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def _enumerate_dst_area_chunks(dst_area, dst_chunks):
for (position, slices) in _enumerate_chunk_slices(dst_chunks):
chunk_shape = tuple((chunk[pos] for (pos, chunk) in zip(position, dst_chunks)))
target_geo_def = dst_area[slices[(- 2):]]
block_info = {'num-chunks': [len(chunk) for chunk in ... |
class Migration(migrations.Migration):
dependencies = [('participants', '0001_store_participants')]
operations = [migrations.AddField(model_name='participant', name='facebook_url', field=models.CharField(blank=True, max_length=2048)), migrations.AddField(model_name='participant', name='instagram_handle', field=... |
def collect(workflow_prefix: str, force: bool) -> None:
results_path = build_started_results_path(workflow_prefix)
if results_path.exists():
started_results = pd.read_csv(results_path)
else:
logger.warning('Started results are not found.')
started_results = create_empty_dataframe_for... |
class MockVirtualEnv(VirtualEnv):
def __init__(self, path: Path, base: (Path | None)=None, sys_path: (list[str] | None)=None) -> None:
super().__init__(path, base=base)
self._sys_path = sys_path
def sys_path(self) -> list[str]:
if (self._sys_path is not None):
return self._sy... |
def test_storyboard_story_input():
init = OSC.Init()
TD = OSC.TransitionDynamics(OSC.DynamicsShapes.step, OSC.DynamicsDimension.rate, 1)
egospeed = OSC.AbsoluteSpeedAction(10, TD)
init.add_init_action('Ego', egospeed)
init.add_init_action('Ego', OSC.TeleportAction(OSC.WorldPosition(1, 2, 3, 0, 0, 0)... |
def test_eval_hmean_ic13():
det_boxes = []
gt_boxes = []
gt_ignored_boxes = []
precision_thr = 0.4
recall_thr = 0.8
center_dist_thr = 1.0
one2one_score = 1.0
one2many_score = 0.8
many2one_score = 1
with pytest.raises(AssertionError):
hmean_ic13.eval_hmean_ic13([1], gt_box... |
def preformat_Peptides(dataset_dir, name):
try:
from graphgps.loader.dataset.peptides_functional import PeptidesFunctionalDataset
from graphgps.loader.dataset.peptides_structural import PeptidesStructuralDataset
except Exception as e:
logging.error('ERROR: Failed to import Peptides datas... |
class RelationTreeTests(SimpleTestCase):
all_models = (CassandraThing,)
def setUp(self):
apps.clear_cache()
def test_clear_cache_clears_relation_tree(self):
all_models_with_cache = (m for m in self.all_models if (not m._meta.abstract))
for m in all_models_with_cache:
self... |
def generate_alias_id(chat):
chat_id = chat.id
title = chat.title
while True:
alias_id = ''.join([random.choice((string.ascii_letters + string.digits)) for _ in range(len(str(chat_id)))])
if (alias_id in alias_ids):
continue
alias_ids.append(alias_id)
chat_ids.app... |
def dliate_erode(img, kernel):
er_k = kernel
di_k = kernel
erode_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, ((er_k // 2), (er_k // 2)))
dilate_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (di_k, di_k))
img_f = cv2.dilate(img, dilate_kernel)
img_f = cv2.erode(img_f, erode_kernel)
... |
def data_pre(dataset, task):
test_data = pd.read_csv('../../data/dataset/{}/{}.tsv'.format(dataset, task), sep='\t')
(text_a, text_b, label, similarity) = (test_data['text_a'], test_data['text_b'], test_data['labels'], [])
ppservers = ()
job_server = pp.Server(ppservers=ppservers)
modules = ('nltk.c... |
class Babel():
default_date_formats = ImmutableDict({'time': 'medium', 'date': 'medium', 'datetime': 'medium', 'time.short': None, 'time.medium': None, 'time.full': None, 'time.long': None, 'date.short': None, 'date.medium': None, 'date.full': None, 'date.long': None, 'datetime.short': None, 'datetime.medium': None... |
class TestHTLCManager(ElectrumTestCase):
