def batch_dynamic_time_warping(distance, shapes=None):
    """full batched DTW without any constraints

    distance:  (batchsize, max_M, max_N) matrix
    shapes: (batchsize,) vector specifying (M, N) for each entry
    """
    # ptr: 0=left, 1=up-left, 2=up
    ptr2dij = {0: (0, -1), 1: (-1, -1), 2: (-1, 0)}

    bsz, m, n = distance.size()
    cumdist = torch.zeros_like(distance)
    backptr = torch.zeros_like(distance).type(torch.int32) - 1

    # initialize
    cumdist[:, 0, :] = distance[:, 0, :].cumsum(dim=-1)
    cumdist[:, :, 0] = distance[:, :, 0].cumsum(dim=-1)
    backptr[:, 0, :] = 0
    backptr[:, :, 0] = 2

    # DP with optimized anti-diagonal parallelization, O(M+N) steps
    for offset in range(2, m + n - 1):
        ind = antidiag_indices(offset, 1, m, 1, n)
        c = torch.stack(
            [
                cumdist[:, ind[0], ind[1] - 1],
                cumdist[:, ind[0] - 1, ind[1] - 1],
                cumdist[:, ind[0] - 1, ind[1]],
            ],
            dim=2,
        )
        v, b = c.min(axis=-1)
        backptr[:, ind[0], ind[1]] = b.int()
        cumdist[:, ind[0], ind[1]] = v + distance[:, ind[0], ind[1]]

    # backtrace
    pathmap = torch.zeros_like(backptr)
    for b in range(bsz):
        i = m - 1 if shapes is None else (shapes[b][0] - 1).item()
        j = n - 1 if shapes is None else (shapes[b][1] - 1).item()
        dtwpath = [(i, j)]
        while (i != 0 or j != 0) and len(dtwpath) < 10000:
            assert i >= 0 and j >= 0
            di, dj = ptr2dij[backptr[b, i, j].item()]
            i, j = i + di, j + dj
            dtwpath.append((i, j))
        dtwpath = dtwpath[::-1]
        indices = torch.from_numpy(np.array(dtwpath))
        pathmap[b, indices[:, 0], indices[:, 1]] = 1

    return cumdist, backptr, pathmap
full batched DTW without any constraints

    distance:  (batchsize, max_M, max_N) matrix
    shapes: (batchsize,) vector specifying (M, N) for each entry
    
understand_icl/fairseq/fairseq/tasks/text_to_speech.py