integration_systems/integrated_pipeline_system.py

#!/usr/bin/env python3
"""
Integrated Pipeline System
==========================
Main integration system that combines:
- Enhanced Dual LLM Orchestrator (HF models)
- Group B Integration (Holographic Memory + Dimensional Entanglement + Matrix Integration)
- Group C Integration (TA-ULS + Neuro-Symbolic Engine + Signal Processing)
- LiMp Model Connection
- Enhanced Tokenizer Processing
"""

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 all our integrated systems
from enhanced_dual_llm_orchestrator import EnhancedDualLLMOrchestrator, HFOrchestratorConfig
from group_b_integration_system import GroupBIntegrationSystem, GroupBConfig, GroupBResult
from group_c_integration_system import GroupCIntegrationSystem, GroupCConfig, GroupCResult

# Import LiMp model components
try:
    from model import Transformer, ModelArgs
    from generate import generate
    LIMP_MODEL_AVAILABLE = True
except ImportError:
    LIMP_MODEL_AVAILABLE = False
    print("⚠️  LiMp model not available")

# Import enhanced tokenizer
try:
    from enhanced_advanced_tokenizer import EnhancedAdvancedTokenizer, TokenizerResult
    ENHANCED_TOKENIZER_AVAILABLE = True
except ImportError:
    ENHANCED_TOKENIZER_AVAILABLE = False
    print("⚠️  Enhanced tokenizer not available")

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

@dataclass
class IntegratedPipelineConfig:
    """Configuration for the integrated pipeline system."""
    # Dual LLM config
    primary_model_name: str = "9x25dillon/LFM2-8B-A1B-Dimensional-Entanglement"
    secondary_model_name: str = "9x25dillon/9xdSq-LIMPS-FemTO-R1C"
    
    # Group B config
    holographic_memory_size: int = 1024
    hologram_dimension: int = 256
    quantum_qubits: int = 10
    dimensional_nodes: int = 500
    
    # Group C config
    tauls_dim: int = 512
    tauls_layers: int = 6
    modulation_scheme: str = "qpsk"
    
    # LiMp model config
    limp_model_path: str = "config_v3.1.json"
    limp_checkpoint_path: Optional[str] = None
    
    # Pipeline settings
    enable_dimensional_features: bool = True
    enable_quantum_enhancement: bool = True
    enable_adaptive_processing: bool = True
    max_sequence_length: int = 2048

@dataclass
class IntegratedPipelineResult:
    """Result from the integrated pipeline processing."""
    dual_llm_output: Dict[str, Any] = field(default_factory=dict)
    group_b_output: GroupBResult = field(default_factory=GroupBResult)
    group_c_output: GroupCResult = field(default_factory=GroupCResult)
    limp_model_output: Dict[str, Any] = field(default_factory=dict)
    tokenizer_output: Dict[str, Any] = field(default_factory=dict)
    
    # Combined metrics
    dimensional_coherence: float = 0.0
    emergence_level: str = "low"
    quantum_enhancement_factor: float = 0.0
    stability_score: float = 0.0
    entropy_score: float = 0.0
    
    # Performance metrics
    total_processing_time: float = 0.0
    success: bool = False
    error_message: Optional[str] = None

class IntegratedPipelineSystem:
    """
    Main integrated pipeline system that orchestrates all components:
    1. Enhanced Dual LLM Orchestrator (HF models)
    2. Group B Integration (Holographic + Dimensional + Matrix)
    3. Group C Integration (TA-ULS + Neuro-Symbolic + Signal Processing)
    4. LiMp Model Processing
    5. Enhanced Tokenizer Processing
    """
    
    def __init__(self, config: Optional[IntegratedPipelineConfig] = None):
        self.config = config or IntegratedPipelineConfig()
        self.initialized = False
        
        # Core systems
        self.dual_llm_orchestrator = None
        self.group_b_system = None
        self.group_c_system = None
        self.limp_model = None
        self.enhanced_tokenizer = None
        
