add tests

tests/unittests/multimodal/test_clip_score.py

@@ -22,7 +22,12 @@
 from transformers import CLIPModel as _CLIPModel
 from transformers import CLIPProcessor as _CLIPProcessor
 
-from torchmetrics.functional.multimodal.clip_score import clip_score
+from torchmetrics.functional.multimodal.clip_score import (
+    _detect_modality,
+    _process_image_data,
+    _process_text_data,
+    clip_score,
+)
 from torchmetrics.multimodal.clip_score import CLIPScore
 from torchmetrics.utilities.imports import _TRANSFORMERS_GREATER_EQUAL_4_10
 from unittests._helpers import seed_all, skip_on_connection_issues
@@ -143,3 +148,63 @@ def test_warning_on_long_caption(self, inputs, model_name_or_path):
             match="Encountered caption longer than max_position_embeddings=77. Will truncate captions to this length.*",
         ):
             metric.update(preds[0], target[0])
+
+
+@pytest.mark.parametrize(
+    ("input_data", "expected"),
+    [
+        (torch.randn(3, 64, 64), "image"),
+        ([torch.randn(3, 64, 64)], "image"),
+        ("some text", "text"),
+        (["text1", "text2"], "text"),
+    ],
+)
+def test_detect_modality(input_data, expected):
+    """Test that modality detection works correctly."""
+    assert _detect_modality(input_data) == expected
+
+    with pytest.raises(ValueError, match="Empty input list"):
+        _detect_modality([])
+
+    with pytest.raises(ValueError, match="Could not automatically determine modality"):
+        _detect_modality(123)
+
+
+@pytest.mark.parametrize(
+    ("images", "expected_len", "should_raise"),
+    [
+        (torch.randn(3, 64, 64), 1, False),
+        (torch.randn(2, 3, 64, 64), 2, False),
+        ([torch.randn(3, 64, 64)], 1, False),
+        ([torch.randn(3, 64, 64), torch.randn(3, 64, 64)], 2, False),
+        (torch.randn(64, 64), 0, True),
+        ([torch.randn(64, 64)], 0, True),
+    ],
+)
+def test_process_image_data(images, expected_len, should_raise):
+    """Test that image processing works correctly."""
+    if should_raise:
+        with pytest.raises(ValueError, match="Expected all images to be 3d"):
+            _process_image_data(images)
+    else:
+        processed = _process_image_data(images)
+        assert isinstance(processed, list)
+        assert len(processed) == expected_len
+        assert all(isinstance(img, Tensor) and img.ndim == 3 for img in processed)
+
+
+@pytest.mark.parametrize(
+    ("texts", "expected_len"),
+    [
+        ("single text", 1),
+        (["text1", "text2"], 2),
+        ([""], 1),
+        ([], 0),
+    ],
+)
+def test_process_text_data(texts, expected_len):
+    """Test that text processing works correctly."""
+    processed = _process_text_data(texts)
+    assert isinstance(processed, list)
+    assert len(processed) == expected_len
+    assert all(isinstance(text, str) for text in processed)