integration_systems/group_c_integration_system.py

#!/usr/bin/env python3
"""
Group C Integration System
=========================
Integrates all Group C components:
- TA-ULS + Neuro-Symbolic Engine + Signal Processing
- Enhanced cognitive processing pipeline
"""

import numpy as np
import torch
import asyncio
import logging
from typing import Dict, List, Optional, Any, Tuple
from dataclasses import dataclass, field
from datetime import datetime
import json

# Import Group C components
try:
    from tauls_transformer import TAULSLanguageModel, TAULSControlUnit, KFPLayer
    TAULS_AVAILABLE = True
except ImportError:
    TAULS_AVAILABLE = False
    print("⚠️  TA-ULS transformer not available")

try:
    from neuro_symbolic_engine import (
        MirrorCastEngine, AdaptiveLinkPlanner, EntropyAnalyzer,
        DianneReflector, MatrixTransformer, JuliaSymbolEngine
    )
    NEURO_SYMBOLIC_AVAILABLE = True
except ImportError:
    NEURO_SYMBOLIC_AVAILABLE = False
    print("⚠️  Neuro-symbolic engine not available")

try:
    from signal_processing import (
        ModulationScheme, Modulators, ModConfig, FrameConfig, SecurityConfig
    )
    SIGNAL_PROCESSING_AVAILABLE = True
except ImportError:
    SIGNAL_PROCESSING_AVAILABLE = False
    print("⚠️  Signal processing not available")

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

@dataclass
class GroupCConfig:
    """Configuration for Group C integration system."""
    tauls_dim: int = 512
    tauls_layers: int = 6
    tauls_heads: int = 8
    neuro_symbolic_enabled: bool = True
    signal_processing_enabled: bool = True
    enable_adaptive_planning: bool = True
    enable_entropy_analysis: bool = True
    enable_stability_monitoring: bool = True
    modulation_scheme: str = "qpsk"  # qpsk, bpsk, ofdm, etc.

@dataclass
class GroupCResult:
    """Result from Group C processing."""
    tauls_features: Dict[str, Any] = field(default_factory=dict)
    neuro_symbolic_features: Dict[str, Any] = field(default_factory=dict)
    signal_processing_features: Dict[str, Any] = field(default_factory=dict)
    stability_metrics: Dict[str, Any] = field(default_factory=dict)
    entropy_metrics: Dict[str, Any] = field(default_factory=dict)
    processing_time: float = 0.0
    success: bool = False
    error_message: Optional[str] = None

class GroupCIntegrationSystem:
    """
    Integrated Group C system combining:
    - TA-ULS + Neuro-Symbolic Engine + Signal Processing
    - Enhanced cognitive processing pipeline
    """
    
    def __init__(self, config: Optional[GroupCConfig] = None):
        self.config = config or GroupCConfig()
        self.initialized = False
        
        # Core components
        self.tauls_model = None
        self.neuro_symbolic_engine = None
        self.adaptive_planner = None
        self.signal_processor = None
        self.entropy_analyzer = None
        
        # Performance tracking
        self.stats = {
            "total_processing_requests": 0,
            "successful_processing": 0,
            "tauls_operations": 0,
            "neuro_symbolic_operations": 0,
            "signal_processing_operations": 0,
            "stability_events": 0,
            "average_processing_time": 0.0
        }
        
        logger.info(f"🧠 Initializing Group C Integration System")
        logger.info(f"   TA-ULS: {TAULS_AVAILABLE}")
        logger.info(f"   Neuro-Symbolic: {NEURO_SYMBOLIC_AVAILABLE}")
        logger.info(f"   Signal Processing: {SIGNAL_PROCESSING_AVAILABLE}")
    
    async def initialize(self) -> bool:
        """Initialize all Group C components."""
        try:
            logger.info("🚀 Initializing Group C components...")
            