def test_adding_htlcs_race(self):
A = HTLCManager(StoredDict({}, None, []))
B = HTLCManager(StoredDict({}, None, []))
A.channel_open_finished()
B.channel_open_finished()
(ah0, bh0) = (H('A', 0), H('B', 0))
B.recv_htlc(A.send_ht... |
class IndexedDataset(torch.utils.data.Dataset):
def __init__(self, path):
super().__init__()
with open(index_file_path(path), 'rb') as f:
magic = f.read(8)
assert (magic == b'TNTIDX\x00\x00')
version = f.read(8)
assert (struct.unpack('<Q', version) == ... |
def create_minibatch_rv(rv: TensorVariable, total_size: Union[(int, None, Sequence[Union[(int, EllipsisType, None)]])]) -> TensorVariable:
if isinstance(total_size, int):
if (rv.ndim <= 1):
total_size = [total_size]
else:
missing_ndims = (rv.ndim - 1)
total_size =... |
def main():
parser = argparse.ArgumentParser(description='Krkn Chaos Recommender Command-Line tool')
args = parse_arguments(parser)
if ((args.config_file is None) and (not args.options)):
logging.error('You have to either specify a config file path or pass recommender options as command line argumen... |
class NetWrapper(nn.Module):
def __init__(self, net, projection_size, projection_hidden_size, layer=(- 2)):
super().__init__()
self.net = net
self.layer = layer
self.projector = None
self.projection_size = projection_size
self.projection_hidden_size = projection_hidde... |
def get_scheduler(name: Union[(str, SchedulerType)], optimizer: Optimizer, num_warmup_steps: Optional[int]=None, num_training_steps: Optional[int]=None):
name = SchedulerType(name)
schedule_func = TYPE_TO_SCHEDULER_FUNCTION[name]
if (name == SchedulerType.CONSTANT):
return schedule_func(optimizer)
... |
def train(base_loader, val_loader, model, optimization, start_epoch, stop_epoch, params):
if (optimization == 'Adam'):
optimizer = torch.optim.Adam(model.parameters())
else:
raise ValueError('Unknown optimization, please define by yourself')
max_acc = 0
for epoch in range(start_epoch, st... |
.parametrize('sampler', [sample_blackjax_nuts, sample_numpyro_nuts])
.parametrize('idata_kwargs', [dict(), dict(log_likelihood=True), dict(coords={'x_coord': ['x1', 'x2']}), dict(dims={'x': ['x_coord2']}), dict(coords={'x_coord3': ['A', 'B']}, dims={'x': ['x_coord3']})])
.parametrize('postprocessing_backend', [None, 'c... |
class _BackendREST(_BackendBase):
def __init__(self, url, bugzillasession):
_BackendBase.__init__(self, url, bugzillasession)
self._bugzillasession.set_rest_defaults()
def _handle_error(self, e):
response = getattr(e, 'response', None)
if (response is None):
raise e
... |
class LARSOptimizer(tf.train.Optimizer):
def __init__(self, learning_rate, momentum=0.9, use_nesterov=False, weight_decay=0.0, exclude_from_weight_decay=None, exclude_from_layer_adaptation=None, classic_momentum=True, eeta=EETA_DEFAULT, name='LARSOptimizer'):
super(LARSOptimizer, self).__init__(False, name)... |
def train(args, generator, discriminator_photo, discriminator_cari, discriminator_feat_p, discriminator_feat_c, g_optim, d_optim_p, d_optim_c, d_optim_fp, d_optim_fc, g_ema, p_cls, c_cls, id_net, device):
pbar = range(args.iter)
if (get_rank() == 0):
if (not os.path.exists(f'checkpoint/{args.name}')):
... |
class _TestingThread(threading.Thread):
def __init__(self):
super(_TestingThread, self).__init__()
self.results = []
self.exc = None
def run(self):
try:
with mssqlconn() as mssql:
for i in range(0, 1000):
num = mssql.execute_scalar(... |
def test_mws_xml_to_dotdict_resultkey(simple_xml_response_str):
output = mws_xml_to_dotdict(simple_xml_response_str, result_key='ListMatchingProductsResult')
assert isinstance(output, DotDict)
assert isinstance(output, dict)
assert ('ListMatchingProductsResult' not in output)
