def lengths_to_encoder_padding_mask(lengths, batch_first=False):
    """
    convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor

    Args:
        lengths: a (B, )-shaped tensor

    Return:
        max_length: maximum length of B sequences
        encoder_padding_mask: a (max_length, B) binary mask, where
        [t, b] = 0 for t < lengths[b] and 1 otherwise

    TODO:
        kernelize this function if benchmarking shows this function is slow
    """
    max_lengths = torch.max(lengths).item()
    bsz = lengths.size(0)
    encoder_padding_mask = torch.arange(
        max_lengths
    ).to(  # a (T, ) tensor with [0, ..., T-1]
        lengths.device
    ).view(  # move to the right device
        1, max_lengths
    ).expand(  # reshape to (1, T)-shaped tensor
        bsz, -1
    ) >= lengths.view(  # expand to (B, T)-shaped tensor
        bsz, 1
    ).expand(
        -1, max_lengths
    )
    if not batch_first:
        return encoder_padding_mask.t(), max_lengths
    else:
        return encoder_padding_mask, max_lengths

    convert lengths (a 1-D Long/Int tensor) to 2-D binary tensor

    Args:
        lengths: a (B, )-shaped tensor

    Return:
        max_length: maximum length of B sequences
        encoder_padding_mask: a (max_length, B) binary mask, where
        [t, b] = 0 for t < lengths[b] and 1 otherwise

    TODO:
        kernelize this function if benchmarking shows this function is slow
    
understand_icl/fairseq/examples/speech_recognition/data/data_utils.py