NVILA-Lite-2B-hf / processing_nvila_lite.py

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import re
from typing import cast

import numpy as np
import transformers.image_transforms as image_transforms
import transformers.image_utils as image_utils
import transformers.video_utils as video_utils
from PIL.Image import Image
from transformers.feature_extraction_utils import BatchFeature
from transformers.image_utils import ImageInput
from transformers.models.qwen2 import Qwen2Tokenizer, Qwen2TokenizerFast
from transformers.models.siglip import SiglipImageProcessor, SiglipImageProcessorFast
from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
from transformers.tokenization_utils_base import BatchEncoding, TextInput
from transformers.video_utils import VideoInput, VideoMetadata


class NVILALiteProcessorKwargs(ProcessingKwargs, total=False):
    _defaults = {}  # type: ignore


class NVILALiteProcessor(ProcessorMixin):
    attributes = [
        "image_processor",
        "tokenizer",
    ]
    image_processor_class = "AutoImageProcessor"
    tokenizer_class = "AutoTokenizer"
    _auto_class = "AutoProcessor"

    def __init__(
        self,
        image_processor: SiglipImageProcessor | SiglipImageProcessorFast,
        tokenizer: Qwen2Tokenizer | Qwen2TokenizerFast,
        chat_template: str | None = None,
        **kwargs,
    ):
        super().__init__(
            image_processor,
            tokenizer,
            chat_template=chat_template,
            **kwargs,
        )

        self.image_processor: SiglipImageProcessor | SiglipImageProcessorFast
        self.tokenizer: Qwen2Tokenizer | Qwen2TokenizerFast

    def __call__(
        self,
        *,
        text: TextInput | list[TextInput],
        images: ImageInput | None = None,
        videos: VideoInput | None = None,
        **kwargs: Unpack[NVILALiteProcessorKwargs],
    ) -> BatchFeature:
        normalized_text, normalized_images, normalized_videos = self._normalize_inputs(
            text=text,
            images=images,
            videos=videos,
        )

        images_inputs, image_token_padding_strategy = (
            self._preprocess_images(
                normalized_images,
                **kwargs,
            )
            if len(normalized_images) > 0
            else (BatchFeature(), [])
        )

        videos_inputs, video_token_padding_strategy = (
            self._preprocess_videos(
                normalized_videos,
                **kwargs,
            )
            if len(normalized_videos) > 0
            else (BatchFeature(), [])
        )

        text_inputs = self._preprocess_text(
            normalized_text,
            image_token_padding_strategy=image_token_padding_strategy,
            video_token_padding_strategy=video_token_padding_strategy,
            **kwargs,
        )

        return BatchFeature(
            {
                **text_inputs,
                **images_inputs,
                **videos_inputs,
            }
        )

    def batch_decode(self, *args, **kwargs) -> list[str]:
        return self.tokenizer.batch_decode(*args, **kwargs)

    def _normalize_inputs(
        self,
        *,
        text: TextInput | list[TextInput],
        images: ImageInput | None,
        videos: VideoInput | None,
    ) -> tuple[list[str], list[Image], list[list[Image]]]:
        if isinstance(text, list):
            normalized_text = text
        else:
            normalized_text = [text]

        if images is not None and images != []:
            image_flat_list = cast(list, image_utils.make_flat_list_of_images(images))
            normalized_images = [cast(Image, image_transforms.to_pil_image(image)) for image in image_flat_list]
        else:
            normalized_images = []

        if videos is not None and videos != []:
            video_list = cast(list[list], video_utils.make_batched_videos(videos))
            normalized_videos = [
                [cast(Image, image_transforms.to_pil_image(image)) for image in video] for video in video_list
            ]
        else:
            normalized_videos = []

        return normalized_text, normalized_images, normalized_videos

    def _preprocess_images(
        self,
        images: list[Image],
        **kwargs: Unpack[NVILALiteProcessorKwargs],
    ) -> tuple[BatchFeature, list[list[int]]]:
        merged_kwargs = self._merge_kwargs(
            NVILALiteProcessorKwargs,  # type: ignore
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        images = [image.convert("RGB") for image in images]

