code: 
  
  import argparse
  
  import torch
  from torch import nn
  
  from transformers import PLBartConfig, PLBartForConditionalGeneration, PLBartForSequenceClassification
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  Dict		       =   [
  	    "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  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__       ,	UpperCAmelCase__:  Optional[int]		       =   emb.weight.shape
  	UpperCAmelCase__:  Optional[int]		       =   nn.Linear(lowercase__  ,lowercase__  ,bias=lowercase__			)
  	UpperCAmelCase__:  int		       =   emb.weight.data
  	return lin_layer
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE="uclanlp/plbart-base"  ,SCREAMING_SNAKE_CASE=False  ,SCREAMING_SNAKE_CASE=False			):
  	UpperCAmelCase__:  Optional[Any]		       =   torch.load(lowercase__  ,map_location="cpu"			)["model"]
  	remove_ignore_keys_(lowercase__			)
  	UpperCAmelCase__:  int		       =   state_dict["encoder.embed_tokens.weight"].shape[0]
  
  	UpperCAmelCase__:  Optional[int]		       =   PLBartConfig.from_pretrained(lowercase__  ,vocab_size=lowercase__			)
  
  	UpperCAmelCase__:  int		       =   state_dict["decoder.embed_tokens.weight"]
  	if not classification:
  		UpperCAmelCase__:  int		       =   PLBartForConditionalGeneration(lowercase__			)
  		model.model.load_state_dict(lowercase__			)
  		if finetuned:
  			UpperCAmelCase__:  Tuple		       =   make_linear_from_emb(model.model.shared			)
  
  	else:
  		UpperCAmelCase__:  Tuple		       =   {}
  		for key, value in state_dict.copy().items():
  			if key.startswith("classification_heads.sentence_classification_head"			):
  				UpperCAmelCase__:  Optional[int]		       =   value
  				state_dict.pop(lowercase__			)
  		UpperCAmelCase__:  Any		       =   PLBartForSequenceClassification(lowercase__			)
  		model.model.load_state_dict(lowercase__			)
  		model.classification_head.load_state_dict(lowercase__			)
  
  	return model
  
  
  if __name__ == "__main__":
  					_lowerCAmelCase     :			Dict    =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     :			int    =parser.parse_args()
  					_lowerCAmelCase     :			Optional[int]    =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: 
  
  _lowerCAmelCase     :			Optional[int]    ={
      """+""": lambda x, y: x + y,
      """-""": lambda x, y: x - y,
      """*""": lambda x, y: x * y,
      """/""": lambda x, y: x / y,
  }
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	return c.isdigit()
  
  
  
  def  _A  (			SCREAMING_SNAKE_CASE			):
  	UpperCAmelCase__:  str		       =   []
  
  	# iterate over the string in reverse order
  	for c in expression.split()[::-1]:
  		# push operand to stack
  		if is_operand(SCREAMING_SNAKE_CASE			):
  			stack.append(int(SCREAMING_SNAKE_CASE			)			)
  
  		else:
  			# pop values from stack can calculate the result
  			# push the result onto the stack again
  			UpperCAmelCase__:  Any		       =   stack.pop()
  			UpperCAmelCase__:  int		       =   stack.pop()
  			stack.append(calc[c](SCREAMING_SNAKE_CASE  ,SCREAMING_SNAKE_CASE			)			)
  
  	return stack.pop()
  
  
  # Driver code
  if __name__ == "__main__":
  					_lowerCAmelCase     :			Optional[int]    ="""+ 9 * 2 6"""
  					print(evaluate(test_expression))
  
  					_lowerCAmelCase     :			Optional[Any]    ="""/ * 10 2 + 4 1 """
  					print(evaluate(test_expression))