
"""simple docstring"""






from sklearn.metrics import mean_squared_error

import datasets


lowerCamelCase__ :       Optional[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"

lowerCamelCase__ :       str						     =			"\\nMean Squared Error(MSE) is the average of the square of difference between the predicted\nand actual values.\n"


lowerCamelCase__ :       str						     =			"\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 				lowercase__(				datasets.Metric   ):
	'''simple docstring'''

	def   __lowerCAmelCase (						self    :Union[str, Any]					)							->			List[Any]:







			'''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    :int					)							->			Union[str, 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    :List[Any]		, lowerCamelCase_    :int		, lowerCamelCase_    :Optional[int]		, lowerCamelCase_    :Optional[Any]=None		, lowerCamelCase_    :int="uniform_average"		, lowerCamelCase_    :Dict=True					)							->			Dict:







			'''simple docstring'''




			SCREAMING_SNAKE_CASE       :     Optional[int]				= mean_squared_error(
			    lowerCamelCase_		, lowerCamelCase_		, sample_weight=lowerCamelCase_		, multioutput=lowerCamelCase_		, squared=lowerCamelCase_					)

			return {"mse": mse}







"""simple docstring"""






from typing import TYPE_CHECKING

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


lowerCamelCase__ :       Optional[Any]						     =			{
    "configuration_funnel": ["FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP", "FunnelConfig"],
    "convert_funnel_original_tf_checkpoint_to_pytorch": [],
    "tokenization_funnel": ["FunnelTokenizer"],
}

try:
					if not is_tokenizers_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Optional[Any]						     =			["FunnelTokenizerFast"]

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Dict						     =			[
					    "FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "FunnelBaseModel",
					    "FunnelForMaskedLM",
					    "FunnelForMultipleChoice",
					    "FunnelForPreTraining",
					    "FunnelForQuestionAnswering",
					    "FunnelForSequenceClassification",
					    "FunnelForTokenClassification",
					    "FunnelModel",
					    "FunnelPreTrainedModel",
					    "load_tf_weights_in_funnel",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCamelCase__ :       Tuple						     =			[
					    "TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TFFunnelBaseModel",
					    "TFFunnelForMaskedLM",
					    "TFFunnelForMultipleChoice",
					    "TFFunnelForPreTraining",
					    "TFFunnelForQuestionAnswering",
					    "TFFunnelForSequenceClassification",
					    "TFFunnelForTokenClassification",
					    "TFFunnelModel",
					    "TFFunnelPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_funnel import FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP, FunnelConfig
					from .tokenization_funnel import FunnelTokenizer

					try:
										if not is_tokenizers_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .tokenization_funnel_fast import FunnelTokenizerFast

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_funnel import (
										    FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    FunnelBaseModel,
										    FunnelForMaskedLM,
										    FunnelForMultipleChoice,
										    FunnelForPreTraining,
										    FunnelForQuestionAnswering,
										    FunnelForSequenceClassification,
										    FunnelForTokenClassification,
										    FunnelModel,
										    FunnelPreTrainedModel,
										    load_tf_weights_in_funnel,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_funnel import (
										    TF_FUNNEL_PRETRAINED_MODEL_ARCHIVE_LIST,
										    TFFunnelBaseModel,
										    TFFunnelForMaskedLM,
										    TFFunnelForMultipleChoice,
										    TFFunnelForPreTraining,
										    TFFunnelForQuestionAnswering,
										    TFFunnelForSequenceClassification,
										    TFFunnelForTokenClassification,
										    TFFunnelModel,
										    TFFunnelPreTrainedModel,
										)

else:
					import sys

					lowerCamelCase__ :       Optional[int]						     =			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





