code stringlengths 281 23.7M |
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class VerticalTabBar(QtWidgets.QTabBar):
def tabSizeHint(self, index: int) -> QtCore.QSize:
return super().tabSizeHint(index).transposed()
def paintEvent(self, event: QtGui.QPaintEvent) -> None:
painter = QtWidgets.QStylePainter(self)
opt = QtWidgets.QStyleOptionTab()
for i in ra... |
class VideoRecord(object):
def __init__(self, video, feature_dir, annot_dir, label_name, test_mode=False):
self.video = video
self.feature_dir = feature_dir
self.annot_dir = annot_dir
self.label_name = label_name
self.test_mode = test_mode
self.path_label = self.get_p... |
def minimize(X, f, args, maxnumlinesearch=None, maxnumfuneval=None, red=1.0, verbose=False):
INT = 0.1
EXT = 3.0
MAX = 20
RATIO = 10
SIG = 0.1
RHO = (SIG / 2)
SMALL = (10.0 ** (- 16))
if (maxnumlinesearch == None):
if (maxnumfuneval == None):
raise 'Specify maxnumline... |
class EESP(nn.Module):
def __init__(self, in_channels, out_channels, stride=1, k=4, r_lim=7, down_method='esp', norm_layer=nn.BatchNorm2d):
super(EESP, self).__init__()
self.stride = stride
n = int((out_channels / k))
n1 = (out_channels - ((k - 1) * n))
assert (down_method in... |
_settings(GUEST_ENABLED=True, GUEST_LIST=['bruce_wayne'])
class TestDefaultGuest(EvenniaTest):
ip = '212.216.134.22'
_settings(GUEST_ENABLED=False)
def test_create_not_enabled(self):
(account, errors) = DefaultGuest.authenticate(ip=self.ip)
self.assertFalse(account, 'Guest account was create... |
class ResConvBlock(nn.Module):
def __init__(self, in_c, out_c, btn_c, kernel_size, stride, act='silu', reparam=False, block_type='k1kx'):
super(ResConvBlock, self).__init__()
self.stride = stride
if (block_type == 'k1kx'):
self.conv1 = ConvKXBN(in_c, btn_c, kernel_size=1, stride=... |
def convert_to_detectron2_names(layer_keys):
output_keys = []
for k in layer_keys:
k = k.replace('_feature_blocks.conv1.', 'stem.conv1.')
k = k.replace('_feature_blocks.bn1.', 'stem.conv1.norm.')
k = k.replace('_feature_blocks.layer1.', 'res2.')
k = k.replace('_feature_blocks.lay... |
class MNIST_Net(nn.Module):
def __init__(self, N=10):
super(MNIST_Net, self).__init__()
self.encoder = nn.Sequential(nn.Conv2d(1, 6, 5), nn.MaxPool2d(2, 2), nn.ReLU(True), nn.Conv2d(6, 16, 5), nn.MaxPool2d(2, 2), nn.ReLU(True))
self.classifier = nn.Sequential(nn.Linear(((16 * 4) * 4), 120), ... |
_transform('imagenet_no_augment')
class ImagenetNoAugmentTransform(ClassyTransform):
def __init__(self, resize: int=ImagenetConstants.RESIZE, crop_size: int=ImagenetConstants.CROP_SIZE, mean: List[float]=ImagenetConstants.MEAN, std: List[float]=ImagenetConstants.STD):
self.transform = transforms.Compose([tr... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--model', default='variationally_sparse_gp', nargs='?', type=str)
parser.add_argument('--dataset', default='boston', nargs='?', type=str)
parser.add_argument('--split', default=0, nargs='?', type=int)
parser.add_argument('--se... |
def voting_test(args):
logger = logging.getLogger(__name__)
logger.info(('Working path: %s' % str(os.getcwd())))
logger.info(('random seed is set to %s ...' % str(args.seed)))
logger.info(('Load %s dataset ...' % args.dataset))
DATA_PATH = hydra.utils.to_absolute_path(args.dataset_dir)
if (args.... |
class F27_RepoData(F21_RepoData):
removedKeywords = F21_RepoData.removedKeywords
