


'''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


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

__magic_name__			=      {
    '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'
        ),
    },
}

__magic_name__			=      {
    '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,
}

__magic_name__			=      {
    '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 	__lowerCAmelCase		(							__SCREAMING_SNAKE_CASE    ):


  '''simple docstring'''







  a_  					=							VOCAB_FILES_NAMES
  a_  					=							PRETRAINED_VOCAB_FILES_MAP
  a_  					=							PRETRAINED_INIT_CONFIGURATION
  a_  					=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a_  					=							ElectraTokenizer






  def __init__(							self :       Any	,_a :       str=None	,_a :       Union[str, Any]=None	,_a :       Optional[int]=True	,_a :       Union[str, Any]="[UNK]"	,_a :       List[Any]="[SEP]"	,_a :       Union[str, Any]="[PAD]"	,_a :       str="[CLS]"	,_a :       Union[str, Any]="[MASK]"	,_a :       Optional[int]=True	,_a :       Tuple=None	,**_a :       List[str]	,):

        '''simple docstring'''




        super().__init__(
            _a	,tokenizer_file=_a	,do_lower_case=_a	,unk_token=_a	,sep_token=_a	,pad_token=_a	,cls_token=_a	,mask_token=_a	,tokenize_chinese_chars=_a	,strip_accents=_a	,**_a	,)

        A_						:  Optional[int]          =				json.loads(self.backend_tokenizer.normalizer.__getstate__() )
        if (
            normalizer_state.get("""lowercase"""	,_a ) != do_lower_case
            or normalizer_state.get("""strip_accents"""	,_a ) != strip_accents
            or normalizer_state.get("""handle_chinese_chars"""	,_a ) != tokenize_chinese_chars
        ):
              A_						:  List[str]          =				getattr(_a	,normalizer_state.pop("""type""" ) )
              A_						:  str          =				do_lower_case
              A_						:  List[Any]          =				strip_accents
              A_						:  Optional[Any]          =				tokenize_chinese_chars
              A_						:  List[str]          =				normalizer_class(**_a )

        A_						:  Dict          =				do_lower_case






  def        _a							(							self :       List[Any]	,_a :       Any	,_a :       int=None ):

        '''simple docstring'''




        A_						:  List[str]          =				[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        _a							(							self :       int	,_a :       List[int]	,_a :       Optional[List[int]] = None ):

        '''simple docstring'''




        A_						:  List[str]          =				[self.sep_token_id]
        A_						:  Dict          =				[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        _a							(							self :       Dict	,_a :       str	,_a :       Optional[str] = None ):

        '''simple docstring'''




        A_						:  Optional[Any]          =				self._tokenizer.model.save(_a	,name=_a )
        return tuple(_a )




'''simple docstring'''


from __future__ import annotations

import math




def      lowerCamelCase					(      lowerCamelCase  :      int):

      if num <= 0:
            A_						:  List[Any]          =				F'{num}: Invalid input, please enter a positive integer.'
            raise ValueError(lowerCamelCase)

      A_						:  str          =				[True] * (num + 1)
      A_						:  Tuple          =				[]
      A_						:  str          =				2
      A_						:  Any          =				int(math.sqrt(lowerCamelCase))

      while start <= end:
            # If start is a prime
            if sieve[start] is True:
                  prime.append(lowerCamelCase)

                  # Set multiples of start be False
                  for i in range(start * start							,						num + 1							,						lowerCamelCase):
                        if sieve[i] is True:
                              A_						:  Union[str, Any]          =				False

            start += 1

      for j in range(end + 1							,						num + 1):
            if sieve[j] is True:
                  prime.append(lowerCamelCase)

      return prime


if __name__ == "__main__":
     print(prime_sieve(int(input('Enter a positive integer: ').strip())))
