# Vendored from transformers' LFM3 code (not in a release yet) at 69da1b47a11e362fbb15bfe5d5c8da0790c688c5, src/transformers/models/lfm3_vl/video_processing_lfm3_vl.py: # its relative imports made absolute, nothing else changed. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/lfm3_vl/modular_lfm3_vl.py. # Do NOT edit this file manually as any edits will be overwritten by the generation of # the file from the modular. If any change should be done, please apply the change to the # modular_lfm3_vl.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # 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 numpy as np import torch from torchvision.transforms.v2 import functional as tvF from transformers.image_processing_utils import BatchFeature from transformers.image_utils import PILImageResampling, SizeDict from transformers.processing_utils import Unpack, VideosKwargs from transformers.utils import TensorType, auto_docstring, logging from transformers.video_processing_utils import BaseVideoProcessor from transformers.video_utils import ( VideoMetadata, group_videos_by_shape, make_batched_metadata, make_batched_videos, reorder_videos, ) logger = logging.get_logger(__name__) class Lfm3VlVideoProcessorInitKwargs(VideosKwargs, total=False): r""" patch_size (`int`, *optional*, defaults to 16): 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_frames (`int`, *optional*, defaults to 4): The minimum number of frames to sample from video. max_frames (`int`, *optional*, defaults to 768): The maximum number of frames to sample from video. cap_pixels_per_frame (`bool`, *optional*): Whether to cap each frame's pixel cost the way the reference implementation (qwen-vl-utils) does: per-frame pixels are limited to `min(max_video_tokens * factor**2, size["longest_edge"] / num_frames)` and floored at `1.05 * size["shortest_edge"]`, so token cost scales with clip duration. Without the cap, videos that sample few frames spend the whole `size["longest_edge"]` budget on those frames and keep near-native per-frame resolution, so a short clip can cost almost as many tokens as a long video. If unset, the current uncapped behavior is kept and a warning is emitted: the default will change to `True` in v5.22, after which the argument will be removed. max_video_tokens (`int`, *optional*, defaults to 768): The per-frame token ceiling applied by `cap_pixels_per_frame`, in vision tokens per frame (qwen-vl-utils' `VIDEO_MAX_TOKEN_NUM`). """ patch_size: int temporal_patch_size: int merge_size: int min_frames: int max_frames: int cap_pixels_per_frame: bool max_video_tokens: int def smart_resize( num_frames: int, height: int, width: int, temporal_factor: int = 2, factor: int = 28, min_pixels: int = 112 * 112, max_pixels: int = 14 * 14 * 2 * 2 * 2 * 6144, ): if num_frames < temporal_factor: raise ValueError(f"t:{num_frames} must be larger than temporal_factor:{temporal_factor}") if height < factor or width < factor: scale = max(factor / height, factor / width) height = int(height * scale) width = int(width * scale) 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 t_bar = round(num_frames / temporal_factor) * temporal_factor if t_bar * h_bar * w_bar > max_pixels: beta = math.sqrt((num_frames * 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 t_bar * h_bar * w_bar < min_pixels: beta = math.sqrt(min_pixels / (num_frames * height * width)) h_bar = math.ceil(height * beta / factor) * factor w_bar = math.ceil(width * beta / factor) * factor return h_bar, w_bar def _check_video_fps(metadata: VideoMetadata) -> None: if metadata.fps is not None and not (math.isfinite(metadata.fps) and metadata.fps > 0): raise ValueError(f"`video_metadata.fps` must be a positive number, got {metadata.fps}") def _check_video_metadata_count(videos, video_metadata) -> None: # the shared sampler pairs clips with metadata by `zip`, which drops the clips past the last entry if video_metadata is None: return videos = make_batched_videos(videos) entries = make_batched_metadata(videos, video_metadata) if video_metadata != [] else [] if len(entries) != len(videos): raise ValueError( f"Received {len(videos)} videos but {len(entries)} `video_metadata` entries; pass one entry per video" ) @auto_docstring class Lfm3VlVideoProcessor(BaseVideoProcessor): resample = PILImageResampling.BICUBIC size = {"shortest_edge": 4_096, "longest_edge": 25_165_824} image_mean = [0.5, 0.5, 0.5] image_std = [0.5, 0.5, 0.5] do_resize = True do_rescale = True do_normalize = True do_convert_rgb = True patch_size = 16 temporal_patch_size = 2 merge_size = 2 min_frames = 4 max_frames = 768 do_sample_frames = True cap_pixels_per_frame = False max_video_tokens = 768 valid_kwargs = Lfm3VlVideoProcessorInitKwargs model_input_names = ["pixel_values_videos", "video_grid_thw"] max_image_size = {"longest_edge": 28 * 28 * 2 * 30000} max_duration = None num_frames = None fps = 2.0 def __init__(self, **kwargs: Unpack[Lfm3VlVideoProcessorInitKwargs]): super().