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def test_multiple_decorators():
_along_last_axis
_type_conversion
def half_vec(x):
assert (x.ndim == 1)
return x[:(len(x) // 2)]
for shape in [(10,), (2, 10), (2, 2, 10)]:
for dtype in [np.float32, np.float16, np.float64]:
x = np.ones(shape, dtype=dtype)
y... |
def attach_object_to_vehicle(object_id: int, vehicle_id: int, offset_x: float, offset_y: float, offset_z: float, rotation_x: float, rotation_y: float, rotation_z: float) -> bool:
return AttachObjectToVehicle(object_id, vehicle_id, offset_x, offset_y, offset_z, rotation_x, rotation_y, rotation_z) |
class WeylQuantizationTest(unittest.TestCase):
def test_weyl_empty(self):
res = weyl_polynomial_quantization('')
self.assertTrue((res == QuadOperator.zero()))
def test_weyl_one_term(self):
op = QuadOperator('q0')
res = weyl_polynomial_quantization('q0')
self.assertTrue((r... |
def orth_reg(net, loss, cof=1):
orth_loss = 0
for m in net.modules():
if isinstance(m, nn.Linear):
w = m.weight
dimension = w.size()[0]
eye_ = Variable(torch.eye(dimension), requires_grad=False).cuda()
diff = (torch.matmul(w, w.t()) - eye_)
mas... |
()
def daily_update_placements(day=None):
(start_date, end_date) = get_day(day)
log.info('Updating PlacementImpressions for %s-%s', start_date, end_date)
queryset = Offer.objects.using(settings.REPLICA_SLUG).filter(date__gte=start_date, date__lt=end_date)
for values in queryset.values('publisher', 'adve... |
class RCC_APB1LPENR(IntEnum):
TIM2LPEN = (1 << 0)
TIM3LPEN = (1 << 1)
TIM4LPEN = (1 << 2)
TIM5LPEN = (1 << 3)
WWDGLPEN = (1 << 11)
SPI2LPEN = (1 << 14)
SPI3LPEN = (1 << 15)
USART2LPEN = (1 << 17)
I2C1LPEN = (1 << 21)
I2C2LPEN = (1 << 22)
I2C3LPEN = (1 << 23)
PWRLPEN = (1 ... |
def build_v2_index_specs():
return [IndexV2TestSpec('v2.list_all_tags', 'GET', PUBLIC_REPO).request_status(200, 200, 200, 200, 200), IndexV2TestSpec('v2.list_all_tags', 'GET', PRIVATE_REPO).request_status(401, 401, 200, 401, 200), IndexV2TestSpec('v2.list_all_tags', 'GET', ORG_REPO).request_status(401, 401, 200, 40... |
class ExitCommand(command.Command):
obj = None
def func(self):
if self.obj.access(self.caller, 'traverse'):
self.obj.at_traverse(self.caller, self.obj.destination)
elif self.obj.db.err_traverse:
self.caller.msg(self.obj.db.err_traverse)
else:
self.obj.... |
def load_data(args, dataset_name):
data_loader = load_partition_data_FashionMNIST
(train_data_num, test_data_num, train_data_global, test_data_global, train_data_local_num_dict, test_data_local_num_dict, train_data_local_dict, test_data_local_dict, class_num_train, class_num_test) = data_loader(args.dataset, ar... |
def dicom_file_loader(accept_multiple_files: bool, stop_before_pixels: bool) -> Sequence['pydicom.Dataset']:
(left_column, right_column) = st.columns(2)
if accept_multiple_files:
file_string = 'files'
else:
file_string = 'file'
with left_column:
st.write(f'## Upload DICOM {file_s... |
class TrueWind(BaseWind):
def __init__(self, client, boatimu):
super(TrueWind, self).__init__(client, 'truewind', boatimu)
def compute_true_wind_direction(water_speed, wind_speed, wind_direction):
rd = math.radians(wind_direction)
windv = ((wind_speed * math.sin(rd)), ((wind_speed * math... |
def test_filewritejson_filewritejson_not_iterable_raises():
context = Context({'k1': 'v1', 'fileWriteJson': 1})
