

import warnings

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





class 					__UpperCAmelCase     (       UpperCAmelCase_						):
   UpperCamelCase        =     ['image_processor', 'tokenizer']
   UpperCamelCase        =     'CLIPImageProcessor'
   UpperCamelCase        =     ('XLMRobertaTokenizer', 'XLMRobertaTokenizerFast')



   def __init__(							self							:     Tuple,		__A							:     str=None,		__A							:     List[Any]=None,		**__A							:     Dict						):
     UpperCAmelCase       :							str  = None
     if "feature_extractor" in kwargs:
       warnings.warn(
           '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
           ''' instead.''',		__UpperCAmelCase,		)
       UpperCAmelCase       :							str  = kwargs.pop('''feature_extractor'''						)

     UpperCAmelCase       :							Union[str, Any]  = image_processor if image_processor is not None else feature_extractor
     if image_processor is None:
       raise ValueError('''You need to specify an `image_processor`.'''						)
     if tokenizer is None:
       raise ValueError('''You need to specify a `tokenizer`.'''						)

     super().__init__(__UpperCAmelCase,		__UpperCAmelCase						)



   def __call__(							self							:     Optional[int],		__A							:     Optional[Any]=None,		__A							:     str=None,		__A							:     Optional[Any]=None,		**__A							:     Dict						):



     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:
       UpperCAmelCase       :							str  = self.tokenizer(__UpperCAmelCase,		return_tensors=__UpperCAmelCase,		**__UpperCAmelCase						)

     if images is not None:
       UpperCAmelCase       :							Union[str, Any]  = self.image_processor(__UpperCAmelCase,		return_tensors=__UpperCAmelCase,		**__UpperCAmelCase						)

     if text is not None and images is not None:
       UpperCAmelCase       :							Union[str, Any]  = image_features.pixel_values
       return encoding
     elif text is not None:
       return encoding
     else:
       return BatchEncoding(data=dict(**__UpperCAmelCase						),		tensor_type=__UpperCAmelCase						)



   def        __magic_name__    (							self							:     Any,		*__A							:     Union[str, Any],		**__A							:     Dict						):
     return self.tokenizer.batch_decode(*__UpperCAmelCase,		**__UpperCAmelCase						)



   def        __magic_name__    (							self							:     Any,		*__A							:     List[Any],		**__A							:     Dict						):
     return self.tokenizer.decode(*__UpperCAmelCase,		**__UpperCAmelCase						)





   @property
   def        __magic_name__    (							self							:     str						):
     UpperCAmelCase       :							Tuple  = self.tokenizer.model_input_names
     UpperCAmelCase       :							Union[str, Any]  = self.image_processor.model_input_names
     return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names						)						)






def    lowerCAmelCase_ (     __A,  __A							)     ->				float:







      '''simple docstring'''


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

            return "".join(__A							)

      # matching characters
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  get_matched_characters(__A,  __A							)
      UpperCAmelCase__								=  len(__A							)

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

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

      # common prefix up to 4 characters
      UpperCAmelCase__								=  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'))



