# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES, ETH Zurich, and University of Toronto
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""Script to train RL agent with Stable Baselines3."""

from __future__ import annotations

"""Launch Isaac Sim Simulator first."""


import argparse
import numpy as np
import os

from omni.isaac.orbit.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with Stable-Baselines3.")
parser.add_argument("--headless", action="store_true", default=False, help="Force display off at all times.")
parser.add_argument("--video", action="store_true", default=False, help="Record videos during training.")
parser.add_argument("--video_length", type=int, default=200, help="Length of the recorded video (in steps).")
parser.add_argument("--video_interval", type=int, default=2000, help="Interval between video recordings (in steps).")
parser.add_argument("--cpu", action="store_true", default=False, help="Use CPU pipeline.")
parser.add_argument("--num_envs", type=int, default=None, help="Number of environments to simulate.")
parser.add_argument("--task", type=str, default=None, help="Name of the task.")
parser.add_argument("--seed", type=int, default=None, help="Seed used for the environment")
args_cli = parser.parse_args()

# launch the simulator
config = {"headless": args_cli.headless}
# load cheaper kit config in headless
if args_cli.headless:
    app_experience = f"{os.environ['EXP_PATH']}/omni.isaac.sim.python.gym.headless.kit"
else:
    app_experience = f"{os.environ['EXP_PATH']}/omni.isaac.sim.python.kit"
# launch omniverse app
app_launcher = AppLauncher(headless=args_cli.headless, experience=app_experience)
simulation_app = app_launcher.app
"""Rest everything follows."""


import gym
import os
from datetime import datetime

from stable_baselines3 import PPO
from stable_baselines3.common.callbacks import CheckpointCallback
from stable_baselines3.common.logger import configure
from stable_baselines3.common.vec_env import VecNormalize

from omni.isaac.orbit.utils.dict import print_dict
from omni.isaac.orbit.utils.io import dump_pickle, dump_yaml

import omni.isaac.contrib_envs  # noqa: F401
import omni.isaac.orbit_envs  # noqa: F401
from omni.isaac.orbit_envs.utils import parse_env_cfg
from omni.isaac.orbit_envs.utils.wrappers.sb3 import Sb3VecEnvWrapper

from config import parse_sb3_cfg


def main():
    """Train with stable-baselines agent."""
    # parse configuration
    env_cfg = parse_env_cfg(args_cli.task, use_gpu=not args_cli.cpu, num_envs=args_cli.num_envs)
    agent_cfg = parse_sb3_cfg(args_cli.task)
    # override configuration with command line arguments
    if args_cli.seed is not None:
        agent_cfg["seed"] = args_cli.seed

    # directory for logging into
    log_dir = os.path.join("logs", "sb3", args_cli.task, datetime.now().strftime("%b%d_%H-%M-%S"))
    # dump the configuration into log-directory
    dump_yaml(os.path.join(log_dir, "params", "env.yaml"), env_cfg)
    dump_yaml(os.path.join(log_dir, "params", "agent.yaml"), agent_cfg)
    dump_pickle(os.path.join(log_dir, "params", "env.pkl"), env_cfg)
    dump_pickle(os.path.join(log_dir, "params", "agent.pkl"), agent_cfg)

    # read configurations about the agent-training
    policy_arch = agent_cfg.pop("policy")
    n_timesteps = agent_cfg.pop("n_timesteps")

    # create isaac environment
    env = gym.make(args_cli.task, cfg=env_cfg)
    # wrap for video recording
    if args_cli.video:
        video_kwargs = {
            "video_folder": os.path.join(log_dir, "videos"),
            "step_trigger": lambda step: step % args_cli.video_interval == 0,
            "video_length": args_cli.video_length,
        }
        print("[INFO] Recording videos during training.")
        print_dict(video_kwargs, nesting=4)
        env = gym.wrappers.RecordVideo(env, **video_kwargs)
    # wrap around environment for stable baselines
    env = Sb3VecEnvWrapper(env)
    # set the seed
    env.seed(seed=agent_cfg["seed"])

    if "normalize_input" in agent_cfg:
        env = VecNormalize(
            env,
            training=True,
            norm_obs="normalize_input" in agent_cfg and agent_cfg.pop("normalize_input"),
            norm_reward="normalize_value" in agent_cfg and agent_cfg.pop("normalize_value"),
            clip_obs="clip_obs" in agent_cfg and agent_cfg.pop("clip_obs"),
            gamma=agent_cfg["gamma"],
            clip_reward=np.inf,
        )

    # create agent from stable baselines
    agent = PPO(policy_arch, env, verbose=1, **agent_cfg)
    # configure the logger
    new_logger = configure(log_dir, ["stdout", "tensorboard"])
    agent.set_logger(new_logger)

    # callbacks for agent
    checkpoint_callback = CheckpointCallback(save_freq=100, save_path=log_dir, name_prefix="model", verbose=2)
    # train the agent
    agent.learn(total_timesteps=n_timesteps, callback=checkpoint_callback)
    # save the final model
    agent.save(os.path.join(log_dir, "model"))

    # close the simulator
    env.close()
    simulation_app.close()


if __name__ == "__main__":
    main()
