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class Trio_Asyncio_Wrapper():
def __init__(self, proc, loop=None):
self.proc = proc
self._loop = loop
def loop(self):
loop = self._loop
if (loop is None):
loop = current_loop.get()
return loop
def __get__(self, obj, cls):
if (obj is None):
... |
def test_transaction_rollback(tmp_path):
filename = f'v{RAIDEN_DB_VERSION}_log.db'
db_path = Path((tmp_path / filename))
storage = SQLiteStorage(db_path)
storage.update_version()
assert (storage.get_version() == RAIDEN_DB_VERSION)
with pytest.raises(RuntimeError):
with storage.transactio... |
class EpochBatchIterating(object):
def __len__(self) -> int:
raise NotImplementedError
def next_epoch_idx(self):
raise NotImplementedError
def next_epoch_itr(self, shuffle=True, pin_memory=False):
raise NotImplementedError
def end_of_epoch(self) -> bool:
raise NotImplemen... |
def find_speaker_f0_median_std(speaker_utt_path, fs, window, hop, voiced_prob_cutoff):
frame_len_samples = int((fs * window))
hop_len_samples = int((fs * hop))
utterance_files = get_speaker_utterance_paths(speaker_utt_path)
k = min(50, len(utterance_files))
utterance_files = random.sample(utterance_... |
def load_matrix(embedding_file_path, word_dict, word_embedding_dim):
embedding_matrix = np.random.uniform(size=((len(word_dict) + 1), word_embedding_dim))
have_word = []
if (embedding_file_path is not None):
with open(embedding_file_path, 'rb') as f:
while True:
line = f.... |
_cache(maxsize=512)
def parse_git_url(url: str) -> ParsedGitUrl:
log.debug('Parsing git url %r', url)
normalizers = [('^(\\w+)', 'ssh://\\1'), ('^git\\+ssh://', 'ssh://'), ('(ssh://(?:\\w+)?[\\w.]+):(?!\\d{1,5}/\\w+/)(.*)$', '\\1/\\2'), ('^([C-Z]:/)|^/(\\w)', 'file:///\\1\\2')]
for (pattern, replacement) in... |
def gen_random_test():
data = []
for i in range(128):
data.append(random.randint(0, ))
asm_code = []
for i in range(50):
a = random.randint(0, 127)
b = random.randint(0, 127)
base = Bits32((8192 + (4 * b)))
offset = Bits16((4 * (a - b)))
result = data[a]
... |
def test_load_rsa_nist_vectors():
vector_data = textwrap.dedent('\n # CAVS 11.4\n # "SigGen PKCS#1 RSASSA-PSS" information\n # Mod sizes selected: 1024 1536 2048 3072 4096\n # SHA Algorithm selected:SHA1 SHA224 SHA256 SHA384 SHA512\n # Salt len: 20\n\n [mod = 1024]\n\n n = bcb47b2e0dafcba81ff2a... |
class SpreadSheetDelegate(QItemDelegate):
def __init__(self, parent=None):
super(SpreadSheetDelegate, self).__init__(parent)
def createEditor(self, parent, styleOption, index):
if (index.column() == 1):
editor = QDateTimeEdit(parent)
editor.setDisplayFormat(self.parent().... |
def evaluate(args):
model.eval()
losses = []
for (step, batch) in enumerate(eval_dataloader):
with torch.no_grad():
outputs = model(batch, labels=batch)
loss = outputs.loss.repeat(args.valid_batch_size)
losses.append(accelerator.gather(loss))
if ((args.max_eval_st... |
def get_alt_az(utc_time, lon, lat):
lon = np.deg2rad(lon)
lat = np.deg2rad(lat)
(ra_, dec) = sun_ra_dec(utc_time)
h__ = _local_hour_angle(utc_time, lon, ra_)
return (np.arcsin(((np.sin(lat) * np.sin(dec)) + ((np.cos(lat) * np.cos(dec)) * np.cos(h__)))), np.arctan2((- np.sin(h__)), ((np.cos(lat) * np... |
class Fates(enum.Enum):
FALSE_POSITIVE = 0
INITIALIZE = 1
TERMINATE = 2
LINK = 3
DIVIDE = 4
APOPTOSIS = 5
MERGE = 6
EXTRUDE = 7
INITIALIZE_BORDER = 10
INITIALIZE_FRONT = 11
INITIALIZE_LAZY = 12
TERMINATE_BORDER = 20
