tjJ*ddlmZddlZddlmZddlmZddlmZddlm Z ddl m Z m Z m Z dd lmZdd lmZdd lmZmZmZdd lmZd dlmZddlmZedeGdde ZedeGdde ZGddejZeGddeZ edGdde Z!edGd d!e e Z"gd"Z#dS)#) dataclassN)nn)ACT2FN)Cache)GenerationMixin)BaseModelOutputWithPastBaseModelOutputWithPooling ModelOutput)PreTrainedModel)Unpack)TransformersKwargsauto_docstringtorch_compilable_check)can_return_tuple) AutoModel)VipLlavaConfigzM Base class for VipLlava outputs, with hidden states and attentions.  custom_introc2eZdZUdZdZejdzed<dS)VipLlavaModelOutputWithPasta past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. Nimage_hidden_states)__name__ __module__ __qualname____doc__rtorch FloatTensor__annotations__l/var/www/html/CA-Chatbot/venv/lib/python3.11/site-packages/transformers/models/vipllava/modeling_vipllava.pyrr&s7  59*T188888r#rzT Base class for VipLlava causal language model (or autoregressive) outputs. ceZdZUdZdZejdzed<dZejdzed<dZ e dzed<dZ e ejdzed<dZ e ejdzed<dZejdzed<dS) VipLlavaCausalLMOutputWithPasta4 loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Language modeling loss (for next-token prediction). logits (`torch.FloatTensor` of shape `(batch_size, sequence_length, config.vocab_size)`): Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax). past_key_values (`Cache`, *optional*, returned when `use_cache=True` is passed or when `config.use_cache=True`): It is a [`~cache_utils.Cache`] instance. For more details, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache). Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see `past_key_values` input) to speed up sequential decoding. image_hidden_states (`torch.FloatTensor`, *optional*): A `torch.FloatTensor` of size `(batch_size, num_images, sequence_length, hidden_size)`. image_hidden_states of the model produced by the vision encoder and after projecting the last hidden state. Nlosslogitspast_key_values hidden_states attentionsr)rrrrr'rr r!r(r)rr*tupler+rr"r#r$r&r&;s  &*D% d ")))'+FE  $+++$(OUT\(((59M5*+d299926Je'(4/66648*T188888r#r&c*eZdZdeffd ZdZxZS)VipLlavaMultiModalProjectorconfigctt|jtrdnt |j}t j||jj z|j |_ t j ||jj z|j j d|_t|j|_t j |j j |j j d|_dS)Nr)epsTbias)super__init__ isinstancevision_feature_layersintlenr LayerNorm vision_config hidden_sizeprojector_layernorm_epsprojector_layernormLinear text_configlinear_1rprojector_hidden_actactlinear_2)selfr/num_feature_layers __class__s r$r5z$VipLlavaMultiModalProjector.__init__Zs ",V-I3"O"OvQQUXY_YuUvUv#%< !5!A AvGe$ $ $   !5!A A   *    &56 &"4"@&BTB`gklll r#c||}||}||}||}|SN)r>rArCrD)rEr*s r$forwardz#VipLlavaMultiModalProjector.forwardisN00??  m44 //  m44 r#)rrrrr5rJ __classcell__rGs@r$r.r.YsZ m~ m m m m m mr#r.c@eZdZUeed<dZdZdZdgZdZ dZ dZ dZ dZ dS)VipLlavaPreTrainedModelr/model)imagetextTr)N)rrrrr!base_model_prefixinput_modalitiessupports_gradient_checkpointing_skip_keys_device_placement_supports_flash_attn_supports_sdpa_can_compile_fullgraph_supports_flex_attn_supports_attention_backendr"r#r$rNrNqsV (&*##4"5N!"&r#rNzx The VipLlava model which consists of a vision backbone and a language model, without a language modeling head. ceZdZdeffd Zeed ddejde e e zdzde e d e ezfd Zd ejd ejd ejfdZee dd ejdzdejdzdejdzdejdzdedzd ejdzde e e zdzdedzde e d e ezfdZxZS) VipLlavaModelr/ct|tj|j|_t ||_tj|j|_ | dSrI) r4r5r from_configr; vision_towerr.multi_modal_projectorr@language_model post_initrEr/rGs r$r5zVipLlavaModel.__init__sm    %1&2FGG%@%H%H"'3F4FGG r#zWObtains image last hidden states from the vision tower and apply multimodal projection.rN pixel_valuesr7kwargsreturnc ,||n |jj}d|d<|j|fi|t|trj|ddddf}n$fd|D}t j|d}||}|_ S)\ pixel_values (`torch.FloatTensor]` of shape `(batch_size, channels, height, width)`): The tensors corresponding to the input images. vision_feature_layers (`Union[int, list[int]]`, *optional*): The vision feature layer, or the list of indexes of the layers to select the vision feature. NToutput_hidden_statesrcBg|]}j|ddddfS)Nr)r*).0index image_outputss r$ z4VipLlavaModel.get_image_features..s2kkkEm9%@ABBGkkkr#)dim) r/r7r_r6r8r*rcatr` pooler_output)rErdr7reimage_featuresrms @r$get_image_featuresz VipLlavaModel.get_image_featuress$&;%F ! !DKLm *.