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class TestRSAVerification():
.supported(only_if=(lambda backend: backend.rsa_padding_supported(padding.PKCS1v15())), skip_message='Does not support PKCS1v1.5.')
.supported(only_if=(lambda backend: backend.signature_hash_supported(hashes.SHA1())), skip_message='Does not support SHA1 signature.')
def test_pkc... |
def export(preprocessor: Union[('PreTrainedTokenizer', 'FeatureExtractionMixin')], model: Union[('PreTrainedModel', 'TFPreTrainedModel')], config: OnnxConfig, opset: int, output: Path, tokenizer: 'PreTrainedTokenizer'=None) -> Tuple[(List[str], List[str])]:
if (not (is_torch_available() or is_tf_available())):
... |
def get_config():
(config, warnings, errors) = process_cline()
(config, warnings, errors) = check_config(config, warnings, errors)
for warning in warnings:
print('WARNING:', warning)
for error in errors:
print('ERROR', error)
if len(errors):
sys.exit(2)
if ('pass' not in ... |
def _run_reader_iter(reader: Any, buf: bytes, do_eof: bool) -> Generator[(Any, None, None)]:
while True:
event = reader(buf)
if (event is None):
break
(yield event)
if (type(event) is EndOfMessage):
break
if do_eof:
assert (not buf)
(yield ... |
def get_control_names(control, allcontrols, textcontrols):
names = []
friendly_class_name = control.friendly_class_name()
names.append(friendly_class_name)
cleaned = control.window_text()
if (cleaned and control.has_title):
names.append(cleaned)
names.append((cleaned + friendly_class... |
class MongoDBCollector(diamond.collector.Collector):
MAX_CRC32 =
def __init__(self, *args, **kwargs):
self.__totals = {}
super(MongoDBCollector, self).__init__(*args, **kwargs)
def get_default_config_help(self):
config_help = super(MongoDBCollector, self).get_default_config_help()
... |
def main(args):
device = torch.device(('cuda' if (torch.cuda.is_available() and (not args.no_cuda)) else 'cpu'))
n_gpu = torch.cuda.device_count()
logger.info('device: {}, n_gpu: {}, 16-bits training: {}'.format(device, n_gpu, args.fp16))
random.seed(args.seed)
np.random.seed(args.seed)
torch.ma... |
def test_teardown_logging(pytester: Pytester) -> None:
pytester.makepyfile("\n import logging\n\n logger = logging.getLogger(__name__)\n\n def test_foo():\n logger.info('text going to logger from call')\n\n def teardown_function(function):\n logger.info('text going ... |
def get_non_trading_days(start, end):
non_trading_rules = []
start = canonicalize_datetime(start)
end = canonicalize_datetime(end)
weekends = rrule.rrule(rrule.YEARLY, byweekday=(rrule.SA, rrule.SU), cache=True, dtstart=start, until=end)
non_trading_rules.append(weekends)
new_years = rrule.rrule... |
class variable_size_graph():
def __init__(self, task_parameters):
vocab_size = task_parameters['Voc']
nb_of_clust = task_parameters['nb_clusters_target']
clust_size_min = task_parameters['size_min']
clust_size_max = task_parameters['size_max']
p = task_parameters['p']
... |
class BufferOperation(enum.IntFlag):
discard_read_buffer = VI_READ_BUF
discard_read_buffer_no_io = VI_READ_BUF_DISCARD
flush_write_buffer = VI_WRITE_BUF
discard_write_buffer = VI_WRITE_BUF_DISCARD
discard_receive_buffer = VI_IO_IN_BUF_DISCARD
discard_receive_buffer2 = VI_IO_IN_BUF
flush_tran... |
class TestWebsiteCollector(CollectorTestCase):
def setUp(self, config=None):
if (config is None):
config = get_collector_config('WebsiteCollector', {'url': ''})
