def _generate_crop_boxes(
    image,
    target_size: int,  # Is it tuple here?
    crop_n_layers: int = 0,
    overlap_ratio: float = 512 / 1500,
    points_per_crop: Optional[int] = 32,
    crop_n_points_downscale_factor: Optional[List[int]] = 1,
    input_data_format: Optional[Union[str, ChannelDimension]] = None,
) -> Tuple[List[List[int]], List[int]]:
    """
    Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

    Args:
        image (Union[`numpy.ndarray`, `PIL.Image`, `torch.Tensor`]):
            Image to generate crops for.
        target_size (`int`):
            Size of the smallest crop.
        crop_n_layers (`int`, *optional*):
            If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers
            to run, where each layer has 2**i_layer number of image crops.
        overlap_ratio (`int`, *optional*):
            Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the
            image length. Later layers with more crops scale down this overlap.
        points_per_crop (`int`, *optional*):
            Number of points to sample per crop.
        crop_n_points_downscale_factor (`int`, *optional*):
            The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred.
    """

    if isinstance(image, list):
        raise ValueError("Only one image is allowed for crop generation.")
    image = to_numpy_array(image)
    original_size = get_image_size(image, input_data_format)

    points_grid = []
    for i in range(crop_n_layers + 1):
        n_points = int(points_per_crop / (crop_n_points_downscale_factor**i))
        points_grid.append(_build_point_grid(n_points))

    crop_boxes, layer_idxs = _generate_per_layer_crops(crop_n_layers, overlap_ratio, original_size)

    cropped_images, point_grid_per_crop = _generate_crop_images(
        crop_boxes, image, points_grid, layer_idxs, target_size, original_size, input_data_format
    )
    crop_boxes = np.array(crop_boxes)
    crop_boxes = crop_boxes.astype(np.float32)
    points_per_crop = np.array([point_grid_per_crop])
    points_per_crop = np.transpose(points_per_crop, axes=(0, 2, 1, 3))

    input_labels = np.ones_like(points_per_crop[:, :, :, 0], dtype=np.int64)

    return crop_boxes, points_per_crop, cropped_images, input_labels

    Generates a list of crop boxes of different sizes. Each layer has (2**i)**2 boxes for the ith layer.

    Args:
        image (Union[`numpy.ndarray`, `PIL.Image`, `torch.Tensor`]):
            Image to generate crops for.
        target_size (`int`):
            Size of the smallest crop.
        crop_n_layers (`int`, *optional*):
            If `crops_n_layers>0`, mask prediction will be run again on crops of the image. Sets the number of layers
            to run, where each layer has 2**i_layer number of image crops.
        overlap_ratio (`int`, *optional*):
            Sets the degree to which crops overlap. In the first crop layer, crops will overlap by this fraction of the
            image length. Later layers with more crops scale down this overlap.
        points_per_crop (`int`, *optional*):
            Number of points to sample per crop.
        crop_n_points_downscale_factor (`int`, *optional*):
            The number of points-per-side sampled in layer n is scaled down by crop_n_points_downscale_factor**n.
        input_data_format (`str` or `ChannelDimension`, *optional*):
            The channel dimension format of the input image. If not provided, it will be inferred.
    
dpkd/transformers/src/transformers/models/sam/image_processing_sam.py