






"""simple docstring"""





import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_electra import ElectraTokenizer


_SCREAMING_SNAKE_CASE					:				int					    =						{'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE					:				Dict					    =						{
    '''vocab_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-generator''': '''https://huggingface.co/google/electra-base-generator/resolve/main/vocab.txt''',
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/vocab.txt'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/vocab.txt'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/vocab.txt'''
        ),
    },
    '''tokenizer_file''': {
        '''google/electra-small-generator''': (
            '''https://huggingface.co/google/electra-small-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-generator''': (
            '''https://huggingface.co/google/electra-base-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-generator''': (
            '''https://huggingface.co/google/electra-large-generator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-small-discriminator''': (
            '''https://huggingface.co/google/electra-small-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-base-discriminator''': (
            '''https://huggingface.co/google/electra-base-discriminator/resolve/main/tokenizer.json'''
        ),
        '''google/electra-large-discriminator''': (
            '''https://huggingface.co/google/electra-large-discriminator/resolve/main/tokenizer.json'''
        ),
    },
}

_SCREAMING_SNAKE_CASE					:				int					    =						{
    '''google/electra-small-generator''': 512,
    '''google/electra-base-generator''': 512,
    '''google/electra-large-generator''': 512,
    '''google/electra-small-discriminator''': 512,
    '''google/electra-base-discriminator''': 512,
    '''google/electra-large-discriminator''': 512,
}

_SCREAMING_SNAKE_CASE					:				Union[str, Any]					    =						{
    '''google/electra-small-generator''': {'''do_lower_case''': True},
    '''google/electra-base-generator''': {'''do_lower_case''': True},
    '''google/electra-large-generator''': {'''do_lower_case''': True},
    '''google/electra-small-discriminator''': {'''do_lower_case''': True},
    '''google/electra-base-discriminator''': {'''do_lower_case''': True},
    '''google/electra-large-discriminator''': {'''do_lower_case''': True},
}






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Dict						= VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE						:     Tuple						= PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE						:     int						= PRETRAINED_INIT_CONFIGURATION
    SCREAMING_SNAKE_CASE						:     List[Any]						= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE						:     Any						= ElectraTokenizer






    def __init__(       self     :							Any       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]=None       ,						__SCREAMING_SNAKE_CASE     :							Dict=None       ,						__SCREAMING_SNAKE_CASE     :							Optional[Any]=True       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]="[UNK]"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]="[SEP]"       ,						__SCREAMING_SNAKE_CASE     :							Union[str, Any]="[PAD]"       ,						__SCREAMING_SNAKE_CASE     :							Tuple="[CLS]"       ,						__SCREAMING_SNAKE_CASE     :							Any="[MASK]"       ,						__SCREAMING_SNAKE_CASE     :							Optional[int]=True       ,						__SCREAMING_SNAKE_CASE     :							List[Any]=None       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]       ,						)     ->		List[str]:
      super().__init__(
          __SCREAMING_SNAKE_CASE       ,						tokenizer_file=__SCREAMING_SNAKE_CASE       ,						do_lower_case=__SCREAMING_SNAKE_CASE       ,						unk_token=__SCREAMING_SNAKE_CASE       ,						sep_token=__SCREAMING_SNAKE_CASE       ,						pad_token=__SCREAMING_SNAKE_CASE       ,						cls_token=__SCREAMING_SNAKE_CASE       ,						mask_token=__SCREAMING_SNAKE_CASE       ,						tokenize_chinese_chars=__SCREAMING_SNAKE_CASE       ,						strip_accents=__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE       ,						)

      lowerCamelCase_         =						json.loads(self.backend_tokenizer.normalizer.__getstate__()					)
      if (
          normalizer_state.get('lowercase'       ,						__SCREAMING_SNAKE_CASE					) != do_lower_case
          or normalizer_state.get('strip_accents'       ,						__SCREAMING_SNAKE_CASE					) != strip_accents
          or normalizer_state.get('handle_chinese_chars'       ,						__SCREAMING_SNAKE_CASE					) != tokenize_chinese_chars
      ):
        lowerCamelCase_         =						getattr(__SCREAMING_SNAKE_CASE       ,						normalizer_state.pop('type'					)					)
        lowerCamelCase_         =						do_lower_case
        lowerCamelCase_         =						strip_accents
        lowerCamelCase_         =						tokenize_chinese_chars
        lowerCamelCase_         =						normalizer_class(**__SCREAMING_SNAKE_CASE					)

      lowerCamelCase_         =						do_lower_case






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[str]=None					)     ->		int:
      lowerCamelCase_         =						[self.cls_token_id] + token_ids_a + [self.sep_token_id]

      if token_ids_a:
        output += token_ids_a + [self.sep_token_id]

      return output






    def    UpperCamelCase       (       self     :							Tuple       ,						__SCREAMING_SNAKE_CASE     :							List[int]       ,						__SCREAMING_SNAKE_CASE     :							Optional[List[int]] = None					)     ->		List[int]:
      lowerCamelCase_         =						[self.sep_token_id]
      lowerCamelCase_         =						[self.cls_token_id]
      if token_ids_a is None:
        return len(cls + token_ids_a + sep					) * [0]
      return len(cls + token_ids_a + sep					) * [0] + len(token_ids_a + sep					) * [1]






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							str       ,						__SCREAMING_SNAKE_CASE     :							Optional[str] = None					)     ->		Tuple[str]:
      lowerCamelCase_         =						self._tokenizer.model.save(__SCREAMING_SNAKE_CASE       ,						name=__SCREAMING_SNAKE_CASE					)
      return tuple(__SCREAMING_SNAKE_CASE					)












"""simple docstring"""





import string







def      lowerCamelCase__       (							_lowerCamelCase       :		str )      ->				None:
  for key in range(len(string.ascii_uppercase ) ):
    lowerCamelCase_         =						''
    for symbol in message:
      if symbol in string.ascii_uppercase:
        lowerCamelCase_         =						string.ascii_uppercase.find(_lowerCamelCase )
        lowerCamelCase_         =						num - key
        if num < 0:
          lowerCamelCase_         =						num + len(string.ascii_uppercase )
        lowerCamelCase_         =						translated + string.ascii_uppercase[num]
      else:
        lowerCamelCase_         =						translated + symbol
    print(F'''Decryption using Key #{key}: {translated}''' )







def      lowerCamelCase__       (							)      ->				None:
  lowerCamelCase_         =						input('Encrypted message: ' )
  lowerCamelCase_         =						message.upper()
  decrypt(_lowerCamelCase )


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  main()