with pytest.raises(ContextError) as err_info:
filewrite.run_step(context)
assert (str(err_info.value) == "context['fileWriteJson'] must exist, be iterable and contain 'path' for pypyr.step... |
def highwaynet(inputs, scope, depth):
with tf.variable_scope(scope):
H = tf.layers.dense(inputs, units=depth, activation=tf.nn.relu, name='H')
T = tf.layers.dense(inputs, units=depth, activation=tf.nn.sigmoid, name='T', bias_initializer=tf.constant_initializer((- 1.0)))
return ((H * T) + (in... |
def test_clean_comment():
from frigate.gen import clean_comment
assert (clean_comment('# hello world') == 'hello world')
assert (clean_comment('hello world') == 'hello world')
assert (clean_comment('## # ## ## hello world') == 'hello world')
assert (clean_comment(' # hello world ') == 'hello world'... |
def test_lambert_conformat_conic_1sp_operation():
aeaop = LambertConformalConic1SPConversion(latitude_natural_origin=1, longitude_natural_origin=2, false_easting=3, false_northing=4, scale_factor_natural_origin=0.5)
assert (aeaop.name == 'unknown')
assert (aeaop.method_name == 'Lambert Conic Conformal (1SP)... |
class TornadoRoleTest(ProvyTestCase):
def setUp(self):
super(TornadoRoleTest, self).setUp()
self.role = TornadoRole(prov=None, context={})
def installs_necessary_packages_to_provision(self):
with self.using_stub(AptitudeRole) as aptitude, self.using_stub(PipRole) as pip:
self... |
def conv2d_transpose(inputs, num_output_channels, kernel_size, scope, stride=[1, 1], padding='SAME', use_xavier=True, stddev=0.001, weight_decay=0.0, activation_fn=tf.nn.relu, bn=False, bn_decay=None, is_training=None):
with tf.variable_scope(scope) as sc:
(kernel_h, kernel_w) = kernel_size
num_in_c... |
('build')
('flavour')
('--logs', '-l', is_flag=True, help='Show build logs')
def build(flavour: str, logs: bool) -> None:
if (not run_checks(flavour)):
return
flavour_config = get_key_values_from_config(flavour)
flavour_dockerfile = create_dockerfile(flavour_config['base'], flavour_config['install']... |
class Cabinet(models.Model):
idc = models.ForeignKey('IDC', related_name='cabinet', on_delete=models.CASCADE)
cabinet_name = models.CharField(max_length=64, unique=True, verbose_name='')
cabinet_memo = models.CharField(max_length=100, blank=True, null=True, verbose_name='')
class Meta():
db_tabl... |
def test_remove_world_from_session(server_app):
session = {'worlds': [1234]}
server_app.session = MagicMock()
server_app.session.return_value.__enter__.return_value = session
world = MagicMock()
world.id = 1234
server_app.remove_world_from_session(world)
assert (session == {'worlds': []}) |
def test_build_overviews_new_file(tmpdir, path_rgb_byte_tif):
dst_file = str(tmpdir.join('test.tif'))
with rasterio.open(path_rgb_byte_tif) as src:
with rasterio.open(dst_file, 'w', **src.profile) as dst:
dst.write(src.read())
overview_factors = [2, 4]
dst.build_overv... |
def main():
batch_size = 16
data_path = './data/nyu_depth_v2_labeled.mat'
learning_rate = 0.0001
monentum = 0.9
weight_decay = 0.0005
num_epochs = 100
(train_lists, val_lists, test_lists) = load_split()
print('Loading data...')
train_loader = torch.utils.data.DataLoader(NyuDepthLoade... |
def dump_dataclass(obj: Any):
assert (dataclasses.is_dataclass(obj) and (not isinstance(obj, type))), 'dump_dataclass() requires an instance of a dataclass.'
