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g d
ZdS )zCLAP model configuration   )PretrainedConfig)loggingc                       sP   e Zd ZdZdZdZ											
										d fdd	Z  ZS )ClapTextConfiga  
    This is the configuration class to store the configuration of a [`ClapTextModel`]. It is used to instantiate a CLAP
    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 CLAP
    [calp-hsat-fused](https://huggingface.co/laion/clap-hsat-fused) 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 30522):
            Vocabulary size of the CLAP model. Defines the number of different tokens that can be represented by the
            `inputs_ids` passed when calling [`ClapTextModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        num_hidden_layers (`int`, *optional*, defaults to 12):
            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.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (often named feed-forward) layer in the Transformer encoder.
        hidden_act (`str` or `Callable`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"relu"`,
            `"relu"`, `"silu"` and `"relu_new"` are supported.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout ratio for the attention probabilities.
        max_position_embeddings (`int`, *optional*, defaults to 512):
            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).
        type_vocab_size (`int`, *optional*, defaults to 2):
            The vocabulary size of the `token_type_ids` passed when calling [`ClapTextModel`].
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        position_embedding_type (`str`, *optional*, defaults to `"absolute"`):
            Type of position embedding. Choose one of `"absolute"`, `"relative_key"`, `"relative_key_query"`. For
            positional embeddings use `"absolute"`. For more information on `"relative_key"`, please refer to
            [Self-Attention with Relative Position Representations (Shaw et al.)](https://arxiv.org/abs/1803.02155).
            For more information on `"relative_key_query"`, please refer to *Method 4* in [Improve Transformer Models
            with Better Relative Position Embeddings (Huang et al.)](https://arxiv.org/abs/2009.13658).
        is_decoder (`bool`, *optional*, defaults to `False`):
            Whether the model is used as a decoder or not. If `False`, the model is used as an encoder.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models). Only
            relevant if `config.is_decoder=True`.
        projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        projection_dim (`int`, *optional*, defaults to 512)
            Dimension of the projection head of the `ClapTextModelWithProjection`.

    Examples:

    ```python
    >>> from transformers import ClapTextConfig, ClapTextModel

    >>> # Initializing a CLAP text configuration
    >>> configuration = ClapTextConfig()

    >>> # Initializing a model (with random weights) from the configuration
    >>> model = ClapTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Zclap_text_modeltext_configY           gelu皙?           ?-q=          absoluteTreluc                    s~   t  jd|||d| || _|| _|| _|| _|| _|| _|| _|| _	|	| _
|
| _|| _|| _|| _|| _|| _|| _d S )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizehidden_sizenum_hidden_layersnum_attention_heads
hidden_actintermediate_sizehidden_dropout_probattention_probs_dropout_probmax_position_embeddingstype_vocab_sizeinitializer_factorlayer_norm_epsposition_embedding_type	use_cacheprojection_hidden_actprojection_dim)selfr   r   r   r   r    r   r!   r"   r#   r$   r%   r&   r*   r   r   r   r'   r(   r)   kwargs	__class__r   Z/var/www/auris/lib/python3.10/site-packages/transformers/models/clap/configuration_clap.pyr   `   s"   
zClapTextConfig.__init__)r   r   r   r   r	   r
   r   r   r   r   r   r   r   r   r   r   r   Tr   __name__
__module____qualname____doc__
model_typeZbase_config_keyr   __classcell__r   r   r-   r/   r      s0    Dr   c                       sl   e Zd ZdZdZdZdddddddgd	d
dg dg dddddddddddddddddf fdd	Z  ZS )ClapAudioConfiga  
    This is the configuration class to store the configuration of a [`ClapAudioModel`]. It is used to instantiate a
    CLAP audio encoder according to the specified arguments, defining the model architecture. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the audio encoder of the CLAP
    [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) architecture.

