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def test_MinMaxScaler_weights(decision_matrix):
dm = decision_matrix(seed=42, min_alternatives=10, max_alternatives=10, min_criteria=20, max_criteria=20, min_objectives_proportion=0.5)
expected = skcriteria.mkdm(matrix=dm.matrix, objectives=dm.objectives, weights=((dm.weights - np.min(dm.weights)) / (np.max(dm.... |
class Entity():
def __init__(self, name, comment=None, tags=[]):
self.id = name
self.comment = None
self.note(comment)
self.tags = []
self.tag(tags)
def note(self, comment):
if ((comment is not None) and (comment.strip() != '')):
self.comment = (commen... |
class JsonWriterTest(Json, 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": "a", "bar": "b"},', '{"foo": "c", "bar": "d"}]'))
()
... |
def _get_update_tickets_errors(response, input: UpdateAttendeeTicketInput) -> UpdateAttendeeTicketErrors:
errors = []
for field in ('attendee_name', 'attendee_email'):
if response.get(field):
errors.append(UpdateAttendeeTicketError(field=field, message=response[field][0]))
if response.ge... |
class Window(QWidget):
def __init__(self, parent=None):
super(Window, self).__init__(parent)
self.setupModel()
nameLabel = QLabel('Na&me:')
nameEdit = QLineEdit()
addressLabel = QLabel('&Address:')
addressEdit = QTextEdit()
typeLabel = QLabel('&Type:')
... |
class PairingManager():
def __init__(self):
self.enabled = False
self.enabled_automatically = False
self.agent_manager = BluezAgentManagerAPI.connect()
def register(self, server: AdvertisingAPI) -> None:
SystemBus().publish_object(PairingAgentAPI.path, PairingAgent(server))
d... |
def index_vars_to_types(entry, slice_ok=True):
if (isinstance(entry, (np.ndarray, Variable)) and hasattr(entry, 'dtype') and (entry.dtype == 'bool')):
raise AdvancedIndexingError('Invalid index type or slice for Subtensor')
if (isinstance(entry, Variable) and ((entry.type in invalid_scal_types) or (entr... |
class Telegraph():
__slots__ = ('_telegraph',)
def __init__(self, access_token=None, domain='telegra.ph'):
self._telegraph = TelegraphApi(access_token, domain)
def get_access_token(self):
return self._telegraph.access_token
async def create_account(self, short_name, author_name=None, aut... |
class AppendDictAction(argparse.Action):
def __init__(self, allow_commas=True, *args, **kwargs):
self.allow_commas = allow_commas
super(AppendDictAction, self).__init__(*args, **kwargs)
def __call__(self, parser, namespace, values, option_string=None):
items = (getattr(namespace, self.de... |
def get_attacker(attack_method, arch, predict, p, epsilon, num_steps, step_size, image_dim, image_size, grid_scale, sample_grid_num, sample_times, momentum=0.0, gamma=1.0, lam=0.0, ti_size=1, m=0, sigma=15):
if ((('SGM' in attack_method) or ('Hybrid' in attack_method)) and (gamma > 1)):
raise Exception('gam... |
def integral_mini_interval_Pprecision_CDFmethod(I, J, E):
integral_min_piece = integral_mini_interval_P_CDFmethod__min_piece(I, J, E)
e_min = min(E)
j_min = min(J)
j_max = max(J)
e_max = max(E)
i_min = min(I)
i_max = max(I)
d_min = max((i_min - j_max), (j_min - i_max))
d_max = max((i... |
def iload_pyrocko_events(file_path, segment, content):
from pyrocko import model as pmodel
for (iev, ev) in enumerate(pmodel.Event.load_catalog(file_path)):
nut = model.make_event_nut(file_segment=0, file_element=iev, codes=model.CodesX((ev.catalog or '')), tmin=ev.time, tmax=ev.time)
if ('event... |
def write_ts_properties(training_set_properties: dict) -> None:
training_set = constants.training_set
dict_path = f'{training_set[:(- 4)]}.csv'
with open(dict_path, 'w') as csv_file:
csv_writer = csv.writer(csv_file, delimiter=';')
for (key, value) in training_set_properties.items():
... |
def test_warn_deprecated_formatting(recwarn_always: pytest.WarningsRecorder) -> None:
warn_deprecated(old, '1.0', issue=1, instead=new)
got = recwarn_always.pop(TrioDeprecationWarning)
assert isinstance(got.message, Warning)
assert ('test_deprecate.old is deprecated' in got.message.args[0])
assert (... |
class ResNetConfig(PretrainedConfig):
model_type = 'resnet'
layer_types = ['basic', 'bottleneck']
def __init__(self, num_channels=3, embedding_size=64, hidden_sizes=[256, 512, 1024, 2048], depths=[3, 4, 6, 3], layer_type='bottleneck', hidden_act='relu', downsample_in_first_stage=False, out_features=None, **... |
_config
def test_hints_setting_unsetting(xmanager, conn):
w = None
def no_input_hint():
nonlocal w
w = conn.create_window(5, 5, 10, 10)
w.map()
conn.conn.flush()
try:
xmanager.create_window(no_input_hint)
assert xmanager.c.window.get_hints()['input']
h... |
class WrappedSubplan(operator):
def __init__(self, database, query, tuple_vars, vars):
self.database = database
self.query = query
self.tuple_vars = tuple_vars
self.vars = vars
def __repr__(self):
return (((((('Wrapped(' + self.query) + ',') + repr([x['tuple_var'] for x i... |
def test_enable_with_flag(hatch, devpi, temp_dir_cache, helpers, published_project_name, config_file):
config_file.model.publish['index']['user'] = devpi.user
config_file.model.publish['index']['auth'] = devpi.auth
config_file.model.publish['index']['ca-cert'] = devpi.ca_cert
config_file.model.publish['... |
class _DictionaryMock(dict):
def __init__(self, item):
super().__init__()
self._value = item
def __setitem__(self, key, item):
self._value = item
def __getitem__(self, key):
return self._value
def __repr__(self):
return repr("{{'*': {0}}}".format(self._value)) |
('evennia.server.server.LoopingCall', MagicMock())
('evennia.server.portal.amp.amp.BinaryBoxProtocol.transport')
class TestAMPClientSend(_TestAMP):
def test_msgserver2portal(self, mocktransport):
self._connect_client(mocktransport)
self.amp_client.send_MsgServer2Portal(self.session, text={'foo': 'ba... |
def get_preprocess_fn(is_training, is_pretrain):
if (FLAGS.image_size <= 32):
test_crop = False
else:
test_crop = True
return functools.partial(data_util.preprocess_image, height=FLAGS.image_size, width=FLAGS.image_size, is_training=is_training, color_distort=is_pretrain, test_crop=test_crop... |
class LogtalkLexer(RegexLexer):
name = 'Logtalk'
url = '
aliases = ['logtalk']
filenames = ['*.lgt', '*.logtalk']
mimetypes = ['text/x-logtalk']
version_added = '0.10'
tokens = {'root': [('^\\s*:-\\s', Punctuation, 'directive'), ('%.*?\\n', Comment), ('/\\*(.|\\n)*?\\*/', Comment), ('\\n', T... |
def on_episode_end(episode_summary, logger, global_step, steps_count):
episode_return = sum(episode_summary['reward'])
steps = (global_step + steps_count)
print('\nFinished episode with return: {}'.format(episode_return))
summary = {'training/episode_return': episode_return}
if ('cost' in episode_su... |
_fixtures(SqlAlchemyFixture, QueryFixture)
def test_query_as_sequence_last_sort_wins(sql_alchemy_fixture, query_fixture):
fixture = query_fixture
with sql_alchemy_fixture.persistent_test_classes(fixture.MyObject):
[object1, object2, object3] = fixture.objects
fixture.query_as_sequence.sort(key=f... |
.parametrize('case', [np.array([[0, 5, 1], [1, 6, 1], [2, 7, 0.5]]), [[0, 5, 'red'], (1, 6, 'blue'), [2, 7, {'this': 'also works'}]], pd.DataFrame([[0, 5, 'red'], [1, 6, 'blue'], [2, 7, 'something']], columns=['lat', 'lng', 'color'])])
def test_fast_marker_cluster_data(case):
data = FastMarkerCluster(case).data
... |
def CISD(mf, frozen=None, mo_coeff=None, mo_occ=None):
if mf.istype('UHF'):
return UCISD(mf, frozen, mo_coeff, mo_occ)
elif mf.istype('ROHF'):
from pyscf import lib
lib.logger.warn(mf, 'RCISD method does not support ROHF method. ROHF object is converted to UHF object and UCISD method is ... |
def get_data_loader(max_bag_size: int=20) -> Generator[(Batch, None, None)]:
for _ in range(EPOCH_SIZE):
values = []
lengths = []
for _ in range(len(TABLES)):
for _ in range(BATCH_SIZE):
length = torch.randint(max_bag_size, (1,))
values.append(torc... |
def max_status(left: TestStatus, right: TestStatus) -> TestStatus:
if (left == right):
return left
elif ((left == TestStatus.TEST_CRASHED) or (right == TestStatus.TEST_CRASHED)):
return TestStatus.TEST_CRASHED
elif ((left == TestStatus.FAILURE) or (right == TestStatus.FAILURE)):
retu... |
class CmdDarkHelp(Command):
key = 'help'
locks = 'cmd:all()'
help_category = 'TutorialWorld'
def func(self):
string = "Can't help you until you find some light! Try looking/feeling around for something to burn. You shouldn't give up even if you don't find anything right away."
self.calle... |
def markup_inline_word(format, pronunc):
pronunc = as_utf8(pronunc)
format = checkSetting(format, 'inline_format', '%s')
if (type(format) in [bytes, unicode]):
if (type(format) == unicode):
format = format.encode('utf-8')
return (format % pronunc)
else:
return format(... |
def _sparse_to_arrays(sparray, ids=None):
sparray = sparray.tocoo(copy=False)
if (ids is not None):
ids = np.asarray(ids)
if (sparray.shape[0] != ids.shape[0]):
raise ValueError(f'The length of ids ({ids.shape[0]}) does not match the shape of sparse {sparray.shape}.')
sorter ... |
def main(path_list, target_file_path, search_item, mask):
script_state = True
while script_state:
dsz.ui.Echo(list_size_status(path_list, mask), dsz.WARNING)
num_to_process = user_prompt()
dsz.ui.Echo('Processing {0} files'.format(num_to_process))
if (num_to_process > len(path_li... |
class Effect3212(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Auto-Targeting Missiles')), 'aoeCloudSize', (container.g... |
class ConcatSentencesDataset(FairseqDataset):
def __init__(self, *datasets):
super().__init__()
self.datasets = datasets
assert all(((len(ds) == len(datasets[0])) for ds in datasets)), 'datasets must have the same length'
def __getitem__(self, index):
return torch.cat([ds[index] ... |
class Result():
extension = None
def _is_valid_type(cls, type_):
return True
def peek(cls, filepath):
return ResultMetadata(*archive.Archiver.peek(filepath))
def extract(cls, filepath, output_dir):
return archive.Archiver.extract(filepath, output_dir)
def load(cls, filepath):... |
def create_window(window):
def create():
browser = BrowserView.BrowserForm(window, cache_dir)
BrowserView.instances[window.uid] = browser
if window.hidden:
browser.Opacity = 0
browser.Show()
browser.Hide()
browser.Opacity = 1
else:
... |
class SwitchGraphDataRegion(GraphDataRegion):
def __init__(self, key, exec_comm_id, pid, tid, comm, thread_id, comm_id):
super(SwitchGraphDataRegion, self).__init__(key)
self.title = ((((str(pid) + ' / ') + str(tid)) + ' ') + comm)
self.ordinal = ((str(pid).rjust(16) + str(exec_comm_id).rjus... |
class PixelShuffleBlcok(nn.Module):
def __init__(self, in_feat, num_feat, num_out_ch):
super(PixelShuffleBlcok, self).__init__()
self.conv_before_upsample = nn.Sequential(nn.Conv2d(in_feat, num_feat, 3, 1, 1), nn.LeakyReLU(inplace=True))
self.upsample = nn.Sequential(nn.Conv2d(num_feat, (4 *... |
_flax
class FlaxBigBirdModelTest(FlaxModelTesterMixin, unittest.TestCase):
all_model_classes = ((FlaxBigBirdForCausalLM, FlaxBigBirdModel, FlaxBigBirdForPreTraining, FlaxBigBirdForMaskedLM, FlaxBigBirdForMultipleChoice, FlaxBigBirdForQuestionAnswering, FlaxBigBirdForSequenceClassification, FlaxBigBirdForTokenClassi... |
class Hotel(Accommodation):
roomNumber: int = 0
def __init__(self, name: str='Hotel'):
self.name = name
def setRoomNumber(self, n: int) -> None:
self.roomNumber = n
def getRoomNumber(self) -> int:
return self.roomNumber
def getLocation(self) -> str:
if (self.roomNumbe... |
(name='test-dist')
def test_dist(session: nox.Session) -> None:
tmp_dir = Path(session.create_tmp())
dist = (tmp_dir / 'dist')
_build(session, dist)
python_versions = (session.posargs or PYTHON_ALL_VERSIONS)
for version in python_versions:
session.notify(f'_test_sdist-{version}', [str(dist)]... |
def test_var_replacement():
X_mean = pm.floatX(np.linspace(0, 10, 10))
y = pm.floatX(np.random.normal((X_mean * 4), 0.05))
inp_size = pytensor.shared(np.array(10, dtype='int64'), name='inp_size')
with pm.Model():
inp = pm.Normal('X', X_mean, size=(inp_size,))
coef = pm.Normal('b', 4.0)
... |
.django_db
def test_django_assert_num_queries_db_connection(django_assert_num_queries: DjangoAssertNumQueries) -> None:
from django.db import connection
with django_assert_num_queries(1, connection=connection):
Item.objects.create(name='foo')
with django_assert_num_queries(1, connection=None):
... |
def build_from_cfg(cfg, registry, default_args=None):
if (not isinstance(cfg, dict)):
raise TypeError(f'cfg must be a dict, but got {type(cfg)}')
if ('NAME' not in cfg):
if ((default_args is None) or ('NAME' not in default_args)):
raise KeyError(f'''`cfg` or `default_args` must conta... |
class GetInlineBotResults():
async def get_inline_bot_results(self: 'pyrogram.Client', bot: Union[(int, str)], query: str='', offset: str='', latitude: float=None, longitude: float=None):
try:
return (await self.invoke(raw.functions.messages.GetInlineBotResults(bot=(await self.resolve_peer(bot))... |
class CocoEval(keras.callbacks.Callback):
def __init__(self, generator, tensorboard=None, threshold=0.05):
self.generator = generator
self.threshold = threshold
self.tensorboard = tensorboard
super(CocoEval, self).__init__()
def on_epoch_end(self, epoch, logs=None):
logs ... |
def prepare_roidb(imdb):
roidb = imdb.roidb
if (not (imdb.name.startswith('coco') or imdb.name.startswith('vg'))):
sizes = [PIL.Image.open(imdb.image_path_at(i)).size for i in range(imdb.num_images)]
for i in range(len(imdb.image_index)):
roidb[i]['img_id'] = imdb.image_id_at(i)
roid... |
class Paint(object):
pen_size = 5.0
color = 'black'
def __init__(self):
self.root = Tk()
self.pen_button = Button(self.root, text='pen', command=self.use_pen)
self.pen_button.grid(row=0, column=0)
self.brush_button = Button(self.root, text='brush', command=self.use_brush)
... |
class TestCygwinCCompiler(support.TempdirManager):
def _get_config_h_filename(self):
return self.python_h
.skipif('sys.platform != "cygwin"')
.skipif('not os.path.exists("/usr/lib/libbash.dll.a")')
def test_find_library_file(self):
from distutils.cygwinccompiler import CygwinCCompiler
... |
_lazy('cudf')
def get_device_memory_objects_register_cudf():
import cudf.core.frame
import cudf.core.index
import cudf.core.multiindex
import cudf.core.series
(cudf.core.frame.Frame)
def get_device_memory_objects_cudf_frame(obj):
ret = []
for col in obj._data.columns:
... |
def process_pattern(tree, vars):
if ((len(tree.children) > 1) and isinstance(tree.children[1], Node) and (tree.children[1].label == 'pattern_object_list')):
list = tree.children[1]
res = []
for l in list.children:
if (not isinstance(l, Node)):
continue
... |
class FocalLoss(nn.Module):
def __init__(self, alpha: float=0.25, gamma: float=2.0, loss_weight: float=2.0) -> None:
super(FocalLoss, self).__init__()
self.alpha = alpha
self.gamma = gamma
self.loss_weight = loss_weight
def forward(self, pred: torch.Tensor, target: torch.Tensor, ... |
class CommonOptions():
head: ((Sequence[VdomDict] | VdomDict) | str) = (html.title('ReactPy'), html.link({'rel': 'icon', 'href': '/_reactpy/assets/reactpy-logo.ico', 'type': 'image/x-icon'}))
url_prefix: str = ''
serve_index_route: bool = True
def __post_init__(self) -> None:
if (self.url_prefix... |
class PurePyShpWrapper(fileio.FileIO):
FORMATS = ['shp', 'shx']
MODES = ['w', 'r', 'wb', 'rb']
def __init__(self, *args, **kwargs):
fileio.FileIO.__init__(self, *args, **kwargs)
self.dataObj = None
if ((self.mode == 'r') or (self.mode == 'rb')):
self.__open()
elif... |
def randomFFD(img_name, ffd_type=1, random_type=0, control_points=(20, 20, 20), num_samples=5, **kwargs):
img_name = os.path.basename(img_name)
image_suffix = kwargs.pop('image_suffix', 'image.nii.gz')
label_suffix = kwargs.pop('label_suffix', 'label.nii.gz')
lab_name = img_name.replace(image_suffix, la... |
def interpolate_background(a, b, blend):
if ((type(a) is Background) and (type(b) is Background)):
return Background(color=interpolate_color(a.color, b.color, blend))
else:
return BackgroundGradient(color_top=interpolate_color(a.color_top, b.color_top, blend), color_bottom=interpolate_color(a.co... |
def node_view_and_apply_settings(wizard):
pp = pprint.PrettyPrinter(indent=4)
saves = False
game_index_txt = 'No changes to save for Game Index.'
if hasattr(wizard, 'game_index_listing'):
if (wizard.game_index_listing != settings.GAME_INDEX_LISTING):
game_index_txt = 'No changes to s... |
def load_kasvs_dh_vectors(vector_data):
vectors = []
data: typing.Dict[(str, typing.Any)] = {'fail_z': False, 'fail_agree': False}
for line in vector_data:
line = line.strip()
if ((not line) or line.startswith('#')):
continue
if line.startswith('P = '):
data['... |
def fmt_phi_structure(ps, title='Phi-structure', subsystem=True):
distinctions = len(ps.distinctions)
if ps.requires_filter_relations:
relations = sum_phi = sum_phi_r = sii = selectivity = '[requires filter]'
elif (ps.relations is None):
relations = sum_phi = sum_phi_r = sii = selectivity = ... |
class TestGUI(WrapperTester):
script_name = 'bar-script.pyw'
wrapper_source = win_launcher_exe('gui')
wrapper_name = 'bar.exe'
script_tmpl = textwrap.dedent("\n #!%(python_exe)s\n import sys\n f = open(sys.argv[1], 'wb')\n bytes_written = f.write(repr(sys.argv[2]).encode('utf... |
class BackgroundKnowledge(object):
def __init__(self):
self.forbidden_rules_specs: Set[Tuple[(Node, Node)]] = set()
self.forbidden_pattern_rules_specs: Set[Tuple[(str, str)]] = set()
self.required_rules_specs: Set[Tuple[(Node, Node)]] = set()
self.required_pattern_rules_specs: Set[Tu... |
def validate_search(args, val_data, device, model):
model.eval()
choice_dict = {}
val_loss = 0.0
val_top1 = AvgrageMeter()
val_top5 = AvgrageMeter()
criterion = nn.CrossEntropyLoss()
choice = random_choice(m=args.m)
while (choice in check_dict):
print('Duplicate Index !')
... |
class DefaultStyle(Style):
name = 'default'
background_color = '#f8f8f8'
styles = {Whitespace: '#bbbbbb', Comment: 'italic #3D7B7B', Comment.Preproc: 'noitalic #9C6500', Keyword: 'bold #008000', Keyword.Pseudo: 'nobold', Keyword.Type: 'nobold #B00040', Operator: '#666666', Operator.Word: 'bold #AA22FF', Nam... |
def export_cli(args):
ip = args.ip
csv_path = args.csv_path
log_level = logging.INFO
logger = logging.getLogger(__name__)
logger.setLevel(log_level)
ch = logging.StreamHandler(sys.stdout)
formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
ch.setFormatter(formatter... |
class _SectBlockElementIterator():
_compiled_blocks_xpath: (etree.XPath | None) = None
_compiled_count_xpath: (etree.XPath | None) = None
def __init__(self, sectPr: CT_SectPr):
self._sectPr = sectPr
def iter_sect_block_elements(cls, sectPr: CT_SectPr) -> Iterator[BlockElement]:
return cl... |
class PresetMenu(QtWidgets.QMenu):
action_customize: QtGui.QAction
action_delete: QtGui.QAction
action_history: QtGui.QAction
action_export: QtGui.QAction
action_duplicate: QtGui.QAction
action_map_tracker: QtGui.QAction
action_required_tricks: QtGui.QAction
action_import: QtGui.QAction
... |
def is_hash160(addr):
if (not addr):
return False
if (not isinstance(addr, str)):
return False
if (not (len(addr) == 40)):
return False
for char in addr:
if (((char < '0') or (char > '9')) and ((char < 'A') or (char > 'F')) and ((char < 'a') or (char > 'f'))):
... |
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, zero_init_residual=False):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.layer1... |
('/gitlab/callback/trigger', methods=['GET'])
_show_if(features.GITLAB_BUILD)
_session_login
def attach_gitlab_build_trigger():
state = request.args.get('state', None)
if (not state):
abort(400)
state = state[len('repo:'):]
try:
[namespace, repository] = state.split('/')
except Value... |
class Checker():
raw_options: InitVar[Optional[Options]] = None
options: Options = field(init=False)
arg_spec_cache: ArgSpecCache = field(init=False)
ts_finder: TypeshedFinder = field(init=False)
reexport_tracker: ImplicitReexportTracker = field(init=False)
callable_tracker: CallableTracker = fi... |
def _create_hypotheses_widgets() -> dict[(str, tuple[(str, QtWidgets.QWidget)])]:
hypotheses = btrack.optimise.hypothesis.H_TYPES
tooltips = ['Hypothesis that a tracklet is a false positive detection. Always required.', 'Hypothesis that a tracklet starts at the beginning of the movie or edge of the field of vie... |
class Pool(object):
def __init__(self, nworkers=0, name='Pool'):
self._closed = False
self._tasks = task_group()
self._pool = ([None] * default_num_threads())
def apply(self, func, args=(), kwds=dict()):
return self.apply_async(func, args, kwds).get()
def map(self, func, iter... |
class Trainer(object):
def __init__(self, ps_rref):
self.ps_rref = ps_rref
self.loss_fn = nn.MSELoss()
self.one_hot_indices = torch.LongTensor(batch_size).random_(0, num_classes).view(batch_size, 1)
def get_next_batch(self):
for _ in range(num_batches):
inputs = torch... |
class TestConvertSelection(EndianTest):
def setUp(self):
self.req_args_0 = {'property': , 'requestor': , 'selection': , 'target': , 'time': }
self.req_bin_0 = b"\x18\x00\x06\x00\x0b'no7\xd6\nPTp4&;\xd2\xbck\xd3\x18\xcaQ"
def testPackRequest0(self):
bin = request.ConvertSelection._request... |
def model_setenv(cpu_only):
import random
random.seed(42)
torch.manual_seed(42)
if cpu_only:
os.environ['DEVICE'] = 'cpu'
elif ((os.environ.get('DEVICE') != 'cuda') and (os.environ.get('DEVICE') != 'cpu')):
os.environ['DEVICE'] = ('cuda' if torch.cuda.is_available() else 'cpu')
i... |
class MobileNetV2(nn.Module):
def __init__(self, num_classes=1000, width_mult=1.0):
super(MobileNetV2, self).__init__()
self.cfgs = [[1, 16, 1, 1], [6, 24, 2, 2], [6, 32, 3, 2], [6, 64, 4, 2], [6, 96, 3, 1], [6, 160, 3, 2], [6, 320, 1, 1]]
input_channel = _make_divisible((32 * width_mult), (... |
def test_admin_session_download_permalink_no_layout(clean_database, mock_emit_session_update, flask_app, mock_audit):
user1 = database.User.create(id=1234, name='The Name')
session = database.MultiplayerSession.create(id=1, name='Debug', state=MultiplayerSessionVisibility.VISIBLE, creator=user1)
database.Mu... |
def print1d(comp, type, wid, label, arr, doinp=False, **kwargs):
if (arr is None):
return
if doinpprt(label, arr, doinp=False, **kwargs):
return
if (label != ' '):
labstr = ('%6s=' % label)
else:
labstr = ' '
(npl, pkstr, fwid) = printpars(type, wid)
i = 0
nd... |
.skipif('sys.platform == "win32" and platform.python_implementation() == "PyPy"')
def test_xdist_no_data_collected(testdir):
testdir.makepyfile(target='x = 123')
script = testdir.makepyfile('\nimport target\ndef test_foobar():\n assert target.x == 123\n')
result = testdir.runpytest('-v', '--cov=target', ... |
class ScrimsSlotReserve(ScrimsView):
def __init__(self, ctx: Context, scrim: Scrim):
super().__init__(ctx)
self.ctx = ctx
self.record = scrim
async def initial_embed(self):
_e = discord.Embed(color=self.bot.color)
_e.description = f'''**{self.record} - Reserved Slots**
... |
class PackageInclude(Include):
def __init__(self, base: Path, include: str, formats: (list[str] | None)=None, source: (str | None)=None, target: (str | None)=None) -> None:
self._package: str
self._is_package = False
self._is_module = False
self._source = source
self._target ... |
def connection_options(func):
('-m', '--metadir', default='yadagemeta', help='directory to store workflow metadata')
('--accept-metadir/--no-accept-metadir', default=True)
('-r', '--controller', default='frommodel')
('-o', '--ctrlopt', multiple=True, default=None, help='options for the workflow controll... |
def check_master_taint(master_nodes, master_label):
schedulable_masters = []
for master_node in master_nodes:
node_info = get_node_info(master_node)
node = node_info.metadata.name
NoSchedule_taint = False
try:
if (node_info.spec is not None):
if (node_... |
class RestoreTest(unittest.TestCase):
def get_rfcs(self):
base_rf = _repo_shadow._RestoreFile(restore_base_rp, restore_base_rp, [])
rfs = base_rf.yield_sub_rfs()
rfcs = []
for rf in rfs:
if (rf.mirror_rp.dirsplit()[1] in [b'dir']):
log.Log("skipping 'dir'"... |
class TestAttributes():
def test_sets_attrs(self):
class C():
x = attr.ib()
assert ('x' == C.__attrs_attrs__[0].name)
assert all((isinstance(a, Attribute) for a in C.__attrs_attrs__))
def test_empty(self):
class C3():
pass
assert ('C3()' == repr(C3... |
_ARCH_REGISTRY.register()
class CamAwareBaseline(Baseline):
def forward(self, batched_inputs):
outputs = super().forward(batched_inputs)
if self.training:
camids = batched_inputs['camids'].long().to(self.device)
outputs['camids'] = camids
return outputs
el... |
def run(params):
dataset = get_criteo_dataset(params)
train_dataset = dataset['train']
test_dataset = dataset['test']
train_data = tf.data.Dataset.from_tensor_slices((dict(train_dataset['x']), train_dataset['labels'], train_dataset['delay_labels']))
train_data = train_data.batch(params['batch_size']... |
class SaveLogger(object):
def __init__(self, file_name, save_every=10, verbose=0):
self.file_name = file_name
self.save_every = save_every
self.verbose = verbose
def __repr__(self):
return ('%s(file_name="%s", save_every=%s)' % (self.__class__.__name__, self.file_name, self.save_... |
