import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torch.nn.functional as F


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

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        x = F.relu(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
    
    def bn_feature(self, x):

        x = F.relu(x)
        residual = x

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

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

        bn_feature = out

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

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

        return out, bn_feature


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, cfg, 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 * Bottleneck.expansion, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * Bottleneck.expansion)
        self.relu = nn.ReLU(inplace=True)

        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        x = F.relu(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 ResNet(nn.Module):
    def __init__(self, depth, num_classes, cfg=None, bottleneck=False):
        super(ResNet, self).__init__()

        if cfg == None:
            cfg = [16, 32, 64]

        self.inplanes = 16

        #print(bottleneck)
        if bottleneck == True:
            n = int((depth - 2) / 9)
            block = Bottleneck
        else:
            n = int((depth - 2) / 6)
            block = BasicBlock

        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.inplanes)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(block, 16, n, cfg[0])
        self.layer2 = self._make_layer(block, 32, n, cfg[1], stride=2)
        self.layer3 = self._make_layer(block, 64, n, cfg[2], stride=2)
        self.avgpool = nn.AvgPool2d(8)
        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, cfg, 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, cfg, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, cfg))


        return nn.Sequential(*layers)

    def forward(self, x):

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

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

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

        return x

    def get_bn_before_relu(self):
        if isinstance(self.layer1[0], Bottleneck):
            bn1 = self.layer1[-1].bn3
            bn2 = self.layer2[-1].bn3
            bn3 = self.layer3[-1].bn3
        elif isinstance(self.layer1[0], BasicBlock):
            bn1 = self.layer1[-1].bn2
            bn2 = self.layer2[-1].bn2
            bn3 = self.layer3[-1].bn2
        else:
            print('ResNet unknown block error !!!')

        return [bn1, bn2, bn3]

    def get_channel_num(self):

        return [16, 32, 64]

    def extract_feature(self, x, preReLU=False):

        x = x.cuda()

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

        feat1 = self.layer1(x)
        feat2 = self.layer2(feat1)
        feat3 = self.layer3(feat2)

        x = F.relu(feat3)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        #out = self.fc(x)

        if not preReLU:
            feat1 = F.relu(feat1)
            feat2 = F.relu(feat2)
            feat3 = F.relu(feat3)

        return [feat1, feat2, feat3]#, out
    
    
    def bn_feature(self,x):

        bn_feature_list = []

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

        for block in self.layer1:
            if isinstance(block,BasicBlock):
                x, bn_feature = block.bn_feature(x)
                temp = bn_feature.cpu().detach().numpy()
                bn_feature_list.append(temp)
        for block in self.layer2:
            if isinstance(block,BasicBlock):
                x, bn_feature = block.bn_feature(x)
                temp = bn_feature.cpu().detach().numpy()
                bn_feature_list.append(temp)
        for block in self.layer3:
            if isinstance(block,BasicBlock):
                x, bn_feature = block.bn_feature(x)
                temp = bn_feature.cpu().detach().numpy()
                bn_feature_list.append(temp)

        return bn_feature_list

        

    def ware(self, x):
        
        x = self.conv1(x)
        x = self.bn1(x)

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

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

        return x.cpu().detach().numpy()
