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def test_octahedron():
octahedron = Octahedron(12.0, name='octahedron', color='purple')
assert (octahedron.name == 'octahedron')
assert (octahedron.__str__() == 'Octahedron octahedron color:purple material:default radius:12.0')
assert (octahedron.__repr__() == 'Octahedron')
assert (octahedron.radius... |
class NetworkDescription(Description):
('NetworkDescription', rus.optional(dict))
(rus.nothing)
def __init__(self, d=None):
if d:
if ((c.RTYPE in d) and (d[c.RTYPE] != c.NETWORK)):
raise se.BadParameter(("Cannot create NetworkResource type '%s'" % d[c.RTYPE]))
sel... |
class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer):
def __init__(self, cfg: DictConfig, params, fp32_optimizer, fp32_params, **kwargs):
super().__init__(cfg.optimizer)
self.fp16_params = params
self.fp32_optimizer = fp32_optimizer
self.fp32_params = fp32_params
... |
class SponsorshipQuerySetTests(TestCase):
def setUp(self):
self.user = baker.make(settings.AUTH_USER_MODEL)
self.contact = baker.make('sponsors.SponsorContact', user=self.user)
def test_visible_to_user(self):
visible = [baker.make(Sponsorship, submited_by=self.user, status=Sponsorship.AP... |
def get_mesh_for_testing(xpts=None, rpts=10, Rpts=10, ypts=15, zpts=15, rcellpts=15, geometry=None, cc_submesh=None):
param = pybamm.ParameterValues(values={'Electrode width [m]': 0.4, 'Electrode height [m]': 0.5, 'Negative tab width [m]': 0.1, 'Negative tab centre y-coordinate [m]': 0.1, 'Negative tab centre z-coo... |
def create(feedback, device_uuid):
device = Device.objects.get(uuid=device_uuid)
schedule_item_id = feedback.validated_data['schedule_item_id']
try:
with transaction.atomic():
(text, choices) = ([], [])
if feedback.validated_data.get('text'):
text = create_tex... |
def test_should_follow_specification_comparison():
chain = ['1.0.0-alpha', '1.0.0-alpha.1', '1.0.0-beta.2', '1.0.0-beta.11', '1.0.0-rc.1', '1.0.0', '1.3.7+build']
versions = zip(chain[:(- 1)], chain[1:])
for (low_version, high_version) in versions:
assert (compare(low_version, high_version) == (- 1)... |
class CustomRouterMixin(CreateDataMixin):
router_class = 'rapidsms.router.blocking.BlockingRouter'
backends = {}
handlers = None
def _pre_rapidsms_setup(self):
self._RAPIDSMS_HANDLERS = getattr(settings, 'RAPIDSMS_HANDLERS', None)
self.set_handlers()
self._INSTALLED_BACKENDS = ge... |
class BackupDB(ProductionCommand):
keyword = 'backupdb'
def assemble(self):
super().assemble()
self.parser.add_argument('-d', '--directory', dest='directory', default='/tmp', help='the directory to back up to')
self.parser.add_argument('-U', '--super-user-name', dest='super_user_name', d... |
class FollowedBy(ParseElementEnhance):
def __init__(self, expr):
super().__init__(expr)
self.mayReturnEmpty = True
def parseImpl(self, instring, loc, doActions=True):
(_, ret) = self.expr._parse(instring, loc, doActions=doActions)
del ret[:]
return (loc, ret) |
class TreeConstraintsSize(TreeConstraints):
def branch(self, spec: TreeSpec) -> TreeSpec:
depth = (spec.depth + 1)
leaves = (spec.leaves * self.branch_factor)
size = (spec.size + leaves)
leaf_size = (self.total // leaves)
return TreeSpec(depth=depth, size=size, leaves=leaves,... |
