# ruff: noqa
# fmt: off

# __import_begin__
import os
import tempfile
from typing import Dict

import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from filelock import FileLock
from torch.utils.data import random_split

import ray
from ray import tune
from ray.tune import Checkpoint
from ray.tune.schedulers import ASHAScheduler

# __import_end__


# __load_data_begin__
DATA_DIR = tempfile.mkdtemp()

def load_data(data_dir):
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
    ])

    # 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__

def load_test_data():
    # Loads a fake dataset for testing so it doesn't rely on external download.
    trainset = torchvision.datasets.FakeData(
        128, (3, 32, 32), num_classes=10, transform=transforms.ToTensor()
    )
    testset = torchvision.datasets.FakeData(
        16, (3, 32, 32), num_classes=10, transform=transforms.ToTensor()
    )
    return trainset, testset


# __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):
    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)

    # Load existing checkpoint through `get_checkpoint()` API.
    if tune.get_checkpoint():
        loaded_checkpoint = tune.get_checkpoint()
        with loaded_checkpoint.as_directory() as loaded_checkpoint_dir:
            model_state, optimizer_state = torch.load(
                os.path.join(loaded_checkpoint_dir, "checkpoint.pt")
            )
            net.load_state_dict(model_state)
            optimizer.load_state_dict(optimizer_state)

    if config["smoke_test"]:
        trainset, testset = load_test_data()
    else:
        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=0 if config["smoke_test"] else 8,
    )
    valloader = torch.utils.data.DataLoader(
        val_subset,
        batch_size=int(config["batch_size"]),
        shuffle=True,
        num_workers=0 if config["smoke_test"] else 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):
            # 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 accessed through in ``get_checkpoint()``
        # in future iterations.
        # Note to save a file like checkpoint, you still need to put it under a directory
        # to construct a checkpoint.
        with tempfile.TemporaryDirectory() as temp_checkpoint_dir:
            path = os.path.join(temp_checkpoint_dir, "checkpoint.pt")
            torch.save(
                (net.state_dict(), optimizer.state_dict()), path
            )
            checkpoint = Checkpoint.from_directory(temp_checkpoint_dir)
            tune.report(
                {"loss": (val_loss / val_steps), "accuracy": correct / total},
                checkpoint=checkpoint,
            )
    print("Finished Training")
# __train_end__


# __test_acc_begin__
def test_best_model(config: Dict, checkpoint: "Checkpoint", smoke_test=False):
    best_trained_model = Net(config["l1"], config["l2"])
    device = "cuda:0" if torch.cuda.is_available() else "cpu"
    best_trained_model.to(device)

    with checkpoint.as_directory() as checkpoint_dir:
        checkpoint_path = os.path.join(checkpoint_dir, "checkpoint.pt")
        model_state, optimizer_state = torch.load(checkpoint_path)
        best_trained_model.load_state_dict(model_state)

    if smoke_test:
        _, testset = load_test_data()
    else:
        _, testset = load_data(DATA_DIR)

    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, smoke_test=False):
    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]),
        "smoke_test": smoke_test,
    }
    scheduler = ASHAScheduler(
        max_t=max_num_epochs,
        grace_period=1,
        reduction_factor=2)

    tuner = tune.Tuner(
        tune.with_resources(
            tune.with_parameters(train_cifar),
            resources={"cpu": 2, "gpu": gpus_per_trial},
        ),
        tune_config=tune.TuneConfig(
            metric="loss",
            mode="min",
            num_samples=num_samples,
            scheduler=scheduler
        ),
        param_space=config,
    )
    results = tuner.fit()
    best_result = results.get_best_result("loss", "min")
    print("Best trial config: {}".format(best_result.config))
    print("Best trial final validation loss: {}".format(
        best_result.metrics["loss"]))
    print("Best trial final validation accuracy: {}".format(
        best_result.metrics["accuracy"]))

    test_best_model(best_result.config, best_result.checkpoint, smoke_test=smoke_test)


# __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)
    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, smoke_test=True)
    else:
        ray.init(args.ray_address)
        # Change this to activate training on GPUs
        main(num_samples=10, max_num_epochs=10, gpus_per_trial=0)