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def test_multinomial_blocks_cutting_plane():
(X, Y) = generate_blocks_multinomial(n_samples=40, noise=0.5, seed=0)
n_labels = len(np.unique(Y))
crf = GridCRF(n_states=n_labels, inference_method=inference_method)
clf = NSlackSSVM(model=crf, max_iter=100, C=100, check_constraints=False, batch_size=1)
... |
def test_index_names():
df = pd.DataFrame([{'stat': 'mean', 'score': 4, 'var': 'var1'}, {'stat': 'sd', 'score': 7, 'var': 'var1'}, {'stat': 'mean', 'score': 1, 'var': 'var2'}, {'stat': 'sd', 'score': 2, 'var': 'var2'}, {'stat': 'mean', 'score': 11, 'var': 'var3'}, {'stat': 'sd', 'score': 14, 'var': 'var3'}])
ex... |
class SplitOperatorTrotterStep(TrotterStep):
def __init__(self, hamiltonian: 'openfermion.DiagonalCoulombHamiltonian') -> None:
quad_ham = ops.QuadraticHamiltonian(hamiltonian.one_body)
(self.orbital_energies, self.basis_change_matrix, _) = quad_ham.diagonalizing_bogoliubov_transform()
super... |
def chp_hash_table_ref_cont(ht, old, env, cont, _vals):
if (_vals.num_values() != 2):
raise SchemeException('hash-ref handler produced the wrong number of results')
(key, post) = _vals.get_all_values()
val = values.Values.make1(key)
after = check_chaperone_results(val, env, imp_hash_table_post_r... |
class SawyerReachPushPickPlaceEnv(SawyerXYZEnv):
def __init__(self):
liftThresh = 0.04
goal_low = ((- 0.1), 0.8, 0.05)
goal_high = (0.1, 0.9, 0.3)
hand_low = ((- 0.5), 0.4, 0.05)
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.1), 0.6, 0.02)
obj_high = (0.1, 0.7, 0.... |
def safe_cacher(maxsize):
def safewrap(uncached):
cached = lru_cache(maxsize=maxsize)(uncached)
def mucked_up_func(*arg, **kwarg):
try:
return cached(*arg, **kwarg)
except:
return uncached(*arg, **kwarg)
mucked_up_func.cache_clear = cac... |
class CheckUpdateWorker(QThread):
infos = pyqtSignal(object, object)
bg_update_infos = pyqtSignal(object, object)
def __init__(self, parent=None):
super(CheckUpdateWorker, self).__init__(parent)
self._ver = ''
self._manual = False
self._mutex = QMutex()
self._is_work ... |
def mainopt_trymac(i):
format = sys.argv[(i + 1)]
if (not (format in lexFormats)):
return (('No such format ' + repr(format)) + ' (use --formats to see a list of formats)')
for resp in getInputText((i + 2), (('phonemes in ' + format) + ' format'), 'maybe'):
mac = convert(resp, format, 'mac')... |
class Timer(object):
def __init__(self):
self.reset()
def tic(self):
self.start_time = time.time()
def toc(self):
self.diff = (time.time() - self.start_time)
self.total_time += self.diff
self.calls += 1
self.average_time = (self.total_time / self.calls)
de... |
(ATTRS_WITH_ALIAS)
def test_alias_old_style():
class WithAliases():
foo = attr.ib(type=int, alias='foo1')
_foo = attr.ib(type=int, alias='foo2')
assert (get_attrs_shape(WithAliases) == Shape(input=InputShape(constructor=WithAliases, kwargs=None, fields=(InputField(type=int, id='foo', default=NoD... |
class FeatureMapResampler(nn.Module):
def __init__(self, in_channels, out_channels, stride, norm=''):
super(FeatureMapResampler, self).__init__()
if (in_channels != out_channels):
self.reduction = Conv2d(in_channels, out_channels, kernel_size=1, bias=(norm == ''), norm=get_norm(norm, out... |
def read_array(dirpath: (pathlib.Path | str)) -> cunumeric.ndarray:
dirpath = pathlib.Path(dirpath)
zarr_ary = zarr.open_array(dirpath, mode='r')
if (zarr_ary.compressor is not None):
raise NotImplementedError("compressor isn't supported")
padded_ary = get_padded_array(zarr_ary)
if (padded_a... |
class Migration(migrations.Migration):
dependencies = [('questions', '0065_data_migration')]
