chore: import upstream snapshot with attribution

tests/multimodal/test_parse.py

@@ -0,0 +1,51 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import numpy as np
+import pytest
+import torch
+from PIL import Image
+
+from vllm.multimodal.parse import ImageProcessorItems, VideoProcessorItems
+
+H, W = 480, 640
+
+
+@pytest.mark.parametrize(
+    "image",
+    [
+        Image.new("RGB", (W, H)),
+        # HWC, e.g. from np.array(PIL.Image)
+        np.zeros((H, W, 3), dtype=np.uint8),
+        torch.zeros((H, W, 3), dtype=torch.uint8),
+        # CHW, standard PyTorch / numpy convention
+        np.zeros((3, H, W), dtype=np.uint8),
+        torch.zeros((3, H, W), dtype=torch.uint8),
+    ],
+)
+def test_image_size_hwc_chw(image):
+    """Image sizes must be channel-layout agnostic.
+
+    `get_image_size` determines the multimodal placeholder count; reading an
+    HWC array (the layout `np.array(PIL.Image)` produces) as CHW yields a
+    bogus size and a placeholder/embedding count mismatch at inference time.
+    """
+    items = ImageProcessorItems([image])
+
+    assert items.get_image_size(0) == (W, H)
+
+
+@pytest.mark.parametrize(
+    "frame",
+    [
+        Image.new("RGB", (W, H)),
+        np.zeros((H, W, 3), dtype=np.uint8),
+        torch.zeros((H, W, 3), dtype=torch.uint8),
+        np.zeros((3, H, W), dtype=np.uint8),
+        torch.zeros((3, H, W), dtype=torch.uint8),
+    ],
+)
+def test_frame_size_hwc_chw(frame):
+    """`get_frame_size` must stay consistent with `get_image_size`."""
+    items = VideoProcessorItems([[frame]])
+
+    assert items.get_frame_size(0) == (W, H)