
    sg"O                         d Z ddlZddlmZmZ er	 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y)zCLVP model configuration    N)TYPE_CHECKINGUnion   )PretrainedConfig)loggingc                        e Zd ZdZdZddgZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Ze	 ddee	e
j                  f   de	dd	fd
       Z xZS )ClvpEncoderConfigak  
    This is the configuration class to store the configuration of a [`ClvpEncoder`]. It is used to instantiate a CLVP
    text or CLVP speech encoder according to the specified arguments. Instantiating a configuration with the defaults
    will yield a similar configuration to that of the encoder of the CLVP
    [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        vocab_size (`int`, *optional*, defaults to 256):
            Vocabulary size of the CLVP Encoder model.
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 1536):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        projection_dim (`int`, *optional*, defaults to 768):
            Dimensionality of the projection vector.
        num_hidden_layers (`int`, *optional*, defaults to 20):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 12):
            Number of attention heads for each attention layer in the Transformer encoder.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"quick_gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the feed-forward layers in [`ClvpEncoderMLP`].
        use_rotary_embedding (`bool`, *optional*, defaults to `True`):
            Whether to use rotary_embedding or not.
        use_attention_bias (`bool`, *optional*, defaults to `False`):
            Whether to use bias in Query, Key and Value layers during self attention.
        summary_type (`str`, *optional*, defaults to `"mean"`):
            What strategy to use to get pooler_output from the last_hidden_state. `"last"`, `"first"`, `"mean"` and
            `"cls_index"` are supported.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
            testing).
        bos_token_id (`int`, *optional*, defaults to 255):
            Beginning of sequence token id.
        eos_token_id (`int`, *optional*, defaults to 0):
            End of sequence token id.

    Example:

    ```python
    >>> from transformers import ClvpEncoderConfig, ClvpEncoder

    >>> # Initializing a ClvpEncoderConfig with susnato/clvp_dev style configuration
    >>> encoder_configuration = ClvpEncoderConfig()

    >>> # Initializing a ClvpEncoder (with random weights) from the susnato/clvp_dev style configuration
    >>> model = ClvpEncoder(encoder_configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```clvp_encodertext_configspeech_configc                 
   || _         || _        || _        || _        || _        || _        || _        || _        || _        |	| _	        |
| _
        || _        || _        || _        || _        || _        t!        | D  d||d| y N)bos_token_ideos_token_id )
vocab_sizehidden_sizeintermediate_sizeprojection_dimnum_hidden_layersnum_attention_headslayer_norm_eps
hidden_actinitializer_factorattention_dropoutdropoutuse_rotary_embeddinguse_attention_biassummary_typer   r   super__init__)selfr   r   r   r   r   r   r   r   r   r   r   r   r   r   r   r   kwargs	__class__s                     ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/clvp/configuration_clvp.pyr!   zClvpEncoderConfig.__init__`   s    ( %&!2,!2#6 ,$"4!2$8!"4(((XlXQWX    pretrained_model_name_or_pathconfig_typereturnr   c                 v   | j                  |        | j                  |fi |\  }}|| j                  vrt        d|       |j	                  d      dk(  r||   }d|v rGt        | d      r;|d   | j                  k7  r)t        j                  d|d    d| j                   d        | j                  |fi |S )NzSWe can only load either 'text_config' or 'speech_config' but you are trying to load
model_typeclvpzYou are using a model of type z  to instantiate a model of type zN. This is not supported for all configurations of models and can yield errors.)
_set_token_in_kwargsget_config_dictbase_config_key
ValueErrorgethasattrr+   loggerwarning	from_dict)clsr'   r(   r#   config_dicts        r%   from_pretrainedz!ClvpEncoderConfig.from_pretrained   s     	  (1c112OZSYZV c111ejuivw 
 ??<(F2%k2K;&73+E+VbJcgjguguJuNN0\1J0KKk>>""pr
 s}}[3F33r&   )      i   r:         geluh㈵>皙?r?   TFmean      ?   r   )r   )__name__
__module____qualname____doc__r+   r/   r!   classmethodr   strosPathLiker8   __classcell__r$   s   @r%   r	   r	      s    ;z  J$o6O ! #%YN Xe4,1#r{{2B,C4RU4	4 4r&   r	   c                   d     e Zd ZdZdZdZdddddd	d
dddddddddd
ddddddddg df fd	Z xZS )ClvpDecoderConfigaN  
    This is the configuration class to store the configuration of a [`ClvpDecoder`]. It is used to instantiate a CLVP
    Decoder 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 Decoder part of the CLVP
    [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    The architecture is similar to GPT2.

