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def ql_syscall_connect_flags(ql: Qiling, pid, coid, mask, bits, *args, **kw):
assert (pid == 0), 'Is it possible to change the connection flags of another process?'
assert (coid in ql.os.connections), 'Connection Id must exist in connections mapping'
assert (mask == 1), 'Is the mask is always FD_CLOEXEC?'
... |
def put_actions(name: str, *, label: str='', buttons: List[Union[(Dict[(str, Any)], Tuple, List, str)]]=None, help_text: str=None, scope: str=None, position: int=OutputPosition.BOTTOM) -> Output:
from pywebio.input import actions
check_dom_name_value(name, 'pin `name`')
single_input_return = actions(name=na... |
def train(args, train_dataset, model, tokenizer, teacher=None):
if (args.local_rank in [(- 1), 0]):
tb_writer = SummaryWriter(log_dir=args.output_dir)
args.train_batch_size = (args.per_gpu_train_batch_size * max(1, args.n_gpu))
train_sampler = (RandomSampler(train_dataset) if (args.local_rank == (- ... |
class TrainerX(SimpleTrainer):
def run_epoch(self):
self.set_model_mode('train')
losses = MetricMeter()
batch_time = AverageMeter()
data_time = AverageMeter()
self.num_batches = len(self.train_loader_x)
end = time.time()
for (self.batch_idx, batch) in enumerat... |
def handle_offchain_secretreveal(target_state: TargetTransferState, state_change: ReceiveSecretReveal, channel_state: NettingChannelState, pseudo_random_generator: random.Random, block_number: BlockNumber) -> TransitionResult[TargetTransferState]:
valid_secret = is_valid_secret_reveal(state_change=state_change, tra... |
def test_format_returns_text_edit_per_line(workspace):
single_space_indent = 'def wow():\n log("x")\n log("hi")'
doc = Document(DOC_URI, workspace, single_space_indent)
res = pylsp_format_document(workspace, doc, options=None)
assert (len(res) == 4)
assert (res[0]['newText'] == '')
assert (res[1... |
def _check_health_group(filename, group_content, health_is_called):
has_error = False
domain = 'enterprise-attack'
if ('domain' in group_content):
if (not (group_content['domain'].lower() in DETTECT_DOMAIN_SUPPORT)):
has_error = _print_error_msg(('[!] INVALID domain value in group admini... |
def test_base_recognizer():
tmp_dir = tempfile.TemporaryDirectory()
dict_file = osp.join(tmp_dir.name, 'fake_chars.txt')
_create_dummy_dict_file(dict_file)
label_convertor = dict(type='CTCConvertor', dict_file=dict_file, with_unknown=False)
preprocessor = None
backbone = dict(type='VeryDeepVgg',... |
def detect(first512):
if (len(first512) < size_record_header):
return False
(label, version, size_record, size_payload, hash, type) = unpack('>4s4sQQ20s20s', first512[:size_record_header])
if ((label == b'YAFF') and (version == b'0000') and (type.strip() == b'trace')):
return True
return... |
class RandomRotate(object):
def __init__(self, degree):
self.degree = degree
def __call__(self, sample):
img = sample['image']
mask = sample['label']
rotate_degree = random.uniform(((- 1) * self.degree), self.degree)
img = img.rotate(rotate_degree, Image.BILINEAR)
... |
class TestJSHandle(BaseTestCase):
async def test_get_property(self):
handle1 = (await self.page.evaluateHandle('() => ({one: 1, two: 2, three: 3})'))
handle2 = (await handle1.getProperty('two'))
self.assertEqual((await handle2.jsonValue()), 2)
async def test_json_value(self):
han... |
def test_perform_per_layer_analysis_by_disabling_quant_ops(cpu_session):
(sim, quant_analyzer) = get_quantsim_and_quantanalyzer(cpu_session)
try:
quant_analyzer._perform_per_op_analysis_by_disabling_quant_ops(sim, results_dir='./tmp/')
assert os.path.isfile('./tmp/per_op_quant_disabled.html')
... |
def test_for_with_continue_in_if_else() -> None:
src = '\n for i in range(10):\n if i > 5:\n print(i)\n else:\n continue\n i -= 1\n '
cfg = build_cfg(src)
expected_blocks = [['range(10)'], ['i'], ['i > 5'], ['print(i)'], ['i -= 1']... |
def test_main_no_spec(capsys: pytest.CaptureFixture[str]) -> None:
with pytest.raises(SystemExit) as excinfo:
find_missing_reqs.main(arguments=[])
expected_code = 2
assert (excinfo.value.code == expected_code)
err = capsys.readouterr().err
assert err.endswith('error: no source files or direc... |
.parametrize('proc_name', ['s1', 's2', 's3'])
def test_runtime_error_on_start_fail(tcp_port, proc_name, xprocess):
restart = False
class Starter(ProcessStarter):
pattern = 'I will not be matched!'
