code: 
  
  """simple docstring"""
  from __future__ import annotations
  
  import random
  import string
  
  class 			lowerCamelCase  :
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    def __init__(self       ,					_lowerCamelCase = None  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Union[str, Any]						=						passcode or self.__passcode_creator()
          UpperCAmelCase__ :       Union[str, Any]						=						self.__make_key_list()
          UpperCAmelCase__ :       int						=						self.__make_shift_key()
  
  
  
    def __str__(self  ):
  
  
          """simple docstring"""
  
          return "".join(self.__passcode  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          for i in range(1       ,					len(_lowerCamelCase  )       ,					2  ):
                iterlist[i] *= -1
          return iterlist
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       int						=						string.ascii_letters + string.digits
          UpperCAmelCase__ :       str						=						[random.choice(_lowerCamelCase  ) for _ in range(random.randint(10       ,					20  )  )]
          return password
  
  
  
    def    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						(
              string.ascii_letters + string.digits + string.punctuation + """ \t\n"""
          )
  
          UpperCAmelCase__ :       Union[str, Any]						=						[]
  
          # creates points known as breakpoints to break the key_list_options at those
          # points and pivot each substring
          UpperCAmelCase__ :       Dict						=						sorted(set(self.__passcode  )  )
          UpperCAmelCase__ :       list[str]						=						[]
  
          # algorithm for creating a new shuffled list, keys_l, out of key_list_options
          for i in key_list_options:
                temp_list.extend(_lowerCamelCase  )
  
                # 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    _a			(self  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       Dict						=						sum(self.__neg_pos([ord(_lowerCamelCase  ) for x in self.__passcode]  )  )
          return num if num > 0 else len(self.__passcode  )
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       List[str]						=						""""""
  
          # decoding shift like Caesar cipher algorithm implementing negative shift or
          # reverse shift or left shift
          for i in encoded_message:
                UpperCAmelCase__ :       Optional[int]						=						self.__key_list.index(_lowerCamelCase  )
                decoded_message += self.__key_list[
                    (position - self.__shift_key) % -len(self.__key_list  )
                ]
  
          return decoded_message
  
  
  
  
    def    _a			(self       ,					_lowerCamelCase  ):
  
  
          """simple docstring"""
  
          UpperCAmelCase__ :       str						=						""""""
  
          # encoding shift like Caesar cipher algorithm implementing positive shift or
          # forward shift or right shift
          for i in plaintext:
                UpperCAmelCase__ :       Optional[int]						=						self.__key_list.index(_lowerCamelCase  )
                encoded_message += self.__key_list[
                    (position + self.__shift_key) % len(self.__key_list  )
                ]
  
          return encoded_message
  
  
  
  
  def 				a__	(	lowerCAmelCase = "Hello, this is a modified Caesar cipher"						)  ->   str:
        UpperCAmelCase__ :       List[Any]						=						ShuffledShiftCipher()
        return cipa.decrypt(cipa.encrypt(lowerCAmelCase						)						)
  
  
  if __name__ == "__main__":
      import doctest
  
      doctest.testmod()
  
  
style_context: 
  
  """simple docstring"""
  import argparse
  import json
  
  from transformers import GPTNeoConfig, GPTNeoForCausalLM, load_tf_weights_in_gpt_neo
  from transformers.utils import logging
  
  
  logging.set_verbosity_info()
  
  
  
  
  def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Dict:
        # Initialise PyTorch model
        UpperCAmelCase__ :       Union[str, Any]						=						json.load(open(lowerCAmelCase ,  """r"""						)						)
        UpperCAmelCase__ :       Any						=						GPTNeoConfig(
            hidden_size=config_json["""n_embd"""] ,  num_layers=config_json["""n_layer"""] ,  num_heads=config_json["""n_head"""] ,  attention_types=config_json["""attention_types"""] ,  max_position_embeddings=config_json["""n_positions"""] ,  resid_dropout=config_json["""res_dropout"""] ,  embed_dropout=config_json["""embed_dropout"""] ,  attention_dropout=config_json["""attn_dropout"""] ,  )
        print(F"""Building PyTorch model from configuration: {config}"""						)
        UpperCAmelCase__ :       Dict						=						GPTNeoForCausalLM(lowerCAmelCase						)
  
        # Load weights from tf checkpoint
        load_tf_weights_in_gpt_neo(lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)
  
        # Save pytorch-model
        print(F"""Save PyTorch model to {pytorch_dump_path}"""						)
        model.save_pretrained(lowerCAmelCase						)
  
  
  if __name__ == "__main__":
      _A						=     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(
          """--config_file""",
          default=None,
          type=str,
          required=True,
          help=(
              """The config json file corresponding to the pre-trained mesh-tf 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."""
      )
      _A						=     parser.parse_args()
      convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
  
  