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.mosaiqdb
def test_session_offsets_for_site(connection):
mock_patient_ident_df = mocks.create_mock_patients()
mock_site_df = mocks.create_mock_treatment_sites(mock_patient_ident_df)
mock_txfield_df = mocks.create_mock_treatment_fields(mock_site_df)
mocks.create_mock_treatment_sessions(mock_site_df, mock... |
class EmptyCudaCache(Callback):
def __init__(self, step_interval: int) -> None:
self._step_interval = step_interval
def on_train_step_end(self, state: State, unit: TTrainUnit) -> None:
total_num_steps_completed = unit.train_progress.num_steps_completed
if (state.entry_point == EntryPoint... |
class TestOverrideIniArgs():
.parametrize('name', 'setup.cfg tox.ini pytest.ini'.split())
def test_override_ini_names(self, pytester: Pytester, name: str) -> None:
section = ('[pytest]' if (name != 'setup.cfg') else '[tool:pytest]')
pytester.path.joinpath(name).write_text(textwrap.dedent('\n ... |
class InferenceContext():
__slots__ = ('path', 'lookupname', 'callcontext', 'boundnode', 'extra_context', 'constraints', '_nodes_inferred')
max_inferred = 100
def __init__(self, path: (set[tuple[(nodes.NodeNG, (str | None))]] | None)=None, nodes_inferred: (list[int] | None)=None) -> None:
if (nodes_... |
(optionalhook=True)
def pytest_selenium_runtest_makereport(item, report, summary, extra):
provider = BrowserStack()
if (not provider.uses_driver(item.config.getoption('driver'))):
return
passed = (report.passed or (report.failed and hasattr(report, 'wasxfail')))
session_id = item._driver.session... |
class ResNet(nn.Module):
def __init__(self, block, layers, n_channels=3, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None, drop_rate=0.0):
super(ResNet, self).__init__()
self.drop_rate = drop_rate
if (norm_layer is N... |
class PoseEstimationEvaluator(chainer.training.extensions.Evaluator):
def comm(self):
if (not hasattr(self, '_comm')):
self._comm = None
return self._comm
def comm(self, value):
self._comm = value
def evaluate(self):
iterator = self._iterators['main']
eval... |
def get_args(driver=None, download_dir=None, download_ftypes=None, firefox_pref=None, firefox_prof_dir=None, remote_url=None, executable=None, headless=False, driver_kwargs=None):
kwargs = {}
firefox_profile_preferences = dict({'browser.download.folderList': 2, 'browser.download.manager.showWhenStarting': False... |
def get_parameter_groups(model):
no_weight_decay_names = ['bias', 'normalization', 'label_embeddings']
parameter_groups = [{'params': [param for (name, param) in model.named_parameters() if (not any(((no_weight_decay_name in name) for no_weight_decay_name in no_weight_decay_names)))]}, {'params': [param for (na... |
def get_baseline_dict_entry(tag):
if (not isinstance(tag, pydicom.tag.BaseTag)):
tag = pydicom.tag.Tag(tag)
try:
return get_baseline_dicom_dict()[tag]
except KeyError:
if (not tag.is_private):
mask_x = pydicom.datadict.mask_match(tag)
if mask_x:
... |
class SwaggerMaskHeaderTest(object):
def test_marshal_with_expose_mask_header(self, app, client):
api = Api(app)
model = api.model('Test', {'name': fields.String, 'age': fields.Integer, 'boolean': fields.Boolean})
('/test/')
class TestResource(Resource):
_with(model)
... |
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, replace_stride_with_dilation=None, norm_layer=None):
super(ResNet, self).__init__()
if (norm_layer is None):
norm_layer = nn.BatchNorm2d
self._norm... |
def _install_one(requirement, cmd, pkgname, modulename):
cmd.args = [requirement]
cmd.ensure_finalized()
cmd.run()
target = cmd.install_dir
dest_path = glob.glob(os.path.join(target, (pkgname + '*.egg')))
assert dest_path
assert os.path.exists(os.path.join(dest_path[0], pkgname, modulename)) |
def grid_partition(x, grid_size: List[int]):
(B, H, W, C) = x.shape
_assert(((H % grid_size[0]) == 0), f'height {H} must be divisible by grid {grid_size[0]}')
_assert(((W % grid_size[1]) == 0), '')
x = x.view(B, grid_size[0], (H // grid_size[0]), grid_size[1], (W // grid_size[1]), C)
windows = x.per... |
def fetch_data_table(api_key, show_progress, retries):
for _ in range(retries):
try:
if show_progress:
log.info('Downloading WIKI metadata.')
