code stringlengths 281 23.7M |
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def dnorm_problem(dim):
(X, constraints) = initialize_constraints_on_dnorm_problem(dim)
Jr = cvxpy.Parameter(((dim ** 2), (dim ** 2)))
Ji = cvxpy.Parameter(((dim ** 2), (dim ** 2)))
objective = cvxpy.Maximize(cvxpy.trace(((Jr.T X.re) + (Ji.T X.im))))
problem = cvxpy.Problem(objective, constraints)... |
.parametrize('min_len', [0, 3])
.parametrize('num_chars', [5, 9])
.parametrize('num_elements', itertools.chain(range(1, 26), [125]))
def test_scattered_hints_count(min_len, num_chars, num_elements):
manager = qutebrowser.browser.hints.HintManager(win_id=0)
chars = string.ascii_lowercase[:num_chars]
hints = ... |
class TestLowRankTwoBodyDecomposition(QiskitNatureTestCase):
(4, 5)
def test_double_factorized_random(self, dim: int):
two_body_tensor = random_two_body_tensor_real(dim, seed=25257)
(diag_coulomb_mats, orbital_rotations) = double_factorized(two_body_tensor)
reconstructed = np.einsum('tpk... |
class GraphLearner(nn.Module):
def __init__(self, input_size, hidden_size, topk=None, epsilon=None, num_pers=16, metric_type='attention', device=None):
super(GraphLearner, self).__init__()
self.device = device
self.topk = topk
self.epsilon = epsilon
self.metric_type = metric_... |
class DomainThermalParameters(BaseParameters):
def __init__(self, domain, main_param):
self.domain = domain
self.main_param = main_param
def _set_parameters(self):
Domain = self.domain.capitalize()
self.h_tab = pybamm.Parameter(f'{Domain} tab heat transfer coefficient [W.m-2.K-1]... |
def default_regression_model(num_values, num_anchors, pyramid_feature_size=256, regression_feature_size=256, name='regression_submodel'):
options = {'kernel_size': 3, 'strides': 1, 'padding': 'same', 'kernel_initializer': keras.initializers.normal(mean=0.0, stddev=0.01, seed=None), 'bias_initializer': 'zeros'}
... |
class Pile(TracesGroup):
def __init__(self):
TracesGroup.__init__(self, None)
self.subpiles = {}
self.open_files = {}
self.listeners = []
self.abspaths = set()
def add_listener(self, obj):
self.listeners.append(util.smart_weakref(obj))
def notify_listeners(sel... |
class ColoredFormatter(logging.Formatter):
def __init__(self, msg, use_color=True):
logging.Formatter.__init__(self, msg)
self.use_color = use_color
def format(self, record):
levelname = record.levelname
if (self.use_color and (levelname in COLORS)):
levelname_color =... |
def post_release_work():
current_version = get_version()
dev_version = f'{current_version.major}.{(current_version.minor + 1)}.0.dev0'
current_version = current_version.base_version
version = input(f'Which version are we developing now? [{dev_version}]')
if (len(version) == 0):
version = dev... |
def taxids_at_ranks(qid, ranks, taxdump):
cid = qid
pid = ''
res = {x: None for x in ranks}
rankset = set(ranks)
while True:
taxon = _get_taxon(cid, taxdump)
rank = taxon['rank']
if (rank in rankset):
res[rank] = cid
pid = taxon['parent']
if ((pid ... |
class InitiatorSetup(NamedTuple):
current_state: State
block_number: typing.BlockNumber
channel: NettingChannelState
channel_map: typing.Dict[(typing.ChannelID, NettingChannelState)]
channels: ChannelSet
available_routes: typing.List[RouteState]
prng: random.Random
lock: HashTimeLockStat... |
class Test_pep440_branch(unittest.TestCase, Testing_branch_renderer_case_mixin):
