code stringlengths 281 23.7M |
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def test_conc_dock() -> None:
a = Mult(Charclass('A'), ONE)
b = Mult(Charclass('B'), ONE)
x = Mult(Charclass('X'), ONE)
x_twice = Mult(Charclass('X'), Multiplier(Bound(2), Bound(2)))
yplus = Mult(Charclass('y'), PLUS)
z = Mult(Charclass('Z'), ONE)
assert (Conc(a, z).dock(Conc(z)) == Conc(a))... |
class LogTestCase(unittest.TestCase):
def setUp(self):
self.out = StringIO()
self.tracker = ClassTracker(stream=self.out)
def output(self):
return self.out.getvalue()
def tearDown(self):
self.tracker.stop_periodic_snapshots()
self.tracker.clear()
def test_dump(sel... |
def _maybe_compute_stride_kjt(keys: List[str], stride: Optional[int], lengths: Optional[torch.Tensor], offsets: Optional[torch.Tensor]) -> int:
if (stride is None):
if (len(keys) == 0):
stride = 0
elif ((offsets is not None) and (offsets.numel() > 0)):
stride = ((offsets.nume... |
def make_xml(filename, path, box_list, labels, w, h, d):
doc = xml.dom.minidom.Document()
root = doc.createElement('annotation')
doc.appendChild(root)
foldername = doc.createElement('folder')
foldername.appendChild(doc.createTextNode('JPEGImages'))
root.appendChild(foldername)
nodeFilename =... |
def collect_stats(model, data_loader, num_batches):
for (name, module) in model.named_modules():
if isinstance(module, quant_nn.TensorQuantizer):
if (module._calibrator is not None):
module.disable_quant()
module.enable_calib()
else:
mo... |
def validate_report(arg):
file_choices = ['annotate', 'html', 'xml', 'json', 'lcov']
term_choices = ['term', 'term-missing']
term_modifier_choices = ['skip-covered']
all_choices = (term_choices + file_choices)
values = arg.split(':', 1)
report_type = values[0]
if (report_type not in (all_cho... |
class Base64BinaryField(TextField):
def db_value(self, value):
if (value is None):
return None
return base64.b64encode(value).decode('ascii')
def python_value(self, value):
if (value is None):
return None
return base64.b64decode(value.encode('ascii')) |
class Cleanup(SquirrelCommand):
def make_subparser(self, subparsers):
headline = 'Remove leftover volatile data entries.'
return subparsers.add_parser('cleanup', help=headline, description=headline)
def run(self, parser, args):
s = sq.Squirrel()
db = s.get_database()
n_re... |
class Effect6062(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.drones.filteredItemBoost((lambda drone: drone.item.requiresSkill('Light Drone Operation')), 'shieldCapacity', ship.getModifiedItemAttr('shipBonusGC2'), skill='Gallente Cruiser', **kwargs) |
class FakeFile():
def __init__(self, name, mode):
self.mode = mode
self.name = name
self.data = BytesIO()
self.size = 0
def __enter__(self):
return self
def __exit__(self, *args):
self.close()
def read(self, length=(- 1)):
if (length == (- 1)):
... |
class sepCEMA():
def __init__(self, num_params, mu_init=None, sigma_init=0.001, pop_size=256, parents=None, elitism=False, antithetic=False):
self.num_params = num_params
if (mu_init is None):
self.mu = np.zeros(self.num_params)
else:
self.mu = np.array(mu_init)
... |
def validate_config_section(config, section):
notifications = (config.get('notifications') or {})
if (section == 'email'):
email_config = (notifications.get('email') or {})
validate(email_config, EMAIL_CONFIG_SCHEMA)
elif (section == 'slack'):
slack_config = (notifications.get('slack... |
def load_NarrativeQA(cache_dir):
f = pd.read_csv('datasets/NarrativeQA_LLMs.csv')
q = f['Question'].tolist()
a_human = f['answers'].tolist()
a_human = [_.split(';')[0] for _ in a_human]
mgt_text_list = []
for detectLLM in ['ChatGPT', 'ChatGLM', 'Dolly', 'ChatGPT-turbo', 'GPT4', 'StableLM']:
... |
class GdbContinue(sublime_plugin.WindowCommand):
def run(self):
global gdb_cursor_position
gdb_cursor_position = 0
update_view_markers()
resume()
def is_enabled(self):
return (is_running() and (gdb_run_status != 'running'))
def is_visible(self):
return is_runn... |
def test_special_generics():
assert_normalize(tuple, tuple, [nt_zero(Any), ...])
