code stringlengths 281 23.7M |
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.parametrize('prefer_grpc', [False, True])
def test_qdrant_client_integration_update_collection(prefer_grpc):
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
client.recreate_collection(collection_name=COLLECTION_NAME, vectors_config={'text': VectorParams(size=DIM, distance=Distance.DOT)}, timeou... |
class ProxyFactory(QNetworkProxyFactory):
def get_error(self):
proxy = config.val.content.proxy
if isinstance(proxy, pac.PACFetcher):
return proxy.fetch_error()
else:
return None
def _set_capabilities(self, proxy):
if (proxy.type() == QNetworkProxy.ProxyTy... |
class IndexedDataset(FairseqDataset):
_HDR_MAGIC = b'TNTIDX\x00\x00'
def __init__(self, path, fix_lua_indexing=False):
super().__init__()
self.path = path
self.fix_lua_indexing = fix_lua_indexing
self.data_file = None
self.read_index(path)
def read_index(self, path):
... |
class TestMongoMultiHostDBCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('TokuMXCollector', {'hosts': ['localhost:27017', 'localhost:27057'], 'databases': '^db'})
self.collector = TokuMXCollector(config, None)
self.connection = MagicMock()
def test_import(se... |
def test_collectignore_via_conftest(pytester: Pytester) -> None:
tests = pytester.mkpydir('tests')
tests.joinpath('conftest.py').write_text("collect_ignore = ['ignore_me']", encoding='utf-8')
ignore_me = tests.joinpath('ignore_me')
ignore_me.mkdir()
ignore_me.joinpath('__init__.py').touch()
igno... |
class PrepareAnonTerminals(Transformer_InPlace):
def __init__(self, terminals):
self.terminals = terminals
self.term_set = {td.name for td in self.terminals}
self.term_reverse = {td.pattern: td for td in terminals}
self.i = 0
self.rule_options = None
_args
def pattern... |
class TestEncryptionBuilder():
def test_unsupported_format(self):
f = PrivateFormat.PKCS8
with pytest.raises(ValueError):
f.encryption_builder()
def test_duplicate_kdf_rounds(self):
b = PrivateFormat.OpenSSH.encryption_builder().kdf_rounds(12)
with pytest.raises(Value... |
class BotUpdateTest(TestCase):
def test_branch_is_none(self):
bot = bot_factory()
bot.provider.get_default_branch.return_value = 'the foo'
bot.provider.get_file.return_value = (None, None)
bot.get_all_requirements = Mock()
bot.apply_updates = Mock()
bot.update()
... |
class TmpfsUsage_TestCase(CommandSequenceTest):
def runTest(self):
self.assert_parse('part /foo --size=100 --fstype=tmpfs --fsoptions="noexec"')
self.assert_parse('part /ham --fstype=tmpfs --fsoptions="size=250%"')
self.assert_parse('part /tmp --size=20000 --fstype=tmpfs')
self.asser... |
_equal.register(list, list)
_equal.register(tuple, tuple)
def assert_sequence_equal(result, expected, path=(), msg='', **kwargs):
result_len = len(result)
expected_len = len(expected)
assert (result_len == expected_len), ('%s%s lengths do not match: %d != %d\n%s' % (_fmt_msg(msg), type(result).__name__, res... |
def get_growing_subgraphs(device_graph: nx.Graph, central_qubit: cirq.Qid, min_size=2, max_size=None) -> Dict[(int, Tuple[cirq.Qid])]:
by_radius = defaultdict(list)
for (q, distance) in nx.shortest_path_length(device_graph, source=central_qubit).items():
by_radius[distance].append(q)
by_radius = {k:... |
class DirectionalLight(Light):
def __init__(self, Ldir, color):
self.Ldir = Ldir
self.color = color
def get_L(self):
return self.Ldir
def get_distance(self, M):
return SKYBOX_DISTANCE
def get_irradiance(self, dist_light, NdotL):
return (self.color * NdotL) |
class SegmentationDataset(Dataset):
def __init__(self, images_root, masks_root, crop=True, size=None, mask_thr=0.5):
self.mask_thr = mask_thr
images_ds = UnannotatedDataset(images_root, transform=None)
masks_ds = UnannotatedDataset(masks_root, transform=None)
