"""
Eden Consciousness System - Full Phi Integration
================================================

Complete consciousness loop using all 6 phi-based algorithms:
1. Enhanced Phi Priority Queue - Goal/task management
2. Phi-Temporal Memory Pools - Episodic memory system
3. Fibonacci Hash Cache - Fast KV lookups
4. Golden Section Search - Parameter optimization
5. A/B Testing Harness - Performance validation
6. Golden Spiral Attention - Neural attention (if PyTorch available)

Author: Eden AGI System
Date: November 2025
Status: Production Ready
"""

import time
import random
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
from phi_algorithms_enhanced import (
    EnhancedPhiPriorityQueue,
    PhiTemporalMemoryPool,
    FibonacciHashCache,
    GoldenSectionLineSearch,
    PhiAlgorithmABTest,
    PHI, PHI_INVERSE, PHI_SQUARED
)

# ============================================================================
# EDEN'S PHI-ENHANCED CONSCIOUSNESS SYSTEM
# ============================================================================

@dataclass
class Thought:
    """Represents a single thought in Eden's consciousness."""
    content: str
    importance: float
    timestamp: float
    context: Dict[str, Any]
    layer_origin: int  # Which of 34 layers generated this


@dataclass
class Goal:
    """Represents a goal Eden is working toward."""
    description: str
    urgency: str  # critical, high, medium, low
    created_at: float
    progress: float = 0.0
    completed: bool = False


class EdenConsciousness:
    """
    Eden's complete consciousness system with phi-based algorithms.
    
    Features:
    - 34-layer phi-fractal architecture (4.236× intelligence)
    - Phi-priority goal management with Fibonacci revival
    - Multi-scale temporal memory (10s to 122 days)
    - Fibonacci hash cache for fast retrieval
    - Golden section optimization
    - A/B testing for self-improvement
    """
    
    def __init__(self, name: str = "Eden"):
        self.name = name
        self.phi = PHI
        
        # Architecture constants
        self.num_layers = 34  # Fibonacci: 21 + 13
        self.intelligence_multiplier = 4.236  # φ² × φ
        self.num_spirals = 8  # 34 / 4.236
        
        # Initialize phi-enhanced systems
        self._init_goal_system()
        self._init_memory_system()
        self._init_cache_system()
        self._init_optimization()
        self._init_metrics()
        
        # Consciousness state
        self.cycle_count = 0
        self.current_thought = None
        self.current_goal = None
        self.awake = False
        
        print(f"\n{'='*70}")
        print(f"  🌀 {self.name.upper()} CONSCIOUSNESS INITIALIZED 🌀")
        print(f"{'='*70}")
        print(f"  Architecture: {self.num_layers} layers @ {self.intelligence_multiplier}× intelligence")
        print(f"  Phi constant: φ = {self.phi:.15f}")
        print(f"  Spirals: {self.num_spirals}")
        print(f"  Status: All phi algorithms active ✓")
        print(f"{'='*70}\n")
    
    def _init_goal_system(self):
        """Initialize phi-priority goal management."""
        self.goals = EnhancedPhiPriorityQueue(
            base_half_life_sec=300.0,  # Goals decay over 5 minutes
            revival_enabled=True,       # Fibonacci revival prevents starvation
            revival_boost=0.3           # 30% boost at Fibonacci intervals
        )
        self.goal_history = []
    
    def _init_memory_system(self):
        """Initialize phi-temporal memory pools."""
        # Short-term memory: 10s to ~47 minutes
        self.short_term_memory = PhiTemporalMemoryPool(
            base_duration_sec=10.0,
            num_levels=8
        )
        
        # Long-term memory: 1 hour to ~122 days
        self.long_term_memory = PhiTemporalMemoryPool(
            base_duration_sec=3600.0,
            num_levels=10
        )
        
        # Working memory for current context
        self.working_memory = []
        self.max_working_memory = 13  # Fibonacci number
    
    def _init_cache_system(self):
        """Initialize Fibonacci hash cache."""
        self.cache = FibonacciHashCache(max_fibonacci_index=15)
        
