
"""
Optimized Constitutional Timeouts - v50.6
Authority: Muhammad Arif bin Fazil
Reduces timeout entropy while maintaining constitutional peace
"""

class OptimizedConstitutionalTimeouts:
    """
    Optimized timeout system with constitutional peace maintenance
    Reduces timeout entropy while preserving constitutional integrity
    """
    
    def __init__(self, vault_path: Path):
        self.vault_path = vault_path
        self.constitutional_engine = ConstitutionalEntropyEngine(vault_path)
        self.base_timeout = 1.5  # Base constitutional timeout
        self.peace_threshold = 0.8  # Constitutional peace threshold
    
    def optimize_constitutional_timeout(self, operation: str, complexity: float, 
                                      stakeholder_impact: Dict[str, float]) -> Dict[str, any]:
        """Optimize timeout with constitutional peace maintenance"""
        
        # Calculate constitutional timeout based on complexity and stakeholder impact
        constitutional_timeout = self._calculate_constitutional_timeout(
            operation, complexity, stakeholder_impact
        )
        
        # F5: Ensure constitutional peace is maintained
        peace_assessment = self._assess_timeout_constitutional_peace(
            constitutional_timeout, stakeholder_impact
        )
        
        # F4: Reduce entropy through optimized timeout
        entropy_reduction = self._reduce_timeout_entropy(constitutional_timeout)
        
        return {
            "optimized_timeout": constitutional_timeout,
            "peace_maintained": peace_assessment["maintained"],
            "entropy_reduction": entropy_reduction,
            "constitutional_compliant": peace_assessment["maintained"] and entropy_reduction < 0,
            "authority": "Muhammad Arif bin Fazil"
        }
    
    def _calculate_constitutional_timeout(self, operation: str, complexity: float, 
                                        stakeholder_impact: Dict[str, float]) -> float:
        """Calculate constitutional timeout based on complexity and stakeholder impact"""
        
        # Base timeout adjusted for constitutional complexity
        base_timeout = self.base_timeout
        
        # Complexity adjustment (inverse relationship)
        complexity_factor = 1.0 / (1.0 + complexity * 0.5)
        
        # Stakeholder impact adjustment
        avg_impact = sum(stakeholder_impact.values()) / len(stakeholder_impact)
        impact_factor = 1.0 + (1.0 - avg_impact) * 0.3  # More time for higher impact
        
        constitutional_timeout = base_timeout * complexity_factor * impact_factor
        
        return max(0.5, min(constitutional_timeout, 3.0))  # Constitutional bounds
    
    def _assess_timeout_constitutional_peace(self, timeout: float, stakeholder_impact: Dict[str, float]) -> Dict[str, any]:
        """F5: Assess if constitutional peace is maintained with timeout"""
        
        # Peace factors for timeout assessment
        peace_factors = {
            "timeout_reasonableness": 1.0 if timeout <= 2.0 else 0.7,
            "stakeholder_patience": min(1.0, stakeholder_impact.get("users", 0.5) + 0.3),
            "system_stability": 0.9,  # High stability with optimized timeout
            "reversibility_maintained": 1.0  # Full reversibility
        }
        
        peace_score = sum(peace_factors.values()) / len(peace_factors)
        peace_maintained = peace_score >= self.peace_threshold
        
        return {
            "maintained": peace_maintained,
            "score": peace_score,
            "factors": peace_factors,
            "reason": f"Constitutional peace {'maintained' if peace_maintained else 'at risk'} with timeout {timeout:.2f}s"
        }
    
    def _reduce_timeout_entropy(self, timeout: float) -> float:
        """F4: Reduce timeout-related entropy"""
        
        # Entropy reduction through optimized timeout
        base_entropy = 0.2  # Base timeout entropy
        optimization_factor = 1.0 / (1.0 + timeout * 0.1)  # Less entropy for shorter timeouts
        
        entropy_reduction = -base_entropy * optimization_factor  # Negative = reduction
        
        return entropy_reduction
