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class TestCAlgorithms(unittest.TestCase):
def test_get_julian_day_from_gregorian(self):
self.assertRaises(ValueError, alg_p.get_julian_day_from_gregorian_date, 2016, 2, 30)
self.assertRaises(ValueError, alg_p.get_julian_day_from_gregorian_date, 2015, 2, 29)
self.assertRaises(ValueError, alg_... |
def test_set_size_custom() -> None:
instance = printer.Dummy()
instance.set_with_default(custom_size=True, width=8, height=7)
expected_sequence = (TXT_SIZE, bytes(((TXT_STYLE['width'][8] + TXT_STYLE['height'][7]),)), TXT_STYLE['flip'][False], TXT_STYLE['smooth'][False], TXT_STYLE['bold'][False], TXT_STYLE['... |
def recursive_render(template_dir: Path, environment: Environment, _root_dir: (str | os.PathLike[str])='.') -> list[str]:
rendered_paths: list[str] = []
for (root, file) in ((Path(root), file) for (root, _, files) in os.walk(template_dir) for file in files if ((not any((elem.startswith('.') for elem in Path(roo... |
class Bookmark():
def for_widget(cls, description, query_arguments=None, **bookmark_kwargs):
return Bookmark('', '', description, query_arguments=query_arguments, ajax=True, **bookmark_kwargs)
def __init__(self, base_path, relative_path, description, query_arguments=None, ajax=False, detour=False, exact... |
class WorkflowEnabledMeta(base.WorkflowEnabledMeta, models.base.ModelBase):
def _find_workflows(mcs, attrs):
workflows = {}
for (k, v) in attrs.items():
if isinstance(v, StateField):
workflows[k] = v
return workflows
def _add_workflow(mcs, field_name, state_fi... |
class QtHandler(QtHandlerBase):
pin_signal = pyqtSignal(object, object)
def __init__(self, win, pin_matrix_widget_class, device):
super(QtHandler, self).__init__(win, device)
self.pin_signal.connect(self.pin_dialog)
self.pin_matrix_widget_class = pin_matrix_widget_class
def get_pin(s... |
class DialogSelectTrack(SimpleBuilderApp):
def __init__(self, data_path=None, tracks=None, okmethod=None, gpx=None):
logging.debug('>>')
self.okmethod = okmethod
self.tracks = tracks
self.gpx = gpx
SimpleBuilderApp.__init__(self, 'selecttrackdialog.ui')
logging.debug(... |
def HeronFit(DB, Gamedata, Saveddata):
print('Creating Heron - RemoteSebo')
item = DB['gamedata_session'].query(Gamedata['Item']).filter((Gamedata['Item'].name == 'Heron')).first()
ship = Saveddata['Ship'](item)
fit = Saveddata['Fit'](ship, 'Heron - RemoteSebo')
mod = Saveddata['Module'](DB['db'].ge... |
def _wait_for_condition(self, condition=None, timeout=None, poll_frequency=0.5, ignored_exceptions=None):
condition = functools.partial((condition or self.visit_condition), self)
timeout = (timeout or self.wait_time)
return wait.WebDriverWait(self.driver, timeout, poll_frequency=poll_frequency, ignored_exce... |
class Ui_MainWindow(QtWidgets.QMainWindow):
def __init__(self):
super(Ui_MainWindow, self).__init__()
def setupUi(self, MainWindow):
MainWindow.setObjectName('MainWindow')
MainWindow.setEnabled(True)
MainWindow.resize(1000, 650)
self.centralwidget = QtWidgets.QWidget(Main... |
def format_version(version: ScmVersion) -> str:
log.debug('scm version %s', version)
log.debug('config %s', version.config)
if version.preformatted:
assert isinstance(version.tag, str)
return version.tag
main_version = _entrypoints._call_version_scheme(version, 'setuptools_scm.version_sc... |
def prepare_class_def(path: str, module_name: str, cdef: ClassDef, errors: Errors, mapper: Mapper) -> None:
ir = mapper.type_to_ir[cdef.info]
info = cdef.info
attrs = get_mypyc_attrs(cdef)
if (attrs.get('allow_interpreted_subclasses') is True):
ir.allow_interpreted_subclasses = True
if (attr... |
class SpatialGroupEnhance(nn.Module):
