code: import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  lowercase__    :       Tuple						=			logging.get_logger(__name__)
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[Any]:
  							lowerCAmelCase				=	WavLMForSequenceClassification.from_pretrained(snake_case__     ,						config=snake_case__							)
  							lowerCAmelCase				=	downstream_dict['''projector.weight''']
  							lowerCAmelCase				=	downstream_dict['''projector.bias''']
  							lowerCAmelCase				=	downstream_dict['''model.post_net.linear.weight''']
  							lowerCAmelCase				=	downstream_dict['''model.post_net.linear.bias''']
  							return model
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			str:
  							lowerCAmelCase				=	WavLMForAudioFrameClassification.from_pretrained(snake_case__     ,						config=snake_case__							)
  							lowerCAmelCase				=	downstream_dict['''model.linear.weight''']
  							lowerCAmelCase				=	downstream_dict['''model.linear.bias''']
  							return model
  
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__							)  ->			int:
  							lowerCAmelCase				=	WavLMForXVector.from_pretrained(snake_case__     ,						config=snake_case__							)
  							lowerCAmelCase				=	downstream_dict['''connector.weight''']
  							lowerCAmelCase				=	downstream_dict['''connector.bias''']
  							for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
  														lowerCAmelCase				=	downstream_dict[
  														    f"model.framelevel_feature_extractor.module.{i}.kernel.weight"
  														]
  														lowerCAmelCase				=	downstream_dict[f"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
  
  							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
  							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
  							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
  							lowerCAmelCase				=	downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
  							lowerCAmelCase				=	downstream_dict['''objective.W''']
  							return model
  
  @torch.no_grad()
  def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__     ,						snake_case__     ,						snake_case__							)  ->			List[str]:
  							lowerCAmelCase				=	torch.load(snake_case__     ,						map_location='''cpu'''							)
  
  							lowerCAmelCase				=	checkpoint['''Downstream''']
  
  							lowerCAmelCase				=	WavLMConfig.from_pretrained(snake_case__							)
  							lowerCAmelCase				=	WavaVecaFeatureExtractor.from_pretrained(
  							    snake_case__     ,						return_attention_mask=snake_case__     ,						do_normalize=snake_case__							)
  
  							lowerCAmelCase				=	hf_config.architectures[0]
  							if arch.endswith('''ForSequenceClassification'''							):
  														lowerCAmelCase				=	convert_classification(snake_case__     ,						snake_case__     ,						snake_case__							)
  							elif arch.endswith('''ForAudioFrameClassification'''							):
  														lowerCAmelCase				=	convert_diarization(snake_case__     ,						snake_case__     ,						snake_case__							)
  							elif arch.endswith('''ForXVector'''							):
  														lowerCAmelCase				=	convert_xvector(snake_case__     ,						snake_case__     ,						snake_case__							)
  							else:
  														raise NotImplementedError(f"S3PRL weights conversion is not supported for {arch}"							)
  
  							if hf_config.use_weighted_layer_sum:
  														lowerCAmelCase				=	checkpoint['''Featurizer''']['''weights''']
  
  							hf_feature_extractor.save_pretrained(snake_case__							)
  							hf_model.save_pretrained(snake_case__							)
  
  
  if __name__ == "__main__":
  	lowercase__    :       List[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.''')
  	lowercase__    :       List[Any]						=			parser.parse_args()
  	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
style_context: def      SCREAMING_SNAKE_CASE_  (		snake_case__     ,						snake_case__							)  ->			int:
  							return (input_a, input_a).count(0							) % 2
  
  def      SCREAMING_SNAKE_CASE_  (		)  ->			None:
  							assert xor_gate(0     ,						0							) == 0
  							assert xor_gate(0     ,						1							) == 1
  							assert xor_gate(1     ,						0							) == 1
  							assert xor_gate(1     ,						1							) == 0
  
  
  if __name__ == "__main__":
  	print(xor_gate(0, 0))
  	print(xor_gate(0, 1))
  
  
  
  