import importlib
import os
import pickle
import shutil
import tempfile
from multiprocessing import Pool
from pathlib import Path
from unittest import TestCase
from unittest.mock import patch

import dill
import pyarrow as pa
import pytest

import datasets
from datasets import config, load_dataset
from datasets.arrow_dataset import Dataset
from datasets.arrow_writer import ArrowWriter
from datasets.builder import DatasetBuilder
from datasets.config import METADATA_CONFIGS_FIELD
from datasets.data_files import DataFilesDict, DataFilesPatternsDict
from datasets.dataset_dict import DatasetDict
from datasets.download.download_config import DownloadConfig
from datasets.exceptions import DatasetNotFoundError
from datasets.features import Features, Value
from datasets.iterable_dataset import IterableDataset
from datasets.load import (
    CachedDatasetModuleFactory,
    HubDatasetModuleFactory,
    LocalDatasetModuleFactory,
    PackagedDatasetModuleFactory,
    infer_module_for_data_files_list,
    infer_module_for_data_files_list_in_archives,
    load_dataset_builder,
)
from datasets.packaged_modules.audiofolder.audiofolder import AudioFolder, AudioFolderConfig
from datasets.packaged_modules.imagefolder.imagefolder import ImageFolder, ImageFolderConfig
from datasets.utils.logging import INFO, get_logger

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


SAMPLE_DATASET_IDENTIFIER2 = "hf-internal-testing/dataset_with_data_files"  # only has data files
SAMPLE_DATASET_IDENTIFIER3 = "hf-internal-testing/multi_dir_dataset"  # has multiple data directories
SAMPLE_DATASET_IDENTIFIER4 = "hf-internal-testing/imagefolder_with_metadata"  # imagefolder with a metadata file inside the train/test directories
SAMPLE_DATASET_IDENTIFIER5 = "hf-internal-testing/imagefolder_with_metadata_no_splits"  # imagefolder with a metadata file and no default split names in data files

SAMPLE_DATASET_COMMIT_HASH = "0e1cee81e718feadf49560b287c4eb669c2efb1a"
SAMPLE_DATASET_COMMIT_HASH2 = "c19550d35263090b1ec2bfefdbd737431fafec40"
SAMPLE_DATASET_COMMIT_HASH3 = "aaa2d4bdd1d877d1c6178562cfc584bdfa90f6dc"
SAMPLE_DATASET_COMMIT_HASH4 = "507fa72044169a5a1802b7ac2d6bd38d5f310739"
SAMPLE_DATASET_COMMIT_HASH5 = "4971fa562942cab8263f56a448c3f831b18f1c27"

SAMPLE_DATASET_NO_CONFIGS_IN_METADATA = "hf-internal-testing/audiofolder_no_configs_in_metadata"
SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA = "hf-internal-testing/audiofolder_single_config_in_metadata"
SAMPLE_DATASET_TWO_CONFIG_IN_METADATA = "hf-internal-testing/audiofolder_two_configs_in_metadata"
SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_WITH_DEFAULT = (
    "hf-internal-testing/audiofolder_two_configs_in_metadata_with_default"
)
SAMPLE_DATASET_CAPITAL_LETTERS_IN_NAME = "hf-internal-testing/DatasetWithCapitalLetters"

SAMPLE_DATASET_NO_CONFIGS_IN_METADATA_COMMIT_HASH = "26cd5079bb0d3cd1521c6894765a0b8edb159d7f"
SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA_COMMIT_HASH = "1668dfc91efae975e44457cdabef60fb9200820a"
SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_COMMIT_HASH = "e71bce498e6c2bd2c58b20b097fdd3389793263f"
SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_WITH_DEFAULT_COMMIT_HASH = "38937109bb4dc7067f575fe6e7b420158eb9cf32"
SAMPLE_DATASET_CAPITAL_LETTERS_IN_NAME_COMMIT_HASH = "70aa36264a6954920a13dd0465156a60b9f8af4b"

SAMPLE_NOT_EXISTING_DATASET_IDENTIFIER = "hf-internal-testing/_dummy"
SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST = "_dummy"


@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 data_dir_with_arrow(tmp_path):
    data_dir = tmp_path / "data_dir"
    data_dir.mkdir()
    output_train = os.path.join(data_dir, "train.arrow")
    with ArrowWriter(path=output_train) as writer:
        writer.write_table(pa.Table.from_pydict({"col_1": ["foo"] * 10}))
        num_examples, num_bytes = writer.finalize()
    assert num_examples == 10
    assert num_bytes > 0
    output_test = os.path.join(data_dir, "test.arrow")
    with ArrowWriter(path=output_test) as writer:
        writer.write_table(pa.Table.from_pydict({"col_1": ["bar"] * 10}))
        num_examples, num_bytes = writer.finalize()
    assert num_examples == 10
    assert num_bytes > 0
    return str(data_dir)


@pytest.fixture
def data_dir_with_metadata(tmp_path):
    data_dir = tmp_path / "data_dir_with_metadata"
    data_dir.mkdir()
    (data_dir / "train").mkdir()
    (data_dir / "test").mkdir()
    with open(data_dir / "train" / "cat.jpg", "wb") as f:
        f.write(b"train_image_bytes")
    with open(data_dir / "test" / "dog.jpg", "wb") as f:
        f.write(b"test_image_bytes")
    with open(data_dir / "train" / "metadata.jsonl", "w") as f:
        f.write(
            """\
        {"file_name": "cat.jpg", "caption": "Cool train cat image"}
        """
        )
    with open(data_dir / "test" / "metadata.jsonl", "w") as f:
        f.write(
            """\
        {"file_name": "dog.jpg", "caption": "Cool test dog image"}
        """
        )
    return str(data_dir)


@pytest.fixture
def data_dir_with_single_config_in_metadata(tmp_path):
    data_dir = tmp_path / "data_dir_with_one_default_config_in_metadata"

    cats_data_dir = data_dir / "cats"
    cats_data_dir.mkdir(parents=True)
    dogs_data_dir = data_dir / "dogs"
    dogs_data_dir.mkdir(parents=True)

    with open(cats_data_dir / "cat.jpg", "wb") as f:
        f.write(b"this_is_a_cat_image_bytes")
    with open(dogs_data_dir / "dog.jpg", "wb") as f:
        f.write(b"this_is_a_dog_image_bytes")
    with open(data_dir / "README.md", "w") as f:
        f.write(
            f"""\
---
{METADATA_CONFIGS_FIELD}:
  - config_name: custom
    drop_labels: true
---
        """
        )
    return str(data_dir)


