code: 
  
  
  def     UpperCAmelCase__		(UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if len(UpperCamelCase_       ) <= 1:
            return data
       else:
            return [
                *quick_sort([e for e in data[1:] if e <= data[0]]       ),
                data[0],
                *quick_sort([e for e in data[1:] if e > data[0]]       ),
            ]
  
  
  if __name__ == "__main__":
   import doctest
  
   doctest.testmod()
  
  
  
  
  
style_context: 
  
  
  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()
  _SCREAMING_SNAKE_CASE    :				List[str]       =					logging.get_logger(__name__)
  
  _SCREAMING_SNAKE_CASE    :				Dict       =					{
      "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__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       for attribute in key.split('''.'''       ):
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       )
  
       if weight_type is not None:
            snake_case					=       getattr(UpperCamelCase_							,UpperCamelCase_       ).shape
       else:
            snake_case					=       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":
            snake_case					=       value
       elif weight_type == "weight_g":
            snake_case					=       value
       elif weight_type == "weight_v":
            snake_case					=       value
       elif weight_type == "bias":
            snake_case					=       value
       else:
            snake_case					=       value
  
       logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       []
       snake_case					=       fairseq_model.state_dict()
  
       snake_case					=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor
  
       for name, value in fairseq_dict.items():
            snake_case					=       False
            if "conv_layers" in name:
                 load_conv_layer(
                     UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,hf_model.config.feat_extract_norm == '''group'''							,)
                 snake_case					=       True
            else:
                 for key, mapped_key in MAPPING.items():
                      snake_case					=       '''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]:
                           snake_case					=       True
                           if "*" in mapped_key:
                                snake_case					=       name.split(UpperCamelCase_       )[0].split('''.'''       )[-2]
                                if not layer_index.isnumeric():
                                     continue
                                snake_case					=       mapped_key.replace('''*'''							,UpperCamelCase_       )
                           if "weight_g" in name:
                                snake_case					=       '''weight_g'''
                           elif "weight_v" in name:
                                snake_case					=       '''weight_v'''
                           elif "weight" in name:
                                snake_case					=       '''weight'''
                           elif "bias" in name:
                                snake_case					=       '''bias'''
                           else:
                                snake_case					=       None
                           set_recursively(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
                      continue
            if not is_used:
                 unused_weights.append(UpperCamelCase_       )
  
       logger.warning(F'''Unused weights: {unused_weights}'''       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       full_name.split('''conv_layers.'''       )[-1]
       snake_case					=       name.split('''.'''       )
       snake_case					=       int(items[0]       )
       snake_case					=       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.'''
                 )
                 snake_case					=       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.'''
                 )
                 snake_case					=       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."
                 )
                 snake_case					=       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.'''
                 )
                 snake_case					=       value
                 logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''       )
       else:
            unused_weights.append(UpperCamelCase_       )
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       snake_case					=       SEWDConfig()
       if is_finetuned:
            snake_case					=       model.wav_encoder.wav_model.cfg
       else:
            snake_case					=       model.cfg
  
       snake_case					=       fs_config.conv_bias
       snake_case					=       eval(fs_config.conv_feature_layers       )
       snake_case					=       [x[0] for x in conv_layers]
       snake_case					=       [x[1] for x in conv_layers]
       snake_case					=       [x[2] for x in conv_layers]
       snake_case					=       '''gelu'''
       snake_case					=       '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
       snake_case					=       0.0
       snake_case					=       fs_config.activation_fn.name
       snake_case					=       fs_config.encoder_embed_dim
       snake_case					=       0.02
       snake_case					=       fs_config.encoder_ffn_embed_dim
       snake_case					=       1e-5
       snake_case					=       fs_config.encoder_layerdrop
       snake_case					=       fs_config.encoder_attention_heads
       snake_case					=       fs_config.conv_pos_groups
       snake_case					=       fs_config.conv_pos
       snake_case					=       len(UpperCamelCase_       )
       snake_case					=       fs_config.encoder_layers
       snake_case					=       fs_config.squeeze_factor
       # DeBERTa-specific parameters:
       snake_case					=       fs_config.max_position_embeddings
       snake_case					=       fs_config.position_buckets
       snake_case					=       fs_config.share_att_key
       snake_case					=       fs_config.relative_attention
       snake_case					=       fs_config.position_biased_input
       snake_case					=       tuple(fs_config.pos_att_type.split('''|'''       )       )
       snake_case					=       fs_config.norm_rel_ebd
  
