code stringlengths 281 23.7M |
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def iter_selections(manifest, selections, *, unique=True):
byname = {b.name: b for b in manifest.benchmarks}
seen = set()
included = []
excluded = set()
for (op, _, kind, parsed) in selections:
matches = _match_selection(manifest, kind, parsed, byname)
if (op == '+'):
for... |
_funcify.register(DimShuffle)
def jax_funcify_DimShuffle(op, **kwargs):
def dimshuffle(x):
res = jnp.transpose(x, op.transposition)
shape = list(res.shape[:len(op.shuffle)])
for augm in op.augment:
shape.insert(augm, 1)
res = jnp.reshape(res, shape)
if (not op.inp... |
class IfNodeTest(_NodeTest):
CODE = '\n if 0:\n print()\n\n if True:\n print()\n else:\n pass\n\n if "":\n print()\n elif []:\n raise\n\n if 1:\n print()\n elif True:\n print()\n elif... |
def search_for_duplicates(inp_path, verbose=False):
headers = swmmio.utils.text.get_inp_sections_details(inp_path)['headers']
dups_found = False
for (header, cols) in headers.items():
if (cols != 'blob'):
df = dataframe_from_inp(inp_path, section=header)
elements = df.index
... |
def get_semisup_dataloaders(train_dataset, test_dataset, val_dataset=None, batch_size=256, batch_size_test=256, num_workers=4, unsup_fraction=0.5):
dataset_size = train_dataset.dataset_size
train_batch_sampler = SemiSupervisedSampler(train_dataset.sup_indices, train_dataset.unsup_indices, batch_size, unsup_frac... |
def configure(dir=None, format_strs=None, comm=None, log_suffix=''):
if (dir is None):
dir = os.getenv('OPENAI_LOGDIR')
if (dir is None):
dir = osp.join(tempfile.gettempdir(), datetime.datetime.now().strftime('openai-%Y-%m-%d-%H-%M-%S-%f'))
assert isinstance(dir, str)
dir = os.path.expan... |
class Migration(migrations.Migration):
dependencies = [('domain', '0047_attribute_locked')]
operations = [migrations.AlterField(model_name='attribute', name='locked', field=models.BooleanField(default=False, help_text='Designates whether this attribute (and its descendants) can be changed.', verbose_name='Locke... |
()
def valid_tmp_game_root(tmp_path):
game_root = tmp_path.joinpath('game_root')
game_root.joinpath('files').mkdir(parents=True)
game_root.joinpath('sys').mkdir()
for f in ['default.dol', 'FrontEnd.pak', 'Metroid1.pak', 'Metroid2.pak']:
game_root.joinpath('files', f).write_bytes(b'')
game_ro... |
class ImgWrapper(gym.ObservationWrapper):
def __init__(self, env=None):
super(ImgWrapper, self).__init__(env)
obs_shape = self.observation_space.shape
self.observation_space = spaces.Box(self.observation_space.low[(0, 0, 0)], self.observation_space.high[(0, 0, 0)], [obs_shape[2], obs_shape[0... |
class KlapEncryptionSession():
def __init__(self, local_seed, remote_seed, user_hash):
self.local_seed = local_seed
self.remote_seed = remote_seed
self.user_hash = user_hash
self._key = self._key_derive(local_seed, remote_seed, user_hash)
(self._iv, self._seq) = self._iv_deri... |
def stat_proxy(path: str) -> os.stat_result:
try:
st = orig_stat(path)
except OSError as err:
print(f'stat({path!r}) -> {err}')
raise
else:
print(('stat(%r) -> (st_mode=%o, st_mtime=%d, st_size=%d)' % (path, st.st_mode, st.st_mtime, st.st_size)))
return st |
def compute_dense_reward(self, action):
reward = 0.0
cube_at_goal = (np.linalg.norm((self.obj.pose.p - self.goal_pos)) <= 0.02)
is_robot_static = (np.max(np.abs(self.agent.robot.get_qvel()[:(- 2)])) <= 0.2)
if (cube_at_goal and is_robot_static):
reward += 2.25
return reward
gripper_p... |
def test_histogrambin():
hb = OSC.parameters._HistogramBin(1, OSC.Range(0, 1))
hb2 = OSC.parameters._HistogramBin(1, OSC.Range(0, 1))
hb3 = OSC.parameters._HistogramBin(1, OSC.Range(0, 2))
assert (hb == hb2)
assert (hb != hb3)
prettyprint(hb)
hb4 = OSC.parameters._HistogramBin.parse(hb.get_e... |
def test_regress_with_steps(ansi_bar: ProgressBar, ansi_io: BufferedIO) -> None:
ansi_bar.start()
ansi_bar.advance(4)
ansi_bar.advance(4)
ansi_bar.advance((- 2))
output = [' 0 [>]', ' 4 [---->]', ' 8 [>]', ' 6 [------>]']
expected = generate_output(output)
assert (expected == ans... |
def test_majorana_operator_with_basis_rotated_by():
H = (numpy.array([[1, 1], [1, (- 1)]]) / numpy.sqrt(2))
a = MajoranaOperator((0, 1), 2.0)
op = a.with_basis_rotated_by(H)
assert (op == MajoranaOperator.from_dict({(0, 1): (- 2.0)}))
b = MajoranaOperator((0,), 2.0)
op = b.with_basis_rotated_by(... |
class PreImport(WorkerPlugin):
def __init__(self, libraries):
if (libraries is None):
libraries = []
elif isinstance(libraries, str):
libraries = libraries.split(',')
self.libraries = libraries
def setup(self, worker=None):
for l in self.libraries:
... |
_config
def test_resize(manager):
manager.c.screen[0].resize(x=10, y=10, w=100, h=100)
(ignore_exceptions=AssertionError, fail_msg="Screen didn't resize")
def run():
d = manager.c.screen[0].info()
assert (d['width'] == 100)
assert (d['height'] == 100)
return d
d = run()
... |
def get_config():
config = get_default_configs()
training = config.training
training.sde = 'vpsde'
training.continuous = True
training.reduce_mean = True
sampling = config.sampling
sampling.method = 'pc'
sampling.predictor = 'euler_maruyama'
sampling.corrector = 'none'
data = con... |
class PlayMultiBlockChange(Packet):
id = 59
to = 1
def __init__(self, chunk_sect_x: int, chunk_sect_y: int, chunk_sect_z: int, trust_edges: bool, blocks: list) -> None:
super().__init__()
self.chunk_sect_x = chunk_sect_x
self.chunk_sect_y = chunk_sect_y
self.chunk_sect_z = ch... |
class Word(entity):
def __init__(self, token, syllables=None, sylls_text=[], broken=False, lang=None):
if (syllables == None):
import prosodic
if (lang == None):
lang = prosodic.lang
w = prosodic.dict[lang].get(token)[0]
if (not len(w.__dict__)... |
def test_misc_object_reader(tmpdir):
tmpcatalog = os.path.join(tmpdir, 'my_catalog.xosc')
cf = xosc.CatalogFile()
cf.create_catalog(tmpcatalog, 'MiscObjectCatalog', 'My first miscobject catalog', 'Mandolin')
orig = xosc.MiscObject('pole', 50, xosc.MiscObjectCategory.pole, xosc.BoundingBox(1, 1, 1, 1, 1,... |
class RunModel():
def __init__(self, model, args, ID2wordVecIdx, ID2char, expName, m_name, m_train='train', m_dev='dev', m_test='test'):
