def antidiag_indices(offset, min_i=0, max_i=None, min_j=0, max_j=None):
    """
    for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs

    offset=2 (1, 1),
    offset=3 (2, 1), (1, 2)
    offset=4 (2, 2), (1, 3)
    offset=5 (2, 3)

    constraints:
        i + j = offset
        min_j <= j < max_j
        min_i <= offset - j < max_i
    """
    if max_i is None:
        max_i = offset + 1
    if max_j is None:
        max_j = offset + 1
    min_j = max(min_j, offset - max_i + 1, 0)
    max_j = min(max_j, offset - min_i + 1, offset + 1)
    j = torch.arange(min_j, max_j)
    i = offset - j
    return torch.stack([i, j])

    for a (3, 4) matrix with min_i=1, max_i=3, min_j=1, max_j=4, outputs

    offset=2 (1, 1),
    offset=3 (2, 1), (1, 2)
    offset=4 (2, 2), (1, 3)
    offset=5 (2, 3)

    constraints:
        i + j = offset
        min_j <= j < max_j
        min_i <= offset - j < max_i
    
structured_prompting/fairseq-version/fairseq/fairseq/tasks/text_to_speech.py