code stringlengths 281 23.7M |
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def decay_batch_step(batch_size, num_intra_steps=2, no_odd=False):
if (batch_size <= 1):
return 0
base_batch_size = int((2 ** (math.log((batch_size - 1)) // math.log(2))))
step_size = max((base_batch_size // num_intra_steps), 1)
batch_size = (base_batch_size + ((((batch_size - base_batch_size) -... |
def collect_results_gpu(result_part, size):
(rank, world_size) = get_dist_info()
part_tensor = torch.tensor(bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
shape_list = [shape_tensor.clone() for _ in range(world_size)]... |
def _read_annotations(csv_reader, classes):
result = OrderedDict()
for (line, row) in enumerate(csv_reader):
line += 1
try:
(img_file, x1, y1, x2, y2, class_name) = row[:6]
except ValueError:
raise_from(ValueError("line {}: format should be 'img_file,x1,y1,x2,y2,c... |
class ProgressModel(object):
def __init__(self, start_date, end_date):
self._start_date = start_date
self._end_date = end_date
self._total_days = ((end_date - start_date).days + 1)
self._progress = 0.0
self._days_completed = 0
self._state = 'init'
self._curren... |
def write_sac_zpk(zeros, poles, constant, filename):
if hasattr(filename, 'write'):
f = filename
else:
f = open('w', filename)
def write_complex(x):
f.write(('%12.8g %12.8g\n' % (complex(x).real, complex(x).imag)))
f.write(('POLES %i\n' % len(poles)))
for p in poles:
... |
class Scenario(ScenarioGenerator):
def __init__(self):
super().__init__()
self.open_scenario_version = 2
def scenario(self, **kwargs):
catalog = xosc.Catalog()
catalog.add_catalog('VehicleCatalog', '../xosc/Catalogs/Vehicles')
road = xosc.RoadNetwork(roadfile='../xodr/e6m... |
def test_override():
class TestObject(object):
def __init__(self):
self.v = None
o = TestObject()
o.v = 'a'
()
def test_body():
assert_eq(o.v, 'a')
(yield None)
with async_override(o, 'v', 'b'):
assert_eq(o.v, 'b')
(yield None)
... |
def patch_norm_fp32(module):
if isinstance(module, (nn.modules.batchnorm._BatchNorm, nn.GroupNorm)):
module.float()
if (isinstance(module, nn.GroupNorm) or (torch.__version__ < '1.3')):
module.forward = patch_forward_method(module.forward, torch.half, torch.float)
for child in module... |
class DataModule(LightningDataModule):
def __init__(self, cfg):
super().__init__()
self.cfg = DefaultTrainer.auto_scale_workers(cfg, comm.get_world_size())
def train_dataloader(self):
return build_detection_train_loader(self.cfg)
def val_dataloader(self):
dataloaders = []
... |
class ServiceDiscoveryConsulTests(unittest.TestCase):
BASE_DIR = os.path.dirname(os.path.abspath(__file__))
def setUp(self):
os.environ[CONFIGMAP_FILE_ENVIRONMENT] = os.path.join(self.BASE_DIR, 'config-tests-service-discovery-consul.yml')
ms = Microservice(path=__file__)
self.ms = ms
... |
def dict2str(opt, indent_l=1):
msg = ''
for (k, v) in opt.items():
if isinstance(v, dict):
msg += (((' ' * (indent_l * 2)) + k) + ':[\n')
msg += dict2str(v, (indent_l + 1))
msg += ((' ' * (indent_l * 2)) + ']\n')
else:
msg += (((((' ' * (indent_l *... |
def main(args):
img_size = args.img_size
z_dim = 128
lamb_obj = 1.0
lamb_app = 1.0
lamb_img = 0.1
num_classes = (184 if (args.dataset == 'coco') else 179)
num_obj = (8 if (args.dataset == 'coco') else 31)
args.out_path = os.path.join(args.out_path, args.dataset, str(args.img_size))
n... |
class MacroElement(Element):
_template = Template('')
def __init__(self):
super().__init__()
self._name = 'MacroElement'
def render(self, **kwargs):
figure = self.get_root()
assert isinstance(figure, Figure), 'You cannot render this Element if it is not in a Figure.'
