'''simple docstring'''

def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a      )  ->      int:
     '''simple docstring'''







     return len_board * len_board_column * row + column



def  _lowerCAmelCase				(				__a			,      __a			,      __a			,      __a			,      __a			,      __a			,      )  ->      bool:
     '''simple docstring'''







     if board[row][column] != word[word_index]:
          return False

     if word_index == len(__a      ) - 1:
          return True

     _UpperCamelCase  :Dict       =[(0, 1), (0, -1), (-1, 0), (1, 0)]
     _UpperCamelCase  :int       =len(__a      )
     _UpperCamelCase  :int       =len(board[0]      )
     for direction in traverts_directions:
          _UpperCamelCase  :int       =row + direction[0]
          _UpperCamelCase  :Dict       =column + direction[1]
          if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
               continue

          _UpperCamelCase  :int       =get_point_key(__a			,      __a			,      __a			,      __a      )
          if key in visited_points_set:
               continue

          visited_points_set.add(__a      )
          if exits_word(__a			,      __a			,      __a			,      __a			,      word_index + 1			,      __a      ):
               return True

          visited_points_set.remove(__a      )

     return False



def  _lowerCAmelCase				(				__a			,      __a      )  ->      bool:
     '''simple docstring'''







     _UpperCamelCase  :int       =(
         """The board should be a non empty matrix of single chars strings."""
     )

     _UpperCamelCase  :Any       =len(__a      )
     if not isinstance(__a			,      __a      ) or len(__a      ) == 0:
          raise ValueError(__a      )

     for row in board:
          if not isinstance(__a			,      __a      ) or len(__a      ) == 0:
               raise ValueError(__a      )

          for item in row:
               if not isinstance(__a			,      __a      ) or len(__a      ) != 1:
                    raise ValueError(__a      )

    # Validate word
     if not isinstance(__a			,      __a      ) or len(__a      ) == 0:
          raise ValueError(
              """The word parameter should be a string of length greater than 0."""      )

     _UpperCamelCase  :Dict       =len(board[0]      )
     for i in range(__a      ):
          for j in range(__a      ):
               if exits_word(
                   __a			,      __a			,      __a			,      __a			,      0			,      {get_point_key(__a			,      __a			,      __a			,      __a      )}      ):
                    return True

     return False


if __name__ == "__main__":
      import doctest

      doctest.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}