metadata = pd.read_csv(format_metadata_url(api_key))
table_url = metadata.loc[(0, 'file.link')]
if sho... |
def replacePassword(actionData, password):
if (actionData['TYPE'] == 'COMMANDS'):
for i in range(len(actionData['COMMANDS'])):
for j in range(len(actionData['COMMANDS'][i])):
while ('VM_PASSWORD' in actionData['COMMANDS'][i][j]):
actionData['COMMANDS'][i][j] =... |
class FortuneThread(QThread):
newFortune = pyqtSignal(str)
error = pyqtSignal(int, str)
def __init__(self, parent=None):
super(FortuneThread, self).__init__(parent)
self.quit = False
self.hostName = ''
self.cond = QWaitCondition()
self.mutex = QMutex()
self.po... |
class FakeResponse(web.Response):
headers = CIMultiDict({'content-type': 'application/json; charset=utf-8', 'x-ratelimit-limit': '10', 'x-ratelimit-remaining': '5', 'x-ratelimit-reset': '1'})
url = 'test URL'
def __init__(self, data=None, **kwargs):
super().__init__(**kwargs)
self._data = da... |
def test_colorscheme_gentoo_workaround(config_stub, gentoo_versions):
config_stub.val.colors.webpage.preferred_color_scheme = 'dark'
darkmode_settings = darkmode.settings(versions=gentoo_versions, special_flags=[])
assert (darkmode_settings['blink-settings'] == [('preferredColorScheme', '0')]) |
class ExampleDataset(Dataset):
def __init__(self):
self.index = 0
self.eval_result = [1, 4, 3, 7, 2, (- 3), 4, 6]
def __getitem__(self, idx):
results = dict(x=torch.tensor([1]))
return results
def __len__(self):
return 1
_autospec
def evaluate(self, results, l... |
(hookwrapper=True)
def pytest_fixture_setup(fixturedef: FixtureDef, request: SubRequest) -> Optional[object]:
if (fixturedef.argname == 'event_loop'):
_add_finalizers(fixturedef, _close_event_loop, _restore_event_loop_policy(asyncio.get_event_loop_policy()), _provide_clean_event_loop)
outcome = (yie... |
_REGISTRY.register()
class CIFAR10C(DatasetBase):
dataset_dir = ''
domains = ['cifar10', 'cifar10_c']
def __init__(self, cfg):
root = osp.abspath(osp.expanduser(cfg.DATASET.ROOT))
self.dataset_dir = root
self.check_input_domains(cfg.DATASET.SOURCE_DOMAINS, cfg.DATASET.TARGET_DOMAINS)... |
_deepspeed
_torch_gpu
class TestDeepSpeedModelZoo(TestCasePlus):
def get_task_cmd(self, task, stage):
if (task not in task_cmds):
raise ValueError(f"don't know of task {task}, have {task_cmds.keys()}")
cmd = task_cmds[task]
args_ds = f'--deepspeed {self.test_file_dir_str}/ds_conf... |
def test_set_inf_nodata(tmpdir):
dst_path = str(tmpdir.join('lol.tif'))
with rasterio.open('tests/data/RGB.byte.tif') as src:
meta = src.meta
meta['dtype'] = 'float32'
meta['nodata'] = float('inf')
with rasterio.open(dst_path, 'w', **meta) as dst:
assert numpy.isinf(d... |
class TestPruningLRUProxiedImagesToAllowBlobUpload():
upstream_registry = 'docker.io'
upstream_repository = 'library/busybox'
orgname = 'proxy-cache'
repository = f'{orgname}/{upstream_repository}'
tag = '1.35.0'
(autouse=True)
def setup(self, app):
self.user = get_user('devtable')
... |
def test_mouse_press_event_small_item_inside_handle_free_center(view, item):
view.scene.addItem(item)
item.setSelected(True)
event = MagicMock()
event.pos.return_value = QtCore.QPointF(10, 10)
event.button.return_value = Qt.MouseButton.LeftButton
with patch('PyQt6.QtWidgets.QGraphicsPixmapItem.m... |
def accuracy(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, (- 1)).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].reshape((- 1)).float().sum(0, ke... |
.parametrize('repeat', [1, 2])
