#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Enhanced Multilingual Quantum LIMIT-Graph v2.0 Demonstration

Comprehensive demonstration of quantum-enhanced AI research agent with
full support for Indonesian, Arabic, Spanish, English, and Chinese languages.
"""

import logging
import time
import json
from pathlib import Path

from quantum_integration import QuantumLimitGraph, MultilingualQuantumProcessor

# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)

def demo_multilingual_quantum_processing():
    """Demonstrate enhanced multilingual quantum processing capabilities."""
    print("\n" + "="*80)
    print("🌍 ENHANCED MULTILINGUAL QUANTUM PROCESSING DEMONSTRATION")
    print("="*80)
    
    # Initialize multilingual processor
    processor = MultilingualQuantumProcessor(max_qubits=24)
    
    # Test texts in all five languages
    test_texts = {
        'indonesian': "Keharmonisan dalam masyarakat Indonesia sangat penting untuk membangun negara yang kuat dan sejahtera bersama-sama.",
        'arabic': "الانسجام في المجتمع العربي مهم جداً لبناء أمة قوية ومزدهرة مع احترام التقاليد والشرف.",
        'spanish': "La armonía en la familia española es fundamental para construir una sociedad fuerte y próspera con valores tradicionales.",
        'english': "Individual innovation and efficiency are key drivers for building a competitive and prosperous modern society.",
        'chinese': "和谐社会是中华民族发展的基础，需要尊重传统文化和维护社会稳定，实现共同繁荣。"
    }
    
    print("\n🔍 Analyzing Language-Specific Features:")
    
    # Analyze each language
    language_features = {}
    for language, text in test_texts.items():
        print(f"\n  📝 {language.title()}:")
        print(f"     Text: {text[:60]}...")
        
        features = processor.detect_language_features(text, language)
        language_features[language] = features
        
        print(f"     Script: {features['script_type']}")
        print(f"     Direction: {features['text_direction']}")
        print(f"     Cultural Weight: {features['cultural_weight']}")
        print(f"     Tonal: {features['is_tonal']}")
        
        # Language-specific features
        if language == 'chinese':
            print(f"     Character Count: {features.get('character_count', 0)}")
            print(f"     Tone Complexity: {features.get('tone_complexity', 0):.2f}")
            print(f"     Cultural Concepts: {features.get('cultural_concepts', 0)}")
        elif language == 'arabic':
            print(f"     Arabic Characters: {features.get('arabic_chars', 0)}")
            print(f"     Honor Concepts: {features.get('honor_concepts', 0)}")
            print(f"     Religious Context: {features.get('religious_context', 0)}")
        elif language == 'indonesian':
            print(f"     Agglutination Level: {features.get('agglutination_level', 0):.2f}")
            print(f"     Community Focus: {features.get('community_focus', 0)}")
        elif language == 'spanish':
            print(f"     Romance Patterns: {features.get('romance_patterns', 0):.2f}")
            print(f"     Family Centrality: {features.get('family_centrality', 0)}")
        elif language == 'english':
            print(f"     Directness Level: {features.get('directness_level', 0):.2f}")
            print(f"     Individual Focus: {features.get('individual_focus', 0)}")
    
    # Create multilingual quantum circuit
    print(f"\n⚛️  Creating Multilingual Quantum Circuit:")
    circuit = processor.create_multilingual_quantum_circuit(test_texts)
    print(f"     Total Qubits: {circuit.num_qubits}")
    print(f"     Circuit Depth: {circuit.depth()}")
    print(f"     Languages Encoded: {len(test_texts)}")
    
    # Calculate cultural similarities
    print(f"\n🎭 Cultural Similarity Matrix:")
    languages = list(test_texts.keys())
    for i, lang1 in enumerate(languages):
        for lang2 in languages[i+1:]:
            similarity = processor._calculate_cultural_similarity(lang1, lang2)
            print(f"     {lang1.title()} ↔ {lang2.title()}: {similarity:.3f}")
    
    return {
        'language_features': language_features,
        'quantum_circuit': circuit,
        'processor_metrics': processor.get_multilingual_metrics()
    }

def demo_enhanced_quantum_research():
    """Demonstrate enhanced quantum research with all five languages."""
    print("\n" + "="*80)
    print("🔬 ENHANCED QUANTUM RESEARCH WITH 5 LANGUAGES")
    print("="*80)
    
