processing_penguinvl.py

"""Processor class for PenguinVL."""

import copy
import importlib.util
import os
import os.path as osp
import warnings
from collections import defaultdict
from typing import Any, List, Union, Dict, Optional, Tuple, TypedDict

import cv2
import ffmpeg
import imageio
import json
import math
import numpy as np
import torch
import transformers
from decord import VideoReader, cpu
from einops import rearrange
from torch import nn
from PIL import Image
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack
from transformers.tokenization_utils_base import PreTokenizedInput, TextInput

try:
    from . import image_processing_penguinvl
    from .image_processing_penguinvl import (
        is_valid_image, is_valid_video,
    )
except ModuleNotFoundError:
    spec = importlib.util.spec_from_file_location(
        "image_processing_penguinvl",
        osp.join(osp.dirname(__file__), "image_processing_penguinvl.py"),
    )
    image_processing_penguinvl = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(image_processing_penguinvl)
    is_valid_image = getattr(image_processing_penguinvl, "is_valid_image")
    is_valid_video = getattr(image_processing_penguinvl, "is_valid_video")

# constants
DEFAULT_IMAGE_TOKEN = "<image>"
IGNORE_INDEX = -100

# Type aliases
Conversation = List[Dict[str, Any]]
SingleImage = Union[Image.Image, np.ndarray, torch.Tensor]
SingleVideo = Union[List[SingleImage], np.ndarray, torch.Tensor]
BatchedImage = List[Union[SingleImage, SingleVideo]]
BatchedNamedImage = List[Tuple[str, Union[SingleImage, SingleVideo]]]


def _custom_import(class_name: str):
    try:
        attribute_class = getattr(transformers, class_name)
    except AttributeError:
        if "image" in class_name.lower():
            attribute_class = getattr(image_processing_penguinvl, class_name)
    return attribute_class


def is_named_image(image) -> bool:
    return isinstance(image, (list, tuple)) and \
        len(image) == 2 and \
        isinstance(image[0], str) and \
        image[0] in ["image", "video"] and \
        (is_valid_image(image[1]) or is_valid_video(image[1]))


def make_batched_images(images) -> List[List[ImageInput]]:
    if isinstance(images, (list, tuple)) and all(is_named_image(image) for image in images):
        # list of named images
        return [image[0] for image in images], [image[1] for image in images]
    elif isinstance(images, (list, tuple)) and all(is_valid_image(image) or is_valid_video(image) for image in images):
        # list of images/videos
        batch = []
        for image in images:
            if is_valid_video(image):
                batch.append(("video", image))
            elif is_valid_image(image):
                batch.append(("image", image))
            else:
                raise ValueError(f"Could not make batched images from {images}")
        return [x[0] for x in batch], [x[1] for x in batch]
    elif is_named_image(images):
        # named images
        return [images[0]], [image[1]]
    elif is_valid_video(images):
        # single video
        return ["video"], [images]
    elif is_valid_image(images):
        # single image
        return ["image"], [images]

    raise ValueError(f"Could not make batched images from {images}")


def frame_sample(duration, mode='uniform', num_frames=None, vid_fps=None, fps=None):
    if mode == 'uniform':
        assert num_frames is not None, "Number of frames must be provided for uniform sampling."
        if duration <= num_frames:
            return np.arange(duration).astype(int)
        # NOTE: v1 version
        # Calculate the size of each segment from which a frame will be extracted
        # if duration <= num_frames:
        #     return np.arange(duration).astype(int)
        # seg_size = float(duration - 1) / num_frames

        # frame_ids = []
        # for i in range(num_frames):
        #     # Calculate the start and end indices of each segment
        #     start = seg_size * i
        #     end   = seg_size * (i + 1)
        #     # Append the middle index of the segment to the list
        #     frame_ids.append((start + end) / 2)

        # return np.round(np.array(frame_ids) + 1e-6).astype(int)
        # NOTE: v0 version
        return np.linspace(0, duration-1, num_frames, dtype=int)
    elif mode == 'fps':
        assert vid_fps is not None, "FPS must be provided for FPS sampling."
        assert fps is not None, "FPS must be provided for FPS sampling."
        segment_len = min(vid_fps // fps, duration)
        return np.arange(segment_len // 2, duration, segment_len, dtype=int)
    else:
        raise ImportError(f'Unsupported frame sampling mode: {mode}')


def load_video_from_ids(video_path, s=None, e=None, fps=None, max_frames=128, temporal_factor=1):
    if s is not None and e is not None:
        s = s if s >= 0. else 0.
        e = e if e >= 0. else 0.
        if s > e:
            s, e = e, s
        elif s == e:
            e = s + 1

    # 1. Loading Video
    if os.path.isdir(video_path):
        frame_files = sorted(os.listdir(video_path))

        vid_fps = 3
        num_frames_of_video = len(frame_files)
    elif video_path.endswith('.gif'):
        gif_reader = imageio.get_reader(video_path)

        vid_fps = 25
        num_frames_of_video = len(gif_reader)
    else:
        vreader = VideoReader(video_path, ctx=cpu(0), num_threads=2)
        # vreader = VideoReader(video_path, ctx=cpu(0), num_threads=1)

        vid_fps = vreader.get_avg_fps()
        num_frames_of_video = len(vreader)

    # 2. Determine frame range & Calculate frame indices
    f_start = 0                       if s is None else max(int(s * vid_fps) - 1, 0)
    f_end   = num_frames_of_video - 1 if e is None else min(int(e * vid_fps) - 1, num_frames_of_video - 1)
    frame_indices = list(range(f_start, f_end + 1))

    duration = len(frame_indices)
    # 3. Sampling frame indices
    if fps is not None and duration / vid_fps < max_frames:
        sampled_frame_indices = [frame_indices[i] for i in frame_sample(duration, mode='fps', vid_fps=vid_fps, fps=fps)]
    else:
        sampled_frame_indices = [frame_indices[i] for i in frame_sample(duration, mode='uniform', num_frames=max_frames)]

    # 4. Acquire frame data
    if os.path.isdir(video_path):
        frames = np.array([cv2.cvtColor(cv2.imread(os.path.join(video_path, frame_files[frame_idx])), cv2.COLOR_BGR2RGB) for frame_idx in sampled_frame_indices])
    elif video_path.endswith('.gif'):
        frames = np.array([cv2.cvtColor(frame, cv2.COLOR_RGBA2RGB) for idx, frame in enumerate(gif_reader) if idx in sampled_frame_indices])
    else:
        frames = vreader.get_batch(sampled_frame_indices).asnumpy()

    frames = frames.transpose(0, 3, 1, 2)
    timestamps = [x / vid_fps for x in sampled_frame_indices]

    if temporal_factor > 1:
        pad_length = temporal_factor - len(frames) % temporal_factor
        frames = np.concatenate([frames, frames[-1:].repeat(pad_length, axis=0)])
        [timestamps.append(timestamps[-1] + 1 / fps) for _ in range(pad_length)]

    frames = [frame for frame in frames]

    return frames, timestamps



def round_by_factor(number: int, factor: int) -> int:
    """Returns the closest integer to 'number' that is divisible by 'factor'."""
    return round(number / factor) * factor


def ceil_by_factor(number: int, factor: int) -> int:
    """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
    return math.ceil(number / factor) * factor


def floor_by_factor(number: int, factor: int) -> int:
    """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
    return math.floor(number / factor) * factor

