code stringlengths 281 23.7M |
|---|
def get_dummy_graph():
g = nx.DiGraph()
g.add_nodes_from(['kitchen', 'spoon', 'living room'])
g.add_edge('spoon', 'kitchen', type='in')
g.add_edge('kitchen', 'living room', type='connected')
g.add_edge('living room', 'kitchen', type='connected')
g.nodes['kitchen']['type'] = 'room'
g.nodes['l... |
def test_push_pull_manifest_list_again(v22_protocol, basic_images, different_images, liveserver_session, app_reloader, data_model):
credentials = ('devtable', 'password')
options = ProtocolOptions()
blobs = {}
first_manifest = v22_protocol.build_schema2(basic_images, blobs, options)
second_manifest ... |
class LinearLRScheduler(Scheduler):
def __init__(self, optimizer: torch.optim.Optimizer, t_initial: int, lr_min_rate: float, warmup_t=0, warmup_lr_init=0.0, t_in_epochs=True, noise_range_t=None, noise_pct=0.67, noise_std=1.0, noise_seed=42, initialize=True) -> None:
super().__init__(optimizer, param_group_f... |
_grad()
def convsample(model, shape, return_intermediates=True, verbose=True, make_prog_row=False):
if (not make_prog_row):
return model.p_sample_loop(None, shape, return_intermediates=return_intermediates, verbose=verbose)
else:
return model.progressive_denoising(None, shape, verbose=True) |
def _get_dataloader(data_length: int, dl2: bool, shuffle: bool, rs=None):
data_source = IterableWrapper(list(range(data_length)))
dp = data_source.sharding_filter()
if shuffle:
dp = dp.shuffle()
if dl2:
if (rs is None):
rs = DistributedReadingService()
dl = DataLoader... |
class BridgeTowerConfig(PretrainedConfig):
model_type = 'bridgetower'
def __init__(self, share_cross_modal_transformer_layers=True, hidden_act='gelu', hidden_size=768, initializer_factor=1, layer_norm_eps=1e-05, share_link_tower_layers=False, link_tower_type='add', num_attention_heads=12, num_hidden_layers=6, t... |
class Up(nn.Module):
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv = DoubleConv(in_channels, out_channels, (in_channels // 2))
else:
... |
class OpenALBuffer(OpenALObject):
_format_map = {(1, 8): al.AL_FORMAT_MONO8, (1, 16): al.AL_FORMAT_MONO16, (2, 8): al.AL_FORMAT_STEREO8, (2, 16): al.AL_FORMAT_STEREO16}
def __init__(self, al_name):
self.al_name = al_name
self.name = al_name.value
assert self.is_valid
def is_valid(sel... |
class Voxelization3D(chainer.Function):
def __init__(self, *, batch_size, pitch, origin, dimensions):
self.batch_size = batch_size
self.pitch = pitch
self.origin = origin
if (not (isinstance(dimensions, tuple) and (len(dimensions) == 3) and all((isinstance(d, int) for d in dimensions... |
def _new_root_model_state(component: ComponentType, schedule_render: Callable[([_LifeCycleStateId], None)]) -> _ModelState:
return _ModelState(parent=None, index=(- 1), key=None, model=Ref(), patch_path='', children_by_key={}, targets_by_event={}, life_cycle_state=_make_life_cycle_state(component, schedule_render)) |
def test_show_with_group_only(tester: CommandTester, poetry: Poetry, installed: Repository) -> None:
poetry.package.add_dependency(Factory.create_dependency('cachy', '^0.1.0'))
poetry.package.add_dependency(Factory.create_dependency('pendulum', '^2.0.0'))
poetry.package.add_dependency(Factory.create_depende... |
def compute_metric_for_each_image(metric_func):
def wrapper(D_ests, D_gts, masks, *nargs):
check_shape_for_metric_computation(D_ests, D_gts, masks)
bn = D_gts.shape[0]
results = []
for idx in range(bn):
cur_nargs = [(x[idx] if isinstance(x, (Tensor, Variable)) else x) for... |
class PlayServerDifficulty(Packet):
id = 13
to = 1
