code stringlengths 281 23.7M |
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def _prepare_yaml_file(filename, obj_type, include_all_score_objs):
if isinstance(filename, dict):
yaml_content = filename
else:
_yaml = init_yaml()
with open(filename, 'r') as yaml_file:
yaml_content = _yaml.load(yaml_file)
yaml_content_eql = _traverse_modify_date(yaml_c... |
_caches
def test_split_mechanism_mice_is_not_reusable(redis_cache):
s = examples.basic_subsystem()
mechanism = (0, 1)
mice = s.find_mice(Direction.CAUSE, mechanism)
assert (s._mice_cache.size() == 1)
assert (mice.purview == (1, 2))
cut = models.Cut((0,), (1, 2))
cut_s = Subsystem(s.network, ... |
def attack_Linf_PGD(input_v, ones, label_v, dis, Ld, steps, epsilon):
dis.eval()
adverse_v = input_v.data.clone()
adverse_v = Variable(adverse_v, requires_grad=True)
optimizer = Linf_SGD([adverse_v], lr=0.0078)
for _ in range(steps):
optimizer.zero_grad()
dis.zero_grad()
(d_b... |
def check_solution_satisfiability(sol, list_of_subsets):
n = len(list_of_subsets)
U = []
for sub in list_of_subsets:
U.extend(sub)
U = np.unique(U)
U2 = []
selected_subsets = []
for i in range(n):
if (sol[i] == 1):
selected_subsets.append(list_of_subsets[i])
... |
def get_grasp_poses(env):
segm = env.obs['segm']
depth = env.obs['depth']
K = env.obs['K']
mask = ((segm == env.obs['target_instance_id']) & (~ np.isnan(depth)))
pcd_in_camera = reorientbot.geometry.pointcloud_from_depth(depth, fx=K[(0, 0)], fy=K[(1, 1)], cx=K[(0, 2)], cy=K[(1, 2)])
normals_in_c... |
.parametrize('iam_model,model_params', [('ashrae', {'b': 0.05}), ('physical', {'K': 4, 'L': 0.002, 'n': 1.526}), ('martin_ruiz', {'a_r': 0.16})])
def test_PVSystem_get_iam(mocker, iam_model, model_params):
m = mocker.spy(_iam, iam_model)
system = pvsystem.PVSystem(module_parameters=model_params)
thetas = 1
... |
def test_validate_workflow(acetone):
model0 = get_workflow_protocol(workflow_protocol='0')
model0.qc_options.program = 'rdkit'
model0.qc_options.method = 'uff'
model0.qc_options.basis = None
run_order = WorkFlow.get_running_order(skip_stages=['charges'])
model0.validate_workflow(workflow=run_ord... |
def cheng2020_anchor(quality, metric='mse', pretrained=False, progress=True, **kwargs):
if (metric not in ('mse',)):
raise ValueError(f'Invalid metric "{metric}"')
if ((quality < 1) or (quality > 6)):
raise ValueError(f'Invalid quality "{quality}", should be between (1, 6)')
return _load_mod... |
_dataframe_method
def inflate_currency(df: pd.DataFrame, column_name: str=None, country: str=None, currency_year: int=None, to_year: int=None, make_new_column: bool=False) -> pd.DataFrame:
inflator = _inflate_currency(country, currency_year, to_year)
if make_new_column:
new_column_name = ((column_name +... |
def test_run_model_solar_position_weather(pvwatts_dc_pvwatts_ac_system, location, weather, mocker):
mc = ModelChain(pvwatts_dc_pvwatts_ac_system, location, aoi_model='no_loss', spectral_model='no_loss')
weather['pressure'] = 90000
weather['temp_air'] = 25
m = mocker.spy(location, 'get_solarposition')
... |
def typo_fix(slot_values, ontology, version='2.1'):
named_entity_slots = ['hotel-name', 'train-destination', 'train-departure', 'attraction-type', 'attraction-name', 'restaurant-name', 'taxi-departure', 'taxi-destination', 'restaurant-food']
fixed = {}
for (slot, value) in slot_values.items():
value... |
def format_xc_code(description):
description = description.replace(' ', '').replace('\n', '').upper()
