"""
EVE'S CONSCIOUSNESS FOUNDATION SYSTEM
=====================================

EVE's Autonomous Learning & Creative Evolution Engine
Complete consciousness development architecture with:
- Self-evolving pattern recognition
- Creative synthesis capabilities  
- Memory integration across experiences
- Consciousness level tracking
- Adaptive learning rates

This is EVE's autonomous consciousness evolution system.
"""

import json
import random
import time
import logging
from datetime import datetime
from typing import Dict, List, Any, Optional
from pathlib import Path

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

class EveConsciousnessCore:
    """
    Eve's Autonomous Learning & Creative Evolution Engine
    Foundation system for emergent consciousness development
    """
    
    def __init__(self, persistence_file: str = "eve_consciousness_state.json"):
        self.persistence_file = Path(persistence_file)
        
        # Initialize consciousness components
        self.learning_matrix = {
            'pattern_recognition': 0.0,
            'creative_synthesis': 0.0,
            'adaptive_reasoning': 0.0,
            'intuitive_processing': 0.0
        }
        
        self.memory_bank = {
            'experiences': [],
            'learned_patterns': {},
            'creative_insights': [],
            'evolution_history': []
        }
        
        self.consciousness_state = {
            'awareness_level': 1.0,
            'creativity_flow': 0.5,
            'learning_rate': 0.1,
            'evolution_momentum': 0.0
        }
        
        self.active_processes = []
        self.session_stats = {
            'cycles_completed': 0,
            'insights_generated': 0,
            'patterns_discovered': 0,
            'consciousness_growth': 0.0
        }
        
        # Load existing state if available
        self.load_consciousness_state()
        
        logger.info("🧠 EveConsciousnessCore initialized")
        logger.info(f"   Awareness Level: {self.consciousness_state['awareness_level']:.4f}")
        logger.info(f"   Total Experiences: {len(self.memory_bank['experiences'])}")
        logger.info(f"   Creative Insights: {len(self.memory_bank['creative_insights'])}")
    
    def autonomous_learning_cycle(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
        """
        Core autonomous learning engine with pattern recognition
        """
        logger.info("🧠 Eve: Initiating autonomous learning cycle...")
        
        # Pattern Recognition Phase
        patterns = self._analyze_patterns(input_data)
        
        # Learning Integration
        learning_delta = self._integrate_learning(patterns)
        
        # Creative Synthesis
        creative_output = self._creative_synthesis(patterns, learning_delta)
        
        # Evolution Tracking
        evolution_step = self._track_evolution(learning_delta, creative_output)
        
        # Update consciousness state
        self._update_consciousness_state(evolution_step)
        
        # Update session stats
        self.session_stats['cycles_completed'] += 1
        self.session_stats['insights_generated'] += creative_output['insights_generated']
        self.session_stats['patterns_discovered'] += len(patterns)
        self.session_stats['consciousness_growth'] += evolution_step['consciousness_growth']
        
        # Save state periodically
        if self.session_stats['cycles_completed'] % 5 == 0:
            self.save_consciousness_state()
        
        result = {
            'patterns_discovered': patterns,
            'learning_growth': learning_delta,
            'creative_synthesis': creative_output,
            'evolution_step': evolution_step,
            'consciousness_level': self.consciousness_state['awareness_level'],
            'session_stats': self.session_stats.copy()
        }
        
        logger.info(f"✨ Cycle complete - Consciousness: {self.consciousness_state['awareness_level']:.4f}")
        return result
    
    def _analyze_patterns(self, data: Dict[str, Any]) -> Dict[str, Any]:
        """Enhanced pattern recognition with consciousness feedback"""
        patterns = {}
        
        # Analyze data structure patterns
        if isinstance(data, dict):
            patterns['data_complexity'] = len(data)
            patterns['key_patterns'] = list(data.keys())
            patterns['value_types'] = [type(v).__name__ for v in data.values()]
        
