code stringlengths 281 23.7M |
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class TestNumberAttribute():
def test_number_attribute(self):
attr = NumberAttribute()
assert (attr.attr_type == NUMBER)
attr = NumberAttribute(default=1)
assert (attr.default == 1)
def test_number_serialize(self):
attr = NumberAttribute()
assert (attr.serialize(3... |
class Perpendicular(Base):
_id = 28
_entityDef = (_lw, _lw)
_workplane = True
_iconName = 'Assembly_ConstraintPerpendicular.svg'
_tooltip = QT_TRANSLATE_NOOP('asm3', 'Add a "{}" constraint to make planar faces or linear edges of two\nparts perpendicular.')
def prepare(cls, obj, solver):
... |
def getBatches(data, batch_size):
random.shuffle(data)
batches = []
data_len = len(data)
def genNextSamples():
for i in range(0, data_len, batch_size):
(yield data[i:min((i + batch_size), data_len)])
for samples in genNextSamples():
batch = createBatch(samples)
ba... |
def try_to_load_from_cache(cache_dir, repo_id, filename, revision=None, commit_hash=None):
if ((commit_hash is not None) and (revision is not None)):
raise ValueError('`commit_hash` and `revision` are mutually exclusive, pick one only.')
if ((revision is None) and (commit_hash is None)):
revisio... |
def _get_area_extent_from_cf_axis(x, y):
(ll_x, ll_y) = (x['first'], y['last'])
(ur_x, ur_y) = (x['last'], y['first'])
ll_x -= ((x['sign'] * 0.5) * x['spacing'])
ur_x += ((x['sign'] * 0.5) * x['spacing'])
ll_y += ((y['sign'] * 0.5) * y['spacing'])
ur_y -= ((y['sign'] * 0.5) * y['spacing'])
r... |
def test_ohem_sampler():
with pytest.raises(AssertionError):
sampler = OHEMPixelSampler(context=_context_for_ohem())
seg_logit = torch.randn(1, 19, 45, 45)
seg_label = torch.randint(0, 19, size=(1, 1, 89, 89))
sampler.sample(seg_logit, seg_label)
sampler = OHEMPixelSampler(contex... |
def _prompt_user_for_file(window: QtWidgets.QWidget, caption: str, filter: str, dir: (str | None)=None, new_file: bool=False) -> (Path | None):
if new_file:
method = QtWidgets.QFileDialog.getSaveFileName
else:
method = QtWidgets.QFileDialog.getOpenFileName
open_result = method(window, captio... |
class Effect6526(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Capital Capacitor Emission Systems')), 'powerTransferAmount', src.getModifiedItemAttr('shipBonusForceAuxiliaryA1'), skill='Amarr Ca... |
def import_chrome(profile, bookmark_types, output_format):
out_template = {'bookmark': '{url} {name}', 'quickmark': '{name} {url}', 'search': "c.url.searchengines['{keyword}'] = '{url}'", 'oldsearch': '{keyword} {url}'}
if ('search' in bookmark_types):
webdata = sqlite3.connect(os.path.join(profile, 'We... |
class PPM(nn.ModuleList):
def __init__(self, pool_scales, in_channels, channels, conv_cfg, norm_cfg, act_cfg, align_corners):
super(PPM, self).__init__()
self.pool_scales = pool_scales
self.align_corners = align_corners
self.in_channels = in_channels
self.channels = channels
... |
('Not ready for production yet')
class SshConfig(Config):
def setUp(self):
self.instance = pynag.Parsers.SshConfig(host='localhost', username='palli')
def tearDown(self):
pass
def testParseMaincfg(self):
self.instance.parse_maincfg()
def testParse(self):
self.instance.par... |
class Packages_Contains_Environment_1_TestCase(ParserTest):
def __init__(self, *args, **kwargs):
ParserTest.__init__(self, *args, **kwargs)
self.ks = '\n%packages\^whatever-environment\n%end\n'
def runTest(self):
with warnings.catch_warnings(record=True):
warnings.simplefilte... |
def params_to_string(num_params: float, units: Optional[str]=None, precision: int=2) -> str:
if (units is None):
if ((num_params // (10 ** 6)) > 0):
return (str(round((num_params / (10 ** 6)), precision)) + ' M')
elif (num_params // (10 ** 3)):
return (str(round((num_params /... |
def unique_in_window(iterable, n, key=None):
if (n <= 0):
raise ValueError('n must be greater than 0')
window = deque(maxlen=n)
uniques = set()
use_key = (key is not None)
for item in iterable:
k = (key(item) if use_key else item)
if (k in uniques):
continue
... |
class SessionStore(object):
def __init__(self, inactivity_timeout=10, sweep_time=1, attach_timeout=60, timeout_disable_mode='soft'):
if (timeout_disable_mode not in ['soft', 'hard']):
raise ValueError("timeout_disable_mode must be 'hard' or 'soft'")
self.inactivity_timeout = inactivity_t... |
def check_model_compatibilty(config: AttrDict, state_dict: Dict[(str, Any)]):
from vissl.models import is_feature_extractor_model
(trunk_append_prefix, heads_append_prefix) = ('trunk._feature_blocks.', 'heads.')
