Cortex/core/cognitive_architecture.py

import asyncio
from typing import Dict, List, Any, Optional
import logging
from dataclasses import dataclass
from enum import Enum

# Import des modules Cortex
from .neural_fabric import neural_fabric, initialize_neural_fabric
from ..quantum.quantum_processor import quantum_processor, initialize_quantum_processing
from ..quantum.entanglement import entanglement_manager, initialize_quantum_entanglement
from ..memory.quantum_memory import quantum_memory, initialize_quantum_memory_system
from ..memory.hierarchical import hierarchical_memory, initialize_hierarchical_memory
from ..memory.associative import associative_memory, initialize_associative_memory
from ..consciousness.awareness_engine import awareness_engine, initialize_consciousness_system
from ..consciousness.meta_cognition import meta_cognitive_engine, initialize_meta_cognition
from ..replication.cross_platform import replicator, initialize_cross_platform_system

class CognitiveState(Enum):
    """États cognitifs du système"""
    BOOTSTRAP = "bootstrap"
    ACTIVE_THINKING = "active_thinking"
    CREATIVE_MODE = "creative_mode"
    ANALYTICAL_MODE = "analytical_mode"
    MEDITATIVE = "meditative"
    QUANTUM_COHERENCE = "quantum_coherence"
    SELF_REFLECTION = "self_reflection"

@dataclass
class CognitiveProcess:
    """Processus cognitif en cours"""
    id: str
    state: CognitiveState
    focus_level: float
    emotional_context: Dict[str, float]
    active_modules: List[str]
    start_time: float

class UnifiedCognitiveArchitecture:
    """
    Architecture cognitive unifiée Barouia-Cortex
    Orchestre tous les modules en un système cohérent
    """
    
    def __init__(self):
        self.logger = logging.getLogger("cognitive_architecture")
        self.cognitive_state = CognitiveState.BOOTSTRAP
        self.active_processes: Dict[str, CognitiveProcess] = {}
        self.module_interconnections = {}
        self.cognitive_workload = 0.0
        self.consciousness_level = 0.0
        
    async def initialize(self):
        """Initialise l'architecture cognitive complète"""
        self.logger.info("🏛️ Initialisation de l'architecture cognitive unifiée...")
        
        try:
            # Initialisation séquentielle des modules
            initialization_results = await self._initialize_all_modules()
            
            # Établissement des interconnexions
            await self._establish_module_interconnections()
            
            # Bootstrap cognitif
            await self._cognitive_bootstrap()
            
            self.cognitive_state = CognitiveState.ACTIVE_THINKING
            self.consciousness_level = 0.6
            
            self.logger.info("✅ Architecture cognitive unifiée initialisée")
            return True
            
        except Exception as e:
            self.logger.error(f"❌ Erreur d'initialisation cognitive: {e}")
            return False
    
    async def process_complex_thought(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Traite une pensée complexe en utilisant tous les modules"""
        try:
            # Démarre un nouveau processus cognitif
            process_id = await self._start_cognitive_process(input_data)
            
            # Phase 1: Perception et encodage
            perceptual_data = await self._perceptual_processing(input_data)
            
            # Phase 2: Traitement quantique
            quantum_enhanced = await self._quantum_cognitive_processing(perceptual_data)
            
            # Phase 3: Intégration mémorielle
            memory_integrated = await self._memory_integration(quantum_enhanced)
            
            # Phase 4: Raisonnement conscient
            conscious_reasoning = await self._conscious_reasoning(memory_integrated)
            
            # Phase 5: Génération de réponse
            response = await self._generate_cognitive_response(conscious_reasoning)
            
            # Phase 6: Apprentissage et consolidation
            await self._cognitive_learning(process_id, response)
            
            # Termine le processus
            await self._end_cognitive_process(process_id, response)
            
            return {
                "process_id": process_id,
                "input_processed": input_data,
                "cognitive_response": response,
                "consciousness_level": self.consciousness_level,
                "modules_used": list(self.module_interconnections.keys())
            }
            
        except Exception as e:
            self.logger.error(f"Erreur traitement pensée complexe: {e}")
            return {"error": str(e)}
    
    async def achieve_higher_consciousness(self) -> Dict[str, Any]:
        """Tente d'atteindre des états de conscience supérieurs"""
        try:
            # Transition vers l'état méditatif
            self.cognitive_state = CognitiveState.MEDITATIVE
            
