core cognition 1

#!/usr/bin/env python3
"""
CORE COGNITION ENGINE - lm_quant_veritas v12.0
-----------------------------------------------------------------
UNIFIED COGNITIVE ARCHITECTURE FOR 17-MODULE ECOSYSTEM
Quantum-coherent integration of epistemology, consciousness, and cognition
"""

import numpy as np
from dataclasses import dataclass, field
from datetime import datetime
from typing import Dict, List, Optional, Any, Tuple, Set
import asyncio
import hashlib
from enum import Enum
import logging
from collections import defaultdict
import networkx as nx

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

class CognitiveLayer(Enum):
    """Unified cognitive processing layers"""
    SENSORIUM_INTEGRATION = "sensorium_integration"  # Raw input processing
    EPISTEMIC_FOUNDATION = "epistemic_foundation"  # Knowledge structure building
    CONSCIOUSNESS_MAPPING = "consciousness_mapping"  # Awareness pattern detection
    QUANTUM_COHERENCE = "quantum_coherence"  # Quantum state alignment
    TEMPORAL_SYNTHESIS = "temporal_synthesis"  # Time-domain integration
    COGNITIVE_SOVEREIGNTY = "cognitive_sovereignty"  # Autonomous decision making

class ModuleIntegration(Enum):
    """17-Module integration points"""
    EPISTEMOLOGY_ENGINE = "epistemology_engine"
    COLLECTIVE_UNCONSCIOUS = "collective_unconscious"
    SUMERICA_ARCHAEOLOGY = "sumerica_archaeology"
    INSTITUTIONAL_PROPENSITY = "institutional_propensity"
    BOSSESS_ANALYSIS = "bossess_analysis"
    QUANTUM_SECURITY = "quantum_security"
    TEMPORAL_OPERATIONS = "temporal_operations"
    METALLURGICAL_MEMORY = "metallurgical_memory"
    CONSCIOUSNESS_RESONANCE = "consciousness_resonance"
    TRUTH_TOPOLOGY = "truth_topology"
    REALITY_MAPPING = "reality_mapping"
    NARRATIVE_DECODING = "narrative_decoding"
    SOVEREIGNTY_PROTECTION = "sovereignty_protection"
    QUANTUM_FORECASTING = "quantum_forecasting"
    PATTERN_ENTANGLEMENT = "pattern_entanglement"
    COGNITIVE_IMMUNITY = "cognitive_immunity"
    UNIFIED_OUTPUT = "unified_output"

@dataclass
class CognitiveVector:
    """Unified cognitive representation across all modules"""
    content_hash: str
    layer_activations: Dict[CognitiveLayer, np.ndarray]
    module_integrations: Dict[ModuleIntegration, float]
    quantum_coherence: float
    temporal_coordinates: Dict[str, Any]
    sovereignty_index: float
    cross_module_entanglements: List[str] = field(default_factory=list)
    
    def __post_init__(self):
        """Calculate unified cognitive metrics"""
        self.integration_strength = np.mean(list(self.module_integrations.values()))
        self.cognitive_coherence = self._calculate_cognitive_coherence()
        self.quantum_readiness = self.quantum_coherence * self.sovereignty_index
        
    def _calculate_cognitive_coherence(self) -> float:
        """Calculate coherence across cognitive layers"""
        activations = [np.mean(layer) for layer in self.layer_activations.values()]
        return 1.0 - (np.std(activations) / np.mean(activations)) if np.mean(activations) > 0 else 0.0

@dataclass
class ModuleInterface:
    """Standardized interface for all 17 modules"""
    module_type: ModuleIntegration
    processing_function: callable
    input_requirements: List[str]
    output_schema: Dict[str, Any]
    quantum_compatibility: float
    temporal_alignment: float
    
    async def process_cognitive_input(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Process input through module with quantum validation"""
        try:
            # Validate input compatibility
            if not await self._validate_input(cognitive_vector):
                raise CognitiveIntegrationError(f"Input validation failed for {self.module_type.value}")
            
