code: 
  
  
  """simple docstring"""
  import heapq
  
  def 							_snake_case			(      __snake_case       :			List[str]	,   __snake_case       :			List[Any]	,   __snake_case       :			Any      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase  :				Dict      =		[(0, start)]  # cost from start node,end node
     _lowerCamelCase  :				Dict      =		set()
     while heap:
        ((_lowerCamelCase)      ,						(_lowerCamelCase))  :				Union[str, Any]      =		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
           _lowerCamelCase  :				int      =		cost + c
           heapq.heappush(_A	,   (next_item, v)      )
     return -1
  
  
  UpperCAmelCase   =	{
      '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]],
  }
  UpperCAmelCase   =	{
      'B': [['C', 1]],
      'C': [['D', 1]],
      'D': [['F', 1]],
      'E': [['B', 1], ['F', 3]],
      'F': [],
  }
  UpperCAmelCase   =	{
      'B': [['C', 1]],
      'C': [['D', 1]],
      'D': [['F', 1]],
      'E': [['B', 1], ['G', 2]],
      'F': [],
      'G': [['F', 1]],
  }
  
  UpperCAmelCase   =	dijkstra(G, """E""", """C""")
  print(short_distance)  # E -- 3 --> F -- 3 --> C == 6
  
  UpperCAmelCase   =	dijkstra(Ga, """E""", """F""")
  print(short_distance)  # E -- 3 --> F == 3
  
  UpperCAmelCase   =	dijkstra(Ga, """E""", """F""")
  print(short_distance)  # E -- 2 --> G -- 1 --> F == 3
  
  if __name__ == "__main__":
        import doctest
  
        doctest.testmod()
  
  
style_context: 
  
  
  """simple docstring"""
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  class   lowercase__							(				utils.Parser							):
     __UpperCAmelCase       					=				"halfcheetah-medium-expert-v2"
     __UpperCAmelCase       					=				"config.offline"
  
  
  
  
  
  
  
  def 							_snake_case			(      __snake_case       :			Any	,   __snake_case       :			Optional[Any]	,   __snake_case       :			Optional[int]	,   __snake_case       :			List[str]	,   __snake_case       :			Union[str, Any]      ):
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     _lowerCamelCase      ,						_lowerCamelCase  :				int      =		utils.load_model(__snake_case	,   __snake_case	,   __snake_case	,   epoch=__snake_case	,   device=__snake_case      )
     _lowerCamelCase  :				List[str]      =		TrajectoryTransformerModel(gpt.config      )
  
     trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
     _lowerCamelCase  :				str      =		gpt.pos_emb
     trajectory_transformer.drop.load_state_dict(gpt.drop.state_dict()      )
     trajectory_transformer.ln_f.load_state_dict(gpt.ln_f.state_dict()      )
     trajectory_transformer.head.load_state_dict(gpt.head.state_dict()      )
  
     for i, block in enumerate(gpt.blocks      ):
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].lna.load_state_dict(gpt.blocks[i].lna.state_dict()      )
        trajectory_transformer.blocks[i].attn.load_state_dict(gpt.blocks[i].attn.state_dict()      )
  
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[0].state_dict()      )
        trajectory_transformer.blocks[i].act.load_state_dict(gpt.blocks[i].mlp[1].state_dict()      )
        trajectory_transformer.blocks[i].la.load_state_dict(gpt.blocks[i].mlp[2].state_dict()      )
        trajectory_transformer.blocks[i].drop.load_state_dict(gpt.blocks[i].mlp[3].state_dict()      )
  
     torch.save(trajectory_transformer.state_dict()	,   """pytorch_model.bin"""      )
  
  
  if __name__ == "__main__":
   UpperCAmelCase   =	Parser().parse_args("""plan""")
   convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
       args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
   )
  
  