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class ImportPlugin(MdfInfo):
name = 'Import plugin'
author = 'Aymeric Rateau'
description = 'Import MDF files'
promote_tab = 'MDF'
file_extensions = set(['.dat', '.mf4', '.mdf'])
def __init__(self):
self.fields = []
def getPreview(self, params):
info = MdfInfo()
if (i... |
def parse_input_update_spec(spec):
for key in spec:
assert (key not in {'action', 'buttons', 'code', 'inline', 'max_size', 'max_total_size', 'multiple', 'name', 'onchange', 'type', 'validate'}), ('%r can not be updated' % key)
attributes = dict(((k, v) for (k, v) in spec.items() if (v is not None)))
... |
def _convert_advanced_activation(inexpr, keras_layer, etab):
act_type = type(keras_layer).__name__
if (act_type == 'Softmax'):
axis = keras_layer.axis
dims = len(keras_layer.input_shape)
if isinstance(axis, list):
raise tvm.error.OpAttributeUnImplemented('Softmax with axes {}... |
def conv2d_transposed(x, shape, outshape, name, strides=[1, 1, 1, 1]):
weight = weight_variable(shape, '{}_W'.format(name))
bias = bias_variable([shape[(- 2)]], '{}_b'.format(name))
return (tf.nn.conv2d_transpose(x, weight, output_shape=outshape, strides=strides, padding='SAME', name=name) + bias) |
class JciHitachiMonthlyPowerConsumptionSensorEntity(JciHitachiEntity, SensorEntity):
def __init__(self, thing, coordinator):
super().__init__(thing, coordinator)
def name(self):
return f'{self._thing.name} Monthly Power Consumption'
def native_value(self):
monthly_data = self._thing.... |
class Iptables(InstanceModule):
def __init__(self):
super().__init__()
self._has_w_argument = None
def _iptables_command(self, version):
if (version == 4):
iptables = 'iptables'
elif (version == 6):
iptables = 'ip6tables'
else:
raise Ru... |
class AsmCmdImportMulti(AsmCmdImportSingle):
_id = 26
_menuText = QT_TRANSLATE_NOOP('asm3', 'Import as multi-document')
_tooltip = QT_TRANSLATE_NOOP('asm3', 'Import assemblies from STEP file into separate document')
_iconName = 'Assembly_ImportMulti.svg'
def importMode(cls):
params = FreeCAD... |
_tokenizers
class CpmTokenizationTest(XLNetModelTest):
def test_pre_tokenization(self):
tokenizer = CpmTokenizer.from_pretrained('TsinghuaAI/CPM-Generate')
text = 'Hugging Face,'
normalized_text = 'Hugging Face,<unk>'
bpe_tokens = 'Hu gg ing F ace , '.split()
token... |
class QuoPageView(QuotientView):
def __init__(self, ctx: Context, *, pages: T.List[PageLine], items: T.Optional[T.List[discord.ui.Item]]=None, embed: discord.Embed, show_count: bool, need_skip: bool):
super().__init__(ctx, timeout=40)
self.pages = pages
self.items = items
self.curren... |
class DirectionalGridCRF(GridCRF, EdgeFeatureGraphCRF):
def __init__(self, n_states=None, n_features=None, inference_method=None, neighborhood=4):
self.neighborhood = neighborhood
n_edge_features = (2 if (neighborhood == 4) else 4)
EdgeFeatureGraphCRF.__init__(self, n_states, n_features, n_e... |
class _NetD(nn.Module):
def __init__(self):
super(_NetD, self).__init__()
self.features = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=64, kernel_size=5, stride=1, padding=2, bias=True), nn.LeakyReLU(0.2, inplace=True), nn.Conv2d(in_channels=64, out_channels=64, kernel_size=4, stride=2, paddi... |
def menu_setattr(menu, choice, obj, string):
attr = (getattr(choice, 'attr', None) if choice else None)
if ((choice is None) or (string is None) or (attr is None) or (menu is None)):
log_err(dedent('\n The `menu_setattr` function was called to set the attribute {} of object {} to {},\n ... |
class TestOutputParser(TestCase):
def setUp(self) -> None:
self.parser = OutputParser([])
def test_parse_pytest(self):
output = get_output('pytest')
failed = list(self.parser.parse_failed('python#pytest', output))
self.assertEqual(failed, [ParseResult(name='test_d', namespaces=['... |
class AlexNet(nn.Module):
def __init__(self, num_classes=100):
super(AlexNet, self).__init__()
