#   Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# code was heavily based on https://github.com/csdwren/PReNet
# Users should be careful about adopting these functions in any commercial matters.

import numpy as np
from math import exp

import paddle
import paddle.nn.functional as F
from .builder import CRITERIONS


def gaussian(window_size, sigma):
    gauss = paddle.to_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()).unsqueeze(0).unsqueeze(0)
    window = paddle.to_tensor(paddle.expand(
        _2D_window, (channel, 1, window_size, window_size)),
                              stop_gradient=False)
    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)


@CRITERIONS.register()
class SSIM(paddle.nn.Layer):

    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.shape

        if channel == self.channel and self.window.dtype == img1.dtype:
            window = self.window
        else:
            window = create_window(self.window_size, channel)
            tt = img1.dtype
            window = paddle.to_tensor(window, dtype=tt)
            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)