def smart_resize(
    height: int,
    width: int,
    factor: int = 14,
    min_pixels: int = 0,
    max_pixels: int = 16384,
):
    """
    Compute target (height, width) such that:
    - Both dimensions are divisible by factor.
    - Total pixels lie in [min_pixels, max_pixels].
    - Aspect ratio is preserved as closely as possible.
    """
    def round_by_factor(number: int, factor: int) -> int:
        """Returns the closest integer to 'number' that is divisible by 'factor'."""
        return round(number / factor) * factor
    def ceil_by_factor(number: int, factor: int) -> int:
        """Returns the smallest integer greater than or equal to 'number' that is divisible by 'factor'."""
        return math.ceil(number / factor) * factor
    def floor_by_factor(number: int, factor: int) -> int:
        """Returns the largest integer less than or equal to 'number' that is divisible by 'factor'."""
        return math.floor(number / factor) * factor

    max_ratio = 200
    if max(height, width) / min(height, width) > max_ratio:
        raise ValueError(
            f"Aspect ratio must be < {max_ratio}, got {max(height, width) / min(height, width)}"
        )
    h = max(factor, round_by_factor(height, factor))
    w = max(factor, round_by_factor(width, factor))
    if h * w > max_pixels:
        scale = math.sqrt((height * width) / max_pixels)
        h = floor_by_factor(height / scale, factor)
        w = floor_by_factor(width / scale, factor)
    elif h * w < min_pixels:
        scale = math.sqrt(min_pixels / (height * width))
        h = ceil_by_factor(height * scale, factor)
        w = ceil_by_factor(width * scale, factor)
    return max(h, factor), max(w, factor)

# Adapted from Keye-VL: https://github.com/Kwai-Keye/Keye
def get_frame_sim(
    frame1: torch.Tensor,
    frame2: torch.Tensor,
    patch_size: int = 14,
    threshold: float = 0.7,
    epsilon: float = 1e-8,
) -> float:
    """Cosine similarity between two frames in HSV, averaged over patches. Returns mean similarity in [0, 1]."""
    assert frame1.dim() == 3 and frame2.dim() == 3, "Frames must be 3D tensors [C, H, W]"

    def to_hsv_tensor(tensor: torch.Tensor) -> torch.Tensor:
        arr = tensor.cpu().permute(1, 2, 0).numpy()
        if arr.dtype in (np.float32, np.float64):
            arr = arr.astype(np.uint8)
        hsv = cv2.cvtColor(arr, cv2.COLOR_RGB2HSV)
        return torch.from_numpy(hsv).permute(2, 0, 1).to(tensor.device).float()

    f1 = to_hsv_tensor(frame1)
    f2 = to_hsv_tensor(frame2)
    patch1 = rearrange(f1, "c (h p1) (w p2) -> h w (c p1 p2)", p1=patch_size, p2=patch_size).float()
    patch2 = rearrange(f2, "c (h p1) (w p2) -> h w (c p1 p2)", p1=patch_size, p2=patch_size).float()

    norm1 = torch.norm(patch1, p=2, dim=-1, keepdim=True) + epsilon
    norm2 = torch.norm(patch2, p=2, dim=-1, keepdim=True) + epsilon
    cos_sim = (patch1 / norm1 * patch2 / norm2).sum(dim=-1)

    both_near_zero = (norm1.squeeze() < 0.01) & (norm2.squeeze() < 0.01)
    similar = torch.ones_like(cos_sim)
    similar[~both_near_zero] = (cos_sim[~both_near_zero] > threshold).float()
    return similar[~both_near_zero].float().mean().item()


# KI: keyframe indices (formerly slow/fast). 0 = key frame, 1 = intermediate frame.
K_PATCH = 14
K_MIN_PIXELS = 10 * 14 * 14
K_MAX_PIXELS = 10240 * 14 * 14

def extract_ki_frames(
    frames: torch.Tensor,
    threshold: float = MIN_FRAME_SIMILARITY,
) -> list:
    """
    Label each frame as keyframe (0) or non-keyframe (1) by comparing to the previous keyframe.
    First frame is always a keyframe; a new keyframe is chosen when similarity drops below threshold.
    """
    assert frames.dim() == 4, "Frames must be 4D tensor [N, C, H, W]"

    def _keyframe_indices(f: torch.Tensor) -> list:
        indices = [0]
        key = f[0]
        for i in range(1, f.size(0)):
            if get_frame_sim(key, f[i]) < threshold:
                indices.append(i)
                key = f[i]
        return indices

    _, _, h, w = frames.shape
    rh, rw = smart_resize(h, w, factor=K_PATCH, min_pixels=K_MIN_PIXELS, max_pixels=K_MAX_PIXELS)
    resized = nn.functional.interpolate(frames, (rh, rw), mode="bilinear", antialias=True).float()
    k_indices = _keyframe_indices(resized)
    frame_types = torch.ones(frames.size(0), dtype=torch.int32)
    frame_types[k_indices] = 0
    return frame_types.tolist()


class ChatTemplateKwargs(TypedDict, total=False):

    chat_template: Optional[str]
    add_system_prompt: Optional[bool]
    add_generation_prompt: Optional[bool]


class PenguinVLQwen3ProcessorKwargs(ProcessingKwargs, ChatTemplateKwargs, total=False):

    chat_template_kwargs: ChatTemplateKwargs = {
        **ChatTemplateKwargs.__annotations__,
    }

    _defaults = {
        "text_kwargs": {
            "padding": False,
        },
        "image_kwargs": {
            "merge_size": None,
        },
        "chat_template_kwargs": {
            "chat_template": None,
            "add_system_prompt": False,
            "add_generation_prompt": False,
        },
    }


class PenguinVLQwen3Processor(ProcessorMixin):

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "PenguinVLImageProcessor"
    tokenizer_class = ("Qwen2Tokenizer", "Qwen2TokenizerFast")
    valid_kwargs = ["chat_template", "image_merge_size", "video_merge_size", "fps", "max_frames"]

    def __init__(
        self,
        image_processor=None,
        tokenizer=None,
        chat_template: str = None,
        image_merge_size: int = 1,
        video_merge_size: int = 2,
        fps: Optional[int] = 1,
        max_frames: Optional[int] = 128,
        use_codec = False,
    ):
        self.image_processor = image_processor
        self.tokenizer = tokenizer
        if chat_template is None:
            chat_template = self.tokenizer.chat_template
        self.chat_template = chat_template

        self.image_merge_size = image_merge_size
        self.video_merge_size = video_merge_size
        self.fps = fps
        self.max_frames = max_frames
        self.use_codec = use_codec
        self.generation_prompt = self._infer_generation_prompt()
        self.generation_prompt_ids = self.tokenizer.encode(self.generation_prompt, return_tensors="pt")
        self.generation_prompt_length = len(self.generation_prompt_ids[0])
        self.image_token_id = self.tokenizer.convert_tokens_to_ids(DEFAULT_IMAGE_TOKEN)
        self.eos_token_id = self.tokenizer.eos_token_id

