tokenization_tapex.py

# coding=utf-8
# Copyright 2022 Microsoft Research and The HuggingFace Inc. team. All rights reserved.
#
# 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.
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"""Tokenization classes for TAPEX."""

import json
import os
import random
from contextlib import contextmanager
from functools import lru_cache
from typing import Dict, List, Optional, Tuple, Union

import regex as re

from ...file_utils import ExplicitEnum, PaddingStrategy, TensorType, add_end_docstrings, is_pandas_available
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import ENCODE_KWARGS_DOCSTRING, BatchEncoding, TextInput, TruncationStrategy
from ...utils import logging


if is_pandas_available():
    import pandas as pd


logger = logging.get_logger(__name__)

VOCAB_FILES_NAMES = {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

PRETRAINED_VOCAB_FILES_MAP = {
    "vocab_file": {
        "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/vocab.json",
    },
    "merges_file": {
        "microsoft/tapex-base": "https://huggingface.co/microsoft/tapex-base/resolve/main/merges.txt",
    },
}

PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
    "microsoft/tapex-base": 512,
}

PRETRAINED_INIT_CONFIGURATION = {
    "microsoft/tapex-base": {"do_lower_case": True},
}


class TapexTruncationStrategy(ExplicitEnum):
    """
    Possible values for the `truncation` argument in [`~TapasTokenizer.__call__`]. Useful for tab-completion in an IDE.
    """

    DROP_ROWS_TO_FIT = "drop_rows_to_fit"


class TokenizerStrategy(ExplicitEnum):

    TOKENIZE_SOURCE = "tokenize_source"
    TOKENIZE_TARGET = "tokenize_target"


TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
            add_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether or not to encode the sequences with the special tokens relative to their model.
            padding (`bool`, `str` or [`~file_utils.PaddingStrategy`], *optional*, defaults to `False`):
                Activates and controls padding. Accepts the following values:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).
            truncation (`bool`, `str`, [`TapexTruncationStrategy`] or [`~tokenization_utils_base.TruncationStrategy`],
                   *optional*, defaults to `False`):

                Activates and controls truncation. Accepts the following values:

                - `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length` or to the
                  maximum acceptable input length for the model if that argument is not provided. This will truncate
                  row by row, removing rows from the table.
                - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `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.
                - `'only_first'`: Truncate to a maximum length specified with the argument `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.
                - `'only_second'`: Truncate to a maximum length specified with the argument `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.
                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                  greater than the model maximum admissible input size).
            max_length (`int`, *optional*):
                Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
                `None`, this will use the predefined model maximum length if a maximum length is required by one of the
                truncation/padding parameters. If the model has no specific maximum input length (like XLNet)
                truncation/padding to a maximum length will be deactivated.
            stride (`int`, *optional*, defaults to 0):
                If set to a number along with `max_length`, the overflowing tokens returned when
                `return_overflowing_tokens=True` will contain some tokens from the end of the truncated sequence
                returned to provide some overlap between truncated and overflowing sequences. The value of this
                argument defines the number of overlapping tokens.
            pad_to_multiple_of (`int`, *optional*):
                If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
                the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
            return_tensors (`str` or [`~file_utils.TensorType`], *optional*):
                If set, will return tensors instead of list of python integers. Acceptable values are:

                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return Numpy `np.ndarray` objects.
"""


@lru_cache()
def bytes_to_unicode():
    """
    Returns list of utf-8 byte and a mapping to unicode strings. We specifically avoids mapping to whitespace/control
    characters the bpe code barfs on. The reversible bpe codes work on unicode strings. This means you need a large #
    of unicode characters in your vocab if you want to avoid UNKs. When you're at something like a 10B token dataset
    you end up needing around 5K for decent coverage. This is a significant percentage of your normal, say, 32K bpe
    vocab. To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
    """
    bs = (
        list(range(ord("!"), ord("~") + 1)) + list(range(ord("¡"), ord("¬") + 1)) + list(range(ord("®"), ord("ÿ") + 1))
    )
    cs = bs[:]
    n = 0
    for b in range(2**8):
        if b not in bs:
            bs.append(b)
            cs.append(2**8 + n)
            n += 1
    cs = [chr(n) for n in cs]
    return dict(zip(bs, cs))


def get_pairs(word):
    """
    Return set of symbol pairs in a word. Word is represented as tuple of symbols (symbols being variable-length
    strings).
    """
    pairs = set()
    prev_char = word[0]
    for char in word[1:]:
        pairs.add((prev_char, char))
        prev_char = char
    return pairs


