code stringlengths 281 23.7M |
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class TemporalModulation(nn.Module):
def __init__(self, in_channels, out_channels, downsample_scale=8):
super().__init__()
self.conv = ConvModule(in_channels, out_channels, (3, 1, 1), stride=(1, 1, 1), padding=(1, 0, 0), bias=False, groups=32, conv_cfg=dict(type='Conv3d'), act_cfg=None)
self... |
class Loss(ABC):
def __call__(self, predict: np.ndarray, target: np.ndarray) -> np.ndarray:
return self.evaluate(predict, target)
def evaluate(self, predict: np.ndarray, target: np.ndarray) -> np.ndarray:
raise NotImplementedError
def _validate_shapes(predict: np.ndarray, target: np.ndarray)... |
def get_sorted_s_r_embed_limit(s_hist, s, r, ent_embeds, limit):
s_hist_len = to_device(torch.LongTensor(list(map(len, s_hist))))
(s_len, s_idx) = s_hist_len.sort(0, descending=True)
num_non_zero = len(torch.nonzero(s_len))
s_len_non_zero = s_len[:num_non_zero]
s_len_non_zero = torch.where((s_len_no... |
def test_read_pinned_buffer(tmpdir):
data_fname = tmpdir.join('test_read.sigmf-data')
actual = cp.random.rand(100).astype(cp.complex64)
actual.tofile(data_fname)
binary = cusignal.read_bin(str(data_fname), dtype=cp.complex64)
buffer = cusignal.get_pinned_mem(binary.shape, cp.complex64)
expect = ... |
def get_train_val_data(data_path, tokenizer, val_data_path=None, val_set_size=1, augment_times=1, load_pre_prompt_dataset=False, vqa=False, add_input_prompt=False, eval_only=False, eval_items=None):
if eval_only:
return (None, get_val_data(val_data_path, tokenizer, val_set_size, eval_items=eval_items))
... |
.parametrize('exc', [ValueError, SystemExit])
def test_wrapper_exception(exc: 'Type[BaseException]') -> None:
out = []
(wrapper=True)
def m1():
out.append('m1 init')
try:
result = (yield)
except BaseException as e:
assert isinstance(e, exc)
raise
... |
def create_palette_from_dict(conf):
palette = QtGui.QPalette()
for (key, value) in conf.items():
(group, role) = key.split(':')
if hasattr(QtGui.QPalette.ColorGroup, group):
palette.setColor(getattr(QtGui.QPalette.ColorGroup, group), getattr(QtGui.QPalette.ColorRole, role), QtGui.QCo... |
class _CanAssignBasedContext():
can_assign_ctx: CanAssignContext
visitor: Optional['NameCheckVisitor'] = None
errors: List[str] = field(default_factory=list)
def on_error(self, message: str, *, code: ErrorCode=ErrorCode.incompatible_call, node: Optional[ast.AST]=None, detail: Optional[str]=..., replacem... |
def test_stream_create(stream):
assert (stream.is_unconnected == True)
assert (stream.is_creating == False)
assert (stream.is_ready == False)
assert (stream.is_failed == False)
assert (stream.is_terminated == False)
with stream.mainloop.lock:
stream.delete()
assert (stream.is_unconne... |
class UnbroadcastLayer(Layer):
def __init__(self, incoming, broadcast_layer, **kwargs):
self.broadcast_layer = broadcast_layer
assert (len(incoming.output_shape) == len(self.broadcast_layer.output_shape))
incoming = AwaitLayer(incoming, layer_to_await=broadcast_layer)
super(Unbroadca... |
class GeoBUGSTextIO(fileio.FileIO):
FORMATS = ['geobugs_text']
MODES = ['r', 'w']
def __init__(self, *args, **kwargs):
args = args[:2]
fileio.FileIO.__init__(self, *args, **kwargs)
self.file = open(self.dataPath, self.mode)
def read(self, n=(- 1)):
self._complain_ifclosed... |
def test_hetero_tuple_validation():
c = Converter(detailed_validation=True)
with pytest.raises(IterableValidationError) as exc:
c.structure(['1', 2, 'a'], Tuple[(int, int, int)])
assert (repr(exc.value.exceptions[0]) == repr(ValueError("invalid literal for int() with base 10: 'a'")))
assert (exc... |
