import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import extension as my

__all__ = ['ResNet_XBNBlock_P1', 'resnet_XBNBlock_P1_18', 'resnet_XBNBlock_P1_34', 'resnet_XBNBlock_P1_50', 'resnet_XBNBlock_P1_101', 'resnet_XBNBlock_P1_152']



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_XBNBlock_P1(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock_XBNBlock_P1, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        #self.bn1 = nn.BatchNorm2d(planes)
        self.bn1 = my.Norm(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_XBNBlock_P1(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck_XBNBlock_P1, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = my.Norm(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 ResNet_XBNBlock_P1(nn.Module):

    def __init__(self, block, layers, num_classes=1000, **kwargs):
        self.inplanes = 64
        super(ResNet_XBNBlock_P1, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = my.Norm(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.avgpool = nn.AvgPool2d(7, stride=1)
        if kwargs.setdefault('last', False):
            self.last_bn = my.Norm(512 * block.expansion, dim=2)
        else:
            self.last_bn = None
        drop_ratio=kwargs.setdefault('dropout', 0)
        self.dropout = nn.Dropout(p=drop_ratio) if drop_ratio > 0 else None
        self.fc = nn.Linear(512 * block.expansion, num_classes)

        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. / 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 = self.avgpool(x)
        x = x.view(x.size(0), -1)
        if self.last_bn is not None:
            x = self.last_bn(x)
        if self.dropout is not None:
            x=self.dropout(x)
        x = self.fc(x)

        return x


def resnet_XBNBlock_P1_18(pretrained=False, **kwargs):
    """Constructs a ResNet_XBNBlock_P1-18 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_XBNBlock_P1(BasicBlock_XBNBlock_P1, [2, 2, 2, 2], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet_XBNBlock_P1_18']))
    return model


def resnet_XBNBlock_P1_34(pretrained=False, **kwargs):
    """Constructs a ResNet_XBNBlock_P1-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_XBNBlock_P1(BasicBlock_XBNBlock_P1, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet_XBNBlock_P1_34']))
    return model


def resnet_XBNBlock_P1_50(pretrained=False, **kwargs):
    """Constructs a ResNet_XBNBlock_P1-50 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_XBNBlock_P1(Bottleneck_XBNBlock_P1, [3, 4, 6, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet_XBNBlock_P1_50']))
    return model


def resnet_XBNBlock_P1_101(pretrained=False, **kwargs):
    """Constructs a ResNet_XBNBlock_P1-101 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_XBNBlock_P1(Bottleneck_XBNBlock_P1, [3, 4, 23, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet_XBNBlock_P1_101']))
    return model


def resnet_XBNBlock_P1_152(pretrained=False, **kwargs):
    """Constructs a ResNet_XBNBlock_P1-152 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet_XBNBlock_P1(Bottleneck_XBNBlock_P1, [3, 8, 36, 3], **kwargs)
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet_XBNBlock_P1_152']))
    return model