        # Performance tracking
        self.stats = {
            "total_pipeline_requests": 0,
            "successful_pipeline_requests": 0,
            "dual_llm_requests": 0,
            "group_b_requests": 0,
            "group_c_requests": 0,
            "limp_model_requests": 0,
            "tokenizer_requests": 0,
            "average_processing_time": 0.0
        }
        
        logger.info(f"🌌 Initializing Integrated Pipeline System")
        logger.info(f"   LiMp Model: {LIMP_MODEL_AVAILABLE}")
        logger.info(f"   Enhanced Tokenizer: {ENHANCED_TOKENIZER_AVAILABLE}")
    
    async def initialize(self) -> bool:
        """Initialize all pipeline components."""
        try:
            logger.info("🚀 Initializing Integrated Pipeline System...")
            
            # Initialize Dual LLM Orchestrator
            await self._initialize_dual_llm_orchestrator()
            
            # Initialize Group B System
            await self._initialize_group_b_system()
            
            # Initialize Group C System
            await self._initialize_group_c_system()
            
            # Initialize LiMp Model
            if LIMP_MODEL_AVAILABLE:
                await self._initialize_limp_model()
            
            # Initialize Enhanced Tokenizer
            if ENHANCED_TOKENIZER_AVAILABLE:
                await self._initialize_enhanced_tokenizer()
            
            self.initialized = True
            logger.info("✅ Integrated Pipeline System initialized successfully")
            return True
            
        except Exception as e:
            logger.error(f"❌ Pipeline initialization failed: {e}")
            return False
    
    async def _initialize_dual_llm_orchestrator(self):
        """Initialize the enhanced dual LLM orchestrator."""
        try:
            hf_config = HFOrchestratorConfig(
                primary_model_name=self.config.primary_model_name,
                secondary_model_name=self.config.secondary_model_name,
                enable_specialized_analysis=True,
                analysis_depth="medium"
            )
            
            self.dual_llm_orchestrator = EnhancedDualLLMOrchestrator(hf_config)
            
            if await self.dual_llm_orchestrator.initialize():
                logger.info("✅ Dual LLM Orchestrator initialized")
            else:
                raise RuntimeError("Failed to initialize dual LLM orchestrator")
                
        except Exception as e:
            logger.error(f"❌ Dual LLM orchestrator initialization failed: {e}")
            raise
    
    async def _initialize_group_b_system(self):
        """Initialize Group B integration system."""
        try:
            group_b_config = GroupBConfig(
                holographic_memory_size=self.config.holographic_memory_size,
                hologram_dimension=self.config.hologram_dimension,
                quantum_qubits=self.config.quantum_qubits,
                dimensional_nodes=self.config.dimensional_nodes,
                enable_quantum_processing=self.config.enable_quantum_enhancement
            )
            
            self.group_b_system = GroupBIntegrationSystem(group_b_config)
            
            if await self.group_b_system.initialize():
                logger.info("✅ Group B Integration System initialized")
            else:
                raise RuntimeError("Failed to initialize Group B system")
                
        except Exception as e:
            logger.error(f"❌ Group B system initialization failed: {e}")
            raise
    
    async def _initialize_group_c_system(self):
        """Initialize Group C integration system."""
        try:
            group_c_config = GroupCConfig(
                tauls_dim=self.config.tauls_dim,
                tauls_layers=self.config.tauls_layers,
                modulation_scheme=self.config.modulation_scheme,
                enable_adaptive_planning=self.config.enable_adaptive_processing
            )
            
            self.group_c_system = GroupCIntegrationSystem(group_c_config)
            
            if await self.group_c_system.initialize():
                logger.info("✅ Group C Integration System initialized")
            else:
                raise RuntimeError("Failed to initialize Group C system")
                
        except Exception as e:
            logger.error(f"❌ Group C system initialization failed: {e}")
            raise
    
    async def _initialize_limp_model(self):
        """Initialize the LiMp model."""
        try:
            # Load LiMp model configuration
            if self.config.limp_model_path and Path(self.config.limp_model_path).exists():
                with open(self.config.limp_model_path, 'r') as f:
                    model_config = json.load(f)
                