else:
config = get_collector_config('WebsiteCollector', config)
self.collector = WebsiteMonitorCollector(co... |
def test_point_distance():
with pytest.raises(AssertionError):
utils.point_distance([1, 2], [1, 2])
with pytest.raises(AssertionError):
p = np.array([1, 2, 3])
utils.point_distance(p, p)
p = np.array([1, 2])
assert (utils.point_distance(p, p) == 0)
p1 = np.array([2, 2])
a... |
class SwaggerDeprecatedTest(object):
def test_doc_parser_parameters(self, api):
parser = api.parser()
parser.add_argument('param', type=int, help='Some param')
with pytest.warns(DeprecationWarning):
('/with-parser/')
class WithParserResource(restx.Resource):
... |
class SeasonalTiltMount(pvsystem.AbstractMount):
monthly_tilts: list
surface_azimuth: float = 180.0
def get_orientation(self, solar_zenith, solar_azimuth):
tilts = [self.monthly_tilts[(m - 1)] for m in solar_zenith.index.month]
return pd.DataFrame({'surface_tilt': tilts, 'surface_azimuth': s... |
class TestEnvVars(EnvironmentTestCase):
def test_run_no_env(self, runner, target):
env = self.run_environ(runner, *target, environ={'USER': 'romain'})
assert (env.get('USER') == 'romain')
def test_run_env(self, runner, target):
env = self.run_environ(runner, *target, '--env', 'USER=serio... |
class ChainBiMapper(SequenceBiMapper):
def __init__(self, first_layer: SequenceMapper, second_layer: SequenceMapper):
self.first_layer = first_layer
self.second_layer = second_layer
def apply(self, is_train, x, mask=None):
with tf.variable_scope('out'):
m1 = self.first_layer.... |
def generate_packets() -> List[bytes]:
out = DNSOutgoing((const._FLAGS_QR_RESPONSE | const._FLAGS_AA))
address = socket.inet_pton(socket.AF_INET, '192.168.208.5')
additionals = [{'name': 'HASS Bridge ZJWH FF5137._hap._tcp.local.', 'address': address, 'port': 51832, 'text': b'\x13md=HASS Bridge ZJWH\x06pv=1.... |
class Config():
(frozen=True)
class InvocationParams():
args: Tuple[(str, ...)]
plugins: Optional[Sequence[Union[(str, _PluggyPlugin)]]]
dir: Path
def __init__(self, *, args: Iterable[str], plugins: Optional[Sequence[Union[(str, _PluggyPlugin)]]], dir: Path) -> None:
... |
class Effect6098(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Missile Launcher Operation')), 'reloadTime', ship.getModifiedItemAttr('shipBonusTacticalDestroyerCaldari2'), skill='Caldari Tactic... |
class Person(QObject):
def __init__(self, parent=None):
super(Person, self).__init__(parent)
self._name = ''
self._shoe = ShoeDescription()
(str)
def name(self):
return self._name
def name(self, name):
self._name = name
(ShoeDescription)
def shoe(self):
... |
class Request(Awaitable[W]):
def __init__(self, pg: dist.ProcessGroup, device: torch.device) -> None:
super().__init__()
self.pg: dist.ProcessGroup = pg
self.req: Optional[dist.Work] = None
self.tensor: Optional[W] = None
self.a2ai = None
self.qcomm_ctx = None
... |
def run_setup(setup_script, args):
setup_dir = os.path.abspath(os.path.dirname(setup_script))
with setup_context(setup_dir):
try:
sys.argv[:] = ([setup_script] + list(args))
sys.path.insert(0, setup_dir)
working_set.__init__()
working_set.callbacks.append(... |
class CIFAR10Policy():
def __init__(self, fillcolor=(128, 128, 128)):
self.policies = [SubPolicy(0.1, 'invert', 7, 0.2, 'contrast', 6, fillcolor), SubPolicy(0.7, 'rotate', 2, 0.3, 'translateX', 9, fillcolor), SubPolicy(0.8, 'sharpness', 1, 0.9, 'sharpness', 3, fillcolor), SubPolicy(0.5, 'shearY', 8, 0.7, 't... |