args = [sys.executable, server_path, tcp_port, '--no-children', '--ignore-sigterm']
with pytest... |
def create_index_file(html_root: Path, builder: str) -> None:
pep_zero_file = ('pep-0000.html' if (builder == 'html') else 'pep-0000/index.html')
try:
pep_zero_text = html_root.joinpath(pep_zero_file).read_text(encoding='utf-8')
except FileNotFoundError:
return None
if (builder == 'dirht... |
_module()
class DAHead(BaseDecodeHead):
def __init__(self, pam_channels, **kwargs):
super(DAHead, self).__init__(**kwargs)
self.pam_channels = pam_channels
self.pam_in_conv = ConvModule(self.in_channels, self.channels, 3, padding=1, conv_cfg=self.conv_cfg, norm_cfg=self.norm_cfg, act_cfg=sel... |
def fold_given_batch_norms(model: tf.keras.Model, layer_pairs: List[PairType]) -> Optional[tf.keras.Model]:
conv_bn_paris = []
bn_conv_pairs = []
def is_batchnorm(layer: tf.keras.layers.Layer) -> bool:
if isinstance(layer, QcQuantizeWrapper):
layer = layer._layer_to_wrap
return i... |
def antlrConverter(antlrGrammarTree):
pyparsingRules = {}
antlrTokens = {}
for antlrToken in antlrGrammarTree.tokens:
antlrTokens[antlrToken.token_ref] = antlrToken.lit
for (antlrTokenName, antlrToken) in list(antlrTokens.items()):
pyparsingRules[antlrTokenName] = Literal(antlrToken)
... |
def choose_conv_method(in1, in2, mode='full', measure=False):
volume = cp.asarray(in1)
kernel = cp.asarray(in2)
if measure:
times = {}
for method in ('fft', 'direct'):
times[method] = _timeit_fast((lambda : convolve(volume, kernel, mode=mode, method=method)))
chosen_metho... |
class XLNetTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
padding_side = 'left'
def __init__(self, vocab_file, do_lower_case=False, remove_space=True, keep_ac... |
class Transformer_Reattention(nn.Module):
def __init__(self, dim, depth, heads, dim_head, mlp_dim=1024, dropout=0.0, num_patches=128):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([PreNorm(dim, ReAttention(dim, heads=h... |
def main():
parser = ArgumentParser(description='COCO Evaluation')
parser.add_argument('result', help='result file path')
parser.add_argument('--ann', help='annotation file path')
parser.add_argument('--types', type=str, nargs='+', choices=['proposal_fast', 'proposal', 'bbox', 'segm', 'keypoint'], defau... |
def test_gdalversion_class_at_least():
assert GDALVersion(2, 1).at_least(GDALVersion(1, 9))
assert GDALVersion(2, 1).at_least((1, 9))
assert GDALVersion(2, 1).at_least('1.9')
assert (not GDALVersion(2, 1).at_least(GDALVersion(2, 2)))
assert (not GDALVersion(2, 1).at_least((2, 2)))
assert (not GD... |
class Indenter(PostLex, ABC):
paren_level: int
indent_level: List[int]
def __init__(self) -> None:
self.paren_level = 0
self.indent_level = [0]
assert (self.tab_len > 0)
def handle_NL(self, token: Token) -> Iterator[Token]:
if (self.paren_level > 0):
return
... |
def check_model_type_doc_match():
model_doc_folder = (Path(PATH_TO_DOC) / 'model_doc')
model_docs = [m.stem for m in model_doc_folder.glob('*.mdx')]
model_types = list(transformers.models.auto.configuration_auto.MODEL_NAMES_MAPPING.keys())
model_types = [(MODEL_TYPE_TO_DOC_MAPPING[m] if (m in MODEL_TYPE... |
class FilesystemStorage(StoragePlugin):
name = 'filesystem'
PATH_BACKEND: type[pathlib.Path] = pathlib.Path
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
def get_lock(self, path: (str | None)=None) -> filelock.FileLock:
if (path is None):
... |
class TestListAttribute():
def test_roundtrip_untyped(self) -> None:
string_list_attribute = ListAttribute()
