tSB(hgko!->P_(Ii&CLYW{zS%3lJL|$utB?&Kwb5RNr&T`q2>j
zE|&L0@uP|f)3X})vRgxDTM-_F0>U!Vg-p$4*L*wNT^(jXQrrn21SrR~gVQzq*WJwe
z^~ALIXN3d3fKr=kbM5TcJ~nx?e#fLJqDkmOC1{^sYQZo#<p3|qqu8J1r5r7N0@0P>
zBD0Gvy%O^b=j3`g4Q*ZO8z1cW;Mu0|fGUZ`A;;Aa<)N0%MoWRZ1SLA|T7$Bp-qOfu
zZ2%*vyqqR^xR{EyZc$>W#X{uh+M+&)R4Cp+*eb&YZ4^Y!I=E*005}ZoJ|+;gWB3HJ
z77QC-iUNt;+8A%9Z)Vg{al3`b)mT|mKKvo7vDpbSYh^<kET#^g-u(IO-Xbyk@GsR+
zcxs^5AwtsP=32UCxBFj>wBi=fHsMlf8l~);W+YcdOVd|AQwx2GLj!_1fQU571!@dt
zMC=-#qMRD_m<_%(?d02w%{^ABR6K6sRW=h+W6ed&EDNK1sQ%cfRJC~UnfAq-aR;9~
z(z+c#JX$0eQ#nDErH4(R2}!{4Rl+3>!t$1Z9=#|GILw}KyETA<c$b&}t5bcB%+!!7
zn~G(u|6@1D()k<gNOJI<?Uc{W;2_iakIyub__Urksi9TtGk3<0MP3iP{x;l<)%ei4
zg?I1~e&jVo<UAlfC7fO=!QSOOINN_|#Rbqx44~EQ6vE<%nU-2ow7+R1hO1-IfwMG-
zluOSHqL60j_WW_SZY?q6z|QLiRswH5mKS({#+HpKXU}qx@u@nWX~8jm;9=6vwqP75
zBF)?blpk6*DS#?qem;qL-VkG7KL~z6wetUbTin_2Z?4&OO2d$E#&|p<K&t;)?}Lxz
zQg!=SR{vQWD1iFiFx(N8dRrGW%Q>QyJ=-5r|JXN^2iJkosnzh|1K`#J-xn8nXC|8|
zyDN(VGnxZgj*s|0K<UeH428j7b?&?%-%Pyoj+Cl(^E2ulal<{a&Os?&KCGLQdFcc3
z7&1^bnVDI!fE!HCaX$bW4(>$7b(*u`<&|dr;EB;v%7GC6)IEVV)F`Z9<w2DDUOmo|
zOwBCW1=9#ja((>fthN|S__DQ@Ai(al1pbG)nX3fA2r@H@pWxUnTv%LzUiCbn>3w}<
z@yjEIC=gkS5|$?<tJ^GJ9zp=-QGQ;SF5`PaO-scyUsrGFAX%AuW}@U2HlzbiY-|7m
zd`Kozv<Nk%<L<Puh=du*K~k|DldAit9dCo<-o+@;ERGGu1P-Jt>+#GUC271qVfmq*
z<-rOku`E%e4)_LC_e}AS8U1%pd;?Dp7ai;NH_LB=*UYW`6HC=E5ydz|^6tCsE4X>)
z?wFkp>RY#!TC!qzOt7fvxPod!LYd2p1*CdTM4Wcdm@+pE2nQ;3()1Lz8WSb38nUuL
zp}~6zpBzkOnO|inN1IzKy0@6j{CW!GJpBfaIsETW6iEDTHI2{q!uq8yYIeR-9bR}?
zK)3a=f4IHiKKN*S*t^M7_V!P|w(%8Qmv`q1*}wcEDUW2#ld#XzYt+Fi_|_X=$1sd|
zZy_c`2#Rvpadq8HZWnCdry%d4=CaP&ntwbqJ`ES)>k?7@u6)Ldit-g$9V^a%fH8;>
zjHoGYIzn6IGhER{XENgR@iZ?gx7ydP3c#ELI3Dz8fuG5Kw-z?;lFGFuTzmVNk+2GJ
zbgxtfglUXQYwc_6xJu5EkM^&0xELl27Wmwq^@b6>!BFlvuQgfwuziv0u=`3p`{m5u
z`lNNeti7TmLZUjc!u(Vxc>VAp*`1rgGOh)*|C46;8hIBk(++6Z2T%@}?)HM=?+azs
zdd2xPxwe|fMuwnUcU;rWDezgXm8YQKP%Al$qkw55q0~Dc7Z0`w#|?VOh8Sji_QN7o
zRp=mCG7=0_zG0wt2718XiR-9WHKRV{JY3dR;|m|k=dCcupcA)QpE^bKZF)cPH4YxU
z@a!+9VQEX7h`49`t_t?k`(IqgX0DiYGbO`E$oc-Je<jse-}Vx9EcS6JCAyD@C!#KF
z`QhsSeIhLlx;L@6-fKkd!x=ZX#K@gT>jUXu5x>Q7m3L$(<@VC<|92M^V3+g)gOXK+
zWN<COn%_fX$!1?lj`DdB2cR!WY#tsUJ!>#yV9Aap6I)Mi3%sJ?5WkFEKAajcXyt5|
zY{xMf?#-LMhwjxk;ZRr@gbk<Y4vDx)W`X(-Ghcy1)5KxA>`fe6=Kx%blgJ#SnEZ4!
zT=hMr_8FGc`IphKMc!D;!;%A0LQ$1)EPUPh@phc+z2vZdivUXP<8O-{RIQu_QklcX
zHiJ^CbtjJ6UO$@>(Ui8fGTBt*c?;=3PmB?b(kH5oFQv*`iu3)@`KX{ul*H<<enT<*
zTNHMw(`Y}zWgU-m>IJGF&FIJfwy@1bHoQw`VJ%%0y)$zXqKu20!lK~R53xADRy58N
zN|Z5}8PsUBf4AMa3`VL48(ov<di}+Uq=t7}ES;18%h`q+<c3MZX77`TOxy0S_`KKS
zRK=A`>t2_EWu}r?iQ_8&&k-wk^pGtUE*C;JeBJH~CS2e}^3ecFt3AP7!UX}_q7nY_
zmZxTe<BX@l;@vf`<1%fFP-S-?LK#OAtJ6x-^;yflz5xYuhP^Mc=caAgwA6})@sth&
zxY~~LcP6;I0`~z28HKkIZb}(U=_l~QsW<+oktKgDFPOlv7`y$1(ya#)pRz=#FKF{P
zr_<bsZK-MUS>fQ*t5TTwRr)9gr^3%H8FqPPVP@u`Ts9@)j)^z>(P2Os$_p8b#sD3@
zpNuK9yrGoLxG9`s)p)>gxuzCw;&vR<gV#Stbv2)RJ$uf2d>SA0ktBL-=<md_|2?DC
zpm0L8<@H7BsluwGcNlJIR-6@61b#dLs604F9mjs0-<SUp)!e5psu&CH@23F(%`C$V
zq~}x!OC%J@JkEJg#yRJQJJhaEeQ@FXmp}I9U?E0|m*rsFR(W2@Kw^C`!I1r2I009M
z0Ha7w;sCV2r+y-~xcw$5-0hDSr3kO>jdCYO5Z+C?S%t%AY6KN9AB)(1Dsp*k?(RuB
z1z-18-Wkq(VtCRWcE40Z0lq-{NMJGf?wd{%4{a(!$^#DCBx2o&V7ve&_FtL?#WO?+
z1H23Tc?{@HLHFm3I8@0~BL<=O2{nvCit<>i(yc;>!jPCC>8-Bncj34iLMRJeg5}7X
zVWD~O5y~s9#=N%Dj1l}5ze-SCa#&|rE#TvLFg^i!RlHF2PiUPdSO?RwjTss>wZRt9
zlFGM<JRmOa4Y^Kh`LdYTHEOR;b04uQ^z=QU1yUJAZO`<>S)+8XM6Z`yF{jjpzF-~+
z6>698dX0ItxuI??4mh+S%QPVxobbP`#98M@)Qm>R>_wQ|>=hOG#FG3C?O+HU2J1k3
z6$osMp>gjm6lYMc7*$~4Z_(;|R_E2fT%!z)MRK{6^tWHB^~y!wG!QHp9P@6vuy04J
z^2eLdeZ+~*m#J$_ocWFs#1e6Ix>tIybix_3I4~*mqQBFPkyjNvulwlLpAR7oIzqvc
zs6Z=#W{Iagdt-n-+2Q(Sp=y!#*st0aCcp$UF`%wQtvFvZlytENmzH!9;j}>#c%0>5
zy~{q;#+wmWUAeQr`&-|}|I};94d>7Z^NQRb;k&!VO!+8Hwy$z2YzK7MsA>G5fFUV|
z5L-h^t;BX!b6t+sEPt8vq=E725uCmg%wqkxmT>KeD>QIGVa1`mac3sHy*VPL7Q4vo
z{QFtnm*wS${Ptr+%JEZ5sS(lc8R0$Fb4|)v?wFM|T~Mdf;m7F3u$Q<JcmhNlXw3)W
z45X(s-%g*D&^^FCF{+bXT%hjQ&U}*0$&Qy&I(P@)^}ZH%KIZ6oR=ANnb@?%XHKl6V
zZ*)q?(d(yk!Z&wvgXGUX>E}{1Rhg4uHGk$~{WF3w6QUu$^&<F5-2=5pkdK9~4J{s5
zKdaML<I@5rx2rBTxq{3CSj9nI?b0_DiAHQoCKm9mFDX~PSFF5u6Uq*e8|a$PZq;iG
zi^vncM^m3-7&{_{@fngyKk%CDPW6NLou>*zATIOMZ0G*%s#if{xVSuSL*fL&CsGPq
zA0HsjkV+$X?0&f7g}y_BMU5y<OU8vB-vJd5Ox&=tgmux&FEP+1*oyhDpTP^0S@}=s
zugRzT{;dBTWY!_vI(0%Igvg<Y5pQ%#(y1qTx_425YIsXGR_mfiq^jI=<YK?Iw7B80
zvf`AQwmQlRj(z$@&wqil2*~0<EvVYI87z=;q`0;&EO%-ay%e~2zAeWy!CdflQj)Fi
z?v9Vto>!xrRi+tI;=7ogL0e9Zxh;A#ZFj(lO<mLH-^vr1Fu>S57_Ac>yBDwt3Om<B
zSmrjO5*6AKwC~nl1P<jbcGuG%OausoO}*Y(L=r!1uvg<z?uYL;Y%)N0ZUSLCMwmwF
z2TcBIig{Xz8@MTvD8qksDmJSvjo{q;tTcD5+Gk&TJ*rJ`^QOi7FYBWV>HaWZ*bFQF
zOX&YWWoD&RY>`8p@HZLO6e4Kg<S9%b@d^nd7bKgue1}={;05{Nqolw=Myv8L_p3U2
zALW7jYlEJTu+wI?g@E%{*N{p_M1hD*4UsbH9RCQNZ}YLl2cWjFCb2G?ksB{X1&|T!
zh_w57&jJY;=fZ{v7j1|8ZF3MUvPD{L+s_${C#5jr4X!&If2Ac#7sUnKBh5frodA4U
zTk&@?Qi25&TGfAakqoKXy3y1L^}+Xbf8$pH^_Uue;~qR9fqe4*l~n?r%i-(r)Gp7Q
z&L@3{4`i=iC%-o26Oz}?E*j_|B5kvUdCI{s@|^qzT&-d-#?SfT{s|R$V&CzKs95z1
z2~eyEn{b*U4XS>Znta;=x|^@DWK|Y6WdMOHFR~9#x;QJG5s@*O37am=vI<?4M}l~U
zZtkW)rw9Wm4_p|=oc?aht=BaXQH$+pLINigJdD&Yd^U>b3k)^`sD-x|5=XIdP%DEm
z69>$it*l^L9<LAR6|nZqMc#KE94K^zxM?7HYL#k-aJ{G#){h{tS&=&_d3(p2?a)|2
z5ida1p<X%vVP;*;tTh70i83=UKEY8|hmsF@ffHrnhZOxkc<wK>1(k^~fQii!8^K@>
z?bcIc$Qqb845AIeNCzfO)4zRsl9UIq3o*w9Azjg!@yZ6j+B_5}Gn6>CFSy{U9|(W-
z1;sy3*^T0x(LFGChbvPTrpds(dnA+ibf?dk6haO^leG*ZSw0kX9)Qf|>aClBe*cNX
z7MLSGN9|Wm(3Ae24VV0m4r(bN%hA|2W%O`LGL#_@J(|vEiId~GimoL5x?((QS@p!@
zBsb`TBTdV7p%n>lF8c#<!Y(p&GBAU@FjE805n0tg57rhFIBC36GX|tgqdad<6ll0E
z3By_diS$klErT#Q`XI5RAD>4f<qL%m>L;9bON&=wnfKF;LFZl~Em}g1m9yMO@!YfB
z^6FJ!kVhYt$3jrus3b+e?L@BA!_C&(LDO>I{%jo&os|#nb$<XMb!Gq&_f=ADBq)E6
z5)`RZOM&?rRpXwLA<Oa~G7PC<g}i-DkV*4!908j$6LhgReP(uhG2L+~V7J&xgcHGH
z=+Lr3EPgoyWWplf`JaAfCnw?1<#)<1jF%*L-5N0;Zx%}!!$Jw!-rIXg%iWjmt{xfP
zi1nD2f=GRP-uQ_$IIB68gFL&F-mJfXBB;?E;3Ac4UOb2Hv^4ROsBN|d<R>k47rng%
zz48g>UeWFBk&)_5N=gdquWr92R}o$bg+Eb%O?c66_ZIN>@j)HQ$5=7mrWBQ|(z$<r
zoEc9T1)k`@*1s4X*P4DUJ~hHu?$*BumvuK$`z+w^CPyyg>XUHqBO|Cn7XQ*<JG}~!
zp4#dWU;U<#tgB01Qxiv=1SjIg^<f6$x_$j``W{SsNw$M_4BjH4E*HiVzM}3lrMkJ$
zO-)bt+29-Qk1#|Uf^N_JtW_OMyS!?B>EqAwbQ72ykrQ<5qYC!V8J0VSC;t2i<{J@4
z1X{||jlv(=TNx(2?YqI{P2DFjyt}zvJu(9$j6C-K>ZN(rHfFf;Y8sKtpJq(sO!!r8
zOxX2VN+uKTGgv}0nA?F&i0J>z#&%Zg>FL!vt;n)#*T8|H8(f$p<z;2EK6iP<EZoOw
zIrFn3H(L>FQ+kfQu|iAQs_+Zt^fi`<$G<Ew<Eh*vofa+_z*7(_5|^t@*L$m+1S}dK
zj4%z<z20?L3<!-N@IOgVkG$VBkzlX0&6S6{qX_d~OEYG1BH_xkE&4d1HZoo)kQX)G
z97<n!7|yK&z%lv(_kujHw#H7VR@_8y%&K3S#wt+{I}%bqo*Tix+u6oqe7vPGKC_*d
zEsB5SRrULov&=S(n=}x@8o&^n_3TN{HS2Y@0f3y(Dx&Dv*x2G%)3dV!u0A1_SCSW{
z?`q*gPZ5>_9{t46$6hw?RkdN1c}yF4P~~{<vOu>Lq*kX+g}^t;>^k3(jG(I|waYh<
zTG<nYzL-%kx9G`q8!%Gayy&{{?ea=0Jk8_9%qJ<!PP?l=Z-xJ*wFxOe>&37#w~x@g
z_}13e;H}e0LH^vUA-++_oNe_6tBH;dnM2z_V-S{fux|gC`>TuU04k&VT`izdI96OF
z83|b+<3yk|Rf1wh&`6_1iRk-=>U}uAEm$a#9Yp(@3AZkT6-MLkpQi8E;SRYj^>82~
zs(hWNU^rAGIN=|gwQht?w?m+VoK+S?1c5?VY5+@z)-6oSDiTOgjljGgI^_|V8h~_e
z51%B_S$nT1t8aFkIln1*1;6#`8%#~15fB4@I(!t!>z<@@_rai#pH16!W=21=EWCp?
z{p?9Sz6k}CES5r>IPMSct46C-#8Kq9TXD#7Z0tbD<<;Q;R>9LsYdC<$OTVH<SZemj
zoDET;)`*A!{>LU%e17UFQWebY1$bA9>CPD}2&KCoGmM)UjS^vV{q=pyN>9;nZfn47
zN!-@Kwev(zn+EfQvAR(btj(R-P6$|iMr{dulI3ZCVJ?Ac{h$~aHRYa+n?KJDWz4PO
zW+5j^O3+I^gXW(gN@#&Jpwy#4bmZ~Kzb#^h8>syG=3f1?kE%MLNFr1PJ?e`IMo7S@
z)2+pg!<$q-RNNb(5&`hK=nAg>|5-&22_jqwBmtv1{DjX3*zjU1dE+N@Q0{`upW(y@
z8M{82N>bjiOU<k)haAIqa@D=7a{lc*+Q>EgLb-p`Bwt5h>}Bb3!v^<g;;YE%T2!WZ
z(as&3z4?EPzhF89C&~stt$H~fs6euJ;xSeHMt&(}|9fmi01iIo{v6N2C1$KVKMe&p
zAo<BtN0Kp?gNq}eAE4Bwf1`eFpn57>DHZ)LD7a=C$SjiA)<*^U2nE>HV}}+P?3>{c
zJQ-cxUuA%k&=v0fvJut4TtQ6B7R01yIm=q)^}4o&bKIF(F8PNafcmR7Bz#Yta^#&X
zl<En=3h8>iAy-h87#7m*&ws9bH_mKW2HqtRT|89-9d47CHMJ8r3f4Ij?*jE@8;Ewl
zLM87bIi3Ul$&(S@t@7*}w@O|Mc(wuFt(R|RCR@7QAZ(Gl8E`;Hx4xU{hIx?Ep#E!b
zJd@yhV)!b66XHtHnw|a(xquKoniLXLidqizNG`u$&vPOGI@&#Ou^sH71G6fg95rWV
z1d`kAZMI?A*b?zG1^eaT-?^s92p><G=i@#8xkPe+nI~+I8X=A&O~rIROT=65uX*>-
zEbxy&210l*Ff=1rp!!|+elLKV0XX1J0G)jZJN0m`%m0;j-D$K4PU2=)2y}DnbWb4z
zz^VZle~l>PqR^A%X{k^idGDw-qIqr<ynlZOP{vQhl}&{8D}9n&zkq-!?4-e}F#Im;
zPcqEBK)Cg3V?tQL^wYCZSU@@4)3dL_VWVAP$EAn0;DbH;zgcenp(7|V#)K3qLLPf~
zr1vC7O@tyZAKS7YJ${frgKtLtY0C4UZ)5}pJHC3_PYo(YjfJ8X!z|A;xGW(z?Oxy5
z*nM>MQjIXC`0+cP+T6lIj5KYaX+heu7~PSc`GhYV9O}*+IT>}Z=W+lK_{ucku@}0e
zaQA@SV|=3Z;R9myH{s%yuL#YkV~!cEt)ekn+qkkk59X@-N7)HiMn8_gY0Eyf;73#c
zqe8F8F8WI728+rp0K0;b>*s$Zmx^qb$_oeZJ?KH-j_}Oq#ri$T(H+<3mEk^Y4KcO-
zqk#HP=yLy(JOSf>N;egk5s52(et!N=z_M#?CC|MXvh?(M!&BqDCa>?ck(|bBj<Mh%
zRHU5Qr&4Pt7;OsqulQN!(1DRr|4Tl80fCdFi^4cj3;+zp6cv<e=#9RW<%;9TpBF0^
zFL6m46$V4cDNuVocQh+2De1h~%8j2a*DBP0va(*{h<v1lyMcjEKfjZVG+p;uzN3D8
z06*OBU0qcwkpeLp7h+ERPZ2c!X^-!rdq$5@?t4=!1i9BpQ9&BznkTItcZY4bFzg`!
zvUxzgTJquQxSgynAf?Usfx%icY^%CHZskW%cLR$57s4aVFa&;!ns_Hg1^lG#KgUP+
z2R{#tjwLbjeFjW-;4lohcNf3gulu#(0y93Zecoe@F|p!HDyDMwCNU~H0g%wOA7>+L
z;G?L>rX}y_$D9{D>@jdrLC>Nh`e_0dR0(^3S=7n%oQBXqI{_j_sVOZ4Jl@N5lkzk1
zoo$T@RkdHv1@&hXkqHP0z${l2LV|OxTfmC6A!Gwy2`b@v(n}I+%1DVr0e-lsjy0tt
zjwa)?7~+NvXj}V9@Q%N#?XdOUPmutn6pr|rgoMQP8qMP`dl>+vYx=di8kdog@$ODA
zl&8w=J@PsXj5#x*9B<@v&~WeV*xm7XrcAh&>#RI+3<<$?x%jNx)r+7JI4ZhV5iIrC
zeMpB;gLeH}s{^5WI{MF__jXG^$l|3QgrgGwwHEtGI*!Bc{n)xm|4dHyK3;2RY7$cx
z_v<w|EIM8>?(11CglvN2BrdgwawI5vy8)AZ<?O~2vu{$%C{}^Sj}Vp04KE5F{|Eo6
zOkH00FF*#uw``>7dCs_7((pf~Y{3Sv0LFiaWsVn$W;qeSPb>`&YV1e3R@T;CWR_W7
ze9l@1?nF^L?U=H)>cN^7+Fb>CX=x&^Oeq|C;X+HEAzOZ2ZTq~w$6cst`}{&eYpr`#
zYgNQ#;Lt>datIS`c6Rpm;o;Jb$cJK;u%4Bf!)xF{RU@N#VDgQ7cO$y|?Zd!@FEHwC
zWxFuG4?sPOIp7jcjOHxyX~@$jqA}TGre%%9i=U#kNuP~Lg`Vd`m#!*5!b=n7buCAj
zLZXeJ^p9Bs&y=ekO~ZBbzj#U)eHy@OS>$SlAaL5O=c~ZoljVWe_|lcST)Qu4!bO;3
zTz|vxb@lf6!4FP^J$J>wX+-k3hBG7Xj=%x3(O`-D*>{?+_y3G~oUHaDc1QT<PMG1N
zbgzf)k{I3oN!32@IPS)L1>`@$S1(SNkJ<)-!4%yGM_s;`b-BG2I(44=Z+<SrlucxQ
z3f$~kyx5^|Xpc!;;7y-UN7tjfuv&QlL(oqejR$fH3g@kC50?w}cN!W*Ok(^;Z~AC%
zl^)HN?CDK!&v(T6_|x;yNXxFNfB}lIX-R++lzYrAejma;8`-}mzMNs%6OIk7V`4xu
z4^+8qJj#7Ie^K?IMr`cA$w!Lia=G<!3ub~VNZqPEi1Y&@*f0SP@Mem6X7h8e#&kxZ
z6+S$}frroT=EK?dB><04!fd5GO#aQANWgrKEh=JK0~|iVM!Q*4LPaS0Ju%H;A!+me
z_rY+#qfx2HA3S6&fG~&!W^(kbI61Ko?Nx&v2ocZEC=a)kq#g%G;vRe_a_>zc02GfK
zIP1-oua{Sg^KX`7(oylV7`2MmSVO-O$o`{&%uI3RV97_|9|No<^8Z#F{rAc(KX+tA
zMO{N<rEU5Cu(KB6L%`icj3(h)s=i_Z9G;Y4oW_7olo3DqNg(_PJ=3`!&JrO2T+Uu#
zP*()a<8}Yt-4viT`~Fd+FPkstY)=+kM^`>E#B~A}@B|=w*N)ra(L6rT-k9%617p!8
zzcsnspS5yu==<|ck;if*3y>(lS<Rw`U>X|BWU<xV%HBS#YHz05dF`Qt3U~t^1y-1*
z4gusF?DLO4jAKCnPnf^~QA=HY|40uorT?@1D(oIJ#ES<5Q(H*@nB<EbPx|04T4D~}
zdaxsax-uC`AO?D_%1Xh?EwrHik9W%qcxaubwa3)}#?~D0RlqaLO@ML5ZSflUKla4`
iT)F)p+8EN#Cy(yjYP*o^foE{Q&zn~&a#ga%!T%48rpf34

literal 72875
zcmcG$cRZK<|33USGm}vyqZA6!FiKWg5tW&ckqDI$*{fkS5Q!o)D;Z^FkA_NCNV1cV
zJ<8@j&g<&)y}$SU`2O*`@5k@=cywJ?mvO$&^YwbZp3mcX9LMtvK7CS^k&cs&L?SV&
z9aqvOk;wf>B(gplYJ5eax$6i1bJ$tgz*)!M%GuTYswGL&+}Yu>z4K+8^V?l4uR7V-
z+Z_~@5Zy1b-P+mN!AV+7?8<-rfT;b|i(+5RXUcIAT8HCjok%1mbK)Pebon$J5*dl4
zrgT)-Eq1Ki)tOCidBfD?<;jtIq#d_-9y^B#>?P-;2@2BIRj|2Db$xy_StOtGX{FmM
zly7e&1Px~z9M#&yMrM;pNxLg7u3NKkvSD`F;#2qsyZ3f141+!m9}ahv9(0}jRCsan
z$+B@s5DmlYefaa~ilTN4qZvnlIsUAj6Cqvr=dZ=}xuh`SOB_EXUz02R^WEr9271!g
zfBtHB@c+mUu5D%)H73<Me$BVhDQG)uY<zcmpmwLE<QA@4^QE~FlHb_aWd>e3E@o!t
zzC#;@&tyL@tbP7IRWqi_kxSJ0)l`nG!0hA^&p#qdzefYZ!ZtZ@1#%y8X?2buk#L2c
zGWW<H*I%s}ZJi07U;W4^_L<k*dTd9{dMiTk)G>jG{)OoQiS7r!RP>~}YDZ%QU(Y{d
z86&B;$2yCcW`EY-Y_@(rl4|Tq&o24~AGy!w^RCN1Wm}U~L#atJt8*<2KV$6odH%Uf
ze9^Vnd;XgH*tWqF@r(2KxQ}e94dH%$gNi=aeby@fQhRo4jPnV{LtN2^odj&#o>3JT
z-V5PA^1XDjg5GWY(<hC)9Q(-=6BGAwFmTAY?Q4japUgYw9zS}AT`XIB*?0B#8L#E>
zBK&e{dhC$9$k~^a!NI|&Gjy#xUh>N=4JZ4Ju1=IrHWgetV=c~k%irI8VXEK9_wtv_
zGmVpuveR!^<vKa<sc6I=V%E~qYO*bOHJV*IH5Bhl?fof5@AqQoOH2B1`8E`h>i+5J
zyJh}-wRm4wH`^f^JtP*^u9Rn0zt5php={y%ooe1W&ExtljIXq=^pu@-UM`>Sd5tF@
z(&@>!Y4OcFyZKv@J@3!P_)y1zcRR$z^HXD-&aEyCgbuf6u>Nd#GO8ozHf1I=-{sUk
z{sJ2x)c-or;(geTq?zVqRf`|*@1GWm#CIM&w#@6u^<DQ_nXg>8l(lOA@XB#u>Vsgs
z8A8JPYwixoE8lLBQBa%BPjnA|D;SlS-zzHm?wo5kRm6u|>nlU@VReHBFV0hW%ne`P
zsT#7KkMCH%cW;IFeydL$S{>Z3U0NxB#Lktlm3z*s+@xky8eCtU^nKwreO@DaAK7?k
zQ7u*`L@Qb4`oN*PT!&{KU-MZUj3V|0YjpGN9ro9kB?o@Do2s^FpL1N8GFMVo4lcCs
zE|G}y{&QCAY_iHdr6TvZ+N$dmj&s9WpQ86u+`oU{Z8WQ>DevMbE@@{P#%+?%Tc^(q
zE*j-oF$$cDyW3OlIZ)7UqNt)mZQ{FnZDqQa_mD#$NmnS+dRZZ^(#(YQl*}|GH3NrV
ze!loFetx}Q3-+9EC@*|`ut)b?+MOZymxftw+Goz5h!W)|&CU1vnno=Nk4{#u3y|`$
z6SkQL_wT<?y^(8Jr@@g+^U;BnZ#=cUS^6g|YD3DiLmh>kij99b9I`Fh8W|oJcamg1
z@;RkV|DvO#V?is0ANI{?#5-qxSzyzKja*ZQD<xy@g@=cyI}J5#o>>2tnZrVocAH8l
z8f(qa*%hg7^JlCh=}J!R{)-ncMra5m<6(n79z1a1xM<WJp3oupUthAewPl}+>9U)b
z7`^i4`Bn+*CWX0*!&iUMsO@6$`1$CfQm4SKT??z1MMeig<?#epIwkGTo;<nHKOo@q
zO(r>m{<SsT!9}M-Tx^`2>fNQTf^kFTgQi~7RAdQKJ#LEm-U*GS`*LX0TGY(V&BZS^
z#L|tHcf26&Q&=Ofa_w;soG2L;y?y&OpZ~F=nlX|oMLiyM7kk5{2C~nUreEnSe5xyC
z^Y->;B_-$n8Y)h4>rFP|ocQsik(RU;{0I$cyr<kqx6!_GRb|hfJs0+I9M96%K_reu
znfgrk$h%G$llRTKW|<M^D-{8*^F;dE@x;@o$G;WWAb#b@etmsmp_bXEjooJCk%`VX
z+^ejj@;R?jZgjR<&%od~c7^|DwZe`aJH!#N(q0P=Rb*!7<~wA6x9aGYx?IuT&Gz8I
z1101JfzVn-C8bi=$v>X4+obH~jCice`DJ*RvW$zvA4}PvR8I-#+qrWOKfk}v`l>4*
zUuCEk3&X?w#2>LtT!+F~SXozjE~cG)LOgZS%la58ofye0$5XVm3=BAAr>ZENr@z0Y
zYutK&ifVQ^IaJ%&m}?Ik-Lo?}JVZuG61$6F<dpL|<nQl)-!Ll``Rp^6V5D4LSoqFU
z?LD|&W7=A4t1h`hrhXRPuh@r_>j==4&BFSpSC@XP@!fC2>U`*TXo@}Tl#ZPsmNL2d
zG)~n>EwAsK`$}1vjK@NMpjuA5r|B8h5U%Ov66tHd*bq^7YieqaXXt8o7G4>dZIoxf
zGT<z2gKXcLaFjw@D3Xnb=OlJo&>Kl}FwVkv^2M!Nl#{EzYm0*2-t*m0+eeA@&oL>t
zUSC^Hl2$j_iNv3%79pUetILKkd$=539h>};%uLPm)DvlC94Psb-yU>KOlgAgUg4J|
zV;U=TQZ*7A<(BV;M?|P$bCDy5M{_E-TyaIvJQlZMk+5wI(iPh59~js&)&DwkTy^N~
z)`Q1|BEvsiV2KnneicU2UGTB)t&lCS@9jw|Gp+Q&p6(J7K5UiQ_RC35J<mYI`B&?n
zkM;FgxvP_9vm410P=<m&p2;!MBn`g_W=)!5l3xiUH6eG@WgV24k0&+Z6TBu)oIIIo
zSYR9cQN6;6Yh$g1uW^^}`YIbM>%i>RLyq5-K3j`lDC(^1E|Sr6U)#tntwgf^{i{uG
ze#BY&*XNWw=^{--V$toC%*|LR)xAcAB1IKOsE-ss#7LKMyZ5$9h3G#wIEpPz@>yMC
zCV9-(ODT8q@7i@5sdTr9$X%=Y$I2*pDL9!rj*k1=a!lh}Z6)d-OWe`VHdf=7c23aG
zGF+telV6#lKs^2GDn7W^v?7+cC_Zi%P9@UJcD^K(S>m`KCt2e(=A(>$D|4TfkSC!M
zt58rgB>2;_e=fN;I{c!RXJ>}4`s+X@_A62@V+Pt8x({MwHA3Z<c4wW<3kYD`rb1FW
zD%LpLTJGr@eteg>`^*I_&}KaK9mB%QTThd$Ul^oY);(lnW<I*U*ywAGlS-Vt?}tL-
zJbvxrW4ZBwI2#u(>?*M9{^l858_Ju8FR+q+br$6m$uxPKxQj(0oklF#41SEvzua+v
zQ^qY<YO15m{otNRb!5Dl`Tc*!J5`k`$of1d%ZFy`B91Sx>|GXKj67qqBV=Is`Uai^
z<e@t#ou^PNbaPB(g+xTke!ACv`t%exdtW6vk;p&a3NG0+#2#)1TAHjld-g20Rnen7
zZf&`D{qv=qa+NH@0={6SW1U6z^IzIB&xqN6dH$%;;#0hQJPxF#r6tw1lPAclutQon
zfi^ha`RCu#sh>KP*41Tv`_7%@i*fD&U!I>;Jn>K@@!h>`5yqlvSXa-{DL0wcVyBDY
z>M7#ae(4fho?+ix(KD|;(%uHZwrAI_TaP6!spM2F36(GYj3HL~`=OK0vBYu)4IFyB
z&z!t`^-pKM*Wy)=sp<{HClAIA5j%W)voH-wcA@VkvBOJad5vYEnQgXvL`6ePd=@PY
z%iQ+!3kZa3XFTooS*|xy{c!2&**vS(WK|}tN}B}l!6qjqT_4idBxQOcRTCdRH}#r{
zmBcCQk>6N>^_VI2#ir<1`gkMqA4EkBb=x6pv_8`l_LzRlZi980i(g*}L_*oUW5;!o
zEq8_w*VWfE4mXZCd(4lvq%lcc`PMol<F&pzPwD3}(RFsBc*aeJC=#Eas>#ilPu0*<
z`hBv>8;&k8-0R4tN=mnBNu8_Sz`HPJG?RQVtcO@29VMk(y;rmJg@1MM@kzT*#P;@N
zeHJyX&>#)JWjCEG&hcLUL+O`oT>O3SiQ~t&7Z_@4XS6Qww8WjuElXNA`7O;)PU=*c
zzosTv02*wU2yKghCgZ#OXM87VuHne!LKpky&C0d~hHTHNXI7||A8%)oyTi{Cd}@<t
zra76J-wKuAT7aXDwstzsY|=|)Lvn>umvLkwoIFw!Qg@jp$~qDkA05d(E)9o9TV4J3
z@axyM1={D&^Zg$0Y&Y66wkfyL!XV$88IL(KGSc(%g+(n3$zvoXN~yEQv6ef@<T=3i
zCX(UH3&BO7#$9xZ?7GS6NT%4x&$b25KR<2NT6}&s4_WZGkL*LKPyO}=+nYRmSEg0T
z6;8=5K0Q^Q(&+83SY=6}5b;3?#ftz`%`ON=<|hNleqp*oDOB`Zf(nngjB$Lpa3Fr>
z<-5CE6<LDcbe<T=>266oH9umrTSzGB%^M|@skr%@*SbrtQTpNW1fHc4af2f<Ij+!X
zqeimc$FW;bP&GfRbNtU%I#Cgkr_s?I9&^pA<(Bb2OMA#Rc?Y}8@ce4ajA|Tx<~rHa
ziU`x1q(T-+Il*rGH8-*7xn8C|8Dm7GUWsa6{tm@}uw#h{sEGC*gLP3TSp*~~x8%>c
z56qx{XiSxD;V_?&JgIEH$5XjeGfw6asR^K0d0B{!jtGdNvEk-qx$=rNxr2Wudry&u
z0j*~rM#iQotf{Ym)jWA}OL>EO>><Y+q$VVRy2Coha>pi}ZV%fs8Qa=ED|(K~ntydr
zeQ9O3(FVD;wancqp0_}$n#%87smt$?4nY<bijO{0n`q^qlA5pyAG2BprcYa2@BTf}
zZKN9)6{TvJ^^nxW>$}pwmzOw6OTRMnEowI|Rjx0~PYz9@%8Gq6&1_pL3kV8Y11?`&
zZJ<-qi<d|CLI!4yjJhjF+O*0+`(u&*kHk^uf-{9rod)&aT9KW(ZkY9F&Hlinu&Ag&
z9$AmRmBrb8k2I35u(Gn=4-e-P>MXd#{(WG;ak6~gqYjw&)Gt4v_P()>JPrw4wwc-4
z`}jp*0RxdOa}(X$<kwdYy2a(p?8pEA7#vK*lW*talJoj{artH5dsGC0$7;XZ&X^t*
zh%m<y?KzO4dmBfRsx=NUC()?Lj)tComgl002@lX1MQH8Kc;B_dUlkdi^_-GlW#g({
z)DR)Nwe@240S<=Y-b(pGyKXwZnTLqdTB3_VGN&hft?)kL7FQd@yi;lX&b@oeuLBrg
zc+O8;<-IxNKKAtm1uEgpk!=M}yG^}+Z<cl*-d;jul-6SY{Y~&Ihko*taWb?d4r%Az
z7Y=NdTU)T0bU{U_{SYBIT}8p<R(HcY8^`abQ(}p(kpHp7DIV3MN6EVEq_FLRkAq)b
zY8QP!c(vJ6k?+14@PE=09Ua{ifb#h_LClE#nZiBt^1J}1htL!sICzkZgtiiOpRyrN
z)@Y)b@j>lCby2U+9RzFwazW3`t5=8aa!SxuuFTLQi&@9hvI-~&Md};4CJYT5evC^C
zpC345>dh}S*^(AITr>Z0uThQJzM!6g6#0Y3qU_k6W?-&c0Rgwav2NRTXR=~hG@%j&
z-hDo=<Fb#SR;2NYeO|{$*@p0C^6vF^Q{T5br)2$61lfs}2KeLF&-$1F`Srz6>(V$`
z&l^GH>MtVV<3n?E_f=lW9CJiZAhWhG02s(iwA!(<o?8Mpa@Sz#`<$h{|MV6+3C8)Z
zxij#{a1a&WXK6(6>C>lff_l2DF&}jzrFZV!x&QndnzH4IQiBSw!|1F-(e`~u!Syp1
zo$rtj8xs54PDz@a9kTy9opr08Jo|>J@9JhWH*eQgm(2?dmFFL62;91L%gtU;KwxPs
zJ}xfg>C;{DRY_x?UWALKBLzO}+!FEO$Gl=1AG?^zn>%6^K~IjjCgtWfr8Rt3PyG?K
zWy=<UZ(_2t+-RtpMx2wzIi>8lPJ3cQ?o_TV)eSkS|M>BPQumbQsOE_iiYQr|cz8@Q
zYNv;AlU2xFL?`S#@|nuU#^#XoaM*77{*JthN0kN}N`x1uW(B3B*sb!m0+3W%9r@hS
z()_|gHGU@i(HJtcbx+x0Gyu(|uJ)I^OE!yDt{fokFpK<f0nwM9USBP9X03;URL1)!
zBO}A1*x08?%>5j?o#qP5Ug4pJClt))02|Q<FP<zOza1J{dqS-IR`tyd$!Lv<D^FA`
z+uh}n<pgS*EA1`#@$+YmZNZZF6R9iT*kl?9KBt^Ge)8lWPp{3iKe*1F=xEo|)@Ip-
znnZU$oOf4Bq<3iNWl0gkd>V8vN>)~>!-1M3jSQe5<h++CTPr>TG;(bWJXPFnH{au%
z<xQsR;p>0wdWKG#$Ial|R?i=%%^lv;UHGw(o@C-N!jDW~{vn*dM_dpU{k27?Y(T-K
z_9d^M58pY)O38jfXiWhZoH-G*l41K~^h?&wdaKt?jj}`aKZvjI+_Q%kpsDhw<C5o^
z(Cp*g?)Z1|x>XH%mAX1MWwNKd`KcOf*`IHh(sj}{ve8jhEd3IGZt$WeO*Q*~{ZkhH
zYkNT`WEmCxDXlN)VH=!wlNs(T62sZt=k!xU+~zZ-Vb+#Mj~;PI*~K>Qr~GWBr6<#v
z3=HMAwm7ufV=8&?sAl`Nprbdbk0v*p;r@m{r_d9rCs^sm@?@oHt6a#y6gY=v*~UnB
zpUm^%O5A?jS9SRDo;lyHn-f`08TZJ%b?ZpCz*nbVld@B5%aayeuN*lJT%fV~6n%Yo
z*czQ3vD^rwqeZ1ZB$m6J8W-nYMeU~LL<bpy(&dGFS@_dw{{lDz&qfXu{}v3{O`kC{
zs-;J#yN76Yuiwckn|B-WvjP?J^_An!)Kl>x4Ns1EMwo|k9oC+E)IaSO78A4Wv}euh
z*X8B0i1;3pZ=V5d$VusXnO`JEwS*#juH5YAC4rTrP#D(Cz8e%o6VKb?=U|Mxpk@wk
z@J$#;hx;|xoMMZJf#1Q44ci3-sPAz~)TC=AZ@+)s%ZDLyaPrmn@86#}XRl;@oZUb|
zr@Vl==Q#ac+4z+M87(dC@#Du8qD0Sa+PqoM+#~O0?b%1|N*`v=EX_{nzrmI+eZM2t
z=Wr<3Y3O8g&d*DXZIO{-VSeBQm%O&e9&tUHf9^xr-D<P+DfVmoDh+T118^IC-LAcx
z2es$~%rBnBM-Un5w6U=Wo#DxNIqs%4H8r)}qN3TljYqGw#yog%%f4cfnu?05eT<6X
zr_7}SL#(#!*TO5?OgzUWyoh3gu3%VE5P_$3>&=@t2{)TTy>T!E(B-uYg8uO4(bmx^
zpN}H;kH>e#adO|(qk|WT6xi(3Hf`)Z^Fc7(v{J6nzE@N_%`&i$m4zkObKcG%OJ7UO
ztYcpN!&IiRC{y~m{{DU<P^RAR3{80TiUUnqE!wCX8%fDYNeRu?A7v{6pO)XUJy&RK
zE<kqGZ#8n8=sG+l6YBB%m)<j3kP&ZZ2J6Z#T}rzJ7gthFM3GM<c9nZddHflpRd{b&
zgb$6bx8h)+o|Gi$p&nBngzT2RhJB&(Yg5IOV8Mv{oby<f=+r*Yb<U*GMHw*j25N0<
zTG_SHFT_Tld?M|SrX={A-c{f2=g*(t#L7ycK=X2btm7sYUw>g|bFU+MFHY20Ah59G
z*(S^`eN|-E;*Zr=_ho;aJ!1%*SKgbYlFUpLhg|d5LR~pyj`}|(rqR2jQX031T;E1k
zN}U_7?z1O~dkvv$QFRT4V*9~^tVdiY<{oy8yOg0rNgDrFc;yZfyogaD!`ruS<$ga{
zi_B`XCEK15sj$LL!Q78S$h5%mk#nCJ2}K#r(!kzXHyMKhTRny4P_Vem^OLnwy<P)O
z@y*t^I>)nhOUbib@sQj3(1!J1Ed4wL?t!}C+eZo}8Na$HF$;i)LmqSK!-moduO?0k
zMV?<<S(JONs&s2*W##JV!{fV%5U5!APG8A;mH#&*$3D^qCZ0DaPUZ7wMYcRQt>i%`
z%j&%cyagXWKNV8JTNLSp)4RRGP0rLh4Ngk9VXd^bk@c5<zgny`$rv~E!sEAXvrBVx
z^KeI=;Fm0;p?P-Sk$LqFr)MoAO(^G;iptlyR4aW}rxu6e?*!=O<>gHy+>?+HlEhw{
zjZzEAskUqXk%=th#p9Z0{CVJgFWazyjwe)ASHI&DDUgYGBY@TK(RR-|u8kWc?Ye@n
zkGIK3z*%|tRz2uh$F{JSv*YC41LD+b9i7N=eVLw41A<=(eKz)BbZ?Pi)^SZu#^J}g
zNHEDLrxzoGQGFCeqXx2ydS)InGk?iGcV}V24Yl#Dk<Z}A&GF#8@#q1#N)U@2I~&`A
z47+F)Jw7zxSYj3U;laJU;HzHd<-PD;cCkMT2v~TfGXP0fPbkqFSk~97F^;jQ^IlrK
z&o`qjpJv&IFD1Q6R16G@jTI@mJNj^LvR<mVIPwWq?BmDkbpAIC^R18KoD1wVDT@Xr
zT4u9Hy|HGmeW9(E7BkxK#u)0K2$wXVoYP3@wCg`;g@F_8z)<KG*`0fQ#8ouQv@#9^
z>j|98G|!3R+R|=3A}tBK?APNtDGO>p^rI#P*exKsV3cy3>Kjz~wrxVcI`YzbTRHjN
zNp(VxEDdS~rkkF)_Kav!%tDi(enJhBL~>We2kVuE>3pktPVgL$=BG~#_0w5xaoG1e
zgh%$<-+Kca*VL4#sC;n81_llV^Z*aVOi%K9&)t`*SM4}k#eZ3H8>p|(qrXAz?m2R#
zGp+m1mc4u=q6<QsM@uj)1pY!tWbUsP2%dMhq~w<vYAYN_ar+(_bszvW3M8jvPCq{-
z64W0S!uxab12^>3v?N>_ZF^Yb>h-vm8LN7hbM=p0&;}5^jx~-JLGXU~@F8i%!>%ZX
zz|rayEjgz0lb)6>shT}kPQ@NN3W}HDZNR@JA>3N(#40sF4s9Czb2}s?m3gO1Q)W(u
zI=EU%S)j-_V83G|#~%-slnv~`ZSnG+qTeEt0DK$yt3Aimj+4XpVBsq592c`*EVUJm
zRgd^S(D^eR?WWq@Xqubv&hNK)N3@}7aUQpEa8w80-Ae{AM}MzWz^_`lzP79;5YY@G
z&Ke6qEE4#>xt3G%!G)vE$$_U_sPj5BP$}5Bxitxng;)^K!|4FbNoR)vI#cksW_qzQ
zh0*q$w8hyWonyzy(QSjuuSjn&vm(1_X?Y){I`ZaXmz2dj266>f!IMt`=wm?dA18Tf
zM?JW3Zujcpmta#kkvGbLSn_>VJvjHDrzA@N6LIHHH_A3YKY#P5M+a@dbmcqrADbNe
z3O4O_;kD9kkobb1N1Vld*SrkNJtX)A1tUyy<{xbx&Cwi0u`RN0=JOa%3>>~E)k82=
zbulL6;INplu>W&fZX91QVE|3aZsL|$Gf?&~>;AcFtgAQzA*hDNA04$SXrgEHucgCC
zVL`)a2E`#RX=k>vfx!Ty`dn&Ic|?qnFA2m`3nFt5AK#4!>ii%Wn*jQU19^QmU74S$
z6VvhXl2O|gPS=|&cq;BGFk{%swB+~ikArKvA@y6)l3amrhs_9@>hj<O)leR8UB2O@
zB)-S8o{|t8{D9y<x@!X?Kti)j@-nZc^s8<ibt$!h_R$Kc2M9vk(CYGhnquHa_aZe-
z&6J7m(g?#WfrJ&MrWDz^CPhj=bdWj|FMO9q(psUtf#i7FW}5*XS={m?o3!_mv;WXu
zS=lE9NdesroKXf^F5-KY!3m0rifW+RQLmJO6HXkAsqYyOUcPR~7v|)VMG6~oH+<!=
z0}mYr>g&kFtD6-oGbcEYxI91Q5D^n|f(S~18$9SER+mO|DE$gAckq3a+`IQ4@lh{c
zyx0|0w?|GcZlZXIXX!`8sj|+pnUBJ)iHaNO7#aD&E@?g%Pb8QW6Z$pdQMa{~*;DFj
zYCE2sROP%EK*%HZA@5nLsr^8L&yZIE7kvhYO>i0z1~i{Y7j!8#Sw=FF=;`T4?yK@7
zV$~5c55mKbp^uR3)bp>mN*;>y3^{LMp$3v3EY)i2u4#6KP0Z&?oPz_Hcu!K_yzVT`
z-3>_O{AcWJvn0s9y#&X!%t_10n1V%G^onDlVkfaMccWF5+hY`Ti}yW$V+tsiA3rWs
zPVV)s{&JmSLplHf+m<aVh%3;61nd0!gYW{x0~E2FyRiV+W`fuLRv>((yQIzDh@Os)
zU@MY=Il%@JG-72=z4_|^=oi`h1ovpQ#Z{t73P-9@0Ie#{$$0Ho>j(SJo9=5t<er+F
zn7zThQC3ZQ7jcHXZ*=BQRe{pN0w^PX;J|@Ryu79@_2$vrzM$4QKo0JQ8d8m{e72>&
zxc^g(6w~ZbqpF?V#9rT{%l9q-t`pT4C{MUNZsMK*K(58RyVdBLE|u(QZwuR@2#H&6
zc`Wa--N$RS*<{bnEoYg4Ql1+XZG-}Hp_=#g1MNTM+E=e+RGTS4xUo&wV;IOp`5$i2
z;e9A_R=Y(rV~gO)SU+?+IP3ph72VU+V3esxi)r_-uOfY%lFq}YTeFPV(YOSBxKM*m
zBNT#-7bseZZt;NPM2#3pM#&gP^s@o4`>Q}<zp(F>6ES|3t8Vj<<EO_0g6~oZZJWU1
z!ySV67qbZJGcwNF?cjMbGsY8YYSkbza4p`df$0R$Z(m<p-f0xk3MlSIYNZGEsl^J>
zmUf3wGjf`>J=2SpzGkZ_bq@}N)YsW$|7A<K^gmk=7S9l1A1Xg;!9Qx{{&%6gp<llm
zzykoToMMx{DF>=OA0HnU64JxjE1_raBmG0wZMNO?DQgG>6P7C8mAmV&Wq{@cTSJUg
z&gbQqqkA$N!}(7%ArZt&v_0Bp1eNULx!x81*W^`K9k|$=deKnYmSpfDwqFx?+Awv@
zssNev^0$222m#F+kQ)0v=B!Co$o=<-Rv_<U<2!uOalYHtG^(~jt77hvZ!d&E$FZ;b
zMGSIDF;ey_U}SAe)QZ>wUtb5-PeLg;o!K^wAF8_a!1qtb#p7yfM?nuH=n82;281o5
z>tfc}%8Hi=T2BB7Rft!|6cwA1!6%p6^~@z>rlHQB|M}@rl7tJH85(-)mQ+TFQsA)e
z6gv%3OU#Pz-OGqva?IIT9PKuz<mI4saC>r{I4zq|myUrS#QHVioUBaxuFrOa{7>eC
zKBkhp>&unvir9!m?TjYCZ@(ytz0=S?zXQZ5ySW|0NrE030(tgz+BlA>GEhKb%Ls7(
zO#oR7X!A+qghZ+FM0zs{J{$_^Mn$43cR8-3@mi3XK>QMz^#T+O-zKS;$MuTd1CWFA
zhI7zDQ&y$P2n%PX#t6Lx6!eQ4U6hJ^9=GYe`u6LPT6cE44?U3^+`oT6GB%T5=fFa8
zo9$}0N1sdS2dlir3qu#}xb-t5Nlm`%%e{~7ZZlE7F!ZnVUd^J^W>pE{QakYIZk*@D
zVZ$Oj?rRfWkHFcDcb68`cn?9UVUn355!g@i@>hkNvKhL+S{L`>t8YrX*-gEUUM-HL
zX5!HRAbpCSAc9D(<O*UH3+E%hRYG|I#VD7V<ps79$fWyP`#GXiKbE}0Nhsmpg9XW-
z%_H%$0X>1zO@{NJrPIDrlR#2X^@9sN2kuGHkQgE55}J*${_`o(>BW(>_}hg~Bra_s
zO8}ME1Yb;fU5ht}?MS&bP$DcO>@o*nXHRL_&+l*VP<&gDv9H(x)eVPWPSjfpbj9QS
z76I0!gcd|<dd(!Sfz6r~t=t{)VHn+9&p4r|p~i?``6liGfpr84B^Bg3LGdG)wVv>V
z1|stlIu$-L<{$0Qe$T6q-`(S$>t8Bp)%!BeMnbw<I!6~W@M?5TS>J^8p7Pz`)!>Ke
zrH`JbrA3vp8CymcnYiZK`IDybk#a-rR(V=rkFWv7+SeXFZ(N3_#Ip`$-YK|9HeIQm
zl|e?i^FZA)GA;rgz-Qd`m`>00vj^d|*<5TaYTtp~HHDyuPV-v5v@8#kgndt}G}OZW
zc~vxE8PH#nc#{Mof{C((lGF3@?p=oPy1}HOC;#2D;Obv%_^{13h2Sh2e+Ke+1VO#u
z%*JNy<O{_DG3JcCf4_EMkL!`BI_NQvQS3y8<bBs<$PyqOe>VC9k$B0g2a0#V1ND^N
zh8xJp+Q*L7>|IxkV<bCM4biO`B88Hws$zz25^|<Np4GiZ7d+i-+4-)UL?!`dyJ?v9
z4iK3{0>}3Xf<j`OEi*UwICsUIRGAl=y)J0CG_<tWRg#Z}^2ko%Gd(8brR;e?1d)-B
zYiP`E)G;0P$R~(U&_t?$s)@)cX(zb}SCd?(DD=#Y(oZ%Ld=)SxmLw@DX$k~J^}uvc
zp#<kWuvlEjjvd>?$$1<6C$+?`E8?8{0e1i%H>jtuFmj4l0e1f?e~5lc7QQVA&{BkE
z;2xUyW9bNA4M1S|;Po#?H-_BH)|N)Co0F)Jmhd4CNRQyM*GSuqdCXoIOu^LT!AsCs
zB(EH@GGL0+(D5p3JD^3zC%#4#$qP>3-!MxBIR=L{7+L_8a}Q|m>8}={?tvAsIP8aO
z>(G;vlae^FwX5^Jlid0vtr-#42D^_O*>>O&1B&u|#`@ey3+@xkYjt`k|Ho1z<Y04%
zZ^zZuZ)j({0XN_8EIkd@C<&iOtgL>fGQ=%{afz>OY;3Fr&NSc0QI?YlO>?QdP+US{
zGw`b_ZVL~F1|bk7^md_Rv2nD#uN+Pw4R}rjk@NVs-;WjV!E2@npieRbe8X*Ew`Y5N
z;Vhb)?B%7tNrtV!R=q<PBk(Nwhr|qOiuufs4?!UzDhP%;?@wk8_bdNg<qAaYCX8hG
z75L@cer2SY6?Lvo<Uz(kFUEzPL2mt8a4BkdZEnt4BTi;3)cgRfo{|#f1bP?S(uv;@
zr{u2d3cZEFpxja@k}g#C4+r!p-l^rpd{BsRmo*QaK7T%GzF4H%kt={<D>;f4mhjHR
z2~Ewn$WX#w&vB(~5GmOKj&};lyU*-`;1L|mDtJtw^zjW>-1v4rFVI)*&*zcN-l8Yj
z56?_`n=QDj_{JU<klA1+k{T<++b@hf9U;^i2o11FiAZliX*~wp4DQIZfBInomjB1l
z(4AkDetUFMtIoez;0_QS$JtkMaghXhND5XWzw`F{ytH~$9aT(y0FsoJJ4zi$=;1@9
z&Liah$98<n2K)n0{SF7w>(>N0TX5)(pjU5ioKJnna`a3!gl+WycL~V?;X?R~a4!sT
zL;Hc_n+wxU#lM3~Aqb$8i&a2F0S%$~3xg({VdLVfRHtNrvmqA}<jK;i**bh(&6U2s
za5QZ}ujjACH%*-Y2&;@HU>}eevH~?)Z)R8_`p+#N6cT#YWkFDe&-H}`G#}qWyl~05
zZG!WHSOYK@0^hz2Om7G0gVg=zrDfd|!rNkTW)M+Z^HdEMNF{=<A0S{T3}n~A34Djk
z=rsTZg>;&J=4M%sIRz^#0fJs6Kp=r-fQcNzq&x?#1yp(q#4?TcHQ8nSn;QTK8OgQF
zF>v0ns=|BuJdABe+l;;gPQS*LoA6wBdF6TRinig2-Xc~5@Jr^1Yz}!J835|O)o^un
z^%+kYqG%${Z}$4GbHi*m|1d2rZ2%oCYgT5lSv5II{f3<?cdI}=-;as81Cccm7d+&(
zun*X#PS!q3%v1&IWrmCcHw(e6BTX%!$uTQ1Ogz0i?8%dRqUTDgYzw}_Fo?FCvd4S=
zTo-na_!$D0?*P)qlUJdACjzh8f?#9A)KgxedA})_<~McogI$pYv^tm(X_%M-J1)N?
zU~Jz_rt>woXd$uHtgrU2@4&Kyhf_jrB%?XHh<?Lis_&@AV{uyaR@Hz$6Z6-ueSbsS
z1@sXF?F5o*LYwW2Yoq!MTMtkY5;Jm}9O;-tK04}2CmVn@giv3j{JIj*&}!TeXeT~#
zaW<m4;PIL|$^hO^`bop`D<tci)n--DW0{JLLqG&1UTln8K-F)8@S}AO&6)~upo!PC
zvVq9MtMQ!#uS+BaqJ$(V-<iUh>qqmh07DtUFg3dn${%Rt5F|OpoOVEpo+=<X2axGl
zJ<_J^G{VzKBv+)dff|2$R3t`_zrlosHo4g?KJUhj8+4u21Pc$}CF$1o66ixPvL;=@
zu9HZIVC)0euVelM>@3(gxK<2`ukwGgI_NP!ajxHozuhPD+>v5*4#=UZ{_WmT#=iP4
z?lnlC6n>4N6=>`d5lKDEb+ZU>3KF;gBA=ouKbm;DuVQ)PwUK?f|J0PjWTC9*pEszo
zHxM)C=m2v&$3e?()(~JNY&=>qDAmALLLSe<oxG7;|H-}MLYZX)mpcph?qZ?c$SZdj
z^uog9S{tPDCya0oBG13YZM^_iOV<@5O_^*fv1t)-p6J?2Y(um48}LncAyqar(f9_@
zbaZqOidS(CiUi4zkP&fQR8QCX>ST0md><27xx>u2dkRb*;YQIqi;Pc}0Chh2BVrn^
zOE`vm4#d=}Ab@s|@{!y4SW13ule~N!zmX(rli8*Y({F2$y{Y}#T*a!j5cB3FW#Gn?
