"""
Based on code from https://github.com/locuslab/fast_adversarial/
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import torchvision.transforms as tf
from . import utils
from models.wideresnet import WideResNet

if torch.cuda.is_available():
    device = torch.device('cuda')
else:
    device = torch.device('cpu')


class DefenseFastAT(torch.nn.Module):
    file_id = '1XM-v4hqi9u8EDrQ2xdCo37XXcM9q-R07'
    destination = 'checkpoints/FastAT/'

    def __init__(self):
        super(DefenseFeaScatter, self).__init__()
        filename = os.path.join(
            self.destination, 'cifar_model_weights_30_epochs.pth')
        if not os.path.exists(filename):
            if not os.path.exists(self.destination):
                os.makedirs(self.destination, exist_ok=True)
            utils.download_file_from_google_drive(self.file_id, filename)
        checkpoint = torch.load(filename)
        state_dict = checkpoint.get('net', checkpoint)
        self.base_model = PreActResNet(PreActBlock, [2, 2, 2, 2])

        def strip_data_parallel(s):
            if s.startswith('module.basic_net.'):
                return s[len('module.basic_net.'):]
            else:
                return s
        state_dict = {strip_data_parallel(k): v for k, v in state_dict.items()}
        self.base_model.load_state_dict(state_dict)
        self.base_model.eval()

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


class PreActBlock(nn.Module):
    '''Pre-activation version of the BasicBlock.'''
    expansion = 1

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBlock, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(
            in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=1, padding=1, bias=False)
        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)
            )

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


class PreActBottleneck(nn.Module):
    '''Pre-activation version of the original Bottleneck module.'''
    expansion = 4

    def __init__(self, in_planes, planes, stride=1):
        super(PreActBottleneck, self).__init__()
        self.bn1 = nn.BatchNorm2d(in_planes)
        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
                               stride=stride, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, self.expansion
                               * planes, kernel_size=1, bias=False)

        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)
            )

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


class PreActResNet(nn.Module):
    def __init__(self, block, num_blocks, num_classes=10):
        super(PreActResNet, self).__init__()
        self.in_planes = 64
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3,
                               stride=1, padding=1, bias=False)
        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.bn = nn.BatchNorm2d(512 * block.expansion)
        self.linear = nn.Linear(512 * block.expansion, num_classes)

    def _make_layer(self, block, planes, num_blocks, stride):
        strides = [stride] + [1]*(num_blocks-1)
        layers = []
        for stride in strides:
            layers.append(block(self.in_planes, planes, stride))
            self.in_planes = planes * block.expansion
        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 = self.layer4(out)
        out = F.relu(self.bn(out))
        out = F.avg_pool2d(out, 4)
        out = out.view(out.size(0), -1)
        out = self.linear(out)
        return out


def PreActResNet18():
    return PreActResNet(PreActBlock, [2, 2, 2, 2])
