code stringlengths 281 23.7M |
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class curvefi():
def __init__(self, *args, a):
self.reserve = list(args)
self.n = len(args)
self.a_inv = a
self.sum_inv = self.get_suminv()
self.prod_inv = ((self.sum_inv / self.n) ** self.n)
self.totalshares = self.sum_inv
def poolsum(self):
return sum(se... |
class VoskHandler(STTHandler):
def __init__(self, settings, pip_path, stt):
self.key = 'vosk'
self.settings = settings
self.pip_path = pip_path
self.stt = stt
def recognize_file(self, path):
from vosk import Model
r = sr.Recognizer()
with sr.AudioFile(path... |
class ResNet_Final_Auxiliary_Classifer(nn.Module):
def __init__(self, block, num_classes):
super(ResNet_Final_Auxiliary_Classifer, self).__init__()
self.conv = conv1x1(((512 * block.expansion) * 4), (512 * block.expansion))
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Li... |
def get_trivial_allowed_durations(utt2dur, args):
lengths = list(set([((int(((float(d) * 1000) - args.frame_length)) / args.frame_shift) + 1) for (key, d) in utt2dur.items()]))
lengths.sort()
allowed_durations = []
with open(os.path.join(args.dir, 'allowed_durs.txt'), 'w', encoding='latin-1') as durs_fp... |
_image_displayer('sixel')
class SixelImageDisplayer(ImageDisplayer, FileManagerAware):
def __init__(self):
self.win = None
self.cache = {}
self.fm.signal_bind('preview.cleared', (lambda signal: self._clear_cache(signal.path)))
def _clear_cache(self, path):
if os.path.exists(path)... |
def lenet(batch_size):
n = caffe.NetSpec()
(n.data, n.label) = L.DummyData(shape=[dict(dim=[batch_size, 1, 28, 28]), dict(dim=[batch_size, 1, 1, 1])], transform_param=dict(scale=(1.0 / 255)), ntop=2)
n.conv1 = L.Convolution(n.data, kernel_size=5, num_output=20, weight_filler=dict(type='xavier'))
n.pool1... |
def test_local_site_to_dict():
vsite = LocalVirtualSite(name='test', orientations=[(0, 1, 2)], p1=(0 * unit.angstrom), p2=(0 * unit.angstrom), p3=(0 * unit.angstrom), o_weights=[1.0, 0.0, 0.0], x_weights=[(- 1.0), 0.5, 0.5], y_weights=[(- 1.0), 0.0, 1.0])
vsite_dict = vsite.to_dict()
assert (vsite_dict['nam... |
class Trainer(object):
def __init__(self, args):
self.args = args
self.device = torch.device(args.device)
self.num_gpus = (int(os.environ['WORLD_SIZE']) if ('WORLD_SIZE' in os.environ) else 1)
if (args.dataset == 'citys'):
train_dataset = CSTrainValSet(args.data, list_pat... |
class ConvolutionalGatingMLP(torch.nn.Module):
def __init__(self, size: int, linear_units: int, kernel_size: int, dropout_rate: float, use_linear_after_conv: bool=False):
super().__init__()
self.norm = LayerNorm(size)
self.channel_proj1 = torch.nn.Sequential(torch.nn.Linear(size, linear_unit... |
class SST(Task):
VERSION = 0
DATASET_PATH = 'glue'
DATASET_NAME = 'sst2'
def has_training_docs(self):
return True
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
if (self._training_docs is None):
... |
def main():
os.environ.setdefault('DJANGO_SETTINGS_MODULE', 'main.settings')
try:
from django.core.management import execute_from_command_line
except ImportError as exc:
raise ImportError("Couldn't import Django. Are you sure it's installed and available on your PYTHONPATH environment variab... |
def raise_on_call_returned_empty(given_block_identifier: BlockIdentifier) -> NoReturn:
msg = f'Either the given address is for a different smart contract, or the contract was not yet deployed at the block {format_block_id(given_block_identifier)}. Either way this call should never have happened.'
