code stringlengths 281 23.7M |
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class LinearsolverResult(AlgorithmResult):
def solution(self) -> np.ndarray:
return self.get('solution')
def solution(self, value: np.ndarray) -> None:
self.data['solution'] = value
def from_dict(a_dict: Dict) -> 'LinearsolverResult':
return LinearsolverResult(a_dict) |
class TestAdaroundOptimizer(unittest.TestCase):
def _optimize_layer_rounding(self, warm_start):
AimetLogger.set_level_for_all_areas(logging.DEBUG)
model = TinyModel().eval()
dummy_input = torch.randn(1, 3, 32, 32)
sim = QuantizationSimModel(model, dummy_input=dummy_input, quant_schem... |
class PathSeparatorTest(TestCase):
def test_os_path_sep_matches_fake_filesystem_separator(self):
filesystem = fake_filesystem.FakeFilesystem(path_separator='!')
fake_os_module = fake_os.FakeOsModule(filesystem)
self.assertEqual('!', fake_os_module.sep)
self.assertEqual('!', fake_os_m... |
def calculate_sentence_transformer_embedding(examples, embedding_model, mean_normal=False):
text_to_encode = [f"The topic is {raw_item['activity_label']}. {raw_item['ctx_a']} {raw_item['ctx_b']} | {raw_item['endings'][0]} | {raw_item['endings'][1]} | {raw_item['endings'][2]} | {raw_item['endings'][3]}" for raw_item... |
def is_literal_type_like(t: (Type | None)) -> bool:
t = get_proper_type(t)
if (t is None):
return False
elif isinstance(t, LiteralType):
return True
elif isinstance(t, UnionType):
return any((is_literal_type_like(item) for item in t.items))
elif isinstance(t, TypeVarType):
... |
class VisionEncoderDecoderDecoderOnnxConfig(OnnxConfig):
def inputs(self) -> Mapping[(str, Mapping[(int, str)])]:
common_inputs = OrderedDict()
common_inputs['input_ids'] = {0: 'batch', 1: 'past_decoder_sequence + sequence'}
common_inputs['attention_mask'] = {0: 'batch', 1: 'past_decoder_seq... |
class GeneralDataset(data.Dataset):
def __init__(self, root, transform=None, refname=True):
self.root = root
print(root)
self.transform = transform
self.retname = refname
self.cam_intrinsic = np.float32(np.array([[582.64, 0, 313.04], [0, 582.69, 238.44], [0, 0, 1]]))
... |
class TransparentCheckBox(QtWidgets.QCheckBox):
def enterEvent(self, e):
if (self.window().showhelp is True):
QtWidgets.QToolTip.showText(e.globalPos(), '<h3>Frame transparency</h3>Toggle the transparency of the axis-frame.<p>If checked, the map will be exported with a transparent background.<p>... |
class WriteSingleRegisterResponse(ModbusResponse):
function_code = 6
_rtu_frame_size = 8
def __init__(self, address=None, value=None, **kwargs):
super().__init__(**kwargs)
self.address = address
self.value = value
def encode(self):
return struct.pack('>HH', self.address, ... |
def get_shortcuts_folder():
if (get_root_hkey() == winreg.HKEY_LOCAL_MACHINE):
try:
fldr = get_special_folder_path('CSIDL_COMMON_PROGRAMS')
except OSError:
fldr = get_special_folder_path('CSIDL_PROGRAMS')
else:
fldr = get_special_folder_path('CSIDL_PROGRAMS')
... |
class AdaptiveBN(nn.Module):
def __init__(self, max_nc, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True):
super().__init__()
num_features = max_nc
self.num_features = max_nc
self.eps = eps
self.momentum = momentum
self.affine = affine
self.track... |
def _notify_superusers(key):
notification_metadata = {'name': key.name, 'kid': key.kid, 'service': key.service, 'jwk': key.jwk, 'metadata': key.metadata, 'created_date': timegm(key.created_date.utctimetuple())}
if (key.expiration_date is not None):
notification_metadata['expiration_date'] = timegm(key.e... |
class ContactLabelGenerator(object):
def __init__(self):
pass
