

import string
from math import logaa






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     int:
	__lowerCamelCase				:				Tuple					       = document.translate(
	    str.maketrans(''	, ''	, string.punctuation			)			).replace('\n'	, ''			)
	__lowerCamelCase				:				int					       = document_without_punctuation.split(' '			)  # word tokenization
	return len([word for word in tokenize_document if word.lower() == term.lower()]			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     tuple[int, int]:
	__lowerCamelCase				:				List[Any]					       = corpus.lower().translate(
	    str.maketrans(''	, ''	, string.punctuation			)			)  # strip all punctuation and replace it with ''
	__lowerCamelCase				:				Union[str, Any]					       = corpus_without_punctuation.split('\n'			)
	__lowerCamelCase				:				Optional[int]					       = term.lower()
	return (len([doc for doc in docs if term in doc]			), len(lowerCamelCase__			))






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=False			)  ->     float:
	if smoothing:
		if n == 0:
			raise ValueError('log10(0) is undefined.'			)
		return round(1 + logaa(n / (1 + df)			)	, 3			)

	if df == 0:
		raise ZeroDivisionError('df must be > 0'			)
	elif n == 0:
		raise ValueError('log10(0) is undefined.'			)
	return round(logaa(n / df			)	, 3			)






def    SCREAMING_SNAKE_CASE__       (  lowerCamelCase__	, lowerCamelCase__			)  ->     float:
	return round(tf * idf	, 3			)







'''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					(						)      ->			Any:
     """simple docstring"""




     _SCREAMING_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=_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        _lowerCAmelCase					(						)      ->			Optional[int]:
     """simple docstring"""




     _SCREAMING_SNAKE_CASE							    =parse_args()

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

     # Patch sys.argv
     _SCREAMING_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()
