bCi >SSKJrJr SSKJrJrJr SSKJr SSK J r J r SSK J r Jr SSKJrJr SSKJrJr \"5(aSS Kr\R,"\5r"S S \ S S 9r"SS\5rS/rg ))OptionalUnion) IMAGE_TOKENPaliGemmaProcessorbuild_string_from_input) BatchFeature) ImageInputmake_flat_list_of_images)ProcessingKwargsUnpack)PreTokenizedInput TextInput)is_torch_availableloggingNc.\rSrSrSS0SSS.SS0S .rS rg ) ColPaliProcessorKwargs"paddinglongestchannels_firstT) data_formatdo_convert_rgbreturn_tensorspt) text_kwargs images_kwargs common_kwargsN)__name__ __module__ __qualname____firstlineno__ _defaults__static_attributes__re/home/james-whalen/.local/lib/python3.13/site-packages/transformers/models/colpali/modular_colpali.pyrr"s, y ," +D1 Ir&rF)totalc Z^\rSrSrSrSS\S\4U4Sjjjr\S\4Sj5rSS\ \ S \ \ \ \\ \\ 4S \\S\4S jjrSS\ \ S \\S\4S jjrS \ \ \\ 4S \\S\4S jrSS\ S\S4S\ S\S4S\S\ SS\ S\4SS4 SjjrSrU=r$)ColPaliProcessor/a Constructs a ColPali processor which wraps a PaliGemmaProcessor and special methods to process images and queries, as well as to compute the late-interaction retrieval score. [`ColPaliProcessor`] offers all the functionalities of [`PaliGemmaProcessor`]. See the [`~PaliGemmaProcessor.__call__`] for more information. Args: image_processor ([`SiglipImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. visual_prompt_prefix (`str`, *optional*, defaults to `"Describe the image."`): A string that gets tokenized and prepended to the image tokens. query_prefix (`str`, *optional*, defaults to `"Question: "`): A prefix to be used for the query. visual_prompt_prefix query_prefixc:>[TU]XUS9 X@lXPlg)N)image_processor tokenizer chat_template)super__init__r,r-)selfr/r0r1r,r- __class__s r'r3ColPaliProcessor.__init__Ds$ ]jk$8!(r&returnc.URR$)zr Return the query augmentation token. Query augmentation buffers are used as reasoning buffers during inference. )r0 pad_token)r4s r'query_augmentation_token)ColPaliProcessor.query_augmentation_tokenPs~~'''r&imagestextkwargsc UR"[4SURR0UD6nUSR SS5nUSLnUcUc [ S5eUbUb [ S5eUGbrUR RU5n[U5nUR/[U5-n UV s/sHoRS5PM nn [X5V V s/sHTup[U URRUR[ [#U [$5(a [U 5OSS 9PMV n n n UR "U40US D6S nUSR'S S5bUSS ==UR- ss'UR"U 4S S0USD6n0UES U0EnU(a.USR)USS:HS5nUR+SU05 [-US9$Ub[#U[.5(aU/nO8[#U[$5(a[#US[.5(d [ S5eUcUR0S-n/nUH@nURRUR2-U-U-S-nUR5U5 MB USR'S S5USS 'UR"U4S S0USD6nU$gs sn fs sn n f)a Main method to prepare for the model either (1) one or several texts, either (2) one or several image(s). This method is a custom wrapper around the PaliGemmaProcessor's [`~PaliGemmaProcessor.__call__`] method adapted for the ColPali model. It cannot process both text and images at the same time. When preparing the text(s), this method forwards the `text` and `kwargs` arguments to LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`]. When preparing the image(s), this method forwards the `images` and `kwargs` arguments to SiglipImageProcessor's [`~SiglipImageProcessor.__call__`]. 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. 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. 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. - **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`. tokenizer_init_kwargsrsuffixNz&Either text or images must be providedz5Only one of text or images can be processed at a timeRGB)prompt bos_token image_seq_len image_token num_imagesr pixel_values max_lengthreturn_token_type_idsF input_idstoken_type_idsrilabels)dataz*Text must be a string or a list of strings  2) _merge_kwargsrr0 init_kwargspop ValueErrorr/ fetch_imagesr r,lenconvertziprrEimage_seq_lengthr isinstancelistget masked_fillupdater strr:r-append)r4r<r=audiovideosr> output_kwargsrArK texts_docimagerD image_list input_stringsrIinputs return_datarN texts_queryquery batch_querys r'__call__ColPaliProcessor.__call__Ys!Z** " "&.."<"<  }-11(DA &d 2 F-f5F223c&kAI8>?ummE*F?+.i*@ +A&F(!"nn66"&"7"7 +2S>SS:^^&+ .F CVB^\BK$ ,88@P9QUV9VX\]""Hf#56[1 1  $$$v t,,DGS1I1I !MNN~66;%'K0043D3DDuLvUX\\""5):G}9U9Y9YZfhj9kM- ( 6..&+ .K  -A@ s 0KAKc *UR"SSU0UD6$)a Prepare for the model one or several image(s). This method is a wrapper around the `__call__` method of the ColPaliProcessor's [`ColPaliProcessor.