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class LoggerDepthLoss():
def __init__(self, type='train', empty_value=0.0):
super(LoggerDepthLoss, self).__init__()
self.type = type
self.empty_value = empty_value
def tick(self, logs, out_rgb, target_rgb, out_depth, target_depth=None):
if (target_depth is None):
retu... |
_cache(maxsize=1)
def _float_max_string_len() -> int:
PA_POS_FLOAT64_MAX_STR_BYTES = pc.binary_length(pc.cast(pa.scalar(np.finfo(np.float64).max, type=pa.float64()), pa.string())).as_py()
PA_NEG_FLOAT64_MAX_STR_BYTES = pc.binary_length(pc.cast(pa.scalar(np.finfo(np.float64).min, type=pa.float64()), pa.string())... |
class UnboundCollector(diamond.collector.ProcessCollector):
def get_default_config_help(self):
config_help = super(UnboundCollector, self).get_default_config_help()
config_help.update({'bin': 'Path to unbound-control binary', 'histogram': 'Include histogram in collection'})
return config_hel... |
class Tiny200_boxes(datasets.ImageFolder):
def __init__(self, root, transform_rcrop, transform_ccrop, init_box=(0.0, 0.0, 1.0, 1.0), **kwargs):
super().__init__(root=root, **kwargs)
self.transform_rcrop = transform_rcrop
self.transform_ccrop = transform_ccrop
self.boxes = torch.tenso... |
.parametrize('iterators', [[[1, 2, 3], [4, 5], [6, 7, 8]], [(i for i in range(1, 7)), (i for i in range(7, 9))]])
def test_flatten(iterators):
source = Stream()
L = source.flatten().sink_to_list()
for iterator in iterators:
source.emit(iterator)
assert (L == [1, 2, 3, 4, 5, 6, 7, 8]) |
class MultiChoiceInstruction(Instruction):
def __init__(self, data_name: str, data_list: List, verbalizer: Dict, instruction: str, keys_order: List[str], data_type: str):
super(MultiChoiceInstruction, self).__init__(data_name, data_list, verbalizer, instruction, keys_order, data_type)
self.NO_ANSWER... |
class CNN_encoder(nn.Module):
def __init__(self, hidden_states=256):
super(CNN_encoder, self).__init__()
self.encoder = nn.Sequential(nn.Conv2d(3, 32, (15, 23), stride=9), nn.ReLU(True), nn.Conv2d(32, 64, 3, stride=(1, 3)), nn.ReLU(True), nn.Conv2d(64, 96, (7, 3), stride=(1, 3)), nn.ReLU(True))
... |
class UserPersonalAccessTokenManager(CreateMixin, RESTManager):
_path = '/users/{user_id}/personal_access_tokens'
_obj_cls = UserPersonalAccessToken
_from_parent_attrs = {'user_id': 'id'}
_create_attrs = RequiredOptional(required=('name', 'scopes'), optional=('expires_at',))
_types = {'scopes': Arra... |
def cache(repository_cache_dir: Path, repository_one: str, mock_caches: None) -> FileCache[dict[(str, str)]]:
cache: FileCache[dict[(str, str)]] = FileCache(path=(repository_cache_dir / repository_one))
cache.remember('cachy:0.1', (lambda : {'name': 'cachy', 'version': '0.1'}), minutes=None)
cache.remember(... |
(st.sets(text))
def test_map_with_pad(tmpdir_factory, keys):
trie = marisa_trie.Trie(keys)
dirname = f'{str(uuid4())}_'
path = str(tmpdir_factory.mktemp(dirname).join('trie.bin'))
trie.save(path)
data = ((b'pad' + open(path, 'rb').read()) + b'pad')
trie2 = marisa_trie.Trie()
trie2.map(memory... |
def _send_twilio(msg, numbers):
twilio_sid = (SMS_CREDENTIALS['sid'].strip().split(' ')[0] if ('sid' in SMS_CREDENTIALS) else None)
twilio_token = (SMS_CREDENTIALS['token'].strip().split(' ')[0] if ('token' in SMS_CREDENTIALS) else None)
twilio_from = (SMS_CREDENTIALS['from'].strip().split(' ')[0] if ('from... |
def test_archs_platform_specific(platform, intercepted_build_args, monkeypatch):
monkeypatch.setenv('CIBW_ARCHS', 'unused')
