vllm.model_executor.models.nano_nemotron_vl ¶

class BaseNanoNemotronVLProcessingInfo(BaseProcessingInfo):
    """Basic image-only ProcessingInfo for InternVL-style models."""

    @abstractmethod
    def get_hf_processor(
        self,
        **kwargs: object,
    ) -> BaseNanoNemotronVLProcessor:
        raise NotImplementedError

    def get_default_tok_params(self) -> TokenizeParams:
        return super().get_default_tok_params().with_kwargs(add_special_tokens=False)

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"image": None}

    def get_image_size_with_most_features(self, max_num_tiles: int) -> ImageSize:
        processor = self.get_hf_processor()

        base_size = processor.image_size
        target_ratios = get_internvl_target_ratios(1, max_num_tiles)

        largest_feature_size, largest_feature_pinpoint = 0, None
        for wr, hr in target_ratios:
            width, height = base_size * wr, base_size * hr

            feat_size = processor.get_num_image_tokens(
                image_width=width, image_height=height, max_num_tiles=max_num_tiles
            )
            if feat_size > largest_feature_size:
                largest_feature_size = feat_size
                largest_feature_pinpoint = ImageSize(width=width, height=height)

        if largest_feature_size == 0 or largest_feature_pinpoint is None:
            raise ValueError("Cannot have a largest feature size of 0!")

        return largest_feature_pinpoint

    def get_max_image_tokens(self) -> int:
        processor = self.get_hf_processor()
        # Use default max_num_tiles for max tokens calculation
        max_num_tiles = processor.max_num_tiles
        target_width, target_height = self.get_image_size_with_most_features(
            max_num_tiles
        )

        return processor.get_num_image_tokens(
            image_width=target_width,
            image_height=target_height,
            max_num_tiles=max_num_tiles,
        )

class BaseNanoNemotronVLProcessor(ABC):
    """
    This model doesn't define its own HF processor,
    so we implement our own one here.

    The code to insert image tokens is based on:
    https://huggingface.co/OpenGVLab/InternVL2-1B/blob/main/modeling_internvl_chat.py#L252
    """

    def __init__(
        self,
        config: PretrainedConfig,
        tokenizer: TokenizerLike,
        *args,
        max_model_len: int,
        max_num_tiles: int | None = None,
        **kwargs,
    ) -> None:
        super().__init__()

        self.config = config
        self.tokenizer = tokenizer

        self.max_num_tiles = max_num_tiles or DEFAULT_NUM_TILES
        image_size: int = config.force_image_size
        patch_size: int = config.patch_size
        downsample_ratio: int = config.downsample_ratio

        self.num_image_token = int(
            (image_size // patch_size) ** 2 * (downsample_ratio**2)
        )
        self.image_size = image_size
        self.use_thumbnail: bool = config.use_thumbnail
        self.norm_mean = torch.Tensor(config.norm_mean).reshape(1, 3, 1, 1)
        self.norm_std = torch.Tensor(config.norm_std).reshape(1, 3, 1, 1)

        self.dynamic_tiler: DynamicResolutionImageTiler | None = None
        if self.use_dynamic_resolution(config):
            self.dynamic_tiler = DynamicResolutionImageTiler(
                max_model_len=max_model_len,
                patch_size=patch_size,
                downsample_ratio=downsample_ratio,
                min_num_patches=config.vision_config.args["min_num_patches"],
                max_num_patches=config.vision_config.args["max_num_patches"],
                norm_mean=config.norm_mean,
                norm_std=config.norm_std,
            )

    @staticmethod
    def use_dynamic_resolution(config: PretrainedConfig) -> bool:
        return "min_num_patches" in config.vision_config.args

    @property
    @abstractmethod
    def image_token_id(self) -> int:
        raise NotImplementedError

