test_load.py

import importlib
import os
import re
import shutil
import tempfile
import time
from hashlib import sha256
from pathlib import Path
from unittest import TestCase
from unittest.mock import patch

import pytest
import requests

import datasets
from datasets import SCRIPTS_VERSION, config, load_dataset, load_from_disk
from datasets.arrow_dataset import Dataset
from datasets.builder import DatasetBuilder
from datasets.data_files import DataFilesDict
from datasets.dataset_dict import DatasetDict, IterableDatasetDict
from datasets.features import Features, Value
from datasets.iterable_dataset import IterableDataset
from datasets.load import (
    CachedDatasetModuleFactory,
    CachedMetricModuleFactory,
    GithubDatasetModuleFactory,
    GithubMetricModuleFactory,
    HubDatasetModuleFactoryWithoutScript,
    HubDatasetModuleFactoryWithScript,
    LocalDatasetModuleFactoryWithoutScript,
    LocalDatasetModuleFactoryWithScript,
    LocalMetricModuleFactory,
    PackagedDatasetModuleFactory,
    infer_module_for_data_files_in_archives,
)
from datasets.utils.file_utils import DownloadConfig, is_remote_url

from .utils import (
    OfflineSimulationMode,
    assert_arrow_memory_doesnt_increase,
    assert_arrow_memory_increases,
    offline,
    set_current_working_directory_to_temp_dir,
)


DATASET_LOADING_SCRIPT_NAME = "__dummy_dataset1__"

DATASET_LOADING_SCRIPT_CODE = """
import os

import datasets
from datasets import DatasetInfo, Features, Split, SplitGenerator, Value


class __DummyDataset1__(datasets.GeneratorBasedBuilder):

    def _info(self) -> DatasetInfo:
        return DatasetInfo(features=Features({"text": Value("string")}))

    def _split_generators(self, dl_manager):
        return [
            SplitGenerator(Split.TRAIN, gen_kwargs={"filepath": os.path.join(dl_manager.manual_dir, "train.txt")}),
            SplitGenerator(Split.TEST, gen_kwargs={"filepath": os.path.join(dl_manager.manual_dir, "test.txt")}),
        ]

    def _generate_examples(self, filepath, **kwargs):
        with open(filepath, "r", encoding="utf-8") as f:
            for i, line in enumerate(f):
                yield i, {"text": line.strip()}
"""

SAMPLE_DATASET_IDENTIFIER = "lhoestq/test"  # has dataset script
SAMPLE_DATASET_IDENTIFIER2 = "lhoestq/test2"  # only has data files
SAMPLE_DATASET_IDENTIFIER3 = "mariosasko/test_multi_dir_dataset"  # has multiple data directories
SAMPLE_NOT_EXISTING_DATASET_IDENTIFIER = "lhoestq/_dummy"
SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST = "_dummy"


METRIC_LOADING_SCRIPT_NAME = "__dummy_metric1__"

METRIC_LOADING_SCRIPT_CODE = """
import datasets
from datasets import MetricInfo, Features, Value


class __DummyMetric1__(datasets.Metric):

    def _info(self):
        return MetricInfo(features=Features({"predictions": Value("int"), "references": Value("int")}))

    def _compute(self, predictions, references):
        return {"__dummy_metric1__": sum(int(p == r) for p, r in zip(predictions, references))}
"""


@pytest.fixture
def data_dir(tmp_path):
    data_dir = tmp_path / "data_dir"
    data_dir.mkdir()
    with open(data_dir / "train.txt", "w") as f:
        f.write("foo\n" * 10)
    with open(data_dir / "test.txt", "w") as f:
        f.write("bar\n" * 10)
    return str(data_dir)


@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 dataset_loading_script_dir(tmp_path):
    script_name = DATASET_LOADING_SCRIPT_NAME
    script_dir = tmp_path / script_name
    script_dir.mkdir()
    script_path = script_dir / f"{script_name}.py"
    with open(script_path, "w") as f:
        f.write(DATASET_LOADING_SCRIPT_CODE)
    return str(script_dir)


@pytest.fixture
def dataset_loading_script_dir_readonly(tmp_path):
    script_name = DATASET_LOADING_SCRIPT_NAME
    script_dir = tmp_path / "readonly" / script_name
    script_dir.mkdir(parents=True)
    script_path = script_dir / f"{script_name}.py"
    with open(script_path, "w") as f:
        f.write(DATASET_LOADING_SCRIPT_CODE)
    dataset_loading_script_dir = str(script_dir)
    # Make this directory readonly
    os.chmod(dataset_loading_script_dir, 0o555)
    os.chmod(os.path.join(dataset_loading_script_dir, f"{script_name}.py"), 0o555)
    return dataset_loading_script_dir