.parametrize('preset_name', [None, 'Starter Preset'])
.parametrize('no_retry', [False, True])
def test_generate_logic(no_retry: bool, preset_name: (str | None), repeat: int, mocker: pytest_mock.MockerFixture, preset_manager):
layout_description = MagicMock()
mock_run = mocker.patc... |
def test_mlp_grad(test, device):
results = load_golden()
torch_weights = results['weights']
torch_weights_grad = results['weights_grad']
torch_bias = results['bias']
torch_bias_grad = results['bias_grad']
torch_x = results['x'].T
torch_x_grad = results['x_grad'].T
torch_y = results['y'].... |
def test_models():
for _ in range(3):
clf = CacheClassifier('clf', SGDClassifier(loss='log'))
check_classifier(clf, has_staged_pp=False, has_importances=False)
reg = CacheRegressor('reg', SGDRegressor())
check_regression(reg, has_staged_predictions=False, has_importances=False)
c... |
def _lru_cache_with_config_path(func: Callable):
_cache()
def _call_without_config_path_wrapper(sensor_name, _):
return func(sensor_name)
def _add_config_path_wrapper(sensor_name: str):
config_path = satpy.config.get('config_path')
config_path = tuple(config_path)
return _cal... |
def update_best_score(new_score, old_score, is_higher_better):
if (not old_score):
(score, updated) = (new_score, True)
elif is_higher_better:
score = max(new_score, old_score)
updated = (new_score > old_score)
else:
score = min(new_score, old_score)
updated = (new_sc... |
class SponsorshipPackageTests(TestCase):
def setUp(self):
self.package = baker.make('sponsors.SponsorshipPackage')
self.package_benefits = baker.make(SponsorshipBenefit, _quantity=3)
self.package.benefits.add(*self.package_benefits)
def test_has_user_customization_if_benefit_from_other_p... |
class ImageNet100(data.Dataset):
def __init__(self, data_dir, dataidxs=None, train=True, transform=None, target_transform=None, download=False, client_num=100, alpha=None):
self.dataidxs = dataidxs
self.client_num = client_num
self.train = train
self.transform = transform
sel... |
class WideResNet1(nn.Module):
def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
super(WideResNet1, self).__init__()
nChannels = [16, (16 * widen_factor), (32 * widen_factor), (64 * widen_factor)]
assert (((depth - 4) % 6) == 0)
n = ((depth - 4) / 6)
block ... |
class FastAIMixedOptim(OptimWrapper):
def create(cls, opt_func, lr, layer_groups, model, flat_master=False, loss_scale=512.0, **kwargs):
opt = OptimWrapper.create(opt_func, lr, layer_groups, **kwargs)
(opt.model_params, opt.master_params) = get_master(layer_groups, flat_master)
opt.flat_mast... |
def majority_vote(nsqls: List, pred_answer_list: List, allow_none_and_empty_answer: bool=False, allow_error_answer: bool=False, answer_placeholder: Union[(str, int)]='<error|empty>', vote_method: str='prob', answer_biased: Union[(str, int)]=None, answer_biased_weight: float=None):
def _compare_answer_vote_simple(a,... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1, drop=False, block_size=4):
super(Bottleneck, self).__init__()
self.drop = drop
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
... |
def test_new_project_does_not_fail_pre_commit(cwd, pre_commit, putup):
name = 'my_project'
run(f'{putup} --pre-commit --dsproject -p my_package --namespace my.ns {name}')
with cwd.join(name).as_cwd():
try:
run(f'{pre_commit} install')
run(f'{pre_commit} run --all')
ex... |
_infer_shape
_useless
_canonicalize
_specialize
_rewriter([Subtensor])
def local_subtensor_of_alloc(fgraph, node):
