


'''simple docstring'''



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    :Union[str, Any]				       =     logging.get_logger(__name__)

__UpperCAmelCase    :Optional[int]				       =     "▁"

__UpperCAmelCase    :List[str]				       =     {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}

__UpperCAmelCase    :int				       =     {
    "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    :Optional[int]				       =     {"vinai/bartpho-syllable": 1_0_2_4}



class 			a       (	_a ):


     """simple docstring"""






     SCREAMING_SNAKE_CASE :				Optional[int]							     =     VOCAB_FILES_NAMES
     SCREAMING_SNAKE_CASE :				int							     =     PRETRAINED_VOCAB_FILES_MAP
     SCREAMING_SNAKE_CASE :				List[str]							     =     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     SCREAMING_SNAKE_CASE :				Union[str, Any]							     =     ["input_ids", "attention_mask"]

     def __init__(       self  :	Dict							,				snake_case  :	Tuple							,				snake_case  :	str							,				snake_case  :	Optional[int]="<s>"							,				snake_case  :	Tuple="</s>"							,				snake_case  :	Tuple="</s>"							,				snake_case  :	int="<s>"							,				snake_case  :	Tuple="<unk>"							,				snake_case  :	Dict="<pad>"							,				snake_case  :	str="<mask>"							,				snake_case  :	Optional[Dict[str, Any]] = None							,				**snake_case  :	Dict							,				)		->							None:
          # Mask token behave like a normal word, i.e. include the space before it
          __UpperCAmelCase			:				Union[str, Any]													=    AddedToken(snake_case							,				lstrip=snake_case							,				rstrip=snake_case    ) if isinstance(snake_case							,				snake_case    ) else mask_token

          __UpperCAmelCase			:				List[str]													=    {} if sp_model_kwargs is None else sp_model_kwargs

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

          __UpperCAmelCase			:				str													=    vocab_file
          __UpperCAmelCase			:				int													=    monolingual_vocab_file
          __UpperCAmelCase			:				Tuple													=    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.Load(str(snake_case    )    )

          # Load the reduced vocab

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

          __UpperCAmelCase			:				Optional[int]													=    {v: k for k, v in self.fairseq_tokens_to_ids.items()}

     def __getstate__(       self  :	Any    )		->							Any:
          __UpperCAmelCase			:				Any													=    self.__dict__.copy()
          __UpperCAmelCase			:				Optional[int]													=    None
          __UpperCAmelCase			:				Any													=    self.sp_model.serialized_model_proto()
          return state

     def __setstate__(       self  :	Any							,				snake_case  :	Any    )		->							Optional[int]:
          __UpperCAmelCase			:				Optional[Any]													=    d

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

          __UpperCAmelCase			:				Optional[int]													=    spm.SentencePieceProcessor(**self.sp_model_kwargs    )
          self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )

     def 				lowerCamelCase__				(       self  :	Tuple							,				snake_case  :	List[int]							,				snake_case  :	Optional[List[int]] = None    )		->							List[int]:

          if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
          __UpperCAmelCase			:				List[Any]													=    [self.cls_token_id]
          __UpperCAmelCase			:				Dict													=    [self.sep_token_id]
          return cls + token_ids_a + sep + sep + token_ids_a + sep

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	List[int]							,				snake_case  :	Optional[List[int]] = None							,				snake_case  :	bool = False    )		->							List[int]:

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

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

     def 				lowerCamelCase__				(       self  :	List[Any]							,				snake_case  :	List[int]							,				snake_case  :	Optional[List[int]] = None    )		->							List[int]:
          __UpperCAmelCase			:				Optional[int]													=    [self.sep_token_id]
          __UpperCAmelCase			:				List[Any]													=    [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 				lowerCamelCase__				(       self  :	str    )		->							Optional[int]:
          return len(self.fairseq_ids_to_tokens    )

     def 				lowerCamelCase__				(       self  :	Dict    )		->							Optional[int]:
          __UpperCAmelCase			:				List[Any]													=    {self.convert_ids_to_tokens(snake_case    ): i for i in range(self.vocab_size    )}
          vocab.update(self.added_tokens_encoder    )
          return vocab

