code stringlengths 281 23.7M |
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def get_cd_loss(pred, gt, radius, alpha):
(dists_forward, _, dists_backward, _) = tf_nndistance.nn_distance(gt, pred)
CD_dist = ((alpha * dists_forward) + ((1 - alpha) * dists_backward))
CD_dist = tf.reduce_mean(CD_dist, axis=1)
CD_dist_norm = (CD_dist / radius)
cd_loss = tf.reduce_mean(CD_dist_norm... |
def test_is_valid_balanceproof_signature():
balance_proof = factories.create(factories.BalanceProofSignedStateProperties())
valid = is_valid_balanceproof_signature(balance_proof, factories.make_address())
assert (not valid), 'Address does not match.'
balance_proof = factories.create(factories.BalancePro... |
class LookupColor(rq.ReplyRequest):
_request = rq.Struct(rq.Opcode(92), rq.Pad(1), rq.RequestLength(), rq.Colormap('cmap'), rq.LengthOf('name', 2), rq.Pad(2), rq.String8('name'))
_reply = rq.Struct(rq.ReplyCode(), rq.Pad(1), rq.Card16('sequence_number'), rq.ReplyLength(), rq.Card16('exact_red'), rq.Card16('exac... |
def test_error_raising_with_one_class():
with pytest.raises(TypeError):
class BadDecoratorArg(StaticProvider):
_provision_action
def _provide(self, mediator: Mediator, request: int):
pass
with pytest.raises(ValueError):
class DoubleDecoration(StaticProvide... |
.skip
def test_interleave_speed():
n_samples = 100000
a = np.arange(0, n_samples)
b = np.arange(1, (n_samples + 1))
c = np.arange(2, (n_samples + 2))
assert (a.shape[0] == b.shape[0] == c.shape[0])
n = a.shape[0]
a_buf = np.empty((n * INT32_BUF_SIZE), dtype=np.uint8)
b_buf = np.empty((n ... |
class DescribeUnmarshaller():
def it_can_unmarshal_from_a_pkg_reader(self, pkg_reader_, pkg_, part_factory_, _unmarshal_parts_, _unmarshal_relationships_, parts_dict_):
_unmarshal_parts_.return_value = parts_dict_
Unmarshaller.unmarshal(pkg_reader_, pkg_, part_factory_)
_unmarshal_parts_.ass... |
def _get_user_repo_permissions(user, limit_to_repository_obj=None, limit_namespace=None, limit_repo_name=None):
UserThroughTeam = User.alias()
base_query = RepositoryPermission.select(RepositoryPermission, Role, Repository, Namespace).join(Role).switch(RepositoryPermission).join(Repository).join(Namespace, on=(... |
class Effect5825(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Missile Launcher Operation')), 'kineticDamage', ship.getModifiedItemAttr('shipBonusGC2'), skill='Gallente Cruiser', **kwargs) |
class TestTwoBitOp(Bloq):
_property
def signature(self) -> Signature:
return Signature.build(ctrl=1, target=1)
def decompose_bloq(self) -> 'CompositeBloq':
raise DecomposeTypeError(f'{self} is atomic')
def add_my_tensors(self, tn: 'qtn.TensorNetwork', tag: Any, *, incoming: Dict[(str, 'S... |
def outputids2words(id_list, vocab, article_oovs):
words = []
for i in id_list:
try:
w = vocab.id2word(i)
except ValueError:
assert (article_oovs is not None), "Error: model produced a word ID that isn't in the vocabulary. This should not happen in baseline (no pointer-ge... |
def get_download_model_command(file_id, file_name):
current_directory = os.getcwd()
save_path = MODEL_DIR
if (not os.path.exists(save_path)):
os.makedirs(save_path)
url = 'wget --load-cookies /tmp/cookies.txt " --quiet --save-cookies /tmp/cookies.txt --keep-session-cookies --no-check-certificate... |
