"""RRDBNet architecture (Real-ESRGAN / ESRGAN generator).

Self-contained re-implementation of `basicsr.archs.rrdbnet_arch.RRDBNet` so that
the OpenVINO export does not require the whole BasicSR package to be installed.

The module layout (parameter names) is identical to the original
`RealESRGAN_x4plus` checkpoint, so `load_state_dict(..., strict=True)` works.
"""

import torch
from torch import nn as nn
from torch.nn import functional as F


class ResidualDenseBlock(nn.Module):
    """Residual dense block used inside every RRDB."""

    def __init__(self, num_feat=64, num_grow_ch=32):
        super().__init__()
        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
        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, num_feat, num_grow_ch=32):
        super().__init__()
        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)

    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):
    """The ESRGAN / Real-ESRGAN generator.

    `RealESRGAN_x4plus` uses num_feat=64, num_block=23, num_grow_ch=32, scale=4.
    """

    def __init__(self, num_in_ch=3, num_out_ch=3, scale=4, num_feat=64, num_block=23, num_grow_ch=32):
        super().__init__()
        self.scale = scale
        if scale == 2:
            num_in_ch = num_in_ch * 4
        elif scale == 1:
            num_in_ch = num_in_ch * 16
        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
        self.body = nn.Sequential(*[RRDB(num_feat, num_grow_ch) for _ in range(num_block)])
        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        feat = self.conv_first(x)
        body_feat = self.conv_body(self.body(feat))
        feat = feat + body_feat
        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
        return self.conv_last(self.lrelu(self.conv_hr(feat)))