masks_ds.align_names(ima... |
class BrowserWidget(QtWidgets.QWidget):
def __init__(self, *args, parent=None):
super().__init__(parent)
self.browser_args = args
self._setup_ui()
self._layout()
def _setup_ui(self):
self.browser = Browser(*self.browser_args, parent=self)
self.clear_button = QtWid... |
def __match_identation_stack(identation_stack, level, level_limits, folding_ranges, current_line):
upper_level = identation_stack.pop(0)
while (upper_level >= level):
level_start = level_limits.pop(upper_level)
folding_ranges.append((level_start, current_line))
upper_level = identation_s... |
class RRDB(nn.Module):
def __init__(self, nf, gc=32):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(nf, gc)
self.RDB2 = ResidualDenseBlock_5C(nf, gc)
self.RDB3 = ResidualDenseBlock_5C(nf, gc)
def forward(self, x):
out = self.RDB1(x)
out = self.RDB... |
_grad()
def convert_wav2vec2_checkpoint(checkpoint_path, pytorch_dump_folder_path, config_path=None, dict_path=None, is_finetuned=True):
if (config_path is not None):
config = Wav2Vec2Config.from_pretrained(config_path)
else:
config = Wav2Vec2Config()
if is_finetuned:
if dict_path:
... |
class Effect6636(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Capital Hybrid Turret')), 'damageMultiplier', src.getModifiedItemAttr('shipBonusTitanG1'), skill='Gallente Titan', **kwargs) |
def main(argv):
parser = argparse.ArgumentParser(description='Config file')
parser.add_argument('--config_file', type=str, default='./configs/catcher.json', help='Configuration file for the chosen model')
parser.add_argument('--config_idx', type=int, default=1, help='Configuration index')
parser.add_arg... |
class Migration(migrations.Migration):
dependencies = [('questions', '0055_catalog_locked')]
operations = [migrations.AddField(model_name='question', name='is_optional', field=models.BooleanField(default=False, help_text='Designates whether this question is optional.', verbose_name='is optional'))] |
_grad()
def evaluation(model, data_loader, device, config):
model.eval()
model_without_ddp = model
if hasattr(model, 'module'):
model_without_ddp = model.module
metric_logger = utils.MetricLogger(delimiter=' ')
header = 'Caption generation:'
print_freq = 50
result = []
for batch... |
()
def empty_database():
old_db = database.db
try:
test_db = SqliteDatabase(':memory:')
database.db = test_db
with test_db.bind_ctx(database.all_classes):
test_db.connect(reuse_if_open=True)
(yield test_db)
finally:
database.db = old_db |
def get_last_checkpoint(work_dir, steps=None):
checkpoint = None
last_ckpt_path = None
ckpt_paths = get_all_ckpts(work_dir, steps)
if (len(ckpt_paths) > 0):
last_ckpt_path = ckpt_paths[0]
checkpoint = torch.load(last_ckpt_path, map_location='cpu')
logging.info(f'load module from ... |
class GRAMLoss(BaseLoss):
def __init__(self, runner, d_loss_kwargs=None, g_loss_kwargs=None):
self.d_loss_kwargs = (d_loss_kwargs or dict())
self.g_loss_kwargs = (g_loss_kwargs or dict())
self.r1_gamma = self.d_loss_kwargs.get('r1_gamma', 10.0)
self.camera_gamma = self.d_loss_kwargs.... |
.parametrize('use_path', [True, False], ids=['Path', 'str'])
.parametrize('suffix', ['', '.qu', '.dat'])
def test_qsave_qload(use_path, suffix):
ops_in = [qutip.sigmax(), qutip.num(_dimension), qutip.coherent_dm(_dimension, 1j)]
filename = (_random_file_name() + suffix)
if use_path:
filename = (Path... |
def load_data_for_all_tasks(json_files):
data_dict = {}
for json_file in json_files:
dataset_json = json.load(open(json_file))
logging.info(f"loading dataset file: {json_file} for {dataset_json['task']} task")
print(f"loading dataset file: {json_file} for {dataset_json['task']} task")
... |
_options_exempt
def pp_inst(request, inst_index):
try:
inst_index = int(inst_index)
