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class NetworkTrainer_acdc(object):
def __init__(self, deterministic=True, fp16=False, seed=12345):
self.fp16 = fp16
self.amp_grad_scaler = None
if deterministic:
np.random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
tor... |
.parametrize('project_id', projects)
def test_project_delete(db, project_id):
project = Project.objects.get(id=project_id)
project_parent_id = (project.parent_id if project.parent else None)
project_children = [child.id for child in project.get_children()]
project.delete()
for child_id in project_ch... |
class MyTestCase(unittest.TestCase):
def test_something(self):
net = nn.Linear(10, 10)
optimizer = make_optimizer(cfg, net)
lr_scheduler = WarmupMultiStepLR(optimizer, [20, 40], warmup_iters=10)
for i in range(50):
lr_scheduler.step()
for j in range(3):
... |
class TestCheckpointFunctions(unittest.TestCase):
def setUp(self):
self.base_dir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.base_dir)
def test_save_and_load_checkpoint(self):
checkpoint_dict = {str(i): (i * 2) for i in range(1000)}
save_checkpoint(self.base_d... |
def token_generator_three(source_path, target_path_l2r, target_path_r2l, token_vocab_src, token_vocab_tgt, eos=None):
eos_list = ([] if (eos is None) else [eos])
pad_list = ([] if (PAD is None) else [PAD])
l2r_list = ([] if (L2R is None) else [L2R])
r2l_list = ([] if (R2L is None) else [R2L])
with t... |
class IO():
def get(cls, file_path):
(_, file_extension) = os.path.splitext(file_path)
if (file_extension in ['.npy']):
return cls._read_npy(file_path)
elif (file_extension in ['.h5']):
return cls._read_h5(file_path)
elif (file_extension in ['.txt']):
... |
class NeuralNet(torch.nn.Module):
def __init__(self, d_in, d_out):
self.d_in = d_in
self.d_out = d_out
super().__init__()
self.norm = torch.nn.LayerNorm(d_in)
self.norm2 = FusedLayerNorm(d_out)
self.linear = torch.nn.Linear(d_in, d_out)
self.linear2 = torch.nn... |
class SimpleParameter(Parameter):
def __init__(self, *args, **kargs):
Parameter.__init__(self, *args, **kargs)
def _interpretValue(self, v):
typ = self.opts['type']
def _missing_interp(v):
return v
interpreter = getattr(builtins, typ, _missing_interp)
return i... |
class DOSTest(unittest.TestCase):
def test_dos_8086_hello(self):
ql = Qiling(['../examples/rootfs/8086/dos/HI.DOS_COM'], '../examples/rootfs/8086/dos', verbose=QL_VERBOSE.DEBUG)
ck = Checklist()
def onenter(ql: Qiling):
ck.visited_onenter = True
def onexit(ql: Qiling):
... |
class AENC(Frame):
_framespec = [Latin1TextSpec('owner'), SizedIntegerSpec('preview_start', size=2, default=0), SizedIntegerSpec('preview_length', size=2, default=0), BinaryDataSpec('data')]
def HashKey(self):
return ('%s:%s' % (self.FrameID, self.owner))
def __bytes__(self):
return self.own... |
class Timezone(BaseOption):
def validate(self, value, **kwargs):
return validatorfuncs.timezone(value, option_key=self.key, **kwargs)
def default(self):
return _TZ_DICT[self.default_value]
def deserialize(self, save_data):
if (save_data not in _TZ_DICT):
raise ValueError(... |
def multitest(url, payloads):
if (urlparse(url).scheme == ''):
url = (' + url)
regexBypassPayloads = generator(url, payloads)
if ('=' in url):
if url.endswith('='):
url += 'r007'
parsedQueries = parse_qs(urlparse(url).query)
keys = [key for key in parsedQueries]
... |
def compute_statistics(model=None, args=None, logger=None, log_time=None):
from utils.norm_stats_utils import ComputeNormStatsHook
compute_stat_hooks = []
list_stat_mean = []
list_stat_var = []
if (args.arch == 'tanet'):