operations = [migrations.AlterField(model_name='question', name='value_type', field=models.CharField(choices=[('text', 'Text'), ('url', 'URL'), ('integer', 'Integer'), ('float', 'Float'), ('boolean', 'Boolean'), ('datet... |
def rtn_strcpy(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('strcpy hooked')
dst = pstate.get_argument_value(0)
src = pstate.get_argument_value(1)
src_str = pstate.memory.read_string(src)
size = len(src_str)
for (i, c) in enumerate(src_str):
pstate.push_constraint((pstat... |
class PreResNet164Drop():
base = PreResNetDrop
args = list()
kwargs = {'depth': 164}
transform_train = transforms.Compose([transforms.Resize(32), transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transforms.ToTensor(), transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994... |
def cal_rouge_path(pred_name, ref_name):
with open(pred_name, 'r') as f:
preds = get_sents_str(f)
with open(ref_name, 'r') as f:
refs = get_sents_str(f)
(ref_ids, pred_ids) = ([], [])
for (ref, pred) in zip(refs, preds):
(ref_id, pred_id) = change_word2id(ref, pred)
ref_i... |
class BSTLexer(RegexLexer):
name = 'BST'
aliases = ['bst', 'bst-pybtex']
filenames = ['*.bst']
version_added = '2.2'
flags = (re.IGNORECASE | re.MULTILINE)
url = '
tokens = {'root': [include('whitespace'), (words(['read', 'sort']), Keyword), (words(['execute', 'integers', 'iterate', 'reverse... |
class DealStack(StackBase):
offset_x = 20
offset_y = 0
spread_from = 0
def setup(self):
self.setPen(QPen(Qt.NoPen))
color = QColor(Qt.black)
color.setAlpha(50)
brush = QBrush(color)
self.setBrush(brush)
def reset(self):
super(DealStack, self).reset()
... |
class PIOSPI():
def __init__(self, sm_id, pin_mosi, pin_miso, pin_sck, cpha=False, cpol=False, freq=1000000):
assert (not (cpol or cpha))
self._sm = rp2.StateMachine(sm_id, spi_cpha0, freq=(4 * freq), sideset_base=Pin(pin_sck), out_base=Pin(pin_mosi), in_base=Pin(pin_sck))
self._sm.active(1)... |
class ImageAndLogitsFolder(datasets.ImageFolder):
def __init__(self, *args, logits_prefix, **kwargs):
super().__init__(*args, **kwargs)
self.logits_prefix = logits_prefix
def logits_path_create(prefix, path):
return ((prefix + path[(path.rfind('/') + 1):path.find('.j')]) + '.npy')
de... |
def get_job_links():
run_id = os.environ['GITHUB_RUN_ID']
url = f'
result = requests.get(url).json()
jobs = {}
try:
jobs.update({job['name']: job['html_url'] for job in result['jobs']})
pages_to_iterate_over = math.ceil(((result['total_count'] - 100) / 100))
for i in range(pa... |
def get_veff(ks_grad, mol=None, dm=None):
if (mol is None):
mol = ks_grad.mol
if (dm is None):
dm = ks_grad.base.make_rdm1()
t0 = (logger.process_clock(), logger.perf_counter())
mf = ks_grad.base
ni = mf._numint
(grids, nlcgrids) = rks_grad._initialize_grids(ks_grad)
mem_now ... |
def train_model(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None):
logger = get_root_logger(cfg.log_level)
dataset = (dataset if isinstance(dataset, (list, tuple)) else [dataset])
loader_cfg = {**dict(seed=cfg.get('seed'), drop_last=False, dist=distributed, num_gpus=len(cfg.... |
def extract_bilibili_video_id(link: str):
'
if ('bilibili.com' in link):
try:
video_id = re.match('.*\\/(.v[0-9]*)', link).group(1)
return video_id
except Exception as e:
print('Not a valid video url', link)
return link
else:
print('Not... |
class ChangeRahPattern(ContextMenuSingle, DamagePatternMixin):
def __init__(self):
self.mainFrame = gui.mainFrame.MainFrame.getInstance()
def display(self, callingWindow, srcContext, mainItem):
if (srcContext != 'fittingModule'):
return False
if (self.mainFrame.getActiveFit()... |
def runas(args):
if ((args['username'] == None) or (args['password'] == None)):
logging.error('username or password has to be given')
else:
printT('Try to run as via creds...')
