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

"""Script to run a trained policy from robomimic."""

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

import argparse

from omni.isaac.orbit.app import AppLauncher

# add argparse arguments
parser = argparse.ArgumentParser(description="Play policy trained using robomimic for Orbit environments.")
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("--task", type=str, default=None, help="Name of the task.")
parser.add_argument("--checkpoint", type=str, default=None, help="Pytorch model checkpoint to load.")
# 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 gymnasium as gym
import torch

import robomimic  # noqa: F401
import robomimic.utils.file_utils as FileUtils
import robomimic.utils.torch_utils as TorchUtils

import omni.isaac.orbit_tasks  # noqa: F401
from omni.isaac.orbit_tasks.utils import parse_env_cfg


def main():
    """Run a trained policy from robomimic with Orbit environment."""
    # parse configuration
    env_cfg = parse_env_cfg(args_cli.task, use_gpu=not args_cli.cpu, num_envs=1, use_fabric=not args_cli.disable_fabric)
    # we want to have the terms in the observations returned as a dictionary
    # rather than a concatenated tensor
    env_cfg.observations.policy.concatenate_terms = False

    # create environment
    env = gym.make(args_cli.task, cfg=env_cfg)

    # acquire device
    device = TorchUtils.get_torch_device(try_to_use_cuda=True)
    # restore policy
    policy, _ = FileUtils.policy_from_checkpoint(ckpt_path=args_cli.checkpoint, device=device, verbose=True)

    # reset environment
    obs_dict, _ = env.reset()
    # robomimic only cares about policy observations
    obs = obs_dict["policy"]
    # simulate environment
    while simulation_app.is_running():
        # run everything in inference mode
        with torch.inference_mode():
            # compute actions
            actions = policy(obs)
            actions = torch.from_numpy(actions).to(device=device).view(1, env.action_space.shape[1])
            # apply actions
            obs_dict = env.step(actions)[0]
            # robomimic only cares about policy observations
            obs = obs_dict["policy"]

    # close the simulator
    env.close()


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