if (not isinstance(node.op, Subtensor)):
return False
u = node.inputs[0]
if (u.owner is None):
return False
if (not isinstance(u.owner.op, Alloc)):
return False
... |
def load_model(model_version=None):
currentDirectory = os.getcwd()
if (model_version == 'chembl'):
model_name = 'chembl_pretrained'
elif (model_version == 'moses'):
model_name = 'moses_pretrained'
elif (model_version == 'new'):
model_name = 'new_model'
else:
print('No... |
class MDConfig(dict):
def __init__(self, config_name='default'):
self.update(DEFAULT_CONFIG)
self.set_configs(config_name)
def set_configs(self, config_name='default'):
configs = getattr(settings, 'MDEDITOR_CONFIGS', None)
if configs:
if isinstance(configs, dict):
... |
def add_instructions(opts: ScaffoldOpts, content: AbstractContent, file_op: FileOp) -> ResolvedLeaf:
text = structure.reify_content(content, opts)
if (text is not None):
i = text.find(INSERT_AFTER)
assert (i > 0), f'''{INSERT_AFTER!r} not found in README template:
{text}'''
j = (i + len(... |
class SsoCharacterMgmt(AuxiliaryFrame):
def __init__(self, parent):
super().__init__(parent, id=wx.ID_ANY, title=_t('SSO Character Management'), pos=wx.DefaultPosition, size=wx.Size(550, 250), resizeable=True)
self.mainFrame = parent
mainSizer = wx.BoxSizer(wx.HORIZONTAL)
self.lcChar... |
class SCPIndexDataset(torch.utils.data.Dataset):
def __init__(self, scp_path_list, concat=4, shared_object=None):
self.scp_path_list = scp_path_list
self._sizes = len(self.scp_path_list)
self._dtype = torch.float32
self.concat = concat
if (shared_object is not None):
... |
def test_keep_alive_return_value(capture):
n_inst = ConstructorStats.detail_reg_inst()
with capture:
p = m.Parent()
assert (capture == 'Allocating parent.')
with capture:
p.returnChild()
assert (ConstructorStats.detail_reg_inst() == (n_inst + 1))
assert (capture == '\n ... |
def test_fermi_hubbard_2x2_spinful_phs():
hubbard_model = fermi_hubbard(2, 2, 1.0, 4.0, chemical_potential=0.5, magnetic_field=0.3, spinless=False, particle_hole_symmetry=True)
assert (str(hubbard_model).strip() == '\n4.0 [] +\n-2.8 [0^ 0] +\n4.0 [0^ 0 1^ 1] +\n-1.0 [0^ 2] +\n-1.0 [0^ 4] +\n-2.2 [1^ 1] +\n-1.0 ... |
def training_loop(run_dir='.', dataset_kwargs={}, data_loader_kwargs={}, network_kwargs={}, loss_kwargs={}, optimizer_kwargs={}, augment_kwargs=None, seed=0, batch_size=512, batch_gpu=None, total_kimg=200000, ema_halflife_kimg=500, ema_rampup_ratio=0.05, lr_rampup_kimg=10000, loss_scaling=1, kimg_per_tick=50, snapshot_... |
def test_alternation_ab(a: FixtureA, b: FixtureB) -> None:
altAB = (a | b)
assert (not altAB.accepts(''))
assert altAB.accepts('a')
assert altAB.accepts('b')
assert (not altAB.accepts('aa'))
assert (not altAB.accepts('ab'))
assert (not altAB.accepts('ba'))
assert (not altAB.accepts('bb')... |
class KnownValues(unittest.TestCase):
def test_h2_gamma(self):
mf = pscf.KRHF(cell).rs_density_fit()
mf.kernel()
self.assertAlmostEqual(mf.e_tot, (- 1.), 7)
def test_h2_kpt1_shiftedcenter(self):
kpts = cell.make_kpts([1, 1, 1], scaled_center=scaled_center)
mf = pscf.KRHF(... |
def Increment(new, mirror, incpref, inc_time=None):
log.Log('Incrementing mirror file {mf}'.format(mf=mirror), log.INFO)
if (((new and new.isdir()) or mirror.isdir()) and (not incpref.lstat())):
incpref.mkdir()
if (not mirror.lstat()):
incrp = _make_missing_increment(incpref, inc_time)
e... |
class EvoNorm2dS1(nn.Module):
def __init__(self, num_features, groups=32, group_size=None, apply_act=True, act_layer=None, eps=1e-05, **_):
