File size: 6,559 Bytes
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import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class DummyBeamDataset(datasets.BeamBasedBuilder):
"""Dummy beam dataset."""
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features({"content": datasets.Value("string")}),
# No default supervised_keys.
supervised_keys=None,
)
def _split_generators(self, dl_manager, pipeline):
return [datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"examples": get_test_dummy_examples()})]
def _build_pcollection(self, pipeline, examples):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(examples)
class NestedBeamDataset(datasets.BeamBasedBuilder):
"""Dummy beam dataset."""
def _info(self):
return datasets.DatasetInfo(
features=datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string")})}),
# No default supervised_keys.
supervised_keys=None,
)
def _split_generators(self, dl_manager, pipeline):
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"examples": get_test_nested_examples()})
]
def _build_pcollection(self, pipeline, examples):
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(examples)
def get_test_dummy_examples():
return [(i, {"content": content}) for i, content in enumerate(["foo", "bar", "foobar"])]
def get_test_nested_examples():
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["foo", "bar", "foobar"])]
class BeamBuilderTest(TestCase):
@require_beam
def test_download_and_prepare(self):
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(tmp_cache_dir, builder.name, "default", "0.0.0", f"{builder.name}-train.arrow")
)
)
self.assertDictEqual(builder.info.features, datasets.Features({"content": datasets.Value("string")}))
dset = builder.as_dataset()
self.assertEqual(dset["train"].num_rows, expected_num_examples)
self.assertEqual(dset["train"].info.splits["train"].num_examples, expected_num_examples)
self.assertDictEqual(dset["train"][0], get_test_dummy_examples()[0][1])
self.assertDictEqual(
dset["train"][expected_num_examples - 1], get_test_dummy_examples()[expected_num_examples - 1][1]
)
self.assertTrue(
os.path.exists(os.path.join(tmp_cache_dir, builder.name, "default", "0.0.0", "dataset_info.json"))
)
del dset
@require_beam
def test_download_and_prepare_sharded(self):
import apache_beam as beam
original_write_parquet = beam.io.parquetio.WriteToParquet
expected_num_examples = len(get_test_dummy_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
with patch("apache_beam.io.parquetio.WriteToParquet") as write_parquet_mock:
write_parquet_mock.side_effect = partial(original_write_parquet, num_shards=2)
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
tmp_cache_dir, builder.name, "default", "0.0.0", f"{builder.name}-train-00000-of-00002.arrow"
)
)
)
self.assertTrue(
os.path.exists(
os.path.join(
tmp_cache_dir, builder.name, "default", "0.0.0", f"{builder.name}-train-00000-of-00002.arrow"
)
)
)
self.assertDictEqual(builder.info.features, datasets.Features({"content": datasets.Value("string")}))
dset = builder.as_dataset()
self.assertEqual(dset["train"].num_rows, expected_num_examples)
self.assertEqual(dset["train"].info.splits["train"].num_examples, expected_num_examples)
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["train"]["content"]), sorted(["foo", "bar", "foobar"]))
self.assertTrue(
os.path.exists(os.path.join(tmp_cache_dir, builder.name, "default", "0.0.0", "dataset_info.json"))
)
del dset
@require_beam
def test_no_beam_options(self):
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = DummyBeamDataset(cache_dir=tmp_cache_dir)
self.assertRaises(datasets.builder.MissingBeamOptions, builder.download_and_prepare)
@require_beam
def test_nested_features(self):
expected_num_examples = len(get_test_nested_examples())
with tempfile.TemporaryDirectory() as tmp_cache_dir:
builder = NestedBeamDataset(cache_dir=tmp_cache_dir, beam_runner="DirectRunner")
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(tmp_cache_dir, builder.name, "default", "0.0.0", f"{builder.name}-train.arrow")
)
)
self.assertDictEqual(
builder.info.features, datasets.Features({"a": datasets.Sequence({"b": datasets.Value("string")})})
)
dset = builder.as_dataset()
self.assertEqual(dset["train"].num_rows, expected_num_examples)
self.assertEqual(dset["train"].info.splits["train"].num_examples, expected_num_examples)
self.assertDictEqual(dset["train"][0], get_test_nested_examples()[0][1])
self.assertDictEqual(
dset["train"][expected_num_examples - 1], get_test_nested_examples()[expected_num_examples - 1][1]
)
self.assertTrue(
os.path.exists(os.path.join(tmp_cache_dir, builder.name, "default", "0.0.0", "dataset_info.json"))
)
del dset
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