



'''simple docstring'''





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


lowercase_	 =						logging.get_logger(__name__)


lowercase_	 =						"▁"

lowercase_	 =						{"vocab_file": "spiece.model"}

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

lowercase_	 =						{
    "google/reformer-crime-and-punishment": 524_288,
}






class __A    (						A		):



 '''simple docstring'''







 __lowerCamelCase					: Any    = VOCAB_FILES_NAMES
 __lowerCamelCase					: str    = PRETRAINED_VOCAB_FILES_MAP
 __lowerCamelCase					: str    = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __lowerCamelCase					: Tuple    = ['input_ids', 'attention_mask']



 def __init__(self				,     A				,     A="</s>"				,     A="<unk>"				,     A=[]				,     A = None				,     **A				,     )					->  None:


      """simple docstring"""



      _a											=						{} if sp_model_kwargs is None else sp_model_kwargs

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

      _a											=						vocab_file
      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(A )



 @property
 def       a__						(self )					->  str:


      """simple docstring"""



      return self.sp_model.get_piece_size()



 def       a__						(self )					->  Dict[str, int]:


      """simple docstring"""



      _a											=						{self.convert_ids_to_tokens(A ): i for i in range(self.vocab_size )}
      vocab.update(self.added_tokens_encoder )
      return vocab



 def __getstate__(self )					->  List[str]:


      """simple docstring"""



      _a											=						self.__dict__.copy()
      _a											=						None
      return state



 def __setstate__(self				,     A )					->  Optional[int]:


      """simple docstring"""



      _a											=						d

      # for backward compatibility
      if not hasattr(self				,     '''sp_model_kwargs''' ):
           _a											=						{}

      _a											=						spm.SentencePieceProcessor(**self.sp_model_kwargs )
      self.sp_model.Load(self.vocab_file )



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



      return self.sp_model.encode(A				,     out_type=A )



 def       a__						(self				,     A )					->  Tuple:


      """simple docstring"""



      return self.sp_model.piece_to_id(A )



 def       a__						(self				,     A )					->  List[str]:


      """simple docstring"""



      if index < self.sp_model.get_piece_size():
           _a											=						self.sp_model.IdToPiece(A )
      return token



 def       a__						(self				,     A )					->  Optional[Any]:


      """simple docstring"""



      _a											=						[]
      _a											=						''''''
      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(A ) + token
                _a											=						[]
           else:
                current_sub_tokens.append(A )
      out_string += self.sp_model.decode(A )
      return out_string.strip()



 def       a__						(self				,     A				,     A = None )					->  Tuple[str]:


      """simple docstring"""



      if not os.path.isdir(A ):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
           return
      _a											=						os.path.join(
          A				,     (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )

      if os.path.abspath(self.vocab_file ) != os.path.abspath(A ) and os.path.isfile(self.vocab_file ):
           copyfile(self.vocab_file				,     A )
      elif not os.path.isfile(self.vocab_file ):
           with open(A				,     '''wb''' ) as fi:
                _a											=						self.sp_model.serialized_model_proto()
                fi.write(A )

      return (out_vocab_file,)








'''simple docstring'''





from __future__ import annotations

lowercase_	 =						[True] * 1_000_001
lowercase_	 =						2
while i * i <= 1_000_000:
  if seive[i]:
    for j in range(i * i, 1_000_001, i):
      lowercase_	 =						False
  i += 1




def lowerCAmelCase						(__A):



     """simple docstring"""



     return seive[n]




def lowerCAmelCase						(__A):



     """simple docstring"""



     return any(digit in '''02468''' for digit in str(__A))




def lowerCAmelCase						(__A = 1_000_000):



     """simple docstring"""



     _a											=						[2]  # result already includes the number 2.
     for num in range(3						,			limit + 1						,			2):
          if is_prime(__A) and not contains_an_even_digit(__A):
               _a											=						str(__A)
               _a											=						[int(str_num[j:] + str_num[:j]) for j in range(len(__A))]
               if all(is_prime(__A) for i in list_nums):
                    result.append(__A)
     return result




def lowerCAmelCase						():



     """simple docstring"""



     return len(find_circular_primes())


if __name__ == "__main__":
  print(F"""{len(find_circular_primes()) = }""")



