def       A__   (						snake_case_			:		list   ):
 SCREAMING_SNAKE_CASE__: Dict=   0
 while len(snake_case_   ) > 1:
  SCREAMING_SNAKE_CASE__: Any=   0
  # Consider two files with minimum cost to be merged
  for _ in range(2   ):
   SCREAMING_SNAKE_CASE__: Optional[int]=   files.index(min(snake_case_   )   )
   temp += files[min_index]
   files.pop(snake_case_   )
  files.append(snake_case_   )
  optimal_merge_cost += temp
 return optimal_merge_cost


if __name__ == "__main__":
      import doctest

      doctest.testmod()



from sklearn.metrics import mean_squared_error

import datasets


A		: List[Any]								=     '''\
@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		: str								=     '''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


A		: 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.

    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    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self  ):
									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    UpperCamelCase_				(      self				,			__UpperCamelCase				,			__UpperCamelCase				,			__UpperCamelCase=None				,			__UpperCamelCase="uniform_average"				,			__UpperCamelCase=True  ):
									_lowercase					       =					mean_squared_error(
									    __UpperCamelCase				,			__UpperCamelCase				,			sample_weight=__UpperCamelCase				,			multioutput=__UpperCamelCase				,			squared=__UpperCamelCase  )

									return {"mse": mse}