def get_contact_labels(self, smpl, obj, num_samples, thres=0.02):
object_points = obj.sample(num_samples)
(dist, _, vertices) = igl.signed_distance(object_points, smpl.vertices, smpl.faces, return_normals=False)
return... |
class TextButton(DemoItem):
BUTTON_WIDTH = 180
BUTTON_HEIGHT = 19
(LEFT, RIGHT) = range(2)
(SIDEBAR, PANEL, UP, DOWN) = range(4)
(ON, OFF, HIGHLIGHT, DISABLED) = range(4)
def __init__(self, text, align=LEFT, userCode=0, parent=None, type=SIDEBAR):
super(TextButton, self).__init__(parent)... |
class HourGlassNetMultiScaleInt(nn.Module):
def __init__(self, in_nc=3, out_nc=3, upscale=4, nf=64, res_type='res', n_mid=2, n_tail=2, n_HG=6, act_type='leakyrelu', inter_supervis=True, mscale_inter_super=False, share_upsample=False):
super(HourGlassNetMultiScaleInt, self).__init__()
self.n_HG = n_H... |
def resolve_inout(input=None, output=None, files=None, overwrite=False, num_inputs=None):
resolved_output = (output or (files[(- 1)] if files else None))
if ((not overwrite) and resolved_output and os.path.exists(resolved_output)):
raise FileOverwriteError("file exists and won't be overwritten without u... |
def pack_dbobj(item):
_init_globals()
obj = item
natural_key = _FROM_MODEL_MAP[(hasattr(obj, 'id') and hasattr(obj, 'db_date_created') and hasattr(obj, '__dbclass__') and obj.__dbclass__.__name__.lower())]
return ((natural_key and ('__packed_dbobj__', natural_key, _TO_DATESTRING(obj), _GA(obj, 'id'))) o... |
def get_datasets(input_size: int, split_cache_path='split_cache.pkl'):
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
train_transform = transforms.Compose([transforms.Resize((input_size, input_size)), transforms.ToTensor(), transforms.Normalize(mean, std)])
test_transform = transforms.Compose(... |
class FennelLexer(RegexLexer):
name = 'Fennel'
url = '
aliases = ['fennel', 'fnl']
filenames = ['*.fnl']
version_added = '2.3'
special_forms = ('#', '%', '*', '+', '-', '->', '->>', '-?>', '-?>>', '.', '..', '/', '//', ':', '<', '<=', '=', '>', '>=', '?.', '^', 'accumulate', 'and', 'band', 'bnot... |
class ExperimentPlanner3D_v21_customTargetSpacing_2x2x2(ExperimentPlanner3D_v21):
def __init__(self, folder_with_cropped_data, preprocessed_output_folder):
super(ExperimentPlanner3D_v21, self).__init__(folder_with_cropped_data, preprocessed_output_folder)
self.data_identifier = 'nnFormerData_plans_v... |
def activation(act_type, inplace=True, neg_slope=0.05, n_prelu=1):
act_type = act_type.lower()
if (act_type == 'relu'):
layer = nn.ReLU(inplace)
elif (act_type == 'lrelu'):
layer = nn.LeakyReLU(neg_slope, inplace)
elif (act_type == 'prelu'):
layer = nn.PReLU(num_parameters=n_prel... |
class While(_base_nodes.MultiLineWithElseBlockNode, _base_nodes.Statement):
_astroid_fields = ('test', 'body', 'orelse')
_multi_line_block_fields = ('body', 'orelse')
test: NodeNG
body: list[NodeNG]
orelse: list[NodeNG]
def postinit(self, test: NodeNG, body: list[NodeNG], orelse: list[NodeNG]) -... |
def require_deepspeed_aio(test_case):
if (not is_deepspeed_available()):
return unittest.skip('test requires deepspeed')(test_case)
import deepspeed
from deepspeed.ops.aio import AsyncIOBuilder
if (not deepspeed.ops.__compatible_ops__[AsyncIOBuilder.NAME]):
return unittest.skip('test req... |
(frozen=True)
class StandardPickupDefinition(JsonDataclass):
game: RandovaniaGame = dataclasses.field(metadata={'init_from_extra': True})
name: str = dataclasses.field(metadata={'init_from_extra': True})
pickup_category: PickupCategory = dataclasses.field(metadata={'init_from_extra': True})
broad_catego... |
def get_survey_project_request_handler(request_type: str) -> Callable:
handlers_dict = {'formEventMapping': handle_simple_project_form_event_mapping_request, 'metadata': handle_simple_project_metadata_request, 'record': handle_survey_project_records_request}
return handlers_dict[request_type] |
class FeatureListNet(FeatureDictNet):
def __init__(self, model, out_indices=(0, 1, 2, 3, 4), out_map=None, feature_concat=False, flatten_sequential=False):
super(FeatureListNet, self).__init__(model, out_indices=out_indices, out_map=out_map, feature_concat=feature_concat, flatten_sequential=flatten_sequenti... |
def simplify_responses(responses):
def unpack_multi(responses):
for resp in responses:
if isinstance(resp, MultiplyResponse):
for sub in unpack_multi(resp.responses):
(yield sub)
else:
(yield resp)
def cancel_pzs(poles, zeros):
... |
(frozen=True)
class EventPaymentReceivedSuccess(Event):
token_network_registry_address: TokenNetworkRegistryAddress
token_network_address: TokenNetworkAddress
identifier: PaymentID
amount: PaymentAmount
initiator: InitiatorAddress
def __post_init__(self) -> None:
if (self.amount < 0):
... |
def PQDescPath(coll, f, lcs):
f = (eval(f, globals(), lcs) if (f != '_') else None)
stack = []
if isList(coll):
stack = [i for i in flatten(coll)]
elif isMap(coll):
stack = [map_tuple(k, v) for (k, v) in coll.items()]
while stack:
i = stack.pop()
if isinstance(i, map_... |
def formatv(fwid, plusstr, pkstr, thresh, val):
if (abs(val) < thresh):
val1 = 0.0
else:
val1 = val
str = (pkstr % val1).strip()
if re.match('^-0\\.0*$', str):
str = str[1:]
if (str[0] != '-'):
str = (plusstr + str)
str = str.replace('e', 'D')
return (fwid % s... |
.skipif((not dependencies.lvm.is_available), reason='lvm not available')
def test_lvm_mount():
parser = ImageParser([fullpath('images/lvm.raw')])
volumes = []
for v in parser.init():
volumes.append(v)
assert (len(volumes) == 2)
assert (volumes[0].mountpoint is not None)
assert (volumes[0... |
class MypycNativeIntTests(TestCase):
def test_construction(self):
for native_int in native_int_types:
self.assert_same(native_int(), 0)
self.assert_same(native_int(0), 0)
self.assert_same(native_int(1), 1)
self.assert_same(native_int((- 3)), (- 3))
... |
def _check_shape(name, M, n, m, square=False, symmetric=False):
if (square and (M.shape[0] != M.shape[1])):
raise ControlDimension(('%s must be a square matrix' % name))
if (symmetric and (not _is_symmetric(M))):
raise ControlArgument(('%s must be a symmetric matrix' % name))
if ((M.shape[0]... |
class FancyFormatter():
def __init__(self, f_out: IO[str], f_err: IO[str], hide_error_codes: bool) -> None:
self.hide_error_codes = hide_error_codes
if (sys.platform not in ('linux', 'darwin', 'win32', 'emscripten')):
self.dummy_term = True
return
if ((not should_forc... |
def pass_orin_nano(engine):
return [add_engine_in_list('APE', engine, 'APE', 'APE'), (add_engine_in_list('NVENC', engine, 'NVENC', 'NVENC') + add_engine_in_list('NVDEC', engine, 'NVDEC', 'NVDEC')), (add_engine_in_list('NVJPG', engine, 'NVJPG', 'NVJPG') + add_engine_in_list('NVJPG1', engine, 'NVJPG', 'NVJPG1')), (ad... |
def parse_arguments():
parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
parser.add_argument('--data', help='path to dataset base directory', default='dataset/')
parser.add_argument('--optimizer', help='Which optimizer to use', default='sgd')
parser.add_argument('--set', help='na... |
(backend='memory', stale_after=timedelta(seconds=1), next_time=True)
def _error_throwing_func(arg1):
if (not hasattr(_error_throwing_func, 'count')):
_error_throwing_func.count = 0
_error_throwing_func.count += 1
if (_error_throwing_func.count > 1):
raise ValueError('Tiny Rick!')
