tokenization_utils_fast

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast

Bases: PreTrainedTokenizerBase

Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).

Inherits from [~tokenization_utils_base.PreTrainedTokenizerBase].

Handles all the shared methods for tokenization and special tokens, as well as methods for downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.

This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).

Source code in mindnlp\transformers\tokenization_utils_fast.py

class PreTrainedTokenizerFast(PreTrainedTokenizerBase):
    """
    Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).

    Inherits from [`~tokenization_utils_base.PreTrainedTokenizerBase`].

    Handles all the shared methods for tokenization and special tokens, as well as methods for
    downloading/caching/loading pretrained tokenizers, as well as adding tokens to the vocabulary.

    This class also contains the added tokens in a unified way on top of all tokenizers so we don't have to handle the
    specific vocabulary augmentation methods of the various underlying dictionary structures (BPE, sentencepiece...).
    """

    vocab_files_names = VOCAB_FILES_NAMES
    slow_tokenizer_class: PreTrainedTokenizer = None

    def __init__(self, *args, **kwargs):
        tokenizer_object = kwargs.pop("tokenizer_object", None)
        slow_tokenizer = kwargs.pop("__slow_tokenizer", None)
        gguf_file = kwargs.pop("gguf_file", None)
        fast_tokenizer_file = kwargs.pop("tokenizer_file", None)
        from_slow = kwargs.pop("from_slow", False)
        added_tokens_decoder = kwargs.pop("added_tokens_decoder", {})

        if from_slow and slow_tokenizer is None and self.slow_tokenizer_class is None:
            raise ValueError(
                "Cannot instantiate this tokenizer from a slow version. If it's based on sentencepiece, make sure you "
                "have sentencepiece installed."
            )

        if tokenizer_object is not None:
            fast_tokenizer = copy.deepcopy(tokenizer_object)
        elif fast_tokenizer_file is not None and not from_slow:
            # We have a serialization from tokenizers which let us directly build the backend
            fast_tokenizer = TokenizerFast.from_file(fast_tokenizer_file)
        elif slow_tokenizer is not None:
            # We need to convert a slow tokenizer to build the backend
            fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)

        elif self.slow_tokenizer_class is not None:
            # We need to create and convert a slow tokenizer to build the backend
            slow_tokenizer = self.slow_tokenizer_class(*args, **kwargs)
            fast_tokenizer = convert_slow_tokenizer(slow_tokenizer)
        else:
            raise ValueError(
                "Couldn't instantiate the backend tokenizer from one of: \n"
                "(1) a `tokenizers` library serialization file, \n"
                "(2) a slow tokenizer instance to convert or \n"
                "(3) an equivalent slow tokenizer class to instantiate and convert. \n"
                "You need to have sentencepiece installed to convert a slow tokenizer to a fast one."
            )

        self._tokenizer = fast_tokenizer

        if slow_tokenizer is not None:
            kwargs.update(slow_tokenizer.init_kwargs)

        self._decode_use_source_tokenizer = False

        _truncation = self._tokenizer.truncation

        if _truncation is not None:
            self._tokenizer.enable_truncation(**_truncation)
            kwargs.setdefault("max_length", _truncation["max_length"])
            kwargs.setdefault("truncation_side", _truncation["direction"])
            kwargs.setdefault("stride", _truncation["stride"])
            kwargs.setdefault("truncation_strategy", _truncation["strategy"])
        else:
            self._tokenizer.no_truncation()

        _padding = self._tokenizer.padding
        if _padding is not None:
            self._tokenizer.enable_padding(**_padding)
            kwargs.setdefault("pad_token", _padding["pad_token"])
            kwargs.setdefault("pad_token_type_id", _padding["pad_type_id"])
            kwargs.setdefault("padding_side", _padding["direction"])
            kwargs.setdefault("max_length", _padding["length"])
            kwargs.setdefault("pad_to_multiple_of", _padding["pad_to_multiple_of"])

        # We call this after having initialized the backend tokenizer because we update it.
        super().__init__(**kwargs)

        # Set the splitting mode for special tokens for the tokenizer to be used throughout the class.
        self._tokenizer.encode_special_tokens = self.split_special_tokens

        # The following logic will be replace with a single add_tokens once a fix is pushed to tokenizers
        # allows converting a slow -> fast, non-legacy: if the `tokenizer.json` does not have all the added tokens
        # uses the information stored in `added_tokens_decoder`.
        # this is costly for fast tokenizers as we re-compute the regex again. But not all tokens are added tokens
        # Use hash to speed up the very slow operation `token not in added_tokens_decoder`.
        added_tokens_decoder_hash = {hash(repr(token)) for token in self.added_tokens_decoder}
        tokens_to_add = [
            token
            for index, token in sorted(added_tokens_decoder.items(), key=lambda x: x[0])
            if hash(repr(token)) not in added_tokens_decoder_hash
        ]
        encoder = list(self.added_tokens_encoder.keys()) + [str(token) for token in tokens_to_add]
        # if some of the special tokens are strings, we check if we don't already have a token
        tokens_to_add += [
            token for token in self.all_special_tokens_extended if token not in encoder and token not in tokens_to_add
        ]

        if len(tokens_to_add) > 0:
            # super hack: if a token.special is set, tokenizer ignores it for now so FIXME @ArthurZ
            # Accumulate added tokens into batches of special/non-special tokens, because calling add_tokens() for
            # individual tokens would repeatedly rebuild a trie, which can be slow.
            is_last_special = None
            tokens = []
            special_tokens = self.all_special_tokens
            for token in tokens_to_add:
                is_special = (
                    (token.special or str(token) in special_tokens)
                    if isinstance(token, AddedToken)
                    else str(token) in special_tokens
                )
                if is_last_special is None or is_last_special == is_special:
                    tokens.append(token)
                else:
                    self._add_tokens(tokens, special_tokens=is_last_special)
                    tokens = [token]
                is_last_special = is_special
            if tokens:
                self._add_tokens(tokens, special_tokens=is_last_special)