       # take care of any params that are overridden by the Wav2VecCtc model
       if is_finetuned:
            snake_case					=       model.cfg
            snake_case					=       fs_config.final_dropout
            snake_case					=       fs_config.layerdrop
       snake_case					=       fs_config.activation_dropout
       snake_case					=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
       snake_case					=       fs_config.attention_dropout
       snake_case					=       fs_config.dropout_input
       snake_case					=       fs_config.dropout
       snake_case					=       fs_config.mask_channel_length
       snake_case					=       fs_config.mask_channel_prob
       snake_case					=       fs_config.mask_length
       snake_case					=       fs_config.mask_prob
  
       snake_case					=       '''Wav2Vec2FeatureExtractor'''
       snake_case					=       '''Wav2Vec2CTCTokenizer'''
  
       return config
  @torch.no_grad()
  def     UpperCAmelCase__		(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_=None							,UpperCamelCase_=None							,UpperCamelCase_=True       ):
  
  
  
       """simple docstring"""
  
  
  
  
  
  
       if is_finetuned:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]							,arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''       )[:-1]       )}       )
       else:
            snake_case				,						snake_case				,						snake_case					=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       )
  
       if config_path is not None:
            snake_case					=       SEWDConfig.from_pretrained(UpperCamelCase_       )
       else:
            snake_case					=       convert_config(model[0]							,UpperCamelCase_       )
       snake_case					=       model[0].eval()
  
       snake_case					=       True if config.feat_extract_norm == '''layer''' else False
       snake_case					=       WavaVecaFeatureExtractor(
           feature_size=1							,sampling_rate=1_60_00							,padding_value=0							,do_normalize=UpperCamelCase_							,return_attention_mask=UpperCamelCase_							,)
  
       if is_finetuned:
            if dict_path:
                 snake_case					=       Dictionary.load(UpperCamelCase_       )
  
                 # important change bos & pad token id since CTC symbol is <pad> and
                 # not <s> as in fairseq
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.pad_index
                 snake_case					=       target_dict.bos_index
                 snake_case					=       target_dict.eos_index
                 snake_case					=       len(target_dict.symbols       )
                 snake_case					=       os.path.join(UpperCamelCase_							,'''vocab.json'''       )
                 if not os.path.isdir(UpperCamelCase_       ):
                      logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase_       )       )
                      return
                 os.makedirs(UpperCamelCase_							,exist_ok=UpperCamelCase_       )
                 with open(UpperCamelCase_							,'''w'''							,encoding='''utf-8'''       ) as vocab_handle:
                      json.dump(target_dict.indices							,UpperCamelCase_       )
                 snake_case					=       WavaVecaCTCTokenizer(
                     UpperCamelCase_							,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=UpperCamelCase_							,)
                 snake_case					=       WavaVecaProcessor(feature_extractor=UpperCamelCase_							,tokenizer=UpperCamelCase_       )
                 processor.save_pretrained(UpperCamelCase_       )
  
            snake_case					=       SEWDForCTC(UpperCamelCase_       )
       else:
            snake_case					=       SEWDModel(UpperCamelCase_       )
            feature_extractor.save_pretrained(UpperCamelCase_       )
  
       recursively_load_weights(UpperCamelCase_							,UpperCamelCase_							,UpperCamelCase_       )
  
       hf_model.save_pretrained(UpperCamelCase_       )
  
  
  if __name__ == "__main__":
   _SCREAMING_SNAKE_CASE    :				List[Any]       =					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"
   )
   _SCREAMING_SNAKE_CASE    :				Optional[int]       =					parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )
  
  
  
  
  