def __init__(self, groups):
super().__init__()
self.groups = groups
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.weight = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.bias = nn.Parameter(torch.zeros(1, groups, 1, 1))
self.sig =... |
def import_gmsh_mesh(filename, analysis=None):
assert (filename[(- 4):] == u'.geo')
mesh_filename = (filename[:(- 4)] + u'.unv')
cmdlist = [u'gmsh', u'-format', u'unv', filename, u'-o', mesh_filename, u'-']
error = _run_command(cmdlist)
if (not error):
if analysis:
docName = anal... |
def create_straight_road(road_id, length=100, junction=(- 1), n_lanes=1, lane_offset=3):
warn('create_straight_road should not be used anymore, please use the create_road function instead', DeprecationWarning, 2)
line1 = Line(length)
planview1 = PlanView()
planview1.add_geometry(line1)
lanesec1 = La... |
class ResponseProcessingOpener(OpenerDirector):
def open(self, fullurl, data=None, timeout=_sockettimeout._GLOBAL_DEFAULT_TIMEOUT):
def bound_open(fullurl, data=None, timeout=_sockettimeout._GLOBAL_DEFAULT_TIMEOUT):
return OpenerDirector.open(self, fullurl, data, timeout)
return wrapped_... |
class PascalVocGenerator(Generator):
def __init__(self, data_dir, set_name, classes=voc_classes, image_extension='.jpg', skip_truncated=False, skip_difficult=False, **kwargs):
self.data_dir = data_dir
self.set_name = set_name
self.classes = classes
self.image_names = [l.strip().split... |
class Scheduler(abc.ABC, Generic[T]):
def __init__(self, backend: str, session_name: str) -> None:
self.backend = backend
self.session_name = session_name
def close(self) -> None:
pass
def submit(self, app: AppDef, cfg: T, workspace: Optional[str]=None) -> str:
resolved_cfg =... |
def test_expand_multiple_levels(df_expand):
expected = df_expand.pivot_wider('id', ('year', 'gender'), 'percentage', names_expand=True, flatten_levels=False)
actual = df_expand.complete('year', 'gender', 'id').pivot(index='id', columns=('year', 'gender'), values='percentage')
assert_frame_equal(actual, expe... |
class BarthezConverter(SpmConverter):
def unk_id(self, proto):
unk_id = 3
return unk_id
def post_processor(self):
return processors.TemplateProcessing(single='<s> $A </s>', pair='<s> $A </s> </s> $B </s>', special_tokens=[('<s>', self.original_tokenizer.convert_tokens_to_ids('<s>')), ('<... |
def confirm_team_invite(code, user_obj):
found = find_matching_team_invite(code, user_obj)
code_found = False
for invite in find_organization_invites(found.team.organization, user_obj):
try:
code_found = True
add_user_to_team(user_obj, invite.team)
except UserAlreadyI... |
class SignUp():
async def sign_up(self: 'pyrogram.Client', phone_number: str, phone_code_hash: str, first_name: str, last_name: str='') -> 'types.User':
phone_number = phone_number.strip(' +')
r = (await self.invoke(raw.functions.auth.SignUp(phone_number=phone_number, first_name=first_name, last_nam... |
class Solution():
def checkPossibility(self, nums: List[int]) -> bool:
n = len(nums)
if ((n == 1) or (n == 2)):
return True
i = 0
nums1 = nums[:]
while (i < (n - 1)):
if (nums[i] <= nums[(i + 1)]):
i += 1
continue
... |
class TestNameCheckVisitor(TestNameCheckVisitorBase):
_passes()
def test_known_ordered(self):
from typing_extensions import OrderedDict
known_ordered = OrderedDict({1: 2})
bad_ordered = OrderedDict({'a': 'b'})
def capybara(arg: OrderedDict[(int, int)]) -> None:
pass
... |
.parametrize('stream', ['stdout', 'stderr'])
def test_exit_successful_output(qtbot, proc, py_proc, stream):
with qtbot.wait_signal(proc.finished, timeout=10000):
proc.start(*py_proc('\n import sys\n print("test", file=sys.{})\n sys.exit(0)\n '.format(stream))) |