@pytest.fixture
def metric_loading_script_dir(tmp_path):
    script_name = METRIC_LOADING_SCRIPT_NAME
    script_dir = tmp_path / script_name
    script_dir.mkdir()
    script_path = script_dir / f"{script_name}.py"
    with open(script_path, "w") as f:
        f.write(METRIC_LOADING_SCRIPT_CODE)
    return str(script_dir)


@pytest.mark.parametrize("data_file, expected_module", [("zip_csv_path", "csv"), ("zip_csv_with_dir_path", "csv")])
def test_infer_module_for_data_files_in_archives(data_file, expected_module, zip_csv_path, zip_csv_with_dir_path):
    data_file_paths = {"zip_csv_path": zip_csv_path, "zip_csv_with_dir_path": zip_csv_with_dir_path}
    data_files = [str(data_file_paths[data_file])]
    inferred_module = infer_module_for_data_files_in_archives(data_files, False)
    assert inferred_module == expected_module


class ModuleFactoryTest(TestCase):
    @pytest.fixture(autouse=True)
    def inject_fixtures(self, jsonl_path, data_dir, dataset_loading_script_dir, metric_loading_script_dir):
        self._jsonl_path = jsonl_path
        self._data_dir = data_dir
        self._dataset_loading_script_dir = dataset_loading_script_dir
        self._metric_loading_script_dir = metric_loading_script_dir

    def setUp(self):
        self.hf_modules_cache = tempfile.mkdtemp()
        self.cache_dir = tempfile.mkdtemp()
        self.download_config = DownloadConfig(cache_dir=self.cache_dir)
        self.dynamic_modules_path = datasets.load.init_dynamic_modules(
            name="test_datasets_modules_" + os.path.basename(self.hf_modules_cache),
            hf_modules_cache=self.hf_modules_cache,
        )

    def test_GithubDatasetModuleFactory(self):
        # "wmt_t2t" has additional imports (internal)
        factory = GithubDatasetModuleFactory(
            "wmt_t2t", download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)

    def test_GithubMetricModuleFactory_with_internal_import(self):
        # "squad_v2" requires additional imports (internal)
        factory = GithubMetricModuleFactory(
            "squad_v2", download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

    def test_GithubMetricModuleFactory_with_external_import(self):
        # "bleu" requires additional imports (external from github)
        factory = GithubMetricModuleFactory(
            "bleu", download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

    def test_LocalMetricModuleFactory(self):
        path = os.path.join(self._metric_loading_script_dir, f"{METRIC_LOADING_SCRIPT_NAME}.py")
        factory = LocalMetricModuleFactory(
            path, download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

    def test_LocalDatasetModuleFactoryWithScript(self):
        path = os.path.join(self._dataset_loading_script_dir, f"{DATASET_LOADING_SCRIPT_NAME}.py")
        factory = LocalDatasetModuleFactoryWithScript(
            path, download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert os.path.isdir(module_factory_result.builder_kwargs["base_path"])

    def test_LocalDatasetModuleFactoryWithoutScript(self):
        factory = LocalDatasetModuleFactoryWithoutScript(self._data_dir)
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert os.path.isdir(module_factory_result.builder_kwargs["base_path"])

    def test_PackagedDatasetModuleFactory(self):
        factory = PackagedDatasetModuleFactory(
            "json", data_files=self._jsonl_path, download_config=self.download_config
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

    def test_PackagedDatasetModuleFactory_with_data_dir(self):
        factory = PackagedDatasetModuleFactory("json", data_dir=self._data_dir, download_config=self.download_config)
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert (
            module_factory_result.builder_kwargs["data_files"] is not None
            and len(module_factory_result.builder_kwargs["data_files"]["train"]) > 0
        )
        assert Path(module_factory_result.builder_kwargs["data_files"]["train"][0]).parent.samefile(self._data_dir)

    def test_HubDatasetModuleFactoryWithoutScript(self):
        factory = HubDatasetModuleFactoryWithoutScript(
            SAMPLE_DATASET_IDENTIFIER2, download_config=self.download_config
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)

