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


"""Utility functions for parsing skrl configuration files."""

import os
import yaml

from skrl.resources.preprocessors.torch import RunningStandardScaler  # noqa: F401
from skrl.resources.schedulers.torch import KLAdaptiveRL  # noqa: F401
from skrl.utils.model_instantiators import Shape  # noqa: F401

from omni.isaac.orbit_envs import ORBIT_ENVS_DATA_DIR

__all__ = ["SKRL_PPO_CONFIG_FILE", "parse_skrl_cfg"]


SKRL_PPO_CONFIG_FILE = {
    # classic
    "Isaac-Cartpole-v0": os.path.join(ORBIT_ENVS_DATA_DIR, "skrl/cartpole_ppo.yaml"),
    "Isaac-Ant-v0": os.path.join(ORBIT_ENVS_DATA_DIR, "skrl/ant_ppo.yaml"),
    "Isaac-Humanoid-v0": os.path.join(ORBIT_ENVS_DATA_DIR, "skrl/humanoid_ppo.yaml"),
    # manipulation
    "Isaac-Lift-Franka-v0": os.path.join(ORBIT_ENVS_DATA_DIR, "skrl/lift_ppo.yaml"),
    "Isaac-Reach-Franka-v0": os.path.join(ORBIT_ENVS_DATA_DIR, "skrl/reach_ppo.yaml"),
    # locomotion
    "Isaac-Velocity-Anymal-C-v0": os.path.join(ORBIT_ENVS_DATA_DIR, "skrl/anymal_ppo.yaml"),
}
"""Mapping from environment names to PPO agent files."""


def parse_skrl_cfg(task_name) -> dict:
    """Parse configuration based on command line arguments.

    Args:
        task_name (str): The name of the environment.

    Returns:
        dict: A dictionary containing the parsed configuration.
    """
    # retrieve the default environment config file
    try:
        config_file = SKRL_PPO_CONFIG_FILE[task_name]
    except KeyError:
        raise ValueError(f"Task not found: {task_name}")

    # parse agent configuration
    with open(config_file) as f:
        cfg = yaml.load(f, Loader=yaml.Loader)

    return cfg


def convert_skrl_cfg(cfg):
    """Convert simple YAML types to skrl classes/components.

    Args:
        cfg (dict): configuration dictionary.

    Returns:
        dict: A dictionary containing the converted configuration.
    """
    _direct_eval = [
        "learning_rate_scheduler",
        "state_preprocessor",
        "value_preprocessor",
        "input_shape",
        "output_shape",
    ]

    def reward_shaper_function(scale):
        def reward_shaper(rewards, timestep, timesteps):
            return rewards * scale

        return reward_shaper

    def update_dict(d):
        for key, value in d.items():
            if isinstance(value, dict):
                update_dict(value)
            else:
                if key in _direct_eval:
                    d[key] = eval(value)
                elif key.endswith("_kwargs"):
                    d[key] = value if value is not None else {}
                elif key in ["rewards_shaper_scale"]:
                    d["rewards_shaper"] = reward_shaper_function(value)

        return d

    # parse agent configuration and convert to classes
    return update_dict(cfg)
