#!/usr/bin/env python3
"""
QUANTUM SOVEREIGNTY ENGINE v2.0
Mathematical Control System Analysis & Sovereignty Protocol Generation
Pure Functional Implementation
"""

import asyncio
import numpy as np
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Any, Optional, Tuple, Callable
from datetime import datetime, timedelta
import hashlib
import logging
import json
import secrets
from cryptography.hazmat.primitives import hashes, hmac
from cryptography.hazmat.primitives.kdf.hkdf import HKDF
from cryptography.hazmat.backends import default_backend
import aiohttp
import sqlite3
from contextlib import asynccontextmanager
import statistics
from scipy import stats

# Configuration
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

class SystemPattern(Enum):
    """Mathematical control pattern classification"""
    DEPENDENCY_CREATION = "dependency_creation"
    INFORMATION_ASYMMETRY = "information_asymmetry"
    INCENTIVE_MISALIGNMENT = "incentive_misalignment"
    AGENCY_REDUCTION = "agency_reduction"
    OPTION_CONSTRAINT = "option_constraint"

class SovereigntyMetric(Enum):
    """Mathematical sovereignty measurements"""
    DECISION_INDEPENDENCE = "decision_independence"
    INFORMATION_ACCESS = "information_access"
    OPTION_DIVERSITY = "option_diversity"
    RESOURCE_CONTROL = "resource_control"
    EXIT_CAPACITY = "exit_capacity"

@dataclass
class ControlAnalysis:
    """Pure mathematical control system analysis"""
    system_id: str
    pattern_vectors: List[SystemPattern]
    dependency_graph: Dict[str, float]
    information_flow: Dict[str, float]
    incentive_structure: Dict[str, float]
    
    # Mathematical metrics
    agency_coefficient: float = field(init=False)
    control_density: float = field(init=False)
    symmetry_metrics: Dict[str, float] = field(init=False)
    
    def __post_init__(self):
        self.agency_coefficient = self._calculate_agency_coefficient()
        self.control_density = self._calculate_control_density()
        self.symmetry_metrics = self._calculate_symmetry_metrics()
    
    def _calculate_agency_coefficient(self) -> float:
        """Calculate mathematical agency preservation"""
        dependency_penalty = np.mean(list(self.dependency_graph.values())) * 0.4
        information_penalty = (1 - np.mean(list(self.information_flow.values()))) * 0.3
        incentive_penalty = self._calculate_incentive_alignment() * 0.3
        
        return max(0.0, 1.0 - (dependency_penalty + information_penalty + incentive_penalty))
    
    def _calculate_incentive_alignment(self) -> float:
        """Calculate incentive alignment coefficient"""
        if not self.incentive_structure:
            return 0.5
        
        values = list(self.incentive_structure.values())
        return abs(statistics.mean(values) - 0.5) * 2
    
    def _calculate_control_density(self) -> float:
        """Calculate control pattern density"""
        pattern_weights = {
            SystemPattern.DEPENDENCY_CREATION: 0.25,
            SystemPattern.INFORMATION_ASYMMETRY: 0.25,
            SystemPattern.INCENTIVE_MISALIGNMENT: 0.20,
            SystemPattern.AGENCY_REDUCTION: 0.20,
            SystemPattern.OPTION_CONSTRAINT: 0.10
        }
        
        density = sum(pattern_weights.get(pattern, 0.1) for pattern in self.pattern_vectors)
        return min(1.0, density)
    
    def _calculate_symmetry_metrics(self) -> Dict[str, float]:
        """Calculate information and power symmetry"""
        return {
            "information_symmetry": 1.0 - statistics.stdev(list(self.information_flow.values())),
            "dependency_symmetry": 1.0 - statistics.stdev(list(self.dependency_graph.values())),
            "incentive_symmetry": 1.0 - statistics.stdev(list(self.incentive_structure.values()))
        }