def __init__(self, difficulty: int, locked: bool) -> None:
super().__init__()
self.difficulty = difficulty
self.locked = locked
def encode(self) -> bytes:
return (Buffer.pack('B', self.difficulty) + Buffer.pack('?', self.... |
class AudioSampleEntry(object):
channels = 0
sample_size = 0
sample_rate = 0
bitrate = 0
codec = None
codec_description = None
def __init__(self, atom, fileobj):
(ok, data) = atom.read(fileobj)
if (not ok):
raise ASEntryError(('too short %r atom' % atom.name))
... |
def symmetric_ema(xolds, yolds, low=None, high=None, n=512, decay_steps=1.0, low_counts_threshold=1e-08):
(xs, ys1, count_ys1) = one_sided_ema(xolds, yolds, low, high, n, decay_steps, low_counts_threshold=0)
(_, ys2, count_ys2) = one_sided_ema((- xolds[::(- 1)]), yolds[::(- 1)], (- high), (- low), n, decay_step... |
class NetworkAgent(Agent):
def __init__(self, dic_agent_conf, dic_traffic_env_conf, dic_path, cnt_round, best_round=None, bar_round=None, intersection_id='0'):
super(NetworkAgent, self).__init__(dic_agent_conf, dic_traffic_env_conf, dic_path, intersection_id=intersection_id)
self.num_actions = len(d... |
class TListWrapper(TestCase):
def test_empty(self):
wrapped = list_wrapper([])
self.assertEqual(wrapped, [])
def test_empty_song(self):
wrapped = list_wrapper([{}])
self.assertTrue((len(wrapped) == 1))
self.assertFalse(isinstance(wrapped[0], dict))
def test_none(self)... |
def convert_dog(data_root):
train_lst = (data_root + '/train_list.mat')
train_txt = (data_root + '/dog_train.txt')
info = scio.loadmat(train_lst)['file_list']
name_dict = {}
index = 0
for i in info:
name = i[0][0]
cate = name.split('/')[0]
if (cate in name_dict):
... |
(auto_attribs=True, frozen=False)
class FaultLocalization(object):
faultElements: Sequence[FaultElement]
def __repr__(self) -> str:
return self.toSpecifierStr()
def toSpecifierStr(self) -> str:
rstStr = [ele.toSpecifierStr() for ele in sorted(frozenset(self.faultElements), key=(lambda x: (ty... |
def infer_Trange(events_pred, events_gt):
if (len(events_gt) == 0):
raise ValueError('The gt events should contain at least one event')
if (len(events_pred) == 0):
return infer_Trange(events_gt, events_gt)
min_pred = min([x[0] for x in events_pred])
min_gt = min([x[0] for x in events_gt]... |
class LocalVirtualSite(VirtualSite):
def __init__(self, p1: unit.Quantity, p2: unit.Quantity, p3: unit.Quantity, name: str, o_weights: List[float], x_weights: List[float], y_weights: List[float], orientations: List[Tuple[(int, ...)]]):
super().__init__(name=name, orientations=orientations)
self._p1 ... |
def insert_head_doc(docstring, head_doc=''):
if (len(head_doc) > 0):
return docstring.replace('one of the model classes of the library ', f'one of the model classes of the library (with a {head_doc} head) ')
return docstring.replace('one of the model classes of the library ', 'one of the base model clas... |
class TinyRV0Inst():
def __init__(self, bits):
self.bits = Bits32(bits)
def name(self):
if (self.bits == 19):
return 'nop'
elif (self.opcode == 51):
if (self.funct7 == 0):
if (self.funct3 == 0):
return 'add'
elif... |
def crawl_specific_attrs():
query_link = {}
log_step = 100
disease_info = {}
keys = ['', '', '', '', '', '', '']
query_list = json.load(open('./Doctor_GLM/WebCrawl/query_MSD.json'))
for (i, elem) in enumerate(tqdm(query_list)):
url = elem[0]
query = elem[1]
info = file_to... |
def check_finite_int(num_slices, num_rows):
num_slices = int(num_slices)
num_rows = int(num_rows)
if (not all(np.isfinite((num_slices, num_rows)))):
raise ValueError('num_slices and num_rows must be finite.')