    def test_HubDatasetModuleFactoryWithoutScript_with_data_dir(self):
        data_dir = "data2"
        factory = HubDatasetModuleFactoryWithoutScript(
            SAMPLE_DATASET_IDENTIFIER3, data_dir=data_dir, download_config=self.download_config
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)
        assert (
            module_factory_result.builder_kwargs["data_files"] is not None
            and len(module_factory_result.builder_kwargs["data_files"]["train"]) > 0
        )
        assert all(
            data_dir in Path(data_file).parts
            for data_file in module_factory_result.builder_kwargs["data_files"]["train"]
        )

    def test_HubDatasetModuleFactoryWithScript(self):
        factory = HubDatasetModuleFactoryWithScript(
            SAMPLE_DATASET_IDENTIFIER,
            download_config=self.download_config,
            dynamic_modules_path=self.dynamic_modules_path,
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)

    def test_CachedDatasetModuleFactory(self):
        path = os.path.join(self._dataset_loading_script_dir, f"{DATASET_LOADING_SCRIPT_NAME}.py")
        factory = LocalDatasetModuleFactoryWithScript(
            path, download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        for offline_mode in OfflineSimulationMode:
            with offline(offline_mode):
                factory = CachedDatasetModuleFactory(
                    DATASET_LOADING_SCRIPT_NAME,
                    dynamic_modules_path=self.dynamic_modules_path,
                )
                module_factory_result = factory.get_module()
                assert importlib.import_module(module_factory_result.module_path) is not None

    def test_CachedMetricModuleFactory(self):
        path = os.path.join(self._metric_loading_script_dir, f"{METRIC_LOADING_SCRIPT_NAME}.py")
        factory = LocalMetricModuleFactory(
            path, download_config=self.download_config, dynamic_modules_path=self.dynamic_modules_path
        )
        module_factory_result = factory.get_module()
        for offline_mode in OfflineSimulationMode:
            with offline(offline_mode):
                factory = CachedMetricModuleFactory(
                    METRIC_LOADING_SCRIPT_NAME,
                    dynamic_modules_path=self.dynamic_modules_path,
                )
                module_factory_result = factory.get_module()
                assert importlib.import_module(module_factory_result.module_path) is not None


@pytest.mark.parametrize(
    "factory_class",
    [
        CachedDatasetModuleFactory,
        CachedMetricModuleFactory,
        GithubDatasetModuleFactory,
        GithubMetricModuleFactory,
        HubDatasetModuleFactoryWithoutScript,
        HubDatasetModuleFactoryWithScript,
        LocalDatasetModuleFactoryWithoutScript,
        LocalDatasetModuleFactoryWithScript,
        LocalMetricModuleFactory,
        PackagedDatasetModuleFactory,
    ],
)
def test_module_factories(factory_class):
    if issubclass(factory_class, (HubDatasetModuleFactoryWithoutScript, HubDatasetModuleFactoryWithScript)):
        name = "dummy_org/dummy_name"
    else:
        name = "dummy_name"
    factory = factory_class(name)
    assert factory.name == name


class LoadTest(TestCase):
    @pytest.fixture(autouse=True)
    def inject_fixtures(self, caplog):
        self._caplog = caplog

    def setUp(self):
        self.hf_modules_cache = tempfile.mkdtemp()
        self.dynamic_modules_path = datasets.load.init_dynamic_modules(
            name="test_datasets_modules2", hf_modules_cache=self.hf_modules_cache
        )

    def tearDown(self):
        shutil.rmtree(self.hf_modules_cache)

    def _dummy_module_dir(self, modules_dir, dummy_module_name, dummy_code):
        assert dummy_module_name.startswith("__")
        module_dir = os.path.join(modules_dir, dummy_module_name)
        os.makedirs(module_dir, exist_ok=True)
        module_path = os.path.join(module_dir, dummy_module_name + ".py")
        with open(module_path, "w") as f:
            f.write(dummy_code)
        return module_dir

