import numpy as np
from math import exp, log10
import torch
import torch.nn.functional as F
from torch.autograd import Variable
from fastai.vision import ImageImageList, Tuple, subplots, plt
from .common import ArcGISMSImage, ArcGISImageListRGB


def gaussian(window_size, sigma):
    gauss = torch.Tensor(
        [
            exp(-((x - window_size // 2) ** 2) / float(2 * sigma ** 2))
            for x in range(window_size)
        ]
    )
    return gauss / gauss.sum()


def create_window(window_size, channel):
    _1D_window = gaussian(window_size, 1.5).unsqueeze(1)
    _2D_window = _1D_window.mm(_1D_window.t()).float().unsqueeze(0).unsqueeze(0)
    window = Variable(
        _2D_window.expand(channel, 1, window_size, window_size).contiguous()
    )
    return window


def _ssim(img1, img2, window, window_size, channel, size_average=True):
    mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
    mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)

    mu1_sq = mu1.pow(2)
    mu2_sq = mu2.pow(2)
    mu1_mu2 = mu1 * mu2

    sigma1_sq = (
        F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq
    )
    sigma2_sq = (
        F.conv2d(img2 * img2, window, padding=window_size // 2, groups=channel) - mu2_sq
    )
    sigma12 = (
        F.conv2d(img1 * img2, window, padding=window_size // 2, groups=channel)
        - mu1_mu2
    )

    C1 = 0.01 ** 2
    C2 = 0.03 ** 2

    ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / (
        (mu1_sq + mu2_sq + C1) * (sigma1_sq + sigma2_sq + C2)
    )

    if size_average:
        return ssim_map.mean()
    else:
        return ssim_map.mean(1).mean(1).mean(1)


class SSIM(torch.nn.Module):
    def __init__(self, window_size=11, size_average=True):
        super(SSIM, self).__init__()
        self.window_size = window_size
        self.size_average = size_average
        self.channel = 1
        self.window = create_window(window_size, self.channel)

    def forward(self, img1, img2):
        (_, channel, _, _) = img1.size()

        if channel == self.channel and self.window.data.type() == img1.data.type():
            window = self.window
        else:
            window = create_window(self.window_size, channel)

            if img1.is_cuda:
                window = window.cuda(img1.get_device())
            window = window.type_as(img1)

            self.window = window
            self.channel = channel

        return _ssim(img1, img2, window, self.window_size, channel, self.size_average)


def ssim(img1, img2, window_size=11, size_average=True):
    (_, channel, _, _) = img1.size()
    window = create_window(window_size, channel)

    if img1.is_cuda:
        window = window.cuda(img1.get_device())
    window = window.type_as(img1)

    return _ssim(img1, img2, window, window_size, channel, size_average)


def psnr(img1, img2):
    mse = F.l1_loss(img1, img2)
    psnr = 10 * log10(1 / mse)
    return psnr


class ImageImageListSR(ImageImageList):
    label_cls = ArcGISImageListRGB

    from fastai.basics import Optional

    def show_xys(
        self,
        xs,
        ys,
        imgsize: int = 4,
        figsize: Optional[Tuple[int, int]] = None,
        **kwargs
    ):
        "Show the `xs` (inputs) and `ys`(targets)  on a figure of `figsize`."
        axs = subplots(
            len(xs), 2, imgsize=imgsize, figsize=figsize, weight="bold", size=14
        )
        for i, (x, y) in enumerate(zip(xs, ys)):
            x.show(ax=axs[i, 0], **kwargs)
            y.show(ax=axs[i, 1], **kwargs)
        axs[0, 0].title.set_text("Low Resolution")
        axs[0, 1].title.set_text("High Resolution")
        plt.tight_layout()

    def open(self, fn):
        return ArcGISMSImage.open(fn, div=255)
