code: 
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE__    :    List[str]								=					logging.get_logger(__name__)
  
  def lowercase						(   SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE					,						config=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['projector.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['projector.bias']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.post_net.linear.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.post_net.linear.bias']
  			return model
  
  def lowercase						(   SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE					,						config=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.linear.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.linear.bias']
  			return model
  
  def lowercase						(   SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE					,						config=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['connector.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['connector.bias']
  			for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
  						SCREAMING_SNAKE_CASE_			=	downstream_dict[
  						    F'model.framelevel_feature_extractor.module.{i}.kernel.weight'
  						]
  						SCREAMING_SNAKE_CASE_			=	downstream_dict[F'model.framelevel_feature_extractor.module.{i}.kernel.bias']
  
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['objective.W']
  			return model
  
  @torch.no_grad()
  def lowercase						(   SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	torch.load(SCREAMING_SNAKE_CASE					,						map_location='cpu'   )
  
  			SCREAMING_SNAKE_CASE_			=	checkpoint['Downstream']
  
  			SCREAMING_SNAKE_CASE_			=	WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	WavaVecaFeatureExtractor.from_pretrained(
  			    SCREAMING_SNAKE_CASE					,						return_attention_mask=SCREAMING_SNAKE_CASE					,						do_normalize=SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	hf_config.architectures[0]
  			if arch.endswith('ForSequenceClassification'   ):
  						SCREAMING_SNAKE_CASE_			=	convert_classification(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif arch.endswith('ForAudioFrameClassification'   ):
  						SCREAMING_SNAKE_CASE_			=	convert_diarization(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif arch.endswith('ForXVector'   ):
  						SCREAMING_SNAKE_CASE_			=	convert_xvector(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			else:
  						raise NotImplementedError(F'S3PRL weights conversion is not supported for {arch}'   )
  
  			if hf_config.use_weighted_layer_sum:
  						SCREAMING_SNAKE_CASE_			=	checkpoint['Featurizer']['weights']
  
  			hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE   )
  			hf_model.save_pretrained(SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    str								=					argparse.ArgumentParser()
  				parser.add_argument(
  				    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  				)
  				parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  				parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  				parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					parser.parse_args()
  				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
style_context: 
  
  
  
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					"\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
  
  SCREAMING_SNAKE_CASE__    :    List[str]								=					[{"type": "code", "content": INSTALL_CONTENT}]
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{
      "{processor_class}": "FakeProcessorClass",
      "{model_class}": "FakeModelClass",
      "{object_class}": "FakeObjectClass",
  }
  