code: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  __lowercase:		List[str]							=     logging.get_logger(__name__)
  
  __lowercase:		Optional[int]							=     {
      "post_extract_proj": "feature_projection.projection",
      "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
      "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
      "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
      "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
      "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
      "self_attn_layer_norm": "encoder.layers.*.layer_norm",
      "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
      "fc2": "encoder.layers.*.feed_forward.output_dense",
      "final_layer_norm": "encoder.layers.*.final_layer_norm",
      "encoder.layer_norm": "encoder.layer_norm",
      "mask_emb": "masked_spec_embed",
  }
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				List[Any]    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Optional[Any]    ,		_UpperCamelCase      :				Tuple   )		->					List[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     for attribute in key.split("."   ):
        UpperCamelCase__ 						=						getattr(_UpperCamelCase    ,		_UpperCamelCase   )
  
     if weight_type is not None:
        UpperCamelCase__ 						=						getattr(_UpperCamelCase    ,		_UpperCamelCase   ).shape
     else:
        UpperCamelCase__ 						=						hf_pointer.shape
  
     assert hf_shape == value.shape, (
         F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
         F' {value.shape} for {full_name}'
     )
  
     if weight_type == "weight":
        UpperCamelCase__ 						=						value
     elif weight_type == "weight_g":
        UpperCamelCase__ 						=						value
     elif weight_type == "weight_v":
        UpperCamelCase__ 						=						value
     elif weight_type == "bias":
        UpperCamelCase__ 						=						value
     else:
        UpperCamelCase__ 						=						value
  
     logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				Tuple   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						[]
     UpperCamelCase__ 						=						fairseq_model.state_dict()
  
     UpperCamelCase__ 						=						hf_model.feature_extractor
  
     for name, value in fairseq_dict.items():
        UpperCamelCase__ 						=						False
        if "conv_layers" in name:
           load_conv_layer(
               _UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		hf_model.config.feat_extract_norm == "group"    ,		)
           UpperCamelCase__ 						=						True
        else:
           for key, mapped_key in MAPPING.items():
              UpperCamelCase__ 						=						mapped_key
  
              if key in name:
                 UpperCamelCase__ 						=						True
                 if "*" in mapped_key:
                    UpperCamelCase__ 						=						name.split(_UpperCamelCase   )[0].split("."   )[-2]
                    UpperCamelCase__ 						=						mapped_key.replace("*"    ,		_UpperCamelCase   )
                 if "weight_g" in name:
                    UpperCamelCase__ 						=						"weight_g"
                 elif "weight_v" in name:
                    UpperCamelCase__ 						=						"weight_v"
                 elif "weight" in name:
                    UpperCamelCase__ 						=						"weight"
                 elif "bias" in name:
                    UpperCamelCase__ 						=						"bias"
                 else:
                    UpperCamelCase__ 						=						None
                 set_recursively(_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase    ,		_UpperCamelCase   )
              continue
        if not is_used:
           unused_weights.append(_UpperCamelCase   )
  
     logger.warning(F'Unused weights: {unused_weights}'   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Dict    ,		_UpperCamelCase      :				Tuple    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				Optional[Any]   )		->					Any:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						full_name.split("conv_layers."   )[-1]
     UpperCamelCase__ 						=						name.split("."   )
     UpperCamelCase__ 						=						int(items[0]   )
     UpperCamelCase__ 						=						int(items[1]   )
  
     if type_id == 0:
        if "bias" in name:
           assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
               F'{full_name} has size {value.shape}, but'
               F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
           )
           UpperCamelCase__ 						=						value
           logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
        elif "weight" in name:
           assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
               F'{full_name} has size {value.shape}, but'
               F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
           )
           UpperCamelCase__ 						=						value
           logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'   )
     elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
           assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
               F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
               " found."
           )
           UpperCamelCase__ 						=						value
           logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
        elif "weight" in name:
           assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
               F'{full_name} has size {value.shape}, but'
               F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
           )
           UpperCamelCase__ 						=						value
           logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'   )
     else:
        unused_weights.append(_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]   )		->					Any:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						HubertConfig()
     UpperCamelCase__ 						=						model.config
  
