
def 			UpperCAmelCase_     (  __lowerCAmelCase		,    __lowerCAmelCase		)       ->  int:
    assert x is not None
    assert y is not None

    __lowercase						:	Dict					=				len(SCREAMING_SNAKE_CASE__		)
    __lowercase						:	List[Any]					=				len(SCREAMING_SNAKE_CASE__		)

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

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

            __lowercase						:	List[str]					=				max(l[i - 1][j]		,    l[i][j - 1]		,    l[i - 1][j - 1] + match		)

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

        if l[i][j] == l[i - 1][j - 1] + match:
            if match == 1:
                __lowercase						:	List[str]					=				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__":
 __lowerCAmelCase    :	Union[str, Any]						=						"AGGTAB"
 __lowerCAmelCase    :	List[Any]						=						"GXTXAYB"
 __lowerCAmelCase    :	str						=						4
 __lowerCAmelCase    :	Any						=						"GTAB"

 __lowerCAmelCase					,      __lowerCAmelCase    :	str						=						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  A						(							_UpperCAmelCase ):







    """simple docstring"""





    def 						snake_case__     (      self :       int,lowercase_ :       Dict=None,lowercase_ :       Tuple=None,lowercase_ :       List[Any]=None,**lowercase_ :       Any		)->    Optional[Any]:





        '''simple docstring'''







        if tokenize_kwargs is None:
            A__          =   {}

        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__          =   truncation

        A__          =   tokenize_kwargs

        A__          =   {}
        if return_tensors is not None:
            A__          =   return_tensors

        return preprocess_params, {}, postprocess_params


    def 						snake_case__     (      self :       Dict,lowercase_ :       List[Any],**lowercase_ :       Tuple		)->    Dict[str, GenericTensor]:





        '''simple docstring'''







        A__          =   self.framework
        A__          =   self.tokenizer(lowercase_,return_tensors=lowercase_,**lowercase_		)
        return model_inputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       int		)->    Optional[Any]:





        '''simple docstring'''







        A__          =   self.model(**lowercase_		)
        return model_outputs


    def 						snake_case__     (      self :       Tuple,lowercase_ :       Tuple,lowercase_ :       List[str]=False		)->    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 :       List[Any],*lowercase_ :       int,**lowercase_ :       Optional[Any]		)->    int:





        '''simple docstring'''







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

