code stringlengths 281 23.7M |
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def git_upstream(destination):
git_url = '/'.join((cli.args.baseurl, default_fork))
git_cmd = ['git', '-C', destination, 'remote', 'add', 'upstream', git_url]
with subprocess.Popen(git_cmd, stderr=subprocess.STDOUT, stdout=subprocess.PIPE, bufsize=1, universal_newlines=True, encoding='utf-8') as p:
... |
class RandomizedSearchTest(unittest.TestCase):
def test_clone_estimator(self):
params = dict(lr=tune.loguniform(0.1, 1))
random_search = TuneSearchCV(SGDClassifier(), param_distributions=params, return_train_score=True, n_jobs=2)
clone(random_search)
random_search = TuneSearchCV(SGDC... |
class TestInstagramPortraitPost(unittest.TestCase):
('nider.models.Image._set_fullpath')
def setUp(self, *mocks):
self.post = InstagramPortraitPost(content=mock.Mock(), fullpath=mock.Mock())
def test_size(self):
self.assertEqual(self.post.width, 1080)
self.assertEqual(self.post.heigh... |
class UT_HAR_GRU(nn.Module):
def __init__(self, hidden_dim=64):
super(UT_HAR_GRU, self).__init__()
self.gru = nn.GRU(90, hidden_dim, num_layers=1)
self.fc = nn.Linear(hidden_dim, 7)
def forward(self, x):
x = x.view((- 1), 250, 90)
x = x.permute(1, 0, 2)
(_, ht) = ... |
class RequestScope(Scope):
def configure(self):
self.context = None
def __call__(self, request):
assert (self.context is None)
self.context = {}
binder = self.injector.get(Binder)
binder.bind(Request, to=request, scope=RequestScope)
(yield)
self.context = ... |
def test_remove(brew_info):
brew_info.brew_input.extend(['aaa', 'bbb', 'ccc'])
brew_info.remove('brew_input', 'bbb')
assert (brew_info.brew_input == ['aaa', 'ccc'])
brew_info.brew_input_opt.update({'aaa': 'aaa', 'bbb': 'bbb', 'ccc': 'ccc'})
brew_info.remove('brew_input_opt', 'bbb')
assert (brew_... |
class F13_TestCase(F12_TestCase):
def __init__(self, *kargs, **kwargs):
F12_TestCase.__init__(self, *kargs, **kwargs)
self.validLevels.append('RAID4')
def runTest(self):
F12_TestCase.runTest(self)
self.assert_parse(('raid / --device=md0 --level=4%s raid.01 raid.02' % (self.bytesP... |
def get_modname_from_path(modpath: pathlib.Path, package_path: pathlib.Path, add_package_name: bool=True) -> str:
package_name: str = package_path.stem
rel_path_parts = modpath.relative_to(package_path).parts
modname = ''
if (len(rel_path_parts) > 0):
for part in rel_path_parts[:(- 1)]:
... |
.parametrize('log_prob_key', [None, 'sample_log_prob', ('nested', 'sample_log_prob'), ('data', 'sample_log_prob')])
def test_nested_keys_probabilistic_delta(log_prob_key):
policy_module = TensorDictModule(nn.Linear(1, 1), in_keys=[('data', 'states')], out_keys=[('data', 'param')])
td = TensorDict({'data': Tenso... |
class GrabPointer(rq.ReplyRequest):
_request = rq.Struct(rq.Opcode(26), rq.Bool('owner_events'), rq.RequestLength(), rq.Window('grab_window'), rq.Card16('event_mask'), rq.Set('pointer_mode', 1, (X.GrabModeSync, X.GrabModeAsync)), rq.Set('keyboard_mode', 1, (X.GrabModeSync, X.GrabModeAsync)), rq.Window('confine_to',... |
class AssignmentStmt(Statement):
__slots__ = ('lvalues', 'rvalue', 'type', 'unanalyzed_type', 'new_syntax', 'is_alias_def', 'is_final_def', 'invalid_recursive_alias')
__match_args__ = ('lvalues', 'rvalues', 'type')
lvalues: list[Lvalue]
rvalue: Expression
type: (mypy.types.Type | None)
unanalyze... |
def test_dealloc_mix1_2_order():
allocs = ([1, 2] * 5)
capacity = sum(allocs)
allocator = RegionAllocator(capacity)
regions = []
for alloc in allocs:
regions.append(allocator.alloc(alloc))
for region in regions:
allocator.dealloc(region)
assert (allocator.get_free_size() == a... |
_exporter
class SceneToPixmapExporter(ExporterBase):
TYPE = ExporterRegistry.DEFAULT_TYPE
def get_user_input(self, parent):
