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	# coding=utf-8
	# Copyright 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.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""Tokenization class for LayoutLMv3. Same as LayoutLMv2, but RoBERTa-like BPE tokenization instead of WordPiece."""

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

	import regex as re

	from ...tokenization_utils import AddedToken, PreTrainedTokenizer
	from ...tokenization_utils_base import (
	BatchEncoding,
	EncodedInput,
	PreTokenizedInput,
	TextInput,
	TextInputPair,
	TruncationStrategy,
	)
	from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging


	logger = logging.get_logger(__name__)

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


	LAYOUTLMV3_ENCODE_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` or [`~tokenization_utils_base.TruncationStrategy`], optional, defaults to `False`):
	Activates and controls truncation. Accepts the following values:

	- `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.
	"""


	LAYOUTLMV3_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 [`~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` or [`~tokenization_utils_base.TruncationStrategy`], optional, defaults to `False`):
	Activates and controls truncation. Accepts the following values:

	- `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 [`~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()
	# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
	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))


	# Copied from transformers.models.roberta.tokenization_roberta.get_pairs
	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 LayoutLMv3Tokenizer(PreTrainedTokenizer):
	r"""
	Construct a LayoutLMv3 tokenizer. Based on [`RoBERTatokenizer`] (Byte Pair Encoding or BPE).
	[`LayoutLMv3Tokenizer`] can be used to turn words, word-level bounding boxes and optional word labels to
	token-level `input_ids`, `attention_mask`, `token_type_ids`, `bbox`, and optional `labels` (for token
	classification).

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

	[`LayoutLMv3Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
	word-level bounding boxes into token-level bounding boxes.

	Args:
	vocab_file (`str`):
	Path to the vocabulary file.
	merges_file (`str`):
	Path to the merges file.
	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 `True`):
	Whether or not to add an initial space to the input. This allows to treat the leading word just as any
	other word. (RoBERTa tokenizer detect beginning of words by the preceding space).
	cls_token_box (`List[int]`, optional, defaults to `[0, 0, 0, 0]`):
	The bounding box to use for the special [CLS] token.
	sep_token_box (`List[int]`, optional, defaults to `[0, 0, 0, 0]`):
	The bounding box to use for the special [SEP] token.
	pad_token_box (`List[int]`, optional, defaults to `[0, 0, 0, 0]`):
	The bounding box to use for the special [PAD] token.
	pad_token_label (`int`, optional, defaults to -100):
	The label to use for padding tokens. Defaults to -100, which is the `ignore_index` of PyTorch's
	CrossEntropyLoss.
	only_label_first_subword (`bool`, optional, defaults to `True`):
	Whether or not to only label the first subword, in case word labels are provided.
	"""

	vocab_files_names = VOCAB_FILES_NAMES
	model_input_names = ["input_ids", "attention_mask", "bbox"]

	def __init__(
	self,
	vocab_file,
	merges_file,
	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=True,
	cls_token_box=[0, 0, 0, 0],
	sep_token_box=[0, 0, 0, 0],
	pad_token_box=[0, 0, 0, 0],
	pad_token_label=-100,
	only_label_first_subword=True,
	**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

	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

	# 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.cls_token_box = cls_token_box
	self.sep_token_box = sep_token_box
	self.pad_token_box = pad_token_box
	self.pad_token_label = pad_token_label
	self.only_label_first_subword = only_label_first_subword

	super().__init__(
	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,
	cls_token_box=cls_token_box,
	sep_token_box=sep_token_box,
	pad_token_box=pad_token_box,
	pad_token_label=pad_token_label,
	only_label_first_subword=only_label_first_subword,
	**kwargs,
	)

	@property
	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size
	def vocab_size(self):
	return len(self.encoder)

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_vocab
	def get_vocab(self):
	vocab = dict(self.encoder).copy()
	vocab.update(self.added_tokens_encoder)
	return vocab

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.bpe
	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

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._tokenize
	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

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_token_to_id
	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))

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer._convert_id_to_token
	def _convert_id_to_token(self, index):
	"""Converts an index (integer) in a token (str) using the vocab."""
	return self.decoder.get(index)

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.convert_tokens_to_string
	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

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.save_vocabulary
	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

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.build_inputs_with_special_tokens
	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 RoBERTa 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

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.get_special_tokens_mask
	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]:
	"""
	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.