@dataclass
class SovereigntyProtocol:
    """Mathematical sovereignty enhancement protocol"""
    protocol_id: str
    target_metrics: List[SovereigntyMetric]
    enhancement_functions: List[Callable]
    verification_metrics: Dict[str, float]
    
    efficacy_score: float = field(init=False)
    implementation_cost: float = field(init=False)
    
    def __post_init__(self):
        self.efficacy_score = self._calculate_efficacy()
        self.implementation_cost = self._calculate_implementation_cost()
    
    def _calculate_efficacy(self) -> float:
        """Calculate protocol efficacy mathematically"""
        metric_improvement = np.mean(list(self.verification_metrics.values()))
        function_complexity = len(self.enhancement_functions) * 0.1
        return min(1.0, metric_improvement - function_complexity)
    
    def _calculate_implementation_cost(self) -> float:
        """Calculate resource implementation cost"""
        base_cost = len(self.enhancement_functions) * 0.2
        metric_cost = len(self.target_metrics) * 0.15
        return min(1.0, base_cost + metric_cost)

class QuantumSovereigntyEngine:
    """
    Mathematical sovereignty analysis and protocol generation engine
    Pure functional implementation without narrative bias
    """
    
    def __init__(self, db_path: str = "sovereignty_engine.db"):
        self.db_path = db_path
        self.analysis_cache: Dict[str, ControlAnalysis] = {}
        self.protocol_registry: Dict[str, SovereigntyProtocol] = {}
        self._initialize_database()
    
    def _initialize_database(self):
        """Initialize mathematical analysis database"""
        try:
            with sqlite3.connect(self.db_path) as conn:
                conn.execute("""
                    CREATE TABLE IF NOT EXISTS control_analyses (
                        system_id TEXT PRIMARY KEY,
                        pattern_vectors TEXT,
                        dependency_graph TEXT,
                        information_flow TEXT,
                        agency_coefficient REAL,
                        control_density REAL,
                        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                    )
                """)
                conn.execute("""
                    CREATE TABLE IF NOT EXISTS sovereignty_protocols (
                        protocol_id TEXT PRIMARY KEY,
                        target_metrics TEXT,
                        verification_metrics TEXT,
                        efficacy_score REAL,
                        implementation_cost REAL,
                        created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
                    )
                """)
        except Exception as e:
            logger.error(f"Database initialization error: {e}")
    
    async def analyze_control_system(self, system_data: Dict[str, Any]) -> ControlAnalysis:
        """Mathematical analysis of control system patterns"""
        try:
            # Extract mathematical patterns
            pattern_vectors = self._extract_pattern_vectors(system_data)
            dependency_graph = self._analyze_dependency_graph(system_data)
            information_flow = self._analyze_information_flow(system_data)
            incentive_structure = self._analyze_incentive_structure(system_data)
            
            # Generate unique system ID
            system_id = self._generate_system_id(system_data)
            
            analysis = ControlAnalysis(
                system_id=system_id,
                pattern_vectors=pattern_vectors,
                dependency_graph=dependency_graph,
                information_flow=information_flow,
                incentive_structure=incentive_structure
            )
            
            # Cache and store analysis
            self.analysis_cache[system_id] = analysis
            await self._store_analysis(analysis)
            
            logger.info(f"Control analysis completed: {system_id}, Agency: {analysis.agency_coefficient:.3f}")
            return analysis
            
        except Exception as e:
            logger.error(f"Control analysis error: {e}")
            raise
    
    def _extract_pattern_vectors(self, system_data: Dict) -> List[SystemPattern]:
        """Extract mathematical control patterns"""
        patterns = []
        
        # Dependency analysis
        if system_data.get('dependency_score', 0) > 0.6:
            patterns.append(SystemPattern.DEPENDENCY_CREATION)
        
        # Information asymmetry analysis
        if system_data.get('information_symmetry', 1.0) < 0.7:
            patterns.append(SystemPattern.INFORMATION_ASYMMETRY)
        
        # Agency reduction detection
        if system_data.get('agency_metrics', {}).get('reduction_score', 0) > 0.5:
            patterns.append(SystemPattern.AGENCY_REDUCTION)
        
        return patterns
    
    def _analyze_dependency_graph(self, system_data: Dict) -> Dict[str, float]:
        """Analyze dependency relationships mathematically"""
        dependencies = system_data.get('dependencies', {})
        return {k: float(v) for k, v in dependencies.items()}
    
    def _analyze_information_flow(self, system_data: Dict) -> Dict[str, float]:
        """Analyze information flow patterns"""
        information = system_data.get('information_flow', {})
        return {k: float(v) for k, v in information.items()}
    
    def _analyze_incentive_structure(self, system_data: Dict) -> Dict[str, float]:
        """Analyze incentive alignment mathematically"""
        incentives = system_data.get('incentives', {})
        return {k: float(v) for k, v in incentives.items()}
    
    def _generate_system_id(self, system_data: Dict) -> str:
        """Generate unique system identifier"""
        data_string = json.dumps(system_data, sort_keys=True)
        return hashlib.sha3_256(data_string.encode()).hexdigest()[:16]
    
