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def main():
parser = argparse.ArgumentParser()
parser.add_argument('--model_type', default=None, type=str, required=True, help=('Model type selected in the list: ' + ', '.join(MODEL_CLASSES.keys())))
parser.add_argument('--model_name_or_path', default=None, type=str, required=True, help='Path to pretrained ... |
class ConvKXBNRELU(nn.Module):
def __init__(self, in_c, out_c, kernel_size, stride, act='silu'):
super(ConvKXBNRELU, self).__init__()
self.conv = ConvKXBN(in_c, out_c, kernel_size, stride)
if (act is None):
self.activation_function = torch.relu
else:
self.acti... |
class _SQLLineageConfigLoader():
config = {'DIRECTORY': (str, os.path.join(os.path.dirname(__file__), 'data')), 'TSQL_NO_SEMICOLON': (bool, False)}
def __getattr__(self, item):
if (item in self.config):
(type_, default) = self.config[item]
return type_(os.environ.get(('SQLLINEAGE... |
def name_to_array(name: str) -> t.Tuple[(str, ...)]:
tags: t.Dict[(str, t.Callable[([str], bool)])] = {'unstable': (lambda i: (i == 'master')), 'latest': (lambda i: (RELEASE_RE.fullmatch(i) is not None)), name: (lambda i: ((RELEASE_RE.fullmatch(i) is not None) or (PRE_RELEASE_RE.fullmatch(i) is not None)))}
ret... |
def load_pickle(pickle_file):
try:
with open(pickle_file, 'rb') as f:
pickle_data = pickle.load(f)
except UnicodeDecodeError as e:
with open(pickle_file, 'rb') as f:
pickle_data = pickle.load(f, encoding='latin1')
except Exception as e:
print('Unable to load d... |
def get_valid_stats(cfg: DictConfig, trainer: Trainer, stats: Dict[(str, Any)]) -> Dict[(str, Any)]:
stats['num_updates'] = trainer.get_num_updates()
if hasattr(checkpoint_utils.save_checkpoint, 'best'):
key = 'best_{0}'.format(cfg.checkpoint.best_checkpoint_metric)
best_function = (max if cfg.c... |
class ConvModule(enn.EquivariantModule):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias='auto', conv_cfg=None, norm_cfg=None, activation='relu', inplace=False, order=('conv', 'norm', 'act')):
super(ConvModule, self).__init__()
assert ((conv... |
def get_bool_opt(options, optname, default=None):
string = options.get(optname, default)
if isinstance(string, bool):
return string
elif isinstance(string, int):
return bool(string)
elif (not isinstance(string, str)):
raise OptionError(('Invalid type %r for option %s; use 1/0, ye... |
def useCycleGetPrefixData(e, prefixE, data):
copyData = list(copy.deepcopy(data))
flage = 0
for i in copyData[:]:
for j in i[:]:
if set('_').issubset(e):
if set([prefixE, e[1]]).issubset(set(j)):
for l in j[:]:
if ((l == prefixE... |
def get_augmentations():
sometimes = (lambda aug: iaa.Sometimes(0.5, aug))
seq = iaa.Sequential([iaa.SomeOf((0, 5), [iaa.OneOf([iaa.GaussianBlur((0, 3.0)), iaa.AverageBlur(k=(2, 7)), iaa.MedianBlur(k=(3, 11))]), iaa.Sharpen(alpha=(0, 1.0), lightness=(0.75, 1.5)), iaa.Emboss(alpha=(0, 1.0), strength=(0, 2.0)), i... |
def test_get_next_page_de_should_return_3_on_page_2(tmp_path):
htmlString = '\n <div class="next-previous-links">\n <div class="previous dl-rounded-borders dl-white-bg">\n <a href="/impfung-covid-19-corona/berlin?ref_visit_motive_ids%5B%5D=6769">\n <svg xmlns=" wi... |
def getAWSGroups(data_path, account_name):
logger.debug("[*] Getting AWS Group data for AWS Account '%s'", account_name)
jqQuery = '.GroupDetailList[]'
data = getAWSIamAccountAuthorizationDetailsInfo(data_path, account_name, jqQuery)
logger.debug("[*] Completed getting AWS Group data for AWS Account '%s... |
class ResBlock_myDFNM(nn.Module):
def __init__(self, dim, norm='in', activation='relu', pad_type='zero'):
super(ResBlock_myDFNM, self).__init__()
model1 = []
model2 = []
model1 += [Conv2dBlock_my(dim, dim, 3, 1, 1, norm=norm, activation=activation, pad_type=pad_type)]
model2 ... |
def test_project_location_complex_set_first_project(hatch, config_file, helpers, temp_dir):
with temp_dir.as_cwd():
result = hatch('config', 'set', 'projects.foo.location', '.')
