




import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging


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


SCREAMING_SNAKE_CASE__       :      List[str]  =   '▁'

SCREAMING_SNAKE_CASE__       :      Any  =   {'vocab_file': 'spiece.model'}

SCREAMING_SNAKE_CASE__       :      Dict  =   {
    'vocab_file': {
        'google/reformer-crime-and-punishment': (
            'https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'
        )
    }
}

SCREAMING_SNAKE_CASE__       :      List[Any]  =   {
    'google/reformer-crime-and-punishment': 524288,
}




class 		UpperCamelCase__	(lowerCAmelCase__     ):

    '''simple docstring'''



    lowerCamelCase_      :	Any	     =      VOCAB_FILES_NAMES
    lowerCamelCase_      :	List[Any]	     =      PRETRAINED_VOCAB_FILES_MAP
    lowerCamelCase_      :	str	     =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    lowerCamelCase_      :	Optional[Any]	     =      ["""input_ids""", """attention_mask"""]







    def __init__(    self     ,  UpperCamelCase__     ,  UpperCamelCase__="</s>"     ,  UpperCamelCase__="<unk>"     ,  UpperCamelCase__=[]     ,  UpperCamelCase__ = None     ,  **UpperCamelCase__     ,  )     ->      None:
           lowerCamelCase   :      List[Any]			 =					{} if sp_model_kwargs is None else sp_model_kwargs

           super().__init__(
               eos_token=UpperCamelCase__     ,  unk_token=UpperCamelCase__     ,  additional_special_tokens=UpperCamelCase__     ,  sp_model_kwargs=self.sp_model_kwargs     ,  **UpperCamelCase__     ,  )

           lowerCamelCase   :      str			 =					vocab_file
           lowerCamelCase   :      List[Any]			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(UpperCamelCase__     )







    @property
    def     _lowercase     (    self     )     ->      Dict:
           return self.sp_model.get_piece_size()







    def     _lowercase     (    self     )     ->      Dict[str, int]:
           lowerCamelCase   :      Optional[Any]			 =					{self.convert_ids_to_tokens(UpperCamelCase__     ): i for i in range(self.vocab_size     )}
           vocab.update(self.added_tokens_encoder     )
           return vocab







    def __getstate__(    self     )     ->      str:
           lowerCamelCase   :      Optional[int]			 =					self.__dict__.copy()
           lowerCamelCase   :      Dict			 =					None
           return state







    def __setstate__(    self     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           lowerCamelCase   :      Any			 =					d

           # for backward compatibility
           if not hasattr(self     ,  "sp_model_kwargs"     ):
                  lowerCamelCase   :      Any			 =					{}

           lowerCamelCase   :      int			 =					spm.SentencePieceProcessor(**self.sp_model_kwargs     )
           self.sp_model.Load(self.vocab_file     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      List[str]:
           return self.sp_model.encode(UpperCamelCase__     ,  out_type=UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Union[str, Any]:
           return self.sp_model.piece_to_id(UpperCamelCase__     )







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      int:
           if index < self.sp_model.get_piece_size():
                  lowerCamelCase   :      Dict			 =					self.sp_model.IdToPiece(UpperCamelCase__     )
           return token







    def     _lowercase     (    self     ,  UpperCamelCase__     )     ->      Dict:
           lowerCamelCase   :      Any			 =					[]
           lowerCamelCase   :      str			 =					""
           for token in tokens:
                  # make sure that special tokens are not decoded using sentencepiece model
                  if token in self.all_special_tokens:
                         out_string += self.sp_model.decode(UpperCamelCase__     ) + token
                         lowerCamelCase   :      Union[str, Any]			 =					[]
                  else:
                         current_sub_tokens.append(UpperCamelCase__     )
           out_string += self.sp_model.decode(UpperCamelCase__     )
           return out_string.strip()







    def     _lowercase     (    self     ,  UpperCamelCase__     ,  UpperCamelCase__ = None     )     ->      Tuple[str]:
           if not os.path.isdir(UpperCamelCase__     ):
                  logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
                  return
           lowerCamelCase   :      Optional[int]			 =					os.path.join(
               UpperCamelCase__     ,  (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]     )

           if os.path.abspath(self.vocab_file     ) != os.path.abspath(UpperCamelCase__     ) and os.path.isfile(self.vocab_file     ):
                  copyfile(self.vocab_file     ,  UpperCamelCase__     )
           elif not os.path.isfile(self.vocab_file     ):
                  with open(UpperCamelCase__     ,  "wb"     ) as fi:
                         lowerCamelCase   :      Tuple			 =					self.sp_model.serialized_model_proto()
                         fi.write(UpperCamelCase__     )

           return (out_vocab_file,)












import random

from .binary_exp_mod import bin_exp_mod

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE=1000    )	-> List[str]:
       if n < 2:
              return False

       if n % 2 == 0:
              return n == 2

       # this means n is odd
       lowerCamelCase   :      List[Any]			 =					n - 1
       lowerCamelCase   :      Dict			 =					0
       while d % 2 == 0:
              d /= 2
              exp += 1

       # n - 1=d*(2**exp)
       lowerCamelCase   :      Optional[Any]			 =					0
       while count < prec:
              lowerCamelCase   :      str			 =					random.randint(2 ,n - 1    )
              lowerCamelCase   :      Dict			 =					bin_exp_mod(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )
              if b != 1:
                     lowerCamelCase   :      str			 =					True
                     for _ in range(_SCREAMING_SNAKE_CASE    ):
                            if b == n - 1:
                                   lowerCamelCase   :      Tuple			 =					False
                                   break
                            lowerCamelCase   :      int			 =					b * b
                            b %= n
                     if flag:
                            return False
                     count += 1
       return True


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__       :      Optional[int]  =   abs(int(input('Enter bound : ').strip()))
       print('Here\'s the list of primes:')
       print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))






