


import os

import pytest

from datasets import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
)


UpperCAmelCase__   	=							pytest.mark.integration
@pytest.mark.parametrize('''path'''     ,						['''paws''', '''csv''']						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[Any]:







 """simple docstring"""


 inspect_dataset(__snake_case     ,						__snake_case						)
 _lowercase							      =path + '''.py'''
 assert script_name in os.listdir(__snake_case						)
 assert "__pycache__" not in os.listdir(__snake_case						)
@pytest.mark.filterwarnings('''ignore:inspect_metric is deprecated:FutureWarning'''						)
@pytest.mark.filterwarnings('''ignore:metric_module_factory is deprecated:FutureWarning'''						)
@pytest.mark.parametrize('''path'''     ,						['''accuracy''']						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Any:







 """simple docstring"""


 inspect_metric(__snake_case     ,						__snake_case						)
 _lowercase							      =path + '''.py'''
 assert script_name in os.listdir(__snake_case						)
 assert "__pycache__" not in os.listdir(__snake_case						)
@pytest.mark.parametrize(
    '''path, config_name, expected_splits'''     ,						[
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =get_dataset_config_info(__snake_case     ,						config_name=__snake_case						)
 assert info.config_name == config_name
 assert list(info.splits.keys()						) == expected_splits
@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''     ,						[
        ('''paws''', None, ValueError),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			int:







 """simple docstring"""


 with pytest.raises(__snake_case						):
  get_dataset_config_info(__snake_case     ,						config_name=__snake_case						)
@pytest.mark.parametrize(
    '''path, expected'''     ,						[
        ('''squad''', '''plain_text'''),
        ('''acronym_identification''', '''default'''),
        ('''lhoestq/squad''', '''plain_text'''),
        ('''lhoestq/test''', '''default'''),
        ('''lhoestq/demo1''', '''lhoestq--demo1'''),
        ('''dalle-mini/wit''', '''dalle-mini--wit'''),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case						)     ->			Optional[int]:







 """simple docstring"""


 _lowercase							      =get_dataset_config_names(__snake_case						)
 assert expected in config_names
@pytest.mark.parametrize(
    '''path, expected_configs, expected_splits_in_first_config'''     ,						[
        ('''squad''', ['''plain_text'''], ['''train''', '''validation''']),
        ('''dalle-mini/wit''', ['''dalle-mini--wit'''], ['''train''']),
        ('''paws''', ['''labeled_final''', '''labeled_swap''', '''unlabeled_final'''], ['''train''', '''test''', '''validation''']),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			List[str]:







 """simple docstring"""


 _lowercase							      =get_dataset_infos(__snake_case						)
 assert list(infos.keys()						) == expected_configs
 _lowercase							      =expected_configs[0]
 assert expected_config in infos
 _lowercase							      =infos[expected_config]
 assert info.config_name == expected_config
 assert list(info.splits.keys()						) == expected_splits_in_first_config
@pytest.mark.parametrize(
    '''path, expected_config, expected_splits'''     ,						[
        ('''squad''', '''plain_text''', ['''train''', '''validation''']),
        ('''dalle-mini/wit''', '''dalle-mini--wit''', ['''train''']),
        ('''paws''', '''labeled_final''', ['''train''', '''test''', '''validation''']),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Tuple:







 """simple docstring"""


 _lowercase							      =get_dataset_infos(__snake_case						)
 assert expected_config in infos
 _lowercase							      =infos[expected_config]
 assert info.config_name == expected_config
 assert list(info.splits.keys()						) == expected_splits



@pytest.mark.parametrize(
    '''path, config_name, expected_exception'''     ,						[
        ('''paws''', None, ValueError),
    ]     ,						)
def 				UpperCAmelCase_					(							__snake_case     ,						__snake_case     ,						__snake_case						)     ->			Union[str, Any]:







 """simple docstring"""


 with pytest.raises(__snake_case						):
  get_dataset_split_names(__snake_case     ,						config_name=__snake_case						)




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}