




from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
	from .features import FeatureType


@dataclass
class    lowerCamelCase    :






			'''simple docstring'''





			UpperCamelCase__								=4_2
			UpperCamelCase__								=None
			# Automatically constructed
			UpperCamelCase__								='''dict'''
			UpperCamelCase__								=None
			UpperCamelCase__								=field(default='''Translation'''				,init=SCREAMING_SNAKE_CASE__				,repr=SCREAMING_SNAKE_CASE__						)

			def __call__( self			:   int  )						->   List[Any]:
								return pa.struct({lang: pa.string() for lang in sorted(self.languages  )}  )


			def 							UpperCAmelCase__       ( self			:   Tuple  )						->   Union["FeatureType", Dict[str, "FeatureType"]]:
								from .features import Value

								return {k: Value('''string'''  ) for k in sorted(self.languages  )}






@dataclass
class    lowerCamelCase    :






			'''simple docstring'''





			UpperCamelCase__								=None
			UpperCamelCase__								=None
			UpperCamelCase__								=None
			# Automatically constructed
			UpperCamelCase__								='''dict'''
			UpperCamelCase__								=None
			UpperCamelCase__								=field(default='''TranslationVariableLanguages'''				,init=SCREAMING_SNAKE_CASE__				,repr=SCREAMING_SNAKE_CASE__						)

			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Any:
								__magic_name__							:	Tuple    							=							sorted(set(self.languages  )  ) if self.languages else None
								__magic_name__							:	Optional[int]    							=							len(self.languages  ) if self.languages else None

			def __call__( self			:   List[str]  )						->   str:
								return pa.struct({'''language''': pa.list_(pa.string()  ), '''translation''': pa.list_(pa.string()  )}  )

			def 							UpperCAmelCase__       ( self			:   List[Any]       ,						lowerCamelCase_			:   Any  )						->   Tuple:
								__magic_name__							:	int    							=							set(self.languages  )
								if self.languages and set(_lowercase  ) - lang_set:
													raise ValueError(
													    F'''Some languages in example ({', '.join(sorted(set(_lowercase  ) - lang_set  )  )}) are not in valid set ({', '.join(_lowercase  )}).'''  )

								# Convert dictionary into tuples, splitting out cases where there are
								# multiple translations for a single language.
								__magic_name__							:	List[Any]    							=							[]
								for lang, text in translation_dict.items():
													if isinstance(_lowercase       ,						_lowercase  ):
																		translation_tuples.append((lang, text)  )
													else:
																		translation_tuples.extend([(lang, el) for el in text]  )

        # Ensure translations are in ascending order by language code.
								__magic_name__							:	str    							=							zip(*sorted(_lowercase  )  )

								return {"language": languages, "translation": translations}


			def 							UpperCAmelCase__       ( self			:   Optional[int]  )						->   Union["FeatureType", Dict[str, "FeatureType"]]:
								from .features import Sequence, Value

								return {
								    "language": Sequence(Value('''string'''  )  ),
								    "translation": Sequence(Value('''string'''  )  ),
								}








from sklearn.metrics import mean_squared_error

import datasets


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


__lowerCamelCase   :						str				=			'''
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    lowerCamelCase    (		datasets.Metric						):






			'''simple docstring'''





			def 							UpperCAmelCase__       ( self			:   Dict  )						->   Tuple:
								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			:   List[Any]  )						->   Any:
								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			:   Union[str, Any]       ,						lowerCamelCase_			:   Optional[Any]       ,						lowerCamelCase_			:   int       ,						lowerCamelCase_			:   int=None       ,						lowerCamelCase_			:   List[Any]="uniform_average"       ,						lowerCamelCase_			:   List[str]=True  )						->   List[str]:
								__magic_name__							:	Tuple    							=							mean_squared_error(
								    lowerCamelCase_       ,						lowerCamelCase_       ,						sample_weight=lowerCamelCase_       ,						multioutput=lowerCamelCase_       ,						squared=lowerCamelCase_  )

								return {"mse": mse}



