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: import glob
  import os
  
  from get_test_info import get_tester_classes
  
  
  if __name__ == "__main__":
  	lowercase__    :       List[Any]						=			[]
  
  	lowercase__    :       Optional[Any]						=			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
  	lowercase__    :       Optional[int]						=			glob.glob(pattern)
  	# TODO: deal with TF/Flax too
  	lowercase__    :       Dict						=			[
  	    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
  	]
  
  	for test_file in test_files:
  		lowercase__    :       Tuple						=			get_tester_classes(test_file)
  		for tester_class in tester_classes:
  			# A few tester classes don't have `parent` parameter in `__init__`.
  			# TODO: deal this better
  			try:
  				lowercase__    :       Union[str, Any]						=			tester_class(parent=None)
  			except Exception:
  				continue
  			if hasattr(tester, '''get_config'''):
  				lowercase__    :       Union[str, Any]						=			tester.get_config()
  				for k, v in config.to_dict().items():
  					if isinstance(v, int):
  						lowercase__    :       int						=			None
  						if k in ["vocab_size"]:
  							lowercase__    :       Optional[int]						=			1_0_0
  						elif k in ["max_position_embeddings"]:
  							lowercase__    :       Union[str, Any]						=			1_2_8
  						elif k in ["hidden_size", "d_model"]:
  							lowercase__    :       Optional[Any]						=			4_0
  						elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
  							lowercase__    :       Dict						=			5
  						if target is not None and v > target:
  							failures.append(
  							    f'{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`'
  							    f' with config["{k}"] = {v} which is too large for testing! Set its value to be smaller'
  							    f' than {target}.'
  							)
  
  	if len(failures) > 0:
  		raise Exception(f'There were {len(failures)} failures:\n' + '''\n'''.join(failures))
  
  
  
  