zv^3AUcOl#-NW&>nCM;JxYq?!d!{Sn2<U02CD9M^o#SRp8YT}ORt!Wm3)&yYv#KSR1
zHo}wzW6-%Ww`QY<VPSo)G#owwFUhm!5sGtL<#uZ=bwx>0mN^cnG%rd$(g1-m(s40<
z^Od3tA6S|XS3t`Kg<+U$cHO!ZIsu5O**#*HkQa8{KR%4&ELKE#G6YFUqv<eE?uG{y
zg@1_K?kuo0*;KJ)>VLOne3+`NHcN<&<!UapE9$&xotm2)1}R{;S(W$qLvJuFlzwR9
zMhLQ<uyMq<>!E|rP`<MTZZ_qi1XTXkTysVHvq+CC6+S}RS7j^%{Q?=b9s^JN`Sa&8
zODwWLxydOsO7jmR%T!8S#yOid86^w_y?3R7<WWqiAU>w5M7YDq6+jqm9?v|zd$_CE
zsplnXItvML7+LhG_u@M=EVLxJGdz1<hR?FSH~nytn_27X)tAH9^pP}JDg8i88#O#)
zxzzRQMUf9U6|u6Z8i_!*;2?<Qd!(fw1G)Wlf&fN@d+2fvz1Sv_kdP2dWR#a=)Ea|u
zbA`7j4or*0DDfe*BpgFDF0xATUNf}yj}8W+1GzTxS$S^p!ILMNzy({L41j>aA&pOO
z04K8KWy;J9VSro#oanPDx4Op%Ea|XKmGjEV%FXZZ@60Lot$VmvEx9=irZOD6X0YAr
zXx(;_<d)IZK7_KjQ@R0tgO07Ot^arzOl)KcM1MEySqc6H?3SKEWBpyA)uSV#R&ZRy
znlU%rOaXiq2y^3=nCR%B2M^e^I$(SOClZR(+isMqodNGXWwQ%xoYj!8_uIDaM(N%u
zCdTCM?w)HPGTdD%<pcduJ&kj>H^Y`YRQNKXfd;*w6#Fa~=XT7w6x2piVmQb{x&x0s
zb?&4gX=kwEzUk9t5$%eAW1F^Y`6=5#+|EeL2A<FjaEdtP26E_@6Y2I)$n3}}`F75e
zv=6|Sz^g~(;?o(*u7m{xR!dr>QYymY$Su=o0WU?;pSSPcB>^0?b4?7m^T2Xcg(Sby
zpt9Fy9)z(!Nc|w6=22&9!c~thqzd_yjHcZ9z#|RuZ8NA|humkLS_jvd#b@Foi*qU`
zQnV+VG}J=xca9UCK4A~`C?dQsKdu%Jm+yeG>G2Kx)c-#(M^M1Z!1wR_u&OKIM&&ML
zoP?EKJ0riQghKW|QcWc!Yc2P9Id7%%4runbs?9c)*PigTdSxt%tw@FpLc_?oxj+!p
z7buhG6nqKy+{DGa4iFCBA&F0i(#8h&40eD%Mn3ij`AZLo0IpBi^*!6BhH=CwWpeYd
z>pLN4st3P!z=c9=(?R2;r`?0xM#{B%#>NaJ!tvVUyY9`fO_D*Ze2#@M`4Q6=FoWK7
zHM*yA6Ah3BEHnh{>2V+8e)ION`EXO>AvmPcHQOAs>v%49^nWo6ZLuvN0EipP6@i?}
zrr&LILm7WzS+}KmSudx3=i$R!VbQGyWi|c9$i8~qg_twIRD<yReo$@%c0(2~?rP2m
zZ!9p{y6u16_JL(e>f{$&xWt(J7{(BPe=2W=|H1c>9Von4L_{Os)Wbs>TD6zl%KyXl
zk=bQF8XFty_bcOaIRtrV;}5M5r99(t>mwr!L{QaK!A*xfd>DX27aVXLeH1<4F?6b{
zU$hR${5)l6CyELRPQ|C*1ugS3gChCV#&!HH*?snB(A~RANRC8+mmwTTxBT?rg~Bo>
zNp6@mUi4%lRblYt4t#1m(Wi(;)lL54&&dq$f6MZ}$@h`epE}bJD1SXR`O=={62QJ2
zfV#zYIX^b2*;w`tfKh~qRD-R)|LD=4C3BsngNiBIi^dz+X$lM(17G*+m|U$tKSG)_
zgfnhz)fZ^Ir+pCscg0Q~q{G;^0(yu<al5x?iSmz&PJR`V&JJJZ1xV6bghznT$PrSF
zo)u6{d?qc`_(8#pX>Qe-8OSbaxYptFuwvrX<#N5l4LI@UNCd=038Y-KD>sq<Zj=9k
zn6Mr3gOc)=Xk&GJ_4T(vUJ1f{(d6&cwM|+nK6AYq1|$PDY#ouf2tm8u#FJo)uS01h
z(gT#8g}L{7Kkw-#J<Wz{{?N1hREMANCZr{B4a%@|SvAB41J)~Kw(&uPM&B@dI6%Qe
z_}T3t_c`F=E~QwQ9!e5_F}^|GfmEN1?IdhKNP|S@U~=Nj=l>;Bh;q9!mFLOT@(skf
zBRYG<0ETKPRGYZD8DWh>1>LcC@7`gTs4}Z~5MA*+^DO*FE#O|fkLUv)<s!&=FkF6k
zBs27z!1G>Bq1?|E<XN9BbCC_sTbYj5<-WlHP%eOi2FwuXb^i#w<~e-z>F~kV=uo7j
zq!Rwz3Jmo0yoaMuR-T`74!x0|L%ne>Y_~+OL@>@i6jczs2yM3pyj1z3lCUrzA36F7
z)UBq`-~Tw(NHvZR$^)i9Mhf?gPJGn4+O;MiOPZn{A$oh1n_SRm%`UJ!U@9on`nwq>
z7F;HK##zU72aRjrz6E`;T~M&LQ&J)N(}VbUQ}ZYA5Z9nH6_RN{o4J*+;lV{M@J~-@
z{VhLZ_|S=H4FRGH2X;jlQf_5*S0%aeT0#-$7LxvFFnnZae~H%KM`FW)gE>lobZB-*
zW`7EID>E5CF*Do8A+(~*{YmU0@KHZn8#fRf%B%w9ny-GH!Vmi_^lxBeXTNjfA%N0`
zd|z-}uwe_gIi2SIEx^y;4{Gk{(WBK8o@6%<H^M=L$tfm6vL=EJ!ufu}?YK0hrmjxT
zyaVjhu$j1!5FPc+YQg3!gh$0Eo}dIKSNYMX-vFlT>eO+73-=SOfcd7^WX=$f0>Wn~
z!tG9jO~;M}Xq;m`<=gZ#^;+o-ATAtO5sIp#uss7FgU9^`v&_u<oiMqmAUO8h_mm}2
zRKc?X7sl1b93?(l>+d(@ug4`{I_&x9d3w<lpi`f_iTKUK(SPn@9u9Q>JmIp4f3$|o
zM)xZ4Dy~8(AfyDG!>@3Z5cWg9_kb{E^JfC;t!B5jk^G>dTVSpuTFzS<=jCih$%#v!
zpVE@dB^^e3euTRa9d(k~Bn&F=-@i|H_<RgVU>XjlWy)H>J!*$*FHJ!OkQTTnT#8YM
zTA*_f&71=!E5t8-`DA1g>2z-C5R~t}z3bqes%E|XuVH<O`o|3xNm=%n-r<u9Zud5F
ziqilwR}-LCy5}Xn1wP1Iptj5QtAt;q?#zkev8h_*Gm30Tt5j%6ljhi5O1aSm7h0F%
zf_$*~0CdfoY|u6wG3~%klE~FdzfW=m(d1OFc~sS&{(|X5P}0gH4l+Eas?PSQg8PQ}
zIDiP4E^f3Ir;glTdEoa(lue}{QjF>&vrrmus$ZFfWwZ~EPq=8H>I;=T+_)*bG(tTE
zJX6K6qdJe2ynJO>2|o)h5dvVIYhs?gGcCmv``z5YbX)rfOtk-HA=w`A|F)3qyYPOK
zRyq7ei=K=;ha9Q|SC!P&>5N6AhRdsGW%P4Q_I?wUmgXcHFN9)QIY&yQPtF6`G%!{m
z0jG}a+Vb9UZnT{Vr>of7Kmv>#`l}s)-oFlJN<^1jzm4Yff$S;%$y%W5GtXo#O_PD&
zXBPRg$0YpF0S49%F#k3?%(d}y*DGeQ9BfpdQETVc{Py%u9=H!ncpn<JsbZBs*t~b3
zu7W|yzE2a(c&^Y=^?*S(%3Dl8P%s&g)u7U+eApOn1!@RJ7>?{Y0N{$Vp;d+1HM8B3
z>i7Vy)|L`D-$+vp#`aAKWatP9XxKda;QsyVz}dI03O2%JdbOC#zWn#YqPs!4F5`x8
zYHO2GCpPogpDp+Jeay}6)hYFSiA!G;;Nt76y592=X!L1n>TS_PLV+il^WXoBR7!fJ
z@pCot^o*iAf+A_0&|hQaybYaWQ5^_+1pHPgddOf=rl}!F;H1+T1C<#op0k>Ec9s{(
z(o$2y0mR|4YC?3V!QPSz!;*dB@6UM-bun3ny1Ghe_IE<&C0r4(+&&@nOoTpW=`J?%
zOcuguClINwjANGq8R#>hz74PtF|<(vinXkhTgDB8MF>d3gq{prZF<+O)RSouu(<3#
za3CDa8Zpt8gktFtnmG6ABRJr2{mkDP$rm1)(_$oz7`=jm@mR+Fz(3O<Nbt5$*%k8q
z_HLmsuxSZE3v-WGK8{c=h*KfwV`pdQKeXA$ifn_9z1o*01Val*>ywZ)62ODpwmP;E
zh8Xnk#Gnx7U3V^x7xkiV+u}frei=&boQEu0oR19+e{AMKa?p^_A^AfCJ$C8K^F#pc
zI8d$z=AZjP>fgS1kN=*EBJyn-7N8m8-R{GOAJNg%2OmEkunqGx#N6G*1};Mkkiv8J
z_)thP(&KXLBA9Yk5{xk7)=k^vmnme^2J0X5^LY$CaV^W!ZHE_xSnZ~!)3CgxB9BhG
z{Oeg!(9U>}dh&@nxMcx2R2r8}I`VBITsz_0I1M?S=<dJ`Src<8K<+JAbz&B&XFMr6
z*#>RM93;HYgz5uU{1BWVXn0D07K={0h49Lsgk1;5dBPLaqelxpVgC0~=id_F?cWt5
z5${Zzxkv`T#to>eN||SJQgKwGNlidVUVnZ^(Ma#uaa4T3o9Naf)kiYY;*yBvz@Tk9
z&^;_QT30?a695VZ7I7F&{?Wg%sMtDa1gDb5F<jdp@ni@3S%RMOfQCiP(hxt2IXOXu
z7rf1xuB8+kHWGsos1w9WdFi_g$;QO&o;q8lqy)=y+6G>)rnD7!rniR-q+_;?5WqY@
z4inRXw`f_FI?<_r^f9TJ`+Nd!i$CKhhzCK%W~p_Q3zhI$aU)l7?Y_p2PUZF%QRCwf
zxCAnEe>rWu_$eCH2m0|jR0Gd5<cpy1$ZaF!@#_L4ag0<F%9RZsp0Y|X=IDpfyg3mE
zz9dy%?*}BpW<rd_Im4mEN`heoDLj002|?Exg8C}Q7&PHIFwD{yXI;R$@sa+Ch#>&)
zdb#m<>1#r5Sw`~@Pf^yBSCJM^P~^d7ity*p&=n$PCyBv|9;Q(R!k=<!5$cv2#z(=}
z$wKPydBC1hDIRpo(l1~mw>mg{FfMyAl>{O5DA6HVmx9ZEge}MN=ti^r{P7{8HSH8H
z%ol81x2l1Do=Wve*B~9=^;DZf)<b+N12r8jZ6bJv7ED@FtW6QKF=)PZFrPq7q{3<U
z&pFv^q;fK?rS#lBGP8ZZOS<h0OPu#e*nEDoOhvHONFHIXG02s_ddiPvwAqsPt$r64
zBkf{2&`*&7sQkcljOeegjem>83AHIOv_*x+kg-49edqe!Phh$Xrm53-M9jiHgBKLv
zQ+{x47u+g@6$Il6Pqj1B(dNv7np(<gye#P%Iti+8zEpGj(3~OJ_QqMlqoj_xs1$8A
zFlrmTF^(+`?JmAgKuRh~<sPRpQULTQYY0Vi$QvxAZ*Uq|cM|id2dx;;$A@E(4NgHK
zy$z#GOxw+V6wWyf8hN+4_yZ{4c-mV?Yui2cE;wZkEP`AmhGrpr!3|%6S$QvQd&+Gj
zYNwEo^rTa9GHNJIg5Z*f`C`n&#an(P|Nr8K!6X{<A`=OM{0ZP`SR41@km+XX@8vvv
zbr&2arb*-Kae*^yAAR(xSV<MIa6N4qSxNo0i1D~|oKnI_ghM(9>S&?t=STWNx41NL
zo}gXtVal4AH(r^TyjpAxOGRtRwMz&O{Qhuu=|mDRV{@7;PiXD(5-~*_8#^|?S^+!Z
zjd*9P!LyY<vJ!S((J;*r{xD4Dd67SxAy>d4H9|WXISqm+F=30;=-+4oxrP}1N(YJe
zR6mm#0VM237+224d1+U3_qA+P1pp@o<jA;(>hJCpYKdxWeHAHu<~!JG6-XEe<Vz)o
z8Ju2!dttGGI9~*J@oKi=2)-&6i}Pd-EA0FF`tm_B>&p8;;;lKRys*txTZGE$x725B
zA#?&5*TB426E2wo2wlW13%atN;3GvsE{tZ3!ZEA_oe%PpX!GdTI8q<HoQw>=bb^uk
zX5%w@2Av25&J^6^7BGHAHZ*(eO!U3Fy#jVAyclA10&_%fL0;Q0_A!~|`CaNlHP(W4
zlknUNM9?BX%7IOohQ+1TxrO6rzyY+UUoT4%1o6f|3QQE#LMh30=&un<IeR{O)Er)n
zwgZ_9DpiigDNeaPmxc1yrt5E0`ALWXHiGJ{LOvwHISVtiIVz)Ep3>HzF72qFBBm8w
zn4{w#b2$RTq#wa>gCitNI_A+a8%wGG>&+ei%{TKuz0M<sQkx3189<C^sg7nb))8+6
z(Wu|S2eVru3@2g7?HVigp}DCBJ0M(Mb>yE{hDeOUmXr>vT2m@nrRxD%A2hLC*h(jp
zMxiLWVUDt#KBjZrMFu$xCLeluezmo=r$D?wY<Xu?3tf<qVTslP$dIs<qeyR$pz={i
z;U@J{>2~<{{PUWTMb}65R#@-aWm%siC3`5UK=oT-Sd`eiAR1!02)+DWoIZlzA*2yP
zRW=9chbCK%-`fu^Xa%Mu(yh&uM8^&{5Wy~;$u_Qz-fyuzs_q5$!mm^7z}TgI90sKK
z#M4VX>Rg=3G7?}iLznYV^xSRa%AIJV2&9SxwUGoDvNFo<3t|ihJRx{}2_n*sE4(^7
zg<k;#eu_QJT3lRwdN&)HY--14mE*^0AUgc}=9HfT)fJWY*Uwe~+8GsIzW(<7QJj0n
z;ies+-M~}UmTE%OJdK!|WDz51A21_IiZ!78_r=s>ZtG2@55vFjZCOu^xk|%9BBl{w
zh<<HXq?p#y4?Ry2xPzDj+08~rjA0dq1_vJnVL{q1EPR4(C9=!(s;N6R1hX{rg&mi(
zQ<MLB$xFcEd{@4#9|Yic_jjr~z%)Q;SH#wU$IKbE5XGbjkWi1jBt<mw3(+ynn%hVi
zRz$HQ0h<y#*n}ZkLcb5XchA3N<W{pa+m(38Z&-Aa!sW{%@L79Lh!y9^!21Mu*~}W;
ze;FQ3xBq>~jHyElwV&m*3==wNbV$cw>n5;#kL%K$jd7V9GrDSO5^<0qizuEv8R8m)
z?cWG#8&(*QctAU@@5Evj{|Vqv^}kZfs2%X;31VW8us6Z#>4gc6uXz`lDi(ik!~9ks
zAygi0l(`A12-6CLvkf1i0%I461mHrf;yyUGh<B?%qH^o^XWs{saChWKPC4m+UxqU}
zn#w?Ox_WgVX2S__7t2Gue}%X{2pe`%E>xo9yI4SNYCNDRxdjy+)jAl>M<T>tSeiJF
zxQJj>Ddn;Bu75A+TfaAAL#{xs%rRnzoo3B@dyASR{Qli;4YR-T$>0=YPPYB;t9t^d
z{(ie?>lV_1e|~LX`W0DPN~)1#-TA*iCbi`fS(qo%N_(^BqYeM0hG?U(x{c8XNEfv<
zi+((;{ri3&+bzteV;R{j$;?KA*BSr5;3xIxq1$9F{8d+SMjm$Q|GkM%m5l?`w?!$w
ztUR{+a3%QfU96Nor}8_@+Se`79p1U+@2i3qU0-q6>|zQmPBpsPY4P`#+S&0*6l@pC
z-n2Vk;`sZfq3F>MS|W2x#aAS{SyI{lK9A0-8<SZDgTbtA3IAQ*|J;*D2OXJ?KvgNe
zsB!qKw!=Sbq{~#q`u-FJotW<4h1o;b<$EbUoq3jJbC+ZEU-Mw9Z4l{%=xOJaUlB6d
zD^z3Ws5!*Q{YR=^$CGoNksZHK_V?j51`qG`o7VoVF|*H-WGX}DN4mz*blIV8^uo%2
zmbAK*Cqm|!ErljEsn0JYltN3>V6yjG&EIvrVR4YI{F>gAA6l^#D}~g<hnSmcX)U|s
zs18Xg_j~+j4X!&LA#<WOlhVuL>7j^S;aXU|Qf0|1;Qdr&(}YEJZ{>}DSJjDr2Ome4
z=%Fr|2M1`6NnCZ%<5uA(7jai;9(;Cc2G8qbLj5C-#jaL-gS5t!`zyZEZ7yGT48^uN
zG2Es3n{>9^w<sKNOWpcNML3p+hc1J1I+0U6RBHRTglEPnCxy*QC|^C-yA<2G<EhKx
z5~}S?+uP|BD>XL+d#uuloP0WH5T<h>P;289XT`7Dss*!u_ppon>L-`6!%yfV>VvJz
z>4b{eT%tDE6f3_Mc1gH6klshWJC5CBMZ}$y{)<s&daJ_^ivD$v?(0Y5Df90qY2vzE
z>S9VSy8ZWMv2d1iT4j{Y)K)#X^Y@t}cJgsGiod?#lw@-2%Ms&edLK`pN_^|@MIUnU
zv163U)~~0Zb??%cnL3o2bN|CUS=2`I`bmmk*Jhm<6bwVC-ny`VKi1Y)RlTEM$d}=h
z#0%lQL^=5>a529m%qE}DFlUA~<WPuWzKOp^@@ADwRCzA4q6vLvQ)eRkLTL`o%TE2)
z@N=cNJizbJc**Ccyuz!WVrlO~aHm|)92hhd44D2oe_cPg?08NxF-02xn)it`zAbZy
zV&lAofP=xztumh<8HN_0&iI*{P#o}Kw~Jq-wmy(s(~Z>^HHm)p{ggs&73JOkJgm;g
zU8j%p=a$Me33*q38y96_(+*zm`)J9kKjU!r*%z7~My_?X1&f`1g%sL{9cnoe%q#s|
z&K&0tJF&~*m3#W2wvwgKhDVt+zEUxCQ9>)<aP52lxi)><duHBeM-A<mf9=UnGB@Kp
zcJe;AnwG~c+aG6Kzq^}{o174RCi7wE<lV4u{F8Sbj${{vH66}BS*16ucR*=W?|uCT
zrN(5P^1-(|?%hA@du!doOz6KZK5%Gb{Qx6&yzf|rvN-)AEYhZX<oMDV3dPdJcr~7o
zXYz-bbL}HHt?c=&P^+deuy$0e|DB`49s2bRZ5?9DgF_a}1L_rT=Q*N<{${l<@+3x~
zxyxLG^%T(;9Tj#&Zr`KsD-jr1%TMX!V$sGM=C;F^x0dW&Javc{jq_s$>zbQ6!jmGo
z-sI|nZijh~ke0W}QS|Ip)n==sFz7zS_blLtT#;a2$6e6%z>FBFdu+FnNS_ZXS%Ua^
z_g&QY4iC)Q^tF<+Ns0VPT)N@8vJDGfbYU?<LaBkS2|+E@*&)|ub`^d*Lym;}{NMa(
zw}-2CheqZr!@ifa<vtDHpXq5(#&%HQuscMP1#?-u%O?b<8+)_=A@6Nqj=JW{V|{?c
zMWP_xJWBOIZcFO7zZuU?S%wKo@a^IBnO#ge)VcE#eKYYj<3ZF4^Fu=HD~GeHQm=oP
z969f|L6Smy%h;;X-waj6sd_)`TgtNt88<11Ig7qVSN}In+Wbuu%n5HD6tsgkeV^J;
zHEDmRj=Fr~n}TPCM;;}-_!s`!ZRU|OYuJ?ClDV<)nE9$nC~J`Y24_#|eXVpAaY@2e
zs!X@*SV}3pe{PU9iq7EqH{V7ZT`y3G{-O19&d}05_DtaU0xg9o8<HRu=O0EngPr6)
z(M++uJ4JaH%?HWV{_lsg5?z*$kh%TL<jj&>UsR+*=vIaCKgZ4vtVL!Dk8<QXJ%07P
zJ6R!Szn(3hj_ltjliJCbu$z8&gG!{JyTPpgg$91vYpO-YjHH``j$}r)G_NaC?k%&N
z9)8xUk-GGsRg?_jOKfkvl4B_Qh9YB~V&ikF*eyF=IMr?NE3^<k)BEULRovU}FXd*h
zFUj6OtUM396jm(ypAV!DHG0+1sDJu*$=#8v#=7gzD{pmbY2IoTWoEpQQ)frnvyD&m
z&Sgplcj1MO8d2e?T9v+qw3^?6-llXFvKs#Xxy;R9kAG+-_1)OC@<CT&d0Jt{+fRwp
zOSUqLY;DQFSkk$C6N$RcYwN<yh;Kl_j=8HWFCNvjll{%lDnDIlu7|xg{$0YQ?58cZ
z691LkTAs|hX<Jh*wdomi>D;VOryV1%E6i_=w2v31J{@Rs&h(J`Zu+%s)_=?7r+Y!i
zhiQcywq&<u(y1~#OkL@ljK97tb&m1ngj+hx`1kAUvh>DsBBH*-tP0zHNO&q$6;Jw`
z>87%IY+wGgL3f9rF}GJ|wBLWCL_Fktm2(ECfP2`cxp7a$=n;An`}`TXg1Quns`i6#
zEziYKK4xO95FJo9o4nfhQ7cjE#cQ|3vIFJ!HJbjjB6K}Fjfc*#Jl9ip+&DEls<4z(
zGae?bP;?P5fVi1ccV&Yl6NT}^OiT6hrcps1>N8QbR9vex3r+{CN^f1-Bw=1%6r92G
zpU0~*+^%t>@0?!F=$nmweIDj2SxVITJXHBUyBEwDH(3o#81aj4%gw*)PM#Nn6Y0->
ztMi&PP5aWwz>S<c|GA8Y=G7zq{XM@B%1PTbY1Yn%U$<){>kqDOH~&B(#~~_u$6k=q
zH}X-m<5j__x=S_dzxE}}>Tb_qkNKgc`1i3St{>+}-P`bNX%|zG;_)BPY1!>uqiK{R
zsUI_J3O}-8G>yM%B<GFg&K{~`tU|r-q`7bP_dGRGs1+ndCeB>julMd>jIdZlU<{VN
zLcQ|ry1?u4QM3_))FP}Ntq(jBMr5noBRx46#vC2bW^TA161v>GW_a7_R)K#+AWhNl
z7gg_>NSaDh*Y>xC{F~)x?fYeGuK9c^d)96#MOK+b)!?<a%jF2bPQsmRrP8ireNpiO
zqP(-kVd4DgB{EeX8n5?C(Y$Ulr&Y*0a%<UO%Juc%qQ0{2vcKP+JOHF1zkFW*;H@q@
zNgJ&t4UQ)#HBp)NF3MKT&%UvgU8fNL#qM>EYRJFmeQZR{_^_3qkobZ}^k_B<J9TRr
zG{i)xnrJEENqvXW6$gO$W=SX@(BO~4g9j!BvP%G7=GZJrh~=QY5}U1|K;GMW(5h-Y
zJC)ysc=IInCIR;!X|xATGdWi}?p0-<tqK*RUk_}fm^eq}`~*DXkO=jc|BJ4-jEeFL
z`-Nxd?(S}ekQlm=l9CWa1W74}PU)dbBn70TL`pzPlo06@1nCAr8U&=?jsBnaIUmkh
z%eDC8S~GLsdtdvyezi}@FoT%uLk<=&884gY+uHHlN=U;Obt<8|*#vljwQi)k8QZ(X
zzo3@@Ww>qO>z*y#suFLbh(>^UFk9M}<d%Qg2PozWP=zys;UfYZ6}Q#{077+Dea1OY
z0vT%m&++k<wDfInVI<Fn*GuuJ><?Qai5tFd3cA{=eAZ~kiLbuz{t2BQCA?10%xf=8
z-sxy@n+4^QpSIco3go02#akBLz7VX@^GSrVvglmYTM+T30z|2|$rT=Z^Yj2#0A8^(
z5K15v&?tzM0Sx`E5)(k;x5kbD01q_+6bo^FXaij15Xh|u7MMj?Knes7vY?||G;8oy
zpWJI5+TLb+M+o%EHwaVYI1J`EvCRV~4F67kcn+1Hr?wPxwx3(v7Pdoq*vEI1Hcq>F
zqBt>DlYId_bB-rEWA`c#kSS6pS0pS^d?<60+5v{xG3LehV6KHQNt<O{2mW6s*k22u
z86mf66+qM2L3;_62V4<QDQIWImrh;52zr~6lz2P+ee_`vw82s^H4KwWzlZf!mY(pk
ze#gPrx^OW}j%6|H<_|^?p$pV+7#nuRYx4PYuhqCe*$+zi(fD3FBc^>}NNMpT`<WG*
z_!<%7DNySG=KdBn8vWQb;+d10iVk8T01iL;0WhLHeSL>6_a)DGn?A3~1_8<>9B|CW
zN5r>-e*;>U-lpXdyxe@UD6CC@H+;zoB3Nwvsj~lWus}&{zoZtK5hixpc}&uP#ELg_
zrn?z0b1q{ycIP)k_DL+w@7!<mVe7A4{y-2QhXo#Z3BQer+`fKrYxMzx=1QxxSQZe~
z=WJg$f=?U3A6OxY@ZTv0d-e{>#R8e%6hxkqRcpU=rio<74dtOKHm_ewQctjuR2X5k
zPeV1^EF&$_Go;9k$(M`C7;^y@1dpz!%NC2Nz&jLZDno4pisfaXKl}nX`Z$1-`1e~`
zSpizoQiMjOH5h0S!B~Fc;to`)de7Nw!!<u}YnB-(mc9ET8FTw}?uA8J)HN?6j&%Fj
zuqJxiQz=va>tp|*K9+=BHp)LBNqC6`HM+RVJ6W6drzk0*s4fsU*v}iBP`gj_D=%w1
zb{fCmmiG~dEr=dX_>b;Xlah?Gx-4M`8_Vr{o%{SZHJHi6z9=7%9sTc&e=9GAKyHQ8
z02dl<s@>;(3kKu|)fP}UH#Z`Bezw6&00si;{FWFAIt{)>A7~X5ek819+FH|8ApCr|
z-jIBWbQqDI54I26+KPZ>RVPRG>pv%~j3wWSS3_wYgcM|<J-%)70&*MqIH;+PF_>Op
zJkCAGskTZMj5R64l)Q&QGxtcQ)q{lEBI<K<0R2%_Gq)WSV7t_}xQ{GQj)}O;PCaBG
zAwj7(-As55BK>Y7KW_b90D}N<5Gv#rSqe_VC<cfu(C<(Hx+(PCJHFdQl$Gv8LO4EN
zmdY0})^GO+&D3moz_i3T3r=`SSM{eJF|3mPATBThV-uJ1Hd%+KZ<-mz@bb^<Tw7|P
ze85b(jY)U#X0fr|C5F`kk7dS(I!oH%C=5f#4IRyJ>7Yl&IFna~n4XP9m+Fqij4{uH
z7T!QDd><WvAeAqGz=2>z=YdCW5&%p;2sj;ZjkN=bEIZBoJsq8HpbM`CB%po0=`AUL
zYmL{Un8HnNy?x3q_{!mPWld`R{EDqu6`TGMQ3&RRP*rC6Q!3iVw;b*m!P)masvVXV
z+7=slCTENb7Nz%l3zy&9Dup{@hS~5#2r5yh6G$t2QN!iyg`s6~`bC1X<!^uNY5l%>
z{t_ct9OpO66RL<NQ8Lr)v%{)>!P3<cp00L+?v^&%JBxfU`GyER!P0UfJkSR--eA%#
z5Cn&BFZd0ikTjz`raWjH;tct<vV0iXfGPJ*11<YnLrrRh4`a&<x?4G<LJYTzfAWZr
zsq7rj<JZLnN9#XK^zF;e!Oovc`N^?WFJI2!=GEDR^n^K?PZf`iokBmqL*tX^*on9b
zcibA!Pc7fhx{2y<mPHk55kG5=OAl`C4_~r}MA>82(bERVti5nVVzW+&T4j_`{Ogll
z{53Msz;q1;VbdZ!k_LL7Z5y>YMPa*^tVlB!r~BM;0;IO<C><jX4R*oPJ7HDbHmLKo
zd_$t81f(RAm{PIdS5OiH(dg#+E(PvGcNn0r2+LnHKh{Rsbc9NNV4IDP`g`6FI13oC
z-VG?lcq+jm^4&DwV->fv0D={+7>iopSvOM7b6!vVLk%m+G!8NyPa0ZbadlE7sQhrc
zZ8G>wEY<N_^l-4;Jn?naocX4L$QQ%P%J(T4%=xwNmTe3(U$y`3Pu4$lyV98|yCKY*
z!Y}!7JL)vJY)AkEfx}O*Y2e1D;)psN_(Tiymo3u=B%<Y~OvhVlOKeE^)cc;@-^H&+
z%dOWYA7!Hkz!lAm8c37gmpc4>@CqN}`kGqplg@*_3RC@-o{*%ott|clGx7hP-Lu=l
zL_VdQh<?ldMH(vD5|(w?ou9xjQj7V{Vn!?fEDbb>=S1+!PlCiN*YS!hcG}DM*Z&Jp
z#8NV_g311ds)Z88?lXD1%>F0Amtqfs_RxoZq5S#8wu+X*9pZ0nxX?%Y3X8d3)8-Ig
z_(t+ygV`XUDXZ&YqUMRhWL7T!Kt^S;)xF4u9`!d26Gs56p>qogYq4ifM2RJh9K+Rf
z@{abSzJtRrIGt^uMZ|#?BEcC#|FY|3L5Rlw<^g+x5arp4=VFJPX7TH59D7N%v(J*D