class FomostoTestCase(unittest.TestCase):
def test_fomosto_usage(self):
common.call_assert_usage('fomosto')
def test_fomosto_ahfull(self):
with common.run_in_temp():
fomosto('init', 'ahfullgreen', 'my_gfs')
with common.chdir('my_gfs'):
common.call_assert_u... |
def boolean_mask(boxlist, indicator, fields=None, scope=None):
with tf.name_scope(scope, 'BooleanMask'):
if (indicator.shape.ndims != 1):
raise ValueError('indicator should have rank 1')
if (indicator.dtype != tf.bool):
raise ValueError('indicator should be a boolean tensor')... |
class JSONBasedEditor(qltk.UniqueWindow):
_WIDTH = 800
_HEIGHT = 400
def __init__(self, proto_cls, values, filename, title):
if self.is_not_unique():
return
super().__init__()
self.proto_cls = proto_cls
self.current = None
self.filename = filename
... |
def main():
args = parse_args()
send_example_telemetry('run_clm_no_trainer', args)
accelerator_log_kwargs = {}
if args.with_tracking:
accelerator_log_kwargs['log_with'] = args.report_to
accelerator_log_kwargs['logging_dir'] = args.output_dir
accelerator = Accelerator(gradient_accumul... |
def _make_circle_one_point(points, p):
c = (p[0], p[1], 0)
for (i, q) in enumerate(points):
if (not _is_in_circle(c, q)):
if (c[2] == 0):
c = _make_diameter(p, q)
else:
c = _make_circle_two_points(points[:(i + 1)], p, q)
return c |
class ProjectUpdateView(ObjectPermissionMixin, RedirectViewMixin, UpdateView):
model = Project
queryset = Project.objects.all()
form_class = ProjectForm
permission_required = 'projects.change_project_object'
def get_form_kwargs(self):
catalogs = Catalog.objects.filter_current_site().filter_g... |
class Tree(nn.Module):
def __init__(self, levels, block, in_channels, out_channels, stride=1, level_root=False, root_dim=0, root_kernel_size=1, dilation=1, root_residual=False):
super(Tree, self).__init__()
if (root_dim == 0):
root_dim = (2 * out_channels)
if level_root:
... |
def conv(x, c):
ksize = c['ksize']
stride = c['stride']
filters_out = c['conv_filters_out']
filters_in = x.get_shape()[(- 1)]
shape = [ksize, ksize, filters_in, filters_out]
initializer = tf.contrib.layers.xavier_initializer()
weights = _get_variable('weights', shape=shape, dtype='float', in... |
def parsefile(file):
(path, filename) = dsz.path.Split(file)
dsz.control.echo.Off()
runsuccess = dsz.cmd.Run(('local run -command "%s\\Tools\\i386-winnt\\SlDecoder.exe %s %s\\GetFiles\\STRANGELAND_Decrypted\\%s.xml"' % (STLA_PATH, file, logdir, filename)), dsz.RUN_FLAG_RECORD)
dsz.control.echo.On()
... |
def init(disp, info):
disp.extension_add_method('display', 'xinerama_query_version', query_version)
disp.extension_add_method('window', 'xinerama_get_state', get_state)
disp.extension_add_method('window', 'xinerama_get_screen_count', get_screen_count)
disp.extension_add_method('window', 'xinerama_get_sc... |
_if_asan_class
class ShardedEmbeddingCollectionParallelTest(MultiProcessTestBase):
((torch.cuda.device_count() <= 1), 'Not enough GPUs, this test requires at least two GPUs')
(verbosity=Verbosity.verbose, max_examples=10, deadline=None)
(use_apply_optimizer_in_backward=st.booleans(), use_index_dedup=st.bool... |
def get_fold(dataset, fold=None, cross_validation_ratio=0.2, num_valid_per_point=4, seed=0, shuffle=True):
