# coding=utf-8 # Copyright 2025 The HuggingFace Inc. team. # # 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. """ Image/Text processor class for SigLIP2. """ from typing import List, Optional, Union from ...feature_extraction_utils import BatchFeature from ...image_utils import ImageInput from ...processing_utils import ImagesKwargs, ProcessingKwargs, ProcessorMixin, Unpack from ...tokenization_utils_base import PreTokenizedInput, TextInput class Siglip2ImagesKwargs(ImagesKwargs, total=False): max_num_patches: Optional[int] patch_size: Optional[int] class Siglip2ProcessorKwargs(ProcessingKwargs, total=False): images_kwargs: Siglip2ImagesKwargs _defaults = { "text_kwargs": { "padding": "max_length", "truncation": True, "max_length": 64, }, "images_kwargs": { "max_num_patches": 256, "patch_size": 16, }, } class Siglip2Processor(ProcessorMixin): r""" Constructs a Siglip2 processor which wraps a Siglip2 image processor and a Gemma tokenizer into a single processor. [`Siglip2Processor`] offers all the functionalities of [`Siglip2ImageProcessor`] and [`GemmaTokenizerFast`]. See the [`~Siglip2Processor.__call__`] and [`~Siglip2Processor.decode`] for more information. Args: image_processor ([`Siglip2ImageProcessor`]): The image processor is a required input. tokenizer ([`GemmaTokenizerFast`]): The tokenizer is a required input. """ attributes = ["image_processor", "tokenizer"] image_processor_class = "AutoImageProcessor" tokenizer_class = "AutoTokenizer" def __init__(self, image_processor, tokenizer): super().__init__(image_processor, tokenizer) def __call__( self, images: Optional[Union[ImageInput, List[ImageInput], List[List[ImageInput]]]] = None, text: Optional[Union[TextInput, "PreTokenizedInput", List[TextInput], List["PreTokenizedInput"]]] = None, audio=None, videos=None, **kwargs: Unpack[Siglip2ProcessorKwargs], ) -> 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 GemmaTokenizerFast's [`~GemmaTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` argument to Siglip2ImageProcessor's [`~Siglip2ImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring of the above two methods for more information. 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). padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `max_length`): Select a strategy to pad the returned sequences (according to the model's padding side and padding index) among: - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum acceptable input length for the model if that argument is not provided. - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence if provided). - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different lengths). max_length (`int`, *optional*, defaults to 64): Maximum length of the returned list and optionally padding length (see above). truncation (`bool`, *optional*, defaults to `True`): Activates truncation to cut input sequences longer than `max_length` to `max_length`. return_tensors (`str` or [`~utils.TensorType`], *optional*, defaults to `'pt'`): 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_attention_mask** -- Attention mask for the pixel values. Returned when `images` is not `None`. - **spatial_shapes** -- The number of horizontal and vertical patches per image. Returned when `images` is not `None`. """ output_kwargs = self._merge_kwargs( Siglip2ProcessorKwargs, tokenizer_init_kwargs=self.tokenizer.init_kwargs, **kwargs, ) if text is None and images is None: raise ValueError("You have to specify either text or images. Both cannot be none.") if text is not None: encoding = self.tokenizer(text, **output_kwargs["text_kwargs"]) if images is not None: image_features = self.image_processor(images, **output_kwargs["images_kwargs"]) if text is not None and images is not None: encoding.update(image_features) return encoding elif text is not None: return encoding else: return_tensors = output_kwargs["common_kwargs"]["return_tensors"] return BatchFeature(data=dict(**image_features), tensor_type=return_tensors) def decode(self, *args, **kwargs): """ This method forwards all its arguments to Siglip2Tokenizer's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.decode(*args, **kwargs) def batch_decode(self, *args, **kwargs): """ This method forwards all its arguments to Siglip2Tokenizer's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. """ return self.tokenizer.batch_decode(*args, **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__ = ["Siglip2Processor"]