
    sgFK                         d Z ddlmZ ddl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CLIP model configuration    OrderedDict)TYPE_CHECKINGAnyMappingOptional   )ProcessorMixin)
TensorType)PretrainedConfig)
OnnxConfig)loggingc                   J     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )CLIPTextConfiga  
    This is the configuration class to store the configuration of a [`CLIPTextModel`]. It is used to instantiate a CLIP
    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 CLIP
    [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-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 CLIP text model. Defines the number of different tokens that can be represented by
            the `inputs_ids` passed when calling [`CLIPModel`].
        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.
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision projection layers.
        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 77):
            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 1):
            Padding token id.
        bos_token_id (`int`, *optional*, defaults to 49406):
            Beginning of stream token id.
        eos_token_id (`int`, *optional*, defaults to 49407):
            End of stream token id.

    Example:

    ```python
    >>> from transformers import CLIPTextConfig, CLIPTextModel

    >>> # Initializing a CLIPTextConfig with openai/clip-vit-base-patch32 style configuration
    >>> configuration = CLIPTextConfig()

    >>> # Initializing a CLIPTextModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
    >>> model = CLIPTextModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```clip_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projection_dimnum_hidden_layersnum_attention_headsmax_position_embeddingslayer_norm_eps
hidden_actinitializer_rangeinitializer_factorattention_dropout)selfr   r   r   r   r   r   r    r"   r!   r%   r#   r$   r   r   r   kwargs	__class__s                    ^/var/www/html/venv/lib/python3.12/site-packages/transformers/models/clip/configuration_clip.pyr   zCLIPTextConfig.__init__a   s~    * 	sl\hslrs$&!2,!2#6 '>$,$!2"4!2    )i      i   r+         M   
quick_geluh㈵>        {Gz?      ?   i  i  __name__
__module____qualname____doc__
model_typebase_config_keyr   __classcell__r(   s   @r)   r   r   !   sP    :x #J#O  " %"3 "3r*   r   c                   F     e Zd ZdZdZdZ	 	 	 	 	 	 	 	 	 	 	 	 	 d fd	Z xZS )CLIPVisionConfiga  
    This is the configuration class to store the configuration of a [`CLIPVisionModel`]. It is used to instantiate a
    CLIP 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 CLIP
    [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-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.
        projection_dim (`int`, *optional*, defaults to 512):
            Dimensionality of text and vision projection layers.
        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):
            The number of input channels.
        image_size (`int`, *optional*, defaults to 224):
            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 CLIPVisionConfig, CLIPVisionModel

    >>> # Initializing a CLIPVisionConfig with openai/clip-vit-base-patch32 style configuration
    >>> configuration = CLIPVisionConfig()

    >>> # Initializing a CLIPVisionModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
    >>> model = CLIPVisionModel(configuration)

    >>> # Accessing the model configuration
    >>> configuration = model.config
    ```clip_vision_modelvision_configc                     t        |   di | || _        || _        || _        || _        || _        || _        || _        || _	        || _
        || _        || _        |
| _        |	| _        y )Nr   )r   r   r   r   r   r   r   num_channels
patch_size
image_sizer#   r$   r%   r!   r"   )r&   r   r   r   r   r   rC   rE   rD   r"   r!   r%   r#   r$   r'   r(   s                  r)   r   zCLIPVisionConfig.__init__   sz    " 	"6"&!2,!2#6 ($$!2"4!2,$r*   )i   i   r+   r,   r,   r	          r/   r0   r1   r2   r3   r5   r=   s   @r)   r?   r?      sH    4l %J%O % %r*   r?   c                   N     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 )	
CLIPConfigaN  
    [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
    a CLIP 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 CLIP
    [openai/clip-vit-base-patch32](https://huggingface.co/openai/clip-vit-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 [`CLIPTextConfig`].
        vision_config (`dict`, *optional*):
            Dictionary of configuration options used to initialize [`CLIPVisionConfig`].
        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 CLIP implementation.
        kwargs (*optional*):
            Dictionary of keyword arguments.

    Example:

    ```python
    >>> from transformers import CLIPConfig, CLIPModel

    >>> # Initializing a CLIPConfig with openai/clip-vit-base-patch32 style configuration
    >>> configuration = CLIPConfig()

    >>> # Initializing a CLIPModel (with random weights) from the openai/clip-vit-base-patch32 style configuration
    >>> model = CLIPModel(configuration)

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

    >>> # We can also initialize a CLIPConfig from a CLIPTextConfig and a CLIPVisionConfig
    >>> from transformers import CLIPTextConfig, CLIPVisionConfig

    >>> # Initializing a CLIPText and CLIPVision configuration
    >>> config_text = CLIPTextConfig()
    >>> config_vision = CLIPVisionConfig()

