+ jv&"^RIHt^RIHt^RIHt^RIHtHt]!RR7]!RR]444t ]!RR7]!R R ]444t ]!RR7]!R R ]444t ]!RR7]!R R]444t .ROt R#))strict)PreTrainedConfig)auto_docstring)CONFIG_MAPPING AutoConfigzyonigozlan/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_configNgeluư>{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)r) backbone_channel_listbackbone_feature_sizesfpn_top_down_levels isinstancer dictgetrrfrom_pretrainedsuper __post_init__selfkwargs __class__s&,r/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/edgetam/configuration_edgetam.pyr(!EdgeTamVisionConfig.__post_init__Ds"&"<"<"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 &#)rNr r r!fpn_hidden_sizefpn_kernel_size fpn_stride fpn_paddingr"num_feature_levels hidden_actlayer_norm_epsinitializer_range)r$rlistintstrfloat)format __classdict__s"r- __annotate__ EdgeTamVisionConfig.__annotate__s8,,t3:9: 9t+2;<!4K.=>?@ABCDEFcT)0GHIJKL MN#Or/)r r r!r")__name__ __module__ __qualname____firstlineno____doc__base_config_keyrr sub_configsr r r!r1r2r3r4r"r5r6r7r8r(__annotate_func____static_attributes____classdictcell__ __classcell__r,r>s@@r-r r sr&&O'J:K7;O.2*.OOJK,0J N#(Sr/r cTa]tRt^XtoRtRtRtRt^t^t ^t Rt Rt ^t V3RltRtVtR #) EdgeTamPromptEncoderConfiga= mask_input_channels (`int`, *optional*, defaults to 16): The number of channels to be fed to the `MaskDecoder` module. num_point_embeddings (`int`, *optional*, defaults to 4): The number of point embeddings to be used. scale (`float`, *optional*, defaults to 1): The scale factor for the prompt encoder. prompt_encoder_configrirrc<V^8dQh/S[;R&S[S[S[,,S[S[S[3,,;R&S[S[S[,,S[S[S[3,,;R&S[;R&S[;R&S[;R&S[;R&S[;R&#) r hidden_size image_size patch_sizemask_input_channelsnum_point_embeddingsr6r7scale)r:r9tupler;r<)r=r>s"r-r?'EdgeTamPromptEncoderConfig.__annotate__Xsd3i%S/18 d3i%S/16!"!#$!%&'( )* N+r/rN)rArBrCrDrErFrQrRrSrTrUr6r7rVrHrIrJr>s@r-rNrNXsB.OK48J46J! !J NE+r/rNcda]tRt^ptoRtRtRtRtRt^t ^t ^t ^t ^t RtRtRtRtV3R ltR tVtR #) EdgeTamMaskDecoderConfigam mlp_dim (`int`, *optional*, defaults to 2048): The dimension of the MLP in the two-way transformer. attention_downsample_rate (`int`, *optional*, defaults to 2): The downsample rate for the attention layers. num_multimask_outputs (`int`, *optional*, defaults to 3): The number of multimask outputs. iou_head_depth (`int`, *optional*, defaults to 3): The depth of the IoU head. iou_head_hidden_dim (`int`, *optional*, defaults to 256): The hidden dimension of the IoU head. dynamic_multimask_via_stability (`bool`, *optional*, defaults to `True`): Whether to use dynamic multimask via stability. dynamic_multimask_stability_delta (`float`, *optional*, defaults to 0.05): The stability delta for the dynamic multimask. dynamic_multimask_stability_thresh (`float`, *optional*, defaults to 0.98): The stability threshold for the dynamic multimask. mask_decoder_configrriTg?g\(\?c<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R &S[;R &S[;R &S[;R &#) rrQr6mlp_dimnum_hidden_layersnum_attention_headsattention_downsample_ratenum_multimask_outputsiou_head_depthiou_head_hidden_dimdynamic_multimask_via_stability!dynamic_multimask_stability_delta"dynamic_multimask_stability_thresh)r:r;boolr<)r=r>s"r-r?%EdgeTamMaskDecoderConfig.__annotate__ps012345678 9: #&;<"=>?@"AB&*0CD(-3EF).4Gr/rN)rArBrCrDrErFrQr6r^r_r`rarbrcrdrerfrgrHrIrJrYs@r-r[r[ps]&,OKJG %&!"N",0#/3%04&Gr/r[cfaa]tRt^toRtRtR]R]R]/t Rt Rt Rt Rt V3RltV3R ltR tVtV;t#) 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) ``` edgetamr rOr\Nrc <\VP\4'd^VPPRR4VPR&\VPR,,!R/VPBVnM%VPf\R,!4Vn\VP \4'd\ R/VP BVnMVP f\ 4Vn\VP\4'd\R/VPBVnMVPf\4Vn\SV`(!R/VBR#)rr Nr) r#r r$r%rrOrNr\r[r'r(r)s&,r-r(EdgeTamConfig.__post_init__s d(($ / //3/A/A/E/ElTj/kD  | ,!/0B0B<0P!Q!gTXTfTf!gD     '!/0F!G!ID  d00$ 7 7)C)adF`F`)aD &  ' ' /)C)ED & d.. 5 5'?'[$BZBZ'[D $  % % -'?'AD $ ''r/c<V^8dQh/S[S[,R,;R&S[S[,R,;R&S[S[,R,;R&S[;R&#)rNr rOr\r8)r$rr<)r=r>s"r-r?EdgeTamConfig.__annotate__sbZ**T18[\ "22T9@]^ 0047>_`#ar/)r\rOr )rArBrCrDrErrrNr[rGr rOr\r8r(rHrIrJrKrLs@@r-rkrksP!FJ!;7K 59M<@:>#(er/rkN)rkr rNr[)huggingface_hub.dataclassesrconfiguration_utilsrutilsrautorrr rNr[rk__all__rr/r-rvs(/3#-238(*8(48(v23!14,23!5/!54!5H23A($A(4A(H mr/