
"""simple docstring"""



import os
import unicodedata
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


UpperCAmelCase__          = logging.get_logger(__name__)
UpperCAmelCase__          = {'vocab_file': 'spiece.model'}

UpperCAmelCase__          = {
    'vocab_file': {
        'albert-base-v1': 'https://huggingface.co/albert-base-v1/resolve/main/spiece.model',
        'albert-large-v1': 'https://huggingface.co/albert-large-v1/resolve/main/spiece.model',
        'albert-xlarge-v1': 'https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model',
        'albert-xxlarge-v1': 'https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model',
        'albert-base-v2': 'https://huggingface.co/albert-base-v2/resolve/main/spiece.model',
        'albert-large-v2': 'https://huggingface.co/albert-large-v2/resolve/main/spiece.model',
        'albert-xlarge-v2': 'https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model',
        'albert-xxlarge-v2': 'https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model',
    }
}

UpperCAmelCase__          = {
    'albert-base-v1': 512,
    'albert-large-v1': 512,
    'albert-xlarge-v1': 512,
    'albert-xxlarge-v1': 512,
    'albert-base-v2': 512,
    'albert-large-v2': 512,
    'albert-xlarge-v2': 512,
    'albert-xxlarge-v2': 512,
}

UpperCAmelCase__          = '▁'

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES







     def __init__(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			Any    ,      _lowerCamelCase			:			List[str]=True    ,      _lowerCamelCase			:			int=True    ,      _lowerCamelCase			:			Tuple=False    ,      _lowerCamelCase			:			str="[CLS]"    ,      _lowerCamelCase			:			List[str]="[SEP]"    ,      _lowerCamelCase			:			List[Any]="<unk>"    ,      _lowerCamelCase			:			int="[SEP]"    ,      _lowerCamelCase			:			Any="<pad>"    ,      _lowerCamelCase			:			Any="[CLS]"    ,      _lowerCamelCase			:			Tuple="[MASK]"    ,      _lowerCamelCase			:			Optional[Dict[str, Any]] = None    ,      **_lowerCamelCase			:			Any    ,      ):
       # Mask token behave like a normal word, i.e. include the space before it and
       # is included in the raw text, there should be a match in a non-normalized sentence.
       _snake_case					     =       (
           AddedToken(_lowerCamelCase    ,      lstrip=_lowerCamelCase    ,      rstrip=_lowerCamelCase    ,      normalized=_lowerCamelCase			)
           if isinstance(_lowerCamelCase    ,      _lowerCamelCase			)
           else mask_token
       )

       _snake_case					     =       {} if sp_model_kwargs is None else sp_model_kwargs

       super().__init__(
           do_lower_case=_lowerCamelCase    ,      remove_space=_lowerCamelCase    ,      keep_accents=_lowerCamelCase    ,      bos_token=_lowerCamelCase    ,      eos_token=_lowerCamelCase    ,      unk_token=_lowerCamelCase    ,      sep_token=_lowerCamelCase    ,      pad_token=_lowerCamelCase    ,      cls_token=_lowerCamelCase    ,      mask_token=_lowerCamelCase    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **_lowerCamelCase    ,      )

       _snake_case					     =       do_lower_case
       _snake_case					     =       remove_space
       _snake_case					     =       keep_accents
       _snake_case					     =       vocab_file

       _snake_case					     =       spm.SentencePieceProcessor(**self.sp_model_kwargs			)
       self.sp_model.Load(_lowerCamelCase			)







     @property
     def      lowercase	(							self			:			Dict			):
       return len(self.sp_model			)







     def      lowercase	(							self			:			str			):
       _snake_case					     =       {self.convert_ids_to_tokens(_lowerCamelCase			): i for i in range(self.vocab_size			)}
       vocab.update(self.added_tokens_encoder			)
       return vocab







     def __getstate__(							self			:			Tuple			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       return state







     def __setstate__(							self			:			List[Any]    ,      _lowerCamelCase			:			Optional[Any]			):
       _snake_case					     =       d

