import numpy as np
import multiprocessing
from typing import Dict, List, Any, Optional

class SIMDAccelerator:
    def __init__(self):
        self.current_width = 16
    
    def vectorized_multiply(self, a: np.ndarray, b: np.ndarray, modulus: int) -> np.ndarray:
        return (a * b) % modulus
    
    def vectorized_add(self, a: np.ndarray, b: np.ndarray, modulus: int) -> np.ndarray:
        return (a + b) % modulus

class GPUAccelerator:
    def __init__(self):
        self.available = False
        
    def check_availability(self) -> bool:
        try:
            import cupy
            self.available = True
        except ImportError:
            self.available = False
        return self.available
    
    def gpu_matrix_multiply(self, a: np.ndarray, b: np.ndarray, modulus: int) -> np.ndarray:
        if not self.available:
            return np.dot(a, b) % modulus
        
        try:
            import cupy as cp
            a_gpu = cp.asarray(a)
            b_gpu = cp.asarray(b)
            result_gpu = cp.dot(a_gpu, b_gpu) % modulus
            return cp.asnumpy(result_gpu)
        except:
            return np.dot(a, b) % modulus

class NumbaAccelerator:
    def __init__(self):
        self.available = self._check_availability()
    
    def _check_availability(self) -> bool:
        try:
            import numba
            return True
        except ImportError:
            return False
    
    def jit_polynomial_multiply(self, a: np.ndarray, b: np.ndarray, modulus: int) -> np.ndarray:
        if not self.available:
            return np.convolve(a, b)[:len(a)] % modulus
        
        try:
            import numba
            
            @numba.jit(nopython=True)
            def fast_multiply(a, b, mod):
                result = np.zeros(len(a), dtype=np.int32)
                for i in range(len(a)):
                    for j in range(len(b)):
                        if i + j < len(result):
                            result[i + j] = (result[i + j] + a[i] * b[j]) % mod
                return result
            
            return fast_multiply(a, b, modulus)
        except:
            return np.convolve(a, b)[:len(a)] % modulus

class HardwareAccelerationManager:
    def __init__(self):
        self.simd = SIMDAccelerator()
        self.gpu = GPUAccelerator()
        self.numba = NumbaAccelerator()
        
        self.acceleration_hierarchy = self._build_hierarchy()
    
    def _build_hierarchy(self) -> List[str]:
        hierarchy = ["simd"]
        
        if self.gpu.check_availability():
            hierarchy.append("gpu")
        
        if self.numba.available:
            hierarchy.append("numba")
        
        return hierarchy
    
    def accelerated_matrix_operations(self, matrix: np.ndarray, vector: np.ndarray, modulus: int) -> np.ndarray:
        if "gpu" in self.acceleration_hierarchy and matrix.shape[0] > 512:
            return self.gpu.gpu_matrix_multiply(matrix, vector, modulus)
        elif "numba" in self.acceleration_hierarchy:
            return self.numba.jit_polynomial_multiply(matrix.flatten(), vector, modulus)
        else:
            return self.simd.vectorized_multiply(np.dot(matrix, vector), np.ones_like(vector), modulus)
    
    def get_performance_summary(self) -> Dict[str, Any]:
        return {
            'cpu_cores': multiprocessing.cpu_count(),
            'simd_width': self.simd.current_width,
            'gpu_available': self.gpu.available,
            'numba_available': self.numba.available,
            'acceleration_methods': self.acceleration_hierarchy,
            'memory_gb': self._estimate_memory()
        }
    
    def _estimate_memory(self) -> float:
        try:
            import psutil
            return psutil.virtual_memory().total / (1024**3)
        except ImportError:
            return 8.0

hardware_manager = HardwareAccelerationManager()