"""
Blood-Brain Barrier Penetration dataset loader.
"""
import os
import logging
import deepchem

logger = logging.getLogger(__name__)

DEFAULT_DIR = deepchem.utils.get_data_dir()
BBBP_URL = 'http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/BBBP.csv'


def load_bbbp(featurizer='ECFP',
              split='random',
              reload=True,
              data_dir=None,
              save_dir=None,
              **kwargs):
  """Load blood-brain barrier penetration datasets """
  # Featurize bbb dataset
  logger.info("About to featurize bbbp dataset.")
  if data_dir is None:
    data_dir = DEFAULT_DIR
  if save_dir is None:
    save_dir = DEFAULT_DIR

  bbbp_tasks = ["p_np"]

  if reload:
    save_folder = os.path.join(save_dir, "bbbp-featurized", featurizer)
    if featurizer == "smiles2img":
      img_spec = kwargs.get("img_spec", "std")
      save_folder = os.path.join(save_folder, img_spec)
    save_folder = os.path.join(save_folder, str(split))

    loaded, all_dataset, transformers = deepchem.utils.save.load_dataset_from_disk(
        save_folder)
    if loaded:
      return bbbp_tasks, all_dataset, transformers

  dataset_file = os.path.join(data_dir, "BBBP.csv")
  if not os.path.exists(dataset_file):
    deepchem.utils.download_url(url=BBBP_URL, dest_dir=data_dir)

  if featurizer == 'ECFP':
    featurizer = deepchem.feat.CircularFingerprint(size=1024)
  elif featurizer == 'GraphConv':
    featurizer = deepchem.feat.ConvMolFeaturizer()
  elif featurizer == 'Weave':
    featurizer = deepchem.feat.WeaveFeaturizer()
  elif featurizer == 'Raw':
    featurizer = deepchem.feat.RawFeaturizer()
  elif featurizer == "smiles2img":
    img_spec = kwargs.get("img_spec", "std")
    img_size = kwargs.get("img_size", 80)
    featurizer = deepchem.feat.SmilesToImage(
        img_size=img_size, img_spec=img_spec)

  loader = deepchem.data.CSVLoader(
      tasks=bbbp_tasks, smiles_field="smiles", featurizer=featurizer)
  dataset = loader.featurize(dataset_file, shard_size=8192)

  if split is None:
    # Initialize transformers
    transformers = [deepchem.trans.BalancingTransformer(dataset=dataset)]

    logger.info("Split is None, about to transform data")
    for transformer in transformers:
      dataset = transformer.transform(dataset)

    return bbbp_tasks, (dataset, None, None), transformers

  splitters = {
      'index': deepchem.splits.IndexSplitter(),
      'random': deepchem.splits.RandomSplitter(),
      'scaffold': deepchem.splits.ScaffoldSplitter()
  }
  splitter = splitters[split]
  logger.info("About to split data with {} splitter.".format(split))
  frac_train = kwargs.get("frac_train", 0.8)
  frac_valid = kwargs.get('frac_valid', 0.1)
  frac_test = kwargs.get('frac_test', 0.1)

  train, valid, test = splitter.train_valid_test_split(
      dataset,
      frac_train=frac_train,
      frac_valid=frac_valid,
      frac_test=frac_test)

  # Initialize transformers
  transformers = [deepchem.trans.BalancingTransformer(dataset=train)]

  for transformer in transformers:
    train = transformer.transform(train)
    valid = transformer.transform(valid)
    test = transformer.transform(test)

  if reload:
    deepchem.utils.save.save_dataset_to_disk(save_folder, train, valid, test,
                                             transformers)
  return bbbp_tasks, (train, valid, test), transformers
