code stringlengths 281 23.7M |
|---|
def _include_extra(req: str, extra: str, condition: str) -> Requirement:
r = Requirement(req)
parts = ((f'({r.marker})' if r.marker else None), (f'({condition})' if condition else None), (f'extra == {extra!r}' if extra else None))
r.marker = Marker(' and '.join((x for x in parts if x)))
return r |
class Identity(UnaryScalarOp):
def impl(self, input):
return input
def c_code(self, node, name, inputs, outputs, sub):
(x,) = inputs
(z,) = outputs
return f'{z} = {x};'
def grad(self, inputs, gout):
(x,) = inputs
(gz,) = gout
if (x.type in continuous_t... |
def LibriSpeech(root: Union[(str, Path)], url: str=URL, folder_in_archive: str=FOLDER_IN_ARCHIVE):
if (url in ['dev-clean', 'dev-other', 'test-clean', 'test-other', 'train-clean-100', 'train-clean-360', 'train-other-500']):
url = ((BASE_URL + url) + '.tar.gz')
root = os.fspath(root)
checksum_dict = ... |
def stop_evennia():
def _portal_stopped(*args):
print('... Portal stopped.\nEvennia shut down.')
_reactor_stop()
def _server_stopped(*args):
print('... Server stopped.\nStopping Portal ...')
send_instruction(PSHUTD, {})
wait_for_status(False, None, _portal_stopped)
de... |
def add_eval_sample_opts(parser):
parser.add_argument('--sample_method', type=str, default='greedy', help='greedy; sample; gumbel; top<int>, top<0-1>')
parser.add_argument('--beam_size', type=int, default=1, help='used when sample_method = greedy, indicates number of beams in beam search. Usually 2 or 3 works w... |
def tail_events(benchmark_tms: QFSeries, examined_tms: QFSeries, tail_percentile: float) -> [QFSeries, QFSeries]:
assert benchmark_tms.index.equals(examined_tms.index)
percentile = np.percentile(benchmark_tms, tail_percentile)
indices_of_tail_events = (benchmark_tms < percentile)
benchmark_tail_tms = be... |
_required
def feed_delete(request, feed_pk, template='yarr/confirm.html'):
feed = get_object_or_404(models.Feed, pk=feed_pk, user=request.user)
if request.POST:
feed.delete()
messages.success(request, 'Feed deleted')
return HttpResponseRedirect(reverse(settings.INDEX_URL))
return ren... |
class LinkedinOAuth2(BaseOAuth2):
name = 'linkedin-oauth2'
AUTHORIZATION_URL = '
ACCESS_TOKEN_URL = '
USER_DETAILS_URL = '
USER_EMAILS_URL = '
ACCESS_TOKEN_METHOD = 'POST'
REDIRECT_STATE = False
DEFAULT_SCOPE = ['r_liteprofile']
EXTRA_DATA = [('id', 'id'), ('expires_in', 'expires'), ... |
def get_distribution(dist):
if isinstance(dist, str):
dist = Requirement.parse(dist)
if isinstance(dist, Requirement):
dist = get_provider(dist)
if (not isinstance(dist, Distribution)):
raise TypeError('Expected string, Requirement, or Distribution', dist)
return dist |
def makeRunCommand(cmd, case_path, source_env=True):
installation_path = getFoamDir()
if (installation_path is None):
raise IOError('OpenFOAM installation directory not found')
source = ''
if source_env:
env_setup_script = '{}/etc/bashrc'.format(installation_path)
source = 'sourc... |
class TestTransform():
.parametrize('ndim', (0, 1))
def test_fallback_log_jac_det(self, ndim):
class SquareTransform(Transform):
name = 'square'
ndim_supp = ndim
def forward(self, value, *inputs):
return pt.power(value, 2)
def backward(self... |
def gen_candidate(level):
global candidate
size = len(freArr[(level - 1)])
start = 0
for i in range(size):
Q = freArr[(level - 1)][start][0:(level - 1)]
R = freArr[(level - 1)][i][1:level]
if (Q != R):
start = binary_search(level, R, 0, (size - 1))
if ((start ... |
class clean(distutils.command.clean.clean):
def run(self):
distutils.command.clean.clean.run(self)
for path in (ROOT_DIR / 'torcharrow').glob('**/*.so'):