__call__`]. This method forwards the `images` and `kwargs` arguments to the image processor. Args: images (`PIL.Image.Image`, `np.ndarray`, `torch.Tensor`, `list[PIL.Image.Image]`, `list[np.ndarray]`, `list[torch.Tensor]`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. In case of a NumPy array/PyTorch tensor, each image should be of shape (C, H, W), where C is a number of channels, H and W are image height and width. return_tensors (`str` or [`~utils.TensorType`], *optional*): If set, will return tensors of a particular framework. Acceptable values are: - `'tf'`: Return TensorFlow `tf.constant` objects. - `'pt'`: Return PyTorch `torch.Tensor` objects. - `'np'`: Return NumPy `np.ndarray` objects. - `'jax'`: Return JAX `jnp.ndarray` objects. Returns: [`BatchFeature`]: A [`BatchFeature`] with the following fields: - **input_ids** -- List of token ids to be fed to a model. - **attention_mask** -- List of indices specifying which tokens should be attended to by the model (when `return_attention_mask=True` or if *"attention_mask"* is in `self.model_input_names` and if `text` is not `None`). - **pixel_values** -- Pixel values to be fed to a model. Returned when `images` is not `None`. r<rro)r4r<r>s r'process_imagesColPaliProcessor.process_imagessB}}5F5f55r&c *UR"SSU0UD6$)a? Prepare for the model one or several texts. This method is a wrapper around the `__call__` method of the ColPaliProcessor's [`ColPaliProcessor.__call__`]. This method forwards the `text` and `kwargs` arguments to the tokenizer. 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). 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. - **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`). r=rrr)r4r=r>s r'process_queries ColPaliProcessor.process_queriess@}}1$1&11r&query_embeddingsz torch.Tensorpassage_embeddings batch_size output_dtypez torch.dtype output_devicez torch.devicec [U5S:Xa [S5e[U5S:Xa [S5eUSRUSR:wa [S5eUSRUSR:wa [S5eUcUSRn/n[ S[U5U5GHn/n[ R RRRXXs-SSS9n [ S[U5U5H}n [ R RRRX*X-SSS9n UR[ R"SX5RS S 9SRS S 95 M UR[ R"US S 9RU5RU55 GM [ R"USS 9$) a Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector query embeddings (`qs`) and passage embeddings (`ps`). For ColPali, a passage is the image of a document page. Because the embedding tensors are multi-vector and can thus have different shapes, they should be fed as: (1) a list of tensors, where the i-th tensor is of shape (sequence_length_i, embedding_dim) (2) a single tensor of shape (n_passages, max_sequence_length, embedding_dim) -> usually obtained by padding the list of tensors. Args: query_embeddings (`Union[torch.Tensor, list[torch.Tensor]`): Query embeddings. passage_embeddings (`Union[torch.Tensor, list[torch.Tensor]`): Passage embeddings. batch_size (`int`, *optional*, defaults to 128): Batch size for computing scores. output_dtype (`torch.dtype`, *optional*, defaults to `torch.float32`): The dtype of the output tensor. If `None`, the dtype of the input embeddings is used. output_device (`torch.device` or `str`, *optional*, defaults to "cpu"): The device of the output tensor. Returns: `torch.Tensor`: A tensor of shape `(n_queries, n_passages)` containing the scores. The score tensor is saved on the "cpu" device. rzNo queries providedzNo passages providedz/Queries and passages must be on the same devicez-Queries and passages must have the same dtypeT) batch_first padding_valuez bnd,csd->bcnsr)dimrC)rXrVdevicedtyperangetorchnnutilsrnn pad_sequencerbeinsummaxsumcatto) r4rxryrzr{r|scoresi batch_scores batch_queriesjbatch_passagess r'score_retrieval ColPaliProcessor.score_retrievals@  A %23 3 ! "a '34 4 A  % %);A)>)E)E ENO O A  $ $(:1(=(C(C CLM M  +A.44L%'q#./:\]"A"##LL-PTTYZT[\]^bbghbi C MM%))La8;;LILL][ \=yyQ''r&)r-r,)NNNzDescribe the image.z Question: )NNNN)N)Ncpu)r r!r"r#__doc__rar3propertyr:rr rrrr]r rr rorsrvintrr% __classcell__)r5s@r'r*r*/s,$9( ) " )  ) )(#(((,^b u$uI0$y/4HYCZZ[u /0 u ur(,!6$!6/0!6  !6F 2ItI./ 2/0 2  2L0449 >(^0D DE>(".$~2F"FG>( >( }- >( ^S01 >( >(>(r&r*)typingrr2transformers.models.paligemma.processing_paligemmarrrfeature_extraction_utilsr image_utilsr r processing_utilsr r tokenization_utils_baserrrrrr get_loggerr loggerrr*__all__rr&r'rsh"#ww4?8C0   H % -U d()d(P  r&