from copy import deepcopy

import torch

from ..utils import load_file_from_url
from .retinaface import RetinaFace


def init_detection_model(model_name, half=False, device="cuda", model_rootpath=None):
    if model_name == "retinaface_resnet50":
        model = RetinaFace(network_name="resnet50", half=half, device=device)
        model_url = "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth"
    elif model_name == "retinaface_mobile0.25":
        model = RetinaFace(network_name="mobile0.25", half=half, device=device)
        model_url = "https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_mobilenet0.25_Final.pth"
    else:
        raise NotImplementedError(f"{model_name} is not implemented.")

    model_path = load_file_from_url(
        url=model_url,
        model_dir="facexlib/weights",
        progress=True,
        file_name=None,
        save_dir=model_rootpath,
    )

    # TODO: clean pretrained model
    load_net = torch.load(model_path, map_location=lambda storage, loc: storage)
    # remove unnecessary 'module.'
    for k, v in deepcopy(load_net).items():
        if k.startswith("module."):
            load_net[k[7:]] = v
            load_net.pop(k)
    model.load_state_dict(load_net, strict=True)
    model.eval()
    model = model.to(device)
    return model