super().__init__()
act_layer = (act_layer or nn.SiLU)
self.apply_act = apply_act
if ((act_layer is not None) and apply_act):
self.ac... |
def _get_weight_tensor_transpose_reshape(conv_linear: LayerType) -> libpymo.TensorParams():
weight_tensor = libpymo.TensorParams()
weight = conv_linear.get_weights()[0]
shape = weight.shape
if isinstance(conv_linear, tf.keras.layers.DepthwiseConv2D):
weight = np.transpose(weight, (2, 3, 0, 1))
... |
class SPSA(Optimizer):
_C0 = ((2 * np.pi) * 0.1)
_OPTIONS = ['save_steps', 'last_avg']
def __init__(self, maxiter: int=1000, save_steps: int=1, last_avg: int=1, c0: float=_C0, c1: float=0.1, c2: float=0.602, c3: float=0.101, c4: float=0, skip_calibration: bool=False, max_trials: Optional[int]=None) -> None:... |
def log_events(klass: Type[QObject]) -> Type[QObject]:
old_event = klass.event
(old_event)
def new_event(self: Any, e: QEvent) -> bool:
log.misc.debug('Event in {}: {}'.format(utils.qualname(klass), qenum_key(QEvent, e.type(), klass=QEvent.Type)))
return old_event(self, e)
klass.event = ... |
class Project(MPTTModel, Model):
objects = ProjectManager()
parent = TreeForeignKey('self', null=True, blank=True, on_delete=models.DO_NOTHING, related_name='children', db_index=True, verbose_name=_('Parent project'), help_text=_('The parent project of this project.'))
user = models.ManyToManyField(settings... |
def collate_fn_all_des(batch):
(obj_point_list, obj_label_list) = ([], [])
rel_label_list = []
(edge_indices, descriptor) = ([], [])
count = 0
for i in batch:
obj_point_list.append(i[0])
obj_label_list.append(i[2])
rel_label_list.append(i[3])
edge_indices.append((i[4]... |
class TestGaussianProcess(GaussianProcessTestCase):
precompute_gaussian_process_data = True
(autouse=True, scope='class')
def base_setup(cls):
numpy.random.seed(8794)
super(TestGaussianProcess, cls).base_setup()
def test_sample_point_from_gp(self):
point_one = SamplePoint([0.0, 1... |
def test_interactive_with_dependencies_and_no_selection(tester: CommandTester, repo: TestRepository) -> None:
repo.add_package(get_package('django-pendulum', '0.1.6-pre4'))
repo.add_package(get_package('pendulum', '2.0.0'))
repo.add_package(get_package('pytest', '3.6.0'))
inputs = ['my-package', '1.2.3'... |
def _label_nodes_by_identity(intralayer_graphs, interlayer_edges, layer_vec):
namedict = {}
backedges = {}
for e in interlayer_edges:
(ei, ej) = (e[0], e[1])
if (ei < ej):
backedges[ej] = (backedges.get(ej, []) + [ei])
else:
backedges[ei] = (backedges.get(ei, ... |
class ControlTabs(QtWidgets.QTabWidget):
def __init__(self, *args, m=None, **kwargs):
super().__init__(*args, **kwargs)
self.m = m
self.tab_compare = CompareTab(m=self.m)
self.tab_open = OpenFileTabs(m=self.m)
self.tab_edit = ArtistEditor(m=self.m)
self.addTab(self.ta... |
class TestHuffman():
def test_request_huffman_decoder(self):
assert (decode_huffman(b'\xf1\xe3\xc2\xe5\xf2:k\xa0\xab\x90\xf4\xff') == b'www.example.com')
assert (decode_huffman(b'\xa8\xeb\x10d\x9c\xbf') == b'no-cache')
assert (decode_huffman(b'%\xa8I\xe9[\xa9}\x7f') == b'custom-key')
... |
def _parse_path(path):
if isinstance(path, _Path):
return path
elif (pathlib and isinstance(path, pathlib.PurePath)):
return _ParsedPath(path.as_posix(), None, None)
elif isinstance(path, str):
if ((sys.platform == 'win32') and re.match('^[a-zA-Z]\\:', path)):
if pathlib:... |
def hydra_init(cfg_name='config') -> None:
cs = ConfigStore.instance()
cs.store(name=cfg_name, node=FairseqConfig)
