code stringlengths 281 23.7M |
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def main():
scene = SceneManager.AddScene('Scene')
scene.gameObjects[1].GetComponent(Light).type = LightType.Point
scene.mainCamera.transform.position = Vector3(0, 3, (- 10))
lookAt = scene.mainCamera.AddComponent(LookAt)
cube = GameObject('Cube')
renderer = cube.AddComponent(MeshRenderer)
r... |
_start_docstrings('The bare RegNet model outputting raw features without any specific head on top.', REGNET_START_DOCSTRING)
class TFRegNetModel(TFRegNetPreTrainedModel):
def __init__(self, config: RegNetConfig, *inputs, **kwargs):
super().__init__(config, *inputs, **kwargs)
self.regnet = TFRegNetMa... |
class ForIterable(ForGenerator):
def need_cleanup(self) -> bool:
return True
def init(self, expr_reg: Value, target_type: RType) -> None:
builder = self.builder
iter_reg = builder.call_c(iter_op, [expr_reg], self.line)
builder.maybe_spill(expr_reg)
self.iter_target = buil... |
def main():
parser = ArgumentParser()
parser.add_argument('-c', '--config', type=Path, required=True)
parser.add_argument('-o', '--output_folder', type=Path, required=True)
parser.add_argument('-n', '--num_seqs', type=int, required=True)
parser.add_argument('-s', '--name_prefix', type=str, default='... |
class OnnxSeq2SeqConfigWithPast(OnnxConfigWithPast):
def outputs(self) -> Mapping[(str, Mapping[(int, str)])]:
common_outputs = super(OnnxConfigWithPast, self).outputs
for (name, axes_names) in common_outputs.items():
sequence_name = ('encoder_sequence' if ('encoder' in name) else 'decod... |
def test_tracker_diverges():
box = np.array([0, 0, 10, 10])
mot = MultiObjectTracker(dt=0.1)
mot.step([Detection(box=box)])
assert (len(mot.trackers) == 1)
first_track_id = mot.active_tracks()[0].id
assert_almost_equal(mot.trackers[0].model.dt, 0.1)
assert (not mot.trackers[0].is_invalid())
... |
_fixtures(WebFixture)
def test_dropdown_menu_with_header(web_fixture):
sub_menu = DropdownMenu(web_fixture.view)
my_header = H(web_fixture.view, 6, text='My header text')
header = sub_menu.add_header(my_header)
assert (header is my_header)
[header] = sub_menu.html_representation.children
assert ... |
.parametrize(('line', 'expected_warnings'), [('Governance', set()), ('Packaging', set()), ('Typing', set()), ('Release', set()), ('Governance, Packaging', set()), ('Packaging, Typing', set()), ('Governance, Governance', {'duplicates'}), ('Release, Release', {'duplicates'}), ('Packaging, Packaging', {'duplicates'}), ('S... |
class Solution(object):
def generateParenthesis(self, n):
if (n == 1):
return ['()']
last_list = self.generateParenthesis((n - 1))
res = []
for t in last_list:
curr = (t + ')')
for index in range(len(curr)):
if (curr[index] == ')'):... |
.end_to_end()
def test_collapsing_of_warnings(tmp_path, runner):
source = '\n import warnings\n from pytask import task\n\n for i in range(6):\n\n \n def task_example():\n warnings.warn("Warning", category=UserWarning)\n '
tmp_path.joinpath('task_example.py').write_text(text... |
(scope='session')
def gitlab_runner(gl):
container = 'gitlab-runner-test'
runner_name = 'python-gitlab-runner'
token = 'registration-token'
url = '
docker_exec = ['docker', 'exec', container, 'gitlab-runner']
register = ['register', '--run-untagged', '--non-interactive', '--registration-token', ... |
class PdfTextSearcher(pdfium_i.AutoCloseable):
def __init__(self, raw, textpage):
self.raw = raw
self.textpage = textpage
super().__init__(pdfium_c.FPDFText_FindClose)
def parent(self):
return self.textpage
def _get_occurrence(self, find_func):
ok = find_func(self)
... |
class TrainLoopDLT():
def __init__(self, accelerator: Accelerator, model, diffusion: JointDiffusionScheduler, train_data, val_data, opt_conf, log_interval: int, save_interval: int, categories_num: int, device: str='cpu', resume_from_checkpoint: str=None):
