from collections import OrderedDict
import math
import random
import torch
import torch.nn as nn
import torch.nn.functional as F

def load_weights_sequential(target, source_state):
    new_dict = OrderedDict()
    for (k1, v1), (k2, v2) in zip(target.state_dict().items(), source_state.items()):
        new_dict[k1] = v2
    target.load_state_dict(new_dict)

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

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.relu(out)

        out = self.conv2(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, dilation=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,
                               padding=dilation, bias=False)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.relu(out)

        out = self.conv3(out)

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

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

        return out


class ResNet(nn.Module):
    def __init__(self, block, layers=(3, 4, 23, 3), inplanes=64, ratio=1):
        inplanes = int(inplanes * ratio)
        self.inplanes = inplanes
        super(ResNet, self).__init__()
        self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, inplanes*1, layers[0])
        self.layer2 = self._make_layer(block, inplanes*2, layers[1], stride=2)
        self.layer3 = self._make_layer(block, inplanes*4, layers[2], stride=1, dilation=2)
        self.layer4 = self._make_layer(block, inplanes*8, layers[3], stride=1, dilation=4)

        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, dilation=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)
            )

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

        return nn.Sequential(*layers)

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

        feature_dict = {}
        feature_dict['conv_s2'] = x

        x = self.maxpool(x)
        x = self.layer1(x)
        feature_dict['conv_s4'] = x
        x = self.layer2(x)
        feature_dict['conv_s8'] = x
        x_3 = self.layer3(x)
        # feature_dict['conv_s16'] = x
        x = self.layer4(x_3)
        # feature_dict['conv_s32'] = x

        return x, feature_dict


def resnet18(pretrained=False, ratio=1):
    model = ResNet(BasicBlock, [2, 2, 2, 2], ratio=ratio)
    return model

def resnet34(pretrained=False, ratio=1):
    model = ResNet(BasicBlock, [3, 4, 6, 3], ratio=ratio)
    return model

def resnet50(pretrained=False, ratio=1):
    model = ResNet(Bottleneck, [3, 4, 6, 3], ratio=ratio)
    return model

def resnet101(pretrained=False, ratio=1):
    model = ResNet(Bottleneck, [3, 4, 23, 3], ratio=ratio)
    return model

def resnet152(pretrained=False, ratio=1):
    model = ResNet(Bottleneck, [3, 8, 36, 3], ratio=ratio)
    return model