



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


_lowerCamelCase						    =  logging.get_logger(__name__)

_lowerCamelCase						    =  """▁"""

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

_lowerCamelCase						    =  {
    """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""",
    },
}

_lowerCamelCase						    =  {"""vinai/bartpho-syllable""": 1024}

class       _SCREAMING_SNAKE_CASE	(UpperCamelCase ):
     lowerCAmelCase					=			VOCAB_FILES_NAMES
     lowerCAmelCase					=			PRETRAINED_VOCAB_FILES_MAP
     lowerCAmelCase					=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowerCAmelCase					=			["""input_ids""", """attention_mask"""]





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

      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	{} 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     ,  )

      __SCREAMING_SNAKE_CASE       :    Tuple       =	vocab_file
      __SCREAMING_SNAKE_CASE       :    int       =	monolingual_vocab_file
      __SCREAMING_SNAKE_CASE       :    List[str]       =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(str(UpperCamelCase				)				)

      # Load the reduced vocab

      # Keep order of special tokens for backward compatibility
      __SCREAMING_SNAKE_CASE       :    List[Any]       =	{}
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	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:
        __SCREAMING_SNAKE_CASE       :    Dict       =	cnt
        cnt += 1
      with open(UpperCamelCase     ,  "r"     ,  encoding="utf-8"				) as f:
       for line in f.readlines():
        __SCREAMING_SNAKE_CASE       :    Optional[int]       =	line.strip().split()[0]
        __SCREAMING_SNAKE_CASE       :    Any       =	len(self.fairseq_tokens_to_ids				)
      if str(UpperCamelCase				) not in self.fairseq_tokens_to_ids:
       __SCREAMING_SNAKE_CASE       :    int       =	len(self.fairseq_tokens_to_ids				)

      __SCREAMING_SNAKE_CASE       :    List[str]       =	{v: k for k, v in self.fairseq_tokens_to_ids.items()}





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





     def __setstate__(       self :							Optional[Any]     ,  UpperCamelCase :							Dict				)->Dict:
      __SCREAMING_SNAKE_CASE       :    str       =	d

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

      __SCREAMING_SNAKE_CASE       :    Any       =	spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.LoadFromSerializedProto(self.sp_model_proto				)





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None				)->List[int]:

      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      __SCREAMING_SNAKE_CASE       :    Optional[int]       =	[self.cls_token_id]
      __SCREAMING_SNAKE_CASE       :    Dict       =	[self.sep_token_id]
      return cls + token_ids_a + sep + sep + token_ids_a + sep





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None     ,  UpperCamelCase :							bool = False				)->List[int]:

      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 	__snake_case    (       self :							List[str]     ,  UpperCamelCase :							List[int]     ,  UpperCamelCase :							Optional[List[int]] = None				)->List[int]:
      __SCREAMING_SNAKE_CASE       :    Any       =	[self.sep_token_id]
      __SCREAMING_SNAKE_CASE       :    Union[str, 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 	__snake_case    (       self :							Union[str, Any]				)->Union[str, Any]:
      return len(self.fairseq_ids_to_tokens				)





     def 	__snake_case    (       self :							Optional[int]				)->List[str]:
      __SCREAMING_SNAKE_CASE       :    int       =	{self.convert_ids_to_tokens(UpperCamelCase				): i for i in range(self.vocab_size				)}
      vocab.update(self.added_tokens_encoder				)
      return vocab





     def 	__snake_case    (       self :							Union[str, Any]     ,  UpperCamelCase :							str				)->List[str]:
      return self.sp_model.encode(UpperCamelCase     ,  out_type=UpperCamelCase				)





     def 	__snake_case    (       self :							Optional[Any]     ,  UpperCamelCase :							List[str]				)->Optional[Any]:
      if token in self.fairseq_tokens_to_ids:
       return self.fairseq_tokens_to_ids[token]
      else:
       return self.unk_token_id





     def 	__snake_case    (       self :							str     ,  UpperCamelCase :							Dict				)->Any:
      return self.fairseq_ids_to_tokens[index]





     def 	__snake_case    (       self :							Dict     ,  UpperCamelCase :							Tuple				)->Any:
      __SCREAMING_SNAKE_CASE       :    Union[str, Any]       =	"".join(UpperCamelCase				).replace(UpperCamelCase     ,  " "				).strip()
      return out_string






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











from math import sqrt






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int     ):



 """simple docstring"""





 if 1 < number < 4:
  # 2 and 3 are primes
  return True
 elif number < 2 or number % 2 == 0 or number % 3 == 0:
  # Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
  return False

 # All primes number are in format of 6k +/- 1
 for i in range(5		,	int(sqrt(__lowerCamelCase     ) + 1     )		,	6     ):
  if number % i == 0 or number % (i + 2) == 0:
   return False
 return True






def 	_lowerCAmelCase						(				__lowerCamelCase   :     int = 10001     ):



 """simple docstring"""





 __SCREAMING_SNAKE_CASE       :    List[Any]       =	0
 __SCREAMING_SNAKE_CASE       :    Dict       =	1
 while count != nth and number < 3:
  number += 1
  if is_prime(__lowerCamelCase     ):
   count += 1
 while count != nth:
  number += 2
  if is_prime(__lowerCamelCase     ):
   count += 1
 return number


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






