




'''simple docstring'''



def   lowercase__(			__UpperCamelCase:					str      ,__UpperCamelCase:					str    ):

      """simple docstring"""






      def get_matched_characters(__UpperCamelCase:					str      ,__UpperCamelCase:					str    ) -> str:
            SCREAMING_SNAKE_CASE   :      Optional[int]    					=						[]
            SCREAMING_SNAKE_CASE   :      List[Any]    					=						min(len(_stra    )      ,len(_stra    )    ) // 2
            for i, l in enumerate(_stra    ):
                  SCREAMING_SNAKE_CASE   :      str    					=						int(max(0      ,i - limit    )    )
                  SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						int(min(i + limit + 1      ,len(_stra    )    )    )
                  if l in _stra[left:right]:
                        matched.append(__UpperCamelCase    )
                        SCREAMING_SNAKE_CASE   :      str    					=						f"{_stra[0:_stra.index(__UpperCamelCase    )]} {_stra[_stra.index(__UpperCamelCase    ) + 1:]}"

            return "".join(__UpperCamelCase    )

      # matching characters
      SCREAMING_SNAKE_CASE   :      Tuple    					=						get_matched_characters(__UpperCamelCase      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      List[str]    					=						get_matched_characters(__UpperCamelCase      ,__UpperCamelCase    )
      SCREAMING_SNAKE_CASE   :      str    					=						len(__UpperCamelCase    )

      # transposition
      SCREAMING_SNAKE_CASE   :      Any    					=						(
          len([(ca, ca) for ca, ca in zip(__UpperCamelCase      ,__UpperCamelCase    ) if ca != ca]    ) // 2
      )

      if not match_count:
            SCREAMING_SNAKE_CASE   :      Union[str, Any]    					=						0.0
      else:
            SCREAMING_SNAKE_CASE   :      Any    					=						(
                1
                / 3
                * (
                    match_count / len(__UpperCamelCase    )
                    + match_count / len(__UpperCamelCase    )
                    + (match_count - transpositions) / match_count
                )
            )

      # common prefix up to 4 characters
      SCREAMING_SNAKE_CASE   :      List[Any]    					=						0
      for ca, ca in zip(stra[:4]      ,stra[:4]    ):
            if ca == ca:
                  prefix_len += 1
            else:
                  break

      return jaro + 0.1 * prefix_len * (1 - jaro)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      print(jaro_winkler("hello", "world"))








'''simple docstring'''



from typing import Dict

from .base import GenericTensor, Pipeline






class 							_a				(			SCREAMING_SNAKE_CASE ):





       '''simple docstring'''






       def        UpperCamelCase_							(      self,							A=None,							A=None,							A=None,							**A  ):







             '''simple docstring'''







             if tokenize_kwargs is None:
                   SCREAMING_SNAKE_CASE   :      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)'  )
                   SCREAMING_SNAKE_CASE   :      Tuple    					=						truncation

             SCREAMING_SNAKE_CASE   :      int    					=						tokenize_kwargs

             SCREAMING_SNAKE_CASE   :      Optional[Any]    					=						{}
             if return_tensors is not None:
                   SCREAMING_SNAKE_CASE   :      Optional[int]    					=						return_tensors

             return preprocess_params, {}, postprocess_params







       def        UpperCamelCase_							(      self,							A,							**A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.framework
             SCREAMING_SNAKE_CASE   :      Tuple    					=						self.tokenizer(A,							return_tensors=A,							**A  )
             return model_inputs







       def        UpperCamelCase_							(      self,							A  ):







             '''simple docstring'''







             SCREAMING_SNAKE_CASE   :      Any    					=						self.model(**A  )
             return model_outputs







       def        UpperCamelCase_							(      self,							A,							A=False  ):







             '''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,							*A,							**A  ):







             '''simple docstring'''







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