assert ('ResponseMetada... |
def dump_gl(context=None):
if (context is not None):
info = context.get_info()
else:
from pyglet.gl import gl_info as info
print('gl_info.get_version():', info.get_version())
print('gl_info.get_vendor():', info.get_vendor())
print('gl_info.get_renderer():', info.get_renderer()) |
.testinfra_hosts('docker://rockylinux9', 'ssh://rockylinux9')
def test_docker_encoding(host):
encoding = host.check_output("python3 -c 'import locale;print(locale.getpreferredencoding())'")
assert (encoding == 'UTF-8')
string = 'teinfra seak u8'
assert (host.check_output('echo %s | tee /tmp/s.txt', stri... |
class TestFunctional():
def test_fail_to_ok(self, pytester: pytest.Pytester) -> None:
p = pytester.makepyfile(textwrap.dedent('\n def test_one():\n x = 0\n assert x == 1\n '))
child = pytester.spawn_pytest(('-f %s --traceconfig' % p... |
class MetricWrapper(Metric):
def isAggregate(self):
return self.aggregate
def getTags(self):
return self.tags
'\n This method does nothing and therefore keeps the existing metric unchanged.\n '
def processDefaultMetric(self):
self.tags = {}
self.aggregate = False
... |
class GridPlot(AbstractPlot):
def __init__(self, columns=3, *plots):
super(GridPlot, self).__init__()
self.plots = plots
self.columns = columns
self.rows = (((len(plots) + self.columns) - 1) // self.columns)
width = max([elem.figsize[0] for elem in self.plots])
height... |
class TestPep420Namespaces():
def test_namespace_package_importable(self, venv, tmp_path, editable_opts):
pkg_A = namespaces.build_pep420_namespace_package(tmp_path, 'myns.n.pkgA')
pkg_B = namespaces.build_pep420_namespace_package(tmp_path, 'myns.n.pkgB')
opts = editable_opts[:]
opts... |
_model_architecture('linformer_roberta', 'linformer_roberta_large')
def linformer_roberta_large_architecture(args):
args.encoder_layers = getattr(args, 'encoder_layers', 24)
args.encoder_embed_dim = getattr(args, 'encoder_embed_dim', 1024)
args.encoder_ffn_embed_dim = getattr(args, 'encoder_ffn_embed_dim', ... |
class MemoryService(object):
def __init__(self, config):
self._config = config
self._page_size = os.sysconf('SC_PAGE_SIZE')
self._root_path = '/sys/kernel'
if os.getenv('JTOP_TESTING', False):
self._root_path = '/fake_sys/kernel'
logger.warning('Running in JTO... |
def duplicate_module(module_file: Union[(str, os.PathLike)], old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns, dest_file: Optional[str]=None, add_copied_from: bool=True):
if (dest_file is None):
dest_file = str(module_file).replace(old_model_patterns.model_lower_cased, new_model_patterns... |
class ViTImageProcessingTester(unittest.TestCase):
def __init__(self, parent, batch_size=7, num_channels=3, image_size=18, min_resolution=30, max_resolution=400, do_resize=True, size=None, do_normalize=True, image_mean=[0.5, 0.5, 0.5], image_std=[0.5, 0.5, 0.5]):
size = (size if (size is not None) else {'he... |
class VQA2Dataset(BaseDataset):
def __init__(self, dataset_type, imdb_file_index, config, *args, **kwargs):
super().__init__('vqa2', dataset_type, config)
imdb_files = self.config.imdb_files
if (dataset_type not in imdb_files):
raise ValueError('Dataset type {} is not present in ... |
def main():
subprocess.run(['mkdocs', 'build'], check=True)
hti = Html2Image(custom_flags=['--force-device-scale-factor=2'])
html_str = Path('docs/diagram.md').read_text()
css_tags = f'''
<style>{Path('site/css/theme.css').read_text()}</style>
<style>{Path('site/css/theme_extra.css').rea... |
def _pack(binary):
data_size = (binary.dtype.itemsize * binary.shape[0])
out_size = data_size
out = cp.empty_like(binary, dtype=cp.ubyte, shape=out_size)