self.model = model
self.tbWriter = None
self.args = args
self.ID2wordVecIdx = ID2wordVecIdx
self.ID2char = ID2char
self.expName = e... |
.parametrize('fixture, result', [('script_callable_legacy_table', []), ('script_callable_legacy_string', []), ('script_reference_console', []), ('script_reference_file', [(Path('bin') / 'script.sh')])])
def test_builder_convert_script_files(fixture: str, result: list[Path]) -> None:
project_root = ((Path(__file__).... |
class QlArchMIPS(QlArch):
type = QL_ARCH.MIPS
bits = 32
def __init__(self, ql: Qiling, endian: QL_ENDIAN):
super().__init__(ql)
self._init_endian = endian
_property
def uc(self) -> Uc:
endian = {QL_ENDIAN.EB: UC_MODE_BIG_ENDIAN, QL_ENDIAN.EL: UC_MODE_LITTLE_ENDIAN}[self.endia... |
def ql_syscall_faccessat(ql: Qiling, dirfd: int, filename: int, mode: int):
vpath = ql.os.utils.read_cstring(filename)
vpath_at = virtual_abspath_at(ql, vpath, dirfd)
if (vpath_at is None):
regreturn = (- 1)
else:
hpath = ql.os.path.virtual_to_host_path(vpath_at)
if (not ql.os.pa... |
class NamespaceReader(abc.TraversableResources):
def __init__(self, namespace_path):
if ('NamespacePath' not in str(namespace_path)):
raise ValueError('Invalid path')
self.path = MultiplexedPath(*list(namespace_path))
def resource_path(self, resource):
return str(self.path.jo... |
class Migration(migrations.Migration):
dependencies = [('successstories', '0008_auto__2000')]
operations = [migrations.AlterField(model_name='story', name='slug', field=models.SlugField(max_length=200, unique=True)), migrations.AlterField(model_name='storycategory', name='slug', field=models.SlugField(max_lengt... |
class TestHTML():
.parametrize('pause, expectation', [(0.4, 400), (1, '^((?:[01]\\d|2[0-3]):[0-5]\\d:[0-5]\\d$)')])
def test_durations(self, pytester, pause, expectation):
pytester.makepyfile(f'''
import time
def test_sleep():
time.sleep({pause})
''')
... |
def test_add_row_button():
widget = QgridWidget(df=create_df())
event_history = init_event_history('row_added', widget=widget)
widget._handle_qgrid_msg_helper({'type': 'add_row'})
assert (event_history == [{'name': 'row_added', 'index': 4, 'source': 'gui'}])
added_index = event_history[0]['index']
... |
def check_connection_end_to_end(wrap_client: Callable[([CA, socket.socket, str], SslSocket)], wrap_server: Callable[([LeafCert, socket.socket], SslSocket)], key_type: KeyType) -> None:
def fake_ssl_client(ca: CA, raw_client_sock: socket.socket, hostname: str) -> None:
try:
wrapped_client_sock = ... |
class DNSlog():
def __init__(self):
self.headers = headers = {'Cookie': 'UM_distinctid=17d9ee9b99ad5-08c6a2266360e7-4c3f2779-1fa400-17d9ee9b99b2b1; CNZZDATA=--%7C; PHPSESSID=kolveuasn829nk9s0jfffjg4n2'}
def getdomain(self):
getdomain = requests.get(url=' headers=self.headers, timeout=60)
... |
class Serial(SerialBase, PlatformSpecific):
def open(self):
if (self._port is None):
raise SerialException('Port must be configured before it can be used.')
if self.is_open:
raise SerialException('Port is already open.')
