




def       UpperCAmelCase__ (snake_case__							:      str	,     snake_case__							:      str					):
  """simple docstring"""

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

    return "".join(snake_case__					)

  # matching characters
  _snake_case							:	Optional[Any]			=						get_matched_characters(snake_case__	,     snake_case__					)
  _snake_case							:	Optional[Any]			=						get_matched_characters(snake_case__	,     snake_case__					)
  _snake_case							:	int			=						len(snake_case__					)

  # transposition
  _snake_case							:	List[str]			=						(
      len([(ca, ca) for ca, ca in zip(snake_case__	,     snake_case__					) if ca != ca]					) // 2
  )

  if not match_count:
    _snake_case							:	Tuple			=						0.0
  else:
    _snake_case							:	str			=						(
        1
        / 3
        * (
            match_count / len(snake_case__					)
            + match_count / len(snake_case__					)
            + (match_count - transpositions) / match_count
        )
    )

  # common prefix up to 4 characters
  _snake_case							:	int			=						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 ...processing_utils import ProcessorMixin






class       lowercase(  __a				):







 '''simple docstring'''





 lowercase__				      =      ["image_processor", "feature_extractor"]
 lowercase__				      =      "TvltImageProcessor"
 lowercase__				      =      "TvltFeatureExtractor"


 def __init__(       self:	Dict,   a_:	Union[str, Any],   a_:	Union[str, Any]	):




   '''simple docstring'''



   super().__init__(image_processor=a_,   feature_extractor=a_	)

   _snake_case							:	Any			=						image_processor
   _snake_case							:	Dict			=						feature_extractor


 def __call__(       self:	int,   a_:	str=None,   a_:	Tuple=None,   a_:	Dict=None,   a_:	str=None,   a_:	Optional[int]=False,   a_:	Tuple=False,   *a_:	List[str],   **a_:	int,   ):




   '''simple docstring'''



   if images is None and audio is None:
     raise ValueError("""You need to specify either an `images` or `audio` input to process."""	)

   _snake_case							:	Optional[int]			=						None
   if images is not None:
     _snake_case							:	Tuple			=						self.image_processor(a_,   mask_pixel=a_,   *a_,   **a_	)
   if images_mixed is not None:
     _snake_case							:	Optional[int]			=						self.image_processor(a_,   is_mixed=a_,   *a_,   **a_	)
   if audio is not None:
     _snake_case							:	Any			=						self.feature_extractor(
         a_,   *a_,   sampling_rate=a_,   mask_audio=a_,   **a_	)

   _snake_case							:	List[str]			=						{}
   if audio is not None:
     output_dict.update(a_	)
   if images is not None:
     output_dict.update(a_	)
   if images_mixed_dict is not None:
     output_dict.update(a_	)
   return output_dict


 @property
 def 				UpperCamelCase_     (       self:	List[str]	):




   '''simple docstring'''



   _snake_case							:	Dict			=						self.image_processor.model_input_names
   _snake_case							:	List[str]			=						self.feature_extractor.model_input_names
   return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names	)	)