style = 'pep440-branch'
expected = {'tagged_0_commits_clean': 'v1.2.3', 'tagged_0_commits_dirty': 'v1.2.3+0.g.dirty', 'tagged_1_commits_clean': 'v1.2.3+1.gabc', 'tagged_1_commits_dirty': 'v1.2.3+1.gabc.dirty', 'untagged_0_commits_c... |
class CfdRunnableFoam(_CfdRunnable):
def __init__(self, solver=None):
super(CfdRunnableFoam, self).__init__(solver)
self.writer = CfdCaseWriterFoam.CfdCaseWriterFoam(self.analysis)
if using_freecad_plot:
from FoamCaseBuilder import FoamResidualPloter
self.ploter = Foa... |
class GCN3D(nn.Module):
def __init__(self, class_num, support_num, neighbor_num):
super().__init__()
self.neighbor_num = neighbor_num
self.conv_0 = gcn3d.Conv_surface(kernel_num=128, support_num=support_num)
self.conv_1 = gcn3d.Conv_layer(128, 128, support_num=support_num)
se... |
class ParallelPoolPerformerTests(TestCase, ParallelPerformerTestsMixin):
def setUp(self):
super(ParallelPoolPerformerTests, self).setUp()
self.pool = ThreadPool()
self.p_performer = partial(perform_parallel_with_pool, self.pool)
self.dispatcher = ComposedDispatcher([base_dispatcher, ... |
def model_composited(t_imgs_dict, t_labels_dict, params=dict()):
net = Parameters()
net.inputs = t_imgs_dict
net.imgs = dict()
net.resi_imgs = dict()
net.resi_imgs_noaug = dict()
net.latent = dict()
net.logits = dict()
net.instr = dict()
net.resi_outs = dict()
net.activations = d... |
(init=False)
class TokenNetworkRegistryState(State):
class Meta():
unknown = marshmallow.EXCLUDE
fields = ['address', 'token_network_list', 'tokennetworkaddresses_to_tokennetworks']
load_only = ['tokennetworkaddresses_to_tokennetworks']
address: TokenNetworkRegistryAddress
token_netw... |
def main(data_dir, client, bc, config):
benchmark(read_tables, data_dir, bc, dask_profile=config['dask_profile'])
query = ' \n\t\tSELECT CASE WHEN pmc > 0.0 THEN CAST (amc AS DOUBLE) / CAST (pmc AS DOUBLE) ELSE -1.0 END AS am_pm_ratio\n\t\tFROM \n\t\t(\n\t\t\tSELECT SUM(amc1) AS amc, SUM(pmc1) AS pmc\n\t\t\tFRO... |
def pad_sequences(sequences, pad_mark=0):
max_len = max(map((lambda x: len(x)), sequences))
(seq_list, seq_len_list) = ([], [])
for seq in sequences:
seq = list(seq)
seq_ = (seq[:max_len] + ([pad_mark] * max((max_len - len(seq)), 0)))
seq_list.append(seq_)
seq_len_list.append... |
class SNConv2d(nn.Conv2d):
Ip = 1
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
super(SNConv2d, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias)
u = Parameter(torch.FloatTensor(1, s... |
def change_nvfancontrol_default(name, value):
with open('/etc/nvfancontrol.conf', 'r') as f:
lines = f.readlines()
with open('/etc/nvfancontrol.conf', 'w') as f:
for line in lines:
match_defaults = re.search(FAN_NVFAN_DEFAULT_RE, line.strip())
if match_defaults:
... |
def _send_invitations(*, queryset, invited_only: bool=False, uninvited_only: bool=False, is_reminder: bool=False):
queryset = queryset.filter(status=ScheduleItem.STATUS.waiting_confirmation, submission__isnull=False, type__in=[ScheduleItem.TYPES.talk, ScheduleItem.TYPES.submission, ScheduleItem.TYPES.training])
... |
def _get_build_status(build_obj):
phase = build_obj.phase
status = {}
error = None
if (not database.BUILD_PHASE.is_terminal_phase(phase)):
try:
status = build_logs.get_status(build_obj.uuid)
except BuildStatusRetrievalError as bsre:
phase = 'cannot_load'
... |