print(f"removing '{path}'")
path.unlink()
build_dirs = [(ROOT_DIR / 'build')]
for path in build_dirs:
... |
class GHMCLoss(nn.Module):
def __init__(self, bins=30, momentum=0.5):
super(GHMCLoss, self).__init__()
self.bins = bins
self.momentum = momentum
self.edges = [(t / bins) for t in range((bins + 1))]
self.edges[(- 1)] += 1e-06
if (momentum > 0):
self.acc_sum... |
class TestLogging(QiskitChemistryTestCase):
def setUp(self):
super().setUp()
self.current_level = get_qiskit_chemistry_logging()
set_qiskit_chemistry_logging(logging.INFO)
def tearDown(self):
set_qiskit_chemistry_logging(self.current_level)
super().tearDown()
def test... |
def main() -> None:
application = Application.builder().token('TOKEN').build()
application.add_handler(ChatMemberHandler(track_chats, ChatMemberHandler.MY_CHAT_MEMBER))
application.add_handler(CommandHandler('show_chats', show_chats))
application.add_handler(ChatMemberHandler(greet_chat_members, ChatMem... |
class NDCGMetricValueTest(unittest.TestCase):
def setUp(self) -> None:
self.non_exponential_ndcg = NDCGMetric(world_size=WORLD_SIZE, my_rank=0, batch_size=BATCH_SIZE, tasks=[DefaultTaskInfo], exponential_gain=False, session_key=SESSION_KEY)
self.exponential_ndcg = NDCGMetric(world_size=WORLD_SIZE, m... |
class BamBlock(nn.Module):
def __init__(self, channels):
super(BamBlock, self).__init__()
self.ch_att = ChannelGate(channels=channels)
self.sp_att = SpatialGate(channels=channels)
self.sigmoid = nn.Sigmoid()
def forward(self, x):
att = (1 + self.sigmoid((self.ch_att(x) * ... |
class TestVMStatCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('VMStatCollector', {'interval': 10})
self.collector = VMStatCollector(config, None)
def test_import(self):
self.assertTrue(VMStatCollector)
('__builtin__.open')
('os.access', Mock(return_... |
def create_door_frame(bm, face, prop):
normal = face.normal.copy()
min_frame_size = (min(calc_face_dimensions(face)) / 2)
prop.frame_thickness = clamp(prop.frame_thickness, 0.01, (min_frame_size - 0.001))
(door_face, frame_faces) = make_door_inset(bm, face, prop)
arch_face = None
if prop.add_arc... |
class BaseBatteryModel(pybamm.BaseModel):
def __init__(self, options=None, name='Unnamed battery model'):
super().__init__(name)
self.options = options
def deserialise(cls, properties: dict):
instance = cls.__new__(cls)
instance.__init__(options=properties['options'], name=(prope... |
def test_do():
rng = np.random.default_rng(seed=435)
with pm.Model() as m_old:
x = pm.Normal('x', 0, 0.001)
y = pm.Normal('y', x, 0.001)
z = pm.Normal('z', (y + x), 0.001)
assert ((- 5) < pm.draw(z, random_seed=rng) < 5)
m_new = do(m_old, {y: (x + 100)})
assert (len(m_new.fre... |
def action_modify(actions):
triple = ['intent', 'slot', 'value1', 'value2']
res = ''
temp = {}
for action in actions:
if (('value1' in action.keys()) and (action['value1'] != '')):
temp[('' + action['value1'])] = random_modify(action['value1'])
for x in triple:
if... |
def optimalK(data, num_fold, maxClusters=5, THRE_PS=0.9):
num_data = data.shape[0]
num_feat = data.shape[1]
pred_strength_avg = np.zeros((maxClusters + 1))
for nf in range(num_fold):
inds_train = np.random.choice(num_data, int((num_data * 0.5)), replace=False)
inds_test = list(set(range(... |
def _create_dummy_icdar_json(json_name):
image_1 = {'id': 0, 'width': 640, 'height': 640, 'file_name': 'fake_name.jpg'}
image_2 = {'id': 1, 'width': 640, 'height': 640, 'file_name': 'fake_name1.jpg'}
annotation_1 = {'id': 1, 'image_id': 0, 'category_id': 0, 'area': 400, 'bbox': [50, 60, 20, 20], 'iscrowd': ... |
def check_match(op_list, op_map=None):
if (not op_list):