TERMINATE_BACK = 21
TERMINATE_LAZY = 22
DEAD... |
def get_nonlinearity_layer(activation_type='PReLU'):
if (activation_type == 'ReLU'):
nonlinearity_layer = nn.ReLU()
elif (activation_type == 'SELU'):
nonlinearity_layer = nn.SELU()
elif (activation_type == 'LeakyReLU'):
nonlinearity_layer = nn.LeakyReLU(0.1)
elif (activation_type... |
def plot_fig(args, test_img, recon_imgs, scores, gts, threshold, save_dir):
num = len(scores)
vmax = (scores.max() * 255.0)
vmin = (scores.min() * 255.0)
for i in range(num):
img = test_img[i]
img = denormalization(img)
recon_img = recon_imgs[i]
recon_img = denormalizatio... |
def set_random_seed(seed, deterministic=False, use_rank_shift=False):
if use_rank_shift:
(rank, _) = mmcv.runner.get_dist_info()
seed += rank
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
os.enviro... |
def get_num_bond_types(mol):
bonds = mol.GetBonds()
num_bonds = 0
num_double = 0
num_triple = 0
num_single = 0
num_aromatic = 0
for b in bonds:
num_bonds += 1
if (b.GetBondType() == rdkit.Chem.rdchem.BondType.SINGLE):
num_single += 1
if (b.GetBondType() ==... |
def test_update_catalog_error(db):
catalog = Catalog.objects.first()
catalog.locked = True
catalog.save()
section = Section.objects.exclude(catalogs=catalog).first()
with pytest.raises(ValidationError):
SectionLockedValidator(section)({'catalogs': [catalog], 'locked': False}) |
class IpAddressMiddleware():
PROVIDER_NAME = 'REMOTE_ADDR header'
def __init__(self, get_response):
self.get_response = get_response
def __call__(self, request):
request.ip_address = self.get_ip_address(request)
response = self.get_response(request)
if (settings.DEBUG or requ... |
def test_mix_resize():
allocator = RegionAllocator(1)
regions = []
for i in range(10):
regions.append(allocator.force_alloc(3))
for region in regions[:5]:
allocator.force_realloc(region, 8)
for i in range(10):
regions.append(allocator.force_alloc((i + 1)))
for region in r... |
class TestCallableGuards(TestNameCheckVisitorBase):
_passes()
def test_callable(self):
from pyanalyze.signature import ANY_SIGNATURE
def capybara(o: object) -> None:
assert_is_value(o, TypedValue(object))
if callable(o):
assert_is_value(o, CallableValue(AN... |
class ImageFilelist(data.Dataset):
def __init__(self, root, flist, transform=None, target_transform=None, flist_reader=default_flist_reader, loader=default_loader):
self.root = root
self.imlist = flist_reader(flist)
self.transform = transform
self.target_transform = target_transform
... |
def test_quantizable_mha_with_mask():
B = 5
T = 8
S = 4
q_inputs = keras.Input(shape=(T, 16))
v_inputs = keras.Input(shape=(S, 16))
k_inputs = keras.Input(shape=(S, 16))
m_inputs = keras.Input(shape=(T, S))
model_output = keras.layers.MultiHeadAttention(key_dim=2, num_heads=2)(q_inputs, ... |
class TestValidationCountingAggregator(unittest.TestCase):
def test_aggregate_scenes(self) -> None:
agg = validators.ValidationCountingAggregator()
mock_validator_output = mock.Mock()
is_valid_scene = mock.PropertyMock(return_value=False)
type(mock_validator_output).is_valid_scene = ... |
class DisabledQuerySet(models.QuerySet):
def __init__(self, *args, **kwargs):
self.missing_scopes = kwargs.pop('missing_scopes', None)
super().__init__(*args, **kwargs)
def error(self, *args, **kwargs):
raise ScopeError('A scope on dimension(s) {} needs to be active for this query.'.form... |
class TestMonteCarloGFormula():
