o ۷i)M@sddlmZmZddlmZddlmZmZddlm Z m Z m Z m Z ddl mZmZddlmZer7ddlZGd d d e d d ZGd dde ZdgZdS))OptionalUnion) BatchFeature) ImageInputis_valid_image)MultiModalDataProcessingKwargsProcessorMixinUnpack)PreTokenizedInput TextInput)is_torch_availableNc@s&eZdZddidddddidZd S) ColQwen2ProcessorKwargspaddinglongestchannels_firstT) data_formatdo_convert_rgbreturn_tensorspt) text_kwargs images_kwargs common_kwargsN)__name__ __module__ __qualname__ _defaultsrrf/home/ubuntu/vllm_env/lib/python3.10/site-packages/transformers/models/colqwen2/processing_colqwen2.pyr#s rF)totalc sLeZdZdZddgZdZdZ     d'deedeeffd d Z    d(d ee d e e e ee ee fd eedefddZd)ddZedefddZ d)d ee d eedefddZd e e ee fd eedefddZ   d*de dedfde dedfdeded d!e d"efddf d#d$Zed%d&ZZS)+ColQwen2Processora Constructs a ColQwen2 processor which wraps a Qwen2VLProcessor and special methods to process images and queries, as well as to compute the late-interaction retrieval score. [`ColQwen2Processor`] offers all the functionalities of [`Qwen2VLProcessor`]. See the [`~Qwen2VLProcessor.__call__`] for more information. Args: image_processor ([`Qwen2VLImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`Qwen2TokenizerFast`], *optional*): The tokenizer is a required input. chat_template (`str`, *optional*): A Jinja template which will be used to convert lists of messages in a chat into a tokenizable string. visual_prompt_prefix (`str`, *optional*): A string that gets tokenized and prepended to the image tokens. query_prefix (`str`, *optional*): A prefix to be used for the query. image_processor tokenizerAutoImageProcessor)Qwen2TokenizerQwen2TokenizerFastNvisual_prompt_prefix query_prefixc sftj|||dt|dsdn|j|_t|dsdn|j|_|dur%d}||_|dur.d}||_dS)N) chat_template image_tokenz <|image_pad|> video_tokenz <|video_pad|>zf<|im_start|>user <|vision_start|><|image_pad|><|vision_end|>Describe the image.<|im_end|><|endoftext|>zQuery: )super__init__hasattrr*r+r'r()selfr"r#r)r'r(kwargs __class__rrr-Hs  zColQwen2Processor.__init__imagestextr0returncKs|jtfd|jji|}|ddd}|du}|dur%|dur%td|dur1|dur1td|durt|r>|g}n$t|trJt|drJnt|tr^t|dtr^t|ddsbtd|j gt |} |j dd |i|d } | d } | dur|j j d } d} t t | D]3}|j| |vr| ||jd | | | d| |<| d7} |j| |vs| |d |j| |<q|j| fddi|d}ti|| d}|d dddf|d ddd f}tt|d|}tjjjj|dd|d<|r|d|ddkd}|d|i|S|durht|tr)|g}nt|tr7t|dts;td|durE|jd}g}|D]}|j||}||qI|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 a custom wrapper around the Qwen2VLProcessor's [`~Qwen2VLProcessor.__call__`] method adapted for the ColQwen2 model. It cannot process both text and images at the same time. When preparing the the text(s), this method forwards the `text` and `kwargs` arguments to Qwen2TokenizerFast's [`~Qwen2TokenizerFast.__call__`]. When preparing the the image(s), this method forwards the `images` and `kwargs` arguments to Qwen2VLImageProcessor's [`~Qwen2VLImageProcessor.__call__`]. Please refer to the doctsring 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 timerzAimages must be an image, list of images or list of list of imagesr3rimage_grid_thwz<|placeholder|>return_token_type_idsF)data pixel_valuesT) batch_first input_idstoken_type_idsilabelsz*Text must be a string or a list of strings r) _merge_kwargsrr# init_kwargspop ValueErrorr isinstancelistr'lenr" merge_sizeranger*replaceprodrtorchsplittolistnnutilsrnn pad_sequence masked_fillupdatestrquery_augmentation_tokenr(append)r/r3r4audiovideosr0 output_kwargsr7r; texts_doc image_inputsr8 merge_lengthindexi text_inputs return_dataoffsetsr=rA texts_queryqueryaugmented_query batch_queryrrr__call__]s- ( (      zColQwen2Processor.__call__c s|i}|dur7tjdi|ddpjjfdd|D}fdd|D}|||dtdi|S) a Computes the number of placeholder tokens needed for multimodal inputs with the given sizes. Args: image_sizes (`list[list[int]]`, *optional*): The input sizes formatted as (height, width) per each image. Returns: `MultiModalData`: A `MultiModalData` object holding number of tokens per each of the provided input modalities, along with other useful data. NrrJcs"g|] }jjg|RqSr)r"get_number_of_image_patches).0 image_size)rr/rr sz@ColQwen2Processor._get_num_multimodal_tokens..csg|]}|dqS)r9r)rk num_patches)rJrrrms)num_image_tokensnum_image_patchesr)rrgetrVr"rJr)r/ image_sizesr0 vision_datarpror)rrJr/r_get_num_multimodal_tokenss   z,ColQwen2Processor._get_num_multimodal_tokenscCs|jjS)z Return the query augmentation token. Query augmentation buffers are used as reasoning buffers during inference. )r# pad_token)r/rrrrXsz*ColQwen2Processor.query_augmentation_tokencK|jdd|i|S)a Prepare for the model one or several image(s). This method is a wrapper around the `__call__` method of the ColQwen2Processor's [`ColQwen2Processor.__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`. r3Nrri)r/r3r0rrrprocess_imagess!z ColQwen2Processor.process_imagescKrv)a Prepare for the model one or several texts. This method is a wrapper around the `__call__` method of the ColQwen2Processor's [`ColQwen2Processor.__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`). r4Nrrw)r/r4r0rrrprocess_queries(s z!ColQwen2Processor.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)a[ Compute the late-interaction/MaxSim score (ColBERT-like) for the given multi-vector query embeddings (`qs`) and passage embeddings (`ps`). For ColQwen2, 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)r> padding_valuez bnd,csd->bcnsr)dimr9r:)rIrFdevicedtyperKrNrQrRrSrTrYeinsummaxsumcatto) r/r|r}r~rrscoresra batch_scores batch_queriesjbatch_passagesrrrscore_retrievalJs2      "z!ColQwen2Processor.score_retrievalcCs&|jj}|jj}dd|D}||S)NcSsg|]}|dvr|qS))pixel_values_videosvideo_grid_thwr)rknamerrrrmsz7ColQwen2Processor.model_input_names..)r#model_input_namesr")r/tokenizer_input_namesimage_processor_input_namesrrrrs z#ColQwen2Processor.model_input_names)NNNNN)NNNN)N)rzNr{)rrr__doc__ attributesimage_processor_classtokenizer_classrrWr-rrr r rHr rrrirtpropertyrXrxryintrr __classcell__rrr1rr!0s    # &  @r!)typingrrfeature_extraction_utilsr image_utilsrrprocessing_utilsrr r r tokenization_utils_baser r rRrrNrr!__all__rrrrs    i