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

    Args:
        window_size (`int`, *optional*, defaults to 8):
            Image size of the spectrogram
        num_mel_bins (`int`, *optional*, defaults to 64):
            Number of mel features used per frames. Should correspond to the value used in the `ClapProcessor` class.
        spec_size (`int`, *optional*, defaults to 256):
            Desired input size of the spectrogram that the model supports. It can be different from the output of the
            `ClapFeatureExtractor`, in which case the input features will be resized. Corresponds to the `image_size`
            of the audio models.
        hidden_act (`str`, *optional*, defaults to `"gelu"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        patch_size (`int`, *optional*, defaults to 4):
            Patch size for the audio spectrogram
        patch_stride (`list`, *optional*, defaults to `[4, 4]`):
            Patch stride for the audio spectrogram
        num_classes (`int`, *optional*, defaults to 527):
            Number of classes used for the head training
        hidden_size (`int`, *optional*, defaults to 768):
            Hidden size of the output of the audio encoder. Correspond to the dimension of the penultimate layer's
            output,which is sent to the projection MLP layer.
        projection_dim (`int`, *optional*, defaults to 512):
            Hidden size of the projection layer.
        depths (`list`, *optional*, defaults to `[2, 2, 6, 2]`):
            Depths used for the Swin Layers of the audio model
        num_attention_heads (`list`, *optional*, defaults to `[4, 8, 16, 32]`):
            Number of attention heads used for the Swin Layers of the audio model
        enable_fusion (`bool`, *optional*, defaults to `False`):
            Whether or not to enable patch fusion. This is the main contribution of the authors, and should give the
            best results.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.1):
            The dropout probability for all fully connected layers in the encoder.
        fusion_type (`[type]`, *optional*):
            Fusion type used for the patch fusion.
        patch_embed_input_channels (`int`, *optional*, defaults to 1):
            Number of channels used for the input spectrogram
        flatten_patch_embeds (`bool`, *optional*, defaults to `True`):
            Whether or not to flatten the patch embeddings
        patch_embeds_hidden_size (`int`, *optional*, defaults to 96):
            Hidden size of the patch embeddings. It is used as the number of output channels.
        enable_patch_layer_norm (`bool`, *optional*, defaults to `True`):
            Whether or not to enable layer normalization for the patch embeddings
        drop_path_rate (`float`, *optional*, defaults to 0.0):
            Drop path rate for the patch fusion
        attention_probs_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        qkv_bias (`bool`, *optional*, defaults to `True`):
            Whether or not to add a bias to the query, key, value projections.
        mlp_ratio (`float`, *optional*, defaults to 4.0):
            Ratio of the mlp hidden dim to embedding dim.
        aff_block_r (`int`, *optional*, defaults to 4):
            downsize_ratio used in the AudioFF block
        num_hidden_layers (`int`, *optional*, defaults to 4):
            Number of hidden layers in the Transformer encoder.
        projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
            The non-linear activation function (function or string) in the projection layer. If string, `"gelu"`,
            `"relu"`, `"silu"` and `"gelu_new"` are supported.
        layer_norm_eps (`[type]`, *optional*, defaults to 1e-05):
            The epsilon used by the layer normalization layers.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            A factor for initializing all weight matrices (should be kept to 1, used internally for initialization
            testing).

    Example:

    ```python
    >>> from transformers import ClapAudioConfig, ClapAudioModel

    >>> # Initializing a ClapAudioConfig with laion/clap-htsat-fused style configuration
    >>> configuration = ClapAudioConfig()