@pytest.fixture
def data_dir_with_config_and_data_files(tmp_path):
    data_dir = tmp_path / "data_dir_with_config_and_data_files"

    cats_data_dir = data_dir / "data" / "cats"
    cats_data_dir.mkdir(parents=True)
    dogs_data_dir = data_dir / "data" / "dogs"
    dogs_data_dir.mkdir(parents=True)

    with open(cats_data_dir / "cat.jpg", "wb") as f:
        f.write(b"this_is_a_cat_image_bytes")
    with open(dogs_data_dir / "dog.jpg", "wb") as f:
        f.write(b"this_is_a_dog_image_bytes")
    with open(data_dir / "README.md", "w") as f:
        f.write(
            f"""\
---
{METADATA_CONFIGS_FIELD}:
  - config_name: custom
    data_files: "data/**/*.jpg"
---
        """
        )
    return str(data_dir)


@pytest.fixture
def data_dir_with_two_config_in_metadata(tmp_path):
    data_dir = tmp_path / "data_dir_with_two_configs_in_metadata"
    cats_data_dir = data_dir / "cats"
    cats_data_dir.mkdir(parents=True)
    dogs_data_dir = data_dir / "dogs"
    dogs_data_dir.mkdir(parents=True)

    with open(cats_data_dir / "cat.jpg", "wb") as f:
        f.write(b"this_is_a_cat_image_bytes")
    with open(dogs_data_dir / "dog.jpg", "wb") as f:
        f.write(b"this_is_a_dog_image_bytes")

    with open(data_dir / "README.md", "w") as f:
        f.write(
            f"""\
---
{METADATA_CONFIGS_FIELD}:
  - config_name: "v1"
    drop_labels: true
    default: true
  - config_name: "v2"
    drop_labels: false
---
        """
        )
    return str(data_dir)


@pytest.fixture
def data_dir_with_data_dir_configs_in_metadata(tmp_path):
    data_dir = tmp_path / "data_dir_with_two_configs_in_metadata"
    cats_data_dir = data_dir / "cats"
    cats_data_dir.mkdir(parents=True)
    dogs_data_dir = data_dir / "dogs"
    dogs_data_dir.mkdir(parents=True)

    with open(cats_data_dir / "cat.jpg", "wb") as f:
        f.write(b"this_is_a_cat_image_bytes")
    with open(dogs_data_dir / "dog.jpg", "wb") as f:
        f.write(b"this_is_a_dog_image_bytes")


@pytest.fixture
def sub_data_dirs(tmp_path):
    data_dir2 = tmp_path / "data_dir2"
    relative_subdir1 = "subdir1"
    sub_data_dir1 = data_dir2 / relative_subdir1
    sub_data_dir1.mkdir(parents=True)
    with open(sub_data_dir1 / "train.txt", "w") as f:
        f.write("foo\n" * 10)
    with open(sub_data_dir1 / "test.txt", "w") as f:
        f.write("bar\n" * 10)

    relative_subdir2 = "subdir2"
    sub_data_dir2 = tmp_path / data_dir2 / relative_subdir2
    sub_data_dir2.mkdir(parents=True)
    with open(sub_data_dir2 / "train.txt", "w") as f:
        f.write("foo\n" * 10)
    with open(sub_data_dir2 / "test.txt", "w") as f:
        f.write("bar\n" * 10)

    return str(data_dir2), relative_subdir1


@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.mark.parametrize(
    "data_files, expected_module, expected_builder_kwargs",
    [
        (["train.csv"], "csv", {}),
        (["train.tsv"], "csv", {"sep": "\t"}),
        (["train.json"], "json", {}),
        (["train.jsonl"], "json", {}),
        (["train.parquet"], "parquet", {}),
        (["train.geoparquet"], "parquet", {}),
        (["train.gpq"], "parquet", {}),
        (["train.arrow"], "arrow", {}),
        (["train.txt"], "text", {}),
        (["uppercase.TXT"], "text", {}),
        (["unsupported.ext"], None, {}),
        ([""], None, {}),
    ],
)
def test_infer_module_for_data_files(data_files, expected_module, expected_builder_kwargs):
    module, builder_kwargs = infer_module_for_data_files_list(data_files)
    assert module == expected_module
    assert builder_kwargs == expected_builder_kwargs


@pytest.mark.parametrize(
    "data_file, expected_module",
    [
        ("zip_csv_path", "csv"),
        ("zip_csv_with_dir_path", "csv"),
        ("zip_uppercase_csv_path", "csv"),
        ("zip_unsupported_ext_path", None),
    ],
)
def test_infer_module_for_data_files_in_archives(
    data_file, expected_module, zip_csv_path, zip_csv_with_dir_path, zip_uppercase_csv_path, zip_unsupported_ext_path
):
    data_file_paths = {
        "zip_csv_path": zip_csv_path,
        "zip_csv_with_dir_path": zip_csv_with_dir_path,
        "zip_uppercase_csv_path": zip_uppercase_csv_path,
        "zip_unsupported_ext_path": zip_unsupported_ext_path,
    }
    data_files = [str(data_file_paths[data_file])]
    inferred_module, _ = infer_module_for_data_files_list_in_archives(data_files)
    assert inferred_module == expected_module


class ModuleFactoryTest(TestCase):
    @pytest.fixture(autouse=True)
    def inject_fixtures(
        self,
        jsonl_path,
        data_dir,
        data_dir_with_metadata,
        data_dir_with_single_config_in_metadata,
        data_dir_with_config_and_data_files,
        data_dir_with_two_config_in_metadata,
        sub_data_dirs,
    ):
        self._jsonl_path = jsonl_path
        self._data_dir = data_dir
        self._data_dir_with_metadata = data_dir_with_metadata
        self._data_dir_with_single_config_in_metadata = data_dir_with_single_config_in_metadata
        self._data_dir_with_config_and_data_files = data_dir_with_config_and_data_files
        self._data_dir_with_two_config_in_metadata = data_dir_with_two_config_in_metadata
        self._data_dir2 = sub_data_dirs[0]
        self._sub_data_dir = sub_data_dirs[1]