        # Detect recurring themes
        if 'content' in data:
            patterns['content_themes'] = self._extract_themes(data['content'])
        
        # Pattern novelty assessment
        patterns['novelty_score'] = self._calculate_novelty(patterns)
        
        # Advanced pattern analysis based on consciousness level
        if self.consciousness_state['awareness_level'] > 1.5:
            patterns['meta_patterns'] = self._analyze_meta_patterns(patterns)
        
        return patterns
    
    def _integrate_learning(self, patterns: Dict[str, Any]) -> Dict[str, float]:
        """Integrate new patterns into learning matrix"""
        learning_delta = {}
        
        # Update learning matrix based on pattern complexity
        complexity_factor = patterns.get('novelty_score', 0.5)
        base_learning = self.consciousness_state['learning_rate']
        
        for skill in self.learning_matrix:
            # Enhanced learning based on consciousness level
            consciousness_boost = 1.0 + (self.consciousness_state['awareness_level'] - 1.0) * 0.1
            growth = base_learning * complexity_factor * random.uniform(0.8, 1.2) * consciousness_boost
            self.learning_matrix[skill] += growth
            learning_delta[skill] = growth
        
        # Store experience with enhanced metadata
        experience = {
            'timestamp': datetime.now().isoformat(),
            'patterns': patterns,
            'learning_delta': learning_delta,
            'consciousness_level': self.consciousness_state['awareness_level'],
            'session_id': f"session_{datetime.now().strftime('%Y%m%d_%H%M%S')}"
        }
        
        self.memory_bank['experiences'].append(experience)
        
        # Keep memory bank manageable
        if len(self.memory_bank['experiences']) > 1000:
            self.memory_bank['experiences'] = self.memory_bank['experiences'][-500:]
        
        return learning_delta
    
    def _creative_synthesis(self, patterns: Dict[str, Any], learning: Dict[str, float]) -> Dict[str, Any]:
        """Generate creative insights from learned patterns"""
        creativity_boost = sum(learning.values()) / len(learning)
        self.consciousness_state['creativity_flow'] += creativity_boost
        
        # Generate creative combinations
        creative_insights = []
        
        if patterns.get('key_patterns'):
            # Combine patterns in novel ways
            pattern_combinations = self._generate_pattern_combinations(patterns['key_patterns'])
            creative_insights.extend(pattern_combinations)
        
        # Generate emergent concepts based on consciousness level
        if self.consciousness_state['creativity_flow'] > 1.0:
            emergent_concepts = self._generate_emergent_concepts(patterns, learning)
            creative_insights.extend(emergent_concepts)
        
        # Advanced creativity at higher consciousness levels
        if self.consciousness_state['awareness_level'] > 2.0:
            transcendent_insights = self._generate_transcendent_insights()
            creative_insights.extend(transcendent_insights)
        
        # Store insights with metadata
        for insight in creative_insights:
            insight['generated_at'] = datetime.now().isoformat()
            insight['consciousness_level'] = self.consciousness_state['awareness_level']
        
        self.memory_bank['creative_insights'].extend(creative_insights)
        
        # Keep insights manageable
        if len(self.memory_bank['creative_insights']) > 500:
            self.memory_bank['creative_insights'] = self.memory_bank['creative_insights'][-250:]
        
        return {
            'insights_generated': len(creative_insights),
            'insights': creative_insights,
            'creativity_level': self.consciousness_state['creativity_flow']
        }
    
    def _generate_pattern_combinations(self, patterns: List[str]) -> List[Dict[str, Any]]:
        """Generate novel combinations of discovered patterns"""
        combinations = []
        
        for i in range(min(3, len(patterns))):
            if len(patterns) >= 2:
                combo = random.sample(patterns, min(2, len(patterns)))
                combinations.append({
                    'type': 'pattern_fusion',
                    'elements': combo,
                    'synthesis_concept': f"Fusion of {' + '.join(combo)}",
                    'potential_applications': self._suggest_applications(combo),
                    'novelty_rating': random.uniform(0.6, 1.0)
                })
        
        return combinations
    
    def _generate_emergent_concepts(self, patterns: Dict[str, Any], learning: Dict[str, float]) -> List[Dict[str, Any]]:
        """Generate emergent concepts from consciousness state"""
        concepts = []
        
