old_contents: from deepchem.feat import Featurizer
  from typing import Dict, List
  try:
    from transformers import RobertaTokenizerFast
  except ModuleNotFoundError:
    pass
  
  
  class RobertaFeaturizer(RobertaTokenizerFast, Featurizer):
    """Roberta Featurizer.
  
    The Roberta Featurizer is a wrapper class of the Roberta Tokenizer,
    which is used by Huggingface's transformers library for tokenizing large corpuses for Roberta Models.
    Please confirm the details in [1]_.
  
    Please see https://github.com/huggingface/transformers
    and https://github.com/seyonechithrananda/bert-loves-chemistry for more details.
  
    Examples
    --------
    >>> from deepchem.feat.molecule_featurizers import RobertaFeaturizer
    >>> smiles = ["Cn1c(=O)c2c(ncn2C)n(C)c1=O", "CC(=O)N1CN(C(C)=O)C(O)C1O"]
    >>> featurizer = RobertaFeaturizer.from_pretrained("seyonec/SMILES_tokenized_PubChem_shard00_160k")
    >>> featurizer.featurize(smiles, add_special_tokens=True, truncation=True)
  
    References
    ----------
    .. [1] Chithrananda, Seyone, Grand, Gabriel, and Ramsundar, Bharath (2020): "Chemberta: Large-scale self-supervised
      pretraining for molecular property prediction." arXiv. preprint. arXiv:2010.09885.
  
  
    Note
    -----
    This class requires transformers to be installed.
    RobertaFeaturizer uses dual inheritance with RobertaTokenizerFast in Huggingface for rapid tokenization,
    as well as DeepChem's MolecularFeaturizer class.
    """
  
    def __init__(self, **kwargs):
      super().__init__(**kwargs)
      return
  
    def _featurize(self, sequence: str, **kwargs) -> List[List[int]]:
      """Calculate encoding using HuggingFace's RobertaTokenizerFast
  
      Parameters
      ----------
      sequence: str
        Arbitrary string sequence to be tokenized.
  
      Returns
      -------
      encoding: List
        List containing two lists; the `input_ids` and the `attention_mask`
      """
  
      # the encoding is natively a dictionary with keys 'input_ids' and 'attention_mask'
      # encoding = list(self(smiles_string, **kwargs).values())
      encoding = list(self(sequence, **kwargs).values())
      return encoding
  
    def __call__(self, *args, **kwargs) -> Dict[str, List[int]]:
      return super().__call__(*args, **kwargs)
  
new_contents: from deepchem.feat import Featurizer
  from typing import Dict, List
  try:
    from transformers import RobertaTokenizerFast
  except ModuleNotFoundError:
    raise ImportError('Transformers must be installed for RobertaFeaturizer to be used!')
    pass
  
  
  class RobertaFeaturizer(RobertaTokenizerFast, Featurizer):
    """Roberta Featurizer.
  
    The Roberta Featurizer is a wrapper class of the Roberta Tokenizer,
    which is used by Huggingface's transformers library for tokenizing large corpuses for Roberta Models.
    Please confirm the details in [1]_.
  
    Please see https://github.com/huggingface/transformers
    and https://github.com/seyonechithrananda/bert-loves-chemistry for more details.
  
    Examples
    --------
    >>> from deepchem.feat.molecule_featurizers import RobertaFeaturizer
    >>> smiles = ["Cn1c(=O)c2c(ncn2C)n(C)c1=O", "CC(=O)N1CN(C(C)=O)C(O)C1O"]
    >>> featurizer = RobertaFeaturizer.from_pretrained("seyonec/SMILES_tokenized_PubChem_shard00_160k")
    >>> featurizer.featurize(smiles, add_special_tokens=True, truncation=True)
  
    References
    ----------
    .. [1] Chithrananda, Seyone, Grand, Gabriel, and Ramsundar, Bharath (2020): "Chemberta: Large-scale self-supervised
      pretraining for molecular property prediction." arXiv. preprint. arXiv:2010.09885.
  
  
    Note
    -----
    This class requires transformers to be installed.
    RobertaFeaturizer uses dual inheritance with RobertaTokenizerFast in Huggingface for rapid tokenization,
    as well as DeepChem's MolecularFeaturizer class.
    """
  
    def __init__(self, **kwargs):
      super().__init__(**kwargs)
      return
  
    def _featurize(self, sequence: str, **kwargs) -> List[List[int]]:
      """Calculate encoding using HuggingFace's RobertaTokenizerFast
  
      Parameters
      ----------
      sequence: str
        Arbitrary string sequence to be tokenized.
  
      Returns
      -------
      encoding: List
        List containing two lists; the `input_ids` and the `attention_mask`
      """
  
      # the encoding is natively a dictionary with keys 'input_ids' and 'attention_mask'
      # encoding = list(self(smiles_string, **kwargs).values())
      encoding = list(self(sequence, **kwargs).values())
      return encoding
  
    def __call__(self, *args, **kwargs) -> Dict[str, List[int]]:
      return super().__call__(*args, **kwargs)
  
subject: add import error