code: 
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	str   ,       snake_case__:	Optional[int]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	val
         # Number of nodes in left subtree
         _lowercase     =	0
         _lowercase     =	None
         _lowercase     =	None
         _lowercase     =	None
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Dict	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         assert self.left_tree_size == other.left_tree_size, "Unequal Sizes of Blocks"
  
         if self.val < other.val:
             _lowercase     =	self.right
             _lowercase     =	None
             if self.right:
                 _lowercase     =	other
             _lowercase     =	other
             _lowercase     =	self.left_tree_size * 2 + 1
             return self
         else:
             _lowercase     =	other.right
             _lowercase     =	None
             if other.right:
                 _lowercase     =	self
             _lowercase     =	self
             _lowercase     =	other.left_tree_size * 2 + 1
             return other
  
  
  
  
  
  
  class 					snake_case_							:
  
  
  
  
  
  
  
     """simple docstring"""
  
  
  
     def __init__(    self:	List[str]   ,       snake_case__:	Tuple=None   ,       snake_case__:	Tuple=None   ,       snake_case__:	Tuple=0	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	heap_size
         _lowercase     =	bottom_root
         _lowercase     =	min_node
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Any   ,       snake_case__:	Union[str, Any]	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         if other.size == 0:
             return None
         if self.size == 0:
             _lowercase     =	other.size
             _lowercase     =	other.bottom_root
             _lowercase     =	other.min_node
             return None
         # Update size
         _lowercase     =	self.size + other.size
  
         # Update min.node
         if self.min_node.val > other.min_node.val:
             _lowercase     =	other.min_node
         # Merge
  
         # Order roots by left_subtree_size
         _lowercase     =	[]
         _lowercase ,    _lowercase     =	self.bottom_root, other.bottom_root
         while i or j:
             if i and ((not j) or i.left_tree_size < j.left_tree_size):
                 combined_roots_list.append((i, True)	)
                 _lowercase     =	i.parent
             else:
                 combined_roots_list.append((j, False)	)
                 _lowercase     =	j.parent
          # Insert links between them
         for i in range(len(snake_case__	) - 1	):
             if combined_roots_list[i][1] != combined_roots_list[i + 1][1]:
                 _lowercase     =	combined_roots_list[i + 1][0]
                 _lowercase     =	combined_roots_list[i][0]
          # Consecutively merge roots with same left_tree_size
         _lowercase     =	combined_roots_list[0][0]
         while i.parent:
             if (
                 (i.left_tree_size == i.parent.left_tree_size) and (not i.parent.parent)
             ) or (
                 i.left_tree_size == i.parent.left_tree_size
                 and i.left_tree_size != i.parent.parent.left_tree_size
             ):
                 # Neighbouring Nodes
                 _lowercase     =	i.left
                 _lowercase     =	i.parent.parent
  
                 # Merging trees
                 _lowercase     =	i.merge_trees(i.parent	)
  
                 # Updating links
                 _lowercase     =	previous_node
                 _lowercase     =	next_node
                 if previous_node:
                     _lowercase     =	i
                 if next_node:
                     _lowercase     =	i
             else:
                 _lowercase     =	i.parent
          # Updating self.bottom_root
         while i.left:
             _lowercase     =	i.left
         _lowercase     =	i
  
         # Update other
         _lowercase     =	self.size
         _lowercase     =	self.bottom_root
         _lowercase     =	self.min_node
  
         # Return the merged heap
         return self
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[str]   ,       snake_case__:	Optional[Any]	)  ->		Union[str, Any]:
  
  
         """simple docstring"""
  
  
  
         if self.size == 0:
             _lowercase     =	Node(snake_case__	)
             _lowercase     =	1
             _lowercase     =	self.bottom_root
         else:
             # Create new node
             _lowercase     =	Node(snake_case__	)
  
             # Update size
             self.size += 1
  
             # update min_node
             if val < self.min_node.val:
                 _lowercase     =	new_node
             # Put new_node as a bottom_root in heap
             _lowercase     =	new_node
             _lowercase     =	self.bottom_root
             _lowercase     =	new_node
  
             # Consecutively merge roots with same left_tree_size
             while (
                 self.bottom_root.parent
                 and self.bottom_root.left_tree_size
                 == self.bottom_root.parent.left_tree_size
             ):
                 # Next node
                 _lowercase     =	self.bottom_root.parent.parent
  
                 # Merge
                 _lowercase     =	self.bottom_root.merge_trees(self.bottom_root.parent	)
  
                 # Update Links
                 _lowercase     =	next_node
                 _lowercase     =	None
                 if next_node:
                     _lowercase     =	self.bottom_root
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Tuple	)  ->		Tuple:
  
  
         """simple docstring"""
  
  
  
         return self.min_node.val
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	List[Any]	)  ->		int:
  
  
         """simple docstring"""
  
  
  
         return self.size == 0
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.min_node.val
  
         # Last element in heap corner case
         if self.size == 1:
             # Update size
             _lowercase     =	0
  
             # Update bottom root
             _lowercase     =	None
  
             # Update min_node
             _lowercase     =	None
  
             return min_value
         # No right subtree corner case
         # The structure of the tree implies that this should be the bottom root
         # and there is at least one other root
         if self.min_node.right is None:
             # Update size
             self.size -= 1
  
