"""
ProTeGi (Prompt Optimization with Textual Gradients) implementation.

Based on: "Automatic Prompt Optimization with 'Gradient Descent' and Beam Search"
https://aclanthology.org/2023.emnlp-main.494/
"""

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


class ProTeGiOptimizer(PromptOptimizer):
    """
    ProTeGi optimizer using textual gradients for iterative prompt refinement.
    
    This approach uses LLM-generated critiques as "gradients" to iteratively
    improve prompts, similar to gradient descent in continuous optimization.
    """
    
    def __init__(self):
        super().__init__(
            name="ProTeGi",
            description="Prompt Optimization with Textual Gradients"
        )
    
    async def optimize(
        self,
        task_description: str,
        seed_prompts: List[str],
        labeled_examples: List[Dict[str, str]],
        technique: Optional[PromptTechnique] = None,
        num_iterations: int = 10,
        beam_width: int = 5,
        **kwargs
    ) -> Dict[str, Any]:
        """
        Optimize prompts using textual gradients.
        
        Implementation coming soon - this is a placeholder.
        """
        log.warning("ProTeGi optimizer is not yet implemented. Using first seed prompt.")
        
        return {
            "best_prompt": seed_prompts[0] if seed_prompts else f"Please {task_description}",
            "score": 0.0,
            "metadata": {
                "algorithm": "ProTeGi",
                "status": "not_implemented",
                "num_iterations": num_iterations,
                "beam_width": beam_width
            },
            "history": [],
            "all_candidates": []
        }
