

from .balanced_parentheses import balanced_parentheses
from .stack import Stack







def     A_   (					_UpperCAmelCase					):
						return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(_UpperCAmelCase       ,			-1					)







def     A_   (					_UpperCAmelCase					):
						if not balanced_parentheses(_UpperCAmelCase					):
												raise ValueError("Mismatched parentheses"					)
						SCREAMING_SNAKE_CASE_:				Stack[str]							= Stack()
						SCREAMING_SNAKE_CASE_:				str							= []
						for char in expression_str:
												if char.isalpha() or char.isdigit():
																		postfix.append(_UpperCAmelCase					)
												elif char == "(":
																		stack.push(_UpperCAmelCase					)
												elif char == ")":
																		while not stack.is_empty() and stack.peek() != "(":
																								postfix.append(stack.pop()					)
																		stack.pop()
												else:
																		while not stack.is_empty() and precedence(_UpperCAmelCase					) <= precedence(stack.peek()					):
																								postfix.append(stack.pop()					)
																		stack.push(_UpperCAmelCase					)
						while not stack.is_empty():
												postfix.append(stack.pop()					)
						return " ".join(_UpperCAmelCase					)


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						lowerCAmelCase							:						Tuple     			=		"""a+b*(c^d-e)^(f+g*h)-i"""

						print("""Infix to Postfix Notation demonstration:\n""")
						print("""Infix notation: """ + expression)
						print("""Postfix notation: """ + infix_to_postfix(expression))









import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path

import torch_xla.distributed.xla_multiprocessing as xmp







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Tuple							= ArgumentParser(
						    description=(
						        "PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
						    )					)

						# Optional arguments for the launch helper
						parser.add_argument("--num_cores"       ,			type=_UpperCAmelCase       ,			default=1       ,			help="Number of TPU cores to use (1 or 8)."					)

						# positional
						parser.add_argument(
						    "training_script"       ,			type=_UpperCAmelCase       ,			help=(
						        "The full path to the single TPU training "
						        "program/script to be launched in parallel, "
						        "followed by all the arguments for the "
						        "training script"
						    )       ,			)

						# rest from the training program
						parser.add_argument("training_script_args"       ,			nargs=_UpperCAmelCase					)

						return parser.parse_args()







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				List[str]							= parse_args()

						# Import training_script as a module.
						SCREAMING_SNAKE_CASE_:				Tuple							= Path(args.training_script					)
						sys.path.append(str(script_fpath.parent.resolve()					)					)
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= script_fpath.stem
						SCREAMING_SNAKE_CASE_:				Optional[Any]							= importlib.import_module(_UpperCAmelCase					)

						# Patch sys.argv
						SCREAMING_SNAKE_CASE_:				int							= [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores					)]

						xmp.spawn(mod._mp_fn       ,			args=()       ,			nprocs=args.num_cores					)


if __name__ == "__main__":
						main()






