import argparse
import torch
import torch.nn as nn
import functools

from inplace_abn import InPlaceABN, InPlaceABNSync
BatchNorm2d = functools.partial(InPlaceABNSync, activation='identity')


class Bottleneck(nn.Module):
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False)
        self.bn2 = BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=False)
        self.relu_inplace = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.dilation = dilation
        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 = out + residual
        out = self.relu_inplace(out)

        return out


class CCNet_resnet50(nn.Module):
    def __init__(self, block, layers):
        self.inplanes = 128
        super(CCNet_resnet50, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=False)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn2 = BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=False)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn3 = BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=False)

        self.relu = nn.ReLU(inplace=False)
#         self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1, ceil_mode=True) # change
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True) # change
        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=1, dilation=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4, multi_grid=(1,1,1))

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=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),
                BatchNorm2d(planes * block.expansion,affine = True))

        layers = []
        generate_multi_grid = lambda index, grids: grids[index%len(grids)] if isinstance(grids, tuple) else 1
        layers.append(block(self.inplanes, planes, stride,dilation=dilation, downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation, multi_grid=generate_multi_grid(i, multi_grid)))

        return nn.Sequential(*layers)

    def forward(self, x):
        outs = []

        x = self.relu1(self.bn1(self.conv1(x)))
        outs.append(x)

        x = self.relu2(self.bn2(self.conv2(x)))
        outs.append(x)

        x = self.relu3(self.bn3(self.conv3(x)))
        outs.append(x)

        x = self.maxpool(x)
        outs.append(x)

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

        return outs

class encoder(nn.Module):
    def __init__(self, params):
        super(encoder, self).__init__()
        self.params = params
        import torchvision.models as models
        if params.encoder == 'densenet121':
            self.base_model = models.densenet121(pretrained=True).features
            self.feat_names = ['relu0', 'pool0', 'transition1', 'transition2', 'norm5']
            self.feat_out_channels = [64, 64, 128, 256, 1024]
        elif params.encoder == 'densenet161':
            self.base_model = models.densenet161(pretrained=True).features
            self.feat_names = ['relu0', 'pool0', 'transition1', 'transition2', 'norm5']
            self.feat_out_channels = [96, 96, 192, 384, 2208]
        elif params.encoder == 'resnet50':
            self.base_model = models.resnet50(pretrained=True)
            self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
            self.feat_out_channels = [64, 256, 512, 1024, 2048]
        elif params.encoder == 'resnet101':
            self.base_model = models.resnet101(pretrained=True)
            self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
            self.feat_out_channels = [64, 256, 512, 1024, 2048]
        elif params.encoder == 'resnext50':
            self.base_model = models.resnext50_32x4d(pretrained=True)
            self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
            self.feat_out_channels = [64, 256, 512, 1024, 2048]
        elif params.encoder == 'resnext101':
            self.base_model = models.resnext101_32x8d(pretrained=True)
            self.feat_names = ['relu', 'layer1', 'layer2', 'layer3', 'layer4']
            self.feat_out_channels = [64, 256, 512, 1024, 2048]
        elif params.encoder == 'mobilenetv2':
            self.base_model = models.mobilenet_v2(pretrained=True).features
            self.feat_inds = [2, 4, 7, 11, 19]
            self.feat_out_channels = [16, 24, 32, 64, 1280]
            self.feat_names = []
        elif params.encoder == 'CCNet_resnet50':
            layers = [3, 4, 23, 3]
            self.base_model = CCNet_resnet50(Bottleneck, layers)
            self.feat_out_channels = [64, 64, 128, 128, 2048]
        else:
            LOGGER.error('Not supported encoder: {}'.format(params.encoder))

    def forward(self, x):
        if self.params.encoder == 'CCNet_resnet50':
            return self.base_model(x)

        feature = x
        skip_feat = []
        i = 1
        for k, v in self.base_model._modules.items():
            if 'fc' in k or 'avgpool' in k:
                continue
            feature = v(feature)
            if self.params.encoder == 'mobilenetv2':
                if i == 2 or i == 4 or i == 7 or i == 11 or i == 19:
                    skip_feat.append(feature)
            else:
                if any(x in k for x in self.feat_names):
                    skip_feat.append(feature)
            i = i + 1
        return skip_feat
