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class Tester(Manager):
def __init__(self, args):
tic = time.time()
super(Tester, self).__init__(args)
self.model.load_state_dict(torch.load(args.PathModel))
self.create_thinningsampler(args.NumSample, args.NumExp)
print(f'time spent on initializatin : {(time.time() - tic):.2f... |
class TestLt(unittest.TestCase):
def test_lt_matrix(self):
base = Ga('a b', g=[1, 1], coords=symbols('x, y', real=True))
(a, b) = base.mv()
A = base.lt([(a + b), ((2 * a) - b)])
assert (str(A) == 'Lt(a) = a + b\nLt(b) = 2*a - b')
assert (str(A.matrix()) == 'Matrix([[1, 2], [1... |
def test_speedaction_rel():
speedaction = OSC.RelativeSpeedAction(1, 'Ego', TD)
prettyprint(speedaction.get_element())
speedaction2 = OSC.RelativeSpeedAction(1, 'Ego', TD)
speedaction3 = OSC.RelativeSpeedAction(1, 'Ego1', TD)
assert (speedaction == speedaction2)
assert (speedaction != speedactio... |
class ins(object):
def ssh(self):
Mylogo()
print((('\n\n\x1b[01;31m [\x1b[01;33m+\x1b[01;31m] \x1b[01;36mOpenSSH \x1b[01;31mis not installed in your ' + system) + '.'))
opt = input('\n\x1b[01;33m Do you want to install OpenSSH [\x1b[01;32mY/n\x1b[01;33m] >>\x1b[01;36m ')
if ((opt =... |
def pretf_blocks(var):
private_label = 'private'
private = (yield resource.aws_security_group[private_label](name='pretf-example-aws-private'))
public_label = 'public'
public = (yield resource.aws_security_group[public_label](name='pretf-example-aws-public'))
for cidr in sorted(set(var.security_grou... |
def initiator_init(raiden: 'RaidenService', transfer_identifier: PaymentID, transfer_amount: PaymentAmount, transfer_secret: Secret, transfer_secrethash: SecretHash, token_network_address: TokenNetworkAddress, target_address: TargetAddress, lock_timeout: BlockTimeout=None, route_states: List[RouteState]=None) -> Tuple[... |
class MetricName(MetricNameBase):
DEFAULT = ''
NE = 'ne'
SEGMENTED_NE = 'segmented_ne'
LOG_LOSS = 'logloss'
THROUGHPUT = 'throughput'
TOTAL_EXAMPLES = 'total_examples'
CTR = 'ctr'
CALIBRATION = 'calibration'
MSE = 'mse'
MAE = 'mae'
RMSE = 'rmse'
AUC = 'auc'
AUPRC = 'a... |
class TestRowWiseNormalize(unittest.TestCase):
def test_3by3_matrix(self):
matrix = np.array([[0.5, 2.0, 3.0], [3.0, 4.0, 5.0], [4.0, 2.0, 1.0]])
adjusted_matrix = refinement.RowWiseNormalize().refine(matrix)
expected = np.array([[0.167, 0.667, 1.0], [0.6, 0.8, 1.0], [1.0, 0.5, 0.25]])
... |
class Notification():
def __init__(self, id: int):
self.device_id = id
self.device_handle: Optional[str] = None
self.host_id = 0
def show(self, data: ShowNotificationData) -> None:
self.device_handle = data.device_handle
actions = []
if (data.positive_action is no... |
def create_vgg16bn(models_path, task, save_type, get_params=False):
print('Creating VGG16BN untrained {} models...'.format(task))
model_params = get_task_params(task)
if (model_params['input_size'] == 32):
model_params['fc_layers'] = [512, 512]
elif (model_params['input_size'] == 64):
mo... |
class MakeClientTests(unittest.TestCase):
def test_no_endpoint(self):
client = metrics.make_client('namespace', None, log_if_unconfigured=False)
self.assertIsInstance(client.transport, metrics.NullTransport)
def test_valid_endpoint(self):
client = metrics.make_client('namespace', EXAMPLE... |
def get_valid_gpu_id(gpu_status):
for (i, _gpu_status) in enumerate(gpu_status):
if (_gpu_status.occupied is False):
current_gpu_id = _gpu_status.gpu_id
gpu_status_id = i
_gpu_status.occupied = True
return (current_gpu_id, gpu_status_id)
return (None, None... |
class Pirani(SensorChannel):
_id = Sources.PIRANI
gas_factor = gas_factor
statistics = Channel.measurement('0PM011', 'Get the sensor statistics as a tuple: wear in percent (negative: corrosion,\n positive: contamination), time since last adjustment in hours.', preprocess_reply=(lambda msg: msg.st... |
class _DiscreteBounded(Discrete):
rv_op = discrete_boundrv
def __new__(cls, *args, **kwargs):
kwargs.setdefault('transform', None)
if (kwargs.get('transform') is not None):
raise ValueError('Cannot transform discrete variable.')