def get_files(**kwargs):
metadata_directory = kwargs.get('metadata_directory', '')
files = []
for f in get_template_files(**kwargs):
if (str(f.path) == 'LICENSE.txt'):
files.append(File(Path(metadata_directory, 'licenses', f.path), f.contents))
if (f.path.parts[0] != kwargs['pack... |
class MovieGenres(Enum):
Action = '28'
Adventure = '12'
Animation = '16'
Comedy = '35'
Crime = '80'
Documentary = '99'
Drama = '18'
Family = '10751'
Fantasy = '14'
History = '36'
Horror = '27'
Music = '10402'
Mystery = '9648'
Romance = '10749'
Science = '878'
... |
def commands_spotting_challenge_validated(specific_features_dir: str, feature_name: str, run_name: str=RUN_NAME, results_dir: str=RESULTS_DIR, models_dir: str=MODELS_DIR, labels_dir: str=LABELS_DIR, splits_dir: str=SPLITS_DIR, base_config_dir: str=BASE_CONFIG_DIR, memory_setup: str=MEMORY_SETUP) -> List[Command]:
d... |
class InceptionV3Aux(InceptionV3):
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0.0, global_pool='avg', aux_logits=True):
super(InceptionV3Aux, self).__init__(num_classes, in_chans, drop_rate, global_pool, aux_logits)
def forward_features(self, x):
x = self.forward_preaux(x)
... |
.skipif((not is_py310_plus), reason='3.10+ union syntax')
def test_roundtrip_generic_with_union() -> None:
c = Converter()
class A():
a: int
class B():
b: int
class Outer(Generic[T]):
member: T
raw = c.unstructure(Outer(A(1)), unstructure_as=Outer[(A | B)])
assert (c.stru... |
class UDPEndpoint():
ip: IPAddress
port: int
def as_python_sockaddr(self) -> (tuple[(str, int)] | tuple[(str, int, int, int)]):
sockaddr: (tuple[(str, int)] | tuple[(str, int, int, int)]) = (self.ip.compressed, self.port)
if isinstance(self.ip, ipaddress.IPv6Address):
sockaddr +=... |
class ModbusSlaveContext(ModbusBaseSlaveContext):
def __init__(self, *_args, **kwargs):
self.store = {}
self.store['d'] = kwargs.get('di', ModbusSequentialDataBlock.create())
self.store['c'] = kwargs.get('co', ModbusSequentialDataBlock.create())
self.store['i'] = kwargs.get('ir', Mod... |
def calculate_loss_for_nq(start_logits, start_labels, end_logits, end_labels, pooled_logits, pooler_labels):
def cross_entropy(logits, labels, reduction=None):
vocab_size = logits.shape[(- 1)]
labels = (labels[(..., None)] == jnp.arange(vocab_size)[None]).astype('f4')
logits = jax.nn.log_sof... |
class TestJit(object):
def test_add_dict(self):
def add_dict(oper):
rets = (oper['x'] + oper['y'])
return {'result': rets}
def add_dict_pyfunc(oper):
rets = (oper['x'] + oper['y'])
return {'result': rets}
a = torch.rand((3, 4))
b = torc... |
def test_RankInvariantChecker_remove_one_alternative():
dm = skc.datasets.load_simple_stock_selection()
dmaker = RemoveAlternativeDMaker(TOPSIS(), ['AA'], 1)
rrt1 = RankInvariantChecker(dmaker, random_state=42, allow_missing_alternatives=True)
result = rrt1.evaluate(dm)
(_, rank) = result.ranks[1]
... |
def init_test_environ():
global _TEMP_DIR, _BUS_INFO, _VDISPLAY
_TEMP_DIR = tempfile.mkdtemp(prefix=fsnative('QL-TEST-'))
os.environ['XDG_CACHE_HOME'] = xdg_get_cache_home()
os.environ['GST_REGISTRY_UPDATE'] = fsnative('no')
if util.is_flatpak():
del os.environ['GST_REGISTRY_UPDATE']
hom... |
class TestBitFlags(unittest.TestCase):
def test_bitflags(self):
from functools import reduce
import numpy as np
from satpy.readers.olci_nc import BitFlags
flag_list = ['SEA_ICE', 'MEGLINT', 'HIGHGLINT', 'CASE2_S', 'CASE2_ANOM', 'HAZE_OVER_WATER', 'WHITECAPS', 'AC_FAIL', 'BPAC_ON', 'W... |
def get_flat_faces(faces, visited):
flat_edges = list({e for f in faces for e in f.edges if ((len(e.link_faces) > 1) and equal(e.calc_face_angle(), 0))})
flat_faces = []
