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.parametrize('section', ['console', 'gui'])
.parametrize('kind', ['win-ia32', 'win-amd64', 'win-arm'])
def test_script_generate_launcher(section, kind):
launcher_data = _read_launcher_data(section, kind)
script = Script('foo', 'foo.bar', 'baz.qux', section=section)
(name, data) = script.generate('#!C:\\path... |
def get_matched_files(refresh=False):
TMP_MATCHED_FILES = '/tmp/matched_files.txt'
if (refresh or (not os.path.exists(TMP_MATCHED_FILES))):
with st.spinner('Refreshing... (this may take a while)'):
matched_files = subprocess.run(['gsutil', 'ls', glob_pattern], capture_output=True)
... |
class SnapshotRollbackView(ObjectPermissionMixin, RedirectViewMixin, DetailView):
model = Snapshot
queryset = Snapshot.objects.all()
permission_required = 'projects.rollback_snapshot_object'
template_name = 'projects/snapshot_rollback.html'
def get_queryset(self):
return Snapshot.objects.fil... |
class TalkieConnectionOwner(object):
def __init__(self):
self._connections = []
def talkie_connect(self, state, path, listener, drop_args=False):
if drop_args:
listener = drop_args_wrapper(listener)
if (not isinstance(path, str)):
return [self.talkie_connect(state... |
def test_instanceloader_yaml_dup_anchor(tmp_path, open_wide):
f = (tmp_path / 'foo.yaml')
f.write_text('a:\n b: &anchor\n - 1\n - 2\n c: &anchor d\n')
loader = InstanceLoader(open_wide(f))
data = list(loader.iter_files())
assert (data == [(str(f), {'a': {'b': [1, 2], 'c': 'd'}})]) |
def test_cross_layer_equalization_stepwise():
orig = tf.keras.applications.ResNet50(input_shape=(224, 224, 3))
(folded_pairs, model) = fold_all_batch_norms(orig)
bn_dict = {}
for (conv_or_linear, bn) in folded_pairs:
bn_dict[conv_or_linear] = bn
(conv1, conv2, conv3) = (model.layers[6], mode... |
class HTLCManager():
def __init__(self, log: 'StoredDict', *, initial_feerate=None):
if (len(log) == 0):
initial = {'adds': {}, 'locked_in': {}, 'settles': {}, 'fails': {}, 'fee_updates': {}, 'revack_pending': False, 'next_htlc_id': 0, 'ctn': (- 1)}
log[LOCAL] = deepcopy(initial)
... |
def get_normalize_mesh(model_file, norm_mesh_sub_dir):
total = 16384
print('[*] loading model with trimesh...', model_file)
mesh_list = trimesh.load_mesh(model_file, process=False)
print('[*] done!', model_file)
mesh = as_mesh(mesh_list)
if (not isinstance(mesh, list)):
mesh_list = [mesh... |
def test_python_option(tester: CommandTester) -> None:
inputs = ['my-package', '1.2.3', 'This is a description', 'n', 'MIT', 'n', 'n', '\n']
tester.execute("--python '~2.7 || ^3.6'", inputs='\n'.join(inputs))
expected = '[tool.poetry]\nname = "my-package"\nversion = "1.2.3"\ndescription = "This is a descrip... |
def test_apply_classical_cbloq():
bb = BloqBuilder()
x = bb.add_register(Register('x', 1, shape=(5,)))
(x, y) = bb.add(ApplyClassicalTest(), x=x)
(y, z) = bb.add(ApplyClassicalTest(), x=y)
cbloq = bb.finalize(x=x, y=y, z=z)
xarr = np.zeros(5)
(x, y, z) = cbloq.call_classically(x=xarr)
np... |
class ChoiceFeedbackQuestionValueFactory(Factory):
class Meta():
model = 'feedback.ChoiceFeedbackQuestionValue'
strategy = factory.CREATE_STRATEGY
question = factory.SubFactory('tests.factories.ChoiceFeedbackQuestionFactory')
title = factory.Sequence((lambda n: 'title{}'.format(n)))
valu... |
def pdb_reformat(reference, target):
from qubekit.molecules.protein import Protein
pro = Protein(reference)
print(pro.pdb_names)
with open(target, 'r') as traj:
lines = traj.readlines()
PRO = False
i = 0
new_traj = open('QUBE_traj.pdb', 'w+')
for line in lines:
if ('MODEL... |
class MitreParser():
def __init__(self, name):
self.graph = QBIxora(name)
self.mitrepath = path.abspath(path.join(path.dirname(__file__), 'mitrefiles'))