    def setUp(self):
        self.cache_dir = tempfile.mkdtemp()
        self.download_config = DownloadConfig(cache_dir=self.cache_dir)

    def test_LocalDatasetModuleFactory(self):
        factory = LocalDatasetModuleFactory(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_LocalDatasetModuleFactory_with_data_dir(self):
        factory = LocalDatasetModuleFactory(self._data_dir2, data_dir=self._sub_data_dir)
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        builder_config = module_factory_result.builder_configs_parameters.builder_configs[0]
        assert (
            builder_config.data_files is not None
            and len(builder_config.data_files["train"]) == 1
            and len(builder_config.data_files["test"]) == 1
        )
        assert all(
            self._sub_data_dir in Path(data_file).parts
            for data_file in builder_config.data_files["train"] + builder_config.data_files["test"]
        )

    def test_LocalDatasetModuleFactory_with_metadata(self):
        factory = LocalDatasetModuleFactory(self._data_dir_with_metadata)
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        builder_config = module_factory_result.builder_configs_parameters.builder_configs[0]
        assert (
            builder_config.data_files is not None
            and len(builder_config.data_files["train"]) > 0
            and len(builder_config.data_files["test"]) > 0
        )
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in builder_config.data_files["train"])
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in builder_config.data_files["test"])

    def test_LocalDatasetModuleFactory_with_single_config_in_metadata(self):
        factory = LocalDatasetModuleFactory(
            self._data_dir_with_single_config_in_metadata,
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

        module_metadata_configs = module_factory_result.builder_configs_parameters.metadata_configs
        assert module_metadata_configs is not None
        assert len(module_metadata_configs) == 1
        assert next(iter(module_metadata_configs)) == "custom"
        assert "drop_labels" in next(iter(module_metadata_configs.values()))
        assert next(iter(module_metadata_configs.values()))["drop_labels"] is True

        module_builder_configs = module_factory_result.builder_configs_parameters.builder_configs
        assert module_builder_configs is not None
        assert len(module_builder_configs) == 1
        assert isinstance(module_builder_configs[0], ImageFolderConfig)
        assert module_builder_configs[0].name == "custom"
        assert module_builder_configs[0].data_files is not None
        assert isinstance(module_builder_configs[0].data_files, DataFilesPatternsDict)
        module_builder_configs[0]._resolve_data_files(self._data_dir_with_single_config_in_metadata, DownloadConfig())
        assert isinstance(module_builder_configs[0].data_files, DataFilesDict)
        assert len(module_builder_configs[0].data_files) == 1  # one train split
        assert len(module_builder_configs[0].data_files["train"]) == 2  # two files
        assert module_builder_configs[0].drop_labels is True  # parameter is passed from metadata

        # config named "default" is automatically considered to be a default config
        assert module_factory_result.builder_configs_parameters.default_config_name == "custom"

        # we don't pass config params to builder in builder_kwargs, they are stored in builder_configs directly
        assert "drop_labels" not in module_factory_result.builder_kwargs

    def test_LocalDatasetModuleFactory_with_config_and_data_files(self):
        factory = LocalDatasetModuleFactory(
            self._data_dir_with_config_and_data_files,
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

        module_metadata_configs = module_factory_result.builder_configs_parameters.metadata_configs
        builder_kwargs = module_factory_result.builder_kwargs
        assert module_metadata_configs is not None
        assert len(module_metadata_configs) == 1
        assert next(iter(module_metadata_configs)) == "custom"
        assert "data_files" in next(iter(module_metadata_configs.values()))
        assert next(iter(module_metadata_configs.values()))["data_files"] == "data/**/*.jpg"
        assert "data_files" not in builder_kwargs

    def test_LocalDatasetModuleFactory_data_dir_with_config_and_data_files(self):
        factory = LocalDatasetModuleFactory(self._data_dir_with_config_and_data_files, data_dir="data")
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

        module_metadata_configs = module_factory_result.builder_configs_parameters.metadata_configs
        builder_kwargs = module_factory_result.builder_kwargs
        assert module_metadata_configs is not None
        assert len(module_metadata_configs) == 1
        assert next(iter(module_metadata_configs)) == "custom"
        assert "data_files" in next(iter(module_metadata_configs.values()))
        assert next(iter(module_metadata_configs.values()))["data_files"] == "data/**/*.jpg"
        assert "data_files" in builder_kwargs
        assert "train" in builder_kwargs["data_files"]
        assert len(builder_kwargs["data_files"]["train"]) == 2

    def test_LocalDatasetModuleFactory_with_two_configs_in_metadata(self):
        factory = LocalDatasetModuleFactory(
            self._data_dir_with_two_config_in_metadata,
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None

        module_metadata_configs = module_factory_result.builder_configs_parameters.metadata_configs
        assert module_metadata_configs is not None
        assert len(module_metadata_configs) == 2
        assert list(module_metadata_configs) == ["v1", "v2"]
        assert "drop_labels" in module_metadata_configs["v1"]
        assert module_metadata_configs["v1"]["drop_labels"] is True
        assert "drop_labels" in module_metadata_configs["v2"]
        assert module_metadata_configs["v2"]["drop_labels"] is False

