# 2021.05.07-Changed for IPT
#            Huawei Technologies Co., Ltd. <foss@huawei.com>
from __future__ import annotations

import math

import torch
import torch.nn as nn


def default_conv(in_channels: int, out_channels: int, kernel_size: int, bias=True):
    return nn.Conv2d(
        in_channels, out_channels, kernel_size, padding=(kernel_size // 2), bias=bias
    )


class MeanShift(nn.Conv2d):
    def __init__(
        self,
        rgb_range,
        rgb_mean=(0.4488, 0.4371, 0.4040),
        rgb_std=(1.0, 1.0, 1.0),
        sign=-1,
    ):
        super().__init__(3, 3, kernel_size=1)
        std = torch.Tensor(rgb_std)
        self.weight.data = torch.eye(3).view(3, 3, 1, 1) / std.view(3, 1, 1, 1)
        self.bias.data = sign * rgb_range * torch.Tensor(rgb_mean) / std  # type: ignore
        for p in self.parameters():
            p.requires_grad = False


class ResBlock(nn.Module):
    def __init__(
        self,
        conv,
        n_feats: int,
        kernel_size: int,
        bias=True,
        bn=False,
        act: nn.Module = nn.ReLU(True),
        res_scale=1,
    ):
        super().__init__()
        m = []
        for i in range(2):
            m.append(conv(n_feats, n_feats, kernel_size, bias=bias))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if i == 0:
                m.append(act)

        self.body = nn.Sequential(*m)
        self.res_scale = res_scale

    def forward(self, x):
        res = self.body(x).mul(self.res_scale)
        res += x

        return res


class Upsampler(nn.Sequential):
    def __init__(
        self,
        conv,
        scale: int,
        n_feats: int,
        bn=False,
        act: str | None = None,
        bias=True,
    ):
        m = []
        if (scale & (scale - 1)) == 0:  # Is scale = 2^n?
            for _ in range(int(math.log(scale, 2))):
                m.append(conv(n_feats, 4 * n_feats, 3, bias))
                m.append(nn.PixelShuffle(2))
                if bn:
                    m.append(nn.BatchNorm2d(n_feats))
                if act == "relu":
                    m.append(nn.ReLU(True))
                elif act == "prelu":
                    m.append(nn.PReLU(n_feats))

        elif scale == 3:
            m.append(conv(n_feats, 9 * n_feats, 3, bias))
            m.append(nn.PixelShuffle(3))
            if bn:
                m.append(nn.BatchNorm2d(n_feats))
            if act == "relu":
                m.append(nn.ReLU(True))
            elif act == "prelu":
                m.append(nn.PReLU(n_feats))
        else:
            raise NotImplementedError

        super().__init__(*m)