code stringlengths 281 23.7M |
|---|
.parametrize('cfg_file', ['kie/sdmgr/sdmgr_novisual_60e_wildreceipt.py', 'kie/sdmgr/sdmgr_unet16_60e_wildreceipt.py'])
def test_sdmgr_pipeline(cfg_file):
model = _get_detector_cfg(cfg_file)
from mmocr.models import build_detector
detector = build_detector(model)
input_shape = (1, 3, 128, 128)
mm_inp... |
class PlotArgs():
def __init__(self, name=None, states=None, sigma_bounds=None, is_angle=None, rad2deg=None, max_length=None, connect=True, symbol='o', symbol_size=2, px_mode=True, color=None, hidden=False):
if (name is not None):
self.name = name
elif (states is not None):
s... |
def preprocess_degreeLists():
logging.info('Recovering degreeList from disk...')
degreeList = restoreVariableFromDisk('degreeList')
logging.info('Creating compactDegreeList...')
dList = {}
dFrequency = {}
for (v, layers) in degreeList.items():
dFrequency[v] = {}
for (layer, degre... |
class screen2Widget(Container):
def __init__(self, **kwargs):
super(screen2Widget, self).__init__(**kwargs)
self.style['position'] = 'absolute'
self.style['overflow'] = 'auto'
self.style['background-color'] = '#ffff80'
self.style['left'] = '10px'
self.style['top'] = '... |
def convert_to_nested_clauses(thought):
if ('Shawn started' in thought):
return 'Shawn, who originally had 5 toys, got 4 more from his parents. 5 + 4 = 9.'
if ('There are originally 3 cars' in thought):
return 'In the parking lot, where there were originally 3 cars, 2 more cars arrived. 3 + 2 = ... |
def lock(func=None, **kwgs):
if (func is None):
return partial(lock, **kwgs)
(func)
def wrapped(self, *args, **kwargs):
self.lock.acquire(**kwgs)
try:
return func(self, *args, **kwargs)
finally:
self.lock.release()
return wrapped |
class QuestionHistory():
def __init__(self) -> None:
self._history: Dict[(DNSQuestion, Tuple[(float, Set[DNSRecord])])] = {}
def add_question_at_time(self, question: DNSQuestion, now: _float, known_answers: Set[DNSRecord]) -> None:
self._history[question] = (now, known_answers)
def suppresse... |
def average_named_params(named_params_list, average_weights_dict_list, inplace=True):
if ((type(named_params_list[0]) is tuple) or (type(named_params_list[0]) is list)):
if inplace:
(_, averaged_params) = named_params_list[0]
else:
(_, averaged_params) = deepcopy(named_params... |
def get_split(metrics, split):
if (split == 'all'):
return metrics
metrics['id'] = metrics.index.str.rsplit(pat='_', n=1).str[1].astype(int)
if (metrics['id'].max() > len(metrics)):
split_id = (math.floor((len(metrics) * 1.5)) / 2)
else:
split_id = (len(metrics) / 2)
if (spli... |
class _ACArray(np.ndarray, abc.Mapping):
def __new__(cls, input_array, skc_slicer):
obj = np.asarray(input_array).view(cls)
obj._skc_slicer = skc_slicer
return obj
_inherit(np.ndarray.__getitem__)
def __getitem__(self, k):
try:
if (k in self):
retu... |
def test_get_package_with_dist_and_universal_py3_wheel() -> None:
repo = MockRepository()
package = repo.package('ipython', Version.parse('7.5.0'))
assert (package.name == 'ipython')
assert (package.version.text == '7.5.0')
assert (package.python_versions == '>=3.5')
expected = [Dependency('appn... |
class SubmissionFactory(DjangoModelFactory):
class Meta():
model = Submission
conference = factory.SubFactory(ConferenceFactory)
title = LanguageFactory('sentence')
abstract = LanguageFactory('text')
elevator_pitch = LanguageFactory('text')
notes = factory.Faker('text')
type = factor... |
def create_config(config_file_env, config_file_exp):
with open(config_file_env, 'r') as stream:
root_dir = yaml.safe_load(stream)['root_dir']