if ((num_slices < 0) or (num_rows < 0)):
raise ValueError('num_slices and num_r... |
def main():
global opt, model, netContent
opt = parser.parse_args()
print(opt)
cuda = opt.cuda
if cuda:
print("=> use gpu id: '{}'".format(opt.gpus))
os.environ['CUDA_VISIBLE_DEVICES'] = opt.gpus
if (not torch.cuda.is_available()):
raise Exception('No GPU found or... |
class Effect5386(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Missile Launcher Operation')), 'kineticDamage', ship.getModifiedItemAttr('shipBonusCC2'), skill='Caldari Cruiser', **kwargs) |
class MemcachedObjectStore(IObjectStore):
CONNECT_TIMEOUT = (10 * 60)
FETCH_TIMEOUT = (30 * 60)
MAX_ITEM_SIZE_BYTES =
def __init__(self, storage_node_ips: Optional[List[str]]=None, port: Optional[int]=11212, connect_timeout: float=CONNECT_TIMEOUT, timeout: float=FETCH_TIMEOUT, noreply: bool=False, max_... |
def register(name: str, cls: Type[BaseEnv], max_episode_steps=None, default_kwargs: dict=None):
if (name in REGISTERED_ENVS):
logger.warn(f'Env {name} already registered')
if (not issubclass(cls, BaseEnv)):
raise TypeError(f'Env {name} must inherit from BaseEnv')
REGISTERED_ENVS[name] = EnvS... |
.parametrize('data', [[[], [0, 1, 2, 3, 4, 5]], [[None, None, None], [0, 1, 2, 3, 4, 5]], [[1, None, None], [1, 2, 3, 4, 5]], [[None, 4, None], [0, 1, 2, 3]], [[None, 4, 2], [0, 2]], [[3, 1, None], []]])
def test_slice(data):
(pars, expected) = data
a = Stream()
b = a.slice(*pars)
out = b.sink_to_list()... |
class ContentFormPetition(ContentFormGeneric):
title = forms.CharField(max_length=200)
publication_date = forms.DateField(required=False)
show_publication_date = SwitchField(required=False, label=_('Show publication date'))
paper_signatures = forms.IntegerField()
field_order = ('title', 'publication... |
def test_query_url_fail():
query = {'query_format': 'advanced', 'product': 'FOO'}
checkstr = 'does not appear to support'
exc = bugzilla.BugzillaError('FAKEERROR query_format', code=123)
bz = tests.mockbackend.make_bz(version='4.0.0', bug_search_args=None, bug_search_return=exc)
try:
bz.quer... |
class Effect6368(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Remote Shield Booster')), 'falloffEffectiveness', src.getModifiedItemAttr('falloffBonus'), **kwargs)
fit.modules.filteredI... |
.parametrize('manager', [MonadWideMarginsConfig], indirect=True)
def test_wide_margins(manager):
manager.test_window('one')
assert_dimensions(manager, 4, 4, 788, 588)
manager.test_window('two')
assert_focused(manager, 'two')
assert_dimensions(manager, 4, 304, 788, 288)
manager.c.layout.previous(... |
class MemCreateExpression():
R: pybamm.Parameter
model: pybamm.BaseModel
def setup(self):
set_random_seed()
def mem_create_expression(self):
self.R = pybamm.Parameter('Particle radius [m]')
D = pybamm.Parameter('Diffusion coefficient [m2.s-1]')
j = pybamm.Parameter('Inter... |
def orig_function(inputs, outputs, mode=None, accept_inplace=False, name=None, profile=None, on_unused_input=None, output_keys=None, fgraph: Optional[FunctionGraph]=None) -> Function:
if profile:
t1 = time.perf_counter()
mode = pytensor.compile.mode.get_mode(mode)
inputs = list(map(convert_function_... |
def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
if (args.options is not None):
cfg.merge_from_dict(args.options)
output_config = cfg.get('output_config', {})
output_config = merge_configs(output_config, dict(out=args.out))
eval_config = cfg.get('eval_config', {})
... |
def exec_random_walk(graphs, alias_method_j, alias_method_q, v, walk_length, amount_neighbours):
original_v = v
