"""
    WideResNet model definition
    ported from https://github.com/meliketoy/wide-resnet.pytorch/blob/master/networks/wide_resnet.py
"""

import torchvision.transforms as transforms
import torch.nn as nn
import torch.nn.init as init
import torch.nn.functional as F
import math

from .masked_layer import MaskedConv2d, MaskedLinear

__all__ = ["WideResNet28x10"]


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


def conv_init(m):
    classname = m.__class__.__name__
    if classname.find("Conv") != -1:
        init.xavier_uniform(m.weight, gain=math.sqrt(2))
        init.constant(m.bias, 0)
    elif classname.find("BatchNorm") != -1:
        init.constant(m.weight, 1)
        init.constant(m.bias, 0)


class WideBasic(nn.Module):
    def __init__(self, in_planes, planes, dropout_rate, stride=1):
        super(WideBasic, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, padding=1, bias=True)
        self.dropout = nn.Dropout(p=dropout_rate)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(
            planes, planes, kernel_size=3, stride=stride, padding=1, bias=True
        )

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

    def forward(self, x):
        out = self.dropout(self.conv1(F.relu(self.bn1(x))))
        out = self.conv2(F.relu(self.bn2(out)))
        out += self.shortcut(x)

        return out


class WideResNet(nn.Module):
    def __init__(self, num_classes=10, depth=28, widen_factor=10, dropout_rate=0.0):
        super(WideResNet, self).__init__()
        self.in_planes = 16

        assert (depth - 4) % 6 == 0, "Wide-resnet depth should be 6n+4"
        n = (depth - 4) / 6
        k = widen_factor

        nstages = [16, 16 * k, 32 * k, 64 * k]

        self.conv1 = conv3x3(3, nstages[0])
        self.layer1 = self._wide_layer(WideBasic, nstages[1], n, dropout_rate, stride=1)
        self.layer2 = self._wide_layer(WideBasic, nstages[2], n, dropout_rate, stride=2)
        self.layer3 = self._wide_layer(WideBasic, nstages[3], n, dropout_rate, stride=2)
        self.bn1 = nn.BatchNorm2d(nstages[3], momentum=0.9)
        self.linear = nn.Linear(nstages[3], num_classes)

    def _wide_layer(self, block, planes, num_blocks, dropout_rate, stride):
        strides = [stride] + [1] * int(num_blocks - 1)
        layers = []

        for stride in strides:
            layers.append(block(self.in_planes, planes, dropout_rate, stride))
            self.in_planes = planes

        return nn.Sequential(*layers)

    def forward(self, x):
        out = self.conv1(x)
        out = self.layer1(out)
        out = self.layer2(out)
        out = self.layer3(out)
        out = F.relu(self.bn1(out))
        out = F.avg_pool2d(out, 8)
        out = out.view(out.size(0), -1)
        out = self.linear(out)

        return out


class WideResNet28x10:
    base = WideResNet
    args = list()
    kwargs = {"depth": 28, "widen_factor": 10}
    transform_train = transforms.Compose(
        [
            transforms.Resize(32),
            transforms.RandomCrop(32, padding=4),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ]
    )
    transform_test = transforms.Compose(
        [
            transforms.Resize(32),
            transforms.ToTensor(),
            transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
        ]
    )
