class_docstr: Construct a "fast" DeBERTa tokenizer (backed by HuggingFace's *tokenizers* library). Based on byte-level
  Byte-Pair-Encoding.
  
  This tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece) so a word will
  be encoded differently whether it is at the beginning of the sentence (without space) or not:
  
  ```
  >>> from transformers import DebertaTokenizerFast
  >>> tokenizer = DebertaTokenizerFast.from_pretrained("microsoft/deberta-base")
  >>> tokenizer("Hello world")['input_ids']
  [15496, 995]
  >>> tokenizer(" Hello world")['input_ids']
  [18435, 995]
  ```
  
  You can get around that behavior by passing `add_prefix_space=True` when instantiating this tokenizer, but since
  the model was not pretrained this way, it might yield a decrease in performance.
  
  <Tip>
  
  When used with `is_split_into_words=True`, this tokenizer needs to be instantiated with `add_prefix_space=True`.
  
  </Tip>
  
  This tokenizer inherits from [`PreTrainedTokenizerFast`] 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.
      merges_file (`str`):
          Path to the merges file.
      tokenizer_file (`str`, *optional*):
          The path to a tokenizer file to use instead of the vocab file.
      errors (`str`, *optional*, defaults to `"replace"`):
          Paradigm to follow when decoding bytes to UTF-8. See
          [bytes.decode](https://docs.python.org/3/library/stdtypes.html#bytes.decode) for more information.
      bos_token (`str`, *optional*, defaults to `"[CLS]"`):
          The beginning of sequence token.
      eos_token (`str`, *optional*, defaults to `"[SEP]"`):
          The end of sequence token.
      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.
      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.
      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.
      add_prefix_space (`bool`, *optional*, defaults to `False`):
          Whether or not to add an initial space to the input. This allows to treat the leading word just as any
          other word. (Deberta tokenizer detect beginning of words by the preceding space).
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docstr: Overriding the default behavior of the mask token to have it eat the space before it.
docstr_tok: ["Overriding","the","default","behavior","of","the","mask","token","to","have","it","eat","the","space","before","it","."]
code: def mask_token(self, value):
      """
      Overriding the default behavior of the mask token to have it eat the space before it.
      """
      # Mask token behave like a normal word, i.e. include the space before it
      # So we set lstrip to True
      value = (
          AddedToken(value, lstrip=True, rstrip=False)
          if isinstance(value, str)
          else value
      )
      self._mask_token = value
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/models/deberta/tokenization_deberta_fast.py
imports: import json
  from typing import TYPE_CHECKING, List, Optional, Tuple
  from tokenizers import pre_tokenizers
  from ...tokenization_utils_base import AddedToken, BatchEncoding
  from ...tokenization_utils_fast import PreTrainedTokenizerFast
  from ...utils import logging
  from .tokenization_deberta import DebertaTokenizer
label_desc: Use image node_id 3 for calling the DebertaTokenizerFast obj's underlying member method code with example usage: obj.mask_token(value) without return types