if (not self.mitrepath.endswith(path.sep)):
self.mitrepath = (self.mitrepath + path.sep)
if (not path.isdir(self.mitrep... |
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] not in {kwargs[... |
def get_beam_indices_of_fraction_group(dicom_dataset, fraction_group_number):
(beam_numbers, _) = get_referenced_beam_sequence(dicom_dataset, fraction_group_number)
beam_sequence_numbers = [beam_sequence.BeamNumber for beam_sequence in dicom_dataset.BeamSequence]
beam_indexes = [beam_sequence_numbers.index(... |
def _load_library():
osp = os.path
if sys.platform.startswith('darwin'):
libfile = osp.abspath(osp.join(osp.dirname(__file__), '../../vendor/mujoco/libglfw.3.dylib'))
elif sys.platform.startswith('linux'):
libfile = osp.abspath(osp.join(osp.dirname(__file__), '../../vendor/mujoco/libglfw.so.... |
def AddExtraLayers(net, use_batchnorm=True):
use_relu = True
from_layer = net.keys()[(- 1)]
out_layer = 'conv6_1'
ConvBNLayer(net, from_layer, out_layer, use_batchnorm, use_relu, 256, 1, 0, 1)
from_layer = out_layer
out_layer = 'conv6_2'
ConvBNLayer(net, from_layer, out_layer, use_batchnorm,... |
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
dataset = build_dataset(cfg.data.val)
data_loader = build_dataloader(dataset, samples_per_gpu=1, workers_per_gpu=cfg.data.workers_per_gpu, dist=False... |
def contractreceivechannelclosed_from_event(canonical_identifier: CanonicalIdentifier, event: DecodedEvent) -> ContractReceiveChannelClosed:
data = event.event_data
args = data['args']
return ContractReceiveChannelClosed(transaction_from=args['closing_participant'], canonical_identifier=canonical_identifier... |
class ASFInfo(StreamInfo):
length = 0.0
sample_rate = 0
bitrate = 0
channels = 0
codec_type = u''
codec_name = u''
codec_description = u''
def __init__(self):
self.length = 0.0
self.sample_rate = 0
self.bitrate = 0
self.channels = 0
self.codec_type... |
def remap(raw_file, remap_dict_file, remap_file):
with open(remap_dict_file, 'rb') as f:
uid_remap_dict = pkl.load(f)
iid_remap_dict = pkl.load(f)
cid_remap_dict = pkl.load(f)
sid_remap_dict = pkl.load(f)
bid_remap_dict = pkl.load(f)
aid_remap_dict = pkl.load(f)
... |
class MsgContainer(TLObject):
ID =
__slots__ = ['messages']
QUALNAME = 'MsgContainer'
def __init__(self, messages: List[Message]):
self.messages = messages
def read(data: BytesIO, *args: Any) -> 'MsgContainer':
count = Int.read(data)
return MsgContainer([Message.read(data) f... |
def exportMultiBuy(fit, options, callback):
itemAmounts = {}
for module in fit.modules:
if module.item:
if module.isMutated:
continue
_addItem(itemAmounts, module.item)
if (module.charge and options[PortMultiBuyOptions.LOADED_CHARGES]):
_addIte... |
class CacheFTPHandler(FTPHandler):
def __init__(self):
self.cache = {}
self.timeout = {}
self.soonest = 0
self.delay = 60
self.max_conns = 16
def setTimeout(self, t):
self.delay = t
def setMaxConns(self, m):
self.max_conns = m
def connect_ftp(self,... |
class Representer(SafeRepresenter):
def represent_str(self, data):
tag = None
style = None
try:
data = unicode(data, 'ascii')
tag = u'tag:yaml.org,2002:str'
except UnicodeDecodeError:
try:
data = unicode(data, 'utf-8')
... |
def test_is_open_on_minute(benchmark):
xhkg = get_calendar('XHKG')
timestamps = [pd.Timestamp('2019-10-11 01:20:00', tz=UTC), pd.Timestamp('2019-10-11 01:30:00', tz=UTC), pd.Timestamp('2019-10-11 01:31:00', tz=UTC), pd.Timestamp('2019-10-11 04:31:00', tz=UTC), pd.Timestamp('2019-10-11 08:00:00', tz=UTC), pd.Tim... |
def make_dataset(path, impl, skip_warmup=False):
if (not IndexedDataset.exists(path)):
print(f'Dataset does not exist: {path}')
print('Path should be a basename that both .idx and .bin can be appended to get full filenames.')
