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

# Training settings
parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
parser.add_argument('--batch-size', type=int, default=64, metavar='N',
                    help='input batch size for training (default: 64)')
parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N',
                    help='input batch size for testing (default: 1000)')
parser.add_argument('--epochs', type=int, default=10, metavar='N',
                    help='number of epochs to train (default: 10)')
parser.add_argument('--lr', type=float, default=0.001, metavar='LR',
                    help='learning rate (default: 0.001)')
parser.add_argument('--no-cuda', action='store_true', default=False,
                    help='disables CUDA training')
parser.add_argument('--seed', type=int, default=1, metavar='S',
                    help='random seed (default: 1)')
parser.add_argument('--log-interval', type=int, default=10, metavar='N',
                    help='how many batches to wait before logging training status')
parser.add_argument('--dni', action='store_true', default=False,
                    help='enable DNI')
parser.add_argument('--context', action='store_true', default=False,
                    help='enable context (label conditioning) in DNI')
args = parser.parse_args()
args.cuda = not args.no_cuda and torch.cuda.is_available()

torch.manual_seed(args.seed)
if args.cuda:
    torch.cuda.manual_seed(args.seed)


kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
train_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=True, download=True,
                   transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.batch_size, shuffle=True, **kwargs)
test_loader = torch.utils.data.DataLoader(
    datasets.MNIST('../data', train=False, transform=transforms.Compose([
                       transforms.ToTensor(),
                       transforms.Normalize((0.1307,), (0.3081,))
                   ])),
    batch_size=args.test_batch_size, shuffle=True, **kwargs)


def one_hot(indexes, n_classes):
    result = torch.FloatTensor(indexes.size() + (n_classes,))
    if args.cuda:
        result = result.cuda()
    result.zero_()
    indexes_rank = len(indexes.size())
    result.scatter_(
        dim=indexes_rank,
        index=indexes.data.unsqueeze(dim=indexes_rank),
        value=1
    )
    return Variable(result)


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.hidden1 = nn.Linear(784, 256, bias=False)
        self.hidden1_bn = nn.BatchNorm1d(256)
        self.hidden2 = nn.Linear(256, 256, bias=False)
        self.hidden2_bn = nn.BatchNorm1d(256)
        if args.dni:
            if args.context:
                context_dim = 10
            else:
                context_dim = None
            self.bidirectional_interface = dni.BidirectionalInterface(
                dni.BasicSynthesizer(
                    output_dim=256, n_hidden=2, trigger_dim=784,
                    context_dim=context_dim
                ),
                dni.BasicSynthesizer(
                    output_dim=256, n_hidden=2, context_dim=context_dim
                )
            )
        self.output = nn.Linear(256, 10, bias=False)
        self.output_bn = nn.BatchNorm1d(10)

    def forward(self, x, y=None):
        input_flat = x.view(x.size()[0], -1)
        x = self.hidden1_bn(self.hidden1(input_flat))
        x = self.hidden2_bn(self.hidden2(F.relu(x)))
        if args.dni and self.training:
            if args.context:
                context = one_hot(y, 10)
            else:
                context = None
            with dni.synthesizer_context(context):
                x = self.bidirectional_interface(x, input_flat)
        x = self.output_bn(self.output(F.relu(x)))
        return F.log_softmax(x)

model = Net()
if args.cuda:
    model.cuda()

optimizer = optim.Adam(model.parameters(), lr=args.lr)

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data, target)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.log_interval == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.data[0]))

def test():
    model.eval()
    test_loss = 0
    correct = 0
    for data, target in test_loader:
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        data, target = Variable(data, volatile=True), Variable(target)
        output = model(data)
        test_loss += F.nll_loss(output, target, size_average=False).data[0] # sum up batch loss
        pred = output.data.max(1, keepdim=True)[1] # get the index of the max log-probability
        correct += pred.eq(target.data.view_as(pred)).cpu().sum()

    test_loss /= len(test_loader.dataset)
    print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
        test_loss, correct, len(test_loader.dataset),
        100. * correct / len(test_loader.dataset)))


for epoch in range(1, args.epochs + 1):
    train(epoch)
    test()
