

'''simple docstring'''
from sklearn.metrics import mean_squared_error

import datasets


_UpperCAmelCase  :		int  =		'''\\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'''

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


_UpperCAmelCase  :		Tuple  =		'''\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 						__magic_name__   (							datasets.Metric ):







 def 						_A(       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 						_A(       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 						_A(       self  , snake_case_  , snake_case_  , snake_case_=None  , snake_case_="uniform_average"  , snake_case_=True   ):
   lowercase									=mean_squared_error(
       _UpperCamelCase  , _UpperCamelCase  , sample_weight=_UpperCamelCase  , multioutput=_UpperCamelCase  , squared=_UpperCamelCase   )

   return {"mse": mse}







def      A__       (    SCREAMING_SNAKE_CASE_   :      int				,	SCREAMING_SNAKE_CASE_   :      int						)  ->  str:






   """simple docstring"""







   if a < 0 or b < 0:
      raise ValueError('''the value of both inputs must be positive'''						)

   _UpperCAmelCase           =   str(bin(SCREAMING_SNAKE_CASE_						)						)[2:]  # remove the leading "0b"
   _UpperCAmelCase           =   str(bin(SCREAMING_SNAKE_CASE_						)						)[2:]  # remove the leading "0b"

   _UpperCAmelCase           =   max(len(SCREAMING_SNAKE_CASE_						)				,	len(SCREAMING_SNAKE_CASE_						)						)

   return "0b" + "".join(
       str(int(char_a == '''1''' and char_b == '''1'''						)						)
       for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE_						)				,	b_binary.zfill(SCREAMING_SNAKE_CASE_						)						)						)


if __name__ == "__main__":
   import doctest

   doctest.testmod()