path = str(temp_dir).replace('\\', '\\\\')
assert (result.exit_code == 0), result.output
assert (result.output == help... |
def total_loss(net, t_inst_dict, params=dict()):
loss_dict_Disc = dict()
loss_dict_Gene = dict()
metrics = dict()
replay_worst = params.get('replay_worst', 0)
t_inst_real = t_inst_dict['instr_real']
t_inst_synt = t_inst_dict['instr_synt']
if replay_worst:
t_inst_wors = t_inst_dict['w... |
def test_shell_sequence_with_ampersands_save_output():
context = Context({'one': 1, 'two': 2, 'three': 3, 'cmd': {'run': 'echo {one} && echo {two} && echo {three}', 'save': True}})
pypyr.steps.shell.run_step(context)
assert (context['cmdOut'].returncode == 0)
assert (context['cmdOut'].stdout == ('1 \n2 ... |
class TestSysModulesSnapshot():
key = 'my-test-module'
def test_remove_added(self) -> None:
original = dict(sys.modules)
assert (self.key not in sys.modules)
snapshot = SysModulesSnapshot()
sys.modules[self.key] = ModuleType('something')
assert (self.key in sys.modules)
... |
def test_merge_pass_nested_with_substitutions():
context = Context({'key1': 'value1', 'key2': 'value2', 'key3': {'k31': 'value31', 'k32': 'value32'}, 'key5': False, 15: 16})
add_me = {'key2': 'value4', 'key3': {'k33': 'value33'}, 'key4': '444_{key1}_444', 'key5': {'k51': PyString('key1')}, 13: 14, 15: 17}
c... |
class GlmMultiResp(Glm):
def get_z(self, x):
return safe_data_mat_coef_mat_dot(X=self.X, coef=x.reshape(self.var_shape_), fit_intercept=self.fit_intercept)
def cat_intercept_coef(self, intercept, coef):
if (intercept.ndim == 1):
intercept = intercept.reshape(1, (- 1))
return ... |
def test_env_only_calls_set():
context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'env': {'set': {'ARB_SET_ME1': 'key2', 'ARB_SET_ME2': 'key1'}}})
with patch.multiple('pypyr.steps.env', env_get=DEFAULT, env_set=DEFAULT, env_unset=DEFAULT) as mock_env:
pypyr.steps.env.run_step(conte... |
def confirm_booleanbased_sqli(base, parameter, payload_detected, url='', data='', headers='', injection_type='', proxy='', is_multipart=False, timeout=30, delay=0, timesec=5, response_time=8, code=None, match_string=None, not_match_string=None, text_only=False, confirmation=False):
_temp = []
Response = collect... |
class TestAccount(potr.context.Account):
contextclass = TestContext
def __init__(self, name, post_office):
self.post_office = post_office
super(TestAccount, self).__init__(name, 'test_protocol', 415)
def loadPrivkey(self):
pass
def savePrivkey(self):
pass |
class MultilabelAccuracy(MulticlassAccuracy):
def __init__(self: TMultilabelAccuracy, *, threshold: float=0.5, criteria: str='exact_match', device: Optional[torch.device]=None) -> None:
super().__init__(device=device)
_multilabel_accuracy_param_check(criteria)
self.threshold = threshold
... |
def get_auth_header(protocol, timestamp, client, api_key, api_secret=None, **kwargs):
header = [('sentry_timestamp', timestamp), ('sentry_client', client), ('sentry_version', protocol), ('sentry_key', api_key)]
if api_secret:
header.append(('sentry_secret', api_secret))
return ('Sentry %s' % ', '.jo... |
def _bfs_for_latest_version_in_history(merge_base: (((Commit | TagObject) | Blob) | Tree), full_release_tags_and_versions: list[tuple[(Tag, Version)]]) -> (Version | None):
def bfs(visited: set[Commit], q: Queue[Commit]) -> (Version | None):
if q.empty():
log.debug('queue is empty, returning non... |
class FakeKeyparser(QObject):
keystring_updated = pyqtSignal(str)
request_leave = pyqtSignal(usertypes.KeyMode, str, bool)
def __init__(self):
super().__init__()
self.passthrough = False