za+w-fYN>zTPIg^7{%a*?1I@hJQlRkIfZb&{TKZO(2_D@8!t818S))kkqhjdE9trvb
z<^KcEV45vS)6B;)T!|TtI5Ob~r}rbs7J76^W%U_LwvB}W-b4y-WI&iewQ=eKbu*ru
z@xv`Eo;>G!`1zFM%fGk!TLBHAdtE<D4>cHqtB<EkR-lfb5G@CgycN<wQhLfCb3NSe
zS+&ZKF@o6P2z#e<`oU|TM<4H3NyvxIw>Ok8ejK6Ae6H9?KZmz@O-E!dch-EVw;8U^
zaHyeWYw3dZ->W42Zyx(3Ibqph+Je3T+H92iT`uqYC6SS_8~G{G_j}bIb=jYOqdNF%
zc@l0j%afI`mY%A(&p8R9=fF9KHaV!Z4X8tv_~mI$PMR}ly8eCt@9VCp>soEM!<tWG
z(r}e|LwUKghrf1)uATyeEYbj_Nx5-d=SrvJ=k$iG@+DG7d4~Knh_qR9Fzgxa=tTZh
z<+l)}J)G@RhjMPhTps1klb3zvC9=h;|K^@1PZQj5FgU3*80sU;)i$%;^&f~9p2jNr
z25(Pq{ewfe>X;17qrP91MDKp48YTbX{92`DTA^6CCM$U1I}UG*M}Qd@ZWZBi4x<^U
z5IsnBhE$W9@FvUuKH&s_p(J*J9KHr`D?777SQ(+K#1V#852O5NQQBPP^;0mSg9h$e
zFM+((7Sm4zWy}=LVZEYiXm73{zx+w7X^~6I>q_NS-GmU47ad6gnmca&|8NucZ6*>9
zA2K`KT8r|nLrY6xB)3vy4uTW0aX%O)dDbjU$b1<z921i;@tlg%5uZ<v(8BkB@u|oO
z(n^JQm5VyX6yNs@?ZGoykLdjGnJ;o-Xr;e>mfYR67sB|M_<B+qE9CJLZ-_$W>XH|P
zQI_d=>?+KfhYWuq0v=6*-M&f#4z3!VV5O|^m6V<kvBD4|LZff>zS&9NRVX|4TV|L4
zTmQ2@iHMxX>O5&%`VDvT;xR}hQRz9b%5;cUH5fSMFau4PVJ-ebjm<ekQ>kG#Q#b(8
zB@g}FXrx&6*nUof{xu=eyzzTtkyjJ{KOmsV_diq^Wxi;3zNLWAGU8cFclf*jlA<w)
zG_UlF2E+G&#VgBY%mhdD*;<w}4?c#;iFp}kvJqbz2#Fhk(Om(0PVzLwH&ugH*n07Q
zrEq=43#YS{z+JcHe5dtW-{`x2Df^p0C3g27<Jt?$PvslzQ}$>uJe8mbw|R%*z2$y@
zN*Gk)rk)|d0114={N`2JoHiHl(}$kANSxL{zWRRW$bVIQFbs=p2pUrx`g(P#>QviB
zOKI#{bj0+ALM+A^`wPd#+~<N+>8%l)x{F=k1MR^`+@`ZoaH#&SqOd(jX?sCfi4i=Z
z@RkTg&QR@SqIcHdzo$wEu#fuK8H(dd8~?pG44liu&*K_nrP_%JOANvWRIT*(hA!IG
zF9}D}8j>-4!d4gTl<Z_VVT`S;XS2S_V0aBdbqr6{gkz5cP>t-qSlr^K<b1cBUxl9H
zo;|P=^6$IH$Dcw}j3JsXMde&G{*#MSZsBWJP)JYW&(LqtE4o-REOgR%4mTk$KBr1|
zxDn)yKCX)SQ0o~;u|*LU`s~d?a+2G6AMuixGOFd(d?`o!<XWN`>$_DwhX+|r&;Q#h
zu7*#NBjpGhFjWXBt@R0%<!zS*?PI>V<{$V_6;6k-hL-mbl$2aru8r~f+Iz@f$XK;4
zZT&V<LDTkn81~_W_XR5v1ZND4f^W(EE=}>OE=}8tTuJ09c~@c9Wsfp~{#!H$25#VE
z(7Om$8Lv@@e>9!(zM(X5<Hp-E?Qy9kk&LH-EFy4870Jd9BUK~_ZqK6Qfo}V=Zi><8
za^iu|gi;E7T3Q;jxHT@66u+gLFAVZ6cSA6qhfj#V4I5)J+cs{O>>Akl@14hosZnk5
zH|z8B`cl=xf&<MHIKEohxAh{_XXrliFq~6xz9S$^W)t2t5GFIFE2S?+ntR4qbrtYp
z-E1m^r}H$^eJ6;dl?k&CqZte{>FoqZ#w_IWiWG69YAFWmhB|*WSVL4L_W=0n)qYHa
zjf>`gB^gh<d}Mp$y-_P{9{-Oc?QxJ(c#3yuIu{~;7vV!DR1KdS;kM>-Yjmw?i_wjz
z^Gl^uU#sZUM7!K4K?F9GHrn_2>)JmV<<Cf!hAeiNFQ!jI9p~j5g3w>#y&GGKDeXiq
z>hyhD&OEF!#suK%{aPOeu4cRkhY;O~?S6Ii-!Sy_Tnz5uju8sAXTQ<tUctw`8xdWr
z!M*EfI^nZVFp~?8?e%6BKcB+3;nlTTH0fT6D9&Hy3FR5JTKJmUs-9s%nV8U?0uE()
z#$XrTpUiWkzN23zs`@{7Qe?aWq)UIJZ}!Cf+&R1e33|j#JMS@^P7Y`2%crEvmxszF
zDTNAkcw*@^+6|aU3d&a#V;2}f43yQL?}#$e#CA3Jk`W3L=Mo5uoROAZb!5H&Ok8EL
z;}P64G!m9p<YkHS54u@j*l@+}Z1uW-^1WObQgIjhRkM2b0RA4<ly^d;Vdu5frofsW
zikfFkoFZJ-m~dwS;!hFX|7QSo>-1ef%i$1q`(uy$c^7eg6*$X@X1L+sp{rY4Wr<DB
z|JLKVH+ecBHb4Q=WT<{dKH`Q(bTo{^;2nnnCy+!bh)4Et)yW$={1k&QCGCA7Y{B+<
zR^50V718$GF;u5}*$XSWT$97nBO5?cWR)i`)v8*bF~Osg223ao!9hAh@)vQ;2#W0I
zV3YDPd20^(=rVy|)NSOSf%K64ZDeNM&A?YK^_eGi60TkLN3ff9#1U5=JdaoAV5LoR
zar5qScjGg9q6zrfhext}Y#!gcp;zfx_<CC*wx>o{jUhq$E#pvY=n&kNO734pGJ7oj
z*nvIK0ibRObFAw3I#>;buTbW4PqJ3wpxK%U7yOT58s%8MD5o!0(XXRe8scrkwCa))
zrRKu>hSBeW68I1|``ol~=RJ1!yK?I%oA24&nAjL}BMo()4><qhBL+^a#BnG87<L0>
z4)x5vcI5tDhNb3)-=x8|VO0|EXetev-n~Xq@e=<>f%z4o%}x$OGlOEp12;)6D`qW-
zpikv+lt#S@b-z0aj#w(Lam1Y!MibfM8+Y#ubq4>dd?$;p(BimqsGetIH6{G;(|%BE
zrF=RkfTUL7ZF$!lynAB`i`yGrv>v`B_Oc>pVn5>AULzHAAVja2-YwA1uz;57MH$$i
zeR(89g8zdb8whB=M%**^)H-BpmaNMfr&U`J82Ebc7e&A$7peVgK}$DP>>>sq&~4fq
zMku0YRAF`2I;)~qsG^K*HHsyodI$x}rd2HTn^FGT?{C5TJ^mvRTW(HCjQIU<aZ1c$
zgjO-H%T<0)Lc#dHwYQR+o0Lc7JjsHD1-EX?^L=ODzhICph9<du6=nF+=14Or%7mIb
z9hbQOUZVm2Flv{Hyeq+5o8(At)U&nv^2v>yDT3Jp#K;8MgX~710}|7{=UN84an7jz
zxyDPY*f?2I`!=&?Vv8dj%v?KydYe0_JmUAK7`1Vz9MFUCSvn0+{w>G8+AL)7;7Y0A
zBL>R-$?mq?J?xjNZ*cN<9^|c|&cBx4V@tpZ{Q$b}4||mPo*#U!<_%xCTdXeYo~m&u
zin#T#lK`fwBhDg7vpu!ZLl;XU_I>ya@~5HP{Fni=q61~wN55^G47Yzf4EuFAe#RZh
z!~BJoF#M?`Pr9iVJg$#SvkA9E2#w$u*3VNRE19a+S<Z_JBiuGoqvieh(_9-O9p2I~
z9w{0j$Y&LNCG8OWb6Z+7>BLHWmi#337kc&~qwC6GEie3WLbhE`+`{WK%~W~Y-{j}C
zp|4AE)K&or?rDEA-Yp3I$0C!`X@06!g@MZ&yhFNv4z_G8pSSt_&<@~03wleGcSM$_
z@Hlt{A=^N;klUx=QiIBqiKdZBW)z93THY^izcSZmml)QsLuz>C`cix;W!pmJM>{F=
zP+sWh`qv^9+T<1dDBQoJLLD7$#};=b##>54NDC!|ptkx@^~e&OQjn%%zo9}7eX2tn
za30dM{q6TKl+g+&&?B=V^*IzuRS7Dc(sTM@tH&*0+5V5|$7p{4rwuDfgs+W;W%tm1
zND+bkX(<sJN{@io;>CT5#wT^m8YO6)OY+KsKOX7!7X|+a6t%zbC)~f1)_eFVJ`nF|
zehxDd&83BwB9hjj2~Qsz6FGodIEbC~GE$XUCBrB>n?)+-*KYaGC&xk^e_Dz0!G_Vh
z_jbd&-=G6g_5PI`ny?o8-`BY=mi|B7cDZ+tn5;qA`T>SAt-(#)hAo(M8o#p<vM}*?
zhdyb+buIcnZ`fa?o4RWgD-t?$+Kgy@nCsgy^a^2VUvKcJTL9NNS(B{?_P$doI0bdw
zjXK*UKj$2IzWAv{XPh8WmVt3RGjg0x%9=yU2Sr-CeHp>3*dM|aV#Wi7B95dX+<>6h
zqTu9O@y@ff%P*+*Ym?yxxJ`Q%`Y%e(H=d(i)#EU&_`mf3fHPmWQ}^p{O@cqNqrR8t
zC2LxG`zED54FC89b63NFHD;LHZPPHXXIB-lwfQvYPTCAEuOqcsLQI*Vm;{zI)FO7W
zs9w)dXT>bPmxFHV@Qco)pbZn<U;Fax?4_x~rEmfU7BY$1B(>nm;wnVR(-NO<|Alb9
zNuuJgbc^>bL}mBodDGi>y$ahpSQnPVO_`DA?2#HANG%p9gzHFWsq)))UV|M<zGLOk
zuZqg>A)FjQ(o|W0Qiv$C{aa$1?hz9MGT!pNgr0!nHvQoQkBwRi_Y42?XJ&|IA`=0=
zJOW-Y?e*>|D+pP5H7+h;gO?m;nh=r9S0{BN=CSVH@l|LL42FO3uOV$qznMNyL5URD
z(-k?0X_!{v<7pI6^o=qDg4J7t&E4e}5!Z^$9U^XDHdDCjnwY++<t3}<-`9BY%vG@i
zx<rExiohnD*SPu>M2LDJ7&XLzc}?4?j7*z{Z>c!G^phlYG0lIg#pZt`03?t0IK9af
zpZ14pEh%fipta4yrRz3?7fvoCz<^Cqk`)P_h$##iM`!QnN;^&UISftOOdo%CheLkO
zty-|AfKFSSup!byx7X6!pDA4fLKLxHrNTO+H~K-u4HvG&2g&Q<I9B{l@U1N`d8vn;
zG)|0?Oqc*C6HT%RbRh^j{_O4%j|C}RNn8jd;m=jxFUc-{{3oepib(2DeD%2QsDI1E
zfOFr{`qJ^fZZFcLG`kzSUX)Znz1rZFK1aJvA*uiHdi$2g<2&fFh^xoMIL@#sI+q98
zE^6#_iq<Y7uxok}Adzs6T^4&e^467Pz>6@Op+P(k?aNauGJOJvA~K*}XbjOk!j`r)
zXLq=ne5GJNvgaxyVwR%?J`!=zp`1n)Mw9c>y77Spa&!v~8a2Xg@xIwL(e(}u%_$1x
z*VY4gi!{EHe)pC{jXsj-%+Q#rSMWbt)8+nuXjloYr2$rv3?b9Je~@ZEdbjYsj{PzX
zuR$_CO5AtwfL^R31C-P+7`68SQXgT~f|k3bs=&YW!qOi3>5m$nhIowr7ke^&BB)q;
z_d^T_MGK};IsR~#xhfTP>&~HksEVGZE++?x_g2^BIQJWp9`=p_t9mn>FpY#&Q;a>)
zXcxlv&Z8)gdKCJ3OuIuYu9?IzjmmegU%rh;Ij)DOpSNb=xwHP|zXqIn0I>FmpILl`
zjSK@jxO}DAwuLn44eiqJ{Zhs<q#_5hUrA7ER@axUfQ%1<fVEJm#**gDVj|LArM!4B
zmhFr=d*IjvnzT&X<%%h$A)xd^4vV9>tHJe60Z?#SsE$f1Zy8og&cR<iRqX7@6`DPK
zQ94LlpV&?^NgYiiLDNO`u{DzYn1>o*!`5V`d4KFUgfR)0hVXoxRN(3-x}0Y5N$aN}
zjw1|xBjH;TnZor5nZjF2MrgqEZ-1IGL%avI8&bygBp~4SF%y#fsE|3g);_AKI`v4J
zMtM6g8TT={1~n$qAOR(dR5|>}w<`*l*l*}ic=(A^yOjMRak8LyhhFyBN$^~k(jT4c
zPb`tNG(}|5FH8{~DY#~0(|W6j_T6RI%lFiy7@R#uQ6`yplm`cmCc5oBZS&jKL(;HD
zG1!v}>SZTc+~{=WG{Sf}XouDw5EVgbtOFwasHODS@Y{7<-Sbmi;U1%LZ#LoK#wXHl
zU^2tqEHH7PTrd8sqso|xuZIXe#_*x+sK=?hhjmmrYgu=&zmJB^sSu}tt(3_i7MXRo
zW-hE>j21@z;Dw=uqW?E>D%(pOVV}id6uF<6%Lp~bQVDJYH&90vi$^2QbVU^>jH@R5
z`Ms6{UZbPQmn4Tojfifr&RSk89B1TC<R<oy`S|#Rs=|<F9r=23?R#%o&?z{FHcXcg
z{&_<;xI5Y(Be8S{AZ>3dW2GFPLh84s`#+;=<Wu@xSx<-r3<%z^{KXSzVY37P+Iyd+
zLJI2;!Ko`R*-s;%d4y>4U8Rs(`b|V8oNw2^d$<VdOymrHdZcc@E3^<tPC12>7J|^9
zCS&Z0ENCN5;Nn!M2u=zb-U^>siU1q_fR`iO(V&#JQZa25BXTB;D~IGoWq(G})%((>
z$U2#7aa*#jyIZ>HD-e`%9PSu8n0@M}$B)^|li)G9qoR(y%kKlxAQ|i!zv5}oRl*nq
zOOY1$4CfD@g(UqQ7<o<^g|*`U!bt=7xbli-DqZl%HXvOlTwEWF8?~zh?-&!5zl{8`
zs~m0sMaYLtU`KYN0^QTEC!FtA6~Z|&Z8!JD<cKiK*gMEHESto#aKZ52ML?MCVp&`h
z8EjUIYQiA}19?B`8hJ0&_jQMa$30^v_a4#I6ZyLETdQajnJ}dyk~H8z(FDXtfYr%W
z((12iGfasMEV;%-enW%68OBh9R2LFa%%h`NIFB8Wr_J)8`#&2h(_>IMpz8lV>7AXO
z;spxTb&mT>W6n2BEL8ETjkp4e>e}!*mtUH^!Qeo8V}!KNA<-s0$BRtUdLuY(rrT81
z(;%6jqHa;sLZl=Ce?*4U(jQ-ICN0*(*zr`I)mqXT(Tvs9rCqw#(Z{Hfnbe}gzQUBQ
zPW(2lb8RU<T|MdkFZuf$*jiqfxtmkJCuw4HZE{{bw!ZtiD}<|+#xIqKuX$El7{@sZ
zU$1@bAn(}tK`#&6^avGMqzH2QRfv^3>3&u{g<^`^rZigZ;M$`LN+MeYT4zO`Y@C{L
z*Z}`OnWy-Rm^7E|A8?#e405+PifkEgimxC05eL%Q+Jr-E6opeuA?EB#2|q$~<54_2
zMWZh4;KycOLNwi5OUp=g3ZxMxM1Co`<LRf+3tU<G`={J|26DKCoOBG_mW-zDYsbMG
z*d)=_$64PxJMG;&ejdnIrG#Nc;p(xP70Rde5+nkjX^gD`LHcQ93|vN5%?zbp6+eD1
z{zyz*TAHXlT?v-jw!=&&H5Vnum2cRGke!S{0L5HW$jjVL@|lUOo4tVA-o+w~2ht>J
z&K!+0XiX_$6YBHzLC_^X)h6ZTZ&{mRV9a(=5ML_?InzO3fzMeNxFG)B;TW~c$#Q$;
zU{q`@h&xjSu?0Zi3!*|nrn(OZpat~cZTwg_ga2(z_sy~%V06(+evLT;A_g=b75Wi=
z#HJZe+CWvv%6Wl9z&M?l{Z4a)$A;-Of~_;0)OeuLz`p%ldTFl;kMwuR8f&*xq!~vS
z<4^Q6Sy9^0+5KGyTBNJ`*vrQ_9@V?;R_Pms$O||e677Ng1?m<}h87wLdA-qoL?<mB
zvgwcoG<=HVZsCsPy1Z{T`)|U1n}%aTKMy;|gz?8O1mEQWeNpJl4(R5Tp1h%O%f#bo
z!S!mud?86rqq<GR`1ZEzOY1KQrhggoyxii<JxBZ|_iiPs`l@a}JC+`=?kZ8`(-aN(
z-t=cQ#r&=i`_?|r)VOpld}T2bzv~D4-Lp?z`WWP6yF!74e+eX}bOE2j^WDcFpn2YB
z1^@Q)3y}Ku1>{R5B`1g82KN+^MBHWY+ho4gG2fb9LBiIp!S^=H0`Q+ppP$14M-1hD
z24DveNG4>8J&;)jPhAV1u)S+mN(1zk2{sv)brQ^Lkk=aTuZW{IUKsysYyXT(oc|Im
zONAUPe3p6*=A=HE$PelB3idDdQJZxqv2q<4+w?T43_j9==JwmYeIK8s2JKMV;K+0c
z=wmSp@HCW@h&vNxSRIZ`Y}^Bg{=k!2j_rvmp~I*~>9i6Yn9GXT-s9LvGY;fTrxHi1
zPh{HC2j=UJ$PtD!Q#G@S5e1_X{DM>sYpRNZ%OF=~4YHX>jZW_iy)tOP37FL%1{gER
z)Y)e|)M?YmCVJpxumJx5Ta(l0Q(|HeFB1)di=n_6i}ClKo~Y>Pb|AMGbX!%+mh{8{
zp&cE-@sR+mH&6(8(8${>D1oo>_F{w2UVPwzh48d^WKj~6B?Q>N`_cHoEY68w-Vyo@
z_m|+0oFvVnML3~>8@3OMfiQQr3j+>z<m?lvY!|{URr~z=eauU6wnx(mXP+As6&c{{
zJlXH=kaQo*ASAz`5&E{dl7Mpd;E~HE>6JnU^oy&mM$rg&5g0<~U<|EQPV(<hf=hd|
zwm#ySKgR6k;Vw9FAd@niSLenrI1wc41p{k-nr4W8H*Ind6?VyY1!qeAmb~jn^66!Z
zBE)L%;g)qR2mt=9i==3<jg+q?slhEbTc)5*%AwPirnzgsAXsIOfcn|(0EZRNy}St+
zGaS!BSnP|`)Z5!SeENZh<~DxnRsaI7?zs)8C#Ip<0*!v2POfZ{8r$t{mVnd$^vYfm
zNDoAP(_P$sKR(rEbD=f(uBA5TH8sni#zyBuQi-f>@}a6AP=gbE@dqtGsAJfK(omdC
zGOq?&7xM=Z_bQSU`6(kL>7|*N35VW3wA5MU8n4QV(k-DCjz;^^V(H$vwB(B*T9(T%
zj6)@6dg%r_Sl!{53BARWknIU8IGqrd91<^71BNt*DYRnIPjiIh)FN*(M+)w1xN^;L
znGT{tSOoS|$J8xlEFyVGcD27*mQO%&v{7W*pJ^+BC!#v)H=Z~q4@>o9RH{mjmO6!(
zB~7c)x<1cUj%ZStb%R>`bzg9Bs2VVUTlZQ8czJPe1MMzsgsE?D#h5u;LBPT76CWD}
zZQVUZvn;8K74vB%mX$<riFcVM8XkWFvILai9{+m};}oJ<T9O#P?kp)VsTGm<m@$CH
zm`nl-Mo9P?Qig`Os(lnS7J^nrOQ$H$L^pEURWrqv!8+Y$>zzNfq-g#^b93Ut?)y8f
zju1}lC{DFEbUx;4-34hxwiy)6eXQ5g&ufPqnTntoNIZ=uv{64xQNrl(BlXT?vI^wr
zI@!tkBLc@rxC$C9(z9(1gRr0n7P)O!sl$_1tuKLph2d{grWl+g8Ejxar$gO2TkX;W
z+4lpP_{88EI}n9z0d5lRa;1CKO>4n3fc&{^(lbG_FKoH2)GO*d=tzBgUc@QlDvIKi
zS__w2yO8-|;b7+_x#_AEL5Q8S3C&y>WZrb|%O7mWzk+fWn1@ZUhKvq%GI&KI?&U^y
zcLmT&OA}V;6BG{Ow`!m;1ov-+(n>E9!*W_Qz3}eOJyhPgo)RSR9etF4zZWc})~B|Y
z1nt+XT7~j=;;AcfA1~?-TqS9+C25kwr(DqEVB9?lSl{~cE;r8li{E2&Rplzf@J`<<
zm0zy{vCMiJE(FMHppaW#O>FE@8o1r`HidoO8E96NFTf==WMpJMr>Ay6@8YwbSxzaG
z_1DN$Li2DKXh858C=2G&M_J!eJ$*xj@1FLlIDQ3A<*ZF+nlr*>t=soTx0j8pPSR4h
zpSUtL43Z>)tD_;SWYvug2~Ncg>Cl6aQW6jnztPWU5Z3`!1k_e=NIJcwcVPhoauP&v
z1}bCM>F1|DKFgc@;PUZ6P`)8Iaru4u@}ztd{xI)#XWv#gbQPlqK{pWBrw^IgDG#k{
z1SfXG-q`cI#n5r_)v;8Lk5rq&@B?iK@3C2wqN}|jcj^&rD1HcoXlZFF12@lYRK>G4
zJFzU+|GYww5s<<Ox~<zAZGj;O9dZdmSr>g7fK8vT!KFwayC}Khyhl(ApKAy`rV_GR
zhRCQru17G;ht59HuJd>`xTI(|qqDhphNI!u(b<dOa1k8(G9N~iNkH;?aVZ3F1fWh@
zlYn6wl@2prAEVV2qx>5xN}h4QVA}yX#<#?R^-5vsc$9mnKvSdq)3B7XOd)MFK`|Cr
zU9Z^<RpkC3y`EE;lJhn@Bv|Mp`Rpja<p-$);|N*;@kFlHG1|*iEFT-fObx1C!>!AO
zVZnxIAY9@D7YPGT{Kp0m2KbCl!VL|CiIjpU;@f*}jLm(~LGt!-aYz}E``l(<LO@6e
z_w6-Ew-*P4SYu@WTF0#m4ICJ^wy6x-*l#$%bd#=PFyPv_E@;<i6f%M);edRbL{+5l
z`UiI5U;>D5#_RQ?Kd6j_1Q(3h?t47P5MuV!GkdR`9T}<Lr_iUaD`kMxRHe1nXI|7-
zLmJ8`zR|B6%Mx6^3{pwFz@4OcSVzcgR1&Ij3*Ut_60x%-nMujJo62K~w;|jx;Y4O`
zsAIG;p_fy;;3Rit${yqn9o<q5$he_yo;3dvMmpBKx$?k)GNSz+4dfc+Y24lo{jota
z!Uh<xVuAE<3G`->T~FFUwi%F?je?we5Wv%)g-QgGJzGS*l}&<hga~Dpb|CB8|4RGp
za=W`j5O0NY7*{4R&CKWM9UAj3z?weTBPP{$gqEWb)2Pco&7sWnBnW=i5dfdo4zE0C
zLfo)Pz~;*BT^n5``{M#)Nf{Ur&Atda&Uat)4J{DgX-m`n&5kpkec@|+8cS2d%oF~W
ze11`<gp9-3?2i0JAVXu^0u*8*B$M>xjJS~;V{eWQuIymg=p@o%?apep;=g0j#OvQv
zFK<sTUrF#sT6mx$hP7Z(&V&lmG2-^mv4@AD)FpM>M|QZ7_+3d4;fS+`4(skO_;#IL
z8xS1E&;zMl>7I1hljkICJoE)88g+>t%myVGB9q#_`WCLsX4*p6jk>|$%UjZ*)A$eN
z?Ja03&+NoEGX6NhP~v{c)a`V)P60hfBT&@!NX`jH$VVXFEE;#X109eg7~WNxdhCez
zrhZ0Yxg>q4^9b%yLu5(APNVFo;z^2S949tT@U7rct6=`i#Fto_jXw)`q!R~rkhkhD
znOjp>16!gd*5$9S!D0e4{Uye|d1;ymFte+c<3^iON^Ur-psrM%X)d5%EPsVShTfjo
zQ<6s<Vkl~cYP=5qD&+TNGZpT}jL97&gPq#a(~|SB;LGn-@qB&9_5*1%2ErIR7cw)L
z;!9J%(lz6%Oqlxp`%+rCt@<e0muB#JicTc{ldMj+Jt@H=a{29bB-)4#TqgWM7>-7!
zK?+0hF`xZjAA?31q9S+ThR+Hl!2jiZ*HtZgQ3ukYhiG-@=Vq<~xd3qK`l<S%H^k{B
zIch!U$kkqju5XO|VqQ|erFRo(V^k)WQg;+Qe5K*Jj^vf-mv%M@)+-En+K)K>E*EHr
z<@G%LxqiWXd>8<7j_lHgn@J1DiCJej+0&IPb=uRf>X(VjJHF{bKkcGpx@b)D7Fg15
zW3qM{aZCZIYygQosxfMhAF(7j;C*H`@8FtN5tB$dc9icmS!O&W`paE7B>!VtP^Yu8
z)P@@t`#)C#C^KZ*(t*obzfmR}B^K3ssBPtgv%OIO4Mex}UHpQBT&S5RNeY4ZVTG3(
zdvNEDLJ0D-^3~F^ZzE7xRe-5o_)Fy5{G*JK-SmTkZtuGd!8ND)pcg}|CKH2sT5(($
zKtq<^N1&G>E=XQd%qQP)=jVFgq9>;xOI3k3=q?*(xxfk8qp0k5lvBvaI3P?o%7rvL
z5k3YL2T?KRO;N{zCpeDr=2v;VB~QmZJ6JBFAsZpfe0gw8@_UC|Ka2W-4ydp#qLUIA
z0UJiiQH<fMMFp!Ed^B5&PQK3QL)|uXN^3ZYD3x6SX4~?TuEqP#Essto@AlnQ*Zn;d
z_?$FEF~bNOnWE%iMEqXMOL;rhKIur-;qkfAcmluYMqywfMO0mjYgt<YY7nJ74|Hf9
zv}z&EOAbJ32L6dejpz65hJqFFVG%x^9Y82~K@O3(H1J1w%OXcMr)9fM=iOXvCU?mP
zL(_P|m&_-2eEBIm4KV(`m1}3OBj7!uBX=>iaAg7TKkhljn>C8gWd!h)b^Kh``*PG%
z+nA(hukua-y``AJEE^@r9eb)c5Tdtq((Vg46PjK;CbHI-?{keB=SIL9sJu!rok=>t
zCbIPOvvBtvG0P0gw_A^%Mo52T{CzhuylW|9?LD@N7-gM?gf$5Pz<7~eFrTQqX`>;H
zx&*)-=az})!ou|nxtVo90}!+_tj<h~#N~I*q8`3(brX_Tm1rd6ZZ6YYmIaZ@%NyDe
zmVJK(I2jIrL@`B*`lSAI|JkroB+?4nK|XW@Z}C;bUAbC`RJ<tNVt;YHm$Gy}1Vwf6
zfC$fxvRNZx<!)Uku*_+Bv42O(#ERU-0<wk_F-kgc8@4zwt%ahL<APV}5cxFe^)sY$
zA9KGGfV{(IR=!rp;u3A2JsgjyxHliH#DIcM{7&1Fqw>dYgf@GmJ`DLyX_1Xikqn1S
zYL=^7Qjb>|Jw5VnO((v2e)paws@~nF3SKKh`#tplf%v*xZ?sd&HOy@708}Z8v?EQ{
zRI2}sZ-y!VKXKF0&GSFR&2_JwLoa3w40rN#>XJYzE(DOnx=!8-u++sT`S_4UDAQVS
zni&trp%k%->VqN&x^>fAc<&V-+G4o5NREEQtTZo{^g9?~9S=1?KJt?_+rx<7CU-iy
zEBK#-$Xv1#uO9^r(X(f=SgE)Ymu3toI>B8_3MuB$mVVTF#Tb-wR1$)HqP(<zriQgV
z|Mrrp@)lZodMX29IwQRBLL)Abn}J{WJ`)6=;qr{Qn@=7n+C`M1{6+l?6o4pMro<g^
zBE%KOfXnFEJ!Il<!1C=PIMy0teKmZ$wdnUv=v=I`c6#{TeC*~_w+*Aju%P<jsCJj=
zLNv)>>GeZ3Ws8^Zc)Yk~mzG5qjBtnD-wjcKg^jK&nnls@J4Uu`?Jw%*<D+2%2%lMl
z8k|F1UX4p$M@jD5(q3LPY>lrnnv!t4SUPcg8c<K(!Ah^2imRr6Mi_C24gL(#fGNG`
zL}FE-z{Cj)C-BSXLRx11M>~(v$q<Tb!pm;Qd;lkAIngL3UuK?mteVy%Kx#LB*VzqX
zCyKB{`Rf#Rc?iXyF{-{f(7FRe%v&n<trrfd!s9O;lib<qDYbtP848Tgm>9a<dkkO<
zjuukmIIo^J`XW(dOsZ9(orA1}BcXAMyiP9S25zD;^e&~=By5ydF)hxy3b?e=Uo1Hn
z&>0*3JF(H>4YXe3gqbN};iN`gN$TuL{ctcEDE~}N(qu>YV}WIFx*ilSt0JY`eWY+)
ztER&8ON`S%Sg~d<TGNFPVCZ-b=*x-*x7@|tT13hUQ30_Zbzu(WDDw*axEJpBskxqb
z$K&Y4c4rI`fb8J$<ktK5KdSHi%TGh6dxCRmBP=WbiVJ4bP-s3>1dnb39683mRf7z!
