3j:;.SrSSKrSSKJr SSKJr SSKJr SSKJrJ r SSK J r SS K J r SS KJr SS KJrJrJrJrJr S S KJr \R0"\5r\"SS\ 55r\"SS9"SS\55r\"SS9"SS\\55r/SQrg)zPyTorch Fuyu model.N)nn)Cache)GenerationMixin)BaseModelOutputWithPoolingCausalLMOutputWithPast)PreTrainedModel) AutoModel)Unpack)TransformersKwargsauto_docstringcan_return_tupleloggingtorch_compilable_check) FuyuConfigcF\rSrSr%\\S'SrSrSrSr Sr Sr Sr /r S/rSrg) FuyuPreTrainedModel configmodel)imagetextTpast_key_valuesN)__name__ __module__ __qualname____firstlineno__r__annotations__base_model_prefixinput_modalitiessupports_gradient_checkpointing_supports_attention_backend_supports_flash_attn_supports_sdpa_supports_flex_attn_no_split_modules_skip_keys_device_placement__static_attributes__r`/home/wildlama/miniconda3/lib/python3.13/site-packages/transformers/models/fuyu/modeling_fuyu.pyrr s@ (&*#"&N#4"5r+rzt The Fuyu model which consists of a vision backbone and a language model, without a language modeling head. ) custom_introcJ^\rSrSrS\4U4SjjrS\RS\\RS\RS\R4Sjr \ \ S \RS \ \S\\-4S j55rS \R$S \RS\R4Sjr\ \ SS \R$S-S\RS-S\RS-S\RS-S\R$S-S\S-S \RS-S\S-S \ \S\\-4Sjj55rSrU=r$) FuyuModel.rc>[TU]U5 URUlURR Ul[ R"UR5Ul[R"URUR-UR-UR5UlSUlUR!5 g)NF)super__init__ pad_token_id padding_idx text_config vocab_sizer from_configlanguage_modelrLinear patch_size num_channels hidden_sizevision_embed_tokensgradient_checkpointing post_initselfr __class__s r,r3FuyuModel.__init__4s  !.. ,,77'33F4F4FG#%99    1 1 1F4G4G GI[I[$  ',# r+word_embeddingscontinuous_embeddingsimage_patch_input_indicesreturnc URS[U5:Xd)[S[U5<SURS<35eUR5n[ URS5Hn[ R "X5S:SS9SnX5UnURSX%RS:a-[SX%R<SUR<SUS 35eX%URUR5XEU4'M U$) ayThis function places the continuous_embeddings into the word_embeddings at the locations indicated by image_patch_input_indices. Different batch elements can have different numbers of continuous embeddings. Args: word_embeddings (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`): Tensor of word embeddings. continuous_embeddings (`torch.FloatTensor` of shape `(batch_size, num_patches, hidden_size)`): Tensor of continuous embeddings. The length of the list is the batch size. Each entry is shape [num_image_embeddings, hidden], and num_image_embeddings needs to match the number of non-negative indices in image_patch_input_indices for that batch element. image_patch_input_indices (`torch.LongTensor` of shape `(batch_size, sequence_length)`): Tensor of indices of the image patches in the input_ids tensor. rz7Batch sizes must match! Got len(continuous_embeddings)=z and word_embeddings.shape[0]=T)as_tuplezGNumber of continuous embeddings continuous_embeddings[batch_idx].shape=zA does not match number of continuous token ids src_indices.shape=z in batch element .) shapelen ValueErrorclonerangetorchnonzerotodevice)rBrErFrGoutput_embeddings batch_idx dst_indices src_indicess r,gather_continuous_embeddings&FuyuModel.gather_continuous_embeddingsAsI( %%a(C0E,FFJs3H/I.KKjQ`QfQfghQiPkl ,11344Q78I --(A(LPQ(Q\`abcdK4>{KK  #&;&F&L&LQ&OO ^7L7W7]7]6_`I6A6G6G5II[\e[ffgi9N8XYd8e8h8h!((9 4 59 ! r+ pixel_valueskwargsc 6URU5n[US9$)z pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, image_size, image_size)`): The tensors corresponding to the input images. )last_hidden_state)r>r)rBr[r\patch_embeddingss r,get_image_featuresFuyuModel.get_image_featuresms! 33LA)