class_docstr: Construct a LayoutLMv2 tokenizer. Based on WordPiece. [`LayoutLMv2Tokenizer`] 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.
  
  [`LayoutLMv2Tokenizer`] runs end-to-end tokenization: punctuation splitting and wordpiece. It also turns the
  word-level bounding boxes into token-level bounding boxes.
class_docstr_tok: ["Construct","a","LayoutLMv2","tokenizer",".","Based","on","WordPiece",".","[","`","LayoutLMv2Tokenizer","`","]","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",".","[","`","LayoutLMv2Tokenizer","`","]","runs","end-to-end","tokenization",":","punctuation","splitting","and","wordpiece",".","It","also","turns","the","word-level","bounding","boxes","into","token-level","bounding","boxes","."]
docstr: 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.
docstr_tok: ["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","."]
code: 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 not None:
          return (
              [1]
              + ([0] * len(token_ids_0))
              + [1]
              + ([0] * len(token_ids_1))
              + [1]
          )
      return [1] + ([0] * len(token_ids_0)) + [1]
  
imports: import collections
  import os
  import sys
  import unicodedata
  from typing import Dict, List, Optional, Tuple, Union
  from ...tokenization_utils import PreTrainedTokenizer, _is_control, _is_punctuation, _is_whitespace
  from ...tokenization_utils_base import BatchEncoding, EncodedInput, PreTokenizedInput, TextInput, TextInputPair, TruncationStrategy
  from ...utils import PaddingStrategy, TensorType, add_end_docstrings, logging
label_desc: Use image node_id 10 for calling the LayoutLMv2Tokenizer obj's underlying member method code with example usage: obj.get_special_tokens_mask(token_ids_0, token_ids_1, already_has_special_tokens) and returns: unknown, super, unknown