text: # coding=utf-8
  # Copyright 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.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  """Tokenization class for LayoutLMv2."""
  
  import collections
  import os
  import sys
  import unicodedata
  from typing import Dict, List, Optional, Tuple, Union
  
  from ...file_utils import PaddingStrategy, TensorType, add_end_docstrings
  from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...tokenization_utils_base import (
      ENCODE_KWARGS_DOCSTRING,
      BatchEncoding,
      EncodedInput,
      PreTokenizedInput,
      TextInput,
      TextInputPair,
      TruncationStrategy,
  )
  from ...utils import logging
  
  
  logger = logging.get_logger(__name__)
  
  VOCAB_FILES_NAMES = {"vocab_file": "vocab.txt"}
  
  PRETRAINED_VOCAB_FILES_MAP = {
      "vocab_file": {
          "microsoft/layoutlmv2-base-uncased": "https://huggingface.co/microsoft/layoutlmv2-base-uncased/resolve/main/vocab.txt",
          "microsoft/layoutlmv2-large-uncased": "https://huggingface.co/microsoft/layoutlmv2-large-uncased/resolve/main/vocab.txt",
      }
  }
  
  
  PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES = {
      "microsoft/layoutlmv2-base-uncased": 512,
      "microsoft/layoutlmv2-large-uncased": 512,
  }
  
  
  PRETRAINED_INIT_CONFIGURATION = {
      "microsoft/layoutlmv2-base-uncased": {"do_lower_case": True},
      "microsoft/layoutlmv2-large-uncased": {"do_lower_case": True},
  }
  
  
  LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING = r"""
              add_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`True`):
                  Whether or not to encode the sequences with the special tokens relative to their model.
              padding (:obj:`bool`, :obj:`str` or :class:`~transformers.file_utils.PaddingStrategy`, `optional`, defaults to :obj:`False`):
                  Activates and controls padding. Accepts the following values:
  
                  * :obj:`True` or :obj:`'longest'`: Pad to the longest sequence in the batch (or no padding if only a
                    single sequence if provided).
                  * :obj:`'max_length'`: Pad to a maximum length specified with the argument :obj:`max_length` or to the
                    maximum acceptable input length for the model if that argument is not provided.
                  * :obj:`False` or :obj:`'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of
                    different lengths).
              truncation (:obj:`bool`, :obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
                  Activates and controls truncation. Accepts the following values:
  
                  * :obj:`True` or :obj:`'longest_first'`: Truncate to a maximum length specified with the argument
                    :obj:`max_length` or to the maximum acceptable input length for the model if that argument is not
                    provided. This will truncate token by token, removing a token from the longest sequence in the pair
                    if a pair of sequences (or a batch of pairs) is provided.
                  * :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
                    the maximum acceptable input length for the model if that argument is not provided. This will only
                    truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                  * :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
                    to the maximum acceptable input length for the model if that argument is not provided. This will only
                    truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                  * :obj:`False` or :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with
                    sequence lengths greater than the model maximum admissible input size).
              max_length (:obj:`int`, `optional`):
                  Controls the maximum length to use by one of the truncation/padding parameters. If left unset or set to
                  :obj:`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 (:obj:`int`, `optional`, defaults to 0):
                  If set to a number along with :obj:`max_length`, the overflowing tokens returned when
                  :obj:`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 (:obj:`int`, `optional`):
                  If set will pad the sequence to a multiple of the provided value. This is especially useful to enable
                  the use of Tensor Cores on NVIDIA hardware with compute capability >= 7.5 (Volta).
              return_tensors (:obj:`str` or :class:`~transformers.file_utils.TensorType`, `optional`):
                  If set, will return tensors instead of list of python integers. Acceptable values are:
  
