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 sklearn.metrics import accuracy_score
  
  import datasets
  
  
  __lowerCamelCase  				=					"""
  Accuracy is the proportion of correct predictions among the total number of cases processed. It can be computed with:
  Accuracy = (TP + TN) / (TP + TN + FP + FN)
   Where:
  TP: True positive
  TN: True negative
  FP: False positive
  FN: False negative
  """
  
  
  __lowerCamelCase  				=					"""
  Args:
      predictions (`list` of `int`): Predicted labels.
      references (`list` of `int`): Ground truth labels.
      normalize (`boolean`): If set to False, returns the number of correctly classified samples. Otherwise, returns the fraction of correctly classified samples. Defaults to True.
      sample_weight (`list` of `float`): Sample weights Defaults to None.
  
  Returns:
      accuracy (`float` or `int`): Accuracy score. Minimum possible value is 0. Maximum possible value is 1.0, or the number of examples input, if `normalize` is set to `True`.. A higher score means higher accuracy.
  
  Examples:
  
      Example 1-A simple example
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0])
          >>> print(results)
          {'accuracy': 0.5}
  
      Example 2-The same as Example 1, except with `normalize` set to `False`.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], normalize=False)
          >>> print(results)
          {'accuracy': 3.0}
  
      Example 3-The same as Example 1, except with `sample_weight` set.
          >>> accuracy_metric = datasets.load_metric(\"accuracy\")
          >>> results = accuracy_metric.compute(references=[0, 1, 2, 0, 1, 2], predictions=[0, 1, 1, 2, 1, 0], sample_weight=[0.5, 2, 0.7, 0.5, 9, 0.4])
          >>> print(results)
          {'accuracy': 0.8778625954198473}
  """
  
  
  __lowerCamelCase  				=					"""
  @article{scikit-learn,
    title={Scikit-learn: Machine Learning in {P}ython},
    author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
           and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
           and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
           Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
    journal={Journal of Machine Learning Research},
    volume={12},
    pages={2825--2830},
    year={2011}
  }
  """
  
  
  
  @datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION					,_KWARGS_DESCRIPTION				)
  class 		_snake_case      (			datasets.Metric				):
  
  
  
  
  
   """simple docstring"""
  
  
  
   def lowercase_		(						self					)	->							Optional[Any]:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return datasets.MetricInfo(
         description=_DESCRIPTION	,    citation=_CITATION	,    inputs_description=_KWARGS_DESCRIPTION	,    features=datasets.Features(
             {
                 '''predictions''': datasets.Sequence(datasets.Value('''int32'''					)					),
                 '''references''': datasets.Sequence(datasets.Value('''int32'''					)					),
             }
             if self.config_name == '''multilabel'''
             else {
                 '''predictions''': datasets.Value('''int32'''					),
                 '''references''': datasets.Value('''int32'''					),
             }					)	,    reference_urls=['''https://scikit-learn.org/stable/modules/generated/sklearn.metrics.accuracy_score.html''']	,    )
  
  
  
   def lowercase_		(						self	,    a	,    a	,    a=True	,    a=None					)	->							Dict:
  
  
  
  
     """simple docstring"""
  
  
  
  
  
  
  
     return {
         "accuracy": float(
             accuracy_score(a	,    a	,    normalize=a	,    sample_weight=a					)					)
     }