





'''simple docstring'''





from __future__ import annotations

from typing import Any

def       A__(		__lowerCAmelCase ):
				if not postfix_notation:
								return 0

				_snake_case :			List[Any]  						=   {'+', '-', '*', '/'}
				_snake_case :			int  						=   []

				for token in postfix_notation:
								if token in operations:
												_snake_case						,			_snake_case :			Optional[int]  						=   stack.pop(), stack.pop()
												if token == "+":
																stack.append(a + b )
												elif token == "-":
																stack.append(a - b )
												elif token == "*":
																stack.append(a * b )
												else:
																if a * b < 0 and a % b != 0:
																				stack.append(a // b + 1 )
																else:
																				stack.append(a // b )
								else:
												stack.append(int(__lowerCAmelCase ) )

				return stack.pop()


if __name__ == "__main__":
		import doctest

		doctest.testmod()













import argparse

import torch

from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging


logging.set_verbosity_info()

def       A__(		__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase ):
				# Initialise PyTorch model
				_snake_case :			Optional[int]  						=   BertConfig.from_json_file(__lowerCAmelCase )
				print(F'''Building PyTorch model from configuration: {config}''' )
				_snake_case :			List[str]  						=   BertForPreTraining(__lowerCAmelCase )

				# Load weights from tf checkpoint
				load_tf_weights_in_bert(__lowerCAmelCase	,			__lowerCAmelCase	,			__lowerCAmelCase )

				# Save pytorch-model
				print(F'''Save PyTorch model to {pytorch_dump_path}''' )
				torch.save(model.state_dict()	,			__lowerCAmelCase )


if __name__ == "__main__":
		lowercase_		:				Any				=						argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--bert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained BERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		lowercase_		:				List[str]				=						parser.parse_args()
		convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)






