"""simple docstring"""



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class 	__UpperCAmelCase						(utils.Parser    ):







			'''simple docstring'''
			_UpperCamelCase     :	str				    =							"halfcheetah-medium-expert-v2"
			_UpperCamelCase     :	str				    =							"config.offline"





def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase       ):
							A__							,       A__					:    int														= utils.load_model(lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     epoch=lowerCAmelCase ,     device=lowerCAmelCase       )
							A__					:    List[Any]														= TrajectoryTransformerModel(gpt.config       )

							trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()       )
							A__					:    Dict														= 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
				)






"""simple docstring"""



def    _A(		lowerCAmelCase       ):
							if not isinstance(lowerCAmelCase ,     lowerCAmelCase       ):
														A__					:    int														= F'''Input value of [number={number}] must be an integer'''
														raise TypeError(lowerCAmelCase       )
							if number < 0 or number % 2 == 1:
														return False
							A__					:    List[Any]														= int(number**0.5       )
							return number == number_sqrt * (number_sqrt + 1)


if __name__ == "__main__":
				import doctest

				doctest.testmod()





