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def build_environment(poetry: CorePoetry, env: (Env | None)=None, io: (IO | None)=None) -> Iterator[Env]:
if ((not env) or poetry.package.build_script):
with ephemeral_environment(executable=(env.python if env else None)) as venv:
overwrite = ((io is not None) and io.output.is_decorated() and (n... |
def levenshtein(s1: str, s2: str) -> int:
if (len(s1) < len(s2)):
return levenshtein(s2, s1)
if (len(s2) == 0):
return len(s1)
previous_row = list(range((len(s2) + 1)))
for (i, c1) in enumerate(s1):
current_row = [(i + 1)]
for (j, c2) in enumerate(s2):
inserti... |
class IMU(object):
def __init__(self, server):
self.client = pypilotClient(server)
self.multiprocessing = server.multiprocessing
if self.multiprocessing:
(self.pipe, pipe) = NonBlockingPipe('imu pipe', self.multiprocessing)
self.process = multiprocessing.Process(targe... |
def collapse_aware_exception_split(exc, etype):
if (not isinstance(exc, BaseExceptionGroup)):
if isinstance(exc, etype):
return (exc, None)
else:
return (None, exc)
(match, rest) = exc.split(etype)
if isinstance(match, BaseExceptionGroup):
match = collapse_exc... |
def test_inconsistent_array_params(location, sapm_module_params, cec_module_params):
module_error = '.* selected for the DC model but one or more Arrays are missing one or more required parameters'
temperature_error = 'could not infer temperature model from system\\.temperature_model_parameters\\. Check that al... |
def list_tags_raw(filenames):
for filename in filenames:
print('Raw IDv2 tag info for', filename)
try:
id3 = mutagen.id3.ID3(filename, translate=False)
except mutagen.id3.ID3NoHeaderError:
print(u'No ID3 header found; skipping.')
except Exception as err:
... |
class CombinedROIHeads(nn.ModuleDict):
def __init__(self, heads):
super().__init__(heads)
if (config.MODEL.INSTANCE2D.ROI_HEADS.ROI_MASK_HEAD.USE and config.MODEL.INSTANCE2D.ROI_HEADS.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR):
self.mask.feature_extractor = self.box.feature_extractor
... |
def extract_feature(model, dataloaders):
features = torch.FloatTensor()
count = 0
for data in dataloaders:
(img, label) = data
(n, c, h, w) = img.size()
count += n
print(count)
if opt.use_dense:
ff = torch.FloatTensor(n, 1024).zero_()
else:
... |
class VOT(object):
def __init__(self, region_format, channels=None):
assert (region_format in [trax.Region.RECTANGLE, trax.Region.POLYGON])
if (channels is None):
channels = ['color']
elif (channels == 'rgbd'):
channels = ['color', 'depth']
elif (channels == '... |
class Effect4045(BaseEffect):
runTime = 'early'
type = ('projected', 'passive')
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: (mod.item.group.name == 'Smart Bomb')), 'empFieldRange', module.getModifiedItemAttr('empFieldRangeMultiplier'), ... |
def test_delete_invalid_driver(path_rgb_byte_tif, tmpdir):
path = str(tmpdir.join('test_invalid_driver.tif'))
rasterio.shutil.copy(path_rgb_byte_tif, path)
with pytest.raises(DriverRegistrationError) as e:
rasterio.shutil.delete(path, driver='trash')
assert ('Unrecognized driver' in str(e.value)... |
class FlaxHybridCLIPModule(nn.Module):
config: HybridCLIPConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
text_config = self.config.text_config
vision_config = self.config.vision_config
self.projection_dim = self.config.projection_dim
self.text_embed_dim = text_config.h... |
def test_op_invalid_input_types():
class TestOp(pytensor.graph.op.Op):
itypes = [dvector, dvector, dvector]
otypes = [dvector]
def perform(self, node, inputs, outputs):
