//  [python]
def convert_deformable_detr_checkpoint(
    checkpoint_path,
    single_scale,
    dilation,
    with_box_refine,
    two_stage,
    pytorch_dump_folder_path,
    push_to_hub,
):
    """
    Copy/paste/tweak model's weights to our Deformable DETR structure.
    """

    # load default config
    config = DeformableDetrConfig()
    # set config attributes
    if single_scale:
        config.num_feature_levels = 1
    config.dilation = dilation
    config.with_box_refine = with_box_refine
    config.two_stage = two_stage
    # set labels
    config.num_labels = 91
    repo_id = "huggingface/label-files"
    filename = "coco-detection-id2label.json"
    id2label = json.load(
        open(cached_download(hf_hub_url(repo_id, filename, repo_type="dataset")), "r")
    )
    id2label = {int(k): v for k, v in id2label.items()}
    config.id2label = id2label
    config.label2id = {v: k for k, v in id2label.items()}

    # load feature extractor
    feature_extractor = DeformableDetrFeatureExtractor(format="coco_detection")

    # prepare image
    img = prepare_img()
    encoding = feature_extractor(images=img, return_tensors="pt")
    pixel_values = encoding["pixel_values"]

    logger.info("Converting model...")

    # load original state dict
    state_dict = torch.load(checkpoint_path, map_location="cpu")["model"]
    # rename keys
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        state_dict[rename_key(key)] = val
    # query, key and value matrices need special treatment
    read_in_q_k_v(state_dict)
    # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
    prefix = "model."
    for key in state_dict.copy().keys():
        if not key.startswith("class_embed") and not key.startswith("bbox_embed"):
            val = state_dict.pop(key)
            state_dict[prefix + key] = val
    # finally, create HuggingFace model and load state dict
    model = DeformableDetrForObjectDetection(config)
    model.load_state_dict(state_dict)
    model.eval()

    device = "cuda" if torch.cuda.is_available() else "cpu"
    model.to(device)
    # verify our conversion
    outputs = model(pixel_values.to(device))

    expected_logits = torch.tensor(
        [
            [-9.6645, -4.3449, -5.8705],
            [-9.7035, -3.8504, -5.0724],
            [-10.5634, -5.3379, -7.5116],
        ]
    )
    expected_boxes = torch.tensor(
        [[0.8693, 0.2289, 0.2492], [0.3150, 0.5489, 0.5845], [0.5563, 0.7580, 0.8518]]
    )

    if single_scale:
        expected_logits = torch.tensor(
            [
                [-9.9051, -4.2541, -6.4852],
                [-9.6947, -4.0854, -6.8033],
                [-10.0665, -5.8470, -7.7003],
            ]
        )
        expected_boxes = torch.tensor(
            [
                [0.7292, 0.4991, 0.5532],
                [0.7959, 0.2426, 0.4236],
                [0.7582, 0.3518, 0.4451],
            ]
        )

    if single_scale and dilation:
        expected_logits = torch.tensor(
            [
                [-8.9652, -4.1074, -5.6635],
                [-9.0596, -4.9447, -6.6075],
                [-10.1178, -4.5275, -6.2671],
            ]
        )
        expected_boxes = torch.tensor(
            [
                [0.7665, 0.4130, 0.4769],
                [0.8364, 0.1841, 0.3391],
                [0.6261, 0.3895, 0.7978],
            ]
        )

    if with_box_refine:
        expected_logits = torch.tensor(
            [
                [-8.8895, -5.4187, -6.8153],
                [-8.4706, -6.1668, -7.6184],
                [-9.0042, -5.5359, -6.9141],
            ]
        )
        expected_boxes = torch.tensor(
            [
                [0.7828, 0.2208, 0.4323],
                [0.0892, 0.5996, 0.1319],
                [0.5524, 0.6389, 0.8914],
            ]
        )

    if with_box_refine and two_stage:
        expected_logits = torch.tensor(
            [
                [-6.7108, -4.3213, -6.3777],
                [-8.9014, -6.1799, -6.7240],
                [-6.9315, -4.4735, -6.2298],
            ]
        )
        expected_boxes = torch.tensor(
            [
                [0.2583, 0.5499, 0.4683],
                [0.7652, 0.9068, 0.4882],
                [0.5490, 0.2763, 0.0564],
            ]
        )

    print("Logits:", outputs.logits[0, :3, :3])

    assert torch.allclose(
        outputs.logits[0, :3, :3], expected_logits.to(device), atol=1e-4
    )
    assert torch.allclose(
        outputs.pred_boxes[0, :3, :3], expected_boxes.to(device), atol=1e-4
    )

    print("Everything ok!")

    # Save model and feature extractor
    logger.info(
        f"Saving PyTorch model and feature extractor to {pytorch_dump_folder_path}..."
    )
    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    model.save_pretrained(pytorch_dump_folder_path)
    feature_extractor.save_pretrained(pytorch_dump_folder_path)

    # Push to hub
    if push_to_hub:
        model_name = "deformable-detr"
        model_name += "-single-scale" if single_scale else ""
        model_name += "-dc5" if dilation else ""
        model_name += "-with-box-refine" if with_box_refine else ""
        model_name += "-two-stage" if two_stage else ""
        print("Pushing model to hub...")
        model.push_to_hub(
            repo_path_or_name=model_name,
            organization="nielsr",
            commit_message="Add model",
        )