def test_error_continuous_treatment(self, sim_t_fixed_data):
with pytest.raises(ValueError):
MonteCarloGFormula(sim_t_fixed_data, idvar='id', exposure='W1', outcome='Y', time_out='t', time_in='t0')
def test_error_continuous_outcome(self, sim_t_fixed_data):
... |
(scope='session')
def special_char_name():
base = 'e-$ e-j'
encoding = ('ascii' if IS_WIN else sys.getfilesystemencoding())
result = ''
for char in base:
try:
trip = char.encode(encoding, errors='strict').decode(encoding)
if (char == trip):
result += char
... |
def rolling_volatility(qf_series: QFSeries, frequency: Frequency=None, annualise: bool=True, window_size: int=None) -> QFSeries:
returns_tms = qf_series.to_log_returns()
if annualise:
assert (frequency is not None)
volatility_values = []
for i in range((window_size - 1), len(returns_tms)):
... |
class CausalLMOutputWithCrossAttentions(ModelOutput):
loss: Optional[torch.FloatTensor] = None
logits: torch.FloatTensor = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
hidden_states: Optional[Tuple[torch.FloatTensor]] = None
attentions: Optional[Tuple[torch.FloatTensor]] = ... |
def init_network_weights(model, init_type='normal', gain=0.02):
def _init_func(m):
classname = m.__class__.__name__
if (hasattr(m, 'weight') and ((classname.find('Conv') != (- 1)) or (classname.find('Linear') != (- 1)))):
if (init_type == 'normal'):
nn.init.normal_(m.weig... |
class OP_RunSynthesis(bpy.types.Operator):
bl_idname = 'genmm.run_synthesis'
bl_label = 'Run synthesis'
bl_description = ''
bl_options = {'REGISTER', 'UNDO'}
def __init__(self) -> None:
super().__init__()
def execute(self, context: bpy.types.Context):
setting = context.scene.sett... |
def patch_plots(function):
from functools import wraps
(function)
def decorated(*args, **kwargs):
with patch('matplotlib.pyplot.show', (lambda *x, **y: None)):
import matplotlib
matplotlib.use('Agg')
return function(*args, **kwargs)
return decorated |
class DistributedTrainingParams(FairseqDataclass):
distributed_world_size: int = field(default=max(1, torch.cuda.device_count()), metadata={'help': 'total number of GPUs across all nodes (default: all visible GPUs)'})
distributed_rank: Optional[int] = field(default=0, metadata={'help': 'rank of the current work... |
def test_substrate_presence_profile():
wavelength = (np.linspace(300, 800, 3) * 1e-09)
GaAs = material('GaAs')(T=300)
my_structure = SolarCell([Layer(si(700, 'nm'), material=GaAs)], substrate=GaAs)
solar_cell_solver(my_structure, 'optics', user_options={'wavelength': wavelength, 'optics_method': 'TMM', ... |
def test_xdg_vars_set_single(freebsd, xdg_env_single):
pp = platform.get_platform_paths('pypyr', 'config.yaml')
assert (pp == platform.PlatformPaths(config_user=Path('/ch//pypyr/config.yaml'), config_common=[Path('/cc/pypyr/config.yaml')], data_dir_user=Path('/dh/pypyr'), data_dir_common=[Path('/dc/pypyr')])) |
def write_results(filename: str, insts):
f = open(filename, 'w', encoding='utf-8')
for inst in insts:
for i in range(len(inst.input)):
words = inst.input.words
tags = inst.input.pos_tags
heads = inst.input.heads
dep_labels = inst.input.dep_labels
... |
class TestWeightFi():
def setup_class(self):
torch.manual_seed(0)
batch_size = 1
workers = 1
channels = 3
img_size = 32
use_gpu = False
(self.model, self.dataset) = CIFAR10_set_up_custom(batch_size, workers)
dataiter = iter(self.dataset)
(self.... |
class TPUDistributedDataParallel(nn.Module):
