"""
This code was copied from open-mmlab/mmsr.
"""

import functools
import torch
import torch.nn as nn
import torch.nn.functional as F


class ResidualBlock_noBN(nn.Module):
    '''Residual block w/o BN
    ---Conv-ReLU-Conv-+-
     |________________|
    '''

    def __init__(self, nf=64):
        super(ResidualBlock_noBN, self).__init__()
        self.conv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)

    def forward(self, x):
        identity = x
        out = F.relu(self.conv1(x), inplace=True)
        out = self.conv2(out)
        return identity + out


class MSRResNet(nn.Module):
    ''' modified SRResNet'''

    def __init__(self, in_nc=3, out_nc=3, nf=64, nb=16, upscale=4):
        super(MSRResNet, self).__init__()
        self.upscale = upscale

        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
        basic_block = functools.partial(ResidualBlock_noBN, nf=nf)
        self.recon_trunk = nn.Sequential(*[basic_block() for _ in range(nb)])

        # upsampling
        if self.upscale == 2:
            self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
            self.pixel_shuffle = nn.PixelShuffle(2)
        elif self.upscale == 3:
            self.upconv1 = nn.Conv2d(nf, nf * 9, 3, 1, 1, bias=True)
            self.pixel_shuffle = nn.PixelShuffle(3)
        elif self.upscale == 4:
            self.upconv1 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
            self.upconv2 = nn.Conv2d(nf, nf * 4, 3, 1, 1, bias=True)
            self.pixel_shuffle = nn.PixelShuffle(2)

        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)

        # activation function
        self.lrelu = nn.LeakyReLU(negative_slope=0.1, inplace=True)

    def forward(self, x):
        fea = self.lrelu(self.conv_first(x))
        out = self.recon_trunk(fea)

        if self.upscale == 4:
            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))
            out = self.lrelu(self.pixel_shuffle(self.upconv2(out)))
        elif self.upscale == 3 or self.upscale == 2:
            out = self.lrelu(self.pixel_shuffle(self.upconv1(out)))

        out = self.conv_last(self.lrelu(self.HRconv(out)))
        base = F.interpolate(x, scale_factor=self.upscale, mode='bilinear', align_corners=False)
        out += base
        return out


class ResidualDenseBlock_5C(nn.Module):
    def __init__(self, nf=64, gc=32, bias=True):
        super(ResidualDenseBlock_5C, self).__init__()
        # gc: growth channel, i.e. intermediate channels
        self.conv1 = nn.Conv2d(nf, gc, 3, 1, 1, bias=bias)
        self.conv2 = nn.Conv2d(nf + gc, gc, 3, 1, 1, bias=bias)
        self.conv3 = nn.Conv2d(nf + 2 * gc, gc, 3, 1, 1, bias=bias)
        self.conv4 = nn.Conv2d(nf + 3 * gc, gc, 3, 1, 1, bias=bias)
        self.conv5 = nn.Conv2d(nf + 4 * gc, nf, 3, 1, 1, bias=bias)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        x1 = self.lrelu(self.conv1(x))
        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5 * 0.2 + x


class RRDB(nn.Module):
    '''Residual in Residual Dense Block'''

    def __init__(self, nf, gc=32):
        super(RRDB, self).__init__()
        self.RDB1 = ResidualDenseBlock_5C(nf, gc)
        self.RDB2 = ResidualDenseBlock_5C(nf, gc)
        self.RDB3 = ResidualDenseBlock_5C(nf, gc)

    def forward(self, x):
        out = self.RDB1(x)
        out = self.RDB2(out)
        out = self.RDB3(out)
        return out * 0.2 + x


class RRDBNet(nn.Module):
    def __init__(self, in_nc, out_nc, nf, nb, gc=32):
        super(RRDBNet, self).__init__()
        RRDB_block_f = functools.partial(RRDB, nf=nf, gc=gc)

        self.conv_first = nn.Conv2d(in_nc, nf, 3, 1, 1, bias=True)
        self.RRDB_trunk = nn.Sequential(*[RRDB_block_f() for _ in range(nb)])
        self.trunk_conv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        #### upsampling
        self.upconv1 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.upconv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.HRconv = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
        self.conv_last = nn.Conv2d(nf, out_nc, 3, 1, 1, bias=True)

        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        fea = self.conv_first(x)
        trunk = self.trunk_conv(self.RRDB_trunk(fea))
        fea = fea + trunk

        fea = self.lrelu(self.upconv1(F.interpolate(fea, scale_factor=2, mode='nearest')))
        fea = self.lrelu(self.upconv2(F.interpolate(fea, scale_factor=2, mode='nearest')))
        out = self.conv_last(self.lrelu(self.HRconv(fea)))

        return out
