



from __future__ import annotations

from collections.abc import Iterator
from typing import Any

class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				Tuple  ,	UpperCamelCase			:				Any					)->None:
      __SCREAMING_SNAKE_CASE							:     Any							=						data
      __SCREAMING_SNAKE_CASE							:     Node | None							=						None






     def __str__(							self			:				int					)->str:
      return F"""{self.data}"""






class    _SCREAMING_SNAKE_CASE						:





     def __init__(							self			:				int					)->None:
      __SCREAMING_SNAKE_CASE							:     Node | None							=						None
      __SCREAMING_SNAKE_CASE							:     Node | None							=						None





     def __iter__(							self			:				Optional[Any]					)->Iterator[Any]:
      __SCREAMING_SNAKE_CASE							:     Any							=						self.front
      while node:
       yield node.data
       __SCREAMING_SNAKE_CASE							:     Tuple							=						node.next





     def __len__(							self			:				Tuple					)->int:
      return len(tuple(iter(self					)					)					)





     def __str__(							self			:				List[str]					)->str:
      return " <- ".join(str(UpperCamelCase					) for item in self					)





     def 						__snake_case     (							self			:				int					)->bool:
      return len(self					) == 0





     def 						__snake_case     (							self			:				Optional[Any]  ,	UpperCamelCase			:				Any					)->None:
      __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						Node(UpperCamelCase					)
      if self.is_empty():
       __SCREAMING_SNAKE_CASE							:     Dict							=						node
      else:
       assert isinstance(self.rear  ,	UpperCamelCase					)
       __SCREAMING_SNAKE_CASE							:     Union[str, Any]							=						node
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						node





     def 						__snake_case     (							self			:				List[Any]					)->Any:
      if self.is_empty():
       raise IndexError("dequeue from empty queue"					)
      assert isinstance(self.front  ,	UpperCamelCase					)
      __SCREAMING_SNAKE_CASE							:     List[str]							=						self.front
      __SCREAMING_SNAKE_CASE							:     int							=						self.front.next
      if self.front is None:
       __SCREAMING_SNAKE_CASE							:     Optional[Any]							=						None
      return node.data






     def 						__snake_case     (							self			:				List[str]					)->None:
      __SCREAMING_SNAKE_CASE							:     List[Any]							=						None


if __name__ == "__main__":
      from doctest import testmod

      testmod()







'''simple docstring'''

from sklearn.metrics import mean_absolute_error

import datasets


_lowerCamelCase :				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}
}
"""

_lowerCamelCase :				List[Any]														= """\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


_lowerCamelCase :				Union[str, Any]														= """
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    lowerCamelCase__  (   datasets.Metric     ):



   def  _UpperCamelCase		(    self     )				->     int:
        """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     )				->     Tuple:
        """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					,      lowerCAmelCase__					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__="uniform_average"     )				->     List[str]:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =mean_absolute_error(lowerCAmelCase__					,      lowerCAmelCase__					,      sample_weight=lowerCAmelCase__					,      multioutput=lowerCAmelCase__     )

        return {"mae": mae_score}