    def test_dataset_module_factory(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            # prepare module from directory path
            dummy_code = "MY_DUMMY_VARIABLE = 'hello there'"
            module_dir = self._dummy_module_dir(tmp_dir, "__dummy_module_name1__", dummy_code)
            dataset_module = datasets.load.dataset_module_factory(
                module_dir, dynamic_modules_path=self.dynamic_modules_path
            )
            dummy_module = importlib.import_module(dataset_module.module_path)
            self.assertEqual(dummy_module.MY_DUMMY_VARIABLE, "hello there")
            self.assertEqual(dataset_module.hash, sha256(dummy_code.encode("utf-8")).hexdigest())
            # prepare module from file path + check resolved_file_path
            dummy_code = "MY_DUMMY_VARIABLE = 'general kenobi'"
            module_dir = self._dummy_module_dir(tmp_dir, "__dummy_module_name1__", dummy_code)
            module_path = os.path.join(module_dir, "__dummy_module_name1__.py")
            dataset_module = datasets.load.dataset_module_factory(
                module_path, dynamic_modules_path=self.dynamic_modules_path
            )
            dummy_module = importlib.import_module(dataset_module.module_path)
            self.assertEqual(dummy_module.MY_DUMMY_VARIABLE, "general kenobi")
            self.assertEqual(dataset_module.hash, sha256(dummy_code.encode("utf-8")).hexdigest())
            # missing module
            for offline_simulation_mode in list(OfflineSimulationMode):
                with offline(offline_simulation_mode):
                    with self.assertRaises((FileNotFoundError, ConnectionError, requests.exceptions.ConnectionError)):
                        datasets.load.dataset_module_factory(
                            "__missing_dummy_module_name__", dynamic_modules_path=self.dynamic_modules_path
                        )

    def test_offline_dataset_module_factory(self):
        with tempfile.TemporaryDirectory() as tmp_dir:
            dummy_code = "MY_DUMMY_VARIABLE = 'hello there'"
            module_dir = self._dummy_module_dir(tmp_dir, "__dummy_module_name2__", dummy_code)
            dataset_module_1 = datasets.load.dataset_module_factory(
                module_dir, dynamic_modules_path=self.dynamic_modules_path
            )
            time.sleep(0.1)  # make sure there's a difference in the OS update time of the python file
            dummy_code = "MY_DUMMY_VARIABLE = 'general kenobi'"
            module_dir = self._dummy_module_dir(tmp_dir, "__dummy_module_name2__", dummy_code)
            dataset_module_2 = datasets.load.dataset_module_factory(
                module_dir, dynamic_modules_path=self.dynamic_modules_path
            )
        for offline_simulation_mode in list(OfflineSimulationMode):
            with offline(offline_simulation_mode):
                self._caplog.clear()
                # allow provide the module name without an explicit path to remote or local actual file
                dataset_module_3 = datasets.load.dataset_module_factory(
                    "__dummy_module_name2__", dynamic_modules_path=self.dynamic_modules_path
                )
                # it loads the most recent version of the module
                self.assertEqual(dataset_module_2.module_path, dataset_module_3.module_path)
                self.assertNotEqual(dataset_module_1.module_path, dataset_module_3.module_path)
                self.assertIn("Using the latest cached version of the module", self._caplog.text)

    def test_load_dataset_from_github(self):
        scripts_version = os.getenv("HF_SCRIPTS_VERSION", SCRIPTS_VERSION)
        with self.assertRaises(FileNotFoundError) as context:
            datasets.load_dataset("_dummy")
        self.assertIn(
            "https://raw.githubusercontent.com/huggingface/datasets/master/datasets/_dummy/_dummy.py",
            str(context.exception),
        )
        with self.assertRaises(FileNotFoundError) as context:
            datasets.load_dataset("_dummy", revision="0.0.0")
        self.assertIn(
            "https://raw.githubusercontent.com/huggingface/datasets/0.0.0/datasets/_dummy/_dummy.py",
            str(context.exception),
        )
        for offline_simulation_mode in list(OfflineSimulationMode):
            with offline(offline_simulation_mode):
                with self.assertRaises(ConnectionError) as context:
                    datasets.load_dataset("_dummy")
                if offline_simulation_mode != OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
                    self.assertIn(
                        f"https://raw.githubusercontent.com/huggingface/datasets/{scripts_version}/datasets/_dummy/_dummy.py",
                        str(context.exception),
                    )

    def test_load_dataset_users(self):
        with self.assertRaises(FileNotFoundError) as context:
            datasets.load_dataset("lhoestq/_dummy")
        self.assertIn(
            "lhoestq/_dummy",
            str(context.exception),
        )
        for offline_simulation_mode in list(OfflineSimulationMode):
            with offline(offline_simulation_mode):
                with self.assertRaises(ConnectionError) as context:
                    datasets.load_dataset("lhoestq/_dummy")
                self.assertIn("lhoestq/_dummy", str(context.exception))


def test_load_dataset_builder_for_absolute_script_dir(dataset_loading_script_dir, data_dir):
    builder = datasets.load_dataset_builder(dataset_loading_script_dir, data_dir=data_dir)
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == DATASET_LOADING_SCRIPT_NAME
    assert builder.info.features == Features({"text": Value("string")})


