






'''simple docstring'''







from sklearn.metrics import mean_absolute_error

import datasets


UpperCamelCase_					=				"""\
@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}
}
"""

UpperCamelCase_					=				"""\
Mean Absolute Error (MAE) is the mean of the magnitude of difference between the predicted and actual
values.
"""


UpperCamelCase_					=				"""
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 					__UpperCAmelCase (		datasets.Metric	):

      '''simple docstring'''





      def 							lowerCamelCase     (					self						):
             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 							lowerCamelCase     (					self						):

             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 							lowerCamelCase     (					self  ,		_UpperCAmelCase  ,		_UpperCAmelCase  ,		_UpperCAmelCase=None  ,		_UpperCAmelCase="uniform_average"						):
             UpperCAmelCase__	:   int												=	mean_absolute_error(__SCREAMING_SNAKE_CASE  ,		__SCREAMING_SNAKE_CASE  ,		sample_weight=__SCREAMING_SNAKE_CASE  ,		multioutput=__SCREAMING_SNAKE_CASE						)

             return {"mae": mae_score}






"""simple docstring"""


def 				a__      (	_UpperCAmelCase						,  _UpperCAmelCase						)     ->	int:

 '''simple docstring'''






 if len(_UpperCAmelCase						) < len(_UpperCAmelCase						):
  return levenshtein_distance(_UpperCAmelCase						,  _UpperCAmelCase						)

 if len(_UpperCAmelCase						) == 0:
  return len(_UpperCAmelCase						)

 snake_case__     				=					list(range(len(_UpperCAmelCase						) + 1						)						)

 for i, ca in enumerate(_UpperCAmelCase						):
  snake_case__     				=					[i + 1]

  for j, ca in enumerate(_UpperCAmelCase						):
   # Calculate insertions, deletions and substitutions
   snake_case__     				=					previous_row[j + 1] + 1
   snake_case__     				=					current_row[j] + 1
   snake_case__     				=					previous_row[j] + (ca != ca)

   # Get the minimum to append to the current row
   current_row.append(min(_UpperCAmelCase						,  _UpperCAmelCase						,  _UpperCAmelCase						)						)

  # Store the previous row
  snake_case__     				=					current_row

 # Returns the last element (distance)
 return previous_row[-1]


if __name__ == "__main__":
   a__       :  Optional[int]       		=					input("""Enter the first word:\n""").strip()
   a__       :  int       		=					input("""Enter the second word:\n""").strip()

   a__       :  Union[str, Any]       		=					levenshtein_distance(first_word, second_word)
   print(f'''Levenshtein distance between {first_word} and {second_word} is {result}''')

