g#2dZddlZddlmZmZmZddlmZddlm Z m Z ddl m Z e j eZd ZGd d eZe ed d dGdd eZe edddGddeZe edddGddeZGddeZdS)zBARK model configurationN)DictOptionalUnion)PretrainedConfig)add_start_docstringslogging)CONFIG_MAPPINGa 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). ceZdZdZdgZdddddZ dfd Ze ddee e j fde ee e j fde de de ee e fde ddfdZxZS)BarkSubModelConfig bark_modulepast_key_values num_heads num_layersinput_vocab_size block_size)num_attention_headsnum_hidden_layers vocab_size window_size@' T{Gz?c  ||_||_||_||_||_||_||_||_| |_| |_ tj di| dS)N) rroutput_vocab_sizerr hidden_sizedropoutbias use_cacheinitializer_rangesuper__init__) selfrrr rrr!r"r#r%r$kwargs __class__s g/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/bark/configuration_bark.pyr'zBarkSubModelConfig.__init__Msr% 0!2$"&  "!2""6"""""NFmainpretrained_model_name_or_path cache_dirforce_downloadlocal_files_onlytokenrevisionreturnrc ||d<||d<||d<||d<||||j|fi|\}}|ddkr||jd}d|vrMt |dr=|d|jkr,t d|dd |jd |j|fi|S) Nr/r0r1r3 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.)_set_token_in_kwargsget_config_dictgetr6hasattrloggerwarning from_dict) clsr.r/r0r1r2r3r) config_dicts r+from_pretrainedz"BarkSubModelConfig.from_pretrainedhs#({#1 %5!"%z   ///1c12OZZSYZZ V ??< ( (F 2 2%&@&@&@AK ; & &73 +E+E &+VbJcgjguJuJu NNr\1Jrr>rrr    s}[33F333r,) rrrrrrrTrT)NFFNr-)__name__ __module__ __qualname__r6keys_to_ignore_at_inference attribute_mapr' classmethodrstrosPathLikerboolrB __classcell__r*s@r+r r Bs*J#4"5 +)(# M  ######68<$!&,044',S"+-='>4E#r{"2344 4  4 c4i() 44 444[44444r,r BarkSemanticConfigBarkSemanticModel)configmodela Example: ```python >>> from transformers import BarkSemanticConfig, BarkSemanticModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkSemanticConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkSemanticModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ceZdZdZdS)rOsemanticNrCrDrEr6rr,r+rOrOs&JJJr,BarkCoarseConfigBarkCoarseModela Example: ```python >>> from transformers import BarkCoarseConfig, BarkCoarseModel >>> # Initializing a Bark sub-module style configuration >>> configuration = BarkCoarseConfig() >>> # Initializing a model (with random weights) from the suno/bark style configuration >>> model = BarkCoarseModel(configuration) >>> # Accessing the model configuration >>> configuration = model.config ```ceZdZdZdS)rVcoarse_acousticsNrUrr,r+rVrVs&$JJJr,BarkFineConfig 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 ```c$eZdZdZdfd ZxZS)rZfine_acousticsTc Z||_||_tjdd|i|dS)Ntie_word_embeddingsr) n_codes_total n_codes_givenr&r')r(rarbrcr)r*s r+r'zBarkFineConfig.__init__s<**KK-@KFKKKKKr,)Tr^r_)rCrDrEr6r'rMrNs@r+rZrZsN0"JLLLLLLLLLLr,c leZdZdZdZ d dedededeffd Zedede de de fd Z xZ 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 ``` r7Nrsemantic_configcoarse_acoustics_configfine_acoustics_config codec_configc |i}td|i}td|i}td|i}tdtdi||_t di||_t di||_d|vr|dnd}t|di||_ ||_ tj di|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.r6encodecr) r=inforOrfrVrgrZrhr rir%r&r') r(rfrgrhrir%r)codec_model_typer*s r+r'zBarkConfig.__init__ s%  " O KKg h h h " *&( # KKm n n n ($& ! KKi j j j  L KKa b b b1DDODD'7'R'R:Q'R'R$%3%L%L6K%L%L"9E9U9U< 55[d*+;<LL|LL!2""6"""""r,c |d||||d|S)z Instantiate a [`BarkConfig`] (or a derived class) from bark sub-models configuration. Returns: [`BarkConfig`]: An instance of a configuration object )rfrgrhrir)to_dict)r@rfrgrhrir)s r+from_sub_model_configsz!BarkConfig.from_sub_model_configs0shs +3355$;$C$C$E$E"7"?"?"A"A%--//       r,)NNNNr)rCrDrE__doc__r6rr'rHrOrVrZrrprMrNs@r+reres22hJ!%(,&*! !#!#!#"&!# $ !#  !#!#!#!#!#!#F + "2  .  '    [     r,re)rqrJtypingrrrconfiguration_utilsrutilsrr autor get_loggerrCr=#BARK_SUBMODELCONFIG_START_DOCSTRINGr formatrOrVrZrerr,r+rys3 ((((((((((33333322222222!!!!!!  H % %#'#LD4D4D4D4D4)D4D4D4N'..6JRe.ff $+%$'..6HPa.bb $$$$$$)$$%$$'..6Fo.^^ .LLLLL'LL/.Lo o o o o !o o o o o r,