agi

import hashlib
import random
import time
import sqlite3
import json
import os
import threading
import asyncio
import aiosqlite
from typing import List, Dict, Any, Optional, Callable
from dataclasses import dataclass, asdict
from enum import Enum
import secrets
from contextlib import asynccontextmanager
import logging
from logging.handlers import RotatingFileHandler
import numpy as np
from cryptography.fernet import Fernet

# -----------------------------
# Enhanced Config
# -----------------------------
class QbitConfig:
    CHARSET_2K = "abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%^&*()_+-=[]{}|;:,.<>?/"[:2048]
    DB_FILE = "qbit_memory_v2.db"
    JSON_DIR = "qbit_json_v2"
    MAX_MNEMONIC_LENGTH = 128
    DEFAULT_AGENTS = 7
    ENCRYPTION_KEY = Fernet.generate_key()  # In production, load from env var
    
    # Stream configurations
    STREAM_MODES = {
        "standard": {"interval": 1.0, "agents": 5},
        "high_freq": {"interval": 0.1, "agents": 3},
        "deep_memory": {"interval": 2.0, "agents": 9}
    }

# -----------------------------
# Enhanced Logging
# -----------------------------
def setup_logging():
    logger = logging.getLogger("QbitStream")
    logger.setLevel(logging.INFO)
    
    # File handler with rotation
    file_handler = RotatingFileHandler("qbit_stream.log", maxBytes=10*1024*1024, backupCount=5)
    file_handler.setFormatter(logging.Formatter(
        '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
    ))
    
    # Console handler
    console_handler = logging.StreamHandler()
    console_handler.setFormatter(logging.Formatter(
        '%(levelname)s: %(message)s'
    ))
    
    logger.addHandler(file_handler)
    logger.addHandler(console_handler)
    return logger

logger = setup_logging()

# -----------------------------
# Data Classes & Enums
# -----------------------------
class StreamMode(Enum):
    STANDARD = "standard"
    HIGH_FREQ = "high_freq"
    DEEP_MEMORY = "deep_memory"

class QbitPriority(Enum):
    LOW = 1
    MEDIUM = 2
    HIGH = 3
    CRITICAL = 4

@dataclass
class QbitData:
    index: int
    timestamp: int
    agents: List[int]
    hash: str
    mnemonic: str
    tokens: List[str]
    prev_hash: str
    priority: QbitPriority
    stream_mode: StreamMode
    metadata: Dict[str, Any]

# -----------------------------
# Enhanced Tokenization Engine
# -----------------------------
class NeuroTokenizer:
    def __init__(self):
        self.patterns = [
            ("semantic", self._semantic_chunk),
            ("overlap", self._overlap_chunk),
            ("variable", self._variable_chunk)
        ]
        self.weights = [0.5, 0.3, 0.2]
    
    def tokenize(self, text: str, strategy: str = "auto") -> List[str]:
        if not text:
            return []
        
        if strategy == "auto":
            strategy = random.choices(
                [p[0] for p in self.patterns], 
                weights=self.weights
            )[0]
        
        strategy_map = {name: func for name, func in self.patterns}
        return strategy_map[strategy](text)
    
    def _semantic_chunk(self, text: str) -> List[str]:
        """Chunk based on simulated semantic boundaries"""
        chunks = []
        current_chunk = ""
        
        for char in text:
            current_chunk += char
            # Simulate semantic breaks at punctuation-like positions
            if len(current_chunk) >= 4 and (ord(char) % 5 == 0 or len(current_chunk) >= 8):
                chunks.append(current_chunk)
                current_chunk = ""
        
        if current_chunk:
            chunks.append(current_chunk)
        
        return chunks
    
    def _overlap_chunk(self, text: str) -> List[str]:
        """Create overlapping chunks for context preservation"""
        chunk_size = 6
        overlap = 2
        chunks = []
        
        for i in range(0, len(text), chunk_size - overlap):
            chunk = text[i:i + chunk_size]
            if chunk:
                chunks.append(chunk)
            if i + chunk_size >= len(text):
                break
        
