class_docstr: Adapted from [`RobertaTokenizer`] and [`XLNetTokenizer`]. Based on
  [SentencePiece](https://github.com/google/sentencepiece).
  
  This tokenizer inherits from [`PreTrainedTokenizer`] which contains most of the main methods. Users should refer to
  this superclass for more information regarding those methods.
  
  Args:
      vocab_file (`str`):
          Path to the vocabulary file.
      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.
      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 `[1000, 1000, 1000, 1000]`):
          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.
      sp_model_kwargs (`dict`, *optional*):
          Will be passed to the `SentencePieceProcessor.__init__()` method. The [Python wrapper for
          SentencePiece](https://github.com/google/sentencepiece/tree/master/python) can be used, among other things,
          to set:
  
          - `enable_sampling`: Enable subword regularization.
          - `nbest_size`: Sampling parameters for unigram. Invalid for BPE-Dropout.
  
            - `nbest_size = {0,1}`: No sampling is performed.
            - `nbest_size > 1`: samples from the nbest_size results.
            - `nbest_size < 0`: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
              using forward-filtering-and-backward-sampling algorithm.
  
          - `alpha`: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
            BPE-dropout.
  
  Attributes:
      sp_model (`SentencePieceProcessor`):
          The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
class_docstr_tok: 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code: 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,
      )
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple, Union
  import sentencepiece
  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
  from ..xlm_roberta.tokenization_xlm_roberta import PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES, PRETRAINED_VOCAB_FILES_MAP, SPIECE_UNDERLINE, VOCAB_FILES_NAMES
label_desc: Use image node_id 17 for calling the LayoutXLMTokenizer obj's underlying member method code with example usage: obj._encode_plus(text, text_pair, boxes, word_labels, add_special_tokens, padding_strategy, truncation_strategy, max_length, stride, pad_to_multiple_of, return_tensors, return_token_type_ids, return_attention_mask, return_overflowing_tokens, return_special_tokens_mask, return_offsets_mapping, return_length, verbose) and returns: self