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def test_create_lane_links_normalroad1():
planview = []
lanec = []
lanel = []
laner = []
lanesec = []
lanes = []
rm = pyodrx.RoadMark(pyodrx.RoadMarkType.solid, 0.2, rule=pyodrx.MarkRule.no_passing)
geom = []
geom.append(pyodrx.Line(50))
geom.append(pyodrx.Arc(0.01, angle=(np.pi ... |
def _pickup_assignment_to_item_locations(region_list: RegionList, pickup_assignment: PickupAssignment, num_players: int) -> dict[(str, dict[(str, str)])]:
items_locations: collections.defaultdict[(str, dict[(str, str)])] = collections.defaultdict(dict)
for (region, area, node) in region_list.all_regions_areas_n... |
class PopcornPopper():
description: str
def __init__(self, description: str):
self.description = description
def on(self) -> None:
print(f'{self.description} on')
def off(self) -> None:
print(f'{self.description} off')
def pop(self) -> None:
print(f'{self.description}... |
class EnsurePackagesDiscovered():
def __init__(self, distribution: 'Distribution'):
self._dist = distribution
self._called = False
def __call__(self):
if (not self._called):
self._called = True
self._dist.set_defaults(name=False)
def __enter__(self):
r... |
class GodelTNormSolver(TNormSolver):
def gettnorm(self, args, function, probs):
def AND(t, dim):
return (t.min(dim)[0] if (dim is not None) else t.min())
def OR(t, dim):
return (t.max(dim)[0] if (dim is not None) else t.max())
(tnorm_dict, lv, rv) = self.base_tnorm(ar... |
def get_bn_params(model: ModelProto, bn: NodeProto, channels: int) -> libpymo.BNParams:
bn_params = libpymo.BNParams()
gamma = numpy_helper.to_array(ParamUtils.get_param(model, bn, WEIGHT_INDEX)).reshape((- 1))
resize = (channels / len(gamma))
bn_params.gamma = np.repeat(gamma, resize)
bn_params.bet... |
_new_faces(MaterialGroup.WALLS)
def create_window_split(bm, face, prop):
(wall_w, wall_h) = calc_face_dimensions(face)
(width, height, offset) = (*prop.size, prop.offset)
h_widths = [(((wall_w / 2) - offset.x) - (width / 2)), width, (((wall_w / 2) + offset.x) - (width / 2))]
h_faces = subdivide_face_hor... |
def _version_logger(save_dir, logger_name=''):
if logger_name:
path = os.path.join(save_dir, logger_name)
else:
path = save_dir
if ((not os.path.exists(path)) or (not os.listdir(path))):
version = 0
else:
try:
versions = [int(v.split('_')[(- 1)]) for v in os.l... |
def get_random_ddf(chunk_size, num_chunks, frac_match, chunk_type, args):
parts = [chunk_size for _ in range(num_chunks)]
device_type = (True if (args.type == 'gpu') else False)
meta = generate_chunk(0, 4, 1, chunk_type, None, device_type)
divisions = ([None] * (len(parts) + 1))
name = ('generate-da... |
class DebertaV2Tokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, do_... |
class Scope():
default: ty.ClassVar['Scope']
default_config: ty.Dict = {'device': 'cpu', 'tracing': False, 'types_to_trace': []}
def __init__(self, config: ty.Union[(dict, str, None)]=None):
if (config is None):
self.config = type(self).default_config
elif isinstance(config, str)... |
class HVT(nn.Module):
def __init__(self, img_size=224, patch_size=16, in_chans=3, num_classes=1000, embed_dim=768, depth=12, num_heads=12, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop_rate=0.0, attn_drop_rate=0.0, drop_path_rate=0.0, norm_layer=nn.LayerNorm, **kwargs):
super().__init__()
self.... |
class Slither(ProblemDetector):
name: str = 'slither'
docker_image: str
dockerCl: Any
threadPool: ClassVar[concurrent.futures.ThreadPoolExecutor] = concurrent.futures.ThreadPoolExecutor()
titleVulDict: ClassVar[Dict[(str, str)]] = {'reentrancy-eth': 'reentrancy', 'reentrancy-no-eth': 'reentrancy', '... |