    @property
    def is_fast(self) -> bool:
        return True

    @property
    def can_save_slow_tokenizer(self) -> bool:
        """
        `bool`: Whether or not the slow tokenizer can be saved. Usually for sentencepiece based slow tokenizer, this
        can only be `True` if the original `"sentencepiece.model"` was not deleted.
        """
        return True

    @property
    def vocab_size(self) -> int:
        """
        `int`: Size of the base vocabulary (without the added tokens).
        """
        return self._tokenizer.get_vocab_size(with_added_tokens=False)

    def get_vocab(self) -> Dict[str, int]:
        return self._tokenizer.get_vocab(with_added_tokens=True)

    @property
    def vocab(self) -> Dict[str, int]:
        return self.get_vocab()

    @property
    def added_tokens_encoder(self) -> Dict[str, int]:
        """
        Returns the sorted mapping from string to index. The added tokens encoder is cached for performance
        optimisation in `self._added_tokens_encoder` for the slow tokenizers.
        """
        return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}

    @property
    def added_tokens_decoder(self) -> Dict[int, AddedToken]:
        """
        Returns the added tokens in the vocabulary as a dictionary of index to AddedToken.

        Returns:
            `Dict[str, int]`: The added tokens.
        """
        return self._tokenizer.get_added_tokens_decoder()

    def get_added_vocab(self) -> Dict[str, int]:
        """
        Returns the added tokens in the vocabulary as a dictionary of token to index.

        Returns:
            `Dict[str, int]`: The added tokens.
        """
        return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}

    def __len__(self) -> int:
        """
        Size of the full vocabulary with the added tokens.
        """
        return self._tokenizer.get_vocab_size(with_added_tokens=True)

    @property
    def backend_tokenizer(self) -> TokenizerFast:
        """
        `tokenizers.implementations.BaseTokenizer`: The Rust tokenizer used as a backend.
        """
        return self._tokenizer

    @property
    def decoder(self) -> DecoderFast:
        """
        `tokenizers.decoders.Decoder`: The Rust decoder for this tokenizer.
        """
        return self._tokenizer.decoder

    def _convert_encoding(
        self,
        encoding: EncodingFast,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
    ) -> Tuple[Dict[str, Any], List[EncodingFast]]:
        """
        Convert the encoding representation (from low-level HuggingFace tokenizer output) to a python Dict and a list
        of encodings, take care of building a batch from overflowing tokens.

        Overflowing tokens are converted to additional examples (like batches) so the output values of the dict are
        lists (overflows) of lists (tokens).

        Output shape: (overflows, sequence length)
        """
        if return_token_type_ids is None:
            return_token_type_ids = "token_type_ids" in self.model_input_names
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        if return_overflowing_tokens and encoding.overflowing is not None:
            encodings = [encoding] + encoding.overflowing
        else:
            encodings = [encoding]

        encoding_dict = defaultdict(list)
        for e in encodings:
            encoding_dict["input_ids"].append(e.ids)

            if return_token_type_ids:
                encoding_dict["token_type_ids"].append(e.type_ids)
            if return_attention_mask:
                encoding_dict["attention_mask"].append(e.attention_mask)
            if return_special_tokens_mask:
                encoding_dict["special_tokens_mask"].append(e.special_tokens_mask)
            if return_offsets_mapping:
                encoding_dict["offset_mapping"].append(e.offsets)
            if return_length:
                encoding_dict["length"].append(len(e.ids))

        return encoding_dict, encodings

    def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
        """
        Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
        vocabulary.

        Args:
            tokens (`str` or `List[str]`): One or several token(s) to convert to token id(s).

        Returns:
            `int` or `List[int]`: The token id or list of token ids.
        """
        if tokens is None:
            return None

        if isinstance(tokens, str):
            return self._convert_token_to_id_with_added_voc(tokens)

        return [self._convert_token_to_id_with_added_voc(token) for token in tokens]

    def _convert_token_to_id_with_added_voc(self, token: str) -> int:
        index = self._tokenizer.token_to_id(token)
        if index is None:
            return self.unk_token_id
        return index

    def _convert_id_to_token(self, index: int) -> Optional[str]:
        return self._tokenizer.id_to_token(int(index))

    def _add_tokens(self, new_tokens: List[Union[str, AddedToken]], special_tokens=False) -> int:
        if special_tokens:
            return self._tokenizer.add_special_tokens(new_tokens)

        return self._tokenizer.add_tokens(new_tokens)

    def num_special_tokens_to_add(self, pair: bool = False) -> int:
        """
        Returns the number of added tokens when encoding a sequence with special tokens.

        <Tip>

        This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put
        this inside your training loop.

        </Tip>

        Args:
            pair (`bool`, *optional*, defaults to `False`):
                Whether the number of added tokens should be computed in the case of a sequence pair or a single
                sequence.

        Returns:
            `int`: Number of special tokens added to sequences.
        """
        return self._tokenizer.num_special_tokens_to_add(pair)

    def convert_ids_to_tokens(
        self, ids: Union[int, List[int]], skip_special_tokens: bool = False
    ) -> Union[str, List[str]]:
        """
        Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
        added tokens.

