


import argparse
from collections import defaultdict






def   _lowercase(					__a				:						Union[str, Any]    ,     __a				:						Dict    ,     __a				:						Union[str, Any]    ,     __a				:						Optional[int]    ,     __a				:						Optional[int]     ):
							a__      =f"""{file}_{class_name}_{test_name}"""
							done_test[_id] += 1

							with open(__a    ,     'r'     ) as f:
														a__      =f.readlines()

							a__      =f"""class {class_name}("""
							a__      =f"""{4 * ' '}def {test_name}("""
							a__      =f"""{8 * ' '}{correct_line.split()[0]}"""
							a__      =f"""{16 * ' '}{correct_line.split()[0]}"""
							a__      =False
							a__      =False
							a__      =False
							a__      =False
							a__      =0
							a__      =0

							a__      =[]
							for line in lines:
														if line.startswith(__a     ):
																					a__      =True
														elif in_class and line.startswith(__a     ):
																					a__      =True
														elif in_class and in_func and (line.startswith(__a     ) or line.startswith(__a     )):
																					a__      =len(line.split(correct_line.split()[0]     )[0]     )
																					count += 1

																					if count == done_test[_id]:
																												a__      =True

														if in_class and in_func and in_line:
																					if ")" not in line:
																												continue
																					else:
																												a__      =True

														if in_class and in_func and in_line and insert_line:
																					new_lines.append(f"""{spaces * ' '}{correct_line}"""     )
																					a__      =a__      =a__      =a__      =False
														else:
																					new_lines.append(__a     )

							with open(__a    ,     'w'     ) as f:
														for line in new_lines:
																					f.write(__a     )






def   _lowercase(					__a				:						int    ,     __a				:						Union[str, Any]=None     ):
							if fail is not None:
														with open(__a    ,     'r'     ) as f:
																					a__      ={l.strip() for l in f.readlines()}
							else:
														a__      =None

							with open(__a    ,     'r'     ) as f:
														a__      =f.readlines()

							a__      =defaultdict(__a     )
							for line in correct_lines:
														a__    ,     a__    ,     a__    ,     a__      =line.split(';'     )
														if test_failures is None or "::".join([file, class_name, test_name]     ) in test_failures:
																					overwrite_file(__a    ,     __a    ,     __a    ,     __a    ,     __a     )


if __name__ == "__main__":
	_lowerCAmelCase:						Tuple   				=       argparse.ArgumentParser()
	parser.add_argument('--correct_filename', help='filename of tests with expected result')
	parser.add_argument('--fail_filename', help='filename of test failures', type=str, default=None)
	_lowerCAmelCase:						int   				=       parser.parse_args()

	main(args.correct_filename, args.fail_filename)



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}