raise ValueError('Empty op_list passed to check_match')
if (not op_map):
op_map = default_op_map
op_type_list = [op.type for op in op_list]
_log.debug('Checking matches for op_type_list: %s', op_type_list)
op_index = op_type_li... |
class ContainerPage(HTML5Page):
def __init__(self, view):
super().__init__(view)
page_layout = PageLayout(contents_layout=CenteredLayout(), header_layout=Container(fluid=True), footer_layout=Container(fluid=True))
self.use_layout(page_layout)
self.layout.header.add_child(P(view, text... |
_infer_shape
_useless
_canonicalize
_rewriter([SpecifyShape])
def local_merge_consecutive_specify_shape(fgraph, node):
if (not isinstance(node.op, SpecifyShape)):
return False
obj = node.inputs[0]
if (not (obj.owner and isinstance(obj.owner.op, SpecifyShape))):
return False
(inner_obj, *... |
class TestDOTARSDet(TestDOTA):
def eval(self):
txt_name = '{}.txt'.format(self.cfgs.VERSION)
real_test_img_list = self.get_test_image()
rsdet = build_whole_network_5p.DetectionNetworkRSDet(cfgs=self.cfgs, is_training=False)
self.test_dota(det_net=rsdet, real_test_img_list=real_test_i... |
def test_dependency_from_pep_508_with_python_full_version_pep440_compatible_release_tilde() -> None:
name = 'pathlib2 ; python_version ~= "3.4" or python_version < "3"'
dep = Dependency.create_from_pep_508(name)
assert (dep.name == 'pathlib2')
assert (str(dep.constraint) == '*')
assert (dep.python_v... |
class _GoogleDocstringToMarkdown(GoogleDocstring):
def _load_custom_sections(self) -> None:
super()._load_custom_sections()
self._sections['registers'] = self._parse_registers_section
def _parse_parameters_section(self, section: str) -> List[str]:
def _template(name, desc_lines):
... |
def test_basic_chain_alt_az(sam_data, cec_inverter_parameters, sapm_temperature_cs5p_220m):
times = pd.date_range(start=' 1200-0700', end=' 1800-0700', freq='6H')
latitude = 32.2
longitude = (- 111)
surface_tilt = 0
surface_azimuth = 0
modules = sam_data['sandiamod']
module_parameters = modu... |
class BaseOptions():
def __init__(self):
self._parser = argparse.ArgumentParser()
self._initialized = False
def initialize(self):
self._parser.add_argument('--load_epoch', type=int, default=(- 1), help='which epoch to load? set to -1 to use latest cached model')
self._parser.add_... |
def conv2d(input_, output_dim, k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02, name='conv2d'):
with tf.variable_scope(name):
w = tf.get_variable('w', [k_h, k_w, input_.get_shape()[(- 1)], output_dim], initializer=tf.truncated_normal_initializer(stddev=stddev))
conv = tf.nn.conv2d(input_, w, strides=[1, d_h... |
.parametrize(('given', 'tag', 'number', 'node', 'dirty'), [('3.3.1-rc26-0-g9df187b', '3.3.1-rc26', 0, 'g9df187b', False), ('17.33.0-rc-17-g38c3047c0', '17.33.0-rc', 17, 'g38c3047c0', False)])
def test_parse_describe_output(given: str, tag: str, number: int, node: str, dirty: bool) -> None:
parsed = git._git_parse_d... |
class CleanChannels(Converter):
_channel_converter = TextChannelConverter()
async def convert(self, ctx: Context, argument: str) -> (Literal['*'] | list[TextChannel]):
if (argument == '*'):
return '*'
return [(await self._channel_converter.convert(ctx, channel)) for channel in argume... |
def test_get_srv_pn(np_junction):
from solcore.sesame_drift_diffusion.process_structure import get_srv, process_structure
from solcore import material, si
from solcore.structure import Junction, Layer
from solcore.state import State
options = State(T=300)
GaAs_p = material('GaAs')(T=300, Na=1e+2... |
def summarize_ratings(ratings_file, out_dir=None):
ratings_file = Path(ratings_file).resolve()
