def test_iterable_dataset_cast_column(generate_examples_fn): ex_iterable = ExamplesIterable(generate_examples_fn, {"label": 10}) features = Features({"id": Value("int64"), "label": Value("int64")}) dataset = IterableDataset(ex_iterable, info=DatasetInfo(features=features)) casted_dataset = dataset.cast_column("label", Value("bool")) casted_features = features.copy() casted_features["label"] = Value("bool") assert list(casted_dataset) == [casted_features.encode_example(ex) for _, ex in ex_iterable]
def test_flatten_with_sequence(self): features = Features( {"foo": Sequence({"bar": { "my_value": Value("int32") }})}) _features = features.copy() flattened_features = features.flatten() assert flattened_features == { "foo.bar": [{ "my_value": Value("int32") }] } assert features == _features, "calling flatten shouldn't alter the current features"
def test_flatten(self): features = Features({ "foo": { "bar1": Value("int32"), "bar2": { "foobar": Value("string") } } }) _features = features.copy() flattened_features = features.flatten() assert flattened_features == { "foo.bar1": Value("int32"), "foo.bar2.foobar": Value("string") } assert features == _features, "calling flatten shouldn't alter the current features"