U 9%e2@sdZddlZddlmZmZmZddlmZddlm Z m Z ddl m Z e eZd d d Zd ZGd ddeZe ejddddGdddeZe ejddddGdddeZe ejddddGdddeZGdddeZdS)z BARK model configurationN)DictOptionalUnion)PretrainedConfig)add_start_docstringslogging)CONFIG_MAPPINGz?https://huggingface.co/suno/bark-small/resolve/main/config.jsonz9https://huggingface.co/suno/bark/resolve/main/config.json)zsuno/bark-smallz suno/barka 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 szeZdZdZdgZdddddZdfdd Zedee e j fe ee e j fe e e ee e fe ddddZZS)BarkSubModelConfigZ bark_moduleZpast_key_values num_heads num_layersinput_vocab_size block_size)Znum_attention_headsZnum_hidden_layersZ vocab_sizeZ window_size@' T{Gz?c  sN||_||_||_||_||_||_||_||_| |_| |_ t j f| dS)N) rroutput_vocab_sizer r hidden_sizedropoutbias use_cacheinitializer_rangesuper__init__) selfrrrr r rrrrrkwargs __class__j/var/www/html/Darija-Ai-API/env/lib/python3.8/site-packages/transformers/models/bark/configuration_bark.pyrRszBarkSubModelConfig.__init__NFmainr)pretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionreturnc Ks||d<||d<||d<||d<||||j|f|\}}|ddkr\||jd}d|krt|dr|d|jkrtd|dd |jd |j|f|S) Nr&r'r(r* model_typebark_configzYou are using a model of type z to instantiate a model of type zN. This is not supported for all configurations of models and can yield errors.)Z_set_token_in_kwargsZget_config_dictgetr,hasattrloggerwarning from_dict) clsr%r&r'r(r)r*rZ config_dictr"r"r#from_pretrainedms   z"BarkSubModelConfig.from_pretrained) rrrrrrrTrT)NFFNr$)__name__ __module__ __qualname__r,Zkeys_to_ignore_at_inferenceZ attribute_mapr classmethodrstrosPathLikerboolr5 __classcell__r"r"r r#r GsB  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@seZdZdZdS)r?ZsemanticNr6r7r8r,r"r"r"r#r?sBarkCoarseConfigZBarkCoarseModela 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@seZdZdZdS)rCZcoarse_acousticsNrBr"r"r"r#rCsBarkFineConfigZ 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"eZdZdZdfdd ZZS)rDZfine_acousticsTc s&||_||_tjfd|i|dS)Ntie_word_embeddings) n_codes_total n_codes_givenrr)rrGrHrIrr r"r#rszBarkFineConfig.__init__)TrErF)r6r7r8r,rr>r"r"r r#rDscsJeZdZdZdZd eeeedfdd Zeee e e ddd Z Z S) 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 ``` r-Nrsemantic_configcoarse_acoustics_configfine_acoustics_config codec_configc s|dkri}td|dkr,i}td|dkrBi}td|dkrXi}tdtf||_tf||_tf||_d|kr|dnd}t|f||_ ||_ t j f|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) r1infor?rLrCrMrDrNr rOrrr)rrLrMrNrOrrZcodec_model_typer r"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 rK)to_dict)r4rLrMrNrOrr"r"r#from_sub_model_configs5sz!BarkConfig.from_sub_model_configs)NNNNr)r6r7r8__doc__r,rrr9r?rCrDrrRr>r"r"r r#rJs&4#rJ)rSr;typingrrrZconfiguration_utilsrutilsrrautor Z get_loggerr6r1Z"BARK_PRETRAINED_CONFIG_ARCHIVE_MAPZ#BARK_SUBMODELCONFIG_START_DOCSTRINGr formatr?rCrDrJr"r"r"r#s6   &G