if (not pexists(ratings_file)):
raise IOError('Ratings file does not exist! : {}'.format(ratings_file))
if (out_dir is None):
out_dir = ratings_file.parents[0]
import re
clean = (lambda lbl... |
(frozen=True)
class ContractSendChannelWithdraw(ContractSendEvent):
canonical_identifier: CanonicalIdentifier
total_withdraw: WithdrawAmount
expiration: BlockExpiration
partner_signature: Signature
def channel_identifier(self) -> ChannelID:
return self.canonical_identifier.channel_identifier... |
def EfficientNet(width_coefficient, depth_coefficient, default_resolution, dropout_rate=0.2, drop_connect_rate=0.2, depth_divisor=8, blocks_args=DEFAULT_BLOCKS_ARGS, model_name='efficientnet', include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000, freeze_bn=False, **kwarg... |
def test_discover_cosine(local_client, grpc_client):
def f(client: QdrantBase, **kwargs: Dict[(str, Any)]) -> List[models.ScoredPoint]:
return client.discover(collection_name=COLLECTION_NAME, target=10, context=[models.ContextExamplePair(positive=11, negative=19)], with_payload=True, limit=10, using='image... |
class StateHandler(object):
def __init__(self, room):
self.room = room
self.current_state_name = (room.db.state or _FIRST_STATE)
self.prev_state_name = room.db.prev_state
self.current_state = None
self.current_state = self.load_state(self.current_state_name)
def load_stat... |
class TraceLocalSpanObserverTests(TraceTestBase):
def setUp(self):
super().setUp()
self.recorder = NullRecorder()
self.mock_context = mock.Mock()
self.span = ServerSpan('test-id', 'test-parent-id', 'test-span-id', None, 0, 'test', self.mock_context)
def test_init_local_component(... |
class CoinCollectorLevel(gym.Env):
metadata = {'render.modes': ['human', 'ansi']}
def __init__(self, level, n_games, game_generator_seed, grammar_flags={}, request_infos=[]):
self.level = level
self.n_games = n_games
self.grammar_flags = grammar_flags
self.game_generator_seed = g... |
def get_externsheet_local_range(bk, refx, blah=0):
try:
info = bk._externsheet_info[refx]
except IndexError:
print(('!!! get_externsheet_local_range: refx=%d, not in range(%d)' % (refx, len(bk._externsheet_info))), file=bk.logfile)
return ((- 101), (- 101))
(ref_recordx, ref_first_sh... |
def verify_interface(test_interface, nodelst, template, kubecli: KrknKubernetes):
pod_index = random.randint(0, (len(nodelst) - 1))
pod_body = yaml.safe_load(template.render(nodename=nodelst[pod_index]))
logging.info(('Creating pod to query interface on node %s' % nodelst[pod_index]))
kubecli.create_pod... |
def add_pyscaffold(config: ConfigUpdater, opts: ScaffoldOpts) -> ConfigUpdater:
if ('pyscaffold' not in config):
config.add_section('pyscaffold')
pyscaffold = config['pyscaffold']
pyscaffold['version'] = pyscaffold_version
extensions = {ext.name for ext in opts.get('extensions', []) if ext.persi... |
class MachoParser():
def __init__(self, ql, path, arch=None):
self.ql = ql
self.binary_file = self.readFile(path)
self.raw_data = self.binary_file
self.archtype = ql.arch.type
self.parseFile()
self.page_zero_size = 0
self.header_address = 0
for seg in ... |
class Audio_Visual_Separation():
def __init__(self):
self.Video_Path = ''
self.Video_Name = ''
self.Audio_Path = ''
self.Audio_Name = ''
def _path_check(path):
FileName = Path(path)
if FileName.exists():
return True
elif FileName.is_file():
... |
class SequentialGraphRewriter(GraphRewriter, UserList):
def warn(cls, exc, self, rewriter):
_logger.error(f'{cls.__name__} apply {rewriter}')
_logger.error('Traceback:')
_logger.error(traceback.format_exc())
if (config.on_opt_error == 'raise'):
raise exc