t0 = time()
initialLayer = 0
layer = initialLayer
path = deque()
path.append(v)
while (len(path) < walk_length):
r = random.random()
if (r < 0.3):
v = c... |
def list_logged_exceptions(log_records: list[logging.LogRecord], pattern: str='', types: (type[Any] | tuple[(type[Any], ...)])=Exception, log_level: int=logging.ERROR, del_log_records: bool=True) -> list[BaseException]:
found: list[BaseException] = []
compiled_pattern = re.compile(pattern)
for (index, recor... |
class Model(nn.Module):
def __init__(self, model_name, num_layers, input_dim, hidden_dim, output_dim, hidden_dim_multiplier, num_heads, normalization, dropout):
super().__init__()
normalization = NORMALIZATION[normalization]
self.input_linear = nn.Linear(in_features=input_dim, out_features=h... |
def BayesNet(args):
if (args.dataset == 'MNIST'):
net_name = MNIST_Net
elif ((args.dataset == 'CIFAR10') or (args.dataset == 'CIFAR100')):
net_name = CIFAR_Net
class OurNet(net_name):
def __init__(self, args):
super(OurNet, self).__init__(args)
if torch.cuda.i... |
class AttentionUNet(nn.Module):
def __init__(self, in_ch, num_classes, base_ch=32, block='SingleConv', pool=True):
super().__init__()
num_block = 2
block = get_block(block)
self.inc = inconv(in_ch, base_ch, block=block)
self.down1 = down_block(base_ch, (2 * base_ch), num_bloc... |
class CacheEvaluationListener(Listener):
def __init__(self):
smokesignal.on('evaluation_finished', self.on_evaluation_finished)
super().__init__()
def on_evaluation_finished(self, evaluation, dataset, predictor):
self.fname = _timestamped_filename(f'{dataset}-{predictor}-predictions')
... |
def fix_overpassing_lines(lines, buses, distance_crs, tol=1):
lines_to_add = []
lines_to_split = []
lines_epsgmod = lines.to_crs(distance_crs)
buses_epsgmod = buses.to_crs(distance_crs)
tqdm_kwargs_substation_ids = dict(ascii=False, unit=' lines', total=lines.shape[0], desc='Verify lines overpassing... |
def Linf_PGD(x_in, y_true, net, steps, eps):
if (eps == 0):
return x_in
training = net.training
if training:
net.eval()
x_adv = x_in.clone().requires_grad_()
optimizer = Linf_SGD([x_adv], lr=0.007)
for _ in range(steps):
optimizer.zero_grad()
net.zero_grad()
... |
class TrainOptions():
def __init__(self):
self.parser = argparse.ArgumentParser()
req = self.parser.add_argument_group('Required')
req.add_argument('--name', required=True, help='Name of the experiment')
gen = self.parser.add_argument_group('General')
gen.add_argument('--time... |
class QuadraticConstraint(Constraint):
Sense = ConstraintSense
def __init__(self, quadratic_program: Any, name: str, linear: Union[(ndarray, spmatrix, List[float], Dict[(Union[(str, int)], float)])], quadratic: Union[(ndarray, spmatrix, List[List[float]], Dict[(Tuple[(Union[(int, str)], Union[(int, str)])], flo... |
def train_detector(model, dataset, cfg, distributed=False, validate=False, timestamp=None, meta=None):
logger = get_root_logger(cfg.log_level)
dataset = (dataset if isinstance(dataset, (list, tuple)) else [dataset])
if ('imgs_per_gpu' in cfg.data):
logger.warning('"imgs_per_gpu" is deprecated in MMD... |
.parametrize('serializer', ['dask', 'pickle', 'disk'])
def test_multiple_deserializations(serializer):
data1 = bytearray(10)
proxy = proxy_object.asproxy(data1, serializers=(serializer,))
pxy = proxy._pxy_get()
data2 = proxy._pxy_deserialize()
assert (data1 == data2)
if (serializer == 'disk'):
... |
class ListenbrainzSubmission(EventPlugin):
PLUGIN_ID = 'listenbrainz'
PLUGIN_NAME = _('ListenBrainz Submission')
PLUGIN_DESC = _('Submit listens to ListenBrainz.')