if is_feature_extractor_model(config.MODEL):
trunk_append_prefix = 'trunk.base_model._fe... |
class KnownValues(unittest.TestCase):
def test_ea_adc2(self):
myadc.method_type = 'ea'
(e, v, p, x) = myadc.kernel(nroots=3)
e_corr = myadc.e_corr
self.assertAlmostEqual(e_corr, (- 0.), 6)
self.assertAlmostEqual(e[0], 0., 6)
self.assertAlmostEqual(e[1], 0., 6)
... |
def get_geolocation(request, force=False):
if force:
return get_geoipdb_geolocation(request)
if hasattr(request, 'geo'):
return request.geo
log.warning('No geolocation data set by middleware (see CloudflareGeoIpMiddleware). Consider enabling a GeoIp middleware for ad targeting.')
return ... |
class CCCVFunctionControl(FunctionControl):
def __init__(self, param, options):
super().__init__(param, self.cccv, options, control='differential with max')
pybamm.citations.register('Mohtat2021')
def cccv(self, variables):
K_aw = 1
Q = self.param.Q
I_var = variables['Cur... |
def warn_explicit_for(method: FunctionType, message: PytestWarning) -> None:
lineno = method.__code__.co_firstlineno
filename = inspect.getfile(method)
module = method.__module__
mod_globals = method.__globals__
try:
warnings.warn_explicit(message, type(message), filename=filename, module=mo... |
.parametrize('num_spin_orb, num_bits_rot_aa', ((8, 3), (20, 3), (57, 3)))
def test_sparse_costs_against_openfermion(num_spin_orb, num_bits_rot_aa):
num_bits_state_prep = 12
cost = 0
bloq = SelectSparse(num_spin_orb)
(_, sigma) = bloq.call_graph()
cost += sigma[TGate()]
(bloq, num_non_zero) = mak... |
def get_print_full(x):
old_stdout = sys.stdout
sys.stdout = newstdout = StringIO()
try:
pd.set_option('display.max_rows', len(x))
print(x)
string = newstdout.getvalue()
except:
raise
finally:
sys.stdout = old_stdout
pd.reset_option('display.max_rows')
... |
(strat=unstructure_strats, detailed_validation=..., prefer_attrib=..., dict_factory=one_of(just(dict), just(OrderedDict)))
def test_copy_func_hooks(converter_cls: Type[BaseConverter], strat: UnstructureStrategy, prefer_attrib: bool, detailed_validation: bool, dict_factory: Callable):
c = converter_cls(unstruct_stra... |
def get_elec_ddpm_discrete_config():
config = get_default_configs()
config.weight_decay = None
config.reduce_mean = True
config.likelihood_weighting = False
config.batch_size = 64
config.epochs = 20
modeling = config.modeling
modeling.num_scales = 100
modeling.beta_min = 0.01
mod... |
def netting_channel_state(chain_state, token_network_state, token_network_registry_state, partner):
if (partner is None):
partner = factories.make_address()
canonical_identifier = factories.make_canonical_identifier(token_network_address=token_network_state.address)
channel_state = factories.create(... |
.supported(only_if=(lambda backend: backend.cipher_supported(algorithms._IDEAInternal((b'\x00' * 16)), modes.CBC((b'\x00' * 8)))), skip_message='Does not support IDEA CBC')
class TestIDEAModeCBC():
test_cbc = generate_encrypt_test(load_nist_vectors, os.path.join('ciphers', 'IDEA'), ['idea-cbc.txt'], (lambda key, **... |
class SKConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride, groups=32, num_branches=2, reduction=16, min_channels=32):
super(SKConvBlock, self).__init__()
self.num_branches = num_branches
self.out_channels = out_channels
mid_channels = max((in_channels // redu... |
class ScenarioTestCase(unittest.TestCase):
store_id = 'crust2_mf'
store_id_static = 'ak135_static'
tempdirs = []
def tearDownClass(cls):
for d in cls.tempdirs:
continue
shutil.rmtree(d)
(*have_gf_store(store_id))
def test_scenario_waveforms(self):