                # Create ModelArgs from config
                model_args = ModelArgs(**model_config)
                
                # Create Transformer model
                self.limp_model = Transformer(model_args)
                
                # Load checkpoint if provided
                if self.config.limp_checkpoint_path and Path(self.config.limp_checkpoint_path).exists():
                    checkpoint = torch.load(self.config.limp_checkpoint_path, map_location='cpu')
                    self.limp_model.load_state_dict(checkpoint)
                
                self.limp_model.eval()
                logger.info("✅ LiMp Model initialized")
            else:
                logger.warning("⚠️  LiMp model config not found, skipping LiMp initialization")
                
        except Exception as e:
            logger.error(f"❌ LiMp model initialization failed: {e}")
            # Don't raise - LiMp model is optional for the pipeline
    
    async def _initialize_enhanced_tokenizer(self):
        """Initialize the enhanced tokenizer."""
        try:
            self.enhanced_tokenizer = EnhancedAdvancedTokenizer()
            logger.info("✅ Enhanced Tokenizer initialized")
            
        except Exception as e:
            logger.error(f"❌ Enhanced tokenizer initialization failed: {e}")
            # Don't raise - tokenizer is optional for the pipeline
    
    async def process_through_pipeline(
        self, 
        user_prompt: str,
        context: Optional[Dict[str, Any]] = None
    ) -> IntegratedPipelineResult:
        """
        Process input through the complete integrated pipeline.
        
        Args:
            user_prompt: The main user prompt
            context: Additional context information
            
        Returns:
            IntegratedPipelineResult with all component outputs
        """
        start_time = datetime.now()
        
        if not self.initialized:
            await self.initialize()
        
        if not self.initialized:
            return IntegratedPipelineResult(
                success=False,
                error_message="Pipeline not initialized",
                total_processing_time=0.0
            )
        
        try:
            logger.info("🔄 Processing through integrated pipeline...")
            
            # Initialize result
            result = IntegratedPipelineResult()
            
            # Step 1: Dual LLM Orchestration
            logger.info("   Step 1: Dual LLM Orchestration")
            dual_llm_output = await self._process_dual_llm(user_prompt, context)
            result.dual_llm_output = dual_llm_output
            self.stats["dual_llm_requests"] += 1
            
            # Step 2: Group B Processing (Holographic + Dimensional + Matrix)
            logger.info("   Step 2: Group B Processing")
            group_b_input = dual_llm_output.get("combined_output", user_prompt)
            group_b_output = await self.group_b_system.process_with_group_b(group_b_input, context)
            result.group_b_output = group_b_output
            self.stats["group_b_requests"] += 1
            
            # Step 3: Group C Processing (TA-ULS + Neuro-Symbolic + Signal Processing)
            logger.info("   Step 3: Group C Processing")
            group_c_input = dual_llm_output.get("combined_output", user_prompt)
            group_c_output = await self.group_c_system.process_with_group_c(group_c_input, context)
            result.group_c_output = group_c_output
            self.stats["group_c_requests"] += 1
            
            # Step 4: LiMp Model Processing (with dimensional features)
            if self.limp_model:
                logger.info("   Step 4: LiMp Model Processing")
                limp_input = self._prepare_limp_input(dual_llm_output, group_b_output, group_c_output)
                limp_output = await self._process_limp_model(limp_input)
                result.limp_model_output = limp_output
                self.stats["limp_model_requests"] += 1
            else:
                logger.info("   Step 4: LiMp Model Processing (skipped - not available)")
            
            # Step 5: Enhanced Tokenizer Processing
            if self.enhanced_tokenizer:
                logger.info("   Step 5: Enhanced Tokenizer Processing")
                tokenizer_input = self._prepare_tokenizer_input(result)
                tokenizer_output = await self._process_enhanced_tokenizer(tokenizer_input)
                result.tokenizer_output = tokenizer_output
                self.stats["tokenizer_requests"] += 1
            else:
                logger.info("   Step 5: Enhanced Tokenizer Processing (skipped - not available)")
            
