from omegaconf import OmegaConf
import torch

from lib.smplfusion import DDIM, share, scheduler
from .common import *


DOWNLOAD_URL = 'https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt?download=true'
MODEL_PATH = f'{MODEL_FOLDER}/sd-1-5-inpainting/sd-v1-5-inpainting.ckpt'

# pre-download
download_file(DOWNLOAD_URL, MODEL_PATH)


def load_model():
    download_file(DOWNLOAD_URL, MODEL_PATH)

    state_dict = torch.load(MODEL_PATH)['state_dict']

    config = OmegaConf.load(f'{CONFIG_FOLDER}/ddpm/v1.yaml')

    print ("Loading model: Stable-Inpainting 1.5")

    unet = load_obj(f'{CONFIG_FOLDER}/unet/inpainting/v1.yaml').eval().cuda()
    vae = load_obj(f'{CONFIG_FOLDER}/vae.yaml').eval().cuda()
    encoder = load_obj(f'{CONFIG_FOLDER}/encoders/clip.yaml').eval().cuda()

    extract = lambda state_dict, model: {x[len(model)+1:]:y for x,y in state_dict.items() if model in x}
    unet_state = extract(state_dict, 'model.diffusion_model')
    encoder_state = extract(state_dict, 'cond_stage_model')
    vae_state = extract(state_dict, 'first_stage_model')

    unet.load_state_dict(unet_state)
    encoder.load_state_dict(encoder_state)
    vae.load_state_dict(vae_state)

    unet = unet.requires_grad_(False)
    encoder = encoder.requires_grad_(False)
    vae = vae.requires_grad_(False)

    ddim = DDIM(config, vae, encoder, unet)
    share.schedule = scheduler.linear(config.timesteps, config.linear_start, config.linear_end)

    return ddim