ret = {'_target_': _convert_target_to_string(type(obj))}
for f in dataclasses.fields(obj):
v = getattr(obj, f.name)
if dataclasses.is_... |
class _ConvNdMtl(Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation, transposed, output_padding, groups, bias):
super(_ConvNdMtl, self).__init__()
if ((in_channels % groups) != 0):
raise ValueError('in_channels must be divisible by groups')
... |
def test_save_action_additional_extensions(default_file):
existing_config(default_file)
opts = dict(author='author', email='email', license='MPL-2.0', my_extension1_opt=5)
extensions = [make_extension('MyExtension1'), make_extension('MyExtension2'), make_extension('MyExtension3', persist=False)]
config.... |
class GeometryOptimizer(lib.StreamObject):
def __init__(self, method):
self.method = method
self.callback = None
self.params = {}
self.converged = False
self.max_cycle = 100
def cell(self):
return self.method.cell
def cell(self, x):
self.method.cell = ... |
class DistributedTest(TestCase):
def _test_fullsync(rank, world_size, backend, q):
dist.init_process_group(backend, rank=rank, world_size=world_size)
data_length = 23
dp = IterableWrapper(list(range(data_length))).sharding_filter()
torch.utils.data.graph_settings.apply_sharding(dp, w... |
class Migration(migrations.Migration):
dependencies = [('adserver', '0003_publisher-advertiser-adtype')]
operations = [migrations.AddField(model_name='adimpression', name='publisher', field=models.ForeignKey(blank=True, null=True, on_delete=django.db.models.deletion.PROTECT, to='adserver.Publisher')), migration... |
def test_geoid_model_name():
wkt = 'COMPOUNDCRS["NAD83 / Pennsylvania South + NAVD88 height",\n PROJCRS["NAD83 / Pennsylvania South",\n BASEGEOGCRS["NAD83",\n DATUM["North American Datum 1983",\n ELLIPSOID["GRS 1980",6378137,298.,\n LENGTHUNIT["metre",1]]],\n ... |
def test_state_wait_secretrequest_valid_amount_and_fee():
fee_amount = 5
setup = setup_initiator_tests(allocated_fee=fee_amount)
state_change = ReceiveSecretRequest(payment_identifier=UNIT_TRANSFER_IDENTIFIER, amount=(setup.lock.amount - fee_amount), expiration=setup.lock.expiration, secrethash=setup.lock.s... |
def test_struct_comparison2():
m = run_mod('\n #lang pycket\n (require racket/private/generic-interfaces)\n\n (struct lead (width height)\n #:methods\n gen:equal+hash\n [(define (equal-proc a b equal?-recur)\n ; compare a and b\n (and (equal?-recur (lead-width a) (lead-width ... |
def test_Join_view():
vals = (set_test_value(pt.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)), set_test_value(pt.matrix(), rng.normal(size=(2, 2)).astype(config.floatX)))
g = ptb.Join(view=1)(1, *vals)
g_fg = FunctionGraph(outputs=[g])
with pytest.raises(NotImplementedError):
compare_... |
def CheckForBadCharacters(filename, lines, error):
for (linenum, line) in enumerate(lines):
if (u'' in line):
error(filename, linenum, 'readability/utf8', 5, 'Line contains invalid UTF-8 (or Unicode replacement character).')
if ('\x00' in line):
error(filename, linenum, 'read... |
class Describe_ChunkParser():
def it_can_construct_from_a_stream(self, stream_, StreamReader_, stream_rdr_, _ChunkParser__init_):
chunk_parser = _ChunkParser.from_stream(stream_)
StreamReader_.assert_called_once_with(stream_, BIG_ENDIAN)
_ChunkParser__init_.assert_called_once_with(ANY, strea... |
def nooper(cls):
def empty_func(*args, **kwargs):
pass
empty_methods = {m_name: empty_func for m_name in cls.__abstractmethods__}
if (not empty_methods):
raise NoopIsANoopException(('nooper implemented no abstract methods on %s' % cls))
return type(cls.__name__, (cls,), empty_methods) |
def system_command_call(command, shell=True):
if (shell and isinstance(command, list)):
command = subprocess.list2cmdline(command)
