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.optim.lr_scheduler import StepLR
from mnist_demo.models.model import Net
from mnist_demo.models.dataset import MyMNIST
import os
import ssl

ssl._create_default_https_context = ssl._create_unverified_context


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()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % 250 == 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()))


def 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)
            output = model(data)
            test_loss += F.nll_loss(output, target, reduction='sum').item()  # sum up batch loss
            pred = output.argmax(dim=1, keepdim=True)  # get the index of the max log-probability
            correct += pred.eq(target.view_as(pred)).sum().item()

    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)))

def main():
    # 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('--epochs', type=int, default=7, metavar='N', help='number of epochs to train (default: 14)')
    parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)')
    parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)')
    parser.add_argument('--use_cuda', type=bool, default=False)


    # Data, model, and output directories
    parser.add_argument('--model_dir', type=str)#, default=os.environ['SM_MODEL_DIR'])
    #parser.add_argument('--train', type=str, default=os.environ['SM_CHANNEL_TRAIN'])
    #parser.add_argument('--test', type=str, default=os.environ['SM_CHANNEL_TEST'])

    args, _ = parser.parse_known_args()
    if args.model_dir is None:
        model_dir = os.getenv('SM_MODEL_DIR')
    else:
        model_dir = args.model_dir

    use_cuda = args.use_cuda and torch.cuda.is_available()

    torch.manual_seed(42)

    device = torch.device("cuda" if use_cuda else "cpu")
    print('Device is {}'.format(device))

    train_kwargs = {'batch_size': args.batch_size}
    test_kwargs = {'batch_size': args.batch_size}
    if use_cuda:
        cuda_kwargs = {'num_workers': 1,
                       'pin_memory': True,
                       'shuffle': True}
        train_kwargs.update(cuda_kwargs)
        test_kwargs.update(cuda_kwargs)

    transform=transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.1307,), (0.3081,))
        ])
    print('Downloading dataset')
    print(os.path.join('../data/MNIST/processed', 'training.pt'))
    dataset1 = MyMNIST(os.path.join('../data/MNIST/processed', 'training.pt'), transform=transform)
    #datasets.MNIST('../data', train=True, download=True, transform=transform)
    dataset2 = MyMNIST(os.path.join('../data/MNIST/processed', 'test.pt'), transform=transform)
    #datasets.MNIST('../data', train=False, transform=transform)
    print('Dataset downloaded successfully')
    train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs)
    test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs)

    model = Net().to(device)
    optimizer = optim.Adadelta(model.parameters(), lr=args.lr)

    scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)
    for epoch in range(1, args.epochs + 1):
        train(args, model, device, train_loader, optimizer, epoch)
        test(model, device, test_loader)
        scheduler.step()

    with open(os.path.join(model_dir, 'model.pth'), 'wb') as f:
        torch.save(model.state_dict(), f)


if __name__ == '__main__':
    main()