code: 
  
  
  
  import argparse
  import os
  
  import torch
  from emmental.modules import ThresholdBinarizer, TopKBinarizer
  
  
  
  def  __a  (					lowerCAmelCase_     :      Dict       )   ->				Optional[Any]:
  
  
  
  
  
  
  						'''simple docstring'''
  
  
  
  						UpperCAmelCase_=     args.serialization_dir
  						UpperCAmelCase_=     args.pruning_method
  						UpperCAmelCase_=     args.threshold
  
  						UpperCAmelCase_=     torch.load(os.path.join(lowerCAmelCase_    ,"""pytorch_model.bin"""       )    ,map_location="""cpu"""       )
  
  						UpperCAmelCase_=     0  # Number of remaining (not pruned) params in the encoder
  						UpperCAmelCase_=     0  # Number of params in the encoder
  
  						print("""name""".ljust(60    ,""" """       )    ,"""Remaining Weights %"""    ,"""Remaining Weight"""       )
  						for name, param in st.items():
  												if "encoder" not in name:
  																		continue
  
  												if "mask_scores" in name:
  																		if pruning_method == "topK":
  																								UpperCAmelCase_=     TopKBinarizer.apply(lowerCAmelCase_    ,lowerCAmelCase_       ).sum().item()
  																		elif pruning_method == "sigmoied_threshold":
  																								UpperCAmelCase_=     ThresholdBinarizer.apply(lowerCAmelCase_    ,lowerCAmelCase_    ,lowerCAmelCase_       ).sum().item()
  																		elif pruning_method == "l0":
  																								UpperCAmelCase_,     UpperCAmelCase_=     -0.1, 1.1
  																								UpperCAmelCase_=     torch.sigmoid(lowerCAmelCase_       )
  																								UpperCAmelCase_=     s * (r - l) + l
  																								UpperCAmelCase_=     s_bar.clamp(min=0.0    ,max=1.0       )
  																								UpperCAmelCase_=     (mask > 0.0).sum().item()
  																		else:
  																								raise ValueError("""Unknown pruning method"""       )
  																		remaining_count += mask_ones
  																		print(name.ljust(60    ,""" """       )    ,str(round(1_00 * mask_ones / param.numel()    ,3       )       ).ljust(20    ,""" """       )    ,str(lowerCAmelCase_       )       )
  												else:
  																		encoder_count += param.numel()
  																		if "bias" in name or "LayerNorm" in name:
  																								remaining_count += param.numel()
  
  						print(""""""       )
  						print("""Remaining Weights (global) %: """    ,1_00 * remaining_count / encoder_count       )
  
  
  if __name__ == "__main__":
  	__A       					=		argparse.ArgumentParser()
  
  	parser.add_argument(
  	    '''--pruning_method''',
  	    choices=['''l0''', '''topK''', '''sigmoied_threshold'''],
  	    type=str,
  	    required=True,
  	    help=(
  	        '''Pruning Method (l0 = L0 regularization, topK = Movement pruning, sigmoied_threshold = Soft movement'''
  	        ''' pruning)'''
  	    ),
  	)
  	parser.add_argument(
  	    '''--threshold''',
  	    type=float,
  	    required=False,
  	    help=(
  	        '''For `topK`, it is the level of remaining weights (in %) in the fine-pruned model.'''
  	        '''For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared.'''
  	        '''Not needed for `l0`'''
  	    ),
  	)
  	parser.add_argument(
  	    '''--serialization_dir''',
  	    type=str,
  	    required=True,
  	    help='''Folder containing the model that was previously fine-pruned''',
  	)
  
  	__A       					=		parser.parse_args()
  
  	main(args)
  
  
  
  
  
style_context: 
  
  
  
  import copy
  from typing import Dict
  
  from ...generation.configuration_utils import GenerationConfig
  from ...utils import logging
  
  
  __A       					=		logging.get_logger(__name__)
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Optional[int]	     =							"semantic"
  
  
  
  
  	def __init__(			self			:		Any		,						__UpperCAmelCase			:		List[str]=10_000		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		int=768		,						__UpperCAmelCase			:		Dict=False		,						__UpperCAmelCase			:		str=False		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		int=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		int=10_048		,						__UpperCAmelCase			:		Dict=129_595		,						__UpperCAmelCase			:		Optional[Any]=129_599		,						__UpperCAmelCase			:		Tuple=10_000		,						__UpperCAmelCase			:		List[Any]=256		,						__UpperCAmelCase			:		Union[str, Any]=49.9		,						**__UpperCAmelCase			:		Optional[Any]		,						)    ->				Tuple:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						eos_token_id=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						max_new_tokens=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     text_encoding_offset
  							UpperCAmelCase_=     text_pad_token
  							UpperCAmelCase_=     eos_token_id
  							UpperCAmelCase_=     semantic_infer_token
  							UpperCAmelCase_=     semantic_vocab_size
  							UpperCAmelCase_=     max_input_semantic_length
  							UpperCAmelCase_=     semantic_rate_hz
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"coarse_acoustics"
  