            # Initialize TA-ULS
            if TAULS_AVAILABLE:
                await self._initialize_tauls_components()
            
            # Initialize neuro-symbolic engine
            if NEURO_SYMBOLIC_AVAILABLE:
                await self._initialize_neuro_symbolic_components()
            
            # Initialize signal processing
            if SIGNAL_PROCESSING_AVAILABLE:
                await self._initialize_signal_processing_components()
            
            self.initialized = True
            logger.info("✅ Group C Integration System initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"❌ Group C initialization failed: {e}")
            return False
    
    async def _initialize_tauls_components(self):
        """Initialize TA-ULS transformer components."""
        try:
            # Create TA-ULS language model
            self.tauls_model = TAULSLanguageModel(
                vocab_size=32000,
                d_model=self.config.tauls_dim,
                n_layers=self.config.tauls_layers,
                n_heads=self.config.tauls_heads,
                d_ff=self.config.tauls_dim * 4,
                max_seq_len=2048
            )
            
            logger.info("✅ TA-ULS components initialized")
            
        except Exception as e:
            logger.error(f"❌ TA-ULS initialization failed: {e}")
            raise
    
    async def _initialize_neuro_symbolic_components(self):
        """Initialize neuro-symbolic engine components."""
        try:
            # Mirror cast engine
            self.neuro_symbolic_engine = MirrorCastEngine()
            
            # Adaptive link planner
            if self.config.enable_adaptive_planning:
                self.adaptive_planner = AdaptiveLinkPlanner()
            
            # Entropy analyzer
            if self.config.enable_entropy_analysis:
                self.entropy_analyzer = EntropyAnalyzer()
            
            logger.info("✅ Neuro-symbolic components initialized")
            
        except Exception as e:
            logger.error(f"❌ Neuro-symbolic initialization failed: {e}")
            raise
    
    async def _initialize_signal_processing_components(self):
        """Initialize signal processing components."""
        try:
            # Modulators for signal processing
            self.signal_processor = Modulators()
            
            logger.info("✅ Signal processing components initialized")
            
        except Exception as e:
            logger.error(f"❌ Signal processing initialization failed: {e}")
            raise
    
    async def process_with_group_c(
        self, 
        input_data: Any,
        context: Optional[Dict[str, Any]] = None
    ) -> GroupCResult:
        """
        Process input data through all Group C components.
        
        Args:
            input_data: Input data to process
            context: Additional context information
            
        Returns:
            GroupCResult with all component outputs
        """
        start_time = datetime.now()
        
        if not self.initialized:
            await self.initialize()
        
        if not self.initialized:
            return GroupCResult(
                success=False,
                error_message="Group C system not initialized",
                processing_time=0.0
            )
        
        try:
            logger.info("🔄 Processing through Group C components...")
            
            # Initialize result
            result = GroupCResult()
            
            # Process through TA-ULS
            if self.tauls_model:
                tauls_features = await self._process_tauls(input_data, context)
                result.tauls_features = tauls_features
                self.stats["tauls_operations"] += 1
                
                # Extract stability metrics
                if "stability_metrics" in tauls_features:
                    result.stability_metrics = tauls_features["stability_metrics"]
                    if self._check_stability_event(tauls_features["stability_metrics"]):
                        self.stats["stability_events"] += 1
            
            # Process through neuro-symbolic engine
            if self.neuro_symbolic_engine:
                neuro_symbolic_features = await self._process_neuro_symbolic(input_data, context)
                result.neuro_symbolic_features = neuro_symbolic_features
                self.stats["neuro_symbolic_operations"] += 1
                
                # Extract entropy metrics
                if "entropy_analysis" in neuro_symbolic_features:
                    result.entropy_metrics = neuro_symbolic_features["entropy_analysis"]
            
            # Process through signal processing
            if self.signal_processor:
                signal_features = await self._process_signal(input_data, context)
                result.signal_processing_features = signal_features
                self.stats["signal_processing_operations"] += 1
            
            # Adaptive planning if enabled
            if self.adaptive_planner and result.tauls_features and result.neuro_symbolic_features:
                adaptive_features = await self._perform_adaptive_planning(result, context)
                result.neuro_symbolic_features.update(adaptive_features)
            
