i4dZddlmZddlmZddlmZmZeje Z edeGdd eZ edeGd d eZ edeGd d eZ gdZy)zCLAP model configuration)strict)PreTrainedConfig)auto_docstringloggingzlaion/clap-htsat-fused) checkpointc2eZdZUdZdZdZdZeed<dZ eed<dZ eed <dZ eed <d Z eed <d Z eed<dZeezed<dZeezed<dZeed<dZeed<dZeed<dZeed<dZeed<dZedzed<dZedzed<d Zeeezdzed!<d"Zeed#<y)$ClapTextConfiga Examples: ```python >>> from transformers import ClapTextConfig, ClapTextModel >>> # Initializing a CLAP text configuration >>> configuration = ClapTextConfig() >>> # Initializing a model (with random weights) from the configuration >>> model = ClapTextModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```clap_text_model text_configiY vocab_size hidden_size num_hidden_layersnum_attention_headsi intermediate_sizegelu hidden_act皙?hidden_dropout_probattention_probs_dropout_probimax_position_embeddingstype_vocab_size?initializer_factorg-q=layer_norm_epsprojection_dimN pad_token_idr bos_token_id eos_token_idreluprojection_hidden_act)__name__ __module__ __qualname____doc__ model_typebase_config_keyr int__annotations__rrrrrstrrfloatrrrrrr r!r"r$listr&|/var/www/vps2.regionflexible.com/Desarrollo/venv/lib/python3.12/site-packages/transformers/models/clap/configuration_clap.pyr r s #J#OJKs!!!s!J'**03 %#+3#&S&OS ##!NE!NC L#*  L#* +,L#S /D(,!'3'r3r ceZdZUdZdZdZdZeed<dZ eed<dZ eed <d Z e ed <d Z eeezeeefzed <dZeeezeedfzed<dZeed<dZeed<dZeed<dZeeeedfzed<dZeeeedfzed<dZeed<dZeezed<dZe dzed <d!Zeed"<d#Zeed$<d%Zeed&<d#Zeed'<d(Zeezed)<d(Z eezed*<d#Z!eed+<d,Z"eed-<d Z#eed.<d Z$eed/<d0Z%e ed1<d2Z&eed3<d4Z'eed5<y)6ClapAudioConfiga 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_config window_size@ num_mel_bins spec_sizerr patch_size)r?r?. patch_stridei num_classesrrrr )r#r#r#depths)r?r9 rF enable_fusionrrN fusion_typerpatch_embed_input_channelsTflatten_patch_embeds`patch_embeds_hidden_sizeenable_patch_layer_normgdrop_path_raterqkv_biasg@ mlp_ratio aff_block_rrr%r&gh㈵>rrr)(r'r(r)r*r+r,r:r-r.r<r>rr/r@r1tuplerArBrr rDrrGboolrr0rHrIrJrLrMrNrrOrPrQrr&rrr2r3r4r6r6Bs( T$J$OKL#IsJ45Jd3i%S/156>> 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) ```clap)r r8Nr r8g$I$I,@logit_scale_init_valuerr r%r&rrc <|j%t|_tjdn4t |jt rtdi|j|_|j %t|_tjdn4t |j t rtdi|j |_|j|j_|j|j _|j|j_ |j|j _ |jj|_ |jjt|j jz|_ t|<di|y)NzO`text_config` is `None`. initializing the `ClapTextConfig` with default values.zQ`audio_config` is `None`. initializing the `ClapAudioConfig` with default values.r2)r r loggerinfo isinstancedictr8r6r r&rrlenrDsuper __post_init__)selfkwargs __class__s r4r_zClapConfig.__post_init__s5    #-/D  KKi j (($ /-A0@0@AD     $ / 1D  KKk l ))4 0 / D$2C2C DD *.*=*='+/+>+>(151K1K.262L2L/++77!%!1!1!C!Cc$J[J[JbJbFc!c ''r3)r'r(r)r*r+r r6 sub_configsr r\rr.r8rWr0r r-r&r/rr_ __classcell__)rbs@r4rUrUsx 2J"0/RK26K((4/637L$))D07$,E,NC!'3' ##((r3rU)r6rUr N)r*huggingface_hub.dataclassesrconfiguration_utilsrutilsrr get_loggerr'rYr r6rU__all__r2r3r4rjs.3,   H %34$(%$(5$(N34H$&H$5H$V348(!8(58(v >r3