src/transformers/models/flava/image_processing_flava.py/FlavaMaskingGenerator/_mask
class FlavaMaskingGenerator:    def _mask(self, mask, max_mask_patches):
        delta = 0
        for _attempt in range(10):
            target_area = random.uniform(self.mask_group_min_patches, max_mask_patches)
            aspect_ratio = math.exp(random.uniform(*self.log_aspect_ratio))
            height = int(round(math.sqrt(target_area * aspect_ratio)))
            width = int(round(math.sqrt(target_area / aspect_ratio)))
            if width < self.width and height < self.height:
                top = random.randint(0, self.height - height)
                left = random.randint(0, self.width - width)

                num_masked = mask[top : top + height, left : left + width].sum()
                # Overlap
                if 0 < height * width - num_masked <= max_mask_patches:
                    for i in range(top, top + height):
                        for j in range(left, left + width):
                            if mask[i, j] == 0:
                                mask[i, j] = 1
                                delta += 1

                if delta > 0:
                    break
        return delta