Zj%/SSKJr SSKJr SSKJrJr \R"\5r \"SS9\"SS\555r \"SS9\"S S \555r \"SS9\"S S \555r /S Qr g))strict)PreTrainedConfig)auto_docstringloggingzCIDAS/clipseg-rd64) checkpointc"\rSrSr%SrSrSrSr\\ S'Sr \\ S'S r \\ S 'S r \\ S 'S r \\ S'Sr\\ S'Sr\\ S'Sr\S-\ S'Sr\\-S-\ S'Sr\\ S'Sr\S-\ S'Sr\S-\ S'Sr\S-\ S'S r\\\-S-\ S!'S"rS#rg)$CLIPSegTextConfiga Example: ```python >>> from transformers import CLIPSegTextConfig, CLIPSegTextModel >>> # Initializing a CLIPSegTextConfig with CIDAS/clipseg-rd64 style configuration >>> configuration = CLIPSegTextConfig() >>> # Initializing a CLIPSegTextModel (with random weights) from the CIDAS/clipseg-rd64 style configuration >>> model = CLIPSegTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clipseg_text_model text_configi vocab_size hidden_sizeintermediate_size num_hidden_layersnum_attention_headsMmax_position_embeddings quick_gelu hidden_acth㈵>Nlayer_norm_epsattention_dropout{Gz?initializer_range?initializer_factor pad_token_idi bos_token_idi eos_token_idcURUR-S:wa&[SURSURS35egzOPart of `@strict`-powered validation. Validates the architecture of the config.rzThe hidden size (z6) is not a multiple of the number of attention heads (z).Nrr ValueErrorselfs ڂ/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/clipseg/configuration_clipseg.pyvalidate_architecture'CLIPSegTextConfig.validate_architectureES   d66 6! ;#D$4$4#5622327  <)__name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyrint__annotations__rrrrrrstrrfloatrr r"r$r%r&listr.__static_attributes__r2r1r-r r s &J#OJK!s!s  #%S%"J"#'NEDL',/sU{T)/#u#'* * !L#* $L#*$+0L#S /D(0r1r c \rSrSr%SrSrSrSr\\ S'Sr \\ S'S r \\ S 'S r \\ S 'S r \\ S 'Sr\\\-\\\4-S-\ S'Sr\\\-\\\4-S-\ S'Sr\\ S'Sr\\ S'Sr\\-S-\ S'Sr\\ S'Sr\\ S'SrSrg)CLIPSegVisionConfigNa Example: ```python >>> from transformers import CLIPSegVisionConfig, CLIPSegVisionModel >>> # Initializing a CLIPSegVisionConfig with CIDAS/clipseg-rd64 style configuration >>> configuration = CLIPSegVisionConfig() >>> # Initializing a CLIPSegVisionModel (with random weights) from the CIDAS/clipseg-rd64 style configuration >>> model = CLIPSegVisionModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clipseg_vision_model vision_configiri rrrrr num_channelsN image_size patch_sizerrrrrrrr r!r"cURUR-S:wa&[SURSURS35egr(r)r+s r-r.)CLIPSegVisionConfig.validate_architectureqr0r1r2)r3r4r5r6r7r8r9rr:r;rrrrErGr>tuplerIrr<rr=rr r"r.r?r2r1r-rArANs (J%OK!s!s!!L#;>Jd3i%S/1D8>;=Jd3i%S/1D8="J" NE ,/sU{T)/#u# ##r1rAc>^\rSrSr%SrSr\\S.rSr \ \-S-\ S'Sr \ \-S-\ S'Sr \S-\ S 'S r\\-S-\ S 'S r\S-\ S 'Sr\\\\S4-\ S'Sr\\ S'Sr\\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\ S'Sr\\ S'U4SjrSrU=r $) CLIPSegConfigza extract_layers (`list[int]`, *optional*, defaults to `[3, 6, 9]`): Layers to extract when forwarding the query image through the frozen visual backbone of CLIP. reduce_dim (`int`, *optional*, defaults to 64): Dimensionality to reduce the CLIP vision embedding. conditional_layer (`int`, *optional*, defaults to 0): The layer to use of the Transformer encoder whose activations will be combined with the condition embeddings using FiLM (Feature-wise Linear Modulation). If 0, the last layer is used. use_complex_transposed_convolution (`bool`, *optional*, defaults to `False`): Whether to use a more complex transposed convolution in the decoder, enabling more fine-grained segmentation.. Example: ```python >>> from transformers import CLIPSegConfig, CLIPSegModel >>> # Initializing a CLIPSegConfig with CIDAS/clipseg-rd64 style configuration >>> configuration = CLIPSegConfig() >>> # Initializing a CLIPSegModel (with random weights) from the CIDAS/clipseg-rd64 style configuration >>> model = CLIPSegModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a CLIPSegConfig from a CLIPSegTextConfig and a CLIPSegVisionConfig >>> # Initializing a CLIPSegText and CLIPSegVision configuration >>> config_text = CLIPSegTextConfig() >>> config_vision = CLIPSegVisionConfig() >>> config = CLIPSegConfig(text_config=config_text, vision_config=config_vision) ```clipseg)r rDNr rDrprojection_dimg/L F@logit_scale_init_valuer!r")r .extract_layers@ reduce_dimdecoder_num_attention_headsrdecoder_attention_dropoutrdecoder_hidden_actrdecoder_intermediate_sizerconditional_layerF"use_complex_transposed_convolutionc >URc0n[RS5 OF[UR[5(aURR 5nO URnUR c0n[RS5 OF[UR [5(aUR R 5nO UR nURSS5nURSS5nUb[ S0UD6R 5nUR5HHupxXr;dM XU:wdMUS:wdMXt;a SUSUS3n OS US 3n [RU 5 MJ URU5 Ub[S0UD6R 5n S U ;a5U S R5VVs0sHupx[U5U_M snnU S 'U R5HHupxXs;dM XU:wdMUS:wdMXu;a SUS US3n OS US 3n [RU 5 MJ URU 5 [ S0UD6Ul[S0UD6Ul[T U]4"S0UD6 gs snnf)NzR`text_config` is `None`. Initializing the `CLIPSegTextConfig` with default values.zV`vision_config` is `None`. initializing the `CLIPSegVisionConfig` with default values.text_config_dictvision_config_dicttransformers_version`zp` is found in both `text_config_dict` and `text_config` but with different values. The value `text_config_dict["z"]` will be used instead.zj`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["z"]` will be overridden.id2labelzv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zp`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. The value `vision_config["r2)r loggerinfo isinstancer to_dictrDrApopitemsupdater<super __post_init__) r,kwargsr rDr`ra_text_config_dictkeyvaluemessage_vision_config_dict __class__s r-rmCLIPSegConfig.__post_init__s    #K KKl m ((*; < <**224K**K    %M KKp q **,? @ @ ..668M ..M "::&8$?#ZZ(rLrWrYrZr[r<r\r]r^boolrmr? __classcell__)rts@r-rNrNzs! FJ"3FYZK37K))D077;M4--4;!$NC$J$17ECK$.7'* *2;NDIc3h/;J'((-0us{0**%)s)s/4&4Q(Q(r1rN)rNr rAN)huggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr3rer rArN__all__r2r1r-rs(/3,   H %/0,(,1,^/0'*'1'T/0G($G(1G(T Hr1