        if len(images) == 1:
            assert self.image_processor.size["height"] == self.image_processor.size["width"]

            image_tiles = dynamic_preprocess(
                images[0],
                min_num=1,
                max_num=12,
                image_size=self.image_processor.size["height"],
            )

            pixel_values = self.image_processor(
                image_tiles,
                **merged_kwargs["images_kwargs"],
            )["pixel_values"]

            images_inputs = BatchFeature(
                {
                    "pixel_values": pixel_values,
                }
            )

            padding_strategy = [[121] * len(image_tiles)]

        else:
            pixel_values = self.image_processor(
                images,
                **merged_kwargs["images_kwargs"],
            )["pixel_values"]

            images_inputs = BatchFeature(
                {
                    "pixel_values": pixel_values,
                }
            )

            padding_strategy = [[121]] * len(images)

        return images_inputs, padding_strategy

    def _preprocess_text(
        self,
        text: list[str],
        *,
        image_token_padding_strategy: list[list[int]],
        video_token_padding_strategy: list[list[int]],
        **kwargs: Unpack[NVILALiteProcessorKwargs],
    ) -> BatchEncoding:
        # Pad media tokens.
        assert isinstance(self.tokenizer.image_token, str)
        assert isinstance(self.tokenizer.video_token, str)

        for media_token, padding_strategy in (
            (self.tokenizer.image_token, image_token_padding_strategy),
            (self.tokenizer.video_token, video_token_padding_strategy),
        ):
            assert sum([s.count(media_token) for s in text]) == len(padding_strategy)

            # Pad to number of tiles.
            pad_lens = [len(x) for x in padding_strategy]
            text = [re.sub(rf"({re.escape(media_token)})", lambda _: media_token * pad_lens.pop(0), s) for s in text]

            # HACK: NVILA mistakenly suffixes line feeds to some media tokens.
            if len(image_token_padding_strategy) == 1 and media_token == self.tokenizer.image_token:
                image_token = self.tokenizer.image_token
                assert isinstance(image_token, str)

                text = [re.sub(rf"({re.escape(image_token)})", r"\1\n", s) for s in text]

            # Pad to number of features.
            pad_lens = [y for x in padding_strategy for y in x]
            pad_lens = [x + 1 for x in pad_lens]  # Reserve for lf ending.
            text = [re.sub(rf"({re.escape(media_token)})", lambda _: media_token * pad_lens.pop(0), s) for s in text]

        merged_kwargs = self._merge_kwargs(
            NVILALiteProcessorKwargs,  # type: ignore
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

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

        # Replace last token id of every image tile with lf token id.
        lf_token_id = self.tokenizer.encode("\n")[0]
        assert isinstance(self.tokenizer.image_token_id, int)
        assert isinstance(self.tokenizer.video_token_id, int)

        input_ids = text_inputs.input_ids

        for media_token_id, padding_strategy in [
            (self.tokenizer.image_token_id, image_token_padding_strategy),
            (self.tokenizer.video_token_id, video_token_padding_strategy),
        ]:
            pad_lens = [y for x in padding_strategy for y in x]

            for i in range(len(input_ids)):
                j = 0
                while j < len(input_ids[i]):
                    if input_ids[i][j] != media_token_id:
                        j += 1
                        continue

                    j += pad_lens.pop(0)
                    input_ids[i][j] = lf_token_id

                    j += 1

        return text_inputs

    def _preprocess_videos(
        self,
        videos: list[list[Image]],
        **kwargs: Unpack[NVILALiteProcessorKwargs],
    ) -> tuple[BatchFeature, list[list[int]]]:
        merged_kwargs = self._merge_kwargs(
            NVILALiteProcessorKwargs,  # type: ignore
            tokenizer_init_kwargs=self.tokenizer.init_kwargs,
            **kwargs,
        )

