





import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel







class 		_lowerCAmelCase(		utils.Parser):

  """simple docstring"""







  lowerCamelCase__     	=       "halfcheetah-medium-expert-v2"
  lowerCamelCase__     	=       "config.offline"
def  __UpperCamelCase    (						snake_case							,			snake_case							,			snake_case							,			snake_case							,			snake_case      )    ->		Union[str, Any]:


   '''simple docstring'''







   __A   ,		__A    =  utils.load_model(snake_case							,			snake_case							,			snake_case							,			epoch=snake_case							,			device=snake_case      )
   __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__":
    _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
    )








from __future__ import annotations
def  __UpperCamelCase    (						snake_case      )    ->		bool:


   '''simple docstring'''







   __A    =  42
   __A    =  list(snake_case      )
   __A   ,		__A    =  _vector[:-1], int(_vector[-1]      )
   __A    =  [int(snake_case      ) for digit in __vector]

   vector.reverse()
   for i, digit in enumerate(snake_case      ):
      if i & 1 == 0:
         __A    =  digit * 2
         if doubled > 9:
            doubled -= 9
         check_digit += doubled
      else:
         check_digit += digit

   return check_digit % 1_0 == 0


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    assert is_luhn("""79927398713""")
    assert not is_luhn("""79927398714""")