def __init__(self, module, process_group):
super().__init__()
self.module = module
self.process_group = process_group
self.world_size = distributed_utils.get_world_size(self.process_group)
def forward(self, *inputs, **kwargs):
... |
class Effect5132(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Medium Projectile Turret')), 'falloff', ship.getModifiedItemAttr('shipBonusMC2'), skill='Minmatar Cruiser', **kwargs) |
class TestURIReferenceComparesToURIReferences():
def test_same_basic_uri(self, basic_uri):
uri = URIReference.from_string(basic_uri)
assert (uri == uri)
def test_different_basic_uris(self, basic_uri, basic_uri_with_port):
uri = URIReference.from_string(basic_uri)
assert ((uri == ... |
class BirthdayParty(QObject):
def __init__(self, parent=None):
super(BirthdayParty, self).__init__(parent)
self._host = None
self._guests = []
(Person)
def host(self):
return self._host
def host(self, host):
self._host = host
(QQmlListProperty)
def guests(... |
def test_direct_junction_offsets_pre_suc_3_left(direct_junction_left_lane_fixture):
(main_road, small_road, junction_creator) = direct_junction_left_lane_fixture
main_road.add_predecessor(xodr.ElementType.junction, junction_creator.id)
small_road.add_successor(xodr.ElementType.junction, junction_creator.id)... |
class ProjectUpdateViewsView(ObjectPermissionMixin, RedirectViewMixin, UpdateView):
model = Project
queryset = Project.objects.all()
form_class = ProjectUpdateViewsForm
permission_required = 'projects.change_project_object'
def get_form_kwargs(self):
views = View.objects.filter_current_site(... |
def init_client(client: QdrantBase, records: List[models.Record], collection_name: str=COLLECTION_NAME, vectors_config: Optional[Union[(Dict[(str, models.VectorParams)], models.VectorParams)]]=None, sparse_vectors_config: Optional[Dict[(str, models.SparseVectorParams)]]=None) -> None:
initialize_fixture_collection(... |
class StoreKeyValuePairAction(argparse.Action):
def __init__(self, option_strings, dest, nargs=None, const=None, default=None, type=None, choices=None, required=False, help=None, metavar=None):
if (type not in (None, str)):
raise ValueError('type for StoreKeyValuePairAction must be str')
... |
class Zoom(object):
def __init__(self, value, interp='bilinear', lazy=False):
if (not isinstance(value, (tuple, list))):
value = (value, value)
self.value = value
self.interp = interp
self.lazy = lazy
def __call__(self, *inputs):
if (not isinstance(self.interp... |
def init_QKV_forward_buffer():
args = get_args()
batch_pp = (args.batch_size // args.summa_dim)
seq_length = args.seq_length
hidden_pp = (args.hidden_size // args.summa_dim)
global _QKV_FORWARD_BUFFER
assert (_QKV_FORWARD_BUFFER is None), '_QKV_FORWARD_BUFFER is already initialized'
space = ... |
def _search_noise_pauses(levels, tsc):
pauses = list()
possible_start = None
for i in range(2, (len(levels) - 2)):
if (((levels[i] - levels[(i - 1)]) >= tsc) and ((levels[(i - 1)] - levels[(i - 2)]) < tsc)):
possible_start = i
if (((levels[i] - levels[(i + 1)]) >= tsc) and ((leve... |
class SetComprehension(Expression):
__slots__ = ('generator',)
__match_args__ = ('generator',)
generator: GeneratorExpr
def __init__(self, generator: GeneratorExpr) -> None:
super().__init__()
self.generator = generator
def accept(self, visitor: ExpressionVisitor[T]) -> T:
re... |
_module()
class GCHead(FCNHead):
def __init__(self, ratio=(1 / 4.0), pooling_type='att', fusion_types=('channel_add',), **kwargs):
