Pg8*ddlmZddlmZmZmZddlZddlZddlm Z ddl m Z ddl m Z ddlmZmZdd lmZd d lmZd d lmZdZdZej2eZdZeGdde ZGdde ZdgZy)) dataclass)ListOptionalUnionN)Cache)$ImageClassifierOutputWithNoAttention)PreTrainedModel)%add_start_docstrings_to_model_forwardlogging)deprecate_kwarg)AutoModelForImageTextToText)ShieldGemma2Configzgoogle/shieldgemma-2-4b-itra Args: input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide it. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) attention_mask (`torch.Tensor` of shape `(batch_size, sequence_length)`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. If `past_key_values` is used, optionally only the last `input_ids` have to be input (see `past_key_values`). If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`] and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more information on the default strategy. - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, config.n_positions - 1]`. [What are position IDs?](../glossary#position-ids) past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*): Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention blocks) that can be used to speed up sequential decoding. This typically consists in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or `config.use_cache=True`. Two formats are allowed: - a [`~cache_utils.Cache`] instance, see our [kv cache guide](https://huggingface.co/docs/transformers/en/kv_cache); - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy cache format. The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the legacy cache format will be returned. If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids` of shape `(batch_size, sequence_length)`. inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. This is useful if you want more control over how to convert `input_ids` indices into associated vectors than the model's internal embedding lookup matrix. use_cache (`bool`, *optional*): If set to `True`, `past_key_values` key value states are returned and can be used to speed up decoding (see `past_key_values`). output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, this tensor is not affected by padding. It is used to update the cache in the correct position and to infer the complete sequence length. c4eZdZUdZdZej ed<y)0ShieldGemma2ImageClassifierOutputWithNoAttentionz^ShieldGemma2 classifies imags as violative or not relative to a specific policy Args: N probabilities)__name__ __module__ __qualname____doc__rtorchTensor__annotations__}/var/www/html/suriana-translation/venv/lib/python3.12/site-packages/transformers/models/shieldgemma2/modeling_shieldgemma2.pyrrrs#'M5<<&rrc! eZdZeZdeffd ZdZdZdZdZ dZ dZ d Z e d d d ee ddej"dej$deej(deej"deeeej$efdeej"deej"deej$deej"deedeedeedeed eeej(fdefdZxZS)"ShieldGemma2ForImageClassificationconfigct||t|dd|_t|dd|_t j ||_y)N)r!yes_token_indexi *no_token_indexi)super__init__getattrr#r$r from_configmodel)selfr! __class__s rr&z+ShieldGemma2ForImageClassification.__init__~sI '&v/@&I%f.>E0<>@@rcN|jjj|yr-)r)r.set_output_embeddings)r*new_embeddingss rr7z8ShieldGemma2ForImageClassification.set_output_embeddingss !!77GrcN|jjj|yr-)r)r. set_decoder)r*decoders rr:z.ShieldGemma2ForImageClassification.set_decoders !!--g6rcJ|jjjSr-)r)r. get_decoderr0s rr=z.ShieldGemma2ForImageClassification.get_decoderzz((4466rcJ|jjjSr-)r)r. tie_weightsr0s rr@z.ShieldGemma2ForImageClassification.tie_weightsr>rnum_logits_to_keepz4.50logits_to_keep)versionnew_name input_ids pixel_valuesattention_mask position_idspast_key_valuestoken_type_idscache_position inputs_embedslabels use_cacheoutput_attentionsoutput_hidden_states return_dictreturnc |jd||||||||| | | | | |d|}|j}|ddd|j|jgf}t j |d}t ||S)aPredicts the binary probability that the image violates the specified policy. 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. )rErFrGrHrIrJrKrLrMrNrOrPrQrBN)dim)logitsrr)r)rVr#r$rsoftmaxr)r*rErFrGrHrIrJrKrLrMrNrOrPrQrB lm_kwargsoutputsrVselected_logitsrs rforwardz*ShieldGemma2ForImageClassification.forwardsR$** %)%+))'/!5#)  " B)=)=t?R?R(S!ST o2> ?"'  r)NNNNNNNNNNNNNr)rrrr config_classr&r/r2r5r7r:r=r@r r SHIELDGEMMA2_INPUTS_DOCSTRINGr LongTensor FloatTensorrrrrrboolintrr[ __classcell__)r+s@rr r {s%LL1L @>AH777)6DTU*+HI'+*.1537KO595959-1$(,0/3&*34> ##> ''> !. > u//0 > "%U->->(?(F"GH > !!1!12> !!1!12>   1 12> ))*> D>> $D>> 'tn> d^> c5<</0> " :#> JV> rr ) dataclassesrtypingrrrrtorch.utils.checkpoint cache_utilsrmodeling_outputsr modeling_utilsr utilsr r utils.deprecationr autorconfiguration_shieldgemma2r_CHECKPOINT_FOR_DOC_CONFIG_FOR_DOC get_loggerrloggerr]rr __all__rrrrrs "((  D-1.:3&   H %H!V '7[' '^ ^ D) r