        # Support sampling frames.
        if merged_kwargs["videos_kwargs"].get("do_sample_frames"):
            videos = [
                self._sample_frames(
                    video,
                    **merged_kwargs["videos_kwargs"],
                )
                for video in videos
            ]

        videos = [[image.convert("RGB") for image in video] for video in videos]

        frames = [image for video in videos for image in video]
        pixel_values_videos = self.image_processor(
            frames,
            **merged_kwargs["images_kwargs"],
        )["pixel_values"]

        videos_inputs = BatchFeature(
            {
                "pixel_values_videos": pixel_values_videos,
            }
        )

        padding_strategy = [[121] * len(video) for video in videos]

        return videos_inputs, padding_strategy

    def _sample_frames(
        self,
        video: list[Image],
        **kwargs: Unpack[VideosKwargs],
    ) -> list[Image]:
        fps = kwargs.get("fps")
        num_frames = kwargs.get("num_frames")

        if num_frames is not None and fps is None:
            indices = np.round(np.linspace(0, len(video) - 1, num_frames)).astype(int)

            return [video[i] for i in indices]

        elif num_frames is None and fps is not None:
            video_metadata = kwargs.get("video_metadata")

            if isinstance(video_metadata, VideoMetadata):
                total_num_frames = video_metadata.total_num_frames
                duration = video_metadata.duration

            elif isinstance(video_metadata, dict):
                total_num_frames = video_metadata.get("total_num_frames")
                duration = video_metadata.get("duration")

                assert total_num_frames is not None
                assert duration is not None

            else:
                raise NotImplementedError

            indices = np.round(np.linspace(0, total_num_frames - 1, int(fps * duration))).astype(int)

            return [video[i] for i in indices]

        else:
            raise NotImplementedError


# NOTE: The following functions are directly copied from VILA codebase.


def dynamic_preprocess(
    image: Image, min_num: int, max_num: int, image_size: int, use_thumbnail: bool = True
) -> list[Image]:
    orig_width, orig_height = image.size
    aspect_ratio = orig_width / orig_height

    # calculate the existing image aspect ratio
    target_ratios = {
        (i, j)
        for n in range(min_num, max_num + 1)
        for i in range(1, n + 1)
        for j in range(1, n + 1)
        if i * j <= max_num and i * j >= min_num
    }
    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

    # find the closest aspect ratio to the target
    target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)

    # calculate the target width and height
    target_width = image_size * target_aspect_ratio[0]
    target_height = image_size * target_aspect_ratio[1]
    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

    # resize the image
    resized_img = image.resize((target_width, target_height))
    processed_images = []
    for i in range(blocks):
        box = (
            (i % (target_width // image_size)) * image_size,
            (i // (target_width // image_size)) * image_size,
            ((i % (target_width // image_size)) + 1) * image_size,
            ((i // (target_width // image_size)) + 1) * image_size,
        )
        # split the image
        split_img = resized_img.crop(box)
        processed_images.append(split_img)
    assert len(processed_images) == blocks
    if use_thumbnail and len(processed_images) != 1:
        thumbnail_img = image.resize((image_size, image_size))
        processed_images.append(thumbnail_img)
    return processed_images


def find_closest_aspect_ratio(
    aspect_ratio: float, target_ratios: list[tuple[int, int]], width: int, height: int, image_size: int
) -> tuple[int, int]:
    best_ratio_diff = float("inf")
    best_ratio = (1, 1)
    area = width * height
    for ratio in target_ratios:
        target_aspect_ratio = ratio[0] / ratio[1]
        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
        if ratio_diff < best_ratio_diff:
            best_ratio_diff = ratio_diff
            best_ratio = ratio
        elif ratio_diff == best_ratio_diff:
            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
                best_ratio = ratio
    return best_ratio

