//  [python]
def convert_convnext_checkpoint(checkpoint_url, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our ConvNext structure.
    """

    # define ConvNext configuration based on URL
    config, expected_shape = get_convnext_config(checkpoint_url)
    # load original state_dict from URL
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url)["model"]
    # rename keys
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        state_dict[rename_key(key)] = val
    # add prefix to all keys expect classifier head
    for key in state_dict.copy().keys():
        val = state_dict.pop(key)
        if not key.startswith("classifier"):
            key = "convnext." + key
        state_dict[key] = val

    # load HuggingFace model
    model = ConvNextForImageClassification(config)
    model.load_state_dict(state_dict)
    model.eval()

    # Check outputs on an image, prepared by ConvNextFeatureExtractor
    size = 224 if "224" in checkpoint_url else 384
    feature_extractor = ConvNextFeatureExtractor(size=size)
    pixel_values = feature_extractor(images=prepare_img(), return_tensors="pt").pixel_values

    logits = model(pixel_values).logits

    # note: the logits below were obtained without center cropping
    if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
        expected_logits = torch.tensor([-0.1210, -0.6605, 0.1918])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
        expected_logits = torch.tensor([-0.4473, -0.1847, -0.6365])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
        expected_logits = torch.tensor([0.4525, 0.7539, 0.0308])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
        expected_logits = torch.tensor([0.3561, 0.6350, -0.0384])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
        expected_logits = torch.tensor([0.4174, -0.0989, 0.1489])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
        expected_logits = torch.tensor([0.2513, -0.1349, -0.1613])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
        expected_logits = torch.tensor([1.2980, 0.3631, -0.1198])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
        expected_logits = torch.tensor([1.2963, 0.1227, 0.1723])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
        expected_logits = torch.tensor([1.7956, 0.8390, 0.2820])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
        expected_logits = torch.tensor([-0.2822, -0.0502, -0.0878])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
        expected_logits = torch.tensor([-0.5672, -0.0730, -0.4348])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
        expected_logits = torch.tensor([0.2681, 0.2365, 0.6246])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
        expected_logits = torch.tensor([-0.2642, 0.3931, 0.5116])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
        expected_logits = torch.tensor([-0.6677, -0.1873, -0.8379])
    elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
        expected_logits = torch.tensor([-0.7749, -0.2967, -0.6444])
    else:
        raise ValueError(f"Unknown URL: {checkpoint_url}")

    assert torch.allclose(logits[0, :3], expected_logits, atol=1e-3)
    assert logits.shape == expected_shape

    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)
    print(f"Saving feature extractor to {pytorch_dump_folder_path}")
    feature_extractor.save_pretrained(pytorch_dump_folder_path)

    print("Pushing model to the hub...")
    model_name = "convnext"
    if "tiny" in checkpoint_url:
        model_name += "-tiny"
    elif "small" in checkpoint_url:
        model_name += "-small"
    elif "base" in checkpoint_url:
        model_name += "-base"
    elif "xlarge" in checkpoint_url:
        model_name += "-xlarge"
    elif "large" in checkpoint_url:
        model_name += "-large"
    if "224" in checkpoint_url:
        model_name += "-224"
    elif "384" in checkpoint_url:
        model_name += "-384"
    if "22k" in checkpoint_url and "1k" not in checkpoint_url:
        model_name += "-22k"
    if "22k" in checkpoint_url and "1k" in checkpoint_url:
        model_name += "-22k-1k"

    model.push_to_hub(
        repo_path_or_name=Path(pytorch_dump_folder_path, model_name),
        organization="nielsr",
        commit_message="Add model",
    )