import math

import torch
import torch.nn as nn


__all__ = ["ResNetBase", "make_resnet_base", "ResNet"]


"""
model_urls = {
    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
"""


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, 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):
        residual = 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:
            residual = self.downsample(x)

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

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=False
        )
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        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)
        return out


class ResNetBase(nn.Module):
    def __init__(self, block, layers):
        self.inplanes = 64
        super(ResNetBase, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)

        self.out_channels = 512 * block.expansion

        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(2.0 / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, 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=stride,
                    bias=False,
                ),
                nn.BatchNorm2d(planes * block.expansion),
            )

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

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

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

        x = x.view(x.shape[0], x.shape[1], -1).mean(dim=2)
        return x


def make_resnet18_base(**kwargs):
    """Constructs a ResNet-18 model."""
    model = ResNetBase(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model


def make_resnet34_base(**kwargs):
    """Constructs a ResNet-34 model."""
    model = ResNetBase(BasicBlock, [3, 4, 6, 3], **kwargs)
    return model


def make_resnet50_base(**kwargs):
    """Constructs a ResNet-50 model."""
    model = ResNetBase(Bottleneck, [3, 4, 6, 3], **kwargs)
    return model


def make_resnet101_base(**kwargs):
    """Constructs a ResNet-101 model."""
    model = ResNetBase(Bottleneck, [3, 4, 23, 3], **kwargs)
    return model


def make_resnet152_base(**kwargs):
    """Constructs a ResNet-152 model."""
    model = ResNetBase(Bottleneck, [3, 8, 36, 3], **kwargs)
    return model


def make_resnet_base(version, pretrained=None):
    maker = {
        "resnet18": make_resnet18_base,
        "resnet34": make_resnet34_base,
        "resnet50": make_resnet50_base,
        "resnet101": make_resnet101_base,
        "resnet152": make_resnet152_base,
    }
    resnet = maker[version]()
    if pretrained is not None:
        sd = torch.load(pretrained)
        sd.pop("fc.weight")
        sd.pop("fc.bias")
        resnet.load_state_dict(sd)
    return resnet


class ResNet(nn.Module):
    def __init__(self, version, num_classes, pretrained=None):
        super().__init__()
        self.resnet_base = make_resnet_base(version, pretrained=pretrained)
        self.fc = nn.Linear(self.resnet_base.out_channels, num_classes)

    def forward(self, x, need_features=False):
        x = self.resnet_base(x)
        feat = x
        x = self.fc(x)
        if need_features:
            return x, feat
        else:
            return x