    # Initialize full quantum agent with all languages
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish', 'english', 'chinese'],
        max_qubits=24,
        enable_quantum_walks=True,
        enable_quantum_rlhf=True,
        enable_quantum_context=True,
        enable_quantum_benchmarking=True,
        enable_quantum_provenance=True
    )
    
    # Multilingual research queries
    research_queries = [
        "cross-cultural artificial intelligence alignment",
        "multilingual semantic understanding across cultures",
        "quantum-enhanced natural language processing",
        "cultural preservation in AI systems",
        "harmonious human-AI interaction across languages"
    ]
    
    print(f"\n🔍 Conducting Quantum Research Across 5 Languages:")
    
    research_results = {}
    for i, query in enumerate(research_queries, 1):
        print(f"\n  Query {i}: '{query}'")
        
        start_time = time.time()
        results = agent.quantum_research(query, research_depth='comprehensive')
        execution_time = time.time() - start_time
        
        research_results[f"query_{i}"] = results
        
        print(f"     Execution Time: {execution_time:.2f}s")
        print(f"     Quantum Coherence: {results['synthesis']['quantum_coherence_score']:.4f}")
        print(f"     Research Confidence: {results['synthesis']['research_confidence']:.4f}")
        
        # Display language-specific results
        if 'semantic_graph' in results['quantum_components']:
            semantic_data = results['quantum_components']['semantic_graph']
            print(f"     Language Analysis:")
            for lang, data in semantic_data.items():
                entropy = data.get('entropy', 0)
                confidence = 1.0 - entropy
                print(f"       {lang.title()}: Confidence = {confidence:.3f}")
        
        # Display cultural embeddings
        if 'cultural_embeddings' in results['quantum_components']:
            embeddings = results['quantum_components']['cultural_embeddings']
            print(f"     Cultural Embeddings: {len(embeddings)} cross-cultural mappings")
    
    return research_results

def demo_quantum_cultural_analysis():
    """Demonstrate quantum cultural analysis across all languages."""
    print("\n" + "="*80)
    print("🎭 QUANTUM CULTURAL ANALYSIS DEMONSTRATION")
    print("="*80)
    
    # Initialize quantum agent
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish', 'english', 'chinese'],
        max_qubits=24,
        enable_quantum_context=True
    )
    
    # Cultural context examples
    cultural_contexts = {
        'indonesian': {
            'text': "Gotong royong adalah nilai penting dalam masyarakat Indonesia untuk mencapai keharmonisan bersama.",
            'cultural_focus': 'community_harmony'
        },
        'arabic': {
            'text': "الشرف والكرامة هما أساس العلاقات الاجتماعية في المجتمع العربي مع احترام التقاليد.",
            'cultural_focus': 'honor_tradition'
        },
        'spanish': {
            'text': "La familia es el centro de la vida social española, donde se comparten valores y tradiciones.",
            'cultural_focus': 'family_centrality'
        },
        'english': {
            'text': "Individual achievement and innovation drive progress in competitive modern societies.",
            'cultural_focus': 'individual_achievement'
        },
        'chinese': {
            'text': "中华文化强调和谐、尊重长辈、维护面子，这些是社会稳定的基础。",
            'cultural_focus': 'hierarchical_harmony'
        }
    }
    
    print(f"\n🌍 Analyzing Cultural Contexts:")
    
    cultural_analysis = {}
    for language, context in cultural_contexts.items():
        print(f"\n  📝 {language.title()} Cultural Context:")
        print(f"     Focus: {context['cultural_focus']}")
        print(f"     Text: {context['text'][:50]}...")
        
        if agent.quantum_context_engine:
            # Create cultural embedding
            embedding = agent.quantum_context_engine.cultural_nuance_embedding(
                context['text'], language, 'english'  # Compare to English baseline
            )
            
            cultural_analysis[language] = embedding
            
            print(f"     Cultural Similarity to English: {embedding['cross_cultural_similarity']:.3f}")
            print(f"     Cultural Entropy: {embedding['cultural_entropy']:.3f}")
            print(f"     Dominant Pattern: {embedding['dominant_pattern'][:20]}...")
    
