def pad_sequences_1d(sequences, dtype=torch.long, device=torch.device("cpu"), fixed_length=None):
    """ Pad a single-nested list or a sequence of n-d array (torch.tensor or np.ndarray)
    into a (n+1)-d array, only allow the first dim has variable lengths.
    Args:
        sequences: list(n-d tensor or list)
        dtype: np.dtype or torch.dtype
        device:
        fixed_length: pad all seq in sequences to fixed length. All seq should have a length <= fixed_length.
            return will be of shape [len(sequences), fixed_length, ...]
    Returns:
        padded_seqs: ((n+1)-d tensor) padded with zeros
        mask: (2d tensor) of the same shape as the first two dims of padded_seqs,
              1 indicate valid, 0 otherwise
    Examples:
        >>> test_data_list = [[1,2,3], [1,2], [3,4,7,9]]
        >>> pad_sequences_1d(test_data_list, dtype=torch.long)
        >>> test_data_3d = [torch.randn(2,3,4), torch.randn(4,3,4), torch.randn(1,3,4)]
        >>> pad_sequences_1d(test_data_3d, dtype=torch.float)
        >>> test_data_list = [[1,2,3], [1,2], [3,4,7,9]]
        >>> pad_sequences_1d(test_data_list, dtype=np.float32)
        >>> test_data_3d = [np.random.randn(2,3,4), np.random.randn(4,3,4), np.random.randn(1,3,4)]
        >>> pad_sequences_1d(test_data_3d, dtype=np.float32)
    """
    if isinstance(sequences[0], list):
        if "torch" in str(dtype):
            sequences = [torch.tensor(s, dtype=dtype, device=device) for s in sequences]
        else:
            sequences = [np.asarray(s, dtype=dtype) for s in sequences]

    extra_dims = sequences[0].shape[1:]  # the extra dims should be the same for all elements
    lengths = [len(seq) for seq in sequences]
    if fixed_length is not None:
        max_length = fixed_length
    else:
        max_length = max(lengths)
    if isinstance(sequences[0], torch.Tensor):
        assert "torch" in str(dtype), "dtype and input type does not match"
        padded_seqs = torch.zeros((len(sequences), max_length) + extra_dims, dtype=dtype, device=device)
        mask = torch.zeros((len(sequences), max_length), dtype=torch.float32, device=device)
    else:  # np
        assert "numpy" in str(dtype), "dtype and input type does not match"
        padded_seqs = np.zeros((len(sequences), max_length) + extra_dims, dtype=dtype)
        mask = np.zeros((len(sequences), max_length), dtype=np.float32)

    for idx, seq in enumerate(sequences):
        end = lengths[idx]
        padded_seqs[idx, :end] = seq
        mask[idx, :end] = 1
    return padded_seqs, mask  # , lengths
 Pad a single-nested list or a sequence of n-d array (torch.tensor or np.ndarray)
    into a (n+1)-d array, only allow the first dim has variable lengths.
    Args:
        sequences: list(n-d tensor or list)
        dtype: np.dtype or torch.dtype
        device:
        fixed_length: pad all seq in sequences to fixed length. All seq should have a length <= fixed_length.
            return will be of shape [len(sequences), fixed_length, ...]
    Returns:
        padded_seqs: ((n+1)-d tensor) padded with zeros
        mask: (2d tensor) of the same shape as the first two dims of padded_seqs,
              1 indicate valid, 0 otherwise
    Examples:
        >>> test_data_list = [[1,2,3], [1,2], [3,4,7,9]]
        >>> pad_sequences_1d(test_data_list, dtype=torch.long)
        >>> test_data_3d = [torch.randn(2,3,4), torch.randn(4,3,4), torch.randn(1,3,4)]
        >>> pad_sequences_1d(test_data_3d, dtype=torch.float)
        >>> test_data_list = [[1,2,3], [1,2], [3,4,7,9]]
        >>> pad_sequences_1d(test_data_list, dtype=np.float32)
        >>> test_data_3d = [np.random.randn(2,3,4), np.random.randn(4,3,4), np.random.randn(1,3,4)]
        >>> pad_sequences_1d(test_data_3d, dtype=np.float32)
    
video_chat2/dataset/utils.py