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