_session
def test_runningmeanstd():
for (x1, x2, x3) in [(np.random.randn(3), np.random.randn(4), np.random.randn(5)), (np.random.randn(3, 2), np.random.randn(4, 2), np.random.randn(5, 2))]:
rms = RunningMeanStd(epsilon=0.0, shape=x1.shape[1:])
U.initialize()
x = np.concatenate([x1, x2, x3],... |
class FC():
_activations = {None: tf.identity, 'ReLU': tf.nn.relu, 'tanh': tf.tanh, 'sigmoid': tf.sigmoid, 'softmax': tf.nn.softmax, 'swish': (lambda x: (x * tf.sigmoid(x)))}
def __init__(self, output_dim, input_dim=None, activation=None, weight_decay=None, ensemble_size=1):
(self.input_dim, self.output... |
def test_generator(game_enum):
from randovania.generator.base_patches_factory import BasePatchesFactory
from randovania.generator.hint_distributor import HintDistributor
from randovania.resolver.bootstrap import Bootstrap
g = game_enum.generator
assert isinstance(g.bootstrap, Bootstrap)
assert i... |
def getDoomsdayMult(mod, tgt, distance, tgtSigRadius):
modRange = mod.maxRange
if ((distance is not None) and modRange and (distance > modRange)):
return 0
if {'superWeaponAmarr', 'superWeaponCaldari', 'superWeaponGallente', 'superWeaponMinmatar'}.intersection(mod.item.effects):
if (tgt.isFi... |
def save_import_snapshot_values(project, snapshots, checked):
for snapshot in snapshots:
assert (snapshot.pk is None)
snapshot.project = project
snapshot.save(copy_values=False)
for value in snapshot.snapshot_values:
if value.attribute:
value_key = f'{valu... |
def test_read_psm3_map_variables():
(data, metadata) = psm3.read_psm3(MANUAL_TEST_DATA, map_variables=True)
columns_mapped = ['Year', 'Month', 'Day', 'Hour', 'Minute', 'dhi', 'ghi', 'dni', 'ghi_clear', 'dhi_clear', 'dni_clear', 'Cloud Type', 'temp_dew', 'solar_zenith', 'Fill Flag', 'albedo', 'wind_speed', 'wind... |
def test_replace_output_layer():
with tempfile.TemporaryDirectory() as tmp_dir:
saved_model_dir = os.path.join(tmp_dir, 'saved_model')
inp = tf.keras.layers.Input(shape=(2,))
x = tf.keras.layers.Dense(units=1)(inp)
x = tf.keras.layers.Dense(units=2)(x)
model = tf.keras.Model(... |
class UnavailableSession(Session):
session_issue: ClassVar[str]
def __init__(self, *args, **kwargs) -> None:
raise ValueError(self.session_issue)
def _get_attribute(self, attr):
raise NotImplementedError()
def _set_attribute(self, attr, value):
raise NotImplementedError()
def... |
def test_annotation_based_injection_works_in_provider_methods():
class MyModule(Module):
def configure(self, binder):
binder.bind(int, to=42)
def provide_str(self, i: int) -> str:
return str(i)
def provide_object(self) -> object:
return object()
inject... |
def torch_full(*args, **kwargs):
args = list(args)
if (isinstance(args[1], torch.Tensor) and (args[1].device == torch.device('meta'))):
args[1] = 1
kwargs_without_device = dict(kwargs)
kwargs_without_device.pop('device', None)
return torch.full(*args, **kwargs_without_device) |
class PeleeBranch2(nn.Module):
def __init__(self, in_channels, out_channels, mid_channels):
super(PeleeBranch2, self).__init__()
self.conv1 = conv1x1_block(in_channels=in_channels, out_channels=mid_channels)
self.conv2 = conv3x3_block(in_channels=mid_channels, out_channels=out_channels)
... |
class MMGNet():
def __init__(self, config):
self.config = config
self.model_name = self.config.NAME
self.mconfig = mconfig = config.MODEL
self.exp = config.exp
self.save_res = config.EVAL
