tokenization_utils_base.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.
"""
Base classes common to both the slow and the fast temporal tokenization classes: TempoPreTrainedTokenizerBase (host all the user
fronting encoding methods) and TempoSpecialTokensMixin (host the special tokens logic)
"""
import copy
import json
import os
import warnings
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union

import numpy as np
import requests
from transformers.file_utils import (
    PushToHubMixin,
    cached_path,
    hf_bucket_url,
    is_remote_url,
    is_tf_available,
    is_torch_available,
)
from transformers.tokenization_utils_base import (
    ADDED_TOKENS_FILE,
    FULL_TOKENIZER_FILE,
    LARGE_INTEGER,
    SPECIAL_TOKENS_MAP_FILE,
    TOKENIZER_CONFIG_FILE,
    VERY_LARGE_INTEGER,
    AddedToken,
    BatchEncoding,
    EncodedInput,
    EncodedInputPair,
    PaddingStrategy,
    PreTokenizedInput,
    PreTokenizedInputPair,
    SpecialTokensMixin,
    TensorType,
    TextInput,
    TextInputPair,
    TruncationStrategy,
    _is_tensorflow,
    _is_torch,
    to_py_obj,
)
from transformers.utils import logging

if TYPE_CHECKING:
    if is_torch_available():
        import torch
    if is_tf_available():
        import tensorflow as tf


logger = logging.get_logger(__name__)


class TempoSpecialTokensMixin(SpecialTokensMixin):
    """
    A mixin derived by :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast` to
    handle specific behaviors related to special tokens. In particular, this class hold the attributes which can be
    used to directly access these special tokens in a model-independent manner and allow to set and update the special
    tokens.

    Args:
        bos_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token representing the beginning of a sentence.
        eos_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token representing the end of a sentence.
        unk_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token representing an out-of-vocabulary token.
        sep_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token separating two different sentences in the same input (used by BERT for instance).
        pad_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
            attention mechanisms or loss computation.
        cls_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token representing the class of the input (used by BERT for instance).
        mask_token (:obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A special token representing a masked token (used by masked-language modeling pretraining objectives, like
            BERT).
        additional_special_tokens (tuple or list of :obj:`str` or :obj:`tokenizers.AddedToken`, `optional`):
            A tuple or a list of additional special tokens.
    """

    SPECIAL_TIMES_COUNT = 2  # The special "times" are: pad and mask

    def __init__(self, verbose=True, **kwargs):
        super().__init__(verbose, **kwargs)

    @property
    def pad_time_id(self) -> int:
        """
        :obj:`int`: Id of the padding "time" in the time vocabulary.
        """
        return self._pad_time_id

    @property
    def mask_time_id(self) -> int:
        """
        :obj:`int`: Id of the mask "time" in the time vocabulary.
        """
        return self._mask_time_id


class TempoPreTrainedTokenizerBase(TempoSpecialTokensMixin, PushToHubMixin):
    """
    Base class for :class:`~transformers.PreTrainedTokenizer` and :class:`~transformers.PreTrainedTokenizerFast`.

    Handles shared (mostly boiler plate) methods for those two classes.
    """

    vocab_files_names: Dict[str, str] = {}
    pretrained_vocab_files_map: Dict[str, Dict[str, str]] = {}
    pretrained_init_configuration: Dict[str, Dict[str, Any]] = {}
    max_model_input_sizes: Dict[str, Optional[int]] = {}
    # first name has to correspond to main model input name
    # to make sure `tokenizer.pad(...)` works correctly
    model_input_names: List[str] = [
        "input_ids",
        "token_type_ids",
        "attention_mask",
        "time_ids",
    ]
    padding_side: str = "right"
    slow_tokenizer_class = None

    def __init__(self, **kwargs):
        # inputs and kwargs for saving and re-loading (see ``from_pretrained`` and ``save_pretrained``)
        self.init_inputs = ()
        self.init_kwargs = copy.deepcopy(kwargs)
        self.name_or_path = kwargs.pop("name_or_path", "")

        # For backward compatibility we fallback to set model_max_length from max_len if provided
        model_max_length = kwargs.pop("model_max_length", kwargs.pop("max_len", None))
        self.model_max_length = (
            model_max_length if model_max_length is not None else VERY_LARGE_INTEGER
        )

        # Padding side is right by default and overridden in subclasses. If specified in the kwargs, it is changed.
        self.padding_side = kwargs.pop("padding_side", self.padding_side)
        assert self.padding_side in [
            "right",
            "left",
        ], f"Padding side should be selected between 'right' and 'left', current value: {self.padding_side}"
        self.model_input_names = kwargs.pop("model_input_names", self.model_input_names)

        self.deprecation_warnings = (
            {}
        )  # Use to store when we have already noticed a deprecation warning (avoid overlogging).

        times = kwargs.pop("times")
        self.time_embedding_type = kwargs.pop("time_embedding_type")
        self.init_times(times)

        super().__init__(**kwargs)

    def init_times(self, times, time_embedding_type=None):
        # Reserve the first IDs to the "special" times (padding and masking)
        # NOTE: the below IDs and `SPECIAL_TIMES_COUNT` are hardcoded
        self.time_to_id = {
            time: i + self.SPECIAL_TIMES_COUNT for i, time in enumerate(times)
        }
        self._pad_time_id = 0
        self._mask_time_id = 1
        if time_embedding_type:
            self.time_embedding_type = time_embedding_type
        if self.time_embedding_type.startswith("prepend_token"):
            special_tokens = [f"<{time}>" for time in times]
            self.add_tokens(special_tokens, special_tokens=True)

    @classmethod
    def from_non_temporal(
        cls, tokenizer, config=None, times=None, time_embedding_type=None
    ):
        tokenizer.__class__ = cls
        if times is None:
            times = config.times
        if time_embedding_type is None:
            time_embedding_type = config.time_embedding_type
        tokenizer.model_input_names = cls.model_input_names
        tokenizer.init_times(times, time_embedding_type)
        if times:
            # TempoBERT always uses a max length of 128 (hardcoded)
            tokenizer.model_max_length = 128
        return tokenizer

    @property
    def max_len_single_sentence(self) -> int:
        """
        :obj:`int`: The maximum length of a sentence that can be fed to the model.
        """
        return self.model_max_length - self.num_special_tokens_to_add(pair=False)

    @property
    def max_len_sentences_pair(self) -> int:
        """
        :obj:`int`: The maximum combined length of a pair of sentences that can be fed to the model.
        """
        return self.model_max_length - self.num_special_tokens_to_add(pair=True)

    @max_len_single_sentence.setter
    def max_len_single_sentence(self, value) -> int:
        # For backward compatibility, allow to try to setup 'max_len_single_sentence'.
        if (
            value == self.model_max_length - self.num_special_tokens_to_add(pair=False)
            and self.verbose
        ):
            if not self.deprecation_warnings.get("max_len_single_sentence", False):
                logger.warning(
                    "Setting 'max_len_single_sentence' is now deprecated. "
                    "This value is automatically set up."
                )
            self.deprecation_warnings["max_len_single_sentence"] = True
        else:
            raise ValueError(
                "Setting 'max_len_single_sentence' is now deprecated. "
                "This value is automatically set up."
            )

    @max_len_sentences_pair.setter
    def max_len_sentences_pair(self, value) -> int:
        # For backward compatibility, allow to try to setup 'max_len_sentences_pair'.
        if (
            value == self.model_max_length - self.num_special_tokens_to_add(pair=True)
            and self.verbose
        ):
            if not self.deprecation_warnings.get("max_len_sentences_pair", False):
                logger.warning(
                    "Setting 'max_len_sentences_pair' is now deprecated. "
                    "This value is automatically set up."
                )
            self.deprecation_warnings["max_len_sentences_pair"] = True
        else:
            raise ValueError(
                "Setting 'max_len_sentences_pair' is now deprecated. "
                "This value is automatically set up."
            )

    def __repr__(self) -> str:
        return (
            f"{'PreTrainedTokenizerFast' if self.is_fast else 'PreTrainedTokenizer'}(name_or_path='{self.name_or_path}', "
            f"vocab_size={self.vocab_size}, model_max_len={self.model_max_length}, is_fast={self.is_fast}, "
            f"padding_side='{self.padding_side}', special_tokens={self.special_tokens_map_extended})"
        )

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

        :obj:`tokenizer.get_vocab()[token]` is equivalent to :obj:`tokenizer.convert_tokens_to_ids(token)` when
        :obj:`token` is in the vocab.