  
  
  
  	def __init__(			self			:		Union[str, Any]		,						__UpperCAmelCase			:		Optional[Any]=True		,						__UpperCAmelCase			:		Optional[int]=False		,						__UpperCAmelCase			:		Union[str, Any]=False		,						__UpperCAmelCase			:		List[str]=False		,						__UpperCAmelCase			:		Any=False		,						__UpperCAmelCase			:		Union[str, Any]=0.7		,						__UpperCAmelCase			:		List[Any]=True		,						__UpperCAmelCase			:		Optional[int]=12_048		,						__UpperCAmelCase			:		Optional[Any]=75		,						__UpperCAmelCase			:		str=2		,						__UpperCAmelCase			:		Optional[int]=12_050		,						__UpperCAmelCase			:		Union[str, Any]=256		,						__UpperCAmelCase			:		int = 630		,						__UpperCAmelCase			:		int = 60		,						**__UpperCAmelCase			:		int		,						)    ->				Union[str, Any]:
  							super().__init__(
  							    temperature=__UpperCAmelCase		,						do_sample=__UpperCAmelCase		,						renormalize_logits=__UpperCAmelCase		,						output_scores=__UpperCAmelCase		,						return_dict_in_generate=__UpperCAmelCase		,						output_hidden_states=__UpperCAmelCase		,						output_attentions=__UpperCAmelCase		,						**__UpperCAmelCase		,						)
  
  							UpperCAmelCase_=     coarse_semantic_pad_token
  							UpperCAmelCase_=     coarse_rate_hz
  							UpperCAmelCase_=     n_coarse_codebooks
  							UpperCAmelCase_=     coarse_infer_token
  							UpperCAmelCase_=     max_coarse_input_length
  							UpperCAmelCase_=     max_coarse_history
  							UpperCAmelCase_=     sliding_window_len
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Tuple	     =							"fine_acoustics"
  
  
  
  
  	def __init__(			self			:		Tuple		,						__UpperCAmelCase			:		Tuple=0.5		,						__UpperCAmelCase			:		Optional[Any]=512		,						__UpperCAmelCase			:		Optional[Any]=1_024		,						__UpperCAmelCase			:		List[str]=8		,						**__UpperCAmelCase			:		str		,						)    ->				Tuple:
  							super().__init__(temperature=__UpperCAmelCase  )
  
  							UpperCAmelCase_=     max_fine_history_length
  							UpperCAmelCase_=     max_fine_input_length
  							UpperCAmelCase_=     n_fine_codebooks
  
  
  
  
  
  
  class 	lowercase		(				snake_case__):
  
  
  
  
  
  
  	"""simple docstring"""
  
  
  	a__   :      Any	     =							"bark"
  	a__   :      List[str]	     =							True
  
  
  
  
  	def __init__(			self			:		List[str]		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		Dict = None		,						__UpperCAmelCase			:		str=24_000		,						__UpperCAmelCase			:		List[str]=1_024		,						**__UpperCAmelCase			:		Optional[int]		,						)    ->				List[str]:
  							if semantic_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""semantic_config is None. initializing the semantic model with default values."""  )
  
  							if coarse_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""coarse_acoustics_config is None. initializing the coarse model with default values."""  )
  
  							if fine_acoustics_config is None:
  													UpperCAmelCase_=     {}
  													logger.info("""fine_acoustics_config is None. initializing the fine model with default values."""  )
  
  							UpperCAmelCase_=     BarkSemanticGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkCoarseGenerationConfig(**__UpperCAmelCase  )
  							UpperCAmelCase_=     BarkFineGenerationConfig(**__UpperCAmelCase  )
  
  							UpperCAmelCase_=     sample_rate
  							UpperCAmelCase_=     codebook_size
  
  
  
  
  	@classmethod
  	def 						_SCREAMING_SNAKE_CASE	(			cls			:		str		,						__UpperCAmelCase			:		BarkSemanticGenerationConfig		,						__UpperCAmelCase			:		BarkCoarseGenerationConfig		,						__UpperCAmelCase			:		BarkFineGenerationConfig		,						**__UpperCAmelCase			:		Any		,						)    ->				Any:
  							return cls(
  							    semantic_config=semantic_config.to_dict()		,						coarse_acoustics_config=coarse_acoustics_config.to_dict()		,						fine_acoustics_config=fine_acoustics_config.to_dict()		,						**__UpperCAmelCase		,						)
  
  
  
  
  	def 						_SCREAMING_SNAKE_CASE	(			self			:		str  )    ->				Optional[Any]:
  							UpperCAmelCase_=     copy.deepcopy(self.__dict__  )
  
  							UpperCAmelCase_=     self.semantic_config.to_dict()
  							UpperCAmelCase_=     self.coarse_acoustics_config.to_dict()
  							UpperCAmelCase_=     self.fine_acoustics_config.to_dict()
  
  							UpperCAmelCase_=     self.__class__.model_type
  							return output
  
  
  
  
  