self.update_2d = config.update_2d
dataset = None
if (config.MODE... |
('pypyr.steps.filewrite.Path')
def test_filewrite_binary(mock_path):
context = Context({'k1': 'v1', 'fileWrite': {'path': '/arb/blah', 'payload': b'one\ntwo\nthree', 'binary': True}})
with io.BytesIO() as out_bytes:
with patch('pypyr.steps.filewrite.open', mock_open()) as mock_output:
mock_o... |
class DockerLexer(RegexLexer):
name = 'Docker'
url = '
aliases = ['docker', 'dockerfile']
filenames = ['Dockerfile', '*.docker']
mimetypes = ['text/x-dockerfile-config']
version_added = '2.0'
_keywords = '(?:MAINTAINER|EXPOSE|WORKDIR|USER|STOPSIGNAL)'
_bash_keywords = '(?:RUN|CMD|ENTRYPO... |
def load_data(data_path, dataset, images):
all_datas = {}
for split in ['train', 'val', 'test']:
datas = []
dropdata = 0
if (not os.path.exists(((data_path + split) + '.json'))):
continue
with open(((data_path + split) + '.json'), 'r', encoding='utf-8') as fin:
... |
class Requirement():
def damage(self, current_resources: ResourceCollection, database: ResourceDatabase) -> int:
raise NotImplementedError
def satisfied(self, current_resources: ResourceCollection, current_energy: int, database: ResourceDatabase) -> bool:
raise NotImplementedError
def patch_... |
class RepairCore(ABC):
problemDectors: Dict[(str, ProblemDetector)]
patchSynthesizers: Dict[(str, PatchSynthesizer)]
def name(self) -> str:
pass
def __init__(self, clsProblemDectors: Iterable[Type[ProblemDetector]], clsPatchSynthesizers: Iterable[Type[PatchSynthesizer]], detectorArgs: Optional[D... |
class TelemetryData(MutableMapping):
def __init__(self, *args, **kwargs):
self.store = dict()
self.update(dict(*args, **kwargs))
def __getitem__(self, key):
value = self.store[self.__keytransform__(key)]
if isinstance(value, dict):
return self.__class__(value)
... |
class CoreAudioSource(StreamingSource):
def __init__(self, filename, file=None):
self._bl = None
self._file = file
self._deleted = False
self._file_obj = None
self._audfile = None
self._audref = None
audref = ExtAudioFileRef()
if (file is None):
... |
def get_example_models():
example_dir = os.path.join(os.path.dirname(__file__), '..', 'examples')
for filename in os.listdir(example_dir):
if (filename.endswith('.py') and (not filename.startswith('run_')) and (not filename.startswith('__'))):
modelname = filename[:(- 3)]
if ((mo... |
class PandaRealSensed435Config(PandaDefaultConfig):
def __init__(self) -> None:
super().__init__()
self.urdf_path = '{PACKAGE_ASSET_DIR}/descriptions/panda_v3.urdf'
def cameras(self):
return CameraConfig(uid='hand_camera', p=[0, 0, 0], q=[1, 0, 0, 0], width=128, height=128, fov=1.57, nea... |
class Tokenizer():
def add_file_to_dictionary(filename, dict, tokenize, append_eos=True):
with open(filename, 'r') as f:
for line in f:
for word in tokenize(line):
dict.add_symbol(word)
if append_eos:
dict.add_symbol(dict.eo... |
class Effect3961(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Repair Systems')), 'armorDamageAmount', module.getModifiedItemAttr('subsystemBonusGallenteDefensive'), skill='Gallente Defensive... |
def main(filename, save):
data = pd.read_csv(filename)
data = data[['hit_x', 'hit_y']]
df = pd.DataFrame([])
df['hit_x'] = data['hit_x']
df['hit_y'] = data['hit_y']
df['hit_area'] = pd.Series(to_area(data['hit_x'], data['hit_y']))
df.to_csv(save, index=False, encoding='utf-8') |
def _build_hint(parser: argparse.ArgumentParser, arg_action: argparse.Action) -> str:
suppress_hint = arg_action.get_suppress_tab_hint()
if (suppress_hint or (arg_action.help == argparse.SUPPRESS)):
return ''
else:
formatter = parser._get_formatter()
formatter.start_section('Hint')
... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size... |
def default_argument_parser():
parser = argparse.ArgumentParser(description='Detectron2 Training')
parser.add_argument('--config-file', default='configs/smoke_gn_vector.yaml', metavar='FILE', help='path to config file')
parser.add_argument('--eval-only', action='store_true', help='perform evaluation only')
... |
class VoteStorage():
def __init__(self, request, name, rate):
self.request = request
self.name = name
self.items = request.session.get(name, [])
self.rate = rate
def add(self, instance):
self.items.append(instance.pk)
self.request.session[self.name] = self.items
... |
class StyleElementDescription():
def __init__(self, name, description, defaultFormat):
self._name = name
self._description = description
self._defaultFormat = StyleFormat(defaultFormat)
def __repr__(self):
return ('<"%s": "%s">' % (self.name, self.defaultFormat))
def name(sel... |
def test_classical_truth_table():
truth_table = []
for (c, t) in itertools.product([0, 1], repeat=2):
(out_c, out_t) = CNOT().call_classically(ctrl=c, target=t)
truth_table.append(((c, t), (out_c, out_t)))
assert (truth_table == [((0, 0), (0, 0)), ((0, 1), (0, 1)), ((1, 0), (1, 1)), ((1, 1),... |
class FeaturesManager():
_TASKS_TO_AUTOMODELS = {}
_TASKS_TO_TF_AUTOMODELS = {}
if is_torch_available():
_TASKS_TO_AUTOMODELS = {'default': AutoModel, 'masked-lm': AutoModelForMaskedLM, 'causal-lm': AutoModelForCausalLM, 'seq2seq-lm': AutoModelForSeq2SeqLM, 'sequence-classification': AutoModelForSeq... |
def test_call_after_hooks_in_correct_order(hookregistry, mocker):
data = []
(order=2)
def second_hook(features):
data.append(2)
(order=1)
def first_hook(step):
data.append(1)
hookregistry.call('after', 'all', False, mocker.MagicMock())
assert (data == [2, 1]) |
class TestAssert_reprcompare_attrsclass():
def test_attrs(self) -> None:
class SimpleDataObject():
field_a = attr.ib()
field_b = attr.ib()
left = SimpleDataObject(1, 'b')
right = SimpleDataObject(1, 'c')
lines = callequal(left, right)
assert (lines is ... |
class Cipher(typing.Generic[Mode]):
def __init__(self, algorithm: CipherAlgorithm, mode: Mode, backend: typing.Any=None) -> None:
if (not isinstance(algorithm, CipherAlgorithm)):
raise TypeError('Expected interface of CipherAlgorithm.')
if (mode is not None):
assert isinstanc... |
def test_filewritejson_empty_path_raises():
context = Context({'fileWriteJson': {'path': None}})
with pytest.raises(KeyInContextHasNoValueError) as err_info:
filewrite.run_step(context)
assert (str(err_info.value) == "context['fileWriteJson']['path'] must have a value for pypyr.steps.filewritejson."... |
_lazy('cudf')
def _register_cudf():
import cudf
(cudf.DataFrame)
(cudf.Series)
(cudf.BaseIndex)
def proxify_device_object_cudf_dataframe(obj, proxied_id_to_proxy, found_proxies, excl_proxies):
return proxify(obj, proxied_id_to_proxy, found_proxies)
try:
from dask.array.dispatch i... |
class EdgeBlock(nn.Module):
def __init__(self, in_chs, out_chs, dilation=1, bottle_ratio=0.5, groups=1, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, attn_layer=None, drop_block=None, drop_path=0.0):
super(EdgeBlock, self).__init__()