	import re
	from typing import cast

	import numpy as np
	import transformers.image_transforms as image_transforms
	import transformers.image_utils as image_utils
	import transformers.video_utils as video_utils
	from PIL.Image import Image
	from transformers.feature_extraction_utils import BatchFeature
	from transformers.image_utils import ImageInput
	from transformers.models.qwen2 import Qwen2Tokenizer, Qwen2TokenizerFast
	from transformers.models.siglip import SiglipImageProcessor, SiglipImageProcessorFast
	from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs
	from transformers.tokenization_utils_base import BatchEncoding, TextInput
	from transformers.video_utils import VideoInput, VideoMetadata


	class NVILALiteProcessorKwargs(ProcessingKwargs, total=False):
	_defaults = {} # type: ignore


	class NVILALiteProcessor(ProcessorMixin):
	attributes = [
	"image_processor",
	"tokenizer",
	]
	image_processor_class = "AutoImageProcessor"
	tokenizer_class = "AutoTokenizer"
	_auto_class = "AutoProcessor"

	def __init__(
	self,
	image_processor: SiglipImageProcessor \| SiglipImageProcessorFast,
	tokenizer: Qwen2Tokenizer \| Qwen2TokenizerFast,
	chat_template: str \| None = None,
	**kwargs,
	):
	super().__init__(
	image_processor,
	tokenizer,
	chat_template=chat_template,
	**kwargs,
	)

	self.image_processor: SiglipImageProcessor \| SiglipImageProcessorFast
	self.tokenizer: Qwen2Tokenizer \| Qwen2TokenizerFast

	def __call__(
	self,
	*,
	text: TextInput \| list[TextInput],
	images: ImageInput \| None = None,
	videos: VideoInput \| None = None,
	**kwargs: Unpack[NVILALiteProcessorKwargs],
	) -> BatchFeature:
	normalized_text, normalized_images, normalized_videos = self._normalize_inputs(
	text=text,
	images=images,
	videos=videos,
	)

	images_inputs, image_token_padding_strategy = (
	self._preprocess_images(
	normalized_images,
	**kwargs,
	)
	if len(normalized_images) > 0
	else (BatchFeature(), [])
	)

	videos_inputs, video_token_padding_strategy = (
	self._preprocess_videos(
	normalized_videos,
	**kwargs,
	)
	if len(normalized_videos) > 0
	else (BatchFeature(), [])
	)

	text_inputs = self._preprocess_text(
	normalized_text,
	image_token_padding_strategy=image_token_padding_strategy,
	video_token_padding_strategy=video_token_padding_strategy,
	**kwargs,
	)

	return BatchFeature(
	{
	**text_inputs,
	**images_inputs,
	**videos_inputs,
	}
	)

	def batch_decode(self, args, *kwargs) -> list[str]:
	return self.tokenizer.batch_decode(args, *kwargs)

	def _normalize_inputs(
	self,
	*,
	text: TextInput \| list[TextInput],
	images: ImageInput \| None,
	videos: VideoInput \| None,
	) -> tuple[list[str], list[Image], list[list[Image]]]:
	if isinstance(text, list):
	normalized_text = text
	else:
	normalized_text = [text]

	if images is not None and images != []:
	image_flat_list = cast(list, image_utils.make_flat_list_of_images(images))
	normalized_images = [cast(Image, image_transforms.to_pil_image(image)) for image in image_flat_list]
	else:
	normalized_images = []

	if videos is not None and videos != []:
	video_list = cast(list[list], video_utils.make_batched_videos(videos))
	normalized_videos = [
	[cast(Image, image_transforms.to_pil_image(image)) for image in video] for video in video_list
	]
	else:
	normalized_videos = []

	return normalized_text, normalized_images, normalized_videos

	def _preprocess_images(
	self,
	images: list[Image],
	**kwargs: Unpack[NVILALiteProcessorKwargs],
	) -> tuple[BatchFeature, list[list[int]]]:
	merged_kwargs = self._merge_kwargs(
	NVILALiteProcessorKwargs, # type: ignore
	tokenizer_init_kwargs=self.tokenizer.init_kwargs,
	**kwargs,
	)

