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

"""Script to play a checkpoint if an RL agent from Stable-Baselines3."""

from __future__ import annotations

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


import argparse
import numpy as np

from omni.isaac.orbit.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from Stable-Baselines3.")
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("--checkpoint", type=str, default=None, help="Path to model checkpoint.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()

# launch omniverse app
app_launcher = AppLauncher(args_cli)
simulation_app = app_launcher.app

"""Rest everything follows."""


import gym
import torch
import traceback

import carb
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import VecNormalize

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

from config import parse_sb3_cfg


def main():
    """Play 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)
    # create isaac environment
    env = gym.make(args_cli.task, cfg=env_cfg)
    # wrap around environment for stable baselines
    env = Sb3VecEnvWrapper(env)
    # parse agent configuration
    agent_cfg = parse_sb3_cfg(args_cli.task)

    # normalize environment (if needed)
    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,
        )

    # check checkpoint is valid
    if args_cli.checkpoint is None:
        raise ValueError("Checkpoint path is not valid.")
    # create agent from stable baselines
    print(f"Loading checkpoint from: {args_cli.checkpoint}")
    agent = PPO.load(args_cli.checkpoint, env, print_system_info=True)

    # reset environment
    obs = env.reset()
    # simulate environment
    while simulation_app.is_running():
        # run everything in inference mode
        with torch.inference_mode():
            # agent stepping
            actions, _ = agent.predict(obs, deterministic=True)
            # env stepping
            obs, _, _, _ = env.step(actions)

    # close the simulator
    env.close()


if __name__ == "__main__":
    try:
        # run the main execution
        main()
    except Exception as err:
        carb.log_error(err)
        carb.log_error(traceback.format_exc())
        raise
    finally:
        # close sim app
        simulation_app.close()
