+ j4Rt^RIHt^RIHt^RIHtHt]P!] 4t ]!RR7]!RR]444t ]!RR7]!RR ]444t ]!RR7]!R R ]444t .R OtR #)zCLAP model configuration)strict)PreTrainedConfig)auto_docstringloggingzlaion/clap-htsat-fused) checkpointc|a]tRt^toRtRtRtRtRt^ t ^ t Rt Rt Rt RtR t^tR tR tR t^t^t^tR tV3RltRtVtR#)ClapTextConfigac Examples: ```python >>> from transformers import ClapTextConfig, ClapTextModel >>> # Initializing a CLAP text configuration >>> configuration = ClapTextConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = ClapTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clap_text_model text_configiYi gelu皙?i?g-q=relucP<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[S[,;R&S[;R &S[;R &S[;R &S[;R &S[;R &S[R,;R&S[R,;R&S[S[S[,,R,;R&S[;R&#) vocab_size hidden_sizenum_hidden_layersnum_attention_headsintermediate_size hidden_acthidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_factorlayer_norm_epsprojection_dimN pad_token_id bos_token_id eos_token_idprojection_hidden_act)intstrfloatlist)format __classdict__s"l/home/luca/screen-checking/.venv/lib/python3.14/site-packages/transformers/models/clap/configuration_clap.py __annotate__ClapTextConfig.__annotate__s ,-./012!34!5678*9:#(#+3;<!&=>?@#AB!CDEF* GH* IJS /D(,KL'MN)__name__ __module__ __qualname____firstlineno____doc__ model_typebase_config_keyrrrrrrrrrrrrrr r!r"r#__annotate_func____static_attributes____classdictcell__r)s@r*rrs{ #J#OJK!!J'*03 #&O #!NN L L+,L!'Mr-rca]tRt^BtoRtRtRt^t^@tRt Rt ^t Rt Rt RtRtRtRtR tR tR t^tR t^`tR tR tR tR tRt^t^tRtRt Rt!V3Rlt"Rt#Vt$R #)ClapAudioConfiga^ window_size (`int`, *optional*, defaults to 8): Image size of the spectrogram spec_size (`int`, *optional*, defaults to 256): Desired input size of the spectrogram that the model supports. It can be different from the output of the `ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size` of the audio models. patch_stride (`list`, *optional*, defaults to `[4, 4]`): Patch stride for the audio spectrogram num_classes (`int`, *optional*, defaults to 527): Number of classes used for the head training enable_fusion (`bool`, *optional*, defaults to `False`): Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the best results. fusion_type (`[type]`, *optional*): Fusion type used for the patch fusion. patch_embed_input_channels (`int`, *optional*, defaults to 1): Number of channels used for the input spectrogram flatten_patch_embeds (`bool`, *optional*, defaults to `True`): Whether or not to flatten the patch embeddings patch_embeds_hidden_size (`int`, *optional*, defaults to 96): Hidden size of the patch embeddings. It is used as the number of output channels. enable_patch_layer_norm (`bool`, *optional*, defaults to `True`): Whether or not to enable layer normalization for the patch embeddings aff_block_r (`int`, *optional*, defaults to 4): downsize_ratio used in the AudioFF block Example: ```python >>> from transformers import ClapAudioConfig, ClapAudioModel >>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration >>> configuration = ClapAudioConfig() >>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration >>> model = ClapAudioModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clap_audio_model audio_configr ir rFr NTgg@rgh㈵>rc<V^8dQh/S[;R&S[;R&S[;R&S[;R&S[S[S[,,S[S[S[3,,;R&S[S[S[,,S[S[R3,,;R&S[;R&S[;R &S[;R &S[S[,S[S[R3,,;R &S[S[,S[S[R3,,;R &S[;R &S[S[,;R&S[R,;R&S[;R&S[;R&S[;R&S[;R&S[S[,;R&S[S[,;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&S[;R&#)r window_size num_mel_bins spec_sizer patch_size. patch_stride num_classesrrdepthsr enable_fusionrN fusion_typepatch_embed_input_channelsflatten_patch_embedspatch_embeds_hidden_sizeenable_patch_layer_normdrop_path_raterqkv_bias mlp_ratio aff_block_rrr#rr)r$r%r'tupleboolr&)r(r)s"r*r+ClapAudioConfig.__annotate__Bs`abcdefghd3i%S/15ijS /E#s(O3>> from transformers import ClapConfig, ClapModel >>> # Initializing a ClapConfig with laion-ai/base style configuration >>> configuration = ClapConfig() >>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration >>> model = ClapModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config >>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig >>> from transformers import ClapTextConfig, ClapAudioConfig >>> # Initializing a ClapText and ClapAudioConfig configuration >>> config_text = ClapTextConfig() >>> config_audio = ClapAudioConfig() >>> config = ClapConfig(text_config=config_text, audio_config=config_audio) ```clapr r=Nrrrc h<VPf&\4Vn\PR4M:\ VP\ 4'd\R/VPBVnVP f&\4Vn\PR4M:\ VP \ 4'd\R/VP BVnVPVPnVPVP nVPVPn VPVP n VPPVn VPP\VP P4,Vn \SV`<!R/VBR#)NzO`text_config` is `None`. initializing the `ClapTextConfig` with default values.zQ`audio_config` is `None`. initializing the `ClapAudioConfig` with default values.r.)r rloggerinfo isinstancedictr=r;rr#rrlenrFsuper __post_init__)selfkwargs __class__s&,r*rcClapConfig.__post_init__s;    #-/D  KKi j (($ / /-A0@0@AD     $ / 1D  KKk l ))4 0 0 / D$2C2C DD *.*=*='+/+>+>(151K1K.262L2L/++77!%!1!1!C!Cc$J[J[JbJbFc!c ''r-c<V^8dQh/S[S[,R,;R&S[S[,R,;R&S[;R&S[;R&S[;R&S[;R&#)rNr r=logit_scale_init_valuerr#r)r`rr&r$r%)r(r)s"r*r+ClapConfig.__annotate__ss>((4/6?@))D07AB",CDEF'GH#Ir-)r=rrr g$I$I,@)r/r0r1r2r3r4rr; sub_configsr r=rirr#rrcr6r7r8 __classcell__)rfr)s@@r*rZrZsM 2J ../RK26K37L$,N!' #(Mr-rZN)r;rZr)r3huggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr/r]rr;rZ__all__r.r-r*rrs.3,   H %34$(%$(5$(N34H$&H$5H$V348(!8(58(v >r-