startupInfo = STARTUPINFO()
startupInfo.cb = sizeof(startupInfo)
processInformation = PROCESS_I... |
.overload(MultiVector)
def MultiVector_ctor(layout, value=None, dtype=None):
if (not isinstance(layout, LayoutType)):
return
if isinstance(value, types.Array):
def impl(layout, value=None, dtype=None):
return MultiVector_basic_ctor(layout, value)
return impl
elif (dtype i... |
def handle_speaker_voucher_email_sent(data):
from conferences.models import SpeakerVoucher
speaker_voucher = SpeakerVoucher.objects.get(id=data['speaker_voucher_id'])
speaker = speaker_voucher.user
voucher_code = speaker_voucher.voucher_code
conference_name = speaker_voucher.conference.name.localize... |
def get_msdnet(blocks, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
assert (blocks == 22)
num_scales = 4
num_feature_blocks = 10
base = 4
step = 2
reduction_rate = 0.5
growth = 6
growth_factor = [1, 2, 4, 4]
use_bottleneck = True
bottl... |
def multipleOf(validator, dB, instance, schema):
if (not validator.is_type(instance, 'number')):
return
if isinstance(dB, float):
quotient = (instance / dB)
try:
failed = (int(quotient) != quotient)
except OverflowError:
failed = ((Fraction(instance) / Fra... |
def delete_all_daemonset_namespace(kubecli: KrknKubernetes, namespace: str):
try:
daemonsets = kubecli.get_daemonset(namespace)
for daemonset in daemonsets:
logging.info(('Deleting daemonset' + daemonset))
kubecli.delete_daemonset(daemonset, namespace)
except Exception as... |
def embedding_lookup(inputs, voca_size, initializer, reuse=False, trainable=True, scope='Embedding'):
with tf.variable_scope(scope, reuse=reuse) as scope:
embedding_tablePAD = tf.get_variable('embedPAD', initializer=initializer[0:1], trainable=False, dtype=tf.float32)
embedding_tableLast = tf.get_va... |
class StructuralTranslatorL3(StructuralTranslatorL2):
def _get_structural_rtlir_gen_pass(s):
return StructuralRTLIRGenL3Pass
def translate_structural(s, tr_top):
s.structural.decl_ifcs = {}
super().translate_structural(tr_top)
def translate_decls(s, m):
m_rtype = m.get_metada... |
def _convert_examples_to_ner_features(examples: List[NERExample], tokenizer: BertTokenizer, args: NERTrainArguments, label_list: List[str], cls_token_at_end: Optional[bool]=False):
label_map = {label: i for (i, label) in enumerate(label_list)}
id_to_label = {i: label for (i, label) in enumerate(label_list)}
... |
class Pin_():
_strict = False
def use_strict(self):
object.__setattr__(self, '_strict', True)
def __getattr__(self, name: str):
if name.startswith('__'):
raise AttributeError(('Pin object has no attribute %r' % name))
return self.__getitem__(name)
def __getitem__(self... |
class F28_ClearPart(F25_ClearPart):
def __init__(self, *args, **kwargs):
super(F28_ClearPart, self).__init__(*args, **kwargs)
self.cdl = kwargs.get('cdl', False)
def __str__(self):
s = super(F28_ClearPart, self).__str__()
if (s and self.cdl):
s = s.rstrip()
... |
def analysis(file_path, chaos_tests_config):
data = load_telemetry_data(file_path)
zscores = calculate_zscores(data)
(outliers_cpu, outliers_memory, outliers_network) = identify_outliers(zscores)
(cpu_services, mem_services) = get_services_above_heatmap_threshold(data, heatmap_cpu_threshold, heatmap_mem... |
def disable_existing_mirrors(func):
(func)
def wrapper(*args, **kwargs):
for mirror in RepoMirrorConfig.select():
mirror.is_enabled = False
mirror.save()
func(*args, **kwargs)
for mirror in RepoMirrorConfig.select():
mirror.is_enabled = True
... |
def web_history(fake_save_manager, tmpdir, database, config_stub, stubs, monkeypatch):