raise RaidenUn... |
def Mine_ItemS(FP, ItemS):
global CanNum
for i in sort_item:
CanNum += 1
count = 0
index_i = GetIndex(i)
for j in range(SeqNum):
count += len(index_i[j])
if (count >= int(minsup)):
p = []
p.append(i)
FP.append(p)
... |
class TestCreatePixmap(EndianTest):
def setUp(self):
self.req_args_0 = {'depth': 161, 'drawable': , 'height': 4764, 'pid': , 'width': 57984}
self.req_bin_0 = b'5\xa1\x00\x04wv\x0b\x1f,\xadRL\xe2\x80\x12\x9c'
def testPackRequest0(self):
bin = request.CreatePixmap._request.to_binary(*(), *... |
class AvahiService():
DBUS_NAME = 'org.freedesktop.Avahi'
DBUS_PATH_SERVER = '/'
DBUS_INTERFACE_ENTRY_GROUP = 'org.freedesktop.Avahi.EntryGroup'
DBUS_INTERFACE_SERVER = 'org.freedesktop.Avahi.Server'
def register(self, name, port, stype):
try:
GLib.Variant('q', port)
exce... |
def on_resize(width, height):
(viewport_width, viewport_height) = window.get_framebuffer_size()
glViewport(0, 0, viewport_width, viewport_height)
glMatrixMode(GL_PROJECTION)
glLoadIdentity()
gluPerspective(45.0, (float(width) / height), 1.0, 100.0)
glMatrixMode(GL_MODELVIEW)
return True |
class IndexURLs():
def __init__(self, repo: str):
self.repo = hyperlink.parse(repo).normalize()
if self.repo.host.endswith('pypi.org'):
repo_url = (self.repo.replace(host='pypi.org') if (self.repo.host == 'upload.pypi.org') else self.repo)
self.simple = repo_url.click('/simpl... |
class Const(_base_nodes.NoChildrenNode, Instance):
_other_fields = ('value', 'kind')
def __init__(self, value: Any, lineno: (int | None)=None, col_offset: (int | None)=None, parent: (NodeNG | None)=None, kind: (str | None)=None, *, end_lineno: (int | None)=None, end_col_offset: (int | None)=None) -> None:
... |
def get_path(prog_name):
try:
return _path_cache[prog_name]
except KeyError:
pass
if (prog_name not in _path_config.keys()):
raise ValueError(('%s is not a known external executable' % prog_name))
path_conf = _path_config[prog_name]
if (path_conf['env_var'] in os.environ):
... |
class Test_avl_del(unittest.TestCase):
def setUp(self):
pass
def testdel_basic(self):
t = avl.new()
t.remove(1)
t.remove((- 2))
self.assertTrue(verify_empty(t))
t = range_tree((- 2000), (+ 2000))
self.assertTrue((t.verify() == 1))
n = len(t)
... |
def _run_basic_get_repeatedly():
from timeit import default_timer
REPEAT = 10000
for _ in range(7):
start = default_timer()
for _ in range(REPEAT):
time_server_basic_get_with_realistic_headers()
finish = default_timer()
print(f'{(REPEAT / (finish - start)):.1f} re... |
class FloScriptLexer(RegexLexer):
name = 'FloScript'
url = '
aliases = ['floscript', 'flo']
filenames = ['*.flo']
version_added = '2.4'
def innerstring_rules(ttype):
return [('%(\\(\\w+\\))?[-#0 +]*([0-9]+|[*])?(\\.([0-9]+|[*]))?[hlL]?[E-GXc-giorsux%]', String.Interpol), ('[^\\\\\\\'"%\\... |
class outputReference(object):
def __init__(self, stepid, pointer):
self.stepid = stepid
self.pointer = pointer
def __repr__(self):
return 'outputReference {}#{}'.format(self.stepid, self.pointer.path)
def fromJSON(cls, data):
return cls(data['stepid'], jsonpointer.JsonPointe... |
def get_input_string(input_data, task_type, additional_column_name_string):
input_strings = []
if (additional_column_name_string is not None):
for additional_column_name in additional_column_name_string.split(','):
input_strings.append(str(input_data[additional_column_name]))
if (task_ty... |
(nopython=True)
def _rescale_and_lookup1d_function(data, scale, offset, lut, out):
(vmin, vmax) = (0, (lut.shape[0] - 1))
for r in range(data.shape[0]):
for c in range(data.shape[1]):
val = ((data[(r, c)] - offset) * scale)
val = min(max(val, vmin), vmax)
out[(r, c)] ... |
def setup_logging(debug, verbose):
handler = StreamHandler(sys.stderr)
handler.setFormatter(Formatter('[%(asctime)s] %(levelname)s (%(module)s:%(lineno)d) %(message)s', '%H:%M:%S'))
