code: 
  
  
  
  '''simple docstring'''
  
  
  
  import csv
  import dataclasses
  import json
  from dataclasses import dataclass
  from typing import List, Optional, Union
  
  from ...utils import is_tf_available, is_torch_available, logging
  
  
  a__						:			Dict										=			logging.get_logger(__name__)
  @dataclass
  class        UpperCAmelCase__  :
      __SCREAMING_SNAKE_CASE					      =				4_2
      __SCREAMING_SNAKE_CASE					      =				4_2
      __SCREAMING_SNAKE_CASE					      =				None
      __SCREAMING_SNAKE_CASE					      =				None
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Any:
       return json.dumps(dataclasses.asdict(self			)		,	indent=2			) + "\n"
  
  
  @dataclass(frozen=A_)
  class        UpperCAmelCase__  :
      __SCREAMING_SNAKE_CASE					      =				4_2
      __SCREAMING_SNAKE_CASE					      =				None
      __SCREAMING_SNAKE_CASE					      =				None
      __SCREAMING_SNAKE_CASE					      =				None
  
  
  
      def    __lowerCamelCase				(  self			)  ->							Optional[int]:
       return json.dumps(dataclasses.asdict(self			)			) + "\n"
  
  
  class        UpperCAmelCase__  :
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase			)  ->							Union[str, Any]:
       raise NotImplementedError()
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase			)  ->							List[Any]:
       raise NotImplementedError()
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase			)  ->							Any:
       raise NotImplementedError()
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase			)  ->							Any:
       raise NotImplementedError()
  
  
  
      def    __lowerCamelCase				(  self			)  ->							str:
       raise NotImplementedError()
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase			)  ->							str:
  
  
  
       if len(self.get_labels()			) > 1:
        __UpperCamelCase				      =  self.get_labels()[int(example.label			)]
       return example
  
  
  
  
      @classmethod
      def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase=None			)  ->							Optional[Any]:
  
  
  
       with open(lowercase		,	"""r"""		,	encoding="""utf-8-sig"""			) as f:
        return list(csv.reader(lowercase		,	delimiter="""\t"""		,	quotechar=lowercase			)			)
  
  
  class        UpperCAmelCase__  (    A_):
  
  
  
      def __init__(  self		,	lowercase=None		,	lowercase=None		,	lowercase="classification"		,	lowercase=False			)  ->							List[Any]:
       __UpperCamelCase				      =  [] if labels is None else labels
       __UpperCamelCase				      =  [] if examples is None else examples
       __UpperCamelCase				      =  mode
       __UpperCamelCase				      =  verbose
  
  
  
      def __len__(  self			)  ->							Any:
       return len(self.examples			)
  
  
  
      def __getitem__(  self		,	lowercase			)  ->							int:
  
  
  
       if isinstance(lowercase		,	lowercase			):
        return SingleSentenceClassificationProcessor(labels=self.labels		,	examples=self.examples[idx]			)
       return self.examples[idx]
  
  
  
      @classmethod
      def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase=""		,	lowercase=0		,	lowercase=1		,	lowercase=None		,	lowercase=False		,	**lowercase			)  ->							Tuple:
       __UpperCamelCase				      =  cls(**lowercase			)
       processor.add_examples_from_csv(
           lowercase		,	split_name=lowercase		,	column_label=lowercase		,	column_text=lowercase		,	column_id=lowercase		,	skip_first_row=lowercase		,	overwrite_labels=lowercase		,	overwrite_examples=lowercase		,	)
       return processor
  
  
  
      @classmethod
      def    __lowerCamelCase				(  cls		,	lowercase		,	lowercase=None		,	**lowercase			)  ->							Any:
       __UpperCamelCase				      =  cls(**lowercase			)
       processor.add_examples(lowercase		,	labels=lowercase			)
       return processor
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=""		,	lowercase=0		,	lowercase=1		,	lowercase=None		,	lowercase=False		,	lowercase=False		,	lowercase=False		,	)  ->							int:
       __UpperCamelCase				      =  self._read_tsv(lowercase			)
       if skip_first_row:
        __UpperCamelCase				      =  lines[1:]
       __UpperCamelCase				      =  []
       __UpperCamelCase				      =  []
       __UpperCamelCase				      =  []
       for i, line in enumerate(lowercase			):
        texts.append(line[column_text]			)
        labels.append(line[column_label]			)
        if column_id is not None:
         ids.append(line[column_id]			)
        else:
         __UpperCamelCase				      =  f"{split_name}-{i}" if split_name else str(lowercase			)
         ids.append(lowercase			)
  
       return self.add_examples(
           lowercase		,	lowercase		,	lowercase		,	overwrite_labels=lowercase		,	overwrite_examples=lowercase			)
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=None		,	lowercase=None		,	lowercase=False		,	lowercase=False			)  ->							List[Any]:
  
  
  
       if labels is not None and len(lowercase			) != len(lowercase			):
        raise ValueError(
            f"Text and labels have mismatched lengths {len(lowercase			)} and {len(lowercase			)}"			)
       if ids is not None and len(lowercase			) != len(lowercase			):
        raise ValueError(f"Text and ids have mismatched lengths {len(lowercase			)} and {len(lowercase			)}"			)
       if ids is None:
        __UpperCamelCase				      =  [None] * len(lowercase			)
       if labels is None:
        __UpperCamelCase				      =  [None] * len(lowercase			)
       __UpperCamelCase				      =  []
       __UpperCamelCase				      =  set()
       for text_or_text_and_label, label, guid in zip(lowercase		,	lowercase		,	lowercase			):
        if isinstance(lowercase		,	(tuple, list)			) and label is None:
         __UpperCamelCase				      =  text_or_text_and_label
        else:
         __UpperCamelCase				      =  text_or_text_and_label
        added_labels.add(lowercase			)
        examples.append(InputExample(guid=lowercase		,	text_a=lowercase		,	text_b=lowercase		,	label=lowercase			)			)
  
