





"""simple docstring"""




import os
import re
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    : List[str]							=							logging.get_logger(__name__)

_lowerCAmelCase    : str							=							{"vocab_file": "spiece.model"}

_lowerCAmelCase    : List[str]							=							{
    "vocab_file": {
        "google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/spiece.model",
        "google/bigbird-roberta-large": (
            "https://huggingface.co/google/bigbird-roberta-large/resolve/main/spiece.model"
        ),
        "google/bigbird-base-trivia-itc": (
            "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/spiece.model"
        ),
    }
}

_lowerCAmelCase    : Optional[Any]							=							{
    "google/bigbird-roberta-base": 40_96,
    "google/bigbird-roberta-large": 40_96,
    "google/bigbird-base-trivia-itc": 40_96,
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       Dict								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Optional[Any]								=			['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE						:       List[int]								=			[]



      def __init__(					self					:     int					,     A					:     Union[str, Any]					,     A					:     List[str]="<unk>"					,     A					:     List[Any]="<s>"					,     A					:     Dict="</s>"					,     A					:     Any="<pad>"					,     A					:     List[str]="[SEP]"					,     A					:     Optional[Any]="[MASK]"					,     A					:     List[str]="[CLS]"					,     A					:     Optional[Dict[str, Any]] = None					,     **A					:     Optional[int]					,     ):
           _UpperCAmelCase      :     str        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else bos_token
           _UpperCAmelCase      :     Optional[Any]        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else eos_token
           _UpperCAmelCase      :     List[Any]        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else unk_token
           _UpperCAmelCase      :     Optional[Any]        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else pad_token
           _UpperCAmelCase      :     Dict        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else cls_token
           _UpperCAmelCase      :     Dict        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else sep_token

           # Mask token behave like a normal word, i.e. include the space before it
           _UpperCAmelCase      :     Optional[Any]        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else mask_token

           _UpperCAmelCase      :     Optional[int]        =   {} if sp_model_kwargs is None else sp_model_kwargs

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

           _UpperCAmelCase      :     int        =   vocab_file

           _UpperCAmelCase      :     Union[str, Any]        =   spm.SentencePieceProcessor(**self.sp_model_kwargs						)
           self.sp_model.Load(A						)



      @property
      def 						snake_case_ (					self					:     List[str]						):
           return self.sp_model.get_piece_size()



      def 						snake_case_ (					self					:     Optional[int]						):
           _UpperCAmelCase      :     Any        =   {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					:     Dict						):
           _UpperCAmelCase      :     Optional[Any]        =   self.__dict__.copy()
           _UpperCAmelCase      :     str        =   None
           return state



      def __setstate__(					self					:     Optional[Any]					,     A					:     Any						):
           _UpperCAmelCase      :     Any        =   d

           # for backward compatibility
           if not hasattr(self					,     "sp_model_kwargs"						):
                _UpperCAmelCase      :     List[str]        =   {}

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



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     str						):
           return self.sp_model.encode(A					,     out_type=A						)



      def 						snake_case_ (					self					:     int					,     A					:     Optional[Any]						):
           return self.sp_model.piece_to_id(A						)



      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     Optional[Any]        =   self.sp_model.IdToPiece(A						)
           return token



      def 						snake_case_ (					self					:     Optional[Any]					,     A					:     Union[str, Any]						):
           _UpperCAmelCase      :     Dict        =   []
           _UpperCAmelCase      :     Optional[Any]        =   ""
           _UpperCAmelCase      :     Any        =   False
           for token in tokens:
                # make sure that special tokens are not decoded using sentencepiece model
                if token in self.all_special_tokens:
                     if not prev_is_special:
                          out_string += " "
                     out_string += self.sp_model.decode(A						) + token
                     _UpperCAmelCase      :     List[Any]        =   True
                     _UpperCAmelCase      :     str        =   []
                else:
                     current_sub_tokens.append(A						)
                     _UpperCAmelCase      :     Dict        =   False
           out_string += self.sp_model.decode(A						)
           return out_string.strip()