... |
_start_docstrings('The bare Cvt Model transformer outputting raw hidden-states without any specific head on top.', CVT_START_DOCSTRING)
class CvtModel(CvtPreTrainedModel):
def __init__(self, config, add_pooling_layer=True):
super().__init__(config)
self.config = config
self.encoder = CvtEnco... |
def package_modpaths(pkgpath, with_pkg=False, with_mod=True, followlinks=True, recursive=True, with_libs=False, check=True):
if isfile(pkgpath):
(yield pkgpath)
else:
if with_pkg:
root_path = join(pkgpath, '__init__.py')
if ((not check) or exists(root_path)):
... |
def get_active_window():
active_window = None
try:
active_window = _app.get_active_window()
except:
return None
active_window_number = active_window.get_id()
for (uid, browser_view_instance) in BrowserView.instances.items():
if (browser_view_instance.window.get_id() == active... |
def build_dataset(config, ratio, charge, model_name, seed):
vocab = pkl.load(open(config.vocab_path, 'rb'))
print(f'Vocab size: {len(vocab)}')
def load_dataset(text, labels, word_idx, word_key, chains, model_name):
contents = []
for i in range(len(text)):
if ((model_name == 'BiLS... |
def save_checkpoint(state, args, is_best, filename='checkpoint.pth.tar'):
directory = ('experiments/segmentation/runs/%s/%s/%s/' % (args.dataset, args.model, args.checkname))
if (not os.path.exists(directory)):
os.makedirs(directory)
filename = (directory + filename)
torch.save(state, filename)
... |
def pauli_string_iterator(num_qubits, max_word_size=2):
if (max_word_size > num_qubits):
raise ValueError('Number of qubits is too few')
if (max_word_size <= 0):
raise ValueError('Word size too small')
qubit_list = list(range(num_qubits))
partitions = partition_iterator(qubit_list, max_w... |
def accuracy(pred, target, topk=1):
assert isinstance(topk, (int, tuple))
if isinstance(topk, int):
topk = (topk,)
return_single = True
else:
return_single = False
maxk = max(topk)
(_, pred_label) = pred.topk(maxk, dim=1)
pred_label = pred_label.t()
correct = pred_lab... |
.parametrize('truncated_dist, lower, upper, shape, expected', [(icdf_normal(0, 1), (- 1), 2, None, 0), (icdf_normal(3, 1), (- 1), 2, (2,), np.full((2,), (3 / 2))), (icdf_normal((- 3), 1), (- 1), None, (2, 3), np.full((2, 3), 0)), (icdf_normal([0, 3, 3], 1), None, [2, 2, 4], (4, 3), np.full((4, 3), [0, 1, 3]))])
def tes... |
.slow
.requires_src
_on_conda_build
def test_update_version_3_0_to_3_1_pretend(tmp_path, with_coverage, venv_mgr):
with chdir(str(tmp_path)):
name = 'my_old_project'
project = (tmp_path / 'my_old_project')
venv_mgr.install_pyscaffold(3, 0).putup(name).uninstall_pyscaffold().install_this_pysc... |
def run_test_commands_with_gui_process(commands):
gui_command = [pmp_test_utils.get_executable_even_when_embedded(), '-m', 'pymedphys', 'gui']
with pmp_test_utils.process(gui_command, cwd=HERE):
for command in commands:
subprocess.check_call(command, cwd=HERE, shell=True) |
def rtn_mempcpy(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('mempcpy hooked')
(dst, dst_ast) = pstate.get_full_argument(0)
src = pstate.get_argument_value(1)
cnt = pstate.get_argument_value(2)
pstate.concretize_argument(2)
for index in range(cnt):
sym_src = pstate.read_... |
class _NetG(nn.Module):
def __init__(self, in_c=1, out_c=1, n_feat=80, scale_unetfeats=48, scale_orsnetfeats=32, num_cab=8, kernel_size=3, reduction=4, bias=False):