        Returns:
            :obj:`Dict[str, int]`: The vocabulary.
        """
        raise NotImplementedError()

    @classmethod
    def from_pretrained(
        cls,
        pretrained_model_name_or_path: Union[str, os.PathLike],
        *init_inputs,
        **kwargs,
    ):
        r"""
        Instantiate a :class:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase` (or a derived class) from
        a predefined tokenizer.

        Args:
            pretrained_model_name_or_path (:obj:`str` or :obj:`os.PathLike`):
                Can be either:

                - A string, the `model id` of a predefined tokenizer hosted inside a model repo on huggingface.co.
                  Valid model ids can be located at the root-level, like ``bert-base-uncased``, or namespaced under a
                  user or organization name, like ``dbmdz/bert-base-german-cased``.
                - A path to a `directory` containing vocabulary files required by the tokenizer, for instance saved
                  using the :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizerBase.save_pretrained`
                  method, e.g., ``./my_model_directory/``.
                - (**Deprecated**, not applicable to all derived classes) A path or url to a single saved vocabulary
                  file (if and only if the tokenizer only requires a single vocabulary file like Bert or XLNet), e.g.,
                  ``./my_model_directory/vocab.txt``.
            cache_dir (:obj:`str` or :obj:`os.PathLike`, `optional`):
                Path to a directory in which a downloaded predefined tokenizer vocabulary files should be cached if the
                standard cache should not be used.
            force_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to force the (re-)download the vocabulary files and override the cached versions if they
                exist.
            resume_download (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to delete incompletely received files. Attempt to resume the download if such a file
                exists.
            proxies (:obj:`Dict[str, str], `optional`):
                A dictionary of proxy servers to use by protocol or endpoint, e.g., :obj:`{'http': 'foo.bar:3128',
                'http://hostname': 'foo.bar:4012'}`. The proxies are used on each request.
            revision(:obj:`str`, `optional`, defaults to :obj:`"main"`):
                The specific model version to use. It can be a branch name, a tag name, or a commit id, since we use a
                git-based system for storing models and other artifacts on huggingface.co, so ``revision`` can be any
                identifier allowed by git.
            subfolder (:obj:`str`, `optional`):
                In case the relevant files are located inside a subfolder of the model repo on huggingface.co (e.g. for
                facebook/rag-token-base), specify it here.
            inputs (additional positional arguments, `optional`):
                Will be passed along to the Tokenizer ``__init__`` method.
            kwargs (additional keyword arguments, `optional`):
                Will be passed to the Tokenizer ``__init__`` method. Can be used to set special tokens like
                ``bos_token``, ``eos_token``, ``unk_token``, ``sep_token``, ``pad_token``, ``cls_token``,
                ``mask_token``, ``additional_special_tokens``. See parameters in the ``__init__`` for more details.

        Examples::

            # We can't instantiate directly the base class `PreTrainedTokenizerBase` so let's show our examples on a derived class: BertTokenizer
            # Download vocabulary from huggingface.co and cache.
            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')

            # Download vocabulary from huggingface.co (user-uploaded) and cache.
            tokenizer = BertTokenizer.from_pretrained('dbmdz/bert-base-german-cased')

            # If vocabulary files are in a directory (e.g. tokenizer was saved using `save_pretrained('./test/saved_model/')`)
            tokenizer = BertTokenizer.from_pretrained('./test/saved_model/')

            # If the tokenizer uses a single vocabulary file, you can point directly to this file
            tokenizer = BertTokenizer.from_pretrained('./test/saved_model/my_vocab.txt')

            # You can link tokens to special vocabulary when instantiating
            tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', unk_token='<unk>')
            # You should be sure '<unk>' is in the vocabulary when doing that.
            # Otherwise use tokenizer.add_special_tokens({'unk_token': '<unk>'}) instead)
            assert tokenizer.unk_token == '<unk>'

        """
        cache_dir = kwargs.pop("cache_dir", None)
        force_download = kwargs.pop("force_download", False)
        resume_download = kwargs.pop("resume_download", False)
        proxies = kwargs.pop("proxies", None)
        local_files_only = kwargs.pop("local_files_only", False)
        revision = kwargs.pop("revision", None)
        subfolder = kwargs.pop("subfolder", None)

        s3_models = list(cls.max_model_input_sizes.keys())
        pretrained_model_name_or_path = str(pretrained_model_name_or_path)
        vocab_files = {}
        init_configuration = {}
        if pretrained_model_name_or_path in s3_models:
            # Get the vocabulary from AWS S3 bucket
            for file_id, map_list in cls.pretrained_vocab_files_map.items():
                vocab_files[file_id] = map_list[pretrained_model_name_or_path]
            if (
                cls.pretrained_init_configuration
                and pretrained_model_name_or_path in cls.pretrained_init_configuration
            ):
                init_configuration = cls.pretrained_init_configuration[
                    pretrained_model_name_or_path
                ].copy()
        else:
            # Get the vocabulary from local files
            logger.info(
                "Model name '{}' not found in model shortcut name list ({}). "
                "Assuming '{}' is a path, a model identifier, or url to a directory containing tokenizer files.".format(
                    pretrained_model_name_or_path,
                    ", ".join(s3_models),
                    pretrained_model_name_or_path,
                )
            )

            if os.path.isfile(pretrained_model_name_or_path) or is_remote_url(
                pretrained_model_name_or_path
            ):
                if len(cls.vocab_files_names) > 1:
                    raise ValueError(
                        f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is not "
                        "supported. Use a model identifier or the path to a directory instead."
                    )
                warnings.warn(
                    f"Calling {cls.__name__}.from_pretrained() with the path to a single file or url is deprecated and "
                    "won't be possible anymore in v5. Use a model identifier or the path to a directory instead.",
                    FutureWarning,
                )
                file_id = list(cls.vocab_files_names.keys())[0]
                vocab_files[file_id] = pretrained_model_name_or_path
            else:
                # At this point pretrained_model_name_or_path is either a directory or a model identifier name
                additional_files_names = {
                    "added_tokens_file": ADDED_TOKENS_FILE,
                    "special_tokens_map_file": SPECIAL_TOKENS_MAP_FILE,
                    "tokenizer_config_file": TOKENIZER_CONFIG_FILE,
                    "tokenizer_file": FULL_TOKENIZER_FILE,
                }
                # Look for the tokenizer files
                for file_id, file_name in {
                    **cls.vocab_files_names,
                    **additional_files_names,
                }.items():
                    if os.path.isdir(pretrained_model_name_or_path):
                        if subfolder is not None:
                            full_file_name = os.path.join(
                                pretrained_model_name_or_path, subfolder, file_name
                            )
                        else:
                            full_file_name = os.path.join(
                                pretrained_model_name_or_path, file_name
                            )
                        if not os.path.exists(full_file_name):
                            logger.info(
                                f"Didn't find file {full_file_name}. We won't load it."
                            )
                            full_file_name = None
                    else:
                        full_file_name = hf_bucket_url(
                            pretrained_model_name_or_path,
                            filename=file_name,
                            subfolder=subfolder,
                            revision=revision,
                            mirror=None,
                        )

                    vocab_files[file_id] = full_file_name

        # Get files from url, cache, or disk depending on the case
        resolved_vocab_files = {}
        unresolved_files = []
        for file_id, file_path in vocab_files.items():
            if file_path is None:
                resolved_vocab_files[file_id] = None
            else:
                try:
                    try:
                        resolved_vocab_files[file_id] = cached_path(
                            file_path,
                            cache_dir=cache_dir,
                            force_download=force_download,
                            proxies=proxies,
                            resume_download=resume_download,
                            local_files_only=local_files_only,
                        )
                    except FileNotFoundError as error:
                        if local_files_only:
                            unresolved_files.append(file_id)
                        else:
                            raise error

                except requests.exceptions.HTTPError as err:
                    if "404 Client Error" in str(err):
                        logger.debug(err)
                        resolved_vocab_files[file_id] = None
                    else:
                        raise err

        if len(unresolved_files) > 0:
            logger.info(
                f"Can't load following files from cache: {unresolved_files} and cannot check if these "
                "files are necessary for the tokenizer to operate."
            )

        if all(
            full_file_name is None for full_file_name in resolved_vocab_files.values()
        ):
            msg = (
                f"Can't load tokenizer for '{pretrained_model_name_or_path}'. Make sure that:\n\n"
                f"- '{pretrained_model_name_or_path}' is a correct model identifier listed on 'https://huggingface.co/models'\n\n"
                f"- or '{pretrained_model_name_or_path}' is the correct path to a directory containing relevant tokenizer files\n\n"
            )
            raise EnvironmentError(msg)

        for file_id, file_path in vocab_files.items():
            if file_id not in resolved_vocab_files:
                continue

            if file_path == resolved_vocab_files[file_id]:
                logger.info(f"loading file {file_path}")
            else:
                logger.info(
                    f"loading file {file_path} from cache at {resolved_vocab_files[file_id]}"
                )

        return cls._from_pretrained(
            resolved_vocab_files,
            pretrained_model_name_or_path,
            init_configuration,
            *init_inputs,
            **kwargs,
        )