self.categories_num = categories_num
self.tr... |
def eval_with_funcs(predictors, nr_eval, get_player_fn):
class Worker(StoppableThread, ShareSessionThread):
def __init__(self, func, queue):
super(Worker, self).__init__()
self.func = func
self.q = queue
def run(self):
with self.default_sess():
... |
def infer_tests_to_run(output_file, diff_with_last_commit=False, filters=None, json_output_file=None):
modified_files = get_modified_python_files(diff_with_last_commit=diff_with_last_commit)
print(f'''
### MODIFIED FILES ###
{_print_list(modified_files)}''')
impacted_modules_map = create_reverse_dependency_... |
class RDKit():
def mol_to_file(rdkit_mol: Chem.Mol, file_name: str) -> None:
file_path = Path(file_name)
if (file_path.suffix == '.pdb'):
return Chem.MolToPDBFile(rdkit_mol, file_name)
elif ((file_path.suffix == '.sdf') or (file_path.suffix == '.mol')):
return Chem.Mo... |
class _XyzTileServiceNonEarth(_XyzTileService):
def __call__(self, *args, **kwargs):
_log.info(f"EOmaps: The WebMap service '{self.name}' shows images from a different celestrial body projected to an earth-based crs! Units used in scalebars, geod_crices etc. represent earth-based units!")
super().__... |
class SceneModel(QtCore.QObject):
sigSceneModelChanged = QtCore.pyqtSignal(object)
sigSceneChanged = QtCore.pyqtSignal()
sigConfigChanged = QtCore.pyqtSignal()
sigFrameChanged = QtCore.pyqtSignal()
sigQuadtreeChanged = QtCore.pyqtSignal()
_sigQuadtreeChanged = QtCore.pyqtSignal()
sigQuadtree... |
class Xpub(MasterPublicKeyMixin):
def __init__(self, *, derivation_prefix: str=None, root_fingerprint: str=None):
self.xpub = None
self.xpub_receive = None
self.xpub_change = None
self._xpub_bip32_node = None
self._derivation_prefix = derivation_prefix
self._root_fing... |
class SawyerDoorUnlockV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'lock_pos': obs[3:6], 'unused_info': obs[6:]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_effort': 3})
action['d... |
def test_commonpath() -> None:
path = Path('/foo/bar/baz/path')
subpath = (path / 'sampledir')
assert (commonpath(path, subpath) == path)
assert (commonpath(subpath, path) == path)
assert (commonpath(Path((str(path) + 'suffix')), path) == path.parent)
assert (commonpath(path, path.parent.parent)... |
def test_entrypoint_injection(pytester, monkeypatch):
(pytester.path / 'test_one.py').write_text('def test_one(): pass\n')
class _FakeEntryPoint():
def __init__(self, name: str, obj: mock.Mock) -> None:
self.name = name
self._obj = obj
def load(self) -> mock.Mock:
... |
class Command(BaseCommand):
def handle(self, *args, **options):
if (len(args) != 2):
raise CommandError('Usage: python manage.py fill_data <in_file> <out_file>')
(in_file, out_file) = args
ticket_nums = [line.rstrip('\n') for line in open(in_file).readlines()]
fh = open(o... |
.dask_deserialize.register(ProxyObject)
.cuda.cuda_deserialize.register(ProxyObject)
def obj_pxy_dask_deserialize(header, frames):
args = pickle.loads(header['obj-pxy-detail'])
if (args['subclass'] is None):
subclass = ProxyObject
else:
subclass = pickle.loads(args['subclass'])
pxy = Pro... |
class Bruggeman(BaseModel):
def __init__(self, param, component, options=None):
super().__init__(param, component, options=options)
def get_coupled_variables(self, variables):
if (self.component == 'Electrolyte'):
tor_dict = {}
for domain in self.options.whole_cell_domain... |
def reformat_to_coco(predictions: List[str], ground_truths: List[List[str]], ids: Union[(List[int], None)]=None) -> Tuple[(List[Dict[(str, Any)]], Dict[(str, Any)])]:
if (ids is None):
ids = range(len(predictions))
pred = []
ref = {'info': {'description': 'Clotho reference captions (2019)'}, 'audio ... |
class VersionCommand(Command):
name = 'version'
description = 'Shows the version of the project or bumps it when a valid bump rule is provided.'
