"""
Hardware optimization and resource management
"""
from typing import Dict, Any, List
import numpy as np
from dataclasses import dataclass
from concurrent.futures import ThreadPoolExecutor

@dataclass
class GPUConfig:
    matrix_size: int = 256
    concurrent_kernels: int = 32
    pipeline_depth: int = 4
    prefetch_size: int = 8192
    cache_line_size: int = 512
    num_cache_lines: int = 2048

@dataclass
class CPUConfig:
    simd_width: int = 512
    thread_affinity: str = 'compact'
    prefetch_distance: int = 16
    cache_size: int = 32768
    numa_aware: bool = True

class HardwareOptimizer:
    def __init__(self, num_gpus: int = 10, num_cpus: int = 5000):
        self.gpus = [GPUConfig() for _ in range(num_gpus)]
        self.cpus = [CPUConfig() for _ in range(num_cpus)]
        self.thread_pool = ThreadPoolExecutor(max_workers=num_cpus)
        
    def optimize_resources(self):
        """Optimize all hardware resources"""
        # Optimize GPUs
        gpu_futures = [
            self.thread_pool.submit(self._optimize_gpu, gpu, idx)
            for idx, gpu in enumerate(self.gpus)
        ]
        
        # Optimize CPUs
        cpu_futures = [
            self.thread_pool.submit(self._optimize_cpu, cpu, idx)
            for idx, cpu in enumerate(self.cpus)
        ]
        
        # Wait for completion
        for f in gpu_futures + cpu_futures:
            f.result()
            
    def _optimize_gpu(self, gpu: GPUConfig, gpu_id: int):
        """Optimize single GPU configuration"""
        # Update tensor core configuration
        self._optimize_tensor_cores(gpu)
        
        # Optimize memory configuration
        self._optimize_gpu_memory(gpu)
        
        # Configure compute kernels
        self._optimize_gpu_compute(gpu)
        
    def _optimize_tensor_cores(self, gpu: GPUConfig):
        """Optimize tensor core configuration"""
        gpu.matrix_size = 256  # Optimal for mining operations
        gpu.concurrent_kernels = 32  # Maximum concurrent tensor operations
        gpu.pipeline_depth = 4  # Tensor operation pipeline depth
        
    def _optimize_gpu_memory(self, gpu: GPUConfig):
        """Optimize GPU memory configuration"""
        gpu.prefetch_size = 8192  # 8KB prefetch size
        gpu.cache_line_size = 512  # 512B cache lines
        gpu.num_cache_lines = 2048  # 1MB L1 cache
        
    def _optimize_gpu_compute(self, gpu: GPUConfig):
        """Optimize GPU compute configuration"""
        # Configure CUDA cores
        cuda_config = {
            'block_size': 256,
            'grid_size': 65535,
            'shared_memory_size': 48 * 1024  # 48KB shared memory
        }
        
        # Configure streaming multiprocessors
        sm_config = {
            'max_blocks_per_sm': 32,
            'max_threads_per_sm': 2048,
            'max_registers_per_thread': 255
        }
        
    def _optimize_cpu(self, cpu: CPUConfig, cpu_id: int):
        """Optimize single CPU configuration"""
        # Configure SIMD
        self._optimize_cpu_simd(cpu)
        
        # Configure threading
        self._optimize_cpu_threading(cpu)
        
        # Configure memory access
        self._optimize_cpu_memory(cpu)
        
    def _optimize_cpu_simd(self, cpu: CPUConfig):
        """Optimize CPU SIMD configuration"""
        cpu.simd_width = 512  # AVX-512 support
        
    def _optimize_cpu_threading(self, cpu: CPUConfig):
        """Optimize CPU threading configuration"""
        cpu.thread_affinity = 'compact'  # Keep threads close
        
    def _optimize_cpu_memory(self, cpu: CPUConfig):
        """Optimize CPU memory configuration"""
        cpu.prefetch_distance = 16  # Prefetch 16 cache lines ahead
        cpu.cache_size = 32768  # 32KB L1 cache
        cpu.numa_aware = True  # Enable NUMA awareness
        
    def get_optimal_config(self) -> Dict[str, Any]:
        """Get optimal configuration for all hardware"""
        return {
            'gpu_config': [vars(gpu) for gpu in self.gpus],
            'cpu_config': [vars(cpu) for cpu in self.cpus],
            'global_config': {
                'total_gpus': len(self.gpus),
                'total_cpus': len(self.cpus),
                'memory_pool_size': self._calculate_memory_pool_size()
            }
        }
        
    def _calculate_memory_pool_size(self) -> int:
        """Calculate optimal memory pool size"""
        gpu_memory = len(self.gpus) * 32 * 1024 * 1024 * 1024  # 32GB per GPU
        cpu_memory = len(self.cpus) * 8 * 1024 * 1024  # 8MB per CPU
        return gpu_memory + cpu_memory