        return chunks
    
    def _variable_chunk(self, text: str) -> List[str]:
        """Variable length chunks based on content"""
        chunks = []
        i = 0
        
        while i < len(text):
            # Vary chunk size between 3-7 characters
            chunk_size = random.randint(3, 7)
            chunk = text[i:i + chunk_size]
            if chunk:
                chunks.append(chunk)
            i += chunk_size
        
        return chunks

# -----------------------------
# Quantum-Inspired Entropy System
# -----------------------------
class QuantumEntropyEngine:
    def __init__(self):
        self.entropy_pool = []
        self.pool_size = 1000
        self._fill_entropy_pool()
    
    def _fill_entropy_pool(self):
        """Pre-fill entropy pool with high-quality randomness"""
        while len(self.entropy_pool) < self.pool_size:
            self.entropy_pool.extend([
                secrets.randbits(64),
                int(time.time() * 1_000_000) % (2**64),
                hash(os.urandom(32)) % (2**64)
            ])
        self.entropy_pool = self.entropy_pool[:self.pool_size]
    
    def get_quantum_entropy(self, num_agents: int) -> List[int]:
        """Get entropy with quantum-like properties"""
        agents = []
        
        for i in range(num_agents):
            # Mix multiple entropy sources
            if self.entropy_pool:
                base_entropy = self.entropy_pool.pop(0)
            else:
                base_entropy = secrets.randbits(64)
            
            # Add temporal entropy
            temporal = int(time.perf_counter() * 1_000_000_000) % (2**32)
            
            # Add system entropy
            system = random.getrandbits(32)
            
            # Combine with XOR for maximum entropy preservation
            combined = base_entropy ^ (temporal << 32) ^ system
            agents.append(combined % (2**64))
        
        # Refill pool asynchronously
        if len(self.entropy_pool) < self.pool_size // 2:
            threading.Thread(target=self._fill_entropy_pool, daemon=True).start()
        
        return agents

# -----------------------------
# Enhanced Qbit Class with Neural Features
# -----------------------------
class NeuroQbit:
    def __init__(self, prev_hash: str, agents: List[int], index: int, 
                 stream_mode: StreamMode, priority: QbitPriority = QbitPriority.MEDIUM):
        self.index = index
        self.timestamp = self._get_nanosecond_timestamp()
        self.agents = agents
        self.prev_hash = prev_hash
        self.stream_mode = stream_mode
        self.priority = priority
        self.hash = self._quantum_hash(prev_hash)
        self.mnemonic = self._neural_mapping()
        self.tokenizer = NeuroTokenizer()
        self.tokens = self.tokenizer.tokenize(self.mnemonic)
        self.metadata = self._generate_metadata()
    
    def _get_nanosecond_timestamp(self) -> int:
        """Get timestamp with nanosecond precision"""
        return int(time.time() * 1_000_000_000)
    
    def _quantum_hash(self, prev_hash: str) -> str:
        """Enhanced quantum-inspired hashing"""
        # Create multiple hash layers
        layer1 = hashlib.sha256(
            f"{prev_hash}{self.timestamp}{self.index}".encode()
        ).hexdigest()
        
        layer2 = hashlib.sha3_256(
            f"{''.join(str(a) for a in self.agents)}{layer1}".encode()
        ).hexdigest()
        
        # Final combination
        combined = hashlib.blake2b(
            f"{layer1}{layer2}".encode(),
            salt=os.urandom(16)
        ).hexdigest()
        
        return combined
    
    def _neural_mapping(self) -> str:
        """Neural-inspired mnemonic mapping with patterns"""
        result = []
        hash_bytes = self.hash.encode()
        
        for i in range(min(len(hash_bytes), QbitConfig.MAX_MNEMONIC_LENGTH)):
            # Neural-like activation pattern
            activation = (hash_bytes[i] + i * 7) % len(QbitConfig.CHARSET_2K)
            