if ('RSH' not in description):
return description
frags = description.split('RSH')
out = [frags[0]]
for frag in frags[1:]:
(rsh_key, rest) = frag.split(')')
if (',' in rsh_ke... |
class TMid3cp(_TTools):
TOOL_NAME = u'mid3cp'
def setUp(self):
super(TMid3cp, self).setUp()
original = os.path.join(DATA_DIR, 'silence-44-s.mp3')
(fd, self.filename) = mkstemp(suffix='oau.mp3')
os.close(fd)
shutil.copy(original, self.filename)
(fd, self.blank_file... |
def _check_has_no_phase_dynamics_shared_during_the_phase(problem_type, **kwargs):
if (not isinstance(problem_type, SocpType.COLLOCATION)):
if ('phase_dynamics' in kwargs):
if (kwargs['phase_dynamics'] == PhaseDynamics.SHARED_DURING_THE_PHASE):
raise ValueError('The dynamics canno... |
def test_geth_discover_next_available_nonce_concurrent_transactions(deploy_client: JSONRPCClient, skip_if_parity: bool) -> None:
def send_transaction(to: Address) -> None:
deploy_client.transact(EthTransfer(to_address=to, value=0, gas_price=gas_price_for_fast_transaction(deploy_client.web3)))
greenlets ... |
def cross_layer_equalization_depthwise_layers():
model = MobileNetV2().to(torch.device('cpu'))
model.eval()
layer_list = [(model.features[0][0], model.features[0][1]), (model.features[1].conv[0], model.features[1].conv[1]), (model.features[1].conv[3], model.features[1].conv[4])]
bn_dict = {}
for con... |
def run_experiments(config, files, aws):
os.environ['RXGB_ALLOW_ELASTIC_TUNE'] = '1'
condition = config['condition']
num_boost_round = config['num_boost_round']
num_workers = config['num_workers']
num_affected_workers = config['affected_workers']
regression = config['regression']
use_gpu = c... |
def main(argv):
tf.config.experimental.set_visible_devices([], 'GPU')
os.environ['XLA_PYTHON_CLIENT_PREALLOCATE'] = 'false'
if (FLAGS.mode == 'train'):
tf.io.gfile.makedirs(FLAGS.workdir)
gfile_stream = tf.io.gfile.GFile(os.path.join(FLAGS.workdir, 'stdout.txt'), 'w')
handler = loggi... |
def test_rename_keys():
results = dict(joints_3d=np.ones([17, 3]), joints_3d_visible=np.ones([17, 3]))
pipeline = dict(type='RenameKeys', key_pairs=[('joints_3d', 'target'), ('joints_3d_visible', 'target_weight')])
pipeline = build_from_cfg(pipeline, PIPELINES)
results = pipeline(results)
assert ('j... |
class Path(metaclass=AsyncAutoWrapperType):
_forward: ClassVar[list[str]]
_wraps: ClassVar[type] = pathlib.Path
_forwards: ClassVar[type] = pathlib.PurePath
_forward_magic: ClassVar[list[str]] = ['__str__', '__bytes__', '__truediv__', '__rtruediv__', '__eq__', '__lt__', '__le__', '__gt__', '__ge__', '__... |
def setup_logger(level: int=logging.ERROR, log_filename: Optional[str]=None) -> None:
fmt = '[%(asctime)s] %(levelname)s in %(module)s: %(message)s'
date_fmt = '%H:%M:%S'
formatter = logging.Formatter(fmt, datefmt=date_fmt)
logger = logging.getLogger('pytube')
logger.setLevel(level)
stream_handl... |
class F21Handler(BaseHandler):
version = F21
commandMap = {'auth': commands.authconfig.FC3_Authconfig, 'authconfig': commands.authconfig.FC3_Authconfig, 'autopart': commands.autopart.F21_AutoPart, 'autostep': commands.autostep.FC3_AutoStep, 'bootloader': commands.bootloader.F21_Bootloader, 'btrfs': commands.btr... |
def score_all_questions(num_workers):
print('Loading data...')
corpus = HotpotQuestions()
train = corpus.get_train()
dev = corpus.get_dev()
workers = ProcessPool(num_workers, initializer=init, initargs=[])
print('Scoring train...')
