def _get_coco_panoptic_separated_meta():
    """
    Returns metadata for "separated" version of the panoptic segmentation dataset.
    """
    stuff_ids = [k["id"] for k in COCO_CATEGORIES if k["isthing"] == 0]
    assert len(stuff_ids) == 53, len(stuff_ids)

    # For semantic segmentation, this mapping maps from contiguous stuff id
    # (in [0, 53], used in models) to ids in the dataset (used for processing results)
    # The id 0 is mapped to an extra category "thing".
    stuff_dataset_id_to_contiguous_id = {k: i + 1 for i, k in enumerate(stuff_ids)}
    # When converting COCO panoptic annotations to semantic annotations
    # We label the "thing" category to 0
    stuff_dataset_id_to_contiguous_id[0] = 0

    # 54 names for COCO stuff categories (including "things")
    stuff_classes = ["things"] + [
        k["name"].replace("-other", "").replace("-merged", "")
        for k in COCO_CATEGORIES
        if k["isthing"] == 0
    ]

    # NOTE: I randomly picked a color for things
    stuff_colors = [[82, 18, 128]] + [k["color"] for k in COCO_CATEGORIES if k["isthing"] == 0]
    ret = {
        "stuff_dataset_id_to_contiguous_id": stuff_dataset_id_to_contiguous_id,
        "stuff_classes": stuff_classes,
        "stuff_colors": stuff_colors,
    }
    ret.update(_get_coco_instances_meta())
    return ret

    Returns metadata for "separated" version of the panoptic segmentation dataset.
    
video_chat_text/video_chat_with_StableLM/models/grit_src/third_party/CenterNet2/detectron2/data/datasets/builtin_meta.py