assert_normalize(Tuple, tuple, [nt_zero(Any), ...])
if HAS_STD_CLASSES_GENERICS:
assert_normalize(tuple[int], tuple, [nt_zero(int)])
assert_normalize(Tuple[int], tuple, [nt_zero(int)])
if HAS_STD_CLASSES_GENERIC... |
class DudenWord():
wordcloud_parts_of_speech = ['substantive', 'verben', 'adjektive']
def __init__(self, soup):
self.soup = soup
def __repr__(self):
return '{} ({})'.format(self.title, self.part_of_speech)
def title(self):
return self.soup.h1.get_text().replace('\xad', '').strip(... |
class RRDBNet(nn.Module):
def __init__(self, in_nc, out_nc, nf, nb, gc=32, upscale=4, norm_type=None, act_type='leakyrelu', mode='CNA', upsample_mode='upconv'):
super(RRDBNet, self).__init__()
n_upscale = int(math.log(upscale, 2))
if (upscale == 3):
n_upscale = 1
fea_conv... |
def evaluate_command(cmd, solver):
if (cmd.name == smtcmd.SET_INFO):
return solver.set_info(cmd.args[0], cmd.args[1])
if (cmd.name == smtcmd.SET_OPTION):
opt = cmd.args[0]
if (opt[0] == ':'):
opt = opt[1:]
return solver.set_option(opt, cmd.args[1])
elif (cmd.name ... |
class PoolFromConfigTests(unittest.TestCase):
def test_empty_config(self):
with self.assertRaises(ConfigurationError):
pool_from_config({})
def test_basic_url(self):
pool = pool_from_config({'memcache.endpoint': 'localhost:1234'})
self.assertEqual(pool.server[0], 'localhost')... |
def test_multitask_gather():
ann_info = dict(image_size=np.array([256, 256]), heatmap_size=np.array([64, 64]), num_joints=17, joint_weights=np.ones((17, 1), dtype=np.float32), use_different_joint_weights=False)
results = dict(joints_3d=np.zeros([17, 3]), joints_3d_visible=np.ones([17, 3]), ann_info=ann_info)
... |
_dataframe_method
_alias(column='column_name')
def convert_unix_date(df: pd.DataFrame, column_name: Hashable) -> pd.DataFrame:
try:
df[column_name] = pd.to_datetime(df[column_name], unit='s')
except OutOfBoundsDatetime:
df[column_name] = pd.to_datetime(df[column_name], unit='ms')
return df |
class StringMixin(object):
__schema_type__ = 'string'
def __init__(self, *args, **kwargs):
self.min_length = kwargs.pop('min_length', None)
self.max_length = kwargs.pop('max_length', None)
self.pattern = kwargs.pop('pattern', None)
super(StringMixin, self).__init__(*args, **kwarg... |
def build_norm_layer(cfg, num_features, postfix=''):
if (not isinstance(cfg, dict)):
raise TypeError('cfg must be a dict')
if ('type' not in cfg):
raise KeyError('the cfg dict must contain the key "type"')
cfg_ = cfg.copy()
layer_type = cfg_.pop('type')
if (layer_type not in NORM_LAY... |
(os.path.exists(DEFAULT_REPO), 'Tatoeba directory does not exist.')