for k in FairseqConfig.__dataclass_fields__:
v = FairseqConfig.__dataclass_fields__[k].default
try:
cs.store(name=k, node=v)
except BaseException:
... |
def init_eigenstate_visualization(eigenstates):
if (eigenstates.type == 'SingleParticle1D'):
return VisualizationSingleParticle1D(eigenstates)
elif (eigenstates.type == 'SingleParticle2D'):
return VisualizationSingleParticle2D(eigenstates)
elif (eigenstates.type == 'SingleParticle3D'):
... |
class JobList(ListView):
template_name = 'jobs_list.html'
context_object_name = 'jobs'
paginate_by = 20
paginator_class = DiggPaginator
model = JobItem
def get_queryset(self):
jobs = super().get_queryset()
search = self.request.GET.get('q')
if search:
filters ... |
class FragDB():
def __init__(self, metadata, options, db, c):
self.metadata = metadata
self.db = db
self.c = c
self.options = options
def get(self, id):
obj = select_fragment_record_by_title(self.c, id)
if (obj is not None):
return obj
return s... |
def multiplicative_rlattention(queries, keys, values, bias, sample, keep_prob=None, name=None, epsilon=1e-06):
with tf.name_scope(name, default_name='multiplicative_rlattention', values=[queries, keys, values, bias]):
logits = tf.matmul(queries, keys, transpose_b=True)
if (bias is not None):
... |
class SmartBulb(SmartDevice):
LIGHT_SERVICE = 'smartlife.iot.smartbulb.lightingservice'
SET_LIGHT_METHOD = 'transition_light_state'
emeter_type = 'smartlife.iot.common.emeter'
def __init__(self, host: str, *, config: Optional[DeviceConfig]=None, protocol: Optional[TPLinkProtocol]=None) -> None:
... |
def load_and_covnert_case(input_image: str, input_seg: str, output_image: str, output_seg: str, min_component_size: int=50):
seg = io.imread(input_seg)
assert ((np.unique(seg)[0] == 0) and ((np.unique(seg)[1] == 255) or (np.unique(seg)[1] == 1)))
seg[(seg == 255)] = 1
image = io.imread(input_image)
... |
def transform_import(builder: IRBuilder, node: Import) -> None:
if node.is_mypy_only:
return
if (not node.is_top_level):
globals = builder.load_globals_dict()
for (mod_id, as_name) in node.ids:
builder.gen_import(mod_id, node.line)
(globals_id, globals_name) = imp... |
class NLLModel(nn.Module):
def __init__(self, args, config):
super().__init__()
self.args = args
self.models = nn.ModuleList()
self.device = [(i % args.n_gpu) for i in range(args.n_model)]
self.loss_fnt = nn.CrossEntropyLoss()
for i in range(args.n_model):
... |
def total_ordering(cls):
assert ('__eq__' in cls.__dict__)
assert ('__lt__' in cls.__dict__)
cls.__le__ = (lambda self, other: ((self == other) or (self < other)))
cls.__gt__ = (lambda self, other: (not ((self == other) or (self < other))))
cls.__ge__ = (lambda self, other: (not (self < other)))
... |
class IndentLoggerAdapter(logging.LoggerAdapter):
def process(self, msg, kwargs):
if get_yf_logger().isEnabledFor(logging.DEBUG):
i = (' ' * self.extra['indent'])
if (not isinstance(msg, str)):
msg = str(msg)
msg = '\n'.join([(i + m) for m in msg.split('\n... |
def is_protocol_implementation(left: Instance, right: Instance, proper_subtype: bool=False, class_obj: bool=False, skip: (list[str] | None)=None, options: (Options | None)=None) -> bool:
assert right.type.is_protocol
if (skip is None):
skip = []
type_state.record_protocol_subtype_check(left.type, ri... |
def parse_args():
parser = argparse.ArgumentParser(description='Calculate the prototype for trained model')
parser.add_argument('config', help='trained model config file path')
