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 __init__(
      self,
      vocab_file,
      bos_token="<s>",
      eos_token="</s>",
      sep_token="</s>",
      cls_token="<s>",
      unk_token="<unk>",
      pad_token="<pad>",
      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,
      sp_model_kwargs: Optional[Dict[str, Any]] = None,
      **kwargs,
  ) -> None:
      # Mask token behave like a normal word, i.e. include the space before it
      mask_token = (
          AddedToken(mask_token, lstrip=True, special=True)
          if isinstance(mask_token, str)
          else mask_token
      )
  
      self.sp_model_kwargs = (
          {} if sp_model_kwargs is None else sp_model_kwargs
      )
  
      self.sp_model = spm.SentencePieceProcessor(**self.sp_model_kwargs)
      self.sp_model.Load(str(vocab_file))
      self.vocab_file = vocab_file
  
      # Original fairseq vocab and spm vocab must be "aligned":
      # Vocab    |    0    |    1    |   2    |    3    |  4  |  5  |  6  |   7   |   8   |  9
      # -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
      # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's'   | '▁de' | '-'
      # spm      | '<unk>' | '<s>'   | '</s>' | ','     | '.' | '▁' | 's' | '▁de' | '-'   | '▁a'
  
      # Mimic fairseq token-to-id alignment for the first 4 token
      self.fairseq_tokens_to_ids = {
          "<s>": 0,
          "<pad>": 1,
          "</s>": 2,
          "<unk>": 3,
      }
  
      # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
      self.fairseq_offset = 1
  
      self.fairseq_tokens_to_ids["<mask>"] = (
          len(self.sp_model) + self.fairseq_offset
      )
      self.fairseq_ids_to_tokens = {
          v: k for k, v in self.fairseq_tokens_to_ids.items()
      }
  
      # 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__(
          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,
          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,
          sp_model_kwargs=self.sp_model_kwargs,
          **kwargs,
      )
  
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 1 to create a new LayoutXLMTokenizer object from inherited base classes: PreTrainedTokenizer with example: obj = LayoutXLMTokenizer(vocab_file, bos_token, eos_token, sep_token, cls_token, unk_token, pad_token, mask_token, cls_token_box, sep_token_box, pad_token_box, pad_token_label, only_label_first_subword, sp_model_kwargs)