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

"""
Original Author: Wei Yang
"""

__all__ = ['wrn']

def conv3x3(in_planes, out_planes, stride=1):
    return nn.Conv2d(in_planes, out_planes, kernel_size=3,
                     stride=stride, padding=1, bias=False)

def conv1x1(in_planes, planes, stride=1):
    return nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False)

def branchBottleNeck(channel_in, channel_out, kernel_size):
    middle_channel = channel_out//4
    return nn.Sequential(
        nn.Conv2d(channel_in, middle_channel, kernel_size=1, stride=1),
        nn.BatchNorm2d(middle_channel),
        nn.ReLU(),

        nn.Conv2d(middle_channel, middle_channel, kernel_size=kernel_size, stride=kernel_size),
        nn.BatchNorm2d(middle_channel),
        nn.ReLU(),

        nn.Conv2d(middle_channel, channel_out, kernel_size=1, stride=1),
        nn.BatchNorm2d(channel_out),
        nn.ReLU(),
    )

class BasicBlock(nn.Module):
    def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
        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.droprate = dropRate
        self.equalInOut = (in_planes == out_planes)
        self.convShortcut = (not self.equalInOut) 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.equalInOut:
            x = self.relu1(self.bn1(x))
        else:
            out = self.relu1(self.bn1(x))
        out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
        if self.droprate > 0:
            out = F.dropout(out, p=self.droprate, training=self.training)
        out = self.conv2(out)
        return torch.add(x if self.equalInOut else self.convShortcut(x), out)


class NetworkBlock(nn.Module):
    def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0):
        super(NetworkBlock, self).__init__()
        self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, stride, dropRate)

    def _make_layer(self, block, in_planes, out_planes, nb_layers, stride, dropRate):
        layers = []
        for i in range(nb_layers):
            layers.append(block(i == 0 and in_planes or out_planes, out_planes, i == 0 and stride or 1, dropRate))
        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, dropRate=0.0):
        super(WideResNet, self).__init__()
        nChannels = [16, 16*widen_factor, 32*widen_factor, 64*widen_factor]
        assert (depth - 4) % 6 == 0, 'depth should be 6n+4'
        n = (depth - 4) // 6
        block = BasicBlock
        # 1st conv before any network block
        self.conv1 = nn.Conv2d(3, nChannels[0], kernel_size=3, stride=1,
                               padding=1, bias=False)
        # 1st block
        self.block1 = NetworkBlock(n, nChannels[0], nChannels[1], block, 1, dropRate)
        # 2nd block
        self.block2 = NetworkBlock(n, nChannels[1], nChannels[2], block, 2, dropRate)
        # 3rd block
        self.block3 = NetworkBlock(n, nChannels[2], nChannels[3], block, 2, dropRate)
        # global average pooling and classifier
        self.bn1 = nn.BatchNorm2d(nChannels[3])
        self.relu = nn.ReLU(inplace=True)
        self.fc = nn.Linear(nChannels[3], num_classes)
        self.nChannels = nChannels[3]


        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_()
            elif isinstance(m, nn.Linear):
                m.bias.data.zero_()

    def get_feat_modules(self):
        feat_m = nn.ModuleList([])
        feat_m.append(self.conv1)
        feat_m.append(self.block1)
        feat_m.append(self.block2)
        feat_m.append(self.block3)
        return feat_m

    def get_bn_before_relu(self):
        bn1 = self.block2.layer[0].bn1
        bn2 = self.block3.layer[0].bn1
        bn3 = self.bn1

        return [bn1, bn2, bn3]

    def forward(self, out, is_feat=False, preact=False, begin=0, end=100):
        if begin <= 0:
            out = self.conv1(out)
            f0 = out
        if end==0:
            return out

        if begin <= 1:
            out = self.block1(out)
            f1 = out
        if end==1:
            return out

        if begin <= 2:
            out = self.block2(out)
            f2 = out
        if end==2:
            return out

        if begin <= 3:
            out = self.block3(out)
            f3 = out
        if end==3:
            return out

        if begin <= 4:
            out = self.relu(self.bn1(out))
            out = F.adaptive_avg_pool2d(out, 1)
            final_fea = out
            out = out.view(-1, self.nChannels)
            f4 = out
        if end==4:
            return f4

        if begin<=5:
            out = self.fc(out)

        if is_feat:
            if preact:
                f1 = self.block2.layer[0].bn1(f1)
                f2 = self.block3.layer[0].bn1(f2)
                f3 = self.bn1(f3)
            return [f0, f1, f2, f3, f4], out
        return out

    def get_hint_channel(self):
        return self.nChannels

def wrn(**kwargs):
    """
    Constructs a Wide Residual Networks.
    """
    model = WideResNet(**kwargs)
    return model


def wrn_40_2(**kwargs):
    model = WideResNet(depth=40, widen_factor=2, **kwargs)
    return model


def wrn_40_1(**kwargs):
    model = WideResNet(depth=40, widen_factor=1, **kwargs)
    return model


def wrn_16_2(**kwargs):
    model = WideResNet(depth=16, widen_factor=2, **kwargs)
    return model


def wrn_16_1(**kwargs):
    model = WideResNet(depth=16, widen_factor=1, **kwargs)
    return model

def wrn_28_10(**kwargs):
    model = WideResNet(depth=28, widen_factor=10, **kwargs)
    return model


if __name__ == '__main__':
    import torch

    x = torch.randn(2, 3, 32, 32)
    net = wrn_28_10(num_classes=100)
    feats, logit = net(x, is_feat=True, preact=True)
    print("{} paramerters in total".format(sum(x.numel() for x in net.parameters())))

    for f in feats:
        print(f.shape, f.min().item())
    print(logit.shape)

    for m in net.get_bn_before_relu():
        if isinstance(m, nn.BatchNorm2d):
            print('pass')
        else:
            print('warning')
