code stringlengths 281 23.7M |
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class ConsoleMessageCollection():
class Message():
type: str
text: str
class View():
def __init__(self, console, msg_type):
self.console = console
self.msg_type = msg_type
def messages(self):
if (self.msg_type is None):
return s... |
class EvolutionFactory():
def build(operator: OperatorBase=None) -> EvolutionBase:
primitives = operator.primitive_strings()
if ('Matrix' in primitives):
return MatrixEvolution()
elif ('Pauli' in primitives):
return PauliTrotterEvolution()
else:
ra... |
def load_checkpoint(model, optimizer, filename, map_location, logger=None):
if os.path.isfile(filename):
logger.info("==> Loading from checkpoint '{}'".format(filename))
checkpoint = torch.load(filename, map_location)
epoch = checkpoint.get('epoch', (- 1))
if ((model is not None) and... |
def check_cookie(node: str, pod_template, br_name, cookie, kubecli: KrknKubernetes) -> str:
pod_body = yaml.safe_load(pod_template.render(nodename=node))
logging.info(('Creating pod to query duplicate rules on node %s' % node))
kubecli.create_pod(pod_body, 'default', 300)
try:
cmd = ['chroot', '... |
def test_icdar_dataset():
tmp_dir = tempfile.TemporaryDirectory()
fake_json_file = osp.join(tmp_dir.name, 'fake_data.json')
_create_dummy_icdar_json(fake_json_file)
dataset = IcdarDataset(ann_file=fake_json_file, pipeline=[])
assert (dataset.CLASSES == 'text')
assert (dataset.img_ids == [0, 1])
... |
def test_c3d():
config = get_recognizer_cfg('c3d/c3d_sports1m_16x1x1_45e_ucf101_rgb.py')
config.model['backbone']['pretrained'] = None
recognizer = build_recognizer(config.model)
recognizer.cfg = config
input_shape = (1, 1, 3, 16, 112, 112)
target_layer_name = 'backbone/conv5a/activate'
_do_... |
def parse_key_value_pair(src: str, pos: Pos, parse_float: ParseFloat) -> Tuple[(Pos, Key, Any)]:
(pos, key) = parse_key(src, pos)
try:
char: Optional[str] = src[pos]
except IndexError:
char = None
if (char != '='):
raise suffixed_err(src, pos, 'Expected "=" after a key in a key/v... |
class BaseReport():
model = None
index = None
order = None
DEFAULT_MAX_RESULTS = 65535
select_related_fields = ('advertisement', 'advertisement__flight')
def __init__(self, queryset, index=None, order=None, max_results=None, export=False, **kwargs):
self.queryset = queryset
if in... |
class SimulationParameters(object):
def __init__(self, start_session, end_session, trading_calendar, capital_base=DEFAULT_CAPITAL_BASE, emission_rate='daily', data_frequency='daily', arena='backtest'):
assert (type(start_session) == pd.Timestamp)
assert (type(end_session) == pd.Timestamp)
as... |
class CommunicationParameter2(DataElementGroup):
service_type = IntCodeField(enum=ServiceType2, max_length=2, _d='Kommunikationsdienst')
address = DataElementField(type='an', max_length=512, _d='Kommunikationsadresse')
address_adjunct = DataElementField(type='an', max_length=512, required=False, _d='Kommuni... |
def eval_with_output_tfms(csv_path, model_config_map, checkpoint_path, labelmap, window_size, num_workers, min_segment_dur, n_timebin_from_onoffset=N_TIMEBINS_FROM_ONOFFSET, split='test', spect_scaler_path=None, device='cuda', spect_key='s', timebins_key='t', logger=None, to_annot=False):
from crowsetta import Sequ... |
_performer
def perform_parallel_with_pool(pool, dispatcher, parallel_effects):
def perform_child(index_and_effect):
(index, effect) = index_and_effect
try:
return sync_perform(dispatcher, effect)
except Exception as e:
raise FirstError(exception=e, index=index)
re... |
def param2stroke(param, H, W, meta_brushes):
b = param.shape[0]
param_list = paddle.split(param, 8, axis=1)
(x0, y0, w, h, theta) = [item.squeeze((- 1)) for item in param_list[:5]]
sin_theta = paddle.sin((math.pi * theta))
cos_theta = paddle.cos((math.pi * theta))
index = paddle.full((b,), (- 1)... |
class Item(Resource):
def __init__(self, client=None):