	Args:
	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]

	# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.create_token_type_ids_from_sequences
	def create_token_type_ids_from_sequences(
	self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
	) -> List[int]:
	"""
	Create a mask from the two sequences passed to be used in a sequence-pair classification task. RoBERTa does not
	make use of token type ids, therefore a list of zeros is returned.

	Args:
	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 the text starts with a token that should not be split, no space is added before the text in any case.
	# It's necessary to match the fast tokenization
	if (
	(is_split_into_words or add_prefix_space)
	and (len(text) > 0 and not text[0].isspace())
	and sum([text.startswith(no_split_token) for no_split_token in self.added_tokens_encoder]) == 0
	):
	text = " " + text
	return (text, kwargs)

	@add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.__call__
	def __call__(
	self,
	text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]],
	text_pair: Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None,
	boxes: Union[List[List[int]], List[List[List[int]]]] = None,
	word_labels: Optional[Union[List[int], List[List[int]]]] = None,
	add_special_tokens: bool = True,
	padding: Union[bool, str, PaddingStrategy] = False,
	truncation: Union[bool, str, TruncationStrategy] = None,
	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 sequence(s) or one or several pair(s) of
	sequences with word-level normalized bounding boxes and optional labels.

	Args:
	text (`str`, `List[str]`, `List[List[str]]`):
	The sequence or batch of sequences to be encoded. Each sequence can be a string, a list of strings
	(words of a single example or questions of a batch of examples) or a list of list of strings (batch of
	words).
	text_pair (`List[str]`, `List[List[str]]`):
	The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
	(pretokenized string).
	boxes (`List[List[int]]`, `List[List[List[int]]]`):
	Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
	word_labels (`List[int]`, `List[List[int]]`, optional):
	Word-level integer labels (for token classification tasks such as FUNSD, CORD).
	"""

	# 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 text_pair is not None:
	# in case text + text_pair are provided, text = questions, text_pair = words
	if not _is_valid_text_input(text):
	raise ValueError("text input must of type `str` (single example) or `List[str]` (batch of examples). ")
	if not isinstance(text_pair, (list, tuple)):
	raise ValueError(
	"Words must be of type `List[str]` (single pretokenized example), "
	"or `List[List[str]]` (batch of pretokenized examples)."
	)
	else:
	# in case only text is provided => must be words
	if not isinstance(text, (list, tuple)):
	raise ValueError(
	"Words must be of type `List[str]` (single pretokenized example), "
	"or `List[List[str]]` (batch of pretokenized examples)."
	)

	if text_pair is not None:
	is_batched = isinstance(text, (list, tuple))
	else:
	is_batched = isinstance(text, (list, tuple)) and text and isinstance(text[0], (list, tuple))

	words = text if text_pair is None else text_pair
	if boxes is None:
	raise ValueError("You must provide corresponding bounding boxes")
	if is_batched:
	if len(words) != len(boxes):
	raise ValueError("You must provide words and boxes for an equal amount of examples")
	for words_example, boxes_example in zip(words, boxes):
	if len(words_example) != len(boxes_example):
	raise ValueError("You must provide as many words as there are bounding boxes")
	else:
	if len(words) != len(boxes):
	raise ValueError("You must provide as many words as there are bounding boxes")

	if is_batched:
	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`:"
	f" {len(text_pair)}."
	)
	batch_text_or_text_pairs = list(zip(text, text_pair)) if text_pair is not None else text
	is_pair = bool(text_pair is not None)
	return self.batch_encode_plus(
	batch_text_or_text_pairs=batch_text_or_text_pairs,
	is_pair=is_pair,
	boxes=boxes,
	word_labels=word_labels,
	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:
	return self.encode_plus(
	text=text,
	text_pair=text_pair,
	boxes=boxes,
	word_labels=word_labels,
	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,
	)

