




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


class        UpperCAmelCase__				(							utils.Parser				):



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





def 							lowerCamelCase    (							UpperCAmelCase_	:      Optional[int]   ,       UpperCAmelCase_	:      List[Any]   ,       UpperCAmelCase_	:      List[str]   ,       UpperCAmelCase_	:      Optional[int]   ,       UpperCAmelCase_	:      List[Any]      )->			Optional[int]:

       """simple docstring"""




       a   ,      a			   =utils.load_model(UpperCAmelCase_   ,       UpperCAmelCase_   ,       UpperCAmelCase_   ,       epoch=UpperCAmelCase_   ,       device=UpperCAmelCase_      )
       a			   =TrajectoryTransformerModel(gpt.config      )

       trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
       a			   =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__":
    _lowerCamelCase							=      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"""

import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel
class      A__   (   utils.Parser  ):
	A_				:					str      =   "halfcheetah-medium-expert-v2"
	A_				:					str      =   "config.offline"




def    snake_case__						(							lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]					,       lowerCAmelCase :		List[str]					,       lowerCAmelCase :		Optional[int]					,       lowerCAmelCase :		List[Any]		):
					__snake_case   ,   __snake_case        =						utils.load_model(lowerCAmelCase					,       lowerCAmelCase					,       lowerCAmelCase					,       epoch=lowerCAmelCase					,       device=lowerCAmelCase		)
					__snake_case        =						TrajectoryTransformerModel(gpt.config		)

					trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()		)
					__snake_case        =						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__			:     int			       =				Parser().parse_args("plan")
		convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(
		    args.logbase, args.dataset, args.gpt_loadpath, args.gpt_epoch, args.device
		)


