from __future__ import print_function
import os
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
import copy

from wideresnet import *
from mart_soso import mart_loss
import numpy as np
import time

os.environ["CUDA_VISIBLE_DEVICES"]="2"

parser = argparse.ArgumentParser(description='PyTorch CIFAR MART Defense')
parser.add_argument('--batch-size', type=int, default=128, metavar='N',
                    help='input batch size for training (default: 128)')
parser.add_argument('--test-batch-size', type=int, default=128, metavar='N',
                    help='input batch size for testing (default: 100)')
parser.add_argument('--epochs', type=int, default=200, metavar='N',
                    help='number of epochs to train')
parser.add_argument('--weight-decay', '--wd', default=5e-4,
                    type=float, metavar='W')
parser.add_argument('--lr', type=float, default=0.1, metavar='LR',
                    help='learning rate')
parser.add_argument('--momentum', type=float, default=0.9, metavar='M',
                    help='SGD momentum')
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')
parser.add_argument('--epsilon', default=8/255,
                    help='perturbation')
parser.add_argument('--num-steps', default=10,
                    help='perturb number of steps')
parser.add_argument('--step-size', default=2/255,
                    help='perturb step size')
parser.add_argument('--beta', default=6.0,
                    help='weight before kl (misclassified examples)')
parser.add_argument('--seed', type=int, default=1, metavar='S',
                    help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=100, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--model', default='./model-cifar10-wideresnet/0.3',
                    help='directory of model for saving checkpoint')
parser.add_argument('--save-freq', '-s', default=1, type=int, metavar='N',
                    help='save frequency')

args = parser.parse_args()

# settings
model_dir = args.model
if not os.path.exists(model_dir):
    os.makedirs(model_dir)
    
use_cuda = not args.no_cuda and torch.cuda.is_available()
torch.manual_seed(args.seed)
device = torch.device("cuda" if use_cuda else "cpu")
kwargs = {'num_workers': 10, 'pin_memory': True} if use_cuda else {}
torch.backends.cudnn.benchmark = True

# setup data loader
transform_train = transforms.Compose([
    transforms.RandomCrop(32, padding=4),
    transforms.RandomHorizontalFlip(),
    transforms.ToTensor(),
])
transform_test = transforms.Compose([
    transforms.ToTensor(),
])
trainset = torchvision.datasets.CIFAR10(root='../data/', train=True, download=True, transform=transform_train)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=args.batch_size, shuffle=True, num_workers=10)
testset = torchvision.datasets.CIFAR10(root='../data/', train=False, download=True, transform=transform_test)
test_loader = torch.utils.data.DataLoader(testset, batch_size=args.test_batch_size, shuffle=False, num_workers=10)


def train(args, model, device, train_loader, optimizer, epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        data, target = data.to(device), target.to(device)

        optimizer.zero_grad()

        # calculate robust loss
        loss = mart_loss(model=model,
                           x_natural=data,
                           y=target,
                           optimizer=optimizer,
                           step_size=args.step_size,
                           epsilon=args.epsilon,
                           perturb_steps=args.num_steps,
                           beta=args.beta)
        loss.backward()
        optimizer.step()

def _pgd_whitebox(model,
                  X,
                  y,
                  epsilon=args.epsilon,
                  num_steps=args.num_steps,
                  step_size=args.step_size):
    model.eval()
    out = model(X)

    X_pgd = Variable(X.data, requires_grad=True)

    random_noise = torch.FloatTensor(*X_pgd.shape).uniform_(-epsilon, epsilon).to(device)
    X_pgd = Variable(X_pgd.data + random_noise, requires_grad=True)

    for _ in range(num_steps):
        opt = optim.SGD([X_pgd], lr=1e-3)
        opt.zero_grad()

        with torch.enable_grad():
            loss2 = nn.CrossEntropyLoss()(model(X_pgd), y)
        loss2.backward()
        eta = step_size * X_pgd.grad.data.sign()
        X_pgd = Variable(X_pgd.data + eta, requires_grad=True)
        eta = torch.clamp(X_pgd.data - X.data, -epsilon, epsilon)
        X_pgd = Variable(X.data + eta, requires_grad=True)
        X_pgd = Variable(torch.clamp(X_pgd, 0, 1.0), requires_grad=True)
    return X_pgd

def eval_test(model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            
            X, y = Variable(data, requires_grad=True), Variable(target)
            
            output = model(X)
            test_loss += F.cross_entropy(output, target, size_average=False).item()
            pred = output.max(1, keepdim=True)[1]
            correct += pred.eq(target.view_as(pred)).sum().item()
    test_loss /= len(test_loader.dataset)
    test_accuracy = correct / len(test_loader.dataset)
    return test_loss, test_accuracy

def adjust_learning_rate(optimizer, epoch):
    """decrease the learning rate"""
    lr = args.lr
    if epoch >= 100:
        lr = args.lr * 0.1
    if epoch >= 150:
        lr = args.lr * 0.01
    for param_group in optimizer.param_groups:
        param_group['lr'] = lr

def main():
    # init model, ResNet18() can be also used here for training
    model = WideResNet().to(device)
    #model = PreActResNet18().to(device)
    optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
    
    tstt = []
    for epoch in range(1, args.epochs + 1):
        start_time = time.time()
        # adjust learning rate for SGD
        adjust_learning_rate(optimizer, epoch)

        # adversarial training
        train(args, model, device, train_loader, optimizer, epoch)

        # evaluation on natural examples
        tstloss, tstacc = eval_test(model, device, test_loader)
        tstt.append(tstacc)
        
        print('Epoch '+str(epoch)+': '+str(int(time.time()-start_time))+'s', end=', ')
        #print('trn_loss: {:.4f}, trn_acc: {:.2f}%'.format(trnloss, 100. * trnacc), end=', ')
        print('test_loss: {:.4f}, test_acc: {:.2f}%'.format(tstloss, 100. * tstacc))
        #print('test_adv_loss: {:.4f}, test_adv_acc: {:.2f}%'.format(tst_adv_loss, 100. * tst_adv_acc))

        # save checkpoint
        if (epoch>99 and epoch%5==0) or (epoch>99 and epoch<110) or (epoch>149 and epoch<160) or (epoch>99 and tstacc==max(tstt)):
            torch.save(model.state_dict(),
                       os.path.join(model_dir, 'epoch{}.pt'.format(epoch)))

if __name__ == '__main__':
    main()