try:
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=shell)
(stdout, stderr) = process.communicate()
if (pr... |
def write_predictions(all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, verbose_logging, version_2_with_negative, null_score_diff_threshold):
logger.info(('Writing predictions to: %s' % output_prediction_file... |
def usersMap(theRequest):
users = []
myUserCount = QgisUser.objects.all().count()
myRandomUser = None
myRandomUsers = QgisUser.objects.exclude(image='').order_by('?')[:1]
if (myRandomUsers.count() > 0):
myRandomUser = myRandomUsers[0]
for user in QgisUser.objects.all():
users.app... |
class Effect7097(BaseEffect):
type = 'passive'
def handler(fit, skill, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Precursor Weapon')), 'damageMultiplier', (skill.getModifiedItemAttr('damageMultiplierBonus') * skill.level), **kwargs) |
class TestBotDescriptionWithoutRequest(TestBotDescriptionBase):
def test_slot_behaviour(self, bot_description):
for attr in bot_description.__slots__:
assert (getattr(bot_description, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(bot_description)) == len(set(mr... |
class HumanoidStandupEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
mujoco_env.MujocoEnv.__init__(self, 'humanoidstandup.xml', 5)
utils.EzPickle.__init__(self)
def _get_obs(self):
data = self.model.data
return np.concatenate([data.qpos.flat[2:], data.qvel.flat, da... |
def test_view_node(caller, **kwargs):
text = ('\n Your name is |g%s|n!\n\n click |lclook|lthere|le to trigger a look command under MXP.\n This node\'s option has no explicit key (nor the "_default" key\n set), and so gets assigned a number automatically. You can infact\n -always- use numbers (1...N) ... |
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000, zero_init_residual=False, groups=1, width_per_group=64, norm_layer=None):
super(ResNet, self).__init__()
if (norm_layer is None):
norm_layer = nn.BatchNorm2d
self.inplanes = 64
self.groups = grou... |
def uiGetFilePath(initial_path=None):
try:
if initial_path:
output = subprocess.check_output('osascript -e \'set strPath to POSIX file "{}"\' -e \'set theDocument to choose file with prompt "Please select a document to process:" default location strPath\' -e \'set theDocument to (the POSIX path ... |
class Login(LoginView):
form_class = LoginForm
template_name = 'dictionary/registration/login.html'
def form_valid(self, form):
remember_me = form.cleaned_data.get('remember_me', False)
session_timeout = ((86400 * 30) if remember_me else 86400)
self.request.session.set_expiry(session... |
def load_openai_model(name: str, precision: Optional[str]=None, device: Optional[Union[(str, torch.device)]]=None, jit: bool=True, cache_dir: Optional[str]=None):
if (device is None):
device = ('cuda' if torch.cuda.is_available() else 'cpu')
if (precision is None):
precision = ('fp32' if (device... |
class TestVersion(unittest.TestCase):
def test_version(self):
version = pyppeteer.version
self.assertTrue(isinstance(version, str))
self.assertEqual(version.count('.'), 2)
def test_version_info(self):
vinfo = pyppeteer.version_info
self.assertEqual(len(vinfo), 3)
... |
def async_wraps(cls: type[object], wrapped_cls: type[object], attr_name: str) -> t.Callable[([CallT], CallT)]:
def decorator(func: CallT) -> CallT:
func.__name__ = attr_name
func.__qualname__ = '.'.join((cls.__qualname__, attr_name))
func.__doc__ = 'Like :meth:`~{}.{}.{}`, but async.\n\n ... |
def get_solcast_historic(latitude, longitude, start, api_key, end=None, duration=None, map_variables=True, **kwargs):
params = dict(latitude=latitude, longitude=longitude, start=start, end=end, duration=duration, api_key=api_key, format='json', **kwargs)
