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# Licensed under the Apache License, Version 2.0 (the "License");
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import numpy as np

from typing import Tuple


def create_arrays(shape: Tuple[int, ...], dtype: np.dtype):
    """创建asnumpy和numpy测试数组"""
    # numpy 基准数据
    m1_np = np.random.normal(0, 1, shape).astype(dtype)
    m2_np = np.random.normal(0, 1, shape).astype(dtype)

    # asnumpy测试数据 - 从 numpy 转换
    import asnumpy as ap
    m1_asnp = ap.ndarray.from_numpy(m1_np)
    m2_asnp = ap.ndarray.from_numpy(m2_np)

    return m1_asnp, m2_asnp, m1_np, m2_np


def calculate_stable_metric(times: list, trim_ratio: float = 0.1) -> float:
    """
    统计策略：取中段最快速度
    1. 排序去除最慢的 10% (受系统调度影响的数据)
    2. 取剩余数据的最小值 (代表硬件峰值性能)
    """
    if not times:
        return 0.0

    sorted_times = sorted(times)
    keep_count = int(len(sorted_times) * (1.0 - trim_ratio))
    if keep_count < 1:
        keep_count = 1

    valid_times = sorted_times[:keep_count]
    return min(valid_times)