


import os
import re
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


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

_snake_case   = {
    "vocab_file": {
        "google/fnet-base": "https://huggingface.co/google/fnet-base/resolve/main/spiece.model",
        "google/fnet-large": "https://huggingface.co/google/fnet-large/resolve/main/spiece.model",
    },
}

_snake_case   = {
    "google/fnet-base": 5_1_2,
    "google/fnet-large": 5_1_2,
}

_snake_case   = "▁"




class    A_   ( a_     ):







 """simple docstring"""





 _UpperCamelCase				: Tuple      =					VOCAB_FILES_NAMES
 _UpperCamelCase				: int      =					PRETRAINED_VOCAB_FILES_MAP
 _UpperCamelCase				: Optional[int]      =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _UpperCamelCase				: Union[str, Any]      =					['''input_ids''', '''token_type_ids''']
 def __init__(			self					: Optional[Any]       ,    _a					: str       ,    _a					: Tuple=False       ,    _a					: Dict=True       ,    _a					: Dict=True       ,    _a					: List[Any]="<unk>"       ,    _a					: Tuple="[SEP]"       ,    _a					: Optional[int]="<pad>"       ,    _a					: Union[str, Any]="[CLS]"       ,    _a					: Union[str, Any]="[MASK]"       ,    _a					: Optional[Dict[str, Any]] = None       ,    **_a					: List[Any]       ,    )    ->			None:
      # 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.
      UpperCAmelCase						:				Tuple		  =			(
          AddedToken(_a       ,    lstrip=_a       ,    rstrip=_a       ,    normalized=_a				)
          if isinstance(_a       ,    _a				)
          else mask_token
      )

      UpperCAmelCase						:				List[str]		  =			{} if sp_model_kwargs is None else sp_model_kwargs

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

      UpperCAmelCase						:				List[Any]		  =			do_lower_case
      UpperCAmelCase						:				int		  =			remove_space
      UpperCAmelCase						:				Optional[int]		  =			keep_accents
      UpperCAmelCase						:				List[str]		  =			vocab_file

      UpperCAmelCase						:				List[str]		  =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(_a				)
 @property
 def   _UpperCamelCase    (			self					: int				)    ->			Optional[int]:
      return len(self.sp_model				)
 def   _UpperCamelCase    (			self					: int				)    ->			Optional[int]:
      UpperCAmelCase						:				Dict		  =			{self.convert_ids_to_tokens(_a				): i for i in range(self.vocab_size				)}
      vocab.update(self.added_tokens_encoder				)
      return vocab
 def __getstate__(			self					: List[str]				)    ->			str:
      UpperCAmelCase						:				str		  =			self.__dict__.copy()
      UpperCAmelCase						:				Optional[Any]		  =			None
      return state
 def __setstate__(			self					: Optional[Any]       ,    _a					: List[Any]				)    ->			int:
      UpperCAmelCase						:				Any		  =			d

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

      UpperCAmelCase						:				List[str]		  =			spm.SentencePieceProcessor(**self.sp_model_kwargs				)
      self.sp_model.Load(self.vocab_file				)
 def   _UpperCamelCase    (			self					: str       ,    _a					: str				)    ->			Any:
      if self.remove_space:
           UpperCAmelCase						:				Dict		  =			" ".join(inputs.strip().split()				)
      else:
           UpperCAmelCase						:				List[str]		  =			inputs
      UpperCAmelCase						:				int		  =			outputs.replace("``"       ,    "\""				).replace("''"       ,    "\""				)

      if not self.keep_accents:
           UpperCAmelCase						:				Dict		  =			unicodedata.normalize("NFKD"       ,    _a				)
           UpperCAmelCase						:				Optional[int]		  =			"".join([c for c in outputs if not unicodedata.combining(_a				)]				)
      if self.do_lower_case:
           UpperCAmelCase						:				List[str]		  =			outputs.lower()

      return outputs
 def   _UpperCamelCase    (			self					: List[Any]       ,    _a					: str				)    ->			List[str]:
      UpperCAmelCase						:				Tuple		  =			self.preprocess_text(_a				)
      UpperCAmelCase						:				Tuple		  =			self.sp_model.encode(_a       ,    out_type=_a				)
      UpperCAmelCase						:				Any		  =			[]
      for piece in pieces:
           if len(_a				) > 1 and piece[-1] == str(","				) and piece[-2].isdigit():
                UpperCAmelCase						:				Union[str, Any]		  =			self.sp_model.EncodeAsPieces(piece[:-1].replace(_a       ,    ""				)				)
                if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0]				) == 1:
                          UpperCAmelCase						:				List[Any]		  =			cur_pieces[1:]
                     else:
                          UpperCAmelCase						:				str		  =			cur_pieces[0][1:]
                cur_pieces.append(piece[-1]				)
                new_pieces.extend(_a				)
           else:
                new_pieces.append(_a				)

      return new_pieces
 def   _UpperCamelCase    (			self					: str       ,    _a					: List[str]				)    ->			Optional[Any]:
      return self.sp_model.PieceToId(_a				)
 def   _UpperCamelCase    (			self					: Dict       ,    _a					: Tuple				)    ->			str:
      return self.sp_model.IdToPiece(_a				)
 def   _UpperCamelCase    (			self					: int       ,    _a					: Any				)    ->			List[str]:
      UpperCAmelCase						:				List[str]		  =			[]
      UpperCAmelCase						:				Dict		  =			""
      UpperCAmelCase						:				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(_a				) + token
                UpperCAmelCase						:				Union[str, Any]		  =			True
                UpperCAmelCase						:				Union[str, Any]		  =			[]
           else:
                current_sub_tokens.append(_a				)
                UpperCAmelCase						:				Dict		  =			False
      out_string += self.sp_model.decode(_a				)
      return out_string.strip()
 def   _UpperCamelCase    (			self					: Optional[Any]       ,    _a					: List[int]       ,    _a					: bool = False       ,    _a					: bool = None       ,    _a					: bool = True       ,    **_a					: Any       ,    )    ->			str:
      UpperCAmelCase						:				Tuple		  =			kwargs.pop("use_source_tokenizer"       ,    _a				)

