code: 
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  class    __A				(							lowerCAmelCase ):
      lowerCAmelCase_							:		str						=							["image_processor", "tokenizer"]
      lowerCAmelCase_							:		Tuple						=							"AutoImageProcessor"
      lowerCAmelCase_							:		List[Any]						=							"AutoTokenizer"
  
  
  
  
  
  
      def __init__(	self			:    Optional[Any]    ,				UpperCAmelCase_			:    Optional[int]=None    ,				UpperCAmelCase_			:    Any=None    ,				**UpperCAmelCase_			:    Optional[int]    ):
          lowerCAmelCase				:			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_    ,				)
              lowerCAmelCase				:			List[Any]    			=      kwargs.pop('feature_extractor'    )
  
          lowerCAmelCase				:			Dict    			=      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_    )
          lowerCAmelCase				:			Any    			=      self.image_processor
          lowerCAmelCase				:			Union[str, Any]    			=      False
  
  
  
  
  
  
      def __call__(	self			:    List[str]    ,				*UpperCAmelCase_			:    Tuple    ,				**UpperCAmelCase_			:    List[str]    ):
          # For backward compatibility
          if self._in_target_context_manager:
              return self.current_processor(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
          lowerCAmelCase				:			Union[str, Any]    			=      kwargs.pop('images'    ,				UpperCAmelCase_    )
          lowerCAmelCase				:			List[str]    			=      kwargs.pop('text'    ,				UpperCAmelCase_    )
          if len(UpperCAmelCase_    ) > 0:
              lowerCAmelCase				:			Union[str, Any]    			=      args[0]
              lowerCAmelCase				:			Dict    			=      args[1:]
  
          if images is None and text is None:
              raise ValueError('You need to specify either an `images` or `text` input to process.'    )
  
          if images is not None:
              lowerCAmelCase				:			int    			=      self.image_processor(UpperCAmelCase_    ,				*UpperCAmelCase_    ,				**UpperCAmelCase_    )
          if text is not None:
              lowerCAmelCase				:			List[Any]    			=      self.tokenizer(UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
          if text is None:
              return inputs
          elif images is None:
              return encodings
          else:
              lowerCAmelCase				:			int    			=      encodings['input_ids']
              return inputs
  
  
  
  
  
  
      def 	lowercase__ (	self			:    List[str]    ,				*UpperCAmelCase_			:    Any    ,				**UpperCAmelCase_			:    str    ):
          return self.tokenizer.batch_decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
  
  
  
  
  
      def 	lowercase__ (	self			:    Dict    ,				*UpperCAmelCase_			:    Optional[Any]    ,				**UpperCAmelCase_			:    Optional[int]    ):
          return self.tokenizer.decode(*UpperCAmelCase_    ,				**UpperCAmelCase_    )
  
  
  
  
  
  
      @contextmanager
      def 	lowercase__ (	self			:    Tuple    ):
          warnings.warn(
              '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
              'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
              'your images inputs, or in a separate call.'    )
          lowerCAmelCase				:			Any    			=      True
          lowerCAmelCase				:			Any    			=      self.tokenizer
          yield
          lowerCAmelCase				:			int    			=      self.image_processor
          lowerCAmelCase				:			Optional[int]    			=      False
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Optional[int]    ):
          warnings.warn(
              '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'    ,				UpperCAmelCase_    ,				)
          return self.image_processor_class
  
  
  
  
  
  
      @property
      def 	lowercase__ (	self			:    Optional[int]    ):
          warnings.warn(
              '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'    ,				UpperCAmelCase_    ,				)
          return self.image_processor
  
  
  
  
  
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available, is_tokenizers_available
  
  
  __A   :     Dict      =			{}
  
  try:
   if not is_sentencepiece_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[Any]      =			['''BarthezTokenizer''']
  
  try:
   if not is_tokenizers_available():
    raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
   pass
  else:
   __A   :     List[str]      =			['''BarthezTokenizerFast''']
  
  
  if TYPE_CHECKING:
   try:
    if not is_sentencepiece_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez import BarthezTokenizer
  
   try:
    if not is_tokenizers_available():
     raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
    pass
   else:
    from .tokenization_barthez_fast import BarthezTokenizerFast
  
  else:
   import sys
  
   __A   :     Tuple      =			_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  
  
  
  
  
  