def _random_segmentation(num_items, num_segments):
            """Partition a sequence of items randomly into non-empty segments.
            Args:
                num_items: an integer scalar > 0
                num_segments: an integer scalar in [1, num_items]
            Returns:
                a Tensor with shape [num_segments] containing positive integers that add
                up to num_items
            """
            mask_indices = np.arange(num_items - 1) < (num_segments - 1)
            np.random.shuffle(mask_indices)
            first_in_segment = np.pad(mask_indices, [[1, 0]])
            segment_id = np.cumsum(first_in_segment)
            # count length of sub segments assuming that list is sorted
            _, segment_length = np.unique(segment_id, return_counts=True)
            return segment_length
Partition a sequence of items randomly into non-empty segments.
            Args:
                num_items: an integer scalar > 0
                num_segments: an integer scalar in [1, num_items]
            Returns:
                a Tensor with shape [num_segments] containing positive integers that add
                up to num_items
            
mGPT/archs/mgpt_lm.py