        Args:
            ids (`int` or `List[int]`):
                The token id (or token ids) to convert to tokens.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.

        Returns:
            `str` or `List[str]`: The decoded token(s).
        """
        if isinstance(ids, int):
            return self._tokenizer.id_to_token(ids)
        tokens = []
        for index in ids:
            index = int(index)
            if skip_special_tokens and index in self.all_special_ids:
                continue
            tokens.append(self._tokenizer.id_to_token(index))
        return tokens

    def tokenize(self, text: str, pair: Optional[str] = None, add_special_tokens: bool = False, **kwargs) -> List[str]:
        return self.encode_plus(text=text, text_pair=pair, add_special_tokens=add_special_tokens, **kwargs).tokens()

    def set_truncation_and_padding(
        self,
        padding_strategy: PaddingStrategy,
        truncation_strategy: TruncationStrategy,
        max_length: int,
        stride: int,
        pad_to_multiple_of: Optional[int],
    ):
        """
        Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
        library) and restore the tokenizer settings afterwards.

        The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
        padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed
        section.

        Args:
            padding_strategy ([`~utils.PaddingStrategy`]):
                The kind of padding that will be applied to the input
            truncation_strategy ([`~tokenization_utils_base.TruncationStrategy`]):
                The kind of truncation that will be applied to the input
            max_length (`int`):
                The maximum size of a sequence.
            stride (`int`):
                The stride to use when handling overflow.
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
                the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
        """
        _truncation = self._tokenizer.truncation
        _padding = self._tokenizer.padding
        # Set truncation and padding on the backend tokenizer
        if truncation_strategy == TruncationStrategy.DO_NOT_TRUNCATE:
            if _truncation is not None:
                self._tokenizer.no_truncation()
        else:
            target = {
                "max_length": max_length,
                "stride": stride,
                "strategy": truncation_strategy.value,
                "direction": self.truncation_side,
            }

            # _truncation might contain more keys that the target `transformers`
            # supports. Use only the target keys to trigger `enable_truncation`.
            # This should enable this code to works on various `tokenizers`
            # targets.
            if _truncation is None:
                current = None
            else:
                current = {k: _truncation.get(k, None) for k in target}

            if current != target:
                self._tokenizer.enable_truncation(**target)

        if padding_strategy == PaddingStrategy.DO_NOT_PAD:
            if _padding is not None:
                self._tokenizer.no_padding()
        else:
            length = max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None
            target = {
                "length": length,
                "direction": self.padding_side,
                "pad_id": self.pad_token_id,
                "pad_token": self.pad_token,
                "pad_type_id": self.pad_token_type_id,
                "pad_to_multiple_of": pad_to_multiple_of,
            }
            if _padding != target:
                self._tokenizer.enable_padding(**target)

    def _batch_encode_plus(
        self,
        batch_text_or_text_pairs: Union[
            List[TextInput], List[TextInputPair], List[PreTokenizedInput], List[PreTokenizedInputPair]
        ],
        add_special_tokens: bool = True,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_split_into_words: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[str] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        split_special_tokens: bool = False,
    ) -> BatchEncoding:
        if not isinstance(batch_text_or_text_pairs, (tuple, list)):
            raise TypeError(
                f"batch_text_or_text_pairs has to be a list or a tuple (got {type(batch_text_or_text_pairs)})"
            )

        # Set the truncation and padding strategy and restore the initial configuration
        self.set_truncation_and_padding(
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
        )

        if self._tokenizer.encode_special_tokens != split_special_tokens:
            self._tokenizer.encode_special_tokens = split_special_tokens

        encodings = self._tokenizer.encode_batch(
            batch_text_or_text_pairs,
            add_special_tokens=add_special_tokens,
            is_pretokenized=is_split_into_words,
        )

        # Convert encoding to dict
        # `Tokens` has type: Tuple[
        #                       List[Dict[str, List[List[int]]]] or List[Dict[str, 2D-Tensor]],
        #                       List[EncodingFast]
        #                    ]
        # with nested dimensions corresponding to batch, overflows, sequence length
        tokens_and_encodings = [
            self._convert_encoding(
                encoding=encoding,
                return_token_type_ids=return_token_type_ids,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_length=return_length,
                verbose=verbose,
            )
            for encoding in encodings
        ]

        # Convert the output to have dict[list] from list[dict] and remove the additional overflows dimension
        # From (variable) shape (batch, overflows, sequence length) to ~ (batch * overflows, sequence length)
        # (we say ~ because the number of overflow varies with the example in the batch)
        #
        # To match each overflowing sample with the original sample in the batch
        # we add an overflow_to_sample_mapping array (see below)
        sanitized_tokens = {}
        for key in tokens_and_encodings[0][0].keys():
            stack = [e for item, _ in tokens_and_encodings for e in item[key]]
            sanitized_tokens[key] = stack
        sanitized_encodings = [e for _, item in tokens_and_encodings for e in item]