(tryfirst=True)
def pytest_load_initial_conftests(args: list[str], early_config: pytest.Config, parser: pytest.Parser) -> None:
for entry in _load_values(early_config):
if (entry.skip_if_set and (entry.key in os.environ)):
continue
os.environ[entry.key] = (entry.value.format(**os.environ... |
def define_G(opt):
gpu_ids = opt['gpu_ids']
opt_net = opt['network_G']
which_model = opt_net['which_model_G']
if (which_model == 'sr_resnet'):
netG = arch.SRResNet(in_nc=opt_net['in_nc'], out_nc=opt_net['out_nc'], nf=opt_net['nf'], nb=opt_net['nb'], upscale=opt_net['scale'], norm_type=opt_net['n... |
class XdgStatic(Static[XdgSurface]):
def __init__(self, core: Core, qtile: Qtile, win: XdgWindow, idle_inhibitor_count: int):
surface = win.surface
Static.__init__(self, core, qtile, surface, win.wid, idle_inhibitor_count=idle_inhibitor_count)
if surface.toplevel.title:
self.name... |
def generate_app(appname, force=False, outpath='..', dbtype='sql', update_only=False, view_type=None):
print((' generating app:' + str(appname)))
import os, sys
base = os.path.normpath(outpath)
print((' base for app: ' + base))
root = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'start... |
def get_parser():
parser = argparse.ArgumentParser(allow_abbrev=True, description='pypyr pipeline runner', formatter_class=argparse.RawTextHelpFormatter)
parser.add_argument('pipeline_name', help=wrap('Name of pipeline to run. Don`t add the .yaml at the end.'))
parser.add_argument(dest='context_args', nargs... |
class TransformerSentenceEncoderLayer(nn.Module):
def __init__(self, embedding_dim: float=768, ffn_embedding_dim: float=3072, num_attention_heads: float=8, dropout: float=0.1, attention_dropout: float=0.1, activation_dropout: float=0.1, activation_fn: str='relu', layer_norm_first: bool=False) -> None:
super... |
class Migration(migrations.Migration):
dependencies = [('adserver', '0086_region_topic_pricing')]
operations = [migrations.AddField(model_name='historicalpublisher', name='allow_multiple_placements', field=models.BooleanField(default=False, help_text='Can this publisher have multiple placements on the same page... |
def filter_rop(ops):
addr = 0
gadgets = r2p.cmdj(('/Rj %s' % ops[0]))
gadgets.reverse()
for gadget in gadgets:
instrs = gadget['opcodes']
for (i, instr) in enumerate(instrs):
rest = [x['opcode'] for x in instrs[i:]]
if (rest == ops):
addr = instr['... |
def save_trees(trees, path, mode='all', replace_newline=True, joiner='***', short_long_sep=''):
assert (mode in ['all', 'final_long', 'final_short'])
num_iterations = max([root.max_depth_from_self() for root in trees])
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, 'w') as wf:
... |
def build_discriminator():
cnn = Sequential()
cnn.add(Conv2D(32, 3, padding='same', strides=2, input_shape=(1, 28, 28)))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Conv2D(64, 3, padding='same', strides=1))
cnn.add(LeakyReLU())
cnn.add(Dropout(0.3))
cnn.add(Conv2D(128, 3, padding=... |
class BotShortDescription(TelegramObject):
__slots__ = ('short_description',)
def __init__(self, short_description: str, *, api_kwargs: Optional[JSONDict]=None):
super().__init__(api_kwargs=api_kwargs)
self.short_description: str = short_description
self._id_attrs = (self.short_descripti... |
class Voxelization_Idx(Function):
def forward(ctx, coords, batchsize, mode=4):
assert coords.is_contiguous()
N = coords.size(0)
output_coords = coords.new()
input_map = torch.IntTensor(N).zero_()
output_map = input_map.new()
PG_OP.voxelize_idx(coords, output_coords, i... |
