code stringlengths 281 23.7M |
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class downResBlock_3x3(nn.Module):
def __init__(self, in_c, out_c, hid_c=None, conv2d=None, norm_layer=None, non_linear=None):
super(downResBlock_3x3, self).__init__()
if (hid_c is None):
hid_c = in_c
if (conv2d is None):
conv2d = nn.Conv2d
if (norm_layer is N... |
def main():
parser = build_parser()
args = parser.parse_args()
files_by_sample = collections.defaultdict(list)
for fname in args.files:
samplename = get_file_samplename(fname, strip_rep=True)
files_by_sample[samplename].append(os.path.abspath(fname))
for (samplename, files) in files_... |
def calculate_coordinates_shell(distance, num_dimensions, distance_step_size):
if (num_dimensions == 1):
return calculate_coordinates_shell_1d(distance)
if (num_dimensions == 2):
return calculate_coordinates_shell_2d(distance, distance_step_size)
if (num_dimensions == 3):
return calc... |
class TestCLIIntegration(TestCase):
def test_license(self):
output = subprocess.check_output([sys.executable, '-m', 'pip', 'show', 'jsonschema'], stderr=subprocess.STDOUT)
self.assertIn(b'License: MIT', output)
def test_version(self):
version = subprocess.check_output([sys.executable, '-... |
class Discriminator_VGG_Patch(nn.Module):
def __init__(self, in_nc, base_nf, norm_type='batch', act_type='leakyrelu', mode='CNA'):
super(Discriminator_VGG_Patch, self).__init__()
conv0 = B.conv_block(in_nc, base_nf, kernel_size=3, norm_type=None, act_type=act_type, mode=mode)
conv1 = B.conv_... |
class TupletMarginLoss(GenericPairLoss):
def __init__(self, margin=5.73, scale=64, **kwargs):
super().__init__(mat_based_loss=False, **kwargs)
c_f.assert_distance_type(self, CosineSimilarity)
self.margin = np.radians(margin)
self.scale = scale
self.add_to_recordable_attribute... |
def extract_args(detector, aligner, in_path, out_path, args=None):
py_exe = sys.executable
_extract_args = ('%s faceswap.py extract -i %s -o %s -D %s -A %s' % (py_exe, in_path, out_path, detector, aligner))
if args:
_extract_args += (' %s' % args)
return _extract_args.split() |
class Configurable():
global_defaults = {}
def __init__(self, **config):
self._variable_defaults = {}
self._user_config = config
def add_defaults(self, defaults):
self._variable_defaults.update(((d[0], copy.copy(d[1])) for d in defaults))
def __getattr__(self, name):
if (... |
def test_user_avatar(api, mock_req):
mock_req({'getUserProfilePhotos': {'ok': True, 'result': {'total_count': 1, 'photos': [[{'file_id': 'aaaaaa', 'width': 50, 'height': 50, 'file_size': 128}, {'file_id': 'bbbbbb', 'width': 25, 'height': 25, 'file_size': 64}]]}}})
user = botogram.objects.User({'id': 123, 'first... |
_fixtures(WebFixture, FileInputButtonFixture)
def test_file_upload_button(web_fixture, file_input_button_fixture):
fixture = file_input_button_fixture
wsgi_app = web_fixture.new_wsgi_app(child_factory=file_input_button_fixture.FileUploadForm.factory(), enable_js=True)
web_fixture.reahl_server.set_app(wsgi_a... |
def vgg_face_dag(weights_path=None, return_layer='fc8', **kwargs):
model = Vgg_face_dag(return_layer)
if weights_path:
state_dict = torch.load(weights_path, map_location=torch.device('cuda'))
try:
model.load_state_dict(state_dict)
except:
from collections import O... |
def initlogging(logfile):
logging.shutdown()
logger = logging.getLogger()
logger.handlers = []
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', filename=logfile, filemode='w')