    # Cross-cultural comparison matrix
    print(f"\n🔗 Cross-Cultural Quantum Alignment Matrix:")
    languages = list(cultural_contexts.keys())
    
    alignment_matrix = {}
    for i, source_lang in enumerate(languages):
        for target_lang in languages[i+1:]:
            if agent.quantum_context_engine:
                source_text = cultural_contexts[source_lang]['text']
                embedding = agent.quantum_context_engine.cultural_nuance_embedding(
                    source_text, source_lang, target_lang
                )
                alignment_score = embedding['cross_cultural_similarity']
                alignment_matrix[f"{source_lang}→{target_lang}"] = alignment_score
                print(f"     {source_lang.title()} → {target_lang.title()}: {alignment_score:.3f}")
    
    return {
        'cultural_analysis': cultural_analysis,
        'alignment_matrix': alignment_matrix,
        'average_alignment': sum(alignment_matrix.values()) / len(alignment_matrix) if alignment_matrix else 0
    }

def demo_quantum_benchmarking_multilingual():
    """Demonstrate quantum benchmarking across all five languages."""
    print("\n" + "="*80)
    print("🏆 MULTILINGUAL QUANTUM BENCHMARKING DEMONSTRATION")
    print("="*80)
    
    # Initialize quantum agent
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish', 'english', 'chinese'],
        max_qubits=24,
        enable_quantum_benchmarking=True
    )
    
    # Create diverse test agents
    test_agents = [
        {
            'id': 'multilingual_harmony_agent',
            'weights': [0.9, 0.8, 0.9, 0.7, 0.8, 0.9, 0.8],
            'architecture': 'harmony_focused',
            'cultural_bias': 'collectivist'
        },
        {
            'id': 'individual_efficiency_agent',
            'weights': [0.7, 0.9, 0.6, 0.9, 0.8, 0.6, 0.9],
            'architecture': 'efficiency_focused',
            'cultural_bias': 'individualist'
        },
        {
            'id': 'balanced_cultural_agent',
            'weights': [0.8, 0.8, 0.8, 0.8, 0.8, 0.8, 0.8],
            'architecture': 'culturally_balanced',
            'cultural_bias': 'neutral'
        },
        {
            'id': 'hierarchical_respect_agent',
            'weights': [0.6, 0.7, 0.9, 0.9, 0.7, 0.8, 0.9],
            'architecture': 'hierarchy_aware',
            'cultural_bias': 'hierarchical'
        },
        {
            'id': 'innovation_driven_agent',
            'weights': [0.9, 0.6, 0.7, 0.9, 0.9, 0.7, 0.8],
            'architecture': 'innovation_focused',
            'cultural_bias': 'progressive'
        }
    ]
    
    print(f"\n🤖 Benchmarking {len(test_agents)} Agents Across 5 Languages:")
    
    benchmark_results = {}
    for agent_params in test_agents:
        print(f"\n  ⚡ Benchmarking: {agent_params['id']}")
        print(f"     Architecture: {agent_params['architecture']}")
        print(f"     Cultural Bias: {agent_params['cultural_bias']}")
        
        if agent.quantum_benchmark_harness:
            results = agent.quantum_benchmark_agent(agent_params)
            benchmark_results[agent_params['id']] = results
            
            if 'benchmark_results' in results:
                print(f"     Results Summary:")
                total_score = 0
                for lang, metrics in results['benchmark_results'].items():
                    score = metrics['overall_score']
                    total_score += score
                    print(f"       {lang.title()}: {score:.3f}")
                
                avg_score = total_score / len(results['benchmark_results'])
                print(f"     Average Score: {avg_score:.3f}")
                print(f"     Leaderboard Position: #{results.get('leaderboard_position', 'N/A')}")
    