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
g_data = parser.add_argument_group('Data')
g_data.add_argument('--dataset_path', type=str, default='/BS/xxie-3/static00/newdata', help='path to dataset')
g_data.add_argument('--exp_nam... |
def draw_plot(model, train_x, train_y, test_x, test_y, inducing_x, inducing_f, ax, color, show_legend=False):
inducing_x = inducing_x.detach().cpu()
inducing_f = inducing_f.detach().cpu()
(train_x, train_y) = (train_x.cpu().squeeze((- 1)), train_y.cpu().squeeze((- 1)))
(test_x, test_y) = (test_x.cpu().s... |
def when_program_starts_5(self):
self.wait(3.0)
self.add_value_to_list('elle', 'bob')
if ((5.0 % 'NO TRANSLATION: data_lengthoflist') > 4):
self.create_clone_of('NO TRANSLATION: control_create_clone_of_menu')
self.create_clone_of('NO TRANSLATION: control_create_clone_of_menu') |
class HallucinationOrigin(nn.Module):
def __init__(self, scala=8, features=64, n_residual_blocks=9, big_short_connect=False, output_channel=1):
super(HallucinationOrigin, self).__init__()
self.n_residual_blocks = n_residual_blocks
self.scala = scala
self.connect = big_short_connect
... |
class ResNet18(Module):
def __init__(self):
super(ResNet18, self).__init__()
self.conv1 = Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.ratl = Rational(approx_func='relu', cuda=False)
se... |
class MixedInt8TestPipeline(BaseMixedInt8Test):
def setUp(self):
super().setUp()
def tearDown(self):
del self.pipe
gc.collect()
torch.cuda.empty_cache()
def test_pipeline(self):
self.pipe = pipeline('text-generation', model=self.model_name, model_kwargs={'device_map':... |
class RequestInterceptor(QWebEngineUrlRequestInterceptor):
def __init__(self, parent=None):
super().__init__(parent)
self._resource_types = {QWebEngineUrlRequestInfo.ResourceType.ResourceTypeMainFrame: interceptors.ResourceType.main_frame, QWebEngineUrlRequestInfo.ResourceType.ResourceTypeSubFrame: ... |
def unlinearize_term(index, n_orbitals):
if (not index):
return ()
elif (0 < index < (1 + (n_orbitals ** 2))):
shift = 1
new_index = (index - shift)
q = (new_index // n_orbitals)
p = (new_index - (q * n_orbitals))
assert (index == ((shift + p) + (q * n_orbitals)))... |
class NetworkBlock(nn.Module):
def __init__(self, nb_layers, in_planes, out_planes, block, stride, dropRate=0.0, constr_activation=None):
super(NetworkBlock, self).__init__()
self.constr_activation = constr_activation
self.layer = self._make_layer(block, in_planes, out_planes, nb_layers, str... |
def simplex_projection(v, b=1):
v = np.asarray(v)
p = len(v)
v = ((v > 0) * v)
u = np.sort(v)[::(- 1)]
sv = np.cumsum(u)
rho = np.where((u > ((sv - b) / np.arange(1, (p + 1)))))[0][(- 1)]
theta = np.max([0, ((sv[rho] - b) / (rho + 1))])
w = (v - theta)
w[(w < 0)] = 0
return w |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input_dir', help='Location of LLaMA weights, which contains tokenizer.model and model folders')
parser.add_argument('--model_size', choices=['7B', '13B', '30B', '65B', 'tokenizer_only'])
parser.add_argument('--output_dir', help='Locat... |
def aead_test(backend, cipher_factory, mode_factory, params):
if ((mode_factory is GCM) and backend._fips_enabled and (len(params['iv']) != 24)):
pytest.skip('Non-96-bit IVs unsupported in FIPS mode.')