pass
msg = '^Invalid input types for Op.*'
with pytest.raises(TypeError, match=msg):
TestOp()(d... |
(reason='data is local')
def test_gacos():
corr = GACOSCorrection()
corr.load('/home/marius/Development/testing/kite/GACOS/.ztd')
grd = corr.grids[0]
d = grd.get_corrections(grd.llLat, grd.llLon, (- grd.dLat), grd.dLon, grd.rows, grd.cols)
d = grd.get_corrections(grd.llLat, grd.llLon, ((- grd.dLat) ... |
def get_pyramidnet_cifar(num_classes, blocks, alpha, bottleneck, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
assert (num_classes in [10, 100])
if bottleneck:
assert (((blocks - 2) % 9) == 0)
layers = ([((blocks - 2) // 9)] * 3)
else:
asse... |
def nvram_listener():
server_address = f'{ROOTFS}/var/cfm_socket'
if os.path.exists(server_address):
os.unlink(server_address)
sock = socket.socket(socket.AF_UNIX, socket.SOCK_STREAM)
sock.bind(server_address)
sock.listen(1)
data = bytearray()
with open('cfm_socket.log', 'wb') as ofi... |
def get_gcn_fact(adj):
adj_ = (adj + np.eye(node_num, node_num))
row_sum = np.array(adj_.sum(1))
d_inv_sqrt = np.power(row_sum, (- 0.5)).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.0
d_mat_inv_sqrt = np.mat(np.diag(d_inv_sqrt))
gcn_fact = ((d_mat_inv_sqrt * adj_) * d_mat_inv_sqrt)
return ... |
class Migration(migrations.Migration):
dependencies = [('conferences', '0010_merge__1807')]
operations = [migrations.AlterField(model_name='conference', name='introduction', field=i18n.fields.I18nTextField(verbose_name='introduction')), migrations.AlterField(model_name='conference', name='name', field=i18n.fiel... |
def Mv_setup_options():
Print_Function()
(o3d, e1, e2, e3) = Ga.build('e_1 e_2 e_3', g=[1, 1, 1])
v = o3d.mv('v', 'vector')
print(v)
(o3d, e1, e2, e3) = Ga.build('e*1|2|3', g=[1, 1, 1])
v = o3d.mv('v', 'vector')
print(v)
(o3d, e1, e2, e3) = Ga.build('e*x|y|z', g=[1, 1, 1])
v = o3d.mv... |
.online
def test_pypi_multiple_pkg(cache_dir):
pypi = service.PyPIService(cache_dir)
deps: list[service.Dependency] = [service.ResolvedDependency('jinja2', Version('2.4.1')), service.ResolvedDependency('flask', Version('0.5'))]
results: dict[(service.Dependency, list[service.VulnerabilityResult])] = dict(py... |
def log_debug_tracing(func):
def wrapper(self, *args, **kwargs):
func_name = ('%s.%s' % (self.__class__.__name__, func.__name__))
self.log(message='On {}, body {}, kwargs {}'.format(func_name, args[0].request.body, str(kwargs)), level=logging.DEBUG)
return func(self, *args, **kwargs)
ret... |
class WIREGUARD(asyncio.DatagramProtocol):
def __init__(self, args):
self.args = args
self.preshared_key = (b'\x00' * 32)
self.ippacket = ip.IPPacket(args)
self.private_key = hashlib.blake2s(args.passwd.encode()).digest()
self.public_key = crypto.X25519(self.private_key, 9)
... |
def validator(package):
try:
if (package.size > PLUGIN_MAX_UPLOAD_SIZE):
raise ValidationError((_('File is too big. Max size is %s Megabytes') % (PLUGIN_MAX_UPLOAD_SIZE / 1000000)))
except AttributeError:
if (package.len > PLUGIN_MAX_UPLOAD_SIZE):
raise ValidationError((_... |
def Var(term=None, *others, dom=None, id=None):
global started_modeling
if ((not started_modeling) and (not options.uncurse)):
cursing()
started_modeling = True
if ((term is None) and (dom is None)):
dom = Domain(math.inf)
assert (not (term and dom))
if (term is not None):
... |
def get_latest_table_version(namespace: str, table_name: str, *args, **kwargs) -> Optional[TableVersion]:
table_versions = list_table_versions(namespace, table_name, *args, **kwargs).all_items()
if (not table_versions):
return None