values = [None, 'foo', '', 42, True, b'foo', {42, 43}, {42.5, 43.5}, {42, 43.5}, {'foo', 'bar'}, {b'foo', b'bar'}, {'foo': 'bar'}, ['foo', 'bar']]
serialized = string_list_attribute.s... |
def make_typeddict(cls_name: str, attrs: Dict[(str, type)], total: bool=True, bases: List=[]) -> TypedDictType:
globs = {'TypedDict': TypedDict}
lines = []
bases_snippet = ', '.join((f'_base{ix}' for ix in range(len(bases))))
for (ix, base) in enumerate(bases):
globs[f'_base{ix}'] = base
if ... |
def build_unique_dict(controls):
name_control_map = UniqueDict()
text_ctrls = [ctrl_ for ctrl_ in controls if (ctrl_.can_be_label and ctrl_.is_visible() and ctrl_.window_text())]
for ctrl in controls:
ctrl_names = get_control_names(ctrl, controls, text_ctrls)
for name in ctrl_names:
... |
class MultiscaleDiscriminator(nn.Module):
def __init__(self, input_nc, ndf=64, norm_type='batch', mode='CNA', num_D=3, n_layers=3, getIntermFeat=False):
super(MultiscaleDiscriminator, self).__init__()
self.num_D = num_D
self.getIntermFeat = getIntermFeat
for i in range(num_D):
... |
def diguipd(k, sample, R):
sup = 0
m = [[(- 1)] for i in range(len(sample))]
ll = 0
i = 0
while (i < len(k)):
l = 0
for j in range((len(sample) - 1), (- 1), (- 1)):
if ((j == 0) and (k[i] == sample[j])):
m[j][ll] = i
break
elif ... |
def test_return_padded_repr():
node_feats = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [9, 10, 11], [11, 11.1, 12.4], [18, 11.1, 22.4], [24, 15.31, 18.4], [16, 10.1, 17.4]])
graph_ids = np.array([0, 0, 0, 1, 1, 2, 2, 2])
edges = {'asingle': np.array([[0, 1, 7, 6, 3, 4], [1, 2, 6, 5, 4, 3]]), 'bdouble': np.a... |
class PeopleList(LoginRequiredMixin, ListView):
template_name = 'dictionary/list/people_list.html'
paginate_by = 15
tab = None
tabs = {'following': gettext_lazy('following list'), 'blocked': gettext_lazy('blocked list')}
def get_queryset(self):
queryset = getattr(self, self.tab)()
if... |
def pytest_addoption(parser):
parser.addoption('--ip', action='store', default=None, help='run against device on given ip')
parser.addoption('--username', action='store', default=None, help='authentication username')
parser.addoption('--password', action='store', default=None, help='authentication password'... |
(scope='module')
def test_image_small_mid_atlantic_K_L(test_area_tiny_eqc_sphere):
arr = xr.DataArray(_get_fake_da(((- 80) + 273.15), (40 + 273.15), (test_area_tiny_eqc_sphere.shape + (1,))), dims=('y', 'x', 'bands'), attrs={'name': 'test-small-mid-atlantic', 'start_time': datetime.datetime(1985, 8, 13, 13, 0), 'ar... |
def annualise_total_return(total_return: float, period_length_in_years: float, returns_type: type) -> float:
assert issubclass(returns_type, ReturnsSeries)
annualised_return = None
if issubclass(returns_type, SimpleReturnsSeries):
annualised_return = (pow((1 + total_return), (1 / period_length_in_ye... |
class _TestAMP(TwistedTestCase):
def setUp(self):
super(_TestAMP, self).setUp()
self.account = mommy.make('accounts.AccountDB', id=1)
self.server = server.Evennia(MagicMock())
self.server.sessions.data_in = MagicMock()
self.server.sessions.data_out = MagicMock()
self.... |
class TestNoselikeTestAttribute():
def test_module_with_global_test(self, pytester: Pytester) -> None:
pytester.makepyfile('\n __test__ = False\n def test_hello():\n pass\n ')
reprec = pytester.inline_run()
assert (not reprec.getfailedcollections()... |
def export_scores(c, test_img, scores, threshold):
image_dirs = os.path.join(OUT_DIR, c.model, ('sc_images_' + datetime.datetime.now().strftime('%Y-%m-%d-%H:%M:%S')))
if (not os.path.isdir(image_dirs)):
print('Exporting scores...')