    @abstractmethod
    def get_image_repl(
        self,
        feature_size: int,
        num_patches: int | None,
    ) -> PromptUpdateDetails[str]:
        raise NotImplementedError

    def get_num_image_tokens(
        self,
        *,
        image_width: int,
        image_height: int,
        max_num_tiles: int,
    ) -> int:
        target_ratios = get_internvl_target_ratios(1, max_num_tiles)

        num_patches, _, _ = calculate_internvl_targets(
            orig_width=image_width,
            orig_height=image_height,
            target_ratios=target_ratios,
            image_size=self.image_size,
            use_thumbnail=self.use_thumbnail,
        )

        return num_patches * self.num_image_token

    def _images_to_pixel_values_lst(
        self,
        images: list[Image.Image],
        max_num_tiles: int,
    ) -> list[torch.Tensor]:
        return [
            image_to_pixel_values(
                image,
                input_size=self.image_size,
                max_num=max_num_tiles,
                use_thumbnail=self.use_thumbnail,
                idx=idx,
            )
            for idx, image in enumerate(images)
        ]

    def _preprocess_image(
        self,
        text: list[str],
        images: list[Image.Image],
        max_num_tiles: int,
    ) -> tuple[list[str], dict[str, Any]]:
        if len(images) == 0:
            image_inputs = {}
            return text, image_inputs

        if tiler := self.dynamic_tiler:
            sans_images = text[0].replace("<image>", "")
            text_prompt_length = len(
                self.tokenizer(sans_images, add_special_tokens=False).input_ids
            )
            pixel_values_lst, num_tokens_per_image = tiler._images_to_pixel_values_lst(
                text_prompt_length=text_prompt_length,
                images=images,
            )
            imgs_sizes = [(pv.shape[-2], pv.shape[-1]) for pv in pixel_values_lst]
            normalized = [
                input_conditioner(img, tiler.norm_mean, tiler.norm_std)
                for img in pixel_values_lst
            ]
            image_num_patches = torch.tensor([1] * len(num_tokens_per_image))
            image_inputs = {
                "pixel_values_flat": normalized,
                "imgs_sizes": imgs_sizes,
                "num_tokens_per_image": num_tokens_per_image,
            }
        else:
            pixel_values_lst = self._images_to_pixel_values_lst(images, max_num_tiles)
            image_num_patches = torch.tensor([len(item) for item in pixel_values_lst])
            pixel_values_flat = input_conditioner(
                torch.cat(pixel_values_lst), self.norm_mean, self.norm_std
            )
            image_inputs = {
                "pixel_values_flat": pixel_values_flat,
                "image_num_patches": image_num_patches,
            }
            num_tokens_per_image = [
                self.num_image_token * len(item) for item in pixel_values_lst
            ]

        assert len(text) == 1, (
            "hf_processor is called on the output of get_dummy_text, "
            "which should be a single string"
        )
        parts = [x for x in re.split(r"(<image>)", text[0]) if x]
        assert parts.count("<image>") == len(pixel_values_lst), (
            "the number of <image> tokens in the text should be the "
            "same as the number of images"
        )

        for i, (feature_size, num_patches) in enumerate(
            zip(num_tokens_per_image, image_num_patches, strict=True)
        ):
            image_repl = self.get_image_repl(feature_size, num_patches)
            parts[i] = parts[i].replace("<image>", image_repl.full)
        text = ["".join(parts)]
        return text, image_inputs

    def _make_batch_input(self, input_item: Any | list[Any] | None = None):
        if input_item is None:
            input_item = []
        if not isinstance(input_item, list):
            input_item = [input_item]
        return input_item

    @abstractmethod
    def __call__(
        self,
        text: str | list[str] | None = None,
        images: Image.Image | list[Image.Image] | None = None,
        return_tensors: str | TensorType | None = None,
        max_num_tiles: int | None = None,
    ) -> BatchFeature:
        raise NotImplementedError

class DynamicResolutionImageTiler:
    CONV_MERGING = False
    PIXEL_SHUFFLE = True
    USE_THUMBNAIL = False

    def __init__(
        self,
        *,
        max_model_len: int,
        patch_size: int,
        min_num_patches: int,
        max_num_patches: int,
        downsample_ratio: int,
        norm_mean: Sequence[float],
        norm_std: Sequence[float],
        factor_max: float = 1.0,
        use_thumbnail: bool = False,
    ) -> None:
        assert use_thumbnail is False, "use_thumbnail is not supported"
        self._patch_size: int = patch_size
        self._max_model_len = max_model_len
        self._min_num_patches = min_num_patches
        self._max_num_patches = max_num_patches if max_num_patches > 0 else float("inf")
        self._factor_max = factor_max
        self.norm_mean = torch.tensor(norm_mean).reshape(3, 1, 1)
        self.norm_std = torch.tensor(norm_std).reshape(3, 1, 1)
        self._transform = T.Compose(
            [
                T.Lambda(lambda img: img.convert("RGB") if img.mode != "RGB" else img),
                T.ToTensor(),
            ]
        )
        assert downsample_ratio < 1
        reduction_factor = 1 / downsample_ratio
        assert reduction_factor == 2.0
        self._downsample_ratio = int(reduction_factor) ** (
            self.PIXEL_SHUFFLE + self.CONV_MERGING
        )
        assert self._downsample_ratio == 2