self.fd = None
try:
self.fd... |
def basecompiledir_ls():
subdirs = []
others = []
for f in os.listdir(config.base_compiledir):
if os.path.isdir(os.path.join(config.base_compiledir, f)):
subdirs.append(f)
else:
others.append(f)
subdirs = sorted(subdirs)
others = sorted(others)
print(f'Bas... |
class _ModelFallbackWrapper(GenerationMixin):
__slots__ = ('_optimized', '_default')
def __init__(self, optimized, default):
self._optimized = optimized
self._default = default
def __call__(self, *args, **kwargs):
if (kwargs['past_key_values'] is None):
return self._defau... |
def loadAWSServiceControlPolicy(neo4j_session, data_path, account_name):
logger.info("[*] Loading AWS Service Control Policy into neo4j instance for AWS account '%s'", account_name)
ingest_aws_service_control_policy = 'merge(scp:AWSPolicy:AWSServiceControlPolicy {Arn:$Arn}) \n ... |
class PedestrianAnimation(_AnimationType):
def __init__(self, motion=None, animation=None):
self.motion = convert_enum(motion, PedestrianMotionType, True)
self.animation = animation
self.gestures = []
def __eq__(self, other):
if isinstance(other, PedestrianAnimation):
... |
class ImportWizard(QtWidgets.QWizard):
def __init__(self):
QtWidgets.QWizard.__init__(self)
self.setMinimumSize(500, 400)
self.resize(700, 500)
self.setPreviewData(None)
self.selectFilePage = SelectFilePage()
self.setParametersPage = SetParametersPage()
self.r... |
(frozen=True)
class SuperMetroidPerGameOptions(PerGameOptions):
input_path: (Path | None) = None
output_directory: (Path | None) = None
output_format: str = 'smc'
def as_json(self):
return {**super().as_json, 'input_path': (str(self.input_path) if (self.input_path is not None) else None), 'outpu... |
def event_data_generator_bert_mrc_mul(input_Xs, Ys, token_type_ids, query_lens):
for index in range(len(input_Xs)):
input_x = input_Xs[index]
y = Ys[index]
token_type_id = token_type_ids[index]
query_len = query_lens[index]
(yield ((input_x, len(input_x), query_len, token_typ... |
def StyleGAN2_FLOPCal(generator_dict):
styled_conv_FLOPs = Styled_Conv_FLOPCal(generator_dict, return_detail=False)
toRGB_FLOPs = ToRGB_Conv_FLOPCal(generator_dict, False)
mapping_network_FLOPs = Mapping_Network_FLOPCal(generator_dict)
style_mod_FLOPs = Style_Modulation_FLOPCal(generator_dict)
all_F... |
def parsexml_(infile, parser=None, **kwargs):
if (parser is None):
try:
parser = etree_.ETCompatXMLParser()
except AttributeError:
parser = etree_.XMLParser()
try:
if isinstance(infile, os.PathLike):
infile = os.path.join(infile)
except AttributeEr... |
def console_progress():
def progress(totalhashed, totalsize):
msg = (' ' * 30)
if (totalhashed < totalsize):
msg = ('%5.1f%% complete' % ((totalhashed * 100.0) / totalsize))
sys.stdout.write((msg + ' \r'))
sys.stdout.flush()
try:
return (progress if sys.stdout... |
class DequantizeFunc(torch.autograd.Function):
def forward(ctx, tensor: torch.Tensor, scale: torch.Tensor, offset: torch.Tensor):
x_dequant = ((tensor + offset) * scale)
ctx.tensor_requires_grad = tensor.requires_grad
ctx.scale_requires_grad = scale.requires_grad
ctx.offset_requires_... |
def str_for_dist(dist: TensorVariable, formatting: str='plain', include_params: bool=True) -> str:
if include_params:
if isinstance(dist.owner.op, RandomVariable):
dist_args = [_str_for_input_var(x, formatting=formatting) for x in dist.owner.inputs[3:]]
else:
dist_args = [_st... |
def init_output_database(output_c, subset):
schema._execute_sql(output_c, fragment_db.get_schema_template())
schema._execute_sql(output_c, '\nATTACH DATABASE ":memory:" AS merge;\n\nCREATE TABLE merge.required_constants (\n constant_smiles TEXT\n);\n\n ')
output_c.executemany('\nINSERT INTO merge.requir... |