config_stub.val.completion.timestamp_format = '%Y-%m-%d'
config_stub.val.completion.web_history.max_items = (- 1)
web_history = history.WebHistory(database, stubs.FakeHistoryProgress())
monkeypatch.setattr(history, 'w... |
class TestMatchChromeUrls():
def up(self):
return urlmatch.UrlPattern('chrome://favicon/*')
def test_attrs(self, up):
assert (up._scheme == 'chrome')
assert (up.host == 'favicon')
assert (not up._match_subdomains)
assert (not up._match_all)
assert (up._path is Non... |
def conduct_admined_team_search(username, query, encountered_teams, results):
matching_teams = model.team.get_matching_admined_teams(query, get_authenticated_user(), limit=5)
for team in matching_teams:
if (team.id in encountered_teams):
continue
encountered_teams.add(team.id)
... |
class LmdbDataset(Dataset):
def __init__(self, root: str, charset: str, max_label_len: int, min_image_dim: int=0, remove_whitespace: bool=True, normalize_unicode: bool=True, unlabelled: bool=False, transform: Optional[Callable]=None):
self._env = None
self.root = root
self.unlabelled = unlab... |
_start_docstrings('\n CamemBERT Model with a span classification head on top for extractive question-answering tasks like SQuAD (a linear\n layers on top of the hidden-states output to compute `span start logits` and `span end logits`\n ', CAMEMBERT_START_DOCSTRING)
class CamembertForQuestionAnswering(RobertaF... |
class AesEncyptionSession():
def create_from_keypair(handshake_key: str, keypair):
handshake_key_bytes: bytes = base64.b64decode(handshake_key.encode('UTF-8'))
private_key_data = base64.b64decode(keypair.get_private_key().encode('UTF-8'))
private_key = serialization.load_der_private_key(priv... |
class Agent():
max_grad_norm = 0.5
def __init__(self):
self.training_step = 0
self.var = 1.0
(self.eval_cnet, self.target_cnet) = (CriticNet().float(), CriticNet().float())
(self.eval_anet, self.target_anet) = (ActorNet().float(), ActorNet().float())
self.memory = Memory(... |
def test_ancestor_with_generic() -> None:
tree = builder.parse('\n from typing import TypeVar, Generic\n T = TypeVar("T")\n class A(Generic[T]):\n def a_method(self):\n print("hello")\n class B(A[T]): pass\n class C(B[str]): pass\n ')
inferred_b = next(tree['B'].infer())
... |
class GetBuiltinSourceTest(unittest.TestCase):
def setUp(self) -> None:
self.test_dir = Path(tempfile.mkdtemp('torchx_specs_finder_test'))
self.orig_cwd = os.getcwd()
os.chdir(os.path.dirname(__file__))
def tearDown(self) -> None:
os.chdir(self.orig_cwd)
shutil.rmtree(sel... |
def test_basics(testdir, tmp_path, pytestconfig):
p = testdir.makepyfile('\n import warnings\n\n def test_ok():\n pass\n\n def test_fail():\n assert 0\n\n def test_warning():\n warnings.warn("message", UserWarning)\n ')
log_file = (tmp_path / '... |
class Stl10Dataset(dataset_mixin.DatasetMixin):
def __init__(self, resize=64):
self.resize = resize
self.image_files = glob('/home/users/ntu/yasin001/project/stl10/*.jpg')
print(len(self.image_files))
def __len__(self):
return len(self.image_files)
def get_example(self, i):
... |
class DataViewer():
def __init__(self, app, dt=0.01, time_window_length=30, plot_period=0.2, data_recording_period=0.1):
self._dt = dt
self._data_window_length = (time_window_length / data_recording_period)
self._update_counter = 0
self._plots_per_row = 4
self._plotter = Plot... |
def get_lasso_pen_max(X, y, loss, fit_intercept, weights=None, sample_weight=None, offsets=None, multi_task=False, groups=None, nuc=False):
assert (sum([multi_task, (groups is not None), nuc]) <= 1)
