code: 
  
  
  """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)
  
  
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
   )
  
  