arguments = [argument('version', 'The version number or the rule to update the version.', optional=True)]
options = [option('short', 's', 'Output the version n... |
class TestGetProvider(SetUpTest, TestCase):
def test_get_provider_should_succeed(self):
with open(self.qlr_file) as f:
self.assertEqual(get_provider(f), 'wms')
def test_get_provider_should_return_none(self):
tf = NamedTemporaryFile(mode='w+t', suffix='.qlr')
tf.write('<!DOCTY... |
def eth_nodes_configuration(blockchain_number_of_nodes, blockchain_key_seed, port_generator, blockchain_type, blockchain_extra_config) -> List[EthNodeDescription]:
eth_nodes = []
for position in range(blockchain_number_of_nodes):
key = keccak(blockchain_key_seed.format(position).encode())
eth_no... |
class TestStat(unittest.TestCase):
def test_silent_file(self):
expected = {'Samples read': 627456, 'Length (seconds)': 14.228027, 'Scaled by': .0, 'Maximum amplitude': 0.010895, 'Minimum amplitude': (- 0.004883), 'Midline amplitude': 0.003006, 'Mean norm': 0.000137, 'Mean amplitude': (- 6.2e-05), 'RMS... |
def test_cancel_merge_when_pipeline_succeeds(project, merge_request_with_pipeline, wait_for_sidekiq):
wait_for_sidekiq(timeout=60)
merge_request_with_pipeline.merge(merge_when_pipeline_succeeds=True)
wait_for_sidekiq(timeout=60)
mr = project.mergerequests.get(merge_request_with_pipeline.iid)
assert ... |
class F_RandomProj(nn.Module):
def __init__(self, backbone, loops_type=None, model_path=None, im_res=256, cout=64, expand=True, proj_type=2, **kwargs):
super().__init__()
self.proj_type = proj_type
self.backbone = backbone
self.loops_type = loops_type
self.cout = cout
... |
def _remove_dup_initializers_from_model(model, model_without_ext, ind_to_replace):
inits_with_data = list(model.graph.initializer)
inits = list(model_without_ext.graph.initializer)
for (i, ref_i) in ind_to_replace:
assert (inits_with_data[i].name == inits[i].name)
assert (inits_with_data[ref... |
def l2_afa_schema(settings=None):
settings = (settings or {})
npix = settings.get('num_pixels', 120)
nacc = settings.get('num_accumulations', 20)
return {'providers': settings.get('providers', {}), 'variable_path': settings.get('variable_path', ''), 'dimensions': accumulation_dimensions(nacc, npix), 'va... |
def rtn_fwrite(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('fwrite hooked')
arg0 = pstate.get_argument_value(0)
arg1 = pstate.get_argument_value(1)
arg2 = pstate.get_argument_value(2)
arg3 = pstate.get_argument_value(3)
size = (arg1 * arg2)
data = pstate.memory.read(arg0, s... |
def _test():
import torch
pretrained = False
models = [(rir_cifar10, 10), (rir_cifar100, 100), (rir_svhn, 10)]
for (model, num_classes) in models:
net = model(pretrained=pretrained)
net.eval()
weight_count = _calc_width(net)
print('m={}, {}'.format(model.__name__, weight_... |
.parametrize('input_type', [(lambda x: x[0]), tuple, list])
def test_run_model_from_effective_irradiance(sapm_dc_snl_ac_system, location, weather, total_irrad, input_type):
data = weather.copy()
data[['poa_global', 'poa_diffuse', 'poa_direct']] = total_irrad
data['effective_irradiance'] = data['poa_global']... |
class SawyerFaucetOpenEnvV2(SawyerXYZEnv):
def __init__(self):
hand_low = ((- 0.5), 0.4, (- 0.15))
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.05), 0.8, 0.0)
obj_high = (0.05, 0.85, 0.0)
self._handle_length = 0.175
self._target_radius = 0.07
super().__init__(sel... |
def get_config(args, logger=None):
if args.resume:
cfg_path = os.path.join(args.experiment_path, 'config.yaml')
if (not os.path.exists(cfg_path)):
print_log('Failed to resume', logger=logger)
