import torch
import torch.nn as nn
import math

__all__ = ['pyramidnet272']


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)


def calc_prob(curr_layer, total_layers, p_l):
    """Calculates drop prob depending on the current layer."""
    return 1 - (float(curr_layer) / total_layers) * p_l


class Bottleneck(nn.Module):
    outchannel_ratio = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None, prob=1.):
        super(Bottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(inplanes)
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        if stride == 1:
            self.conv2 = nn.Conv2d(planes, (planes * 1), kernel_size=3, stride=stride,
                                   padding=1, bias=False)
        else:
            self.conv2 = nn.Sequential(nn.ZeroPad2d((0, 1, 0, 1)),
                                       nn.Conv2d(planes, (planes * 1), kernel_size=3, stride=stride,
                                                 padding=0, bias=False))
        self.bn3 = nn.BatchNorm2d((planes * 1))
        self.conv3 = nn.Conv2d((planes * 1), planes * Bottleneck.outchannel_ratio, kernel_size=1, bias=False)
        self.bn4 = nn.BatchNorm2d(planes * Bottleneck.outchannel_ratio)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride
        self.prob = prob
        self.padding = None

    def forward(self, x):

        out = self.bn1(x)
        out = self.conv1(out)

        out = self.bn2(out)
        out = self.relu(out)
        out = self.conv2(out)

        out = self.bn3(out)
        out = self.relu(out)
        out = self.conv3(out)

        out = self.bn4(out)

        # shake drop inference
        # we may support shake drop training in a future version
        assert not self.training
        out = out * self.prob

        if self.downsample is not None:
            shortcut = self.downsample(x)
            featuremap_size = shortcut.size()[2:4]
        else:
            shortcut = x
            featuremap_size = out.size()[2:4]

        batch_size = out.size()[0]
        residual_channel = out.size()[1]
        shortcut_channel = shortcut.size()[1]
        if residual_channel != shortcut_channel:
            self.padding = torch.zeros(batch_size, residual_channel - shortcut_channel,
                                       featuremap_size[0], featuremap_size[1])
            self.padding = self.padding.to(x.device)
            out += torch.cat((shortcut, self.padding), 1)
        else:
            out += shortcut

        return out


class PyramidNet(nn.Module):

    def __init__(self, depth, alpha, num_classes):
        super(PyramidNet, self).__init__()
        self.inplanes = 16
        n = int((depth - 2) / 9)
        block = Bottleneck

        self.addrate = alpha / (3 * n * 1.0)

        self.input_featuremap_dim = self.inplanes
        self.conv1 = nn.Conv2d(3, self.input_featuremap_dim, kernel_size=3, stride=1, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(self.input_featuremap_dim)

        self.featuremap_dim = self.input_featuremap_dim

        self.p_l = 0.5
        self.layer_num = 1
        self.total_layers = n * 3

        self.layer1 = self.pyramidal_make_layer(block, n)
        self.layer2 = self.pyramidal_make_layer(block, n, stride=2)
        self.layer3 = self.pyramidal_make_layer(block, n, stride=2)

        self.final_featuremap_dim = self.input_featuremap_dim
        self.bn_final = nn.BatchNorm2d(self.final_featuremap_dim)
        self.relu_final = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(8)
        self.fc = nn.Linear(self.final_featuremap_dim, num_classes)

        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 pyramidal_make_layer(self, block, block_depth, stride=1):
        downsample = None
        if stride != 1:  # or self.inplanes != int(round(featuremap_dim_1st)) * block.outchannel_ratio:
            downsample = nn.AvgPool2d((2, 2), stride=(2, 2), ceil_mode=True)

        layers = []
        self.featuremap_dim = self.featuremap_dim + self.addrate
        prob = calc_prob(self.layer_num, self.total_layers, self.p_l)
        layers.append(block(self.input_featuremap_dim, int(round(self.featuremap_dim)), stride, downsample, prob))
        self.layer_num += 1
        for i in range(1, block_depth):
            temp_featuremap_dim = self.featuremap_dim + self.addrate
            prob = calc_prob(self.layer_num, self.total_layers, self.p_l)
            layers.append(
                block(int(round(self.featuremap_dim)) * block.outchannel_ratio, int(round(temp_featuremap_dim)), 1,
                      prob=prob))
            self.layer_num += 1
            self.featuremap_dim = temp_featuremap_dim
        self.input_featuremap_dim = int(round(self.featuremap_dim)) * block.outchannel_ratio

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)

        x = self.bn_final(x)
        x = self.relu_final(x)
        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def pyramidnet272(**kwargs):
    return PyramidNet(depth=272, alpha=200, **kwargs)

torch.nn.Linear torch.zeros torch.cat torch.nn.Sequential torch.nn.AvgPool2d torch.nn.BatchNorm2d torch.nn.ReLU torch.nn.Conv2d torch.nn.ZeroPad2d