self.features = nn.Sequential(nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=2), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2), nn.Conv2d(64, 192, kernel_size=5, padding=2), nn.ReLU(inpla... |
def is_html(ct_headers, url, allow_xhtml=False):
if (not ct_headers):
return is_html_file_extension(url, allow_xhtml)
headers = split_header_words(ct_headers)
if (len(headers) < 1):
return is_html_file_extension(url, allow_xhtml)
first_header = headers[0]
first_parameter = first_head... |
.parametrize('q', [quantize(symmetric=True, initialized=False), quantize_dequantize(symmetric=True, initialized=False)])
def test_compute_encodings_updates_parameters_upon_exit(q: _QuantizerBase, x: torch.Tensor):
assert (q.get_min() is None)
assert (q.get_max() is None)
assert (q.get_scale() is None)
a... |
class Migration(migrations.Migration):
dependencies = [('jobs', '0009_auto__1815')]
operations = [migrations.AlterField(model_name='job', name='company_description_markup_type', field=models.CharField(max_length=30, choices=[('', '--'), ('html', 'HTML'), ('plain', 'Plain'), ('markdown', 'Markdown'), ('restructu... |
def example_interaction_with_policy(policy='random'):
assert (policy in ('random', 'user'))
def random_policy(state):
from helper import get_candidates
lfs = get_candidates(state)
path = random.choice(lfs)[0]
return path
def user_policy(state):
from helper import get_... |
def format_xlabel(wunit, plot_medium):
if (wunit == 'cm-1'):
xlabel = 'Wavenumber (cm-1)'
elif (wunit == 'nm'):
if (plot_medium and (plot_medium != 'vacuum_only')):
xlabel = 'Wavelength [air] (nm)'
else:
xlabel = 'Wavelength (nm)'
elif (wunit == 'nm_vac'):
... |
def weight2subspace(weight, ratio=0.7, num=(- 1)):
dim = len(weight)
threshold = (ratio * np.sum(weight))
sorted_idx = np.argsort(weight)
sorted_idx = [sorted_idx[((dim - i) - 1)] for i in range(dim)]
if (num != (- 1)):
exp_subspace = sorted_idx[:num]
exp_subspace = list(np.sort(exp_... |
def _test_sharding(tables: List[EmbeddingBagConfig], initial_state_dict: Dict[(str, Any)], rank: int, world_size: int, kjt_input_per_rank: List[KeyedJaggedTensor], sharder: ModuleSharder[nn.Module], backend: str, constraints: Optional[Dict[(str, ParameterConstraints)]]=None, local_size: Optional[int]=None, is_data_para... |
def parse_fatal_stacktrace(text):
lines = ['(?P<type>Fatal Python error|Windows fatal exception): (?P<msg>.*)', ' *', '(Current )?[Tt]hread [^ ]* \\(most recent call first\\): *', ' File ".*", line \\d+ in (?P<func>.*)']
m = re.search('\n'.join(lines), text)
if (m is None):
return ('', '')
else... |
(scope='class')
def request_chat():
return KeyboardButtonRequestChat(TestKeyboardButtonRequestChatBase.request_id, TestKeyboardButtonRequestChatBase.chat_is_channel, TestKeyboardButtonRequestChatBase.chat_is_forum, TestKeyboardButtonRequestChatBase.chat_has_username, TestKeyboardButtonRequestChatBase.chat_is_create... |
def adj_loglikelihood_scalar(disp, X, y, mu, sign):
n = (1 / disp)
p = (n / (n + mu))
loglik = sum(nbinom.logpmf(y, n, p))
diagVec = (mu / (1 + (mu * disp)))
diagWM = np.diag(diagVec)
xtwx = np.dot(np.dot(X.T, diagWM), X)
coxreid = (0.5 * np.log(np.linalg.det(xtwx)))
ret = ((loglik - cox... |
def get_address_metadata(address: Address, route_states: List[RouteState]) -> Optional[AddressMetadata]:
for route_state in route_states:
recipient_metadata = route_state.address_to_metadata.get(address, None)
if (recipient_metadata is not None):
return recipient_metadata
return None |
def _decode(inputpath, coder, show, device, output=None):
decode_func = {CodecType.IMAGE_CODEC: decode_image, CodecType.VIDEO_CODEC: decode_video}
compressai.set_entropy_coder(coder)
dec_start = time.time()
with Path(inputpath).open('rb') as f:
(model, metric, quality) = parse_header(read_uchars... |
def get_ms():
try:
client = docker.DockerClient(base_url=('tcp://%s:2376' % docker_host_ip))
for ms in client.containers.list():
if (ms.name == sys.argv[1]):
cms.append(ms)
return cms[0]
except:
print("Can't connect to docker API, Exiting!")