ch = logging.StreamHandler()
ch.setLevel(logging.CRITICAL)
ch.setFormatte... |
def _parse_string(data: str, type_comments: bool=True) -> tuple[(ast.Module, ParserModule)]:
parser_module = get_parser_module(type_comments=type_comments)
try:
parsed = parser_module.parse((data + '\n'), type_comments=type_comments)
except SyntaxError as exc:
if ((exc.args[0] != MISPLACED_T... |
_tokenizer('moses', dataclass=MosesTokenizerConfig)
class MosesTokenizer(object):
def __init__(self, cfg: MosesTokenizerConfig):
self.cfg = cfg
try:
from sacremoses import MosesTokenizer, MosesDetokenizer
self.tok = MosesTokenizer(cfg.source_lang)
self.detok = Mos... |
class GlobalAttention(nn.Module):
def __init__(self, dim, heads=8, dim_head=64, dropout=0.0, k=7):
super().__init__()
inner_dim = (dim_head * heads)
self.heads = heads
self.scale = (dim_head ** (- 0.5))
self.to_q = nn.Conv2d(dim, inner_dim, 1, bias=False)
self.to_kv =... |
class ConnectionItem(GraphicsObject):
def __init__(self, source, target=None):
GraphicsObject.__init__(self)
self.setFlags((self.GraphicsItemFlag.ItemIsSelectable | self.GraphicsItemFlag.ItemIsFocusable))
self.source = source
self.target = target
self.length = 0
self.... |
class TestDiverseSiblingsSearch(TestDiverseBeamSearch):
def assertHypoScore(self, hypo, pos_probs, sibling_rank, diversity_rate, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
pos_scores.sub_((torch.Tensor(sibling_rank) * diversity_rate))
self.assertAlmostEqual... |
def test_setup_show_with_KeyboardInterrupt_in_test(pytester: Pytester) -> None:
p = pytester.makepyfile('\n import pytest\n \n def arg():\n pass\n def test_arg(arg):\n raise KeyboardInterrupt()\n ')
result = pytester.runpytest('--setup-show', p, no_reraise_ct... |
def prune_episodes(episodes, scene, metrics, num_good_episodes):
good_episodes = []
for episode in episodes:
episode_full_id = f"{scene}_{episode['episode_id']}"
try:
episode_stats = metrics.loc[episode_full_id]
except KeyError:
continue
if (not math.isclo... |
def convert_classification(base_model_name, hf_config, downstream_dict):
model = WavLMForSequenceClassification.from_pretrained(base_model_name, config=hf_config)
model.projector.weight.data = downstream_dict['projector.weight']
model.projector.bias.data = downstream_dict['projector.bias']
model.classif... |
class HacktoberStats(commands.Cog):
linked_accounts = RedisCache()
def __init__(self, bot: Bot):
self.bot = bot
_month(Month.SEPTEMBER, Month.OCTOBER, Month.NOVEMBER)
(name='hacktoberstats', aliases=('hackstats',), invoke_without_command=True)
async def hacktoberstats_group(self, ctx: comman... |
class ForRange(ForGenerator):
def init(self, start_reg: Value, end_reg: Value, step: int) -> None:
builder = self.builder
self.start_reg = start_reg
self.end_reg = end_reg
self.step = step
self.end_target = builder.maybe_spill(end_reg)
if (is_short_int_rprimitive(star... |
class TestIPython(unittest.TestCase):
def test_init(self):
try:
from IPython.testing.globalipapp import get_ipython
except ImportError:
import pytest
pytest.skip()
ip = get_ipython()
ip.run_line_magic('load_ext', 'line_profiler')
ip.run_cel... |
class NewWindow(QtWidgets.QMainWindow):
def __init__(self, *args, m=None, title=None, on_close=None, **kwargs):
super().__init__(*args, **kwargs)
self.m = m
self.setWindowTitle('OpenFile')
self.showhelp = False
self.toolbar = ToolBar(title=title, on_close=on_close)
se... |
def _do_query(bz, opt, parser):