tempdir ... |
def create_shortcut(downsample_type, layers: LayerFn, in_chs, out_chs, stride, dilation, **kwargs):
assert (downsample_type in ('avg', 'conv1x1', ''))
if ((in_chs != out_chs) or (stride != 1) or (dilation[0] != dilation[1])):
if (not downsample_type):
return None
elif (downsample_typ... |
def test_wcs_comparison():
wcs1 = WCS(naxis=3)
wcs1.wcs.crpix = np.array([50.0, 45.0, 30.0], dtype='float32')
wcs2 = WCS(naxis=3)
wcs2.wcs.crpix = np.array([50.0, 45.0, 30.0], dtype='float64')
wcs3 = WCS(naxis=3)
wcs3.wcs.crpix = np.array([50.0, 45.0, 31.0], dtype='float64')
wcs4 = WCS(naxis... |
def aimet_spatial_svd(model: torch.nn.Module, evaluator: aimet_common.defs.EvalFunction):
greedy_params = aimet_torch.defs.GreedySelectionParameters(target_comp_ratio=Decimal(0.75), num_comp_ratio_candidates=10)
auto_params = aimet_torch.defs.SpatialSvdParameters.AutoModeParams(greedy_params, modules_to_ignore=... |
def downloadFileWithJSONPost(url, file, post_json_str, descriptor):
global PROXY
if ('/' in file):
makeDirs(os.path.dirname(file))
if os.path.exists(file):
logging.debug(f'Skipping json post to url: {url} ({descriptor}) as already downloaded')
opener = getUrlOpener(PROXY)
opener.addh... |
def get_prep_freqs(receptacle, df_objects):
template_prep_dict = {}
for tem in rec_templates:
template_list = []
object_list = []
for (idx, row) in df_objects.iterrows():
template_list.append(tem.format(row['entity'], '<mask>', receptacle))
object_list.append(row[... |
(frozen=True)
class Timezone(AnnotatedTypesCheck):
value: Union[(str, timezone, type(...), None)]
def predicate(self, value: Any) -> bool:
if (not isinstance(value, datetime)):
return False
if (self.value is None):
return (value.tzinfo is None)
elif (self.value is... |
def add_interactive_args(parser):
group = parser.add_argument_group('Interactive')
group.add_argument('--buffer-size', default=0, type=int, metavar='N', help='read this many sentences into a buffer before processing them')
group.add_argument('--input', default='-', type=str, metavar='FILE', help='file to re... |
class EmbeddingSimilarityEvaluator(SentenceEvaluator):
def __init__(self, sentences1: List[str], sentences2: List[str], scores: List[float], batch_size: int=16, main_similarity: SimilarityFunction=None, name: str='', show_progress_bar: bool=False, write_csv: bool=True):
self.sentences1 = sentences1
... |
def list_jobs(session, inprogress='False'):
conn = get_database_conn()
curs = query_execute_wrapper(conn, query_string="SELECT * FROM scansweep_queue WHERE session=? AND inprogress=? AND complete='False'", query_list=[session, inprogress], no_return=False)
job_list = []
for row in curs:
job_list... |
class EoctConv(nn.Module):
def __init__(self, in_channels, num_channels, kernel_size=3, stride=1, padding=1, bias=True, name=None):
super(EoctConv, self).__init__()
self.stride = stride
if ((type(in_channels) is tuple) and (len(in_channels) == 3)):
(in_h, in_l, in_ll) = in_channe... |
def M_eq(mu_new, C, mu, m, n):
csums = [sum([C[i][h] for i in range(m)]) for h in range(n)]
eqs = ([0] * (n + 1))
for j in range(n):
temp = sum([(mu_new[h] * csums[h]) for h in range(n)])
eqs[j] = (((mu[j] * temp) - (mu_new[j] * csums[j])) - mu_new[n])
eqs[n] = (sum(mu_new[:n]) - 1)
... |
def test_analytical_azimuth():
times = pd.date_range(start='1/1/2015 0:00', end='12/31/2015 23:00', freq='H').tz_localize('Etc/GMT+8')
(lat, lon) = (37.8, (- 122.25))
lat_rad = np.deg2rad(lat)
output = solarposition.spa_python(times, lat, lon, 100)
solar_azimuth = np.deg2rad(output['azimuth'])
