def encoder_padding_mask_to_lengths(
    encoder_padding_mask, max_lengths, batch_size, device
):
    """
    convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor

    Conventionally, encoder output contains a encoder_padding_mask, which is
    a 2-D mask in a shape (T, B), whose (t, b) element indicate whether
    encoder_out[t, b] is a valid output (=0) or not (=1). Occasionally, we
    need to convert this mask tensor to a 1-D tensor in shape (B, ), where
    [b] denotes the valid length of b-th sequence

    Args:
        encoder_padding_mask: a (T, B)-shaped binary tensor or None; if None,
        indicating all are valid
    Return:
        seq_lengths: a (B,)-shaped tensor, where its (b, )-th element is the
        number of valid elements of b-th sequence

        max_lengths: maximum length of all sequence, if encoder_padding_mask is
        not None, max_lengths must equal to encoder_padding_mask.size(0)

        batch_size: batch size; if encoder_padding_mask is
        not None, max_lengths must equal to encoder_padding_mask.size(1)

        device: which device to put the result on
    """
    if encoder_padding_mask is None:
        return torch.Tensor([max_lengths] * batch_size).to(torch.int32).to(device)

    assert encoder_padding_mask.size(0) == max_lengths, "max_lengths does not match"
    assert encoder_padding_mask.size(1) == batch_size, "batch_size does not match"

    return max_lengths - torch.sum(encoder_padding_mask, dim=0)

    convert encoder_padding_mask (2-D binary tensor) to a 1-D tensor

    Conventionally, encoder output contains a encoder_padding_mask, which is
    a 2-D mask in a shape (T, B), whose (t, b) element indicate whether
    encoder_out[t, b] is a valid output (=0) or not (=1). Occasionally, we
    need to convert this mask tensor to a 1-D tensor in shape (B, ), where
    [b] denotes the valid length of b-th sequence

    Args:
        encoder_padding_mask: a (T, B)-shaped binary tensor or None; if None,
        indicating all are valid
    Return:
        seq_lengths: a (B,)-shaped tensor, where its (b, )-th element is the
        number of valid elements of b-th sequence

        max_lengths: maximum length of all sequence, if encoder_padding_mask is
        not None, max_lengths must equal to encoder_padding_mask.size(0)

        batch_size: batch size; if encoder_padding_mask is
        not None, max_lengths must equal to encoder_padding_mask.size(1)

        device: which device to put the result on
    
understand_icl/fairseq/examples/speech_recognition/data/data_utils.py