super(_NetG, self).__init__()
act = nn.PReLU()
self.shallow_feat1 = nn.Sequential(conv(1, n_feat, kernel_size, bias=bias), CAB(... |
def cvt_list_toavi(dirpath):
filenames_dict = {}
for file in os.listdir(dirpath):
if ((file == 'mapping_table') or (file == 'avi_txt')):
continue
else:
old_txt = open(file, 'r')
clip_names = old_txt.read()
clip_names = clip_names.split('\n')
... |
.slow
.xfail(reason='Memory test is not stable')
def test_memory_leak_on_unsuccessful_connect():
p = psutil.Process()
m0 = p.memory_full_info()
for i in range(10):
gc.collect()
try:
pymssql.connect(server='www.google.com', port=81, user='username', password='password', login_time... |
def parse_args():
parser = argparse.ArgumentParser()
data_group = parser.add_argument_group(title='Data-related configuration')
model_group = parser.add_argument_group(title='Model-related configuration')
atk_group = parser.add_argument_group(title='Attack-related configuration')
add_data_group(data... |
('beeref.scene.BeeGraphicsScene.clearSelection')
('PyQt6.QtGui.QClipboard.text')
('PyQt6.QtGui.QClipboard.image')
def test_on_action_paste_when_empty(img_mock, text_mock, clear_mock, view):
view.scene.cancel_crop_mode = MagicMock()
img_mock.return_value = QtGui.QImage()
text_mock.return_value = ''
view.... |
class CandlestickItem(pg.GraphicsObject):
def __init__(self, data):
pg.GraphicsObject.__init__(self)
self.data = data
self.generatePicture()
def generatePicture(self):
self.picture = QtGui.QPicture()
p = QtGui.QPainter(self.picture)
p.setPen(pg.mkPen('w'))
... |
class Memory():
data_pointer = 0
isfull = False
def __init__(self, capacity):
self.memory = np.empty(capacity, dtype=object)
self.capacity = capacity
def update(self, transition):
self.memory[self.data_pointer] = transition
self.data_pointer += 1
if (self.data_poi... |
def f_conv2d_bias(in_channels, out_channels):
def padding_same(kernel, stride):
return [((((k - 1) * s) + 1) // 2) for (k, s) in zip(kernel, stride)]
padding = padding_same([3, 3], [1, 1])
assert (padding == [1, 1]), padding
return nn.Sequential(nn.Conv2d(in_channels=in_channels, out_channels=ou... |
def _timed_dedupe(object_ids: List[Any], sort_keys: List[SortKey], num_materialize_buckets: int, dedupe_task_index: int, enable_profiler: bool, object_store: Optional[IObjectStore], **kwargs):
task_id = get_current_ray_task_id()
worker_id = get_current_ray_worker_id()
with (memray.Tracker(f'dedupe_{worker_i... |
def _instance_init_in_callstack(instance: Any) -> bool:
frame = inspect.currentframe().f_back
while frame:
frame_context_name = inspect.getframeinfo(frame).function
frame_context_self = frame.f_locals.get('self')
frame_context_vars = frame.f_code.co_varnames
if ((frame_context_na... |
class BrowserStack(Provider):
API = '
def auth(self):
return (self.username, self.key)
def executor(self):
return '
def username(self):
return self.get_credential('username', ['BROWSERSTACK_USERNAME', 'BROWSERSTACK_USR'])
def key(self):
return self.get_credential('key... |
def read_batchfile(pythonpath, file_ending='.py'):
abspaths = utils.pypath_to_realpath(pythonpath, file_ending, settings.BASE_BATCHPROCESS_PATHS)
if (not abspaths):
raise IOError('Absolute batchcmd paths could not be found.')