os.makedirs(image_dirs, exist_ok=True)
num = len(test_im... |
class install(Command):
description = 'install everything from build directory'
user_options = [('prefix=', None, 'installation prefix'), ('exec-prefix=', None, '(Unix only) prefix for platform-specific files'), ('home=', None, '(Unix only) home directory to install under'), ('install-base=', None, 'base instal... |
def test_create_binst_graph():
(cxns, signature) = _manually_make_test_cbloq_cxns()
binst1 = cxns[2].left.binst
binst2 = cxns[2].right.binst
binst_graph = _create_binst_graph(cxns)
assert nx.is_isomorphic(binst_graph, CompositeBloq(cxns, signature)._binst_graph)
binst_generations = list(nx.topol... |
class ProcessMonitor():
def __init__(self):
self.proclist = dict()
self.running = False
def procstat(self):
c = ['cat /proc/[1-9]*/stat 2>/dev/null']
process = Popen(c, shell=True, stdout=PIPE)
running = dict()
for line in process.stdout:
data = ascii(... |
def simplify_links(n, costs, renewable_config, hvdc_as_lines, config_lines, config_links, output, exclude_carriers=[], aggregation_strategies=dict()):
logger.info('Simplifying connected link components')
if n.links.empty:
with open(output.connection_costs, 'w') as fp:
pass
return (n,... |
class PreprocessImage(ObservationWrapper):
def __init__(self, env, height=64, width=64, grayscale=True, crop=(lambda img: img)):
super(PreprocessImage, self).__init__(env)
self.img_size = (height, width)
self.grayscale = grayscale
self.crop = crop
n_colors = (1 if self.graysc... |
class BaseAgent(ExtendedModule):
def __init__(self, *args, **kwargs):
super(BaseAgent, self).__init__(*args, **kwargs)
self._device_ids = None
self._be_data_parallel = False
self._tmp_attrs = {}
self.obs_processor = None
self.obs_rms = None
self.rew_rms = None... |
def test_binder_install():
class ModuleA(Module):
def configure(self, binder):
binder.bind(str, to='hello world')
class ModuleB(Module):
def configure(self, binder):
binder.install(ModuleA())
injector = Injector([ModuleB()])
assert (injector.get(str) == 'hello wor... |
class HakushHsrCharacterSkillTree(Struct):
Anchor: str
DefaultUnlock: bool
Icon: str
LevelUpSkillID: List[int]
MaterialList: List[Union[(HakushHsrMaterial, None)]]
MaxLevel: int
ParamList: List[float]
PointID: int
PointName: str
PointDesc: str
PointTriggerKey: int
PointTy... |
class AttrVI_ATTR_FILE_APPEND_EN(BooleanAttribute):
resources = [(constants.InterfaceType.gpib, 'INSTR'), (constants.InterfaceType.gpib, 'INTFC'), (constants.InterfaceType.asrl, 'INSTR'), (constants.InterfaceType.tcpip, 'INSTR'), (constants.InterfaceType.tcpip, 'SOCKET'), (constants.InterfaceType.usb, 'INSTR'), (co... |
class MainWindow(QMainWindow):
def __init__(self, parent=None):
super(MainWindow, self).__init__(parent)
self.createMenu()
self.completingTextEdit = TextEdit()
self.completer = QCompleter(self)
self.completer.setModel(self.modelFromFile(':/resources/wordlist.txt'))
se... |
def default_zero_weight_decay_condition(module_name, module, parameter_name, parameter):
del module_name, parameter
return (parameter_name.endswith('bias') or isinstance(module, (nn.BatchNorm1d, nn.LayerNorm, nn.InstanceNorm1d, rtdl.CLSToken, rtdl.NumericalFeatureTokenizer, rtdl.CategoricalFeatureTokenizer, Per... |
class Receiver(QDialog):
def __init__(self, parent=None):
super(Receiver, self).__init__(parent)
self.statusLabel = QLabel('Listening for broadcasted messages')
