
"""simple docstring"""


import heapq






def    lowercase	(  lowerCAmelCase__     :   List[Any]  ,lowerCAmelCase__     :   Optional[int]  ,lowerCAmelCase__     :   int	):
			lowerCamelCase_ 		=     [(0, start)]  # cost from start node,end node
			lowerCamelCase_ 		=     set()
			while heap:
						(lowerCamelCase_) 		=     heapq.heappop(UpperCAmelCase_	)
						if u in visited:
									continue
						visited.add(UpperCAmelCase_	)
						if u == end:
									return cost
						for v, c in graph[u]:
									if v in visited:
												continue
									lowerCamelCase_ 		=     cost + c
									heapq.heappush(UpperCAmelCase_  ,(next_item, v)	)
			return -1


A_						       =				{
    """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]],
}
A_						       =				{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""F""", 3]],
    """F""": [],
}
A_						       =				{
    """B""": [["""C""", 1]],
    """C""": [["""D""", 1]],
    """D""": [["""F""", 1]],
    """E""": [["""B""", 1], ["""G""", 2]],
    """F""": [],
    """G""": [["""F""", 1]],
}

A_						       =				dijkstra(G, """E""", """C""")
print(short_distance)  # E -- 3 --> F -- 3 --> C == 6

A_						       =				dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 3 --> F == 3

A_						       =				dijkstra(Ga, """E""", """F""")
print(short_distance)  # E -- 2 --> G -- 1 --> F == 3

if __name__ == "__main__":
						import doctest

						doctest.testmod()





"""simple docstring"""


import copy

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import logging


A_						       =				logging.get_logger(__name__)




class    __lowerCamelCase     (   lowerCAmelCase ):
				a__:			List[str]									=						'hybrid-clip'
				a__:			List[Any]									=						True




				def __init__(							self       ,	UpperCAmelCase=512       ,	**UpperCAmelCase      ):
							super().__init__(**UpperCAmelCase      )

							if "text_config" not in kwargs:
										raise ValueError('''`text_config` can not be `None`.'''      )

							if "vision_config" not in kwargs:
										raise ValueError('''`vision_config` can not be `None`.'''      )

							lowerCamelCase_ 		=     kwargs.pop('''text_config'''      )
							lowerCamelCase_ 		=     kwargs.pop('''vision_config'''      )

							lowerCamelCase_ 		=     text_config.pop('''model_type'''      )
							lowerCamelCase_ 		=     vision_config.pop('''model_type'''      )

							from transformers import AutoConfig

							lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      )

							if vision_model_type == "clip":
										lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      ).vision_config
							elif vision_model_type == "clip_vision_model":
										from transformers import CLIPVisionConfig

										lowerCamelCase_ 		=     CLIPVisionConfig(**UpperCAmelCase      )
							else:
										lowerCamelCase_ 		=     AutoConfig.for_model(UpperCAmelCase       ,	**UpperCAmelCase      )

							lowerCamelCase_ 		=     projection_dim
							lowerCamelCase_ 		=     1.0




				@classmethod
				def 					UpperCAmelCase__	(							cls       ,	UpperCAmelCase       ,	UpperCAmelCase       ,	**UpperCAmelCase      ):
							return cls(text_config=text_config.to_dict()       ,	vision_config=vision_config.to_dict()       ,	**UpperCAmelCase      )





				def 					UpperCAmelCase__	(							self      ):
							lowerCamelCase_ 		=     copy.deepcopy(self.__dict__      )
							lowerCamelCase_ 		=     self.text_config.to_dict()
							lowerCamelCase_ 		=     self.vision_config.to_dict()
							lowerCamelCase_ 		=     self.__class__.model_type
							return output