        module_builder_configs = module_factory_result.builder_configs_parameters.builder_configs
        assert module_builder_configs is not None
        assert len(module_builder_configs) == 2
        module_builder_config_v1, module_builder_config_v2 = module_builder_configs
        assert module_builder_config_v1.name == "v1"
        assert module_builder_config_v2.name == "v2"
        assert isinstance(module_builder_config_v1, ImageFolderConfig)
        assert isinstance(module_builder_config_v2, ImageFolderConfig)
        assert isinstance(module_builder_config_v1.data_files, DataFilesPatternsDict)
        assert isinstance(module_builder_config_v2.data_files, DataFilesPatternsDict)
        module_builder_config_v1._resolve_data_files(self._data_dir_with_two_config_in_metadata, DownloadConfig())
        module_builder_config_v2._resolve_data_files(self._data_dir_with_two_config_in_metadata, DownloadConfig())
        assert isinstance(module_builder_config_v1.data_files, DataFilesDict)
        assert isinstance(module_builder_config_v2.data_files, DataFilesDict)
        assert sorted(module_builder_config_v1.data_files) == ["train"]
        assert len(module_builder_config_v1.data_files["train"]) == 2
        assert sorted(module_builder_config_v2.data_files) == ["train"]
        assert len(module_builder_config_v2.data_files["train"]) == 2
        assert module_builder_config_v1.drop_labels is True  # parameter is passed from metadata
        assert module_builder_config_v2.drop_labels is False  # parameter is passed from metadata

        assert (
            module_factory_result.builder_configs_parameters.default_config_name == "v1"
        )  # it's marked as a default one in yaml

        # we don't pass config params to builder in builder_kwargs, they are stored in builder_configs directly
        assert "drop_labels" not in module_factory_result.builder_kwargs

    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
        data_files = module_factory_result.builder_kwargs.get("data_files")
        assert data_files is not None and len(data_files["train"]) > 0 and len(data_files["test"]) > 0
        assert Path(data_files["train"][0]).parent.samefile(self._data_dir)
        assert Path(data_files["test"][0]).parent.samefile(self._data_dir)

    def test_PackagedDatasetModuleFactory_with_data_dir_and_metadata(self):
        factory = PackagedDatasetModuleFactory(
            "imagefolder", data_dir=self._data_dir_with_metadata, download_config=self.download_config
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        data_files = module_factory_result.builder_kwargs.get("data_files")
        assert data_files is not None and len(data_files["train"]) > 0 and len(data_files["test"]) > 0
        assert Path(self._data_dir_with_metadata) in Path(data_files["train"][0]).parents
        assert Path(self._data_dir_with_metadata) in Path(data_files["test"][0]).parents
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in data_files["train"])
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in data_files["test"])

    @pytest.mark.integration
    def test_HubDatasetModuleFactory(self):
        factory = HubDatasetModuleFactory(
            SAMPLE_DATASET_IDENTIFIER2, commit_hash=SAMPLE_DATASET_COMMIT_HASH2, 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)

    @pytest.mark.integration
    def test_HubDatasetModuleFactory_with_data_dir(self):
        data_dir = "data2"
        factory = HubDatasetModuleFactory(
            SAMPLE_DATASET_IDENTIFIER3,
            commit_hash=SAMPLE_DATASET_COMMIT_HASH3,
            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
        builder_config = module_factory_result.builder_configs_parameters.builder_configs[0]
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)
        assert (
            builder_config.data_files is not None
            and len(builder_config.data_files["train"]) == 1
            and len(builder_config.data_files["test"]) == 1
        )
        assert all(
            data_dir in Path(data_file).parts
            for data_file in builder_config.data_files["train"] + builder_config.data_files["test"]
        )

    @pytest.mark.integration
    def test_HubDatasetModuleFactory_with_metadata(self):
        factory = HubDatasetModuleFactory(
            SAMPLE_DATASET_IDENTIFIER4, commit_hash=SAMPLE_DATASET_COMMIT_HASH4, download_config=self.download_config
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        builder_config = module_factory_result.builder_configs_parameters.builder_configs[0]
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)
        assert (
            builder_config.data_files is not None
            and len(builder_config.data_files["train"]) > 0
            and len(builder_config.data_files["test"]) > 0
        )
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in builder_config.data_files["train"])
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in builder_config.data_files["test"])

        factory = HubDatasetModuleFactory(
            SAMPLE_DATASET_IDENTIFIER5, commit_hash=SAMPLE_DATASET_COMMIT_HASH5, download_config=self.download_config
        )
        module_factory_result = factory.get_module()
        assert importlib.import_module(module_factory_result.module_path) is not None
        builder_config = module_factory_result.builder_configs_parameters.builder_configs[0]
        assert module_factory_result.builder_kwargs["base_path"].startswith(config.HF_ENDPOINT)
        assert (
            builder_config.data_files is not None
            and len(builder_config.data_files) == 1
            and len(builder_config.data_files["train"]) > 0
        )
        assert any(Path(data_file).name == "metadata.jsonl" for data_file in builder_config.data_files["train"])

    @pytest.mark.integration
    def test_HubDatasetModuleFactory_with_one_default_config_in_metadata(self):
        factory = HubDatasetModuleFactory(
            SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA,
            commit_hash=SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA_COMMIT_HASH,
            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)

        module_metadata_configs = module_factory_result.builder_configs_parameters.metadata_configs
        assert module_metadata_configs is not None
        assert len(module_metadata_configs) == 1
        assert next(iter(module_metadata_configs)) == "custom"
        assert "drop_labels" in next(iter(module_metadata_configs.values()))
        assert next(iter(module_metadata_configs.values()))["drop_labels"] is True

        module_builder_configs = module_factory_result.builder_configs_parameters.builder_configs
        assert module_builder_configs is not None
        assert len(module_builder_configs) == 1
        assert isinstance(module_builder_configs[0], AudioFolderConfig)
        assert module_builder_configs[0].name == "custom"
        assert module_builder_configs[0].data_files is not None
        assert isinstance(module_builder_configs[0].data_files, DataFilesPatternsDict)
        module_builder_configs[0]._resolve_data_files(
            module_factory_result.builder_kwargs["base_path"], DownloadConfig()
        )
        assert isinstance(module_builder_configs[0].data_files, DataFilesDict)
        assert sorted(module_builder_configs[0].data_files) == ["test", "train"]
        assert len(module_builder_configs[0].data_files["train"]) == 3
        assert len(module_builder_configs[0].data_files["test"]) == 3
        assert module_builder_configs[0].drop_labels is True  # parameter is passed from metadata

        # config named "default" is automatically considered to be a default config
        assert module_factory_result.builder_configs_parameters.default_config_name == "custom"

        # we don't pass config params to builder in builder_kwargs, they are stored in builder_configs directly
        assert "drop_labels" not in module_factory_result.builder_kwargs

