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test_data_files.py
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test_data_files.py
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import os
from itertools import chain
from pathlib import Path, PurePath
from unittest.mock import patch
import fsspec
import pytest
from huggingface_hub.hf_api import DatasetInfo
from datasets.data_files import (
DataFilesDict,
DataFilesList,
Url,
_get_data_files_patterns,
resolve_patterns_in_dataset_repository,
resolve_patterns_locally_or_by_urls,
)
from datasets.fingerprint import Hasher
from datasets.utils.file_utils import hf_hub_url
_TEST_PATTERNS = ["*", "**", "**/*", "*.txt", "data/*", "**/*.txt", "**/train.txt"]
_FILES_TO_IGNORE = {".dummy", "README.md", "dummy_data.zip", "dataset_infos.json"}
_TEST_PATTERNS_SIZES = dict(
[("*", 0), ("**", 2), ("**/*", 2), ("*.txt", 0), ("data/*", 2), ("**/*.txt", 2), ("**/train.txt", 1)]
)
_TEST_URL = "https://raw.githubusercontent.com/huggingface/datasets/9675a5a1e7b99a86f9c250f6ea5fa5d1e6d5cc7d/setup.py"
@pytest.fixture
def complex_data_dir(tmp_path):
data_dir = tmp_path / "complex_data_dir"
data_dir.mkdir()
(data_dir / "data").mkdir()
with open(data_dir / "data" / "train.txt", "w") as f:
f.write("foo\n" * 10)
with open(data_dir / "data" / "test.txt", "w") as f:
f.write("bar\n" * 10)
with open(data_dir / "README.md", "w") as f:
f.write("This is a readme")
with open(data_dir / ".dummy", "w") as f:
f.write("this is a dummy file that is not a data file")
return str(data_dir)
@pytest.fixture
def pattern_results(complex_data_dir):
# We use fsspec glob as a reference for data files resolution from patterns.
# This is the same as dask for example.
#
# /!\ Here are some behaviors specific to fsspec glob that are different from glob.glob, Path.glob, Path.match or fnmatch:
# - '*' matches only first level items
# - '**' matches all items
# - '**/*' matches all at least second level items
#
# More generally:
# - '*' matches any character except a forward-slash (to match just the file or directory name)
# - '**' matches any character including a forward-slash /
return {
pattern: sorted(
str(Path(path).resolve())
for path in fsspec.filesystem("file").glob(os.path.join(complex_data_dir, pattern))
if Path(path).name not in _FILES_TO_IGNORE and Path(path).is_file()
)
for pattern in _TEST_PATTERNS
}
@pytest.fixture
def hub_dataset_info(complex_data_dir):
return DatasetInfo(
siblings=[
{"rfilename": path.relative_to(complex_data_dir).as_posix()}
for path in Path(complex_data_dir).rglob("*")
if path.is_file()
],
sha="foobarfoobar",
id="foo",
)
@pytest.fixture
def hub_dataset_info_patterns_results(hub_dataset_info, complex_data_dir, pattern_results):
return {
pattern: [
hf_hub_url(
hub_dataset_info.id, Path(path).relative_to(complex_data_dir).as_posix(), revision=hub_dataset_info.sha
)
for path in pattern_results[pattern]
]
for pattern in pattern_results
}
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_pattern_results_fixture(pattern_results, pattern):
assert len(pattern_results[pattern]) == _TEST_PATTERNS_SIZES[pattern]
assert all(Path(path).is_file() for path in pattern_results[pattern])
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_resolve_patterns_locally_or_by_urls(complex_data_dir, pattern, pattern_results):
try:
resolved_data_files = resolve_patterns_locally_or_by_urls(complex_data_dir, [pattern])
assert sorted(str(f) for f in resolved_data_files) == pattern_results[pattern]
assert all(isinstance(path, Path) for path in resolved_data_files)
except FileNotFoundError:
assert len(pattern_results[pattern]) == 0
def test_resolve_patterns_locally_or_by_urls_with_absolute_path(tmp_path, complex_data_dir):
abs_path = os.path.join(complex_data_dir, "data", "train.txt")
resolved_data_files = resolve_patterns_locally_or_by_urls(str(tmp_path / "blabla"), [abs_path])
assert len(resolved_data_files) == 1
@pytest.mark.parametrize("pattern,size,extensions", [("**", 2, ["txt"]), ("**", 2, None), ("**", 0, ["blablabla"])])
def test_resolve_patterns_locally_or_by_urls_with_extensions(complex_data_dir, pattern, size, extensions):
if size > 0:
resolved_data_files = resolve_patterns_locally_or_by_urls(
complex_data_dir, [pattern], allowed_extensions=extensions
)
assert len(resolved_data_files) == size
else:
with pytest.raises(FileNotFoundError):
resolve_patterns_locally_or_by_urls(complex_data_dir, pattern, allowed_extensions=extensions)
def test_fail_resolve_patterns_locally_or_by_urls(complex_data_dir):
with pytest.raises(FileNotFoundError):