if (args.stat_type in ['temp', 'temp_v2']):
candidate_layer... |
class MultiHeadedDotAttention(nn.Module):
def __init__(self, h, d_model, dropout=0.1, scale=1, project_k_v=1, use_output_layer=1, do_aoa=0, norm_q=0, dropout_aoa=0.3):
super(MultiHeadedDotAttention, self).__init__()
assert (((d_model * scale) % h) == 0)
self.d_k = ((d_model * scale) // h)
... |
class GetChatPhotosCount():
async def get_chat_photos_count(self: 'pyrogram.Client', chat_id: Union[(int, str)]) -> int:
peer_id = (await self.resolve_peer(chat_id))
if isinstance(peer_id, raw.types.InputPeerChannel):
r = (await self.invoke(raw.functions.messages.GetSearchCounters(peer=p... |
class LogReturnsSeries(ReturnsSeries):
def _constructor(self):
return LogReturnsSeries
def _constructor_expanddim(self):
from qf_lib.containers.dataframe.log_returns_dataframe import LogReturnsDataFrame
return LogReturnsDataFrame
def to_log_returns(self) -> 'LogReturnsSeries':
... |
class YamlMigrations(QObject):
changed = pyqtSignal()
def __init__(self, settings: Any, parent: QObject=None) -> None:
super().__init__(parent)
self._settings = settings
def migrate(self) -> None:
self._migrate_configdata()
self._migrate_bindings_default()
self._migra... |
def render_pep440_branch(pieces):
if pieces['closest-tag']:
rendered = pieces['closest-tag']
if (pieces['distance'] or pieces['dirty']):
if (pieces['branch'] != 'master'):
rendered += '.dev0'
rendered += plus_or_dot(pieces)
rendered += ('%d.g%s' % ... |
class ZeroconfIPv6Address(IPv6Address):
__slots__ = ('_str', '_is_link_local', '_is_unspecified')
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self._str = super().__str__()
self._is_link_local = super().is_link_local
self._is_unspecifie... |
class GraphicsWidgetAnchor(object):
def __init__(self):
self.__parent = None
self.__parentAnchor = None
self.__itemAnchor = None
self.__offset = (0, 0)
if hasattr(self, 'geometryChanged'):
self.geometryChanged.connect(self.__geometryChanged)
def anchor(self, i... |
class HumanReadableMtimeLinemode(LinemodeBase):
name = 'humanreadablemtime'
def filetitle(self, fobj, metadata):
return fobj.relative_path
def infostring(self, fobj, metadata):
if (fobj.stat is None):
return '?'
return human_readable_time(fobj.stat.st_mtime) |
def test_regular_bind_and_provider_dont_work_with_multibind():
Names = NewType('Names', List[str])
Passwords = NewType('Passwords', Dict[(str, str)])
class MyModule(Module):
with pytest.raises(Error):
def provide_strs(self) -> List[str]:
return []
with pytest.rais... |
def adagrad_window(loss_or_grads=None, params=None, learning_rate=0.001, epsilon=0.1, n_win=10):
if ((loss_or_grads is None) and (params is None)):
return partial(adagrad_window, **_get_call_kwargs(locals()))
elif ((loss_or_grads is None) or (params is None)):
raise ValueError('Please provide bo... |
class JordanWignerSparseTest(unittest.TestCase):
def test_jw_sparse_0create(self):
expected = csc_matrix(([1], ([1], [0])), shape=(2, 2))
self.assertTrue(numpy.allclose(jordan_wigner_sparse(FermionOperator('0^')).A, expected.A))
def test_jw_sparse_1annihilate(self):
expected = csc_matrix... |
class OrderWeighted(Reorder, OrderRemembered):
name = 'weighted'
display_name = _('Prefer higher rated')
accelerated_name = _('Prefer _higher rated')
def next(self, playlist, iter):
super().next(playlist, iter)
remaining = self.remaining(playlist)
if (not remaining):
... |
def test_collect_ref_counts():
source = Stream()
collector = source.collect()
refs = []
for i in range(10):
r = RefCounter()
refs.append(r)
source.emit(i, metadata=[{'ref': r}])
