"""
        Outlier Exposure (OE)
        
    Code for the models from https://arxiv.org/abs/1812.04606
    almost the same as for the other ResNet Models but since they normalize their CIFAR10
    at dataloading time I include this preprocessing step explicitly in the architecture
"""


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


class BasicBlock(nn.Module):
    expansion = 1

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

        self.shortcut = nn.Sequential()
        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,
                ),
                nn.BatchNorm2d(self.expansion * planes),
            )

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


class ResNet(nn.Module):
    def __init__(
        self, block, num_blocks, num_classes=10, num_of_channels=3, dataset="CIFAR10"
    ):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1 = nn.Conv2d(
            num_of_channels, 64, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn1 = nn.BatchNorm2d(64)
        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.linear = nn.Linear(512 * block.expansion, num_classes)

        if dataset in ["CIFAR10", "CIFAR100"]:
            self.mean = torch.tensor([x / 255 for x in [125.3, 123.0, 113.9]])
            self.std = torch.tensor([x / 255 for x in [63.0, 62.1, 66.7]])
        elif dataset in ["SVHN"]:
            self.mean = torch.tensor([x for x in [0.0, 0.0, 0.0]])
            self.std = torch.tensor([x for x in [1.0, 1.0, 1.0]])
        else:
            self.mean = torch.tensor([x for x in [0.0]])
            self.std = torch.tensor([x for x in [1.0]])

    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 = (x - self.mean[None, :, None, None]) / self.std[None, :, None, None]
        out = F.relu(self.bn1(self.conv1(out)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = self.layer4(out)
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out

    def to(self, device):
        self.mean = self.mean.to(device)
        self.std = self.std.to(device)
        return super(ResNet, self).to(device)


def ResNet18(num_of_channels=3, num_classes=10, dataset="CIFAR10"):
    return ResNet(
        BasicBlock,
        [2, 2, 2, 2],
        num_of_channels=num_of_channels,
        num_classes=num_classes,
        dataset=dataset,
    )


class MNIST_ConvNet(nn.Module):
    def __init__(self):
        super(MNIST_ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = torch.relu(F.max_pool2d(self.conv1(x), 2))
        x = torch.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = torch.relu(self.fc1(x))
        # x = F.dropout(x)
        x = self.fc2(x)
        return x
