

import pytest

from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested

from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):  # picklable for multiprocessing
						return i + 1


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def   __SCREAMING_SNAKE_CASE					():
						with parallel_backend('''spark'''       ):
												assert ParallelBackendConfig.backend_name == "spark"

						snake_case_   =	[1, 2, 3]
						with pytest.raises(SCREAMING_SNAKE_CASE__       ):
												with parallel_backend('''unsupported backend'''       ):
																		map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=2       )

						with pytest.raises(SCREAMING_SNAKE_CASE__       ):
												with parallel_backend('''unsupported backend'''       ):
																		map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=-1       )


@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize('''num_proc'''  ,		[2, -1]       )
def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	[1, 2]
						snake_case_   =	{'''a''': 1, '''b''': 2}
						snake_case_   =	{'''a''': [1, 2], '''b''': [3, 4]}
						snake_case_   =	{'''a''': {'''1''': 1}, '''b''': 2}
						snake_case_   =	{'''a''': 1, '''b''': 2, '''c''': 3, '''d''': 4}
						snake_case_   =	[2, 3]
						snake_case_   =	{'''a''': 2, '''b''': 3}
						snake_case_   =	{'''a''': [2, 3], '''b''': [4, 5]}
						snake_case_   =	{'''a''': {'''1''': 2}, '''b''': 3}
						snake_case_   =	{'''a''': 2, '''b''': 3, '''c''': 4, '''d''': 5}

						with parallel_backend('''spark'''       ):
												assert map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=SCREAMING_SNAKE_CASE__       ) == expected_map_nested_sa
												assert map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=SCREAMING_SNAKE_CASE__       ) == expected_map_nested_sa
												assert map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=SCREAMING_SNAKE_CASE__       ) == expected_map_nested_sa
												assert map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=SCREAMING_SNAKE_CASE__       ) == expected_map_nested_sa
												assert map_nested(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		num_proc=SCREAMING_SNAKE_CASE__       ) == expected_map_nested_sa


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					):







			'''simple docstring'''

			def 				snake_case__(     self :				Optional[int]							)      ->List[Any]:
									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 :				List[Any]							)      ->Optional[int]:
									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 :				int							,   _UpperCamelCase :				int							,   _UpperCamelCase :				Union[str, Any]							,   _UpperCamelCase :				Any=None							,   _UpperCamelCase :				Optional[int]="uniform_average"							,   _UpperCamelCase :				Tuple=True							)      ->Tuple:
									snake_case_   =	mean_squared_error(
									    _UpperCamelCase							,   _UpperCamelCase							,   sample_weight=_UpperCamelCase							,   multioutput=_UpperCamelCase							,   squared=_UpperCamelCase							)

									return {"mse": mse}