





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   int:




    """simple docstring"""




    return len_board * len_board_column * row + column





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    )       ->   bool:




    """simple docstring"""




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

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

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

        _A    =       get_point_key(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)
        if key in visited_points_set:
            continue

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

        visited_points_set.remove(_SCREAMING_SNAKE_CASE							)

    return False





def 							__lowerCAmelCase(					_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							)       ->   bool:




    """simple docstring"""




    _A    =       (
        'The board should be a non empty matrix of single chars strings.'
    )

    _A    =       len(_SCREAMING_SNAKE_CASE							)
    if not isinstance(_SCREAMING_SNAKE_CASE       ,    _SCREAMING_SNAKE_CASE							) or len(_SCREAMING_SNAKE_CASE							) == 0:
        raise ValueError(_SCREAMING_SNAKE_CASE							)

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

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

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

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

    return False


if __name__ == "__main__":
 import doctest

 doctest.testmod()












from sklearn.metrics import accuracy_score

import datasets


__A       : Union[str, Any]						    = "\nAccuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:\nAccuracy = (TP + TN) / (TP + TN + FP + FN)\n Where:\nTP: True positive\nTN: True negative\nFP: False positive\nFN: False negative\n"


__A       : List[Any]						    = "\nArgs:\n    predictions (`list` of `int`): Predicted labels.\n    references (`list` of `int`): Ground truth labels.\n    normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.\n    sample_weight (`list` of `float`): Sample weights Defaults to None.\n\nReturns:\n    accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.\n\nExamples:\n\n    Example 1-A simple example\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])\n        >>> print(results)\n        {'accuracy': 0.5}\n\n    Example 2-The same as Example 1, except with `normalize` set to `False`.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)\n        >>> print(results)\n        {'accuracy': 3.0}\n\n    Example 3-The same as Example 1, except with `sample_weight` set.\n        >>> accuracy_metric = datasets.load_metric(\"accuracy\")\n        >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])\n        >>> print(results)\n        {'accuracy': 0.8778625954198473}\n"


__A       : List[str]						    = "\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"







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


     def 					lowerCAmelCase__		(						self       ):
         return datasets.MetricInfo(
             description=_DESCRIPTION							,    citation=_CITATION							,    inputs_description=_KWARGS_DESCRIPTION							,    features=datasets.Features(
                 {
                     'predictions': datasets.Sequence(datasets.Value('int32'       )       ),
                     'references': datasets.Sequence(datasets.Value('int32'       )       ),
                 }
                 if self.config_name == 'multilabel'
                 else {
                     'predictions': datasets.Value('int32'       ),
                     'references': datasets.Value('int32'       ),
                 }       )							,    reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html']							,    )




     def 					lowerCAmelCase__		(						self							,    snake_case_							,    snake_case_							,    snake_case_=True							,    snake_case_=None       ):
         return {
             "accuracy": float(
                 accuracy_score(snake_case_							,    snake_case_							,    normalize=snake_case_							,    sample_weight=snake_case_       )       )
         }





