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class DictionaryParsingTests(unittest.TestCase):
simple_dict_values = [('Test-String', 1, 'string'), ('Test-Octets', 2, 'octets'), ('Test-Integer', 3, 'integer'), ('Test-Ip-Address', 4, 'ipaddr'), ('Test-Ipv6-Address', 5, 'ipv6addr'), ('Test-If-Id', 6, 'ifid'), ('Test-Date', 7, 'date'), ('Test-Abinary', 8, 'abinary... |
def do_train(cfg, model, resume=False):
model = check_if_freeze_model(model, cfg)
model.train()
if cfg.SOLVER.USE_CUSTOM_SOLVER:
optimizer = build_custom_optimizer(cfg, model)
else:
assert (cfg.SOLVER.OPTIMIZER == 'SGD')
assert (cfg.SOLVER.CLIP_GRADIENTS.CLIP_TYPE != 'full_model'... |
def test_convoluted_quantities_units(*args, **kwargs):
from radis.test.utils import getTestFile
s = load_spec(getTestFile('CO_Tgas1500K_mole_fraction0.5.spec'), binary=True)
s.update(verbose=False)
assert (s.units['radiance_noslit'] == 'mW/cm2/sr/nm')
assert (s.units['transmittance_noslit'] == '')
... |
class TestLinkpred():
def teardown_method(self):
smokesignal.clear_all()
def config_file(self, training=False, test=False, **kwargs):
config = {'predictors': ['Random'], 'label': 'testing'}
for (var, name, fname, data) in ((training, 'training', 'foo.net', b'*Vertices 3\n1 A\n2 B\n3 C\n*... |
()
('--config-name', '-cn', required=True, type=str)
('--config-dir', '-cd', default=None, type=str)
('--seeds', '-s', default='42,43,44', type=str)
('--monitor_key', '-k', multiple=True, default=['test/mean_score'])
('--ray_address', '-ra', default='auto')
('--num_cpus', '-nc', default=7, type=float)
('--num_gpus', '-... |
def generate_latin_hypercube_points(num_points, domain_bounds):
if (num_points == 0):
return numpy.array([])
points = numpy.zeros((num_points, len(domain_bounds)), dtype=numpy.float64)
for (i, interval) in enumerate(domain_bounds):
subcube_edge_length = old_div(interval.length, float(num_poi... |
_jit(nogil=True)
def _ld_matrix_jit(x: ArrayLike, scores: ArrayLike, chunk_window_starts: ArrayLike, chunk_window_stops: ArrayLike, abs_chunk_start: int, chunk_max_window_start: int, index_dtype: DType, value_dtype: DType, threshold: float) -> List[Any]:
rows = list()
no_threshold = np.isnan(threshold)
for ... |
def lookup_secscan_notification_severities(repository_id):
try:
repo = Repository.get(id=repository_id)
except Repository.DoesNotExist:
return None
event_kind = ExternalNotificationEvent.get(name='vulnerability_found')
for event in RepositoryNotification.select().where((RepositoryNotific... |
class Load_From_URL_To_File_TestCase(Load_From_URL_Test):
def setUp(self):
super(Load_From_URL_To_File_TestCase, self).setUp()
(handle, self._target_path) = tempfile.mkstemp(prefix='testfile', text=True)
os.close(handle)
def runTest(self):
target_path = load.load_to_file(self._ur... |
def setup_handlers(web_app):
host_pattern = '.*$'
base_url = web_app.settings['base_url']
handlers = []
if (apps.gpu.ngpus > 0):
route_pattern_gpu_util = url_path_join(base_url, URL_PATH, 'gpu_utilization')
route_pattern_gpu_usage = url_path_join(base_url, URL_PATH, 'gpu_usage')
... |
def main(data_dir, client, c, config):
benchmark(read_tables, config, c)
query_1 = '\n SELECT i_item_sk,\n CAST(i_category_id AS TINYINT) AS i_category_id\n FROM item\n '
item_df = c.sql(query_1)
item_df = item_df.persist()
wait(item_df)
c.create_table('item_df', item... |
('/json/load_config', endpoint='load_config')
_required('SETTINGS')
def load_config():
category = flask.request.args.get('category')
section = flask.request.args.get('section')
if ((category not in ('core', 'plugin')) or (not section)):
return (jsonify(False), 500)
conf = None
api = flask.cu... |
(simple_typed_classes(defaults=True))
