code: 
  
  import torch
  import trajectory.utils as utils
  
  from transformers import TrajectoryTransformerModel
  
  
  
  
  
  
  
  class 		__magic_name__    (							utils.Parser							):
  
     '''simple docstring'''
  
  
  
  
     lowerCamelCase__				:			str												=     "halfcheetah-medium-expert-v2"
     lowerCamelCase__				:			str												=     "config.offline"
  
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				int			,   _A   :				int			,   _A   :				List[str]			,   _A   :				List[str]			,   _A   :				Optional[int]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__,   a__						=utils.load_model(_A			,   _A			,   _A			,   epoch=_A			,   device=_A							)
    a__						=TrajectoryTransformerModel(gpt.config							)
  
    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()							)
    a__						=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__":
         lowerCamelCase     =      Parser().parse_args('''plan''')
         convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
             args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
         )
  
style_context: 
  
  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()
  