super(Item, self).__init__(client)
self.base_url = (URL.V1 + URL.ITEM_URL)
def create(self, data={}, **kwargs):
url = self.base_url
return self.post_url(url, data, **kwargs)
def fetch(self, item_id, data={}, **kwargs):
... |
.skipif((not PY_3_8_PLUS), reason='cached_property is 3.8+')
def test_slots_getattr_in_superclass__is_called_for_missing_attributes_when_cached_property_present():
(slots=True)
class A():
x = attr.ib()
def __getattr__(self, item):
return item
(slots=True)
class B(A):
... |
def generate_model_output_multiple_sessions() -> Dict[(str, torch._tensor.Tensor)]:
return {'predictions': torch.tensor([[0.1, 0.2, 0.3, 0.4, 0.5, 0.1, 0.2, 0.3]]), 'session_ids': torch.tensor([[1, 1, 1, 1, 1, 2, 2, 2]]), 'labels': torch.tensor([[0.0, 1.0, 0.0, 0.0, 2.0, 2.0, 1.0, 0.0]]), 'weights': torch.tensor([[... |
class AverageMeter(object):
def __init__(self):
self.val = None
self.avg = None
self.sum = None
self.count = None
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
se... |
def _weighted_calibration_update(input: torch.Tensor, target: torch.Tensor, weight: Union[(float, int, torch.Tensor)], *, num_tasks: int) -> Tuple[(torch.Tensor, torch.Tensor)]:
_weighted_calibration_input_check(input, target, weight, num_tasks=num_tasks)
if (isinstance(weight, float) or isinstance(weight, int)... |
def _test_rx(dut, divisor):
def tick(cb=None):
for _ in range(divisor):
if (cb is not None):
(yield from cb())
else:
(yield)
def bit(d, cb=None):
(yield dut.rx.eq(d))
(yield from tick(cb))
def bits(d, cb=None):
for dd in... |
def _get_new_logger(name, filename=None):
new_logger = logging.getLogger(name)
if (filename is None):
handler = logging.StreamHandler()
else:
handler = logging.FileHandler(filename)
handler.setFormatter(LOG_FORMATTER)
new_logger.addHandler(handler)
return new_logger |
class PointnetFPModule(nn.Module):
def __init__(self, mlp, bn=True):
super(PointnetFPModule, self).__init__()
self.mlp = build_shared_mlp(mlp, bn=bn)
def forward(self, unknown, known, unknow_feats, known_feats):
if (known is not None):
(dist, idx) = pointnet2_utils.three_nn(u... |
class TestReadFunc(unittest.TestCase):
def setUp(self):
file = tempfile.NamedTemporaryFile(delete=False)
file.write(TEXT.encode('utf-8'))
file.close()
self._path_str = file.name
self._path_obj = pathlib.Path(self._path_str)
def tearDown(self):
self._path_obj.unlin... |
_settings(PRETIX_WEBHOOK_SECRET='secret')
def test_pretix_webhook_does_not_allow_method(rest_api_client):
rest_api_client.basic_auth('pretix', 'secret')
for method in ['get', 'delete', 'patch']:
response = getattr(rest_api_client, method)(reverse('pretix-webhook'))
assert (response.status_code =... |
.integration
def test_import_and_delete_records(simple_project):
new_record_ids = [4, 5, 6]
test_records = [{'record_id': i} for i in new_record_ids]
res = simple_project.import_records(test_records)
assert (res['count'] == len(test_records))
res = simple_project.import_records(test_records, return_... |
class AIFFChunk(IffChunk):
def parse_header(cls, header):
return struct.unpack('>4sI', header)
def get_class(cls, id):
if (id == 'FORM'):
return AIFFFormChunk
else:
return cls
def write_new_header(self, id_, size):
self._fileobj.write(pack('>4sI', id_,... |
def setup_kubernetes(kubeconfig_path):
if (kubeconfig_path is None):
kubeconfig_path = config.KUBE_CONFIG_DEFAULT_LOCATION
kubeconfig = config.kube_config.KubeConfigMerger(kubeconfig_path)
if (kubeconfig.config is None):
raise Exception(('Invalid kube-config file: %s. No configuration found.... |
def test_filter(hatch, helpers, temp_dir, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), result.output
project_path = (temp... |
def test_section_descendants(db):
instances = Section.objects.all()
for instance in instances:
descendant_ids = []
for section_page in instance.section_pages.order_by('order'):
page = section_page.page
descendant_ids.append(page.id)
page_elements = sorted([*pa... |