raise FileNotFoundError()
print_log(f'Resume yaml from {cfg_path}', logger=l... |
class RedundantAssignmentChecker(BaseChecker):
name = 'redundant_assignment'
msgs = {'E9959': ('This assignment statement is redundant; You can remove it from the program.', 'redundant-assignment', 'This assignment statement is redundant; You can remove it from the program.')}
def __init__(self, linter=None... |
def sphinx_built_file(test_dir, test_file):
os.chdir('tests/{0}'.format(test_dir))
try:
app = Sphinx(srcdir='.', confdir='.', outdir='_build/text', doctreedir='_build/.doctrees', buildername='html', verbosity=1)
app.build(force_all=True)
with io.open(test_file, encoding='utf-8') as fin:
... |
class DataTrainingArguments():
data_dir: str = field(metadata={'help': 'The input data dir. Should contain the .txt files for a CoNLL-2003-formatted task.'})
labels: Optional[str] = field(default=None, metadata={'help': 'Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.'})
... |
def singleton(cls):
(cls)
def wrapper_singleton(*args, **kwargs):
if (not wrapper_singleton.instance):
try:
wrapper_singleton.instance = cls(*args, **kwargs)
except TypeError:
wrapper_singleton.instance = data(cls)(*args, **kwargs)
return w... |
def besselFilter(data, cutoff, order=1, dt=None, btype='low', bidir=True):
try:
import scipy.signal
except ImportError:
raise Exception('besselFilter() requires the package scipy.signal.')
if (dt is None):
try:
tvals = data.xvals('Time')
dt = ((tvals[(- 1)] - ... |
class TestAtmosphere():
def test_standard_atmosphere(self):
a = Atmosphere()
assert (a.temperature == 293.15)
assert (a.pressure == 101.325)
assert (a.relative_humidity == 0.0)
assert (abs((a.soundspeed - 343.2)) < 1e-09)
assert (abs((a.saturation_pressure - 2.)) < 1e... |
def cnn_decoder(lstm1_out, lstm2_out, lstm3_out, lstm4_out):
d_filter4 = tensor_variable([2, 2, 128, 256], 'd_filter4')
dec4 = cnn_decoder_layer(lstm4_out, d_filter4, [1, 8, 8, 128], (1, 2, 2, 1))
dec4_concat = tf.concat([dec4, lstm3_out], axis=3)
d_filter3 = tensor_variable([2, 2, 64, 256], 'd_filter3'... |
_model
def caformer_b36_in21k(pretrained=False, **kwargs):
model = MetaFormer(depths=[3, 12, 18, 3], dims=[128, 256, 512, 768], token_mixers=[SepConv, SepConv, Attention, Attention], head_fn=MlpHead, **kwargs)
model.default_cfg = default_cfgs['caformer_b36_in21k']
if pretrained:
state_dict = torch.h... |
class TestHome():
def test_default(self):
actual = pystiche.home()
desired = path.expanduser(path.join('~', '.cache', 'pystiche'))
assert (actual == desired)
def test_env(self):
tmp_dir = tempfile.mkdtemp()
pystiche_home = os.getenv('PYSTICHE_HOME')
os.environ['PY... |
_BBOX_CODERS.register_module()
class CSLCoder(BaseBBoxCoder):
def __init__(self, angle_version, omega=1, window='gaussian', radius=6):
super().__init__()
self.angle_version = angle_version
assert (angle_version in ['oc', 'le90', 'le135'])
assert (window in ['gaussian', 'triangle', 'r... |
class JuliaLexer(RegexLexer):
name = 'Julia'
url = '
aliases = ['julia', 'jl']
filenames = ['*.jl']
mimetypes = ['text/x-julia', 'application/x-julia']
version_added = '1.6'
tokens = {'root': [('\\n', Whitespace), ('[^\\S\\n]+', Whitespace), ('#=', Comment.Multiline, 'blockcomment'), ('#.*$'... |
def test_commented_extension(monkeypatch):
config = {'comment': ['--option'], 'ignore': []}
monkeypatch.setattr(interactive, 'get_config', (lambda x: config[x]))
parser = ArgumentParser()
fake_extension = Mock(flag='--option')
action = parser.add_argument('--option', dest='extensions', action='appen... |
def print_cuda_usage():