	@add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.batch_encode_plus
	def batch_encode_plus(
	self,
	batch_text_or_text_pairs: Union[
	List[TextInput],
	List[TextInputPair],
	List[PreTokenizedInput],
	],
	is_pair: bool = None,
	boxes: Optional[List[List[List[int]]]] = None,
	word_labels: Optional[Union[List[int], List[List[int]]]] = None,
	add_special_tokens: bool = True,
	padding: Union[bool, str, PaddingStrategy] = False,
	truncation: Union[bool, str, TruncationStrategy] = None,
	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:
	# 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,
	is_pair=is_pair,
	boxes=boxes,
	word_labels=word_labels,
	add_special_tokens=add_special_tokens,
	padding_strategy=padding_strategy,
	truncation_strategy=truncation_strategy,
	max_length=max_length,
	stride=stride,
	pad_to_multiple_of=pad_to_multiple_of,
	return_tensors=return_tensors,
	return_token_type_ids=return_token_type_ids,
	return_attention_mask=return_attention_mask,
	return_overflowing_tokens=return_overflowing_tokens,
	return_special_tokens_mask=return_special_tokens_mask,
	return_offsets_mapping=return_offsets_mapping,
	return_length=return_length,
	verbose=verbose,
	**kwargs,
	)

	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._batch_encode_plus
	def _batch_encode_plus(
	self,
	batch_text_or_text_pairs: Union[
	List[TextInput],
	List[TextInputPair],
	List[PreTokenizedInput],
	],
	is_pair: bool = None,
	boxes: Optional[List[List[List[int]]]] = None,
	word_labels: Optional[List[List[int]]] = 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."
	)

	batch_outputs = self._batch_prepare_for_model(
	batch_text_or_text_pairs=batch_text_or_text_pairs,
	is_pair=is_pair,
	boxes=boxes,
	word_labels=word_labels,
	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(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._batch_prepare_for_model
	def _batch_prepare_for_model(
	self,
	batch_text_or_text_pairs,
	is_pair: bool = None,
	boxes: Optional[List[List[int]]] = None,
	word_labels: Optional[List[List[int]]] = 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:
	"""
	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:
	batch_ids_pairs: list of tokenized input ids or input ids pairs
	"""

	batch_outputs = {}
	for idx, example in enumerate(zip(batch_text_or_text_pairs, boxes)):
	batch_text_or_text_pair, boxes_example = example
	outputs = self.prepare_for_model(
	batch_text_or_text_pair[0] if is_pair else batch_text_or_text_pair,
	batch_text_or_text_pair[1] if is_pair else None,
	boxes_example,
	word_labels=word_labels[idx] if word_labels is not None else None,
	add_special_tokens=add_special_tokens,
	padding=PaddingStrategy.DO_NOT_PAD.value, # we pad in batch afterward
	truncation=truncation_strategy.value,
	max_length=max_length,
	stride=stride,
	pad_to_multiple_of=None, # we pad in batch afterward
	return_attention_mask=False, # we pad in batch afterward
	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(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING)
	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode
	def encode(
	self,
	text: Union[TextInput, PreTokenizedInput],
	text_pair: Optional[PreTokenizedInput] = None,
	boxes: Optional[List[List[int]]] = None,
	word_labels: Optional[List[int]] = None,
	add_special_tokens: bool = True,
	padding: Union[bool, str, PaddingStrategy] = False,
	truncation: Union[bool, str, TruncationStrategy] = None,
	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,
	) -> List[int]:
	encoded_inputs = self.encode_plus(
	text=text,
	text_pair=text_pair,
	boxes=boxes,
	word_labels=word_labels,
	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,
	)

	return encoded_inputs["input_ids"]