     UpperCamelCase__ 						=						fs_config.activation_dropout
     UpperCamelCase__ 						=						False
     UpperCamelCase__ 						=						fs_config.attention_dropout
     UpperCamelCase__ 						=						False
     UpperCamelCase__ 						=						eval(fs_config.extractor_conv_feature_layers   )
     UpperCamelCase__ 						=						[x[0] for x in conv_layers]
     UpperCamelCase__ 						=						[x[1] for x in conv_layers]
     UpperCamelCase__ 						=						[x[2] for x in conv_layers]
     UpperCamelCase__ 						=						"gelu"
     UpperCamelCase__ 						=						"layer" if fs_config.extractor_mode == "layer_norm" else "group"
     UpperCamelCase__ 						=						False
     UpperCamelCase__ 						=						0.0
     UpperCamelCase__ 						=						0.0
     UpperCamelCase__ 						=						fs_config.activation_fn
     UpperCamelCase__ 						=						fs_config.dropout
     UpperCamelCase__ 						=						fs_config.encoder_embed_dim
     UpperCamelCase__ 						=						0.0_2
     UpperCamelCase__ 						=						fs_config.encoder_ffn_embed_dim
     UpperCamelCase__ 						=						1e-5
     UpperCamelCase__ 						=						0.0
     UpperCamelCase__ 						=						fs_config.encoder_attention_heads
     UpperCamelCase__ 						=						fs_config.conv_pos_groups
     UpperCamelCase__ 						=						fs_config.conv_pos
     UpperCamelCase__ 						=						len(_UpperCamelCase   )
     UpperCamelCase__ 						=						fs_config.encoder_layers
  
     return config
  
  
  
  @torch.no_grad()
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				str    ,		_UpperCamelCase      :				Dict=None   )		->					Optional[int]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						distilhubert().model.model
  
     if config_path is not None:
        UpperCamelCase__ 						=						HubertConfig.from_pretrained(_UpperCamelCase   )
     else:
        UpperCamelCase__ 						=						convert_config(_UpperCamelCase   )
     UpperCamelCase__ 						=						model.eval()
  
     UpperCamelCase__ 						=						WavaVecaFeatureExtractor(
         feature_size=1    ,		sampling_rate=1_60_00    ,		padding_value=0    ,		do_normalize=_UpperCamelCase    ,		return_attention_mask=_UpperCamelCase    ,		)
     UpperCamelCase__ 						=						HubertModel(_UpperCamelCase   )
  
     recursively_load_weights(_UpperCamelCase    ,		_UpperCamelCase   )
  
     feature_extractor.save_pretrained(_UpperCamelCase   )
     hf_model.save_pretrained(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
         __lowercase:		Optional[int]							=     argparse.ArgumentParser()
         parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
         parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
         __lowercase:		Dict							=     parser.parse_args()
         convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
style_context: 
  
  
  '''simple docstring'''
  
  from __future__ import annotations
  
  import random
  from collections.abc import Iterable
  from typing import Any, Generic, TypeVar
  
  __lowercase:		Optional[Any]							=     TypeVar("T", bound=bool)
  
  
  
  
  
  
  
  class  UpperCAmelCase (       Generic[T]):
  
   def __init__( self :	int,					a_ :	T					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						value
      UpperCamelCase__ 						=						None
      UpperCamelCase__ 						=						None
  
   @property
   def 		lowercase_						( self :	Dict					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self._value
  
   @staticmethod
   def 		lowercase_						( a_ :	RandomizedHeapNode[T] | None,					a_ :	RandomizedHeapNode[T] | None					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not roota:
         return roota
  
      if not roota:
         return roota
  
      if roota.value > roota.value:
         UpperCamelCase__						,		UpperCamelCase__ 						=						roota, roota
  
      if random.choice([True, False]					):
         UpperCamelCase__						,		UpperCamelCase__ 						=						roota.right, roota.left
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(roota.left,					a_					)
  
      return roota
  
  
  
  
  
  
  
  class  UpperCAmelCase (       Generic[T]):
  
   def __init__( self :	List[Any],					a_ :	Iterable[T] | None = ()					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						None
  
      if data:
         for item in data:
            self.insert(a_					)
  
   def 		lowercase_						( self :	Any,					a_ :	T					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root,					RandomizedHeapNode(a_					)					)
  
   def 		lowercase_						( self :	Any					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.top()
  
      if self._root is None:
         return None
  
      UpperCamelCase__ 						=						RandomizedHeapNode.merge(self._root.left,					self._root.right					)
  
      return result
  
   def 		lowercase_						( self :	Union[str, Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if not self._root:
         raise IndexError("Can't get top element for the empty heap."					)
      return self._root.value
  
   def 		lowercase_						( self :	Tuple					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						None
  
   def 		lowercase_						( self :	int					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						[]
      while self:
         result.append(self.pop()					)
  
      return result
  
   def __bool__( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return self._root is not None
  
  
  if __name__ == "__main__":
         import doctest
  
         doctest.testmod()