dialog = widgets.SceneToPixmapExporterDialog(parent=parent, default_size=self.default_size)
if dialog.exec():
size = dialog.value()
logger.debug(f'G... |
class FakePathlibPathModule():
fake_pathlib = None
def __init__(self, filesystem=None):
if (self.fake_pathlib is None):
self.__class__.fake_pathlib = FakePathlibModule(filesystem)
def __call__(self, *args, **kwargs):
return self.fake_pathlib.Path(*args, **kwargs)
def __getatt... |
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--seed', type=int, default=1, help='random seed')
args = parser.parse_args()
pp.connect()
_utils.init_simulation(camera_distance=1)
unique_ids = _utils.create_pile(class_ids=... |
def eval_model(model, filepaths, entropy_estimation=False, half=False, savedir=''):
device = next(model.parameters()).device
metrics = defaultdict(float)
for (idx, f) in enumerate(sorted(filepaths)):
x = read_image(f).to(device)
if (not entropy_estimation):
print('evaluating inde... |
def apply_rc(mediator: Mediator, request: BaseNameLayoutRequest, request_checker: RequestChecker, field: BaseField) -> bool:
owner_type = request.loc_map[TypeHintLoc].type
filter_request = NameMappingFilterRequest(loc_map=field_to_loc_map(owner_type, field))
try:
request_checker.check_request(ExtraS... |
def add_data_args(parser):
parser.add_argument('--dataset', type=str, choices=DATASET_CHOICES, help='dataset format')
parser.add_argument('--data-dir', type=str, help='data directory')
parser.add_argument('--split-sizes', type=float, nargs=3, default=[0.8, 0.1, 0.1], help='train/val/test proportions for dat... |
def squad_convert_examples_to_features(examples, tokenizer, max_seq_length, doc_stride, max_query_length, is_training, return_dataset=False, threads=1):
features = []
threads = min(threads, cpu_count())
with Pool(threads, initializer=squad_convert_example_to_features_init, initargs=(tokenizer,)) as p:
... |
class MaxOrderSize(TradingControl):
def __init__(self, on_error, asset=None, max_shares=None, max_notional=None):
super(MaxOrderSize, self).__init__(on_error, asset=asset, max_shares=max_shares, max_notional=max_notional)
self.asset = asset
self.max_shares = max_shares
self.max_notio... |
class MaximumEntropyInverseRL():
def __init__(self, agent):
self.agent = agent
def train(self, sess):
start = time.time()
max_epoch = AbstractLearning.max_epochs
dataset_size = AbstractLearning.dataset_size
tuning_size = AbstractLearning.validation_datasize
train_... |
class Document(ElementProxy):
def __init__(self, element: CT_Document, part: DocumentPart):
super(Document, self).__init__(element)
self._element = element
self._part = part
self.__body = None
def add_heading(self, text: str='', level: int=1):
if (not (0 <= level <= 9)):
... |
class CifarNet(nn.Module):
def __init__(self):
super(CifarNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3)
self.conv2 = nn.Conv2d(64, 64, kernel_size=3)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3)
self.conv4 = nn.Conv2d(128, 128, kernel_size=3)
se... |
def test_trafficstopaction():
tsa = OSC.TrafficStopAction('hej')
tsa2 = OSC.TrafficStopAction('hej')
tsa3 = OSC.TrafficStopAction('hey')
prettyprint(tsa)
assert (tsa == tsa2)
assert (tsa != tsa3)
tsa4 = OSC.TrafficStopAction.parse(tsa.get_element())
prettyprint(tsa4.get_element())
as... |
class TestTopPSamplingSearch(TestSequenceGeneratorBase):
def setUp(self):
d = test_utils.dummy_dictionary(vocab_size=2)
self.assertEqual(d.pad(), 1)
self.assertEqual(d.eos(), 2)
self.assertEqual(d.unk(), 3)
self.eos = d.eos()
self.w1 = 4
self.w2 = 5
se... |
class DocumentationLink(ModelReprMixin, models.Model):
package = models.CharField(primary_key=True, max_length=50, validators=(package_name_validator,), help_text='The Python package name that this documentation link belongs to.')
