
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


__lowerCAmelCase			:     Optional[int]           =   logging.get_logger(__name__)

__lowerCAmelCase			:     List[str]           =   '▁'

__lowerCAmelCase			:     Optional[Any]           =   {'vocab_file': 'sentencepiece.bpe.model'}

__lowerCAmelCase			:     Optional[int]           =   {
    'vocab_file': {
        'facebook/xglm-564M': 'https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model',
    }
}

__lowerCAmelCase			:     Optional[Any]           =   {
    'facebook/xglm-564M': 2048,
}




class        UpperCAmelCase_			( _A     ):


  '''simple docstring'''







  a__           =		VOCAB_FILES_NAMES
  a__           =		PRETRAINED_VOCAB_FILES_MAP
  a__           =		PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a__           =		["""input_ids""", """attention_mask"""]
  def __init__( self			: Optional[Any]			,							UpperCamelCase__			: List[str]			,							UpperCamelCase__			: str="<s>"			,							UpperCamelCase__			: Optional[int]="</s>"			,							UpperCamelCase__			: str="</s>"			,							UpperCamelCase__			: Optional[Any]="<s>"			,							UpperCamelCase__			: Optional[int]="<unk>"			,							UpperCamelCase__			: Optional[int]="<pad>"			,							UpperCamelCase__			: Optional[Dict[str, Any]] = None			,							**UpperCamelCase__			: str			,							)			->	None:







    """simple docstring"""






    __magic_name__				     =       {} if sp_model_kwargs is None else sp_model_kwargs

    # Compatibility with the original tokenizer
    __magic_name__				     =       7
    __magic_name__				     =       [F'''<madeupword{i}>''' for i in range(self.num_madeup_words    )]

    __magic_name__				     =       kwargs.get("""additional_special_tokens"""			,							[]    )
    kwargs["additional_special_tokens"] += [
        word for word in madeup_words if word not in kwargs["additional_special_tokens"]
    ]

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

    __magic_name__				     =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.Load(str(UpperCamelCase__    )    )
    __magic_name__				     =       vocab_file

    # Original fairseq vocab and spm vocab must be "aligned":
    # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
    # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
    # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
    # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'

    # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
    __magic_name__				     =       1

    # Mimic fairseq token-to-id alignment for the first 4 token
    __magic_name__				     =       {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}

    __magic_name__				     =       len(self.sp_model    )
    __magic_name__				     =       {F'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words    )}
    self.fairseq_tokens_to_ids.update(UpperCamelCase__    )

    __magic_name__				     =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}
  def __getstate__( self			: Union[str, Any]    )			->	List[str]:







    """simple docstring"""






    __magic_name__				     =       self.__dict__.copy()
    __magic_name__				     =       None
    __magic_name__				     =       self.sp_model.serialized_model_proto()
    return state
  def __setstate__( self			: List[str]			,							UpperCamelCase__			: str    )			->	List[Any]:







    """simple docstring"""






    __magic_name__				     =       d

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

    __magic_name__				     =       spm.SentencePieceProcessor(**self.sp_model_kwargs    )
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto    )
  def 							_lowercase							( self			: Union[str, Any]			,							UpperCamelCase__			: List[int]			,							UpperCamelCase__			: Optional[List[int]] = None    )			->	List[int]:







    """simple docstring"""






    if token_ids_a is None:
      return [self.sep_token_id] + token_ids_a
    __magic_name__				     =       [self.sep_token_id]
    return sep + token_ids_a + sep + sep + token_ids_a
  def 							_lowercase							( self			: Optional[Any]			,							UpperCamelCase__			: List[int]			,							UpperCamelCase__			: Optional[List[int]] = None			,							UpperCamelCase__			: bool = False    )			->	List[int]:







    """simple docstring"""






    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__    ))
    return [1] + ([0] * len(UpperCamelCase__    )) + [1, 1] + ([0] * len(UpperCamelCase__    ))
  def 							_lowercase							( self			: int			,							UpperCamelCase__			: List[int]			,							UpperCamelCase__			: Optional[List[int]] = None    )			->	List[int]:







    """simple docstring"""






    __magic_name__				     =       [self.sep_token_id]

    if token_ids_a is None:
      return len(sep + token_ids_a    ) * [0]
    return len(sep + token_ids_a + sep + sep + token_ids_a    ) * [0]
  @property
  def 							_lowercase							( self			: Tuple    )			->	List[Any]:







    """simple docstring"""






    return len(self.sp_model    ) + self.fairseq_offset + self.num_madeup_words
  def 							_lowercase							( self			: Optional[int]    )			->	Tuple:







    """simple docstring"""






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







    """simple docstring"""






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







    """simple docstring"""






    if token in self.fairseq_tokens_to_ids:
      return self.fairseq_tokens_to_ids[token]
    __magic_name__				     =       self.sp_model.PieceToId(UpperCamelCase__    )

    # Need to return unknown token if the SP model returned 0
    return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
  def 							_lowercase							( self			: str			,							UpperCamelCase__			: str    )			->	Tuple:







    """simple docstring"""






    if index in self.fairseq_ids_to_tokens:
      return self.fairseq_ids_to_tokens[index]
    return self.sp_model.IdToPiece(index - self.fairseq_offset    )
  def 							_lowercase							( self			: Dict			,							UpperCamelCase__			: Any    )			->	Optional[Any]:







    """simple docstring"""






    __magic_name__				     =       """""".join(UpperCamelCase__    ).replace(UpperCamelCase__			,							""" """    ).strip()
    return out_string





  def 							_lowercase							( self			: Optional[int]			,							UpperCamelCase__			: str			,							UpperCamelCase__			: Optional[str] = None    )			->	Tuple[str]:







    """simple docstring"""






    if not os.path.isdir(UpperCamelCase__    ):
      logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''    )
      return
    __magic_name__				     =       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:
        __magic_name__				     =       self.sp_model.serialized_model_proto()
        fi.write(UpperCamelCase__    )

    return (out_vocab_file,)









"""simple docstring"""





from __future__ import annotations







def      lowerCamelCase__       (							_lowerCamelCase       :		int )      ->				list[int]:
  lowerCamelCase_         =						[True] * limit
  lowerCamelCase_         =						False
  lowerCamelCase_         =						False
  lowerCamelCase_         =						True

  for i in range(3		,				int(limit**0.5 + 1 )		,				2 ):
    lowerCamelCase_         =						i * 2
    while index < limit:
      lowerCamelCase_         =						False
      lowerCamelCase_         =						index + i

  lowerCamelCase_         =						[2]

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

  return primes







def      lowerCamelCase__       (							_lowerCamelCase       :		int = 1000000 )      ->				int:
  lowerCamelCase_         =						prime_sieve(_lowerCamelCase )
  lowerCamelCase_         =						0
  lowerCamelCase_         =						0

  for i in range(len(_lowerCamelCase ) ):
    for j in range(i + length		,				len(_lowerCamelCase ) ):
      lowerCamelCase_         =						sum(primes[i:j] )
      if sol >= ceiling:
        break

      if sol in primes:
        lowerCamelCase_         =						j - i
        lowerCamelCase_         =						sol

  return largest


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