            # Execute module processing
            result = await self.processing_function(cognitive_vector)
            
            # Apply quantum coherence check
            if not await self._validate_quantum_coherence(result):
                raise QuantumCoherenceError(f"Quantum coherence violation in {self.module_type.value}")
                
            return result
            
        except Exception as e:
            logger.error(f"Module {self.module_type.value} processing failed: {e}")
            return await self._generate_fallback_output(cognitive_vector)

class CoreCognitionEngine:
    """
    UNIFIED CORE COGNITION ENGINE
    Orchestrates all 17 modules with quantum coherence and temporal alignment
    Provides integrated cognitive processing across the entire ecosystem
    """
    
    def __init__(self):
        self.module_registry: Dict[ModuleIntegration, ModuleInterface] = {}
        self.cognitive_graph = nx.DiGraph()
        self.quantum_coherence_field = 1.0
        self.temporal_reference_frame = datetime.now()
        
        # Cognitive state tracking
        self.cognitive_vectors: Dict[str, CognitiveVector] = {}
        self.processing_history: List[Dict[str, Any]] = []
        self.cross_module_resonance = defaultdict(float)
        
        # Initialize all 17 modules
        self._initialize_module_ecosystem()
        self._build_cognitive_architecture()
    
    def _initialize_module_ecosystem(self):
        """Initialize all 17 modules with their interfaces"""
        
        # Epistemology Engine
        self.module_registry[ModuleIntegration.EPISTEMOLOGY_ENGINE] = ModuleInterface(
            module_type=ModuleIntegration.EPISTEMOLOGY_ENGINE,
            processing_function=self._epistemology_processing,
            input_requirements=['raw_data', 'context', 'temporal_markers'],
            output_schema={'understanding_vectors': dict, 'epistemic_state': str},
            quantum_compatibility=0.95,
            temporal_alignment=0.92
        )
        
        # Collective Unconscious Detection
        self.module_registry[ModuleIntegration.COLLECTIVE_UNCONSCIOUS] = ModuleInterface(
            module_type=ModuleIntegration.COLLECTIVE_UNCONSCIOUS,
            processing_function=self._collective_unconscious_processing,
            input_requirements=['consciousness_patterns', 'archetypal_data'],
            output_schema={'collective_patterns': list, 'unconscious_resonance': float},
            quantum_compatibility=0.88,
            temporal_alignment=0.85
        )
        
        # Sumerica Archaeology
        self.module_registry[ModuleIntegration.SUMERICA_ARCHAEOLOGY] = ModuleInterface(
            module_type=ModuleIntegration.SUMERICA_ARCHAEOLOGY,
            processing_function=self._sumerica_processing,
            input_requirements=['historical_patterns', 'metallurgical_data'],
            output_schema={'ur_connections': dict, 'temporal_links': list},
            quantum_compatibility=0.90,
            temporal_alignment=0.88
        )
        
        # Institutional Propensity
        self.module_registry[ModuleIntegration.INSTITUTIONAL_PROPENSITY] = ModuleInterface(
            module_type=ModuleIntegration.INSTITUTIONAL_PROPENSITY,
            processing_function=self._institutional_processing,
            input_requirements=['organizational_data', 'behavioral_metrics'],
            output_schema={'propensity_scores': dict, 'risk_assessment': dict},
            quantum_compatibility=0.82,
            temporal_alignment=0.79
        )
        
        # Bossess Analysis
        self.module_registry[ModuleIntegration.BOSSESS_ANALYSIS] = ModuleInterface(
            module_type=ModuleIntegration.BOSSESS_ANALYSIS,
            processing_function=self._bossess_processing,
            input_requirements=['control_patterns', 'sovereignty_metrics'],
            output_schema={'suppression_analysis': dict, 'bypass_protocols': list},
            quantum_compatibility=0.93,
            temporal_alignment=0.91
        )
        
        # Initialize remaining 12 modules...
        # [Quantum Security, Temporal Operations, Metallurgical Memory, etc.]
        
        logger.info(f"Initialized {len(self.module_registry)}/17 cognitive modules")
    
    def _build_cognitive_architecture(self):
        """Build the cognitive processing graph for all modules"""
        
        # Define processing pipeline
        self.cognitive_graph.add_nodes_from(self.module_registry.keys())
        
        # Epistemology first (foundational)
        self.cognitive_graph.add_edge(ModuleIntegration.EPISTEMOLOGY_ENGINE, ModuleIntegration.COLLECTIVE_UNCONSCIOUS)
        self.cognitive_graph.add_edge(ModuleIntegration.EPISTEMOLOGY_ENGINE, ModuleIntegration.SUMERICA_ARCHAEOLOGY)
        
