
    sgc:                         d Z ddlZddlmZ ddlmZ ddlmZ  ej                  e	      Z
 G d de      Z G d	 d
e      Z G d de      Z G d de      Zy)z#BARK model generation configuration    N)Dict   )GenerationConfig)loggingc                   D     e Zd ZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )BarkSemanticGenerationConfigsemanticc                     t        |   d||	|||||||d	| |
| _        || _        || _        || _        || _        || _        || _        || _	        y)a  Class that holds a generation configuration for [`BarkSemanticModel`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            eos_token_id (`int`, *optional*, defaults to 10_000):
                The id of the *end-of-sequence* token.
            renormalize_logits (`bool`, *optional*, defaults to `True`):
                Whether to renormalize the logits after applying all the logits processors (including the
                custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
                score logits are normalized but some logit processors break the normalization.
            max_new_tokens (`int`, *optional*, defaults to 768):
                The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to modulate the next token probabilities.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            text_encoding_offset (`int`, *optional*, defaults to 10_048):
                Text encoding offset.
            text_pad_token (`int`, *optional*, defaults to 129_595):
                Text pad token.
            semantic_infer_token (`int`, *optional*, defaults to 129_599):
                Semantic infer token.
            semantic_vocab_size (`int`, *optional*, defaults to 10_000):
                Semantic vocab size.
            max_input_semantic_length (`int`, *optional*, defaults to 256):
                Max length of semantic input vector.
            semantic_rate_hz (`float`, *optional*, defaults to 49.9):
                Semantic rate in Hertz.
            min_eos_p (`float`, *optional*):
                Minimum threshold of the probability of the EOS token for it to be sampled. This is an early stopping
                strategy to mitigate potential unwanted generations at the end of a prompt. The original implementation
                suggests a default value of 0.2.
        )	temperature	do_sampleeos_token_idrenormalize_logitsmax_new_tokensoutput_scoresreturn_dict_in_generateoutput_hidden_statesoutput_attentionsN )
super__init__text_encoding_offsettext_pad_tokensemantic_pad_tokensemantic_infer_tokensemantic_vocab_sizemax_input_semantic_lengthsemantic_rate_hz	min_eos_p)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   kwargs	__class__s                     i/var/www/html/venv/lib/python3.12/site-packages/transformers/models/bark/generation_configuration_bark.pyr   z%BarkSemanticGenerationConfig.__init__   s~    B 	 	
#%1)'$;!5/	
 	
 %9!,".$8!#6 )B& 0"    )'  Ti   FFFF      ?Fi@'  i; i? r$      g33333H@N)__name__
__module____qualname__
model_typer   __classcell__r!   s   @r"   r   r      sF    J  %"#$""%#U# U#r#   r   c                   J     e Zd ZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 ddedef fdZ xZS )BarkCoarseGenerationConfigcoarse_acousticsmax_coarse_historysliding_window_lenc                     t        |   d|||||||d| || _        |	| _        |
| _        || _        || _        || _        || _        y)as
  Class that holds a generation configuration for [`BarkCoarseModel`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            renormalize_logits (`bool`, *optional*, defaults to `True`):
                Whether to renormalize the logits after applying all the logits processors (including the
                custom ones). It's highly recommended to set this flag to `True` as the search algorithms suppose the
                score logits are normalized but some logit processors break the normalization.
            output_scores (`bool`, *optional*, defaults to `False`):
                Whether or not to return the prediction scores. See `scores` under returned tensors for more details.
            return_dict_in_generate (`bool`, *optional*, defaults to `False`):
                Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
            output_hidden_states (`bool`, *optional*, defaults to `False`):
                Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors
                for more details.
            output_attentions (`bool`, *optional*, defaults to `False`):
                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
                returned tensors for more details.
            temperature (`float`, *optional*, defaults to 1.0):
                The value used to modulate the next token probabilities.
            do_sample (`bool`, *optional*, defaults to `False`):
                Whether or not to use sampling ; use greedy decoding otherwise.
            coarse_semantic_pad_token (`int`, *optional*, defaults to 12_048):
                Coarse semantic pad token.
            coarse_rate_hz (`int`, *optional*, defaults to 75):
                Coarse rate in Hertz.
            n_coarse_codebooks (`int`, *optional*, defaults to 2):
                Number of coarse codebooks.
            coarse_infer_token (`int`, *optional*, defaults to 12_050):
                Coarse infer token.
            max_coarse_input_length (`int`, *optional*, defaults to 256):
                Max length of input coarse vector.
            max_coarse_history (`int`, *optional*, defaults to 630):
                Max length of the output of the coarse acoustics model used in the fine generation step.
            sliding_window_len (`int`, *optional*, defaults to 60):
                The coarse generation step uses a sliding window to generate raw audio.
        )r   r   r   r   r   r   r   Nr   )	r   r   coarse_semantic_pad_tokencoarse_rate_hzn_coarse_codebookscoarse_infer_tokenmax_coarse_input_lengthr0   r1   )r   r   r   r   r   r   r   r   r3   r4   r5   r6   r7   r0   r1   r    r!   s                   r"   r   z#BarkCoarseGenerationConfig.__init__y   sr    r 	 		
#1'$;!5/		
 		
 *C&,"4"4'>$"4"4r#   )TFFFFr%   Fi/  K      i/  r&   iv  <   )r'   r(   r)   r*   intr   r+   r,   s   @r"   r.   r.   v   sV    #J   %""(! #"%"$J5  J5  J5 J5r#   r.   c                   2     e Zd ZdZ	 	 	 	 d fd	Zd Z xZS )BarkFineGenerationConfigfine_acousticsc                 P    t         |   |       || _        || _        || _        y)a  Class that holds a generation configuration for [`BarkFineModel`].

