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.
      additional_special_tokens (`List[str]`, *optional*, defaults to `["<s>NOTUSED", "</s>NOTUSED"]`):
          Additional special tokens used by the tokenizer.
      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: ["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","can","not","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",".","additional_special_tokens","(","`","List","[","str","]","`",",","*","optional","*",",","defaults","to","`","[","``","<","s",">","NOTUSED","''",",","``","<","\/s",">","NOTUSED","''","]","`",")",":","Additional","special","tokens","used","by","the","tokenizer",".","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",")","."]
docstr: Create a mask from the two sequences passed to be used in a sequence-pair classification task. XLM-RoBERTa does
  not make use of token type ids, therefore a list of zeros is returned.
  
  Args:
      token_ids_0 (`List[int]`):
          List of IDs.
      token_ids_1 (`List[int]`, *optional*):
          Optional second list of IDs for sequence pairs.
  
  Returns:
      `List[int]`: List of zeros.
docstr_tok: ["Create","a","mask","from","the","two","sequences","passed","to","be","used","in","a","sequence-pair","classification","task",".","XLM-RoBERTa","does","not","make","use","of","token","type","ids",",","therefore","a","list","of","zeros","is","returned",".","Args",":","token_ids_0","(","`","List","[","int","]","`",")",":","List","of","IDs",".","token_ids_1","(","`","List","[","int","]","`",",","*","optional","*",")",":","Optional","second","list","of","IDs","for","sequence","pairs",".","Returns",":","`","List","[","int","]","`",":","List","of","zeros","."]
code: 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. XLM-RoBERTa does
      not make use of token type ids, therefore a list of zeros is returned.
  
      Args:
          token_ids_0 (`List[int]`):
              List of IDs.
          token_ids_1 (`List[int]`, *optional*):
              Optional second list of IDs for sequence pairs.
  
      Returns:
          `List[int]`: List of zeros.
  
      """
  
      sep = [self.sep_token_id]
      cls = [self.cls_token_id]
  
      if token_ids_1 is None:
          return len(cls + token_ids_0 + sep) * [0]
      return len(cls + token_ids_0 + sep + sep + token_ids_1 + sep) * [
          0
      ]
  
imports: import os
  from shutil import copyfile
  from typing import Any, Dict, List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import AddedToken, PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 6 for calling the XLMRobertaTokenizer obj's underlying member method code with example usage: obj.create_token_type_ids_from_sequences(token_ids_0, token_ids_1) and returns: unknown, unknown