                  * :obj:`'tf'`: Return TensorFlow :obj:`tf.constant` objects.
                  * :obj:`'pt'`: Return PyTorch :obj:`torch.Tensor` objects.
                  * :obj:`'np'`: Return Numpy :obj:`np.ndarray` objects.
  """
  
  
  def load_vocab(vocab_file):
      """Loads a vocabulary file into a dictionary."""
      vocab = collections.OrderedDict()
      with open(vocab_file, "r", encoding="utf-8") as reader:
          tokens = reader.readlines()
      for index, token in enumerate(tokens):
          token = token.rstrip("\n")
          vocab[token] = index
      return vocab
  
  
  def whitespace_tokenize(text):
      """Runs basic whitespace cleaning and splitting on a piece of text."""
      text = text.strip()
      if not text:
          return []
      tokens = text.split()
      return tokens
  
  
  table = dict.fromkeys(i for i in range(sys.maxunicode) if unicodedata.category(chr(i)).startswith("P"))
  
  
  def subfinder(mylist, pattern):
      matches = []
      indices = []
      for idx, i in enumerate(range(len(mylist))):
          if mylist[i] == pattern[0] and mylist[i : i + len(pattern)] == pattern:
              matches.append(pattern)
              indices.append(idx)
      if matches:
          return matches[0], indices[0]
      else:
          return None, 0
  
  
  class LayoutLMv2Tokenizer(PreTrainedTokenizer):
      r"""
      Construct a LayoutLMv2 tokenizer. Based on WordPiece. :class:`~transformers.LayoutLMv2Tokenizer` can be used to
      turn words, word-level bounding boxes and optional word labels to token-level :obj:`input_ids`,
      :obj:`attention_mask`, :obj:`token_type_ids`, :obj:`bbox`, and optional :obj:`labels` (for token classification).
  
      This tokenizer inherits from :class:`~transformers.PreTrainedTokenizer` which contains most of the main methods.
      Users should refer to this superclass for more information regarding those methods.
  
      :class:`~transformers.LayoutLMv2Tokenizer` runs end-to-end tokenization: punctuation splitting and wordpiece. It
      also turns the word-level bounding boxes into token-level bounding boxes.
  
      """
  
      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
  
      def __init__(
          self,
          vocab_file,
          do_lower_case=True,
          do_basic_tokenize=True,
          never_split=None,
          unk_token="[UNK]",
          sep_token="[SEP]",
          pad_token="[PAD]",
          cls_token="[CLS]",
          mask_token="[MASK]",
          cls_token_box=[0, 0, 0, 0],
          sep_token_box=[1000, 1000, 1000, 1000],
          pad_token_box=[0, 0, 0, 0],
          pad_token_label=-100,
          only_label_first_subword=True,
          tokenize_chinese_chars=True,
          strip_accents=None,
          model_max_length: int = 512,
          additional_special_tokens: Optional[List[str]] = None,
          **kwargs
      ):
          super().__init__(
              do_lower_case=do_lower_case,
              do_basic_tokenize=do_basic_tokenize,
              never_split=never_split,
              unk_token=unk_token,
              sep_token=sep_token,
              pad_token=pad_token,
              cls_token=cls_token,
              mask_token=mask_token,
              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,
              tokenize_chinese_chars=tokenize_chinese_chars,
              strip_accents=strip_accents,
              model_max_length=model_max_length,
              additional_special_tokens=additional_special_tokens,
              **kwargs,
          )
  
          if not os.path.isfile(vocab_file):
              raise ValueError(
                  f"Can't find a vocabulary file at path '{vocab_file}'. To load the vocabulary from a Google pretrained "
                  "model use `tokenizer = BertTokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"
              )
          self.vocab = load_vocab(vocab_file)
          self.ids_to_tokens = collections.OrderedDict([(ids, tok) for tok, ids in self.vocab.items()])
          self.do_basic_tokenize = do_basic_tokenize
          if do_basic_tokenize:
              self.basic_tokenizer = BasicTokenizer(
                  do_lower_case=do_lower_case,
                  never_split=never_split,
                  tokenize_chinese_chars=tokenize_chinese_chars,
                  strip_accents=strip_accents,
              )
          self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab, unk_token=self.unk_token)
  
          # 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
  
      @property
      def do_lower_case(self):
          return self.basic_tokenizer.do_lower_case
  