super(GCHead, self).__init__(num_convs=2, **kwargs)
self.ratio = ratio
self.pooling_type = pooling_type
self.fusion_types = fusion_types
self.gc_... |
class Effect4809(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'ECM')), 'scanGravimetricStrengthBonus', module.getModifiedItemAttr('ecmStrengthBonusPercent'), stackingPenalties=True, **kwargs... |
class FeatureGraphNet(nn.Module):
def __init__(self, model, out_indices, out_map=None):
super().__init__()
assert has_fx_feature_extraction, 'Please update to PyTorch 1.10+, torchvision 0.11+ for FX feature extraction'
self.feature_info = _get_feature_info(model, out_indices)
if (out... |
def nice_value(x):
if (x == 0.0):
return 0.0
exp = 1.0
sign = 1
if (x < 0.0):
x = (- x)
sign = (- 1)
while (x >= 1.0):
x /= 10.0
exp *= 10.0
while (x < 0.1):
x *= 10.0
exp /= 10.0
if (x >= 0.75):
return ((sign * 1.0) * exp)
... |
def make_tcl_script_vis_annot(subject_id, hemi, out_vis_dir, annot_file='aparc.annot'):
script_file = (out_vis_dir / f'vis_annot_{hemi}.tcl')
vis = dict()
for view in cfg.tksurfer_surface_vis_angles:
vis[view] = (out_vis_dir / f'{subject_id}_{hemi}_{view}.tif')
img_format = 'tiff'
cmds = lis... |
def residual_bottleneck(feature_dim=256, num_blocks=1, l2norm=True, final_conv=False, norm_scale=1.0, out_dim=None, interp_cat=False, final_relu=False, final_pool=False):
if (out_dim is None):
out_dim = feature_dim
feat_layers = []
if interp_cat:
feat_layers.append(InterpCat())
for i in ... |
class RegexLexerMeta(LexerMeta):
def _process_regex(cls, regex, rflags, state):
if isinstance(regex, Future):
regex = regex.get()
return re.compile(regex, rflags).match
def _process_token(cls, token):
assert ((type(token) is _TokenType) or callable(token)), ('token type must ... |
def get_protocol_member(left: Instance, member: str, class_obj: bool) -> (ProperType | None):
if ((member == '__call__') and class_obj):
from mypy.checkmember import type_object_type
def named_type(fullname: str) -> Instance:
return Instance(left.type.mro[(- 1)], [])
return type_... |
def load_lvis_v1_json(json_file, image_root, dataset_name=None):
from lvis import LVIS
json_file = PathManager.get_local_path(json_file)
timer = Timer()
lvis_api = LVIS(json_file)
if (timer.seconds() > 1):
logger.info('Loading {} takes {:.2f} seconds.'.format(json_file, timer.seconds()))
... |
class TwoStepParameters6(TwoStepParametersCommon):
zka_id = DataElementField(type='an', max_length=32, _d='ZKA TAN-Verfahren')
zka_version = DataElementField(type='an', max_length=10, _d='Version ZKA TAN-Verfahren')
name = DataElementField(type='an', max_length=30, _d='Name des Zwei-Schritt-Verfahrens')
... |
def create_pytensor_params(dist_params, obs, size):
dist_params_at = []
for p in dist_params:
p_aet = pt.as_tensor(p).type()
p_aet.tag.test_value = p
dist_params_at.append(p_aet)
size_at = []
for s in size:
s_aet = pt.iscalar()
s_aet.tag.test_value = s
siz... |
class SplAtConv2d(Module):
def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2, reduction_factor=4, rectify=False, rectify_avg=False, norm_layer=None, dropblock_prob=0.0, **kwargs):
super(SplAtConv2d, self).__init__()
pa... |
class TestTupleEqual(TestCase):
def test_simple(self):
assert (100 == klm)
assert (456 == (aaa and bbb))
assert (789 == (ccc or ddd))
assert (123 == (True if You else False))
def test_simple_msg(self):
assert (klm == 100), 'This is wrong!'
