

import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


UpperCAmelCase__ :     Optional[Any]				    =    logging.get_logger(__name__)

UpperCAmelCase__ :     Any				    =    {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCAmelCase__ :     Optional[Any]				    =    {
    'tokenizer_file': {
        'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/tokenizer.json',
    },
}

UpperCAmelCase__ :     List[Any]				    =    {
    'gpt-neox-20b': 2_048,
}







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : str			       =VOCAB_FILES_NAMES
  UpperCAmelCase__      : str			       =PRETRAINED_VOCAB_FILES_MAP
  UpperCAmelCase__      : Union[str, Any]			       =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  UpperCAmelCase__      : List[Any]			       =['input_ids', 'attention_mask']


  def __init__(			self    : Any				,				UpperCAmelCase__    : Dict=None				,				UpperCAmelCase__    : List[str]=None				,				UpperCAmelCase__    : str=None				,				UpperCAmelCase__    : str="<|endoftext|>"				,				UpperCAmelCase__    : Dict="<|endoftext|>"				,				UpperCAmelCase__    : str="<|endoftext|>"				,				UpperCAmelCase__    : int=False				,				**UpperCAmelCase__    : Optional[int]				,				)					->Optional[Any]:
        """simple docstring"""







        super().__init__(
            UpperCAmelCase__				,				UpperCAmelCase__				,				tokenizer_file=UpperCAmelCase__				,				unk_token=UpperCAmelCase__				,				bos_token=UpperCAmelCase__				,				eos_token=UpperCAmelCase__				,				add_prefix_space=UpperCAmelCase__				,				**UpperCAmelCase__				,				)

        SCREAMING_SNAKE_CASE :      Optional[int]       =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()		)
        if pre_tok_state.get("""add_prefix_space"""				,				UpperCAmelCase__		) != add_prefix_space:
              SCREAMING_SNAKE_CASE :      List[str]       =   getattr(UpperCAmelCase__				,				pre_tok_state.pop("""type"""		)		)
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   add_prefix_space
              SCREAMING_SNAKE_CASE :      List[Any]       =   pre_tok_class(**UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   add_prefix_space


  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : str				,				UpperCAmelCase__    : Optional[str] = None		)					->Tuple:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      List[str]       =   self._tokenizer.model.save(UpperCAmelCase__				,				name=UpperCAmelCase__		)
        return tuple(UpperCAmelCase__		)




  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : "Conversation"		)					->Optional[int]:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Optional[Any]       =   []
        for is_user, text in conversation.iter_texts():
              input_ids.extend(self.encode(UpperCAmelCase__				,				add_special_tokens=UpperCAmelCase__		) + [self.eos_token_id]		)

        if len(UpperCAmelCase__		) > self.model_max_length:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   input_ids[-self.model_max_length :]
        return input_ids







def 					__lowercase      (     _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[Any]       =   """"""
      for ch in key:
            if ch == " " or ch not in key_no_dups and ch.isalpha():
                  key_no_dups += ch
      return key_no_dups






def 					__lowercase      (     _A			)     ->						dict[str, str]:
      SCREAMING_SNAKE_CASE :      int       =   [chr(i + 65			) for i in range(26			)]
      # Remove duplicate characters from key
      SCREAMING_SNAKE_CASE :      List[str]       =   remove_duplicates(key.upper()			)
      SCREAMING_SNAKE_CASE :      Dict       =   len(_A			)
      # First fill cipher with key characters
      SCREAMING_SNAKE_CASE :      int       =   {alphabet[i]: char for i, char in enumerate(_A			)}
      # Then map remaining characters in alphabet to
      # the alphabet from the beginning
      for i in range(len(_A			)						,    26			):
            SCREAMING_SNAKE_CASE :      List[Any]       =   alphabet[i - offset]
            # Ensure we are not mapping letters to letters previously mapped
            while char in key:
                  offset -= 1
                  SCREAMING_SNAKE_CASE :      str       =   alphabet[i - offset]
            SCREAMING_SNAKE_CASE :      List[Any]       =   char
      return cipher_alphabet






def 					__lowercase      (     _A						,    _A			)     ->						str:
      return "".join(cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     _A						,    _A			)     ->						str:
      SCREAMING_SNAKE_CASE :      Optional[int]       =   {v: k for k, v in cipher_map.items()}
      return "".join(rev_cipher_map.get(_A						,    _A			) for ch in message.upper()			)






def 					__lowercase      (     )     ->						None:
      SCREAMING_SNAKE_CASE :      List[Any]       =   input("""Enter message to encode or decode: """			).strip()
      SCREAMING_SNAKE_CASE :      str       =   input("""Enter keyword: """			).strip()
      SCREAMING_SNAKE_CASE :      Dict       =   input("""Encipher or decipher? E/D:"""			).strip()[0].lower()
      try:
            SCREAMING_SNAKE_CASE :      int       =   {"""e""": encipher, """d""": decipher}[option]
      except KeyError:
            raise KeyError("""invalid input option"""			)
      SCREAMING_SNAKE_CASE :      Optional[int]       =   create_cipher_map(_A			)
      print(func(_A						,    _A			)			)


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




