# Copyright (c) 2022-2024, The Isaac Lab Project Developers.
# All rights reserved.
#
# SPDX-License-Identifier: BSD-3-Clause

"""
Script to play a checkpoint of an RL agent from skrl.

Visit the skrl documentation (https://skrl.readthedocs.io) to see the examples structured in
a more user-friendly way.
"""

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


import argparse

from omni.isaac.lab.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Play a checkpoint of an RL agent from skrl.")
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("--cpu", action="store_true", default=False, help="Use CPU pipeline.")
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(
    "--ml_framework",
    type=str,
    default="torch",
    choices=["torch", "jax", "jax-numpy"],
    help="The ML framework used for training the skrl agent.",
)

# 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 os
import torch

import skrl

if args_cli.ml_framework.startswith("torch"):
    from skrl.agents.torch.ppo import PPO, PPO_DEFAULT_CONFIG
    from skrl.utils.model_instantiators.torch import deterministic_model, gaussian_model, shared_model
elif args_cli.ml_framework.startswith("jax"):
    from skrl.agents.jax.ppo import PPO, PPO_DEFAULT_CONFIG
    from skrl.utils.model_instantiators.jax import deterministic_model, gaussian_model

from omni.isaac.lab.utils.dict import print_dict

import omni.isaac.lab_tasks  # noqa: F401
from omni.isaac.lab_tasks.utils import get_checkpoint_path, load_cfg_from_registry, parse_env_cfg
from omni.isaac.lab_tasks.utils.wrappers.skrl import SkrlVecEnvWrapper, process_skrl_cfg


def main():
    """Play with skrl agent."""
    # configure the ML framework into the global skrl variable
    if args_cli.ml_framework.startswith("jax"):
        skrl.config.jax.backend = "jax" if args_cli.ml_framework == "jax" else "numpy"
    # parse configuration
    env_cfg = parse_env_cfg(
        args_cli.task, use_gpu=not args_cli.cpu, num_envs=args_cli.num_envs, use_fabric=not args_cli.disable_fabric
    )
    experiment_cfg = load_cfg_from_registry(args_cli.task, "skrl_cfg_entry_point")

    # specify directory for logging experiments (load checkpoint)
    log_root_path = os.path.join("logs", "skrl", experiment_cfg["agent"]["experiment"]["directory"])
    log_root_path = os.path.abspath(log_root_path)
    print(f"[INFO] Loading experiment from directory: {log_root_path}")
    # get checkpoint path
    if args_cli.checkpoint:
        resume_path = os.path.abspath(args_cli.checkpoint)
    else:
        resume_path = get_checkpoint_path(log_root_path, other_dirs=["checkpoints"])
    log_dir = os.path.dirname(os.path.dirname(resume_path))

    # create isaac environment
    env = gym.make(args_cli.task, cfg=env_cfg, render_mode="rgb_array" if args_cli.video else None)
    # 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 skrl
    env = SkrlVecEnvWrapper(env, ml_framework=args_cli.ml_framework)  # same as: `wrap_env(env, wrapper="isaaclab")`

    # instantiate models using skrl model instantiator utility
    # https://skrl.readthedocs.io/en/latest/api/utils/model_instantiators.html
    models = {}
    if args_cli.ml_framework.startswith("jax"):
        experiment_cfg["models"]["separate"] = True  # shared model is not supported in JAX
    # non-shared models
    if experiment_cfg["models"]["separate"]:
        models["policy"] = gaussian_model(
            observation_space=env.observation_space,
            action_space=env.action_space,
            device=env.device,
            **process_skrl_cfg(experiment_cfg["models"]["policy"], ml_framework=args_cli.ml_framework),
        )
        models["value"] = deterministic_model(
            observation_space=env.observation_space,
            action_space=env.action_space,
            device=env.device,
            **process_skrl_cfg(experiment_cfg["models"]["value"], ml_framework=args_cli.ml_framework),
        )
    # shared models
    else:
        models["policy"] = shared_model(
            observation_space=env.observation_space,
            action_space=env.action_space,
            device=env.device,
            structure=None,
            roles=["policy", "value"],
            parameters=[
                process_skrl_cfg(experiment_cfg["models"]["policy"], ml_framework=args_cli.ml_framework),
                process_skrl_cfg(experiment_cfg["models"]["value"], ml_framework=args_cli.ml_framework),
            ],
        )
        models["value"] = models["policy"]
    # instantiate models' state dict
    if args_cli.ml_framework.startswith("jax"):
        for role, model in models.items():
            model.init_state_dict(role)

    # configure and instantiate PPO agent
    # https://skrl.readthedocs.io/en/latest/api/agents/ppo.html
    agent_cfg = PPO_DEFAULT_CONFIG.copy()
    experiment_cfg["agent"]["rewards_shaper"] = None  # avoid 'dictionary changed size during iteration'
    agent_cfg.update(process_skrl_cfg(experiment_cfg["agent"], ml_framework=args_cli.ml_framework))

    agent_cfg["state_preprocessor_kwargs"].update({"size": env.observation_space, "device": env.device})
    agent_cfg["value_preprocessor_kwargs"].update({"size": 1, "device": env.device})
    agent_cfg["experiment"]["write_interval"] = 0  # don't log to Tensorboard
    agent_cfg["experiment"]["checkpoint_interval"] = 0  # don't generate checkpoints

    agent = PPO(
        models=models,
        memory=None,  # memory is optional during evaluation
        cfg=agent_cfg,
        observation_space=env.observation_space,
        action_space=env.action_space,
        device=env.device,
    )

    # initialize agent
    agent.init()
    print(f"[INFO] Loading model checkpoint from: {resume_path}")
    agent.load(resume_path)
    # set agent to evaluation mode
    agent.set_running_mode("eval")

    # reset environment
    obs, _ = env.reset()
    timestep = 0
    # simulate environment
    while simulation_app.is_running():
        # run everything in inference mode
        with torch.inference_mode():
            # agent stepping
            actions = agent.act(obs, timestep=0, timesteps=0)[0]
            # 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

    # close the simulator
    env.close()


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