code: 
  
  
  '''simple docstring'''
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  __lowercase			:		Any =			logging.get_logger(__name__)
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     Optional[int]     ):
  					__a    : Dict      					=						WavLMForSequenceClassification.from_pretrained(_SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE     )
  					__a    : Dict      					=						downstream_dict['projector.weight']
  					__a    : List[Any]      					=						downstream_dict['projector.bias']
  					__a    : str      					=						downstream_dict['model.post_net.linear.weight']
  					__a    : int      					=						downstream_dict['model.post_net.linear.bias']
  					return model
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Any		,   _SCREAMING_SNAKE_CASE   :     Optional[Any]		,   _SCREAMING_SNAKE_CASE   :     Tuple     ):
  					__a    : List[str]      					=						WavLMForAudioFrameClassification.from_pretrained(_SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE     )
  					__a    : Any      					=						downstream_dict['model.linear.weight']
  					__a    : Tuple      					=						downstream_dict['model.linear.bias']
  					return model
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int		,   _SCREAMING_SNAKE_CASE   :     Union[str, Any]		,   _SCREAMING_SNAKE_CASE   :     int     ):
  					__a    : Optional[Any]      					=						WavLMForXVector.from_pretrained(_SCREAMING_SNAKE_CASE		,   config=_SCREAMING_SNAKE_CASE     )
  					__a    : List[str]      					=						downstream_dict['connector.weight']
  					__a    : Optional[Any]      					=						downstream_dict['connector.bias']
  					for i, kernel_size in enumerate(hf_config.tdnn_kernel     ):
  										__a    : Optional[int]      					=						downstream_dict[
  										    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  										]
  										__a    : Optional[int]      					=						downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
  					__a    : int      					=						downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
  					__a    : Union[str, Any]      					=						downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
  					__a    : str      					=						downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
  					__a    : int      					=						downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
  					__a    : Union[str, Any]      					=						downstream_dict['objective.W']
  					return model
  
  
  
  
  
  
  @torch.no_grad()
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Dict		,   _SCREAMING_SNAKE_CASE   :     Optional[int]		,   _SCREAMING_SNAKE_CASE   :     List[str]		,   _SCREAMING_SNAKE_CASE   :     str     ):
  					__a    : Any      					=						torch.load(_SCREAMING_SNAKE_CASE		,   map_location='cpu'     )
  
  					__a    : Dict      					=						checkpoint['Downstream']
  
  					__a    : Union[str, Any]      					=						WavLMConfig.from_pretrained(_SCREAMING_SNAKE_CASE     )
  					__a    : str      					=						WavaVecaFeatureExtractor.from_pretrained(
  					    _SCREAMING_SNAKE_CASE		,   return_attention_mask=_SCREAMING_SNAKE_CASE		,   do_normalize=_SCREAMING_SNAKE_CASE     )
  
  					__a    : str      					=						hf_config.architectures[0]
  					if arch.endswith('ForSequenceClassification'     ):
  										__a    : Union[str, Any]      					=						convert_classification(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					elif arch.endswith('ForAudioFrameClassification'     ):
  										__a    : Optional[int]      					=						convert_diarization(_SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE		,   _SCREAMING_SNAKE_CASE     )
  					elif arch.endswith('ForXVector'     ):
  										__a    : Dict      					=						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:
  										__a    : str      					=						checkpoint['Featurizer']['weights']
  
  					hf_feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE     )
  					hf_model.save_pretrained(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	__lowercase			:		Union[str, Any] =			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.')
  	__lowercase			:		Optional[Any] =			parser.parse_args()
  	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
style_context: 
  
  
  '''simple docstring'''
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     int = 30     ):
  					__a    : Dict      					=						exponent + 2
  					__a    : List[Any]      					=						(1 + 5**0.5) / 2
  					__a    : Any      					=						(phi**fibonacci_index - (phi - 1) ** fibonacci_index) / 5**0.5
  
  					return int(_SCREAMING_SNAKE_CASE     )
  
  
  if __name__ == "__main__":
  	print(f'''{solution() = }''')
  
  
  
  