




from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class        UpperCamelCase_					(				UpperCAmelCase__  ):

    '''simple docstring'''






    UpperCAmelCase__													=   ['''image_processor''', '''tokenizer''']
    UpperCAmelCase__													=   '''AutoImageProcessor'''
    UpperCAmelCase__													=   '''AutoTokenizer'''


    def __init__(    self   :			Union[str, Any]      ,				UpperCAmelCase__   :			Optional[int]      ,				UpperCAmelCase__   :			Tuple)		->Optional[Any]:





           '''simple docstring'''

           super().__init__(UpperCAmelCase__      ,				UpperCAmelCase__)
           A__   				=					self.image_processor


    def __call__(    self   :			Any      ,				UpperCAmelCase__   :			Union[str, Any]=None      ,				UpperCAmelCase__   :			Any=None      ,				UpperCAmelCase__   :			Optional[int]=None      ,				**UpperCAmelCase__   :			Dict)		->Any:





           '''simple docstring'''

           if text is None and images is None:
                  raise ValueError('''You have to specify either text or images. Both cannot be none.''')

           if text is not None:
                  A__   				=					self.tokenizer(UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__      ,				**UpperCAmelCase__)

           if images is not None:
                  A__   				=					self.image_processor(UpperCAmelCase__      ,				return_tensors=UpperCAmelCase__      ,				**UpperCAmelCase__)

           if text is not None and images is not None:
                  A__   				=					image_features.pixel_values
                  return encoding
           elif text is not None:
                  return encoding
           else:
                  return BatchEncoding(data=dict(**UpperCAmelCase__)      ,				tensor_type=UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[Any]      ,				*UpperCAmelCase__   :			Any      ,				**UpperCAmelCase__   :			int)		->Optional[Any]:





           '''simple docstring'''

           return self.tokenizer.batch_decode(*UpperCAmelCase__      ,				**UpperCAmelCase__)


    def  SCREAMING_SNAKE_CASE							(    self   :			Optional[int]      ,				*UpperCAmelCase__   :			int      ,				**UpperCAmelCase__   :			Tuple)		->Union[str, Any]:





           '''simple docstring'''

           return self.tokenizer.decode(*UpperCAmelCase__      ,				**UpperCAmelCase__)





    @property
    def  SCREAMING_SNAKE_CASE							(    self   :			List[str])		->Optional[Any]:





           '''simple docstring'''

           return ["input_ids", "attention_mask", "pixel_values"]








def SCREAMING_SNAKE_CASE       ( lowercase_				,       lowercase_		)     -> float:



       """simple docstring"""

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

              return "".join(lowercase_		)

       # matching characters
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					get_matched_characters(lowercase_				,       lowercase_		)
       A__   				=					len(lowercase_		)

       # transposition
       A__   				=					(
           len([(ca, ca) for ca, ca in zip(lowercase_				,       lowercase_		) if ca != ca]		) // 2
       )

       if not match_count:
              A__   				=					0.0
       else:
              A__   				=					(
                  1
                  / 3
                  * (
                      match_count / len(lowercase_		)
                      + match_count / len(lowercase_		)
                      + (match_count - transpositions) / match_count
                  )
              )

       # common prefix up to 4 characters
       A__   				=					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"""))