class Effect6794(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Shield Command')), 'warfareBuff4Value', src.getModifiedItemAttr('shipBonusORECapital3'), skill='Capital Industrial Ships', **kwargs... |
def Frame(name, widget):
def hx(value):
return hex(int((value * 255)))[2:]
f = Gtk.Frame()
qltk.add_css(f, '* {opacity: 0.9}')
l = Gtk.Label()
l.set_markup(util.escape(name))
qltk.add_css(l, ' * {opacity: 0.6; padding: 0px 2px;}')
f.set_label_widget(l)
a = Align(top=6, left=12, b... |
def test_TMM_dielectric_model():
drud = Drude(An=24.317, Brn=0.12574)
model = DielectricConstantModel(e_inf=3.4837, oscillators=[drud])
wavelength = (2 * np.logspace(3, 4, 10))
n = model.n_and_k(wavelength)
data = ((0.3737771 + 2.0726883j), (0. + 3.j), (0. + 4.j), (1. + 5.j), (1. + 6.8504966j), (2. ... |
class SpecialGuestSection():
id: strawberry.ID
title: str
guest_name: str
guest_job_title: str
event_date: datetime.date
cta: (CTA | None)
_block: strawberry.Private[Any]
def guest_photo(self) -> str:
guest_photo = self._block.value['guest_photo']
return guest_photo.get_r... |
def _check_service_key(app):
if (not app.config.get('SETUP_COMPLETE', False)):
return (True, 'Stack not fully setup; skipping check')
try:
kid = instance_keys.local_key_id
except IOError as ex:
return (True, 'Stack not fully setup; skipping check')
try:
key_is_valid = boo... |
def check_dicom_agrees(ds1, ds2):
assert (ds1.SOPInstanceUID == ds2.SOPInstanceUID)
assert (ds1.SeriesInstanceUID == ds2.SeriesInstanceUID)
assert (ds1.StudyInstanceUID == ds2.StudyInstanceUID)
assert (ds1.PatientID == ds2.PatientID)
assert (ds1.Modality == ds2.Modality)
assert (ds1.Manufacturer... |
(help='Try ./bin/projects.py docs/data/projects.yml')
('input', type=click.File('r'))
('--online/--no-online', default=True, help='Get info from GitHub')
('--auth', help='GitHub authentication token')
('--dry-run', default=False, help='Print the output, rather than writing it to files in the repo')
def projects(input: ... |
class ChecklistParameterItem(GroupParameterItem):
def __init__(self, param, depth):
self.btnGrp = QtWidgets.QButtonGroup()
self.btnGrp.setExclusive(False)
self._constructMetaBtns()
super().__init__(param, depth)
def _constructMetaBtns(self):
self.metaBtnWidget = QtWidgets... |
class FatigueModel(ABC):
def __init__(self, scaling: float=1, state_only: bool=None, apply_to_joint_dynamics: bool=None):
self.scaling = scaling
self.state_only = (self.default_state_only() if (state_only is None) else state_only)
self.apply_to_joint_dynamics = (self.default_apply_to_joint_d... |
class TestSequentialNodeRewriter():
def test_optimizer_verbose(self, capsys):
x = MyVariable('x')
y = MyVariable('y')
o1 = op1(x, y)
fgraph = FunctionGraph([x, y], [o1], clone=False)
_rewriter(None)
def local_rewrite_1(fgraph, node):
if (node.inputs[0] == ... |
def test_set_pos_center_when_scaled(qapp, item):
item.setScale(2)
with patch.object(item, 'bounding_rect_unselected', return_value=QtCore.QRectF(0, 0, 200, 100)):
item.set_pos_center(QtCore.QPointF(0, 0))
assert (item.pos().x() == (- 200))
assert (item.pos().y() == (- 100)) |
class Migration(migrations.Migration):
dependencies = [('conditions', '0017_data_migration')]
operations = [migrations.AlterField(model_name='condition', name='comment', field=models.TextField(blank=True, help_text='Additional internal information about this condition.', verbose_name='Comment')), migrations.Alt... |
