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 class for fast temporal tokenizers.
"""

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

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,
)

import utils
from tokenization_utils_base import (
    BatchEncoding,
    PaddingStrategy,
    PreTokenizedInput,
    PreTokenizedInputPair,
    SpecialTokensMixin,
    TempoPreTrainedTokenizerBase,
    TextInput,
    TextInputPair,
    TruncationStrategy,
)
from transformers.convert_slow_tokenizer import convert_slow_tokenizer
from transformers.tokenization_utils import PreTrainedTokenizer
from transformers.tokenization_utils_base import AddedToken
from transformers.tokenization_utils_fast import ADDED_TOKENS_FILE, TOKENIZER_FILE
from transformers.utils import logging

logger = logging.get_logger(__name__)


def noop(x=None, *args, **kwargs):
    """Do nothing"""
    return x


def prepend(texts, time_ids, stringify_time_id=noop):
    """Prepend the time to each text"""
    texts = [
        f"{stringify_time_id(time_id)} {text}" for text, time_id in zip(texts, time_ids)
    ]
    return texts


def prepend_tuples(text_pairs, time_id_pairs, stringify_time_id=noop):
    """Prepend the time to each text, where the inputs are pairs"""
    texts = [
        prepend(text_pair, time_id_pair, stringify_time_id)
        for text_pair, time_id_pair in zip(text_pairs, time_id_pairs)
    ]
    return texts


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


class TempoPreTrainedTokenizerFast(TempoPreTrainedTokenizerBase):
    """
    Base class for all fast tokenizers (wrapping HuggingFace tokenizers library).

    Inherits from :class:`~transformers.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...).
    """

    slow_tokenizer_class: PreTrainedTokenizer = None
    can_save_slow_tokenizer: bool = True

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

        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 = 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)

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

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

    @property
    def vocab_size(self) -> int:
        """
        :obj:`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()

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

        Returns:
            :obj:`Dict[str, int]`: The added tokens.
        """
        base_vocab = self._tokenizer.get_vocab(with_added_tokens=False)
        full_vocab = self._tokenizer.get_vocab(with_added_tokens=True)
        added_vocab = dict(
            (tok, index) for tok, index in full_vocab.items() if tok not in base_vocab
        )
        return added_vocab

    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:
        """
        :obj:`tokenizers.implementations.BaseTokenizer`: The Rust tokenizer used as a backend.
        """
        return self._tokenizer

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

    def _convert_encoding(
        self,
        encoding: EncodingFast,
        return_token_type_ids: Optional[bool] = None,
        return_time_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_time_ids is None:
            return_time_ids = "time_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_time_ids and hasattr(e, "time_ids"):
                encoding_dict["time_ids"].append(e.time_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 (:obj:`str` or :obj:`List[str]`): One or several token(s) to convert to token id(s).

        Returns:
            :obj:`int` or :obj:`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)

        ids = []
        for token in tokens:
            ids.append(self._convert_token_to_id_with_added_voc(token))
        return ids

    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.

        .. note::
            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.

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

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

        Returns:
            :obj:`str` or :obj:`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 (:class:`~transformers.tokenization_utils_base.PaddingStrategy`):
                The kind of padding that will be applied to the input
            truncation_strategy (:class:`~transformers.tokenization_utils_base.TruncationStrategy`):
                The kind of truncation that will be applied to the input
            max_length (:obj:`int`):
                The maximum size of a sequence.
            stride (:obj:`int`):
                The stride to use when handling overflow.
            pad_to_multiple_of (:obj:`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,
            }
            if _truncation != 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],
        ],
        batch_time_id: Union[List[str], List[Tuple[str, str]]] = 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[str] = None,
        return_token_type_ids: Optional[bool] = None,
        return_time_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,
    ) -> BatchEncoding:

        if not isinstance(batch_text_or_text_pairs, list):
            raise TypeError(
                f"batch_text_or_text_pairs has to be a list (got {type(batch_text_or_text_pairs)})"
            )

        if self.time_embedding_type.startswith("prepend") and batch_time_id:
            kwargs = {}
            if self.time_embedding_type.startswith(("prepend_token", "append_token")):
                kwargs["stringify_time_id"] = lambda time_id: f"<{time_id}>"
            if utils.check_type(batch_text_or_text_pairs, List[TextInput]):
                texts = prepend(batch_text_or_text_pairs, batch_time_id, **kwargs)
            elif utils.check_type(batch_text_or_text_pairs, List[TextInputPair]):
                texts = prepend_tuples(
                    batch_text_or_text_pairs, batch_time_id, **kwargs
                )
            else:
                raise 'Unsupported batch_text_or_text_pairs'
            batch_text_or_text_pairs = texts

        # 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,
        )

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

        if utils.is_time_id_necessary(self.time_embedding_type):
            if not batch_time_id:
                raise ValueError("batch_time_id is missing")
            # Add time ids to the encoding (use the same format as of the other properties)
            for seq_i, encoding in enumerate(encodings):
                time_id = batch_time_id[seq_i]
                encoding.time_ids = (
                    [
                        self.time_to_id[time_id]
                        if sequence_id is not None
                        else self.pad_time_id
                        for sequence_id in encoding.sequence_ids
                    ]
                    if not isinstance(time_id, tuple)
                    else [
                        self.time_to_id[time_id[sequence_id]]
                        if sequence_id is not None
                        else self.pad_time_id
                        for sequence_id in encoding.sequence_ids
                    ]
                )
                # Replace any unknown time_id with the time_id of its nearby token
                for i, time_id in enumerate(encoding.time_ids):
                    if time_id == self.pad_time_id:
                        nearby_index = i + 1 if i == 0 else i - 1
                        encoding.time_ids[i] = encoding.time_ids[nearby_index]

        # 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_time_ids=return_time_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],
        time_id: str = None,
        text_pair: Optional[Union[TextInput, PreTokenizedInput]] = None,
        time_id_pair: Optional[str] = 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_time_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,
        **kwargs,
    ) -> BatchEncoding:

        batched_input = [(text, text_pair)] if text_pair else [text]
        if not time_id:
            batched_time_id = None
        else:
            batched_time_id = [(time_id, time_id_pair)] if text_pair else [time_id]
        batched_output = self._batch_encode_plus(
            batched_input,
            batched_time_id,
            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_time_ids=return_time_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,
            **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 = True,
        **kwargs,
    ) -> str:
        if isinstance(token_ids, int):
            token_ids = [token_ids]
        text = self._tokenizer.decode(
            token_ids, skip_special_tokens=skip_special_tokens
        )

        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,
            )
            added_vocab = self.get_added_vocab()
            if added_vocab:
                with open(added_tokens_file, "w", encoding="utf-8") as f:
                    out_str = json.dumps(added_vocab, ensure_ascii=False)
                    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,
        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 :obj:`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 (:obj:`int`):
                The size of the vocabulary you want for your tokenizer.
            new_special_tokens (list of :obj:`str` or :obj:`AddedToken`, `optional`):
                A list of new special tokens to add to the tokenizer you are training.
            special_tokens_map (:obj:`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:
                Additional keyword arguments passed along to the trainer from the 🤗 Tokenizers library.
        Returns:
            :class:`~transformers.PreTrainedTokenizerFast`: A new tokenizer of the same type as the original one,
            trained on :obj:`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

        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, 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,
                    )
                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)