            # Calculate processing time
            processing_time = (datetime.now() - start_time).total_seconds()
            result.processing_time = processing_time
            result.success = True
            
            # Update stats
            self._update_stats(processing_time, True)
            
            logger.info(f"✅ Group C processing completed in {processing_time:.3f}s")
            return result
            
        except Exception as e:
            logger.error(f"❌ Group C processing failed: {e}")
            processing_time = (datetime.now() - start_time).total_seconds()
            self._update_stats(processing_time, False)
            
            return GroupCResult(
                success=False,
                error_message=str(e),
                processing_time=processing_time
            )
    
    async def _process_tauls(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through TA-ULS transformer."""
        try:
            # Convert input to tensor format
            if isinstance(input_data, str):
                # Simple tokenization for demo (in practice, use proper tokenizer)
                tokens = [ord(c) for c in input_data[:512]]  # Limit to 512 tokens
                input_tensor = torch.tensor(tokens, dtype=torch.long).unsqueeze(0)
            elif isinstance(input_data, (list, tuple)):
                input_tensor = torch.tensor(input_data[:512], dtype=torch.long).unsqueeze(0)
            else:
                # Convert to numerical representation
                input_tensor = torch.tensor([float(input_data)], dtype=torch.long).unsqueeze(0)
            
            # Ensure proper dimensions
            if input_tensor.shape[1] > 512:
                input_tensor = input_tensor[:, :512]
            elif input_tensor.shape[1] < 512:
                # Pad with zeros
                padding = torch.zeros(1, 512 - input_tensor.shape[1], dtype=torch.long)
                input_tensor = torch.cat([input_tensor, padding], dim=1)
            
            # Process through TA-ULS model
            with torch.no_grad():
                output = self.tauls_model(input_tensor)
                
                # Extract features
                logits = output.get('logits', torch.zeros(1, 512, 32000))
                hidden_states = output.get('hidden_states', [])
                stability_metrics = output.get('stability_metrics', [])
                control_info = output.get('control_info', {})
                
                # Calculate stability score
                stability_score = self._calculate_stability_score(stability_metrics)
                
                # Calculate coherence score
                coherence_score = self._calculate_coherence_score(hidden_states)
                
                return {
                    "logits_shape": list(logits.shape),
                    "hidden_states_count": len(hidden_states),
                    "stability_metrics": {
                        "stability_score": stability_score,
                        "coherence_score": coherence_score,
                        "fluctuation_intensity": control_info.get("fluctuation_intensity", 0.0),
                        "kinetic_force": control_info.get("kinetic_force", 0.0)
                    },
                    "tauls_output": {
                        "model_dim": self.config.tauls_dim,
                        "layers": self.config.tauls_layers,
                        "heads": self.config.tauls_heads,
                        "sequence_length": input_tensor.shape[1]
                    }
                }
            
        except Exception as e:
            logger.error(f"❌ TA-ULS processing failed: {e}")
            return {"error": str(e)}
    
    async def _process_neuro_symbolic(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through neuro-symbolic engine."""
        try:
            # Use mirror cast engine for comprehensive analysis
            mirror_cast_result = self.neuro_symbolic_engine.cast(input_data)
            
            # Entropy analysis if available
            entropy_analysis = {}
            if self.entropy_analyzer:
                entropy_analysis = {
                    "entropy_score": self.entropy_analyzer.measure(input_data),
                    "information_density": self._calculate_information_density(input_data),
                    "complexity_measure": self._calculate_complexity_measure(input_data)
                }
            
