def resize_annotation(
    annotation: Dict[str, Any],
    orig_size: Tuple[int, int],
    target_size: Tuple[int, int],
    threshold: float = 0.5,
    resample: PILImageResampling = PILImageResampling.NEAREST,
):
    """
    Resizes an annotation to a target size.

    Args:
        annotation (`Dict[str, Any]`):
            The annotation dictionary.
        orig_size (`Tuple[int, int]`):
            The original size of the input image.
        target_size (`Tuple[int, int]`):
            The target size of the image, as returned by the preprocessing `resize` step.
        threshold (`float`, *optional*, defaults to 0.5):
            The threshold used to binarize the segmentation masks.
        resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
            The resampling filter to use when resizing the masks.
    """
    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(target_size, orig_size))
    ratio_height, ratio_width = ratios

    new_annotation = {}
    new_annotation["size"] = target_size

    for key, value in annotation.items():
        if key == "boxes":
            boxes = value
            scaled_boxes = boxes * np.asarray([ratio_width, ratio_height, ratio_width, ratio_height], dtype=np.float32)
            new_annotation["boxes"] = scaled_boxes
        elif key == "area":
            area = value
            scaled_area = area * (ratio_width * ratio_height)
            new_annotation["area"] = scaled_area
        elif key == "masks":
            masks = value[:, None]
            masks = np.array([resize(mask, target_size, resample=resample) for mask in masks])
            masks = masks.astype(np.float32)
            masks = masks[:, 0] > threshold
            new_annotation["masks"] = masks
        elif key == "size":
            new_annotation["size"] = target_size
        else:
            new_annotation[key] = value

    return new_annotation

    Resizes an annotation to a target size.

    Args:
        annotation (`Dict[str, Any]`):
            The annotation dictionary.
        orig_size (`Tuple[int, int]`):
            The original size of the input image.
        target_size (`Tuple[int, int]`):
            The target size of the image, as returned by the preprocessing `resize` step.
        threshold (`float`, *optional*, defaults to 0.5):
            The threshold used to binarize the segmentation masks.
        resample (`PILImageResampling`, defaults to `PILImageResampling.NEAREST`):
            The resampling filter to use when resizing the masks.
    
dpkd/transformers/src/transformers/models/conditional_detr/image_processing_conditional_detr.py