




'''simple docstring'''







import warnings

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


class 			_a  (	__a ):
       __a				:     int   				=					["""image_processor""", """tokenizer"""]
       __a				:     Union[str, Any]   				=					"""ChineseCLIPImageProcessor"""
       __a				:     List[Any]   				=					("""BertTokenizer""", """BertTokenizerFast""")
       def __init__(	self						:	Dict   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	Dict=None   ,				**lowercase						:	Optional[Any]				):



           '''simple docstring'''

           UpperCAmelCase			       =		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   ,				)
               UpperCAmelCase			       =		kwargs.pop('''feature_extractor'''				)

           UpperCAmelCase			       =		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				)
           UpperCAmelCase			       =		self.image_processor
       def __call__(	self						:	Tuple   ,				lowercase						:	Optional[Any]=None   ,				lowercase						:	Union[str, Any]=None   ,				lowercase						:	int=None   ,				**lowercase						:	Dict				):



           '''simple docstring'''

           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:
               UpperCAmelCase			       =		self.tokenizer(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if images is not None:
               UpperCAmelCase			       =		self.image_processor(lowercase   ,				return_tensors=lowercase   ,				**lowercase				)

           if text is not None and images is not None:
               UpperCAmelCase			       =		image_features.pixel_values
               return encoding
           elif text is not None:
               return encoding
           else:
               return BatchEncoding(data=dict(**lowercase				)   ,				tensor_type=lowercase				)
       def     A      (	self						:	int   ,				*lowercase						:	Tuple   ,				**lowercase						:	List[str]				):



           '''simple docstring'''

           return self.tokenizer.batch_decode(*lowercase   ,				**lowercase				)
       def     A      (	self						:	Optional[Any]   ,				*lowercase						:	int   ,				**lowercase						:	Optional[int]				):



           '''simple docstring'''

           return self.tokenizer.decode(*lowercase   ,				**lowercase				)
       @property
       def     A      (	self						:	Optional[int]				):



           '''simple docstring'''

           UpperCAmelCase			       =		self.tokenizer.model_input_names
           UpperCAmelCase			       =		self.image_processor.model_input_names
           return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)







       @property
       def     A      (	self						:	List[Any]				):



           '''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









from __future__ import annotations

__UpperCAmelCase          =       {
    '''A''': ['''B''', '''C''', '''E'''],
    '''B''': ['''A''', '''D''', '''E'''],
    '''C''': ['''A''', '''F''', '''G'''],
    '''D''': ['''B'''],
    '''E''': ['''A''', '''B''', '''D'''],
    '''F''': ['''C'''],
    '''G''': ['''C'''],
}



class 				lowerCAmelCase_		:
       def __init__(			self,			SCREAMING_SNAKE_CASE_,			SCREAMING_SNAKE_CASE_       )     ->		None:
          UpperCamelCase    :  Union[str, Any]							=	graph
          # mapping node to its parent in resulting breadth first tree
          UpperCamelCase    :  dict[str, str | None]							=	{}
          UpperCamelCase    :  Union[str, Any]							=	source_vertex
       def 					snake_case_       (			self       )     ->		None:
          UpperCamelCase    :  str							=	{self.source_vertex}
          UpperCamelCase    :  str							=	None
          UpperCamelCase    :  int							=	[self.source_vertex]  # first in first out queue

          while queue:
             UpperCamelCase    :  Dict							=	queue.pop(0       )
             for adjacent_vertex in self.graph[vertex]:
                if adjacent_vertex not in visited:
                   visited.add(SCREAMING_SNAKE_CASE_       )
                   UpperCamelCase    :  Tuple							=	vertex
                   queue.append(SCREAMING_SNAKE_CASE_       )






       def 					snake_case_       (			self,			SCREAMING_SNAKE_CASE_       )     ->		str:
          if target_vertex == self.source_vertex:
             return self.source_vertex

          UpperCamelCase    :  Optional[Any]							=	self.parent.get(SCREAMING_SNAKE_CASE_       )
          if target_vertex_parent is None:
             UpperCamelCase    :  Union[str, Any]							=	(
                 F"""No path from vertex: {self.source_vertex} to vertex: {target_vertex}"""
             )
             raise ValueError(SCREAMING_SNAKE_CASE_       )

          return self.shortest_path(SCREAMING_SNAKE_CASE_       ) + F"""->{target_vertex}"""


if __name__ == "__main__":
    __UpperCAmelCase          =       Graph(graph, '''G''')
    g.breath_first_search()
    print(g.shortest_path('''D'''))
    print(g.shortest_path('''G'''))
    print(g.shortest_path('''Foo'''))