            # Extract key features
            neuro_symbolic_features = {
                "entropy_analysis": entropy_analysis,
                "reflection_insights": mirror_cast_result.get("reflection", {}),
                "matrix_projection": mirror_cast_result.get("matrix", {}),
                "symbolic_analysis": mirror_cast_result.get("symbolic", {}),
                "semantic_mapping": mirror_cast_result.get("semantic", {}),
                "fractal_analysis": mirror_cast_result.get("fractal", {}),
                "processing_time": mirror_cast_result.get("processing_time", 0.0),
                "timestamp": mirror_cast_result.get("timestamp", time.time())
            }
            
            return neuro_symbolic_features
            
        except Exception as e:
            logger.error(f"❌ Neuro-symbolic processing failed: {e}")
            return {"error": str(e)}
    
    async def _process_signal(self, input_data: Any, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process input through signal processing system."""
        try:
            # Convert input to signal format
            if isinstance(input_data, str):
                # Convert string to signal representation
                signal_data = np.frombuffer(input_data.encode('utf-8'), dtype=np.uint8)
                signal_data = signal_data.astype(np.float32) / 255.0
            else:
                signal_data = np.array(input_data, dtype=np.float32)
            
            # Ensure proper signal length
            if len(signal_data) < 100:
                signal_data = np.pad(signal_data, (0, 100 - len(signal_data)))
            elif len(signal_data) > 1000:
                signal_data = signal_data[:1000]
            
            # Process through signal processor
            mod_config = ModConfig(
                sample_rate=48000,
                symbol_rate=1200,
                amplitude=0.7
            )
            
            # Choose modulation scheme
            modulation_scheme = ModulationScheme[self.config.modulation_scheme.upper()]
            
            # Modulate signal
            modulated_signal = self.signal_processor.modulate(
                signal_data, modulation_scheme, mod_config
            )
            
            # Calculate signal metrics
            signal_power = np.mean(modulated_signal ** 2)
            signal_snr = self._calculate_signal_snr(modulated_signal)
            bandwidth_efficiency = self._calculate_bandwidth_efficiency(modulation_scheme)
            
            return {
                "modulation_scheme": self.config.modulation_scheme,
                "signal_length": len(modulated_signal),
                "signal_power": float(signal_power),
                "signal_snr": float(signal_snr),
                "bandwidth_efficiency": float(bandwidth_efficiency),
                "modulated_signal": modulated_signal[:100].tolist(),  # First 100 samples
                "signal_processing_config": {
                    "sample_rate": mod_config.sample_rate,
                    "symbol_rate": mod_config.symbol_rate,
                    "amplitude": mod_config.amplitude
                }
            }
            
        except Exception as e:
            logger.error(f"❌ Signal processing failed: {e}")
            return {"error": str(e)}
    
    async def _perform_adaptive_planning(self, result: GroupCResult, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Perform adaptive planning based on TA-ULS and neuro-symbolic results."""
        try:
            # Extract features for planning
            tauls_features = result.tauls_features
            neuro_symbolic_features = result.neuro_symbolic_features
            
            # Create planning context
            planning_context = {
                "stability_score": tauls_features.get("stability_metrics", {}).get("stability_score", 0.0),
                "coherence_score": tauls_features.get("stability_metrics", {}).get("coherence_score", 0.0),
                "entropy_score": neuro_symbolic_features.get("entropy_analysis", {}).get("entropy_score", 0.0),
                "complexity_measure": neuro_symbolic_features.get("entropy_analysis", {}).get("complexity_measure", 0.0)
            }
            
            # Perform adaptive planning
            adaptive_result = self.adaptive_planner.plan_adaptive(planning_context)
            
            return {
                "adaptive_planning": adaptive_result,
                "planning_context": planning_context,
                "recommendations": self._generate_recommendations(adaptive_result)
            }
            
        except Exception as e:
            logger.error(f"❌ Adaptive planning failed: {e}")
            return {"error": str(e)}
    
    def _calculate_stability_score(self, stability_metrics: List[Dict]) -> float:
        """Calculate overall stability score from TA-ULS metrics."""
        if not stability_metrics:
            return 0.5  # Neutral score
        