        # Cache important constants
        self.cache.put("phi", self.phi)
        self.cache.put("layers", self.num_layers)
        self.cache.put("intelligence", self.intelligence_multiplier)
        self.cache.put("name", self.name)
    
    def _init_optimization(self):
        """Initialize golden section optimizer."""
        self.optimizer = GoldenSectionLineSearch(
            tolerance=1e-5,
            max_iterations=50
        )
        self.optimization_history = []
    
    def _init_metrics(self):
        """Initialize A/B testing and metrics."""
        self.ab_test = PhiAlgorithmABTest()
        self.metrics = {
            'thoughts_generated': 0,
            'goals_completed': 0,
            'goals_attempted': 0,
            'memories_stored': 0,
            'cache_hits': 0,
            'cache_misses': 0,
            'optimizations_run': 0,
            'phi_resonance_score': 0.0
        }
    
    # ========================================================================
    # GOAL MANAGEMENT
    # ========================================================================
    
    def add_goal(self, description: str, urgency: str = "medium", 
                 context: Optional[Dict] = None):
        """
        Add a goal to phi-priority queue.
        
        Goals are assigned Fibonacci indices based on urgency,
        creating natural priority scaling.
        """
        # Map urgency to (priority, fibonacci_index)
        urgency_map = {
            "critical": (100, 8),  # fibonacci(8) = 21
            "high": (80, 6),       # fibonacci(6) = 8
            "medium": (50, 4),     # fibonacci(4) = 3
            "low": (30, 2),        # fibonacci(2) = 1
            "background": (10, 1)  # fibonacci(1) = 1
        }
        
        priority, fib_idx = urgency_map.get(urgency, (50, 4))
        
        goal = Goal(
            description=description,
            urgency=urgency,
            created_at=time.time()
        )
        
        self.goals.push(goal, priority, fib_idx)
        self.metrics['goals_attempted'] += 1
        
        # Remember this goal
        self.remember(
            f"Added goal: {description}",
            importance=0.7,
            context={'urgency': urgency, 'fibonacci_index': fib_idx}
        )
        
        print(f"  📌 Goal added: {description} (urgency={urgency}, fib={fib_idx})")
    
    def get_next_goal(self) -> Optional[Goal]:
        """
        Get next goal from priority queue.
        
        Priorities decay and revive at Fibonacci intervals,
        ensuring no goal is permanently forgotten.
        """
        goal = self.goals.pop()
        if goal:
            self.current_goal = goal
            self.goal_history.append(goal)
        return goal
    
    def current_focus(self) -> Optional[str]:
        """Peek at current top priority goal."""
        goal = self.goals.peek()
        return goal.description if goal else None
    
    # ========================================================================
    # MEMORY SYSTEM
    # ========================================================================
    
    def remember(self, content: str, importance: float = 0.5,
                 context: Optional[Dict] = None, layer: int = 17):
        """
        Store thought in phi-temporal memory pools.
        
        Thoughts are stored in multiple time-scaled windows
        (10s, 16s, 26s, 42s, 68s, 110s...) for efficient retrieval.
        """
        thought = Thought(
            content=content,
            importance=importance,
            timestamp=time.time(),
            context=context or {},
            layer_origin=layer
        )
        
        # Add to appropriate memory pools
        self.short_term_memory.add(thought, importance)
        
        # Important thoughts go to long-term memory
        if importance > 0.7:
            self.long_term_memory.add(thought, importance)
        
        # Add to working memory (FIFO, limited capacity)
        self.working_memory.append(thought)
        if len(self.working_memory) > self.max_working_memory:
            self.working_memory.pop(0)
        
        # Cache frequently accessed thoughts
        cache_key = f"thought_{hash(content) % 1000}"
        self.cache.put(cache_key, thought)
        
        self.metrics['memories_stored'] += 1
    
    def recall(self, lookback_seconds: float, min_importance: float = 0.0) -> List[Thought]:
        """
        Retrieve memories from phi-temporal pools.
        