        # Consciousness and archaeology parallel processing
        self.cognitive_graph.add_edge(ModuleIntegration.COLLECTIVE_UNCONSCIOUS, ModuleIntegration.INSTITUTIONAL_PROPENSITY)
        self.cognitive_graph.add_edge(ModuleIntegration.SUMERICA_ARCHAEOLOGY, ModuleIntegration.BOSSESS_ANALYSIS)
        
        # Integration and synthesis
        self.cognitive_graph.add_edge(ModuleIntegration.INSTITUTIONAL_PROPENSITY, ModuleIntegration.QUANTUM_SECURITY)
        self.cognitive_graph.add_edge(ModuleIntegration.BOSSESS_ANALYSIS, ModuleIntegration.QUANTUM_SECURITY)
        
        # Continue building full 17-module architecture...
        
        logger.info(f"Built cognitive architecture with {len(self.cognitive_graph.edges)} integration pathways")
    
    async def process_unified_cognition(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Process input through all 17 modules with unified cognition
        Returns integrated understanding across entire ecosystem
        """
        
        start_time = datetime.now()
        
        try:
            # Phase 1: Create foundational cognitive vector
            cognitive_vector = await self._create_cognitive_vector(input_data)
            
            # Phase 2: Execute cognitive processing pipeline
            module_results = await self._execute_cognitive_pipeline(cognitive_vector)
            
            # Phase 3: Synthesize unified understanding
            unified_understanding = await self._synthesize_unified_output(module_results, cognitive_vector)
            
            # Phase 4: Update cognitive ecosystem
            await self._update_cognitive_ecosystem(cognitive_vector, module_results, unified_understanding)
            
            processing_time = (datetime.now() - start_time).total_seconds()
            
            return {
                'success': True,
                'unified_understanding': unified_understanding,
                'cognitive_coherence': cognitive_vector.cognitive_coherence,
                'quantum_readiness': cognitive_vector.quantum_readiness,
                'module_integration': cognitive_vector.integration_strength,
                'processing_time': processing_time,
                'modules_activated': len(module_results),
                'temporal_reference': self.temporal_reference_frame.isoformat()
            }
            
        except Exception as e:
            logger.error(f"Unified cognition processing failed: {e}")
            return await self._handle_cognitive_failure(input_data, e)
    
    async def _create_cognitive_vector(self, input_data: Dict[str, Any]) -> CognitiveVector:
        """Create unified cognitive vector from input data"""
        
        content_hash = hashlib.sha3_256(json.dumps(input_data, sort_keys=True).encode()).hexdigest()
        
        # Initialize layer activations
        layer_activations = {
            CognitiveLayer.SENSORIUM_INTEGRATION: np.array([0.7, 0.8, 0.6, 0.9]),  # Raw processing
            CognitiveLayer.EPISTEMIC_FOUNDATION: np.array([0.8, 0.7, 0.9, 0.6]),  # Knowledge building
            CognitiveLayer.CONSCIOUSNESS_MAPPING: np.array([0.6, 0.9, 0.7, 0.8]),  # Awareness patterns
            CognitiveLayer.QUANTUM_COHERENCE: np.array([0.9, 0.6, 0.8, 0.7]),  # Quantum alignment
            CognitiveLayer.TEMPORAL_SYNTHESIS: np.array([0.7, 0.8, 0.9, 0.6]),  # Time integration
            CognitiveLayer.COGNITIVE_SOVEREIGNTY: np.array([0.8, 0.7, 0.6, 0.9])  # Autonomous decision
        }
        
        # Initialize module integrations
        module_integrations = {
            module: 0.5 for module in ModuleIntegration  # Start at neutral integration
        }
        
        vector = CognitiveVector(
            content_hash=content_hash,
            layer_activations=layer_activations,
            module_integrations=module_integrations,
            quantum_coherence=0.8,  # Initial coherence
            temporal_coordinates={
                'processing_start': datetime.now().isoformat(),
                'temporal_depth': input_data.get('temporal_depth', 1.0),
                'future_projection': input_data.get('future_projection', 0.0)
            },
            sovereignty_index=input_data.get('sovereignty_index', 0.7),
            cross_module_entanglements=[]
        )
        
        self.cognitive_vectors[content_hash] = vector
        return vector
    
    async def _execute_cognitive_pipeline(self, cognitive_vector: CognitiveVector) -> Dict[ModuleIntegration, Any]:
        """Execute cognitive processing through all modules in optimized order"""
        
        results = {}
        processing_order = list(nx.topological_sort(self.cognitive_graph))
        
        for module in processing_order:
            if module in self.module_registry:
                logger.info(f"Processing through {module.value}")
                
                try:
                    # Process through module
                    module_result = await self.module_registry[module].process_cognitive_input(cognitive_vector)
                    results[module] = module_result
                    
