# Copyright 2024 Bytedance Ltd. and/or its affiliates
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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from dataclasses import is_dataclass
from typing import Any, Optional

from omegaconf import DictConfig, ListConfig, OmegaConf

__all__ = ["omega_conf_to_dataclass"]


def omega_conf_to_dataclass(config: DictConfig | dict, dataclass_type: Optional[type[Any]] = None) -> Any:
    """
    Convert an OmegaConf DictConfig to a dataclass.

    Args:
        config: The OmegaConf DictConfig or dict to convert.
        dataclass_type: The dataclass type to convert to. When dataclass_type is None,
            the DictConfig must contain _target_ to be instantiated via hydra.instantiate API.

    Returns:
        The dataclass instance.
    """
    # Got an empty config
    if not config:
        return dataclass_type if dataclass_type is None else dataclass_type()
    # Got an object
    if not isinstance(config, DictConfig | ListConfig | dict | list):
        return config

    if dataclass_type is None:
        assert "_target_" in config, (
            "When dataclass_type is not provided, config must contain _target_."
            "See trainer/config/ppo_trainer.yaml algorithm section for an example."
        )
        from hydra.utils import instantiate

        return instantiate(config, _convert_="partial")

    if not is_dataclass(dataclass_type):
        raise ValueError(f"{dataclass_type} must be a dataclass")
    cfg = OmegaConf.create(config)  # in case it's a dict
    cfg_from_dataclass = OmegaConf.structured(dataclass_type)
    # let cfg override the existing vals in `cfg_from_dataclass`
    cfg_merged = OmegaConf.merge(cfg_from_dataclass, cfg)
    # now convert to `dataclass_type`
    config_object = OmegaConf.to_object(cfg_merged)
    return config_object


def update_dict_with_config(dictionary: dict, config: DictConfig):
    for key in dictionary:
        if hasattr(config, key):
            dictionary[key] = getattr(config, key)