def SVHN(train=True, batch_size=None, augm_flag=True, val_size=None):
if (batch_size == None):
if train:
batch_size = train_batch_size
else:
batch_size = test_batch_size
if train:
split = 'train'
else:
split = 'test'
transform_base = [transforms.To... |
class Effect6764(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
lvl = src.level
fit.drones.filteredItemBoost((lambda mod: mod.item.requiresSkill('Ice Harvesting Drone Specialization')), 'duration', (src.getModifiedItemAttr('rofBonus') * lvl), **kwargs)
... |
_tag()
def vendor(vendor_key):
vendor_config = settings.VENDOR[vendor_key]
tags = []
if ('js' in vendor_config):
for file in vendor_config['js']:
if settings.VENDOR_CDN:
tag = '<script src="{url}/{path}" integrity="{sri}" crossorigin="anonymous"></script>'.format(url=vend... |
class TestDebugging():
class _FakePdb():
quitting: bool = False
calls: list[str] = []
def __init__(self, *_: object, **__: object) -> None:
self.calls.append('init')
def reset(self) -> None:
self.calls.append('reset')
def interaction(self, *_: object) ... |
def _convert_xml(in_path: str, out_path: str):
with open(in_path) as f, open(out_path, 'w') as f_o:
for s in f:
ss = s.strip()
if (not ss.startswith('<seg')):
continue
ss = ss.replace('</seg>', '').split('">')
assert (len(ss) == 2)
... |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--data_dir', default='./data', type=str)
parser.add_argument('--model_name_or_path', default='bert-base-cased', type=str)
parser.add_argument('--max_seq_length', default=512, type=int)
parser.add_argument('--batch_size', default=64,... |
.parametrize('given, expected, uncertainty', [(0.0, 1.0, 0.0), (((1.0 / 2.0) * np.pi), 0.0, 0.0), (np.pi, (+ 1.0), 0.0), (((3.0 / 2.0) * np.pi), 0.0, 0.0), ((2.0 * np.pi), (+ 1.0), 0.0)])
def test_figure_eight(given, expected, uncertainty):
assert (figure_eight(given) == pytest.approx(expected, uncertainty)) |
def myConvTranspose(nf, n_dims, prefix=None, suffix=None, ks=3, strides=1, kernel_initializer=None, bias_initializer=None):
if (kernel_initializer is None):
kernel_initializer = 'glorot_uniform'
if (bias_initializer is None):
bias_initializer = 'zeros'
if (n_dims == 2):
if (not isins... |
def _get_entityv2_instances_meta():
thing_ids = [k['id'] for k in EntityV2_instance_CATEGORIES]
thing_dataset_id_to_contiguous_id = {k: i for (i, k) in enumerate(thing_ids)}
thing_classes = [k['name'] for k in EntityV2_instance_CATEGORIES]
ret = {'thing_dataset_id_to_contiguous_id': thing_dataset_id_to_... |
.parametrize('examplefun', examplefunctions)
.filterwarnings('ignore:numpy.dtype size changed')
.filterwarnings('ignore:numpy.ufunc size changed')
def test_example(examplefun, capsys, recwarn):
examplefun()
captured = capsys.readouterr()
failconditions = [((not (len(captured.out) > 0)), 'Example {} did not ... |
('a tab_stops having {count} tab stops')
def given_a_tab_stops_having_count_tab_stops(context, count):
paragraph_idx = {'0': 0, '3': 1}[count]
document = Document(test_docx('tab-stops'))
paragraph_format = document.paragraphs[paragraph_idx].paragraph_format
context.tab_stops = paragraph_format.tab_stops |
def test_thread_cache_deref() -> None:
res = [False]
class del_me():
def __call__(self) -> int:
return 42
def __del__(self) -> None:
res[0] = True
q: Queue[Outcome[int]] = Queue()
def deliver(outcome: Outcome[int]) -> None:
q.put(outcome)
start_thread_... |
def assert_applied_techniques(output_model, acc, encoding_path, target_acc, bn_folded_acc, cle_acc, adaround_acc, results_dir):
html_path = os.path.join(results_dir, 'diagnostics.html')
with open(html_path) as f:
html_parsed = BeautifulSoup(f.read(), features='html.parser')
assert output_model.appli... |
class WeightTensorUtils():
def get_tensor_index_in_given_op(input_op: tf.Operation) -> int:
if (input_op.type not in constants.OP_WEIGHT_INDICES):
raise ValueError((('Op type: ' + input_op.type) + ' does not contain weights!'))