class IndexedRowTableLinearize:
    """
    FORMAT: col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...
    """

    def process_table(self, table_content: Dict):
        """
        Given a table, TableLinearize aims at converting it into a flatten sequence with special symbols.
        """
        assert "header" in table_content and "rows" in table_content, self.PROMPT_MESSAGE
        # process header
        table_str = self.process_header(table_content["header"]) + " "
        # process rows
        for i, row_example in enumerate(table_content["rows"]):
            # NOTE: the row should start from row 1 instead of 0
            table_str += self.process_row(row_example, row_index=i + 1) + " "
        return table_str.strip()

    def process_header(self, headers: List):
        """
        Given a list of headers, TableLinearize aims at converting it into a flatten sequence with special symbols.
        """
        return "col : " + " | ".join(headers)

    def process_row(self, row: List, row_index: int):
        """
        Given a row, TableLinearize aims at converting it into a flatten sequence with special symbols.
        """
        row_str = ""
        row_cell_values = []
        for cell_value in row:
            if isinstance(cell_value, int):
                row_cell_values.append(str(cell_value))
            else:
                row_cell_values.append(cell_value)
        row_str += " | ".join(row_cell_values)
        return "row " + str(row_index) + " : " + row_str


class TapexTokenizer(PreTrainedTokenizer):
    r"""
    Construct a TAPEX tokenizer. Based on byte-level Byte-Pair-Encoding (BPE).

    This tokenizer can be used to flatten one or more table(s) and concatenate them with one or more related sentences
    to be used by TAPEX models. The format that the TAPEX tokenizer creates is the following:

    sentence col: col1 | col2 | col 3 row 1 : val1 | val2 | val3 row 2 : ...

    The tokenizer supports a single table + single query, a single table and multiple queries (in which case the table
    will be duplicated for every query), a single query and multiple tables (in which case the query will be duplicated
    for every table), and multiple tables and queries. In other words, you can provide a batch of tables + questions to
    the tokenizer for instance to prepare them for the model.

    Tokenization itself is based on the BPE algorithm. It is identical to the one used by BART, RoBERTa and GPT-2.

    This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
    this superclass for more information regarding those methods.

    Args:
        vocab_file (`str`):
            Path to the vocabulary file.
        merges_file (`str`):
            Path to the merges file.
        do_lower_case (`bool`, *optional*, defaults to `True`):
            Whether or not to lowercase the input when tokenizing.
        errors (`str`, *optional*, defaults to `"replace"`):
            Paradigm to follow when decoding bytes to UTF-8. See
            [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
        bos_token (`str`, *optional*, defaults to `"<s>"`):
            The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the beginning of
            sequence. The token used is the `cls_token`.

            </Tip>

        eos_token (`str`, *optional*, defaults to `"</s>"`):
            The end of sequence token.

            <Tip>

            When building a sequence using special tokens, this is not the token that is used for the end of sequence.
            The token used is the `sep_token`.

            </Tip>

        sep_token (`str`, *optional*, defaults to `"</s>"`):
            The separator token, which is used when building a sequence from multiple sequences, e.g. two sequences for
            sequence classification or for a text and a question for question answering. It is also used as the last
            token of a sequence built with special tokens.
        cls_token (`str`, *optional*, defaults to `"<s>"`):
            The classifier token which is used when doing sequence classification (classification of the whole sequence
            instead of per-token classification). It is the first token of the sequence when built with special tokens.
        unk_token (`str`, *optional*, defaults to `"<unk>"`):
            The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
            token instead.
        pad_token (`str`, *optional*, defaults to `"<pad>"`):
            The token used for padding, for example when batching sequences of different lengths.
        mask_token (`str`, *optional*, defaults to `"<mask>"`):
            The token used for masking values. This is the token used when training this model with masked language
            modeling. This is the token which the model will try to predict.
        add_prefix_space (`bool`, *optional*, defaults to `False`):
            Whether or not to add an initial space to the input. This allows to treat the leading word just as any
            other word. (BART tokenizer detect beginning of words by the preceding space).
        max_cell_length (`int`, *optional*, defaults to 15):
            Maximum number of characters per cell when linearizing a table. If this number is exceeded, truncation
            takes place.
    """

    vocab_files_names = VOCAB_FILES_NAMES
    pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
    max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
    model_input_names = ["input_ids", "attention_mask"]