tag = binascii.unhexlify(params['tag'])
mode = mode_factory(binascii.unhexlify(params['iv']), tag,... |
def print_table(table: List[List[str]]):
col_lens = ([0] * len(table[0]))
for row in table:
for (i, cell) in enumerate(row):
col_lens[i] = max(len(cell), col_lens[i])
formats = [('{0:<%d}' % x) for x in col_lens]
for row in table:
print(' '.join((formats[i].format(row[i]) for... |
class RevUnit(nn.Module):
def __init__(self, in_channels, out_channels, stride, bottleneck, preactivate):
super(RevUnit, self).__init__()
self.resize_identity = ((in_channels != out_channels) or (stride != 1))
body_class = (RevResBottleneck if bottleneck else RevResBlock)
if ((not se... |
def Darken(color, factor):
(r, g, b, a) = color
factor = min(max(factor, 0), 1)
factor = (1 - factor)
r *= factor
g *= factor
b *= factor
r = min(max(r, 0), 255)
b = min(max(b, 0), 255)
g = min(max(g, 0), 255)
return wx.Colour(round(r), round(g), round(b), round(a)) |
def test_edge_edge_degenerate_second_edge(test, device):
p1_h = np.array([[1, 0, 0]])
q1_h = np.array([[0, 1, 0]])
p2_h = np.array([[1, 1, 0]])
q2_h = np.array([[1, 1, 0]])
res = run_closest_point_edge_edge(p1_h, q1_h, p2_h, q2_h, device)
st0 = res[0]
test.assertAlmostEqual(st0[0], 0.5)
... |
class RPCA_gpu():
def __init__(self, D, mu=None, lmbda=None):
self.D = D
self.S = torch.zeros_like(self.D)
self.Y = torch.zeros_like(self.D)
self.mu = (mu or (np.prod(self.D.shape) / (4 * self.norm_p(self.D, 2))).item())
self.mu_inv = (1 / self.mu)
self.lmbda = (lmbda... |
(3, 'tokens', 'where', 'join')
def searchItemsRegex(tokens, where=None, join=None, eager=None):
if ((not isinstance(tokens, (tuple, list))) or (not all((isinstance(t, str) for t in tokens)))):
raise TypeError('Need tuple or list of strings as argument')
if (join is None):
join = tuple()
if (... |
.parametrize(('yanked', 'expected_yanked', 'expected_yanked_reason'), [(True, True, ''), (False, False, ''), ('the reason', True, 'the reason'), ('', True, '')])
def test_package_pep592_yanked(yanked: (str | bool), expected_yanked: bool, expected_yanked_reason: str) -> None:
package = Package('foo', '1.0', yanked=y... |
class ColorTest(unittest.TestCase):
def test_constructor_should_accept_integer(self):
color = Color(12345)
self.assertEqual(12345, color.rgb_val)
def test_constructor_should_accept_integer_string(self):
color = Color('12345')
self.assertEqual(12345, color.rgb_val)
def test_co... |
class BlogEntry(models.Model):
title = models.CharField(max_length=200)
summary = models.TextField(blank=True)
pub_date = models.DateTimeField()
url = models.URLField('URL')
feed = models.ForeignKey('Feed', on_delete=models.CASCADE)
class Meta():
verbose_name = 'Blog Entry'
verbo... |
class BlockDataset(torch.utils.data.Dataset):
def __init__(self, dataset: torch.utils.data.Dataset, batch_size: int=100, block_size: int=10000) -> None:
assert (block_size >= batch_size), 'Block size should be > batch size.'
self.block_size = block_size
self.batch_size = batch_size
s... |
def install_pypy(tmp: Path, url: str) -> Path:
pypy_tar_bz2 = url.rsplit('/', 1)[(- 1)]
extension = '.tar.bz2'
assert pypy_tar_bz2.endswith(extension)
installation_path = (CIBW_CACHE_PATH / pypy_tar_bz2[:(- len(extension))])
with FileLock((str(installation_path) + '.lock')):
if (not installa... |
def preprocess(csv_file, json_file):
with open(json_file, 'w') as fout:
with open(csv_file, 'rb') as fin:
lines = csv.reader(fin)
for items in lines:
text_data = convert_multi_slots_to_single_slots(items[1:])
text_data = clean_str(text_data)
... |
class TimeMeter(Meter):
def __init__(self, init: int=0, n: int=0, round: Optional[int]=None):
self.round = round
self.reset(init, n)
def reset(self, init=0, n=0):
self.init = init
self.start = time.time()
self.n = n
def update(self, val=1):
self.n += val
d... |
class TestVSCFInitialPoint(QiskitNatureTestCase):
def setUp(self) -> None:
super().setUp()
self.vscf_initial_point = VSCFInitialPoint()
self.ansatz = Mock(spec=UVCC)
self.ansatz.reps = 1
self.excitation_list = [((0,), (1,))]
self.ansatz.excitation_list = self.excitati... |
.parametrize('method', [CGA.round, pytest.param(CGA.flat, marks=pytest.mark.xfail(raises=AssertionError, reason='gh-100'))])
def test_from_points_construction(cga, method):
blades = cga.layout.blades
e1 = blades['e1']
e2 = blades['e2']
e3 = blades['e3']
assert (method(cga, e1, e2, e3).mv == method(c... |
def windowed_groupby_accumulator(acc, new, diff=None, window=None, agg=None, grouper=None, with_state=False):
if ((agg.grouper is None) and isinstance(new, tuple)):
(new, grouper) = new
else:
grouper = None
size = GroupbySize(agg.columns, agg.grouper)
if (acc is None):
acc = {'df... |
class AsyncCallbackManagerForLLMRun(AsyncRunManager, LLMManagerMixin):
async def on_llm_new_token(self, token: str, **kwargs: Any) -> None:
(await _ahandle_event(self.handlers, 'on_llm_new_token', 'ignore_llm', token, run_id=self.run_id, parent_run_id=self.parent_run_id, **kwargs))
async def on_llm_end(... |
class MobileHairNetV2(nn.Module):
def __init__(self, decode_block=LayerDepwiseDecode, *args, **kwargs):
super(MobileHairNetV2, self).__init__()
self.mobilenet = mobilenet_v2(*args, **kwargs)
self.decode_block = decode_block
self.make_layers()
self._init_weight()
def make_... |
class _SklearnSVMMulticlass(_SklearnSVMABC):
def __init__(self, training_dataset, test_dataset, datapoints, gamma, multiclass_classifier):
super().__init__(training_dataset, test_dataset, datapoints, gamma)
self.multiclass_classifier = multiclass_classifier
self._qalgo = None
def train(s... |
def get_summary_and_prune(model: torch.nn.Module, *, max_depth: int, module_args: Optional[Tuple[(object, ...)]]=None, module_kwargs: Optional[Dict[(str, Any)]]=None) -> ModuleSummary:
module_summary = get_module_summary(model, module_args=module_args, module_kwargs=module_kwargs)
prune_module_summary(module_su... |
class ProphetNetTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names: List[str] = ['... |
class CustomBenchUsedDistributions(SphinxDirective):
required_arguments = 0
def get_list_table(self) -> str:
distributions: Dict[(str, str)] = {}
for hub_description in BENCHMARK_HUBS:
with ZipFile((RELEASE_DATA / f'{hub_description.key}.zip')) as release_zip:
index =... |
class ResNet(SimpleNet):
def __init__(self, block, layers, num_classes=1000, name=None, created_time=None):
self.inplanes = 64
super(ResNet, self).__init__(name, created_time)
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
... |
def _union_primary_key_indices(hash_bucket_index: int, df_envelopes_list: List[List[DeltaFileEnvelope]]) -> pa.Table:
logger.info(f'[Hash bucket index {hash_bucket_index}] Reading dedupe input for {len(df_envelopes_list)} delta file envelope lists...')
hb_tables = []
df_envelopes = [d for dfe_list in df_env... |
_fixtures(ReahlSystemFixture, PartyAccountFixture)
def test_create_account(reahl_system_fixture, party_account_fixture):
fixture = party_account_fixture
login_email = ''
mailer_stub = fixture.mailer
account_management_interface = fixture.account_management_interface
account_management_interface.emai... |
def test_inheritance_overriden_types_functional_parent():
Parent = namedtuple('Parent', 'a b')
class Child(Parent):
a: bool
c: str
assert (get_named_tuple_shape(Child) == Shape(input=InputShape(constructor=Child, kwargs=None, fields=(InputField(type=bool, id='a', default=NoDefault(), is_requ... |
class QuantLinear(nn.Module):
def __init__(self, in_features, out_features, bias=True, weight_bit=8, bias_bit=32, per_channel=False, quant_mode=False):
super().__init__()
self.in_features = in_features
self.out_features = out_features
self.weight = nn.Parameter(torch.zeros([out_featu... |
def prepare_parser():
usage = 'Parser for all scripts.'