def handle(self, evt: QKeyEvent, *, dry_run: bool=False) -> QKeySequence.SequenceMatch:
retur... |
class TPreferencesWindow(TestCase):
def setUp(self):
config.init()
init_fake_app()
set_columns(['artist', 'title'])
self.win = PreferencesWindow(None)
def test_ctr(self):
pass
def tearDown(self):
destroy_fake_app()
self.win.destroy()
config.qui... |
def test_valid_manifestlist():
manifestlist = DockerSchema2ManifestList(Bytes.for_string_or_unicode(MANIFESTLIST_BYTES))
assert (len(manifestlist.manifests(retriever)) == 2)
assert (manifestlist.media_type == 'application/vnd.docker.distribution.manifest.list.v2+json')
assert (manifestlist.bytes.as_enco... |
def load_module_from_name(dotted_name: str) -> types.ModuleType:
try:
return sys.modules[dotted_name]
except KeyError:
pass
with redirect_stderr(io.StringIO()) as stderr, redirect_stdout(io.StringIO()) as stdout:
module = importlib.import_module(dotted_name)
stderr_value = stderr... |
class LinearQubitOperatorOptions(object):
def __init__(self, processes=10, pool=None):
if (processes <= 0):
raise ValueError('Invalid number of processors specified {} <= 0'.format(processes))
self.processes = min(processes, multiprocessing.cpu_count())
self.pool = pool
def g... |
def get_logger(args):
logger = logging.getLogger()
logger.setLevel(logging.INFO)
formatter = logging.Formatter('%(asctime)s %(levelname)s: - %(message)s', datefmt='%m-%d %H:%M:%S')
fh = logging.FileHandler('./logs/{}.txt'.format(args.model_id), mode='w+')
fh.setLevel(logging.INFO)
fh.setFormatte... |
def ld2table(header, ld, html_id=None, html_class=None, widths={}):
table = '<table'
if html_id:
table += (' id="%s"' % html_id)
if html_class:
table += (' class="%s"' % html_class)
table += '>\n\t<thead>\n\t\t<tr>'
for h in header:
if (not (h in widths)):
table +... |
def load_properties_data(file_name='../../resources/properties_data.json'):
abs_dir_path = os.path.dirname(os.path.abspath(__file__))
file_name_all = os.path.join(abs_dir_path, file_name)
with open(file_name_all, 'r') as f:
pd_dict = json.load(f)
return {key: [Property[p] for p in props] for... |
def test_set_current():
with expected_protocol(AdvantestR6246, [('di 1,0,2.1100e-04,2.1300e-04', None), ('spot 1,2.3120e-03', None), (None, 'ABCD 7.311e-4')]) as inst:
inst.ch_A.current_source(0, 0.000211, 0.000213)
inst.ch_A.change_source_current = 0.002312
assert (inst.read_measurement() =... |
def _build_message(*args, **kwargs):
string = kwargs.get('string', None)
t = _escape(kwargs.get('t', ''))
expected = _escape(kwargs.get('expected', ''))
result = _escape(kwargs.get('result', ''))
if (string is None):
string = "The expected output of '{t}'\n\t\tShould be '{expected}'\n\t\tAct... |
class _XXZZLattice(_RotatedLattice):
stabilizer_shortnames = {'mx': _XXXX, 'mz': _ZZZZ}
def reset_x(self) -> None:
self.circ.reset(self.qregisters['data'])
self.circ.h(self.qregisters['data'])
self.circ.barrier()
def reset_z(self) -> None:
self.circ.reset(self.qregisters['dat... |
class Evaluator(object):
def eval_annotation(self, annotation, output):
captions = json.load(open(annotation, 'r'))['audios']
key2refs = {}
for audio_idx in range(len(captions)):
audio_id = captions[audio_idx]['audio_id']
key2refs[audio_id] = []
for captio... |
def make_save_dirs(args, prefix, suffix=None, with_time=False):
time_str = (datetime.now().strftime('%Y-%m-%dT%H-%M-%S') if with_time else '')
suffix = (suffix if (suffix is not None) else '')
result_path = make_dir(os.path.join(args.result_path, prefix, suffix, time_str))
image_path = make_dir(os.path.... |
class Qtile(CommandObject):
