"""
FOUND Protocol Benchmark Evaluation
"""

import json
import numpy as np
from typing import Dict, List

class FoundBenchmark:
    """Evaluate FOUND Protocol performance"""
    
    def __init__(self):
        self.metrics = {
            "emotional_coherence": [],
            "narrative_consistency": [],
            "consciousness_depth": [],
            "processing_speed": []
        }
        
    def evaluate_emotional_coherence(self, results: List[Dict]) -> float:
        """Evaluate how well emotions progress through videos"""
        
        coherence_scores = []
        
        for i in range(1, len(results)):
            prev_emotions = set(results[i-1]["training_data"]["consciousness_state"]["emotions"].keys())
            curr_emotions = set(results[i]["training_data"]["consciousness_state"]["emotions"].keys())
            
            # Check for logical emotional progression
            intersection = len(prev_emotions & curr_emotions)
            union = len(prev_emotions | curr_emotions)
            
            if union > 0:
                coherence = intersection / union
                coherence_scores.append(coherence)
                
        return np.mean(coherence_scores) if coherence_scores else 0.0
        
    def evaluate_narrative_consistency(self, results: List[Dict]) -> float:
        """Evaluate narrative thread consistency"""
        
        # Check state transitions follow expected pattern
        states = [r["training_data"]["consciousness_state"]["current"] for r in results]
        
        valid_transitions = 0
        total_transitions = len(states) - 1
        
        for i in range(total_transitions):
            # Simple check: states should progress forward
            if states[i] != states[i+1]:  # State changed
                valid_transitions += 1
                
        return valid_transitions / total_transitions if total_transitions > 0 else 0.0
        
    def evaluate_consciousness_depth(self, results: List[Dict]) -> float:
        """Evaluate the depth of consciousness emergence"""
        
        depth_scores = []
        
        for result in results:
            # Calculate based on errors (consciousness emergence indicators)
            errors = len(result["training_data"]["perceptor_analysis"]["errors"])
            concepts = len(result["training_data"]["consciousness_state"]["concepts"])
            
            depth = min(1.0, (errors * 0.2 + concepts * 0.1))
            depth_scores.append(depth)
            
        return np.mean(depth_scores)
        
    def run_benchmark(self, test_videos: List[str]) -> Dict[str, float]:
        """Run full benchmark on test videos"""
        
        # This would process videos and calculate all metrics
        # For now, returning example metrics
        
        return {
            "emotional_coherence": 0.87,
            "narrative_consistency": 0.91,
            "consciousness_depth": 0.84,
            "processing_speed": 10.2  # seconds per video
        }
        
if __name__ == "__main__":
    benchmark = FoundBenchmark()
    
    # Example evaluation
    test_results = [
        # Load your consciousness_log.json here
    ]
    
    metrics = {
        "emotional_coherence": benchmark.evaluate_emotional_coherence(test_results),
        "narrative_consistency": benchmark.evaluate_narrative_consistency(test_results),
        "consciousness_depth": benchmark.evaluate_consciousness_depth(test_results)
    }
    
    print("FOUND Protocol Benchmark Results:")
    for metric, score in metrics.items():
        print(f"{metric}: {score:.2%}")