def _check_and_preprocess_points(
        self,
        input_points=None,
        input_labels=None,
        input_boxes=None,
    ):
        r"""
        Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they
        are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`,
        it is converted to a `numpy.ndarray` and then to a `list`.
        """
        if input_points is not None:
            if hasattr(input_points, "numpy"):  # Checks for TF or Torch tensor
                input_points = input_points.numpy().tolist()

            if not isinstance(input_points, list) or not isinstance(input_points[0], list):
                raise ValueError("Input points must be a list of list of floating points.")
            input_points = [np.array(input_point) for input_point in input_points]
        else:
            input_points = None

        if input_labels is not None:
            if hasattr(input_labels, "numpy"):
                input_labels = input_labels.numpy().tolist()

            if not isinstance(input_labels, list) or not isinstance(input_labels[0], list):
                raise ValueError("Input labels must be a list of list integers.")
            input_labels = [np.array(label) for label in input_labels]
        else:
            input_labels = None

        if input_boxes is not None:
            if hasattr(input_boxes, "numpy"):
                input_boxes = input_boxes.numpy().tolist()

            if (
                not isinstance(input_boxes, list)
                or not isinstance(input_boxes[0], list)
                or not isinstance(input_boxes[0][0], list)
            ):
                raise ValueError("Input boxes must be a list of list of list of floating points.")
            input_boxes = [np.array(box).astype(np.float32) for box in input_boxes]
        else:
            input_boxes = None

        return input_points, input_labels, input_boxes

        Check and preprocesses the 2D points, labels and bounding boxes. It checks if the input is valid and if they
        are, it converts the coordinates of the points and bounding boxes. If a user passes directly a `torch.Tensor`,
        it is converted to a `numpy.ndarray` and then to a `list`.
        
dpkd/transformers/src/transformers/models/sam/processing_sam.py