o Zh >@sddlmZmZmZmZddlmZmZmZddl m Z ddl m Z m Z mZddlmZmZddlmZmZddlmZmZerEdd lZeeZGd d d ed d ZGdddeZdgZd S))ClassVarListOptionalUnion) IMAGE_TOKENPaliGemmaProcessorbuild_string_from_input) BatchFeature) ImageInputis_valid_imagemake_flat_list_of_images)ProcessingKwargsUnpack)PreTokenizedInput TextInput)is_torch_availableloggingNc@s&eZdZddidddddidZd S) ColPaliProcessorKwargspaddinglongestZchannels_firstT)Z data_formatZdo_convert_rgbZreturn_tensorspt) text_kwargs images_kwargsZ common_kwargsN)__name__ __module__ __qualname__ _defaultsrrZ/var/www/auris/lib/python3.10/site-packages/transformers/models/colpali/modular_colpali.pyr0s rF)totalc @seZdZUdZdZeeed<dZeeed<e defddZ dd e d e e eee eefd eedefd dZ d d e d eedefddZd e e ee fd eedefddZ  d!de dedfde dedfdededde defddf ddZd S)"ColPaliProcessora 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. zDescribe the image.visual_prompt_prefixz Question: query_prefixreturncCs|jjS)z Return the query augmentation token. Query augmentation buffers are used as reasoning buffers during inference. ) tokenizerZ pad_tokenselfrrrquery_augmentation_tokenQsz)ColPaliProcessor.query_augmentation_tokenNimagestextkwargsc snjtfdjji|}|ddd}|durdnd}|dur)|dur)td|dur5|dur5td|durt|rA|g}n$t|trMt|d rMnt|trat|d trat|d d setd j gt |} d d |D}fd d t | |D} t |}j |fi|dd} |ddddur|ddj7<j| fddi|d} i| d| i} |r| d| dd kd}| d|it| dS|dur5t|tr|g}nt|trt|d tstd|durjd}g}|D]}jjj|}||7}|d7}||q|ddd|dd<j|fddi|d}|SdS)a Main method to prepare for the model either (1) one or several texts, either (2) one or several image(s). This method is 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`. Ztokenizer_init_kwargsrsuffixNTFz&Either text or images must be providedz5Only one of text or images can be processed at a timerzAimages must be an image, list of images or list of list of imagescSsg|]}|dqS)RGB)convert).0imagerrr sz-ColPaliProcessor.__call__..c s:g|]\}}t|jjjtt|trt|nddqS))prompt bos_tokenZ image_seq_lenZ image_tokenZ num_images)rr%r4image_seq_lengthr isinstancelistlen)r/r3Z image_listr&rrr1sr pixel_values max_lengthreturn_token_type_idsZ input_idsZtoken_type_idsilabels)dataz*Text must be a string or a list of strings  2)Z _merge_kwargsrr%Z init_kwargspop ValueErrorr r6r7r"r8zipr Zimage_processorgetr5Z masked_fillupdater strr(r4r#append)r'r)r*ZaudioZvideosr+Z output_kwargsr,r;Z texts_docZ input_stringsr9ZinputsZ return_datar<Z texts_queryqueryZ batch_queryrr&r__call__Zs-(      zColPaliProcessor.__call__cK|jdd|i|S)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 SiglipImageProcessor's [`~SiglipImageProcessor.__call__`]. 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)NrrI)r'r)r+rrrprocess_imagess!zColPaliProcessor.process_imagescKrJ)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 LlamaTokenizerFast's [`~LlamaTokenizerFast.__call__`]. 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*NrrK)r'r*r+rrrprocess_queriess z ColPaliProcessor.process_queriescpuquery_embeddingsz torch.Tensorpassage_embeddings batch_size output_dtypez torch.dtype output_devicez torch.devicec Cs@t|dkr tdt|dkrtd|dj|djkr"td|dj|djkr0td|dur9|dj}g}tdt||D]U}g}tjjjj ||||ddd} tdt||D]'} tjjjj || | |ddd} | t d | | j d d dj d d q`| tj|d d ||qCtj|dd S)aZ 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 dtypeNT)Z batch_firstZ padding_valuez bnd,csd->bcnsr )dimr2)r8rBZdeviceZdtyperangetorchnnutilsZrnnZ pad_sequencerGZeinsummaxsumcatto) r'rPrQrRrSrTZscoresiZ batch_scoresZ batch_queriesjZbatch_passagesrrrscore_retrievals2      "z ColPaliProcessor.score_retrieval)NNNN)N)rNNrO)rrr__doc__r"rrF__annotations__r#propertyr(r rrrrrrr rIrLrMintrrarrrrr!=sf    # & r!)typingrrrrZ2transformers.models.paligemma.processing_paligemmarrrZfeature_extraction_utilsr Z image_utilsr r r Zprocessing_utilsrrZtokenization_utils_baserrrZrrrXZ get_loggerrloggerrr!__all__rrrrs    %