#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Simplified Demo: Quantum-Scaling RL Hybrid Agent
Demonstrates the architecture without requiring quantum dependencies
"""
import numpy as np
from dataclasses import dataclass
from typing import Dict, List, Any


@dataclass
class QuantumRLConfig:
    """Configuration for Quantum-Scaling RL Hybrid"""
    qaoa_depth: int = 2
    qsvm_feature_dim: int = 8
    qec_code_distance: int = 5
    learning_rate: float = 1e-5
    batch_size: int = 8
    kl_coef: float = 0.1
    backends: List[str] = None
    
    def __post_init__(self):
        if self.backends is None:
            self.backends = ['ibm', 'russian']


def simulate_quantum_optimization(edit: Dict, corpus: List[Dict], backend: str) -> Dict:
    """Simulate quantum optimization step"""
    # Simulate QAOA coherence
    qaoa_coherence = np.random.uniform(0.6, 0.9)
    qaoa_latency = np.random.uniform(30, 100)
    
    # Simulate QSVM hallucination detection
    qsvm_valid_prob = np.random.uniform(0.7, 0.95)
    
    # Simulate QEC correction
    qec_logical_error_rate = np.random.uniform(0.001, 0.01)
    qec_success = qec_logical_error_rate < 0.008
    
    return {
        'optimized_edit': edit,
        'quantum_metrics': {
            'qaoa_coherence': qaoa_coherence,
            'qaoa_latency_ms': qaoa_latency,
            'qsvm_valid_prob': qsvm_valid_prob,
            'qec_logical_error_rate': qec_logical_error_rate,
            'qec_success': qec_success,
            'total_quantum_time_ms': qaoa_latency + 20
        }
    }


def simulate_rlhf_adaptation(edit: Dict, quantum_metrics: Dict, backend: str, 
                            backend_history: Dict, kl_coef: float) -> Dict:
    """Simulate RLHF adaptation step"""
    # Calculate reward signals
    edit_reliability = 1.0 - quantum_metrics['qec_logical_error_rate']
    latency_reduction = 1.0 / (1.0 + quantum_metrics['qaoa_latency_ms'] / 100)
    contributor_agreement = quantum_metrics['qsvm_valid_prob']
    
    # Combined reward
    base_reward = (
        0.4 * edit_reliability +
        0.3 * latency_reduction +
        0.3 * contributor_agreement
    )
    
    # KL penalty
    kl_penalty = 0.0
    if backend_history.get(backend):
        historical_perf = np.mean(backend_history[backend][-10:])
        kl_penalty = kl_coef * abs(base_reward - historical_perf)
    
    reward = base_reward - kl_penalty
    
    return {
        'reward': reward,
        'rl_metrics': {
            'edit_reliability_delta': edit_reliability,
            'latency_reduction': latency_reduction,
            'contributor_agreement_score': contributor_agreement,
            'base_reward': base_reward,
            'kl_penalty': kl_penalty,
            'final_reward': reward,
            'adaptation_time_ms': 15
        }
    }


def simulate_scaling_budgeting(edit: Dict, quantum_metrics: Dict, rl_metrics: Dict,
                              batch_size: int) -> Dict:
    """Simulate scaling RL budgeting step"""
    # Calculate model size proxy
    edit_complexity = len(str(edit)) / 1000
    model_size_proxy = max(1.0, edit_complexity)
    
    # Optimal batch size
    optimal_batch_size = int(batch_size * np.sqrt(model_size_proxy))
    
    # Compute efficiency
    total_compute_time = quantum_metrics['total_quantum_time_ms'] + rl_metrics['adaptation_time_ms']
    compute_efficiency = rl_metrics['final_reward'] / (total_compute_time / 1000 + 1e-6)
    
    return {
        'scaling_metrics': {
            'optimal_batch_size': optimal_batch_size,
            'compute_efficiency': compute_efficiency,
            'total_compute_time_ms': total_compute_time,
            'budgeting_time_ms': 5
        }
    }


def main():
    print("=" * 80)
    print("Quantum-Scaling RL Hybrid Agent - Simplified Demo")
    print("=" * 80)
    print()
    print("NOTE: This is a simplified demo that simulates quantum operations")
    print("      For full quantum functionality, install: pip install qiskit")
    print()
    
    # Initialize configuration
    config = QuantumRLConfig(
        qaoa_depth=2,
        qsvm_feature_dim=8,
        qec_code_distance=5,
        learning_rate=1e-5,
        batch_size=8,
        kl_coef=0.1,
        backends=['ibm', 'russian']
    )
    
    print("✓ Configuration initialized")
    print(f"  - QAOA depth: {config.qaoa_depth}")
    print(f"  - QSVM feature dim: {config.qsvm_feature_dim}")
    print(f"  - QEC code distance: {config.qec_code_distance}")
    print(f"  - Backends: {config.backends}")
    print()
    
    # Generate sample data
    languages = ['en', 'ru', 'zh', 'es', 'fr']
    corpus = [
        {
            'id': f'doc_{i}',
            'lang': np.random.choice(languages),
            'text': f'Sample document {i}',
            'embedding': np.random.randn(768)
        }
        for i in range(20)
    ]
    
    print(f"✓ Generated corpus with {len(corpus)} documents")
    print(f"  - Languages: {set(doc['lang'] for doc in corpus)}")
    print()
    
    # Track performance
    backend_performance = {b: [] for b in config.backends}
    learned_heuristics = {}
    edit_history = []
    
    # Run edit cycles
    print("=" * 80)
    print("Running Edit Cycles")
    print("=" * 80)
    print()
    
    num_cycles = 15
    for i in range(num_cycles):
        print(f"--- Edit Cycle {i+1}/{num_cycles} ---")
        
