from typing import Iterable

import numpy as np
from PIL import Image
from frozendict import frozendict

from image_prediction.estimator.preprocessor.utils import image_to_normalized_tensor


def transform_equal(a, b):
    return (list(a) if isinstance(a, map) else a) == b


def images_equal(im1: Image, im2: Image, **kwargs):
    return np.allclose(image_to_normalized_tensor(im1), image_to_normalized_tensor(im2), **kwargs)


def metadata_equal(mdat1: Iterable, mdat2: Iterable):
    return set(map(frozendict, mdat1)) == set(map(frozendict, mdat2))


def image_sets_equal(ims1, ims2):
    return all(any(images_equal(im1, im2) for im2 in ims2) for im1 in ims1)