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import glob
from unittest import TestCase
from unittest.mock import patch
import pytest
import requests
import yaml
from evaluate.hub import push_to_hub
from tests.test_metric import DummyMetric
minimum_metadata = {
"model-index": [
{
"results": [
{
"task": {"type": "dummy-task"},
"dataset": {"type": "dataset_type", "name": "dataset_name"},
"metrics": [
{"type": "dummy_metric", "value": 1.0, "name": "Pretty Metric Name"},
],
}
]
}
]
}
extras_metadata = {
"model-index": [
{
"results": [
{
"task": {"type": "dummy-task", "name": "task_name"},
"dataset": {
"type": "dataset_type",
"name": "dataset_name",
"config": "fr",
"split": "test",
"revision": "abc",
"args": {"a": 1, "b": 2},
},
"metrics": [
{
"type": "dummy_metric",
"value": 1.0,
"name": "Pretty Metric Name",
"config": "default",
"args": {"hello": 1, "world": 2},
},
],
}
]
}
]
}
@patch("evaluate.hub.HF_HUB_ALLOWED_TASKS", ["dummy-task"])
@patch("evaluate.hub.dataset_info", lambda x: True)
@patch("evaluate.hub.model_info", lambda x: True)
@patch("evaluate.hub.metadata_update")
class TestHub(TestCase):
@pytest.fixture(autouse=True)
def inject_fixtures(self, caplog):
self._caplog = caplog
def setUp(self):
self.metric = DummyMetric()
self.metric.add()
self.args = {"hello": 1, "world": 2}
self.result = self.metric.compute()
def test_push_metric_required_arguments(self, metadata_update):
push_to_hub(
model_id="username/repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dataset_type",
task_type="dummy-task",
)
metadata_update.assert_called_once_with(repo_id="username/repo", metadata=minimum_metadata, overwrite=False)
def test_push_metric_missing_arguments(self, metadata_update):
with pytest.raises(TypeError):
push_to_hub(
model_id="username/repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dummy-task",
)
def test_push_metric_invalid_arguments(self, metadata_update):
with pytest.raises(TypeError):
push_to_hub(
model_id="username/repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dataset_type",
task_type="dummy-task",
random_value="incorrect",
)
def test_push_metric_extra_arguments(self, metadata_update):
push_to_hub(
model_id="username/repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dataset_type",
dataset_config="fr",
dataset_split="test",
dataset_revision="abc",
dataset_args={"a": 1, "b": 2},
task_type="dummy-task",
task_name="task_name",
metric_config=self.metric.config_name,
metric_args=self.args,
)
metadata_update.assert_called_once_with(repo_id="username/repo", metadata=extras_metadata, overwrite=False)
def test_push_metric_invalid_task_type(self, metadata_update):
with pytest.raises(ValueError):
push_to_hub(
model_id="username/repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dataset_type",
task_type="audio-classification",
)
def test_push_metric_invalid_dataset_type(self, metadata_update):
with patch("evaluate.hub.dataset_info") as mock_dataset_info:
mock_dataset_info.side_effect = requests.HTTPError()
push_to_hub(
model_id="username/repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dataset_type",
task_type="dummy-task",
)
assert "Dataset dataset_type not found on the Hub at hf.co/datasets/dataset_type" in self._caplog.text
metadata_update.assert_called_once_with(
repo_id="username/repo", metadata=minimum_metadata, overwrite=False
)
def test_push_metric_invalid_model_id(self, metadata_update):
with patch("evaluate.hub.model_info") as mock_model_info:
mock_model_info.side_effect = requests.HTTPError()
with pytest.raises(ValueError):
push_to_hub(
model_id="username/bad-repo",
metric_value=self.result["accuracy"],
metric_name="Pretty Metric Name",
metric_type=self.metric.name,
dataset_name="dataset_name",
dataset_type="dataset_type",
task_type="dummy-task",
)
class ValidateYaml(TestCase):
def setUp(self):
pass
def testLoadingCards(self):
readme_filepaths = []
for glob_path in ["measurements/*/README.md", "metrics/*/README.md", "comparisons/*/README.md"]:
readme_filepaths.extend(glob.glob(glob_path))
for readme_file in readme_filepaths:
with open(readme_file, encoding="utf8") as f_yaml:
x = yaml.safe_load_all(f_yaml)
self.assertIsInstance(next(x), dict)
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