
    sg9                         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Blip model configuration   )PretrainedConfig)loggingc                   T     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )BlipTextConfiga  
    This is the configuration class to store the configuration of a [`BlipTextModel`]. It is used to instantiate a BLIP
    text 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 `BlipText` used by the [base
    architectures](https://huggingface.co/Salesforce/blip-vqa-base).

    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 30524):
            Vocabulary size of the `Blip` text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`BlipModel`].
        hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers and the pooler layer.
        encoder_hidden_size (`int`, *optional*, defaults to 768):
            Dimensionality of the encoder layers from the vision model.
        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 8):
            Number of attention heads for each attention layer in the Transformer encoder.
        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).
        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"` `"gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-12):
            The epsilon used by the layer normalization layers.
        hidden_dropout_prob (`float`, *optional*, defaults to 0.0):
            The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 0.02):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
        bos_token_id (`int`, *optional*, defaults to 30522):
            The id of the `beginning-of-sequence` token.
        eos_token_id (`int`, *optional*, defaults to 2):
            The id of the `end-of-sequence` token.
        pad_token_id (`int`, *optional*, defaults to 0):
            The id of the `padding` token.
        sep_token_id (`int`, *optional*, defaults to 102):
            The id of the `separator` token.
        is_decoder (`bool`, *optional*, defaults to `True`):
            Whether the model is used as a decoder.
        use_cache (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return the last key/values attentions (not used by all models).
        label_smoothing (float, *optional*):
            A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
            become a mixture of the original ground truth and a uniform distribution as described in
            `Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.

    Example:

    ```python
    >>> from transformers import BlipTextConfig, BlipTextModel

    >>> # Initializing a BlipTextConfig with Salesforce/blip-vqa-base style configuration
    >>> configuration = BlipTextConfig()

    >>> # Initializing a BlipTextModel (with random weights) from the Salesforce/blip-vqa-base style configuration
    >>> model = BlipTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```blip_text_modeltext_configc                    t        |   d||||d| || _        || _        || _        || _        || _        || _        || _        || _	        || _
        |
| _        |	| _        || _        || _        || _        || _        || _        y )N)pad_token_idbos_token_ideos_token_idsep_token_id )super__init__
vocab_sizehidden_sizeencoder_hidden_sizeintermediate_sizeprojection_dimhidden_dropout_probnum_hidden_layersnum_attention_headsmax_position_embeddingslayer_norm_eps
hidden_actinitializer_rangeattention_probs_dropout_prob
is_decoder	use_cachelabel_smoothing)selfr   r   r   r   r   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/blip/configuration_blip.pyr   zBlipTextConfig.__init__b   s    0 	 	
%%%%		

 	
 %&#6 !2,#6 !2#6 '>$,$!2,H)$".    )i<w     r&      r&            gelug-q=        r,   {Gz?i:w         f   TTr,   __name__
__module____qualname____doc__
model_typebase_config_keyr   __classcell__r#   s   @r$   r   r      s^    DL #J#O  #%(+// //r%   r   c                   B     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )BlipVisionConfiga
  
    This is the configuration class to store the configuration of a [`BlipVisionModel`]. It is used to instantiate a
    BLIP vision model according to the specified arguments, defining the model architecture. Instantiating a
    configuration defaults will yield a similar configuration to that of the Blip-base
    [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) 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.
        image_size (`int`, *optional*, defaults to 384):
            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"`):
            The non-linear activation function (function or string) in the encoder and pooler. If string, `"gelu"`,
            `"relu"`, `"selu"` and `"gelu_new"` `"gelu"` are supported.
        layer_norm_eps (`float`, *optional*, defaults to 1e-5):
            The epsilon used by the layer normalization layers.
        attention_dropout (`float`, *optional*, defaults to 0.0):
            The dropout ratio for the attention probabilities.
        initializer_range (`float`, *optional*, defaults to 1e-10):
            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.