resolve_patterns_locally_or_by_urls(complex_data_dir, ["blablabla"])
def test_resolve_patterns_locally_or_by_urls_sorted_files(tmp_path_factory):
path = str(tmp_path_factory.mktemp("unsorted_text_files"))
unsorted_names = ["0.txt", "2.txt", "3.txt"]
for name in unsorted_names:
with open(os.path.join(path, name), "w"):
pass
resolved_data_files = resolve_patterns_locally_or_by_urls(path, ["*"])
resolved_names = [os.path.basename(data_file) for data_file in resolved_data_files]
assert resolved_names == sorted(unsorted_names)
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_resolve_patterns_in_dataset_repository(hub_dataset_info, pattern, hub_dataset_info_patterns_results):
try:
resolved_data_files = resolve_patterns_in_dataset_repository(hub_dataset_info, [pattern])
assert sorted(str(f) for f in resolved_data_files) == hub_dataset_info_patterns_results[pattern]
assert all(isinstance(url, Url) for url in resolved_data_files)
except FileNotFoundError:
assert len(hub_dataset_info_patterns_results[pattern]) == 0
@pytest.mark.parametrize("pattern,size,extensions", [("**", 2, ["txt"]), ("**", 2, None), ("**", 0, ["blablabla"])])
def test_resolve_patterns_in_dataset_repository_with_extensions(hub_dataset_info, pattern, size, extensions):
if size > 0:
resolved_data_files = resolve_patterns_in_dataset_repository(
hub_dataset_info, [pattern], allowed_extensions=extensions
)
assert len(resolved_data_files) == size
else:
with pytest.raises(FileNotFoundError):
resolved_data_files = resolve_patterns_in_dataset_repository(
hub_dataset_info, [pattern], allowed_extensions=extensions
)
def test_fail_resolve_patterns_in_dataset_repository(hub_dataset_info):
with pytest.raises(FileNotFoundError):
resolve_patterns_in_dataset_repository(hub_dataset_info, "blablabla")
def test_resolve_patterns_in_dataset_repository_sorted_files():
unsorted_names = ["0.txt", "2.txt", "3.txt"]
siblings = [{"rfilename": name} for name in unsorted_names]
datasets_infos = DatasetInfo(id="test_unsorted_files", siblings=siblings, sha="foobar")
resolved_data_files = resolve_patterns_in_dataset_repository(datasets_infos, ["*"])
resolved_names = [os.path.basename(data_file) for data_file in resolved_data_files]
assert resolved_names == sorted(unsorted_names)
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesList_from_hf_repo(hub_dataset_info, hub_dataset_info_patterns_results, pattern):
try:
data_files_list = DataFilesList.from_hf_repo([pattern], hub_dataset_info)
assert sorted(str(f) for f in data_files_list) == hub_dataset_info_patterns_results[pattern]
assert all(isinstance(url, Url) for url in data_files_list)
assert len(data_files_list.origin_metadata) > 0
except FileNotFoundError:
assert len(hub_dataset_info_patterns_results[pattern]) == 0
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesList_from_local_or_remote(complex_data_dir, pattern_results, pattern):
try:
data_files_list = DataFilesList.from_local_or_remote([pattern], complex_data_dir)
assert sorted(str(f) for f in data_files_list) == pattern_results[pattern]
assert all(isinstance(path, Path) for path in data_files_list)
assert len(data_files_list.origin_metadata) > 0
except FileNotFoundError:
assert len(pattern_results[pattern]) == 0
def test_DataFilesList_from_local_or_remote_with_extra_files(complex_data_dir, text_file):
data_files_list = DataFilesList.from_local_or_remote([_TEST_URL, str(text_file)], complex_data_dir)
assert list(data_files_list) == [Url(_TEST_URL), Path(text_file)]
assert len(data_files_list.origin_metadata) == 2
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesDict_from_hf_repo(hub_dataset_info, hub_dataset_info_patterns_results, pattern):
split_name = "train"
try:
data_files = DataFilesDict.from_hf_repo({split_name: [pattern]}, hub_dataset_info)
assert all(isinstance(data_files_list, DataFilesList) for data_files_list in data_files.values())
assert sorted(str(f) for f in data_files[split_name]) == hub_dataset_info_patterns_results[pattern]
assert all(isinstance(url, Url) for url in data_files[split_name])
except FileNotFoundError:
assert len(hub_dataset_info_patterns_results[pattern]) == 0
@pytest.mark.parametrize("pattern", _TEST_PATTERNS)
def test_DataFilesDict_from_local_or_remote(complex_data_dir, pattern_results, pattern):
split_name = "train"
try:
data_files = DataFilesDict.from_local_or_remote({split_name: [pattern]}, complex_data_dir)
assert all(isinstance(data_files_list, DataFilesList) for data_files_list in data_files.values())
assert sorted(str(f) for f in data_files[split_name]) == pattern_results[pattern]