data = _get_solcast(endpoint='historic/radiation_and_weat... |
def CheckSectionSpacing(filename, clean_lines, class_info, linenum, error):
if (((class_info.last_line - class_info.starting_linenum) <= 24) or (linenum <= class_info.starting_linenum)):
return
matched = Match('\\s*(public|protected|private):', clean_lines.lines[linenum])
if matched:
prev_li... |
class IDBH(tc.nn.Module):
def __init__(self, version):
super().__init__()
if (version == 'cifar10-weak'):
layers = [T.RandomHorizontalFlip(), CropShift(0, 11), ColorShape('color'), T.ToTensor(), T.RandomErasing(p=0.5)]
elif (version == 'cifar10-strong'):
layers = [T.R... |
def build_dataset(is_train, args, infer_no_resize=False):
transform = build_transform(is_train, args, infer_no_resize)
if (args.data_set == 'CIFAR100'):
dataset = datasets.CIFAR100(args.data_path, train=is_train, transform=transform, download=True)
nb_classes = 100
elif (args.data_set == 'CI... |
def test_should_show_fixtures_used_by_test(pytester: Pytester) -> None:
pytester.makeconftest('\n import pytest\n \n def arg1():\n """arg1 from conftest"""\n \n def arg2():\n """arg2 from conftest"""\n ')
p = pytester.makepyfile('\n import pytes... |
def test_recompute_equilibrium(verbose=True, warnings=True, plot=True, *args, **kwargs):
if plot:
import matplotlib.pyplot as plt
plt.ion()
s1 = load_spec(getTestFile('CO_Tgas1500K_mole_fraction0.01.spec'))
s1.rescale_path_length(100)
assert s1.is_at_equilibrium()
s1.update('emisscoe... |
class ImageToWordModel(OnnxInferenceModel):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def predict(self, image: np.ndarray):
image = cv2.resize(image, self.input_shape[:2][::(- 1)])
image_pred = np.expand_dims(image, axis=0).astype(np.float32)
preds = ... |
class SubQueryLineageHolder(ColumnLineageMixin):
def __init__(self) -> None:
self.graph = nx.DiGraph()
def __or__(self, other):
self.graph = nx.compose(self.graph, other.graph)
return self
def _property_getter(self, prop) -> Set[Union[(SubQuery, Table)]]:
return {t for (t, at... |
def compute_checksum(filename, hashtype):
file = os.fsdecode(filename)
if (not exists(file)):
return None
buf = fsbsize(filename)
if (hashtype in ('adler32', 'crc32')):
hf = getattr(zlib, hashtype)
last = 0
with open(file, mode='rb') as fp:
for chunk in iter((... |
def test_context():
with pm.Model():
pm.Normal('x')
ctx = multiprocessing.get_context('spawn')
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*number of samples.*', UserWarning)
pm.sample(tune=2, draws=2, chains=2, cores=2, mp_ctx=ctx) |
def test_componentanimation():
vc = OSC.utils._VehicleComponent(OSC.VehicleComponentType.doorFrontLeft)
vc2 = OSC.utils._VehicleComponent(OSC.VehicleComponentType.doorRearRight)
udc = OSC.UserDefinedComponent('my_component')
udc2 = OSC.UserDefinedComponent('my_component2')
udc3 = OSC.UserDefinedComp... |
def prime_factors(obj):
visited = set((obj,))
ef = getattr(obj, '_e_factors', None)
if (not ef):
return
fn = ef[0]
e = getattr(obj, fn, None)
if (e in visited):
raise RecursiveFactor(obj, e)
visited.add(e)
(yield (fn, e))
while (e is not None):
ef = getattr(ob... |
class GroupPointTest(tf.test.TestCase):
def test(self):
pass
def test_grad(self):
with tf.device('/gpu:0'):
points = tf.constant(np.random.random((1, 128, 16)).astype('float32'))
print(points)
xyz1 = tf.constant(np.random.random((1, 128, 3)).astype('float32'))... |
def rand_reach():
vp = np.random.uniform(low=0, high=360)
goal = np.concatenate([np.random.uniform(low=(- 1.1), high=(- 0.5), size=1), np.random.uniform(low=0.5, high=1.1, size=1)]).tolist()
armcolor = getcolor()
bgcolor = getcolor()
while (np.linalg.norm((bgcolor - armcolor)) < 0.5):
bgcolo... |
class LR_Scheduler(object):
