

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
       )



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()
