src/transformers/models/mobilevit/convert_mlcvnets_to_pytorch.py/get_mobilevit_config
def get_mobilevit_config(mobilevit_name):
    config = MobileViTConfig()

    # size of the architecture
    if "mobilevit_s" in mobilevit_name:
        config.hidden_sizes = [144, 192, 240]
        config.neck_hidden_sizes = [16, 32, 64, 96, 128, 160, 640]
    elif "mobilevit_xs" in mobilevit_name:
        config.hidden_sizes = [96, 120, 144]
        config.neck_hidden_sizes = [16, 32, 48, 64, 80, 96, 384]
    elif "mobilevit_xxs" in mobilevit_name:
        config.hidden_sizes = [64, 80, 96]
        config.neck_hidden_sizes = [16, 16, 24, 48, 64, 80, 320]
        config.hidden_dropout_prob = 0.05
        config.expand_ratio = 2.0

    if mobilevit_name.startswith("deeplabv3_"):
        config.image_size = 512
        config.output_stride = 16
        config.num_labels = 21
        filename = "pascal-voc-id2label.json"
    else:
        config.num_labels = 1000
        filename = "imagenet-1k-id2label.json"

    repo_id = "huggingface/label-files"
    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()}

    return config