


"""simple docstring"""
UpperCAmelCase   =	{
    0: """underwear""",
    1: """pants""",
    2: """belt""",
    3: """suit""",
    4: """shoe""",
    5: """socks""",
    6: """shirt""",
    7: """tie""",
    8: """watch""",
}

UpperCAmelCase   =	[[1, 4], [2, 4], [3], [], [], [4], [2, 7], [3], []]

UpperCAmelCase   =	[0 for x in range(len(graph))]
UpperCAmelCase   =	[]

def 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			Tuple      ):




   """simple docstring"""







   _lowerCamelCase  :				List[Any]      =		1
   while stack:
      _lowerCamelCase  :				str      =		stack.pop()
      print(__snake_case	,   clothes[current_clothing]      )
      order += 1

def 							_snake_case			(      __snake_case       :			str	,   __snake_case       :			Optional[Any]	,   __snake_case       :			List[Any]      ):




   """simple docstring"""







   _lowerCamelCase  :				Union[str, Any]      =		1
   for v in graph[u]:
      if not visited[v]:
         depth_first_search(__snake_case	,   __snake_case	,   __snake_case      )

   stack.append(__snake_case      )

def 							_snake_case			(      __snake_case       :			Union[str, Any]	,   __snake_case       :			Dict      ):




   """simple docstring"""







   for v in range(len(__snake_case      )      ):
      if not visited[v]:
         depth_first_search(__snake_case	,   __snake_case	,   __snake_case      )


if __name__ == "__main__":
      topological_sort(graph, visited)
      print(stack)
      print_stack(stack, clothes)





"""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
 )

