# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/video_llama_3/modular_video_llama_3.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_video_llama_3.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # Copyright 2025 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 numpy as np from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import auto_docstring, logging from ...video_utils import VideoInput logger = logging.get_logger(__name__) class VideoLlama3ProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "padding": False, "return_mm_token_type_ids": False, }, "videos_kwargs": {"return_metadata": True}, } @auto_docstring class VideoLlama3Processor(ProcessorMixin): def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs): self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token self.image_token_id = ( tokenizer.image_token_id if getattr(tokenizer, "image_token_id", None) else tokenizer.convert_tokens_to_ids(self.image_token) ) self.video_token_id = ( tokenizer.video_token_id if getattr(tokenizer, "video_token_id", None) else tokenizer.convert_tokens_to_ids(self.video_token) ) super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template) @auto_docstring def __call__( self, images: ImageInput = None, text: TextInput | PreTokenizedInput | list[TextInput] | list[PreTokenizedInput] = None, videos: VideoInput = None, **kwargs: Unpack[VideoLlama3ProcessorKwargs], ) -> BatchFeature: r""" Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is not `None`. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. - **pixel_values_videos** -- Pixel values of videos to be fed to a model. Returned when `videos` is not `None`. - **image_grid_thw** -- List of image 3D grid in LLM. Returned when `images` is not `None`. - **video_grid_thw** -- List of video 3D grid in LLM. Returned when `videos` is not `None`. """ output_kwargs = self._merge_kwargs( VideoLlama3ProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) image_inputs = videos_inputs = {} if images is not None: image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) image_grid_thw = image_inputs["image_grid_thw"] image_merge_sizes = image_inputs["image_merge_sizes"] else: image_grid_thw = image_merge_sizes = [] if videos is not None: videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) num_video_tokens = [ grid_thw.prod() // merge_size**2 for grid_thw, merge_size in zip(videos_inputs["video_grid_thw"], videos_inputs["video_merge_sizes"]) ] video_compression_masks = videos_inputs["video_compression_mask"].split(num_video_tokens) if not kwargs.get("return_metadata"): video_metadata = videos_inputs.pop("video_metadata") else: video_metadata = videos_inputs["video_metadata"] timestamps = [] for metadata in video_metadata: if metadata.fps is None: logger.warning_once( "VideoLLaMA4 requires frame timestamps to construct prompts, but the `fps` of the input video could not be inferred. " "Probably `video_metadata` was missing from inputs and you passed pre-sampled frames. " "Defaulting to `fps=1`. Please provide `video_metadata` for more accurate results." ) metadata.fps = 1 if metadata.fps is None else metadata.fps timestamps.append(metadata.timestamps) else: video_compression_masks = timestamps = [] if not isinstance(text, list): text = [text] text = text.copy() # below lines change text in-place if images is not None: image_index = 0 for i in range(len(text)): while self.image_token in text[i]: num_image_tokens = image_grid_thw[image_index].prod() // (image_merge_sizes[image_index] ** 2) text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) image_index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if videos is not None: video_index = 0 for i in range(len(text)): while self.video_token in text[i]: frame_compression_masks = video_compression_masks[video_index].split( len(video_compression_masks[video_index]) // len(timestamps[video_index]) ) num_frame_tokens = [x.sum() for x in frame_compression_masks] frame_prompts = [ f"Time {t:.1f}s:" + "<|placeholder|>" * n for n, t in zip(num_frame_tokens, timestamps[video_index]) ] text[i] = text[i].replace(self.video_token, ",".join(frame_prompts), 1) video_index += 1 text[i] = text[i].replace("<|placeholder|>", self.video_token) return_tensors = output_kwargs["text_kwargs"].pop("return_tensors", None) return_mm_token_type_ids = output_kwargs["text_kwargs"].pop("return_mm_token_type_ids", False) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"], return_tensors=None) self._check_special_mm_tokens(text, text_inputs, modalities=["image", "video"]) if return_mm_token_type_ids: array_ids = np.array(text_inputs["input_ids"]) mm_token_type_ids = np.zeros_like(text_inputs["input_ids"]) mm_token_type_ids[array_ids == self.image_token_id] = 1 text_inputs["mm_token_type_ids"] = mm_token_type_ids.tolist() return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}, tensor_type=return_tensors) def _get_num_multimodal_tokens(self, image_sizes=None, video_sizes=None, **kwargs): """ Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. Args: image_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (height, width) per each image. video_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (num_frames, height, width) per each video. Returns: `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided input modalities, along with other useful data. """ vision_data = {} if image_sizes is not None: images_kwargs = VideoLlama3ProcessorKwargs._defaults.get("images_kwargs", {}) images_kwargs.update(kwargs) merge_size = images_kwargs.get("merge_size", None) or self.image_processor.merge_size num_image_patches = [ self.image_processor.get_number_of_image_patches(*image_size, images_kwargs) for image_size in image_sizes ] num_image_tokens = [(num_patches // merge_size**2) for num_patches in num_image_patches] vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) if video_sizes is not None: videos_kwargs = VideoLlama3ProcessorKwargs._defaults.get("videos_kwargs", {}) videos_kwargs.update(kwargs) num_video_patches = [ self.video_processor.get_number_of_video_patches(*video_size, videos_kwargs) for video_size in video_sizes ] num_video_tokens = [(num_patches // merge_size**2) for num_patches in num_video_patches] vision_data["num_video_tokens"] = num_video_tokens return MultiModalData(**vision_data) def post_process_image_text_to_text( self, generated_outputs, skip_special_tokens=True, clean_up_tokenization_spaces=False, **kwargs ): """ Post-process the output of the model to decode the text. Args: generated_outputs (`torch.Tensor` or `np.ndarray`): The output of the model `generate` function. The output is expected to be a tensor of shape `(batch_size, sequence_length)` or `(sequence_length,)`. skip_special_tokens (`bool`, *optional*, defaults to `True`): Whether or not to remove special tokens in the output. Argument passed to the tokenizer's `batch_decode` method. clean_up_tokenization_spaces (`bool`, *optional*, defaults to `False`): Whether or not to clean up the tokenization spaces. Argument passed to the tokenizer's `batch_decode` method. **kwargs: Additional arguments to be passed to the tokenizer's `batch_decode method`. Returns: `list[str]`: The decoded text. """ return self.tokenizer.batch_decode( generated_outputs, skip_special_tokens=skip_special_tokens, clean_up_tokenization_spaces=clean_up_tokenization_spaces, **kwargs, ) __all__ = ["VideoLlama3Processor"]