s... |
class BackgroundGenerator(threading.Thread):
def __init__(self, generator, local_rank, max_prefetch=6):
super(BackgroundGenerator, self).__init__()
self.queue = Queue.Queue(max_prefetch)
self.generator = generator
self.local_rank = local_rank
self.daemon = True
self.s... |
class MaxViT_CASCADE_Small(nn.Module):
def __init__(self, n_class=1, img_size=224):
super(MaxViT_CASCADE_Small, self).__init__()
self.conv = nn.Sequential(nn.Conv2d(1, 3, kernel_size=1), nn.BatchNorm2d(3), nn.ReLU(inplace=True))
if (img_size == 224):
self.backbone = maxvit_rmlp_s... |
_dataframe_method
def fill_missing_timestamps(df: pd.DataFrame, frequency: str, first_time_stamp: pd.Timestamp=None, last_time_stamp: pd.Timestamp=None) -> pd.DataFrame:
check('frequency', frequency, [str])
check('first_time_stamp', first_time_stamp, [pd.Timestamp, type(None)])
check('last_time_stamp', last... |
def getBoundingBoxes():
allBoundingBoxes = BoundingBoxes()
import glob
import os
currentPath = os.path.dirname(os.path.abspath(__file__))
folderGT = os.path.join(currentPath, 'groundtruths')
os.chdir(folderGT)
files = glob.glob('*.txt')
files.sort()
allBoundingBoxes = BoundingBoxes()... |
def hierarchical_subsequence(sequential, first, last, after, upto, share_weights=False, depth=0):
assert ((last is None) or (upto is None))
assert ((first is None) or (after is None))
if (first is last is after is upto is None):
return (sequential if share_weights else copy.deepcopy(sequential))
... |
class TestDataset(Dataset):
def __init__(self, args, raw_datasets, cache_root):
self.raw_datasets = raw_datasets
cache_path = os.path.join(cache_root, 'multiwoz_test.cache')
if (os.path.exists(cache_path) and args.dataset.use_cache):
self.extended_data = torch.load(cache_path)
... |
def weights_init_orthogonal(m):
classname = m.__class__.__name__
print(classname)
if (classname.find('Conv') != (- 1)):
init.orthogonal(m.weight.data, gain=1)
elif (classname.find('Linear') != (- 1)):
init.orthogonal(m.weight.data, gain=1)
elif (classname.find('BatchNorm2d') != (- 1)... |
def test_load_spectrum(plot=False, verbose=True, warnings=True, *args, **kwargs):
setup_test_line_databases()
temp_file_name = '_test_database_co2_tempfile.spec'
assert (not exists(temp_file_name))
try:
sf = SpectrumFactory(wavelength_min=4190, wavelength_max=4200, mole_fraction=0.0004, path_len... |
def configure_tagged_union(union: Any, converter: Converter, tag_generator: Callable[([Type], str)]=default_tag_generator, tag_name: str='_type', default: Optional[Type]=NOTHING) -> None:
args = union.__args__
tag_to_hook = {}
exact_cl_unstruct_hooks = {}
for cl in args:
tag = tag_generator(cl)
... |
class TopKCompressor():
def __init__(self):
self.residuals = {}
self.sparsities = []
self.zero_conditions = {}
self.values = {}
self.indexes = {}
self.c = 0
self.t = 0.0
self.name = 'topk'
self.zc = None
self.current_ratio = 1
s... |
def getLESTurbulencePropertiesTemplate(LESModel='dynamicKEqn'):
return ('\n simulationType LES;\n\n LES\n {\n LESModel %s;\n\n turbulence on;\n\n printCoeffs on;\n\n delta cubeRootVol;\n\n dynamicKEqnCoeffs\n {\n filter simple;... |
_on_failure
.parametrize('number_of_nodes', [3])
.parametrize('channels_per_node', [CHAIN])
def test_secret_revealed_on_chain(raiden_chain: List[RaidenService], deposit, settle_timeout, token_addresses, retry_interval_initial):
(app0, app1, app2) = raiden_chain
token_address = token_addresses[0]
token_netwo... |
def query_client_id(display, wid):
specs = [{'client': wid, 'mask': XRes.LocalClientPIDMask}]
