# flake8: noqa
# Original Code: https://github.com/pytorch/examples/blob/master/mnist/main.py

# fmt: off
# __tutorial_imports_begin__
import numpy as np
import torch
import torch.optim as optim
import torch.nn as nn
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import torch.nn.functional as F

from ray import air, tune
from ray.air import session
from ray.tune.schedulers import ASHAScheduler
# __tutorial_imports_end__
# fmt: on


# fmt: off
# __model_def_begin__
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        # In this example, we don't change the model architecture
        # due to simplicity.
        self.conv1 = nn.Conv2d(1, 3, kernel_size=3)
        self.fc = nn.Linear(192, 10)

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 3))
        x = x.view(-1, 192)
        x = self.fc(x)
        return F.log_softmax(x, dim=1)
# __model_def_end__
# fmt: on

# fmt: off
# __train_def_begin__

# Change these values if you want the training to run quicker or slower.
EPOCH_SIZE = 512
TEST_SIZE = 256

def train(model, optimizer, train_loader):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.train()
    for batch_idx, (data, target) in enumerate(train_loader):
        # We set this just for the example to run quickly.
        if batch_idx * len(data) > EPOCH_SIZE:
            return
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = F.nll_loss(output, target)
        loss.backward()
        optimizer.step()


def test(model, data_loader):
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
    model.eval()
    correct = 0
    total = 0
    with torch.no_grad():
        for batch_idx, (data, target) in enumerate(data_loader):
            # We set this just for the example to run quickly.
            if batch_idx * len(data) > TEST_SIZE:
                break
            data, target = data.to(device), target.to(device)
            outputs = model(data)
            _, predicted = torch.max(outputs.data, 1)
            total += target.size(0)
            correct += (predicted == target).sum().item()

    return correct / total
# __train_def_end__


# __train_func_begin__
def train_mnist(config):
    # Data Setup
    mnist_transforms = transforms.Compose(
        [transforms.ToTensor(),
         transforms.Normalize((0.1307, ), (0.3081, ))])

    train_loader = DataLoader(
        datasets.MNIST("~/data", train=True, download=True, transform=mnist_transforms),
        batch_size=64,
        shuffle=True)
    test_loader = DataLoader(
        datasets.MNIST("~/data", train=False, transform=mnist_transforms),
        batch_size=64,
        shuffle=True)

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

    model = ConvNet()
    model.to(device)

    optimizer = optim.SGD(
        model.parameters(), lr=config["lr"], momentum=config["momentum"])
    for i in range(10):
        train(model, optimizer, train_loader)
        acc = test(model, test_loader)

        # Send the current training result back to Tune
        session.report({"mean_accuracy": acc})

        if i % 5 == 0:
            # This saves the model to the trial directory
            torch.save(model.state_dict(), "./model.pth")
# __train_func_end__
# fmt: on

# __eval_func_begin__
search_space = {
    "lr": tune.sample_from(lambda spec: 10 ** (-10 * np.random.rand())),
    "momentum": tune.uniform(0.1, 0.9),
}

# Uncomment this to enable distributed execution
# `ray.init(address="auto")`

# Download the dataset first
datasets.MNIST("~/data", train=True, download=True)

tuner = tune.Tuner(
    train_mnist,
    param_space=search_space,
)
results = tuner.fit()
# __eval_func_end__

# __plot_begin__
dfs = {result.log_dir: result.metrics_dataframe for result in results}
[d.mean_accuracy.plot() for d in dfs.values()]
# __plot_end__

# __run_scheduler_begin__
tuner = tune.Tuner(
    train_mnist,
    tune_config=tune.TuneConfig(
        num_samples=20,
        scheduler=ASHAScheduler(metric="mean_accuracy", mode="max"),
    ),
    param_space=search_space,
)
results = tuner.fit()

# Obtain a trial dataframe from all run trials of this `tune.run` call.
dfs = {result.log_dir: result.metrics_dataframe for result in results}
# __run_scheduler_end__

# fmt: off
# __plot_scheduler_begin__
# Plot by epoch
ax = None  # This plots everything on the same plot
for d in dfs.values():
    ax = d.mean_accuracy.plot(ax=ax, legend=False)
# __plot_scheduler_end__
# fmt: on

# __run_searchalg_begin__
from hyperopt import hp
from ray.tune.search.hyperopt import HyperOptSearch

space = {
    "lr": hp.loguniform("lr", -10, -1),
    "momentum": hp.uniform("momentum", 0.1, 0.9),
}

hyperopt_search = HyperOptSearch(space, metric="mean_accuracy", mode="max")

tuner = tune.Tuner(
    train_mnist,
    tune_config=tune.TuneConfig(
        num_samples=10,
        search_alg=hyperopt_search,
    ),
)
results = tuner.fit()

# To enable GPUs, use this instead:
# analysis = tune.run(
#     train_mnist, config=search_space, resources_per_trial={'gpu': 1})

# __run_searchalg_end__

# __run_analysis_begin__
import os

logdir = results.get_best_result("mean_accuracy", mode="max").log_dir
state_dict = torch.load(os.path.join(logdir, "model.pth"))

model = ConvNet()
model.load_state_dict(state_dict)
# __run_analysis_end__

from ray.tune.examples.mnist_pytorch_trainable import TrainMNIST

# __trainable_run_begin__
search_space = {
    "lr": tune.sample_from(lambda spec: 10 ** (-10 * np.random.rand())),
    "momentum": tune.uniform(0.1, 0.9),
}

tuner = tune.Tuner(
    TrainMNIST,
    run_config=air.RunConfig(stop={"training_iteration": 10}),
    param_space=search_space,
)
results = tuner.fit()
# __trainable_run_end__
