from __future__ import print_function
import argparse
import torch
import torch.utils.data
from torch import nn, optim
from torch.nn import functional as F
from torchvision import datasets, transforms
from torchvision.utils import save_image
import os


class VAE(nn.Module):
    def __init__(self):
        super(VAE, self).__init__()

        self.fc1 = nn.Linear(784, 400)
        self.fc21 = nn.Linear(400, 20)
        self.fc22 = nn.Linear(400, 20)
        self.fc3 = nn.Linear(20, 400)
        self.fc4 = nn.Linear(400, 784)

    def encode(self, x):
        h1 = F.relu(self.fc1(x))
        return self.fc21(h1), self.fc22(h1)

    def reparameterize(self, mu, logvar):
        if self.training:
            std = torch.exp(0.5*logvar)
            eps = torch.randn_like(std)
            return eps.mul(std).add_(mu)
        else:
            return mu

    def decode(self, z):
        h3 = F.relu(self.fc3(z))
        return torch.sigmoid(self.fc4(h3))
    
    def save(self,epoch):
        save_dir = os.path.join('models','mnist', 'VAE',str(epoch))

        if not os.path.exists(save_dir):
            os.makedirs(save_dir)

        torch.save(self.state_dict(), os.path.join(save_dir, 'VAE.pkl'))

    def forward(self, z):
        return self.decode(z).view(-1, 1, 28, 28)
        # mu, logvar = self.encode(x.view(-1, 784))
        # z = self.reparameterize(mu, logvar)
        # return self.decode(z), mu, logvar

    def load(self,save_path, **options):
        self.load_state_dict(torch.load(save_path, **options))
     
# class VAE(nn.Module):
#     def __init__(self):
#         super(VAE, self).__init__()
# 
#         self.fc1 = nn.Linear(784, 400)
#         self.fc21 = nn.Linear(400, 20)
#         self.fc22 = nn.Linear(400, 20)
#         self.fc3 = nn.Linear(20, 400)
#         self.fc4 = nn.Linear(400, 784)
# 
#     def encode(self, x):
#         h1 = F.relu(self.fc1(x))
#         return self.fc21(h1), self.fc22(h1)
# 
#     def reparameterize(self, mu, logvar):
#         if self.training:
#             std = torch.exp(0.5*logvar)
#             eps = torch.randn_like(std)
#             return eps.mul(std).add_(mu)
#         else:
#             return mu
# 
#     def decode(self, z):
#         h3 = F.relu(self.fc3(z))
#         return torch.sigmoid(self.fc4(h3))
#     
#     def save(self,epoch):
#         save_dir = os.path.join('models','mnist', 'VAE',str(epoch))
# 
#         if not os.path.exists(save_dir):
#             os.makedirs(save_dir)
# 
#         torch.save(self.state_dict(), os.path.join(save_dir, 'VAE.pkl'))
# 
#     def forward(self, z):
#         mu, logvar = self.encode(x.view(-1, 784))
#         z = self.reparameterize(mu, logvar)
#         return self.decode(z), mu, logvar
# 
#     def load(self,save_path, **options):
#         self.load_state_dict(torch.load(save_path, **options))
 
# model = VAE().to(device)
# optimizer = optim.Adam(model.parameters(), lr=1e-2)


# Reconstruction + KL divergence losses summed over all elements and batch
def loss_function(recon_x, x, mu, logvar):
    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')

    # see Appendix B from VAE paper:
    # Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
    # https://arxiv.org/abs/1312.6114
    # 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())

    return BCE + KLD


def train(epoch):
    model.train()
    train_loss = 0
    for batch_idx, (data, _) in enumerate(train_loader):
        data = data.to(device)
        optimizer.zero_grad()
        recon_batch, mu, logvar = model(data)
        loss = loss_function(recon_batch, data, mu, logvar)
        loss.backward()
        train_loss += loss.item()
        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.item() / len(data)))

    print('====> Epoch: {} Average loss: {:.4f}'.format(
          epoch, train_loss / len(train_loader.dataset)))


def test(epoch):
    model.eval()
    test_loss = 0
    with torch.no_grad():
        for i, (data, _) in enumerate(test_loader):
            data = data.to(device)
            recon_batch, mu, logvar = model(data)
            test_loss += loss_function(recon_batch, data, mu, logvar).item()
            if i == 0:
                n = min(data.size(0), 8)
                comparison = torch.cat([data[:n],
                                      recon_batch.view(args.batch_size, 1, 28, 28)[:n]])
                #save_image(comparison.cpu(),
                #         'results/mnist/reconstruction_' + str(epoch) + '.png', nrow=n)

    test_loss /= len(test_loader.dataset)
    print('====> Test set loss: {:.4f}'.format(test_loss))

def main():
    parser = argparse.ArgumentParser(description='VAE MNIST Example')
    parser.add_argument('--batch-size', type=int, default=128, metavar='N',
                        help='input batch size for training (default: 128)')
    parser.add_argument('--epochs', type=int, default=25, metavar='N',
                        help='number of epochs to train (default: 10)')
    parser.add_argument('--no-cuda', action='store_true', default=False,
                        help='enables 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)

    device = torch.device("cuda" if args.cuda else "cpu")

    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.ToTensor()),
        batch_size=args.batch_size, shuffle=True, **kwargs)
    test_loader = torch.utils.data.DataLoader(
        datasets.MNIST('../data', train=False, transform=transforms.ToTensor()),
        batch_size=args.batch_size, shuffle=True, **kwargs)

    for epoch in range(1, args.epochs + 1):
        train(epoch)
        model.save(epoch)
        test(epoch)
        if not os.path.exists('results/mnist/VAE'):
            os.makedirs('results/mnist/VAE')
        with torch.no_grad():
            sample = torch.randn(64, 20).to(device)
            sample = model.decode(sample).cpu()
            save_image(sample.view(64, 1, 28, 28),
                       'results/mnist/VAE/VAE_epoch%03d.png' % epoch)

if __name__ == "__main__":
    main()