    Args:
        vocab_size (`int`, *optional*, defaults to 8194):
            Vocabulary size of the model.
        max_position_embeddings (`int`, *optional*, defaults to 608):
            The maximum sequence length of mel tokens that this model might ever be used with. Similar to `n_positions`
            in `GPT2Config`.
        max_text_tokens (`int`, *optional*, defaults to 404):
            The maximum sequence length of text tokens that this model might ever be used with. Similar to
            `n_positions` in `GPT2Config`.
        hidden_size (`int`, *optional*, defaults to 1024):
            Dimensionality of the embeddings and hidden states.
        num_hidden_layers (`int`, *optional*, defaults to 30):
            Number of hidden layers in the Transformer encoder.
        num_attention_heads (`int`, *optional*, defaults to 16):
            Number of attention heads for each attention layer in the Transformer encoder.
        n_inner (`int`, *optional*):
            Dimensionality of the inner feed-forward layers. `None` will set it to 4 times `hidden_size`.
        num_mel_attn_blocks (`int`, *optional*, defaults to 6):
            Denotes the number of self attention layers in [`ClvpConditioningEncoder`].
        activation_function (`str`, *optional*, defaults to `"gelu_new"`):
            Activation function, to be selected in the list `["relu", "silu", "gelu", "tanh", "gelu_new"]`.
        resid_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        embd_pdrop (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the embeddings.
        attention_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention.
        layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
            The epsilon to use in the layer normalization layers.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        summary_type (`string`, *optional*, defaults to `"cls_index"`):
            Argument used when doing sequence summary.

            Has to be one of the following options:

                - `"last"`: Take the last token hidden state (like XLNet).
                - `"first"`: Take the first token hidden state (like BERT).
                - `"mean"`: Take the mean of all tokens hidden states.
                - `"cls_index"`: Supply a Tensor of classification token position (like GPT/GPT-2).
                - `"attn"`: Not implemented now, use multi-head attention.
        summary_use_proj (`bool`, *optional*, defaults to `True`):
            Whether or not to add a projection after the vector extraction.
        summary_activation (`str`, *optional*):
            Pass `"tanh"` for a tanh activation to the output, any other value will result in no activation.
        summary_proj_to_labels (`bool`, *optional*, defaults to `True`):
            Whether the projection outputs should have `config.num_labels` or `config.hidden_size` classes.
        summary_first_dropout (`float`, *optional*, defaults to 0.1):
            The dropout ratio to be used after the projection and activation.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        bos_token_id (`int`, *optional*, defaults to 8192):
            Beginning of sequence token id, used at the start of the generation.
        eos_token_id (`int`, *optional*, defaults to 8193):
            End of sequence token id, used in the method
            [`ClvpModelForConditionalGeneration.fix_speech_decoder_output()`] to correct decoder outputs.
        feature_size (`int`, *optional*, defaults to 80):
            The feature dimension of the extracted mel features. This value is used in [`ClvpConditioningEncoder`].
        use_attention_bias (`bool`, *optional*, defaults to `True`):
            Whether to use bias in Query, Key and Value layers during self attention.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
            testing).
        decoder_fixing_codes (`list`, *optional*, defaults to `[83, 45, 45, 248]`):
            These values are used in the method `fix_speech_decoder_output` to fix decoder generated outputs.

    Example:

    ```python
    >>> from transformers import ClvpDecoderConfig, ClvpDecoder

    >>> # Initializing a ClvpDecoderConfig with susnato/clvp_dev style configuration
    >>> decoder_configuration = ClvpDecoderConfig()

