code: 
  
  
  import argparse
  
  import torch
  from torch import nn
  
  from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			Union[str, Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    [
  		    'encoder.version',
  		    'decoder.version',
  		    'model.encoder.version',
  		    'model.decoder.version',
  		    '_float_tensor',
  		    'decoder.output_projection.weight',
  		]
  		for k in ignore_keys:
  				state_dict.pop(lowercase__     ,				lowercase__			)
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__			)		->			List[Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase,							__lowercase=    emb.weight.shape
  		__lowercase=    nn.Linear(lowercase__     ,				lowercase__     ,				bias=lowercase__			)
  		__lowercase=    emb.weight.data
  		return lin_layer
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__="uclanlp/plbart-base"     ,				lowercase__=False     ,				lowercase__=False			)		->			Optional[Any]:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		__lowercase=    torch.load(lowercase__     ,				map_location='cpu'			)['model']
  		remove_ignore_keys_(lowercase__			)
  		__lowercase=    state_dict['encoder.embed_tokens.weight'].shape[0]
  
  		__lowercase=    PLBartConfig.from_pretrained(lowercase__     ,				vocab_size=lowercase__			)
  
  		__lowercase=    state_dict['decoder.embed_tokens.weight']
  		if not classification:
  				__lowercase=    PLBartForConditionalGeneration(lowercase__			)
  				model.model.load_state_dict(lowercase__			)
  				if finetuned:
  						__lowercase=    make_linear_from_emb(model.model.shared			)
  
  		else:
  				__lowercase=    {}
  				for key, value in state_dict.copy().items():
  						if key.startswith('classification_heads.sentence_classification_head'			):
  								__lowercase=    value
  								state_dict.pop(lowercase__			)
  				__lowercase=    PLBartForSequenceClassification(lowercase__			)
  				model.model.load_state_dict(lowercase__			)
  				model.classification_head.load_state_dict(lowercase__			)
  
  		return model
  
  
  if __name__ == "__main__":
  	lowerCAmelCase					 =	argparse.ArgumentParser()
  	# Required parameters
  	parser.add_argument('''fairseq_path''', type=str, help='''model.pt on local filesystem.''')
  	parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
  	parser.add_argument(
  	    '''--hf_config''',
  	    default='''uclanlp/plbart-base''',
  	    type=str,
  	    help='''Which huggingface architecture to use: plbart-base''',
  	)
  	parser.add_argument('''--finetuned''', action='''store_true''', help='''whether the model is a fine-tuned checkpoint''')
  	parser.add_argument(
  	    '''--classification''', action='''store_true''', help='''whether the model is a classification checkpoint'''
  	)
  	lowerCAmelCase					 =	parser.parse_args()
  	lowerCAmelCase					 =	convert_fairseq_plbart_checkpoint_from_disk(
  	    args.fairseq_path,
  	    hf_config_path=args.hf_config,
  	    finetuned=args.finetuned,
  	    classification=args.classification,
  	)
  	model.save_pretrained(args.pytorch_dump_folder_path)
  
  
  
  
  
  
  
style_context: 
  
  
  from __future__ import annotations
  
  from enum import Enum
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							List[Any] =24
  							UpperCamelCase_     :							List[Any] =21
  							UpperCamelCase_     :							int =18
  							UpperCamelCase_     :							Optional[int] =15
  							UpperCamelCase_     :							Tuple =12
  							UpperCamelCase_     :							List[str] =9
  							UpperCamelCase_     :							Any =6
  							UpperCamelCase_     :							List[str] =3
  							UpperCamelCase_     :							str =2
  							UpperCamelCase_     :							str =1
  							UpperCamelCase_     :							Optional[int] =-1
  							UpperCamelCase_     :							List[Any] =-2
  							UpperCamelCase_     :							str =-3
  							UpperCamelCase_     :							Tuple =-6
  							UpperCamelCase_     :							Tuple =-9
  							UpperCamelCase_     :							Optional[int] =-12
  							UpperCamelCase_     :							str =-15
  							UpperCamelCase_     :							Optional[Any] =-18
  							UpperCamelCase_     :							Any =-21
  							UpperCamelCase_     :							List[str] =-24
  
  
  
  
  
  class    A					(     A_				):
  							UpperCamelCase_     :							str =8
  							UpperCamelCase_     :							Optional[Any] =7
  							UpperCamelCase_     :							Tuple =6
  							UpperCamelCase_     :							List[Any] =5
  							UpperCamelCase_     :							Optional[Any] =4
  							UpperCamelCase_     :							Union[str, Any] =3
  							UpperCamelCase_     :							Union[str, Any] =2
  							UpperCamelCase_     :							int =1
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    SIUnit[known_prefix.lower()]
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    SIUnit[unknown_prefix.lower()]
  		__lowercase=    known_amount * (
  		    1_0 ** (known_prefix.value - unknown_prefix.value)
  		)
  		return unknown_amount
  
  
  
  
  
  
  
  def  _lowerCamelCase(			lowercase__     ,				lowercase__     ,				lowercase__     ,				)		->			float:
  
  		'''simple docstring'''
  
  
  
  
  
  
  
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    BinaryUnit[known_prefix.lower()]
  		if isinstance(lowercase__     ,				lowercase__			):
  				__lowercase=    BinaryUnit[unknown_prefix.lower()]
  		__lowercase=    known_amount * (
  		    2 ** ((known_prefix.value - unknown_prefix.value) * 1_0)
  		)
  		return unknown_amount
  
  
  if __name__ == "__main__":
  	import doctest
  
  	doctest.testmod()
  
  
  
  
  
  
  