q = {}
u = opt.from_url
if u:
q = bz.url_to_query(u)
if opt.components_file:
clist = []
f = open(opt.components_file, 'r')
for line in f.readlines():
line = line.rstrip('\n')
clist.append(line)
opt.component ... |
class SubModules():
def CryptUnprotectData(encrypted_data: bytes, optional_entropy: str=None) -> bytes:
class DATA_BLOB(ctypes.Structure):
_fields_ = [('cbData', ctypes.c_ulong), ('pbData', ctypes.POINTER(ctypes.c_ubyte))]
pDataIn = DATA_BLOB(len(encrypted_data), ctypes.cast(encrypted_da... |
def test_signature(workspace):
sig_position = {'line': 10, 'character': 5}
doc = Document(DOC_URI, workspace, DOC)
sig_info = signature.pylsp_signature_help(doc._config, doc, sig_position)
sigs = sig_info['signatures']
assert (len(sigs) == 1)
assert (sigs[0]['label'] == 'main(param1, param2)')
... |
class SendEnterKeyTest(unittest.TestCase):
def setUp(self):
Timings.fast()
self.app = Application()
self.app.start(_notepad_exe())
self.dlg = self.app.UntitledNotepad
self.ctrl = HwndWrapper(self.dlg.Edit.handle)
def tearDown(self):
self.dlg.menu_select('File -> E... |
def pad_if_smaller(img, size, fill=0):
size = ((size, size) if isinstance(size, int) else size)
(original_width, original_height) = img.size
pad_height = ((size[1] - original_height) if (original_height < size[1]) else 0)
pad_width = ((size[0] - original_width) if (original_width < size[0]) else 0)
... |
def test_pages_site_not_found(graphql_client):
query = '\n query Page ($hostname: String!, $language: String!) {\n cmsPages(hostname: $hostname, language: $language){\n body {\n ...on TextSection {\n title\n }\n }\n }\n }\n ... |
def process_ground_paras(retrieved='../data/wq_finetuneq_train_10000.txt', save_path='../data/wq_ft_train_matched.txt', raw_data='../data/wq-train.txt', num_workers=40, debug=False, k=10000, match='string'):
retrieved = [json.loads(l) for l in open(retrieved).readlines()]
raw_data = [json.loads(l) for l in open... |
def add_idol_config(cfg):
cfg.MODEL.IDOL = CN()
cfg.MODEL.IDOL.NUM_CLASSES = 80
cfg.INPUT.SAMPLING_FRAME_NUM = 1
cfg.INPUT.SAMPLING_FRAME_RANGE = 10
cfg.INPUT.SAMPLING_INTERVAL = 1
cfg.INPUT.SAMPLING_FRAME_SHUFFLE = False
cfg.INPUT.AUGMENTATIONS = []
cfg.INPUT.COCO_PRETRAIN = False
c... |
def try_load_beams(data):
try:
from radio_beam import Beam
except ImportError:
warnings.warn('radio_beam is not installed. No beam can be created.', ImportError)
if isinstance(data, fits.BinTableHDU):
if ('BPA' in data.data.names):
beam_table = data.data
retur... |
def _get_pixel_navigation_parameters(point, im_nav_params):
obs_time = get_observation_time(point, im_nav_params.static.scan_params)
(attitude, orbit) = interpolate_navigation_prediction(attitude_prediction=im_nav_params.predicted.attitude, orbit_prediction=im_nav_params.predicted.orbit, observation_time=obs_ti... |
def test_connect_plain():
class Top(ComponentLevel3):
def construct(s):
s.src = TestSource(Bits32, [4, 3, 2, 1, 4, 3, 2, 1])
s.sink = TestSink(Bits32, [5, 4, 3, 2, 5, 4, 3, 2])
s.wire0 = Wire(32)
def up_from_src():
s.wire0 = (s.src.out + 1)
... |
def add_object(model_path, rot_mat=((1, 0, 0), (0, 1, 0), (0, 0, 1)), trans_vec=(0, 0, 0), scale=1, name=None):
if model_path.endswith('.obj'):
bpy.ops.import_scene.obj(filepath=model_path, axis_forward='-Z', axis_up='Y')
else:
raise NotImplementedError('Importing model of this type')
obj_li... |
class BigBirdConfig(PretrainedConfig):
model_type = 'big_bird'
