code: 
  
  
  
  
  """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 :		Optional[Any]      							=    logging.get_logger(__name__)
  __A :		List[str]      							=    {'''vocab_file''': '''spiece.model'''}
  
  __A :		Optional[int]      							=    {
      '''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 :		Dict      							=    {
      '''google/fnet-base''': 512,
      '''google/fnet-large''': 512,
  }
  
  __A :		List[str]      							=    '''▁'''
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				VOCAB_FILES_NAMES
     lowercase       :  Union[str, Any]      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  str      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  Tuple      =				['input_ids', 'token_type_ids']
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_=False    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_=True    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="[SEP]"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="[CLS]"    ,      SCREAMING_SNAKE_CASE_="[MASK]"    ,      SCREAMING_SNAKE_CASE_ = None    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # 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				:   Optional[Any]          =							(
             AddedToken(SCREAMING_SNAKE_CASE_    ,      lstrip=SCREAMING_SNAKE_CASE_    ,      rstrip=SCREAMING_SNAKE_CASE_    ,      normalized=SCREAMING_SNAKE_CASE_ )
             if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ )
             else mask_token
         )
  
         UpperCamelCase				:   int          =							{} if sp_model_kwargs is None else sp_model_kwargs
  
         super().__init__(
             do_lower_case=SCREAMING_SNAKE_CASE_    ,      remove_space=SCREAMING_SNAKE_CASE_    ,      keep_accents=SCREAMING_SNAKE_CASE_    ,      unk_token=SCREAMING_SNAKE_CASE_    ,      sep_token=SCREAMING_SNAKE_CASE_    ,      pad_token=SCREAMING_SNAKE_CASE_    ,      cls_token=SCREAMING_SNAKE_CASE_    ,      mask_token=SCREAMING_SNAKE_CASE_    ,      sp_model_kwargs=self.sp_model_kwargs    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   List[str]          =							do_lower_case
         UpperCamelCase				:   Any          =							remove_space
         UpperCamelCase				:   Dict          =							keep_accents
         UpperCamelCase				:   str          =							vocab_file
  
         UpperCamelCase				:   List[str]          =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return len(self.sp_model )
  
  
  
  
  
  
  
     def       a_							(							self ):
         UpperCamelCase				:   Optional[Any]          =							{self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
         vocab.update(self.added_tokens_encoder )
         return vocab
  
  
  
  
  
  
  
     def __getstate__(							self ):
         UpperCamelCase				:   Union[str, Any]          =							self.__dict__.copy()
         UpperCamelCase				:   Optional[int]          =							None
         return state
  
  
  
  
  
  
  
     def __setstate__(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[int]          =							d
  
         # for backward compatibility
         if not hasattr(self    ,      """sp_model_kwargs""" ):
             UpperCamelCase				:   str          =							{}
  
         UpperCamelCase				:   List[str]          =							spm.SentencePieceProcessor(**self.sp_model_kwargs )
         self.sp_model.Load(self.vocab_file )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         if self.remove_space:
             UpperCamelCase				:   str          =							""" """.join(inputs.strip().split() )
         else:
             UpperCamelCase				:   List[str]          =							inputs
         UpperCamelCase				:   Optional[int]          =							outputs.replace("""``"""    ,      """\"""" ).replace("""''"""    ,      """\"""" )
  
         if not self.keep_accents:
             UpperCamelCase				:   int          =							unicodedata.normalize("""NFKD"""    ,      SCREAMING_SNAKE_CASE_ )
             UpperCamelCase				:   List[str]          =							"""""".join([c for c in outputs if not unicodedata.combining(SCREAMING_SNAKE_CASE_ )] )
         if self.do_lower_case:
             UpperCamelCase				:   List[Any]          =							outputs.lower()
  