      @property
      def vocab_size(self):
          return len(self.vocab)
  
      def get_vocab(self):
          return dict(self.vocab, **self.added_tokens_encoder)
  
      def _tokenize(self, text):
          split_tokens = []
          if self.do_basic_tokenize:
              for token in self.basic_tokenizer.tokenize(text, never_split=self.all_special_tokens):
  
                  # If the token is part of the never_split set
                  if token in self.basic_tokenizer.never_split:
                      split_tokens.append(token)
                  else:
                      split_tokens += self.wordpiece_tokenizer.tokenize(token)
          else:
              split_tokens = self.wordpiece_tokenizer.tokenize(text)
          return split_tokens
  
      def _convert_token_to_id(self, token):
          """Converts a token (str) in an id using the vocab."""
          return self.vocab.get(token, self.vocab.get(self.unk_token))
  
      def _convert_id_to_token(self, index):
          """Converts an index (integer) in a token (str) using the vocab."""
          return self.ids_to_tokens.get(index, self.unk_token)
  
      def convert_tokens_to_string(self, tokens):
          """Converts a sequence of tokens (string) in a single string."""
          out_string = " ".join(tokens).replace(" ##", "").strip()
          return out_string
  
      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 BERT sequence has the following format:
  
          - single sequence: ``[CLS] X [SEP]``
          - pair of sequences: ``[CLS] A [SEP] B [SEP]``
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs to which the special tokens will be added.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
  
          Returns:
              :obj:`List[int]`: List of `input IDs <../glossary.html#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 + 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]:
          """
          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 (:obj:`List[int]`):
                  List of IDs.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
              already_has_special_tokens (:obj:`bool`, `optional`, defaults to :obj:`False`):
                  Whether or not the token list is already formatted with special tokens for the model.
  
          Returns:
              :obj:`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 not None:
              return [1] + ([0] * len(token_ids_0)) + [1] + ([0] * len(token_ids_1)) + [1]
          return [1] + ([0] * len(token_ids_0)) + [1]
  
      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. A BERT sequence
          pair mask has the following format: :: 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 | first sequence | second
          sequence | If :obj:`token_ids_1` is :obj:`None`, this method only returns the first portion of the mask (0s).
  
          Args:
              token_ids_0 (:obj:`List[int]`):
                  List of IDs.
              token_ids_1 (:obj:`List[int]`, `optional`):
                  Optional second list of IDs for sequence pairs.
  
          Returns:
              :obj:`List[int]`: List of `token type IDs <../glossary.html#token-type-ids>`_ according to the given
              sequence(s).
          """
          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) * [0] + len(token_ids_1 + sep) * [1]
  
      def save_vocabulary(self, save_directory: str, filename_prefix: Optional[str] = None) -> Tuple[str]:
          index = 0
          if os.path.isdir(save_directory):
              vocab_file = os.path.join(
                  save_directory, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]
              )
          else:
              vocab_file = (filename_prefix + "-" if filename_prefix else "") + save_directory
          with open(vocab_file, "w", encoding="utf-8") as writer:
              for token, token_index in sorted(self.vocab.items(), key=lambda kv: kv[1]):
                  if index != token_index:
                      logger.warning(
                          f"Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive."
                          " Please check that the vocabulary is not corrupted!"
                      )
                      index = token_index
                  writer.write(token + "\n")
                  index += 1
          return (vocab_file,)
  
      @add_end_docstrings(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
      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] = 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 sequence(s) or one or several pair(s) of
          sequences with word-level normalized bounding boxes and optional labels.
  
