# flake8: noqa
# yapf: disable

# __import_begin__
from functools import partial
import numpy as np
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from filelock import FileLock
from torch.utils.data import random_split
import torchvision
import torchvision.transforms as transforms
import ray
from ray import tune
from ray.tune.schedulers import ASHAScheduler
# __import_end__


# __load_data_begin__
def load_data(data_dir="./data"):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
    ])

    # We add FileLock here because multiple workers will want to
    # download data, and this may cause overwrites since
    # DataLoader is not threadsafe.
    with FileLock(os.path.expanduser("~/.data.lock")):
        trainset = torchvision.datasets.CIFAR10(
            root=data_dir, train=True, download=True, transform=transform)

        testset = torchvision.datasets.CIFAR10(
            root=data_dir, train=False, download=True, transform=transform)

    return trainset, testset
# __load_data_end__


# __net_begin__
class Net(nn.Module):
    def __init__(self, l1=120, l2=84):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, l1)
        self.fc2 = nn.Linear(l1, l2)
        self.fc3 = nn.Linear(l2, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x
# __net_end__


# __train_begin__
def train_cifar(config, checkpoint_dir=None):
    net = Net(config["l1"], config["l2"])

    device = "cpu"
    if torch.cuda.is_available():
        device = "cuda:0"
        if torch.cuda.device_count() > 1:
            net = nn.DataParallel(net)
    net.to(device)

    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9)

    # The `checkpoint_dir` parameter gets passed by Ray Tune when a checkpoint
    # should be restored.
    if checkpoint_dir:
        checkpoint = os.path.join(checkpoint_dir, "checkpoint")
        model_state, optimizer_state = torch.load(checkpoint)
        net.load_state_dict(model_state)
        optimizer.load_state_dict(optimizer_state)

    data_dir = os.path.abspath("./data")
    trainset, testset = load_data(data_dir)

    test_abs = int(len(trainset) * 0.8)
    train_subset, val_subset = random_split(
        trainset, [test_abs, len(trainset) - test_abs])

    trainloader = torch.utils.data.DataLoader(
        train_subset,
        batch_size=int(config["batch_size"]),
        shuffle=True,
        num_workers=8)
    valloader = torch.utils.data.DataLoader(
        val_subset,
        batch_size=int(config["batch_size"]),
        shuffle=True,
        num_workers=8)

    for epoch in range(10):  # loop over the dataset multiple times
        running_loss = 0.0
        epoch_steps = 0
        for i, data in enumerate(trainloader, 0):
            # get the inputs; data is a list of [inputs, labels]
            inputs, labels = data
            inputs, labels = inputs.to(device), labels.to(device)

            # zero the parameter gradients
            optimizer.zero_grad()

            # forward + backward + optimize
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()

            # print statistics
            running_loss += loss.item()
            epoch_steps += 1
            if i % 2000 == 1999:  # print every 2000 mini-batches
                print("[%d, %5d] loss: %.3f" % (epoch + 1, i + 1,
                                                running_loss / epoch_steps))
                running_loss = 0.0

        # Validation loss
        val_loss = 0.0
        val_steps = 0
        total = 0
        correct = 0
        for i, data in enumerate(valloader, 0):
            with torch.no_grad():
                inputs, labels = data
                inputs, labels = inputs.to(device), labels.to(device)

                outputs = net(inputs)
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()

                loss = criterion(outputs, labels)
                val_loss += loss.cpu().numpy()
                val_steps += 1

        # Here we save a checkpoint. It is automatically registered with
        # Ray Tune and will potentially be passed as the `checkpoint_dir`
        # parameter in future iterations.
        with tune.checkpoint_dir(step=epoch) as checkpoint_dir:
            path = os.path.join(checkpoint_dir, "checkpoint")
            torch.save(
                (net.state_dict(), optimizer.state_dict()), path)

        tune.report(loss=(val_loss / val_steps), accuracy=correct / total)
    print("Finished Training")
# __train_end__


# __test_acc_begin__

def test_best_model(best_trial):
    best_trained_model = Net(best_trial.config["l1"], best_trial.config["l2"])
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    best_trained_model.to(device)

    checkpoint_path = os.path.join(best_trial.checkpoint.value, "checkpoint")

    model_state, optimizer_state = torch.load(checkpoint_path)
    best_trained_model.load_state_dict(model_state)

    trainset, testset = load_data()

    testloader = torch.utils.data.DataLoader(
        testset, batch_size=4, shuffle=False, num_workers=2)

    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            images, labels = images.to(device), labels.to(device)
            outputs = best_trained_model(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()


    print("Best trial test set accuracy: {}".format(correct / total))

# __test_acc_end__

# __main_begin__
def main(num_samples=10, max_num_epochs=10, gpus_per_trial=2):
    config = {
        "l1": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
        "l2": tune.sample_from(lambda _: 2 ** np.random.randint(2, 9)),
        "lr": tune.loguniform(1e-4, 1e-1),
        "batch_size": tune.choice([2, 4, 8, 16])
    }
    scheduler = ASHAScheduler(
        max_t=max_num_epochs,
        grace_period=1,
        reduction_factor=2)
    result = tune.run(
        tune.with_parameters(train_cifar),
        resources_per_trial={"cpu": 2, "gpu": gpus_per_trial},
        config=config,
        metric="loss",
        mode="min",
        num_samples=num_samples,
        scheduler=scheduler
    )

    best_trial = result.get_best_trial("loss", "min", "last")
    print("Best trial config: {}".format(best_trial.config))
    print("Best trial final validation loss: {}".format(
        best_trial.last_result["loss"]))
    print("Best trial final validation accuracy: {}".format(
        best_trial.last_result["accuracy"]))

    if ray.util.client.ray.is_connected():
        # If using Ray Client, we want to make sure checkpoint access
        # happens on the server. So we wrap `test_best_model` in a Ray task.
        # We have to make sure it gets executed on the same node that
        # ``tune.run`` is called on.
        from ray.util.ml_utils.node import force_on_current_node
        remote_fn = force_on_current_node(ray.remote(test_best_model))
        ray.get(remote_fn.remote(best_trial))
    else:
        test_best_model(best_trial)


# __main_end__


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--smoke-test", action="store_true", help="Finish quickly for testing")
    parser.add_argument(
        "--ray-address",
        help="Address of Ray cluster for seamless distributed execution.",
        required=False)
    parser.add_argument(
        "--server-address",
        type=str,
        default=None,
        required=False,
        help="The address of server to connect to if using "
             "Ray Client.")
    args, _ = parser.parse_known_args()

    if args.smoke_test:
        ray.init(num_cpus=2)
        main(num_samples=1, max_num_epochs=1, gpus_per_trial=0)
    else:
        if args.server_address:
            # Connect to a remote server through Ray Client.
            ray.init(f"ray://{args.server_address}")
        elif args.ray_address:
            # Run directly on the Ray cluster.
            ray.init(args.ray_address)
        # Change this to activate training on GPUs
        main(num_samples=10, max_num_epochs=10, gpus_per_trial=0)