         return outputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   List[Any]          =							self.preprocess_text(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							self.sp_model.encode(SCREAMING_SNAKE_CASE_    ,      out_type=SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							[]
         for piece in pieces:
             if len(SCREAMING_SNAKE_CASE_ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
                 UpperCamelCase				:   Union[str, Any]          =							self.sp_model.EncodeAsPieces(piece[:-1].replace(SCREAMING_SNAKE_CASE_    ,      """""" ) )
                 if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
                     if len(cur_pieces[0] ) == 1:
                         UpperCamelCase				:   Optional[int]          =							cur_pieces[1:]
                     else:
                         UpperCamelCase				:   Union[str, Any]          =							cur_pieces[0][1:]
                 cur_pieces.append(piece[-1] )
                 new_pieces.extend(SCREAMING_SNAKE_CASE_ )
             else:
                 new_pieces.append(SCREAMING_SNAKE_CASE_ )
  
         return new_pieces
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Tuple          =							[]
         UpperCamelCase				:   List[Any]          =							""""""
         UpperCamelCase				:   Dict          =							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(SCREAMING_SNAKE_CASE_ ) + token
                 UpperCamelCase				:   List[str]          =							True
                 UpperCamelCase				:   Optional[Any]          =							[]
             else:
                 current_sub_tokens.append(SCREAMING_SNAKE_CASE_ )
                 UpperCamelCase				:   Optional[int]          =							False
         out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_ )
         return out_string.strip()
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = False    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = True    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   Optional[int]          =							kwargs.pop("""use_source_tokenizer"""    ,      SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Any          =							self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_    ,      skip_special_tokens=SCREAMING_SNAKE_CASE_ )
  
         # 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				:   Union[str, Any]          =							[]
         UpperCamelCase				:   Optional[Any]          =							[]
         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(SCREAMING_SNAKE_CASE_ ) )
                     UpperCamelCase				:   int          =							[]
                 sub_texts.append(SCREAMING_SNAKE_CASE_ )
             else:
                 current_sub_text.append(SCREAMING_SNAKE_CASE_ )
         if current_sub_text:
             sub_texts.append(self.convert_tokens_to_string(SCREAMING_SNAKE_CASE_ ) )
  
         # Mimic the behavior of the Rust tokenizer:
         # No space after <unk>
         if spaces_between_special_tokens:
             UpperCamelCase				:   List[Any]          =							re.sub(r"""(<unk>) """    ,      r"""\1"""    ,      """ """.join(SCREAMING_SNAKE_CASE_ ) )
         else:
             UpperCamelCase				:   Optional[int]          =							"""""".join(SCREAMING_SNAKE_CASE_ )
  
         UpperCamelCase				:   Any          =							(
             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(SCREAMING_SNAKE_CASE_ )
             return clean_text
         else:
             return text
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase				:   int          =							[self.sep_token_id]
         UpperCamelCase				:   str          =							[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    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = False ):
  
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=SCREAMING_SNAKE_CASE_    ,      token_ids_a=SCREAMING_SNAKE_CASE_    ,      already_has_special_tokens=SCREAMING_SNAKE_CASE_ )
  
         if token_ids_a is not None:
             return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
         return [1] + ([0] * len(SCREAMING_SNAKE_CASE_ )) + [1]
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         UpperCamelCase				:   str          =							[self.sep_token_id]
         UpperCamelCase				:   List[str]          =							[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    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   Any          =							os.path.join(
             SCREAMING_SNAKE_CASE_    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
             copyfile(self.vocab_file    ,      SCREAMING_SNAKE_CASE_ )
         elif not os.path.isfile(self.vocab_file ):
             with open(SCREAMING_SNAKE_CASE_    ,      """wb""" ) as fi:
                 UpperCamelCase				:   Union[str, Any]          =							self.sp_model.serialized_model_proto()
                 fi.write(SCREAMING_SNAKE_CASE_ )
  
         return (out_vocab_file,)
  
  
  
  
  
style_context: 
  
  
  
  
  """simple docstring"""
  