log.addHandler(handler)
if debug:
log.setLevel(DEBUG)
elif verbose:
log.setLevel(INFO)
else:
... |
_REGISTRY.register()
class ADDA(TrainerXU):
def __init__(self, cfg):
super().__init__(cfg)
self.open_layers = ['backbone']
if isinstance(self.model.head, nn.Module):
self.open_layers.append('head')
self.source_model = copy.deepcopy(self.model)
self.source_model.ev... |
class Task():
def __init__(self, fn, args, kwargs):
self._fn = fn
self._args = args
self._kwargs = kwargs
self.has_run = Event()
self._result = self._exception = None
def __call__(self):
try:
self._result = self._fn(*self._args, **self._kwargs)
... |
class Glucose4(object):
def __init__(self, bootstrap_with=None, use_timer=False, incr=False, with_proof=False, warm_start=False):
self.glucose = None
self.status = None
self.prfile = None
self.new(bootstrap_with, use_timer, incr, with_proof, warm_start)
def __enter__(self):
... |
class TestWithCExtension():
def _simulate_package_with_extension(self, tmp_path):
files = ['benchmarks/file.py', 'docs/Makefile', 'docs/requirements.txt', 'docs/source/conf.py', 'proj/header.h', 'proj/file.py', 'py/proj.cpp', 'py/other.cpp', 'py/file.py', 'py/py.typed', 'py/tests/test_proj.py', 'README.rst'... |
def test_checkpoint_hook(tmp_path):
loader = DataLoader(torch.ones((5, 2)))
runner = _build_demo_runner('EpochBasedRunner', max_epochs=1)
runner.meta = dict()
checkpointhook = CheckpointHook(interval=1, by_epoch=True)
runner.register_hook(checkpointhook)
runner.run([loader], [('train', 1)])
... |
class E(object):
def __init__(self, begin, end, fmt, dummy=False):
self.advance = 1
if dummy:
self.advance = 0
self.position = (begin - 1)
if (end is not None):
self.length = ((end - begin) + 1)
else:
self.length = None
self.end = e... |
def resnet_v1_200(inputs, num_classes=None, is_training=True, global_pool=True, output_stride=None, spatial_squeeze=True, reuse=None, scope='resnet_v1_200'):
blocks = [resnet_v1_block('block1', base_depth=64, num_units=3, stride=2), resnet_v1_block('block2', base_depth=128, num_units=24, stride=2), resnet_v1_block(... |
def test_QobjEvo_isherm_flag_knowcase():
assert (QobjEvo(sigmax())(0)._isherm is True)
non_hermitian = (sigmax() + 1j)
non_hermitian.isherm
assert (QobjEvo(non_hermitian)(0)._isherm is False)
assert (QobjEvo([sigmax(), sigmaz()])(0)._isherm is True)
assert (QobjEvo([sigmax(), 't'])(0)._isherm is... |
def test_042_parseModifier_nonstd():
def report(mod_group):
return Metar.Metar((sta_time + mod_group))
assert (report('RTD').mod == 'RTD')
assert (report('TEST').mod == 'TEST')
assert (report('CCA').mod == 'CCA')
assert (report('CCB').mod == 'CCB')
assert (report('CCC').mod == 'CCC')
... |
def _get_satellite_unit_vector_z(attitude, orbit):
v1950 = _get_satellite_z_axis_1950(attitude.angle_between_sat_spin_and_z_axis, attitude.angle_between_sat_spin_and_yz_plane)
vcorr = _correct_nutation_precession(v1950, orbit.nutation_precession)
return _rotate_to_greenwich(vcorr, orbit.angles.greenwich_sid... |
def load_archive_file(archive_file):
try:
resolved_archive_file = cached_path(archive_file, cache_dir=None)
except EnvironmentError:
print("Archive name '{}' was not found in archive name list. We assumed '{}' was a path or URL but couldn't find any file associated to this path or URL.".format(a... |
class AxialImageTransformer(nn.Module):
def __init__(self, dim, depth, heads=8, dim_heads=None, dim_index=1, reversible=True, axial_pos_emb_shape=None):
super().__init__()
permutations = calculate_permutations(2, dim_index)
get_ff = (lambda : nn.Sequential(ChanLayerNorm(dim), nn.Conv2d(dim, ... |
def chamfer(query, target_feature, comparator=False):
query = torch.Tensor(query).cuda()
target_feature = torch.Tensor(target_feature).cuda()
simmatrix = torch.einsum('ik,jk->ij', [query, target_feature])
