fThKSrSSKJrJrJr SSKJr SSKJr SSK J r J r J r J r SSKJrJr SSKJr "S S \ S S 9r\R("\5r"S S\ 5rS/rg)z$ Image/Text processor class for GIT )ListOptionalUnion) BatchFeature) ImageInput)ProcessingKwargsProcessorMixinUnpack!_validate_images_text_input_order)PreTokenizedInput TextInput)loggingc\rSrSr0rSrg)GitProcessorKwargsN)__name__ __module__ __qualname____firstlineno__ _defaults__static_attributes__r^/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/git/processing_git.pyrrsIrrF)totalc ^\rSrSrSrSS/rSrSrU4SjrSS\ \ S \ \ \ \ \\ \\ 4S \\S \4S jjrS rSr\S5rSrU=r$) GitProcessor#a Constructs a GIT processor which wraps a CLIP image processor and a BERT tokenizer into a single processor. [`GitProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`BertTokenizerFast`]. See the [`~GitProcessor.__call__`] and [`~GitProcessor.decode`] for more information. Args: image_processor ([`AutoImageProcessor`]): The image processor is a required input. tokenizer ([`AutoTokenizer`]): The tokenizer is a required input. image_processor tokenizerAutoImageProcessor AutoTokenizercF>[TU]X5 URUlg)N)super__init__r current_processor)selfr r! __class__s rr&GitProcessor.__init__5s 4!%!5!5rimagestextkwargsreturnc zUcUc [S5e[X5upUR"[4SURR 0UD6n0nUb'UR"U40USD6nUR U5 Ub'UR"U40USD6n UR U 5 [XvSRS5S9$)a, Main method to prepare for the model one or several sequences(s) and image(s). This method forwards the `text` and `kwargs` 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` and `kwrags` arguments to CLIPImageProcessor's [`~CLIPImageProcessor.__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 (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`, *optional*): 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: [`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`. z?You have to specify either text or images. Both cannot be none.tokenizer_init_kwargs text_kwargs images_kwargs common_kwargsreturn_tensors)data tensor_type) ValueErrorr _merge_kwargsrr! init_kwargsupdater rget) r(r+r,audiovideosr- output_kwargsr5 text_featuresimage_featuress r__call__GitProcessor.__call__9sR >r)r')NNNN)rrrr__doc__ attributesimage_processor_classtokenizer_classr&rrrrr rr rrrArDrIpropertyrOr __classcell__)r)s@rrr#s $[1J0%O6 (,hl =i$=iuY(94 ?DQbLccde=i +, =i =i~<6??rrN)rQtypingrrrfeature_extraction_utilsr image_utilsrprocessing_utilsr r r r tokenization_utils_baser rutilsrr get_loggerrloggerr__all__rrrr`s^)(4%kkC)   H %e?>e?P  r