eThSrSSKJrJr SSKJr SSKJrJrJ r J r SSK J r J r Jr "SS\S S 9r"S S \5rS /rg )z& Image/Text processor class for ALIGN )ListUnion) ImageInput)ProcessingKwargsProcessorMixinUnpack!_validate_images_text_input_order) BatchEncodingPreTokenizedInput TextInputc"\rSrSrSSSS.0rSrg)AlignProcessorKwargs text_kwargs max_length@)paddingrN)__name__ __module__ __qualname____firstlineno__ _defaults__static_attributes__rb/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/align/processing_align.pyrrs # IrrF)totalc ^\rSrSrSrSS/rSrSrU4SjrSS\ S \ \ \ \ \ \ \ 4S \\S \4S jjrS rSr\S5rSrU=r$)AlignProcessor$a Constructs an ALIGN processor which wraps [`EfficientNetImageProcessor`] and [`BertTokenizer`]/[`BertTokenizerFast`] into a single processor that inherits both the image processor and tokenizer functionalities. See the [`~AlignProcessor.__call__`] and [`~OwlViTProcessor.decode`] for more information. The preferred way of passing kwargs is as a dictionary per modality, see usage example below. ```python from transformers import AlignProcessor from PIL import Image model_id = "kakaobrain/align-base" processor = AlignProcessor.from_pretrained(model_id) processor( images=your_pil_image, text=["What is that?"], images_kwargs = {"crop_size": {"height": 224, "width": 224}}, text_kwargs = {"padding": "do_not_pad"}, common_kwargs = {"return_tensors": "pt"}, ) ``` Args: image_processor ([`EfficientNetImageProcessor`]): The image processor is a required input. tokenizer ([`BertTokenizer`, `BertTokenizerFast`]): The tokenizer is a required input. image_processor tokenizerEfficientNetImageProcessor) BertTokenizerBertTokenizerFastc$>[TU]X5 gN)super__init__)selfr"r# __class__s rr*AlignProcessor.__init__Fs 4rimagestextkwargsreturnc UcUc [S5e[X5upUR"[4SURR 0UD6nUbUR"U40USD6nUbUR "U40USD6nSUS;aUSRSS5n UbUbWRWS'U$UbW$[[S 0WD6W S 9$) a Main method to prepare text(s) and image(s) to be fed as input to the model. This method forwards the `text` arguments to BertTokenizerFast's [`~BertTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` arguments to EfficientNetImageProcessor's [`~EfficientNetImageProcessor.__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]`): 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). 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: [`BatchEncoding`]: A [`BatchEncoding`] 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`. Nz'You must specify either text or images.tokenizer_init_kwargsr images_kwargsreturn_tensors common_kwargs pixel_values)data tensor_typer) ValueErrorr _merge_kwargsrr# init_kwargsr"popr7r dict) r+r.r/audiovideosr0 output_kwargsencodingimage_featuresr5s r__call__AlignProcessor.__call__IsL fromkeys)r+tokenizer_input_namesimage_processor_input_namess rrO AlignProcessor.model_input_namess< $ @ @&*&:&:&L&L#DMM"7"UVWWrr)NNNN)rrrr__doc__ attributesimage_processor_classtokenizer_classr*rrr r rr rr rDrGrLpropertyrOr __classcell__)r,s@rr r $s:$[1J8r`sH%kkRR+5zX^zXz  r