class TooManyStoppingSequences(ErrorReason):
def __init__(self, num_stopping_sequences: int, max_num_stopping_sequences: int) -> None:
self.num_stopping_sequences = num_stopping_sequences
self.max_num_stopping_sequences = max_num_stopping_sequences
def get_message(self) -> str:
return f'... |
class NNVFunction(MLPFunction):
def __init__(self, env_spec, hidden_layer_sizes=(100, 100), name='vf', batchnormvf=False, dropoutvf_keep_prob=1.0):
Serializable.quick_init(self, locals())
self._Do = env_spec.observation_space.flat_dim
self._obs_pl = tf.placeholder(tf.float32, shape=[None, se... |
def train(model, train_loader, myloss, optimizer, epoch):
model.train()
for (batch_idx, train_data) in enumerate(train_loader):
train_data = Variable(train_data).type(torch.cuda.DoubleTensor).squeeze().view(175, 50, 34).permute(1, 0, 2)
optimizer.zero_grad()
output = model(train_data)
... |
_attention('dot')
class DotAttention(BaseAttention):
def __init__(self, decoder_hidden_state_dim, context_dim, **kwargs):
super().__init__(decoder_hidden_state_dim, context_dim)
self.input_proj = None
force_projection = kwargs.get('force_projection', False)
if (force_projection or (d... |
def _get_text_feedback(schedule_item):
questions = TextFeedbackQuestion.objects.filter(schedule_item_type__title=schedule_item.type)
text = [{'question': question, 'values': ScheduleItemTextFeedback.objects.filter(question=question, schedule_item=schedule_item)} for question in questions]
return text |
class BatchEasyHardMiner(BaseTupleMiner):
HARD = 'hard'
SEMIHARD = 'semihard'
EASY = 'easy'
ALL = 'all'
all_batch_mining_strategies = [HARD, SEMIHARD, EASY, ALL]
def __init__(self, pos_strategy=EASY, neg_strategy=SEMIHARD, allowed_pos_range=None, allowed_neg_range=None, **kwargs):
super(... |
class ColorShape(tc.nn.Module):
ColorBiased = [(0.125, 'color', 0.1, 1.9), (0.125, 'brightness', 0.5, 1.9), (0.125, 'contrast', 0.5, 1.9), (0.125, 'sharpness', 0.1, 1.9), (0.125, 'autocontrast'), (0.125, 'equalize'), (0.125, 'shear', 0.05, 0.15), (0.125, 'rotate', 1, 11)]
ShapeBiased = [(0.08, 'color', 0.1, 1.9... |
def read_output(meteor_output_path, n_repeats):
n_combinations = (math.factorial(n_repeats) / (math.factorial(2) * math.factorial((n_repeats - 2))))
raw_scores = []
average_scores = []
for line in open(meteor_output_path):
if (not line.startswith('Segment ')):
continue
score ... |
def decode_dxt1_rgb(data, width, height):
out = (ctypes.c_uint16 * (width * height))()
image_offset = 0
for (c0_lo, c0_hi, c1_lo, c1_hi, b0, b1, b2, b3) in split_8byte.findall(data):
color0 = (ord(c0_lo) | (ord(c0_hi) << 8))
color1 = (ord(c1_lo) | (ord(c1_hi) << 8))
bits = (((ord(b0)... |
def _get_new_season_streams(config, db):
handlers = services.get_service_handlers()
for service in db.get_services():
if (service.key not in handlers):
warning('Service handler for {} not installed'.format(service.key))
continue
if service.enabled:
handler = h... |
def parse_args():
parser = argparse.ArgumentParser(description='mmrotate benchmark a model')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--repeat-num', type=int, default=1, help='number of repeat times of meas... |
def main() -> None:
application = Application.builder().token('TOKEN').build()
application.add_handler(CommandHandler('start', start))
application.add_handler(CommandHandler('help', help_command))
application.add_handler(MessageHandler((filters.TEXT & (~ filters.COMMAND)), echo))
application.run_pol... |
class CeilingFan():
HIGH: Final[int] = 3
MEDIUM: Final[int] = 2
LOW: Final[int] = 1
OFF: Final[int] = 0
location: str = ''
speed: int = 0
def __init__(self, location: str):
self.location = location
def high(self) -> None:
self.speed = self.HIGH
print(f'{self.locat... |
.slow
def test_pinnacle_cli_missing_trial(data):
output_path = tempfile.mkdtemp()
for pinn_dir in data.joinpath('Pt1').joinpath('Pinnacle').iterdir():
command = (([str(pmp_test_utils.get_executable_even_when_embedded()), '-m'] + 'pymedphys pinnacle export'.split()) + ['-o', output_path, '-t', 'nonexiste... |
class HarmonicPotential(PotentialBase):
def __init__(self, molecule: Molecule) -> None:
self.k = 0.0
self.m_shift = 0.0
self.r_0 = 0.0
self.d_e: Optional[float] = None
if (molecule.masses is not None):
self._m_a = molecule.masses[0]
self._m_b = molecul... |
((not torch.cuda.is_available()), 'test requires a GPU')
class TestTranslationGPU(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fp16_multigpu(self):
with contextlib.redirect_stdout(StringIO()):
... |
def main():
torch.set_default_dtype(torch.double)
np.set_printoptions(precision=3)
import notears.utils as ut
ut.set_random_seed(123)
(n, d, s0, graph_type, sem_type) = (200, 5, 9, 'ER', 'mim')
B_true = ut.simulate_dag(d, s0, graph_type)
np.savetxt('W_true.csv', B_true, delimiter=',')
X ... |
def test_import_visitor():
source = 'import operator\nimport itertools as itools\n\nimport urllib.parse\nimport tests.arbpack.arbmod as z\n\n# from mod import submod\nfrom tests.arbpack import arbmod2\nfrom tests.arbpack import arbmod3 as ab3\nfrom tests.arbpack import arbmod4_avoid\n\n# from mod import attr\nfrom ... |
class LoginForm(Form):
def __init__(self, view, login_session):
super().__init__(view, 'login')
self.use_layout(FormLayout())
if self.exception:
self.layout.add_alert_for_domain_exception(self.exception)
self.layout.add_input(TextInput(self, login_session.fields.email_add... |
class APISession(requests.Session):
base_url = '
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.headers.update({'Accept': 'application/json', 'User-Agent': f'pythondotorg/create_initial_data ({requests.utils.default_user_agent()})'})
def request(self, method, url... |
def _dataclass_from_dict(klass, in_val):
if is_dataclass(klass):
fieldtypes = {f.name: f.type for f in fields(klass)}
val = {}
for dict_key in in_val:
if ((dict_key in fieldtypes) and hasattr(fieldtypes[dict_key], 'from_dict')):
val[dict_key] = fieldtypes[dict_key... |
def find_dynamicsymbols(expression, exclude=None):
t_set = set([dynamicsymbols._t])
if exclude:
if iterable(exclude):
exclude_set = set(exclude)
else:
raise TypeError('exclude kwarg must be iterable')
else:
exclude_set = set()
return (set([i for i in expre... |
def json_content():
return [{'id': 102, 'project': {'id': 2, 'name': 'Gitlab Ce', 'name_with_namespace': 'Gitlab Org / Gitlab Ce', 'path': 'gitlab-ce', 'path_with_namespace': 'gitlab-org/gitlab-ce'}, 'author': {'name': 'Administrator', 'username': 'root', 'id': 1}, 'action_name': 'marked', 'target_type': 'MergeRequ... |
class PythonCause(IncompatibilityCause):
def __init__(self, python_version: str, root_python_version: str) -> None:
self._python_version = python_version
self._root_python_version = root_python_version
def python_version(self) -> str:
return self._python_version
def root_python_versi... |
class CalcAddCargoCommand(wx.Command):
def __init__(self, fitID, cargoInfo):
wx.Command.__init__(self, True, 'Add Cargo')
self.fitID = fitID
self.cargoInfo = cargoInfo
def Do(self):
pyfalog.debug('Doing addition of cargo {} to fit {}'.format(self.cargoInfo, self.fitID))
f... |
class ShardedTensorIOPreparerTest(unittest.TestCase):
