a h.@sdZddlmZddlmZddlmZmZddlm Z m Z e e Z dZGd d d eZeejd d d dGdd d eZeejddd dGdddeZeejddd dGdddeZGdddeZgdZdS)zBARK model configuration)Optional)PretrainedConfig)add_start_docstringslogging)CONFIG_MAPPING AutoConfiga This is the configuration class to store the configuration of a [`{model}`]. It is used to instantiate the model according to the specified arguments, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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. num_layers (`int`, *optional*, defaults to 12): Number of hidden layers in the given sub-model. num_heads (`int`, *optional*, defaults to 12): Number of attention heads for each attention layer in the Transformer architecture. hidden_size (`int`, *optional*, defaults to 768): Dimensionality of the "intermediate" (often named feed-forward) layer in the architecture. dropout (`float`, *optional*, defaults to 0.0): The dropout probability for all fully connected layers in the embeddings, encoder, and pooler. bias (`bool`, *optional*, defaults to `True`): Whether or not to use bias in the linear layers and layer norm layers. initializer_range (`float`, *optional*, defaults to 0.02): The standard deviation of the truncated_normal_initializer for initializing all weight matrices. use_cache (`bool`, *optional*, defaults to `True`): Whether or not the model should return the last key/values attentions (not used by all models). c s2eZdZdgZdddddZdfdd ZZS)BarkSubModelConfigZpast_key_values num_heads num_layersinput_vocab_size block_size)Znum_attention_headsZnum_hidden_layersZ vocab_sizeZ window_size@' T{Gz?c  sR||_||_||_||_||_||_||_||_| |_| |_ t j fi| dS)N) rr output_vocab_sizer r hidden_sizedropoutbias use_cacheinitializer_rangesuper__init__) selfrr rr r rrrrrkwargs __class__g/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/bark/configuration_bark.pyrKszBarkSubModelConfig.__init__) rrrrrrrTrT)__name__ __module__ __qualname__Zkeys_to_ignore_at_inferenceZ attribute_mapr __classcell__r!r!rr"r As" r BarkSemanticConfigZBarkSemanticModel)configmodela 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 ```c@seZdZdZdZdS)r'Zsemanticsemantic_configNr#r$r% model_typebase_config_keyr!r!r!r"r'gsBarkCoarseConfigZBarkCoarseModela Example: ```python >>> from transformers import BarkCoarseConfig, BarkCoarseModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkCoarseConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkCoarseModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```c@seZdZdZdZdS)r.Zcoarse_acousticscoarse_acoustics_configNr+r!r!r!r"r.~sBarkFineConfigZ BarkFineModela n_codes_total (`int`, *optional*, defaults to 8): The total number of audio codebooks predicted. Used in the fine acoustics sub-model. n_codes_given (`int`, *optional*, defaults to 1): The number of audio codebooks predicted in the coarse acoustics sub-model. Used in the acoustics sub-models. Example: ```python >>> from transformers import BarkFineConfig, BarkFineModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkFineConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkFineModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```cs&eZdZdZdZdfdd ZZS) r0Zfine_acousticsfine_acoustics_configTc s&||_||_tjfd|i|dS)Ntie_word_embeddings) n_codes_total n_codes_givenrr)rr4r5r6rrr!r"rszBarkFineConfig.__init__)Tr2r3)r#r$r%r,r-rr&r!r!rr"r0scsheZdZdZdZeeeedZ d e e e e e e e e dfdd Z e eeeeddd ZZS) BarkConfiga This is the configuration class to store the configuration of a [`BarkModel`]. It is used to instantiate a Bark model according to the specified sub-models configurations, defining the model architecture. Instantiating a configuration with the defaults will yield a similar configuration to that of the Bark [suno/bark](https://huggingface.co/suno/bark) architecture. Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information. Args: 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.from_sub_model_configs( ... 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 ``` Zbarkr*r/r1 codec_configNrc s|duri}td|dur,i}td|durBi}td|durXi}tdtfi||_tfi||_tfi||_|dd}t |fi||_ ||_ t j fi|dS)NzMsemantic_config is None. initializing the semantic model with default values.zScoarse_acoustics_config is None. initializing the coarse model with default values.zOfine_acoustics_config is None. initializing the fine model with default values.zGcodec_config is None. initializing the codec model with default values.r,Zencodec)loggerinfor'r*r.r/r0r1getrr9rrr)rr*r/r1r9rrZcodec_model_typerr!r"rs&      zBarkConfig.__init__cKs(|f||||d|S)z Instantiate a [`BarkConfig`] (or a derived class) from bark sub-models configuration. Returns: [`BarkConfig`]: An instance of a configuration object r8)to_dict)clsr*r/r1r9rr!r!r"from_sub_model_configssz!BarkConfig.from_sub_model_configs)NNNNr)r#r$r%__doc__r,r'r.r0r Z sub_configsrdictr classmethodrr?r&r!r!rr"r7s04 #r7)r.r7r0r'N)r@typingrZconfiguration_utilsrutilsrrautorr Z get_loggerr#r:Z#BARK_SUBMODELCONFIG_START_DOCSTRINGr formatr'r.r0r7__all__r!r!r!r"s0   &&    x