from __future__ import annotations

from transformers import PretrainedConfig


class MrBalanceConfig(PretrainedConfig):
    """
    Configuration for the MrBalance continuous-control Actor-Critic MLP.
    """

    model_type = "mrbalance"

    def __init__(
        self,
        observation_size: int = 64,
        hidden_size: int = 128,
        intermediate_size: int = 128,
        bottleneck_size: int = 64,
        action_size: int = 2,
        actor_log_std_init: float = -0.75,
        activation: str = "silu",
        action_squashing: str = "tanh",
        action_names=None,
        **kwargs,
    ):
        self.observation_size = observation_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.bottleneck_size = bottleneck_size
        self.action_size = action_size
        self.actor_log_std_init = actor_log_std_init
        self.activation = activation
        self.action_squashing = action_squashing
        self.action_names = (
            action_names
            if action_names is not None
            else ["roll", "pitch"]
        )

        super().__init__(**kwargs)