src/transformers/models/beit/convert_beit_unilm_to_pytorch.py/convert_beit_checkpoint
def convert_beit_checkpoint(checkpoint_url, pytorch_dump_folder_path):
    """
    Copy/paste/tweak model's weights to our BEiT structure.
    """

    # define default BEiT configuration
    config = BeitConfig()
    has_lm_head = False
    repo_id = "datasets/huggingface/label-files"
    # set config parameters based on URL
    if checkpoint_url[-9:-4] == "pt22k":
        # masked image modeling
        config.use_shared_relative_position_bias = True
        config.use_mask_token = True
        has_lm_head = True
    elif checkpoint_url[-9:-4] == "ft22k":
        # intermediate fine-tuning on ImageNet-22k
        config.use_relative_position_bias = True
        config.num_labels = 21841
        filename = "imagenet-22k-id2label.json"
        id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename)), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        # this dataset contains 21843 labels but the model only has 21841
        # we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
        del id2label[9205]
        del id2label[15027]
        config.id2label = id2label
        config.label2id = {v: k for k, v in id2label.items()}
    elif checkpoint_url[-8:-4] == "to1k":
        # fine-tuning on ImageNet-1k
        config.use_relative_position_bias = True
        config.num_labels = 1000
        filename = "imagenet-1k-id2label.json"
        id2label = json.load(open(cached_download(hf_hub_url(repo_id, filename)), "r"))
        id2label = {int(k): v for k, v in id2label.items()}
        config.id2label = id2label
        config.label2id = {v: k for k, v in id2label.items()}
        if "384" in checkpoint_url:
            config.image_size = 384
        if "512" in checkpoint_url:
            config.image_size = 512
    else:
        raise ValueError("Checkpoint not supported, URL should either end with 'pt22k', 'ft22k' or 'to1k'")

    # size of the architecture
    if "base" in checkpoint_url:
        pass
    elif "large" in checkpoint_url:
        config.hidden_size = 1024
        config.intermediate_size = 4096
        config.num_hidden_layers = 24
        config.num_attention_heads = 16
    else:
        raise ValueError("Should either find 'base' or 'large' in checkpoint URL")

    # load state_dict of original model, remove and rename some keys
    state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu", check_hash=True)["model"]
    rename_keys = create_rename_keys(config, has_lm_head=has_lm_head)
    for src, dest in rename_keys:
        rename_key(state_dict, src, dest)
    read_in_q_k_v(state_dict, config, has_lm_head=has_lm_head)

    # load HuggingFace model
    if checkpoint_url[-9:-4] == "pt22k":
        model = BeitForMaskedImageModeling(config)
    else:
        model = BeitForImageClassification(config)
    model.eval()
    model.load_state_dict(state_dict)

    # Check outputs on an image
    feature_extractor = BeitFeatureExtractor(size=config.image_size, resample=Image.BILINEAR, do_center_crop=False)
    encoding = feature_extractor(images=prepare_img(), return_tensors="pt")
    pixel_values = encoding["pixel_values"]

    outputs = model(pixel_values)
    logits = outputs.logits

    # verify logits
    expected_shape = torch.Size([1, 1000])
    if checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k"):
        expected_shape = torch.Size([1, 196, 8192])
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k"):
        expected_shape = torch.Size([1, 196, 8192])
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22k"):
        expected_shape = torch.Size([1, 21841])
        expected_logits = torch.tensor([2.2288, 2.4671, 0.7395])
        expected_class_idx = 2397
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22k"):
        expected_shape = torch.Size([1, 21841])
        expected_logits = torch.tensor([1.6881, -0.2787, 0.5901])
        expected_class_idx = 2396
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft1k"):
        expected_logits = torch.tensor([0.1241, 0.0798, -0.6569])
        expected_class_idx = 285
    elif checkpoint_url[:-4].endswith("beit_base_patch16_224_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-1.2385, -1.0987, -1.0108])
        expected_class_idx = 281
    elif checkpoint_url[:-4].endswith("beit_base_patch16_384_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-1.5303, -0.9484, -0.3147])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft1k"):
        expected_logits = torch.tensor([0.4610, -0.0928, 0.2086])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith("beit_large_patch16_224_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-0.4804, 0.6257, -0.1837])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith("beit_large_patch16_384_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([[-0.5122, 0.5117, -0.2113]])
        expected_class_idx = 761
    elif checkpoint_url[:-4].endswith("beit_large_patch16_512_pt22k_ft22kto1k"):
        expected_logits = torch.tensor([-0.3062, 0.7261, 0.4852])
        expected_class_idx = 761
    else:
        raise ValueError("Can't verify logits as model is not supported")

    assert logits.shape == expected_shape, "Shape of logits not as expected"
    print("Shape of logits:", logits.shape)
    if not has_lm_head:
        print("Predicted class idx:", logits.argmax(-1).item())
        assert torch.allclose(logits[0, :3], expected_logits, atol=1e-3), "First elements of logits not as expected"
        assert logits.argmax(-1).item() == expected_class_idx, "Predicted class index not as expected"

    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)