... |
class FocalLoss(nn.Module):
def __init__(self, num_classes, w, epsilon=0.1, use_gpu=True, label_smooth=True, gamma=0.5):
super(FocalLoss, self).__init__()
self.num_classes = num_classes
self.epsilon = (epsilon if label_smooth else 0)
self.use_gpu = use_gpu
self.sigmoid = nn.S... |
_fixtures(SqlAlchemyFixture, DeferredActionFixture)
def test_deferred_action_completes_with_shared_requirements(sql_alchemy_fixture, deferred_action_fixture):
fixture = deferred_action_fixture
with sql_alchemy_fixture.persistent_test_classes(fixture.MyDeferredAction, fixture.SomeObject):
requirements1 =... |
def test_Array_dc_ohms_from_percent(mocker):
mocker.spy(pvsystem, 'dc_ohms_from_percent')
expected = 0.1425
array = pvsystem.Array(pvsystem.FixedMount(0, 180), array_losses_parameters={'dc_ohmic_percent': 3}, module_parameters={'I_mp_ref': 8, 'V_mp_ref': 38})
out = array.dc_ohms_from_percent()
pvsys... |
class DataTrainingArguments():
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to use (via the datasets library).'})
dataset_config_name: Optional[str] = field(default=None, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}... |
def create_table(table: str, namespace: Optional[str]=None, lifecycle_state: Optional[LifecycleState]=None, schema: Optional[Union[(pa.Schema, str, bytes)]]=None, schema_consistency: Optional[Dict[(str, SchemaConsistencyType)]]=None, partition_keys: Optional[List[Dict[(str, Any)]]]=None, primary_keys: Optional[Set[str]... |
class PreviewForm(QDialog):
def __init__(self, parent):
super(PreviewForm, self).__init__(parent)
self.encodingComboBox = QComboBox()
encodingLabel = QLabel('&Encoding:')
encodingLabel.setBuddy(self.encodingComboBox)
self.textEdit = QTextEdit()
self.textEdit.setLineWr... |
def test_huge_dataset():
candidates = CompletedKeys((1024 * 1024))
start_time = datetime.now()
iterations = 0
with pytest.raises(NoAvailableKeysError):
while ((datetime.now() - start_time) < timedelta(seconds=10)):
start = candidates.get_block_start_index(1024)
assert can... |
def make_rotating_equity_info(num_assets, first_start, frequency, periods_between_starts, asset_lifetime, exchange='TEST'):
return pd.DataFrame({'symbol': [chr((ord('A') + i)) for i in range(num_assets)], 'start_date': pd.date_range(first_start, freq=(periods_between_starts * frequency), periods=num_assets), 'end_d... |
class Effect11947(BaseEffect):
type = ('projected', 'passive')
def handler(fit, beacon, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: mod.item.requiresSkill('Vorton Projector Operation')), 'aoeCloudSize', beacon.getModifiedItemAttr('aoeCloudSizeMultiplier'), stacking... |
class ProxySimple(ProxyDirect):
def __init__(self, jump, protos, cipher, users, rule, bind, host_name, port, unix, lbind, sslclient, sslserver):
super().__init__(lbind)
self.protos = protos
self.cipher = cipher
self.users = users
self.rule = (compile_rule(rule) if rule else N... |
def test_load_backoff_callable_bare():
with pytest.raises(ValueError) as err:
backoffcache.load_backoff_callable('local_test_arb_callable')
assert (str(err.value) == "Trying to find back-off strategy 'local_test_arb_callable'. If this is a built-in back-off strategy, are you sure you got the name right?... |
.skipif(kvikio.defaults.compat_mode(), reason='cannot test `set_compat_mode` when already running in compatibility mode')
def test_set_compat_mode_between_io(tmp_path):
with kvikio.defaults.set_compat_mode(False):