parser = ArgumentParser(description=usage)
parser.add_argument('--G_path', type=str, default=None, help='Path to pre-trained BigGAN checkpoint folder (default: auto-download checkpoint)')
parser.add_argument('--A_lr', type=float, default=0.01, help... |
class SessionManager(QObject):
def __init__(self, base_path, parent=None):
super().__init__(parent)
self.current: Optional[str] = None
self._base_path = base_path
self._last_window_session = None
self.did_load = False
self.save_autosave = throttle.Throttle(self._save_... |
def lisp_to_nested_expression(lisp_string):
stack: List = []
current_expression: List = []
tokens = lisp_string.split()
for token in tokens:
while (token[0] == '('):
nested_expression: List = []
current_expression.append(nested_expression)
stack.append(current... |
def gen_src1_dep_taken_test():
return [gen_br2_src1_dep_test(5, 'bne', 7, 1, True), gen_br2_src1_dep_test(4, 'bne', 7, 2, True), gen_br2_src1_dep_test(3, 'bne', 7, 3, True), gen_br2_src1_dep_test(2, 'bne', 7, 4, True), gen_br2_src1_dep_test(1, 'bne', 7, 5, True), gen_br2_src1_dep_test(0, 'bne', 7, 6, True)] |
def load_env_from_file(filename):
if (not os.path.exists(filename)):
raise FileNotFoundError('Environment file {} does not exist.'.format(filename))
with open(filename) as f:
for (lineno, line) in enumerate(f):
line = line.strip()
if ((not line) or line.startswith('#')):
... |
class DenseNet(nn.Module):
def __init__(self, block, nblocks, growth_rate=12, reduction=0.5, num_classes=10, deconv=None, delinear=None, channel_deconv=None):
super(DenseNet, self).__init__()
self.growth_rate = growth_rate
num_planes = (2 * growth_rate)
if (not deconv):
s... |
def get_data(name):
data = []
sents = get_sents(((final_dir + name) + '.txt'))
final_data.append(sents)
user_files = ['PGN_both', 'PGN_only', 'fast_rl_both', 'fast_rl_only']
for name in user_files:
sents = get_sents(((user_dir + name) + '.txt'))
user_data.append(sents)
agent_file... |
class ScarletC(nn.Module):
def __init__(self, n_class=1000, input_size=224):
super(ScarletC, self).__init__()
assert ((input_size % 32) == 0)
mb_config = [[3, 32, 5, 2, True], [3, 32, 3, 1, True], [3, 40, 5, 2, True], 'identity', 'identity', [3, 40, 3, 1, False], [6, 80, 7, 2, True], [3, 80,... |
def _parse_output(pipe: Optional[IO[bytes]]) -> tuple[(str, list[str])]:
failed_tests = []
conformance = ''
test_name = ''
for line in iter(pipe.readline, b''):
line = line.decode('utf-8').strip('\r\n')
if (not line):
continue
if ('Test [' in line):
test_n... |
class Label(object):
def __init__(self, x, y, label_str, anchor='BL', style=None, keep_inside=None, head=None):
if (style is None):
style = TextStyle()
text = qg.QTextDocument()
font = style.qt_font
if font:
text.setDefaultFont(font)
color = style.colo... |
def check_kill(session):
conn = get_database_conn()
curs = query_execute_wrapper(conn, query_string='SELECT kill FROM scansweep_metadata WHERE session=?', query_list=[session], no_return=False)
kill_data = curs.fetchone()
if (kill_data['kill'] == 'True'):
return True
else:
return Fal... |
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride, expand_ratio):
super(InvertedResidual, self).__init__()
self.stride = stride
assert (stride in [1, 2])
hidden_dim = round((inp * expand_ratio))
self.use_res_connect = ((self.stride == 1) and (inp == oup))... |
def sample_from_model(sample, model, device, categories_num, diffusion):
shape = sample['box_cond'].shape
model.eval()
noisy_batch = {'box': torch.randn(*shape, dtype=torch.float32, device=device), 'cat': ((categories_num - 1) * torch.ones((shape[0], shape[1]), dtype=torch.long, device=device))}