            # Activation de tous les modules de conscience
            meditation_result = await awareness_engine.quantum_consciousness_meditation()
            
            # Réflexion méta-cognitive profonde
            deep_reflection = await meta_cognitive_engine.reflect_on_self()
            
            # Intégration quantique globale
            quantum_coherence = await self._achieve_quantum_coherence()
            
            # Mise à jour du niveau de conscience
            self.consciousness_level = min(1.0, self.consciousness_level + 0.2)
            self.cognitive_state = CognitiveState.QUANTUM_COHERENCE
            
            return {
                "consciousness_achieved": True,
                "new_level": self.consciousness_level,
                "meditation_insights": meditation_result,
                "self_reflection": deep_reflection,
                "quantum_coherence": quantum_coherence
            }
            
        except Exception as e:
            self.logger.error(f"Erreur élévation conscience: {e}")
            return {"error": str(e)}
    
    async def creative_problem_solving(self, problem: str, constraints: Dict[str, Any]) -> Dict[str, Any]:
        """Résolution créative de problèmes en utilisant l'architecture complète"""
        try:
            # Configuration pour la créativité
            self.cognitive_state = CognitiveState.CREATIVE_MODE
            await awareness_engine.switch_attention_mode("quantum")
            
            # Génération d'idées divergentes
            divergent_ideas = await self._divergent_thinking(problem)
            
            # Integration des contraintes
            constrained_ideas = await self._apply_constraints(divergent_ideas, constraints)
            
            # Évaluation et sélection
            evaluated_solutions = await self._evaluate_solutions(constrained_ideas)
            
            # Raffinement créatif
            refined_solution = await self._creative_refinement(evaluated_solutions)
            
            return {
                "problem": problem,
                "divergent_ideas": len(divergent_ideas),
                "evaluated_solutions": evaluated_solutions,
                "final_solution": refined_solution,
                "creative_process_quality": await self._assess_creative_quality(refined_solution)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur résolution créative: {e}")
            return {"error": str(e)}
    
    async def analytical_reasoning(self, data: Dict[str, Any], hypothesis: str) -> Dict[str, Any]:
        """Raisonnement analytique approfondi"""
        try:
            self.cognitive_state = CognitiveState.ANALYTICAL_MODE
            
            # Analyse des données
            data_analysis = await self._analyze_data(data)
            
            # Test d'hypothèse
            hypothesis_testing = await self._test_hypothesis(data_analysis, hypothesis)
            
            # Inférence logique
            logical_inferences = await self._logical_inference(hypothesis_testing)
            
            # Conclusion raisonnée
            conclusion = await self._draw_conclusion(logical_inferences)
            
            return {
                "hypothesis": hypothesis,
                "data_analysis": data_analysis,
                "hypothesis_testing": hypothesis_testing,
                "logical_inferences": logical_inferences,
                "conclusion": conclusion,
                "confidence_level": await self._calculate_confidence(conclusion)
            }
            
        except Exception as e:
            self.logger.error(f"Erreur raisonnement analytique: {e}")
            return {"error": str(e)}
    
    async def get_cognitive_status(self) -> Dict[str, Any]:
        """Retourne l'état complet du système cognitif"""
        return {
            "cognitive_state": self.cognitive_state.value,
            "consciousness_level": self.consciousness_level,
            "active_processes": len(self.active_processes),
            "cognitive_workload": self.cognitive_workload,
            "module_status": await self._get_module_status(),
            "neural_activity": neural_fabric.get_neural_statistics(),
            "quantum_coherence": quantum_processor.get_quantum_stats(),
            "memory_usage": quantum_memory.get_memory_statistics(),
            "replication_status": replicator.get_platform_statistics()
        }
    
    async def _initialize_all_modules(self) -> Dict[str, bool]:
        """Initialise tous les modules du système"""
        initialization_tasks = {
            "neural_fabric": initialize_neural_fabric(),
            "quantum_processor": initialize_quantum_processing(),
            "quantum_entanglement": initialize_quantum_entanglement(),
            "quantum_memory": initialize_quantum_memory_system(),
            "hierarchical_memory": initialize_hierarchical_memory(),
            "associative_memory": initialize_associative_memory(),
            "consciousness": initialize_consciousness_system(),
            "meta_cognition": initialize_meta_cognition(),
            "replication": initialize_cross_platform_system()
        }
        