return constants.OP_WEIGHT_INDICES[input_op.type]
def ge... |
def test_cells():
row_key = b'cell-test'
col = b'cf1:col1'
table.put(row_key, {col: b'old'}, timestamp=1234)
table.put(row_key, {col: b'new'})
with assert_raises(TypeError):
table.cells(row_key, col, versions='invalid')
with assert_raises(TypeError):
table.cells(row_key, col, ver... |
class warmupLR(toptim._LRScheduler):
def __init__(self, optimizer, lr, warmup_steps, momentum, decay):
self.optimizer = optimizer
self.lr = lr
self.warmup_steps = warmup_steps
self.momentum = momentum
self.decay = decay
if (self.warmup_steps < 1):
self.war... |
def get_color(colorscale, loc):
cv = ColorscaleValidator('colorscale', '')
colorscale = cv.validate_coerce(colorscale)
(locs, colors) = zip(*colorscale)
colors = standardize_colors(colors, colortype='rgb')
colorscale = list(zip(locs, colors))
if isinstance(loc, Iterable):
return [_get_co... |
def mix_slices_in_checkers(slice1, slice2, checker_size=cfg.default_checkerboard_size):
checkers = _get_checkers(slice1.shape, checker_size)
if ((slice1.shape != slice2.shape) or (slice2.shape != checkers.shape)):
raise ValueError('size mismatch between cropped slices and checkers!!!')
mixed = slice... |
def convert_weight_and_push(name: str, config: ResNetConfig, save_directory: Path, push_to_hub: bool=True):
print(f'Converting {name}...')
with torch.no_grad():
from_model = timm.create_model(name, pretrained=True).eval()
our_model = ResNetForImageClassification(config).eval()
module_tra... |
class Tencode_endian(TestCase):
def test_other(self):
assert (encode_endian(u'a', 'latin-1') == b'\xe4')
assert (encode_endian(u'a', 'utf-8') == b'\xc3\xa4')
with self.assertRaises(LookupError):
encode_endian(u'', 'nopenope')
with self.assertRaises(UnicodeEncodeError):
... |
class TestOtherFS(fake_filesystem_unittest.TestCase):
def setUp(self):
self.setUpPyfakefs()
.dict(os.environ, {'HOME': '/home/john'})
def test_real_file_with_home(self):
self.fs.is_windows_fs = (os.name != 'nt')
if self.fs.is_windows_fs:
self.fs.is_macos = False
s... |
def stop_server_only(when_stopped=None, interactive=False):
def _server_stopped(*args):
if when_stopped:
when_stopped()
else:
print('... Server stopped.')