          Args:
              text (:obj:`str`, :obj:`List[str]`, :obj:`List[List[str]]`):
                  The sequence or batch of sequences to be encoded. Each sequence can be a string, 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 (:obj:`List[str]`, :obj:`List[List[str]]`):
                  The sequence or batch of sequences to be encoded. Each sequence should be a list of strings
                  (pretokenized string).
              boxes (:obj:`List[List[int]]`, :obj:`List[List[List[int]]]`):
                  Word-level bounding boxes. Each bounding box should be normalized to be on a 0-1000 scale.
              word_labels (:obj:`List[int]`, :obj:`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
          assert boxes is not None, "You must provide corresponding bounding boxes"
          if is_batched:
              assert len(words) == len(boxes), "You must provide words and boxes for an equal amount of examples"
              for words_example, boxes_example in zip(words, boxes):
                  assert len(words_example) == len(
                      boxes_example
                  ), "You must provide as many words as there are bounding boxes"
          else:
              assert len(words) == len(boxes), "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`: {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(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
      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] = 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:
          """ """
  
          # 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,
          )
  
      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(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
      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(ENCODE_KWARGS_DOCSTRING)
      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] = 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
      ) -> 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(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_ENCODE_PLUS_ADDITIONAL_KWARGS_DOCSTRING)
      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] = 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:
          """
          Tokenize and prepare for the model a sequence or a pair of sequences. .. warning:: This method is deprecated,
          ``__call__`` should be used instead.
  
          Args:
              text (:obj:`str`, :obj:`List[str]`, :obj:`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 (:obj:`List[str]` or :obj:`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,
          )
  
      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(ENCODE_KWARGS_DOCSTRING, LAYOUTLMV2_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] = False,
          max_length: Optional[int] = None,
          stride: int = 0,
          pad_to_multiple_of: Optional[int] = None,
          return_tensors: Optional[Union[str, TensorType]] = None,
          return_token_type_ids: Optional[bool] = None,
          return_attention_mask: Optional[bool] = None,
          return_overflowing_tokens: bool = False,
          return_special_tokens_mask: bool = False,
          return_offsets_mapping: bool = False,
          return_length: bool = False,
          verbose: bool = True,
          prepend_batch_axis: bool = False,
          **kwargs
      ) -> BatchEncoding:
          """
          Prepares a sequence 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 :obj:`boxes` are turned into token-level :obj:`bbox`. If provided, word-level :obj:`word_labels` are
          turned into token-level :obj:`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 (:obj:`str`, :obj:`List[str]`, :obj:`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 (:obj:`List[str]` or :obj:`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 = pair_token_boxes + [self.sep_token_box]
              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 [])
  
          # Build output dictionary
          encoded_inputs["input_ids"] = sequence
          encoded_inputs["bbox"] = token_boxes + pair_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
  
      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 (:obj:`List[int]`):
                  Tokenized input ids of the first sequence. Can be obtained from a string by chaining the ``tokenize``
                  and ``convert_tokens_to_ids`` methods.
              token_boxes (:obj:`List[List[int]]`):
                  Bounding boxes of the first sequence.
              pair_ids (:obj:`List[int]`, `optional`):
                  Tokenized input ids of the second sequence. Can be obtained from a string by chaining the ``tokenize``
                  and ``convert_tokens_to_ids`` methods.
              pair_token_boxes (:obj:`List[List[int]]`, `optional`):
                  Bounding boxes of the second sequence.
              labels (:obj:`List[int]`, `optional`):
                  Labels of the first sequence (for token classification tasks).
              num_tokens_to_remove (:obj:`int`, `optional`, defaults to 0):
                  Number of tokens to remove using the truncation strategy.
              truncation_strategy (:obj:`str` or :class:`~transformers.tokenization_utils_base.TruncationStrategy`, `optional`, defaults to :obj:`False`):
                  The strategy to follow for truncation. Can be:
  
                  * :obj:`'longest_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
                    to the maximum acceptable input length for the model if that argument is not provided. This will
                    truncate token by token, removing a token from the longest sequence in the pair if a pair of
                    sequences (or a batch of pairs) is provided.
                  * :obj:`'only_first'`: Truncate to a maximum length specified with the argument :obj:`max_length` or to
                    the maximum acceptable input length for the model if that argument is not provided. This will only
                    truncate the first sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                  * :obj:`'only_second'`: Truncate to a maximum length specified with the argument :obj:`max_length` or
                    to the maximum acceptable input length for the model if that argument is not provided. This will only
                    truncate the second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
                  * :obj:`'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                    greater than the model maximum admissible input size).
              stride (:obj:`int`, `optional`, defaults to 0):
                  If set to a positive number, the overflowing tokens returned will contain some tokens from the main
                  sequence returned. The value of this argument defines the number of additional tokens.
  
