

'''simple docstring'''



from __future__ import annotations

UpperCAmelCase					:			Optional[int]       =	{
    'A': ['B', 'C', 'E'],
    'B': ['A', 'D', 'E'],
    'C': ['A', 'F', 'G'],
    'D': ['B'],
    'E': ['A', 'B', 'D'],
    'F': ['C'],
    'G': ['C'],
}







class 		lowerCAmelCase__  :



 """simple docstring"""



 def __init__(						self   :			List[Any]   ,				__SCREAMING_SNAKE_CASE   :			dict[str, list[str]]   ,				__SCREAMING_SNAKE_CASE   :			str     )    ->					None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = graph
      # mapping node to its parent in resulting breadth first tree
      __SCREAMING_SNAKE_CASE      = {}
      __SCREAMING_SNAKE_CASE      = source_vertex
 def  UpperCAmelCase__						(						self   :			Union[str, Any]     )    ->					None:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = {self.source_vertex}
      __SCREAMING_SNAKE_CASE      = None
      __SCREAMING_SNAKE_CASE      = [self.source_vertex]  # first in first out queue

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


 def  UpperCAmelCase__						(						self   :			List[str]   ,				__SCREAMING_SNAKE_CASE   :			str     )    ->					str:


      """simple docstring"""




      if target_vertex == self.source_vertex:
           return self.source_vertex

      __SCREAMING_SNAKE_CASE      = self.parent.get(__SCREAMING_SNAKE_CASE     )
      if target_vertex_parent is None:
           __SCREAMING_SNAKE_CASE      = (
               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					:			Optional[int]       =	Graph(graph, 'G')
  g.breath_first_search()
  print(g.shortest_path('D'))
  print(g.shortest_path('G'))
  print(g.shortest_path('Foo'))









'''simple docstring'''



import warnings

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







class 		lowerCAmelCase__  ( a   ):



 """simple docstring"""



 lowerCAmelCase__       = ["image_processor", "tokenizer"]
 lowerCAmelCase__       = "CLIPImageProcessor"
 lowerCAmelCase__       = ("XLMRobertaTokenizer", "XLMRobertaTokenizerFast")
 def __init__(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			str=None   ,				**__SCREAMING_SNAKE_CASE   :			Tuple     )    ->					Dict:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = None
      if "feature_extractor" in kwargs:
           warnings.warn(
               """The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
               """ instead."""   ,				__SCREAMING_SNAKE_CASE   ,				)
           __SCREAMING_SNAKE_CASE      = kwargs.pop("""feature_extractor"""     )

      __SCREAMING_SNAKE_CASE      = 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__(__SCREAMING_SNAKE_CASE   ,				__SCREAMING_SNAKE_CASE     )
 def __call__(						self   :			Union[str, Any]   ,				__SCREAMING_SNAKE_CASE   :			Union[str, Any]=None   ,				__SCREAMING_SNAKE_CASE   :			Any=None   ,				__SCREAMING_SNAKE_CASE   :			Optional[Any]=None   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					int:


      """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:
           __SCREAMING_SNAKE_CASE      = self.tokenizer(__SCREAMING_SNAKE_CASE   ,				return_tensors=__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )

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

      if text is not None and images is not None:
           __SCREAMING_SNAKE_CASE      = image_features.pixel_values
           return encoding
      elif text is not None:
           return encoding
      else:
           return BatchEncoding(data=dict(**__SCREAMING_SNAKE_CASE     )   ,				tensor_type=__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[str]   ,				*__SCREAMING_SNAKE_CASE   :			Tuple   ,				**__SCREAMING_SNAKE_CASE   :			List[Any]     )    ->					Optional[int]:


      """simple docstring"""




      return self.tokenizer.batch_decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )
 def  UpperCAmelCase__						(						self   :			List[Any]   ,				*__SCREAMING_SNAKE_CASE   :			Union[str, Any]   ,				**__SCREAMING_SNAKE_CASE   :			str     )    ->					Optional[Any]:


      """simple docstring"""




      return self.tokenizer.decode(*__SCREAMING_SNAKE_CASE   ,				**__SCREAMING_SNAKE_CASE     )


 @property
 def  UpperCAmelCase__						(						self   :			List[Any]     )    ->					List[Any]:


      """simple docstring"""




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






