# TAKEN FROM https://github.com/VITA-Group/BNN_NoBN/blob/main/models/Qa_reactnet_18_bf.py
"""
React-birealnet-18(modified from resnet)

BN setting: remove all BatchNorm layers
Conv setting: replace conv2d with ScaledstdConv2d (add alpha beta each blocks)
Binary setting: only activation are binarized

"""

import torch.nn as nn
from layers import (
    BinaryActivation,
    HardBinaryScaledStdConv2d,
    LearnableBias,
    ScaledStdConv2d,
)


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return ScaledStdConv2d(
        in_planes,
        out_planes,
        kernel_size=3,
        stride=stride,
        padding=1,
        bias=False,
    )


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return ScaledStdConv2d(
        in_planes, out_planes, kernel_size=1, stride=stride, bias=False
    )


def binaryconv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return HardBinaryScaledStdConv2d(
        in_planes, out_planes, kernel_size=3, stride=stride, padding=1
    )


def binaryconv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return HardBinaryScaledStdConv2d(
        in_planes, out_planes, kernel_size=1, stride=stride, padding=0
    )


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self, inplanes, planes, alpha, beta, stride=1, downsample=None
    ):
        super(BasicBlock, self).__init__()

        self.alpha = alpha
        self.beta = beta

        self.move0 = LearnableBias(inplanes)
        self.binary_activation = BinaryActivation()
        self.binary_conv = conv3x3(inplanes, planes, stride=stride)
        self.move1 = LearnableBias(planes)
        self.prelu = nn.PReLU(planes)
        self.move2 = LearnableBias(planes)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x
        x_in = x * self.beta

        out = self.move0(x_in)
        out = self.binary_activation(out)
        out = self.binary_conv(out)

        if self.downsample is not None:
            residual = self.downsample(x_in)

        out = out * self.alpha + residual
        out = self.move1(out)
        out = self.prelu(out)
        out = self.move2(out)

        return out


class BiRealNet(nn.Module):
    def __init__(
        self, block, layers, imagenet=True, alpha=0.2, num_classes=1000
    ):
        super(BiRealNet, self).__init__()
        self.inplanes = 64

        if imagenet:
            self.conv1 = ScaledStdConv2d(
                3, 64, kernel_size=7, stride=2, padding=3, bias=False
            )
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        else:
            self.conv1 = ScaledStdConv2d(
                3, 64, kernel_size=3, stride=1, padding=1, bias=False
            )
            self.maxpool = nn.Identity()

        expected_var = 1.0
        self.layer1, expected_var = self._make_layer(
            block, 64, layers[0], alpha, expected_var
        )
        self.layer2, expected_var = self._make_layer(
            block, 128, layers[1], alpha, expected_var, stride=2
        )
        self.layer3, expected_var = self._make_layer(
            block, 256, layers[2], alpha, expected_var, stride=2
        )
        self.layer4, expected_var = self._make_layer(
            block, 512, layers[3], alpha, expected_var, stride=2
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, blocks, alpha, expected_var, stride=1):
        beta = 1.0 / expected_var**0.5
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.AvgPool2d(kernel_size=2, stride=stride),
                conv1x1(self.inplanes, planes * block.expansion),
            )
            # Reset expected var at a transition block
            expected_var = 1.0

        layers = []
        layers.append(
            block(self.inplanes, planes, alpha, beta, stride, downsample)
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            beta = 1.0 / expected_var**0.5
            layers.append(block(self.inplanes, planes, alpha, beta))
            expected_var += alpha**2

        return nn.Sequential(*layers), expected_var

    def forward(self, x):
        x = self.conv1(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def birealnet18(pretrained=False, **kwargs):
    """Constructs a BiRealNet-18 model."""
    model = BiRealNet(BasicBlock, [4, 4, 4, 4], **kwargs)
    return model
