class_docstr: [`CLIPConfig`] is the configuration class to store the configuration of a [`CLIPModel`]. It is used to instantiate
  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 (`dict`, *optional*):
          Dictionary of configuration options used to initialize [`CLIPTextConfig`].
      vision_config_dict (`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
  
  >>> # Initializing a CLIPText and CLIPVision configuration
  >>> config_text = CLIPTextConfig()
  >>> config_vision = CLIPVisionConfig()
  
  >>> config = CLIPConfig.from_text_vision_configs(config_text, config_vision)
  ```
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code: def __init__(
      self,
      text_config_dict=None,
      vision_config_dict=None,
      projection_dim=512,
      logit_scale_init_value=2.6592,
      **kwargs,
  ):
      super().__init__(
          text_config_dict=text_config_dict,
          vision_config_dict=vision_config_dict,
          **kwargs,
      )
  
      if text_config_dict is None:
          text_config_dict = {}
          logger.info(
              "text_config_dict is None. Initializing the CLIPTextConfig with default values."
          )
  
      if vision_config_dict is None:
          vision_config_dict = {}
          logger.info(
              "vision_config_dict is None. initializing the CLIPVisionConfig with default values."
          )
  
      self.text_config = CLIPTextConfig(**text_config_dict)
      self.vision_config = CLIPVisionConfig(**vision_config_dict)
  
      self.projection_dim = projection_dim
      self.logit_scale_init_value = logit_scale_init_value
      self.initializer_factor = 1.0
  
imports: import copy
  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 1 to create a new CLIPConfig object from inherited base classes: PretrainedConfig with example: obj = CLIPConfig(text_config_dict, vision_config_dict, projection_dim, logit_scale_init_value)