base_url = models.URLField(help_text='The base URL from which documentation will ... |
class TestTupleNames(unittest.TestCase):
def setUp(self) -> None:
self.inst_a = RInstance(ClassIR('A', '__main__'))
self.inst_b = RInstance(ClassIR('B', '__main__'))
def test_names(self) -> None:
assert (RTuple([int_rprimitive, int_rprimitive]).unique_id == 'T2II')
assert (RTuple... |
class FC3_RaidData(BaseData):
removedKeywords = BaseData.removedKeywords
removedAttrs = BaseData.removedAttrs
def __init__(self, *args, **kwargs):
BaseData.__init__(self, *args, **kwargs)
self.device = kwargs.get('device', None)
self.fstype = kwargs.get('fstype', '')
self.lev... |
class GaussNewtonCG(ConjugateGradientBase):
def __init__(self, problem: L2Problem, variable: TensorList, cg_eps=0.0, fletcher_reeves=True, standard_alpha=True, direction_forget_factor=0, debug=False, analyze=False, plotting=False, visdom=None):
super().__init__(fletcher_reeves, standard_alpha, direction_for... |
def is_same_side(p1: ColorXY, p2: ColorXY, a: ColorXY, b: ColorXY) -> bool:
vector_ab = [(y - x) for (x, y) in zip(a, b)]
vector_ap1 = [(y - x) for (x, y) in zip(a, p1)]
vector_ap2 = [(y - x) for (x, y) in zip(a, p2)]
cross_vab_ap1 = ((vector_ab[0] * vector_ap1[1]) - (vector_ab[1] * vector_ap1[0]))
... |
def plot_segments(onsets, offsets, labels, palette, y=0.5, seg_height=0.1, ax=None, patch_kwargs=None):
if (patch_kwargs is None):
patch_kwargs = {}
if (ax is None):
(fig, ax) = plt.subplots
segments = [Rectangle(xy=(on, y), height=seg_height, width=(off - on)) for (on, off) in zip(onsets, o... |
def platipy_cli():
if ((len(sys.argv) == 1) or (not (sys.argv[1] in tools))):
print('')
print(' PlatiPy CLI (Command Line Interface)')
print(' ')
print('')
print(' Usage: platipy [tool]')
print('')
print(' Supply the name of the desired tool:')
for... |
_fixtures(WebFixture, AccessDomainFixture, AccessUIFixture)
def test_edit_and_add_own(web_fixture, access_domain_fixture, access_ui_fixture):
browser = access_ui_fixture.browser
fixture = access_domain_fixture
account = fixture.account
address_book = fixture.address_book
web_fixture.log_in(browser=b... |
def format_to_lines_new(args):
json_dir_init = os.path.abspath(args.raw_path)
dataset_split = ['test', 'valid', 'train']
lang_split = ['eng', 'chn']
(train_files, valid_files, test_files) = ({}, {}, {})
for data_sp in dataset_split:
for lan_sp in lang_split:
if (data_sp == 'train... |
def test_model_gradient_descent_limited_evaluations():
x0 = np.random.randn(10)
sample_radius = 0.1
rate = 0.1
result = model_gradient_descent(sum_of_squares, x0, sample_radius=sample_radius, n_sample_points=10, rate=rate, tol=1e-08, known_values=None, max_evaluations=15)
assert isinstance(result.x,... |
class SeviriL2AMVBufrData():
(sys.platform.startswith('win'), "'eccodes' not supported on Windows")
def __init__(self, filename):
from satpy.readers.seviri_l2_bufr import SeviriL2BufrFileHandler
with mock.patch('satpy.readers.seviri_l2_bufr.np.fromfile'):
self.fh = SeviriL2BufrFileHa... |
class DirectoryFormat(FormatBase, metaclass=_DirectoryMeta):
def validate(self, level='max'):
_check_validation_level(level)
if (not self.path.is_dir()):
raise ValidationError(('%s is not a directory.' % self.path))
collected_paths = {p: None for p in self.path.glob('**/*') if ((... |