assert all(((r.count == 1) for r in refs))
collector.flush()
assert all(((r.count == 0) fo... |
def _decode_host(host):
if (not host):
return u''
try:
host_bytes = host.encode('ascii')
except UnicodeEncodeError:
host_text = host
else:
try:
host_text = idna_decode(host_bytes, uts46=True)
except ValueError:
host_text = host
return h... |
class CT_Style(BaseOxmlElement):
_tag_seq = ('w:name', 'w:aliases', 'w:basedOn', 'w:next', 'w:link', 'w:autoRedefine', 'w:hidden', 'w:uiPriority', 'w:semiHidden', 'w:unhideWhenUsed', 'w:qFormat', 'w:locked', 'w:personal', 'w:personalCompose', 'w:personalReply', 'w:rsid', 'w:pPr', 'w:rPr', 'w:tblPr', 'w:trPr', 'w:tc... |
class PublishFilter(SimpleListFilter):
title = _('Publish status')
parameter_name = 'published'
def lookups(self, request, model_admin):
return [('yes', gettext('Published')), ('no', gettext('Waiting for publication date'))]
def queryset(self, request, queryset):
if (self.value() == 'yes... |
class Car():
def __init__(self, world, init_angle, init_x, init_y):
self.world = world
self.hull = self.world.CreateDynamicBody(position=(init_x, init_y), angle=init_angle, fixtures=[fixtureDef(shape=polygonShape(vertices=[((x * SIZE), (y * SIZE)) for (x, y) in HULL_POLY1]), density=1.0), fixtureDef... |
class TestModels(TestCase):
(QF_LRA)
def test_get_model(self):
varA = Symbol('A', BOOL)
varB = Symbol('B', REAL)
zero = Real(0)
f1 = Implies(varA, And(GT(varB, zero), LT(varB, zero)))
model = None
for solver_name in get_env().factory.all_solvers(logic=QF_UFLIRA):
... |
class TensorBoardLogger(MetricLogger):
def __init__(self: TensorBoardLogger, path: str, *args: Any, **kwargs: Any) -> None:
self._writer: Optional[SummaryWriter] = None
self._rank: int = get_global_rank()
self._sync_path_to_workers(path)
if (self._rank == 0):
logger.info(... |
.parametrize('locale, time, expected_period_id', [('de', time(7, 42), 'morning1'), ('de', time(3, 11), 'night1'), ('fi', time(0), 'midnight'), ('en_US', time(12), 'noon'), ('en_US', time(21), 'night1'), ('en_US', time(5), 'night1'), ('en_US', time(6), 'morning1'), ('agq', time(10), 'am'), ('agq', time(22), 'pm'), ('am'... |
def make_d_label_spk2uttr(lines):
idx = 0
dic_label = {}
list_label = []
dic_spk2utt = {}
for line in lines:
spk = get_spk(line)
if (spk not in dic_label):
dic_label[spk] = idx
list_label.append(spk)
idx += 1
if (spk not in dic_spk2utt):
... |
class ModelArguments():
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
config_name: Optional[str] = field(default=None, metadata={'help': 'Pretrained config name or path if not the same as model_name'})
tokenizer_name: Optional[s... |
_tf
_sentencepiece
_tokenizers
class TFMT5ModelIntegrationTest(unittest.TestCase):
def test_small_integration_test(self):
model = TFAutoModelForSeq2SeqLM.from_pretrained('google/mt5-small')
tokenizer = AutoTokenizer.from_pretrained('google/mt5-small')
input_ids = tokenizer('Hello there', ret... |
class UrlPattern():
_DEFAULT_PORTS = {' 443, ' 80, 'ftp': 21}
_SCHEMES_WITHOUT_HOST = ['about', 'file', 'data', 'javascript']
def __init__(self, pattern: str) -> None:
self._pattern = pattern
self._match_all = False
self._match_subdomains: bool = False
self._scheme: Optional[... |
_grad()
def load_mtl(fn, clear_ks=True):
import re
mtl_path = os.path.dirname(fn)
with open(fn, 'r') as f:
lines = f.readlines()
materials = []
for line in lines:
split_line = re.split(' +|\t+|\n+', line.strip())
prefix = split_line[0].lower()
data = split_line[1:]
... |
def parse_locale(identifier: str, sep: str='_') -> (tuple[(str, (str | None), (str | None), (str | None))] | tuple[(str, (str | None), (str | None), (str | None), (str | None))]):
