# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/Keye/modular_Keye.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_Keye.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # 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. from typing import List, Union import numpy as np from transformers.feature_extraction_utils import BatchFeature #from transformers.image_utils import ImageInput, VideoInput from transformers.processing_utils import ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs from transformers.tokenization_utils_base import PreTokenizedInput, TextInput from .image_processing_keye import SiglipImageProcessor import torch from itertools import chain 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 KeyeVideosProcessorKwargs(VideosKwargs, total=False): fps: Union[List[float], float] class KeyeProcessorKwargs(ProcessingKwargs, total=False): videos_kwargs: KeyeVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, }, "videos_kwargs": {"fps": 2.0}, } class KeyeProcessor(ProcessorMixin): r""" [`KeyeProcessor`] offers all the functionalities of [`SiglipImageProcessor`] and [`Qwen2TokenizerFast`]. See the [`~KeyeProcessor.__call__`] and [`~KeyeProcessor.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 = ("Qwen2Tokenizer", "Qwen2TokenizerFast") def __init__(self, image_processor=None, tokenizer=None, chat_template=None, **kwargs): self.image_token = "<|image_pad|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token self.vision_start_token = "<|vision_start|>" if not hasattr(tokenizer, "vision_start_token") else tokenizer.vision_start_token self.video_token = "<|video_pad|>" if not hasattr(tokenizer, "video_token") else tokenizer.video_token self.frame_token = "<|frame|>" if not hasattr(tokenizer, "frame_token") else tokenizer.frame_token self.fast_video_token = "<|fast_video_pad|>" if not hasattr(tokenizer, "fast_video_token") else tokenizer.fast_video_token self.fast_start = "<|fast_start|>" if not hasattr(tokenizer, "fast_start") else tokenizer.fast_start self.fast_end = "<|fast_end|>" if not hasattr(tokenizer, "fast_end") else tokenizer.fast_end self.image_info_tag = "<|image_info|>" if not hasattr(tokenizer, "image_info_tag") else tokenizer.image_info_tag super().__init__(image_processor, tokenizer, chat_template=chat_template) # self.fast_patch_size = 16 # self.fast_image_processor = SiglipImageProcessor(patch_size=self.fast_patch_size) self.slowfast = True def __call__( self, images: ImageInput = None, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, videos: VideoInput = None, get_resolution_from_cropped_image_size = None, **kwargs: Unpack[KeyeProcessorKwargs], ) -> 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( KeyeProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: slow_images = images image_inputs = self.image_processor(images=slow_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 all_slow_videos = [] all_fast_videos = [] # 这个是因为视频会划分为多张图片，需要在这个地方提前统计好token量，后面就不清楚界限在哪了 slow_videos_token_nums = [[] for i in range(len(videos))] fast_videos_token_nums = [[] for i in range(len(videos))] all_position = [] for current_index, current_video in enumerate(videos): if len(current_video) == 4: # slow_frames, fast_frames, time_position, slow_fast_order, 这里需要注意的是fast_frames，有可能和slow的长度不等，需要靠slow_fast_order来进行识别 slow_frames, fast_frames, time_position, slow_fast_order = current_video[0], current_video[1], current_video[2], current_video[3] all_position.append((time_position, slow_fast_order)) ####### slow part ######### if slow_frames is not None: slow_videos_inputs = self.image_processor(images=None, videos=slow_frames, **output_kwargs["images_kwargs"]) slow_video_grid_thw = slow_videos_inputs["video_grid_thw"] all_slow_videos.append(slow_videos_inputs) slow_videos_token_nums[current_index] = slow_video_grid_thw.prod(dim=1).tolist() # 当前这个视频的所有token数 else: all_slow_videos.append(None) # 这样的话，slow_fast_order都是1了，这里应该不会用到的 slow_videos_token_nums[current_index] = None # 如果全为fast？但目前不存在这种情况 ########################### ####### fast part ######### if self.slowfast: if fast_frames is not None: fast_videos_inputs = self.image_processor(images=None, videos=fast_frames, **output_kwargs["images_kwargs"]) fast_video_grid_thw = fast_videos_inputs["video_grid_thw"] all_fast_videos.append(fast_videos_inputs) fast_videos_token_nums[current_index] = fast_video_grid_thw.prod(dim=1).tolist() # 当前这个视频的fast的所有token数 else: all_fast_videos.append(None) # 如果全为slow fast_videos_token_nums[current_index] = None ########################### else: slow_frames, fast_frames, slow_fast_order = current_video[0], current_video[1], current_video[2] if kwargs.get("image_video_pad", False): fast_frames = slow_frames slow_fast_order += [1] all_position.append((None, slow_fast_order)) ####### slow part ######### if slow_frames is not None: for each_image in slow_frames: if kwargs.get("image_video_pad", False): slow_videos_inputs = self.image_processor.preprocess(images=None, videos=[each_image], size = {"height": 28, "width": 28}, **output_kwargs["images_kwargs"]) else: slow_videos_inputs = self.image_processor(images=None, videos=[each_image], **output_kwargs["images_kwargs"]) slow_video_grid_thw = slow_videos_inputs["video_grid_thw"] all_slow_videos.append(slow_videos_inputs) slow_videos_token_nums[current_index].append(slow_video_grid_thw.prod(dim=1).item()) # 这里因为没在前面split开，所以要这么写 ########################### else: all_slow_videos.append(None) # 这样的话，slow_fast_order都是1了，这里应该不会用到的 slow_videos_token_nums[current_index] = None # 如果全为fast？