code: 
  
  import warnings
  from contextlib import contextmanager
  
  from ...processing_utils import ProcessorMixin
  
  
  
  
  
  
  
  class 		__magic_name__    (							lowerCamelCase__							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			Union[str, Any]												=     ['image_processor', 'tokenizer']
     lowerCamelCase__				:			str												=     'AutoImageProcessor'
     lowerCamelCase__				:			Optional[int]												=     'AutoTokenizer'
  
  
  
     def __init__(       self,					lowercase_=None,					lowercase_=None,					**lowercase_ )							->					Tuple:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       a__						=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_,					)
         a__						=kwargs.pop('''feature_extractor''' )
  
       a__						=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_ )
       a__						=self.image_processor
       a__						=False
  
  
  
     def __call__(       self,					*lowercase_,					**lowercase_ )							->					str:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if self._in_target_context_manager:
         return self.current_processor(*lowercase_,					**lowercase_ )
  
       a__						=kwargs.pop('''images''',					lowercase_ )
       a__						=kwargs.pop('''text''',					lowercase_ )
       if len(lowercase_ ) > 0:
         a__						=args[0]
         a__						=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:
         a__						=self.image_processor(lowercase_,					*lowercase_,					**lowercase_ )
       if text is not None:
         a__						=self.tokenizer(lowercase_,					**lowercase_ )
  
       if text is None:
         return inputs
       elif images is None:
         return encodings
       else:
         a__						=encodings['''input_ids''']
         return inputs
  
  
  
     def 				_UpperCAmelCase				(       self,					*lowercase_,					**lowercase_ )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.tokenizer.batch_decode(*lowercase_,					**lowercase_ )
  
  
  
     def 				_UpperCAmelCase				(       self,					*lowercase_,					**lowercase_ )							->					List[Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       return self.tokenizer.decode(*lowercase_,					**lowercase_ )
  
  
  
     @contextmanager
     def 				_UpperCAmelCase				(       self )							->					Union[str, Any]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       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.''' )
       a__						=True
       a__						=self.tokenizer
       yield
       a__						=self.image_processor
       a__						=False
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					List[str]:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       warnings.warn(
           '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''',					lowercase_,					)
       return self.image_processor_class
  
  
  
     @property
     def 				_UpperCAmelCase				(       self )							->					Dict:
  
  
  
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       warnings.warn(
           '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''',					lowercase_,					)
       return self.image_processor
  
style_context: 
  
  import heapq
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Optional[Any]			,   _A   :				str			,   _A   :				Optional[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[(0, start)]  # cost from start node,end node
    a__						=set()
    while heap:
      ((a__),   (a__))						=heapq.heappop(_A							)
      if u in visited:
        continue
      visited.add(_A							)
      if u == end:
        return cost
      for v, c in graph[u]:
        if v in visited:
          continue
        a__						=cost + c
        heapq.heappush(_A			,   (next_item, v)							)
    return -1
  
  
  lowerCamelCase     =      {
      '''A''': [['''B''', 2], ['''C''', 5]],
      '''B''': [['''A''', 2], ['''D''', 3], ['''E''', 1], ['''F''', 1]],
      '''C''': [['''A''', 5], ['''F''', 3]],
      '''D''': [['''B''', 3]],
      '''E''': [['''B''', 4], ['''F''', 3]],
      '''F''': [['''C''', 3], ['''E''', 3]],
  }
  lowerCamelCase     =      {
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''F''', 3]],
      '''F''': [],
  }
  lowerCamelCase     =      {
      '''B''': [['''C''', 1]],
      '''C''': [['''D''', 1]],
      '''D''': [['''F''', 1]],
      '''E''': [['''B''', 1], ['''G''', 2]],
      '''F''': [],
      '''G''': [['''F''', 1]],
  }
  
  lowerCamelCase     =      dijkstra(G, '''E''', '''C''')
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  lowerCamelCase     =      dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 3 --> F == 3
  
  lowerCamelCase     =      dijkstra(Ga, '''E''', '''F''')
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()
  