def train(epoch, model, model_ema, vnet, optimizer_model, optimizer_vnet, train_loader, train_meta_loader, meta_lr):
print(('\nEpoch: %d' % epoch))
train_loss = 0
meta_loss = 0
train_meta_loader_iter = iter(train_meta_loader)
for (batch_idx, (inputs, targets, _, index)) in enumerate(train_loader):
... |
def get_worker_config(dask_worker):
from .proxify_host_file import ProxifyHostFile
plugin_vals = dask_worker.plugins.values()
ret = {}
for p in plugin_vals:
config = {v: getattr(p, v) for v in dir(p) if (not (v.startswith('_') or (v in {'setup', 'cores'})))}
try:
pickle.dumps... |
class FConvEncoder(FairseqEncoder):
def __init__(self, dictionary, embed_dim=512, max_positions=1024, convolutions=(((512, 3),) * 20), dropout=0.1, attention=False, attention_nheads=1):
super().__init__(dictionary)
self.dropout = dropout
self.num_attention_layers = None
num_embedding... |
def draw_experiment(env_axes, experiment_statistics, algo):
for (ax, metric_name, label) in zip(env_axes, ['objectives', 'mean_sum_costs', 'average_costs'], ['Average reward return', 'Average cost return', 'Cost regret']):
draw(ax, experiment_statistics['timesteps'], experiment_statistics[(metric_name + '_m... |
class SNResNetDiscriminator(chainer.Chain):
def __init__(self, ch=64, activation=F.relu):
super(SNResNetDiscriminator, self).__init__()
self.activation = activation
initializer = chainer.initializers.GlorotUniform()
with self.init_scope():
self.block1 = OptimizedBlock(3, ... |
def test_move_to_device_trivial() -> None:
in_dict = {'k1': torch.zeros(10), 'k2': torch.ones(4)}
for k in in_dict:
assert isinstance(in_dict[k], torch.Tensor)
assert (in_dict[k].device == torch.device('cpu'))
out_dict = move_to_device(in_dict, torch.device('cpu'))
assert np.alltrue((lis... |
class Effect11392(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Hybrid Turret')), 'maxRange', ship.getModifiedItemAttr('shipBonusNavyDestroyerCaldari2'), skill='Caldari Destroyer', **kwar... |
class TestFork(TestCase):
def test_fork(self):
f1 = jsons.fork()
f2 = jsons.fork()
f3 = jsons.fork(fork_inst=f1)
jsons.set_serializer((lambda *_, **__: 'f1'), str, fork_inst=f1)
jsons.set_serializer((lambda *_, **__: 'f2'), str, fork_inst=f2)
jsons.set_serializer((lam... |
def test_patched_errwindow(capfd, mocker, monkeypatch):
monkeypatch.setattr(checkpyver.sys, 'hexversion', )
monkeypatch.setattr(checkpyver.sys, 'exit', (lambda status: None))
try:
import tkinter
except ImportError:
tk_mock = mocker.patch('qutebrowser.misc.checkpyver.Tk', spec=['withdraw'... |
def test_get_secret():
secret1 = factories.make_secret()
secret2 = factories.make_secret()
secrethash3 = factories.make_secret_hash()
secrethash4 = factories.make_secret_hash()
lock_state = HashTimeLockState(amount=10, expiration=10, secrethash=factories.UNIT_SECRETHASH)
end_state = factories.cr... |
class History():
def __init__(self, project, maxundos=None):
self.project = project
self._undo_list = []
self._redo_list = []
self._maxundos = maxundos
self._load_history()
self.project.data_files.add_write_hook(self.write)
self.current_change = None
def _... |
def test_perform_indexing_needs_reindexing(initialized_db, set_secscan_config):
secscan = V4SecurityScanner(application, instance_keys, storage)
secscan._secscan_api = mock.Mock()
secscan._secscan_api.state.return_value = {'state': 'xyz'}
secscan._secscan_api.index.return_value = ({'err': None, 'state':... |
