






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				:	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
   )













import warnings

from .generation import GenerationMixin




class        snake_case_					(						__UpperCamelCase			):

						"""simple docstring"""



						warnings.warn(
						    """Importing `GenerationMixin` from `src/transformers/generation_utils.py` is deprecated and will """
						    """be removed in Transformers v5. Import as `from transformers import GenerationMixin` instead."""		,       __UpperCamelCase		,       )