monkeypatch.setenv('CIBW_ARCHS_LINUX', 'ppc64le')
monkeypatch.setenv('CIBW_ARCHS_WINDOWS', 'x86')
monkeypatch.setenv('CIBW_ARCHS_MACOS', 'x86_64')
main()
options = intercep... |
('/api_save', methods=['POST'])
def api_save():
flag = request.form['flag']
req_host = request.form['host']
req_api_name = request.form['api_name']
req_project_name = request.form['project_name']
req_url = request.form['url']
req_method = request.form['method']
req_data = request.form['data'... |
class InitializationSection(BasePathMixin):
tag = ext_x_map
def __init__(self, base_uri, uri, byterange=None):
self.base_uri = base_uri
self.uri = uri
self.byterange = byterange
def __str__(self):
output = []
if self.uri:
output.append(('URI=' + quoted(sel... |
def convert_net_g(ori_net, crt_net):
for (crt_k, crt_v) in crt_net.items():
if ('style_mlp' in crt_k):
ori_k = crt_k.replace('style_mlp', 'style')
elif ('constant_input.weight' in crt_k):
ori_k = crt_k.replace('constant_input.weight', 'input.input')
elif ('style_conv1... |
def get_word_list(line, dictionary):
splitted_words = json.loads(line.lower()).split()
words = ['<bos>']
for word in splitted_words:
word = filter_symbols.search(word)[0]
if (len(word) > 1):
if dictionary.word2idx.get(word, False):
words.append(word)
e... |
def import_question(element, save=False, user=None):
try:
question = Question.objects.get(uri=element.get('uri'))
except Question.DoesNotExist:
question = Question()
set_common_fields(question, element)
set_foreign_field(question, 'attribute', element)
question.is_collection = (eleme... |
def pytest_configure(config: pytest.Config):
markers = []
if config.option.skip_generation_tests:
markers.append('not skip_generation_tests')
if config.option.skip_resolver_tests:
markers.append('not skip_resolver_tests')
if config.option.skip_gui_tests:
markers.append('not skip_... |
class CPythonmacOsFramework(CPython, metaclass=ABCMeta):
def can_describe(cls, interpreter):
return (is_mac_os_framework(interpreter) and super().can_describe(interpreter))
def create(self):
super().create()
target = self.desired_mach_o_image_path()
current = self.current_mach_o_... |
class BackBone(nn.Module):
def __init__(self, opt):
super(BackBone, self).__init__()
self._name = 'BackBone'
self._opt = opt
self.model = self._init_create_networks()
def _init_create_networks(self):
if ((self._opt.pretrained_dataset == 'ferplus') or (self._opt.pretrained... |
.fast
def test_line_survey(verbose=True, plot=False, warnings=True, *args, **kwargs):
_temp_file = 'radis_test_line_survey.html'
if exists(_temp_file):
os.remove(_temp_file)
s = load_spec(getTestFile('CO_Tgas1500K_mole_fraction0.01.spec'), binary=True)
s.line_survey(overlay='abscoeff', writefile... |
def pytest_configure(config: Config) -> None:
if config.getvalue('lsof'):
checker = LsofFdLeakChecker()
if checker.matching_platform():
config.pluginmanager.register(checker)
config.addinivalue_line('markers', 'pytester_example_path(*path_segments): join the given path segments to `p... |
def convert_folder_with_preds_back_to_BraTS_labeling_convention(input_folder: str, output_folder: str, num_processes: int=12):
maybe_mkdir_p(output_folder)
nii = subfiles(input_folder, suffix='.nii.gz', join=False)
with multiprocessing.get_context('spawn').Pool(num_processes) as p:
p.starmap(load_co... |
class AutoTokenizer(object):
def __init__(self):
raise EnvironmentError('AutoTokenizer is designed to be instantiated using the `AutoTokenizer.from_pretrained(pretrained_model_name_or_path)` method.')