        # If returning overflowing tokens, we need to return a mapping
        # from the batch idx to the original sample
        if return_overflowing_tokens:
            overflow_to_sample_mapping = []
            for i, (toks, _) in enumerate(tokens_and_encodings):
                overflow_to_sample_mapping += [i] * len(toks["input_ids"])
            sanitized_tokens["overflow_to_sample_mapping"] = overflow_to_sample_mapping

        for input_ids in sanitized_tokens["input_ids"]:
            self._eventual_warn_about_too_long_sequence(input_ids, max_length, verbose)
        return BatchEncoding(sanitized_tokens, sanitized_encodings, tensor_type=return_tensors)

    def _encode_plus(
        self,
        text: Union[TextInput, PreTokenizedInput],
        text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
        add_special_tokens: bool = True,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        truncation_strategy: TruncationStrategy = TruncationStrategy.DO_NOT_TRUNCATE,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_split_into_words: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[bool] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        split_special_tokens: bool = False,
        **kwargs,
    ) -> BatchEncoding:
        batched_input = [(text, text_pair)] if text_pair else [text]
        batched_output = self._batch_encode_plus(
            batched_input,
            is_split_into_words=is_split_into_words,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            return_token_type_ids=return_token_type_ids,
            return_attention_mask=return_attention_mask,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            split_special_tokens=split_special_tokens,
            **kwargs,
        )

        # Return tensor is None, then we can remove the leading batch axis
        # Overflowing tokens are returned as a batch of output so we keep them in this case
        if return_tensors is None and not return_overflowing_tokens:
            batched_output = BatchEncoding(
                {
                    key: value[0] if len(value) > 0 and isinstance(value[0], list) else value
                    for key, value in batched_output.items()
                },
                batched_output.encodings,
            )

        self._eventual_warn_about_too_long_sequence(batched_output["input_ids"], max_length, verbose)

        return batched_output

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        return self.backend_tokenizer.decoder.decode(tokens)

    def _decode(
        self,
        token_ids: Union[int, List[int]],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = None,
        **kwargs,
    ) -> str:
        self._decode_use_source_tokenizer = kwargs.pop("use_source_tokenizer", False)

        if isinstance(token_ids, int):
            token_ids = [token_ids]
        text = self._tokenizer.decode(token_ids, skip_special_tokens=skip_special_tokens)

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

    def _save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
        file_names: Tuple[str],
        legacy_format: Optional[bool] = None,
        filename_prefix: Optional[str] = None,
    ) -> Tuple[str]:
        """
        Save a tokenizer using the slow-tokenizer/legacy format: vocabulary + added tokens as well as in a unique JSON
        file containing {config + vocab + added-tokens}.
        """
        save_directory = str(save_directory)

        if self.slow_tokenizer_class is None and legacy_format is True:
            raise ValueError(
                "Your tokenizer does not have a legacy version defined and therefore cannot register this version. You"
                " might consider leaving the legacy_format at `None` or setting it to `False`."
            )

        save_slow = (
            (legacy_format is None or legacy_format is True)
            and self.slow_tokenizer_class is not None
            and self.can_save_slow_tokenizer
        )
        save_fast = legacy_format is None or legacy_format is False

        if save_slow:
            added_tokens_file = os.path.join(
                save_directory, (filename_prefix + "-" if filename_prefix else "") + ADDED_TOKENS_FILE
            )
            # make sure to be foward compatible
            added_vocab = {tok: index for tok, index in self.added_tokens_encoder.items() if index >= self.vocab_size}
            if added_vocab:
                with open(added_tokens_file, "w", encoding="utf-8") as f:
                    out_str = json.dumps(added_vocab, indent=2, sort_keys=True, ensure_ascii=False) + "\n"
                    f.write(out_str)

            vocab_files = self.save_vocabulary(save_directory, filename_prefix=filename_prefix)
            file_names = file_names + vocab_files + (added_tokens_file,)

        if save_fast:
            tokenizer_file = os.path.join(
                save_directory, (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_FILE
            )
            self.backend_tokenizer.save(tokenizer_file)
            file_names = file_names + (tokenizer_file,)

        return file_names

    def train_new_from_iterator(
        self,
        text_iterator,
        vocab_size,
        length=None,
        new_special_tokens=None,
        special_tokens_map=None,
        **kwargs,
    ):
        """
        Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline)
        as the current one.

        Args:
            text_iterator (generator of `List[str]`):
                The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts
                if you have everything in memory.
            vocab_size (`int`):
                The size of the vocabulary you want for your tokenizer.
            length (`int`, *optional*):
                The total number of sequences in the iterator. This is used to provide meaningful progress tracking
            new_special_tokens (list of `str` or `AddedToken`, *optional*):
                A list of new special tokens to add to the tokenizer you are training.
            special_tokens_map (`Dict[str, str]`, *optional*):
                If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special
                token name to new special token name in this argument.
            kwargs (`Dict[str, Any]`, *optional*):
                Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.

        Returns:
            [`PreTrainedTokenizerFast`]: A new tokenizer of the same type as the original one, trained on
            `text_iterator`.

        """
        tokenizer_json = json.loads(self._tokenizer.to_str())
        # Remove added tokens for now (uses IDs of tokens)
        added_tokens = tokenizer_json.pop("added_tokens")
        # Remove post processor for now (uses IDs of tokens)
        post_processor = tokenizer_json.pop("post_processor")

        unk_token = None
        # Remove vocab
        if tokenizer_json["model"]["type"] == "BPE":
            tokenizer_json["model"]["vocab"] = {}
            tokenizer_json["model"]["merges"] = []
        elif tokenizer_json["model"]["type"] == "Unigram":
            if tokenizer_json["model"]["unk_id"] is not None:
                unk_id = tokenizer_json["model"]["unk_id"]
                unk_token = tokenizer_json["model"]["vocab"][unk_id][0]
                if special_tokens_map is not None and unk_token in special_tokens_map:
                    unk_token = special_tokens_map[unk_token]
                tokenizer_json["model"]["unk_id"] = 0
                tokenizer_json["model"]["vocab"] = [[unk_token, 0.0]]
        elif tokenizer_json["model"]["type"] in ["WordLevel", "WordPiece"]:
            tokenizer_json["model"]["vocab"] = {}
        else:
            raise ValueError(
                f"This method does not support this type of tokenizer (found {tokenizer_json['model']['type']}) "
                "only BPE, Unigram, WordLevel and WordPiece."
            )

