def test_building_cli_from_function(monkeypatch):
monkeypatch.setattr(sys, 'argv', [
'entry_point',
'--command',
'run',
'--path',
'file.txt',
'--flag',
'1',
])
params = {}
def run(command: str, path: str = 'default.txt', flag: bool = False):
nonlocal params
params['command'] = command
params['path'] = path
params['flag'] = flag
make_cli(run)
assert len(params) == 3
assert params['command'] == 'run'
assert params['path'] == 'file.txt'
assert params['flag']
def test_function_signature_without_args():
def no_args():
pass
spec = make_cli(no_args, spec_only=True)
assert not spec
def test_function_signature_with_required_and_default_params():
def some_optional(first: int, second: bool = False):
pass
spec = make_cli(some_optional, spec_only=True)
assert len(spec) == 2
assert spec[0] == {'name': 'first', 'type': int, 'required': True}
assert spec[1] == {'name': 'second', 'type': bool, 'default': False}
def test_function_signature_without_annotations():
def no_annot(a, b=2.0, c=3):
pass
spec = make_cli(no_annot, spec_only=True)
assert len(spec) == 3
assert spec[0] == {'name': 'a', 'type': str, 'required': True}
assert spec[1] == {'name': 'b', 'type': float, 'default': 2.0}
assert spec[2] == {'name': 'c', 'type': int, 'default': 3}
def test_function_signature_with_all_required_params():
def all_required(integer: int, floating: float, string: str):
pass
spec = make_cli(all_required, spec_only=True)
assert len(spec) == 3
assert spec[0] == {'name': 'integer', 'type': int, 'required': True}
assert spec[1] == {'name': 'floating', 'type': float, 'required': True}
assert spec[2] == {'name': 'string', 'type': str, 'required': True}
def test_function_signature_with_default_params():
def all_optional(x: int = 1, y: int = 2, z: int = 3):
pass
spec = make_cli(all_optional, spec_only=True)
assert len(spec) == 3
assert spec[0] == {'name': 'x', 'type': int, 'default': 1}
assert spec[1] == {'name': 'y', 'type': int, 'default': 2}
assert spec[2] == {'name': 'z', 'type': int, 'default': 3}
def test_printing_parameters_passed_info_function(monkeypatch):
monkeypatch.setattr(
sys, 'argv',
['entry_point', '--first', 'a', '--second', 'b', '--third', 'c'])
expected_output = textwrap.dedent('''
invoked with parameters
-----------------------
first=a
second=b
third=c
''')
def run(first: str, second: str, third: str):
pass
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
make_cli(run, show_params=True)
captured = buf.getvalue()
assert captured == expected_output[1:] # exclude first newline char
anime += [(0, 1)]
playlist = open(m3u, 'w')
playlist.write('#EXTM3U\n\n\n')
playlist.mode = 'a'
for url, title in anime[start - 1:end]:
if url == 0:
break
html = openurl(url).text
title = title.replace('–', '-')
video = findall(r"src='(.+?)'", html)[1]
print(url, title)
print()
print(video)
print('\n')
playlist.write(f'#EXTINF:-1, ANIM3U - {title}\n{video}\n\n')
playlist.close()
make_cli(anim3u)
import importlib
import sys
from ancli import make_cli
try:
entry_point = sys.argv[1]
except IndexError:
print('Error: no entry point name provided!')
sys.exit(1)
try:
module_path, function_name = entry_point.split(':')
except ValueError:
print('Error: entry point name should have format a.b.c:function')
sys.exit(1)
mod = importlib.import_module(module_path)
try:
func = getattr(mod, function_name)
except AttributeError:
print(f'Error: function \'{function_name}\' is not found')
sys.exit(1)
sys.argv = [sys.argv[0]] + sys.argv[2:]
make_cli(func)
bar.update(1)
vis.line(X=[iteration], Y=[avg_loss],
win='loss', name='avg_loss', update='append')
val_dl = dataset['valid']
n = len(val_dl)
model.eval()
with torch.no_grad():
matches = []
with tqdm(total=n) as bar:
for batch in val_dl:
x = batch['features'].to(device)
y = batch['targets'].to(device)
out = model(x)
y_pred = out.softmax(dim=1).argmax(dim=1)
matched = (y == y_pred).detach().cpu().numpy().tolist()
matches.extend(matched)
bar.update(1)
acc = np.mean(matches)
vis.line(X=[epoch], Y=[acc], win='acc', name='val_acc', update='append')
print(f'validation accuracy: {acc:2.2%}')
acc_str = str(int(round(acc * 10_000, 0)))
path = os.path.join(logdir, f'train.{epoch}.{acc_str}.pth')
torch.save(model.state_dict(), path)
if __name__ == '__main__':
if not is_notebook():
ancli.make_cli(train)