


'''simple docstring'''



from sklearn.metrics import mean_absolute_error

import datasets


A__		: Tuple												=  """\
@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}
}
"""

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


A__		: Optional[int]												=  """
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        UpperCAmelCase_  (datasets.Metric  ):





    """simple docstring"""






    def 				lowercase_    (							self     )    ->					int:
         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 				lowercase_    (							self     )    ->					Dict:
         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 				lowercase_    (							self      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_=None      ,					SCREAMING_SNAKE_CASE_="uniform_average"     )    ->					Optional[Any]:
         __lowerCamelCase       :     Union[str, Any]        =		mean_absolute_error(SCREAMING_SNAKE_CASE_      ,					SCREAMING_SNAKE_CASE_      ,					sample_weight=SCREAMING_SNAKE_CASE_      ,					multioutput=SCREAMING_SNAKE_CASE_     )

         return {"mae": mae_score}










'''simple docstring'''



from typing import Literal

A__		: Tuple												=  """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    Literal["encrypt", "decrypt"]							)					->		str:
     __lowerCamelCase       :     Dict        =		LETTERS if mode == 'decrypt' else key
     __lowerCamelCase       :     Tuple        =		key if mode == 'decrypt' else LETTERS
     __lowerCamelCase       :     Optional[int]        =		''
     # loop through each symbol in the message
     for symbol in message:
          if symbol.upper() in chars_a:
               # encrypt/decrypt the symbol
               __lowerCamelCase       :     Optional[int]        =		chars_a.find(symbol.upper()							)
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               # symbol is not in LETTERS, just add it
               translated += symbol
     return translated



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    str							)					->		str:
     return translate_message(UpperCAmelCase_				,   UpperCAmelCase_				,   'encrypt'							)



def       UpperCAmelCase__ (     UpperCAmelCase_   :    str				,   UpperCAmelCase_   :    str							)					->		str:
     return translate_message(UpperCAmelCase_				,   UpperCAmelCase_				,   'decrypt'							)



def       UpperCAmelCase__ (     )					->		None:
     __lowerCamelCase       :     Dict        =		'Hello World'
     __lowerCamelCase       :     str        =		'QWERTYUIOPASDFGHJKLZXCVBNM'
     __lowerCamelCase       :     Any        =		'decrypt'  # set to 'encrypt' or 'decrypt'

     if mode == "encrypt":
          __lowerCamelCase       :     Optional[int]        =		encrypt_message(UpperCAmelCase_				,   UpperCAmelCase_							)
     elif mode == "decrypt":
          __lowerCamelCase       :     List[str]        =		decrypt_message(UpperCAmelCase_				,   UpperCAmelCase_							)
     print(F'Using the key {key}, the {mode}ed message is: {translated}'							)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()






