

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

import datasets


_lowerCamelCase      					=     """\
@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}
}
"""

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


_lowerCamelCase      					=     """
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 					_snake_case		(datasets.Metric):






			def 		UpperCamelCase__							(  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 		UpperCamelCase__							(  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 		UpperCamelCase__							(  self			,_snake_case			,_snake_case			,_snake_case=None			,_snake_case="uniform_average"			,_snake_case=True     ):
				UpperCAmelCase_     :      str						=  mean_squared_error(
				    _snake_case			,_snake_case			,sample_weight=_snake_case			,multioutput=_snake_case			,squared=_snake_case     )

				return {"mse": mse}







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


A_			:					Tuple    			=     {
    'configuration_xlm': ['XLM_PRETRAINED_CONFIG_ARCHIVE_MAP', 'XLMConfig', 'XLMOnnxConfig'],
    'tokenization_xlm': ['XLMTokenizer'],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					Dict    			=     [
	    'XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'XLMForMultipleChoice',
	    'XLMForQuestionAnswering',
	    'XLMForQuestionAnsweringSimple',
	    'XLMForSequenceClassification',
	    'XLMForTokenClassification',
	    'XLMModel',
	    'XLMPreTrainedModel',
	    'XLMWithLMHeadModel',
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	A_			:					str    			=     [
	    'TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST',
	    'TFXLMForMultipleChoice',
	    'TFXLMForQuestionAnsweringSimple',
	    'TFXLMForSequenceClassification',
	    'TFXLMForTokenClassification',
	    'TFXLMMainLayer',
	    'TFXLMModel',
	    'TFXLMPreTrainedModel',
	    'TFXLMWithLMHeadModel',
	]


if TYPE_CHECKING:
	from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
	from .tokenization_xlm import XLMTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm import (
		    XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMForMultipleChoice,
		    XLMForQuestionAnswering,
		    XLMForQuestionAnsweringSimple,
		    XLMForSequenceClassification,
		    XLMForTokenClassification,
		    XLMModel,
		    XLMPreTrainedModel,
		    XLMWithLMHeadModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm import (
		    TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMForMultipleChoice,
		    TFXLMForQuestionAnsweringSimple,
		    TFXLMForSequenceClassification,
		    TFXLMForTokenClassification,
		    TFXLMMainLayer,
		    TFXLMModel,
		    TFXLMPreTrainedModel,
		    TFXLMWithLMHeadModel,
		)

else:
	import sys

	A_			:					Tuple    			=     _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