class TatoebaConversionTester(unittest.TestCase):
_property
def resolver(self):
tmp_dir = tempfile.mkdtemp()
return TatoebaConverter(save_dir=tmp_dir)
def test_resolver(self):
self.resolver.convert_models(['heb-eng']... |
class FontConfig():
def __init__(self):
self._fontconfig = self._load_fontconfig_library()
self._search_cache = OrderedDict()
self._cache_size = 20
def dispose(self):
while (len(self._search_cache) > 0):
self._search_cache.popitem().dispose()
self._fontconfig.... |
class LayerOutput():
def __init__(self, session: tf.compat.v1.Session, starting_op_names: List[str], output_op_names: List[str], dir_path: str):
self.session = session
(self.activation_tensor_names, self.activation_tensors) = LayerOutput.get_activation_tensor_info(session, starting_op_names, output_... |
def benchmark(mapping, start_pt, run_num):
topo = TopoGraphGen(mapping, max_raycast_dist=1.5)
topo.test_detect_collisions(start_pt)
topo.node_expansion(start_pt, False)
s = time.time()
topo.node_expansion_benchmark(start_pt, False, run_num=run_num)
dt = (time.time() - s)
print(f'avg node exp... |
def KUGW(mf, freq_int='ac', frozen=None):
if (freq_int.lower() == 'ac'):
return kugw_ac.KUGWAC(mf, frozen)
elif (freq_int.lower() == 'cd'):
raise RuntimeError('GWCD does not support UHF or UKS methods.')
else:
raise RuntimeError(("GW frequency integration method %s not recognized. Wi... |
class VPG(BatchPolopt, Serializable):
def __init__(self, env, policy, baseline, optimizer=None, optimizer_args=None, **kwargs):
Serializable.quick_init(self, locals())
if (optimizer is None):
default_args = dict(batch_size=None, max_epochs=1)
if (optimizer_args is None):
... |
def daily_analyze_urls(days=30, min_visits=100):
dt_cutoff = (timezone.now() - datetime.timedelta(days=days))
analyzed_urls = AnalyzedUrl.objects.filter(last_analyzed_date__lt=dt_cutoff, visits_since_last_analyzed__gte=min_visits).select_related()
log.debug('URLs to analyze: %s', analyzed_urls.count())
... |
class Effect5331(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
for layer in ('shieldCapacity', 'armorHP', 'hp'):
fit.drones.filteredItemBoost((lambda drone: drone.item.requiresSkill('Drones')), layer, ship.getModifiedItemAttr('shipBonusABC2'), skil... |
def lr0_goto(I, x):
g = _lr_goto_cache.get((id(I), x), None)
if g:
return g
s = _lr_goto_cache.get(x, None)
if (not s):
s = {}
_lr_goto_cache[x] = s
gs = []
for p in I:
n = p.lr_next
if (n and (n.lrbefore == x)):
s1 = s.get(id(n), None)
... |
def send_validation(strategy, backend, code, partial_token):
url = '{}?verification_code={}&partial_token={}'.format(reverse('social:complete', args=(backend.name,)), code.code, partial_token)
url = strategy.request.build_absolute_uri(url)
send_mail('Validate your account', f'Validate your account {url}', s... |
class StyleGAN2Model(BaseModel):
def __init__(self, opt):
super(StyleGAN2Model, self).__init__(opt)
self.net_g = define_network(deepcopy(opt['network_g']))
self.net_g = self.model_to_device(self.net_g)
self.print_network(self.net_g)
load_path = self.opt['path'].get('pretrain_... |
def test__getting_started__example_multinode_constraints():
from bioptim.examples.getting_started import example_multinode_constraints as ocp_module
bioptim_folder = os.path.dirname(ocp_module.__file__)
ocp_module.prepare_ocp(biorbd_model_path=(bioptim_folder + '/models/cube.bioMod'), phase_dynamics=PhaseDy... |
class MetadataTest(unittest.TestCase):
def make_temp(self):
global tempdir
if tempdir.lstat():
tempdir.delete()
tempdir.mkdir()
def testQuote(self):
filenames = [b'foo', b'.', b'hello\nthere', b'\\', b'\\\\\\', b'h\no\t\x87\n', b' ']
for filename in filenames:... |
class JSONFormatter(logging.Formatter):
def __init__(self):
pass
def format(self, record):
event = {'timestamp': self.getTimestamp(record.created), 'message': record.getMessage(), 'level': record.levelname, 'logger': record.name}