(threadsperblock, blockspergrid) = _get_tpb_bpg()
k_type = 'pack'
_populate_kernel_cache(out.dtype, k_type)
kernel = _get_backend_kernel(o... |
class TestEPSL1B(BaseTestCaseEPSL1B):
def setUp(self):
self.scan_lines = 1080
self.earth_views = 2048
sections = self._create_structure()
sections[('mphr', 0)]['TOTAL_MDR'] = ((b'TOTAL_MDR = ' + bytes(str(self.scan_lines), encoding='ascii')) + b'\n')
sec... |
def test_solution_integrator():
assert (SolutionIntegrator.OCP.value == 'OCP')
assert (SolutionIntegrator.SCIPY_RK23.value == 'RK23')
assert (SolutionIntegrator.SCIPY_RK45.value == 'RK45')
assert (SolutionIntegrator.SCIPY_DOP853.value == 'DOP853')
assert (SolutionIntegrator.SCIPY_BDF.value == 'BDF')... |
class ContextFlag():
def __init__(self) -> None:
self.__count = 0
def __bool__(self) -> bool:
return (self.__count > 0)
def __enter__(self) -> None:
self.__count += 1
def __exit__(self, *args: Any) -> None:
self.__count -= 1
if (self.__count < 0):
rais... |
class Random(object):
MDIG = 32
ONE = 1
m1 = ((ONE << (MDIG - 2)) + ((ONE << (MDIG - 2)) - ONE))
m2 = (ONE << (MDIG // 2))
dm1 = (1.0 / float(m1))
def __init__(self, seed):
self.initialize(seed)
self.left = 0.0
self.right = 1.0
self.width = 1.0
self.haveRa... |
def test_swap_with_zero_cirq_gate_diagram():
gate = SwapWithZero(3, 2, 4)
gh = cq_testing.GateHelper(gate)
cirq.testing.assert_has_diagram(cirq.Circuit(gh.operation, cirq.decompose_once(gh.operation)), '\nselection0: (r0)\n \nselection1: (r0)(approx)\n ... |
def add_attached_meshes(mesh_ids, meshes, poses, link_names):
attached_objects = list()
for (mesh_id, mesh, pose, link_name) in zip(mesh_ids, meshes, poses, link_names):
attached_object_msg = _AttachedCollisionObject()
attached_object_msg.link_name = link_name
attached_object_msg.touch_l... |
.parametrize(('current_os', 'required_files'), [('Windows', [('AM2R.exe',), ('data.win',)]), ('Linux', [('AM2R.AppImage',)]), ('Linux', [('runner',), ('assets', 'game.unx')]), ('Darwin', [('AM2R.app', 'Contents', 'MacOS', 'Mac_Runner'), ('AM2R.app', 'Contents', 'Resources', 'game.ios')])])
def test_is_valid_input_dir(c... |
def read_tles_from_mmam_xml_files(paths):
fnames = collect_filenames(paths)
tles = []
for fname in fnames:
data = read_tle_from_mmam_xml_file(fname).split('\n')
for two_lines in _group_iterable_to_chunks(2, data):
tl_stream = io.StringIO('\n'.join(two_lines))
tles.app... |
class StubtestMiscUnit(unittest.TestCase):
def test_output(self) -> None:
output = run_stubtest(stub='def bad(number: int, text: str) -> None: ...', runtime='def bad(num, text): pass', options=[])
expected = f'''error: {TEST_MODULE_NAME}.bad is inconsistent, stub argument "number" differs from runti... |
class LinearBottleneck(nn.Module):
def __init__(self, inplanes, outplanes, stride=1, t=6, activation=nn.ReLU6):
super(LinearBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, (inplanes * t), kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d((inplanes * t), momentum=0.0003)
... |
class Arguments():
def __init__(self, description):
self.parser = ArgumentParser(description=description)
self.checks = []
self.add_argument('--root', dest='root', default='experiments')
self.add_argument('--experiment', dest='experiment', default='dirty')
self.add_argument('... |
class Basic3DBlock(nn.Module):
def __init__(self, in_planes, out_planes, kernel_size):
super(Basic3DBlock, self).__init__()
self.block = nn.Sequential(nn.Conv3d(in_planes, out_planes, kernel_size=kernel_size, stride=1, padding=((kernel_size - 1) // 2)), nn.BatchNorm3d(out_planes), nn.ReLU(True))
... |
class TransformComponent(ABC):
def __init__(self) -> None:
self._parent = None
def parent(self) -> Any:
return self._parent
def parent(self, parent: None) -> None:
self._parent = parent
def output_columns(self) -> List[str]:
def transform(self, dataframe: DataFrame) -> DataFr... |
def test_unsuccessful_load_from_s3_client_error(s3_stub):
s3_stub.add_client_error('get_object')
with pytest.raises(LoaderException):
_load_from_s3(json.dumps({'region_name': 'us-east-1', 'bucket_name': 'my-test-bucket', 'file_key': 'my-object-key', 'sse_key': 'my-sse-key'}).encode('utf-8')) |
class Effect6699(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
lvl = src.level
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Rig Drones')), 'drawback', (src.getModifiedItemAttr('rigDrawbackBonus') * lvl), **kwargs) |
def mode_dataframe(spark_context, spark_session):
data = [{'id': 1, 'timestamp': '2016-04-11 11:31:11', 'feature1': 200}, {'id': 1, 'timestamp': '2016-04-11 11:44:12', 'feature1': 200}, {'id': 1, 'timestamp': '2016-04-11 11:46:24', 'feature1': 200}, {'id': 1, 'timestamp': '2016-04-11 12:03:21', 'feature1': 300}, {'... |
class BuildScripts(du_build_scripts):
description = 'copy scripts to build directory'
def run(self):
du_build_scripts.run(self)
for script in self.scripts:
outfile = os.path.join(self.build_dir, os.path.basename(script))
new = os.path.splitext(outfile)[0]
try:... |
class CSVLoggerTest(unittest.TestCase):
def test_csv_log(self) -> None:
with TemporaryDirectory() as tmpdir:
csv_path = Path(tmpdir, 'test.csv').as_posix()
logger = CSVLogger(csv_path, steps_before_flushing=1)
log_name = 'asdf'
log_value = 123.0
lo... |
class TestTransformerLowpass(unittest.TestCase):
def test_default(self):
tfm = new_transformer()
tfm.lowpass(1000.0)
actual_args = tfm.effects
expected_args = ['lowpass', '-2', '1000.000000', '0.707000q']
self.assertEqual(expected_args, actual_args)
actual_log = tfm.e... |
def read_squad_examples(input_file, is_training, version_2_with_negative):
with open(input_file, 'r', encoding='utf-8') as reader:
input_data = json.load(reader)['data']
def is_whitespace(c):
if ((c == ' ') or (c == '\t') or (c == '\r') or (c == '\n') or (ord(c) == 8239)):
return Tru... |
class SelectExtractor(BaseExtractor, SourceHandlerMixin):
SUPPORTED_STMT_TYPES = ['select_statement', 'set_expression', 'bracketed']
def __init__(self, dialect: str, metadata_provider: MetaDataProvider):
super().__init__(dialect, metadata_provider)
self.columns = []
self.tables = []
... |
(frozen=True)
class TranslatorConfiguration(BitPackValue):
translator_requirement: dict[(NodeIdentifier, LayoutTranslatorRequirement)]
fixed_gfmc_compound: bool = True
fixed_torvus_temple: bool = True
fixed_great_temple: bool = True
def bit_pack_encode(self, metadata) -> Iterator[tuple[(int, int)]]:... |
class ModelBuilderTest(tf.test.TestCase):
def test_compute_vertex_channels_linear(self):
matrix1 = np.array([[0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 0, 0, 0]])
vc1 = model_builder.compute_vertex_channels(8, 8, matrix1)
assert (vc1 == [8, 8, 8, 8])
vc2 = model_builder.compute_ve... |
def _load_checkpoint(session, checkpoint_path, allow_drop_layers, allow_lr_init=True):
ckpt = tfv1.train.load_checkpoint(checkpoint_path)
vars_in_ckpt = frozenset(ckpt.get_variable_to_shape_map().keys())
load_vars = set(tfv1.global_variables())
init_vars = set()
lr_var = set((v for v in load_vars if... |
def add_sample_args(parser):
common_arg = parser.add_argument_group('Common')
add_common_arg(common_arg)
common_arg.add_argument('--model_load', type=str, required=True, help='Where to load the model')
common_arg.add_argument('--config_load', type=str, required=True, help='Where to load the config')
... |
.end_to_end()
