


import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__lowerCAmelCase									=				logging.get_logger(__name__)

__lowerCAmelCase									=				{
    '''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   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							Any:
       for attribute in key.split('.'	):
              lowercase__:  Dict         =  getattr(snake_case     ,				snake_case	)

       if weight_type is not None:
              lowercase__:  Optional[int]         =  getattr(snake_case     ,				snake_case	).shape
       else:
              lowercase__:  List[Any]         =  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":
              lowercase__:  int         =  value
       elif weight_type == "weight_g":
              lowercase__:  int         =  value
       elif weight_type == "weight_v":
              lowercase__:  List[Any]         =  value
       elif weight_type == "bias":
              lowercase__:  Any         =  value
       else:
              lowercase__:  Union[str, Any]         =  value

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




def   snake_case_	(    snake_case     ,				snake_case	)    ->							Dict:
       lowercase__:  int         =  []
       lowercase__:  Union[str, Any]         =  fairseq_model.state_dict()

       lowercase__:  Optional[Any]         =  hf_model.feature_extractor

       for name, value in fairseq_dict.items():
              lowercase__:  Optional[int]         =  False
              if "conv_layers" in name:
                     load_conv_layer(
                         snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				hf_model.config.feat_extract_norm == 'group'     ,				)
                     lowercase__:  Optional[Any]         =  True
              else:
                     for key, mapped_key in MAPPING.items():
                            lowercase__:  List[Any]         =  mapped_key

                            if key in name:
                                   lowercase__:  Any         =  True
                                   if "*" in mapped_key:
                                          lowercase__:  int         =  name.split(snake_case	)[0].split('.'	)[-2]
                                          lowercase__:  str         =  mapped_key.replace('*'     ,				snake_case	)
                                   if "weight_g" in name:
                                          lowercase__:  List[str]         =  'weight_g'
                                   elif "weight_v" in name:
                                          lowercase__:  List[str]         =  'weight_v'
                                   elif "weight" in name:
                                          lowercase__:  int         =  'weight'
                                   elif "bias" in name:
                                          lowercase__:  List[str]         =  'bias'
                                   else:
                                          lowercase__:  Dict         =  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   snake_case_	(    snake_case     ,				snake_case     ,				snake_case     ,				snake_case     ,				snake_case	)    ->							Tuple:
       lowercase__:  Any         =  full_name.split('conv_layers.'	)[-1]
       lowercase__:  Optional[Any]         =  name.split('.'	)
       lowercase__:  int         =  int(items[0]	)
       lowercase__:  Tuple         =  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.'
                     )
                     lowercase__:  Tuple         =  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.'
                     )
                     lowercase__:  Optional[Any]         =  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."
                     )
                     lowercase__:  Union[str, Any]         =  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.'
                     )
                     lowercase__:  List[Any]         =  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   snake_case_	(    snake_case	)    ->							List[Any]:
       lowercase__:  List[Any]         =  HubertConfig()
       lowercase__:  str         =  model.config

       lowercase__:  Any         =  fs_config.activation_dropout
       lowercase__:  Tuple         =  False
       lowercase__:  List[str]         =  fs_config.attention_dropout
       lowercase__:  List[Any]         =  False
       lowercase__:  Optional[Any]         =  eval(fs_config.extractor_conv_feature_layers	)
       lowercase__:  Dict         =  [x[0] for x in conv_layers]
       lowercase__:  List[Any]         =  [x[1] for x in conv_layers]
       lowercase__:  Optional[int]         =  [x[2] for x in conv_layers]
       lowercase__:  str         =  'gelu'
       lowercase__:  int         =  'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
       lowercase__:  List[str]         =  False
       lowercase__:  Dict         =  0.0
       lowercase__:  Tuple         =  0.0
       lowercase__:  Tuple         =  fs_config.activation_fn
       lowercase__:  str         =  fs_config.dropout
       lowercase__:  Optional[int]         =  fs_config.encoder_embed_dim
       lowercase__:  int         =  0.0_2
       lowercase__:  Dict         =  fs_config.encoder_ffn_embed_dim
       lowercase__:  str         =  1e-5
       lowercase__:  List[str]         =  0.0
       lowercase__:  List[Any]         =  fs_config.encoder_attention_heads
       lowercase__:  Any         =  fs_config.conv_pos_groups
       lowercase__:  Any         =  fs_config.conv_pos
       lowercase__:  Tuple         =  len(snake_case	)
       lowercase__:  Dict         =  fs_config.encoder_layers

       return config




@torch.no_grad()
def   snake_case_	(    snake_case     ,				snake_case=None	)    ->							Optional[Any]:
       lowercase__:  List[str]         =  distilhubert().model.model

       if config_path is not None:
              lowercase__:  Tuple         =  HubertConfig.from_pretrained(snake_case	)
       else:
              lowercase__:  Optional[int]         =  convert_config(snake_case	)
       lowercase__:  Union[str, Any]         =  model.eval()

       lowercase__:  Any         =  WavaVecaFeatureExtractor(
           feature_size=1     ,				sampling_rate=1_60_00     ,				padding_value=0     ,				do_normalize=snake_case     ,				return_attention_mask=snake_case     ,				)
       lowercase__:  int         =  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__":
 __lowerCAmelCase									=				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									=				parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






from typing import Callable




def   snake_case_	(    snake_case	)    ->							Tuple:

       def wrapper(snake_case	):
              lowercase__:  Tuple         =  function_with_docstring.__doc__
              return target_function

       return wrapper


