code: 
  
  
  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
  
  
  SCREAMING_SNAKE_CASE : str          =    logging.get_logger(__name__)
  SCREAMING_SNAKE_CASE : Union[str, Any]          =    {"""vocab_file""": """spiece.model"""}
  
  SCREAMING_SNAKE_CASE : 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""",
      },
  }
  
  SCREAMING_SNAKE_CASE : Any          =    {
      """google/fnet-base""": 5_12,
      """google/fnet-large""": 5_12,
  }
  
  SCREAMING_SNAKE_CASE : Optional[int]          =    """▁"""
  
  
  
  
  
  class 					snake_case__  ( _SCREAMING_SNAKE_CASE       ):
  	UpperCAmelCase   :							str         = VOCAB_FILES_NAMES
  	UpperCAmelCase   :							str         = PRETRAINED_VOCAB_FILES_MAP
  	UpperCAmelCase   :							List[str]         = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  	UpperCAmelCase   :							List[str]         = ["""input_ids""", """token_type_ids"""]
  
  
  
  
  
  	def __init__(  self				,    UpperCamelCase_				,    UpperCamelCase_=False				,    UpperCamelCase_=True				,    UpperCamelCase_=True				,    UpperCamelCase_="<unk>"				,    UpperCamelCase_="[SEP]"				,    UpperCamelCase_="<pad>"				,    UpperCamelCase_="[CLS]"				,    UpperCamelCase_="[MASK]"				,    UpperCamelCase_ = None				,    **UpperCamelCase_				,    )		->			None:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Tuple			=	(
  								    AddedToken(__A				,    lstrip=__A				,    rstrip=__A				,    normalized=__A							)
  								    if isinstance(__A				,    __A							)
  								    else mask_token
  								)
  
  								a_       :   Any			=	{} 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				,    )
  
  								a_       :   Tuple			=	do_lower_case
  								a_       :   str			=	remove_space
  								a_       :   Tuple			=	keep_accents
  								a_       :   Dict			=	vocab_file
  
  								a_       :   int			=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  								self.sp_model.Load(__A							)
  
  
  
  
  
  	@property
  	def     A				(  self							)		->			Optional[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								return len(self.sp_model							)
  
  
  
  
  
  	def     A				(  self							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	{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[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Union[str, Any]			=	self.__dict__.copy()
  								a_       :   Any			=	None
  								return state
  
  
  
  
  
  	def __setstate__(  self				,    UpperCamelCase_							)		->			Any:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   int			=	d
  
  								# for backward compatibility
  								if not hasattr(self				,    """sp_model_kwargs"""							):
  															a_       :   Dict			=	{}
  
  								a_       :   List[Any]			=	spm.SentencePieceProcessor(**self.sp_model_kwargs							)
  								self.sp_model.Load(self.vocab_file							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Optional[Any]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if self.remove_space:
  															a_       :   Dict			=	""" """.join(inputs.strip().split()							)
  								else:
  															a_       :   str			=	inputs
  								a_       :   Dict			=	outputs.replace("""``"""				,    """\""""							).replace("""\'\'"""				,    """\""""							)
  
  								if not self.keep_accents:
  															a_       :   List[Any]			=	unicodedata.normalize("""NFKD"""				,    __A							)
  															a_       :   List[str]			=	"""""".join([c for c in outputs if not unicodedata.combining(__A							)]							)
  								if self.do_lower_case:
  															a_       :   List[Any]			=	outputs.lower()
  
  								return outputs
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			List[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[int]			=	self.preprocess_text(__A							)
  								a_       :   int			=	self.sp_model.encode(__A				,    out_type=__A							)
  								a_       :   Optional[int]			=	[]
  								for piece in pieces:
  															if len(__A							) > 1 and piece[-1] == str(""","""							) and piece[-2].isdigit():
  																						a_       :   Dict			=	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:
  																																				a_       :   Any			=	cur_pieces[1:]
  																													else:
  																																				a_       :   List[str]			=	cur_pieces[0][1:]
  																						cur_pieces.append(piece[-1]							)
  																						new_pieces.extend(__A							)
  															else:
  																						new_pieces.append(__A							)
  
  								return new_pieces
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.sp_model.PieceToId(__A							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			Dict:
  								"""simple docstring"""
  
  
  
  
  
  
  								return self.sp_model.IdToPiece(__A							)
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_							)		->			int:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Optional[Any]			=	[]
  								a_       :   int			=	""""""
  								a_       :   str			=	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
  																						a_       :   Optional[Any]			=	True
  																						a_       :   str			=	[]
  															else:
  																						current_sub_tokens.append(__A							)
  																						a_       :   Optional[Any]			=	False
  								out_string += self.sp_model.decode(__A							)
  								return out_string.strip()
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = False				,    UpperCamelCase_ = None				,    UpperCamelCase_ = True				,    **UpperCamelCase_				,    )		->			str:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	kwargs.pop("""use_source_tokenizer"""				,    __A							)
  
  								a_       :   Union[str, Any]			=	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
  								a_       :   str			=	[]
  								a_       :   str			=	[]
  								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							)							)
  																													a_       :   str			=	[]
  																						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:
  															a_       :   List[str]			=	re.sub(R"""(<unk>) """				,    R"""\1"""				,    """ """.join(__A							)							)
  								else:
  															a_       :   List[str]			=	"""""".join(__A							)
  
  								a_       :   Union[str, Any]			=	(
  								    clean_up_tokenization_spaces
  								    if clean_up_tokenization_spaces is not None
  								    else self.clean_up_tokenization_spaces
  								)
  								if clean_up_tokenization_spaces:
  															a_       :   Dict			=	self.clean_up_tokenization(__A							)
  															return clean_text
  								else:
  															return text
  
  
  
  
  
  	def     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   str			=	[self.sep_token_id]
  								a_       :   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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None				,    UpperCamelCase_ = False							)		->			List[int]:
  								"""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				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			List[int]:
  								"""simple docstring"""
  
  
  
  
  
  
  								a_       :   Dict			=	[self.sep_token_id]
  								a_       :   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     A				(  self				,    UpperCamelCase_				,    UpperCamelCase_ = None							)		->			Tuple[str]:
  								"""simple docstring"""
  
  
  
  
  
  
  								if not os.path.isdir(__A							):
  															logger.error(f"""Vocabulary path ({save_directory}) should be a directory"""							)
  															return
  								a_       :   Dict			=	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:
  																						a_       :   List[Any]			=	self.sp_model.serialized_model_proto()
  																						fi.write(__A							)
  
  								return (out_vocab_file,)
  
  
  
style_context: 
  
  
  def 						_lowerCamelCase		(							SCREAMING_SNAKE_CASE_				: list[int]      ):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  							a_       :   List[Any]			=	len(SCREAMING_SNAKE_CASE_      )
  							if n in (0, 1):
  														return 0
  							return sum(query * (n - i - 1) for i, query in enumerate(sorted(SCREAMING_SNAKE_CASE_      )      )      )
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  