


"""simple docstring"""






from __future__ import annotations

import random
import string


class      __lowerCamelCase	:

     '''simple docstring'''


     def __init__(   self     ,					__UpperCAmelCase = None				)							->				None:
          _a     						=					passcode or self.__passcode_creator()
          _a     						=					self.__make_key_list()
          _a     						=					self.__make_shift_key()


     def __str__(   self				)							->				str:
          return "".join(self.__passcode				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				list[int]:
          for i in range(1     ,					len(__UpperCAmelCase				)     ,					2				):
               iterlist[i] *= -1
          return iterlist


     def 				_UpperCAmelCase   (   self				)							->				list[str]:
          _a     						=					string.ascii_letters + string.digits
          _a     						=					[random.choice(__UpperCAmelCase				) for _ in range(random.randint(10     ,					20				)				)]
          return password


     def 				_UpperCAmelCase   (   self				)							->				list[str]:
          _a     						=					(
              string.ascii_letters + string.digits + string.punctuation + ''' \t\n'''
          )

          _a     						=					[]

          # creates points known as breakpoints to break the key_list_options at those
          # points and pivot each substring
          _a     						=					sorted(set(self.__passcode				)				)
          _a     						=					[]

          # algorithm for creating a new shuffled list, keys_l, out of key_list_options
          for i in key_list_options:
               temp_list.extend(__UpperCAmelCase				)

               # checking breakpoints at which to pivot temporary sublist and add it into
               # keys_l
               if i in breakpoints or i == key_list_options[-1]:
                    keys_l.extend(temp_list[::-1]				)
                    temp_list.clear()

        # returning a shuffled keys_l to prevent brute force guessing of shift key
          return keys_l


     def 				_UpperCAmelCase   (   self				)							->				int:
          _a     						=					sum(self.__neg_pos([ord(__UpperCAmelCase				) for x in self.__passcode]				)				)
          return num if num > 0 else len(self.__passcode				)


     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				str:
          _a     						=					''''''

          # decoding shift like Caesar cipher algorithm implementing negative shift or
          # reverse shift or left shift
          for i in encoded_message:
               _a     						=					self.__key_list.index(__UpperCAmelCase				)
               decoded_message += self.__key_list[
                   (position - self.__shift_key) % -len(self.__key_list				)
               ]

          return decoded_message





     def 				_UpperCAmelCase   (   self     ,					__UpperCAmelCase				)							->				str:
          _a     						=					''''''

          # encoding shift like Caesar cipher algorithm implementing positive shift or
          # forward shift or right shift
          for i in plaintext:
               _a     						=					self.__key_list.index(__UpperCAmelCase				)
               encoded_message += self.__key_list[
                   (position + self.__shift_key) % len(self.__key_list				)
               ]

          return encoded_message







def    A_ ( _lowerCAmelCase  :       str = "Hello, this is a modified Caesar cipher"							):






     """simple docstring"""

     _a     						=					ShuffledShiftCipher()
     return cipa.decrypt(cipa.encrypt(_lowerCAmelCase							)							)


if __name__ == "__main__":
   import doctest

   doctest.testmod()



"""simple docstring"""






import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()
def    A_ ( _lowerCAmelCase  :       Dict,  _lowerCAmelCase  :       Tuple,  _lowerCAmelCase  :       Optional[Any]							):






     """simple docstring"""

     _a     						=					RoFormerConfig.from_json_file(_lowerCAmelCase							)
     print(f'Building PyTorch model from configuration: {config}'							)
     _a     						=					RoFormerForMaskedLM(_lowerCAmelCase							)

     # Load weights from tf checkpoint
     load_tf_weights_in_roformer(_lowerCAmelCase,  _lowerCAmelCase,  _lowerCAmelCase							)

     # Save pytorch-model
     print(f'Save PyTorch model to {pytorch_dump_path}'							)
     torch.save(model.state_dict(),  _lowerCAmelCase,  _use_new_zipfile_serialization=_lowerCAmelCase							)


if __name__ == "__main__":
   __snake_case												=							argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
   )
   parser.add_argument(
       '''--bert_config_file''',
       default=None,
       type=str,
       required=True,
       help=(
           '''The config json file corresponding to the pre-trained BERT model. \n'''
           '''This specifies the model architecture.'''
       ),
   )
   parser.add_argument(
       '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
   )
   __snake_case												=							parser.parse_args()
   convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)