

'''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


A_				  =							logging.get_logger(__name__)
A_				  =							{"vocab_file": "spiece.model"}

A_				  =							{
    "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",
    }
}

A_				  =							{
    "albert-base-v1": 5_12,
    "albert-large-v1": 5_12,
    "albert-xlarge-v1": 5_12,
    "albert-xxlarge-v1": 5_12,
    "albert-base-v2": 5_12,
    "albert-large-v2": 5_12,
    "albert-xlarge-v2": 5_12,
    "albert-xxlarge-v2": 5_12,
}

A_				  =							"▁"





class 							_snake_case       (				A_  ):
   _A				:   List[Any]			      =    VOCAB_FILES_NAMES
   _A				:   Optional[Any]			      =    PRETRAINED_VOCAB_FILES_MAP
   _A				:   int			      =    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   def __init__(       self	:					Union[str, Any]   ,SCREAMING_SNAKE_CASE__	:					Any   ,SCREAMING_SNAKE_CASE__	:					List[str]=True   ,SCREAMING_SNAKE_CASE__	:					int=True   ,SCREAMING_SNAKE_CASE__	:					Tuple=False   ,SCREAMING_SNAKE_CASE__	:					str="[CLS]"   ,SCREAMING_SNAKE_CASE__	:					List[str]="[SEP]"   ,SCREAMING_SNAKE_CASE__	:					List[Any]="<unk>"   ,SCREAMING_SNAKE_CASE__	:					int="[SEP]"   ,SCREAMING_SNAKE_CASE__	:					Any="<pad>"   ,SCREAMING_SNAKE_CASE__	:					Any="[CLS]"   ,SCREAMING_SNAKE_CASE__	:					Tuple="[MASK]"   ,SCREAMING_SNAKE_CASE__	:					Optional[Dict[str, Any]] = None   ,**SCREAMING_SNAKE_CASE__	:					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.
     SCREAMING_SNAKE_CASE:Optional[int]  					=    (
         AddedToken(_lowerCamelCase   ,lstrip=_lowerCamelCase   ,rstrip=_lowerCamelCase   ,normalized=_lowerCamelCase							)
         if isinstance(_lowerCamelCase   ,_lowerCamelCase							)
         else mask_token
     )

     SCREAMING_SNAKE_CASE:List[str]  					=    {} 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   ,)

     SCREAMING_SNAKE_CASE:List[str]  					=    do_lower_case
     SCREAMING_SNAKE_CASE:List[Any]  					=    remove_space
     SCREAMING_SNAKE_CASE:Dict  					=    keep_accents
     SCREAMING_SNAKE_CASE:List[str]  					=    vocab_file

     SCREAMING_SNAKE_CASE:List[Any]  					=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(_lowerCamelCase							)
   @property
   def 			__UpperCamelCase  (       self	:					Dict							):
     return len(self.sp_model							)
   def 			__UpperCamelCase  (       self	:					str							):
     SCREAMING_SNAKE_CASE:str  					=    {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							):
     SCREAMING_SNAKE_CASE:Dict  					=    self.__dict__.copy()
     SCREAMING_SNAKE_CASE:Dict  					=    None
     return state
   def __setstate__(       self	:					List[Any]   ,SCREAMING_SNAKE_CASE__	:					Optional[Any]							):
     SCREAMING_SNAKE_CASE:Any  					=    d

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

     SCREAMING_SNAKE_CASE:Optional[int]  					=    spm.SentencePieceProcessor(**self.sp_model_kwargs							)
     self.sp_model.Load(self.vocab_file							)
   def 			__UpperCamelCase  (       self	:					Tuple   ,SCREAMING_SNAKE_CASE__	:					List[Any]							):
     if self.remove_space:
       SCREAMING_SNAKE_CASE:List[str]  					=    " ".join(inputs.strip().split()							)
     else:
       SCREAMING_SNAKE_CASE:Union[str, Any]  					=    inputs
     SCREAMING_SNAKE_CASE:Dict  					=    outputs.replace("``"   ,"\""							).replace("\'\'"   ,"\""							)