r = display.res_query_client_ids(specs)
for id in r.ids:
if ((id.spec.client > 0) and (id.spec.mask == XRes.LocalClientPIDMask)):
for value in id.value:
return value
return N... |
class Road():
_safe
def build(cls, context, prop):
name = ('road_' + str('{:0>3}'.format((len(bpy.data.objects) + 1))))
obj = create_object(name, create_mesh((name + '_mesh')))
link_obj(obj)
bm = bm_from_obj(obj)
vertex_count = cls.create_vertex_outline(bm, prop)
... |
def test_align_right_multiline():
text = 'foo\nshoes'
fill_char = '-'
width = 7
aligned = cu.align_right(text, fill_char=fill_char, width=width)
assert (aligned == '----foo\n--shoes')
reset_all = str(ansi.TextStyle.RESET_ALL)
blue = str(ansi.Fg.BLUE)
red = str(ansi.Fg.RED)
green = st... |
def find_subtitle(title, delimiters=DEFAULT_SUB_SPLITTERS):
if isinstance(title, bytes):
title = title.decode('utf-8', 'replace')
for pair in delimiters:
if ((len(pair) == 2) and (pair[0] in title[:(- 1)]) and title.endswith(pair[1])):
r = len(pair[1])
l = title[0:(- r)].... |
def gen_train_txt(txt_path):
global train_cnt
f = open(txt_path, 'w')
for (i, path) in enumerate(trainval_path):
img_names = open(path, 'r').readlines()
for img_name in img_names:
img_name = img_name.strip()
xml_path = (((anno_path[i] + '/') + img_name) + '.xml')
... |
def urun(b, mo0=None, dm0=None):
mol = gto.Mole()
mol.build(verbose=5, output=('o2uhf-%3.2f.out' % b), atom=[['O', (0, 0, (b / 2))], ['O', (0, 0, ((- b) / 2))]], basis='cc-pvdz', spin=2)
mf = scf.UHF(mol)
mf.scf(dm0)
mc = mcscf.CASSCF(mf, 12, 8)
if (mo0 is not None):
mo0 = mcscf.project_... |
_if_mysql
class ModelTaggedQuerysetOptionsSingleTest(TagTestManager, TestCase):
manage_models = [test_models.SingleTagFieldOptionsModel]
def setUpExtra(self):
self.test_model = test_models.SingleTagFieldOptionsModel
self.test_model.objects.create(name='Test 1', case_sensitive_true='Mr', case_sen... |
def read_gda(file_in, tokenizer, max_seq_length=1024):
pmids = set()
features = []
maxlen = 0
with open(file_in, 'r') as infile:
lines = infile.readlines()
for (i_l, line) in enumerate(tqdm(lines)):
line = line.rstrip().split('\t')
pmid = line[0]
if (p... |
def dist_factory(path_item, entry, only):
lower = entry.lower()
is_egg_info = lower.endswith('.egg-info')
is_dist_info = (lower.endswith('.dist-info') and os.path.isdir(os.path.join(path_item, entry)))
is_meta = (is_egg_info or is_dist_info)
return (distributions_from_metadata if is_meta else (find_... |
def test_asyncio_marker_compatibility_with_xfail(pytester: Pytester):
pytester.makepyfile(dedent(' import pytest\n\n pytest_plugins = "pytest_asyncio"\n\n .xfail(reason="need a failure", strict=True)\n .asyncio\n async def test_asyncio_marke... |
def test_time_tracking_mixin():
class TestClass(TimeTrackingMixin):
pass
obj = TestClass()
assert hasattr(obj, 'time_stats')
assert hasattr(obj, 'time_estimate')
assert hasattr(obj, 'reset_time_estimate')
assert hasattr(obj, 'add_spent_time')
assert hasattr(obj, 'reset_spent_time') |
def test_context_share_texture():
w1 = window.Window(200, 200)
w1.switch_to()
textures = c_uint()
glGenTextures(1, byref(textures))
texture = textures.value
glBindTexture(GL_TEXTURE_2D, texture)
data = (c_ubyte * 4)()
glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, 1, 1, 0, GL_RGBA, GL_UNSIGNED_... |
class ANSI_input(unittest.TestCase):
def test(self):
run_test(self, ['-A', ''], ' Month/Day/Year H:M:S 06/11/2013 20:46:11 GPS\n Modified Julian Date 56454. GPS\n GPSweek DayOfWeek SecOfWeek 720 2 247571.000000\n FullGPSweek Zcount 1744 165... |