with open(config_file_exp, 'r') as stream:
config = yaml.safe_load(stream)
cfg = EasyDict()
for (k, v) in config.items():
cfg[k] = v
o... |
def test_system():
syst = {'height': 1.0, 'pitch': 2.0, 'surface_tilt': 30.0, 'surface_azimuth': 180.0, 'rotation': (- 30.0)}
syst['gcr'] = (1.0 / syst['pitch'])
pts = np.linspace(0, 1, num=3)
sqr3 = (np.sqrt(3) / 4)
c00 = (((- 2) - sqr3) / np.sqrt(((1.25 ** 2) + ((2 + sqr3) ** 2))))
c01 = ((- s... |
class TypeInfoMap(Dict[(str, TypeInfo)]):
def __str__(self) -> str:
a: list[str] = ['TypeInfoMap(']
for (x, y) in sorted(self.items()):
ti = ('\n' + ' ').join(str(y).split('\n'))
a.append(f' {x} : {ti}')
a[(- 1)] += ')'
return '\n'.join(a) |
class SmilesRnnDistributionLearner():
def __init__(self, output_dir: str, n_epochs=10, hidden_size=512, n_layers=3, max_len=100, batch_size=64, rnn_dropout=0.2, lr=0.001, valid_every=100) -> None:
self.n_epochs = n_epochs
self.output_dir = output_dir
self.hidden_size = hidden_size
se... |
def _run_testcases(plot=True, close_plots=False, verbose=True, *args, **kwargs):
test_against_specair_convolution(*args, plot=plot, close_plots=close_plots, verbose=verbose, **kwargs)
test_normalisation_mode(*args, plot=plot, close_plots=close_plots, verbose=verbose, **kwargs)
test_slit_energy_conservation(... |
def extract_answer_from_response(response, task_config: TaskConfig) -> str:
if (task_config.prompt_config.inter_example_sep and (task_config.prompt_config.inter_example_sep in response)):
answer = response.split(task_config.prompt_config.inter_example_sep)[0]
else:
answer = response
return a... |
def query_param(name, help_str, type=reqparse.text_type, default=None, choices=(), required=False):
def add_param(func):
if ('__api_query_params' not in dir(func)):
func.__api_query_params = []
func.__api_query_params.append({'name': name, 'type': type, 'help': help_str, 'default': defau... |
class NetworkLock(Lock):
def __init__(self, *args, **kwargs):
if ('timeout' in kwargs):
timeout = kwargs['timeout']
del kwargs['timeout']
else:
timeout = pysat.params['file_timeout']
super(NetworkLock, self).__init__(*args, timeout=timeout, **kwargs)
... |
class IDPMenu(menus.Menu):
def __init__(self, send_channel: QuoTextChannel, role: QuoRole):
super().__init__(timeout=60, delete_message_after=False, clear_reactions_after=True)
self.embed = None
self._id = 'Not Set!'
self._pass = 'Not Set!'
self.msg = None
self.send_c... |
class w2v_api(object):
def load_word2vec(self, binary=True):
if (self.word_vec_path is None):
return
raw_word2vec = gensim.models.KeyedVectors.load_word2vec_format(self.word_vec_path, binary=binary)
print('load w2v done')
self.word2vec = []
oov_cnt = 0
for... |
class LLMHandler():
def __init__(self, settings, path, llm):
self.history = []
self.propmts = []
self.settings = settings
self.path = path
self.llm = llm
def stream_enabled(self):
enabled = self.get_setting('streaming')
if (enabled is None):
re... |
class Package(OpcPackage):
def after_unmarshal(self):
self._gather_image_parts()
def get_or_add_image_part(self, image_descriptor: (str | IO[bytes])) -> ImagePart:
return self.image_parts.get_or_add_image_part(image_descriptor)
def image_parts(self) -> ImageParts:
return ImageParts()... |
def align_dfmesh_scanpc(df_mesh, df_resolution, scan_pc):
pts_min = np.amin(scan_pc, axis=0)
pts_max = np.amax(scan_pc, axis=0)
pc_extents = (pts_max - pts_min)
pc_bbox_center = ((pts_max + pts_max) / 2.0)
max_pc_size = np.max(pc_extents)
df_mesh_extents = df_mesh.bounding_box.extents
max_me... |
def custom(path: Union[(PurePath, str)], context: Optional[dict]=None) -> CompletedProcess:
with import_file(path) as module:
try:
func = getattr(module, 'pretf_workflow')
except AttributeError:
raise log.bad(f"workflow: {path} does not have a 'pretf_workflow' function")
... |
class Effect5317(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Projectile Turret')), 'damageMultiplier', ship.getModifiedItemAttr('shipBonusMD1'), skill='Minmatar Destroyer', **kwargs) |
def replay_citations(dag: ProvDAG, out_fp: str, deduplicate: bool=True, suppress_header: bool=False):
bib_db = collect_citations(dag, deduplicate=deduplicate)
boundary = ('#' * 79)
header = []
footer = []
extra = ['', '# This bibtex-formatted citation file can be imported into popular citation ', '#... |
def test_json_xmlrpc(run_cli):
cmd = 'bugzilla query --json --id 1165434'
timestr = 'T19:12:12'
dateobj = datetime.datetime.strptime(timestr, '%Y%m%dT%H:%M:%S')
attachfile = tests.utils.tests_path('data/bz-attach-get1.txt')
attachdata = open(attachfile, 'rb').read()
bugid = 1165434
data = {'... |
class Tree():
def __init__(self, left: (Tree | None), value: int, right: (Tree | None)) -> None:
self.left = left
self.value = value
self.right = right
async def __aiter__(self) -> AsyncIterator[int]:
if self.left:
async for i in self.left:
(yield i)
... |
class TestLength():
def test_sanity_check(self):
mySymbolicMatricesList = TypedListType(TensorType(pytensor.config.floatX, shape=(None, None)))()
z = Length()(mySymbolicMatricesList)
f = pytensor.function([mySymbolicMatricesList], z)
x = rand_ranged_matrix((- 1000), 1000, [100, 101])... |
def test_podman_vfs(tmp_path: Path, monkeypatch, container_engine):
if (container_engine.name != 'podman'):
pytest.skip('only runs with podman')
vfs_path = (tmp_path / 'podman_vfs')
vfs_path.mkdir()
vfs_containers_conf_data = {'containers': {'default_capabilities': ['CHOWN', 'DAC_OVERRIDE', 'FOW... |
class Node():
def __init__(self, state, parent=None):
self.visits = 1
self.reward = 0.0
self.state = state
self.children = []
self.parent = parent
def add_child(self, child_state):
child = Node(child_state, self)
self.children.append(child)
def update(... |
class RecvIfcRTL(CalleeIfcRTL):
def construct(s, Type):
super().construct(en=True, rdy=True, MsgType=Type, RetType=None)
def connect(s, other, parent):
if isinstance(other, CallerIfcCL):
m = RecvCL2SendRTL(s.MsgType)
if hasattr(parent, 'RecvCL2SendRTL_count'):
... |
.parametrize('has_changelog', [False, True])
def test_on_menu_action_changelog(default_main_window, monkeypatch, has_changelog):
mock_show = MagicMock()
monkeypatch.setattr(QtWidgets.QWidget, 'show', mock_show)
if has_changelog:
default_main_window.all_change_logs = {}
default_main_window._on_me... |
def render_venv_config(cfg):
lines = [f'home = {cfg.home}', f'version = {cfg.version}', f'include-system-site-packages = {cfg.system_site_packages}']
if (cfg.prompt is not None):
lines.append(f'prompt = {cfg.prompt}')
if (cfg.executable is not None):
lines.append(f'executable = {cfg.executab... |
class StableDiffusionPipeline(DiffusionPipeline):
def __init__(self, vae: AutoencoderKL, text_encoder: CLIPTextModel, tokenizer: CLIPTokenizer, unet: UNet2DConditionModel, scheduler: Union[(DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler)], safety_checker, feature_extractor: CLIPFeatureExtractor):
super(... |
class ODConv2d(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size, stride=1, padding=0, dilation=1, groups=1, reduction=0.0625, kernel_num=4):
super(ODConv2d, self).__init__()
self.in_planes = in_planes
self.out_planes = out_planes
self.kernel_size = kernel_size
