


'''simple docstring'''







from collections import OrderedDict
from dataclasses import fields
from typing import Any, Tuple

import numpy as np

from .import_utils import is_torch_available


def      SCREAMING_SNAKE_CASE_ (  _UpperCAmelCase     :     int  )  ->      Union[str, Any]:
       if is_torch_available():
              import torch

              if isinstance(_UpperCAmelCase   ,torch.Tensor  ):
                     return True

       return isinstance(_UpperCAmelCase   ,np.ndarray  )







class A__	(						UpperCAmelCase__				):






      def __UpperCAmelCase	(			self      :Optional[Any]     )	-> Optional[int]:




             '''simple docstring'''
             _a   :			Union[str, Any]					 =fields(self     )

             # Safety and consistency checks
             if not len(SCREAMING_SNAKE_CASE     ):
                    raise ValueError(f"{self.__class__.__name__} has no fields."     )

             _a   :			Any					 =getattr(self					,						class_fields[0].name     )
             _a   :			Any					 =all(getattr(self					,						field.name     ) is None for field in class_fields[1:]     )

             if other_fields_are_none and isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
                    for key, value in first_field.items():
                           _a   :			List[str]					 =value
             else:
                    for field in class_fields:
                           _a   :			Optional[int]					 =getattr(self					,						field.name     )
                           if v is not None:
                                  _a   :			Union[str, Any]					 =v






      def __delitem__(			self      :Optional[Any]					,						*SCREAMING_SNAKE_CASE      :Optional[int]					,						**SCREAMING_SNAKE_CASE      :Tuple     )	-> Union[str, Any]:




             '''simple docstring'''
             raise Exception(f"You cannot use ``__delitem__`` on a {self.__class__.__name__} instance."     )






      def __UpperCAmelCase	(			self      :int					,						*SCREAMING_SNAKE_CASE      :List[str]					,						**SCREAMING_SNAKE_CASE      :str     )	-> Optional[int]:




             '''simple docstring'''
             raise Exception(f"You cannot use ``setdefault`` on a {self.__class__.__name__} instance."     )






      def __UpperCAmelCase	(			self      :Optional[Any]					,						*SCREAMING_SNAKE_CASE      :Dict					,						**SCREAMING_SNAKE_CASE      :int     )	-> Dict:




             '''simple docstring'''
             raise Exception(f"You cannot use ``pop`` on a {self.__class__.__name__} instance."     )






      def __UpperCAmelCase	(			self      :Optional[Any]					,						*SCREAMING_SNAKE_CASE      :Any					,						**SCREAMING_SNAKE_CASE      :str     )	-> List[str]:




             '''simple docstring'''
             raise Exception(f"You cannot use ``update`` on a {self.__class__.__name__} instance."     )






      def __getitem__(			self      :Tuple					,						SCREAMING_SNAKE_CASE      :Dict     )	-> Optional[Any]:




             '''simple docstring'''
             if isinstance(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     ):
                    _a   :			Optional[int]					 =dict(self.items()     )
                    return inner_dict[k]
             else:
                    return self.to_tuple()[k]






      def __setattr__(			self      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :Optional[Any]					,						SCREAMING_SNAKE_CASE      :List[str]     )	-> List[str]:




             '''simple docstring'''
             if name in self.keys() and value is not None:
                    # Don't call self.__setitem__ to avoid recursion errors
                    super().__setitem__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
             super().__setattr__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __setitem__(			self      :Any					,						SCREAMING_SNAKE_CASE      :Tuple					,						SCREAMING_SNAKE_CASE      :Union[str, Any]     )	-> Tuple:




             '''simple docstring'''
             # Will raise a KeyException if needed
             super().__setitem__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )
             # Don't call self.__setattr__ to avoid recursion errors
             super().__setattr__(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE     )






      def __UpperCAmelCase	(			self      :Any     )	-> Tuple[Any]:




             '''simple docstring'''
             return tuple(self[k] for k in self.keys()     )






'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


A__:   Dict    =	'''\
@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__:   Union[str, Any]    =	'''\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
'''


A__:   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.

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 A__	(						datasets.Metric				):






      def __UpperCAmelCase	(			self      :List[Any]     )	-> Dict:




             '''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 __UpperCAmelCase	(			self      :Dict     )	-> List[str]:




             '''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 __UpperCAmelCase	(			self      :Any					,						SCREAMING_SNAKE_CASE      :Union[str, Any]					,						SCREAMING_SNAKE_CASE      :str					,						SCREAMING_SNAKE_CASE      :Union[str, Any]=None					,						SCREAMING_SNAKE_CASE      :Any="uniform_average"     )	-> List[Any]:




             '''simple docstring'''
             _a   :			Tuple					 =mean_absolute_error(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						sample_weight=SCREAMING_SNAKE_CASE					,						multioutput=SCREAMING_SNAKE_CASE     )

             return {"mae": mae_score}