	@add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.encode_plus
	def encode_plus(
	self,
	text: Union[TextInput, PreTokenizedInput],
	text_pair: Optional[PreTokenizedInput] = None,
	boxes: Optional[List[List[int]]] = None,
	word_labels: Optional[List[int]] = None,
	add_special_tokens: bool = True,
	padding: Union[bool, str, PaddingStrategy] = False,
	truncation: Union[bool, str, TruncationStrategy] = None,
	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:
	"""
	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 (`str`, `List[str]`, `List[List[str]]`):
	The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
	text_pair (`List[str]` or `List[int]`, optional):
	Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
	list of list of strings (words of a batch of examples).
	"""

	# 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,
	boxes=boxes,
	text_pair=text_pair,
	word_labels=word_labels,
	add_special_tokens=add_special_tokens,
	padding_strategy=padding_strategy,
	truncation_strategy=truncation_strategy,
	max_length=max_length,
	stride=stride,
	pad_to_multiple_of=pad_to_multiple_of,
	return_tensors=return_tensors,
	return_token_type_ids=return_token_type_ids,
	return_attention_mask=return_attention_mask,
	return_overflowing_tokens=return_overflowing_tokens,
	return_special_tokens_mask=return_special_tokens_mask,
	return_offsets_mapping=return_offsets_mapping,
	return_length=return_length,
	verbose=verbose,
	**kwargs,
	)

	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._encode_plus
	def _encode_plus(
	self,
	text: Union[TextInput, PreTokenizedInput],
	text_pair: Optional[PreTokenizedInput] = None,
	boxes: Optional[List[List[int]]] = None,
	word_labels: Optional[List[int]] = 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"
	)

	return self.prepare_for_model(
	text=text,
	text_pair=text_pair,
	boxes=boxes,
	word_labels=word_labels,
	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,
	)

	@add_end_docstrings(LAYOUTLMV3_ENCODE_KWARGS_DOCSTRING, LAYOUTLMV3_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
	def prepare_for_model(
	self,
	text: Union[TextInput, PreTokenizedInput],
	text_pair: Optional[PreTokenizedInput] = None,
	boxes: Optional[List[List[int]]] = None,
	word_labels: Optional[List[int]] = None,
	add_special_tokens: bool = True,
	padding: Union[bool, str, PaddingStrategy] = False,
	truncation: Union[bool, str, TruncationStrategy] = None,
	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 or a pair of sequences 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. Please Note, for text_pair different than `None` and
	truncation_strategy = longest_first or `True`, it is not possible to return overflowing tokens. Such a
	combination of arguments will raise an error.

	Word-level `boxes` are turned into token-level `bbox`. If provided, word-level `word_labels` are turned into
	token-level `labels`. The word label is used for the first token of the word, while remaining tokens are
	labeled with -100, such that they will be ignored by the loss function.

	Args:
	text (`str`, `List[str]`, `List[List[str]]`):
	The first sequence to be encoded. This can be a string, a list of strings or a list of list of strings.
	text_pair (`List[str]` or `List[int]`, optional):
	Optional second sequence to be encoded. This can be a list of strings (words of a single example) or a
	list of list of strings (words of a batch of examples).
	"""

	# 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,
	)

	tokens = []
	pair_tokens = []
	token_boxes = []
	pair_token_boxes = []
	labels = []

	if text_pair is None:
	if word_labels is None:
	# CASE 1: document image classification (training + inference) + CASE 2: token classification (inference)
	for word, box in zip(text, boxes):
	if len(word) < 1: # skip empty words
	continue
	word_tokens = self.tokenize(word)
	tokens.extend(word_tokens)
	token_boxes.extend([box] * len(word_tokens))
	else:
	# CASE 2: token classification (training)
	for word, box, label in zip(text, boxes, word_labels):
	if len(word) < 1: # skip empty words
	continue
	word_tokens = self.tokenize(word)
	tokens.extend(word_tokens)
	token_boxes.extend([box] * len(word_tokens))
	if self.only_label_first_subword:
	# Use the real label id for the first token of the word, and padding ids for the remaining tokens
	labels.extend([label] + [self.pad_token_label] * (len(word_tokens) - 1))
	else:
	labels.extend([label] * len(word_tokens))
	else:
	# CASE 3: document visual question answering (inference)
	# text = question
	# text_pair = words
	tokens = self.tokenize(text)
	token_boxes = [self.pad_token_box for _ in range(len(tokens))]

	for word, box in zip(text_pair, boxes):
	if len(word) < 1: # skip empty words
	continue
	word_tokens = self.tokenize(word)
	pair_tokens.extend(word_tokens)
	pair_token_boxes.extend([box] * len(word_tokens))