    def _get_num_embeddings(self, width: int, height: int) -> int:
        num_patches = (width // self._patch_size) * (height // self._patch_size)
        num_tokens = num_patches // (self._downsample_ratio**2)
        return num_tokens

    def width_and_height_for_max_num_tokens_available(
        self,
        target_num_tokens_post_shuffle: int,
    ) -> tuple[int, int]:
        """
        TODO: optimize this so it squeezes closer to target number of tokens.
        Calculate image dimensions that produce approximately `target` tokens after
        pixel_shuffle.

        With pixel_shuffle enabled, each 2x2 patch grid becomes 1 token, so we
        need 4*B patches to get B tokens.

        Examples:
        >>> PATCH_SIZE = 16
        >>> DOWNSAMPLE_RATIO = 0.5
        >>> tiler = DynamicResolutionImageTiler(
        ...     max_model_len=16384,
        ...     patch_size=PATCH_SIZE,
        ...     downsample_ratio=DOWNSAMPLE_RATIO,
        ...     min_num_patches=4,
        ...     max_num_patches=0,
        ... )
        >>> width, height = tiler.width_and_height_for_max_num_tokens_available(
        ...     target_num_tokens_post_shuffle=8192,
        ... )
        >>> assert width, height == (2880, 2880)
        >>> assert (width // PATCH_SIZE) * (
        ...     height // PATCH_SIZE
        ... ) // 2**2 == 8100  # tokens post-shuffle
        >>> assert tiler._get_num_embeddings(width=width, height=height) == 8100
        """
        side_pixels = (
            math.isqrt(target_num_tokens_post_shuffle)
            * self._downsample_ratio
            * self._patch_size
        )
        assert isinstance(side_pixels, int) and side_pixels % self._patch_size == 0
        return side_pixels, side_pixels

    def max_num_tokens_available(self, text_prompt_length: int) -> int:
        return self._max_model_len - text_prompt_length - 4

    def _images_to_pixel_values_lst(
        self,
        text_prompt_length: int,
        images: list[Image.Image],
    ) -> tuple[list[torch.Tensor], list[int]]:
        num_tokens_available = self.max_num_tokens_available(text_prompt_length)
        params_per_image = self.compute_params(images, num_tokens_available)

        feature_sizes = []
        images = []
        for param in params_per_image:
            for t in self.apply_params(param):
                assert t.ndim == 3, f"{t.ndim=}: expected 3 dim tensor"
                images.append(t)
                feature_sizes.append(param.num_embeddings)
        return images, feature_sizes

    feature_size_cache: dict[Image.Image, int] = {}

    @classmethod
    def get_cached_feature_size(cls, image: Image.Image) -> int:
        feature_size = cls.feature_size_cache[id(image)]
        # hard assert that we only use the feature size once
        del cls.feature_size_cache[id(image)]
        return feature_size

    @dataclass
    class DynamicResolutionParams:
        media: Image.Image
        num_tiles: int
        num_embeddings: int
        patch_size: tuple[int, int]

    def apply_params(self, params: DynamicResolutionParams) -> list[torch.Tensor]:
        resized_img = params.media.resize(
            (
                params.patch_size[0] * self._patch_size,
                params.patch_size[1] * self._patch_size,
            )
        )
        processed_images = [resized_img]

        return [self._transform(img) for img in processed_images]

    def process_media(
        self,
        media: Image.Image,
        num_tokens_available: int,
    ) -> tuple[DynamicResolutionParams, int]:
        """Process a single media item and return its parameters.