def __init__(self, mode, base_lr, num_epochs, iters_per_epoch=0, lr_step=0, warmup_epochs=0):
self.mode = mode
print('Using {} LR Scheduler!'.format(self.mode))
self.lr = base_lr
if (mode == 'step'):
assert lr_step
self.lr_step = lr_ste... |
class _AttributeCollector():
def __init__(self, type):
self.attributes = {}
self.type = type
def __call__(self, name, returned=None, function=None, argnames=['self'], check_existence=True, parent=None):
try:
builtin = getattr(self.type, name)
except AttributeError:
... |
class VSA_Module(nn.Module):
def __init__(self, opt={}):
super(VSA_Module, self).__init__()
channel_size = opt['multiscale']['multiscale_input_channel']
out_channels = opt['multiscale']['multiscale_output_channel']
embed_dim = opt['embed']['embed_dim']
self.LF_conv = nn.Conv2... |
def register_mot_instances(name, metadata, json_file, image_root):
assert isinstance(name, str), name
assert isinstance(json_file, (str, os.PathLike)), json_file
assert isinstance(image_root, (str, os.PathLike)), image_root
DatasetCatalog.register(name, (lambda : load_video_json(json_file, image_root, n... |
def set_project(apps, schema_editor):
Value = apps.get_model('projects', 'Value')
Snapshot = apps.get_model('projects', 'Snapshot')
for value in Value.objects.all():
value.project = value.snapshot.project
value.snapshot = None
value.save()
for snapshot in Snapshot.objects.all():
... |
class CmdCancel(SubCommand):
def add_arguments(self, subparser: argparse.ArgumentParser) -> None:
subparser.add_argument('app_handle', type=str, help='torchx app handle (e.g. local://session-name/app-id)')
def run(self, args: argparse.Namespace) -> None:
app_handle = args.app_handle
runn... |
class Attention(nn.Module):
def __init__(self, dim, num_heads=8, qkv_bias=False, qk_scale=None, attn_drop=0.0, proj_drop=0.0, sr_ratio=1, apply_transform=False):
super().__init__()
self.num_heads = num_heads
head_dim = (dim // num_heads)
self.scale = (qk_scale or (head_dim ** (- 0.5)... |
.parametrize(('prefer_metroids', 'prefer_stronger_metroids', 'prefer_bosses', 'expected_max_slider'), [(True, False, False, 25), (False, True, False, 14), (False, False, True, 4), (True, True, False, 39), (True, False, True, 29), (False, True, True, 18), (True, True, True, 39)])
def test_preferred_dna(skip_qtbot, msr_g... |
def load_tf_sess_variables_to_keras_single_gpu(path: 'str', compressed_ops: List['str']) -> tf.compat.v1.keras.Model:
to_ignore = map(change_name_of_compressed_op, compressed_ops)
class Model(tf.compat.v1.keras.Model):
def __init__(self):
super(Model, self).__init__()
self.import... |
def test_edit_file_with_spaces(base_app, request, monkeypatch):
base_app.editor = 'fooedit'
m = mock.MagicMock(name='Popen')
monkeypatch.setattr('subprocess.Popen', m)
test_dir = os.path.dirname(request.module.__file__)
filename = os.path.join(test_dir, 'my commands.txt')
run_cmd(base_app, 'edit... |
def write_data(flag, image, text_only):
def read_post(flag):
stop_words = stopwordslist()
pre_path = '../Data/weibo/tweets/'
file_list = [(pre_path + 'test_nonrumor.txt'), (pre_path + 'test_rumor.txt'), (pre_path + 'train_nonrumor.txt'), (pre_path + 'train_rumor.txt')]
if (flag == 't... |
.parametrize('value, kwargs, result', (('5,6,7', {}, [5, 6, 7]), ('5.6.7', {'separator': '.'}, [5, 6, 7]), ('5,6,7', {'cast': str}, ['5', '6', '7']), ('X,Y,Z', {}, ['X', 'Y', 'Z']), ('X,Y,Z', {'cast': str}, ['X', 'Y', 'Z']), ('X.Y.Z', {'separator': '.'}, ['X', 'Y', 'Z']), ('0,5,7.1', {'cast': bool}, [False, True, True]... |
class Table(QtWidgets.QTableView):
supported_formats = {'CSV file (*.csv)': 'csv', 'Excel file (*.xlsx)': 'excel', 'HTML file (*.html *.htm)': 'html', 'JSON file (*.json)': 'json', 'LaTeX file (*.tex)': 'latex', 'Markdown file (*.md)': 'markdown', 'XML file (*.xml)': 'xml'}