class VGG(nn.Module):
def __init__(self, features, num_classes=1000, init_weights=True):
super(VGG, self).__init__()
self.features = features
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(nn.Linear(((512 * 7) * 7), 4096), nn.ReLU(True), nn.Dropout(), nn.... |
('PyQt6.QtWidgets.QGraphicsScene.mousePressEvent')
def test_mouse_press_event_when_left_click_over_no_item_in_crop_mode(mouse_mock, view, item):
view.scene.addItem(item)
view.scene.cancel_crop_mode = MagicMock()
view.scene.crop_item = item
view.scene.itemAt = MagicMock(return_value=None)
event = Mag... |
def evaluate_annotation(key2refs, scorer):
if (scorer.method() == 'Bleu'):
scores = np.array([0.0 for n in range(4)])
else:
scores = 0
num_cap_per_audio = len(next(iter(key2refs.values())))
for i in range(num_cap_per_audio):
if (i > 0):
for key in key2refs:
... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--input_text', default='input_text.txt')
parser.add_argument('--length', default=50, type=int)
parser.add_argument('--batch_size', default=1, type=int)
parser.add_argument('--temperature', default=0.7, type=float)
parser.a... |
class InputOutputOracleREST(InputOutputOracle):
def __init__(self, grammar: TritonGrammar, inputs: List[Input], f_name: str=''):
super(InputOutputOracleREST, self).__init__(grammar, inputs, f_name)
self.session = requests.Session()
self._size = 0
def create(filename: Union[(str, Path)], ... |
def parse_init(init_file):
with open(init_file, 'r', encoding='utf-8', newline='\n') as f:
lines = f.readlines()
line_index = 0
while ((line_index < len(lines)) and (not lines[line_index].startswith('_import_structure = {'))):
line_index += 1
if (line_index >= len(lines)):
return... |
class CodeLogger():
def __init__(self, name):
self.logger = logging.getLogger(name)
self.logger.setLevel(logging.INFO)
if (not self.logger.handlers):
handler = logging.StreamHandler(sys.stdout)
handler.setFormatter(CustomFormatter('%(message)s'))
self.logg... |
class AdvertiserTopicReportView(AdvertiserAccessMixin, BaseReportView):
template_name = 'adserver/reports/advertiser-topic.html'
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
advertiser_slug = kwargs.get('advertiser_slug', '')
advertiser = get_object_... |
class RuleCommandTests(unittest.IsolatedAsyncioTestCase):
def setUp(self) -> None:
self.bot = helpers.MockBot()
self.cog = information.Information(self.bot)
self.ctx = helpers.MockContext(author=helpers.MockMember(id=1, name='Bellaluma'))
self.full_rules = [('First rule', ['first', '... |
def test_edge_edge_degenerate_first_edge(test, device):
p1_h = np.array([[0, 0, 0]])
q1_h = np.array([[0, 0, 0]])
p2_h = np.array([[0, 1, 0]])
q2_h = np.array([[1, 0, 0]])
res = run_closest_point_edge_edge(p1_h, q1_h, p2_h, q2_h, device)
st0 = res[0]
test.assertAlmostEqual(st0[0], 0.0)
t... |
def test_filter_end_block_inclusive(deploy_client: JSONRPCClient) -> None:
(contract_proxy, _) = deploy_rpc_test_contract(deploy_client, 'RpcTest')
estimated_transaction1 = deploy_client.estimate_gas(contract_proxy, 'createEvent', {}, 1)
assert estimated_transaction1
transaction_1 = deploy_client.transa... |
def cyclic_learning_rate(global_step, learning_rate=0.01, max_lr=0.1, step_size=20.0, gamma=0.99994, mode='triangular', name=None):
if (global_step is None):
raise ValueError('global_step is required for cyclic_learning_rate.')
