


def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)		->int:


 '''simple docstring'''
 return len_board * len_board_column * row + column






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  )		->bool:


 '''simple docstring'''

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

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

 snake_case :			Tuple							=   [(0, 1), (0, -1), (-1, 0), (1, 0)]
 snake_case :			List[str]							=   len(__lowerCamelCase							)
 snake_case :			Optional[int]							=   len(board[0]							)
 for direction in traverts_directions:
  snake_case :			str							=   row + direction[0]
  snake_case :			Optional[Any]							=   column + direction[1]
  if not (0 <= next_i < len_board and 0 <= next_j < len_board_column):
   continue

  snake_case :			Any							=   get_point_key(__lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase			,  __lowerCamelCase							)
  if key in visited_points_set:
   continue

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

  visited_points_set.remove(__lowerCamelCase							)

 return False






def  _UpperCAmelCase						( __lowerCamelCase			,  __lowerCamelCase							)		->bool:


 '''simple docstring'''
 snake_case :			List[Any]							=   (
     '''The board should be a non empty matrix of single chars strings.'''
 )

 snake_case :			Union[str, Any]							=   len(__lowerCamelCase							)
 if not isinstance(__lowerCamelCase			,  __lowerCamelCase							) or len(__lowerCamelCase							) == 0:
  raise ValueError(__lowerCamelCase							)

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

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

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

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

 return False


if __name__ == "__main__":
  import doctest

  doctest.testmod()










from sklearn.metrics import mean_absolute_error

import datasets


a					:				Optional[Any]       =      '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n         and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n         and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n         Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'

a					:				int       =      '\\nMean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual\nvalues.\n'


a					:				Any       =      '\nArgs:\n    predictions: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Estimated target values.\n    references: array-like of shape (n_samples,) or (n_samples, n_outputs)\n        Ground truth (correct) target values.\n    sample_weight: array-like of shape (n_samples,), default=None\n        Sample weights.\n    multioutput: {"raw_values", "uniform_average"} or array-like of shape (n_outputs,), default="uniform_average"\n        Defines aggregating of multiple output values. Array-like value defines weights used to average errors.\n\n                 "raw_values" : Returns a full set of errors in case of multioutput input.\n\n                 "uniform_average" : Errors of all outputs are averaged with uniform weight.\n\nReturns:\n    mae : mean absolute error.\n        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.\n        MAE output is non-negative floating point. The best value is 0.0.\nExamples:\n\n    >>> mae_metric = datasets.load_metric("mae")\n    >>> predictions = [2.5, 0.0, 2, 8]\n    >>> references = [3, -0.5, 2, 7]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.5}\n\n    If you\'re using multi-dimensional lists, then set the config as follows :\n\n    >>> mae_metric = datasets.load_metric("mae", "multilist")\n    >>> predictions = [[0.5, 1], [-1, 1], [7, -6]]\n    >>> references = [[0, 2], [-1, 2], [8, -5]]\n    >>> results = mae_metric.compute(predictions=predictions, references=references)\n    >>> print(results)\n    {\'mae\': 0.75}\n    >>> results = mae_metric.compute(predictions=predictions, references=references, multioutput=\'raw_values\')\n    >>> print(results)\n    {\'mae\': array([0.5, 1. ])}\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION,      _KWARGS_DESCRIPTION  )
class   lowerCamelCase						(	datasets.Metric  ):

       def 					UpperCAmelCase					(      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 					UpperCAmelCase					(      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 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__=None						,    a__="uniform_average"       ):






        '''simple docstring'''






        snake_case :			str							=   mean_absolute_error(a__						,    a__						,    sample_weight=a__						,    multioutput=a__       )

        return {"mae": mae_score}






