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


def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=dilation, groups=groups, bias=False, dilation=dilation)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
                 base_width=64, dilation=1, norm_layer=None, dcn=None):
        super(BasicBlock, self).__init__()
        if norm_layer is None:
            norm_layer = nn.BatchNorm2d
        if groups != 1 or base_width != 64:
            raise ValueError('BasicBlock only supports groups=1 and base_width=64')
        if dilation > 1:
            raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = norm_layer(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = norm_layer(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        identity = 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:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1,
                 downsample=None, norm_layer=nn.BatchNorm2d, dcn=None):
        super(Bottleneck, self).__init__()
        self.dcn = dcn
        self.with_dcn = dcn is not None

        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = norm_layer(planes, momentum=0.1)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)

        self.bn2 = norm_layer(planes, momentum=0.1)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = norm_layer(planes * 4, momentum=0.1)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = F.relu(self.bn1(self.conv1(x)), inplace=True)
        if not self.with_dcn:
            out = F.relu(self.bn2(self.conv2(out)), inplace=True)
        elif self.with_modulated_dcn:
            offset_mask = self.conv2_offset(out)
            offset = offset_mask[:, :18 * self.deformable_groups, :, :]
            mask = offset_mask[:, -9 * self.deformable_groups:, :, :]
            mask = mask.sigmoid()
            out = F.relu(self.bn2(self.conv2(out, offset, mask)))
        else:
            offset = self.conv2_offset(out)
            out = F.relu(self.bn2(self.conv2(out, offset)), inplace=True)

        out = self.conv3(out)
        out = self.bn3(out)

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

        out += residual
        out = F.relu(out)

        return out


class ResNet(nn.Module):
    """ ResNet """

    def __init__(self, architecture, norm_layer=nn.BatchNorm2d, dcn=None, stage_with_dcn=(False, False, False, False)):
        super(ResNet, self).__init__()
        self._norm_layer = norm_layer
        assert architecture in ["resnet18", "resnet34", "resnet50", "resnet101", 'resnet152']
        layers = {
            'resnet18': [2, 2, 2, 2],
            'resnet34': [3, 4, 6, 3],
            'resnet50': [3, 4, 6, 3],
            'resnet101': [3, 4, 23, 3],
            'resnet152': [3, 8, 36, 3],
        }
        self.inplanes = 64
        if architecture == "resnet18" or architecture == 'resnet34':
            self.block = BasicBlock
        else:
            self.block = Bottleneck
        self.layers = layers[architecture]

        self.conv1 = nn.Conv2d(3, 64, kernel_size=7,
                               stride=2, padding=3, bias=False)
        self.bn1 = norm_layer(64, eps=1e-5, momentum=0.1, affine=True)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        stage_dcn = [dcn if with_dcn else None for with_dcn in stage_with_dcn]

        self.layer1 = self.make_layer(
            self.block, 64, self.layers[0], dcn=stage_dcn[0])
        self.layer2 = self.make_layer(
            self.block, 128, self.layers[1], stride=2, dcn=stage_dcn[1])
        self.layer3 = self.make_layer(
            self.block, 256, self.layers[2], stride=2, dcn=stage_dcn[2])

        self.layer4 = self.make_layer(
            self.block, 512, self.layers[3], stride=2, dcn=stage_dcn[3])

    def forward(self, x):
        x = self.maxpool(self.relu(self.bn1(self.conv1(x))))  # 64 * h/4 * w/4
        x = self.layer1(x)  # 256 * h/4 * w/4
        x = self.layer2(x)  # 512 * h/8 * w/8
        x = self.layer3(x)  # 1024 * h/16 * w/16
        x = self.layer4(x)  # 2048 * h/32 * w/32
        return x

    def stages(self):
        return [self.layer1, self.layer2, self.layer3, self.layer4]

    def make_layer(self, block, planes, blocks, stride=1, dcn=None):
        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),
                self._norm_layer(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample,
                            norm_layer=self._norm_layer, dcn=dcn))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes,
                                norm_layer=self._norm_layer, dcn=dcn))

        return nn.Sequential(*layers)