return None
if (impl == 'infer'):
impl = infer_dataset... |
class UperNetPyramidPoolingBlock(nn.Module):
def __init__(self, pool_scale: int, in_channels: int, channels: int) -> None:
super().__init__()
self.layers = [nn.AdaptiveAvgPool2d(pool_scale), UperNetConvModule(in_channels, channels, kernel_size=1)]
for (i, layer) in enumerate(self.layers):
... |
class Bottleneck(_Bottleneck):
expansion = 4
def __init__(self, inplanes, planes, groups=1, base_width=4, base_channels=64, **kwargs):
super(Bottleneck, self).__init__(inplanes, planes, **kwargs)
if (groups == 1):
width = self.planes
else:
width = (math.floor((sel... |
def dL3_hat(mu, C_hat, r, m, n):
vals = numpy.zeros(2)
for i in range(m):
numer0 = (C_hat[i][0] - C_hat[i][2])
numer1 = (C_hat[i][1] - C_hat[i][2])
denom = ((((C_hat[i][0] - C_hat[i][(n - 1)]) * mu[0]) + ((C_hat[i][1] - C_hat[i][(n - 1)]) * mu[1])) + C_hat[i][2])
vals[0] += (r[i]... |
class TestInhibitAnyPolicyExtension():
def test_inhibit_any_policy(self, backend):
cert = _load_cert(os.path.join('x509', 'custom', 'inhibit_any_policy_5.pem'), x509.load_pem_x509_certificate)
iap = cert.extensions.get_extension_for_class(x509.InhibitAnyPolicy).value
assert (iap.skip_certs =... |
def get_hyperplanes(p1, p2, wrap):
d = len(wrap)
p2_list = [p2]
wrap_amount = (2.0 * np.pi)
for i in range(d):
if wrap[i]:
list_lo = []
list_hi = []
for p_ in p2_list:
lo = np.copy(p_)
lo[i] -= wrap_amount
list_l... |
def remap_ids(w, old2new, id_order=[], **kwargs):
if (not isinstance(w, W)):
raise Exception('w must be a spatial weights object')
new_neigh = {}
new_weights = {}
for (key, value) in list(w.neighbors.items()):
new_values = [old2new[i] for i in value]
new_key = old2new[key]
... |
class F39_Network(F27_Network):
removedKeywords = F27_Network.removedKeywords
removedAttrs = F27_Network.removedAttrs
def _getParser(self):
op = F27_Network._getParser(self)
op.add_argument('--ipv4-dns-search', default=None, version=F39, dest='ipv4_dns_search', help='\n ... |
def _find_dists(dists: List[str]) -> List[str]:
uploads = []
for filename in dists:
if os.path.exists(filename):
uploads.append(filename)
continue
files = glob.glob(filename)
if (not files):
raise exceptions.InvalidDistribution(("Cannot find file (or e... |
class Axicon(DOE):
def __init__(self, period, radius=None, aberration=None):
global bd
from ..util.backend_functions import backend as bd
self.period = period
self.radius = radius
def get_transmittance(self, xx, yy, ):
t = 1
if (self.radius != None):
t... |
def cache_intermediate_datasets(cached_dataset, cache_on_cpu, model, module_name, forward_fn, path=None):
cached_data = []
iterator = iter(cached_dataset)
for idx in range(len(cached_dataset)):
def fn(_, inputs):
inputs = [*inputs]
if cache_on_cpu:
cached_data... |
.parametrize('src_order', [15, 20, 25, 30])
.parametrize('src_gamma', [(- 1.0), (- 0.5), 0.0])
.parametrize('dst_order', [15, 20, 25, 30])
.parametrize('dst_gamma', [(- 1.0), (- 0.5), 0.0])
def test_gc2gc(src_order, src_gamma, dst_order, dst_gamma):
np.random.seed(98765)
src = np.random.rand((src_order + 1))
... |
.parametrize('has_output_dir', [False, True])
def test_on_output_file_button_exists(skip_qtbot, tmp_path, mocker, has_output_dir):