def test_load_dataset_builder_for_relative_script_dir(dataset_loading_script_dir, data_dir):
    with set_current_working_directory_to_temp_dir():
        relative_script_dir = DATASET_LOADING_SCRIPT_NAME
        shutil.copytree(dataset_loading_script_dir, relative_script_dir)
        builder = datasets.load_dataset_builder(relative_script_dir, data_dir=data_dir)
        assert isinstance(builder, DatasetBuilder)
        assert builder.name == DATASET_LOADING_SCRIPT_NAME
        assert builder.info.features == Features({"text": Value("string")})


def test_load_dataset_builder_for_script_path(dataset_loading_script_dir, data_dir):
    builder = datasets.load_dataset_builder(
        os.path.join(dataset_loading_script_dir, DATASET_LOADING_SCRIPT_NAME + ".py"), data_dir=data_dir
    )
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == DATASET_LOADING_SCRIPT_NAME
    assert builder.info.features == Features({"text": Value("string")})


def test_load_dataset_builder_for_absolute_data_dir(complex_data_dir):
    builder = datasets.load_dataset_builder(complex_data_dir)
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == "text"
    assert builder.config.name == Path(complex_data_dir).name
    assert isinstance(builder.config.data_files, DataFilesDict)
    assert len(builder.config.data_files["train"]) > 0
    assert len(builder.config.data_files["test"]) > 0


def test_load_dataset_builder_for_relative_data_dir(complex_data_dir):
    with set_current_working_directory_to_temp_dir():
        relative_data_dir = "relative_data_dir"
        shutil.copytree(complex_data_dir, relative_data_dir)
        builder = datasets.load_dataset_builder(relative_data_dir)
        assert isinstance(builder, DatasetBuilder)
        assert builder.name == "text"
        assert builder.config.name == relative_data_dir
        assert isinstance(builder.config.data_files, DataFilesDict)
        assert len(builder.config.data_files["train"]) > 0
        assert len(builder.config.data_files["test"]) > 0


def test_load_dataset_builder_for_community_dataset_with_script():
    builder = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER)
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == SAMPLE_DATASET_IDENTIFIER.split("/")[-1]
    assert builder.config.name == "default"
    assert builder.info.features == Features({"text": Value("string")})
    namespace = SAMPLE_DATASET_IDENTIFIER[: SAMPLE_DATASET_IDENTIFIER.index("/")]
    assert builder._relative_data_dir().startswith(namespace)
    assert SAMPLE_DATASET_IDENTIFIER.replace("/", "--") in builder.__module__


def test_load_dataset_builder_for_community_dataset_without_script():
    builder = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER2)
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == "text"
    assert builder.config.name == SAMPLE_DATASET_IDENTIFIER2.replace("/", "--")
    assert isinstance(builder.config.data_files, DataFilesDict)
    assert len(builder.config.data_files["train"]) > 0
    assert len(builder.config.data_files["test"]) > 0


def test_load_dataset_builder_fail():
    with pytest.raises(FileNotFoundError):
        datasets.load_dataset_builder("blabla")


@pytest.mark.parametrize("keep_in_memory", [False, True])
def test_load_dataset_local(dataset_loading_script_dir, data_dir, keep_in_memory, caplog):
    with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
        dataset = load_dataset(dataset_loading_script_dir, data_dir=data_dir, keep_in_memory=keep_in_memory)
    assert isinstance(dataset, DatasetDict)
    assert all(isinstance(d, Dataset) for d in dataset.values())
    assert len(dataset) == 2
    assert isinstance(next(iter(dataset["train"])), dict)
    for offline_simulation_mode in list(OfflineSimulationMode):
        with offline(offline_simulation_mode):
            caplog.clear()
            # Load dataset from cache
            dataset = datasets.load_dataset(DATASET_LOADING_SCRIPT_NAME, data_dir=data_dir)
            assert len(dataset) == 2
            assert "Using the latest cached version of the module" in caplog.text
    with pytest.raises(FileNotFoundError) as exc_info:
        datasets.load_dataset(SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST)
    m_combined_path = re.search(
        rf"http\S*{re.escape(SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST + '/' + SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST + '.py')}\b",
        str(exc_info.value),
    )
    assert m_combined_path is not None and is_remote_url(m_combined_path.group())
    assert os.path.abspath(SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST) in str(exc_info.value)