removedAttrs = F21_RepoData.removedAttrs
def __init__(self, *args, **kwargs):
F21_RepoData.__init__(self, *args, **kwargs)
self.metalink = kwargs.get('metalink', False)
def _getArgsAsStr(self):
retva... |
class PBEnc(object):
def _update_vids(cls, cnf, inp, vpool):
(top, vmap) = (max((inp + [vpool.top])), {})
inp = set([abs(l) for l in inp])
while (top < cnf.nv):
top += 1
if (top in inp):
vmap[top] = top
continue
vpool.top +=... |
class InventoryTestCase(CommonAPIRequestTools, unittest.TestCase):
api_class = mws.Inventory
def test_list_inventory_supply(self):
now = datetime.datetime.utcnow()
skus = ['thing1', 'thing2']
response_group = 'Detailed'
params = self.api.list_inventory_supply(skus, now, response_... |
def print_presence_view(chain_state: Any, translator: Optional[Translator]=None) -> None:
if (translator is None):
trans = (lambda s: s)
else:
trans = translator.translate
def network_state_to_color(network_state: NetworkState) -> Optional[str]:
if (network_state == NetworkState.REAC... |
class TestUnaryOperators(TestCase):
def test_unary_operator(self):
a = pybamm.Symbol('a', domain=['test'])
un = pybamm.UnaryOperator('unary test', a)
self.assertEqual(un.children[0].name, a.name)
self.assertEqual(un.domain, a.domain)
a = pybamm.InputParameter('a')
abs... |
class HgWorkdir(Workdir):
def from_potential_worktree(cls, wd: _t.PathT) -> (HgWorkdir | None):
res = _run(['hg', 'root'], wd)
if res.returncode:
return None
return cls(Path(res.stdout))
def get_meta(self, config: Configuration) -> (ScmVersion | None):
node: str
... |
def test_issue940_metaclass_values_funcdef() -> None:
node = builder.extract_node("\n class BaseMeta(type):\n def __members__(cls):\n return ['a', 'func']\n class Parent(metaclass=BaseMeta):\n pass\n Parent.__members__()\n ")
inferred = next(node.infer())
assert isinstan... |
class TokenizerHubInterface(object):
def __init__(self, tokenizer, **kwargs):
super().__init__()
args = argparse.Namespace(tokenizer=tokenizer, **kwargs)
self.tokenizer = encoders.build_tokenizer(args)
assert (self.tokenizer is not None)
def encode(self, sentence: str) -> str:
... |
(strategies.lists(min_size=0, max_size=3, elements=strategies.integers(min_value=0, max_value=(2 ** 31))))
def test_first_last_item(counts):
model = completionmodel.CompletionModel()
for c in counts:
cat = mock.Mock(spec=['layoutChanged', 'layoutAboutToBeChanged'])
cat.rowCount = mock.Mock(retur... |
def test_standard(hatch, config_file, helpers):
result = hatch('config', 'set', 'project', 'foo')
assert (result.exit_code == 0), result.output
assert (result.output == helpers.dedent('\n New setting:\n project = "foo"\n '))
config_file.load()
assert (config_file.model.project =... |
def read_from_memory(addr, size):
inferior = get_inferior()
if ((inferior == (- 1)) or (addr == 0)):
print('Error happens in read_from_memory: addr = {0:x}'.format(int(addr)))
return None
try:
string = inferior.read_memory(addr, size)
return string
except gdb.MemoryError:... |
class test_io(unittest.TestCase):
def test_process_tuple(self):
def funpass(cause, procs, tup, col):
pass
self.assertEqual(tuple(process_tuple((), (), funpass)), ())
self.assertEqual(tuple(process_tuple((int,), ('100',), funpass)), (100,))
self.assertEqual(tuple(process_t... |
def get_bu(model, X_test, X_test_noisy, X_test_adv):
print('Getting Monte Carlo dropout variance predictions...')