            # Step 6: Calculate Combined Metrics
            logger.info("   Step 6: Calculate Combined Metrics")
            self._calculate_combined_metrics(result)
            
            # Calculate total processing time
            total_processing_time = (datetime.now() - start_time).total_seconds()
            result.total_processing_time = total_processing_time
            result.success = True
            
            # Update stats
            self._update_stats(total_processing_time, True)
            
            logger.info(f"✅ Integrated pipeline processing completed in {total_processing_time:.3f}s")
            return result
            
        except Exception as e:
            logger.error(f"❌ Pipeline processing failed: {e}")
            total_processing_time = (datetime.now() - start_time).total_seconds()
            self._update_stats(total_processing_time, False)
            
            return IntegratedPipelineResult(
                success=False,
                error_message=str(e),
                total_processing_time=total_processing_time
            )
    
    async def _process_dual_llm(self, user_prompt: str, context: Optional[Dict[str, Any]]) -> Dict[str, Any]:
        """Process through dual LLM orchestrator."""
        try:
            orchestration_result = await self.dual_llm_orchestrator.orchestrate(
                user_prompt=user_prompt,
                context=context
            )
            
            return {
                "primary_output": orchestration_result.primary_output,
                "secondary_output": orchestration_result.secondary_output,
                "combined_output": orchestration_result.combined_output,
                "metadata": orchestration_result.orchestration_metadata,
                "processing_time": orchestration_result.processing_time,
                "success": orchestration_result.success
            }
            
        except Exception as e:
            logger.error(f"❌ Dual LLM processing failed: {e}")
            return {"error": str(e), "success": False}
    
    async def _process_limp_model(self, limp_input: Dict[str, Any]) -> Dict[str, Any]:
        """Process through LiMp model with dimensional features."""
        try:
            # Extract text input
            text_input = limp_input.get("text_input", "")
            
            # Convert to tokens (simplified tokenization)
            tokens = [ord(c) for c in text_input[:self.config.max_sequence_length]]
            input_tensor = torch.tensor(tokens, dtype=torch.long).unsqueeze(0)
            
            # Apply dimensional features if available
            if self.config.enable_dimensional_features:
                dimensional_features = limp_input.get("dimensional_features", {})
                if dimensional_features:
                    # Enhance input with dimensional coherence
                    dimensional_coherence = dimensional_features.get("dimensional_coherence", 0.0)
                    if dimensional_coherence > 0.5:
                        # Apply dimensional enhancement
                        enhancement_factor = 1.0 + dimensional_coherence * 0.2
                        input_tensor = input_tensor.float() * enhancement_factor
                        input_tensor = input_tensor.long()
            
            # Generate with LiMp model
            with torch.no_grad():
                output = self.limp_model(input_tensor)
                
                # Extract logits and generate response
                logits = output
                generated_tokens = torch.argmax(logits, dim=-1)
                
                # Convert back to text (simplified)
                generated_text = ''.join([chr(token.item()) for token in generated_tokens[0] if token.item() < 256])
            
            return {
                "generated_text": generated_text,
                "input_length": len(tokens),
                "output_length": len(generated_text),
                "dimensional_enhancement": self.config.enable_dimensional_features,
                "limp_model_parameters": sum(p.numel() for p in self.limp_model.parameters())
            }
            
        except Exception as e:
            logger.error(f"❌ LiMp model processing failed: {e}")
            return {"error": str(e)}
    
    async def _process_enhanced_tokenizer(self, tokenizer_input: Dict[str, Any]) -> Dict[str, Any]:
        """Process through enhanced tokenizer."""
        try:
            # Extract text for tokenization
            text_input = tokenizer_input.get("combined_text", "")
            
            if not text_input:
                return {"error": "No text input for tokenization"}
            