quitButton = QPushButton('&Quit')
self.udpSocket = QUdpSocket(self)
self.udpSocket.bind(45454)
self.udpSock... |
class DatasetMapperTTA():
def __init__(self, min_sizes: List[int], max_size: int, flip: bool):
self.min_sizes = min_sizes
self.max_size = max_size
self.flip = flip
def from_config(cls, cfg):
return {'min_sizes': cfg.TEST.AUG.MIN_SIZES, 'max_size': cfg.TEST.AUG.MAX_SIZE, 'flip': c... |
def add_target(domain):
for word in wordlist:
patterns = [word]
if args.alt:
probes = ['dev', 'prod', 'stg', 'qa', 'uat', 'api', 'alpha', 'beta', 'cms', 'test', 'internal', 'staging', 'origin', 'stage']
for probe in probes:
if (probe not in word):
... |
def evaluate_model(model, generator, save_path, score_threshold, iou_threshold=0.5, max_detections=100, diameter_threshold=0.1):
(average_precisions, add_metric, add_s_metric, metric_5cm_5degree, translation_diff_metric, rotation_diff_metric, metric_2d_projection, mixed_add_and_add_s_metric, average_point_distance_... |
def min_sigma():
global sequence_num
global sigmasize
global sigma
global list
counter = {'a': 0, 'b': 0, 'c': 0, 'd': 0, 'e': 0, 'f': 0, 'g': 0, 'h': 0, 'i': 0, 'j': 0, 'k': 0, 'l': 0, 'm': 0, 'n': 0, 'o': 0, 'p': 0, 'q': 0, 'r': 0, 's': 0, 't': 0, 'u': 0, 'v': 0, 'w': 0, 'x': 0, 'y': 0, 'z': 0}
... |
class Inference():
def __init__(self, op, approx, tf, **kwargs):
self.hist = np.asarray(())
self.objective = op(approx, **kwargs)(tf)
self.state = None
approx = property((lambda self: self.objective.approx))
def _maybe_score(self, score):
returns_loss = self.objective.op.retu... |
class FileDownload(Response):
chunk_size = 4096
def __init__(self, a_file):
self.file = a_file
super().__init__(app_iter=self, conditional_response=True)
self.content_type = (self.file.mime_type if self.file.mime_type else None)
self.charset = (self.file.encoding if self.file.enc... |
def transform_index_expr(builder: IRBuilder, expr: IndexExpr) -> Value:
index = expr.index
base_type = builder.node_type(expr.base)
is_list = is_list_rprimitive(base_type)
can_borrow_base = (is_list and is_borrow_friendly_expr(builder, index))
base = builder.accept(expr.base, can_borrow=can_borrow_b... |
class closeable_response():
closeable_response = None
def __init__(self, fp, headers, url, code, msg):
self._set_fp(fp)
self._headers = headers
self._url = url
self.code = code
self.msg = msg
def _set_fp(self, fp):
self.fp = fp
self.read = self.fp.read... |
class VGGTrunk(nn.Module):
def __init__(self):
super(VGGTrunk, self).__init__()
def _make_layers(self, batch_norm=True):
layers = []
in_channels = self.in_channels
for tup in self.cfg:
assert (len(tup) == 2)
(out, dilation) = tup
sz = self.conv... |
def get_quad_operator(operator, hbar=1.0):
quad_operator = QuadOperator()
if isinstance(operator, BosonOperator):
for (term, coefficient) in operator.terms.items():
tmp = QuadOperator('', coefficient)
for (i, d) in term:
tmp *= ((1.0 / numpy.sqrt((2.0 * hbar))) * ... |
def npairs_loss(labels, embeddings_anchor, embeddings_positive, reg_lambda=0.003, print_losses=False):
reg_anchor = math_ops.reduce_mean(math_ops.reduce_sum(math_ops.square(embeddings_anchor), 1))
reg_positive = math_ops.reduce_mean(math_ops.reduce_sum(math_ops.square(embeddings_positive), 1))
l2loss = math... |
def modify_model_bn_mutable(model: tf.keras.Model):
for layer in model.layers:
if isinstance(layer, tf.keras.layers.BatchNormalization):
momentum = layer.momentum
