import os

# Precomputes a list of the 100 first triangular numbers
lowerCAmelCase			:							List[Any] =[int(0.5 * n * (n + 1)) for n in range(1, 101)]







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[Any] 						=						os.path.dirname(os.path.realpath(_SCREAMING_SNAKE_CASE	)	)
    _lowerCamelCase     :		Optional[Any] 						=						os.path.join(_SCREAMING_SNAKE_CASE   , """words.txt"""	)

    _lowerCamelCase     :		Optional[int] 						=						""""""
    with open(_SCREAMING_SNAKE_CASE	) as f:
        _lowerCamelCase     :		Any 						=						f.readline()

    _lowerCamelCase     :		int 						=						[word.strip("""\""""	) for word in words.strip("""\r\n"""	).split(""","""	)]
    _lowerCamelCase     :		List[Any] 						=						[
        word
        for word in [sum(ord(_SCREAMING_SNAKE_CASE	) - 64 for x in word	) for word in words]
        if word in TRIANGULAR_NUMBERS
    ]
    return len(_SCREAMING_SNAKE_CASE	)


if __name__ == "__main__":
    print(solution())





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'''
    _lowerCamelCase     :		Optional[int] 						=						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=__A   , default=1   , help="""Number of TPU cores to use (1 or 8)."""	)

    # positional
    parser.add_argument(
        """training_script"""   , type=__A   , 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=__A	)

    return parser.parse_args()







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		List[str] 						=						parse_args()

    # Import training_script as a module.
    _lowerCamelCase     :		List[Any] 						=						Path(args.training_script	)
    sys.path.append(str(script_fpath.parent.resolve()	)	)
    _lowerCamelCase     :		Optional[Any] 						=						script_fpath.stem
    _lowerCamelCase     :		Dict 						=						importlib.import_module(__A	)

    # Patch sys.argv
    _lowerCamelCase     :		Union[str, Any] 						=						[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()