if comparator:
simmatrix = comparator(simmatrix).detach()
sim = (simmatrix.max(dim=1)[0... |
def test_func(model_f, y_label, X_test_f):
y_pred = []
y_label = th.Tensor(y_label)
print('Testing:')
print('')
with tqdm(range(0, len(X_test_f), 1)) as tepoch:
for i in tepoch:
with th.no_grad():
x = [0, 0]
x[0] = X_test_f[i][0].to(device)
... |
class DeliverSM(SubmitSM):
params = {'service_type': Param(type=str, max=6), 'source_addr_ton': Param(type=int, size=1), 'source_addr_npi': Param(type=int, size=1), 'source_addr': Param(type=str, max=21), 'dest_addr_ton': Param(type=int, size=1), 'dest_addr_npi': Param(type=int, size=1), 'destination_addr': Param(t... |
class LossMixin():
def _process_y(self, X, y, sample_weight=None, copy=True, check_input=True):
loss_config = get_base_config(get_loss_config(self.loss))
if (loss_config.name in ['lin_reg', 'huber']):
return process_y_lin_reg(X=X, y=y, standardize=self.standardize, fit_intercept=self.fit... |
class TestSubschemaLDIF(unittest.TestCase):
def test_subschema_file(self):
for test_file in TEST_SUBSCHEMA_FILES:
with open(test_file, 'rb') as ldif_file:
ldif_parser = ldif.LDIFRecordList(ldif_file, max_entries=1)
ldif_parser.parse()
(_, subschema_sub... |
def truncated_cifar10(nperclass, nperclassvalid, args):
((xtrain, ytrain), (xvalid, yvalid)) = cifar10.load_data()
(ytrain, yvalid) = (np.squeeze(ytrain), np.squeeze(yvalid))
(inputs, labels) = ([], [])
counts = [0 for _ in range(10)]
for (x, y) in zip(xtrain, ytrain):
if all([(count == nper... |
def test_smooth() -> None:
instance = printer.Dummy()
instance.set_with_default(smooth=True)
expected_sequence = (TXT_NORMAL, TXT_STYLE['size']['normal'], TXT_STYLE['flip'][False], TXT_STYLE['smooth'][True], TXT_STYLE['bold'][False], TXT_STYLE['underline'][0], SET_FONT(b'\x00'), TXT_STYLE['align']['left'], ... |
def test_strip_examples(mocker):
p = asyncio.run(get_device_for_file('KP303(UK)_1.0_1.0.3.json', 'IOT'))
mocker.patch('kasa.smartstrip.SmartStrip', return_value=p)
mocker.patch('kasa.smartstrip.SmartStrip.update')
res = xdoctest.doctest_module('kasa.smartstrip', 'all')
assert (not res['failed']) |
class GroundStateEigensolver(GroundStateSolver):
def __init__(self, transformation: Transformation, solver: Union[(MinimumEigensolver, MinimumEigensolverFactory)]) -> None:
super().__init__(transformation)
self._solver = solver
def solver(self) -> Union[(MinimumEigensolver, MinimumEigensolverFac... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--save_path', default='/apdcephfs/share_1316500/donchaoyang/code3/SpecVQGAN/vocoder_audioset/logs/audioset')
parser.add_argument('--load_path', default='/apdcephfs/share_1316500/donchaoyang/code3/SpecVQGAN/vocoder_audioset/logs/audios... |
def fast_encode(texts, tokenizer, chunk_size=256, maxlen=512, enable_padding=False):
tokenizer.enable_truncation(max_length=maxlen)
if enable_padding:
tokenizer.enable_padding(max_length=maxlen)
all_ids = []
for i in tqdm(range(0, len(texts), chunk_size)):
text_chunk = texts[i:(i + chunk... |
def load_model():
base_model = InceptionV3(include_top=False, weights='imagenet', input_shape=IMSIZE)
for layer in base_model.layers:
layer.trainable = False
x = base_model.output
x = Flatten()(x)
predictions = Dense(N_CLASSES, activation='softmax')(x)
model = Model(inputs=base_model.inp... |
def makeCfdSolverFoam(name='OpenFOAM'):
obj = FreeCAD.ActiveDocument.addObject('Fem::FemSolverObjectPython', name)
CfdSolverFoam(obj)
if FreeCAD.GuiUp:
from cfdguiobjects._ViewProviderCfdSolverFoam import _ViewProviderCfdSolverFoam
_ViewProviderCfdSolverFoam(obj.ViewObject)
return obj |
def render_frames(frames, prediction):
rendered_frames = []
for frame in frames:
img = np.array(frame)
(height, width, _) = img.shape