def _verify_subdivided_shards(self, subdivided: List[Tuple[(torch.Tensor, List[int], List[int])]], dim: int, expected_num_sub_shards: int, expected_combined: torch.Tensor, expected_offsets: List[int], expected_sizes: List[int]) -> None:
(_, offsets, size... |
def create_new_database(game_enum: RandovaniaGame, output_path: Path) -> GameDescription:
items = [ItemResourceInfo(0, 'Powerful Weapon', 'Weapon', 1), ItemResourceInfo(1, 'Victory Key', 'VictoryKey', 1), ItemResourceInfo(2, 'Health', 'Health', 500)]
resource_database = ResourceDatabase(game_enum=game_enum, ite... |
class Effect6701(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
lvl = src.level
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Rig Projectile Weapon')), 'drawback', (src.getModifiedItemAttr('rigDrawbackBonus') * lvl), **kwargs) |
def test_refund_transfer_with_reroute():
transfer_amount = TokenAmount(1000)
block_number = BlockNumber(10)
our_address = factories.ADDR
(refund_pkey, refund_address) = factories.make_privkey_address()
prng = random.Random()
transfer_description = create(TransferDescriptionProperties(secret=UNIT... |
def measure_with_final_permutation(circuit: cirq.Circuit, qubits: List[cirq.Qid], *, mutate=False) -> Tuple[(cirq.Circuit, List[cirq.Qid])]:
if mutate:
c2 = circuit
else:
c2 = circuit.copy()
(mom_classes, stats) = validate_well_structured(c2, allow_terminal_permutations=True)
if stats.ha... |
def test_CheckParameter():
mu = pt.constant(0)
sigma = pt.scalar('sigma')
x_rv = pt.random.normal(mu, sigma, name='x')
x_vv = pt.constant(0)
x_logp = logp(x_rv, x_vv)
x_logp_fn = function([sigma], x_logp)
with pytest.raises(ParameterValueError, match='sigma > 0'):
x_logp_fn((- 1)) |
def test_tree_set():
tree = SumSegmentTree(4)
tree[2] = 1.0
tree[3] = 3.0
assert np.isclose(tree.sum(), 4.0)
assert np.isclose(tree.sum(0, 2), 0.0)
assert np.isclose(tree.sum(0, 3), 1.0)
assert np.isclose(tree.sum(2, 3), 1.0)
assert np.isclose(tree.sum(2, (- 1)), 1.0)
assert np.isclo... |
def get_dataset(dataset: str, split: str) -> Dataset:
if (dataset == 'imagenet'):
return _imagenet(split)
elif (dataset == 'imagenet32'):
return _imagenet32(split)
elif (dataset == 'cifar10'):
return _cifar10(split)
elif (dataset == 'cifar10_vit'):
return _cifar10vit(spli... |
class FloorplanGenerator():
_props = None
def __init__(self):
self.context = bpy.context
self.scene = bpy.context.scene
self._register()
def __del__(self):
self._unregister()
def _unregister():
del bpy.types.Scene.prop_floorplan
def _register(self):
tr... |
_traceback
def pq_compute_single_core(proc_id, annotation_set, gt_folder, pred_folder, categories, ow_eval, simi_matrix_path):
pq_stat = PQStat()
idx = 0
if ow_eval:
simiAccess = SIMIaccess(simi_matrix_path)
'\n\n file = os.path.join("/home/xp4/open-metrics/fc-clip/output/", "per_image_pq-sq-... |
class PhoneDecoder():
def __init__(self, model_path, inference_config):
self.model_path = Path(model_path)
self.config = inference_config
self.inventory = Inventory(model_path, inference_config)
self.unit = self.inventory.unit
def compute(self, logits, lang_id=None, topk=1, emit=... |
def tokenize(sentence, regex=SENTENCE_SPLIT_REGEX, keep=["'s"], remove=[',', '?']):
sentence = sentence.lower()
for token in keep:
sentence = sentence.replace(token, (' ' + token))
for token in remove:
sentence = sentence.replace(token, '')
tokens = regex.split(sentence)
tokens = [t.... |
def get_data_loaders(dataset, data_root=None, augment=False, batch_size=64, num_workers=8, shuffle=True, load_in_mem=False, hdf5=False, pin_memory=True, drop_last=True, start_itr=0, num_epochs=500, use_multiepoch_sampler=False, **kwargs):