table_versions.sort(reverse=True, key=(lambda v: int(v.table_version... |
class GraphvizLexer(RegexLexer):
name = 'Graphviz'
url = '
aliases = ['graphviz', 'dot']
filenames = ['*.gv', '*.dot']
mimetypes = ['text/x-graphviz', 'text/vnd.graphviz']
version_added = '2.8'
tokens = {'root': [('\\s+', Whitespace), ('(#|//).*?$', Comment.Single), ('/(\\\\\\n)?[*](.|\\n)*?... |
.parametrize('broken_role', [qt_api.QtCore.Qt.ItemDataRole.ToolTipRole, qt_api.QtCore.Qt.ItemDataRole.StatusTipRole, qt_api.QtCore.Qt.ItemDataRole.WhatsThisRole, qt_api.QtCore.Qt.ItemDataRole.SizeHintRole, qt_api.QtCore.Qt.ItemDataRole.FontRole, qt_api.QtCore.Qt.ItemDataRole.BackgroundRole, qt_api.QtCore.Qt.ItemDataRol... |
.skipif((literal_eval(os.getenv('TEST_SAGEMAKER', 'False')) is not True), reason='Skipping test because should only be run when releasing minor transformers version')
.usefixtures('sm_env')
_class([{'framework': 'pytorch', 'script': 'run_glue_model_parallelism.py', 'model_name_or_path': 'roberta-large', 'instance_type'... |
_grad()
def evaluate(model, criterion, postprocessors, data_loader, evaluator_list, device, args):
model.eval()
criterion.eval()
metric_logger = utils.MetricLogger(delimiter=' ')
header = 'Test:'
predictions = []
for (samples, targets) in metric_logger.log_every(data_loader, 10, header):
... |
def plot_hyperparam(hyperparam_to_plot, fig=None, ax_arr=None, big_ax=None, ylims=YLIMS, legend=False, dpi=300, figsize=(6, 5.5)):
if ((fig is None) and (ax_arr is None)):
(fig, ax_arr) = plt.subplots(2, 2, dpi=dpi, figsize=figsize)
for ax_ in ax_arr.flatten():
ax_.tick_params(pad=0.1)
if (b... |
def load_plugin_elements_by_name(plugin_name: str):
assert (plugin_name in PluginName.__members__), 'Unknown plugin name {}.'.format(plugin_name)
plugin_dir_name = PluginName[plugin_name].value
plugin_file_path = os.path.join(CURRENT_PATH, plugin_dir_name)
data_model_file_path = os.path.join(CURRENT_PAT... |
class FileUploader():
def __init__(self, stream=False):
self.total = 0
self.uploaded = 0
self.percent = 0
self.session = boto3.Session(aws_access_key_id=AWSKEY, aws_secret_access_key=AWSSECRET)
self.s3 = boto3.client('s3')
self.stream = stream
def upload_callback(... |
class HoverXRefStandardDomainMixin(HoverXRefBaseDomain):
def resolve_xref(self, env, fromdocname, builder, typ, target, node, contnode):
if (typ in self.hoverxref_types):
resolver = self._resolve_ref_xref
return resolver(env, fromdocname, builder, typ, target, node, contnode)
... |
def train(num_epochs, model, optimizer, train_loader, val_loader, fabric):
for epoch in range(num_epochs):
train_acc = torchmetrics.Accuracy(task='multiclass', num_classes=10).to(fabric.device)
model.train()
for (batch_idx, (features, targets)) in enumerate(train_loader):
model.t... |
class RoIPointPool3d(nn.Module):
def __init__(self, num_sampled_points=512, pool_extra_width=1.0):
super().__init__()
self.num_sampled_points = num_sampled_points
self.pool_extra_width = pool_extra_width
def forward(self, points, point_features, boxes3d):
return RoIPointPool3dFun... |
class LoadNPYImaged(MapTransform):
def __init__(self, keys, allow_missing_keys: bool=False):
super().__init__(keys, allow_missing_keys)
self.keys = keys
def __call__(self, data):
d = dict(data)
data_npy = None
for key in data.keys():
file_path = d[key]
... |
class Solution(object):
def sumNumbers(self, root):
if (root is None):
return 0
res = 0
queue = [(root, root.val)]
while (len(queue) > 0):
(curr, curr_value) = queue.pop(0)
if ((curr.left is None) and (curr.right is None)):
res += c... |