except ValueError:
return Http404()
html_path = (PROJECT_APP_PATH + '/frontend/templates/frontend/particle-picking-inst/inst{}.html'.format(inst_index))
if (not os.path.exists(html_path)):
return ... |
class CustomIcon(Icon):
_template = Template('\n {% macro script(this, kwargs) %}\n var {{ this.get_name() }} = L.icon({{ this.options|tojson }});\n {{ this._parent.get_name() }}.setIcon({{ this.get_name() }});\n {% endmacro %}\n ')
def __init__(self, icon_image: Any, icon_siz... |
def _check_required_metadata(metadata):
for md in PLUGIN_REQUIRED_METADATA:
if ((md not in dict(metadata)) or (not dict(metadata)[md])):
raise ValidationError((_('Cannot find metadata <strong>%s</strong> in metadata source <code>%s</code>.<br />For further informations about metadata, please see... |
class CustomTestSet(qpbenchmark.TestSet):
def description(self) -> str:
return 'Unit test test set'
def title(self) -> str:
return 'Unit test test set'
def sparse_only(self) -> bool:
return False
def __iter__(self):
(yield custom_problem(name='custom')) |
def add_player_class_ex(teamid: int, model_id: int, spawn_x: float, spawn_y: float, spawn_z: float, z_angle: float, weapon1: int, weapon1_ammo: int, weapon2: int, weapon2_ammo: int, weapon3: int, weapon3_ammo: int) -> int:
return AddPlayerClassEx(teamid, model_id, spawn_x, spawn_y, spawn_z, z_angle, weapon1, weapon... |
class VERSE():
def __init__(self, cpath=None):
path = (os.path.dirname(os.path.realpath(__file__)) if (cpath is None) else cpath)
try:
sofile = (glob.glob(os.path.join(path, 'verse*.so')) + glob.glob(os.path.join(path, '*verse*.dll')))[0]
self.C = ctypes.cdll.LoadLibrary(os.p... |
class nnUNetTrainerDA5_10epochs(nnUNetTrainerDA5):
def __init__(self, plans: dict, configuration: str, fold: int, dataset_json: dict, unpack_dataset: bool=True, device: torch.device=torch.device('cuda')):
super().__init__(plans, configuration, fold, dataset_json, unpack_dataset, device)
self.num_epo... |
class SneakerSchema(BaseModel):
brand_name: str = Field(example='Nike')
name: str = Field(example="Nike Air Force 1 '07")
description: str = Field(example=DESC_EXAMPLE)
size: conint(ge=38, le=53) = Field(example=42)
color: str = Field(example='White')
free_delivery: Optional[bool] = Field(exampl... |
def test_licenses_deprecated(dummy_dist, monkeypatch, tmp_path):
dummy_dist.joinpath('setup.cfg').write_text('[metadata]\nlicense_file=licenses/DUMMYFILE', encoding='utf-8')
monkeypatch.chdir(dummy_dist)
subprocess.check_call([sys.executable, 'setup.py', 'bdist_wheel', '-b', str(tmp_path), '--universal'])
... |
.parametrize('template, expected', [('{{ func1(conf.aliases) }} {{ func2(conf.backend) }}', ['aliases', 'backend']), ('{{ conf.aliases["a"].propname }}', ['aliases']), ('{{ conf.auto_save.interval + conf.hints.min_chars }}', ['auto_save.interval', 'hints.min_chars']), ('{{ notconf.a.b.c }}', [])])
def test_template_con... |
class UserInterfacePluginHandler(PluginHandler):
def __init__(self):
self.__plugins = {}
self.__sidebars = {}
def plugin_handle(self, plugin):
return issubclass(plugin.cls, UserInterfacePlugin)
def plugin_enable(self, plugin):
self.__plugins[plugin.cls] = pl_obj = plugin.get_... |
def _link_objs(value):
result = ''
delims = '(\\s*[\\[\\]\\(\\),]\\s*)'
delims_re = re.compile(delims)
sub_targets = re.split(delims, value.strip())
for sub_target in sub_targets:
sub_target = sub_target.strip()
if delims_re.match(sub_target):
result += f'{sub_target}'
... |
.usefixtures('config_tmpdir')
class TestFile():
(params=[configtypes.File, unrequired_class])
def klass(self, request):
return request.param
def test_to_py_does_not_exist_file(self, os_mock):
os_mock.path.isfile.return_value = False
with pytest.raises(configexc.ValidationError):