.parametrize(('package_repo', 'dependency_repo', 'result'), [('pypi', None, True), ('private', None, True), ('pypi', 'pypi', True), ('private', 'private', True), ('pypi', 'private', False), ('private', 'pypi', False)])
def test_package_satisfies_on_repositories(package_repo: str, dependency_repo: (str | None), result: ... |
def test_create_left_lane_split_second_lane():
lanedef = xodr.LaneDef(10, 20, 1, 2, 2)
lanes = xodr.create_lanes_merge_split(0, [lanedef], 30, xodr.std_roadmark_solid_solid(), 3, 3)
assert (len(lanes.lanesections) == 3)
assert (lanes.lanesections[0].s == 0)
assert (lanes.lanesections[1].s == 10)
... |
class Scenario(ScenarioGenerator):
def __init__(self):
super().__init__()
def road(self, **kwargs):
roads = []
roads.append(xodr.create_road(xodr.Line(100), id=0, left_lanes=1, right_lanes=2))
roads.append(xodr.create_road(xodr.Line(100), id=1, left_lanes=0, right_lanes=1))
... |
def determine_bpi(data, frames, EMPTY=(b'\x00' * 10)):
o = 0
asbpi = 0
while (o < (len(data) - 10)):
part = data[o:(o + 10)]
if (part == EMPTY):
bpioff = (- ((len(data) - o) % 10))
break
(name, size, flags) = struct.unpack('>4sLH', part)
size = BitPadd... |
class _SingleResponse():
def __init__(self, cert: x509.Certificate, issuer: x509.Certificate, algorithm: hashes.HashAlgorithm, cert_status: OCSPCertStatus, this_update: datetime.datetime, next_update: (datetime.datetime | None), revocation_time: (datetime.datetime | None), revocation_reason: (x509.ReasonFlags | Non... |
def blank_fill(img: np.ndarray, start_coordination: (int, int)) -> np.ndarray:
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (9, 9))
last_temp_img = np.zeros_like(img)
last_temp_img[start_coordination] = 1
current_temp_img = cv2.dilate(last_temp_img, kernel)
while (current_temp_img != last_temp... |
class OmniglotClassDataset(ClassDataset):
folder = 'omniglot'
download_url_prefix = '
zips_md5 = {'images_background': '68d2efa1b9178cc56df9314c21c6e718', 'images_evaluation': '6b91aef0f799c5bb55b94e3f2daec811'}
filename = 'data.hdf5'
filename_labels = '{0}_labels.json'
def __init__(self, root, ... |
.fast
def test_normalisation_mode(plot=True, close_plots=True, verbose=True, *args, **kwargs):
from radis.test.utils import getTestFile
_clean(plot, close_plots)
(w, I) = np.loadtxt(getTestFile('calc_N2C_spectrum_Trot1200_Tvib3000.txt')).T
s = calculated_spectrum(w, I, conditions={'Tvib': 3000, 'Trot': ... |
def get_database(data, subset, root_path, video_path_formatter):
video_ids = []
video_paths = []
annotations = []
for (key, value) in data['database'].items():
this_subset = value['subset']
if (this_subset == subset):
video_ids.append(key)
annotations.append(value... |
def _get_proposal_section_choices(conference, action='edit'):
if (action == 'create'):
return [(str(cps.id), cps.name) for cps in ProposalSection.objects.filter(conferences=conference)]
else:
return [(str(cps.id), cps.name) for cps in ProposalSection.objects.filter(conferences=conference)] |
class GuiAddImplantSetCommand(wx.Command):
def __init__(self, fitID, itemIDs):
wx.Command.__init__(self, True, 'Add Implant Set')
self.internalHistory = InternalCommandHistory()
self.fitID = fitID
self.itemIDs = itemIDs
def Do(self):
results = []
for itemID in sel... |
class Reaction():
def __init__(self, template=None, rxnname=None, smiles=None, reference=None):
if (template is not None):
self.smirks = template
self.rxnname = rxnname
self.smiles = smiles
self.reference = reference
rxn = AllChem.ReactionFromSmart... |
class TestGenerateFunction(unittest.TestCase):
def setUp(self) -> None:
self.arg = RuntimeArg('arg', int_rprimitive)
self.reg = Register(int_rprimitive, 'arg')
