+ j$|Rt^RIt^RIHt^RIHt^RIHt^RI H t ^RI H t ^RI Ht^RIHtHt^R IHt^R IHtHt^R IHtHtHt^R IHtHtHtHtH t H!t!H"t"H#t#]!R R7]!RR]444t$]!R R7]!RR]444t%]!R R7]!RR]444t&]!R R7]!RR]444t'!RR]4t(!RR]"4t)!RR]4t*!RR]!4t+!RR ]4t,]!R!R"] 44t-]!R#R$7!R%R&]#44t.!R'R(]4t/.R)Ot0R#)*zPyTorch SAM 2 model.N)strict)initialization)PreTrainedConfig)PreTrainedModel)Unpack)auto_docstring)TransformersKwargsmerge_with_config_defaults)capture_outputs)CONFIG_MAPPING AutoConfig) Sam2ConfigSam2MaskDecoderConfigSam2PromptEncoderConfig) Sam2AttentionSam2FeedForward Sam2LayerNorm Sam2ModelSam2PreTrainedModelSam2TwoWayAttentionBlockSam2VisionEncoderOutputSam2VisionModelzyonigozlan/EdgeTAM-hf) checkpointcaa]tRt^(toRtRtRtR]/tRt Rt Rt Rt ^t ^t^tRt^tRtRtR tV3R ltV3R ltR tVtV;t#) EdgeTamVisionConfiga backbone_channel_list (`List[int]`, *optional*, defaults to `[384, 192, 96, 48]`): The list of channel dimensions for the backbone. backbone_feature_sizes (`List[List[int]]`, *optional*, defaults to `[[256, 256], [128, 128], [64, 64]]`): The spatial sizes of the feature maps from the backbone. fpn_hidden_size (`int`, *optional*, defaults to 256): The hidden dimension of the FPN. fpn_kernel_size (`int`, *optional*, defaults to 1): The kernel size for the convolutions in the neck. fpn_stride (`int`, *optional*, defaults to 1): The stride for the convolutions in the neck. fpn_padding (`int`, *optional*, defaults to 0): The padding for the convolutions in the neck. fpn_top_down_levels (`List[int]`, *optional*, defaults to `[2, 3]`): The levels for the top-down FPN connections. num_feature_levels (`int`, *optional*, defaults to 3): The number of feature levels from the FPN to use. vision_configedgetam_vision_modelbackbone_configNgelugư>g{Gz?c h<VPf.R OM VPVnVPf RR.^^.^@^@..M VPVnVPf^^.M VPVn\VP\ 4'd^VPP RR4VPR&\VPR,,!R /VPBVnM3VPf&\P!RR^RRR.R O/R 7Vn\SV`,!R /VBR#) Nr model_type timm_wrapperztimm/repvit_m1.dist_in1kin_chans features_onlyT out_indices) model_args)i`0)) backbone_channel_listbackbone_feature_sizesfpn_top_down_levels isinstancerdictgetr r from_pretrainedsuper __post_init__)selfkwargs __class__s&,l/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/edgetam/modular_edgetam.pyr7!EdgeTamVisionConfig.__post_init__Qs"&"<"<"D $JdJd "372M2M2Uc3Z#sb"X .[_[v[v #.2-E-E-MAq6SWSkSk d**D 1 1151E1E1I1I,Xf1gD  .#1$2F2F|2T#U#mX\XlXl#mD  ! ! )#-#=#=*&?D-Q]^$D  ''c<V^8dQh/S[S[,R,;R&S[S[,R,;R&S[R,;R&S[;R&S[;R&S[;R&S[;R&S[S[,R,;R &S[;R &S[;R &S[;R &S[;R &#)r-Nrr/r0fpn_hidden_sizefpn_kernel_size fpn_stride fpn_paddingr1num_feature_levels hidden_actlayer_norm_epsinitializer_range)r3rlistintstrfloat)format __classdict__s"r; __annotate__ EdgeTamVisionConfig.__annotate__(s8,,t3:9: 9t+2;<!4K.=>?@ABCDEFcT)0GHIJKL MN#Or=)r/rr0r1)__name__ __module__ __qualname____firstlineno____doc__base_config_keyr!r sub_configsrr/r0r?r@rArBr1rCrDrErFr7__annotate_func____static_attributes____classdictcell__ __classcell__)r:rLs@@r;rr(sr&&O'J:K7;O.2*.OOJK,0J N#(Sr=rc]tRt^etRtR#)EdgeTamPromptEncoderConfigr*NrOrPrQrRrWr*r=r;r[r[e r=r[c]tRt^ktRtR#)EdgeTamMaskDecoderConfigr*Nr\r*r=r;r_r_kr]r=r_c]tRt^qtRtRtR#) EdgeTamConfiga] prompt_encoder_config (Union[`dict`, `EdgeTamPromptEncoderConfig`], *optional*): Dictionary of configuration options used to initialize [`EdgeTamPromptEncoderConfig`]. mask_decoder_config (Union[`dict`, `EdgeTamMaskDecoderConfig`], *optional*): Dictionary