(identifier, _, modifier) = identifier.partition('')
if ('.' in identifier):
identifier = identifier.split('.', 1)[0]
pa... |
class ConnectionPair():
def __init__(self) -> None:
self.conn = {CLIENT: Connection(CLIENT), SERVER: Connection(SERVER)}
self.other = {CLIENT: SERVER, SERVER: CLIENT}
def conns(self) -> ValuesView[Connection]:
return self.conn.values()
def send(self, role: Type[Sentinel], send_events... |
def linux_distribution(full_distribution_name: bool=True) -> Tuple[(str, str, str)]:
warnings.warn("distro.linux_distribution() is deprecated. It should only be used as a compatibility shim with Python's platform.linux_distribution(). Please use distro.id(), distro.version() and distro.name() instead.", Deprecation... |
class StopwatchMeter(Meter):
def __init__(self, round: Optional[int]=None):
self.round = round
self.sum = 0
self.n = 0
self.start_time = None
def start(self):
self.start_time = time.perf_counter()
def stop(self, n=1, prehook=None):
if (self.start_time is not N... |
.parametrize('book__name', ['PyTest for Dummies'])
.parametrize('book__price', [1.0])
.parametrize('author__name', ['Bill Gates'])
.parametrize('edition__year', [2000])
def test_parametrized(book: Book):
assert (book.name == 'PyTest for Dummies')
assert (book.price == 1.0)
assert (book.author.name == 'Bill ... |
class CombinedROIHeads(torch.nn.ModuleDict):
def __init__(self, cfg, heads):
super(CombinedROIHeads, self).__init__(heads)
self.cfg = cfg.clone()
if (cfg.MODEL.MASK_ON and cfg.MODEL.ROI_MASK_HEAD.SHARE_BOX_FEATURE_EXTRACTOR):
self.mask.feature_extractor = self.box.feature_extract... |
class MHAtt(nn.Module):
def __init__(self, __C):
super(MHAtt, self).__init__()
self.__C = __C
self.linear_v = nn.Linear(__C['fusion']['mca_HIDDEN_SIZE'], __C['fusion']['mca_HIDDEN_SIZE'])
self.linear_k = nn.Linear(__C['fusion']['mca_HIDDEN_SIZE'], __C['fusion']['mca_HIDDEN_SIZE'])
... |
def process_form(form, comp=True):
max2theta = form.max2theta.data
min2theta = form.min2theta.data
if (min2theta > max2theta):
min2theta = 0
flash(Markup('<span class="glyphicon glyphicon-warning-sign" aria-hidden="true"></span><span class="sr-only">Error:</span> 2<i>&... |
class ConvTranspose2d_same(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=(5, 5), stride=(2, 2)):
super(ConvTranspose2d_same, self).__init__()
output_padding = [abs(((k % 2) - (s % 2))) for (k, s) in zip(kernel_size, stride)]
padding = [(((k - s) + o) // 2) for (k, s, ... |
class RtlSdrTcpBase(object):
DEFAULT_PORT = 1235
def __init__(self, device_index=0, test_mode_enabled=False, hostname='127.0.0.1', port=None):
self.device_index = device_index
self.test_mode_enabled = test_mode_enabled
self.hostname = hostname
self.port = port
if (self.po... |
def bottleneck_v1b(input_x, base_channel, scope, stride=1, projection=False, avg_down=True):
with tf.variable_scope(scope):
if DEBUG:
debug_dict[input_x.op.name] = tf.transpose(input_x, [0, 3, 1, 2])
net = slim.conv2d(input_x, num_outputs=base_channel, kernel_size=[1, 1], stride=1, paddi... |
def _getShieldResists(ship):
em = (1 - ship.getModifiedItemAttr('shieldEmDamageResonance'))
therm = (1 - ship.getModifiedItemAttr('shieldThermalDamageResonance'))
kin = (1 - ship.getModifiedItemAttr('shieldKineticDamageResonance'))
explo = (1 - ship.getModifiedItemAttr('shieldExplosiveDamageResonance'))... |
def _gen_pairings_between_partitions(parta, partb):
if (len((parta + partb)) < 5):
(yield (tuple(parta), tuple(partb)))
splita = [parta[:(len(parta) // 2)], parta[(len(parta) // 2):]]
