# flake8: noqa

"""ResNet in PyTorch.
ImageNet-Style ResNet
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
    Deep Residual Learning for Image Recognition. arXiv:1512.03385
Adapted from: https://github.com/bearpaw/pytorch-classification
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from geowatch.tasks.rutgers_material_seg.models.tex_refine import TeRN


class ASPP(nn.Module):

    def __init__(self, C, depth, num_classes, conv=nn.Conv2d,
                 norm=nn.BatchNorm2d, momentum=0.0003, mult=1):
        super(ASPP, self).__init__()
        self._C = C
        self._depth = depth
        self._num_classes = num_classes

        self.global_pooling = nn.AdaptiveAvgPool2d(1)
        self.relu = nn.ReLU(inplace=True)
        self.aspp1 = conv(C, depth, kernel_size=1, stride=1, bias=False)
        self.aspp2 = conv(C, depth, kernel_size=3, stride=1,
                          dilation=int(6 * mult), padding=int(6 * mult),
                          bias=False)
        self.aspp3 = conv(C, depth, kernel_size=3, stride=1,
                          dilation=int(12 * mult), padding=int(12 * mult),
                          bias=False)
        self.aspp4 = conv(C, depth, kernel_size=3, stride=1,
                          dilation=int(18 * mult), padding=int(18 * mult),
                          bias=False)
        self.aspp5 = conv(C, depth, kernel_size=1, stride=1, bias=False)
        self.aspp1_bn = norm(depth, momentum)
        self.aspp2_bn = norm(depth, momentum)
        self.aspp3_bn = norm(depth, momentum)
        self.aspp4_bn = norm(depth, momentum)
        self.aspp5_bn = norm(depth, momentum)
        self.conv2 = conv(depth * 5, depth, kernel_size=1, stride=1,
                          bias=False)
        self.bn2 = norm(depth, momentum)
        self.conv3 = nn.Conv2d(depth, num_classes, kernel_size=1, stride=1)

    def forward(self, x):
        x1 = self.aspp1(x)
        x1 = self.aspp1_bn(x1)
        x1 = self.relu(x1)
        x2 = self.aspp2(x)
        x2 = self.aspp2_bn(x2)
        x2 = self.relu(x2)
        x3 = self.aspp3(x)
        x3 = self.aspp3_bn(x3)
        x3 = self.relu(x3)
        x4 = self.aspp4(x)
        x4 = self.aspp4_bn(x4)
        x4 = self.relu(x4)
        x5 = self.global_pooling(x)
        x5 = self.aspp5(x5)
        x5 = self.aspp5_bn(x5)
        x5 = self.relu(x5)
        x5 = nn.Upsample((x.shape[2], x.shape[3]), mode='bilinear',
                         align_corners=True)(x5)
        x = torch.cat((x1, x2, x3, x4, x5), 1)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.conv3(x)

        return x


class DoubleConv(nn.Module):
    """(convolution => [BN] => ReLU) * 2"""

    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.double_conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.GroupNorm(32, out_channels),
            nn.LeakyReLU(0.2),
            nn.Dropout(0.5),
            nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),
            nn.GroupNorm(32, out_channels),
            nn.LeakyReLU(0.2),
        )

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


class Up(nn.Module):
    """Upscaling then double conv"""

    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()

        # if bilinear, use the normal convolutions to reduce the number of
        # channels
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels // 2, in_channels // 2, kernel_size=2, stride=2)

        self.conv = DoubleConv(in_channels, out_channels)
        # Given transposed=1, weight of size [48, 48, 2, 2], 48 -> 32+64//2, instead,
        # expected input[4, 64, 128, 128] to have 48 channels, but got 64
        # channels instead

    def forward(self, x1, x2):
        # print(x1.shape)
        x1 = self.up(x1)
        # input is CHW
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]

        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])

        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, is_last=False):
        super(BasicBlock, self).__init__()
        self.is_last = is_last
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, 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.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        out += self.shortcut(x)
        preact = out
        out = F.relu(out)
        if self.is_last:
            return out, preact
        else:
            return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, in_planes, planes, stride=1, is_last=False):
        super(Bottleneck, self).__init__()
        self.is_last = is_last
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(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, self.expansion * planes, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(self.expansion * planes)

        self.shortcut = nn.Sequential()
        if stride != 1 or in_planes != self.expansion * planes:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(self.expansion * planes)
            )

    def forward(self, x):
        out = F.relu(self.bn1(self.conv1(x)))
        out = F.relu(self.bn2(self.conv2(out)))
        out = self.bn3(self.conv3(out))
        out += self.shortcut(x)
        preact = out
        out = F.relu(out)
        if self.is_last:
            return out, preact
        else:
            return out


class ResNet(nn.Module):
    def __init__(self, block, num_blocks, num_channels=3, zero_init_residual=False,
                pretrained=False, num_classes=None, beta=False, weight_std=False,
                num_groups=32, out_dim=128, feats=[64, 128, 256, 512, 256]):
        super(ResNet, self).__init__()
        self.in_planes = 64

        self.conv1_diff = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1,
                               bias=False)
        self.conv1_cat = nn.Conv2d(num_channels, 64, kernel_size=3, stride=1, padding=1,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
        self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
        self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
        self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
        self.drop1 = nn.Dropout(0.35)
        self.drop2 = nn.Dropout(0.2)
        self.drop3 = nn.Dropout(0.35)
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        # self.fc = nn.Linear(512 * block.expansion, num_classes)
        # def _norm(planes, momentum=0.05):
        #     return nn.BatchNorm2d(planes, momentum=momentum)
        # self.norm = _norm
        # self.conv = nn.Conv2d
        self.aspp = ASPP(feats[3], 256, 256)
        self._aff = TeRN(num_iter=10, dilations=[1, 1, 2, 4, 6, 8])

        self.up1 = Up(feats[4] + feats[3], feats[3], bilinear=True)
        self.up2 = Up(feats[2] + feats[3], feats[2], bilinear=True)
        self.up3 = Up(feats[1] + feats[2], feats[1], bilinear=True)
        self.up4 = Up(feats[0] + feats[1], feats[0], bilinear=True)
        self.outconv = nn.Conv2d(feats[0], num_classes, kernel_size=1, stride=1, bias=False)