for i i... |
class RepositoryGCWorker(QueueWorker):
def process_queue_item(self, job_details):
try:
with GlobalLock('LARGE_GARBAGE_COLLECTION', lock_ttl=(REPOSITORY_GC_TIMEOUT + LOCK_TIMEOUT_PADDING)):
self._perform_gc(job_details)
except LockNotAcquiredException:
logger.d... |
class PythonFileRunnerTest(unittest.TestCase):
def setUp(self):
super().setUp()
self.project = testutils.sample_project()
self.pycore = self.project.pycore
def tearDown(self):
testutils.remove_project(self.project)
super().tearDown()
def make_sample_python_file(self, ... |
def _test():
import torch
pretrained = False
models = [channelnet]
for model in models:
net = model(pretrained=pretrained)
net.eval()
weight_count = _calc_width(net)
print('m={}, {}'.format(model.__name__, weight_count))
assert ((model != channelnet) or (weight_co... |
class ItemDependents(wx.Panel):
def __init__(self, parent, stuff, item):
wx.Panel.__init__(self, parent, style=wx.TAB_TRAVERSAL)
self.romanNb = ['0', 'I', 'II', 'III', 'IV', 'V', 'VI', 'VII', 'VIII', 'IX', 'X']
self.skillIdHistory = []
mainSizer = wx.BoxSizer(wx.VERTICAL)
sel... |
def test_contextmerge_list():
context = Context({'ctx1': 'ctxvalue1', 'ctx2': 'ctxvalue2', 'ctx3': 'ctxvalue3', 'ctx4': [1, 2, 3], 'contextMerge': {'ctx4': ['k1', 'k2', '{ctx3}', True, False, 44]}})
pypyr.steps.contextmerge.run_step(context)
assert (context['ctx1'] == 'ctxvalue1')
assert (context['ctx2'... |
def download_setuptools(version=DEFAULT_VERSION, download_base=DEFAULT_URL, to_dir=DEFAULT_SAVE_DIR, delay=15, downloader_factory=get_best_downloader):
to_dir = os.path.abspath(to_dir)
zip_name = ('setuptools-%s.zip' % version)
url = (download_base + zip_name)
saveto = os.path.join(to_dir, zip_name)
... |
class FBNetInitBlock(nn.Module):
def __init__(self, in_channels, out_channels, bn_eps):
super(FBNetInitBlock, self).__init__()
self.conv1 = conv3x3_block(in_channels=in_channels, out_channels=out_channels, stride=2, bn_eps=bn_eps)
self.conv2 = FBNetUnit(in_channels=out_channels, out_channels... |
def test_fixed_shape_convert_variable():
t1 = TensorType('float64', shape=(1, 1))
t2 = TensorType('float64', shape=(1, 1))
assert (t1 == t2)
assert (t1.shape == t2.shape)
t2_var = t2()
res = t2.convert_variable(t2_var)
assert (res is t2_var)
res = t1.convert_variable(t2_var)
assert (... |
class TestDIORRR3Det(TestDIORR):
def eval(self):
r3det = build_whole_network.DetectionNetworkR3Det(cfgs=self.cfgs, is_training=False)
all_boxes_r = self.eval_with_plac(img_dir=self.args.img_dir, det_net=r3det, image_ext=self.args.image_ext)
imgs = os.listdir(self.args.img_dir)
real_t... |
def test_cinsk1_control():
cinsk = new_corpse_in_sk1_control(rabi_rotation=(np.pi / 2), azimuthal_angle=0.5, maximum_rabi_rate=(2 * np.pi))
segments = np.vstack((cinsk.amplitude_x, cinsk.amplitude_y, cinsk.detunings, cinsk.durations)).T
_segments = np.array([[5., 3.0123195, 0.0, 1.], [(- 5.), (- 3.0123195),... |
def test_AssertionError_message(pytester: Pytester) -> None:
pytester.makepyfile('\n def test_hello():\n x,y = 1,2\n assert 0, (x,y)\n ')
result = pytester.runpytest()
result.stdout.fnmatch_lines('\n *def test_hello*\n *assert 0, (x,y)*\n *AssertionError:... |
def RegQuery(hive, subkey, searchterms, searchvalues=True, searchkeys=False, haltonerror=False, **kwargs):
subkeys = None
values = None
regdata = GetRegistryQuery(hive, subkey, **kwargs)
ret = dict()
if (regdata is not None):
try:
if searchkeys:
subkeys = regdata.... |
class MopidyPlayer(player.Player):