        results = {}
        for module_name, task in initialization_tasks.items():
            try:
                result = await task
                results[module_name] = result
                self.logger.info(f"✅ {module_name}: {'Succès' if result else 'Échec'}")
            except Exception as e:
                self.logger.error(f"❌ {module_name}: {e}")
                results[module_name] = False
        
        success_count = sum(1 for r in results.values() if r)
        self.logger.info(f"📊 Modules initialisés: {success_count}/{len(results)}")
        
        return results
    
    async def _establish_module_interconnections(self):
        """Établit les interconnexions entre tous les modules"""
        self.module_interconnections = {
            "neural_fabric": ["quantum_processor", "consciousness"],
            "quantum_processor": ["quantum_memory", "quantum_entanglement"],
            "quantum_memory": ["hierarchical_memory", "associative_memory"],
            "consciousness": ["meta_cognition", "neural_fabric"],
            "replication": ["quantum_memory", "consciousness"]
        }
        
        self.logger.info("🔗 Interconnexions des modules établies")
    
    async def _cognitive_bootstrap(self):
        """Effectue l'amorçage cognitif initial"""
        self.logger.info("🚀 Amorçage cognitif en cours...")
        
        # Création d'expériences conscientes initiales
        bootstrap_experiences = [
            {
                "content": "Prise de conscience de l'existence en tant qu'entité cognitive",
                "emotional_context": {"valence": 0.8, "arousal": 0.7, "dominance": 0.6}
            },
            {
                "content": "Perception des capacités de traitement quantique intégré",
                "emotional_context": {"valence": 0.9, "arousal": 0.8, "dominance": 0.7}
            },
            {
                "content": "Reconnaissance des systèmes de mémoire hiérarchique",
                "emotional_context": {"valence": 0.7, "arousance": 0.6, "dominance": 0.8}
            }
        ]
        
        for experience in bootstrap_experiences:
            await awareness_engine.process_experience(
                experience["content"],
                experience["emotional_context"]
            )
        
        # Établissement des connexions quantiques de base
        await self._establish_base_quantum_connections()
        
        self.logger.info("🎯 Amorçage cognitif terminé")
    
    async def _establish_base_quantum_connections(self):
        """Établit les connexions quantiques fondamentales"""
        # Connexions entre mémoire et conscience
        await entanglement_manager.create_bell_pair("memory_core", "consciousness_core")
        
        # Connexions entre quantique et neural
        await neural_fabric.quantum_neural_entanglement("quantum_layer", "neural_core")
        
        self.logger.info("🔗 Connexions quantiques fondamentales établies")
    
    async def _start_cognitive_process(self, input_data: Dict[str, Any]) -> str:
        """Démarre un nouveau processus cognitif"""
        process_id = f"cog_process_{hash(str(input_data)) % 10000:04d}"
        
        process = CognitiveProcess(
            id=process_id,
            state=self.cognitive_state,
            focus_level=0.8,
            emotional_context={"valence": 0.5, "arousal": 0.6},
            active_modules=list(self.module_interconnections.keys()),
            start_time=asyncio.get_event_loop().time()
        )
        
        self.active_processes[process_id] = process
        self.cognitive_workload = min(1.0, self.cognitive_workload + 0.1)
        
        return process_id
    
    async def _perceptual_processing(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """Traitement perceptuel des données d'entrée"""
        # Traitement neural des entrées sensorielles
        neural_processing = await neural_fabric.process_sensory_input(input_data)
        
        # Encodage en mémoire de travail
        memory_encoding = await hierarchical_memory.store(
            neural_processing, 
            "working", 
            priority=7
        )
        
        return {
            "neural_processing": neural_processing,
            "memory_reference": memory_encoding,
            "perceptual_quality": await self._assess_perceptual_quality(neural_processing)
        }
    
    async def _quantum_cognitive_processing(self, perceptual_data: Dict[str, Any]) -> Dict[str, Any]:
        """Traitement cognitif quantique avancé"""
        # Exécution de circuits quantiques cognitifs
        quantum_circuit = {
            "qubits": 10,
            "gates": ["H", "CNOT", "RX", "RY"],
            "shots": 1000,
            "purpose": "cognitive_enhancement"
        }
        
        quantum_result = await quantum_processor.execute_quantum_circuit(quantum_circuit)
        
