

def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= word.upper()
						SCREAMING_SNAKE_CASE_:				Tuple							= 0
						SCREAMING_SNAKE_CASE_:				Any							= {}
						SCREAMING_SNAKE_CASE_:				List[str]							= []

						for letter in word:
												if letter not in letter_nums:
																		SCREAMING_SNAKE_CASE_:				Tuple							= str(_UpperCAmelCase					)
																		next_num += 1
												word_pattern.append(letter_nums[letter]					)
						return ".".join(_UpperCAmelCase					)


if __name__ == "__main__":
	import pprint
	import time

	lowerCAmelCase							:						Any     			=		time.time()
	with open("""dictionary.txt""") as in_file:
		lowerCAmelCase							:						Optional[Any]     			=		in_file.read().splitlines()

	lowerCAmelCase							:						dict     			=		{}
	for word in word_list:
		lowerCAmelCase							:						List[str]     			=		get_word_pattern(word)
		if pattern in all_patterns:
			all_patterns[pattern].append(word)
		else:
			lowerCAmelCase							:						int     			=		[word]

	with open("""word_patterns.txt""", """w""") as out_file:
		out_file.write(pprint.pformat(all_patterns))

	lowerCAmelCase							:						int     			=		round(time.time() - start_time, 2)
	print(f'''Done!  {len(all_patterns):,} word patterns found in {total_time} seconds.''')
	# Done!  9,581 word patterns found in 0.58 seconds.









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()