          Returns:
              :obj:`Tuple[List[int], List[int], List[int]]`: The truncated ``ids``, the truncated ``pair_ids`` and the
              list of overflowing tokens. 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(
                  f"Be aware, overflowing tokens are not returned for the setting you have chosen,"
                  f" i.e. sequence pairs with the '{TruncationStrategy.LONGEST_FIRST.value}' "
                  f"truncation strategy. So the returned list will always be empty even if some "
                  f"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}, "
                      f"for instance 'longest_first' or 'only_first'."
                  )
  
          return (
              ids,
              token_boxes,
              pair_ids,
              pair_token_boxes,
              labels,
              overflowing_tokens,
              overflowing_token_boxes,
              overflowing_labels,
          )
  
      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
  
  
  # Copied from transformers.models.bert.tokenization_bert.BasicTokenizer
  class BasicTokenizer(object):
      """
      Constructs a BasicTokenizer that will run basic tokenization (punctuation splitting, lower casing, etc.).
  
      Args:
          do_lower_case (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether or not to lowercase the input when tokenizing.
          never_split (:obj:`Iterable`, `optional`):
              Collection of tokens which will never be split during tokenization. Only has an effect when
              :obj:`do_basic_tokenize=True`
          tokenize_chinese_chars (:obj:`bool`, `optional`, defaults to :obj:`True`):
              Whether or not to tokenize Chinese characters.
  
              This should likely be deactivated for Japanese (see this `issue
              <https://github.com/huggingface/transformers/issues/328>`__).
          strip_accents: (:obj:`bool`, `optional`):
              Whether or not to strip all accents. If this option is not specified, then it will be determined by the
              value for :obj:`lowercase` (as in the original BERT).
      """
  
      def __init__(self, do_lower_case=True, never_split=None, tokenize_chinese_chars=True, strip_accents=None):
          if never_split is None:
              never_split = []
          self.do_lower_case = do_lower_case
          self.never_split = set(never_split)
          self.tokenize_chinese_chars = tokenize_chinese_chars
          self.strip_accents = strip_accents
  
      def tokenize(self, text, never_split=None):
          """
          Basic Tokenization of a piece of text. Split on "white spaces" only, for sub-word tokenization, see
          WordPieceTokenizer.
  
          Args:
              **never_split**: (`optional`) list of str
                  Kept for backward compatibility purposes. Now implemented directly at the base class level (see
                  :func:`PreTrainedTokenizer.tokenize`) List of token not to split.
          """
          # union() returns a new set by concatenating the two sets.
          never_split = self.never_split.union(set(never_split)) if never_split else self.never_split
          text = self._clean_text(text)
  
          # This was added on November 1st, 2018 for the multilingual and Chinese
          # models. This is also applied to the English models now, but it doesn't
          # matter since the English models were not trained on any Chinese data
          # and generally don't have any Chinese data in them (there are Chinese
          # characters in the vocabulary because Wikipedia does have some Chinese
          # words in the English Wikipedia.).
          if self.tokenize_chinese_chars:
              text = self._tokenize_chinese_chars(text)
          orig_tokens = whitespace_tokenize(text)
          split_tokens = []
          for token in orig_tokens:
              if token not in never_split:
                  if self.do_lower_case:
                      token = token.lower()
                      if self.strip_accents is not False:
                          token = self._run_strip_accents(token)
                  elif self.strip_accents:
                      token = self._run_strip_accents(token)
              split_tokens.extend(self._run_split_on_punc(token, never_split))
  
          output_tokens = whitespace_tokenize(" ".join(split_tokens))
          return output_tokens
  