with ops.name_scope(name, 'CyclicLearningRate', [learning_rate, global_step]... |
def extract_pairs(pair_text, phashes_dict):
pairs = []
els = pair_text.split('-')
if (len(els) > 2):
for i in range(len(els)):
pair = '-'.join(els[0:i])
if (pair in phashes_dict):
pairs.append(pair)
break
pair2 = '-'.join(els[i:])
... |
class TCompileMatch(TestCase):
def test_basics_default(self):
assert compile('foo')('foo')
assert compile('foo')('fooo')
assert (not compile('foo')('fo'))
def test_ignore_case(self):
assert compile('foo', ignore_case=True)('Foo')
assert (not compile('foo', ignore_case=Fal... |
class GroupOverSampleKaiming(object):
def __init__(self, crop_size, scale_size=None):
self.crop_size = (crop_size if (not isinstance(crop_size, int)) else (crop_size, crop_size))
if (scale_size is not None):
self.scale_worker = GroupScale(scale_size)
else:
self.scale_... |
_torch
_retrieval
class RagModelSaveLoadTests(unittest.TestCase):
def tearDown(self):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def get_rag_config(self):
question_encoder_config = AutoConfig.from_pretrained('facebook/dpr-question_encoder-single-nq-base')
ge... |
def _get_cached_and_pending_stats(discover_deltas_pending: List[ObjectRef[DeltaStatsCacheResult]], deltacat_storage=unimplemented_deltacat_storage) -> Tuple[(List[DeltaStats], List[ObjectRef[DeltaStats]])]:
delta_stats_processed: List[DeltaStats] = []
delta_stats_pending: List[ObjectRef[DeltaStats]] = []
wh... |
def test_force_locale_with_threading_and_app_context():
app = flask.Flask(__name__)
babel.Babel(app, locale_selector=(lambda : 'de_DE'))
semaphore = Semaphore(value=0)
def first_app_context():
with app.app_context():
with babel.force_locale('en_US'):
assert (str(babel... |
def RCISD(mf, frozen=None, mo_coeff=None, mo_occ=None):
from pyscf.df.df_jk import _DFHF
mf = mf.remove_soscf()
if (not mf.istype('RHF')):
mf = mf.to_rhf()
if (isinstance(mf, _DFHF) and mf.with_df):
from pyscf import lib
lib.logger.warn(mf, f'DF-RCISD for DFHF method {mf} is not ... |
class RerankerTokenizer():
def __init__(self, total_maxlen, base):
self.total_maxlen = total_maxlen
self.tok = AutoTokenizer.from_pretrained(base)
def tensorize(self, questions, passages):
assert (type(questions) in [list, tuple]), type(questions)
assert (type(passages) in [list,... |
class CSRNet_LCM(nn.Module):
def __init__(self, load_weights=True):
super(CSRNet_LCM, self).__init__()
self.seen = 0
self.frontend_feat = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512]
self.backend_feat = ['M', 512, 512, 'M', 512, 256, 'M', 128, 64]
self.fron... |
def test_regression_mediator_task_no_routes():
pseudo_random_generator = random.Random()
channels = make_channel_set([NettingChannelStateProperties(our_state=NettingChannelEndStateProperties(balance=0), partner_state=NettingChannelEndStateProperties(balance=UNIT_TRANSFER_AMOUNT, address=HOP2, privatekey=HOP2_KE... |
_vision
_torch
class AlignModelIntegrationTest(unittest.TestCase):
def test_inference(self):
model_name = 'kakaobrain/align-base'
model = AlignModel.from_pretrained(model_name).to(torch_device)
processor = AlignProcessor.from_pretrained(model_name)
image = prepare_img()
texts... |
def _schedule_item_status_to_message(status: str):
from schedule.models import ScheduleItem
if (status == ScheduleItem.STATUS.confirmed):
return 'I am happy with the time slot.'
if (status == ScheduleItem.STATUS.maybe):
return 'I can make this time slot work if it is not possible to change'
... |
def test_cwd(tmp_path):
project_dir = (tmp_path / 'project')