    >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
    ```clipr   rA   c                    |j                  dd       }|j                  dd       }t        |   di | ||i }t        di |j	                         }|j                         D ]A  \  }	}
|	|v s|
||	   k7  s|	dvs|	|v r
d|	 d|	 d}nd|	 d}t        j                  |       C |j                  |       ||i }t        di |j	                         }d	|v r3|d	   j                         D 	
ci c]  \  }	}
t        |	      |
 c}
}	|d	<   |j                         D ]A  \  }	}
|	|v s|
||	   k7  s|	dvs|	|v r
d|	 d
|	 d}nd|	 d}t        j                  |       C |j                  |       |i }t        j                  d       |i }t        j                  d       t        di || _        t        di || _        || _        || _        d| _        y c c}
}	w )Ntext_config_dictvision_config_dict)transformers_version`zp` is found in both `text_config_dict` and `text_config` but with different values. The value `text_config_dict["z"]` will be used instead.zj`text_config_dict` is provided which will be used to initialize `CLIPTextConfig`. The value `text_config["z"]` will be overridden.id2labelzv` is found in both `vision_config_dict` and `vision_config` but with different values. The value `vision_config_dict["zp`vision_config_dict` is provided which will be used to initialize `CLIPVisionConfig`. The value `vision_config["zO`text_config` is `None`. Initializing the `CLIPTextConfig` with default values.zS`vision_config` is `None`. initializing the `CLIPVisionConfig` with default values.r3   r   )popr   r   r   to_dictitemsloggerinfoupdater?   strr   rA   r   logit_scale_init_valuer$   )r&   r   rA   r   rY   r'   rM   rN   _text_config_dictkeyvaluemessage_vision_config_dictr(   s                r)   r   zCLIPConfig.__init__  sp    "::&8$?#ZZ(<dC"6"
 '"  !/ B1A B J J L 0557 )
U+%%;s3C*CSkHk..u %<<?5@Y[  336%7NP   KK()" 01)$ " #3"H5G"H"P"P"R006I*6U6[6[6]3(2UCHeO3#J/
 2779 )
U-'E]35G,GCWoLo00u %FFIUJce  99<=TV   KK()"   !45KKKij MKKmn)8K8->>,&<#"%K3s   2Gr   rA   c                 P     | d|j                         |j                         d|S )z
        Instantiate a [`CLIPConfig`] (or a derived class) from clip text model configuration and clip vision model
        configuration.

        Returns:
            [`CLIPConfig`]: An instance of a configuration object
        rK   r   )rS   )clsr   rA   r'   s       r)   from_text_vision_configsz#CLIPConfig.from_text_vision_configsk  s,     f{224MDYDYD[f_effr*   )NNr+   g/L
F@)r6   r7   r8   r9   r:   r   r?   sub_configsr   classmethodra   r<   r=   s   @r)   rI   rI      sP    +Z J"0CSTK `fV&p 	g> 	gRb 	g 	gr*   rI   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 )CLIPOnnxConfig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   r4   pixel_valuesrC   heightwidth)r   r4      r	   attention_maskr   r&   s    r)   inputszCLIPOnnxConfig.inputsy  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   ri   logits_per_texttext_embedsimage_embedsr   rp   s    r)   outputszCLIPOnnxConfig.outputs  sD    #a\2"QL1G-!W.	
 	
r*   c                      y)Ng-C6?r   rp   s    r)   atol_for_validationz"CLIPOnnxConfig.atol_for_validation  s    r*   	processorr
   
batch_size
seq_length	frameworkr   c                     t         |   |j                  |||      }t         |   |j                  ||      }i ||S )N)r{   r|   r}   )r{   r}   )r   generate_dummy_inputs	tokenizerimage_processor)r&   rz   r{   r|   r}   text_input_dictimage_input_dictr(   s          r)   r   z$CLIPOnnxConfig.generate_dummy_inputs  s`      '7J:Yb 8 
 !78%%*	 9 
 7/6%566r*   c                      y)N   r   rp   s    r)   default_onnx_opsetz!CLIPOnnxConfig.default_onnx_opset  s    r*   )r   N)r6   r7   r8   propertyr   rX   intrq   rw   floatry   r   r   r   r   r<   r=   s   @r)   re   re   x  s    
WS#X%6 67 
 
 
gc3h&7!78 
 
 U   ,07#7 7 	7
 L)7 
c	7 C  r*   re   N)r9   collectionsr   typingr   r   r   r   processing_utilsr
   utilsr   configuration_utilsr   onnxr   r   
get_loggerr6   rU   r   r?   rI   re   r   r*   r)   <module>r      sy     # 8 8 2# 3   
		H	%b3% b3JY%' Y%xSg! Sgl+Z +r*   