elif (con... |
class GraphicsLayoutWidget(GraphicsView):
def __init__(self, **kwds):
super().__init__(**kwds)
self.gfxLayout = graphicsItems.GraphicsLayout.GraphicsLayout()
for n in ['nextRow', 'nextCol', 'nextColumn', 'addItem', 'getItem', 'addLayout', 'addLabel', 'removeItem', 'itemIndex', 'clear']:
... |
_REGISTRY.register()
class SDLModel(SRModel):
def init_training_settings(self):
self.net_g.train()
train_opt = self.opt['train']
self.ema_decay = train_opt.get('ema_decay', 0)
if (self.ema_decay > 0):
logger = get_root_logger()
logger.info(f'Use Exponential Mo... |
def check_mopidy_extensions() -> Dict[(str, Tuple[(bool, str)])]:
try:
subprocess.check_call(['systemctl', 'is-active', 'mopidy'], stdout=subprocess.DEVNULL)
except subprocess.CalledProcessError:
extensions = _check_mopidy_extensions_user()
else:
extensions = _check_mopidy_extensions... |
class BucketStopwatchMeter(object):
def __init__(self, increment, max_length, sentences_per_batch):
self.increment = increment
self.n_buckets = ((max_length // increment) + 1)
self.sentences_per_batch = sentences_per_batch
self.reset()
def start(self):
self.start_time = t... |
class WebKitCaret(browsertab.AbstractCaret):
_widget: webview.WebView
def __init__(self, tab: 'WebKitTab', mode_manager: modeman.ModeManager, parent: QWidget=None) -> None:
super().__init__(tab, mode_manager, parent)
self._selection_state = browsertab.SelectionState.none
(usertypes.KeyMode)
... |
class TensorboardLoggerHook(LoggerHook):
def __init__(self, log_dir=None, interval=10, ignore_last=True, reset_flag=True):
super(TensorboardLoggerHook, self).__init__(interval, ignore_last, reset_flag)
self.log_dir = log_dir
def before_run(self, runner):
if ((torch.__version__ >= '1.1') ... |
def _test():
import torch
pretrained = False
models = [(shakedropresnet20_cifar10, 10), (shakedropresnet20_cifar100, 100), (shakedropresnet20_svhn, 10)]
for (model, num_classes) in models:
net = model(pretrained=pretrained)
net.eval()
weight_count = _calc_width(net)
print... |
def test_asdict_modify_dict_does_not_change_object(fake_object):
result = fake_object.asdict()
result['attr1'] = 'testing'
result['alist'].append(4)
assert (result == {'attr1': 'testing', 'alist': [1, 2, 3, 4]})
assert (fake_object.attr1 == 'foo')
assert (fake_object.alist == [1, 2, 3]) |
def _parsemeta_tmy2(columns, line):
rawmeta = ' '.join(line.split()).split(' ')
meta = rawmeta[:3]
meta.append(int(rawmeta[3]))
longitude = ((float(rawmeta[5]) + (float(rawmeta[6]) / 60)) * ((2 * (rawmeta[4] == 'N')) - 1))
latitude = ((float(rawmeta[8]) + (float(rawmeta[9]) / 60)) * ((2 * (rawmeta[7... |
def test_multiand_consistent_apply_classical():
rs = np.random.RandomState(52)
n = 5
all_cvs = rs.choice([0, 1], size=(2, n))
ctrl_strings = rs.choice([0, 1], size=(10, n))
for (cvs, ctrl_string) in itertools.product(all_cvs, ctrl_strings):
bloq = MultiAnd(cvs=cvs)
cbloq = bloq.decom... |
class Vgg16(torch.nn.Module):
def __init__(self, requires_grad=False):
super(Vgg16, self).__init__()
vgg_pretrained_features = models.vgg16(pretrained=True).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
... |
class Diffusion(LightningModule):
def __init__(self, model, channels=3, timesteps=1000, initial_lr=0.0002, training_target='x0', noise_schedule='cosine', auto_sample=False, sample_every_n_steps=1000, sample_size=(32, 32)):
super().__init__()
self.step_counter = 0
self.auto_sample = auto_samp... |
class ReahlWSGIApplication():
def from_directory(cls, directory, strict_checking=True, start_on_first_request=False):
config = StoredConfiguration(directory, strict_checking=strict_checking)