    >>> # Initializing a ClvpDecoder (with random weights) from the susnato/clvp_dev style configuration
    >>> model = ClvpDecoder(decoder_configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```clvp_decoderdecoder_configi   i`  i  i         N   gelu_newr?   r>   g{Gz?	cls_indexTi    i   P   rA   )S   -   rX      c                    || _         || _        || _        || _        || _        || _        || _        || _        |	| _        |
| _	        || _
        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        || _        t5        | l  d||d| y r   )r   max_position_embeddingsmax_text_tokensr   r   r   n_innernum_mel_attn_blocksactivation_functionresid_pdrop
embd_pdropr   layer_norm_epsiloninitializer_ranger   summary_use_projsummary_activationsummary_first_dropoutsummary_proj_to_labels	use_cachefeature_sizer   r   decoder_fixing_codesr   r   r    r!   )r"   r   r[   r\   r   r   r   r]   r^   r_   r`   ra   r   rb   rc   r   rd   re   rg   rf   rh   r   r   ri   r   r   rj   r#   r$   s                               r%   r!   zClvpDecoderConfig.__init__  s    < %'>$.&!2#6 #6 #6 &$!2"4!2( 0"4%:"&<#"("4"4$8!((XlXQWXr&   )rC   rD   rE   rF   r+   r/   r!   rK   rL   s   @r%   rN   rN      sr    Zx  J&O  #& #!.7:Y :Yr&   rN   c                   ^     e Zd ZdZdZeeedZ	 	 	 	 	 	 d	 fd	Ze	dededefd       Z
 xZS )

ClvpConfigaQ
  
    [`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It
    is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and
    decoder model configs. Instantiating a configuration with the defaults will yield a similar configuration to that
    of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) architecture.

    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
    documentation from [`PretrainedConfig`] for more information.

    Args:
        text_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize the CLVP text encoder.
        speech_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize CLVP speech encoder.
        decoder_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`ClvpDecoderConfig`].
        projection_dim (`int`, *optional*, defaults to 768):
            Dimensionality of text and speech projection layers.
        logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
            The initial value of the *logit_scale* parameter. Default is used as per the original CLVP implementation.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
            testing).
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration

    >>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration
    >>> configuration = ClvpConfig()

    >>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style configuration
    >>> model = ClvpModelForConditionalGeneration(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config

    >>> # We can also initialize a CLVPConfig from a CLVPTextConfig, CLVPSpeechConfig and a CLVPAutoRegressiveConfig
    >>> from transformers import ClvpEncoderConfig, ClvpDecoderConfig

    >>> # Initializing a CLVP text, CLVP speech and CLVP decoder configuration
    >>> config_text = ClvpEncoderConfig()
    >>> config_speech = ClvpEncoderConfig()
    >>> decoder_config = ClvpDecoderConfig()

    >>> config = ClvpConfig.from_sub_model_configs(config_text, config_speech, decoder_config)
    ```r,   r   r   rP   c                 D   t        |   di | |i }t        j                  d       |i }t        j                  d       |i }t        j                  d       t	        di || _        t	        di || _        t        di || _        || _	        || _
        || _        y )NzR`text_config` is `None`. Initializing the `ClvpEncoderConfig` with default values.zT`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values.zU`decoder_config` is `None`. initializing the `ClvpDecoderConfig` with default values.r   )r    r!   r3   infor	   r   r   rN   rP   r   logit_scale_init_valuer   )	r"   r   r   rP   r   rp   r   r#   r$   s	           r%   r!   zClvpConfig.__init__{  s     	"6"KKKlm MKKno!NKKop,;{;.??/A.A,&<#"4r&   r   r   rP   c                 n     | d|j                         |j                         |j                         d|S )a  
        Instantiate a [`ClvpConfig`] (or a derived class) from CLVP text model configuration, CLVP speech model
        configuration and CLVP decoder model configuration.

        Args:
            text_config (`ClvpEncoderConfig`):
                Text model configuration of type [`ClvpEncoderConfig`].
            speech_config (`ClvpEncoderConfig`):
                Speech model configuration of type [`ClvpEncoderConfig`].
            decoder_config (`ClvpDecoderConfig`):
                Decoder model configuration of type [`ClvpDecoderConfig`].

        Returns:
            [`ClvpConfig`]: An instance of a configuration object
        rm   r   )to_dict)r6   r   r   rP   r#   s        r%   from_sub_model_configsz!ClvpConfig.from_sub_model_configs  sD    0  
#++-'//1)113
 	
 	
r&   )NNNr:   g/L
F@rA   )rC   rD   rE   rF   r+   r	   rN   sub_configsr!   rG   rs   rK   rL   s   @r%   rl   rl   @  sj    1f J(*+K %5@ 
&
 )
 *	
 
r&   rl   )rF   rI   typingr   r   configuration_utilsr   utilsr   
get_loggerrC   r3   r	   rN   rl   r   r&   r%   <module>ry      sc     	 '  3  
		H	%A4( A4HZY( ZYzx
! x
r&   