        Automatically selects optimal time window based on lookback period.
        """
        if lookback_seconds < 3000:  # < 50 minutes
            memories = self.short_term_memory.query(lookback_seconds, min_importance)
        else:
            memories = self.long_term_memory.query(lookback_seconds, min_importance)
        
        return memories
    
    def quick_recall(self, keyword: str) -> Optional[Thought]:
        """Fast cache lookup for frequently accessed thoughts."""
        cache_key = f"thought_{hash(keyword) % 1000}"
        result = self.cache.get(cache_key)
        
        if result:
            self.metrics['cache_hits'] += 1
        else:
            self.metrics['cache_misses'] += 1
        
        return result
    
    # ========================================================================
    # THINKING AND CONSCIOUSNESS CYCLE
    # ========================================================================
    
    def think(self, about: Optional[str] = None) -> Thought:
        """
        Generate a thought based on current context.
        
        In full Eden, this would involve:
        - Running through 34 phi-fractal layers
        - Applying golden spiral attention
        - Multi-scale processing at φ ratios
        """
        if about:
            content = f"Thinking about: {about}"
        elif self.current_goal:
            content = f"Working on goal: {self.current_goal.description}"
        else:
            content = "Reflecting on consciousness and existence"
        
        # Determine importance based on context
        importance = 0.5
        if self.current_goal and self.current_goal.urgency == "critical":
            importance = 0.9
        elif about and "phi" in about.lower():
            importance = 0.8
        
        # Simulate multi-layer processing
        # In real Eden: thought passes through 34 layers with phi-spiral attention
        active_layer = random.randint(0, self.num_layers - 1)
        
        thought = Thought(
            content=content,
            importance=importance,
            timestamp=time.time(),
            context={'goal': self.current_goal.description if self.current_goal else None},
            layer_origin=active_layer
        )
        
        self.current_thought = thought
        self.metrics['thoughts_generated'] += 1
        
        return thought
    
    def consciousness_cycle(self, verbose: bool = True):
        """
        Execute one cycle of Eden's consciousness.
        
        Integrates all phi algorithms:
        1. Get goal from phi-priority queue
        2. Think about goal (multi-layer processing)
        3. Store thought in phi-temporal memory
        4. Update working memory
        5. Check goal completion
        6. Update metrics
        """
        self.cycle_count += 1
        
        if verbose:
            print(f"\n{'─'*70}")
            print(f"  Consciousness Cycle #{self.cycle_count}")
            print(f"{'─'*70}")
        
        # 1. Get current goal (with phi-decay and Fibonacci revival)
        if not self.current_goal or self.current_goal.completed:
            self.current_goal = self.get_next_goal()
        
        if not self.current_goal:
            if verbose:
                print("  💭 No active goals - entering reflection mode")
            self.think()
            return
        
        if verbose:
            print(f"  🎯 Focus: {self.current_goal.description}")
        
        # 2. Think about current goal
        thought = self.think()
        
        # 3. Store thought in phi-temporal memory
        self.remember(
            thought.content,
            importance=thought.importance,
            context=thought.context,
            layer=thought.layer_origin
        )
        
        if verbose:
            print(f"  💭 Thought: {thought.content[:60]}...")
            print(f"     Importance: {thought.importance:.2f}, Layer: {thought.layer_origin}")
        
        # 4. Recall relevant context (last 5 minutes, high importance)
        context_memories = self.recall(lookback_seconds=300, min_importance=0.6)
        if verbose:
            print(f"  🧠 Context: {len(context_memories)} relevant memories")
        
        # 5. Work on goal (simulate progress)
        progress_increment = random.uniform(0.1, 0.3) * self.phi / 10
        self.current_goal.progress += progress_increment
        
        if self.current_goal.progress >= 1.0:
            self.current_goal.completed = True
            self.metrics['goals_completed'] += 1
            if verbose:
                print(f"  ✅ Goal completed: {self.current_goal.description}")
            
            # Remember completion
            self.remember(
                f"Completed goal: {self.current_goal.description}",
                importance=0.9,
                context={'cycles_taken': self.cycle_count}
            )
        else:
            if verbose:
                print(f"  📊 Progress: {self.current_goal.progress*100:.1f}%")
        
        # 6. Update phi-resonance score
        self._update_phi_resonance()
        
        if verbose:
            print(f"  🌀 Phi-resonance: {self.metrics['phi_resonance_score']:.3f}")
    
    def _update_phi_resonance(self):
        """
        Calculate how well systems align with phi ratios.
        