print('Memory Allocated:', (torch.cuda.memory_allocated() / (1024 * 1024)))
print('Max Memory Allocated:', (torch.cuda.max_memory_allocated() / (1024 * 1024)))
print('Memory Cached:', (torch.cuda.memory_cached() / (1024 * 1024)))
print('Max Memory Cached:', (torch.cuda.max_memory... |
class CifarPairTransform():
def __init__(self, train_transform=True, pair_transform=True):
if (train_transform is True):
self.transform = transforms.Compose([transforms.RandomResizedCrop(32), transforms.RandomHorizontalFlip(p=0.5), transforms.RandomApply([transforms.ColorJitter(0.4, 0.4, 0.4, 0.... |
.skipif((not is_py310_plus), reason='3.10+ union syntax')
(simple_typed_classes(defaults=False))
def test_310_optional_field_roundtrip(cl_and_vals):
converter = Converter()
(cl, vals, kwargs) = cl_and_vals
class C():
a: (cl | None)
inst = C(a=cl(*vals, **kwargs))
assert (inst == converter.st... |
def a2c_train_step(agent, abstractor, loader, opt, grad_fn, gamma=0.99, reward_fn=compute_rouge_l, stop_reward_fn=compute_rouge_n(n=1), stop_coeff=1.0):
opt.zero_grad()
indices = []
probs = []
baselines = []
ext_sents = []
(art_batch, abs_batch) = next(loader)
for raw_arts in art_batch:
... |
def test_nested_start_rc():
checker = StackEndRC([AnyRequestChecker(), StackEndRC([create_request_checker(bool), create_request_checker(int), create_request_checker(str)]), create_request_checker(bool)])
checker.check_request(create_mediator(LocatedRequest(loc_map=LocMap(TypeHintLoc(bool))), LocatedRequest(loc_... |
.parametrize(**test_case_table)
def test_2port(test_params, cmdline_opts):
msgs0 = test_params.msg_func(4096)
msgs1 = test_params.msg_func(8192)
run_sim(TestHarness(MagicMemoryRTL, 2, ([(req_cls, resp_cls)] * 2), [msgs0[::2], msgs1[::2]], [msgs0[1::2], msgs1[1::2]], test_params.stall, test_params.extra_lat,... |
def run_louvain_multilayer(intralayer_graph, interlayer_graph, layer_vec, weight='weight', resolution=1.0, omega=1.0, nruns=1):
logging.debug('Shuffling node ids')
t = time()
mu = (np.sum(intralayer_graph.es[weight]) + interlayer_graph.ecount())
use_RBCweighted = hasattr(louvain, 'RBConfigurationVertexP... |
def test_solvers():
cnf = CNF(from_clauses=[[1, 2, 3], [(- 1), 2], [(- 2)]])
for name in solvers:
with Solver(name=name, bootstrap_with=cnf) as solver:
assert solver.solve(), 'wrong outcome by {0}'.format(name)
assert (solver.get_model() == [(- 1), (- 2), 3]), 'wrong model by {0}... |
class EigenstateResult(AlgorithmResult):
def eigenenergies(self) -> Optional[np.ndarray]:
return self.get('eigenenergies')
def eigenenergies(self, value: np.ndarray) -> None:
self.data['eigenenergies'] = value
def eigenstates(self) -> Optional[List[Union[(str, dict, Result, list, np.ndarray,... |
class SawyerBoxCloseEnv(SawyerXYZEnv):
def __init__(self):
liftThresh = 0.12
goal_low = ((- 0.1), 0.85, 0.1329)
goal_high = (0.1, 0.95, 0.1331)
hand_low = ((- 0.5), 0.4, 0.05)
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.05), 0.55, 0.02)
obj_high = (0.05, 0.6, 0.... |
class DataCollator():
pad_id: int
max_length: int = 4096
def __call__(self, batch):
batch = self.collate_fn(batch)
batch = jax.tree_util.tree_map(shard, batch)
return batch
def collate_fn(self, features):
(input_ids, attention_mask) = self.fetch_inputs(features['input_ids... |
def test_receive_order_paid_of_period_outside_current_one(requests_mock):
user = UserFactory(email='')
with time_machine.travel('2023-12-16 01:04:50Z', tick=False):
requests_mock.get(f'{settings.PRETIX_API}organizers/test-organizer/events/local-conf-test/orders/9YKZK/', json=ORDER_DATA_WITH_MEMBERSHIP)
... |
class NonLocal2D(nn.Module):