def __init__(self, refresh_time=0.2, ... |
def prepare_batch_inputs_audio(batched_model_inputs, device, non_blocking=False):
model_inputs = dict(src_txt=batched_model_inputs['query_feat'][0].to(device, non_blocking=non_blocking), src_txt_mask=batched_model_inputs['query_feat'][1].to(device, non_blocking=non_blocking), src_vid=batched_model_inputs['video_fea... |
class RetrievalRecall(Metric[torch.Tensor]):
def __init__(self: TRetrievalRecall, *, empty_target_action: Union[(Literal['neg'], Literal['pos'], Literal['skip'], Literal['err'])]='neg', k: Optional[int]=None, limit_k_to_size: bool=False, num_queries: int=1, avg: Optional[Union[(Literal['macro'], Literal['none'])]]=... |
class DataSuite():
files: list[str]
base_path = test_temp_dir
data_prefix = test_data_prefix
required_out_section = False
native_sep = False
test_name_suffix = ''
def setup(self) -> None:
def run_case(self, testcase: DataDrivenTestCase) -> None:
raise NotImplementedError |
.fast
def test_all_slit_shapes(FWHM=0.4, verbose=True, plot=True, close_plots=True, *args, **kwargs):
_clean(plot, close_plots)
from radis.spectrum.spectrum import Spectrum
from radis.test.utils import getTestFile
s = Spectrum.from_txt(getTestFile('calc_N2C_spectrum_Trot1200_Tvib3000.txt'), quantity='ra... |
def keras_sequential_conv_net():
model = tf.keras.Sequential([tf.keras.layers.Input(shape=(28, 28, 3)), tf.keras.layers.Conv2D(4, kernel_size=3, activation=None), tf.keras.layers.BatchNormalization(), tf.keras.layers.Activation('relu'), tf.keras.layers.AvgPool2D(), tf.keras.layers.Dense(10)])
return model |
class L2Step(AttackerStep):
def project(self, x):
diff = (x - self.orig_input)
diff = diff.renorm(p=2, dim=0, maxnorm=self.eps)
return (self.orig_input + diff)
def make_step(self, g):
g_norm = ch.norm(g.view(g.shape[0], (- 1)), dim=1).view((- 1), 1, 1, 1)
scaled_g = (g / ... |
class SawyerDoorCloseEnv(SawyerDoorEnv):
def __init__(self):
super().__init__()
self.init_config = {'obj_init_angle': 0.3, 'obj_init_pos': np.array([0.1, 0.95, 0.1], dtype=np.float32), 'hand_init_pos': np.array([0, 0.6, 0.2], dtype=np.float32)}
self.goal = np.array([0.2, 0.8, 0.15])
... |
def test_plot_area_def_w_swath_def(create_test_swath):
swath_def = _gen_swath_def_numpy(create_test_swath)
with mock.patch('matplotlib.pyplot.savefig') as mock_savefig:
plot_area_def(swath_def, fmt='svg')
mock_savefig.assert_called_with(ANY, format='svg', bbox_inches='tight') |
def _path_tree_for_react_dnd_treeview(tree: list, id_to_path_dict: dict, path: str, parent: int, highlighted_files: list=[]) -> list:
for item in os.listdir(path):
if item.startswith('.'):
continue
item_path = os.path.join(path, item)
droppable = os.path.isdir(item_path)
... |
.parametrize('repo, commit_parser, translator, commit_messages,prerelease, expected_new_version', xdist_sort_hack([(lazy_fixture(repo_fixture_name), lazy_fixture(parser_fixture_name), translator, commit_messages, prerelease, expected_new_version) for ((repo_fixture_name, parser_fixture_name, translator), values) in {('... |
class AnyExpressionsReporter(AbstractReporter):
def __init__(self, reports: Reports, output_dir: str) -> None:
super().__init__(reports, output_dir)
self.counts: dict[(str, tuple[(int, int)])] = {}
self.any_types_counter: dict[(str, collections.Counter[int])] = {}
def on_file(self, tree:... |
def construct_pred_set(predicted_args, cur_event, context_words, doc, args):
trigger_start = cur_event['trigger']['start']
trigger_end = cur_event['trigger']['end']
predicted_set = set()
lowercased_context_words = [w.lower() for w in context_words]
lowercased_doc = (nlp(' '.join(lowercased_context_w... |
class TestWeightSvdPruning(unittest.TestCase):
def test_prune_layer(self):