def from_pretrained(cls, pretrained_model_name_or_path, *inputs, **kwargs):
if ('t5' in pretrai... |
class BotConfigureTest(TestCase):
def test_kwargs(self):
bot = bot_factory()
bot.provider.get_file.return_value = (None, None)
bot.configure(branch='bogus-branch', pin='bogus-pin', close_prs='bogus-close')
self.assertEqual(bot.config.branch, 'bogus-branch')
self.assertEqual(b... |
class ForwardSchedule(object):
def __init__(self, timesteps, beta_start=0.0001, beta_end=0.02, mode='linear'):
self.timesteps = timesteps
self.beta_start = beta_start
self.beta_end = beta_end
self.mode = mode.lower()
self.calc_vars()
def get_scheduler(self):
if (s... |
def test_first_query_delay():
type_ = '_
zeroconf_browser = Zeroconf(interfaces=['127.0.0.1'])
_wait_for_start(zeroconf_browser)
old_send = zeroconf_browser.async_send
first_query_time = None
def send(out, addr=const._MDNS_ADDR, port=const._MDNS_PORT):
nonlocal first_query_time
i... |
def test_outer_loading_bad_item_quantity():
bad_quantity_data = change(outer_sample_data, ['items', 0, 'quantity'], Decimal(0))
raises_exc(AggregateLoadError(f'while loading model {Receipt}', [with_trail(AggregateLoadError(f'while loading iterable {list}', [with_trail(AggregateLoadError(f'while loading model {R... |
def performance(ob, fo, grade_list=[1e-30], member_list=None, save_path=None, show=False, dpi=300, title=''):
sup_fontsize = 10
hfmc_array = hfmc(ob, fo, grade_list)
pod = pod_hfmc(hfmc_array)
sr = sr_hfmc(hfmc_array)
leftw = 0.6
rightw = 2
uphight = 1.2
lowhight = 1.2
axis_size_x = ... |
def update_alpha(gamma, p, Ap, has_converged, xnp):
denom = xnp.sum((xnp.conj(p) * Ap), axis=(- 2), keepdims=True)
alpha = do_safe_div(gamma, denom, xnp=xnp)
device = xnp.get_device(p)
alpha = xnp.where(has_converged, xnp.array(0.0, dtype=p.dtype, device=device), alpha)
return alpha |
class QAReplayMemory(object):
def __init__(self, capacity=100000, priority_fraction=0.0, seed=None):
self.rng = np.random.RandomState(seed)
self.priority_fraction = priority_fraction
self.alpha_capacity = int((capacity * priority_fraction))
self.beta_capacity = (capacity - self.alpha... |
class KubernetesPodmanExecutor(KubernetesExecutor):
def __init__(self, *args, **kwargs):
super(KubernetesExecutor, self).__init__(*args, **kwargs)
self.namespace = self.executor_config.get('BUILDER_NAMESPACE', 'builder')
self.image = self.executor_config.get('BUILDER_CONTAINER_IMAGE', 'quay.... |
.functions
def test_truncate_datetime_dataframe_all_parts():
x = datetime(2022, 3, 21, 9, 1, 15, 666)
df = pd.DataFrame({'dt': [x], 'foo': [np.nan]}, copy=False)
result = df.truncate_datetime_dataframe('second')
assert (result.loc[(0, 'dt')] == datetime(2022, 3, 21, 9, 1, 15, 0))
result = df.truncat... |
class TestLanguageModeling(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def test_fconv_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory('test_fconv_lm') as... |
class A(HTMLElement):
def from_bookmark(cls, view, bookmark):
if bookmark.is_page_internal:
raise ProgrammerError('You cannot use page-internal Bookmarks directly, first add it to a Bookmark to a View')
return cls(view, bookmark.href, description=bookmark.description, ajax=bookmark.ajax,... |
('pytube.cli._download')
('pytube.cli.YouTube')
def test_download_audio_none(youtube, download):
youtube_instance = youtube.return_value
youtube_instance.streams.filter.return_value.order_by.return_value.last.return_value = None
with pytest.raises(SystemExit):
cli.download_audio(youtube_instance, 'f... |
def _math_define_validator(value, values):
if (not isinstance(value, tuple)):
raise ValueError('Input value {} of trigger_select should be a tuple'.format(value))
