






"""simple docstring"""


import torch
import trajectory.utils as utils

from transformers import TrajectoryTransformerModel


class    snake_case						(				utils.Parser		):


   '''simple docstring'''

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





def    lowerCAmelCase							(       __UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase					):




  """simple docstring"""







  __A     ,       __A		     =					utils.load_model(__UpperCamelCase						,			__UpperCamelCase						,			__UpperCamelCase						,			epoch=__UpperCamelCase						,			device=__UpperCamelCase					)
  __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__":
       lowercase_								=	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 typing import Literal

__UpperCamelCase       :    int         =		"ABCDEFGHIJKLMNOPQRSTUVWXYZ"


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str,				_snake_case		:       Literal["encrypt", "decrypt"]       ):
   _lowercase							     =       LETTERS if mode == "decrypt" else key
   _lowercase							     =       key if mode == "decrypt" else LETTERS
   _lowercase							     =       ""
   # loop through each symbol in the message
   for symbol in message:
      if symbol.upper() in chars_a:
         # encrypt/decrypt the symbol
         _lowercase							     =       chars_a.find(symbol.upper()       )
         if symbol.isupper():
            translated += chars_b[sym_index].upper()
         else:
            translated += chars_b[sym_index].lower()
      else:
         # symbol is not in LETTERS, just add it
         translated += symbol
   return translated


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"encrypt"       )


def   __UpperCAmelCase     (	_snake_case		:       str,				_snake_case		:       str       ):
   return translate_message(_snake_case,				_snake_case,				"decrypt"       )


def   __UpperCAmelCase     (	):
   _lowercase							     =       "Hello World"
   _lowercase							     =       "QWERTYUIOPASDFGHJKLZXCVBNM"
   _lowercase							     =       "decrypt"  # set to 'encrypt' or 'decrypt'

   if mode == "encrypt":
      _lowercase							     =       encrypt_message(_snake_case,				_snake_case       )
   elif mode == "decrypt":
      _lowercase							     =       decrypt_message(_snake_case,				_snake_case       )
   print(f"""Using the key {key}, the {mode}ed message is: {translated}"""       )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()