self.block = BasicBlock(0)
def test_simple(self) -> None:
self.block.ops.append(Return(self.reg))
fn = FuncIR(Fu... |
def _complex_compact(variables):
compact_form = ''
sequence = None
for x in variables:
if (sequence is None):
sequence = SequenceOfSuccessiveVariables(x)
elif (not sequence.can_be_extended_with(x.id)):
compact_form += (str(sequence) if (compact_form == '') else (' ' +... |
def main(args):
at_step = args.step
output_dir_name = args.output_dir
layer_name = args.layer_name
block_type = args.block_type
postfix = args.postfix
probe_type = args.probe_type
normalized = args.normalized
smoothed = args.smoothed
lasso = (True if (args.lasso == 'yes') else False)... |
class _FindFlags():
case_sensitive: bool = False
backward: bool = False
def to_qt(self):
flags: _FindFlagType = QWebEnginePage.FindFlag(0)
if self.case_sensitive:
flags |= QWebEnginePage.FindFlag.FindCaseSensitively
if self.backward:
flags |= QWebEnginePage.Fi... |
def perturb_logistic(net, x_nat, target):
net.eval()
x = (x_nat.detach() + (0.001 * torch.randn(x_nat.shape).cuda().detach()))
for _ in range(args.num_steps):
x.requires_grad_()
with torch.enable_grad():
loss = torch.mean((1 + torch.exp((((- 1.0) * target.float()) * net(x).squeez... |
class TCovers(PluginTestCase):
def setUp(self) -> None:
self.song = A_SONG
self.blank_song = AudioFile()
def test_cover_path_lastfm(self):
plugin_cls = self.plugins['lastfm-cover'].cls
assert isinstance(plugin_cls(self.song).cover_path, fsnative)
assert isinstance(plugin_... |
class IDirectSound3DBuffer(com.pIUnknown):
_methods_ = [('GetAllParameters', com.STDMETHOD(LPDS3DBUFFER)), ('GetConeAngles', com.STDMETHOD(LPDWORD, LPDWORD)), ('GetConeOrientation', com.STDMETHOD(PD3DVECTOR)), ('GetConeOutsideVolume', com.STDMETHOD(LPLONG)), ('GetMaxDistance', com.STDMETHOD(PD3DVALUE)), ('GetMinDis... |
_if_nothing_inferred
def const_infer_binary_op(self: nodes.Const, opnode: (nodes.AugAssign | nodes.BinOp), operator: str, other: InferenceResult, context: InferenceContext, _: SuccessfulInferenceResult) -> Generator[((ConstFactoryResult | util.UninferableBase), None, None)]:
not_implemented = nodes.Const(NotImpleme... |
def gurobi_solve_problem(problem: Problem, initvals: Optional[np.ndarray]=None, verbose: bool=False, **kwargs) -> Solution:
if (initvals is not None):
warnings.warn('warm-start values are ignored by this wrapper')
model = gurobipy.Model()
if (not verbose):
model.setParam(GRB.Param.OutputFlag... |
def parse_args():
parser = argparse.ArgumentParser(description='Finetune a transformers model on a text classification task')
parser.add_argument('--dataset_name', type=str, default=None, help='The name of the dataset to use (via the datasets library).')
parser.add_argument('--predict_with_generate', type=b... |
def test_inferaugassign_picking_parent_instead_of_stmt() -> None:
code = "\n from collections import namedtuple\n SomeClass = namedtuple('SomeClass', ['name'])\n items = [SomeClass(name='some name')]\n\n some_str = ''\n some_str += ', '.join(__(item) for item in items)\n "
node = extract_node(... |
class Effect4640(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
damageTypes = ('Em', 'Explosive', 'Kinetic', 'Thermal')
for damageType in damageTypes:
fit.ship.boostItemAttr('armor{0}DamageResonance'.format(damageType), ship.getModifiedItemA... |
class ScalarBias(torch.autograd.Function):
def forward(ctx, input, dim, bias_init):
size = list(input.size())
size[dim] += 1
output = input.new(*size).fill_(bias_init)
output.narrow(dim, 1, (size[dim] - 1)).copy_(input)
ctx.dim = dim
return output
def backward(ctx... |
class Freezer(object):
def param_to_buffer(module, name):
split_name = name.split('.')
