"""This example demonstrates the usage of conditional search spaces with Tune.

It also checks that it is usable with a separate scheduler.

Requires the HyperOpt library to be installed (`pip install hyperopt`).

For an example of using a Tune search space, see
:doc:`/tune/examples/hyperopt_example`.
"""

import time

from hyperopt import hp

import ray
from ray import tune
from ray.tune.schedulers import AsyncHyperBandScheduler
from ray.tune.search import ConcurrencyLimiter
from ray.tune.search.hyperopt import HyperOptSearch


def f_unpack_dict(dct: dict) -> dict:
    """Unpacks all sub-dictionaries in given dictionary recursively.
    There should be no duplicated keys across all nested
    subdictionaries, or some instances will be lost without warning

    Source: https://www.kaggle.com/fanvacoolt/tutorial-on-hyperopt

    Args:
        dct: dictionary to unpack

    Returns:
        dict: unpacked dictionary
    """

    res = {}
    for k, v in dct.items():
        if isinstance(v, dict):
            res = {**res, **f_unpack_dict(v)}
        else:
            res[k] = v

    return res


def evaluation_fn(step, width, height, mult=1):
    return (0.1 + width * step / 100) ** (-1) + height * 0.1 * mult


def easy_objective(config_in):
    # Hyperparameters
    config = f_unpack_dict(config_in)
    width, height, mult = config["width"], config["height"], config.get("mult", 1)
    print(config)

    for step in range(config["steps"]):
        # Iterative training function - can be any arbitrary training procedure
        intermediate_score = evaluation_fn(step, width, height, mult)
        # Feed the score back back to Tune.
        tune.report({"iterations": step, "mean_loss": intermediate_score})
        time.sleep(0.1)


config_space = {
    "activation": hp.choice(
        "activation",
        [
            {"activation": "relu", "mult": hp.uniform("mult", 1, 2)},
            {"activation": "tanh"},
        ],
    ),
    "width": hp.uniform("width", 0, 20),
    "height": hp.uniform("heright", -100, 100),
    "steps": 100,
}


def run_hyperopt_tune(config_dict=config_space, smoke_test=False):
    algo = HyperOptSearch(space=config_dict, metric="mean_loss", mode="min")
    algo = ConcurrencyLimiter(algo, max_concurrent=4)
    scheduler = AsyncHyperBandScheduler()
    tuner = tune.Tuner(
        easy_objective,
        tune_config=tune.TuneConfig(
            metric="mean_loss",
            mode="min",
            search_alg=algo,
            scheduler=scheduler,
            num_samples=10 if smoke_test else 100,
        ),
    )
    results = tuner.fit()
    print("Best hyperparameters found were: ", results.get_best_result().config)


if __name__ == "__main__":
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        "--smoke-test", action="store_true", help="Finish quickly for testing"
    )
    args, _ = parser.parse_known_args()

    ray.init(configure_logging=False)

    run_hyperopt_tune(smoke_test=args.smoke_test)