if (fold is not None):
indices = fold.split('_')[1:]
sweep_ind = int(indices[0])
fold_ind = int(indices[1])
assert ((sweep_ind is None) or (sweep_ind < num_valid_per_point))
... |
class Experiment(object):
def get_model_name(self, model_name):
model_name = model_name.replace(' ', '').replace(')', '').replace('(', '').replace('[', '').replace(']', '').replace(',', '-').replace("'", '')
return model_name
def __init__(self, data_params, arch_params, loaded_from_dir=None, exp... |
class Migration(migrations.Migration):
dependencies = [('conferences', '0011_auto__2340'), ('cms', '0004_auto__0814')]
operations = [migrations.CreateModel(name='FAQ', fields=[('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('created', model_utils.fields.AutoCr... |
def binary_CIFAR100(cls1, cls2, train=False, batch_size=None, augm_flag=False, val_size=None):
if (batch_size == None):
if train:
batch_size = train_batch_size
else:
batch_size = test_batch_size
transform_base = [transforms.ToTensor()]
transform_train = transforms.Com... |
class _Webhooks(EnvConfig, env_prefix='webhooks_'):
big_brother: Webhook = Webhook(id=, channel=Channels.big_brother)
dev_log: Webhook = Webhook(id=, channel=Channels.dev_log)
duck_pond: Webhook = Webhook(id=, channel=Channels.duck_pond)
incidents: Webhook = Webhook(id=, channel=Channels.incidents)
... |
class ReactionDrivenODE(BaseModel):
def __init__(self, param, options, x_average):
super().__init__(param, options)
self.x_average = x_average
def get_fundamental_variables(self):
eps_dict = {}
for domain in self.options.whole_cell_domains:
Domain = domain.capitalize(... |
class PDControlWithRate():
def __init__(self, kp=0.0, kd=0.0, limit=1.0):
self.kp = kp
self.kd = kd
self.limit = limit
def update(self, y_ref, y, ydot):
u = ((self.kp * (y_ref - y)) - (self.kd * ydot))
u_sat = self._saturate(u)
return u_sat
def _saturate(self,... |
def test_record_property(pytester: Pytester, run_and_parse: RunAndParse) -> None:
pytester.makepyfile('\n import pytest\n\n \n def other(record_property):\n record_property("bar", 1)\n def test_record(record_property, other):\n record_property("foo", "<1");\n ')
... |
class FC6_DmRaidData(BaseData):
removedKeywords = BaseData.removedKeywords
removedAttrs = BaseData.removedAttrs
def __init__(self, *args, **kwargs):
BaseData.__init__(self, *args, **kwargs)
self.name = kwargs.get('name', '')
self.devices = kwargs.get('devices', [])
self.dmset... |
def demo_model_parallel(rank, world_size):
print(f'Running DDP with model parallel example on rank {rank}.')
setup(rank, world_size)
dev0 = (rank * 2)
dev1 = ((rank * 2) + 1)
mp_model = ToyMpModel(dev0, dev1)
ddp_mp_model = DDP(mp_model)
loss_fn = nn.MSELoss()
optimizer = optim.SGD(ddp_m... |
class Basic():
def __init__(self):
self.__accessToken = ''
self.__leftTime = 0
def __real_get_access_token(self):
appId = 'xxxxxxxxxxxxx'
appSecret = 'xxxxxxxxxxxxxxxxxxxxx'
postUrl = (' % (appId, appSecret))
urlResp = urllib.urlopen(postUrl)
urlResp = jso... |
class ModelVarClass(VariableClass, metaclass=RegisteringChoiceType):
var_name = 'model'
_var(argument='(?P<dataaug>[a-zA-Z0-9]+-)?(?P<loss>[a-zA-Z0-9\\.]+)-tor-(?P<arch>[a-zA-Z0-9_]+)(?P<hyper>-[a-zA-Z0-9\\.]+)?')