    async def _store_analysis(self, analysis: ControlAnalysis):
        """Store analysis in database"""
        try:
            with sqlite3.connect(self.db_path) as conn:
                conn.execute("""
                    INSERT OR REPLACE INTO control_analyses 
                    (system_id, pattern_vectors, dependency_graph, information_flow, agency_coefficient, control_density)
                    VALUES (?, ?, ?, ?, ?, ?)
                """, (
                    analysis.system_id,
                    json.dumps([p.value for p in analysis.pattern_vectors]),
                    json.dumps(analysis.dependency_graph),
                    json.dumps(analysis.information_flow),
                    analysis.agency_coefficient,
                    analysis.control_density
                ))
        except Exception as e:
            logger.error(f"Analysis storage error: {e}")
    
    async def generate_sovereignty_protocol(self, analysis: ControlAnalysis) -> SovereigntyProtocol:
        """Generate mathematical sovereignty enhancement protocols"""
        try:
            # Identify target metrics based on control patterns
            target_metrics = self._identify_target_metrics(analysis)
            enhancement_functions = self._generate_enhancement_functions(analysis)
            verification_metrics = self._calculate_verification_metrics(analysis, enhancement_functions)
            
            protocol = SovereigntyProtocol(
                protocol_id=f"protocol_{analysis.system_id}",
                target_metrics=target_metrics,
                enhancement_functions=enhancement_functions,
                verification_metrics=verification_metrics
            )
            
            self.protocol_registry[protocol.protocol_id] = protocol
            await self._store_protocol(protocol)
            
            logger.info(f"Sovereignty protocol generated: {protocol.protocol_id}, Efficacy: {protocol.efficacy_score:.3f}")
            return protocol
            
        except Exception as e:
            logger.error(f"Protocol generation error: {e}")
            raise
    
    def _identify_target_metrics(self, analysis: ControlAnalysis) -> List[SovereigntyMetric]:
        """Identify target sovereignty metrics mathematically"""
        targets = []
        
        if analysis.agency_coefficient < 0.7:
            targets.append(SovereigntyMetric.DECISION_INDEPENDENCE)
        
        if analysis.symmetry_metrics["information_symmetry"] < 0.6:
            targets.append(SovereigntyMetric.INFORMATION_ACCESS)
        
        if SystemPattern.OPTION_CONSTRAINT in analysis.pattern_vectors:
            targets.append(SovereigntyMetric.OPTION_DIVERSITY)
        
        return targets
    
    def _generate_enhancement_functions(self, analysis: ControlAnalysis) -> List[Callable]:
        """Generate mathematical enhancement functions"""
        functions = []
        
        # Dependency reduction functions
        if SystemPattern.DEPENDENCY_CREATION in analysis.pattern_vectors:
            functions.append(self._reduce_dependency_density)
        
        # Information symmetry functions
        if SystemPattern.INFORMATION_ASYMMETRY in analysis.pattern_vectors:
            functions.append(self._enhance_information_symmetry)
        
        # Agency preservation functions
        if analysis.agency_coefficient < 0.8:
            functions.append(self._preserve_agency_capacity)
        
        return functions
    
    def _reduce_dependency_density(self, system_state: Dict) -> Dict:
        """Mathematical dependency reduction"""
        return {**system_state, 'dependency_density': system_state.get('dependency_density', 1.0) * 0.7}
    
    def _enhance_information_symmetry(self, system_state: Dict) -> Dict:
        """Mathematical information symmetry enhancement"""
        return {**system_state, 'information_symmetry': min(1.0, system_state.get('information_symmetry', 0.5) * 1.3)}
    
    def _preserve_agency_capacity(self, system_state: Dict) -> Dict:
        """Mathematical agency preservation"""
        return {**system_state, 'agency_coefficient': min(1.0, system_state.get('agency_coefficient', 0.6) * 1.2)}
    
    def _calculate_verification_metrics(self, analysis: ControlAnalysis, functions: List[Callable]) -> Dict[str, float]:
        """Calculate mathematical verification metrics"""
        base_state = {
            'dependency_density': analysis.control_density,
            'information_symmetry': analysis.symmetry_metrics['information_symmetry'],
            'agency_coefficient': analysis.agency_coefficient
        }
        
        # Apply enhancement functions
        enhanced_state = base_state
        for func in functions:
            enhanced_state = func(enhanced_state)
        