    @classmethod
    def _get_arguments_from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
        args = []
        for attribute_name in cls.attributes:
            class_name = getattr(cls, f"{attribute_name}_class")
            if isinstance(class_name, tuple):
                classes = tuple(_custom_import(n) if n is not None else None for n in class_name)
                use_fast = kwargs.get("use_fast", True)
                if use_fast and classes[1] is not None:
                    attribute_class = classes[1]
                else:
                    attribute_class = classes[0]
            else:
                attribute_class = _custom_import(class_name)

            args.append(attribute_class.from_pretrained(pretrained_model_name_or_path, **kwargs))
        return args

    def get_generation_prompt(self):
        return self.generation_prompt

    def get_generation_prompt_ids(self):
        return self.generation_prompt_ids

    def _infer_generation_prompt(self):
        pseudo_message = [{"role": "user", "content": ""}]
        instruction = self.apply_chat_template(pseudo_message, tokenize=False, add_generation_prompt=True)
        conversation = self.apply_chat_template(pseudo_message, tokenize=False, add_generation_prompt=False)
        return instruction.replace(conversation, "")

    def _get_downsampled_grid_sizes(self, image_inputs: Dict[str, Any]):
        grid_sizes = []
        for grid_size, merge_size in zip(image_inputs.get("grid_sizes", []), image_inputs.get("merge_sizes", [])):
            if not torch.all(grid_size[1:] % merge_size == 0):
                warnings.warn(f"Grid size {grid_size} is not divisible by merge size. Some undesired errors may occur.")
            if grid_size[0] == 1:
                grid_sizes.append(grid_size[1:] / merge_size)
            elif grid_size[0] > 1:
                grid_sizes.extend([grid_size[1:] / merge_size] * grid_size[0])
        return grid_sizes

    def _get_visual_seq_len(self, grid_size: torch.Tensor):
        num_tokens = int(grid_size.prod().item())
        return num_tokens

    def load_images(self, image_path: Union[str, List[str], Image.Image, List[Image.Image]]):
        if isinstance(image_path, str) and os.path.isfile(image_path):
            # images = [cv2.cvtColor(cv2.imread(image_path), cv2.COLOR_BGR2RGB)]
            images = [Image.open(image_path).convert('RGB')]
        elif isinstance(image_path, str) and os.path.isdir(image_path):
            # images = [cv2.cvtColor(cv2.imread(os.path.join(image_path, f)), cv2.COLOR_BGR2RGB) for f in sorted(os.listdir(image_path))]
            images = [Image.open(os.path.join(image_path, f)).convert('RGB') for f in sorted(os.listdir(image_path))]
        elif isinstance(image_path, list) and isinstance(image_path[0], str):
            # images = [cv2.cvtColor(cv2.imread(f), cv2.COLOR_BGR2RGB) for f in image_path]
            images = [Image.open(f).convert('RGB') for f in image_path]
        elif isinstance(image_path, list) and isinstance(image_path[0], Image.Image):
            images = [np.array(x) for x in image_path]
        elif isinstance(image_path, Image.Image):
            images = [np.array(image_path)]
        else:
            raise ValueError(f"Unsupported image path type: {type(image_path)}")
        return images

    def load_video(
        self,
        video_path: str,
        start_time: Optional[float] = None,
        end_time: Optional[float] = None,
        fps: Optional[float] = None,
        max_frames: Optional[float] = None,
        size: Optional[int] = None,
        size_divisible: int = 1,
        precise_time: bool = False,
        verbose: bool = False,
        temporal_factor: int = 1
    ):
        """
        Load and process a video file and return the frames and the timestamps of each frame.
        Args:
            video_path (str): Path to the video file.
            start_time (float, optional): Start time in seconds. Defaults to None.
            end_time (float, optional): End time in seconds. Defaults to None.
            fps (float, optional): Frames per second. Defaults to None.
            num_frames (float, optional): Number of frames to sample. Defaults to None.
            size (int, optional): Size of the shortest side. Defaults to None.
            size_divisible (int, optional): Size divisible by this number. Defaults to 1.
            precise_time (bool, optional): Whether to use precise time. Defaults to False.
            verbose (bool, optional): Print ffmpeg output. Defaults to False.
        Returns:
            frames (List[PIL.Image]): List of frames.
            timestamps (List[float]): List of timestamps.
        """
        if self.use_codec:
            return self.load_video_with_codec(**locals())
        fps = self.fps if fps is None else fps
        max_frames = self.max_frames if max_frames is None else max_frames

        if start_time is not None and end_time is not None and end_time - start_time < 1:
            return load_video_from_ids(video_path, start_time, end_time, fps=fps, max_frames=max_frames)
        if os.path.isdir(video_path):
            return load_video_from_ids(video_path, start_time, end_time, fps=fps, max_frames=max_frames)
        if video_path.endswith('.gif'):
            return load_video_from_ids(video_path, start_time, end_time, fps=fps, max_frames=max_frames)
        probe = ffmpeg.probe(video_path)
        duration = float(probe['format']['duration'])
        video_stream = next((stream for stream in probe['streams'] if stream['codec_type'] == 'video'), None)
        w, h = int(video_stream['width']), int(video_stream['height'])

        kwargs, input_kwargs, output_kwargs = {}, {}, {}
        do_trim = start_time is not None or end_time is not None
        if start_time is not None:
            new_start_time = max(float(video_stream['start_time']), start_time)
            duration -= new_start_time - start_time
            start_time = new_start_time
        else:
            start_time = float(video_stream['start_time'])
        if end_time is not None:
            duration = min(duration, end_time - start_time)
        else:
            duration = duration
        if do_trim:
            kwargs = {'ss': start_time, 't': duration}
        if precise_time:
            output_kwargs.update(kwargs)
        else:
            input_kwargs.update(kwargs)

        if size is not None:
            scale_factor = size / min(w, h)
            new_w, new_h = round(w * scale_factor), round(h * scale_factor)
        else:
            new_w, new_h = w, h
        new_w = new_w // size_divisible * size_divisible
        new_h = new_h // size_divisible * size_divisible

        # NOTE: It may result in unexpected number of frames in ffmpeg
        # if calculate the fps directly according to max_frames
        # if max_frames is not None and (fps is None or duration * fps > 2 * max_frames):
        #     fps = round(max_frames / duration * 2)

        stream = ffmpeg.input(video_path, **input_kwargs)
        if fps is not None:
            stream = ffmpeg.filter(stream, "fps", fps=fps, round="down")
        if new_w != w or new_h != h:
            stream = ffmpeg.filter(stream, 'scale', new_w, new_h)
        stream = ffmpeg.output(stream, "pipe:", format="rawvideo", pix_fmt="rgb24", **output_kwargs)
        out, _ = ffmpeg.run(stream, capture_stdout=True, quiet=not verbose)

        frames = np.frombuffer(out, np.uint8).reshape([-1, new_h, new_w, 3]).transpose([0, 3, 1, 2])

        if fps is not None:
            timestamps = np.arange(start_time, start_time + duration + 1 / fps, 1 / fps)[:len(frames)]
        else:
            timestamps = np.linspace(start_time, start_time + duration, len(frames))

        if max_frames is not None and len(frames) > max_frames:
            indices = np.linspace(0, len(frames) - 1, max_frames, dtype=int)
            frames = frames[indices]
            timestamps = timestamps[indices]

        if temporal_factor > 1:
            pad_length = temporal_factor - len(frames) % temporal_factor
            frames = np.concatenate([frames, frames[-1:].repeat(pad_length, axis=0)])
            timestamps = np.concatenate([timestamps, timestamps[-1:].repeat(pad_length) + np.arange(1, pad_length + 1) / fps])

        frames_tensor = torch.from_numpy(frames.copy()).float()
        frame_types = extract_ki_frames(frames_tensor)

        frames = [frame for frame in frames]
        timestamps = [timestamp for timestamp in timestamps]