splitb = [partb[:(len(partb) // 2)], partb[(len(partb) // 2):]]
for (a, b) in ((0, 0), (0, 1), (1, 0), (1, 1)):
... |
class ErlangShellLexer(Lexer):
name = 'Erlang erl session'
aliases = ['erl']
filenames = ['*.erl-sh']
mimetypes = ['text/x-erl-shellsession']
url = '
version_added = '1.1'
_prompt_re = re.compile('(?:\\([\\_.]+\\))?\\d+>(?=\\s|\\Z)')
def get_tokens_unprocessed(self, text):
erlexe... |
def test_log_file_cli(pytester: Pytester) -> None:
pytester.makepyfile('\n import pytest\n import logging\n def test_log_file(request):\n plugin = request.config.pluginmanager.getplugin(\'logging-plugin\')\n assert plugin.log_file_handler.level == logging.WARNING\n ... |
class TestNumericField(TestCase):
def setUp(self):
self.field = fields.NumericField()
def test_deserialize_float(self):
arbitrary_float = '214.8'
actual_value = self.field.deserialize(arbitrary_float)
expected_value = 214.8
self.assertEqual(actual_value, expected_value)
... |
def packet_processor(pkt):
if pkt.haslayer(IP):
src = pkt[IP].src
dst = pkt[IP].dst
else:
src = pkt.src
dst = pkt.dst
key = tuple(sorted([src, dst]))
packet_counts.update([key])
pkt_no = sum(packet_counts.values())
gateway = netifaces.gateways()['default'][2][0]
... |
def init_model():
model = stylegan2.models.load('../mymodels/Gs_ffhq.pth')
model = utils.unwrap_module(model).to(device)
model.eval()
prior = cnf(512, '512-512-512-512-512', 17, 1)
prior.load_state_dict(torch.load('../flow_weight/modellarge10k.pt'))
prior.to(device)
prior.eval()
return (... |
def evaluate_folder(folder_with_gts: str, folder_with_predictions: str, labels: tuple, **metric_kwargs):
files_gt = subfiles(folder_with_gts, suffix='.nii.gz', join=False)
files_pred = subfiles(folder_with_predictions, suffix='.nii.gz', join=False)
assert all([(i in files_pred) for i in files_gt]), 'files m... |
.parametrize('username,password', users)
.parametrize('export_format', export_formats)
def test_detail_export(db, client, username, password, export_format):
client.login(username=username, password=password)
instance = Condition.objects.first()
url = ((reverse(urlnames['detail_export'], args=[instance.pk])... |
class _job_state_monitor(threading.Thread):
def __init__(self, log):
self._log = log
self._lock = threading.Lock()
self._term = threading.Event()
self._jobs = dict()
self._cnt = 0
super(_job_state_monitor, self).__init__()
self.setDaemon(True)
def stop(sel... |
def test_get_imgformat_jpg_when_setting_jpg(qapp, settings, item):
settings.setValue('Items/image_storage_format', 'jpg')
img = MagicMock(hasAlphaChannel=MagicMock(return_value=True), height=MagicMock(return_value=100), width=MagicMock(return_value=100))
assert (item.get_imgformat(img) == 'jpg') |
(cc=STDCALL, params={'hwnd': HWND, 'pszPath': LPWSTR, 'csidl': INT, 'fCreate': BOOL})
def hook_SHGetSpecialFolderPathW(ql: Qiling, address: int, params):
directory_id = params['csidl']
dst = params['pszPath']
if (directory_id == CSIDL_COMMON_APPDATA):
path = ntpath.join(ql.os.userprofile, 'AppData\\... |
class TestItems():
def neighborlist(self):
return usertypes.NeighborList([1, 2, 3, 4, 5], default=3)
def test_curitem(self, neighborlist):
assert (neighborlist._idx == 2)
assert (neighborlist.curitem() == 3)
assert (neighborlist._idx == 2)
def test_nextitem(self, neighborlist... |
class BertConfig(PretrainedConfig):
pretrained_config_archive_map = BERT_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self, vocab_size_or_config_json_file=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropo... |