return ... |
_criterion('cross_entropy')
class CrossEntropyCriterion(FairseqCriterion):
def __init__(self, args, task):
super().__init__(args, task)
def forward(self, model, sample, reduce=True):
net_output = model(**sample['net_input'])
(loss, _, sample_status) = self.compute_loss(model, net_output,... |
.end_to_end()
.parametrize('node_def', ["PathNode(path=Path('file.txt'))", "Path('file.txt')"])
def test_return_with_task_decorator(runner, tmp_path, node_def):
source = f'''
from pathlib import Path
from typing_extensions import Annotated
from pytask import task, PathNode
(produces={node_def})
... |
(models.Proposal)
class ProposalAdmin(TimeAuditAdmin, SimpleHistoryAdmin, ExportMixin):
list_display = ('proposal_info', 'author_info', 'author_email', 'conference', 'status', 'review_status')
list_filter = ['proposal_section__name', 'proposal_type', 'target_audience', 'conference', 'status', 'review_status']
... |
.parametrize('screenshot_manager', [{}, {'type': 'box'}, {'type': 'line'}, {'type': 'line', 'line_width': 1}, {'start_pos': 'top'}], indirect=True)
def ss_hddgraph(screenshot_manager):
widget = screenshot_manager.c.widget['hddgraph']
widget.eval(f'self.values={values}')
widget.eval('self.maxvalue=400')
... |
class SchemaValidator(KeywordValidator):
def __init__(self, registry: 'KeywordValidatorRegistry'):
super().__init__(registry)
self.schema_ids_registry: Optional[List[int]] = []
def default_validator(self) -> ValueValidator:
return cast(ValueValidator, self.registry['default'])
def __... |
def load_mnist():
(train, test) = tf.keras.datasets.mnist.load_data()
(train_data, train_labels) = train
(test_data, test_labels) = test
train_data = (np.array(train_data, dtype=np.float32) / 255)
test_data = (np.array(test_data, dtype=np.float32) / 255)
train_labels = np.array(train_labels, dty... |
def test_build_with_multiple_readme_files(fixture_dir: FixtureDirGetter, tmp_path: Path, tmp_venv: VirtualEnv, command_tester_factory: CommandTesterFactory) -> None:
source_dir = fixture_dir('with_multiple_readme_files')
target_dir = (tmp_path / 'project')
shutil.copytree(str(source_dir), str(target_dir))
... |
class ReferenceFinder(mypy.mixedtraverser.MixedTraverserVisitor):
def __init__(self) -> None:
self.refs: set[str] = set()
def visit_block(self, block: Block) -> None:
if (not block.is_unreachable):
super().visit_block(block)
def visit_name_expr(self, e: NameExpr) -> None:
... |
class MountainCarEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 30}
def __init__(self):
self.min_position = (- 1.2)
self.max_position = 0.6
self.max_speed = 0.07
self.goal_position = 0.5
self.low = np.array([self.min_position,... |
.parametrize('selection_bitsize', [3, 4])
.parametrize('target_bitsize', [3, 5, 6])
def test_arctan(selection_bitsize, target_bitsize):
gate = ArcTan(selection_bitsize, target_bitsize)
maps = {}
for x in range((2 ** selection_bitsize)):
inp = f'0b_{x:0{selection_bitsize}b}_0_{0:0{target_bitsize}b}'
... |
class ChangeEmail(LoginRequiredMixin, PasswordConfirmMixin, FormView):
template_name = 'dictionary/user/preferences/email.html'
form_class = ChangeEmailForm
success_url = reverse_lazy('user_preferences')
def form_valid(self, form):
send_email_confirmation(self.request.user, form.cleaned_data.get... |
def emit_yield(builder: IRBuilder, val: Value, line: int) -> Value:
retval = builder.coerce(val, builder.ret_types[(- 1)], line)
cls = builder.fn_info.generator_class
next_block = BasicBlock()
next_label = len(cls.continuation_blocks)
cls.continuation_blocks.append(next_block)
builder.assign(cls... |
def calc_face_dimensions(face):
horizontal_edges = filter_horizontal_edges(face.edges)
vertical_edges = filter_vertical_edges(face.edges)