    def __init__(
        self,
        vocab_file,
        merges_file,
        do_lower_case=True,
        errors="replace",
        bos_token="<s>",
        eos_token="</s>",
        sep_token="</s>",
        cls_token="<s>",
        unk_token="<unk>",
        pad_token="<pad>",
        mask_token="<mask>",
        add_prefix_space=False,
        max_cell_length=15,
        **kwargs
    ):
        bos_token = AddedToken(bos_token, lstrip=False, rstrip=False) if isinstance(bos_token, str) else bos_token
        eos_token = AddedToken(eos_token, lstrip=False, rstrip=False) if isinstance(eos_token, str) else eos_token
        sep_token = AddedToken(sep_token, lstrip=False, rstrip=False) if isinstance(sep_token, str) else sep_token
        cls_token = AddedToken(cls_token, lstrip=False, rstrip=False) if isinstance(cls_token, str) else cls_token
        unk_token = AddedToken(unk_token, lstrip=False, rstrip=False) if isinstance(unk_token, str) else unk_token
        pad_token = AddedToken(pad_token, lstrip=False, rstrip=False) if isinstance(pad_token, str) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        mask_token = AddedToken(mask_token, lstrip=True, rstrip=False) if isinstance(mask_token, str) else mask_token

        super().__init__(
            vocab_file=vocab_file,
            merges_file=merges_file,
            do_lower_case=do_lower_case,
            errors=errors,
            bos_token=bos_token,
            eos_token=eos_token,
            unk_token=unk_token,
            sep_token=sep_token,
            cls_token=cls_token,
            pad_token=pad_token,
            mask_token=mask_token,
            add_prefix_space=add_prefix_space,
            max_cell_length=max_cell_length,
            **kwargs,
        )

        with open(vocab_file, encoding="utf-8") as vocab_handle:
            self.encoder = json.load(vocab_handle)
        self.decoder = {v: k for k, v in self.encoder.items()}
        self.errors = errors  # how to handle errors in decoding
        self.byte_encoder = bytes_to_unicode()
        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
        with open(merges_file, encoding="utf-8") as merges_handle:
            bpe_merges = merges_handle.read().split("\n")[1:-1]
        bpe_merges = [tuple(merge.split()) for merge in bpe_merges]
        self.bpe_ranks = dict(zip(bpe_merges, range(len(bpe_merges))))
        self.cache = {}
        self.add_prefix_space = add_prefix_space
        self.do_lower_case = do_lower_case

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        self.pat = re.compile(r"""'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+""")

        # additional properties
        self.max_cell_length = max_cell_length
        self.table_linearize = IndexedRowTableLinearize()

        # property to decide using which call function
        self.current_tokenizer = TokenizerStrategy.TOKENIZE_SOURCE

    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. A TAPEX sequence has the following format:
        - single sequence: `<s> X </s>`
        - pair of sequences: `<s> A </s></s> B </s>`
        Args:
            token_ids_0 (`List[int]`):
                List of IDs to which the special tokens will be added.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
        Returns:
            `List[int]`: List of [input IDs](../glossary#input-ids) with the appropriate special tokens.
        """
        if token_ids_1 is None:
            return [self.cls_token_id] + token_ids_0 + [self.sep_token_id]
        cls = [self.cls_token_id]
        sep = [self.sep_token_id]
        return cls + token_ids_0 + sep + sep + token_ids_1 + sep

    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]:
        """
        Args:
        Retrieve 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` method.
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
            already_has_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not the token list is already formatted with special tokens for the model.
        Returns:
            `List[int]`: A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.
        """
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_0=token_ids_0, token_ids_1=token_ids_1, already_has_special_tokens=True
            )

        if token_ids_1 is None:
            return [1] + ([0] * len(token_ids_0)) + [1]
        return [1] + ([0] * len(token_ids_0)) + [1, 1] + ([0] * len(token_ids_1)) + [1]

    def create_token_type_ids_from_sequences(
        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
    ) -> List[int]:
        """
        Args:
        Create a mask from the two sequences passed to be used in a sequence-pair classification task. TAPEX does not:
        make use of token type ids, therefore a list of zeros is returned.
            token_ids_0 (`List[int]`):
                List of IDs.
            token_ids_1 (`List[int]`, *optional*):
                Optional second list of IDs for sequence pairs.
        Returns:
            `List[int]`: List of zeros.
        """
        sep = [self.sep_token_id]
        cls = [self.cls_token_id]

        if token_ids_1 is None:
            return len(cls + token_ids_0 + sep) * [0]
        return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [0]

    def prepare_for_tokenization(self, text, is_split_into_words=False, **kwargs):
        add_prefix_space = kwargs.pop("add_prefix_space", self.add_prefix_space)
        if (is_split_into_words or add_prefix_space) and (len(text) > 0 and not text[0].isspace()):
            text = " " + text
        return (text, kwargs)

