fTh%SrSSKJrJr SSKJr SSKJrJrJ r SSK J r J r J r Jr SSKJrJr SSKJr \R("\5r"S S \ S S 9r"S S\ 5rS/rg)z Processor class for Llava. )ListUnion) BatchFeature) ImageInputget_image_sizeto_numpy_array)ProcessingKwargsProcessorMixinUnpack!_validate_images_text_input_order)PreTokenizedInput TextInput)loggingc"\rSrSrSS00S.rSrg)LlavaProcessorKwargspaddingF) text_kwargs images_kwargsN)__name__ __module__ __qualname____firstlineno__ _defaults__static_attributes__rb/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/llava/processing_llava.pyrrs u  IrrF)totalc ^\rSrSrSrSS/r/SQrSrSrSU4Sjjr SS \ S \ \ \ \\ \\ 4S \\S \4S jjrSrSr\S5rSrU=r$)LlavaProcessor(a Constructs a LLaVa processor which wraps a LLaVa image processor and a LLaMa tokenizer into a single processor. [`LlavaProcessor`] offers all the functionalities of [`LlavaImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~LlavaProcessor.__call__`] and [`~LlavaProcessor.decode`] for more information. Args: image_processor ([`LlavaImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. patch_size (`int`, *optional*): Patch size from the vision tower. vision_feature_select_strategy (`str`, *optional*): The feature selection strategy used to select the vision feature from the vision backbone. Should be same as in model's config chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. image_token (`str`, *optional*, defaults to `""`): Special token used to denote image location. num_additional_image_tokens (`int`, *optional*, defaults to 0): Number of additional tokens added to the image embeddings, such as CLS (+1). If the backbone has no CLS or other extra tokens appended, no need to set this arg. image_processor tokenizer) chat_template patch_sizevision_feature_select_strategy image_tokennum_additional_image_tokensAutoImageProcessor AutoTokenizerc >X0lXplX@l[US5(a UROUUl[ USS5(a UR OURUR5Ul[T U]%XUS9 g)Nr)image_token_id)r&) r'r*r(hasattrr)getattrr.convert_tokens_to_idssuper__init__) selfr$r%r'r(r&r)r*kwargs __class__s rr3LlavaProcessor.__init__Ms%+F(.L+4;I}4U4U900[fy"2D99  $ $001A1AB  =Qrimagestextr5returnc rUcUc [S5e[X5upUR"[4SURR 0UD6nUbUR "U40USD6nO0n[U[5(aU/nO8[U[5(d#[US[5(d [S5eUnURS5bUSn [[U S55upXR-XR--UR-n URS:XaU S -n /nUH=n U R!UR"UR"U -5n UR%U 5 M? US R'S S5nUR"U40US D6nUR)XS /S 9 [+0UEUEUS9$)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 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 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 (`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). 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`. Nz7You have to specify at least one of `images` or `text`.tokenizer_init_kwargsrrzAInvalid input text. Please provide a string, or a list of strings pixel_valuesdefaultrreturn_tensorsimage) modalities)data tensor_type) ValueErrorr _merge_kwargsrr% init_kwargsr$ isinstancestrlistgetrr r'r*r(replacer)appendpop_check_special_mm_tokensr)r4r8r9audiovideosr5 output_kwargs image_inputsprompt_stringsr=heightwidthnum_image_tokenssampler@ text_inputss r__call__LlavaProcessor.__call__csN >dlVW W9F **  "&.."<"<   //Y-:XYLL dC 6DD$'' 47C0H0H`a a   N + 7'7L*>,q/+JKMF &// 9( 00 1 22i? A% N(8(8$:J:JM]:]^%%f-'}599:JDQnn^T}]7ST  %%nwi%X!@K!@!% @ @&*&:&:&L&L#DMM"7"UVWWr)r)r.r*r'r()NNNNNzr)NNNN)rrrr__doc__ attributes valid_kwargsimage_processor_classtokenizer_classr3rrrrrr rrrZr]rbpropertyrer __classcell__)r6s@rr"r"(s2$[1JL1%O'+$%R0"^b P^P^I0$y/4HYCZZ[P^ -. P^ P^f<6XXrr"N)rktypingrrfeature_extraction_utilsr image_utilsrrr processing_utilsr r r r tokenization_utils_baserrutilsr get_loggerrloggerrr"__all__rrrr{s`4EEkkC   H %+5bX^bXJ  r