"""simple docstring"""







import operator as op


def      __lowerCAmelCase(		__UpperCAmelCase			):






	"""simple docstring"""





	_lowercase :    List[str]							=							[]
	_lowercase :    List[str]							=							lambda __UpperCAmelCase     ,__UpperCAmelCase			: int(x / y			)  # noqa: E731 integer division operation
	_lowercase :    Any							=							{
	    '^': op.pow,
	    '*': op.mul,
	    '/': div,
	    '+': op.add,
	    '-': op.sub,
	}  # operators & their respective operation

	# print table header
	print('Symbol'.center(8			)     ,'Action'.center(12			)     ,'Stack'     ,sep=' | '			)
	print('-' * (30 + len(__UpperCAmelCase			))			)

	for x in post_fix:
		if x.isdigit():  # if x in digit
			stack.append(__UpperCAmelCase			)  # append x to stack
			# output in tabular format
			print(x.rjust(8			)     ,('push(' + x + ')').ljust(12			)     ,','.join(__UpperCAmelCase			)     ,sep=' | '			)
		else:
			_lowercase :    Optional[Any]							=							stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8			)     ,('pop(' + b + ')').ljust(12			)     ,','.join(__UpperCAmelCase			)     ,sep=' | '			)

			_lowercase :    Optional[Any]							=							stack.pop()  # pop stack
			# output in tabular format
			print(''.rjust(8			)     ,('pop(' + a + ')').ljust(12			)     ,','.join(__UpperCAmelCase			)     ,sep=' | '			)

			stack.append(
			    str(opr[x](int(__UpperCAmelCase			)     ,int(__UpperCAmelCase			)			)			)			)  # evaluate the 2 values popped from stack & push result to stack
			# output in tabular format
			print(
			    x.rjust(8			)     ,('push(' + a + x + b + ')').ljust(12			)     ,','.join(__UpperCAmelCase			)     ,sep=' | '     ,)

	return int(stack[0]			)


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE									=						input('\n\nEnter a Postfix Equation (space separated) = ').split(' ')
						print('\n\tResult = ', solve(Postfix))




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


SCREAMING_SNAKE_CASE									=						{
    'configuration_biogpt': ['BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'BioGptConfig'],
    'tokenization_biogpt': ['BioGptTokenizer'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE									=						[
						    'BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'BioGptForCausalLM',
						    'BioGptForTokenClassification',
						    'BioGptForSequenceClassification',
						    'BioGptModel',
						    'BioGptPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
						from .tokenization_biogpt import BioGptTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_biogpt import (
												    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
												    BioGptForCausalLM,
												    BioGptForSequenceClassification,
												    BioGptForTokenClassification,
												    BioGptModel,
												    BioGptPreTrainedModel,
												)


else:
						import sys

						SCREAMING_SNAKE_CASE									=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



