





"""simple docstring"""




import operator as op






def    _SCREAMING_SNAKE_CASE	(			_lowercase		:					Union[str, Any]      )				->Any:






	'''simple docstring'''






	a   :	str			=       []
	a   :	List[str]			=       lambda _lowercase					, _lowercase      : int(x / y      )  # noqa: E731 integer division operation
	a   :	Union[str, 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(_lowercase      ))      )

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

			a   :	List[str]			=       stack.pop()  # pop stack
			# output in tabular format
			print("".rjust(8      )					, ("pop(" + a + ")").ljust(12      )					, ",".join(_lowercase      )					, sep=" | "      )

			stack.append(
			    str(opr[x](int(_lowercase      )					, int(_lowercase      )      )      )      )  # 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(_lowercase      )					, sep=" | "					, )

	return int(stack[0]      )


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








"""simple docstring"""




import time
import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch, torch_device

from ..test_modeling_common import ids_tensor


if is_torch_available():
		import torch

		from transformers.generation import (
		    MaxLengthCriteria,
		    MaxNewTokensCriteria,
		    MaxTimeCriteria,
		    StoppingCriteriaList,
		    validate_stopping_criteria,
		)


@require_torch
class  __UpperCamelCase       (		unittest.TestCase		):






							def 					__a     (     self   ,      lowerCAmelCase__   )			->				Optional[int]:
								a   :	str			=       3
								a   :	str			=       250

								a   :	List[Any]			=       ids_tensor((batch_size, length)   ,      lowerCAmelCase__   )
								a   :	Optional[Any]			=       torch.ones((batch_size, length)   ,      device=lowerCAmelCase__   ,      dtype=torch.float   ) / length
								return input_ids, scores






							def 					__a     (     self   )			->				List[Any]:
								a, a   :	str			=       self._get_tensors(5   )

								a   :	Any			=       StoppingCriteriaList(
								    [
								        MaxLengthCriteria(max_length=10   ),
								        MaxTimeCriteria(max_time=0.1   ),
								    ]   )

								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	str			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[Any]:
								a   :	Optional[Any]			=       MaxLengthCriteria(max_length=10   )

								a, a   :	int			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	Union[str, Any]			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				List[str]:
								a   :	Tuple			=       MaxNewTokensCriteria(start_length=5   ,      max_new_tokens=5   )

								a, a   :	str			=       self._get_tensors(5   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(9   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a, a   :	int			=       self._get_tensors(10   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	List[Any]			=       StoppingCriteriaList([criteria]   )
								self.assertEqual(criteria_list.max_length   ,      10   )






							def 					__a     (     self   )			->				str:
								a, a   :	Tuple			=       self._get_tensors(5   )

								a   :	str			=       MaxTimeCriteria(max_time=0.1   )
								self.assertFalse(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )

								a   :	Optional[int]			=       MaxTimeCriteria(max_time=0.1   ,      initial_timestamp=time.time() - 0.2   )
								self.assertTrue(criteria(lowerCAmelCase__   ,      lowerCAmelCase__   )   )






							def 					__a     (     self   )			->				str:
								validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      10   )

								with self.assertWarns(lowerCAmelCase__   ):
									validate_stopping_criteria(StoppingCriteriaList([MaxLengthCriteria(10   )]   )   ,      11   )

								a   :	Optional[int]			=       validate_stopping_criteria(StoppingCriteriaList()   ,      11   )

								self.assertEqual(len(lowerCAmelCase__   )   ,      1   )


