processing_qwen3_vl.py

import numpy as np

from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from transformers.utils import logging
from transformers.utils.auto_docstring import auto_docstring
from transformers.video_utils import VideoInput

logger = logging.get_logger(__name__)


class Qwen3VLProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = { # type: ignore
        "text_kwargs": {
            "padding": False,
            "return_token_type_ids": False,
            "return_mm_token_type_ids": False,
        },
        "videos_kwargs": {"return_metadata": True},
    }


@auto_docstring
class ZFQwen3VLProcessor(ProcessorMixin):
    def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs):
        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token # type: ignore
        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token # type: ignore
        self.image_token_id = (
            tokenizer.image_token_id # type: ignore
            if getattr(tokenizer, "image_token_id", None)
            else tokenizer.convert_tokens_to_ids(self.image_token) # type: ignore
        )
        self.video_token_id = (
            tokenizer.video_token_id # type: ignore
            if getattr(tokenizer, "video_token_id", None)
            else tokenizer.convert_tokens_to_ids(self.video_token) # type: ignore
        )
        super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template)
        self.vision_start_token = (
            "<|vision_start|>" if not hasattr(tokenizer, "vision_start_token") else tokenizer.vision_start_token # type: ignore
        )
        self.vision_end_token = (
            "<|vision_end|>" if not hasattr(tokenizer, "vision_end_token") else tokenizer.vision_end_token # type: ignore
        )
        self.vision_start_token_id = (
            tokenizer.vision_start_token_id # type: ignore
            if getattr(tokenizer, "vision_start_token_id", None)
            else tokenizer.convert_tokens_to_ids(self.vision_start_token) # type: ignore
        )
        self.vision_end_token_id = (
            tokenizer.vision_end_token_id # type: ignore
            if getattr(tokenizer, "vision_end_token_id", None)
            else tokenizer.convert_tokens_to_ids(self.vision_end_token) # type: ignore
        )

    @auto_docstring
    def __call__( # type: ignore
        self,
        images: ImageInput = None, # type: ignore
        text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, # type: ignore
        videos: VideoInput = None, # type: ignore
        **kwargs: Unpack[Qwen3VLProcessorKwargs],
    ) -> BatchFeature:
        r"""
        Returns:
            [`BatchFeature`]: A [`BatchFeature`] with the following fields:

            - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`.
            - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when
              `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not
              `None`).
            - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`.
            - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`.
            - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`.
            - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`.
        """
        output_kwargs = self._merge_kwargs(
            Qwen3VLProcessorKwargs, # type: ignore
            tokenizer_init_kwargs=self.tokenizer.init_kwargs, # type: ignore
            **kwargs,
        )
        if images is not None:
            image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) # type: ignore
            image_grid_thw = image_inputs["image_grid_thw"]
        else:
            image_inputs = {}
            image_grid_thw = None

        if videos is not None:
            videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) # type: ignore
            video_grid_thw = videos_inputs["video_grid_thw"]
            # If user has not requested video metadata, pop it
            if not kwargs.get("return_metadata"):
                video_metadata = videos_inputs.pop("video_metadata")
            else:
                video_metadata = videos_inputs["video_metadata"]
        else:
            videos_inputs = {}
            video_grid_thw = None

        if not isinstance(text, list):
            text = [text]

        text = text.copy()  # below lines change text in-place
        if image_grid_thw is not None:
            merge_length = self.image_processor.merge_size**2 # type: ignore
            index = 0
            for i in range(len(text)):
                while self.image_token in text[i]:
                    num_image_tokens = image_grid_thw[index].prod() // merge_length
                    text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) # type: ignore
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.image_token) # type: ignore

        if video_grid_thw is not None:
            merge_length = self.video_processor.merge_size**2 # type: ignore
            index = 0
            for i in range(len(text)):
                while self.video_token in text[i]:
                    metadata = video_metadata[index] # type: ignore
                    if metadata.fps is None:
                        logger.warning_once( # type: ignore
                            "Qwen3VL requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. "
                            "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. "
                            "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results."
                        )
                        metadata.fps = 24 if metadata.fps is None else metadata.fps

                    # if timestamps are not provided, calculate them
                    curr_timestamp = self._calculate_timestamps(
                        metadata.frames_indices,
                        metadata.fps,
                        self.video_processor.merge_size, # type: ignore
                        self.video_processor.focus_size, # type: ignore
                    )
                    video_placeholder = ""
                    frame_seqlen = video_grid_thw[index][1:].prod() // merge_length
                    for frame_idx in range(video_grid_thw[index][0]):
                        curr_time = curr_timestamp[frame_idx]
                        video_placeholder += f"<{curr_time:.1f} seconds>"
                        video_placeholder += (
                            self.vision_start_token + "<|placeholder|>" * frame_seqlen + self.vision_end_token
                        )
                    if f"{self.vision_start_token}{self.video_token}{self.vision_end_token}" in text[i]:
                        text[i] = text[i].replace( # type: ignore
                            f"{self.vision_start_token}{self.video_token}{self.vision_end_token}", video_placeholder, 1
                        )
                    else:
                        # vllm may input video token directly
                        text[i] = text[i].replace(self.video_token, video_placeholder, 1) # type: ignore
                    index += 1

                text[i] = text[i].replace("<|placeholder|>", self.video_token) # type: ignore

        return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None)
        return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", None)
        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) # type: ignore
        self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"]) # type: ignore

        if return_mm_token_type_ids:
            array_ids = np.array(text_inputs["input_ids"])
            mm_token_type_ids = np.zeros_like(text_inputs["input_ids"])
            mm_token_type_ids[array_ids == self.image_token_id] = 1
            text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist()

        return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors)

    def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs):
        """
        Computes the number of placeholder tokens needed for multimodal inputs with the given sizes.
        Args:
            image_sizes (`list[list[int]]`, *optional*):
                The input sizes formatted as (height, width) per each image.
            video_sizes (`list[list[int]]`, *optional*):
                The input sizes formatted as (num_frames, height, width) per each video.
        Returns:
            `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided
            input modalities, along with other useful data.
        """

        vision_data = {}
        if image_sizes is not None:
            images_kwargs = Qwen3VLProcessorKwargs._defaults.get("images_kwargs", {})
            images_kwargs.update(kwargs)
            merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size # type: ignore

            num_image_patches = [
                self.image_processor.get_number_of_image_patches(*image_size, images_kwargs) # type: ignore
                for image_size in image_sizes
            ]
            num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches]
            vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches})

        if video_sizes is not None:
            videos_kwargs = Qwen3VLProcessorKwargs._defaults.get("videos_kwargs", {})
            videos_kwargs.update(kwargs)
            num_video_patches = [
                self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs) # type: ignore
                for video_size in video_sizes
            ]
            num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches] # type: ignore
            vision_data["num_video_tokens"] = num_video_tokens

        return MultiModalData(**vision_data)

    def post_process_image_text_to_text(
        self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs
    ):
        """
        Post-process the output of the model to decode the text.

        Args:
            generated_outputs (`torch.Tensor` or `np.ndarray`):
                The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)`
                or `(sequence_length,)`.
            skip_special_tokens (`bool`, *optional*, defaults to `True`):
                Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method.
            clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`):
                Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method.
            **kwargs:
                Additional arguments to be passed to the tokenizer's `batch_decode method`.

        Returns:
            `list[str]`: The decoded text.
        """
        return self.tokenizer.batch_decode( # type: ignore
            generated_outputs,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

    def _calculate_timestamps(
        self,
        indices: list[int] | np.ndarray,
        video_fps: float,
        merge_size: int = 2,
        focus_size: int = 2
    ):
        if not isinstance(indices, list):
            indices = indices.tolist()
        if len(indices) % (merge_size * focus_size) != 0:
            indices.extend( # type: ignore
                indices[-1] for _ in range((merge_size * focus_size) - len(indices) % (merge_size * focus_size))
            )
        timestamps = [idx / video_fps for idx in indices]
        # @JJJYmmm frames are merged by self.merge_size, \
        # so we need to average the timestamps between the first/last frame within the temporal patch
        timestamps = [
            (timestamps[i] + timestamps[i + merge_size - 1]) / 2 for i in range(0, len(timestamps), merge_size)
        ]
        return timestamps


__all__ = ["ZFQwen3VLProcessor"]