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": 1_0_2_4}




class  __magic_name__  (					__lowerCAmelCase    ):





   """simple docstring"""




   lowerCAmelCase      :    List[Any]  			=   VOCAB_FILES_NAMES
   lowerCAmelCase      :    Tuple  			=   PRETRAINED_VOCAB_FILES_MAP
   lowerCAmelCase      :    List[str]  			=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   lowerCAmelCase      :    List[str]  			=   ['''input_ids''', '''attention_mask''']



   def __init__(	self					:  str		,  _lowercase					:  List[Any]		,  _lowercase					:  Optional[Any]		,  _lowercase					:  Any="<s>"		,  _lowercase					:  List[str]="</s>"		,  _lowercase					:  Any="</s>"		,  _lowercase					:  Tuple="<s>"		,  _lowercase					:  List[str]="<unk>"		,  _lowercase					:  Tuple="<pad>"		,  _lowercase					:  Any="<mask>"		,  _lowercase					:  Optional[Dict[str, Any]] = None		,  **_lowercase					:  Optional[int]		,  ):



    """simple docstring"""



    _UpperCamelCase:     List[Any]							= AddedToken(_UpperCamelCase		,  lstrip=_UpperCamelCase		,  rstrip=_UpperCamelCase							) if isinstance(_UpperCamelCase		,  _UpperCamelCase							) else mask_token

    _UpperCamelCase:     Union[str, Any]							= {} if sp_model_kwargs is None else sp_model_kwargs

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

    _UpperCamelCase:     Union[str, Any]							= vocab_file
    _UpperCamelCase:     Optional[int]							= monolingual_vocab_file
    _UpperCamelCase:     Optional[Any]							= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.Load(str(_UpperCamelCase							)							)

    # Load the reduced vocab

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

    _UpperCamelCase:     List[str]							= {v: k for k, v in self.fairseq_tokens_to_ids.items()}



   def __getstate__(	self					:  Union[str, Any]							):



    """simple docstring"""



    _UpperCamelCase:     int							= self.__dict__.copy()
    _UpperCamelCase:     Any							= None
    _UpperCamelCase:     str							= self.sp_model.serialized_model_proto()
    return state



   def __setstate__(	self					:  Union[str, Any]		,  _lowercase					:  Dict							):



    """simple docstring"""



    _UpperCamelCase:     Union[str, Any]							= d

    # for backward compatibility
    if not hasattr(self		,  '''sp_model_kwargs'''							):
     _UpperCamelCase:     Optional[Any]							= {}

    _UpperCamelCase:     Union[str, Any]							= spm.SentencePieceProcessor(**self.sp_model_kwargs							)
    self.sp_model.LoadFromSerializedProto(self.sp_model_proto							)



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None							):



    """simple docstring"""



    if token_ids_a is None:
     return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
    _UpperCamelCase:     Dict							= [self.cls_token_id]
    _UpperCamelCase:     List[Any]							= [self.sep_token_id]
    return cls + token_ids_a + sep + sep + token_ids_a + sep



   def   lowerCAmelCase						(	self					:  Union[str, Any]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None		,  _lowercase					:  bool = False							):



    """simple docstring"""



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

    if token_ids_a is None:
     return [1] + ([0] * len(_UpperCamelCase							)) + [1]
    return [1] + ([0] * len(_UpperCamelCase							)) + [1, 1] + ([0] * len(_UpperCamelCase							)) + [1]



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[int]		,  _lowercase					:  Optional[List[int]] = None							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= [self.sep_token_id]
    _UpperCamelCase:     List[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]



   @property
   def   lowerCAmelCase						(	self					:  Optional[int]							):



    """simple docstring"""



    return len(self.fairseq_ids_to_tokens							)



   def   lowerCAmelCase						(	self					:  str							):



    """simple docstring"""



    _UpperCamelCase:     Optional[Any]							= {self.convert_ids_to_tokens(_UpperCamelCase							): i for i in range(self.vocab_size							)}
    vocab.update(self.added_tokens_encoder							)
    return vocab



   def   lowerCAmelCase						(	self					:  List[Any]		,  _lowercase					:  str							):



    """simple docstring"""



    return self.sp_model.encode(_UpperCamelCase		,  out_type=_UpperCamelCase							)



   def   lowerCAmelCase						(	self					:  int		,  _lowercase					:  Tuple							):



    """simple docstring"""



    if token in self.fairseq_tokens_to_ids:
     return self.fairseq_tokens_to_ids[token]
    else:
     return self.unk_token_id



   def   lowerCAmelCase						(	self					:  Tuple		,  _lowercase					:  Union[str, Any]							):



    """simple docstring"""



    return self.fairseq_ids_to_tokens[index]



   def   lowerCAmelCase						(	self					:  List[str]		,  _lowercase					:  List[Any]							):



    """simple docstring"""



    _UpperCamelCase:     Dict							= """""".join(_UpperCamelCase							).replace(_UpperCamelCase		,  ''' '''							).strip()
    return out_string



   def   lowerCAmelCase						(	self					:  Optional[int]		,  _lowercase					:  str		,  _lowercase					:  Optional[str] = None							):



    """simple docstring"""



    if not os.path.isdir(_UpperCamelCase							):
     logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
     return
    _UpperCamelCase:     Dict							= os.path.join(
        _UpperCamelCase		,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
    _UpperCamelCase:     str							= os.path.join(
        _UpperCamelCase		,  (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file''']		,  )

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

    if os.path.abspath(self.monolingual_vocab_file							) != os.path.abspath(
        _UpperCamelCase							) and os.path.isfile(self.monolingual_vocab_file							):
     copyfile(self.monolingual_vocab_file		,  _UpperCamelCase							)
    elif not os.path.isfile(self.monolingual_vocab_file							):
     with open(_UpperCamelCase		,  '''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(_UpperCamelCase							)} \n"""							)

    return out_vocab_file, out_monolingual_vocab_file
def       lowerCAmelCase_     (		lowercase:							int = 10**9		)    ->				int:







 '''simple docstring'''
 _UpperCamelCase:     List[Any]							= 1
 _UpperCamelCase:     Dict							= 2

 _UpperCamelCase:     Tuple							= 0
 _UpperCamelCase:     int							= 0
 _UpperCamelCase:     Dict							= 0
 while perimeter <= max_perimeter:
  perimeters_sum += perimeter

  prev_value += 2 * value
  value += prev_value

  _UpperCamelCase:     str							= 2 * value + 2 if i % 2 == 0 else 2 * value - 2
  i += 1

 return perimeters_sum


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