cv2.putText(img, prediction, (1, int((height / 8))), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
rendered_frames.append(img)
return rende... |
class YGate(Bloq):
_property
def signature(self) -> 'Signature':
return Signature.build(q=1)
def add_my_tensors(self, tn: qtn.TensorNetwork, tag: Any, *, incoming: Dict[(str, SoquetT)], outgoing: Dict[(str, SoquetT)]):
tn.add(qtn.Tensor(data=_PAULIY, inds=(outgoing['q'], incoming['q']), tags... |
def gdrive_download(id='1n_oKgR81BJtqk75b00eAjdv03qVCQn2f', name='coco128.zip'):
t = time.time()
print(('Downloading as %s... ' % (id, name)), end='')
(os.remove(name) if os.path.exists(name) else None)
(os.remove('cookie') if os.path.exists('cookie') else None)
out = ('NUL' if (platform.system() =... |
class SmartProtocol(TPLinkProtocol):
SLEEP_SECONDS_AFTER_TIMEOUT = 1
def __init__(self, *, transport: BaseTransport) -> None:
super().__init__(transport=transport)
self._terminal_uuid: str = base64.b64encode(md5(uuid.uuid4().bytes)).decode()
self._request_id_generator = SnowflakeId(1, 1)... |
class DiscriminatorS(torch.nn.Module):
def __init__(self, use_spectral_norm=False):
super(DiscriminatorS, self).__init__()
norm_f = (weight_norm if (use_spectral_norm == False) else spectral_norm)
self.convs = nn.ModuleList([norm_f(Conv1d(1, 128, 15, 1, padding=7)), norm_f(Conv1d(128, 128, 4... |
def test_get_solarposition_no_kwargs(expected_solpos, golden):
times = pd.date_range(datetime.datetime(2003, 10, 17, 13, 30, 30), periods=1, freq='D', tz=golden.tz)
ephem_data = solarposition.get_solarposition(times, golden.latitude, golden.longitude)
expected_solpos.index = times
expected_solpos = np.r... |
def test_solver_chooses_most_recent_version_amongst_repositories(package: ProjectPackage, io: NullIO) -> None:
package.python_versions = '^3.7'
package.add_dependency(Factory.create_dependency('tomlkit', {'version': '^0.5'}))
repo = MockLegacyRepository()
pool = RepositoryPool([repo, MockPyPIRepository(... |
def get_ordered_lists_of_conv_fc(model: torch.nn.Module, input_shapes: Tuple, dummy_input: Union[(torch.Tensor, Tuple)]=None) -> List:
device = get_device(model)
if (dummy_input is None):
dummy_input = create_rand_tensors_given_shapes(input_shapes, device)
module_list = get_ordered_list_of_modules(m... |
.skip(reason='unknown')
class TestTools():
def checkit(self, p, q, rng):
N = (p + q)
(layout, blades) = Cl(p, q)
A = layout.randomV(n=N, rng=rng)
R = (5.0 * layout.randomRotor(rng=rng))
B = [((R * a) * (~ R)) for a in A]
(R_found, rs) = of2v(A, B)
self.assertT... |
def main(data_dir, client, bc, config):
benchmark(read_tables, data_dir, bc, dask_profile=config['dask_profile'])
query_1 = '\n SELECT i_item_sk,\n CAST(i_category_id AS TINYINT) AS i_category_id\n FROM item\n '
item_df = bc.sql(query_1)
item_df = item_df.persist()
wait(i... |
def test_no_color_env_var_overrides_cli_option(runner, monkeypatch, mock_cli_exec, boxed_context, in_tmp_dir, tmp_path):
monkeypatch.setenv('NO_COLOR', '1')
touch_files(tmp_path, 'foo.json')
runner.invoke(cli_main, ['--color=always', '--schemafile', 'schema.json', 'foo.json'])
assert (boxed_context.ref.... |
class AvailabilitiesPage(JsonPage):
def find_best_slot(self, start_date=None, end_date=None, excluded_weekdays=[]):
for a in self.doc['availabilities']:
date = parse_date(a['date']).date()
if ((start_date and (date < start_date)) or (end_date and (date > end_date))):
... |
def sample_for_query(qid, ranking, args_positives, depth, permissive, biased):
(positives, negatives, triples) = ([], [], [])
for (pid, rank, *_, label) in ranking:
assert (rank >= 1), f'ranks should start at 1 got rank = {rank}'
assert (label in [0, 1])
if (rank > depth):
... |
def _treat_X_doc(doc: Optional[str]) -> Optional[str]:
if doc:
doc = doc.replace('Data to predict with.', 'Data to predict with. Can also be a ``RayDMatrix``.')