    @property
    def vocab_size(self):
        return len(self.encoder)

    def get_vocab(self):
        return dict(self.encoder, **self.added_tokens_encoder)

    def bpe(self, token):
        if token in self.cache:
            return self.cache[token]
        word = tuple(token)
        pairs = get_pairs(word)

        if not pairs:
            return token

        while True:
            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
            if bigram not in self.bpe_ranks:
                break
            first, second = bigram
            new_word = []
            i = 0
            while i < len(word):
                try:
                    j = word.index(first, i)
                except ValueError:
                    new_word.extend(word[i:])
                    break
                else:
                    new_word.extend(word[i:j])
                    i = j

                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
                    new_word.append(first + second)
                    i += 2
                else:
                    new_word.append(word[i])
                    i += 1
            new_word = tuple(new_word)
            word = new_word
            if len(word) == 1:
                break
            else:
                pairs = get_pairs(word)
        word = " ".join(word)
        self.cache[token] = word
        return word

    def _tokenize(self, text):
        """Tokenize a string."""
        bpe_tokens = []
        for token in re.findall(self.pat, text):
            token = "".join(
                self.byte_encoder[b] for b in token.encode("utf-8")
            )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(token).split(" "))
        return bpe_tokens

    def _convert_token_to_id(self, token):
        """Converts a token (str) in an id using the vocab."""
        return self.encoder.get(token, self.encoder.get(self.unk_token))

    def _convert_id_to_token(self, index):
        """Converts an index (integer) in a token (str) using the vocab."""
        return self.decoder.get(index)

    def convert_tokens_to_string(self, tokens):
        """Converts a sequence of tokens (string) in a single string."""
        text = "".join(tokens)
        text = bytearray([self.byte_decoder[c] for c in text]).decode("utf-8", errors=self.errors)
        return text

    def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
        if not os.path.isdir(save_directory):
            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
            return
        vocab_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
        )
        merge_file = os.path.join(
            save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]
        )

        with open(vocab_file, "w", encoding="utf-8") as f:
            f.write(json.dumps(self.encoder, indent=2, sort_keys=True, ensure_ascii=False) + "\n")

        index = 0
        with open(merge_file, "w", encoding="utf-8") as writer:
            writer.write("#version: 0.2\n")
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items(), key=lambda kv: kv[1]):
                if index != token_index:
                    logger.warning(
                        f"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                        " Please check that the tokenizer is not corrupted!"
                    )
                    index = token_index
                writer.write(" ".join(bpe_tokens) + "\n")
                index += 1

        return vocab_file, merge_file

    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
    def __call__(
        self,
        table: Union["pd.DataFrame", List["pd.DataFrame"]] = None,
        query: Optional[Union[TextInput, List[TextInput]]] = None,
        answer: 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,
        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 table-sequence pair(s).

        Args:
            table (`pd.DataFrame`, `List[pd.DataFrame]`):
                Table(s) containing tabular data.
            query (`str` or `List[str]`, *optional*):
                Sentence or batch of sentences related to one or more table(s) to be encoded. Note that the number of
                sentences must match the number of tables.
            answer (`str` or `List[str]`, *optional*):
                Optionally, the corresponding answer to the questions as supervision.
        """

        if self.current_tokenizer == TokenizerStrategy.TOKENIZE_SOURCE:
            if table is None:
                raise ValueError("Please ensure that the table is not empty if you use TAPEX to encode source.")
            return self.source_call_func(
                table=table,
                query=query,
                answer=answer,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=pad_to_multiple_of,
                return_tensors=return_tensors,
                return_token_type_ids=return_token_type_ids,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_length=return_length,
                verbose=verbose,
                **kwargs,
            )
        else:
            if answer is None:
                raise ValueError("Please ensure that the answer is not empty if you use TAPEX to encode target.")
            return self.target_call_func(
                answer=answer,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=pad_to_multiple_of,
                return_tensors=return_tensors,
                return_token_type_ids=return_token_type_ids,
                return_attention_mask=return_attention_mask,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_offsets_mapping=return_offsets_mapping,
                return_length=return_length,
                verbose=verbose,
                **kwargs,
            )

    def source_call_func(
        self,
        table: Union["pd.DataFrame", List["pd.DataFrame"]],
        query: Optional[Union[TextInput, List[TextInput]]] = None,
        answer: 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,
        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:
        # Input type checking for clearer error
        valid_table = False
        valid_query = False

