

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)






_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))