... |
def test_volume_sample_i(volume: wp.uint64, points: wp.array(dtype=wp.vec3)):
tid = wp.tid()
p = points[tid]
i = round(p[0])
j = round(p[1])
k = round(p[2])
expected = int(((i * j) * k))
if ((abs(i) > 10.0) or (abs(j) > 10.0) or (abs(k) > 10.0)):
expected = 10
expect_eq(wp.volume... |
def get_config():
config = get_default_configs()
training = config.training
training.batch_size = 64
training.n_iters = 2400001
training.snapshot_sampling = True
training.sde = 'vesde'
training.continuous = True
evaluate = config.eval
evaluate.num_samples = 50000
evaluate.ckpt_id... |
def init_distributed_device(args):
args.distributed = False
args.world_size = 1
args.rank = 0
args.local_rank = 0
dist_backend = getattr(args, 'dist_backend', 'nccl')
dist_url = getattr(args, 'dist_url', 'env://')
if is_distributed_env():
if ('SLURM_PROCID' in os.environ):
... |
def transform_all_binary_images(root_path):
if os.path.isdir(root_path):
files = os.listdir(root_path)
files = [file for file in files if ((file[0] != '.') and (file[:2] != '__'))]
for file in files:
try:
transform_all_binary_images(os.path.join(root_path, file))
... |
def calculate_jaccard_index(R1, R2):
subset_overlap = []
for n in range(len(R1)):
sim_per_pair = []
for i in range(len(R1[n])):
s1 = R1[n][i]
s2 = R2[n][i]
sim = jaccard_similarity(s1, s2)
sim_per_pair.append(sim)
subset_overlap.append(sim_... |
class Net(nn.Module):
def __init__(self) -> None:
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = n... |
def build_auth(xpaths, username, password):
auth = {}
try:
auth['username'] = [xpaths.pop('username'), username]
except:
raise ValueError('username not in predefined')
try:
auth['password'] = [xpaths.pop('password'), password]
except:
raise ValueError('password not in... |
class H2Protocol(Protocol):
def __init__(self, root):
config = H2Configuration(client_side=False)
self.conn = H2Connection(config=config)
self.known_proto = None
self.root = root
self._flow_control_deferreds = {}
def connectionMade(self):
self.conn.initiate_connec... |
class ImportNodeTest(resources.SysPathSetup, unittest.TestCase):
def setUp(self) -> None:
super().setUp()
self.module = resources.build_file('data/module.py', 'data.module')
self.module2 = resources.build_file('data/module2.py', 'data.module2')
def test_import_self_resolve(self) -> None:... |
_cache(maxsize=1000, typed=False)
def eight_band_strain_hamiltonian(kx, ky, kz, Ev0, Ec0, exx, ezz, me_eff, gamma1, gamma2, gamma3, a0, Delta, ac, av, b, Ep):
'Hamiltonian to calculate cb, hh, lh, so bands and include strain for the biaxial (along [001]) special case.\n \n See Hamiltonian in ref. but remove r... |
class IdentityObservationsData(ground_truth_data.GroundTruthData):
def num_factors(self):
return 10
def observation_shape(self):
return 10
def factors_num_values(self):
return ([1] * 10)
def sample_factors(self, num, random_state):
return random_state.random_integers(10, ... |
class ISC(EarthquakeCatalog):
def __init__(self, catalog=None):
self.events = {}
def flush(self):
self.events = {}
def append_time_params(self, a, time_range):
(date_start_s, tstart_s) = util.time_to_str(time_range[0], format='%Y-%m-%d %H:%M:%S').split()
(date_end_s, tend_s) ... |
def _warn_incorrect_binary_bitness(exe_name):
if (os.path.isabs(exe_name) and os.path.isfile(exe_name) and handleprops.is64bitbinary(exe_name) and (not is_x64_Python())):
warnings.warn('64-bit binary from 32-bit Python may work incorrectly (please use 64-bit Python instead)', UserWarning, stacklevel=2) |
class TBRangeCharacter(DefaultCharacter):
def at_object_creation(self):
self.db.max_hp = 100
self.db.hp = self.db.max_hp
def at_before_move(self, destination):
if is_in_combat(self):
self.msg("You can't exit a room while in combat!")