loss_func = get_glm_loss_func(config=loss, X=X, y=y, fit_intercept=fit_intercept, sample_weight=sample_weight, of... |
class ProductOfExpertGaussian(layers.Layer):
def __init__(self, **kwargs):
super(ProductOfExpertGaussian, self).__init__(**kwargs)
def call(self, inputs):
(mu_list, std_list) = zip(*inputs)
prec_list = [(1 / (std ** 2)) for std in std_list]
poe_mu = K.sum([((mu * prec) / K.sum(pr... |
class CNN16(nn.Module):
def __init__(self, input_dim=256):
super(CNN16, self).__init__()
outputdim = input_dim
self.layer1 = nn.Sequential(nn.Conv2d(IN_DIM, outputdim, 3, padding=1, stride=1), nn.GroupNorm(num_groups=(outputdim // 8), num_channels=outputdim), nn.ReLU(), nn.MaxPool2d(kernel_s... |
def adjust_learning_rate(args, optimizer, epoch):
lr = args.learning_rate
if args.cosine:
eta_min = (lr * (args.lr_decay_rate ** 3))
lr = (eta_min + (((lr - eta_min) * (1 + math.cos(((math.pi * epoch) / args.epochs)))) / 2))
else:
steps = np.sum((epoch > np.asarray(args.lr_decay_epoc... |
def test_get_prefixed_keys(orchestrator):
keys_to_generate = 10
key_prefix = 'building/'
generated_keys = set()
for x in range(keys_to_generate):
orchestrator.set_key(slash_join(key_prefix, str(x)), 'test_val')
generated_keys.add(slash_join(key_prefix, str(x)))
assert (len(orchestrat... |
def on_event(args):
if isinstance(args, KeyboardEvent):
if ((args.current_key == 'A') and (args.event_type == 'key down') and ('Lcontrol' in args.pressed_key)):
print('Ctrl + A was pressed')
if ((args.current_key == 'K') and (args.event_type == 'key down')):
print('K was pres... |
class MoselLexer(RegexLexer):
name = 'Mosel'
aliases = ['mosel']
filenames = ['*.mos']
url = '
version_added = '2.6'
tokens = {'root': [('\\n', Text), ('\\s+', Text.Whitespace), ('!.*?\\n', Comment.Single), ('\\(!(.|\\n)*?!\\)', Comment.Multiline), (words(('and', 'as', 'break', 'case', 'count', ... |
class SvgStop(Tag):
_attribute_decorator('WidgetSpecific', 'Gradient color', 'ColorPicker', {})
def css_stop_color(self):
return self.style.get('stop-color', None)
_stop_color.setter
def css_stop_color(self, value):
self.style['stop-color'] = str(value)
_stop_color.deleter
def cs... |
def get_win_folder_from_registry(csidl_name: str) -> str:
shell_folder_name = {'CSIDL_APPDATA': 'AppData', 'CSIDL_COMMON_APPDATA': 'Common AppData', 'CSIDL_LOCAL_APPDATA': 'Local AppData', 'CSIDL_PERSONAL': 'Personal'}.get(csidl_name)
if (shell_folder_name is None):
raise ValueError(f'Unknown CSIDL name... |
def parse_measurement_systems(data, tree):
measurement_systems = data.setdefault('measurement_systems', {})
for measurement_system in tree.findall('.//measurementSystemNames/measurementSystemName'):
type = measurement_system.attrib['type']
if (not _should_skip_elem(measurement_system, type=type,... |
class Effect298(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Gunnery')), 'falloff', (container.getModifiedItemAttr('fallof... |
def _pil_interp_torch10(method):
if (method == 'bicubic'):
return InterpolationMode.BICUBIC
elif (method == 'lanczos'):
return InterpolationMode.LANCZOS
elif (method == 'hamming'):
return InterpolationMode.HAMMING
else:
return InterpolationMode.BILINEAR |
def compute_trans_list(theta, img_data, use_voronoi):
N = theta['N']
K = theta['K']
n = theta['n']
theta['trans_list'] = [[None for _ in range(K)] for _ in range(N)]
tasks = {}
for k_ in range(K):
A_k = theta['A'][k_]
for (j, d) in enumerate(img_data['dj']):
t = dict(... |
def raw_hyperprior(quality, metric='mse', pretrained=False, progress=True, **kwargs):
if (metric not in ('mse', 'ms-ssim')):
raise ValueError(f'Invalid metric "{metric}"')
if ((quality < 1) or (quality > 8)):