return super().__new__(cls, *args, **kwargs)
... |
class TransitionWithSkip(nn.Module):
def __init__(self, module):
super().__init__()
self.module = module
def forward(self, x):
for module in self.module:
x = module(x)
if isinstance(module, nn.ReLU):
skip = x
return (x, skip) |
def load_checkpoint(model_load_path, model):
my_model_dict = model.state_dict()
pre_weight = torch.load(model_load_path)
part_load = {}
match_size = 0
nomatch_size = 0
for k in pre_weight.keys():
value = pre_weight[k]
if ((k in my_model_dict) and (my_model_dict[k].shape == value.... |
class Effect6655(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Capital Projectile Turret')), 'speed', src.getModifiedItemAttr('shipBonusTitanM2'), skill='Minmatar Titan', **kwargs) |
class TestForestPlot():
def plotter(self):
return EffectMeasurePlot
def data(self):
labs = ['Overall', 'Adjusted', '', '2012-2013', 'Adjusted', '', '2013-2014', 'Adjusted', '', '2014-2015', 'Adjusted']
measure = [np.nan, 0.94, np.nan, np.nan, 1.22, np.nan, np.nan, 0.59, np.nan, np.nan, 1... |
class BasicBlock(nn.Module):
outchannel_ratio = 1
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn2 = nn.BatchNorm2d(planes)
self.... |
def main():
pgen_dataset_id = '2020-03-19'
dcol_dataset_id = datetime.datetime.now().isoformat(timespec='minutes')
hardware_grid_problem_task = HardwareGridProblemGenerationTask(dataset_id=pgen_dataset_id, device_name='Sycamore23', instance_i=0, n_qubits=23)
sk_problem_task = SKProblemGenerationTask(dat... |
class SSDPResponse(object):
class _FakeSocket(io.BytesIO):
def makefile(self, *args, **kw):
return self
def __init__(self, response):
r =
r.begin()
self.location = r.getheader('location')
self.usn = r.getheader('usn')
self.st = r.getheader('st')
... |
def simplify_network_to_base_voltage(n, linetype, base_voltage):
logger.info(f'Mapping all network lines onto a single {int(base_voltage)}kV layer')
n.lines['type'] = linetype
n.lines['v_nom'] = base_voltage
n.lines['i_nom'] = n.line_types.i_nom[linetype]
n.lines['num_parallel'] = n.lines.eval('s_no... |
def create_train_op(loss, optimizer, global_step, params):
with tf.name_scope('create_train_op'):
grads_and_vars = optimizer.compute_gradients(loss, tf.trainable_variables(params.trainable_scope), colocate_gradients_with_ops=True)
gradients = [item[0] for item in grads_and_vars]
variables = ... |
class Effect6799(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
types = ('thermal', 'em', 'explosive', 'kinetic')
for type in types:
fit.modules.filteredChargeMultiply((lambda mod: (mod.charge.requiresSkill('Rockets') or mod.charge.require... |
class PtableTourney(models.Model):
class Meta():
table = 'ptable_tourney'
id = fields.IntField(pk=True)
guild_id = fields.BigIntField()
associative_id = fields.CharField(max_length=10)
title = fields.CharField(max_length=100)
secondary_title = fields.CharField(max_length=100)
footer ... |
def attention(query, key, value, mask=None, dropout=None):
d_k = query.size((- 1))
scores = (torch.matmul(query, key.transpose((- 2), (- 1))) / math.sqrt(d_k))
if (mask is not None):
scores = scores.masked_fill((mask > 0), (- .0))