      UpperCAmelCase						:				Optional[int]		  =			self.convert_ids_to_tokens(_a       ,    skip_special_tokens=_a				)

      # To avoid mixing byte-level and unicode for byte-level BPT
      # we need to build string separately for added tokens and byte-level tokens
      # cf. https://github.com/huggingface/transformers/issues/1133
      UpperCAmelCase						:				Optional[int]		  =			[]
      UpperCAmelCase						:				Dict		  =			[]
      for token in filtered_tokens:
           if skip_special_tokens and token in self.all_special_ids:
                continue
           if token in self.added_tokens_encoder:
                if current_sub_text:
                     sub_texts.append(self.convert_tokens_to_string(_a				)				)
                     UpperCAmelCase						:				Optional[Any]		  =			[]
                sub_texts.append(_a				)
           else:
                current_sub_text.append(_a				)
      if current_sub_text:
           sub_texts.append(self.convert_tokens_to_string(_a				)				)

      # Mimic the behavior of the Rust tokenizer:
      # No space after <unk>
      if spaces_between_special_tokens:
           UpperCAmelCase						:				int		  =			re.sub(R"(<unk>) "       ,    R"\1"       ,    " ".join(_a				)				)
      else:
           UpperCAmelCase						:				int		  =			"".join(_a				)

      UpperCAmelCase						:				str		  =			(
          clean_up_tokenization_spaces
          if clean_up_tokenization_spaces is not None
          else self.clean_up_tokenization_spaces
      )
      if clean_up_tokenization_spaces:
           UpperCAmelCase						:				List[str]		  =			self.clean_up_tokenization(_a				)
           return clean_text
      else:
           return text
 def   _UpperCamelCase    (			self					: List[str]       ,    _a					: List[int]       ,    _a					: Optional[List[int]] = None				)    ->			List[int]:
      UpperCAmelCase						:				List[Any]		  =			[self.sep_token_id]
      UpperCAmelCase						:				Union[str, Any]		  =			[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					: str       ,    _a					: List[int]       ,    _a					: Optional[List[int]] = None       ,    _a					: bool = False				)    ->			List[int]:

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

      if token_ids_a is not None:
           return [1] + ([0] * len(_a				)) + [1] + ([0] * len(_a				)) + [1]
      return [1] + ([0] * len(_a				)) + [1]
 def   _UpperCamelCase    (			self					: List[str]       ,    _a					: List[int]       ,    _a					: Optional[List[int]] = None				)    ->			List[int]:
      UpperCAmelCase						:				Optional[int]		  =			[self.sep_token_id]
      UpperCAmelCase						:				Optional[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				) * [0] + len(token_ids_a + sep				) * [1]

 def   _UpperCamelCase    (			self					: Any       ,    _a					: str       ,    _a					: Optional[str] = None				)    ->			Tuple[str]:
      if not os.path.isdir(_a				):
           logger.error(f'Vocabulary path ({save_directory}) should be a directory'				)
           return
      UpperCAmelCase						:				Tuple		  =			os.path.join(
          _a       ,    (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]				)

      if os.path.abspath(self.vocab_file				) != os.path.abspath(_a				) and os.path.isfile(self.vocab_file				):
           copyfile(self.vocab_file       ,    _a				)
      elif not os.path.isfile(self.vocab_file				):
           with open(_a       ,    "wb"				) as fi:
                UpperCAmelCase						:				Optional[Any]		  =			self.sp_model.serialized_model_proto()
                fi.write(_a				)

      return (out_vocab_file,)







import math







def   A   (		A__ ):
     UpperCAmelCase						:				List[str]		  =			math.log(2		,		1_0 ) * number
     UpperCAmelCase						:				int		  =			round((log_number - int(A__ ))		,		1_5 )

     return difference







def   A   (		A__ = 6_7_8_9_1_0 ):
     UpperCAmelCase						:				str		  =			9_0
     UpperCAmelCase						:				Any		  =			0

     UpperCAmelCase						:				Dict		  =			math.log(1.23		,		1_0 )
     UpperCAmelCase						:				Optional[Any]		  =			math.log(1.24		,		1_0 )
     UpperCAmelCase						:				Tuple		  =			0

     while position < number:
          UpperCAmelCase						:				Tuple		  =			log_difference(A__ )

          if difference >= upper_limit:
               power_iterator += 9_3

          elif difference < lower_limit:
               power_iterator += 1_9_6

          else:
               UpperCAmelCase						:				Any		  =			power_iterator
               power_iterator += 1_9_6
               position += 1

     return previous_power


if __name__ == "__main__":
      import doctest

      doctest.testmod()

      print(f'''{solution() = }''')