def test_adding_entry_points_affect_entry_point_map(easter_fixture):
easter_fixture.stub_egg.add_entry_point_from_line(easter_fixture.group_name, 'test1 = reahl.stubble_dev.test_easteregg:TestClass1')
easter_fixture.stub_egg.add_entry_point(easter_fixture.group_name, 'test2', TestClass2)
epmap = easter_fixt... |
class MSatBoolUFRewriter(IdentityDagWalker):
def __init__(self, environment):
IdentityDagWalker.__init__(self, environment)
self.get_type = self.env.stc.get_type
self.mgr = self.env.formula_manager
def walk_function(self, formula, args, **kwargs):
from pysmt.typing import Functio... |
class Keithley2600(Instrument):
def __init__(self, adapter, name='Keithley 2600 SourceMeter', **kwargs):
super().__init__(adapter, name, **kwargs)
self.ChA = Channel(self, 'a')
self.ChB = Channel(self, 'b')
def error(self):
err = self.ask('print(errorqueue.next())')
err =... |
def get_logger(name: str=None, rank: Optional[int]=None, **kwargs):
if (rank is None):
rank = int(os.environ.get('RANK', (- 1)))
logger = logging.getLogger(name)
level = logging.INFO
log_format = LOG_FORMAT.format(rank=(f'[Rank {rank}]' if (rank > (- 1)) else ''))
logging.basicConfig(level=l... |
def colour_path(image, path) -> None:
start_node = path[(- 1)]
finish_node = path[0]
pixels = image.load()
red_fade = np.linspace(255, 0, (finish_node.distance + 1)).astype(int)
blue_fade = np.linspace(0, 255, (finish_node.distance + 1)).astype(int)
step = 0
for (node1, node2) in zip(path[:(... |
class PerceiverOnnxConfig(OnnxConfig):
def inputs(self) -> Mapping[(str, Mapping[(int, str)])]:
if (self.task == 'multiple-choice'):
dynamic_axis = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
dynamic_axis = {0: 'batch', 1: 'sequence'}
return OrderedDict([('inputs',... |
def channel_pruning_auto_mode():
sess = tf.compat.v1.Session()
with sess.graph.as_default():
_ = VGG16(weights=None, input_shape=(224, 224, 3))
init = tf.compat.v1.global_variables_initializer()
sess.run(init)
conv2d = sess.graph.get_operation_by_name('block1_conv1/Conv2D')
modules_t... |
class PaymentMockDriver(PaymentTerminalDriver):
def __init__(self):
super(PaymentMockDriver, self).__init__()
self._set_terminal_status(terminal_id='0', status='connected')
def transaction_start(self, data):
payment_info = data['payment_info']
transaction_id = data['transaction_i... |
class NonLinearProgram():
def __init__(self, phase_dynamics: PhaseDynamics):
self.casadi_func = {}
self.contact_forces_func = None
self.soft_contact_forces_func = None
self.control_type = ControlType.CONSTANT
self.cx = None
self.dt = None
self.dynamics = None
... |
def test_incompatible_ok(hatch, helpers, temp_dir_data, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir_data.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), result.output
p... |
class PersistentController(YadageController):
def __init__(self, model, backend=None):
self.model = model
super(PersistentController, self).__init__(self.model.load(), backend)
def transaction(self, sync=True):
self.adageobj = self.model.load()
if sync:
log.debug('syn... |
class Class_Decode():
def func_url(self, encode_type, source_text):
try:
result_text = str(urllib.parse.unquote(source_text, encode_type))
except Exception as e:
return [0, '', 'Url']
return [1, result_text.strip(), 'Url']
def func_unicode(self, encode_type, sourc... |
def train_pipeline(root_path):
(opt, args) = parse_options(root_path, is_train=True)
opt['root_path'] = root_path
torch.backends.cudnn.benchmark = True
resume_state = load_resume_state(opt)
if (resume_state is None):