... |
.skipif((not torch.cuda.is_available()), reason='requires CUDA support')
def test_three_nn():
known = torch.tensor([[[(- 1.8373), 3.5605, (- 0.7867)], [0.7615, 2.942, 0.2314], [(- 0.6503), 3.6637, (- 1.0622)], [(- 1.8373), 3.5605, (- 0.7867)], [(- 1.8373), 3.5605, (- 0.7867)]], [[(- 1.3399), 1.9991, (- 0.3698)], [(... |
class AddressBookPanel(Div):
def __init__(self, view):
super().__init__(view)
number_of_addresses = Session.query(Address).count()
self.add_child(H(view, 1, text=_.ngettext('Address', 'Addresses', number_of_addresses)))
self.add_child(AddressForm(view))
for address in Session... |
def test_locker_properly_loads_subdir(locker: Locker) -> None:
content = '[[package]]\nname = "git-package-subdir"\nversion = "1.2.3"\ndescription = ""\noptional = false\npython-versions = "*"\ndevelop = false\nfiles = []\n\n[package.source]\ntype = "git"\nurl = " = "develop"\nresolved_reference = "123456"\nsubdire... |
class ContextX509(cpi.Context):
def __init__(self, api, adaptor):
_cpi_base = super(ContextX509, self)
_cpi_base.__init__(api, adaptor)
_CALL
def init_instance(self, adaptor_state, type):
if (not (type.lower() in (schema.lower() for schema in _ADAPTOR_SCHEMAS))):
raise Ba... |
def test_game_session_collect_pickup_for_self(flask_app, two_player_session, generic_pickup_category, default_generator_params, echoes_resource_database, mocker):
sa = MagicMock()
sa.get_current_user.return_value = database.User.get_by_id(1234)
mock_emit: MagicMock = mocker.patch('flask_socketio.emit')
... |
class HandlerFactory():
def create(vim: Nvim) -> 'Handler':
client = VimClient(vim)
file_parser = FileParser(client)
process_manager = ProcessManager(client)
output_parser = OutputParser(client.sync_eval('g:ultest_disable_grouping'))
runner = PositionRunner(vim=client, proces... |
def getConfig():
parser = argparse.ArgumentParser()
parser.add_argument('--output_dir', required=True, type=str, help='Name of the output directory')
parser.add_argument('--weights_type', default='', type=str, help='Which probe weights to use for intervention')
cfg = parser.parse_args()
return cfg |
class Composite(ScalarInnerGraphOp):
init_param: tuple[(str, ...)] = ('inputs', 'outputs')
def __init__(self, inputs, outputs, name='Composite'):
self.name = name
self._name = None
for i in inputs:
assert (i not in outputs)
if ((len(outputs) > 1) or (not any((isinstan... |
class ExactGPModel(gpytorch.models.ExactGP):
def __init__(self, train_x, train_y, likelihood):
super(ExactGPModel, self).__init__(train_x, train_y, likelihood)
self.mean_module = gpytorch.means.ConstantMean()
self.covar_module = gpytorch.kernels.ScaleKernel(gpytorch.kernels.RBFKernel())
... |
class SingleSentenceClassificationProcessor(DataProcessor):
def __init__(self, labels=None, examples=None, mode='classification', verbose=False):
self.labels = ([] if (labels is None) else labels)
self.examples = ([] if (examples is None) else examples)
self.mode = mode
self.verbose ... |
class Bottleneck_Res(nn.Module):
def __init__(self, in_channel, depth, stride):
super(Bottleneck_Res, self).__init__()
if (in_channel == depth):
self.shortcut_layer = nn.MaxPool1d(1, stride)
else:
self.shortcut_layer = nn.Sequential(nn.Conv1d(in_channel, depth, 1, str... |
(is_wine(), 'hangs under wine')
(is_osx(), 'crashy on macOS')
class Tchooser(TestCase):
def test_choose_files(self):
w = Gtk.Window()
with with_response(Gtk.ResponseType.CANCEL):
assert (choose_files(w, 'title', 'action') == [])
def test_choose_folders(self):
w = Gtk.Window()... |
def main():
root_path = args.root_path
