def test_parse_args_from_docstring_normal(): args_help = parse_args_from_docstring( """Constrain image dataset Args: root: Root directory of dataset where ``MNIST/processed/training.pt`` and ``MNIST/processed/test.pt`` exist. train: If ``True``, creates dataset from ``training.pt``, otherwise from ``test.pt``. normalize: mean and std deviation of the MNIST dataset. download: If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again. num_samples: number of examples per selected class/digit digits: list selected MNIST digits/classes Examples: >>> dataset = TrialMNIST(download=True) >>> len(dataset) 300 >>> sorted(set([d.item() for d in dataset.targets])) [0, 1, 2] >>> torch.bincount(dataset.targets) tensor([100, 100, 100]) """ ) expected_args = ['root', 'train', 'normalize', 'download', 'num_samples', 'digits'] assert len(args_help.keys()) == len(expected_args) assert all([x == y for x, y in zip(args_help.keys(), expected_args)]) assert args_help['root'] == 'Root directory of dataset where ``MNIST/processed/training.pt``' \ ' and ``MNIST/processed/test.pt`` exist.' assert args_help['normalize'] == 'mean and std deviation of the MNIST dataset.'
def test_parse_args_from_docstring_empty(): args_help = parse_args_from_docstring("""Constrain image dataset Args: Returns: Examples: """) assert len(args_help.keys()) == 0