doc = doc.replace('Feature matrix.', 'Feature matrix. Can also be a ``RayDMatrix``.')
doc = doc.replace('Feature matrix', 'Feature ... |
def _create_fileset(fullname, struct, recurse={}):
set_path = SetPath(fullname, struct, recurse, struct.get('rec', {}))
if (set_path.get_type() == 'directory'):
_create_directory(set_path, always_delete=True)
for name in struct.get('contents', {}):
_multi_create_fileset(fullname, nam... |
("Too dangerous to modify 2.0.x's SourceGroups, this test will fail for them")
class SourceGroupTestCase(unittest.TestCase):
def test_empty(self):
group = SourceGroup()
self.assertIsNone(group.get_audio_data(2048))
def test_functionality(self):
fake_data = ((b'a', 1000, 0.5), (b'b', 4000... |
def _server_maintenance():
global EVENNIA, _MAINTENANCE_COUNT, _FLUSH_CACHE, _GAMETIME_MODULE
if (not _FLUSH_CACHE):
from evennia.utils.idmapper.models import conditional_flush as _FLUSH_CACHE
if (not _GAMETIME_MODULE):
from evennia.utils import gametime as _GAMETIME_MODULE
_MAINTENANCE_... |
def get_id_fromjson(jsonobject, method=DEFAULT_ID_METHOD):
method = os.environ.get('YADAGE_ID_METHOD', method)
if (method == 'uuid'):
return str(uuid.uuid4())
elif (method == 'jsonhash'):
return json_hash(jsonobject)
else:
raise NotImplementedError('unkown id generation method {}... |
def render_trailing_newlines(msg, _node, source_lines=None):
start_line = (msg.line - 1)
(yield from render_context((start_line - 2), start_line, source_lines))
(yield from ((line, slice(None, None), LineType.OTHER, source_lines[(line - 1)]) for line in range(start_line, (len(source_lines) + 1)))) |
def verify_str_arg(value, arg=None, valid_values=None, custom_msg=None):
if (not isinstance(value, torch._six.string_classes)):
if (arg is None):
msg = 'Expected type str, but got type {type}.'
else:
msg = 'Expected type str for argument {arg}, but got type {type}.'
m... |
def dump_readme(path):
readme = "# Pyrocko Earthquake Scenario\n\nThe directory structure of a scenario is layed out as follows:\n\n## Map of the scenario\nA simple map is generated from `pyrocko.automap` in map.pdf\n\n## Earthquake Sources\n\nCan be found as events.txt and sources.yml hosts the pyrocko.gf sources.... |
def test_make_unique_obj_list():
object_list = [type('SomeObjectClass', (object,), {'propertyName': '1'}), type('SomeObjectClass', (object,), {'propertyName': '2'}), type('SomeObjectClass', (object,), {'propertyName': '1'})]
value_list = utils.make_unique_obj_list(object_list, (lambda x: x.propertyName))
va... |
def main():
in_q = Queue()
out_q = Queue()
t = threading.Thread(target=worker, args=(in_q, out_q))
t.start()
while True:
start = time.monotonic()
for _ in range(COUNT):
in_q.put((lambda : None))
out_q.get()
end = time.monotonic()
print(f'{(((en... |
class ReadOnlyObjectDict(ObjectDictProxy):
def __delitem__(self, key):
raise NotImplementedError
def __delattr__(self, item):
raise NotImplementedError
def __setitem__(self, key, item):
raise NotImplementedError
def __setattr__(self, key, value):
raise NotImplementedError |
def test_channelstate_lockedtransfer_invalid_chainid():
(our_model1, _) = create_model(70)
(partner_model1, privkey2) = create_model(100)
channel_state = create_channel_from_models(our_model1, partner_model1, privkey2)
distributable = channel.get_distributable(channel_state.partner_state, channel_state.... |