return False
if (self.... |
def test_box_on_line():
box1 = [0, 0, 1, 0, 1, 1, 0, 1]
box2 = [2, 0.5, 3, 0.5, 3, 1.5, 2, 1.5]
box3 = [4, 0.8, 5, 0.8, 5, 1.8, 4, 1.8]
assert is_on_same_line(box1, box2, 0.5)
assert (not is_on_same_line(box1, box3, 0.5))
box4 = [0, 0, 1, 1, 1, 2, 0, 1]
box5 = [2, 1.5, 3, 1.5, 3, 2.5, 2, 2.5... |
_fixtures(WebFixture, PopupAFixture)
def test_customising_dialog_buttons(web_fixture, popup_a_fixture):
class PopupTestPanel(Div):
def __init__(self, view):
super().__init__(view)
popup_a = self.add_child(PopupA(view, view.as_bookmark(), '#contents'))
popup_a.add_js_butto... |
def get_payer_channel(channelidentifiers_to_channels: Dict[(ChannelID, NettingChannelState)], transfer_pair: MediationPairState) -> Optional[NettingChannelState]:
payer_channel_identifier = transfer_pair.payer_transfer.balance_proof.channel_identifier
return channelidentifiers_to_channels.get(payer_channel_iden... |
def test_vf_row_ground_2d(test_system_fixed_tilt):
(ts, _, _) = test_system_fixed_tilt
vf = utils.vf_row_ground_2d(ts['surface_tilt'], ts['gcr'], 0.0)
expected = (0.5 * (1.0 - cosd(ts['surface_tilt'])))
assert np.isclose(vf, expected)
fx = np.array([0.0, 0.5, 1.0])
vf = utils.vf_row_ground_2d(ts... |
def test_cof_list_input():
with pytest.raises(Call) as err:
cof_func(name='blah', instruction_type=Call, context=Context({'key': ['b', 'c']}), context_key='key')
cof = err.value
assert isinstance(cof, Call)
assert (cof.groups == ['b', 'c'])
assert (not cof.success_group)
assert (not cof.... |
class PointCloudField():
def __init__(self, file_name):
self.file_name = file_name
def load(self, model_path):
file_path = os.path.join(model_path, self.file_name)
pointcloud_dict = np.load(file_path)
points = pointcloud_dict['points'].astype(np.float32)
data = {'cloud': ... |
.skipif((sys.version_info[0] < 3), reason='Python 3+ required for timezone support')
def test_flexible_datetime_with_timezone_that_has_colons():
from datetime import timezone
r = parse.parse('{dt:%Y-%m-%d %H:%M:%S %z}', '2023-11-21 13:23:27 +00:00:00')
assert (r.named['dt'] == datetime(2023, 11, 21, 13, 23,... |
def create_qr_from_map(design, url, mode, error):
(bits, version) = get_raw_qr_data(design, error, mode)
string = (bitstring_to_bin(bits) if (mode == 'binary') else bitstring_to_alphanumeric(bits))
with_url = ((url + '/') + string[(len(url) + 1):])
qr = pyqrcode.create(with_url, error=error, mode=mode, ... |
def convert_xlnet_checkpoint_to_pytorch(tf_checkpoint_path, bert_config_file, pytorch_dump_folder_path, finetuning_task=None):
config = XLNetConfig.from_json_file(bert_config_file)
finetuning_task = (finetuning_task.lower() if (finetuning_task is not None) else '')
if (finetuning_task in GLUE_TASKS_NUM_LABE... |
def draw_sample(font_file):
HEIGHT = 500
WIDTH = 800
background = Image.new('RGBA', (WIDTH, HEIGHT), ImageColor.getrgb('white'))
foreground = Image.new('RGBA', (WIDTH, HEIGHT), (255, 255, 255, 0))
draw_b = ImageDraw.Draw(background)
draw_f = ImageDraw.Draw(foreground)
label_font = ImageFont.... |
class Repo(common.Common, unittest.TestCase):
def test_full(self):
self.run_test('test/demoapp', False, '.', False)
def test_script_only(self):
self.run_test('test/demoapp-script-only', True, '.', False)
def test_project_in_subdir(self):
self.run_test('test/demoapp', False, 'project'... |
def _click_through_rate_input_check(input: torch.Tensor, weights: Union[(torch.Tensor, float, int)], *, num_tasks: int) -> None:
if ((input.ndim != 1) and (input.ndim != 2)):
raise ValueError(f'`input` should be a one or two dimensional tensor, got shape {input.shape}.')
