code: 
  
  
  '''simple docstring'''
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  lowerCAmelCase   :List[str]								=    logging.get_logger(__name__)
  
  lowerCAmelCase   :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 lowerCamelCase    (    lowerCAmelCase       :			str	,	lowerCAmelCase       :			Union[str, Any]	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			int	,	lowerCAmelCase       :			List[Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	for attribute in key.split('.'	):
  		__magic_name__ :					int  			=  getattr(lowerCAmelCase	,	lowerCAmelCase	)
  
  	if weight_type is not None:
  		__magic_name__ :					Optional[Any]  			=  getattr(lowerCAmelCase	,	lowerCAmelCase	).shape
  	else:
  		__magic_name__ :					str  			=  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":
  		__magic_name__ :					int  			=  value
  	elif weight_type == "weight_g":
  		__magic_name__ :					Union[str, Any]  			=  value
  	elif weight_type == "weight_v":
  		__magic_name__ :					str  			=  value
  	elif weight_type == "bias":
  		__magic_name__ :					Optional[int]  			=  value
  	else:
  		__magic_name__ :					int  			=  value
  
  	logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			Optional[int]	,	lowerCAmelCase       :			Union[str, Any]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Dict  			=  []
  	__magic_name__ :					Union[str, Any]  			=  fairseq_model.state_dict()
  
  	__magic_name__ :					Optional[int]  			=  hf_model.feature_extractor
  
  	for name, value in fairseq_dict.items():
  		__magic_name__ :					Dict  			=  False
  		if "conv_layers" in name:
  			load_conv_layer(
  			    lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	hf_model.config.feat_extract_norm == 'group'	,	)
  			__magic_name__ :					str  			=  True
  		else:
  			for key, mapped_key in MAPPING.items():
  				__magic_name__ :					List[str]  			=  mapped_key
  
  				if key in name:
  					__magic_name__ :					str  			=  True
  					if "*" in mapped_key:
  						__magic_name__ :					Optional[int]  			=  name.split(lowerCAmelCase	)[0].split('.'	)[-2]
  						__magic_name__ :					Tuple  			=  mapped_key.replace('*'	,	lowerCAmelCase	)
  					if "weight_g" in name:
  						__magic_name__ :					Tuple  			=  'weight_g'
  					elif "weight_v" in name:
  						__magic_name__ :					Union[str, Any]  			=  'weight_v'
  					elif "weight" in name:
  						__magic_name__ :					Any  			=  'weight'
  					elif "bias" in name:
  						__magic_name__ :					List[str]  			=  'bias'
  					else:
  						__magic_name__ :					Optional[int]  			=  None
  					set_recursively(lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	,	lowerCAmelCase	)
  				continue
  		if not is_used:
  			unused_weights.append(lowerCAmelCase	)
  
  	logger.warning(f'Unused weights: {unused_weights}'	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[Any]	,	lowerCAmelCase       :			List[str]	,	lowerCAmelCase       :			Tuple	,	lowerCAmelCase       :			Optional[Any]	,	lowerCAmelCase       :			str	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					int  			=  full_name.split('conv_layers.'	)[-1]
  	__magic_name__ :					int  			=  name.split('.'	)
  	__magic_name__ :					Tuple  			=  int(items[0]	)
  	__magic_name__ :					str  			=  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.'
  			)
  			__magic_name__ :					Union[str, Any]  			=  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.'
  			)
  			__magic_name__ :					List[str]  			=  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."
  			)
  			__magic_name__ :					Dict  			=  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.'
  			)
  			__magic_name__ :					Any  			=  value
  			logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
  	else:
  		unused_weights.append(lowerCAmelCase	)
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			List[str]	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					List[str]  			=  HubertConfig()
  	__magic_name__ :					Dict  			=  model.config
  
