# This code is from https://github.com/pytorch/examples/blob/master/mnist/main.py
# with modification to do training using onnxruntime as backend on cuda device.

import argparse
import os

import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms

import onnxruntime
from onnxruntime.training import ORTTrainer, ORTTrainerOptions, optim


# Pytorch model
class NeuralNet(nn.Module):
    def __init__(self, input_size, hidden_size, num_classes):
        super(NeuralNet, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, num_classes)

    def forward(self, input1):
        out = self.fc1(input1)
        out = self.relu(out)
        out = self.fc2(out)
        return out


# ONNX Runtime training
def mnist_model_description():
    return {
        "inputs": [("input1", ["batch", 784]), ("label", ["batch"])],
        "outputs": [("loss", [], True), ("probability", ["batch", 10])],
    }


def my_loss(x, target):
    return F.nll_loss(F.log_softmax(x, dim=1), target)


# Helpers
def train(log_interval, trainer, device, train_loader, epoch, train_steps):
    for batch_idx, (data, target) in enumerate(train_loader):
        if batch_idx == train_steps:
            break

        # Fetch data
        data, target = data.to(device), target.to(device)
        data = data.reshape(data.shape[0], -1)

        # Train step
        loss, prob = trainer.train_step(data, target)

        # Stats
        if batch_idx % log_interval == 0:
            print(
                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
                    epoch, batch_idx * len(data), len(train_loader.dataset), 100.0 * batch_idx / len(train_loader), loss
                )
            )


def test(trainer, device, test_loader):
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            data = data.reshape(data.shape[0], -1)

            # Using fetches around without eval_step to not pass 'target' as input
            trainer._train_step_info.fetches = ["probability"]
            output = F.log_softmax(trainer.eval_step(data), dim=1)
            trainer._train_step_info.fetches = []

            # Stats
            test_loss += F.nll_loss(output, target, reduction="sum").item()
            pred = output.argmax(dim=1, keepdim=True)
            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), 100.0 * correct / len(test_loader.dataset)
        )
    )


def main():
    # Training settings
    parser = argparse.ArgumentParser(description="ONNX Runtime MNIST Example")
    parser.add_argument(
        "--train-steps",
        type=int,
        default=-1,
        metavar="N",
        help="number of steps to train. Set -1 to run through whole dataset (default: -1)",
    )
    parser.add_argument(
        "--batch-size", type=int, default=20, metavar="N", help="input batch size for training (default: 20)"
    )
    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=1, metavar="N", help="number of epochs to train (default: 1)")
    parser.add_argument("--lr", type=float, default=0.01, metavar="LR", help="learning rate (default: 0.01)")
    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("--save-path", type=str, default="", help="Path for Saving the current Model state")

    # Basic setup
    args = parser.parse_args()
    if not args.no_cuda and torch.cuda.is_available():
        device = "cuda"
    else:
        device = "cpu"
    torch.manual_seed(args.seed)
    onnxruntime.set_seed(args.seed)

    # Data loader
    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,
    )

    if args.test_batch_size > 0:
        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,
        )

    # Modeling
    model = NeuralNet(784, 500, 10)
    model_desc = mnist_model_description()
    optim_config = optim.SGDConfig(lr=args.lr)
    opts = {"device": {"id": device}}
    opts = ORTTrainerOptions(opts)

    trainer = ORTTrainer(model, model_desc, optim_config, loss_fn=my_loss, options=opts)

    # Train loop
    for epoch in range(1, args.epochs + 1):
        train(args.log_interval, trainer, device, train_loader, epoch, args.train_steps)
        if args.test_batch_size > 0:
            test(trainer, device, test_loader)

    # Save model
    if args.save_path:
        torch.save(model.state_dict(), os.path.join(args.save_path, "mnist_cnn.pt"))


if __name__ == "__main__":
    main()
