code: 
  
  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     UpperCAmelCase__       (    _A   :				Any			,   _A   :				Tuple			,   _A   :				Optional[int]			,   _A   :				List[Any]			,   _A   :				Union[str, Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    for attribute in key.split('''.'''							):
      a__						=getattr(_A			,   _A							)
  
    if weight_type is not None:
      a__						=getattr(_A			,   _A							).shape
    else:
      a__						=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":
      a__						=value
    elif weight_type == "weight_g":
      a__						=value
    elif weight_type == "weight_v":
      a__						=value
    elif weight_type == "bias":
      a__						=value
    else:
      a__						=value
  
    logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				Dict			,   _A   :				Optional[int]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=[]
    a__						=fairseq_model.state_dict()
  
    a__						=hf_model.feature_extractor
  
    for name, value in fairseq_dict.items():
      a__						=False
      if "conv_layers" in name:
        load_conv_layer(
            _A			,   _A			,   _A			,   _A			,   hf_model.config.feat_extract_norm == '''group'''			,   )
        a__						=True
      else:
        for key, mapped_key in MAPPING.items():
          a__						=mapped_key
  
          if key in name:
            a__						=True
            if "*" in mapped_key:
              a__						=name.split(_A							)[0].split('''.'''							)[-2]
              a__						=mapped_key.replace('''*'''			,   _A							)
            if "weight_g" in name:
              a__						='''weight_g'''
            elif "weight_v" in name:
              a__						='''weight_v'''
            elif "weight" in name:
              a__						='''weight'''
            elif "bias" in name:
              a__						='''bias'''
            else:
              a__						=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     UpperCAmelCase__       (    _A   :				Optional[int]			,   _A   :				str			,   _A   :				Optional[Any]			,   _A   :				Dict			,   _A   :				List[Any]							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=full_name.split('''conv_layers.'''							)[-1]
    a__						=name.split('''.'''							)
    a__						=int(items[0]							)
    a__						=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."""
        )
        a__						=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."""
        )
        a__						=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."
        )
        a__						=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."""
        )
        a__						=value
        logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""							)
    else:
      unused_weights.append(_A							)
  
  
  
  
  def     UpperCAmelCase__       (    _A   :				str							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=HubertConfig()
    a__						=model.config
  
    a__						=fs_config.activation_dropout
    a__						=False
    a__						=fs_config.attention_dropout
    a__						=False
    a__						=eval(fs_config.extractor_conv_feature_layers							)
    a__						=[x[0] for x in conv_layers]
    a__						=[x[1] for x in conv_layers]
    a__						=[x[2] for x in conv_layers]
    a__						='''gelu'''
    a__						='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    a__						=False
    a__						=0.0
    a__						=0.0
    a__						=fs_config.activation_fn
    a__						=fs_config.dropout
    a__						=fs_config.encoder_embed_dim
    a__						=0.02
    a__						=fs_config.encoder_ffn_embed_dim
    a__						=1E-5
    a__						=0.0
    a__						=fs_config.encoder_attention_heads
    a__						=fs_config.conv_pos_groups
    a__						=fs_config.conv_pos
    a__						=len(_A							)
    a__						=fs_config.encoder_layers
  
    return config
  
  
  
  
  @torch.no_grad()
  def     UpperCAmelCase__       (    _A   :				Tuple			,   _A   :				List[Any]=None							):
  
  
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
  
    a__						=distilhubert().model.model
  
    if config_path is not None:
      a__						=HubertConfig.from_pretrained(_A							)
    else:
      a__						=convert_config(_A							)
    a__						=model.eval()
  
    a__						=WavaVecaFeatureExtractor(
        feature_size=1			,   sampling_rate=1_60_00			,   padding_value=0			,   do_normalize=_A			,   return_attention_mask=_A			,   )
    a__						=HubertModel(_A							)
  
    recursively_load_weights(_A			,   _A							)
  
    feature_extractor.save_pretrained(_A							)
    hf_model.save_pretrained(_A							)
  
  
  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)
  
style_context: 
  
  from typing import TYPE_CHECKING
  
  from ...utils import (
      OptionalDependencyNotAvailable,
      _LazyModule,
      is_tf_available,
      is_tokenizers_available,
      is_torch_available,
  )
  
  
  lowerCamelCase     =      {
      '''configuration_deberta_v2''': ['''DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaV2Config''', '''DebertaV2OnnxConfig'''],
      '''tokenization_deberta_v2''': ['''DebertaV2Tokenizer'''],
  }
  
  try:
         if not is_tokenizers_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      ['''DebertaV2TokenizerFast''']
  
  try:
         if not is_tf_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''TFDebertaV2ForMaskedLM''',
             '''TFDebertaV2ForQuestionAnswering''',
             '''TFDebertaV2ForSequenceClassification''',
             '''TFDebertaV2ForTokenClassification''',
             '''TFDebertaV2Model''',
             '''TFDebertaV2PreTrainedModel''',
         ]
  
  try:
         if not is_torch_available():
                raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
         pass
  else:
         lowerCamelCase     =      [
             '''DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
             '''DebertaV2ForMaskedLM''',
             '''DebertaV2ForMultipleChoice''',
             '''DebertaV2ForQuestionAnswering''',
             '''DebertaV2ForSequenceClassification''',
             '''DebertaV2ForTokenClassification''',
             '''DebertaV2Model''',
             '''DebertaV2PreTrainedModel''',
         ]
  
  
  if TYPE_CHECKING:
         from .configuration_deberta_va import (
             DEBERTA_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
             DebertaVaConfig,
             DebertaVaOnnxConfig,
         )
         from .tokenization_deberta_va import DebertaVaTokenizer
  
         try:
                if not is_tokenizers_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .tokenization_deberta_va_fast import DebertaVaTokenizerFast
  
         try:
                if not is_tf_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_tf_deberta_va import (
                    TF_DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    TFDebertaVaForMaskedLM,
                    TFDebertaVaForQuestionAnswering,
                    TFDebertaVaForSequenceClassification,
                    TFDebertaVaForTokenClassification,
                    TFDebertaVaModel,
                    TFDebertaVaPreTrainedModel,
                )
  
         try:
                if not is_torch_available():
                       raise OptionalDependencyNotAvailable()
         except OptionalDependencyNotAvailable:
                pass
         else:
                from .modeling_deberta_va import (
                    DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
                    DebertaVaForMaskedLM,
                    DebertaVaForMultipleChoice,
                    DebertaVaForQuestionAnswering,
                    DebertaVaForSequenceClassification,
                    DebertaVaForTokenClassification,
                    DebertaVaModel,
                    DebertaVaPreTrainedModel,
                )
  
  else:
         import sys
  
         lowerCamelCase     =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
  