def __init__(self, in_channels, reduction=2, use_scale=True, conv_cfg=None, norm_cfg=None, mode='embedded_gaussian'):
super(NonLocal2D, self).__init__()
self.in_channels = in_channels
self.reduction = reduction
self.use_scale = use_scale
self.inte... |
def check_imports(filename):
with open(filename, 'r', encoding='utf-8') as f:
content = f.read()
imports = re.findall('^\\s*import\\s+(\\S+)\\s*$', content, flags=re.MULTILINE)
imports += re.findall('^\\s*from\\s+(\\S+)\\s+import', content, flags=re.MULTILINE)
imports = [imp.split('.')[0] for im... |
(Gst, 'GStreamer missing')
class TGStreamerSink(TestCase):
def test_simple(self):
sinks = ['gconfaudiosink', 'alsasink']
for n in filter(Gst.ElementFactory.find, sinks):
(obj, name) = gstreamer_sink(n)
self.assertTrue(obj)
self.assertEqual(name, n)
def test_in... |
def main(args, override_args=None):
utils.import_user_module(args)
assert ((args.max_tokens is not None) or (args.batch_size is not None)), 'Must specify batch size either with --max-tokens or --batch-size'
use_fp16 = args.fp16
use_cuda = (torch.cuda.is_available() and (not args.cpu))
if use_cuda:
... |
class TestHeaderInclusion(unittest.TestCase):
def test_primitives_included_in_header(self) -> None:
base_dir = os.path.join(os.path.dirname(__file__), '..', 'lib-rt')
with open(os.path.join(base_dir, 'CPy.h')) as f:
header = f.read()
with open(os.path.join(base_dir, 'pythonsuppor... |
class TextEncoder(tf.keras.Model):
def __init__(self, strategy, trainable=False):
self.strategy = strategy
with self.strategy.scope():
super(TextEncoder, self).__init__()
self.encoder = hub.KerasLayer(' trainable=trainable)
def __call__(self, inp):
with self.strat... |
def structure(t, fieldproc=unescape):
d = {}
if (t[0] is not None):
d['scheme'] = t[0]
if (t[1] is not None):
uphp = split_netloc(t[1], fieldproc=fieldproc)
if (uphp[0] is not None):
d['user'] = uphp[0]
if (uphp[1] is not None):
d['password'] = uphp[1]... |
def get_config(path: str) -> Dict[(str, RepositoryConfig)]:
realpath = os.path.realpath(os.path.expanduser(path))
parser = configparser.RawConfigParser()
try:
with open(realpath) as f:
parser.read_file(f)
logger.info(f'Using configuration from {realpath}')
except FileNotF... |
def _datetime_offset_inst(obj: str, pattern: str) -> datetime:
(dat_str, tim_str) = obj.split('T')
(splitter, factor) = (('+', 1) if ('+' in tim_str) else ('-', (- 1)))
(naive_tim_str, offset) = tim_str.split(splitter)
naive_dattim_str = '{}T{}'.format(dat_str, naive_tim_str)
dattim_obj = datetime.s... |
def create_model(model_name: str, pretrained: Optional[str]=None, precision: str='fp32', device: Union[(str, torch.device)]='cpu', jit: bool=False, force_quick_gelu: bool=False, force_custom_clip: bool=False, force_patch_dropout: Optional[float]=None, pretrained_image: str='', pretrained_text: str='', pretrained_hf: bo... |
def train_epoch(gpu, train_loader, model, base_optimizer, epoch, args, lr_scheduler=None, grad_rho_scheduler=None, grad_norm_rho_scheduler=None, optimizer=None):
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('', ':6.2f')
Lr = AverageMeter('Lr', ':.4e')
progress = ProgressMeter(len(train_load... |
_required
def plugin_update(request, package_name):
plugin = get_object_or_404(Plugin, package_name=package_name)
if (not check_plugin_access(request.user, plugin)):
return render(request, 'plugins/plugin_permission_deny.html', {})
if (request.method == 'POST'):
form = PluginForm(request.POS... |
def get_examples(path, sub_sample_train=3000, sub_sample_eval=256):
with open(path) as f:
raw_dataset = json.load(f)
positive_examples = raw_dataset['Positive Examples']
