import torch.nn as nn
import torch.nn.functional as F
import math
import torch.utils.model_zoo as model_zoo
from models.adaptive_avgmax_pool import AdaptiveAvgMaxPool2d

__all__ = ['ResNeXt', 'resnext50', 'resnext101', 'resnext152']


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 ResNeXtBottleneckC(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, cardinality=32, base_width=4):
        super(ResNeXtBottleneckC, self).__init__()

        width = math.floor(planes * (base_width / 64)) * cardinality

        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width)
        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,
                               padding=1, bias=False, groups=cardinality)
        self.bn2 = nn.BatchNorm2d(width)
        self.conv3 = nn.Conv2d(width, 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 ResNeXt(nn.Module):

    def __init__(self, block, layers, num_classes=1000, cardinality=32, base_width=4,
                 drop_rate=0., global_pool='avg'):
        self.num_classes = num_classes
        self.inplanes = 64
        self.cardinality = cardinality
        self.base_width = base_width
        self.drop_rate = drop_rate
        super(ResNeXt, 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.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
        self.num_features = 512 * block.expansion
        self.fc = nn.Linear(self.num_features, num_classes)

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1.)
                nn.init.constant_(m.bias, 0.)

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

        return nn.Sequential(*layers)

    def get_classifier(self):
        return self.fc

    def reset_classifier(self, num_classes, global_pool='avg'):
        self.avgpool = AdaptiveAvgMaxPool2d(pool_type=global_pool)
        self.num_classes = num_classes
        del self.fc
        if num_classes:
            self.fc = nn.Linear(self.num_features, num_classes)
        else:
            self.fc = None

    def forward_features(self, x, pool=True):
        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)
        if pool:
            x = self.avgpool(x)
            x = x.view(x.size(0), -1)
        return x

    def forward(self, x):
        x = self.forward_features(x)
        if self.drop_rate > 0.:
            x = F.dropout(x, p=self.drop_rate, training=self.training)
        x = self.fc(x)

        return x


def resnext50(cardinality=32, base_width=4, pretrained=False, **kwargs):
    """Constructs a ResNeXt-50 model.

    Args:
        cardinality (int): Cardinality of the aggregated transform
        base_width (int): Base width of the grouped convolution
    """
    model = ResNeXt(
        ResNeXtBottleneckC, [3, 4, 6, 3], cardinality=cardinality, base_width=base_width, **kwargs)
    return model


def resnext101(cardinality=32, base_width=4, pretrained=False, **kwargs):
    """Constructs a ResNeXt-101 model.

    Args:
        cardinality (int): Cardinality of the aggregated transform
        base_width (int): Base width of the grouped convolution
    """
    model = ResNeXt(
        ResNeXtBottleneckC, [3, 4, 23, 3], cardinality=cardinality, base_width=base_width, **kwargs)
    return model


def resnext152(cardinality=32, base_width=4, pretrained=False, **kwargs):
    """Constructs a ResNeXt-152 model.

    Args:
        cardinality (int): Cardinality of the aggregated transform
        base_width (int): Base width of the grouped convolution
    """
    model = ResNeXt(
        ResNeXtBottleneckC, [3, 8, 36, 3], cardinality=cardinality, base_width=base_width, **kwargs)
    return model