

import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple

import sentencepiece as spm

from ...tokenization_utils import PreTrainedTokenizer


if TYPE_CHECKING:
      from ...tokenization_utils_base import TextInput
from ...utils import logging


a_     :    int     		=   logging.get_logger(__name__)

a_     :    Optional[int]     		=   {"vocab_file": "spiece.model"}

a_     :    List[Any]     		=   {
    "vocab_file": {
        "t5-small": "https://huggingface.co/t5-small/resolve/main/spiece.model",
        "t5-base": "https://huggingface.co/t5-base/resolve/main/spiece.model",
        "t5-large": "https://huggingface.co/t5-large/resolve/main/spiece.model",
        "t5-3b": "https://huggingface.co/t5-3b/resolve/main/spiece.model",
        "t5-11b": "https://huggingface.co/t5-11b/resolve/main/spiece.model",
    }
}


# TODO(PVP) - this should be removed in Transformers v5
a_     :    List[str]     		=   {
    "t5-small": 512,
    "t5-base": 512,
    "t5-large": 512,
    "t5-3b": 512,
    "t5-11b": 512,
}

a_     :    List[Any]     		=   "▁"




class   UpperCamelCase			(  SCREAMING_SNAKE_CASE ):
    __UpperCamelCase    =VOCAB_FILES_NAMES
    __UpperCamelCase    =PRETRAINED_VOCAB_FILES_MAP
    __UpperCamelCase    =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCamelCase    =["input_ids", "attention_mask"]

    def __init__(      self			:   str				,     snake_case__			:   Any				,     snake_case__			:   Optional[Any]="</s>"				,     snake_case__			:   Any="<unk>"				,     snake_case__			:   Union[str, Any]="<pad>"				,     snake_case__			:   Tuple=1_0_0				,     snake_case__			:   Optional[int]=None				,     snake_case__			:   Optional[Dict[str, Any]] = None				,     snake_case__			:   List[Any]=True				,     **snake_case__			:   int				,     ):

     """simple docstring"""






     if extra_ids > 0 and additional_special_tokens is None:
      SCREAMING_SNAKE_CASE		    =      [F"""<extra_id_{i}>""" for i in range(snake_case__     )]
     elif extra_ids > 0 and additional_special_tokens is not None:
      # Check that we have the right number of extra_id special tokens
      SCREAMING_SNAKE_CASE		    =      len(set(filter(lambda snake_case__     : bool('extra_id' in str(snake_case__     )     )				,     snake_case__     )     )     )
      if extra_tokens != extra_ids:
       raise ValueError(
           F"""Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"""
           ' provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids'
           ' tokens'     )
     if legacy:
      logger.warning_once(
          F"""You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to"""
          ' read the related pull request available at https://github.com/huggingface/transformers/pull/24565'     )

     SCREAMING_SNAKE_CASE		    =      legacy
     SCREAMING_SNAKE_CASE		    =      {} if sp_model_kwargs is None else sp_model_kwargs

     super().__init__(
         eos_token=snake_case__				,     unk_token=snake_case__				,     pad_token=snake_case__				,     extra_ids=snake_case__				,     additional_special_tokens=snake_case__				,     sp_model_kwargs=self.sp_model_kwargs				,     legacy=snake_case__				,     **snake_case__				,     )

     SCREAMING_SNAKE_CASE		    =      vocab_file
     SCREAMING_SNAKE_CASE		    =      extra_ids

     SCREAMING_SNAKE_CASE		    =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.Load(snake_case__     )

    @staticmethod
    def UpperCamelCase      (      snake_case__			:   Dict				,     snake_case__			:   str				,     snake_case__			:   Any     ):

     """simple docstring"""






     if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
      SCREAMING_SNAKE_CASE		    =      TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
      if init_max_model_length is not None and init_max_model_length != max_model_length:
       return init_max_model_length
      elif init_max_model_length is None:
       warnings.warn(
           'This tokenizer was incorrectly instantiated with a model max length of'
           F""" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"""
           ' behavior is kept to avoid breaking backwards compatibility when padding/encoding with'
           ' `truncation is True`.\n- Be aware that you SHOULD NOT rely on'
           F""" {pretrained_model_name_or_path} automatically truncating your input to"""
           F""" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"""
           F""" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"""
           ' `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please'
           ' instantiate this tokenizer with `model_max_length` set to your preferred value.'				,     snake_case__				,     )

     return max_model_length

    @property
    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     return self.sp_model.get_piece_size() + self._extra_ids

    def UpperCamelCase      (      self			:   List[str]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      {self.convert_ids_to_tokens(snake_case__     ): i for i in range(self.vocab_size     )}
     vocab.update(self.added_tokens_encoder     )
     return vocab

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None				,     snake_case__			:   bool = False     ):

     """simple docstring"""






     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__     )

     # normal case: some special tokens
     if token_ids_a is None:
      return ([0] * len(snake_case__     )) + [1]
     return ([0] * len(snake_case__     )) + [1] + ([0] * len(snake_case__     )) + [1]

    def UpperCamelCase      (      self			:   str     ):