assert all(isinstance(url, Path) for url in data_files[split_name])
except FileNotFoundError:
assert len(pattern_results[pattern]) == 0
def test_DataFilesDict_from_hf_repo_hashing(hub_dataset_info):
patterns = {"train": ["**/train.txt"], "test": ["**/test.txt"]}
data_files1 = DataFilesDict.from_hf_repo(patterns, hub_dataset_info)
data_files2 = DataFilesDict.from_hf_repo(patterns, hub_dataset_info)
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
data_files2 = DataFilesDict(sorted(data_files1.items(), reverse=True))
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
patterns2 = {"train": ["data/train.txt"], "test": ["data/test.txt"]}
data_files2 = DataFilesDict.from_hf_repo(patterns2, hub_dataset_info)
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
patterns2 = {"train": ["data/train.txt"], "test": ["data/train.txt"]}
data_files2 = DataFilesDict.from_hf_repo(patterns2, hub_dataset_info)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
with patch.object(hub_dataset_info, "id", "blabla"):
data_files2 = DataFilesDict.from_hf_repo(patterns, hub_dataset_info)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
with patch.object(hub_dataset_info, "sha", "blabla"):
data_files2 = DataFilesDict.from_hf_repo(patterns, hub_dataset_info)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
def test_DataFilesDict_from_hf_local_or_remote_hashing(text_file):
patterns = {"train": [_TEST_URL], "test": [str(text_file)]}
data_files1 = DataFilesDict.from_local_or_remote(patterns)
data_files2 = DataFilesDict.from_local_or_remote(patterns)
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
data_files2 = DataFilesDict(sorted(data_files1.items(), reverse=True))
assert Hasher.hash(data_files1) == Hasher.hash(data_files2)
patterns2 = {"train": [_TEST_URL], "test": [_TEST_URL]}
data_files2 = DataFilesDict.from_local_or_remote(patterns2)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
with patch("datasets.data_files.request_etag") as mock_request_etag:
mock_request_etag.return_value = "blabla"
data_files2 = DataFilesDict.from_local_or_remote(patterns)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
with patch("datasets.data_files.os.path.getmtime") as mock_getmtime:
mock_getmtime.return_value = 123
data_files2 = DataFilesDict.from_local_or_remote(patterns)
assert Hasher.hash(data_files1) != Hasher.hash(data_files2)
@pytest.mark.parametrize(
"data_file_per_split",
[
# === Main cases ===
# file named afetr split at the root
{"train": "train.txt", "test": "test.txt", "validation": "valid.txt"},
# file named after split in a directory
{
"train": "data/train.txt",
"test": "data/test.txt",
"validation": "data/valid.txt",
},
# directory named after split
{
"train": "train/split.txt",
"test": "test/split.txt",
"validation": "valid/split.txt",
},
# sharded splits
{
"train": [f"data/train_{i}.txt" for i in range(3)],
"test": [f"data/test_{i}.txt" for i in range(3)],
},
# sharded splits with standard format (+ custom split name)
{
"train": [f"data/train-0000{i}-of-00003.txt" for i in range(3)],
"random": [f"data/random-0000{i}-of-00003.txt" for i in range(3)],
},
# === Secondary cases ===
# Default to train split
{"train": "dataset.txt"},
{"train": "data/dataset.txt"},
# With prefix or suffix in directory or file names
{"train": "my_train_dir/dataset.txt"},
{"train": "data/my_train_file.txt"},
{"test": "my_test_dir/dataset.txt"},
{"test": "data/my_test_file.txt"},
{"validation": "my_validation_dir/dataset.txt"},
{"validation": "data/my_validation_file.txt"},
# With test<>eval aliases
{"test": "eval.txt"},
{"test": "data/eval.txt"},
{"test": "eval/dataset.txt"},
# With valid<>dev aliases
{"validation": "dev.txt"},
{"validation": "data/dev.txt"},
{"validation": "dev/dataset.txt"},
# With other extensions
{"train": "train.parquet", "test": "test.parquet", "validation": "valid.parquet"},
],
)
def test_get_data_files_patterns(data_file_per_split):
data_file_per_split = {k: v if isinstance(v, list) else [v] for k, v in data_file_per_split.items()}
def resolver(pattern):
return [PurePath(path) for path in chain(*data_file_per_split.values()) if PurePath(path).match(pattern)]
patterns_per_split = _get_data_files_patterns(resolver)
assert sorted(patterns_per_split.keys()) == sorted(data_file_per_split.keys())
for split, patterns in patterns_per_split.items():
matched = [
path
for path in chain(*data_file_per_split.values())
for pattern in patterns
if PurePath(path).match(pattern)
]
assert len(matched) == len(data_file_per_split[split])
assert matched == data_file_per_split[split]