"""
DPO (Direct Preference Optimization)
-----------------------------------
An RL method that directly optimizes model outputs based on preference data, often used in LLM fine-tuning.

Example usage:
    >>> from langvision.concepts.dpo import DPO
    >>> import torch
    >>> class MyDPO(DPO):
    ...     def optimize_with_preferences(self, model, preferences, optimizer):
    ...         super().optimize_with_preferences(model, preferences, optimizer)
    >>> model = torch.nn.Linear(2, 1)
    >>> preferences = [(torch.tensor([1.0, 2.0]), 1.0), (torch.tensor([2.0, 3.0]), -1.0)]
    >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
    >>> dpo = MyDPO()
    >>> dpo.optimize_with_preferences(model, preferences, optimizer)
"""
from abc import ABC, abstractmethod
from typing import Any, List
import torch

class DPO(ABC):
    """
    Abstract base class for Direct Preference Optimization (DPO).
    """
    @abstractmethod
    def optimize_with_preferences(self, model: torch.nn.Module, preferences: List[Any], optimizer: torch.optim.Optimizer) -> None:
        """
        Optimize the model using preference data.
        """
        for x, pref in preferences:
            optimizer.zero_grad()
            output = model(x)
            # Simple preference loss: maximize output if preferred, minimize if not
            loss = -pref * output.sum()
            loss.backward()
            optimizer.step() 