p_attn = torch.softmax(scores, dim=(- 1))
if (dropout is not ... |
(trylast=True)
def pytask_extend_command_line_interface(cli: click.Group) -> None:
for command in ('build', 'clean', 'collect', 'dag', 'profile'):
cli.commands[command].params.extend((_PATH_ARGUMENT, _DATABASE_URL_OPTION))
for command in ('build', 'clean', 'collect', 'dag', 'markers', 'profile'):
... |
class SelectSponsorshipApplicationBenefitsViewTests(TestCase):
url = reverse_lazy('select_sponsorship_application_benefits')
def setUp(self):
self.current_year = SponsorshipCurrentYear.get_year()
self.psf = baker.make('sponsors.SponsorshipProgram', name='PSF')
self.wk = baker.make('spons... |
def retrieve_wikisql_query_answer_tapas(table, example) -> List:
(answer_coordinates, aggregation_op) = _get_answer_coordinates(table, example)
float_answer = _get_float_answer(table, answer_coordinates, aggregation_op)
answer_text = _get_answer_text(table, answer_coordinates, float_answer)
if (len(answ... |
def updown_linear_approx(eigvals_lower, eigvals_upper, nv):
nal = len(eigvals_lower)
nau = len(eigvals_upper)
if (nv < (nal + nau)):
raise ValueError('Number of supplied eigenvalues ({0} lower and {1} upper) is higher than number of nodes ({2})!'.format(nal, nau, nv))
ret = np.zeros(nv)
ret[... |
.parametrize('extra_headers', [[], [(b'connection', b'Keep-Alive')]])
def test_handshake_response_broken_connection_header(extra_headers: Headers) -> None:
with pytest.raises(RemoteProtocolError) as excinfo:
_make_handshake(101, ([(b'upgrade', b'websocket')] + extra_headers))
assert (str(excinfo.value) ... |
def test_sep_fcn_head():
head = DepthwiseSeparableFCNHead(in_channels=128, channels=128, concat_input=False, num_classes=19, in_index=(- 1), norm_cfg=dict(type='BN', requires_grad=True, momentum=0.01))
x = [torch.rand(2, 128, 32, 32)]
output = head(x)
assert (output.shape == (2, head.num_classes, 32, 32... |
class TestCopyGC(EndianTest):
def setUp(self):
self.req_args_0 = {'dst_gc': , 'mask': , 'src_gc': }
self.req_bin_0 = b'9\x00\x04\x00q$\xbaF\xbb\xbc\xf5\\x047'
def testPackRequest0(self):
bin = request.CopyGC._request.to_binary(*(), **self.req_args_0)
self.assertBinaryEqual(bin, s... |
def test__check_initialpaths_for_relpath() -> None:
cwd = Path.cwd()
class FakeSession1():
_initialpaths = frozenset({cwd})
session = cast(pytest.Session, FakeSession1)
assert (nodes._check_initialpaths_for_relpath(session, cwd) == '')
sub = (cwd / 'file')
class FakeSession2():
_... |
def convert_checkpoint_to_pytorch(tf_checkpoint_path: str, config_path: str, pytorch_dump_path: str):
def get_masked_lm_array(name: str):
full_name = f'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
array = tf.train.load_variable(tf_checkpoint_path, full_name)
if ('kernel' in name):
... |
class MetaMapAttribute(AttributeContainerMeta):
def __init__(self, name, bases, namespace, discriminator=None):
super().__init__(name, bases, namespace, discriminator=discriminator)
for (attr_name, attr) in self._attributes.items():
if (isinstance(attr, (BinaryAttribute, BinarySetAttribu... |
def prompt_for_value(prompt, values, default):