raise ValueError(f'Invalid quality "{quality}", should be between (1, 8)')
return _... |
def test_replace_node(game_editor):
region_list = game_editor.game.region_list
loc = AreaIdentifier('Temple Grounds', 'Landing Site')
loc2 = AreaIdentifier('Temple Grounds', 'Service Access')
landing_site = region_list.area_by_area_location(loc)
source = landing_site.node_with_name('Save Station')
... |
def generator_sampler(opt):
opt.batchSize = 64
opt.folderSize = 600
opt.overWrite = False
opt.outf = g.default_repo_dir
opt = addDataInfo(opt)
netG = get_generator_model(opt)
netG.load_state_dict(torch.load(get_generator_loc(opt)))
netG.eval()
opt.name = (((((opt.outf + 'samples/') +... |
class TestSendEvent(EndianTest):
def setUp(self):
self.req_args_0 = {'destination': , 'event': event.Expose(count=50227, height=24760, sequence_number=0, type=12, width=10272, window=, x=40165, y=13291), 'event_mask': , 'propagate': 0}
self.req_bin_0 = b'\x19\x00\x0b\x001_\x0bE\x9fG\rz\x0c\x00\x00\x... |
def test_macro_create_with_alias_name(base_app):
macro = 'my_macro'
run_cmd(base_app, 'alias create {} help'.format(macro))
(out, err) = run_cmd(base_app, 'macro create {} help'.format(macro))
assert ('Macro cannot have the same name as an alias' in err[0])
assert (base_app.last_result is False) |
def transform_return_stmt(builder: IRBuilder, stmt: ReturnStmt) -> None:
if stmt.expr:
retval = builder.accept(stmt.expr)
else:
retval = builder.builder.none()
retval = builder.coerce(retval, builder.ret_types[(- 1)], stmt.line)
builder.nonlocal_control[(- 1)].gen_return(builder, retval,... |
class MenuManager(QObject):
(ROOT, MENU1, MENU2, LAUNCH, DOCUMENTATION, QUIT, FULLSCREEN, UP, DOWN, BACK, LAUNCH_QML) = range(11)
pInstance = None
def __init__(self):
super(MenuManager, self).__init__()
self.contentsDoc = None
self.assistantProcess = QProcess()
self.helpRootU... |
class RangeProperty(Property):
def __init__(self, name, initial, min_value, max_value, **kwargs):
self.min_value = min_value
self.max_value = max_value
if ((initial < min_value) or (initial > max_value)):
print(_('invalid initial value for range property'), name, initial)
... |
def test_use_correct_python_version_string(tmpdir, tmpdir_cwd, monkeypatch):
dist = Distribution()
cmd = dist.get_command_obj('easy_install')
cmd.args = ['ok']
cmd.optimize = 0
cmd.user = True
cmd.install_userbase = str(tmpdir)
cmd.install_usersite = None
install_cmd = dist.get_command_o... |
def get_users_handler(config, _, override_config_dir):
authentication_type = config.get('AUTHENTICATION_TYPE', 'Database')
if (authentication_type == 'Database'):
return DatabaseUsers()
if (authentication_type == 'LDAP'):
ldap_uri = config.get('LDAP_URI', 'ldap://localhost')
base_dn ... |
class FilterTests(unittest.TestCase):
def setUp(self) -> None:
member = MockMember(id=123)
channel = MockTextChannel(id=345)
message = MockMessage(author=member, channel=channel)
self.ctx = FilterContext(Event.MESSAGE, member, channel, '', message)
def test_role_bypass_is_off_for... |
.skipif('sys.platform == "win32" or platform.python_implementation() == "PyPy"')
def test_dist_combine_racecondition(testdir):
script = testdir.makepyfile(('\nimport pytest\n\.parametrize("foo", range(1000))\ndef test_foo(foo):\n' + '\n'.join((f'''
if foo == {i}:
assert True
''' for i in range(1000)))))... |
def downgrade(op, tables, tester):
op.drop_index('organizationrhskus_subscription_id', table_name='organizationrhskus')
op.drop_index('organizationrhskus_subscription_id_org_id', table_name='organizationrhskus')