        if (
            special_tokens_map is not None
            and "unk_token" in tokenizer_json["model"]
            and tokenizer_json["model"]["unk_token"] in special_tokens_map
        ):
            tokenizer_json["model"]["unk_token"] = special_tokens_map[tokenizer_json["model"]["unk_token"]]

        tokenizer = TokenizerFast.from_str(json.dumps(tokenizer_json))

        # Get the special tokens from the current tokenizer if none are specified.
        special_tokens = []
        for added_token in added_tokens:
            special = added_token.pop("special", None)
            _ = added_token.pop("id", None)
            if tokenizer_json["model"]["type"] != "Unigram" and not special:
                continue
            if special_tokens_map is not None and added_token["content"] in special_tokens_map:
                added_token["content"] = special_tokens_map[added_token["content"]]
            special_tokens.append(AddedToken(**added_token))

        if new_special_tokens is not None:
            special_tokens.extend(new_special_tokens)

        # Trainer needs to know the end of word / continuing subword thingies in BPE
        if (
            tokenizer_json["model"]["type"] == "BPE"
            and "continuing_subword_prefix" not in kwargs
            and tokenizer_json["model"]["continuing_subword_prefix"] is not None
        ):
            kwargs["continuing_subword_prefix"] = tokenizer_json["model"]["continuing_subword_prefix"]
        if (
            tokenizer_json["model"]["type"] == "BPE"
            and "end_of_word_suffix" not in kwargs
            and tokenizer_json["model"]["end_of_word_suffix"] is not None
        ):
            kwargs["end_of_word_suffix"] = tokenizer_json["model"]["end_of_word_suffix"]
        if tokenizer_json["model"]["type"] == "Unigram" and unk_token is not None:
            kwargs["unk_token"] = unk_token
        if tokenizer_json["pre_tokenizer"] is not None and tokenizer_json["pre_tokenizer"]["type"] == "ByteLevel":
            kwargs["initial_alphabet"] = pre_tokenizers_fast.ByteLevel.alphabet()

        trainer_class = MODEL_TO_TRAINER_MAPPING[tokenizer_json["model"]["type"]]
        trainer = trainer_class(vocab_size=vocab_size, special_tokens=special_tokens, **kwargs)
        tokenizer.train_from_iterator(text_iterator, length=length, trainer=trainer)

        if post_processor is not None:
            trained_tokenizer_json = json.loads(tokenizer.to_str())
            # Almost done, we just have to adjust the token IDs in the post processor
            if "special_tokens" in post_processor:
                for key in post_processor["special_tokens"]:
                    tokens = post_processor["special_tokens"][key]["tokens"]
                    if special_tokens_map is not None:
                        tokens = [special_tokens_map.get(token, token) for token in tokens]
                    post_processor["special_tokens"][key]["tokens"] = tokens
                    post_processor["special_tokens"][key]["ids"] = [tokenizer.token_to_id(token) for token in tokens]

            for special_token in ["cls", "sep"]:
                if special_token in post_processor:
                    token, _ = post_processor[special_token]
                    if special_tokens_map is not None and token in special_tokens_map:
                        token = special_tokens_map[token]
                    token_id = tokenizer.token_to_id(token)
                    post_processor[special_token] = [token, token_id]

            trained_tokenizer_json["post_processor"] = post_processor
            tokenizer = TokenizerFast.from_str(json.dumps(trained_tokenizer_json))

        kwargs = self.init_kwargs.copy()
        # Map pad/cls/mask token at the Transformers level
        special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
        special_tokens_list.remove("additional_special_tokens")
        for token in special_tokens_list:
            # Get the private one to avoid unnecessary warnings.
            if getattr(self, f"_{token}") is not None:
                special_token = getattr(self, token)
                if special_tokens_map is not None and special_token in special_tokens_map:
                    special_token = special_tokens_map[special_token]

                special_token_full = getattr(self, f"_{token}")
                if isinstance(special_token_full, AddedToken):
                    # Create an added token with the same parameters except the content
                    kwargs[token] = AddedToken(
                        special_token,
                        single_word=special_token_full.single_word,
                        lstrip=special_token_full.lstrip,
                        rstrip=special_token_full.rstrip,
                        normalized=special_token_full.normalized,
                        special=True,
                    )
                else:
                    kwargs[token] = special_token

        additional_special_tokens = self.additional_special_tokens
        if new_special_tokens is not None:
            additional_special_tokens.extend(new_special_tokens)
        if len(additional_special_tokens) > 0:
            kwargs["additional_special_tokens"] = additional_special_tokens

        return self.__class__(tokenizer_object=tokenizer, **kwargs)

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.added_tokens_decoder: Dict[int, AddedToken] property

Returns the added tokens in the vocabulary as a dictionary of index to AddedToken.

RETURNS	DESCRIPTION
Dict[int, AddedToken]	Dict[str, int]: The added tokens.

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.added_tokens_encoder: Dict[str, int] property

Returns the sorted mapping from string to index. The added tokens encoder is cached for performance optimisation in self._added_tokens_encoder for the slow tokenizers.

