import torch
import numpy as np


def generate_meshgrid_2d(h: int, w: int, device) -> torch.tensor:
    x = torch.linspace(-1, 1, h, device=device)
    y = torch.linspace(-1, 1, w, device=device)
    grid_x, grid_y = torch.meshgrid(x, y)
    grid = torch.stack([grid_x, grid_y], dim=2)
    return grid


def his_match(src, dst):
    src = src * 255.0
    dst = dst * 255.0
    src = src.astype(np.uint8)
    dst = dst.astype(np.uint8)
    res = np.zeros_like(dst)

    cdf_src = np.zeros((3, 256))
    cdf_dst = np.zeros((3, 256))
    cdf_res = np.zeros((3, 256))
    kw = dict(bins=256, range=(0, 256), density=True)
    for ch in range(3):
        his_src, _ = np.histogram(src[:, :, ch], **kw)
        hist_dst, _ = np.histogram(dst[:, :, ch], **kw)
        cdf_src[ch] = np.cumsum(his_src)
        cdf_dst[ch] = np.cumsum(hist_dst)
        index = np.searchsorted(cdf_src[ch], cdf_dst[ch], side="left")
        np.clip(index, 0, 255, out=index)
        res[:, :, ch] = index[dst[:, :, ch]]
        his_res, _ = np.histogram(res[:, :, ch], **kw)
        cdf_res[ch] = np.cumsum(his_res)
    return res / 255.0