        # Check that table have a valid type
        if isinstance(table, pd.DataFrame):
            valid_table = True
        elif isinstance(table, (list, tuple)) and isinstance(table[0], pd.DataFrame):
            valid_table = True

        # Check that query have a valid type
        if query is None or isinstance(query, str):
            valid_query = True
        elif isinstance(query, (list, tuple)):
            if len(query) == 0 or isinstance(query[0], str):
                valid_query = True

        if not valid_table:
            raise ValueError(
                "table input must of type `pd.DataFrame` (single example), `List[pd.DataFrame]` (batch of examples). "
            )
        if not valid_query:
            raise ValueError("query input must of type `str` (single example), `List[str]` (batch of examples). ")
        is_batched = isinstance(table, (list, tuple)) or isinstance(query, (list, tuple))

        if is_batched:
            return self.batch_encode_plus(
                table=table,
                query=query,
                answer=answer,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                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(
                table=table,
                query=query,
                answer=answer,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                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,
            )

    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
    def batch_encode_plus(
        self,
        table: Union["pd.DataFrame", List["pd.DataFrame"]],
        query: Optional[List[TextInput]] = None,
        answer: List[str] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str] = False,
        max_length: Optional[int] = None,
        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:
        """
        <Tip warning={true}>

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

        </Tip>
        """
        # 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(
            table=table,
            query=query,
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            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,
        table: Union["pd.DataFrame", List["pd.DataFrame"]],
        query: Optional[List[TextInput]] = None,
        answer: Optional[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,
        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:

        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers. "
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast."
            )

        if isinstance(table, pd.DataFrame) and isinstance(query, (list, tuple)):
            # single table, many queries case
            # duplicate table for every query
            table = [table] * len(query)
        if isinstance(table, (list, tuple)) and isinstance(query, str):
            # many tables, single query case
            # duplicate query for every table
            query = [query] * len(table)

        batch_outputs = self._batch_prepare_for_model(
            table=table,
            query=query,
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            return_tensors=return_tensors,
            verbose=verbose,
        )

        return BatchEncoding(batch_outputs)

    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
    def _batch_prepare_for_model(
        self,
        table: Union["pd.DataFrame", List["pd.DataFrame"]],
        query: Optional[Union[TextInput, List[TextInput]]] = None,
        answer: Optional[Union[str, 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,
        pad_to_multiple_of: Optional[int] = None,
        return_tensors: Optional[str] = None,
        return_token_type_ids: Optional[bool] = None,
        return_attention_mask: Optional[bool] = None,
        return_overflowing_tokens: bool = False,
        return_special_tokens_mask: bool = False,
        return_length: bool = False,
        verbose: bool = True,
    ) -> BatchEncoding:
        """
        This method 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.
        """
        batch_outputs = {}
        if answer is None:
            answer = [None] * len(table)
        for _table, _query, _answer in zip(table, query, answer):
            text = self.prepare_table_query(
                _table, _query, _answer, truncation_strategy=truncation_strategy, max_length=max_length
            )

            if self.do_lower_case:
                text = text.lower()

            tokens = self.tokenize(text)
            outputs = self.prepare_for_model(
                ids=self.convert_tokens_to_ids(tokens),
                add_special_tokens=add_special_tokens,
                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterwards
                truncation=truncation_strategy.value,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=None,  # we pad in batch afterwards
                return_attention_mask=False,  # we pad in batch afterwards
                return_token_type_ids=return_token_type_ids,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_length=return_length,
                return_tensors=None,  # We convert the whole batch to tensors at the end
                prepend_batch_axis=False,
                verbose=verbose,
            )

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

        batch_outputs = self.pad(
            batch_outputs,
            padding=padding_strategy.value,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )

        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)

        return batch_outputs

    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING)
    def encode(
        self,
        table: "pd.DataFrame",
        query: Optional[TextInput] = None,
        answer: Optional[str] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs
    ) -> List[int]:
        """
        Prepare a table, a string and possible answer for the model. This method does not return token type IDs,
        attention masks, etc. which are necessary for the model to work correctly. Use this method if you want to build
        your processing on your own, otherwise refer to `__call__`.
        """
        encoded_inputs = self.encode_plus(
            table,
            query=query,
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            return_tensors=return_tensors,
            **kwargs,
        )

        return encoded_inputs["input_ids"]

