


"""simple docstring"""






import operator as op


def __lowercase     (      lowerCamelCase_  :      Tuple  ):
				SCREAMING_SNAKE_CASE__     							=				[]
				SCREAMING_SNAKE_CASE__     							=				lambda lowerCamelCase_						,					lowerCamelCase_  : int(x / y  )  # noqa: E731 integer division operation
				SCREAMING_SNAKE_CASE__     							=				{
				    "^": 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(lowerCamelCase_  ))  )

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

												SCREAMING_SNAKE_CASE__     							=				stack.pop()  # pop stack
												# output in tabular format
												print("".rjust(8  )						,					("pop(" + a + ")").ljust(12  )						,					",".join(lowerCamelCase_  )						,					sep=" | "  )

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

				return int(stack[0]  )


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




"""simple docstring"""






import json
import os
import tempfile

import transformers

import datasets
from utils import generate_example_dataset, get_duration


_lowerCamelCase     =  500000

_lowerCamelCase     ,     _lowerCamelCase     =  os.path.split(__file__)
_lowerCamelCase     =  os.path.join(RESULTS_BASEPATH, 'results', RESULTS_FILENAME.replace('.py', '.json'))


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.map(**lowerCamelCase_  )


@get_duration
def __lowercase     (      lowerCamelCase_  :      datasets.Dataset						,					**lowerCamelCase_  :      Optional[int]  ):
				SCREAMING_SNAKE_CASE__     							=				dataset.filter(**lowerCamelCase_  )


def __lowercase     (      ):
				SCREAMING_SNAKE_CASE__     							=				{"num examples": SPEED_TEST_N_EXAMPLES}
				with tempfile.TemporaryDirectory() as tmp_dir:
								SCREAMING_SNAKE_CASE__     							=				datasets.Features({"text": datasets.Value("string"  ), "numbers": datasets.Value("float32"  )}  )
								SCREAMING_SNAKE_CASE__     							=				generate_example_dataset(
								    os.path.join(lowerCamelCase_						,					"dataset.arrow"  )						,					lowerCamelCase_						,					num_examples=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				transformers.AutoTokenizer.from_pretrained("bert-base-cased"						,					use_fast=lowerCamelCase_  )

								def tokenize(lowerCamelCase_  :      Union[str, Any]  ):
												return tokenizer(examples["text"]  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="numpy"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="pandas"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="torch"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								with dataset.formatted_as(type="tensorflow"						,					columns="numbers"  ):
												SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lambda lowerCamelCase_  : None						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				map(lowerCamelCase_						,					function=lowerCamelCase_						,					batched=lowerCamelCase_  )

								SCREAMING_SNAKE_CASE__     							=				filter(lowerCamelCase_  )

								# Activate later when tokenizer support batched inputs
								# with dataset.formatted_as(type='numpy'):
								#     times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)

				with open(lowerCamelCase_						,					"wb"  ) as f:
								f.write(json.dumps(lowerCamelCase_  ).encode("utf-8"  )  )


if __name__ == "__main__":  # useful to run the profiler
	benchmark_map_filter()
