




from sklearn.metrics import mean_absolute_error

import datasets


__SCREAMING_SNAKE_CASE     : List[str]      ="""\
@article{scikit-learn,
 title={Scikit-learn: Machine Learning in {P}ython},
 author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
 journal={Journal of Machine Learning Research},
 volume={12},
 pages={2825--2830},
 year={2011}
}
"""

__SCREAMING_SNAKE_CASE     : Union[str, Any]      ="""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


__SCREAMING_SNAKE_CASE     : Tuple      ="""
Args:
    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Estimated target values.
    references: array-like of shape (n_samples,) or (n_samples, n_outputs)
        Ground truth (correct) target values.
    sample_weight: array-like of shape (n_samples,), default=None
        Sample weights.
    multioutput: {\"raw_values\", \"uniform_average\"} or array-like of shape (n_outputs,), default=\"uniform_average\"
        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.

                 \"raw_values\" : Returns a full set of errors in case of multioutput input.

                 \"uniform_average\" : Errors of all outputs are averaged with uniform weight.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""






@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION      ,    _KWARGS_DESCRIPTION		)
class       A      (		datasets.Metric		):
     """simple docstring"""

     def 			__snake_case (     self	: List[str]      )					->							Dict:
           return datasets.MetricInfo(
               description=_DESCRIPTION						,	citation=_CITATION						,	inputs_description=_KWARGS_DESCRIPTION						,	features=datasets.Features(self._get_feature_types()      )						,	reference_urls=[
                   'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_absolute_error.html'
               ]						,	)




     def 			__snake_case (     self	: int      )					->							Any:
           if self.config_name == "multilist":
                 return {
                     "predictions": datasets.Sequence(datasets.Value('float'      )      ),
                     "references": datasets.Sequence(datasets.Value('float'      )      ),
                 }
           else:
                 return {
                     "predictions": datasets.Value('float'      ),
                     "references": datasets.Value('float'      ),
                 }




     def 			__snake_case (     self	: Union[str, Any]						,	A__	: Tuple						,	A__	: Tuple						,	A__	: str=None						,	A__	: str="uniform_average"      )					->							Optional[Any]:
           lowerCamelCase__   	=					mean_absolute_error(A__						,	A__						,	sample_weight=A__						,	multioutput=A__      )

           return {"mae": mae_score}





from string import ascii_uppercase

def       lowerCAmelCase_      (	lowerCamelCase:							str  ,					lowerCamelCase:							str  ,					lowerCamelCase:							bool = False  ,					lowerCamelCase:							str = ascii_uppercase    )       ->      str:


      '''simple docstring'''




      lowerCamelCase__   	=					keyword.upper()
      lowerCamelCase__   	=					plaintext.upper()
      lowerCamelCase__   	=					set(lowerCamelCase    )

      # create a list of unique characters in the keyword - their order matters
      # it determines how we will map plaintext characters to the ciphertext
      lowerCamelCase__   	=					[]
      for char in keyword:
            if char in alphabet_set and char not in unique_chars:
                  unique_chars.append(lowerCamelCase    )
    # the number of those unique characters will determine the number of rows
      lowerCamelCase__   	=					len(lowerCamelCase    )

      # create a shifted version of the alphabet
      lowerCamelCase__   	=					unique_chars + [
          char for char in alphabet if char not in unique_chars
      ]

      # create a modified alphabet by splitting the shifted alphabet into rows
      lowerCamelCase__   	=					[
          shifted_alphabet[k : k + num_unique_chars_in_keyword]
          for k in range(0  ,					26  ,					lowerCamelCase    )
      ]

      # map the alphabet characters to the modified alphabet characters
      # going 'vertically' through the modified alphabet - consider columns first
      lowerCamelCase__   	=					{}
      lowerCamelCase__   	=					0
      for column in range(lowerCamelCase    ):
            for row in modified_alphabet:
                  # if current row (the last one) is too short, break out of loop
                  if len(lowerCamelCase    ) <= column:
                        break

                  # map current letter to letter in modified alphabet
                  lowerCamelCase__   	=					row[column]
                  letter_index += 1

      if verbose:
            print(lowerCamelCase    )
      # create the encrypted text by mapping the plaintext to the modified alphabet
      return "".join(mapping[char] if char in mapping else char for char in plaintext    )


if __name__ == "__main__":
      # example use
      print(mixed_keyword("""college""", """UNIVERSITY"""))