module_name_hierarchy = split_name[:(- 1)]
param_name = split_name[(- 1)]
tgt_module = module
for module_name in module_name_hierarchy:
tgt_module = getattr(tgt_module, module_n... |
class AbstractMonitor(metaclass=ABCMeta):
def real_time_update(self, timestamp: datetime):
raise NotImplementedError('Should implement real_time_update()')
def end_of_day_update(self, timestamp: datetime):
raise NotImplementedError('Should implement end_of_day_update()')
def end_of_trading_u... |
class Bottleneck(nn.Module):
def __init__(self, in_channels, out_channels, expansion=4, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN')):
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
assert (style in ['pytorch', 'caffe']... |
('baseplate.lib.thrift_pool.RetryPolicy.new')
class MaxRetriesRenameTests(unittest.TestCase):
def test_default_is_3(self, new_retry_policy):
thrift_pool.ThriftConnectionPool(EXAMPLE_ENDPOINT)
new_retry_policy.assert_called_with(attempts=3)
def test_default_through_parser(self, new_retry_policy):... |
def build(cfg, registry, default_args=None):
if (cfg is None):
return None
elif isinstance(cfg, (list, tuple)):
modules = [build_from_cfg(cfg_, registry, default_args) for cfg_ in cfg]
return modules
else:
return build_from_cfg(cfg, registry, default_args) |
class GetFromCacheTests(unittest.TestCase):
def test_bogus_url(self):
url = '
with self.assertRaisesRegex(ValueError, 'Connection error'):
_ = get_from_cache(url)
def test_file_not_found(self):
url = hf_bucket_url(MODEL_ID, filename='missing.bin')
with self.assertRais... |
class ChildFilterLALR(ChildFilter):
def __call__(self, children):
filtered = []
for (i, to_expand, add_none) in self.to_include:
if add_none:
filtered += ([None] * add_none)
if to_expand:
if filtered:
filtered += children[i]... |
class ROIBoxHead(torch.nn.Module):
def __init__(self, in_channels):
super().__init__()
self.feature_extractor = make_roi_box_feature_extractor()
self.predictor = make_roi_box_predictor(self.feature_extractor.out_channels)
self.post_processor = make_roi_box_post_processor()
se... |
class CassandraConcurrentTests(unittest.TestCase):
def setUp(self):
self.baseplate_observer = TestBaseplateObserver()
baseplate = Baseplate({'cassandra.contact_points': cassandra_endpoint.address.host})
baseplate.register(self.baseplate_observer)
baseplate.configure_context({'cassand... |
class TestSuper_td():
N = 3
t1 = qutip.QobjEvo([(qutip.qeye(N) * (1 + 0.1j)), [(qutip.create(N) * (1 - 0.1j)), f]])
t2 = qutip.QobjEvo([(qutip.destroy(N) * (1 - 0.2j))])
t3 = qutip.QobjEvo([[(qutip.num(N) * (1 + 0.2j)), f]])
q1 = (qutip.qeye(N) * (1 + 0.3j))
q2 = (qutip.destroy(N) * (1 - 0.3j))
... |
class Index(CtrlNode):
nodeName = 'Index'
uiTemplate = [('axis', 'intSpin', {'value': 0, 'min': 0, 'max': 1000000}), ('index', 'intSpin', {'value': 0, 'min': 0, 'max': 1000000})]
def processData(self, data):
s = self.stateGroup.state()
ax = s['axis']
ind = s['index']
if (ax =... |
class OdeSolverBase():
def __init__(self, allow_free_variables: bool=False, duplicate_starting_point: bool=False):
self.allow_free_variables = allow_free_variables
self.duplicate_starting_point = duplicate_starting_point
def integrator(self):
raise RuntimeError('This method should be imp... |
class CodeWriter():