config.configure()
return cls(config, start_on_first_request=start_on_first_request)
def __init__... |
def Popen23(*args, **kwargs):
if PY3:
(yield Popen(*args, **kwargs))
return
else:
popen2 = Popen(*args, **kwargs)
try:
(yield popen2)
finally:
if popen2.stdout:
popen2.stdout.close()
if popen2.stderr:
popen2.stderr.close()
t... |
class ClassyHubInterface():
def __init__(self, task: Optional[ClassyTask]=None, model: Optional[ClassyModel]=None) -> None:
self.task = task
if (task is None):
assert (model is not None), 'Need to specify a model if task is None'
self.model = model
else:
a... |
class _cupy_convolve_2d_wrapper(object):
def __init__(self, grid, block, kernel):
if isinstance(grid, int):
grid = (grid,)
if isinstance(block, int):
block = (block,)
self.grid = grid
self.block = block
self.kernel = kernel
def __call__(self, d_inp... |
class Prev(ScrimsButton):
def __init__(self, ctx: Context, row: int=None):
super().__init__(emoji='<:double_left:>', row=row)
self.ctx = ctx
async def callback(self, interaction: discord.Interaction):
(await interaction.response.defer())
_ids = [_.pk async for _ in Scrim.filter(g... |
class FakeMonitor(object):
def __init__(self, device_to_emit):
(self._event_source, self._event_sink) = os.pipe()
self.device_to_emit = device_to_emit
self.started = False
def trigger_event(self):
os.write(self._event_sink, b'\x01')
def fileno(self):
return self._even... |
_ignore_inferred
def _infer_assignment(assignment, pymodule):
result = _follow_pyname(assignment, pymodule)
if (result is None):
return None
(pyname, pyobject) = result
pyobject = _follow_evaluations(assignment, pyname, pyobject)
if (pyobject is None):
return None
return _follow_... |
class ModuleLoadedBreakpoint():
def __init__(self, target):
breakpoint = target.BreakpointCreateByName('oe_debug_module_loaded_hook')
breakpoint.SetScriptCallbackFunction('lldb_sgx_plugin.ModuleLoadedBreakpoint.onHit')
def onHit(frame, bp_loc, dict):
library_image_addr = frame.FindValue(... |
def _set_legacy_defaults(args, cls):
if (not hasattr(cls, 'add_args')):
return
import argparse
parser = argparse.ArgumentParser(argument_default=argparse.SUPPRESS, allow_abbrev=False)
cls.add_args(parser)
defaults = argparse.Namespace()
for action in parser._actions:
if (action.d... |
def async_relational_query():
foriegn_child = reactpy_django.hooks.use_query(async_get_foriegn_child_query)
relational_parent = reactpy_django.hooks.use_query(async_get_relational_parent_query)
if ((not relational_parent.data) or (not foriegn_child.data)):
return
mtm = relational_parent.data.man... |
def test_transformer__operations__scope_remarks():
transformer = TransformerGroup(28356, 7856).transformers[0]
assert (transformer.scope is None)
assert ([op.scope for op in transformer.operations] == ['Engineering survey, topographic mapping.', 'Transformation of GDA94 coordinates that have been derived th... |
class TensorBoardLoggerTest(unittest.TestCase):
def test_log(self: TensorBoardLoggerTest) -> None:
with tempfile.TemporaryDirectory() as log_dir:
logger = TensorBoardLogger(path=log_dir)
for i in range(5):
logger.log('test_log', (float(i) ** 2), i)
logger.... |
.parametrize('x, full_matrices, compute_uv, exc', [(set_test_value(pt.dmatrix(), (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype('float64'))), True, True, None), (set_test_value(pt.dmatrix(), (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype('float64'))), False, True, None), (set_test_value(pt.lmatrix(), (lam... |
class STM32F4xxRccV3(STM32F4xxRcc):
class Type(ctypes.Structure):
