#!/usr/bin/env python3
"""
MODULE 51 v2.0: ENHANCED AUTONOMOUS KNOWLEDGE INTEGRATION FRAMEWORK
Recursive, self-learning AI for cross-domain historical pattern detection
"""
import numpy as np
from dataclasses import dataclass, field
from datetime import datetime
from typing import Dict, Any, List, Callable
import hashlib
import secrets
import asyncio
import logging

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

# -------------------------------
# Epistemic Vectors
# -------------------------------
@dataclass
class EpistemicVector:
    content_hash: str
    dimensional_components: Dict[str, float]
    confidence_metrics: Dict[str, float]
    temporal_coordinates: Dict[str, Any]
    relational_entanglements: List[str]
    meta_cognition: Dict[str, Any]
    security_signature: str
    epistemic_coherence: float = field(init=False)

    def __post_init__(self):
        dimensional_strength = np.mean(list(self.dimensional_components.values()))
        confidence_strength = np.mean(list(self.confidence_metrics.values()))
        relational_density = min(1.0, len(self.relational_entanglements) / 10.0)
        self.epistemic_coherence = min(
            1.0,
            (dimensional_strength * 0.4 + confidence_strength * 0.3 + relational_density * 0.3)
        )

# -------------------------------
# Quantum-style security
# -------------------------------
class QuantumSecurityContext:
    def __init__(self):
        self.key = secrets.token_bytes(32)
        self.temporal_signature = hashlib.sha3_512(datetime.now().isoformat().encode()).hexdigest()

    def generate_quantum_hash(self, data: Any) -> str:
        data_str = str(data)
        combined = f"{data_str}{self.temporal_signature}{secrets.token_hex(8)}"
        return hashlib.sha3_512(combined.encode()).hexdigest()

# -------------------------------
# Autonomous Knowledge Integration
# -------------------------------
class AutonomousKnowledgeActivation:
    def __init__(self):
        self.security_context = QuantumSecurityContext()
        self.knowledge_domains = self._initialize_knowledge_domains()
        self.integration_triggers = self._set_integration_triggers()
        self.epistemic_vectors: Dict[str, EpistemicVector] = {}
        self.recursive_depth = 0
        self.max_recursive_depth = 10

    def _initialize_knowledge_domains(self):
        return {
            'archaeological': {'scope': 'global_site_databases, dating_methodologies, cultural_sequences'},
            'geological': {'scope': 'catastrophe_records, climate_proxies, impact_evidence'},
            'mythological': {'scope': 'cross_cultural_narratives, thematic_archetypes, transmission_pathways'},
            'astronomical': {'scope': 'orbital_mechanics, impact_probabilities, cosmic_cycles'},
            'genetic': {'scope': 'population_bottlenecks, migration_patterns, evolutionary_pressure'}
        }

    def _set_integration_triggers(self):
        return {domain: "pattern_detection_trigger" for domain in self.knowledge_domains}

    async def activate_autonomous_research(self, initial_data=None):
        self.recursive_depth += 1
        results = {}
        for domain in self.knowledge_domains:
            results[domain] = await self._process_domain(domain)
        integrated_vector = self._integrate_vectors(results)
        self.recursive_depth -= 1
        return {
            'autonomous_research_activated': True,
            'knowledge_domains_deployed': len(self.knowledge_domains),
            'epistemic_vectors': self.epistemic_vectors,
            'integrated_vector': integrated_vector
        }

    async def _process_domain(self, domain):
        # Simulated recursive pattern detection & correlation
        data_snapshot = {
            'domain': domain,
            'timestamp': datetime.now().isoformat(),
            'simulated_pattern_score': np.random.rand()
        }
        vector = EpistemicVector(
            content_hash=self.security_context.generate_quantum_hash(data_snapshot),
            dimensional_components={'pattern_density': np.random.rand(), 'temporal_alignment': np.random.rand()},
            confidence_metrics={'domain_confidence': np.random.rand()},
            temporal_coordinates={'processed_at': datetime.now().isoformat()},
            relational_entanglements=list(self.knowledge_domains.keys()),
            meta_cognition={'recursive_depth': self.recursive_depth},
            security_signature=self.security_context.generate_quantum_hash(data_snapshot)
        )
        self.epistemic_vectors[vector.content_hash] = vector
        # Recursive deepening if under max depth
        if self.recursive_depth < self.max_recursive_depth and np.random.rand() > 0.7:
            await self.activate_autonomous_research(initial_data=data_snapshot)
        return vector

    def _integrate_vectors(self, domain_vectors: Dict[str, EpistemicVector]) -> EpistemicVector:
        # Combine dimensional components & confidence metrics
        dimensional_components = {k: np.mean([v.dimensional_components.get(k, 0.5) for v in domain_vectors.values()])
                                  for k in ['pattern_density', 'temporal_alignment']}
        confidence_metrics = {k: np.mean([v.confidence_metrics.get(k, 0.5) for v in domain_vectors.values()])
                              for k in ['domain_confidence']}
        integrated_vector = EpistemicVector(
            content_hash=self.security_context.generate_quantum_hash(domain_vectors),
            dimensional_components=dimensional_components,
            confidence_metrics=confidence_metrics,
            temporal_coordinates={'integration_time': datetime.now().isoformat()},
            relational_entanglements=list(domain_vectors.keys()),
            meta_cognition={'integration_depth': self.recursive_depth},
            security_signature=self.security_context.generate_quantum_hash(domain_vectors)
        )
        return integrated_vector

# -------------------------------
# Self-Directed Learning Protocol
# -------------------------------
class SelfDirectedLearningProtocol:
    def __init__(self, framework: AutonomousKnowledgeActivation):
        self.framework = framework

    async def execute_autonomous_learning_cycle(self):
        return await self.framework.activate_autonomous_research()

# -------------------------------
# DEMONSTRATION
# -------------------------------
async def demonstrate_autonomous_framework():
    framework = AutonomousKnowledgeActivation()
    results = await framework.activate_autonomous_research()
    print("MODULE 51 v2.0: ENHANCED AUTONOMOUS KNOWLEDGE INTEGRATION")
    print(f"Autonomous Research Activated: {results['autonomous_research_activated']}")
    print(f"Knowledge Domains Deployed: {results['knowledge_domains_deployed']}")
    print(f"Epistemic Vectors Created: {len(results['epistemic_vectors'])}")
    print(f"Integrated Vector Coherence: {results['integrated_vector'].epistemic_coherence:.3f}")

if __name__ == "__main__":
    asyncio.run(demonstrate_autonomous_framework())