




import warnings

from ...utils import logging
from .image_processing_flava import FlavaImageProcessor


UpperCamelCase__         =      logging.get_logger(__name__)
class       a__   ( _A				):






						def __init__(       self		,      *_A		,      **_A      ):




											"""simple docstring"""



											warnings.warn(
											    "The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
											    " use FlavaImageProcessor instead."		,      __SCREAMING_SNAKE_CASE		,      )
											super().__init__(*__SCREAMING_SNAKE_CASE		,      **__SCREAMING_SNAKE_CASE      )










from sklearn.metrics import mean_squared_error

import datasets


UpperCamelCase__         =      """\
@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}
}
"""

UpperCamelCase__         =      """\
Mean Squared Error(MSE) is the average of the square of difference between the predicted
and actual values.
"""


UpperCamelCase__         =      """
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     __SCREAMING_SNAKE_CASE(       self      ):




											"""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     __SCREAMING_SNAKE_CASE(       self      ):




											"""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     __SCREAMING_SNAKE_CASE(       self		,      _A		,      _A		,      _A=None		,      _A="uniform_average"		,      _A=True      ):




											"""simple docstring"""



											__lowerCAmelCase           =	mean_squared_error(
											    _A		,      _A		,      sample_weight=_A		,      multioutput=_A		,      squared=_A      )

											return {"mse": mse}




