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"