data_root += ('/%s' % root_dict[dataset])
print(('Using dataset root loca... |
class FitSpawner(gui.multiSwitch.TabSpawner):
def __init__(self, multiSwitch):
self.multiSwitch = multiSwitch
self.mainFrame = mainFrame = gui.mainFrame.MainFrame.getInstance()
mainFrame.Bind(EVT_FIT_SELECTED, self.fitSelected)
self.multiSwitch.tabs_container.handleDrag = self.handle... |
(scope='module')
def test_image_rgba_merc(test_area_merc):
arr = xr.DataArray(_get_fake_da((- 80), 40, (test_area_merc.shape + (4,))), dims=('y', 'x', 'bands'), coords={'bands': ['R', 'G', 'B', 'A']}, attrs={'name': 'test-rgba', 'start_time': datetime.datetime(2013, 2, 22, 12, 0), 'area': test_area_merc, 'mode': 'R... |
def print_platform_version_info():
import scipy
import platform
import matplotlib
from visualqc import __version__
print('version info: visualqc {}'.format(__version__))
print('numpy {} / scipy {} / matplotlib {}\npython {}'.format(np.__version__, scipy.__version__, matplotlib.__version__, sys.v... |
class ResNet(nn.Module):
def __init__(self, depth=28, widen_factor=10, dropout_rate=0):
super(ResNet, self).__init__()
self.in_planes = 16
assert (((depth - 4) % 6) == 0), 'Wide-resnet depth should be 6n+4'
n = int(((depth - 4) / 6))
k = widen_factor
print(('Wide-Resn... |
def objective(x_train, y_train, W1, b1, z1, a1, W2, b2, z2, a2, W3, b3, z3, u, v1, v2, rho):
r1 = torch.sum((((z1 - torch.matmul(W1, x_train)) - b1) * ((z1 - torch.matmul(W1, x_train)) - b1)))
r2 = torch.sum((((z2 - torch.matmul(W2, a1)) - b2) * ((z2 - torch.matmul(W2, a1)) - b2)))
r3 = torch.sum((((z3 - to... |
def initialize(forced_gui: (GUIType | None)=None):
def import_gtk():
global guilib
try:
import webview.platforms.gtk as guilib
logger.debug('Using GTK')
return True
except (ImportError, ValueError):
logger.exception('GTK cannot be loaded')
... |
class WID2Section(Section):
wid2 = WID2.T()
sta2 = STA2.T(optional=True)
eid2s = List.T(EID2.T())
bea2 = BEA2.T(optional=True)
dat2 = DAT2.T()
chk2 = CHK2.T()
def read(cls, reader):
blocks = dict(eid2s=[])
expect = [(b'WID2 ', WID2, 1)]
if (reader.version_dialect[0] =... |
_REGISTRY.register()
class DANN(TrainerXU):
def __init__(self, cfg):
super().__init__(cfg)
self.build_critic()
self.ce = nn.CrossEntropyLoss()
self.bce = nn.BCEWithLogitsLoss()
def build_critic(self):
cfg = self.cfg
print('Building critic network')
fdim = ... |
class PreActBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
nn.init.kaiming_normal_(self.conv1.weight, mode='fan_out')
... |
def test_replace_alphabet_2() -> None:
fsm1 = Fsm(alphabet={Charclass('z'), (~ Charclass('z'))}, states={0, 1, 2}, initial=0, finals={1}, map={0: {Charclass('z'): 2, (~ Charclass('z')): 1}, 1: {Charclass('z'): 2, (~ Charclass('z')): 1}, 2: {Charclass('z'): 2, (~ Charclass('z')): 2}})
fsm2 = fsm1.replace_alphabe... |
class BatteryIcon(base._TextBox):
orientations = base.ORIENTATION_HORIZONTAL
defaults = [('battery', 0, 'Which battery should be monitored'), ('update_interval', 60, 'Seconds between status updates'), ('theme_path', default_icon_path(), 'Path of the icons')]
icon_names = ('battery-missing', 'battery-caution... |
def traj_segment_generator(pi, env, horizon, nenvs, stochastic, dropoutpi_keep_prob, dropoutvf_keep_prob, isbnpitrainmode, isbnvftrainmode):
t = 0
ac = ([env.action_space.sample()] * nenvs)
new = ([True] * nenvs)
rew = ([0.0] * nenvs)
ob = env.reset()
cur_ep_ret = []
cur_ep_len = []
ep_r... |
class RotationLogarithmicModel(RotationCostModel):
slope: float
overhead: float
gateset: Optional[str] = None