def __init__(self, vocab_size=50358, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu_new', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=4096, type_vocab_si... |
def test_set(func, qtbot):
throttled = throttle.Throttle(func, DELAY)
throttled.set_delay(DELAY)
throttled('foo')
throttled('foo')
throttled('foo')
throttled('bar')
func.assert_called_once_with('foo')
func.reset_mock()
qtbot.wait(int((1.5 * DELAY)))
func.assert_called_once_with('... |
class DCUN_TFC_GPoCM_TDF_Framework(DenseCUNet_GPoCM_Framework):
def __init__(self, n_fft, hop_length, num_frame, spec_type, spec_est_mode, optimizer, lr, auto_lr_schedule, train_loss, val_loss, **kwargs):
valid_kwargs = inspect.signature(DCUN_TFC_GPoCM_TDF.__init__).parameters
tfc_tdf_net_kwargs = d... |
def moving_code_with_imports(project, resource, source):
import_tools = importutils.ImportTools(project)
pymodule = libutils.get_string_module(project, source, resource)
lines = codeanalyze.SourceLinesAdapter(source)
start = 1
while ((start < lines.length()) and lines.get_line(start).startswith('#')... |
def add_image_net_computational_nodes_in_graph(session: tf.compat.v1.Session, logits_name: str, num_classes: int):
with session.graph.as_default():
y_hat = session.graph.get_tensor_by_name(logits_name)
y_hat_argmax = tf.compat.v1.argmax(y_hat, axis=1)
y = tf.compat.v1.placeholder(tf.compat.v... |
def test_get_all_speakers_user_ids(schedule_item_factory, submission_factory, conference_factory, schedule_item_additional_speaker_factory):
schedule_item_1 = schedule_item_factory(type='talk', submission=submission_factory())
schedule_item_2 = schedule_item_factory(type='talk', conference=schedule_item_1.confe... |
def importESI(string):
sMkt = Market.getInstance()
fitobj = Fit()
refobj = json.loads(string)
items = refobj['items']
fitobj.name = refobj['name']
fitobj.notes = refobj['description']
try:
ship = refobj['ship_type_id']
try:
fitobj.ship = Ship(sMkt.getItem(ship))
... |
class Solution():
def titleToNumber(self, s: str) -> int:
char = {'A': 1, 'B': 2, 'C': 3, 'D': 4, 'E': 5, 'F': 6, 'G': 7, 'H': 8, 'I': 9, 'J': 10, 'K': 11, 'L': 12, 'M': 13, 'N': 14, 'O': 15, 'P': 16, 'Q': 17, 'R': 18, 'S': 19, 'T': 20, 'U': 21, 'V': 22, 'W': 23, 'X': 24, 'Y': 25, 'Z': 26}
length = ... |
class nnUNetTrainerDA5Segord0(nnUNetTrainerDA5):
def get_dataloaders(self):
patch_size = self.configuration_manager.patch_size
dim = len(patch_size)
deep_supervision_scales = self._get_deep_supervision_scales()
(rotation_for_DA, do_dummy_2d_data_aug, initial_patch_size, mirror_axes) ... |
class VolumeShareSlippageTestCase(WithCreateBarData, WithSimParams, WithDataPortal, ZiplineTestCase):
START_DATE = pd.Timestamp('2006-01-05 14:31', tz='utc')
END_DATE = pd.Timestamp('2006-01-05 14:36', tz='utc')
SIM_PARAMS_CAPITAL_BASE = 100000.0
SIM_PARAMS_DATA_FREQUENCY = 'minute'
SIM_PARAMS_EMISS... |
def test_main_prefix(fancy_wheel, tmp_path):
destdir = (tmp_path / 'dest')
main([str(fancy_wheel), '-d', str(destdir), '-p', '/foo'], 'python -m installer')
installed_py_files = list(destdir.rglob('*.py'))
for f in installed_py_files:
assert str(f.parent).startswith(str((destdir / 'foo'))), f'pa... |
def testHistogramLUTWidget():
pg.mkQApp()
win = QtWidgets.QMainWindow()
win.show()
cw = QtWidgets.QWidget()
win.setCentralWidget(cw)
l = QtWidgets.QGridLayout()
cw.setLayout(l)
l.setSpacing(0)
v = pg.GraphicsView()
vb = pg.ViewBox()
vb.setAspectLocked()
v.setCentralItem(v... |