_db
def test_submit_talk_with_not_valid_language_code(graphql_client, user, conference_factory, topic_factory):
graphql_client.force_login(user)
conference = conference_factory(topics=('my-topic',), languages=('it',), submission_types=('tutorial',), active_cfp=True, durations=('50',), audience_levels=('Beginner... |
('read-linklet-bundle-hash', [values.W_InputPort], simple=False)
def read_linklet_bundle_hash(in_port, env, cont):
from pycket.racket_entry import get_primitive
from pycket.fasl import Fasl
from pycket.util import console_log
current_load_relative_dir_path = get_primitive('current-load-relative-director... |
class DataTable():
def __init__(self, num_rows: int, num_columns: int, column_names: list, bokeh_document: Optional[BokehDocument], row_index_names: list=None):
self.total = (num_rows * num_columns)
self.row_names = row_index_names
if row_index_names:
data_frame = pd.DataFrame(in... |
def random_inj_per_layer(pfi: core.FaultInjection, min_val: int=(- 1), max_val: int=1):
(batch, layer_num, c_rand, h_rand, w_rand, value) = ([] for i in range(6))
b = random_batch_element(pfi)
for i in range(pfi.get_total_layers()):
(layer, C, H, W) = random_neuron_location(pfi, layer=i)
bat... |
def _create_view(tensor, stride, inner_dims):
outdim = ((tensor.size(0) - stride) + 1)
size = (outdim, stride, *inner_dims)
inner_dim_prod = int(np.prod(inner_dims))
multidim_stride = ([inner_dim_prod, inner_dim_prod] + ([1] * len(inner_dims)))
return torch.as_strided(tensor, size=size, stride=multi... |
def loadLSTMLMCheckpoint(pathLSTMCheckpoint, pathData):
model_args = argparse.Namespace(task='language_modeling', output_dictionary_size=(- 1), data=pathData, path=pathLSTMCheckpoint)
task = tasks.setup_task(model_args)
(models, _model_args) = checkpoint_utils.load_model_ensemble([model_args.path], task=tas... |
class SpinBox(Input):
_attribute_decorator('WidgetSpecific', 'Defines the actual value for the spin box.', float, {'possible_values': '', 'min': (- 65535), 'max': 65535, 'default': 0, 'step': 1})
def attr_value(self):
return self.attributes.get('value', '0')
_value.setter
def attr_value(self, va... |
('the deleted latent style is not in the latent styles collection')
def then_the_deleted_latent_style_is_not_in_the_collection(context):
latent_styles = context.latent_styles
try:
latent_styles['Normal']
except KeyError:
return
raise AssertionError('Latent style not deleted') |
def init_argparse():
parser = argparse.ArgumentParser(usage='%(prog)s --domain example.com --file subdomains2ips.txt', description='Generate Network Graph For Sudomy.')
parser.add_argument('--domain', type=str)
parser.add_argument('--file', type=str, help='subdomains2ips.txt')
return parser |
def build_dataset_iter(datasets, fields, opt, is_train=True, task_type='task'):
if is_train:
if (task_type == 'task'):
batch_size = opt.batch_size
else:
batch_size = opt.batch_size2
else:
batch_size = opt.valid_batch_size
if (is_train and (opt.batch_type == 't... |
def _create_completion(model: str, messages: list, stream: bool, temperature: float=0.7, **kwargs):
payload = {'temperature': 0.7, 'messages': messages, 'model': model, 'stream': True}
headers = {'user-agent': 'ChatX/39 CFNetwork/1408.0.4 Darwin/22.5.0'}
response = requests.post(' json=payload, headers=head... |
def cylinder(bm, radius=1, height=2, segs=10):
circle = bmesh.ops.create_circle(bm, cap_ends=True, cap_tris=False, segments=segs, radius=radius)
verts = circle['verts']
face = list(verts[0].link_faces)
cylinder = bmesh.ops.extrude_discrete_faces(bm, faces=face)
bmesh.ops.translate(bm, verts=cylinder... |
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