def completer(text, state):
for val in values:
if (text.upper() in val.upper()):
if (not state):
return val
else:
state -= 1
return None
readline.set_completer_delim... |
class GatedTanh(nn.Module):
def __init__(self, in_dim, out_dim):
super(GatedTanh, self).__init__()
self.fc = nn.Linear(in_dim, out_dim)
self.gate_fc = nn.Linear(in_dim, out_dim)
def forward(self, x):
y_tilda = torch.tanh(self.fc(x))
gated = torch.sigmoid(self.gate_fc(x))
... |
def cdnod(data: ndarray, c_indx: ndarray, alpha: float=0.05, indep_test: str=fisherz, stable: bool=True, uc_rule: int=0, uc_priority: int=2, mvcdnod: bool=False, correction_name: str='MV_Crtn_Fisher_Z', background_knowledge: Optional[BackgroundKnowledge]=None, verbose: bool=False, show_progress: bool=True, **kwargs) ->... |
class PPO():
def __init__(self, config):
self.lr = config.lr
self.gamma = config.gamma
self.gae_lambda = config.gae_lambda
self.eps_clip = config.eps_clip
self.k_epochs = config.k_epochs
self.tau = config.tau
self.ploss_coef = config.ploss_coef
self.vl... |
def validate_environment_marker(em):
clauses = re.split('\\s+(?:and|or)\\s+', em)
problems = []
for c in clauses:
parts = MARKER_OP.split(c.strip('()'))
if (len(parts) != 3):
problems.append('Invalid expression in environment marker: {!r}'.format(c))
continue
... |
class ProbMaskConv(nn.Conv2d):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.scores = nn.Parameter(torch.Tensor(self.weight.size()))
self.subnet = None
self.train_weights = False
if (parser_args.score_init_constant is not None):
self.... |
(wrapper=True, tryfirst=True)
def pytest_runtest_makereport(item: Item, call) -> Generator[(None, TestReport, TestReport)]:
rep = (yield)
assert (rep.when is not None)
empty: Dict[(str, bool)] = {}
item.stash.setdefault(tmppath_result_key, empty)[rep.when] = rep.passed
return rep |
class ItemCircleAnimation(DemoItem):
def __init__(self, parent=None):
super(ItemCircleAnimation, self).__init__(parent)
self.letterList = []
self.letterCount = Colors.tickerLetterCount
self.scale = 1.0
self.showCount = (- 1)
self.tickOnPaint = False
self.pause... |
class KeypointBoxCoderTest(tf.test.TestCase):
def test_get_correct_relative_codes_after_encoding(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
keypoints = [[[15.0, 12.0], [10.0, 15.0]], [[0.5, 0.3], [0.2, 0.4]]]
num_keypoints = len(keypoints[0])
anchors = [[15.0, 12... |
class UncertaintyProblem(CircuitFactory, ABC):
def __init__(self, num_qubits: int) -> None:
warnings.warn('The {0} is deprecated as of Aqua 0.8.0 and will be removed no earlier than 3 months after the release date. Instead, you can construct the circuits manually using the respective circuit components. See... |
class TestUCASAODKF(TestUCASAOD):
def eval(self):
kf = build_whole_network.DetectionNetworkKF(cfgs=self.cfgs, is_training=False)
all_boxes_r = self.eval_with_plac(img_dir=self.args.img_dir, det_net=kf, image_ext=self.args.image_ext)
imgs = os.listdir(self.args.img_dir)
real_test_imgn... |
class DriveLetterSupportTest(TestCase):
def setUp(self):
self.filesystem = fake_filesystem.FakeFilesystem(path_separator='!')
self.filesystem.alternative_path_separator = '^'
self.filesystem.is_windows_fs = True
def test_initial_value(self):
filesystem = fake_filesystem.FakeFiles... |
def get_data_lst(data, data_root, entities=None):
if (type(entities) == str):
entities_lst = entities.split(',')
elif (type(entities) == list):
entities_lst = entities
else:
raise ValueError('wrong entities')
name_lst = []
train_df_lst = []
test_df_lst = []
label_lst ... |
.parametrize('test_input,expected', [([1, 2, 3], b'OUTP#13\x01\x02\x03'), (range(100), (b'OUTP#3100' + bytes(range(100))))])
def test_adapter_write_binary_values(adapter, test_input, expected):
adapter.write_binary_values('OUTP', test_input, datatype='B')
assert (adapter.connection.read((len(expected) + 10)) ==... |
def main():
(gen, dis) = load_models()
(loader, loader_test) = make_dataset()
optimizer = get_optimizer(dis.parameters())
loss_f = nn.CrossEntropyLoss()
noise = torch.FloatTensor(opt.batch_size, opt.nz).zero_().cuda()
noise_v = Variable(noise)
noise_y = torch.LongTensor(opt.batch_size).zero_... |
def _make_json_rpc_null_response(succeed_at: int) -> Callable[([PreparedRequest], Tuple[(int, Dict[(str, Any)], str)])]:
request_count = 0
def make_response(request: PreparedRequest) -> Tuple[(int, Dict[(str, Any)], str)]:
nonlocal request_count
assert isinstance(request.body, bytes), MYPY_ANNOT... |
class SawyerPlateSlideV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'puck_pos': obs[3:6], 'shelf_x': obs[(- 3)], 'unused_info': obs[[6, 7, 8, 10, 11]]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3)... |
def test_reject_vote_when_voting_is_not_open(graphql_client, user, conference_factory, submission_factory, requests_mock):
graphql_client.force_login(user)
conference = conference_factory()
requests_mock.post(f'{settings.PRETIX_API}organizers/{conference.pretix_organizer_id}/events/{conference.pretix_event_... |
class ESPNetv2(nn.Module):
def __init__(self, channels, init_block_channels, final_block_channels, final_block_groups, dilations, dropout_rate=0.2, in_channels=3, in_size=(224, 224), num_classes=1000):
super(ESPNetv2, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
... |
class Array(BaseRTLIRType):
def __init__(s, dim_sizes, sub_type, obj=None, unpacked=False):
assert isinstance(sub_type, BaseRTLIRType), f'array subtype {sub_type} is not RTLIR type!'
assert (not isinstance(sub_type, Array)), f'array subtype {sub_type} should not be array RTLIR type!'
assert ... |
def get_zone_id(longitude, latitude):
taxi_zone_shape_dic = get_taxi_zones_dic('./Data/mh-180/mh-180.shp')
find_count = 0
for obj_id in taxi_zone_shape_dic:
if taxi_zone_shape_dic[obj_id].intersects(Point(longitude, latitude)):
taxi_zone = obj_id
find_count = (find_count + 1)... |
class PEMachineType(Enum):
UNKNOWN = 0
I386 = 332
R3000 = 354
R4000 = 358
R10000 = 360
WCEMIPSV2 = 361
ALPHA = 388
SH3 = 418
SH3DSP = 419
SH3E = 420
SH4 = 422
SH5 = 424
ARM = 448
AARCH64 = 43620
THUMB = 450
ARMNT = 452
AM33 = 467
POWERPC = 496
... |
class ROIKeypointHead(torch.nn.Module):
def __init__(self, cfg):
super(ROIKeypointHead, self).__init__()
self.cfg = cfg.clone()
self.feature_extractor = make_roi_keypoint_feature_extractor(cfg)
self.predictor = make_roi_keypoint_predictor(cfg)
self.post_processor = make_roi_k... |
def handle_deprecations(doc):
if ('parser' in doc):
warnings.warn('The parser attribute is deprecated, use expect instead', DeprecationWarning, stacklevel=2)
doc['expect'] = (doc.get('expect', []) + [doc.pop('parser')])
if ('body' in doc):
warnings.warn('The body attribute is deprecated,... |
class Spinner():
busy = False
delay = 0.1
def spinning_cursor():
while 1:
for cursor in '|/-\\':
(yield cursor)
def __init__(self, delay=None):
