eTh SSKJrJrJrJr SSKJr SSKJr SSK J r J r J r SSK JrJr SSKJr SS KJr "S S \ S S 9r"SS\ 5rS/rg))DictListOptionalUnion) BatchFeature) ImageInput)ProcessingKwargsProcessorMixinUnpack)PreTokenizedInput TextInput) TensorType) AutoTokenizerc>\rSrSrSS0SSS.\R S.rSrg) AriaProcessorKwargspaddingF)max_image_size split_image) text_kwargs images_kwargsreturn_tensorsN)__name__ __module__ __qualname____firstlineno__rPYTORCH _defaults__static_attributes__r`/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/aria/processing_aria.pyrrs- u "  %,, Ir$rF)totalc ^\rSrSrSrSS/rSS/rSrSrSS\ \ \ 4S\ \ S\ \ \ \\4\44U4S jjjrSS \ \\\\\\4S \ \S \\S \4SjjrSrSr\S5rSrU=r$) AriaProcessor,a AriaProcessor is a processor for the Aria model which wraps the Aria image preprocessor and the LLama slow tokenizer. Args: image_processor (`AriaImageProcessor`, *optional*): The AriaImageProcessor to use for image preprocessing. tokenizer (`PreTrainedTokenizerBase`, *optional*): An instance of [`PreTrainedTokenizerBase`]. This should correspond with the model's text model. 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. size_conversion (`Dict`, *optional*): A dictionary indicating size conversions for images. image_processor tokenizer chat_templatesize_conversionAriaImageProcessorrc,>UcSSS.nUR5VVs0sHupV[U5U_M snnUlURUlURUlUbUR cUR Ul[TU]!XUS9 gs snnf)N)ir)r,) itemsintr- image_tokenimage_token_id pad_token unk_tokensuper__init__)selfr*r+r,r-kv __class__s r%r9AriaProcessor.__init__@s  "$'c2O6E6K6K6MN6MdaA 6MN$00'66  Y%8%8%@"+"5"5I  =Q OsBtextimageskwargsreturnc UR"[4SURR0UD6n[ U[ 5(aU/nO8[ U[ 5(d#[ US[ 5(d [S5eUbUR"U40USD6nURURRSn/n URS5U-n UHQn U RURRURRU -5n U RU 5 MS O0nUn USRS S5n UR"U 40USD6n UR!XS /S 9 [#0U EUEU S 9$) a Main method to prepare for the model one or several sequences(s) and image(s). Args: text (`TextInput`, `PreTokenizedInput`, `List[TextInput]`, `List[PreTokenizedInput]`): 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 (`ImageInput`): 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. 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_mask** -- Pixel mask to be fed to a model. Returned when `images` is not `None`. tokenizer_init_kwargsrzAInvalid input text. Please provide a string, or a list of stringsNrr num_cropsrrimage) modalities)data tensor_type) _merge_kwargsrr+ init_kwargs isinstancestrlist ValueErrorr*r- pixel_valuesshapepopreplacer4append_check_special_mm_tokensr)r:r?r@audiovideosrA output_kwargs image_inputstokens_per_imageprompt_stringsrEsampler text_inputss r%__call__AriaProcessor.__call__Rs<**  "&.."<"<  dC 6DD$'' 47C0H0H`a a  //0L $33L4M4M4S4STU4VW N$((58HHI(B(BDNND^D^ajDjk%%f- L!N&}599:JDQnn^T}]7ST  %%nwi%X!@K!@? RR*(, >^I0$y/4HYCZZ[>^$>^ ,- >^ >^@<6XXr$r(N)typingrrrrimage_processing_utilsr image_utilsr processing_utilsr r r tokenization_utilsr rutilsrautorrr(__all__rr$r%rsL*/.2%HH>  *% |XN|X~  r$