def torch_model(auto_var, inter_var, dataaug, loss, arch, hyper, trnX, trny, n_channels, multi... |
.end_to_end()
def test_scheduling_w_mixed_priorities(runner, tmp_path):
source = '\n import pytask\n\n .try_last\n .try_first\n def task_mixed(): pass\n '
tmp_path.joinpath('task_module.py').write_text(textwrap.dedent(source))
result = runner.invoke(cli, [tmp_path.as_posix()])
assert (res... |
class CCZ2TFactory(MagicStateFactory):
distillation_l1_d: int = 15
distillation_l2_d: int = 31
qec_scheme: qec.QuantumErrorCorrectionSchemeSummary = qec.FowlerSuperconductingQubits
def l0_state_injection_error(self, phys_err: float) -> float:
return phys_err
def l0_topo_error_t_gate(self, ph... |
class FPNFFConv(nn.Module):
def __init__(self, in_channels):
super(FPNFFConv, self).__init__()
inter_channels = (in_channels // 4)
out_channels = in_channels
self.relu = nn.ReLU(inplace=True)
self.bottleneck = nn.Sequential(nn.Conv2d(in_channels, inter_channels, kernel_size=1... |
def test_put_automatic_versioning(registry_storage):
name = 'test'
type1 = StructType(fields=[IntType(bits=32)])
type2 = StructType(fields=[IntType(bits=24)])
version1 = registry_storage.put(name, type1)
version2 = registry_storage.put(name, type2)
assert (version2 == (version1 + 1)) |
def _process_message(message: Dict[(str, Any)], ws: WebSocketT) -> None:
if ('call' in message):
error_info = {}
try:
return_val = _exposed_functions[message['name']](*message['args'])
status = 'ok'
except Exception as e:
err_traceback = traceback.format_e... |
def DBindex(cl_data_file):
class_list = cl_data_file.keys()
cl_num = len(class_list)
cl_means = []
stds = []
DBs = []
for cl in class_list:
cl_means.append(np.mean(cl_data_file[cl], axis=0))
stds.append(np.sqrt(np.mean(np.sum(np.square((cl_data_file[cl] - cl_means[(- 1)])), axis=... |
def pytest_configure(config):
config.addinivalue_line('markers', "flaky(reruns=1, reruns_delay=0): mark test to re-run up to 'reruns' times. Add a delay of 'reruns_delay' seconds between re-runs.")
if (config.pluginmanager.hasplugin('xdist') and HAS_PYTEST_HANDLECRASHITEM):
config.pluginmanager.register... |
class HashAlgorithm(DataElementGroup):
usage_hash = DataElementField(type='code', max_length=3)
hash_algorithm = DataElementField(type='code', max_length=3)
algorithm_parameter_name = DataElementField(type='code', max_length=3)
algorithm_parameter_value = DataElementField(type='bin', max_length=512, req... |
class TestCRUD(TestCase):
def setUp(self):
self.image_temp = tempfile.NamedTemporaryFile(suffix='.png').name
self.xml_temp = tempfile.NamedTemporaryFile(suffix='.xml').name
self.marker_type = StyleType.objects.create(symbol_type='marker', name='Marker', description='a marker for testing purp... |
class CmdFind(COMMAND_DEFAULT_CLASS):
key = 'find'
aliases = 'search, locate'
switch_options = ('room', 'exit', 'char', 'exact', 'loc', 'startswith')
locks = 'cmd:perm(find) or perm(Builder)'
help_category = 'Building'
def func(self):
caller = self.caller
switches = self.switches... |
def _unpack_sequence_value(value: SequenceValue, target_length: int, post_starred_length: Optional[int]) -> Union[(Sequence[Value], CanAssignError)]:
head = []
tail = []
while (len(head) < target_length):
if (len(head) >= len(value.members)):
return CanAssignError(f'{value} must have at ... |
def deselect_by_mark(items: 'List[Item]', config: Config) -> None:
matchexpr = config.option.markexpr
if (not matchexpr):
return
expr = _parse_expression(matchexpr, "Wrong expression passed to '-m'")
remaining: List[Item] = []
deselected: List[Item] = []
for item in items:
if exp... |
def parse_args():
parser = argparse.ArgumentParser(description='Initialize MS COCO dataset.', epilog='Example: python mscoco.py --download-dir ~/mscoco', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--download-dir', type=str, default=None, help='dataset directory on disk')
pa... |
class one_conv(nn.Module):
def __init__(self, in_ch, out_ch, normaliz=False):
super(one_conv, self).__init__()
ops = []
ops += [nn.Conv2d(in_ch, out_ch, 3, padding=1)]
if normaliz:
ops += [nn.BatchNorm2d(out_ch)]
ops += [nn.ReLU(inplace=True)]
self.conv = ... |
class BoundarySelector(_Selector):
def __init__(self):
super(BoundarySelector, self).__init__()
self.setWindowTitle(self.tr('Select Faces/Edges/Vertexes'))
self.setHelpText(self.tr('To add references: select them in the 3D view and click "Add".'))
