
'''simple docstring'''
import argparse

import torch

from transformers import ImageGPTConfig, ImageGPTForCausalLM, load_tf_weights_in_imagegpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()







def 							A_  (    __SCREAMING_SNAKE_CASE    :	Dict      ,  __SCREAMING_SNAKE_CASE    :	List[Any]      ,  __SCREAMING_SNAKE_CASE    :	List[Any]			)					->							Optional[int]:





  """simple docstring"""

  __A		:  Optional[Any]       				=		{"""small""": (512, 8, 24), """medium""": (1024, 8, 36), """large""": (1536, 16, 48)}
  __A				,       __A				,       __A		:  Dict       				=		MODELS[model_size]  # set model hyperparameters
  __A		:  Dict       				=		ImageGPTConfig(n_embd=__SCREAMING_SNAKE_CASE      ,  n_layer=__SCREAMING_SNAKE_CASE      ,  n_head=__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		ImageGPTForCausalLM(__SCREAMING_SNAKE_CASE			)

  # Load weights from numpy
  load_tf_weights_in_imagegpt(__SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE      ,  __SCREAMING_SNAKE_CASE			)

  # Save pytorch-model
  __A		:  List[str]       				=		pytorch_dump_folder_path + """/""" + WEIGHTS_NAME
  __A		:  str       				=		pytorch_dump_folder_path + """/""" + CONFIG_NAME
  print(F"Save PyTorch model to {pytorch_weights_dump_path}"			)
  torch.save(model.state_dict()      ,  __SCREAMING_SNAKE_CASE			)
  print(F"Save configuration file to {pytorch_config_dump_path}"			)
  with open(__SCREAMING_SNAKE_CASE      ,  """w"""      ,  encoding="""utf-8"""			) as f:
    f.write(config.to_json_string()			)


if __name__ == "__main__":
     A__ :		Union[str, Any]					=argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--imagegpt_checkpoint_path',
         default=None,
         type=str,
         required=True,
         help='Path to the TensorFlow checkpoint path.',
     )
     parser.add_argument(
         '--model_size',
         default=None,
         type=str,
         required=True,
         help='Size of the model (can be either \'small\', \'medium\' or \'large\').',
     )
     parser.add_argument(
         '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     A__ :		Optional[Any]					=parser.parse_args()
     convert_imagegpt_checkpoint_to_pytorch(
         args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
     )





'''simple docstring'''
A__ :		Optional[Any]					=[chr(i) for i in range(32, 126)]
A__ :		Tuple					=list(range(len(alphabets)))
A__ :		int					=list(range(len(alphabets)))
A__ :		Optional[Any]					=list(range(len(alphabets)))
A__ :		Any					=list(reversed(range(len(alphabets))))
A__ :		Optional[int]					=[]
A__ :		Tuple					=0







def 							A_  (    )					->							Any:





  """simple docstring"""

  global gear_one_pos
  global gear_two_pos
  global gear_three_pos
  __A		:  Any       				=		gear_one[0]
  gear_one.append(__SCREAMING_SNAKE_CASE			)
  del gear_one[0]
  gear_one_pos += 1
  if gear_one_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
    __A		:  Any       				=		gear_two[0]
    gear_two.append(__SCREAMING_SNAKE_CASE			)
    del gear_two[0]
    gear_two_pos += 1
    if gear_two_pos % int(len(__SCREAMING_SNAKE_CASE			)			) == 0:
      __A		:  str       				=		gear_three[0]
      gear_three.append(__SCREAMING_SNAKE_CASE			)
      del gear_three[0]
      gear_three_pos += 1







def 							A_  (    __SCREAMING_SNAKE_CASE    :	str			)					->							List[str]:





  """simple docstring"""

  __A		:  Union[str, Any]       				=		alphabets.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Dict       				=		gear_one[target]
  __A		:  int       				=		gear_two[target]
  __A		:  List[str]       				=		gear_three[target]
  __A		:  List[Any]       				=		reflector[target]
  __A		:  List[Any]       				=		gear_three.index(__SCREAMING_SNAKE_CASE			)
  __A		:  Tuple       				=		gear_two.index(__SCREAMING_SNAKE_CASE			)
  __A		:  List[str]       				=		gear_one.index(__SCREAMING_SNAKE_CASE			)
  code.append(alphabets[target]			)
  rotator()


if __name__ == "__main__":
     A__ :		int					=list(input('Type your message:\n'))
     while True:
          try:
               A__ :		Any					=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!'
     )