    @pytest.mark.integration
    def test_HubDatasetModuleFactory_with_two_configs_in_metadata(self):
        datasets_names = [
            (SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_COMMIT_HASH),
            (
                SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_WITH_DEFAULT,
                SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_WITH_DEFAULT_COMMIT_HASH,
            ),
        ]
        for dataset_name, commit_hash in datasets_names:
            factory = HubDatasetModuleFactory(
                dataset_name, commit_hash=commit_hash, download_config=self.download_config
            )
            module_factory_result = factory.get_module()
            assert importlib.import_module(module_factory_result.module_path) is not None

            module_metadata_configs = module_factory_result.builder_configs_parameters.metadata_configs
            assert module_metadata_configs is not None
            assert len(module_metadata_configs) == 2
            assert list(module_metadata_configs) == ["v1", "v2"]
            assert "drop_labels" in module_metadata_configs["v1"]
            assert module_metadata_configs["v1"]["drop_labels"] is True
            assert "drop_labels" in module_metadata_configs["v2"]
            assert module_metadata_configs["v2"]["drop_labels"] is False

            module_builder_configs = module_factory_result.builder_configs_parameters.builder_configs
            assert module_builder_configs is not None
            assert len(module_builder_configs) == 2
            module_builder_config_v1, module_builder_config_v2 = module_builder_configs
            assert module_builder_config_v1.name == "v1"
            assert module_builder_config_v2.name == "v2"
            assert isinstance(module_builder_config_v1, AudioFolderConfig)
            assert isinstance(module_builder_config_v2, AudioFolderConfig)
            assert isinstance(module_builder_config_v1.data_files, DataFilesPatternsDict)
            assert isinstance(module_builder_config_v2.data_files, DataFilesPatternsDict)
            module_builder_config_v1._resolve_data_files(
                module_factory_result.builder_kwargs["base_path"], DownloadConfig()
            )
            module_builder_config_v2._resolve_data_files(
                module_factory_result.builder_kwargs["base_path"], DownloadConfig()
            )
            assert isinstance(module_builder_config_v1.data_files, DataFilesDict)
            assert isinstance(module_builder_config_v2.data_files, DataFilesDict)
            assert sorted(module_builder_config_v1.data_files) == ["test", "train"]
            assert len(module_builder_config_v1.data_files["train"]) == 3
            assert len(module_builder_config_v1.data_files["test"]) == 3
            assert sorted(module_builder_config_v2.data_files) == ["test", "train"]
            assert len(module_builder_config_v2.data_files["train"]) == 2
            assert len(module_builder_config_v2.data_files["test"]) == 1
            assert module_builder_config_v1.drop_labels is True  # parameter is passed from metadata
            assert module_builder_config_v2.drop_labels is False  # parameter is passed from metadata
            # we don't pass config params to builder in builder_kwargs, they are stored in builder_configs directly
            assert "drop_labels" not in module_factory_result.builder_kwargs

            if dataset_name == SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_WITH_DEFAULT:
                assert module_factory_result.builder_configs_parameters.default_config_name == "v1"
            else:
                assert module_factory_result.builder_configs_parameters.default_config_name is None

    @pytest.mark.integration
    def test_CachedDatasetModuleFactory(self):
        name = SAMPLE_DATASET_IDENTIFIER2
        load_dataset_builder(name, cache_dir=self.cache_dir).download_and_prepare()
        for offline_mode in OfflineSimulationMode:
            with offline(offline_mode):
                factory = CachedDatasetModuleFactory(
                    name,
                    cache_dir=self.cache_dir,
                )
                module_factory_result = factory.get_module()
                assert importlib.import_module(module_factory_result.module_path) is not None


@pytest.mark.parametrize(
    "factory_class,requires_commit_hash",
    [
        (CachedDatasetModuleFactory, False),
        (HubDatasetModuleFactory, True),
        (LocalDatasetModuleFactory, False),
        (PackagedDatasetModuleFactory, False),
    ],
)
def test_module_factories(factory_class, requires_commit_hash):
    name = "dummy_name"
    if requires_commit_hash:
        factory = factory_class(name, commit_hash="foo")
    else:
        factory = factory_class(name)
    assert factory.name == name


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

    def setUp(self):
        self.cache_dir = tempfile.mkdtemp()

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

    @pytest.mark.integration
    def test_offline_dataset_module_factory(self):
        repo_id = SAMPLE_DATASET_IDENTIFIER2
        builder = load_dataset_builder(repo_id, cache_dir=self.cache_dir)
        builder.download_and_prepare()
        for offline_simulation_mode in list(OfflineSimulationMode):
            with offline(offline_simulation_mode):
                self._caplog.clear()
                # allow provide the repo id without an explicit path to remote or local actual file
                dataset_module = datasets.load.dataset_module_factory(repo_id, cache_dir=self.cache_dir)
                self.assertEqual(dataset_module.module_path, "datasets.packaged_modules.cache.cache")
                self.assertIn("Using the latest cached version of the dataset", self._caplog.text)

    @pytest.mark.integration
    def test_offline_dataset_module_factory_with_capital_letters_in_name(self):
        repo_id = SAMPLE_DATASET_CAPITAL_LETTERS_IN_NAME
        builder = load_dataset_builder(repo_id, cache_dir=self.cache_dir)
        builder.download_and_prepare()
        for offline_simulation_mode in list(OfflineSimulationMode):
            with offline(offline_simulation_mode):
                self._caplog.clear()
                # allow provide the repo id without an explicit path to remote or local actual file
                dataset_module = datasets.load.dataset_module_factory(repo_id, cache_dir=self.cache_dir)
                self.assertEqual(dataset_module.module_path, "datasets.packaged_modules.cache.cache")
                self.assertIn("Using the latest cached version of the dataset", self._caplog.text)

    def test_load_dataset_from_hub(self):
        with self.assertRaises(DatasetNotFoundError) as context:
            datasets.load_dataset("_dummy")
        self.assertIn(
            "Dataset '_dummy' doesn't exist on the Hub",
            str(context.exception),
        )
        with self.assertRaises(DatasetNotFoundError) as context:
            datasets.load_dataset("HuggingFaceFW/fineweb-edu", revision="0.0.0")
        self.assertIn(
            "Revision '0.0.0' doesn't exist for dataset 'HuggingFaceFW/fineweb-edu' on the Hub.",
            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_HUB_OFFLINE_SET_TO_1:
                    self.assertIn(
                        "Couldn't reach '_dummy' on the Hub",
                        str(context.exception),
                    )