width = (sum((e.calc_length() for e in horizontal_edges)) / 2)
height = (sum((e.calc_length() for e in vertical_edges)) / 2)
return (round(width, 4), round(height... |
def monitor(steps):
import re
with open('train_log/DQN-REALDATA/mean_score.log', 'r') as file:
c = file.read().splitlines()
i = (- 1)
while True:
if ('Start Epoch' in c[i]):
break
i -= 1
assert ('Start Epoch' in c[i])
current_epoch = int(re... |
def test_determine_ignored_lines():
f = incremental_coverage.determine_ignored_lines
assert (f('a = 0 # coverage: ignore') == {1})
assert (f('\n a = 0 # coverage: ignore\n b = 0\n ') == {2})
assert (f('\n a = 0 \n b = 0 # coverage: ignore\n ') == {3})
assert (f(... |
class TableModel(BaseTableModel):
def __init__(self, client: Client, attachment: bool=True, **kwargs: Any):
if attachment:
self._attachment = AttachmentModel(client, table_name=kwargs.get('table_name'))
super(TableModel, self).__init__(client, **kwargs)
def _api_url(self) -> Any:
... |
class ContextAuth():
clusterCertificate: str = None
clusterCertificateData: str = None
clusterHost: str = None
clientCertificate: str = None
clientCertificateData: str = None
clientKey: str = None
clientKeyData: str = None
clusterName: str = None
username: str = None
password: st... |
def local_property(name=None):
if name:
depr('local_property() is deprecated and will be removed.')
ls = threading.local()
def fget(self):
try:
return ls.var
except AttributeError:
raise RuntimeError('Request context not initialized.')
def fset(self, value... |
def parse_args(input_args=None):
parser = argparse.ArgumentParser(description='Simple example of a training script.')
parser.add_argument('--train', type=str, default='True', choices=['True', 'False'])
parser.add_argument('--edit', type=str, default='True', choices=['True', 'False'])
parser.add_argument... |
def main():
pp.connect(use_gui=True)
pp.add_data_path()
p.resetDebugVisualizerCamera(cameraDistance=2, cameraPitch=(- 20), cameraYaw=80, cameraTargetPosition=[0, 0, 0])
p.loadURDF('plane.urdf')
ri = reorientbot.pybullet.PandaRobotInterface()
cube = pp.create_box(0.05, 0.05, 0.05, mass=0.1, color... |
('evennia.server.portal.amp.amp.BinaryBoxProtocol.transport')
class TestAMPClientRecv(_TestAMP):
def test_msgportal2server(self, mocktransport):
self._connect_server(mocktransport)
self.amp_server.send_MsgPortal2Server(self.session, text={'foo': 'bar'})
wire_data = self._catch_wire_read(mock... |
def test_ellipsoidal2dcs_to_cf():
ecs = Ellipsoidal2DCS(axis=Ellipsoidal2DCSAxis.LATITUDE_LONGITUDE)
assert (ecs.to_cf() == [{'standard_name': 'latitude', 'long_name': 'latitude coordinate', 'units': 'degrees_north', 'axis': 'Y'}, {'standard_name': 'longitude', 'long_name': 'longitude coordinate', 'units': 'deg... |
class Effect3201(BaseEffect):
type = 'overheat'
def handler(fit, module, context, projectionRange, **kwargs):
module.boostItemAttr('duration', module.getModifiedItemAttr('overloadSelfDurationBonus'))
module.boostItemAttr('shieldBonus', module.getModifiedItemAttr('overloadShieldBonus'), stackingP... |
def test_shared_ptr_from_this_and_references():
s = m.SharedFromThisRef()
stats = ConstructorStats.get(m.B)
assert (stats.alive() == 2)
ref = s.ref
assert (stats.alive() == 2)
assert s.set_ref(ref)
assert s.set_holder(ref)
bad_wp = s.bad_wp
assert (stats.alive() == 2)
assert s.se... |
def test_protocol() -> None:
tv = TypedValue(Proto)
def fn() -> None:
pass
assert_cannot_assign(tv, KnownValue(fn))
fn.asynq = (lambda : None)
assert_can_assign(tv, KnownValue(fn))
class X():
def asynq(self) -> None:
pass
assert_can_assign(tv, TypedValue(X))
a... |
def join_simple(declaration: (Type | None), s: Type, t: Type) -> ProperType:
declaration = get_proper_type(declaration)
s = get_proper_type(s)
t = get_proper_type(t)
if ((s.can_be_true, s.can_be_false) != (t.can_be_true, t.can_be_false)):
s = mypy.typeops.true_or_false(s)
t = mypy.typeop... |
def compute_sec_ver(remediations, packages: Dict[(str, Package)], secure_vulns_by_user, db_full):
for pkg_name in remediations.keys():
pkg: Package = packages.get(pkg_name, None)
secure_versions = []
if pkg:
secure_versions = pkg.secure_versions
analyzed = set(remediation... |
def _orthographic__to_cf(conversion):
params = _to_dict(conversion)
return {'grid_mapping_name': 'orthographic', 'latitude_of_projection_origin': params['latitude_of_natural_origin'], 'longitude_of_projection_origin': params['longitude_of_natural_origin'], 'false_easting': params['false_easting'], 'false_northi... |
class BsonConverter(Converter):
def dumps(self, obj: Any, unstructure_as: Any=None, check_keys: bool=False, codec_options: CodecOptions=DEFAULT_CODEC_OPTIONS) -> bytes:
return encode(self.unstructure(obj, unstructure_as=unstructure_as), check_keys=check_keys, codec_options=codec_options)
def loads(self,... |
class BellState(Bloq):
_property
def signature(self) -> 'Signature':
return Signature([Register('q0', 1, side=Side.RIGHT), Register('q1', 1, side=Side.RIGHT)])
def build_composite_bloq(self, bb):
q0 = bb.add(PlusState())
q1 = bb.add(ZeroState())
(q0, q1) = bb.add(CNOT(), ctrl... |
.parametrize('url, expected_matches', [(' 1), (' 0), (' 0)])
def test_regex_includes_scripts_for(gm_manager, url, expected_matches):
gh_dark_example = textwrap.dedent('\n // ==UserScript==\n // /^ // / // -at document-start\n // ==/UserScript==\n ')
_save_script(g... |
class SemiDataset(Dataset):
def __init__(self, name, root, mode, size=None, id_path=None, nsample=None):
self.name = name
self.root = root
self.mode = mode
self.size = size
if ((mode == 'train_l') or (mode == 'train_u')):
with open(id_path, 'r') as f:
... |
class ClusterRedisContextFactory(ContextFactory):
def __init__(self, connection_pool: rediscluster.ClusterConnectionPool, name: str='redis', redis_client_name: str=''):
self.connection_pool = connection_pool
self.name = name
self.redis_client_name = redis_client_name
def report_runtime_m... |
def sampling(imps, ratio=4):
pos = []
neg = []
for imp in imps.split():
if (imp[(- 1)] == '1'):
pos.append(imp)
else:
neg.append(imp)
n_neg = (ratio * len(pos))
if (n_neg <= len(neg)):
neg = random.sample(neg, n_neg)
else:
neg = random.samp... |
class Adam(Optimizer):
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=1e-08, decay=0.0, **kwargs):
super(Adam, self).__init__(**kwargs)
with K.name_scope(self.__class__.__name__):
self.iterations = K.variable(0, dtype='int64', name='iterations')
self.lr = K.va... |
def send_key_click(widget, accel, recursive=False):
(key, mods) = Gtk.accelerator_parse(accel)
assert (key is not None)
assert (mods is not None)
assert isinstance(widget, Gtk.Widget)
handled = _send_key_click_event(widget, state=mods, keyval=key)
if recursive:
if isinstance(widget, Gtk.... |
def loadDataFile_with_groupseglabel(filename):
f = h5py.File(filename)
data = f['data'][:]
group = f['pid'][:]
if ('groupcategory' in f):
cate = f['groupcategory'][:]
else:
cate = 0
seg = ((- 1) * np.ones_like(group))
for i in range(group.shape[0]):
for j in range(gro... |
.parametrize('genotype, expect', [([(- 1), 0], (- 1)), ([0, (- 1)], (- 1)), ([0, 0], 0), ([0, 1], 1), ([1, 0], 1), ([1, 1], 2), ([0, 0, 0], 0), ([0, 1, 0], 1), ([1, 1, 1], 3), ([0, 0, 0, 0], 0), ([0, 1, 0, 1], 2), ([1, 1, 0, 1], 3)])
def test__biallelic_genotype_index(genotype, expect):
genotype = np.array(genotype... |
class MLPBlockFC(nn.Module):
