code: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import (
      WavaVecaFeatureExtractor,
      WavLMConfig,
      WavLMForAudioFrameClassification,
      WavLMForSequenceClassification,
      WavLMForXVector,
      logging,
  )
  
  
  logging.set_verbosity_info()
  __A  							=  logging.get_logger(__name__)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	Optional[int]     ,			A_:	Optional[int]		)  ->							Union[str, Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=WavLMForSequenceClassification.from_pretrained(A_     ,			config=A_		)
         __UpperCAmelCase    							=downstream_dict["""projector.weight"""]
         __UpperCAmelCase    							=downstream_dict["""projector.bias"""]
         __UpperCAmelCase    							=downstream_dict["""model.post_net.linear.weight"""]
         __UpperCAmelCase    							=downstream_dict["""model.post_net.linear.bias"""]
         return model
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Optional[Any]     ,			A_:	int     ,			A_:	List[str]		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=WavLMForAudioFrameClassification.from_pretrained(A_     ,			config=A_		)
         __UpperCAmelCase    							=downstream_dict["""model.linear.weight"""]
         __UpperCAmelCase    							=downstream_dict["""model.linear.bias"""]
         return model
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	int     ,			A_:	str     ,			A_:	List[Any]		)  ->							Optional[Any]:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=WavLMForXVector.from_pretrained(A_     ,			config=A_		)
         __UpperCAmelCase    							=downstream_dict["""connector.weight"""]
         __UpperCAmelCase    							=downstream_dict["""connector.bias"""]
         for i, kernel_size in enumerate(hf_config.tdnn_kernel		):
                __UpperCAmelCase    							=downstream_dict[
                    F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
                ]
                __UpperCAmelCase    							=downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']
  
         __UpperCAmelCase    							=downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
         __UpperCAmelCase    							=downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
         __UpperCAmelCase    							=downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
         __UpperCAmelCase    							=downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
         __UpperCAmelCase    							=downstream_dict["""objective.W"""]
         return model
  
  
  
  
  
  
  
  @torch.no_grad()
  def 			lowercase__  (				A_:	List[Any]     ,			A_:	List[Any]     ,			A_:	Tuple     ,			A_:	Optional[int]		)  ->							Dict:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=torch.load(A_     ,			map_location="""cpu"""		)
  
         __UpperCAmelCase    							=checkpoint["""Downstream"""]
  
         __UpperCAmelCase    							=WavLMConfig.from_pretrained(A_		)
         __UpperCAmelCase    							=WavaVecaFeatureExtractor.from_pretrained(
             A_     ,			return_attention_mask=A_     ,			do_normalize=A_		)
  
         __UpperCAmelCase    							=hf_config.architectures[0]
         if arch.endswith("""ForSequenceClassification"""		):
                __UpperCAmelCase    							=convert_classification(A_     ,			A_     ,			A_		)
         elif arch.endswith("""ForAudioFrameClassification"""		):
                __UpperCAmelCase    							=convert_diarization(A_     ,			A_     ,			A_		)
         elif arch.endswith("""ForXVector"""		):
                __UpperCAmelCase    							=convert_xvector(A_     ,			A_     ,			A_		)
         else:
                raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''		)
  
         if hf_config.use_weighted_layer_sum:
                __UpperCAmelCase    							=checkpoint["""Featurizer"""]["""weights"""]
  
         hf_feature_extractor.save_pretrained(A_		)
         hf_model.save_pretrained(A_		)
  
  
  if __name__ == "__main__":
     __A  							=  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.")
     __A  							=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  
  
  
  
  
style_context: 
  
  
  
  
  
  
  from __future__ import annotations
  
  __A  							=  [2, 1, 4, 5, 6, 0, 8, 9, 1, 2, 0, 6, 4, 2, 0, 6, 5, 3, 2, 7]
  
  
  
  class 							_A :
      """simple docstring"""
  
      def __init__(							self			:    List[str]   ,    __SCREAMING_SNAKE_CASE			:    int			)			->       None:
             __UpperCAmelCase    							=-1
             __UpperCAmelCase    							=-1
             __UpperCAmelCase    							=[-1] * length
             __UpperCAmelCase    							=None
             __UpperCAmelCase    							=None
  
  
  
      def __repr__(							self			:    Optional[int]			)			->       str:
             return f'''Node(min_value={self.minn} max_value={self.maxx})'''
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	list[int]		)  ->							Node | None:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         __UpperCAmelCase    							=Node(len(A_		)		)
         __UpperCAmelCase							,   __UpperCAmelCase    							=min(A_		), max(A_		)
         # Leaf node case where the node contains only one unique value
         if root.minn == root.maxx:
                return root
         __UpperCAmelCase    							=(root.minn + root.maxx) // 2
  
         __UpperCAmelCase    							=[]
         __UpperCAmelCase    							=[]
  
         for index, num in enumerate(A_		):
                if num <= pivot:
                       left_arr.append(A_		)
                else:
                       right_arr.append(A_		)
                __UpperCAmelCase    							=len(A_		)
         __UpperCAmelCase    							=build_tree(A_		)
         __UpperCAmelCase    							=build_tree(A_		)
         return root
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if index < 0 or node is None:
                return 0
         # Leaf node cases
         if node.minn == node.maxx:
                return index + 1 if node.minn == num else 0
         __UpperCAmelCase    							=(node.minn + node.maxx) // 2
         if num <= pivot:
                # go the left subtree and map index to the left subtree
                return rank_till_index(node.left     ,			A_     ,			node.map_left[index] - 1		)
         else:
                # go to the right subtree and map index to the right subtree
                return rank_till_index(node.right     ,			A_     ,			index - node.map_left[index]		)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if start > end:
                return 0
         __UpperCAmelCase    							=rank_till_index(A_     ,			A_     ,			A_		)
         __UpperCAmelCase    							=rank_till_index(A_     ,			A_     ,			start - 1		)
         return rank_till_end - rank_before_start
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if index > (end - start) or start > end or node is None:
                return -1
         # Leaf node case
         if node.minn == node.maxx:
                return node.minn
         # Number of elements in the left subtree in interval [start, end]
         __UpperCAmelCase    							=node.map_left[end] - (
             node.map_left[start - 1] if start else 0
         )
         if num_elements_in_left_tree > index:
                return quantile(
                    node.left     ,			A_     ,			(node.map_left[start - 1] if start else 0)     ,			node.map_left[end] - 1     ,			)
         else:
                return quantile(
                    node.right     ,			index - num_elements_in_left_tree     ,			start - (node.map_left[start - 1] if start else 0)     ,			end - node.map_left[end]     ,			)
  
  
  
  
  
  
  
  def 			lowercase__  (				A_:	Node | None     ,			A_:	int     ,			A_:	int     ,			A_:	int     ,			A_:	int		)  ->							int:
  
  
  
  
  
  
  
         """simple docstring"""
  
  
  
  
  
  
         if (
             start > end
             or node is None
             or start_num > end_num
             or node.minn > end_num
             or node.maxx < start_num
         ):
                return 0
         if start_num <= node.minn and node.maxx <= end_num:
                return end - start + 1
         __UpperCAmelCase    							=range_counting(
             node.left     ,			(node.map_left[start - 1] if start else 0)     ,			node.map_left[end] - 1     ,			A_     ,			A_     ,			)
         __UpperCAmelCase    							=range_counting(
             node.right     ,			start - (node.map_left[start - 1] if start else 0)     ,			end - node.map_left[end]     ,			A_     ,			A_     ,			)
         return left + right
  
  
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
  
  
  
  