//  [python]
def convert_videomae_checkpoint(checkpoint_url, pytorch_dump_folder_path, model_name, push_to_hub):
    config = get_videomae_config(model_name)

    if "finetuned" in model_name:
        model = VideoMAEForVideoClassification(config)
    else:
        model = VideoMAEForPreTraining(config)

    # download original checkpoint, hosted on Google Drive
    output = "pytorch_model.bin"
    gdown.cached_download(checkpoint_url, output, quiet=False)
    files = torch.load(output, map_location="cpu")
    if "model" in files:
        state_dict = files["model"]
    else:
        state_dict = files["module"]
    new_state_dict = convert_state_dict(state_dict, config)

    model.load_state_dict(new_state_dict)
    model.eval()

    # verify model on basic input
    feature_extractor = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5])
    video = prepare_video()
    inputs = feature_extractor(video, return_tensors="pt")

    if "finetuned" not in model_name:
        local_path = hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos", filename="bool_masked_pos.pt")
        inputs["bool_masked_pos"] = torch.load(local_path)

    outputs = model(**inputs)
    logits = outputs.logits

    model_names = [
        # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
        "videomae-base-short",
        "videomae-base-short-finetuned-kinetics",
        "videomae-base",
        "videomae-base-finetuned-kinetics",
        "videomae-large",
        "videomae-large-finetuned-kinetics",
        # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
        "videomae-base-short-ssv2",
        "videomae-base-short-finetuned-ssv2",
        "videomae-base-ssv2",
        "videomae-base-finetuned-ssv2",
    ]

    # NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
    if model_name == "videomae-base":
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]])
    elif model_name == "videomae-base-short":
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]])
        # we verified the loss both for normalized and unnormalized targets for this one
        expected_loss = torch.tensor([0.5142]) if config.norm_pix_loss else torch.tensor([0.6469])
    elif model_name == "videomae-large":
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]])
    elif model_name == "videomae-large-finetuned-kinetics":
        expected_shape = torch.Size([1, 400])
        expected_slice = torch.tensor([0.0771, 0.0011, -0.3625])
    elif model_name == "videomae-base-short-finetuned-kinetics":
        expected_shape = torch.Size([1, 400])
        expected_slice = torch.tensor([0.6588, 0.0990, -0.2493])
    elif model_name == "videomae-base-finetuned-kinetics":
        expected_shape = torch.Size([1, 400])
        expected_slice = torch.tensor([0.3669, -0.0688, -0.2421])
    elif model_name == "videomae-base-short-ssv2":
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]])
    elif model_name == "videomae-base-short-finetuned-ssv2":
        expected_shape = torch.Size([1, 174])
        expected_slice = torch.tensor([-0.0537, -0.1539, -0.3266])
    elif model_name == "videomae-base-ssv2":
        expected_shape = torch.Size([1, 1408, 1536])
        expected_slice = torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]])
    elif model_name == "videomae-base-finetuned-ssv2":
        expected_shape = torch.Size([1, 174])
        expected_slice = torch.tensor([0.1961, -0.8337, -0.6389])
    else:
        raise ValueError(f"Model name not supported. Should be one of {model_names}")

    # verify logits
    assert logits.shape == expected_shape
    if "finetuned" in model_name:
        assert torch.allclose(logits[0, :3], expected_slice, atol=1e-4)
    else:
        print("Logits:", logits[0, :3, :3])
        assert torch.allclose(logits[0, :3, :3], expected_slice, atol=1e-4)
    print("Logits ok!")

    # verify loss, if applicable
    if model_name == "videomae-base-short":
        loss = outputs.loss
        assert torch.allclose(loss, expected_loss, atol=1e-4)
        print("Loss ok!")

    if pytorch_dump_folder_path is not None:
        print(f"Saving model and feature extractor to {pytorch_dump_folder_path}")
        feature_extractor.save_pretrained(pytorch_dump_folder_path)
        model.save_pretrained(pytorch_dump_folder_path)

    if push_to_hub:
        print("Pushing to the hub...")
        model.push_to_hub(model_name, organization="nielsr")