def build_state_prediction_dataset(args):
playthroughs = (json.loads(line.rstrip(',\n')) for line in open(args.input) if (len(line.strip()) > 1))
graph_dataset = GraphDataset()
dataset = []
for example in next_example(playthroughs):
(root, candidates) = (example[0], example[1:])
if (len(... |
class JobListCategory(JobCategoryMenu, JobMixin, ListView):
paginate_by = 25
template_name = 'jobs/job_category_list.html'
def get_queryset(self):
self.current_category = get_object_or_404(JobCategory, slug=self.kwargs['slug'])
return Job.objects.visible().select_related().filter(category__s... |
class BaseOptions():
def __init__(self):
self.parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
self.initialized = False
def initialize(self):
self.parser.add_argument('--batch_size', type=int, default=1, help='input batch size')
self.parser... |
class CurricSampler(Sampler):
def __init__(self, data_source, num_samples_cls=1):
num_classes = len(np.unique(data_source.labels))
self.class_iter = RandomCycleIter(range(num_classes))
cls_data_list = [list() for _ in range(num_classes)]
for (i, label) in enumerate(data_source.labels... |
_families
def test_record_testsuite_property(pytester: Pytester, run_and_parse: RunAndParse, xunit_family: str) -> None:
pytester.makepyfile('\n def test_func1(record_testsuite_property):\n record_testsuite_property("stats", "all good")\n\n def test_func2(record_testsuite_property):\n ... |
def test_pipe_Bits():
B1 = mk_bits(1)
B32 = mk_bits(32)
run_tv_test(NormalQueueRTL(Bits32, 2), [[B1(1), B1(1), B32(123), B1(0), B1(0), '?'], [B1(1), B1(1), B32(345), B1(0), B1(1), B32(123)], [B1(0), B1(0), B32(567), B1(0), B1(1), B32(123)], [B1(0), B1(0), B32(567), B1(1), B1(1), B32(123)], [B1(0), B1(1), B3... |
class TimeElements():
def setup(self):
test_file_path = mm.datasets.get_path('bubenec')
self.df_buildings = gpd.read_file(test_file_path, layer='buildings')
self.df_tessellation = gpd.read_file(test_file_path, layer='tessellation')
self.df_streets = gpd.read_file(test_file_path, laye... |
def test_majorana_operator_pow():
a = (MajoranaOperator((0, 1, 5), 1.5) + MajoranaOperator((1, 2, 7), (- 0.5)))
assert ((a ** 2).terms == {(): (- 2.5), (0, 2, 5, 7): (- 1.5)})
with pytest.raises(TypeError):
_ = (a ** (- 1))
with pytest.raises(TypeError):
_ = (a ** 'a') |
class OCSPResponseBuilder():
def __init__(self, response: (_SingleResponse | None)=None, responder_id: (tuple[(x509.Certificate, OCSPResponderEncoding)] | None)=None, certs: (list[x509.Certificate] | None)=None, extensions: list[x509.Extension[x509.ExtensionType]]=[]):
self._response = response
self... |
def find_cuda():
cuda_home = (os.environ.get('CUDA_HOME') or os.environ.get('CUDA_PATH'))
if ((cuda_home is not None) and os.path.isfile(os.path.join(cuda_home, 'bin', 'nvcc'))):
return cuda_home
location = shutil.which('nvcc')
if (location is not None):
cuda_home = os.path.join(os.path.... |
class DynamicOITest(unittest.TestCase):
def setUp(self):
super().setUp()
self.project = testutils.sample_project(validate_objectdb=True)
self.pycore = self.project.pycore
def tearDown(self):
testutils.remove_project(self.project)
super().tearDown()
def test_simple_dti... |
class DrawMap():
def __init__(self, map, window):
self.window = window
fullMesh = np.array([], dtype=np.float32).reshape(0, 3, 3)
fullMeshColors = np.array([], dtype=np.float32).reshape(0, 3, 4)
for i in range(0, map.num_city_blocks):
for j in range(0, map.num_city_blocks... |
def test_state_transition():
lock_amount = 7
block_number = 1
initiator = factories.make_address()
