
def 	UpperCamelCase__  (     SCREAMING_SNAKE_CASE_	:	str       )  ->     str:
						_lowercase					       =					0

						# if input_string is "aba" than new_input_string become "a|b|a"
						_lowercase					       =					""""""
						_lowercase					       =					""""""

						# append each character + "|" in new_string for range(0, length-1)
						for i in input_string[: len(SCREAMING_SNAKE_CASE_       ) - 1]:
												new_input_string += i + "|"
						# append last character
						new_input_string += input_string[-1]

						# we will store the starting and ending of previous furthest ending palindromic
						# substring
						_lowercase       ,						_lowercase					       =					0, 0

						# length[i] shows the length of palindromic substring with center i
						_lowercase					       =					[1 for i in range(len(SCREAMING_SNAKE_CASE_       )       )]

						# for each character in new_string find corresponding palindromic string
						_lowercase					       =					0
						for j in range(len(SCREAMING_SNAKE_CASE_       )       ):
												_lowercase					       =					1 if j > r else min(length[l + r - j] // 2		,   r - j + 1       )
												while (
												    j - k >= 0
												    and j + k < len(SCREAMING_SNAKE_CASE_       )
												    and new_input_string[k + j] == new_input_string[j - k]
												):
																		k += 1

												_lowercase					       =					2 * k - 1

												# does this string is ending after the previously explored end (that is r) ?
												# if yes the update the new r to the last index of this
												if j + k - 1 > r:
																		_lowercase					       =					j - k + 1  # noqa: E741
																		_lowercase					       =					j + k - 1

												# update max_length and start position
												if max_length < length[j]:
																		_lowercase					       =					length[j]
																		_lowercase					       =					j

    # create that string
						_lowercase					       =					new_input_string[start - max_length // 2 : start + max_length // 2 + 1]
						for i in s:
												if i != "|":
																		output_string += i

						return output_string


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}