if (len(value) != 3):
raise ValueError('Number of parameters {} different from 3'.format(len(value)))
output = (sanitize_source(v... |
def test_request_pattern_generic_arg():
check_request_pattern(P[Dict].generic_arg(0, str), [LocatedRequest(loc_map=LocMap(TypeHintLoc(Dict))), LocatedRequest(loc_map=LocMap(TypeHintLoc(str), GenericParamLoc(0)))], fail=False)
check_request_pattern(P[Dict].generic_arg(0, str), [LocatedRequest(loc_map=LocMap(Type... |
.parametrize('key', FUNCTION_METHODS)
def test_given_function_is_set_then_reading_avaliable(resetted_dmm6500, key):
if (key[(- 2):] == 'ac'):
getattr(resetted_dmm6500, FUNCTION_METHODS[key])(ac=True)
elif (key[(- 2):] == '4W'):
getattr(resetted_dmm6500, FUNCTION_METHODS[key])(wires=4)
else:
... |
class BloqExample():
_func: Callable[([], Bloq)] = field(repr=False, hash=False)
name: str
bloq_cls: Type[Bloq]
generalizer: Callable[([Bloq], Optional[Bloq])] = (lambda x: x)
def make(self) -> Bloq:
return self._func()
def __call__(self) -> Bloq:
return self.make() |
.parametrize('aoi_model', ['sapm', 'ashrae', 'physical', 'martin_ruiz'])
def test_aoi_models_singleon_weather_single_array(sapm_dc_snl_ac_system, location, aoi_model, weather):
mc = ModelChain(sapm_dc_snl_ac_system, location, dc_model='sapm', aoi_model=aoi_model, spectral_model='no_loss')
mc.run_model(weather=[... |
def test_kaiming_init():
conv_module = nn.Conv2d(3, 16, 3)
kaiming_init(conv_module, bias=0.1)
assert conv_module.bias.allclose(torch.full_like(conv_module.bias, 0.1))
kaiming_init(conv_module, distribution='uniform')
with pytest.raises(AssertionError):
kaiming_init(conv_module, distribution... |
def get_external_models():
mmcv_home = _get_mmcv_home()
default_json_path = osp.join(mmcv.__path__[0], 'model_zoo/open_mmlab.json')
default_urls = load_file(default_json_path)
assert isinstance(default_urls, dict)
external_json_path = osp.join(mmcv_home, 'open_mmlab.json')
if osp.exists(external... |
class Properties(PymiereBaseObject):
def __init__(self, pymiere_id=None):
super(Properties, self).__init__(pymiere_id)
def bind(self, eventName, function):
self._check_type(eventName, str, 'arg "eventName" of function "Properties.bind"')
self._check_type(function, any, 'arg "function" of... |
def test_set_scale(qapp, imgfilename3x3):
item = BeePixmapItem(QtGui.QImage(imgfilename3x3), imgfilename3x3)
item.prepareGeometryChange = MagicMock()
item.setScale(3)
assert (item.scale() == 3)
assert (item.pos().x() == 0)
assert (item.pos().y() == 0)
item.prepareGeometryChange.assert_called... |
class JWNumberRestrictOperatorTest(unittest.TestCase):
def test_jw_restrict_operator(self):
n_qubits = 4
target_electrons = 2
penalty_const = 10.0
number_sparse = jordan_wigner_sparse(number_operator(n_qubits))
bias_sparse = jordan_wigner_sparse(sum([FermionOperator(((i, 1), ... |
class FlowchartViewBox(ViewBox):
def __init__(self, widget, *args, **kwargs):
ViewBox.__init__(self, *args, **kwargs)
self.widget = widget
def getMenu(self, ev):
self._fc_menu = QtWidgets.QMenu()
self._subMenus = self.getContextMenus(ev)
for menu in self._subMenus:
... |
class ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):
def __init__(self, dataset, indices=None, num_samples=None):
self.indices = (list(range(len(dataset))) if (indices is None) else indices)
self.num_samples = (len(self.indices) if (num_samples is None) else num_samples)
label_t... |
def test_get_transfer_secret_none_for_none_transfer_state(chain_state):
secret = factories.make_secret()
transfer = factories.create(factories.LockedTransferUnsignedStateProperties(secret=secret))
secrethash = transfer.lock.secrethash