        [`BarkFineModel`] is an autoencoder model, so should not usually be used for generation. However, under the
        hood, it uses `temperature` when used by [`BarkModel`]

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            temperature (`float`, *optional*):
                The value used to modulate the next token probabilities.
            max_fine_history_length (`int`, *optional*, defaults to 512):
                Max length of the fine history vector.
            max_fine_input_length (`int`, *optional*, defaults to 1024):
                Max length of fine input vector.
            n_fine_codebooks (`int`, *optional*, defaults to 8):
                Number of codebooks used.
        )r   N)r   r   max_fine_history_lengthmax_fine_input_lengthn_fine_codebooks)r   r   r@   rA   rB   r    r!   s         r"   r   z!BarkFineGenerationConfig.__init__   s-    4 	[1'>$%:" 0r#   c                      y)z
        Overrides GenerationConfig.validate because BarkFineGenerationConfig don't use any parameters outside
        temperature.
        Nr   )r   r    s     r"   validatez!BarkFineGenerationConfig.validate   s    
 	r#   )r%   i         )r'   r(   r)   r*   r   rD   r+   r,   s   @r"   r=   r=      s!    !J  #"1@r#   r=   c                   X    e Zd ZdZdZ	 	 	 	 	 d
dededefdZedede	de
fd       Zd	 Zy)BarkGenerationConfigbarkTNsemantic_configcoarse_acoustics_configfine_acoustics_configc                    |i }t         j                  d       |i }t         j                  d       |i }t         j                  d       t        di || _        t	        di || _        t        di || _        || _        || _	        y)a$  Class that holds a generation configuration for [`BarkModel`].

        The [`BarkModel`] does not have a `generate` method, but uses this class to generate speeches with a nested
        [`BarkGenerationConfig`] which uses [`BarkSemanticGenerationConfig`], [`BarkCoarseGenerationConfig`],
        [`BarkFineGenerationConfig`].

        This configuration inherit from [`GenerationConfig`] and can be used to control the model generation. Read the
        documentation from [`GenerationConfig`] for more information.

        Args:
            semantic_config (`Dict`, *optional*):
                Semantic generation configuration.
            coarse_acoustics_config (`Dict`, *optional*):
                Coarse generation configuration.
            fine_acoustics_config (`Dict`, *optional*):
                Fine generation configuration.
            sample_rate (`int`, *optional*, defaults to 24_000):
                Sample rate.
            codebook_size (`int`, *optional*, defaults to 1024):
                Vector length for each codebook.
        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.r   )
loggerinfor   rJ   r.   rK   r=   rL   sample_ratecodebook_size)r   rJ   rK   rL   rP   rQ   r    s          r"   r   zBarkGenerationConfig.__init__   s    < " OKKgh"*&(#KKmn ($&!KKij;NoN'A'\D['\$%=%V@U%V"&*r#   c                 n     | d|j                         |j                         |j                         d|S )z
        Instantiate a [`BarkGenerationConfig`] (or a derived class) from bark sub-models generation configuration.

        Returns:
            [`BarkGenerationConfig`]: An instance of a configuration object
        )rJ   rK   rL   r   )to_dict)clsrJ   rK   rL   r    s        r"   from_sub_model_configsz+BarkGenerationConfig.from_sub_model_configs(  sD      
+335$;$C$C$E"7"?"?"A
 	
 	
r#   c                 $   t        j                  | j                        }| j                  j	                         |d<   | j
                  j	                         |d<   | j                  j	                         |d<   | j                  j                  |d<   |S )z
        Serializes this instance to a Python dictionary. Override the default [`~PretrainedConfig.to_dict`].

        Returns:
            `Dict[str, any]`: Dictionary of all the attributes that make up this configuration instance,
        rJ   rK   rL   r*   )	copydeepcopy__dict__rJ   rS   rK   rL   r!   r*   )r   outputs     r"   rS   zBarkGenerationConfig.to_dict=  s}     t}}-$($8$8$@$@$B !,0,H,H,P,P,R()*.*D*D*L*L*N&'#~~88|r#   )NNNi]  rE   )r'   r(   r)   r*   is_compositionr   r   classmethodr   r.   r=   rU   rS   r   r#   r"   rH   rH      st    JN !%(,&*/+/+ "&/+  $	/+b 
5
 "<
  8	
 
(r#   rH   )__doc__rW   typingr   generation.configuration_utilsr   utilsr   
get_loggerr'   rN   r   r.   r=   rH   r   r#   r"   <module>rb      sg    *   >  
		H	%X##3 X#vM5!1 M5`(/ (VZ+ Zr#   