bn_momentum_var = tf.Variable(momentum, trainable=False, name=(layer.name + _BN_MOMENTUM_NAME))
layer.momentu... |
class SparseConvTranspose2d(SparseConvolution):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, indice_key=None):
super(SparseConvTranspose2d, self).__init__(2, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, t... |
class TestSetInputFocus(EndianTest):
def setUp(self):
self.req_args_0 = {'focus': , 'revert_to': 2, 'time': }
self.req_bin_0 = b'*\x02\x00\x03S\xa5m\xe7}\xfa ('
def testPackRequest0(self):
bin = request.SetInputFocus._request.to_binary(*(), **self.req_args_0)
self.assertBinaryEqu... |
def create_data(source_sents, target_sents):
(de2idx, idx2de) = load_de_vocab()
(en2idx, idx2en) = load_en_vocab()
(x_list, y_list, Sources, Targets) = ([], [], [], [])
for (source_sent, target_sent) in zip(source_sents, target_sents):
x = [de2idx.get(word, 1) for word in (source_sent + u' </S>'... |
def record_tabular_misc_stat(key, values):
record_tabular((key + 'Average'), np.average(values))
record_tabular((key + 'Std'), np.std(values))
record_tabular((key + 'Median'), np.median(values))
record_tabular((key + 'Min'), np.amin(values))
record_tabular((key + 'Max'), np.amax(values)) |
class JobOfferListCreateAPIView(APIView):
def get(self, request):
jobs = JobOffer.objects.filter(available=True)
serializer = JobOfferSerializer(jobs, many=True)
return Response(serializer.data)
def post(self, request):
serializer = JobOfferSerializer(data=request.data)
i... |
def get_viirs_sdr__1229(base_dir=None, channels=('I01', 'I02', 'I03', 'I04', 'I05', 'M01', 'M02', 'M03', 'M04', 'M05', 'M06', 'M07', 'M08', 'M09', 'M10', 'M11', 'M12', 'M13', 'M14', 'M15', 'M16', 'DNB'), granules=(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)):
base_dir = (base_dir or config.get('demo_data_dir', '.'))
subdir ... |
class BoundFunction(torch.autograd.Function):
def forward(ctx, x, lower_bound, upper_bound):
ctx.save_for_backward(x, torch.tensor(lower_bound).to(x.device), torch.tensor(upper_bound).to(x.device))
return bound_fwd(x, lower_bound, upper_bound)
def backward(ctx, grad_output):
(x, lower_bo... |
def test_object(accum):
retort = Retort(recipe=[accum])
loader = retort.get_loader(ExampleObject)
assert (loader({'field1': 1, 'field2': 1}) == ExampleObject(field1=1, field2=1))
dumper = retort.get_dumper(ExampleObject)
assert (dumper(ExampleObject(field1=1, field2=1)) == {'field1': 1, 'field2': 1}... |
class ViewProviderAsmBase(object):
def __init__(self, vobj):
vobj.Visibility = False
self.attach(vobj)
vobj.Proxy = self
def canReplaceObject(self, _old, _new):
return False
def replaceObject(self, _old, _new):
return False
def canReorderObject(self, _obj, _before... |
def _check_errors(response):
errors = {'02': 'Command does not exist or is not executable.', '03': 'Register number does not exist.', '04': 'Out of setpoint range.', '05': 'Out of data number range.', '06': 'Executed monitor without specifying what to monitor.', '08': 'Illegal parameter is set.', '42': 'Sum does no... |
_serializable
class TFResNetMainLayer(tf.keras.layers.Layer):
config_class = ResNetConfig
def __init__(self, config: ResNetConfig, **kwargs) -> None:
super().__init__(**kwargs)
self.config = config
self.embedder = TFResNetEmbeddings(config, name='embedder')
self.encoder = TFResNe... |
class HelpTextsTest(TestCase):
def test_help_ndarray(self):
def func(arr: NDArray[(Shape['2, 2'], Int)]):
...