def test_omit_default_roundtrip(cl_and_vals):
converter = Converter(omit_if_default=True)
(cl, vals, kwargs) = cl_and_vals
class C():
a: int = 1
b: cl = Factory((lambda : cl(*vals, **kwargs)))
inst = C()
unstructured = converter.unstructure(inst)... |
def get_contents(filename):
documents = []
with bz2.open(filename, mode='rt') as f:
for line in f:
doc = json.loads(line)
doc = preprocess_sentences(doc)
if (not doc):
continue
documents.append((doc['title'], serialize_object(doc['sentences... |
def build_trainer(args, device_id, model, optim, tokenizer):
grad_accum_count = args.accum_count
n_gpu = args.world_size
if (device_id >= 0):
gpu_rank = int(args.gpu_ranks[device_id])
else:
gpu_rank = 0
n_gpu = 0
print(('gpu_rank %d' % gpu_rank))
tensorboard_log_dir = arg... |
class NMTModel(nn.Module):
def __init__(self, encoder, decoder):
super(NMTModel, self).__init__()
self.encoder = encoder
self.decoder = decoder
def forward(self, src, seg, speaker, adj_coo, edge_types, rels, tgt, lengths, bptt=False):
tgt = tgt[:(- 1)]
(enc_state, memory_... |
.script
def _update(input: torch.Tensor, target: torch.Tensor, sample_weight: Optional[torch.Tensor]) -> Tuple[(torch.Tensor, torch.Tensor)]:
squared_error = torch.square((target - input))
if (sample_weight is None):
sum_squared_error = squared_error.sum(dim=0)
sum_weight = torch.tensor(target.s... |
def test_get_function_call_str():
class TestObject():
def __str__(self):
raise NotImplementedError()
def __repr__(self):
return 'test'
def test_function():
pass
function_str_kv = qcore.inspection.get_function_call_str(test_function, (1, 2, 3), {'k': 'v'})
... |
def test__torque_driven_ocp__minimize_segment_velocity():
from bioptim.examples.torque_driven_ocp import example_minimize_segment_velocity as ocp_module
bioptim_folder = os.path.dirname(ocp_module.__file__)
ocp_module.prepare_ocp(biorbd_model_path=(bioptim_folder + '/models/triple_pendulum.bioMod'), n_shoot... |
class QUDevMonitorObserverMixin(MonitorObserverMixin):
def _setup_notifier(self, monitor, notifier_class):
MonitorObserverMixin._setup_notifier(self, monitor, notifier_class)
self._action_signal_map = {'add': self.deviceAdded, 'remove': self.deviceRemoved, 'change': self.deviceChanged, 'move': self.... |
def get_assign_value(node):
try:
targets = node.targets
except AttributeError:
targets = [node.target]
if (len(targets) == 1):
target = targets[0]
if isinstance(target, astroid.nodes.AssignName):
name = target.name
elif isinstance(target, astroid.nodes.Ass... |
.parametrize('prefer_grpc', [False, True])
def test_record_upload(prefer_grpc):
records = (Record(id=idx, vector=np.random.rand(DIM).tolist(), payload=one_random_payload_please(idx)) for idx in range(NUM_VECTORS))
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
client.recreate_collection(col... |
class BehavioralRTLIRTypeCheckVisitorL3(BehavioralRTLIRTypeCheckVisitorL2):
def __init__(s, component, freevars, accessed, tmpvars, rtlir_getter):
super().__init__(component, freevars, accessed, tmpvars, rtlir_getter)
s.type_expect['Attribute'] = (('value', (rt.Component, rt.Signal), 'the base of an... |
def mkl_spmv(A, x):
(m, _) = A.shape
data = A.data.ctypes.data_as(ndpointer(np.complex128, ndim=1, flags='C'))
indptr = A.indptr.ctypes.data_as(POINTER(c_int))
indices = A.indices.ctypes.data_as(POINTER(c_int))
if (x.ndim == 1):
y = np.empty(m, dtype=np.complex128, order='C')
elif ((x.nd... |
def convert_examples_to_features(examples: List[InputExample], label_list: List[str], max_length: int, tokenizer: PreTrainedTokenizer) -> List[InputFeatures]:
label_map = {label: i for (i, label) in enumerate(label_list)}
features = []
for (ex_index, example) in tqdm.tqdm(enumerate(examples), desc='convert ... |
