ZjH-fSrSSKJr SSKJr SSKJrJr SSKJ r J r \R"\ 5r \"SS 9\"S S \555r\"SS 9\"S S \555r\"SS 9\"SS\555r\"SS 9\"SS\555r\"SS 9\"SS\555r/SQrg)zBARK model configuration)strict)PreTrainedConfig)auto_docstringlogging)CONFIG_MAPPING AutoConfigz suno/bark) checkpointc\rSrSr%SrS/rSSSSS.rS r\\ S'S r \\ S'S r \\ S 'S r \\ S'S r \\ S'S r\\ S'Sr\\-\ S'Sr\\ S'Sr\\ S'Sr\\ S'Srg)BarkSubModelConfiga block_size (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers. past_key_values num_heads num_layersinput_vocab_size block_size)num_attention_headsnum_hidden_layers vocab_size window_sizeii@'output_vocab_size i hidden_sizegdropoutTbias{Gz?initializer_range use_cacheN)__name__ __module__ __qualname____firstlineno____doc__keys_to_ignore_at_inference attribute_maprint__annotations__rrrrrrfloatrboolrr__static_attributes__r |/root/GenerationalWealth/GenerationalWealth/venv/lib/python3.13/site-packages/transformers/models/bark/configuration_bark.pyr r s $5"5 +)(# MJ"c"#s#JIsKGUS[D$#u#Itr-r c \rSrSrSrSrSrSrg)BarkSemanticConfigBaS block_size (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers Example: ```python >>> from transformers import BarkSemanticConfig, BarkSemanticModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkSemanticConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkSemanticModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```semanticsemantic_configr Nr!r"r#r$r% model_typebase_config_keyr,r r-r.r0r0Bs <J'Or-r0c \rSrSrSrSrSrSrg)BarkCoarseConfiggaK block_size (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers Example: ```python >>> from transformers import BarkCoarseConfig, BarkCoarseModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkCoarseConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkCoarseModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```coarse_acousticscoarse_acoustics_configr Nr4r r-r.r8r8gs <$J/Or-r8cL\rSrSr%SrSrSrSr\\ S'Sr \ \ S'S r \ \ S 'S r g ) BarkFineConfigau block_size (`int`, *optional*, defaults to 1024): The maximum sequence length that this model might ever be used with. Typically set this to something large just in case (e.g., 512 or 1024 or 2048). input_vocab_size (`int`, *optional*, defaults to 10_048): Vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the `inputs_ids` passed when calling [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. output_vocab_size (`int`, *optional*, defaults to 10_048): Output vocabulary size of a Bark sub-model. Defines the number of different tokens that can be represented by the: `output_ids` when passing forward a [`{model}`]. Defaults to 10_048 but should be carefully thought with regards to the chosen sub-model. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers n_codes_total (`int`, *optional*, defaults to 8): The total number of audio codebooks predicted. Used in the fine acoustics sub-model. n_codes_given (`int`, *optional*, defaults to 1): The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics sub-models. Example: ```python >>> from transformers import BarkFineConfig, BarkFineModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkFineConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkFineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```fine_acousticsfine_acoustics_configTtie_word_embeddings n_codes_total n_codes_givenr N)r!r"r#r$r%r5r6rAr+r)rCr(rEr,r r-r.r=r=s3! F"J-O $$M3M3r-r=c^\rSrSr%SrSr\\\\ S.r Sr \ \ -S-\S'Sr\ \ -S-\S'Sr\ \ -S-\S'Sr\ \ -S-\S 'S r\\S 'U4S jrS rU=r$) BarkConfiga semantic_config ([`BarkSemanticConfig`], *optional*): Configuration of the underlying semantic sub-model. coarse_acoustics_config ([`BarkCoarseConfig`], *optional*): Configuration of the underlying coarse acoustics sub-model. fine_acoustics_config ([`BarkFineConfig`], *optional*): Configuration of the underlying fine acoustics sub-model. codec_config ([`AutoConfig`], *optional*): Configuration of the underlying codec sub-model. Example: ```python >>> from transformers import ( ... BarkSemanticConfig, ... BarkCoarseConfig, ... BarkFineConfig, ... BarkModel, ... BarkConfig, ... AutoConfig, ... ) >>> # Initializing Bark sub-modules configurations. >>> semantic_config = BarkSemanticConfig() >>> coarse_acoustics_config = BarkCoarseConfig() >>> fine_acoustics_config = BarkFineConfig() >>> codec_config = AutoConfig.from_pretrained("facebook/encodec_24khz") >>> # Initializing a Bark module style configuration >>> configuration = BarkConfig( ... semantic_config, coarse_acoustics_config, fine_acoustics_config, codec_config ... ) >>> # Initializing a model (with random weights) >>> model = BarkModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ``` bark)r3r;r@ codec_configNr3r;r@rJrrc >URc%[5Ul[RS5 O9[ UR[ 5(a[S0URD6UlUR c%[5Ul[RS5 O9[ UR [ 5(a[S0UR D6UlURc%[5Ul[RS5 O9[ UR[ 5(a[S0URD6UlURc)[S"5Ul [RS5 OY[ UR[ 5(a:URRSS5n[U"S0URD6Ul [TU]8"S0UD6 g)NzW`semantic_config` is `None`. Initializing the `BarkSemanticConfig` with default values.z]`coarse_acoustics_config` is `None`. Initializing the `BarkCoarseConfig` with default values.zY`fine_acoustics_config` is `None`. Initializing the `BarkFineConfig` with default values.encodeczN`codec_config` is `None`. Initializing the `codec_config` with default values.r5r )r3r0loggerinfo isinstancedictr;r8r@r=rJr getsuper __post_init__)selfkwargscodec_model_type __class__s r.rSBarkConfig.__post_init__so    '#5#7D KKq r ,,d 3 3#5#M8L8L#MD  ' ' /+;+=D ( KKo 44d ; ;+;+[d>Z>Z+[D (  % % -)7)9D & KKs t 22D 9 9)7)U$:T:T)UD &    $ .y 9 ;D  KKh i ))4 0 0#0044\9M  ./? @ U4CTCT UD  ''r-)r;rJr@r3)r!r"r#r$r%r5r0r8r=r sub_configsr3rPrr)r;r@rJrr*rSr, __classcell__)rWs@r.rGrGs(TJ-#3!/" K 7;OT,,t3:>BT$44t;B<@4"22T9@37L$))D07#u#((r-rG)r8rGr=r0N)r%huggingface_hub.dataclassesrconfiguration_utilsrutilsrrautor r get_loggerr!rMr r0r8r=rG__all__r r-r.ras.3,-   H %;'#)#(#L;' (+ (( (F;' 0) 0( 0F;')')()X;'T(!T((T(n Ur-