import torch
import torch.nn as nn
import numpy as np


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 BasicBlock(nn.Module):
    expansion = 1

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

    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)

        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):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, 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, 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

        # TODO hack for old model
        self.conv1.padding_mode = 'zeros'
        self.conv2.padding_mode = 'zeros'
        self.conv3.padding_mode = 'zeros'
        if self.downsample is not None:
            self.downsample[0].padding_mode = 'zeros'

        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 SELayer(nn.Module):
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1, 1)
        return x * y


class BottleneckSE(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
        super(BottleneckSE, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se = SELayer(planes * 4, reduction)
        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)
        out = self.se(out)

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

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

        return out


class CBAMModule(nn.Module):
    def __init__(self, channels, reduction):
        super(CBAMModule, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1,
                             padding=0)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1,
                             padding=0)
        self.sigmoid_channel = nn.Sigmoid()
        self.conv_after_concat = nn.Conv2d(2, 1, kernel_size=7, stride=1, padding=3)
        self.sigmoid_spatial = nn.Sigmoid()

    def forward(self, x):
        module_input = x
        avg = self.avg_pool(x)
        mx = self.max_pool(x)
        avg = self.fc1(avg)
        mx = self.fc1(mx)
        avg = self.relu(avg)
        mx = self.relu(mx)
        avg = self.fc2(avg)
        mx = self.fc2(mx)
        x = avg + mx
        x = self.sigmoid_channel(x)
        x = module_input * x
        module_input = x
        avg = torch.mean(x, 1, True)
        mx, _ = torch.max(x, 1, True)
        x = torch.cat((avg, mx), 1)
        x = self.conv_after_concat(x)
        x = self.sigmoid_spatial(x)
        x = module_input * x
        return x


class BottleneckCBAM(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, reduction=16):
        super(BottleneckCBAM, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, 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, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.se = CBAMModule(planes * 4, reduction)
        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)
        out = self.se(out)

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

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

        return out


class BBoxTransform(nn.Module):
    def __init__(self, mean=None, std=None, is_cuda=True):
        super(BBoxTransform, self).__init__()
        if mean is None:
            self.mean = torch.from_numpy(np.array([0, 0, 0, 0]).astype(np.float32))
            if is_cuda:
                self.mean = self.mean.cuda()
        else:
            self.mean = mean
        if std is None:
            self.std = torch.from_numpy(np.array([0.1, 0.1, 0.2, 0.2]).astype(np.float32))
            if is_cuda:
                self.std = self.std.cuda()
        else:
            self.std = std

    def forward(self, boxes, deltas):
        widths = boxes[:, :, 2] - boxes[:, :, 0]
        heights = boxes[:, :, 3] - boxes[:, :, 1]
        ctr_x = boxes[:, :, 0] + 0.5 * widths
        ctr_y = boxes[:, :, 1] + 0.5 * heights

        dx = deltas[:, :, 0] * self.std[0] + self.mean[0]
        dy = deltas[:, :, 1] * self.std[1] + self.mean[1]
        dw = deltas[:, :, 2] * self.std[2] + self.mean[2]
        dh = deltas[:, :, 3] * self.std[3] + self.mean[3]

        pred_ctr_x = ctr_x + dx * widths
        pred_ctr_y = ctr_y + dy * heights
        pred_w = torch.exp(dw) * widths
        pred_h = torch.exp(dh) * heights

        pred_boxes_x1 = pred_ctr_x - 0.5 * pred_w
        pred_boxes_y1 = pred_ctr_y - 0.5 * pred_h
        pred_boxes_x2 = pred_ctr_x + 0.5 * pred_w
        pred_boxes_y2 = pred_ctr_y + 0.5 * pred_h

        pred_boxes = torch.stack([pred_boxes_x1, pred_boxes_y1, pred_boxes_x2, pred_boxes_y2], dim=2)

        return pred_boxes


class ClipBoxes(nn.Module):
    def __init__(self):
        super(ClipBoxes, self).__init__()

    def forward(self, boxes, img):
        batch_size, num_channels, height, width = img.shape

        boxes[:, :, 0] = torch.clamp(boxes[:, :, 0], min=0)
        boxes[:, :, 1] = torch.clamp(boxes[:, :, 1], min=0)

        boxes[:, :, 2] = torch.clamp(boxes[:, :, 2], max=width)
        boxes[:, :, 3] = torch.clamp(boxes[:, :, 3], max=height)

        return boxes