parser.add_argument('checkpoint', help='checkpoint file path')
parser.add_argument('--round', type=int, default=1, help='save d... |
class TestPDFJSVersion():
def test_not_found(self, mocker):
mocker.patch('qutebrowser.utils.version.pdfjs.get_pdfjs_res_and_path', side_effect=pdfjs.PDFJSNotFound('/build/pdf.js'))
assert (version._pdfjs_version() == 'no')
def test_unknown(self, monkeypatch):
monkeypatch.setattr('qutebro... |
def _serialize(value: Any, memo: Optional[SerializeMemoizer]) -> Any:
if isinstance(value, Serialize):
return value.serialize(memo)
elif isinstance(value, list):
return [_serialize(elem, memo) for elem in value]
elif isinstance(value, frozenset):
return list(value)
elif isinstanc... |
def test_unmeargeable_dimshuffles():
x = pt.random.dirichlet(np.ones((3,)), size=(4, 2))
y = x.dimshuffle((0, 2, 1))
z = pt.cumsum(y, axis=(- 2))
w = z.dimshuffle((1, 0, 2))
w_vv = w.clone()
with pytest.raises(RuntimeError, match='could not be derived'):
conditional_logp({w: w_vv}) |
_tag()
def render_lang_template(template_name, escape_html=False):
loc = to_locale(get_language())
lst = [(((template_name + '_') + loc) + '.html'), (((template_name + '_') + settings.LANGUAGES[0][0]) + '.html'), (template_name + '_en.html'), (template_name + '.html')]
for el in lst:
try:
... |
def run_cmd(cmd):
dsz.ui.Echo('Searching for files')
dsz.control.echo.Off()
dsz.cmd.Run(cmd, dsz.RUN_FLAG_RECORD)
dsz.control.echo.On()
try:
dir_path = dsz.cmd.data.Get('DirItem::path', dsz.TYPE_STRING)
dsz.ui.Echo('Found {0} archive(s)'.format(str(len(dir_path))))
return dir... |
def _parse_pvgis_hourly_csv(src, map_variables):
inputs = {}
inputs['latitude'] = float(src.readline().split(':')[1])
inputs['longitude'] = float(src.readline().split(':')[1])
inputs['elevation'] = float(src.readline().split(':')[1])
inputs['radiation_database'] = src.readline().split(':')[1].strip(... |
class RNNAgent(nn.Module):
def __init__(self, input_shape, args):
super(RNNAgent, self).__init__()
self.args = args
self.fc1 = nn.Linear(input_shape, args.hidden_dim)
if self.args.use_rnn:
self.rnn = nn.GRUCell(args.hidden_dim, args.hidden_dim)
else:
s... |
def test(val_loader, criterion, val_text_features, clip_model, clip_preprocess, clip_device):
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
end = time.time()
bar = Bar('Processing', max=len(val_loader))
avg_accuracy_per_class = None
... |
class _PtqSession(_EvalSession):
def __init__(self, *args, **kwargs):
super(_PtqSession, self).__init__(*args, **kwargs)
self._ptq_result = None
def ptq_result(self) -> PtqResult:
if (self._ptq_result is None):
raise RuntimeError
return self._ptq_result
def set_pt... |
class Tformat_locale(TestCase):
def test_format_int_locale(self):
assert isinstance(util.format_int_locale(1024), str)
def test_format_float_locale(self):
assert isinstance(util.format_float_locale(1024.1024), str)
def test_format_time_seconds(self):
assert isinstance(util.format_tim... |
def compile_rule(filename):
if (filename.startswith('{') and filename.endswith('}')):
return re.compile(filename[1:(- 1)]).match
with open(filename) as f:
return re.compile((('(:?' + ''.join('|'.join((i.strip() for i in f if (i.strip() and (not i.startswith('#'))))))) + ')$')).match |
class GDN(nn.Module):
def __init__(self, in_channels, inverse=False, beta_min=1e-06, gamma_init=0.1):
super().__init__()
beta_min = float(beta_min)
gamma_init = float(gamma_init)
self.inverse = bool(inverse)
self.beta_reparam = NonNegativeParametrizer(minimum=beta_min)
