


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel



class 							lowercase__(						utils.Parser			):






      '''simple docstring'''







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




def 	_lowerCamelCase  (		A_  :	Optional[int]							,  A_  :	Dict							,  A_  :	Union[str, Any]							,  A_  :	Dict							,  A_  :	Tuple      )			->			Union[str, Any]:

   '''simple docstring'''






   UpperCamelCase__						,	UpperCamelCase__ :			Any 						=utils.load_model(a__							,  a__							,  a__							,  epoch=a__							,  device=a__      )
   UpperCamelCase__ :			Optional[int] 						=TrajectoryTransformerModel(gpt.config      )

   trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()      )
   UpperCamelCase__ :			List[str] 						=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'''

from ..utils import DummyObject, requires_backends
class 	SCREAMING_SNAKE_CASE      (    metaclass=__lowercase):







		"""simple docstring"""
		lowercase :   List[Any]							=				['tensorflow_text']






		def __init__( self		,    *__A		,    **__A					)			->  List[Any]:
								requires_backends(self		,    ['tensorflow_text']					)





