






import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class        snake_case_					(						utils.Parser			):

						"""simple docstring"""



						snake_case__   					=			"halfcheetah-medium-expert-v2"
						snake_case__   					=			"config.offline"







def 						a_      (__A							,					__A							,					__A							,					__A							,					__A			)							->	Optional[Any]:



					"""simple docstring"""
					__a			, __a				:	Optional[Any]			=				utils.load_model(__A							,					__A							,					__A							,					epoch=__A							,					device=__A			)
					__a				:	int			=				TrajectoryTransformerModel(gpt.config			)

					trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
					__a				:	List[Any]			=				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
			)













def 						a_      (__A			)							->	list:



					"""simple docstring"""
					if len(__A			) <= 1:
										return data
					else:
										return [
										    *quick_sort([e for e in data[1:] if e <= data[0]]			),
										    data[0],
										    *quick_sort([e for e in data[1:] if e > data[0]]			),
										]


if __name__ == "__main__":
			import doctest

			doctest.testmod()