  	__magic_name__ :					Union[str, Any]  			=  fs_config.activation_dropout
  	__magic_name__ :					Any  			=  False
  	__magic_name__ :					str  			=  fs_config.attention_dropout
  	__magic_name__ :					List[str]  			=  False
  	__magic_name__ :					Optional[Any]  			=  eval(fs_config.extractor_conv_feature_layers	)
  	__magic_name__ :					Tuple  			=  [x[0] for x in conv_layers]
  	__magic_name__ :					Tuple  			=  [x[1] for x in conv_layers]
  	__magic_name__ :					Optional[Any]  			=  [x[2] for x in conv_layers]
  	__magic_name__ :					Any  			=  'gelu'
  	__magic_name__ :					Any  			=  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
  	__magic_name__ :					List[str]  			=  False
  	__magic_name__ :					List[str]  			=  0.0
  	__magic_name__ :					int  			=  0.0
  	__magic_name__ :					List[str]  			=  fs_config.activation_fn
  	__magic_name__ :					List[Any]  			=  fs_config.dropout
  	__magic_name__ :					str  			=  fs_config.encoder_embed_dim
  	__magic_name__ :					Optional[int]  			=  0.02
  	__magic_name__ :					Union[str, Any]  			=  fs_config.encoder_ffn_embed_dim
  	__magic_name__ :					Optional[int]  			=  1e-5
  	__magic_name__ :					Tuple  			=  0.0
  	__magic_name__ :					int  			=  fs_config.encoder_attention_heads
  	__magic_name__ :					Tuple  			=  fs_config.conv_pos_groups
  	__magic_name__ :					Optional[int]  			=  fs_config.conv_pos
  	__magic_name__ :					Optional[Any]  			=  len(lowerCAmelCase	)
  	__magic_name__ :					int  			=  fs_config.encoder_layers
  
  	return config
  
  
  
  
  
  @torch.no_grad()
  def lowerCamelCase    (    lowerCAmelCase       :			Any	,	lowerCAmelCase       :			List[str]=None	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	__magic_name__ :					Any  			=  distilhubert().model.model
  
  	if config_path is not None:
  		__magic_name__ :					Any  			=  HubertConfig.from_pretrained(lowerCAmelCase	)
  	else:
  		__magic_name__ :					Union[str, Any]  			=  convert_config(lowerCAmelCase	)
  	__magic_name__ :					Dict  			=  model.eval()
  
  	__magic_name__ :					Optional[Any]  			=  WavaVecaFeatureExtractor(
  	    feature_size=1	,	sampling_rate=1_6000	,	padding_value=0	,	do_normalize=lowerCAmelCase	,	return_attention_mask=lowerCAmelCase	,	)
  	__magic_name__ :					Tuple  			=  HubertModel(lowerCAmelCase	)
  
  	recursively_load_weights(lowerCAmelCase	,	lowerCAmelCase	)
  
  	feature_extractor.save_pretrained(lowerCAmelCase	)
  	hf_model.save_pretrained(lowerCAmelCase	)
  
  
  if __name__ == "__main__":
  							lowerCAmelCase   :Any								=    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''')
  							lowerCAmelCase   :List[str]								=    parser.parse_args()
  							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
style_context: 
  
  
  '''simple docstring'''
  
  import math
  
  
  
  
  
  def lowerCamelCase    (    lowerCAmelCase       :			int	):
  
  
  
  
  
  	"""simple docstring"""
  
  
  
  
  
  
  
  	if not isinstance(lowerCAmelCase	,	lowerCAmelCase	):
  		__magic_name__ :					str  			=  f'Input value of [number={number}] must be an integer'
  		raise TypeError(lowerCAmelCase	)
  	if number < 1:
  		__magic_name__ :					Tuple  			=  f'Input value of [number={number}] must be a positive integer'
  		raise ValueError(lowerCAmelCase	)
  	__magic_name__ :					Tuple  			=  [number]
  	while number != 1:
  		if number % 2 == 0:
  			__magic_name__ :					Optional[int]  			=  math.floor(math.sqrt(lowerCAmelCase	)	)
  		else:
  			__magic_name__ :					Union[str, Any]  			=  math.floor(
  			    math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	) * math.sqrt(lowerCAmelCase	)	)
  		sequence.append(lowerCAmelCase	)
  	return sequence
  
  
  if __name__ == "__main__":
  							import doctest
  
  							doctest.testmod()