

from sklearn.metrics import mean_squared_error

import datasets


snake_case_        =			'''\
@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_        =			'''\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
'''


snake_case_        =			'''
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 				SCREAMING_SNAKE_CASE__ (datasets.Metric					):





    def 				snake_case_    (				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 				snake_case_    (				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 				snake_case_    (				self						,       a						,       a						,       a=None						,       a="uniform_average"						,       a=True):
         lowercase__   :       List[Any]				=	mean_squared_error(
             a						,       a						,       sample_weight=a						,       multioutput=a						,       squared=a)

         return {"mse": mse}




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


snake_case_        =			{'''configuration_encoder_decoder''': ['''EncoderDecoderConfig''']}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''EncoderDecoderModel''']

try:
       if not is_tf_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''TFEncoderDecoderModel''']

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       snake_case_        =			['''FlaxEncoderDecoderModel''']

if TYPE_CHECKING:
       from .configuration_encoder_decoder import EncoderDecoderConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_encoder_decoder import EncoderDecoderModel

       try:
              if not is_tf_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_tf_encoder_decoder import TFEncoderDecoderModel

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_encoder_decoder import FlaxEncoderDecoderModel

else:
       import sys

       snake_case_        =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)

