a hd.@sdZddlmZddlZddlmZddlmZm Z ddl m Z m Z m Z mZddlmZmZdd lmZmZer~d d lmZeeZGd d d e ddZedddZddZGddde ZdgZdS)z Processor class for Pixtral. )UnionN) BatchFeature) ImageInputis_valid_image)MultiModalDataProcessingKwargsProcessorMixinUnpack)PreTokenizedInput TextInput)is_vision_availablelogging)get_resize_output_image_sizec@s"eZdZdddiddidZdS)PixtralProcessorKwargsF)paddingreturn_mm_token_type_idsreturn_tensorspt) text_kwargs images_kwargsZ common_kwargsN)__name__ __module__ __qualname__ _defaultsrrj/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/pixtral/processing_pixtral.pyr*srF)total)returncCst|to|dS)Nhttp) isinstancestr startswith)valrrris_url8sr%cCst|pt|S)N)r%r)elemrrris_image_or_image_url=sr'cs~eZdZdZddgZdZdZdeed fd d Zde e e e e e e e feeedddZdddZeddZZS)PixtralProcessorab Constructs a Pixtral processor which wraps a Pixtral image processor and a Pixtral tokenizer into a single processor. [`PixtralProcessor`] offers all the functionalities of [`CLIPImageProcessor`] and [`LlamaTokenizerFast`]. See the [`~PixtralProcessor.__call__`] and [`~PixtralProcessor.decode`] for more information. Args: image_processor ([`PixtralImageProcessor`], *optional*): The image processor is a required input. tokenizer ([`LlamaTokenizerFast`], *optional*): The tokenizer is a required input. patch_size (`int`, *optional*, defaults to 16): Patch size from the vision tower. spatial_merge_size (`int`, *optional*, defaults to 1): The downsampling factor for the spatial merge operation. 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 `"[IMG]"`): Special token used to denote image location. image_break_token (`str`, *optional*, defaults to `"[IMG_BREAK]"`): Special token used to denote the end of a line of pixels in an image. image_end_token (`str`, *optional*, defaults to `"[IMG_END]"`): Special token used to denote the end of an image input. image_processor tokenizerZAutoImageProcessorZ AutoTokenizerNr[IMG] [IMG_BREAK] [IMG_END]) patch_sizespatial_merge_sizec  s~||_||_||_||j|_||_||_||j|_||j|_||j|_|j|j|jg|_ t j |||ddS)N) chat_template) r/r0 image_tokenZconvert_tokens_to_idsZimage_token_idimage_break_tokenimage_end_tokenZimage_break_token_idZimage_end_token_id image_idssuper__init__) selfr)r*r/r0r1r2r3r4kwargs __class__rrr7_s zPixtralProcessor.__init__)imagestextr9rcKs|jtfd|jji|}|j|j}|durJ|j|fd|i|d}ni}t|tr`|g}n t|t st|dtst d|} | ddurbt |d} g} g} |D]} |j | vr0t| \} }| |}||}|j g||jgg|}d d |D}|j|d <d |}| || |j d d} qd | vrV| d}| d |d} q0| | q|ddd}|ddd}|j| fi|dddi}|j| |dgd|rt|d}t|d}d|t||j<||d<ti|||dS)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`. Ztokenizer_init_kwargsNr/rrzAInvalid input text. Please provide a string, or a list of stringsZ pixel_values image_sizescSsg|]}|D]}|q qSrr).0Zsublistitemrrr z-PixtralProcessor.__call__..z rrrrFimage)Z modalitiesZ input_idsmm_token_type_ids)dataZ tensor_type)Z _merge_kwargsrr*Z init_kwargsr/r0r)r!r"list TypeErrorgetiterr2nextr3r4joinappendreplacepopZ_check_special_mm_tokensnparrayZ zeros_likeisinr5tolistr)r8r<r=ZaudioZvideosr9Z output_kwargsr/Z image_inputsZprompt_stringsr>Zreplace_stringssampleheightwidthnum_height_tokensnum_width_tokensZreplace_tokensZ replace_strrrZ text_inputsZ array_idsrFrrr__call__ws`)            zPixtralProcessor.__call__cKsi}|durtjdi}|||ddp6|jj}|j|j}g}|D]X\}} tt || df|d|df||fd\} } | |} | |} | | d| qLdgt |}|||dt fi|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. NrsizerZ longest_edge)r[r/r)num_image_tokensnum_image_patches)rrrJupdater)r[r/r0rrQZzerosrNlenr)r8r>r9Z vision_datarr[r/r\rVrWZresized_heightZ resized_widthrXrYr]rrr_get_num_multimodal_tokenss&     z+PixtralProcessor._get_num_multimodal_tokenscCs|jj}|jj}||dgS)Nr>)r*model_input_namesr))r8Ztokenizer_input_namesZimage_processor_input_namesrrrrasz"PixtralProcessor.model_input_names)NNr+rNr,r-r.)NNNN)N)rrr__doc__ attributesZimage_processor_classZtokenizer_classintr7rrr r rHr rrrZr`propertyra __classcell__rrr:rr(As: d $r() rbtypingrnumpyrQZfeature_extraction_utilsrZ image_utilsrrZprocessing_utilsrrr r Ztokenization_utils_baser r utilsr rZimage_processing_pixtralrZ get_loggerrloggerrboolr%r'r(__all__rrrrs     F