#!/usr/bin/env python3
# BenchFlow Interface for MMLU
# This file is a template for integrating MMLU with BenchFlow

import os
import json
import time
from typing import Dict, List, Any, Optional, Union

class BenchflowInterface:
    """
    BenchFlow interface for MMLU
    
    This class provides methods to:
    1. Load the benchmark
    2. Run evaluations
    3. Format results
    """
    
    def __init__(self, config_path: Optional[str] = None):
        """
        Initialize the BenchFlow interface
        
        Args:
            config_path: Path to configuration file (optional)
        """
        self.config = {}
        if config_path and os.path.exists(config_path):
            with open(config_path, 'r') as f:
                self.config = json.load(f)
        
        # Default configuration
        self.config.setdefault("max_samples", 100)
        self.config.setdefault("output_dir", "./results")
        
        # Create output directory if it doesn't exist
        os.makedirs(self.config["output_dir"], exist_ok=True)
    
    def load_benchmark(self) -> bool:
        """
        Load the benchmark data
        
        Returns:
            bool: True if successful, False otherwise
        """
        try:
            # TODO: Implement benchmark loading logic
            print(f"Loading MMLU benchmark...")
            return True
        except Exception as e:
            print(f"Error loading benchmark: {e}")
            return False
    
    def evaluate(self, model_outputs: List[Dict[str, Any]]) -> Dict[str, Any]:
        """
        Evaluate model outputs against the benchmark
        
        Args:
            model_outputs: List of model outputs to evaluate
            
        Returns:
            Dict containing evaluation results
        """
        # TODO: Implement evaluation logic
        results = {
            "benchmark": "MMLU",
            "version": "1.0.0",
            "timestamp": time.strftime("%Y-%m-%d %H:%M:%S"),
            "metrics": {
                "accuracy": 0.0,
                "f1_score": 0.0,
                "precision": 0.0,
                "recall": 0.0
            },
            "samples_evaluated": len(model_outputs)
        }
        
        return results
    
    def save_results(self, results: Dict[str, Any], filename: str = "results.json") -> str:
        """
        Save evaluation results to file
        
        Args:
            results: Evaluation results to save
            filename: Name of the output file
            
        Returns:
            Path to the saved file
        """
        output_path = os.path.join(self.config["output_dir"], filename)
        with open(output_path, 'w') as f:
            json.dump(results, f, indent=2)
        
        return output_path
    
    def get_benchmark_info(self) -> Dict[str, Any]:
        """
        Get information about the benchmark
        
        Returns:
            Dict containing benchmark metadata
        """
        return {
            "name": "MMLU",
            "description": "A benchmark for knowledge evaluation",
            "version": "1.0.0",
            "category": "knowledge",
            "github": "https://github.com/hendrycks/test",
            "paper": "https://arxiv.org/abs/2009.03300",
            "website": ""
        }

# Example usage
if __name__ == "__main__":
    interface = BenchflowInterface()
    if interface.load_benchmark():
        print("Benchmark loaded successfully")
        
        # Example model outputs
        model_outputs = [
            {"input": "Example input 1", "output": "Example output 1"},
            {"input": "Example input 2", "output": "Example output 2"},
        ]
        
        # Evaluate
        results = interface.evaluate(model_outputs)
        
        # Save results
        output_path = interface.save_results(results)
        print(f"Results saved to {output_path}")
        
        # Print benchmark info
        print(json.dumps(interface.get_benchmark_info(), indent=2))