def test_custom_css(pytester, css_file_path, expandvar):
result = run(pytester, 'report.html', cmd_flags=['--css', expandvar, '--css', 'two.css'])
result.assert_outcomes(passed=1)
path = pytester.path.joinpath('assets', 'style.css')
with open(str(path)) as f:
css = f.read()
assert_that(c... |
def average(dj_init=None, img_db=None, djs_file=None, avgs_file=None, pcas_file=None, op=None):
djs = load_dict(op['data_checkpoint'])
avgs = load_dict(op['average']['checkpoint'])
if ((- 1) not in djs):
assert (len(djs) == 0)
djs[(- 1)] = dj_init
AIF.pickle_dump(djs, op['data_checkp... |
class Trainer():
def __init__(self, G, D, latent_size, dataset, device, Gs=None, Gs_beta=(0.5 ** (32 / 10000)), Gs_device=None, batch_size=32, device_batch_size=4, label_size=0, data_workers=4, G_loss='logistic_ns', D_loss='logistic', G_reg='pathreg:2', G_reg_interval=4, G_opt_class='Adam', G_opt_kwargs={'lr': 0.00... |
def main(args):
print(args)
split_name = ('dev' if args.dev else 'train')
dataset_break = DatasetBreak(args.qdmr_path, split_name)
dataset_spider = DatasetSpider(args.spider_path, split_name)
if args.input_grounding:
partial_grounding = load_grounding_from_file(args.input_grounding)
else... |
class AlgorithmSummary():
algorithm_qubits: float = _PRETTY_FLOAT
measurements: float = _PRETTY_FLOAT
t_gates: float = _PRETTY_FLOAT
toffoli_gates: float = _PRETTY_FLOAT
rotation_gates: float = _PRETTY_FLOAT
rotation_circuit_depth: float = _PRETTY_FLOAT
def __mul__(self, other: int) -> 'Algo... |
class JSONFormatter(logging.Formatter):
def __init__(self, fmt=None, datefmt=None):
self.datefmt = datefmt
def formatException(self, ei, strip_newlines=True):
lines = traceback.format_exception(*ei)
if strip_newlines:
lines = [itertools.ifilter((lambda x: x), line.rstrip().sp... |
class InferGroupedEmbeddingsLookup(InferGroupedLookupMixin, BaseEmbeddingLookup[(KJTList, List[torch.Tensor])], TBEToRegisterMixIn):
def __init__(self, grouped_configs_per_rank: List[List[GroupedEmbeddingConfig]], world_size: int, fused_params: Optional[Dict[(str, Any)]]=None, device: Optional[torch.device]=None) -... |
class WeightedLottery(Generic[T]):
def __init__(self, items: Iterable[T], weight_key: Callable[([T], int)]):
self.weights: List[int] = []
self.items = list(items)
if (not self.items):
raise ValueError('items must not be empty')
accumulated_weight = 0
for item in s... |
.parametrize('username,password', users)
.parametrize('project_id', projects)
.parametrize('membership_id', memberships)
def test_detail(db, client, username, password, project_id, membership_id):
client.login(username=username, password=password)
membership = Membership.objects.filter(project_id=project_id, id... |
class _NeuralNetwork(NeuralNetwork):
def _forward(self, input_data, weights):
batch_size = (input_data.shape[0] if (input_data is not None) else 1)
return np.zeros((batch_size, *self.output_shape))
def _backward(self, input_data, weights):
input_grad = None
batch_size = (input_da... |
def test_create_hints_item_joke(empty_patches, players_config):
asset_id = 1000
(logbook_node, _, region_list) = _create_region_list(asset_id, PickupIndex(50))
patches = dataclasses.replace(empty_patches, hints={region_list.identifier_for_node(logbook_node): Hint(HintType.JOKE, None)})
rng = MagicMock()... |
def test_delete_existing_proposal_by_different_author(settings, login, conferences):
client = login[0]
conference = conferences['future']
section = conference.proposal_sections.all()[0]
proposal_type = conference.proposal_types.all()[0]
user = f.create_user()
proposal = f.create_proposal(confere... |