    @classmethod
    def _from_pretrained(
        cls,
        resolved_vocab_files,
        pretrained_model_name_or_path,
        init_configuration,
        *init_inputs,
        **kwargs,
    ):
        # We instantiate fast tokenizers based on a slow tokenizer if we don't have access to the tokenizer.json
        # file or if `from_slow` is set to True.
        from_slow = kwargs.get("from_slow", False)
        has_tokenizer_file = (
            resolved_vocab_files.get("tokenizer_file", None) is not None
        )
        if (
            from_slow or not has_tokenizer_file
        ) and cls.slow_tokenizer_class is not None:
            slow_tokenizer = (cls.slow_tokenizer_class)._from_pretrained(
                copy.deepcopy(resolved_vocab_files),
                pretrained_model_name_or_path,
                copy.deepcopy(init_configuration),
                *init_inputs,
                **(copy.deepcopy(kwargs)),
            )
        else:
            slow_tokenizer = None

        # Prepare tokenizer initialization kwargs
        # Did we saved some inputs and kwargs to reload ?
        tokenizer_config_file = resolved_vocab_files.pop("tokenizer_config_file", None)
        if tokenizer_config_file is not None:
            with open(
                tokenizer_config_file, encoding="utf-8"
            ) as tokenizer_config_handle:
                init_kwargs = json.load(tokenizer_config_handle)
            # First attempt. We get tokenizer_class from tokenizer_config to check mismatch between tokenizers.
            config_tokenizer_class = init_kwargs.get("tokenizer_class")
            init_kwargs.pop("tokenizer_class", None)
            saved_init_inputs = init_kwargs.pop("init_inputs", ())
            if not init_inputs:
                init_inputs = saved_init_inputs
        else:
            config_tokenizer_class = None
            init_kwargs = init_configuration

        if config_tokenizer_class is None:
            from transformers.models.auto.configuration_auto import AutoConfig

            # Second attempt. If we have not yet found tokenizer_class, let's try to use the config.
            try:
                config = AutoConfig.from_pretrained(pretrained_model_name_or_path)
                config_tokenizer_class = config.tokenizer_class
            except (OSError, ValueError, KeyError):
                # skip if an error occured.
                config = None
            if config_tokenizer_class is None:
                # Third attempt. If we have not yet found the original type of the tokenizer,
                # we are loading we see if we can infer it from the type of the configuration file
                from transformers.models.auto.configuration_auto import CONFIG_MAPPING
                from transformers.models.auto.tokenization_auto import TOKENIZER_MAPPING

                if hasattr(config, "model_type"):
                    config_class = CONFIG_MAPPING.get(config.model_type)
                else:
                    # Fallback: use pattern matching on the string.
                    config_class = None
                    for pattern, config_class_tmp in CONFIG_MAPPING.items():
                        if pattern in str(pretrained_model_name_or_path):
                            config_class = config_class_tmp
                            break

                if config_class in TOKENIZER_MAPPING.keys():
                    (
                        config_tokenizer_class,
                        config_tokenizer_class_fast,
                    ) = TOKENIZER_MAPPING[config_class]
                    if config_tokenizer_class is not None:
                        config_tokenizer_class = config_tokenizer_class.__name__
                    else:
                        config_tokenizer_class = config_tokenizer_class_fast.__name__

        if config_tokenizer_class is not None:
            if cls.__name__.replace("Fast", "") != config_tokenizer_class.replace(
                "Fast", ""
            ):
                logger.warning(
                    "The tokenizer class you load from this checkpoint is not the same type as the class this function is called from. "
                    "It may result in unexpected tokenization. \n"
                    f"The tokenizer class you load from this checkpoint is '{config_tokenizer_class}'. \n"
                    f"The class this function is called from is '{cls.__name__}'."
                )

        # Update with newly provided kwargs
        init_kwargs.update(kwargs)

        # Convert AddedTokens serialized as dict to class instances
        def convert_added_tokens(obj: Union[AddedToken, Any]):
            if (
                isinstance(obj, dict)
                and "__type" in obj
                and obj["__type"] == "AddedToken"
            ):
                obj.pop("__type")
                return AddedToken(**obj)
            elif isinstance(obj, (list, tuple)):
                return list(convert_added_tokens(o) for o in obj)
            elif isinstance(obj, dict):
                return {k: convert_added_tokens(v) for k, v in obj.items()}
            return obj

        init_kwargs = convert_added_tokens(init_kwargs)

        # Set max length if needed
        if pretrained_model_name_or_path in cls.max_model_input_sizes:
            # if we're using a pretrained model, ensure the tokenizer
            # wont index sequences longer than the number of positional embeddings
            model_max_length = cls.max_model_input_sizes[pretrained_model_name_or_path]
            if model_max_length is not None and isinstance(
                model_max_length, (int, float)
            ):
                init_kwargs["model_max_length"] = min(
                    init_kwargs.get("model_max_length", int(1e30)), model_max_length
                )

        # Merge resolved_vocab_files arguments in init_kwargs.
        added_tokens_file = resolved_vocab_files.pop("added_tokens_file", None)
        for args_name, file_path in resolved_vocab_files.items():
            if args_name not in init_kwargs:
                init_kwargs[args_name] = file_path

        if slow_tokenizer is not None:
            init_kwargs["__slow_tokenizer"] = slow_tokenizer

        init_kwargs["name_or_path"] = pretrained_model_name_or_path

        # Instantiate tokenizer.
        try:
            tokenizer = cls(*init_inputs, **init_kwargs)
        except OSError:
            raise OSError(
                "Unable to load vocabulary from file. "
                "Please check that the provided vocabulary is accessible and not corrupted."
            )

        # Save inputs and kwargs for saving and re-loading with ``save_pretrained``
        # Removed: Now done at the base class level
        # tokenizer.init_inputs = init_inputs
        # tokenizer.init_kwargs = init_kwargs

        # If there is a complementary special token map, load it
        special_tokens_map_file = resolved_vocab_files.pop(
            "special_tokens_map_file", None
        )
        if special_tokens_map_file is not None:
            with open(
                special_tokens_map_file, encoding="utf-8"
            ) as special_tokens_map_handle:
                special_tokens_map = json.load(special_tokens_map_handle)
            for key, value in special_tokens_map.items():
                if isinstance(value, dict):
                    value = AddedToken(**value)
                elif isinstance(value, list):
                    value = [
                        AddedToken(**token) if isinstance(token, dict) else token
                        for token in value
                    ]
                setattr(tokenizer, key, value)

        # Add supplementary tokens.
        special_tokens = tokenizer.all_special_tokens
        if added_tokens_file is not None:
            with open(added_tokens_file, encoding="utf-8") as added_tokens_handle:
                added_tok_encoder = json.load(added_tokens_handle)

            # Sort added tokens by index
            added_tok_encoder_sorted = list(
                sorted(added_tok_encoder.items(), key=lambda x: x[1])
            )

            for token, index in added_tok_encoder_sorted:
                if (
                    has_tokenizer_file
                    and index != len(tokenizer)
                    and tokenizer.convert_tokens_to_ids(token) != index
                ):
                    # Tokenizer fast: added token needs to either be in the vocabulary with the proper index or the
                    # index is the current length of the tokenizer (not in vocabulary)
                    raise ValueError(
                        f"Wrong index found for {token}: should be {tokenizer.convert_tokens_to_ids(token)} but found "
                        f"{index}."
                    )
                elif not has_tokenizer_file and index != len(tokenizer):
                    # Tokenizer slow: added token cannot already be in the vocabulary so its index needs to be the
                    # current length of the tokenizer.
                    raise ValueError(
                        f"Non-consecutive added token '{token}' found. "
                        f"Should have index {len(tokenizer)} but has index {index} in saved vocabulary."
                    )

                # Safe to call on a tokenizer fast even if token already there.
                tokenizer.add_tokens(
                    token, special_tokens=bool(token in special_tokens)
                )

        # Check all our special tokens are registered as "no split" token (we don't cut them) and are in the vocab
        added_tokens = tokenizer.sanitize_special_tokens()
        if added_tokens:
            logger.warning(
                "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained."
            )

        return tokenizer

    def save_pretrained(
        self,
        save_directory: Union[str, os.PathLike],
        legacy_format: Optional[bool] = None,
        filename_prefix: Optional[str] = None,
        push_to_hub: bool = False,
        **kwargs,
    ) -> Tuple[str]:
        """
        Save the full tokenizer state.