test_projects.new_c_project().generate(project_dir)
actual_wheels = utils.cibuildwheel_run(project_dir, add_env={'CIBW_BEFORE_ALL': f'python -c "import os; assert os.getcwd() == {str(project_dir)!r}"', 'CIBW_BEFORE_ALL_LINUX': 'python -c "import o... |
class FC():
_activations = {None: tf.identity, 'ReLU': tf.nn.relu, 'tanh': tf.tanh, 'sigmoid': tf.sigmoid, 'softmax': tf.nn.softmax, 'swish': (lambda x: (x * tf.sigmoid(x)))}
def __init__(self, output_dim, input_dim=None, activation=None, weight_decay=None, ensemble_size=1):
(self.input_dim, self.output... |
def _get_boundaries(x_values, y_values, round_val):
x1 = np.min((np.floor(((x_values - 0.5) / round_val)) * round_val))
x2 = np.max((np.ceil(((x_values + 0.5) / round_val)) * round_val))
y1 = np.min((np.floor(((y_values - 0.5) / round_val)) * round_val))
y2 = np.max((np.ceil(((y_values + 0.5) / round_va... |
def test_simulationtimecondition():
cond = OSC.SimulationTimeCondition(1.2, OSC.Rule.greaterThan)
prettyprint(cond.get_element())
cond2 = OSC.SimulationTimeCondition(1.2, OSC.Rule.greaterThan)
cond3 = OSC.SimulationTimeCondition(1.3, OSC.Rule.greaterThan)
assert (cond == cond2)
assert (cond != c... |
def ssh(function=None, **kwargs):
def decorator(func, *args, **kwargs):
hostname = kwargs['host']
username = kwargs['user']
sshkey = kwargs['key']
python = (kwargs['python'] if ('python' in kwargs) else 'python3.8')
logging.debug('ssh: func: %s', func.func)
if (not fu... |
.unit()
.parametrize(('markers', 'expected'), [(None, []), ([], []), ([pytask.mark.produces(), pytask.mark.depends_on()], [pytask.mark.produces(), pytask.mark.depends_on()]), ([pytask.mark.produces(), pytask.mark.produces(), pytask.mark.depends_on()], [pytask.mark.produces(), pytask.mark.produces(), pytask.mark.depends... |
class ChatAnthropic(BaseChatModel, _AnthropicCommon):
stop: Optional[List[str]] = None
class Config():
extra = Extra.ignore
def _llm_type(self) -> str:
return 'anthropic-chat'
def _convert_one_message_to_text(self, message: BaseMessage) -> str:
if isinstance(message, ChatMessage)... |
class CasadiAlgebraicSolver(pybamm.BaseSolver):
def __init__(self, tol=1e-06, extra_options=None):
super().__init__()
self.tol = tol
self.name = 'CasADi algebraic solver'
self.algebraic_solver = True
self.extra_options = (extra_options or {})
pybamm.citations.register... |
class EventLoopManager():
current = None
exceptions = []
exceptionLock = threading.RLock()
waitingLock = threading.RLock()
def __init__(self):
self.threads = []
self.loops = []
self.separateLoops = []
self.waiting = {}
self.pending = []
self.updates = ... |
def SynthesizeAddSecondOrder(NetworkPrefixCounter):
trajectories = []
for vessel in range(vessels):
trajectory = []
for step in range(steps):
if (len(trajectory) == 0):
port = random.randint(0, 99)
elif (len(trajectory) == 1):
prev = trajec... |
def main():
parser = argparse.ArgumentParser(description='significant test')
parser.add_argument('-d', '--Domain', required=True, type=str, help='which domain to work on?')
parser.add_argument('-fn', '--FolderName', required=True, type=str, help='base name of the folder to store result?')
parser.add_arg... |
def splitZip(path):
components = os.path.normpath(path).split(os.sep)
for (index, component) in enumerate(components):
if component.endswith('.zip'):
zipPath = os.sep.join(components[0:(index + 1)])
archivePath = ''.join([(x + '/') for x in components[(index + 1):]])
... |
class Issue(DataClassDictMixin):
id: int
node_id: str
url: str
repository_url: str
labels_url: str
comments_url: str
events_url: str