    @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, TAPEX_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
    def encode_plus(
        self,
        table: "pd.DataFrame",
        query: Optional[TextInput] = None,
        answer: Optional[str] = None,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str] = False,
        max_length: Optional[int] = None,
        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_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs
    ) -> BatchEncoding:
        # 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(
            table=table,
            query=query,
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            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_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def _encode_plus(
        self,
        table: "pd.DataFrame",
        query: Optional[TextInput] = None,
        answer: 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,
        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:
        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers. "
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast. "
                "More information on available tokenizers at "
                "https://github.com/huggingface/transformers/pull/2674"
            )

        text = self.prepare_table_query(
            table, query, answer, truncation_strategy=truncation_strategy, max_length=max_length
        )

        # if necessary, perform lower case
        if self.do_lower_case:
            text = text.lower()

        tokens = self.tokenize(text)

        return self.prepare_for_model(
            ids=self.convert_tokens_to_ids(tokens),
            add_special_tokens=add_special_tokens,
            padding=padding_strategy.value,
            truncation=truncation_strategy.value,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            prepend_batch_axis=True,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            verbose=verbose,
        )

    def target_call_func(
        self,
        answer: Union[str, List[str]],
        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,
        **kwargs
    ) -> BatchEncoding:
        """
        The method tokenizes and prepares the answer label for the model.

        Args:
            answer (`str` or `List[str]`):
                Corresponding answer supervision to the queries for training the model.
        """
        is_batched = isinstance(answer, (list, tuple))

        if is_batched:
            return self.target_batch_encode_plus(
                answer=answer,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                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.target_encode_plus(
                answer=answer,
                add_special_tokens=add_special_tokens,
                padding=padding,
                truncation=truncation,
                max_length=max_length,
                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 target_batch_encode_plus(
        self,
        answer: List[str],
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str] = False,
        max_length: Optional[int] = None,
        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:
        """
        Prepare answer strings for the model.

        Args:
            answer `List[str]`:
                Corresponding answer supervision to the queries for training the model.
        """
        # 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._target_batch_encode_plus(
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            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 _target_batch_encode_plus(
        self,
        answer: List[str],
        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,
        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:

        batch_outputs = {}
        for text in answer:

            if self.do_lower_case:
                text = text.lower()

            tokens = self.tokenize(text)
            outputs = self.prepare_for_model(
                ids=self.convert_tokens_to_ids(tokens),
                add_special_tokens=add_special_tokens,
                padding=PaddingStrategy.DO_NOT_PAD.value,  # we pad in batch afterwards
                truncation=truncation_strategy.value,
                max_length=max_length,
                stride=stride,
                pad_to_multiple_of=None,  # we pad in batch afterwards
                return_attention_mask=False,  # we pad in batch afterwards
                return_token_type_ids=return_token_type_ids,
                return_overflowing_tokens=return_overflowing_tokens,
                return_special_tokens_mask=return_special_tokens_mask,
                return_length=return_length,
                return_tensors=None,  # We convert the whole batch to tensors at the end
                prepend_batch_axis=False,
                verbose=verbose,
            )

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

        batch_outputs = self.pad(
            batch_outputs,
            padding=padding_strategy.value,
            max_length=max_length,
            pad_to_multiple_of=pad_to_multiple_of,
            return_attention_mask=return_attention_mask,
        )

        batch_outputs = BatchEncoding(batch_outputs, tensor_type=return_tensors)

        return BatchEncoding(batch_outputs)

    def target_encode(
        self,
        answer: str,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str, TruncationStrategy, TapexTruncationStrategy] = False,
        max_length: Optional[int] = None,
        return_tensors: Optional[Union[str, TensorType]] = None,
        **kwargs
    ) -> List[int]:
        """
        Prepare the answer string for the model. This method does not return token type IDs, attention masks, etc.
        which are necessary for the model to work correctly. Use this method if you want to build your processing on
        your own, otherwise refer to `__call__`.

        Args:
            answer `str`:
                Corresponding answer supervision to the queries for training the model
        """
        encoded_outputs = self.target_encode_plus(
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding=padding,
            truncation=truncation,
            max_length=max_length,
            return_tensors=return_tensors,
            **kwargs,
        )

        return encoded_outputs["input_ids"]

    def target_encode_plus(
        self,
        answer: str,
        add_special_tokens: bool = True,
        padding: Union[bool, str, PaddingStrategy] = False,
        truncation: Union[bool, str] = False,
        max_length: Optional[int] = None,
        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_special_tokens_mask: bool = False,
        return_offsets_mapping: bool = False,
        return_length: bool = False,
        verbose: bool = True,
        **kwargs
    ) -> BatchEncoding:
        """
        Prepare a answer string for the model.

