"""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       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     # Construct configuration depending on size
     snake_case_       =      {'small': (512, 8, 24), 'medium': (1024, 8, 36), 'large': (1536, 16, 48)}
     snake_case_ ,			snake_case_ ,			snake_case_       =      MODELS[model_size]  # set model hyperparameters
     snake_case_       =      ImageGPTConfig(n_embd=UpperCAmelCase		,      n_layer=UpperCAmelCase		,      n_head=UpperCAmelCase      )
     snake_case_       =      ImageGPTForCausalLM(UpperCAmelCase      )

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

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


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


"""simple docstring"""


import random
import sys

from . import cryptomath_module as cryptomath

__UpperCamelCase								=    (
    r''' !"#$%&\'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`'''
    r'''abcdefghijklmnopqrstuvwxyz{|}~'''
)







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase		,      UpperCAmelCase      )       ->   None:
     if mode == "encrypt":
          if key_a == 1:
               sys.exit(
                   'The affine cipher becomes weak when key '
                   'A is set to 1. Choose different key'      )
          if key_b == 0:
               sys.exit(
                   'The affine cipher becomes weak when key '
                   'B is set to 0. Choose different key'      )
     if key_a < 0 or key_b < 0 or key_b > len(UpperCAmelCase      ) - 1:
          sys.exit(
              'Key A must be greater than 0 and key B must '
              f'be between 0 and {len(UpperCAmelCase      ) - 1}.'      )
     if cryptomath.gcd(UpperCAmelCase		,      len(UpperCAmelCase      )      ) != 1:
          sys.exit(
              f'Key A {key_a} and the symbol set size {len(UpperCAmelCase      )} '
              'are not relatively prime. Choose a different key.'      )







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     snake_case_ ,			snake_case_       =      divmod(UpperCAmelCase		,      len(UpperCAmelCase      )      )
     check_keys(UpperCAmelCase		,      UpperCAmelCase		,      'encrypt'      )
     snake_case_       =      ''
     for symbol in message:
          if symbol in SYMBOLS:
               snake_case_       =      SYMBOLS.find(UpperCAmelCase      )
               cipher_text += SYMBOLS[(sym_index * key_a + key_b) % len(UpperCAmelCase      )]
          else:
               cipher_text += symbol
     return cipher_text







def       UpperCAmelCase					(	UpperCAmelCase		,      UpperCAmelCase      )       ->   str:
     snake_case_ ,			snake_case_       =      divmod(UpperCAmelCase		,      len(UpperCAmelCase      )      )
     check_keys(UpperCAmelCase		,      UpperCAmelCase		,      'decrypt'      )
     snake_case_       =      ''
     snake_case_       =      cryptomath.find_mod_inverse(UpperCAmelCase		,      len(UpperCAmelCase      )      )
     for symbol in message:
          if symbol in SYMBOLS:
               snake_case_       =      SYMBOLS.find(UpperCAmelCase      )
               plain_text += SYMBOLS[
                   (sym_index - key_b) * mod_inverse_of_key_a % len(UpperCAmelCase      )
               ]
          else:
               plain_text += symbol
     return plain_text







def       UpperCAmelCase					(	)       ->   int:
     while True:
          snake_case_       =      random.randint(2		,      len(UpperCAmelCase      )      )
          snake_case_       =      random.randint(2		,      len(UpperCAmelCase      )      )
          if cryptomath.gcd(UpperCAmelCase		,      len(UpperCAmelCase      )      ) == 1 and key_b % len(UpperCAmelCase      ) != 0:
               return key_b * len(UpperCAmelCase      ) + key_b







def       UpperCAmelCase					(	)       ->   None:
     snake_case_       =      input('Enter message: '      ).strip()
     snake_case_       =      int(input('Enter key [2000 - 9000]: '      ).strip()      )
     snake_case_       =      input('Encrypt/Decrypt [E/D]: '      ).strip().lower()

     if mode.startswith('e'      ):
          snake_case_       =      'encrypt'
          snake_case_       =      encrypt_message(UpperCAmelCase		,      UpperCAmelCase      )
     elif mode.startswith('d'      ):
          snake_case_       =      'decrypt'
          snake_case_       =      decrypt_message(UpperCAmelCase		,      UpperCAmelCase      )
     print(f'\n{mode.title()}ed text: \n{translated}'      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()
    # main()