for e in flat_edges:
for f in e.link_faces:
if (not visited.get(f, False)):
visited[f] = True
... |
def cnn():
datagen = ImageDataGenerator(rescale=(1.0 / 255))
train_generator = datagen.flow_from_directory(train_data_dir, target_size=(img_width, img_height), batch_size=batch_size, class_mode='binary')
validation_generator = datagen.flow_from_directory(validation_data_dir, target_size=(img_width, img_heig... |
class configuration():
def __init__(self):
self.currentFile = ''
self.currentLabelFile = ''
self.currentCorrectionFile = ''
self.csPath = ''
self.city = ''
self.cityName = ''
self.gtType = ''
self.split = ''
self.labelPath = ''
self.cor... |
class Backforward(textbase.TextBase):
def __init__(self, parent=None):
super().__init__(parent)
self.enabled = False
def on_tab_cur_url_changed(self, tabs):
tab = tabs.widget.currentWidget()
if (tab is None):
self.setText('')
self.hide()
return... |
class DistortionDataset(data.Dataset):
def __init__(self, distorted_image_dir, corrected_image_dir, transform):
self.distorted_image_paths = []
self.corrected_image_paths = []
for fs in os.listdir(distorted_image_dir):
self.distorted_image_paths.append(os.path.join(distorted_imag... |
class TransactionMined():
from_address: Address
data: Union[(SmartContractCall, ByteCode, EthTransfer)]
eth_node: Optional[EthClient]
extra_log_details: Dict[(str, Any)]
startgas: int
gas_price: int
nonce: Nonce
transaction_hash: TransactionHash
receipt: TxReceipt
chain_id: Chain... |
def KD_loss(args, teacher_g, noise, fake_img, fake_img_list, percept_loss):
fake_img_teacher_list = teacher_g(noise, return_rgb_list=True)
fake_img_teacher = fake_img_teacher_list[(- 1)]
fake_img_teacher.requires_grad = True
if (args.kd_l1_mode == 'Output_Only'):
kd_l1_loss = (args.kd_l1_lambda ... |
def main(args):
savefolder = args.savefolder
device = args.device
os.makedirs(savefolder, exist_ok=True)
if (not torch.cuda.is_available()):
print('CUDA is not available! use CPU instead')
else:
cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
torch.b... |
class Obelisk(TutorialObject):
def at_object_creation(self):
super().at_object_creation()
self.db.tutorial_info = 'This object changes its desc randomly, and makes sure to remember which one you saw.'
self.db.puzzle_descs = ['You see a normal stone slab']
self.locks.add('get:false()'... |
def main():
parser = argparse.ArgumentParser(description='Process some stuff')
parser.add_argument('-r', '--run', dest='model_to_run', nargs='+')
parser.add_argument('-rhs', '--run_hotstart', dest='hotstart_model_to_run', nargs='+')
parser.add_argument('-sp', '--start_pool', dest='start_pool', nargs='+'... |
def test_fix_cmd_darwin():
export_file_content = '\nC++ geobase5::details::lookup_impl::*\nC++ geobase5::hardcoded_service\n'
filename = write_temp_file(export_file_content)
args = ['-Wl,--version-script={}'.format(filename)]
assert (fix_cmd('DARWIN', args) == ['-Wl,-exported_symbol,__ZN8geobase57detail... |
_model_architecture('transformer_lm', 'transformer_lm_gpt3_large')
def transformer_lm_gpt3_large(args):
args.decoder_layers = getattr(args, 'decoder_layers', 24)
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 1536)
args.decoder_attention_heads = getattr(args, 'decoder_attention_heads', 16)
... |
def test_method_ports():
incr = IncrMethodPorts()
incr.apply(DefaultPassGroup())
print('\n==== Schedule ====')
for blk in incr._sched.update_schedule:
if (not blk.__name__.startswith('s')):
print(blk.__name__)
print('\n==== Line trace ====')
print(' buf1 buf2')
incr.... |
('/list/view_domain', methods=['POST'])
_wrapper_json
def list_domain_name():
