from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from torch.autograd import Function
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo

__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
           'resnet152']

model_urls = {
    'resnet18': 'https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth',
    'resnet34': 'https://s3.amazonaws.com/pytorch/models/resnet34-333f7ec4.pth',
    'resnet50': 'https://s3.amazonaws.com/pytorch/models/resnet50-19c8e357.pth',
    'resnet101':
        'https://s3.amazonaws.com/pytorch/models/resnet101-5d3b4d8f.pth',
    'resnet152':
        'https://s3.amazonaws.com/pytorch/models/resnet152-b121ed2d.pth',
}


def init_weights(m):
    classname = m.__class__.__name__
    if classname.find('Conv2d') != -1 or \
       classname.find('ConvTranspose2d') != -1:
        nn.init.kaiming_uniform_(m.weight)
        nn.init.zeros_(m.bias)
    elif classname.find('BatchNorm') != -1:
        nn.init.normal_(m.weight, 1.0, 0.02)
        nn.init.zeros_(m.bias)
    elif classname.find('Linear') != -1:
        nn.init.xavier_normal_(m.weight)


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 GradReverse(Function):
    def __init__(self, lambd):
        self.lambd = lambd

    def forward(self, x):
        return x.view_as(x)

    def backward(self, grad_output):
        return (grad_output * -self.lambd)


def grad_reverse(x, lambd=1.0):
    return GradReverse(lambd)(x)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, nobn=False):
        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
        self.nobn = nobn

    def forward(self, x, source=True):

        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 ScaleLayer(nn.Module):
    def __init__(self, init_value=1e-3):
        super(ScaleLayer, self).__init__()
        self.scale = nn.Parameter(torch.FloatTensor([init_value]))

    def forward(self, input):
        print(self.scale)
        return input * self.scale


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, nobn=False):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1,
                               stride=stride, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)

        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, 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
        self.nobn = nobn

    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 feat_bootleneck(nn.Module):
    def __init__(self, feature_dim, bottleneck_dim=256, type="bn"):
        super(feat_bootleneck, self).__init__()
        self.bn = nn.BatchNorm1d(bottleneck_dim, affine=True)
        self.relu = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(p=0.1)
        self.bottleneck = nn.Linear(feature_dim, bottleneck_dim)
        self.bottleneck.apply(init_weights)
        self.type = type

    def forward(self, x):
        x = self.bottleneck(x)
        if self.type == "bn":
            x = self.bn(x)
            x = self.dropout(x)
        return x
class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes=1000,bottleneck=256):
        self.inplanes = 64
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.in1 = nn.InstanceNorm2d(64)
        self.in2 = nn.InstanceNorm2d(128)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2,
                                    padding=0, ceil_mode=True)
        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)
        self.fc = nn.Linear(512 * block.expansion, num_classes)
        self.features=nn.Sequential(
        self.conv1 ,
        self.bn1 ,
        self.in1 ,
        self.in2 ,
        self.relu ,
        self.maxpool ,
        self.layer1 ,
        self.layer2,
        self.layer3 ,
        self.layer4 ,
        self.avgpool,
        )
        self.bottle_neck=feat_bootleneck(512 * block.expansion, bottleneck)
        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, nobn=False):
        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, nobn=nobn))
        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=self.features(x)
        x = x.view(x.size(0), -1)
        x=self.bottle_neck(x)
        return x


def resnet18(pretrained=True):
    """Constructs a ResNet-18 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2])
    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls['resnet18'])
        model_dict = model.state_dict()

        # 1. filter out unnecessary keys
        pretrained_dict = {k: v for k, v in pretrained_dict.items()
                           if k in model_dict}

        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)
    return model

def resnet34(pretrained=True,args=None):
    """Constructs a ResNet-34 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3],bottleneck=args.bottleneck)
    if pretrained:
        # model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
        pretrained_dict = model_zoo.load_url(model_urls['resnet34'])
        model_dict = model.state_dict()
        # 1. filter out unnecessary keys

        pretrained_dict = {k: v for k, v in pretrained_dict.items()
                       if k in model_dict}
        # 2. overwrite entries in the existing state dict
        model_dict.update(pretrained_dict)
        # 3. load the new state dict
        model.load_state_dict(model_dict)
    return model


def resnet50(pretrained=True,args=None):
    """Constructs a ResNet-50 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 6, 3],bottleneck=args.bottleneck)
    if pretrained:
        pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
        model_dict = model.state_dict()
        pretrained_dict = {k: v for k, v in pretrained_dict.items()
                           if k in model_dict}
        model_dict.update(pretrained_dict)
        model.load_state_dict(model_dict)
    return model


def resnet101(pretrained=False):
    """Constructs a ResNet-101 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 4, 23, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))
    return model


def resnet152(pretrained=False):
    """Constructs a ResNet-152 model.
    Args:
      pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(Bottleneck, [3, 8, 36, 3])
    if pretrained:
        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))
    return model
