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

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 = SplitBatchNorm(planes, num_splits=4)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=1, padding=1, bias=False
        )
        self.bn2 = SplitBatchNorm(planes, num_splits=4)

        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,
                ),
                SplitBatchNorm(self.expansion * planes, num_splits=4),
            )

    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 Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(in_planes, 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, self.expansion * planes, kernel_size=1, bias=False
        )
        self.bn3 = nn.BatchNorm2d(self.expansion * 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 = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        out = F.relu(out)
        return out


class ResNet18(nn.Module):
    def __init__(self, block=BasicBlock, num_blocks=None, num_classes=10, dim_aux=0):
        super(ResNet18, self).__init__()
        if num_blocks is None:
            num_blocks = [2, 2, 2, 2]
        self.in_planes = 64

        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = SplitBatchNorm(64, num_splits=8)
        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=1)
        self.avgpool = nn.AvgPool2d(4)
        self.mlp = nn.Sequential(nn.Linear(2048, 2048), nn.ReLU())
        self.fc = nn.Linear(2048, num_classes)
        self.fc_rep = nn.Linear(2048, 128)
        self.dim_aux = dim_aux
        if self.dim_aux > 0:
            hidden_size = self.fc.in_features
            self.fc_aux = nn.Linear(hidden_size, self.dim_aux)
        
        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, 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, return_feature=False, return_aux=False):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        featconv = self.layer4(out)
        out = self.avgpool(featconv)
        feat = out.view(out.size(0), -1)
        feat = self.mlp(feat)
        logits_cls = self.fc(feat)
        logits_rep = self.fc_rep(feat)
        if return_aux:
           logits_aux = self.fc_aux(feat)
           if return_feature:
              return logits_cls, logits_aux, feat, logits_rep
           else:
              return logits_cls, logits_aux
        else:
           return logits_cls