



import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 						snake_case  (		utils.Parser					):



   '''simple docstring'''






   snake_case_   :	str       =						"halfcheetah-medium-expert-v2"
   snake_case_   :	str       =						"config.offline"



def  lowercase    (				SCREAMING_SNAKE_CASE__    :		Any			,    SCREAMING_SNAKE_CASE__    :		Tuple			,    SCREAMING_SNAKE_CASE__    :		int			,    SCREAMING_SNAKE_CASE__    :		Optional[Any]			,    SCREAMING_SNAKE_CASE__    :		Optional[int]			)					->       str:
 _snake_case		,      _snake_case      :					Any					=   utils.load_model(snake_case__			,    snake_case__			,    snake_case__			,    epoch=snake_case__			,    device=snake_case__			)
 _snake_case      :					int					=   TrajectoryTransformerModel(gpt.config			)

 trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()			)
 _snake_case      :					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__":
  a__						=		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  lowercase    (				SCREAMING_SNAKE_CASE__    :		float			,    SCREAMING_SNAKE_CASE__    :		float			)					->       float:
 _snake_case      :					str					=   float()
 try:
  _snake_case      :					str					=   mass * acceleration
 except Exception:
  return -0.0
 return force


if __name__ == "__main__":
  import doctest

  # run doctest
  doctest.testmod()

  # demo
  a__      =		12.5
  a__      =		10
  a__      =		newtons_second_law_of_motion(mass, acceleration)
  print("""The force is """, force, """N""")
