

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

import torch_xla.distributed.xla_multiprocessing as xmp





def a():





	'''simple docstring'''
	snake_case_      		= 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=lowercase__			, default=1			, help='Number of TPU cores to use (1 or 8).'	)

	# positional
	parser.add_argument(
	    'training_script'			, type=lowercase__			, 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=lowercase__	)

	return parser.parse_args()





def a():





	'''simple docstring'''
	snake_case_      		= parse_args()

	# Import training_script as a module.
	snake_case_      		= Path(args.training_script	)
	sys.path.append(str(script_fpath.parent.resolve()	)	)
	snake_case_      		= script_fpath.stem
	snake_case_      		= importlib.import_module(lowercase__	)

	# Patch sys.argv
	snake_case_      		= [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()



from collections import defaultdict





def a(lowercase__			, lowercase__	):





	'''simple docstring'''
	snake_case_      		= first_str.lower().strip()
	snake_case_      		= second_str.lower().strip()

	# Remove whitespace
	snake_case_      		= first_str.replace(' '			, ''	)
	snake_case_      		= second_str.replace(' '			, ''	)

	# Strings of different lengths are not anagrams
	if len(lowercase__	) != len(lowercase__	):
		return False

	# Default values for count should be 0
	snake_case_      		= defaultdict(lowercase__	)

	# For each character in input strings,
	# increment count in the corresponding
	for i in range(len(lowercase__	)	):
		count[first_str[i]] += 1
		count[second_str[i]] -= 1

	return all(_count == 0 for _count in count.values()	)


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

		testmod()
		A											=     input('Enter the first string ').strip()
		A											=     input('Enter the second string ').strip()

		A											=     check_anagrams(input_a, input_b)
		print(f"""{input_a} and {input_b} are {"" if status else "not "}anagrams.""")