  
  
  
  import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  
  from tokenizers import processors
  
  from ...tokenization_utils import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import is_sentencepiece_available, logging
  
  
  if is_sentencepiece_available():
    from .tokenization_mbartaa import MBartaaTokenizer
  else:
    __A :		Tuple      							=    None
  
  
  __A :		Optional[Any]      							=    logging.get_logger(__name__)
  
  __A :		Optional[Any]      							=    {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
  
  __A :		Optional[Any]      							=    {
      '''vocab_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/sentencepiece.bpe.model'''
          ),
      },
      '''tokenizer_file''': {
          '''facebook/mbart-large-50-one-to-many-mmt''': (
              '''https://huggingface.co/facebook/mbart-large-50-one-to-many-mmt/resolve/main/tokenizer.json'''
          ),
      },
  }
  
  __A :		List[Any]      							=    {
      '''facebook/mbart-large-50-one-to-many-mmt''': 1024,
  }
  
  # fmt: off
  __A :		Dict      							=    ['''ar_AR''', '''cs_CZ''', '''de_DE''', '''en_XX''', '''es_XX''', '''et_EE''', '''fi_FI''', '''fr_XX''', '''gu_IN''', '''hi_IN''', '''it_IT''', '''ja_XX''', '''kk_KZ''', '''ko_KR''', '''lt_LT''', '''lv_LV''', '''my_MM''', '''ne_NP''', '''nl_XX''', '''ro_RO''', '''ru_RU''', '''si_LK''', '''tr_TR''', '''vi_VN''', '''zh_CN''', '''af_ZA''', '''az_AZ''', '''bn_IN''', '''fa_IR''', '''he_IL''', '''hr_HR''', '''id_ID''', '''ka_GE''', '''km_KH''', '''mk_MK''', '''ml_IN''', '''mn_MN''', '''mr_IN''', '''pl_PL''', '''ps_AF''', '''pt_XX''', '''sv_SE''', '''sw_KE''', '''ta_IN''', '''te_IN''', '''th_TH''', '''tl_XX''', '''uk_UA''', '''ur_PK''', '''xh_ZA''', '''gl_ES''', '''sl_SI''']
  class 							lowerCamelCase		( _UpperCAmelCase				):
     lowercase       :  List[Any]      =				VOCAB_FILES_NAMES
     lowercase       :  Optional[Any]      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase       :  List[Any]      =				PRETRAINED_VOCAB_FILES_MAP
     lowercase       :  Tuple      =				['input_ids', 'attention_mask']
     lowercase       :  Optional[int]      =				MBartaaTokenizer
  
     lowercase       :  List[int]      =				[]
     lowercase       :  List[int]      =				[]
  
  
  
  
  
  
  
     def __init__(							self    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_=None    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="</s>"    ,      SCREAMING_SNAKE_CASE_="<s>"    ,      SCREAMING_SNAKE_CASE_="<unk>"    ,      SCREAMING_SNAKE_CASE_="<pad>"    ,      SCREAMING_SNAKE_CASE_="<mask>"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         # Mask token behave like a normal word, i.e. include the space before it
         UpperCamelCase				:   Optional[int]          =							AddedToken(SCREAMING_SNAKE_CASE_    ,      lstrip=SCREAMING_SNAKE_CASE_    ,      rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ ) else mask_token
  
         UpperCamelCase				:   Dict          =							kwargs.get("""additional_special_tokens"""    ,      [] )
         kwargs["additional_special_tokens"] += [
             code for code in FAIRSEQ_LANGUAGE_CODES if code not in kwargs["additional_special_tokens"]
         ]
  
         super().__init__(
             SCREAMING_SNAKE_CASE_    ,      src_lang=SCREAMING_SNAKE_CASE_    ,      tgt_lang=SCREAMING_SNAKE_CASE_    ,      tokenizer_file=SCREAMING_SNAKE_CASE_    ,      eos_token=SCREAMING_SNAKE_CASE_    ,      sep_token=SCREAMING_SNAKE_CASE_    ,      cls_token=SCREAMING_SNAKE_CASE_    ,      unk_token=SCREAMING_SNAKE_CASE_    ,      pad_token=SCREAMING_SNAKE_CASE_    ,      mask_token=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_    ,      )
  