current_screen: Screen
dgroups: DGroups
_eventloop: asyncio.AbstractEventLoop
def __init__(self, kore: base.Core, config: Config, no_spawn: bool=False, state: (str | None)=None, socket_path: (str | None)=None) -> None:
self.core = kore
self.config = config
... |
class Time2BumpDistanceGetter(Time2BumpSpeedGetter):
def _calculatePoint(self, x, miscParams, src, tgt, commonData):
tgtMass = (miscParams['tgtMass'] / (10 ** 6))
tgtInertia = miscParams['tgtInertia']
tgtSpeed = Time2BumpSpeedGetter._calculatePoint(self, x=x, miscParams=miscParams, src=src, ... |
class DistributedTrainingConfig(FairseqDataclass):
distributed_world_size: int = field(default=max(1, torch.cuda.device_count()), metadata={'help': 'total number of GPUs across all nodes (default: all visible GPUs)'})
distributed_rank: Optional[int] = field(default=0, metadata={'help': 'rank of the current work... |
def allocator_hparams():
return PlacerParams(hidden_size=512, forget_bias_init=1.0, grad_bound=1.0, lr=0.01, lr_dec=1.0, decay_steps=50, start_decay_step=400, optimizer_type='adam', name='hierarchical_controller', keep_prob=1.0, seed=1, model_size='small', random_prob=1.0, max_degree=100, epoches=10000, dropout=0.0... |
def gather_tensors_fake(tensor):
tensors_gather = [torch.ones_like(tensor) for _ in range(comm.world_size)]
dist.all_gather(tensors_gather, tensor, async_op=False)
tensors_gather[comm.rank] = tensor
output = torch.cat(tensors_gather, dim=0)
output = torch.cat([output, output.detach()], 0)
return... |
class BaseClientFactoriesTests(unittest.TestCase):
def setUp(self):
transport = mock.Mock(spec=metrics.NullTransport)
self.client = metrics.BaseClient(transport, 'namespace')
def test_make_timer(self):
timer = self.client.timer('some_timer')
self.assertIsInstance(timer, metrics.T... |
class TestWaitForNavigation(BaseTestCase):
async def test_wait_for_navigatoin(self):
(await self.page.goto((self.url + 'empty')))
results = (await asyncio.gather(self.page.waitForNavigation(), self.page.evaluate('(url) => window.location.href = url', self.url)))
response = results[0]
... |
.parametrize('username,password', users)
.parametrize('value_id', values)
def test_file(db, client, files, username, password, value_id):
client.login(username=username, password=password)
value = Value.objects.get(pk=value_id)
url = reverse(urlnames['file'], args=[value_id])
response = client.get(url)
... |
class SchemasTest():
def test_lazyness(self):
schema = schemas.LazySchema('oas-2.0.json')
assert (schema._schema is None)
('' in schema)
assert (schema._schema is not None)
assert isinstance(schema._schema, dict)
def test_oas2_schema_is_present(self):
assert hasat... |
class TestWalletHistory_DoubleSpend(TestCaseForTestnet):
transactions = {'a3849040fba12a4389310b58a17b78025d81116a3338595bdefa1625': 'b7ebb40209c234344f57a3365669c8883a3d511fbde5155f11f64dfdffffff024c400fb50d21483fb5e088db90bf766ea79219fb377fef40420faaf5fc4a6297375c32403a9c2768e7029c8dbdefd510954b289829f8f778163b98... |
.skipif((sys.platform != 'win32'), reason='no Windows registry')
.usefixtures('_mock_registry')
def test_pep514():
from virtualenv.discovery.windows.pep514 import discover_pythons
interpreters = list(discover_pythons())
assert (interpreters == [('ContinuumAnalytics', 3, 10, 32, 'C:\\Users\\user\\Miniconda3\... |
class InvalidOpcode(Opcode):
mnemonic = 'INVALID'
gas_cost = 0
def __init__(self, value: int) -> None:
self.value = value
super().__init__()
def __call__(self, computation: ComputationAPI) -> None:
raise InvalidInstruction(f'Invalid opcode 0x{self.value:x} {(computation.code.pro... |