        This method make sure the full tokenizer can then be re-loaded using the
        :meth:`~transformers.tokenization_utils_base.PreTrainedTokenizer.from_pretrained` class method..

        .. Warning::
           This won't save modifications you may have applied to the tokenizer after the instantiation (for instance,
           modifying :obj:`tokenizer.do_lower_case` after creation).

        Args:
            save_directory (:obj:`str` or :obj:`os.PathLike`): The path to a directory where the tokenizer will be saved.
            legacy_format (:obj:`bool`, `optional`):
                Only applicable for a fast tokenizer. If unset (default), will save the tokenizer in the unified JSON
                format as well as in legacy format if it exists, i.e. with tokenizer specific vocabulary and a separate
                added_tokens files.
                If :obj:`False`, will only save the tokenizer in the unified JSON format. This format is incompatible
                with "slow" tokenizers (not powered by the `tokenizers` library), so the tokenizer will not be able to
                be loaded in the corresponding "slow" tokenizer.
                If :obj:`True`, will save the tokenizer in legacy format. If the "slow" tokenizer doesn't exits, a
                value error is raised.
            filename_prefix: (:obj:`str`, `optional`):
                A prefix to add to the names of the files saved by the tokenizer.

        Returns:
            A tuple of :obj:`str`: The files saved.
        """
        if os.path.isfile(save_directory):
            logger.error(
                f"Provided path ({save_directory}) should be a directory, not a file"
            )
            return
        os.makedirs(save_directory, exist_ok=True)

        special_tokens_map_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "")
            + SPECIAL_TOKENS_MAP_FILE,
        )
        tokenizer_config_file = os.path.join(
            save_directory,
            (filename_prefix + "-" if filename_prefix else "") + TOKENIZER_CONFIG_FILE,
        )

        tokenizer_config = copy.deepcopy(self.init_kwargs)
        if len(self.init_inputs) > 0:
            tokenizer_config["init_inputs"] = copy.deepcopy(self.init_inputs)
        for file_id in self.vocab_files_names.keys():
            tokenizer_config.pop(file_id, None)

        # Sanitize AddedTokens
        def convert_added_tokens(obj: Union[AddedToken, Any], add_type_field=True):
            if isinstance(obj, AddedToken):
                out = obj.__getstate__()
                if add_type_field:
                    out["__type"] = "AddedToken"
                return out
            elif isinstance(obj, (list, tuple)):
                return list(
                    convert_added_tokens(o, add_type_field=add_type_field) for o in obj
                )
            elif isinstance(obj, dict):
                return {
                    k: convert_added_tokens(v, add_type_field=add_type_field)
                    for k, v in obj.items()
                }
            return obj

        # add_type_field=True to allow dicts in the kwargs / differentiate from AddedToken serialization
        tokenizer_config = convert_added_tokens(tokenizer_config, add_type_field=True)

        # Add tokenizer class to the tokenizer config to be able to reload it with from_pretrained
        tokenizer_class = self.__class__.__name__
        # Remove the Fast at the end unless we have a special `PreTrainedTokenizerFast`
        if (
            tokenizer_class.endswith("Fast")
            and tokenizer_class != "PreTrainedTokenizerFast"
        ):
            tokenizer_class = tokenizer_class[:-4]
        tokenizer_config["tokenizer_class"] = tokenizer_class

        with open(tokenizer_config_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(tokenizer_config, ensure_ascii=False))

        # Sanitize AddedTokens in special_tokens_map
        write_dict = convert_added_tokens(
            self.special_tokens_map_extended, add_type_field=False
        )
        with open(special_tokens_map_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(write_dict, ensure_ascii=False))

        file_names = (tokenizer_config_file, special_tokens_map_file)

        save_files = self._save_pretrained(
            save_directory=save_directory,
            file_names=file_names,
            legacy_format=legacy_format,
            filename_prefix=filename_prefix,
        )

        if push_to_hub:
            # Annoyingly, the return contains files that don't exist.
            existing_files = [f for f in save_files if os.path.isfile(f)]
            url = self._push_to_hub(save_files=existing_files, **kwargs)
            logger.info(f"Tokenizer pushed to the hub in this commit: {url}")

        return save_files

    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.

        Fast tokenizers can also be saved in a unique JSON file containing {config + vocab + added-tokens} using the
        specific :meth:`~transformers.tokenization_utils_fast.PreTrainedTokenizerFast._save_pretrained`
        """
        if legacy_format is False:
            raise ValueError(
                "Only fast tokenizers (instances of PreTrainedTokenizerFast) can be saved in non legacy format."
            )

        save_directory = str(save_directory)

        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
        )

        return file_names + vocab_files + (added_tokens_file,)

    def save_vocabulary(
        self, save_directory: str, filename_prefix: Optional[str] = None
    ) -> Tuple[str]:
        """
        Save only the vocabulary of the tokenizer (vocabulary + added tokens).

        This method won't save the configuration and special token mappings of the tokenizer. Use
        :meth:`~transformers.PreTrainedTokenizerFast._save_pretrained` to save the whole state of the tokenizer.

        Args:
            save_directory (:obj:`str`):
                The directory in which to save the vocabulary.
            filename_prefix (:obj:`str`, `optional`):
                An optional prefix to add to the named of the saved files.

        Returns:
            :obj:`Tuple(str)`: Paths to the files saved.
        """
        raise NotImplementedError

    def tokenize(
        self,
        text: str,
        pair: Optional[str] = None,
        add_special_tokens: bool = False,
        **kwargs,
    ) -> List[str]:
        """
        Converts a string in a sequence of tokens, replacing unknown tokens with the :obj:`unk_token`.

        Args:
            text (:obj:`str`):
                The sequence to be encoded.
            pair (:obj:`str`, `optional`):
                A second sequence to be encoded with the first.
            add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to add the special tokens associated with the corresponding model.
            kwargs (additional keyword arguments, `optional`):
                Will be passed to the underlying model specific encode method. See details in
                :meth:`~transformers.PreTrainedTokenizerBase.__call__`

        Returns:
            :obj:`List[str]`: The list of tokens.
        """
        raise NotImplementedError

    def encode(
        self,
        text: Union[TextInput, PreTokenizedInput, EncodedInput],
        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs,
    ) -> List[int]:
        """
        Converts a string to a sequence of ids (integer), using the tokenizer and vocabulary.

        Same as doing ``self.convert_tokens_to_ids(self.tokenize(text))``.

        Args:
            text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`):
                The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
                ``tokenize`` method) or a list of integers (tokenized string ids using the ``convert_tokens_to_ids``
                method).
            text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`):
                Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
                the ``tokenize`` method) or a list of integers (tokenized string ids using the
                ``convert_tokens_to_ids`` method).
        """
        encoded_inputs = self.encode_plus(
            text,
            text_pair=text_pair,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            stride=stride,
            return_tensors=return_tensors,
            **kwargs,
        )

        return encoded_inputs["input_ids"]

    def num_special_tokens_to_add(self, pair: bool = False) -> int:
        raise NotImplementedError

    def _get_padding_truncation_strategies(
        self,
        padding=False,
        truncation=False,
        max_length=None,
        pad_to_multiple_of=None,
        verbose=True,
        **kwargs,
    ):
        """
        Find the correct padding/truncation strategy with backward compatibility for old arguments (truncation_strategy
        and pad_to_max_length) and behaviors.
        """
        old_truncation_strategy = kwargs.pop("truncation_strategy", "do_not_truncate")
        old_pad_to_max_length = kwargs.pop("pad_to_max_length", False)

        # Backward compatibility for previous behavior, maybe we should deprecate it:
        # If you only set max_length, it activates truncation for max_length
        if max_length is not None and padding is False and truncation is False:
            if verbose:
                if not self.deprecation_warnings.get(
                    "Truncation-not-explicitly-activated", False
                ):
                    logger.warning(
                        "Truncation was not explicitly activated but `max_length` is provided a specific value, "
                        "please use `truncation=True` to explicitly truncate examples to max length. "
                        "Defaulting to 'longest_first' truncation strategy. "
                        "If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy "
                        "more precisely by providing a specific strategy to `truncation`."
                    )
                self.deprecation_warnings["Truncation-not-explicitly-activated"] = True
            truncation = "longest_first"

