
    sg7                         d Z ddlmZ ddlmZmZmZmZmZ erddl	m
Z
 ddlm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OWL-ViT model configuration    OrderedDict)TYPE_CHECKINGAnyDictMappingOptional   )ProcessorMixin)
TensorType)PretrainedConfig)
OnnxConfig)loggingc                   H     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )OwlViTTextConfiga  
    This is the configuration class to store the configuration of an [`OwlViTTextModel`]. It is used to instantiate an
    OwlViT 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 OwlViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 49408):
            Vocabulary size of the OWL-ViT text model. Defines the number of different tokens that can be represented
            by the `inputs_ids` passed when calling [`OwlViTTextModel`].
        hidden_size (`int`, *optional*, defaults to 512):
            Dimensionality of the encoder layers and the pooler layer.
        intermediate_size (`int`, *optional*, defaults to 2048):
            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 16):
            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 `"quick_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 0.0):
            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.
        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).
        pad_token_id (`int`, *optional*, defaults to 0):
            The id of the padding token in the input sequences.
        bos_token_id (`int`, *optional*, defaults to 49406):
            The id of the beginning-of-sequence token in the input sequences.
        eos_token_id (`int`, *optional*, defaults to 49407):
            The id of the end-of-sequence token in the input sequences.

    Example:

    ```python
    >>> from transformers import OwlViTTextConfig, OwlViTTextModel

    >>> # Initializing a OwlViTTextModel with google/owlvit-base-patch32 style configuration
    >>> configuration = OwlViTTextConfig()

    >>> # Initializing a OwlViTTextConfig from the google/owlvit-base-patch32 style configuration
    >>> model = OwlViTTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```owlvit_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
hidden_actlayer_norm_epsattention_dropoutinitializer_rangeinitializer_factor)selfr   r   r   r   r   r    r!   r"   r#   r$   r%   r   r   r   kwargs	__class__s                   b/var/www/html/venv/lib/python3.12/site-packages/transformers/models/owlvit/configuration_owlvit.pyr   zOwlViTTextConfig.__init__`   sv    $ 	sl\hslrs$&!2!2#6 '>$$,!2!2"4    )i      i            
quick_geluh㈵>        {Gz?      ?r   i  i  __name__
__module____qualname____doc__
model_typebase_config_keyr   __classcell__r(   s   @r)   r   r   !   sK    9v %J#O  "5 5r*   r   c                   D     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )OwlViTVisionConfigah  
    This is the configuration class to store the configuration of an [`OwlViTVisionModel`]. It is used to instantiate
    an OWL-ViT image 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 OWL-ViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 768):
            The size (resolution) of each image.
        patch_size (`int`, *optional*, defaults to 32):
            The size (resolution) of each patch.
        hidden_act (`str` or `function`, *optional*, defaults to `"quick_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 0.0):
            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.
        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 OwlViTVisionConfig, OwlViTVisionModel

    >>> # Initializing a OwlViTVisionModel with google/owlvit-base-patch32 style configuration
    >>> configuration = OwlViTVisionConfig()

