




def    A						(__A   :    str   ,				__A   :    str       )   ->      Tuple:







     """simple docstring"""


     assert x is not None
     assert y is not None

     UpperCAmelCase_          =							len(__A       )
     UpperCAmelCase_          =							len(__A       )

     # declaring the array for storing the dp values
     UpperCAmelCase_          =							[[0] * (n + 1) for _ in range(m + 1       )]  # noqa: E741

     for i in range(1   ,				m + 1       ):
          for j in range(1   ,				n + 1       ):
               UpperCAmelCase_          =							1 if x[i - 1] == y[j - 1] else 0

               UpperCAmelCase_          =							max(l[i - 1][j]   ,				l[i][j - 1]   ,				l[i - 1][j - 1] + match       )

     UpperCAmelCase_          =							''''''
     UpperCAmelCase_     ,       UpperCAmelCase_          =							m, n
     while i > 0 and j > 0:
          UpperCAmelCase_          =							1 if x[i - 1] == y[j - 1] else 0

          if l[i][j] == l[i - 1][j - 1] + match:
               if match == 1:
                    UpperCAmelCase_          =							x[i - 1] + seq
               i -= 1
               j -= 1
          elif l[i][j] == l[i - 1][j]:
               i -= 1
          else:
               j -= 1

     return l[m][n], seq


if __name__ == "__main__":
    snake_case_ : List[str]    		=       "AGGTAB"
    snake_case_ : List[Any]    		=       "GXTXAYB"
    snake_case_ : str    		=       4
    snake_case_ : str    		=       "GTAB"

    snake_case_		,       snake_case_ : Union[str, Any]    		=       longest_common_subsequence(a, b)
    print("len =", ln, ", sub-sequence =", subseq)
    import doctest

    doctest.testmod()










from typing import Dict

from .base import GenericTensor, Pipeline






class   _lowerCamelCase (					UpperCamelCase			):




       """simple docstring"""



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       _SCREAMING_SNAKE_CASE=None				,       **_SCREAMING_SNAKE_CASE		)->List[Any]:




         '''simple docstring'''





         if tokenize_kwargs is None:
           A_      :				Optional[int]		     =					{}

         if truncation is not None:
           if "truncation" in tokenize_kwargs:
             raise ValueError(
                 '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''		)
           A_      :				List[str]		     =					truncation

         A_      :				str		     =					tokenize_kwargs

         A_      :				Optional[Any]		     =					{}
         if return_tensors is not None:
           A_      :				Union[str, Any]		     =					return_tensors

         return preprocess_params, {}, postprocess_params



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Dict[str, GenericTensor]:




         '''simple docstring'''





         A_      :				str		     =					self.framework
         A_      :				Any		     =					self.tokenizer(_SCREAMING_SNAKE_CASE				,       return_tensors=_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)
         return model_inputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE		)->Optional[Any]:




         '''simple docstring'''





         A_      :				Optional[int]		     =					self.model(**_SCREAMING_SNAKE_CASE		)
         return model_outputs



       def        _snake_case       (      self				,       _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE=False		)->List[Any]:




         '''simple docstring'''





         if return_tensors:
           return model_outputs[0]
         if self.framework == "pt":
           return model_outputs[0].tolist()
         elif self.framework == "tf":
           return model_outputs[0].numpy().tolist()







       def __call__(      self				,       *_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)->Union[str, Any]:




         '''simple docstring'''





         return super().__call__(*_SCREAMING_SNAKE_CASE				,       **_SCREAMING_SNAKE_CASE		)