        Args:
            media: The media item to process
            num_tokens_available: Number of tokens available for this media
        Returns:
            DynamicResolutionParams for the media
        """
        current_num_tokens_available = num_tokens_available
        assert isinstance(media, Image.Image), (
            "Dynamic resolution is only supported for image media"
        )
        orig_width, orig_height = media.width, media.height
        closest_patch_height = round(orig_height / self._patch_size + 0.5)
        closest_patch_width = round(orig_width / self._patch_size + 0.5)
        patches = closest_patch_height * closest_patch_width

        factor = min(
            math.sqrt(current_num_tokens_available / patches), self._factor_max
        )
        target_patch_height = math.floor(factor * closest_patch_height)
        target_patch_width = math.floor(factor * closest_patch_width)

        # Consider self._min_num_patches if > current_num_tokens_available.
        if (
            current_num_tokens_available > self._min_num_patches
            and target_patch_height * target_patch_width < self._min_num_patches
        ):
            up_factor = math.sqrt(
                self._min_num_patches / (target_patch_height * target_patch_width)
            )
            target_patch_height = math.ceil(up_factor * target_patch_height)
            target_patch_width = math.ceil(up_factor * target_patch_width)

        # Round patch grid to be divisible by 2 (pixel-shuffle OR conv-merging)
        # or by 4 when BOTH are enabled (two successive 2x reductions)
        if self.PIXEL_SHUFFLE or self.CONV_MERGING:
            required_divisor = 4 if (self.PIXEL_SHUFFLE and self.CONV_MERGING) else 2

            rem_h = target_patch_height % required_divisor
            if rem_h != 0:
                inc_h = required_divisor - rem_h
                if (
                    target_patch_height + inc_h
                ) * target_patch_width <= current_num_tokens_available:
                    target_patch_height += inc_h
                else:
                    target_patch_height = max(
                        required_divisor, target_patch_height - rem_h
                    )

            rem_w = target_patch_width % required_divisor
            if rem_w != 0:
                inc_w = required_divisor - rem_w
                if (
                    target_patch_height * (target_patch_width + inc_w)
                    <= current_num_tokens_available
                ):
                    target_patch_width += inc_w
                else:
                    target_patch_width = max(
                        required_divisor, target_patch_width - rem_w
                    )

        # Calculate embeddings for the main dynamic resolution image
        num_embeddings = self._get_num_embeddings(
            target_patch_width * self._patch_size,
            target_patch_height * self._patch_size,
        )

        token_count = target_patch_width * target_patch_height

        # Add thumbnail embeddings if enabled and image area is below threshold
        num_tiles = 1  # Base dynamic resolution image

        return self.DynamicResolutionParams(
            media=media,
            num_tiles=num_tiles,
            num_embeddings=num_embeddings,
            patch_size=(target_patch_width, target_patch_height),
        ), token_count

    def compute_params(
        self,
        media_list: list[Image.Image],
        num_tokens_available: int | None = None,
    ) -> list[DynamicResolutionParams]:
        """Compute parameters for all media with iterative token budgeting.

        Args:
            media_list: List of media items to process
            num_tokens_available: Total number of tokens available across all media
        Returns:
            List of ImageTilingParams for each media item
        """
        num_tokens_available = (
            num_tokens_available
            * (4 if self.PIXEL_SHUFFLE else 1)
            * (4 if self.CONV_MERGING else 1)
        )
        # When the number of available token is too small,
        # allow self._min_num_patches per media and let the sample be truncated.
        num_tokens_available = max(
            num_tokens_available, self._min_num_patches * len(media_list)
        )

        # Clip the number of tokens available per media to >min and <max patches.
        num_tokens_available_per_media = [
            max(min(num_tokens_available, self._max_num_patches), self._min_num_patches)
            for _ in range(len(media_list))
        ]

        # prevent infinite loop in any case
        for _ in range(10):
            # Step 1: Process each media with current token budget
            params = []
            token_counts = []

            for media, tokens_for_media in zip(
                media_list, num_tokens_available_per_media
            ):
                param, token_count = self.process_media(media, tokens_for_media)
                params.append(param)
                token_counts.append(token_count)
                self.feature_size_cache[id(param.media)] = param.num_embeddings

            # Step 2: Check if total tokens is within budget
            total_tokens = sum(token_counts)

            if total_tokens <= num_tokens_available:
                # We're within budget, return the params
                return params

            # Step 3: We're over budget, need to scale down
            # Calculate scaling factor to get under budget
            scaling_factor = num_tokens_available / total_tokens

