



'''simple docstring'''



from __future__ import annotations







def _lowercase   (  __A       ):



 '''simple docstring'''



 assert (
     isinstance(__A							,__A       ) and mask > 0
 ), f"mask needs to be positive integer, your input {mask}"
 __UpperCamelCase				      =  []
 __UpperCamelCase				      =  mask

 while submask:
  all_submasks.append(__A       )
  __UpperCamelCase				      =  (submask - 1) & mask

 return all_submasks


if __name__ == "__main__":
 import doctest

 doctest.testmod()










'''simple docstring'''



import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a__						:			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 _lowercase   (  __A							,__A							,__A							,__A							,__A       ):



 '''simple docstring'''



 for attribute in key.split("""."""       ):
  __UpperCamelCase				      =  getattr(__A							,__A       )

 if weight_type is not None:
  __UpperCamelCase				      =  getattr(__A							,__A       ).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 _lowercase   (  __A							,__A       ):



 '''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(
       __A							,__A							,__A							,__A							,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(__A       )[0].split("""."""       )[-2]
      __UpperCamelCase				      =  mapped_key.replace("""*"""							,__A       )
     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(__A							,__A							,__A							,__A							,__A       )
    continue
  if not is_used:
   unused_weights.append(__A       )

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







def _lowercase   (  __A							,__A							,__A							,__A							,__A       ):



 '''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(__A       )







def _lowercase   (  __A       ):



 '''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				      =  0.02
 __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(__A       )
 __UpperCamelCase				      =  fs_config.encoder_layers

 return config







@torch.no_grad()
def _lowercase   (  __A							,__A=None       ):



 '''simple docstring'''



 __UpperCamelCase				      =  distilhubert().model.model

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

 __UpperCamelCase				      =  WavaVecaFeatureExtractor(
     feature_size=1							,sampling_rate=16_000							,padding_value=0							,do_normalize=__A							,return_attention_mask=__A							,)
 __UpperCamelCase				      =  HubertModel(__A       )

 recursively_load_weights(__A							,__A       )

 feature_extractor.save_pretrained(__A       )
 hf_model.save_pretrained(__A       )


if __name__ == "__main__":
 a__						:			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')
 a__						:			Optional[Any]										=			parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)





