class_docstr: :class:`HybridCLIPConfig` is the configuration class to store the configuration of a
  :class:`~HybridCLIPModel`. It is used to instantiate HybridCLIPModel model according to the specified arguments,
  defining the text model and vision model configs.
  
  Configuration objects inherit from :class:`~transformers.PretrainedConfig` and can be used to control the model
  outputs. Read the documentation from :class:`~transformers.PretrainedConfig` for more information.
  
  Args:
      text_config_dict (:obj:`dict`):
          Dictionary of configuration options that defines text model config.
      vision_config_dict (:obj:`dict`):
          Dictionary of configuration options that defines vison model config.
      projection_dim (:obj:`int`, `optional`, defaults to 512):
          Dimentionality of text and vision projection layers.
      kwargs (`optional`):
          Dictionary of keyword arguments.
  
  Examples::
  
      >>> from transformers import BertConfig, CLIPConfig, HybridCLIPConfig, FlaxHybridCLIP
  
      >>> # Initializing a BERT and CLIP configuration
      >>> config_text = BertConfig()
      >>> config_vision = CLIPConfig()
  
      >>> config = HybridCLIPConfig.from_text_vision_configs(config_text, config_vision, projection_dim=512)
  
      >>> # Initializing a BERT and CLIPVision model
      >>> model = EncoderDecoderModel(config=config)
  
      >>> # Accessing the model configuration
      >>> config_text = model.config.text_config
      >>> config_vision  = model.config.vision_config
  
      >>> # Saving the model, including its configuration
      >>> model.save_pretrained('my-model')
  
      >>> # loading model and config from pretrained folder
      >>> encoder_decoder_config = HybridCLIPConfig.from_pretrained('my-model')
      >>> model = FlaxHybridCLIP.from_pretrained('my-model', config=encoder_decoder_config)
code: def __init__(self, projection_dim=512, **kwargs):
      super().__init__(**kwargs)
  
      if "text_config" not in kwargs:
          raise ValueError("`text_config` can not be `None`.")
  
      if "vision_config" not in kwargs:
          raise ValueError("`vision_config` can not be `None`.")
  
      text_config = kwargs.pop("text_config")
      vision_config = kwargs.pop("vision_config")
  
      text_model_type = text_config.pop("model_type")
      vision_model_type = vision_config.pop("model_type")
  
      from transformers import AutoConfig
  
      self.text_config = AutoConfig.for_model(
          text_model_type, **text_config
      )
  
      if vision_model_type == "clip":
          self.vision_config = AutoConfig.for_model(
              vision_model_type, **vision_config
          ).vision_config
      elif vision_model_type == "clip_vision_model":
          from transformers import CLIPVisionConfig
  
          self.vision_config = CLIPVisionConfig(**vision_config)
      else:
          self.vision_config = AutoConfig.for_model(
              vision_model_type, **vision_config
          )
  
      self.projection_dim = projection_dim
      self.initializer_factor = 1.0
  
imports: import copy
  from transformers.configuration_utils import PretrainedConfig
  from transformers.utils import logging
label_desc: Use image node_id 1 to create a new HybridCLIPConfig object from inherited base classes: PretrainedConfig with example: obj = HybridCLIPConfig(projection_dim)