help_text = pydoc.render_doc(func)
self.assertIn("arr: NDArray[Shape['2, 2'], Int]", help_text)
self.assertEqual('nptyping.ndarray', NDArray.__module__)
def t... |
def extract_stations(fns):
import io
import sys
from pyrocko.model import Station
from pyrocko.guts import dump_all
stations = {}
for fn in fns:
sta_name = os.path.splitext(fn)[1].lstrip('.')
if (sta_name in stations):
logger.warning('Cube %s already in list!', sta_na... |
class GetScreenSize(rq.ReplyRequest):
_request = rq.Struct(rq.Card8('opcode'), rq.Opcode(3), rq.RequestLength(), rq.Window('window'), rq.Card32('screen'))
_reply = rq.Struct(rq.ReplyCode(), rq.Pad(1), rq.Card16('sequence_number'), rq.Card32('length'), rq.Card32('width'), rq.Card32('height'), rq.Window('window')... |
def apply_memit_to_model(model: AutoModelForCausalLM, tok: AutoTokenizer, requests: List[Dict], hparams: MEMITHyperParams, copy=False, return_orig_weights=False, cache_template: Optional[str]=None) -> Tuple[(AutoModelForCausalLM, Dict[(str, Any)])]:
weights_copy = {}
if copy:
model = deepcopy(model)
... |
class TestOptimizerWrapper(unittest.TestCase):
def test_load_state_dict(self) -> None:
param_1_t = torch.tensor([1.0, 2.0])
param_1 = Variable(param_1_t)
keyed_optimizer = KeyedOptimizer({'param_1': param_1}, {param_1: {'one': 1.0}}, [{'params': [param_1], 'param_group_val_0': 2.0}])
... |
.parametrize(['alias', 'dtype'], zip(dtype_names, dtype_types), ids=[str(dtype) for dtype in dtype_names])
.parametrize(['func', 'args'], [(qutip.basis, (5, 1)), (qutip.fock, (5, 1)), (qutip.fock_dm, (5, 1)), (qutip.coherent, (5, 1)), (qutip.coherent_dm, (5, 1)), (qutip.thermal_dm, (5, 1)), (qutip.maximally_mixed_dm, (... |
def get_logger(setting_getter, name, fail_to_local=False, filter=None):
global got_logger
if got_logger:
return got_logger
if filter:
def log_filter(r, h):
if server_pipe_log_filter_re.search(r.message):
return False
return filter(r, h)
else:
... |
class WavefrontDetailView(ResourceMixin, ResourceBaseDetailView):
is_3d_model = True
js = ({'src': 'wavefront/js/3d_view.js', 'type': 'module'},)
css = ('wavefront/css/wavefront.css',)
def get_context_data(self, **kwargs):
context = super(WavefrontDetailView, self).get_context_data()
obj... |
def main():
cv2.setNumThreads(1)
p = create_config(args.config_env, args.config_exp)
sys.stdout = Logger(p['log_file'])
print('Python script is {}'.format(os.path.abspath(__file__)))
print(colored(p, 'red'))
print(colored('Retrieve model', 'blue'))
model = get_model(p)
print(model)
m... |
def create_model(args):
model = AsyncTFBase(args.extract_feat_dim, args.s_class, args.o_class, args.v_class).cuda()
optimizer = torch.optim.SGD(model.parameters(), args.lr, momentum=args.momentum, weight_decay=args.weight_decay)
(rgb_model, rgb_optimizer) = sub_create_model(args)
criterion = AsyncTFCrit... |
def get_commit_info(show_modified_files=False, show_untracked_files=False):
import git
try:
repo = git.Repo(PACKAGE_DIR.parent)
except git.InvalidGitRepositoryError as err:
logger.warning('mani_skill2 is not installed with git.')
return None
else:
commit_info = {}
... |
def test_userdefinedaction():
cca = OSC.CustomCommandAction('custom_command', 'content')
cca2 = OSC.CustomCommandAction('another_custom_command', 'content')
uda = OSC.UserDefinedAction(cca)
prettyprint(uda)
uda2 = OSC.UserDefinedAction(cca)
assert (uda == uda2)
uda3 = OSC.UserDefinedAction(c... |
(is_safe=True)
def render_email(value):
if value:
(mailbox, domain) = value.split('')
mailbox_tokens = mailbox.split('.')