class TestEvMenu(TestCase):
menutree = {}
startnode = 'start'
cmdset_mergetype = 'Replace'
cmdset_priority = 1
auto_quit = True
auto_look = True
auto_help = True
cmd_on_exit = 'look'
persistent = False
startnode_input = ''
kwargs = {}
expect_all_nodes = False
expected... |
def main():
parser = build_parser()
args = parser.parse_args()
(_, input_ext) = os.path.splitext(args.input)
if (input_ext == '.h5ad'):
x = ad.read_h5ad(args.input)
elif (input_ext == '.h5'):
x = sc.read_10x_h5(args.input)
else:
raise ValueError(f'Unrecognized file extens... |
def main():
args = parse_args()
max_length = 5
num_beams = 4
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO)
logger.setLevel(logging.INFO)
transformers.utils.logging.set_verbosity_error()
device = torch.devic... |
def load_xml(p):
tree = ET.parse(p)
root = tree.getroot()
(title, byline, abs, paras) = ([], [], [], [])
title_node = list(root.iter('hedline'))
if (len(title_node) > 0):
try:
title = [p.text.lower().split() for p in list(title_node[0].iter('hl1'))][0]
except:
... |
def get_thumbnail_from_file(fileobj, boundary) -> (GdkPixbuf.Pixbuf | None):
assert fileobj
try:
path = fileobj.name
assert isinstance(path, fsnative), path
return get_thumbnail(path, boundary)
except GLib.GError:
try:
loader = GdkPixbuf.PixbufLoader()
... |
class Subtensor(COp):
check_input = False
view_map = {0: [0]}
_f16_ok = True
__props__ = ('idx_list',)
def __init__(self, idx_list):
self.idx_list = tuple(map(index_vars_to_types, idx_list))
def make_node(self, x, *inputs):
x = as_tensor_variable(x)
inputs = tuple((as_non... |
def _group_ptr_queries_with_known_answers(now_millis: float_, multicast: bool_, question_with_known_answers: _QuestionWithKnownAnswers) -> List[DNSOutgoing]:
query_by_size: Dict[(DNSQuestion, int)] = {question: (question.max_size + sum((answer.max_size_compressed for answer in known_answers))) for (question, known_... |
class TestForbiddenIOFunctionNoAllowedChecker(pylint.testutils.CheckerTestCase):
CHECKER_CLASS = IOFunctionChecker
CONFIG = {}
def setup(self):
self.setup_method()
def test_message_function_no_allowed(self):
src = '\n def my_function(string: str):\n string = "hello"\n ... |
def parse_source_file(mod: StubSource, mypy_options: MypyOptions) -> None:
assert (mod.path is not None), 'Not found module was not skipped'
with open(mod.path, 'rb') as f:
data = f.read()
source = mypy.util.decode_python_encoding(data)
errors = Errors(mypy_options)
mod.ast = mypy.parse.pars... |
def test_build_wheel_extended() -> None:
with temporary_directory() as tmp_dir, cwd(os.path.join(fixtures, 'extended')):
filename = api.build_wheel(tmp_dir)
whl = (Path(tmp_dir) / filename)
assert whl.exists()
validate_wheel_contents(name='extended', version='0.1', path=whl.as_posix(... |
def run(train_batch_size, epochs, lr, weight_decay, config, exp_id, log_dir, disable_gpu=False):
if (config['test_ratio'] is not None):
(train_loader, val_loader, test_loader) = get_data_loaders(config, train_batch_size, exp_id)
else:
(train_loader, val_loader) = get_data_loaders(config, train_b... |
class OpenFF(Parametrisation):
type: Literal['OpenFF'] = 'OpenFF'
force_field: str = 'openff_unconstrained-2.0.0.offxml'
def start_message(self, **kwargs) -> str:
return f'Parametrising molecule and fragments with {self.force_field}.'