__init__(**kwargs) def sample_frames( self, metadata: VideoMetadata, num_frames: int | None = None, fps: int | float | None = None, **kwargs ): """Decoded frames whose metadata carries no fps are sampled and timestamped at the 24 fps the shared sampler assumes (with a warning); pass `video_metadata` with the clip's fps for accurate timestamps.""" _check_video_fps(metadata) if fps is not None and not (math.isfinite(fps) and fps > 0): raise ValueError(f"`fps` must be a positive number, got {fps}") if fps is not None and num_frames is not None: raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!") total_num_frames = metadata.total_num_frames fps = fps if fps is not None else self.fps # If num_frames is not given but fps is, calculate num_frames from fps if num_frames is None and fps is not None: if metadata.fps is None: metadata.fps = 24 logger.warning_once( "Asked to sample `fps` frames per second but no video metadata was provided which is required when sampling with `fps`. " "Defaulting to `fps=24`. Please provide `video_metadata` for more accurate results." ) num_frames = int(total_num_frames / metadata.fps * fps) num_frames = min(max(num_frames, self.min_frames), self.max_frames, total_num_frames) if num_frames is None: num_frames = min(max(total_num_frames, self.min_frames), self.max_frames) indices = np.linspace(0, total_num_frames - 1, num_frames).round().astype(int) return indices def resize( self, videos: "torch.Tensor", size: SizeDict, resample: "PILImageResampling | tvF.InterpolationMode | int | None", factor: int, temporal_factor: int, cap_pixels_per_frame: bool | None = None, **kwargs, ) -> "torch.Tensor": """Resize dynamically based on input video 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}.") num_frames = videos.shape[1] max_pixels = size.longest_edge if cap_pixels_per_frame: # per-frame pixels are capped at `max_video_tokens` patches or the budget's even share per frame frame_cap = self.max_video_tokens * factor * factor pixels_per_frame = max(min(frame_cap, size.longest_edge // num_frames), int(size.shortest_edge * 1.05)) max_pixels = pixels_per_frame * num_frames height, width = videos.shape[-2:] resized_height, resized_width = smart_resize( height=height, width=width, num_frames=num_frames, factor=factor, temporal_factor=temporal_factor, min_pixels=size.shortest_edge, max_pixels=max_pixels, ) return super().resize( image=videos, size=SizeDict(height=resized_height, width=resized_width), resample=resample, ) def patchify( self, videos: "torch.Tensor", patch_size: int, merge_size: int, temporal_patch_size: int, ) -> tuple["torch.Tensor", int, int]: "Patchifies each video into flat layout of shape (`seq_len`, `patch_dim`) so we can concat dynamically shaped pixels." batch_size, num_frames, channel, resized_height, resized_width = videos.shape # Check that videos have `num_frames` divisible by `temporal_patch_size` if pad := -num_frames % temporal_patch_size: repeats = videos[:, -1:].expand(-1, pad, -1, -1, -1) videos = torch.cat((videos, repeats), dim=1) num_frames += pad grid_t = num_frames // temporal_patch_size grid_h, grid_w = resized_height // patch_size, resized_width // patch_size patches = videos.view( batch_size, grid_t, temporal_patch_size, channel, grid_h // merge_size, merge_size, patch_size, grid_w // merge_size, merge_size, patch_size, ) patches = patches.permute(0, 1, 4, 7, 5, 8, 3, 2, 6, 9) flatten_patches = patches.reshape( batch_size, grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size, ) return flatten_patches, grid_t, grid_h, grid_w def _preprocess( self, videos: 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 | None = None, temporal_patch_size: int | None = None, merge_size: int | None = None, cap_pixels_per_frame: bool | None = None, return_tensors: str | TensorType | None = None, **kwargs, ): if cap_pixels_per_frame is None: logger.warning_once( "Lfm3Vl video processing does not apply the per-frame pixel cap the reference " "implementation (qwen-vl-utils) applies, so some videos cost far more tokens than they " "would there. In v5.22 the capped behavior will become the default and " "`cap_pixels_per_frame` will be removed. Pass `cap_pixels_per_frame=True` to adopt the " "reference behavior now, or `False` to keep the current behavior and silence this " "warning." ) cap_pixels_per_frame = False # Group videos by size for batched resizing grouped_videos, grouped_videos_index = group_videos_by_shape(videos) resized_videos_grouped = {} for shape, stacked_videos in grouped_videos.items(): if do_resize: stacked_videos = self.resize( videos=stacked_videos, size=size, resample=resample, factor=patch_size * merge_size, temporal_factor=temporal_patch_size, cap_pixels_per_frame=cap_pixels_per_frame, ) resized_videos_grouped[shape] = stacked_videos resized_videos = reorder_videos(resized_videos_grouped, grouped_videos_index) # Group videos by size for further processing # Needed in case do_resize is False, or resize returns videos with different sizes grouped_videos, grouped_videos_index = group_videos_by_shape(resized_videos) processed_videos_grouped = {} processed_grids = {} for shape, stacked_videos in grouped_videos.items(): # Fused rescale and normalize stacked_videos = self.rescale_and_normalize( stacked_videos, do_rescale, rescale_factor, do_normalize, image_mean, image_std ) patches, grid_t, grid_h, grid_w = self.patchify( stacked_videos, patch_size=patch_size, merge_size=merge_size, temporal_patch_size=temporal_patch_size, ) processed_videos_grouped[shape] = patches processed_grids[shape] = [[grid_t, grid_h, grid_w]] * len(stacked_videos) processed_videos = reorder_videos(processed_videos_grouped, grouped_videos_index) processed_grids = reorder_videos(processed_grids, grouped_videos_index) pixel_values_videos = torch.cat(processed_videos, dim=0) video_grid_thw = torch.tensor(processed_grids) return BatchFeature( data={"pixel_values_videos": pixel_values_videos, "video_grid_thw": video_grid_thw}, tensor_type=return_tensors, ) def get_num_of_video_patches(self, num_frames: int, height: int, width: int, videos_kwargs=None): """ A utility that returns number of video patches a given video size. Args: num_frames (`int`): Number of frames in the input video. height (`int`): Height of the input video. width (`int`): Width of the input video. videos_kwargs (`dict`, *optional*) Any kwargs to override defaults of the video processor. Returns: `int`: Number of video patches per video. """ videos_kwargs = videos_kwargs if videos_kwargs is not None else {} size = videos_kwargs.get("size", None) or self.size min_pixels = size["shortest_edge"] max_pixels = size["longest_edge"] patch_size = videos_kwargs.get("patch_size", None) or self.patch_size merge_size = videos_kwargs.get("merge_size", None) or self.merge_size temporal_patch_size = videos_kwargs.get("temporal_patch_size", None) or self.temporal_patch_size cap_pixels_per_frame = videos_kwargs.get("cap_pixels_per_frame", None) or self.cap_pixels_per_frame factor = patch_size * merge_size if cap_pixels_per_frame: # Keep the count in sync with `resize` when the per-frame cap is active. frame_cap = self.max_video_tokens * factor * factor pixels_per_frame = max(min(frame_cap, max_pixels // num_frames), int(min_pixels * 1.05)) max_pixels = pixels_per_frame * num_frames resized_height, resized_width = smart_resize( num_frames, height, width, temporal_factor=temporal_patch_size, factor=factor, min_pixels=min_pixels, max_pixels=max_pixels, ) grid_h, grid_w = resized_height // patch_size, resized_width // patch_size grid_t = (num_frames + -num_frames % temporal_patch_size) // temporal_patch_size return grid_t * grid_h * grid_w def preprocess(self, videos, **kwargs): _check_video_metadata_count(videos, kwargs.get("video_metadata")) # the shared `preprocess` fills every unset option from the instance before `sample_frames` runs, so the # caller's options are told apart from the instance ones here: a caller option beats an instance one, and a # configured `num_frames` beats the default fps num_frames, fps = kwargs.get("num_frames"), kwargs.get("fps") if num_frames is not None and fps is not None: raise ValueError("`num_frames` and `fps` are mutually exclusive arguments, please use only one!") if num_frames is not None or (fps is None and self.num_frames is not None): kwargs["fps"] = None elif fps is not None: kwargs["num_frames"] = None return super().preprocess(videos, **kwargs) __all__ = ["Lfm3VlVideoProcessor"]