        # Get padding strategy
        if padding is False and old_pad_to_max_length:
            if verbose:
                warnings.warn(
                    "The `pad_to_max_length` argument is deprecated and will be removed in a future version, "
                    "use `padding=True` or `padding='longest'` to pad to the longest sequence in the batch, or "
                    "use `padding='max_length'` to pad to a max length. In this case, you can give a specific "
                    "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to pad to the "
                    "maximal input size of the model (e.g. 512 for Bert).",
                    FutureWarning,
                )
            if max_length is None:
                padding_strategy = PaddingStrategy.LONGEST
            else:
                padding_strategy = PaddingStrategy.MAX_LENGTH
        elif padding is not False:
            if padding is True:
                padding_strategy = (
                    PaddingStrategy.LONGEST
                )  # Default to pad to the longest sequence in the batch
            elif not isinstance(padding, PaddingStrategy):
                padding_strategy = PaddingStrategy(padding)
            elif isinstance(padding, PaddingStrategy):
                padding_strategy = padding
        else:
            padding_strategy = PaddingStrategy.DO_NOT_PAD

        # Get truncation strategy
        if truncation is False and old_truncation_strategy != "do_not_truncate":
            if verbose:
                warnings.warn(
                    "The `truncation_strategy` argument is deprecated and will be removed in a future version, "
                    "use `truncation=True` to truncate examples to a max length. You can give a specific "
                    "length with `max_length` (e.g. `max_length=45`) or leave max_length to None to truncate to the "
                    "maximal input size of the model (e.g. 512 for Bert). "
                    " If you have pairs of inputs, you can give a specific truncation strategy selected among "
                    "`truncation='only_first'` (will only truncate the first sentence in the pairs) "
                    "`truncation='only_second'` (will only truncate the second sentence in the pairs) "
                    "or `truncation='longest_first'` (will iteratively remove tokens from the longest sentence in the pairs).",
                    FutureWarning,
                )
            truncation_strategy = TruncationStrategy(old_truncation_strategy)
        elif truncation is not False:
            if truncation is True:
                truncation_strategy = (
                    TruncationStrategy.LONGEST_FIRST
                )  # Default to truncate the longest sequences in pairs of inputs
            elif not isinstance(truncation, TruncationStrategy):
                truncation_strategy = TruncationStrategy(truncation)
            elif isinstance(truncation, TruncationStrategy):
                truncation_strategy = truncation
        else:
            truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE

        # Set max length if needed
        if max_length is None:
            if padding_strategy == PaddingStrategy.MAX_LENGTH:
                if self.model_max_length > LARGE_INTEGER:
                    if verbose:
                        if not self.deprecation_warnings.get(
                            "Asking-to-pad-to-max_length", False
                        ):
                            logger.warning(
                                "Asking to pad to max_length but no maximum length is provided and the model has no predefined maximum length. "
                                "Default to no padding."
                            )
                        self.deprecation_warnings["Asking-to-pad-to-max_length"] = True
                    padding_strategy = PaddingStrategy.DO_NOT_PAD
                else:
                    max_length = self.model_max_length

            if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE:
                if self.model_max_length > LARGE_INTEGER:
                    if verbose:
                        if not self.deprecation_warnings.get(
                            "Asking-to-truncate-to-max_length", False
                        ):
                            logger.warning(
                                "Asking to truncate to max_length but no maximum length is provided and the model has no predefined maximum length. "
                                "Default to no truncation."
                            )
                        self.deprecation_warnings[
                            "Asking-to-truncate-to-max_length"
                        ] = True
                    truncation_strategy = TruncationStrategy.DO_NOT_TRUNCATE
                else:
                    max_length = self.model_max_length

        # Test if we have a padding token
        if padding_strategy != PaddingStrategy.DO_NOT_PAD and (
            not self.pad_token or self.pad_token_id < 0
        ):
            raise ValueError(
                "Asking to pad but the tokenizer does not have a padding token. "
                "Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` "
                "or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`."
            )

        # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided
        if (
            truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
            and padding_strategy != PaddingStrategy.DO_NOT_PAD
            and pad_to_multiple_of is not None
            and max_length is not None
            and (max_length % pad_to_multiple_of != 0)
        ):
            raise ValueError(
                f"Truncation and padding are both activated but "
                f"truncation length ({max_length}) is not a multiple of pad_to_multiple_of ({pad_to_multiple_of})."
            )

        return padding_strategy, truncation_strategy, max_length, kwargs

    def __call__(
        self,
        text: Union[
            TextInputPair,
            PreTokenizedInput,
            List[TextInputPair],
            List[PreTokenizedInput],
        ],
        time_id: Union[str, List[str]] = None,
        text_pair: Optional[
            Union[
                TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]
            ]
        ] = None,
        time_id_pair: Optional[Union[str, List[str]]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_split_into_words: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = 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,
        **kwargs,
    ) -> BatchEncoding:
        """
        Main method to tokenize and prepare for the model one or several sequence(s) or one or several pair(s) of
        sequences.

        Args:
            text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                :obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            text_pair (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                :obj:`is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
        """
        # Input type checking for clearer error
        def _is_valid_text_input(t):
            if isinstance(t, str):
                # Strings are fine
                return True
            elif isinstance(t, (list, tuple)):
                # List are fine as long as they are...
                if len(t) == 0:
                    # ... empty
                    return True
                elif isinstance(t[0], str):
                    # ... list of strings
                    return True
                elif isinstance(t[0], (list, tuple)):
                    # ... list with an empty list or with a list of strings
                    return len(t[0]) == 0 or isinstance(t[0][0], str)
                else:
                    return False
            else:
                return False

        if not _is_valid_text_input(text):
            raise ValueError(
                "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
                "or `List[List[str]]` (batch of pretokenized examples)."
            )

        if text_pair is not None and not _is_valid_text_input(text_pair):
            raise ValueError(
                "text input must of type `str` (single example), `List[str]` (batch or single pretokenized example) "
                "or `List[List[str]]` (batch of pretokenized examples)."
            )

        if is_split_into_words:
            is_batched = (
                isinstance(text, (list, tuple))
                and text
                and isinstance(text[0], (list, tuple))
            )
        else:
            is_batched = isinstance(text, (list, tuple))

        is_batched = bool(
            (not is_split_into_words and isinstance(text, (list, tuple)))
            or (
                is_split_into_words
                and isinstance(text, (list, tuple))
                and text
                and isinstance(text[0], (list, tuple))
            )
        )

        if is_batched:
            if isinstance(text_pair, str):
                raise TypeError(
                    "when tokenizing batches of text, `text_pair` must be a list or tuple with the same length as `text`."
                )
            if text_pair is not None and len(text) != len(text_pair):
                raise ValueError(
                    f"batch length of `text`: {len(text)} does not match batch length of `text_pair`: {len(text_pair)}."
                )
            batch_text_or_text_pairs = (
                list(zip(text, text_pair)) if text_pair is not None else text
            )
            batch_time_id = (
                list(zip(time_id, time_id_pair)) if text_pair is not None else time_id
            )
            return self.batch_encode_plus(
                batch_text_or_text_pairs=batch_text_or_text_pairs,
                batch_time_id=batch_time_id,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                is_split_into_words=is_split_into_words,
                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,
                **kwargs,
            )
        else:
            return self.encode_plus(
                text=text,
                time_id=time_id,
                text_pair=text_pair,
                time_id_pair=time_id_pair,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                is_split_into_words=is_split_into_words,
                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,
                **kwargs,
            )

    def encode_plus(
        self,
        text: Union[TextInput, PreTokenizedInput, EncodedInput],
        time_id: str = None,
        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = None,
        time_id_pair: Optional[str] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_split_into_words: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = 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,
        **kwargs,
    ) -> BatchEncoding:
        """
        Tokenize and prepare for the model a sequence or a pair of sequences.

        .. warning::
            This method is deprecated, ``__call__`` should be used instead.

