





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

import sentencepiece as spm

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


UpperCAmelCase_           =	logging.get_logger(__name__)

UpperCAmelCase_           =	"▁"

UpperCAmelCase_           =	{"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

UpperCAmelCase_           =	{
    "vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
    },
    "monolingual_vocab_file": {
        "vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
    },
}

UpperCAmelCase_           =	{"vinai/bartpho-syllable": 10_24}




class 			__UpperCamelCase    (							A__    ):
     __A	:  List[Any]							  =   VOCAB_FILES_NAMES
     __A	:  Dict							  =   PRETRAINED_VOCAB_FILES_MAP
     __A	:  Optional[Any]							  =   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __A	:  int							  =   ["""input_ids""", """attention_mask"""]





     def __init__(     self				,							_UpperCamelCase				,							_UpperCamelCase				,							_UpperCamelCase="<s>"				,							_UpperCamelCase="</s>"				,							_UpperCamelCase="</s>"				,							_UpperCamelCase="<s>"				,							_UpperCamelCase="<unk>"				,							_UpperCamelCase="<pad>"				,							_UpperCamelCase="<mask>"				,							_UpperCamelCase = None				,							**_UpperCamelCase				,							):
        # Mask token behave like a normal word, i.e. include the space before it
        _UpperCAmelCase           =   AddedToken(_UpperCamelCase				,							lstrip=_UpperCamelCase				,							rstrip=_UpperCamelCase					) if isinstance(_UpperCamelCase				,							_UpperCamelCase					) else mask_token

        _UpperCAmelCase           =   {} if sp_model_kwargs is None else sp_model_kwargs

        super().__init__(
            bos_token=_UpperCamelCase				,							eos_token=_UpperCamelCase				,							unk_token=_UpperCamelCase				,							sep_token=_UpperCamelCase				,							cls_token=_UpperCamelCase				,							pad_token=_UpperCamelCase				,							mask_token=_UpperCamelCase				,							sp_model_kwargs=self.sp_model_kwargs				,							**_UpperCamelCase				,							)

        _UpperCAmelCase           =   vocab_file
        _UpperCAmelCase           =   monolingual_vocab_file
        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.Load(str(_UpperCamelCase					)					)

        # Load the reduced vocab

        # Keep order of special tokens for backward compatibility
        _UpperCAmelCase           =   {}
        _UpperCAmelCase           =   0
        for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
           if str(_UpperCamelCase					) not in self.fairseq_tokens_to_ids:
              _UpperCAmelCase           =   cnt
              cnt += 1
        with open(_UpperCamelCase				,							'''r'''				,							encoding='''utf-8'''					) as f:
           for line in f.readlines():
              _UpperCAmelCase           =   line.strip().split()[0]
              _UpperCAmelCase           =   len(self.fairseq_tokens_to_ids					)
        if str(_UpperCamelCase					) not in self.fairseq_tokens_to_ids:
           _UpperCAmelCase           =   len(self.fairseq_tokens_to_ids					)

        _UpperCAmelCase           =   {v: k for k, v in self.fairseq_tokens_to_ids.items()}





     def __getstate__(     self					):
        _UpperCAmelCase           =   self.__dict__.copy()
        _UpperCAmelCase           =   None
        _UpperCAmelCase           =   self.sp_model.serialized_model_proto()
        return state





     def __setstate__(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   d

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

        _UpperCAmelCase           =   spm.SentencePieceProcessor(**self.sp_model_kwargs					)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):

        if token_ids_a is None:
           return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCAmelCase           =   [self.cls_token_id]
        _UpperCAmelCase           =   [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None				,							_UpperCamelCase = False					):

        if already_has_special_tokens:
           return super().get_special_tokens_mask(
               token_ids_a=_UpperCamelCase				,							token_ids_a=_UpperCamelCase				,							already_has_special_tokens=_UpperCamelCase					)

        if token_ids_a is None:
           return [1] + ([0] * len(_UpperCamelCase					)) + [1]
        return [1] + ([0] * len(_UpperCamelCase					)) + [1, 1] + ([0] * len(_UpperCamelCase					)) + [1]





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        _UpperCAmelCase           =   [self.sep_token_id]
        _UpperCAmelCase           =   [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 + sep + token_ids_a + sep					) * [0]





     @property
     def 		UpperCamelCase(     self					):
        return len(self.fairseq_ids_to_tokens					)





     def 		UpperCamelCase(     self					):
        _UpperCAmelCase           =   {self.convert_ids_to_tokens(_UpperCamelCase					): i for i in range(self.vocab_size					)}
        vocab.update(self.added_tokens_encoder					)
        return vocab





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        return self.sp_model.encode(_UpperCamelCase				,							out_type=_UpperCamelCase					)





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        if token in self.fairseq_tokens_to_ids:
           return self.fairseq_tokens_to_ids[token]
        else:
           return self.unk_token_id





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        return self.fairseq_ids_to_tokens[index]





     def 		UpperCamelCase(     self				,							_UpperCamelCase					):
        _UpperCAmelCase           =   ''''''.join(_UpperCamelCase					).replace(_UpperCamelCase				,							''' '''					).strip()
        return out_string





     def 		UpperCamelCase(     self				,							_UpperCamelCase				,							_UpperCamelCase = None					):
        if not os.path.isdir(_UpperCamelCase					):
           logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''					)
           return
        _UpperCAmelCase           =   os.path.join(
            _UpperCamelCase				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']					)
        _UpperCAmelCase           =   os.path.join(
            _UpperCamelCase				,							(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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:
              _UpperCAmelCase           =   self.sp_model.serialized_model_proto()
              fi.write(_UpperCamelCase					)

        if os.path.abspath(self.monolingual_vocab_file					) != os.path.abspath(
            _UpperCamelCase					) and os.path.isfile(self.monolingual_vocab_file					):
           copyfile(self.monolingual_vocab_file				,							_UpperCamelCase					)
        elif not os.path.isfile(self.monolingual_vocab_file					):
           with open(_UpperCamelCase				,							'''w'''				,							encoding='''utf-8'''					) as fp:
              for token in self.fairseq_tokens_to_ids:
                 if token not in self.all_special_tokens:
                    fp.write(f'''{str(_UpperCamelCase					)} \n'''					)

        return out_vocab_file, out_monolingual_vocab_file






def      A__       (    SCREAMING_SNAKE_CASE_   :      int = 2_00_00_00						)  ->  int:






   """simple docstring"""







   _UpperCAmelCase           =   [0 for i in range(n + 1						)]
   _UpperCAmelCase           =   1
   _UpperCAmelCase           =   1

   for i in range(2				,	int(n**0.5						) + 1						):
      if primality_list[i] == 0:
         for j in range(i * i				,	n + 1				,	SCREAMING_SNAKE_CASE_						):
            _UpperCAmelCase           =   1
   _UpperCAmelCase           =   0
   for i in range(SCREAMING_SNAKE_CASE_						):
      if primality_list[i] == 0:
         sum_of_primes += i
   return sum_of_primes


if __name__ == "__main__":
   print(f'''{solution() = }''')