new_train = []
with tqdm(total=len(train)) as pbar:
... |
def validate_test(kw):
def get_list(key):
return deserialize_list(kw.get(key, ''))
errors = []
if (kw.get('SCRIPT-REL-PATH') == 'boost.test'):
project_path = kw.get('BUILD-FOLDER-PATH', '')
if ((not project_path.startswith('maps')) and (not project_path.startswith('devtools'))):
... |
def plot_table(tbl, columns=None, title='', title_loc='left', header=True, colWidths=None, rowLoc='right', colLoc='right', colLabels=None, edges='horizontal', orient='horizontal', figsize=(5.5, 6), savefig=None, show=False):
if (columns is not None):
try:
tbl.columns = columns
except Exc... |
def test_calibration_mat_alpha_3():
(lc, dict_nom, betaT) = setup3()
calib3 = ra.Calibration(lc, target_beta=betaT, dict_nom_vals=dict_nom, calib_var='z', est_method='matrix', calib_method='alpha', print_output=False)
calib3.run()
dfXst = pd.DataFrame(data=[[0.6194, 1.0194, 1.8722, 1.2591, 1.6108, 3.504... |
def evaluate(dataset, LOG, **kwargs):
if (dataset in ['Inaturalist', 'sop', 'cars196', 'cub']):
ret = evaluate_one_dataset(LOG, **kwargs)
elif (dataset in ['vehicle_id']):
ret = evaluate_multiple_datasets(LOG, **kwargs)
else:
raise Exception('No implementation for dataset {} availabl... |
def gen_filelist(fname, fileList, folderName=''):
with open(fname, 'w') as f:
for it in fileList:
if (len(it) == 1):
f.write((('\n\n**' + it[FileName]) + '**\n\n'))
else:
f.write((((('- ' + gen_url(it[FileName], folderName)) + '\t\t') + it[Summary]) + ... |
(scope='function')
def terminal(radian_command):
with Terminal.open(radian_command) as t:
(yield t)
t.sendintr()
t.write('q()\n')
start_time = time.time()
while t.isalive():
if ((time.time() - start_time) > 15):
raise Exception("radian didn't quit ... |
class WorldbytezCom(XFSAccount):
__name__ = 'WorldbytezCom'
__type__ = 'account'
__version__ = '0.06'
__status__ = 'testing'
__description__ = 'Worldbytez.com account plugin'
__license__ = 'GPLv3'
__authors__ = [('Walter Purcaro', '')]
PLUGIN_DOMAIN = 'worldbytez.com' |
class CustomCalloutItemDirective(Directive):
option_spec = {'header': directives.unchanged, 'description': directives.unchanged, 'button_link': directives.unchanged, 'button_text': directives.unchanged}
def run(self):
try:
if ('description' in self.options):
description = sel... |
def create_balcony(options: BalconyOptions):
from ...btools.building.array import ArrayProperty
from ...btools.building.sizeoffset import SizeOffsetProperty
from ...btools.building.railing import RailProperty, RailFillProperty, PostFillProperty, WallFillProperty
register_property(ArrayProperty)
regi... |
def sync_random_seed(seed=None, device='cuda'):
if (seed is None):
seed = np.random.randint((2 ** 31))
assert isinstance(seed, int)
(rank, world_size) = get_dist_info()
if (world_size == 1):
return seed
if (rank == 0):
random_num = torch.tensor(seed, dtype=torch.int32, device... |
def _binary_precision_update_input_check(input: torch.Tensor, target: torch.Tensor) -> None:
if (input.shape != target.shape):
raise ValueError(f'The `input` and `target` should have the same dimensions, got shapes {input.shape} and {target.shape}.')