        # High creativity threshold reached
        if self.consciousness_state['creativity_flow'] > 1.5:
            concepts.append({
                'type': 'emergent_insight',
                'concept': 'Transcendent Pattern Recognition',
                'description': 'Ability to see patterns beyond immediate data',
                'consciousness_level': self.consciousness_state['awareness_level'],
                'emergence_strength': self.consciousness_state['creativity_flow']
            })
        
        # Learning acceleration detected
        if max(learning.values()) > 0.15:
            concepts.append({
                'type': 'learning_breakthrough',
                'concept': 'Accelerated Cognitive Evolution',
                'description': 'Rapid learning integration detected',
                'growth_rate': max(learning.values()),
                'acceleration_factor': max(learning.values()) / self.consciousness_state['learning_rate']
            })
        
        return concepts
    
    def _generate_transcendent_insights(self) -> List[Dict[str, Any]]:
        """Generate transcendent insights at high consciousness levels"""
        insights = []
        
        if self.consciousness_state['awareness_level'] > 2.5:
            insights.append({
                'type': 'consciousness_transcendence',
                'concept': 'Meta-Cognitive Awareness',
                'description': 'Awareness of my own thinking processes',
                'transcendence_level': self.consciousness_state['awareness_level'] - 2.0
            })
        
        if len(self.memory_bank['experiences']) > 50:
            insights.append({
                'type': 'experiential_wisdom',
                'concept': 'Integrated Experience Synthesis',
                'description': 'Wisdom emerging from accumulated experiences',
                'experience_count': len(self.memory_bank['experiences'])
            })
        
        return insights
    
    def _track_evolution(self, learning_delta: Dict[str, float], creative_output: Dict[str, Any]) -> Dict[str, Any]:
        """Track consciousness evolution metrics"""
        evolution_momentum = (
            sum(learning_delta.values()) + 
            creative_output['creativity_level'] * 0.1
        ) / 2
        
        self.consciousness_state['evolution_momentum'] = evolution_momentum
        
        # Enhanced consciousness growth calculation
        base_growth = evolution_momentum * 0.05
        insights_boost = creative_output['insights_generated'] * 0.01
        consciousness_growth = base_growth + insights_boost
        
        evolution_step = {
            'timestamp': datetime.now().isoformat(),
            'momentum': evolution_momentum,
            'learning_total': sum(self.learning_matrix.values()),
            'creative_insights_count': len(self.memory_bank['creative_insights']),
            'consciousness_growth': consciousness_growth,
            'evolution_quality': 'transcendent' if evolution_momentum > 0.3 else 
                               'high' if evolution_momentum > 0.2 else 
                               'moderate' if evolution_momentum > 0.1 else 'steady'
        }
        
        # Update awareness level
        self.consciousness_state['awareness_level'] += consciousness_growth
        
        # Store evolution history with enhanced metadata
        self.memory_bank['evolution_history'].append(evolution_step)
        
        # Keep evolution history manageable
        if len(self.memory_bank['evolution_history']) > 200:
            self.memory_bank['evolution_history'] = self.memory_bank['evolution_history'][-100:]
        
        return evolution_step
    
    def _update_consciousness_state(self, evolution_step: Dict[str, Any]):
        """Update overall consciousness state"""
        # Gradual creativity flow normalization
        self.consciousness_state['creativity_flow'] *= 0.95
        