uncerts_normal = get_mc_predictions(model, X_test, batch_size=args.batch_size).var(axis=0).mean(axis=1)
uncerts_noisy = get_mc_predictions(model, X_test_noisy, batch_size=args.batch_size).var(ax... |
def get_version(config: NsJailConfig) -> int:
cgroup_mounts = (config.cgroup_mem_mount, config.cgroup_pids_mount, config.cgroup_net_cls_mount, config.cgroup_cpu_mount)
v1_exists = any((Path(mount).exists() for mount in cgroup_mounts))
controllers_path = Path(config.cgroupv2_mount, 'cgroup.controllers')
... |
class InputReaderBuilderTest(tf.test.TestCase):
def create_tf_record(self):
path = os.path.join(self.get_temp_dir(), 'tfrecord')
writer = tf.python_io.TFRecordWriter(path)
image_tensor = np.random.randint(255, size=(4, 5, 3)).astype(np.uint8)
with self.test_session():
enc... |
def __encoding_name(codepage: int) -> str:
encodings = {CP_ACP: 'mbcs', CP_OEMCP: 'oem', CP_THREAD_ACP: 'mbcs', CP_UTF16: 'utf-16', CP_UTF16BE: 'utf-16be', CP_ASCII: 'ascii', CP_UTF7: 'utf-7', CP_UTF8: 'utf-8'}
if (codepage in encodings):
encname = encodings[codepage]
else:
encname = f'cp{co... |
def create_repository(namespace, name, creating_user, visibility='private', repo_kind='image', description=None):
namespace_user = User.get(username=namespace)
yesterday = (datetime.now() - timedelta(days=1))
try:
with db_transaction():
existing = get_repository(namespace, name)
... |
class DataPortalTestBase(WithDataPortal, WithTradingSessions):
ASSET_FINDER_EQUITY_SIDS = (1, 2, 3)
DIVIDEND_ASSET_SID = 3
START_DATE = pd.Timestamp('2016-08-01')
END_DATE = pd.Timestamp('2016-08-08')
TRADING_CALENDAR_STRS = ('NYSE', 'us_futures')
EQUITY_DAILY_BAR_SOURCE_FROM_MINUTE = True
O... |
class TestTestFramework(test.SimpleTest):
def test_create(self):
workflow.delete_files('*.json')
with self.create('one.tf.json'):
one = (yield variable.one(default=True))
(yield output.one(value=one))
self.tf.init()
outputs = self.tf.apply()
assert (ou... |
.parametrize('direction,mechanism,purview,probability', [(Direction.CAUSE, (0,), (1,), 0.), (Direction.CAUSE, (0,), (2,), 0.), (Direction.CAUSE, (0,), (1, 2), 0.3333333), (Direction.EFFECT, (1,), (0,), 1), (Direction.EFFECT, (2,), (0,), 1), (Direction.EFFECT, (1, 2), (0,), 1)])
def test_probability(direction, mechanism... |
class SpatialGate(nn.Module):
def __init__(self, gate_channel, reduction_ratio=16, dilation_conv_num=2, dilation_val=4):
super(SpatialGate, self).__init__()
self.gate_s = nn.Sequential()
self.gate_s.add_module('gate_s_conv_reduce0', nn.Conv2d(gate_channel, (gate_channel // reduction_ratio), ... |
def load_checkpoint(filepath: Path) -> Dict[(str, torch.Tensor)]:
checkpoint = torch.load(filepath, map_location='cpu')
if ('network' in checkpoint):
state_dict = checkpoint['network']
elif ('state_dict' in checkpoint):
state_dict = checkpoint['state_dict']
else:
state_dict = che... |
_start_docstrings('The bare Cvt Model transformer outputting raw hidden-states without any specific head on top.', TFCVT_START_DOCSTRING)
class TFCvtModel(TFCvtPreTrainedModel):
def __init__(self, config: CvtConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.cvt = TFCvtMain... |
def abandonedShoppingCarts(df, DYNAMIC_CAT_CODE, ORDER_CAT_CODE):
filtered_df = df[((df['wp_type_codes'] == ORDER_CAT_CODE) | (df['wp_type_codes'] == DYNAMIC_CAT_CODE))]
filtered_df['wp_type_codes'] = filtered_df['tstamp_inSec'].astype('string').str.cat(filtered_df['wp_type_codes'].astype('string'), sep=':')
... |
class TransformerClassifier(nn.Module):
def __init__(self, encoder, generator=None, mpc=False, **kwargs):
super().__init__()
self.encoder = encoder
self.generator = generator
self.num_classes = self.generator[0].linear.weight.size(0)
