# Modified from https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/qwen2_5_vl/processing_qwen2_5_vl.py # Copyright 2025 The Qwen Team and The HuggingFace Inc. team. All rights reserved. # # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX # and OPT implementations in this library. It has been modified from its # original forms to accommodate minor architectural differences compared # to GPT-NeoX and OPT used by the Meta AI team that trained the model. # # 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 import torch from transformers import Qwen2_5_VLProcessor from transformers.feature_extraction_utils import BatchFeature from transformers.models.qwen2_5_vl.processing_qwen2_5_vl import ( Qwen2_5_VLProcessorKwargs, ) class TimeLensProcessor(Qwen2_5_VLProcessor): r""" Constructs a Qwen2.5-VL processor which wraps a Qwen2.5-VL image processor and a Qwen2 tokenizer into a single processor. [`Qwen2_5_VLProcessor`] offers all the functionalities of [`Qwen2VLImageProcessor`] and [`Qwen2TokenizerFast`]. See the [`~Qwen2_5_VLProcessor.__call__`] and [`~Qwen2_5_VLProcessor.decode`] for more information. Args: image_processor ([`Qwen2VLImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`Qwen2TokenizerFast`], *optional*): The tokenizer is a required input. video_processor ([`Qwen2_5_VLVideoProcessor`], *optional*): The video processor 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. """ def __init__( self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs, ): super().__init__( image_processor, tokenizer, video_processor, chat_template, **kwargs ) # ============ [TimeLens] Modification BEGIN ============ self.vision_start = ( "<|vision_start|>" if not hasattr(tokenizer, "vision_start") else tokenizer.vision_start ) self.vision_end = ( "<|vision_end|>" if not hasattr(tokenizer, "vision_end") else tokenizer.vision_end ) # ============ [TimeLens] Modification END ============== def __call__( self, images=None, text=None, videos=None, **kwargs, ) -> 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 `kwargs` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__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( Qwen2_5_VLProcessorKwargs, 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"] if videos is not None: # ============ [TimeLens] Modification BEGIN ============ # videos is a list of (video_tensor, metadata) tuples videos, metadata = [v[0] for v in videos], [v[1] for v in videos] # Duplicate frames at even indices for cur_video_tensor in videos: cur_video_tensor[1::2] = cur_video_tensor[::2] # Calculate sampled timestamps for each video frames_timestamps = [ [ idx / cur_metadata["fps"] for idx in cur_metadata["frames_indices"][::2] ] for cur_metadata in metadata ] videos_inputs = self.video_processor( videos=videos, **output_kwargs["videos_kwargs"] ) video_grid_thw = videos_inputs["video_grid_thw"] # ============ [TimeLens] Modification END ============== if not isinstance(text, list): text = [text] text = text.copy() # below lines change text in-place if images is not None: merge_length = self.image_processor.merge_size**2 index = 0 for i in range(len(text)): while self.image_token in text[i]: num_image_tokens = image_grid_thw[index].prod() // merge_length text[i] = text[i].replace( self.image_token, "<|placeholder|>" * num_image_tokens, 1 ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) if videos is not None: merge_length = self.video_processor.merge_size**2 index = 0 # ============ [TimeLens] Modification BEGIN ============ for i in range(len(text)): while self.video_token in text[i]: cur_video_tokens = "" num_tokens_per_frame = ( video_grid_thw[index][1:].prod() // merge_length ) per_frame_tokens = ( self.vision_start + "<|placeholder|>" * num_tokens_per_frame + self.vision_end ) for cur_frames_timestamp in frames_timestamps[index]: cur_video_tokens += ( f"{cur_frames_timestamp:.1f}s: " + per_frame_tokens ) text[i] = text[i].replace( self.vision_start + self.video_token + self.vision_end, cur_video_tokens, 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) # modeling_qwen2_5_vl.py calls `.item()` on image_grid_thw to convert t, h, w from tensor to int, so we create image_grid_thw as Tensor to be compatible with `.item()` call image_grid_thw = torch.tensor( [ [1, grid_h, grid_w] for grid_t, grid_h, grid_w in video_grid_thw for _ in range(grid_t) ], dtype=torch.long, ) image_inputs = { "pixel_values": videos_inputs[ "pixel_values_videos" ], # [grid_t * grid_h * grid_w, channel * temporal_patch_size * patch_size * patch_size] = [num_patches, dim] "image_grid_thw": image_grid_thw, } videos_inputs = {} # ============ [TimeLens] Modification END ============== 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", None ) text_inputs = self.tokenizer(text, **output_kwargs["text_kwargs"]) 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, )