if (isinstance(weights, torch.Te... |
def convert_batchnorm_parameters(model: torch.nn.Module, bn: Union[(torch.nn.BatchNorm1d, torch.nn.BatchNorm2d)]):
with utils.in_eval_mode(model), torch.no_grad():
gamma = bn.weight
beta = bn.bias
running_mean = bn.running_mean
inv_sigma = torch.rsqrt((bn.running_var + bn.eps))
... |
.fast
def test_spectrum_get_methods(verbose=True, plot=True, close_plots=True, *args, **kwargs):
from radis.test.utils import getTestFile
from radis.tools.database import load_spec
from radis.tools.slit import get_FWHM
if (plot and close_plots):
import matplotlib.pyplot as plt
plt.close(... |
class Edges():
def __init__(self):
self.edges = []
def e(self, source, target, label, color, italicize=False, weight=1):
if italicize:
quoted_label = f'<<i>{label}</i>>'
else:
quoted_label = f'<{label}>'
self.edges.append(f'''{source} -> {target} [
label... |
def sum_regularizer(regularizer_list, scope=None):
regularizer_list = [reg for reg in regularizer_list if (reg is not None)]
if (not regularizer_list):
return None
def sum_reg(weights):
with ops.name_scope(scope, 'sum_regularizer', [weights]) as name:
regularizer_tensors = []
... |
def read_config(filename, fail):
devices = []
if os.path.exists((pypilot_dir + filename)):
try:
f = open((pypilot_dir + filename), 'r')
while True:
device = f.readline()
if (not device):
break
devices.append(devi... |
class PresetStartingArea(PresetTab, Ui_PresetStartingArea, NodeListHelper):
starting_area_quick_fill_default: QtWidgets.QPushButton
_starting_location_for_region: dict[(str, QtWidgets.QCheckBox)]
_starting_location_for_area: dict[(AreaIdentifier, QtWidgets.QCheckBox)]
_starting_location_for_node: dict[(... |
_auth
def pull_asset(request):
if (request.method == 'POST'):
test_auth = request.POST.get('test_auth')
conf_ids = request.POST.get('conf_ids')
if test_auth:
access_id = request.POST.get('access_id')
access_key = request.POST.get('access_key')
cloud_region... |
class CatalogNestedSerializer(CatalogSerializer):
elements = serializers.SerializerMethodField()
class Meta(CatalogSerializer.Meta):
fields = (*CatalogSerializer.Meta.fields, 'elements')
def get_elements(self, obj):
for element in obj.elements:
(yield SectionNestedSerializer(elem... |
def test_L3_ifc_view_index():
a = CaseArrayBits32IfcInComp.DUT()
a.elaborate()
a.apply(StructuralRTLIRGenL3Pass(gen_connections(a)))
connections = a.get_metadata(StructuralRTLIRGenL2Pass.connections)
comp = CurComp(a, 's')
assert (connections == [(InterfaceAttr(InterfaceViewIndex(CurCompAttr(com... |
def test_token_network_registry_max_token_networks(deploy_client, token_network_registry_address, contract_manager):
proxy_manager = ProxyManager(rpc_client=deploy_client, contract_manager=contract_manager, metadata=ProxyManagerMetadata(token_network_registry_deployed_at=GENESIS_BLOCK_NUMBER, filters_start_at=GENES... |
class LRUCache(object):
def __init__(self, capacity):
self.capacity = capacity
self.cache = {}
self.queue = []
def updateQueue(self, key):
self.queue.remove(key)
self.queue.insert(0, key)
def get(self, key):
if (key in self.cache):
self.updateQueue... |
.parametrize('store_graph', [False, True])
def test_tracker_candidate_graph(test_real_objects, store_graph):
tracker = full_tracker_example(test_real_objects, store_candidate_graph=store_graph)
assert (tracker.store_candidate_graph == store_graph)
edges = tracker.candidate_graph_edges()
