def convert_cvt_checkpoint(cvt_model, image_size, cvt_file_name, pytorch_dump_folder):
    """
    Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
    """
    img_labels_file = "imagenet-1k-id2label.json"
    num_labels = 1000

    repo_id = "huggingface/label-files"
    num_labels = num_labels
    id2label = json.load(open(cached_download(hf_hub_url(repo_id, img_labels_file, repo_type="dataset")), "r"))
    id2label = {int(k): v for k, v in id2label.items()}

    id2label = id2label
    label2id = {v: k for k, v in id2label.items()}

    config = config = CvtConfig(num_labels=num_labels, id2label=id2label, label2id=label2id)

    # For depth size 13 (13 = 1+2+10)
    if cvt_model.rsplit("/", 1)[-1][4:6] == "13":
        config.depth = [1, 2, 10]

    # For depth size 21 (21 = 1+4+16)
    elif cvt_model.rsplit("/", 1)[-1][4:6] == "21":
        config.depth = [1, 4, 16]

    # For wide cvt (similar to wide-resnet) depth size 24 (w24 = 2 + 2 20)
    else:
        config.depth = [2, 2, 20]
        config.num_heads = [3, 12, 16]
        config.embed_dim = [192, 768, 1024]

    model = CvtForImageClassification(config)
    image_processor = AutoImageProcessor.from_pretrained("facebook/convnext-base-224-22k-1k")
    image_processor.size["shortest_edge"] = image_size
    original_weights = torch.load(cvt_file_name, map_location=torch.device("cpu"))

    huggingface_weights = OrderedDict()
    list_of_state_dict = []

    for idx in range(len(config.depth)):
        if config.cls_token[idx]:
            list_of_state_dict = list_of_state_dict + cls_token(idx)
        list_of_state_dict = list_of_state_dict + embeddings(idx)
        for cnt in range(config.depth[idx]):
            list_of_state_dict = list_of_state_dict + attention(idx, cnt)

    list_of_state_dict = list_of_state_dict + final()
    for gg in list_of_state_dict:
        print(gg)
    for i in range(len(list_of_state_dict)):
        huggingface_weights[list_of_state_dict[i][0]] = original_weights[list_of_state_dict[i][1]]

    model.load_state_dict(huggingface_weights)
    model.save_pretrained(pytorch_dump_folder)
    image_processor.save_pretrained(pytorch_dump_folder)

    Fucntion to convert the microsoft cvt checkpoint to huggingface checkpoint
    
dpkd/transformers/src/transformers/models/cvt/convert_cvt_original_pytorch_checkpoint_to_pytorch.py