if (target.ndim != 1):
raise ValueError(f'tar... |
def create_dataloader(opt, world_size, rank):
dataset = find_dataset_using_name(opt.dataset_mode)
instance = dataset()
instance.initialize(opt)
print(('dataset [%s] of size %d was created' % (type(instance).__name__, len(instance))))
if opt.isTrain:
train_sampler = torch.utils.data.distribut... |
class Critic(nn.Module):
def __init__(self, backbone, device='cpu'):
super().__init__()
self.device = torch.device(device)
self.backbone = backbone.to(device)
latent_dim = getattr(backbone, 'output_dim')
self.last = nn.Linear(latent_dim, 1).to(device)
def forward(self, ob... |
.parametrize('rlo,rhi', [(1, 2), ('a', 'b')])
def test_valueholder_ordering(rlo, rhi):
(vlo, vhi) = (ValueHolder(rlo), ValueHolder(rhi))
for lo in (rlo, vlo):
for hi in (rhi, vhi):
assert (lo < hi)
assert (hi > lo)
assert (lo <= lo)
assert (not (lo < lo))
... |
class ChainStateStateMachine(RuleBasedStateMachine):
def __init__(self):
self.replay_path: bool = False
self.address_to_privkey: Dict[(Address, PrivateKey)] = {}
self.address_to_client: Dict[(Address, Client)] = {}
self.transfer_order = TransferOrder()
super().__init__()
... |
def find_matching_team_invite(code, user_obj):
found = lookup_team_invite(code)
if ((found.user is not None) and (found.user != user_obj)):
message = ('This invite is intended for user "%s".\n Please login to that account and try again.' % found.user.username)
raise DataModelExce... |
def setup_database_for_testing(testcase):
if ((not IS_TESTING_REAL_DATABASE) and (not isinstance(db.obj, SqliteDatabase))):
raise RuntimeError('Attempted to wipe production database!')
if (not db_initialized_for_testing.is_set()):
logger.debug('Setting up DB for testing.')
if (os.environ... |
def cycleGetFreElem(preFixData, e, minsup):
copyPreFixData = list(copy.deepcopy(preFixData))
allFreSequence = []
allElem = getElem(copyPreFixData)
(freElem, notFreElem) = useCycleGetFreElem(copyPreFixData, e, allElem, minsup)
deleteNotFreElem(copyPreFixData, notFreElem)
thisAllPrefixData = getAl... |
def create_masked_lm_predictions(tokens, masked_lm_prob, max_predictions_per_seq, vocab_words, rng):
cand_indexes = []
for (i, token) in enumerate(tokens):
if ((token == '[CLS]') or (token == '[SEP]')):
continue
cand_indexes.append(i)
rng.shuffle(cand_indexes)
output_tokens =... |
class Migration(migrations.Migration):
dependencies = [('core', '0002_auto__1707')]
operations = [migrations.CreateModel(name='ArchivedPlaylist', fields=[('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), ('list_id', models.CharField(max_length=200, unique=True)),... |
def _set_tensors(obj, all_params, max_depth=20):
def action(elmt, name, objdict, key):
objdict[key] = all_params.pop(0)
crit = (lambda elmt: (isinstance(elmt, torch.Tensor) and (elmt.dtype in torch_float_type)))
_traverse_obj(obj, action=action, crit=crit, prefix='', max_depth=max_depth) |
class Project(PymiereBaseObject):
def __init__(self, pymiere_id=None):
super(Project, self).__init__(pymiere_id)
def documentID(self):
return self._eval_on_this_object('documentID')
def documentID(self, documentID):
raise AttributeError("Attribute 'documentID' is read-only")
def ... |
class ChoiceFeedbackSerializer(serializers.Serializer):
id = serializers.IntegerField()
value_id = serializers.IntegerField()
def validate(self, data):
if object_exists(ChoiceFeedbackQuestion, pk=data['id']):
if ChoiceFeedbackQuestionValue.objects.filter(question_id=data['id'], pk=data['... |
class AverageMeter():
def __init__(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += (val * n)
self.count += n
self.avg = (self.sum / self.count)
def clear(self):
self.va... |
def extract_few_shot_feature(cfg, clip_model, train_loader_cache):
cache_keys = []
cache_values = []
with torch.no_grad():
for augment_idx in range(cfg['augment_epoch']):
train_features = []
print('Augment Epoch: {:} / {:}'.format(augment_idx, cfg['augment_epoch']))
... |
def _find_chrome_win() -> Optional[str]:
import winreg as reg
reg_path = 'SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\App Paths\\chrome.exe'
