class_docstr: Construct a "fast" RoFormer tokenizer (backed by HuggingFace's *tokenizers* library).
  
  [`RoFormerTokenizerFast`] is almost identical to [`BertTokenizerFast`] and runs end-to-end tokenization:
  punctuation splitting and wordpiece. There are some difference between them when tokenizing Chinese.
  
  This tokenizer inherits from [`PreTrainedTokenizerFast`] which contains most of the main methods. Users should
  refer to this superclass for more information regarding those methods.
  
  Example:
  
  ```python
  >>> from transformers import RoFormerTokenizerFast
  
  >>> tokenizer = RoFormerTokenizerFast.from_pretrained("junnyu/roformer_chinese_base")
  >>> tokenizer.tokenize("今天天气非常好。")
  ['今', '天', '天', '气', '非常', '好', '。']
  ```
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docstr: Create a mask from the two sequences passed to be used in a sequence-pair classification task. A RoFormer
  sequence pair mask has the following format:
  
  ```
  0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
  | first sequence    | second sequence |
  ```
  
  If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
  
  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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
docstr_tok: ["Create","a","mask","from","the","two","sequences","passed","to","be","used","in","a","sequence-pair","classification","task",".","A","RoFormer","sequence","pair","mask","has","the","following","format",":","``","`","0","0","0","0","0","0","0","0","0","0","0","1","1","1","1","1","1","1","1","1","|","first","sequence","|","second","sequence","|","``","`","If","`","token_ids_1","`","is","`","None","`",",","this","method","only","returns","the","first","portion","of","the","mask","(","0s",")",".","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","[","token","type","IDs","]","(","..","\/glossary","#","token-type-ids",")","according","to","the","given","sequence","(","s",")","."]
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. A RoFormer
      sequence pair mask has the following format:
  
      ```
      0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1
      | first sequence    | second sequence |
      ```
  
      If `token_ids_1` is `None`, this method only returns the first portion of the mask (0s).
  
      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 [token type IDs](../glossary#token-type-ids) according to the given sequence(s).
      """
      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) * [0] + len(
          token_ids_1 + sep
      ) * [1]
  
file_path: transformers/src/transformers/models/roformer/tokenization_roformer_fast.py
imports: import json
  from typing import List, Optional, Tuple
  from tokenizers import normalizers
  from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_roformer import RoFormerTokenizer
  from .tokenization_utils import JiebaPreTokenizer
label_desc: Use image node_id 5 for calling the RoFormerTokenizerFast 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