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


class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(
            in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(
            out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.equal_in_out = in_planes == out_planes
        self.conv_shortcut = (
            (not self.equal_in_out)
            and nn.Conv2d(
                in_planes,
                out_planes,
                kernel_size=1,
                stride=stride,
                padding=0,
                bias=False,
            )
            or None
        )

    def forward(self, x):
        if not self.equal_in_out:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equal_in_out else x)))
        out = self.conv2(out)
        return torch.add(x if self.equal_in_out else self.conv_shortcut(x), out)


class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(
                block(
                    i == 0 and in_planes or out_planes,
                    out_planes,
                    i == 0 and stride or 1,
                )
            )
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class WideResNet(nn.Module):
    def __init__(self, depth, num_classes, widen_factor=1):
        super(WideResNet, self).__init__()
        n_channels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert (depth - 4) % 6 == 0
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(
            3, n_channels[0], kernel_size=3, stride=1, padding=1, bias=False
        )
        # 1st block
        self.block1 = NetworkBlock(n, n_channels[0], n_channels[1], block, 1)
        # 2nd block
        self.block2 = NetworkBlock(n, n_channels[1], n_channels[2], block, 2)
        # 3rd block
        self.block3 = NetworkBlock(n, n_channels[2], n_channels[3], block, 2)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(n_channels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(n_channels[3], num_classes)
        self.n_channels = n_channels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward_features(self, x):
        x = self.conv1(x)
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.relu(self.bn1(x))
        x = F.avg_pool2d(x, 8, 1, 0)
        x = x.view(-1, self.n_channels)
        return x

    def forward_classifier(self, x):
        return self.fc(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_classifier(x)
        return x


def wide_resnet_28x10(num_classes):
    return WideResNet(depth=28, widen_factor=10, num_classes=num_classes)

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


class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride):
        super(BasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(
            in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(out_planes)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(
            out_planes, out_planes, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.equal_in_out = in_planes == out_planes
        self.conv_shortcut = (
            (not self.equal_in_out)
            and nn.Conv2d(
                in_planes,
                out_planes,
                kernel_size=1,
                stride=stride,
                padding=0,
                bias=False,
            )
            or None
        )

    def forward(self, x):
        if not self.equal_in_out:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equal_in_out else x)))
        out = self.conv2(out)
        return torch.add(x if self.equal_in_out else self.conv_shortcut(x), out)


class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride):
        layers = []
        for i in range(int(nb_layers)):
            layers.append(
                block(
                    i == 0 and in_planes or out_planes,
                    out_planes,
                    i == 0 and stride or 1,
                )
            )
        return nn.Sequential(*layers)

    def forward(self, x):
        return self.layer(x)


class WideResNet(nn.Module):
    def __init__(self, depth, num_classes, widen_factor=1):
        super(WideResNet, self).__init__()
        n_channels = [16, 16 * widen_factor, 32 * widen_factor, 64 * widen_factor]
        assert (depth - 4) % 6 == 0
        n = (depth - 4) / 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(
            3, n_channels[0], kernel_size=3, stride=1, padding=1, bias=False
        )
        # 1st block
        self.block1 = NetworkBlock(n, n_channels[0], n_channels[1], block, 1)
        # 2nd block
        self.block2 = NetworkBlock(n, n_channels[1], n_channels[2], block, 2)
        # 3rd block
        self.block3 = NetworkBlock(n, n_channels[2], n_channels[3], block, 2)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(n_channels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(n_channels[3], num_classes)
        self.n_channels = n_channels[3]

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def forward_features(self, x):
        x = self.conv1(x)
        x = self.block1(x)
        x = self.block2(x)
        x = self.block3(x)
        x = self.relu(self.bn1(x))
        x = F.avg_pool2d(x, 8, 1, 0)
        x = x.view(-1, self.n_channels)
        return x

    def forward_classifier(self, x):
        return self.fc(x)

    def forward(self, x):
        x = self.forward_features(x)
        x = self.forward_classifier(x)
        return x


def wide_resnet_28x10(num_classes):
    return WideResNet(depth=28, widen_factor=10, num_classes=num_classes)

# 1st conv before any network block # 1st block # 2nd block # 3rd block # global average pooling and classifier