class EMAImage(DifferentiableImage):
def __init__(self, width, height, tensor, decay):
super().__init__(width, height)
self.tensor = nn.Parameter(tensor)
self.register_buffer('biased', torch.zeros_like(tensor))
self.register_buffer('average', torch.zeros_like(tensor))
self.de... |
def rgb2lab(c):
R = c[0]
G = c[1]
B = c[2]
eps = (216.0 / 24389.0)
k = (24389.0 / 27.0)
Xr = 0.964221
Yr = 1.0
Zr = 0.825211
r = (R / 255.0)
g = (G / 255.0)
b = (B / 255.0)
if (r <= 0.04045):
r = (r / 12)
else:
r = (((r + 0.055) / 1.055) ** 2.4)
if... |
class TestParseLDAPUrl(unittest.TestCase):
parse_ldap_url_tests = [('ldap://root.openldap.org/dc=openldap,dc=org', LDAPUrl(hostport='root.openldap.org', dn='dc=openldap,dc=org')), ('ldap://root.openldap.org/dc%3dboolean%2cdc%3dnet???%28objectClass%3d%2a%29', LDAPUrl(hostport='root.openldap.org', dn='dc=boolean,dc=n... |
def parse_worker(q):
parser = DependencyTreeParser(model_path=('Stanford Library/stanford-parser-full-%s/edu/stanford/nlp/models/lexparser/englishPCFG.ser.gz' % DATE))
parser = MetricalTreeParser(parser)
for filename in iter(q.get, 'STOP'):
print(('Working on %s...' % filename))
sents = []
... |
def tpdm_antisymmetry_constraint(dim: int) -> DualBasis:
dbe_list = []
for (p, q, r, s) in product(range(dim), repeat=4):
if (((p * dim) + q) <= ((r * dim) + s)):
if ((p < q) and (r < s)):
tensor_elements = [tuple(indices) for indices in _coord_generator(p, q, r, s)]
... |
.parametrize('load_manager', [{'format': '{time}: {load:.1f}'}], indirect=True)
def test_load_times_formatting(load_manager):
widget = load_manager.c.widget['load']
assert (widget.info()['text'] == '1m: 0.7')
widget.next_load()
assert (widget.info()['text'] == '5m: 0.8')
widget.next_load()
asser... |
def test_none_Constant():
o1 = Constant(NoneTypeT(), None, name='NoneConst')
o2 = Constant(NoneTypeT(), None, name='NoneConst')
assert o1.equals(o2)
assert NoneConst.equals(o1)
assert o1.equals(NoneConst)
assert NoneConst.equals(o2)
assert o2.equals(NoneConst)
import pickle
import py... |
('pypyr.steps.filewritetoml.Path')
def test_filewritetoml_pass_no_payload(mock_path):
context = Context({'k1': 'v1', 'fileWriteToml': {'path': '/arb/blah'}})
with io.BytesIO() as out_bytes:
with patch('pypyr.toml.open', mock_open()) as mock_output:
mock_output.return_value.write.side_effect ... |
class TBBasicCharacter(DefaultCharacter):
def at_object_creation(self):
self.db.max_hp = 100
self.db.hp = self.db.max_hp
def at_before_move(self, destination):
if is_in_combat(self):
self.msg("You can't exit a room while in combat!")
return False
if (self.... |
def calculate_shard_sizes_and_offsets(tensor: torch.Tensor, world_size: int, local_world_size: int, sharding_type: str, col_wise_shard_dim: Optional[int]=None) -> Tuple[(List[List[int]], List[List[int]])]:
(rows, columns) = tensor.shape
if (sharding_type == ShardingType.DATA_PARALLEL.value):
return (([[... |
class TestCallable(unittest.TestCase):
def test_callable(self) -> None:
expected = [('callable(len)', True), ('callable("a")', False), ('callable(callable)', True), ('callable(lambda x, y: x+y)', True), ('import os; __(callable(os))', False), ('callable(int)', True), ('\n def test(): pass\n ... |
class TestPortfolioDiversification(QiskitFinanceTestCase):
def setUp(self):
super().setUp()
self.num_assets = 4
self.expected_returns = [0., (- 0.), 0., 0.]