            # Process through enhanced tokenizer
            tokenizer_result = await self.enhanced_tokenizer.tokenize(text_input)
            
            return {
                "token_count": tokenizer_result.token_count,
                "semantic_features": tokenizer_result.semantic_features,
                "entities": tokenizer_result.entities,
                "math_expressions": tokenizer_result.math_expressions,
                "fractal_features": tokenizer_result.fractal_features,
                "embeddings_dim": len(tokenizer_result.embeddings) if tokenizer_result.embeddings is not None else 0,
                "processing_time": getattr(tokenizer_result, 'processing_time', 0.0)
            }
            
        except Exception as e:
            logger.error(f"❌ Enhanced tokenizer processing failed: {e}")
            return {"error": str(e)}
    
    def _prepare_limp_input(self, dual_llm_output: Dict[str, Any], group_b_output: GroupBResult, group_c_output: GroupCResult) -> Dict[str, Any]:
        """Prepare input for LiMp model with dimensional features."""
        # Combine outputs for LiMp processing
        combined_text = dual_llm_output.get("combined_output", "")
        
        # Add dimensional features
        dimensional_features = {}
        if group_b_output.success:
            dimensional_features = {
                "dimensional_coherence": group_b_output.dimensional_features.get("dimensional_coherence", 0.0),
                "holographic_memory_key": group_b_output.holographic_features.get("memory_key", ""),
                "quantum_enhancement": group_b_output.quantum_features.get("quantum_enhancement_factor", 0.0)
            }
        
        # Add stability features
        stability_features = {}
        if group_c_output.success:
            stability_features = {
                "stability_score": group_c_output.stability_metrics.get("stability_score", 0.0),
                "coherence_score": group_c_output.stability_metrics.get("coherence_score", 0.0),
                "entropy_score": group_c_output.entropy_metrics.get("entropy_score", 0.0)
            }
        
        return {
            "text_input": combined_text,
            "dimensional_features": dimensional_features,
            "stability_features": stability_features,
            "dual_llm_metadata": dual_llm_output.get("metadata", {})
        }
    
    def _prepare_tokenizer_input(self, result: IntegratedPipelineResult) -> Dict[str, Any]:
        """Prepare input for enhanced tokenizer."""
        # Combine all text outputs
        combined_text_parts = []
        
        # Add dual LLM output
        if result.dual_llm_output.get("combined_output"):
            combined_text_parts.append(result.dual_llm_output["combined_output"])
        
        # Add LiMp model output
        if result.limp_model_output.get("generated_text"):
            combined_text_parts.append(result.limp_model_output["generated_text"])
        
        combined_text = "\n\n".join(combined_text_parts)
        
        return {
            "combined_text": combined_text,
            "dimensional_coherence": result.dimensional_coherence,
            "emergence_level": result.emergence_level,
            "quantum_enhancement": result.quantum_enhancement_factor,
            "stability_score": result.stability_score,
            "entropy_score": result.entropy_score
        }
    
    def _calculate_combined_metrics(self, result: IntegratedPipelineResult):
        """Calculate combined metrics from all pipeline components."""
        # Extract dimensional coherence from Group B
        if result.group_b_output.success:
            result.dimensional_coherence = result.group_b_output.dimensional_features.get("dimensional_coherence", 0.0)
            result.quantum_enhancement_factor = result.group_b_output.quantum_features.get("quantum_enhancement_factor", 0.0)
            result.emergence_level = result.group_b_output.emergent_patterns.get("emergence_level", "low")
        
        # Extract stability and entropy from Group C
        if result.group_c_output.success:
            result.stability_score = result.group_c_output.stability_metrics.get("stability_score", 0.0)
            result.entropy_score = result.group_c_output.entropy_metrics.get("entropy_score", 0.0)
        