mock_prompt = mocker.patch('randovania.gui.lib.common_qt_lib.prompt_user_for_output_file', autospec=True)
if has_output_dir:
output_directory = tmp_path.joinpath('output_pat... |
def test_git_clone_fails_for_non_existent_revision(source_url: str) -> None:
revision = sha1(uuid.uuid4().bytes).hexdigest()
with pytest.raises(PoetryConsoleError) as e:
Git.clone(url=source_url, revision=revision)
assert (f"Failed to clone {source_url} at '{revision}'" in str(e.value)) |
(scope='session')
def gerb_l2_hr_h5_dummy_file(tmp_path_factory):
filename = (tmp_path_factory.mktemp('data') / FNAME)
with h5py.File(filename, 'w') as fid:
fid.create_group('/Angles')
fid['/Angles/Relative Azimuth'] = np.ones(shape=(1237, 1237), dtype=np.dtype('>i2'))
fid['/Angles/Relat... |
_tests('aes_cbc_pkcs5_test.json')
def test_aes_cbc_pkcs5(backend, wycheproof):
key = binascii.unhexlify(wycheproof.testcase['key'])
iv = binascii.unhexlify(wycheproof.testcase['iv'])
msg = binascii.unhexlify(wycheproof.testcase['msg'])
ct = binascii.unhexlify(wycheproof.testcase['ct'])
padder = padd... |
class HP11713A(Instrument):
ATTENUATOR_X = {}
ATTENUATOR_Y = {}
channels = Instrument.MultiChannelCreator(SwitchDriverChannel, list(range(0, 9)))
def __init__(self, adapter, name='Hewlett-Packard HP11713A', **kwargs):
super().__init__(adapter, name, includeSCPI=False, send_end=True, **kwargs)
... |
def pretix_quotas():
return {'count': 2, 'next': None, 'previous': None, 'results': [{'id': 1, 'name': 'Ticket Quota', 'size': 200, 'available_number': 118, 'items': [1], 'variations': [], 'subevent': None, 'close_when_sold_out': False, 'closed': False}, {'id': 2, 'name': 'T-shirt Quota', 'size': 200, 'available_nu... |
class TasksRendererMixin():
def render_task(self, xml, task):
if (task['uri'] not in self.uris):
self.uris.add(task['uri'])
xml.startElement('task', {'dc:uri': task['uri']})
self.render_text_element(xml, 'uri_prefix', {}, task['uri_prefix'])
self.render_text_e... |
def inspect_node(mixed, *, _partial=None):
if isconfigurabletype(mixed, strict=True):
(inst, typ) = (None, mixed)
elif isconfigurable(mixed):
(inst, typ) = (mixed, type(mixed))
elif hasattr(mixed, 'func'):
return inspect_node(mixed.func, _partial=(mixed.args, mixed.keywords))
els... |
class TweetEncoder(nn.Module):
def __init__(self, output_dim=512):
super().__init__()
self._hidden_size = 768
self.bertweet = AutoModel.from_pretrained('vinai/bertweet-base')
self.linear_transform = nn.Linear(self._hidden_size, output_dim)
nn.init.xavier_normal_(self.linear_t... |
def aggregate_text_similarities(result_dict):
all_averages = [result_dict[prompt]['text_similarities'] for prompt in result_dict]
all_averages = np.array(all_averages).flatten()
total_average = np.average(all_averages)
total_std = np.std(all_averages)
return (total_average, total_std) |
def reduce_leaky_relu(_, op_tensor_tuple: Tuple[(Op, List[tf.Tensor])], _op_mask) -> (str, tf.Operation, tf.Operation):
name = ('reduced_' + op_tensor_tuple[0].dotted_name)
alpha = op_tensor_tuple[0].get_module().get_attr('alpha')
assert (alpha is not None)
new_tensor = tf.nn.leaky_relu(op_tensor_tuple[... |
def test_show_issue() -> None:
class RESTObject():
def __init__(self, manager: str, attrs: int) -> None:
...
class Mixin(RESTObject):
...
with pytest.raises(TypeError) as exc_info:
class Wrongv4Object(RESTObject, Mixin):
...
assert ('MRO' in exc_info.excon... |
def verify_build_token(token, aud, token_type, instance_keys):
try:
headers = jwt.get_unverified_header(token)
except jwtutil.InvalidTokenError as ite:
logger.error('Invalid token reason: %s', ite)
raise InvalidBuildTokenException(ite)
kid = headers.get('kid', None)
if (kid is No... |
class VolSDFTrainRunner():
def __init__(self, **kwargs):
torch.set_default_dtype(torch.float32)
torch.set_num_threads(1)
f = open(kwargs['conf'])
conf_text = f.read()
conf_text = conf_text.replace('SCAN_ID', str(kwargs['scan_id']))
conf_text = conf_text.replace('VIEW_... |
.parametrize('converter_cls', [BaseConverter, Converter])
.parametrize('unstructure_strat', [UnstructureStrategy.AS_DICT, UnstructureStrategy.AS_TUPLE])
def test_unstructure_attrs_mappings(benchmark, converter_cls, unstructure_strat):
class FrozenCls():
a: int
class C():
a: Mapping[(int, str)]
... |
.parametrize('username,password', users)
.parametrize('value_id', values)
def test_detail(db, client, username, password, value_id):
client.login(username=username, password=password)
value = Value.objects.get(pk=value_id)
url = reverse(urlnames['detail'], args=[value_id])
response = client.get(url)
... |
def _update_user_newsletter(graphql_client, user, open_to_newsletter):
query = '\n mutation(\n $open_to_newsletter: Boolean!,\n $open_to_recruiting: Boolean!,\n $date_birth: String\n ){\n update(input: {\n openToNewsletter: $open_to_newsletter,\n openToRecrui... |
def load_ed25519_vectors(vector_data):
data = []
for line in vector_data:
(secret_key, public_key, message, signature, _) = line.split(':')
secret_key = secret_key[0:64]
signature = signature[0:128]
data.append({'secret_key': secret_key, 'public_key': public_key, 'message': messa... |
class NoneCoalesceTernaryVisitor(ast.NodeVisitor):
def __init__(self, file_, callback):
self.__file = file_
self.__callback = callback
def visit_IfExp(self, ifexp):
if isinstance(ifexp.test, ast.Compare):
op = ifexp.test.ops[0]
if (isinstance(op, (ast.Is, ast.IsNo... |
_fixtures(ConfigWithFiles)
def test_config_defaults_dangerous(config_with_files):
fixture = config_with_files
fixture.set_config_spec(easter_egg, 'reahl.component_dev.test_config:ConfigWithDangerousDefaultedSetting')
config = StoredConfiguration(fixture.config_dir.name)
with CallMonitor(logging.getLogge... |
def test_parse_empty_string_default(default_parser):
line = ''
statement = default_parser.parse(line)
assert (statement == '')
assert (statement.args == statement)
assert (statement.raw == line)
assert (statement.command == '')
assert (statement.arg_list == [])
assert (statement.multilin... |
class Migration(migrations.Migration):
dependencies = [migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('schedule', '0002_auto__0043')]
operations = [migrations.CreateModel(name='ScheduleItemType', fields=[('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True... |
class TestBiasCorrection(unittest.TestCase):
def test_get_output_data(self):
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session(graph=tf.Graph())
input_op_names = ['input_1']
output_op_name = 'scope_1/conv2d_2/Conv2D'
with sess.graph.as_default():
_ = ... |
class CssDjangoLexer(DelegatingLexer):
name = 'CSS+Django/Jinja'
aliases = ['css+django', 'css+jinja']
filenames = ['*.css.j2', '*.css.jinja2']
version_added = ''
alias_filenames = ['*.css']
mimetypes = ['text/css+django', 'text/css+jinja']
url = '
def __init__(self, **options):
... |
_rewriter([sparse.mul_s_v])
def local_mul_s_v(fgraph, node):
if (node.op == sparse.mul_s_v):
(x, y) = node.inputs
x_is_sparse_variable = _is_sparse_variable(x)
if x_is_sparse_variable:
svar = x
dvar = y
else:
svar = y
dvar = x
i... |
class TarDataset(data.Dataset):
def download_or_unzip(cls, root):
path = os.path.join(root, cls.dirname)
if (not os.path.isdir(path)):
tpath = os.path.join(root, cls.filename)
if (not os.path.isfile(tpath)):
print('downloading')
urllib.request.... |
def test_ConnectionState_inconsistent_protocol_switch() -> None:
for (client_switches, server_switch) in [([], _SWITCH_CONNECT), ([], _SWITCH_UPGRADE), ([_SWITCH_UPGRADE], _SWITCH_CONNECT), ([_SWITCH_CONNECT], _SWITCH_UPGRADE)]:
cs = ConnectionState()
for client_switch in client_switches:
... |
_module()
class Compose(object):
def __init__(self, transforms):
assert isinstance(transforms, collections.abc.Sequence)
self.transforms = []
for transform in transforms:
if isinstance(transform, dict):
transform = build_from_cfg(transform, PIPELINES)
... |
class KeyPair():
def create_key_pair(key_size: int=1024):
private_key = rsa.generate_private_key(public_exponent=65537, key_size=key_size)
public_key = private_key.public_key()
private_key_bytes = private_key.private_bytes(encoding=serialization.Encoding.DER, format=serialization.PrivateForm... |
def file_content():
try:
return importlib.resources.files('pytest_html').joinpath('resources', 'style.css').read_bytes().decode('utf-8').strip()
except AttributeError:
import pkg_resources
return pkg_resources.resource_string('pytest_html', os.path.join('resources', 'style.css')).decode(... |
def _model_variable_getter(getter, name, shape=None, dtype=None, initializer=None, regularizer=None, trainable=True, collections=None, caching_device=None, partitioner=None, rename=None, use_resource=None, synchronization=tf_variables.VariableSynchronization.AUTO, aggregation=tf_variables.VariableAggregation.NONE, **_)... |
def get_resnet(blocks, bottleneck=None, conv1_stride=True, width_scale=1.0, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
if (bottleneck is None):
bottleneck = (blocks >= 50)
if (blocks == 10):
layers = [1, 1, 1, 1]
elif (blocks == 12):
lay... |
def plot_sapm(sapm_data, effective_irradiance):
(fig, axes) = plt.subplots(2, 3, figsize=(16, 10), sharex=False, sharey=False, squeeze=False)
plt.subplots_adjust(wspace=0.2, hspace=0.3)
ax = axes[(0, 0)]
sapm_data.filter(like='i_').plot(ax=ax)
ax.set_ylabel('Current (A)')
ax = axes[(0, 1)]
s... |
class DescribeCT_Tc():
def it_can_merge_to_another_tc(self, tr_, _span_dimensions_, _tbl_, _grow_to_, top_tc_):
top_tr_ = tr_
(tc, other_tc) = (element('w:tc'), element('w:tc'))
(top, left, height, width) = (0, 1, 2, 3)
_span_dimensions_.return_value = (top, left, height, width)
... |
def main():
parser = argparse.ArgumentParser(description='Baseline')
parser.add_argument('--conf_path', type=str, metavar='conf_path', help='input the path of config file')
parser.add_argument('--id', type=int, metavar='experiment_id', help='Experiment ID')
args = parser.parse_args()
option = Option... |
class SawyerDialTurnV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'dial_pos': obs[3:6], 'goal_pos': obs[6:]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_pow': 3})
action['delta_pos... |
def test_itransform_pipeline_radians():
trans = Transformer.from_pipeline('+proj=pipeline +step +inv +proj=cart +ellps=WGS84 +step +proj=unitconvert +xy_in=rad +xy_out=deg')
assert_almost_equal(list(trans.itransform([((- 2704026.01), (- 4253051.81), 3895878.82)], radians=True)), [((- 2.), 0., (- 20.))])