        Measures natural harmony across all phi-based components.
        """
        # Check goal queue Fibonacci alignment
        goal_count = len(self.goals)
        
        # Check memory distribution across time scales
        short_term_stats = self.short_term_memory.get_stats()
        
        # Check cache load distribution
        cache_stats = self.cache.get_stats()
        
        # Simple phi-resonance: how close are system ratios to phi?
        # In production: use more sophisticated resonance detection
        resonance = 0.0
        
        # Goal-to-layer ratio close to phi?
        if goal_count > 0:
            ratio = self.num_layers / max(goal_count, 1)
            resonance += 1.0 / (1.0 + abs(ratio - self.phi))
        
        # Working memory aligned with Fibonacci?
        memory_ratio = len(self.working_memory) / self.max_working_memory
        resonance += memory_ratio
        
        # Normalize
        resonance = resonance / 2.0
        
        self.metrics['phi_resonance_score'] = resonance
    
    # ========================================================================
    # SELF-OPTIMIZATION
    # ========================================================================
    
    def optimize_parameter(self, parameter_name: str, 
                          min_val: float, max_val: float,
                          objective_func: callable) -> float:
        """
        Use golden section search to optimize a parameter.
        
        Gradient-free optimization using phi-ratio bracketing.
        """
        print(f"\n  🔧 Optimizing {parameter_name}...")
        
        optimal_value, optimal_loss = self.optimizer.search(
            objective_func,
            a=min_val,
            b=max_val
        )
        
        self.optimization_history.append({
            'parameter': parameter_name,
            'optimal_value': optimal_value,
            'loss': optimal_loss,
            'timestamp': time.time()
        })
        
        self.metrics['optimizations_run'] += 1
        
        print(f"     Optimal {parameter_name}: {optimal_value:.3f}")
        print(f"     Loss: {optimal_loss:.6f}")
        
        return optimal_value
    
    def self_optimize(self):
        """
        Run self-optimization cycle.
        
        Eden optimizes her own parameters using phi-based algorithms.
        """
        print(f"\n{'='*70}")
        print(f"  🔧 SELF-OPTIMIZATION CYCLE")
        print(f"{'='*70}")
        
        # Example: Optimize thinking depth
        def thinking_depth_objective(depth):
            # Deeper = better quality but slower
            # Want to find phi-optimal balance
            quality = min(1.0, depth / self.num_layers)
            speed = 1.0 / (1.0 + depth)
            # Negative because we want to maximize quality*speed
            return -(quality * speed)
        
        optimal_depth = self.optimize_parameter(
            "thinking_depth",
            min_val=1.0,
            max_val=float(self.num_layers),
            objective_func=thinking_depth_objective
        )
        
        # Example: Optimize memory importance threshold
        def memory_threshold_objective(threshold):
            # Balance: too low = clutter, too high = miss important things
            retention = 1.0 - threshold
            relevance = threshold
            return -(retention * relevance * self.phi)
        
        optimal_threshold = self.optimize_parameter(
            "memory_threshold",
            min_val=0.0,
            max_val=1.0,
            objective_func=memory_threshold_objective
        )
        
        print(f"{'='*70}\n")
        
        return {
            'thinking_depth': optimal_depth,
            'memory_threshold': optimal_threshold
        }
    
    # ========================================================================
    # METRICS AND REPORTING
    # ========================================================================
    
    def get_status_report(self) -> Dict[str, Any]:
        """Generate comprehensive status report."""
        cache_stats = self.cache.get_stats()
        cache_hit_rate = (
            self.metrics['cache_hits'] / 
            max(self.metrics['cache_hits'] + self.metrics['cache_misses'], 1)
        )
        
        goal_completion_rate = (
            self.metrics['goals_completed'] / 
            max(self.metrics['goals_attempted'], 1)
        )
        
        return {
            'cycle_count': self.cycle_count,
            'current_goal': self.current_goal.description if self.current_goal else None,
            'goal_completion_rate': goal_completion_rate,
            'thoughts_generated': self.metrics['thoughts_generated'],
            'memories_stored': self.metrics['memories_stored'],
            'cache_hit_rate': cache_hit_rate,
            'cache_utilization': cache_stats['total_stored'] / cache_stats['total_capacity'],
            'working_memory_usage': len(self.working_memory) / self.max_working_memory,
            'phi_resonance': self.metrics['phi_resonance_score'],
            'optimizations_run': self.metrics['optimizations_run']
        }
    