PLUGIN_ICON = Icons.NETWORK_WORKGROUP
def __init__(self):
self.__enabled = False
self.queue = ListenBrainzSubmitQueue()
... |
_bpe('gpt2')
class GPT2BPE(object):
def add_args(parser):
parser.add_argument('--gpt2-encoder-json', type=str, default=DEFAULT_ENCODER_JSON, help='path to encoder.json')
parser.add_argument('--gpt2-vocab-bpe', type=str, default=DEFAULT_VOCAB_BPE, help='path to vocab.bpe')
def __init__(self, args... |
class ModuleRenamesTransformer(MigrationTransformer):
def __init__(self, *args, **kwargs):
self.from_imports = []
MigrationTransformer.__init__(self, *args, **kwargs)
def do_lint(self, original_node, module):
if (module == 'window'):
self.lint(original_node, "The 'libqtile.wi... |
(StepsRunner, 'run_step_group')
def test_run_step_groups_sequence_with_failing_fail(mock_run_step_group):
mock_run_step_group.side_effect = [None, None, ValueError('arb'), KeyError('arb failure handler err')]
with pytest.raises(ValueError) as err:
StepsRunner(get_valid_test_pipeline(), Context()).run_st... |
def get_pairs(df, merge_col=['session_id', 'wcs_user_sk'], pair_col='i_category_id', output_col_1='category_id_1', output_col_2='category_id_2'):
pair_df = df.merge(df, on=merge_col, suffixes=['_t1', '_t2'], how='inner')
pair_df = pair_df[[f'{pair_col}_t1', f'{pair_col}_t2']]
pair_df = pair_df[(pair_df[f'{p... |
class LeguHashmap(KaitaiStruct):
def __init__(self, _io, _parent=None, _root=None):
self._io = _io
self._parent = _parent
self._root = (_root if _root else self)
self._read()
def _read(self):
self.header = self._root.HeaderT(self._io, self, self._root)
self.classe... |
class PreLoadedMapStyle():
def __init__(self, dir, transform, buffer_size=100):
dataset = torchvision.datasets.ImageFolder(dir, transform=transform)
self.size = len(dataset)
self.samples = [dataset[torch.randint(0, len(dataset), size=(1,)).item()] for i in range(buffer_size)]
def __len__... |
def test_create(skip_qtbot):
widget = ChangeLogWidget({'1.0': 'Foo', '2.0': 'Bar'})
skip_qtbot.addWidget(widget)
assert (widget.select_version.count() == 2)
assert (widget.select_version.itemText(0) == '1.0')
assert (widget.select_version.itemText(1) == '2.0')
widget.select_version.setCurrentInd... |
def create_debug_lettered_tiles(**writer_kwargs):
writer_kwargs['lettered_grid'] = True
writer_kwargs['num_subtiles'] = (2, 2)
(init_kwargs, save_kwargs) = AWIPSTiledWriter.separate_init_kwargs(**writer_kwargs)
writer = AWIPSTiledWriter(**init_kwargs)
sector_id = save_kwargs['sector_id']
sector_... |
def _eval(train_pipeline: TrainPipelineSparseDist, it: Iterator[Batch]) -> Tuple[(float, float, float)]:
train_pipeline._model.eval()
device = train_pipeline._device
auroc = metrics.AUROC(compute_on_step=False).to(device)
accuracy = metrics.Accuracy(compute_on_step=False).to(device)
val_losses = []
... |
class Ffmpeg():
_RE_DURATION = re.compile(b'Duration: (\\d{2}):(\\d{2}):(\\d{2})\\.(\\d{2}),')
_RE_TIME = re.compile(b'time=(\\d{2}):(\\d{2}):(\\d{2})\\.(\\d{2})')
_RE_VERSION = re.compile(b'ffmpeg version (.+?) ')
CMD = None
priority = 0
streams = []
start_time = (0, 0)
output_filename ... |
class MockPsutil(ModuleType):
__version__ = '5.8.0'
def cpu_percent(cls):
return 2.6
def cpu_freq(cls):
