"""
RLPrompt implementation for reinforcement learning-based prompt optimization.

Based on: "RLPrompt: Optimizing Discrete Text Prompts with Reinforcement Learning"
https://arxiv.org/abs/2205.12548
"""

from typing import List, Dict, Any, Optional
from . import PromptOptimizer
from ..base import PromptTechnique
from ..utils import log


class RLPromptOptimizer(PromptOptimizer):
    """
    RLPrompt optimizer using reinforcement learning for discrete prompt discovery.
    
    This approach treats prompt optimization as an RL problem where actions
    are token selections and rewards are based on task performance.
    """
    
    def __init__(self):
        super().__init__(
            name="RLPrompt",
            description="Reinforcement Learning-based Prompt Optimization"
        )
    
    async def optimize(
        self,
        task_description: str,
        seed_prompts: List[str],
        labeled_examples: List[Dict[str, str]],
        technique: Optional[PromptTechnique] = None,
        num_episodes: int = 100,
        learning_rate: float = 0.001,
        **kwargs
    ) -> Dict[str, Any]:
        """
        Optimize prompts using reinforcement learning.
        
        Note: Full implementation requires integration with the RLPrompt
        repository (https://github.com/mingkaid/rl-prompt).
        """
        log.warning("RLPrompt optimizer requires external dependencies. Using first seed prompt.")
        
        return {
            "best_prompt": seed_prompts[0] if seed_prompts else f"Please {task_description}",
            "score": 0.0,
            "metadata": {
                "algorithm": "RLPrompt",
                "status": "requires_external_setup",
                "num_episodes": num_episodes,
                "learning_rate": learning_rate,
                "note": "Full implementation requires RLPrompt repository setup"
            },
            "history": [],
            "all_candidates": []
        }