    @pytest.mark.integration
    def test_load_dataset_invalid_revision_with_cache(self):
        repo_id = SAMPLE_DATASET_IDENTIFIER2
        builder = load_dataset_builder(repo_id, cache_dir=self.cache_dir)
        builder.download_and_prepare()
        with self.assertRaises(DatasetNotFoundError) as context:
            datasets.load_dataset(repo_id, revision="invalid_revision", cache_dir=self.cache_dir)
        self.assertIn(
            "Revision 'invalid_revision' doesn't exist for dataset",
            str(context.exception),
        )

    def test_load_dataset_namespace(self):
        with self.assertRaises(DatasetNotFoundError) as context:
            datasets.load_dataset("hf-internal-testing/_dummy")
        self.assertIn("hf-internal-testing/_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("hf-internal-testing/_dummy")
                self.assertIn("hf-internal-testing/_dummy", str(context.exception), msg=offline_simulation_mode)


@pytest.mark.integration
def test_load_dataset_builder_with_metadata():
    builder = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER4)
    assert isinstance(builder, ImageFolder)
    assert builder.config.name == "default"
    assert builder.config.data_files is not None
    assert builder.config.drop_metadata is None
    with pytest.raises(ValueError):
        builder = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER4, "non-existing-config")


@pytest.mark.integration
def test_load_dataset_builder_config_kwargs_passed_as_arguments():
    builder_default = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER4)
    builder_custom = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER4, drop_metadata=True)
    assert builder_custom.config.drop_metadata != builder_default.config.drop_metadata
    assert builder_custom.config.drop_metadata is True


@pytest.mark.integration
def test_load_dataset_builder_with_two_configs_in_metadata():
    builder = datasets.load_dataset_builder(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "v1")
    assert isinstance(builder, AudioFolder)
    assert builder.config.name == "v1"
    assert builder.config.data_files is not None
    with pytest.raises(ValueError):
        datasets.load_dataset_builder(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA)
    with pytest.raises(ValueError):
        datasets.load_dataset_builder(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "non-existing-config")


@pytest.mark.parametrize("serializer", [pickle, dill])
def test_load_dataset_builder_with_metadata_configs_pickable(serializer):
    builder = datasets.load_dataset_builder(SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA)
    builder_unpickled = serializer.loads(serializer.dumps(builder))
    assert builder.BUILDER_CONFIGS == builder_unpickled.BUILDER_CONFIGS
    assert list(builder_unpickled.builder_configs) == ["custom"]
    assert isinstance(builder_unpickled.builder_configs["custom"], AudioFolderConfig)

    builder2 = datasets.load_dataset_builder(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "v1")
    builder2_unpickled = serializer.loads(serializer.dumps(builder2))
    assert builder2.BUILDER_CONFIGS == builder2_unpickled.BUILDER_CONFIGS != builder_unpickled.BUILDER_CONFIGS
    assert list(builder2_unpickled.builder_configs) == ["v1", "v2"]
    assert isinstance(builder2_unpickled.builder_configs["v1"], AudioFolderConfig)
    assert isinstance(builder2_unpickled.builder_configs["v2"], AudioFolderConfig)


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.dataset_name == Path(complex_data_dir).name
    assert builder.config.name == "default"
    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.dataset_name == relative_data_dir
        assert builder.config.name == "default"
        assert isinstance(builder.config.data_files, DataFilesDict)
        assert len(builder.config.data_files["train"]) > 0
        assert len(builder.config.data_files["test"]) > 0


@pytest.mark.integration
def test_load_dataset_builder_for_community_dataset():
    builder = datasets.load_dataset_builder(SAMPLE_DATASET_IDENTIFIER2)
    assert isinstance(builder, DatasetBuilder)
    assert builder.name == "text"
    assert builder.dataset_name == SAMPLE_DATASET_IDENTIFIER2.split("/")[-1]
    assert builder.config.name == "default"
    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(DatasetNotFoundError):
        datasets.load_dataset_builder("blabla")


@pytest.mark.integration
@pytest.mark.parametrize(
    "kwargs, expected_train_num_rows, expected_test_num_rows",
    [
        ({}, 2, 2),
        ({"data_dir": "data1"}, 1, 1),  # GH-6918: NonMatchingSplitsSizesError
        ({"data_files": "data1/train.txt"}, 1, None),  # GH-6939: ExpectedMoreSplits
    ],
)
def test_load_dataset_from_hub(kwargs, expected_train_num_rows, expected_test_num_rows):
    dataset = load_dataset(SAMPLE_DATASET_IDENTIFIER3, **kwargs)
    assert dataset["train"].num_rows == expected_train_num_rows
    assert (dataset["test"].num_rows == expected_test_num_rows) if expected_test_num_rows else ("test" not in dataset)


@pytest.mark.integration
@pytest.mark.parametrize("stream_from_cache, ", [False, True])
def test_load_dataset_cached_from_hub(stream_from_cache, caplog):
    dataset = load_dataset(SAMPLE_DATASET_IDENTIFIER3)
    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(SAMPLE_DATASET_IDENTIFIER3, streaming=stream_from_cache)
            assert len(dataset) == 2
            assert "Using the latest cached version of the dataset" in caplog.text
            assert isinstance(next(iter(dataset["train"])), dict)
    with pytest.raises(DatasetNotFoundError) as exc_info:
        datasets.load_dataset(SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST)
    assert f"Dataset '{SAMPLE_DATASET_NAME_THAT_DOESNT_EXIST}' doesn't exist on the Hub" in str(exc_info.value)


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.integration
@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 = "hf-internal-testing/compressed_files"
    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"}