self.mpc = mpc
if mpc:
... |
class StatusBarTestCases(unittest.TestCase):
def setUp(self):
Timings.fast()
app = Application()
app.start(os.path.join(controlspy_folder, 'Status bar.exe'))
self.texts = ['Long text', '', 'Status Bar']
self.part_rects = [RECT(0, 2, 65, 22), RECT(67, 2, 90, 22), RECT(92, 2, 2... |
def test_find_files_stop_at_root_hg(wd: WorkDir, monkeypatch: pytest.MonkeyPatch) -> None:
wd.commit_testfile()
project = (wd.cwd / 'project')
project.mkdir()
project.joinpath('setup.cfg').touch()
assert (setuptools_scm._file_finders.find_files(str(project)) == [])
wd.add_and_commit()
monkey... |
class Screen(metaclass=ImmutableStruct):
_names = ['width', 'height', 'size', 'aspect']
width = config.size[0]
height = config.size[1]
size = Vector2(config.size)
aspect = (config.size[0] / config.size[1])
def _edit(cls, width, height):
cls._set('width', width)
cls._set('height',... |
class UtilTestCase(unittest.TestCase):
def setUpClass(cls):
cls.tempdir = tempfile.mkdtemp()
def tearDownClass(cls):
shutil.rmtree(cls.tempdir)
def fpath(self, fn):
return os.path.join(self.tempdir, fn)
def testTime(self):
for (fmt, accu) in zip(['%Y-%m-%d %H:%M:%S.3FRAC'... |
def test_ashrae():
thetas = np.array([(- 90.0), (- 67.5), (- 45.0), (- 22.5), 0.0, 22.5, 45.0, 67.5, 89.0, 90.0, np.nan])
expected = np.array([0, 0.9193437, 0., 0., 1.0, 0., 0., 0.9193437, 0, 0, np.nan])
iam = _iam.ashrae(thetas, 0.05)
assert_allclose(iam, expected, equal_nan=True)
iam_series = _iam... |
def convert_weights(layer, weights):
if (layer.__class__.__name__ == 'GRU'):
W = [np.split(w, 3, axis=(- 1)) for w in weights]
return sum(map(list, zip(*W)), [])
elif (layer.__class__.__name__ in ('LSTM', 'ConvLSTM2D')):
W = [np.split(w, 4, axis=(- 1)) for w in weights]
for w in ... |
class SponsorshipAssetsAPIListTests(APITestCase):
def setUp(self):
self.user = baker.make('users.User')
token = Token.objects.get(user=self.user)
self.permission = Permission.objects.get(name='Can access sponsor placement API')
self.user.user_permissions.add(self.permission)
... |
.slow
def test_api_with_venv(tmpfolder):
venv_path = (Path(tmpfolder) / 'proj/.venv')
assert (not venv_path.exists())
api.create_project(project_path='proj', extensions=[venv.Venv()], venv_install=['pytest>=6.0.0'])
assert venv_path.is_dir()
assert list(venv_path.glob('*/python*'))
assert list(v... |
class Splat2DFunction(ag.Function):
def forward(ctx, input, coordinates, values, sigma, soft_normalize=False):
_splat = _import_splat()
assert (('FloatTensor' in coordinates.type()) and ('FloatTensor' in values.type())), 'Splat2D only takes float coordinates and values, got {} and {} instead.'.forma... |
.parametrize('n, initial', [(3, (1, [0, 1])), (3, (1, [0, 1])), (5, (1, [0, 3, 4])), (6, (1, [0, 1, 2, 3])), (7, (1, [0, 1, 5, 6])), (9, (1, [2, 4, 6]))])
def test_ffft_multi_fermionic_mode_non_power_of_2(n, initial):
initial_state = _multi_fermionic_mode_base_state(n, *initial)
expected_state = _fourier_transf... |
_attr(allow_interpreted_subclasses=True)
class TraverserVisitor(NodeVisitor[None]):
def __init__(self) -> None:
pass
def visit_mypy_file(self, o: MypyFile) -> None:
for d in o.defs:
d.accept(self)
def visit_block(self, block: Block) -> None:
for s in block.body:
... |
.requires_internet
def test_post_install_commands(hatch, helpers, temp_dir, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), resu... |
def main(args):
if args.use_pasd_light:
from models.pasd_light.unet_2d_condition import UNet2DConditionModel
from models.pasd_light.controlnet import ControlNetModel
else:
from models.pasd.unet_2d_condition import UNet2DConditionModel
from models.pasd.controlnet import ControlNet... |