        # Extract fluctuation intensity scores
        fluctuation_scores = []
        for metric in stability_metrics:
            if "stability_info" in metric:
                fluctuation_intensity = metric["stability_info"]
                # Convert to stability score (lower fluctuation = higher stability)
                stability_score = max(0.0, 1.0 - fluctuation_intensity.mean().item())
                fluctuation_scores.append(stability_score)
        
        return np.mean(fluctuation_scores) if fluctuation_scores else 0.5
    
    def _calculate_coherence_score(self, hidden_states: List[torch.Tensor]) -> float:
        """Calculate coherence score from hidden states."""
        if not hidden_states:
            return 0.5  # Neutral score
        
        # Calculate coherence between consecutive hidden states
        coherence_scores = []
        for i in range(1, len(hidden_states)):
            state1 = hidden_states[i-1]
            state2 = hidden_states[i]
            
            # Calculate cosine similarity
            if state1.numel() > 0 and state2.numel() > 0:
                state1_flat = state1.flatten()
                state2_flat = state2.flatten()
                
                # Ensure same length
                min_len = min(len(state1_flat), len(state2_flat))
                state1_flat = state1_flat[:min_len]
                state2_flat = state2_flat[:min_len]
                
                # Calculate cosine similarity
                dot_product = torch.dot(state1_flat, state2_flat)
                norm1 = torch.norm(state1_flat)
                norm2 = torch.norm(state2_flat)
                
                if norm1 > 0 and norm2 > 0:
                    cosine_sim = dot_product / (norm1 * norm2)
                    coherence_scores.append(cosine_sim.item())
        
        return np.mean(coherence_scores) if coherence_scores else 0.5
    
    def _calculate_information_density(self, data: Any) -> float:
        """Calculate information density of input data."""
        data_str = str(data)
        if not data_str:
            return 0.0
        
        # Calculate unique character ratio
        unique_chars = len(set(data_str))
        total_chars = len(data_str)
        
        return unique_chars / total_chars if total_chars > 0 else 0.0
    
    def _calculate_complexity_measure(self, data: Any) -> float:
        """Calculate complexity measure of input data."""
        data_str = str(data)
        if not data_str:
            return 0.0
        
        # Simple complexity measure based on structure
        complexity = 0.0
        
        # Add complexity for special characters
        special_chars = sum(1 for c in data_str if not c.isalnum() and not c.isspace())
        complexity += special_chars / len(data_str) * 0.3
        
        # Add complexity for numbers
        numbers = sum(1 for c in data_str if c.isdigit())
        complexity += numbers / len(data_str) * 0.2
        
        # Add complexity for mixed case
        has_upper = any(c.isupper() for c in data_str)
        has_lower = any(c.islower() for c in data_str)
        complexity += 0.1 if has_upper and has_lower else 0.0
        
        return min(1.0, complexity)
    
    def _calculate_signal_snr(self, signal: np.ndarray) -> float:
        """Calculate signal-to-noise ratio."""
        signal_power = np.mean(signal ** 2)
        noise_power = np.var(signal - np.mean(signal))
        
        if noise_power > 0:
            snr = 10 * np.log10(signal_power / noise_power)
            return max(0.0, snr)  # Ensure non-negative
        
        return 0.0
    
    def _calculate_bandwidth_efficiency(self, modulation_scheme: ModulationScheme) -> float:
        """Calculate bandwidth efficiency for modulation scheme."""
        efficiency_map = {
            ModulationScheme.BFSK: 0.5,
            ModulationScheme.BPSK: 1.0,
            ModulationScheme.QPSK: 2.0,
            ModulationScheme.QAM16: 4.0,
            ModulationScheme.OFDM: 3.5,
            ModulationScheme.DSSS_BPSK: 0.8
        }
        
        return efficiency_map.get(modulation_scheme, 1.0)
    
    def _check_stability_event(self, stability_metrics: Dict[str, Any]) -> bool:
        """Check if a stability event occurred."""
        stability_score = stability_metrics.get("stability_score", 0.5)
        return stability_score < 0.3  # Low stability threshold
    
    def _generate_recommendations(self, adaptive_result: Dict[str, Any]) -> List[str]:
        """Generate recommendations based on adaptive planning result."""
        recommendations = []
        