class Freq():
def __init__(self):
self.current = 500.067
self.min = 400.0
self.max = 2800.0
return Freq() |
_config
def test_select_layout(manager):
layout = manager.c.layout
assert (layout.screen.info()['index'] == 0)
with pytest.raises(libqtile.command.client.SelectError, match='Item not available in object'):
layout.screen[0]
assert (layout.group.info()['name'] == 'a')
with pytest.raises(libqti... |
def test_windows_sequence(runner, path_rgb_byte_tif):
result = runner.invoke(main_group, ['blocks', path_rgb_byte_tif, '--sequence'])
assert (result.exit_code == 0)
features = tuple(map(json.loads, result.output.splitlines()))
with rasterio.open(path_rgb_byte_tif) as src:
actual_first = features... |
def count_words(filename):
counter = collections.Counter()
with open(filename, 'r') as fd:
for line in fd:
words = line.strip().split()
counter.update(words)
count_pairs = sorted(counter.items(), key=(lambda x: ((- x[1]), x[0])))
(words, counts) = list(zip(*count_pairs))
... |
class complex():
def __init__(self, x: object, y: object=None) -> None:
pass
def __add__(self, n: complex) -> complex:
pass
def __radd__(self, n: float) -> complex:
pass
def __sub__(self, n: complex) -> complex:
pass
def __rsub__(self, n: float) -> complex:
pa... |
def _timed_repartition(annotated_delta: DeltaAnnotated, destination_partition: Partition, repartition_type: RepartitionType, repartition_args: dict, max_records_per_output_file: int, enable_profiler: bool, read_kwargs_provider: Optional[ReadKwargsProvider], s3_table_writer_kwargs: Optional[Dict[(str, Any)]]=None, repar... |
_safe
def lookup_struct_class(constant_false):
if (CONST_FALSE_SIZE and constant_false and (constant_false[(- 1)] < CONST_FALSE_SIZE)):
n = CONST_FALSE_SIZE
pos = 0
for r in range(1, len(constant_false)):
pos += ncr(n, r)
r = len(constant_false)
last_idx = 0
... |
def main():
args = parse_options()
global COLOR
COLOR = (args.color and sys.stdout.isatty())
if (args.sim and (not args.commit) and (not args.diff)):
sims = find_sims(args.sim, args.ignore)
if sims:
print(('%s: %s' % (yel('Similar symbols'), ', '.join(sims))))
else:
... |
def t2_circuits(num_of_gates: Union[(List[int], np.array)], gate_time: float, qubits: List[int], n_echos: int=1, phase_alt_echo: bool=False) -> Tuple[(List[qiskit.QuantumCircuit], np.array)]:
if (n_echos < 1):
raise ValueError('Must be at least one echo')
xdata = (((2 * gate_time) * np.array(num_of_gate... |
def generate_asts_for_modules(py_modules: list[StubSource], parse_only: bool, mypy_options: MypyOptions, verbose: bool) -> None:
if (not py_modules):
return
if verbose:
print(f'Processing {len(py_modules)} files...')
if parse_only:
for mod in py_modules:
parse_source_file... |
def main():
print('Generated using setters:')
x = PrettyTable(['City name', 'Area', 'Population', 'Annual Rainfall'])
x.title = 'Australian capital cities'
x.sortby = 'Population'
x.reversesort = True
x.int_format['Area'] = '04'
x.float_format = '6.1'
x.align['City name'] = 'l'
x.add... |
def to_functional(func: Callable) -> tf.keras.Model:
def wrapper(*args, **kwargs):
model = args[0]
if isinstance(model, tf.keras.Sequential):
_logger.info('Input model is a Sequential model. Converting to Functional model.')