        Args:
            answer `str`:
                Corresponding answer supervision to the queries for training the model.
        """
        # 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._target_encode_plus(
            answer=answer,
            add_special_tokens=add_special_tokens,
            padding_strategy=padding_strategy,
            truncation_strategy=truncation_strategy,
            max_length=max_length,
            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_special_tokens_mask=return_special_tokens_mask,
            return_offsets_mapping=return_offsets_mapping,
            return_length=return_length,
            verbose=verbose,
            **kwargs,
        )

    def _target_encode_plus(
        self,
        answer: str,
        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,
        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:
        if return_offsets_mapping:
            raise NotImplementedError(
                "return_offset_mapping is not available when using Python tokenizers. "
                "To use this feature, change your tokenizer to one deriving from "
                "transformers.PreTrainedTokenizerFast. "
                "More information on available tokenizers at "
                "https://github.com/huggingface/transformers/pull/2674"
            )

        text = answer

        # if necessary, perform lower case
        if self.do_lower_case:
            text = text.lower()

        tokens = self.tokenize(text)

        return self.prepare_for_model(
            ids=self.convert_tokens_to_ids(tokens),
            add_special_tokens=add_special_tokens,
            padding=padding_strategy.value,
            truncation=truncation_strategy.value,
            max_length=max_length,
            stride=stride,
            pad_to_multiple_of=pad_to_multiple_of,
            return_tensors=return_tensors,
            prepend_batch_axis=True,
            return_attention_mask=return_attention_mask,
            return_token_type_ids=return_token_type_ids,
            return_overflowing_tokens=return_overflowing_tokens,
            return_special_tokens_mask=return_special_tokens_mask,
            return_length=return_length,
            verbose=verbose,
        )

    @contextmanager
    def as_target_tokenizer(self):
        """
        Temporarily sets the tokenizer for encoding the targets. Useful for tokenizer associated to
        sequence-to-sequence models that need a slightly different processing for the labels.
        """
        self.current_tokenizer = TokenizerStrategy.TOKENIZE_TARGET
        yield
        # restore the call function
        self.current_tokenizer = TokenizerStrategy.TOKENIZE_SOURCE

    def prepare_table_query(
        self,
        table,
        query,
        answer=None,
        truncation_strategy=Union[str, TruncationStrategy, TapexTruncationStrategy],
        max_length=None,
    ):
        """
        This method can be used to linearize a table and add a corresponding query.

        Optionally, it also handles truncation of the table (cells).

        An answer can be provided for more precise truncation.
        """
        if not table.empty:
            # step 1: create table dictionary
            table_content = {"header": list(table.columns), "rows": [list(row.values) for i, row in table.iterrows()]}

            # step 2: modify table internally
            # always truncate table cells based on self.max_cell_length
            # optionally truncate rows if truncation_strategy is set to it
            self.truncate_table_cells(table_content, query, answer)
            if truncation_strategy == TapexTruncationStrategy.DROP_ROWS_TO_FIT:
                self.truncate_table_rows(table_content, query, answer, max_length=max_length)

            # step 3: linearize table
            linear_table = self.table_linearize.process_table(table_content)
        else:
            linear_table = ""

        if linear_table == "":
            logger.warning(
                "You provide an empty table, or all cells contain much tokens (e.g., >= 1024 tokens). "
                + f"Please carefully check the corresponding table with the query : {query}."
            )
        if query == "":
            logger.warning("You provide nothing to query with respect to the table.")
        # step 4: concatenate query with linear_table
        separator = " " if query and linear_table else ""
        joint_input = (query + separator + linear_table) if query else linear_table

        return joint_input

    def truncate_table_cells(self, table_content: Dict, question: str, answer: List):
        # TODO (Qian): is it possible to revert the original cell if it is in the final answer?
        cell_mapping = {}
        for row in table_content["rows"]:
            for i, cell in enumerate(row):
                truncate_cell = self.truncate_cell(cell)
                if truncate_cell is not None:
                    cell_mapping[cell] = truncate_cell
                    row[i] = truncate_cell