mid_chs = int(round((out_chs * bottle_ratio)))
ckwargs = d... |
def test_compile_single_qubit_gates():
q = cirq.LineQubit(0)
c1 = cirq.Circuit()
for _ in range(10):
c1.append((random.choice([cirq.X, cirq.Y, cirq.Z])(q) ** random.random()))
c2 = compile_single_qubit_gates(c1)
assert (c1 != c2)
assert (len(c2) == 2)
assert isinstance(c2[0].operatio... |
class AtspiMeta(BaseMeta):
control_type_to_cls = {}
def __init__(cls, name, bases, attrs):
BaseMeta.__init__(cls, name, bases, attrs)
for t in cls._control_types:
AtspiMeta.control_type_to_cls[t] = cls
def find_wrapper(element):
try:
wrapper_match = AtspiMeta.... |
class PornhubCom(SimpleDownloader):
__name__ = 'PornhubCom'
__type__ = 'downloader'
__version__ = '0.62'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fallback ... |
class FacesDisplay():
def __init__(self, size, padding, tk_vars):
logger.trace('Initializing %s: (size: %s, padding: %s, tk_vars: %s)', self.__class__.__name__, size, padding, tk_vars)
self.size = size
self.display_dims = (1, 1)
self.tk_vars = tk_vars
self.padding = padding
... |
def test_exclude_glob(pytester: Pytester) -> None:
hellodir = pytester.mkdir('hello')
hellodir.joinpath('test_hello.py').write_text('x y syntaxerror', encoding='utf-8')
hello2dir = pytester.mkdir('hello2')
hello2dir.joinpath('test_hello2.py').write_text('x y syntaxerror', encoding='utf-8')
hello3dir... |
(host=st.one_of(st.ip_addresses(), st.text()), port=st.one_of(st.none(), st.integers()), raises=st.booleans())
def test_setup_url_for_address(host: str, port: (int | None), raises: bool) -> None:
kwargs = {}
if raises:
kwargs['side_effect'] = gaierror
else:
kwargs['return_value'] = '127.0.0.... |
def statistics_match(df1, df2):
matches = []
bounds = {'red_light': {'type': 'discrete', 'eps': 0.005}, 'hazard_stop': {'type': 'discrete', 'eps': 0.005}, 'speed_sign': {'type': 'discrete', 'eps': 0.005}, 'center_distance': {'type': 'cont', 'eps': 0.02}, 'relative_angle': {'type': 'cont', 'eps': 0.01}, 'veh_dis... |
class ResNet18(chainer.Chain):
def __init__(self):
super(ResNet18, self).__init__(conv1_relu=ConvolutionBlock(1, 32), res2a_relu=ResidualBlock(32, 32), res2b_relu=ResidualBlock(32, 32), res3a_relu=ResidualBlockB(32, 64), res3b_relu=ResidualBlock(64, 64), res4a_relu=ResidualBlockB(64, 128), res4b_relu=Residu... |
.parametrize('qurl, expected', [(QUrl('ftp://example.com/'), ('ftp', 'example.com', 21)), (QUrl('ftp://example.com:2121/'), ('ftp', 'example.com', 2121)), (QUrl(' (' 'qutebrowser.org', 8010)), (QUrl(' (' 'example.com', 443)), (QUrl(' (' 'example.com', 4343)), (QUrl(' (' 'qutebrowser.org', 80))])
def test_host_tuple_val... |
def get_argument_parser():
parser = argparse.ArgumentParser()
parser.add_argument('-a', '--all', action='store_const', const=True, default=False)
parser.add_argument('-b', '--black', action='store_const', const=True, default=False)
parser.add_argument('-f', '--flake', action='store_const', const=True, d... |
def degrade(species, kdeg):
def degrade_name_func(rule_expression):
cps = rule_expression.reactant_pattern.complex_patterns
return '_'.join((_complex_pattern_label(cp) for cp in cps))
if isinstance(species, Monomer):
species = species()
species = as_complex_pattern(species)
retur... |
def make_vocab(name, filenames, size, tokenizer, num_workers=1):
if (name == 'source'):
vocab = onmt.Dict([opt.src_pad_token, opt.src_unk_token, opt.src_bos_token, opt.src_eos_token], lower=opt.lower)