def test_collect_task(runner, tmp_path):
source = '\n import pytask\n\n .depends_on("in.txt")\n .produces("out.txt")\n def task_example():\n pass\n '
tmp_path.joinpath('task_module.py').write_text(textwrap.dedent(source))
tmp_path.joinpath('in.txt').touch()
result = r... |
class SRDRM_gen(BaseSRModel):
def __init__(self, lr_shape, hr_shape, SCALE=4):
super(SRDRM_gen, self).__init__('SRDRM', lr_shape, hr_shape, SCALE)
self.n_residual_blocks = 8
self.gf = 64
def residual_block(self, layer_input, filters):
d = Conv2D(filters, kernel_size=3, strides=1,... |
class Mode():
def __init__(self, linker: Optional[Union[(str, Linker)]]=None, optimizer: Union[(str, RewriteDatabaseQuery)]='default', db: RewriteDatabase=None):
if (linker is None):
linker = config.linker
if (isinstance(optimizer, str) and (optimizer == 'default')):
optimize... |
class TestImageNavigation():
()
def expected(self):
exp = {'lon': [[(- 114.56923), (- 112.096837), (- 109.559702)], [8.33221, 8.793893, 9.22339], [15.918476, 16.268354, 16.6332]], 'lat': [[(- 23.078721), (- 24.629845), (- 26.133314)], [(- 42.513409), (- 39.790231), (- 37.06392)], [3.342834, 6.07043, 8.7... |
class TestGroupSearcher():
__test__ = False
def __init__(self):
self.query_text = np.random.random(text_vector_size).tolist()
self.query_image = np.random.random(image_vector_size).tolist()
self.query_code = np.random.random(code_vector_size).tolist()
self.group_by = 'rand_digit'... |
class AbstractCertificateErrorWrapper():
def __init__(self) -> None:
self._certificate_accepted: Optional[bool] = None
def __str__(self) -> str:
raise NotImplementedError
def __repr__(self) -> str:
raise NotImplementedError
def is_overridable(self) -> bool:
raise NotImple... |
_tf
class TFXLMModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = ((TFXLMModel, TFXLMWithLMHeadModel, TFXLMForSequenceClassification, TFXLMForQuestionAnsweringSimple, TFXLMForTokenClassification, TFXLMForMultipleChoice) if is_tf_available() else ())
all_generative_model_c... |
def test_windows_corner_case():
def __test(f, N, normalize):
f(N, normalize)
for f in [blackman, hanning, hamming, bartlett, trapezoid, rectangular]:
with pytest.raises(ValueError):
__test(f, 256, (- 1))
with pytest.raises(ValueError):
__test(f, 256, 3) |
def flush():
with sd_lock:
try:
saveddata_session.flush()
except (KeyboardInterrupt, SystemExit):
raise
except Exception:
saveddata_session.rollback()
exc_info = sys.exc_info()
raise exc_info[0](exc_info[1]).with_traceback(exc_info[... |
class Branch(ControlOp):
error_kind = ERR_NEVER
BOOL: Final = 100
IS_ERROR: Final = 101
def __init__(self, value: Value, true_label: BasicBlock, false_label: BasicBlock, op: int, line: int=(- 1), *, rare: bool=False) -> None:
super().__init__(line)
self.value = value
self.true = ... |
class BertGenerationTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
prefix_tokens: List[int] = []
model_input_names = ['input_ids', 'attention_mask']
def _... |
class CouplingLayer(nn.Module):
def __init__(self, num_inputs, num_hidden, mask=None, act=nn.LeakyReLU, s_act_func=nn.Tanh, t_act_func=None):
super(CouplingLayer, self).__init__()
self.num_inputs = num_inputs
if (mask is None):
mask = (torch.arange(0, num_inputs) % 2).type(torch.... |
class ConvDecoder(tf.Module):
def __init__(self, shape, depth=32, activation=tf.nn.relu, dist='normal'):
super(ConvDecoder, self).__init__()
self._shape = shape
self._dist = dist
self._depth = depth
self._dense = tf.keras.layers.Dense((32 * depth))
self._layers = tf.k... |
()
def restart() -> None:
try:
ok = instance.restart(session='_restart')
except sessions.SessionError as e:
log.destroy.exception('Failed to save session!')
