
"""simple docstring"""



import os
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__          = '▁'

UpperCAmelCase__          = {'vocab_file': 'sentencepiece.bpe.model', 'monolingual_vocab_file': 'dict.txt'}

UpperCAmelCase__          = {
    'vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model',
    },
    'monolingual_vocab_file': {
        'vinai/bartpho-syllable': 'https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt',
    },
}

UpperCAmelCase__          = {'vinai/bartpho-syllable': 1024}

class       lowerCAmelCase__				(						A_	):
     __a						=					VOCAB_FILES_NAMES
     __a						=					PRETRAINED_VOCAB_FILES_MAP
     __a						=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     __a						=					["""input_ids""", """attention_mask"""]







     def __init__(							self			:			List[str]    ,      _lowerCamelCase			:			Optional[Any]    ,      _lowerCamelCase			:			int    ,      _lowerCamelCase			:			Dict="<s>"    ,      _lowerCamelCase			:			List[Any]="</s>"    ,      _lowerCamelCase			:			str="</s>"    ,      _lowerCamelCase			:			Dict="<s>"    ,      _lowerCamelCase			:			Optional[Any]="<unk>"    ,      _lowerCamelCase			:			Union[str, Any]="<pad>"    ,      _lowerCamelCase			:			Optional[Any]="<mask>"    ,      _lowerCamelCase			:			Optional[Dict[str, Any]] = None    ,      **_lowerCamelCase			:			Dict    ,      ):
       # Mask token behave like a normal word, i.e. include the space before it
       _snake_case					     =       AddedToken(_lowerCamelCase    ,      lstrip=_lowerCamelCase    ,      rstrip=_lowerCamelCase			) if isinstance(_lowerCamelCase    ,      _lowerCamelCase			) else mask_token

       _snake_case					     =       {} if sp_model_kwargs is None else sp_model_kwargs

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

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

       # Load the reduced vocab

       # Keep order of special tokens for backward compatibility
       _snake_case					     =       {}
       _snake_case					     =       0
       for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
         if str(_lowerCamelCase			) not in self.fairseq_tokens_to_ids:
           _snake_case					     =       cnt
           cnt += 1
       with open(_lowerCamelCase    ,      '''r'''    ,      encoding='''utf-8'''			) as f:
         for line in f.readlines():
           _snake_case					     =       line.strip().split()[0]
           _snake_case					     =       len(self.fairseq_tokens_to_ids			)
       if str(_lowerCamelCase			) not in self.fairseq_tokens_to_ids:
         _snake_case					     =       len(self.fairseq_tokens_to_ids			)

       _snake_case					     =       {v: k for k, v in self.fairseq_tokens_to_ids.items()}







     def __getstate__(							self			:			Union[str, Any]			):
       _snake_case					     =       self.__dict__.copy()
       _snake_case					     =       None
       _snake_case					     =       self.sp_model.serialized_model_proto()
       return state







     def __setstate__(							self			:			List[str]    ,      _lowerCamelCase			:			Union[str, 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.LoadFromSerializedProto(self.sp_model_proto			)







     def      lowercase	(							self			:			Optional[Any]    ,      _lowerCamelCase			:			List[int]    ,      _lowerCamelCase			:			Optional[List[int]] = None			):

       if token_ids_a is None:
         return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
       _snake_case					     =       [self.cls_token_id]
       _snake_case					     =       [self.sep_token_id]
       return cls + token_ids_a + sep + sep + token_ids_a + sep







     def      lowercase	(							self			:			int    ,      _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 None:
         return [1] + ([0] * len(_lowerCamelCase			)) + [1]
       return [1] + ([0] * len(_lowerCamelCase			)) + [1, 1] + ([0] * len(_lowerCamelCase			)) + [1]







     def      lowercase	(							self			:			Any    ,      _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 + sep + token_ids_a + sep			) * [0]







     @property
     def      lowercase	(							self			:			Optional[int]			):
       return len(self.fairseq_ids_to_tokens			)







     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      lowercase	(							self			:			Any    ,      _lowerCamelCase			:			str			):
       return self.sp_model.encode(_lowerCamelCase    ,      out_type=_lowerCamelCase			)







     def      lowercase	(							self			:			Tuple    ,      _lowerCamelCase			:			Optional[Any]			):
       if token in self.fairseq_tokens_to_ids:
         return self.fairseq_tokens_to_ids[token]
       else:
         return self.unk_token_id







     def      lowercase	(							self			:			List[str]    ,      _lowerCamelCase			:			Optional[int]			):
       return self.fairseq_ids_to_tokens[index]







     def      lowercase	(							self			:			str    ,      _lowerCamelCase			:			Dict			):
       _snake_case					     =       ''''''.join(_lowerCamelCase			).replace(_lowerCamelCase    ,      ''' '''			).strip()
       return out_string







     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''']			)
       _snake_case					     =       os.path.join(
           _lowerCamelCase    ,      (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_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			)

       if os.path.abspath(self.monolingual_vocab_file			) != os.path.abspath(
           _lowerCamelCase			) and os.path.isfile(self.monolingual_vocab_file			):
         copyfile(self.monolingual_vocab_file    ,      _lowerCamelCase			)
       elif not os.path.isfile(self.monolingual_vocab_file			):
         with open(_lowerCamelCase    ,      '''w'''    ,      encoding='''utf-8'''			) as fp:
           for token in self.fairseq_tokens_to_ids:
             if token not in self.all_special_tokens:
               fp.write(f'''{str(_lowerCamelCase			)} \n'''			)

       return out_vocab_file, out_monolingual_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() = }")