elif (name == 'target'):
vocab = onmt.Dict([opt.tgt_pad_token, opt.tgt_unk_token, opt.tgt_bos_t... |
class _RopeConfigSource(Source):
name: str = 'config.py'
run_globals: Dict
def __init__(self, ropefolder: Folder):
self.ropefolder = ropefolder
self.run_globals = {}
def _read(self) -> bool:
if ((self.ropefolder is None) or (not self.ropefolder.has_child('config.py'))):
... |
class CLexer(object):
def __init__(self, cparser):
self.cparser = cparser
self.type_names = set()
def input(self, tokens):
self.tokens = tokens
self.pos = 0
def token(self):
while (self.pos < len(self.tokens)):
t = self.tokens[self.pos]
self.po... |
class SourceLinesAdapter():
def __init__(self, source_code):
self.code = source_code
self.starts = None
self._initialize_line_starts()
def _initialize_line_starts(self):
self.starts = []
self.starts.append(0)
try:
i = 0
while True:
... |
def kasten96_lt(airmass_absolute, precipitable_water, aod_bb):
delta_cda = ((- 0.101) + (0.235 * (airmass_absolute ** (- 0.16))))
delta_w = ((0.112 * (airmass_absolute ** (- 0.55))) * (precipitable_water ** 0.34))
delta_a = aod_bb
lt = (((- (9.4 + (0.9 * airmass_absolute))) * np.log(np.exp(((- airmass_a... |
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv3d(1, 32, 3, padding=1)
self.conv2 = nn.Conv3d(32, 32, 3, padding=1)
self.conv3 = nn.Conv3d(32, 64, 3, padding=1)
self.conv4 = nn.Conv3d(64, 64, 3, padding=1)
self.conv5 = nn.Con... |
.network
def test_get_transform_grid_list__source_id():
grids = get_transform_grid_list(bbox=BBox(170, (- 90), (- 170), 90), source_id='us_noaa', include_already_downloaded=True)
assert (len(grids) > 5)
source_ids = set()
for grid in grids:
source_ids.add(grid['properties']['source_id'])
ass... |
def to_cpu(list_of_tensor):
if isinstance(list_of_tensor[0], list):
list_list_of_tensor = list_of_tensor
list_of_tensor = [to_cpu(list_of_tensor) for list_of_tensor in list_list_of_tensor]
else:
list_of_tensor = [tensor.cpu() for tensor in list_of_tensor]
return list_of_tensor |
def wsproto_demo(host: str, port: int) -> None:
print(f'Connecting to {host}:{port}')
conn = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
conn.connect((host, port))
print('Opening WebSocket')
ws = WSConnection(ConnectionType.CLIENT)
net_send(ws.send(Request(host=host, target='server')), con... |
def test_hotstart():
HSF_PATH = MODEL_WEIR_SETTING_PATH.replace('.inp', '.hsf')
if os.path.exists(HSF_PATH):
os.remove(HSF_PATH)
assert (not os.path.exists(HSF_PATH))
with Simulation(MODEL_WEIR_SETTING_PATH) as sim:
J1 = Nodes(sim)['J1']
for (ind, step) in enumerate(sim):
... |
class ChartElement(Element):
def __init__(self, chart: Chart, figsize: Tuple[(float, float)]=None, dpi=250, optimise=False, grid_proportion=GridProportion.Eight, comment: str='', html_figsize: Tuple[(float, float)]=None, float_setting: str=None, **savefig_settings):
super().__init__(grid_proportion)
... |
_stabilize
_specialize
_rewriter([log])
def local_log1p(fgraph, node):
if (node.op == log):
(log_arg,) = node.inputs
if (log_arg.owner and (log_arg.owner.op == add)):
(scalars, scalar_inputs, nonconsts) = scalarconsts_rest(log_arg.owner.inputs, only_process_constants=True)
if... |
class CleoNamespaceNotFoundError(CleoUserError):
def __init__(self, name: str, namespaces: (list[str] | None)=None) -> None:
message = f'There are no commands in the "{name}" namespace.'