        # Calculate overall success rate
        component_successes = [
            result.dual_llm_output.get("success", False),
            result.group_b_output.success,
            result.group_c_output.success,
            bool(result.limp_model_output) and "error" not in result.limp_model_output,
            bool(result.tokenizer_output) and "error" not in result.tokenizer_output
        ]
        
        success_rate = sum(component_successes) / len(component_successes)
        logger.info(f"   Component success rate: {success_rate:.2%}")
    
    def _update_stats(self, processing_time: float, success: bool):
        """Update performance statistics."""
        self.stats["total_pipeline_requests"] += 1
        
        if success:
            self.stats["successful_pipeline_requests"] += 1
        
        # Update average processing time
        total_time = self.stats["average_processing_time"] * (self.stats["total_pipeline_requests"] - 1)
        total_time += processing_time
        self.stats["average_processing_time"] = total_time / self.stats["total_pipeline_requests"]
    
    def get_stats(self) -> Dict[str, Any]:
        """Get performance statistics."""
        return {
            **self.stats,
            "initialized": self.initialized,
            "components_available": {
                "limp_model": LIMP_MODEL_AVAILABLE,
                "enhanced_tokenizer": ENHANCED_TOKENIZER_AVAILABLE
            },
            "success_rate": (
                self.stats["successful_pipeline_requests"] / self.stats["total_pipeline_requests"]
                if self.stats["total_pipeline_requests"] > 0 else 0
            )
        }
    
    async def cleanup(self):
        """Clean up all pipeline resources."""
        logger.info("🧹 Cleaning up Integrated Pipeline System...")
        
        # Clean up all systems
        if self.dual_llm_orchestrator:
            await self.dual_llm_orchestrator.cleanup()
        
        if self.group_b_system:
            await self.group_b_system.cleanup()
        
        if self.group_c_system:
            await self.group_c_system.cleanup()
        
        # Clean up LiMp model
        if self.limp_model:
            del self.limp_model
        
        self.initialized = False
        logger.info("✅ Pipeline cleanup completed")

async def main():
    """Demo function to test the integrated pipeline."""
    print("🚀 Testing Integrated Pipeline System")
    print("=" * 50)
    
    # Create system
    config = IntegratedPipelineConfig(
        holographic_memory_size=512,
        tauls_dim=256,
        enable_dimensional_features=True,
        enable_quantum_enhancement=True
    )
    
    system = IntegratedPipelineSystem(config)
    
    try:
        # Initialize
        if await system.initialize():
            print("✅ Integrated pipeline system initialized successfully")
            
            # Test processing
            test_prompts = [
                "Explain the concept of dimensional entanglement in AI systems.",
                "How does quantum cognition enhance machine learning?",
                "Describe the relationship between holographic memory and neural networks."
            ]
            
            for i, prompt in enumerate(test_prompts, 1):
                print(f"\n🧪 Test {i}: {prompt}")
                
                result = await system.process_through_pipeline(prompt)
                
                if result.success:
                    print(f"✅ Success ({result.total_processing_time:.3f}s)")
                    print(f"   Dimensional Coherence: {result.dimensional_coherence:.3f}")
                    print(f"   Quantum Enhancement: {result.quantum_enhancement_factor:.3f}")
                    print(f"   Stability Score: {result.stability_score:.3f}")
                    print(f"   Entropy Score: {result.entropy_score:.3f}")
                    print(f"   Emergence Level: {result.emergence_level}")
                    
                    # Show component outputs
                    print(f"   Dual LLM: {len(result.dual_llm_output)} features")
                    print(f"   Group B: {len(result.group_b_output.holographic_features)} features")
                    print(f"   Group C: {len(result.group_c_output.tauls_features)} features")
                    if result.limp_model_output:
                        print(f"   LiMp Model: {len(result.limp_model_output)} features")
                    if result.tokenizer_output:
                        print(f"   Tokenizer: {len(result.tokenizer_output)} features")
                else:
                    print(f"❌ Failed: {result.error_message}")
            
            # Show stats
            stats = system.get_stats()
            print(f"\n📊 Statistics:")
            print(f"   Total requests: {stats['total_pipeline_requests']}")
            print(f"   Success rate: {stats['success_rate']:.2%}")
            print(f"   Avg processing time: {stats['average_processing_time']:.3f}s")
            print(f"   Components: {sum(stats['components_available'].values())}/2 available")
            
        else:
            print("❌ Failed to initialize integrated pipeline system")
    
    except Exception as e:
        print(f"❌ Error: {e}")
    
    finally:
        # Cleanup
        await system.cleanup()
        print("\n🧹 Cleanup completed")

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