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.backend_tokenizer: TokenizerFast property

tokenizers.implementations.BaseTokenizer: The Rust tokenizer used as a backend.

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.can_save_slow_tokenizer: bool property

bool: Whether or not the slow tokenizer can be saved. Usually for sentencepiece based slow tokenizer, this can only be True if the original "sentencepiece.model" was not deleted.

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.decoder: DecoderFast property

tokenizers.decoders.Decoder: The Rust decoder for this tokenizer.

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.vocab_size: int property

int: Size of the base vocabulary (without the added tokens).

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.__len__()

Size of the full vocabulary with the added tokens.

Source code in mindnlp\transformers\tokenization_utils_fast.py

def __len__(self) -> int:
    """
    Size of the full vocabulary with the added tokens.
    """
    return self._tokenizer.get_vocab_size(with_added_tokens=True)

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.convert_ids_to_tokens(ids, skip_special_tokens=False)

Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and added tokens.

PARAMETER	DESCRIPTION
ids	The token id (or token ids) to convert to tokens. TYPE: `int` or `List[int]`
skip_special_tokens	Whether or not to remove special tokens in the decoding. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

RETURNS	DESCRIPTION
Union[str, List[str]]	str or List[str]: The decoded token(s).

Source code in mindnlp\transformers\tokenization_utils_fast.py

def convert_ids_to_tokens(
    self, ids: Union[int, List[int]], skip_special_tokens: bool = False
) -> Union[str, List[str]]:
    """
    Converts a single index or a sequence of indices in a token or a sequence of tokens, using the vocabulary and
    added tokens.

    Args:
        ids (`int` or `List[int]`):
            The token id (or token ids) to convert to tokens.
        skip_special_tokens (`bool`, *optional*, defaults to `False`):
            Whether or not to remove special tokens in the decoding.

    Returns:
        `str` or `List[str]`: The decoded token(s).
    """
    if isinstance(ids, int):
        return self._tokenizer.id_to_token(ids)
    tokens = []
    for index in ids:
        index = int(index)
        if skip_special_tokens and index in self.all_special_ids:
            continue
        tokens.append(self._tokenizer.id_to_token(index))
    return tokens

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.convert_tokens_to_ids(tokens)

Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the vocabulary.

PARAMETER	DESCRIPTION
tokens	One or several token(s) to convert to token id(s). TYPE: `str` or `List[str]`

RETURNS	DESCRIPTION
Union[int, List[int]]	int or List[int]: The token id or list of token ids.

Source code in mindnlp\transformers\tokenization_utils_fast.py

def convert_tokens_to_ids(self, tokens: Union[str, List[str]]) -> Union[int, List[int]]:
    """
    Converts a token string (or a sequence of tokens) in a single integer id (or a sequence of ids), using the
    vocabulary.

    Args:
        tokens (`str` or `List[str]`): One or several token(s) to convert to token id(s).

    Returns:
        `int` or `List[int]`: The token id or list of token ids.
    """
    if tokens is None:
        return None

    if isinstance(tokens, str):
        return self._convert_token_to_id_with_added_voc(tokens)

    return [self._convert_token_to_id_with_added_voc(token) for token in tokens]

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.get_added_vocab()

Returns the added tokens in the vocabulary as a dictionary of token to index.

RETURNS	DESCRIPTION
Dict[str, int]	Dict[str, int]: The added tokens.

Source code in mindnlp\transformers\tokenization_utils_fast.py

def get_added_vocab(self) -> Dict[str, int]:
    """
    Returns the added tokens in the vocabulary as a dictionary of token to index.

    Returns:
        `Dict[str, int]`: The added tokens.
    """
    return {k.content: v for v, k in sorted(self.added_tokens_decoder.items(), key=lambda item: item[0])}

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.num_special_tokens_to_add(pair=False)

Returns the number of added tokens when encoding a sequence with special tokens.

This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put this inside your training loop.

PARAMETER	DESCRIPTION
pair	Whether the number of added tokens should be computed in the case of a sequence pair or a single sequence. TYPE: `bool`, *optional*, defaults to `False` DEFAULT: False

RETURNS	DESCRIPTION
int	int: Number of special tokens added to sequences.

Source code in mindnlp\transformers\tokenization_utils_fast.py

def num_special_tokens_to_add(self, pair: bool = False) -> int:
    """
    Returns the number of added tokens when encoding a sequence with special tokens.

    <Tip>

    This encodes a dummy input and checks the number of added tokens, and is therefore not efficient. Do not put
    this inside your training loop.

    </Tip>

    Args:
        pair (`bool`, *optional*, defaults to `False`):
            Whether the number of added tokens should be computed in the case of a sequence pair or a single
            sequence.

    Returns:
        `int`: Number of special tokens added to sequences.
    """
    return self._tokenizer.num_special_tokens_to_add(pair)

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.set_truncation_and_padding(padding_strategy, truncation_strategy, max_length, stride, pad_to_multiple_of)

Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers library) and restore the tokenizer settings afterwards.

The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed section.

PARAMETER	DESCRIPTION
padding_strategy	The kind of padding that will be applied to the input TYPE: [`~utils.PaddingStrategy`]
truncation_strategy	The kind of truncation that will be applied to the input TYPE: [`~tokenization_utils_base.TruncationStrategy`]
max_length	The maximum size of a sequence. TYPE: `int`
stride	The stride to use when handling overflow. TYPE: `int`
pad_to_multiple_of	If set will pad the sequence to a multiple of the provided value. This is especially useful to enable the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta). TYPE: `int`, *optional*

Source code in mindnlp\transformers\tokenization_utils_fast.py

def set_truncation_and_padding(
    self,
    padding_strategy: PaddingStrategy,
    truncation_strategy: TruncationStrategy,
    max_length: int,
    stride: int,
    pad_to_multiple_of: Optional[int],
):
    """
    Define the truncation and the padding strategies for fast tokenizers (provided by HuggingFace tokenizers
    library) and restore the tokenizer settings afterwards.