def test_simple_msg2(self):
... |
def inception_v1(inputs, num_classes=1000, is_training=True, dropout_keep_prob=0.8, prediction_fn=slim.softmax, spatial_squeeze=True, reuse=None, scope='InceptionV1'):
with tf.variable_scope(scope, 'InceptionV1', [inputs, num_classes], reuse=reuse) as scope:
with slim.arg_scope([slim.batch_norm, slim.dropou... |
class BackgroundDataset(Dataset):
def __getitem__(self, index):
texture_img_path = self.data[index]
texture_img = cv2.imread(texture_img_path)
texture_img = cv2.cvtColor(texture_img, cv2.COLOR_BGR2RGB)
texture_img = self.resize(texture_img)
if self.random:
texture... |
class Migration(migrations.Migration):
dependencies = [('typeclasses', '0010_delete_old_player_tables')]
operations = [migrations.DeleteModel(name='DefaultAccount'), migrations.DeleteModel(name='DefaultCharacter'), migrations.DeleteModel(name='DefaultExit'), migrations.DeleteModel(name='DefaultGuest'), migratio... |
def generate_and_save(env_name, traj_len, cache_size=100000, qpos_only=False, qpos_qvel=False, delta=True, whiten=True, pixels=False, source_img_width=64):
dataset = GymData(env_name, traj_len, cache_size, qpos_only, qpos_qvel, delta, whiten, pixels, source_img_width)
print('Generating dataset to save.')
fo... |
class LinePrecisionReporter(AbstractReporter):
def __init__(self, reports: Reports, output_dir: str) -> None:
super().__init__(reports, output_dir)
self.files: list[FileInfo] = []
def on_file(self, tree: MypyFile, modules: dict[(str, MypyFile)], type_map: dict[(Expression, Type)], options: Optio... |
class PersianSecondDirective(PersianNumberDirective):
def format(self, d):
return super(PersianSecondDirective, self).format(d.second)
def post_parser(self, ctx, formatter):
super(PersianSecondDirective, self).post_parser(ctx, formatter)
if ((self.name in ctx) and ctx[self.name]):
... |
def parse_unit_patterns(data, tree):
unit_patterns = data.setdefault('unit_patterns', {})
compound_patterns = data.setdefault('compound_unit_patterns', {})
unit_display_names = data.setdefault('unit_display_names', {})
for elem in tree.findall('.//units/unitLength'):
unit_length_type = elem.attr... |
def main(options, arguments):
devices = ['/gpu:0', '/gpu:1']
if (options.device == None):
device = devices[0]
else:
device = devices[int(options.device)]
if (options.distance == None):
distance = 8
else:
distance = int(options.distance)
global DISTANCE_THRESHOLD
... |
class BaseResampler():
def __init__(self, source_geo_def: Union[(SwathDefinition, AreaDefinition)], target_geo_def: Union[(CoordinateDefinition, AreaDefinition)]):
self.source_geo_def = source_geo_def
self.target_geo_def = target_geo_def
def get_hash(self, source_geo_def=None, target_geo_def=Non... |
class RedisOrchestrator(Orchestrator):
def __init__(self, host='127.0.0.1', port=6379, password=None, db=0, cert_and_key=None, ca_cert=None, ssl=False, skip_keyspace_event_setup=False, canceller_only=False, **kwargs):
self.is_canceller_only = canceller_only
(cert, key) = (tuple(cert_and_key) if (cer... |
class PerfTimer():
def __init__(self, timer_name: str, perf_stats: Optional['PerfStats']):
self.skip: bool = False
if (perf_stats is None):
self.skip = True
return
self.name: str = timer_name
self.elapsed: float = 0.0
self._last_interval: float = 0.0
... |
class RTLIRGetter():
ifc_primitive_types = (dsl.InPort, dsl.OutPort, dsl.Interface)
def __init__(self, cache=True):
if cache:
self._rtlir_cache = {}
self.get_rtlir = self._get_rtlir_cached
else:
self.get_rtlir = self._get_rtlir_uncached
self._RTLIR_ifc... |
class ExecAccuracyEvaluationResult(evaluate.EvaluationResult):
def __init__(self, prompts, scores):
self.prompts = prompts
self.scores = scores
def _agg_scores(self, method):
if (method == 'mean'):
return [np.mean(s) for s in self.scores]