label_name = args.label_name
if (args.cnn == 'resnet50'):
feature_root = '/media/newssd/OMG_experiments/Extracted_features/resnet50_ferplus_features_fps=30_pool5_7x7_s1'
elif (args.cnn == 'vgg'):
feature_root = '/media/newssd/OMG_experiments/Extract... |
def train(model, train_loader, test_loader, gt, logger):
if (not os.path.exists(cfg.save_dir)):
os.makedirs(cfg.save_dir)
criterion = torch.nn.BCELoss()
criterion2 = torch.nn.KLDivLoss(reduction='batchmean')
optimizer = optim.Adam(model.parameters(), lr=cfg.lr)
scheduler = optim.lr_scheduler... |
class Rule():
def __init__(self, match: (Match | list[Match]), group: (_Group | None)=None, float: bool=False, intrusive: bool=False, break_on_match: bool=True) -> None:
if isinstance(match, Match):
self.matchlist = [match]
else:
self.matchlist = match
self.group = gr... |
class LightMaps(QWidget):
def __init__(self, parent=None):
super(LightMaps, self).__init__(parent)
self.pressed = False
self.snapped = False
self.zoomed = False
self.invert = False
self._normalMap = SlippyMap(self)
self._largeMap = SlippyMap(self)
self... |
def load_x963_vectors(vector_data):
vectors = []
hashname = None
vector = {}
for line in vector_data:
line = line.strip()
if line.startswith('[SHA'):
hashname = line[1:(- 1)]
shared_secret_len = 0
shared_info_len = 0
key_data_len = 0
... |
class MMapIndexedDatasetBuilder(object):
def __init__(self, out_file, dtype=np.int64):
self._data_file = open(out_file, 'wb')
self._dtype = dtype
self._sizes = []
self._doc_idx = [0]
def add_item(self, tensor):
np_array = np.array(tensor.numpy(), dtype=self._dtype)
... |
def setup_logging(training_args):
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', handlers=[logging.StreamHandler(sys.stdout)])
logger.setLevel((logging.INFO if is_main_process(training_args.local_rank) else logging.WARN))
logger.warning((f'P... |
class _ExtractInfo():
def __init__(self, project, resource, start, end, new_name, variable, similar, make_global):
self.project = project
self.resource = resource
self.pymodule = project.get_pymodule(resource)
self.global_scope = self.pymodule.get_scope()
self.source = self.p... |
class KnowValues(unittest.TestCase):
def test_symm_orb_h2o(self):
atoms = [['O', (1.0, 0.0, 0.0)], [1, (0.0, (- 0.757), 0.587)], [1, (0.0, 0.757, 0.587)]]
basis = {'H': gto.basis.load('cc_pvqz', 'C'), 'O': gto.basis.load('cc_pvqz', 'C')}
self.assertEqual(get_so(atoms, basis)[0], 165)
... |
def find_python_executable(python: Optional[str]=None) -> str:
if (not python):
python = os.environ.get('FLIT_INSTALL_PYTHON')
if (not python):
return sys.executable
if os.path.isabs(python):
return python
resolved_python = shutil.which(python)
if (resolved_python is None):
... |
def get_iterator(args):
with open((osp.join(args.data, args.split) + '.tsv'), 'r') as fp:
lines = fp.read().split('\n')
root = lines.pop(0).strip()
files = [osp.join(root, line.split('\t')[0]) for line in lines if (len(line) > 0)]
num = len(files)
reader = Wav2VecFeatureReade... |
class ErrorHandlerTests(unittest.IsolatedAsyncioTestCase):
def setUp(self):
self.bot = MockBot()
self.ctx = MockContext(bot=self.bot)
self.cog = error_handler.ErrorHandler(self.bot)
async def test_error_handler_already_handled(self):
self.ctx.reset_mock()
error = errors.C... |
class MPNN(nn.Module):
def __init__(self, n_node_hidden, n_edge_hidden, n_layers):
super().__init__()
self.n_layers = n_layers
edge_network = nn.Sequential(nn.Linear(n_edge_hidden, n_edge_hidden), nn.ReLU(), nn.Linear(n_edge_hidden, (n_node_hidden * n_node_hidden)))
self.conv = NNCon... |
class MIMOSA_Optimizer(BaseOptimizer):