     """simple docstring"""






     return list(
         set(filter(lambda snake_case__     : bool(re.search(r'<extra_id_\d+>'				,     snake_case__     )     ) is not None				,     self.additional_special_tokens     )     )     )

    def UpperCamelCase      (      self			:   Any     ):

     """simple docstring"""






     return [self._convert_token_to_id(snake_case__     ) for token in self.get_sentinel_tokens()]

    def UpperCamelCase      (      self			:   Optional[int]				,     snake_case__			:   List[int]     ):

     """simple docstring"""






     if len(snake_case__     ) > 0 and token_ids[-1] == self.eos_token_id:
      warnings.warn(
          F"""This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated"""
          ' eos tokens being added.'     )
      return token_ids
     else:
      return token_ids + [self.eos_token_id]

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      [self.eos_token_id]

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

    def UpperCamelCase      (      self			:   Any				,     snake_case__			:   List[int]				,     snake_case__			:   Optional[List[int]] = None     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self._add_eos_if_not_present(snake_case__     )
     if token_ids_a is None:
      return token_ids_a
     else:
      SCREAMING_SNAKE_CASE		    =      self._add_eos_if_not_present(snake_case__     )
      return token_ids_a + token_ids_a

    def __getstate__(      self			:   Any     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      self.__dict__.copy()
     SCREAMING_SNAKE_CASE		    =      None
     return state

    def __setstate__(      self			:   Optional[int]				,     snake_case__			:   List[Any]     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      d

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

     SCREAMING_SNAKE_CASE		    =      spm.SentencePieceProcessor(**self.sp_model_kwargs     )
     self.sp_model.Load(self.vocab_file     )

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   "TextInput"				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     if not self.legacy:
      SCREAMING_SNAKE_CASE		    =      SPIECE_UNDERLINE + text.replace(snake_case__				,     ' '     )
     return super().tokenize(snake_case__				,     **snake_case__     )

    def UpperCamelCase      (      self			:   Tuple				,     snake_case__			:   Union[str, Any]				,     **snake_case__			:   Dict     ):

     """simple docstring"""






     if not self.legacy:
      SCREAMING_SNAKE_CASE		    =      text.startswith(snake_case__     )
      if is_first:
       SCREAMING_SNAKE_CASE		    =      text[1:]

     SCREAMING_SNAKE_CASE		    =      self.sp_model.encode(snake_case__				,     out_type=snake_case__     )

     if not self.legacy and not is_first and not text.startswith(' '     ) and tokens[0].startswith(snake_case__     ):
      SCREAMING_SNAKE_CASE		    =      ([tokens[0][1:]] if len(tokens[0]     ) > 1 else []) + tokens[1:]
     return tokens

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   str     ):

     """simple docstring"""






     if token.startswith('<extra_id_'     ):
      SCREAMING_SNAKE_CASE		    =      re.match(r'<extra_id_(\d+)>'				,     snake_case__     )
      SCREAMING_SNAKE_CASE		    =      int(match.group(1     )     )
      return self.vocab_size - num - 1
     return self.sp_model.piece_to_id(snake_case__     )

    def UpperCamelCase      (      self			:   str				,     snake_case__			:   Union[str, Any]     ):

     """simple docstring"""






     if index < self.sp_model.get_piece_size():
      SCREAMING_SNAKE_CASE		    =      self.sp_model.IdToPiece(snake_case__     )
     else:
      SCREAMING_SNAKE_CASE		    =      F"""<extra_id_{self.vocab_size - 1 - index}>"""
     return token

    def UpperCamelCase      (      self			:   List[str]				,     snake_case__			:   str     ):

     """simple docstring"""






     SCREAMING_SNAKE_CASE		    =      []
     SCREAMING_SNAKE_CASE		    =      ''
     SCREAMING_SNAKE_CASE		    =      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(snake_case__     ) + token
       SCREAMING_SNAKE_CASE		    =      True
       SCREAMING_SNAKE_CASE		    =      []
      else:
       current_sub_tokens.append(snake_case__     )
       SCREAMING_SNAKE_CASE		    =      False
     out_string += self.sp_model.decode(snake_case__     )
     return out_string.strip()




    def UpperCamelCase      (      self			:   List[Any]				,     snake_case__			:   str				,     snake_case__			:   Optional[str] = None     ):

     """simple docstring"""






     if not os.path.isdir(snake_case__     ):
      logger.error(F"""Vocabulary path ({save_directory}) should be a directory"""     )
      return
     SCREAMING_SNAKE_CASE		    =      os.path.join(
         snake_case__				,     (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['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:
       SCREAMING_SNAKE_CASE		    =      self.sp_model.serialized_model_proto()
       fi.write(snake_case__     )

     return (out_vocab_file,)




def 							__lowerCAmelCase     (							_UpperCamelCase		:	List[str]=2_81_23						)		->				str:
 '''simple docstring'''


 SCREAMING_SNAKE_CASE		    =      [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

 SCREAMING_SNAKE_CASE		    =      set()
 SCREAMING_SNAKE_CASE		    =      0

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

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

 return res


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