       # Update examples
       if overwrite_examples:
        __UpperCamelCase				      =  examples
       else:
        self.examples.extend(lowercase			)
  
       # Update labels
       if overwrite_labels:
        __UpperCamelCase				      =  list(lowercase			)
       else:
        __UpperCamelCase				      =  list(set(self.labels			).union(lowercase			)			)
  
       return self.examples
  
  
  
  
      def    __lowerCamelCase				(  self		,	lowercase		,	lowercase=None		,	lowercase=False		,	lowercase=0		,	lowercase=True		,	lowercase=None		,	)  ->							Union[str, Any]:
  
  
  
       if max_length is None:
        __UpperCamelCase				      =  tokenizer.max_len
  
       __UpperCamelCase				      =  {label: i for i, label in enumerate(self.labels			)}
  
       __UpperCamelCase				      =  []
       for ex_index, example in enumerate(self.examples			):
        if ex_index % 1_0_0_0_0 == 0:
         logger.info(f"Tokenizing example {ex_index}"			)
  
        __UpperCamelCase				      =  tokenizer.encode(
            example.text_a		,	add_special_tokens=lowercase		,	max_length=min(lowercase		,	tokenizer.max_len			)		,	)
        all_input_ids.append(lowercase			)
  
       __UpperCamelCase				      =  max(len(lowercase			) for input_ids in all_input_ids			)
  
       __UpperCamelCase				      =  []
       for ex_index, (input_ids, example) in enumerate(zip(lowercase		,	self.examples			)			):
        if ex_index % 1_0_0_0_0 == 0:
         logger.info(f"Writing example {ex_index}/{len(self.examples			)}"			)
        # The mask has 1 for real tokens and 0 for padding tokens. Only real
        # tokens are attended to.
        __UpperCamelCase				      =  [1 if mask_padding_with_zero else 0] * len(lowercase			)
  
        # Zero-pad up to the sequence length.
        __UpperCamelCase				      =  batch_length - len(lowercase			)
        if pad_on_left:
         __UpperCamelCase				      =  ([pad_token] * padding_length) + input_ids
         __UpperCamelCase				      =  ([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
        else:
         __UpperCamelCase				      =  input_ids + ([pad_token] * padding_length)
         __UpperCamelCase				      =  attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)
  
        if len(lowercase			) != batch_length:
         raise ValueError(f"Error with input length {len(lowercase			)} vs {batch_length}"			)
        if len(lowercase			) != batch_length:
         raise ValueError(f"Error with input length {len(lowercase			)} vs {batch_length}"			)
  
        if self.mode == "classification":
         __UpperCamelCase				      =  label_map[example.label]
        elif self.mode == "regression":
         __UpperCamelCase				      =  float(example.label			)
        else:
         raise ValueError(self.mode			)
  
        if ex_index < 5 and self.verbose:
         logger.info("""*** Example ***"""			)
         logger.info(f"guid: {example.guid}"			)
         logger.info(f"input_ids: {' '.join([str(lowercase			) for x in input_ids]			)}"			)
         logger.info(f"attention_mask: {' '.join([str(lowercase			) for x in attention_mask]			)}"			)
         logger.info(f"label: {example.label} (id = {label})"			)
  
        features.append(InputFeatures(input_ids=lowercase		,	attention_mask=lowercase		,	label=lowercase			)			)
  
       if return_tensors is None:
        return features
       elif return_tensors == "tf":
        if not is_tf_available():
         raise RuntimeError("""return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported"""			)
        import tensorflow as tf
  
        def gen():
         for ex in features:
          yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)
  
        __UpperCamelCase				      =  tf.data.Dataset.from_generator(
            lowercase		,	({"""input_ids""": tf.intaa, """attention_mask""": tf.intaa}, tf.intaa)		,	({"""input_ids""": tf.TensorShape([None]			), """attention_mask""": tf.TensorShape([None]			)}, tf.TensorShape([]			))		,	)
        return dataset
       elif return_tensors == "pt":
        if not is_torch_available():
         raise RuntimeError("""return_tensors set to \'pt\' but PyTorch can\'t be imported"""			)
        import torch
        from torch.utils.data import TensorDataset
  
        __UpperCamelCase				      =  torch.tensor([f.input_ids for f in features]		,	dtype=torch.long			)
        __UpperCamelCase				      =  torch.tensor([f.attention_mask for f in features]		,	dtype=torch.long			)
        if self.mode == "classification":
         __UpperCamelCase				      =  torch.tensor([f.label for f in features]		,	dtype=torch.long			)
        elif self.mode == "regression":
         __UpperCamelCase				      =  torch.tensor([f.label for f in features]		,	dtype=torch.float			)
  
        __UpperCamelCase				      =  TensorDataset(lowercase		,	lowercase		,	lowercase			)
        return dataset
       else:
        raise ValueError("""return_tensors should be one of \'tf\' or \'pt\'"""			)
  
  
  
  
  
  
style_context: 
  
  
  
  '''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)
  
  
  
  
  
  