f = kvikio.CuFile((tmp_path / 'test-file'), 'w')
assert (not f.closed)
assert ... |
(ORDERS_PATH)
def handle_create_order() -> dict[(str, Any)]:
env_vars: MyHandlerEnvVars = get_environment_variables(model=MyHandlerEnvVars)
logger.debug('environment variables', env_vars=env_vars.model_dump())
my_configuration = parse_configuration(model=MyConfiguration)
logger.debug('fetched dynamic co... |
class Instrument():
def __init__(self, ip_addr: str, timeout: Optional[float]=None, port: int=PORT, sub_address: str='hislip0') -> None:
timeout = (timeout or 5.0)
self._sync = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
self._sync.connect((ip_addr, port))
self._sync.settimeout... |
.remote_data
.flaky(reruns=RERUNS, reruns_delay=RERUNS_DELAY)
def test_get_acis_station_data():
(df, meta) = get_acis_station_data('ORD', '2020-01-10', '2020-01-12', trace_val=(- 99))
expected = pd.DataFrame([[10.0, 2.0, 6.0, np.nan, 21.34, 0.0, 0.0, 0.0, 59.0, 0.0], [3.0, (- 4.0), (- 0.5), np.nan, 9.4, 5.3, 0.... |
def makefile(filepath, mode=448, size=None, exist_ok=False):
(dirname, _) = os.path.split(filepath)
makedirs(dirname, mode, exist_ok=True)
try:
mkfile(filepath, size)
except OSError as exc:
if ((not os.path.isfile(filepath)) or (not exist_ok)):
raise OSError(exc) |
def un_serialize(folder):
txt_lst = []
msg = ''
folder_lst = os.listdir(folder)
for item in folder_lst:
_file = os.path.join(folder, item)
if os.path.isfile(_file):
if _file.endswith('.txt'):
txt_lst.append(_file)
record_file = get_minimum_file(txt_lst)
... |
class Question():
def __init__(self, data: QuestionData):
self._data = data
self._guesses: dict[(int, UserGuess)] = {}
self._started = None
def number(self) -> str:
return self._data['number']
def description(self) -> str:
return self._data['description']
def answ... |
def build_encoder(cfg, default_args=None):
backbone = build_from_cfg(cfg['backbone'], BACKBONES, default_args)
enhance_cfg = cfg.get('enhance')
if enhance_cfg:
enhance_module = build_from_cfg(enhance_cfg, ENHANCE_MODULES, default_args)
encoder = nn.Sequential(backbone, enhance_module)
el... |
class LimitTest(ConfigurableNodeTest, TestCase):
def setUpClass(cls):
cls.NodeType = bonobo.Limit
def test_execution_default(self):
object_list = [object() for _ in range(42)]
with self.execute() as context:
context.write_sync(*object_list)
assert (context.get_buffer(... |
class JpegXr(Codec):
codec_id = 'imagecodecs_jpegxr'
def __init__(self, level=None, photometric=None, hasalpha=None, resolution=None, fp2int=None):
self.level = level
self.photometric = photometric
self.hasalpha = hasalpha
self.resolution = resolution
self.fp2int = fp2int... |
class RatingOutcomeModelGenerator(keras.utils.Sequence):
def __init__(self, data_root, phase, batch_size, use_feature=True, use_exposure=True, shuffle=True):
assert (phase in ['train', 'val', 'test'])
self.phase = phase
self.batch_size = batch_size
self.use_feature = use_feature
... |
.ddblocal
def test_transaction_write_with_version_attribute_condition_failure(connection):
foo = Foo(21)
foo.save()
foo2 = Foo(21)
with pytest.raises(TransactWriteError) as exc_info:
with TransactWrite(connection=connection) as transaction:
transaction.save(Foo(21))
assert (exc_i... |
_db
def test_cannot_propose_a_talk_as_unlogged_user(graphql_client, conference_factory):
conference = conference_factory(topics=('my-topic',), languages=('it',), submission_types=('talk',), durations=('50',), audience_levels=('Beginner',))
(resp, _) = _submit_talk(graphql_client, conference)
assert (resp['e... |
def test_get_rect_from_points_given_topright_bottomleft():
rect = utils.get_rect_from_points(QtCore.QPointF(50, (- 20)), QtCore.QPointF((- 30), 40))
assert (rect.topLeft().x() == (- 30))
assert (rect.topLeft().y() == (- 20))
assert (rect.bottomRight().x() == 50)
assert (rect.bottomRight().y() == 40) |
def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None):
end_points = {}
def add_and_check_final(name, net):
end_points[name] = net
return (name == final_endpoint)
with tf.variable_scope(scope, 'InceptionV4', [inputs]):
with slim.arg_scope([slim.conv2d, slim.max_pool2d, ... |
def correlated_to_datum_inner(e):
if isinstance(e, W_Correlated):
return correlated_to_datum_inner(e.get_obj())
elif isinstance(e, W_List):
a = correlated_to_datum_inner(e.car())
d = correlated_to_datum_inner(e.cdr())
if ((a is e.car()) and (d is e.cdr())):
return e
... |
('document.paragraphs is a list containing three paragraphs')
def then_document_paragraphs_is_a_list_containing_three_paragraphs(context):
document = context.document
paragraphs = document.paragraphs
assert isinstance(paragraphs, list)
assert (len(paragraphs) == 3)
for paragraph in paragraphs:
... |
class Line():
def __init__(self, v1, v2):
self.a = (v2.y - v1.y)
self.b = (v1.x - v2.x)
self.c = v2.cross(v1)
def __call__(self, p):
return (((self.a * p.x) + (self.b * p.y)) + self.c)
def intersection(self, other):
if (not isinstance(other, Line)):
return... |
def test_lambert_cylindrical_equal_area_scale_operation__defaults():
lceaop = LambertCylindricalEqualAreaScaleConversion()
assert (lceaop.name == 'unknown')
assert (lceaop.method_name == 'Lambert Cylindrical Equal Area')
assert (_to_dict(lceaop) == {'Latitude of 1st standard parallel': 0.0, 'Longitude o... |
class TestClique(QiskitOptimizationTestCase):
def setUp(self):
super().setUp()
self.k = 5
self.seed = 100
aqua_globals.random_seed = self.seed
self.num_nodes = 5
self.w = random_graph(self.num_nodes, edge_prob=0.8, weight_range=10)
(self.qubit_op, self.offset)... |
class MarkGenerator():
if TYPE_CHECKING:
skip: _SkipMarkDecorator
skipif: _SkipifMarkDecorator
xfail: _XfailMarkDecorator
parametrize: _ParametrizeMarkDecorator
usefixtures: _UsefixturesMarkDecorator
filterwarnings: _FilterwarningsMarkDecorator
def __init__(self, ... |
class TaggedInlineSingleAdminTest(AdminTestManager, TagTestManager, TestCase):
admin_cls = test_admin.SimpleMixedTestSingletagAdmin
tagged_model = test_models.SimpleMixedTest
model = test_models.SimpleMixedTest.singletag.tag_model
def setUpExtra(self):
self.site = admin.AdminSite(name='tagulous_... |
_params(node='x')
def test_outside_if(condition: str, satisfy_val: (int | None), fail_val: (int | None)) -> None:
nodes_ = builder.extract_node(f'''
def f1(x = {fail_val}):
if {condition}:
pass
return (
x #
)
def f2(x = {satisfy_val}):
if {condition}:... |
def unit_impulse(shape, idx=None, dtype=float):
shape = np.atleast_1d(shape)
if (idx is None):
idx = ((0,) * len(shape))
elif (idx == 'mid'):
idx = tuple((shape // 2))
elif (not hasattr(idx, '__iter__')):
idx = ((idx,) * len(shape))
return _unit_impulse_kernel(idx[0], size=sh... |
def ql_syscall_socketpair(ql: Qiling, domain: int, socktype: int, protocol: int, sv: int):
unpopulated_fd = (i for i in range(NR_OPEN) if (ql.os.fd[i] is None))
idx1 = next(unpopulated_fd, (- 1))
idx2 = next(unpopulated_fd, (- 1))
regreturn = (- 1)
if ((idx1 != (- 1)) and (idx2 != (- 1))):
v... |
class MinValueConstraint(ValidationConstraint):
name = 'minvalue'
def __init__(self, min_value, error_message=None):
error_message = (error_message or _('$label should be $min_value or greater'))
super().__init__(error_message=error_message)
self.min_value = min_value
def validate_pa... |
class AttrVI_ATTR_TCPIP_HOSTNAME(Attribute):
resources = [(constants.InterfaceType.tcpip, 'INSTR'), (constants.InterfaceType.tcpip, 'SOCKET')]
py_name = ''
visa_name = 'VI_ATTR_TCPIP_HOSTNAME'
visa_type = 'ViString'
default = NotAvailable
(read, write, local) = (True, False, False) |
class VaultClientFactory():
def __init__(self, base_url: str, role: str, auth_type: Authenticator, mount_point: str):
self.base_url = base_url
self.role = role
self.auth_type = auth_type
self.mount_point = mount_point
self.session = requests.Session()
self.session.hea... |
class SimpleMAStrategy(AbstractStrategy):
def __init__(self, ts: BacktestTradingSession, ticker: Ticker):
super().__init__(ts)
self.broker = ts.broker
self.order_factory = ts.order_factory
self.data_handler = ts.data_handler
self.ticker = ticker
def calculate_and_place_or... |
def test_run_shortcut_skip_parse(mock_pipe, monkeypatch):
shortcuts = {'arb pipe': {'pipeline_name': 'sc pipe', 'skip_parse': True}}
monkeypatch.setattr('pypyr.config.config.shortcuts', shortcuts)
out = run(pipeline_name='arb pipe')
assert (type(out) is Context)
assert (out == {})
assert (not ou... |
class TestParallel(TestNested):
def setUp(self):
super(TestParallel, self).setUp()
self.states = ['A', 'B', {'name': 'C', 'parallel': [{'name': '1', 'children': ['a', 'b'], 'initial': 'a', 'transitions': [['go', 'a', 'b']]}, {'name': '2', 'children': ['a', 'b'], 'initial': 'a', 'transitions': [['go'... |
def token_network_registry_state(chain_state, token_network_registry_address):
token_network_registry = TokenNetworkRegistryState(token_network_registry_address, [])
chain_state.identifiers_to_tokennetworkregistries[token_network_registry_address] = token_network_registry
return token_network_registry |
class RNN(nn.Module):
def __init__(self, input_size, hidden_size, output_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear((input_size + hidden_size), hidden_size)
self.i2o = nn.Linear((input_size + hidden_size), output_size)
self.softmax ... |
def test_check_unique():
with pytest.raises(NameNonUniqueError):
repair_names([np.nan], repair='check_unique')
with pytest.raises(NameNonUniqueError):
repair_names([''], repair='check_unique')
with pytest.raises(NameNonUniqueError):
repair_names(['a', 'a'], repair='check_unique')
... |
def resnext101_32x8d(deconv, delinear, channel_deconv, pretrained=False, progress=True, **kwargs):
kwargs['groups'] = 32
kwargs['width_per_group'] = 8
return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3], pretrained, progress, deconv=deconv, delinear=delinear, channel_deconv=channel_deconv, **kwargs... |
def test_plugin_config_repo_override(hatch, devpi, temp_dir_cache, helpers, published_project_name, config_file):
config_file.model.publish['index']['user'] = 'foo'
config_file.model.publish['index']['auth'] = 'bar'
config_file.model.publish['index']['ca-cert'] = 'cert'
config_file.model.publish['index'... |
def require_version(minver: str='0.0.0', maxver: str='4.0.0') -> Callable:
def parse(python_version: str) -> tuple[(int, ...)]:
try:
return tuple((int(v) for v in python_version.split('.')))
except ValueError as e:
msg = f'{python_version} is not a correct version : should be... |
class OCIModel(RegistryDataInterface):
def __init__(self):
self._legacy_image_id_handler = SyntheticIDHandler()
def set_id_hash_salt(self, id_hash_salt):
self._legacy_image_id_handler = SyntheticIDHandler(id_hash_salt)
def _resolve_legacy_image_id_to_manifest_row(self, legacy_image_id):
... |
def BuildHON(InputFileName, OutputNetworkFile):
RawTrajectories = ReadSequentialData(InputFileName)
(TrainingTrajectory, TestingTrajectory) = BuildTrainingAndTesting(RawTrajectories)
VPrint(len(TrainingTrajectory))
Rules = BuildRulesFastParameterFree.ExtractRules(TrainingTrajectory, MaxOrder, MinSupport... |
def get_perturbation_results(args, data, mask_model, mask_tokenizer, base_model, base_tokenizer, span_length=10, n_perturbations=1, method='DetectGPT'):
load_mask_model(args, mask_model)
torch.manual_seed(0)
np.random.seed(0)
train_text = data['train']['text']
train_label = data['train']['label']
... |
def doctest():
extension = 'sphinx.ext.doctest'
doctest_global_setup = '\nimport torch\nfrom torch import nn\n\nimport pystiche\n\nimport warnings\nwarnings.filterwarnings("ignore", category=FutureWarning)\n\nfrom unittest import mock\n\npatcher = mock.patch(\n "pystiche.enc.models.utils.ModelMultiLayerEncod... |
def test_register_action(mocker):
from solcore import registries
mock_gr = mocker.patch('solcore.registries.generic_register')
name = 'pre-process'
overwrite = False
reason_to_exclude = None
_action(name, overwrite=overwrite, reason_to_exclude=reason_to_exclude)
def solver(*args, **kwargs):
... |
def color_jitter_nonrand(image, brightness=0, contrast=0, saturation=0, hue=0):
with tf.name_scope('distort_color'):
def apply_transform(i, x, brightness, contrast, saturation, hue):
if ((brightness != 0) and (i == 0)):
x = tf.image.random_brightness(x, max_delta=brightness)
... |
def test_dataclass_with_field_init_is_false() -> None:
(first, second, second_child, third_child, third) = astroid.extract_node('\n from dataclasses import dataclass, field\n\n\n \n class First:\n a: int\n\n \n class Second(First):\n a: int = field(init=False, default=1)\n\n \n cl... |
def test_dependency_from_pep_508_with_not_in_op_marker() -> None:
name = 'jinja2 (>=2.7,<2.8); python_version not in "3.0,3.1,3.2" and extra == "export"'
dep = Dependency.create_from_pep_508(name)
assert (dep.name == 'jinja2')
assert (str(dep.constraint) == '>=2.7,<2.8')
assert (dep.in_extras == ['e... |
def _apply_bpe(model_path: str, in_path: str, out_path: str):
Args = namedtuple('Args', ['sentencepiece_model'])
args = Args(sentencepiece_model=model_path)
tokenizer = SentencepieceBPE(args)
with open(in_path) as f, open(out_path, 'w') as f_o:
for s in f:
f_o.write((tokenizer.encode... |
.parametrize('converter_cls', [BaseConverter, Converter])
def test_structure_literal_enum(converter_cls):
converter = converter_cls()
class Foo(Enum):
FOO = 1
BAR = 2
class ClassWithLiteral():
literal_field: Literal[Foo.FOO] = Foo.FOO
assert (converter.structure({'literal_field':... |
class DistModel(BaseModel):
def name(self):
return self.model_name
def initialize(self, model='net-lin', net='alex', colorspace='Lab', pnet_rand=False, pnet_tune=False, model_path=None, use_gpu=True, printNet=False, spatial=False, is_train=False, lr=0.0001, beta1=0.5, version='0.1', gpu_ids=[0]):
... |
def load_cpp_ext(ext_name):
root_dir = os.path.join(os.path.split(__file__)[0])
src_dir = os.path.join(root_dir, 'cpp_ht2im')
tar_dir = os.path.join(src_dir, 'build', ext_name)
os.makedirs(tar_dir, exist_ok=True)
srcs = (glob(f'{src_dir}/*.cu') + glob(f'{src_dir}/*.cpp'))
with warnings.catch_war... |
class LockTimeEdit(QWidget):
def __init__(self, parent=None):
QWidget.__init__(self, parent)
hbox = QHBoxLayout()
self.setLayout(hbox)
hbox.setContentsMargins(0, 0, 0, 0)
hbox.setSpacing(0)
self.locktime_raw_e = LockTimeRawEdit(self)
self.locktime_height_e = L... |
class LocalResource(SchemaBase):
cores: PositiveInt = Field(4, description='The number of cores to be allocated to the computation.')
