import torch.nn as nn
import torch
import torch.nn.functional as F
from networks.dropblock import DropBlock

def conv3x3(in_planes, out_planes, stride = 1):
    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, args, inplanes, planes, stride = 1, downsample = None, drop_rate = 0.0, flag_drop_block = False, block_size = 1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.LeakyReLU(0.1)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = conv3x3(planes, planes)
        self.bn3 = nn.BatchNorm2d(planes)
        self.maxpool = nn.MaxPool2d(stride)
        self.downsample = downsample
        self.stride = stride
        self.drop_rate = drop_rate
        self.num_batches_tracked = 0
        self.flag_drop_block = flag_drop_block
        self.block_size = block_size

        self.DropBlock = DropBlock(args, block_size = self.block_size)

    def forward(self, x):
        self.num_batches_tracked += 1

        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)
        out = self.maxpool(out)

        if self.drop_rate > 0:
            if self.flag_drop_block == True:
                feat_size = out.size()[2]
                keep_rate = max(1.0 - self.drop_rate / (20 * 2000) * (self.num_batches_tracked), 1.0 - self.drop_rate)
                gamma = (1 - keep_rate) / self.block_size ** 2 * feat_size ** 2 / (feat_size - self.block_size + 1) ** 2
                out = self.DropBlock(out, gamma = gamma)
            else:
                out = F.dropout(out, p = self.drop_rate, training = self.training, inplace = True)

        return out



class MyNetwork(nn.Module):
    def __init__(self, args, block=BasicBlock, keep_prob=1.0, avg_pool=True, drop_rate=0.1, block_size=5):
        self.inplanes = 3
        self.args = args
        super(MyNetwork, self).__init__()

        self.layer1 = self._make_layer(block, 64, stride=2, drop_rate=drop_rate)
        self.layer2 = self._make_layer(block, 160, stride=2, drop_rate=drop_rate)
        self.layer3 = self._make_layer(block, 320, stride=2, drop_rate=drop_rate, flag_drop_block=True, block_size=block_size)
        self.layer4 = self._make_layer(block, 640, stride=2, drop_rate=drop_rate, flag_drop_block=True, block_size=block_size)
        if avg_pool:
            self.avgpool = nn.AvgPool2d(5, stride=1)
        self.keep_prob = keep_prob
        self.keep_avg_pool = avg_pool
        self.dropout = nn.Dropout(p=1 - self.keep_prob, inplace=False)
        self.drop_rate = drop_rate

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='leaky_relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _make_layer(self, block, planes, stride=1, drop_rate=0.0, flag_drop_block=False, block_size=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 = 1, bias = False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.args, self.inplanes, planes, stride, downsample, drop_rate, flag_drop_block, block_size))
        self.inplanes = planes * block.expansion

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)
        if self.keep_avg_pool:
            y = self.avgpool(x)
        y = y.view(y.size(0), -1)
        return y