_reactor_stop()
def _portal_running(response):
(_, srun, _, _, _, _) = _parse_status(response)
... |
def balanced_accuracy(tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str]=None, class_weights: Optional[List[float]]=None, zero_division: Union[(str, float)]=1.0) -> torch.Tensor:
return _compute_metric(_balanced_accuracy, tp, fp, fn, tn, reduction=reduct... |
class DemoTextItem(DemoItem):
(STATIC_TEXT, DYNAMIC_TEXT) = range(2)
def __init__(self, text, font, textColor, textWidth, parent=None, type=STATIC_TEXT, bgColor=QColor()):
super(DemoTextItem, self).__init__(parent)
self.type = type
self.text = text
self.font = font
self.t... |
def test_with_constraint() -> None:
dependency = Dependency('foo', '^1.2.3', optional=True, groups=['dev'], allows_prereleases=True, extras=['bar', 'baz'])
dependency.marker = parse_marker('python_version >= "3.6" and python_version < "4.0"')
dependency.transitive_marker = parse_marker('python_version >= "3... |
class TimeOracle():
def __init__(self, timeline_file):
self.__timeline = Timeline.from_pickle(timeline_file)
self.__costs = self.__timeline_analyser(self.__timeline)
def __timeline_analyser(timeline):
costs = {}
for device in timeline._run_metadata.step_stats.dev_stats:
... |
def test_font_file():
ff1 = _fontfinder.FontFile('x', 'Foo Sans', 'Regular', {1, 2, 3})
assert (ff1.filename == 'x')
assert (ff1.name == 'FooSans-Regular')
assert (ff1.family == 'Foo Sans')
assert (ff1.variant == 'Regular')
assert (ff1.weight == 400)
assert (ff1.style == 'normal')
assert... |
def init(disp, info):
disp.extension_add_method('display', 'xtest_get_version', get_version)
disp.extension_add_method('window', 'xtest_compare_cursor', compare_cursor)
disp.extension_add_method('display', 'xtest_fake_input', fake_input)
disp.extension_add_method('display', 'xtest_grab_control', grab_co... |
(frozen=True)
class ContractReceiveChannelBatchUnlock(ContractReceiveStateChange):
canonical_identifier: CanonicalIdentifier
receiver: Address
sender: Address
locksroot: Locksroot
unlocked_amount: TokenAmount
returned_tokens: TokenAmount
def __post_init__(self) -> None:
super().__pos... |
class Model(nn.Module):
def __init__(self, feature_dim=128, resnet_depth=18):
super(Model, self).__init__()
self.f = []
if (resnet_depth == 18):
my_resnet = resnet18()
resnet_output_dim = 512
elif (resnet_depth == 34):
my_resnet = resnet34()
... |
def share_file(comm, path):
(localrank, _) = get_local_rank_size(comm)
if (comm.Get_rank() == 0):
with open(path, 'rb') as fh:
data = fh.read()
comm.bcast(data)
else:
data = comm.bcast(None)
if (localrank == 0):
os.makedirs(os.path.dirname(path), exist... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--profile-tool', metavar='TOOL', action='store', choices=['kcachegrind', 'snakeviz', 'gprof2dot', 'tuna', 'none'], default='snakeviz', help='The tool to use to view the profiling data')
parser.add_argument('--profile-file', metavar='F... |
def test_tags_command(capsys, wheelpath):
args = ['tags', '--python-tag', 'py3', '--abi-tag', 'cp33m', '--platform-tag', 'linux_x86_64', '--build', '7', str(wheelpath)]
p = parser()
args = p.parse_args(args)
args.func(args)
assert wheelpath.exists()
newname = capsys.readouterr().out.strip()
... |
_grad()
def evaluate_a2d(model, data_loader, postprocessor, device, args):
model.eval()
predictions = []
metric_logger = utils.MetricLogger(delimiter=' ')
header = 'Test:'
for (samples, targets) in metric_logger.log_every(data_loader, 10, header):
image_ids = [t['image_id'] for t in targets... |
class BreakRop(Op):
__props__ = ()
def make_node(self, x):