memory: PositiveInt = Field(10, description='The amount of memory that should be allocated to the computation in GB.')
def local_options(self) -> Dict[(str, int)]:
re... |
.parametrize('file_format, filename, content', [('json', 'foo.json', '{"a":\n'), ('yaml', 'foo.yaml', 'a: {b\n'), ('yaml', 'foo.yaml', 'a: b\nc\n'), ('json5', 'foo.json5', '{"a":\n'), ('toml', 'foo.toml', 'abc\n')])
def test_instanceloader_invalid_data(tmp_path, file_format, filename, content, open_wide):
if ((file... |
class ExecutionUsage(Usage):
def __init__(self, asynchronous=False):
super().__init__(asynchronous)
self._recorder = dict()
def render(self, flush: bool=False) -> dict:
records = self._recorder
if flush:
self._recorder = dict()
return records
def usage_var... |
def decode_train(example):
features = tf.parse_single_example(example, features={'label': tf.FixedLenFeature([], tf.int64), 'FEA_SrcItemId': tf.FixedLenFeature([], tf.string), 'FEA_SrcItemCp': tf.FixedLenFeature([], tf.string), 'FEA_SrcItemFirstCat': tf.FixedLenFeature([], tf.string), 'FEA_SrcItemSecondCat': tf.Fix... |
def test_get_trail():
exc = Exception()
pytest.raises(AttributeError, (lambda : _raw_trail(exc)))
assert (list(get_trail(exc)) == [])
append_trail(exc, 'foo')
assert (list(get_trail(exc)) == ['foo'])
new_exc = Exception()
append_trail(new_exc, 'bar')
assert (list(get_trail(new_exc)) == [... |
def random_in_unit_spherical_caps(shape, origin, importance_sampled_list):
l = len(importance_sampled_list)
mask = (np.random.rand(shape) * l).astype(int)
mask_list = ([None] * l)
cosmax_list = ([None] * l)
ax_u_list = ([None] * l)
ax_v_list = ([None] * l)
ax_w_list = ([None] * l)
for i ... |
class MultiStepLrUpdater(BaseLrUpdater):
def __init__(self, milestones=[], gamma=0.1, **kwargs):
assert isinstance(milestones, (tuple, list))
self.milestones = milestones
self.gamma = gamma
super().__init__(**kwargs)
def get_lr(self, base_lr, cur_step, steps):
num_steps =... |
def _x_and_y_from_pubkey_bytes(pubkey: bytes) -> Tuple[(int, int)]:
assert isinstance(pubkey, bytes), f'pubkey must be bytes, not {type(pubkey)}'
pubkey_ptr = create_string_buffer(64)
ret = _libsecp256k1.secp256k1_ec_pubkey_parse(_libsecp256k1.ctx, pubkey_ptr, pubkey, len(pubkey))
if (not ret):
... |
def Get_Visual_Response(generator, num_img, layer_id):
LATENT_DIM = 512
noise_z = torch.randn(num_img, LATENT_DIM)
if torch.cuda.is_available():
noise_z = noise_z.to('cuda')
layer_response = Get_Layer_Output(generator, noise_z, layer_id)
(img_tensor, _) = generator([noise_z])
generated_i... |
def simulate_full_curve(parameters, Geff, Tcell, ivcurve_pnts=1000):
sde_args = pvsystem.calcparams_desoto(Geff, Tcell, alpha_sc=parameters['alpha_sc'], a_ref=parameters['a_ref'], I_L_ref=parameters['I_L_ref'], I_o_ref=parameters['I_o_ref'], R_sh_ref=parameters['R_sh_ref'], R_s=parameters['R_s'])
kwargs = {'bre... |
def spatial_svd_cp_example(config: argparse.Namespace):
data_pipeline = ImageNetDataPipeline(config)
model = models.resnet18(pretrained=True)
if config.use_cuda:
model.to(torch.device('cuda'))
model.eval()
accuracy = data_pipeline.evaluate(model, use_cuda=config.use_cuda)
logger.info('Or... |
class TestHTTPJSONCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('HTTPJSONCollector', {})
self.collector = HTTPJSONCollector(config, None)
def test_import(self):
self.assertTrue(HTTPJSONCollector)
(Collector, 'publish')
def test_should_work_with_real... |
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