        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.GroupNorm)):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        # Zero-initialize the last BN in each residual branch,
        # so that the residual branch starts with zeros, and each residual block behaves
        # like an identity. This improves the model by 0.2~0.3% according to:
        # https://arxiv.org/abs/1706.02677
        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1] * (num_blocks - 1)
        layers = []
        for i in range(num_blocks):
            stride = strides[i]
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        return nn.Sequential(*layers)

    def forward(self, img1, img2, layer=100):
        x1_1 = F.relu(self.bn1(self.conv1_diff(img1)))
        x1_2 = F.relu(self.bn1(self.conv1_diff(img2)))
        # x1_cont = torch.abs(x1_1 - x1_2)
        x11_1 = self.layer1(x1_1)
        x11_2 = self.layer1(x1_2)
        x1_cont = torch.abs(x11_1 - x11_2)

        # x2 = self.layer2(x1)
        x2_1 = self.layer2(x11_1)
        x2_2 = self.layer2(x11_2)
        x2_cont = torch.abs(x2_1 - x2_2)
        x3_1 = self.layer3(x2_1)
        x3_2 = self.layer3(x2_2)
        x3_cont = torch.abs(x3_1 - x3_2)
        x4_1 = self.layer4(x3_1)
        x4_2 = self.layer4(x3_2)
        x4_cont = torch.abs(x4_1 - x4_2)
        x4_cont = self.avgpool(x4_cont)
        x_feats = torch.flatten(x4_cont, 1)
        x_aspp = self.aspp(x4_cont)
        x = self.up1(x_aspp, x4_cont)
        x = self.drop1(x)
        x = self.up2(x, x3_cont)
        # x = self.drop2(x)
        x = self.up3(x, x2_cont)
        # x = self.drop3(x)
        x = self.up4(x, x1_cont)
        x = self.outconv(x)
        # classifer = self.fc(x)

        return x  # , x_feats

    # def forward(self, x, layer=100):
    #     # x1 = F.relu(self.bn1(self.conv1(x)))
    #     x1 = F.relu(self.bn1(self.conv1_cat(x)))

    #     # x1 = self._aff(x, x1)

    #     x1 = self.layer1(x1)
    #     x2 = self.layer2(x1)
    #     x3 = self.layer3(x2)
    #     x4 = self.layer4(x3)
    #     x4 = self.avgpool(x4)
    #     x_feats = torch.flatten(x4, 1)
    #     x_aspp = self.aspp(x4)
    #     x = self.up1(x_aspp, x4)
    #     x = self.up2(x, x3)
    #     x = self.up3(x, x2)
    #     x = self.up4(x, x1)
    #     x = self.outconv(x)
    #     # classifer = self.fc(x)

    #     return x, x_feats


def resnet18(**kwargs):
    return ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)


def resnet34(pretrained=False, **kwargs):
    """Constructs a ResNet-34 model.

    Args:
        pretrained (bool): If True, returns a model pre-trained on ImageNet
    """
    model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
    # if pretrained:
    #     model.load_state_dict(model_zoo.load_url(model_urls['resnet50']))
    if pretrained:
        model_dict = model.state_dict()
        # /home/native/projects/data/smart_watch/models/experiments_onera/tasks_experiments_onera_2021-10-18-13:27/experiments_epoch_0_loss_11.28138166103723_valmF1_0.6866047574166068_valChangeF1_0.49019877611815305_time_2021-10-18-14:15:27.pth
#
        # pretrained_path = "/home/native/projects/data/smart_watch/models/experiments_onera/tasks_experiments_onera_2021-10-07-10:23/experiments_epoch_8_loss_3394.9326448260613_valmIoU_0.5388350590429163_time_2021-10-07-22:05:00.pth"
        pretrained_path = "/home/native/projects/data/smart_watch/models/experiments_onera/tasks_experiments_onera_2021-10-18-13:27/experiments_epoch_0_loss_11.28138166103723_valmF1_0.6866047574166068_valChangeF1_0.49019877611815305_time_2021-10-18-14:15:27.pth"
        pretrained_dict = torch.load(pretrained_path)['model']
        # pretrained_dict = model_zoo.load_url(model_urls['resnet50'])
        overlap_dict = {k[7:]: v for k, v in pretrained_dict.items()
                        if k[7:] in model_dict}
        # for k, v in overlap_dict.items():
        #     v.requires_grad=False
        model_dict.update(overlap_dict)
        model.load_state_dict(model_dict)
        print(f"loaded {len(overlap_dict)}/{len(pretrained_dict)} layers")
    return model


def resnet50(**kwargs):
    return ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)


def resnet101(**kwargs):
    return ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)


model_dict = {
    'resnet18': [resnet18, 512],
    'resnet34': [resnet34, 512],
    'resnet50': [resnet50, 2048],
    'resnet101': [resnet101, 2048],
}