z@u*AJq)HxE%hJ#j&(cDGQ9s~+?=kSR0`LvJ3qlyYAp?{eN`TNRNl_wy`*p#Wf{Guu
zN$v&)TUApzW!&v&kYJIw$cbHMH6?!LYBo!T8USLHBrz*j1ncv3oqv2Xl_Oo}Mop%V
z4XmnEV8j`c<CILm{Udh9_k`t`2i?PnbCrYuJfW5u)P`^PIT{gv^h^P63e3WnId%#F
zSTc<!tru`one7s2BPNt8`6{r~sb^3nK;|YvvZ_r4l}tS=S4|-^HEf?u`lp3N_0y(`
zz{a~Rp-npmaHknKPaf0A5=Dd{usD1;aN%+`Ns@|#eCf<qHm>>^i)+}qzG~^!NK|Yj
z&%KuP%yT)WHw0r;7_mr1TL6HmXz5r|{e(`F=XXWwuA?h9V~1yNhUfhwpZZOl+*G;m
z{u%du{j;{Um@^!Wgw%nFgxvsLfrv;Cs?t@;bj@{Gzh5tY980dN_bPPU0?QB&B#w}b
zdDl97{TcYS=QnhI<vOhG^`2H={r&34O2?|ff!X2bxpwQ*gZ^@BSF(g*VVvw1x~7W~
z7-90PYHo&Sf1WD!rhmzN;+wQT+ZAMOx3T59w{&>tCgoW}e<K!2(T@=>Arr!=+74F1
zKC;)WFBFOF{G26}G!&fIdBlF*2tWu#hc^=D<OX*KAC{r*zA8IzAgJ4wsbBF?Ba9GJ
zC_8`z^-;vG`CFprbv^ObIsiGefyn2Y&t0;25qkWMlU|5L3jP83eUVKP<yx%g#>w5I
z&O@xSf%$u5@$So7FC3L7h?&sp+Y0H6lue@FZwP*Gp_j|Cb=g#GcKv#9x7W-#Nb2H(
zL2^(Y@|B4`x@}P{I=>XH^7y_>4zNsx!<~A6?>GODd+7apyE`Z@OH4YK(}_Oc(<0OM
zN4Ix&y@`ym!-x1Zlg(njV-{N}q4?zP-W5^UtVD;tfYq}Nl((#tLQ;OrPEAR*35P#<
z5qmF77KBpn#)uI5I?-i=Uu0j6GV-KGx)^Rg^W9>TGe+wvPe}i9U+{OPLSG#en^e^w
zR`&-jmqsek44=<#zGrnFlEIbv4f{R$sW>H{+@?`qmr%y+Zk{lzdb+cBKvMM28xD7k
zft+h45e=n)l_7SDLz09M15`>IJPpd&YQ@lPtU8P${5~qh$^6|;+O+xJC{r`<-72#)
z*_4O2T!sb#k*15pjRfq&`LuQe9#16ojAk`y`#$wk-xt;1x)fOQWK@zq75E`L{f2f@
z&HGc>%g>QI3s;B7?wti&l;gV#gcDC{W}m%-)IH!!<ldeC;Cz?+BAq4e4|dacHD*B!
zhR^i}t>}3|QS)?(xOPq7?`1x<Ih|TJqkc2%(Wt~ZL<RFtd7j8O$@iP|w*dZ>sCl~r
zLVwQ`CcUo^T8A$0xUB9(=ltH)DxThoT=pu5+66xQbppj0tB(7Nbqe`+;RIGh(Hp%(
zxKz`t*!3IBxfh>5uHmC`J_;ussACZm<<-JR(z%eIx0r0!c<1Y+@V@;N%ZYx}njuvC
zrMUb$RR5x-EB@1k(3h3Rfw?;mcPG9P>M3^^a#rNmb{z`-w+3B3X2d36(GqxWmQcqh
zCvNy1FUK{xbcpYnnbccJ4Dnfvh5c6Ov5M$lXc_#$cVSlh3~A>0L8PA#U!Mn}C}@rk
zua?del^aY{VYD$ec+lbzrtUI#b@a<;_9ZjdTf2#om9aaVdaCZ-0gS`MaVat+HW_>D
zZ%iypgqOQ6ua}>_liIcPCe59?7IRyvuFWO3JAVaF)qfW=9-d_<$)<6~2w%5vgaOZI
zYf<BM^)WVGPX3$3fR1uvyVkS(ix8jGj*%a{G9IVk4V}i22ZPNeq*C~+rbcf)0STMq
zD#^9g8doGjvBJ}<lz944wRpUyHrQ0bjs`<>d-uF`Iy<K{<BGNtZCq(kUv9nl2Z09d
zn{au;v##$gIF6J{)!v;)PjfFxytG82jkXIoM{_=dtcVMH&j}t5+sbsk6LzbK;G)33
z#NRiu8*d()dw&R8I5zUDr?<{5CNJ_>iQ6fh@Vw*GNuK+Xb9+{3L5W(Ob7(4Qwl|Ug
z5_hn%+RMb{byq{fHQvVS01VG_qRT2JwbFa!sdBMhht*d9iUf1`VqCoEZ-JAh=b!q0
zfZScpT}5iB)CczPZl6zrHMlg8Z_%QOD5zWgkZaffQq1nHNG2=*LrSmjiGWqn<EcU{
zW<*X>mH&o&v(!De_A23;12!)tj5n->&uVwxbY9fRW(I)_<0Woovr-&B2=cqJn{)sC
zFbHST`9h*N0_6iH_`h*3*+*}3Se@)s5;EJ%(O|SR><;;nViC12SHgp5Oqs@ZH|kZk
zmdo6A{=HXsTt0Dz`dRKQ2kud8f?smgi36C1!wjpB42NHKI%g#(nT@N*brAHu)-u-H
zh)mTVcb|4Xkz<Z#jUrSW^OYYyq>tugkG%@tCajApU=W}*KFm2J&2$$dDvIH~+Iau0
zpxtCh@S)N3gWZhnnkh}Hia+H~h&5g-O>qf*i<y!%U5eD9<viOH*E2o)-E0^5``zcz
zJ}N0*7)f+HQHjLNU7l|8pu0mqapPIh4To+1q~t<B_vPovsqapz3wtiaXYFDCq?l=4
zTfz(^1m8Q!;@^vSU!%8NS0-7R;^V?R#uHNeeK-DFw%;eMrdGTq)CSfvBn&Ku;RR{U
z=RapY%H9xu9U#eCneo-RGEUNGnS~?`c(OLT#-}RuiC(0uyALXgx4#JW>$Q5zppPr_
zDiz|s??7XGx!^hcXL(=q{Mk9%tVFw7Q>Pkxp2<nC+ez#5Qw+`VProTIN0>JHvw}jZ
z^;mFU{gOJVZ@Ur!4_xD3=`)n>>u23hd_^p7*i8A-y4m92cNg0tWpE#TnBdu#zES?p
znKRS*p;?LFe!k@5sdL8DZaiU?AMP16I4|^>&<uXM9?Z(Qng`<RuI>cN`e5+Zuplf8
z7)Wel%#-g-#y8n62uDf@vDz>Wa}T^u|AQT-T;o5rR<;m8?;`ua-)qaj=jOLuL8_F&
zoqNS1>XknlX6-&v9-R%|8=4=3fA=1?Z#;jAI9XsGlfB1Gy>Rx#*3{~Bq4E4R;<8|l
z_WRxjE<Z*8DJd@m{J|bX9Xyf|#%-L^oN5>sAW<+Q%IQ2dwtX7TAl1Ens=oj0(WdLf
zJUc;!vZE$b)+=_{l<dIo?}ooFw;q#}GE8H^HX_fGKB1*X;H9&%FEAToC~2oiUJg6s
z&en`r)nrwVL}QypIxOlR(rf>G)PBe1s;Aj*!Q=PCCZoOL1)a=NKP-!<N#MnZiEHwm
z+<%*|SsNgJ(D0t{(_y!ux7ESH6n9j~wPZ=e!~MM~*CQD{>ab}H#~pC?;ndt7f!Wp4
zQ_>3uem&oG7*@XiB*4Y3Exoox$)Ch#usVCq@ztNwFJSpkr^j-3%`2VFrY|N#ug_)U
zJ8wD@ke@jr1cF_1EpkIUPwrGEt!j*FlBbRo(%N7_Nfe}pM!qh#B%4tun=P^TUk3h>
z+8zqDL+~_6Upt4t*0lSeG~qoN{)~S=>JD;4QVT!z!6t{t%Et%#dfaBG(8g=jF2z{d
z4qrLUl2*u@PQh94m399Pe)8|Iz_q}SJ>ctSKY-fTJkVBSh>?D5g~uRamqO92>UTzp
zk1}aOD&2qOF}Sc7g%9eG8EK>`ZRK=}GUw0_F@1AVi-|TRma~pu+}Ug&T;nB>0)9H2
zq9k`IMxT*)v-gqpx1w=dzM;{_rlauVZsHk}O}pThqYg?caUX1U|J-?lPcf(NHQ-em
zDI3OCLNj|Z*|d#Spf*bLW?U&z>u1XcZ$pBHzz;-04`IQapNZd+Ztn)|eH}av51zJm
z{d_UU`{4l}A*T@pEPK*~i{uUe&U~@N1s1hb6Gwbh$DQRNCL5P^rX3{eszwE=_uVfG
z2Tl(NDvqD3aBsLclN7}$@e*5!GBTsFhRI}q+NYM4J9i)OBx`c@$-4G^n}2sx;*G2J
zE6P-fLVOe@-l2?lNUd?yWvx4t(Y>j*wURvxhr6D87cZsb3Hun=#%m-k<%%eI>v|v1
zSf>P}=j-d8XYFx#EaM5n$MRK`gN}#_4H++mrFX=cb~=BT$ap9YR#DUj&bJzygQd@!
z`1cqKU-9w%beBkxsFC3DN6*(%6n>2fZQg9;pG&t+lC!l-l<d3&$TQztz3WrvH{;wL
z`Y~p)*AZ-<Ei}PJ!c$=I((zv5uUcU)o-iewM3G4}j`d$kjH_{}yu?)6B=>*e!}96W
z;)u{*D?5g5U?tNTyhX69aqrW&??&^qD!;In@O^y{H2%2e-oQKm$5jJ?v0uuC=$hk<
zo}-0=0v4gF#>;c)FN0}2gI&R8RYxOe`kUJP?D6ZSOUvKU>D3<BvHNt9bq9?F^)0a0
zGbjsP>9gRz&9K&&phu+ow?PoIGQ+3#reP^k>;3~?PrP&tGT)_VY<~$@N|O+bD0$W+
zIA<bhqRfWo4Lim*Q;<2Hl>Y4^J)7TRdcNHyaVIB3J0-skRM6y&JdZ@@;xAu+H=NaE
zCXNa6Lq6S?%&me`5;YTqDK9Y*Vf}fkT^yrAZlyK)e#kw0;@1+qU&3aee?I>vy3p{)
z<DZlVhOPwEGz-BgBzM(R%b_`NE8ir*U}x+o&&n=v4&o2S^m8<H-v52YD}^Br5tKbr
zfqgzu7wLm$j&WJprv!M#8~+eC3$#GCAxcaCNX(=9)PNt#_TLYs%h`A+;qZHqs`Gjc
zx&ycRNFiu&_yUE(5Br{7o37;WtmR;UIEOI?GulyWxI&uyP#VXHVPb2OcBt5^HrJ_^
zDb7A}!-Xz)v`pc;!nmTQr$f!>a%Y*Fk`Rh7Sd7?@2P)=!t!r#&jgLu?d+KQy*i<3H
z5h4P~%5GM=rlfwb+Su4j^9%m%`IUFn>**7;dL8ICh@vm4DA-JG<e|}~ZMr|~l6dHt
zd*W=lh@|<ig>^D7&|a&LbXYDFQy!Y!WY(p#q71WZki~|jWGxd+GMY8nWo7s)`U%$6
z?EkKPz{kn<-^OG0{p3&4>~ysE3i^vPrpO%Ut6+1pfe@8)bpYqXu3%*c(CYWrTh?u@
zXhg4ItxI7y40uU+zE@>Oj7~EJhw-8om<gFR_nUGc&|jeHn4b%BZtvy2FoWfOjXYLa
zjZS>Yu}br&tYB3l&JdNLfknRp99#4weM!9WtZV`#j~69&`X^gIFZ2byp6L=3!Ke_e
zBs;78;@RdM>*XfbPhFJ`T5wyNBuXQJ11Rj(b4I1sR>-??o%Ihd&HJ5u1)mCZrM?1{
zz@ikBVeoyoBFG`?bat!o?f9_dPZ_fIf@M%i7UAv$T7sUxy_v)kyfQL(V<bU;y+sIg
zL?04T_E&7Q{AmQX7b6&OU9~Gk*@NnZc$qxt-E{7##ekzlHQID@8LOQ>mRHLl%+o#M
zD-}Wglk0D-Da8!?P1CVcjvUxlD%l%vnm8s^JfA5QMx|{lQ@4uTeHu2FC+dVSI>Wuj
z8f%%KG|cW|+>p}?$P<lX`8r4ywxJOYy(`Z-RGJ<DS~hvm<w&XW!ee#!=G1xm1Dub$
zoAUoyk0<AmpV+5J|9P4~Bxr&=nI`v&Jr{nTufj!Iyy3k28K#ti<qpaCry#ip;5SzP
z9S|g#C8j%{HH%u*<R<YBTFb1a5ce8*5pgDtC>DAU+kB7F=lP^5fruW<es$ba-|mTa
zlC0^Uw4~8qSwj;a)8A0?(BmcE<Z5>7<<rZL{UzIS3YB;%oM*9@l-!z;qshbe6GV2e
zl({inhYp$82Sw-D?xGqlu-}g`;0`po#@#rW^W57=_r0Sj0!0c(ByT`aif}86Q#ig=
z)7fZ=_Bk<fuc)az?lMi}a}Q1FZp>_v$=<otC@wEy<+Z|-2zAN+79aCCe`Cby&$d{Q
z%w0h>{E0T3PqYhbl#LO=TX2Javk+?dzmjY7k-<%m|3R(SLD5q2jEhyEmz*2dn{*K3
z)m2xJk{YNXpa)9e7CU7H-W*&B8b8vR8ro&v@zpjuXN8=Oc}mcrpgDk5N9uSO&(0lU
zX6%a-W|1A3{DP;edZS{CTcOt*D_-d}7(g|%d4Kc&eoCmHB9;w1JqTJ|sPZSg$-WU}
z)YIuCW>QHI%frLdum6BY(o8b>!a$RRn+-D!)5j!hgFtp@{riN+>#G~8w^~eSv${>e
zZNzlg=I%H%3U2r~VcYL0T{C$o9)iyh@7+DbMceNinL;uDd2x6Ic4{Ksznx0Mj5ZiU
z2sF!)@CpXfipF0io3*n#y{QFyhd)&%P*e)=`dd@{Hj@cNc<x(IR}1%)qQk&~@>t16
zcA*dbxFO7*Kj(ow(%Yz260p?~`>HROpUxF0gqHAys>gHmt)VID8nr&8IpYWIA>N*T
zP!HEmy*n@KdD&&fv4@bm9ang+koQ%CAQO7|On34lE6<32SmB+=OPP^!iYBK*STq%T
zV^3YIl6Zz@AK2L>eM`_cuuY0$afBNlf6M0TEsN;-UMUn|{_2iCcbbi~3Ew0W+jwyd
zm$>7mQ_ZFPliPM(qDGIpj!<D1#iDT;R8)-gAn!EoY`v35W_J;N2JubWijGO63BqG$
zcMJ}}k9%5~@zicy6SnZtJvzFmywUz=r-3JC@UXk=O#zJA9NSeT{r{utEyJSf-ndaf
zq(MMHx&|br8ziNM4(Sk(k{Y^8O1g$FX#r^vhVD)gkZy*O?mV04|6cDo=L4U{>vFHX
z*1qEx_tGS*Lt(;lXFV?oq3k10FnDb#m<@&c%0sQQZQBM_N?%n7pB|aBZ;;*ch@j)E
z5`1Nki+i#BMOv-mahcUSY8Hw4gjKBOSfc2bAF&GG-eUVFKV+{2o_MLjRi5@EBueu|
z-p>yeJQW}{u=AZyitx5`G76LV3jI_FFpC5y@gi=9+FYYn((Z?@=ELG$+MNwhrZN5p
zO)#)MNbiuN<j?rdJClckDdD#E>q1D{U~pRj8GMmz!Ms{~qzQX%tr<rFsKSujpyb)c
zoAkp}1>jgIi>P+BaroQXa@;RPe+RJNqwY#Tt+j5^A9xrb5uWdvUOHAlruh8Fk1B*`
z?Yy`h#{6-jd)$#>oYdkKS`S><70CVZR@kuLa@IdkC{+~Nd!!?CIzXT|lq*)EDM?xE
z;&QP{jxk4aBDw`hP?4}2cn4n2hjEi(NSeS@Puh0JP=MYlmpZzYmamti42-<SZkFlW
zB=)QE@iCwn&HG<5DpG@{hf1?gbv0bV%;zZkg;E_2E0mGNV&PXbbhnq}%vv&?MMy35
zsB7CI#6ItOCTjcWPoeFn_}J3oIFdJrjA~Jvf-wc81UXZ08Pr30Gz1!X%0zQnJT3M?
zA{mwC!c**p?E_gB7_tPWuaNPB@Ce!?RA>vSwMh%LE4iyCRCb2ZVxy$CrmpAzl<7c0
znfReL&fXxxoD;o)+$2$x{5rOc?oV>gqls?OTt=9GxmSt_iwCB!csQ#A4VzUgeyaJ(
z`6~j*#AM`uVWvmIW@l8=AsfWC=Rt0Wt)a&~r5uqaV7AjQh?;yCKs|^BIuZ5by5X3O
zq}t*R(vWi+)&7&#sQx`z*E@=@7ysvsG=@AqB%Y};M%+3OvG2FFU-K2i{@AImtLV`d
ztRPRIMz=Xf1|x6-tqu>3CymQSi<2HdJ#Y9Yu{09ZcutRmprnu+4I3MX-eGAm8@I~2
zMUn-aW-Yyc8mWDYK0RSJ>yfk;F+LM-I>y=CYi*x9NiO<UecZJA{?q^R9;aNB^t1~7
zm6!E97m~`5MZT3nGV>l-9+8JjVpP!Z3nFh4Y<9Tm1ygpq4|=!+t6yg9x8WJ&99(JL
zF$na}pE$FLvSCWc3eG;&6MBaYhq<g}*dckP;<92SX^aT?8WE>gtAYT9=B2v2=7A=s
zzDIUT$Sf>}ns&A39=j*4V6)jrDrf9iy~mntZy$cywO)A&JTFW?;~?#bdnMXhnp6c0
zz+(sGl|R!&Flm0o+SSdl5e=IW|JPgQ<Cu-4*DFx-M*C3Wl`q0)F%gCPh78;xAvgA2
zp}BE9+6RWj6Dovao6CJ==lvu~5#B}{nWOCy@PxGWIg|mYOvZ()%u9i?967G&nd1*v
ze1013{!3?1%Pj%py9^1ciwgUa=jB9*vtvK78OtY|Qc-{IvsJo^^(JO@fgm$j+)Vn4
zWojrT7C#KGiDnciTLtn-y`CRd{|`mQHBZBhbcl+zkE}Z8Qgg*g8AsiqUNaE79sdVI
zGH(l?q{FyRFh*Un!%G_~zPgG;nPlxa78dBT-9Mm|m^Sftglk_Dp2&|)t{{EV<+&L@
zjcjBV4mYrY0w;m>mXb<y<j*ea`pV)8h+6+nJ(9Unqa7zEmeVWCgJ+Q*;bq8Fgi*a0
zCnFo)>@o9Gd&xIqb2=&wm37cI$!Z9+#Wb&Sueksm&)hA)k>q`!jn=s|l8Lw`?)ySh
z=I9{|xP;el^}otj<V)&NkI(MR%cciW{VaNB`ww4NC%`#cP^ZJ0?etYaz2l3-Oqkn<
z4|`F{=D<gdZTrmXT(!4gOyidFHCnh!dx*?2;Zw<dm<LgkdY1U-53QLz?7zZe35H|t
z{pyG{J9_6*JPWT6nrffe#D=2}=w>kTXNF}hFD#HC=97A&PV&=A9%W~h2Yk|Z*L<z<
z?r`>11Py_hj!BfD4%d!&4qco^NIx2b5<$Fe79+zTp>@THq5P`QK8qiMN&a&kA9TG7
zT!~3V_rEuf5yiRa@k41od+od`Os704NhGXF&9sQH+<dwM>vz3igK{QkD;M=3VGM*r
zJRM$_mno4@3u}`sF(^#gba~LYR{sY(V9DvsG6gxs(Qtay{MaL*aSWm1N3nEe1LSb=
zhH+|oE$r7DuJE?O_tEaV+FS!X*)OsQ=wQ0W5+XHQEaH_oFE9`R0IE<(#<K@D9yU^Y
z9V`^<GyPWPn<)%5#w%|z;5s5_{w+SD;qpBIoLK&P%`N9V<hQolU{|20iMn6*ZyB<)
zc0=>rC!_rDYxt_wtmJ;%Z0qEVJl0f+jywwPFyLR`CJEEGZ0>=X)=+Y^COAXrO-oX~
z^B~)+ePL1PuB~A|`FT@$+NtEQ1u>%42vrdpFJLoRS5(6^%+mzI+<bsObO#swW|y+>
zyY^jU`?eo6GIM`6YyU~9CFmFbpa;7NEGK~}T0-2YMb1gPH0(34W_O3R6IECbLAZv=
zk4<OTlg5EAIfSunP3XIJLm)4PHqk$0=`|7^7TEnbg#z)5AjR^+tUppI0#hSy_z8;~
zWnVJI+Okfn?jIgHb%E94|F^mz)1|IZ7Heqhoh4HquZyMX*;J~nIBsZvE9)??_nW&#
zzJ>gpZJuFKg;wZ-g)pluQy)QF|9l5deQXZz)8N~P`{hwg<LzI+V<(JS?MqVD<s>S2
zkpiJ#tM$G~)GiJW#0ovildr4XU;PvwPFkyNH0h4SMIq9|>-?hSz&<$XRqtg5G+8ag
zq&;}5N$8c(6zA&ew>jMKv!laU4RmrnCulZ*M*+HA;1T+13vMm|KgS_gd?#uIDqG;r
zLnN$j`d5X#`AG=D-`B}3UZYajn}1?n^l0ptzDTI|sMcZaKmLxqT4r>?Y;=}Bd;F(Z
z^deU%7ASuL1-wi)22OkXWYW0#W=JZjj0eg=hvIkCKL}Rv_7hlRipVS%xn@n;GH{y`
z)@ovpFzmB;xfa4X&_ra{OH$utjGwYP^5RQnU_5m>!<c=^J;@2bs)&Qm<?G*q3EM!{
z$fC`C%pcMam&|@qnANQ>JvPs=63t|7ZOuJ}?a!p?gr^c?bPx!3sg>dez=g^(T6=3n
zK#m688krf_Ob_U!E{Ml;wqHUS?T*RO4An+ZX(7Slm{N`DrnJabhMJ(OchWu9G0>{N
z{C}ig0l$R#aezs2CY1?_UwJDwpwyx0#wXbV>z6(Gj{K>4X&bm{+XA>*x8-)aa{FVY
z7a=g&%IC+JMYRrPFS+htD_N!uU}&xLH}2l-5VpwH;Nlz+w{cq4tqDP7S>0yxlf!23
zgpf`M+%(5G($`0}CSkJxCPQo`t6Qx|vnq08Xk`5uX`;4yWmGc0^{^+qqegqo_*<Bc
zuB7i1F`ZyG2(L_mM9ty{GR}0uc%rxVS=n;fEX9q=>6^2UX9DCj=t(pm(?jefGnqto
zRna@#>E$croPs`l!ipN^t8^C~)NL9#dOsrYwAcR4_CPU|T!Mcre5yqU9}j5-Z{Xu3
z92|%nV)=;)Lc;7_heDDiP)Ov3Y-I9*qM5hpkxsAadVV6ByMW+B3GLvKW6m5<IfZ(v
zA{}o~Jqci9V%`t8-(wyDg9|<Tg_k>f-x!bB7@d)3Ki$JOJl!@%Om8H5h`XbwDCX6M
ze#H;zMONcC+Mcl-=<hjixP(Q1>8$?O-^t}tBmv(S^;MB4nkxTBR=m&{^QnchhD#2Q
z*g{wur4l5;)XnD`U?N051G<m)qFga&@cKw@0p1Wo8ppHxXgh8C9pB@RWQqZao?3*Q
z+2f~OrQVKZX4G>52hDc0F8Xuw4qSMI7L@bw`aFSL8|+kwr@ksL(d*8EGRDzxTILg=
zTD&RXL(`Z#{6Mw+CtB%Ov{u@`9)YaXEs0Q0)l&*`rjpx)Bng=ctO^j5cfyw)E<J4G
zifdLJ{9ActVohBLKY@N*Spo1s_~qDbAanzpbKv;IY)OnG#6(9s&Y7n?|DBXO_(oo}
zUA4BX6IOoLD?il`yU@7rV!uZMZb0&59Qy*CCwgE9rpggF>W_&78G=8x5K%7pjU>_z
zg8nUU_#S0{uI5rD*V)^U>zs+_3WQIPcKGnqjByQ>Dk!83^4=?)x@ctvsFY&L!S<<g
zaN@Q@dyS$%6dWkkq@BskAssIf#qfR8BT;hn-Lv`OmF79MgfuY)S6@N&eZ&R09x*|1
zA*=-QLTrH2+!U5$+U*ydvUm-1f=w;;5P~Yjr}=B?XQ3}%jmIC5mviW$>m2k4AL5$v
z=8zRNv$~nxT34s4W0E6{=aPD0rT8Mr;y2+aG$|KWCn}ewqG6*2a)!EPa_VC4(vvjz
z?eh#>Qmq4Zs}5xieOy`^Nk!v2#`7O}|JvNRA0PiB<VA6o8>#<wnEycK<6fnxW^4^m
zsBp*|ZLWp7XG{xA?g+A8Cgwd@DaJ1?WW01f`Zr)P2QNVH)EO<#VI*p1q8Br-$ciJA
zvWeqLP&~7_&0i(<lX?(Ak^kE7|HBL(|FK#`A^C+eX9`>{T%6+@A+LL=d!K*RA+NEf
z<r(}YGEV)IRthJsctt#Z0WMTz@WcD>Da@w^8cf39MAm#<U(5=?xeYFWQPyJL{KL&H
zp-V|9Z|o^7>b7hD3`hLnGyG7W#l=}?vFEehhob#Y_w7opT+5z<4*N~iv`;YZNH4yi
z7yesEaj=1P>h1?kpOnr$#eCd}AIO>2)%I`1S&4=nf$xlCm`n5|T;+KAsx9T|@#ty$
zXYJT}w1W?b-Ft5M$Lv0W&w&}$hNa)O<#Qe37l39Eek2FvO7xfq8@e$J8vGqJqDzyS
zo$UO<7*m*kO1p6pZ@EMt1*YKyQh~$|DAh1fOFu}H^SpRn74Lb;iN0vw?Bs$J_SqLX
zgH!sC>?+3c=jCz1lk)ws<FlI#tMIom`IGaXBEButw!ev1#r>&u3vN^Bb+^LCQYkxt
zulM?!>9DUSmmZXhWZP%Pdu5~^)4$0Q>C??1MMGSWh4mlx%ki!<HOtCFX|>ah^1g9N
zXOi4@qILd7F8(9QM^BQGZVvD+<T{j9A_G5Aa56#_GPwj@R^B)QFT^YXl^y}OZ)`&E
zEK%$x(ZKg&I@9-ZuHV3UQu@uL6%XZz8St<2qW~p<S^fwRj2G^QnwS$gLxIbGG|KRz
zJ28fXF7n0_6HfFz6r<*x#J18OhFLGv0S0E;9q3Wc@uhhoe<@dnFj1*2x>%7Qyv1iv
zrwY1xF)Wem>w5#njxiv9qTak<UEcF46lFNha>XMcl@7crq1tLcPlQm&_oke`4k8Lc
zulqDTJPr&sF4-@}DCY%@1;F=SId|UIBe-O~t-jRRE55GiPL>Hj@#?so+;fyZI{A?z
z@{5OsTyNKTUNbGbWCDl5aVemNeXd<(6*@?h?RU!fkC9=Fz_AGJeb|KaVLKs^hdoDt
z{Ir_$Fqd<({-!<kn9`>ifixNgxbF-e<QWD04*eg!OXQ7ymwdbbD?nR|=<f)A@l4rI
ztkwAkmh*L6yAsggMUz#7#a!`gbDxV#Y5SZb;IqYSMK07@XboJK)q0G^yx>J()quB(
z#5y>ka0<rY24~ZO3AewkoyNDKYM`}sWvHyCXI?e>%SoNjE-OUz@Aoe32(z_;&@yR*
zK9D(UPaprdIuyd!#>f*WcH4<;p?wrEU+Sm69-H94);qhQquqpdn3qx}SdRidww}(t
ztM?T%`~>+VJ|uNZ$5~%U(##`se(%ct=`<j>W}Jj<UyiA~HxgK%xCkTSb%b`ydae*4
z3HO9TjH9#q_i0qbJZ(o&Atej>Y40F<aU7hQ*9%K7jm_!(-YMn7lauzdR90%(hJb7Z
zGWtlQ-epDqsctV(EfX|sqCTFB=aWsfzH)8S#N0#J`>Sj+4$x=IYkU)vCBU<nRTtqo
zX06!1IP@>~esxs-ATqm?IG9MDXzlbdmCw7(*{>@VluIdBJ%8&)Z*^@g+BJn5!uU_?