return Apply(self, [x], [x.type()])
def perform(self, node, inp, out_):
(x,) = inp
(out,) = out_
out[0] = x
def grad(self, inp, grads):
return [grad_undefined(self, 0, inp[0])]
def R_op(self, inputs, e... |
class BiSeNet_res18(nn.Module):
def __init__(self, input_h, input_w, n_classes=19):
super().__init__()
self.spatial_path = SpatialPath()
self.context_path = ContextPath(input_h, input_w)
self.ffm = FFM(input_h, input_w, 1152)
self.pred = nn.Conv2d(1152, n_classes, kernel_size... |
def process_one_shard(corpus_params, params):
(corpus_type, fields, src_reader, tgt_reader, opt, existing_fields, src_vocab, tgt_vocab) = corpus_params
(i, (src_shard, tgt_shard, maybe_id, filter_pred)) = params
sub_sub_counter = defaultdict(Counter)
assert (len(src_shard) == len(tgt_shard))
logger.... |
()
('new_version')
def bump_version(new_version: str) -> None:
base_dir = pathlib.Path(__file__).parent
replace_version((base_dir / 'pyproject.toml'), 'version', new_version)
replace_version((base_dir / 'src/cryptography/__about__.py'), '__version__', new_version)
replace_version((base_dir / 'vectors/py... |
def gen_src0_dep_nottaken_test():
return [gen_br2_src0_dep_test(5, 'bne', 1, 1, False), gen_br2_src0_dep_test(4, 'bne', 2, 2, False), gen_br2_src0_dep_test(3, 'bne', 3, 3, False), gen_br2_src0_dep_test(2, 'bne', 4, 4, False), gen_br2_src0_dep_test(1, 'bne', 5, 5, False), gen_br2_src0_dep_test(0, 'bne', 6, 6, False)... |
class TestRateShiftCoefficient():
def assert_f_equals_rate_shift(f, coeffs, tlist, **kw):
def g(t):
return (2 * np.abs(min(([0] + [np.real(c(t)) for c in coeffs]))))
assert_functions_equal(f, g, tlist, **kw)
def test_call(self, rates):
rs = RateShiftCoefficient(rates.coeffs)
... |
class FrankWolfeSSVM(BaseSSVM):
def __init__(self, model, max_iter=1000, C=1.0, verbose=0, n_jobs=1, show_loss_every=0, logger=None, batch_mode=False, line_search=True, check_dual_every=10, tol=0.001, do_averaging=True, sample_method='perm', random_state=None):
if (n_jobs != 1):
warnings.warn('F... |
class CovarianceNotPosDefWarning(QtWidgets.QMessageBox):
def __init__(self, model, *args, **kwargs):
QtWidgets.QMessageBox.__init__(self, *args, **kwargs)
self.setIcon(QtWidgets.QMessageBox.Warning)
self.setWindowTitle('Covariance Warning')
self.setText('<b><span style="font-family: ... |
def test_setuptools_version_keyword_ensures_regex(wd: WorkDir, monkeypatch: pytest.MonkeyPatch) -> None:
wd.commit_testfile('test')
wd('git tag 1.0')
monkeypatch.chdir(wd.cwd)
from setuptools_scm._integration.setuptools import version_keyword
import setuptools
dist = setuptools.Distribution({'na... |
def install_sundials(download_dir, install_dir):
logger = logging.getLogger('scikits.odes setup')
sundials_version = '6.5.0'
try:
subprocess.run(['cmake', '--version'])
except OSError:
raise RuntimeError('CMake must be installed to build SUNDIALS.')
url = (' + 'sundials/releases/down... |
class CocoGenerator(Generator):
def __init__(self, data_dir, set_name, **kwargs):
self.data_dir = data_dir
self.set_name = set_name
self.coco = COCO(os.path.join(data_dir, 'annotations', (('instances_' + set_name) + '.json')))
self.image_ids = self.coco.getImgIds()
self.load_... |
def Unit(st, *args, **kwargs):
import astropy.units as u
try:
st = st.replace('', 'u')
st = st.replace('/molecule', '')
st = st.replace('/molec', '')
except AttributeError:
pass
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*multiple slashes.... |
def reduce_embd_id_len(E1, tasks, cutoff=100):
if (len(tasks) > 1):
raise NotImplementedError('Not implemented minimum length with multiple tasks yet.')
