# Copyright (C) 2022-present Naver Corporation. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).

import torch
from models.croco import CroCoNet
from PIL import Image
import torchvision.transforms
from torchvision.transforms import ToTensor, Normalize, Compose


def main():
    device = torch.device("cuda:0" if torch.cuda.is_available() and torch.cuda.device_count() > 0 else "cpu")

    # load 224x224 images and transform them to tensor
    imagenet_mean = [0.485, 0.456, 0.406]
    imagenet_mean_tensor = torch.tensor(imagenet_mean).view(1, 3, 1, 1).to(device, non_blocking=True)
    imagenet_std = [0.229, 0.224, 0.225]
    imagenet_std_tensor = torch.tensor(imagenet_std).view(1, 3, 1, 1).to(device, non_blocking=True)
    trfs = Compose([ToTensor(), Normalize(mean=imagenet_mean, std=imagenet_std)])
    image1 = trfs(Image.open("assets/Chateau1.png").convert("RGB")).to(device, non_blocking=True).unsqueeze(0)
    image2 = trfs(Image.open("assets/Chateau2.png").convert("RGB")).to(device, non_blocking=True).unsqueeze(0)

    # load model
    ckpt = torch.load("pretrained_models/CroCo_V2_ViTLarge_BaseDecoder.pth", "cpu")
    model = CroCoNet(**ckpt.get("croco_kwargs", {})).to(device)
    model.eval()
    msg = model.load_state_dict(ckpt["model"], strict=True)

    # forward
    with torch.inference_mode():
        out, mask, target = model(image1, image2)

    # the output is normalized, thus use the mean/std of the actual image to go back to RGB space
    patchified = model.patchify(image1)
    mean = patchified.mean(dim=-1, keepdim=True)
    var = patchified.var(dim=-1, keepdim=True)
    decoded_image = model.unpatchify(out * (var + 1.0e-6) ** 0.5 + mean)
    # undo imagenet normalization, prepare masked image
    decoded_image = decoded_image * imagenet_std_tensor + imagenet_mean_tensor
    input_image = image1 * imagenet_std_tensor + imagenet_mean_tensor
    ref_image = image2 * imagenet_std_tensor + imagenet_mean_tensor
    image_masks = model.unpatchify(model.patchify(torch.ones_like(ref_image)) * mask[:, :, None])
    masked_input_image = (1 - image_masks) * input_image

    # make visualization
    visualization = torch.cat(
        (ref_image, masked_input_image, decoded_image, input_image), dim=3
    )  # 4*(B, 3, H, W) -> B, 3, H, W*4
    B, C, H, W = visualization.shape
    visualization = visualization.permute(1, 0, 2, 3).reshape(C, B * H, W)
    visualization = torchvision.transforms.functional.to_pil_image(torch.clamp(visualization, 0, 1))
    fname = "demo_output.png"
    visualization.save(fname)
    print("Visualization save in " + fname)


if __name__ == "__main__":
    main()