event_data = getattr(record, 'event_data', None)
i... |
def require_gdal_version(version, param=None, values=None, is_max_version=False, reason=''):
if (values is not None):
if (param is None):
raise ValueError('require_gdal_version: param must be provided with values')
if (not isinstance(values, (tuple, list, set))):
raise ValueE... |
def _find_nodes_to(state: EnvironmentState, node: Node, relations: List[Relation]):
nodes = []
for src_node in AnyNode().enumerate(state):
for r in relations:
nl = state.get_nodes_from(src_node, r)
if (node in nl):
nodes.append(src_node)
return nodes |
def contains(shape, other):
if (not hasattr(shape, GEO_INTERFACE_ATTR)):
raise TypeError((SHAPE_TYPE_ERR % shape))
if (not hasattr(other, GEO_INTERFACE_ATTR)):
raise TypeError((SHAPE_TYPE_ERR % shape))
o = geom.shape(shape)
o2 = geom.shape(other)
return o.contains(o2) |
def task_02(self):
if 0:
self._subscriber_base.subscribe()
while (self._subscriber_base_points is None):
pass
self._subscriber_base.unsubscribe()
obj = safepicking.pybullet.create_bin(X=0.3, Y=0.3, Z=0.11, color=(0.7, 0.7, 0.7, 1))
safepicking.pybullet.set_pose(obj, ((0.,... |
('pyinaturalist.v1.observations.get_observation')
('pyinaturalist.v1.observations.put')
def test_update_observation__with_photo_ids(mock_put, mock_get_observation):
mock_get_observation.return_value = {'photos': [{'id': 1234}]}
update_observation(1234, access_token='token', photo_ids=5678)
payload = mock_pu... |
def read_kaldi_datadir(dir):
if os.path.isfile(os.path.join(dir, 'segments')):
logger.info("The data directory '{}' seems to use a 'segments' file. This script does not yet support a 'segments' file. You'll need to use utils/data/extract_wav_segments_data_dir.sh to convert the data dir so it does not use a ... |
class TimeSignal(VBObject):
def VBBJECT_TYPE(self):
return 'TimeSignal'
def __init__(self, path=None, sampling_rate=None):
VBObject.__init__(self, path=path)
if sampling_rate:
self.sampling_rate = sampling_rate
def initializeBlank(self):
VBObject.initializeBlank(s... |
def _validate_netloc(value: str, skip_ipv6_addr: bool, skip_ipv4_addr: bool, may_have_port: bool, simple_host: bool, rfc_1034: bool, rfc_2782: bool):
if ((not value) or (value.count('') > 1)):
return False
if (value.count('') < 1):
return hostname((value if (_confirm_ipv6_skip(value, skip_ipv6_a... |
_dimension
def test_triangulation_of_standard_simplex(dim):
t = Triangulation(_make_standard_simplex(dim))
expected_simplex = tuple(range((dim + 1)))
assert (t.simplices == {expected_simplex})
_check_triangulation_is_valid(t)
assert np.isclose(t.volume(expected_simplex), _standard_simplex_volume(dim... |
def create_data(client: Client, args, name='balanced-df') -> Tuple[(int, dask.dataframe.DataFrame)]:
chunksize = (args.partition_size // np.float64().nbytes)
workers = list(client.scheduler_info()['workers'].keys())
assert (len(workers) > 0)
dist = args.partition_distribution
if (dist is None):
... |
def main():
parser = argparse.ArgumentParser(description='PyTorch Lightning DDP')
parser.add_argument('--epochs', default=1, type=int, metavar='N', help='number of total epochs to run')
parser.add_argument('-b', '--batch_size', default=128, type=int, metavar='N')
parser.add_argument('--learning_rate', d... |
class RNet(nn.Module):
def __init__(self):
super(RNet, self).__init__()
self.features = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(3, 28, 3, 1)), ('prelu1', nn.PReLU(28)), ('pool1', nn.MaxPool2d(3, 2, ceil_mode=True)), ('conv2', nn.Conv2d(28, 48, 3, 1)), ('prelu2', nn.PReLU(48)), ('pool2', nn.Ma... |
def differentiable_graph_to_smiles_purely_randomwalk(origin_smiles, differentiable_graph, leaf_extend_idx_pair, leaf_nonleaf_lst, topk=3, epsilon=0.7):
leaf2nonleaf = {leaf: nonleaf for (leaf, nonleaf) in leaf_nonleaf_lst}
