o ۷i"@sdZddlZddlmZmZddlmZddlmZddl m Z m Z m Z ddl mZmZmZdd lmZd d lmZeeZGd d d e ddZGddde ZdgZdS)zq Processor class for InstructBLIP. Largely copy of Blip2Processor with addition of a tokenizer for the Q-Former. N)OptionalUnion) BatchFeature) ImageInput)ProcessingKwargsProcessorMixinUnpack) AddedTokenPreTokenizedInput TextInput)logging) AutoTokenizerc @s*eZdZdddddddddd idZdS)InstructBlipProcessorKwargsTFr) add_special_tokenspaddingstridereturn_overflowing_tokensreturn_special_tokens_maskreturn_offsets_mappingreturn_token_type_ids return_lengthverbose) text_kwargs images_kwargsN)__name__ __module__ __qualname__ _defaultsr r n/home/ubuntu/vllm_env/lib/python3.10/site-packages/transformers/models/instructblip/processing_instructblip.pyr!s rF)totalc seZdZdZgdZdZdZdZdfdd Z    dde e d e e e ee ee fd eed efd d ZeddZfddZefddZZS)InstructBlipProcessora Constructs an InstructBLIP processor which wraps a BLIP image processor and a LLaMa/T5 tokenizer into a single processor. [`InstructBlipProcessor`] offers all the functionalities of [`BlipImageProcessor`] and [`AutoTokenizer`]. See the docstring of [`~BlipProcessor.__call__`] and [`~BlipProcessor.decode`] for more information. Args: image_processor (`BlipImageProcessor`): An instance of [`BlipImageProcessor`]. The image processor is a required input. tokenizer (`AutoTokenizer`): An instance of ['PreTrainedTokenizer`]. The tokenizer is a required input. qformer_tokenizer (`AutoTokenizer`): An instance of ['PreTrainedTokenizer`]. The Q-Former tokenizer is a required input. num_query_tokens (`int`, *optional*):" Number of tokens used by the Qformer as queries, should be same as in model's config. )image_processor tokenizerqformer_tokenizer)BlipImageProcessorBlipImageProcessorFastrNc sPt|dstdddd|_|j|jgddn|j|_||_t|||dS)N image_tokenzFT) normalizedspecial)special_tokens)hasattrr r) add_tokensnum_query_tokenssuper__init__)selfr$r%r&r/kwargs __class__r r!r1Js zInstructBlipProcessor.__init__imagestextr3returnc  s|dur |dur td|jtfd|jji|}|ddd}i}|durt|tr0|g}nt|ts@t|dts@td|j |fi|d} | d|d <| d |d <|d d durm|dd |j 8<|j|fi|d} |dur|j j |j } d |dd<d |dd<d |dd<|j| fi|d| D]fdd| D| <q|| |dur|j|fi|d} || t||d}|S)a This method uses [`BlipImageProcessor.__call__`] method to prepare image(s) for the model, and [`BertTokenizerFast.__call__`] to prepare text for the model. Please refer to the docstring of the above two methods for more information. Args: images (`ImageInput`): The image or batch of images to be prepared. Each image can be a PIL image, NumPy array or PyTorch tensor. Both channels-first and channels-last formats are supported. text (`TextInput`, `PreTokenizedInput`, `list[TextInput]`, `list[PreTokenizedInput]`): The sequence or batch of sequences to be encoded. Each sequence can be a string or a list of strings (pretokenized string). If the sequences are provided as list of strings (pretokenized), you must set `is_split_into_words=True` (to lift the ambiguity with a batch of sequences). Nz,You have to specify at least images or text.tokenizer_init_kwargsrreturn_tensorsrzAInvalid input text. Please provide a string, or a list of strings input_idsqformer_input_idsattention_maskqformer_attention_mask max_lengthFrr truncationcsg|]}|qSr r ).0sampleimage_text_encodingkr r! sz2InstructBlipProcessor.__call__..r) tensor_type) ValueError _merge_kwargsrr% init_kwargspop isinstancestrlistr&getr/r)contentupdater$r) r2r6r7audiovideosr3 output_kwargsr:encodingqformer_text_encoding text_encoding image_tokensimage_encodingr rCr!__call__TsH       zInstructBlipProcessor.__call__cCs$|jj}|jj}ddg}|||S)Nr<r>)r%model_input_namesr$)r2tokenizer_input_namesimage_processor_input_namesqformer_input_namesr r r!r[s z'InstructBlipProcessor.model_input_namesc stj|rtd|dtj|ddtj|d}|j|d|jv}|r/|j dt j|fi|}|rC|jdg7_|S)NzProvided path (z#) should be a directory, not a fileT)exist_okr&) ospathisfilerHmakedirsjoinr&save_pretrained attributesremover0)r2save_directoryr3qformer_tokenizer_pathqformer_presentoutputsr4r r!res    z%InstructBlipProcessor.save_pretrainedc s>tj|fi|}t|tr|d}tj|dd}||_|S)Nrr&) subfolder)r0from_pretrainedrLtuplerr&)clspretrained_model_name_or_pathr3 processorr&r4r r!rms  z%InstructBlipProcessor.from_pretrained)N)NNNN)rrr__doc__rfimage_processor_classtokenizer_classqformer_tokenizer_classr1rrrr r rNr rrrZpropertyr[re classmethodrm __classcell__r r r4r!r#2s2  C  r#)rrr`typingrrimage_processing_utilsr image_utilsrprocessing_utilsrrr tokenization_utils_baser r r utilsr autor get_loggerrloggerrr#__all__r r r r!s