venv/lib/python3.12/site-packages/transformers/tokenization_utils_fast.py

# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Tokenization classes for fast tokenizers (provided by HuggingFace's tokenizers library). For slow (python) tokenizers
see tokenization_utils.py
"""

import copy
import json
import os
from collections import defaultdict
from typing import Any, Dict, List, Optional, Tuple, Union

import tokenizers.pre_tokenizers as pre_tokenizers_fast
from tokenizers import Encoding as EncodingFast
from tokenizers import Tokenizer as TokenizerFast
from tokenizers.decoders import Decoder as DecoderFast
from tokenizers.trainers import BpeTrainer, UnigramTrainer, WordLevelTrainer, WordPieceTrainer

from .convert_slow_tokenizer import convert_slow_tokenizer
from .integrations.ggml import convert_gguf_tokenizer
from .modeling_gguf_pytorch_utils import load_gguf_checkpoint
from .tokenization_utils import PreTrainedTokenizer
from .tokenization_utils_base import (
    INIT_TOKENIZER_DOCSTRING,
    AddedToken,
    BatchEncoding,
    PreTokenizedInput,
    PreTokenizedInputPair,
    PreTrainedTokenizerBase,
    SpecialTokensMixin,
    TextInput,
    TextInputPair,
    TruncationStrategy,
)
from .utils import PaddingStrategy, add_end_docstrings, logging


logger = logging.get_logger(__name__)

# Fast tokenizers (provided by HuggingFace tokenizer's library) can be saved in a single file
TOKENIZER_FILE = "tokenizer.json"
SPECIAL_TOKENS_MAP_FILE = "special_tokens_map.json"
TOKENIZER_CONFIG_FILE = "tokenizer_config.json"

# Slow tokenizers have an additional added tokens files
ADDED_TOKENS_FILE = "added_tokens.json"

INIT_TOKENIZER_DOCSTRING += """
        tokenizer_object ([`tokenizers.Tokenizer`]):
            A [`tokenizers.Tokenizer`] object from 🤗 tokenizers to instantiate from. See [Using tokenizers from 🤗
            tokenizers](../fast_tokenizers) for more information.
        tokenizer_file ([`str`]):
            A path to a local JSON file representing a previously serialized [`tokenizers.Tokenizer`] object from 🤗
            tokenizers.
"""

MODEL_TO_TRAINER_MAPPING = {
    "BPE": BpeTrainer,
    "Unigram": UnigramTrainer,
    "WordLevel": WordLevelTrainer,
    "WordPiece": WordPieceTrainer,
}

VOCAB_FILES_NAMES = {"tokenizer_file": TOKENIZER_FILE}


@add_end_docstrings(INIT_TOKENIZER_DOCSTRING)
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 gguf_file is not None:
            # We need to convert a slow tokenizer to build the backend
            gguf_param = load_gguf_checkpoint(kwargs.get("vocab_file"))
            architecture = gguf_param["config"]["model_type"]
            tokenizer_dict = gguf_param["tokenizer"]
            fast_tokenizer, additional_kwargs = convert_gguf_tokenizer(architecture, tokenizer_dict)

            if len(additional_kwargs) > 0:
                kwargs.update(additional_kwargs)

        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)