def getSelection(self):
selecti... |
class SpatialSvdModuleSplitter():
def split_module(model: tf.keras.Model, layer: Layer, rank: int) -> (tf.keras.layers.Layer, tf.keras.layers.Layer):
(h, v) = SpatialSvdModuleSplitter.get_svd_matrices(layer, rank)
(conv_a_stride, conv_b_stride) = get_strides_for_split_conv_ops(layer=layer.module)
... |
class PluginPipelines(PluginActions):
def register_function(self, function, inputs, parameters, outputs, name, description, input_descriptions=None, parameter_descriptions=None, output_descriptions=None, citations=None, deprecated=False, examples=None):
if (citations is None):
citations = ()
... |
class AttrVI_ATTR_MEM_SPACE(EnumAttribute):
resources = [(constants.InterfaceType.vxi, 'INSTR')]
py_name = ''
visa_name = 'VI_ATTR_MEM_SPACE'
visa_type = 'ViUInt16'
default = constants.VI_A16_SPACE
(read, write, local) = (True, False, False)
enum_type = constants.AddressSpace |
def keytext_to_keyinfo_and_event(keytext):
keyinfo = keysyms.common.make_KeyPress_from_keydescr(keytext)
if ((len(keytext) == 3) and (keytext[0] == '"') and (keytext[2] == '"')):
event = Event(keytext[1])
else:
event = Event(keyinfo.tuple()[3])
return (keyinfo, event) |
class Connection():
def __repr__(self):
return self.__str__()
def __str__(self):
return 'Simple Connection'
def __bool__(self):
return True
def _eval(funcstring):
funclist = funcstring.split('.')
firstelem = funclist.pop(0)
if isinstance(__builtins__, dict... |
def hkdf_derive_test(backend, algorithm, params):
hkdf = HKDF(algorithm, int(params['l']), salt=(binascii.unhexlify(params['salt']) or None), info=(binascii.unhexlify(params['info']) or None), backend=backend)
okm = hkdf.derive(binascii.unhexlify(params['ikm']))
assert (okm == binascii.unhexlify(params['okm... |
.parametrize('name', all_regression_models)
.parametrize('N', [100, 6000])
def test_models_regression(name, N):
(S, Ns, D) = (5, 3, 2)
model = get_regression_model(name)(is_test=True)
model.fit(np.random.randn(N, D), np.random.randn(N, 1))
model.fit(np.random.randn(N, D), np.random.randn(N, 1))
(m, ... |
def get_densenet(blocks, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
if (blocks == 121):
init_block_channels = 64
growth_rate = 32
layers = [6, 12, 24, 16]
elif (blocks == 161):
init_block_channels = 96
growth_rate = 48
... |
def RADC(mf, frozen=None, mo_coeff=None, mo_occ=None):
__doc__ = radc.RADC.__doc__
if (not ((frozen is None) or (frozen == 0))):
raise NotImplementedError
mf = mf.remove_soscf()
if (not mf.istype('RHF')):
mf = mf.to_rhf()
return radc.RADC(mf, frozen, mo_coeff, mo_occ) |
def test_tar_archive_one_pass_with_interpolation():
context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'tar': {'archive': [{'in': '{key2}/to/dir', 'out': './blah.tar.{key1}'}]}})
with patch('tarfile.open') as mock_tarfile:
pypyr.steps.tar.run_step(context)
mock_tarfile.assert_c... |
class Benchmark(object):
def __init__(self, prefix=None):
self.prefix = (prefix or '')
self.results = []
def __call__(self, func):
def stopwatch(*args):
t0 = time.time()
name = (self.prefix + func.__name__)
result = func(*args)
elapsed = (t... |
class CoverSearch():
def __init__(self, callback):
self.engine_list = []
self._stop = False
def wrap(*args, **kwargs):
if (not self._stop):
return callback(*args, **kwargs)
self.callback = wrap
self.finished = 0
def add_engine(self, engine, que... |
def attr(accessing_obj, accessed_obj, *args, **kwargs):
if (not args):
return False
attrname = args[0].strip()
value = None
if (len(args) > 1):
value = args[1].strip()
compare = 'eq'
if kwargs:
compare = kwargs.get('compare', 'eq')
def valcompare(val1, val2, typ='eq')... |
def senstivity_check():
np.random.seed(101)
mcrr = MonteCarloRR(observed_RR=0.73322, sample=10000)
mcrr.confounder_RR_distribution(trapezoidal(mini=0.9, mode1=1.1, mode2=1.7, maxi=1.8, size=10000))
mcrr.prop_confounder_exposed(trapezoidal(mini=0.25, mode1=0.28, mode2=0.32, maxi=0.35, size=10000))
mc... |
def train(train_iter, dev_iter, mixed_test_iter, model, args, text_field, aspect_field, sm_field, predict_iter):
time_stamps = []
optimizer = torch.optim.Adagrad(model.parameters(), lr=args.lr, weight_decay=args.l2, lr_decay=args.lr_decay)
steps = 0
model.train()
start_time = time.time()
(dev_ac... |
class AnActor():
(num_returns=2)
def genData(self, rank, nranks, nrows):
(data, labels) = datasets.load_breast_cancer(return_X_y=True)
(train_x, _, train_y, _) = train_test_split(data, labels, test_size=0.25)
train_y = train_y.reshape((train_y.shape[0], 1))
train = np.hstack([tra... |
class ContentManageableAdmin():
def save_model(self, request, obj, form, change):
if (not change):
obj.creator = request.user
else:
obj.last_modified_by = request.user
return super().save_model(request, obj, form, change)
def get_readonly_fields(self, request, obj... |
def set_backend(name: str) -> _ContextManager:
if (name not in _SUPPORTED_BACKENDS):
supported_backend_names = ', '.join(_SUPPORTED_BACKENDS.keys())
raise RuntimeError(f"Backend '{name}' is not supported. Please choose one of: {supported_backend_names}")
old_backend = _CURRENT_BACKEND
action... |
def gen_value(t):
if (t == 'INT8'):
val = randint((- 128), (- 1))
elif (t == 'INT16'):
val = randint((- 32768), (- 256))
elif (t == 'INT32'):
val = randint((- ), (- 65536))
elif ((t == 'CARD8') or (t == 'BYTE')):
val = randint(128, 255)
elif (t == 'CARD16'):
v... |
class Migration(migrations.Migration):
dependencies = [('api', '0054_user_invalidate_unknown_role')]
operations = [migrations.AddField(model_name='reminder', name='mentions', field=django.contrib.postgres.fields.ArrayField(base_field=models.BigIntegerField(validators=[django.core.validators.MinValueValidator(li... |
def code_assist(project, source_code, offset, resource=None, templates=None, maxfixes=1, later_locals=True):
if (templates is not None):
warnings.warn('Codeassist no longer supports templates', DeprecationWarning, stacklevel=2)
assist = _PythonCodeAssist(project, source_code, offset, resource=resource, ... |
class VOC12AffinityDataset(VOC12SegmentationDataset):
def __init__(self, img_name_list_path, label_dir, crop_size, voc12_root, indices_from, indices_to, rescale=None, img_normal=TorchvisionNormalize(), hor_flip=False, crop_method=None):
super().__init__(img_name_list_path, label_dir, crop_size, voc12_root, ... |
class TestUpgradeToFloat():
unary_ops_vals = [(reciprocal, (list(range((- 127), 0)) + list(range(1, 127)))), (sqrt, list(range(0, 128))), (log, list(range(1, 128))), (log2, list(range(1, 128))), (log10, list(range(1, 128))), (log1p, list(range(0, 128))), (exp, list(range((- 127), 89))), (exp2, list(range((- 127), 8... |
def load(args, base_model, logits_model, base_optimizer, logits_optimizer):
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
chkpoint = torch.load(args.resume)
if (isinstance(chkpoint, dict) and ('base_state_dict' in ... |
def parser_train():
parser = argparse.ArgumentParser(description='Standard + Adversarial Training.')
parser.add_argument('--augment', type=str, default='base', choices=['none', 'base', 'cutout', 'autoaugment', 'randaugment', 'idbh'], help='Augment training set.')
