image_processing_lfm3_vl.py

# Copyright 2026 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import math
import warnings
from collections.abc import Iterable

import torch
from torchvision.transforms.v2 import functional as tvF

from transformers.image_processing_backends import TorchvisionBackend
from transformers.image_processing_utils import BatchFeature
from transformers.image_transforms import group_images_by_shape, reorder_images
from transformers.image_utils import ImageInput, PILImageResampling, SizeDict
from transformers.processing_utils import ImagesKwargs, Unpack
from transformers.utils import TensorType, auto_docstring


class Lfm3VlImageProcessorKwargs(ImagesKwargs, total=False):
    r"""
    min_pixels (`int`, *optional*, defaults to `56 * 56`):
        The min pixels of the image to resize the image.
    max_pixels (`int`, *optional*, defaults to `28 * 28 * 1280`):
        The max pixels of the image to resize the image.
    patch_size (`int`, *optional*, defaults to 14):
        The spatial patch size of the vision encoder.
    temporal_patch_size (`int`, *optional*, defaults to 2):
        The temporal patch size of the vision encoder.
    merge_size (`int`, *optional*, defaults to 2):
        The merge size of the vision encoder to llm encoder.
    """

    min_pixels: int
    max_pixels: int
    patch_size: int
    temporal_patch_size: int
    merge_size: int


def smart_resize(
    height: int, width: int, factor: int = 28, min_pixels: int = 56 * 56, max_pixels: int = 14 * 14 * 4 * 1280
):
    """Rescales the image so that the following conditions are met:

    1. Both dimensions (height and width) are divisible by 'factor'.

    2. The total number of pixels is within the range ['min_pixels', 'max_pixels'].

    3. The aspect ratio of the image is maintained as closely as possible.

    """
    if max(height, width) / min(height, width) > 200:
        raise ValueError(
            f"absolute aspect ratio must be smaller than 200, got {max(height, width) / min(height, width)}"
        )
    h_bar = round(height / factor) * factor
    w_bar = round(width / factor) * factor
    if h_bar * w_bar > max_pixels:
        beta = math.sqrt((height * width) / max_pixels)
        h_bar = max(factor, math.floor(height / beta / factor) * factor)
        w_bar = max(factor, math.floor(width / beta / factor) * factor)
    elif h_bar * w_bar < min_pixels:
        beta = math.sqrt(min_pixels / (height * width))
        h_bar = math.ceil(height * beta / factor) * factor
        w_bar = math.ceil(width * beta / factor) * factor
    return h_bar, w_bar


@auto_docstring
class Lfm3VlImageProcessor(TorchvisionBackend):
    do_resize = True
    resample = PILImageResampling.BICUBIC
    size = {"shortest_edge": 65_536, "longest_edge": 16_777_216}
    default_to_square = False
    do_rescale = True
    do_normalize = True
    image_mean = [0.5, 0.5, 0.5]
    image_std = [0.5, 0.5, 0.5]
    do_convert_rgb = True
    patch_size = 16
    temporal_patch_size = 2
    merge_size = 2
    valid_kwargs = Lfm3VlImageProcessorKwargs
    model_input_names = ["pixel_values", "image_grid_thw"]

    def __init__(self, **kwargs: Unpack[Lfm3VlImageProcessorKwargs]):
        # backward compatibility: override size with min_pixels and max_pixels if they are provided
        size = kwargs.pop("size", None)
        size = dict(self.size) if size is None else size
        if (min_pixels := kwargs.pop("min_pixels", None)) is not None:
            size["shortest_edge"] = min_pixels
            size.pop("min_pixels", None)
        if (max_pixels := kwargs.pop("max_pixels", None)) is not None:
            size["longest_edge"] = max_pixels
            size.pop("max_pixels", None)
        super().__init__(size=size, **kwargs)

    def _standardize_kwargs(
        self,
        size: int | Iterable[int] | dict[str, int] | SizeDict | None = None,
        min_pixels: int | None = None,
        max_pixels: int | None = None,
        **kwargs,
    ) -> dict:
        if min_pixels is not None or max_pixels is not None:
            warnings.warn(
                "Passing `min_pixels` and `max_pixels` to a processor call is deprecated and will be removed in v5.23. "
                "Pass in `size={'longest_edge': xxx, 'shortest_edge': xxx} to override the target size.`",
                FutureWarning,
            )

            size_dict = dict(size) if isinstance(size, (dict, SizeDict)) else {}
            if min_pixels is not None:
                size_dict["shortest_edge"] = min_pixels
            if max_pixels is not None:
                size_dict["longest_edge"] = max_pixels
            size = SizeDict(**size_dict)
        return super()._standardize_kwargs(size=size, **kwargs)

    @auto_docstring
    def preprocess(
        self,
        images: ImageInput,
        **kwargs: Unpack[Lfm3VlImageProcessorKwargs],
    ) -> BatchFeature:
        return super().preprocess(images, **kwargs)

    def resize(
        self,
        images: "torch.Tensor",
        size: SizeDict,
        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
        factor: int,
        **kwargs,
    ) -> "torch.Tensor":
        """Resize dynamically based on input image aspect ratio."""
        if not size.shortest_edge or not size.longest_edge:
            raise ValueError(f"`size` dict must contain 'shortest_edge' and 'longest_edge' keys but got {size}.")