def test_load_dataset_streaming(dataset_loading_script_dir, data_dir):
    dataset = load_dataset(dataset_loading_script_dir, streaming=True, data_dir=data_dir)
    assert isinstance(dataset, IterableDatasetDict)
    assert all(isinstance(d, IterableDataset) for d in dataset.values())
    assert len(dataset) == 2
    assert isinstance(next(iter(dataset["train"])), dict)


def test_load_dataset_streaming_gz_json(jsonl_gz_path):
    data_files = jsonl_gz_path
    ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
    assert isinstance(ds, IterableDataset)
    ds_item = next(iter(ds))
    assert ds_item == {"col_1": "0", "col_2": 0, "col_3": 0.0}


@pytest.mark.parametrize(
    "path", ["sample.jsonl", "sample.jsonl.gz", "sample.tar", "sample.jsonl.xz", "sample.zip", "sample.jsonl.zst"]
)
def test_load_dataset_streaming_compressed_files(path):
    repo_id = "albertvillanova/datasets-tests-compression"
    data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{path}"
    if data_files[-3:] in ("zip", "tar"):  # we need to glob "*" inside archives
        data_files = data_files[-3:] + "://*::" + data_files
        return  # TODO(QL, albert): support re-add support for ZIP and TAR archives streaming
    ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
    assert isinstance(ds, IterableDataset)
    ds_item = next(iter(ds))
    assert ds_item == {
        "tokens": ["Ministeri", "de", "Justícia", "d'Espanya"],
        "ner_tags": [1, 2, 2, 2],
        "langs": ["ca", "ca", "ca", "ca"],
        "spans": ["PER: Ministeri de Justícia d'Espanya"],
    }


@pytest.mark.parametrize("path_extension", ["csv", "csv.bz2"])
@pytest.mark.parametrize("streaming", [False, True])
def test_load_dataset_streaming_csv(path_extension, streaming, csv_path, bz2_csv_path):
    paths = {"csv": csv_path, "csv.bz2": bz2_csv_path}
    data_files = str(paths[path_extension])
    features = Features({"col_1": Value("string"), "col_2": Value("int32"), "col_3": Value("float32")})
    ds = load_dataset("csv", split="train", data_files=data_files, features=features, streaming=streaming)
    assert isinstance(ds, IterableDataset if streaming else Dataset)
    ds_item = next(iter(ds))
    assert ds_item == {"col_1": "0", "col_2": 0, "col_3": 0.0}


@pytest.mark.parametrize("streaming", [False, True])
@pytest.mark.parametrize("data_file", ["zip_csv_path", "zip_csv_with_dir_path", "csv_path"])
def test_load_dataset_zip_csv(data_file, streaming, zip_csv_path, zip_csv_with_dir_path, csv_path):
    data_file_paths = {
        "zip_csv_path": zip_csv_path,
        "zip_csv_with_dir_path": zip_csv_with_dir_path,
        "csv_path": csv_path,
    }
    data_files = str(data_file_paths[data_file])
    expected_size = 8 if data_file.startswith("zip") else 4
    features = Features({"col_1": Value("string"), "col_2": Value("int32"), "col_3": Value("float32")})
    ds = load_dataset("csv", split="train", data_files=data_files, features=features, streaming=streaming)
    if streaming:
        ds_item_counter = 0
        for ds_item in ds:
            if ds_item_counter == 0:
                assert ds_item == {"col_1": "0", "col_2": 0, "col_3": 0.0}
            ds_item_counter += 1
        assert ds_item_counter == expected_size
    else:
        assert ds.shape[0] == expected_size
        ds_item = next(iter(ds))
        assert ds_item == {"col_1": "0", "col_2": 0, "col_3": 0.0}


@pytest.mark.parametrize("streaming", [False, True])
@pytest.mark.parametrize("data_file", ["zip_jsonl_path", "zip_jsonl_with_dir_path", "jsonl_path"])
def test_load_dataset_zip_jsonl(data_file, streaming, zip_jsonl_path, zip_jsonl_with_dir_path, jsonl_path):
    data_file_paths = {
        "zip_jsonl_path": zip_jsonl_path,
        "zip_jsonl_with_dir_path": zip_jsonl_with_dir_path,
        "jsonl_path": jsonl_path,
    }
    data_files = str(data_file_paths[data_file])
    expected_size = 8 if data_file.startswith("zip") else 4
    features = Features({"col_1": Value("string"), "col_2": Value("int32"), "col_3": Value("float32")})
    ds = load_dataset("json", split="train", data_files=data_files, features=features, streaming=streaming)
    if streaming:
        ds_item_counter = 0
        for ds_item in ds:
            if ds_item_counter == 0:
                assert ds_item == {"col_1": "0", "col_2": 0, "col_3": 0.0}
            ds_item_counter += 1
        assert ds_item_counter == expected_size
    else:
        assert ds.shape[0] == expected_size
        ds_item = next(iter(ds))
        assert ds_item == {"col_1": "0", "col_2": 0, "col_3": 0.0}


