# coding=utf-8 # Copyright 2025 Deepseek AI and The HuggingFace 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 Janus. """ from typing import List, Union from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ProcessingKwargs, ProcessorMixin, TextKwargs, Unpack from ...tokenization_utils_base import ( PreTokenizedInput, TextInput, ) from ...utils import logging logger = logging.get_logger(__name__) DEFAULT_SYSTEM_PROMPT = ( "You are a helpful language and vision assistant. " "You are able to understand the visual content that the user provides, " "and assist the user with a variety of tasks using natural language.\n\n" ) class JanusTextKwargs(TextKwargs, total=False): generation_mode: str class JanusProcessorKwargs(ProcessingKwargs, total=False): text_kwargs: JanusTextKwargs _defaults = { "text_kwargs": {"padding": False, "generation_mode": "text"}, "common_kwargs": {"return_tensors": "pt"}, } class JanusProcessor(ProcessorMixin): r""" Constructs a Janus processor which wraps a Janus Image Processor and a Llama tokenizer into a single processor. [`JanusProcessor`] offers all the functionalities of [`JanusImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~JanusProcessor.__call__`] and [`~JanusProcessor.decode`] for more information. Args: image_processor ([`JanusImageProcessor`]): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`]): 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. use_default_system_prompt (`str`, *optional*, defaults to `False`): Use default system prompt for Text Generation. """ attributes = ["image_processor", "tokenizer"] valid_kwargs = ["chat_template", "use_default_system_prompt"] image_processor_class = "JanusImageProcessor" tokenizer_class = "LlamaTokenizerFast" def __init__(self, image_processor, tokenizer, chat_template=None, use_default_system_prompt=False, **kwargs): self.num_image_tokens = 576 self.image_token = tokenizer.image_token self.image_start_token = tokenizer.boi_token self.image_end_token = tokenizer.eoi_token self.use_default_system_prompt = use_default_system_prompt super().__init__(image_processor, tokenizer, chat_template=chat_template) def __call__( self, text: Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None, images: ImageInput = None, videos=None, audio=None, **kwargs: Unpack[JanusProcessorKwargs], ) -> 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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to JanusImageProcessor's [`~JanusImageProcessor.__call__`] if `images` is not `None`. Please refer to the doctsring of the above two methods for more information. Args: 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). 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. 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`. """ output_kwargs = self._merge_kwargs( JanusProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs ) if text is None and images is None: raise ValueError("You must specify either text or images.") if text is not None: if isinstance(text, str): text = [text] elif not (isinstance(text, (list, tuple)) and all(isinstance(t, str) for t in text)): raise ValueError("Invalid input text. Please provide a string, or a list of strings") generation_mode = output_kwargs["text_kwargs"].pop("generation_mode") # Replace the image token with expanded image tokens. prompt_strings = [] one_img_tokens = self.image_start_token + (self.image_token * self.num_image_tokens) + self.image_end_token for prompt in text: prompt = prompt.replace(self.image_token, one_img_tokens) if self.use_default_system_prompt and generation_mode == "text": prompt = DEFAULT_SYSTEM_PROMPT + prompt if generation_mode == "image": prompt += self.image_start_token prompt_strings.append(prompt) data = self.tokenizer(prompt_strings, **output_kwargs["text_kwargs"]) # Process images if pixel values are provided. if images is not None and generation_mode != "image": data["pixel_values"] = self.image_processor(images=images, **output_kwargs["images_kwargs"])[ "pixel_values" ] return BatchFeature(data=data) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to LlamaTokenizerFast'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 LlamaTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def postprocess(self, images: ImageInput, **kwargs): """ Forwards all arguments to the image processor's `postprocess` method. Refer to the original method's docstring for more details. """ return self.image_processor.postprocess(images, **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__ = ["JanusProcessor"]