leaf2extend = {leaf: extend for (leaf, extend) in leaf_extend_idx_pair}
new_smiles_se... |
class QPE(QuantumAlgorithm, MinimumEigensolver):
def __init__(self, operator: Optional[Union[(OperatorBase, LegacyBaseOperator)]]=None, state_in: Optional[Union[(InitialState, QuantumCircuit)]]=None, iqft: Optional[QuantumCircuit]=None, num_time_slices: int=1, num_ancillae: int=1, expansion_mode: str='trotter', exp... |
('satpy.multiscene._multiscene.get_enhanced_image')
def test_save_mp4(smg, tmp_path):
from satpy import MultiScene
area = _create_test_area()
scenes = _create_test_scenes(area=area)
smg.side_effect = _fake_get_enhanced_image
scenes[1]['ds3'] = _create_test_dataset('ds3')
for ds_id in ['ds1', 'ds... |
class IndexV2TestSpec(object):
def __init__(self, index_name, method_name, repo_name, scope=None, **kwargs):
self.index_name = index_name
self.repo_name = repo_name
self.method_name = method_name
default_scope = ('push,pull' if ((method_name != 'GET') and (method_name != 'HEAD')) els... |
.parametrize('lhs,rhs,result', [(1, 1, True), (1, 1.1, True), (1.1, 1, False), (1.0, 1.0, True), ('abc', 'def', True), ('abc', '', False), ([], [1], True), ((1, 2), (2, 3), True), ((1, 0), (1,), False), (True, True, True), (True, False, False), (False, 1, True), ((1 + 0j), (2 + 0j), util.Uninferable), ((+ 0.0), (- 0.0)... |
class _BasePrompt(QWidget):
KEY_MODE = usertypes.KeyMode.prompt
def __init__(self, question, parent=None):
super().__init__(parent)
self.question = question
self._vbox = QVBoxLayout(self)
self._vbox.setSpacing(15)
self._key_grid = None
def __repr__(self):
retu... |
class AttrVI_ATTR_SEND_END_EN(BooleanAttribute):
resources = [(constants.InterfaceType.asrl, 'INSTR'), (constants.InterfaceType.gpib, 'INSTR'), (constants.InterfaceType.gpib, 'INTFC'), (constants.InterfaceType.tcpip, 'INSTR'), (constants.InterfaceType.tcpip, 'SOCKET'), (constants.InterfaceType.usb, 'INSTR'), (const... |
def test_creating_new_catalog():
cf = OSC.CatalogFile()
cf.create_catalog('my_catalog.xml', 'VehicleCatalog', 'My first vehicle catalog', 'Mandolin')
bb = OSC.BoundingBox(2, 5, 1.8, 2.0, 0, 0.9)
fa = OSC.Axle(0., 0.8, 1.68, 2.98, 0.4)
ba = OSC.Axle(0., 0.8, 1.68, 0, 0.4)
white_veh = OSC.Vehicle(... |
class Evaluator(object):
def __init__(self, args, num_gpus):
self.args = args
self.num_gpus = num_gpus
self.device = torch.device(args.device)
val_dataset = CSValSet(args.data, os.path.join(os.getcwd(), '../dataset/list/cityscapes/val.lst'), crop_size=(1024, 2048))
val_sample... |
def call_main(cfg: FairseqConfig, main, **kwargs):
if (cfg.distributed_training.distributed_init_method is None):
infer_init_method(cfg.distributed_training)
if (cfg.distributed_training.distributed_init_method is not None):
if (not cfg.distributed_training.distributed_no_spawn):
sta... |
class GlBuffer():
def __init__(self, target=gl.GL_ARRAY_BUFFER, usage=gl.GL_STATIC_DRAW):
self.id_ = gl.glGenBuffers(1)
self.target_ = target
self.usage_ = usage
def assign(self, array):
gl.glBindBuffer(self.target_, self.id_)
gl.glBufferData(self.target_, array, self.usa... |
class Encoder(nn.Module):
def __init__(self, normalize=False):
super(Encoder, self).__init__()
self.f = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=128, kernel_size=3, stride=1, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=128, out_channels=128, kernel_size=3, stride=1, padding=1... |
.parametrize('membership_role', membership_roles)
def test_project_join_user_error(db, client, membership_role):
client.login(username='user', password='user')
project = Project.objects.get(id=1)
user = get_user_model().objects.get(username='user')
invite = Invite(project=project, user=get_user_model().... |
class SnliProcessor(DataProcessor):
def get_train_examples(self, data_dir):
return self._create_examples(pd.read_csv(os.path.join(data_dir, 'train.csv'), sep='\t', header=None, keep_default_na=False).values.tolist(), 'train')