     if not self.keep_accents:
       SCREAMING_SNAKE_CASE:Optional[Any]  					=    unicodedata.normalize("NFKD"   ,_lowerCamelCase							)
       SCREAMING_SNAKE_CASE:Tuple  					=    "".join([c for c in outputs if not unicodedata.combining(_lowerCamelCase							)]							)
     if self.do_lower_case:
       SCREAMING_SNAKE_CASE:Any  					=    outputs.lower()

     return outputs
   def 			__UpperCamelCase  (       self	:					Union[str, Any]   ,SCREAMING_SNAKE_CASE__	:					str							):
     SCREAMING_SNAKE_CASE:Optional[int]  					=    self.preprocess_text(_lowerCamelCase							)
     SCREAMING_SNAKE_CASE:Optional[Any]  					=    self.sp_model.encode(_lowerCamelCase   ,out_type=_lowerCamelCase							)
     SCREAMING_SNAKE_CASE:Optional[int]  					=    []
     for piece in pieces:
       if len(_lowerCamelCase							) > 1 and piece[-1] == str(","							) and piece[-2].isdigit():
         SCREAMING_SNAKE_CASE:List[Any]  					=    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:
             SCREAMING_SNAKE_CASE:Optional[Any]  					=    cur_pieces[1:]
           else:
             SCREAMING_SNAKE_CASE:Tuple  					=    cur_pieces[0][1:]
         cur_pieces.append(piece[-1]							)
         new_pieces.extend(_lowerCamelCase							)
       else:
         new_pieces.append(_lowerCamelCase							)

     return new_pieces
   def 			__UpperCamelCase  (       self	:					int   ,SCREAMING_SNAKE_CASE__	:					Any							):
     return self.sp_model.PieceToId(_lowerCamelCase							)
   def 			__UpperCamelCase  (       self	:					Optional[int]   ,SCREAMING_SNAKE_CASE__	:					str							):
     return self.sp_model.IdToPiece(_lowerCamelCase							)
   def 			__UpperCamelCase  (       self	:					str   ,SCREAMING_SNAKE_CASE__	:					List[str]							):
     SCREAMING_SNAKE_CASE:Dict  					=    []
     SCREAMING_SNAKE_CASE:Optional[int]  					=    ""
     SCREAMING_SNAKE_CASE: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(_lowerCamelCase							) + token
         SCREAMING_SNAKE_CASE:Tuple  					=    True
         SCREAMING_SNAKE_CASE:Union[str, Any]  					=    []
       else:
         current_sub_tokens.append(_lowerCamelCase							)
         SCREAMING_SNAKE_CASE:List[Any]  					=    False
     out_string += self.sp_model.decode(_lowerCamelCase							)
     return out_string.strip()
   def 			__UpperCamelCase  (       self	:					int   ,SCREAMING_SNAKE_CASE__	:					List[int]   ,SCREAMING_SNAKE_CASE__	:					Optional[List[int]] = None							):
     SCREAMING_SNAKE_CASE:List[str]  					=    [self.sep_token_id]
     SCREAMING_SNAKE_CASE:Dict  					=    [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 			__UpperCamelCase  (       self	:					Dict   ,SCREAMING_SNAKE_CASE__	:					List[int]   ,SCREAMING_SNAKE_CASE__	:					Optional[List[int]] = None   ,SCREAMING_SNAKE_CASE__	:					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 			__UpperCamelCase  (       self	:					int   ,SCREAMING_SNAKE_CASE__	:					List[int]   ,SCREAMING_SNAKE_CASE__	:					Optional[List[int]] = None							):
     SCREAMING_SNAKE_CASE:str  					=    [self.sep_token_id]
     SCREAMING_SNAKE_CASE: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]







   def 			__UpperCamelCase  (       self	:					Optional[int]   ,SCREAMING_SNAKE_CASE__	:					str   ,SCREAMING_SNAKE_CASE__	:					Optional[str] = None							):
     if not os.path.isdir(_lowerCamelCase							):
       logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''							)
       return
     SCREAMING_SNAKE_CASE:List[Any]  					=    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:
         SCREAMING_SNAKE_CASE:int  					=    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() = }")