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     List[int]					,     A					:     bool = False					,     A					:     bool = None					,     A					:     bool = True					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     int        =   kwargs.pop("use_source_tokenizer"					,     A						)

           _UpperCAmelCase      :     Dict        =   self.convert_ids_to_tokens(A					,     skip_special_tokens=A						)

           # To avoid mixing byte-level and unicode for byte-level BPT
           # we need to build string separately for added tokens and byte-level tokens
           # cf. https://github.com/huggingface/transformers/issues/1133
           _UpperCAmelCase      :     List[Any]        =   []
           _UpperCAmelCase      :     Tuple        =   []
           for token in filtered_tokens:
                if skip_special_tokens and token in self.all_special_ids:
                     continue
                if token in self.added_tokens_encoder:
                     if current_sub_text:
                          sub_texts.append(self.convert_tokens_to_string(A						)						)
                          _UpperCAmelCase      :     Union[str, Any]        =   []
                     sub_texts.append(A						)
                else:
                     current_sub_text.append(A						)
           if current_sub_text:
                sub_texts.append(self.convert_tokens_to_string(A						)						)

           # Mimic the behavior of the Rust tokenizer:
           # No space before [MASK] and [SEP]
           if spaces_between_special_tokens:
                _UpperCAmelCase      :     List[Any]        =   re.sub(R" (\[(MASK|SEP)\])"					,     R"\1"					,     " ".join(A						)						)
           else:
                _UpperCAmelCase      :     Optional[Any]        =   "".join(A						)

           _UpperCAmelCase      :     Optional[int]        =   (
               clean_up_tokenization_spaces
               if clean_up_tokenization_spaces is not None
               else self.clean_up_tokenization_spaces
           )
           if clean_up_tokenization_spaces:
                _UpperCAmelCase      :     str        =   self.clean_up_tokenization(A						)
                return clean_text
           else:
                return text



      def 						snake_case_ (					self					:     Dict					,     A					:     str					,     A					:     Optional[str] = None						):
           if not os.path.isdir(A						):
                logger.error(f'Vocabulary path ({save_directory}) should be a directory'						)
                return
           _UpperCAmelCase      :     Tuple        =   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:
                     _UpperCAmelCase      :     Tuple        =   self.sp_model.serialized_model_proto()
                     fi.write(A						)

           return (out_vocab_file,)



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           if token_ids_a is None:
                return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
           _UpperCAmelCase      :     Optional[Any]        =   [self.cls_token_id]
           _UpperCAmelCase      :     str        =   [self.sep_token_id]
           return cls + token_ids_a + sep + token_ids_a + sep



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     List[int]					,     A					:     Optional[List[int]] = None					,     A					:     bool = False						):
           if already_has_special_tokens:
                return super().get_special_tokens_mask(
                    token_ids_a=A					,     token_ids_a=A					,     already_has_special_tokens=A						)

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



      def 						snake_case_ (					self					:     Any					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Union[str, Any]        =   [self.sep_token_id]
           _UpperCAmelCase      :     str        =   [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						) * [0] + len(token_ids_a + sep						) * [1]





"""simple docstring"""




def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Dict=28_123    )  ->       List[str]:





     '''simple docstring'''

     _UpperCAmelCase      :     List[Any]        =   [1] * (limit + 1)

     for i in range(2						,  int(limit**0.5    ) + 1    ):
          sum_divs[i * i] += i
          for k in range(i + 1						,  limit // i + 1    ):
               sum_divs[k * i] += k + i

     _UpperCAmelCase      :     Any        =   set()
     _UpperCAmelCase      :     List[str]        =   0

     for n in range(1						,  limit + 1    ):
          if sum_divs[n] > n:
               abundants.add(SCREAMING_SNAKE_CASE__    )

          if not any((n - a in abundants) for a in abundants    ):
               res += n

     return res


if __name__ == "__main__":
 print(solution())