def fmt_ac_ria(ria, extended_purview=None):
causality = {Direction.CAUSE: ((fmt_mechanism(ria.purview, ria.node_labels) if (extended_purview is None) else fmt_extended_purview(ria.extended_purview, ria.node_labels)), ARROW_LEFT, fmt_mechanism(ria.mechanism, ria.node_labels)), Direction.EFFECT: (fmt_mechanism(ria.me... |
def state_bind_checkbox(owner, state, path, widget):
def make_funcs():
def update_state(widget, state):
state.set(path, bool(widget.isChecked()))
def update_widget(state, widget):
widget.blockSignals(True)
widget.setChecked(state.get(path))
widget.bloc... |
def test_persist_history_permission_error(hist_file, mocker, capsys):
app = cmd2.Cmd(persistent_history_file=hist_file)
run_cmd(app, 'help')
mock_open = mocker.patch('builtins.open')
mock_open.side_effect = PermissionError
app._persist_history()
(out, err) = capsys.readouterr()
assert (not o... |
class Command(BaseCommand):
def handle(self, *args, **options):
try:
git_path = Path(settings.BASE_DIR).parent
os.chdir(git_path)
run('git checkout master -q && git pull -q ')
version_cmd = 'curl -s | grep \'tag_name\' | cut -d : -f2,3 | tr -d \\" | tr -d ,'
... |
class TestDeprecation(object):
def setup_method(self):
warnings.simplefilter('always', DeprecationWarning)
return
def teardown_method(self):
return
def test_convert_timestamp_to_datetime(self):
warn_msgs = [' '.join(['New kwargs added to `pysat.utils.io.load_netCDF4`', 'for g... |
class MlpGeLUFunctionBLASLT(torch.autograd.Function):
_fwd(cast_inputs=torch.float16)
def forward(ctx, p, *args):
outputs = mlp_gelu_blaslt.forward(p, args)
ctx.save_for_backward(*args)
ctx.outputs = outputs
dropout_mask = outputs[(- 1)]
ctx.p = p
return (outputs[... |
def test_tan_hhduc(fints_client):
with fints_client:
accounts = fints_client.get_sepa_accounts()
a = fints_client.simple_sepa_transfer(accounts[0], 'DE', 'GENODE23X42', 'Test Receiver', Decimal('5.23'), 'Test Sender', 'Test transfer hhduc 2step')
from fints.hhd.flicker import parse
a... |
def window_sorter(win):
patterns = (('.', 'E-mail'), ('Gmail', 'E-mail'), ('SquirrelMail', 'E-mail'), ('zeromq', 'Docs'), ('PyYAML', 'Docs'), ('documentation', 'Docs'), ('-ietf-', 'Docs'), ('GNOME Live!', 'Docs'), ('Guide', 'Docs'))
for (k, v) in patterns:
if (k in win.name):
return v |
class StocktickerArgs(_QtileMigrator):
ID = 'UpdateStocktickerArgs'
SUMMARY = 'Updates ``StockTicker`` argument signature.'
HELP = '\n The ``StockTicker`` widget had a keyword argument called ``function``. This needs to be\n renamed to ``func`` to prevent clashes with the ``function()`` method of ``Co... |
class SomeMinionCL(Component):
def recv(s, msg):
assert (s.entry is None)
s.entry = msg
def recv_rdy(s):
return (s.entry is None)
def read(s, addr):
addr = int(addr)
return s.reg_file[addr]
def write(s, addr, data):
addr = int(addr)
s.reg_file[addr... |
.parametrize('x,y,expected', [(np.array([0.0, 1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 3.0, 4.0, 5.0]), np.array([2.0, 1.0, 0.0, 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 3.0]), (np.array([[0.0, (- 1.0), 2.0], [(- 0.5), (- 1.0), 1.0], [(- 0.75), (- 0.5), 3.0], [0.75, (- 1.5), 0.375], [0.125, (- 1.25), 2.5625], [1.5, 0.0, 0.0], [(- 0.5), (- 1... |
def get_parser():
parser = argparse.ArgumentParser(description="\n Convert a config file with Tdnn components to their equivalent\n Affine/Linear components. Useful when we are using MACE (a deep learning\n inference framework using Kaldi's trained models) that doesn't\n support Tdnn com... |
def get_legends(img, colors, palette):
rtn = []
rtn_lines = 1
draw = ImageDraw.Draw(img)
if (platform.system() == 'Windows'):