        return frames, timestamps, frame_types
    
    def load_video_with_codec(
        self,
        video_path: str,
        start_time: Optional[float] = None,
        end_time: Optional[float] = None,
        fps: Optional[float] = None,
        max_frames: Optional[float] = None,
        size: Optional[int] = None,
        size_divisible: int = 1,
        precise_time: bool = False,
        verbose: bool = False,
        temporal_factor: int = 1,
        slow_fast: bool = True
    ):
        """
        Load a video by prioritizing I-frames (keyframes) and dynamically sampling
        additional frames between adjacent I-frames up to `max_frames`.
        Notes:
        - Real codec I-frames (keyframes) are always used as-is and do NOT follow `fps`.
        - If `fps` is provided, it controls how we sample additional non-I frames between
        adjacent I-frames (and still respects `max_frames`).
        - This function does NOT call `load_video_from_ids`.
        Returns:
            frames: List[np.ndarray] where each is CHW (3, H, W) uint8
            timestamps: List[float] timestamps in seconds for each returned frame
            frame_types: List[int] where 0 = I-frame (keyframe), 1 = non-I-frame (sampled)
        """
        return_frame_types = slow_fast
        max_frames = int(max_frames if max_frames is not None else self.max_frames)
        if max_frames <= 0:
            return ([], [], []) if return_frame_types else ([], [])

        def _coerce_range(s: Optional[float], e: Optional[float]):
            if s is not None and e is not None:
                s = s if s >= 0.0 else 0.0
                e = e if e >= 0.0 else 0.0
                if s > e:
                    s, e = e, s
                elif s == e:
                    e = s + 1.0
            return s, e

        # Fallbacks for non-standard "videos"
        if os.path.isdir(video_path):
            # Directory input is a sequence of images; there is no keyframe/I-frame concept.
            # We mimic `load_video_from_ids` semantics: interpret start/end in seconds using a
            # small assumed FPS, then uniformly sample up to `max_frames` within that range.
            start_time, end_time = _coerce_range(start_time, end_time)
            dir_fps = 3.0

            all_entries = sorted(os.listdir(video_path))
            frame_files = []
            for name in all_entries:
                p = os.path.join(video_path, name)
                if not os.path.isfile(p):
                    continue
                if not name.lower().endswith((".jpg", ".jpeg", ".png", ".bmp", ".webp")):
                    continue
                frame_files.append(name)

            if len(frame_files) == 0:
                return ([], [], []) if return_frame_types else ([], [])

            num_frames_of_video = len(frame_files)
            f_start = 0 if start_time is None else max(int(start_time * dir_fps) - 1, 0)
            f_end = (num_frames_of_video - 1) if end_time is None else min(int(end_time * dir_fps) - 1, num_frames_of_video - 1)
            if f_end < f_start:
                return ([], [], []) if return_frame_types else ([], [])

            frame_indices = list(range(f_start, f_end + 1))
            duration = len(frame_indices)
            sampled = frame_sample(duration, mode="uniform", num_frames=max_frames)
            sampled_frame_indices = [frame_indices[i] for i in sampled.tolist()]

            frames = []
            timestamps = []
            for i in sampled_frame_indices:
                img = cv2.imread(os.path.join(video_path, frame_files[i]))
                if img is None:
                    continue
                frames.append(cv2.cvtColor(img, cv2.COLOR_BGR2RGB).transpose(2, 0, 1))
                timestamps.append(float(i) / dir_fps)

            # No keyframe concept for image directories; treat all as non-keyframes.
            frame_types = [1] * len(frames)
            return (frames, timestamps, frame_types) if return_frame_types else (frames, timestamps)

        if video_path.endswith('.gif'):
            gif_reader = imageio.get_reader(video_path)
            num_frames_of_video = len(gif_reader)
            if num_frames_of_video == 0:
                return ([], [], []) if return_frame_types else ([], [])
            n = min(max_frames, num_frames_of_video)
            idxs = np.linspace(0, num_frames_of_video - 1, n, dtype=int).tolist()
            frames = [
                cv2.cvtColor(frame, cv2.COLOR_RGBA2RGB).transpose(2, 0, 1)
                for idx, frame in enumerate(gif_reader) if idx in set(idxs)
            ]
            # crude timestamps for gif; i-frame concept not applicable
            timestamps = [float(i) for i in range(len(frames))]
            # GIF frames are intra-coded; treat them as keyframes.
            frame_types = [0] * len(frames)
            return (frames, timestamps, frame_types) if return_frame_types else (frames, timestamps)

        def _get_video_stream_info(path: str):
            probe = ffmpeg.probe(path)
            fmt_duration = float(probe["format"]["duration"])
            vstream = next((st for st in probe["streams"] if st.get("codec_type") == "video"), None)
            if vstream is None:
                raise ValueError(f"No video stream found in: {path}")
            w, h = int(vstream["width"]), int(vstream["height"])
            stream_start = float(vstream.get("start_time") or 0.0)
            return probe, vstream, fmt_duration, (w, h), stream_start

        def _safe_float(x) -> Optional[float]:
            if x is None:
                return None
            try:
                return float(x)
            except Exception:
                return None

        def _get_iframe_timestamps(path: str, s: float, e: float) -> List[float]:
            """
            Return sorted I-frame timestamps within [s, e].
            Uses ffprobe with skip_frame=nokey to avoid scanning all frames.
            """
            try:
                p = ffmpeg.probe(
                    path,
                    select_streams="v:0",
                    skip_frame="nokey",
                    show_frames=None,
                    show_entries="frame=pict_type,pkt_pts_time,best_effort_timestamp_time,key_frame,pkt_size",
                    of="json",
                )
            except ffmpeg.Error as ex:
                print("ffprobe keyframe scan failed:", ex)
                return []
            frames_meta = p.get("frames") or []
            out_ts = []
            for fr in frames_meta:
                # Prefer pict_type == I; fall back to key_frame == 1 if pict_type missing.
                pict_type = fr.get("pict_type")
                is_i = (pict_type == "I") or (pict_type is None and str(fr.get("key_frame")) == "1")
                if not is_i:
                    continue
                ts = _safe_float(fr.get("pkt_pts_time"))
                if ts is None:
                    ts = _safe_float(fr.get("best_effort_timestamp_time"))
                if ts is None:
                    continue
                if ts < s or ts > e:
                    continue
                size_bytes = int(fr.get("pkt_size", 0))
                out_ts.append((ts, size_bytes))

            out_ts.sort(key=lambda x: x[0])
            out_sizes = [x[1] for x in out_ts]
            return [x[0] for x in out_ts], out_sizes

        def _normalize_uint8_nchw(data: torch.Tensor) -> torch.Tensor:
            """
            Ensure tensor is NCHW uint8 on CPU with values in [0, 255].
            torchcodec may return float in [0,1] or [0,255] depending on backend.
            """
            if not isinstance(data, torch.Tensor):
                raise TypeError(f"Expected torch.Tensor, got {type(data)}")
            if data.ndim != 4:
                raise ValueError(f"Expected NCHW tensor, got shape {tuple(data.shape)}")
            if data.device.type != "cpu":
                data = data.cpu()
            if data.dtype != torch.uint8:
                d = data
                if d.is_floating_point():
                    mx = float(d.max().item()) if d.numel() > 0 else 0.0
                    if mx <= 1.0 + 1e-6:
                        d = d * 255.0
                    d = d.round()
                data = d.clamp(0, 255).to(torch.uint8)
            return data


