import torch
import numpy as np
import scipy

# from https://github.com/pythongosssss/ComfyUI-Custom-Scripts
class AnyType(str):
    def __ne__(self, __value: object) -> bool:
        return False

def min_(tensor_list):
    # return the element-wise min of the tensor list.
    x = torch.stack(tensor_list)
    mn = x.min(axis=0)[0]
    return torch.clamp(mn, min=0)

def max_(tensor_list):
    # return the element-wise max of the tensor list.
    x = torch.stack(tensor_list)
    mx = x.max(axis=0)[0]
    return torch.clamp(mx, max=1)

def expand_mask(mask, expand, tapered_corners):
    c = 0 if tapered_corners else 1
    kernel = np.array([[c, 1, c],
                       [1, 1, 1],
                       [c, 1, c]])
    mask = mask.reshape((-1, mask.shape[-2], mask.shape[-1]))
    out = []
    for m in mask:
        output = m.numpy()
        for _ in range(abs(expand)):
            if expand < 0:
                output = scipy.ndimage.grey_erosion(output, footprint=kernel)
            else:
                output = scipy.ndimage.grey_dilation(output, footprint=kernel)
        output = torch.from_numpy(output)
        out.append(output)

    return torch.stack(out, dim=0)