	# Create ids + pair_ids
	ids = self.convert_tokens_to_ids(tokens)
	pair_ids = self.convert_tokens_to_ids(pair_tokens) if pair_tokens else None

	if (
	return_overflowing_tokens
	and truncation_strategy == TruncationStrategy.LONGEST_FIRST
	and pair_ids is not None
	):
	raise ValueError(
	"Not possible to return overflowing tokens for pair of sequences with the "
	"`longest_first`. Please select another truncation strategy than `longest_first`, "
	"for instance `only_second` or `only_first`."
	)

	# Compute the total size of the returned encodings
	pair = bool(pair_ids is not None)
	len_ids = len(ids)
	len_pair_ids = len(pair_ids) if pair else 0
	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 = []
	overflowing_token_boxes = []
	overflowing_labels = []
	if truncation_strategy != TruncationStrategy.DO_NOT_TRUNCATE and max_length and total_len > max_length:
	(
	ids,
	token_boxes,
	pair_ids,
	pair_token_boxes,
	labels,
	overflowing_tokens,
	overflowing_token_boxes,
	overflowing_labels,
	) = self.truncate_sequences(
	ids,
	token_boxes,
	pair_ids=pair_ids,
	pair_token_boxes=pair_token_boxes,
	labels=labels,
	num_tokens_to_remove=total_len - max_length,
	truncation_strategy=truncation_strategy,
	stride=stride,
	)

	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 = {}

	if return_overflowing_tokens:
	encoded_inputs["overflowing_tokens"] = overflowing_tokens
	encoded_inputs["overflowing_token_boxes"] = overflowing_token_boxes
	encoded_inputs["overflowing_labels"] = overflowing_labels
	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)
	token_boxes = [self.cls_token_box] + token_boxes + [self.sep_token_box]
	if pair_token_boxes:
	pair_token_boxes = [self.sep_token_box] + pair_token_boxes + [self.sep_token_box]
	token_boxes = token_boxes + pair_token_boxes if pair else token_boxes
	if labels:
	labels = [self.pad_token_label] + labels + [self.pad_token_label]
	else:
	sequence = ids + pair_ids if pair else ids
	token_type_ids = [0] * len(ids) + ([0] * len(pair_ids) if pair else [])
	token_boxes = token_boxes + pair_token_boxes if pair else token_boxes

	# Build output dictionary
	encoded_inputs["input_ids"] = sequence
	encoded_inputs["bbox"] = token_boxes
	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)

	if labels:
	encoded_inputs["labels"] = labels

	# 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

	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer.truncate_sequences
	def truncate_sequences(
	self,
	ids: List[int],
	token_boxes: List[List[int]],
	pair_ids: Optional[List[int]] = None,
	pair_token_boxes: Optional[List[List[int]]] = None,
	labels: 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 (`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.
	token_boxes (`List[List[int]]`):
	Bounding boxes of the first sequence.
	pair_ids (`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.
	pair_token_boxes (`List[List[int]]`, optional):
	Bounding boxes of the second sequence.
	labels (`List[int]`, optional):
	Labels of the first sequence (for token classification tasks).
	num_tokens_to_remove (`int`, optional, defaults to 0):
	Number of tokens to remove using the truncation strategy.
	truncation_strategy (`str` or [`~tokenization_utils_base.TruncationStrategy`], optional, defaults to `False`):
	The strategy to follow for truncation. Can be:

	- `'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.
	- `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths greater
	than the model maximum admissible input size).
	stride (`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:
	`Tuple[List[int], List[int], List[int]]`: The truncated `ids`, the truncated `pair_ids` and the list of
	overflowing tokens. Note: The longest_first strategy returns empty list of overflowing tokens if a pair
	of sequences (or a batch of pairs) is provided.
	"""
	if num_tokens_to_remove <= 0:
	return ids, token_boxes, pair_ids, pair_token_boxes, labels, [], [], []

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

	overflowing_tokens = []
	overflowing_token_boxes = []
	overflowing_labels = []
	if truncation_strategy == TruncationStrategy.ONLY_FIRST or (
	truncation_strategy == TruncationStrategy.LONGEST_FIRST and pair_ids is None
	):
	if len(ids) > num_tokens_to_remove:
	window_len = min(len(ids), stride + num_tokens_to_remove)
	overflowing_tokens = ids[-window_len:]
	overflowing_token_boxes = token_boxes[-window_len:]
	overflowing_labels = labels[-window_len:]
	ids = ids[:-num_tokens_to_remove]
	token_boxes = token_boxes[:-num_tokens_to_remove]
	labels = labels[:-num_tokens_to_remove]
	else:
	error_msg = (
	f"We need to remove {num_tokens_to_remove} to truncate the input "
	f"but the first sequence has a length {len(ids)}. "
	)
	if truncation_strategy == TruncationStrategy.ONLY_FIRST:
	error_msg = (
	error_msg + "Please select another truncation strategy than "
	f"{truncation_strategy}, for instance 'longest_first' or 'only_second'."
	)
	logger.error(error_msg)
	elif truncation_strategy == TruncationStrategy.LONGEST_FIRST:
	logger.warning(
	"Be aware, overflowing tokens are not returned for the setting you have chosen,"
	f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
	"truncation strategy. So the returned list will always be empty even if some "
	"tokens have been removed."
	)
	for _ in range(num_tokens_to_remove):
	if pair_ids is None or len(ids) > len(pair_ids):
	ids = ids[:-1]
	token_boxes = token_boxes[:-1]
	labels = labels[:-1]
	else:
	pair_ids = pair_ids[:-1]
	pair_token_boxes = pair_token_boxes[:-1]
	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:]
	overflowing_token_boxes = pair_token_boxes[-window_len:]
	pair_ids = pair_ids[:-num_tokens_to_remove]
	pair_token_boxes = pair_token_boxes[:-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}, "
	"for instance 'longest_first' or 'only_first'."
	)

	return (
	ids,
	token_boxes,
	pair_ids,
	pair_token_boxes,
	labels,
	overflowing_tokens,
	overflowing_token_boxes,
	overflowing_labels,
	)

	# Copied from transformers.models.layoutlmv2.tokenization_layoutlmv2.LayoutLMv2Tokenizer._pad
	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

	# Initialize attention mask if not present.
	if return_attention_mask and "attention_mask" not in encoded_inputs:
	encoded_inputs["attention_mask"] = [1] * len(required_input)

	if needs_to_be_padded:
	difference = max_length - len(required_input)
	if self.padding_side == "right":
	if return_attention_mask:
	encoded_inputs["attention_mask"] = encoded_inputs["attention_mask"] + [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 "bbox" in encoded_inputs:
	encoded_inputs["bbox"] = encoded_inputs["bbox"] + [self.pad_token_box] * difference
	if "labels" in encoded_inputs:
	encoded_inputs["labels"] = encoded_inputs["labels"] + [self.pad_token_label] * 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 + encoded_inputs["attention_mask"]
	if "token_type_ids" in encoded_inputs:
	encoded_inputs["token_type_ids"] = [self.pad_token_type_id] * difference + encoded_inputs[
	"token_type_ids"
	]
	if "bbox" in encoded_inputs:
	encoded_inputs["bbox"] = [self.pad_token_box] * difference + encoded_inputs["bbox"]
	if "labels" in encoded_inputs:
	encoded_inputs["labels"] = [self.pad_token_label] * difference + encoded_inputs["labels"]
	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))

	return encoded_inputs