Modified validation logic for OneHotFeaturizer, modified string check

    datapoints = list(datapoints)

    if (len(datapoints) < 1):

      return np.array([])

    if (len(self.charset) > self.max_length):

      logger.warning("Invalid for max_length to be larger than len(charset). Returning empty array.")

      return np.array([])

    # Featurize data using featurize() in parent class

    return Featurizer.featurize(self, datapoints, log_every_n)

  def _featurize(self, datapoint: Any):

    # Featurize str data

    if (type(datapoint) == str):

    if (isinstance(datapoint, (str, numpy.str_)):

      return self._featurize_string(datapoint)

    # Featurize mol data

    else:

      The shape is `(max_length, len(charset) + 1)`.

      The index of unknown character is `len(charset)`.

    """

    # validation

    if (len(string) > self.max_length):

      logger.info(

          "The length of {} is longer than `max_length`. So we return an empty array."

      )

      return np.array([])

    string = self.pad_string(string)  # Padding

    return np.array([

        one_hot_encode(val, self.charset, include_unknown_set=True)