


from sklearn.metrics import mean_squared_error

import datasets


a_ :Tuple  							=			'\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

a_ :Optional[Any]  							=			'\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n'


a_ :int  							=			'\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n        "raw_values" : Returns a full set of errors in case of multioutput input.\n\n        "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\n    squared : bool, default=True\n        If True returns MSE value, if False returns RMSE (Root Mean Squared Error) value.\n\nReturns:\n    mse : mean squared error.\nExamples:\n\n    >>> mse_metric = datasets.load_metric("mse")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.375}\n    >>> rmse_result = mse_metric.compute(predictions=predictions, references=references, squared=False)\n    >>> print(rmse_result)\n    {\'mse\': 0.6123724356957945}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mse_metric = datasets.load_metric("mse", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mse_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mse\': 0.7083333333333334}\n    >>> results = mse_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results) # doctest: +NORMALIZE_WHITESPACE\n    {\'mse\': array([0.41666667, 1. ])}\n'

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







     def 							lowercase__       (						self :						Dict      ):
           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 							lowercase__       (						self :						Dict      ):
           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 							lowercase__       (						self :						str   ,   _lowercase :						str   ,   _lowercase :						int   ,   _lowercase :						int=None   ,   _lowercase :						Dict="uniform_average"   ,   _lowercase :						List[str]=True      ):
           SCREAMING_SNAKE_CASE__       :  str						     =		mean_squared_error(
               _lowercase   ,   _lowercase   ,   sample_weight=_lowercase   ,   multioutput=_lowercase   ,   squared=_lowercase      )

           return {"mse": mse}








'''simple docstring'''
from string import ascii_lowercase, ascii_uppercase







def 		A_(     A						:		str):
							if not sentence:
														return ""
							UpperCamelCase   			=     dict(zip(A		,     A))
							return lower_to_upper.get(sentence[0]		,     sentence[0]) + sentence[1:]


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

						testmod()