font = ImageFont.truetype('arial.ttf', 15)
elif (platform.system() == 'Linux'):
font = ImageFont.truetype('DejaVuSans.ttf', 14)
else:
assert False... |
class CheckParametersConvergence(Callback):
def __init__(self, every=100, tolerance=0.001, diff='relative', ord=np.inf):
self._diff = _diff[diff]
self.ord = ord
self.every = every
self.prev = None
self.tolerance = tolerance
def __call__(self, approx, _, i) -> None:
... |
(models.ProposalSectionReviewerVote)
class ProposalSectionReviewerVoteAdmin(TimeAuditAdmin):
list_filter = ['vote_value', 'proposal__proposal_type__name']
list_display = (('proposal', 'voter', 'role', 'vote_value') + TimeAuditAdmin.list_display)
def get_queryset(self, request):
qs = super(ProposalSe... |
def _parse_env_kwarg(kwargs, keyword, env_name, env_type):
if (keyword not in kwargs):
env_value = os.environ.get(env_name, None)
if (env_value is not None):
if (env_type is bool):
kwargs[keyword] = ((env_value == '1') or (env_value.lower() == 'true'))
elif (e... |
class Information(Cog):
def __init__(self, bot: Bot):
self.bot = bot
def get_channel_type_counts(guild: Guild) -> defaultdict[(str, int)]:
channel_counter = defaultdict(int)
for channel in guild.channels:
if is_staff_channel(channel):
channel_counter['staff'] ... |
class Encoder(nn.Module):
def __init__(self, n_feat, kernel_size, reduction, act, bias, scale_unetfeats, csff):
super(Encoder, self).__init__()
self.encoder_level1 = [CAB(n_feat, kernel_size, reduction, bias=bias, act=act) for _ in range(2)]
self.encoder_level2 = [CAB((n_feat + scale_unetfea... |
def evaluate(config, workdir, eval_folder='eval'):
eval_dir = os.path.join(workdir, eval_folder)
tf.io.gfile.makedirs(eval_dir)
rng = jax.random.PRNGKey((config.seed + 1))
(train_ds, eval_ds, _) = datasets.get_dataset(config, additional_dim=1, uniform_dequantization=config.data.uniform_dequantization, e... |
class TestSplitWindowPriceLST(unittest.TestCase):
sample_band_10 = np.zeros((5, 5))
mask = np.random.randint(0, high=1, size=(5, 5), dtype=int)
mask = (mask == 1)
def test_that_output_and_input_size_equal(self):
output = SplitWindowPriceLST()(emissivity_10=self.sample_band_10, emissivity_11=self... |
class Library():
_one = None
def one(cls, *args, **kwargs):
if (cls._one is None):
cls._one = cls(*args, **kwargs)
return cls._one
def __init__(self, db=None):
if self._one:
warnings.warn('to guarantee consistency, Library should be used as a singleton through... |
def find_caller():
def current_frame():
try:
raise Exception
except:
return sys.exc_info()[2].tb_frame.f_back
f = current_frame()
if (f is not None):
f = f.f_back
rv = ('(unknown file)', 0, '(unknown function)')
while hasattr(f, 'f_code'):
co =... |
def ql_syscall_recvmsg(ql: Qiling, sockfd: int, msg_addr: int, flags: int):
if (sockfd not in range(NR_OPEN)):
return (- 1)
sock: Optional[ql_socket] = ql.os.fd[sockfd]
if (sock is None):
return (- 1)
abits = ql.arch.bits
endian = ql.arch.endian
msghdr = make_msghdr(abits, endian... |
def get_outputs_after_fold(model, test_data):
onnx.checker.check_model(model.model)
filename = './onnx_test_model.onnx'
onnx.save(model.model, filename)
(conv_bn, bn_conv) = fold_all_batch_norms_to_weight(model.model)
pairs = (conv_bn + bn_conv)
onnx.checker.check_model(model.model)
folded_f... |
class PointNetfeat(nn.Module):
def __init__(self, global_feat=True, feature_transform=False):
super(PointNetfeat, self).__init__()
self.conv1 = torch.nn.Conv1d(3, 64, 1)
self.conv2 = torch.nn.Conv1d(64, 128, 1)
self.conv3 = torch.nn.Conv1d(128, 256, 1)
self.bn1 = nn.InstanceN... |
('/v1/find/all')
class ConductSearch(ApiResource):
_args()
_param('query', 'The search query.', type=str, default='')