class TestInstallRequires():
def test_setup_install_includes_dependencies(self, tmp_path, mock_index):
project_root = (tmp_path / 'project')
project_root.mkdir(exist_ok=True)
install_root = (tmp_path / 'install')
install_root.mkdir(exist_ok=True)
self.create_project(project_r... |
def main(opt: argparse.Namespace) -> None:
utils.set_gpu(opt.gpu)
device = torch.device('cuda')
run_name = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
run_path = os.path.join(opt.output_root, run_name)
print(f'Start training {run_path}')
print(vars(opt))
os.makedirs(run_path, exist_ok=True)... |
class Migration(migrations.Migration):
dependencies = [('sponsors', '0094_sponsorship_locked')]
operations = [migrations.AlterModelOptions(name='benefitfeatureconfiguration', options={'base_manager_name': 'non_polymorphic', 'verbose_name': 'Benefit Feature Configuration', 'verbose_name_plural': 'Benefit Feature... |
class WebhookResponseValidator(BaseWebhookResponseValidator):
def iter_errors(self, request: WebhookRequest, response: Response) -> Iterator[Exception]:
try:
(_, operation, _, _, _) = self._find_path(request)
except PathError as exc:
(yield exc)
return
(yi... |
class SignIn():
async def sign_in(self: 'pyrogram.Client', phone_number: str, phone_code_hash: str, phone_code: str) -> Union[('types.User', 'types.TermsOfService', bool)]:
phone_number = phone_number.strip(' +')
r = (await self.invoke(raw.functions.auth.SignIn(phone_number=phone_number, phone_code_... |
class TestDagMethods(unittest.TestCase):
def test_exists_trek(self):
node_names = ['x1', 'x2', 'x3', 'x4']
nodes = []
for name in node_names:
node = GraphNode(name)
nodes.append(node)
dag = Dag(nodes)
node1 = dag.get_node('x1')
node2 = dag.get_... |
def model_with_legacy_bn_layers_is_training_bool(is_training, is_fused):
inputs = tf.keras.Input(shape=(32, 32, 3))
x = tf.keras.layers.Conv2D(32, (3, 3))(inputs)
layer = normalization_layers.BatchNormalization(momentum=0.3, epsilon=0.65, fused=is_fused)
x = layer.apply(x, training=is_training)
x = ... |
def solve():
problem = Problem()
problem.addVariables(range(1, 21), ['A', 'B', 'C', 'D', 'E'])
problem.addConstraint(SomeInSetConstraint(['A'], 4, True))
problem.addConstraint(SomeInSetConstraint(['B'], 4, True))
problem.addConstraint(SomeInSetConstraint(['C'], 4, True))
problem.addConstraint(So... |
def featurize(smi, fingerprint, radius, length) -> Optional[np.ndarray]:
mol = Chem.MolFromSmiles(smi)
if (mol is None):
return None
if (fingerprint == 'morgan'):
fp = rdmd.GetMorganFingerprintAsBitVect(mol, radius=radius, nBits=length, useChirality=True)
elif (fingerprint == 'pair'):
... |
class inp(SWMMIOFile):
def __init__(self, file_path):
self._options_df = None
self._files_df = None
self._raingages_df = None
self._evaporation_df = None
self._losses_df = None
self._report_df = None
self._conduits_df = None
self._xsections_df = None
... |
_optimizer('adafactor')
class FairseqAdafactor(LegacyFairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
print('using adafactor')
self._optimizer = Adafactor(params, **self.optimizer_config)
def add_args(parser):
parser.add_argument('--adafactor-eps', defa... |
def build_model(cfg, isTrain=True, dataset_num_overwrite=None):
if (dataset_num_overwrite is None):
dataset_num_overwrite = len(cfg.DATASETS.TRAIN)
if isTrain:
model = Generatic_Model(cfg, cfg.INPUT.SIZE_TRAIN[0], cfg.INPUT.SIZE_TRAIN[1], cfg.MODEL.FEATURE_DIM, use_dir=cfg.INPUT.USE_DIR, dataset... |