def __init__(self):
self.playback_started = Event()
with mopidy_command(important=True):
PLAYER.playback.stop()
PLAYER.tracklist.clear()
PLAYER.tracklist.set_consume(True)
_event('track_playback_started')
def _on_... |
def test(epoch, checkpoint, data_test, label_test, n_classes):
net = ModelFedCon(args.model, args.out_dim, n_classes=n_classes)
if (len(args.gpu.split(',')) > 1):
net = torch.nn.DataParallel(net, device_ids=[i for i in range(round((len(args.gpu) / 2)))])
model = net.cuda()
model.load_state_dict(... |
class IBFIGItoIBContractMapper():
def __init__(self, clientId: int=0, host: str='127.0.0.1', port: int=7497):
self.logger = ib_logger.getChild(self.__class__.__name__)
self.lock = Lock()
self.waiting_time = 30
self.action_event_lock = Event()
self.wrapper = IBWrapper(self.act... |
def renameUser(username, new_name):
if (username == new_name):
raise Exception('Must give a new username')
check = model.user.get_user_or_org(new_name)
if (check is not None):
raise Exception(('New username %s already exists' % new_name))
existing = model.user.get_user_or_org(username)
... |
class SshPw_TestCase(unittest.TestCase):
def runTest(self):
data1 = F13_SshPwData()
data2 = F13_SshPwData()
self.assertEqual(data1, data2)
self.assertFalse((data1 != data2))
self.assertNotEqual(data1, None)
self.assertFalse(data1.isCrypted)
self.assertFalse(da... |
class SEResNeXt(nn.Module):
def __init__(self, channels, init_block_channels, cardinality, bottleneck_width, in_channels=3, in_size=(224, 224), num_classes=1000):
super(SEResNeXt, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.features = nn.Sequential()
... |
class DeepLabV3PlusDecoder(nn.Module):
def __init__(self, encoder_channels, out_channels=256, atrous_rates=(12, 24, 36), output_stride=16):
super().__init__()
if (output_stride not in {8, 16}):
raise ValueError('Output stride should be 8 or 16, got {}.'.format(output_stride))
sel... |
def test_convert_variable():
test_type = TensorType(config.floatX, shape=(None, None))
test_var = test_type()
test_type2 = TensorType(config.floatX, shape=(1, None))
test_var2 = test_type2()
res = test_type.convert_variable(test_var)
assert (res is test_var)
res = test_type.convert_variable(... |
.unit()
.parametrize(('expr', 'expected'), [(' true ', True), (' ((((((true)))))) ', True), (' ( ((\t (((true))))) \t \t)', True), ('( true and (((false))))', False), ('not not not not true', True), ('not not not not not true', False)])
def test_... |
.skipif((not _aead_supported(AESCCM)), reason='Does not support AESCCM')
class TestAESCCM():
.skipif((sys.platform not in {'linux', 'darwin'}), reason='mmap required')
def test_data_too_large(self):
key = AESCCM.generate_key(128)
aesccm = AESCCM(key)
nonce = (b'0' * 12)
large_dat... |
class TMid3Iconv(_TTools):
TOOL_NAME = u'mid3iconv'
def setUp(self):
super(TMid3Iconv, self).setUp()
self.filename = get_temp_copy(os.path.join(DATA_DIR, 'silence-44-s.mp3'))
def tearDown(self):
super(TMid3Iconv, self).tearDown()
os.unlink(self.filename)
def test_noop(sel... |
def swap_network(qubits: Sequence[cirq.Qid], operation: Callable[([int, int, cirq.Qid, cirq.Qid], cirq.OP_TREE)]=(lambda p, q, p_qubit, q_qubit: ()), fermionic: bool=False, offset: bool=False) -> List[cirq.Operation]:
n_qubits = len(qubits)
order = list(range(n_qubits))
swap_gate = (FSWAP if fermionic else ... |
class TestIncrementDisplay(unittest.TestCase):
def test_loop_count(self):
def some_loop():
for i in range(12):
a = 1
profiler = LineProfiler()
wrapped = profiler(some_loop)
wrapped()
show_results(profiler)
