



"""simple docstring"""





from __future__ import annotations

from collections.abc import Iterator
from typing import Any


class       __lowerCamelCase	:







   def __init__(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		data
      _lowerCAmelCase					     =		None







   def __str__(self				):




      '''simple docstring'''




      return f"""{self.data}"""





class       __lowerCamelCase	:







   def __init__(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		None
      _lowerCAmelCase					     =		None







   def __iter__(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		self.front
      while node:
         yield node.data
         _lowerCAmelCase					     =		node.next







   def __len__(self				):




      '''simple docstring'''




      return len(tuple(iter(self				)				)				)







   def __str__(self				):




      '''simple docstring'''




      return " <- ".join(str(lowerCamelCase				) for item in self				)







   def 							A__						(self				):




      '''simple docstring'''




      return len(self				) == 0







   def 							A__						(self       ,	lowerCamelCase				):




      '''simple docstring'''




      _lowerCAmelCase					     =		Node(lowerCamelCase				)
      if self.is_empty():
         _lowerCAmelCase					     =		_lowerCAmelCase					     =		node
      else:
         assert isinstance(self.rear       ,	lowerCamelCase				)
         _lowerCAmelCase					     =		node
         _lowerCAmelCase					     =		node







   def 							A__						(self				):




      '''simple docstring'''




      if self.is_empty():
         raise IndexError("""dequeue from empty queue"""				)
      assert isinstance(self.front       ,	lowerCamelCase				)
      _lowerCAmelCase					     =		self.front
      _lowerCAmelCase					     =		self.front.next
      if self.front is None:
         _lowerCAmelCase					     =		None
      return node.data







   def 							A__						(self				):




      '''simple docstring'''




      _lowerCAmelCase					     =		_lowerCAmelCase					     =		None


if __name__ == "__main__":
  from doctest import testmod

  testmod()




"""simple docstring"""





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE				:     str          =	logging.get_logger(__name__)

SCREAMING_SNAKE_CASE				:     List[str]          =	{
    '''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 	__UpperCAmelCase					(       snake_case_  :  List[str]   ,	snake_case_  :  Dict   ,	snake_case_  :  Any   ,	snake_case_  :  str   ,	snake_case_  :  Tuple   )							->	str:






   """simple docstring"""


   for attribute in key.split("""."""   ):
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   )

   if weight_type is not None:
      _lowerCAmelCase					     =		getattr(snake_case_   ,	snake_case_   ).shape
   else:
      _lowerCAmelCase					     =		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":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_g":
      _lowerCAmelCase					     =		value
   elif weight_type == "weight_v":
      _lowerCAmelCase					     =		value
   elif weight_type == "bias":
      _lowerCAmelCase					     =		value
   else:
      _lowerCAmelCase					     =		value

   logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""   )






def 	__UpperCAmelCase					(       snake_case_  :  str   ,	snake_case_  :  Any   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		[]
   _lowerCAmelCase					     =		fairseq_model.state_dict()

   _lowerCAmelCase					     =		hf_model.feature_extractor

   for name, value in fairseq_dict.items():
      _lowerCAmelCase					     =		False
      if "conv_layers" in name:
         load_conv_layer(
             snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	hf_model.config.feat_extract_norm == """group"""   ,	)
         _lowerCAmelCase					     =		True
      else:
         for key, mapped_key in MAPPING.items():
            _lowerCAmelCase					     =		mapped_key

            if key in name:
               _lowerCAmelCase					     =		True
               if "*" in mapped_key:
                  _lowerCAmelCase					     =		name.split(snake_case_   )[0].split("""."""   )[-2]
                  _lowerCAmelCase					     =		mapped_key.replace("""*"""   ,	snake_case_   )
               if "weight_g" in name:
                  _lowerCAmelCase					     =		"""weight_g"""
               elif "weight_v" in name:
                  _lowerCAmelCase					     =		"""weight_v"""
               elif "weight" in name:
                  _lowerCAmelCase					     =		"""weight"""
               elif "bias" in name:
                  _lowerCAmelCase					     =		"""bias"""
               else:
                  _lowerCAmelCase					     =		None
               set_recursively(snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   ,	snake_case_   )
            continue
      if not is_used:
         unused_weights.append(snake_case_   )