        # Generate edit
        language = np.random.choice(languages)
        edit = {
            'id': f'edit_{i}',
            'language': language,
            'start_node': f'doc_{np.random.randint(0, 20)}',
            'end_node': f'doc_{np.random.randint(0, 20)}',
            'text': f'Edit {i}: Modify semantic relationship'
        }
        
        print(f"Edit ID: {edit['id']}, Language: {edit['language']}")
        
        # Select backend (use learned heuristics if available)
        if language in learned_heuristics:
            backend = learned_heuristics[language]['preferred_backend']
        else:
            backend = np.random.choice(config.backends)
        
        # Step 1: Quantum Optimization
        quantum_result = simulate_quantum_optimization(edit, corpus, backend)
        
        # Step 2: RLHF Adaptation
        rlhf_result = simulate_rlhf_adaptation(
            quantum_result['optimized_edit'],
            quantum_result['quantum_metrics'],
            backend,
            backend_performance,
            config.kl_coef
        )
        
        # Step 3: ScalingRL Budgeting
        scaling_result = simulate_scaling_budgeting(
            quantum_result['optimized_edit'],
            quantum_result['quantum_metrics'],
            rlhf_result['rl_metrics'],
            config.batch_size
        )
        
        # Update performance tracking
        reward = rlhf_result['reward']
        backend_performance[backend].append(reward)
        
        # Update learned heuristics
        if language not in learned_heuristics:
            learned_heuristics[language] = {
                'preferred_backend': backend,
                'avg_reward': reward,
                'edit_count': 1
            }
        else:
            heuristic = learned_heuristics[language]
            heuristic['edit_count'] += 1
            heuristic['avg_reward'] = (
                (heuristic['avg_reward'] * (heuristic['edit_count'] - 1) + reward) /
                heuristic['edit_count']
            )
            if reward > heuristic['avg_reward']:
                heuristic['preferred_backend'] = backend
        
        # Calculate performance delta
        performance_delta = reward - 0.5
        
        # Store history
        edit_history.append({
            'edit_id': edit['id'],
            'backend': backend,
            'performance_delta': performance_delta,
            'reward': reward
        })
        
        # Display results
        print(f"Backend: {backend}")
        print(f"Performance Delta: {performance_delta:+.3f}")
        print(f"Quantum Metrics:")
        print(f"  - QAOA Coherence: {quantum_result['quantum_metrics']['qaoa_coherence']:.3f}")
        print(f"  - QEC Logical Error: {quantum_result['quantum_metrics']['qec_logical_error_rate']:.4f}")
        print(f"  - QSVM Valid Prob: {quantum_result['quantum_metrics']['qsvm_valid_prob']:.3f}")
        print(f"RL Metrics:")
        print(f"  - Final Reward: {rlhf_result['rl_metrics']['final_reward']:.3f}")
        print(f"  - Edit Reliability: {rlhf_result['rl_metrics']['edit_reliability_delta']:.3f}")
        print(f"  - KL Penalty: {rlhf_result['rl_metrics']['kl_penalty']:.4f}")
        print(f"Scaling Metrics:")
        print(f"  - Compute Efficiency: {scaling_result['scaling_metrics']['compute_efficiency']:.3f}")
        print(f"  - Optimal Batch Size: {scaling_result['scaling_metrics']['optimal_batch_size']}")
        print()
    
    # Display final statistics
    print("=" * 80)
    print("Final Statistics")
    print("=" * 80)
    print()
    
    print(f"Total Edits: {len(edit_history)}")
    
    # Calculate performance trend
    recent_deltas = [e['performance_delta'] for e in edit_history[-5:]]
    trend = np.mean(recent_deltas)
    if trend > 0.1:
        trend_str = "improving"
    elif trend < -0.1:
        trend_str = "declining"
    else:
        trend_str = "stable"
    print(f"Performance Trend: {trend_str}")
    print()
    
    print("Backend Performance:")
    for backend, perfs in backend_performance.items():
        if perfs:
            print(f"  {backend}:")
            print(f"    - Mean Reward: {np.mean(perfs):.3f}")
            print(f"    - Std Reward: {np.std(perfs):.3f}")
            print(f"    - Edit Count: {len(perfs)}")
    print()
    
    print("Learned Heuristics:")
    for lang, heuristic in learned_heuristics.items():
        print(f"  {lang}:")
        print(f"    - Preferred Backend: {heuristic['preferred_backend']}")
        print(f"    - Avg Reward: {heuristic['avg_reward']:.3f}")
        print(f"    - Edit Count: {heuristic['edit_count']}")
    print()
    
    print("Recent Performance (last 5 edits):")
    for edit_info in edit_history[-5:]:
        print(f"  {edit_info['edit_id']}: {edit_info['performance_delta']:+.3f} ({edit_info['backend']})")
    print()
    
    print("=" * 80)
    print("Demo Complete!")
    print("=" * 80)
    print()
    print("Key Insights:")
    print("1. Quantum modules optimize semantic paths and detect hallucinations")
    print("2. RLHF adapts backend selection based on multilingual feedback")
    print("3. Scaling laws optimize compute budgets and batch sizes")
    print("4. Feedback loop creates self-improving behavior")
    print()
    print("The agent learns which backends work best for each language")
    print("and continuously improves edit quality through the RL loop.")
    print()
    print("For full quantum functionality, install dependencies:")
    print("  pip install qiskit qiskit-machine-learning torch transformers")


if __name__ == '__main__':
    main()