code stringlengths 281 23.7M |
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.parametrize('args', [{}, {'options': {'zticks': [1]}}, {'x_basis': [1, 2, 3, 4, 5]}, {'y_basis': [1, 2, 3, 4, 5]}, {'limits': [0, 1]}, {'color_limits': [0, 1]}, {'color_style': 'phase'}, {'options': {'threshold': 0.1}}, {'color_style': 'real', 'colorbar': True}, {'color_style': 'img', 'colorbar': True}, {'color_style'... |
def test_pyproject_toml_invalid_priority() -> None:
toml: dict[(str, Any)] = TOMLFile((FIXTURE_DIR / 'complete_invalid_priority.toml')).read()
content = toml['tool']['poetry']
assert (Factory.validate(content) == {'errors': ["data.source[0].priority must be one of ['primary', 'default', 'secondary', 'supple... |
(cc=STDCALL, params={'hDlg': HWND, 'nIDDlgItem': INT, 'lpString': LPSTR, 'cchMax': INT})
def hook_GetDlgItemTextA(ql: Qiling, address: int, params):
lpString = params['lpString']
cchMax = params['cchMax']
ql.os.stdout.write(b'Input DlgItemText :\n')
string = ql.os.stdin.readline().strip()[:cchMax]
q... |
def raw_checkboard(quality, metric='mse', pretrained=False, progress=True, **kwargs):
if (metric not in ('mse', 'ms-ssim')):
raise ValueError(f'Invalid metric "{metric}"')
if ((quality < 1) or (quality > 8)):
raise ValueError(f'Invalid quality "{quality}", should be between (1, 8)')
return _... |
class TxsETHAPPRSpider(TxsETHSpider):
name = 'txs.eth.appr'
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.task_map = dict()
self.alpha = float(kwargs.get('alpha', 0.15))
self.epsilon = float(kwargs.get('epsilon', 0.001))
def start_requests(self):
if (s... |
def test_hook_tracing(he_pm: PluginManager) -> None:
saveindent = []
class api1():
def he_method1(self):
saveindent.append(he_pm.trace.root.indent)
class api2():
def he_method1(self):
saveindent.append(he_pm.trace.root.indent)
raise ValueError()
he_pm.... |
_tokenizers
class TokenizerVersioningTest(unittest.TestCase):
def test_local_versioning(self):
tokenizer = AutoTokenizer.from_pretrained('bert-base-cased')
json_tokenizer = json.loads(tokenizer._tokenizer.to_str())
json_tokenizer['model']['vocab']['huggingface'] = len(tokenizer)
with... |
class TorchvisionDataset(Dataset):
def __init__(self, cfg: AttrDict, data_source: str, path: str, split: str, dataset_name: str):
super().__init__()
assert PathManager.isdir(path), f'Directory {path} does not exist'
self.dataset_name = dataset_name
self.path = path
self.split... |
def _find_foldable_bn_pair_and_bn_picked_for_folding(connected_graph: ConnectedGraph) -> Tuple[(List[Tuple[(LayerType, BatchNormType)]], List[Tuple[(BatchNormType, LayerType)]], Set)]:
conv_linear_bn_activation_info_dict = find_all_conv_bn_with_activation_in_graph(connected_graph)
bn_picked_for_folding = set()
... |
class TestRequestRepoBuild(ApiTestCase):
def test_requestbuild_noidurl(self):
self.login(ADMIN_ACCESS_USER)
self.postResponse(RepositoryBuildList, params=dict(repository=(ADMIN_ACCESS_USER + '/simple')), data=dict(), expected_code=400)
def test_requestbuild_invalidurls(self):
self.login(... |
class DistanceEmbed(nn.Module):
def __init__(self, n_rbf, cutoff, feat_dim, dropout):
super().__init__()
rbf = PainnRadialBasis(n_rbf=n_rbf, cutoff=cutoff)
dense = Dense(in_features=n_rbf, out_features=feat_dim, bias=True, dropout_rate=dropout)
self.block = nn.Sequential(rbf, dense)
... |
def mainopt_festival_dictionary_to_espeak(i):
try:
festival_location = sys.argv[(i + 1)]
except IndexError:
return 'Error: --festival-dictionary-to-espeak must be followed by the location of the festival OALD file (see help text)'
try:
open(festival_location)
except:
retu... |
class IfElseIfElseIf(Op):
def __init__(self, inplace=False):
self.inplace = inplace
assert (not self.inplace)
def make_node(self, c1, t1, c2, t2, c3, t3, f3):
assert (t1.type == f3.type)
assert (t2.type == t3.type)
assert (t3.type == f3.type)
return Apply(self, [c... |
class PreActResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(PreActResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride... |
class NC_ABI_BASE(BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info):
super(NC_ABI_BASE, self).__init__(filename, filename_info, filetype_info)
platform_shortname = filename_info['platform_shortname']
self.platform_name = PLATFORM_NAMES.get(platform_shortname.lower(... |
def update_LD_LIBRARY_PATH(install_dir):
export_statement = f'export LD_LIBRARY_PATH={install_dir}/lib:$LD_LIBRARY_PATH'
venv_path = os.environ.get('VIRTUAL_ENV')
if venv_path:
script_path = os.path.join(venv_path, 'bin/activate')
else:
script_path = os.path.join(os.environ.get('HOME'), ... |
def _encode_codepage(codepage, text):
assert isinstance(text, text_type)
if (not text):
return b''
size = (len(text.encode('utf-16-le', _surrogatepass)) // ctypes.sizeof(winapi.WCHAR))
length = winapi.WideCharToMultiByte(codepage, 0, text, size, None, 0, None, None)
if (length == 0):
... |
class ConditionViewSet(ModelViewSet):
permission_classes = ((HasModelPermission | HasObjectPermission),)
serializer_class = ConditionSerializer
queryset = Condition.objects.select_related('source', 'target_option').prefetch_related('optionsets', 'pages', 'questionsets', 'questions', 'tasks', 'editors')
... |
def test_read_header():
keys = ('SatelliteId', 'NominalLongitude', 'SatelliteStatus')
values = (324, 0.0, 1)
expected = dict(zip(keys, values))
types = (np.uint16, np.float32, np.uint8)
dtypes = np.dtype([(k, t) for (k, t) in zip(keys, types)])
hdr_data = np.array([values], dtype=dtypes)
wit... |
class FilterValidationTests(AuthenticatedAPITestCase):
def test_filter_validation(self) -> None:
test_sequences = get_test_sequences()
base_filter = test_sequences['filter']
base_filter_list = test_sequences['filter_list1']
cases = (({'infraction_reason': 'hi'}, {}, 400), ({'infracti... |
def override_eval_lm_args(args: Namespace) -> Tuple[(List[str], List[str])]:
overrides = []
overrides.extend(_override_attr('params.common', CommonParams, args))
overrides.extend(_override_attr('params.dataset', DatasetParams, args))
overrides.extend(_override_attr('params.distributed_training', Distrib... |
def detect_project_name():
logdir = dsz.lp.GetLogsDirectory()
projectdir = os.path.split(logdir)[0]
[logroot, project] = os.path.split(projectdir)
if ((logroot.lower()[3:] != 'logs') or (not project)):
print()
dsz.ui.Echo('', dsz.ERROR)
dsz.ui.Echo('ERROR: You did not correctly c... |
class MDTextField(models.TextField):
def __init__(self, *args, **kwargs):
self.config_name = kwargs.pop('config_name', 'default')
super(MDTextField, self).__init__(*args, **kwargs)
def formfield(self, **kwargs):
defaults = {'form_class': MDTextFormField, 'config_name': self.config_name}
... |
def configure_converter(converter: BaseConverter):
def gen_unstructure_mapping(cl: Any, unstructure_to=None):
key_handler = str
args = getattr(cl, '__args__', None)
if args:
if issubclass(args[0], str):
key_handler = None
elif issubclass(args[0], bytes... |
def get_git_version() -> Optional[str]:
dir = os.path.dirname(os.path.realpath(__file__))
try:
version = subprocess.check_output(['git', 'describe', '--always', '--dirty'], cwd=dir)
version = str(version, 'utf8').strip()
except Exception:
version = None
return version |
def convert_ndarray_to_list_in_data(data: np.ndarray):
new_data = []
for item in data:
if isinstance(item, np.ndarray):
new_item = convert_ndarray_to_list_in_data(item)
elif isinstance(item, dict):
new_item = {}
for (key, value) in item.items():
... |
def cl_parse(command, args, setup=None, details=None):
usage = subcommand_usages[command]
descr = subcommand_descriptions[command]
if isinstance(usage, str):
usage = [usage]
susage = ('%s %s' % (program_name, usage[0]))
for s in usage[1:]:
susage += ('\n%s%s %s' % ((' ' * 7), program... |
class Effect6054(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.drones.filteredItemBoost((lambda drone: drone.item.requiresSkill('Heavy Drone Operation')), 'hp', ship.getModifiedItemAttr('shipBonusGC2'), skill='Gallente Cruiser', **kwargs) |
def test_conftest_found_with_double_dash(pytester: Pytester) -> None:
sub = pytester.mkdir('sub')
sub.joinpath('conftest.py').write_text(textwrap.dedent(' def pytest_addoption(parser):\n parser.addoption("--hello-world", action="store_true")\n '), encoding='utf-8')
p = s... |
def test_do_cleanups_on_setup_failure(pytester: Pytester) -> None:
testpath = pytester.makepyfile('\n import unittest\n class MyTestCase(unittest.TestCase):\n values = []\n def setUp(self):\n def cleanup():\n self.values.append(1)\n ... |
def test_slowfast():
config = get_recognizer_cfg('slowfast/slowfast_r50_4x16x1_256e_kinetics400_rgb.py')
recognizer = build_recognizer(config.model)
recognizer.cfg = config
input_shape = (1, 1, 3, 32, 32, 32)
target_layer_name = 'backbone/slow_path/layer4/1/relu'
_do_test_3D_models(recognizer, t... |
class Depth():
def __init__(self, depth_to_copy=None, direction=1):
self.depth_to_copy = depth_to_copy
self.direction = direction
self.gate_list = []
def add_gate(self, gate):
self.gate_list.append(gate)
def add_gate_list(self, gate_list):
self.gate_list.extend(gate_l... |
def test_get_decimal_symbol():
assert (numbers.get_decimal_symbol('en_US') == '.')
assert (numbers.get_decimal_symbol('en_US', numbering_system='default') == '.')
assert (numbers.get_decimal_symbol('en_US', numbering_system='latn') == '.')
assert (numbers.get_decimal_symbol('sv_SE') == ',')
assert (... |
def adjust_learning_rate(p, optimizer, epoch):
lr = p['optimizer_kwargs']['lr']
if (p['scheduler'] == 'step'):
steps = np.sum((epoch > np.array(p['scheduler_kwargs']['lr_decay_epochs'])))
if (steps > 0):
lr = (lr * (p['scheduler_kwargs']['lr_decay_rate'] ** steps))
elif (p['sched... |
def test_sort_by_keywords():
keywords = {'KEY1': 2, 'KEY2': 0, 'KEY3': 1}
args = 'aaaa bbbb KEY2 KEY1 kkk10 kkk11 ccc ddd KEY3 kkk3 eee'.split()
(flat, spec) = sort_by_keywords(keywords, args)
assert (flat == ['aaaa', 'bbbb', 'ccc', 'ddd', 'eee'])
assert (spec == {'KEY1': ['kkk10', 'kkk11'], 'KEY2':... |
class Loss(object):
__metaclass__ = ABCMeta
def __call__(self, prediction_tensor, target_tensor, ignore_nan_targets=True, scope=None, **params):
with tf.name_scope(scope, 'Loss', [prediction_tensor, target_tensor, params]) as scope:
if ignore_nan_targets:
target_tensor = tf.w... |
def dropout_sparse(x, keep_prob, num_nonzero_elems):
noise_shape = [num_nonzero_elems]
random_tensor = keep_prob
random_tensor += tf.random_uniform(noise_shape)
dropout_mask = tf.cast(tf.floor(random_tensor), dtype=tf.bool)
pre_out = tf.sparse_retain(x, dropout_mask)
return (pre_out * (1.0 / kee... |
((not torch.cuda.is_available()), 'test requires a GPU')
class TestQuantization(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_quantization(self):
with contextlib.redirect_stdout(StringIO()):
... |
def test_background_plotting_add_callback(qtbot, monkeypatch, plotting):
class CallBack(object):
def __init__(self, sphere):
self.sphere = weakref.ref(sphere)
def __call__(self):
self.sphere().points[:] = (self.sphere().points * 0.5)
update_count = [0]
orig_update_app... |
def optimizer_kwargs_gf(parsed_args):
return {'optim': parsed_args.optim, 'lr': parsed_args.lr_gf, 'weight_decay': parsed_args.weight_decay, 'momentum': parsed_args.momentum, 'sgd_dampening': parsed_args.sgd_dampening, 'sgd_nesterov': parsed_args.sgd_nesterov, 'rmsprop_alpha': parsed_args.rmsprop_alpha, 'adam_beta1... |
class ResBlock(nn.Module):
def __init__(self, num_channels, kernel_size=3, bias=True, bn=False, act=nn.ReLU(True), res_scale=1, **kwargs):
super(ResBlock, self).__init__()
m = []
for i in range(2):
m.append(nn.Conv2d(num_channels, num_channels, kernel_size, stride=1, padding=1, b... |
class VOCDataset(BaseDataSet):
def __init__(self, **kwargs):
self.num_classes = 21
self.palette = palette.get_voc_palette(self.num_classes)
super(VOCDataset, self).__init__(**kwargs)
def _set_files(self):
self.root = os.path.join(self.root, 'VOCdevkit/VOC2012')
self.image... |
def get_norm(norm, out_channels, **kwargs):
if isinstance(norm, str):
if (len(norm) == 0):
return None
norm = {'BN': BatchNorm, 'syncBN': SyncBatchNorm, 'GhostBN': GhostBatchNorm, 'FrozenBN': FrozenBatchNorm, 'GN': (lambda channels, **args: nn.GroupNorm(32, channels))}[norm]
return n... |
def state_dict() -> Dict[(str, Any)]:
state = base.state_dict()
musiq_state: Dict[(str, Any)] = {}
musiq_state['paused'] = storage.get('paused')
musiq_state['shuffle'] = storage.get('shuffle')
musiq_state['repeat'] = storage.get('repeat')
musiq_state['autoplay'] = storage.get('autoplay')
mus... |
def convert_to_one_fraction_group(dicom_dataset, fraction_group_number):
created_dicom = deepcopy(dicom_dataset)
(beam_sequence, _) = get_fraction_group_beam_sequence_and_meterset(dicom_dataset, fraction_group_number)
created_dicom.BeamSequence = beam_sequence
fraction_group_index = get_fraction_group_i... |
def generate_stub_for_py_module(mod: StubSource, target: str, *, parse_only: bool=False, inspect: bool=False, include_private: bool=False, export_less: bool=False, include_docstrings: bool=False, doc_dir: str='', all_modules: list[str]) -> None:
if inspect:
ngen = InspectionStubGenerator(module_name=mod.mod... |
class Effect4490(BaseEffect):
dealsDamage = True
type = 'active'
def handler(fit, mod, context, projectionRange, **kwargs):
fit.ship.boostItemAttr('maxVelocity', mod.getModifiedItemAttr('speedFactor'), stackingPenalties=True, **kwargs)
fit.ship.increaseItemAttr('warpScrambleStatus', mod.getM... |
class AddressType():
def __init__(self):
self.hot = {}
hot_file = os.environ.get('PYUNIT_ADDRESS_HOT_FILE', None)
if hot_file:
with open(hot_file, encoding='utf-8') as fp:
for line in fp.readlines():
(name, addr) = line.strip().split()
... |
class TestIndex():
def test_sanity_check(self):
mySymbolicMatricesList = TypedListType(TensorType(pytensor.config.floatX, shape=(None, None)))()
myMatrix = matrix()
z = Index()(mySymbolicMatricesList, myMatrix)
f = pytensor.function([mySymbolicMatricesList, myMatrix], z)
x = ... |
class TestTreeItem(unittest.TestCase):
def test_init(self):
widget = gui.TreeItem('test tree item')
widget.append(gui.TreeItem('2nd tree item'))
self.assertIn('test tree item', widget.repr())
self.assertIn('2nd tree item', widget.repr())
assertValidHTML(widget.repr()) |
def smart_contract_filters_from_node_state(chain_state: ChainState, secret_registry_address: SecretRegistryAddress, service_registry: Optional[ServiceRegistry]) -> RaidenContractFilter:
token_network_registries = chain_state.identifiers_to_tokennetworkregistries.values()
token_networks = [tn for tnr in token_ne... |
class BirthdayParty(QObject):
Q_CLASSINFO('DefaultProperty', 'guests')
partyStarted = pyqtSignal(QTime, arguments=['time'])
def __init__(self, parent=None):
super(BirthdayParty, self).__init__(parent)
self._host = None
self._guests = []
hostChanged = pyqtSignal()
(Person, not... |
def write_file(path: str, contents: str) -> None:
encoded_contents = contents.encode('utf-8')
try:
with open(path, 'rb') as f:
old_contents: (bytes | None) = f.read()
except OSError:
old_contents = None
if (old_contents != encoded_contents):
os.makedirs(os.path.dirnam... |
def augment_and_mix_transform(config_str, hparams):
magnitude = 3
width = 3
depth = (- 1)
alpha = 1.0
blended = False
config = config_str.split('-')
assert (config[0] == 'augmix')
config = config[1:]
for c in config:
cs = re.split('(\\d.*)', c)
if (len(cs) < 2):
... |
def begin_level(options):
global level_stack, level_list, bgcolor
if (not level_stack):
level_list = []
default_level_color = None
default_level_style = None
default_level_fill = bgcolor
else:
default_level_color = level_stack[(- 1)].get('color', None)
default... |
_task('fasthubert_pretraining', dataclass=FastHubertPretrainingConfig)
class HubertFbankPretrainingTask(HubertPretrainingTask):
cfg: FastHubertPretrainingConfig
def __init__(self, cfg: FastHubertPretrainingConfig) -> None:
super().__init__(cfg)
def setup_task(cls, cfg: FastHubertPretrainingConfig, *... |
class WithFutureMinuteBarData(WithAssetFinder, WithTradingCalendars):
FUTURE_MINUTE_BAR_LOOKBACK_DAYS = 0
FUTURE_MINUTE_BAR_START_DATE = alias('START_DATE')
FUTURE_MINUTE_BAR_END_DATE = alias('END_DATE')
def make_future_minute_bar_data(cls):
trading_calendar = get_calendar('us_futures')
... |
def clip_grad_norm_dp(named_parameters, target_params, max_norm, norm_type=2):
parameters = list(filter((lambda p: (p[1] - target_params[p[0]])), named_parameters))
max_norm = float(max_norm)
norm_type = float(norm_type)
if (norm_type == float('inf')):
total_norm = max((p.grad.data.abs().max() f... |
class TestAdamWOptimizer(TestOptimizer, unittest.TestCase):
def _check_momentum_buffer(self):
return False
def _get_config(self):
return {'name': 'adamw', 'num_epochs': 90, 'lr': 0.1, 'betas': (0.9, 0.99), 'eps': 1e-08, 'weight_decay': 0.0001, 'amsgrad': False}
def _instance_to_test(self):
... |
class ComplexWebQuestions(datasets.GeneratorBasedBuilder):
VERSION = datasets.Version('1.0.0')
BUILDER_CONFIGS = [datasets.BuilderConfig(name='compwebq', version=VERSION, description='ComplexWebQuestions Dataset')]
def __init__(self, *args, writer_batch_size=None, **kwargs):
super().__init__(*args, ... |
class WindowCreateFullScreenEventSequenceTest(EventSequenceTest, unittest.TestCase):
last_sequence = 3
def on_resize(self, width, height):
self.check_sequence(1, 'on_resize')
def on_show(self):
self.check_sequence(2, 'on_show')
def on_expose(self):
self.check_sequence(3, 'on_expo... |
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if ((len(sys.argv) == 2) and sys.argv[1].endswith('.json')):
(model_args, data_args, training_args) = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
(model_args, data_args,... |
def model_fn_builder(bert_config, num_labels, init_checkpoint, learning_rate, num_train_steps, num_warmup_steps, use_tpu, use_one_hot_embeddings):
def model_fn(features, labels, mode, params):
tf.logging.info('*** Features ***')
for name in sorted(features.keys()):
tf.logging.info((' na... |
def gable_process_box(bm, roof_faces, prop):
top_faces = [f for f in roof_faces if f.normal.z]
result = bmesh.ops.extrude_face_region(bm, geom=top_faces).get('geom')
bmesh.ops.translate(bm, verts=filter_geom(result, BMVert), vec=(0, 0, prop.thickness))
bmesh.ops.delete(bm, geom=top_faces, context='FACES... |
def _getsafeword(agearg):
targetgmt = ops.system.clocks.gmtime()
ageseconds = ops.timehelper.get_seconds_from_age(agearg)
afterdatetime = (targetgmt - timedelta(seconds=ageseconds))
beforedatetime = (targetgmt + timedelta(seconds=0))
return (afterdatetime.strftime('%Y-%m-%d %H:%M:%S'), beforedatetim... |
class Parser(object):
_extensions = []
_shebangKeywords = []
_keywords = []
def getParserName(cls):
name = cls.__name__
if (name.endswith('Parser') and (len(name) >= 6)):
name = name[:(- 6)].lower()
return name
def disambiguate(cls, text):
return cls.getPa... |
class StaffAdvertiserReportView(BaseReportView):
impression_model = AdvertiserImpression
report = OptimizedAdvertiserReport
template_name = 'adserver/reports/staff-advertisers.html'
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
impressions = self.get_... |
def test_one_accumulator_while_loop() -> None:
number = 10
test_list = [10, 20, 30]
sum_so_far = 0
with AccumulationTable(['number', 'sum_so_far']) as table:
while (number in test_list):
sum_so_far = (sum_so_far + number)
number += 10
assert (table.loop_accumulators =... |
class MCTCTFeatureExtractor(SequenceFeatureExtractor):
model_input_names = ['input_features', 'attention_mask']
def __init__(self, feature_size=80, sampling_rate=16000, padding_value=0.0, hop_length=10, win_length=25, win_function='hamming_window', frame_signal_scale=32768.0, preemphasis_coeff=0.97, mel_floor=1... |
class AsmCmdBase(with_metaclass(AsmCmdManager, object)):
_id = (- 1)
_active = None
_toolbarName = 'Assembly3'
_menuGroupName = ''
_contextMenuName = 'Assembly'
_accel = None
_cmdType = None
_iconName = None
def checkActive(cls):
cls._active = True
def getIconName(cls):
... |
def get_possible_variants(typ: Type) -> list[Type]:
typ = get_proper_type(typ)
if isinstance(typ, TypeVarType):
if (len(typ.values) > 0):
return typ.values
else:
return [typ.upper_bound]
elif isinstance(typ, ParamSpecType):
return [typ.upper_bound]
elif is... |
def test_export_compound_crs():
crs = CRS('urn:ogc:def:crs,crs:EPSG::2393,crs:EPSG::5717')
expected_cf = {'semi_major_axis': 6378388.0, 'semi_minor_axis': crs.ellipsoid.semi_minor_metre, 'inverse_flattening': 297.0, 'reference_ellipsoid_name': 'International 1924', 'longitude_of_prime_meridian': 0.0, 'prime_mer... |
class Baseline(object):
def wrap_dataset(self, dataset):
return dataset
def unwrap_batch(self, batch):
return (batch, None)
def eval(self, x, c):
raise NotImplementedError('Override this method')
def get_learnable_parameters(self):
return []
def epoch_callback(self, m... |
class RestoreFormerModel(pl.LightningModule):
def __init__(self, ddconfig, lossconfig, ckpt_path=None, ignore_keys=[], image_key='lq', colorize_nlabels=None, monitor=None, special_params_lr_scale=1.0, comp_params_lr_scale=1.0, schedule_step=[80000, 200000]):
super().__init__()
self.image_key = image... |
def test_color_yes_collection_on_non_atty(pytester, request) -> None:
tr = request.config.pluginmanager.getplugin('terminalreporter')
if (not hasattr(tr, 'isatty')):
pytest.skip('only valid for newer pytest versions')
pytester.makepyfile("\n import pytest\n .parametrize('i', range(10))... |
def create_cancel_build_in_queue(build_phase, build_queue_id, build_queue):
def cancel_build():
cancelled = False
if (build_queue_id is not None):
cancelled = build_queue.cancel(build_queue_id)
if (build_phase != BUILD_PHASE.WAITING):
return False
return cance... |
class Solution():
def dig_sum(self, n):
total = 0
while (n > 0):
rem = (n % 10)
total += rem
n = (n // 10)
return total
def countLargestGroup(self, n: int) -> int:
from collections import Counter
d = dict()
for i in range(1, (n ... |
class ScopeTimer():
def __init__(self, name):
self.name = name
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, *args):
self.end = time.time()
self.interval = (self.end - self.start)
print('{} {:.3E}'.format(self.name, self.interval... |
.script
def _multilabel_recall_at_fixed_precision_compute(input: torch.Tensor, target: torch.Tensor, num_labels: int, min_precision: float) -> Tuple[(List[torch.Tensor], List[torch.Tensor])]:
(precision, recall, thresholds) = _multilabel_precision_recall_curve_compute(input, target, num_labels)
(max_recall, bes... |
class WebRTCManager(Runnable):
def __init__(self, node_address: Address, process_messages: Callable[([List[ReceivedRaidenMessage]], None)], signaling_send: Callable[([Address, str], None)], stop_event: GEvent) -> None:
super().__init__()
self.node_address = node_address
self._process_message... |
class Solution(object):
def backspaceCompare(self, S, T):
if (S == T):
return True
s_stack = []
t_stack = []
for c in S:
if (c != '#'):
s_stack.append(c)
elif (len(s_stack) != 0):
s_stack.pop((- 1))
for c in ... |
def print_timer(rb_node, idx):
timerqueue = utils.container_of(rb_node, timerqueue_node_type.pointer(), 'node')
timer = utils.container_of(timerqueue, hrtimer_type.pointer(), 'node')
function = str(timer['function']).split(' ')[1].strip('<>')
softexpires = timer['_softexpires']
expires = timer['node... |
class _BlockInfo(object):
def __init__(self, seen_open_brace):
self.seen_open_brace = seen_open_brace
self.open_parentheses = 0
self.inline_asm = _NO_ASM
def CheckBegin(self, filename, clean_lines, linenum, error):
pass
def CheckEnd(self, filename, clean_lines, linenum, error... |
class TestMongoDBCollectorWithReplica(CollectorTestCase):
def setUp(self):
config = get_collector_config('MongoDBCollector', {'host': 'localhost:27017', 'databases': '^db', 'replica': True})
self.collector = MongoDBCollector(config, None)
self.connection = MagicMock()
def test_import(sel... |
def _run(main_wrapper: Callable[([TextIO, TextIO], None)]) -> tuple[(str, str, int)]:
stdout = StringIO()
stderr = StringIO()
try:
main_wrapper(stdout, stderr)
exit_status = 0
except SystemExit as system_exit:
assert isinstance(system_exit.code, int)
exit_status = system_... |
def get_app_from_path(request_path, base, index, reload=False):
path = valid_and_norm_path(base, request_path)
if (path is None):
return ('error', 403)
if os.path.isdir(path):
if (not request_path.endswith('/')):
return ('error', 404)
if os.path.isfile(os.path.join(path, ... |
class Generator(nn.Module):
def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='64', n_classes=1000, num_G_SVs=1, num_G_SV_itrs=1, G_shared=True, shared_dim=0, hier=False, cross_replica=False, mybn=False, G_activation=nn.ReLU(inplace=False), G_lr=5e-05, G_B1=0.0, G_B2=0.9... |
class PooledEmbeddingsAllToAllTest(MultiProcessTestBase):
def _run_test_dist(cls, rank: int, world_size: int, _input: torch.Tensor, output: torch.Tensor, backend: str, dim_sum_per_rank: List[int], batch_size_per_rank: List[int], qcomms_config: Optional[QCommsConfig]=None) -> None:
dist.init_process_group(ra... |
def group_norm(out_channels, affine=True, divisor=1):
out_channels = (out_channels // divisor)
dim_per_gp = (config.MODEL.GROUP_NORM.DIM_PER_GP // divisor)
num_groups = (config.MODEL.GROUP_NORM.NUM_GROUPS // divisor)
eps = config.MODEL.GROUP_NORM.EPSILON
return torch.nn.GroupNorm(get_group_gn(out_ch... |
.parametrize('recap_type, expected_proto_type', [(NullType(name='some_field'), 'google.protobuf.NullValue'), (BoolType(name='some_field'), 'bool'), (IntType(signed=True, bits=32, name='some_field'), 'int32'), (IntType(signed=True, bits=64, name='some_field'), 'int64'), (IntType(signed=False, bits=32, name='some_field')... |
def _msg_sendv(ql: Qiling, coid, smsg, sparts, rmsg, rparts, *args, **kw):
assert (coid in ql.os.connections), 'Connection Id must exist in connections mapping'
conn = ql.os.connections[coid]
if ((conn.pid == SYSMGR_PID) and (conn.chid == SYSMGR_CHID)):
sbody = get_message_body(ql, smsg, sparts)
... |
def test_mult_multiplication() -> None:
assert (parse('(a{2,3}){1,1}').reduce() == parse('a{2,3}').reduce())
assert (parse('(a{2,3}){1}').reduce() == parse('a{2,3}').reduce())
assert (parse('(a{2,3})').reduce() == parse('a{2,3}').reduce())
assert (parse('(a{2,3}){4,5}').reduce() == parse('a{8,15}').redu... |
def xdg_get_system_data_dirs():
'
if (os.name == 'nt'):
from gi.repository import GLib
dirs = []
for dir_ in GLib.get_system_data_dirs():
dirs.append(dir_)
return dirs
data_dirs = os.getenv('XDG_DATA_DIRS')
if data_dirs:
return [os.path.abspath(d) for ... |
class LossValley(SWADBase):
def __init__(self, n_converge, n_tolerance, tolerance_ratio):
self.n_converge = n_converge
self.n_tolerance = n_tolerance
self.tolerance_ratio = tolerance_ratio
self.converge_Q = deque(maxlen=n_converge)
self.smooth_Q = deque(maxlen=n_tolerance)
... |
class Effect6076(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredChargeMultiply((lambda mod: mod.charge.requiresSkill('Missile Launcher Operation')), 'maxVelocity', (1 / module.getModifiedItemAttr('modeMaxRangePostDiv')), stackingPenalties=... |
def print_array_diagnostics(array, indices, comparator=(lambda a, b: (a < b))):
ordered_array = reindex_array(array, indices)
info = [('Original array', array), ('Ordered array', ordered_array), ('Is sorted?', is_sorted(array, indices, comparator)), ('Sort score', sort_score(array, indices, comparator)), ('Has ... |
def get_test_dependencies(test_fname):
with open(os.path.join(PATH_TO_TRANFORMERS, test_fname), 'r', encoding='utf-8') as f:
content = f.read()
relative_imports = re.findall('from\\s+(\\.\\S+)\\s+import\\s+([^\\n]+)\\n', content)
relative_imports = [test for (test, imp) in relative_imports if ('# te... |
def shape_text_hb(text, font_filename, direction=None):
ref_size = REF_GLYPH_SIZE
buf = uharfbuzz.Buffer()
buf.add_str(text)
buf.guess_segment_properties()
is_horizontal = True
if (direction is not None):
buf.direction = direction
is_horizontal = (direction in ('ltr', 'rtl'))
... |
class BILSTMONLY(object):
def __init__(self, params: dict):
self.char_embedding = tf.Variable(np.load(params['embedding_path']), dtype=tf.float32, name='input_char_embedding')
self.word_embedding = tf.Variable(np.load(params['word_embedding_path']), dtype=tf.float32, name='input_word_embedding')
... |
class LazyString(object):
def __init__(self, func, *args, **kwargs):
self._func = func
self._args = args
self._kwargs = kwargs
def __getattr__(self, attr):
if (attr == '__setstate__'):
raise AttributeError(attr)
string = str(self)
if hasattr(string, at... |
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