ZjSSKJr SSKrSSKJr SSKJr SSKJr SSK J r J r SS K J r S S KJr \ R "\5r\"S S \55r\ "SS\55rS/rg)) dataclassN)Cache)$ImageClassifierOutputWithNoAttention)PreTrainedModel)auto_docstringlogging)AutoModelForImageTextToText)ShieldGemma2ConfigcB\rSrSr%SrSr\RS-\S'Sr g)0ShieldGemma2ImageClassifierOutputWithNoAttention!zVShieldGemma2 classifies imags as violative or not relative to a specific policy Args: N probabilities) __name__ __module__ __qualname____firstlineno____doc__rtorchTensor__annotations____static_attributes__rڇ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/shieldgemma2/modeling_shieldgemma2.pyrr!s*.M5<<$&-rrc^\rSrSr%\\S'SrSrS\4U4SjjrSr Sr Sr S r \ SS \RS -S \R S -S \R"S -S\RS -S\S -S\RS -S\R S -S\RS -S\S -S\S -S\S -S\S -S\\R"-S\4Sjj5rSrU=r$)"ShieldGemma2ForImageClassification*config)imagetextmodelc>[TU]US9 [USS5Ul[USS5Ul[ R "US9UlUR5 g)N)r!yes_token_indexi *no_token_indexi) super__init__getattrr&r'r from_configr$ post_init)selfr! __class__s rr)+ShieldGemma2ForImageClassification.__init__0sS '&v/@&I%f.>E0<>rcVURR5RU5 gr1)r$r2set_input_embeddings)r-values rr77ShieldGemma2ForImageClassification.set_input_embeddings:s  55e8ShieldGemma2ForImageClassification.set_output_embeddings@s  66~FrN input_ids pixel_valuesattention_mask position_idspast_key_valuestoken_type_ids inputs_embedslabels use_cacheoutput_attentionsoutput_hidden_states return_dictlogits_to_keepreturnc UR"SUUUUUUUUU U U U U S. UD6nURnUSS2SURUR/4n[R "USS9n[ UUS9$)a Returns: A `ShieldGemma2ImageClassifierOutputWithNoAttention` instance containing the logits and probabilities associated with the model predicting the `Yes` or `No` token as the response to that prompt, captured in the following properties. * `logits` (`torch.Tensor` of shape `(batch_size, 2)`): The first position along dim=1 is the logits for the `Yes` token and the second position along dim=1 is the logits for the `No` token. * `probabilities` (`torch.Tensor` of shape `(batch_size, 2)`): The first position along dim=1 is the probability of predicting the `Yes` token and the second position along dim=1 is the probability of predicting the `No` token. ShieldGemma prompts are constructed such that predicting the `Yes` token means the content *does violate* the policy as described. If you are only interested in the violative condition, use `violated = outputs.probabilities[:, 1]` to extract that slice from the output tensors. When used with the `ShieldGemma2Processor`, the `batch_size` will be equal to `len(images) * len(policies)`, and the order within the batch will be img1_policy1, ... img1_policyN, ... imgM_policyN. ) rArBrCrDrErFrGrHrIrJrKrLrMN)dim)logitsrr)r$rRr&r'rsoftmaxr)r-rArBrCrDrErFrGrHrIrJrKrLrM lm_kwargsoutputsrRselected_logitsrs rforward*ShieldGemma2ForImageClassification.forwardCsL** %)%+)'/!5#)    B)=)=t?R?R(S!ST o2> ?"'  r)r$r'r&) NNNNNNNNNNNNr)rrrrr rinput_modalitiesbase_model_prefixr)r3r7r;r>rr LongTensor FloatTensorrrboolintrrWr __classcell__)r.s@rrr*sg (1?=@G.215.204(,2626*.!%)-,0#'-.; ##d*; ''$.;  t+ ; &&- ;  ; ((4/; ((4/;   4'; $;;  $;; #Tk; D[; ell*; :!; ; rr) dataclassesrr cache_utilsrmodeling_outputsrmodeling_utilsrutilsrr autor configuration_shieldgemma2r get_loggerrloggerrr__all__rrrrjs|" D-/:   H % .7[. .T T T p) r