html_url: str
number: int
state: IssueState
state_reason: Optional[StateReason]
title: str
user: Optional[SimpleUser]
labels: List[... |
class SimpleWire(ComponentLevel4):
def construct(s):
s.read = CalleePort(method=s.rd)
s.write = CalleePort(method=s.wr)
s.v = 0
s.add_constraints((M(s.rd) > M(s.wr)))
def wr(s, v):
s.v = v
def rd(s):
return s.v
def line_trace(s):
return ('%d' % s.v... |
def main():
parser = argparse.ArgumentParser(description='Networks')
parser.add_argument('--modelname', default='SETR_ConvFormer', type=str, help='type of model')
parser.add_argument('--task', default='ICH', help='task or dataset name')
args = parser.parse_args()
opt = get_config(args.task)
opt.... |
def test_unionize_dataframe_categories_single(uniontest_df1, uniontest_df2, uniontest_df3):
(udf1, udf2, udf3) = janitor.unionize_dataframe_categories(uniontest_df1, uniontest_df2, uniontest_df3, column_names='fruits')
assert (set(udf1['fruits'].dtype.categories) == set(udf2['fruits'].dtype.categories))
ass... |
def unet_resnext_50_lovasz(input_shape, freeze_encoder):
(resnet_base, hyper_list) = Unet(backbone_name='resnext50', input_shape=input_shape, input_tensor=None, encoder_weights='imagenet', freeze_encoder=freeze_encoder, skip_connections='default', decoder_block_type='transpose', decoder_filters=(128, 64, 32, 16, 8)... |
class Migration(migrations.Migration):
dependencies = [('options', '0012_meta')]
operations = [migrations.AlterModelOptions(name='option', options={'ordering': ('optionset__order', 'optionset__key', 'order', 'key'), 'permissions': (('view_option', 'Can view Option'),), 'verbose_name': 'Option', 'verbose_name_pl... |
class TestGetOrganization(ApiTestCase):
def test_unknownorg(self):
self.login(ADMIN_ACCESS_USER)
self.getResponse(Organization, params=dict(orgname='notvalid'), expected_code=404)
def test_cannotaccess(self):
self.login(NO_ACCESS_USER)
self.getResponse(Organization, params=dict(o... |
class _composite_rays(Function):
_fwd(cast_inputs=torch.float32)
def forward(ctx, n_alive, n_step, rays_alive, rays_t, sigmas, rgbs, deltas, weights_sum, depth, image, T_thresh=0.01):
_backend.composite_rays(n_alive, n_step, T_thresh, rays_alive, rays_t, sigmas, rgbs, deltas, weights_sum, depth, image)
... |
class VcfSpeedSuite():
def setup(self) -> None:
asv_env_dir = os.environ['ASV_ENV_DIR']
path = Path(asv_env_dir, 'project/sgkit/tests/io/vcf/data/1000G.phase3.broad.withGenotypes.chr20..vcf.gz')
tmp_path = Path(tempfile.mkdtemp())
self.input_vcf = tmp_path.joinpath('1000G.in.vcf').as... |
class PoseHighResolutionNet(nn.Module):
def __init__(self, cfg, **kwargs):
self.inplanes = 64
extra = cfg['MODEL']['EXTRA']
self.cfg = cfg
super(PoseHighResolutionNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1, bias=False)
self.... |
def download_file_from_google_drive(id, destination):
URL = '
session = requests.Session()
response = session.get(URL, params={'id': id}, stream=True)
token = get_confirm_token(response)
if token:
params = {'id': id, 'confirm': token}
response = session.get(URL, params=params, stream... |
.parametrize('size1, size2, axis, concatenate', [((5,), (3,), 0, True), ((5,), (3,), (- 1), True), ((5, 2), (3, 2), 0, True), ((2, 5), (2, 3), 1, True), ((2, 5), (2, 5), 0, False), ((2, 5), (2, 5), 1, False), ((2, 5), (2, 5), 2, False)])
def test_measurable_join_univariate(size1, size2, axis, concatenate):
base1_rv... |
class OctaveMatrixGenerator(MatrixGenerator):
_idx_start = 1
_idx_delim = '()'
_base_printer = OctaveCodePrinter
_type_declar = ''
_line_contin = ' ...'