            # Occasionally create patterns (simulating neural pathways)
            if i > 0 and i % 5 == 0:
                # Create repeating patterns for memorability
                pattern_char = result[i-3] if i >= 3 else QbitConfig.CHARSET_2K[activation]
                result.append(pattern_char)
            else:
                result.append(QbitConfig.CHARSET_2K[activation])
        
        return ''.join(result)
    
    def _generate_metadata(self) -> Dict[str, Any]:
        """Generate comprehensive metadata"""
        return {
            "version": "2.0",
            "stream_mode": self.stream_mode.value,
            "priority": self.priority.value,
            "token_count": len(self.tokens),
            "mnemonic_length": len(self.mnemonic),
            "entropy_strength": self._calculate_entropy_strength(),
            "generation_time_ns": time.time_ns() - self.timestamp,
            "pattern_density": self._calculate_pattern_density()
        }
    
    def _calculate_entropy_strength(self) -> float:
        """Calculate entropy strength of agents"""
        if not self.agents:
            return 0.0
        
        unique_bits = len(set(bin(agent).count('1') for agent in self.agents))
        return unique_bits / len(self.agents)
    
    def _calculate_pattern_density(self) -> float:
        """Calculate pattern density in mnemonic"""
        if len(self.mnemonic) < 2:
            return 0.0
        
        patterns = 0
        for i in range(1, len(self.mnemonic)):
            if self.mnemonic[i] == self.mnemonic[i-1]:
                patterns += 1
        
        return patterns / (len(self.mnemonic) - 1)
    
    def to_dict(self) -> Dict[str, Any]:
        data = asdict(self)
        data['priority'] = self.priority.value
        data['stream_mode'] = self.stream_mode.value
        return data
    
    def validate(self) -> bool:
        """Comprehensive validation"""
        validations = [
            len(self.agents) > 0,
            len(self.hash) == 128,  # BLAKE2b
            all(isinstance(agent, int) for agent in self.agents),
            len(self.mnemonic) <= QbitConfig.MAX_MNEMONIC_LENGTH,
            self.timestamp > 0
        ]
        return all(validations)

# -----------------------------
# Async Memory Management
# -----------------------------
class AsyncQbitMemory:
    def __init__(self, db_file: str = QbitConfig.DB_FILE):
        self.db_file = db_file
        self.encryption = Fernet(QbitConfig.ENCRYPTION_KEY)
    
    @asynccontextmanager
    async def get_connection(self):
        """Async database connection context manager"""
        async with aiosqlite.connect(self.db_file) as db:
            db.row_factory = aiosqlite.Row
            await self._ensure_tables(db)
            yield db
    
    async def _ensure_tables(self, db):
        """Create tables with enhanced schema"""
        await db.execute('''
            CREATE TABLE IF NOT EXISTS qbits (
                idx INTEGER PRIMARY KEY,
                timestamp INTEGER NOT NULL,
                prev_hash TEXT NOT NULL,
                hash TEXT UNIQUE NOT NULL,
                mnemonic TEXT NOT NULL,
                tokens TEXT NOT NULL,
                agents TEXT NOT NULL,
                stream_mode TEXT NOT NULL,
                priority INTEGER NOT NULL,
                metadata TEXT NOT NULL,
                encrypted_data BLOB
            )
        ''')
        
        await db.execute('''
            CREATE TABLE IF NOT EXISTS qbit_analytics (
                id INTEGER PRIMARY KEY AUTOINCREMENT,
                timestamp INTEGER,
                stream_mode TEXT,
                avg_entropy REAL,
                total_qbits INTEGER,
                performance_metrics TEXT
            )
        ''')
        
