

"""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       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										={'small': (512, 8, 24), 'medium': (1024, 8, 36), 'large': (1536, 16, 48)}
     SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=MODELS[model_size]  # set model hyperparameters
     SCREAMING_SNAKE_CASE										=ImageGPTConfig(n_embd=lowerCAmelCase_,   n_layer=lowerCAmelCase_,   n_head=lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=ImageGPTForCausalLM(lowerCAmelCase_      )

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

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


if __name__ == "__main__":
 _lowerCamelCase								=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."
 )
 _lowerCamelCase								=parser.parse_args()
 convert_imagegpt_checkpoint_to_pytorch(
     args.imagegpt_checkpoint_path, args.model_size, args.pytorch_dump_folder_path
 )




import random
import sys

_lowerCamelCase								="ABCDEFGHIJKLMNOPQRSTUVWXYZ"







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=input('Enter message: '      )
     SCREAMING_SNAKE_CASE										='LFWOAYUISVKMNXPBDCRJTQEGHZ'
     SCREAMING_SNAKE_CASE										=input('Encrypt/Decrypt [e/d]: '      )

     check_valid_key(lowerCAmelCase_      )

     if resp.lower().startswith('e'      ):
          SCREAMING_SNAKE_CASE										='encrypt'
          SCREAMING_SNAKE_CASE										=encrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )
     elif resp.lower().startswith('d'      ):
          SCREAMING_SNAKE_CASE										='decrypt'
          SCREAMING_SNAKE_CASE										=decrypt_message(lowerCAmelCase_,   lowerCAmelCase_      )

     print(F'\n{mode.title()}ion: \n{translated}'      )







def       snake_case__    (       lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     key_list.sort()
     letters_list.sort()

     if key_list != letters_list:
          sys.exit('Error in the key or symbol set.'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'encrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     return translate_message(lowerCAmelCase_,   lowerCAmelCase_,   'decrypt'      )







def       snake_case__    (       lowerCAmelCase_,   lowerCAmelCase_,   lowerCAmelCase_      ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=''
     SCREAMING_SNAKE_CASE										=LETTERS
     SCREAMING_SNAKE_CASE										=key

     if mode == "decrypt":
          SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE										=chars_b, chars_a

     for symbol in message:
          if symbol.upper() in chars_a:
               SCREAMING_SNAKE_CASE										=chars_a.find(symbol.upper()      )
               if symbol.isupper():
                    translated += chars_b[sym_index].upper()
               else:
                    translated += chars_b[sym_index].lower()
          else:
               translated += symbol

     return translated







def       snake_case__    (       ):





     """simple docstring"""







     SCREAMING_SNAKE_CASE										=list(lowerCAmelCase_      )
     random.shuffle(lowerCAmelCase_      )
     return "".join(lowerCAmelCase_      )


if __name__ == "__main__":
 main()