def is_available(cls) -> bool:
off = which_import('op... |
def test_context_formatting(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_... |
class BoolAsk(Bool):
def __init__(self, *, none_ok: bool=False, completions: _Completions=None) -> None:
super().__init__(none_ok=none_ok, completions=completions)
self.valid_values = ValidValues('true', 'false', 'ask')
def to_py(self, value: Union[(bool, str)]) -> Union[(bool, str, None)]:
... |
class TestTextInput(unittest.TestCase):
def test_init(self):
widget = gui.TextInput(single_line=True, hint='test text input')
self.assertIn('test text input', widget.repr())
assertValidHTML(widget.repr())
widget = gui.TextInput(single_line=False, hint='test text input')
self.... |
class TrainWithLogger():
def reset_log(self):
self.log_components = OrderedDict()
def log_append(self, log_key, length, loss_components):
for (key, value) in loss_components.items():
key_name = f'{log_key}/{key}'
(count, sum) = self.log_components.get(key_name, (0, 0.0))
... |
class HeightCompression(nn.Module):
def __init__(self, model_cfg, **kwargs):
super().__init__()
self.model_cfg = model_cfg
self.num_bev_features = self.model_cfg.NUM_BEV_FEATURES
def forward(self, batch_dict):
encoded_spconv_tensor = batch_dict['encoded_spconv_tensor']
sp... |
class _ExtractMethodParts(ast.RopeNodeVisitor):
def __init__(self, info):
self.info = info
self.info_collector = self._create_info_collector()
self.info.kind = self._get_kind_by_scope()
self._check_constraints()
def _get_kind_by_scope(self):
if self._extacting_from_static... |
def create_ctth_alti_pal_variable_with_fill_value_color(nc_file, var_name):
var = nc_file.create_variable(var_name, ('pal_colors_250', 'pal_rgb'), np.uint8)
var[:] = PAL_ARRAY
var.attrs['palette_meanings'] = CTTH_PALETTE_MEANINGS
var.attrs['fill_value_color'] = [0, 0, 0]
var.attrs['scale_factor'] = ... |
def test_Anything():
assert_eq(Anything, None)
assert_eq(Anything, [])
assert_eq(None, Anything)
assert_eq([], Anything)
assert (not (Anything != None))
assert (not (Anything != []))
assert (not (None != Anything))
assert (not ([] != Anything))
assert_eq('<Anything>', repr(Anything)) |
class AllDatasetBatchesIterator(MultiIterator):
def __init__(self, individual_dataloaders: Mapping[(str, Union[(DataLoader, Iterable)])], iteration_strategy: AllDatasetBatches) -> None:
super().__init__(individual_dataloaders, iteration_strategy)
self.iteration_strategy = iteration_strategy
... |
class Gmetric():
type = ('', 'string', 'uint16', 'int16', 'uint32', 'int32', 'float', 'double', 'timestamp')
protocol = ('udp', 'multicast')
def __init__(self, host, port, protocol):
if (protocol not in self.protocol):
raise ValueError(('Protocol must be one of: ' + str(self.protocol)))
... |
class FormTagFieldTest(TagTestManager, TestCase):
def test_required(self):
self.assertTrue(tag_forms.TagField(required=True).required)
self.assertTrue(tag_forms.TagField(required=True).widget.is_required)
self.assertFalse(tag_forms.TagField(required=False).required)
self.assertFalse(... |
class Migration(migrations.Migration):
dependencies = [('adserver_auth', '0003_allow-blank')]
operations = [migrations.CreateModel(name='HistoricalUser', fields=[('id', models.IntegerField(auto_created=True, blank=True, db_index=True, verbose_name='ID')), ('password', models.CharField(max_length=128, verbose_na... |
def test_pipeline_runner_main_all(pipeline_cache_reset):
expected_notify_output = ['sg1', 'sg1.2', 'success_handler']
with patch_logger('pypyr.steps.echo', logging.NOTIFY) as mock_log:
pipelinerunner.run(pipeline_name='pipelines/api/main-all', args_in=['A', 'B', 'C'], groups=['sg1'], success_group='sh',... |
def run(scenarios_list, config, wait_duration, kubecli: KrknKubernetes, telemetry: KrknTelemetryKubernetes) -> (list[str], list[ScenarioTelemetry]):
failed_post_scenarios = ''
logging.info('Runing the Network Chaos tests')
failed_post_scenarios = ''
scenario_telemetries: list[ScenarioTelemetry] = []
... |
class Effect7013(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Missile Launcher Operation')), 'kineticDamage', src.getModifiedItemAttr('eliteBonusGunship1'), skill='Assault Frigates', **kwar... |
class FGM():
def __init__(self, model):
self.model = model
self.backup = {}
def attack(self, epsilon=1.0, emb_name='word_embeddings'):
for (name, param) in self.model.named_parameters():
if (param.requires_grad and (emb_name in name)):
self.backup[name] = para... |
class S3Path(_S3Path):
keep_file = '.s3keep'
def mkdir(self, mode: int=511, parents: bool=False, exist_ok: bool=False) -> None:
self.joinpath(self.keep_file).touch()
def glob(self, pattern: str) -> Iterator[S3Path]:
bucket_name = self.bucket
(resource, _) = self._accessor.configurati... |
class Solution():
def twoSum(self, nums, target):
for i in nums:
a = (target - i)
m = nums.index(i)
if (a in nums):
try:
n = nums.index(a, (m + 1), len(nums))
return (m, n)
except:
... |
def test_annotation_long(testdir):
testdir.makepyfile("\n import pytest\n pytest_plugins = 'pytest_github_actions_annotate_failures'\n\n def f(x):\n return x\n\n def test_fail():\n x = 1\n x += 1\n x += 1\n x += 1\n x += 1... |
class TestRegisteringSubclass(BaseTestCase):
def test_handling_duplicate(self):
with pytest.raises(ValueError) as e:
rname.register_subclass(rname.GPIBInstr)
assert ('Class already registered for' in e.exconly())
def test_handling_duplicate_default(self) -> None:
with pytest.... |
def _standardize_weights(y, sample_weight=None, class_weight=None, sample_weight_mode=None):
if (sample_weight_mode is not None):
if (sample_weight_mode != 'temporal'):
raise ValueError(('"sample_weight_mode should be None or "temporal". Found: ' + str(sample_weight_mode)))
if (len(y.sha... |
def _expval_with_stddev(coeffs: np.ndarray, probs: np.ndarray, shots: int) -> Tuple[(float, float)]:
expval = coeffs.dot(probs)
sq_expval = (coeffs ** 2).dot(probs)
variance = ((sq_expval - (expval ** 2)) / shots)
if ((variance < 0) and (not np.isclose(variance, 0))):
logger.warning('Encountered... |
def test_infer_generated_setter() -> None:
code = '\n class A:\n \n def test(self):\n pass\n A.test.setter\n '
node = extract_node(code)
inferred = next(node.infer())
assert isinstance(inferred, nodes.FunctionDef)
assert isinstance(inferred.args, nodes.Arguments)
... |
class AccreditationFitter():
def __init__(self):
self._counts_all = {}
self._counts_accepted = {}
self._Ntraps = None
self._Nrejects = []
self._Nruns = 0
self._Nacc = 0
self._g = 1.0
self.flag = None
self.outputs = None
self.num_runs = ... |
def test_cc_head():
head = CCHead(in_channels=32, channels=16, num_classes=19)
assert (len(head.convs) == 2)
assert hasattr(head, 'cca')
if (not torch.cuda.is_available()):
pytest.skip('CCHead requires CUDA')
inputs = [torch.randn(1, 32, 45, 45)]
(head, inputs) = to_cuda(head, inputs)
... |
class Imagefolder_modified(DatasetFolder):
def __init__(self, root, transform=None, target_transform=None, loader=default_loader, is_valid_file=None, cached=False, number=None):
super(Imagefolder_modified, self).__init__(root, loader, (IMG_EXTENSIONS if (is_valid_file is None) else None), transform=transfor... |
def _on_raw(func_name):