_fields_ = [('CR', ctypes.c_uint32), ('PLLCFGR', ctypes.c_uint32), ('CFGR', ctypes.c_uint32), ('CIR', ctypes.c_uint32), ('AHB1RSTR', ctypes.c_uint32), ('AHB2RSTR', ctypes.c_uint32), ('AHB3RSTR', ctypes.c_uint32), ('RESERVED0', ctypes.c_uint32... |
class SecuredFunction(FunctionWrapper):
__bound_function_wrapper__ = SecuredMethod
def __init__(self, wrapped, read_check, write_check):
super().__init__(wrapped, self.check_call_wrapped)
self.check_and_setup_check(read_check)
self._self_read_check = self.read_check = read_check
... |
def get_operator(mdl: Model, auto_penalty: bool=True, default_penalty: float=100000.0) -> Tuple[(WeightedPauliOperator, float)]:
_validate_input_model(mdl)
if auto_penalty:
penalty = _auto_define_penalty(mdl, default_penalty)
else:
penalty = default_penalty
sign = 1
if mdl.is_maximiz... |
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--dist_url', type=str, default='tcp://127.0.0.1:10002')
parser.add_argument('--num_gpu', type=int, default=8, help='num of gpus for cluter training')
parser.add_ar... |
def test_unavailable_chats(api, mock_req):
mock_req({'sendMessage': {'ok': True, 'result': {}}, 'forwardMessage': {'ok': False, 'error_code': 123, 'description': 'This is a message!'}, 'sendPhoto': {'ok': False, 'error_code': 403, 'description': 'This is not the message you want!'}, 'sendAudio': {'ok': False, 'erro... |
class UnetSkipConnectionBlock(nn.Module):
def __init__(self, outer_nc, inner_nc, act, gpu_ids, input_nc=None, submodule=None, outermost=False, innermost=False, norm_layer=nn.BatchNorm2d, use_dropout=False):
super(UnetSkipConnectionBlock, self).__init__()
self.gpulist = gpu_ids
use_bias = (no... |
def tracing_v2_enabled(session_name: Optional[str]=None, *, example_id: Optional[Union[(str, UUID)]]=None, tenant_id: Optional[str]=None, session_extra: Optional[Dict[(str, Any)]]=None) -> Generator[(TracerSession, None, None)]:
warnings.warn('The experimental tracing v2 is in development. This is not yet stable an... |
def _query_sponsors(client, conference_code):
return client.query('query Sponsors($code: String!) {\n conference(code: $code) {\n sponsorsByLevel {\n level\n sponsors {\n name\n image\n ... |
class _ChildEnv():
def __init__(self, id):
(self._pipe, child_pipe) = mp.Pipe()
self._process = mp.Process(target=_child, args=(id, child_pipe))
self._process.start()
def call(self, method, *args):
self._pipe.send(('call', method, args))
def get(self, attr):
self._pip... |
class MultipleReducers(BaseReducer):
def __init__(self, reducers, default_reducer=None, **kwargs):
super().__init__(**kwargs)
self.reducers = torch.nn.ModuleDict(reducers)
self.default_reducer = (MeanReducer() if (default_reducer is None) else default_reducer)
def forward(self, loss_dict... |
class GlobalContextVit(nn.Module):
def __init__(self, in_chans: int=3, num_classes: int=1000, global_pool: str='avg', img_size: Tuple[(int, int)]=224, window_ratio: Tuple[(int, ...)]=(32, 32, 16, 32), window_size: Tuple[(int, ...)]=None, embed_dim: int=64, depths: Tuple[(int, ...)]=(3, 4, 19, 5), num_heads: Tuple[(... |
def test_laneoffset_rel():
laneoffset = OSC.RelativeLaneOffsetAction(1, 'Ego', OSC.DynamicsShapes.step, 3, False)
prettyprint(laneoffset.get_element(), None)
laneoffset2 = OSC.RelativeLaneOffsetAction(1, 'Ego', OSC.DynamicsShapes.step, 3, False)
laneoffset3 = OSC.RelativeLaneOffsetAction(1, 'Ego', OSC.D... |
class DevDataset(Dataset):
def __init__(self, args, raw_datasets, cache_root):
self.raw_datasets = raw_datasets
self.tab_processor = get_default_processor(max_cell_length=100, tokenizer=AutoTokenizer.from_pretrained(args.bert.location, use_fast=False), max_input_length=args.seq2seq.table_truncation_... |