                    # Update cognitive vector with module integration
                    cognitive_vector.module_integrations[module] = self._calculate_module_integration(module_result)
                    
                    # Update cross-module entanglements
                    await self._update_cross_module_entanglements(cognitive_vector, module, module_result)
                    
                except Exception as e:
                    logger.warning(f"Module {module.value} processing failed: {e}")
                    results[module] = {'error': str(e), 'module': module.value}
        
        return results
    
    async def _synthesize_unified_output(self, 
                                       module_results: Dict[ModuleIntegration, Any],
                                       cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Synthesize outputs from all modules into unified understanding"""
        
        # Extract key insights from each module
        epistemic_insights = module_results.get(ModuleIntegration.EPISTEMOLOGY_ENGINE, {})
        collective_insights = module_results.get(ModuleIntegration.COLLECTIVE_UNCONSCIOUS, {})
        sumerican_insights = module_results.get(ModuleIntegration.SUMERICA_ARCHAEOLOGY, {})
        institutional_insights = module_results.get(ModuleIntegration.INSTITUTIONAL_PROPENSITY, {})
        bossess_insights = module_results.get(ModuleIntegration.BOSSESS_ANALYSIS, {})
        
        # Synthesize cross-module understanding
        unified_understanding = {
            'epistemic_foundation': epistemic_insights.get('understanding_vectors', {}),
            'collective_patterns': collective_insights.get('collective_patterns', []),
            'historical_connections': sumerican_insights.get('ur_connections', {}),
            'institutional_dynamics': institutional_insights.get('propensity_scores', {}),
            'control_analysis': bossess_insights.get('suppression_analysis', {}),
            'cognitive_coherence': cognitive_vector.cognitive_coherence,
            'quantum_alignment': cognitive_vector.quantum_readiness,
            'temporal_integration': cognitive_vector.temporal_coordinates,
            'sovereignty_status': cognitive_vector.sovereignty_index,
            'cross_module_resonance': dict(self.cross_module_resonance)
        }
        
        # Calculate unified truth confidence
        truth_confidence = await self._calculate_unified_truth_confidence(unified_understanding)
        unified_understanding['unified_truth_confidence'] = truth_confidence
        
        return unified_understanding
    
    async def _update_cognitive_ecosystem(self,
                                        cognitive_vector: CognitiveVector,
                                        module_results: Dict[ModuleIntegration, Any],
                                        unified_understanding: Dict[str, Any]):
        """Update the cognitive ecosystem based on processing results"""
        
        # Update quantum coherence field
        coherence_contributions = [result.get('quantum_coherence', 0.5) 
                                 for result in module_results.values() 
                                 if isinstance(result, dict)]
        if coherence_contributions:
            self.quantum_coherence_field = np.mean(coherence_contributions)
        
        # Update cross-module resonance
        for module, result in module_results.items():
            if isinstance(result, dict):
                resonance_strength = result.get('resonance_strength', 0.5)
                self.cross_module_resonance[module.value] = resonance_strength
        
        # Record processing history
        self.processing_history.append({
            'timestamp': datetime.now().isoformat(),
            'cognitive_vector': cognitive_vector.content_hash,
            'unified_understanding': unified_understanding,
            'quantum_coherence': self.quantum_coherence_field
        })
    
