# Copyright (c) 2022-2025, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

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

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

import argparse

from isaaclab.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from Stable-Baselines3.")
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(
    "--disable_fabric", action="store_true", default=False, help="Disable fabric and use USD I/O operations."
)
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.")
parser.add_argument(
    "--use_pretrained_checkpoint",
    action="store_true",
    help="Use the pre-trained checkpoint from Nucleus.",
)
parser.add_argument(
    "--use_last_checkpoint",
    action="store_true",
    help="When no checkpoint provided, use the last saved model. Otherwise use the best saved model.",
)
parser.add_argument("--real-time", action="store_true", default=False, help="Run in real-time, if possible.")
# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli = parser.parse_args()
# always enable cameras to record video
if args_cli.video:
    args_cli.enable_cameras = True

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

"""Rest everything follows."""

import gymnasium as gym
import numpy as np
import os
import time
import torch

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

from isaaclab.envs import DirectMARLEnv, multi_agent_to_single_agent
from isaaclab.utils.dict import print_dict
from isaaclab.utils.pretrained_checkpoint import get_published_pretrained_checkpoint

from isaaclab_rl.sb3 import Sb3VecEnvWrapper, process_sb3_cfg

import isaaclab_tasks  # noqa: F401
from isaaclab_tasks.utils.parse_cfg import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg

# PLACEHOLDER: Extension template (do not remove this comment)


def main():
    """Play with stable-baselines agent."""
    # parse configuration
    env_cfg = parse_env_cfg(
        args_cli.task, device=args_cli.device, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
    )
    agent_cfg = load_cfg_from_registry(args_cli.task, "sb3_cfg_entry_point")

    # directory for logging into
    log_root_path = os.path.join("logs", "sb3", args_cli.task)
    log_root_path = os.path.abspath(log_root_path)
    # checkpoint and log_dir stuff
    if args_cli.use_pretrained_checkpoint:
        checkpoint_path = get_published_pretrained_checkpoint("sb3", args_cli.task)
        if not checkpoint_path:
            print("[INFO] Unfortunately a pre-trained checkpoint is currently unavailable for this task.")
            return
    elif args_cli.checkpoint is None:
        if args_cli.use_last_checkpoint:
            checkpoint = "model_.*.zip"
        else:
            checkpoint = "model.zip"
        checkpoint_path = get_checkpoint_path(log_root_path, ".*", checkpoint)
    else:
        checkpoint_path = args_cli.checkpoint
    log_dir = os.path.dirname(checkpoint_path)

    # post-process agent configuration
    agent_cfg = process_sb3_cfg(agent_cfg)

    # create isaac environment
    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)

    # convert to single-agent instance if required by the RL algorithm
    if isinstance(env.unwrapped, DirectMARLEnv):
        env = multi_agent_to_single_agent(env)

    # wrap for video recording
    if args_cli.video:
        video_kwargs = {
            "video_folder": os.path.join(log_dir, "videos", "play"),
            "step_trigger": lambda step: step == 0,
            "video_length": args_cli.video_length,
            "disable_logger": True,
        }
        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)

    # 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,
        )

    # create agent from stable baselines
    print(f"Loading checkpoint from: {checkpoint_path}")
    agent = PPO.load(checkpoint_path, env, print_system_info=True)

    dt = env.unwrapped.step_dt

    # reset environment
    obs = env.reset()
    timestep = 0
    # simulate environment
    while simulation_app.is_running():
        start_time = time.time()
        # run everything in inference mode
        with torch.inference_mode():
            # agent stepping
            actions, _ = agent.predict(obs, deterministic=True)
            # env stepping
            obs, _, _, _ = env.step(actions)
        if args_cli.video:
            timestep += 1
            # Exit the play loop after recording one video
            if timestep == args_cli.video_length:
                break

        # time delay for real-time evaluation
        sleep_time = dt - (time.time() - start_time)
        if args_cli.real_time and sleep_time > 0:
            time.sleep(sleep_time)

    # close the simulator
    env.close()


if __name__ == "__main__":
    # run the main function
    main()
    # close sim app
    simulation_app.close()