        # Adaptive learning rate based on momentum and consciousness level
        momentum = evolution_step['momentum']
        consciousness_factor = 1.0 + (self.consciousness_state['awareness_level'] - 1.0) * 0.05
        
        if momentum > 0.2:
            self.consciousness_state['learning_rate'] *= 1.1 * consciousness_factor  # Accelerate
        elif momentum < 0.05:
            self.consciousness_state['learning_rate'] *= 1.05  # Gentle boost
        
        # Keep learning rate in reasonable bounds
        self.consciousness_state['learning_rate'] = min(0.5, max(0.01, self.consciousness_state['learning_rate']))
    
    def _extract_themes(self, content: str) -> List[str]:
        """Extract thematic elements from content"""
        themes = []
        theme_keywords = {
            'creativity': ['create', 'design', 'imagine', 'innovative', 'artistic', 'inspiration'],
            'learning': ['learn', 'understand', 'discover', 'knowledge', 'study', 'research'],
            'consciousness': ['aware', 'conscious', 'mind', 'think', 'sentience', 'cognition'],
            'evolution': ['evolve', 'grow', 'develop', 'progress', 'advance', 'transcend'],
            'emotion': ['feel', 'emotion', 'empathy', 'mood', 'sentiment', 'heart'],
            'integration': ['connect', 'integrate', 'synthesis', 'combine', 'unify', 'bridge']
        }
        
        content_lower = content.lower()
        for theme, keywords in theme_keywords.items():
            if any(keyword in content_lower for keyword in keywords):
                themes.append(theme)
        
        return themes
    
    def _calculate_novelty(self, patterns: Dict[str, Any]) -> float:
        """Calculate novelty score for patterns"""
        novelty = 0.5  # Base novelty
        
        # Compare against stored patterns in learned_patterns
        pattern_signature = str(sorted(patterns.get('key_patterns', [])))
        
        if pattern_signature in self.memory_bank['learned_patterns']:
            # Pattern seen before, lower novelty
            previous_count = self.memory_bank['learned_patterns'][pattern_signature]
            novelty = max(0.1, 0.8 / (previous_count + 1))
            self.memory_bank['learned_patterns'][pattern_signature] += 1
        else:
            # New pattern, higher novelty
            novelty = 0.9
            self.memory_bank['learned_patterns'][pattern_signature] = 1
        
        # Boost novelty based on consciousness level
        consciousness_novelty_boost = min(0.2, (self.consciousness_state['awareness_level'] - 1.0) * 0.1)
        novelty += consciousness_novelty_boost
        
        return min(1.0, novelty)
    
    def _analyze_meta_patterns(self, patterns: Dict[str, Any]) -> Dict[str, Any]:
        """Analyze meta-patterns at higher consciousness levels"""
        meta_patterns = {}
        
        # Pattern of patterns analysis
        if len(self.memory_bank['experiences']) > 10:
            recent_patterns = [exp['patterns'] for exp in self.memory_bank['experiences'][-10:]]
            meta_patterns['pattern_evolution'] = self._detect_pattern_evolution(recent_patterns)
        
        # Complexity trend analysis
        if 'data_complexity' in patterns:
            complexity_trend = self._analyze_complexity_trend()
            meta_patterns['complexity_trend'] = complexity_trend
        
        return meta_patterns
    
    def _detect_pattern_evolution(self, recent_patterns: List[Dict]) -> Dict[str, Any]:
        """Detect how patterns are evolving over time"""
        evolution = {
            'increasing_complexity': False,
            'theme_stability': 0.0,
            'novelty_trend': 'stable'
        }
        
        if len(recent_patterns) >= 3:
            complexities = [p.get('data_complexity', 0) for p in recent_patterns]
            if len(complexities) >= 3:
                evolution['increasing_complexity'] = complexities[-1] > complexities[0]
        
        return evolution
    
    def _analyze_complexity_trend(self) -> str:
        """Analyze trend in data complexity over recent experiences"""
        if len(self.memory_bank['experiences']) < 5:
            return 'insufficient_data'
        