approximation_protocol: Optional[str] = None
reference: Optional[str] = None
def rotation_cost(self, error_budget: float) -> AlgorithmSummary:
return AlgorithmSummary(t_gates=mat... |
.parametrize(('local_config', 'fresh'), [({}, True), ({'dependencies': [uuid.uuid4().hex]}, True), ({'dependencies': [uuid.uuid4().hex], 'dev-dependencies': [uuid.uuid4().hex]}, True), ({'dependencies': [uuid.uuid4().hex], 'dev-dependencies': None}, True), ({'dependencies': [uuid.uuid4().hex], 'groups': [uuid.uuid4().h... |
def test_music_settings(skip_qtbot: pytestqt.qtbot.QtBot) -> None:
cosmetic_patches = SuperMetroidCosmeticPatches()
dialog = SuperCosmeticPatchesDialog(None, cosmetic_patches)
skip_qtbot.addWidget(dialog)
for (music_mode, radio_button) in dialog.radio_buttons.items():
assert ((music_mode == dial... |
class DuckFactory(AbstractDuckFactory):
def createMallardDuck(self) -> Quackable:
return MallardDuck()
def createRedheadDuck(self) -> Quackable:
return RedheadDuck()
def createDuckCall(self) -> Quackable:
return DuckCall()
def createRubberDuck(self) -> Quackable:
return R... |
class GRU_encoder(nn.Module):
def __init__(self, hidden_states=256):
super(GRU_encoder, self).__init__()
self.encoder = nn.GRU(342, 64, num_layers=1)
self.mapping = nn.Linear(64, hidden_states)
self.bn = nn.BatchNorm1d(hidden_states)
def forward(self, x, flag='unsupervised'):
... |
def visualize_detection_results(result_dict, tag, global_step, categories, summary_dir='', export_dir='', agnostic_mode=False, show_groundtruth=False, min_score_thresh=0.2, max_num_predictions=20):
if (not set(['original_image', 'detection_boxes', 'detection_scores', 'detection_classes']).issubset(set(result_dict.k... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, bn_norm, with_ibn, baseWidth, cardinality, stride=1, downsample=None):
super(Bottleneck, self).__init__()
D = int(math.floor((planes * (baseWidth / 64))))
C = cardinality
self.conv1 = nn.Conv2d(inplan... |
def _get_callee_type(call: CallExpr) -> (CallableType | None):
callee_node: (Node | None) = call.callee
if isinstance(callee_node, RefExpr):
callee_node = callee_node.node
if isinstance(callee_node, Decorator):
callee_node = callee_node.func
if (isinstance(callee_node, (Var, SYMBOL_FUNCB... |
def act_quantization(b, grid, power=True):
def uniform_quant(x, b=3):
xdiv = x.mul(((2 ** b) - 1))
xhard = xdiv.round().div(((2 ** b) - 1))
return xhard
def power_quant(x, grid):
shape = x.shape
xhard = x.view((- 1))
value_s = grid.type_as(x)
idxs = (xhard... |
def from_ieee_block(block: Union[(bytes, bytearray)], datatype: BINARY_DATATYPES='f', is_big_endian: bool=False, container: Callable[([Iterable[Union[(int, float)]]], Sequence[Union[(int, float)]])]=list) -> Sequence[Union[(int, float)]]:
(offset, data_length) = parse_ieee_block_header(block)
if (data_length ==... |
.requires_user_action
class CaretColorInitTestCase(InteractiveTestCase, _DRYHelperMixin):
def test_caret_color_init_rgb(self):
color = (255, 0, 0)
self.build_window(color)
app.run()
self.ask_color(color)
def test_caret_color_init_rgba(self):
color = (255, 0, 0, 80)
... |
def get_checkpoint_id(key):
if (key in all_methods):
setting = 'hr_to_lr'
method = key
elif ((key in [(method + '-inst') for method in all_methods]) or (key in [(method + '-instruction') for method in all_methods])):
setting = 'hr_to_lr_inst_all'
method = '-'.join(key.split('-')[... |
(name='help-analysis')
_readme_flag
def help_analysis(readme):
