"""
Multi-GPU optimization and workload distribution
"""
from typing import List, Dict, Any, Optional
from dataclasses import dataclass
import numpy as np
import time
import threading
from queue import Queue

@dataclass
class GPUStats:
    device_id: int
    util_percent: float
    memory_used: int
    memory_total: int
    temperature: float
    power_usage: float
    
@dataclass
class WorkUnit:
    nonce_range: tuple
    block_template: Dict[str, Any]
    difficulty: int
    priority: int = 0
    
class GPUManager:
    def __init__(self, num_gpus: int):
        self.num_gpus = num_gpus
        self.gpu_stats = {}
        self.work_queues = [Queue() for _ in range(num_gpus)]
        self.results_queue = Queue()
        self.active = True
        
        # Start worker threads
        self.workers = []
        for gpu_id in range(num_gpus):
            worker = threading.Thread(
                target=self._gpu_worker,
                args=(gpu_id,)
            )
            worker.daemon = True
            worker.start()
            self.workers.append(worker)
            
    def update_gpu_stats(self, gpu_id: int, stats: GPUStats):
        """Update statistics for a GPU"""
        self.gpu_stats[gpu_id] = stats
        
    def add_work(self, work_unit: WorkUnit):
        """Add work unit to appropriate GPU queue"""
        gpu_id = self._select_gpu()
        self.work_queues[gpu_id].put(work_unit)
        
    def _select_gpu(self) -> int:
        """Select best GPU for next work unit"""
        if not self.gpu_stats:
            return 0
            
        # Find GPU with lowest utilization
        best_gpu = min(
            self.gpu_stats.items(),
            key=lambda x: x[1].util_percent
        )
        return best_gpu[0]
        
    def _gpu_worker(self, gpu_id: int):
        """Worker thread for GPU mining"""
        while self.active:
            try:
                work = self.work_queues[gpu_id].get(timeout=1)
                result = self._process_work_unit(gpu_id, work)
                if result:
                    self.results_queue.put(result)
            except Queue.Empty:
                continue
                
    def _process_work_unit(self, gpu_id: int, work: WorkUnit) -> Optional[Dict]:
        """Process a work unit on GPU"""
        # Simulate mining work
        time.sleep(0.1)  # Placeholder
        return None
        
class WorkloadOptimizer:
    def __init__(self, num_gpus: int):
        self.gpu_manager = GPUManager(num_gpus)
        self.current_difficulty = 0
        self.block_template = None
        
    def optimize_distribution(self) -> Dict[str, Any]:
        """Optimize work distribution across GPUs"""
        stats = {
            'gpu_stats': self._get_gpu_stats(),
            'queue_lengths': self._get_queue_stats(),
            'throughput': self._calculate_throughput()
        }
        
        # Adjust work distribution based on stats
        self._balance_workload()
        
        return stats
        
    def _get_gpu_stats(self) -> Dict[int, Dict[str, Any]]:
        """Get current GPU statistics"""
        return {
            gpu_id: {
                'util': stats.util_percent,
                'memory': stats.memory_used / stats.memory_total,
                'temp': stats.temperature,
                'power': stats.power_usage
            }
            for gpu_id, stats in self.gpu_manager.gpu_stats.items()
        }
        
    def _get_queue_stats(self) -> Dict[int, int]:
        """Get work queue statistics"""
        return {
            i: q.qsize()
            for i, q in enumerate(self.gpu_manager.work_queues)
        }
        
    def _calculate_throughput(self) -> float:
        """Calculate current mining throughput"""
        # Placeholder - implement actual throughput calculation
        return 0.0
        
    def _balance_workload(self):
        """Balance workload across GPUs"""
        queue_sizes = self._get_queue_stats()
        gpu_stats = self._get_gpu_stats()
        
        # Find overloaded and underloaded GPUs
        avg_queue_size = sum(queue_sizes.values()) / len(queue_sizes)
        
        for gpu_id, size in queue_sizes.items():
            if size > avg_queue_size * 1.2:  # Overloaded
                self._redistribute_work(gpu_id, queue_sizes)
                
    def _redistribute_work(self, from_gpu: int, queue_sizes: Dict[int, int]):
        """Redistribute work from overloaded GPU"""
        # Find GPU with shortest queue
        to_gpu = min(queue_sizes.items(), key=lambda x: x[1])[0]
        
        # Move some work units
        num_to_move = (queue_sizes[from_gpu] - queue_sizes[to_gpu]) // 2
        for _ in range(num_to_move):
            if not self.gpu_manager.work_queues[from_gpu].empty():
                work = self.gpu_manager.work_queues[from_gpu].get()
                self.gpu_manager.work_queues[to_gpu].put(work)