        Args:
            text (:obj:`str`, :obj:`List[str]` or :obj:`List[int]` (the latter only for not-fast tokenizers)):
                The first sequence to be encoded. This can be a string, a list of strings (tokenized string using the
                ``tokenize`` method) or a list of integers (tokenized string ids using the ``convert_tokens_to_ids``
                method).
            text_pair (:obj:`str`, :obj:`List[str]` or :obj:`List[int]`, `optional`):
                Optional second sequence to be encoded. This can be a string, a list of strings (tokenized string using
                the ``tokenize`` method) or a list of integers (tokenized string ids using the
                ``convert_tokens_to_ids`` method).
        """

        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        (
            padding_strategy,
            truncation_strategy,
            max_length,
            kwargs,
        ) = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        return self._encode_plus(
            text=text,
            time_id=time_id,
            text_pair=text_pair,
            time_id_pair=time_id_pair,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            is_split_into_words=is_split_into_words,
            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,
            **kwargs,
        )

    def _encode_plus(
        self,
        text: Union[TextInput, PreTokenizedInput, EncodedInput],
        time_id: str = None,
        text_pair: Optional[Union[TextInput, PreTokenizedInput, EncodedInput]] = 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[Union[str, TensorType]] = 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,
        **kwargs,
    ) -> BatchEncoding:
        raise NotImplementedError

    def batch_encode_plus(
        self,
        batch_text_or_text_pairs: Union[
            List[TextInput],
            List[TextInputPair],
            List[PreTokenizedInput],
            List[PreTokenizedInputPair],
            List[EncodedInput],
            List[EncodedInputPair],
        ],
        batch_time_id: List[str] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        is_split_into_words: bool = False,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = 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,
        **kwargs,
    ) -> BatchEncoding:
        """
        Tokenize and prepare for the model a list of sequences or a list of pairs of sequences.

        .. warning::
            This method is deprecated, ``__call__`` should be used instead.

        Args:
            batch_text_or_text_pairs (:obj:`List[str]`, :obj:`List[Tuple[str, str]]`, :obj:`List[List[str]]`, :obj:`List[Tuple[List[str], List[str]]]`, and for not-fast tokenizers, also :obj:`List[List[int]]`, :obj:`List[Tuple[List[int], List[int]]]`):
                Batch of sequences or pair of sequences to be encoded. This can be a list of
                string/string-sequences/int-sequences or a list of pair of string/string-sequences/int-sequence (see
                details in ``encode_plus``).
        """

        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        (
            padding_strategy,
            truncation_strategy,
            max_length,
            kwargs,
        ) = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        return self._batch_encode_plus(
            batch_text_or_text_pairs=batch_text_or_text_pairs,
            batch_time_id=batch_time_id,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            is_split_into_words=is_split_into_words,
            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,
            **kwargs,
        )

    def _batch_encode_plus(
        self,
        batch_text_or_text_pairs: Union[
            List[TextInput],
            List[TextInputPair],
            List[PreTokenizedInput],
            List[PreTokenizedInputPair],
            List[EncodedInput],
            List[EncodedInputPair],
        ],
        batch_time_id: List[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[Union[str, TensorType]] = 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,
        **kwargs,
    ) -> BatchEncoding:
        raise NotImplementedError

    def pad(
        self,
        encoded_inputs: Union[
            BatchEncoding,
            List[BatchEncoding],
            Dict[str, EncodedInput],
            Dict[str, List[EncodedInput]],
            List[Dict[str, EncodedInput]],
        ],
        padding: Union[bool, str, PaddingStrategy] = True,
        max_length: Optional[int] = None,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        verbose: bool = True,
    ) -> BatchEncoding:
        """
        Pad a single encoded input or a batch of encoded inputs up to predefined length or to the max sequence length
        in the batch.

        Padding side (left/right) padding token ids are defined at the tokenizer level (with ``self.padding_side``,
        ``self.pad_token_id`` and ``self.pad_token_type_id``)

        .. note::

            If the ``encoded_inputs`` passed are dictionary of numpy arrays, PyTorch tensors or TensorFlow tensors, the
            result will use the same type unless you provide a different tensor type with ``return_tensors``. In the
            case of PyTorch tensors, you will lose the specific device of your tensors however.

        Args:
            encoded_inputs (:class:`~transformers.BatchEncoding`, list of :class:`~transformers.BatchEncoding`, :obj:`Dict[str, List[int]]`, :obj:`Dict[str, List[List[int]]` or :obj:`List[Dict[str, List[int]]]`):
                Tokenized inputs. Can represent one input (:class:`~transformers.BatchEncoding` or :obj:`Dict[str,
                List[int]]`) or a batch of tokenized inputs (list of :class:`~transformers.BatchEncoding`, `Dict[str,
                List[List[int]]]` or `List[Dict[str, List[int]]]`) so you can use this method during preprocessing as
                well as in a PyTorch Dataloader collate function.

                Instead of :obj:`List[int]` you can have tensors (numpy arrays, PyTorch tensors or TensorFlow tensors),
                see the note above for the return type.
            padding (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.PaddingStrategy`, `optional`, defaults to :obj:`True`):
                 Select a strategy to pad the returned sequences (according to the model's padding side and padding
                 index) among:

                * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                  single sequence if provided).
                * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                  maximum acceptable input length for the model if that argument is not provided.
                * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                  different lengths).
            max_length (:obj:`int`, `optional`):
                Maximum length of the returned list and optionally padding length (see above).
            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).
            return_attention_mask (:obj:`bool`, `optional`):
                Whether to return the attention mask. If left to the default, will return the attention mask according
                to the specific tokenizer's default, defined by the :obj:`return_outputs` attribute.

                `What are attention masks? <../glossary.html#attention-mask>`__
            return_tensors (:obj:`str` or :class:`~transformers.tokenization_utils_base.TensorType`, `optional`):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
            verbose (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to print more information and warnings.
        """
        # If we have a list of dicts, let's convert it in a dict of lists
        # We do this to allow using this method as a collate_fn function in PyTorch Dataloader
        if isinstance(encoded_inputs, (list, tuple)) and isinstance(
            encoded_inputs[0], (dict, BatchEncoding)
        ):
            encoded_inputs = {
                key: [example[key] for example in encoded_inputs]
                for key in encoded_inputs[0].keys()
            }

        # The model's main input name, usually `input_ids`, has be passed for padding
        if self.model_input_names[0] not in encoded_inputs:
            raise ValueError(
                "You should supply an encoding or a list of encodings to this method "
                f"that includes {self.model_input_names[0]}, but you provided {list(encoded_inputs.keys())}"
            )

        required_input = encoded_inputs[self.model_input_names[0]]

        if not required_input:
            if return_attention_mask:
                encoded_inputs["attention_mask"] = []
            return encoded_inputs

        # If we have PyTorch/TF/NumPy tensors/arrays as inputs, we cast them as python objects
        # and rebuild them afterwards if no return_tensors is specified
        # Note that we lose the specific device the tensor may be on for PyTorch

        first_element = required_input[0]
        if isinstance(first_element, (list, tuple)):
            # first_element might be an empty list/tuple in some edge cases so we grab the first non empty element.
            index = 0
            while len(required_input[index]) == 0:
                index += 1
            if index < len(required_input):
                first_element = required_input[index][0]
        # At this state, if `first_element` is still a list/tuple, it's an empty one so there is nothing to do.
        if not isinstance(first_element, (int, list, tuple)):
            if is_tf_available() and _is_tensorflow(first_element):
                return_tensors = "tf" if return_tensors is None else return_tensors
            elif is_torch_available() and _is_torch(first_element):
                return_tensors = "pt" if return_tensors is None else return_tensors
            elif isinstance(first_element, np.ndarray):
                return_tensors = "np" if return_tensors is None else return_tensors
            else:
                raise ValueError(
                    f"type of {first_element} unknown: {type(first_element)}. "
                    f"Should be one of a python, numpy, pytorch or tensorflow object."
                )

            for key, value in encoded_inputs.items():
                encoded_inputs[key] = to_py_obj(value)

        # Convert padding_strategy in PaddingStrategy
        padding_strategy, _, max_length, _ = self._get_padding_truncation_strategies(
            padding=padding, max_length=max_length, verbose=verbose
        )

        required_input = encoded_inputs[self.model_input_names[0]]
        if required_input and not isinstance(required_input[0], (list, tuple)):
            encoded_inputs = self._pad(
                encoded_inputs,
                max_length=max_length,
                padding_strategy=padding_strategy,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )
            return BatchEncoding(encoded_inputs, tensor_type=return_tensors)

        batch_size = len(required_input)
        assert all(
            len(v) == batch_size for v in encoded_inputs.values()
        ), "Some items in the output dictionary have a different batch size than others."

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = max(len(inputs) for inputs in required_input)
            padding_strategy = PaddingStrategy.MAX_LENGTH

        batch_outputs = {}
        for i in range(batch_size):
            inputs = dict((k, v[i]) for k, v in encoded_inputs.items())
            outputs = self._pad(
                inputs,
                max_length=max_length,
                padding_strategy=padding_strategy,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )

            for key, value in outputs.items():
                if key not in batch_outputs:
                    batch_outputs[key] = []
                batch_outputs[key].append(value)

        return BatchEncoding(batch_outputs, tensor_type=return_tensors)

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Create the token type IDs corresponding to the sequences passed. `What are token type IDs?
        <../glossary.html#token-type-ids>`__

        Should be overridden in a subclass if the model has a special way of building those.