json_data = request.get_json(force=True)
domain_name = json_data.get('domain_name', '').strip()
server_rooms = json_data.get('server_rooms', [])
isps = json_data.get('isps', [])
select_cdn = json_data.get('select_cdn', Tru... |
class SmtPrinter(TreeWalker):
def __init__(self, stream, annotations=None):
TreeWalker.__init__(self)
self.stream = stream
self.write = self.stream.write
self.mgr = get_env().formula_manager
self.annotations = annotations
def printer(self, f):
self.walk(f)
def... |
def compute_dense_reward(self, action, obs):
handle_dist = np.linalg.norm((obs[:3] - obs[4:7]))
window_diff = np.linalg.norm((obs[4:7] - self.env._get_pos_goal()))
action_reg = np.linalg.norm(action)
(w1, w2, w3) = (1.0, 1.0, 0.1)
reward = ((((- w1) * handle_dist) - (w2 * window_diff)) - (w3 * actio... |
def get_array_date(scn_data, utc_date=None):
if (utc_date is None):
try:
utc_date = scn_data.attrs['start_time']
except KeyError:
try:
utc_date = scn_data.attrs['scheduled_time']
except KeyError:
raise KeyError('Scene has no start_t... |
def chat_member_administrator():
return ChatMemberAdministrator(CMDefaults.user, CMDefaults.can_be_edited, CMDefaults.is_anonymous, CMDefaults.can_manage_chat, CMDefaults.can_delete_messages, CMDefaults.can_manage_video_chats, CMDefaults.can_restrict_members, CMDefaults.can_promote_members, CMDefaults.can_change_in... |
def run(config):
config['resolution'] = utils.imsize_dict[config['dataset']]
config['n_classes'] = utils.nclass_dict[config['dataset']]
config['G_activation'] = utils.activation_dict[config['G_nl']]
config['D_activation'] = utils.activation_dict[config['D_nl']]
if config['resume']:
print('Sk... |
('time.sleep')
def test_wait_until_true_invoke_inline(mock_time_sleep):
mock = MagicMock()
mock.side_effect = ['test string 1', 'test string 2', 'test string 3', 'expected value', 'test string 5']
def decorate_me(arg1, arg2):
assert (arg1 == 'v1')
assert (arg2 == 'v2')
return (mock(a... |
class DropBertModel():
def __init__(self, args, network, state_dict=None, num_train_step=(- 1)):
self.args = args
self.train_loss = AverageMeter()
self.step = 0
self.updates = 0
self.network = network
if (state_dict is not None):
print('Load Model!')
... |
class SawyerButtonPressTopdownWallEnvV2(SawyerXYZEnv):
def __init__(self):
hand_low = ((- 0.5), 0.4, 0.05)
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.1), 0.8, 0.115)
obj_high = (0.1, 0.9, 0.115)
super().__init__(self.model_name, hand_low=hand_low, hand_high=hand_high)
... |
class Solution(object):
def largestDivisibleSubset(self, nums):
ls = len(nums)
S = {(- 1): set()}
for num in sorted(nums):
candicate = []
for key in S:
if ((num % key) == 0):
candicate.append(S[key])
S[num] = (max(candic... |
class Vgg19(vgg19.Vgg19):
def build(self, gray, train=False):
start_time = time.time()
print('build model started')
net = dict()
with tf.variable_scope('vgg19', reuse=tf.AUTO_REUSE):
gray_scaled = ((gray + 0.5) * 255.0)
bgr = tf.concat([(gray_scaled - VGG_MEAN... |
def test_simple():
def foo(a: int, /, b: int, c: str='', *, d: int=0, e: str, **kwargs: int) -> int:
pass
assert (get_callable_shape(foo) == Shape(input=InputShape(fields=(InputField(id='a', type=int, default=NoDefault(), metadata=MappingProxyType({}), original=ANY, is_required=True), InputField(id='b',... |
class Light_estimation(nn.Module):
def __init__(self):
super(Light_estimation, self).__init__()
self.dense = models.densenet121(pretrained=True).features
self.pool = nn.AvgPool2d(8)
self.color1 = nn.Linear(1024, 512, bias=False)
self.relu1 = nn.ReLU()
self.color2 = nn... |
def test_poetry_with_explicit_pypi_and_other(fixture_dir: FixtureDirGetter, with_simple_keyring: None) -> None:
io = BufferedIO()
poetry = Factory().create_poetry(fixture_dir('with_explicit_pypi_and_other'), io=io)
assert (len(poetry.pool.repositories) == 1)
assert (len(poetry.pool.all_repositories) == ... |
class CustomErrorCodePlugin(Plugin):
def get_function_hook(self, fullname: str) -> (Callable[([FunctionContext], Type)] | None):
if fullname.endswith('.main'):
return self.emit_error
return None
def emit_error(self, ctx: FunctionContext) -> Type:
ctx.api.fail('Custom error', ... |
def prune_completely_outside_window(boxlist, window, scope=None):
with tf.name_scope(scope, 'PruneCompleteleyOutsideWindow'):
(y_min, x_min, y_max, x_max) = tf.split(value=boxlist.get(), num_or_size_splits=4, axis=1)
(win_y_min, win_x_min, win_y_max, win_x_max) = tf.unstack(window)
coordinat... |
class FireUnit(nn.Module):
def __init__(self, in_channels, squeeze_channels, expand1x1_channels, expand3x3_channels, residual):
super(FireUnit, self).__init__()
self.residual = residual
self.squeeze = FireConv(in_channels=in_channels, out_channels=squeeze_channels, kernel_size=1, padding=0)
... |
def test_L1_bit_selection():
a = CaseConnectBitSelToOutComp.DUT()
a.elaborate()
a.apply(StructuralRTLIRGenL1Pass(gen_connections(a)))
connections = a.get_metadata(StructuralRTLIRGenL1Pass.connections)
comp = sexp.CurComp(a, 's')
assert (connections == [(sexp.PartSelection(sexp.CurCompAttr(comp, ... |
def tensor4(name: Optional[str]=None, *, dtype: Optional['DTypeLike']=None, shape: Optional[tuple[(ST, ST, ST, ST)]]=(None, None, None, None)) -> 'TensorVariable':
if (dtype is None):
dtype = config.floatX
shape = _validate_static_shape(shape, ndim=4)
type = TensorType(dtype, shape=shape)
return... |
class TransformerEncoderLayer(nn.Module):
def __init__(self, args):
super().__init__()
self.embed_dim = args.encoder_embed_dim
self.lstm_san = args.encoder_lstm_san
if self.lstm_san:
self.self_attn = nn.LSTM(input_size=self.embed_dim, hidden_size=self.embed_dim, bidirecti... |
class TestChangeProperty(EndianTest):
def setUp(self):
self.req_args_0 = {'data': (8, b''), 'mode': 0, 'property': , 'type': , 'window': }
self.req_bin_0 = b'\x12\x00\x00\x06\x1dRr|i1\xd3\xd5\x04\x1c\xdcQ\x08\x00\x00\x00\x00\x00\x00\x00'
self.req_args_1 = {'data': (8, b'foo'), 'mode': 1, 'pr... |
def test_base_history(base_app):
run_cmd(base_app, 'help')
run_cmd(base_app, 'shortcuts')
(out, err) = run_cmd(base_app, 'history')
expected = normalize('\n 1 help\n 2 shortcuts\n')
assert (out == expected)
(out, err) = run_cmd(base_app, 'history he')
expected = normalize('\n 1 h... |
def convert_urdf(file: Path, urdf_dir: Path, out_dir: Path):
tree = ET.parse(file)
root = tree.getroot()
for mesh_node in root.findall('.//mesh'):
obj_file = mesh_node.attrib['filename']
obj_file = str(Path(obj_file).with_suffix('.obj'))
glb_file = str(Path(obj_file).with_suffix('.gl... |
def _node_to_pattern(node):
if hasattr(node.op, 'connection_pattern'):
connection_pattern = node.op.connection_pattern(node)
if (not isinstance(connection_pattern, list)):
raise TypeError((('Op.connection_pattern should return ' + f'list of list of bool, but for Op={node.op}') + f'got {c... |
def test_repeated_show():
mesh = gfx.Mesh(gfx.sphere_geometry(), gfx.MeshPhongMaterial())
camera = gfx.PerspectiveCamera()
scene = gfx.Scene()
scene.add(mesh, camera.add(gfx.DirectionalLight()))
camera.show_object(scene)
cam_width = camera.width
scene_radius = scene.get_world_bounding_sphere... |
_HEADS.register_module()
class ResLayer(nn.Module):
def __init__(self, depth, stage=3, stride=2, dilation=1, style='pytorch', norm_cfg=dict(type='BN', requires_grad=True), norm_eval=True, with_cp=False, dcn=None):