model = mnist_model.Net()
input_shape = (1, 1, 28, 28)
dummy_input = create_rand_tensors_given_shapes(input_shape, get_device(model))
orig_layer_db = LayerDatabase(model, dummy_input)
comp_layer_db = c... |
def test_eigen_transform_ket():
N = 5
a = qutip.destroy(N)
op = (((a * a.dag()) + a) + a.dag())
eigenT = _EigenBasisTransform(qutip.QobjEvo(op))
op_diag = qutip.qdiags(eigenT.eigenvalues(0), [0])
state = qutip.coherent(N, 1.1)
expected = (op state).full()
computed = eigenT.from_eigbasis... |
def test_atlas_glyps():
assert isinstance(global_atlas, GlyphAtlas)
atlas = GlyphAtlas()
gs = 50
array_id = id(atlas._array)
assert (atlas.get_index_from_hash('0') is None)
i0 = atlas.store_region_with_hash('0', glyphgen(gs))
assert isinstance(i0, int)
(atlas.get_index_from_hash('0') == ... |
def eval_video_single(cfg, models, device, test_loader, interp, fixed_test_size, verbose):
if (cfg.SOURCE == 'Viper'):
palette = [128, 64, 128, 244, 35, 232, 70, 70, 70, 190, 153, 153, 250, 170, 30, 220, 220, 0, 107, 142, 35, 152, 251, 152, 70, 130, 180, 220, 20, 60, 0, 0, 142, 0, 0, 70, 0, 60, 100, 0, 0, 2... |
def test_emit_warning_when_event_loop_fixture_is_redefined(pytester: Pytester):
pytester.makepyfile(dedent(' import asyncio\n import pytest\n\n \n def event_loop():\n loop = asyncio.new_event_loop()\n yield loop\n loop.close()\... |
class Plugin(KeepKeyPlugin, QtPlugin):
icon_paired = 'keepkey.png'
icon_unpaired = 'keepkey_unpaired.png'
def create_handler(self, window):
return QtHandler(window, self.pin_matrix_widget_class(), self.device)
def pin_matrix_widget_class(self):
from keepkeylib.qt.pinmatrix import PinMatr... |
def configInputQueue():
def captureInput(iqueue):
while True:
c = getch()
if ((c == '\x03') or (c == '\x04')):
log.debug('Break received (\\x{0:02X})'.format(ord(c)))
iqueue.put(c)
break
log.debug("Input Char '{}' received".... |
class KeywordImpression(BaseImpression):
keyword = models.CharField(_('Keyword'), max_length=1000)
publisher = models.ForeignKey(Publisher, related_name='keyword_impressions', on_delete=models.PROTECT)
advertisement = models.ForeignKey(Advertisement, related_name='keyword_impressions', on_delete=models.PROT... |
class HotelRoom():
id: str
name: str = strawberry.field(resolver=make_localized_resolver('name'))
description: str = strawberry.field(resolver=make_localized_resolver('description'))
price: str
is_sold_out: bool
capacity_left: int
def available_bed_layouts(self) -> List[BedLayout]:
r... |
def test_quantsim_export_quantizer_args():
if (version.parse(tf.version.VERSION) >= version.parse('2.00')):
model = dense_functional()
rand_inp = np.random.randn(100, 5)
qsim = QuantizationSimModel(model, quant_scheme=QuantScheme.post_training_tf_enhanced, default_param_bw=16, default_output... |
.parametrize('use_enemy_attribute_randomizer', [False, True])
def test_on_preset_changed(skip_qtbot, preset_manager, use_enemy_attribute_randomizer):
base = preset_manager.default_preset_for_game(RandovaniaGame.METROID_PRIME).get_preset()
preset = dataclasses.replace(base, uuid=uuid.UUID('b41fde84-1f57-4b79-8cd... |
def register_model(name, dataclass=None):
def register_model_cls(cls):
if (name in MODEL_REGISTRY):
raise ValueError('Cannot register duplicate model ({})'.format(name))
if (not issubclass(cls, BaseFairseqModel)):
raise ValueError('Model ({}: {}) must extend BaseFairseqModel'... |
class dist_info(Command):
description = 'DO NOT CALL DIRECTLY, INTERNAL ONLY: create .dist-info directory'
user_options = [('output-dir=', 'o', 'directory inside of which the .dist-info will becreated (default: top of the source tree)'), ('tag-date', 'd', 'Add date stamp (e.g. ) to version number'), ('tag-build... |
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