def __init__(self, args=None):
super().__init__(args)
self.model_name = 'mimosa'
def _optimize(self, oracle, config):
self.oracle.assign_evaluator(oracle)
all_smiles_score_list = []
model_ckpt = os.path.join(path_here, 'pretrained_mo... |
class DataQuery():
def __init__(self, **kwargs):
self._dict = kwargs.copy()
self._fields = tuple(self._dict.keys())
self._values = tuple(self._dict.values())
def __getitem__(self, key):
return self._dict[key]
def __eq__(self, other):
sdict = self._asdict()
try... |
def select_2(train_embs, one_test_emb, downstream_train_examples, one_test_example, tag, given_context, phase2_selection):
cos = nn.CosineSimilarity(dim=1, eps=1e-06)
if (not os.path.isdir(f'cache/{tag}/prompts')):
os.makedirs(f'cache/{tag}/prompts', exist_ok=True)
prompt_string = f'''{conversion(ta... |
def test_add_with_strings_update():
context = Context({'arbset': {1, 2}, 'add': {'set': PyString('arbset'), 'addMe': 'xy', 'unpack': True}})
add.run_step(context)
context['add']['unpack'] = False
context['add']['addMe'] = 'z'
add.run_step(context)
assert (context['arbset'] == {1, 2, 'x', 'y', 'z... |
def test_inheritance():
class Parent(NamedTuple):
a: int
class Child(Parent):
b: str
assert (get_named_tuple_shape(Child) == Shape(input=InputShape(constructor=Child, kwargs=None, fields=(InputField(type=int, id='a', default=NoDefault(), is_required=True, metadata=MappingProxyType({}), origi... |
class Lz4f(Codec):
codec_id = 'imagecodecs_lz4f'
def __init__(self, level=None, blocksizeid=False, contentchecksum=None, blockchecksum=None):
self.level = level
self.blocksizeid = blocksizeid
self.contentchecksum = contentchecksum
self.blockchecksum = blockchecksum
def encode... |
def query_yes_no(question):
valid = {'yes': True, 'y': True, 'ye': True, 'no': False, 'n': False}
prompt = ' [y/n] '
while True:
sys.stdout.write((question + prompt))
choice = input().lower()
if (choice in valid):
return valid[choice]
else:
sys.stdout.... |
class TestRopLop(RopLopChecker):
def test_max(self):
self.check_mat_rop_lop(pt_max(self.mx, axis=0), (self.mat_in_shape[1],))
self.check_mat_rop_lop(pt_max(self.mx, axis=1), (self.mat_in_shape[0],))
def test_argmax(self):
self.check_nondiff_rop(argmax(self.mx, axis=1))
def test_subte... |
class StackAsserter(Provider):
request_type: Type[Request]
expected_stack: Sequence[Request]
send_next: Optional[Request]
def apply_provider(self, mediator: Mediator, request: Request):
if (not isinstance(request, self.request_type)):
raise CannotProvide
assert (list(self.exp... |
def draw(response, axes_amplitude=None, axes_phase=None, fmin=0.01, fmax=100.0, nf=100, normalize=False, style={}, label=None, show_breakpoints=False, color_pool=None, label_pool=None):
f = num.exp(num.linspace(num.log(fmin), num.log(fmax), nf))
resp_fmax = response.get_fmax()
if (resp_fmax is not None):
... |
def save_dataset(data_items, name):
if (not data_items):
return
out_filepath = os.path.join(settings.DATASET_FOLDER, name)
data = {'links': data_items}
if (not os.path.exists(os.path.dirname(out_filepath))):
os.makedirs(os.path.dirname(out_filepath))
with open(out_filepath, 'w') as f... |
_sentencepiece
class BertGenerationTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BertGenerationTokenizer
test_rust_tokenizer = False
test_sentencepiece = True
def setUp(self):
super().setUp()
tokenizer = BertGenerationTokenizer(SAMPLE_VOCAB, keep_accents=Tr... |
def test_multiple_root_event_handlers():
root_called = 0
def pointer_leave_callback(event):
nonlocal root_called
root_called += 1
root_handler = RootEventHandler()