def __init__(self, project, templates, language='core'):
super().__init__()
self.project = project
self.templates = templates
self.language = language
self.comments = []
self.last_id = 0
self.current_sprite = ''
self.jinja_environme... |
class AlexOutputBlock(nn.Module):
def __init__(self, in_channels, classes):
super(AlexOutputBlock, self).__init__()
mid_channels = 4096
self.fc1 = AlexDense(in_channels=in_channels, out_channels=mid_channels)
self.fc2 = AlexDense(in_channels=mid_channels, out_channels=mid_channels)
... |
_auth
def db_del(request, pk):
if (request.method == 'DELETE'):
try:
DBConfig.objects.get(id=pk).delete()
return JsonResponse({'code': 200, 'data': None, 'msg': '!'})
except Exception as e:
return JsonResponse({'code': 500, 'data': None, 'msg': '!{}'.format(e)}) |
class PolyvoreModel(object):
def __init__(self, config, mode, train_inception=False):
assert (mode in ['train', 'eval', 'inference'])
self.config = config
self.mode = mode
self.train_inception = train_inception
self.reader = tf.TFRecordReader()
self.initializer = tf.r... |
def online_kurtosis(data):
n = 0
mean = 0
M2 = 0
M3 = 0
M4 = 0
for x in data:
n1 = n
n = (n + 1)
delta = (x - mean)
delta_n = (delta / n)
delta_n2 = (delta_n * delta_n)
term1 = ((delta * delta_n) * n1)
mean = (mean + delta_n)
M4 = (... |
def _create_random_dataset(shape, channel_per_class):
tmp = NamedTemporaryFile(delete=False)
with h5py.File(tmp.name, 'w') as f:
l_shape = w_shape = shape
if (len(shape) == 4):
l_shape = shape[1:]
w_shape = shape[1:]
if channel_per_class:
l_shape = ((2... |
def list_ready_nodes(cli, label_selector=None):
nodes = []
try:
if label_selector:
ret = cli.list_node(pretty=True, label_selector=label_selector)
else:
ret = cli.list_node(pretty=True)
except ApiException as e:
logging.error(('Exception when calling CoreV1Api... |
def save_coeffs(coeffs, out_dir=''):
for platform in coeffs.keys():
fname = os.path.join(out_dir, ('%s_calibration_data.h5' % platform))
fid = h5py.File(fname, 'w')
for chan in coeffs[platform].keys():
fid.create_group(chan)
fid[chan]['datetime'] = coeffs[platform][ch... |
class SelectiveKernelAttn(nn.Module):
def __init__(self, channels, num_paths=2, attn_channels=32, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d):
super(SelectiveKernelAttn, self).__init__()
self.num_paths = num_paths
self.fc_reduce = nn.Conv2d(channels, attn_channels, kernel_size=1, bias=Fals... |
class TestForceDocumentEnd():
def _get_script(self, *, namespace, name):
source = textwrap.dedent('\n // ==UserScript==\n // {}\n // {}\n // ==/UserScript==\n '.format(namespace, name))
_save_script(source, 'force.user.js')
gm_manager = gr... |
def verify_message_with_address(address: str, sig65: bytes, message: bytes, *, net=None):
from .bitcoin import pubkey_to_address
assert_bytes(sig65, message)
if (net is None):
net = constants.net
try:
h = sha256d(msg_magic(message))
(public_key, compressed) = ECPubkey.from_signat... |
class LMDBDataset(data.Dataset):
def __init__(self, db_path, noise_model=None, size=None, repeat=1, ratio_used_list=None):
import lmdb
self.db_path = db_path
self.env = lmdb.open(db_path, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False)
with self.env.begin(wr... |
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