      def _run_strip_accents(self, text):
          """Strips accents from a piece of text."""
          text = unicodedata.normalize("NFD", text)
          output = []
          for char in text:
              cat = unicodedata.category(char)
              if cat == "Mn":
                  continue
              output.append(char)
          return "".join(output)
  
      def _run_split_on_punc(self, text, never_split=None):
          """Splits punctuation on a piece of text."""
          if never_split is not None and text in never_split:
              return [text]
          chars = list(text)
          i = 0
          start_new_word = True
          output = []
          while i < len(chars):
              char = chars[i]
              if _is_punctuation(char):
                  output.append([char])
                  start_new_word = True
              else:
                  if start_new_word:
                      output.append([])
                  start_new_word = False
                  output[-1].append(char)
              i += 1
  
          return ["".join(x) for x in output]
  
      def _tokenize_chinese_chars(self, text):
          """Adds whitespace around any CJK character."""
          output = []
          for char in text:
              cp = ord(char)
              if self._is_chinese_char(cp):
                  output.append(" ")
                  output.append(char)
                  output.append(" ")
              else:
                  output.append(char)
          return "".join(output)
  
      def _is_chinese_char(self, cp):
          """Checks whether CP is the codepoint of a CJK character."""
          # This defines a "chinese character" as anything in the CJK Unicode block:
          #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
          #
          # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
          # despite its name. The modern Korean Hangul alphabet is a different block,
          # as is Japanese Hiragana and Katakana. Those alphabets are used to write
          # space-separated words, so they are not treated specially and handled
          # like the all of the other languages.
          if (
              (cp >= 0x4E00 and cp <= 0x9FFF)
              or (cp >= 0x3400 and cp <= 0x4DBF)  #
              or (cp >= 0x20000 and cp <= 0x2A6DF)  #
              or (cp >= 0x2A700 and cp <= 0x2B73F)  #
              or (cp >= 0x2B740 and cp <= 0x2B81F)  #
              or (cp >= 0x2B820 and cp <= 0x2CEAF)  #
              or (cp >= 0xF900 and cp <= 0xFAFF)
              or (cp >= 0x2F800 and cp <= 0x2FA1F)  #
          ):  #
              return True
  
          return False
  
      def _clean_text(self, text):
          """Performs invalid character removal and whitespace cleanup on text."""
          output = []
          for char in text:
              cp = ord(char)
              if cp == 0 or cp == 0xFFFD or _is_control(char):
                  continue
              if _is_whitespace(char):
                  output.append(" ")
              else:
                  output.append(char)
          return "".join(output)
  
  
  # Copied from transformers.models.bert.tokenization_bert.WordpieceTokenizer
  class WordpieceTokenizer(object):
      """Runs WordPiece tokenization."""
  
      def __init__(self, vocab, unk_token, max_input_chars_per_word=100):
          self.vocab = vocab
          self.unk_token = unk_token
          self.max_input_chars_per_word = max_input_chars_per_word
  
      def tokenize(self, text):
          """
          Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform
          tokenization using the given vocabulary.
  
          For example, :obj:`input = "unaffable"` wil return as output :obj:`["un", "##aff", "##able"]`.
  
          Args:
            text: A single token or whitespace separated tokens. This should have
              already been passed through `BasicTokenizer`.
  
          Returns:
            A list of wordpiece tokens.
          """
  
          output_tokens = []
          for token in whitespace_tokenize(text):
              chars = list(token)
              if len(chars) > self.max_input_chars_per_word:
                  output_tokens.append(self.unk_token)
                  continue
  
              is_bad = False
              start = 0
              sub_tokens = []
              while start < len(chars):
                  end = len(chars)
                  cur_substr = None
                  while start < end:
                      substr = "".join(chars[start:end])
                      if start > 0:
                          substr = "##" + substr
                      if substr in self.vocab:
                          cur_substr = substr
                          break
                      end -= 1
                  if cur_substr is None:
                      is_bad = True
                      break
                  sub_tokens.append(cur_substr)
                  start = end
  
              if is_bad:
                  output_tokens.append(self.unk_token)
              else:
                  output_tokens.extend(sub_tokens)
          return output_tokens
  