import imgviz
import numpy as np
from utils import render_transient
import matplotlib.pyplot as plt
import torchvision
import torch
import os 


def _atomic_torch_save(obj, path):
    folder = os.path.dirname(path) or "."
    os.makedirs(folder, exist_ok=True)
    tmp_path = os.path.join(folder, "." + os.path.basename(path) + ".tmp")
    torch.save(obj, tmp_path)
    os.replace(tmp_path, path)


@torch.no_grad()
def write_summary_histogram(radiance_field, occupancy_grid, writer, test_dataset, step, render_step_size, args):
    img_scale = args.img_scale
    radiance_field.eval()
    occupancy_grid.eval()
    rgb_images = []
    depth_images = [] 
    gt_imgs = []
    accs = []

    pixels_to_plot = args.pixels_to_plot
    plotting_transients = []
    plotting_transients_depth = []

    plotting_transients_gt = []
    mse_list = []

    test_list = list(range(len(test_dataset)))
    # if args.version == "simulated":
    #     color_channels = 3
    # else:
    #     color_channels = 1
    # n_output_dim = args.n_bins*(color_channels)
    with torch.no_grad():

        # sample transients from network
        for ind, i in enumerate(test_list):
            data = test_dataset[i]
            render_bkgd = data["color_bkgd"]
            rays = data["rays"]
            pixels = data["pixels"]
            pixels = pixels.reshape(rays.origins.shape[0], rays.origins.shape[1], -1, 3)
            valid_mask = data.get("valid_mask", None)
            if valid_mask is not None:
                valid_mask = valid_mask.to(torch.bool).cpu()
                if valid_mask.ndim == 1:
                    valid_mask = valid_mask.reshape(rays.origins.shape[0], rays.origins.shape[1])


            # # rendering
            out = render_transient(
            radiance_field,
            occupancy_grid,
            rays,
            near_plane=args.near_plane,
            far_plane=args.far_plane,
            render_step_size=render_step_size,
            cone_angle=args.cone_angle,
            alpha_thre=args.alpha_thre,
            use_normals = False, 
            args = args
            )

            rgb, acc, n_rendering_samples, comp_weights, depth = [out[key] for key in ['colors', 'opacities', 'n_rendering_samples', "comp_weights", "depths"]]
            del out

            rgb = rgb.reshape(rays.origins.shape[0], rays.origins.shape[1], -1, 3)

            _atomic_torch_save(rgb, os.path.join(args.outpath, f"test_{ind}_conv.pt"))
            _atomic_torch_save(depth, os.path.join(args.outpath, f"test_{ind}_depth.pt"))

            # if color_channels ==1:
            #     gt_imgs.append(torch.clip(pixels.sum(-2).cpu().repeat(1, 1, 3).permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2))
            #     rgb_images.append(torch.clip(rgb.sum(-2).cpu().repeat(1, 1, 3).permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2))
            # else:
            gt_img = torch.clip(pixels.sum(-2).cpu().permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2)
            rgb_img = torch.clip(rgb.sum(-2).cpu().permute(2, 0, 1)/img_scale, 0, 1)**(1/2.2)
            if valid_mask is not None:
                valid3 = valid_mask[None, ...].expand_as(gt_img)
                gt_imgs.append(gt_img * valid3.to(gt_img.dtype))
                rgb_images.append(rgb_img * valid3.to(rgb_img.dtype))
                if valid3.any():
                    mse_list.append(((gt_img - rgb_img) ** 2)[valid3].mean())
                else:
                    mse_list.append(torch.tensor(float("nan")))
            else:
                gt_imgs.append(gt_img)
                rgb_images.append(rgb_img)
                mse_list.append(torch.mean((gt_img - rgb_img) ** 2))
            accs.append(acc.repeat(1, 1, 3).permute(2, 0, 1).cpu())
            dp = imgviz.depth2rgb(depth.cpu().squeeze().numpy(), colormap="inferno")
            depth_images.append(torch.from_numpy(dp).permute(2, 0, 1))

            if ind == 0:
                for pixel in pixels_to_plot:
                    plotting_transients.append(rgb[pixel[0], pixel[1], :, 0])
                    plotting_transients_gt.append(pixels[pixel[0], pixel[1], :, 0])
                    plotting_transients_depth.append(depth[pixel[0], pixel[1]])


        images = torchvision.utils.make_grid(torch.stack(gt_imgs + rgb_images + depth_images + accs), nrow=len(test_list), normalize=False)
        mse = torch.stack(mse_list)
        valid_mse = torch.isfinite(mse)
        if valid_mse.any():
            psnr = -10.0 * torch.log10(torch.clamp(mse[valid_mse], min=1e-12))
            print(f"image psnr (masked): {psnr.mean():.2f}\n")
            writer.add_scalar("Metric/psnr_masked", psnr.mean().item(), step)
        else:
            print("image psnr (masked): nan (no valid pixels)\n")
        writer.add_image('rgbdn', images, step)

        figure = plt.figure(figsize=((len(pixels_to_plot)+1), 4), dpi=250)

        # plot the predicted intensity
        plt.subplot(2, (len(pixels_to_plot)+1)//2, 1)
        plt.imshow(gt_imgs[0].permute(1, 2, 0))
        for i, pixel in enumerate(pixels_to_plot):
            plt.plot(pixel[1], pixel[0], '.', markersize=10, color='red')
            plt.text(pixel[1], pixel[0], str(i), color="yellow", fontsize=10)
        plt.gca().set_aspect(1.0/plt.gca().get_data_ratio(), adjustable='box')
        plt.title('gt intensity')

        for i, pixel in enumerate(pixels_to_plot):
            # plot transients
            plt.subplot(2, (len(pixels_to_plot)+1)//2, i+2)
            plt.plot(np.arange(args.n_bins), plotting_transients[i].detach().cpu(), label='pred', linewidth=0.5)
            plt.plot(np.arange(args.n_bins), plotting_transients_gt[i].detach().cpu(), label='gt', linewidth=0.5)
            plt.axvline(x = (plotting_transients_depth[i]/args.exposure_time).detach().cpu().numpy(), color = 'y')
            plt.title(f"pixel {i}")
            plt.ylabel('intensity')
            plt.legend(borderpad=0, labelspacing=0)
            plt.gca().set_aspect(1.0 / plt.gca().get_data_ratio(), adjustable='box')
        
        plt.tight_layout()
        writer.add_figure("transient_plots", figure, step)


    radiance_field.train()
    occupancy_grid.train()