code: 
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  lowerCAmelCase__	:  Tuple 		=logging.get_logger(__name__)
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      WavLMForSequenceClassification.from_pretrained(lowercase_							,     config=lowercase_     )
  							__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     (				a__							,     a__							,     a__     )       ->				int:
  							__SCREAMING_SNAKE_CASE								=      WavLMForAudioFrameClassification.from_pretrained(lowercase_							,     config=lowercase_     )
  							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.linear.weight''']
  							__SCREAMING_SNAKE_CASE								=      downstream_dict['''model.linear.bias''']
  							return model
  
  
  
  
  
  
  def 			__lowercase     (				a__							,     a__							,     a__     )       ->				Dict:
  							__SCREAMING_SNAKE_CASE								=      WavLMForXVector.from_pretrained(lowercase_							,     config=lowercase_     )
  							__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     (				a__							,     a__							,     a__							,     a__     )       ->				Any:
  							__SCREAMING_SNAKE_CASE								=      torch.load(lowercase_							,     map_location='cpu'     )
  
  							__SCREAMING_SNAKE_CASE								=      checkpoint['''Downstream''']
  
  							__SCREAMING_SNAKE_CASE								=      WavLMConfig.from_pretrained(lowercase_     )
  							__SCREAMING_SNAKE_CASE								=      WavaVecaFeatureExtractor.from_pretrained(
  							    lowercase_							,     return_attention_mask=lowercase_							,     do_normalize=lowercase_     )
  
  							__SCREAMING_SNAKE_CASE								=      hf_config.architectures[0]
  							if arch.endswith('ForSequenceClassification'     ):
  														__SCREAMING_SNAKE_CASE								=      convert_classification(lowercase_							,     lowercase_							,     lowercase_     )
  							elif arch.endswith('ForAudioFrameClassification'     ):
  														__SCREAMING_SNAKE_CASE								=      convert_diarization(lowercase_							,     lowercase_							,     lowercase_     )
  							elif arch.endswith('ForXVector'     ):
  														__SCREAMING_SNAKE_CASE								=      convert_xvector(lowercase_							,     lowercase_							,     lowercase_     )
  							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(lowercase_     )
  							hf_model.save_pretrained(lowercase_     )
  
  
  if __name__ == "__main__":
  			lowerCAmelCase__	:  int 		=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.''')
  			lowerCAmelCase__	:  int 		=parser.parse_args()
  			convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  
  
style_context: 
  
  
  
  def 			__lowercase     (				a__							,     a__     )       ->				List[str]:
  							if n == 0:
  														return 1
  
  							elif n % 2 == 1:
  														return binary_exponentiation(a__							,     n - 1     ) * a
  
  							else:
  														__SCREAMING_SNAKE_CASE								=      binary_exponentiation(a__							,     n / 2     )
  														return b * b
  
  
  if __name__ == "__main__":
  			try:
  						lowerCAmelCase__	:  str 		=int(input('''Enter Base : ''').strip())
  						lowerCAmelCase__	:  Optional[int] 		=int(input('''Enter Power : ''').strip())
  			except ValueError:
  						print('''Invalid literal for integer''')
  
  			lowerCAmelCase__	:  Any 		=binary_exponentiation(BASE, POWER)
  			print(F'''{BASE}^({POWER}) : {RESULT}''')
  
  
  
  
  
  
  