def test_load_real_metric(self, metric_name):
with tempfile.TemporaryDirectory() as temp_data_dir:
download_config = DownloadConfig()
download_config.download_mode = GenerateMode.FORCE_REDOWNLOAD
load_metric(metric_name,
data_dir=temp_data_dir,
download_config=download_config)
def test_load_real_dataset(self, dataset_name):
with tempfile.TemporaryDirectory() as temp_data_dir:
download_config = DownloadConfig()
download_config.download_mode = GenerateMode.FORCE_REDOWNLOAD
dataset = load_dataset(dataset_name, data_dir=temp_data_dir, download_config=download_config)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
def test_load_real_dataset_local(self, dataset_name):
with tempfile.TemporaryDirectory() as temp_data_dir:
download_config = DownloadConfig()
download_config.download_mode = GenerateMode.FORCE_REDOWNLOAD
download_and_prepare_kwargs = {"download_config": download_config}
dataset = load_dataset(
"./datasets/" + dataset_name,
data_dir=temp_data_dir,
download_and_prepare_kwargs=download_and_prepare_kwargs,
)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
def test_load_real_dataset(self, dataset_name):
if "/" not in dataset_name:
logging.info("Skip {} because it is a canonical dataset")
return
with tempfile.TemporaryDirectory() as temp_data_dir:
download_config = DownloadConfig()
download_config.download_mode = GenerateMode.FORCE_REDOWNLOAD
download_and_prepare_kwargs = {"download_config": download_config}
dataset = load_dataset(
dataset_name,
data_dir=temp_data_dir,
download_and_prepare_kwargs=download_and_prepare_kwargs)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
def test_load_real_metric(self, metric_name):
with tempfile.TemporaryDirectory() as temp_data_dir:
download_config = DownloadConfig()
download_config.force_download = True
name = None
if metric_name == "glue":
name = "sst2"
metric = load_metric(metric_name,
name=name,
data_dir=temp_data_dir,
download_config=download_config)
parameters = inspect.signature(metric._compute).parameters
self.assertTrue("predictions" in parameters)
self.assertTrue("references" in parameters)
self.assertTrue(
all([p.kind != p.VAR_KEYWORD
for p in parameters.values()])) # no **kwargs
def load_builder_class(self, dataset_name, is_local=False):
# Download/copy dataset script
if is_local is True:
module_path = prepare_module("./datasets/" + dataset_name)
else:
module_path = prepare_module(dataset_name, download_config=DownloadConfig(force_download=True))
# Get dataset builder class
builder_cls = import_main_class(module_path)
# Instantiate dataset builder
return builder_cls
def test_load_real_dataset(self, dataset_name):
path = dataset_name
module_path, hash = prepare_module(
path,
download_config=DownloadConfig(force_download=True),
dataset=True)
builder_cls = import_main_class(module_path, dataset=True)
name = builder_cls.BUILDER_CONFIGS[
0].name if builder_cls.BUILDER_CONFIGS else None
with tempfile.TemporaryDirectory() as temp_cache_dir:
dataset = load_dataset(path,
name=name,
cache_dir=temp_cache_dir,
download_mode=GenerateMode.FORCE_REDOWNLOAD)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
def test_load_real_dataset_all_configs(self, dataset_name):
path = "./datasets/" + dataset_name
module_path, hash = prepare_module(
path,
download_config=DownloadConfig(local_files_only=True),
dataset=True)
builder_cls = import_main_class(module_path, dataset=True)
config_names = ([
config.name for config in builder_cls.BUILDER_CONFIGS
] if len(builder_cls.BUILDER_CONFIGS) > 0 else [None])
for name in config_names:
with tempfile.TemporaryDirectory() as temp_cache_dir:
dataset = load_dataset(
path,
name=name,
cache_dir=temp_cache_dir,
download_mode=GenerateMode.FORCE_REDOWNLOAD)
for split in dataset.keys():
self.assertTrue(len(dataset[split]) > 0)
