def add_optional_chunk_mask(
    xs: torch.Tensor,
    masks: torch.Tensor,
    use_dynamic_chunk: bool,
    use_dynamic_left_chunk: bool,
    decoding_chunk_size: int,
    static_chunk_size: int,
    num_decoding_left_chunks: int,
):
    """Apply optional mask for encoder.

    Args:
        xs (torch.Tensor): padded input, (B, L, D), L for max length
        mask (torch.Tensor): mask for xs, (B, 1, L)
        use_dynamic_chunk (bool): whether to use dynamic chunk or not
        use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
            training.
        decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
            0: default for training, use random dynamic chunk.
            <0: for decoding, use full chunk.
            >0: for decoding, use fixed chunk size as set.
        static_chunk_size (int): chunk size for static chunk training/decoding
            if it's greater than 0, if use_dynamic_chunk is true,
            this parameter will be ignored
        num_decoding_left_chunks: number of left chunks, this is for decoding,
            the chunk size is decoding_chunk_size.
            >=0: use num_decoding_left_chunks
            <0: use all left chunks

    Returns:
        torch.Tensor: chunk mask of the input xs.
    """
    # Whether to use chunk mask or not
    if use_dynamic_chunk:
        max_len = xs.size(1)
        if decoding_chunk_size < 0:
            chunk_size = max_len
            num_left_chunks = -1
        elif decoding_chunk_size > 0:
            chunk_size = decoding_chunk_size
            num_left_chunks = num_decoding_left_chunks
        else:
            # chunk size is either [1, 25] or full context(max_len).
            # Since we use 4 times subsampling and allow up to 1s(100 frames)
            # delay, the maximum frame is 100 / 4 = 25.
            chunk_size = torch.randint(1, max_len, (1,)).item()
            num_left_chunks = -1
            if chunk_size > max_len // 2:
                chunk_size = max_len
            else:
                chunk_size = chunk_size % 25 + 1
                if use_dynamic_left_chunk:
                    max_left_chunks = (max_len - 1) // chunk_size
                    num_left_chunks = torch.randint(0, max_left_chunks, (1,)).item()
        chunk_masks = subsequent_chunk_mask(
            xs.size(1), chunk_size, num_left_chunks, xs.device
        )  # (L, L)
        chunk_masks = chunk_masks.unsqueeze(0)  # (1, L, L)
        chunk_masks = masks & chunk_masks  # (B, L, L)
    elif static_chunk_size > 0:
        num_left_chunks = num_decoding_left_chunks
        chunk_masks = subsequent_chunk_mask(
            xs.size(1), static_chunk_size, num_left_chunks, xs.device
        )  # (L, L)
        chunk_masks = chunk_masks.unsqueeze(0)  # (1, L, L)
        chunk_masks = masks & chunk_masks  # (B, L, L)
    else:
        chunk_masks = masks
    return chunk_masks
Apply optional mask for encoder.

Args:
xs (torch.Tensor): padded input, (B, L, D), L for max length
mask (torch.Tensor): mask for xs, (B, 1, L)
use_dynamic_chunk (bool): whether to use dynamic chunk or not
use_dynamic_left_chunk (bool): whether to use dynamic left chunk for
training.
decoding_chunk_size (int): decoding chunk size for dynamic chunk, it's
0: default for training, use random dynamic chunk.
<0: for decoding, use full chunk.
>0: for decoding, use fixed chunk size as set.
static_chunk_size (int): chunk size for static chunk training/decoding
if it's greater than 0, if use_dynamic_chunk is true,
this parameter will be ignored
num_decoding_left_chunks: number of left chunks, this is for decoding,
the chunk size is decoding_chunk_size.
>=0: use num_decoding_left_chunks
<0: use all left chunks

Returns:
torch.Tensor: chunk mask of the input xs.
modules/wenet_extractor/utils/mask.py