# Deep Residual Learning for Image Recognition, CVPR 2016
import torch.nn as nn
from .initialization import initialize_resnet


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


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


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(
        self, inplanes, planes, stride=1, downsample=None, groups=1, base_width=64
    ):
        super(BasicBlock, self).__init__()
        if groups != 1 or base_width != 64:
            raise ValueError("BasicBlock only supports groups=1 and base_width=64")
        # Both self.conv1 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(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, groups=1, base_width=64
    ):
        super(Bottleneck, self).__init__()
        width = int(planes * (base_width / 64.0)) * groups
        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
        self.conv1 = conv1x1(inplanes, width)
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = conv3x3(width, width, stride, groups)
        self.bn2 = nn.BatchNorm2d(width)
        self.conv3 = conv1x1(width, planes * self.expansion)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu = nn.ReLU(inplace=True)
        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)
        out = self.relu(out)

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

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

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

        return out


class ResNet(nn.Module):
    def __init__(
        self,
        block_name,
        layers,
        deep_stem,
        num_classes,
        zero_init_residual,
        groups,
        width_per_group,
    ):
        super(ResNet, self).__init__()

        # planes = [int(width_per_group * groups * 2 ** i) for i in range(4)]
        if block_name == "BasicBlock":
            block = BasicBlock
        elif block_name == "Bottleneck":
            block = Bottleneck
        else:
            raise ValueError("invalid block-name : {:}".format(block_name))

        if not deep_stem:
            self.conv = nn.Sequential(
                nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False),
                nn.BatchNorm2d(64),
                nn.ReLU(inplace=True),
            )
        else:
            self.conv = nn.Sequential(
                nn.Conv2d(3, 32, kernel_size=3, stride=2, padding=1, bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
                nn.Conv2d(32, 32, kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(inplace=True),
                nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1, bias=False),
                nn.BatchNorm2d(64),
                nn.ReLU(inplace=True),
            )
        self.inplanes = 64
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(
            block, 64, layers[0], stride=1, groups=groups, base_width=width_per_group
        )
        self.layer2 = self._make_layer(
            block, 128, layers[1], stride=2, groups=groups, base_width=width_per_group
        )
        self.layer3 = self._make_layer(
            block, 256, layers[2], stride=2, groups=groups, base_width=width_per_group
        )
        self.layer4 = self._make_layer(
            block, 512, layers[3], stride=2, groups=groups, base_width=width_per_group
        )
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.message = (
            "block = {:}, layers = {:}, deep_stem = {:}, num_classes = {:}".format(
                block, layers, deep_stem, num_classes
            )
        )

        self.apply(initialize_resnet)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves like an identity.
        # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
        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, blocks, stride, groups, base_width):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            if stride == 2:
                downsample = nn.Sequential(
                    nn.AvgPool2d(kernel_size=2, stride=2, padding=0),
                    conv1x1(self.inplanes, planes * block.expansion, 1),
                    nn.BatchNorm2d(planes * block.expansion),
                )
            elif stride == 1:
                downsample = nn.Sequential(
                    conv1x1(self.inplanes, planes * block.expansion, stride),
                    nn.BatchNorm2d(planes * block.expansion),
                )
            else:
                raise ValueError("invalid stride [{:}] for downsample".format(stride))

        layers = []
        layers.append(
            block(self.inplanes, planes, stride, downsample, groups, base_width)
        )
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes, 1, None, groups, base_width))

        return nn.Sequential(*layers)

    def get_message(self):
        return self.message

    def forward(self, x):
        x = self.conv(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        features = self.avgpool(x)
        features = features.view(features.size(0), -1)
        logits = self.fc(features)

        return features, logits