if namespaces:
suggestions = _suggest_similar_names(name, namespaces)
if suggestions:
... |
def test_index(stream):
df = pd.DataFrame({'x': [1, 2, 3], 'y': [4, 5, 6]})
a = DataFrame(example=df, stream=stream)
b = (a.index + 5)
L = b.stream.gather().sink_to_list()
a.emit(df)
a.emit(df)
wait_for((lambda : (len(L) > 1)), timeout=2, period=0.05)
assert_eq(L[0], (df.index + 5))
... |
def channel_deposit_with_the_same_token_network(deposit_queue: List[ChannelDeposit]) -> None:
while deposit_queue:
to_delete = []
for (pos, channel_deposit) in enumerate(deposit_queue):
channel = channel_details(channel_deposit.endpoint, channel_deposit.token_address, channel_deposit.par... |
.parametrize('shape', [(), (3, 4)])
.parametrize('dtype', [None, torch.float, torch.double, torch.int])
.parametrize('device', ([None] + get_available_devices()))
.parametrize('from_path', [True, False])
class TestConstructors():
.parametrize('shape_arg', ['expand', 'arg', 'kwarg'])
def test_zeros(self, shape, ... |
def evaluate(ground_truth_path, result_path, subset, top_k, ignore):
(ground_truth, class_labels_map) = load_ground_truth(ground_truth_path, subset)
result = load_result(result_path, top_k, class_labels_map)
n_ground_truth = len(ground_truth)
ground_truth = remove_nonexistent_ground_truth(ground_truth, ... |
class ErrorCode(enum.Enum):
bad_star_import = 1
cant_import = 2
unexpected_node = 3
undefined_name = 4
undefined_attribute = 5
attribute_is_never_set = 6
duplicate_dict_key = 7
unhashable_key = 8
bad_unpack = 9
unsupported_operation = 10
not_callable = 11
incompatible_cal... |
def test_scenarios_none_found(pytester, pytest_params):
testpath = pytester.makepyfile("\n import pytest\n from pytest_bdd import scenarios\n\n scenarios('.')\n ")
result = pytester.runpytest_subprocess(testpath, *pytest_params)
result.assert_outcomes(errors=1)
result.stdout.fnma... |
class PLMSSampler(object):
def __init__(self, model, schedule='linear', **kwargs):
super().__init__()
self.model = model
self.ddpm_num_timesteps = model.num_timesteps
self.schedule = schedule
def register_buffer(self, name, attr):
if (type(attr) == torch.Tensor):
... |
class StateActionDynEVAE(nn.Module):
def __init__(self, traj_size, action_embed_size, state_embed_size, stack=4):
super().__init__()
self.traj_size = traj_size
self.action_embed_size = action_embed_size
self.state_embed_size = state_embed_size
self.stack = stack
self.... |
def create_train_state(model: FlaxAutoModelForSequenceClassification, learning_rate_fn: Callable[([int], float)], is_regression: bool, num_labels: int, weight_decay: float) -> train_state.TrainState:
class TrainState(train_state.TrainState):
logits_fn: Callable = struct.field(pytree_node=False)
loss... |
def test_get_module_raises():
with pytest.raises(PyModuleNotFoundError) as err:
moduleloader.get_module('unlikelyblahmodulenameherexxssz')
assert (str(err.value) == "unlikelyblahmodulenameherexxssz.py should be in your pipeline dir, or in your working dir, or it should be installed in the current python... |
def construct_odenet(dims):
layers = []
for (in_dim, out_dim) in zip(dims[:(- 1)], dims[1:]):
layers.append(diffeq_layers.ConcatLinear(in_dim, out_dim))
layers.append(basic_layers.TimeDependentSwish(out_dim))
layers = layers[:(- 1)]
return container_layers.SequentialDiffEq(*layers) |
class LxDeviceListClass(gdb.Command):
def __init__(self):
super(LxDeviceListClass, self).__init__('lx-device-list-class', gdb.COMMAND_DATA)
def invoke(self, arg, from_tty):
if (not arg):
for cls in for_each_class():
gdb.write('class {}:\t{}\n'.format(cls['name'].strin... |
class Signature(Generic[T]):
def __init__(self) -> None:
self.pos: list[T] = []
self.kwonly: dict[(str, T)] = {}
self.varpos: (T | None) = None
self.varkw: (T | None) = None
def __str__(self) -> str:
def get_name(arg: Any) -> str:
if isinstance(arg, inspect.Pa... |
def assert_column_equality(output_df: DataFrame, target_df: DataFrame, output_column: Column, target_column: Column) -> None:
if (not ((output_df.select(output_column).count() == target_df.select(target_column).count()) and (len(target_df.columns) == len(output_df.columns)))):
raise AssertionError(f'''DataF... |
class DataCollatorForWav2Vec2Pretraining():
model: Wav2Vec2ForPreTraining
feature_extractor: Wav2Vec2FeatureExtractor
padding: Union[(bool, str)] = 'longest'
pad_to_multiple_of: Optional[int] = None
max_length: Optional[int] = None
def __call__(self, features: List[Dict[(str, Union[(List[int], t... |
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