super(ResLayer, self).__init__()
self.norm_eval = norm_eval
self.norm_cfg = no... |
(suppress_health_check=[HealthCheck.function_scoped_fixture], deadline=timedelta(seconds=10))
(args=st.lists(st.integers(min_value=0, max_value=1), max_size=2))
.filterwarnings('ignore:.*:pytest.PytestUnraisableExceptionWarning')
def test_as_completed(ray_context, args):
args = sorted(args, reverse=True)
expect... |
.parametrize('setting,expect_value', [(None, None), ('1', False), ('0', False)])
def test_no_color_env_var(runner, monkeypatch, setting, expect_value, boxed_context, in_tmp_dir, tmp_path):
if (setting is None):
monkeypatch.delenv('NO_COLOR', raising=False)
else:
monkeypatch.setenv('NO_COLOR', se... |
class AstroidIndexError(AstroidError):
def __init__(self, message: str='', node: ((nodes.NodeNG | bases.Instance) | None)=None, index: (nodes.Subscript | None)=None, context: (InferenceContext | None)=None, **kws: Any) -> None:
self.node = node
self.index = index
self.context = context
... |
def run(kubeconfig_path, scenario, pre_action_output=''):
if (scenario.endswith('.yaml') or scenario.endswith('.yml')):
logging.error('Powerfulseal support has recently been removed. Please switch to using plugins instead.')
elif scenario.endswith('.py'):
action_output = runcommand.invoke(('pyth... |
class CalendarWrapperTests(unittest.TestCase):
NO_HOLIDAYS_IN_MONTH = 0
CALENDARBK_WIDTH_COEFF = 9
CALENDARBK_HEIGHT_OFFSET = 112
TITLEBK_WIDTH_COEFF = 2
TITLEBK_HEIGHT_COEFF = 26.67
def setUp(self):
self.app = Application().start(os.path.join(mfc_samples_folder, u'CmnCtrl1.exe'))
... |
('document.tables is a list containing three tables')
def then_document_tables_is_a_list_containing_three_tables(context):
document = context.document
tables = document.tables
assert isinstance(tables, list)
assert (len(tables) == 3)
for table in tables:
assert isinstance(table, Table) |
class VBObject(AObject):
FEATURE_FUNCS = {}
def __init__(self, path=None):
AObject.__init__(self, path=path)
def initializeBlank(self):
AObject.initializeBlank(self)
self.a_info.update({'VBObjectType': self.VBOBJECT_TYPE()})
self.features = AFuncDict(owner=self, name='feature... |
def test_describe_evaluated_once(testdir):
testdir.makepyfile('\n count = 0\n def describe_is_evaluated_only_once():\n global count\n count += 1\n def one():\n assert count == 1\n def two():\n assert count == 1\n def ... |
class TabHistoryItem():
def __init__(self, url, title, *, original_url=None, active=False, user_data=None, last_visited=None):
self.url = url
if (original_url is None):
self.original_url = url
else:
self.original_url = original_url
self.title = title
s... |
class ConvolutionalBoxPredictorBuilderTest(tf.test.TestCase):
def test_box_predictor_calls_conv_argscope_fn(self):
conv_hyperparams_text_proto = '\n regularizer {\n l1_regularizer {\n weight: 0.0003\n }\n }\n initializer {\n truncated_normal_initializer {\n ... |
def load_results(root_dir_or_dirs, enable_progress=True, enable_monitor=True, verbose=False):
import re
if isinstance(root_dir_or_dirs, str):
rootdirs = [osp.expanduser(root_dir_or_dirs)]
else:
rootdirs = [osp.expanduser(d) for d in root_dir_or_dirs]
allresults = []
for rootdir in ro... |
def test_call_super_creates_temp_invalid(pe_bio_teacher):
pe_bio_teacher.teach_new_classes(['Computer Science', 'Video Production', 'Life Science'])
assert (len(pe_bio_teacher.currently_teaching) == 3)
assert (pe_bio_teacher.wage == (pe_bio_teacher.wage_per_class * len(pe_bio_teacher.currently_teaching))) |
class FrameManager(EventEmitter):