def get_dev_examples(self, data_dir):
return self._create_examples(pd.... |
def verify_opt(opt):
assert (opt.nz >= opt.infogan_nz)
assert ((opt.infogan_nz == 0) or (opt.infogan_lambda > 0))
assert ((len(opt.layers_to_reg) == 0) or (min(opt.layers_to_reg) >= 1))
assert ((opt.epsilon > 0) or (opt.hp_lambda == 0))
assert ((opt.num_rademacher_samples >= 2) or (opt.hp_lambda == ... |
def main():
args = parser.parse_args()
with open(args.config_file) as f:
config_template = Template(f.read())
args.out_dir.mkdir(parents=True, exist_ok=True)
print('Writing to directory', args.out_dir)
for (scene, split) in scenes:
config = config_template.substitute(scene=scene, spl... |
class TestShowFixtures():
def test_funcarg_compat(self, pytester: Pytester) -> None:
config = pytester.parseconfigure('--funcargs')
assert config.option.showfixtures
def test_show_help(self, pytester: Pytester) -> None:
result = pytester.runpytest('--fixtures', '--help')
assert (... |
class AsyncQdrantClient(AsyncQdrantFastembedMixin):
def __init__(self, location: Optional[str]=None, url: Optional[str]=None, port: Optional[int]=6333, grpc_port: int=6334, prefer_grpc: bool=False, Optional[bool]=None, api_key: Optional[str]=None, prefix: Optional[str]=None, timeout: Optional[float]=None, host: Op... |
class KTI1(DataElementGroup):
iban = DataElementField(type='an', max_length=34, required=False, _d='IBAN')
bic = DataElementField(type='an', max_length=11, required=False, _d='BIC')
account_number = DataElementField(type='id', required=False, _d='Konto-/Depotnummer')
subaccount_number = DataElementField... |
class Mixed_7a(nn.Module):
def __init__(self):
super(Mixed_7a, self).__init__()
self.branch0 = nn.Sequential(BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicConv2d(256, 384, kernel_size=3, stride=2))
self.branch1 = nn.Sequential(BasicConv2d(1088, 256, kernel_size=1, stride=1), BasicCon... |
.parametrize('example, error_msg', [('\n [project]\n name = "myproj"\n version = "1.2"\n requires = [\'pywin32; platform_system=="Windows"\' ]\n ', 'configuration error: .project. must not contain ..requires.. properties')])
def test_invalid_example(tmp_path, examp... |
class AoA_Refiner_Layer(nn.Module):
def __init__(self, size, self_attn, feed_forward, dropout):
super(AoA_Refiner_Layer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.use_ff = 0
self.batch_info = []
self.att_layer_idx = 0
if... |
_test
def test_optimizer_get_updates_legacy_interface():
for optimizer_cls in [keras.optimizers.RMSprop, keras.optimizers.SGD, keras.optimizers.Adadelta, keras.optimizers.Adam, keras.optimizers.Adagrad, keras.optimizers.Nadam, keras.optimizers.Adamax]:
optimizer = optimizer_cls()
param = keras.backe... |
class SuffixImporter():
scheme = 'suffix'
suffix = None
path_entry = None
def trigger_url(cls):
if (cls.suffix is None):
raise ValueError(('%s.suffix is not set' % cls.__name__))
return ('suffix:%s' % cls.suffix)
def register(cls):
sys.path_hooks.append(cls)
... |
def test_interactive_with_git_dependencies_with_reference(tester: CommandTester, repo: TestRepository) -> None:
repo.add_package(get_package('pendulum', '2.0.0'))
repo.add_package(get_package('pytest', '3.6.0'))
inputs = ['my-package', '1.2.3', 'This is a description', 'n', 'MIT', '~2.7 || ^3.6', '', 'git+ ... |
def creatMultiItemUserAdj(dataset, cv):
(trainMat, _, _, trainMat_time, _) = loadData(dataset, cv)
ratingClass = np.unique(trainMat.data).size
(userNum, itemNum) = trainMat.shape
multi_adj = sp.lil_matrix(((ratingClass * itemNum), userNum), dtype=np.int)
uidList = trainMat.tocoo().row
iidList = ... |
def main():
opt = parser.parse_args()
opt.cuda = (opt.gpu > (- 1))
if opt.cuda:
torch.cuda.set_device(opt.gpu)
opt.n_best = opt.beam_size
if (opt.output == 'stdout'):
outF = sys.stdout
else:
outF = open(opt.output, 'w')
(pred_score_total, pred_words_total, gold_score_... |
()
('name', default=None, nargs=(- 1))
_options
_options
('--submit/--no-submit')
def apply_stage(name, metadir, accept_metadir, controller, ctrlopt, modelsetup, modelopt, backend, local, verbosity, submit):
handle_common_options(verbosity)
ys = handle_connection_options(metadir, accept_metadir, controller, ctr... |
def test_used_with_session_scope(testdir: Any) -> None:
testdir.makeini('\n [pytest]\n asyncio_mode=auto\n ')
testdir.makepyfile('\n import pytest\n import random\n\n def get_random_number():\n return random.randint(0, 1)\n\n (autouse=True, scope="sess... |
class EEGSupervisedPretrainLoader(torch.utils.data.Dataset):
def __init__(self, tuev_data, chb_mit_data, iiic_data, tuab_data):
(tuev_root, tuev_files) = tuev_data
self.tuev_root = tuev_root
self.tuev_files = tuev_files
self.tuev_size = len(self.tuev_files)
(chb_mit_root, chb... |
def train(model, data, target, optimizer, coreset_theta):
model.train()
optimizer.zero_grad()
output = model(data)
acc1 = mean_accuracy(output, target)
loss = torch.sum(((F.binary_cross_entropy_with_logits(output, target, reduction='none') * coreset_theta) / coreset_theta.sum()))
loss.backward()... |
def test_cf_rotated_latlon__grid():
crs = CRS.from_cf(dict(grid_mapping_name='rotated_latitude_longitude', grid_north_pole_latitude=32.5, grid_north_pole_longitude=1.0, north_pole_grid_longitude=170.0))
with pytest.warns(UserWarning):
proj_dict = crs.to_dict()
assert (proj_dict == {'proj': 'ob_tran'... |
class _Cached():
def __init__(self, func, count):
self.func = func
self.cache = []
self.count = count
def __call__(self, *args, **kwds):
key = (args, kwds)
for (cached_key, cached_result) in self.cache:
if (cached_key == key):
return cached_res... |
class BroadcastUDPClient(Client):
def __init__(self, bcastaddr, prog, vers):
self.pmap = BroadcastUDPPortMapperClient(bcastaddr)
self.pmap.set_reply_handler(self.my_reply_handler)
self.prog = prog
self.vers = vers
self.user_reply_handler = None
self.addpackers()
d... |
class VNet(nn.Module):
def __init__(self, classes_num=2):
classes = classes_num
super(VNet, self).__init__()
self.in_block = VNetInBlock(1, 32, 1)
self.down_block1 = VNetDownBlock(32, 32, 2)
self.down_block2 = VNetDownBlock(32, 64, 3)
self.down_block3 = VNetDownBlock(... |
((types.Array(types.float64, 1, 'C', readonly=True), types.int32, types.float64), nopython=True)
def _numba_sampen(sequence, order, r):
size = sequence.size
numerator = 0
denominator = 0
for offset in range(1, (size - order)):
n_numerator = int((abs((sequence[order] - sequence[(order + offset)])... |
class Solution():
def fizzBuzz(self, n: int) -> List[str]:
res = []
for i in range(1, (n + 1)):
if (((i % 3) == 0) and ((i % 5) == 0)):
res.append('FizzBuzz')
elif ((i % 3) == 0):
res.append('Fizz')
elif ((i % 5) == 0):
... |
class PyCoreScopesTest(unittest.TestCase):
def setUp(self):
super().setUp()
self.project = testutils.sample_project()
self.pycore = self.project.pycore
def tearDown(self):
testutils.remove_project(self.project)
super().tearDown()
def test_simple_scope(self):
c... |
def true_or_false(t: Type) -> ProperType:
t = get_proper_type(t)
if isinstance(t, UnionType):
new_items = [true_or_false(item) for item in t.items]
return make_simplified_union(new_items, line=t.line, column=t.column)
new_t = copy_type(t)
new_t.can_be_true = new_t.can_be_true_default()
... |
def fit_svm(features, y, MAX_SAMPLES=10000):
nb_classes = np.unique(y, return_counts=True)[1].shape[0]
train_size = features.shape[0]
svm = SVC(C=np.inf, gamma='scale')
if (((train_size // nb_classes) < 5) or (train_size < 50)):
return svm.fit(features, y)
else:
grid_search = GridSea... |
def get_rtlir_dtype(obj):
try:
assert (not isinstance(obj, list)), 'array datatype object should be a field of some struct!'