self.spinner_generator = self.spinning_cursor()
if (delay and float(delay)):
self.delay = delay
... |
def add_rorp(rorp, dest_rorp=None):
if ((not rorp.isreg()) or (rorp.getnumlinks() < 2)):
return None
rp_inode_key = _get_inode_key(rorp)
if (rp_inode_key not in _inode_index):
if (not dest_rorp):
dest_key = None
else:
if (dest_rorp.getnumlinks() == 1):
... |
.parametrize('username,password', users)
def test_create(db, client, username, password):
client.login(username=username, password=password)
url = reverse(urlnames['list'])
data = {'title': 'Lorem ipsum dolor sit amet', 'description': 'At vero eos et accusam et justo duo dolores et ea rebum.', 'catalog': ca... |
def test_hash_algo_off(main):
main(['--hash-algo=off'])
assert (main.app._pypiserver_config.hash_algo is None)
main(['--hash-algo=0'])
assert (main.app._pypiserver_config.hash_algo is None)
main(['--hash-algo=no'])
assert (main.app._pypiserver_config.hash_algo is None)
main(['--hash-algo=fal... |
def find_evaluated_item_indexes_by_schema(validator, instance, schema):
if validator.is_type(schema, 'boolean'):
return []
evaluated_indexes = []
if ('items' in schema):
return list(range(0, len(instance)))
ref = schema.get('$ref')
if (ref is not None):
resolved = validator._... |
('beeref.selection.SelectableMixin.mouseMoveEvent')
def test_mouse_move_when_not_crop_mode(mouse_mock, qapp, item):
event = MagicMock()
event.pos.return_value = QtCore.QPointF(30, 50)
item.mouseMoveEvent(event)
event.accept.assert_not_called()
mouse_mock.assert_called_once_with(event) |
class GrapevineBot(Bot):
factory_path = 'evennia.server.portal.grapevine.RestartingWebsocketServerFactory'
def start(self, ev_channel=None, grapevine_channel=None):
if (not _GRAPEVINE_ENABLED):
self.delete()
return
global _SESSIONS
if (not _SESSIONS):
... |
class AutocleanView(ScrimsView):
def __init__(self, ctx: Context, scrim: Scrim):
super().__init__(ctx)
self.ctx = ctx
self.record = scrim
async def initial_embed(self) -> discord.Embed:
autoclean_time = (self.record.autoclean_time.strftime('%I:%M %p') if self.record.autoclean_tim... |
def check_marian_cfg_assumptions(marian_cfg):
assumed_settings = {'layer-normalization': False, 'right-left': False, 'transformer-ffn-depth': 2, 'transformer-aan-depth': 2, 'transformer-no-projection': False, 'transformer-postprocess-emb': 'd', 'transformer-postprocess': 'dan', 'transformer-preprocess': '', 'type':... |
def lamda_scheduler(start_warmup_value, base_value, epochs, niter_per_ep, warmup_epochs=5):
warmup_schedule = np.array([])
warmup_iters = (warmup_epochs * niter_per_ep)
if (warmup_epochs > 0):
warmup_schedule = np.linspace(start_warmup_value, base_value, warmup_iters)
schedule = (np.ones(((epoch... |
def test_team_member_sync_info_synced_superuser(app):
with mock_ldap() as ldap:
with patch('endpoints.api.team.authentication', ldap):
with client_with_identity('devtable', app) as cl:
resp = conduct_api_call(cl, TeamMemberList, 'GET', SYNCED_TEAM_PARAMS)
assert (... |
def main_validation(default_evaluation_params_fn, validate_data_fn):
try:
p = dict([s[1:].split('=') for s in sys.argv[1:]])
evalParams = default_evaluation_params_fn()
if ('p' in p.keys()):
evalParams.update((p['p'] if isinstance(p['p'], dict) else json.loads(p['p'])))
v... |
def _test():
import torch
pretrained = False
models = [resnet10, resnet12, resnet14, resnetbc14b, resnet16, resnet18_wd4, resnet18_wd2, resnet18_w3d4, resnet18, resnet26, resnetbc26b, resnet34, resnetbc38b, resnet50, resnet50b, resnet101, resnet101b, resnet152, resnet152b, resnet200, resnet200b]
for mod... |
def count(object, cache=None):
cache = (cache or [])
if (isstruct(object) or isinstance(object, (dict, list))):
if (id(object) in cache):
raise RuntimeError('recursion!')
