
from sklearn.metrics import mean_absolute_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__       :   str   						=					"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


A__       :   Optional[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.

Returns:
    mae : mean absolute error.
        If multioutput is \"raw_values\", then mean absolute error is returned for each output separately. If multioutput is \"uniform_average\" or an ndarray of weights, then the weighted average of all output errors is returned.
        MAE output is non-negative floating point. The best value is 0.0.
Examples:

    >>> mae_metric = datasets.load_metric(\"mae\")
    >>> predictions = [2.5, 0.0, 2, 8]
    >>> references = [3, -0.5, 2, 7]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.5}

    If you're using multi-dimensional lists, then set the config as follows :

    >>> mae_metric = datasets.load_metric(\"mae\", \"multilist\")
    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]
    >>> references = [[0, 2], [-1, 2], [8, -5]]
    >>> results = mae_metric.compute(predictions=predictions, references=references)
    >>> print(results)
    {'mae': 0.75}
    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput='raw_values')
    >>> print(results)
    {'mae': array([0.5, 1. ])}
"""





@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION	,		_KWARGS_DESCRIPTION					)
class 						lowercase       (  datasets.Metric					):
					def lowercase_			(		self    ):
								"""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_absolute_error.html'''
								    ] ,	)
					def lowercase_			(		self    ):
								"""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 lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=None ,	SCREAMING_SNAKE_CASE__="uniform_average"    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			mean_absolute_error(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	sample_weight=SCREAMING_SNAKE_CASE__ ,	multioutput=SCREAMING_SNAKE_CASE__    )

								return {"mae": mae_score}





import abc
import copy
import dataclasses
from dataclasses import dataclass
from typing import ClassVar, Dict, Type, TypeVar

from ..features import Features


A__       :   Tuple   						=					TypeVar("""T""", bound="""TaskTemplate""")





@dataclass(frozen=__UpperCamelCase					)
class 						lowercase       (  abc.ABC					):
					# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
					__a					=    42
					__a					=    42
					__a					=    42
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								return copy.deepcopy(self    )
					@property
					def lowercase_			(		self    ):
								"""simple docstring"""







								return Features(**self.input_schema ,	**self.label_schema    )
					@property
					@abc.abstractmethod
					def lowercase_			(		self    ):
								"""simple docstring"""







								raise NotImplementedError
					@classmethod
					def lowercase_			(		cls ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[Any]				      =			{f.name for f in dataclasses.fields(cls    )}
								return cls(**{k: v for k, v in template_dict.items() if k in field_names}    )



