
    sgd.                         d 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
y
)zSiglip model configuration   )PretrainedConfig)loggingc                   D     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )SiglipTextConfiga6  
    This is the configuration class to store the configuration of a [`SiglipTextModel`]. It is used to instantiate a
    Siglip text 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 text encoder of the Siglip
    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 32000):
            Vocabulary size of the Siglip text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`SiglipModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        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.
        max_position_embeddings (`int`, *optional*, defaults to 64):
            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).
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            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-06):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        pad_token_id (`int`, *optional*, defaults to 1):
            The id of the padding token in the vocabulary.
        bos_token_id (`int`, *optional*, defaults to 49406):
            The id of the beginning-of-sequence token in the vocabulary.
        eos_token_id (`int`, *optional*, defaults to 49407):
            The id of the end-of-sequence token in the vocabulary.

    Example:

    ```python
    >>> from transformers import SiglipTextConfig, SiglipTextModel

    >>> # Initializing a SiglipTextConfig with google/siglip-base-patch16-224 style configuration
    >>> configuration = SiglipTextConfig()

    >>> # Initializing a SiglipTextModel (with random weights) from the google/siglip-base-patch16-224 style configuration
    >>> model = SiglipTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```siglip_text_modeltext_configc                     t        |   d|
||d| || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        y )N)pad_token_idbos_token_ideos_token_id )super__init__
vocab_sizehidden_sizeintermediate_sizenum_hidden_layersnum_attention_headsmax_position_embeddingslayer_norm_eps
hidden_actattention_dropout)selfr   r   r   r   r   r   r   r   r   r
   r   r   kwargs	__class__s                 b/var/www/html/venv/lib/python3.12/site-packages/transformers/models/siglip/configuration_siglip.pyr   zSiglipTextConfig.__init__Q   sf    $ 	sl\hslrs$&!2!2#6 '>$,$!2    )i }           r    @   gelu_pytorch_tanhư>           i  i  __name__
__module____qualname____doc__
model_typebase_config_keyr   __classcell__r   s   @r   r   r      sG    3j %J#O  "& 3 3r   r   c                   @     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )SiglipVisionConfiga'
  
    This is the configuration class to store the configuration of a [`SiglipVisionModel`]. It is used to instantiate a
    Siglip vision 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 vision encoder of the Siglip
    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) architecture.

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

    Args:
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 3072):
            Dimensionality of the "intermediate" (i.e., feed-forward) layer in the Transformer encoder.
        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.
        num_channels (`int`, *optional*, defaults to 3):
            Number of channels in the input images.
        image_size (`int`, *optional*, defaults to 224):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 16):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"gelu_pytorch_tanh"`):
            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-06):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.

    Example:

    ```python
    >>> from transformers import SiglipVisionConfig, SiglipVisionModel

    >>> # Initializing a SiglipVisionConfig with google/siglip-base-patch16-224 style configuration
    >>> configuration = SiglipVisionConfig()

    >>> # Initializing a SiglipVisionModel (with random weights) from the google/siglip-base-patch16-224 style configuration
    >>> model = SiglipVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```siglip_vision_modelvision_configc                     t        |   di | || _        || _        || _        || _        || _        || _        || _        |
| _	        |	| _
        || _        y )Nr   )r   r   r   r   r   r   num_channels
patch_size
image_sizer   r   r   )r   r   r   r   r   r4   r6   r5   r   r   r   r   r   s               r   r   zSiglipVisionConfig.__init__   sb     	"6"&!2!2#6 ($$!2,$r   )
r   r   r    r    r         r"   r#   r$   r&   r.   s   @r   r0   r0   p   s?    -^ 'J%O &% %r   r0   c                   L     e Zd ZdZdZeedZd fd	Ze	dedefd       Z
 xZS )	SiglipConfigaC  
    [`SiglipConfig`] is the configuration class to store the configuration of a [`SiglipModel`]. It is used to
    instantiate a Siglip model according to the specified arguments, defining the text model and vision model configs.
    Instantiating a configuration with the defaults will yield a similar configuration to that of the Siglip
    [google/siglip-base-patch16-224](https://huggingface.co/google/siglip-base-patch16-224) 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 [`SiglipTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`SiglipVisionConfig`].
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import SiglipConfig, SiglipModel

    >>> # Initializing a SiglipConfig with google/siglip-base-patch16-224 style configuration
    >>> configuration = SiglipConfig()

    >>> # Initializing a SiglipModel (with random weights) from the google/siglip-base-patch16-224 style configuration
    >>> model = SiglipModel(configuration)

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

    >>> # We can also initialize a SiglipConfig from a SiglipTextConfig and a SiglipVisionConfig
    >>> from transformers import SiglipTextConfig, SiglipVisionConfig

    >>> # Initializing a SiglipText and SiglipVision configuration
    >>> config_text = SiglipTextConfig()
    >>> config_vision = SiglipVisionConfig()

    >>> config = SiglipConfig.from_text_vision_configs(config_text, config_vision)
    ```siglipr   r2   c                     t        |   di | |i }t        j                  d       |i }t        j                  d       t	        di || _        t        di || _        d| _        y )NzQ`text_config` is `None`. Initializing the `SiglipTextConfig` with default values.zU`vision_config` is `None`. initializing the `SiglipVisionConfig` with default values.g      ?r   )	r   r   loggerinfor   r   r0   r2   initializer_factor)r   r   r2   r   r   s       r   r   zSiglipConfig.__init__   sk    "6"KKKkl MKKop+:k:/@-@"%r   r   r2   c                 P     | d|j                         |j                         d|S )z
        Instantiate a [`SiglipConfig`] (or a derived class) from siglip text model configuration and siglip vision
        model configuration.

        Returns:
            [`SiglipConfig`]: An instance of a configuration object
        r<   r   )to_dict)clsr   r2   r   s       r   from_text_vision_configsz%SiglipConfig.from_text_vision_configs   s,     f{224MDYDYD[f_effr   )NN)r'   r(   r)   r*   r+   r   r0   sub_configsr   classmethodrD   r-   r.   s   @r   r:   r:      sH    'R J"2EWXK&  	g3C 	gTf 	g 	gr   r:   N)r*   configuration_utilsr   utilsr   
get_loggerr'   r>   r   r0   r:   r   r   r   <module>rJ      sU    ! 3  
		H	%U3' U3pL%) L%^Gg# Ggr   