text = None
decoderr = []
for abspath in abspaths:
for fil... |
class EventMarker(Marker):
def __init__(self, event, kind=0, event_hash=None):
Marker.__init__(self, [], event.time, event.time, kind)
self._event = event
self.active = False
self._event_hash = event_hash
def get_event_hash(self):
if (self._event_hash is not None):
... |
def get_component_unique_name(c_rtype):
full_name = get_component_full_name(c_rtype)
special_chars = [' ', '<', '>', '.', '[', ']']
if ((len(full_name) < 64) and (not any([(c in full_name) for c in special_chars]))):
return full_name
comp_name = c_rtype.get_name()
param_hash = blake2b(digest... |
class InternalBaseplateSession(BaseplateSession):
def _add_span_context(self, span: Span, request: PreparedRequest) -> None:
request.headers['X-Trace'] = str(span.trace_id)
request.headers['X-Parent'] = str(span.parent_id)
request.headers['X-Span'] = str(span.id)
if span.sampled:
... |
class _GroupBase(base._TextBox, base.PaddingMixin, base.MarginMixin):
defaults: list[tuple[(str, Any, str)]] = [('borderwidth', 3, 'Current group border width'), ('center_aligned', True, 'center-aligned group box')]
def __init__(self, **config):
base._TextBox.__init__(self, **config)
self.add_de... |
def noneuclidian_distance_calculation():
from sympy import solve, sqrt
metric = '0 # #,# 0 #,# # 1'
(X, Y, e) = MV.setup('X Y e', metric)
print('g_{ij} =', MV.metric)
print('(X^Y)**2 =', ((X ^ Y) * (X ^ Y)))
L = ((X ^ Y) ^ e)
B = (L * e)
print('B =', B)
Bsq = (B * B)
print('B**2 ... |
class Post(models.Model):
title = models.CharField(max_length=70, verbose_name='', unique=True)
html_content = models.TextField(verbose_name='HTML')
md_content = models.TextField(verbose_name='markdown')
created_time = models.DateTimeField(auto_now_add=True, verbose_name='')
modified_time = models.D... |
def main():
parser = HfArgumentParser((DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments), description=DESCRIPTION)
if ((len(sys.argv) == 2) and sys.argv[1].endswith('.json')):
(data_args, teacher_args, student_args, training_args) = parser.parse_json_file(jso... |
def get_conv_output_size(input_size, kernel_size, stride, padding, dilation):
ndim = len(input_size)
output_size = []
for i in range(ndim):
size = (((((input_size[i] + (2 * padding[i])) - (dilation[i] * (kernel_size[i] - 1))) - 1) // stride[i]) + 1)
if (kernel_size[i] == (- 1)):
... |
class Predictor_length(nn.Module):
def __init__(self, opt, key_name):
super(Predictor_length, self).__init__()
self.net = nn.Sequential(nn.Linear(opt['dim_hidden'], opt['dim_hidden']), nn.ReLU(), nn.Dropout(opt['hidden_dropout_prob']), nn.Linear(opt['dim_hidden'], opt['max_len']))
self.key_n... |
def test_defaults():
assert (pressure('1000').value() == 1000.0)
assert (pressure('1000', 'HPA').value() == 1000.0)
assert (pressure('30', 'in').value() == 30.0)
assert (pressure('30', 'in').string() == '30.00 inches')
assert (pressure('1000').value('MB') == 1000)
assert (pressure('1000').string... |
def get_train_op_for_scope(loss, optimizer, scopes, clip_gradient_norm):
for var in tf.trainable_variables():
if (not (var in tf.model_variables())):
tf.contrib.framework.add_model_variable(var)
is_trainable = (lambda x: (x in tf.trainable_variables()))
var_list = []
update_ops = []
... |
class TestMPM(TestCase):
def test_well_posed(self):
options = {'thermal': 'isothermal'}
model = pybamm.lithium_ion.MPM(options)
model.check_well_posedness()
model = pybamm.lithium_ion.MPM(build=False)
model.build_model()
model.check_well_posedness()
def test_defau... |
def _get_display_cls(format):
dummy = (lambda *args, **kwargs: None)
try:
import IPython.display as display
except ImportError:
return dummy
if (format in IPYTHON_NO_DISPLAY_FORMATS):
return dummy
elif (format in IPYTHON_IMAGE_FORMATS):
return partial(display.Image, f... |
def test_triggeringentities():
cond = OSC.TriggeringEntities(OSC.TriggeringEntitiesRule.all)
cond.add_entity('ego')
prettyprint(cond.get_element())
cond2 = OSC.TriggeringEntities(OSC.TriggeringEntitiesRule.all)
cond2.add_entity('ego')
cond3 = OSC.TriggeringEntities(OSC.TriggeringEntitiesRule.all... |
def _camel_killer(attr):
try:
attr = str(attr)
except UnicodeEncodeError:
attr = attr.encode('utf-8', 'ignore')
s1 = _first_cap_re.sub('\\1_\\2', attr)
s2 = _all_cap_re.sub('\\1_\\2', s1)
return re.sub('_+', '_', (s2.casefold() if hasattr(s2, 'casefold') else s2.lower())) |
def build(image_resizer_config):
if (not isinstance(image_resizer_config, image_resizer_pb2.ImageResizer)):
raise ValueError('image_resizer_config not of type image_resizer_pb2.ImageResizer.')