... |
class Callback(object):
def __init__(self):
self.validation_data = None
def set_params(self, params):
self.params = params
def set_model(self, model):
self.model = model
def on_epoch_begin(self, epoch, logs=None):
pass
def on_epoch_end(self, epoch, logs=None):
... |
class SyntaxHighlighting(object):
_styleElements = Manager.getStyleElementDescriptionsForAllParsers()
def parser(self):
try:
return self.__parser
except AttributeError:
return None
_option(None)
def setParser(self, parserName=''):
self.__parser = Manager.g... |
class AdditionsExportAll(ContextMenuUnconditional):
visibilitySetting = 'additionsCopyPaste'
def __init__(self):
self.mainFrame = gui.mainFrame.MainFrame.getInstance()
self.viewSpecMap = {'droneItemMisc': (_t('Drones'), (lambda cw: cw.drones), exportDrones), 'fighterItemMisc': (_t('Fighters'), (... |
def test_manual_response_limits():
out = manual_response.plot()
axs = ct.get_plot_axes(out)
for i in range(manual_response.noutputs):
for j in range(1, manual_response.ninputs):
assert (axs[((i * 2), 0)].get_ylim() == axs[((i * 2), j)].get_ylim())
assert (axs[(((i * 2) + 1), ... |
def LoadObjectInfos(file):
with open(file) as f:
contents = f.read().rstrip().splitlines()
data = []
attrs = {}
struct = {}
(name, uuid) = contents.pop(0).split(' : ')
for line in contents:
if ('#' in line):
quotes = 0
for (i, char) in enumerate(line):
... |
class ManagedCollisionModule(nn.Module):
def __init__(self, device: torch.device) -> None:
super().__init__()
self._device = device
def preprocess(self, features: Dict[(str, JaggedTensor)]) -> Dict[(str, JaggedTensor)]:
pass
def device(self) -> torch.device:
return self._devi... |
class ElasticConditionParser(BaseConditionParser):
def build_condition(self, and_subfilters: Optional[List[Any]], or_subfilters: Optional[List[Any]]) -> Optional[Any]:
return {'bool': {'must': and_subfilters, 'should': or_subfilters}}
def build_exact_match_filter(self, field_name: str, value: FieldValue... |
class AbstractBasicLexer(Lexer):
terminals_by_name: Dict[(str, TerminalDef)]
def __init__(self, conf: 'LexerConf', comparator=None) -> None:
...
def next_token(self, lex_state: LexerState, parser_state: Any=None) -> Token:
...
def lex(self, state: LexerState, parser_state: Any) -> Iterat... |
def mod_import(module):
if (not module):
return None
if isinstance(module, types.ModuleType):
return module
if (module.endswith('.py') and os.path.exists(module)):
return mod_import_from_path(module)
try:
return importlib.import_module(module)
except ImportError:
... |
class TerminalIniter(IniterBase):
def prompt_text(self, prompt, default, validator, retry_msg='Try again.'):
if (default is not None):
p = '{} [{}]: '.format(prompt, default)
else:
p = (prompt + ': ')
while True:
response = input(p)
if ((respon... |
def get_filter_args_for_specific_event_from_channel(token_network_address: TokenNetworkAddress, channel_identifier: ChannelID, event_name: str, contract_manager: ContractManager, from_block: BlockIdentifier=GENESIS_BLOCK_NUMBER, to_block: BlockIdentifier=BLOCK_ID_LATEST) -> FilterParams:
event_abi = contract_manage... |
def edit_rest(file_, key):
key = ('description' if key.strip().startswith('d') else 'summary')
if file_.startswith(URL_REPO):
file_ = file_.replace(URL_REPO, '')
with open(file_) as fp:
data = json.load(fp)
data[key] = get_edited_text(data[key])
with open(file_, 'w') as fp:
j... |
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