elif (method == 'median'):
... |
class GaussianDiffusion():
def __init__(self, *, betas, model_mean_type, model_var_type, loss_type, rescale_timesteps=False):
self.model_mean_type = model_mean_type
self.model_var_type = model_var_type
self.loss_type = loss_type
self.rescale_timesteps = rescale_timesteps
beta... |
class RepeatListForever(Repeat):
name = 'repeat_all'
display_name = _('Repeat all')
accelerated_name = _('Repeat _all')
def next(self, playlist, iter):
next = self.wrapped.next(playlist, iter)
if next:
return next
self.wrapped.reset(playlist)
print_d('Restarti... |
class SyncPerformerTests(TestCase):
def test_success(self):
_performer
def succeed(dispatcher, intent):
return intent
dispatcher = (lambda _: succeed)
result = sync_perform(dispatcher, Effect('foo'))
self.assertEqual(result, 'foo')
def test_failure(self):
... |
_REGISTRY.register()
def build_mit_backbone(cfg, input_shape):
if (cfg.MODEL.MIT_BACKBONE.NAME == 'b0'):
return mit_b0()
elif (cfg.MODEL.MIT_BACKBONE.NAME == 'b1'):
return mit_b1()
elif (cfg.MODEL.MIT_BACKBONE.NAME == 'b2'):
return mit_b2()
elif (cfg.MODEL.MIT_BACKBONE.NAME == 'b... |
def test_no_linecache():
class A():
a: int
c = Converter()
before = len(linecache.cache)
c.structure(c.unstructure(A(1)), A)
after = len(linecache.cache)
assert (after == (before + 2))
class B():
a: int
before = len(linecache.cache)
c.register_structure_hook(B, make_d... |
def test_delete_cur_item_no_func():
callback = mock.Mock(spec=[])
model = completionmodel.CompletionModel()
cat = listcategory.ListCategory('', [('foo', 'bar')], delete_func=None)
model.rowsAboutToBeRemoved.connect(callback)
model.rowsRemoved.connect(callback)
model.add_category(cat)
parent ... |
class dense121(torch.nn.Module):
def __init__(self, requires_grad=False):
super(dense121, self).__init__()
dense_pretrained_features = light_estimation.module.dense
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.slice3 = torch.nn.Sequential()
... |
.skipif(utils.is_windows, reason="current CPython/win can't recover from SIGSEGV")
def test_debug_crash_segfault():
caught = False
def _handler(num, frame):
nonlocal caught
caught = (num == signal.SIGSEGV)
with _trapped_segv(_handler):
with pytest.raises(Exception, match='Segfault fa... |
def get_arch(filename: Union[(str, Path)]) -> List[ArchitectureType]:
this_platform = sys.platform
if this_platform.startswith('win'):
machine_type = get_shared_library_arch(filename)
if (machine_type == PEMachineType.I386):
return [ArchitectureType.I386]
elif (machine_type =... |
def plot(*args, **kargs):
mkQApp()
pwArgList = ['title', 'labels', 'name', 'left', 'right', 'top', 'bottom', 'background']
pwArgs = {}
dataArgs = {}
for k in kargs:
if (k in pwArgList):
pwArgs[k] = kargs[k]
else:
dataArgs[k] = kargs[k]
windowTitle = pwArgs... |
def main(data_dir, client, bc, config):
benchmark(read_tables, data_dir, bc, dask_profile=config['dask_profile'])
query_date = f'''
select min(d_date_sk) as min_d_date_sk,
max(d_date_sk) as max_d_date_sk
from date_dim
where d_year = {q17_year}
and d_moy = {q17_month}
... |
def InceptionV3Body(net, from_layer, output_pred=False):
use_scale = False
out_layer = 'conv'
ConvBNLayer(net, from_layer, out_layer, use_bn=True, use_relu=True, num_output=32, kernel_size=3, pad=0, stride=2, use_scale=use_scale)
from_layer = out_layer
out_layer = 'conv_1'
ConvBNLayer(net, from_... |
def EnsureNonBlockingDataPipe(validated_datapipe):
if (not isinstance(validated_datapipe, IterDataPipe)):
raise Exception(('Not Iterable DataPipe ' + str(validated_datapipe.__class__)))
if isinstance(validated_datapipe, NonBlocking):
return validated_datapipe
if (not hasattr(validated_datapi... |
_kernel_api(params={'handle': UINT})
def hook__sflt_unregister(ql, address, params):
handle = str(params['handle']).encode()