.parametrize('density,expected', [(0, ((- 1684649.41338), (- 350356.81377), 1684649.41338, 2234551.18559)), (100, ((- 1684649.41338), (- ), 1684649.41338, 2234551.18559))])
def test_transform_bounds_densify(density, expected):
transformer = Transformer.from_crs('EPSG:4326', '+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 ... |
class BucketTestCase(unittest.TestCase):
q = Auth(access_key, secret_key)
bucket = BucketManager(q)
def test_list(self):
(ret, eof, info) = self.bucket.list(bucket_name, limit=4)
assert (eof is False)
assert (len(ret.get('items')) == 4)
(ret, eof, info) = self.bucket.list(buc... |
def loss(logits, labels):
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=labels, logits=logits)
cross_entropy_mean = tf.reduce_mean(cross_entropy)
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
loss_ = tf.add_n(([cross_entropy_mean] + regularization_... |
class Env():
total_cards = sorted(((to_char(np.arange(3, 16)) * 4) + ['*', '$']), key=(lambda k: Card.cards_to_value[k]))
def __init__(self, agent_names=('agent1', 'agent2', 'agent3')):
seed = ((id(self) + int(datetime.now().strftime('%Y%m%d%H%M%S%f'))) % )
np.random.seed(seed)
self.agen... |
def _expr(expr):
node = type(expr)
if (node is ast.Name):
return _build_atomic(expr.id)
if (node is ast.Call):
args = _parse_args(expr.args)
kwargs = _parse_kwargs(expr.keywords)
return _build_predicate(expr.func.id, args, kwargs)
if (node is ast.Subscript):
field... |
def test_L1_bits_connection():
a = CaseConnectBitsConstToOutComp.DUT()
a.elaborate()
a.apply(StructuralRTLIRGenL1Pass(gen_connections(a)))
connections = a.get_metadata(StructuralRTLIRGenL1Pass.connections)
comp = sexp.CurComp(a, 's')
assert (connections == [(sexp.ConstInstance(Bits32(0), 0), sex... |
def is_ast_cont_with_surrounding_lambda(k):
from pycket import interpreter as i
cs = [i.LetCont, i.LetrecCont, i.BeginCont, i.Begin0Cont, i.Begin0BodyCont, i.WCMKeyCont, i.WCMValCont]
for c in cs:
if isinstance(k, c):
a = k.get_ast()
if (isinstance(a, i.AST) and a.surrounding... |
_torch
class TestTrainerExt(TestCasePlus):
def run_seq2seq_quick(self, distributed=False, extra_args_str=None, predict_with_generate=True, do_train=True, do_eval=True, do_predict=True):
output_dir = self.run_trainer(eval_steps=1, max_len=12, model_name=MBART_TINY, num_train_epochs=1, distributed=distributed... |
def resnet_retinanet(num_classes, backbone='resnet50', modifier=None, **kwargs):
inputs = keras.layers.Input(shape=(None, None, 3))
if (backbone == 'resnet50'):
resnet = keras_resnet.models.ResNet50(inputs, include_top=False, freeze_bn=True)
elif (backbone == 'resnet101'):
resnet = keras_res... |
def test_it_cannot_solve_other_solver_errors() -> None:
from poetry.mixology.solutions.providers import PythonRequirementSolutionProvider
incompatibility = Incompatibility([Term(Dependency('foo', '^1.0'), True)], NoVersionsCause())
exception = SolverProblemError(SolveFailure(incompatibility))
provider =... |
def test_dynamic_property_values_update_in_one_instance_leaves_other_unchanged():
generic1 = FakeBase()
generic2 = FakeBase()
generic1.fake_ctrl_values = (0, 33)
generic1.fake_ctrl = 50
generic2.fake_ctrl = 50
assert (generic1.fake_ctrl == 33)
assert (generic2.fake_ctrl == 10) |
class ModelFormTagFieldOptionsTest(TagTestManager, TestCase):
manage_models = [test_models.TagFieldOptionsModel]
def setUpExtra(self):
self.form = test_forms.TagFieldOptionsModelForm
self.model = test_models.TagFieldOptionsModel
_if_mysql
def test_case_sensitive_true(self):
tag_m... |
class MessageBroker():
def __init__(self, stage):
self.stage = stage
self._messages = []
def broadcast(self, message):
self._messages.append(message)
def get_messages(self):
return self._messages
def mark_completed(self):
self._messages.clear() |
def events_for_close(channel_state: NettingChannelState, block_number: BlockNumber, block_hash: BlockHash) -> List[Event]:
events: List[Event] = []
if (get_status(channel_state) in CHANNEL_STATES_PRIOR_TO_CLOSED):
channel_state.close_transaction = TransactionExecutionStatus(block_number, None, None)
... |
def make_migration(name):
try:
with Capturing() as output:
call_command('makemigrations', '--name={}'.format(name), app_name, verbosity=0)
except Exception as e:
print('>> makemigration failed for {}:'.format(name))
print('\n'.join(output))
print('')
raise e
... |
class LetsuploadCo(SimpleDownloader):
__name__ = 'LetsuploadCo'
__type__ = 'downloader'
__version__ = '0.03'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fallb... |
def find_executable_batch_size(function: callable=None, starting_batch_size: int=128, auto_find_batch_size: bool=False):
if (function is None):
return functools.partial(find_executable_batch_size, starting_batch_size=starting_batch_size, auto_find_batch_size=auto_find_batch_size)
if auto_find_batch_size... |
def nearest_unequal_elements(dts, dt):
if (not dts.is_unique):
raise ValueError('dts must be unique')
if (not dts.is_monotonic_increasing):
raise ValueError('dts must be sorted in increasing order')
if (not len(dts)):
return (None, None)
sortpos = dts.searchsorted(dt, side='left'... |
class CoTAttention(nn.Module):
def __init__(self, dim=512, kernel_size=3):
super().__init__()
self.dim = dim
self.kernel_size = kernel_size
self.key_embed = nn.Sequential(nn.Conv2d(dim, dim, kernel_size=kernel_size, padding=(kernel_size // 2), groups=4, bias=False), nn.BatchNorm2d(di... |
def inherit_signature(c, method_name):
m = getattr(c, method_name)
sig = inspect.signature(m)
params = []
for param in sig.parameters.values():
if ((param.name == 'self') or (param.annotation is not param.empty)):
params.append(param)
continue
for ancestor in insp... |
def form_loads(explode: bool, name: str, schema_type: str, location: Mapping[(str, Any)]) -> Any:
explode_type = (explode, schema_type)
if (explode_type == (False, 'array')):
return split(location[name], separator=',')
elif (explode_type == (True, 'array')):
if (name not in location):
... |
.parametrize('available', [True, False])
def test_is_available(available, mocker):
mock = mocker.patch.object(pdfjs, 'get_pdfjs_res', autospec=True)
if available:
mock.return_value = b'foo'
else:
mock.side_effect = pdfjs.PDFJSNotFound('build/pdf.js')
assert (pdfjs.is_available() == avail... |
class AttrVI_ATTR_GPIB_ATN_STATE(EnumAttribute):
resources = [(constants.InterfaceType.gpib, 'INTFC')]
py_name = 'atn_state'
visa_name = 'VI_ATTR_GPIB_ATN_STATE'
visa_type = 'ViInt16'
default = NotAvailable
(read, write, local) = (True, False, False)
enum_type = constants.LineState |
class TestOptionMarker():
.options(debug=False)
def test_not_debug_app(self, app):
assert (not app.debug), 'Ensure the app not in debug mode'
.options(foo=42)
def test_update_application_config(self, request, app, config):
assert (config['FOO'] == 42)
def test_application_config_tear... |
def create_strategy(name=None):
import logging
from bonobo.execution.strategies.base import Strategy
if isinstance(name, Strategy):
return name
if (name is None):
name = DEFAULT_STRATEGY
logging.debug('Creating execution strategy {!r}...'.format(name))
try:
factory = STRA... |
def create_table(table: str, namespace: Optional[str]=None, catalog: 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, pr... |
class CudaRNGStatesTracker():
def __init__(self):
self.states_ = {}
self.seeds_ = set()
def reset(self):
self.states_ = {}
self.seeds_ = set()
def get_states(self):
states = {}
for name in self.states_:
states[name] = self.states_[name]
ret... |
def set_werkzeug_hostname(f):
(f)
def wrapper(*args, **kwargs):
try:
hostname = json.loads(request.form['data'])['hostname']
except Exception:
hostname = None
ret = f(*args, **kwargs)
if hostname:
request.environ['REMOTE_ADDR'] = hostname
... |
class MrpcPVP(PVP):
VERBALIZER = {'0': ['Alas'], '1': ['Rather']}
def get_parts(self, example: InputExample) -> FilledPattern:
text_a = self.shortenable(example.text_a)
text_b = self.shortenable(example.text_b)
if (self.pattern_id == 1):
string_list_a = [text_a, '.', self.mas... |
class TestNFP(unittest.TestCase):
def test_enviar(self):
client = SoapClient(wsdl=WSDL, soap_ns='soap12env')
client['Autenticacao'] = SimpleXMLElement((HEADER_XML % ('user', 'password', 'fed_tax_num', 1)))
response = client.Enviar(NomeArquivo='file_name', ConteudoArquivo='content', EnvioNorm... |
class ReplayBuffer():
def __init__(self, max_size=50):
assert (max_size > 0), 'Empty buffer or trying to create a black hole. Be careful.'
self.max_size = max_size
self.data = []
def push_and_pop(self, data):
to_return = []
for element in data.data:
element = ... |
class PororoStoryDataset(Dataset):
def __init__(self, args, split, tokenizer):
self.args = args
self.root = args.dataset_dir
self.feature_extractor = utils.get_feature_extractor_for_model(args.visual_model, image_size=args.image_size, train=False)
self.image_size = args.image_size
... |
def train_ubr_model(ubr_training_monitor, epoch_num, sess, eval_iter_num, taker, lr, train_batch_size, rec_model, ubr_model, target_train_file, user_feat_dict_file, item_feat_dict_file, context_dict_file, summary_writer, step, b_num):
loss_step = []
reward_step = []
for i in range(epoch_num):
data_l... |
class LinesToReadline():
def __init__(self, lines, start):
self.lines = lines
self.current = start
def readline(self):
if (self.current <= self.lines.length()):
self.current += 1
return (self.lines.get_line((self.current - 1)) + '\n')
return ''
def __c... |
def get_latest_checkpoint(directory: pathlib.PosixPath, args: argparse.Namespace):
latest_checkpoint = None
checkpoint_files = list(directory.glob(f'*{args.checkpoint_id_pattern}*'))
if checkpoint_files:
latest_checkpoint = 0
for checkpoint_file in checkpoint_files:
checkpoint_fi... |
def register_dataframe_method(method):
def inner(*args, **kwargs):
class AccessorMethod():
def __init__(self, pyspark_obj):
self._obj = pyspark_obj
(method)
def __call__(self, *args, **kwargs):
return method(self._obj, *args, **kwargs)
... |
def export_quant_table(quantizers: dict, quant_dir: str, format: str='toml'):
table = {}
def save_tensor(name: str, tensor):
np.save(os.path.join(quant_dir, name), tensor.numpy())
return '{}.npy'.format(name)
for (key, value) in quantizers.items():
quantizer = value[0]
dump =... |
def test_allows_post_releases_with_post_and_local_min() -> None:
one = Version.parse('3.0.0+local.1')
two = Version.parse('3.0.0-1')
three = Version.parse('3.0.0-1+local.1')
four = Version.parse('3.0.0+local.2')
assert (not VersionRange(min=one, include_min=True).allows(two))
assert VersionRange... |
def main():
min_cost_flow = pywrapgraph.SimpleMinCostFlow()
start_nodes = (([0, 0, 0, 0] + [1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4]) + [5, 6, 7, 8])
end_nodes = (([1, 2, 3, 4] + [5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8, 5, 6, 7, 8]) + [9, 9, 9, 9])
capacities = (([1, 1, 1, 1] + [1, 1, 1, 1, 1, 1, 1, ... |
class TerminalView(View):
def __init__(self, app):
super().__init__(app)
def cleanup(self):
print('Goodbye')
def print(self, txt):
if txt:
print('\x1b[92m{}\x1b[39m'.format(txt))
def detail(self, txt):
print(txt)
def error(self, e):
if (not e):
... |
def test_invalid_next_name_ignored():
packet = b'\x00\x00\x00\x00\x00\x01\x00\x02\x00\x00\x00\x00\x07Android\x05local\x00\x00\xff\x00\x01\xc0\x0c\x00/\x00\x01\x00\x00\x00x\x00\x08\xc02\x00\\x00\x00\x08\xc0\x0c\x00\x01\x00\x01\x00\x00\x00x\x00\x04\xc0\xa8X<'
parsed = r.DNSIncoming(packet)
assert (len(parsed.... |
def inference_detector(model, imgs):
if isinstance(imgs, (list, tuple)):
is_batch = True
else:
imgs = [imgs]
is_batch = False
cfg = model.cfg
device = next(model.parameters()).device
if isinstance(imgs[0], np.ndarray):
cfg = cfg.copy()
cfg.data.test.pipeline[0... |
class NPMRole(Role):
time_format = '%d-%m-%y %H:%M:%S'
key = 'npm-up-to-date'
def provision(self):
self.provision_role(NodeJsRole)
def is_package_installed(self, package_name, version=None):
with settings(warn_only=True):
if version:
package_name = ('%%s' % (p... |
def upsampleG(fieldmap, activation_data, shape=None):
(offset, size, step) = fieldmap
input_count = activation_data.shape[0]
if (shape is None):
shape = upsampled_shape(fieldmap, activation_data.shape[1:])
activations = numpy.zeros(((input_count,) + shape))
activations[((slice(None),) + cent... |
def tokenize_stories(stories, add_speaker):
(total_count, avg_sum_len, avg_context_len, avg_sum_sent, avg_context_sent) = (0, 0.0, 0.0, 0.0, 0.0)
speaker_count = {}
with open(stories, 'r') as f:
data = json.load(f)
processed_data = []
for sample in data:
total_count += 1
sum ... |
def test_class_method_inherited() -> None:
nodes_ = builder.extract_node('\n class A:\n \n def method(cls):\n return cls\n\n class B(A):\n pass\n\n A().method() #\n A.method() #\n\n B().method() #\n B.method() #\n ')
expected_names = ['A', 'A', 'B', 'B']
... |
def get_args():
parser = argparse.ArgumentParser(description='This script creates the\n text form of a subword lexicon FST to be compiled by fstcompile using\n the appropriate symbol tables (phones.txt and words.txt). It will mostly\n be invoked indirectly via utils/prepare_lang_subword.sh. The... |
def main():
global FLAGS
with open(FLAGS.cluster_spec_file, 'r') as fp:
cluster_spec_str = json.load(fp)
config = tf.ConfigProto()
if (FLAGS.job_name == 'ps'):
config.inter_op_parallelism_threads = 768
config.intra_op_parallelism_threads = 0
config.device_count['GPU'] = 0... |
class App(models.Model):
module = models.CharField(max_length=100, unique=True)
active = models.BooleanField(default=False)
class Meta():
app_label = 'rapidsms'
def __str__(self):
return self.module
def __repr__(self):
return ('<%s: %s>' % (type(self).__name__, self)) |
class TestHypenation(unittest.TestCase):
def test_hypenation(self):
assert (hyphenation('begegnen', Hyphenator('de_DE')) == ['be', 'geg', 'nen'])
assert (hyphenation(".b,e~g'eg*nen, ", Hyphenator('de_DE')) == ['.b,e', "~g'eg", '*nen, '])
assert (hyphenation('Abend, ', Hyphenator('de_AT')) ==... |
class VGG(tf.keras.Model):
def __init__(self, vgg_name, num_classes, weight_decay):
super(VGG, self).__init__()
self.vgg_name = vgg_name
self.num_classes = num_classes
self.wd = weight_decay
self.convlayers = self._make_convlayers(cfg[vgg_name])
self.fc_layers = self.... |
_module()
class RawframeDataset(BaseDataset):
def __init__(self, ann_file, pipeline, data_prefix=None, test_mode=False, filename_tmpl='img_{:05}.jpg', with_offset=False, multi_class=False, num_classes=None, start_index=1, modality='RGB'):
self.filename_tmpl = filename_tmpl
self.with_offset = with_of... |
class VisaIOError(Error):
def __init__(self, error_code: int) -> None:
(abbreviation, description) = completion_and_error_messages.get(error_code, ('?', 'Unknown code.'))