            # Recalculate token budgets for each media based on scaling
            # Each media gets a proportional share of the total budget
            scaled_down_num_tokens_available_per_media = [
                max(self._min_num_patches, int(token_count * scaling_factor))
                for token_count in token_counts
            ]
            scaled_down = any(
                [
                    scaled_down_num_tokens_available_per_media[i]
                    < num_tokens_available_per_media[i]
                    for i in range(len(num_tokens_available_per_media))
                ]
            )
            # If there wasn't scaling down, we're stuck with min_num_patches per media,
            # else try with the scaled down num_tokens_available_per_media.
            if not scaled_down:
                num_tokens_available_per_media = [self._min_num_patches] * len(
                    media_list
                )
            else:
                num_tokens_available_per_media = (
                    scaled_down_num_tokens_available_per_media
                )
        ctx = f"{params=} {total_tokens=} {num_tokens_available=}"
        raise ValueError(
            f"Should be unreachable - `return params` above must be reached: {ctx}"
        )

    @staticmethod
    def stack(images: list[torch.Tensor], patch_size: int) -> torch.Tensor:
        assert len(images) > 0, "No images to stack"

        def rearrange_img(x):
            py = x.shape[-2] // patch_size
            px = x.shape[-1] // patch_size
            x = einops.rearrange(
                x,
                "c (py yy) (px xx) -> (py px) (c yy xx)",
                py=py,
                yy=patch_size,
                px=px,
                xx=patch_size,
            )
            return x

        imgs = [rearrange_img(img) for img in images]
        pixel_values_flat = torch.cat(imgs, dim=0).unsqueeze(0)
        return pixel_values_flat

class NanoNemotronBaseVLMultiModalProcessor(BaseMultiModalProcessor[_I]):
    """Basic image-only MultiModalProcessor for InternVL-style models."""

    @cached_property
    def is_dynamic_tiler(self) -> bool:
        return self.info.get_hf_processor().dynamic_tiler is not None

    def _get_mm_fields_config(
        self,
        hf_inputs: BatchFeature,
        hf_processor_mm_kwargs: Mapping[str, object],
    ) -> Mapping[str, MultiModalFieldConfig]:
        if self.is_dynamic_tiler:
            pixel_values_flat = MultiModalFieldConfig.batched("image")
        else:
            image_num_patches = hf_inputs.get("image_num_patches", torch.empty(0))
            pixel_values_flat = MultiModalFieldConfig.flat_from_sizes(
                "image", image_num_patches
            )

        return dict(
            pixel_values_flat=pixel_values_flat,
            image_num_patches=MultiModalFieldConfig.batched("image"),
            image_embeds=MultiModalFieldConfig.batched("image"),
            num_tokens_per_image=MultiModalFieldConfig.batched("image"),
            imgs_sizes=MultiModalFieldConfig.batched("image"),
        )

    def _get_prompt_updates(
        self,
        mm_items: MultiModalDataItems,
        hf_processor_mm_kwargs: Mapping[str, object],
        out_mm_kwargs: MultiModalKwargsItems,
    ) -> Sequence[PromptUpdate]:
        hf_processor = self.info.get_hf_processor(**hf_processor_mm_kwargs)

        out_mm_data = out_mm_kwargs.get_data()
        if "image_num_patches" in out_mm_data:
            image_num_patches = out_mm_data["image_num_patches"]
            assert isinstance(image_num_patches, torch.Tensor)
            image_num_patches = image_num_patches.tolist()
        elif "image_embeds" in out_mm_data:
            # to compute num_patches (similar to Qwen2-VL)
            image_num_patches = [None] * len(out_mm_data["image_embeds"])
        else:
            image_num_patches = []

        def get_replacement_custom(item_idx: int):
            images = mm_items.get_items(
                "image", (ImageEmbeddingItems, ImageProcessorItems)
            )

            if isinstance(images, ImageEmbeddingItems):
                feature_size = images.get_feature_size(item_idx)
            elif tiler := hf_processor.dynamic_tiler:
                image = images.get(item_idx)
                feature_size = tiler.get_cached_feature_size(image)
            else:
                image_size = images.get_image_size(item_idx)
                # Extract max_num_tiles from kwargs, default to 12
                max_num_tiles = hf_processor_mm_kwargs.get(
                    "max_num_tiles", hf_processor.max_num_tiles
                )
                feature_size = hf_processor.get_num_image_tokens(
                    image_width=image_size.width,
                    image_height=image_size.height,
                    max_num_tiles=max_num_tiles,
                )