Events = SimpleNamespace(FrameAttached='frameattached', FrameNavigated='framenavigated', FrameDetached='framedetached', LifecycleEvent='lifecycleevent', FrameNavigatedWithinDocument='framenavigatedwithindocument')
def __init__(self, client: CDPSession, frameTree: Dict, page: An... |
class CIFAR_ResNet50_BiFPN(nn.Module):
def __init__(self, num_classes=100):
super(CIFAR_ResNet50_BiFPN, self).__init__()
self.backbone = CIFAR_ResNet50(num_classes=num_classes)
self.bifpn = BiFPNc(self.backbone.network_channels, num_classes, repeat=1, depth=([1] * 3), width=1)
def forwar... |
def dequantize(arr, min_val, max_val, levels, dtype=np.float64):
if (not (isinstance(levels, int) and (levels > 1))):
raise ValueError(f'levels must be a positive integer, but got {levels}')
if (min_val >= max_val):
raise ValueError(f'min_val ({min_val}) must be smaller than max_val ({max_val})'... |
def get_rel_loc_tensor(lat1, lon1, lat, lon, zoom):
scale = (1 << zoom)
(ul_proj_x, ul_proj_y) = project_with_scale_tensor(lat1, lon1, scale)
ul_tile_x = (ul_proj_x // 1)
ul_tile_y = (ul_proj_y // 1)
ul_pixel_x = (ul_tile_x * TILE_SIZE)
ul_pixel_y = (ul_tile_y * TILE_SIZE)
(br_proj_x, br_pro... |
class ContainedInput(PrimitiveInput):
is_contained = True
def __init__(self, containing_input, choice, refresh_widget=None):
self.choice = choice
self.containing_input = containing_input
super().__init__(containing_input.form, choice.field, registers_with_form=False, refresh_widget=refre... |
def fixed_padding(inputs, kernel_size, rate):
kernel_size_effective = (kernel_size + ((kernel_size - 1) * (rate - 1)))
pad_total = (kernel_size_effective - 1)
pad_beg = (pad_total // 2)
pad_end = (pad_total - pad_beg)
padded_inputs = F.pad(inputs, (pad_beg, pad_end, pad_beg, pad_end))
return pad... |
def summary_eval(model, loader, dset):
model.eval()
with torch.no_grad():
loss_list = []
top1_list = []
iter = 0
prob = []
target = []
file_name = []
for batch_data in loader:
(loss, info) = model(batch_data)
loss_list.append(loss.c... |
_kernel_api(params={'oidp': POINTER})
def hook__sysctl_register_oid(ql, address, params):
oidp = sysctl_oid_t(ql, params['oidp'])
oidp = oidp.loadFromMem()
oid_name = ql.mem.string(oidp.oid_name.value)
oid_parent = b''
for (symname, symb) in ql.loader.kernel_extrn_symbols_detail.items():
if ... |
class Bottleneck2D(nn.Module):
expansion = 2
def __init__(self, inplanes, planes, stride=1, resample=None):
super(Bottleneck2D, self).__init__()
self.bn1 = nn.BatchNorm2d(inplanes)
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1)
self.bn2 = nn.BatchNorm2d(planes)
s... |
.parametrize('rv_op, dist_params, base_size, cdf_name, params_conv', [(ptr.beta, [set_test_value(pt.dvector(), np.array([1.0, 2.0], dtype=np.float64)), set_test_value(pt.dscalar(), np.array(1.0, dtype=np.float64))], (2,), 'beta', (lambda *args: args)), (ptr.cauchy, [set_test_value(pt.dvector(), np.array([1.0, 2.0], dty... |
def copy_data_to_device(data: T, device: torch.device, *args: Any, **kwargs: Any) -> T:
if (_is_named_tuple(data) and isinstance(data, tuple)):
return type(data)(**copy_data_to_device(data._asdict(), device, *args, **kwargs))
elif isinstance(data, (list, tuple)):
return type(data)((copy_data_to_... |
def make_patched_web3_get_block(original_func: Callable[([BlockIdentifier, bool], BlockData)]) -> Callable[([BlockIdentifier, bool], BlockData)]:
def patched_web3_get_block(block_identifier: BlockIdentifier, full_transactions: bool=False) -> BlockData:
last_ex: Optional[Exception] = None
for remaini... |
class Migration(migrations.Migration):
dependencies = [('help', '0001_initial')]