of configuration options used to initialize [`EdgeTamMaskDecoderConfig`]. Example: ```python >>> from transformers import ( ... EdgeTamVisionConfig, ... EdgeTamPromptEncoderConfig, ... EdgeTamMaskDecoderConfig, ... EdgeTamModel, ... ) >>> # Initializing a EdgeTamConfig with `"facebook/edgetam.1_hiera_tiny"` style configuration >>> configuration = EdgeTamConfig() >>> # Initializing a EdgeTamModel (with random weights) from the `"facebook/edgetam.1_hiera_tiny"` style configuration >>> model = EdgeTamModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a EdgeTamConfig from a EdgeTamVisionConfig, EdgeTamPromptEncoderConfig, and EdgeTamMaskDecoderConfig >>> # Initializing EDGETAM vision encoder, memory attention, and memory encoder configurations >>> vision_config = EdgeTamVisionConfig() >>> prompt_encoder_config = EdgeTamPromptEncoderConfig() >>> mask_decoder_config = EdgeTamMaskDecoderConfig() >>> config = EdgeTamConfig(vision_config, prompt_encoder_config, mask_decoder_config) ``` r*N)rOrPrQrRrSrWr*r=r;raraqs !F r=rac]tRt^tRtR#)EdgeTamLayerNormr*Nr\r*r=r;rcrcr=rcc]tRt^tRtR#)EdgeTamVisionEncoderOutputr*Nr\r*r=r;rfrfrdr=rfc]tRt^tRtR#)EdgeTamAttentionr*Nr\r*r=r;rhrhrdr=rhc]tRt^tRtR#)EdgeTamTwoWayAttentionBlockr*Nr\r*r=r;rjrjrdr=rjc]tRt^tRtR#)EdgeTamFeedForwardr*Nr\r*r=r;rlrlrdr=rlcRa]tRt^toRt]P !4R4tRtVt R#)EdgeTamPreTrainedModelNcB\P!W4\V\4'd3VPe#\ P !VP4R#R#\VR4'd/\ P!VPVPR7R#R#)Npositional_embedding)std) r _init_weightsr2 EdgeTamModelno_memory_embeddinginitzeros_hasattrnormal_rpscale)r8modules&&r;rr$EdgeTamPreTrainedModel._init_weightssk%%d3 fl + +))5 F6676 V3 4 4 LL44&,, G5r=r*) rOrPrQrR"_keys_to_ignore_on_load_unexpectedtorchno_gradrrrWrXrLs@r;rnrns%)-& ]]_HHr=rnzN The vision model from EdgeTAM without any head or projection on top. ) custom_introc\a]tRt^to]tRt/tRt] ] RV3RlRll44t Rt Vt R#)EdgeTamVisionModel pixel_valuesc\R4hz2Can't get input embeddings from timm wrapper modelNotImplementedErrorr8s&r;get_input_embeddings'EdgeTamVisionModel.get_input_embeddings!"VWWr=Ncn<V^8dQhRS[PR,RS[S[,RS[S[,/#)r-rNr9return)r} FloatTensorrrtuplerf)rKrLs"r;rMEdgeTamVisionModel.__annotate__s@  ''$. +,  + +  r=c vVf \R4hVP!V3/VBpVPpVUu.uFqUP^^^^4NK ppVP V4wrgW`P )RRRR1,pWpP )RRRR1,p\ VR,VVVPR7#uupi)Nz You have to specify pixel_values)last_hidden_statefpn_hidden_statesfpn_position_encoding hidden_states) ValueErrorbackbonerpermuteneckrCrfr)r8rr9backbone_outputintermediate_hidden_states hidden_staterrs&&, r;forwardEdgeTamVisionModel.forwards  ?@ @-- ??%4%F%F"[u%v[u<&:&:1aA&F[u"%v3799=W3X0-/F/F.F.HI$B$O 57N7N6N6P QRVTVRV W)8</"7)77   &wsB6r*)N)rOrPrQrRr config_classmain_input_name_can_record_outputsrr r rrWrXrs@r;rrs@ 'L$OX     r=rc.a]tRt^to.ROtRtRtVtR#)rsc\R4hrrrs&r;r!EdgeTamModel.get_input_embeddingsrr=r*N)z ^memory_.*z^mask_downsample.*zspatial_perceiver.*z^object_pointer_proj.*z0^temporal_positional_encoding_projection_layer.*no_memory_positional_encodingno_object_pointer%occlusion_spatial_embedding_parameter)rOrPrQrRr|rrWrXrs@r;rsrss *&XXr=rs)rsrrnrarr[r_)1rSr}huggingface_hub.dataclassesrrruconfiguration_utilsrmodeling_utilsrprocessing_utilsrutilsr utils.genericrr utils.output_capturingr autor r sam2.configuration_sam2r rrsam2.modeling_sam2rrrrrrrrrr[r_rarcrfrhrjrlrnrrs__all__r*r=r;rs~ .&3-&#K5-``   238(*8(48(v23 !8 4 23 4 4 23$ J$ 4$ N }  !8  }  ":    H0 H H # #  # L X9 X  r=