import torch.nn as nn

# This ResNet network was designed following the practice of the following papers:
# TADAM: Task dependent adaptive metric for improved few-shot learning (Oreshkin et al., in NIPS 2018) and
# A Simple Neural Attentive Meta-Learner (Mishra et al., in ICLR 2018).
from torchvision.models.resnet import Bottleneck


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)
        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

    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)
        out = self.maxpool(out)

        return out


class ResNet(nn.Module):

    def __init__(self, args, block=BasicBlock,zero_init_residual=False):
        self.inplanes = 3
        super(ResNet, self).__init__()

        self.args = args
        self.layer1 = self._make_layer(block, 64, stride=2)
        self.layer2 = self._make_layer(block, 160, stride=2)
        self.layer3 = self._make_layer(block, 320, stride=2)
        self.layer4 = self._make_layer(block, 640, stride=2)

        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)
        if zero_init_residual:
            for m in self.modules():
                # if isinstance(m, Bottleneck):
                #     nn.init.constant_(m.bn3.weight, 0)
                # elif isinstance(m, BasicBlock):
                nn.init.constant_(m.bn2.weight, 0)
    def _make_layer(self, block, planes, 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=1, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        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)
        return x
