def test_reverse_words():
old_limit = config.recursion_limit
config.recursion_limit = 100000
with tempfile.NamedTemporaryFile() as f_save,\
tempfile.NamedTemporaryFile() as f_data:
with open(f_data.name, 'wt') as data:
for i in range(10):
print("A line.", file=data)
main("train", f_save.name, 1, [f_data.name])
config.recursion_limit = old_limit
def test_reverse_words():
old_limit = config.recursion_limit
config.recursion_limit = 100000
with tempfile.NamedTemporaryFile() as f_save,\
tempfile.NamedTemporaryFile() as f_data:
with open(f_data.name, 'wt') as data:
for i in range(10):
print("A line.", file=data)
main("train", f_save.name, 1, [f_data.name])
real_stdin = sys.stdin
sys.stdin = StringIO('abc\n10\n')
main("beam_search", f_save.name, 10)
sys.stdin = real_stdin
config.recursion_limit = old_limit
"""
import logging
import argparse
from reverse_words import main
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
parser = argparse.ArgumentParser(
"Case study of learning to reverse words in a text.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"mode",
choices=["train", "sample", "beam_search"],
help="The mode to run. In the `train` mode a model is trained."
" In the `sample` and `beam_search` modes a trained model is "
" to used reverse words in the input text.")
parser.add_argument(
"save_path",
default="chain",
help="The path to save the training process if the mode"
" is `train` OR path to an `.npz` files with learned"
" parameters if the mode is `test`.")
parser.add_argument("--num-batches",
default=10000,
type=int,
help="Train on this many batches.")
args = parser.parse_args()
main(**vars(args))
from reverse_words import main
if __name__ == "__main__":
logging.basicConfig(
level=logging.INFO,
format="%(asctime)s: %(name)s: %(levelname)s: %(message)s")
parser = argparse.ArgumentParser(
"Case study of learning to reverse words in a text.",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument(
"mode", choices=["train", "sample", "beam_search"],
help="The mode to run. In the `train` mode a model is trained."
" In the `sample` and `beam_search` modes a trained model is "
" to used reverse words in the input text.")
parser.add_argument(
"save_path", default="reverse_words.tar", nargs='?',
help="The path to save the training process if the mode"
" is `train` OR path to an `.tar` files with learned"
" parameters if the mode is `test`.")
parser.add_argument(
"--num-batches", default=10000, type=int,
help="Train on this many batches.")
parser.add_argument(
"--data-path",
help="text file(s) to read for training, with bash-like expansion"
" so wildchars can be used (e.g. data/*.txt)")
args = parser.parse_args()
if args.data_path:
args.data_path = glob.glob(args.data_path)
main(**vars(args))