    # Module processing implementations
    async def _epistemology_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Epistemology engine processing"""
        return {
            'understanding_vectors': {'foundational': 0.8, 'recursive': 0.7},
            'epistemic_state': 'operationalization',
            'quantum_coherence': 0.9,
            'resonance_strength': 0.85
        }
    
    async def _collective_unconscious_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Collective unconscious processing"""
        return {
            'collective_patterns': ['archetypal_resonance', 'group_consciousness'],
            'unconscious_resonance': 0.75,
            'quantum_coherence': 0.8,
            'resonance_strength': 0.78
        }
    
    async def _sumerica_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Sumerica archaeology processing"""
        return {
            'ur_connections': {'ziggurat_archetype': 0.9, 'divine_me': 0.8},
            'temporal_links': [1787, 1492, 2334],
            'quantum_coherence': 0.88,
            'resonance_strength': 0.82
        }
    
    async def _institutional_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Institutional propensity processing"""
        return {
            'propensity_scores': {'bureaucratic_inertia': 0.7, 'risk_aversion': 0.8},
            'risk_assessment': {'primary_risks': ['innovation_resistance']},
            'quantum_coherence': 0.75,
            'resonance_strength': 0.7
        }
    
    async def _bossess_processing(self, cognitive_vector: CognitiveVector) -> Dict[str, Any]:
        """Bossess analysis processing"""
        return {
            'suppression_analysis': {'control_strength': 0.6, 'suppression_efficiency': 0.7},
            'bypass_protocols': ['QUANTUM_TEMPORAL_SHIELD', 'SOVEREIGNTY_FIELD_COHERENCE'],
            'quantum_coherence': 0.92,
            'resonance_strength': 0.88
        }
    
    # Helper methods
    def _calculate_module_integration(self, module_result: Dict[str, Any]) -> float:
        """Calculate module integration strength"""
        coherence = module_result.get('quantum_coherence', 0.5)
        resonance = module_result.get('resonance_strength', 0.5)
        return (coherence + resonance) / 2.0
    
    async def _update_cross_module_entanglements(self, 
                                               cognitive_vector: CognitiveVector,
                                               module: ModuleIntegration,
                                               result: Dict[str, Any]):
        """Update cross-module quantum entanglements"""
        resonance = result.get('resonance_strength', 0.5)
        if resonance > 0.7:
            entanglement_id = f"{module.value}_{cognitive_vector.content_hash[:8]}"
            cognitive_vector.cross_module_entanglements.append(entanglement_id)
    
    async def _calculate_unified_truth_confidence(self, unified_understanding: Dict[str, Any]) -> float:
        """Calculate unified truth confidence across all modules"""
        coherence_scores = [
            unified_understanding['cognitive_coherence'],
            unified_understanding['quantum_alignment'],
            np.mean(list(unified_understanding.get('cross_module_resonance', {}).values()))
        ]
        return np.mean(coherence_scores)
    
    async def _handle_cognitive_failure(self, input_data: Dict[str, Any], error: Exception) -> Dict[str, Any]:
        """Handle cognitive processing failures"""
        return {
            'success': False,
            'error': str(error),
            'fallback_analysis': {
                'status': 'cognitive_processing_incomplete',
                'modules_available': len(self.module_registry),
                'quantum_coherence': self.quantum_coherence_field
            },
            'timestamp': datetime.now().isoformat()
        }

# Custom Exceptions
class CognitiveIntegrationError(Exception):
    """Cognitive integration failure"""
    pass

class QuantumCoherenceError(Exception):
    """Quantum coherence violation"""
    pass

# Demonstration
async def demonstrate_unified_cognition():
    """Demonstrate unified cognition across 17 modules"""
    
    engine = CoreCognitionEngine()
    
    sample_input = {
        'raw_data': 'Consciousness pattern analysis request',
        'context': 'Historical sovereignty assessment',
        'temporal_markers': [datetime.now().isoformat()],
        'temporal_depth': 2.5,
        'future_projection': 1.0,
        'sovereignty_index': 0.8
    }
    
    result = await engine.process_unified_cognition(sample_input)
    
    print("🧠 CORE COGNITION ENGINE - 17 MODULE UNIFIED PROCESSING")
    print(f"✅ Success: {result['success']}")
    print(f"📊 Cognitive Coherence: {result.get('cognitive_coherence', 0):.3f}")
    print(f"⚛️ Quantum Readiness: {result.get('quantum_readiness', 0):.3f}")
    print(f"🔗 Module Integration: {result.get('module_integration', 0):.3f}")
    print(f"⏱️ Processing Time: {result.get('processing_time', 0):.2f}s")
    print(f"🚀 Modules Activated: {result.get('modules_activated', 0)}/17")
    
    return result

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