# coding=utf-8 # Copyright 2025 Meta Platforms, Inc. and the HuggingFace Inc. 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. """ Processor class for PerceptionLM. """ from typing import Iterable, Union import numpy as np from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput, get_image_size, to_numpy_array from ...processing_utils import MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...utils import logging from ...video_utils import VideoInput logger = logging.get_logger(__name__) class PerceptionLMProcessorKwargs(ProcessingKwargs, total=False): _defaults = { "text_kwargs": { "padding": False, "return_mm_token_type_ids": False, }, } class PerceptionLMProcessor(ProcessorMixin): r""" Constructs a PerceptionLM processor which wraps a PerceptionLM image processor, a PerceptionLM video processor, and a tokenizer into a single processor. [`PerceptionLMProcessor`] offers all the functionalities of [`PerceptionLMImageProcessorFast`], [`PerceptionLMVideoProcessor`], and the tokenizer (e.g. [`LlamaTokenizerFast`]). See the [`~PerceptionLMProcessor.__call__`] and [`~PerceptionLMProcessor.decode`] for more information. Args: video_processor ([`PerceptionLMVideoProcessor`], *optional*): The video processor to process video inputs. image_processor ([`PerceptionLMImageProcessorFast`], *optional*): The image processor to process image inputs. tokenizer ([`LlamaTokenizerFast`] or similar, *optional*): The tokenizer to process text inputs. patch_size (`int`, *optional*): Patch size from the vision tower. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. pooling_ratio (`int`, *optional*, defaults to 2): Pooling ratio for vision tokens. If not 1, 2D adaptive pooling is applied over projected vision tokens. """ attributes = ["video_processor", "image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" video_processor_class = "AutoVideoProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, video_processor=None, image_processor=None, tokenizer=None, patch_size=None, chat_template=None, pooling_ratio=2, **kwargs, ): self.patch_size = patch_size self.pooling_ratio = pooling_ratio self.image_token = tokenizer.image_token self.video_token = tokenizer.video_token self.image_token_id = tokenizer.image_token_id self.video_token_id = tokenizer.video_token_id super().__init__(video_processor, image_processor, tokenizer, chat_template=chat_template) def __call__( self, images: ImageInput = None, text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None, audio=None, videos: VideoInput = None, **kwargs: Unpack[PerceptionLMProcessorKwargs], ) -> BatchFeature: """ Prepares a batch containing one or more sequences of text and/or images and/or videos. If `text` is provided, it is tokenized using the tokenizer. If `images` is provided, they are processed using the image processor. If `videos` is provided, they are processed using the video processor. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`, *optional*): The image or batch of images to be processed. Each image can be a PIL image, NumPy array, or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`str`, `List[str]`, *optional*): The sequence or batch of sequences to be tokenized. Each sequence can be a string. videos (`Any`, *optional*): The video or batch of videos to be processed. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. Returned when `text` is provided. - **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 provided). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is provided. - **pixel_values_videos** -- Video pixel values to be fed to a model. Returned when `videos` is provided. """ if text is None: raise ValueError( "You have to specify at least `text` input. Optionally, you can also specify `images` or `videos`." ) output_kwargs = self._merge_kwargs( PerceptionLMProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) else: image_inputs = {} if videos is not None: videos_inputs = self.video_processor(videos, **output_kwargs["videos_kwargs"]) else: videos_inputs = {} if isinstance(text, str): text = [text] elif not isinstance(text, list) and not isinstance(text[0], str): raise ValueError("Invalid input text. Please provide a string, or a list of strings") # try to expand inputs in processing if we have the necessary parts prompt_strings = [] pixel_values = iter(image_inputs.get("pixel_values", [])) pixel_values_videos = iter(videos_inputs.get("pixel_values_videos", [])) for sample in text: # Replace the media token with the expanded media token sequence sample = self._expand_media_tokens(sample, self.tokenizer.image_token, pixel_values) sample = self._expand_media_tokens(sample, self.tokenizer.video_token, pixel_values_videos) prompt_strings.append(sample) 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(prompt_strings, **output_kwargs["text_kwargs"], return_tensors=None) self._check_special_mm_tokens(prompt_strings, 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}, tensor_type=return_tensors) def _expand_media_tokens(self, sample, media_token: str, media_iter: Iterable): media_count = sample.count(media_token) if media_count > 0: media_list = [next(media_iter) for _ in range(media_count)] sample_splits = sample.split(media_token) media_token_list = [] for media in media_list: height, width = get_image_size(to_numpy_array(media)) num_tiles = media.shape[0] num_media_tokens = ( (height // self.patch_size // self.pooling_ratio) * (width // self.patch_size // self.pooling_ratio) * num_tiles ) media_token_list.append(num_media_tokens) sample = "" for i, num_media_tokens in enumerate(media_token_list): sample += sample_splits[i] sample += media_token * num_media_tokens sample += sample_splits[-1] return sample def _get_num_multimodal_tokens(self, image_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. 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 = PerceptionLMProcessorKwargs._defaults.get("images_kwargs", {}) images_kwargs.update(kwargs) tile_size = images_kwargs.get("tile_size", None) or self.image_processor.tile_size num_image_tokens = [] num_image_patches = [] for height, width in image_sizes: if self.image_processor.vision_input_type == "thumb+tile": aspect_ratio = self.image_processor._fit_image_to_canvas( img_width=width, img_height=height, tile_size=tile_size ) if aspect_ratio is None: aspect_ratio = self.image_processor._find_closest_aspect_ratio( img_width=width, img_height=height, tile_size=tile_size ) num_tiles = aspect_ratio[0] * aspect_ratio[1] + 1 # base image and tiles else: num_tiles = 1 num_image_tokens.append( (tile_size // self.patch_size // self.pooling_ratio) * (tile_size // self.patch_size // self.pooling_ratio) * num_tiles ) num_image_patches.append(num_tiles) vision_data.update({"num_image_tokens": num_image_tokens, "num_image_patches": num_image_patches}) return MultiModalData(**vision_data) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to PerceptionLMTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **kwargs) def decode(self, *args, **kwargs): """ This method forwards all its arguments to PerceptionLMTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **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)) __all__ = ["PerceptionLMProcessor"]