code: 
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  SCREAMING_SNAKE_CASE__    :    List[str]								=					logging.get_logger(__name__)
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	WavLMForSequenceClassification.from_pretrained(SCREAMING_SNAKE_CASE					,						config=SCREAMING_SNAKE_CASE   )
  			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						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       int:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	WavLMForAudioFrameClassification.from_pretrained(SCREAMING_SNAKE_CASE					,						config=SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.linear.weight']
  			SCREAMING_SNAKE_CASE_			=	downstream_dict['model.linear.bias']
  			return model
  
  def lowercase						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Tuple:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	WavLMForXVector.from_pretrained(SCREAMING_SNAKE_CASE					,						config=SCREAMING_SNAKE_CASE   )
  			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						(     SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   ) ->       Optional[int]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	torch.load(SCREAMING_SNAKE_CASE					,						map_location='cpu'   )
  
  			SCREAMING_SNAKE_CASE_			=	checkpoint['Downstream']
  
  			SCREAMING_SNAKE_CASE_			=	WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE   )
  			SCREAMING_SNAKE_CASE_			=	WavaVecaFeatureExtractor.from_pretrained(
  			    SCREAMING_SNAKE_CASE					,						return_attention_mask=SCREAMING_SNAKE_CASE					,						do_normalize=SCREAMING_SNAKE_CASE   )
  
  			SCREAMING_SNAKE_CASE_			=	hf_config.architectures[0]
  			if arch.endswith('ForSequenceClassification'   ):
  						SCREAMING_SNAKE_CASE_			=	convert_classification(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif arch.endswith('ForAudioFrameClassification'   ):
  						SCREAMING_SNAKE_CASE_			=	convert_diarization(SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE					,						SCREAMING_SNAKE_CASE   )
  			elif arch.endswith('ForXVector'   ):
  						SCREAMING_SNAKE_CASE_			=	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:
  						SCREAMING_SNAKE_CASE_			=	checkpoint['Featurizer']['weights']
  
  			hf_feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE   )
  			hf_model.save_pretrained(SCREAMING_SNAKE_CASE   )
  
  
  if __name__ == "__main__":
  				SCREAMING_SNAKE_CASE__    :    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.")
  				SCREAMING_SNAKE_CASE__    :    Optional[Any]								=					parser.parse_args()
  				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
style_context: 
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  SCREAMING_SNAKE_CASE__    :    List[Any]								=					{"configuration_detr": ["DETR_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetrConfig", "DetrOnnxConfig"]}
  
  try:
  				if not is_vision_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    int								=					["DetrFeatureExtractor"]
  				SCREAMING_SNAKE_CASE__    :    Any								=					["DetrImageProcessor"]
  
  try:
  				if not is_torch_available():
  								raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
  				pass
  else:
  				SCREAMING_SNAKE_CASE__    :    List[Any]								=					[
  				    "DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
  				    "DetrForObjectDetection",
  				    "DetrForSegmentation",
  				    "DetrModel",
  				    "DetrPreTrainedModel",
  				]
  
  
  if TYPE_CHECKING:
  				from .configuration_detr import DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DetrConfig, DetrOnnxConfig
  
  				try:
  								if not is_vision_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .feature_extraction_detr import DetrFeatureExtractor
  								from .image_processing_detr import DetrImageProcessor
  
  				try:
  								if not is_torch_available():
  												raise OptionalDependencyNotAvailable()
  				except OptionalDependencyNotAvailable:
  								pass
  				else:
  								from .modeling_detr import (
  								    DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
  								    DetrForObjectDetection,
  								    DetrForSegmentation,
  								    DetrModel,
  								    DetrPreTrainedModel,
  								)
  
  else:
  				import sys
  
  				SCREAMING_SNAKE_CASE__    :    Tuple								=					_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  