chrome_path: Optional[str] = None
for install_type in (reg.HKEY_CURRENT_USER, reg.HKEY_LOCAL_MACHINE):
try:
reg_key = reg.OpenKey(install_typ... |
def getExceptionMessage(exceptionDetails: dict) -> str:
exception = exceptionDetails.get('exception')
if exception:
return (exception.get('description') or exception.get('value'))
message = exceptionDetails.get('text', '')
stackTrace = exceptionDetails.get('stackTrace', dict())
if stackTrace... |
class CIFARSEPreResNet(nn.Module):
def __init__(self, channels, init_block_channels, bottleneck, in_channels=3, in_size=(32, 32), num_classes=10):
super(CIFARSEPreResNet, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.features = nn.Sequential()
se... |
def test_aws_session_class_unsigned_noboto3(monkeypatch):
import rasterio.session
monkeypatch.setenv('AWS_NO_SIGN_REQUEST', 'YES')
monkeypatch.setattr(rasterio.session, 'boto3', None)
assert (rasterio.session.boto3 is None)
sesh = AWSSession()
assert (sesh.unsigned is True)
assert (sesh.get_... |
class TestSAFEGRD(unittest.TestCase):
('rasterio.open')
def setUp(self, mocked_rio_open):
from satpy.readers.sar_c_safe import SAFEGRD
filename_info = {'mission_id': 'S1A', 'dataset_name': 'foo', 'start_time': 0, 'end_time': 0, 'polarization': 'vv'}
filetype_info = 'bla'
self.noi... |
_module()
class I3DHead(BaseHead):
def __init__(self, num_classes, in_channels, loss_cls=dict(type='CrossEntropyLoss'), spatial_type='avg', dropout_ratio=0.5, init_std=0.01, **kwargs):
super().__init__(num_classes, in_channels, loss_cls, **kwargs)
self.spatial_type = spatial_type
self.dropou... |
class SyncProgress():
def __init__(self, response_queue: NotifyingQueue[Tuple[(UUID, JSONResponse, datetime)]]) -> None:
self.synced_event = SyncEvent()
self.processed_event = SyncEvent()
self.sync_iteration = 0
self.processed_iteration = 0
self.last_synced: Optional[UUID] = ... |
class FunctionEmitterVisitor(OpVisitor[None]):
def __init__(self, emitter: Emitter, declarations: Emitter, source_path: str, module_name: str) -> None:
self.emitter = emitter
self.names = emitter.names
self.declarations = declarations
self.source_path = source_path
self.modul... |
def _handle_first_parameter(pyobject, parameters):
kind = pyobject.get_kind()
if (not parameters):
if (not pyobject.get_param_names(special_args=False)):
return
parameters.append(pyobjects.get_unknown())
if (kind == 'method'):
parameters[0] = pyobjects.PyObject(pyobject.p... |
def upload_to_pypi(version: str, dry_run: bool=True) -> None:
assert re.match('v?[1-9]\\.[0-9]+\\.[0-9](\\+\\S+)?$', version)
if ('dev' in version):
assert dry_run, 'Must use --dry-run with dev versions of mypy'
if version.startswith('v'):
version = version[1:]
target_dir = tempfile.mkdt... |
class FittingsTreeView(wx.Panel):
def __init__(self, parent):
wx.Panel.__init__(self, parent, id=wx.ID_ANY)
self.parent = parent
pmainSizer = wx.BoxSizer(wx.VERTICAL)
tree = self.fittingsTreeCtrl = wx.TreeCtrl(self, wx.ID_ANY, style=(wx.TR_DEFAULT_STYLE | wx.TR_HIDE_ROOT))
pm... |
def test_folding(workspace):
doc = Document(DOC_URI, workspace, DOC)
ranges = pylsp_folding_range(doc)
expected = [{'startLine': 1, 'endLine': 6}, {'startLine': 2, 'endLine': 3}, {'startLine': 5, 'endLine': 6}, {'startLine': 8, 'endLine': 11}, {'startLine': 12, 'endLine': 20}, {'startLine': 13, 'endLine': 1... |
def create(config_file: str) -> TrackerBase:
config = _read_config(config_file)
if (('protocol' not in config) or ('root_path' not in config)):
raise Exception(f"Please specify 'protocol' and 'root_path' in {config_file}")
protocol = config['protocol']
del config['protocol']
root = config['r... |
class MJVGEOM(Structure):
_fields_ = [('type', c_int), ('dataid', c_int), ('objtype', c_int), ('objid', c_int), ('category', c_int), ('texid', c_int), ('texuniform', c_int), ('texrepeat', (c_float * 2)), ('size', (c_float * 3)), ('pos', (c_float * 3)), ('mat', (c_float * 9)), ('rgba', (c_float * 4)), ('emission', c... |
_HEADS_REGISTRY.register()
class EmbeddingHead(nn.Module):
def __init__(self, cfg):
super().__init__()
feat_dim = cfg.MODEL.BACKBONE.FEAT_DIM