pseudo_random_generator = random.Random()
channels = make_channel_set([channel_properties2])
from_transfer = make_target_transfer(channels[0], amount=lock_amount, initiator=initiator)
init = Ac... |
.usefixtures('cmdline_opts')
class ChecksumCLSrcSink_Tests():
def setup_class(cls):
cls.DutType = ChecksumCL
def run_sim(s, th):
run_sim(th, s.__class__.cmdline_opts)
def test_srcsink_simple(s):
words = [b16(x) for x in [1, 2, 3, 4, 5, 6, 7, 8]]
bits = words_to_b128(words)
... |
_datapipe('shuffled_flatmap')
class ShuffledFlatMapperIterDataPipe(IterDataPipe):
datapipe: IterDataPipe
fn: Optional[Callable]
buffer_size: int
_buffer: List[Iterator]
_enabled: bool
_seed: Optional[int]
_rng: random.Random
_no_op_fn: bool = False
def __init__(self, datapipe: IterDa... |
def test_read_write(tmpdir):
tif1 = str(tmpdir.join('test.tif'))
tif2 = str(tmpdir.join('test2.tif'))
with rasterio.open('tests/data/RGB.byte.tif') as src:
kwargs = src.meta.copy()
del kwargs['transform']
del kwargs['crs']
with rasterio.open(tif1, 'w', **kwargs) as dst:
... |
def main() -> int:
checkers = {'git': check_git, 'vcs': check_vcs_conflict, 'spelling': check_spelling, 'pyqt-imports': check_pyqt_imports, 'userscript-descriptions': check_userscripts_descriptions, 'userscript-shebangs': check_userscript_shebangs, 'changelog-urls': check_changelog_urls, 'vim-modelines': check_vim_... |
def eval_base_encoder(dataset, device):
print('Evaluating base encoder...')
base_encoder = torchvision.models.resnet152(pretrained=True)
base_encoder.fc = nn.Identity()
cars_encoder = CarsEncoder(base_encoder)
cars_encoder.to(device=device)
cars_encoder.eval()
result = Quaterion.evaluate(eva... |
def test_load_security_information_api_returns_none(initialized_db, set_secscan_config):
repository_ref = registry_model.lookup_repository('devtable', 'simple')
tag = registry_model.get_repo_tag(repository_ref, 'latest')
manifest = registry_model.get_manifest_for_tag(tag)
ManifestSecurityStatus.create(m... |
class JoinGroupCall(Scaffold):
async def join_group_call(self, chat_id: Union[(int, str)], stream: Optional[Stream]=None, invite_hash: Optional[str]=None, join_as=None, auto_start: bool=True):
if (join_as is None):
join_as = self._cache_local_peer
chat_id = (await self._resolve_chat_id(c... |
class DataTrainingArguments():
dataset_name: Optional[str] = field(default='nateraw/image-folder', metadata={'help': 'Name of a dataset from the datasets package'})
dataset_config_name: Optional[str] = field(default=None, metadata={'help': 'The configuration name of the dataset to use (via the datasets library)... |
def train(opt):
ArgumentParser.validate_train_opts(opt)
ArgumentParser.update_model_opts(opt)
ArgumentParser.validate_model_opts(opt)
if opt.train_from:
logger.info(('Loading checkpoint from %s' % opt.train_from))
checkpoint = torch.load(opt.train_from, map_location=(lambda storage, loc:... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--data-root', type=str, required=True)
parser.add_argument('--annot-path', type=str, required=True)
parser.add_argument('--det-stride', type=float, default=1)
parser.add_argument('--in-scale', type=float, default=None)
par... |
def test_create_org_policy(initialized_db, app):
with client_with_identity('devtable', app) as cl:
response = conduct_api_call(cl, OrgAutoPrunePolicies, 'POST', {'orgname': 'sellnsmall'}, {'method': 'creation_date', 'value': '2w'}, 201).json
assert (response['uuid'] is not None)
assert (mode... |
class ModelArguments():
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