def wrapped(self, *args, **kwargs):
args = list(args)
try:
string = args.pop(0)
if hasattr(string, '_raw_string'):
args.insert(0, string.raw())
else:
args.insert(0, string)
except IndexError:
... |
class TestNormalDistribution(QiskitTestCase):
def assertDistributionIsCorrect(self, circuit, num_qubits, mu, sigma, bounds, upto_diag):
if (not isinstance(num_qubits, (list, np.ndarray))):
num_qubits = [num_qubits]
if (not isinstance(mu, (list, np.ndarray))):
mu = [mu]
... |
class PizzaTestDrive():
def main(*args) -> None:
nyStore: PizzaStore = NYPizzaStore()
chicagoStore: PizzaStore = ChicagoPizzaStore()
pizza: Pizza = nyStore.orderPizza('cheese')
print(f'''Ethan ordered a {pizza.getName()}
''')
pizza = chicagoStore.orderPizza('cheese')
... |
def test_logins_fails_with_invalid_email(graphql_client):
UserFactory(email='', password='test')
response = graphql_client.query('mutation($input: LoginInput!) {\n login(input: $input) {\n __typename\n ... on LoginErrors {\n errors {\n ... |
class JavaLexer(RegexLexer):
name = 'Java'
url = '
aliases = ['java']
filenames = ['*.java']
mimetypes = ['text/x-java']
version_added = ''
flags = (re.MULTILINE | re.DOTALL)
tokens = {'root': [('(^\\s*)((?:(?:public|private|protected|static|strictfp)(?:\\s+))*)(record)\\b', bygroups(Whi... |
def find_paths(path_patterns: Sequence[str], exclude_name_patterns: Sequence[str]=[], cwd: Optional[Union[(Path, str)]]=None) -> Generator[(Path, None, None)]:
if (cwd is None):
cwd = Path.cwd()
elif isinstance(cwd, str):
cwd = Path(cwd)
for pattern in path_patterns:
for path in cwd.... |
class GANLoss(nn.Module):
def __init__(self, use_lsgan=True, gan_mode='lsgan', target_real_label=1.0, target_fake_label=0.0):
super(GANLoss, self).__init__()
self.register_buffer('real_label', torch.tensor(target_real_label))
self.register_buffer('fake_label', torch.tensor(target_fake_label)... |
class FunctionTest(unittest.TestCase):
def test_asized(self):
self.assertEqual(list(asizeof.asized(detail=2)), [])
self.assertRaises(KeyError, asizeof.asized, **{'all': True})
sized = asizeof.asized(Foo(42), detail=2)
self.assertEqual(sized.name, 'Foo')
refs = [ref for ref in... |
class AttentionBlock(nn.Module):
def __init__(self, channels, num_heads=1, use_checkpoint=False):
super().__init__()
self.channels = channels
self.num_heads = num_heads
self.use_checkpoint = use_checkpoint
self.norm = normalization(channels)
self.qkv = conv_nd(1, chan... |
def should_stop_early(cfg: DictConfig, valid_loss: float) -> bool:
if (valid_loss is None):
return False
if (cfg.checkpoint.patience <= 0):
return False
def is_better(a, b):
return ((a > b) if cfg.checkpoint.maximize_best_checkpoint_metric else (a < b))
prev_best = getattr(should... |
def module_name_from_dir(dirname, err=True, files=None):
if (files is None):
try:
files = os.listdir(dirname)
except OSError as e:
if ((e.errno == 2) and (not err)):
return None
names = [file for file in files if (file.endswith('.so') or file.endswith('.py... |
def get_gml_graph(location):
try:
g = nx_pydot.read_dot(location).to_undirected()
temp = sorted(g)
mapping = {}
for node in temp:
node_name = node
if (',' in node_name):
node_name = (('"' + node_name) + '"')
if (node_name[(- 1)] == ... |
def run_data_migration(apps, schema_editor):
QuestionSet = apps.get_model('questions', 'QuestionSet')
Question = apps.get_model('questions', 'Question')
VerboseName = apps.get_model('domain', 'VerboseName')
Range = apps.get_model('domain', 'Range')
for questionset in QuestionSet.objects.exclude(attr... |
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