flake8

from deepchem.utils.typing import OneOrMany

from deepchem.utils.data_utils import load_image_files, load_csv_files, load_json_files, load_sdf_files

from deepchem.utils.genomics_utils import encode_bio_sequence

from deepchem.feat import UserDefinedFeaturizer, Featurizer

from deepchem.data import Dataset, DiskDataset, NumpyDataset, ImageDataset

from deepchem.feat.molecule_featurizers import OneHotFeaturizer

    shard[feature_fields] = shard[feature_fields].apply(pd.to_numeric)

    X_shard = shard[feature_fields].to_numpy()

    time2 = time.time()

    logger.info("TIMING: user specified processing took %0.3f s" %

                (time2 - time1))

    logger.info(

        "TIMING: user specified processing took %0.3f s" % (time2 - time1))

    return (X_shard, np.ones(len(X_shard), dtype=bool))

    Iterator[pd.DataFrame]

      Iterator over shards

    """

    return load_sdf_files(input_files=input_files,

    return load_sdf_files(

        input_files=input_files,

        clean_mols=self.sanitize,

        tasks=self.tasks,

        shard_size=shard_size)

  learning tasks.

  """

  def __init__(self,

               featurizer: Featurizer = OneHotFeaturizer,

               charset: str = "ATCGN"):

  def __init__(self, featurizer: Featurizer = OneHotFeaturizer, charset: str = "ATCGN"):

    """Initialize FASTALoader.

    Parameters

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

                  'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X',

                  'Y', 'Z', '*', '-'),

        "nucleic": ('A', 'C', 'G', 'T', 'U', '(i)', 'R', 'Y', 'K', 'M', 'S',

                    'W', 'B', 'D', 'H', 'V', 'N', '-'),

      "nucleic": ('A', 'C', 'G', 'T', 'U', '(i)', 'R', 'Y', 'K', 'M', 'S', 'W',

                  'B', 'D', 'H', 'V', 'N', '-'),

      "ATCGN": ('A', 'T', 'C', 'G', 'N')

    }

      self.user_specified_features = featurizer.feature_fields

    if (featurizer == OneHotFeaturizer):

      logger.warning(f"CHARSET IS {self.charset}")

      featurizer = OneHotFeaturizer(self.charset, max_length)

    else:

      featurizer = featurizer()

      for input_file in input_files:

        sequences = np.append(sequences, _read_file(input_file))

      X = self.featurizer(sequences)

      logger.warning(X)

      ids = np.ones(len(X))

      # (X, y, w, ids)

      yield X, None, None, ids

      """

      Convert the FASTA file to a numpy array of FASTA-format strings.

      """

      def _generate_sequences(fasta_file, header_mark=">") -> np.array:

        """

        Uses a fasta_file to create a numpy array of annotated FASTA-format strings

            header_read = True

            sequences = _add_sequence(sequences, sequence)

            sequence = []

          elif header_read == True:  # Line contains sequence in FASTA format

          elif header_read:  # Line contains sequence in FASTA format

            if line[-1:] == '\n':  # Check last character in string

              line = line[0:-1]  # Remove last character

            sequence.append(line)

      def _add_sequence(sequences: np.array, sequence: list) -> np.array:

        # Handle empty sequence

        if sequence == None or len(sequence) <= 0:

          logger.warning(

              "Attempting to add empty sequence, returning empty array...")

        if sequence is None or len(sequence) <= 0:

          logger.warning("Attempting to add empty sequence, returning empty array...")

          return np.array([])

        # Annotate start/stop of sequence

        if auto_add_annotations:

    if in_memory:

      if data_dir is None:

        return NumpyDataset(load_image_files(image_files),

                            y=labels,

                            w=weights,

                            ids=image_files)

        return NumpyDataset(

            load_image_files(image_files), y=labels, w=weights, ids=image_files)

      else:

        dataset = DiskDataset.from_numpy(load_image_files(image_files),

        dataset = DiskDataset.from_numpy(

            load_image_files(image_files),

            y=labels,

            w=weights,

            ids=image_files,

  # FIXME: Signature of "_featurize_shard" incompatible with supertype "DataLoader"

  def _featurize_shard(  # type: ignore[override]

      self, shard: List, global_index: int) -> Tuple[np.ndarray, np.ndarray,

                                                     np.ndarray, np.ndarray]:

      self, shard: List, global_index: int

  ) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:

    """Featurizes a shard of an input data.

    Parameters