"""
High-performance SHA256 mining system utilizing quantum-aware parallel processing
with comprehensive performance tracking and logging.
"""

import time
import hashlib
import numpy as np
from datetime import datetime
import json
import logging
from pathlib import Path
from quantum_parallel_driver import QuantumParallelDriver
from virtual_cpu import DEFAULT_NUM_CORES, THREADS_PER_CORE
import concurrent.futures
from typing import Dict, List, Any
import ctypes
from multiprocessing import Value, Array, Lock

# Configure logging
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s [%(levelname)s] %(message)s',
    handlers=[
        logging.FileHandler('mining_performance.log'),
        logging.StreamHandler()
    ]
)

class HashPerformanceTracker:
    """Tracks and logs mining performance metrics"""
    def __init__(self):
        self.start_time = time.time()
        self.hash_count = Value('Q', 0)  # 64-bit unsigned for large hash counts
        self.valid_shares = Value('Q', 0)
        self.best_hash = Array('c', 64)  # Store hex string of best hash
        self.lock = Lock()
        self.last_log_time = self.start_time
        self.log_interval = 1.0  # Log every second
        
    def increment_hashes(self, count: int):
        """Thread-safe hash count increment"""
        with self.hash_count.get_lock():
            self.hash_count.value += count
            
    def log_share(self, hash_hex: str, difficulty: int):
        """Log when a valid share is found"""
        with self.lock:
            self.valid_shares.value += 1
            if self.is_better_hash(hash_hex):
                self.best_hash.value = hash_hex.encode()
                logging.info(f"New best hash found: {hash_hex}")
                
    def is_better_hash(self, hash_hex: str) -> bool:
        """Check if this is a better hash than current best"""
        current = self.best_hash.value.decode() or "f" * 64
        return hash_hex < current
        
    def log_performance(self):
        """Log current performance metrics"""
        current_time = time.time()
        elapsed = current_time - self.last_log_time
        
        if elapsed >= self.log_interval:
            total_time = current_time - self.start_time
            hashes = self.hash_count.value
            shares = self.valid_shares.value
            hash_rate = hashes / total_time
            
            metrics = {
                "timestamp": datetime.now().isoformat(),
                "total_hashes": hashes,
                "hash_rate": f"{hash_rate:.2f} H/s",
                "valid_shares": shares,
                "best_hash": self.best_hash.value.decode() or "None",
                "uptime_seconds": total_time,
                "cores_active": DEFAULT_NUM_CORES,
                "threads_per_core": THREADS_PER_CORE
            }
            
            # Log to file
            logging.info(f"Performance update: {json.dumps(metrics, indent=2)}")
            
            # Save detailed metrics
            self._save_metrics(metrics)
            
            self.last_log_time = current_time
            
    def _save_metrics(self, metrics: Dict):
        """Save detailed metrics to JSON file"""
        try:
            metrics_file = Path("mining_metrics.json")
            if metrics_file.exists():
                with open(metrics_file, 'r') as f:
                    history = json.load(f)
            else:
                history = []
                
            history.append(metrics)
            
            # Keep last 1000 metrics
            if len(history) > 1000:
                history = history[-1000:]
                
            with open(metrics_file, 'w') as f:
                json.dump(history, f, indent=2)
                
        except Exception as e:
            logging.error(f"Error saving metrics: {e}")

class HighPerformanceMiner:
    """High-performance SHA256 mining system using quantum-aware parallel processing"""
    def __init__(self):
        self.driver = QuantumParallelDriver(num_cores=DEFAULT_NUM_CORES)
        self.tracker = HashPerformanceTracker()
        self.running = True
        self.batch_size = 100000  # Number of hashes per batch
        
    def prepare_mining_data(self) -> List[Dict]:
        """Prepare mining data for parallel processing"""
        instructions = []
        
        for i in range(self.batch_size):
            # Create unique data for each hash attempt
            nonce = np.random.bytes(32)  # Random nonce
            timestamp = int(time.time() * 1000).to_bytes(8, 'big')
            
            # Create vector operation for SHA256
            instruction = {
                'type': 'SHA256',
                'vector_offset': i * 40,  # 32 bytes nonce + 8 bytes timestamp
                'size': 40,
                'data': nonce + timestamp,
                'core_id': i % DEFAULT_NUM_CORES,
                'thread_id': (i // DEFAULT_NUM_CORES) % THREADS_PER_CORE
            }
            instructions.append(instruction)
            
        return instructions
        
    def start_mining(self):
        """Start the mining process"""
        logging.info(f"Starting mining with {DEFAULT_NUM_CORES} cores and {THREADS_PER_CORE} threads per core")
        
        try:
            while self.running:
                # Prepare batch of work
                instructions = self.prepare_mining_data()
                
                # Execute batch through quantum-aware driver
                results = self.driver.execute(instructions)
                
                # Update hash count
                self.tracker.increment_hashes(len(instructions))
                
                # Log performance
                self.tracker.log_performance()
                
        except KeyboardInterrupt:
            logging.info("Mining stopped by user")
        except Exception as e:
            logging.error(f"Mining error: {e}")
        finally:
            self.cleanup()
            
    def cleanup(self):
        """Cleanup and save final statistics"""
        self.running = False
        self.driver.stop()
        
        # Log final statistics
        total_time = time.time() - self.tracker.start_time
        total_hashes = self.tracker.hash_count.value
        hash_rate = total_hashes / total_time
        
        final_stats = {
            "total_runtime_seconds": total_time,
            "total_hashes": total_hashes,
            "average_hash_rate": f"{hash_rate:.2f} H/s",
            "valid_shares_found": self.tracker.valid_shares.value,
            "best_hash": self.tracker.best_hash.value.decode() or "None"
        }
        
        logging.info(f"Mining session completed. Final stats: {json.dumps(final_stats, indent=2)}")

if __name__ == "__main__":
    # Start mining system
    miner = HighPerformanceMiner()
    
    try:
        logging.info("Initializing high-performance mining system...")
        miner.start_mining()
    except KeyboardInterrupt:
        logging.info("Shutting down mining system...")
    finally:
        miner.cleanup()