if (image_resizer_config.WhichOneof('image_resizer_oneof') == 'keep_aspect_ratio_resizer'):
keep_aspect... |
.parametrize('is_no_update', [False, True])
def test_lock_with_incompatible_lockfile(command_tester_factory: CommandTesterFactory, poetry_with_incompatible_lockfile: Poetry, repo: TestRepository, is_no_update: bool) -> None:
repo.add_package(get_package('sampleproject', '1.3.1'))
locker = Locker(lock=(poetry_wi... |
class InformationRetrievalEvaluator(SentenceEvaluator):
def __init__(self, queries: Dict[(str, str)], corpus: Dict[(str, str)], relevant_docs: Dict[(str, Set[str])], query_chunk_size: int=1000, corpus_chunk_size: int=500000, mrr_at_k: List[int]=[10], ndcg_at_k: List[int]=[10], accuracy_at_k: List[int]=[1, 3, 5, 10]... |
def happy_path_fixture(chain_state, token_network_state, our_address):
(token_network_state, addresses, channel_states) = create_square_network_topology(token_network_state=token_network_state, our_address=our_address)
(address1, address2, address3, address4) = addresses
chain_state.nodeaddresses_to_network... |
def make_casa_mask(SpecCube, outname, append_to_image=True, img=None, add_stokes=True, stokes_posn=None, overwrite=False):
try:
from casatools import image
ia = image()
except ImportError:
try:
from taskinit import ia
except ImportError:
raise ImportError(... |
def linkify(weburl_match):
(domain, path) = (weburl_match.group(1), (weburl_match.group(2) or ''))
if (domain.endswith(settings.DOMAIN) and (len(path) > 7)):
if (permalink := re.match('^/entry/([0-9]+)/?$', path)):
return f'({SEE}: <a href="{path}">#{permalink.group(1)}</a>)'
if (top... |
('inspector-superior?', [values_struct.W_StructInspector, values_struct.W_StructInspector])
def inspector_superior_huh(w_inspector, maybe_subinspector):
if (w_inspector is maybe_subinspector):
return values.w_false
s = maybe_subinspector.w_super
while (s is not None):
if (w_inspector is s):
... |
class PlaneAlignment(BaseCascade):
_id = 37
_iconName = 'Assembly_ConstraintAlignment.svg'
_props = (['Cascade', 'Offset'] + _AngleProps)
_tooltip = QT_TRANSLATE_NOOP('asm3', 'Add a "{}" constraint to align planar faces of two or more parts.\nThe faces become coplanar or parallel with an optional distan... |
class TargetWeightMolecule(Molecule):
def __init__(self, target_weight, **kwargs):
super(TargetWeightMolecule, self).__init__(**kwargs)
self.target_weight = target_weight
def _reward(self):
molecule = Chem.MolFromSmiles(self._state)
if (molecule is None):
return (- (s... |
class InnerProductTest(unittest.TestCase):
def test_inner_product(self):
state_1 = numpy.array([1.0, 1j])
state_2 = numpy.array([1.0, (- 1j)])
self.assertAlmostEqual(inner_product(state_1, state_1), 2.0)
self.assertAlmostEqual(inner_product(state_1, state_2), 0.0) |
def get_f1_score(prediction, ground_truth):
prediction_tokens = normalize_prediction(prediction, lowercase=True).split()
ground_truth_tokens = normalize_prediction(ground_truth, lowercase=True).split()
common = (Counter(prediction_tokens) & Counter(ground_truth_tokens))
num_same = sum(common.values())
... |
def test_pythontag_in_setup_cfg(temp_pkg):
temp_pkg.joinpath('setup.cfg').write_text('[bdist_wheel]\npython_tag=py32', encoding='utf-8')
subprocess.check_call([sys.executable, 'setup.py', 'bdist_wheel'], cwd=str(temp_pkg))
dist_dir = temp_pkg.joinpath('dist')
assert dist_dir.is_dir()
wheels = list(d... |
def from_csv(fp, field_names=None, **kwargs):
fmtparams = {}
for param in ['delimiter', 'doublequote', 'escapechar', 'lineterminator', 'quotechar', 'quoting', 'skipinitialspace', 'strict']:
if (param in kwargs):
fmtparams[param] = kwargs.pop(param)
if fmtparams:
reader = csv.read... |
def parse_type_comment(type_comment: str, line: int, column: int, errors: (Errors | None)) -> tuple[((list[str] | None), (ProperType | None))]:
try:
typ = ast3_parse(type_comment, '<type_comment>', 'eval')
except SyntaxError:
if (errors is not None):
stripped_type = type_comment.spli... |
def reshape_for_gwas(spark, label_df):
assert check_argument_types()
if (label_df.index.nlevels == 1):
transposed_df = label_df.T
column_names = ['label', 'values']
elif (label_df.index.nlevels == 2):
ordered_cols = pd.unique(label_df.index.get_level_values(0))
transposed_df ... |
def process_url(item, exclude_websites):
source = item.get('source').get('href')
if (not all([(not re.match(website, source)) for website in [f'^ for website in exclude_websites]])):
return
url = item.get('link')
if re.match(GOOGLE_NEWS_REGEX, url):
url = requests.head(url).headers.get('... |
def multiply_inv_gaussians_batch(mus, lambdas):
assert (len(mus) == len(lambdas))
batch_size = mus[0].shape.as_list()[:(- 1)]
d_z = lambdas[0].shape.as_list()[(- 1)]
identity_matrix = tf.tile(tf.expand_dims(tf.expand_dims(tf.eye(d_z), axis=0), axis=0), (batch_size + [1, 1]))
lambda_new = (tf.reduce_... |
def get_cams(latitude, longitude, start, end, email, identifier='mcclear', altitude=None, time_step='1h', time_ref='UT', verbose=False, integrated=False, label=None, map_variables=True, server=URL, timeout=30):
try:
time_step_str = TIME_STEPS_MAP[time_step]
except KeyError:
raise ValueError(f'Ti... |
def merge_pks(string):
curdir = os.getcwd()
files = os.listdir(curdir)
relevant_files = sorted([fl for fl in files if (string in fl)])
dfs = [pickle.load(open(fl, 'rb')) for fl in relevant_files]
merged_dfs = {}
for df in dfs:
for (key, value) in df.items():
if (key == 'bss_e... |
def fmt_relation(relation):
labels = relation.subsystem.node_labels
body = fmt_relata(relation.relata, node_labels=labels)
data = [('', relation.phi), ('Purview', fmt_mechanism(relation.purview, node_labels=labels)), ('Relata', '')]
data = '\n'.join(align_columns(data))
body = center(header(data, bo... |
class LeakyReLUBNConv2d(nn.Module):
def __init__(self, n_in, n_out, kernel_size, stride, padding=0):
super(LeakyReLUBNConv2d, self).__init__()
model = []
model += [nn.Conv2d(n_in, n_out, kernel_size=kernel_size, stride=stride, padding=padding, bias=True)]
model += [nn.BatchNorm2d(n_o... |
.parametrize('username,password', users)
def test_create_empty(db, client, username, password):
client.login(username=username, password=password)
url = reverse(urlnames['list'])
response = client.post(url, {})
assert (response.status_code == status_map['create_error'][username]), response.json() |
class RandConv2d(nn.Module):
def __init__(self, sigma_0, N, init_s, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
super(RandConv2d, self).__init__()
if ((in_channels % groups) != 0):
raise ValueError('in_channels must be divisible by group... |
class GPSPilot(AutopilotPilot):
def __init__(self, ap):
super(GPSPilot, self).__init__('gps', ap)
self.wind_gps_offset = HeadingOffset()
self.true_wind_gps_offset = HeadingOffset()
self.gains = {}
self.PosGain('P', 0.003, 0.02)
self.PosGain('D', 0.1, 1.0)
self... |
def has_arg(fn, name, accept_all=False):
if (sys.version_info < (3,)):
arg_spec = inspect.getargspec(fn)
if (accept_all and (arg_spec.keywords is not None)):
return True
return (name in arg_spec.args)
elif (sys.version_info < (3, 3)):