    >>> # Initializing a ClapAudioModel (with random weights) from the laion/clap-htsat-fused style configuration
    >>> model = ClapAudioModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```Zclap_audio_modelaudio_config   @      r
      i  r   r   )r   r      r   )r<   r9          Fr   Nr   T`   g        g      @r   gh㈵>r   c                    s   t  jdi | || _|| _|| _|| _|| _|| _|| _|
| _	|| _
|| _|| _|| _|| _|| _|| _|	| _|| _|| _|| _|| _|| _|| _|| _|| _|| _|| _|| _|| _d S )Nr   )r   r   window_sizenum_mel_bins	spec_size
patch_sizepatch_stridenum_classesr   depthsr   r   enable_fusionfusion_typer   r!   r*   flatten_patch_embedspatch_embeds_hidden_sizeenable_patch_layer_normdrop_path_rater"   qkv_bias	mlp_ratiopatch_embed_input_channelsaff_block_rr&   r%   r)   )r+   rA   rB   rC   r   rD   rE   rF   r   r*   rG   r   rH   r!   rI   rP   rJ   rK   rL   rM   r"   rN   rO   rQ   r   r)   r&   r%   r,   r-   r   r/   r      s:   
zClapAudioConfig.__init__r0   r   r   r-   r/   r7      s@    Wr7   c                       sR   e Zd ZdZdZeedZ						d fd	d
	Ze	dedefddZ
  ZS )
ClapConfiga*	  
    [`ClapConfig`] is the configuration class to store the configuration of a [`ClapModel`]. It is used to instantiate
    a CLAP model according to the specified arguments, defining the text model and audio model configs. Instantiating a
    configuration with the defaults will yield a similar configuration to that of the CLAP
    [laion/clap-htsat-fused](https://huggingface.co/laion/clap-htsat-fused) 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 [`ClapTextConfig`].
        audio_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`ClapAudioConfig`].
        logit_scale_init_value (`float`, *optional*, defaults to 14.29):
            The initial value of the *logit_scale* parameter. Default is used as per the original CLAP implementation.
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and audio projection layers.
        projection_hidden_act (`str`, *optional*, defaults to `"relu"`):
            Activation function for the projection layers.
        initializer_factor (`float`, *optional*, defaults to 1.0):
            Factor to scale the initialization of the model weights.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import ClapConfig, ClapModel

    >>> # Initializing a ClapConfig with laion-ai/base style configuration
    >>> configuration = ClapConfig()

    >>> # Initializing a ClapModel (with random weights) from the laion-ai/base style configuration
    >>> model = ClapModel(configuration)

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

    >>> # We can also initialize a ClapConfig from a ClapTextConfig and a ClapAudioConfig
    >>> from transformers import ClapTextConfig, ClapAudioConfig

    >>> # Initializing a ClapText and ClapAudioConfig configuration
    >>> config_text = ClapTextConfig()
    >>> config_audio = ClapAudioConfig()

    >>> config = ClapConfig.from_text_audio_configs(config_text, config_audio)
    ```clapr   r8   N$I$I,@r   r   r   c                    s   t  jdi | |d u ri }td |d u ri }td tdi || _tdi || _|| j_|| j_|| j_	|| j_	|| _|| _	| jj
| _
|| _|| _| jjt| jj | _d S )NzItext_config is None. Initializing the ClapTextConfig with default values.zKaudio_config is None. initializing the ClapAudioConfig with default values.r   )r   r   loggerinfor   r   r7   r8   r*   r)   r   logit_scale_init_valuer%   r   lenrG   )r+   r   r8   rX   r*   r)   r%   r,   r-   r   r/   r   Y  s&   

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
zClapConfig.__init__r   r8   c                 K   s   | d|  |  d|S )z
        Instantiate a [`ClapConfig`] (or a derived class) from clap text model configuration and clap audio model
        configuration.

        Returns:
            [`ClapConfig`]: An instance of a configuration object
        rT   Nr   )to_dict)clsr   r8   r,   r   r   r/   from_text_audio_configs}  s   
z"ClapConfig.from_text_audio_configs)NNrU   r   r   r   )r1   r2   r3   r4   r5   r   r7   Zsub_configsr   classmethodr\   r6   r   r   r-   r/   rR   $  s    1
$rR   )r7   rR   r   N)r4   Zconfiguration_utilsr   utilsr   Z
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