class TwilioViewTestCase(TestCase):
def setUp(self):
self.regular_caller = G(Caller, phone_number='+', blacklisted=False)
self.blocked_caller = G(Caller, phone_number='+', blacklisted=True)
self.factory = TwilioRequestFactory(token=settings.TWILIO_AUTH_TOKEN, enforce_csrf_checks=True)
... |
class DependenciesModel(QtCore.QAbstractTableModel):
_headers = ('Dependency', 'Version', 'License')
def __init__(self, parent):
super().__init__(parent)
self._packages = [(dist.name, dist.version, _get_license(dist)) for dist in importlib.metadata.distributions()]
def columnCount(self, pare... |
def validate(val_list, model, criterion):
print('begin test')
test_loader = torch.utils.data.DataLoader(dataset.listDataset(val_list, shuffle=False, transform=transforms.Compose([transforms.ToTensor(), transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])]), train=False), batch_size=args.b... |
class _BaseAutoModelClass():
_model_mapping = None
def __init__(self, *args, **kwargs):
raise EnvironmentError(f'{self.__class__.__name__} is designed to be instantiated using the `{self.__class__.__name__}.from_pretrained(pretrained_model_name_or_path)` or `{self.__class__.__name__}.from_config(config)... |
def override_json(args, json_path, check_consistency=False):
json_params = json.load(open(json_path))
params = vars(args)
if check_consistency:
missing_keys = []
for key in json_params:
if (key not in params):
missing_keys.append(key)
assert (not missing_k... |
def test_json_skipped_dep(vuln_data_skipped_dep):
json_format = format.JsonFormat(False)
expected_json = {'dependencies': [{'name': 'foo', 'version': '1.0', 'vulns': [{'id': 'VULN-0', 'fix_versions': ['1.1', '1.4']}]}, {'name': 'bar', 'skip_reason': 'skip-reason'}], 'fixes': []}
assert (json_format.format(v... |
class GumballMachine():
soldOutState: State
noQuarterState: State
hasQuarterState: State
soldState: State
winnerState: State
state: State = SoldOutState
count: int = 0
def __init__(self, numberGumballs: int):
self.soldOutState = SoldOutState(self)
self.noQuarterState = No... |
def _apply_min_max(df: pd.DataFrame, old_min: ((int | float) | pd.Series), old_max: ((int | float) | pd.Series), new_min: ((int | float) | pd.Series), new_max: ((int | float) | pd.Series)) -> pd.DataFrame:
old_range = (old_max - old_min)
new_range = (new_max - new_min)
return ((((df - old_min) * new_range) ... |
class Timer():
def __init__(self, name='task', verbose=True):
self.name = name
self.verbose = verbose
def __enter__(self):
self.start = time.time()
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self.verbose:
print('[Time] {} consumes {:.4f}... |
class PairAccumulator(Accumulator):
def __init__(self):
super().__init__()
self._labels = []
self._pairs = []
self._subgroups = []
self._accumulated_size = 0
def state(self) -> Dict[(str, torch.Tensor)]:
state = super().state
state.update({'labels': self.l... |
def zenodo_api_with_helpful_fallback(url, method, **kwargs):
hostname = urllib.parse.urlparse(url).hostname
access_token = get_zenodo_access_token(hostname)
kwargs['params'] = {'access_token': access_token}
r = getattr(requests, method)(url, **kwargs)
if (r.status_code == 401):
print('The ac... |
def counting_context_manager():
nitems = 50
with progress.task('counting (context manager)', nitems, logger=logger) as task:
for iitem in range(nitems):
if (iitem > (nitems // 2)):
message = 'over half already done!'