def pytest_unconfigure(config: Config) -> None:
import faulthandler
faulthandler.disable()
if (fault_handler_stderr_fd_key in config.stash):
os.close(config.stash[fault_handler_stderr_fd_key])
del config.stash[fault_handler_stderr_fd_key]
if (fault_handler_original_stderr_fd_key in confi... |
def _get_frame_recognized_actions_view_from_video(frame_time: float, video_recognized_actions: VideoSoccerRecognizedActions, label_map: LabelMap) -> Optional[FrameRecognizedActionsView]:
action_times = video_recognized_actions.keys()
(time_diff, best_time) = min(((abs((action_time - frame_time)), action_time) f... |
def test_do_not_mistake_JSDoc_for_django_comment(lexer):
text = '/**\n * {*} cool\n */\n func = function(cool) {\n };\n\n /**\n * {*} stuff\n */\n fun = function(stuff) {\n };'
tokens = lex... |
def count_conseq_double(mol):
bonds = mol.GetBonds()
previous_BType = None
count_conseq_doub = 0
for b in bonds:
curr_BType = b.GetBondType()
if ((previous_BType == curr_BType) and (curr_BType == rdkit.Chem.rdchem.BondType.DOUBLE)):
count_conseq_doub += 1
previous_BTy... |
def integral_interval_probaCDF_recall(I, J, E):
def f(J_cut):
if (J_cut is None):
return 0
else:
return integral_mini_interval_Precall_CDFmethod(I, J_cut, E)
def f0(J_middle):
if (J_middle is None):
return 0
else:
return (max(J_midd... |
def _generate_filler(key_type: bytes, hops_data: Sequence[OnionHopsDataSingle], shared_secrets: Sequence[bytes]) -> bytes:
num_hops = len(hops_data)
filler_size = 0
for hop_data in hops_data[:(- 1)]:
filler_size += len(hop_data.to_bytes())
filler = bytearray(filler_size)
for i in range(0, (n... |
class ConvLayer(torch.nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride):
super(ConvLayer, self).__init__()
reflection_padding = (kernel_size // 2)
self.reflection_pad = torch.nn.ReflectionPad2d(reflection_padding)
self.conv2d = torch.nn.Conv2d(in_channels... |
(id='vmware-node-terminate', name='Reboot VMware VM', description='Wait for the node to be terminated', outputs={'success': NodeScenarioSuccessOutput, 'error': NodeScenarioErrorOutput})
def node_terminate(cfg: NodeScenarioConfig) -> typing.Tuple[(str, typing.Union[(NodeScenarioSuccessOutput, NodeScenarioErrorOutput)])]... |
class MiniImagenet(CombinationMetaDataset):
def __init__(self, root, num_classes_per_task=None, meta_train=False, meta_val=False, meta_test=False, meta_split=None, transform=None, target_transform=None, dataset_transform=None, class_augmentations=None, download=False):
dataset = MiniImagenetClassDataset(roo... |
def model_with_compat_bn_layers(training_as_placeholder, is_fused):
training = True
if training_as_placeholder:
training = tf.compat.v1.placeholder_with_default(True, shape=())
inputs = tf.keras.Input(shape=(32, 32, 3))
x = tf.keras.layers.Conv2D(32, (3, 3))(inputs)
x = tf.compat.v1.layers.b... |
def load(model, dataset_name, uid, root='models_checkpoints', optimizer=None):
checkpoint_path = os.path.join(root, dataset_name, model.name)
save_path = os.path.join(checkpoint_path, ('%s_%s_%s.pth.tar' % (dataset_name, model.name, uid)))
checkpoint = torch.load(save_path)
model.load_state_dict(checkpo... |
def run_train():
parser = argparse.ArgumentParser()
parser.add_argument('--model_dir', '-md', default=None, required=True, type=str)
parser.add_argument('--resume_weights', '-r', default=None, type=int)
os.environ['MASTER_ADDR'] = '127.0.0.1'
os.environ['MASTER_PORT'] = '8888'
os.environ['NCCL_I... |
class OptionViewSet(ModelViewSet):
permission_classes = ((HasModelPermission | HasObjectPermission),)
serializer_class = OptionSerializer