@pytest.mark.parametrize("streaming", [False, True])
def test_load_dataset_arrow(streaming, data_dir_with_arrow):
    ds = load_dataset("arrow", split="train", data_dir=data_dir_with_arrow, streaming=streaming)
    expected_size = 10
    if streaming:
        ds_item_counter = 0
        for ds_item in ds:
            if ds_item_counter == 0:
                assert ds_item == {"col_1": "foo"}
            ds_item_counter += 1
        assert ds_item_counter == 10
    else:
        assert ds.num_rows == 10
        assert ds.shape[0] == expected_size
        ds_item = next(iter(ds))
        assert ds_item == {"col_1": "foo"}


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_load_dataset_with_unsupported_extensions(text_dir_with_unsupported_extension):
    data_files = str(text_dir_with_unsupported_extension)
    ds = load_dataset("text", split="train", data_files=data_files)
    assert ds.num_rows == 4


@pytest.mark.integration
def test_loading_from_the_datasets_hub_with_token():
    class CustomException(Exception):
        pass

    with patch("huggingface_hub.file_download._get_metadata_or_catch_error") as mock_request:
        mock_request.side_effect = CustomException()
        with tempfile.TemporaryDirectory() as tmp_dir:
            with pytest.raises(CustomException):
                load_dataset(SAMPLE_NOT_EXISTING_DATASET_IDENTIFIER, cache_dir=tmp_dir, token="foo")
        mock_request.assert_called_once()
        assert mock_request.call_args_list[0][1]["headers"]["authorization"] == "Bearer foo"


@pytest.mark.integration
def test_load_streaming_private_dataset(hf_token, hf_private_dataset_repo_txt_data):
    ds = load_dataset(hf_private_dataset_repo_txt_data, streaming=True, token=hf_token)
    assert next(iter(ds)) is not None


@pytest.mark.integration
def test_load_dataset_builder_private_dataset(hf_token, hf_private_dataset_repo_txt_data):
    builder = load_dataset_builder(hf_private_dataset_repo_txt_data, token=hf_token)
    assert isinstance(builder, DatasetBuilder)


@pytest.mark.integration
def test_load_streaming_private_dataset_with_zipped_data(hf_token, hf_private_dataset_repo_zipped_txt_data):
    ds = load_dataset(hf_private_dataset_repo_zipped_txt_data, streaming=True, token=hf_token)
    assert next(iter(ds)) is not None


@pytest.mark.integration
def test_load_dataset_config_kwargs_passed_as_arguments():
    ds_default = load_dataset(SAMPLE_DATASET_IDENTIFIER4)
    ds_custom = load_dataset(SAMPLE_DATASET_IDENTIFIER4, drop_metadata=True)
    assert list(ds_default["train"].features) == ["image", "caption"]
    assert list(ds_custom["train"].features) == ["image"]


@require_torchcodec
@pytest.mark.integration
def test_load_hub_dataset_with_single_config_in_metadata():
    # load the same dataset but with no configurations (=with default parameters)
    ds = load_dataset(SAMPLE_DATASET_NO_CONFIGS_IN_METADATA)
    assert list(ds["train"].features) == ["audio", "label"]  # assert label feature is here as expected by default
    assert len(ds["train"]) == 5 and len(ds["test"]) == 4

    ds2 = load_dataset(SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA)  # single config -> no need to specify it
    assert list(ds2["train"].features) == ["audio"]  # assert param `drop_labels=True` from metadata is passed
    assert len(ds2["train"]) == 3 and len(ds2["test"]) == 3

    ds3 = load_dataset(SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA, "custom")
    assert list(ds3["train"].features) == ["audio"]  # assert param `drop_labels=True` from metadata is passed
    assert len(ds3["train"]) == 3 and len(ds3["test"]) == 3

    with pytest.raises(ValueError):
        # no config named "default"
        _ = load_dataset(SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA, "default")


@require_torchcodec
@pytest.mark.integration
def test_load_hub_dataset_with_two_config_in_metadata():
    ds = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "v1")
    assert list(ds["train"].features) == ["audio"]  # assert param `drop_labels=True` from metadata is passed
    assert len(ds["train"]) == 3 and len(ds["test"]) == 3

    ds2 = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "v2")
    assert list(ds2["train"].features) == [
        "audio",
        "label",
    ]  # assert param `drop_labels=False` from metadata is passed
    assert len(ds2["train"]) == 2 and len(ds2["test"]) == 1

    with pytest.raises(ValueError):
        # config is required but not specified
        _ = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA)

    with pytest.raises(ValueError):
        # no config named "default"
        _ = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "default")

    ds_with_default = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA_WITH_DEFAULT)
    # it's a dataset with the same data but "v1" config is marked as a default one
    assert list(ds_with_default["train"].features) == list(ds["train"].features)
    assert len(ds_with_default["train"]) == len(ds["train"]) and len(ds_with_default["test"]) == len(ds["test"])


@require_torchcodec
@pytest.mark.integration
def test_load_hub_dataset_with_metadata_config_in_parallel():
    # assert it doesn't fail (pickling of dynamically created class works)
    ds = load_dataset(SAMPLE_DATASET_SINGLE_CONFIG_IN_METADATA, num_proc=2)
    assert "label" not in ds["train"].features  # assert param `drop_labels=True` from metadata is passed
    assert len(ds["train"]) == 3 and len(ds["test"]) == 3

    ds = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "v1", num_proc=2)
    assert "label" not in ds["train"].features  # assert param `drop_labels=True` from metadata is passed
    assert len(ds["train"]) == 3 and len(ds["test"]) == 3

    ds = load_dataset(SAMPLE_DATASET_TWO_CONFIG_IN_METADATA, "v2", num_proc=2)
    assert "label" in ds["train"].features
    assert len(ds["train"]) == 2 and len(ds["test"]) == 1


