class_docstr: Construct a RemBERT tokenizer. 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`):
          [SentencePiece](https://github.com/google/sentencepiece) file (generally has a *.spm* extension) that
          contains the vocabulary necessary to instantiate a tokenizer.
      bos_token (`str`, *optional*, defaults to `"[CLS]"`):
          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 `"[SEP]"`):
          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>
  
      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.
      sep_token (`str`, *optional*, defaults to `"[SEP]"`):
          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.
      pad_token (`str`, *optional*, defaults to `"<pad>"`):
          The token used for padding, for example when batching sequences of different lengths.
      cls_token (`str`, *optional*, defaults to `"[CLS]"`):
          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.
      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.
  
  Attributes:
      sp_model (`SentencePieceProcessor`):
          The *SentencePiece* processor that is used for every conversion (string, tokens and IDs).
class_docstr_tok: ["Construct","a","RemBERT","tokenizer",".","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","`",")",":","[","SentencePiece","]","(","https",":","\/\/github.com\/google\/sentencepiece",")","file","(","generally","has","a","*",".spm","*","extension",")","that","contains","the","vocabulary","necessary","to","instantiate","a","tokenizer",".","bos_token","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","[","CLS","]","''","`",")",":","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","`","``","[","SEP","]","''","`",")",":","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",">","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",".","sep_token","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","[","SEP","]","''","`",")",":","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",".","pad_token","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","<","pad",">","''","`",")",":","The","token","used","for","padding",",","for","example","when","batching","sequences","of","different","lengths",".","cls_token","(","`","str","`",",","*","optional","*",",","defaults","to","`","``","[","CLS","]","''","`",")",":","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",".","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",".","Attributes",":","sp_model","(","`","SentencePieceProcessor","`",")",":","The","*","SentencePiece","*","processor","that","is","used","for","every","conversion","(","string",",","tokens","and","IDs",")","."]
code: def __getstate__(self):
      state = self.__dict__.copy()
      state["sp_model"] = None
      return state
  
imports: import os
  from shutil import copyfile
  from typing import List, Optional, Tuple
  import sentencepiece
  from ...tokenization_utils import PreTrainedTokenizer
  from ...utils import logging
label_desc: Use image node_id 4 for calling the RemBertTokenizer obj's underlying member method code with example usage: obj.__getstate__() and returns: state