
    rh                     x    d Z ddlZddlmZ ddlmZ ddlmZmZ  ej                  e
      Z G d d	e      Zd	gZy)
zColPali model configuration    N)deepcopy   )PretrainedConfig   )CONFIG_MAPPING
AutoConfigc                   >     e Zd ZdZdZeedZ	 	 	 ddef fdZ	 xZ
S )ColPaliConfigab  
    Configuration class to store the configuration of a [`ColPaliForRetrieval`]. It is used to instantiate an instance
    of `ColPaliForRetrieval` according to the specified arguments, defining the model architecture following the methodology
    from the "ColPali: Efficient Document Retrieval with Vision Language Models" paper.

    Creating a configuration with the default settings will result in a configuration where the VLM backbone is set to the
    default PaliGemma configuration, i.e the one from [vidore/colpali-v1.2](https://huggingface.co/vidore/colpali-v1.2).

    Note that contrarily to what the class name suggests (actually the name refers to the ColPali **methodology**), you can
    use a different VLM backbone model than PaliGemma by passing the corresponding VLM configuration to the class constructor.

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

    Args:
        vlm_config (`PretrainedConfig`, *optional*):
            Configuration of the VLM backbone model.
        text_config (`PretrainedConfig`, *optional*):
            Configuration of the text backbone model. Overrides the `text_config` attribute of the `vlm_config` if provided.
        embedding_dim (`int`, *optional*, defaults to 128):
            Dimension of the multi-vector embeddings produced by the model.

    Example:

    ```python
    from transformers.models.colpali import ColPaliConfig, ColPaliForRetrieval

    config = ColPaliConfig()
    model = ColPaliForRetrieval(config)
    ```
    colpali)
vlm_configtext_configembedding_dimc                 F   |#t        d          }t        j                  d       nt        |t              rIt        |      }d|vrt        d      |d   t         vrt        d|d    d      t        |d      d
i |}n+t        |t              r|}nt        dt        |       d      || _        ||n|j                  | _        t        | j                  t              r+|j                  dd	      |d<   t        |d      d
i || _        || _        t        | @  d
i | y )N	paligemmaza`vlm_config` is `None`. Initializing `vlm_config` with the `PaliGemmaConfig` with default values.
model_typez^The `model_type` key is missing in the `vlm_config` dictionary. Please provide the model type.zThe model type `z6` is not supported. Please provide a valid model type.zWInvalid type for `vlm_config`. Expected `PretrainedConfig`, `dict`, or `None`, but got .gemma )r   loggerinfo
isinstancedictr   KeyError
ValueErrorr   	TypeErrortyper   r   getr   super__init__)selfr   r   r   kwargs	__class__s        /var/www/html/ai-insurance-compliance-backend/venv/lib/python3.12/site-packages/transformers/models/colpali/configuration_colpali.pyr   zColPaliConfig.__init__?   sD    '46JKKs 
D)!*-J:-t  L)? &z,'?&@@vw  (
<(@AOJOJ
$45#Jijnoyjzi{{|}  %*5*A;zG]G]d&&-(3g(NK%-k,.GHW;WD*"6"    )NN   )__name__
__module____qualname____doc__r   r   r   sub_configsintr   __classcell__)r"   s   @r#   r
   r
      s9    @ J!1*MK  	&# 	&# &#r$   r
   )r)   loggingcopyr   configuration_utilsr   autor   r   	getLoggerr&   r   r
   __all__r   r$   r#   <module>r3      sD    "   3 - 
		8	$J#$ J#Z 
r$   