o Zh@sXdZddlZddlmZddlmZddlmZmZe e Z Gdd d eZ d gZ dS) zColPali model configurationN)deepcopy)PretrainedConfig)CONFIG_MAPPING AutoConfigcs<eZdZdZdZeedZ   d deffdd Z Z S) ColPaliConfiga 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). The ColPali config is very similar to [`PaligemmaConfig`], but with an extra attribute defining the embedding dimension. 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) ``` Zcolpali) vlm_config text_configN embedding_dimc s|durtd}tdn?t|trs    O