import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

import torch


def gauss_kernel(size=5, channels=3):
    kernel = torch.tensor(
        [
            [1.0, 4.0, 6.0, 4.0, 1],
            [4.0, 16.0, 24.0, 16.0, 4.0],
            [6.0, 24.0, 36.0, 24.0, 6.0],
            [4.0, 16.0, 24.0, 16.0, 4.0],
            [1.0, 4.0, 6.0, 4.0, 1.0],
        ]
    )
    kernel /= 256.0
    kernel = kernel.repeat(channels, 1, 1, 1)
    kernel = kernel.to(device)
    return kernel


def downsample(x):
    return x[:, :, ::2, ::2]


def upsample(x):
    cc = torch.cat([x, torch.zeros(x.shape[0], x.shape[1], x.shape[2], x.shape[3]).to(device)], dim=3)
    cc = cc.view(x.shape[0], x.shape[1], x.shape[2] * 2, x.shape[3])
    cc = cc.permute(0, 1, 3, 2)
    cc = torch.cat([cc, torch.zeros(x.shape[0], x.shape[1], x.shape[3], x.shape[2] * 2).to(device)], dim=3)
    cc = cc.view(x.shape[0], x.shape[1], x.shape[3] * 2, x.shape[2] * 2)
    x_up = cc.permute(0, 1, 3, 2)
    return conv_gauss(x_up, 4 * gauss_kernel(channels=x.shape[1]))


def conv_gauss(img, kernel):
    img = torch.nn.functional.pad(img, (2, 2, 2, 2), mode="reflect")
    out = torch.nn.functional.conv2d(img, kernel, groups=img.shape[1])
    return out


def laplacian_pyramid(img, kernel, max_levels=3):
    current = img
    pyr = []
    for level in range(max_levels):
        filtered = conv_gauss(current, kernel)
        down = downsample(filtered)
        up = upsample(down)
        diff = current - up
        pyr.append(diff)
        current = down
    return pyr


class LapLoss(torch.nn.Module):
    def __init__(self, max_levels=5, channels=3):
        super(LapLoss, self).__init__()
        self.max_levels = max_levels
        self.gauss_kernel = gauss_kernel(channels=channels)

    def forward(self, input, target):
        pyr_input = laplacian_pyramid(img=input, kernel=self.gauss_kernel, max_levels=self.max_levels)
        pyr_target = laplacian_pyramid(img=target, kernel=self.gauss_kernel, max_levels=self.max_levels)
        return sum(torch.nn.functional.l1_loss(a, b) for a, b in zip(pyr_input, pyr_target))