"""Abstract base class for all steering methods."""

from abc import ABC, abstractmethod
from typing import Optional

import numpy as np


class SteeringMethod(ABC):
    """Base class for activation steering methods.

    Subclasses implement `extract_vector()` which takes probing data
    (H_pos, H_neg) and returns a steering direction vector.
    """

    @property
    @abstractmethod
    def name(self) -> str:
        """Human-readable method name."""
        ...

    @property
    @abstractmethod
    def method_id(self) -> str:
        """Method ID (M0–M11)."""
        ...

    @property
    def is_training_free(self) -> bool:
        """Whether this method requires no training."""
        return True

    @abstractmethod
    def extract_vector(
        self,
        h_pos: np.ndarray,
        h_neg: np.ndarray,
        **kwargs,
    ) -> Optional[np.ndarray]:
        """Compute the steering vector from probing data.

        Args:
            h_pos: Positive hidden states, shape (N, d)
            h_neg: Negative hidden states, shape (N, d)

        Returns:
            Steering vector of shape (d,), or None for prompt-only methods.
        """
        ...

    def train(self, train_data: dict) -> None:
        """Train the method (for non-training-free methods).

        Default: no-op. Override in M8, M9, M10.
        """
        pass

    def __repr__(self) -> str:
        return f"{self.__class__.__name__}(id={self.method_id}, training_free={self.is_training_free})"