



'''simple docstring'''





import warnings

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






class        lowerCAmelCase_(				lowerCamelCase_     ):
  '''simple docstring'''




  __lowercase							:      Optional[int]				=      ['''image_processor''', '''tokenizer''']
  __lowercase							:      Union[str, Any]				=      '''CLIPImageProcessor'''
  __lowercase							:      Any				=      ('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')




  def __init__(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  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."""  ,_lowercase  ,)
          lowerCAmelCase__ :  Dict							      =					kwargs.pop("""feature_extractor"""      )

      lowerCAmelCase__ :  str							      =					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__(_lowercase  ,_lowercase      )




  def __call__(    self  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,__UpperCAmelCase=None  ,**__UpperCAmelCase      )    ->		int:






      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:
          lowerCAmelCase__ :  List[Any]							      =					self.tokenizer(_lowercase  ,return_tensors=_lowercase  ,**_lowercase      )

      if images is not None:
          lowerCAmelCase__ :  Tuple							      =					self.image_processor(_lowercase  ,return_tensors=_lowercase  ,**_lowercase      )

      if text is not None and images is not None:
          lowerCAmelCase__ :  Optional[Any]							      =					image_features.pixel_values
          return encoding
      elif text is not None:
          return encoding
      else:
          return BatchEncoding(data=dict(**_lowercase      )  ,tensor_type=_lowercase      )




  def 							UpperCAmelCase_	(    self  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		Union[str, Any]:
      return self.tokenizer.batch_decode(*_lowercase  ,**_lowercase      )




  def 							UpperCAmelCase_	(    self  ,*__UpperCAmelCase  ,**__UpperCAmelCase      )    ->		Optional[Any]:
      return self.tokenizer.decode(*_lowercase  ,**_lowercase      )






  @property
  def 							UpperCAmelCase_	(    self      )    ->		Union[str, Any]:
      lowerCAmelCase__ :  Dict							      =					self.tokenizer.model_input_names
      lowerCAmelCase__ :  Tuple							      =					self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )



def 	__SCREAMING_SNAKE_CASE  (					__UpperCamelCase    :       int ,     __UpperCamelCase    :       int	)							->					str:







  """simple docstring"""





  if a < 0 or b < 0:
    raise ValueError("""the value of both inputs must be positive"""	)

  SCREAMING_SNAKE_CASE__										=      str(bin(__UpperCamelCase	)	)[2:]  # remove the leading "0b"
  SCREAMING_SNAKE_CASE__										=      str(bin(__UpperCamelCase	)	)[2:]  # remove the leading "0b"

  SCREAMING_SNAKE_CASE__										=      max(len(__UpperCamelCase	) ,     len(__UpperCamelCase	)	)

  return "0b" + "".join(
      str(int(char_a != char_b	)	)
      for char_a, char_b in zip(a_binary.zfill(__UpperCamelCase	) ,     b_binary.zfill(__UpperCamelCase	)	)	)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