     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	str    )		->							List[str]:
          return self.sp_model.encode(snake_case							,				out_type=snake_case    )

     def 				lowerCamelCase__				(       self  :	Any							,				snake_case  :	List[Any]    )		->							Any:
          if token in self.fairseq_tokens_to_ids:
               return self.fairseq_tokens_to_ids[token]
          else:
               return self.unk_token_id

     def 				lowerCamelCase__				(       self  :	Union[str, Any]							,				snake_case  :	int    )		->							List[str]:
          return self.fairseq_ids_to_tokens[index]

     def 				lowerCamelCase__				(       self  :	Optional[int]							,				snake_case  :	Any    )		->							str:
          __UpperCAmelCase			:				List[str]													=    ''''''.join(snake_case    ).replace(snake_case							,				''' '''    ).strip()
          return out_string






     def 				lowerCamelCase__				(       self  :	Optional[Any]							,				snake_case  :	str							,				snake_case  :	Optional[str] = None    )		->							Tuple[str]:
          if not os.path.isdir(snake_case    ):
               logger.error(f'Vocabulary path ({save_directory}) should be a directory'    )
               return
          __UpperCAmelCase			:				Optional[int]													=    os.path.join(
              snake_case							,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']    )
          __UpperCAmelCase			:				Union[str, Any]													=    os.path.join(
              snake_case							,				(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']							,				)

          if os.path.abspath(self.vocab_file    ) != os.path.abspath(snake_case    ) and os.path.isfile(self.vocab_file    ):
               copyfile(self.vocab_file							,				snake_case    )
          elif not os.path.isfile(self.vocab_file    ):
               with open(snake_case							,				'''wb'''    ) as fi:
                    __UpperCAmelCase			:				Optional[Any]													=    self.sp_model.serialized_model_proto()
                    fi.write(snake_case    )

          if os.path.abspath(self.monolingual_vocab_file    ) != os.path.abspath(
              snake_case    ) and os.path.isfile(self.monolingual_vocab_file    ):
               copyfile(self.monolingual_vocab_file							,				snake_case    )
          elif not os.path.isfile(self.monolingual_vocab_file    ):
               with open(snake_case							,				'''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(snake_case    )} \n'    )

          return out_vocab_file, out_monolingual_vocab_file



'''simple docstring'''



from __future__ import annotations




def 			_a			(		_lowercase      :			int						):





     '''simple docstring'''






     __UpperCAmelCase			:				str													=    [True] * limit
     __UpperCAmelCase			:				Tuple													=    False
     __UpperCAmelCase			:				List[str]													=    False
     __UpperCAmelCase			:				List[Any]													=    True

     for i in range(3    ,      int(limit**0.5 + 1						)    ,      2						):
          __UpperCAmelCase			:				Dict													=    i * 2
          while index < limit:
               __UpperCAmelCase			:				Optional[int]													=    False
               __UpperCAmelCase			:				Union[str, Any]													=    index + i

     __UpperCAmelCase			:				Optional[Any]													=    [2]

     for i in range(3    ,      _lowercase    ,      2						):
          if is_prime[i]:
               primes.append(_lowercase						)

     return primes




def 			_a			(		_lowercase      :			int = 1000000						):





     '''simple docstring'''






     __UpperCAmelCase			:				List[str]													=    prime_sieve(_lowercase						)
     __UpperCAmelCase			:				Optional[Any]													=    0
     __UpperCAmelCase			:				int													=    0

     for i in range(len(_lowercase						)						):
          for j in range(i + length    ,      len(_lowercase						)						):
               __UpperCAmelCase			:				Optional[Any]													=    sum(primes[i:j]						)
               if sol >= ceiling:
                    break

               if sol in primes:
                    __UpperCAmelCase			:				Any													=    j - i
                    __UpperCAmelCase			:				List[Any]													=    sol

     return largest


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