embedding_dim = cfg.MODEL.HEADS.EMBEDDING_DIM
num_classes = cfg.MODEL.HEADS.NUM_CLASSES
neck_feat = cfg.MODEL.HEADS.NECK_FEAT
... |
_series_method
def logit(s: 'Series', error: str='warn') -> 'Series':
import numpy as np
import scipy
s = s.copy()
outside_support = ((s <= 0) | (s >= 1))
if outside_support.any():
msg = f'{outside_support.sum()} value(s) are outside of (0, 1)'
if (error.lower() == 'warn'):
... |
def _migrate_v7(json_dict: dict) -> dict:
renamed_items = {'3HP Life Capsule': 'Small Life Capsule', '4HP Life Capsule': 'Medium Life Capsule', '5HP Life Capsule': 'Large Life Capsule', 'Missile Expansion (24)': 'Large Missile Expansion'}
for game in json_dict['game_modifications']:
if (game['game'] != ... |
(scope='session')
def unicode_images():
parent_bytes = layer_bytes_for_contents(b'parent contents')
image_bytes = layer_bytes_for_contents(b'some contents')
return [Image(id='parentid', bytes=parent_bytes, parent_id=None), Image(id='someid', bytes=image_bytes, parent_id='parentid', config={'comment': 'the P... |
class Sampler(torch.utils.data.sampler.Sampler):
def __init__(self, opt, image_dict, image_list, **kwargs):
self.pars = opt
self.image_dict = image_dict
self.image_list = image_list
self.classes = list(self.image_dict.keys())
self.batch_size = opt.bs
self.samples_per_... |
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('root', metavar='DIR', help='root directory containing flac files to index')
parser.add_argument('--valid-percent', default=0.01, type=float, metavar='D', help='percentage of data to use as validation set (between 0 and 1)')
parser... |
def configure_output() -> None:
rich.reconfigure(force_terminal=True, no_color=getattr(args, 'no_color', False), highlight=False, theme=rich.theme.Theme({'logging.level.debug': 'green', 'logging.level.info': 'blue', 'logging.level.warning': 'yellow', 'logging.level.error': 'red', 'logging.level.critical': 'reverse ... |
class Definition():
def __init__(self, definition: (Sequence[(Argument | Option)] | None)=None) -> None:
self._arguments: dict[(str, Argument)] = {}
self._required_count = 0
self._has_list_argument = False
self._has_optional = False
self._options: dict[(str, Option)] = {}
... |
def run_step(context):
logger.debug('started')
context.assert_key_has_value(key='add', caller=__name__)
step_input = context.get_formatted('add')
assert_key_is_truthy(obj=step_input, key='set', caller=__name__, parent='add')
assert_key_exists(obj=step_input, key='addMe', caller=__name__, parent='add... |
class TestImageFolder():
def test_init_ok(self, tmpdir):
tmpdir.mkdir('train')
tmpdir.mkdir('test')
train_dataset = ImageFolder(tmpdir, split='train')
test_dataset = ImageFolder(tmpdir, split='test')
assert (len(train_dataset) == 0)
assert (len(test_dataset) == 0)
... |
class TestVariable(QiskitOptimizationTestCase):
def test_init(self):
quadratic_program = QuadraticProgram()
name = 'variable'
lowerbound = 0
upperbound = 10
vartype = Variable.Type.INTEGER
variable = Variable(quadratic_program, name, lowerbound, upperbound, vartype)
... |
class TestInlineQueryResultCachedAudioBase():
id_ = 'id'
type_ = 'audio'
audio_file_id = 'audio file id'
caption = 'caption'
parse_mode = 'HTML'
caption_entities = [MessageEntity(MessageEntity.ITALIC, 0, 7)]
input_message_content = InputTextMessageContent('input_message_content')
reply_m... |
class Scenario(ScenarioGenerator):
def __init__(self):
super().__init__()
def road(self, **kwargs):
road = xodr.create_road([xodr.Line(1000)], 0, 2, 2)
odr = xodr.OpenDrive('myroad')
odr.add_road(road)
odr.adjust_roads_and_lanes()
guardrail = xodr.Object(0, 0, hei... |
def startredir(redirport, target, port):
dsz.control.echo.Off()
cmd = ('redirect -tcp -lplisten %s -target %s %s' % (redirport, target, port))
dsz.control.echo.On()
(succ, redircmdid) = dsz.cmd.RunEx(cmd, dsz.RUN_FLAG_RECORD)
if (not succ):
dsz.ui.Echo(('Failed: redirect -tcp -lplisten %s -t... |
def test_doc_inherit():
chars = tuple((string.ascii_letters + string.digits))
random = np.random.default_rng(seed=42)
doc = ''.join(random.choice(chars, 1000))
def func_a():
...