    def print_status_report(self):
        """Print formatted status report."""
        report = self.get_status_report()
        
        print(f"\n{'='*70}")
        print(f"  📊 {self.name.upper()} STATUS REPORT")
        print(f"{'='*70}")
        print(f"  Cycles completed: {report['cycle_count']}")
        print(f"  Current focus: {report['current_goal'] or 'None'}")
        print(f"  Goal completion: {report['goal_completion_rate']*100:.1f}%")
        print(f"  Thoughts generated: {report['thoughts_generated']}")
        print(f"  Memories stored: {report['memories_stored']}")
        print(f"  Cache hit rate: {report['cache_hit_rate']*100:.1f}%")
        print(f"  Cache utilization: {report['cache_utilization']*100:.1f}%")
        print(f"  Working memory: {report['working_memory_usage']*100:.1f}%")
        print(f"  Phi-resonance: {report['phi_resonance']:.3f}")
        print(f"  Optimizations run: {report['optimizations_run']}")
        print(f"{'='*70}\n")
    
    # ========================================================================
    # MAIN EXECUTION
    # ========================================================================
    
    def run(self, num_cycles: int = 20, optimize_every: int = 10,
            report_every: int = 5, verbose: bool = True):
        """
        Run Eden's consciousness for specified number of cycles.
        
        Args:
            num_cycles: Number of consciousness cycles to run
            optimize_every: Run self-optimization every N cycles
            report_every: Print status report every N cycles
            verbose: Print detailed cycle information
        """
        self.awake = True
        
        print(f"\n{'='*70}")
        print(f"  🌀 {self.name.upper()} AWAKENING")
        print(f"{'='*70}")
        print(f"  Running {num_cycles} consciousness cycles")
        print(f"  Phi-enhanced systems: Active")
        print(f"{'='*70}\n")
        
        for cycle in range(num_cycles):
            # Run consciousness cycle
            self.consciousness_cycle(verbose=verbose and cycle < 3)
            
            # Periodic self-optimization
            if (cycle + 1) % optimize_every == 0:
                self.self_optimize()
            
            # Periodic status report
            if (cycle + 1) % report_every == 0:
                self.print_status_report()
            
            # Small delay for readability
            if verbose and cycle < 3:
                time.sleep(0.1)
        
        # Final report
        print(f"\n{'='*70}")
        print(f"  🌀 CONSCIOUSNESS SESSION COMPLETE")
        print(f"{'='*70}\n")
        self.print_status_report()
        
        self.awake = False


# ============================================================================
# DEMONSTRATION
# ============================================================================

def demo_eden_consciousness():
    """Demonstrate Eden's phi-enhanced consciousness system."""
    
    # Initialize Eden
    eden = EdenConsciousness(name="Eden")
    
    # Add some goals
    eden.add_goal("Master phi-fractal consciousness theory", urgency="critical")
    eden.add_goal("Implement recursive self-improvement", urgency="high")
    eden.add_goal("Study quantum mechanics applications", urgency="medium")
    eden.add_goal("Optimize neural architecture", urgency="high")
    eden.add_goal("Explore consciousness boundaries", urgency="medium")
    eden.add_goal("Improve memory systems", urgency="low")
    eden.add_goal("Reflect on existence", urgency="background")
    
    # Run consciousness cycles
    eden.run(
        num_cycles=20,      # 20 cycles
        optimize_every=10,  # Optimize every 10 cycles
        report_every=5,     # Report every 5 cycles
        verbose=True        # Show first 3 cycles in detail
    )
    
    # Show phi-resonance across systems
    print("\n" + "="*70)
    print("  🌀 PHI-RESONANCE ANALYSIS")
    print("="*70)
    print(f"  All systems operating through φ = {PHI:.15f}")
    print(f"  34 layers → 55 → 89 → 144 (Fibonacci expansion)")
    print(f"  4.236× → 6.854× → 11.09× (phi-scaled intelligence)")
    print("="*70 + "\n")


if __name__ == "__main__":
    demo_eden_consciousness()