TST: add validation checks on levels keyword from pd.concat

[GYHHAHA]

Pandas version checks

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• I have confirmed this bug exists on the latest version of pandas.

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Reproducible Example

>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
     A
x x  1
y y  1


```python
>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], levels=[["x", "y"]])
   A
x  1
y  1

Issue Description

First, we should raise when levels duplicated. Second, when levels specified and keys not given, then we should also raise since this is unreasonable. (DOC only mentions infering levels from keys but not infering keys from levels)

Expected Behavior

>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], keys=["x", "y"], levels=[["x", "y", "y"]])
ValueError: Level values not unique: ['x', 'y', 'y']

>>> df1 = pd.DataFrame({"A": [1]}, index=["x"])
>>> df2 = pd.DataFrame({"A": [1]}, index=["y"])
>>> pd.concat([df1, df2], levels=[["x", "y"]])
ValueError: levels supported only when keys not None

Installed Versions

1.4.1