super(VisaIOError, self).__init__(('%s (%d): %s' % (abbreviation, error_code, description)))
self.error_code = error_code... |
class FixHasKey(fixer_base.BaseFix):
BM_compatible = True
PATTERN = "\n anchor=power<\n before=any+\n trailer< '.' 'has_key' >\n trailer<\n '('\n ( not(arglist | argument<any '=' any>) arg=any\n | arglist<(not argument<any '=' any>) arg=any ','>\n ... |
class Conv1d(Mapper):
def __init__(self, num_filters, filter_size, keep_probs, activation='relu'):
self.keep_probs = keep_probs
self.num_filters = num_filters
self.filter_size = filter_size
self.activation = activation
def apply(self, is_train, x, mask=None):
num_channels... |
class KernelInclude(Include):
def run(self):
path = os.path.realpath(os.path.expandvars(self.arguments[0]))
if path.startswith((os.sep + 'etc')):
raise self.severe(('Problems with "%s" directive, prohibited path: %s' % (self.name, path)))
self.arguments[0] = path
return s... |
def flatten_nested_unions(types: list[RType]) -> list[RType]:
if (not any((isinstance(t, RUnion) for t in types))):
return types
flat_items: list[RType] = []
for t in types:
if isinstance(t, RUnion):
flat_items.extend(flatten_nested_unions(t.items))
else:
flat... |
class FeatureExtraction(torch.nn.Module):
def __init__(self, train_fe=False, feature_extraction_cnn='vgg', normalization=True, last_layer='', use_cuda=True):
super(FeatureExtraction, self).__init__()
self.normalization = normalization
if (feature_extraction_cnn == 'vgg'):
self.mo... |
class TagReader():
label2id_map = {'<START>': 0}
def read_inst(cls, file, is_labeled, number, opinion_offset):
insts = []
inputs = []
outputs = []
total_p = 0
original_p = 0
f = open(file, 'r', encoding='utf-8')
for line in f:
line = line.strip... |
class Effect5486(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Medium Projectile Turret')), 'damageMultiplier', ship.getModifiedItemAttr('shipBonusMBC2'), skill='Minmatar Battlecruiser', **kwar... |
def aggregate_results(filepaths: List[Path]) -> Dict[(str, Any)]:
metrics = defaultdict(list)
for f in filepaths:
with f.open('r') as fd:
data = json.load(fd)
for (k, v) in data['results'].items():
metrics[k].append(v)
agg = {k: np.mean(v) for (k, v) in metrics.items(... |
class DeformRoIPoolFunction(Function):
def symbolic(g, input, rois, offset, output_size, spatial_scale, sampling_ratio, gamma):
return g.op('mmcv::MMCVDeformRoIPool', input, rois, offset, pooled_height_i=output_size[0], pooled_width_i=output_size[1], spatial_scale_f=spatial_scale, sampling_ratio_f=sampling_... |
def test_dimensions_missing_params():
with pytest.raises(ValueError):
calculate_default_transform('epsg:4326', 'epsg:3857', width=1, height=1, gcps=[1], resolution=1, dst_width=1, dst_height=None)
with pytest.raises(ValueError):
calculate_default_transform('epsg:4326', 'epsg:3857', width=1, heig... |
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