def assign_boxes_to_levels(
    box_lists: List[Boxes],
    min_level: int,
    max_level: int,
    canonical_box_size: int,
    canonical_level: int,
):
    """
    Map each box in `box_lists` to a feature map level index and return the assignment
    vector.

    Args:
        box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes,
            where N is the number of images in the batch.
        min_level (int): Smallest feature map level index. The input is considered index 0,
            the output of stage 1 is index 1, and so.
        max_level (int): Largest feature map level index.
        canonical_box_size (int): A canonical box size in pixels (sqrt(box area)).
        canonical_level (int): The feature map level index on which a canonically-sized box
            should be placed.

    Returns:
        A tensor of length M, where M is the total number of boxes aggregated over all
            N batch images. The memory layout corresponds to the concatenation of boxes
            from all images. Each element is the feature map index, as an offset from
            `self.min_level`, for the corresponding box (so value i means the box is at
            `self.min_level + i`).
    """
    box_sizes = torch.sqrt(cat([boxes.area() for boxes in box_lists]))
    # Eqn.(1) in FPN paper
    level_assignments = torch.floor(
        canonical_level + torch.log2(box_sizes / canonical_box_size + 1e-8)
    )
    # clamp level to (min, max), in case the box size is too large or too small
    # for the available feature maps
    level_assignments = torch.clamp(level_assignments, min=min_level, max=max_level)
    return level_assignments.to(torch.int64) - min_level

    Map each box in `box_lists` to a feature map level index and return the assignment
    vector.

    Args:
        box_lists (list[Boxes] | list[RotatedBoxes]): A list of N Boxes or N RotatedBoxes,
            where N is the number of images in the batch.
        min_level (int): Smallest feature map level index. The input is considered index 0,
            the output of stage 1 is index 1, and so.
        max_level (int): Largest feature map level index.
        canonical_box_size (int): A canonical box size in pixels (sqrt(box area)).
        canonical_level (int): The feature map level index on which a canonically-sized box
            should be placed.

    Returns:
        A tensor of length M, where M is the total number of boxes aggregated over all
            N batch images. The memory layout corresponds to the concatenation of boxes
            from all images. Each element is the feature map index, as an offset from
            `self.min_level`, for the corresponding box (so value i means the box is at
            `self.min_level + i`).
    
ControlNet-v1-1-nightly/annotator/oneformer/detectron2/modeling/poolers.py