        Args:
            token_ids_0 (:obj:`List[int]`): The first tokenized sequence.
            token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence.

        Returns:
            :obj:`List[int]`: The token type ids.
        """
        if token_ids_1 is None:
            return len(token_ids_0) * [0]
        return [0] * len(token_ids_0) + [1] * len(token_ids_1)

    def build_inputs_with_special_tokens(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
        adding special tokens.

        This implementation does not add special tokens and this method should be overridden in a subclass.

        Args:
            token_ids_0 (:obj:`List[int]`): The first tokenized sequence.
            token_ids_1 (:obj:`List[int]`, `optional`): The second tokenized sequence.

        Returns:
            :obj:`List[int]`: The model input with special tokens.
        """
        if token_ids_1 is None:
            return token_ids_0
        return token_ids_0 + token_ids_1

    def prepare_for_model(
        self,
        ids: List[int],
        pair_ids: Optional[List[int]] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy] = False,
        max_length: Optional[int] = None,
        stride: int = 0,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = 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,
        prepend_batch_axis: bool = False,
        **kwargs,
    ) -> BatchEncoding:
        """
        Prepares a sequence of input id, or a pair of sequences of inputs ids so that it can be used by the model. It
        adds special tokens, truncates sequences if overflowing while taking into account the special tokens and
        manages a moving window (with user defined stride) for overflowing tokens

        Args:
            ids (:obj:`List[int]`):
                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the ``tokenize``
                and ``convert_tokens_to_ids`` methods.
            pair_ids (:obj:`List[int]`, `optional`):
                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the ``tokenize``
                and ``convert_tokens_to_ids`` methods.
        """

        # Backward compatibility for 'truncation_strategy', 'pad_to_max_length'
        (
            padding_strategy,
            truncation_strategy,
            max_length,
            kwargs,
        ) = self._get_padding_truncation_strategies(
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            verbose=verbose,
            **kwargs,
        )

        pair = bool(pair_ids is not None)
        len_ids = len(ids)
        len_pair_ids = len(pair_ids) if pair else 0

        if return_token_type_ids and not add_special_tokens:
            raise ValueError(
                "Asking to return token_type_ids while setting add_special_tokens to False "
                "results in an undefined behavior. Please set add_special_tokens to True or "
                "set return_token_type_ids to None."
            )

        # Load from model defaults
        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

        encoded_inputs = {}

        # Compute the total size of the returned encodings
        total_len = (
            len_ids
            + len_pair_ids
            + (self.num_special_tokens_to_add(pair=pair) if add_special_tokens else 0)
        )

        # Truncation: Handle max sequence length
        overflowing_tokens = []
        if (
            truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE
            and max_length
            and total_len > max_length
        ):
            ids, pair_ids, overflowing_tokens = self.truncate_sequences(
                ids,
                pair_ids=pair_ids,
                num_tokens_to_remove=total_len - max_length,
                truncation_strategy=truncation_strategy,
                stride=stride,
            )

        if return_overflowing_tokens:
            encoded_inputs["overflowing_tokens"] = overflowing_tokens
            encoded_inputs["num_truncated_tokens"] = total_len - max_length

        # Add special tokens
        if add_special_tokens:
            sequence = self.build_inputs_with_special_tokens(ids, pair_ids)
            token_type_ids = self.create_token_type_ids_from_sequences(ids, pair_ids)
        else:
            sequence = ids + pair_ids if pair else ids
            token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])

        # Build output dictionary
        encoded_inputs["input_ids"] = sequence
        if return_token_type_ids:
            encoded_inputs["token_type_ids"] = token_type_ids
        if return_special_tokens_mask:
            if add_special_tokens:
                encoded_inputs["special_tokens_mask"] = self.get_special_tokens_mask(
                    ids, pair_ids
                )
            else:
                encoded_inputs["special_tokens_mask"] = [0] * len(sequence)

        # Check lengths
        self._eventual_warn_about_too_long_sequence(
            encoded_inputs["input_ids"], max_length, verbose
        )

        # Padding
        if padding_strategy != PaddingStrategy.DO_NOT_PAD or return_attention_mask:
            encoded_inputs = self.pad(
                encoded_inputs,
                max_length=max_length,
                padding=padding_strategy.value,
                pad_to_multiple_of=pad_to_multiple_of,
                return_attention_mask=return_attention_mask,
            )

        if return_length:
            encoded_inputs["length"] = len(encoded_inputs["input_ids"])

        batch_outputs = BatchEncoding(
            encoded_inputs,
            tensor_type=return_tensors,
            prepend_batch_axis=prepend_batch_axis,
        )

        return batch_outputs

    def truncate_sequences(
        self,
        ids: List[int],
        pair_ids: Optional[List[int]] = None,
        num_tokens_to_remove: int = 0,
        truncation_strategy: Union[str, TruncationStrategy] = "longest_first",
        stride: int = 0,
    ) -> Tuple[List[int], List[int], List[int]]:
        """
        Truncates a sequence pair in-place following the strategy.

        Args:
            ids (:obj:`List[int]`):
                Tokenized input ids of the first sequence. Can be obtained from a string by chaining the ``tokenize``
                and ``convert_tokens_to_ids`` methods.
            pair_ids (:obj:`List[int]`, `optional`):
                Tokenized input ids of the second sequence. Can be obtained from a string by chaining the ``tokenize``
                and ``convert_tokens_to_ids`` methods.
            num_tokens_to_remove (:obj:`int`, `optional`, defaults to 0):
                Number of tokens to remove using the truncation strategy.
            truncation_strategy (:obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
                The strategy to follow for truncation. Can be:

                * :obj:`'longest_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
                  to the maximum acceptable input length for the model if that argument is not provided. This will
                  truncate token by token, removing a token from the longest sequence in the pair if a pair of
                  sequences (or a batch of pairs) is provided.
                * :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
                  the maximum acceptable input length for the model if that argument is not provided. This will only
                  truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                * :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
                  to the maximum acceptable input length for the model if that argument is not provided. This will only
                  truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                * :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                  greater than the model maximum admissible input size).
            stride (:obj:`int`, `optional`, defaults to 0):
                If set to a positive number, the overflowing tokens returned will contain some tokens from the main
                sequence returned. The value of this argument defines the number of additional tokens.

        Returns:
            :obj:`Tuple[List[int], List[int], List[int]]`: The truncated ``ids``, the truncated ``pair_ids`` and the
            list of overflowing tokens.
        """
        if num_tokens_to_remove <= 0:
            return ids, pair_ids, []

        if not isinstance(truncation_strategy, TruncationStrategy):
            truncation_strategy = TruncationStrategy(truncation_strategy)

        overflowing_tokens = []
        if truncation_strategy == TruncationStrategy.LONGEST_FIRST:
            for _ in range(num_tokens_to_remove):
                if pair_ids is None or len(ids) > len(pair_ids):
                    if not overflowing_tokens:
                        window_len = min(len(ids), stride + 1)
                    else:
                        window_len = 1
                    overflowing_tokens.extend(ids[-window_len:])
                    ids = ids[:-1]
                else:
                    if not overflowing_tokens:
                        window_len = min(len(pair_ids), stride + 1)
                    else:
                        window_len = 1
                    overflowing_tokens.extend(pair_ids[-window_len:])
                    pair_ids = pair_ids[:-1]
        elif truncation_strategy == TruncationStrategy.ONLY_FIRST:
            if len(ids) > num_tokens_to_remove:
                window_len = min(len(ids), stride + num_tokens_to_remove)
                overflowing_tokens = ids[-window_len:]
                ids = ids[:-num_tokens_to_remove]
            else:
                logger.error(
                    f"We need to remove {num_tokens_to_remove} to truncate the input"
                    f"but the first sequence has a length {len(ids)}. "
                    f"Please select another truncation strategy than {truncation_strategy}, "
                    f"for instance 'longest_first' or 'only_second'."
                )
        elif (
            truncation_strategy == TruncationStrategy.ONLY_SECOND
            and pair_ids is not None
        ):
            if len(pair_ids) > num_tokens_to_remove:
                window_len = min(len(pair_ids), stride + num_tokens_to_remove)
                overflowing_tokens = pair_ids[-window_len:]
                pair_ids = pair_ids[:-num_tokens_to_remove]
            else:
                logger.error(
                    f"We need to remove {num_tokens_to_remove} to truncate the input"
                    f"but the second sequence has a length {len(pair_ids)}. "
                    f"Please select another truncation strategy than {truncation_strategy}, "
                    f"for instance 'longest_first' or 'only_first'."
                )

        return (ids, pair_ids, overflowing_tokens)

    def _pad(
        self,
        encoded_inputs: Union[Dict[str, EncodedInput], BatchEncoding],
        max_length: Optional[int] = None,
        padding_strategy: PaddingStrategy = PaddingStrategy.DO_NOT_PAD,
        pad_to_multiple_of: Optional[int] = None,
        return_attention_mask: Optional[bool] = None,
    ) -> dict:
        """
        Pad encoded inputs (on left/right and up to predefined length or max length in the batch)

        Args:
            encoded_inputs: Dictionary of tokenized inputs (`List[int]`) or batch of tokenized inputs (`List[List[int]]`).
            max_length: maximum length of the returned list and optionally padding length (see below).
                Will truncate by taking into account the special tokens.
            padding_strategy: PaddingStrategy to use for padding.