            num_patches = None
            local_image_num_patches = image_num_patches
            if isinstance(local_image_num_patches, torch.Tensor):
                local_image_num_patches = local_image_num_patches.tolist()
            if isinstance(local_image_num_patches, (list, tuple)) and item_idx < len(
                local_image_num_patches
            ):
                num_patches = int(local_image_num_patches[item_idx])

            return hf_processor.get_image_repl(feature_size, num_patches)

        return [
            PromptReplacement(
                modality="image",
                target="<image>",
                replacement=get_replacement_custom,
            )
        ]

class NanoNemotronVLAudioFeatureInputs(TensorSchema):
    """
    Dimensions:
        - b: Number of audio clips
        - t: Audio feature length
        - f: Feature size (mel bins)
    """

    type: Literal["audio_features"] = "audio_features"
    input_audio_features: Annotated[torch.Tensor, TensorShape("b", "t", "f")]
    feature_attention_mask: Annotated[torch.Tensor, TensorShape("b", "t")]
    audio_feature_lengths: Annotated[torch.Tensor, TensorShape("b")]

class NanoNemotronVLDummyInputsBuilder(
    NanoNemotronVLDummyInputsBuilder[NanoNemotronVLProcessingInfo]
):
    """DummyInputsBuilder extended for video support"""

    def get_dummy_text(self, mm_counts: Mapping[str, int]) -> str:
        num_videos = mm_counts.get("video", 0)
        num_audios = mm_counts.get("audio", 0)

        return (
            super().get_dummy_text(mm_counts)
            + "<video>" * num_videos
            + AUDIO_CONTEXT * num_audios
        )

    def _get_dummy_videos(
        self,
        *,
        width: int,
        height: int,
        num_frames: int,
        num_videos: int,
        overrides: VideoDummyOptions | None = None,
    ) -> list[VideoItem]:
        video = super()._get_dummy_videos(
            width=width,
            height=height,
            num_frames=num_frames,
            num_videos=1,
            overrides=overrides,
        )[0]
        video_items = []
        for _ in range(num_videos):
            video_metadata = {
                "total_num_frames": num_frames,
                "fps": 2,
                "duration": num_frames / 2.0,
                "video_backend": "opencv_dynamic",
                "frames_indices": [i for i in range(num_frames)],
                "do_sample_frames": False,
            }
            video_item = (video.copy(), video_metadata)
            video_items.append(video_item)

        return video_items

    def get_dummy_mm_data(
        self,
        seq_len: int,
        mm_counts: Mapping[str, int],
        mm_options: Mapping[str, BaseDummyOptions],
    ) -> MultiModalDataDict:
        dummy_image = super().get_dummy_mm_data(seq_len, mm_counts, mm_options)
        if self.info.supports_video:
            config = self.info.get_hf_config()
            image_size: int = config.force_image_size
            target_num_frames = self.info.get_num_frames_with_most_features(
                seq_len, mm_counts
            )
            num_videos = mm_counts.get("video", 0)
            video_overrides = mm_options.get("video")
            dummy_video = {
                "video": self._get_dummy_videos(
                    width=image_size,
                    height=image_size,
                    num_frames=target_num_frames,
                    num_videos=num_videos,
                    overrides=video_overrides,
                )
            }
        else:
            dummy_video = {}

        if extractor := self.info.audio_extractor:
            num_audios = mm_counts.get("audio", 0)
            audio_overrides = mm_options.get("audio") if mm_options else None
            tokens_per_audio = max(1, seq_len // max(num_audios, 1))
            max_audio_num_samples = MAX_AUDIO_LEN_S * extractor.sampling_rate
            calculated_max_audio_num_samples = extractor.audio_length(tokens_per_audio)
            audio_len = min(max_audio_num_samples, calculated_max_audio_num_samples)
            dummy_audio = {
                "audio": self._get_dummy_audios(
                    length=audio_len,
                    num_audios=num_audios,
                    overrides=audio_overrides,
                )
            }
        else:
            dummy_audio = {}

        return {**dummy_image, **dummy_video, **dummy_audio}