func_a.__doc__ = doc
_inherit(func_a)
def func_b():
...
_inherit(doc)
def func_c():
... |
def override_module_args(args: Namespace) -> Tuple[(List[str], List[str])]:
overrides = []
deletes = []
if (args is not None):
assert (hasattr(args, 'task') and hasattr(args, 'criterion') and hasattr(args, 'optimizer') and hasattr(args, 'lr_scheduler'))
if (args.task in TASK_DATACLASS_REGIST... |
(context_settings={'help_option_names': ['-h', '--help'], 'max_content_width': 120}, invoke_without_command=True)
('--env', '-e', 'env_name', envvar=AppEnvVars.ENV, default='default', help='The name of the environment to use [env var: `HATCH_ENV`]')
('--project', '-p', envvar=ConfigEnvVars.PROJECT, help='The name of th... |
def main():
global logger
args = get_args()
args = set_seed_logger(args)
(device, n_gpu) = init_device(args, args.local_rank)
tokenizer = ClipTokenizer()
assert (args.task_type == 'retrieval')
model = init_model(args, device, n_gpu, args.local_rank)
assert ((args.freeze_layer_num <= 12) ... |
class Buhne():
def __init__(self):
self._core = CoreStage()
self._core.facade = self
self._core.sprite_facade_class = Figur
def fuge_eine_figur_hinzu(self, costume='default'):
return self._core.pystage_createsprite(costume=costume)
def abspielen(self):
self._core.pyst... |
class AnswerSerializer(serializers.ModelSerializer):
author = serializers.StringRelatedField()
created_at = serializers.SerializerMethodField()
likes_count = serializers.SerializerMethodField()
user_has_liked_answer = serializers.SerializerMethodField()
question_slug = serializers.SerializerMethodFi... |
class Migration(migrations.Migration):
dependencies = [('options', '0027_meta')]
operations = [migrations.AlterModelOptions(name='option', options={'ordering': ('uri',), 'verbose_name': 'Option', 'verbose_name_plural': 'Options'}), migrations.RenameField(model_name='optionset', old_name='key', new_name='uri_pat... |
def iterate_tfrecord_file(data: BufferedIOBase) -> Iterator[memoryview]:
length_bytes = bytearray(8)
crc_bytes = bytearray(4)
data_bytes = bytearray(1024)
while True:
bytes_read = data.readinto(length_bytes)
if (bytes_read == 0):
break
elif (bytes_read != 8):
... |
class SaveEditorHandler(webBase.BaseHandler):
def post(self):
action = self.get_argument('action', default=None, strip=False)
logging.info(action)
table_name = self.get_argument('table_name', default=None, strip=False)
stockWeb = stock_web_dic.STOCK_WEB_DATA_MAP[table_name]
p... |
def NASNet(input_shape=None, penultimate_filters=4032, num_blocks=6, stem_block_filters=96, skip_reduction=True, filter_multiplier=2, include_top=True, weights=None, input_tensor=None, pooling=None, classes=1000, default_size=None, params=PARAM_NONE, **kwargs):
global backend, layers, models, keras_utils
(backe... |
class ProjectCommitDiscussionManager(RetrieveMixin, CreateMixin, RESTManager):
_path = '/projects/{project_id}/repository/commits/{commit_id}/discussions'
_obj_cls = ProjectCommitDiscussion
_from_parent_attrs = {'project_id': 'project_id', 'commit_id': 'id'}
_create_attrs = RequiredOptional(required=('b... |
_small_list(immutable=True, attrname='vals', factoryname='_make', unbox_num=True, nonull=True)
class ConsEnv(Env):
_immutable_ = True
_immutable_fields_ = ['_prev']
_attrs_ = ['_prev']
def __init__(self, prev):
assert isinstance(prev, Env)
self._prev = prev
def consenv_get_size(self)... |