@pytest.mark.parametrize("streaming", [False, True])
@pytest.mark.parametrize("data_file", ["zip_text_path", "zip_text_with_dir_path", "text_path"])
def test_load_dataset_zip_text(data_file, streaming, zip_text_path, zip_text_with_dir_path, text_path):
    data_file_paths = {
        "zip_text_path": zip_text_path,
        "zip_text_with_dir_path": zip_text_with_dir_path,
        "text_path": text_path,
    }
    data_files = str(data_file_paths[data_file])
    expected_size = 8 if data_file.startswith("zip") else 4
    ds = load_dataset("text", split="train", data_files=data_files, streaming=streaming)
    if streaming:
        ds_item_counter = 0
        for ds_item in ds:
            if ds_item_counter == 0:
                assert ds_item == {"text": "0"}
            ds_item_counter += 1
        assert ds_item_counter == expected_size
    else:
        assert ds.shape[0] == expected_size
        ds_item = next(iter(ds))
        assert ds_item == {"text": "0"}


def test_load_dataset_text_with_unicode_new_lines(text_path_with_unicode_new_lines):
    data_files = str(text_path_with_unicode_new_lines)
    ds = load_dataset("text", split="train", data_files=data_files)
    assert ds.num_rows == 3


def test_loading_from_the_datasets_hub():
    with tempfile.TemporaryDirectory() as tmp_dir:
        dataset = load_dataset(SAMPLE_DATASET_IDENTIFIER, cache_dir=tmp_dir)
        assert len(dataset["train"]) == 2
        assert len(dataset["validation"]) == 3
        del dataset


def test_loading_from_the_datasets_hub_with_use_auth_token():
    from requests import get

    def assert_auth(url, *args, headers, **kwargs):
        assert headers["authorization"] == "Bearer foo"
        return get(url, *args, headers=headers, **kwargs)

    with patch("requests.get") as mock_head:
        mock_head.side_effect = assert_auth
        with tempfile.TemporaryDirectory() as tmp_dir:
            with offline():
                with pytest.raises((ConnectionError, requests.exceptions.ConnectionError)):
                    load_dataset(SAMPLE_NOT_EXISTING_DATASET_IDENTIFIER, cache_dir=tmp_dir, use_auth_token="foo")
        mock_head.assert_called()


@pytest.mark.skipif(
    os.name == "nt", reason="skip on windows because of SSL issues with moon-staging.huggingface.co:443"
)
def test_load_streaming_private_dataset(hf_token, hf_private_dataset_repo_txt_data):
    with pytest.raises(FileNotFoundError):
        load_dataset(hf_private_dataset_repo_txt_data, streaming=True)
    ds = load_dataset(hf_private_dataset_repo_txt_data, streaming=True, use_auth_token=hf_token)
    assert next(iter(ds)) is not None


@pytest.mark.skipif(
    os.name == "nt", reason="skip on windows because of SSL issues with moon-staging.huggingface.co:443"
)
def test_load_streaming_private_dataset_with_zipped_data(hf_token, hf_private_dataset_repo_zipped_txt_data):
    with pytest.raises(FileNotFoundError):
        load_dataset(hf_private_dataset_repo_zipped_txt_data, streaming=True)
    ds = load_dataset(hf_private_dataset_repo_zipped_txt_data, streaming=True, use_auth_token=hf_token)
    assert next(iter(ds)) is not None


def test_load_dataset_then_move_then_reload(dataset_loading_script_dir, data_dir, tmp_path, caplog):
    cache_dir1 = tmp_path / "cache1"
    cache_dir2 = tmp_path / "cache2"
    dataset = load_dataset(dataset_loading_script_dir, data_dir=data_dir, split="train", cache_dir=cache_dir1)
    fingerprint1 = dataset._fingerprint
    del dataset
    os.rename(cache_dir1, cache_dir2)
    caplog.clear()
    dataset = load_dataset(dataset_loading_script_dir, data_dir=data_dir, split="train", cache_dir=cache_dir2)
    assert "Reusing dataset" in caplog.text
    assert dataset._fingerprint == fingerprint1, "for the caching mechanism to work, fingerprint should stay the same"
    dataset = load_dataset(dataset_loading_script_dir, data_dir=data_dir, split="test", cache_dir=cache_dir2)
    assert dataset._fingerprint != fingerprint1