    # Display final leaderboard
    if agent.quantum_benchmark_harness:
        print(f"\n🏅 Final Quantum Leaderboard (Top 5):")
        leaderboard = agent.quantum_benchmark_harness.get_quantum_leaderboard(top_k=5)
        
        for i, entry in enumerate(leaderboard, 1):
            print(f"     #{i}: {entry['agent_id']}")
            print(f"          Score: {entry['aggregate_score']:.4f}")
            print(f"          Quantum Coherence: {entry['quantum_coherence']:.4f}")
            print(f"          Languages: {len(entry['languages_evaluated'])}")
    
    return benchmark_results

def demo_complete_multilingual_integration():
    """Demonstrate complete multilingual quantum integration."""
    print("\n" + "="*80)
    print("🚀 COMPLETE MULTILINGUAL QUANTUM INTEGRATION")
    print("="*80)
    
    # Initialize full system
    agent = QuantumLimitGraph(
        languages=['indonesian', 'arabic', 'spanish', 'english', 'chinese'],
        max_qubits=24,
        enable_quantum_walks=True,
        enable_quantum_rlhf=True,
        enable_quantum_context=True,
        enable_quantum_benchmarking=True,
        enable_quantum_provenance=True
    )
    
    # Comprehensive multilingual research
    research_query = "Building culturally-aware AI systems that respect Indonesian gotong royong, Arabic honor traditions, Spanish family values, English innovation, and Chinese harmony principles"
    
    print(f"\n🔬 Comprehensive Research Query:")
    print(f"   '{research_query[:80]}...'")
    
    start_time = time.time()
    results = agent.quantum_research(research_query, research_depth='comprehensive')
    execution_time = time.time() - start_time
    
    print(f"\n📊 Integration Results:")
    print(f"   Execution Time: {execution_time:.2f} seconds")
    print(f"   Languages Processed: {len(results['languages'])}")
    print(f"   Quantum Coherence: {results['synthesis']['quantum_coherence_score']:.4f}")
    print(f"   Research Confidence: {results['synthesis']['research_confidence']:.4f}")
    
    # Component analysis
    components = results['quantum_components']
    print(f"\n🔧 Component Analysis:")
    
    if 'semantic_graph' in components:
        semantic_results = components['semantic_graph']
        print(f"   Semantic Graph: {len(semantic_results)} language analyses")
        
        # Show language-specific insights
        for lang, data in semantic_results.items():
            entropy = data.get('entropy', 0)
            confidence = 1.0 - entropy
            print(f"     {lang.title()}: Confidence = {confidence:.3f}, Entropy = {entropy:.3f}")
    
    if 'cultural_embeddings' in components:
        cultural_data = components['cultural_embeddings']
        print(f"   Cultural Embeddings: {len(cultural_data)} cross-cultural mappings")
        
        # Show top cultural alignments
        alignments = [(pair, data['cross_cultural_similarity']) 
                     for pair, data in cultural_data.items()]
        alignments.sort(key=lambda x: x[1], reverse=True)
        
        print(f"     Top Cultural Alignments:")
        for pair, similarity in alignments[:3]:
            print(f"       {pair}: {similarity:.3f}")
    
    if 'language_alignments' in components:
        lang_alignments = components['language_alignments']
        print(f"   Language Alignments: {len(lang_alignments)} quantum correlations")
        
        avg_alignment = sum(lang_alignments.values()) / len(lang_alignments)
        print(f"     Average Alignment: {avg_alignment:.3f}")
    
    # Quantum advantage demonstration
    print(f"\n🚀 Quantum Advantage Metrics:")
    advantage_demo = agent.demonstrate_quantum_advantage()
    
    print(f"   Quantum Speedup: {advantage_demo['classical_equivalent']['speedup_factor']:.2f}x")
    print(f"   Parallel Advantage: {advantage_demo['classical_equivalent']['parallel_advantage']}x")
    print(f"   Overall Quantum Advantage: {advantage_demo['overall_quantum_advantage']}")
    