queryset = Option.objects.annotate(values_count=models.Count('values')).annotate(projects_count=models.Count('values__project', distinct=True)).prefetch_related('optionse... |
class Product(Bloq):
a_bitsize: int
b_bitsize: int
def signature(self):
return Signature([Register('a', self.a_bitsize), Register('b', self.b_bitsize), Register('result', (2 * max(self.a_bitsize, self.b_bitsize)), side=Side.RIGHT)])
def short_name(self) -> str:
return 'a*b'
def t_com... |
class DEVISR(IntEnum):
SUSP = (1 << 0)
MSOF = (1 << 1)
SOF = (1 << 2)
EORST = (1 << 3)
WAKEUP = (1 << 4)
EORSM = (1 << 5)
UPRSM = (1 << 6)
PEP_0 = (1 << 12)
PEP_1 = (1 << 13)
PEP_2 = (1 << 14)
PEP_3 = (1 << 15)
PEP_4 = (1 << 16)
PEP_5 = (1 << 17)
PEP_6 = (1 << 18)... |
class Generator_Prune(nn.Module):
def __init__(self, cfg_mask, n_residual_blocks=9):
super(Generator_Prune, self).__init__()
first_conv_out = int(sum(cfg_mask[0]))
model = [nn.ReflectionPad2d(3), nn.Conv2d(3, first_conv_out, 7), nn.InstanceNorm2d(first_conv_out), nn.ReLU(inplace=True)]
... |
def main():
args = parse_args()
net = caffe_pb2.NetParameter()
text_format.Merge(open(args.input_net_proto_file).read(), net)
print(('Drawing net to %s' % args.output_image_file))
phase = None
if (args.phase == 'TRAIN'):
phase = caffe.TRAIN
elif (args.phase == 'TEST'):
phase ... |
class SDFNetwork(nn.Module):
def __init__(self, encoding='hashgrid', num_layers=3, skips=[], hidden_dim=64, clip_sdf=None):
super().__init__()
self.num_layers = num_layers
self.skips = skips
self.hidden_dim = hidden_dim
self.clip_sdf = clip_sdf
(self.encoder, self.in_... |
class DataTrainingArguments():
source_lang: str = field(default=None, metadata={'help': 'Source language id for translation.'})
target_lang: str = field(default=None, metadata={'help': 'Target language id for translation.'})
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of th... |
class IDR(IntEnum):
EOC0 = (1 << 0)
EOC1 = (1 << 1)
EOC2 = (1 << 2)
EOC3 = (1 << 3)
EOC4 = (1 << 4)
EOC5 = (1 << 5)
EOC6 = (1 << 6)
EOC7 = (1 << 7)
EOC8 = (1 << 8)
EOC9 = (1 << 9)
EOC10 = (1 << 10)
EOC11 = (1 << 11)
EOC12 = (1 << 12)
EOC13 = (1 << 13)
EOC14 = ... |
.requires_user_action
class EVENT_MOVE(InteractiveTestCase):
def on_move(self, x, y):
print(('Window moved to %dx%d.' % (x, y)))
def test_move(self):
w = Window(200, 200)
try:
w.push_handlers(self)
while (not w.has_exit):
w.dispatch_events()
... |
class F13_TestCase(CommandTest):
command = 'sshpw'
def runTest(self):
self.assert_parse('sshpw --username=someguy --iscrypted secrethandshake', 'sshpw --username=someguy --iscrypted secrethandshake\n')
self.assertFalse((self.assert_parse('sshpw --username=A --iscrypted secrethandshake') is None)... |
def test_add_mod_n_protocols():
with pytest.raises(ValueError, match='must be between'):
_ = AddConstantMod(3, 10)
add_one = AddConstantMod(3, 5, 1)
add_two = AddConstantMod(3, 5, 2, cvs=[1, 0])
assert (add_one == AddConstantMod(3, 5, 1))
assert (add_one != add_two)
assert (hash(add_one)... |
def parse_args():
parser = argparse.ArgumentParser(description='Train Meta R-CNN network')
parser.add_argument('--dataset', dest='dataset', help='training dataset:coco2017,coco,pascal_07_12', default='pascal_voc_0712', type=str)
parser.add_argument('--net', dest='net', help='metarcnn', default='metarcnn', t... |
class TestPostgresqlCollector(CollectorTestCase):
def setUp(self, allowed_names=None):
if (not allowed_names):
allowed_names = []
config = get_collector_config('PostgresqlCollector', {})
self.collector = PostgresqlCollector(config, None)
def test_import(self):
