def convert_deit_checkpoint(deit_name, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our DeiT structure.
    """

    # define default DeiT configuration
    config = DeiTConfig()
    # all deit models have fine-tuned heads
    base_model = False
    # dataset (fine-tuned on ImageNet 2012), patch_size and image_size
    config.num_labels = 1000
    repo_id = "huggingface/label-files"
    filename = "imagenet-1k-id2label.json"
    id2label = json.load(open(hf_hub_download(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()}
    config.patch_size = int(deit_name[-6:-4])
    config.image_size = int(deit_name[-3:])
    # size of the architecture
    if deit_name[9:].startswith("tiny"):
        config.hidden_size = 192
        config.intermediate_size = 768
        config.num_hidden_layers = 12
        config.num_attention_heads = 3
    elif deit_name[9:].startswith("small"):
        config.hidden_size = 384
        config.intermediate_size = 1536
        config.num_hidden_layers = 12
        config.num_attention_heads = 6
    if deit_name[9:].startswith("base"):
        pass
    elif deit_name[4:].startswith("large"):
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16

    # load original model from timm
    timm_model = timm.create_model(deit_name, pretrained=True)
    timm_model.eval()

    # load state_dict of original model, remove and rename some keys
    state_dict = timm_model.state_dict()
    rename_keys = create_rename_keys(config, base_model)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, base_model)

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

    # Check outputs on an image, prepared by DeiTImageProcessor
    size = int(
        (256 / 224) * config.image_size
    )  # to maintain same ratio w.r.t. 224 images, see https://github.com/facebookresearch/deit/blob/ab5715372db8c6cad5740714b2216d55aeae052e/datasets.py#L103
    image_processor = DeiTImageProcessor(size=size, crop_size=config.image_size)
    encoding = image_processor(images=prepare_img(), return_tensors="pt")
    pixel_values = encoding["pixel_values"]
    outputs = model(pixel_values)

    timm_logits = timm_model(pixel_values)
    assert timm_logits.shape == outputs.logits.shape
    assert torch.allclose(timm_logits, outputs.logits, atol=1e-3)

    Path(pytorch_dump_folder_path).mkdir(exist_ok=True)
    print(f"Saving model {deit_name} to {pytorch_dump_folder_path}")
    model.save_pretrained(pytorch_dump_folder_path)
    print(f"Saving image processor to {pytorch_dump_folder_path}")
    image_processor.save_pretrained(pytorch_dump_folder_path)

    Copy/paste/tweak model's weights to our DeiT structure.
    
dpkd/transformers/src/transformers/models/deit/convert_deit_timm_to_pytorch.py