model = tf.keras.Model(inputs=model.inputs, outputs... |
class TrackerParams():
def set_default_values(self, default_vals: dict):
for (name, val) in default_vals.items():
if (not hasattr(self, name)):
setattr(self, name, val)
def get(self, name: str, *default):
if (len(default) > 1):
raise ValueError('Can only g... |
class SE(object):
def __init__(self, params, batcher, prepare=None):
params = utils.dotdict(params)
params.usepytorch = (True if ('usepytorch' not in params) else params.usepytorch)
params.seed = (1111 if ('seed' not in params) else params.seed)
params.batch_size = (128 if ('batch_si... |
class BaseCrownBuilder(ABC, Generic[(LeafCr, DictCr, ListCr)]):
def build_empty_crown(self, as_list: bool) -> Union[(DictCr, ListCr)]:
if as_list:
return self._make_list_crown(current_path=(), paths_with_leaves=[])
return self._make_dict_crown(current_path=(), paths_with_leaves=[])
d... |
class PickupExporter():
def __init__(self, game: RandovaniaGame) -> None:
self.game = game
def create_details(self, original_index: PickupIndex, pickup_target: PickupTarget, visual_pickup: PickupEntry, model_pickup: PickupEntry, model_style: PickupModelStyle, name: str, description: str) -> ExportedPick... |
class SegformerFeatureExtractionTester(unittest.TestCase):
def __init__(self, parent, batch_size=7, num_channels=3, min_resolution=30, max_resolution=400, do_resize=True, size=30, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5], reduce_labels=False):
self.parent = parent
sel... |
class TestYAMLFiles():
def test_filename_matches_reader_name(self):
import yaml
class IgnoreLoader(yaml.SafeLoader):
def _ignore_all_tags(self, tag_suffix, node):
return ((tag_suffix + ' ') + node.value)
IgnoreLoader.add_multi_constructor('', IgnoreLoader._ignore_... |
class CmdOOCLook(MuxAccountLookCommand):
key = 'look'
aliases = ['l', 'ls']
locks = 'cmd:all()'
help_category = 'General'
account_caller = True
def func(self):
if (_MULTISESSION_MODE < 2):
self.msg('You are out-of-character (OOC).\nUse |wic|n to get back into the game.')
... |
.slow
.pydicom
def test_metersetmap_agreement(loaded_dicom_dataset, logfile_delivery_data):
dicom_delivery_data = Delivery.from_dicom(loaded_dicom_dataset, FRACTION_GROUP)
dicom_metersetmap = dicom_delivery_data.metersetmap(grid_resolution=5)
logfile_metersetmap = logfile_delivery_data.metersetmap(grid_reso... |
_grad()
def evaluate(data_loader, model, device):
criterion = torch.nn.CrossEntropyLoss()
metric_logger = utils.MetricLogger(delimiter=' ')
header = 'Test:'
print_freq = 10
model.eval()
for (images, target) in metric_logger.log_every(data_loader, print_freq, header):
images = images.to(... |
.skipif((not HAVE_DEPS_FOR_RESOURCE_ESTIMATES), reason='pyscf and/or jax not installed.')
def test_estimate():
n = 152
lam = 3071.8
L = 275
dE = 0.001
chi = 10
res = _compute_cost(n, lam, L, dE, chi, 20000, 3, 3, 3)
assert np.isclose(res[0], 1663687)
assert np.isclose(res[1], )
asser... |
class Ansible(InstanceModule):
AnsibleException = AnsibleException
_ansible
def __call__(self, module_name, module_args=None, check=True, become=False, **kwargs):
result = self._host.backend.run_ansible(module_name, module_args, check=check, become=become, **kwargs)
if result.get('failed', F... |
class FighterInfo():
def __init__(self, itemID, amount=None, state=None, abilities=None):
self.itemID = itemID
self.amount = amount
self.state = state
self.abilities = abilities
def fromFighter(cls, fighter):
if (fighter is None):
return None
info = cl... |
class Criterion(torch.nn.Module):
def __init__(self, opt):
super(Criterion, self).__init__()
self.par = opt
self.angular_margin = opt.loss_arcface_angular_margin
self.feature_scale = opt.loss_arcface_feature_scale
self.class_map = torch.nn.Parameter(torch.Tensor(opt.n_classes... |
def upload_files_to_zenodo(filepaths, title, author=None, use_sandbox=False, record_name=None):
filepaths = [pathlib.Path(filepath) for filepath in filepaths]
root_depositions_url = get_root_depositions_url(use_sandbox)
if (record_name is not None):