arg_spec = inspect.getfullargspec... |
class TermsOfService(Object):
def __init__(self, *, id: str, text: str, entities: List['types.MessageEntity']):
super().__init__()
self.id = id
self.text = text
self.entities = entities
def _parse(terms_of_service: 'raw.types.help.TermsOfService') -> 'TermsOfService':
ret... |
_module()
class BaseDecoder(BaseModule):
def __init__(self, init_cfg=None, **kwargs):
super().__init__(init_cfg=init_cfg)
def forward_train(self, feat, out_enc, targets_dict, img_metas):
raise NotImplementedError
def forward_test(self, feat, out_enc, img_metas):
raise NotImplementedE... |
.mongo
def test_mongo_core_keywords():
(mongetter=_test_mongetter)
def _test_mongo_caching(arg_1, arg_2):
return ((random() + arg_1) + arg_2)
_test_mongo_caching.clear_cache()
val1 = _test_mongo_caching(1, arg_2=2)
val2 = _test_mongo_caching(1, arg_2=2)
assert (val1 == val2)
val3 = _... |
def select_cond_path(mode):
path = 'data/example_conditioning'
path = os.path.join(path, mode)
onlyfiles = [f for f in sorted(os.listdir(path))]
selected = widgets.RadioButtons(options=onlyfiles, description='Select conditioning:', disabled=False)
display(selected)
selected_path = os.path.join(p... |
(params=[pytest.param(('linux', 'linux', 'x86_64', '64'), id='linux-64'), pytest.param(('linux', 'linux', 'i686', '32'), id='linux-32'), pytest.param(('linux', 'linux', 'aarch64', 'arm'), id='linux-arm'), pytest.param(('macos', 'darwin', 'x86_64', '64'), id='macos-64'), pytest.param(('macos', 'darwin', 'arm64', 'arm'),... |
class KGESmoothCELoss(nn.Module):
def __init__(self, smoothing=0.001, mode='multiply'):
super(KGESmoothCELoss, self).__init__()
self.loss_function = CESmoothLossOnevsAll(smoothing=smoothing)
self.mode = mode
def forward(self, head_emb, tail_emb, all_rel_emb, labels):
if (self.mod... |
class HardSwishJitAutoFn(torch.autograd.Function):
def forward(ctx, x):
ctx.save_for_backward(x)
return hard_swish_jit_fwd(x)
def backward(ctx, grad_output):
x = ctx.saved_tensors[0]
return hard_swish_jit_bwd(x, grad_output)
def symbolic(g, self):
input = g.op('Add', ... |
def str_for_potential_or_deterministic(var: TensorVariable, formatting: str='plain', include_params: bool=True, dist_name: str='Deterministic') -> str:
print_name = (var.name if (var.name is not None) else '<unnamed>')
if ('latex' in formatting):
print_name = (('\\text{' + _latex_escape(print_name.strip... |
class SHHA(data.Dataset):
def __init__(self, data_path, mode, main_transform=None, img_transform=None, gt_transform=None, data_augment=1):
self.img_path = (data_path + '/img')
self.gt_path = (data_path + '/den')
self.data_files = [filename for filename in os.listdir(self.img_path) if os.path... |
def save_embedding(word_list, word_embedding, word_list_file='embedding/yelp_words.txt', word_embedding_file='embedding/yelp_embedding.txt'):
with open(word_list_file, 'w') as fopen:
for w in word_list:
fopen.write((w + '\n'))
with open(word_embedding_file, 'w') as fopen:
for i in ra... |
class ReportQuerysetMixin():
impression_model = None
def get_queryset(self, **kwargs):
queryset = self.impression_model.objects.all()
if (('start_date' in kwargs) and kwargs['start_date']):
queryset = queryset.filter(date__gte=kwargs['start_date'])
if (('end_date' in kwargs) ... |
class TestDataHandler(TestCase):
def setUpClass(cls):
cls.spx_index_ticker = BloombergTicker('SPX Index')
cls.google_ticker = BloombergTicker('GOOGL US Equity')
cls.microsoft_ticker = BloombergTicker('MSFT US Equity')
cls.start_date = str_to_date('2018-01-02')
cls.end_date = ... |
def read_dataset(dname):