_comment_char = '%'
_m_template = 'function [{output_args}] = {prefix}({input_args})\n% function [{output_args}] = {prefix}({input_args})\n... |
class ReduceLRWDOnPlateau(ReduceLROnPlateau):
def epoch_step(self, metrics, epoch):
current = metrics
if (current is None):
warnings.warn('Learning Rate Plateau Reducing requires metrics available!', RuntimeWarning)
else:
if self.in_cooldown():
self.co... |
def test_default_image_optimizer():
torch.manual_seed(0)
image = torch.rand(1, 3, 128, 128)
optimizer = optim.default_image_optimizer(image)
assert isinstance(optimizer, torch.optim.Optimizer)
actual = optimizer.param_groups[0]['params'][0]
desired = image
ptu.assert_allclose(actual, desired... |
def parse_args():
parser = argparse.ArgumentParser(description='AB3DMOT')
parser.add_argument('--dataset', type=str, default='nuScenes', help='KITTI, nuScenes')
parser.add_argument('--split', type=str, default='val', help='train, val, test')
parser.add_argument('--det_name', type=str, default='centerpoi... |
def _validate_pickup_pool_size(item_pool: list[PickupEntry], game: GameDescription, configuration: BaseConfiguration) -> None:
min_starting_pickups = configuration.standard_pickup_configuration.minimum_random_starting_pickups
if (len(item_pool) > (game.region_list.num_pickup_nodes + min_starting_pickups)):
... |
def can_symlink(local_resource_dir: Path) -> bool:
if (not WINDOWS):
return True
if (local_resource_dir not in _can_symlink_cache):
with TemporaryDirectory(dir=local_resource_dir) as d:
p = Path(d)
target = (p / 'a')
target.touch()
lnk = (p / 'b')
... |
class YosysBehavioralTranslatorL2(YosysBehavioralTranslatorL1, VBehavioralTranslatorL2):
def _get_rtlir2v_visitor(s):
return YosysBehavioralRTLIRToVVisitorL2
def rtlir_tr_behavioral_tmpvars(s, tmpvars):
_tmpvars = []
for tmpvar in tmpvars:
_tmpvars += tmpvar
make_inde... |
class AndRequestChecker(RequestChecker):
def __init__(self, request_checkers: Iterable[RequestChecker]):
self._request_checkers = request_checkers
def check_request(self, mediator: DirectMediator, request: Request) -> None:
for checker in self._request_checkers:
checker.check_request... |
class LearningSchedulesTest(tf.test.TestCase):
def testExponentialDecayWithBurnin(self):
global_step = tf.placeholder(tf.int32, [])
learning_rate_base = 1.0
learning_rate_decay_steps = 3
learning_rate_decay_factor = 0.1
burnin_learning_rate = 0.5
burnin_steps = 2
... |
def test_update_questionset_error_section(db):
questionset = QuestionSet.objects.exclude(pages=None).first()
page = questionset.pages.first()
section = page.sections.first()
section.locked = True
section.save()
question = Question.objects.exclude(questionsets=questionset).first()
with pytest... |
def build_dataset():
noise_label_path = os.path.join('noisy_labels', args.noise_label_file)
noise_y = np.load(noise_label_path)
print('Load noisy label from {}'.format(noise_label_path))
transform_train = transforms.Compose([transforms.RandomCrop(32, padding=4), transforms.RandomHorizontalFlip(), transf... |
class Bottleneck(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=True)
self.bn2 = nn.BatchNorm2d(planes)
... |
.parametrize('text,result', [('1', PEP440Version(release=Release.from_parts(1))), ('1.2.3', PEP440Version(release=Release.from_parts(1, 2, 3))), ('1.2.3-1', PEP440Version(release=Release.from_parts(1, 2, 3), post=ReleaseTag('post', 1))), ('1.2.3.dev1', PEP440Version(release=Release.from_parts(1, 2, 3), dev=ReleaseTag('... |
class TestEntryPoints(unittest.TestCase):
def __init__(self, *args):
super().__init__(*args)
self.ep = importlib_metadata.EntryPoint(name='name', value='value', group='group')
def test_entry_point_pickleable(self):
revived = pickle.loads(pickle.dumps(self.ep))
assert (revived == ... |
def _check_chain(r, chain):
chain = list(reversed(chain))
while chain:
elem = chain.pop()
if (elem is None):
if (r.owner is not None):
return False
elif (r.owner is None):
return False
elif isinstance(elem, Op):
if (r.owner.op !... |
class _PreparedIterableCursor():
def __init__(self, prepared, params, kwargs):
self._prepared = prepared
self._params = params
self._kwargs = kwargs
def __aiter__(self):
return getattr(self._prepared, '_get_iterator')(*self._params, **self._kwargs)
def __await__(self):
... |
def test_vector_arg_types(v2: wp.vec2, v3: wp.vec3, v4: wp.vec4, m22: wp.mat22, m33: wp.mat33, m44: wp.mat44):
wp.expect_eq(v2, wp.vec2(1.0, 2.0))
wp.expect_eq(v3, wp.vec3(1.0, 2.0, 3.0))
wp.expect_eq(v4, wp.vec4(1.0, 2.0, 3.0, 4.0))
wp.expect_eq(m22, wp.mat22(1.0, 2.0, 3.0, 4.0))
wp.expect_eq(m33, ... |
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