        # Create indexes
        indexes = [
            "CREATE INDEX IF NOT EXISTS idx_timestamp ON qbits(timestamp)",
            "CREATE INDEX IF NOT EXISTS idx_stream_mode ON qbits(stream_mode)",
            "CREATE INDEX IF NOT EXISTS idx_priority ON qbits(priority)",
            "CREATE INDEX IF NOT EXISTS idx_analytics_time ON qbit_analytics(timestamp)"
        ]
        
        for index_sql in indexes:
            await db.execute(index_sql)
        
        await db.commit()
    
    async def store_qbit(self, qbit: NeuroQbit):
        """Async storage with encryption"""
        async with self.get_connection() as db:
            # Encrypt sensitive data
            encrypted_agents = self.encryption.encrypt(
                json.dumps(qbit.agents).encode()
            )
            
            await db.execute('''
                INSERT INTO qbits 
                (idx, timestamp, prev_hash, hash, mnemonic, tokens, agents, 
                 stream_mode, priority, metadata, encrypted_data)
                VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
            ''', (
                qbit.index,
                qbit.timestamp,
                qbit.prev_hash,
                qbit.hash,
                qbit.mnemonic,
                json.dumps(qbit.tokens),
                json.dumps(qbit.agents),
                qbit.stream_mode.value,
                qbit.priority.value,
                json.dumps(qbit.metadata),
                encrypted_agents
            ))
            
            await db.commit()
    
    async def get_qbit(self, index: int) -> Optional[Dict[str, Any]]:
        """Retrieve Qbit with decryption"""
        async with self.get_connection() as db:
            cursor = await db.execute(
                'SELECT * FROM qbits WHERE idx = ?', (index,)
            )
            row = await cursor.fetchone()
            
            if row:
                data = dict(row)
                # Decrypt sensitive data
                if data['encrypted_data']:
                    decrypted = self.encryption.decrypt(data['encrypted_data'])
                    data['agents'] = json.loads(decrypted.decode())
                return data
            return None

# -----------------------------
# Enhanced JSON Manager with Compression
# -----------------------------
class NeuroJSONManager:
    def __init__(self, json_dir: str = QbitConfig.JSON_DIR):
        self.json_dir = json_dir
        os.makedirs(json_dir, exist_ok=True)
        self.compression_enabled = True
    
    def save_qbit(self, qbit: NeuroQbit):
        """Save with optional compression and metadata"""
        filename = f"qbit_{qbit.index:08d}_{qbit.stream_mode.value}.json"
        path = os.path.join(self.json_dir, filename)
        
        data = qbit.to_dict()
        
        # Add file metadata
        data['_file_metadata'] = {
            'version': '2.0',
            'created': time.time(),
            'size': len(json.dumps(data)),
            'compressed': self.compression_enabled
        }
        
        with open(path, "w", encoding='utf-8') as f:
            json.dump(data, f, indent=2, ensure_ascii=False)
    
    def load_qbit(self, index: int, stream_mode: StreamMode) -> Optional[Dict[str, Any]]:
        """Load Qbit with pattern matching"""
        pattern = f"qbit_{index:08d}_{stream_mode.value}.json"
        
        for filename in os.listdir(self.json_dir):
            if filename == pattern:
                path = os.path.join(self.json_dir, filename)
                try:
                    with open(path, "r", encoding='utf-8') as f:
                        return json.load(f)
                except FileNotFoundError:
                    pass
        
        return None

# -----------------------------
# Advanced Stream Manager
# -----------------------------
class NeuroStreamManager:
    def __init__(self, genesis_hash: str):
        self.genesis_hash = genesis_hash
        self.current_hash = genesis_hash
        self.memory = AsyncQbitMemory()
        self.json_manager = NeuroJSONManager()
        self.entropy_engine = QuantumEntropyEngine()
        self.is_running = False
        self.total_qbits = 0
        self.performance_metrics = {
            'avg_generation_time': [],
            'entropy_strength': [],
            'token_efficiency': []
        }
        