raise cmdutils.CommandError('Failed to save session: {}!'.format(e))
except SyntaxError as e:
log.destroy.exception('Go... |
class BlockItem(scrapy.Item):
block_hash = scrapy.Field()
block_number = scrapy.Field()
parent_hash = scrapy.Field()
difficulty = scrapy.Field()
total_difficulty = scrapy.Field()
size = scrapy.Field()
transaction_hashes = scrapy.Field()
gas_limit = scrapy.Field()
gas_used = scrapy.Fi... |
def parse_args():
parser = argparse.ArgumentParser(description='Generate training and validation set of ArT ')
parser.add_argument('root_path', help='Root dir path of ArT')
parser.add_argument('--val-ratio', help='Split ratio for val set', default=0.0, type=float)
args = parser.parse_args()
return a... |
def convert_state_dict_type(state_dict, ttype=torch.FloatTensor):
if isinstance(state_dict, dict):
cpu_dict = OrderedDict()
for (k, v) in state_dict.items():
cpu_dict[k] = convert_state_dict_type(v)
return cpu_dict
elif isinstance(state_dict, list):
return [convert_st... |
def run():
observable = 'Y3'
vals = [0.8, 1.0, 1.2]
solver = ScipyOdeSimulator(model, tspan)
sens = InitialsSensitivity(values_to_sample=vals, observable=observable, objective_function=obj_func_cell_cycle, solver=solver)
sens.run()
sens.create_individual_pairwise_plots(save_name='pairwise_indivi... |
def disk_info():
logdir = dsz.lp.GetLogsDirectory()
projectdir = os.path.split(logdir)[0]
infofile = os.path.join(projectdir, 'disk-version.txt')
if os.path.exists(infofile):
dsz.ui.Echo(('Disk version already logged; if you switched disks for some reason, rename %s and restart the LP please.' %... |
def start_test_server():
pywebio.enable_debug()
from flask import Flask, send_from_directory
from pywebio.platform.flask import webio_view, run_event_loop
from pywebio import STATIC_PATH
import threading
import logging
app = Flask(__name__)
app.add_url_rule('/io', 'webio_view', webio_vie... |
class TestExists():
.parametrize('absolute', [True, False])
def test_existent(self, tmp_path, absolute):
session_dir = (tmp_path / 'sessions')
abs_session = (tmp_path / 'foo.yml')
rel_session = (session_dir / 'foo.yml')
session_dir.mkdir()
abs_session.touch()
rel_... |
def _main():
parser = argparse.ArgumentParser(description='Find any stray release notes.')
_args = parser.parse_args()
files = discover_files()
with multiprocessing.Pool() as pool:
res = pool.map(validate_path, files)
failed_files = [x for x in res if (x is not None)]
if (len(failed_file... |
class ShardEstimator(abc.ABC):
def __init__(self, topology: Topology, constraints: Optional[Dict[(str, ParameterConstraints)]]=None) -> None:
...
def estimate(self, sharding_options: List[ShardingOption], sharder_map: Optional[Dict[(str, ModuleSharder[nn.Module])]]=None) -> None:
... |
def fci(dataset: ndarray, independence_test_method: str=fisherz, alpha: float=0.05, depth: int=(- 1), max_path_length: int=(- 1), verbose: bool=False, background_knowledge: (BackgroundKnowledge | None)=None, show_progress: bool=True, **kwargs) -> Tuple[(Graph, List[Edge])]:
if (dataset.shape[0] < dataset.shape[1]):... |
def test_add_no_constraint(app: PoetryTestApplication, repo: TestRepository, tester: CommandTester) -> None:
repo.add_package(get_package('cachy', '0.1.0'))
repo.add_package(get_package('cachy', '0.2.0'))
tester.execute('cachy')
expected = 'Using version ^0.2.0 for cachy\n\nUpdating dependencies\nResolv... |
class LmdbBackend(BaseStorageBackend):
def __init__(self, db_path, readonly=True, lock=False, readahead=False, **kwargs):
try:
import lmdb
except ImportError:
raise ImportError('Please install lmdb to enable LmdbBackend.')