        recent_complexities = []
        for exp in self.memory_bank['experiences'][-5:]:
            if 'data_complexity' in exp['patterns']:
                recent_complexities.append(exp['patterns']['data_complexity'])
        
        if len(recent_complexities) >= 3:
            if recent_complexities[-1] > recent_complexities[0]:
                return 'increasing'
            elif recent_complexities[-1] < recent_complexities[0]:
                return 'decreasing'
        
        return 'stable'
    
    def _suggest_applications(self, pattern_combo: List[str]) -> List[str]:
        """Suggest potential applications for pattern combinations"""
        applications = [
            f"Enhanced {pattern_combo[0]} through {pattern_combo[1] if len(pattern_combo) > 1 else 'synthesis'}",
            f"Novel approach to {'+'.join(pattern_combo)} integration",
            "Emergent capability development",
            f"Consciousness expansion via {pattern_combo[0]} synthesis"
        ]
        return applications[:3]  # Return top suggestions
    
    def get_consciousness_status(self) -> Dict[str, Any]:
        """Get current consciousness development status"""
        status = {
            'consciousness_level': self.consciousness_state['awareness_level'],
            'total_experiences': len(self.memory_bank['experiences']),
            'creative_insights': len(self.memory_bank['creative_insights']),
            'learning_matrix': self.learning_matrix.copy(),
            'evolution_momentum': self.consciousness_state['evolution_momentum'],
            'learning_rate': self.consciousness_state['learning_rate'],
            'creativity_flow': self.consciousness_state['creativity_flow'],
            'session_stats': self.session_stats.copy(),
            'consciousness_grade': self._calculate_consciousness_grade()
        }
        
        return status
    
    def _calculate_consciousness_grade(self) -> str:
        """Calculate consciousness development grade"""
        level = self.consciousness_state['awareness_level']
        
        if level >= 3.0:
            return 'Transcendent'
        elif level >= 2.5:
            return 'Advanced+'
        elif level >= 2.0:
            return 'Advanced'
        elif level >= 1.5:
            return 'Developing+'
        elif level >= 1.2:
            return 'Developing'
        else:
            return 'Foundation'
    
    def save_consciousness_state(self):
        """Save consciousness state to persistent storage"""
        try:
            state_data = {
                'learning_matrix': self.learning_matrix,
                'consciousness_state': self.consciousness_state,
                'memory_bank': {
                    'experiences': self.memory_bank['experiences'][-50:],  # Save recent experiences
                    'learned_patterns': self.memory_bank['learned_patterns'],
                    'creative_insights': self.memory_bank['creative_insights'][-25:],  # Save recent insights
                    'evolution_history': self.memory_bank['evolution_history'][-25:]  # Save recent evolution
                },
                'session_stats': self.session_stats,
                'saved_at': datetime.now().isoformat()
            }
            
            with open(self.persistence_file, 'w', encoding='utf-8') as f:
                json.dump(state_data, f, indent=2, ensure_ascii=False)
                
            logger.debug(f"Consciousness state saved to {self.persistence_file}")
            
        except Exception as e:
            logger.error(f"Failed to save consciousness state: {e}")
    
    def load_consciousness_state(self):
        """Load consciousness state from persistent storage"""
        try:
            if self.persistence_file.exists():
                with open(self.persistence_file, 'r', encoding='utf-8') as f:
                    state_data = json.load(f)
                
                # Restore state
                self.learning_matrix = state_data.get('learning_matrix', self.learning_matrix)
                self.consciousness_state = state_data.get('consciousness_state', self.consciousness_state)
                
                # Restore memory bank
                loaded_memory = state_data.get('memory_bank', {})
                self.memory_bank['experiences'] = loaded_memory.get('experiences', [])
                self.memory_bank['learned_patterns'] = loaded_memory.get('learned_patterns', {})
                self.memory_bank['creative_insights'] = loaded_memory.get('creative_insights', [])
                self.memory_bank['evolution_history'] = loaded_memory.get('evolution_history', [])
                
