code: 
  
  """simple docstring"""
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  lowercase__  :					int												=	logging.get_logger(__name__)
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      WavLMForSequenceClassification.from_pretrained(lowercase__    ,							config=lowercase__   )
  					A    =      downstream_dict["projector.weight"]
  					A    =      downstream_dict["projector.bias"]
  					A    =      downstream_dict["model.post_net.linear.weight"]
  					A    =      downstream_dict["model.post_net.linear.bias"]
  					return model
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      WavLMForAudioFrameClassification.from_pretrained(lowercase__    ,							config=lowercase__   )
  					A    =      downstream_dict["model.linear.weight"]
  					A    =      downstream_dict["model.linear.bias"]
  					return model
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      WavLMForXVector.from_pretrained(lowercase__    ,							config=lowercase__   )
  					A    =      downstream_dict["connector.weight"]
  					A    =      downstream_dict["connector.bias"]
  					for i, kernel_size in enumerate(hf_config.tdnn_kernel   ):
  										A    =      downstream_dict[
  										    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
  										]
  										A    =      downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]
  
  					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
  					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
  					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
  					A    =      downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
  					A    =      downstream_dict["objective.W"]
  					return model
  @torch.no_grad()
  def      __SCREAMING_SNAKE_CASE		(		lowercase__    ,							lowercase__    ,							lowercase__    ,							lowercase__   ):
  					"""simple docstring"""
  					A    =      torch.load(lowercase__    ,							map_location="cpu"   )
  
  					A    =      checkpoint["Downstream"]
  
  					A    =      WavLMConfig.from_pretrained(lowercase__   )
  					A    =      WavaVecaFeatureExtractor.from_pretrained(
  					    lowercase__    ,							return_attention_mask=lowercase__    ,							do_normalize=lowercase__   )
  
  					A    =      hf_config.architectures[0]
  					if arch.endswith("ForSequenceClassification"   ):
  										A    =      convert_classification(lowercase__    ,							lowercase__    ,							lowercase__   )
  					elif arch.endswith("ForAudioFrameClassification"   ):
  										A    =      convert_diarization(lowercase__    ,							lowercase__    ,							lowercase__   )
  					elif arch.endswith("ForXVector"   ):
  										A    =      convert_xvector(lowercase__    ,							lowercase__    ,							lowercase__   )
  					else:
  										raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""   )
  
  					if hf_config.use_weighted_layer_sum:
  										A    =      checkpoint["Featurizer"]["weights"]
  
  					hf_feature_extractor.save_pretrained(lowercase__   )
  					hf_model.save_pretrained(lowercase__   )
  
  
  if __name__ == "__main__":
  	lowercase__  :					Dict												=	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__  :					Dict												=	parser.parse_args()
  	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
  
style_context: 
  
  """simple docstring"""
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
  
  
  __A  :					int												=	{
      'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
  }
  
  try:
  	if not is_torch_available():
  		raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  	pass
  else:
  	__A  :					Any												=	[
  	    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
  	    'NystromformerForMaskedLM',
  	    'NystromformerForMultipleChoice',
  	    'NystromformerForQuestionAnswering',
  	    'NystromformerForSequenceClassification',
  	    'NystromformerForTokenClassification',
  	    'NystromformerLayer',
  	    'NystromformerModel',
  	    'NystromformerPreTrainedModel',
  	]
  
  
  if TYPE_CHECKING:
  	from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig
  
  	try:
  		if not is_torch_available():
  			raise OptionalDependencyNotAvailable()
  	except OptionalDependencyNotAvailable:
  		pass
  	else:
  		from .modeling_nystromformer import (
  		    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
  		    NystromformerForMaskedLM,
  		    NystromformerForMultipleChoice,
  		    NystromformerForQuestionAnswering,
  		    NystromformerForSequenceClassification,
  		    NystromformerForTokenClassification,
  		    NystromformerLayer,
  		    NystromformerModel,
  		    NystromformerPreTrainedModel,
  		)
  
  
  else:
  	import sys
  
  	__A  :					Optional[Any]												=	_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
  
  
  
  
  
  