_scope(scopes.READ_REPO)
('conductSearch')
def get(self, parsed_args):
query = parsed_args['query']
if (not query):
return {'results': []}
usernam... |
def qtwe_version_patcher(monkeypatch):
try:
from qutebrowser.qt import webenginecore
except ImportError:
pytest.skip('QtWebEngine not available')
def patch(ver, chromium_version=None):
monkeypatch.setattr(configfiles.version, 'qtwebengine_versions', (lambda avoid_init=False: version.... |
class ModuleMock(nn.Module):
def __init__(self, *methods):
super().__init__()
self._call_args_list = []
for method in methods:
setattr(self, method, unittest.mock.MagicMock(name=f'{type(self).__name__}.{method}'))
def forward(self, *args, **kwargs):
self._call_args_li... |
def test_get_eval_class():
context = Context({'c': 789})
context.pystring_globals_update({'A': ArbClassForEvalTest})
assert (context.get_eval_string('A.a') == 123)
assert (context.get_eval_string('A().b') == 456)
assert (context.get_eval_string('A().dothing(1)') == 124)
assert (context.get_eval_... |
def create_data(tracker, iterations=20, obj_per_iteration=100):
objects = []
for x in range(iterations):
for y in range(obj_per_iteration):
objects.append(Alpha())
objects.append(Beta())
objects.append(Gamma())
tracker.create_snapshot()
return objects |
def test_everything_annotated() -> None:
pyanalyze_dir = Path(__file__).parent
failures = []
for filename in sorted(files_with_extension_from_directory('py', pyanalyze_dir)):
tree = annotate_file(filename, show_errors=True)
for node in ast.walk(tree):
if (hasattr(node, 'lineno') ... |
def test_list_value():
p = ListParameter('Test', choices=[1, 2.2, 'three', 'and four'])
p.value = 1
assert (p.value == 1)
p.value = 2.2
assert (p.value == 2.2)
p.value = '1'
assert (p.value == 1)
p.value = '2.2'
assert (p.value == 2.2)
p.value = 'three'
assert (p.value == 'th... |
class DiscreteAgent(Agent):
def __init__(self, xs, ys, map_matrix, obs_range=3, n_channels=3, seed=1, flatten=False):
self.random_state = np.random.RandomState(seed)
self.xs = xs
self.ys = ys
self.eactions = [0, 1, 2, 3, 4]
self.motion_range = [[(- 1), 0], [1, 0], [0, 1], [0,... |
class CallableArgument(ProperType):
__slots__ = ('typ', 'name', 'constructor')
typ: Type
name: (str | None)
constructor: (str | None)
def __init__(self, typ: Type, name: (str | None), constructor: (str | None), line: int=(- 1), column: int=(- 1)) -> None:
super().__init__(line, column)
... |
class OnlineStats(object):
def __init__(self, init_func=(lambda : 0), update_func=(lambda x, y: (x + y)), readout_func=(lambda x, y: (x / y))):
super(OnlineStats, self).__init__()
self.num_steps = 0
self.update_func = update_func
self.readout_func = readout_func
self.init_fun... |
def dump_data(features, labels, user_negative, num_neg, is_training):
if (not os.path.exists(DATA_PATH)):
os.makedirs(DATA_PATH)
(features, labels) = add_negative(features, user_negative, labels, num_neg, is_training)
data_dict = dict([('user', features['user']), ('item', features['item']), ('label'... |
class ScalarMeanTracker(object):
def __init__(self) -> None:
self._sums = {}
self._counts = {}
def add_scalars(self, scalars):
for k in scalars:
if (k != 'tools'):
if (k not in self._sums):
self._sums[k] = scalars[k]
sel... |
def lang(category: str, key: str, replacements: typing.Optional[dict]=None, default=None, user: typing.Optional[tweepy.models.User]=None):
string = _language_config.get(category, key, fallback=default)
if string:
if replacements:
for (rkey, rvalue) in replacements.items():
st... |
def test_py_string_with_imports():
context = Context({'a': (- 3), 'b': 4})
from math import sqrt
context.pystring_globals_update({'squareroot': sqrt})