%&))      +S 1 1 ?*89NOPQPQPQSTSUSUPUVNNlkkkUjkkkN"Y~2>>>N33NCC&4 #r# input_ids inputs_embedsrsc |e||tj|jjtj|jk}|d}n||jjk}|}|j d|j dz}| d |j}t||j dz| kd|d||S)z Obtains multimodal placeholder mask from `input_ids` or `inputs_embeds`, and checks that the placeholder token count is equal to the length of multimodal features. If the lengths are different, an error is raised. N)dtypedevicerorrz6Image features and image tokens do not match, tokens: z , features: )get_input_embeddingsrtensorr/image_token_idlongryallsumshape unsqueezetornumel)rErurvrsspecial_image_maskn_image_tokensn_image_featuress r$get_placeholder_maskz"VipLlavaModel.get_placeholder_masks  !.2M$2K2K2M2M T[7uzR_Rfggg33" "4!7!7!;!;  !*dk.H!H +//11)/2^5I!5LL/99"==@@AUVV ]04 48L8L8N8N N s^ s saq s s   "!r#attention_mask position_idsr) use_cache lm_kwargsc ||n |jj}|du|duz rtd|||}|j|||j} | |j|j} | ||| } | | | }|j d|||||d| } t| j | j| j| j|| nd} | S)z vision_feature_layers (`Union[int, list[int]]`, *optional*): The vision feature layer, or the list of indexes of the layers to select the vision feature. Nz:You must specify exactly one of input_ids or inputs_embedsrdr7)rvrs)rrr)rvr)last_hidden_stater)r*r+rr")r/r7 ValueErrorrzrtrrrryrxrmasked_scatterrarrr)r*r+)rErurdrrr)rvr7rrrsroutputsoutputs r$rJzVipLlavaModel.forwardsb(&;%F ! !DKLm   -t"; < [YZZ Z  7D5577 BBM  #!44)AV5 ,..}/C]EXYYN!%!:!:~";"" *889K^\\M+>4+>, )%+' , ,  , , -%7#3!/)2>2JPT     r#rI)NNNNNNNN)rrrrr5rrrr r8listr rr,r rt LongTensorrTensorrboolrrJrKrLs@r$r\r\s ~^n 9=""'" #T#Y5"+, " + + """"H")":?:K"]b]n""""0.215.204(,268>> import torch >>> from PIL import Image >>> import httpx >>> from io import BytesIO >>> from transformers import AutoProcessor, VipLlavaForConditionalGeneration >>> model = VipLlavaForConditionalGeneration.from_pretrained("llava-hf/vip-llava-7b-hf", device_map="auto", dtype=torch.float16) >>> processor = AutoProcessor.from_pretrained("llava-hf/vip-llava-7b-hf") >>> prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.###Human: \n{}###Assistant:" >>> question = "Can you please describe this image?" >>> prompt = prompt.format(question) >>> url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/compel-neg.png" >>> with httpx.stream("GET", url) as response: ... image = Image.open(BytesIO(response.read())) >>> inputs = processor(text=text, images=image, return_tensors="pt").to(0, torch.float16) >>> # Generate >>> generate_ids = model.generate(**inputs, max_new_tokens=20) >>> processor.decode(generate_ids[0][len(inputs["input_ids"][0]):], skip_special_tokens=True) The image features a brown and white cat sitting on a green surface, with a red ball in its ```N)rurdrrr)rvrr7)r(rr)r'r(r)r*r+rr")r/r7rOrr6r8slicer loss_functionr@rr&r)r*r+r)rErurdrrr)rvr7rrrrrr* slice_indicesr(r's r$rJz(VipLlavaForConditionalGeneration.forward,sj&;%F ! !DKLm 0:tz 0 %)%+'"7 0 0  0 0  1 8B>SV8W8Wk~ot444]k mAAA}aaa,?@AA  %%VFt{OfOq%rrD-#3!/) ' ;     r#Fc tj|f|||||d|} |s|dds|| d<| S)N)r)rvrris_first_iterationrTrd)r4prepare_inputs_for_generationget) rErur)rvrdrrrre model_inputsrGs r$rz>VipLlavaForConditionalGeneration.prepare_inputs_for_generationst=uww<  +'))1       8VZZ T%B%B 8 ,8L (r#rI) NNNNNNNNNr)NNNNNF)rrr_tied_weights_keysrr5rModulerrrr r8rr rr,r rtrrrrrr&rJrrKrLs@r$rr s< +,VW~ ry9=  '  #T#Y5 +,  + +    ^ ".215.204(,268<*.!%-.R R #d*R '$.R  t+ R &- R  R (4/R  #T#Y5R  4'R $;R el*R ./R  / /R R R ^R n r#r)r\rrN)$ dataclassesrrr activationsr cache_utilsr generationrmodeling_outputsr r r modeling_utilsr processing_utilsr utilsrrr utils.genericrautorconfiguration_vipllavarrr&rr.rNr\r__all__r"r#r$rs*"!!!!! !!!!!! ))))))``````````------&&&&&&OOOOOOOOOO------222222   9 9 9 9 9"9 9 9   9  99999[99   90")0 ' ' ' ' 'o ' ' ' @@@@@+@@  @F RRRRR'>RR  Rj [ Z Zr#