



from sklearn.metrics import mean_squared_error

import datasets


__snake_case		:Tuple   	=  '''\
@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}
}
'''

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


__snake_case		:Dict   	=  '''
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 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           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 	_lowerCamelCase    (	self							:  Any):


           '''simple docstring'''

           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 	_lowerCamelCase    (	self							:  Tuple  ,	__SCREAMING_SNAKE_CASE							:  List[Any]  ,	__SCREAMING_SNAKE_CASE							:  str  ,	__SCREAMING_SNAKE_CASE							:  Union[str, Any]=None  ,	__SCREAMING_SNAKE_CASE							:  Tuple="uniform_average"  ,	__SCREAMING_SNAKE_CASE							:  List[str]=True):


           '''simple docstring'''

           __a		 =       mean_squared_error(
               __SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE  ,	sample_weight=__SCREAMING_SNAKE_CASE  ,	multioutput=__SCREAMING_SNAKE_CASE  ,	squared=__SCREAMING_SNAKE_CASE)

           return {"mse": mse}










# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union


__snake_case		:List[str]   	=  re.compile(r'''^(?P<major>\d+)''' r'''\.(?P<minor>\d+)''' r'''\.(?P<patch>\d+)$''')

@total_ordering
@dataclass
class 					_A :
       UpperCamelCase__						:					str
       UpperCamelCase__						:					Optional[str]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None
       UpperCamelCase__						:					Optional[Union[str, int]]		      =		None



       def 	_lowerCamelCase    (	self							:  Union[str, Any]):


           '''simple docstring'''

           __a ,     __a ,     __a		 =       _str_to_version_tuple(self.version_str)



       def __repr__(	self							:  Tuple):


           '''simple docstring'''

           return F'{self.tuple[0]}.{self.tuple[1]}.{self.tuple[2]}'



       @property
       def 	_lowerCamelCase    (	self							:  Optional[int]):


           '''simple docstring'''

           return self.major, self.minor, self.patch



       def 	_lowerCamelCase    (	self							:  Any  ,	__SCREAMING_SNAKE_CASE							:  Optional[int]):


           '''simple docstring'''

           if isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               return Version(__SCREAMING_SNAKE_CASE)
           elif isinstance(__SCREAMING_SNAKE_CASE  ,	__SCREAMING_SNAKE_CASE):
               return other
           raise TypeError(F'{other} (type {type(__SCREAMING_SNAKE_CASE)}) cannot be compared to version.')



       def __eq__(	self							:  int  ,	__SCREAMING_SNAKE_CASE							:  Any):


           '''simple docstring'''

           try:
               __a		 =       self._validate_operand(__SCREAMING_SNAKE_CASE)
           except (TypeError, ValueError):
               return False
           else:
               return self.tuple == other.tuple



       def __lt__(	self							:  str  ,	__SCREAMING_SNAKE_CASE							:  Tuple):


           '''simple docstring'''

           __a		 =       self._validate_operand(__SCREAMING_SNAKE_CASE)
           return self.tuple < other.tuple



       def __hash__(	self							:  Optional[Any]):


           '''simple docstring'''

           return hash(_version_tuple_to_str(self.tuple))



       @classmethod
       def 	_lowerCamelCase    (	cls							:  List[str]  ,	__SCREAMING_SNAKE_CASE							:  Optional[Any]):


           '''simple docstring'''

           __a		 =       {f.name for f in dataclasses.fields(cls)}
           return cls(**{k: v for k, v in dic.items() if k in field_names})





       def 	_lowerCamelCase    (	self							:  int):


           '''simple docstring'''

           return self.version_str





def   __snake_case			(    _UpperCAmelCase       ):
    __a		 =       _VERSION_REG.match(_UpperCAmelCase       )
    if not res:
        raise ValueError(f'Invalid version \'{version_str}\'. Format should be x.y.z with {{x,y,z}} being digits.'       )
    return tuple(int(_UpperCAmelCase       ) for v in [res.group('''major'''       ), res.group('''minor'''       ), res.group('''patch'''       )]       )





def   __snake_case			(    _UpperCAmelCase       ):
    return ".".join(str(_UpperCAmelCase       ) for v in version_tuple       )





