# Copyright (c) 2025 PaddlePaddle Authors. 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. from typing import List, Union import numpy as np import torch from transformers.feature_extraction_utils import BatchFeature from transformers.processing_utils import ( ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs, ) from transformers.tokenization_utils_base import PreTokenizedInput, TextInput ImageInput = Union[ "PIL.Image.Image", np.ndarray, "torch.Tensor", List["PIL.Image.Image"], List[np.ndarray], List["torch.Tensor"], ] # noqa VideoInput = Union[ List["PIL.Image.Image"], "np.ndarray", "torch.Tensor", List["np.ndarray"], List["torch.Tensor"], List[List["PIL.Image.Image"]], List[List["np.ndarrray"]], List[List["torch.Tensor"]], ] # noqa class PaddleOCRVLVideosProcessorKwargs(VideosKwargs, total=False): fps: Union[List[float], float] class PaddleOCRVLProcessorKwargs(ProcessingKwargs, total=False): videos_kwargs: PaddleOCRVLVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, }, "videos_kwargs": {"fps": 2.0}, } class PaddleOCRVLProcessor(ProcessorMixin): r""" [`PaddleOCRVLProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`Qwen2TokenizerFast`]. See the [`~PaddleOCRVLProcessor.__call__`] and [`~PaddleOCRVLProcessor.decode`] for more information. Args: image_processor ([`SiglipImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`Qwen2TokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = [ "chat_template", "image_std", "min_pixels", "image_mean", "merge_size", "image_processor_type", "temporal_patch_size", "patch_size", "max_pixels", ] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__( self, image_processor=None, tokenizer=None, chat_template=None, **kwargs ): self.image_token = ( "<|IMAGE_PLACEHOLDER|>" 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 ) super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, images: ImageInput = None, text: Union[ TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput] ] = None, videos: VideoInput = None, **kwargs: Unpack[PaddleOCRVLProcessorKwargs], ) -> BatchFeature: """ Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the vision inputs, this method forwards the `vision_infos` and `kwrags` arguments to SiglipImageProcessor's [`~SiglipImageProcessor.__call__`] if `vision_infos` is not `None`. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of images to be prepared. 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]`, `List[List[str]]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). videos (`np.ndarray`, `torch.Tensor`, `List[np.ndarray]`, `List[torch.Tensor]`): The image or batch of videos to be prepared. Each video can be a 4D NumPy array or PyTorch tensor, or a nested list of 3D frames. Both channels-first and channels-last formats are supported. 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 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`. - **second_per_grid_ts** -- List of video seconds per time grid. Returned when `videos` is not `None`. """ output_kwargs = self._merge_kwargs( PaddleOCRVLProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor(images=images, return_tensors="pt") image_inputs["pixel_values"] = image_inputs["pixel_values"] image_grid_thw = image_inputs["image_grid_thw"] else: image_inputs = {} image_grid_thw = None if videos is not None: # TODO: add video processing videos_inputs = self.image_processor( images=None, videos=videos, **output_kwargs["images_kwargs"] ) video_grid_thw = videos_inputs["video_grid_thw"] fps = output_kwargs["videos_kwargs"].pop("fps", 2.0) if isinstance(fps, (int, float)): second_per_grid_ts = [ self.image_processor.temporal_patch_size / fps ] * len(video_grid_thw) elif hasattr(fps, "__len__") and len(fps) == len(video_grid_thw): second_per_grid_ts = [ self.image_processor.temporal_patch_size / tmp for tmp in fps ] else: raise ValueError( f"The length of fps ({len(fps) if hasattr(fps, '__len__') else fps}) must be equal to the length of video_grid_thw ({len(video_grid_thw)}) or fps should be a single number." ) videos_inputs.update( {"second_per_grid_ts": torch.tensor(second_per_grid_ts)} ) else: videos_inputs = {} video_grid_thw = None if not isinstance(text, list): text = [text] if image_grid_thw is not None: index = 0 for i in range(len(text)): while self.image_token in text[i]: text[i] = text[i].replace( self.image_token, "<|placeholder|>" * ( image_grid_thw[index].prod() // self.image_processor.merge_size // self.image_processor.merge_size ), 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if video_grid_thw is not None: index = 0 for i in range(len(text)): while self.video_token in text[i]: text[i] = text[i].replace( self.video_token, "<|placeholder|>" * ( video_grid_thw[index].prod() // self.image_processor.merge_size // self.image_processor.merge_size ), 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.video_token) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) return BatchFeature(data={**text_inputs, **image_inputs, **videos_inputs}) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Qwen2TokenizerFast'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 Qwen2TokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) 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, ) @property def model_input_names(self): tokenizer_input_names = self.tokenizer.model_input_names image_processor_input_names = self.image_processor.model_input_names names_from_processor = list( dict.fromkeys(tokenizer_input_names + image_processor_input_names) ) return names_from_processor + ["second_per_grid_ts"] __all__ = ["PaddleOCRVLProcessor", "PaddleOCRVLProcessor"]