else:
message = None
... |
class ResourceBaseDeleteView(LoginRequiredMixin, ResourceBaseContextMixin, DeleteView):
context_object_file = 'object'
template_name = 'base/confirm_delete.html'
def dispatch(self, request, *args, **kwargs):
object = self.get_object()
user = self.request.user
if (not check_resources_... |
class StateWrapper(object):
def __init__(self, state, workflow):
self.state = state
self.workflow = workflow
for st in workflow.states:
setattr(self, ('is_%s' % st.name), (st.name == self.state.name))
def __eq__(self, other):
if isinstance(other, self.__class__):
... |
def test_unsupported_not_forwarded() -> None:
class FakeFile(io.RawIOBase):
def unsupported_attr(self) -> None:
pass
async_file = trio.wrap_file(FakeFile())
assert hasattr(async_file.wrapped, 'unsupported_attr')
with pytest.raises(AttributeError):
async_file.unsupported_attr |
class PotentialPair1plus1D(BasePotentialPair):
def __init__(self, param):
super().__init__(param)
def set_boundary_conditions(self, variables):
phi_s_cn = variables['Negative current collector potential [V]']
phi_s_cp = variables['Positive current collector potential [V]']
param ... |
def eval_metrics(results, gt_seg_maps, num_classes, ignore_index, metrics=['mIoU'], nan_to_num=None, label_map=dict(), reduce_zero_label=False, beta=1):
if isinstance(metrics, str):
metrics = [metrics]
allowed_metrics = ['mIoU', 'mDice', 'mFscore']
if (not set(metrics).issubset(set(allowed_metrics))... |
def assert_focus_path(self, *names):
for i in names:
self.c.group.next_window()
assert_focused(self, i)
for i in names:
self.c.group.next_window()
assert_focused(self, i)
for i in reversed(names):
assert_focused(self, i)
self.c.group.prev_window()
for i in... |
def draw_pareto_changing_b(b_set, num_classes, max=1000, min=1, head=0.0, tail=0.99, save_name='./pareto_ref.jpg'):
(fig, ax) = plt.subplots(1, 1)
classes = np.linspace(0, num_classes, (10 * num_classes))
for (i, b) in enumerate(b_set):
rv = pareto(b)
classes_x = np.linspace(pareto.ppf(head,... |
def get_micronet_score(net, weight_bits, activation_bits, weight_strategy=None, activation_strategy=None, input_res=(3, 224, 224), baseline_params=6900000, baseline_MAC=):
flops_model = add_flops_counting_methods(net)
flops_model.eval().start_flops_count()
batch = torch.ones(()).new_empty((1, *input_res), d... |
class UnivariatePiecewiseLinearObjective(CircuitFactory):
def __init__(self, num_state_qubits: int, min_state_value: float, max_state_value: float, breakpoints: Union[(List[float], np.ndarray)], slopes: Union[(List[float], np.ndarray)], offsets: Union[(List[float], np.ndarray)], f_min: float, f_max: float, c_approx... |
def test_query_grant(graphql_client, user, conference, grant_factory):
graphql_client.force_login(user)
grant = grant_factory(user_id=user.id, conference=conference)
response = graphql_client.query('query($conference: String!) {\n me {\n grant(conference: $conference) {\n ... |
class Deterministic_Wallet(Abstract_Wallet):
def __init__(self, db, storage, *, config):
self._ephemeral_addr_to_addr_index = {}
Abstract_Wallet.__init__(self, db, storage, config=config)
self.gap_limit = db.get('gap_limit', 20)
self.synchronize()
if self.can_have_lightning()... |
class Migration(migrations.Migration):
dependencies = [('questions', '0085_section_pages')]
operations = [migrations.CreateModel(name='PageQuestionSet', fields=[('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('order', models.IntegerField(default=0)), ('page', ... |
class TerminusPasteFromHistoryCommand(sublime_plugin.TextCommand):
def run(self, edit):
paste_list = g_clipboard_history.get()
keys = [x[0] for x in paste_list]
self.view.show_popup_menu(keys, (lambda choice_index: self.paste_choice(choice_index)))
def is_enabled(self):
return (n... |
def test_loop_variable_initialized_in_loop() -> None:
with AccumulationTable(['i']) as table:
for number in [10, 20, 30, 40, 50, 60]:
i = number
assert (table.loop_variables == {'number': ['N/A', 10, 20, 30, 40, 50, 60]})