        height, width = images.shape[-2:]
        resized_height, resized_width = smart_resize(
            height,
            width,
            factor=factor,
            min_pixels=size.shortest_edge,
            max_pixels=size.longest_edge,
        )
        return super().resize(
            image=images,
            size=SizeDict(height=resized_height, width=resized_width),
            resample=resample,
        )

    def patchify(
        self,
        images: "torch.Tensor",
        patch_size: int,
        merge_size: int,
        temporal_patch_size: int,
    ) -> tuple["torch.Tensor", int, int]:
        """Patchifies each image into flat layout of shape (`seq_len`, `patch_dim`) so we can concat dynamically shaped pixels."""
        batch_size, channel, resized_height, resized_width = images.shape
        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
        patches = images.reshape(
            batch_size,
            channel,
            grid_h // merge_size,
            merge_size,
            patch_size,
            grid_w // merge_size,
            merge_size,
            patch_size,
        )
        # Reorder dimensions to group grid and patch information for subsequent flattening.
        # [batch, grid_h/merge, grid_w/merge, merge, merge, channel, patch, patch]
        patches = patches.permute(0, 2, 5, 3, 6, 1, 4, 7)

        flatten_patches = (
            patches.unsqueeze(6)
            .expand(-1, -1, -1, -1, -1, -1, temporal_patch_size, -1, -1)
            .reshape(
                batch_size,
                grid_h * grid_w,
                channel * temporal_patch_size * patch_size * patch_size,
            )
        )
        return flatten_patches, grid_h, grid_w

    def _preprocess(
        self,
        images: list["torch.Tensor"],
        do_resize: bool,
        size: SizeDict,
        resample: "PILImageResampling | tvF.InterpolationMode | int | None",
        do_rescale: bool,
        rescale_factor: float,
        do_normalize: bool,
        image_mean: float | list[float] | None,
        image_std: float | list[float] | None,
        patch_size: int,
        temporal_patch_size: int,
        merge_size: int,
        disable_grouping: bool | None,
        return_tensors: str | TensorType | None,
        **kwargs,
    ) -> BatchFeature:
        grouped_images, grouped_images_index = group_images_by_shape(images, disable_grouping=disable_grouping)
        resized_images_grouped = {}
        for shape, stacked_images in grouped_images.items():
            if do_resize:
                stacked_images = self.resize(
                    images=stacked_images,
                    size=size,
                    resample=resample,
                    factor=patch_size * merge_size,
                )
            resized_images_grouped[shape] = stacked_images
        resized_images = reorder_images(resized_images_grouped, grouped_images_index)

        grouped_images, grouped_images_index = group_images_by_shape(resized_images, disable_grouping=disable_grouping)
        processed_images_grouped = {}
        processed_grids = {}
        for shape, stacked_images in grouped_images.items():
            stacked_images = self.rescale_and_normalize(
                stacked_images, do_rescale, rescale_factor, do_normalize, image_mean, image_std
            )
            patches, grid_h, grid_w = self.patchify(
                stacked_images,
                patch_size=patch_size,
                merge_size=merge_size,
                temporal_patch_size=temporal_patch_size,
            )

            processed_images_grouped[shape] = patches
            processed_grids[shape] = [[1, grid_h, grid_w]] * len(stacked_images)

        processed_images = reorder_images(processed_images_grouped, grouped_images_index)
        processed_grids_ordered = reorder_images(processed_grids, grouped_images_index)
        pixel_values = processed_images[0] if len(processed_images) == 1 else torch.cat(processed_images, dim=0)
        image_grid_thw = torch.tensor(processed_grids_ordered, dtype=torch.long)

        return BatchFeature(
            data={"pixel_values": pixel_values, "image_grid_thw": image_grid_thw}, tensor_type=return_tensors
        )

    def get_number_of_image_patches(self, height: int, width: int, images_kwargs: dict | None = None) -> int:
        """
        A utility that returns number of image patches for a given image size.

        Note: Do not remove this method! It is used by vLLM to infer the number of patches and placeholders
        without an image input.

        Args:
            height (`int`):
                Height of the input image.
            width (`int`):
                Width of the input image.
            images_kwargs (`dict`, *optional*)
                Any kwargs to override defaults of the image processor.
        Returns:
            `int`: Number of image patches per image.
        """
        # `size` is the supported override (`min_pixels` / `max_pixels` are deprecated): count with the budget the
        # preprocessing applies
        images_kwargs = dict(images_kwargs or {})
        size = images_kwargs.pop("size", None) or self.size
        images_kwargs.setdefault("min_pixels", size["shortest_edge"])
        images_kwargs.setdefault("max_pixels", size["longest_edge"])
        images_kwargs = images_kwargs or {}
        min_pixels = images_kwargs["min_pixels"] if "min_pixels" in images_kwargs else self.size["shortest_edge"]
        max_pixels = images_kwargs["max_pixels"] if "max_pixels" in images_kwargs else self.size["longest_edge"]
        patch_size = images_kwargs.get("patch_size", self.patch_size)
        merge_size = images_kwargs.get("merge_size", self.merge_size)

        factor = patch_size * merge_size
        resized_height, resized_width = smart_resize(
            height, width, factor, min_pixels=min_pixels, max_pixels=max_pixels
        )
        grid_h, grid_w = resized_height // patch_size, resized_width // patch_size
        return grid_h * grid_w


__all__ = ["Lfm3VlImageProcessor"]