    The provided tokenizer has no padding / truncation strategy before the managed section. If your tokenizer set a
    padding / truncation strategy before, then it will be reset to no padding / truncation when exiting the managed
    section.

    Args:
        padding_strategy ([`~utils.PaddingStrategy`]):
            The kind of padding that will be applied to the input
        truncation_strategy ([`~tokenization_utils_base.TruncationStrategy`]):
            The kind of truncation that will be applied to the input
        max_length (`int`):
            The maximum size of a sequence.
        stride (`int`):
            The stride to use when handling overflow.
        pad_to_multiple_of (`int`, *optional*):
            If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
            the use of Tensor Cores on NVIDIA hardware with compute capability `>= 7.5` (Volta).
    """
    _truncation = self._tokenizer.truncation
    _padding = self._tokenizer.padding
    # Set truncation and padding on the backend tokenizer
    if truncation_strategy == TruncationStrategy.DO_NOT_TRUNCATE:
        if _truncation is not None:
            self._tokenizer.no_truncation()
    else:
        target = {
            "max_length": max_length,
            "stride": stride,
            "strategy": truncation_strategy.value,
            "direction": self.truncation_side,
        }

        # _truncation might contain more keys that the target `transformers`
        # supports. Use only the target keys to trigger `enable_truncation`.
        # This should enable this code to works on various `tokenizers`
        # targets.
        if _truncation is None:
            current = None
        else:
            current = {k: _truncation.get(k, None) for k in target}

        if current != target:
            self._tokenizer.enable_truncation(**target)

    if padding_strategy == PaddingStrategy.DO_NOT_PAD:
        if _padding is not None:
            self._tokenizer.no_padding()
    else:
        length = max_length if padding_strategy == PaddingStrategy.MAX_LENGTH else None
        target = {
            "length": length,
            "direction": self.padding_side,
            "pad_id": self.pad_token_id,
            "pad_token": self.pad_token,
            "pad_type_id": self.pad_token_type_id,
            "pad_to_multiple_of": pad_to_multiple_of,
        }
        if _padding != target:
            self._tokenizer.enable_padding(**target)

mindnlp.transformers.tokenization_utils_fast.PreTrainedTokenizerFast.train_new_from_iterator(text_iterator, vocab_size, length=None, new_special_tokens=None, special_tokens_map=None, **kwargs)

Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline) as the current one.

PARAMETER	DESCRIPTION
text_iterator	The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts if you have everything in memory. TYPE: generator of `List[str]`
vocab_size	The size of the vocabulary you want for your tokenizer. TYPE: `int`
length	The total number of sequences in the iterator. This is used to provide meaningful progress tracking TYPE: `int`, *optional* DEFAULT: None
new_special_tokens	A list of new special tokens to add to the tokenizer you are training. TYPE: list of `str` or `AddedToken`, *optional* DEFAULT: None
special_tokens_map	If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special token name to new special token name in this argument. TYPE: `Dict[str, str]`, *optional* DEFAULT: None
kwargs	Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library. TYPE: `Dict[str, Any]`, *optional* DEFAULT: {}

RETURNS	DESCRIPTION
	[PreTrainedTokenizerFast]: A new tokenizer of the same type as the original one, trained on
	text_iterator.

Source code in mindnlp\transformers\tokenization_utils_fast.py

def train_new_from_iterator(
    self,
    text_iterator,
    vocab_size,
    length=None,
    new_special_tokens=None,
    special_tokens_map=None,
    **kwargs,
):
    """
    Trains a tokenizer on a new corpus with the same defaults (in terms of special tokens or tokenization pipeline)
    as the current one.

    Args:
        text_iterator (generator of `List[str]`):
            The training corpus. Should be a generator of batches of texts, for instance a list of lists of texts
            if you have everything in memory.
        vocab_size (`int`):
            The size of the vocabulary you want for your tokenizer.
        length (`int`, *optional*):
            The total number of sequences in the iterator. This is used to provide meaningful progress tracking
        new_special_tokens (list of `str` or `AddedToken`, *optional*):
            A list of new special tokens to add to the tokenizer you are training.
        special_tokens_map (`Dict[str, str]`, *optional*):
            If you want to rename some of the special tokens this tokenizer uses, pass along a mapping old special
            token name to new special token name in this argument.
        kwargs (`Dict[str, Any]`, *optional*):
            Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.

    Returns:
        [`PreTrainedTokenizerFast`]: A new tokenizer of the same type as the original one, trained on
        `text_iterator`.