         UpperCamelCase				:   int          =							vocab_file
         UpperCamelCase				:   Optional[Any]          =							False if not self.vocab_file else True
  
         UpperCamelCase				:   List[Any]          =							{
             lang_code: self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) for lang_code in FAIRSEQ_LANGUAGE_CODES
         }
  
         UpperCamelCase				:   List[str]          =							src_lang if src_lang is not None else """en_XX"""
         UpperCamelCase				:   Union[str, Any]          =							tgt_lang
         UpperCamelCase				:   Union[str, Any]          =							self.lang_code_to_id[self._src_lang]
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     @property
     def       a_							(							self ):
         return self._src_lang
  
  
  
  
  
  
  
     @src_lang.setter
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   str          =							new_src_lang
         self.set_src_lang_special_tokens(self._src_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if token_ids_a is None:
             return self.prefix_tokens + token_ids_a + self.suffix_tokens
         # We don't expect to process pairs, but leave the pair logic for API consistency
         return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = "en_XX"    ,      SCREAMING_SNAKE_CASE_ = None    ,      SCREAMING_SNAKE_CASE_ = "ro_RO"    ,      **SCREAMING_SNAKE_CASE_    ,      ):
         UpperCamelCase				:   str          =							src_lang
         UpperCamelCase				:   Optional[Any]          =							tgt_lang
         return super().prepare_seqaseq_batch(SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_src_lang_special_tokens(self.src_lang )
  
  
  
  
  
  
  
     def       a_							(							self ):
         return self.set_tgt_lang_special_tokens(self.tgt_lang )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Optional[Any]          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Tuple          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Optional[int]          =							[self.eos_token_id]
  
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Any          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Any          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_ ):
         UpperCamelCase				:   Any          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Optional[Any]          =							[self.cur_lang_code_id]
         UpperCamelCase				:   Tuple          =							[self.eos_token_id]
  
         UpperCamelCase				:   str          =							self.convert_ids_to_tokens(self.prefix_tokens )
         UpperCamelCase				:   Dict          =							self.convert_ids_to_tokens(self.suffix_tokens )
  
         UpperCamelCase				:   Dict          =							processors.TemplateProcessing(
             single=prefix_tokens_str + ["""$A"""] + suffix_tokens_str    ,      pair=prefix_tokens_str + ["""$A""", """$B"""] + suffix_tokens_str    ,      special_tokens=list(zip(prefix_tokens_str + suffix_tokens_str    ,      self.prefix_tokens + self.suffix_tokens ) )    ,      )
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ ):
         if src_lang is None or tgt_lang is None:
             raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
         UpperCamelCase				:   Optional[int]          =							src_lang
         UpperCamelCase				:   Optional[int]          =							self(SCREAMING_SNAKE_CASE_    ,      add_special_tokens=SCREAMING_SNAKE_CASE_    ,      return_tensors=SCREAMING_SNAKE_CASE_    ,      **SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   Dict          =							self.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ )
         UpperCamelCase				:   str          =							tgt_lang_id
         return inputs
  
  
  
  
  
  
  
     def       a_							(							self    ,      SCREAMING_SNAKE_CASE_    ,      SCREAMING_SNAKE_CASE_ = None ):
         if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer.""" )
  
         if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
             logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
             return
         UpperCamelCase				:   Optional[int]          =							os.path.join(
             SCREAMING_SNAKE_CASE_    ,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
  
         if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ):
             copyfile(self.vocab_file    ,      SCREAMING_SNAKE_CASE_ )
  
         return (out_vocab_file,)
  
  
  
  
  