processing_videollama3.py

"""Processor class for VideoLLaMA3."""

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 numpy as np
import torch
import transformers
from decord import VideoReader, cpu
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_videollama3
    from .image_processing_videollama3 import (
        is_valid_image, is_valid_video,
    )
except ModuleNotFoundError:
    spec = importlib.util.spec_from_file_location(
        "image_processing_videollama3",
        osp.join(osp.dirname(__file__), "image_processing_videollama3.py"),
    )
    image_processing_videollama3 = importlib.util.module_from_spec(spec)
    spec.loader.exec_module(image_processing_videollama3)
    is_valid_image = getattr(image_processing_videollama3, "is_valid_image")
    is_valid_video = getattr(image_processing_videollama3, "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:
        attribute_class = getattr(image_processing_videollama3, 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


class ChatTemplateKwargs(TypedDict, total=False):

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


class Videollama3Qwen2ProcessorKwargs(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 Videollama3Qwen2Processor(ProcessorMixin):

    attributes = ["image_processor", "tokenizer"]
    image_processor_class = "Videollama3ImageProcessor"
    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,
    ):
        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.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.
        """
        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 = [frame for frame in frames]
        timestamps = [timestamp for timestamp in timestamps]

        return 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":
                # TODO: start_time is None?
                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 = self.load_video(**load_args)

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

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

        return new_conversation

    def _gather_multimodal_data(self, conversation: Conversation):
        images = []
        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))
                if content["type"] == "image":
                    image = content["image"]
                    images.append(("image", image))
        images = images if len(images) > 0 else None
        return images

    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(
            Videollama3Qwen2ProcessorKwargs,
            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(
            Videollama3Qwen2ProcessorKwargs,
            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[Videollama3Qwen2ProcessorKwargs],
    ) -> BatchFeature:
        assert isinstance(conversation, list), "Conversation must be a list of messages."

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

        output_kwargs = self._merge_kwargs(
            Videollama3Qwen2ProcessorKwargs,
            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 = {}

        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[Videollama3Qwen2ProcessorKwargs],
    ) -> 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(
            Videollama3Qwen2ProcessorKwargs,
            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)
        image_inputs["modals"] = 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[Videollama3Qwen2ProcessorKwargs],
    ) -> 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,
        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,
            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