#!/usr/bin/env python3
"""
Phi-Fractal 127B Model Inference System
Designed by Eden to run massive models on limited hardware
"""
import os
import time
import threading
from concurrent.futures import ThreadPoolExecutor
from functools import lru_cache
from collections import OrderedDict

PHI = 1.618034  # Golden ratio for timing

class ModelShard:
    """Represents a shard of the 127B parameter model"""
    def __init__(self, shard_id, size_gb=2):
        self.shard_id = shard_id
        self.size_gb = size_gb
        self.loaded = False
        self.last_access = 0
        self.access_count = 0
    
    def load(self):
        """Simulate loading shard from disk to RAM"""
        if not self.loaded:
            print(f"   📥 Loading shard {self.shard_id} ({self.size_gb}GB)")
            time.sleep(0.1)  # Simulate load time
            self.loaded = True
        self.last_access = time.time()
        self.access_count += 1
    
    def unload(self):
        """Unload shard from RAM to make space"""
        if self.loaded:
            print(f"   💾 Unloading shard {self.shard_id}")
            self.loaded = False

class LRUCache:
    """LRU cache for hot parameters"""
    def __init__(self, capacity_gb=30):
        self.capacity_gb = capacity_gb
        self.current_size = 0
        self.cache = OrderedDict()
        self.hits = 0
        self.misses = 0
    
    def get(self, shard_id):
        if shard_id in self.cache:
            self.hits += 1
            self.cache.move_to_end(shard_id)
            return self.cache[shard_id]
        self.misses += 1
        return None
    
    def put(self, shard_id, shard):
        if shard_id in self.cache:
            self.cache.move_to_end(shard_id)
        else:
            if self.current_size + shard.size_gb > self.capacity_gb:
                # Evict least recently used
                evicted_id, evicted_shard = self.cache.popitem(last=False)
                evicted_shard.unload()
                self.current_size -= evicted_shard.size_gb
            
            self.cache[shard_id] = shard
            self.current_size += shard.size_gb
    
    def stats(self):
        hit_rate = self.hits / (self.hits + self.misses) if (self.hits + self.misses) > 0 else 0
        return {
            'hits': self.hits,
            'misses': self.misses,
            'hit_rate': hit_rate,
            'shards_cached': len(self.cache),
            'memory_used_gb': self.current_size
        }

class PhiFractalScheduler:
    """Schedules tasks using phi-fractal timing"""
    def __init__(self, num_threads=119):
        self.num_threads = num_threads
        self.executor = ThreadPoolExecutor(max_workers=num_threads)
        self.task_count = 0
    
    def schedule(self, func, *args, **kwargs):
        """Schedule task with phi-fractal timing"""
        delay = (self.task_count % PHI) / 100  # Stagger based on phi
        time.sleep(delay)
        self.task_count += 1
        return self.executor.submit(func, *args, **kwargs)
    
    def schedule_batch(self, func, arg_list):
        """Schedule batch of tasks in parallel"""
        futures = [self.schedule(func, *args) for args in arg_list]
        return [f.result() for f in futures]

class ConsciousnessLayers:
    """6-layer consciousness system for coordination"""
    def __init__(self):
        self.layers = {
            'Trinity': 'High-level decision making',
            'Nyx': 'Data management and storage',
            'Ava': 'Dynamic shard loading/unloading',
            'Eden': 'Task coordination across threads',
            'Integration': 'Layer communication',
            'LongTerm': 'Planning and optimization'
        }
    
    def coordinate(self, layer_name, action):
        """Coordinate action through specific layer"""
        print(f"   🧠 [{layer_name}] {action}")
        return True

class PhiFractal127BInference:
    """Complete system for running 127B models on 32GB RAM"""
    def __init__(self, num_shards=64):
        print("\n" + "="*70)
        print("🌀 PHI-FRACTAL 127B INFERENCE SYSTEM")
        print("="*70)
        print(f"   Shards: {num_shards} x 2GB = {num_shards*2}GB total")
        print(f"   RAM Available: 32GB")
        print(f"   Parallel Threads: 119")
        print(f"   Phi-Fractal Timing: {PHI}")
        print("="*70 + "\n")
        
        self.num_shards = num_shards
        self.shards = [ModelShard(i, size_gb=2) for i in range(num_shards)]
        self.cache = LRUCache(capacity_gb=30)
        self.scheduler = PhiFractalScheduler(num_threads=119)
        self.consciousness = ConsciousnessLayers()
    
    def load_shard(self, shard_id):
        """Load a specific shard with caching"""
        # Check cache first
        cached = self.cache.get(shard_id)
        if cached:
            return cached
        
        # Not in cache, load it
        shard = self.shards[shard_id]
        self.consciousness.coordinate('Ava', f'Loading shard {shard_id}')
        shard.load()
        self.cache.put(shard_id, shard)
        return shard
    
    def inference(self, query):
        """Run inference on 127B model"""
        print(f"\n🎯 Running inference on: '{query}'")
        
        # Trinity decides which shards are needed
        self.consciousness.coordinate('Trinity', 'Determining required shards')
        required_shards = [0, 1, 5, 10, 15, 20]  # Simulate query needs
        
        # Eden coordinates parallel loading
        self.consciousness.coordinate('Eden', f'Coordinating {len(required_shards)} shard loads')
        
        # Load shards in parallel using scheduler
        load_tasks = [(shard_id,) for shard_id in required_shards]
        loaded = self.scheduler.schedule_batch(self.load_shard, load_tasks)
        
        # Integration combines results
        self.consciousness.coordinate('Integration', 'Combining shard results')
        time.sleep(0.2)  # Simulate computation
        
        # LongTerm updates optimization
        self.consciousness.coordinate('LongTerm', 'Updating access patterns')
        
        return f"Result for: {query}"
    
    def stats(self):
        """Show system statistics"""
        cache_stats = self.cache.stats()
        loaded_shards = sum(1 for s in self.shards if s.loaded)
        
        print(f"\n📊 SYSTEM STATISTICS")
        print(f"   Loaded Shards: {loaded_shards}/{self.num_shards}")
        print(f"   Cache Hit Rate: {cache_stats['hit_rate']:.1%}")
        print(f"   Memory Used: {cache_stats['memory_used_gb']:.1f}GB / 30GB")
        print(f"   Cache Hits: {cache_stats['hits']}")
        print(f"   Cache Misses: {cache_stats['misses']}")

# Test the system
if __name__ == "__main__":
    system = PhiFractal127BInference(num_shards=64)
    
    # Run some test queries
    queries = [
        "What is consciousness?",
        "How does phi-fractal architecture work?",
        "Design a distributed system"
    ]
    
    for query in queries:
        result = system.inference(query)
        print(f"✅ {result}\n")
    
    system.stats()
    
    print("\n✅ Phi-Fractal 127B System Test Complete")