# The model with 50 stochastic binary elements,
# 5 ands used all of the 50 binary elements, and the final expression pipe them together with ors

# But we have a problem. The gradient vanishes. The architecture does not seem to preserve
# the gradient very well.

# Let's try a simplified model with Ands.
# If we fail, we can go read the XOR paper or look into LSTM implmenetation.

from __future__ import print_function
import argparse
import torchvision
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
from binary.continuous.cnf import *
from binary.continuous.reserved import MulticlassReserved,one_hot


# 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('--tttest-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=0.01, metavar='LR',
                    help='learning rate (default: 0.01)')
parser.add_argument('--momentum', type=float, default=0.5, metavar='M',
                    help='SGD momentum (default: 0.5)')
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')
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)


'''
The net is some dense convolution until the contact layer.
Contact-binary contains a binarize operation, conducted by neural network, that outputs value from 0 to 1.
There on, the binary units form up my expression. (\Sum_i{\Prod_j B_ij^C_ij})*T_1 + T_2
I might need to pay attention to the initialization, or multi-stage training.
I highly doubt that the CNF would lead to neat asymptomatic behavior that leads to discovery of Cartesian spaces.
'''


class SimplifiedCNFNet(nn.Module):

    # SimplifiedCNFNet will have a simpler domain
    # Also, it would predict two classes only
    # So we need to work on the dataset too.

    def __init__(self):
        super(SimplifiedCNFNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50,5)
        self.AND1 = And_CNF(5)
        self.AND2 = And_CNF(5)

    def forward(self,input):
        x = F.relu(F.max_pool2d(self.conv1(input), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        # binarize the values
        x = F.sigmoid(self.fc2(self.fc1(x)))

        ret=[]
        ret+=[self.AND1(x)]
        ret+=[self.AND2(x)]
        x=torch.stack(ret,dim=1)

        return x



# class Simplified_CNF_Net(nn.Module):
#     def __init__(self,num_classes):
#         super(Multiclass_CNF_Net, self).__init__()
#         self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
#         self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
#         self.conv2_drop = nn.Dropout2d()
#         self.fc1 = nn.Linear(320, 50)
#         self.num_classes=num_classes
#         for i in range(0, self.num_classes):
#             setattr(self, 'cnf{}'.format(i),CNF(50,5))
#
#     def forward(self,input):
#         x = F.relu(F.max_pool2d(self.conv1(input), 2))
#         x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
#         x = x.view(-1, 320)
#         # binarize the values
#         x = F.sigmoid(self.fc1(x))
#
#         # CNF values
#         ret=[]
#         for i in range(0, self.num_classes):
#             a = getattr(self,'cnf{}'.format(i))(x)
#             a = F.sigmoid(a)
#             ret+=[a]
#         x=torch.stack(ret,dim=1)
#         return x


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

optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum=args.momentum)

def train(epoch):
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        if args.cuda:
            data, target = data.cuda(), target.cuda()
        target=target%2
        target=one_hot(target,2)
        data, target = Variable(data), Variable(target)
        optimizer.zero_grad()
        output = model(data)
        MR=MulticlassReserved(2,0)
        loss = MR(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()
        target=target%2
        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()


'''
The gradient flows correctly, and there is no problem regarding implementation.
Does this architecture achieve what I want? Namely, learning something 
significant in terms given AND expression?
No. The tttest result is very bad.

'''