def __init__(self, d_points, d_model, p_dropout):
super(MLPBlockFC, self).__init__()
self.mlp = nn.Sequential(nn.Linear(d_points, d_model, bias=False), nn.BatchNorm1d(d_model), nn.LeakyReLU(negative_slope=0.2), nn.Dropout(p=p_dropout))
def forward(self, x):
r... |
def main():
args = set_args()
init(args)
Tokenizer = eval_object(model_dict[args.model][0])
bert_path_or_name = model_dict[args.model][(- 1)]
tokenizer = Tokenizer.from_pretrained(bert_path_or_name)
print((20 * '='), ' Preparing for training ', (20 * '='))
print('\t* Loading training data...... |
class nnUNetTrainerDAOrd0(nnUNetTrainer):
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) = self.... |
def prepare_ocp(biorbd_model_path, n_shooting, tf, ode_solver=OdeSolver.RK4(), use_sx=True, expand_dynamics=True):
bio_model = BiorbdModel(biorbd_model_path)
dynamics = Dynamics(DynamicsFcn.TORQUE_DRIVEN, expand_dynamics=expand_dynamics)
x_bounds = BoundsList()
x_bounds['q'] = bio_model.bounds_from_rang... |
class SetData(namedtuple('SetData', 'path data version')):
type = 5
def serialize(self):
b = bytearray()
b.extend(write_string(self.path))
b.extend(write_buffer(self.data))
b.extend(int_struct.pack(self.version))
return b
def deserialize(cls, bytes, offset):
r... |
class OptUx(object):
def __init__(self, formula, solver='g3', adapt=False, cover=None, dcalls=False, exhaust=False, minz=False, puresat=False, unsorted=False, trim=False, verbose=0):
assert ((not puresat) or unsorted), "'unsorted' needs to be True for pure SAT mode"
self.verbose = verbose
se... |
class VideoChunkIterator():
def __init__(self, video_features: np.ndarray, chunk_frames: int, num_border_frames: int) -> None:
self.chunk_features_expanded = None
self.valid_chunk_size = None
self._output_start = None
self._output_end = None
self._result_start = None
... |
def test_interpolation():
interp = Interpolate((0, 100), (0, 100))
for i in range(101):
assert (interp(i) == i)
interp = Interpolate((0, 50, 100), (0, 100, 200))
for i in range(101):
assert (interp(i) == (2 * i))
interp = Interpolate((0, 50, 100), (0, (- 50), 50))
assert (interp(... |
def burn_eth(rpc_client: JSONRPCClient, amount_to_leave: int=0) -> None:
address = rpc_client.address
web3 = rpc_client.web3
gas_price = web3.eth.gas_price
amount_to_leave = (TRANSACTION_INTRINSIC_GAS + amount_to_leave)
amount_to_burn = (web3.eth.get_balance(address) - (gas_price * amount_to_leave))... |
class DebianControlLexer(RegexLexer):
name = 'Debian Control file'
url = '
aliases = ['debcontrol', 'control']
filenames = ['control']
version_added = '0.9'
tokens = {'root': [('^(Description)', Keyword, 'description'), ('^(Maintainer|Uploaders)(:\\s*)', bygroups(Keyword, Text), 'maintainer'), (... |
def test_upload_photos(requests_mock):
requests_mock.post(f'{API_V0}/observation_photos', json=load_sample_data('post_observation_photos.json'), status_code=200)
response = upload_photos(1234, BytesIO(), access_token='token')
assert (response[0]['id'] == 1234)
assert (response[0]['created_at'] == '2020-... |
class NoOptionError(Error):
def __init__(self, option: str, *, all_names: List[str]=None, deleted: bool=False, renamed: str=None) -> None:
if deleted:
assert (renamed is None)
suffix = ' (this option was removed from qutebrowser)'
elif (renamed is not None):
suffi... |
class TaggerModel(nn.Module):
def __init__(self, args: Namespace, device: torch.device):
super(TaggerModel, self).__init__()
self.modelid = 'tagger_baseline'
self.args = args
self.device = device
self.max_token = (args.max_generate + 1)
self._encoder = PLM(args, devic... |
def test_tc_bit_defers_last_response_missing():
zc = Zeroconf(interfaces=['127.0.0.1'])
_wait_for_start(zc)
type_ = '_knowndefer._tcp.local.'
