"""Example of using a custom model with batch norm."""

import argparse
import os

import ray
from ray import air, tune
from ray.rllib.examples.models.batch_norm_model import (
    BatchNormModel,
    KerasBatchNormModel,
    TorchBatchNormModel,
)
from ray.rllib.models import ModelCatalog
from ray.rllib.utils.framework import try_import_tf
from ray.rllib.utils.test_utils import check_learning_achieved

tf1, tf, tfv = try_import_tf()

parser = argparse.ArgumentParser()
parser.add_argument(
    "--run", type=str, default="PPO", help="The RLlib-registered algorithm to use."
)
parser.add_argument(
    "--framework",
    choices=["tf", "tf2", "tfe", "torch"],
    default="tf",
    help="The DL framework specifier.",
)
parser.add_argument(
    "--as-test",
    action="store_true",
    help="Whether this script should be run as a test: --stop-reward must "
    "be achieved within --stop-timesteps AND --stop-iters.",
)
parser.add_argument(
    "--stop-iters", type=int, default=200, help="Number of iterations to train."
)
parser.add_argument(
    "--stop-timesteps", type=int, default=100000, help="Number of timesteps to train."
)
parser.add_argument(
    "--stop-reward", type=float, default=150.0, help="Reward at which we stop training."
)

if __name__ == "__main__":
    args = parser.parse_args()
    ray.init()

    ModelCatalog.register_custom_model(
        "bn_model",
        TorchBatchNormModel
        if args.framework == "torch"
        else KerasBatchNormModel
        if args.run != "PPO"
        else BatchNormModel,
    )

    config = {
        "env": "Pendulum-v1" if args.run in ["DDPG", "SAC"] else "CartPole-v0",
        "model": {
            "custom_model": "bn_model",
        },
        "lr": 0.0003,
        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
        "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
        "num_workers": 0,
        "framework": args.framework,
    }

    stop = {
        "training_iteration": args.stop_iters,
        "timesteps_total": args.stop_timesteps,
        "episode_reward_mean": args.stop_reward,
    }

    tuner = tune.Tuner(
        args.run,
        param_space=config,
        run_config=air.RunConfig(
            verbose=2,
            stop=stop,
        ),
    )
    results = tuner.fit()

    if args.as_test:
        check_learning_achieved(results, args.stop_reward)

    ray.shutdown()