class AbstractComparisonNodeRecorder(NumpyArrayNodeRecorder):
def __init__(self, model, node, observed, **kwargs):
super(AbstractComparisonNodeRecorder, self).__init__(model, node, **kwargs)
self.observed = observed
self._aligned_observed = None
def setup(self):
super(AbstractCom... |
class Command(BaseCommand):
def add_arguments(self, parser):
parser.add_argument('username', type=str)
parser.add_argument('org', type=str)
def handle(self, *args, **options):
try:
user = PytitionUser.objects.get(user__username=options['username'])
except PytitionUser... |
def run(video_path: str, detect_labels, video_downscale: float=1.0, architecture: str='ssdlite320', confidence_threshold: float=0.5, tracker_min_iou: float=0.25, show_detections: bool=False, track_text_verbose: int=0, device: str='cpu', viz_wait_ms: int=1):
detector = CocoObjectDetector(class_ids=get_class_ids(dete... |
def make_fake_hdf_epic(fname):
fid = h5py.File(fname, 'w')
g1 = fid.create_group('Band317nm')
g1.create_dataset('Image', shape=(100, 100), dtype=np.float32, data=b317_data)
g2 = fid.create_group('Band688nm')
g2.create_dataset('Image', shape=(100, 100), dtype=np.float32, data=b688_data)
g3 = g2.c... |
_server.route('/services/<service>/keys/<kid>', methods=['DELETE'])
def delete_service_key(service, kid):
jwt_header = request.headers.get(JWT_HEADER_NAME, '')
match = jwtutil.TOKEN_REGEX.match(jwt_header)
if (match is None):
abort(400)
encoded_jwt = match.group(1)
signer_kid = _signer_kid(e... |
class HashBucketInput(Dict):
def of(annotated_delta: DeltaAnnotated, primary_keys: List[str], num_hash_buckets: int, num_hash_groups: int, enable_profiler: Optional[bool]=False, metrics_config: Optional[MetricsConfig]=None, read_kwargs_provider: Optional[ReadKwargsProvider]=None, object_store: Optional[IObjectStore... |
def main():
global args, best_prec1
args = parser.parse_args()
if args.tensorboard:
configure(('runs/%s' % args.name))
normalize = transforms.Normalize(mean=[(x / 255.0) for x in [125.3, 123.0, 113.9]], std=[(x / 255.0) for x in [63.0, 62.1, 66.7]])
if args.augment:
transform_train =... |
.fast
def test_progress_bar(*args, **kwargs):
from time import sleep
from numpy.random import rand
from radis.misc.progress_bar import ProgressBar
print('Testing progress bar')
a = 0
r = list(range(200))
N = len(r)
pb = ProgressBar(N)
for i in r:
pb.update(i, modulo=10)
... |
class FusedScaleMaskSoftmax(torch.nn.Module):
def __init__(self, input_in_fp16, upper_triang_mask, mask_func, softmax_in_fp32, scale):
super(FusedScaleMaskSoftmax, self).__init__()
self.input_in_fp16 = input_in_fp16
self.upper_triang_mask = upper_triang_mask
self.mask_func = mask_fun... |
def plot_graph(filename, type_graph, output_filename):
(my_techniques, name, _, _) = load_techniques(filename)
graph_values = []
for t in my_techniques.values():
for item in t[type_graph]:
date = get_latest_date(item)
score = get_latest_score(item)
if (date and (s... |
class TransformerLanguageModelConfig(FairseqDataclass):
activation_fn: ChoiceEnum(utils.get_available_activation_fns()) = field(default='relu', metadata={'help': 'activation function to use'})
dropout: float = field(default=0.1, metadata={'help': 'dropout probability'})
attention_dropout: float = field(defa... |
class _SSHFormatEd25519():
def get_public(self, data: memoryview) -> tuple[(tuple, memoryview)]:
(point, data) = _get_sshstr(data)
return ((point,), data)
def load_public(self, data: memoryview) -> tuple[(ed25519.Ed25519PublicKey, memoryview)]:
((point,), data) = self.get_public(data)
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.