             # Update bottom root
             _lowercase     =	self.bottom_root.parent
             _lowercase     =	None
  
             # Update min_node
             _lowercase     =	self.bottom_root
             _lowercase     =	self.bottom_root.parent
             while i:
                 if i.val < self.min_node.val:
                     _lowercase     =	i
                 _lowercase     =	i.parent
             return min_value
         # General case
         # Find the BinomialHeap of the right subtree of min_node
         _lowercase     =	self.min_node.right
         _lowercase     =	None
         _lowercase     =	bottom_of_new
         _lowercase     =	1
  
         # Size, min_node and bottom_root
         while bottom_of_new.left:
             _lowercase     =	size_of_new * 2 + 1
             _lowercase     =	bottom_of_new.left
             if bottom_of_new.val < min_of_new.val:
                 _lowercase     =	bottom_of_new
          # Corner case of single root on top left path
         if (not self.min_node.left) and (not self.min_node.parent):
             _lowercase     =	size_of_new
             _lowercase     =	bottom_of_new
             _lowercase     =	min_of_new
             # print("Single root, multiple nodes case")
             return min_value
         # Remaining cases
         # Construct heap of right subtree
         _lowercase     =	BinomialHeap(
             bottom_root=snake_case__   ,       min_node=snake_case__   ,       heap_size=snake_case__	)
  
         # Update size
         _lowercase     =	self.size - 1 - size_of_new
  
         # Neighbour nodes
         _lowercase     =	self.min_node.left
         _lowercase     =	self.min_node.parent
  
         # Initialize new bottom_root and min_node
         _lowercase     =	previous_node or next_node
         _lowercase     =	next_node
  
         # Update links of previous_node and search below for new min_node and
         # bottom_root
         if previous_node:
             _lowercase     =	next_node
  
             # Update bottom_root and search for min_node below
             _lowercase     =	previous_node
             _lowercase     =	previous_node
             while self.bottom_root.left:
                 _lowercase     =	self.bottom_root.left
                 if self.bottom_root.val < self.min_node.val:
                     _lowercase     =	self.bottom_root
         if next_node:
             _lowercase     =	previous_node
  
             # Search for new min_node above min_node
             _lowercase     =	next_node
             while i:
                 if i.val < self.min_node.val:
                     _lowercase     =	i
                 _lowercase     =	i.parent
          # Merge heaps
         self.merge_heaps(snake_case__	)
  
         return min_value
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict	)  ->		Optional[Any]:
  
  
         """simple docstring"""
  
  
  
         _lowercase     =	self.bottom_root
         while top_root.parent:
             _lowercase     =	top_root.parent
         # preorder
         _lowercase     =	[]
         self.__traversal(snake_case__   ,       snake_case__	)
         return heap_pre_order
  
     def 			SCREAMING_SNAKE_CASE__				(    self:	Dict   ,       snake_case__:	Dict   ,       snake_case__:	Optional[Any]   ,       snake_case__:	int=0	)  ->		Dict:
  
  
         """simple docstring"""
  
  
  
         if curr_node:
             preorder.append((curr_node.val, level)	)
             self.__traversal(curr_node.left   ,       snake_case__   ,       level + 1	)
             self.__traversal(curr_node.right   ,       snake_case__   ,       level + 1	)
         else:
             preorder.append(('#', level)	)
  
     def __str__(    self:	List[Any]	)  ->		Optional[int]:
  
  
         """simple docstring"""
  
  
  
         if self.is_empty():
             return ""
         _lowercase     =	self.pre_order()
  
         return "\n".join(('-' * level + str(snake_case__	)) for value, level in preorder_heap	)
  
  
  # Unit Tests
  if __name__ == "__main__":
     import doctest
  
     doctest.testmod()
  
style_context: 
  
  
  
  
  
  
  import argparse
  
  import torch
  
  from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCamelCase										=  logging.get_logger(__name__)
  
  lowerCamelCase										=  ['''UtteranceLevel''']
  @torch.no_grad()
  def        UpperCAmelCase      (   _lowercase,      _lowercase,      _lowercase,      _lowercase      ):
  
  
      '''simple docstring'''
  
  
  
      _lowercase     =	torch.load(_lowercase,      map_location='cpu'      )
      if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
          raise NotImplementedError(f'''The supported s3prl models are {SUPPORTED_MODELS}'''      )
  
      _lowercase     =	checkpoint['Downstream']
  
      _lowercase     =	HubertConfig.from_pretrained(_lowercase      )
      _lowercase     =	HubertForSequenceClassification.from_pretrained(_lowercase,      config=_lowercase      )
      _lowercase     =	WavaVecaFeatureExtractor.from_pretrained(
          _lowercase,      return_attention_mask=_lowercase,      do_normalize=_lowercase      )
  
      if hf_congfig.use_weighted_layer_sum:
          _lowercase     =	checkpoint['Featurizer']['weights']
  
      _lowercase     =	downstream_dict['projector.weight']
      _lowercase     =	downstream_dict['projector.bias']
      _lowercase     =	downstream_dict['model.post_net.linear.weight']
      _lowercase     =	downstream_dict['model.post_net.linear.bias']
  
      hf_feature_extractor.save_pretrained(_lowercase      )
      hf_model.save_pretrained(_lowercase      )
  
  
  if __name__ == "__main__":
     lowerCamelCase										=  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.''')
     lowerCamelCase										=  parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
  