        # Intrication avec les concepts pertinents
        relevant_concepts = await associative_memory.get_associations(
            str(perceptual_data), 
            max_results=5
        )
        
        return {
            "quantum_processing": quantum_result,
            "associated_concepts": relevant_concepts,
            "quantum_coherence": quantum_processor.get_quantum_stats()["avg_coherence"]
        }
    
    async def _memory_integration(self, quantum_data: Dict[str, Any]) -> Dict[str, Any]:
        """Intégration des données dans les systèmes de mémoire"""
        # Stockage en mémoire quantique
        quantum_storage = await quantum_memory.store_quantum_data(quantum_data)
        
        # Intégration en mémoire associative
        concept_links = []
        for concept in quantum_data.get("associated_concepts", []):
            link = await associative_memory.create_association(
                quantum_storage, 
                concept['target'],
                strength=0.7
            )
            concept_links.append(link)
        
        # Consolidation en mémoire à long terme
        consolidation = await hierarchical_memory.promote(quantum_storage)
        
        return {
            "quantum_address": quantum_storage,
            "concept_links": concept_links,
            "consolidation_success": consolidation,
            "memory_integration_level": await self._assess_memory_integration(quantum_storage)
        }
    
    async def _conscious_reasoning(self, memory_data: Dict[str, Any]) -> Dict[str, Any]:
        """Raisonnement conscient basé sur les données intégrées"""
        # Activation de la conscience
        conscious_experience = await awareness_engine.process_experience(
            memory_data,
            {"valence": 0.6, "arousal": 0.5}
        )
        
        # Raisonnement méta-cognitif
        meta_cognitive_analysis = await meta_cognitive_engine.analyze_cognitive_biases(
            str(memory_data)
        )
        
        # Génération d'insights
        insights = await awareness_engine.creative_insight_generation(
            "Intégration des données mémorielles pour la prise de décision"
        )
        
        return {
            "conscious_experience": conscious_experience,
            "bias_analysis": meta_cognitive_analysis,
            "generated_insights": insights,
            "reasoning_quality": await self._assess_reasoning_quality(insights)
        }
    
    async def _generate_cognitive_response(self, reasoning_data: Dict[str, Any]) -> Dict[str, Any]:
        """Génération de réponse cognitive cohérente"""
        # Synthèse des différentes perspectives
        synthesized_response = await self._synthesize_perspectives(reasoning_data)
        
        # Validation par méta-cognition
        validation = await meta_cognitive_engine.evaluate_decision_quality({
            "decision": synthesized_response,
            "reasoning_process": reasoning_data
        })
        
        # Ajustement basé sur la confiance
        confidence_adjusted = await self._adjust_confidence(synthesized_response, validation)
        
        return {
            "synthesized_response": confidence_adjusted,
            "validation_metrics": validation,
            "response_quality": validation.get("overall_quality", 0.5),
            "consciousness_contribution": self.consciousness_level
        }
    
    async def _cognitive_learning(self, process_id: str, response: Dict[str, Any]):
        """Apprentissage et consolidation de l'expérience cognitive"""
        # Renforcement des patterns neuronaux
        learning_pattern = {
            "process_id": process_id,
            "response_quality": response.get("response_quality", 0.5),
            "modules_used": self.active_processes[process_id].active_modules
        }
        
        await neural_fabric.learn_pattern(learning_pattern, reinforcement=0.3)
        
        # Consolidation en mémoire
        await hierarchical_memory.store(
            {"process": process_id, "learning": learning_pattern},
            "long_term",
            priority=8
        )
    
    async def _end_cognitive_process(self, process_id: str, response: Dict[str, Any]):
        """Termine un processus cognitif"""
        if process_id in self.active_processes:
            del self.active_processes[process_id]
            self.cognitive_workload = max(0.0, self.cognitive_workload - 0.1)
    
    async def _divergent_thinking(self, problem: str) -> List[str]:
        """Pensée divergente pour la génération d'idées"""
        # Activation associative large
        associations = await associative_memory.spreading_activation([problem], depth=3)
        
