"""
Example of a curriculum learning setup using the `TaskSettableEnv` API
and the env_task_fn config.

This example shows:
  - Writing your own curriculum-capable environment using gym.Env.
  - Defining a env_task_fn that determines, whether and which new task
    the env(s) should be set to (using the TaskSettableEnv API).
  - Using Tune and RLlib to curriculum-learn this env.

You can visualize experiment results in ~/ray_results using TensorBoard.
"""
import argparse
import numpy as np
import os

import ray
from ray import tune
from ray.rllib.env.apis.task_settable_env import TaskSettableEnv, TaskType
from ray.rllib.env.env_context import EnvContext
from ray.rllib.examples.env.curriculum_capable_env import CurriculumCapableEnv
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import check_learning_achieved

tf1, tf, tfv = try_import_tf()
torch, nn = try_import_torch()

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=50, help="Number of iterations to train."
)
parser.add_argument(
    "--stop-timesteps", type=int, default=200000, help="Number of timesteps to train."
)
parser.add_argument(
    "--stop-reward",
    type=float,
    default=10000.0,
    help="Reward at which we stop training.",
)
parser.add_argument(
    "--local-mode",
    action="store_true",
    help="Init Ray in local mode for easier debugging.",
)


def curriculum_fn(
    train_results: dict, task_settable_env: TaskSettableEnv, env_ctx: EnvContext
) -> TaskType:
    """Function returning a possibly new task to set `task_settable_env` to.

    Args:
        train_results: The train results returned by Algorithm.train().
        task_settable_env: A single TaskSettableEnv object
            used inside any worker and at any vector position. Use `env_ctx`
            to get the worker_index, vector_index, and num_workers.
        env_ctx: The env context object (i.e. env's config dict
            plus properties worker_index, vector_index and num_workers) used
            to setup the `task_settable_env`.

    Returns:
        TaskType: The task to set the env to. This may be the same as the
            current one.
    """
    # Our env supports tasks 1 (default) to 5.
    # With each task, rewards get scaled up by a factor of 10, such that:
    # Level 1: Expect rewards between 0.0 and 1.0.
    # Level 2: Expect rewards between 1.0 and 10.0, etc..
    # We will thus raise the level/task each time we hit a new power of 10.0
    new_task = int(np.log10(train_results["episode_reward_mean"]) + 2.1)
    # Clamp between valid values, just in case:
    new_task = max(min(new_task, 5), 1)
    print(
        f"Worker #{env_ctx.worker_index} vec-idx={env_ctx.vector_index}"
        f"\nR={train_results['episode_reward_mean']}"
        f"\nSetting env to task={new_task}"
    )
    return new_task


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

    # Can also register the env creator function explicitly with:
    # register_env(
    #     "curriculum_env", lambda config: CurriculumCapableEnv(config))

    config = {
        "env": CurriculumCapableEnv,  # or "curriculum_env" if registered above
        "env_config": {
            "start_level": 1,
        },
        "num_workers": 2,  # parallelism
        "num_envs_per_worker": 5,
        "env_task_fn": curriculum_fn,
        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
        "num_gpus": int(os.environ.get("RLLIB_NUM_GPUS", "0")),
        "framework": args.framework,
    }

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

    results = tune.run(args.run, config=config, stop=stop, verbose=2)

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