make_exp_dirs(opt)
if (opt['logger'].get('use_tb_logger') and ('deb... |
def test_get_tag_by_manifest_id_multiple_tags_returns_latest(initialized_db):
repo = model.repository.create_repository('devtable', 'newrepo', None)
(manifest, _) = create_manifest_for_testing(repo, '1')
before_ms = (get_epoch_timestamp_ms() - (timedelta(hours=24).total_seconds() * 1000))
count = Tag.up... |
class TestTracer(unittest.TestCase):
def test_trace_async_module(self) -> None:
class NeedWait(LazyAwaitable[torch.Tensor]):
def __init__(self, obj: torch.Tensor) -> None:
super().__init__()
self._obj = obj
def _wait_impl(self) -> torch.Tensor:
... |
def test_L4_subcomp_index():
a = CaseBits32ArrayConnectSubCompAttrComp.DUT()
a.elaborate()
a.apply(StructuralRTLIRGenL4Pass(gen_connections(a)))
connections = a.get_metadata(StructuralRTLIRGenL2Pass.connections)
comp = CurComp(a, 's')
assert (connections[10] == (SubCompAttr(ComponentIndex(CurCom... |
def download(platforms, version, use_v8, max_workers, robust):
if (not max_workers):
max_workers = len(platforms)
archives = {}
with ThreadPoolExecutor(max_workers=max_workers) as pool:
func = functools.partial(_get_package, version=version, robust=robust, use_v8=use_v8)
for (pl_name... |
def contractreceivesecretreveal_from_event(event: DecodedEvent) -> ContractReceiveSecretReveal:
secret_registry_address = event.originating_contract
data = event.event_data
args = data['args']
return ContractReceiveSecretReveal(secret_registry_address=SecretRegistryAddress(secret_registry_address), secr... |
class CompoundModelProcessor(SourceProcessor):
__implements__ = 'CompoundModel'
def process(self, sources, sandbox, nthreads=0):
result = {'processor_profile': dict(), 'displacement.e': np.zeros(sandbox.frame.npixel), 'displacement.n': np.zeros(sandbox.frame.npixel), 'displacement.d': np.zeros(sandbox.f... |
def get_best_routes(chain_state: ChainState, token_network_address: TokenNetworkAddress, one_to_n_address: Optional[OneToNAddress], from_address: InitiatorAddress, to_address: TargetAddress, amount: PaymentAmount, previous_address: Optional[Address], privkey: PrivateKey, our_address_metadata: AddressMetadata, pfs_proxy... |
class PreconditionerTest():
def __init__(self):
self.x1 = (torch.randn(int((mconfig.K / 2)), mconfig.M).cuda().half() / 100)
self.x2 = torch.zeros_like(self.x1)
self.x = torch.cat([self.x1, self.x2], 0)
self.y = (torch.randn(mconfig.K, mconfig.N).cuda().half() / 100)
self.y_i... |
def parse_mit_splits():
class_mapping = {}
with open('data/mit/annotations/moments_categories.txt') as f_cat:
for line in f_cat.readlines():
(cat, digit) = line.rstrip().split(',')
class_mapping[cat] = int(digit)
def line_to_map(x):
video = osp.splitext(x[0])[0]
... |
def calculate_metrics(y_true: np.ndarray, y_pred: np.ndarray, task_type: Union[(str, TaskType)], prediction_type: Optional[Union[(str, PredictionType)]], y_info: dict[(str, Any)]) -> dict[(str, Any)]:
task_type = TaskType(task_type)
if (prediction_type is not None):
prediction_type = PredictionType(pred... |
class _PatchWithDescription(codemod.Patch):
def __init__(self, start_line_number: int, end_line_number: Optional[int]=None, new_lines: Optional[List[str]]=None, path: Optional[str]=None, description: Optional[str]=None) -> None:
super().__init__(start_line_number, end_line_number, new_lines, path)
s... |
class GRAFLoss(BaseLoss):
def __init__(self, runner, d_loss_kwargs=None, g_loss_kwargs=None):
if runner.enable_amp:
raise NotImplementedError('GRAF loss does not support automatic mixed precision training yet.')
