import argparse
import sys
import os
import random
from datetime import datetime

import gymnasium as gym
import skrl
from packaging import version

from isaaclab.app import AppLauncher
from isaaclab.envs import (
    DirectMARLEnv,
    DirectMARLEnvCfg,
    DirectRLEnvCfg,
    ManagerBasedRLEnvCfg,
    multi_agent_to_single_agent,
)
from isaaclab.utils.assets import retrieve_file_path
from isaaclab.utils.dict import print_dict
from isaaclab.utils.io import dump_pickle, dump_yaml

from isaaclab_rl.skrl import SkrlVecEnvWrapper

from isaaclab_tasks.utils.hydra import hydra_task_config

# add argparse arguments
parser = argparse.ArgumentParser(description="Train an RL agent with 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(
    "--video_interval",
    type=int,
    default=2000,
    help="Interval between video recordings (in steps).",
)
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"
)
parser.add_argument(
    "--distributed",
    action="store_true",
    default=False,
    help="Run training with multiple GPUs or nodes.",
)
parser.add_argument(
    "--checkpoint",
    type=str,
    default=None,
    help="Path to model checkpoint to resume training.",
)
parser.add_argument(
    "--max_iterations",
    type=int,
    default=None,
    help="RL Policy training iterations.",
)
parser.add_argument(
    "--ml_framework",
    type=str,
    default="torch",
    choices=["torch", "jax", "jax-numpy"],
    help="The ML framework used for training the skrl agent.",
)
parser.add_argument(
    "--algorithm",
    type=str,
    default="PPO",
    choices=["AMP", "PPO", "IPPO", "MAPPO"],
    help="The RL algorithm used for training the skrl agent.",
)

# append AppLauncher cli args
AppLauncher.add_app_launcher_args(parser)
# parse the arguments
args_cli, hydra_args = parser.parse_known_args()
# always enable cameras to record video
if args_cli.video:
    args_cli.enable_cameras = True

# clear out sys.argv for Hydra
sys.argv = [sys.argv[0]] + hydra_args

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

# check for minimum supported skrl version
SKRL_VERSION = "1.4.2"
if version.parse(skrl.__version__) < version.parse(SKRL_VERSION):
    skrl.logger.error(
        f"Unsupported skrl version: {skrl.__version__}. "
        f"Install supported version using 'pip install skrl>={SKRL_VERSION}'"
    )
    exit()

if args_cli.ml_framework.startswith("torch"):
    from skrl.utils.runner.torch import Runner
elif args_cli.ml_framework.startswith("jax"):
    from skrl.utils.runner.jax import Runner
else:
    raise ValueError(f"Unsupported ML framework: {args_cli.ml_framework}")

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

# config shortcuts
algorithm = args_cli.algorithm.lower()
agent_cfg_entry_point = (
    "skrl_cfg_entry_point"
    if algorithm in ["ppo"]
    else f"skrl_{algorithm}_cfg_entry_point"
)


@hydra_task_config(args_cli.task, agent_cfg_entry_point)
def main(
    env_cfg: ManagerBasedRLEnvCfg | DirectRLEnvCfg | DirectMARLEnvCfg,
    agent_cfg: dict,
):
    """Train with skrl agent."""
    # override configurations with non-hydra CLI arguments
    env_cfg.scene.num_envs = (
        args_cli.num_envs if args_cli.num_envs is not None else env_cfg.scene.num_envs
    )
    env_cfg.sim.device = (
        args_cli.device if args_cli.device is not None else env_cfg.sim.device
    )

    # multi-gpu training config
    if args_cli.distributed:
        env_cfg.sim.device = f"cuda:{app_launcher.local_rank}"
    # max iterations for training
    if args_cli.max_iterations:
        agent_cfg["trainer"]["timesteps"] = (
            args_cli.max_iterations * agent_cfg["agent"]["rollouts"]
        )
    agent_cfg["trainer"]["close_environment_at_exit"] = False
    # 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"

    # randomly sample a seed if seed = -1
    if args_cli.seed == -1:
        args_cli.seed = random.randint(0, 10000)

    # set the agent and environment seed from command line
    # note: certain randomization occur in the environment initialization so we set the seed here
    agent_cfg["seed"] = (
        args_cli.seed if args_cli.seed is not None else agent_cfg["seed"]
    )
    env_cfg.seed = agent_cfg["seed"]

    # specify directory for logging experiments
    log_root_path = os.path.join(
        "logs", "skrl", agent_cfg["agent"]["experiment"]["directory"]
    )
    log_root_path = os.path.abspath(log_root_path)
    print(f"[INFO] Logging experiment in directory: {log_root_path}")
    # specify directory for logging runs: {time-stamp}_{run_name}
    log_dir = (
        datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
        + f"_{algorithm}_{args_cli.ml_framework}"
    )
    # The Ray Tune workflow extracts experiment name using the logging line below, hence, do not change it (see PR #2346, comment-2819298849)
    print(f"Exact experiment name requested from command line: {log_dir}")
    if agent_cfg["agent"]["experiment"]["experiment_name"]:
        log_dir += f'_{agent_cfg["agent"]["experiment"]["experiment_name"]}'
    # set directory into agent config
    agent_cfg["agent"]["experiment"]["directory"] = log_root_path
    agent_cfg["agent"]["experiment"]["experiment_name"] = log_dir
    # update log_dir
    log_dir = os.path.join(log_root_path, log_dir)

    # 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)

    # get checkpoint path (to resume training)
    resume_path = (
        retrieve_file_path(args_cli.checkpoint) if args_cli.checkpoint else None
    )

    # 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) and algorithm in ["ppo"]:
        env = multi_agent_to_single_agent(env)  # type: ignore

    # wrap for video recording
    if args_cli.video:
        video_kwargs = {
            "video_folder": os.path.join(log_dir, "videos", "train"),
            "step_trigger": lambda step: step % args_cli.video_interval == 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  # type: ignore
    )  # same as: `wrap_env(env, wrapper="auto")`

    # configure and instantiate the skrl runner
    # https://skrl.readthedocs.io/en/latest/api/utils/runner.html
    runner = Runner(env, agent_cfg)  # type: ignore

    # load checkpoint (if specified)
    if resume_path:
        print(f"[INFO] Loading model checkpoint from: {resume_path}")
        runner.agent.load(resume_path)

    # run training
    runner.run()

    # close the simulator
    env.close()