get_wrapper(readme)('\nThe overall process is:\n\n1) Fragment structures in a SMILES file, to produce fragments.\n\n2) Index the fragments to produces matched molecular pairs.\n(you might include property information at this point)\n\n3) Load property i... |
class TInternetRadio(TestCase):
def setUp(self):
quodlibet.config.init()
self.bar = InternetRadio(SongLibrary())
def test_can_filter(self):
self.assertTrue(self.bar.can_filter('foo'))
self.assertTrue(self.bar.can_filter_text())
def test_status_bar_text(self):
self.ass... |
class CallContext():
__slots__ = ('args', 'keywords', 'callee')
def __init__(self, args: list[NodeNG], keywords: (list[Keyword] | None)=None, callee: (InferenceResult | None)=None):
self.args = args
if keywords:
arg_value_pairs = [(arg.arg, arg.value) for arg in keywords]
els... |
def _validate_setting(setting, value):
if (setting == SettingCodes.ENABLE_PUSH):
if (value not in (0, 1)):
return ErrorCodes.PROTOCOL_ERROR
elif (setting == SettingCodes.INITIAL_WINDOW_SIZE):
if (not (0 <= value <= )):
return ErrorCodes.FLOW_CONTROL_ERROR
elif (settin... |
class _BaseMedium(TelegramObject):
__slots__ = ('file_id', 'file_size', 'file_unique_id')
def __init__(self, file_id: str, file_unique_id: str, file_size: Optional[int]=None, *, api_kwargs: Optional[JSONDict]=None):
super().__init__(api_kwargs=api_kwargs)
self.file_id: str = str(file_id)
... |
class M4CDecodingBCEWithMaskLoss(nn.Module):
def __init__(self):
super().__init__()
self.one = torch.Tensor([1.0])
def forward(self, scores, targets, loss_mask):
assert ((scores.dim() == 3) and (loss_mask.dim() == 2))
losses = F.binary_cross_entropy_with_logits(scores, targets, r... |
class KeyMapper(QtCore.QObject):
keyMappingChanged = QtCore.Signal()
def setShortcut(self, action):
if (action.menuPath in pyzo.config.shortcuts2):
shortcuts = pyzo.config.shortcuts2[action.menuPath]
action.setShortcuts(shortcuts.split(','))
pyzo.main.addAction(action... |
def main():
with open(FLAGS.hq_replay_set) as f:
replay_list = sorted(json.load(f))
race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]
global_feature_vec_path = os.path.join(FLAGS.parsed_replay_path, 'SpatialFeatureTensor', race_vs_race)
races = set(race_vs_race.split('_vs_'))
... |
class CollectiveUtilsTest(unittest.TestCase):
_and_log
def setUp(self) -> None:
os.environ['MASTER_ADDR'] = str(MASTER_ADDR)
os.environ['MASTER_PORT'] = str(get_free_port())
os.environ['GLOO_DEVICE_TRANSPORT'] = 'TCP'
os.environ['NCCL_SOCKET_IFNAME'] = 'lo'
self.WORLD_SIZ... |
_fixtures(WebFixture, DisclosedInputFixture)
def test_input_values_retained_upon_domain_exception(web_fixture, disclosed_input_fixture):
fixture = disclosed_input_fixture
fixture.raise_domain_exception_on_submit = True
fixture.default_trigger_field_value = False
wsgi_app = web_fixture.new_wsgi_app(enabl... |
def get_func_target(builder: IRBuilder, fdef: FuncDef) -> AssignmentTarget:
if fdef.original_def:
return builder.lookup(fdef.original_def)
if (builder.fn_info.is_generator or builder.fn_info.add_nested_funcs_to_env):
return builder.lookup(fdef)
return builder.add_local_reg(fdef, object_rprim... |
def do_parse(file_path):
try:
with io.open(file_path, 'rb') as fp:
json_data = fp.read()
except Exception as e:
return (str(e), None)
h = {}
h['encoding'] = 'unknown'
try:
h = chardet.detect(json_data)
try:
with io.open(file_path, 'r', encoding... |
class TestConnTrackCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('ConnTrackCollector', {'interval': 10, 'bin': 'true', 'dir': self.getFixtureDirPath()})
self.collector = ConnTrackCollector(config, None)
def test_import(self):
self.assertTrue(ConnTrackCollec... |
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