evs = ql.os.ev_manager.get_events_by_name(b'', keyword=handle)
for found in evs:
ql.os.ev_manager.emit(found.name, MacOSEventType.EV_SFLT_UNREGISTERED, [params['handle']])
... |
def test_run_all(mock_pipe):
out = run(pipeline_name='arb pipe', args_in='arb context input', parse_args=True, dict_in={'a': 'b'}, groups=['g'], success_group='sg', failure_group='fg', loader='arb loader', py_dir='arb/dir')
assert (type(out) is Context)
assert (out == Context({'a': 'b'}))
assert (not ou... |
def configure_stabilization_augs(img, init_image_pil, params, loss_augs):
stabilization_augs = ['direct_stabilization_weight', 'depth_stabilization_weight', 'edge_stabilization_weight']
stabilization_augs = [build_loss(x, params[x], 'stabilization', img, init_image_pil) for x in stabilization_augs if (params[x]... |
class StopOrder(ExecutionStyle):
def __init__(self, stop_price, asset=None, exchange=None):
check_stoplimit_prices(stop_price, 'stop')
self.stop_price = stop_price
self._exchange = exchange
self.asset = asset
def get_limit_price(self, _is_buy):
return None
def get_sto... |
def tw_mock():
class TWMock():
WRITE = object()
def __init__(self):
self.lines = []
self.is_writing = False
def sep(self, sep, line=None):
self.lines.append((sep, line))
def write(self, msg, **kw):
self.lines.append((TWMock.WRITE, msg))... |
(scope='module')
def inline_query_result_contact():
return InlineQueryResultContact(TestInlineQueryResultContactBase.id_, TestInlineQueryResultContactBase.phone_number, TestInlineQueryResultContactBase.first_name, last_name=TestInlineQueryResultContactBase.last_name, thumbnail_url=TestInlineQueryResultContactBase.t... |
_arg_scope
def batch_norm(inputs, decay=0.999, center=True, scale=False, epsilon=0.001, activation_fn=None, param_initializers=None, param_regularizers=None, updates_collections=ops.GraphKeys.UPDATE_OPS, is_training=True, reuse=None, variables_collections=None, outputs_collections=None, trainable=True, batch_weights=No... |
def get_example_spectral_response(wavelength=None):
SR_DATA = np.array([[290, 0.0], [350, 0.27], [400, 0.37], [500, 0.52], [650, 0.71], [800, 0.88], [900, 0.97], [950, 1.0], [1000, 0.93], [1050, 0.58], [1100, 0.21], [1150, 0.05], [1190, 0.0]]).transpose()
if (wavelength is None):
resolution = 5.0
... |
class ResNet3dPathway(ResNet3d):
def __init__(self, *args, lateral=False, speed_ratio=8, channel_ratio=8, fusion_kernel=5, **kwargs):
self.lateral = lateral
self.speed_ratio = speed_ratio
self.channel_ratio = channel_ratio
self.fusion_kernel = fusion_kernel
super().__init__(*... |
class SCMIFileHandler(BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info):
super(SCMIFileHandler, self).__init__(filename, filename_info, filetype_info)
self.nc = xr.open_dataset(self.filename, decode_cf=True, mask_and_scale=False, chunks={'x': LOAD_CHUNK_SIZE, 'y': LOAD_CHU... |
class MathSATOptions(SolverOptions):
def __init__(self, **base_options):
SolverOptions.__init__(self, **base_options)
def _set_option(msat_config, name, value):
check = mathsat.msat_set_option(msat_config, name, value)
if (check != 0):
raise PysmtValueError(("Error setting th... |
_module()
class CosineAnnealingLrUpdaterHook(LrUpdaterHook):
def __init__(self, min_lr=None, min_lr_ratio=None, **kwargs):
assert ((min_lr is None) ^ (min_lr_ratio is None))
self.min_lr = min_lr
self.min_lr_ratio = min_lr_ratio
super(CosineAnnealingLrUpdaterHook, self).__init__(**kwa... |
def test_change_rv_size_default_update():
rng = pytensor.shared(np.random.default_rng(0))
x = normal(rng=rng)
rng.default_update = x.owner.outputs[0]
new_x = change_dist_size(x, new_size=(2,))
new_rng = new_x.owner.inputs[0]
assert (rng.default_update is x.owner.outputs[0])
assert (new_rng.d... |
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