z*YOgC*m%|$5#JpSnnWh^wihQr=TexN2Q&7pw4$$rXdxc9FJh_4w)@gVf%qT!fV=_(
z*|NFI3SeCmBHM}LkGGXi>0-C}Vy13x$^GvS9q*@&9|(a<%$7mEtdU!IYmcPDv9nTa
zr7!E=SPtq(o||<VpxS5JPenUlvR{#uq5P42HOur<-9T3T6d9v&Z;YM~>m`tF^vD2E
zCy0kZSFfWo7GYu>%YQg_<Hxs!UM(n>ob?2OouM6;g{#cCqZYS2lizFy$NR^*yY~Gt
z-`cr8?@|YGa0=b9Bl0FVkt}G4=0!mq!LJeNm#Pt63aViF3a5&p@m0wR_|R!=%?VTG
zdO^HU(2I9%zE&aK<sM4n?qq`X9UG1rVSk%BzQ3_lTneII1^^CNbBvWOVtbbWWK#8}
zlfE3d6mr<nLlLv%mG6#}f!myU3;g}j8gNMxeEjKt9JZOD(0DPUPeMiIUl40qE%H-@
z+6>MrQ}^okfER0iqg_R_){+xNr;7r|ijqJbz8bcHfp#1`RMA>bZODnea9U-jM-aw^
zmgpS*NaZJRi?tHPzK+61Gsf#ZO`OzW{?}QnH&3Rl`KK;cK%77f%`m2f6;{jg16_*H
zijMmx|CtPqaAK4<;662v+oq;hCn9NUVnj;F7p$p0kQ30QG-W<IedkFuk7j?R0{?fY
zpDWUJ6N#S0VO~L23t*qffYbsBNqZu~xeex!2D6W9CKY7RVPB2-xe@29n`O8~!Sk?M
zQHgG{&;n#j8aU@Wgr{?rCuq(cEN5f7(vKJ6$aCEL`n*_dRP13&>?EW8CL{KEmo+&o
z_Rj9W7VCWbyD%ofSDSGke?!+Yh%(-JVw90OF<<PpXr!8CS<a*iCTNFossLue5azM9
zns3V)hDaR!Doz-qmh@sxAfX11>m4CsIziy<8xtp|+k$F`JWu>9visiicotxNEIsj`
z4n-VBD=Xi?0lr^&2&lAw4sl7T35u-WtK*?po>y$jj(q$u52skh!S_$~8l)c+2@NvU
z-TDP%!(2i($Eu$w%-Ke+82`Zg$urIg;wio(&&LPu_jz)z1nXtwO^wP!1-JK!0MbD9
zER`_8I^F4C{p@LSLyP%8XLT@J+qG#$(_X3U@n@pp?(qjMH@7L;cK+DeirqfJv!zrE
zdV$h<rh$3@1;9#q|J$JU@3`u`DmGo%0!a*k=7%ALQ=k_)<E0B_Gk=V>JWIMaPz5Bc
zlI!vkMc6UGcE3HQOXCZX!!)S-jrBW9>~y(Xetn|p>R8V&@^ASRR%_m988vr0-IyAw
zdQ#;mCgswl@7I<PV~E%wqa7|JT9ELaK`59_t;kARI(R{y-J~!_o&D}Vs#6&n4-Ds%
zk>fqqQA5wtnT$ED7Zd{-uK%X<oS+6f`N%G8;yg1Y$H1Z^BO@6LQ&UrQu5QA^(d}Vd
z$s28N<?$2fbnhCcc72S;t*U**1ij|+-w((+N$*hjS5u#pCwsl9*?O@`p0OFvtW-1`
znZh`2g&jli^8?AJW2cZwNFznO&8R)j>HZ)pNiKFr%EiXD_6<2vEf@IB+W|DjyH$a&
z;2nVH)YZnbBt^V7GTHON;XhG0eb^tnIJ|#pV3Hm1VU<}rv)@B~U9gwkq{AIKo;g_t
ztCw}{$HD2B8|&<YMYN8e)~A#gKK70(MG*>Ia=Q!>UOQ^94Cd98i+BNO%E+nYnvHjN
zQi&@Wl_~K8R|O2=jeJP8=motB4Od8{rikJkaW`{|nYML$j_^7jBN!d1UCqlEY|A%e
z?xMf@t8^%N+>I{>lGavF5mp@kLNbX8_t?3KD1edAszR5Q*eV!MKGsY8(!j9a5gC?V
zK<AN2CY25d(HqA~)oR(2spg9$LcV!UZ9bL2Pu|s%X}?IF{$=m@#52X}f-j^dGw3Xz
z#DtX<LOsFcpK(uw<6aoWno47o^T15P7Q=X$VxtO^sN7@ekGHvGQzs(p84?#C$$|Mx
z8)B)-oH{<xJ!*&~YF-l;T4SpZwROw!em3L&T#^#c6peUmJ+_wgZBkF17sBM(8@2YS
ze(Aj~yV^VLM!~F4ja-V~=#(S&cJXZ9G_RTftpN^YEfU1Vg`nRtH6ug+>IMcA1a6a!
zy-ROs*?RXJ6>X^8g(&zOmhXMv7Xu%QbBlQ_<M3SV1bk08#baK6!$tvBvJg3lqzTj6
zR%mGW&<apVxQ|?=+Vle=6y~<io^eyrTy>n#_u)rqT;@By=*3P9&Pf`(^yynIgU#30
zhXiE1q^LZbdB>fYBE`oG3Ivh_&Y{*7@tanKgY|0+EtRKS&NoeGE%d(FE4&MWT%{g8
zeOuo|6uF|0z)0G-#imM`gVW0nrjiwZV&PcTvF&Q?t8tl<XyG5eV*{<aL6{xLTzrD^
zT;5rF+FjmRDS@R)O(xwYoj%rp3^*`@?sLEF7#RL74!T=<&CK^cWB4K#FTWq%WqKdN
z3_Q1zT2^1OBZ%9KCTSZDk}V&H0v`z)(pPGM!&%m3S<IE;{-f;V_oadTo(rTtCo|F&
zbL}H9g=L6;+wF{2O+@|(1>`?0{dBtI|7;pu3yzzFt3*EKrmR+MZ^@(;kvC@IvmXDv
zev#QF`pDz^ZY8OiBWBgDK1-9NZjzC4zNW@%v<Zo;IQ8kwP`)(s%hosD?f1($0CH2m
zj`jGrC8}c5#@Q!^HR}85(j~J2)1b4|D!4}TMDm2bg2=LJMUggm&A2vFqN%YWh^P0$
ztUkaV`g#i^%A`#rD(FL69+4MTl86=x6H5?5A+~&SEpUG&o?rCIyJDx)L%XP1slG7q
z|NBm;JIqrI`<{`T?Biet2v887!2KWL=XVeF<)0$AKA2TdoJ6;>kQ?mgb7F;1W~3+8
z@BO~gyR?G7@UxOld>@t~XW>w!^p$}g_{q$4Az;J)@e6X87A-k)8ltS!Qjo&$VHo4B
z2%BK_33`orO9C~-5O_8~xawG^n%JY8yXd36?unDjG63q`A89|Ky9#x+ya~DIvLNYm
z)*44~5C5q4O~U%H6p0eAsCd}-9(t$7$1y&4i?jUJk8;J4nDNy(USo~i1JD5k9^u|5
z*#8755OAhRorTBrY0*KmjcyOVmL-dGshPgV(1yweQ}a>HWHo@pM{dP$11Zw@G$Ey`
zs;XmQ<<ZT;(ozt(2@nQc=NPMt1rS@v_Ckx>pjq;Dh;ZJ4AXFs9KDG9*!mQ7I9TO2>
z7YMv-6=hMokV`b?cv(bKYC^FR|3}ijJZnDsj5lZp6p1+sRwJbP9ARJ$Zj;JiUlYX9
zPgwArT$RGItVQsV%^ng<hgZ{urlCdLv}o1oWo@$U6LMJD4QOjSmjw=A`cOG=9T}yp
z+#T(u`6wDhxpZ=d0{gI{OmnSO`3Aqh$XDj~M$#>}NMaeHmD*O~t>kk@?Ioc8U6Y2E
zLUz3gXEZ^j_Mmz~LFb@Q@sH1TGn2lo!q0OqFUL@>bfswgi-|39@&v9SmYiQ1nC{W@
z7&$=X)I3Qg3P5o1=q5X?-m<CAWE`mni8_ta;IO{>E2`e-PN80?8_qQZLqS;-SXw(6
zmAa0h8zV?stq^-uo&k}QzYPb8$Z{B<|EOg1CVp|3f&vIm^9c(DBv%C!CS0v#Nnm`7
zqHm@YoSe#acP$HG>(aMVB6i0s2p{aTo+$iDjK@?M_KOuOFv<bF5KfPHsWyx2sT|JH
z-iTrL$=FV88o}`X^4V}r!*7Oyy*Kot{b+eDaq(Z9OMOjwDV2ZvL2|FzDnI5FF-6#I
zvk#x$G`ce_RgJ{4FnQvS<DF|nGnmwoa!oUG5;PHaIMa2TSIS}F1>pTrpsdXz&hHdJ
zj9hygn$G(x`MvT2$LHSXH}dBs^-TcLaCi)7sKX*`L{ZrEPA+YN{ZS|EHw}ayP|I{A
zJ-1_*+cbQ9LjZ?eGg?U<P5aH_B!LK}8!V>Va8SLd0(%sX+{dZlkRt?`t-1pM5BS@#
z2v1JKfiWR#55D_g$O}mg=eGa2A%fiqJK<@oU866kQG`)wj7Bh<K(_!<jSO{24_*k8
zh&noyID6NipKR$#=gM#8o$=VLVB|OQaGEJnS-Cn+<m{k942O0Q(uAt!BQ7MjC7i#R
z3i(qg=kor&fb&)&y6@njllVK<`v))im>oh>_c|hVOhbJ0okGFrgz?P}9j5FiZDh5U
zxhktf+!0Np^2>`ozvA%$AtkR-!>{M>MlBp55Qou~ZQ>ju^m&`Y*X}m|FldM|-g{N&
zQCJoXLHhcH0B|#HuG`j43qmdhwb8mAY%-7C@&h!d$uRR@ktlqxQq+d3c;s{_3#`g_
zKagN_$HZeK*@l4KlZ9KBpuS=3?kbkK=QdGFnGq8ST4HZUdCe{YwA$rR<I9pbX9<J5
z==r7JI`m?BwBI%FFAs|rT?eg3pF7RiY9kX&I>hu52k96Ra}vk+Fw8IqcJPldE%sR)
zo~vvmo|5#;oBVw(M+5xVnlDmn`LQ{t<OSW!hQVT69pGT{fPFL>W>JlcxGy`um7Iz~
z!B`1c|Flm+8@^}70U`?n{<X|^*f#s7K%T9C=7lp?>0mN8Qagg5VQ(8V(9R22Ay9R8
z!4KABzl!>~`<oAx^hexZ&+Adu9yosPG%LxW4f(En>KNSN__8}@y!^}%8tSz(8T)$7
z)^M;EC5Ntiqx>;rCxR=f!>m1x_KUQpNJ@vRl_ohLs%N1HEff96n9n`%jVlD1oXHc`
znE6J?Soez3FOr|*pKTX!g21>VGIE7f1p$mxI)(#j7mdJl8icESvF*ubynW<>7Y_{J
zIF@}Rn;@uml8Wi3{%Dmi$33dfUPw`x*<|t!(>=yCYR$%-buvanhJ{9|uq)^o!eSc7
z+($+j+SlMDEA|N>Oc(@{I;5<XY7e-zYAX<d12Or34#X;$ls?uzvzALP71CW#_}4Fi
zdnrPok&7Dga(2;!1ydr-(9Ar$svKrtvN2GDx=8p>3)_IG2|e3cn~+7VSEyVoJ=!9j
zYFu<+`x*fOwMV9utG7|gmIU4UmD8^%O#yd>pgFe@Q?Ww4Ce{o5+Y)A5RS{G?OU3k;
zk2z;=_Lmplcp&-P_+@sy2B;LhO0B0Tnkf`T6UldRRv33F2@H-dBPs*zr#@%A<hg=9
z#5tFK#4V;X6YekdGt9N}GU?d);-pl5D-itCDK7`bYQD&B@rJ%X^95v8XRW5(UInBR
zDZHU?e`&CVWKMIOzn1>RT+}(CbV@5UZhr?*S+X?GvK9sbK=0gVk=E-6PJoTLnvv4y
zD3V;Vx&Sz_ZF%Rb*aoIV&eqHcO1jMEde5l(VLbIh`J^sqa;`#>0>O_fFKGa2ZTw01
zWU(js9zfgT8Ba=%VT-E1Ty6WRqrB>9d**<Z!)G6mvfBg36QX9w55hHYAP6}SbZvO#
zo7qVL;BR~9N-mqYF+6DGNc|EDQnmO1FuyJ!Ylf@0DpZ7Pgrq#va8|qApZZ;g6eg41
z%e(wiQR~I}p9#_e#TlfU>E+`-8Mz7?jR^3M)E@xDXEE-Rv(1;Y7NO#t(f&61y%-&{
zl4JkBR$gOO8szDG#hN5Oz=x4wU<NQDu`<u_qB0~H*T&fwL1J$rJi;X=$*+s#LUjBu
zHOYxo2aPQ>)mqvH300s7|F4RZg&wDTkrg%<PJ?OR7ev6LAl`rf`}BxA?+s$3jA+a&
z&vDCDthKiorqZQ6{}@3AqbOrMfPtfLpD>SyVT=b%#<tCDl>XEB^m^eyU9|gUqx#oL
zX^b#jx}gcyH|3uLP2!jhcGrcz6JRytu@8!MYBb^mn1^1+J((@4dy(srA-!}g67Nuj
z{YXvrL8U>i2o?ylVCrzBo1Op^S^y@pSD448$L^_05w=GsG<1^H$+_@H7SJ^x4Y<w?
zJU5$GU4XA(3pL>Xm0358{7N$lS9J`l{c6tl5@)I41MeW9OF@#T#phUw76gjLHVxZa
zN^9d}kuW!Cg7rYO$%g8H3xh=k8Mhn|b0qEE-KEmc5U)afrZOe7iHG{AKgb9Vb&9th
zm?%~kveMO=bwUz0niIk~;7>nWJeRz8(&Dr&9cX)HF(4FiU7hX}KH3wk&2qXrb%0j_
z%{Ocfu+*RxEpS%u_<!qnq#*6oUSv2KT%B7@;)DUQHFqDcF~MmSA0GOF1nCWz`8Q{2
zYRokTWa#@8`-FJP6T)+V|3`13Wq_$&&e&;CweC3fP&vYNM(q*wfph=v`P_?91GS|x
zf9MNLJ7tVFbD9pEy6m~wte2*@@?#Yz7}MWzCAt4aIs7w5jQI6smFO{mU$KFtAVPzo
zY{X4Hk7vVrThAWT9pbH-&%0or^F)qvs;(}%7xSqk=Qk<c<~(2w@JWe)e@85ouzJzO
zF%^EZdu=?obaM;v?(&zWZFy8-`)G?FXRT>4s%IH*h<O%d%K*;}(Jd!g7+A5v6i_h{
zj_2Z=`!KgdbA8gr`He>EPY5cYQ7MtE%ZzH!7h!rkHRg?7lI%sVNt)zCkHC9?+J2LJ
zz)7ZzMjfbc+xELAEPg~)2p>&;ob^?Bs8`$)&;VM_(4n>^1VHy4`{jQJKj!mOU)uVu
z{+0T_L(BcDZl&-@uHJ-!ZyU8da%t8K2c+)d&b?r<Lfw-&Q9X&Dy*32Nj4{FXd4l=#
zCjI69{hCK_-IA2y-T4lm=)oivQ4^8QM0ZR#U$X7enMS@`+Sz0M69Ao)8!+K^nUv|Q
zr@;N;sb^0F?J88!pvc)Ke1rZ@VnFu%h1$9|J-&4Obp!v+OdwH~U(f6L9MJyA87%ir
zBBRnQ$(BRt@;3q;&~)BII+GI15H4Da!3GRp7UN5xLi!smu8n#GCgfJS;>~8o8DG5z
zXm~i&7yU8ht83>3S#85&{TD=c=xv}mK`s5rWkA!HEX(p=^y4_oN+l~69${^m7)BLn
zsp`<-{lv(q#_kEUjz0ZTXcF&^0UA_|6d-vKQ-6k`VXU+;P6M(=fpP25uZB}p8{xw_
zU7RG~+k)4zX5RTd_m5C}DECqfqExSQ#OUfr<gX(9=ck!G{8-w)%e)@K^+xVXb58?L
zFYW*6JK{BhX@cL*#_i`l><b}rkRQ?kP>x%aCUFIFrgm0;^P>vm)g>ZC;h$vYp2fTx
zm}TsQ)0Pa~kz&x6fyRZfeuDno;H?b}VC2AQcv-@Ao{$MsuK5C?;4rwbDnzpU%+Rd7
z9N36@;vNiq?9%msp^$|y-B3RNU~cHYLh)Ib<n)zmCH&_1yR(9SBz!Q61P)6hjCnPg
zU!PKcqGI9qIza*TA2}010lTmitqWr%y{`*QbL&xO2iysc6^!pzhR#r^Ohv@7#J#T_
z7d?k;bfKWdUk+)7_LAgHALL`;7|Fj)ccsALk$tM)B<!#(cQZT{jvn^M|2b^u>n}_E
ziJi-o5(ZE7;hLk+V1dc<G!ef?Nzeiwz(pTO%<YB$a9?){C`#sz_ZHrgd{#7O=_6>m
zqMp-Tj)=JgU4Y*K5<vqRVrEZGq?80W*EAZ_9h-1JGgKt}2pknSL*464A1~H*yvaBA
z_fO+Ep$JWIy%!Mn8UB-WGEnys+s}(d>NI5;)_0+fui|>)1cG+WBa{<r&3<;-G8DZg
ze79~u29$qR$?qTGm(n=zWLaO~JG~r@7#G}r>sNPXj~tr)<9MmOvg4qIICyp2cX*L=
zmi{9tg9b6R^0Xdv7|Y8TkSYdw{`IS)fBl(vIlmqehq2=6NvN6bCY0#R%0AX9Lro^G
zD}wksO5rPgV;3T4eNTD0+6X%H$2|885BMe88<#KMc{%p|ipOAx))rihnQNW^4f4Py
zc@#uJ(*Y%3a_?A^xsZ`oTm`EBShi5o-ec5`33QtTac7v@tO9)P|CB|7lN0Hei+;CL
z{M<xMXc%Sj2@E96H$cEEZBt+v`um5m*1H7^WyYyTv9;C`^er7~!1*PYWI_*i3@Ola
z`o`5e?IjEsG+X?_cP4Q|zW2bXoV`yI;6U}o3@b`2s7pDdi~V>Y50Kg2_B~fEPFF*&
zvX59QYwcZcAj!rIdzPQU8kh#mfeM*6!GQY@0}0#~xi42^5JYfYaR2iW&GnXb5bv?~
z!O<dtTi_}NG40R{>wvVfVwBU~K6&mh8qB(=-$`scDq5=7jorQ8yxFm@a|;pkA?nj-
zOctKhe|?LMRUNLVS{_4(oIoXX?l*oyk=9@L@6&V6>wnI9A875R?r=RDWQ#6@as&fE
zePR5bvk3^T?WjLu#1p<78}teXR3Cq9CoD@+elx;;j<BDdAF%k?U58w&J2jSh7T*nP
zmb#`_6y$6IeyIHEaXGHuB8zVzx+Q_`Pmn^oS?QOMZ<JKWPe+`xelSml3|TS}`^GN$
z*B^0Mu?7A{SpEF<-B<hfo+Yv2HE!~YH>gV4WIRY~Dc1pBY#O&5Iq~CNXW+&X)1s^i
z6(?Y_zfor8$18+_I9sb=V4G^DQyW3V5q6usBH#5Fy8b=j7J>jJdUfgen%fBfp#iN%
zdZaQ*`#A0KfXKFsP1SZuW3}2ZOq&1O0fh$)efLFMCqifgofOaw@)qXT_?HFt1IZ3>
z=Ms5wvIIf(R?D~U-8M2Sm)Zu@-8<4+D^IsKHK6i328?H(N3?Z(R+S@Gqn3=vsrzq{
zvp*M6<U?~`Z*9*13~y&0=jb@{INCaK8gBwUIX=F=j}?rpfuA-iNh$I&vtGQ0GHu`6
zr|qu^<G%p(j*R@!_hHyxF)4*qYZ|wb6-kzXL_YF4O6fWBOU{GpN~@$^WkhVS-y<&T
zW~DxW-M{9j=5MUER!e;)HrHU*4Y7ieNjwN@k>{uFvkb)SIA#m&mrK(1t43uWKuBj_
zY7!R)&+2wP_qTr)JZ=Zgw2+@`Uc%?7AM19jFp&mGe?>oH8ur=HglXLdNB?e8GP1+U
zWf%0n<0y6@riEZWH3P04Z3pA;{O-bHi9V?mUSbFhX`?>x=f=Rvs;A_2cmR^REe|-h
zCnHpNA>XDl-Jpq|F|ZpYAvMCoN6kk@?gr*`>>KVJ_~dp7-MLArlV8G<{Du@6au42w
zQ_5EiKqbI|>`q2jZ3DGaEGMd1gE+H}D6n{|e8&Qvasj}#@pg<L7mn|hpbW(;Ot0}z
zAy#(o{`{U*<dr_&C^&#I7V?b-=L1WWW<+YU%$)ISf7NgM6=Q%|p-4Z-d-ROA4+4l$
z+@Ljfn`|08;8md0uW~5y&j~4b#uyEr`#}-CK~pQL;5zq_l;8kBj}K5siY#5u6k1+P
z3J<~d2^yCaMSC<c1c1uBuOqvA*tYpBsD<a&!GM5M^l6OVpkf>}iIiniW3V9{F~O2I
zi5qG_R)9-PJr&Z?LoB-4dXeh<;B*^#z^&mCBaU%w5FYonJ1cc<OLU1*)~4HPiQ$+|
z-hQHwgIA#EHR_SM`sfQtkVweY4|ra14<W9N)T)p`O;z}0U)**CFmi-{EH2MVT8ah%
zI~1tVfq)1|P#4>v&QsrInqMPD0InR({GQGv#O0;+dG$g^3VGghE9t9j_yXx?K>idp
z+hvBFjr$cK$#a7Ldj<db_C)46P`sQcvR;n7H{vrm5p=gn1T3_ThpQMtnrhhZe_?B5
zh&4+L)!%6gxZBjq^<G~Z*VNy&BltvW+<2dfY=6vBB#DtD&zHae97%>3IIPTJ@)hxU
zKSZ+U+S834#cB>&0W$RlbTic>NrM1@0Tn%XWrWQF@XH&@*YyFTqxegjYcPBjY>)N(
zFv0Q*bA)-#8>?f<wvC)oCw|bZbh8cuniRjN`pb8v0reg9Lp)gmAnlM;MW<14`|;(1
zppmq8-!7a+cRoy0?UW8wAD|3fLz76h`8v(lq{~~C{WDD9>m3xHv6-%E-IcF*BKz74
zC-Py_=zquHfA1h&&MS2Wv8nZOo>sz+H|Np=Xi0&VLMp$<YP#Q*9Gq=ngFaqIdpUA7
z$EUYFzg=5WzSz~TFb<!}GPKe_E6aDVLokmJO6Lp}_=xp86Bx!IGAj0?fr_c7{G{~0
zVJDR<hN&hvn)5oQCkfviE096Xsg+qZlJQrDg%F^GHk0+iEOx~>xf(r264me%?H)DB
zEJTc>Vak&xM~5M;yu%^!VgoM9iver|-Gr~qd7PJs|EE^|E1+z&FpzS2X(j9XcHez;
z`=8+f;L4D9k`f!76VRlc4!JG^T;J*DL|%iwLuc54jD&#GD{1v_yZVOW5(AAjP!0Ug
z0~w1__jSx9`3+93W!pI5Et?AYPFfiPD2zFLGA~W|$8>kz_L475s*e%Nz}tpA9AYw-
z;MKW!zu6tuufDWPv+z-K8R(+bHqRE;g_1|(Cde^CIq!d-F=Y?m-Y@%Wjjx|Qa-&Oy
zAH4lM1RSk>IVbaPQ&==F=~Vz-LVZUedE;l@uK1zt<<-w^s*iY=UJQ<54H;fLr<whB
zpQ<xXOUQ5%%Ot<o=V29M4yV-|ap_$+27fY-Q%5&o6SM=eKj!2?)1STuadRm9at&m0
zu7#WL4<ewm!(2*TAoAi-pnQFSzX709&}F#D%KuMEvL6kQQ5$!3bnk#<vDWjPE92jO
zk+{PhUJ{)>G|qiv$-{yesBe2iskb{7p+2@WFCv3H=57WU(*jKoNAbR5D`ymsJ#d3q
zR{E$L=ftvH_H6lh!9EYl^6cS`^aW+_<Yfc+Lg-!xpm1S-CovDXLh*MRi8qdGdEM`L
zoFqXKvVq}ZA8{jsmCL&QWWc0452%~wUW?doo!MqQB8Ty}g(UVf$S1AE#OH{krGDco
z*r=LN0zT9rKzn|Hstr`9R`nbfY^Q-Q(|#l84kufLcnx;D8v>rAE*j!_teAeV*h6-$
z=d&3nFG@i<=kwF=fL^~SkTj>Ur`V}a2reK^8UujD6?Ok5qGKQawsFbd&ybn&bB{#H
zq^ZT~*%Q)v3b>711<by<mc9ADDF|c?J(dR8sId(y`;`WOyyw2Q1j={j?9I)Pr|ry;
z*tIkQdV@Am7p7TP8^skK*$OPMbwW#(<7CMADXHvyr^UsrvA6%AmSFqgKzmkOgw2RO
zIpWwC8!a2DL046nq*)viQl(X-jBfRoaN(=dQ&?=R5*5PgpDlJLg{?x57;5bhY(>HI
zzkkr?+IgtKyg`8BO$kbb1o=q`s8h8vH_$u#*6`~)SfWzugSXund1uDFj4jXjuu}xx
z_0J?o1OQb9puhQ>{%X}!tkZq-0YBjxAAmeNfllQn^aBo`1^@hGX-9n_AJN^*v}mlf
zkN|LLNx*nvuhq9=Se-qWs4=))xUufcA*v^&V}9dsU5rJnGeI~+Zfyp}=J=m7T>R%@
z`c+~T^Jc`}N}4JA(dm=*63Mn=&}>0_OaX2<J_o_O&t_VNn!%lpI!k|e8OtFqLoAy~
z_up<XDrP>{^lT-;Ps&ddOQe}9Sbzf$py9f&c8VkPErfx+q}U)Fa|^qXY5TxGD4)#x
z`XWGP2l4BTa#x_OxB`~iVa%D7?tsT`1@}tMTuT`3xI5rgoBUFcrG)g1{kZ+Ny~_VH
z%2eLDl%8`AQp&z=OwiKQtV6I*wLeKCv)rM_NRz#HyJG)=i#Nz+OnmEDp$eafCX{0Q
zk{d~Y15A54rOw~<-r?z&(;yb>F0GpKp%vh3zq**K<SLhiyiA>@tR@Q4u|=k=01<x{
z+?FHrboIX0^d)0g5*=#PMOs}r)Fl_#;ys9Ef!1aMI-uk=u4g+F_f!mkJxj3+{kX-M
zV!=st&Cz&kGvbB;+Usy0sDC@iYocQ^NK&L{!?B=r{kt4<A1j3_99X2JoE{)6=?;7b
zy}JWvLh2P9Vm5S}7d2}7@e*2e&`u}@mqRFXT1}Ee?7MW{QUAblEcC;9JxSp+;Z14H
z7u>Uok@_Y0d_7ZrbsgLZk&fGpFxaPkY4-88VWxkQws(%v)yQ~>^Ns#P*GIK}J^#P)
zy(6D?z|6Vb)Ki-gT`Uu-o7c>vm*-l2qxg25J;i%sovx=?*?Oera@#H`+yI+K7AlEc
z1pxNKU;t1V1lpH#orB)<#LSZ10EyCFIzU5@SDzsJ_iqO!?}x%o$KM~Ka}(U7)@rgw
zpBs1YzUTOKr}X<>sUI_}Um+&tz%dw9)R&P?$Q-yCVRLnwe|7|KI`+XC;EtDsXq=Jw
zG&thP3rjL7y>|h|P2|FVQ*H{($9)OH1?J`;&6>n*@BA2EIUCDE_8?#fFUu&ZrV1*A
zOXO?MhQ!)p!~3ch$5oq-S2Sca-u5NA0+8|e4%OQ@QGP7rF3D{*M|w%g%rAOsGW=r$
z&e(Z61datA?k@omREIN~!QgM|!q?3Q#AQ_ujvaQxp<3oF*lK1@1D5*`PuCae8>DOt
zb0UD?OHlN=EL~yO#98X*&}o`g2zk~};WVK5TtQO21`Bna2B9V;Ih>kKbDVm4&JPTY
z=iBWdo)>&2LH8y87!~KU%toULKqH_i_R*FSr<1tJF@~$@hf5mmbiUe&M|k5`s)!lw
zhey5EIQCSA)mwZ9ZANU^q#VYYdjYP_X~Z1N{wGIYRj<tn>CpyMu}AoHc1npnnC#Se
z9lta@3=yz*PqA=18fo50%H{>`CLt1zo9xRK9=~$goaZkj@~F#)Mt2jIzSf_tUSBjc
z14OdGq>l<Oca_j#H$K1ur4!Tp-==kbA`zM*PMV57^X7FmIxP0N`;^fd`5vcXJx|E;
z`+=|`PDs=yMa!^%?Cp>{q7GKW>CL3Jsi{1Ra*5ZNo|raMv=dz$0nkj;aT1$B3aN*9
zK~25F&F$VJBt~TSc&(ahuGAW<80hL|JfCF^8D4tCu;PoUii|&yvTuWtxVYA87Sguj
z<}qYmleAiBQZ%tW@R&6o)RLPYr2pX3JcR@!AYM$oWO|k{D$`6D$WBasBvM$=-C-^Y
z#Sa~(oIZXe`9C|R`#f)8SV}&dXogKbY|GCpM=>kqj>)*qHE?0ONlv!PykKNa>jT3O
zCo^3B-#FwkaC|j2)8N*p&4)=_J3D5GJi%a6#hK^~ndW65LHQrA)hd5a@j3S&VQkJz
z!CKH`y$6%*p@tcX8+K-g9>vT)rg_M)L99dq4Z;dxDi)NnOUy@G{gOU=1Lzg`KHqt^
zo;m&sCqcOMU^t=L%Arm+i^AA>E0_x*E`MoD%Emnvfo?DefH_VS07oRCu8wV35CEVY
z<^^5UYQ4M?atk@1I)kB25x@*{r80Diq>lQ~;pc2X{XCn>laT}ER`#bM&+{aUKQOhE
z$$68wEuaTBrw4m<&6tF;i1u|*IKRiKz(Nxlgv?nMy#|GR(x~kT9czG9U7#aq$&77B
z=Ci|XoGF*wWZv-r!@}Y=3x;qTzjnC(<uWp1bb+lwQVgus?f#}8!R>A<fpu>f^jLN)
zOk&6gp)bgSHIc#?HG<XS_Pba(;=a}#1q4h2Uc1m?k}0a~!9ndfVlU`T>XU)}4+A+~
z$eiZI<UGehxS+RlkR6Edl3f-+>?{T?3)Z>on|4e%0QNL;37ILfHz8}|<0TIGKqV~I
zrdV-?zSx0$eI!~6;n{!H{09)H0ALT`h)G6O5MnI#n?v}NRpR{41pv#fWv#0{%Yb_>
zXloXa^DPwt76w)SMIE1}RIDZmioDDq3$p=gY3xSEZpg^2a=y>dt0_Iw)d3vhZgsKV
zczmW@I{F7q#IOHA=6!oKsM@qb52-84k5HwNa^cg;6#uX!)`E5UJVox6@RPz0cZ$$1
zT5WB{px@~-a}2}S6)bV;_}4THwN&A2`={ih2k%q(KE7Qtm4y5voQkQ<3t^!LATZl6
z$(_Vsa%F4R%iqD!kO7Kt=DQAt{4u)hFV5xj@MKh5vdWXXC9{L<9~J1r|7W$7|E)G+
zM!L7My<>8QO;$0>SG&g2L>#)+sE$vcZl4LvRpHJ;L(JsS1&v{>ZOi|yI#tT9J?9h^
za3kU5H9)5-ZwW4iJA&B(hme3e*Bs-diqm!z`q!@XLbcOlgF$NUx?7TBoQk#LM1NiP
z&2}DkU=1f8)+ReCO!9Myfd31yLIT90lzB>$U#L39tm6+9Ho^v}4*|H87@hol?V*Ri
z$&D2nG}t|D^>_^q`_SjDYRLEK!!j&|0n9erb4UPy(gA||#lC*s^hBW=&T$5r4wD`A
zKN24X_(*j0MugD+Z?Te#ftR@(dVo1Ekx7YvD7+@K2F|q*BVYIS+>kl2+kjy$wA+r$
z@Nb#s8GUx!Q;_XH!&e<QS>uk<5B&f%+)4+Gx^QQ`)Y)bDJX8f!qpu`|hISAJm;GWI
z4vN}^3HrI+@XuyhD=QzD-KIzgh{w;7H9q(AUN{w?T?Ud!z%o8)R<}3B_%XaT6SJn$
zxpPSN#%p)b=d0xuX~9TAH)|Q`1DynhgzCx3Qdc|2kV(rSFsZ96>1+4DR2GLccv^AP
zRD_6y-el9y4OmZp-HA)K>kx|1EKDVDjLr2~V;b~4N^0xzyVDN&SF0^b_7jS5uFzfy
z@H>d^UNN1X#ziFds(}<Q<b>|03#}Wg(cq23ll&)qDCFAfAHL{@Xpo;+ZqD{~Ot;6o
z@7oHOY@emS$S}xQOF@~SY)&Hh<{mOf8Y05Yrc0iFvz{*+B1a2!l~*P0sf|Ym3Cq~V
zlcsOFE#|r%>42#*HNCo@_k{hsKRjt6xD_>k40<Wf-q(D5YKsSVc)a`6L`Vp0ZW|8u
z3STR2x3Fq8Vf@^d0#JsIfEnHU4GO?tXqigrWt-aWqb#)51Jytn$@7W58!D~G6ne@|
znU+kE8H5)#O7*4yUFd|XPW>7P{4rjvxh2K&T&#zvo+<zsav^Hu%f$0*(vWf@Mgtgv
zr*;a&Mnwjc2)j9-l5w4PKq4xD<&lq{#B@Fxy2m)YRM|9EU<X9%)?&THKnX{zN=mM)
ziQ|jfRFbC#VD2TIxxIWF4gTM1^>QTAGZn85)I4#FFS4V8q<A?7@ULT}@(<RU699H&
zh`LzKDeX(}Kt8j3w!Tj7h$}v2``jA(Lb}U`=pQ`tt9QSA0f#zkEW&d=a3zyXAWvz1
zC2Giq3?QLS51oYD&FIz@=tyA(08ma7;Ffs?UDlq<yLufNSIOfa#q#I1PB`zQDODl%
z#Y8cY5EN<9hrJi?>tv})0+V{-F1=tIK?~a>>X;^frEvEGivx%~y4H?yb@g`uy_ss!