	images = [image.convert("RGB") for image in images]

	if len(images) == 1:
	assert self.image_processor.size["height"] == self.image_processor.size["width"]

	image_tiles = dynamic_preprocess(
	images[0],
	min_num=1,
	max_num=12,
	image_size=self.image_processor.size["height"],
	)

	pixel_values = self.image_processor(
	image_tiles,
	**merged_kwargs["images_kwargs"],
	)["pixel_values"]

	images_inputs = BatchFeature(
	{
	"pixel_values": pixel_values,
	}
	)

	padding_strategy = [[121] * len(image_tiles)]

	else:
	pixel_values = self.image_processor(
	images,
	**merged_kwargs["images_kwargs"],
	)["pixel_values"]

	images_inputs = BatchFeature(
	{
	"pixel_values": pixel_values,
	}
	)

	padding_strategy = [[121]] * len(images)

	return images_inputs, padding_strategy

	def _preprocess_text(
	self,
	text: list[str],
	*,
	image_token_padding_strategy: list[list[int]],
	video_token_padding_strategy: list[list[int]],
	**kwargs: Unpack[NVILALiteProcessorKwargs],
	) -> BatchEncoding:
	# Pad media tokens.
	assert isinstance(self.tokenizer.image_token, str)
	assert isinstance(self.tokenizer.video_token, str)

	for media_token, padding_strategy in (
	(self.tokenizer.image_token, image_token_padding_strategy),
	(self.tokenizer.video_token, video_token_padding_strategy),
	):
	assert sum([s.count(media_token) for s in text]) == len(padding_strategy)

	# Pad to number of tiles.
	pad_lens = [len(x) for x in padding_strategy]
	text = [re.sub(rf"({re.escape(media_token)})", lambda _: media_token * pad_lens.pop(0), s) for s in text]

	# HACK: NVILA mistakenly suffixes line feeds to some media tokens.
	if len(image_token_padding_strategy) == 1 and media_token == self.tokenizer.image_token:
	image_token = self.tokenizer.image_token
	assert isinstance(image_token, str)

	text = [re.sub(rf"({re.escape(image_token)})", r"\1\n", s) for s in text]

	# Pad to number of features.
	pad_lens = [y for x in padding_strategy for y in x]
	pad_lens = [x + 1 for x in pad_lens] # Reserve for lf ending.
	text = [re.sub(rf"({re.escape(media_token)})", lambda _: media_token * pad_lens.pop(0), s) for s in text]

	merged_kwargs = self._merge_kwargs(
	NVILALiteProcessorKwargs, # type: ignore
	tokenizer_init_kwargs=self.tokenizer.init_kwargs,
	**kwargs,
	)

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

	# Replace last token id of every image tile with lf token id.
	lf_token_id = self.tokenizer.encode("\n")[0]
	assert isinstance(self.tokenizer.image_token_id, int)
	assert isinstance(self.tokenizer.video_token_id, int)

	input_ids = text_inputs.input_ids

	for media_token_id, padding_strategy in [
	(self.tokenizer.image_token_id, image_token_padding_strategy),
	(self.tokenizer.video_token_id, video_token_padding_strategy),
	]:
	pad_lens = [y for x in padding_strategy for y in x]

	for i in range(len(input_ids)):
	j = 0
	while j < len(input_ids[i]):
	if input_ids[i][j] != media_token_id:
	j += 1
	continue

	j += pad_lens.pop(0)
	input_ids[i][j] = lf_token_id

	j += 1

	return text_inputs

	def _preprocess_videos(
	self,
	videos: list[list[Image]],
	**kwargs: Unpack[NVILALiteProcessorKwargs],
	) -> tuple[BatchFeature, list[list[int]]]:
	merged_kwargs = self._merge_kwargs(
	NVILALiteProcessorKwargs, # type: ignore
	tokenizer_init_kwargs=self.tokenizer.init_kwargs,
	**kwargs,
	)