def load_binary_dataset(train_file, tokenizer, dev_file=None, train_limit=None, dev_limit=None, max_seq_length=MAX_SEQ_LENGTH):
logger.info('Read binary dataset')
train_samples = []
dev_samples = []
train_objs = utils.JsonL.load(train_file)
if train_limit:
random.shuffle(train_objs)
... |
def test__poa_ground_shadows():
(poa_ground, f_gnd_beam, df, vf_gnd_sky) = (300.0, 0.5, 0.5, 0.2)
result = infinite_sheds._poa_ground_shadows(poa_ground, f_gnd_beam, df, vf_gnd_sky)
expected = (300.0 * ((0.5 * 0.5) + (0.5 * 0.2)))
assert np.isclose(result, expected)
poa_ground = np.array([300.0, 300... |
def scan(fn, sequences=None, outputs_info=None, non_sequences=None, n_steps=None, truncate_gradient=(- 1), go_backwards=False, mode=None, name=None, profile=False, allow_gc=None, strict=False, return_list=False):
def wrap_into_list(x):
if (x is None):
return []
elif (not isinstance(x, (l... |
def parse_manifest_from_bytes(manifest_bytes, media_type, validate=True, sparse_manifest_support=False, ignore_unknown_mediatypes=False):
assert isinstance(manifest_bytes, Bytes)
if (is_manifest_list_type(media_type) and sparse_manifest_support):
return SparseManifestList(manifest_bytes, media_type)
... |
class StateTomographyFitter(TomographyFitter):
def __init__(self, result: Result, circuits: List[QuantumCircuit], meas_basis: Union[(TomographyBasis, str)]='Pauli'):
super().__init__(result, circuits, meas_basis, None)
def fit(self, method: str='auto', standard_weights: bool=True, beta: float=0.5, **kwa... |
class GoogledriveCom(BaseDownloader):
__name__ = 'GoogledriveCom'
__type__ = 'downloader'
__version__ = '0.35'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fal... |
class CPUDataset():
def __init__(self, data, targets, transforms=[], batch_size=args.batch_size, use_hd=False):
self.data = data
if torch.is_tensor(data):
self.length = data.shape[0]
else:
self.length = len(self.data)
self.targets = targets
assert (sel... |
def particle_picking_visualization_main(p: PPVisRequest):
item = particlePickingPool.get(p.path)
if (p.subvol_num == (- 1)):
result = item.pick.view_subtom(p.subvol_num)
else:
result = item.pick.view_subtom(p.subvol_num)
with open(result, 'rb') as f:
b64 = base64.b64encode(f.read... |
def pyramid_block(pyramid_filters=256, segmentation_filters=128, upsample_rate=2, use_batchnorm=False):
def layer(c, m=None):
x = Conv2D(pyramid_filters, (1, 1))(c)
if (m is not None):
up = UpSampling2D((upsample_rate, upsample_rate))(m)
x = Add()([x, up])
p = Conv(se... |
class process(object):
def __init__(self):
pass
def process_train(self):
c = 0
common_feat_dict = {}
with open(common_feat_path.format('train'), 'r') as fr:
for line in fr:
line_list = line.strip().split(',')
kv = np.array(re.split('\x0... |
_client_parallelize(1)
_channel('purerpc_port')
def test_metadata_grpc_client(greeter_pb2, greeter_pb2_grpc, channel):
stub = greeter_pb2_grpc.GreeterStub(channel)
response = stub.SayHello(greeter_pb2.HelloRequest(name='World'), metadata=METADATA)
received_metadata = pickle.loads(base64.b64decode(response.m... |
def _test_helper(res):
assert (((2 * 40), (2048 * 2)) == res['1'].shape)
assert ('reflectance' == res['1'].attrs['calibration'])
assert ('%' == res['1'].attrs['units'])
assert (((2 * 40), (2048 * 2)) == res['2'].shape)
assert ('reflectance' == res['2'].attrs['calibration'])
assert ('%' == res['2... |
class EnableCloudPassword():
async def enable_cloud_password(self: 'pyrogram.Client', password: str, hint: str='', email: str=None) -> bool:
r = (await self.invoke(raw.functions.account.GetPassword()))
if r.has_password:
raise ValueError('There is already a cloud password enabled')
... |
class AutoModelWithLMHead(_AutoModelWithLMHead):
def from_config(cls, config):
warnings.warn('The class `AutoModelWithLMHead` is deprecated and will be removed in a future version. Please use `AutoModelForCausalLM` for causal language models, `AutoModelForMaskedLM` for masked language models and `AutoModelF... |
class KeyboardButtonRequestChat(TelegramObject):
__slots__ = ('request_id', 'chat_is_channel', 'chat_is_forum', 'chat_has_username', 'chat_is_created', 'user_administrator_rights', 'bot_administrator_rights', 'bot_is_member')
def __init__(self, request_id: int, chat_is_channel: bool, chat_is_forum: Optional[boo... |
class Meteor():
def __init__(self):
self.meteor_cmd = ['java', '-jar', '-Xmx2G', METEOR_JAR, '-', '-', '-stdio', '-l', 'en', '-norm']
self.meteor_p = subprocess.Popen(self.meteor_cmd, cwd=os.path.dirname(os.path.abspath(__file__)), stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIP... |
class ConcatOutput(nn.Module):
def __init__(self, channel):
super(ConcatOutput, self).__init__()
self.upsample = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
self.conv_upsample1 = BasicConv2d(channel, channel, 3, padding=1)
self.conv_upsample2 = BasicConv2d(channe... |
def set_panning(self, crtc, left, top, width, height, track_left, track_top, track_width, track_height, border_left, border_top, border_width, border_height, timestamp=X.CurrentTime):
return SetPanning(display=self.display, opcode=self.display.get_extension_major(extname), crtc=crtc, left=left, top=top, width=width... |
def postprocess_text(mode, preds, golds):
predictions = {}
for (pred, gold) in zip(preds, golds):
dial_id = gold['ID']
if (dial_id not in predictions):
predictions[dial_id] = {}
predictions[dial_id]['domains'] = gold['domains']
predictions[dial_id]['turns'] = ... |
class STM32F4xxSdio(QlConnectivityPeripheral):
class Type(ctypes.Structure):
_fields_ = [('POWER', ctypes.c_uint32), ('CLKCR', ctypes.c_uint32), ('ARG', ctypes.c_uint32), ('CMD', ctypes.c_uint32), ('RESPCMD', ctypes.c_uint32), ('RESP1', ctypes.c_uint32), ('RESP2', ctypes.c_uint32), ('RESP3', ctypes.c_uint32... |
def digit_version(version_str: str, length: int=4):
assert ('parrots' not in version_str)
version = parse(version_str)
assert version.release, f'failed to parse version {version_str}'
release = list(version.release)
release = release[:length]
if (len(release) < length):
release = (releas... |
def get_cmdclass():
if ('versioneer' in sys.modules):
del sys.modules['versioneer']
cmds = {}
from distutils.core import Command
class cmd_version(Command):
description = 'report generated version string'
user_options = []
boolean_options = []
def initialize_optio... |
class TrappingPotential():
def get_potential(self, sites_count: int) -> np.ndarray:
def as_quadratic_hamiltonian(self, sites_count: int, j: Union[(Real, Iterable[Real])]) -> openfermion.QuadraticHamiltonian:
return _potential_to_quadratic_hamiltonian(self.get_potential(sites_count), j) |
class EllipsisType(ProperType):
__slots__ = ()
def accept(self, visitor: TypeVisitor[T]) -> T:
assert isinstance(visitor, SyntheticTypeVisitor)
ret: T = visitor.visit_ellipsis_type(self)
return ret
def serialize(self) -> JsonDict:
assert False, "Synthetic types don't serializ... |
_meter('accuracy_list_meter')
class AccuracyListMeter(ClassyMeter):
def __init__(self, num_meters: int, topk_values: List[int], meter_names: List[str]):
super().__init__()
assert is_pos_int(num_meters), 'num_meters must be positive'
assert isinstance(topk_values, list), 'topk_values must be ... |
