class_docstr: [`ClvpConfig`] is the configuration class to store the configuration of a [`ClvpModelForConditionalGeneration`]. It
  is used to instantiate a CLVP model according to the specified arguments, defining the text model, speech model and
  decoder model configs. Instantiating a configuration with the defaults will yield a similar configuration to that
  of the CLVP [susnato/clvp_dev](https://huggingface.co/susnato/clvp_dev) 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 the CLVP text encoder.
      speech_config (`dict`, *optional*):
          Dictionary of configuration options used to initialize CLVP speech encoder.
      decoder_config (`dict`, *optional*):
          Dictionary of configuration options used to initialize [`ClvpDecoderConfig`].
      projection_dim (`int`, *optional*, defaults to 768):
          Dimentionality of text and speech 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 CLVP implementation.
      initializer_factor (`float`, *optional*, defaults to 1.0):
          A factor for initializing all weight matrices (should be kept to 1.0, used internally for initialization
          testing).
      kwargs (*optional*):
          Dictionary of keyword arguments.
  
  Example:
  
  ```python
  >>> from transformers import ClvpConfig, ClvpModelForConditionalGeneration
  
  >>> # Initializing a ClvpConfig with susnato/clvp_dev style configuration
  >>> configuration = ClvpConfig()
  
  >>> # Initializing a ClvpModelForConditionalGeneration (with random weights) from the susnato/clvp_dev style configuration
  >>> model = ClvpModelForConditionalGeneration(configuration)
  
  >>> # Accessing the model configuration
  >>> configuration = model.config
  
  >>> # We can also initialize a CLVPConfig from a CLVPTextConfig, CLVPSpeechConfig and a CLVPAutoRegressiveConfig
  >>> from transformers import ClvpEncoderConfig, ClvpDecoderConfig
  
  >>> # Initializing a CLVP text, CLVP speech and CLVP decoder configuration
  >>> config_text = ClvpEncoderConfig()
  >>> config_speech = ClvpEncoderConfig()
  >>> decoder_config = ClvpDecoderConfig()
  
  >>> config = ClvpConfig.from_sub_model_configs(config_text, config_speech, decoder_config)
  ```
code: def __init__(
      self,
      text_config=None,
      speech_config=None,
      decoder_config=None,
      projection_dim=768,
      logit_scale_init_value=2.6592,
      initializer_factor=1.0,
      **kwargs,
  ):
      super().__init__(**kwargs)
  
      if text_config is None:
          text_config = {}
          logger.info(
              "`text_config` is `None`. Initializing the `ClvpEncoderConfig` with default values."
          )
  
      if speech_config is None:
          speech_config = {}
          logger.info(
              "`speech_config` is `None`. initializing the `ClvpEncoderConfig` with default values."
          )
  
      if decoder_config is None:
          decoder_config = {}
          logger.info(
              "`decoder_config` is `None`. initializing the `ClvpDecoderConfig` with default values."
          )
  
      self.text_config = ClvpEncoderConfig(**text_config)
      self.speech_config = ClvpEncoderConfig(**speech_config)
      self.decoder_config = ClvpDecoderConfig(**decoder_config)
  
      self.projection_dim = projection_dim
      self.logit_scale_init_value = logit_scale_init_value
      self.initializer_factor = initializer_factor
  
file_path: transformers/src/transformers/models/clvp/configuration_clvp.py
imports: import os
  from typing import TYPE_CHECKING, Union
  from ...configuration_utils import PretrainedConfig
  from ...utils import logging
label_desc: Use image node_id 1 to create a new ClvpConfig object from inherited base classes: PretrainedConfig with example: obj = ClvpConfig(text_config, speech_config, decoder_config, projection_dim, logit_scale_init_value, initializer_factor)