E1_short = []
for sub in E1:
d = np.delete(sub, np.s_[cutoff:], 1)
E1_short.append(d)
assert (E1_short[(- 1)].shape == (E1[(- 1... |
class ClassDefTransformer(ast.NodeTransformer):
def __init__(self, class_replace_map: Optional[Dict[(str, str)]]):
self.class_replace_map = (class_replace_map if (class_replace_map is not None) else {})
def visit_ClassDef(self, node: ast.ClassDef) -> ast.AST:
for (old_value, new_value) in self.c... |
def _convert_stix_campaigns_to_dict(stix_attack_data):
attack_data = []
for stix_campaign in stix_attack_data:
campaign = json.loads(stix_campaign.serialize(), object_hook=_date_hook)
campaign['campaign_id'] = get_attack_id(stix_campaign)
attack_data.append(campaign)
return attack_da... |
def test_FullMultiplicativeForm_only_minimize():
dm = skcriteria.mkdm(matrix=[[1, 2, 3], [4, 5, 6], [7, 8, 9]], objectives=[min, min, min])
expected = RankResult('FullMultiplicativeForm', ['A0', 'A1', 'A2'], [1, 2, 3], {'score': np.log([398., 19., 4.])})
transformer = VectorScaler(target='matrix')
dm = ... |
def _filter_commands(ctx, commands=None):
lookup = getattr(ctx.command, 'commands', {})
if ((not lookup) and isinstance(ctx.command, click.MultiCommand)):
lookup = _get_lazyload_commands(ctx.command)
if (commands is None):
return sorted(lookup.values(), key=(lambda item: item.name))
name... |
class WeightNormLinear(nn.Linear):
def __init__(self, in_features, out_features, init_scale=1.0, polyak_decay=0.9995):
super(WeightNormLinear, self).__init__(in_features, out_features, bias=True)
self.V = self.weight
self.g = Parameter(torch.Tensor(out_features))
self.b = self.bias
... |
('/v1/user/quota/<quota_id>/limit')
_if(features.SUPER_USERS)
_if(features.QUOTA_MANAGEMENT)
class UserQuotaLimitList(ApiResource):
_user_admin()
('listUserQuotaLimit')
def get(self, quota_id):
parent = get_authenticated_user()
quota = get_quota(parent.username, quota_id)
return [lim... |
class TestSnapshotWithDTensor(DTensorTestBase):
def _create_model(self, seed: int, optim_lr: float, device_mesh: Optional[DeviceMesh]=None):
torch.manual_seed(seed)
if device_mesh:
model = FSDP(DummyModel().cuda(), device_mesh=device_mesh, sharding_strategy=ShardingStrategy.HYBRID_SHARD)... |
def purerpc_server_wrong_method_name_port(greeter_pb2):
service = purerpc.Service('Greeter')
('SomeOtherMethod')
async def say_hello(message: greeter_pb2.HelloRequest) -> greeter_pb2.HelloReply:
return greeter_pb2.HelloReply(message=('Hello, ' + message.name))
with run_purerpc_service_in_process... |
def load_groups(cli, manage_dict):
cli.manage_groups = {}
groups = manage_dict.get('groups')
if (not groups):
return
is_dict = isinstance(groups[0], dict)
for group in groups:
if is_dict:
for (group_name, data) in group.items():
data = (data or {})
... |
def test_run_strict_exception_groups_nursery_override() -> None:
async def main() -> NoReturn:
async with _core.open_nursery(strict_exception_groups=False):
raise Exception('foo')
with pytest.raises(Exception, match='^foo$'):
_core.run(main, strict_exception_groups=True) |
class RTLIRConversionError(Exception):
def __init__(self, obj, msg):
obj = str(obj)
(_, _, tb) = sys.exc_info()
tb_info = traceback.extract_tb(tb)
(fname, line, func, text) = tb_info[(- 1)]
return super().__init__(f'''
In file {fname}, Line {line}, Method {func}:
Error trying... |
def _concat_dataset(cfg):
ann_files = cfg['ann_file']
img_prefixes = cfg.get('img_prefix', None)
partial_files = cfg.get('partial_file', None)
pseudo_files = cfg.get('pseudo_file', None)
datasets = []
num_dset = len(ann_files)
for i in range(num_dset):
data_cfg = copy.deepcopy(cfg)
... |
def normal_ordered_ladder_term(term, coefficient, parity=(- 1)):
