import torch
import torch.nn as nn
import math
from torch.nn import functional as F


#reproducible_state()
def conv3x3(in_planes, out_planes, stride=1):
    " 3x3 convolution with padding "
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion=1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

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

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion=4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes*4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes*4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

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

        out += residual
        out = self.relu(out)

        return out


class PreActBasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(PreActBasicBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = conv3x3(planes, planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.bn1(x)
        out = self.relu(out)

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

        out = self.conv1(out)

        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv2(out)

        out += residual

        return out


class PreActBottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(PreActBottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.conv1 = nn.Conv2d(inplanes, 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, planes*4, kernel_size=1, bias=False)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.bn1(x)
        out = self.relu(out)

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

        out = self.conv1(out)

        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv2(out)

        out = self.bn3(out)
        out = self.relu(out)
        out = self.conv3(out)

        out += residual

        return out


class ResNet_Cifar(nn.Module):

    def __init__(self, block, layers, num_classes=10):
        super(ResNet_Cifar, self).__init__()
        self.inplanes = 16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16, layers[0])
        self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        self.avgpool = nn.AvgPool2d(8, stride=1)
        self.fc = nn.Linear(64 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion)
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        layer1_output = self.layer1(x)
        layer2_output = self.layer2(layer1_output)
        layer3_output = self.layer3(layer2_output)

        x = self.avgpool(layer3_output)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x,layer1_output,layer2_output,layer3_output

    def activations(self, x):
        # outputs activation this is not necessary
        z1 = self.conv1(x)
        b1 = self.bn1(z1)
        a1 = self.relu(b1)

        layer1_output = self.layer1(a1)
        layer2_output = self.layer2(layer1_output)
        layer3_output = self.layer3(layer2_output)

        #avg = self.avgpool(layer3_output)
        #out_view = avg.view(avg.size(0), -1)
        return layer1_output,layer2_output,layer3_output






class ResNet_Cifar_Multi_Classifier(nn.Module):

    def __init__(self, block, layers, num_classes=10):
        super(ResNet_Cifar_Multi_Classifier, self).__init__()
        self.inplanes = 16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(16)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16, layers[0])
        self.prob_layer_1 = nn.Linear(16 * 32 * 32, 10)
        self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
        self.prob_layer_2 = nn.Linear(32 * 16 * 16, 10)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        self.prob_layer_3 = nn.Linear(64 * 8 * 8, 10)
        self.avgpool = nn.AvgPool2d(8, stride=1)
        self.fc = nn.Linear(64 * block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion)
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)

        layer1_output = self.layer1(x)
        linear_layer_1 = F.softmax(self.prob_layer_1(layer1_output.view(layer1_output.size(0), -1)), dim=1)

        layer2_output = self.layer2(layer1_output)
        linear_layer_2 = F.softmax(self.prob_layer_2(layer2_output.view(layer2_output.size(0), -1)), dim=1)

        layer3_output = self.layer3(layer2_output)
        linear_layer_3 = F.softmax(self.prob_layer_3(layer3_output.view(layer3_output.size(0), -1)), dim=1)


        x = self.avgpool(layer3_output)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x,linear_layer_1,linear_layer_2,linear_layer_3


class PreAct_ResNet_Cifar(nn.Module):

    def __init__(self, block, layers, num_classes=10):
        super(PreAct_ResNet_Cifar, self).__init__()
        self.inplanes = 16
        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
        self.layer1 = self._make_layer(block, 16, layers[0])
        self.layer2 = self._make_layer(block, 32, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 64, layers[2], stride=2)
        self.bn = nn.BatchNorm2d(64*block.expansion)
        self.relu = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(8, stride=1)
        self.fc = nn.Linear(64*block.expansion, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes*block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes*block.expansion, kernel_size=1, stride=stride, bias=False)
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes*block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))
        return nn.Sequential(*layers)

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

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

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

        return x




def resnet8_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [1, 1, 1], **kwargs)
    return model



def resnet14_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [2, 2, 2], **kwargs)
    return model


def resnet20_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [3, 3, 3], **kwargs)
    return model


def resnet32_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [5, 5, 5], **kwargs)
    return model


def resnet32_cifar_multi_classifier(**kwargs):
    model = ResNet_Cifar_Multi_Classifier(BasicBlock, [5, 5, 5], **kwargs)
    return model


def resnet44_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [7, 7, 7], **kwargs)
    return model


def resnet56_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [9, 9, 9], **kwargs)
    return model


def resnet110_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [18, 18, 18], **kwargs)
    return model


def resnet1202_cifar(**kwargs):
    model = ResNet_Cifar(BasicBlock, [200, 200, 200], **kwargs)
    return model


def resnet164_cifar(**kwargs):
    model = ResNet_Cifar(Bottleneck, [18, 18, 18], **kwargs)
    return model


def resnet1001_cifar(**kwargs):
    model = ResNet_Cifar(Bottleneck, [111, 111, 111], **kwargs)
    return model


def preact_resnet110_cifar(**kwargs):
    model = PreAct_ResNet_Cifar(PreActBasicBlock, [18, 18, 18], **kwargs)
    return model


def preact_resnet164_cifar(**kwargs):
    model = PreAct_ResNet_Cifar(PreActBottleneck, [18, 18, 18], **kwargs)
    return model


def preact_resnet1001_cifar(**kwargs):
    model = PreAct_ResNet_Cifar(PreActBottleneck, [111, 111, 111], **kwargs)
    return model