cache_dir: Optional[str] = field(default=None, metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'})
freeze_fe... |
_flags(compute_test_value='raise')
.parametrize('dist_op, dist_params, size', [(normal, [np.array(1.0, dtype=config.floatX), np.array(5.0, dtype=config.floatX)], []), (normal, [np.array([0.0, 1.0], dtype=config.floatX), np.array(5.0, dtype=config.floatX)], []), (normal, [np.array([0.0, 1.0], dtype=config.floatX), np.ar... |
class BaseOptimisationWrapper(object):
def __init__(self, pywr_model_json, *args, **kwargs):
uid = kwargs.pop('uid', None)
self.pywr_model_klass = kwargs.pop('model_klass', Model)
self.pywr_model_kwargs = kwargs.pop('model_kwargs', {})
super(BaseOptimisationWrapper, self).__init__(*a... |
def install_jetson_clocks(args):
if (not os.path.isfile('/usr/bin/jetson_clocks')):
shutil.copy('tests/jetson_clocks', '/usr/bin/jetson_clocks')
print('Copied test/jetson_clocks')
else:
print('/usr/bin/jetson_clocks already exists')
pytest.exit('I cannot install a fake jetson_clo... |
def validate_op_return_output(output: TxOutput, *, max_size: int=None) -> None:
script = output.scriptpubkey
if (script[0] != opcodes.OP_RETURN):
raise UserFacingException(_('Only OP_RETURN scripts are supported.'))
if ((max_size is not None) and (len(script) > max_size)):
raise UserFacingEx... |
class HookError(RadishError):
def __init__(self, hook_function, failure):
self.hook_function = hook_function
self.failure = failure
super(HookError, self).__init__("Hook '{0}' from {1}:{2} raised: '{3}: {4}'".format(hook_function.__name__, hook_function.__code__.co_filename, hook_function.__... |
def test_cannot_manage_subscription_if_not_subscribed_via_stripe(graphql_client):
membership = MembershipFactory(status=MembershipStatus.ACTIVE)
graphql_client.force_login(membership.user)
query = 'mutation {\n manageUserSubscription {\n __typename\n }\n }'
response = graphql... |
def test_export_methods_handle_empty_data_error(simple_project, mocker):
mocker.patch.object(simple_project, '_call_api', return_value='\n')
dataframe = simple_project.export_records(format_type='df')
assert dataframe.empty
dataframe = simple_project.export_instrument_event_mappings(format_type='df')
... |
class OUT2(Block):
_format = [E(1, 4, x_fixed(b'OUT2'), dummy=True), E(6, 28, x_date_time), E(30, 34, 'a5'), E(36, 38, 'a3'), E(40, 43, 'a4'), E(45, 55, 'f11.3')]
time = Timestamp.T()
station = String.T(help='station code (5 characters)')
channel = String.T(help='channel code (3 characters)')
locati... |
class UserPushShowPvar(BaseHandler):
.authenticated
async def post(self, userid):
try:
user = (await self.db.user.get(userid, fields=('role', 'email')))
envs = {}
for (k, _) in self.request.body_arguments.items():
envs[k] = self.get_body_argument(k)
... |
class Effect172(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Energy Turret')), 'damageMultiplier', (container.getMod... |
def _add_run_pair_metric_page(report_index_file, pair_output_paths, pair_name, pair_data_frame, pair_report_data_list):
pair_index_path = pair_output_paths.index_path
out_dir = pair_output_paths.output_paths.out_dir
report_index_file.write(('<a href="%s">%s</a><br>\n' % (os.path.relpath(pair_index_path, out... |
class QQP(Task):
VERSION = 0
DATASET_PATH = 'glue'
DATASET_NAME = 'qqp'
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if (self._training_docs is None):
... |
def test_newtype_structure_hooks(converter: BaseConverter):
assert (converter.structure('0', int) == 0)
assert (converter.structure('0', PositiveIntNewType) == 0)
assert (converter.structure('0', BigPositiveIntNewType) == 0)