payment_state = InitiatorPaymentState(initiator_transfers={secrethash... |
class VQModel(pl.LightningModule):
def __init__(self, ddconfig, lossconfig, n_embed, embed_dim, ckpt_path=None, ignore_keys=[], image_key='image', colorize_nlabels=None, monitor=None, remap=None, sane_index_shape=False):
super().__init__()
self.image_key = image_key
self.encoder = Encoder(**... |
class MessageDataModel():
def _count_tokens(test_string: str) -> int:
enc = tiktoken.get_encoding('cl100k_base')
tokens = len(enc.encode(test_string))
return tokens
def _get_num_tokens_from_messages(cls, buffer: List[BaseMessage]) -> int:
return sum([cls._count_tokens(m.content) ... |
def model_fixture(request: SubRequest, factory_name: str) -> Any:
factoryboy_request: FactoryboyRequest = request.getfixturevalue('factoryboy_request')
factoryboy_request.evaluate(request)
assert request.fixturename
fixture_name = request.fixturename
prefix = ''.join((fixture_name, SEPARATOR))
f... |
def fold_all_batch_norms(sess: tf.compat.v1.Session, input_op_names: Union[(str, List[str])], output_op_names: Union[(str, List[str])]) -> Tuple[(tf.compat.v1.Session, List[Tuple[(tf.Operation, tf.Operation)]])]:
if (not isinstance(input_op_names, (str, List))):
logger.error('start op names must be passed a... |
class PipelineTestCase(TestCase):
if PY2:
def assertRaisesRegex(self, *args, **kwargs):
return self.assertRaisesRegexp(*args, **kwargs)
def test_construction(self):
p0 = Pipeline()
self.assertEqual(p0.columns, {})
self.assertIs(p0.screen, None)
columns = {'f':... |
def parse_config():
parser = ArgumentParser()
parser.add_argument('--gpu', type=int, nargs='+', default=(0,), help='specify gpu devices')
parser.add_argument('--seed', default=0, type=int)
parser.add_argument('--config_path', default='config/2DPASS-semantickitti.yaml')
parser.add_argument('--log_dir... |
class ResNet(nn.Module):
def __init__(self, *, d_numerical: int, categories: ty.Optional[ty.List[int]], d_embedding: int, d: int, d_hidden_factor: float, n_layers: int, activation: str, normalization: str, hidden_dropout: float, residual_dropout: float, d_out: int) -> None:
super().__init__()
def ma... |
class PyTorchClassifier(object):
def __init__(self, inputdim, nclasses, l2reg=0.0, batch_size=64, seed=1111, cudaEfficient=False):
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
self.inputdim = inputdim
self.nclasses = nclasses
self.l2reg = ... |
def INSgrow_Gap(sub_ptn, I):
global compnum
global NumbS
global sDB
global IPLUS
compnum = (compnum + 1)
support = 0
global ptn_len
ptn_len = len(sub_ptn)
p = sub_ptn[(ptn_len - 1)].end
IPLUS = copy.deepcopy(I)
for i in range(0, NumbS):
if (len(sDB[i].S) > 0):
... |
def main():
connection = establish_tcp_connection()
h2_connection = h2.connection.H2Connection()
settings_header_value = h2_connection.initiate_upgrade_connection()
send_initial_request(connection, settings_header_value)
extra_data = get_upgrade_response(connection)
connection.sendall(h2_connect... |
_sentencepiece
_tokenizers
class MBartTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = MBartTokenizer
rust_tokenizer_class = MBartTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = True
def setUp(self):
super().setUp()
tokenizer = MBartTokenizer... |
class IfExp(NodeNG):
_astroid_fields = ('test', 'body', 'orelse')
test: NodeNG
body: NodeNG
orelse: NodeNG
def postinit(self, test: NodeNG, body: NodeNG, orelse: NodeNG) -> None:
self.test = test
self.body = body
self.orelse = orelse
def get_children(self):
(yield... |
def dbref(inp, reqhash=True):
if (reqhash and (not (isinstance(inp, str) and inp.startswith('#')))):
return None
if isinstance(inp, str):
inp = inp.lstrip('#')
try:
if (int(inp) < 0):
