



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 				lowerCAmelCase__	(						utils.Parser    ):
					__A  :     str     =    "halfcheetah-medium-expert-v2"
					__A  :     str     =    "config.offline"




def 						snake_case__							(       __lowercase				,  __lowercase				,  __lowercase				,  __lowercase				,  __lowercase							)		->		Any:







						"""simple docstring"""

						A__     ,							A__						:				List[Any]         =			utils.load_model(__lowercase				,  __lowercase				,  __lowercase				,  epoch=__lowercase				,  device=__lowercase							)
						A__						:				List[str]         =			TrajectoryTransformerModel(gpt.config							)

						trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()							)
						A__						:				Optional[int]         =			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__":
		snake_case     :    Tuple						=    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 						snake_case__							(       __lowercase = 1_0_0_0							)		->		int:







						"""simple docstring"""

						A__						:				int         =			0
						A__						:				Optional[int]         =			0
						while 1:
												num += 3
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
												num += 1
												if num >= n:
																		break
												total += num
												num += 2
												if num >= n:
																		break
												total += num
												num += 3
												if num >= n:
																		break
												total += num
						return total


if __name__ == "__main__":
		print(f"""{solution() = }""")