code: 
  
  
  
  import argparse
  
  import torch
  from saprl.hub import distilhubert
  
  from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging
  
  
  logging.set_verbosity_info()
  A_    :					Optional[int]             =logging.get_logger(__name__)
  
  A_    :					Union[str, Any]             ={
      """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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						for attribute in key.split("""."""      ):
  												a_					=		getattr(__a       ,    __a      )
  
  						if weight_type is not None:
  												a_					=		getattr(__a       ,    __a      ).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   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		[]
  						a_					=		fairseq_model.state_dict()
  
  						a_					=		hf_model.feature_extractor
  
  						for name, value in fairseq_dict.items():
  												a_					=		False
  												if "conv_layers" in name:
  																		load_conv_layer(
  																		    __a       ,    __a       ,    __a       ,    __a       ,    hf_model.config.feat_extract_norm == """group"""       ,    )
  																		a_					=		True
  												else:
  																		for key, mapped_key in MAPPING.items():
  																								a_					=		mapped_key
  
  																								if key in name:
  																														a_					=		True
  																														if "*" in mapped_key:
  																																				a_					=		name.split(__a      )[0].split("""."""      )[-2]
  																																				a_					=		mapped_key.replace("""*"""       ,    __a      )
  																														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(__a       ,    __a       ,    __a       ,    __a       ,    __a      )
  																								continue
  												if not is_used:
  																		unused_weights.append(__a      )
  
  						logger.warning(F"Unused weights: {unused_weights}"      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__       ,    UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						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(__a      )
  
  
  
  
  def   lowerCamelCase_       (					UpperCAmelCase__      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		HubertConfig()
  						a_					=		model.config
  
  						a_					=		fs_config.activation_dropout
  						a_					=		False
  						a_					=		fs_config.attention_dropout
  						a_					=		False
  						a_					=		eval(fs_config.extractor_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_					=		False
  						a_					=		0.0
  						a_					=		0.0
  						a_					=		fs_config.activation_fn
  						a_					=		fs_config.dropout
  						a_					=		fs_config.encoder_embed_dim
  						a_					=		0.02
  						a_					=		fs_config.encoder_ffn_embed_dim
  						a_					=		1e-5
  						a_					=		0.0
  						a_					=		fs_config.encoder_attention_heads
  						a_					=		fs_config.conv_pos_groups
  						a_					=		fs_config.conv_pos
  						a_					=		len(__a      )
  						a_					=		fs_config.encoder_layers
  
  						return config
  
  
  
  
  @torch.no_grad()
  def   lowerCamelCase_       (					UpperCAmelCase__       ,    UpperCAmelCase__=None      ):
  
  
  
  						"""simple docstring"""
  
  
  
  
  						a_					=		distilhubert().model.model
  
  						if config_path is not None:
  												a_					=		HubertConfig.from_pretrained(__a      )
  						else:
  												a_					=		convert_config(__a      )
  						a_					=		model.eval()
  
  						a_					=		WavaVecaFeatureExtractor(
  						    feature_size=1       ,    sampling_rate=16_000       ,    padding_value=0       ,    do_normalize=__a       ,    return_attention_mask=__a       ,    )
  						a_					=		HubertModel(__a      )
  
  						recursively_load_weights(__a       ,    __a      )
  
  						feature_extractor.save_pretrained(__a      )
  						hf_model.save_pretrained(__a      )
  
  
  if __name__ == "__main__":
  	A_    :					int             =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_    :					Union[str, Any]             =parser.parse_args()
  	convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)
style_context: 
  
  
  from typing import List
  
  from ...processing_utils import ProcessorMixin
  from ...utils import is_torch_available
  
  
  if is_torch_available():
  	import torch
  
  
  
  
  
  class        lowercase_				( UpperCamelCase__):
  
  
  
  
  
  
  							"""simple docstring"""
  
  
  
  
  
  
  
  							snake_case_        =		['''image_processor''', '''tokenizer''']
  							snake_case_        =		'''OneFormerImageProcessor'''
  							snake_case_        =		('''CLIPTokenizer''', '''CLIPTokenizerFast''')
  
  
  							def __init__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase = 77			,    _UpperCAmelCase = 77			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if image_processor is None:
  																			raise ValueError("""You need to specify an `image_processor`."""		)
  													if tokenizer is None:
  																			raise ValueError("""You need to specify a `tokenizer`."""		)
  
  													a_					=		max_seq_length
  													a_					=		task_seq_length
  
  													super().__init__(_UpperCAmelCase			,    _UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=77		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if text_list is None:
  																			raise ValueError("""tokens cannot be None."""		)
  
  													a_					=		self.tokenizer(_UpperCAmelCase			,    padding="""max_length"""			,    max_length=_UpperCAmelCase			,    truncation=_UpperCAmelCase		)
  
  													a_			,   a_					=		tokens["""attention_mask"""], tokens["""input_ids"""]
  
  													a_					=		[]
  													for attn_mask, input_id in zip(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		torch.tensor(_UpperCAmelCase		) * torch.tensor(_UpperCAmelCase		)
  																			token_inputs.append(token.unsqueeze(0		)		)
  
  													a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  													return token_inputs
  
  
  							def __call__(   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    _UpperCAmelCase=None			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													if task_inputs is None:
  																			raise ValueError("""You have to specify the task_input. Found None."""		)
  													elif images is None:
  																			raise ValueError("""You have to specify the image. Found None."""		)
  
  													if not all(task in ["""semantic""", """instance""", """panoptic"""] for task in task_inputs		):
  																			raise ValueError("""task_inputs must be semantic, instance, or panoptic."""		)
  
  													a_					=		self.image_processor.encode_inputs(_UpperCAmelCase			,    _UpperCAmelCase			,    _UpperCAmelCase			,    **_UpperCAmelCase		)
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		):
  																			a_					=		[task_inputs]
  
  													if isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) and all(isinstance(_UpperCAmelCase			,    _UpperCAmelCase		) for task_input in task_inputs		):
  																			a_					=		[]
  																			for task in task_inputs:
  																									a_					=		f"the task is {task}"
  																									task_token_inputs.append(_UpperCAmelCase		)
  																			a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.task_seq_length		)
  													else:
  																			raise TypeError("""Task Inputs should be a string or a list of strings."""		)
  
  													if hasattr(_UpperCAmelCase			,    """text_inputs"""		):
  																			a_					=		encoded_inputs.text_inputs
  
  																			a_					=		[]
  																			for texts in texts_list:
  																									a_					=		self._preprocess_text(_UpperCAmelCase			,    max_length=self.max_seq_length		)
  																									text_inputs.append(text_input_list.unsqueeze(0		)		)
  
  																			a_					=		torch.cat(_UpperCAmelCase			,    dim=0		)
  
  													return encoded_inputs
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_semantic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_instance_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)
  
  
  
  
  
  
  							def 						lowercase__     (   self			,    *_UpperCAmelCase			,    **_UpperCAmelCase		):
  
  
  
  
  
  
  
  													"""simple docstring"""
  
  
  													return self.image_processor.post_process_panoptic_segmentation(*_UpperCAmelCase			,    **_UpperCAmelCase		)