


'''simple docstring'''







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


a__    :   Dict								=			logging.get_logger(__name__)
a__    :   Optional[Any]								=			{'vocab_file': 'spiece.model'}

a__    :   Tuple								=			{
    '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',
    },
}

a__    :   str								=			{
    'google/fnet-base': 5_1_2,
    'google/fnet-large': 5_1_2,
}

a__    :   int								=			'▁'
class    lowercase_	(       a__  ):
    __UpperCAmelCase       =      VOCAB_FILES_NAMES
    __UpperCAmelCase       =      PRETRAINED_VOCAB_FILES_MAP
    __UpperCAmelCase       =      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __UpperCAmelCase       =      ['input_ids', 'token_type_ids']


    def __init__(	self    ,   a    ,   a=False    ,   a=True    ,   a=True    ,   a="<unk>"    ,   a="[SEP]"    ,   a="<pad>"    ,   a="[CLS]"    ,   a="[MASK]"    ,   a = None    ,   **a    ,   ):
          # 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__      =							(
              AddedToken(a    ,   lstrip=a    ,   rstrip=a    ,   normalized=a       )
              if isinstance(a    ,   a       )
              else mask_token
          )

          UpperCamelCase__      =							{} 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__      =							do_lower_case
          UpperCamelCase__      =							remove_space
          UpperCamelCase__      =							keep_accents
          UpperCamelCase__      =							vocab_file

          UpperCamelCase__      =							spm.SentencePieceProcessor(**self.sp_model_kwargs       )
          self.sp_model.Load(a       )


    @property
    def   __a				(	self       ):
          return len(self.sp_model       )


    def   __a				(	self       ):
          UpperCamelCase__      =							{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       ):
          UpperCamelCase__      =							self.__dict__.copy()
          UpperCamelCase__      =							None
          return state


    def __setstate__(	self    ,   a       ):
          UpperCamelCase__      =							d

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

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


    def   __a				(	self    ,   a       ):
          if self.remove_space:
                UpperCamelCase__      =							" ".join(inputs.strip().split()       )
          else:
                UpperCamelCase__      =							inputs
          UpperCamelCase__      =							outputs.replace("``"    ,   "\""       ).replace("''"    ,   "\""       )

          if not self.keep_accents:
                UpperCamelCase__      =							unicodedata.normalize("NFKD"    ,   a       )
                UpperCamelCase__      =							"".join([c for c in outputs if not unicodedata.combining(a       )]       )
          if self.do_lower_case:
                UpperCamelCase__      =							outputs.lower()

          return outputs


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							self.preprocess_text(a       )
          UpperCamelCase__      =							self.sp_model.encode(a    ,   out_type=a       )
          UpperCamelCase__      =							[]
          for piece in pieces:
                if len(a       ) > 1 and piece[-1] == str(","       ) and piece[-2].isdigit():
                      UpperCamelCase__      =							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__      =							cur_pieces[1:]
                            else:
                                  UpperCamelCase__      =							cur_pieces[0][1:]
                      cur_pieces.append(piece[-1]       )
                      new_pieces.extend(a       )
                else:
                      new_pieces.append(a       )

          return new_pieces


    def   __a				(	self    ,   a       ):
          return self.sp_model.PieceToId(a       )


    def   __a				(	self    ,   a       ):
          return self.sp_model.IdToPiece(a       )


    def   __a				(	self    ,   a       ):
          UpperCamelCase__      =							[]
          UpperCamelCase__      =							""
          UpperCamelCase__      =							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__      =							True
                      UpperCamelCase__      =							[]
                else:
                      current_sub_tokens.append(a       )
                      UpperCamelCase__      =							False
          out_string += self.sp_model.decode(a       )
          return out_string.strip()


    def   __a				(	self    ,   a    ,   a = False    ,   a = None    ,   a = True    ,   **a    ,   ):
          UpperCamelCase__      =							kwargs.pop("use_source_tokenizer"    ,   a       )

          UpperCamelCase__      =							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__      =							[]
          UpperCamelCase__      =							[]
          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__      =							[]
                      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__      =							re.sub(r"(<unk>) "    ,   r"\1"    ,   " ".join(a       )       )
          else:
                UpperCamelCase__      =							"".join(a       )

          UpperCamelCase__      =							(
              clean_up_tokenization_spaces
              if clean_up_tokenization_spaces is not None
              else self.clean_up_tokenization_spaces
          )
          if clean_up_tokenization_spaces:
                UpperCamelCase__      =							self.clean_up_tokenization(a       )
                return clean_text
          else:
                return text


    def   __a				(	self    ,   a    ,   a = None       ):
          UpperCamelCase__      =							[self.sep_token_id]
          UpperCamelCase__      =							[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   __a				(	self    ,   a    ,   a = None    ,   a = False       ):

          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   __a				(	self    ,   a    ,   a = None       ):
          UpperCamelCase__      =							[self.sep_token_id]
          UpperCamelCase__      =							[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   __a				(	self    ,   a    ,   a = None       ):
          if not os.path.isdir(a       ):
                logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''       )
                return
          UpperCamelCase__      =							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__      =							self.sp_model.serialized_model_proto()
                      fi.write(a       )

          return (out_vocab_file,)









'''simple docstring'''







import math
from timeit import timeit







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      UpperCamelCase__      =							0
      UpperCamelCase__      =							abs(__A       )
      while True:
            UpperCamelCase__      =							n // 10
            digits += 1
            if n == 0:
                  break
      return digits







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      return 1 if n == 0 else math.floor(math.log(abs(__A       ) ,     10       ) + 1       )







def _UpperCamelCase			(	__A       )				->						int:


      '''simple docstring'''



      return len(str(abs(__A       )       )       )







def _UpperCamelCase			(	)				->						None:


      '''simple docstring'''



      from collections.abc import Callable

      def benchmark_a_function(__A ,     __A       ) -> None:
            UpperCamelCase__      =							F'''{func.__name__}({value})'''
            UpperCamelCase__      =							timeit(F'''__main__.{call}''' ,     setup="import __main__"       )
            print(F'''{call}: {func(__A       )} -- {timing} seconds'''       )

      for value in (262144, 1125899906842624, 1267650600228229401496703205376):
            for func in (num_digits, num_digits_fast, num_digits_faster):
                  benchmark_a_function(__A ,     __A       )
            print()


if __name__ == "__main__":
  import doctest

  doctest.testmod()
  benchmark()