term = list(term)
if (parity == (- 1)):
Op = FermionOperator
elif (parity == 1):
Op = BosonOperator
ordered_term = Op()
for i in range(1, len(term)):
for j in range(i, 0, (- 1)):
right_operator = ter... |
def shortestdistance(ifst, reverse=False, source=_fst.NO_STATE_ID, queue_type='auto', delta=_weight.DELTA):
try:
queue_type = _getters.GetQueueType(queue_type)
except ValueError:
raise ValueError('Unknown queue type: {!r}'.format(queue_type))
return ifst._ops.shortestdistance(ifst, reverse, ... |
class NNQFunction(MLPFunction):
def __init__(self, env_spec, hidden_layer_sizes=(100, 100), name='qf', observation_ph=None, action_ph=None):
Serializable.quick_init(self, locals())
self._Da = env_spec.action_space.flat_dim
self._Do = env_spec.observation_space.flat_dim
self._obs_pl =... |
def determineIndentationAndTrailingWS(text):
text = text[:32768]
indents = {}
indents[(- 1)] = 0
trailing = 0
lines = text.splitlines()
lines.insert(0, '')
for i in range(len(lines)):
line = lines[i]
lineA = line.lstrip()
lineB = line.rstrip()
lineC = lineA.rs... |
def setup_sentry() -> None:
sentry_logging = LoggingIntegration(level=logging.DEBUG, event_level=logging.WARNING)
sentry_sdk.init(dsn=constants.Bot.sentry_dsn, integrations=[sentry_logging, RedisIntegration()], release=f'{constants.GIT_SHA}', traces_sample_rate=0.5, _experiments={'profiles_sample_rate': 0.5}) |
class ModuleNodeTest(ModuleLoader, unittest.TestCase):
def test_special_attributes(self) -> None:
self.assertEqual(len(self.module.getattr('__name__')), 2)
self.assertIsInstance(self.module.getattr('__name__')[0], nodes.Const)
self.assertEqual(self.module.getattr('__name__')[0].value, 'data.... |
class TestCheng2020():
.parametrize('func,cls', ((cheng2020_anchor, Cheng2020Anchor), (cheng2020_attn, Cheng2020Attention)))
def test_anchor_ok(self, func, cls):
for i in range(1, 4):
net = func(i, metric='mse')
assert isinstance(net, cls)
assert (net.state_dict()['g_... |
def D_r1(D, reals, real_labels=None, gamma=10, *args, **kwargs):
loss = None
reg = None
if gamma:
reals.requires_grad_(True)
real_scores = D(reals, labels=real_labels)
reg = _grad_reg(input=reals, output=real_scores, gamma=gamma, retain_graph=False).float()
return (loss, reg) |
class Cholesky(Op):
__props__ = ('lower', 'destructive', 'on_error')
gufunc_signature = '(m,m)->(m,m)'
def __init__(self, *, lower=True, on_error='raise'):
self.lower = lower
self.destructive = False
if (on_error not in ('raise', 'nan')):
raise ValueError('on_error must b... |
def deserialize_exports(w_exports):
(r_exports, exports_len) = to_rpython_list(w_exports)
exports = {}
for (i, exp) in enumerate(r_exports):
if looks_like_an_export(exp):
k = exp.cdr().car()
gen_int_id = exp.cdr().cdr().car()
ext_id = exp.cdr().cdr().cdr().car()
... |
(hookwrapper=True, trylast=True)
def pytest_runtest_teardown(item):
def report():
gevent.util.print_run_info()
raise RetryTestError(f'Teardown timeout >{item.timeout_setup_and_call}s. This must not happen, when the teardown times out not all finalizers got a chance to run. This means not all fixture... |
def test_struct_inheritance2():
m = run_mod("\n #lang pycket\n (require racket/private/kw)\n\n (struct posn (x y))\n (define (raven-constructor super-type)\n (struct raven ()\n #:super super-type\n #:transparent\n #:property prop:procedure (lambda ... |
def build(setup_kwargs: Any) -> None:
if os.environ.get('SKIP_CYTHON', False):
return
try:
from Cython.Build import cythonize
setup_kwargs.update(dict(ext_modules=cythonize(['src/zeroconf/_dns.py', 'src/zeroconf/_cache.py', 'src/zeroconf/_history.py', 'src/zeroconf/_record_update.py', 's... |
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