converter.register_structure_hook(PositiveIntNewType, (lambda v, _: (int(v) if ... |
def test_difference() -> None:
v = Version.parse('1.2.3')
assert v.difference(v).is_empty()
assert (v.difference(Version.parse('0.8.0')) == v)
assert v.difference(VersionRange(Version.parse('1.1.4'), Version.parse('1.2.4'))).is_empty()
assert (v.difference(VersionRange(Version.parse('1.4.0'), Versio... |
class Effect6597(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.requiresSkill('Skirmish Command') or mod.item.requiresSkill('Armored Command'))), 'warfareBuff2Value', src.getModifiedItemAttr('shipBonusCarrierG... |
.unit()
.parametrize(('outcome', 'outcome_enum', 'total_description'), ([(outcome, TaskOutcome, 'description') for outcome in TaskOutcome] + [(outcome, CollectionOutcome, 'description') for outcome in CollectionOutcome]))
def test_create_summary_panel(capsys, outcome, outcome_enum, total_description):
counts = {out... |
def setup(parser):
parser.add_squirrel_selection_arguments()
parser.add_squirrel_query_arguments(without=['time'])
style_choices = ['visual', 'summary', 'yaml']
parser.add_argument('--style', dest='style', choices=style_choices, default='visual', help=('Set style of presentation. Choices: %s' % ldq(styl... |
class BuildNoProvenanceUsageTests(CustomAssertions):
def setUpClass(cls):
cls.das = DummyArtifacts()
cls.tempdir = cls.das.tempdir
cls.pm = PluginManager()
def tearDownClass(cls):
cls.das.free()
def test_build_no_provenance_node_usage_w_complete_node(self):
ns = Names... |
class Project(_Project):
def __init__(self, projectroot, fscommands=None, ropefolder='.ropeproject', **prefs):
if (projectroot != '/'):
projectroot = _realpath(projectroot).rstrip('/\\')
assert isinstance(projectroot, str)
self._address = projectroot
self._ropefolder_name... |
class EnrSpace(Space):
_stored_dims = {}
def __init__(self, dims, excitations):
self.dims = tuple(dims)
self.n_excitations = excitations
enr_dicts = enr_state_dictionaries(dims, excitations)
(self.size, self.state2idx, self.idx2state) = enr_dicts
self.issuper = False
... |
.parametrize('masked, secrets', [(_secrets, _secrets), ((re.compile('token.+?(?=\\s|$)'), re.compile('secret.+?(?=\\s|$)')), _secrets)])
def test_multiple_secrets_with_same_mask(masked, secrets):
masker = MaskingFilter(_use_named_masks=True)
for mask in masked:
masker.add_mask_for(mask, 'ksam')
test... |
class QuantileReg(Glm):
GLM_LOSS_CLASS = Quantile
def __init__(self, X, y, fit_intercept=True, sample_weight=None, offsets=None, quantile=0.5):
super().__init__(X=X, y=y, fit_intercept=fit_intercept, sample_weight=sample_weight, offsets=offsets, quantile=quantile)
def intercept_at_coef_eq0(self):
... |
def _parse_marker_op(tokenizer: Tokenizer) -> Op:
if tokenizer.check('IN'):
tokenizer.read()
return Op('in')
elif tokenizer.check('NOT'):
tokenizer.read()
tokenizer.expect('WS', expected="whitespace after 'not'")
tokenizer.expect('IN', expected="'in' after 'not'")
... |
class TestParseEntryPoints():
.parametrize(('script', 'expected'), [pytest.param('', [], id='empty'), pytest.param('\n [foo]\n foo = foo.bar\n ', [], id='unrelated'), pytest.param('\n [console_scripts]\n package = package.__m... |
class PreparedBuild(object):
def __init__(self, trigger=None):
self._dockerfile_id = None
self._archive_url = None
self._tags = None
self._build_name = None
self._subdirectory = None
self._context = None
self._metadata = None
self._trigger = trigger
... |
def run(settings):
settings.description = 'Default train settings for DiMP with ResNet50 as backbone.'