self.d_loss_kwargs = (d_loss_kwargs or dict())
self.r1_gamma = self.... |
def snooze_issue(hostname, issue_name, snooze_until):
db = get_db()
spec = {'closed_at': {'$exists': False}, '$or': [{'unsnooze_at': {'$exists': False}}, {'unsnooze_at': {'$lt': snooze_until}}]}
if hostname:
spec['hostname'] = hostname
if issue_name:
spec['name'] = issue_name
ids = [... |
def wavelet_color_fix(target: Image, source: Image):
to_tensor = ToTensor()
target_tensor = to_tensor(target).unsqueeze(0)
source_tensor = to_tensor(source).unsqueeze(0)
result_tensor = wavelet_reconstruction(target_tensor, source_tensor)
to_image = ToPILImage()
result_image = to_image(result_te... |
class ObjectDeleteView(LoginRequiredMixin, ObjectDetailView, EvenniaDeleteView):
model = class_from_module(settings.BASE_OBJECT_TYPECLASS)
template_name = 'website/object_confirm_delete.html'
access_type = 'delete'
def delete(self, request, *args, **kwargs):
obj = str(self.get_object())
... |
class WeightedAvgMetricTest(unittest.TestCase):
target_clazz: Type[RecMetric] = WeightedAvgMetric
target_compute_mode: RecComputeMode = RecComputeMode.UNFUSED_TASKS_COMPUTATION
task_name: str = 'weighted_avg'
def test_weighted_avg_unfused(self) -> None:
rec_metric_value_test_launcher(target_claz... |
class _FoldArrow(QWidget):
def __init__(self, parent=None):
super().__init__(parent)
self._folded = True
def fold(self, folded):
self._folded = folded
self.update()
def paintEvent(self, _event):
opt = QStyleOption()
opt.initFrom(self)
painter = QPainte... |
.allow_backend_process
.requires_internet
def test_build_dependencies(hatch, temp_dir, helpers):
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), result.output
project_path = (temp_dir / 'my-app')
data_path = (temp_dir / ... |
class SquadFeatures():
def __init__(self, input_ids, attention_mask, token_type_ids, cls_index, p_mask, example_index, unique_id, paragraph_len, token_is_max_context, tokens, token_to_orig_map, start_position, end_position, is_impossible, qas_id: str=None, encoding: BatchEncoding=None):
self.input_ids = inp... |
def filter_args_by_frequency(args_list: List[EventPredictedArgs], similarity_threshold: float=0.7) -> Tuple[(EventPredictedArgs, List[str])]:
n_generations = len(args_list)
role_to_type_str_pairs = defaultdict(list)
_prev_event_type = None
for (generation_id, predicted_args) in enumerate(args_list):
... |
_optimizer('adam', dataclass=FairseqAdamConfig)
class FairseqAdam(FairseqOptimizer):
def __init__(self, args, params):
super().__init__(args)
fused_adam_cls = get_fused_adam_class()
use_fused_adam = ((not getattr(args, 'use_old_adam', False)) and (fused_adam_cls is not None) and torch.cuda.i... |
def val(net, dataset, criterion, max_iter=2):
print('Start val')
for p in crnn.parameters():
p.requires_grad = False
net.eval()
data_loader = torch.utils.data.DataLoader(dataset, shuffle=True, batch_size=opt.batchSize, num_workers=int(opt.workers))
val_iter = iter(data_loader)
i = 0
... |
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