self.as... |
class DocDB(object):
def __init__(self, db_path=None, full_docs=False):
self.path = (db_path or config.DOC_DB)
self.full_docs = full_docs
self.connection = sqlite3.connect(self.path, check_same_thread=False)
def __enter__(self):
return self
def __exit__(self, *args):
... |
def test_serializer_create(db):
class MockedView():
project = Project.objects.get(id=project_id)
value = Value.objects.get(project_id=project_id, snapshot=None, attribute__path=attribute_path)
validator = ValueConflictValidator()
serializer = ValueSerializer()
serializer.context['view'] = Mo... |
class MultisourceLanguagePairDataset(data.LanguagePairDataset):
def __getitem__(self, i):
source = [src_sent.long() for src_sent in self.src[i]]
res = {'id': i, 'source': source}
if self.tgt:
res['target'] = self.tgt[i].long()
return res
def collater(self, samples):
... |
class Rouge():
def __init__(self):
self.beta = 1.2
def calc_score(self, candidate, refs):
assert (len(candidate) == 1)
assert (len(refs) > 0)
prec = []
rec = []
token_c = candidate[0].split(' ')
for reference in refs:
token_r = reference.split(... |
def render_missing_space_in_doctest(msg, _node, source_lines=None):
line = msg.line
(yield from render_context((line - 2), line, source_lines))
(yield (line, slice(None, None), LineType.ERROR, source_lines[(line - 1)]))
(yield from render_context((line + 1), (line + 3), source_lines)) |
.skipif((sys.platform == 'win32'), reason='symlinks to files not supported on windows')
def test_symlink_file(inwd: WorkDir) -> None:
((inwd.cwd / 'adir') / 'file1link').symlink_to('../file1')
inwd.add_and_commit()
assert (set(find_files('adir')) == _sep({'adir/filea', 'adir/file1link'})) |
(config_path='config', config_name='config')
def main(opt):
print(opt.pretty())
pl.seed_everything(42, workers=True)
torch.set_num_threads(10)
datamodule = hydra.utils.instantiate(opt.datamodule, opt.datamodule)
datamodule.setup(stage='fit')
np.savez('meta_info.npz', **datamodule.meta_info)
... |
class abstractmethod(IncludeMixin):
def __init__(self, func):
if (not callable(func)):
raise ABCException(f'Function is not callable: {func}')
self.func = func
self.args = self.getargs(func)
def __eq__(self, other):
if isinstance(other, abstractmethod):
re... |
class CecaModule(nn.Module):
def __init__(self, channels=None, kernel_size=3, gamma=2, beta=1, act_layer=None, gate_layer='sigmoid'):
super(CecaModule, self).__init__()
if (channels is not None):
t = int((abs((math.log(channels, 2) + beta)) / gamma))
kernel_size = max((t if (... |
class TestUpdateLon(object):
def setup_method(self):
self.py_inst = None
self.inst_time = pysat.instruments.pysat_testing._test_dates['']['']
return
def teardown_method(self):
del self.py_inst, self.inst_time
return
.parametrize('name', ['testing', 'testing_xarray', '... |
def test_conc_str() -> None:
assert (str(Conc(Mult(Charclass('a'), ONE), Mult(Charclass('b'), ONE), Mult(Charclass('c'), ONE), Mult(Charclass('d'), ONE), Mult(Charclass('e'), ONE), Mult((~ Charclass('fg')), STAR), Mult(Charclass('h'), Multiplier(Bound(5), Bound(5))), Mult(Charclass('abcdefghijklmnopqrstuvwxyz'), PL... |
def temp_link_blob(repository_id, blob_digest, link_expiration_s):
assert blob_digest
with db_transaction():
try:
storage = ImageStorage.get(content_checksum=blob_digest)
except ImageStorage.DoesNotExist:
return None
_temp_link_blob(repository_id, storage, link_ex... |
class PPLScorer(Scorer):
def __init__(self):
super(PPLScorer, self).__init__(float('inf'), 'ppl')
def is_improving(self, stats):
return (stats.ppl() < self.best_score)
