





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


class 					__magic_name__  (							utils.Parser							):





     """simple docstring"""



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




def      __snake_case						(					_lowerCAmelCase : Dict				,				_lowerCAmelCase : Union[str, Any]				,				_lowerCAmelCase : Tuple				,				_lowerCAmelCase : Any				,				_lowerCAmelCase : List[str]	) ->						int:
   A_      ,						A_					:				Optional[Any]          =			utils.load_model(_lowerCAmelCase				,				_lowerCAmelCase				,				_lowerCAmelCase				,				epoch=_lowerCAmelCase				,				device=_lowerCAmelCase	)
   A_					:				Any          =			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__":
 _lowerCAmelCase							:      List[Any]						=	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      __snake_case						(					_lowerCAmelCase : float				,				_lowerCAmelCase : float				,				_lowerCAmelCase : int	) ->						float:
   A_					:				List[str]          =			[1 / first_term]
   A_					:				Tuple          =			1 / first_term
   for _ in range(number_of_terms - 1	):
      first_term += common_difference
      arithmetic_progression.append(_lowerCAmelCase	)
   A_					:				List[str]          =			[1 / step for step in arithmetic_progression]
   return sum(_lowerCAmelCase	)


if __name__ == "__main__":
 import doctest

 doctest.testmod()
 print(sum_of_harmonic_progression(1 / 2, 2, 2))
