from sklearn.metrics import mean_squared_error

import datasets


__magic_name__								='''\
@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}
}
'''

__magic_name__								='''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


__magic_name__								='''
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.

    squared : bool, default=True
        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.

Returns:
    mse : mean squared error.
Examples:

    >>> mse_metric = datasets.load_metric("mse")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.375}
    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)
    >>> print(rmse_result)
    {\'mse\': 0.6123724356957945}

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

    >>> mse_metric = datasets.load_metric("mse", "multilist")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mse_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {\'mse\': 0.7083333333333334}
    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')
    >>> print(results) # doctest: +NORMALIZE_WHITESPACE
    {\'mse\': array([0.41666667, 1. ])}
'''



@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	, _KWARGS_DESCRIPTION							)
class     _A   (    datasets.Metric							):






  def 		_a					(self	)		->      List[str]:





       '''simple docstring'''





       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_squared_error.html'''
           ]   ,				)






  def 		_a					(self	)		->      Any:





       '''simple docstring'''





       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 		_a					(self   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_=None   ,				SCREAMING_SNAKE_CASE_="uniform_average"   ,				SCREAMING_SNAKE_CASE_=True	)		->      List[Any]:





       '''simple docstring'''





       UpperCamelCase__										=      mean_squared_error(
           SCREAMING_SNAKE_CASE_   ,				SCREAMING_SNAKE_CASE_   ,				sample_weight=SCREAMING_SNAKE_CASE_   ,				multioutput=SCREAMING_SNAKE_CASE_   ,				squared=SCREAMING_SNAKE_CASE_	)

       return {"mse": mse}




from __future__ import annotations







def  __UpperCamelCase    (						A ,							A							):
     UpperCamelCase__										=      get_failure_array(A							)

     # 2) Step through text searching for pattern
     UpperCamelCase__						,       UpperCamelCase__										=      0, 0  # index into text, pattern
     while i < len(A							):
          if pattern[j] == text[i]:
               if j == (len(A							) - 1):
                    return True
               j += 1

          # if this is a prefix in our pattern
          # just go back far enough to continue
          elif j > 0:
               UpperCamelCase__										=      failure[j - 1]
               continue
          i += 1
     return False







def  __UpperCamelCase    (						A							):
     UpperCamelCase__										=      [0]
     UpperCamelCase__										=      0
     UpperCamelCase__										=      1
     while j < len(A							):
          if pattern[i] == pattern[j]:
               i += 1
          elif i > 0:
               UpperCamelCase__										=      failure[i - 1]
               continue
          j += 1
          failure.append(A							)
     return failure


if __name__ == "__main__":
     # Test 1)
     __magic_name__								='''abc1abc12'''
     __magic_name__								='''alskfjaldsabc1abc1abc12k23adsfabcabc'''
     __magic_name__								='''alskfjaldsk23adsfabcabc'''
     assert kmp(pattern, texta) and not kmp(pattern, texta)

     # Test 2)
     __magic_name__								='''ABABX'''
     __magic_name__								='''ABABZABABYABABX'''
     assert kmp(pattern, text)

     # Test 3)
     __magic_name__								='''AAAB'''
     __magic_name__								='''ABAAAAAB'''
     assert kmp(pattern, text)

     # Test 4)
     __magic_name__								='''abcdabcy'''
     __magic_name__								='''abcxabcdabxabcdabcdabcy'''
     assert kmp(pattern, text)

     # Test 5)
     __magic_name__								='''aabaabaaa'''
     assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]



