from functools import partial
from os.path import join

from numpy.random import randint
from ray import tune
from ray.tune import CLIReporter
from ray.tune.schedulers import ASHAScheduler
from torch import nn, load, save
from torch.cuda import is_available

from NeuralNetwork import NeuralNetwork
from dataset import get_data
from tests import test_accuracy
from training import training

device = "cuda:0" if is_available() else "cpu"
print(f"Using {device} device")


def main(data_root, num_samples=10, max_num_epochs=10, gpus_per_trial=1):
    get_data(data_root, True)

    config = {
        "l1": tune.sample_from(lambda _: 2 ** randint(2, 9)),
        "l2": tune.sample_from(lambda _: 2 ** randint(2, 9)),
        "lr": tune.loguniform(1e-4, 1e-1),
        "batch_size": tune.choice([2, 4, 8, 16])
    }
    scheduler = ASHAScheduler(
        metric="loss",
        mode="min",
        max_t=max_num_epochs,
        grace_period=1,
        reduction_factor=2)
    reporter = CLIReporter(
        # parameter_columns=["l1", "l2", "lr", "batch_size"],
        metric_columns=["loss", "accuracy", "training_iteration"])
    result = tune.run(
        partial(training, data_root=data_root, device=device),
        resources_per_trial={"cpu": 2, "gpu": gpus_per_trial},
        config=config,
        num_samples=num_samples,
        scheduler=scheduler,
        progress_reporter=reporter)

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

    best_trained_model = NeuralNetwork(best_trial.config["l1"], best_trial.config["l2"])
    if is_available():
        if gpus_per_trial > 1:
            best_trained_model = nn.DataParallel(best_trained_model)
    best_trained_model.to(device)

    best_checkpoint_dir = best_trial.checkpoint.value
    model_state, optimizer_state = load(join(
        best_checkpoint_dir, "checkpoint"))
    best_trained_model.load_state_dict(model_state)

    # If Pytorch don't save the end
    print("In case saving...")
    save(best_trained_model, "/home/flifloo/IA/model.pth")

    print("Testing accuracy...")
    print(f"Best trial test set accuracy: {test_accuracy(best_trained_model, data_root, device)}")


if __name__ == "__main__":
    main("/home/flifloo/IA/data")