(d, ext) = op.splitext(dname)
if (ext.lower() == '.csv'):
dname = d
if (dname not in dts['dataset'].to_numpy()):
raise ValueError('Dataset does not exist. Valid datasets names are', dts['dataset'].to_numpy())
return pd.read_csv(op.join(ddir, (dname + '.csv')), se... |
def test__shaded_fraction_array():
solar_zenith = np.array([0.0, 60.0, 90.0, 60.0])
solar_azimuth = np.array([180.0, 180.0, 180.0, 180.0])
surface_azimuth = np.array([180.0, 180.0, 180.0, 210.0])
surface_tilt = np.array([30.0, 60.0, 0.0, 30.0])
gcr = 1.0
result = infinite_sheds._shaded_fraction(... |
def extract_connecting_borders_between_points(cell_min_point, cell_length_x, cell_length_y, point_begin, point_end, zero_tolerance):
if (point_begin == point_end):
return ([], [])
border_id_p_begin = (- 1)
border_id_p_end = (- 1)
if (cwt(point_begin[0], cell_min_point[0], zero_tolerance) == 0):
... |
(suggest_parser)
def do_suggest(args: argparse.Namespace) -> None:
response = request(args.status_file, 'suggest', function=args.function, json=args.json, callsites=args.callsites, no_errors=args.no_errors, no_any=args.no_any, flex_any=args.flex_any, use_fixme=args.use_fixme, max_guesses=args.max_guesses)
check... |
def mmd(datasetA, datasetB, kernel):
KAA = kernel.compute_K_symm(datasetA)
KAA_corrected = (KAA - np.diag(np.diag(KAA)))
KBB = kernel.compute_K_symm(datasetB)
KBB_corrected = (KBB - np.diag(np.diag(KBB)))
KAB = kernel.compute_K(datasetA, datasetB)
M = KAA.shape[0]
return np.sum(((((KAA_corre... |
def test_connect_lambda():
class Top(ComponentLevel3):
def construct(s, x):
s.in_ = InPort(Bits32)
s.out = OutPort(Bits32)
s.out //= (lambda : ((s.in_ + x) + globalvar))
x = Top(3)
x.elaborate()
simple_sim_pass(x)
x.in_ = 10
x.tick()
assert (x.out ... |
class TestSidekiqCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('SidekiqWebCollector', {'password': 'TEST_PASSWORD'})
self.collector = SidekiqCollector(config, None)
def test_import(self):
self.assertTrue(SidekiqCollector)
_only_if_redis_is_available
... |
class CatEmbeddings(nn.Module):
def __init__(self, _cardinalities_and_maybe_dimensions: Union[(list[int], list[tuple[(int, int)]])], d_embedding: Optional[int]=None, *, stack: bool=False) -> None:
assert _cardinalities_and_maybe_dimensions
spec = _cardinalities_and_maybe_dimensions
if (not (... |
.cli
_CLI_ENDPONTS
.parametrize('option', [['-h'], []])
def test_sync(input_command, option, tmpdir):
with tmp_chdir(str(tmpdir)):
output = subprocess.check_output(((input_command + ['sync']) + option), stderr=subprocess.STDOUT).decode('utf-8')
assert ('Tool for synchronizing PROJ datum and transformati... |
def get_saver(cfg: DictConfig) -> ModelCheckpoint:
args = dict(cfg[__key__].saver)
args['filename'] = args['filename'].format(experiment=cfg[__key__].name)
args = {str(k).lower(): v for (k, v) in args.items()}
args['dirpath'] = cfg.disk.model_dir
saver = ModelCheckpoint(**args)
if cfg.train_all:... |
def get_pose_net(cfg, is_train, **kwargs):
num_layers = cfg.MODEL.EXTRA.NUM_LAYERS
style = cfg.MODEL.STYLE
kwargs['groups'] = cfg.MODEL.GROUPS
kwargs['width_per_group'] = cfg.MODEL.WIDTH_PER_GROUP
(block_class, layers) = resnet_spec[num_layers]
if (style == 'caffe'):
block_class = Bottle... |
class RawMetadata(TypedDict, total=False):
metadata_version: str
name: str
version: str
platforms: List[str]
summary: str
description: str
keywords: List[str]
home_page: str
author: str
author_email: str
license: str
supported_platforms: List[str]
download_url: str
... |
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