def test_interleave_datasets(dataset: IterableDataset, probas, seed, expected_length):
d1 = dataset
d2 = dataset.map(lambda x: {"id+1": x["id"] + 1, **x})
d3 = dataset.with_format("python")
datasets = [d1, d2, d3]
merged_dataset = interleave_datasets(datasets, probabilities=probas, seed=seed)
# Check the examples iterable
assert isinstance(
merged_dataset._ex_iterable, (CyclingMultiSourcesExamplesIterable, RandomlyCyclingMultiSourcesExamplesIterable)
)
# Check that it is deterministic
if seed is not None:
merged_dataset2 = interleave_datasets([d1, d2, d3], probabilities=probas, seed=seed)
assert list(merged_dataset) == list(merged_dataset2)
# Check first example
if seed is not None:
rng = np.random.default_rng(seed)
i = next(iter(RandomlyCyclingMultiSourcesExamplesIterable._iter_random_indices(rng, len(datasets), p=probas)))
assert next(iter(merged_dataset)) == next(iter(datasets[i]))
else:
assert any(next(iter(merged_dataset)) == next(iter(dataset)) for dataset in datasets)
# Compute length it case it's random
if expected_length is None:
expected_length = 0
counts = [len(list(d)) for d in datasets]
rng = np.random.default_rng(seed)
for i in RandomlyCyclingMultiSourcesExamplesIterable._iter_random_indices(rng, len(datasets), p=probas):
if counts[i] == 0:
break
counts[i] -= 1
expected_length += 1
# Check length
assert len(list(merged_dataset)) == expected_length
def test_iterable_dataset_with_format(dataset: IterableDataset, format_type):
formatted_dataset = dataset.with_format(format_type)
assert formatted_dataset._format_type == format_type
if format_type == "torch":
import torch
assert isinstance(formatted_dataset, torch.utils.data.IterableDataset)