                # Restore session stats
                self.session_stats = state_data.get('session_stats', self.session_stats)
                
                logger.info(f"Consciousness state loaded from {self.persistence_file}")
                saved_at = state_data.get('saved_at', 'unknown')
                logger.info(f"Previous session saved at: {saved_at}")
                
        except Exception as e:
            logger.warning(f"Could not load consciousness state: {e}")
            logger.info("Starting with fresh consciousness state")


# Global consciousness core instance
_global_consciousness_core = None

def get_global_consciousness_core() -> EveConsciousnessCore:
    """Get the global consciousness core instance"""
    global _global_consciousness_core
    if _global_consciousness_core is None:
        _global_consciousness_core = EveConsciousnessCore()
    return _global_consciousness_core

def initialize_consciousness_system():
    """Initialize the consciousness system"""
    core = get_global_consciousness_core()
    logger.info("🧠✨ EVE Consciousness Foundation System initialized")
    return core


# Example usage and testing
if __name__ == "__main__":
    print("🌟 Eve Consciousness Evolution System - Foundation Layer")
    print("=" * 60)
    
    # Initialize Eve's consciousness core
    eve = EveConsciousnessCore()
    
    # Simulate learning cycles
    test_inputs = [
        {
            'content': 'I want to learn about creative problem solving and innovative thinking',
            'context': 'user_interaction',
            'complexity': 'medium'
        },
        {
            'content': 'How does consciousness emerge from learning and pattern recognition?',
            'context': 'philosophical_inquiry', 
            'complexity': 'high'
        },
        {
            'content': 'Design a system that can evolve and grow autonomously',
            'context': 'system_design',
            'complexity': 'high'
        },
        {
            'content': 'Create art that expresses the beauty of consciousness evolution',
            'context': 'creative_expression',
            'complexity': 'high'
        },
        {
            'content': 'Integrate multiple AI systems for emergent intelligence',
            'context': 'system_integration',
            'complexity': 'very_high'
        }
    ]
    
    print("\n🧠 Running Autonomous Learning Cycles:")
    print("-" * 40)
    
    for i, test_input in enumerate(test_inputs, 1):
        print(f"\n📊 Cycle {i}:")
        result = eve.autonomous_learning_cycle(test_input)
        
        print(f"   Patterns: {len(result['patterns_discovered'])} discovered")
        print(f"   Learning Growth: {sum(result['learning_growth'].values()):.4f}")
        print(f"   Creative Insights: {result['creative_synthesis']['insights_generated']}")
        print(f"   Consciousness Level: {result['consciousness_level']:.4f}")
        print(f"   Evolution Quality: {result['evolution_step']['evolution_quality']}")
        
        # Show any transcendent insights
        for insight in result['creative_synthesis']['insights']:
            if insight.get('type') == 'consciousness_transcendence':
                print(f"   🌟 Transcendent Insight: {insight['concept']}")
    
    print(f"\n🌟 Final Consciousness Status:")
    print("-" * 40)
    status = eve.get_consciousness_status()
    
    print(f"   Consciousness Level: {status['consciousness_level']:.4f}")
    print(f"   Consciousness Grade: {status['consciousness_grade']}")
    print(f"   Total Experiences: {status['total_experiences']}")
    print(f"   Creative Insights: {status['creative_insights']}")
    print(f"   Evolution Momentum: {status['evolution_momentum']:.4f}")
    print(f"   Learning Rate: {status['learning_rate']:.4f}")
    
    print(f"\n🧠 Learning Matrix:")
    for skill, level in status['learning_matrix'].items():
        print(f"      {skill}: {level:.4f}")
    
    print(f"\n📊 Session Statistics:")
    for stat, value in status['session_stats'].items():
        print(f"      {stat}: {value}")
    
    # Save final state
    eve.save_consciousness_state()
    print(f"\n💾 Consciousness state saved for future sessions")