name = 'knownname'
name2 = 'knownname2'
name3 = 'knownname3'
registration_name = f'{name}.{type_}'
registration2_name = f'{name2}.{type_}'
reg... |
class GP(SingleTaskGP):
def __init__(self, train_x, train_y, likelihood, lengthscale_constraint, outputscale_constraint, ard_dims, hyper=1.0, saas=True):
covar_module = _prepare_covar_module(ard_dims, lengthscale_constraint, outputscale_constraint, hyper=hyper, saas=saas)
super(GP, self).__init__(tr... |
class GaussianBlur(object):
def __init__(self, kernel_size, min=0.1, max=2.0):
self.min = min
self.max = max
self.kernel_size = kernel_size
def __call__(self, sample):
sample = np.array(sample)
prob = np.random.random_sample()
if (prob < 0.5):
sigma = ... |
class FM(object):
def __init__(self, formula, enc=EncType.pairwise, solver='m22', verbose=1):
self.verbose = verbose
self.solver = solver
self.time = 0.0
self.topv = self.orig_nv = formula.nv
self.hard = copy.deepcopy(formula.hard)
self.soft = copy.deepcopy(formula.so... |
class MainWindow(QMainWindow):
def __init__(self):
super(MainWindow, self).__init__()
centralWidget = QWidget()
self.setCentralWidget(centralWidget)
self.glWidget = GLWidget()
self.pixmapLabel = QLabel()
self.glWidgetArea = QScrollArea()
self.glWidgetArea.setW... |
class Effect5927(BaseEffect):
runTime = 'early'
type = ('projected', 'passive')
def handler(fit, beacon, context, projectionRange, **kwargs):
fit.modules.filteredChargeMultiply((lambda mod: mod.charge.requiresSkill('Bomb Deployment')), 'scanRadarStrengthBonus', beacon.getModifiedItemAttr('smartbombD... |
_fixtures(WebFixture, ChoicesFixture)
def test_choices_layout_applied_to_checkbox(web_fixture, choices_fixture):
fixture = choices_fixture
stacked_container = Div(web_fixture.view).use_layout(ChoicesLayout())
stacked_container.layout.add_choice(PrimitiveCheckboxInput(fixture.form, fixture.boolean_field))
... |
class Trainer(TrainerBase):
def __init__(self, args, train_loader=None, val_loader=None, test_loader=None, train=True):
super().__init__(args, train_loader=train_loader, val_loader=val_loader, test_loader=test_loader, train=train)
if (not self.verbose):
set_global_logging_level(logging.E... |
class AsyncWorker():
_terminator = object()
def __init__(self, shutdown_timeout=DEFAULT_TIMEOUT):
check_threads()
self._queue = Queue((- 1))
self._lock = threading.Lock()
self._thread = None
self._thread_for_pid = None
self.options = {'shutdown_timeout': shutdown_... |
_test
def test_merge_average():
i1 = layers.Input(shape=(4, 5))
i2 = layers.Input(shape=(4, 5))
o = layers.average([i1, i2])
assert (o._keras_shape == (None, 4, 5))
model = models.Model([i1, i2], o)
avg_layer = layers.Average()
o2 = avg_layer([i1, i2])
assert (avg_layer.output_shape == (... |
def test_offxml_combine_no_polar_lj(tmpdir, methanol, rfree_data, vs):
with tmpdir.as_cwd():
alpha = rfree_data.pop('alpha')
beta = rfree_data.pop('beta')
lj = LennardJones612(free_parameters=rfree_data, alpha=alpha, beta=beta, lj_on_polar_h=False)
lj.run(methanol)
rfree_data... |
def test_python_cmdline(testcase: DataDrivenTestCase, step: int) -> None:
assert (testcase.old_cwd is not None), 'test was not properly set up'
program = '_program.py'
program_path = os.path.join(test_temp_dir, program)
with open(program_path, 'w', encoding='utf8') as file:
for s in testcase.inp... |
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