def _request(url, post=False, **kwargs):
logger.debug(('Accessing URL %s' % url))
if post:
logger.debug(('POST data: \n%s' % post))
req = requests.Request('POST', url=url, params=kwargs, data=post)
else:
req = requests.Request('GET', url=url, params=kwargs)
ses = requests.Session... |
def test_revalidate_vercel_frontend_when_vercel_is_down_doesnt_crash(caplog, requests_mock, locale):
parent = PageFactory()
page = PageFactory(slug='test123', locale=locale('en'), parent=parent)
site = SiteFactory(hostname='pycon', root_page=page)
italian_page = page.copy_for_translation(locale=locale('... |
class MilvusUploader(BaseUploader):
client = None
upload_params = {}
collection: Collection = None
distance: str = None
def get_mp_start_method(cls):
return ('forkserver' if ('forkserver' in mp.get_all_start_methods()) else 'spawn')
def init_client(cls, host, distance, connection_params,... |
def test_connect_two_chains():
g = Graph()
(a1, a2, b1, b2) = get_pseudo_nodes('a1', 'a2', 'b1', 'b2')
g.add_chain(a1, a2, _input=None, _output=None)
g.add_chain(b1, b2, _input=None, _output=None)
assert (len(g.outputs_of(a2)) == 0)
g.add_chain(_input=a2, _output=b1)
assert (g.outputs_of(a2)... |
class Blade(metaclass=_GradedTypesMeta):
def __init__(self, layout):
self.layout = layout
def _repr_skip_members(self):
return {'layout'}
def __new__(cls, *args, **kwargs):
return super().__new__(cls)
def __repr__(self):
members = self.__dict__.copy()
for name in ... |
class Propagator():
def __init__(self, system, *, c_ops=(), args=None, options=None, memoize=10, tol=1e-14):
if isinstance(system, MultiTrajSolver):
raise TypeError('Non-deterministic solvers cannot be used as a propagator system')
elif isinstance(system, HEOMSolver):
raise N... |
def _get_epsilon_for_un_fused_bn(graph_def: tf.Graph, bn_conn_graph_op: Op) -> Union[(None, float)]:
epsilon = None
bn_op_name = (bn_conn_graph_op.name + '/batchnorm/add/y')
for node in graph_def.node:
if (bn_op_name == node.name):
epsilon = node.attr['value'].tensor.float_val[0]
... |
def main() -> None:
args = _get_command_line_arguments()
splits_dir = Path(args[ARG_SPLITS_DIR])
spotting_game_paths = _read_spotting_game_paths_dict(splits_dir)
segmentation_game_paths_set = _read_segmentation_game_paths_set(splits_dir)
out_rows = _prepare_out_rows(spotting_game_paths, segmentation... |
class LogNormal(PositiveContinuous):
rv_op = lognormal
def dist(cls, mu=0, sigma=None, tau=None, *args, **kwargs):
(tau, sigma) = get_tau_sigma(tau=tau, sigma=sigma)
mu = pt.as_tensor_variable(floatX(mu))
sigma = pt.as_tensor_variable(floatX(sigma))
return super().dist([mu, sigma... |
class TestTrainingExtensionsQcQuantizeOp():
def test_qc_quantize_op_cpu(self):
graph = tf.Graph()
config = tf.compat.v1.ConfigProto(log_device_placement=False)
sess = tf.compat.v1.Session(graph=graph, config=config)
bitwidth = 8
use_symm_encoding = True
with graph.as_... |
.parametrize('status, raising, message', [(QDataStream.Status.Ok, False, None), (QDataStream.Status.ReadPastEnd, True, 'The data stream has read past the end of the data in the underlying device.'), (QDataStream.Status.ReadCorruptData, True, 'The data stream has read corrupt data.'), (QDataStream.Status.WriteFailed, Tr... |
class TestNoReassignmentChecker(pylint.testutils.CheckerTestCase):
CHECKER_CLASS = example_plugin.NoReassignmentChecker
def test_finds_reassigned_variable(self):
(assign_node_a, assign_node_b) = astroid.extract_node('\n test = 1 #\n test = 2 #\n ')
self.checker.visit_ass... |
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