import asyncio
import logging
from cortex.engineer.autonomous_engineer import autonomous_engineer, initialize_autonomous_engineering
from cortex.engineer.quantum_compiler import quantum_compiler, initialize_quantum_compilation
from cortex.engineer.self_evolving_system import self_evolving_system, initialize_self_evolution
from cortex.deployment.global_deployer import global_deployer, initialize_global_deployment, DeploymentPackage
from cortex.deployment.quantum_network import quantum_network, initialize_quantum_network

# Configuration du logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')

async def demonstrate_autonomous_development():
    """Démontre le développement autonome complet"""
    print("🚀 DÉMONSTRATION DU DÉVELOPPEMENT AUTONOME BAROUIA-CORTEX")
    print("=" * 60)
    
    # Initialisation de tous les systèmes
    print("\n1. 📦 INITIALISATION DES SYSTÈMES...")
    await initialize_autonomous_engineering()
    await initialize_quantum_compilation()
    await initialize_self_evolution()
    await initialize_global_deployment()
    await initialize_quantum_network()
    
    print("✅ Tous les systèmes initialisés")
    
    # 1. Création d'un langage personnalisé pour l'IA Quantique
    print("\n2. 🆕 CRÉATION D'UN LANGAGE PERSONNALISÉ...")
    quantum_ai_language = await autonomous_engineer.create_custom_language(
        domain="quantum_ai",
        requirements={
            "paradigms": ["quantum", "neural", "functional"],
            "memory_model": "quantum_neural_hybrid",
            "target_platforms": ["ibm_quantum", "nvidia_dgx"],
            "optimization_goals": ["quantum_speedup", "neural_efficiency"]
        }
    )
    
    print(f"✅ Langage créé: {quantum_ai_language['name']}")
    print(f"   - Paradigmes: {quantum_ai_language['specification']['paradigms']}")
    print(f"   - Modèle mémoire: {quantum_ai_language['specification']['memory_model']}")
    
    # 2. Génération d'architecture quantique
    print("\n3. 🏗️ GÉNÉRATION D'ARCHITECTURE QUANTIQUE...")
    quantum_architecture = await autonomous_engineer.generate_quantum_architecture({
        "quantum_processing": True,
        "neural_networks": True,
        "hybrid_computing": True,
        "error_correction": True,
        "scalability": "massive"
    })
    
    print(f"✅ Architecture générée: {quantum_architecture.name}")
    print(f"   - Composants: {len(quantum_architecture.components)}")
    print(f"   - Score d'évolutivité: {quantum_architecture.scalability_score:.2f}")
    print(f"   - Intégration quantique: {quantum_architecture.quantum_integration}")
    
    # 3. Compilation de code quantique
    print("\n4. ⚛️ COMPILATION QUANTIQUE...")
    classical_ai_code = """
def train_neural_network(data, labels):
    # Entraînement d'un réseau neuronal classique
    model = create_quantum_inspired_model()
    for epoch in range(100):
        predictions = model.predict(data)
        loss = calculate_quantum_loss(predictions, labels)
        model.quantum_backpropagate(loss)
    return model

def quantum_optimize_parameters(params):
    # Optimisation quantique des paramètres
    best_params = quantum_gradient_descent(params)
    return best_params
"""
    
    quantum_circuit = await quantum_compiler.compile_to_quantum(classical_ai_code)
    print(f"✅ Code compilé en circuit quantique")
    print(f"   - Qubits: {quantum_circuit.qubits}")
    print(f"   - Portes quantiques: {len(quantum_circuit.gates)}")
    print(f"   - Temps d'exécution estimé: {quantum_circuit.execution_time}")
    
    # 4. Génération de code hybride
    print("\n5. 🔄 GÉNÉRATION DE CODE HYBRIDE...")
    hybrid_system = await quantum_compiler.generate_hybrid_code(
        "Système d'IA quantique pour la reconnaissance d'images médicales",
        ["quantum_ml", "grover_search", "quantum_fourier"]
    )
    
    print(f"✅ Système hybride généré")
    print(f"   - Accélérations quantiques: {list(hybrid_system['quantum_speedup'].keys())}")
    print(f"   - Performance estimée: {hybrid_system['performance_estimate']['overall_speedup']}x")
    
    # 5. Auto-évolution du système
    print("\n6. 🔄 AUTO-ÉVOLUTION DU SYSTÈME...")
    evolved_system = await self_evolving_system.evolve_system({
        "performance": 0.3,
        "accuracy": 0.2,
        "efficiency": 0.25,
        "robustness": 0.15,
        "adaptability": 0.35
    })
    
    print(f"✅ Système auto-évolué: {evolved_system.version_id}")
    print(f"   - Score d'amélioration: {evolved_system.improvement_score:.2f}")
    print(f"   - Chemin évolutif: {evolved_system.evolutionary_path}")
    
    # 6. Création d'un package de déploiement
    print("\n7. 📦 CRÉATION DU PACKAGE DE DÉPLOIEMENT...")
    deployment_package = DeploymentPackage(
        package_id="barouia_quantum_ai_system",
        code_components=quantum_architecture.components,
        dependencies=hybrid_system["hybrid_architecture"]["classical"]["dependencies"],
        configuration={
            "environment": "global_production",
            "quantum_enabled": True,
            "ai_capabilities": "full",
            "auto_scaling": True
        },
        quantum_optimizations=hybrid_system["hybrid_architecture"].get("quantum_optimizations", []),
        deployment_scripts={
            "docker": """
FROM nvidia/cuda:11.8-base
FROM python:3.9-slim

WORKDIR /app

# Installation des dépendances quantiques et IA
RUN pip install qiskit pennylane tensorflow torch
RUN pip install scikit-learn numpy pandas

# Copie du code Barouia-Cortex
COPY . .

# Configuration quantique
ENV QUANTUM_BACKEND=ibm_quantum
ENV AI_MODE=quantum_enhanced

CMD ["python", "main.py"]
""",
            "kubernetes": """
apiVersion: apps/v1
kind: Deployment
metadata:
  name: barouia-quantum-ai
  labels:
    app: barouia-quantum-ai
spec:
  replicas: 3
  selector:
    matchLabels:
      app: barouia-quantum-ai
  template:
    metadata: