processing_keye_vl_2.py

# coding=utf-8
# Copyright 2026 The Kwai Keye Team and The HuggingFace Inc. team. All rights reserved.
#
# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
# and OPT implementations in this library. It has been modified from its
# original forms to accommodate minor architectural differences compared
# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import List, Union, Optional

from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.video_utils import VideoInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput
from .image_processing_keye_vl_2 import KeyeVL2ImageProcessor
import torch
import torch.nn as nn
import numpy as np
from itertools import chain

class KeyeVL2VideosProcessorKwargs(VideosKwargs, total=False):
    fps: Optional[Union[List[float], float]]
    # target resize width (slow)
    width: Optional[Union[List[int], int]]
    # target resize height (slow)
    height: Optional[Union[List[int], int]]
    # target resize width (fast)
    fast_width: Optional[Union[List[int], int]]
    # target resize height (fast)
    fast_height: Optional[Union[List[int], int]]
    # per-frame timestamps; length equals the number of frames
    timestamps: Optional[Union[List[torch.Tensor], torch.Tensor]]
    # per-frame type marker: slow=0, fast=1
    frame_types: Optional[Union[List[torch.Tensor], torch.Tensor]]


class KeyeVL2ProcessorKwargs(ProcessingKwargs, total=False):
    videos_kwargs: KeyeVL2VideosProcessorKwargs
    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
        "videos_kwargs": {"fps": 2.0},
    }

def select_slow_fast_frames(frames: torch.Tensor, frame_types: torch.Tensor):
    """
    Selects frames from a tensor based on a mask list.

    Args:
        frames (torch.Tensor): A tensor of shape (nframes, c, h, w).
        frame_types (torch.Tensor): A int tensor of shape (nframes,)

    Returns:
        tuple[torch.Tensor, torch.Tensor]: A tuple containing two tensors:
            - slow_frames: Frames which the type is 0.
            - fast_frames: Frames where the type is 1.
    """
    nframes, _, _, _ = frames.shape
    if frame_types.shape[-1] != nframes:
        raise ValueError("Length of mask must be equal to the number of frames.")

    mask = (frame_types == 0)

    slow_frames = frames[mask]
    fast_frames = frames[~mask]

    return slow_frames, fast_frames

def split_thw(tensor):
    """Split grid_thw in t dimension, the result tensor should like [[1, h, w],...]"""
    repeats = tensor[:, 0]
    new_thw = torch.cat([
        torch.ones(tensor.shape[0], 1, dtype=tensor.dtype,
            device=tensor.device),
        tensor[:, 1:]
    ], dim=1)
    return torch.repeat_interleave(new_thw, repeats, dim=0)

class KeyeVL2Processor(ProcessorMixin):
    r"""
    [`KeyeVL2Processor`] offers all the functionalities of [`KeyeVL2ImageProcessor`] and [`Qwen2TokenizerFast`]. See the
    [`~KeyeVL2Processor.__call__`] and [`~KeyeVL2Processor.decode`] for more information.
    Args:
        image_processor ([`KeyeVL2ImageProcessor`], *optional*):
            The image processor is a required input.
        tokenizer ([`Qwen2TokenizerFast`], *optional*):
            The tokenizer is a required input.
        chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages
            in a chat into a tokenizable string.
    """

    attributes = ["image_processor", "tokenizer"]
    valid_kwargs = [
        "chat_template","image_std", "min_pixels", "image_mean", "merge_size", "image_processor_type",
        "temporal_patch_size", "patch_size", "max_pixels"
    ]

    image_processor_class = "AutoImageProcessor"
    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")

    def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs):
        self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token
        self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token
        self.frame_token = "<|frame|>" if not hasattr(tokenizer, "frame_token") else tokenizer.frame_token
        self.fast_video_token = "<|fast_video_pad|>" if not hasattr(tokenizer, "fast_video_token") else tokenizer.fast_video_token
        self.fast_start = "<|fast_start|>" if not hasattr(tokenizer, "fast_start") else tokenizer.fast_start
        self.fast_end = "<|fast_end|>" if not hasattr(tokenizer, "fast_end") else tokenizer.fast_end
        super().__init__(image_processor, tokenizer, chat_template=chat_template)

        self.slowfast = True

    def __call__(
        self,
        text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,
        images: ImageInput = None,
        videos: VideoInput = None,
        **kwargs: Unpack[KeyeVL2ProcessorKwargs],
    ) -> BatchFeature:
        """
        Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text`
        and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode
        the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to
        KeyeVL2ImageProcessor's [`~KeyeVL2ImageProcessor.__call__`] if `vision_infos` is not `None`.

        Args:
            text (`str`, `List[str]`, `List[List[str]]`):
                The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings
                (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set
                `is_split_into_words=True` (to lift the ambiguity with a batch of sequences).
            images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch
                tensor. Both channels-first and channels-last formats are supported.
            videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`):
                The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch
                tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported.
            return_tensors (`str` or [`~utils.TensorType`], *optional*):
                If set, will return tensors of a particular framework. Acceptable values are:
                - `'tf'`: Return TensorFlow `tf.constant` objects.
                - `'pt'`: Return PyTorch `torch.Tensor` objects.
                - `'np'`: Return NumPy `np.ndarray` objects.
                - `'jax'`: Return JAX `jnp.ndarray` objects.

        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`.
            - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`.
        """
        output_kwargs = self._merge_kwargs(
            KeyeVL2ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        if images is not None:
            image_inputs = self.image_processor(images=images, return_tensors="pt")
            image_inputs['pixel_values'] = image_inputs['pixel_values']
            image_grid_thw = image_inputs["image_grid_thw"]
        else:
            image_inputs = {}
            image_grid_thw = None

        num_frames = []
        if videos is not None:
            batch_slow_frames = []
            batch_fast_frames = []

            videos_kwargs = output_kwargs["videos_kwargs"]
            num_videos = len(videos)
            batch_frame_types = videos_kwargs.get("frame_types", [None] * num_videos)
            batch_timestamps = videos_kwargs.get("timestamps", [None] * num_videos)
            batch_width = videos_kwargs.get("width", [None] * num_videos)
            batch_height = videos_kwargs.get("height", [None] * num_videos)
            batch_fast_width = videos_kwargs.get("fast_width", [None] * num_videos)
            batch_fast_height = videos_kwargs.get("fast_height", [None] * num_videos)

            for index, frames in enumerate(videos):
                if isinstance(frames, np.ndarray):
                    frames = torch.from_numpy(frames)
                nframes = frames.shape[0]
                num_frames.append(nframes)
                assert nframes > 0, "No frames in video"
                if batch_frame_types[index] is None:
                    # default to all slow frames
                    batch_frame_types[index] = torch.zeros((nframes, ), dtype=torch.long)
                frame_types = batch_frame_types[index]
                slow_frames, fast_frames = select_slow_fast_frames(frames, frame_types)
                has_fast_frames = fast_frames.shape[0] > 0
                # resize slow frames
                resized_width = batch_width[index]
                resized_height = batch_height[index]
                if resized_width is not None and resized_height is not None:
                    slow_frames = nn.functional.interpolate(
                        slow_frames,
                        [resized_height, resized_width],
                        mode="bilinear",
                        antialias=True,
                    ).float()
                    do_resize = False
                else:
                    slow_frames = slow_frames.float()
                    do_resize = True
                # Tensor(N, C, H, W) -> Tuple[Tensor(1, C, H, W)]; splitting is done inside the model
                slow_video_inputs = self.image_processor(
                    images=None, videos=[slow_frames], **output_kwargs["images_kwargs"], do_resize=do_resize)
                slow_video_grid_thw = slow_video_inputs["video_grid_thw"]
                batch_slow_frames.append(slow_video_inputs)

                if has_fast_frames:
                    fast_resized_width = batch_fast_width[index]
                    fast_resized_height = batch_fast_height[index]
                    if fast_resized_width is not None and fast_resized_height is not None:
                        fast_frames = nn.functional.interpolate(
                            fast_frames,
                            [fast_resized_height, fast_resized_width],
                            mode="bilinear",
                            antialias=True,
                        ).float()
                        do_fast_resize = False
                    else:
                        fast_frames = fast_frames.float()
                        do_fast_resize = True
                    # Tensor(N, C, H, W) -> Tuple[Tensor(1, C, H, W)]
                    fast_video_inputs = self.image_processor(
                        images=None, videos=[fast_frames], **output_kwargs["images_kwargs"], do_resize=do_fast_resize)
                    fast_video_grid_thw = fast_video_inputs["video_grid_thw"]
                    batch_fast_frames.append(fast_video_inputs)

            assert len(batch_slow_frames) > 0, "Slow frames should not be empty."
            slow_pixel_values_videos_list = [
                video["pixel_values_videos"] for video in batch_slow_frames if video is not None]
            slow_video_grid_thw_list = [
                video["video_grid_thw"] for video in batch_slow_frames if video is not None]

            slow_pixel_values_videos = torch.concat(slow_pixel_values_videos_list, dim=0)
            slow_video_grid_thw = torch.concat(slow_video_grid_thw_list, dim=0)

            if has_fast_frames:
                fast_pixel_values_videos_list = [
                    video["pixel_values_videos"] for video in batch_fast_frames \
                        if video is not None]
                fast_video_grid_thw_list = [
                    video["video_grid_thw"] for video in batch_fast_frames \
                        if video is not None]

                fast_pixel_values_videos = \
                    torch.concat(fast_pixel_values_videos_list, dim=0)
                fast_video_grid_thw = \
                    torch.concat(fast_video_grid_thw_list, dim=0)
            else:
                fast_video_grid_thw = None
        else:
            slow_video_grid_thw = None
            fast_video_grid_thw = None

        if not isinstance(text, list):
            text = [text]
        if image_grid_thw is not None:
            index = 0
            for i in range(len(text)):
                while self.image_token in text[i]:
                    image_place_holder_tempale = "<|placeholder|>" * (
                        image_grid_thw[index].prod() // self.image_processor.merge_size ** 2)
                    text[i] = text[i].replace(
                        self.image_token,
                        image_place_holder_tempale,
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.image_token)

        pixel_values_videos = []
        video_grid_thw = []
        videos_inputs = {}
        if slow_video_grid_thw is not None:
            slow_video_grid_thw = split_thw(slow_video_grid_thw)
            if fast_video_grid_thw is not None:
                fast_video_grid_thw = split_thw(fast_video_grid_thw)
            index = 0
            slow_index = 0
            fast_index = 0
            slow_pixels_index = 0
            fast_pixels_index = 0
            for i in range(len(text)):
                while self.video_token in text[i]:
                    video_place_holder_tempale = ""

                    # timestamp format: HMS <00:00:03>
                    use_hms_format = True

                    for j in range(batch_frame_types[index].shape[-1]):
                        if batch_timestamps[index] is not None:
                            # current frame timestamp value (float)
                            curr_ts = batch_timestamps[index][j]
                            if isinstance(curr_ts, torch.Tensor):
                                curr_ts = curr_ts.item()

                            # --- formatting logic ---
                            if use_hms_format:
                                # format B: HMS <00:00:03>
                                total_seconds = int(curr_ts)
                                m, s = divmod(total_seconds, 60)
                                h, m = divmod(m, 60)
                                # standard HMS shown; add milliseconds yourself if needed
                                time_str = "{:02d}:{:02d}:{:02d}".format(h, m, s)
                                video_place_holder_tempale += self.frame_token + f"<{time_str}>"
                            else:
                                # format A: Seconds <3.0 seconds>
                                video_place_holder_tempale += self.frame_token + f"<{curr_ts:.1f} seconds>"
                        else:
                            # fallback when no timestamps: keep the original frame_token
                            video_place_holder_tempale += self.frame_token


                        # current frame is slow
                        if batch_frame_types[index][j] == 0:
                            num_patches = int(slow_video_grid_thw[slow_index].prod())
                            video_place_holder_tempale += "<|placeholder|>" * (
                                num_patches // self.image_processor.merge_size ** 2)
                            pixel_values_videos.append(
                                slow_pixel_values_videos[slow_pixels_index:slow_pixels_index + num_patches])
                            slow_pixels_index = slow_pixels_index + num_patches
                            video_grid_thw.append(slow_video_grid_thw[slow_index].tolist())
                            slow_index += 1

                        # current frame is fast
                        elif batch_frame_types[index][j] == 1:
                            num_patches = int(fast_video_grid_thw[fast_index].prod()) 
                            video_place_holder_tempale += self.fast_start + "<|placeholder|>" * (
                                num_patches // self.image_processor.merge_size ** 2) + \
                                self.fast_end
                            pixel_values_videos.append(
                                fast_pixel_values_videos[fast_pixels_index:fast_pixels_index + num_patches])
                            fast_pixels_index = fast_pixels_index + num_patches
                            video_grid_thw.append(fast_video_grid_thw[fast_index].tolist())
                            fast_index += 1
                    text[i] = text[i].replace(
                        self.video_token,
                        video_place_holder_tempale,
                        1,
                    )
                    index += 1
                text[i] = text[i].replace("<|placeholder|>", self.video_token)

            videos_inputs["pixel_values_videos"] = torch.cat(pixel_values_videos, dim=0)
            videos_inputs["video_grid_thw"] = torch.tensor(video_grid_thw)
            videos_inputs["num_frames"] = torch.tensor(num_frames)

        text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"])

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

    def batch_decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please
        refer to the docstring of this method for more information.
        """
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        """
        This method forwards all its arguments to Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to
        the docstring of this method for more information.
        """
        return self.tokenizer.decode(*args, **kwargs)

    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(
            generated_outputs,
            skip_special_tokens=skip_special_tokens,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
            **kwargs,
        )

    @property
    def model_input_names(self):
        tokenizer_input_names = self.tokenizer.model_input_names
        image_processor_input_names = self.image_processor.model_input_names
        names_from_processor = list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))
        return names_from_processor



__all__ = ["KeyeVL2Processor"]