if isinstance(obj, (dsl.Signal, dsl.Const)):
Type = obj._dsl.Type
assert isinstance(Type, type)
if issubclass(Type, Bits):
... |
class DiscourseTransformer(Transformer):
def forward(self, batch, target_mask=None, streaming=False, zero_encoder=False, mirror=False, streaming_state=None, nce=False, factorize=True, **kwargs):
if ((self.switchout > 0) and self.training):
batch.switchout(self.switchout, self.src_vocab_size, sel... |
def reset_version_parts(version: Version, **kwargs: Any) -> None:
internal_version = version._version
parts: dict[(str, Any)] = {}
ordered_part_names = ('epoch', 'release', 'pre', 'post', 'dev', 'local')
reset = False
for part_name in ordered_part_names:
if reset:
parts[part_name... |
_specialize
_canonicalize
_rewriter([Subtensor])
def local_subtensor_inc_subtensor(fgraph, node):
if isinstance(node.op, Subtensor):
x = node.inputs[0]
if ((not x.owner) or (not isinstance(x.owner.op, IncSubtensor))):
return
if (not x.owner.op.set_instead_of_inc):
ret... |
_ordering
class Scope(Enum):
Function: _ScopeName = 'function'
Class: _ScopeName = 'class'
Module: _ScopeName = 'module'
Package: _ScopeName = 'package'
Session: _ScopeName = 'session'
def next_lower(self) -> 'Scope':
index = _SCOPE_INDICES[self]
if (index == 0):
rais... |
class Command(BaseCommand):
columns = ('id', 'username', 'first_name', 'last_name', 'email', 'date_joined', 'last_login')
def add_arguments(self, parser):
parser.add_argument('since', type=(lambda s: pytz.utc.localize(datetime.strptime(s, '%Y-%m-%d'))), help='Date since the users have been inactive (for... |
class TestSetSelectionOwner(EndianTest):
def setUp(self):
self.req_args_0 = {'selection': , 'time': , 'window': }
self.req_bin_0 = b'\x16\x00\x04\x00\xaf4\\xfa\x88a\x9a\x10\xdf\x16'
def testPackRequest0(self):
bin = request.SetSelectionOwner._request.to_binary(*(), **self.req_args_0)
... |
def test_slice_inference_in_for_loops_not_working() -> None:
ast_nodes = extract_node('\n from unknown import Unknown\n for a, *b in something:\n b #\n for a, *b in Unknown:\n b #\n for a, *b in (1):\n b #\n ')
for node in ast_nodes:
inferred = next(node.infer())
... |
def parse():
parser = argparse.ArgumentParser(description='variational autoencoder')
parser.add_argument('-model_dir', default='train_model', help='output model weight dir')
parser.add_argument('-model_path', help='latest model path')
parser.add_argument('-batch_size', default=96, type=int, help='batch ... |
def check_valid(config):
data_fn_splits = os.path.basename(config['data_path']).split('.')[0].split('_')
if (len(data_fn_splits) > 1):
clip_arch = data_fn_splits[(- 1)].upper()
if (clip_arch[:3] == 'VIT'):
assert ('' not in clip_arch), f'TODO: {clip_arch}'
(_, model_type,... |
def rename_key(key):
if key.startswith('module.'):
key = key[7:]
if ('.downsample.' in key):
return key.replace('downsample', 'skip')
if key.startswith('entropy_bottleneck.'):
if key.startswith('entropy_bottleneck._biases.'):
return f'entropy_bottleneck._bias{key[(- 1)]}'... |
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