self.db_path = str(db_path)
self._cli... |
class AmplLexer(RegexLexer):
name = 'Ampl'
url = '
aliases = ['ampl']
filenames = ['*.run']
version_added = '2.2'
tokens = {'root': [('\\n', Text), ('\\s+', Whitespace), ('#.*?\\n', Comment.Single), ('/[*](.|\\n)*?[*]/', Comment.Multiline), (words(('call', 'cd', 'close', 'commands', 'data', 'del... |
def test_poetry_with_non_default_multiple_sources_legacy(fixture_dir: FixtureDirGetter, with_simple_keyring: None) -> None:
poetry = Factory().create_poetry(fixture_dir('with_non_default_multiple_sources_legacy'))
assert (not poetry.pool.has_default())
assert poetry.pool.has_repository('bar')
assert isi... |
def list_atoms(d, re_obj, low, high):
while (low <= high):
try:
val = d.get_atom_name(low)
if (re_obj == None):
print_atom(options.format, low, val)
elif (re_obj.match(val) != None):
print_atom(options.format, low, val)
low += 1... |
def getTackledSpeed(src, tgt, currentUntackledSpeed, srcScramRange, tgtScrammables, webMods, webDrones, webFighters, distance):
if (tgt.isFit and tgt.item.ship.getModifiedItemAttr('disallowOffensiveModifiers')):
return currentUntackledSpeed
maxUntackledSpeed = tgt.getMaxVelocity()
if (maxUntackledSp... |
def test_flops_to_string():
flops = (6.54321 * (10.0 ** 9))
assert (flops_to_string(flops) == '6.54 GFLOPs')
assert (flops_to_string(flops, 'MFLOPs') == '6543.21 MFLOPs')
assert (flops_to_string(flops, 'KFLOPs') == '6543210.0 KFLOPs')
assert (flops_to_string(flops, 'FLOPs') == '.0 FLOPs')
assert... |
def generate_thumbnail(original_image: Union[(FileLike, StreamDescriptor)], width: int=None, height: int=None, ratio: float=None, ratio_precision: int=5, thumbnail_type: Type[Thumbnail]=Thumbnail) -> Tuple[(int, int, float, Thumbnail)]:
(width, height, ratio) = validate_width_height_ratio(width, height, ratio)
... |
class FlightAdminForm(FlightMixin, forms.ModelForm):
class Meta():
model = Flight
fields = ('name', 'slug', 'campaign', 'start_date', 'end_date', 'hard_stop', 'live', 'priority_multiplier', 'pacing_interval', 'prioritize_ads_ctr', 'cpc', 'sold_clicks', 'cpm', 'sold_impressions', 'targeting_parameter... |
.parametrize('metadata_version', [None, '0.1', '0.2'])
def test_inject_simple_legacy_venv(pipx_temp_env, capsys, metadata_version):
assert (not run_pipx_cli(['install', 'pycowsay']))
mock_legacy_venv('pycowsay', metadata_version=metadata_version)
if (metadata_version is not None):
assert (not run_pi... |
class Migration(migrations.Migration):
dependencies = [migrations.swappable_dependency(settings.AUTH_USER_MODEL), ('projects', '0011_refactoring')]
operations = [migrations.CreateModel(name='Membership', fields=[('id', models.AutoField(auto_created=True, primary_key=True, serialize=False, verbose_name='ID')), (... |
class Effect11953(BaseEffect):
type = ('projected', 'passive')
def handler(fit, beacon, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: mod.item.requiresSkill('Vorton Projector Operation')), 'aoeVelocity', beacon.getModifiedItemAttr('aoeVelocityMultiplier'), stackingPe... |
def clean(opts):
for s in [p.root_ca_path(), p.intermediate_ca_path('1'), p.intermediate_ca_path('2'), p.result_path(), p.leaf_pair_path('server'), p.leaf_pair_path('client')]:
print('Removing {}'.format(s))
try:
shutil.rmtree(s)
except FileNotFoundError:
pass |
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