adding slow tests

    - name: PyTest

      if: ${{ (success() || failure()) && (steps.install.outcome == 'failure' || steps.install.outcome == 'success') }}

      shell: bash -l {0}

      run: pytest -v -m "not slow and not jax and not torch and not tensorflow" deepchem

      run: pytest -v -m "not jax and not torch and not tensorflow" deepchem

    - name: PyTest

      if: ${{ (success() || failure()) && (steps.install.outcome == 'failure' || steps.install.outcome == 'success') }}

      shell: bash -l {0}

      run: pytest -v -m 'jax and not slow' deepchem

      run: pytest -v -m 'jax' deepchem

    - name: PyTest

      if: ${{ (success() || failure()) && (steps.install.outcome == 'failure' || steps.install.outcome == 'success') }}

      shell: bash -l {0}

      run: pytest -v -m 'tensorflow and not slow' deepchem

      run: pytest -v -m 'tensorflow' deepchem

    - name: PyTest

      if: ${{ (success() || failure()) && (steps.install.outcome == 'failure' || steps.install.outcome == 'success') }}

      shell: bash -l {0}

      run: pytest -v -m 'torch and not slow' deepchem

      run: pytest -v -m 'torch' deepchem

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

from deepchem.utils.genomics_utils import encode_bio_sequence

logger = logging.getLogger(__name__)

  learning tasks.

  """

  def __init__(self):

    """Initialize loader."""

    pass

  def __init__(self,

               featurizer: Optional[Featurizer] = None,

               auto_add_annotations: bool = False,

               legacy: bool = True):

    """Initialize FASTALoader.

    Parameters

    ----------

    featurizer: Featurizer (default: None)

      The Featurizer to be used for the loaded FASTA data.

      If featurizer is None and legacy is True, the original featurization

      logic is used, creating a one hot encoding of all included FASTA strings

      of shape

      (number of FASTA sequences, number of channels + 1, sequence length, 1).

      If featurizer is None and legacy is False, the featurizer is initialized

      as a OneHotFeaturizer object with charset ("A", "C", "T", "G") and

      max_length = None.

    auto_add_annotations: bool (default False)

      Whether create_dataset will automatically add [CLS] and [SEP] annotations

      to the sequences it reads in order to assist tokenization.

      Keep False if your FASTA file already includes [CLS] and [SEP] annotations.

    legacy: bool (default True)

      Whether to use legacy logic for featurization. Legacy mode will create

      a one hot encoding of the FASTA content of shape

      (number of FASTA sequences, number of channels + 1, max length, 1).

      Legacy mode is only tested for ACTGN charsets, and will be deprecated.

   """

    # Process legacy toggle

    if legacy:

      warnings.warn(

          """

                    Legacy mode is deprecated and will be removed in

                    DeepChem 3.0. Disable legacy mode by passing legacy=False

                    during construction of FASTALoader object.

                    """, FutureWarning)

      if featurizer is not None or auto_add_annotations:

        raise ValueError(f"""

                          featurizer option must be None and

                          auto_add_annotations must be false when legacy mode

                          is enabled. You set featurizer to {featurizer} and

                          auto_add_annotations to {auto_add_annotations}.

                          """)

    # Set attributes

    self.legacy = legacy

    self.auto_add_annotations = auto_add_annotations

    self.user_specified_features = None

    # Handle special featurizer cases

    if isinstance(featurizer, UserDefinedFeaturizer):  # User defined featurizer

      self.user_specified_features = featurizer.feature_fields

    elif featurizer is None:  # Default featurizer

      featurizer = OneHotFeaturizer(

          charset=["A", "C", "T", "G"], max_length=None)

    # Set self.featurizer

    self.featurizer = featurizer

  def create_dataset(self,

                     input_files: OneOrMany[str],

                     shard_size: Optional[int] = None) -> DiskDataset:

    """Creates a `Dataset` from input FASTA files.

    At present, FASTA support is limited and only allows for one-hot

    featurization, and doesn't allow for sharding.

    At present, FASTA support is limited and doesn't allow for sharding.

    Parameters

    ----------

    if isinstance(input_files, str):

      input_files = [input_files]

    def shard_generator():

    def shard_generator():  # TODO Enable sharding with shard size parameter

      for input_file in input_files:

        if self.legacy:

          X = encode_bio_sequence(input_file)

        else:

          sequences = _read_file(input_file)

          X = self.featurizer(sequences)

        ids = np.ones(len(X))

        # (X, y, w, ids)

        yield X, None, None, ids

    def _read_file(input_file: str, auto_add_annotations: bool = False):

      """

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

      """

      # TODO don't convert all sequences into np array (allow shards)

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

        """

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

        """

        sequences = np.array([])

        sequence = np.array([])

        header_read = False

        for line in fasta_file:

          # Check if line is a header

          if line.startswith(header_mark):  # New header line

            header_read = True

            sequences = _add_sequence(sequences, sequence)

            sequence = np.array([])

          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 = np.append(sequence, line)

        sequences = _add_sequence(sequences, sequence)  # Add last sequence

        return sequences

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

        # Handle empty sequence

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

          # TODO log attempts to add empty sequences every shard

          return np.array([])

        # Annotate start/stop of sequence

        if auto_add_annotations:

          sequence = np.insert(sequence, 0, "[CLS]")

          sequence = np.append(sequence, "[SEP]")

        new_sequence = ''.join(sequence)

        new_sequences = np.append(sequences, new_sequence)

        return new_sequences

      with open(input_file, 'r') as f:  # Read FASTA file

        return _generate_sequences(f)

    return DiskDataset.create_dataset(shard_generator(), data_dir)