ass... |
.skipif((not fs_supports_symlink()), reason='symlink is not supported')
def test_py_info_cached_symlink_error(mocker, tmp_path, session_app_data):
spy = mocker.spy(cached_py_info, '_run_subprocess')
with pytest.raises(RuntimeError):
PythonInfo.from_exe(str(tmp_path), session_app_data)
symlinked = (t... |
def list2mat(input, undirected, sep, format):
nodes = set()
with open(input, 'r') as inf:
for line in inf:
if (line.startswith('#') or line.startswith('%')):
continue
line = line.strip()
splt = line.split(sep)
if (not splt):
... |
def get_r50_l16_config():
config = get_l16_config()
config.patches.grid = (16, 16)
config.resnet = ml_collections.ConfigDict()
config.resnet.num_layers = (3, 4, 9)
config.resnet.width_factor = 1
config.classifier = 'seg'
config.resnet_pretrained_path = '../model/vit_checkpoint/imagenet21k/R5... |
def _box_aug_per_img(img, target, aug_type=None, scale_ratios=None, scale_splits=None, img_prob=0.1, box_prob=0.3, level=1):
if (random.random() > img_prob):
return (img, target)
img_mean = torch.Tensor(pixel_mean).reshape(3, 1, 1).to(img.device)
img = (img + img_mean)
img /= 255.0
bboxes = ... |
def get_model_params(model, cfg):
optim_cfg = cfg.SOLVER
base_lr = optim_cfg.BASE_LR
params = []
for (key, value) in model.named_parameters():
if (not value.requires_grad):
continue
key_lr = [base_lr]
if ('bias' in key):
key_lr.append((cfg.SOLVER.BASE_LR *... |
def format_rouge_scores(scores):
return '\n\n****** ROUGE SCORES ******\n\n** ROUGE 1\nF1 >> {:.3f}\nPrecision >> {:.3f}\nRecall >> {:.3f}\n\n** ROUGE 2\nF1 >> {:.3f}\nPrecision >> {:.3f}\nRecall >> {:.3f}\n\n** ROUGE L\nF1 >> {:.3f}\nPrecision >> {:.3f}\nRecall >> {:.3f}'.format(score... |
class UserDal(object):
ALLOWED_ROLES = [u'owner', u'dev']
def __init__(self):
pass
def get_roles():
return {item.id: item.name_ch for item in Role.query.all()}
def get_role_name(role_id):
role = Role.query.get(role_id)
if role:
return role.name
else:
... |
class NegativeBaseEntryTestCase(unittest.TestCase):
def setUpClass(cls):
cls.entry = BaseEntry(**{'name': 'vw bully', 'value': (- 6000), 'date': '2000-01-01'})
def test_name(self):
self.assertEqual(self.entry.name, 'vw bully')
def test_value(self):
self.assertEqual(self.entry.value, ... |
()
def _check_alazy_constant_ttl():
global constant_call_count
constant_call_count = 0
_constant(ttl=100000)
()
def constant():
global constant_call_count
constant_call_count += 1
return constant_call_count
assert_eq(1, (yield constant.asynq()))
assert_eq(1, (yield co... |
(scope='module')
def chat_join_request(bot, time):
cjr = ChatJoinRequest(chat=TestChatJoinRequestBase.chat, from_user=TestChatJoinRequestBase.from_user, date=time, bio=TestChatJoinRequestBase.bio, invite_link=TestChatJoinRequestBase.invite_link, user_chat_id=TestChatJoinRequestBase.from_user.id)
cjr.set_bot(bot... |
.parametrize('progress, load_status, expected_visible', [(15, usertypes.LoadStatus.loading, True), (100, usertypes.LoadStatus.success, False), (100, usertypes.LoadStatus.error, False), (100, usertypes.LoadStatus.warn, False), (100, usertypes.LoadStatus.none, False)])
def test_tab_changed(fake_web_tab, progress_widget, ... |
class SendContact():
async def send_contact(self: 'pyrogram.Client', chat_id: Union[(int, str)], phone_number: str, first_name: str, last_name: str=None, vcard: str=None, disable_notification: bool=None, reply_to_message_id: int=None, schedule_date: datetime=None, protect_content: bool=None, reply_markup: Union[('t... |
def test_num_threads():
before = kvikio.defaults.get_num_threads()
with kvikio.defaults.set_num_threads(3):
assert (kvikio.defaults.get_num_threads() == 3)
kvikio.defaults.num_threads_reset(4)