	# Support sampling frames.
	if merged_kwargs["videos_kwargs"].get("do_sample_frames"):
	videos = [
	self._sample_frames(
	video,
	**merged_kwargs["videos_kwargs"],
	)
	for video in videos
	]

	videos = [[image.convert("RGB") for image in video] for video in videos]

	frames = [image for video in videos for image in video]
	pixel_values_videos = self.image_processor(
	frames,
	**merged_kwargs["images_kwargs"],
	)["pixel_values"]

	videos_inputs = BatchFeature(
	{
	"pixel_values_videos": pixel_values_videos,
	}
	)

	padding_strategy = [[121] * len(video) for video in videos]

	return videos_inputs, padding_strategy

	def _sample_frames(
	self,
	video: list[Image],
	**kwargs: Unpack[VideosKwargs],
	) -> list[Image]:
	fps = kwargs.get("fps")
	num_frames = kwargs.get("num_frames")

	if num_frames is not None and fps is None:
	indices = np.round(np.linspace(0, len(video) - 1, num_frames)).astype(int)

	return [video[i] for i in indices]

	elif num_frames is None and fps is not None:
	video_metadata = kwargs.get("video_metadata")

	if isinstance(video_metadata, VideoMetadata):
	total_num_frames = video_metadata.total_num_frames
	duration = video_metadata.duration

	elif isinstance(video_metadata, dict):
	total_num_frames = video_metadata.get("total_num_frames")
	duration = video_metadata.get("duration")

	assert total_num_frames is not None
	assert duration is not None

	else:
	raise NotImplementedError

	indices = np.round(np.linspace(0, total_num_frames - 1, int(fps * duration))).astype(int)

	return [video[i] for i in indices]

	else:
	raise NotImplementedError


	# NOTE: The following functions are directly copied from VILA codebase.


	def dynamic_preprocess(
	image: Image, min_num: int, max_num: int, image_size: int, use_thumbnail: bool = True
	) -> list[Image]:
	orig_width, orig_height = image.size
	aspect_ratio = orig_width / orig_height

	# calculate the existing image aspect ratio
	target_ratios = {
	(i, j)
	for n in range(min_num, max_num + 1)
	for i in range(1, n + 1)
	for j in range(1, n + 1)
	if i * j <= max_num and i * j >= min_num
	}
	target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])

	# find the closest aspect ratio to the target
	target_aspect_ratio = find_closest_aspect_ratio(aspect_ratio, target_ratios, orig_width, orig_height, image_size)

	# calculate the target width and height
	target_width = image_size * target_aspect_ratio[0]
	target_height = image_size * target_aspect_ratio[1]
	blocks = target_aspect_ratio[0] * target_aspect_ratio[1]

	# resize the image
	resized_img = image.resize((target_width, target_height))
	processed_images = []
	for i in range(blocks):
	box = (
	(i % (target_width // image_size)) * image_size,
	(i // (target_width // image_size)) * image_size,
	((i % (target_width // image_size)) + 1) * image_size,
	((i // (target_width // image_size)) + 1) * image_size,
	)
	# split the image
	split_img = resized_img.crop(box)
	processed_images.append(split_img)
	assert len(processed_images) == blocks
	if use_thumbnail and len(processed_images) != 1:
	thumbnail_img = image.resize((image_size, image_size))
	processed_images.append(thumbnail_img)
	return processed_images


	def find_closest_aspect_ratio(
	aspect_ratio: float, target_ratios: list[tuple[int, int]], width: int, height: int, image_size: int
	) -> tuple[int, int]:
	best_ratio_diff = float("inf")
	best_ratio = (1, 1)
	area = width * height
	for ratio in target_ratios:
	target_aspect_ratio = ratio[0] / ratio[1]
	ratio_diff = abs(aspect_ratio - target_aspect_ratio)
	if ratio_diff < best_ratio_diff:
	best_ratio_diff = ratio_diff
	best_ratio = ratio
	elif ratio_diff == best_ratio_diff:
	if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
	best_ratio = ratio
	return best_ratio