operations = [migrations.AlterField(model_name='helpentry', name='db_tags', field=models.ManyToManyField(blank=True, help_text='tags on this object. Tags are simple string markers to identify, group and alias objects.', to='typecla... |
def get_executable(name: str, prefix: PathLike=sys.prefix, include_path=True) -> Optional[str]:
executable = shutil.which(name)
if (include_path and executable):
return executable
candidates = list(Path(prefix).resolve().glob(f'*/{name}*'))
if candidates:
path = (f.parent for f in sorted... |
_model()
_legacy_interface(weights=('pretrained', ResNet34_Weights.IMAGENET1K_V1))
def resnet34(*, weights: Optional[ResNet34_Weights]=None, progress: bool=True, **kwargs: Any) -> ResNet:
weights = ResNet34_Weights.verify(weights)
return _resnet(BasicBlock, [3, 4, 6, 3], weights, progress, **kwargs) |
def test_single_dihedral(tmpdir):
with tmpdir.as_cwd():
mol = Ligand.from_file(get_data('ethane.sdf'))
qc_spec = QCOptions(program='rdkit', method='uff', basis=None)
local_ops = LocalResource(cores=1, memory=1)
tdrive = TorsionDriver(n_workers=1, grid_spacing=60)
t_scan = Tor... |
.xfail(reason='list-like is converted to list.')
def test_type_index(df_checks_output):
with pytest.raises(TypeError):
df_checks_output.pivot_wider(index={'geoid'}, names_from='variable')
with pytest.raises(TypeError):
df_checks_output.pivot_wider(index=('geoid', 'name'), names_from='variable') |
class TestApp():
def test_create_and_delete_app(self):
_name_create = 'appjust4testcreate'
_uri_create = ''
_args = {'name': _name_create, 'title': 'whatever', 'region': app_region}
with Call(acc_client, 'create_app', _args) as r:
assert (r[0] is not None)
_ur... |
class ImageModel(QAbstractTableModel):
def __init__(self, parent=None):
super(ImageModel, self).__init__(parent)
self.modelImage = QImage()
def setImage(self, image):
self.beginResetModel()
self.modelImage = QImage(image)
self.endResetModel()
def rowCount(self, parent... |
class RandomForestWrapper(RandomForestClassifier):
def __init__(self, sampler=None, n_estimators=10, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, bootstrap=True, ... |
def _get_socketname(basedir):
if utils.is_windows:
return _get_socketname_windows(basedir)
parts_to_hash = [getpass.getuser()]
if (basedir is not None):
parts_to_hash.append(basedir)
data_to_hash = '-'.join(parts_to_hash).encode('utf-8')
md5 = hashlib.md5(data_to_hash).hexdigest()
... |
def nested_field_condition_1() -> models.Condition:
value = random_real_word()
lt = random.randint(1, 10)
return models.NestedCondition(nested=models.Nested(key='nested.array', filter=models.Filter(must=[models.FieldCondition(key='word', match=models.MatchValue(value=value)), models.FieldCondition(key='numb... |
.parametrize('bitsize', [2, 3, 4, 5])
.parametrize('arctan_bitsize', [5, 6, 7])
def test_phase_oracle(bitsize: int, arctan_bitsize: int):
phase_oracle = ComplexPhaseOracle(ExampleSelect(bitsize), arctan_bitsize)
g = cq_testing.GateHelper(phase_oracle)
assert_valid_bloq_decomposition(phase_oracle)
circui... |
def test_no_monitor_reset_unless_done():
def assert_reset_raises(env):
errored = False
try:
env.reset()
except error.Error:
errored = True
assert errored, "Env allowed a reset when it shouldn't have"
with helpers.tempdir() as temp:
env = gym.make('... |
def oncreate_init_py(unit, *args):
keywords = {'DESTINATION': 1, 'INCLUDING_DEST_DIR': 0, 'RESULT': 1}
(flat_args, spec_args) = sort_by_keywords(keywords, args)
generated = []
dest_dir = (spec_args['DESTINATION'][0] if ('DESTINATION' in spec_args) else '$ARCADIA_BUILD_ROOT')
if ('INCLUDING_DEST_DIR'... |
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