class_docstr: [`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):
          Dimentionality of text and vision projection layers.
      logit_scale_init_value (`float`, *optional*, defaults to 2.6592):
          The inital value of the *logit_scale* paramter. 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)
  ```
docstr: 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
docstr_tok: ["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"]
code: def from_text_vision_configs(
      cls,
      text_config: CLIPTextConfig,
      vision_config: CLIPVisionConfig,
      **kwargs,
  ):
      r"""
      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
      """
  
      return cls(
          text_config=text_config.to_dict(),
          vision_config=vision_config.to_dict(),
          **kwargs,
      )
  
imports: import os
  from collections import OrderedDict
  from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
  from ...configuration_utils import PretrainedConfig
  from ...onnx import OnnxConfig
  from ...utils import logging
label_desc: Use image node_id 2 for calling the CLIPConfig obj's underlying member method code with example usage: obj.from_text_vision_configs(cls, text_config, vision_config) and returns: cls