"""
Example of a custom gym environment and model. Run this for a demo.

This example shows:
  - using a custom environment
  - using a custom model
  - using Tune for grid search to try different learning rates

You can visualize experiment results in ~/ray_results using TensorBoard.

Run example with defaults:
$ python custom_env.py
For CLI options:
$ python custom_env.py --help
"""
import argparse
import os
import random

import gymnasium as gym
import ray
from gym.spaces import Box, Discrete
from ray import tune
from ray.rllib.algorithms.ppo import PPOConfig
from ray.rllib.algorithms.algorithm_config import AlgorithmConfig
from ray.rllib.env.env_context import EnvContext
from ray.rllib.models import ModelCatalog
from ray.rllib.models.tf.fcnet import FullyConnectedNetwork
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
from ray.rllib.models.torch.fcnet import FullyConnectedNetwork as TorchFC
from ray.rllib.models.torch.torch_modelv2 import TorchModelV2
from ray.rllib.utils.framework import try_import_tf, try_import_torch
from ray.rllib.utils.test_utils import check_learning_achieved
from ray.tune.logger import pretty_print

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=100000, help="Number of timesteps to train."
)
parser.add_argument(
    "--stop-reward", type=float, default=0.1, help="Reward at which we stop training."
)
parser.add_argument(
    "--no-tune",
    action="store_true",
    help="Run without Tune using a manual train loop instead. In this case,"
    "use PPO without grid search and no TensorBoard.",
)
parser.add_argument(
    "--local-mode",
    action="store_true",
    help="Init Ray in local mode for easier debugging.",
)


class SimpleCorridor(gym.Env):
    """Example of a custom env in which you have to walk down a corridor.

    You can configure the length of the corridor via the env config."""

    def __init__(self, config: EnvContext):
        self.end_pos = config["corridor_length"]
        self.cur_pos = 0
        self.action_space = Discrete(2)
        self.observation_space = Box(0.0, self.end_pos, shape=(1,), dtype=float)
        # Set the seed. This is only used for the final (reach goal) reward.
        self.seed(config.worker_index * config.num_workers)

    def reset(self, *, seed=None, options=None):
        self.cur_pos = 0
        info_dict = {}
        return [self.cur_pos], info_dict

    def step(self, action):
        assert action in [0, 1], action
        if action == 0 and self.cur_pos > 0:
            self.cur_pos -= 1
        elif action == 1:
            self.cur_pos += 1
        terminated = self.cur_pos >= self.end_pos
        truncated = False
        # Produce a random reward when we reach the goal.
        return (
            [self.cur_pos], 
            random.random() * 2 if terminated else -0.1, 
            terminated, 
            truncated, 
            {}
        )

    def seed(self, seed=None):
        random.seed(seed)


class CustomModel(TFModelV2):
    """Example of a keras custom model that just delegates to an fc-net."""

    def __init__(self, obs_space, action_space, num_outputs, model_config, name):
        super(CustomModel, self).__init__(
            obs_space, action_space, num_outputs, model_config, name
        )
        self.model = FullyConnectedNetwork(
            obs_space, action_space, num_outputs, model_config, name
        )

    def forward(self, input_dict, state, seq_lens):
        return self.model.forward(input_dict, state, seq_lens)

    def value_function(self):
        return self.model.value_function()


class TorchCustomModel(TorchModelV2, nn.Module):
    """Example of a PyTorch custom model that just delegates to a fc-net."""

    def __init__(self, obs_space, action_space, num_outputs, model_config, name):
        TorchModelV2.__init__(
            self, obs_space, action_space, num_outputs, model_config, name
        )
        nn.Module.__init__(self)

        self.torch_sub_model = TorchFC(
            obs_space, action_space, num_outputs, model_config, name
        )

    def forward(self, input_dict, state, seq_lens):
        input_dict["obs"] = input_dict["obs"].float()
        fc_out, _ = self.torch_sub_model(input_dict, state, seq_lens)
        return fc_out, []

    def value_function(self):
        return torch.reshape(self.torch_sub_model.value_function(), [-1])


if __name__ == "__main__":
    args = parser.parse_args()
    print(f"Running with following CLI options: {args}")

    ray.init(local_mode=args.local_mode)

    # Can also register the env creator function explicitly with:
    # register_env("corridor", lambda config: SimpleCorridor(config))
    ModelCatalog.register_custom_model(
        "my_model", TorchCustomModel if args.framework == "torch" else CustomModel
    )

    run_config = (
        AlgorithmConfig()
        .environment(env=SimpleCorridor,  # or "corridor" if registered above
                     env_config={"corridor_length": 5}, 
                  )
        # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
        .resources(num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0")))
        .training(model={"custom_model": "my_model", 
                         "vf_share_layers": True}
                  )
        .rollouts(num_rollout_workers=1, rollout_fragment_length="auto")
        .framework(args.framework)
    )

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

    if args.run == "PPO":
        run_config = PPOConfig().from_dict(run_config.to_dict())
        run_config.training(sgd_minibatch_size=2)

    if args.no_tune:
        # manual training with train loop using PPO and fixed learning rate
        if args.run != "PPO":
            raise ValueError("Only support --run PPO with --no-tune.")
        print("Running manual train loop without Ray Tune.")
        # use fixed learning rate instead of grid search (needs tune)
        run_config.training(lr=1e-3)
        trainer = run_config.build()
        # run manual training loop and print results after each iteration
        for _ in range(args.stop_iters):
            result = trainer.train()
            print(pretty_print(result))
            # stop training of the target train steps or reward are reached
            if (
                result["timesteps_total"] >= args.stop_timesteps
                or result["episode_reward_mean"] >= args.stop_reward
            ):
                break
    else:
        # automated run with Tune and grid search and TensorBoard
        print("Training automatically with Ray Tune")
        results = tune.run(args.run, config=run_config.to_dict(), stop=stop)

        if args.as_test:
            print("Checking if learning goals were achieved")
            check_learning_achieved(results, args.stop_reward)

        results.results_df.to_csv("custom_env_results.csv")

    ray.shutdown()