domain_tokens = domain.split('.')
mailbox = '<span>.</span>'.join(mailbox_tokens)
domain = '<span>.</span>'.join(domain_tokens)
return format_html... |
class main(list):
def __init__(self, domains, campaign, mod, project_id):
global module
global domain_names
global campaign_list
campaign_list = campaign
domain_names = domains
if (mod is not None):
module = mod
i = cmd_main()
i.prompt = ((... |
def test_register_service_with_custom_ttl():
zc = Zeroconf(interfaces=['127.0.0.1'])
type_ = '_homeassistant._tcp.local.'
name = 'MyTestHome'
info_service = r.ServiceInfo(type_, f'{name}.{type_}', 80, 0, 0, {'path': '/~paulsm/'}, 'ash-90.local.', addresses=[socket.inet_aton('10.0.1.2')])
zc.register... |
_start_docstrings('\n CamemBERT Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.\n for Named-Entity-Recognition (NER) tasks.\n ', CAMEMBERT_START_DOCSTRING)
class TFCamembertForTokenClassification(TFRobertaForTokenClassification):
config_class = Camembe... |
def check_dataset(args):
if (args.dataset.lower() == 'msvd'):
args.dataset = 'Youtube2Text'
assert (args.dataset in ['Youtube2Text', 'MSRVTT']), 'We now only support Youtube2Text (MSVD) and MSRVTT datasets.'
if args.default:
if (args.dataset == 'Youtube2Text'):
args.beta = [0, 1]... |
def transform_member_expr(builder: IRBuilder, expr: MemberExpr) -> Value:
final = builder.get_final_ref(expr)
if (final is not None):
(fullname, final_var, native) = final
value = builder.emit_load_final(final_var, fullname, final_var.name, native, builder.types[expr], expr.line)
if (val... |
def find_targets_recursive(manager: BuildManager, graph: Graph, triggers: set[str], deps: dict[(str, set[str])], up_to_date_modules: set[str]) -> tuple[(dict[(str, set[FineGrainedDeferredNode])], set[str], set[TypeInfo])]:
result: dict[(str, set[FineGrainedDeferredNode])] = {}
worklist = triggers
processed:... |
def get_fix_hint_for_unpinned(remediation):
secure_options: List[str] = [str(fix) for fix in remediation.get('other_recommended_versions', [])]
fixes_hint = f"Version {remediation.get('recommended_version')} has no known vulnerabilities and falls within your current specifier range."
if (len(secure_options)... |
def test_estimate_parallel_two_qubit_xeb_fidelity_on_grid_no_noise(tmpdir):
base_dir = os.path.abspath(tmpdir)
qubits = cirq.GridQubit.square(2)
two_qubit_gate = (cirq.ISWAP ** 0.5)
cycles = [5, 10, 15]
data_collection_id = collect_grid_parallel_two_qubit_xeb_data(sampler=cirq.Simulator(seed=34310, ... |
def create_logger(logdir, phase='train'):
os.makedirs(logdir, exist_ok=True)
log_file = osp.join(logdir, f'{phase}_log.txt')
head = '%(asctime)-15s %(message)s'
logging.basicConfig(filename=log_file, format=head)
logger = logging.getLogger()
logger.setLevel(logging.INFO)
console = logging.St... |
def test_nl_head():
head = NLHead(in_channels=32, channels=16, num_classes=19)
assert (len(head.convs) == 2)
assert hasattr(head, 'nl_block')
inputs = [torch.randn(1, 32, 45, 45)]
if torch.cuda.is_available():
(head, inputs) = to_cuda(head, inputs)
outputs = head(inputs)
assert (outp... |
_metaclass(ABCMeta)
class PermissionDataInterface(object):
def get_repo_permissions_by_user(self, namespace_name, repository_name):
def get_repo_roles(self, username, namespace_name, repository_name):
def get_repo_permission_for_user(self, username, namespace_name, repository_name):
def set_repo_permiss... |
class SimpleDownloader(BaseDownloader):
__name__ = 'SimpleDownloader'
__type__ = 'downloader'
__version__ = '2.42'
__status__ = 'stable'
__pattern__ = '^unmatchable$'
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallba... |
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