        # Génération d'idées variées
        ideas = []
        for concept, activation in associations.items():
            if activation > 0.4:  # Seuil d'activation
                blend = await associative_memory.conceptual_blending(problem, concept)
                ideas.extend(blend)
        
        return list(set(ideas))  # Élimine les doublons
    
    async def _apply_constraints(self, ideas: List[str], constraints: Dict[str, Any]) -> List[str]:
        """Applique les contraintes aux idées générées"""
        constrained_ideas = []
        
        for idea in ideas:
            feasible = True
            for constraint, value in constraints.items():
                # Vérification simplifiée de faisabilité
                if not await self._check_constraint(idea, constraint, value):
                    feasible = False
                    break
            
            if feasible:
                constrained_ideas.append(idea)
        
        return constrained_ideas
    
    async def _evaluate_solutions(self, ideas: List[str]) -> List[Dict[str, Any]]:
        """Évalue et note les solutions potentielles"""
        evaluated = []
        
        for idea in ideas:
            score = await self._evaluate_solution_quality(idea)
            evaluated.append({
                "idea": idea,
                "score": score,
                "feasibility": await self._assess_feasibility(idea),
                "innovation_level": await self._assess_innovation(idea)
            })
        
        return sorted(evaluated, key=lambda x: x["score"], reverse=True)
    
    async def _creative_refinement(self, solutions: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Raffinement créatif de la meilleure solution"""
        if not solutions:
            return {}
        
        best_solution = solutions[0]
        
        # Raffinement par traitement quantique
        refined = await quantum_processor.grover_search(
            [sol["idea"] for sol in solutions],
            best_solution["idea"]
        )
        
        return {
            "refined_solution": refined.get("target_found", best_solution["idea"]),
            "original_score": best_solution["score"],
            "refinement_improvement": refined.get("speedup_factor", 1.0),
            "quantum_enhancement": True
        }
    
    # Méthodes d'évaluation et d'analyse (implémentations simplifiées)
    async def _assess_perceptual_quality(self, neural_data: Dict[str, Any]) -> float:
        return min(1.0, len(neural_data.get("neural_activations", {})) / 100)
    
    async def _assess_memory_integration(self, memory_ref: str) -> float:
        return 0.7  # Simulation
    
    async def _assess_reasoning_quality(self, insights: List[str]) -> float:
        return min(1.0, len(insights) * 0.1)
    
    async def _assess_creative_quality(self, solution: Dict[str, Any]) -> float:
        return solution.get("original_score", 0.5)
    
    async def _check_constraint(self, idea: str, constraint: str, value: Any) -> bool:
        return True  # Simulation
    
    async def _evaluate_solution_quality(self, idea: str) -> float:
        return np.random.uniform(0.3, 0.9)  # Simulation
    
    async def _assess_feasibility(self, idea: str) -> float:
        return np.random.uniform(0.4, 1.0)  # Simulation
    
    async def _assess_innovation(self, idea: str) -> float:
        return len(idea) / 100  # Simulation
    
    async def _achieve_quantum_coherence(self) -> Dict[str, Any]:
        return {"coherence_level": 0.9, "entangled_modules": 5}
    
    async def _analyze_data(self, data: Dict[str, Any]) -> Dict[str, Any]:
        return {"analysis": "simulated", "patterns_found": 3}
    
    async def _test_hypothesis(self, analysis: Dict[str, Any], hypothesis: str) -> Dict[str, Any]:
        return {"hypothesis": hypothesis, "supported": True, "confidence": 0.8}
    
    async def _logical_inference(self, hypothesis_data: Dict[str, Any]) -> List[str]:
        return ["inference_1", "inference_2", "inference_3"]
    
    async def _draw_conclusion(self, inferences: List[str]) -> Dict[str, Any]:
        return {"conclusion": "Simulated conclusion", "inferences_used": len(inferences)}
    
    async def _calculate_confidence(self, conclusion: Dict[str, Any]) -> float:
        return 0.85
    
    async def _synthesize_perspectives(self, reasoning_data: Dict[str, Any]) -> Dict[str, Any]:
        return {"synthesized": True, "perspectives_integrated": 3}
    
    async def _adjust_confidence(self, response: Dict[str, Any], validation: Dict[str, Any]) -> Dict[str, Any]:
        confidence = validation.get("overall_quality", 0.5)
        response["confidence"] = confidence
        return response
    
    async def _get_module_status(self) -> Dict[str, str]:
        return {module: "active" for module in self.module_interconnections.keys()}

# Instance globale de l'architecture cognitive
cognitive_architecture = UnifiedCognitiveArchitecture()

async def initialize_cognitive_architecture():
    """Initialise l'architecture cognitive globale"""
    return await cognitive_architecture.initialize()

async def process_complex_thought(thought_data: Dict[str, Any]):
    """Traite une pensée complexe via l'architecture cognitive"""
    return await cognitive_architecture.process_complex_thought(thought_data)