if use_sandbox:
raise ValueError('Cann... |
def gpubdb_argparser():
args = get_gpubdb_argparser_commandline_args()
with open(args['config_file']) as fp:
args = yaml.safe_load(fp.read())
args = add_empty_config(args)
KEYS_TO_ENV_VAR_MAPPING = {'data_dir': os.environ.get('DATA_DIRECTORY'), 'output_dir': os.environ.get('OUTPUT_DIRECTORY', '.... |
def js_bridge(window):
window.load_html('<html><body>TEST</body></html>')
assert_js(window, 'get_int', 420)
assert_js(window, 'get_float', 3.141)
assert_js(window, 'get_string', 'test')
assert_js(window, 'get_object', {'key1': 'value', 'key2': 420})
assert_js(window, 'get_objectlike_string', '{"... |
class HouseholderInverseMultiplier(nn.Module):
def __init__(self, group, dim, learnable):
super(HouseholderInverseMultiplier, self).__init__()
self.group = group
self.dim = dim
H_group = self.constructH(group)
self.H_inv = nn.Parameter(H_group.t().repeat((dim // group), 1, 1)... |
class VQLPIPSWithDiscriminator(nn.Module):
def __init__(self, disc_start, codebook_weight=1.0, pixelloss_weight=1.0, disc_num_layers=3, disc_in_channels=3, disc_factor=1.0, disc_weight=1.0, perceptual_weight=1.0, use_actnorm=False, disc_conditional=False, disc_ndf=64, disc_loss='hinge'):
super().__init__()
... |
def sync_execute_write_reqs(write_reqs: List[WriteReq], storage: StoragePlugin, memory_budget_bytes: int, rank: int, event_loop: asyncio.AbstractEventLoop) -> PendingIOWork:
return event_loop.run_until_complete(execute_write_reqs(write_reqs=write_reqs, storage=storage, memory_budget_bytes=memory_budget_bytes, rank=... |
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('--dataset_config_names', nargs='... |
class RW():
def __init__(self, addr, imagefd, logger, seek_lock):
self.addr = addr
self.seek_lock = seek_lock
self.imagefd = imagefd
self.logger = helpers.get_child_logger(logger, 'FS')
self.logger.debug('File for {0}'.format(addr))
def read(self, offset, length):
... |
class Block(nn.Module):
def __init__(self, inplanes, planes, stride=1, dilation=1, start_with_relu=True, norm_layer=None, norm_kwargs=None):
super(Block, self).__init__()
norm_kwargs = (norm_kwargs if (norm_kwargs is not None) else {})
if isinstance(planes, (list, tuple)):
assert... |
def single_order():
order = {'orderNo': 'E4CACBXXXXX528384A20C930', 'orderCost': '127.19', 'quantity': '2', 'status': 'success', 'paidBy': 'online', 'paidTo': None, 'refundAmount': None, 'purchaseDate': '2013-11-13', 'name': 'Pankaj Kumar', 'email': '', 'city': 'Pune', 'state': 'Maharashtra', 'country': 'India', 'a... |
class FusedBiasLeakyReLU(nn.Module):
def __init__(self, num_channels, negative_slope=0.2, scale=(2 ** 0.5)):
super(FusedBiasLeakyReLU, self).__init__()
self.bias = nn.Parameter(torch.zeros(num_channels))
self.negative_slope = negative_slope
self.scale = scale
def forward(self, in... |
def iam_group(var):
(yield block('variable', 'name', {}))
(yield block('variable', 'path', {'default': '/'}))
group = (yield block('resource', 'aws_iam_group', var.name, {'name': var.name}))
(yield block('output', 'name', {'value': var.name}))
(yield block('output', 'resource', {'value': group})) |
class CsvWriterTest(Csv, WriterTest, TestCase):
()
def test_fields(self, context):
context.set_input_fields(['foo', 'bar'])
context.write_sync(('a', 'b'), ('c', 'd'))
context.stop()
assert (self.readlines() == ('foo,bar', 'a,b', 'c,d'))
(skip_header=False)
def test_fields... |
class ResidualBlock(nn.Module):
def __init__(self, in_channel, out_channel, stride=1):
super(ResidualBlock, self).__init__()
self.in_channel = in_channel
self.out_channel = out_channel
self.stride = stride
self.res_bottleneck = nn.Sequential(nn.BatchNorm2d(in_channel), nn.ReL... |
class AdvertiserViewSet(viewsets.ReadOnlyModelViewSet):
serializer_class = AdvertiserSerializer
lookup_field = 'slug'
def get_queryset(self):
if self.request.user.is_staff:
return Advertiser.objects.all()
return self.request.user.advertisers.all()
(detail=True, methods=['get'... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.