cache.append(id(object))
n = 1
if (isstruct(object) or isinstance(object, dict)):
for key in obje... |
def generate_dispatch_glue_native_function(builder: IRBuilder, fitem: FuncDef, callable_class_decl: FuncDecl, dispatch_name: str) -> FuncIR:
line = fitem.line
builder.enter()
callable_class = builder.load_global_str(dispatch_name, line)
decl = builder.mapper.func_to_decl[fitem]
arg_info = get_args(b... |
def get_input_device(input):
if isinstance(input, list):
for item in input:
input_device = get_input_device(item)
if (input_device != (- 1)):
return input_device
return (- 1)
elif isinstance(input, torch.Tensor):
return (input.get_device() if input... |
def resolve_locale_filename(name: (os.PathLike[str] | str)) -> str:
name = os.path.basename(name)
if ((sys.platform == 'win32') and _windows_reserved_name_re.match(os.path.splitext(name)[0])):
raise ValueError(f'Name {name} is invalid on Windows')
return os.path.join(_dirname, f'{name}.dat') |
class ButtonMask(object):
def __init__(self, value, length):
self._value = value
self._length = length
def __len__(self):
return self._length
def __getitem__(self, key):
return (self._value & (1 << key))
def __str__(self):
return repr(self)
def __repr__(self):... |
class Effect3864(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Afterburner')), 'speedFactor', module.getModifiedItemAttr('subsystemBonusAmarrPropulsion'), skill='Amarr Propulsion Systems', **... |
def xavier_init(module, gain=1, bias=0, distribution='normal'):
assert (distribution in ['uniform', 'normal'])
if (distribution == 'uniform'):
nn.init.xavier_uniform_(module.weight, gain=gain)
else:
nn.init.xavier_normal_(module.weight, gain=gain)
if hasattr(module, 'bias'):
nn.i... |
def project():
extension = None
metadata = extract_metadata('pystiche')
project = metadata['name']
author = metadata['author']
copyright = f'{datetime.now().year}, {author}'
release = metadata['version']
version = release.split('.dev')[0]
config = dict(project=project, author=author, cop... |
def tarfile_samples(src, handler=wds.handlers.reraise_exception, select_files=None, rename_files=rename):
streams = wds.tariterators.url_opener(src, handler=handler)
files = wds.tariterators.tar_file_expander(streams, handler=handler, select_files=select_files, rename_files=rename_files)
samples = wds.tarit... |
def add_docs_parser(dev_subparsers):
parser = dev_subparsers.add_parser('docs')
parser.add_argument('--output', help='Custom output directory for the built docs.')
parser.add_argument('--clean', help='Delete all of the built files.', action='store_true')
parser.add_argument('--prep', help='Undergo prepa... |
_rewriter([IfElse])
def cond_merge_ifs_false(fgraph, node):
op = node.op
if (not isinstance(op, IfElse)):
return False
f_ins = node.inputs[1:][op.n_outs:]
replace = {}
for (idx, fval) in enumerate(f_ins):
if (fval.owner and isinstance(fval.owner.op, IfElse) and (fval.owner.inputs[0] ... |
class CatalogRendererMixin():
def render_catalog(self, xml, catalog):
if (catalog['uri'] not in self.uris):
self.uris.add(catalog['uri'])
xml.startElement('catalog', {'dc:uri': catalog['uri']})
self.render_text_element(xml, 'uri_prefix', {}, catalog['uri_prefix'])
... |
def install_packages(struct: Structure, opts: ScaffoldOpts) -> ActionParams:
packages = opts.get('venv_install')
if (not packages):
return (struct, opts)
pretend = opts.get('pretend')
venv_path = get_path(opts)
if (not pretend):
pip = get_command('pip', venv_path, include_path=False)... |
def _soquet_to_proto(soq: Soquet, bloq_to_idx: Dict[(Bloq, int)]) -> bloq_pb2.Soquet:
if isinstance(soq.binst, DanglingT):
return bloq_pb2.Soquet(dangling_t=repr(soq.binst), register=registers.register_to_proto(soq.reg), index=soq.idx)
else:
return bloq_pb2.Soquet(bloq_instance=_bloq_instance_to... |
def test():
import hydra
from omegaconf import OmegaConf
OmegaConf.register_new_resolver('eval', eval, replace=True)
with hydra.initialize('../diffusion_policy/config'):
cfg = hydra.compose('train_robomimic_real_image_workspace')
OmegaConf.resolve(cfg)
dataset = hydra.utils.insta... |
def get_real_obs_resolution(shape_meta: dict) -> Tuple[(int, int)]:
out_res = None
obs_shape_meta = shape_meta['obs']
for (key, attr) in obs_shape_meta.items():
type = attr.get('type', 'low_dim')
shape = attr.get('shape')
if (type == 'rgb'):
(co, ho, wo) = shape
... |
def _adapt_splice_sites(exons, genome, strand='+'):
for e in range(1, (len(exons) - 1)):
if (strand == '-'):
genome = ((genome[:exons[(e - 1)][1]] + 'CT') + genome[(exons[(e - 1)][1] + 2):])
genome = ((genome[:(exons[e][0] - 2)] + 'AC') + genome[exons[e][0]:])
else:
... |
class INTF(IntEnum):
GIF0 = (1 << 0)
FTFIF0 = (1 << 1)
HTFIF0 = (1 << 2)
ERRIF0 = (1 << 3)
GIF1 = (1 << 4)
FTFIF1 = (1 << 5)
HTFIF1 = (1 << 6)
ERRIF1 = (1 << 7)
GIF2 = (1 << 8)
FTFIF2 = (1 << 9)
HTFIF2 = (1 << 10)
ERRIF2 = (1 << 11)
GIF3 = (1 << 12)
FTFIF3 = (1 <<... |
class AsyncQdrantFastembedMixin(AsyncQdrantBase):
DEFAULT_EMBEDDING_MODEL = 'BAAI/bge-small-en'
embedding_models: Dict[(str, 'DefaultEmbedding')] = {}
def __init__(self, **kwargs: Any):
self.embedding_model_name = self.DEFAULT_EMBEDDING_MODEL
super().__init__(**kwargs)
def set_model(self... |
def test_make_iou():
privkey = bytes(([2] * 32))
sender = Address(privatekey_to_address(privkey))
receiver = Address(bytes(([1] * 20)))
one_to_n_address = Address(bytes(([2] * 20)))
chain_id = ChainID(4)
max_fee = 100
pfs_config_copy = replace(PFS_CONFIG)
pfs_config_copy.info = replace(p... |
.parametrize('map_variables', [True, False])
.parametrize('endpoint,function,params,json_response', [('historic/radiation_and_weather', pvlib.iotools.get_solcast_historic, dict(api_key='1234', latitude=(- 33.856784), longitude=51.215297, start='2023-01-01T08:00', duration='P1D', period='PT1H', output_parameters='dni'),... |
class CAB(nn.Module):
def __init__(self, in_channels, out_channels):
super(CAB, self).__init__()
self.global_pooling = nn.AdaptiveAvgPool2d(1)
self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0)
self.relu = nn.ReLU()
self.conv2 = nn.Conv2d(ou... |
class TestSetFontPath(EndianTest):
def setUp(self):
self.req_args_0 = {'path': ['foo', 'bar', 'gazonk']}
self.req_bin_0 = b'3\x00\x06\x00\x03\x00\x00\x00\x03foo\x03bar\x06gazonk\x00'
self.req_args_1 = {'path': []}
self.req_bin_1 = b'3\x00\x02\x00\x00\x00\x00\x00'
def testPackRequ... |
class CmdArmPuzzle(MuxCommand):
key = ''
locks = 'cmd:perm(armpuzzle) or perm(Builder)'
help_category = 'Puzzles'
def func(self):
caller = self.caller
if ((self.args is None) or (not utils.dbref(self.args))):
caller.msg("A puzzle recipe's #dbref must be specified")
... |
class Process(InstanceModule):
def filter(self, **filters):
match = []
for attrs in self._get_processes(**filters):
for (key, value) in filters.items():
if (str(attrs[key]) != str(value)):
break
else:
attrs['_get_process_att... |
def _init_notebook(path_stem: str, overwrite=False, directory: str='.') -> Tuple[(nbformat.NotebookNode, Path)]:
nb_path = (Path(f'{directory}') / f'{path_stem}.ipynb')
if overwrite:
nb_path.unlink(missing_ok=True)
if nb_path.exists():
print(f'[{path_stem}] Loading existing: {nb_path}')
... |
class ListPortInfo(object):
def __init__(self, device, skip_link_detection=False):
self.device = device
self.name = os.path.basename(device)
self.description = 'n/a'
self.hwid = 'n/a'
self.vid = None
self.pid = None
self.serial_number = None
self.locat... |
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