





'''simple docstring'''







def    _lowerCAmelCase						(						lowercase	)					->					str:
			__lowerCAmelCase											=				word.upper()
			__lowerCAmelCase											=				0
			__lowerCAmelCase											=				{}
			__lowerCAmelCase											=				[]

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


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

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

	_a					: dict      =    {}
	for word in word_list:
		_a					: Dict      =    get_word_pattern(word)
		if pattern in all_patterns:
			all_patterns[pattern].append(word)
		else:
			_a					: Tuple      =    [word]

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

	_a					: Tuple      =    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.








'''simple docstring'''







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

import torch_xla.distributed.xla_multiprocessing as xmp





def    _lowerCAmelCase						(						)					->					List[str]:
			__lowerCAmelCase											=				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    _lowerCAmelCase						(						)					->					Any:
			__lowerCAmelCase											=				parse_args()

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

			# Patch sys.argv
			__lowerCAmelCase											=				[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()

