

"""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()




"""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)