return None
except Exception:
return None
return inp |
class Data(object):
time = 0
host = None
plugin = None
plugininstance = None
type = None
typeinstance = None
def __init__(self, **kw):
[setattr(self, k, v) for (k, v) in kw.items()]
def datetime(self):
return datetime.fromtimestamp(self.time)
def source(self):
... |
class RacketLexer(RegexLexer):
name = 'Racket'
url = '
aliases = ['racket', 'rkt']
filenames = ['*.rkt', '*.rktd', '*.rktl']
mimetypes = ['text/x-racket', 'application/x-racket']
version_added = '1.6'
_keywords = ('#%app', '#%datum', '#%declare', '#%expression', '#%module-begin', '#%plain-ap... |
class CodeGenOnnxConfig(OnnxConfigWithPast):
def __init__(self, config: PretrainedConfig, task: str='default', patching_specs: List[PatchingSpec]=None, use_past: bool=False):
super().__init__(config, task=task, patching_specs=patching_specs, use_past=use_past)
if (not getattr(self._config, 'pad_toke... |
def _create_session_with_discord_token(sa: ServerApp, sid: (str | None)) -> User:
discord_user = sa.discord.fetch_user()
if (sa.enforce_role is not None):
if (not sa.enforce_role.verify_user(discord_user.id)):
logger().info('User %s is not authorized for connecting to the server', discord_us... |
def ground_sort(i_op, qdmr, grounding_out):
assert (qdmr.ops[i_op] == 'sort')
if (len(qdmr.args[i_op]) == 3):
(data_arg, sort_arg, sort_dir_arg) = qdmr.args[i_op]
else:
(data_arg, sort_arg) = qdmr.args[i_op]
sort_dir_arg = None
if (sort_dir_arg is not None):
is_ascending_... |
_module()
class XMLDataset(CustomDataset):
def __init__(self, min_size=None, **kwargs):
assert (self.CLASSES or kwargs.get('classes', None)), 'CLASSES in `XMLDataset` can not be None.'
super(XMLDataset, self).__init__(**kwargs)
self.cat2label = {cat: i for (i, cat) in enumerate(self.CLASSES)... |
class BoW(nn.Module):
def __init__(self, vocab: List[str], word_weights: Dict[(str, float)]={}, unknown_word_weight: float=1, cumulative_term_frequency: bool=True):
super(BoW, self).__init__()
vocab = list(set(vocab))
self.config_keys = ['vocab', 'word_weights', 'unknown_word_weight', 'cumul... |
class LineEditDelegate(QtWidgets.QStyledItemDelegate):
def createEditor(self, parent, option, index):
editor = QtWidgets.QLineEdit(parent)
editor.setValidator(ExpressionValidator())
return editor
def setEditorData(self, editor, index):
value = index.data(QtCore.Qt.ItemDataRole.Di... |
def check_resp(resp, value, frequency, limit_db, prelude, context):
try:
value_resp = num.abs(evaluate1(resp, frequency))
except response.InvalidResponseError as e:
return Delivery(log=[('warning', ('Could not check response: %s' % str(e)), context)])
if (value_resp == 0.0):
return D... |
class Panorama(Primitive):
def __init__(self, panorama, center=vec3(0.0, 0.0, 0.0), light_intensity=0.0, blur=0.0):
super().__init__(center, SkyBox_Material(panorama, light_intensity, blur), shadow=False)
l = SKYBOX_DISTANCE
self.light_intensity = light_intensity
self.collider_list +... |
def find_memory_type(phys_addr):
if (phys_addr == 0):
return 'N/A'
if is_system_ram(phys_addr):
return 'System RAM'
if is_persistent_mem(phys_addr):
return 'Persistent Memory'
f.seek(0, 0)
for j in f:
m = re.split('-|:', j, 2)
if (int(m[0], 16) <= phys_addr <=... |
class Blocks():
def __init__(self, tessellation, edges, buildings, id_name, unique_id):
self.tessellation = tessellation
self.edges = edges
self.buildings = buildings
self.id_name = id_name
self.unique_id = unique_id
if (id_name in buildings.columns):
rais... |
class Target(object):
def __init__(self, targetInfo: Dict, browserContext: 'BrowserContext', sessionFactory: Callable[([], Coroutine[(Any, Any, CDPSession)])], ignoreHTTPSErrors: bool, defaultViewport: Optional[Dict], screenshotTaskQueue: List, loop: asyncio.AbstractEventLoop) -> None:
self._targetInfo = ta... |
def extract_and_save_image(dataset, save_dir):
if osp.exists(save_dir):
print('Folder "{}" already exists'.format(save_dir))
return
print('Extracting images to "{}" ...'.format(save_dir))
mkdir_if_missing(save_dir)
for i in range(len(dataset)):
(img, label) = dataset[i]
c... |
class ResNet101FeatureExtractor(nn.Module):
def __init__(self, use_input_norm=True, device=torch.device('cpu')):
super(ResNet101FeatureExtractor, self).__init__()
model = torchvision.models.resnet101(pretrained=True)
self.use_input_norm = use_input_norm
if self.use_input_norm:
... |
class CbEnterpriseEdr(Product):
product: str = 'cbc'
profile: str = 'default'
token: Optional[str] = None
org_key: Optional[str] = None
_device_group: Optional[list[str]] = None
_device_policy: Optional[list[str]] = None
_conn: CBCloudAPI
_limit: int = (- 1)
_raw: bool = False
de... |
def _best_effort_input_batch_size(flat_input):
for input_ in flat_input:
shape = input_.shape
if (shape.ndims is None):
continue
if (shape.ndims < 2):
raise ValueError(('Expected input tensor %s to have rank at least 2' % input_))
batch_size = shape[1].value
... |
def main(options, arguments):
global previous_time
previous_time = time.time()
phases_path = options.input
if (options.output == None):
outfile = phases_path.replace('.txt', '-diffs.json')
else:
outfile = options.output
hashes_dic = read_phashes_manifest(phases_path)
hashes =... |
def extract_T1_features(wf, feature_type='histogram_whole_scan'):
feature_type = feature_type.lower()
basename = (lambda name: splitext(name)[0])
if (wf.mri_name is not None):
prefix = (basename(wf.mri_name) + '_')
else:
prefix = ''
out_csv_name = '{}{}_features.csv'.format(prefix, f... |
class ToggleValidationFixture(FileUploadInputFixture):
make_validation_fail = False
def new_domain_object(self):
fixture = self
class DomainObject():
fields = ExposedNames()
def make_field(self):
field = FileField(allow_multiple=True, label='Attached files... |
class PlayQueryBlockNBT(Packet):
id = 1
to = 0
def __init__(self, transaction_id: int, x: int, y: int, z: int) -> None:
super().__init__()
self.transaction_id = transaction_id
(self.x, self.y, self.z) = (x, y, z)
def decode(cls, buf: Buffer) -> PlayQueryBlockNBT:
return c... |
def icon_name(name):
return {'stackoverflow': 'stack-overflow', 'google-oauth': 'google', 'google-oauth2': 'google', 'google-openidconnect': 'google', 'yahoo-oauth': 'yahoo', 'facebook-app': 'facebook', 'email': 'envelope', 'vimeo': 'vimeo-square', 'linkedin-oauth2': 'linkedin', 'vk-oauth2': 'vk', 'live': 'windows'... |
class Net():
def __init__(self, points, features, is_training, setting):
bn_decay = setting.get_bn_decay(tf.train.get_global_step())
l0_xyz = points
l0_points = None
num_class = setting.num_class
(l1_xyz, l1_points) = pointnet_sa_module_msg(l0_xyz, l0_points, 512, [0.1, 0.2, ... |
_reduction
_guvectorize(['(uint8[:], int64[:], int8[:], float64[:])', '(uint64[:], int64[:], int8[:], float64[:])', '(int8[:], int64[:], int8[:], float64[:])', '(int64[:], int64[:], int8[:], float64[:])', '(float32[:], int64[:], int8[:], float32[:])', '(float64[:], int64[:], int8[:], float64[:])'], '(n),(n),(c)->(c)')
... |
def update_world(world, time_elapsed):
num_substeps = world.env.get_num_update_substeps()
timestep = (time_elapsed / num_substeps)
num_substeps = (1 if (time_elapsed == 0) else num_substeps)
for i in range(num_substeps):
world.update(timestep)
valid_episode = world.env.check_valid_episod... |
class Continuous_MountainCarEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 30}