op.drop_index('organizationrhskus_subscription_id_org_id_user_id', table_name='organizationrhsku... |
class GRUencoder(nn.Module):
def __init__(self, d_emb, d_out, num_layers):
super(GRUencoder, self).__init__()
self.gru = nn.GRU(input_size=d_emb, hidden_size=d_out, bidirectional=True, num_layers=num_layers, dropout=0.3)
def forward(self, sent, sent_lens):
device = sent.device
se... |
def test_get_arguments_from_argument_str():
argument_str = 'LClass;,10,String,new-array(),3.14,1'
descriptor = '(I Ljava/lang/String; [B F Z)'
arguments = get_arguments_from_argument_str(argument_str, descriptor)
assert (arguments == ['LClass;', 10, 'String', 'new-array()', 3.14, True]) |
.parametrize(('ctype', 'vconv'), (('VELO-F2W', u.doppler_optical), ('VELO-F2V', u.doppler_relativistic), ('VRAD', u.doppler_radio), ('VOPT', u.doppler_optical), ('VELO', u.doppler_relativistic), ('WAVE', u.doppler_optical), ('WAVE-F2W', u.doppler_optical), ('WAVE-V2W', u.doppler_optical), ('FREQ', u.doppler_radio), ('F... |
def test_save_options(skip_qtbot, tmp_path):
options = Options(tmp_path)
window = DreadGameExportDialog(options, {}, 'MyHash', True, [])
window.atmosphere_radio.setChecked(True)
window.save_options()
game_options = options.options_for_game(RandovaniaGame.METROID_DREAD)
assert isinstance(game_opt... |
def test_basic_notification_endtoend(initialized_db):
assert (not model.user_has_local_notifications('public'))
notification_uuid = model.create_notification_for_testing('public')
event_data = {}
worker = NotificationWorker(None)
worker.process_queue_item({'notification_uuid': notification_uuid, 'ev... |
class PasswdUtilsTest(unittest.TestCase):
def check_if_two_generated_salts_are_different(self):
self.assertNotEqual(random_salt_function(), random_salt_function())
def check_random_add_function_output_is_as_specified(self):
self.assertEqual(len(random_salt_function(salt_len=125)), 125)
def c... |
class TestCmdLineTools(BaseTestCase):
def test_visa_main_argument_handling(self):
from pyvisa.cmd_line_tools import visa_main
old = sys.argv = ['python']
try:
with pytest.raises(ValueError):
visa_main('unknown')
finally:
sys.argv = old
def ... |
def test_typed_value() -> None:
val = TypedValue(str)
assert (val.typ is str)
assert (str(val) == 'str')
assert val.is_type(str)
assert (not val.is_type(int))
assert_can_assign(val, val)
assert_cannot_assign(val, TypedValue(int))
assert_can_assign(val, KnownValue('x'))
assert_can_ass... |
_test
def test_unit_norm():
unit_norm_instance = constraints.unit_norm()
normalized = unit_norm_instance(K.variable(get_example_array()))
norm_of_normalized = np.sqrt(np.sum((K.eval(normalized) ** 2), axis=0))
difference = (norm_of_normalized - 1.0)
largest_difference = np.max(np.abs(difference))
... |
def ZFNetBody(net, from_layer, need_fc=True, fully_conv=False, reduced=False, dilated=False, dropout=True, need_fc8=False, freeze_layers=[]):
kwargs = {'param': [dict(lr_mult=1, decay_mult=1), dict(lr_mult=2, decay_mult=0)], 'weight_filler': dict(type='xavier'), 'bias_filler': dict(type='constant', value=0)}
as... |
_required('task.add_ansibleplaybook', raise_exception=True)
def playbook_upload(request):
if (request.method == 'POST'):
playbook_file = request.FILES.get('playbook_file')
playbook_name = request.POST.get('playbook_name')
if playbook_file:
playbook = AnsiblePlaybook.objects.creat... |
class TruncatedDataset(Dataset):
def __init__(self, base_dataset, pc):
self.base_dataset = base_dataset
self.len = int((len(self.base_dataset) * pc))
self.random_order = np.random.choice(len(self.base_dataset), size=self.len, replace=False)
