# coding=utf-8 # Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved. # # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from dataclasses import dataclass from typing import List, Optional, Union import torch import torch.utils.checkpoint from ...cache_utils import Cache from ...modeling_outputs import ImageClassifierOutputWithNoAttention from ...modeling_utils import PreTrainedModel from ...utils import ( add_start_docstrings_to_model_forward, logging, ) from ...utils.deprecation import deprecate_kwarg from ..auto import AutoModelForImageTextToText from .configuration_shieldgemma2 import ShieldGemma2Config _CHECKPOINT_FOR_DOC = "google/shieldgemma-2-4b-it" _CONFIG_FOR_DOC = "ShieldGemma2Config" logger = logging.get_logger(__name__) SHIELDGEMMA2_INPUTS_DOCSTRING = r""" 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. """ @dataclass class ShieldGemma2ImageClassifierOutputWithNoAttention(ImageClassifierOutputWithNoAttention): """ShieldGemma2 classifies imags as violative or not relative to a specific policy Args: """ probabilities: torch.Tensor = None class ShieldGemma2ForImageClassification(PreTrainedModel): config_class = ShieldGemma2Config def __init__(self, config: ShieldGemma2Config): super().__init__(config=config) self.yes_token_index = getattr(config, "yes_token_index", 10_784) self.no_token_index = getattr(config, "no_token_index", 3771) self.model = AutoModelForImageTextToText.from_config(config=config) def get_input_embeddings(self): return self.model.language_model.get_input_embeddings() def set_input_embeddings(self, value): self.model.language_model.set_input_embeddings(value) def get_output_embeddings(self): return self.model.language_model.get_output_embeddings() def set_output_embeddings(self, new_embeddings): self.model.language_model.set_output_embeddings(new_embeddings) def set_decoder(self, decoder): self.model.language_model.set_decoder(decoder) def get_decoder(self): return self.model.language_model.get_decoder() def tie_weights(self): return self.model.language_model.tie_weights() @deprecate_kwarg("num_logits_to_keep", version="4.50", new_name="logits_to_keep") @add_start_docstrings_to_model_forward(SHIELDGEMMA2_INPUTS_DOCSTRING) def forward( self, input_ids: torch.LongTensor = None, pixel_values: torch.FloatTensor = None, attention_mask: Optional[torch.Tensor] = None, position_ids: Optional[torch.LongTensor] = None, past_key_values: Optional[Union[List[torch.FloatTensor], Cache]] = None, token_type_ids: Optional[torch.LongTensor] = None, cache_position: Optional[torch.LongTensor] = None, inputs_embeds: Optional[torch.FloatTensor] = None, labels: Optional[torch.LongTensor] = None, use_cache: Optional[bool] = None, output_attentions: Optional[bool] = None, output_hidden_states: Optional[bool] = None, return_dict: Optional[bool] = None, logits_to_keep: Union[int, torch.Tensor] = 0, **lm_kwargs, ) -> ShieldGemma2ImageClassifierOutputWithNoAttention: """Predicts 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. """ outputs = self.model( input_ids=input_ids, pixel_values=pixel_values, attention_mask=attention_mask, position_ids=position_ids, past_key_values=past_key_values, token_type_ids=token_type_ids, cache_position=cache_position, inputs_embeds=inputs_embeds, labels=labels, use_cache=use_cache, output_attentions=output_attentions, output_hidden_states=output_hidden_states, return_dict=return_dict, logits_to_keep=logits_to_keep, **lm_kwargs, ) logits = outputs.logits selected_logits = logits[:, -1, [self.yes_token_index, self.no_token_index]] probabilities = torch.softmax(selected_logits, dim=-1) return ShieldGemma2ImageClassifierOutputWithNoAttention( logits=selected_logits, probabilities=probabilities, ) __all__ = [ "ShieldGemma2ForImageClassification", ]