import safetensors
import safetensors.torch
import torch
from omegaconf import OmegaConf

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

MODEL_PATH = f'{MODEL_FOLDER}/sd-2-0-inpainting/512-inpainting-ema.safetensors'
DOWNLOAD_URL = 'https://huggingface.co/stabilityai/stable-diffusion-2-inpainting/resolve/main/512-inpainting-ema.safetensors?download=true'

# pre-download
download_file(DOWNLOAD_URL, MODEL_PATH)


def load_model(dtype=torch.float16, device='cuda:0'):
    print ("Loading model: Stable-Inpainting 2.0")

    download_file(DOWNLOAD_URL, MODEL_PATH)

    state_dict = safetensors.torch.load_file(MODEL_PATH)

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

    unet = load_obj(f'{CONFIG_FOLDER}/unet/inpainting/v2.yaml').eval().cuda()
    vae = load_obj(f'{CONFIG_FOLDER}/vae.yaml').eval().cuda()
    encoder = load_obj(f'{CONFIG_FOLDER}/encoders/openclip.yaml').eval().cuda()
    ddim = DDIM(config, vae, encoder, unet)

    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)

    if dtype == torch.float16:
        unet.convert_to_fp16()
    unet.to(device=device)
    vae.to(dtype=dtype, device=device)
    encoder.to(dtype=dtype, device=device)
    encoder.device = device

    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)

    print('Stable-Inpainting 2.0 loaded')
    return ddim