def __getitem__(self, item):
assert (it... |
def test_param_scaling():
param = ParameterList()
with pytest.raises(ValueError, match='Parameter scaling must be a VariableScaling'):
param.add('gravity_z', my_parameter_function, size=1, scaling='a')
with pytest.raises(ValueError, match='Parameter scaling must be a VariableScaling'):
param... |
class Snake(Converter):
snakes = None
special_cases = None
async def convert(self, ctx: Context, name: str) -> str:
(await self.build_list())
name = name.lower()
if (name == 'python'):
return 'Python (programming language)'
def get_potential(iterable: Iterable, *,... |
def select_device(device='', batch_size=None):
cpu_request = (device.lower() == 'cpu')
if (device and (not cpu_request)):
os.environ['CUDA_VISIBLE_DEVICES'] = device
assert torch.cuda.is_available(), ('CUDA unavailable, invalid device %s requested' % device)
cuda = (False if cpu_request else... |
class TesttestPurePyShpWrapper():
def setup_method(self):
test_file = pysal_examples.get_path('10740.shp')
self.test_file = test_file
self.shp_obj = PurePyShpWrapper(test_file, 'r')
f = tempfile.NamedTemporaryFile(suffix='.shp')
shpcopy = f.name
f.close()
self... |
def create_patterns_for_ops():
for (pattern_op_type, info_dict) in op_type_templates.items():
input_shape = info_dict['input_shape']
constructor_string = info_dict['constructor']
additional_starting_ops = info_dict.get('additional_starting_ops', [])
op_type = info_dict['op_type']
... |
class MockProposer(Proposer):
def load(self, search_space: List[ShardingOption], enumerator: Optional[Enumerator]=None) -> None:
pass
def feedback(self, partitionable: bool, plan: Optional[List[ShardingOption]]=None, perf_rating: Optional[float]=None, storage_constraint: Optional[Topology]=None) -> None... |
class Canvas(object):
def __init__(self, width, height):
self.bytes = array.array('B', ([0] * ((width * height) * 3)))
for i in range((width * height)):
self.bytes[((i * 3) + 2)] = 255
self.width = width
self.height = height
def plot(self, x, y, r, g, b):
i = ... |
class PFGeneralPref(PreferenceView):
def populatePanel(self, panel):
self.title = _t('Database')
self.dirtySettings = False
mainSizer = wx.BoxSizer(wx.VERTICAL)
self.stTitle = wx.StaticText(panel, wx.ID_ANY, self.title, wx.DefaultPosition, wx.DefaultSize, 0)
self.stTitle.Wrap... |
.unit()
.parametrize('decorator', [pytask.mark.depends_on, pytask.mark.produces])
.parametrize(('values', 'expected'), [({'objects': 'a'}, ['a']), ({'objects': ['b']}, [['b']]), ({'objects': ['e', 'f']}, [['e', 'f']])])
def test_extract_kwargs_from_mark(decorator, values, expected):
(**values)
def task_example(... |
def init_summary_writer(config):
makedirs(config.summary_dir)
makedirs(config.checkpoint_dir)
print(config.checkpoint, os.path.exists(config.checkpoint))
if (not os.path.exists(config.checkpoint)):
os.makedirs(config.checkpoint)
path = os.path.dirname(os.path.abspath(__file__))
path_mode... |
def get_metrics(image_features, text_features, logit_scale):
metrics = {}
logits_per_image = ((logit_scale * image_features) text_features.t()).detach().cpu()
logits_per_text = logits_per_image.t().detach().cpu()
logits = {'image_to_text': logits_per_image, 'text_to_image': logits_per_text}
ground_... |
def command_tttextract(args):
def setup(parser):
parser.add_option('--output', dest='output_fn', metavar='TEMPLATE', help='output to text files instead of stdout (example TEMPLATE: "extracted/%(args)s.txt")')
(parser, options, args) = cl_parse('tttextract', args, setup=setup)
try:
sdef = arg... |
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