# 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # This file was automatically generated from src/transformers/models/glm4v/modular_glm4v.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_glm4v.py file directly. One of our CI enforces this. # 🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨🚨 # coding=utf-8 # Copyright 2025 The ZhipuAI Inc. team and 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. from typing import Optional, Union import numpy as np from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ImagesKwargs, MultiModalData, ProcessingKwargs, ProcessorMixin, Unpack, VideosKwargs from ...tokenization_utils_base import PreTokenizedInput, TextInput from ...video_utils import VideoInput class Glm4vVideosProcessorKwargs(VideosKwargs, total=False): fps: Union[list[float], float] class Glm4vImagesKwargs(ImagesKwargs): patch_size: Optional[int] temporal_patch_size: Optional[int] merge_size: Optional[int] class Glm4vProcessorKwargs(ProcessingKwargs, total=False): images_kwargs: Glm4vImagesKwargs videos_kwargs: Glm4vVideosProcessorKwargs _defaults = { "text_kwargs": { "padding": False, "return_mm_token_type_ids": False, }, } class Glm4vProcessor(ProcessorMixin): r""" Constructs a GLM-4V processor which wraps a GLM-4V image processor and a GLM-4 tokenizer into a single processor. [`~Glm4vProcessor.__call__`] and [`~Glm4vProcessor.decode`] for more information. Args: image_processor ([`Glm4vProcessor`], *optional*): The image processor is a required input. tokenizer ([`PreTrainedTokenizerFast`], *optional*): The tokenizer is a required input. video_processor ([`Glm4vVideoProcessor`], *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. """ attributes = ["image_processor", "tokenizer", "video_processor"] image_processor_class = "AutoImageProcessor" video_processor_class = "AutoVideoProcessor" tokenizer_class = ("PreTrainedTokenizer", "PreTrainedTokenizerFast") def __init__(self, image_processor=None, tokenizer=None, video_processor=None, chat_template=None, **kwargs): super().__init__(image_processor, tokenizer, video_processor, chat_template=chat_template) self.image_token = "<|image|>" if not hasattr(tokenizer, "image_token") else tokenizer.image_token self.video_token = "<|video|>" 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) ) def __call__( self, images: ImageInput = None, text: Union[TextInput, PreTokenizedInput, list[TextInput], list[PreTokenizedInput]] = None, videos: VideoInput = None, **kwargs: Unpack[Glm4vProcessorKwargs], ) -> 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 PreTrainedTokenizerFast's [`~PreTrainedTokenizerFast.__call__`] if `text` is not `None` to encode the text. 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`. """ output_kwargs = self._merge_kwargs( Glm4vProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if images is not None: image_inputs = self.image_processor(images=images, **output_kwargs["images_kwargs"]) image_grid_thw = image_inputs["image_grid_thw"] else: image_inputs = {} image_grid_thw = None if videos is not None: videos_inputs = self.video_processor(videos=videos, **output_kwargs["videos_kwargs"]) timestamps = videos_inputs.pop("timestamps") video_grid_thw = videos_inputs["video_grid_thw"] else: videos_inputs = {} timestamps = [] video_grid_thw = None if not isinstance(text, list): text = [text] text = text.copy() # below lines change text in-place if image_grid_thw 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 video_grid_thw is not None: merge_length = self.video_processor.merge_size**2 video_index = 0 for i in range(len(text)): while self.video_token in text[i]: num_frames = video_grid_thw[video_index][0] video_structure = "" if hasattr(timestamps, "tolist"): timestamps_list = timestamps.tolist()[0] else: timestamps_list = timestamps[0] if isinstance(timestamps[0], list) else timestamps unique_timestamps = [] for idx in range(0, len(timestamps_list)): unique_timestamps.append(timestamps_list[idx]) selected_timestamps = unique_timestamps[:num_frames] while len(selected_timestamps) < num_frames: selected_timestamps.append(selected_timestamps[-1] if selected_timestamps else 0) for frame_idx in range(num_frames): timestamp_sec = selected_timestamps[frame_idx] frame_structure = f"<|begin_of_image|>{self.image_token}<|end_of_image|>{timestamp_sec}" video_structure += frame_structure text[i] = text[i].replace(self.video_token, video_structure, 1) num_image_tokens = ( video_grid_thw[video_index].prod() // merge_length // video_grid_thw[video_index][0] ) for frame_idx in range(num_frames): if self.image_token in text[i]: text[i] = text[i].replace(self.image_token, "<|placeholder|>" * num_image_tokens, 1) video_index += 1 text[i] = text[i].replace("<|placeholder|>", self.image_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"]) 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 = Glm4vProcessorKwargs._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 = Glm4vProcessorKwargs._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 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__ = ["Glm4vProcessor"]