        def _allocate_remaining_floor_ratio(widths: np.ndarray, remaining: int) -> list[int]:
            """
            Allocate `remaining` frames across windows proportionally by window width using floor,
            without redistributing leftover.
            This matches the spec:
            - prioritize large I-frame windows
            - use floor so the sum does not exceed `remaining`
            """
            nwin = int(widths.shape[0])
            if nwin == 0 or remaining <= 0:
                return [0] * nwin
            widths = np.maximum(widths.astype(float), 0.0)
            wsum = float(widths.sum())
            if wsum <= 0.0:
                return [0] * nwin
            alloc = np.floor(float(remaining) * (widths / wsum)).astype(int)
            # Defensive clamp (should already be <= remaining by construction)
            s = int(alloc.sum())
            if s > remaining:
                # remove extras from smallest windows first
                order = np.argsort(widths)  # ascending
                i = 0
                while s > remaining and i < nwin:
                    j = int(order[i])
                    if alloc[j] > 0:
                        alloc[j] -= 1
                        s -= 1
                    else:
                        i += 1
            return alloc.tolist()

        def _uniform_inside(a: float, b: float, k: int) -> List[float]:
            """k points uniformly spaced inside (a, b), excluding endpoints."""
            if k <= 0:
                return []
            if b <= a:
                return []
            step = (b - a) / (k + 1)
            return [a + step * (j + 1) for j in range(k)]

        def _sample_inside_fps(a: float, b: float, fps_val: float) -> List[float]:
            """Sample points at `fps_val` within (a, b), excluding endpoints."""
            if fps_val is None:
                return []
            try:
                fps_f = float(fps_val)
            except Exception:
                return []
            if not (fps_f > 0.0):
                return []
            if b <= a:
                return []
            step = 1.0 / fps_f
            t = a + step
            out = []
            # avoid producing a huge list if `fps` is absurd; we'll downsample anyway,
            # but keep a reasonable cap based on the window size.
            # (This cap is still safe because we always keep I-frames.)
            max_points = int(max(0.0, (b - a) * fps_f)) + 2
            n = 0
            while t < b and n < max_points:
                out.append(float(t))
                t += step
                n += 1
            return out

        start_time, end_time = _coerce_range(start_time, end_time)
        probe, video_stream, fmt_duration, (w, h), stream_start = _get_video_stream_info(video_path)

        # Use absolute timestamps in seconds.
        if start_time is None:
            start_time = float(stream_start)
        else:
            start_time = max(float(stream_start), float(start_time))

        if end_time is None:
            end_time = float(stream_start) + float(fmt_duration)
        else:
            end_time = float(end_time)

        if end_time <= start_time:
            end_time = start_time + 1e-3

        # Output scaling (same logic as `load_video`)
        if size is not None:
            scale_factor = size / min(w, h)
            new_w, new_h = round(w * scale_factor), round(h * scale_factor)
        else:
            new_w, new_h = w, h
        new_w = new_w // size_divisible * size_divisible
        new_h = new_h // size_divisible * size_divisible

        # 1) Extract all I-frames in [start_time, end_time]
        iframe_ts, iframe_sizes = _get_iframe_timestamps(video_path, start_time, end_time)

        # 2) Decide timestamps to decode, and frame_types aligned to timestamps
        timestamps: List[float] = []
        frame_types: List[int] = []

        if len(iframe_ts) == 0:
            # No I-frames detected by ffprobe (rare / container oddities). Fall back to uniform time sampling.
            if end_time <= start_time:
                return ([], [], []) if return_frame_types else ([], [])
            if fps is None:
                n = max_frames
                timestamps = np.linspace(start_time, end_time, n, endpoint=False, dtype=float).tolist()
            else:
                try:
                    fps_f = float(fps)
                except Exception:
                    fps_f = 0.0
                if fps_f > 0.0:
                    step = 1.0 / fps_f
                    timestamps = np.arange(start_time, end_time, step, dtype=float).tolist()
                    if len(timestamps) > max_frames:
                        idxs = np.linspace(0, len(timestamps) - 1, max_frames, dtype=int).tolist()
                        idxs = list(dict.fromkeys(idxs))
                        timestamps = [timestamps[i] for i in idxs][:max_frames]
                else:
                    timestamps = np.linspace(start_time, end_time, max_frames, endpoint=False, dtype=float).tolist()
            # No I-frames detected; treat all as non-keyframes.
            frame_types = [1] * len(timestamps)
        elif len(iframe_ts) >= max_frames:
            # Too many I-frames: uniformly sample among all available keyframes.
            idxs = np.linspace(0, len(iframe_ts) - 1, max_frames, dtype=int).tolist()
            idxs = list(dict.fromkeys(idxs))
            if len(idxs) != max_frames:
                missing = max_frames - len(idxs)
                all_idxs = np.arange(len(iframe_ts), dtype=int).tolist()
                remain = [i for i in all_idxs if i not in set(idxs)]
                if len(remain) > 0 and missing > 0:
                    fill = np.linspace(0, len(remain) - 1, missing, dtype=int).tolist()
                    idxs.extend([remain[i] for i in fill])
                idxs = sorted(idxs)[:max_frames]
            timestamps = [iframe_ts[i] for i in idxs]
            frame_types = [0] * len(timestamps)
        else:
            # Use all I-frames, then allocate remaining between adjacent I-frames.
            timestamps = list(iframe_ts)
            frame_types = [0] * len(iframe_ts)
            remaining = max_frames - len(iframe_ts)

            if len(iframe_ts) >= 2 and remaining > 0:
                left = np.array(iframe_ts[:-1], dtype=float)
                right = np.array(iframe_ts[1:], dtype=float)

                widths = (right - left).astype(float)
                extra_ts: List[float] = []
                if fps is None:
                    # Spec: allocate remaining frames by window size ratio using floor (no leftover redistribution).
                    alloc = _allocate_remaining_floor_ratio(widths, remaining)
                    for a, b, k in zip(left.tolist(), right.tolist(), alloc):
                        extra_ts.extend(_uniform_inside(float(a), float(b), int(k)))
                else:
                    # Spec: prioritize large windows; sample at fixed fps inside each window until `max_frames` is reached
                    # or all windows are exhausted.
                    order = np.argsort(-widths).tolist()  # descending widths
                    rem = int(remaining)
                    for j in order:
                        if rem <= 0:
                            break
                        a = float(left[j])
                        b = float(right[j])
                        cand = _sample_inside_fps(a, b, fps)
                        if len(cand) == 0:
                            continue
                        if len(cand) > rem:
                            cand = cand[:rem]
                        extra_ts.extend(cand)
                        rem -= len(cand)

                # Drop samples too close to any I-frame timestamp to avoid collisions at decode.
                if len(extra_ts) > 0:
                    iframe_set = [float(x) for x in iframe_ts]
                    def _far_from_iframes(t: float) -> bool:
                        return all(abs(float(t) - it) > 1e-3 for it in iframe_set)
                    extra_ts = [t for t in extra_ts if _far_from_iframes(t)]

                timestamps.extend(extra_ts)
                frame_types.extend([1] * len(extra_ts))
            elif remaining > 0:
                # Only 1 I-frame: sample the rest uniformly across the range, avoiding exact collision.
                if end_time > start_time:
                    it = float(iframe_ts[0])
                    if fps is None:
                        extra_ts = np.linspace(start_time, end_time, remaining + 2, endpoint=True, dtype=float)[1:-1].tolist()
                    else:
                        extra_ts = _sample_inside_fps(float(start_time), float(end_time), fps)
                        # Keep at most `remaining` samples.
                        if len(extra_ts) > remaining and remaining > 0:
                            idxs = np.linspace(0, len(extra_ts) - 1, remaining, dtype=int).tolist()
                            idxs = list(dict.fromkeys(idxs))
                            extra_ts = [extra_ts[i] for i in idxs][:remaining]
                        elif remaining <= 0:
                            extra_ts = []

