import gdown, os, torch
from ..label_mapper import id2label_dict

model_urls = {
    'UNet_ResNet50_default': 'https://drive.google.com/file/d/1Y9zubvMzkYHoAqz-NvV6vniH5FKAF2iV/view?usp=drive_link'
}

def get_model(model_name, gpus=''):
    """
    Function to load a model by name and optionally move it to specified GPUs.

    Args:
        model_name (str): Name of the model to load.
        gpus (str): String containing GPU device IDs separated by commas.
                    If empty or 'cpu', the model will be loaded on CPU.

    Returns:
        torch.nn.Module: Loaded model.
    """
    assert model_name.split('_')[0] in list(model_getter.keys())

    model = model_getter[model_name.split('_')[0]](model_name)
    download_weights(model_name)
    model = load_weights(model, model_name, map_location='cpu' if 'cpu' in gpus else 'cuda')

    if 'cpu' not in gpus:
        gpus = [int(i) for i in gpus.split(',') if len(i) > 0]

        if len(gpus) > 1:
            assert torch.cuda.is_available()
            model.to(gpus[0])
            model = torch.nn.DataParallel(model, device_ids=gpus).cuda()
        elif len(gpus) == 1:
            assert torch.cuda.is_available()
            model.to(gpus[0])

    return model

def get_unet(model_name):
    """
    Function to get a U-Net model.

    Args:
        model_name (str): Name of the U-Net model.

    Returns:
        torch.nn.Module: U-Net model.
    """
    from .UNet.backbone_unet import BackboneUNet
    return BackboneUNet(model_name, len(id2label_dict.keys()))

def download_weights(model_name: str) -> None:
    """
    Function to download model weights.

    Args:
        model_name (str): Name of the model.
    """
    if "CXAS_PATH" in os.environ:
        store_path = os.path.join(os.environ['CXAS_PATH'], '.cxas')
    else:
        store_path = os.path.join(os.environ['HOME'], '.cxas')
    os.makedirs(os.path.join(store_path, 'weights/'), exist_ok=True)
    out_path = os.path.join(store_path, 'weights/{}'.format(model_name + '.pth'))
    if os.path.isfile(out_path):
        return
    else:
        gdown.download(model_urls[model_name], out_path, quiet=False, fuzzy=True)
        return

def load_weights(model, model_name: str, map_location: str = 'cuda:0'):
    """
    Function to load model weights.

    Args:
        model (torch.nn.Module): Model to load weights into.
        model_name (str): Name of the model.
        map_location (str): Location to map tensors to (default: 'cuda:0' if available, else 'cpu').

    Returns:
        torch.nn.Module: Model with loaded weights.
    """
    if "CXAS_PATH" in os.environ:
        store_path = os.path.join(os.environ['CXAS_PATH'], '.cxas')
    else:
        store_path = os.path.join(os.environ['HOME'], '.cxas')
    out_path = os.path.join(store_path, 'weights/{}'.format(model_name + '.pth'))
    assert os.path.isfile(out_path)

    checkpoint = torch.load(out_path, weights_only=False, map_location=map_location)

    if 'module' in list(checkpoint['model'].keys())[0]:
        for i in list(checkpoint['model'].keys()):
            checkpoint['model'][i[len('module.'):]] = checkpoint['model'].pop(i)
    model.load_state_dict(checkpoint['model'], strict=False)
    return model

# Dictionary containing model getter functions
model_getter = {
    'UNet': get_unet,
}