negative_examples = raw_dataset['Negative Examples']
all_examples = raw_dataset['Instances']
n = len(all_examples)
eva... |
class CustomJsonFormatter(jsonlogger.JsonFormatter):
service_name = ''
tracer = ''
def add_fields(self, log_record, record, message_dict):
super().add_fields(log_record, record, message_dict)
if (not log_record.get('timestamp')):
now = datetime.datetime.utcnow().strftime('%Y-%m-%... |
def parse_extension_item_param(header: str, pos: int, header_name: str) -> Tuple[(ExtensionParameter, int)]:
(name, pos) = parse_token(header, pos, header_name)
pos = parse_OWS(header, pos)
value: Optional[str] = None
if (peek_ahead(header, pos) == '='):
pos = parse_OWS(header, (pos + 1))
... |
def init_visualization(argument):
if isinstance(argument, Eigenstates):
eigenstates = argument
return init_eigenstate_visualization(eigenstates)
elif isinstance(argument, TimeSimulation):
simulation = argument
return init_timesimulation_visualization(simulation) |
class InstrumentDumpFetch():
def __init__(self):
self.conn = redis.StrictRedis(host='localhost', port=6379)
def data_dump(self, symbol, instrument_data):
self.conn.set(symbol, json.dumps(instrument_data))
def symbol_data(self, symbol):
try:
contract_detail = json.loads(se... |
def get_dataset(data_args: argparse.Namespace, processor: Union[(Union[(PyGameTextRenderer, PangoCairoTextRenderer)], PreTrainedTokenizerFast)], modality: Modality, split: Split, config: PretrainedConfig):
if (modality == Modality.IMAGE):
transforms = get_transforms(do_resize=True, size=(processor.pixels_pe... |
def calculate_class_abstract_status(typ: TypeInfo, is_stub_file: bool, errors: Errors) -> None:
typ.is_abstract = False
typ.abstract_attributes = []
if typ.typeddict_type:
return
concrete: set[str] = set()
abstract: list[tuple[(str, int)]] = []
abstract_in_this_class: list[str] = []
... |
def _run(handle_data, initialize, before_trading_start, analyze, algofile, algotext, defines, data_frequency, capital_base, bundle, bundle_timestamp, start, end, output, trading_calendar, print_algo, metrics_set, local_namespace, environ, blotter, benchmark_spec):
bundle_data = bundles.load(bundle, environ, bundle_... |
class UVCCSD(UVCC):
def __init__(self, num_modals: (list[int] | None)=None, qubit_mapper: (QubitMapper | None)=None, *, reps: int=1, initial_state: (QuantumCircuit | None)=None) -> None:
super().__init__(num_modals=num_modals, excitations='sd', qubit_mapper=qubit_mapper, reps=reps, initial_state=initial_sta... |
def finite_loss(ival: Interval, loss: float, x_scale: float) -> tuple[(float, Interval)]:
if (math.isinf(loss) or math.isnan(loss)):
loss = ((ival[1] - ival[0]) / x_scale)
if (len(ival) == 3):
loss /= ival[2]
round_fac = .0
loss = (int(((loss * round_fac) + 0.5)) / round_fac)
... |
class TestVehicleRouting(QiskitOptimizationTestCase):
def setUp(self):
super().setUp()
random.seed(600)
low = 0
high = 100
pos = {i: (random.randint(low, high), random.randint(low, high)) for i in range(4)}
self.graph = nx.random_geometric_graph(4, (np.hypot((high - l... |
_canonicalize
_specialize
_rewriter([AdvancedSubtensor1])
def local_adv_sub1_adv_inc_sub1(fgraph, node):
if (not isinstance(node.op, AdvancedSubtensor1)):
return
inp = node.inputs[0]
if ((not inp.owner) or (not isinstance(inp.owner.op, AdvancedIncSubtensor1))):
return
idx = node.inputs[1... |
def test_apply_along_last_axis():
_along_last_axis
def f(x):
assert (x.ndim == 1)
return (x[:(len(x) // 2)] + np.arange((len(x) // 2)))
for shape in [(10,), (2, 10), (2, 2, 10)]:
x = np.ones(shape)
y = f(x)
xshape = x.shape
