# Source code for deeprobust.image.netmodels.preact_resnet

    """
    This is an reimplementaiton of Pre-activation ResNet.
    """
    import torch
    import torch.nn as nn
    import torch.nn.functional as F

    class PreActBlock(nn.Module):
        '''Pre-activation version of the BasicBlock.'''
        expansion = 1

        def __init__(self, in_planes, planes, stride=1):
            super(PreActBlock, self).__init__()
            self.bn1 = nn.BatchNorm2d(in_planes)
            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)

            if stride != 1 or in_planes != self.expansion*planes:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
                )

        def forward(self, x):
            out = F.relu(self.bn1(x))
            shortcut = self.shortcut(x) if hasattr(self, 'shortcut') else x
            out = self.conv1(out)
            out = self.conv2(F.relu(self.bn2(out)))
            out += shortcut
            return out

    class PreActBottleneck(nn.Module):
        '''Pre-activation version of the original Bottleneck module.'''
        expansion = 4

        def __init__(self, in_planes, planes, stride=1):
            super(PreActBottleneck, self).__init__()
            self.bn1 = nn.BatchNorm2d(in_planes)
            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
            self.bn3 = nn.BatchNorm2d(planes)
            self.conv3 = nn.Conv2d(planes, self.expansion*planes, kernel_size=1, bias=False)

            if stride != 1 or in_planes != self.expansion*planes:
                self.shortcut = nn.Sequential(
                    nn.Conv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False)
                )

        def forward(self, x):
            out = F.relu(self.bn1(x))
            shortcut = self.shortcut(out) if hasattr(self, 'shortcut') else x
            out = self.conv1(out)
            out = self.conv2(F.relu(self.bn2(out)))
            out = self.conv3(F.relu(self.bn3(out)))
            out += shortcut
            return out

    class PreActResNet(nn.Module):
        """PreActResNet.
        """

        def __init__(self, block, num_blocks, num_classes=10):
            super(PreActResNet, self).__init__()
            self.in_planes = 64
            self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
            self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
            self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
            self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
            self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
            self.bn = nn.BatchNorm2d(512 * block.expansion)
            self.linear = nn.Linear(512 * block.expansion, num_classes)

        def _make_layer(self, block, planes, num_blocks, stride):
            strides = [stride] + [1]*(num_blocks-1)
            layers = []
            for stride in strides:
                layers.append(block(self.in_planes, planes, stride))
                self.in_planes = planes * block.expansion
            return nn.Sequential(*layers)

        def forward(self, x):
            out = self.conv1(x)
            out = self.layer1(out)
            out = self.layer2(out)
            out = self.layer3(out)
            out = self.layer4(out)
            out = F.relu(self.bn(out))
            out = F.avg_pool2d(out, 4)
            out = out.view(out.size(0), -1)
            out = self.linear(out)
            return out

    def PreActResNet18():
        """PreActResNet18.
        """
        return PreActResNet(PreActBlock, [2,2,2,2])