self.covariances = [[0., 7.e-05, 0., (- 9.e-05)], [7.e-05, 0., 5.e-05, 4.e-05], [0., 5.e-05, 0., (- 0.)], [(- 9.e-05), 4.e-05, ... |
def test_pype_no_pipe_arg(mock_pipe):
context = Context({'pype': {'name': 'pipe name', 'pipeArg': None, 'useParentContext': False, 'skipParse': False, 'raiseError': True}})
with patch_logger('pypyr.steps.pype', logging.INFO) as mock_logger_info:
with get_arb_pipeline_scope(context):
pype.run... |
_LOSS.register_module()
class CeLoss(BaseLoss):
def __init__(self, weight=1.0, ignore_label=(- 100), use_weight=False, cls_weight=None, input_dict=None, **kwargs):
super().__init__(weight)
if (input_dict is None):
self.input_dict = {'ce_inputs': 'ce_inputs', 'ce_labels': 'ce_labels'}
... |
def catch_the_response_if_user_want_evaluate(update, context):
query = update.callback_query
if (query.data == (PATTERN_TO_CATCH_IF_USER_WANT_RATE_THE_PRODUCT + 'OK')):
send_a_rating_message(update, context, PATTERN_TO_CATCH_THE_RATE)
elif (query.data == (PATTERN_TO_CATCH_IF_USER_WANT_RATE_THE_PRODU... |
_mode()
def multiclass_recall(input: torch.Tensor, target: torch.Tensor, *, num_classes: Optional[int]=None, average: Optional[str]='micro') -> torch.Tensor:
_recall_param_check(num_classes, average)
(num_tp, num_labels, num_predictions) = _recall_update(input, target, num_classes, average)
return _recall_c... |
class QdbClientServer(QdbServerBase):
def __init__(self, session_store, host='localhost', port=8002, route=DEFAULT_ROUTE, auth_fn=None, auth_timeout=60):
self.auth_fn = (auth_fn or self.NO_AUTH)
self.auth_timeout = auth_timeout
self.route = re.compile(route, re.IGNORECASE)
self.sessi... |
def setup(app):
generate_keybinding_images()
if os.getenv('QTILE_BUILD_SCREENSHOTS', False):
generate_widget_screenshots()
else:
print('Skipping screenshot builds...')
app.add_directive('qtile_class', QtileClass)
app.add_directive('qtile_hooks', QtileHooks)
app.add_directive('qti... |
def test_toml_parser_pass():
in_bytes = b'[table]\nkey= "value"'
with patch('pypyr.toml.open', mock_open(read_data=in_bytes)) as mocked_open:
out = toml_file.get_parsed_context(['./myfile.toml'])
mocked_open.assert_called_once_with('./myfile.toml', 'rb')
assert (out == {'table': {'key': 'value'}... |
.parametrize('to_test', [qutip.basis, qutip.fock, qutip.fock_dm])
.parametrize('size, n', [([2, 2], [0, 1]), ([2, 3, 4], [1, 2, 0])])
def test_implicit_tensor_basis_like(to_test, size, n):
implicit = to_test(size, n)
explicit = qutip.tensor(*[to_test([ss], [nn]) for (ss, nn) in zip(size, n)])
assert (implic... |
def setup(args):
cfg = get_cfg()
add_centernet_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
if ('/auto' in cfg.OUTPUT_DIR):
file_name = os.path.basename(args.config_file)[:(- 5)]
cfg.OUTPUT_DIR = cfg.OUTPUT_DIR.replace('/auto', '/{}'.format(file_na... |
def test_get_formatted_iterable_with_memo():
arb_dict = {'key4.1': 'value4.1', '{ctx2}_key4.2': 'value_{ctx3}_4.2', 'key4.3': {'4.3.1': '4.3.1value', '4.3.2': '4.3.2_{ctx1}_value'}}
arb_list = [0, 1, 2]
arb_string = 'arb string'
arb_string_with_formatting = 'a {ctx1} string'
input_obj = {'k1': arb_s... |
class UNet3D(AbstractUNet):
def __init__(self, in_channels, out_channels, final_sigmoid=True, f_maps=64, layer_order='gcr', num_groups=8, num_levels=4, is_segmentation=True, conv_padding=1, conv_upscale=2, upsample='default', dropout_prob=0.1, **kwargs):
super(UNet3D, self).__init__(in_channels=in_channels,... |
class Solution(object):
def findMin(self, nums):
(l, r) = (0, (len(nums) - 1))
while ((l < r) and (nums[l] >= nums[r])):
mid = ((l + r) / 2)
if (nums[mid] > nums[r]):
l = (mid + 1)
elif (nums[mid] < nums[l]):
r = mid
els... |
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