import torch


def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbose=True, device=None):
    """Creates a specified YOLOv5 model
    Arguments:
        name (str): name of model, i.e. 'yolov5s'
        pretrained (bool): load pretrained weights into the model
        channels (int): number of input channels
        classes (int): number of model classes
        autoshape (bool): apply YOLOv5 .autoshape() wrapper to model
        verbose (bool): print all information to screen
        device (str, torch.device, None): device to use for model parameters
    Returns:
        YOLOv5 pytorch model
    """
    from pathlib import Path

    from models.experimental import attempt_load
    from models.yolo import Model
    from yolo_utils.torch_utils import select_device

    file = Path(__file__).absolute()

    save_dir = Path("") if str(name).endswith(".pt") else file.parent
    path = (save_dir / name).with_suffix(".pt")  # checkpoint path
    try:
        device = "cpu"

        if pretrained and channels == 3 and classes == 80:
            model = attempt_load(path, map_location=device)  # download/load FP32 model
        else:
            cfg = list((Path(__file__).parent / "models").rglob(f"{name}.yaml"))[0]  # model.yaml path
            model = Model(cfg, channels, classes)  # create model
        if autoshape:
            model = model.autoshape()  # for file/URI/PIL/cv2/np inputs and NMS
        return model.to(device)

    except Exception as e:
        help_url = "https://github.com/ultralytics/yolov5/issues/36"
        s = "Cache may be out of date, try `force_reload=True`. See %s for help." % help_url
        raise Exception(s) from e


def custom(path="path/to/model.pt", autoshape=True, verbose=True, device=None):
    # YOLOv5 custom or local model
    return _create(path, autoshape=autoshape, verbose=verbose, device=device)