parser.add_argument('--batch-size', type=int... |
class Lorenz96(DynSys):
def rhs(self, X, t):
Xdot = np.zeros_like(X)
Xdot[0] = ((((X[1] - X[(- 2)]) * X[(- 1)]) - X[0]) + self.f)
Xdot[1] = ((((X[2] - X[(- 1)]) * X[0]) - X[1]) + self.f)
Xdot[(- 1)] = ((((X[0] - X[(- 3)]) * X[(- 2)]) - X[(- 1)]) + self.f)
Xdot[2:(- 1)] = ((((... |
_module()
class FasterRCNN(TwoStageDetector):
'Implementation of `Faster R-CNN <
def __init__(self, backbone, rpn_head, roi_head, train_cfg, test_cfg, neck=None, pretrained=None):
super(FasterRCNN, self).__init__(backbone=backbone, neck=neck, rpn_head=rpn_head, roi_head=roi_head, train_cfg=train_cfg, te... |
class UploaderTestCase(unittest.TestCase):
mime_type = 'text/plain'
params = {'x:a': 'a'}
metadata = {'x-qn-meta-name': 'qiniu', 'x-qn-meta-age': '18'}
q = Auth(access_key, secret_key)
bucket = BucketManager(q)
def test_put(self):
key = 'a\\b\\c"hello'
data = 'hello bubby!'
... |
('pyproj.sync.urlretrieve', autospec=True)
.parametrize('verbose', [True, False])
def test_download_resource_file(urlretrieve_mock, verbose, tmp_path, capsys):
def dummy_urlretrieve(url, local_path):
with open(local_path, 'w') as testf:
testf.write('TEST')
urlretrieve_mock.side_effect = dumm... |
class SemiDataset(Dataset):
def __init__(self, name, root, mode, size=None, id_path=None, nsample=None):
self.name = name
self.root = root
self.mode = mode
self.size = size
if ((mode == 'train_l') or (mode == 'train_u')):
with open(id_path, 'r') as f:
... |
def get_env_group_title(env):
s = env.unwrapped.spec.entry_point
if ('gym_ple' in s):
group_title = 'gym_ple'
elif ('gym_pygame' in s):
group_title = 'gym_pygame'
elif ('gym_minatar' in s):
group_title = 'gym_minatar'
elif ('gym_exploration' in s):
group_title = 'gym_... |
class _GitlabProject():
def __init__(self, status):
self.commits = {REF: self._Commit(status)}
self.tags = self._Tags()
self.releases = self._Releases()
class _Commit():
def __init__(self, status):
self.statuses = self._Statuses(status)
class _Statuses():
... |
class TranslationEvaluator(SentenceEvaluator):
def __init__(self, source_sentences: List[str], target_sentences: List[str], show_progress_bar: bool=False, batch_size: int=16, name: str='', print_wrong_matches: bool=False, write_csv: bool=True):
self.source_sentences = source_sentences
self.target_se... |
def test_rexx_can_guess_from_text():
lx = get_lexer_by_name('rexx')
assert (lx.analyse_text('/* */') == pytest.approx(0.01))
assert (lx.analyse_text('/* Rexx */\n say "hello world"') == pytest.approx(1.0))
val = lx.analyse_text('/* */\nhello:pRoceduRe\n say "hello world"')
assert (val > ... |
def tensorclass(cls: T) -> T:
def __torch_function__(cls, func: Callable, types: tuple[(type, ...)], args: tuple[(Any, ...)]=(), kwargs: (dict[(str, Any)] | None)=None) -> Callable:
if ((func not in _TD_PASS_THROUGH) or (not all((issubclass(t, (Tensor, cls)) for t in types)))):
return NotImpleme... |
class Capture(object):
def __init__(self, tee=False):
self.file = StringIO()
self.tee = tee
def __enter__(self):
self.orig_stdout = sys.stdout
self.orig_exit = sys.exit
sys.stdout = self
def my_exit(res):
raise PyrockoExit(res)
sys.exit = my_ex... |
_model('lightconv_lm')
class LightConvLanguageModel(FairseqLanguageModel):
def __init__(self, decoder):
super().__init__(decoder)
def add_args(parser):
parser.add_argument('--dropout', default=0.1, type=float, metavar='D', help='dropout probability')
parser.add_argument('--attention-drop... |
class Solution():
def search(self, nums: List[int], target: int) -> int:
low = 0
high = (len(nums) - 1)
if ((target < nums[0]) or (target > nums[(- 1)])):
return (- 1)
while (low <= high):
