code: 
  
  
  
  import argparse
  import json
  import os
  
  import fairseq
  import torch
  from fairseq.data import Dictionary
  
  # Register SEW's fairseq modules
  from sew_asapp import tasks  # noqa: F401
  
  from transformers import (
      SEWDConfig,
      SEWDForCTC,
      SEWDModel,
      WavaVecaCTCTokenizer,
      WavaVecaFeatureExtractor,
      WavaVecaProcessor,
      logging,
  )
  
  
  logging.set_verbosity_info()
  __lowerCamelCase  				=					logging.get_logger(__name__)
  
  __lowerCamelCase  				=					{
      """post_extract_proj""": """feature_projection""",
      """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
      """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
      """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
      """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
      """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
      """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
      """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
      """output.dense""": """encoder.encoder.layer.*.output.dense""",
      """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
      """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
      """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
      """encoder.upsample.0""": """encoder.upsample.projection""",
      """encoder.layer_norm""": """encoder.layer_norm""",
      """w2v_model.layer_norm""": """layer_norm""",
      """w2v_encoder.proj""": """lm_head""",
      """mask_emb""": """masked_spec_embed""",
  }
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> Tuple:
    for attribute in key.split('''.'''							):
      _A           =    getattr(__snake_case						,				__snake_case							)
  
    if weight_type is not None:
      _A           =    getattr(__snake_case						,				__snake_case							).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__ (							__snake_case						,				__snake_case						,				__snake_case							)   -> Dict:
    _A           =    []
    _A           =    fairseq_model.state_dict()
  
    _A           =    hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
    for name, value in fairseq_dict.items():
      _A           =    False
      if "conv_layers" in name:
        load_conv_layer(
            __snake_case						,				__snake_case						,				__snake_case						,				__snake_case						,				hf_model.config.feat_extract_norm == '''group'''						,				)
        _A           =    True
      else:
        for key, mapped_key in MAPPING.items():
          _A           =    '''sew_d.''' + mapped_key if (is_finetuned and mapped_key != '''lm_head''') else mapped_key
  
          if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
            _A           =    True
            if "*" in mapped_key:
              _A           =    name.split(__snake_case							)[0].split('''.'''							)[-2]
              if not layer_index.isnumeric():
                continue
              _A           =    mapped_key.replace('''*'''						,				__snake_case							)
            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(__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						,				__snake_case						,				__snake_case						,				__snake_case						,				__snake_case							)   -> int:
    _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(__snake_case							)
  
  
  
  
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case							)   -> List[Any]:
    _A           =    SEWDConfig()
    if is_finetuned:
      _A           =    model.wav_encoder.wav_model.cfg
    else:
      _A           =    model.cfg
  
    _A           =    fs_config.conv_bias
    _A           =    eval(fs_config.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           =    0.0
    _A           =    fs_config.activation_fn.name
    _A           =    fs_config.encoder_embed_dim
    _A           =    0.02
    _A           =    fs_config.encoder_ffn_embed_dim
    _A           =    1e-5
    _A           =    fs_config.encoder_layerdrop
    _A           =    fs_config.encoder_attention_heads
    _A           =    fs_config.conv_pos_groups
    _A           =    fs_config.conv_pos
    _A           =    len(__snake_case							)
    _A           =    fs_config.encoder_layers
    _A           =    fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    _A           =    fs_config.max_position_embeddings
    _A           =    fs_config.position_buckets
    _A           =    fs_config.share_att_key
    _A           =    fs_config.relative_attention
    _A           =    fs_config.position_biased_input
    _A           =    tuple(fs_config.pos_att_type.split('''|'''							)							)
    _A           =    fs_config.norm_rel_ebd
  
    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
      _A           =    model.cfg
      _A           =    fs_config.final_dropout
      _A           =    fs_config.layerdrop
    _A           =    fs_config.activation_dropout
    _A           =    fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    _A           =    fs_config.attention_dropout
    _A           =    fs_config.dropout_input
    _A           =    fs_config.dropout
    _A           =    fs_config.mask_channel_length
    _A           =    fs_config.mask_channel_prob
    _A           =    fs_config.mask_length
    _A           =    fs_config.mask_prob
  
    _A           =    '''Wav2Vec2FeatureExtractor'''
    _A           =    '''Wav2Vec2CTCTokenizer'''
  
    return config
  
  
  
  
  @torch.no_grad()
  def     UpperCAmelCase__ (							__snake_case						,				__snake_case						,				__snake_case=None						,				__snake_case=None						,				__snake_case=True							)   -> List[Any]:
  
    if is_finetuned:
      _A    ,   _A    ,   _A           =    fairseq.checkpoint_utils.load_model_ensemble_and_task(
          [checkpoint_path]						,				arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''							)[:-1]							)}							)
    else:
      _A    ,   _A    ,   _A           =    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]							)
  
    if config_path is not None:
      _A           =    SEWDConfig.from_pretrained(__snake_case							)
    else:
      _A           =    convert_config(model[0]						,				__snake_case							)
    _A           =    model[0].eval()
  
    _A           =    True if config.feat_extract_norm == '''layer''' else False
    _A           =    WavaVecaFeatureExtractor(
        feature_size=1						,				sampling_rate=16_000						,				padding_value=0						,				do_normalize=__snake_case						,				return_attention_mask=__snake_case						,				)
  
    if is_finetuned:
      if dict_path:
        _A           =    Dictionary.load(__snake_case							)
  
        # important change bos & pad token id since CTC symbol is <pad> and
        # not <s> as in fairseq
        _A           =    target_dict.pad_index
        _A           =    target_dict.bos_index
        _A           =    target_dict.pad_index
        _A           =    target_dict.bos_index
        _A           =    target_dict.eos_index
        _A           =    len(target_dict.symbols							)
        _A           =    os.path.join(__snake_case						,				'''vocab.json'''							)
        if not os.path.isdir(__snake_case							):
          logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(__snake_case							)							)
          return
        os.makedirs(__snake_case						,				exist_ok=__snake_case							)
        with open(__snake_case						,				'''w'''						,				encoding='''utf-8'''							) as vocab_handle:
          json.dump(target_dict.indices						,				__snake_case							)
        _A           =    WavaVecaCTCTokenizer(
            __snake_case						,				unk_token=target_dict.unk_word						,				pad_token=target_dict.pad_word						,				bos_token=target_dict.bos_word						,				eos_token=target_dict.eos_word						,				word_delimiter_token='''|'''						,				do_lower_case=__snake_case						,				)
        _A           =    WavaVecaProcessor(feature_extractor=__snake_case						,				tokenizer=__snake_case							)
        processor.save_pretrained(__snake_case							)
  
      _A           =    SEWDForCTC(__snake_case							)
    else:
      _A           =    SEWDModel(__snake_case							)
      feature_extractor.save_pretrained(__snake_case							)
  
    recursively_load_weights(__snake_case						,				__snake_case						,				__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("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
      parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
      parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
      parser.add_argument(
          """--is_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
      )
      __lowerCamelCase  				=					parser.parse_args()
      convert_sew_checkpoint(
          args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
      )
style_context: 
  
  
  
  from ..utils import requires_backends
  
  
  __lowerCamelCase  				=					None
  
  
  
  class 		_snake_case      :
  
  
  
  
  
   """simple docstring"""
  
  
  
   def __init__(						self	,    *a	,    **a					)	->							Tuple:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     requires_backends(self	,    ['''detectron2''']					)
  
  
  
   @classmethod
   def lowercase_		(						cls	,    *a	,    **a					)	->							Optional[int]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     requires_backends(cls	,    ['''detectron2''']					)