root_handler.add_event_handler(pointer_leave_callback, 'pointer_leave')
alt_handler = RootEventHandler()
root_handle... |
.parametrize('actual, expected', [(reactpy.vdom('div', [reactpy.vdom('div')]), {'tagName': 'div', 'children': [{'tagName': 'div'}]}), (reactpy.vdom('div', {'style': {'backgroundColor': 'red'}}), {'tagName': 'div', 'attributes': {'style': {'backgroundColor': 'red'}}}), (reactpy.vdom('div', [reactpy.vdom('div'), 1], (rea... |
def evaluate(args):
torch.cuda.set_device(args.gpu)
s_r = args.se_ratio
arch_def = [['ds_r1_k3_s1_e1_c16'], [('ir_r1_k3_s2_e6_c32_se%f_nsw' % s_r)], [('ir_r1_k3_s1_e3_c32_se%f_nsw' % s_r)], [('ir_r1_k5_s2_e6_c40_se%f_nsw' % s_r), ('ir_r3_k3_s1_e6_c40_se%f_nsw' % s_r)], [('ir_r1_k5_s2_e6_c80_se%f_nsw' % s_r)... |
class UFID(Frame):
_framespec = [Latin1TextSpec('owner'), BinaryDataSpec('data')]
def HashKey(self):
return ('%s:%s' % (self.FrameID, self.owner))
def __eq__(s, o):
if isinstance(o, UFI):
return ((s.owner == o.owner) and (s.data == o.data))
else:
return (s.dat... |
def validate_dicts(ground_truth: dict, predicted: dict) -> bool:
valid = True
num_agents_gt = len(ground_truth)
num_agents_pred = len(predicted)
if (num_agents_gt != num_agents_pred):
print(f'Incorrect number of rows in inference csv. Expected {num_agents_gt}, Got {num_agents_pred}')
val... |
class CheckAndRaise(COp):
_f16_ok = True
__props__ = ('msg', 'exc_type')
view_map = {0: [0]}
check_input = False
params_type = ParamsType(exc_type=exception_type)
def __init__(self, exc_type, msg=''):
if (not issubclass(exc_type, Exception)):
raise ValueError('`exc_type` must... |
.parametrize('pretty_json', (True, False))
.parametrize('verbosity', (0, 1, 2))
def test_json_format_validation_error_nested(capsys, pretty_json, verbosity):
validator = Draft7Validator({'anyOf': [{'properties': {'foo': {'oneOf': [{'type': 'string'}, {'type': 'integer'}]}}}, {'properties': {'bar': {'oneOf': [{'type... |
(fov=ShowInInspector(int), orthoSize=ShowInInspector(float))
class Camera(SingleComponent):
near = ShowInInspector(float, 0.05)
far = ShowInInspector(float, 200)
clearColor = ShowInInspector(Color, RGB(0, 0, 0))
shader = ShowInInspector(Shader, shaders['Standard'])
skyboxEnabled = ShowInInspector(bo... |
def test_log_vehicle_leave():
events = telemetry.events_from_type('LogVehicleLeave')
for (idx, ev) in enumerate(events):
if (ev.fellow_passengers and (ev.vehicle.fuel_percent != 0)):
data = events[idx]
break
else:
assert False
assert isinstance(data, LogVehicleLea... |
def get_batches(targets, sources, batch_size, source_pad_int, target_pad_int):
for batch_i in range(0, (len(sources) // batch_size)):
start_i = (batch_i * batch_size)
sources_batch = sources[start_i:(start_i + batch_size)]
targets_batch = targets[start_i:(start_i + batch_size)]
pad_s... |
def get_files_in_tree(tree, repo):
files = set()
for entry in tree:
if (entry.type == 'tree'):
sub_files = [(f[0], '{}/{}'.format(entry.name, f[1])) for f in get_files_in_tree(repo[entry.id], repo)]
files.update(sub_files)
else:
blob = repo[entry.id]
... |
class Tunnel(XodrBase):
def __init__(self, s: float, length: float, id: str, name: str, tunnel_type: TunnelType=TunnelType.standard, daylight: float=0.5, lighting: float=0.5):
super().__init__()
self.s = s
self.length = length
self.id = id
self.name = name
self.tunnel... |
class Product(Space):
def __init__(self, *components):
if isinstance(components[0], (list, tuple)):
assert (len(components) == 1)
components = components[0]
self._components = tuple(components)
dtypes = [c.new_tensor_variable('tmp', extra_dims=0).dtype for c in compon... |
