handle optional charset, make max_length optional

  learning tasks.

  """

  def __init__(self, featurizer = OneHotFeaturizer, charset: str = "ATCGN", max_length = 58): # TODO: REMOVE MAX_LENGTH ARGUMENT BEFORE PR MERGE

  def __init__(self, featurizer = OneHotFeaturizer, charset: Optional[str] = "ATCGN", max_length: Optional[int] = None):

    """Initialize FASTALoader.

    Parameters

      Whether or not the sequence passed in is a protein sequence. If False,

      it is treated as a nucleic acid sequence.

    charset: str (default: ATCGN)

    charset: Optional[str] (default: "ATCGN")

      The charset used in the loaded FASTA file. Currently, we support ATCGN,

      full FASTA-format protein sequences, and full FASTA format nucleic acid

      sequences.

      Currently acceptable charsets are: "protein", "nucleic", and "ATCGN".

      This argument is currently only used if featurizer = OneHotFeaturizer.

    max_length: Optional[int] (default: None)

      max_length is only used if featurizer = OneHotFeaturizer.

      The length that all strings are padded to before featurization.

      max_length must be larger than the length of the longest string that is

      being featurized.

    """

    charsets = {

      "protein": ('A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L',

    }

    try:

      self.charset = charsets[charset]

    except:

    except KeyError:

      if (featurizer == OneHotFeaturizer):

        logger.exception("charset is invalid. charset must be 'protein', 'nucleic', or 'ATCGN'.")

      else:

        logger.warning("charset is invalid, ignoring...")

    self.user_specified_features = None

    if isinstance(featurizer, UserDefinedFeaturizer):

    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)