src/transformers/models/swin/convert_swin_timm_to_pytorch.py/get_swin_config
def get_swin_config(swin_name):
    config = SwinConfig()
    name_split = swin_name.split("_")

    model_size = name_split[1]
    img_size = int(name_split[4])
    window_size = int(name_split[3][-1])

    if model_size == "tiny":
        embed_dim = 96
        depths = (2, 2, 6, 2)
        num_heads = (3, 6, 12, 24)
    elif model_size == "small":
        embed_dim = 96
        depths = (2, 2, 18, 2)
        num_heads = (3, 6, 12, 24)
    elif model_size == "base":
        embed_dim = 128
        depths = (2, 2, 18, 2)
        num_heads = (4, 8, 16, 32)
    else:
        embed_dim = 192
        depths = (2, 2, 18, 2)
        num_heads = (6, 12, 24, 48)

    if "in22k" in swin_name:
        num_classes = 21841
    else:
        num_classes = 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.image_size = img_size
    config.num_labels = num_classes
    config.embed_dim = embed_dim
    config.depths = depths
    config.num_heads = num_heads
    config.window_size = window_size

    return config