   logger.warning(F"""Unused weights: {unused_weights}"""   )






def 	__UpperCAmelCase					(       snake_case_  :  Any   ,	snake_case_  :  List[Any]   ,	snake_case_  :  Dict   ,	snake_case_  :  Union[str, Any]   ,	snake_case_  :  Optional[Any]   )							->	List[Any]:






   """simple docstring"""


   _lowerCAmelCase					     =		full_name.split("""conv_layers."""   )[-1]
   _lowerCAmelCase					     =		name.split("""."""   )
   _lowerCAmelCase					     =		int(items[0]   )
   _lowerCAmelCase					     =		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."""
         )
         _lowerCAmelCase					     =		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."""
         )
         _lowerCAmelCase					     =		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."
         )
         _lowerCAmelCase					     =		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."""
         )
         _lowerCAmelCase					     =		value
         logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""   )
   else:
      unused_weights.append(snake_case_   )






def 	__UpperCAmelCase					(       snake_case_  :  Optional[int]   )							->	Any:






   """simple docstring"""


   _lowerCAmelCase					     =		HubertConfig()
   _lowerCAmelCase					     =		model.config

   _lowerCAmelCase					     =		fs_config.activation_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		fs_config.attention_dropout
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		eval(fs_config.extractor_conv_feature_layers   )
   _lowerCAmelCase					     =		[x[0] for x in conv_layers]
   _lowerCAmelCase					     =		[x[1] for x in conv_layers]
   _lowerCAmelCase					     =		[x[2] for x in conv_layers]
   _lowerCAmelCase					     =		"""gelu"""
   _lowerCAmelCase					     =		"""layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
   _lowerCAmelCase					     =		False
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.activation_fn
   _lowerCAmelCase					     =		fs_config.dropout
   _lowerCAmelCase					     =		fs_config.encoder_embed_dim
   _lowerCAmelCase					     =		0.0_2
   _lowerCAmelCase					     =		fs_config.encoder_ffn_embed_dim
   _lowerCAmelCase					     =		1e-5
   _lowerCAmelCase					     =		0.0
   _lowerCAmelCase					     =		fs_config.encoder_attention_heads
   _lowerCAmelCase					     =		fs_config.conv_pos_groups
   _lowerCAmelCase					     =		fs_config.conv_pos
   _lowerCAmelCase					     =		len(snake_case_   )
   _lowerCAmelCase					     =		fs_config.encoder_layers

   return config






@torch.no_grad()
def 	__UpperCAmelCase					(       snake_case_  :  Optional[Any]   ,	snake_case_  :  Union[str, Any]=None   )							->	List[str]:






   """simple docstring"""


   _lowerCAmelCase					     =		distilhubert().model.model

   if config_path is not None:
      _lowerCAmelCase					     =		HubertConfig.from_pretrained(snake_case_   )
   else:
      _lowerCAmelCase					     =		convert_config(snake_case_   )
   _lowerCAmelCase					     =		model.eval()

   _lowerCAmelCase					     =		WavaVecaFeatureExtractor(
       feature_size=1   ,	sampling_rate=16000   ,	padding_value=0   ,	do_normalize=snake_case_   ,	return_attention_mask=snake_case_   ,	)
   _lowerCAmelCase					     =		HubertModel(snake_case_   )

   recursively_load_weights(snake_case_   ,	snake_case_   )

   feature_extractor.save_pretrained(snake_case_   )
   hf_model.save_pretrained(snake_case_   )


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE				:     List[str]          =	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''')
  SCREAMING_SNAKE_CASE				:     Optional[int]          =	parser.parse_args()
  convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)