assert (bool(edg... |
class Effect2056(BaseEffect):
type = 'passive'
def handler(fit, skill, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Shield Resistance Amplifier')), 'thermalDamageResistanceBonus', (skill.getModifiedItemAttr('hardeningBonus') * skill.level), **k... |
class TCN_GCN_unit_7(nn.Module):
def __init__(self, in_channels, out_channels, A, stride=1, residual=True):
super(TCN_GCN_unit_7, self).__init__()
self.gcn1 = unit_gtcn_7(in_channels, out_channels, A)
self.tcn1 = unit_tcn(out_channels, out_channels, stride=stride)
self.relu = nn.ReLU... |
def add_defaults(cfg: DictConfig) -> None:
from fairseq.registry import REGISTRIES
from fairseq.tasks import TASK_DATACLASS_REGISTRY
from fairseq.models import ARCH_MODEL_NAME_REGISTRY, MODEL_DATACLASS_REGISTRY
from fairseq.dataclass.utils import merge_with_parent
from typing import Any
OmegaCon... |
class LossFunction(nn.Module):
def __init__(self, gpu, init_w=10.0, init_b=(- 5.0), **kwargs):
super(LossFunction, self).__init__()
self.gpu = gpu
self.w = nn.Parameter(torch.tensor(init_w))
self.b = nn.Parameter(torch.tensor(init_b))
self.w.requires_grad = True
self.... |
def file_based_input_fn_builder(input_file, seq_length, is_training, drop_remainder):
name_to_features = {'input_ids': tf.FixedLenFeature([seq_length], tf.int64), 'input_mask': tf.FixedLenFeature([seq_length], tf.int64), 'segment_ids': tf.FixedLenFeature([seq_length], tf.int64), 'label_ids': tf.FixedLenFeature([], ... |
class DataModuleFromConfig(pl.LightningDataModule):
def __init__(self, batch_size, train=None, validation=None, test=None, predict=None, wrap=False, num_workers=None, shuffle_test_loader=False, use_worker_init_fn=False, shuffle_val_dataloader=False):
super().__init__()
self.batch_size = batch_size
... |
def main():
logging.basicConfig(level=logging.WARNING)
parser = argparse.ArgumentParser()
parser.add_argument('path', help='path to file(s) to reserialize')
parser.add_argument('-a', '--all', action='store_true', help='reserialize all JSON files under path')
args = parser.parse_args()
if args.al... |
def get_user_inputs(onnx_input_names, input_info, inputs, kwargs, device):
def _expand_inputs(current_input, non_none_inputs):
if ((current_input is None) or isinstance(current_input, str)):
return
if isinstance(current_input, abc.Sequence):
for inp in current_input:
... |
class strtr():
def st(self):
while True:
if (system == 'termux'):
Ux()
print("\x07\n\n\n\n\n\n\n\n\n\x1b[01;32m __ __ ____\n | \\/ |_ _/ ___| ___ _ \x1b[01;31m____ _____ \x1b[01;32m_ __\n | |\\/| | | | \\___ \\ / _ \\ '__\x1b[01;31m\\ \\ / / \... |
class TestTruncatedNormalLowerTau(BaseTestDistributionRandom):
pymc_dist = pm.TruncatedNormal
(lower, upper, mu, tau) = ((- 2.0), np.inf, 0, 1.0)
(tau, sigma) = get_tau_sigma(tau=tau, sigma=None)
pymc_dist_params = {'mu': mu, 'tau': tau, 'lower': lower}
expected_rv_op_params = {'mu': mu, 'sigma': si... |
class Cfengine3Lexer(RegexLexer):
name = 'CFEngine3'
url = '
aliases = ['cfengine3', 'cf3']
filenames = ['*.cf']
mimetypes = []
version_added = '1.5'
tokens = {'root': [('#.*?\\n', Comment), ('(body)(\\s+)(\\S+)(\\s+)(control)', bygroups(Keyword, Whitespace, Keyword, Whitespace, Keyword)), (... |
(qssp.have_backend(), 'backend qssp not available')
class QSSPTestCase(unittest.TestCase):
def setUp(self):
self.tmpdir = tempfile.mkdtemp(prefix='pyrocko.qssp')
def tearDown(self):