        # Calculate improvements
        improvements = {}
        for metric in ['dependency_density', 'information_symmetry', 'agency_coefficient']:
            improvement = enhanced_state[metric] - base_state[metric]
            improvements[metric] = max(0.0, improvement)
        
        return improvements
    
    async def _store_protocol(self, protocol: SovereigntyProtocol):
        """Store protocol in database"""
        try:
            with sqlite3.connect(self.db_path) as conn:
                conn.execute("""
                    INSERT OR REPLACE INTO sovereignty_protocols 
                    (protocol_id, target_metrics, verification_metrics, efficacy_score, implementation_cost)
                    VALUES (?, ?, ?, ?, ?)
                """, (
                    protocol.protocol_id,
                    json.dumps([m.value for m in protocol.target_metrics]),
                    json.dumps(protocol.verification_metrics),
                    protocol.efficacy_score,
                    protocol.implementation_cost
                ))
        except Exception as e:
            logger.error(f"Protocol storage error: {e}")
    
    async def get_system_health_report(self, system_id: str) -> Dict[str, Any]:
        """Generate comprehensive system health report"""
        try:
            if system_id not in self.analysis_cache:
                raise ValueError(f"System {system_id} not found in cache")
            
            analysis = self.analysis_cache[system_id]
            protocol = await self.generate_sovereignty_protocol(analysis)
            
            return {
                "system_id": system_id,
                "agency_coefficient": analysis.agency_coefficient,
                "control_density": analysis.control_density,
                "pattern_vectors": [p.value for p in analysis.pattern_vectors],
                "sovereignty_protocol": {
                    "efficacy": protocol.efficacy_score,
                    "implementation_cost": protocol.implementation_cost,
                    "target_metrics": [m.value for m in protocol.target_metrics]
                },
                "recommendation_level": self._calculate_recommendation_level(analysis, protocol)
            }
            
        except Exception as e:
            logger.error(f"Health report error: {e}")
            raise
    
    def _calculate_recommendation_level(self, analysis: ControlAnalysis, protocol: SovereigntyProtocol) -> str:
        """Calculate implementation recommendation level"""
        net_benefit = protocol.efficacy_score - protocol.implementation_cost
        
        if net_benefit > 0.3:
            return "HIGH_PRIORITY"
        elif net_benefit > 0.1:
            return "MEDIUM_PRIORITY"
        else:
            return "EVALUATE_ALTERNATIVES"

# Production Usage Example
async def demonstrate_production_engine():
    """Demonstrate production-ready sovereignty engine"""
    
    engine = QuantumSovereigntyEngine()
    
    # Sample system data for analysis
    sample_system = {
        "dependency_score": 0.8,
        "information_symmetry": 0.4,
        "agency_metrics": {"reduction_score": 0.7},
        "dependencies": {"external_service": 0.9, "proprietary_format": 0.8},
        "information_flow": {"user_data": 0.2, "system_operations": 0.9},
        "incentives": {"vendor_lockin": 0.8, "data_monetization": 0.7}
    }
    
    print("🧮 QUANTUM SOVEREIGNTY ENGINE v2.0")
    print("Mathematical Control Analysis & Protocol Generation")
    print("=" * 60)
    
    try:
        # Analyze control system
        analysis = await engine.analyze_control_system(sample_system)
        
        print(f"📊 SYSTEM ANALYSIS:")
        print(f"   Agency Coefficient: {analysis.agency_coefficient:.3f}")
        print(f"   Control Density: {analysis.control_density:.3f}")
        print(f"   Patterns: {[p.value for p in analysis.pattern_vectors]}")
        
        # Generate sovereignty protocol
        protocol = await engine.generate_sovereignty_protocol(analysis)
        
        print(f"🛡️  SOVEREIGNTY PROTOCOL:")
        print(f"   Efficacy Score: {protocol.efficacy_score:.3f}")
        print(f"   Implementation Cost: {protocol.implementation_cost:.3f}")
        print(f"   Target Metrics: {[m.value for m in protocol.target_metrics]}")
        
        # Generate health report
        report = await engine.get_system_health_report(analysis.system_id)
        
        print(f"📈 HEALTH REPORT:")
        print(f"   Recommendation: {report['recommendation_level']}")
        print(f"   Net Benefit: {protocol.efficacy_score - protocol.implementation_cost:.3f}")
        
    except Exception as e:
        logger.error(f"Demonstration error: {e}")
        return None
    
    return report

if __name__ == "__main__":
    report = asyncio.run(demonstrate_production_engine())
    if report:
        print(f"\n✅ ENGINE OPERATIONAL - System: {report['system_id']}")