    Example:

    ```python
    >>> from transformers import BlipVisionConfig, BlipVisionModel

    >>> # Initializing a BlipVisionConfig with Salesforce/blip-vqa-base style configuration
    >>> configuration = BlipVisionConfig()

    >>> # Initializing a BlipVisionModel (with random weights) from the Salesforce/blip-vqa-base style configuration
    >>> model = BlipVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```blip_vision_modelvision_configc                     t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |
| _
        |	| _        || _        y )Nr   )r   r   r   r   r   r   r   
patch_size
image_sizer   attention_dropoutr   r   )r!   r   r   r   r   r   r@   r?   r   r   rA   r   r"   r#   s                r$   r   zBlipVisionConfig.__init__   sj     	"6"&!2,!2#6 $$!2!2,$r%   )r&   r'   r*   r(   r(   i     r+   gh㈵>r,   g|=r1   r9   s   @r$   r;   r;      sB    .` %J%O % %r%   r;   c                   X     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 )	
BlipConfigaO
  
    [`BlipConfig`] is the configuration class to store the configuration of a [`BlipModel`]. It is used to instantiate
    a BLIP 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 BLIP-base
    [Salesforce/blip-vqa-base](https://huggingface.co/Salesforce/blip-vqa-base) 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 [`BlipTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`BlipVisionConfig`].
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision 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 BLIP implementation.
        image_text_hidden_size (`int`, *optional*, defaults to 256):
            Dimensionality of the hidden state of the image-text fusion layer.
        label_smoothing (float, optional, *optional*, defaults to 0.0):
            A float in [0.0, 1.0]. Specifies the amount of smoothing when computing the loss, where 0.0 means no smoothing. The targets
            become a mixture of the original ground truth and a uniform distribution as described in
            `Rethinking the Inception Architecture for Computer Vision <https://arxiv.org/abs/1512.00567>`__. Default: :math:`0.0`.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import BlipConfig, BlipModel

    >>> # Initializing a BlipConfig with Salesforce/blip-vqa-base style configuration
    >>> configuration = BlipConfig()

    >>> # Initializing a BlipPModel (with random weights) from the Salesforce/blip-vqa-base style configuration
    >>> model = BlipModel(configuration)

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

    >>> # We can also initialize a BlipConfig from a BlipTextConfig and a BlipVisionConfig

    >>> # Initializing a BLIPText and BLIPVision configuration
    >>> config_text = BlipTextConfig()
    >>> config_vision = BlipVisionConfig()

    >>> config = BlipConfig.from_text_vision_configs(config_text, config_vision)
    ```blipr   r=   c                 f   t        |   di | |i }t        j                  d       |i }t        j                  d       t	        di || _        t        di || _        | j                  j                  | j
                  _	        || _
        || _        d| _        d| _        || _        || _        y )NzO`text_config` is `None`. Initializing the `BlipTextConfig` with default values.zS`vision_config` is `None`. Initializing the `BlipVisionConfig` with default values.g      ?r-   r   )r   r   loggerinfor   r   r;   r=   r   r   r   logit_scale_init_valueinitializer_factorr   image_text_hidden_sizer    )	r!   r   r=   r   rJ   rL   r    r"   r#   s	           r$   r   zBlipConfig.__init__  s     	"6"KKKij MKKmn)8K8->>/3/A/A/M/M,,&<#"%!%&<#.r%   r   r=   c                 P     | d|j                         |j                         d|S )z
        Instantiate a [`BlipConfig`] (or a derived class) from blip text model configuration and blip vision model
        configuration.

        Returns:
            [`BlipConfig`]: An instance of a configuration object
        rF   r   )to_dict)clsr   r=   r"   s       r$   from_text_vision_configsz#BlipConfig.from_text_vision_configs<  s,     f{224MDYDYD[f_effr%   )NNr*   g/L
F@   r,   )r2   r3   r4   r5   r6   r   r;   sub_configsr   classmethodrP   r8   r9   s   @r$   rD   rD      s\    0d J"0CSTK %"/@ 	g> 	gRb 	g 	gr%   rD   N)r5   configuration_utilsr   utilsr   
get_loggerr2   rH   r   r;   rD   r   r%   r$   <module>rW      sU     3  
		H	%y/% y/xO%' O%d`g! `gr%   