        # Add stability recommendations
        if "stability_improvement" in adaptive_result:
            recommendations.append("Consider stability enhancement techniques")
        
        # Add performance recommendations
        if "performance_optimization" in adaptive_result:
            recommendations.append("Apply performance optimization strategies")
        
        # Add modulation recommendations
        if "modulation_adjustment" in adaptive_result:
            recommendations.append("Adjust modulation scheme for better efficiency")
        
        return recommendations
    
    def _update_stats(self, processing_time: float, success: bool):
        """Update performance statistics."""
        self.stats["total_processing_requests"] += 1
        
        if success:
            self.stats["successful_processing"] += 1
        
        # Update average processing time
        total_time = self.stats["average_processing_time"] * (self.stats["total_processing_requests"] - 1)
        total_time += processing_time
        self.stats["average_processing_time"] = total_time / self.stats["total_processing_requests"]
    
    def get_stats(self) -> Dict[str, Any]:
        """Get performance statistics."""
        return {
            **self.stats,
            "initialized": self.initialized,
            "components_available": {
                "tauls": TAULS_AVAILABLE,
                "neuro_symbolic": NEURO_SYMBOLIC_AVAILABLE,
                "signal_processing": SIGNAL_PROCESSING_AVAILABLE
            },
            "success_rate": (
                self.stats["successful_processing"] / self.stats["total_processing_requests"]
                if self.stats["total_processing_requests"] > 0 else 0
            )
        }
    
    async def cleanup(self):
        """Clean up Group C resources."""
        logger.info("🧹 Cleaning up Group C components...")
        
        # Clean up TA-ULS model
        if self.tauls_model:
            del self.tauls_model
        
        self.initialized = False
        logger.info("✅ Group C cleanup completed")

async def main():
    """Demo function to test Group C integration."""
    print("🚀 Testing Group C Integration System")
    print("=" * 50)
    
    # Create system
    config = GroupCConfig(
        tauls_dim=256,
        tauls_layers=4,
        tauls_heads=8,
        modulation_scheme="qpsk"
    )
    
    system = GroupCIntegrationSystem(config)
    
    try:
        # Initialize
        if await system.initialize():
            print("✅ Group C system initialized successfully")
            
            # Test processing
            test_inputs = [
                "Explain the concept of dimensional entanglement in AI systems.",
                "How does quantum cognition enhance machine learning?",
                [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
            ]
            
            for i, test_input in enumerate(test_inputs, 1):
                print(f"\n🧪 Test {i}: {str(test_input)[:50]}...")
                
                result = await system.process_with_group_c(test_input)
                
                if result.success:
                    print(f"✅ Success ({result.processing_time:.3f}s)")
                    print(f"   TA-ULS: {len(result.tauls_features)} features")
                    print(f"   Neuro-Symbolic: {len(result.neuro_symbolic_features)} features")
                    print(f"   Signal Processing: {len(result.signal_processing_features)} features")
                    print(f"   Stability Score: {result.stability_metrics.get('stability_score', 0.0):.3f}")
                    print(f"   Entropy Score: {result.entropy_metrics.get('entropy_score', 0.0):.3f}")
                else:
                    print(f"❌ Failed: {result.error_message}")
            
            # Show stats
            stats = system.get_stats()
            print(f"\n📊 Statistics:")
            print(f"   Total requests: {stats['total_processing_requests']}")
            print(f"   Success rate: {stats['success_rate']:.2%}")
            print(f"   Avg processing time: {stats['average_processing_time']:.3f}s")
            print(f"   Stability events: {stats['stability_events']}")
            print(f"   Components: {sum(stats['components_available'].values())}/3 available")
            
        else:
            print("❌ Failed to initialize Group C system")
    
    except Exception as e:
        print(f"❌ Error: {e}")
    
    finally:
        # Cleanup
        await system.cleanup()
        print("\n🧹 Cleanup completed")

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