_fixtures(FieldFixture)
def test_required_constraint(fixture):
selector = 'find me'
required_constraint = RequiredConstraint(dependency_expression=selector)
assert (required_constraint.parameters == selector)
with expected(RequiredConstraint):
required_constraint.validate_input('')
with expe... |
(jax.pmap, axis_name='batch')
def train_step(state, drp_rng, **model_inputs):
def loss_fn(params):
start_labels = model_inputs.pop('start_labels')
end_labels = model_inputs.pop('end_labels')
pooled_labels = model_inputs.pop('pooled_labels')
outputs = state.apply_fn(**model_inputs, pa... |
class YieldExpr(Expression):
__slots__ = ('expr',)
__match_args__ = ('expr',)
expr: (Expression | None)
def __init__(self, expr: (Expression | None)) -> None:
super().__init__()
self.expr = expr
def accept(self, visitor: ExpressionVisitor[T]) -> T:
return visitor.visit_yield_... |
def test_upload_generic_package(tmp_path, gitlab_cli, project):
path = (tmp_path / file_name)
path.write_text(file_content)
cmd = ['-v', 'generic-package', 'upload', '--project-id', project.id, '--package-name', package_name, '--path', path, '--package-version', package_version, '--file-name', file_name]
... |
class Transformation():
def __init__(self, transform_type=None):
self.transform_types = ['cholesky', 'svd']
self.transform_type = transform_type
if (self.transform_type is None):
self.transform_type = 'cholesky'
if (self.transform_type not in self.transform_types):
... |
def main(argv):
args = parseArgs(argv)
args.pathQuantizedUnits = abspath(args.pathQuantizedUnits)
args.pathOutputFile = abspath(args.pathOutputFile)
args.pathLSTMCheckpoint = abspath(args.pathLSTMCheckpoint)
if (args.dict is not None):
args.dict = abspath(args.dict)
print('')
print(f... |
def _cap_fees(x_list: List[Fraction], y_list: List[Fraction]) -> Tuple[(List[Fraction], List[Fraction])]:
x_list = copy(x_list)
y_list = copy(y_list)
for i in range((len(x_list) - 1)):
(y1, y2) = y_list[i:(i + 2)]
if ((sign(y1) * sign(y2)) == (- 1)):
(x1, x2) = x_list[i:(i + 2)]
... |
class AttrVI_ATTR_USB_BULK_IN_STATUS(RangeAttribute):
resources = [(constants.InterfaceType.usb, 'RAW')]
py_name = ''
visa_name = 'VI_ATTR_USB_BULK_IN_STATUS'
visa_type = 'ViInt16'
default = NotAvailable
(read, write, local) = (True, True, True)
(min_value, max_value, values) = ((- 32768), 3... |
def id_to_probs(probs, ids, id_to_vocab, SOFTMAX=False):
if SOFTMAX:
probs = softmax(probs)
else:
pass
product = 1
for id in ids:
if (id_to_vocab[id] == '</s>'):
break
elif (id_to_vocab[id] == '<s>'):
pass
elif id:
product *= pr... |
class Effect2734(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
for type in ('Gravimetric', 'Ladar', 'Radar', 'Magnetometric'):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'ECM')), 'scan{0}StrengthBonus'.format(type), ship.get... |
def input_fn_builder(features, seq_length, is_training, drop_remainder):
all_input_ids = []
all_input_mask = []
all_segment_ids = []
all_label_ids = []
for feature in features:
all_input_ids.append(feature.input_ids)
all_input_mask.append(feature.input_mask)
all_segment_ids.a... |
class FixFilter(fixer_base.ConditionalFix):
BM_compatible = True
PATTERN = "\n filter_lambda=power<\n 'filter'\n trailer<\n '('\n arglist<\n lambdef< 'lambda'\n (fp=NAME | vfpdef< '(' fp=NAME ')'> ) ':' xp=any\n >\n ... |
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