






import warnings

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


class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Any			=	['''image_processor''', '''tokenizer''']
     UpperCamelCase_							:		str			=	'''CLIPImageProcessor'''
     UpperCamelCase_							:		Any			=	('''XLMRobertaTokenizer''', '''XLMRobertaTokenizerFast''')


     def __init__(				self       :	Any					, UpperCAmelCase_       :	Dict=None					, UpperCAmelCase_       :	int=None					, **UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Any							=    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_					, )
         SCREAMING_SNAKE_CASE       :      int							=    kwargs.pop("feature_extractor"      )

       SCREAMING_SNAKE_CASE       :      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       :	int					, UpperCAmelCase_       :	List[Any]=None					, UpperCAmelCase_       :	Tuple=None					, UpperCAmelCase_       :	int=None					, **UpperCAmelCase_       :	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:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self.tokenizer(UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, **UpperCAmelCase_      )

       if images is not None:
         SCREAMING_SNAKE_CASE       :      Dict							=    self.image_processor(UpperCAmelCase_					, return_tensors=UpperCAmelCase_					, **UpperCAmelCase_      )

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


     def 				_A       (				self       :	List[Any]					, *UpperCAmelCase_       :	Optional[Any]					, **UpperCAmelCase_       :	List[str]      ):
       return self.tokenizer.batch_decode(*UpperCAmelCase_					, **UpperCAmelCase_      )


     def 				_A       (				self       :	List[Any]					, *UpperCAmelCase_       :	Any					, **UpperCAmelCase_       :	int      ):
       return self.tokenizer.decode(*UpperCAmelCase_					, **UpperCAmelCase_      )






     @property
     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.tokenizer.model_input_names
       SCREAMING_SNAKE_CASE       :      Any							=    self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )














def   lowerCamelCase__			(     lowercase						,     lowercase				):

  """simple docstring"""






  return numa ^ numa < 0


if __name__ == "__main__":
   import doctest

   doctest.testmod()