                - PaddingStrategy.LONGEST Pad to the longest sequence in the batch
                - PaddingStrategy.MAX_LENGTH: Pad to the max length (default)
                - PaddingStrategy.DO_NOT_PAD: Do not pad
                The tokenizer padding sides are defined in self.padding_side:

                    - 'left': pads on the left of the sequences
                    - 'right': pads on the right of the sequences
            pad_to_multiple_of: (optional) Integer if set will pad the sequence to a multiple of the provided value.
                This is especially useful to enable the use of Tensor Core on NVIDIA hardware with compute capability
                >= 7.5 (Volta).
            return_attention_mask: (optional) Set to False to avoid returning attention mask (default: set to model specifics)
        """
        # Load from model defaults
        if return_attention_mask is None:
            return_attention_mask = "attention_mask" in self.model_input_names

        required_input = encoded_inputs[self.model_input_names[0]]

        if padding_strategy == PaddingStrategy.LONGEST:
            max_length = len(required_input)

        if (
            max_length is not None
            and pad_to_multiple_of is not None
            and (max_length % pad_to_multiple_of != 0)
        ):
            max_length = ((max_length // pad_to_multiple_of) + 1) * pad_to_multiple_of

        needs_to_be_padded = (
            padding_strategy != PaddingStrategy.DO_NOT_PAD
            and len(required_input) != max_length
        )

        if needs_to_be_padded:
            difference = max_length - len(required_input)
            if self.padding_side == "right":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [1] * len(required_input) + [
                        0
                    ] * difference
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = (
                        encoded_inputs["token_type_ids"]
                        + [self.pad_token_type_id] * difference
                    )
                if "time_ids" in encoded_inputs:
                    encoded_inputs["time_ids"] = (
                        encoded_inputs["time_ids"] + [0] * difference
                    )
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = (
                        encoded_inputs["special_tokens_mask"] + [1] * difference
                    )
                encoded_inputs[self.model_input_names[0]] = (
                    required_input + [self.pad_token_id] * difference
                )
            elif self.padding_side == "left":
                if return_attention_mask:
                    encoded_inputs["attention_mask"] = [0] * difference + [1] * len(
                        required_input
                    )
                if "token_type_ids" in encoded_inputs:
                    encoded_inputs["token_type_ids"] = [
                        self.pad_token_type_id
                    ] * difference + encoded_inputs["token_type_ids"]
                if "time_ids" in encoded_inputs:
                    encoded_inputs["time_ids"] = [0] * difference + encoded_inputs[
                        "time_ids"
                    ]
                if "special_tokens_mask" in encoded_inputs:
                    encoded_inputs["special_tokens_mask"] = [
                        1
                    ] * difference + encoded_inputs["special_tokens_mask"]
                encoded_inputs[self.model_input_names[0]] = [
                    self.pad_token_id
                ] * difference + required_input
            else:
                raise ValueError("Invalid padding strategy:" + str(self.padding_side))
        elif return_attention_mask and "attention_mask" not in encoded_inputs:
            encoded_inputs["attention_mask"] = [1] * len(required_input)

        return encoded_inputs

    def convert_tokens_to_string(self, tokens: List[str]) -> str:
        """
        Converts a sequence of tokens in a single string. The most simple way to do it is ``" ".join(tokens)`` but
        we often want to remove sub-word tokenization artifacts at the same time

        Args:
            tokens (:obj:`List[str]`): The token to join in a string.
        Returns:
            :obj:`str`: The joined tokens.
        """
        raise NotImplementedError

    def batch_decode(
        self,
        sequences: Union[
            List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"
        ],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        **kwargs,
    ) -> List[str]:
        """
        Convert a list of lists of token ids into a list of strings by calling decode.

        Args:
            sequences (:obj:`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the ``__call__`` method.
            skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to clean up the tokenization spaces.
            kwargs (additional keyword arguments, `optional`):
                Will be passed to the underlying model specific decode method.

        Returns:
            :obj:`List[str]`: The list of decoded sentences.
        """
        return [
            self.decode(
                seq,
                skip_special_tokens=skip_special_tokens,
                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                **kwargs,
            )
            for seq in sequences
        ]

    def decode(
        self,
        token_ids: Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        **kwargs,
    ) -> str:
        """
        Converts a sequence of ids in a string, using the tokenizer and vocabulary with options to remove special
        tokens and clean up tokenization spaces.

        Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.

        Args:
            token_ids (:obj:`Union[int, List[int], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the ``__call__`` method.
            skip_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (:obj:`bool`, `optional`, defaults to :obj:`True`):
                Whether or not to clean up the tokenization spaces.
            kwargs (additional keyword arguments, `optional`):
                Will be passed to the underlying model specific decode method.

        Returns:
            :obj:`str`: The decoded sentence.
        """
        # Convert inputs to python lists
        token_ids = to_py_obj(token_ids)

        return self._decode(
            token_ids=token_ids,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

    def _decode(
        self,
        token_ids: Union[int, List[int]],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = True,
        **kwargs,
    ) -> str:
        raise NotImplementedError

    def get_special_tokens_mask(
        self,
        token_ids_0: List[int],
        token_ids_1: Optional[List[int]] = None,
        already_has_special_tokens: bool = False,
    ) -> List[int]:
        """
        Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
        special tokens using the tokenizer ``prepare_for_model`` or ``encode_plus`` methods.

        Args:
            token_ids_0 (:obj:`List[int]`):
                List of ids of the first sequence.
            token_ids_1 (:obj:`List[int]`, `optional`):
                List of ids of the second sequence.
            already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                Whether or not the token list is already formatted with special tokens for the model.

        Returns:
            A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        assert already_has_special_tokens and token_ids_1 is None, (
            "You cannot use ``already_has_special_tokens=False`` with this tokenizer. "
            "Please use a slow (full python) tokenizer to activate this argument."
            "Or set `return_special_tokens_mask=True` when calling the encoding method "
            "to get the special tokens mask in any tokenizer. "
        )

        all_special_ids = self.all_special_ids  # cache the property

        special_tokens_mask = [
            1 if token in all_special_ids else 0 for token in token_ids_0
        ]

        return special_tokens_mask

    @staticmethod
    def clean_up_tokenization(out_string: str) -> str:
        """
        Clean up a list of simple English tokenization artifacts like spaces before punctuations and abbreviated forms.

        Args:
            out_string (:obj:`str`): The text to clean up.

        Returns:
            :obj:`str`: The cleaned-up string.
        """
        out_string = (
            out_string.replace(" .", ".")
            .replace(" ?", "?")
            .replace(" !", "!")
            .replace(" ,", ",")
            .replace(" ' ", "'")
            .replace(" n't", "n't")
            .replace(" 'm", "'m")
            .replace(" 's", "'s")
            .replace(" 've", "'ve")
            .replace(" 're", "'re")
        )
        return out_string

    def _eventual_warn_about_too_long_sequence(
        self, ids: List[int], max_length: Optional[int], verbose: bool
    ):
        """
        Depending on the input and internal state we might trigger a warning about a sequence that is too long for its
        corresponding model
        Args:
            ids (:obj:`List[str]`): The ids produced by the tokenization
            max_length (:obj:`int`, `optional`): The max_length desired (does not trigger a warning if it is set)
            verbose (:obj:`bool`): Whether or not to print more information and warnings.
        """
        if max_length is None and len(ids) > self.model_max_length and verbose:
            if not self.deprecation_warnings.get(
                "sequence-length-is-longer-than-the-specified-maximum", False
            ):
                logger.warning(
                    f"Token indices sequence length is longer than the specified maximum sequence length "
                    f"for this model ({len(ids)} > {self.model_max_length}). Running this sequence through the model will result in "
                    f"indexing errors"
                )
            self.deprecation_warnings[
                "sequence-length-is-longer-than-the-specified-maximum"
            ] = True