def is_decreasing(self, stats):
return (stats.ppl() > self.best_score)
def _caller(self, stats):
return ... |
('I can iterate over the inline shape collection')
def then_can_iterate_over_inline_shape_collection(context):
inline_shapes = context.inline_shapes
shape_count = 0
for inline_shape in inline_shapes:
shape_count += 1
assert isinstance(inline_shape, InlineShape)
expected_count = 5
ass... |
class EqualPointLineDistance(BaseSketch):
_id = 13
_entityDef = (_p, _l, _p, _l)
_workplane = True
_iconName = 'Assembly_ConstraintEqualPointLineDistance.svg'
_tooltip = QT_TRANSLATE_NOOP('asm3', 'Add a "{}" to constrain the distance between a point and a\nline to be the same as the distance between... |
class ProjectMetadata():
def __str__(self):
return 'Default project metadata provider'
def __init__(self, project):
self.project = project
def project_name(self):
return os.path.basename(self.directory)
def directory(self):
return self.project.directory
def version(se... |
_end_docstrings(PIPELINE_INIT_ARGS)
class ImageClassificationPipeline(Pipeline):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, 'vision')
self.check_model_type((TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if (self.framework == 'tf') else MODEL_FO... |
class DNSRecord(DNSEntry):
__slots__ = ('ttl', 'created')
def __init__(self, name: str, type_: int, class_: int, ttl: Union[(float, int)], created: Optional[float]=None) -> None:
super().__init__(name, type_, class_)
self.ttl = ttl
self.created = (created or current_time_millis())
de... |
def draw_meter(x, y, dx, dy, style=None, fill='bgcolor'):
global bgcolor
if (fill == 'bgcolor'):
fill = bgcolor
draw_rectangle(x, y, dx, dy, None, style=style, fill=fill)
radius = min((0.5 * dx), (0.5 * dy))
if style:
style_str = (',' + style)
else:
style_str = ''
pri... |
def get_default_config():
config = {'logdir': 'ppo_torch', 'idx': 0, 'seed': 0, 'num_timesteps': int(.0), 'episode_length': 1000, 'discounting': 0.97, 'learning_rate': 0.0003, 'entropy_cost': 0.01, 'unroll_length': 5, 'batch_size': 1024, 'num_minibatches': 32, 'num_update_epochs': 4, 'reward_scaling': 10, 'lambda_'... |
class TestProtLib(EvenniaTest):
def setUp(self):
super(TestProtLib, self).setUp()
self.obj1.attributes.add('testattr', 'testval')
self.prot = spawner.prototype_from_object(self.obj1)
def test_prototype_to_str(self):
prstr = protlib.prototype_to_str(self.prot)
self.assertT... |
def parse_kinetics_splits(level, dataset):
def convert_label(s, keep_whitespaces=False):
if (not keep_whitespaces):
return s.replace('"', '').replace(' ', '_')
return s.replace('"', '')
def line_to_map(x, test=False):
if test:
video = f'{x[1]}_{int(float(x[2])):06... |
def import_optionset(element, save=False, user=None):
try:
optionset = OptionSet.objects.get(uri=element.get('uri'))
except OptionSet.DoesNotExist:
optionset = OptionSet()
set_common_fields(optionset, element)
optionset.order = (element.get('order') or 0)
optionset.provider_key = (el... |
class TestOOVQE(QiskitChemistryTestCase):
def setUp(self):
super().setUp()
self.driver1 = HDF5Driver(hdf5_input=self.get_resource_path('test_oovqe_h4.hdf5'))
self.driver2 = HDF5Driver(hdf5_input=self.get_resource_path('test_oovqe_lih.hdf5'))
self.driver3 = HDF5Driver(hdf5_input=self.... |
class Record(object):
def __init__(self, mod, mde, dat, sum, values):
self._mod = mod
self._mde = mde
self._dat = dat
self._sum = sum
self._values = values
self._approx_system_time = None
self._approx_gps_time = None
self._gps = None
def set_approx... |
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