assert (kvikio.defaults.get_num_threads() == 4)
assert (before == kvikio.defaults.get_num_t... |
def test_attn_label_convertor():
tmp_dir = tempfile.TemporaryDirectory()
dict_file = osp.join(tmp_dir.name, 'fake_dict.txt')
_create_dummy_dict_file(dict_file)
with pytest.raises(NotImplementedError):
AttnConvertor(5)
with pytest.raises(AssertionError):
AttnConvertor('DICT90', dict_f... |
class LRUCacheDataset(BaseWrapperDataset):
def __init__(self, dataset, token=None):
super().__init__(dataset)
_cache(maxsize=8)
def __getitem__(self, index):
return self.dataset[index]
_cache(maxsize=8)
def collater(self, samples):
return self.dataset.collater(samples) |
def test_accepts_none(msg):
a = m.NoneTester()
assert (m.no_none1(a) == 42)
assert (m.no_none2(a) == 42)
assert (m.no_none3(a) == 42)
assert (m.no_none4(a) == 42)
assert (m.no_none5(a) == 42)
assert (m.ok_none1(a) == 42)
assert (m.ok_none2(a) == 42)
assert (m.ok_none3(a) == 42)
a... |
class RMSpropTFOptimizer(Optimizer):
def __init__(self, params, lr=0.01, alpha=0.99, eps=1e-08, weight_decay=0, momentum=0, centered=False):
if (not (0.0 <= lr)):
raise ValueError('Invalid learning rate: {}'.format(lr))
if (not (0.0 <= eps)):
raise ValueError('Invalid epsilon... |
class VisaIOWarning(Warning):
def __init__(self, error_code: int) -> None:
(abbreviation, description) = completion_and_error_messages.get(error_code, ('?', 'Unknown code.'))
super(VisaIOWarning, self).__init__(('%s (%d): %s' % (abbreviation, error_code, description)))
self.error_code = erro... |
.parametrize('schema', [{'required': ['\x00']}, {'properties': {'\x00': {'type': 'integer'}}}, {'dependencies': {'\x00': ['a']}}, {'dependencies': {'\x00': {'type': 'integer'}}}, {'required': ['y']}, {'properties': {'y': {'type': 'integer'}}}, {'dependencies': {'y': ['a']}}, {'dependencies': {'y': {'type': 'integer'}}}... |
def make_dict_unstructure_fn(cl: type[T], converter: BaseConverter, _cattrs_omit_if_default: bool=False, _cattrs_use_linecache: bool=True, _cattrs_use_alias: bool=False, _cattrs_include_init_false: bool=False, **kwargs: AttributeOverride) -> Callable[([T], dict[(str, Any)])]:
origin = get_origin(cl)
attrs = ada... |
class IRBuilderVisitor(IRVisitor):
builder: IRBuilder
def visit_mypy_file(self, mypyfile: MypyFile) -> None:
assert False, 'use transform_mypy_file instead'
def visit_class_def(self, cdef: ClassDef) -> None:
transform_class_def(self.builder, cdef)
def visit_import(self, node: Import) -> ... |
def sync_states(states: Dict[(str, Dict[(str, Any)])], devices: Dict[(str, torch.device)], metrics_traversal_order: List[Tuple[(str, str)]], process_group: Optional[dist.ProcessGroup]=None, rank: Optional[int]=None) -> Optional[List[Dict[(str, Dict[(str, Any)])]]]:
gathered_states = [_get_empty_metric_state_collect... |
def main(argv):
args = setup_args().parse_args(argv)
if ((not args.show) and (not args.output)):
raise ValueError('select output file destination or --show')
scatters = []
for f in args.results_file:
rv = parse_json_file(f, args.metric)
scatters.append(rv)
ylabel = f'{args.me... |
class PermanentCheckBox(QtWidgets.QCheckBox):
def enterEvent(self, e):
if (self.window().showhelp is True):
QtWidgets.QToolTip.showText(e.globalPos(), '<h3>Permanent Markers / Annotations</h3>If checked, the markers and annotations created with the Pick/Click callbacks will be permanent.(e.g. th... |
def _create_pr_data_frame(evaluation_runs: List[EvaluationRun], methods: List[str]) -> DataFrame:
data_frames = []
for (evaluation_run, name) in zip(evaluation_runs, methods):
spotting_evaluation = evaluation_run.evaluation.spotting_evaluation
pr_data_frame = spotting_evaluation.pr_data_frame
... |
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