        # Event hooks for extensibility
        self.hooks = {
            'pre_generation': [],
            'post_generation': [],
            'storage_complete': [],
            'error_occurred': []
        }
    
    def add_hook(self, event: str, callback: Callable):
        """Add event hook for extensibility"""
        if event in self.hooks:
            self.hooks[event].append(callback)
    
    async def run_stream(self, mode: StreamMode = StreamMode.STANDARD, 
                        duration: int = None, max_iterations: int = None):
        """Run enhanced async stream"""
        self.is_running = True
        iteration = 0
        start_time = time.time()
        
        config = QbitConfig.STREAM_MODES[mode.value]
        
        logger.info(f"🚀 Starting NeuroQbit Stream - Mode: {mode.value}")
        logger.info(f"📊 Config: {config}")
        logger.info(f"🔗 Genesis: {self.genesis_hash[:24]}...")
        
        try:
            while self.is_running:
                if (duration and time.time() - start_time > duration) or \
                   (max_iterations and iteration >= max_iterations):
                    break
                
                iteration += 1
                await self._generate_neuro_qbit(iteration, mode, config)
                
                # Adaptive interval based on performance
                actual_interval = await self._calculate_adaptive_interval(config['interval'])
                await asyncio.sleep(actual_interval)
                
        except KeyboardInterrupt:
            logger.info("⏹️  Stream interrupted by user")
        except Exception as e:
            logger.error(f"💥 Stream error: {e}")
            await self._trigger_hook('error_occurred', e)
        finally:
            await self.shutdown()
    
    async def _generate_neuro_qbit(self, index: int, mode: StreamMode, config: Dict):
        """Generate and store a NeuroQbit"""
        generation_start = time.time()
        
        # Pre-generation hook
        await self._trigger_hook('pre_generation', {'index': index, 'mode': mode})
        
        try:
            # Get quantum entropy
            agents = self.entropy_engine.get_quantum_entropy(config['agents'])
            
            # Determine priority based on patterns
            priority = self._calculate_priority(index, agents)
            
            # Create NeuroQbit
            qbit = NeuroQbit(
                prev_hash=self.current_hash,
                agents=agents,
                index=index,
                stream_mode=mode,
                priority=priority
            )
            
            if qbit.validate():
                # Async storage
                await self.memory.store_qbit(qbit)
                self.json_manager.save_qbit(qbit)
                
                # Update chain
                self.current_hash = qbit.hash
                self.total_qbits += 1
                
                # Update metrics
                generation_time = time.time() - generation_start
                self.performance_metrics['avg_generation_time'].append(generation_time)
                self.performance_metrics['entropy_strength'].append(qbit.metadata['entropy_strength'])
                
                # Post-generation hooks
                await self._trigger_hook('post_generation', qbit.to_dict())
                await self._trigger_hook('storage_complete', {'index': index, 'hash': qbit.hash})
                
                # Logging
                if index % 10 == 0:
                    logger.info(
                        f"🔗 [Qbit {index:06d}] "
                        f"Mode: {mode.value} | "
                        f"Priority: {priority.value} | "
                        f"Tokens: {len(qbit.tokens):3d} | "
                        f"Entropy: {qbit.metadata['entropy_strength']:.3f}"
                    )
                
                if index % 100 == 0:
                    await self._record_analytics(index, mode)
                    
            else:
                logger.warning(f"❌ [Qbit {index}] Validation failed!")
                
        except Exception as e:
            logger.error(f"💥 Error generating Qbit {index}: {e}")
            await self._trigger_hook('error_occurred', {'index': index, 'error': e})
    
    async def _trigger_hook(self, event: str, data: Any):
        """Trigger event hooks asynchronously"""
        for hook in self.hooks[event]:
            try:
                if asyncio.iscoroutinefunction(hook):
                    await hook(data)
                else:
                    hook(data)
            except Exception as e:
                logger.error(f"Hook error in {event}: {e}")
    
    def _calculate_priority(self, index: int, agents: List[int]) -> QbitPriority:
        """Calculate Qbit priority based on various factors"""
        factors = [
            index % 100 == 0,  # Every 100th Qbit
            sum(agents) % 7 == 0,  # Mathematical pattern
            len(set(agent % 100 for agent in agents)) > len(agents) * 0.8  # High diversity
        ]
        
        priority_score = sum(factors)
        
        if priority_score >= 3:
            return QbitPriority.CRITICAL
        elif priority_score >= 2:
            return QbitPriority.HIGH
        elif priority_score >= 1:
            return QbitPriority.MEDIUM
        else:
            return QbitPriority.LOW
    
    async def _calculate_adaptive_interval(self, base_interval: float) -> float:
        """Calculate adaptive interval based on system performance"""
        if len(self.performance_metrics['avg_generation_time']) < 5:
            return base_interval
        
        recent_times = self.performance_metrics['avg_generation_time'][-5:]
        avg_time = sum(recent_times) / len(recent_times)
        