z-#uOei!rBZHufPNbu?fWLVZKdEL&@s9uIcM!Nf+R#Rkiba&vG57neDsH_k;(KV*)<
zg}!zvm52Yj2Dh<<EBMgxE1NdTitEID+&Zm{8yCS0i5Hq550FSqJwH9eO^dJl#2qNT
zYCoN~8v7IIhYpMQIVak@Vx0<)C^cL~BW*MJj(oNhrIM1}9hdBrdz;TA!|zlCO0||f
z*9ZZv{#3&D(dJkJqqRiI>^ND`o0@?>7bRA)v}dghJi0A8uT=U&D?`Vr#n%fXZj>b%
z#s-r*7vF#VOUy%Rh8)Q9_b+30W<NAR+O$FW>ZG!`VQsMS%=fcpNW@VU7o!DAhItO8
zzITE3uI^T_giin_L-jyyw)2*xexkojWmPc~!^GoHO0?m2)iZyD@O<z#;x8jhjVS>G
z4JM+cgLNF!D0Z0sx5i;-)F=74U)LZwuXLtf;h2AFiaNU|av2dbpr$Fz`K?08uiW5B
z5oVOhJ&iVr1`SL{ZBKf_;dR!Q=IY&ev$-H|n6oyKzpk*Z=nPj^Q*tpkftfNg2Y|^m
zPpC5KiUdd8oXXQuq<qc_j0^Rb1_$-|oZtp2A%0PCa5`ZQ5C8d8)Eto_F9wz9cs$P#
z+q{*KyfXB22pUf)-K>B_h=ecZLh)N`&9C*`<L>Lc!YLqmCNeT};a9*EE7n&po)4dF
zzH9u-g0aU$18K#zh@*buXr!_ELg1C!j4#hud3P~)A$=W^0$`d?w>cFqkk4~7CKQ}&
z7J+6268!zOIq(3Z-SlITk700EvBpC||FuUfe(&O3F2j+Hr1z$UgRXc_p@GPOaH6?p
z;%EdJs}tu_4H31)k)v&~VLRty)mMgXGk^Ow`u9fa%>eSFNG44g3JUqIb-E)%BA;C=
zA~(lDkia4}=uVRI^@H`H&d!Da{)!{Vv^?o(bS96UvE^@(XI`ruvo7ByEBEaG8W=64
zfjJk|K2*<UYeC6Hqk1>_cDQ+8Rr1mPpyb5odGd^l1<{`4r+cREDe3e1%;rE9oYtsb
zmlw1wdX<nKiHRx^IZW=&melDf80|QA+Hbn7mwx4Ecl%?Gm<u;%Pp*wI`hPBwiS2fZ
z1lw4`xujQxu44iffypsCqy-=3$D9`(j@8cu!L;=KCS?YnV`u2fx*Xdwsuop9%0hy(
z3ye;I>XT=fQoiV$lM=%az&p*Wp`aNs2>=s+DzGCx2U>_hyg0lsENvUhN-d+-fH7(R
z7|j>t%#{eVkW%daKZ8-Lo~aMh8`0;9G<h|W*S+Q5t>-&FD{h$p1%bfBB6=~wQsXE4
zBJ|1!%lY3O9y9$LO5;+|j0_nz3G|eBf&OT9ByH0=z;n!{ezMCT*eYNdxBchEn{mh8
z{Y>p`DmZ>y+abAwvw!r9s9dL@VqSI0*3C7!KwRmlg?Lz#-eC6?0a~ZMdv?k;l|`{2
z0+Kzi{*CO3*~p+gxQvM!z}WLBwg6%S5XIYAQv`j$+lq+)BF;aivqMjA5N-uE2$js4
zNIA%~!cAeY&LB;jX;iP8h)%O($P_Zim7Ms0T6@c&x|S$h5J|8g!68@(esOmQ!Gc>L
zxCD21IS_&cOK>Ly3&Gvp^<cqWgF6RjntR`ysd+!%)JzRURf-(gy}Nt$THms-|Cf+%
zpNsWd>u*}6fCTctId7ILKnlv-*mw0=g$0(Uoyq0`)BN(<@H+Cy8vs0*ID~ob4kxY`
z#t8<hd9<Frdjt7u#!?VZ9YE)Z)Bc+AnJV4DaoXBLGZ*Vz4Fc_{_#I>i6uowWzJSca
zR+L<f>(`y%U+26<0fl5f)0FC8wg1MLq&ThobfJ8fDO49j5hfBAX!f?R^)JP0;i=F+
zS<nQpJ`dryB$a09!i%p)qnrS0t{yes)(lEkZ1U1?cc57T)rtE=#TD-5AqFJbqE)Cl
z;upU-<tV368%+KL9{1t&FQN*Ebo;%FS#Iqn0rM<ha0d|#A)fv<&>3*Bm_Haj0!t@F
zt2fS4`pJd+2M*rFi%i?MJt|B9*iN6kYqhve;*!e|Dmi?0XjZBTXpN{)VR}bhPdmsx
z7>GyttcQs0b;Q;*5@eVK6HFC)rc>n-X_dyQj^*%0f<&Cz<!=waOo;={d2C0%Lk=hq
zjp@#Kb4>axVjZ1pn&13XKchFMsN6QEV^TQ`MC(&X)^t||Vq}$PI+!lktM`|*|AOwM
zpC255aU0524yd_Bm!-Wg*}>ps=R!K2NjO-1uD2q4_vde&ti!&6m}A~?dWRrj1=M6(
zrg)lW&3|F~&A)d`-s|sM>??6zng=V!71Mg7@VJ$12TdGp3yKqB^BTO1u{?awt5RxX
zJ`+1sKt%Z*Q2(Zy6rpo~N;#Rl*-@08?yn%#(c&|YAb9&YXlmu_cjTQ;c;b2g?46S8
z)QU{N%NSL-1A61~eC4-k)_FSY$vl7*7sE(a4~m=M&dH5e@iDX4dmL@KpGS?cK5gF{
zWayriRGLkL_E}Kzj&uT2H0ZyM2o|TFDwO2X>Mc>UGCRI^hF&M%GW~sJ8XXzQ@h{~U
z<KA3b4-tr|3HDlX4J@(4Kqn~hDg*KT6M8XaN`KvKp4|Ic{^pvsVysTw2vVuEb6yI-
zDl5KMfKxow?^Zu~@i(8pgsQ(PZ|4?p=GmpGVd%<c489&FX1U#vno#1VQ~L8wt1A_=
z89t;@dn_FNWN(d^XaiH0p$Z-vz{kskfy09ykD?cHt^ckukpN!;9)Ef@`LNd04`xjC
z?1;pRF8dE9S8$yRPks-7<S5NVp5_Y5kHjr7RzcQJf%<W!86H)mUQJKthGKk6A%@w>
zqB7%kqD~U`fR8_ohVF4(-l2@lXquNNg1l3!-3yNv63!2o)3)e3Od!&n1+ub(SfHw8
zn+=_S;+WY-daeH_!WJ4|<*ho4?j1@%)aZLV(v~lUDNv(82bnW?8mQLrJvRul0&Y6%
zZ#XwGx|Uv!igf+NrKmOJcdWE_o{tUN_dKG#!~4`wt{>kEi`A6`HuCz84`_(hq0iN-
z)T^iVnOr@qABc320iV!cTf_I~M5VT?=aH@m)1ZK?`32%7^94{qLogiLJsKWvaaGeK
zz>e}1chNA*c?If(r)n~Mr*nB)o~w}Q2<{<byR2V)1AXx|TZnZtO2=gVBjqU<@PJo0
zAp{-(1ko9Z;!m9)&9QcQsz*(m2r4)#Lns8!l_qrx5f~SGrg>XZ1kdrfmwPQFS^H<X
zK6JpSIg5!DutNw;zfQfDL`;8Q-aNTUSNa&_3azj7!h~4qk2s}K#<#mhh0==s#aHjn
zijn>+>tw1qpb8F~T&MHm<EHdYW;dNUYV=WrXy#r@;;0OIydSm<-Iq3ZQV_ad+30@-
z`iFKjx_s!irs_e9T>ie`vl76A2uJx0)U$c@2RWbw^8`BESYHO9H2hmR(ncHbB$!nG
zKrkufKWJ2WgPH`d>oI+Z^xeH%)W<qc8CA<=DHg;Q_S1JI|A@x(1yeH$-C;73sH!u$
z+1SKcNb?5@<&%4T@Xef6e8)TDq4xkka-dgxm<(C)5xfYq-5<dNEgPD<ban3xs{pwN
zgXAh|k~LQ_hz|&;=FK0SrR2668hfGm@0>FDI8PMrAjR_ad_(^PQV_~>E8dbc&R}t!
zn>tEP{u!?5okk*#tQagQw<>@66CGrCf&1@V6U|;>L>KpJ`ON$B?X&ck9oo(R*PMiI
z!X*b`BASh)(b>%p9K{9Za+6ZLmEeI4`Dr7?W31&L?To~@`n0h*4~TLxOraq14e0c|
z97h0G+n18gvhA@*=LF8~1Q*uU@2%d8XO}jf>_?HAfcs4TQjG@_q>&1LRMCD6wu=Eh
z?k3C<#F30-F}hiiMozz;p_mDz5>E~4>U&elkz{Du+#@ton-`=iW9kC;J$wKA#tn;O
zZEpKg5WK9lTlDlxyidi{pek`j5arsfDImOq@=#811Wf=K1b%i9t~m`9C%`{gy0tiP
z5{kOKA$j5CMs~+bn<olQRP;qQusrxl?j<3<Jr?fVhp+M*L_|qK-hKH-gzi|vaNX_H
z^IGMqgc{1R#B+t%Gkn9}1r=GPKxK3dhL!$s?RduvXhu{o^>%aL5Y}xpat*S~OARt%
zaR{jN@Go?bb4!VqaB81i+iV8;Eco(NXR-5mbk`KK2X_`HEfBdyX-w?{{q!H>aGhg=
z9BaRY#%Jym<R9t&bxi@XG=>eanrdcG791Y9tb<|E>_NpKe0^(t;M6n^Xo@DnRTY(5
zhii`RsQelb+P2yie6o4PX5IOm*I$Bduf?hcAZrpKQ6nzaI+Eix1Gv~yUhVRk68rSY
z=Z71bXsjuNPTtLA8-0y5a;u&_$}rVF3HJB+C0u}){4JHsN2*y;o*Ocj^v91Qp{1K{
zk6bKJzj&$c-Yfi@dL<c>wW8yBoqd|~m<Z-!4z<ECa_N^SDe?aGieGpn?Ho^@i{V6l
z4>o@NEQ@C<4)956`oRH|>GUbqg}pe?#rxj%%nQpT`UlCj3qii;Kmd%r!36_O#S_sz
z<5asP-oDoo>)SiH&(yr=s*?Tjpo8`P1H1kU6i`=^;+K*g%L7Ti8)+~neM#bx_{bP%
z`o7z}ugVBe)u>|uAWm;P!>*l0S_hD0Iz6D)|KmPWer}q<c1wvM{Fcfhh~%#XJMP6d
zx}#ShE<>tIUN$2KhawcBFpOhvYo+`mT5ka~*!R5tSetsU8d+C?eeleB`hRRiJ4gr)
zhJ|U2CYyO0nJqRgjlIC^gjJd{*xjNl-Cr)R><}@Nn$<La+p%T+SxPn+GS4L|qLSC=
zs)8{=#9<02P8r?x`xvi@?H)bxILH<DSh;Ju@XhWmgCqeV^B=Q<m!7GwXaE=jd0l8c
zXb8e-03e8?T}UbQ!J0>^7QXDmTn`ytYr^KKn_Dlx(j0$BuoNoDahG$vhYw&}y%A?_
z@_uPAQ&ND$@`r8Z?ULc|n*J0HU||N0zu=x`UbMozTYG~D_21X)=8aj9!cq{CF&%bt
z6IEFfL2K<cuirXv-<i)Mx53SLBHwnJGs<Uwy+*eaz-MuE3-K5Xq|*+#PSw&+S_Pfu
z0640l>e5<K#{cMBZ+3e`8@xaFTbvSG-$~lswJYI=Ve!|U(XmG<m2eH<6`M+<=`w^H
z6sp*$qCG{diAKyk=U1nlW&9GaZohiGX;pnx!}QiEK*Fr&>9SP(i%O~`@srF7gQ)ac
z$a0(B+=ggvUqK<m@sx-VgKUfD7Lpn{W=l`(&>Qv9>GnA!lvp^7Z+KQj)kr-1%S$}Z
zHq&U^Y<zGP!R!nZrwU{FxDuPYbtDNo<NcS_@2HBxdqN#qsKCDMl&0vyV$;A43f2Qp
zY%rof(!SxhaVdD7gc}S!C)8~deOMPIKXc8+OVsybh>eZCJ9>Oz<>cgSI*l*wIur7H
zgh43$1&^9y<yf2Jrkf94eO3@DRt6YLH_RcI=E*53YcX;xo^{JHn=Rk+K>})AD{<KS
z+-DvLj`!9rpz4AxV2<Ga9eL|aGI$>+_*==$lw-@v5ouWCJ=Fn#S1STIVL$NA<)_4D
z{Z5YZtXAISytM*-QiBN(F?u`9d*mmKr_4+oU-t7sB>R*!#WY%;xGH$l?_CK7)#jUd
zSYwEwNkbosV!V_&$;=vh<$lyjv4W_A^QRXXI3U9HJF9o(WI(t@l+tk9BS<5T9Cy2h
z$TIDF`o4gt^y=#N&3~ttTpY!C2r)AS^2KxccBz+S`ASN=c&Y&kxvb3G<cE4<hG0>c
zefosp4+Cx6P1nQrW=2Nv%P`Ng)a9Xk(R)j~rjwrP>E`oX(28y4c)gjpoWBhk8H0m^
zEo$c{_bx*AeYFmQ*+&iNyHXhDO4I4L3u{@GQFUbt6X>LIO~3lZoW(5~2Zfz1CO;7P
zTqwQWW)i(Us=IWMXB`1Og@JOu+A6^u!P<YzjjKAAy5SvDo+ukj*1uVU@>syTkCS=>
zx>`?`OB(M*ceWk)3D;!9z6D`d;jJ`cnl5MQpL&c(83}~tX<a(x8h+b~K;n-@X5DsP
zl4Qcd1NDs~vK=<sW;G<hUPX<e%}f1;&25RSner`1g826iEkixeFNe6Te+w%$=?){<
z|6Md8+Oo$-5iv%8SdaQgz3yc5jC$G!IF|Yi9R0_e>9fTnzr<eAhZ9}bKkB+ZC*6Dc
z?)@~0$346)C*((|)TlBsMZOC=F65_`I)_sKhTIMB^P>m0gvkRKEhQ!K4=m&-X>XTX
z+n3Mk*Va$zL$LO<5~Df)(2s?OlP>eAxvYIE_|!t;yfj7IEa7-&Ls*RQ`kTNi*`R%v
z&7pt}lkA<@^#M~if>Hod4!s#7cQDs9kuj(@@Am82TE&U_2J%=F<Mx&Jf{*^0B@P}E
zm5|+8CM?wig~$n>gxL&@B)e`<H4Z?OW_XAoxjT&GJSRuDgrj_unkOQX?Sqr0G=JrR
zNBT^q2J2JuWM&0f6>el?qy!hzHrHEOS-E_c0ywDgB;#arjSp{2+?&JQTh7v#^VM{w
zvbfL9vUs5<MS4*qTf+L!+4^6MtqT?+j1hS)-;=iUJ|c<k8NS@lyfVT(@Z;x1?AI)2
zvtO!ICZhI^(rqry`lD2D+tTa6-}agfVdPM^UoxCR7dBLyd2Uwnr_rcIj$8SBf|Mf6
z<yjPcS7w}~H34D^-K?;_F*M!%bOZ6o+-4^guBr*uEB*k5^4D|n1A3YP**M=sh2eKO
zmpHC>1GIb1=&A8=%dR_ho%=F{Oe`BHqH7I^s1wEQb6e*<QnNMQPt_yrUD;A_4_o#R
zw2)(D$gyx~{q<HV|7M&Tc^)p=pw7}M0ZZe4MRl}bI%X=by<gYJ8KT?*XhT+aicTc$
z-x97Gr)x^>O>YPB5TXJk>iVA(a;U9Wn7=uL+IACp%VCq#l^=w|0NwCJv50t9uP$pA
zelB2eQVVuk0XZ!Jm4Wun&~E*KN_P*TgVa(3_k`qKOCO_N`<EUpXA`zx@FLX3U3h!q
z5vG~VP&ji#ImNr(J@2-VB1f}ow$+3#f?*}L<VW-8J|cVi^3|$R2qjd?$&?>o32l=z
zrdl7}+f{y_E5wQCT@w(Qk5~=f;o*h4fNZ&Zk=Ki!<WJCdiJ<2Skk?nfdGcGy5rwRt
zt(4x+m7Yuh3InkD;Lgs4d~XFsMW>5DA4_i+z}MU~;N{zJmNrl;osQPIM+DaAHA>Vi
z_n^4{Ko%#)*lDx=O)~*$p3c}D&)&f6Zfpms<ub{-{pC|*ru{qiZz42?##9?^PMN~t
z!cHI@Wio*Gg8XOFqVt!WjrKFa$C04rCaXpM7T<P*iZ!LN_or_Xc{P||xX(tFoY8-1
zRQixnq6&3xD7S$gpo7{6GODvo<R5yXVfXpX88i^wp{o8kN{8;<v*(9aG-pWzTZ?=g
zXVbqDWd0%P|Gd>u+ME0-kIZo@kMbX}lit?<I|o)k`u$VFp2C5g8^OWGCZfE&JY++k
zP}c$eUz2=yC$zr44yWtbjX$~Pswj9+Rc$SDmJBWOUc~>X+YSuT@wc>gCsAT1!ZoFB
znBT!az&mlS<(W2H)c#I8_~Jz@hE<9gTmRcB5bzjO->pYi&kUlg;iySsQzx8!7aeoN
zro~zgsh3`nMGEV1eOAv?hx3!koFwGBe$&0!`*O9Ib!Fx7Bf?7}amMivh|yLn4t2R&
z`!wj|q|oo6D~-Xy;Tb4{IMGIBSY6quoSAA#L7a!g?ERzk>9=;)Gmu1-yT@BT0rBDN
zKi^{Lri*j{d*)=v`s0{FeBSotad0iw;>D8hy#%24qiHSs$q)F00iW8=R9jDr))PAM
zmyU#M5+&tx@Za1t#U$nPSN*zI+DiU}K2(Y|Jf|u%x-Ik5dl>ak&>oJl9>2}bYAxMN
z)t-j8UWHrZH$55NrrX7%Ed_foT+uU5(N4ma<L4rGt5|0{N{}0DAM<Fj(;N#JEtXA|
zS>4*i!WL)`iqUl(Y?mAnxXVoAf)Q_NjBK|``9&$l8F$b!!^g{4PO@@%xksu00=?5A
z;^6uepz)}aY0s%8mqNC9*G0|6{VWzaTU{PRm49QD`ugPjgB%<$rjf*Vi<vyWar0<@
zG_s!k`{+Kpb!0!8nM4bPnknG(*Uz8n>jG4(tN+pSsj8`|!z(28UfeC!_O7ml)p+RG
zR@bOEW=&h^v5q>~GH!m=c0|l!BEWkY@a#Q;7G3iF-2>mtq;GJz!SWqVnbFeZu8OqF
z*QD^CFz=RS=yX>^cNk4fL=>d&!#0ae_s3DZuN+l2Ts>XeC{ngpqIUaf{Y+RM7FN;a
zrtX44B7~-5gsYk{J<1!S#RmI!`+>hh5AAZPj8^te^92*+$FalL9cLZgI0OGW&bjWV
zhQ5y?`UZcT;eEIoMt7N6?6q5vk8li-4x8;9*S?!zI05q-xQo?fAdcb6N5pJ6r5o1z
zct4U!%>L=w<8ABXU~%ozfH;<L7q}=MAOyc`a(~}$u3`*wTR}mc#`6TDw`@=`i!)h8
zZy3A}_THEuH31z?xblYc1bMhIT)6~7=7a)$f}yk=QERUlwcVQ2+=;+JmGpWNt^E6<
z)xFipGw;kZ14+GWE=8y9qt?jgN+)r&=E-V_V~`A-)}Aw-u|>MD#U?G+XnCpA#lPhg
zL&lSE$d3;hvtaDw*Tdsqt@@2HHC3f;e)dU~piJ?(<LtqgpQvd(u~|Va<l3!7D~thc
zhG^)4l+UM9T2@&M2C{WShJOer{GeWG-xl#$kBIWc#qK9VY``+CDTr2Py%?R#o$eSu
zo?=}^EUcEBQSaJaoj#VbL5HtJtnHMtl}k#vBnrm({ncj%J@CCyJL)F<)RcDPXC?Zx
zlM&?kYr9S|hR&lJtTkBtqoQ2b>*c+T4a#eeG`BY=_mxiBa`#0ub9L^v8DeVQaa))4
z@sWddKTi1X%usLxhiM2$ea8QwQ>e}nZ(!QYj=zdBt|X3ozL>_v=Rdg0cTU`^$hz_I
z^WeN^K?%*!IK~B+y99%<VRmmx0;XG)1d3N$L?H^f%l>@ABIR#61bGUbpWOa$oOCCa
zml0i%l##%NdfL{MlW*ON^}KTJgzq*LQFx6$mC-^3Agpz1HD##{Q<z`x?Y{c5pf$6+
z`_6YT_AJZ<Cx1eWFJUD+*zHpowecWgTG?5&?#yvW+$zOzYrJjA_YdC9=!}lo=X{pY
zv=*4X4s$l>%;zfm?iB?hUC4qu{mudo0_gwPMCwi+Q|AtY*7-Bc&B(8X#LfdB6qzsG
zkm^gAb-wzryg8xvo_o_&%iP~(6oh{2UP%ru6EiI1KAWZ~iboQbCsA`6?I+Kc4NMcB
z8=gxcsM*KN{nFVmRrc=&Qlj~f^L7k->on%LiyRr@e+XEV5rPmg2J7|eWTE{$;S2Iq
zOqn+Z3l`RcgLhp-wTRa2hJ#Gnu0e3F++oM!xktj#X@{w3=C@i+4G-xs(JYTvD@3C|
z*WSZ`4G(nVplaCJ{>)<j+`HMfU17|-u3L8^dP0*TL!@XWt>3=&75Xxo81MR$LScGu
zeSbmBdI~yrCKWmUBV-5Oy_U`!{j~AF)2Id)XUvFQ`Prb3fpeW^z3f{3m&kI;wUZ~i
zsIO?@<%Y^U>1NK4K!uBgDoJCHMYohW<NaL}wRzIK*HAI7QSpq4sClDpTvL%e8$Xok
z8eUbAwdP84nwXCrmSfbm--*T~Vq`ke`e6e64((qoQ*3_MA}kz>>Ep;)SiDf%cP+0u
zSN-SJvT1jjH3NzEaK_fLSfjg+z$Y79^i`*szo9h!&T|6dKLW(Epo`a<d`a7IsfeG&
z+jP3V|J^Z(77r4~#_q>J^LN_+;-*uqHzZ-!V}12f|CXK02kwL|qWPJ>2K1#{&@RJy
zJ$MkYx>Z>=e@Bh*0hhZbW$#f9XWeM{yq)sIWV@!wTGNH7BC+4zuhKH(t8I8Tf1$+4
zyi02JAd%0WBY{yL<){_hxI>7w42`dlp=+oaLxLkJ`*zV4JP?7xzzI}IT$g=2^KZ-P
z72fVj^aI`$yjszbi*9K=B-frSNZXAkjm4MD3<{ae-7kN^d7C}-?auIGe_zOhqfaJ$
zp;PHMHZ>GMh}HR(&hVmgD#|F4V*l2A!Bv=W=EzM|&x#I5Nt4b3Zg`bwOcS9NzLt9-
zP;_ea`Yg|AJ)YA~+Id0Dh-qwA`KGOgG4i0m`eFf-1j7~^&8@X}GCG5jXj#E%Nsue!