    >>> # Initializing a OwlViTVisionModel model from the google/owlvit-base-patch32 style configuration
    >>> model = OwlViTVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```owlvit_vision_modelvision_configc                     t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        |	| _
        |
| _        || _        || _        y )Nr   )r   r   r   r   r   r   num_channels
image_size
patch_sizer!   r"   r#   r$   r%   )r&   r   r   r   r   rB   rC   rD   r!   r"   r#   r$   r%   r'   r(   s                 r)   r   zOwlViTVisionConfig.__init__   sr      	"6"&!2!2#6 ($$$,!2!2"4r*   )   i   r,   r,   r
   rE       r/   r0   r1   r2   r3   r4   r<   s   @r)   r>   r>      sE    2h 'J%O 5 5r*   r>   c                   V     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 )	OwlViTConfiga  
    [`OwlViTConfig`] is the configuration class to store the configuration of an [`OwlViTModel`]. It is used to
    instantiate an OWL-ViT 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 OWL-ViT
    [google/owlvit-base-patch32](https://huggingface.co/google/owlvit-base-patch32) 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 [`OwlViTTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`OwlViTVisionConfig`].
        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 OWL-ViT
            implementation.
        return_dict (`bool`, *optional*, defaults to `True`):
            Whether or not the model should return a dictionary. If `False`, returns a tuple.
        kwargs (*optional*):
            Dictionary of keyword arguments.
    owlvit)r   r@   c                     t        |   di | |i }t        j                  d       |i }t        j                  d       t	        di || _        t        di || _        || _        || _	        || _
        d| _        y )NzKtext_config is None. Initializing the OwlViTTextConfig with default values.zOvision_config is None. initializing the OwlViTVisionConfig with default values.r3   r   )r   r   loggerinfor   r   r>   r@   projection_dimlogit_scale_init_valuereturn_dictr%   )r&   r   r@   rM   rN   rO   r'   r(   s          r)   r   zOwlViTConfig.__init__   s     	"6"KKKef MKKij+:k:/@-@,&<#&"%r*   r   r@   c                 @    i }||d<   ||d<    | j                   |fi |S )z
        Instantiate a [`OwlViTConfig`] (or a derived class) from owlvit text model configuration and owlvit vision
        model configuration.

        Returns:
            [`OwlViTConfig`]: An instance of a configuration object
        r   r@   )	from_dict)clsr   r@   r'   config_dicts        r)   from_text_vision_configsz%OwlViTConfig.from_text_vision_configs  s3     %0M"'4O$s}}[3F33r*   )NNr+   g/L
F@T)r5   r6   r7   r8   r9   r   r>   sub_configsr   classmethodr   rT   r;   r<   s   @r)   rH   rH      sR    2 J"2EWXK %&6 44 4 4 4r*   rH   c                        e Zd Zedeeeeef   f   fd       Zedeeeeef   f   fd       Zede	fd       Z
	 	 	 ddddeded	ed
   deeef   f
 fdZedefd       Z xZS )OwlViTOnnxConfigreturnc           	      @    t        ddddfdddddd	fd
dddfg      S )N	input_idsbatchsequence)r      pixel_valuesrB   heightwidth)r   r^      r
   attention_maskr   r&   s    r)   inputszOwlViTOnnxConfig.inputs"  s@    'j9:WHQX!YZ!w:#>?
 	
r*   c                 @    t        dddifdddifdddifdddifg      S )Nlogits_per_imager   r\   logits_per_texttext_embedsimage_embedsr   rd   s    r)   outputszOwlViTOnnxConfig.outputs,  sD    #a\2"QL1G-!W.	
 	
r*   c                      y)Ng-C6?r   rd   s    r)   atol_for_validationz$OwlViTOnnxConfig.atol_for_validation7  s    r*   	processorr   
batch_size
seq_length	frameworkr   c                     t         |   |j                  |||      }t         |   |j                  ||      }i ||S )N)ro   rp   rq   )ro   rq   )r   generate_dummy_inputs	tokenizerimage_processor)r&   rn   ro   rp   rq   text_input_dictimage_input_dictr(   s          r)   rs   z&OwlViTOnnxConfig.generate_dummy_inputs;  s`      '7J:Yb 8 
 !78%%*	 9 
 7/6%566r*   c                      y)N   r   rd   s    r)   default_onnx_opsetz#OwlViTOnnxConfig.default_onnx_opsetJ  s    r*   )r{   N)r5   r6   r7   propertyr   strintre   rk   floatrm   r	   r   rs   rz   r;   r<   s   @r)   rX   rX   !  s    
WS#X%6 67 
 
 
gc3h&7!78 
 
 U   ,07#7 7 	7
 L)7 
c	7 C  r*   rX   N)r8   collectionsr   typingr   r   r   r   r	   processing_utilsr   utilsr   configuration_utilsr   onnxr   r   
get_loggerr5   rK   r   r>   rH   rX   r   r*   r)   <module>r      sw    " # > > 2# 3   
		H	%]5' ]5@U5) U5pE4# E4P+z +r*   