class TestSklearnSVM(QiskitAquaTestCase):
def setUp(self):
super().setUp()
aqua_globals.random_seed = 10598
pass
def test_binary(self):
training_input = {'A': np.asarray([[0.6560706, 0.], [0., 0.], [0., 0.], [0., (- 0.)], [0.3994399, 0.], [0., (- 0.)], [0., 0.], [0., 0.], [0., 0.... |
class F9_Network(F8_Network):
removedKeywords = F8_Network.removedKeywords
removedAttrs = F8_Network.removedAttrs
def __init__(self, writePriority=0, *args, **kwargs):
F8_Network.__init__(self, writePriority, *args, **kwargs)
self.bootprotoList.append(BOOTPROTO_QUERY)
def _getParser(self... |
.skipif((sys.platform == 'win32'), reason='Windows only applies R/O to files')
def test_populated_read_only_cache_and_copied_app_data(tmp_path, current_fastest, temp_app_data):
dest = (tmp_path / 'venv')
cmd = ['--seeder', 'app-data', '--creator', current_fastest, '-vv', '-p', 'python', str(dest)]
assert cl... |
class Room(models.Model):
TYPES = Choices(('talk', _('Talk room')), ('training', _('Training room')))
name = models.CharField(_('name'), max_length=100)
type = models.CharField(_('type'), choices=TYPES, max_length=10, default=TYPES.talk)
def __str__(self):
return self.name
class Meta():
... |
def imread(filename, flags=cv2.IMREAD_COLOR):
global _im_zfile
path = filename
pos_at = path.index('')
if (pos_at == (- 1)):
print(("character '' is not found from the given path '%s'" % path))
assert 0
path_zip = path[0:pos_at]
if (not os.path.isfile(path_zip)):
print(("... |
class DeployLog(models.Model):
d_types = (('deploy', ''), ('rollback', ''))
project_config = models.ForeignKey('ProjectConfig', on_delete=models.CASCADE)
deploy_user = models.ForeignKey('users.UserProfile', on_delete=models.CASCADE)
d_type = models.CharField(max_length=10, choices=d_types, verbose_name=... |
def create_optimizer(init_lr: float, num_train_steps: int, num_warmup_steps: int, min_lr_ratio: float=0.0, adam_beta1: float=0.9, adam_beta2: float=0.999, adam_epsilon: float=1e-08, weight_decay_rate: float=0.0, power: float=1.0, include_in_weight_decay: Optional[List[str]]=None):
lr_schedule = tf.keras.optimizers.... |
def get_lr_scheduler(scheduler_type: str, optimizer: torch.optim.Optimizer, warmup_steps: Optional[int]=0, max_steps: Optional[bool]=None, base_lr: float=0.0001, max_lr: float=0.001, step_size_up: int=2000) -> torch.optim.lr_scheduler:
if (scheduler_type == 'linear'):
return get_linear_schedule_with_warmup(... |
def cache_data(hparams, filename, flag):
if (hparams.data_format == 'ffm'):
cache_obj = FfmCache()
elif (hparams.data_format == 'din'):
cache_obj = DinCache()
elif (hparams.data_format == 'cccfnet'):
cache_obj = CCCFNetCache()
else:
raise ValueError('data format must be f... |
_REGISTRY.register()
class HiFaceGANModel(SRModel):
def init_training_settings(self):
train_opt = self.opt['train']
self.ema_decay = train_opt.get('ema_decay', 0)
if (self.ema_decay > 0):
raise NotImplementedError('HiFaceGAN does not support EMA now. Pass')
self.net_g.tra... |
class TestStripPickler():
def setup_method(self):
self.origdir = os.getcwd()
self.tmpdir = mkdtemp()
os.chdir(self.tmpdir)
def teardown_method(self):
os.chdir(self.origdir)
if (self.tmpdir is not None):
shutil.rmtree(self.tmpdir)
def test_basic(self):
... |
class TestBrowserCrash(unittest.TestCase):
async def test_browser_crash_send(self):
browser = (await launch(args=['--no-sandbox']))
page = (await browser.newPage())
(await page.goto('about:blank'))
(await page.querySelector('title'))
browser.process.terminate()
browse... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.