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

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







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			List[Any]			):
       if self.remove_space:
         _snake_case					     =       ''' '''.join(inputs.strip().split()			)
       else:
         _snake_case					     =       inputs
       _snake_case					     =       outputs.replace('''``'''    ,      '''"'''			).replace('''\'\''''    ,      '''"'''			)

       if not self.keep_accents:
         _snake_case					     =       unicodedata.normalize('''NFKD'''    ,      _lowerCamelCase			)
         _snake_case					     =       ''''''.join([c for c in outputs if not unicodedata.combining(_lowerCamelCase			)]			)
       if self.do_lower_case:
         _snake_case					     =       outputs.lower()

       return outputs







     def      lowercase	(							self			:			Union[str, Any]    ,      _lowerCamelCase			:			str			):
       _snake_case					     =       self.preprocess_text(_lowerCamelCase			)
       _snake_case					     =       self.sp_model.encode(_lowerCamelCase    ,      out_type=_lowerCamelCase			)
       _snake_case					     =       []
       for piece in pieces:
         if len(_lowerCamelCase			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
           _snake_case					     =       self.sp_model.EncodeAsPieces(piece[:-1].replace(_lowerCamelCase    ,      ''''''			)			)
           if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
             if len(cur_pieces[0]			) == 1:
               _snake_case					     =       cur_pieces[1:]
             else:
               _snake_case					     =       cur_pieces[0][1:]
           cur_pieces.append(piece[-1]			)
           new_pieces.extend(_lowerCamelCase			)
         else:
           new_pieces.append(_lowerCamelCase			)

       return new_pieces







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			Any			):
       return self.sp_model.PieceToId(_lowerCamelCase			)







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			str			):
       return self.sp_model.IdToPiece(_lowerCamelCase			)







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			List[str]			):
       _snake_case					     =       []
       _snake_case					     =       ''''''
       _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(_lowerCamelCase			) + token
           _snake_case					     =       True
           _snake_case					     =       []
         else:
           current_sub_tokens.append(_lowerCamelCase			)
           _snake_case					     =       False
       out_string += self.sp_model.decode(_lowerCamelCase			)
       return out_string.strip()







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       if token_ids_a is None:
         return cls + token_ids_a + sep
       return cls + token_ids_a + sep + token_ids_a + sep







     def      lowercase	(							self			:			Dict    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None    ,      _lowerCamelCase			:			bool = False			):

       if already_has_special_tokens:
         return super().get_special_tokens_mask(
             token_ids_a=_lowerCamelCase    ,      token_ids_a=_lowerCamelCase    ,      already_has_special_tokens=_lowerCamelCase			)

       if token_ids_a is not None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			int    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):
       _snake_case					     =       [self.sep_token_id]
       _snake_case					     =       [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]







     def      lowercase	(							self			:			Optional[int]    ,      _lowerCamelCase			:			str    ,      _lowerCamelCase			:			Optional[str] = None			):
       if not os.path.isdir(_lowerCamelCase			):
         logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''			)
         return
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']			)

       if os.path.abspath(self.vocab_file			) != os.path.abspath(_lowerCamelCase			) and os.path.isfile(self.vocab_file			):
         copyfile(self.vocab_file    ,      _lowerCamelCase			)
       elif not os.path.isfile(self.vocab_file			):
         with open(_lowerCamelCase    ,      '''wb'''			) as fi:
           _snake_case					     =       self.sp_model.serialized_model_proto()
           fi.write(_lowerCamelCase			)

       return (out_vocab_file,)





"""simple docstring"""



def 					_UpperCAmelCase      ( __lowerCamelCase   :       int = 1_00_00_00		)     ->					int:
  _snake_case					     =       limit + 1
  _snake_case					     =       [0] * limit
  for first_term in range(1 ,					__lowerCamelCase		):
    for n in range(__lowerCamelCase ,					__lowerCamelCase ,					__lowerCamelCase		):
      _snake_case					     =       first_term + n / first_term
      if common_difference % 4:  # d must be divisble by 4
        continue
      else:
        common_difference /= 4
        if (
            first_term > common_difference
            and first_term < 4 * common_difference
        ):  # since x,y,z are positive integers
          frequency[n] += 1  # so z>0 and a>d ,also 4d<a

  _snake_case					     =       sum(1 for x in frequency[1:limit] if x == 10		)

  return count


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