                    # drop timestamps extremely close to the I-frame timestamp
                    extra_ts = [t for t in extra_ts if abs(float(t) - it) > 1e-3]
                    # if we dropped some, refill with tiny offsets (to preserve count behavior)
                    while len(extra_ts) < remaining:
                        extra_ts.append(min(end_time, max(start_time, it + 1e-3 * (len(extra_ts) + 1))))
                    timestamps.extend(extra_ts[:remaining])
                    frame_types.extend([1] * min(remaining, len(extra_ts)))

            # Sort by time and keep types aligned
            order = np.argsort(np.array(timestamps, dtype=float)).tolist()
            timestamps = [float(timestamps[i]) for i in order]
            frame_types = [int(frame_types[i]) for i in order]

        # 3) Decode frames at chosen timestamps with torchcodec (batch decode).
        #    We keep the same return format: List[np.ndarray] CHW uint8.
        if len(timestamps) == 0:
            return ([], [], []) if return_frame_types else ([], [])

        try:
            from torchcodec.decoders import VideoDecoder  # type: ignore
        except Exception as ex:
            raise ImportError(
                "torchcodec is required for video decoding in mm_utils.load_video. "
                "Please install torchcodec (https://github.com/pytorch/torchcodec)."
            ) from ex

        # if precise_time and verbose:
        #     # torchcodec selects frames at/around the requested playback times; there's no ffmpeg-style
        #     # input-vs-output seek mode. We keep the flag for API compatibility.
        #     print("[mm_utils.load_video_dynamic] note: `precise_time=True` has no special effect with torchcodec.")
        if not os.path.exists(video_path):
            raise FileNotFoundError(f"Video file not found: {video_path}")
        data: torch.Tensor
        decoder = VideoDecoder(video_path, seek_mode="exact" if precise_time else "approximate")
        stream_end_time = decoder.metadata.end_stream_seconds
        stream_start_time = decoder.metadata.begin_stream_seconds
        # torchcodec accepts list[float] or a torch tensor.
        if start_time != 0:
            t_req = [max(stream_start_time + 0.001, min(float(t), stream_end_time - 0.001)) for t in timestamps]
        else:
            t_req = [min(float(t), stream_end_time - 0.001) for t in timestamps]
        try:
            batch = decoder.get_frames_played_at(torch.tensor(t_req, dtype=torch.float32))
        except Exception:
            batch = decoder.get_frames_played_at(t_req)

        raw = getattr(batch, "data", None)
        if raw is None:
            raise RuntimeError("torchcodec FrameBatch missing `.data` attribute.")
        if not isinstance(raw, torch.Tensor):
            raise RuntimeError(f"torchcodec FrameBatch `.data` is not a torch.Tensor (got {type(raw)}).")
        data = _normalize_uint8_nchw(raw)

        # Optional resize to match existing `size` / `size_divisible` behavior.
        _, _, H, W = data.shape
        if int(new_h) != int(H) or int(new_w) != int(W):
            data_f = data.to(torch.float32)
            data_f = torch.nn.functional.interpolate(
                data_f,
                size=(int(new_h), int(new_w)),
                mode="bilinear",
                align_corners=False,
            )
            data = data_f.round().clamp(0, 255).to(torch.uint8)

        n_out = int(data.shape[0])
        # torchcodec should return 1:1 with requested timestamps, but be defensive.
        n_keep = min(n_out, len(t_req), len(frame_types))
        data = data[:n_keep]
        timestamps = t_req[:n_keep]
        frame_types = frame_types[:n_keep]

        frames: List[np.ndarray] = [data[i].numpy() for i in range(n_keep)]

        # 4) Temporal padding (keep types aligned)
        if temporal_factor > 1 and len(frames) > 0:
            pad_length = (temporal_factor - (len(frames) % temporal_factor)) % temporal_factor
            if pad_length > 0:
                if len(timestamps) >= 2:
                    dt = float(timestamps[-1] - timestamps[-2])
                    dt = dt if dt > 0 else 1e-3
                else:
                    dt = 1e-3
                for _ in range(pad_length):
                    frames.append(frames[-1].copy())
                    timestamps.append(float(timestamps[-1] + dt))
                    frame_types.append(int(frame_types[-1]))

        return (frames, timestamps, frame_types) if return_frame_types else (frames, timestamps)

    def _load_multimodal_data(self, conversation: Conversation):
        multimodal_info = defaultdict(list)
        new_conversation = []
        for message in conversation:
            new_message = {"role": message["role"]}
            if not isinstance(message["content"], (list, tuple)):
                new_message["content"] = message["content"]
                new_conversation.append(new_message)
                continue

            new_contents = []
            for content in message["content"]:
                if not isinstance(content, dict):
                    new_contents.append(content)
                    continue
                assert "type" in content, "Content must have 'type' field."
                if content["type"] in ["image", "video"] and content["type"] in content and isinstance(content[content["type"]], dict):
                    # TODO: support other types which are not compatible with json
                    load_args = content[content["type"]]
                    data_id = json.dumps({k: v for k, v in load_args.items() if not k in ["start_time", "end_time"]})
                    new_content = copy.deepcopy(content)
                    multimodal_info[data_id].append(new_content)
                    new_contents.append(new_content)
                else:
                    new_contents.append(content)

            new_message["content"] = new_contents
            new_conversation.append(new_message)

        for data_id, contents in multimodal_info.items():
            data_type = contents[0]["type"]
            if data_type == "image":
                image = self.load_images(contents[0][data_type]["image_path"])[0]
                for content in contents:
                    content["image"] = [image.copy()]

            elif data_type == "video":
                start_times = [content["video"].get("start_time", 0.) for content in contents]
                end_times = [content["video"].get("end_time", float("inf")) for content in contents]

                load_args = contents[0][data_type]
                start_time, end_time = min(start_times), max(end_times)
                if start_time > 0:
                    load_args["start_time"] = start_time
                if end_time < float("inf"):
                    load_args["end_time"] = end_time
                images, timestamps, frame_types = self.load_video(**load_args)

                for content, start_time, end_time in zip(contents, start_times, end_times):
                    cur_images, cur_timestamps, cur_frame_types = [], [], []
                    for image, timestamp, frame_type in zip(images, timestamps, frame_types):
                        if start_time <= timestamp <= end_time:
                            cur_images.append(image.copy())
                            cur_timestamps.append(timestamp)
                            cur_frame_types.append(frame_type)

                    content[data_type] = cur_images
                    content["num_frames"] = len(cur_images)
                    content["timestamps"] = cur_timestamps
                    content["frame_types"] = cur_frame_types

        return new_conversation

    def _gather_multimodal_data(self, conversation: Conversation):
        images = []
        clip_frame_types = []
        for message in conversation:
            if not isinstance(message["content"], (list, tuple)):
                continue
            for content in message["content"]:
                if not isinstance(content, dict):
                    continue
                if content["type"] == "video":
                    video = content["video"]
                    assert is_valid_video(video), f"Invalid video data: {video}."
                    images.append(("video", video))
                    clip_frame_types.append(content.get("frame_types", None))
                elif content["type"] == "image":
                    image = content["image"]
                    images.append(("image", image))
                    clip_frame_types.append(None)
        if len(images) == 0:
            return None, None
        return images, clip_frame_types

    def _process_conversation_with_label(
        self,
        conversation: Conversation,
        image_inputs: Dict[str, Any],
        **kwargs,
    ):
        assert kwargs.pop("return_tensors", "pt") == "pt", "Only PyTorch tensors are supported when return_labels=True."
        assert not "add_generation_prompt" in kwargs, "'add_generation_prompt' argument is not supported when return_labels=True."