yshape = y.shape
assert (len... |
def data_collator(features):
if (not isinstance(features[0], dict)):
features = [vars(f) for f in features]
first = features[0]
batch = {}
if (('label' in first) and (first['label'] is not None)):
label = (first['label'].item() if isinstance(first['label'], torch.Tensor) else first['labe... |
class ConvOps(BaseOp):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, transposed=False, depthwised=False, dropout_rate=0, ops_order='weight_norm_act'):
super().__init__(in_channels, out_channels, dropout_rate, ops_order)
self.depthwised = depthwised
paddin... |
_bool('is_required_a')
def test_extra_extract(debug_ctx, debug_trail, trail_select, is_required_a, acc_schema):
dumper_getter = make_dumper_getter(shape=shape(TestField('a', acc_schema.accessor_maker('a', is_required=is_required_a)), TestField('b', acc_schema.accessor_maker('b', is_required=True))), name_layout=Out... |
class HIKOM4(FinTS3Segment):
bank_identifier = DataElementGroupField(type=BankIdentifier, _d='Kreditinstitutskennung')
default_language = CodeField(enum=Language2, max_length=3, _d='Standardsprache')
communication_parameters = DataElementGroupField(type=CommunicationParameter2, min_count=1, max_count=9, _d=... |
def loadData(datasetStr):
DIR = os.path.join(os.getcwd(), 'dataset', datasetStr)
log(DIR)
with open((DIR + '/train.pkl'), 'rb') as fs:
trainMat = pk.load(fs)
with open((DIR + '/test_data.pkl'), 'rb') as fs:
testData = pk.load(fs)
with open((DIR + '/valid_data.pkl'), 'rb') as fs:
... |
class Solution(object):
def mostCommonWord(self, paragraph, banned):
banned = set(banned)
count = collections.Counter((word for word in re.split("[ !?',;.]", paragraph.lower()) if word))
return max((item for item in count.items() if (item[0] not in banned)), key=operator.itemgetter(1))[0] |
_onnx
class OnnxUtilsTestCaseV2(TestCase):
_torch
('transformers.onnx.convert.is_torch_onnx_dict_inputs_support_available', return_value=False)
def test_ensure_pytorch_version_ge_1_8_0(self, mock_is_torch_onnx_dict_inputs_support_available):
self.assertRaises(AssertionError, export, None, None, None... |
class Distance2EcmStrMaxGetter(SmoothPointGetter):
_baseResolution = 50
_extraDepth = 2
ECM_ATTRS_GENERAL = ('scanGravimetricStrengthBonus', 'scanLadarStrengthBonus', 'scanMagnetometricStrengthBonus', 'scanRadarStrengthBonus')
ECM_ATTRS_FIGHTERS = ('fighterAbilityECMStrengthGravimetric', 'fighterAbility... |
def validate(gpu, val_loader, model, criterion, test=True, args=None):
if test:
batch_time = AverageMeter('Time', ':6.3f')
losses = AverageMeter('Loss', ':.4e')
top1 = AverageMeter('', ':6.2f')
top5 = AverageMeter('', ':6.2f')
progress = ProgressMeter(len(val_loader), [batch_... |
class AoA_Decoder_Core(nn.Module):
def __init__(self, opt):
super(AoA_Decoder_Core, self).__init__()
self.drop_prob_lm = opt.drop_prob_lm
self.d_model = opt.rnn_size
self.use_multi_head = opt.use_multi_head
self.multi_head_scale = opt.multi_head_scale
self.use_ctx_dro... |
def recurse_artifacts(artifacts: list, root) -> Iterable[Path]:
for raw_artifact in artifacts:
artifact = Path(raw_artifact)
if (not artifact.is_absolute()):
artifact = (root / artifact)
if artifact.is_file():
(yield artifact)
elif artifact.is_dir():
... |
def test_read_bsrn_logical_records_not_found():
(data, metadata) = read_bsrn((DATA_DIR / 'bsrn-lr0100-pay0616.dat'), logical_records=['0300', '0500'])
assert data.empty
assert ('uva_global' in data.columns)
assert ('uvb_reflected_std' in data.columns)
assert ('uva_global_max' in data.columns)
as... |
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