






'''simple docstring'''




import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel




class 					a__   (     utils.Parser				):
     lowerCamelCase						:  str							="halfcheetah-medium-expert-v2"
     lowerCamelCase						:  str							="config.offline"






def __lowerCAmelCase				(							UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       )			->					str:
    __lowerCamelCase			,						__lowerCamelCase						=						utils.load_model(UpperCamelCase__       ,						UpperCamelCase__       ,						UpperCamelCase__       ,						epoch=UpperCamelCase__       ,						device=UpperCamelCase__       )
    __lowerCamelCase						=						TrajectoryTransformerModel(gpt.config       )

    trajectory_transformer.tok_emb.load_state_dict(gpt.tok_emb.state_dict()       )
    __lowerCamelCase						=						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'''




__UpperCAmelCase  							=[chr(i) for i in range(3_2, 1_2_6)]
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(range(len(alphabets)))
__UpperCAmelCase  							=list(reversed(range(len(alphabets))))
__UpperCAmelCase  							=[]
__UpperCAmelCase  							=__UpperCAmelCase  							=__UpperCAmelCase  							=0






def __lowerCAmelCase				(							)			->					List[Any]:
    global gear_one_pos
    global gear_two_pos
    global gear_three_pos
    __lowerCamelCase						=						gear_one[0]
    gear_one.append(UpperCamelCase__       )
    del gear_one[0]
    gear_one_pos += 1
    if gear_one_pos % int(len(UpperCamelCase__       )       ) == 0:
        __lowerCamelCase						=						gear_two[0]
        gear_two.append(UpperCamelCase__       )
        del gear_two[0]
        gear_two_pos += 1
        if gear_two_pos % int(len(UpperCamelCase__       )       ) == 0:
            __lowerCamelCase						=						gear_three[0]
            gear_three.append(UpperCamelCase__       )
            del gear_three[0]
            gear_three_pos += 1






def __lowerCAmelCase				(							UpperCamelCase__       )			->					Optional[Any]:
    __lowerCamelCase						=						alphabets.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_one[target]
    __lowerCamelCase						=						gear_two[target]
    __lowerCamelCase						=						gear_three[target]
    __lowerCamelCase						=						reflector[target]
    __lowerCamelCase						=						gear_three.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_two.index(UpperCamelCase__       )
    __lowerCamelCase						=						gear_one.index(UpperCamelCase__       )
    code.append(alphabets[target]       )
    rotator()


if __name__ == "__main__":
  __UpperCAmelCase  							=list(input("Type your message:\n"))
  while True:
    try:
      __UpperCAmelCase  							=int(input("Please set token:(must be only digits)\n"))
      break
    except Exception as error:
      print(error)
  for _ in range(token):
    rotator()
  for j in decode:
    engine(j)
  print("\n" + "".join(code))
  print(
      f'\nYour Token is {token} please write it down.\nIf you want to decode '
      "this message again you should input same digits as token!"
  )