def __init__(self):
self.min_action = (- 1.0)
self.max_action = 1.0
self.min_position = (- 1.2)
self.max_position = 0.6
self.max_speed = 0.07
... |
_cell_magic
def workspacefile(line: str, cell: str) -> None:
workspace = get_workspace()
(fs, path) = fsspec.core.url_to_fs(workspace)
path = posixpath.join(path, line)
base = posixpath.dirname(path)
if (not fs.exists(base)):
fs.mkdirs(base, exist_ok=True)
with fs.open(path, 'wt') as f:
... |
class Stem(nn.Module):
def __init__(self, in_channels, stem_channels, out_channels, expand_ratio, conv_cfg=None, norm_cfg=dict(type='BN'), with_cp=False):
super().__init__()
self.in_channels = in_channels
self.out_channels = out_channels
self.conv_cfg = conv_cfg
self.norm_cfg... |
class VContainer(SplitContainer):
def __init__(self, area):
SplitContainer.__init__(self, area, QtCore.Qt.Orientation.Vertical)
def type(self):
return 'vertical'
def updateStretch(self):
x = 0
y = 0
sizes = []
for i in range(self.count()):
(wx, wy)... |
def _set_image_or_guide(self, image_or_guide: torch.Tensor, attr: str, comparison_only: bool=False, **kwargs: Any) -> None:
for op in self._losses():
if (comparison_only and (not isinstance(op, loss.ComparisonLoss))):
continue
setter = getattr(op, f'set_{attr}')
setter(image_or_g... |
class WeaviateUploader(BaseUploader):
client = None
upload_params = {}
def init_client(cls, host, distance, connection_params, upload_params):
url = f" WEAVIATE_DEFAULT_PORT)}"
cls.client = Client(url, **connection_params)
cls.upload_params = upload_params
cls.connection_para... |
def fake_validator(*errors):
errors = list(reversed(errors))
class FakeValidator():
def __init__(self, *args, **kwargs):
pass
def iter_errors(self, instance):
if errors:
return errors.pop()
return []
def check_schema(self, schema):
... |
.usefixtures('mock_os_environ')
def test_file_argument_force_overwrite(testdir):
testdir.makeini('\n [pytest]\n env_files =\n myenv.txt\n ')
testdir.maketxtfile(myenv='FOO=BAR\nSPAM=EGGS')
tmp_env_file = testdir.maketxtfile(tmpenv='FOO=BAZ\nBAR=SPAM')
testdir.makepyfile("\n ... |
def collate_fn_mmg(batch):
(obj_point_list, obj_label_list, obj_2d_feats) = ([], [], [])
rel_label_list = []
(edge_indices, descriptor) = ([], [])
batch_ids = []
count = 0
for (i, b) in enumerate(batch):
obj_point_list.append(b[0])
obj_2d_feats.append(b[1])
obj_label_list... |
class MultiSelfAttention(SequenceMapper):
def __init__(self, n_heads: int, project_size: Optional[int], memory_size: Optional[int]=None, shared_project: bool=False, project_bias: bool=False, bilinear_comp: bool=False, init='glorot_uniform', merge: Optional[MergeLayer]=None, scale=True, bias=True):
self.n_he... |
def tensor6(name: Optional[str]=None, *, dtype: Optional['DTypeLike']=None, shape: Optional[tuple[(ST, ST, ST, ST, ST, ST)]]=(None, None, None, None, None, None)) -> 'TensorVariable':
if (dtype is None):
dtype = config.floatX
shape = _validate_static_shape(shape, ndim=6)
type = TensorType(dtype, sha... |
def test_fips_metadata_excludes_md5_and_blake2(monkeypatch):
replaced_blake2b = pretend.raiser(ValueError('fipsmode'))
replaced_md5 = pretend.raiser(ValueError('fipsmode'))
monkeypatch.setattr(package_file.hashlib, 'md5', replaced_md5)
monkeypatch.setattr(package_file.hashlib, 'blake2b', replaced_blake2... |
def test_all_coarse_grains_for_blackbox():
blackbox = macro.Blackbox(((0, 1),), (0, 1))
assert (list(macro.all_coarse_grains_for_blackbox(blackbox)) == [macro.CoarseGrain(((0, 1),), (((0, 1), (2,)),)), macro.CoarseGrain(((0, 1),), (((0, 2), (1,)),)), macro.CoarseGrain(((0, 1),), (((0,), (1, 2)),))]) |
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