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2b06d1d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 | import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, IterableDatasetDict, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.info import DatasetInfo
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _check_parquet_dataset(dataset, expected_features):
assert isinstance(dataset, Dataset)
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_dataset_from_parquet_keep_in_memory(keep_in_memory, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = ParquetDatasetReader(parquet_path, cache_dir=cache_dir, keep_in_memory=keep_in_memory).read()
_check_parquet_dataset(dataset, expected_features)
@pytest.mark.parametrize(
"features",
[
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
],
)
def test_dataset_from_parquet_features(features, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = ParquetDatasetReader(parquet_path, features=features, cache_dir=cache_dir).read()
_check_parquet_dataset(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_dataset_from_parquet_split(split, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
dataset = ParquetDatasetReader(parquet_path, cache_dir=cache_dir, split=split).read()
_check_parquet_dataset(dataset, expected_features)
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("path_type", [str, list])
def test_dataset_from_parquet_path_type(path_type, parquet_path, tmp_path):
if issubclass(path_type, str):
path = parquet_path
elif issubclass(path_type, list):
path = [parquet_path]
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
dataset = ParquetDatasetReader(path, cache_dir=cache_dir).read()
_check_parquet_dataset(dataset, expected_features)
def _check_parquet_datasetdict(dataset_dict, expected_features, splits=("train",)):
assert isinstance(dataset_dict, (DatasetDict, IterableDatasetDict))
for split in splits:
dataset = dataset_dict[split]
assert len(list(dataset)) == 4
assert dataset.features is not None
assert set(dataset.features) == set(expected_features)
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_parquet_datasetdict_reader_keep_in_memory(keep_in_memory, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
dataset = ParquetDatasetReader(
{"train": parquet_path}, cache_dir=cache_dir, keep_in_memory=keep_in_memory
).read()
_check_parquet_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("streaming", [False, True])
@pytest.mark.parametrize(
"features",
[
None,
{"col_1": "string", "col_2": "int64", "col_3": "float64"},
{"col_1": "string", "col_2": "string", "col_3": "string"},
{"col_1": "int32", "col_2": "int32", "col_3": "int32"},
{"col_1": "float32", "col_2": "float32", "col_3": "float32"},
],
)
def test_parquet_datasetdict_reader_features(streaming, features, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
expected_features = features.copy() if features else default_expected_features
features = (
Features({feature: Value(dtype) for feature, dtype in features.items()}) if features is not None else None
)
dataset = ParquetDatasetReader(
{"train": parquet_path}, features=features, cache_dir=cache_dir, streaming=streaming
).read()
_check_parquet_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("streaming", [False, True])
@pytest.mark.parametrize("columns", [None, ["col_1"]])
@pytest.mark.parametrize("pass_features", [False, True])
@pytest.mark.parametrize("pass_info", [False, True])
def test_parquet_datasetdict_reader_columns(streaming, columns, pass_features, pass_info, parquet_path, tmp_path):
cache_dir = tmp_path / "cache"
default_expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
info = (
DatasetInfo(features=Features({feature: Value(dtype) for feature, dtype in default_expected_features.items()}))
if pass_info
else None
)
expected_features = (
{col: default_expected_features[col] for col in columns} if columns else default_expected_features
)
features = (
Features({feature: Value(dtype) for feature, dtype in expected_features.items()}) if pass_features else None
)
dataset = ParquetDatasetReader(
{"train": parquet_path},
columns=columns,
features=features,
info=info,
cache_dir=cache_dir,
streaming=streaming,
).read()
_check_parquet_datasetdict(dataset, expected_features)
@pytest.mark.parametrize("split", [None, NamedSplit("train"), "train", "test"])
def test_parquet_datasetdict_reader_split(split, parquet_path, tmp_path):
if split:
path = {split: parquet_path}
else:
split = "train"
path = {"train": parquet_path, "test": parquet_path}
cache_dir = tmp_path / "cache"
expected_features = {"col_1": "string", "col_2": "int64", "col_3": "float64"}
dataset = ParquetDatasetReader(path, cache_dir=cache_dir).read()
_check_parquet_datasetdict(dataset, expected_features, splits=list(path.keys()))
assert all(dataset[split].split == split for split in path.keys())
def test_parquet_write(dataset, tmp_path):
writer = ParquetDatasetWriter(dataset, tmp_path / "foo.parquet")
assert writer.write() > 0
pf = pq.ParquetFile(tmp_path / "foo.parquet")
output_table = pf.read()
assert dataset.data.table == output_table
def test_dataset_to_parquet_keeps_features(shared_datadir, tmp_path):
image_path = str(shared_datadir / "test_image_rgb.jpg")
data = {"image": [image_path]}
features = Features({"image": Image()})
dataset = Dataset.from_dict(data, features=features)
writer = ParquetDatasetWriter(dataset, tmp_path / "foo.parquet")
assert writer.write() > 0
reloaded_dataset = Dataset.from_parquet(str(tmp_path / "foo.parquet"))
assert dataset.features == reloaded_dataset.features
reloaded_iterable_dataset = ParquetDatasetReader(str(tmp_path / "foo.parquet"), streaming=True).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"feature, expected",
[
(Features({"foo": Value("int32")}), None),
(Features({"image": Image(), "foo": Value("int32")}), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"nested": Sequence(Audio())}), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
],
)
def test_get_writer_batch_size(feature, expected):
assert get_writer_batch_size(feature) == expected
|