add unittests

tests/unittests/multimodal/test_clip_score.py

@@ -149,6 +149,36 @@ def test_warning_on_long_caption(self, inputs, model_name_or_path):
         ):
             metric.update(preds[0], target[0])
 
+    @skip_on_connection_issues()
+    def test_clip_score_image_to_image(self, inputs, model_name_or_path):
+        """Test CLIP score for image-to-image comparison."""
+        metric = CLIPScore(model_name_or_path=model_name_or_path)
+        preds, _ = inputs
+        score = metric(preds[0][0], preds[0][1])
+        assert score.detach().round() == torch.tensor(96.0)
+
+    @skip_on_connection_issues()
+    def test_clip_score_text_to_text(self, inputs, model_name_or_path):
+        """Test CLIP score for text-to-text comparison."""
+        metric = CLIPScore(model_name_or_path=model_name_or_path)
+        _, target = inputs
+        score = metric(target[0][0], target[0][1])
+        assert score.detach().round() == torch.tensor(65.0)
+
+    @skip_on_connection_issues()
+    def test_clip_score_functional_image_to_image(self, inputs, model_name_or_path):
+        """Test functional implementation of image-to-image CLIP score."""
+        preds, _ = inputs
+        score = clip_score(preds[0][0], preds[0][1], model_name_or_path=model_name_or_path)
+        assert score.detach().round() == torch.tensor(96.0)
+
+    @skip_on_connection_issues()
+    def test_clip_score_functional_text_to_text(self, inputs, model_name_or_path):
+        """Test functional implementation of text-to-text CLIP score."""
+        _, target = inputs
+        score = clip_score(target[0][0], target[0][1], model_name_or_path=model_name_or_path)
+        assert score.detach().round() == torch.tensor(65.0)
+
 
 @pytest.mark.parametrize(
     ("input_data", "expected"),