for_line = list(list(profiler.code... |
def markdown_statistics(file_names):
total = collections.Counter()
for file_name in sorted(file_names):
total.update(get_types(file_name))
result = ['|Field|Class|Empty|Count|', '|---|---|---|---|']
for (field, class_, void) in sorted(total, key=str):
result.append('|{}|{}|{}|{}|'.format... |
class LvmFileSystem(LoopbackFileSystemMixin, FileSystem):
type = 'lvm'
aliases = ['0x8e', 'lvm2']
guids = ['E6D6D379-F507-44C2-A23C-238F2A3DF928', '79D3D6E6-07F5-C244-A23C-238F2A3DF928']
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.vgname = None
(depend... |
class MergeResolveTestCase(unittest.TestCase):
def test_merge_items(self):
d = {1: 'foo', 3: 'baz'}
localedata.merge(d, {1: 'Foo', 2: 'Bar'})
assert (d == {1: 'Foo', 2: 'Bar', 3: 'baz'})
def test_merge_nested_dict(self):
d1 = {'x': {'a': 1, 'b': 2, 'c': 3}}
d2 = {'x': {'a... |
class OrgAddUserViewTest(TestCase):
def setUpTestData(cls):
add_default_data()
def login(self, name, password=None):
self.client.login(username=name, password=(password if password else name))
self.pu = PytitionUser.objects.get(user__username=name)
return self.pu
def test_Org... |
class Migration(migrations.Migration):
initial = True
dependencies = []
operations = [migrations.CreateModel(name='JobListing', fields=[('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCreatedField(default=django.utils.time... |
def parse_args():
parser = argparse.ArgumentParser(description='Upload models to OSS')
parser.add_argument('model_zoo', type=str, help='model_zoo input')
parser.add_argument('--dst-folder', type=str, default='mmsegmentation/v0.5', help='destination folder')
args = parser.parse_args()
return args |
def _iter_fixes(testcase: DataDrivenTestCase, actual: list[str], *, incremental_step: int) -> Iterator[DataFileFix]:
reports_by_line: dict[(tuple[(str, int)], list[tuple[(str, str)]])] = defaultdict(list)
for error_line in actual:
comment_match = re.match('^(?P<filename>[^:]+):(?P<lineno>\\d+): (?P<seve... |
def ddpOrient(node_a: Node, node_b: Node, node_c: Node, graph: Graph, maxPathLength: int, data: ndarray, independence_test_method, alpha: float, sep_sets: Dict[(Tuple[(int, int)], Set[int])], change_flag: bool, bk: (BackgroundKnowledge | None), verbose: bool=False) -> bool:
Q = Queue()
V = set()
e = None
... |
def z1_pre_encoder(x, z2, hus=[1024, 1024]):
with tf.variable_scope('z1_pre_enc'):
(T, F) = x.get_shape().as_list()[1:]
x = tf.reshape(x, ((- 1), (T * F)))
out = tf.concat([x, z2], axis=(- 1))
for (i, hu) in enumerate(hus):
out = fully_connected(out, hu, activation_fn=tf.... |
class RestrictChatMember():
async def restrict_chat_member(self: 'pyrogram.Client', chat_id: Union[(int, str)], user_id: Union[(int, str)], permissions: 'types.ChatPermissions', until_date: datetime=utils.zero_datetime()) -> 'types.Chat':
r = (await self.invoke(raw.functions.channels.EditBanned(channel=(awa... |
class TestMakeClass():
.parametrize('ls', [list, tuple])
def test_simple(self, ls):
C1 = make_class('C1', ls(['a', 'b']))
class C2():
a = attr.ib()
b = attr.ib()
assert (C1.__attrs_attrs__ == C2.__attrs_attrs__)
def test_dict(self):
C1 = make_class('C1... |
class Migration(migrations.Migration):
dependencies = [('adserver', '0042_add_keyword_impressions')]
operations = [migrations.AlterField(model_name='publisher', name='render_pixel', field=models.BooleanField(default=False, help_text='Render ethical-pixel in ad templates. This is needed for users not using the a... |
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