assert (table.loop_accumulators == {'i': ['N/A', 10, 20, 30, 40, 5... |
def minimize(fun: Callable[(..., float)], x0: np.ndarray, args: Tuple=(), method: Optional[str]=None, **kwargs) -> scipy.optimize.OptimizeResult:
if (method.lower() in OPTIMIZERS):
optimizer = OPTIMIZERS[method.lower()]
return optimizer(fun, x0, args=args, **kwargs)
return scipy.optimize.minimiz... |
class ThreadState():
def __init__(self, name, trace):
self.root = CallNode({}, OrderedDict(), None)
self.calltree = self.root
self.curr = self.calltree
self.context_switch = 0
self.name = name
if trace:
self.depth = (len(trace) - 1)
for call in... |
def test_voting_open_and_user_can_vote(graphql_client, submission_factory, user, other_user, mocker):
submission = _submission(submission_factory, user)
graphql_client.force_login(other_user)
can_vote_mock = mocker.patch('api.submissions.permissions.check_if_user_can_vote', return_value=True)
data = _qu... |
def initialize_decoder(module):
for m in module.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_uniform_(m.weight, mode='fan_in', nonlinearity='relu')
if (m.bias is not None):
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.BatchNorm2d):
... |
('/suite', methods=['GET', 'POST'])
def suite():
if (not session.get('logged_in')):
return redirect(url_for('login'))
if (request.method == 'GET'):
project = [elem[0] for elem in g.db.execute('select name from project;').fetchall()]
return render_template('suite.html', projects=project)
... |
def findContours(*args, **kwargs):
if cv2.__version__.startswith('4'):
(contours, hierarchy) = cv2.findContours(*args, **kwargs)
elif cv2.__version__.startswith('3'):
(_, contours, hierarchy) = cv2.findContours(*args, **kwargs)
else:
raise AssertionError('cv2 must be either version 3... |
def test(strng):
print(strng)
try:
iniFile = open(strng)
iniData = ''.join(iniFile.readlines())
bnf = inifile_BNF()
tokens = bnf.parseString(iniData)
pp.pprint(tokens.asList())
except ParseException as err:
print(err.line)
print(((' ' * (err.column - 1... |
def _get_example(language: str) -> str:
if (language.lower() in _parsing.PY_LANG_CODES):
log.trace(f'Code block has a Python language specifier `{language}`.')
content = _EXAMPLE_PY.format(lang=language)
elif language:
log.trace(f'Code block has a foreign language specifier `{language}`.... |
def _generate_mock_adapters():
mock_lo0 = Mock(spec=ifaddr.Adapter)
mock_lo0.nice_name = 'lo0'
mock_lo0.ips = [ifaddr.IP('127.0.0.1', 8, 'lo0')]
mock_lo0.index = 0
mock_eth0 = Mock(spec=ifaddr.Adapter)
mock_eth0.nice_name = 'eth0'
mock_eth0.ips = [ifaddr.IP(('2001:db8::', 1, 1), 8, 'eth0')]
... |
def temporary_failure(count=1):
return f'''
import py
path = py.path.local(__file__).dirpath().ensure('test.res')
count = path.read() or 1
if int(count) <= {count}:
path.write(int(count) + 1)
raise Exception('Failure: {{0}}'.format(coun... |
class Money():
def __init__(self, money=None, chntext=None):
self.money = money
self.chntext = chntext
def money2chntext(self):
money = self.money
pattern = re.compile('(\\d+(\\.\\d+)?)')
matchers = pattern.findall(money)
if matchers:
for matcher in ma... |
class Time2CapAmountGetter(SmoothPointGetter):
def getRange(self, xRange, miscParams, src, tgt):
if (not miscParams['useCapsim']):
return super().getRange(xRange=xRange, miscParams=miscParams, src=src, tgt=tgt)
capAmountT0 = (miscParams['capAmountT0'] or 0)
capSimDataRaw = src.it... |
def test_prevent_redundant_quantity(blank_game_description):
db = blank_game_description.resource_database
(res_a, id_req_a) = make_req_a(db)
(res_b, id_req_b) = make_req_b(db)
the_set = RequirementSet([RequirementList([id_req_a]), RequirementList([id_req_a, id_req_b]), RequirementList([ResourceRequirem... |
class MultiResolutionDataset(Dataset):
def __init__(self, path, transform, resolution=8):
self.env = lmdb.open(path, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
if (not self.env):
raise IOError('Cannot open lmdb dataset', path)
with self.env.begin(w... |
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