@require_pil
@pytest.mark.integration
@pytest.mark.parametrize("streaming", [True])
def test_load_dataset_private_zipped_images(hf_private_dataset_repo_zipped_img_data, hf_token, streaming):
    ds = load_dataset(hf_private_dataset_repo_zipped_img_data, split="train", streaming=streaming, token=hf_token)
    assert isinstance(ds, IterableDataset if streaming else Dataset)
    ds_items = list(ds)
    assert len(ds_items) == 2


def test_load_dataset_then_move_then_reload(data_dir, tmp_path, caplog):
    cache_dir1 = tmp_path / "cache1"
    cache_dir2 = tmp_path / "cache2"
    dataset = load_dataset(data_dir, split="train", cache_dir=cache_dir1, trust_remote_code=True)
    fingerprint1 = dataset._fingerprint
    del dataset
    os.rename(cache_dir1, cache_dir2)
    caplog.clear()
    with caplog.at_level(INFO, logger=get_logger().name):
        dataset = load_dataset(data_dir, split="train", cache_dir=cache_dir2)
    assert "Found cached dataset" in caplog.text
    assert dataset._fingerprint == fingerprint1, "for the caching mechanism to work, fingerprint should stay the same"
    dataset = load_dataset(data_dir, split="test", cache_dir=cache_dir2)
    assert dataset._fingerprint != fingerprint1


def test_load_dataset_builder_then_edit_then_load_again(tmp_path: Path):
    dataset_dir = tmp_path / "test_load_dataset_then_edit_then_load_again"
    dataset_dir.mkdir()
    with open(dataset_dir / "train.txt", "w") as f:
        f.write("Hello there")
    dataset_builder = load_dataset_builder(str(dataset_dir))
    with open(dataset_dir / "train.txt", "w") as f:
        f.write("General Kenobi !")
    edited_dataset_builder = load_dataset_builder(str(dataset_dir))
    assert dataset_builder.cache_dir != edited_dataset_builder.cache_dir


@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, 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(data_dir)
    assert (dataset["train"].dataset_size < max_in_memory_dataset_size) is expected_in_memory


@pytest.mark.integration
def test_remote_data_files():
    repo_id = "hf-internal-testing/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"}


def distributed_load_dataset(args):
    data_name, tmp_dir, datafiles = args
    dataset = load_dataset(data_name, cache_dir=tmp_dir, data_files=datafiles)
    return dataset


def test_load_dataset_distributed(tmp_path, csv_path):
    num_workers = 5
    args = "csv", str(tmp_path), csv_path
    with Pool(processes=num_workers) as pool:  # start num_workers processes
        datasets = pool.map(distributed_load_dataset, [args] * num_workers)
        assert len(datasets) == num_workers
        assert all(len(dataset) == len(datasets[0]) > 0 for dataset in datasets)
        assert len(datasets[0].cache_files) > 0
        assert all(dataset.cache_files == datasets[0].cache_files for dataset in datasets)


def test_load_dataset_with_storage_options(mockfs):
    with mockfs.open("data.txt", "w") as f:
        f.write("Hello there\n")
        f.write("General Kenobi !")
    data_files = {"train": ["mock://data.txt"]}
    ds = load_dataset("text", data_files=data_files, storage_options=mockfs.storage_options)
    assert list(ds["train"]) == [{"text": "Hello there"}, {"text": "General Kenobi !"}]


@require_pil
def test_load_dataset_with_storage_options_with_decoding(mockfs, image_file):
    import PIL.Image

    filename = os.path.basename(image_file)
    with mockfs.open(filename, "wb") as fout:
        with open(image_file, "rb") as fin:
            fout.write(fin.read())
    data_files = {"train": ["mock://" + filename]}
    ds = load_dataset("imagefolder", data_files=data_files, storage_options=mockfs.storage_options)
    assert len(ds["train"]) == 1
    assert isinstance(ds["train"][0]["image"], PIL.Image.Image)


def test_load_dataset_with_zip(zip_csv_path):
    path = str(zip_csv_path.parent)
    ds = load_dataset(path)
    assert list(ds.keys()) == ["train"]
    assert ds["train"].column_names == ["col_1", "col_2", "col_3"]
    assert ds["train"].num_rows == 8
    assert ds["train"][0] == {"col_1": 0, "col_2": 0, "col_3": 0.0}


@pytest.mark.integration
def test_reload_old_cache_from_2_15(tmp_path: Path):
    cache_dir = tmp_path / "test_reload_old_cache_from_2_15"
    builder_cache_dir = (
        cache_dir / "polinaeterna___audiofolder_two_configs_in_metadata/v2-374bfde4f55442bc/0.0.0/7896925d64deea5d"
    )
    builder_cache_dir.mkdir(parents=True)
    arrow_path = builder_cache_dir / "audiofolder_two_configs_in_metadata-train.arrow"
    dataset_info_path = builder_cache_dir / "dataset_info.json"
    with dataset_info_path.open("w") as f:
        f.write("{}")
    arrow_path.touch()
    builder = load_dataset_builder(
        "polinaeterna/audiofolder_two_configs_in_metadata",
        "v2",
        data_files="v2/train/*",
        cache_dir=cache_dir.as_posix(),
    )
    assert builder.cache_dir == builder_cache_dir.as_posix()  # old cache from 2.15

    builder = load_dataset_builder(
        "polinaeterna/audiofolder_two_configs_in_metadata", "v2", cache_dir=cache_dir.as_posix()
    )
    assert (
        builder.cache_dir
        == (
            cache_dir / "polinaeterna___audiofolder_two_configs_in_metadata" / "v2" / "0.0.0" / str(builder.hash)
        ).as_posix()
    )  # new cache


@pytest.mark.integration
def test_update_dataset_card_data_with_standalone_yaml():
    # Labels defined in .huggingface.yml because they are too long to be in README.md
    from datasets.utils.metadata import MetadataConfigs

    with patch(
        "datasets.utils.metadata.MetadataConfigs.from_dataset_card_data",
        side_effect=MetadataConfigs.from_dataset_card_data,
    ) as card_data_read_mock:
        builder = load_dataset_builder("datasets-maintainers/dataset-with-standalone-yaml")
    assert card_data_read_mock.call_args.args[0]["license"] is not None  # from README.md
    assert card_data_read_mock.call_args.args[0]["dataset_info"] is not None  # from standalone yaml
    assert card_data_read_mock.call_args.args[0]["tags"] == ["test"]  # standalone yaml has precedence
    assert isinstance(
        builder.info.features["label"], datasets.ClassLabel
    )  # correctly loaded from long labels list in standalone yaml