z64y$H42T@8Mo&h07v`{A_Bl?0f>FdSCyV(V*huJ3n;8*TRS>G=TEssWtL}nfU9mH1
z<oh()Su1^J_Yx8kQC|>#m_%8=n`g0Dl<DE|v@yUeee9YOLSS=xC4ZmCO;z*JeH$Sp
zn|4+6bNh2o|1i;g!-n!SR1!!$htI9&W0d=lr#zpbTWJB+Y<+un!=!Z9boVK9B!l$@
z>33RM`v+!I@1ovb+YvDv5>65Wee&#6d4W%!Q|s#<K#Yoedv71`4|HI>O*3*rmKB0{
zx%5z(^6BowOM1Z+st<_*GvrvR>?Y5iKQGAl3=It>Wq$s)Kf(efrADlY%GtQY13_b`
z@?rKPg84F9XP%qiJlBp5+yRVlW|1K4ll|N9BVUQVD&jBmmkxWqwfX3auSkbu*kKNr
z-#D6bc;XJ{+;Vd}1@wzYw|m&KaxU#Z_+CAW@(86yV*Jdh$!<Ywd@6VHsd#zNdCmgu
z0OGT7UB7y7Jc~{e7)U-xg?j1Ib=*jLrHffpJ8x;nnw|5@-%<Rmnny`Of<qBrIZBZO
zp1FE-$>9o?S9t28dA;ngw&`#)t>(4psuxWdP6x~A_-^uEjGU+Qbut&f<K7W}^MyS_
zX9@p0yqouL2dOm%^W8&h`o%v1(Mf@k)<oA(1B91o5hk`5HX1dF<D&#d@2P|HQ+s=n
zw`xnG3lYib{?LfHReiBUqqF?+MWAVjo@FC#EU=34BiLuu=WlnKqiC&*4LA~#Pqf`N
zeeZwg>y*kVePPT*f@lrQH(ZX)*P5~7DB*nVDzJ}KCR!X}rdUiqb#4sSA4({n*2#D0
zawR>LUqmFk3a!sQHhDELLWx&oX1+;dSgAHk(9Wgs^MtxlyOSY0x-nyJY;k?pzc);f
zFI2b&=ks@J{F7%By>ZGL?iAgv^4QqlGZ^4xZ*bW6sG}X6&3fc|j1C^#PM5U=gTtl8
z#Bwj1Syqpfo6n@;ZwhSD9UpoqR7dap2B6HTr;O<K?B6xlj?H(;Ew;50j_y*vve%Z=
z#I>n?>&=U`Uo^m?E%*2=)A^N1jr?^2m$yhYB8mrT9FmZYJ^Cs`UuRV#syE|%zIzXa
z*oMoO*>Zt%S&VPq^u{6l$f27w6h0LuDAt2RibmFfnvap!1=|^1&FB9xqA@SI5woh#
ze`Gu&l+Q2cmfvTqbY}QzdpuIQdLKm8h8jR<LEre5Oo5D{GU~Qt;}-{HTPj+d9lc1e
zn~a^u_3DHWtKHZgC#uW!RY+*)Iq~zi{2b+F+<Sz!gF_yR)o%P}NJM9rdr6h#E_&j~
zhH_u{52~%p={nM<V=@qI4EzW(JBM?cQzbDy^9}<<yfBtLQ0ClqN_%}?L5VAqla9~p
z)ULi6RfPZScteX9ZFErb%k`DCw|u^1Xz2S)Rk%2DPKCl^xp@|jsW}?rkZazz>jZnQ
zyueP6yJF~F_d-6+gq*1^olT*-JFn&JJR_U3Kg!^7C(S7yLGz<H5+<gG(5E>Ien>-m
zi~TfID3RM9HI@N;Zrk}gwSNcEpof!%zOkJuG7$0s65qMyzx!e(6C(7hdT@1LM|`@>
znsGv7IqkH$XH88@;EpGl;5ypExmWDrBm(~dT~PjtBh3q+PnGu*sbkXqKc%B+5M-{h
zDNE8fZ(^8%Jt#Bj5a_U7rW(yQO)0ZPea2Xn-VC?i`fObUnZ-a`oERysVkfbVmsPR;
z;c^E{#hQTPdMj#VF6NekU#S7<oh#dpHvOw)4L#wC#^PiNB{t6Wu+UHwf3WMj7GJhf
zhHBidZ<UPZ1(o?oMPSy}1UAbb1$G}$U)XK5@z-O844Eu`V=TJxfQNoB?G2Y+X{O6E
zqS6(bmQ6T>)4j^v`7=p{c$d4vD!P$J0HoVS84gcQ$V&JyvdYA)2~nr9t5aj9u*IJ$
zGi8rsD%@bX)y#qQ%o_4@XegU)@xr6}sC^GhQy3lI>SG7Eo^<*TZa&n?cztMaA?eMV
zH^ez`^rIT~>DAV>oSgaqT#Z~$JuLa{%$!5YHf{^1M;=)sQUc&}E)2OB(CvlMDDGyx
z4#=LFFQ(NWo6i*YF0_ax69cWZL8JmVWLl27cekWT?`%9(8uI)xM#DPMPEva{Oiry7
zA3c!uZfZ^<Cd$|PW#T<)hl0Nm87uGgo<?Ny3Pah{P42Cxj)VmD3p_Yy6%JHe!QvXa
zWUGS;=nr;fzpvGMuDc4-lXIJ|2O;L^@IF71TNhdbsP+2!ihRb=Ab|IAw(r!jnWT`h
z(fWus`fezK?#-KVWbiD7x^C^;gisTUCR#>ag@&oQF>9jZg4?#SaI>D_`PJD%)zZk8
zmw6Dn`|=paJ%}x)wZ`GXe3l!2L@S4Qr3=!P>}xj7R3QlolQu9x{II$|bGgP)6%6A;
zL3OH(w%QAPAx83c6keTT<B^tbFSUkMDA#JKd;3(aYANKJ-<r<WJrnXPlH!77rTRAO
z#wW=k*R}W#Z;uf-O~0@7St9Sh!g9|3yiIqsr56oyYu4>$2{1uk!^`3e9e%*z6f@oQ
z%HQL`fizomwo%M_IZ(1TPhap%if=Pq*w0VTI!SX};A`=D+Z1cLo8|*wk#D<pAdy{_
z7lxbV)`E5LV#>)`qKTOhkQ^ozIH8Pgswv|?=v8jBxL+%r<Pc3MSl6oN2+QfHs4?gs
zi<0`Ij5zs+4c@6VRNt=-OcKj9lC;$Pn7?H3ey5`0v+!PrPbv!<Id4RUT-(epp(Y*2
zNk)OA`Ea~MwO6J+BA*;vf4tovvZCzpcLNg}oA?S3E?PPdHOs3I)n^I*I~*J-x{QR_
z2WKSh#w1#s-!D|^cZhrT@2KFIgVwJs_Ay+pkS69P+rjLEI`yQdLXI-SS1xYVO7+e=
z$)mr_!>(;ti2`4ovXzNYLoIucB2{ZHx1U|^`}*v&23Fb!{^s=ZZY;1-w2qb57c=X@
z#m3$NLJ-Y6=2n>HTqJBPUK6}NSsfe}RTpP8e55RR=REmQQz0GW$LMxm3Aem8pWk0+
zr+HZ0k!Ye9*rmGjcJZ39S=e=uh`agKd@r%J@%wcrdGw!jKIE|EX>IFsjx6Wc5aIv!
z5EsqAH{4`A<>bYmSU?&!pV&E78=B!HTyBD+^CJ`@?|Jp(?S|Wo#qN6?1=3G7Cy^nB
zi+7XO23L#N>GdQE)L}HSbz~avt9rF%_z>Z~_=;a!?Az4L1v{Kx_1C3OQO!}!F^%Ke
zp1#EXzMh7#a`JkiskT~<OMIQXJ+<`<mH|?m4hMXGamsrknT)fQ*|PTwbKh1g+&RN*
zy>Xj7^meNqGeZiNh1Q&O-AEKuBQ3Z(`cgzy$~Pfb+c}S~@kpPy)r_B6Mt6mE;c!(+
z0$jImg{ihou;W+xUI{&wdwrX6XYso$&>4td5LVtMgo+Qk{k0ex^2kFaA?!?TEqh0U
z6md)Hsh+Q*O=9G2i~@zXK8BN*e<h9`OmKtZfq8XcLc1Eh{6%k1Rdl`ZteAGp5?Id&
zc^jj?-<i1R({Rd7>+0{?Rg#h>xm@9Bk8G`TxJmPC3J~)bd-`xhP2rUW%)W<pqBUX#
z9RA?m%U2)=`Wq1s4*B<}U14>2a^H{#v*5~+Xu!ddc4@d&NB#2Waa?x)T~%ZSG8H#2
z*PQ(hTXe}|@3aqzH<wB&ww{RkErPCh&PV>`i8%%zt1#Bn4LC3FML!whI6j-@+O<ky
z9HGIH1BhSDS1w5mo>!zcu&0JdW`f!>Ws=Kn#c>A3Bh3TtbY^VP3_lh3G$e;iR#HL&
ziQ{=&04;g>$WdgCnVmc*Dx@AaMWjZtpU;*c;!$-VxlvMt5H|$h?Q+$6w~V|PlleUN
za@BjSV$v_LMfGa@sF-SE{NjYe<DPDG$sb-qO24mRptOu??adTHyJaOjZ6Jajz%DzX
zG?rmq^rsDua~J?E&Wz#o961^qk~V2|3!;3+pWC2(^M+p<-r)zELSe7od`U!=n~c#+
z(biyLW{|U8?OcXScHj0-hq}VsVf;rAq$NMD3uKYA*B|8n`s!sNS=jrpyyV+<TM$=V
z|9~rXaDVrnsoi`2)k$MI5m6Ve3{GBHr=1}Yl3!wALKG3%7`1+4vvZ|vmqYsxm8r2O
zA>n@y@9;V_xJawjL)xq-+_mj%Z`Kc-9>1Oj4OiL^8ICi<Sixdeg^5$?$04k_EtXgO
z$Nfk7t=~K3wc&Z$X1shm%;9iF939QYw-H5KvzTRtEZq(<-DYfOFakX~x;EJy<}l{6
zX-`aW%5cL{yP7unWP3j|ievnI+Je{5+a8#b{S&mU37FJsTCIgOY!~4bDI)l=7BuAI
zs}NExJ09=pY_wy%?&XxUINdDKCtgLx&NPR$HDVbZ%|tqO3l-vr8wKLB9-WxO4ltK$
z1^J<tRH9Hxah;@3jp3RdU3Q!fGd5jhB0Sw^#3~x{?OARJ;h~`$aA4$rUVZLiPPRT6
zA2T^PT&$b%I$#7#zDV&fUT#gslosn{yT;AtsG;4tk19Fal>Pk<1FEcdD{W);`HOJ$
z=;g&T=`^>QOss#AB|~E@@^&LQj9g#dbScndTv@<0%Ggt+Tb8@)D8<mLJ%C;(&4-&Y
zkQ7fwh2)`9umtl$T43^qh1D<e4H6&1i2M13Ak}}IkKcY|MMxAa&bs{a(?<Uu$ymfA
z;;jiC97cEk+2OZB2bVZbc06Ghs7Mj&;I68{z@bU$VEgDhERwxgmz8`5KJ?U`+=i|}
zFsN)`M%6AJHa0&w104BLN=-*0OlxoNtCaSErmWI?NE((w@JGKSFWVlN;YL+e2nVhs
zjvS;bC#4nsr7M1g>AuY?IcUzy%3C*P;lFn76~jA5cUQQ-=$R>#w!03>R90NAwg8|o
zVSV1V{@wD>0Q!*+y~n_E_%ibGldN?j6^>oNV++%Twj;OCW%+sUTp>5wNfZH`8%F&%
z+w2zE4$FhSgBra-BbP(Ps4mn-ImfL;wH0TEV}Qxd-@+ezsI|5WxWk(4byl$qPxqH1
zFGflY|4bB2i`>%3MJPauY6J=i4fWwcEJ9t~z!-Kchl-!>GO82(*JhOk-QC|%i$*NO
z4fYi{j8Q-dznOw(+-grDEb48x7eyn49xQ4qP;@%$@wh}>bEUx^$0-<-{b7V1jjh4I
z864V$>-KqKGCsXpFx_xHqY4qysbfs-1hGzL*;byXoy>*v5SqD+a*fa_tWN&CduBAC
zuoG>Zay(9^Tq$&M^DzV2K;~xsU>3dfo@n>Mve~XM&?Cve*Ln1CaRkRpK&MX7Q&Quq
za<8@=U~_?U-tD(j{|cdnZC;xJlKHX~x2xjx<>v7?fF}y~v=O&HH^$vmWv4P%y%a^2
zU(@yZ)=3;Xat&uLGnn@UPb-5Ooji&rWW6Rjq<}{cbZr$Dq0TC_tX(EEaH3S9wOURH
zLCF{X4RRXu{%N~9tF{=UUL-~5p!<<MKIfec-`-ZB*~_P;3nbF$!-&g=3;<6%)I}AJ
z)FKA8Y_=Fe4%4!@YD>vwJm~I;a6K!=phCWxglI{3TqM}P?CM-$uKBzi>g?^aM+qf3
z9M}xGlXdtVof#V1HwK<TALFQbFJ$BQoaM2;I(ur%|LjN+Qgpd^e9IE;7LtaP*|MCX
zARl7+?m+ez-*8I2^YzNl`YEPGY@wgRPw%v3SUYun{dJ;H4ouD~2afOMq<tBW+WwM7
z^TV+{+vP+v$zC<G;eDoRsoqJ~LjwDbMPXp8D2h&Ed;Oz5L?KWLR#H=Pibl!z;Rzy2
zaL7X{FU`v_HAS$d4s8$CFx!$J5i;xe?#o|^e0kg%$2yK<ZBORS5uFMs*84;@C2x{L
zbMNT&$z3N^NT_`%k^$(=bUe7*!f5@#RzKu9H?7h_R{sKCt;$b3?`aL&Bd4_65q)hb
zJx(V|t)XTf2Y$c2m%a3OFh}aizDj||ilHATer_eTu@w3G<SJ0*!Oc)4(=t7#<3HS)
zI$YW+{|S#Irf5bAKay$YIbgZ!l58&AyAL|<fnL_SCHn}cd1&42AvLa_i;2<jHCkgZ
zgBME90S_TY@;SXd9x-M+**({3ln_6@qA8gEj6bC<+E@v5#gDEam&6hroLk*vJ-FAr
zC2}j|S^kkt@wBW_p$LW$b!F3HnB4RLozb;D0CSgYahYikn%^~Ms_h`gI~B1jXRS4#
z4QV~limLsjai6Rxwe1s{N+3HPiZ7U!QVe-<DAQ_LVcD^jTeffh(l!wKIN~Mr0;oK<
z=_Lj7X3ao)@T$_bZN*i~6{aty_1wv*q#|CkvPSRjS{*jS^nZ?74BTpz>JRg8rv>(|
zXH`_hP~88OMvlAyjPIOo`<g=JSag{_9IVafss@WvTS<A<Bc;|?ji*>_>r3aI20H20
z*Czd~;?h*lki?s)#JgXuFb2io;yr*VR#>zR!<87cW|(mF8%GQG{8Gu*Y;<F;G>6jS
zp*cR@!w^Tu$2&j~OkV^sr^or2v{t1V#<adS^YiD=bs_f`a7;Qi5salxG4;;dr^!&C
z;QUO#lw?lx3a3rko=B3!VI3hSL5<GL2kwixjajx;#+~Rtde(`k1_5fVpN411nDe12
z7tXo$87faQIKrj??w7)r-UGRQ_GqC~6p-pgTKO(bzIxbrWVSt$UZ<<!QGYffs8wbN
zzc*cqyb^A3_a;j@R5F~vHOhG_*_^|u;~BZ{`54vBLP7$;>Dig3ogFRFkOQ=#M!=$A
z=fOLSNXYxo0|C3?L05Y9FL(Gs`Hr9ShQ0snxz6(O!E}`Qgzn25IQFNQeztBLAK?QO
zj@WyCbOisYO&H-&(b<_(Qc^O#blK-jVMG|VFmjCS0B+hSH|j)sMM5ID%xT;c5$DL#
z@b}G<&)IMbLqjdk9kJhS|KzdzDx_`yaqoUV#VC*xw^*y8(mrU=FCqEK<%lUqg8&h@
zAM*Epw(timo9ESPh#<uy!u~>iEH;%Y32tJ1ysKYnS=qOAL3cNO!DC@T_rv{@$~WIp
zzQ4~obCr`rQflQ(bHKfW4NY3-(zTh2BBu)9pICj=5{Ak4XqNUuP<wp-;XKgXX8~Sr
z)93%>o1<iT2o6vorxOJ`_LcfZ+rtV93Rc10PFr6YOAIgfXN4ou_?@>$XhcO@&k8Fl
zPLm+CaP|+UE61>UgU%u8aV*GpjXm8}$=bHU$LIZZu8W65@>gf^!0G%~xNeI5I1xQ=
z4{Du3)|%o}m`XZo|89G3PCHL2b7ZnW?HaK4$Dg*Go;tZ5%*8v|F7KV5o{G1aFT)z0
z!7*^0x2<(;v9q+~uc35+N*{M{7fdUrp_(tpJZK`<xb2jY2dcTnRA-DP1S}&xWoWEo
zK@6!6Zqy#ZP(}uohDN$ZqbM&gcxNJ?({W8=YdAHec(A@)El>VQoM;KcS6#Fff(cpm
zhmzQh>6w`JeV{Qka&?;tIsy>8leKR7WRBcksB7b5-QpqFQ4yj1#N0;t!NKI-$7YnJ
zwewzUpim$2Y7X_5HI<)l`@BHPbULqo#bX^CQc8WFzzKFV2LFBw`}q6sf&!ztD$5V*
z>Q5(1&A<RUk}jw=b?AFNXwb9i<hbZMU2eB1kl}l&Meg-CD{i(i)xIUU^=^|@=(r8B
z2%OF%-y?4(gO;>Zy9Nf5+W6i3=LM@r`#P&Pxy>&iz-XOS79E>Aw^>#KZPYJ+<&0e1
z+x(y!BnlKMd=A)T@`CPg!b-<=DIU9pK9EvWYpE^GR`Bx^VQszRaNZg+TI&i!f5jCF
zXv6a>kzcog&GEe1$-h2yZyf}cCS+34<inqzzqpacYWr!0f*GXQ_-Az15w9}pxjNfl
zT#d|D`D(Xveo6?+20r<{CYbs7c2u~C|7=XPh<^x9bX&^yY7X@Cpt;_QTK{=_;Syo(
zC9#)9e-}rL!5jL$^@T%TLH|Me?!f!_2642BY!23@<*kHzVzR<pO9d6c@-X-XK-+hC
zIe>v)S_ajU2Vi(ZIW7sry9<(u=gR_Gb@%R`^feWlKVmXav>y#;J8yLw(;SG?9EI!d
zq(xf<Ez+00xvXCT^_;V;0!P}XA^O7pNVvA{$H3f%N=Cq9Gf#f?2vo=A>Iok8`DerN
zeT!@vMGK(QMauW<7WjN%;Tc|RyinCI4gyAXx!~jdS#)5Ia7%0Sy~8hS!cIu<XE38(
zV?qQGqobaNF;IgeMOnl4IFix4KxV^L7J3z&Y3h4q(V1z42uZ%keewa{@QcBhwF;J1
zzi2C~G#vdq6mfv2S*-W(DAf7%aF^nFmg5!q<gh~6>g+k;-p^X1kp^O^H1Bf?Lubvs
zyZ?TXjsfnH)0Nq1%Ff>GF0+)T)wS8xwl9b;f9b7}5fXxq{&)O`{hM4ukV8_Jg};BV
zCzq5<vTJ!58Y6o1>U8Qc0wcS%onZU@o!}>V7fFWG3#a_jPUDMnXJihYDu8*=`Oy6D
zK~k%z*fOi0YT)=21?3}u5d#q_k6<AC?AZT|c~~C)VJpHQ4{&<&^Rcnt!j~<B^tsB!
zq@rb8U^(lrgM)5!L*>8DYSXXsxJl`f+`qKiu(bY}>rM{fVFzpBw4e;PPY3dmV-h&g
zmA6Pkb|H~@n0vw@rp|AFAoz3>m#98%GV;^Q%o}x@)?!D|9M@w#n1b{TpY4=4CPV4V
z&*IfjJ0#)dlEg*no7=W$@;)H3VTvu!GS3IvmBWsg#kE4YvO_sDyQusDBd8Pq$p~(I
zi^ID??PMe3rG782^M$!l{DZ(cpF%8%Mi|q7US7{>n4BIK!TdLpiJW@c^ptVQKrj?l
zeRa-eE01bztv%CR80O$jlt0`v`i|mHZcCs}Tiu<8HKDn_8`=|^ftP_V%VfatN+Nfd
z&?Ti=!s`WZf6S+94nn-o7~6R1tzDnQt}PdC28%dYEMWTiiCv0_H(HSv04dNt9Po(s
zKoGewa@T5y#%3PKg10vDSA*M!p&!S;H7w(K$^yNY0s>>MNP+j^Uqu*d5(nVRCPUd=
zBls}XBq@X8@?(@8kN>|vfe`Q0pG2v^fs=*Z&yMB~cHj-a6udHcG<7dM9C)A54Wj37
z!KdFzv*DgFN{lfc9FS}Yr-=Vof4tu31`m#93S3oHl}SCA3pT&%?ZeU%4CcY23MYhC
zTwDzIK|{mr-;ZQIT3a(-UtiC%P%)x&Caxc;$kN76PEFyUJYz$rn%{J>vb20cK(_NW
z*yu==dWx9?o8HKa5SC@|Ex2)czkS*)ess9ZrJ${&Q}F9oo7v#-ABPlpAvB)U$Sm;?
zv&IPW=Yj2MchT@0m(78PqKP12c0}fM``~=NM0tM;{gd44>%@8a@ATRJ&*^)RIyEjX
zcpo(bTVRtozU_3qBnHbIeEP<$_kFDQIr?#X#B7TICI=QX1^$1(sBP;D;5q^+b#;95
z=#ch6<g+J3I5#(EllX_O#%8v|by}}C6q~AVa4@@}Aq9+@Xu*|oFn-g8<#&vjr<neA
zdN23KkJs~?q|U8Z-K<0+K78OooFjaJ;HoRSd76tb0MR0!ifq4rMMLj2Z2$axmZbnX
zlrES!p(L9A_Vrs!O9oE!5tGhfj9HdKsL*L2RhrWPLpsK{eEH;#OmJDDO=3JgY|)(}
z*vMg!xz?mH4qhDon2*=@#s$>0@blQrw1u;_^yMj}UUd+*d^@PP>AJbtlc9|jx|}gC
z(y5Kgd^pb+y7`;!vn{yX?`G-y=2fIqH~vS5^+;Z`#yI1{S@X=znt8kRCGR8C&4Ks`
z3ek*kmWI%Vh6bBNx1peSls(ZDP*%T_@Tb+*_q|Zx8cOcev2X5P>kbd={-o=%8uaB|
zmQCUmT`(rufG=#Z_bWc5W{VI1b|yq5(ZG)%Tu(U&#@$_XU*0V5C0~zIQ|xzRKn!Q#
zH(l)r$}=J|<9aph#-MeQO>y5x%Kk~BX7I1t+14=U+1Xj8UJpCB=c$SFNE&~7DQ?&o
zEfp0bV6%C{)h~qG(nKMCHG_RA4GjXgYzE-UHJij3?x3KcEqlMSRGY*xb0B%cW7*QF
z;e@O_P{AXiv#nJ75jPpcU^6)9z6@b+4^Rx0B;fjwVm{~mritW#pmLT)oP+*VWW}d$
zG_#?kG0$PMN_OKO^d}t2q(SRjTwhQ8n(i@z4hJ>EbBp+0)=Pk6`rY__&A>bAI`z{q
zKpr;x=;5~f?IE}Gpr+_+c=&9CE8`17mLWG8t#V-d$^EX3T23PEO&am~e*_03;e<Ui
zH8o|J9vm2$sdqL{_#M-dAWsXf($V4DE2<>owbQK`+~KsIDr!H1t-CeWuLhGOi9$r^
zGCV%j4DRAWh0f3!KbDvrF4WJmn7#!*t^Z-unId{8-^MhM1;J%VFPZx>*x!@WL0041
z`RlpqmSBE=EPol;1H6@$Ro&HEc>UT}#w1~Hw}gL0y_)OYzw5G@kC0B4=nZm0g<=m>
zZzcgU)2z0_w{N}Y;a&2esx%v3yN4}9-L!faYHa80K#iy;Sj=M&p;;F5x8o`fh|0dc
z!r=BukqvLDDAKUOO@Y5Rmk09&MMWmF6{d)n>|zZt109{gD$5DJvms8;yOZAXS){|q
zqn+W>`}Jti5$4*3cWP=A>?JIMKga0xOA`t8JpzD3<EEd<ZB3w{puo?)ZcBu8whdLf
zhJAZs*RVrNz@pn{UE16OPHcsqqQ-C%dm!-K$i4T=Z4#a2nKjG&p?;T@GY6L=)?pF|
zoor>ZHWF{X#~t;zlg_fd9sdaUpJilZO8t0}<q=+nNpNVXX3B0qSX)^oiTDcO(kYrO
zHF+Wm+_jJ0&ry`>vJgJ~SAPsmdOKaRe1%Cr4tR&WeFD1PILj0tP(yC=aCgn5*TA*i
zNuk*aLq3HpM^8Q#=cjO6tCr|F4|7M52_`%V2^vHi1eN$MFX!#g03dd{oHbX|)cp5M
z*FtW2e-{=a@9!H3fUQd6v3&(p#rnhTQGQEH%bEA)=B8Frk-g1gL%gu}Wtr=q1`wt~
zjba4D$zLj0_YeC7iAJ@HZlGs%_|tffjC_VrGBSnFH)Z-R?rGiFgYl^Pni?K}MQ5&W
zvZF;l{rRnqhmWtKt{&e#5Y^1@ymfp&p;YOx`VJxB?RE<E?#>4|1{J!C<?g^OI4Mx_
z{rda&Kj4%-VN|UK7Y4}vo<XJF96KS(GfeHhdi!E;mST9}e`XiIF!he_{wW*=E<h@9
z-16oRctG*J-I7Bm#ugBZN<Hn&BwqUrRF~WM7oZ65&p~tR=>X#_%R!O{aKVPbC=;7b
z$+2`wJj-5t#WsnPbo$^{9Xm+6IS9!-O(WyM2b#*uI-h$H21H<rl*cA%$EkiOCI;v1
z^9vR>1HbD{ph>x{5M#-U|A$dP^cIVIGQl|t|9{!qeRTr^B~MR&QBcAuSjwzZgPu6p
z4P2QD&E(McW;;xV*xwPG&d243()eRd2NT%MhKOk8liD^D4MvpwPGfBnY2U>B3jp+n
zk~u?K?+@(dQ@Fyot*4HGSgg4<R_Y~$zVCU01TJG^W9@a0>oYaBB#atGD;u#&K(T>T
zY%VBxd!uJFR}}`_yO@4zof;dUoWKY9#Bagq64@%WN*P06P;rSk&Csz#u3iJRl2uj3
zHJ=+6YVrgp`rr@i)-m-xR-ZqdL_=0!xBN#<XMIpmZSYfamPJ8=+V~trQ^KS)S1$2%
zB@pWrjEsYhR6c7XbiEPV^l-EHWW#1zl)x`?A^rze{^!5{pFqq11LAq4aAi~3t)C(M
R3J1PqBo!q}-Wh)WKLA|(KK=jz

diff --git a/doc/theoretical_description_regression.rst b/doc/theoretical_description_regression.rst
index 09c55e74c..410e9f52e 100644
--- a/doc/theoretical_description_regression.rst
+++ b/doc/theoretical_description_regression.rst
@@ -100,7 +100,7 @@ Estimating the prediction intervals is carried out in three main steps:
   
 .. math:: \hat{\mu}(X_{n+1}) \pm ((1-\alpha) \textrm{ quantile of } |Y_1-\hat{\mu}_{-1}(X_1)|, ..., |Y_n-\hat{\mu}_{-n}(X_n)|)
 
-The resulting confidence interval can therefore be summarized as follows
+The resulting prediction interval can therefore be summarized as follows
 
 .. math:: \hat{C}_{n, \alpha}^{\rm jackknife}(X_{n+1}) = [ \hat{q}_{n, \alpha}^-\{\hat{\mu}(X_{n+1}) - R_i^{\rm LOO} \}, \hat{q}_{n, \alpha}^+\{\hat{\mu}(X_{n+1}) + R_i^{\rm LOO} \}] 
 
@@ -123,7 +123,7 @@ Unlike the standard jackknife method which estimates a prediction interval cente
 around the prediction of the model trained on the entire dataset, the so-called jackknife+ 
 method uses each leave-one-out prediction on the new test point to take the variability of the 
 regression function into account.
-The resulting confidence interval can therefore be summarized as follows
+The resulting prediction interval can therefore be summarized as follows
 
 .. math:: \hat{C}_{n, \alpha}^{\rm jackknife+}(X_{n+1}) = [ \hat{q}_{n, \alpha}^-\{\hat{\mu}_{-i}(X_{n+1}) - R_i^{\rm LOO} \}, \hat{q}_{n, \alpha}^+\{\hat{\mu}_{-i}(X_{n+1}) + R_i^{\rm LOO} \}]