def test_load_dataset_readonly(dataset_loading_script_dir, dataset_loading_script_dir_readonly, data_dir, tmp_path):
    cache_dir1 = tmp_path / "cache1"
    cache_dir2 = tmp_path / "cache2"
    dataset = load_dataset(dataset_loading_script_dir, data_dir=data_dir, split="train", cache_dir=cache_dir1)
    fingerprint1 = dataset._fingerprint
    del dataset
    # Load readonly dataset and check that the fingerprint is the same.
    dataset = load_dataset(dataset_loading_script_dir_readonly, data_dir=data_dir, split="train", cache_dir=cache_dir2)
    assert dataset._fingerprint == fingerprint1, "Cannot load a dataset in a readonly folder."


@pytest.mark.parametrize("max_in_memory_dataset_size", ["default", 0, 50, 500])
def test_load_dataset_local_with_default_in_memory(
    max_in_memory_dataset_size, dataset_loading_script_dir, data_dir, monkeypatch
):
    current_dataset_size = 148
    if max_in_memory_dataset_size == "default":
        max_in_memory_dataset_size = 0  # default
    else:
        monkeypatch.setattr(datasets.config, "IN_MEMORY_MAX_SIZE", max_in_memory_dataset_size)
    if max_in_memory_dataset_size:
        expected_in_memory = current_dataset_size < max_in_memory_dataset_size
    else:
        expected_in_memory = False

    with assert_arrow_memory_increases() if expected_in_memory else assert_arrow_memory_doesnt_increase():
        dataset = load_dataset(dataset_loading_script_dir, data_dir=data_dir)
    assert (dataset["train"].dataset_size < max_in_memory_dataset_size) is expected_in_memory


@pytest.mark.parametrize("max_in_memory_dataset_size", ["default", 0, 100, 1000])
def test_load_from_disk_with_default_in_memory(
    max_in_memory_dataset_size, dataset_loading_script_dir, data_dir, tmp_path, monkeypatch
):
    current_dataset_size = 512  # arrow file size = 512, in-memory dataset size = 148
    if max_in_memory_dataset_size == "default":
        max_in_memory_dataset_size = 0  # default
    else:
        monkeypatch.setattr(datasets.config, "IN_MEMORY_MAX_SIZE", max_in_memory_dataset_size)
    if max_in_memory_dataset_size:
        expected_in_memory = current_dataset_size < max_in_memory_dataset_size
    else:
        expected_in_memory = False

    dset = load_dataset(dataset_loading_script_dir, data_dir=data_dir, keep_in_memory=True)
    dataset_path = os.path.join(tmp_path, "saved_dataset")
    dset.save_to_disk(dataset_path)

    with assert_arrow_memory_increases() if expected_in_memory else assert_arrow_memory_doesnt_increase():
        _ = load_from_disk(dataset_path)


def test_remote_data_files():
    repo_id = "albertvillanova/tests-raw-jsonl"
    filename = "wikiann-bn-validation.jsonl"
    data_files = f"https://huggingface.co/datasets/{repo_id}/resolve/main/{filename}"
    ds = load_dataset("json", split="train", data_files=data_files, streaming=True)
    assert isinstance(ds, IterableDataset)
    ds_item = next(iter(ds))
    assert ds_item.keys() == {"langs", "ner_tags", "spans", "tokens"}


@pytest.mark.parametrize("deleted", [False, True])
def test_load_dataset_deletes_extracted_files(deleted, jsonl_gz_path, tmp_path):
    data_files = jsonl_gz_path
    cache_dir = tmp_path / "cache"
    if deleted:
        download_config = DownloadConfig(delete_extracted=True, cache_dir=cache_dir / "downloads")
        ds = load_dataset(
            "json", split="train", data_files=data_files, cache_dir=cache_dir, download_config=download_config
        )
    else:  # default
        ds = load_dataset("json", split="train", data_files=data_files, cache_dir=cache_dir)
    assert ds[0] == {"col_1": "0", "col_2": 0, "col_3": 0.0}
    assert (sorted((cache_dir / "downloads" / "extracted").iterdir()) == []) is deleted