pass kwargs to tokenizder

  as well as DeepChem's MolecularFeaturizer class.

  """

  def __init__(self, input_ids, attention_mask):

    self.input_ids = input_ids

    self.attention_mask = attention_mask

  def __init__(self, **kwargs):

    super().__init__(**kwargs)    

    return

  def _featurize(self, sequence: str) -> List[List[int]]:

  def _featurize(self, sequence: str, **kwargs) -> List[List[int]]:

    """Calculate encoding using HuggingFace's RobertaTokenizerFast

    Parameters

    # -> make this a list of two lists to allow np to handle it

    # encoding = list(self(smiles_string, **kwargs).values())

    encoding = list(

        self(sequence, self.input_ids, self.attention_mask).values())

        self(sequence, **kwargs).values())

    return encoding

  def __call__(self, *args) -> Dict[str, List[int]]:

    return super().__call__(*args)

  def __call__(self, *args, **kwargs) -> Dict[str, List[int]]:

    return super().__call__(*args, **kwargs)

 No newline at end of file

import unittest

from deepchem.feat import RobertaFeaturizer

from deepchem.feat.roberta_tokenizer import RobertaFeaturizer

import pytest