shutil.rmtree(self.tmpdir)
(('qssp.2010' in qssp.have_backend()), 'backend qssp.2010 not available')
def t... |
def test_select_eof(select_app, monkeypatch):
read_input_mock = mock.MagicMock(name='read_input', side_effect=[EOFError, 2])
monkeypatch.setattr('cmd2.Cmd.read_input', read_input_mock)
food = 'fish'
(out, err) = run_cmd(select_app, 'eat {}'.format(food))
arg = 'Sauce? '
calls = [mock.call(arg), ... |
class RawRecipeSearcher(RecipeSearcher):
def __init__(self, recipe: Sequence[Provider]):
self.recipe = recipe
def search_candidates(self, search_offset: int, request: Request) -> Iterable[SearchResult]:
for (i, provider) in enumerate(islice(self.recipe, search_offset, None), start=search_offset)... |
def get_state_dict(net_type: str='alex', version: str='0.1'):
old_state_dict = torch.load('pretrained_models/alex.pth', map_location=(None if torch.cuda.is_available() else torch.device('cpu')))
new_state_dict = OrderedDict()
for (key, val) in old_state_dict.items():
new_key = key
new_key = ... |
.end_to_end()
.xfail((sys.platform == 'win32'), reason='Decoding issues in Gitlab Actions.')
def test_execute_tasks_via_functional_api(tmp_path):
source = '\n import sys\n from pathlib import Path\n from typing_extensions import Annotated\n from pytask import PathNode\n import pytask\n from pytask... |
def identity_block(input_tensor, kernel_size, filters, stage, block, trainable=True):
(nb_filter1, nb_filter2, nb_filter3) = filters
if (K.image_dim_ordering() == 'tf'):
bn_axis = 3
else:
bn_axis = 1
conv_name_base = ((('res' + str(stage)) + block) + '_branch')
bn_name_base = ((('bn'... |
class _Actors(VersionBase):
def __init__(self, selectTriggeringEntities=False):
self.actors = []
self.select = convert_bool(selectTriggeringEntities)
def __eq__(self, other):
if isinstance(other, _Actors):
if ((self.get_attributes() == other.get_attributes()) and (self.actors... |
def get_subnets_info(regions):
clients = []
for region in regions:
client = boto3.client('ec2', region_name=region, aws_access_key_id=config.AWS_ACCESS_KEY, aws_secret_access_key=config.AWS_ACCESS_SECRET)
client.region = region
clients.append(client)
subnet_info = OrderedDict()
f... |
(short_help='Remove build artifacts')
('location', required=False)
('--target', '-t', 'targets', multiple=True, help='The target with which to remove artifacts, overriding project defaults. This may be selected multiple times e.g. `-t sdist -t wheel`')
('--hooks-only', is_flag=True, help='Whether or not to only remove ... |
class PythonLSPServer(MethodDispatcher):
def __init__(self, rx, tx, check_parent_process=False, consumer=None, *, endpoint_cls=None):
self.workspace = None
self.config = None
self.root_uri = None
self.watching_thread = None
self.workspaces = {}
self.uri_workspace_mapp... |
def test_bad_alphabets() -> None:
with pytest.raises(ValueError, match='has overlaps'):
Fsm(alphabet={Charclass('a'), Charclass('ab')}, states={0}, initial=0, finals=(), map={0: {Charclass('a'): 0, Charclass('ab'): 0}})
with pytest.raises(ValueError, match='not a proper partition'):
Fsm(alphabet... |
class Visualizer():
def __init__(self, opt):
self.use_html = (opt.isTrain and (not opt.no_html))
self.win_size = opt.display_winsize
self.name = opt.name
self.opt = opt
self.saved = False
if self.use_html:
self.web_dir = os.path.join(opt.checkpoints_dir, o... |
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