settings.batch_size = 10
settings.num_workers = 8
settings.multi_gpu = False
settings.print_interval = 1
settings.normalize_mean = [0.485, 0.456, 0.406]
settings.normalize_std = [0.229, 0.2... |
def Xception71(num_classes=None, global_pool=True, keep_prob=0.5, output_stride=None, regularize_depthwise=False, multi_grid=None, scope='xception_71'):
blocks = [xception_block('entry_flow/block1', in_channels=64, depth_list=[128, 128, 128], skip_connection_type='conv', activation_fn_in_separable_conv=False, regul... |
class TestInferenceDropout(unittest.TestCase):
def setUp(self):
(self.task, self.parser) = get_dummy_task_and_parser()
TransformerModel.add_args(self.parser)
self.args = self.parser.parse_args([])
self.args.encoder_layers = 2
self.args.decoder_layers = 1
logging.disab... |
class BilmDataset(Dataset):
def worker(self, proc_id, start, end):
print(('Worker %d is building dataset ... ' % proc_id))
set_seed(self.seed)
pos = 0
f_write = open((('dataset-tmp-' + str(proc_id)) + '.pt'), 'wb')
with open(self.corpus_path, mode='r', encoding='utf-8') as f:... |
def stderr_for_metric(metric, bootstrap_iters):
bootstrappable = [median, matthews_corrcoef, f1_score, perplexity, bleu, chrf, ter]
if (metric in bootstrappable):
return (lambda x: bootstrap_stderr(metric, x, iters=bootstrap_iters))
stderr = {mean: mean_stderr, acc_all: acc_all_stderr}
return st... |
def get_value_from_params_dir(params_dir, param_name):
def _load_params(params_file, loader, mode):
with tf.io.gfile.GFile(params_file, mode) as f:
params = loader(f)
logging.info('Found params file %s', params_file)
return params[param_name]
try:
try:
ret... |
class InnerPrepareSingleFactorization(Bloq):
num_aux: int
num_spin_orb: int
num_bits_state_prep: int
num_bits_rot_aa: int = 8
adjoint: bool = False
kp1: int = 1
kp2: int = 1
def pretty_name(self) -> str:
dag = ('' if self.adjoint else '')
return f'In-Prep{dag}'
_prope... |
def test_autoload_commands(command_sets_app):
(cmds_cats, cmds_doc, cmds_undoc, help_topics) = command_sets_app._build_command_info()
assert ('Alone' in cmds_cats)
assert ('elderberry' in cmds_cats['Alone'])
assert ('main' in cmds_cats['Alone'])
result = command_sets_app.app_cmd('main sub')
asse... |
class AboutDialog(Gtk.AboutDialog):
def __init__(self, parent, app):
super().__init__()
self.set_transient_for(parent)
self.set_program_name(app.name)
self.set_version(quodlibet.get_build_description())
self.set_authors(const.AUTHORS)
self.set_artists(const.ARTISTS)
... |
def test_instrument_before_after_run() -> None:
record = []
class BeforeAfterRun(_abc.Instrument):
def before_run(self) -> None:
record.append('before_run')
def after_run(self) -> None:
record.append('after_run')
async def main() -> None:
pass
_core.run(ma... |
('pypyr.moduleloader.get_module')
('pypyr.cache.loadercache.Loader.get_pipeline')
def test_get_parsed_context_parser_pass(mock_get_pipeline, mock_moduleloader):
contextparser_cache.clear()
mock_moduleloader.return_value.get_parsed_context = mock_parser_arb
mock_get_pipeline.return_value = get_pipe_def({'con... |
def G_logistic_nonsaturating(G, D, opt, training_set, minibatch_size):
latents = tf.random_normal(([minibatch_size] + G.input_shapes[0][1:]))
labels = training_set.get_random_labels_tf(minibatch_size)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out = fp32(D.get_outp... |
class LdjsonReaderListsTest(Ldjson, ReaderTest, TestCase):
input_data = '[1,2,3]\n[4,5,6]'
()
def test_nofields(self, context):
context.write_sync(EMPTY)
context.stop()
assert (context.get_buffer() == [([1, 2, 3],), ([4, 5, 6],)])
(output_type=tuple)
def test_output_type(self... |
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