
    sg .                        d Z ddlmZ ddlmZ ddlmZmZ ddlm	Z	m
Z
  ej                  e      ZdZ G d	 d
e      Z eej!                  dd      d       G d de             Z eej!                  dd      d       G d de             Z eej!                  dd      d       G d de             Z G d de      Zy)zBARK model configuration    )Dict   )PretrainedConfig)add_start_docstringslogging   )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                   H     e Zd ZdgZdddddZ	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )	BarkSubModelConfigpast_key_values	num_heads
num_layersinput_vocab_size
block_size)num_attention_headsnum_hidden_layers
vocab_sizewindow_sizec                     || _         || _        || _        || _        || _        || _        || _        || _        |
| _        |	| _	        t        | ,  di | y )N )r   r   output_vocab_sizer   r   hidden_sizedropoutbias	use_cacheinitializer_rangesuper__init__)selfr   r   r   r   r   r   r   r   r   r   kwargs	__class__s               ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/bark/configuration_bark.pyr   zBarkSubModelConfig.__init__K   s_     % 0!2$"&	"!2"6"    )
i   @'  r%      r&   i   g        T{Gz?T)__name__
__module____qualname__keys_to_ignore_at_inferenceattribute_mapr   __classcell__r"   s   @r#   r   r   A   sK    #4"5  +)(#	M  # #r$   r   BarkSemanticConfigBarkSemanticModel)configmodela  
    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                       e Zd ZdZdZy)r/   semanticsemantic_configNr(   r)   r*   
model_typebase_config_keyr   r$   r#   r/   r/   g   s    & J'Or$   BarkCoarseConfigBarkCoarseModela  
    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                       e Zd ZdZdZy)r9   coarse_acousticscoarse_acoustics_configNr6   r   r$   r#   r9   r9   ~   s    & $J/Or$   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                   (     e Zd ZdZdZd fd	Z xZS )r>   fine_acousticsfine_acoustics_configc                 D    || _         || _        t        |   dd|i| y )Ntie_word_embeddingsr   )n_codes_totaln_codes_givenr   r   )r    rD   rE   rF   r!   r"   s        r#   r   zBarkFineConfig.__init__   s)    **K-@KFKr$   )T      )r(   r)   r*   r7   r8   r   r-   r.   s   @r#   r>   r>      s    0 "J-OL Lr$   c            	       t     e Zd ZdZdZeeeedZ		 	 	 	 	 d
de
de
de
de
f fdZededededefd	       Z x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
    ```
    barkr5   r=   rB   codec_configr5   r=   rB   rM   c                    |i }t         j                  d       |i }t         j                  d       |i }t         j                  d       |i }t         j                  d       t        di || _        t	        di || _        t        di || _        d|v r|d   nd}t        |   di || _	        || _
        t        | 0  di | y )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.r7   encodecr   )loggerinfor/   r5   r9   r=   r>   rB   r	   rM   r   r   r   )	r    r5   r=   rB   rM   r   r!   codec_model_typer"   s	           r#   r   zBarkConfig.__init__   s     " OKKgh"*&(#KKmn ($&!KKijLKKab1DOD'7'R:Q'R$%3%L6K%L"9E9U<5[d*+;<L|L!2"6"r$   c                      | d|j                         |j                         |j                         |j                         d|S )z
        Instantiate a [`BarkConfig`] (or a derived class) from bark sub-models configuration.

        Returns:
            [`BarkConfig`]: An instance of a configuration object
        rL   r   )to_dict)clsr5   r=   rB   rM   r!   s         r#   from_sub_model_configsz!BarkConfig.from_sub_model_configs  sP      
+335$;$C$C$E"7"?"?"A%--/	

 
 	
r$   )NNNNr'   )r(   r)   r*   __doc__r7   r/   r9   r>   r
   sub_configsr   r   classmethodr   rV   r-   r.   s   @r#   rJ   rJ      s    2h J-#3!/"	K !%(,&*!!#!# "&!#  $	!#
 !#F 
+
 "2
  .	

 '
 
r$   rJ   N)rW   typingr   configuration_utilsr   utilsr   r   autor	   r
   
get_loggerr(   rP   #BARK_SUBMODELCONFIG_START_DOCSTRINGr   formatr/   r9   r>   rJ   r   r$   r#   <module>ra      s     3 2 - 
		H	%#' #L##) ##L '..6JRe.f$(+ (%$(
 '..6HPa.b$0) 0%$0
 '..6Fo.^.L' L/.Lu
! u
r$   