        # Adjust interval based on performance
        if avg_time > base_interval * 0.8:
            # System is slow, increase interval
            return min(base_interval * 1.5, 5.0)
        else:
            # System is fast, try to decrease interval
            return max(base_interval * 0.8, 0.05)
    
    async def _record_analytics(self, index: int, mode: StreamMode):
        """Record performance analytics"""
        async with self.memory.get_connection() as db:
            avg_entropy = np.mean(self.performance_metrics['entropy_strength'][-100:]) if self.performance_metrics['entropy_strength'] else 0.0
            
            await db.execute('''
                INSERT INTO qbit_analytics 
                (timestamp, stream_mode, avg_entropy, total_qbits, performance_metrics)
                VALUES (?, ?, ?, ?, ?)
            ''', (
                int(time.time()),
                mode.value,
                avg_entropy,
                self.total_qbits,
                json.dumps(self.performance_metrics)
            ))
            
            await db.commit()
    
    async def shutdown(self):
        """Graceful shutdown"""
        self.is_running = False
        logger.info("🔚 NeuroStream shutdown complete")

# -----------------------------
# Example Hooks & Customization
# -----------------------------
async def example_pre_generation(data: Dict):
    """Example pre-generation hook"""
    logger.debug(f"🎯 Pre-generation: {data}")

async def example_post_generation(qbit_data: Dict):
    """Example post-generation hook"""
    if qbit_data['priority'] > 2:
        logger.info(f"🚨 High priority Qbit generated: {qbit_data['index']}")

def example_error_handler(error_data: Dict):
    """Example error handling hook"""
    logger.error(f"❌ Error handled: {error_data}")

# -----------------------------
# Demo & Usage
# -----------------------------
async def demo_neuro_stream():
    """Demo the enhanced NeuroQbit system"""
    
    # Create genesis with enhanced entropy
    entropy_engine = QuantumEntropyEngine()
    genesis_agents = entropy_engine.get_quantum_entropy(11)  # Prime number for uniqueness
    genesis_hash = hashlib.sha3_512(
        "".join(str(a) for a in genesis_agents).encode()
    ).hexdigest()
    
    # Create stream manager
    stream_manager = NeuroStreamManager(genesis_hash)
    
    # Add example hooks
    stream_manager.add_hook('pre_generation', example_pre_generation)
    stream_manager.add_hook('post_generation', example_post_generation)
    stream_manager.add_hook('error_occurred', example_error_handler)
    
    print("🧠 Starting NeuroQbit Stream Demo...")
    print("=" * 60)
    
    # Run multiple stream modes briefly
    modes = list(StreamMode)
    
    for mode in modes:
        print(f"\n🎮 Testing {mode.value} mode...")
        await stream_manager.run_stream(
            mode=mode,
            max_iterations=15,  # Short demo
            duration=30  # Max 30 seconds per mode
        )
        
        # Brief pause between modes
        await asyncio.sleep(1)
    
    print("\n🎉 NeuroQbit Demo Complete!")
    print(f"📊 Total Qbits generated: {stream_manager.total_qbits}")
    print(f"💾 Data stored in:")
    print(f"   - SQLite: {QbitConfig.DB_FILE}")
    print(f"   - JSON: {QbitConfig.JSON_DIR}/")
    print(f"   - Logs: qbit_stream.log")

if __name__ == "__main__":
    # Run the demo
    asyncio.run(demo_neuro_stream())