mid = ((low + high) // 2)
if (nums[mid] == target):
... |
.parametrize('history_num_frames_ego', [0, 1, 2, 3, 4])
.parametrize('history_num_frames_agents', [0, 1, 2, 3, 4])
def test_vector_ego_agents(zarr_dataset: ChunkedDataset, dmg: LocalDataManager, cfg: dict, history_num_frames_ego: int, history_num_frames_agents: int) -> None:
cfg['model_params']['history_num_frames_... |
_auth
def delete_user_role(request, pk):
if (request.method == 'DELETE'):
try:
UserRole.objects.get(id=pk).delete()
return JsonResponse({'code': 200, 'data': None, 'msg': '!'})
except Exception as e:
return JsonResponse({'code': 500, 'data': None, 'msg': ',:{}'.fo... |
class CmdQuit(COMMAND_DEFAULT_CLASS):
key = 'quit'
switch_options = ('all',)
locks = 'cmd:all()'
account_caller = True
def func(self):
account = self.account
if ('all' in self.switches):
account.msg('|RQuitting|n all sessions. Hope to see you soon again.', session=self.se... |
class NoCapture(CaptureBase[str]):
EMPTY_BUFFER = ''
def __init__(self, fd: int) -> None:
pass
def start(self) -> None:
pass
def done(self) -> None:
pass
def suspend(self) -> None:
pass
def resume(self) -> None:
pass
def snap(self) -> str:
retu... |
def version_raises_exception(monkeypatch, pyscaffold):
def raise_exeception(name):
raise metadata.PackageNotFoundError('No version mock')
monkeypatch.setattr(metadata, 'version', raise_exeception)
reload(pyscaffold)
try:
(yield)
finally:
monkeypatch.undo()
reload(pysc... |
def write_reward_csv(rewards, split):
results_df = []
for training in rewards:
for model in rewards[training][split]:
if (model == 'random'):
scores = rewards[training][split][model]['scores'][(- 1)]
smis = rewards[training][split][model]['smis'][(- 1)]
... |
def string_to_bool(v):
if isinstance(v, bool):
return v
if (v.lower() in ('yes', 'true', 't', 'y', '1')):
return True
elif (v.lower() in ('no', 'false', 'f', 'n', '0')):
return False
else:
raise ArgumentTypeError(f'Truthy value expected: got {v} but expected one of yes/no... |
class Effect93(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: (mod.item.group.name == 'Hybrid Weapon')), 'damageMultiplier', module.getModifiedItemAttr('damageMultiplier'), stackingPenalties=True, **kwargs) |
class ContextRenderCORTEX_M(ContextRenderARM, ArchCORTEX_M):
def __init__(self, ql, predictor):
super().__init__(ql, predictor)
ArchCORTEX_M.__init__(self)
self.regs_a_row = 3
_printer('[ REGISTERS ]')
def context_reg(self, saved_reg_dump):
cur_regs = self.dump_regs()
... |
class ResNet_ImageNet(nn.Module):
def __init__(self, block, num_blocks, pretrained=False, norm=False, Embed=True, feat_dim=512, embed_dim=512):
super(ResNet_ImageNet, self).__init__()
self.in_planes = 64
self.layer0_conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
... |
class TestPipe():
def test_success(self):
c = pipe(str, to_bool, bool)
assert (True is c('True') is c(True))
def test_fail(self):
c = pipe(str, to_bool)
with pytest.raises(ValueError):
c(33)
with pytest.raises(ValueError):
c('33')
def test_suga... |
_partition_types.register('GENERAL_BIDIRECTIONAL')
def general_bidirectional(node_indices, node_labels=None):
(yield CompleteGeneralKCut(node_indices, node_labels=node_labels))
for cut_matrix in _cut_matrices(len(node_indices), symmetric=True):
(yield GeneralKCut(node_indices, cut_matrix, node_labels=no... |
class PayloadTest(object):
def assert_errors(self, client, url, data, *errors):
out = client.post_json(url, data, status=400)
assert ('message' in out)
assert ('errors' in out)
for error in errors:
assert (error in out['errors'])
def test_validation_false_on_construct... |
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