assert (PyString('abs(a) + squareroot(b)').get_value(context) == 5)
assert (context == {'a': (- 3), 'b': 4})
assert (context._pystring_globals ==... |
class SmoothedValue():
def __init__(self, window_size=20, fmt=None):
if (fmt is None):
fmt = '{median:.4f} ({global_avg:.4f})'
self.deque = deque(maxlen=window_size)
self.total = 0.0
self.count = 0
self.fmt = fmt
def update(self, value, n=1):
self.dequ... |
def safe_inspect_signature(runtime: Any) -> (inspect.Signature | None):
try:
try:
return inspect.signature(runtime)
except ValueError:
if (hasattr(runtime, '__text_signature__') and ('<unrepresentable>' in runtime.__text_signature__)):
sig = runtime.__text_sig... |
def get_mean_width(X):
n = X.shape[0]
Xmed = X
G = np.sum((Xmed * Xmed), 1).reshape(n, 1)
Q = np.tile(G, (1, n))
R = np.tile(G.T, (n, 1))
dists = ((Q + R) - (2 * np.dot(Xmed, Xmed.T)))
dists = (dists - np.tril(dists))
dists = dists.reshape((n ** 2), 1)
width_x = np.sqrt((0.5 * np.mea... |
class Editable(BaseEditable):
def __init__(self, module: nn.Module, loss_function, optimizer=IngraphGradientDescent(0.01), max_steps=float('inf'), get_editable_parameters=(lambda module: module.parameters()), is_edit_finished=(lambda loss, **kwargs: (loss.item() <= 0))):
super().__init__()
(self.mod... |
def import_view(element, save=False, user=None):
try:
view = View.objects.get(uri=element.get('uri'))
except View.DoesNotExist:
view = View()
set_common_fields(view, element)
view.order = (element.get('order') or 0)
view.template = element.get('template')
set_lang_field(view, 'ti... |
def train(args, model, train_features, benchmarks):
train_dataloader = DataLoader(train_features, batch_size=args.train_batch_size, shuffle=True, collate_fn=collate_fn, drop_last=True)
total_steps = int(((len(train_dataloader) * args.num_train_epochs) // args.gradient_accumulation_steps))
warmup_steps = int... |
def test_option_subscribe():
opt = Option('A_FAKE_OPTION', 'default')
calls = []
opt.subscribe(calls.append)
assert (calls == ['default'])
opt.current = 'default'
assert (calls == ['default'])
opt.current = 'new-1'
opt.current = 'new-2'
assert (calls == ['default', 'new-1', 'new-2'])... |
(dbus, 'dbus missing')
class TDbusUtils(TestCase):
def test_prop_sig(self):
value = apply_signature(2, 'u')
self.assertTrue(isinstance(value, dbus.UInt32))
value = apply_signature({'a': 'b'}, 'a{ss}')
self.assertEqual(value.signature, 'ss')
self.assertTrue(isinstance(value, d... |
def test_simple_1d_dataset_cutting_plane():
X = np.random.uniform(size=(30, 1))
Y = (X.ravel() > 0.5).astype(np.int)
X = np.hstack([X, np.ones((X.shape[0], 1))])
pbl = MultiClassClf(n_features=2)
svm = NSlackSSVM(pbl, check_constraints=True, C=10000)
svm.fit(X, Y)
assert_array_equal(Y, np.hs... |
def build_pom_and_export_to_maven(**kwargs):
target_path = kwargs.get('target_path')
target = kwargs.get('target')
pom_path = kwargs.get('pom_path')
source_dirs = kwargs.get('source_dirs')
output_dir = kwargs.get('output_dir')
final_name = kwargs.get('final_name')
packaging = kwargs.get('pac... |
class UpDownCore(nn.Module):
def __init__(self, opt, use_maxout=False):
super(UpDownCore, self).__init__()
self.drop_prob_lm = opt.drop_prob_lm
self.att_lstm = nn.LSTMCell((opt.input_encoding_size + (opt.rnn_size * 2)), opt.rnn_size)
self.lang_lstm = nn.LSTMCell((opt.rnn_size * 2), o... |
class TestDocumentDataSuppressionMethods(unittest.TestCase):
def test_remove_section_with_default_args(self):
document = parse(USER_ONLY)
self.assertEqual(2, document.count('user'))
document.remove('user')
user = document.get('user')
expected_body = ['id = 1', "name = 'alex'"... |
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