



"""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


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

lowerCamelCase				:     List[Any]								=  {
    """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""",
    },
}

lowerCamelCase				:     Optional[int]								=  {
    """google/fnet-base""": 5_1_2,
    """google/fnet-large""": 5_1_2,
}

lowerCamelCase				:     int								=  """▁"""




class    __snake_case(					__A       ):
    _A       = VOCAB_FILES_NAMES
    _A       = PRETRAINED_VOCAB_FILES_MAP
    _A       = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    _A       = ['''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_    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			(
          AddedToken(A_    ,							lstrip=A_    ,							rstrip=A_    ,							normalized=A_			)
          if isinstance(A_    ,							A_			)
          else mask_token
      )

      _SCREAMING_SNAKE_CASE        =			{} 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_    ,							)

      _SCREAMING_SNAKE_CASE        =			do_lower_case
      _SCREAMING_SNAKE_CASE        =			remove_space
      _SCREAMING_SNAKE_CASE        =			keep_accents
      _SCREAMING_SNAKE_CASE        =			vocab_file

      _SCREAMING_SNAKE_CASE        =			spm.SentencePieceProcessor(**self.sp_model_kwargs			)
      self.sp_model.Load(A_			)




    @property
    def   A   (   self			):





      '''simple docstring'''



      return len(self.sp_model			)




    def   A   (   self			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			{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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.__dict__.copy()
      _SCREAMING_SNAKE_CASE        =			None
      return state




    def __setstate__(   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			d

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

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




    def   A   (   self    ,							A_			):





      '''simple docstring'''



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

      if not self.keep_accents:
        _SCREAMING_SNAKE_CASE        =			unicodedata.normalize('''NFKD'''    ,							A_			)
        _SCREAMING_SNAKE_CASE        =			''''''.join([c for c in outputs if not unicodedata.combining(A_			)]			)
      if self.do_lower_case:
        _SCREAMING_SNAKE_CASE        =			outputs.lower()

      return outputs




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			self.preprocess_text(A_			)
      _SCREAMING_SNAKE_CASE        =			self.sp_model.encode(A_    ,							out_type=A_			)
      _SCREAMING_SNAKE_CASE        =			[]
      for piece in pieces:
        if len(A_			) > 1 and piece[-1] == str(''','''			) and piece[-2].isdigit():
          _SCREAMING_SNAKE_CASE        =			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:
              _SCREAMING_SNAKE_CASE        =			cur_pieces[1:]
            else:
              _SCREAMING_SNAKE_CASE        =			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_			):





      '''simple docstring'''



      return self.sp_model.PieceToId(A_			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      return self.sp_model.IdToPiece(A_			)




    def   A   (   self    ,							A_			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			[]
      _SCREAMING_SNAKE_CASE        =			''''''
      _SCREAMING_SNAKE_CASE        =			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
          _SCREAMING_SNAKE_CASE        =			True
          _SCREAMING_SNAKE_CASE        =			[]
        else:
          current_sub_tokens.append(A_			)
          _SCREAMING_SNAKE_CASE        =			False
      out_string += self.sp_model.decode(A_			)
      return out_string.strip()




    def   A   (   self    ,							A_    ,							A_ = False    ,							A_ = None    ,							A_ = True    ,							**A_    ,							):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			kwargs.pop('''use_source_tokenizer'''    ,							A_			)

      _SCREAMING_SNAKE_CASE        =			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
      _SCREAMING_SNAKE_CASE        =			[]
      _SCREAMING_SNAKE_CASE        =			[]
      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_			)			)
            _SCREAMING_SNAKE_CASE        =			[]
          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:
        _SCREAMING_SNAKE_CASE        =			re.sub(r'''(<unk>) '''    ,							r'''\1'''    ,							''' '''.join(A_			)			)
      else:
        _SCREAMING_SNAKE_CASE        =			''''''.join(A_			)

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




    def   A   (   self    ,							A_    ,							A_ = None			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			[self.sep_token_id]
      _SCREAMING_SNAKE_CASE        =			[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			):





      '''simple docstring'''



      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			):





      '''simple docstring'''



      _SCREAMING_SNAKE_CASE        =			[self.sep_token_id]
      _SCREAMING_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			) * [0] + len(token_ids_a + sep			) * [1]




    def   A   (   self    ,							A_    ,							A_ = None			):





      '''simple docstring'''



      if not os.path.isdir(A_			):
        logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''			)
        return
      _SCREAMING_SNAKE_CASE        =			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:
          _SCREAMING_SNAKE_CASE        =			self.sp_model.serialized_model_proto()
          fi.write(A_			)

      return (out_vocab_file,)





"""simple docstring"""






import math


def 							A__ (     UpperCamelCase__    ):


  '''simple docstring'''


  if not isinstance(UpperCamelCase__       ,							UpperCamelCase__    ):
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be an integer'''
    raise TypeError(UpperCamelCase__    )
  if number < 1:
    _SCREAMING_SNAKE_CASE        =			F'''Input value of [number={number}] must be a positive integer'''
    raise ValueError(UpperCamelCase__    )
  _SCREAMING_SNAKE_CASE        =			[number]
  while number != 1:
    if number % 2 == 0:
      _SCREAMING_SNAKE_CASE        =			math.floor(math.sqrt(UpperCamelCase__    )    )
    else:
      _SCREAMING_SNAKE_CASE        =			math.floor(
          math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    ) * math.sqrt(UpperCamelCase__    )    )
    sequence.append(UpperCamelCase__    )
  return sequence


if __name__ == "__main__":
    import doctest

    doctest.testmod()
