

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



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()