        # modify the answer list
        if answer is not None:
            for i, case in enumerate(answer):
                if case in cell_mapping.keys():
                    answer[i] = cell_mapping[case]

    def truncate_cell(self, cell_value):
        # do not process on these cases
        if isinstance(cell_value, int) or isinstance(cell_value, float):
            return cell_value
        if cell_value.strip() != "":
            try_tokens = self.tokenize(cell_value)
            if len(try_tokens) >= self.max_cell_length:
                retain_tokens = try_tokens[: self.max_cell_length]
                retain_cell_value = self.convert_tokens_to_string(retain_tokens)
                return retain_cell_value
            else:
                return None
        else:
            return cell_value

    def truncate_table_rows(
        self, table_content: Dict, question: str, answer: Optional[Union[str, List[str]]] = None, max_length=None
    ):
        """
        Args:

        table_content:
            {"header": xxx, "rows": xxx, "id" (Optionally): xxx}

        question:
            natural language sentence

        answer:
            if for training, is the supervision; otherwise will be empty
        """
        delete_ratio, remain_token_len = self.estimate_delete_ratio(table_content, question, max_length)
        # randomly delete unrelated rows
        self.delete_unrelated_rows(table_content, question, answer, delete_ratio)
        # guarantee the result < max_length
        maximum_keep_rows = 0
        for ind, row_example in enumerate(table_content["rows"]):
            value_string = self.table_linearize.process_row(row_example, ind + 1)
            value_token_len = len(self.tokenize(value_string))
            # over the size limit, and take action
            if value_token_len > remain_token_len:
                break
            remain_token_len -= value_token_len
            maximum_keep_rows += 1
        del table_content["rows"][maximum_keep_rows:]

    def estimate_delete_ratio(self, table_content: Dict, question: str, max_length=None):
        if "header" not in table_content or "rows" not in table_content:
            raise ValueError("The table content should contain both 'header' and 'rows' keys.")
        # calculate the tokens of header, special tokens will only be pre-prepended into question
        question_tokens = self.tokenize(question, add_special_tokens=True)
        # calculate the tokens of header
        header_string = self.table_linearize.process_header(table_content["header"])
        header_tokens = self.tokenize(header_string, add_special_tokens=False)
        # split all cell values into tokens and see how many can be accommodated
        used_token_len = len(question_tokens) + len(header_tokens)
        # remaining token space for rows
        remain_token_len = max_length - used_token_len

        value_string = ""
        for _, row_example in enumerate(table_content["rows"]):
            # use a general index to roughly estimate the overall token len
            value_string += self.table_linearize.process_row(row_example, 100) + " "
        value_token_len = len(self.tokenize(value_string))

        if value_token_len < remain_token_len:
            # no row will be deleted
            return 0.0, remain_token_len
        else:
            # calc a roughly delete rate
            return 1.0 - remain_token_len / value_token_len, remain_token_len

    def delete_unrelated_rows(self, table_content: Dict, question: str, answer: List, delete_ratio: float):
        """
        The argument answer is used only during training.
        """
        truncated_unrelated_indices = []
        related_indices = []
        if answer is None or len(answer) == 0:
            answer_set = set([])
        else:
            answer_set = set([ans_ex.lower() for ans_ex in answer])
        # add question key words into answer set
        if question is not None:
            answer_set.update(question.split())
        question_set = set(question.strip("?!.,").split(" "))
        row_max_len = len(table_content["rows"])
        for _row_idx, row in enumerate(table_content["rows"]):
            lower_row = set([str(cell).lower() for cell in row])
            if len(lower_row & answer_set) == 0 and len(lower_row & question_set) == 0:
                truncated_unrelated_indices.append(_row_idx)
            else:
                # add neighbours to preserve information aggressively
                related_indices.extend([_row_idx - 2, _row_idx - 1, _row_idx, _row_idx + 1, _row_idx + 2])

        # remove the neighbours
        truncated_unrelated_indices = [
            _row_idx for _row_idx in truncated_unrelated_indices if _row_idx not in related_indices
        ]
        # select some cases to drop
        drop_items = min(len(truncated_unrelated_indices), int(len(table_content["rows"]) * delete_ratio))
        drop_row_indices = random.choices(truncated_unrelated_indices, k=drop_items)

        for _row_idx in reversed(range(row_max_len)):
            if _row_idx in drop_row_indices:
                del table_content["rows"][_row_idx]

        # only when the drop ratio is too large, logging for warning.
        if "id" in table_content and len(drop_row_indices) > 0:
            logger.warning("Delete {:.2f} rows in table {}".format(len(drop_row_indices), table_content["id"]))