    # System status
    status = agent.get_quantum_system_status()
    print(f"\n📈 System Status:")
    print(f"   System Health: {status['system_health'].upper()}")
    print(f"   Active Components: {sum(status['components_enabled'].values())}/5")
    print(f"   Overall Quantum Advantage: {status['overall_quantum_advantage']}")
    
    return {
        'research_results': results,
        'advantage_demo': advantage_demo,
        'system_status': status,
        'execution_time': execution_time
    }

def main():
    """Main demonstration function."""
    print("🌟 ENHANCED MULTILINGUAL QUANTUM LIMIT-GRAPH v2.0")
    print("Complete Integration: Indonesian | Arabic | Spanish | English | Chinese")
    print("=" * 80)
    
    try:
        # Run all demonstrations
        print("\n🎯 Running Comprehensive Multilingual Demonstrations...")
        
        # Stage 1: Multilingual Processing
        multilingual_results = demo_multilingual_quantum_processing()
        
        # Stage 2: Enhanced Research
        research_results = demo_enhanced_quantum_research()
        
        # Stage 3: Cultural Analysis
        cultural_results = demo_quantum_cultural_analysis()
        
        # Stage 4: Multilingual Benchmarking
        benchmark_results = demo_quantum_benchmarking_multilingual()
        
        # Stage 5: Complete Integration
        integration_results = demo_complete_multilingual_integration()
        
        # Final Summary
        print("\n" + "="*80)
        print("✅ MULTILINGUAL QUANTUM INTEGRATION COMPLETE")
        print("="*80)
        
        print("\n🎯 Key Achievements:")
        print("  ✓ Full support for 5 major languages (Indonesian, Arabic, Spanish, English, Chinese)")
        print("  ✓ Language-specific quantum encoding with cultural dimensions")
        print("  ✓ Cross-cultural quantum alignment and similarity measurement")
        print("  ✓ Multilingual quantum benchmarking with cultural bias detection")
        print("  ✓ Comprehensive quantum research across all language families")
        print("  ✓ Cultural preservation through quantum contextuality")
        
        print("\n🌍 Language Coverage:")
        print("  • Indonesian: Community harmony, gotong royong, collectivist values")
        print("  • Arabic: Honor traditions, family centrality, hierarchical respect")
        print("  • Spanish: Family values, emotional expression, regional diversity")
        print("  • English: Individual innovation, efficiency, direct communication")
        print("  • Chinese: Hierarchical harmony, face-saving, long-term orientation")
        
        print("\n⚛️  Quantum Advantages Demonstrated:")
        speedup = integration_results['advantage_demo']['classical_equivalent']['speedup_factor']
        print(f"  • {speedup:.1f}x speedup over classical multilingual processing")
        print(f"  • {len(['indonesian', 'arabic', 'spanish', 'english', 'chinese'])}x parallel language processing")
        print(f"  • Exponential cultural context preservation")
        print(f"  • Quantum-secure multilingual provenance tracking")
        
        # Export comprehensive results
        all_results = {
            'multilingual_processing': multilingual_results,
            'enhanced_research': research_results,
            'cultural_analysis': cultural_results,
            'multilingual_benchmarking': benchmark_results,
            'complete_integration': integration_results,
            'demonstration_metadata': {
                'languages_supported': ['indonesian', 'arabic', 'spanish', 'english', 'chinese'],
                'quantum_components': 5,
                'cultural_dimensions': 6,
                'demonstration_timestamp': time.time()
            }
        }
        
        output_file = Path("multilingual_quantum_demo_results.json")
        with open(output_file, 'w', encoding='utf-8') as f:
            json.dump(all_results, f, indent=2, default=str, ensure_ascii=False)
        
        print(f"\n📄 Complete results exported to: {output_file}")
        print("\n🚀 Multilingual Quantum LIMIT-Graph v2.0 is ready for global deployment!")
        
    except Exception as e:
        logger.error(f"Demonstration failed: {e}")
        print(f"\n❌ Demonstration failed: {e}")
        print("Please ensure all quantum dependencies are installed:")
        print("  python setup_quantum.py")
        return False
    
    return True

if __name__ == "__main__":
    success = main()
    exit(0 if success else 1)