class_docstr: :class:`~transformers.EncoderDecoderConfig` is the configuration class to store the configuration of a
  :class:`~transformers.EncoderDecoderModel`. It is used to instantiate an Encoder Decoder model according to the
  specified arguments, defining the encoder and decoder 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:
      kwargs (`optional`):
          Dictionary of keyword arguments. Notably:
  
              - **encoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
                object that defines the encoder config.
              - **decoder** (:class:`~transformers.PretrainedConfig`, `optional`) -- An instance of a configuration
                object that defines the decoder config.
  
  Examples::
  
      >>> from transformers import BertConfig, EncoderDecoderConfig, EncoderDecoderModel
  
      >>> # Initializing a BERT bert-base-uncased style configuration
      >>> config_encoder = BertConfig()
      >>> config_decoder = BertConfig()
  
      >>> config = EncoderDecoderConfig.from_encoder_decoder_configs(config_encoder, config_decoder)
  
      >>> # Initializing a Bert2Bert model from the bert-base-uncased style configurations
      >>> model = EncoderDecoderModel(config=config)
  
      >>> # Accessing the model configuration
      >>> config_encoder = model.config.encoder
      >>> config_decoder  = model.config.decoder
      >>> # set decoder config to causal lm
      >>> config_decoder.is_decoder = True
      >>> config_decoder.add_cross_attention = True
  
      >>> # Saving the model, including its configuration
      >>> model.save_pretrained('my-model')
  
      >>> # loading model and config from pretrained folder
      >>> encoder_decoder_config = EncoderDecoderConfig.from_pretrained('my-model')
      >>> model = EncoderDecoderModel.from_pretrained('my-model', config=encoder_decoder_config)
code: def __init__(self, **kwargs):
      super().__init__(**kwargs)
      assert (
          "encoder" in kwargs and "decoder" in kwargs
      ), "Config has to be initialized with encoder and decoder config"
      encoder_config = kwargs.pop("encoder")
      encoder_model_type = encoder_config.pop("model_type")
      decoder_config = kwargs.pop("decoder")
      decoder_model_type = decoder_config.pop("model_type")
  
      from ..auto.configuration_auto import AutoConfig
  
      self.encoder = AutoConfig.for_model(
          encoder_model_type, **encoder_config
      )
      self.decoder = AutoConfig.for_model(
          decoder_model_type, **decoder_config
      )
      self.is_encoder_decoder = True
  
imports: import copy
  from ...configuration_utils import PretrainedConfig
  from fastNLP.core.log import logger
label_desc: Use image node_id 1 to create a new EncoderDecoderConfig object from inherited base classes: PretrainedConfig with example: obj = EncoderDecoderConfig()