        output_kwargs = self._merge_kwargs(
            PenguinVLQwen3ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        output_kwargs["chat_template_kwargs"].pop("add_generation_prompt")

        grid_sizes = self._get_downsampled_grid_sizes(image_inputs)
        text_inputs = {"input_ids": [], "labels": []}
        sample_types_list = []
        image_idx = 0

        for message_idx, message in enumerate(conversation):
            prompt = self.apply_chat_template(
                [message],
                tokenize=False,
                add_generation_prompt=False,
                **output_kwargs["chat_template_kwargs"],
            )
            prompt_chunks = prompt.split(DEFAULT_IMAGE_TOKEN)
            prompt = []
            for chunk_idx in range(len(prompt_chunks) - 1):
                prompt.append(prompt_chunks[chunk_idx])
                num_tokens = self._get_visual_seq_len(grid_sizes[image_idx])
                prompt.append(DEFAULT_IMAGE_TOKEN * num_tokens)
                image_idx += 1
            prompt.append(prompt_chunks[-1])
            prompt = "".join(prompt)

            # TODO: support attention_mask, position_ids, etc.
            input_ids = self.tokenizer.encode(prompt, return_tensors="pt", **output_kwargs["text_kwargs"])[0]
            text_inputs["input_ids"].append(input_ids)

            targets = torch.full_like(input_ids, IGNORE_INDEX)
            sample_types = torch.full_like(input_ids, IGNORE_INDEX)
            if message["role"] == "assistant":
                targets[self.generation_prompt_length:-1] = input_ids[self.generation_prompt_length:-1].clone()
            # elif message["role"] == "stream":
            #     diff = torch.diff((input_ids == self.image_token_id).float())
            #     image_end_indices = torch.nonzero(diff < 0)[:, 0]
            #     targets[image_end_indices + 1] = input_ids[image_end_indices + 1]
            #     sample_types = targets.clone()
            #     sample_types[torch.logical_and(sample_types > 0, sample_types != self.eos_token_id)] = 0
            #     targets[-2] = input_ids[-2]    # <|im_end|>

            if message_idx > 0 and conversation[message_idx - 1]["role"] == "stream":
                targets[0] = input_ids[0]
                # TODO: consider non-special tokens
                sample_types[0] = input_ids[0]

            text_inputs["labels"].append(targets)
            sample_types_list.append(sample_types)

        # Negative sampling for streaming data
        text_inputs = {k: torch.cat(v) for k, v in text_inputs.items()}
        sample_types = torch.cat(sample_types_list)
        types, counts = torch.unique(sample_types[sample_types > -1], return_counts=True)

        if len(types) > 0:
            target_num_samples = counts.amin()
            for type_id, type_count in zip(types, counts):
                if type_count > target_num_samples:
                    indices = torch.nonzero(sample_types == type_id)[:, 0]
                    random_selector = torch.randperm(indices.size(0))[:-target_num_samples]
                    text_inputs["labels"][indices[random_selector]] = IGNORE_INDEX
                    # sample_types[indices[random_selector]] = -1

        assert len(grid_sizes) == image_idx, "Number of images does not match the number of image tokens in the text."

        return text_inputs

    def _process_conversation_without_label(
        self,
        conversation: Conversation,
        image_inputs: Dict[str, Any],
        **kwargs,
    ):
        output_kwargs = self._merge_kwargs(
            PenguinVLQwen3ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )
        prompt = self.apply_chat_template(
            conversation,
            tokenize=False,
            **output_kwargs["chat_template_kwargs"],
        )
        return self.process_text(prompt, image_inputs, **output_kwargs["text_kwargs"])

    def _process_conversation(
        self,
        conversation: Conversation,
        images: Optional[Union[BatchedImage, BatchedNamedImage]] = None,
        return_labels: bool = False,
        **kwargs: Unpack[PenguinVLQwen3ProcessorKwargs],
    ) -> BatchFeature:
        assert isinstance(conversation, list), "Conversation must be a list of messages."

        frame_types = None
        if images is None:
            conversation = self._load_multimodal_data(conversation)
            images, frame_types = self._gather_multimodal_data(conversation)

        output_kwargs = self._merge_kwargs(
            PenguinVLQwen3ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        if images is not None:
            image_kwargs = output_kwargs["images_kwargs"]
            if frame_types is not None:
                image_kwargs["frame_types"] = frame_types
            image_inputs = self.process_images(images, **image_kwargs)
        else:
            image_inputs = {}

        if return_labels:
            text_inputs = self._process_conversation_with_label(conversation, image_inputs, **kwargs)
        else:
            text_inputs = self._process_conversation_without_label(conversation, image_inputs, **kwargs)

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

    def _process_plain(
        self,
        text: Union[TextInput, PreTokenizedInput] = None,
        images: Optional[Union[BatchedImage, BatchedNamedImage]] = None,
        return_labels: bool = False,
        **kwargs: Unpack[PenguinVLQwen3ProcessorKwargs],
    ) -> BatchFeature:
        if text is None:
            raise ValueError("You must provide 'text' or 'message'.")
        if return_labels:
            raise ValueError("return_labels is not supported for plain text processing.")

        output_kwargs = self._merge_kwargs(
            PenguinVLQwen3ProcessorKwargs,
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        if images is not None:
            image_inputs = self.process_images(images, **output_kwargs["images_kwargs"])
        else:
            image_inputs = {}

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

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

    def process_images(self, images: Union[BatchedImage, BatchedNamedImage], **kwargs):
        modals, images = make_batched_images(images)
        if not "merge_size" in kwargs:
            kwargs["merge_size"] = [
                self.image_merge_size if modal == "image" else self.video_merge_size
                for modal in modals
            ]
        image_inputs = self.image_processor(images=images, **kwargs)
        expanded_modals = []
        for modal, img in zip(modals, images):
            num_frames = len(img) if is_valid_video(img) else 1
            expanded_modals.extend([modal] * num_frames)
        image_inputs["modals"] = expanded_modals
        return image_inputs

    def process_text(
        self,
        text: TextInput,
        image_inputs: Dict[str, Any],
        **kwargs,
    ):
        grid_sizes = self._get_downsampled_grid_sizes(image_inputs)

        kwargs.pop("padding")
        kwargs.pop("padding_side")

        image_idx = 0
        while DEFAULT_IMAGE_TOKEN in text:
            num_tokens = self._get_visual_seq_len(grid_sizes[image_idx])
            text = text.replace(DEFAULT_IMAGE_TOKEN, "<placeholder>" * num_tokens, 1)
            image_idx += 1
        text = text.replace("<placeholder>", DEFAULT_IMAGE_TOKEN)
 
        assert len(grid_sizes) == image_idx, "Number of images does not match the number of image tokens in the text."

