def convert_vit_checkpoint(vit_name, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our ViT structure.
    """

    # define default ViT configuration
    config = ViTConfig()
    base_model = False
    # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
    if vit_name[-5:] == "in21k":
        base_model = True
        config.patch_size = int(vit_name[-12:-10])
        config.image_size = int(vit_name[-9:-6])
    else:
        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(vit_name[-6:-4])
        config.image_size = int(vit_name[-3:])
    # size of the architecture
    if "deit" in vit_name:
        if vit_name[9:].startswith("tiny"):
            config.hidden_size = 192
            config.intermediate_size = 768
            config.num_hidden_layers = 12
            config.num_attention_heads = 3
        elif vit_name[9:].startswith("small"):
            config.hidden_size = 384
            config.intermediate_size = 1536
            config.num_hidden_layers = 12
            config.num_attention_heads = 6
        else:
            pass
    else:
        if vit_name[4:].startswith("small"):
            config.hidden_size = 768
            config.intermediate_size = 2304
            config.num_hidden_layers = 8
            config.num_attention_heads = 8
        elif vit_name[4:].startswith("base"):
            pass
        elif vit_name[4:].startswith("large"):
            config.hidden_size = 1024
            config.intermediate_size = 4096
            config.num_hidden_layers = 24
            config.num_attention_heads = 16
        elif vit_name[4:].startswith("huge"):
            config.hidden_size = 1280
            config.intermediate_size = 5120
            config.num_hidden_layers = 32
            config.num_attention_heads = 16

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

    # load state_dict of original model, remove and rename some keys
    state_dict = timm_model.state_dict()
    if base_model:
        remove_classification_head_(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
    if vit_name[-5:] == "in21k":
        model = ViTModel(config).eval()
    else:
        model = ViTForImageClassification(config).eval()
    model.load_state_dict(state_dict)

    # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
    if "deit" in vit_name:
        image_processor = DeiTImageProcessor(size=config.image_size)
    else:
        image_processor = ViTImageProcessor(size=config.image_size)
    encoding = image_processor(images=prepare_img(), return_tensors="pt")
    pixel_values = encoding["pixel_values"]
    outputs = model(pixel_values)

    if base_model:
        timm_pooled_output = timm_model.forward_features(pixel_values)
        assert timm_pooled_output.shape == outputs.pooler_output.shape
        assert torch.allclose(timm_pooled_output, outputs.pooler_output, atol=1e-3)
    else:
        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 {vit_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 ViT structure.
    
dpkd/transformers/src/transformers/models/vit/convert_vit_timm_to_pytorch.py