



'''simple docstring'''




import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.text import TextDatasetReader

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases



def    __lowerCamelCase						(      _lowercase			,       _lowercase	)   ->    List[Any]:
							assert isinstance(_lowercase			,       _lowercase	)
							assert dataset.num_rows == 4
							assert dataset.num_columns == 1
							assert dataset.column_names == ["text"]
							for feature, expected_dtype in expected_features.items():
														assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory"""			,       [False, True]	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Union[str, Any]:
							UpperCAmelCase  :	Any							  =	tmp_path / """cache"""
							UpperCAmelCase  :	Union[str, Any]							  =	{"""text""": """string"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase  :	int							  =	TextDatasetReader(_lowercase			,       cache_dir=_lowercase			,       keep_in_memory=_lowercase	).read()
							_check_text_dataset(_lowercase			,       _lowercase	)



@pytest.mark.parametrize(
    """features"""			,       [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,       )
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Any:
							UpperCAmelCase  :	List[str]							  =	tmp_path / """cache"""
							UpperCAmelCase  :	Dict							  =	{"""text""": """string"""}
							UpperCAmelCase  :	Any							  =	features.copy() if features else default_expected_features
							UpperCAmelCase  :	List[str]							  =	(
							    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
							)
							UpperCAmelCase  :	Union[str, Any]							  =	TextDatasetReader(_lowercase			,       features=_lowercase			,       cache_dir=_lowercase	).read()
							_check_text_dataset(_lowercase			,       _lowercase	)



@pytest.mark.parametrize("""split"""			,       [None, NamedSplit("""train"""	), """train""", """test"""]	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							UpperCAmelCase  :	List[Any]							  =	tmp_path / """cache"""
							UpperCAmelCase  :	Dict							  =	{"""text""": """string"""}
							UpperCAmelCase  :	List[Any]							  =	TextDatasetReader(_lowercase			,       cache_dir=_lowercase			,       split=_lowercase	).read()
							_check_text_dataset(_lowercase			,       _lowercase	)
							assert dataset.split == split if split else "train"



@pytest.mark.parametrize("""path_type"""			,       [str, list]	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[str]:
							if issubclass(_lowercase			,       _lowercase	):
														UpperCAmelCase  :	int							  =	text_path
							elif issubclass(_lowercase			,       _lowercase	):
														UpperCAmelCase  :	Optional[int]							  =	[text_path]
							UpperCAmelCase  :	int							  =	tmp_path / """cache"""
							UpperCAmelCase  :	Dict							  =	{"""text""": """string"""}
							UpperCAmelCase  :	List[Any]							  =	TextDatasetReader(_lowercase			,       cache_dir=_lowercase	).read()
							_check_text_dataset(_lowercase			,       _lowercase	)



def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase=("train",)	)   ->    Any:
							assert isinstance(_lowercase			,       _lowercase	)
							for split in splits:
														UpperCAmelCase  :	int							  =	dataset_dict[split]
														assert dataset.num_rows == 4
														assert dataset.num_columns == 1
														assert dataset.column_names == ["text"]
														for feature, expected_dtype in expected_features.items():
																					assert dataset.features[feature].dtype == expected_dtype



@pytest.mark.parametrize("""keep_in_memory"""			,       [False, True]	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    int:
							UpperCAmelCase  :	Tuple							  =	tmp_path / """cache"""
							UpperCAmelCase  :	Optional[Any]							  =	{"""text""": """string"""}
							with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
														UpperCAmelCase  :	List[str]							  =	TextDatasetReader({"""train""": text_path}			,       cache_dir=_lowercase			,       keep_in_memory=_lowercase	).read()
							_check_text_datasetdict(_lowercase			,       _lowercase	)



@pytest.mark.parametrize(
    """features"""			,       [
        None,
        {"""text""": """string"""},
        {"""text""": """int32"""},
        {"""text""": """float32"""},
    ]			,       )
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    Optional[int]:
							UpperCAmelCase  :	int							  =	tmp_path / """cache"""
							# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
							UpperCAmelCase  :	Optional[int]							  =	{"""text""": """string"""}
							UpperCAmelCase  :	List[Any]							  =	features.copy() if features else default_expected_features
							UpperCAmelCase  :	str							  =	(
							    Features({feature: Value(_lowercase	) for feature, dtype in features.items()}	) if features is not None else None
							)
							UpperCAmelCase  :	str							  =	TextDatasetReader({"""train""": text_path}			,       features=_lowercase			,       cache_dir=_lowercase	).read()
							_check_text_datasetdict(_lowercase			,       _lowercase	)



@pytest.mark.parametrize("""split"""			,       [None, NamedSplit("""train"""	), """train""", """test"""]	)
def    __lowerCamelCase						(      _lowercase			,       _lowercase			,       _lowercase	)   ->    List[Any]:
							if split:
														UpperCAmelCase  :	Any							  =	{split: text_path}
							else:
														UpperCAmelCase  :	Optional[int]							  =	"""train"""
														UpperCAmelCase  :	Optional[int]							  =	{"""train""": text_path, """test""": text_path}
							UpperCAmelCase  :	str							  =	tmp_path / """cache"""
							UpperCAmelCase  :	str							  =	{"""text""": """string"""}
							UpperCAmelCase  :	Optional[Any]							  =	TextDatasetReader(_lowercase			,       cache_dir=_lowercase	).read()
							_check_text_datasetdict(_lowercase			,       _lowercase			,       splits=list(path.keys()	)	)
							assert all(dataset[split].split == split for split in path.keys()	)






'''simple docstring'''




from sklearn.metrics import mean_squared_error

import datasets


a     :    Union[str, Any]      						=					"""\
@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}
}
"""

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


a     :    int      						=					"""
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     UpperCamelCase_				(							datasets.Metric    ):





	def 				_lowercase(						self					)    ->    List[str]:
								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 				_lowercase(						self					)    ->    List[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 				_lowercase(						self	,	A	,	A	,	A=None	,	A="uniform_average"	,	A=True					)    ->    List[Any]:
								UpperCAmelCase  :	List[Any]							  =	mean_squared_error(
								    A	,	A	,	sample_weight=A	,	multioutput=A	,	squared=A					)

								return {"mse": mse}