但目前不存在这种情况 ####### fast part ######### if self.slowfast: if fast_frames is not None: for each_image in fast_frames: if kwargs.get("image_video_pad", False): fast_videos_inputs = self.image_processor.preprocess(images=None, videos=[each_image], size = {"height": 28, "width": 28}, **output_kwargs["images_kwargs"]) else: fast_videos_inputs = self.image_processor.preprocess(images=None, videos=[each_image], **output_kwargs["images_kwargs"]) fast_video_grid_thw = fast_videos_inputs["video_grid_thw"] all_fast_videos.append(fast_videos_inputs) fast_videos_token_nums[current_index].append(fast_video_grid_thw.prod(dim=1).item()) else: all_fast_videos.append(None) fast_videos_token_nums[current_index] = None ########################### # todo: zdj debug 多次concat速度会慢很多 slow_pixel_values_videos_list = [single_slow_video["pixel_values_videos"] for single_slow_video in all_slow_videos if single_slow_video is not None] slow_video_grid_thw_list = [single_slow_video["video_grid_thw"] for single_slow_video in all_slow_videos if single_slow_video is not None] total_slow_pixel_values_videos = torch.concat(slow_pixel_values_videos_list, dim=0) total_slow_video_grid_thw = torch.concat(slow_video_grid_thw_list, dim=0) # todo: zdj debug end if len(total_slow_pixel_values_videos): videos_inputs = { "pixel_values_videos": total_slow_pixel_values_videos, "video_grid_thw": total_slow_video_grid_thw, } video_grid_thw = videos_inputs["video_grid_thw"] else: videos_inputs = {} video_grid_thw = None if self.slowfast: # todo: zdj debug 多次concat速度会慢很多 fast_pixel_values_videos_list = [single_fast_video["pixel_values_videos"] for single_fast_video in all_fast_videos if single_fast_video is not None] fast_video_grid_thw_list = [single_fast_video["video_grid_thw"] for single_fast_video in all_fast_videos if single_fast_video is not None] # fast_second_per_grid_ts = torch.tensor(list(chain(*fast_second_per_grid_ts_list))) if len(fast_pixel_values_videos_list): videos_inputs["fast_pixel_values_videos"] = torch.concat(fast_pixel_values_videos_list, dim=0) videos_inputs["fast_video_grid_thw"] = torch.concat(fast_video_grid_thw_list, dim=0) fast_video_grid_thw = videos_inputs["fast_video_grid_thw"] else: fast_video_grid_thw = None # todo: zdj debug end else: videos_inputs = {} video_grid_thw = None fast_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]: image_downsample_ratio = self.image_processor.merge_size * self.image_processor.patch_size _, h_merged, w_merged = image_grid_thw[index]// self.image_processor.merge_size image_place_holder_tempale = f"{image_downsample_ratio*h_merged.item()},{image_downsample_ratio*w_merged.item()}" if get_resolution_from_cropped_image_size is not None: raise NotImplementedError image_place_holder_tempale = "" for i_h in range(h_merged.item()): image_place_holder_tempale += "<|mm_pos_start|>" + f"{i_h},{w_merged}" + "<|mm_pos_end|>" + "<|placeholder|>" * w_merged text[i] = text[i].replace( self.image_token, image_place_holder_tempale, 1, ) index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_token) # text[0].count("<|placeholder|>") if video_grid_thw is not None or fast_video_grid_thw is not None: index = 0 for i in range(len(text)): while self.video_token in text[i]: video_place_holder_tempale = "" slow_index = 0 fast_index = 0 for j in range(len(all_position[index][1])): if all_position[index][0] is not None: # 如果有时间戳 video_place_holder_tempale += self.frame_token + format(all_position[index][0][j], ".1f") else: video_place_holder_tempale += self.frame_token if all_position[index][1][j] == 0: # 当前帧是slow？ video_place_holder_tempale += "<|placeholder|>" * (slow_videos_token_nums[index][slow_index]//self.image_processor.merge_size//self.image_processor.merge_size) slow_index += 1 elif all_position[index][1][j] == 1: # 当前帧是fast？ video_place_holder_tempale += self.fast_start + "<|fast_placeholder|>" * (fast_videos_token_nums[index][fast_index]//self.image_processor.merge_size//self.image_processor.merge_size) + self.fast_end fast_index += 1 text[i] = text[i].replace( self.video_token, video_place_holder_tempale, 1, ) index += 1 # self.tokenizer.decode(191678) # self.tokenizer.encode("<|fast_video_pad|>") text[i] = text[i].replace("<|placeholder|>", self.video_token) text[i] = text[i].replace("<|fast_placeholder|>", self.fast_video_token) # text[0].count(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__ = ["KeyeProcessor", "KeyeProcessor_moonvit", "KeyeProcessor"]