    """
    tokenizer_json = json.loads(self._tokenizer.to_str())
    # Remove added tokens for now (uses IDs of tokens)
    added_tokens = tokenizer_json.pop("added_tokens")
    # Remove post processor for now (uses IDs of tokens)
    post_processor = tokenizer_json.pop("post_processor")

    unk_token = None
    # Remove vocab
    if tokenizer_json["model"]["type"] == "BPE":
        tokenizer_json["model"]["vocab"] = {}
        tokenizer_json["model"]["merges"] = []
    elif tokenizer_json["model"]["type"] == "Unigram":
        if tokenizer_json["model"]["unk_id"] is not None:
            unk_id = tokenizer_json["model"]["unk_id"]
            unk_token = tokenizer_json["model"]["vocab"][unk_id][0]
            if special_tokens_map is not None and unk_token in special_tokens_map:
                unk_token = special_tokens_map[unk_token]
            tokenizer_json["model"]["unk_id"] = 0
            tokenizer_json["model"]["vocab"] = [[unk_token, 0.0]]
    elif tokenizer_json["model"]["type"] in ["WordLevel", "WordPiece"]:
        tokenizer_json["model"]["vocab"] = {}
    else:
        raise ValueError(
            f"This method does not support this type of tokenizer (found {tokenizer_json['model']['type']}) "
            "only BPE, Unigram, WordLevel and WordPiece."
        )

    if (
        special_tokens_map is not None
        and "unk_token" in tokenizer_json["model"]
        and tokenizer_json["model"]["unk_token"] in special_tokens_map
    ):
        tokenizer_json["model"]["unk_token"] = special_tokens_map[tokenizer_json["model"]["unk_token"]]

    tokenizer = TokenizerFast.from_str(json.dumps(tokenizer_json))

    # Get the special tokens from the current tokenizer if none are specified.
    special_tokens = []
    for added_token in added_tokens:
        special = added_token.pop("special", None)
        _ = added_token.pop("id", None)
        if tokenizer_json["model"]["type"] != "Unigram" and not special:
            continue
        if special_tokens_map is not None and added_token["content"] in special_tokens_map:
            added_token["content"] = special_tokens_map[added_token["content"]]
        special_tokens.append(AddedToken(**added_token))

    if new_special_tokens is not None:
        special_tokens.extend(new_special_tokens)

    # Trainer needs to know the end of word / continuing subword thingies in BPE
    if (
        tokenizer_json["model"]["type"] == "BPE"
        and "continuing_subword_prefix" not in kwargs
        and tokenizer_json["model"]["continuing_subword_prefix"] is not None
    ):
        kwargs["continuing_subword_prefix"] = tokenizer_json["model"]["continuing_subword_prefix"]
    if (
        tokenizer_json["model"]["type"] == "BPE"
        and "end_of_word_suffix" not in kwargs
        and tokenizer_json["model"]["end_of_word_suffix"] is not None
    ):
        kwargs["end_of_word_suffix"] = tokenizer_json["model"]["end_of_word_suffix"]
    if tokenizer_json["model"]["type"] == "Unigram" and unk_token is not None:
        kwargs["unk_token"] = unk_token
    if tokenizer_json["pre_tokenizer"] is not None and tokenizer_json["pre_tokenizer"]["type"] == "ByteLevel":
        kwargs["initial_alphabet"] = pre_tokenizers_fast.ByteLevel.alphabet()

    trainer_class = MODEL_TO_TRAINER_MAPPING[tokenizer_json["model"]["type"]]
    trainer = trainer_class(vocab_size=vocab_size, special_tokens=special_tokens, **kwargs)
    tokenizer.train_from_iterator(text_iterator, length=length, trainer=trainer)

    if post_processor is not None:
        trained_tokenizer_json = json.loads(tokenizer.to_str())
        # Almost done, we just have to adjust the token IDs in the post processor
        if "special_tokens" in post_processor:
            for key in post_processor["special_tokens"]:
                tokens = post_processor["special_tokens"][key]["tokens"]
                if special_tokens_map is not None:
                    tokens = [special_tokens_map.get(token, token) for token in tokens]
                post_processor["special_tokens"][key]["tokens"] = tokens
                post_processor["special_tokens"][key]["ids"] = [tokenizer.token_to_id(token) for token in tokens]

        for special_token in ["cls", "sep"]:
            if special_token in post_processor:
                token, _ = post_processor[special_token]
                if special_tokens_map is not None and token in special_tokens_map:
                    token = special_tokens_map[token]
                token_id = tokenizer.token_to_id(token)
                post_processor[special_token] = [token, token_id]

        trained_tokenizer_json["post_processor"] = post_processor
        tokenizer = TokenizerFast.from_str(json.dumps(trained_tokenizer_json))

    kwargs = self.init_kwargs.copy()
    # Map pad/cls/mask token at the Transformers level
    special_tokens_list = SpecialTokensMixin.SPECIAL_TOKENS_ATTRIBUTES.copy()
    special_tokens_list.remove("additional_special_tokens")
    for token in special_tokens_list:
        # Get the private one to avoid unnecessary warnings.
        if getattr(self, f"_{token}") is not None:
            special_token = getattr(self, token)
            if special_tokens_map is not None and special_token in special_tokens_map:
                special_token = special_tokens_map[special_token]

            special_token_full = getattr(self, f"_{token}")
            if isinstance(special_token_full, AddedToken):
                # Create an added token with the same parameters except the content
                kwargs[token] = AddedToken(
                    special_token,
                    single_word=special_token_full.single_word,
                    lstrip=special_token_full.lstrip,
                    rstrip=special_token_full.rstrip,
                    normalized=special_token_full.normalized,
                    special=True,
                )
            else:
                kwargs[token] = special_token

    additional_special_tokens = self.additional_special_tokens
    if new_special_tokens is not None:
        additional_special_tokens.extend(new_special_tokens)
    if len(additional_special_tokens) > 0:
        kwargs["additional_special_tokens"] = additional_special_tokens

    return self.__class__(tokenizer_object=tokenizer, **kwargs)