




'''simple docstring'''







from __future__ import annotations



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase							):
     assert n > 1 and a > 0 and greatest_common_divisor(__lowercase    ,      __lowercase							) == 1
     ((snake_case)   ,		(snake_case)   ,		(snake_case)) :			Union[str, Any]					   = extended_gcd(__lowercase    ,      __lowercase							)  # Implemented below
     snake_case :			Any					   = (b * s) % n
     return x



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     ((snake_case)   ,		(snake_case)) :			Any					   = extended_euclid(__lowercase    ,      __lowercase							)  # Implemented below
     if b < 0:
          snake_case :			Optional[Any]					   = (b % n + n) % n
     return b



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase    ,      __lowercase							):
     snake_case :			str					   = invert_modulo(__lowercase    ,      __lowercase							)
     snake_case :			Union[str, Any]					   = (b * s) % n
     return x



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     assert a >= 0 and b >= 0

     if b == 0:
          snake_case   ,		snake_case   ,		snake_case :			List[Any]					   = a, 1, 0
     else:
          ((snake_case)   ,		(snake_case)   ,		(snake_case)) :			Dict					   = extended_gcd(__lowercase    ,      a % b							)
          snake_case :			Optional[Any]					   = q
          snake_case :			int					   = p - q * (a // b)

     assert a % d == 0 and b % d == 0
     assert d == a * x + b * y

     return (d, x, y)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     if b == 0:
          return (1, 0)
     ((snake_case)   ,		(snake_case)) :			Dict					   = extended_euclid(__lowercase    ,      a % b							)
     snake_case :			Optional[Any]					   = a // b
     return (y, x - k * y)



def 					lowerCamelCase__							(			__lowercase    ,      __lowercase							):
     if a < b:
          snake_case   ,		snake_case :			List[Any]					   = b, a

     while a % b != 0:
          snake_case   ,		snake_case :			Dict					   = b, a % b

     return b


if __name__ == "__main__":
      from doctest import testmod

      testmod(name="""modular_division""", verbose=True)
      testmod(name="""modular_division2""", verbose=True)
      testmod(name="""invert_modulo""", verbose=True)
      testmod(name="""extended_gcd""", verbose=True)
      testmod(name="""extended_euclid""", verbose=True)
      testmod(name="""greatest_common_divisor""", verbose=True)









'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


lowercase			:      int			=					"""\
@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}
}
"""

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


lowercase			:      List[Any]			=					"""
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       )			->							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  snake_case_			(							self       )			->							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       ,__a       ,__a       ,__a=None       ,__a="uniform_average"       )			->							str:
          snake_case :			int					   = mean_absolute_error(__a       ,__a       ,sample_weight=__a       ,multioutput=__a       )

          return {"mae": mae_score}