        text_inputs = self.tokenizer(text, **kwargs)
        return text_inputs

    def __call__(
        self,
        text: Optional[TextInput] = None,
        conversation: Optional[Conversation] = None,
        images: Optional[Union[BatchedImage, BatchedNamedImage]] = None,
        return_labels: bool = False,
        **kwargs: Unpack[PenguinVLQwen3ProcessorKwargs],
    ) -> BatchFeature:
        if conversation is not None:
            if text is not None:
                raise ValueError("You cannot provide 'message' with 'text'.")
            return self._process_conversation(conversation, images, return_labels, **kwargs)
        return self._process_plain(text, images, return_labels, **kwargs)

    def batch_decode(self, *args, **kwargs):
        return self.tokenizer.batch_decode(*args, **kwargs)

    def decode(self, *args, **kwargs):
        return self.tokenizer.decode(*args, **kwargs)

    def apply_chat_template(
        self,
        conversation: Conversation,
        chat_template: Optional[str] = None,
        tokenize: bool = False,
        add_system_prompt: bool = False,
        add_generation_prompt: bool = False,
        add_think_prompt: bool = False,
        image_token: Optional[str] = DEFAULT_IMAGE_TOKEN,
        **kwargs,
    ) -> str:
        """
        Similar to the `apply_chat_template` method on tokenizers, this method applies a Jinja template to input
        conversations to turn them into a single tokenizable string.
        Args:
            conversation (`List[Dict, str, str]`):
                The conversation to format.
            chat_template (`Optional[str]`, *optional*):
                The Jinja template to use for formatting the conversation. If not provided, the tokenizer's
                chat template is used.
            tokenize (`bool`, *optional*, defaults to `False`):
                Whether to tokenize the output or not.
            add_system_prompt (`bool`, *optional*, defaults to `False`):
                Whether to add the system prompt to the output or not.
            add_generation_prompt (`bool`, *optional*, defaults to `False`):
                Whether to add the generation prompt to the output or not.
            image_token (`Optional[str]`, *optional*, defaults to `<image>`):
                The token to use for indicating images in the conversation.
            **kwargs:
                Additional keyword arguments
        """

        if chat_template is None:
            if self.chat_template is not None:
                chat_template = self.chat_template
            else:
                raise ValueError(
                    "No chat template is set for this processor. Please either set the `chat_template` attribute, "
                    "or provide a chat template as an argument. See "
                    "https://huggingface.co/docs/transformers/main/en/chat_templating for more information."
                )
        return self.tokenizer.apply_chat_template(
            conversation,
            chat_template=chat_template,
            tokenize=tokenize,
            add_system_prompt=add_system_prompt,
            add_generation_prompt=add_generation_prompt,
            add_think_prompt=add_think_prompt,
            image_token=image_token,
            **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
        return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names)) + ["modals"]

    # modified from transformers.ProcessorMixin
    def _merge_kwargs(
        self,
        ModelProcessorKwargs: ProcessingKwargs,
        tokenizer_init_kwargs: Optional[Dict] = None,
        **kwargs,
    ) -> Dict[str, Dict]:
        """
        Method to merge dictionaries of kwargs cleanly separated by modality within a Processor instance.
        The order of operations is as follows:
            1) kwargs passed as before have highest priority to preserve BC.
                ```python
                high_priority_kwargs = {"crop_size" = {"height": 222, "width": 222}, "padding" = "max_length"}
                processor(..., **high_priority_kwargs)
                ```
            2) kwargs passed as modality-specific kwargs have second priority. This is the recommended API.
                ```python
                processor(..., text_kwargs={"padding": "max_length"}, images_kwargs={"crop_size": {"height": 222, "width": 222}}})
                ```
            3) kwargs passed during instantiation of a modality processor have fourth priority.
                ```python
                tokenizer = tokenizer_class(..., {"padding": "max_length"})
                image_processor = image_processor_class(...)
                processor(tokenizer, image_processor) # will pass max_length unless overriden by kwargs at call
                ```
            4) defaults kwargs specified at processor level have lowest priority.
                ```python
                class MyProcessingKwargs(ProcessingKwargs, CommonKwargs, TextKwargs, ImagesKwargs, total=False):
                    _defaults = {
                        "text_kwargs": {
                            "padding": "max_length",
                            "max_length": 64,
                        },
                    }
                ```
        Args:
            ModelProcessorKwargs (`ProcessingKwargs`):
                Typed dictionary of kwargs specifically required by the model passed.
            tokenizer_init_kwargs (`Dict`, *optional*):
                Dictionary of kwargs the tokenizer was instantiated with and need to take precedence over defaults.
        Returns:
            output_kwargs (`Dict`):
                Dictionary of per-modality kwargs to be passed to each modality-specific processor.
        """
        # Initialize dictionaries
        output_kwargs = {
            "text_kwargs": {},
            "images_kwargs": {},
            "audio_kwargs": {},
            "videos_kwargs": {},
            "chat_template_kwargs": {},
            "common_kwargs": {},
        }

        default_kwargs = {
            "text_kwargs": {},
            "images_kwargs": {},
            "audio_kwargs": {},
            "videos_kwargs": {},
            "chat_template_kwargs": {},
            "common_kwargs": {},
        }

        used_keys = set()

        # get defaults from set model processor kwargs if they exist
        for modality in default_kwargs:
            default_kwargs[modality] = ModelProcessorKwargs._defaults.get(modality, {}).copy()
            # update defaults with arguments from tokenizer init
            for modality_key in ModelProcessorKwargs.__annotations__[modality].__annotations__.keys():
                # init with tokenizer init kwargs if necessary
                if modality_key in tokenizer_init_kwargs:
                    value = (
                        getattr(self.tokenizer, modality_key)
                        if hasattr(self.tokenizer, modality_key)
                        else tokenizer_init_kwargs[modality_key]
                    )
                    default_kwargs[modality][modality_key] = value
        # now defaults kwargs are updated with the tokenizers defaults.
        # pass defaults to output dictionary
        output_kwargs.update(default_kwargs)

        # update modality kwargs with passed kwargs
        non_modality_kwargs = set(kwargs) - set(output_kwargs)
        for modality in output_kwargs:
            for modality_key in ModelProcessorKwargs.__annotations__[modality].__annotations__.keys():
                # check if we received a structured kwarg dict or not to handle it correctly
                if modality in kwargs:
                    kwarg_value = kwargs[modality].pop(modality_key, "__empty__")
                    # check if this key was passed as a flat kwarg.
                    if kwarg_value != "__empty__" and modality_key in non_modality_kwargs:
                        raise ValueError(
                            f"Keyword argument {modality_key} was passed two times:\n"
                            f"in a dictionary for {modality} and as a **kwarg."
                        )
                elif modality_key in kwargs:
                    # we get a modality_key instead of popping it because modality-specific processors
                    # can have overlapping kwargs
                    kwarg_value = kwargs.get(modality_key, "__empty__")
                else:
                    kwarg_value = "__empty__"
                if kwarg_value != "__empty__":
                    output_kwargs[modality][modality_key] = kwarg_value
                    used_keys.add(modality_key)

        # Determine if kwargs is a flat dictionary or contains nested dictionaries
        if any(key in default_kwargs for key in kwargs):
            # kwargs is dictionary-based, and some keys match modality names
            for modality, subdict in kwargs.items():
                if modality in default_kwargs:
                    for subkey, subvalue in subdict.items():
                        if subkey not in used_keys:
                            output_kwargs[modality][subkey] = subvalue
                            used_keys.add(subkey)
        else:
            # kwargs is a flat dictionary
            for key in kwargs:
                if key not in used_keys:
                    output_kwargs["common_kwargs"][key] = kwargs[key]

        # all modality-specific kwargs are updated with common kwargs
        for modality in output_kwargs:
            output_kwargs[modality].update(output_kwargs["common_kwargs"])
        return output_kwargs