def load_o365_json(json_file, image_root, dataset_name=None):
    '''
    Load Object365 class name text for object description for GRiT
    '''

    json_file = PathManager.get_local_path(json_file)

    timer = Timer()
    lvis_api = LVIS(json_file)
    if timer.seconds() > 1:
        logger.info("Loading {} takes {:.2f} seconds.".format(
            json_file, timer.seconds()))

    class_names = {}
    sort_cat = sorted(lvis_api.dataset['categories'], key=lambda x: x['id'])
    for x in sort_cat:
        if '/' in x['name']:
            text = ''
            for xx in x['name'].split('/'):
                text += xx
                text += ' '
            text = text[:-1]
        else:
            text = x['name']
        class_names[x['id']] = text

    img_ids = sorted(lvis_api.imgs.keys())
    imgs = lvis_api.load_imgs(img_ids)
    anns = [lvis_api.img_ann_map[img_id] for img_id in img_ids]

    ann_ids = [ann["id"] for anns_per_image in anns for ann in anns_per_image]
    assert len(set(ann_ids)) == len(ann_ids), \
        "Annotation ids in '{}' are not unique".format(json_file)

    imgs_anns = list(zip(imgs, anns))
    logger.info("Loaded {} images in the LVIS v1 format from {}".format(
        len(imgs_anns), json_file))

    dataset_dicts = []

    for (img_dict, anno_dict_list) in imgs_anns:
        record = {}
        if "file_name" in img_dict:
            file_name = img_dict["file_name"]
            record["file_name"] = os.path.join(image_root, file_name)

        record["height"] = int(img_dict["height"])
        record["width"] = int(img_dict["width"])
        image_id = record["image_id"] = img_dict["id"]

        objs = []
        for anno in anno_dict_list:
            assert anno["image_id"] == image_id
            if anno.get('iscrowd', 0) > 0:
                continue
            obj = {"bbox": anno["bbox"], "bbox_mode": BoxMode.XYWH_ABS}
            obj["category_id"] = 0
            obj["object_description"] = class_names[anno['category_id']]

            objs.append(obj)
        record["annotations"] = objs
        if len(record["annotations"]) == 0:
            continue
        record["task"] = "ObjectDet"
        dataset_dicts.append(record)

    return dataset_dicts

    Load Object365 class name text for object description for GRiT
    
video_chat_text/video_chat_with_StableLM/models/grit_src/grit/data/datasets/object365.py