



"""simple docstring"""




import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


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


_lowerCAmelCase    : Union[str, Any]							=							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
_lowerCAmelCase    : Any							=							{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

_lowerCAmelCase    : List[Any]							=							{
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Tuple								=			VOCAB_FILES_NAMES
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			PRETRAINED_VOCAB_FILES_MAP
      __SCREAMING_SNAKE_CASE						:       str								=			PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      __SCREAMING_SNAKE_CASE						:       Tuple								=			['input_ids', 'attention_mask']
      __SCREAMING_SNAKE_CASE						:       Union[str, Any]								=			BartTokenizer



      def __init__(					self					:     Tuple					,     A					:     List[str]=None					,     A					:     Any=None					,     A					:     Optional[int]=None					,     A					:     int="replace"					,     A					:     Any="<s>"					,     A					:     str="</s>"					,     A					:     int="</s>"					,     A					:     Dict="<s>"					,     A					:     Tuple="<unk>"					,     A					:     Optional[int]="<pad>"					,     A					:     Optional[Any]="<mask>"					,     A					:     Dict=False					,     A					:     List[Any]=True					,     **A					:     Any					,     ):
           super().__init__(
               A					,     A					,     tokenizer_file=A					,     errors=A					,     bos_token=A					,     eos_token=A					,     sep_token=A					,     cls_token=A					,     unk_token=A					,     pad_token=A					,     mask_token=A					,     add_prefix_space=A					,     trim_offsets=A					,     **A					,     )

           _UpperCAmelCase      :     Optional[Any]        =   json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()						)
           if pre_tok_state.get("add_prefix_space"					,     A						) != add_prefix_space:
                _UpperCAmelCase      :     int        =   getattr(A					,     pre_tok_state.pop("type"						)						)
                _UpperCAmelCase      :     Tuple        =   add_prefix_space
                _UpperCAmelCase      :     Dict        =   pre_tok_class(**A						)

           _UpperCAmelCase      :     str        =   add_prefix_space

           # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
           _UpperCAmelCase      :     Any        =   "post_processor"
           _UpperCAmelCase      :     List[Any]        =   getattr(self.backend_tokenizer					,     A					,     A						)
           if tokenizer_component_instance:
                _UpperCAmelCase      :     List[Any]        =   json.loads(tokenizer_component_instance.__getstate__()						)

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                     _UpperCAmelCase      :     Tuple        =   tuple(state["sep"]						)
                if "cls" in state:
                     _UpperCAmelCase      :     str        =   tuple(state["cls"]						)

                _UpperCAmelCase      :     List[Any]        =   False

                if state.get("add_prefix_space"					,     A						) != add_prefix_space:
                     _UpperCAmelCase      :     Any        =   add_prefix_space
                     _UpperCAmelCase      :     Any        =   True

                if state.get("trim_offsets"					,     A						) != trim_offsets:
                     _UpperCAmelCase      :     List[Any]        =   trim_offsets
                     _UpperCAmelCase      :     Dict        =   True

                if changes_to_apply:
                     _UpperCAmelCase      :     str        =   getattr(A					,     state.pop("type"						)						)
                     _UpperCAmelCase      :     Any        =   component_class(**A						)
                     setattr(self.backend_tokenizer					,     A					,     A						)



      @property
      def 						snake_case_ (					self					:     Optional[Any]						):
           if self._mask_token is None:
                if self.verbose:
                     logger.error("Using mask_token, but it is not set yet."						)
                return None
           return str(self._mask_token						)



      @mask_token.setter
      def 						snake_case_ (					self					:     Optional[int]					,     A					:     List[Any]						):
           _UpperCAmelCase      :     Tuple        =   AddedToken(A					,     lstrip=A					,     rstrip=A						) if isinstance(A					,     A						) else value
           _UpperCAmelCase      :     str        =   value



      def 						snake_case_ (					self					:     Tuple					,     *A					:     Dict					,     **A					:     Union[str, Any]						):
           _UpperCAmelCase      :     Optional[Any]        =   kwargs.get("is_split_into_words"					,     A						)

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                    "to use it with pretokenized inputs."						)

           return super()._batch_encode_plus(*A					,     **A						)



      def 						snake_case_ (					self					:     Union[str, Any]					,     *A					:     Tuple					,     **A					:     str						):
           _UpperCAmelCase      :     Union[str, Any]        =   kwargs.get("is_split_into_words"					,     A						)

           if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                    "to use it with pretokenized inputs."						)

           return super()._encode_plus(*A					,     **A						)



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     str					,     A					:     Optional[str] = None						):
           _UpperCAmelCase      :     int        =   self._tokenizer.model.save(A					,     name=A						)
           return tuple(A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     Dict					,     A					:     Any=None						):
           _UpperCAmelCase      :     Optional[Any]        =   [self.bos_token_id] + token_ids_a + [self.eos_token_id]
           if token_ids_a is None:
                return output

           return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



      def 						snake_case_ (					self					:     Dict					,     A					:     List[int]					,     A					:     Optional[List[int]] = None						):
           _UpperCAmelCase      :     Optional[int]        =   [self.sep_token_id]
           _UpperCAmelCase      :     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]





"""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())
