fThDSrSSKrSSKJr SSKJr "SS\5rS/rg)z( Image/Text processor class for CLIPSeg N)ProcessorMixin) BatchEncodingcv^\rSrSrSrSS/rSrSrSU4SjjrSSjr S r S r \ S 5r \ S 5rS rU=r$)CLIPSegProcessora  Constructs a CLIPSeg processor which wraps a CLIPSeg image processor and a CLIP tokenizer into a single processor. [`CLIPSegProcessor`] offers all the functionalities of [`ViTImageProcessor`] and [`CLIPTokenizerFast`]. See the [`~CLIPSegProcessor.__call__`] and [`~CLIPSegProcessor.decode`] for more information. Args: image_processor ([`ViTImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`CLIPTokenizerFast`], *optional*): The tokenizer is a required input. image_processor tokenizer)ViTImageProcessorViTImageProcessorFast) CLIPTokenizerCLIPTokenizerFastc >SnSU;a,[R"S[5 URS5nUbUOUnUc [ S5eUc [ S5e[ TU]X5 g)Nfeature_extractorzhThe `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor` instead.z)You need to specify an `image_processor`.z"You need to specify a `tokenizer`.)warningswarn FutureWarningpop ValueErrorsuper__init__)selfr r kwargsr __class__s f/var/www/auris/envauris/lib/python3.13/site-packages/transformers/models/clipseg/processing_clipseg.pyrCLIPSegProcessor.__init__+su  & ( MM  !' +> ? -<-H/N_  "HI I  AB B 4c UcUcUc [S5eUbUb [S5eUbUR"U4SU0UD6nUbUR"U4SU0UD6nUbUR"U4SU0UD6nUbUbWRWRS.nU$UbUbWRWS'U$UbW$UbSWR0nU$[ [ S0WD6US9$) 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 CLIPTokenizerFast's [`~CLIPTokenizerFast.__call__`] if `text` is not `None` to encode the text. To prepare the image(s), this method forwards the `images` and `kwrags` arguments to ViTImageProcessor's [`~ViTImageProcessor.__call__`] if `images` is not `None`. Please refer to the docstring of the above two methods for more information. Args: 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). 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. visual_prompt (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `List[PIL.Image.Image]`, `List[np.ndarray]`, `List[torch.Tensor]`): The visual prompt image or batch of images to be prepared. Each visual prompt image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. 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`. z9You have to specify either text, visual prompt or images.zMYou have to specify exactly one type of prompt. Either text or visual prompt.return_tensors) pixel_valuesconditional_pixel_valuesr r!)data tensor_type)rr r r rdict) rtextimages visual_promptrrencodingprompt_featuresimage_featuress r__call__CLIPSegProcessor.__call__=s!L TVTH  $"22=jQ_jcijO  !11&bb[abN  $); . ; ;,;,H,HHO  &"4'5'B'BH^ $O  O  &*O,H,HHO d&<^&<.Y Yrc:URR"U0UD6$)z This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.batch_decode`]. Please refer to the docstring of this method for more information. )r batch_decoderargsrs rr/CLIPSegProcessor.batch_decodes ~~**D;F;;rc:URR"U0UD6$)z This method forwards all its arguments to CLIPTokenizerFast's [`~PreTrainedTokenizer.decode`]. Please refer to the docstring of this method for more information. )r decoder0s rr4CLIPSegProcessor.decodes ~~$$d5f55rcP[R"S[5 UR$)Nzg`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.)rrrimage_processor_classrs rfeature_extractor_class(CLIPSegProcessor.feature_extractor_classs" u  )))rcP[R"S[5 UR$)Nz[`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.)rrrr r8s rr"CLIPSegProcessor.feature_extractors" i  ###rr$)NN)NNNN)__name__ __module__ __qualname____firstlineno____doc__ attributesr7tokenizer_classrr,r/r4propertyr9r__static_attributes__ __classcell__)rs@rrrs^ $[1JJrJs..4H$~H$V  r