def _format_perf_breakdown(perf: Perf) -> str:
breakdown = [perf.fwd_compute, perf.fwd_comms, perf.bwd_compute, perf.bwd_comms]
breakdown_string = ','.join([(str(round(num)) if (num >= 1) else round_to_one_sigfig(num)) for num in breakdown])
return f'{str(round(perf.total, 3))} ({breakdown_string})' |
(Grant)
class GrantAdmin(ExportMixin, admin.ModelAdmin):
change_list_template = 'admin/grants/grant/change_list.html'
speaker_ids = []
resource_class = GrantResource
form = GrantAdminForm
list_display = ('user_display_name', 'country', 'is_speaker', 'conference', 'status', 'approved_type', 'ticket_a... |
class PdfDocument(pdfium_i.AutoCloseable):
def __init__(self, input, password=None, autoclose=False):
if isinstance(input, str):
input = Path(input)
if isinstance(input, Path):
input = input.expanduser().resolve()
if (not input.is_file()):
raise Fi... |
def rescaleData(data, scale, offset, dtype, clip):
data_out = np.empty_like(data, dtype=dtype)
key = (data.dtype.name, data_out.dtype.name)
func = rescale_functions.get(key)
if (func is None):
func = numba.guvectorize([f'{key[0]}[:],f8,f8,f8,f8,{key[1]}[:]'], '(n),(),(),(),()->(n)', nopython=Tru... |
class DiscriminatorBlock(chainer.Chain):
def __init__(self, in_channels, out_channels, hidden_channels=None, ksize=3, pad=1, activation=F.relu, downsample=False, sn=True):
super(DiscriminatorBlock, self).__init__()
initializer = chainer.initializers.GlorotUniform(np.sqrt(2))
initializer_sc =... |
_commands.command(name='cluster-undeploy')
('--namespace', '-n', default='default', help='Kubernetes namespace [default]')
('--instance-name', default='reana', help='REANA instance name')
def cluster_undeploy(namespace, instance_name):
helm_releases = run_command(f'helm ls --short -n {namespace}', 'reana', return_o... |
class CondenseLinear(nn.Module):
def __init__(self, in_features, out_features, drop_rate=0.5):
super(CondenseLinear, self).__init__()
drop_in_features = int((in_features * drop_rate))
self.linear = nn.Linear(in_features=drop_in_features, out_features=out_features)
self.register_buffe... |
def get_context_with_bottleneck_to_question_model(rnn_dim: int, q2c: bool, res_rnn: bool, res_self_att: bool):
recurrent_layer = CudnnGru(rnn_dim, w_init=TruncatedNormal(stddev=0.05))
answer_encoder = BinaryAnswerEncoder()
res_model = get_res_fc_seq_fc(model_rnn_dim=rnn_dim, rnn=res_rnn, self_att=res_self_a... |
def main(args):
utils.init_distributed_mode(args)
os.makedirs(args.output_dir, exist_ok=True)
os.environ['output_dir'] = args.output_dir
logger = setup_logger(output=os.path.join(args.output_dir, 'info.txt'), distributed_rank=args.rank, color=False, name='DAB-DETR')
logger.info('git:\n {}\n'.format... |
_handler('resource')
def qute_resource(url: QUrl) -> _HandlerRet:
path = url.path().lstrip('/')
mimetype = utils.guess_mimetype(path, fallback=True)
try:
data = resources.read_file_binary(path)
except FileNotFoundError as e:
raise NotFoundError(str(e))
return (mimetype, data) |
def preprocess(image):
(w, h) = image.size
(w, h) = map((lambda x: (x - (x % 32))), (w, h))
image = image.resize((w, h), resample=PIL_INTERPOLATION['lanczos'])
image = (np.array(image).astype(np.float32) / 255.0)
image = image[None].transpose(0, 3, 1, 2)
image = torch.from_numpy(image)
retur... |
class _ConstantCumulativeRiskMetric(object):
def __init__(self, field, value):
self._field = field
self._value = value
def end_of_bar(self, packet, *args):
packet['cumulative_risk_metrics'][self._field] = self._value
def end_of_session(self, packet, *args):
packet['cumulative... |
def test_cyclonedx_fix(monkeypatch, vuln_data, fix_data):
import pip_audit._format.cyclonedx as cyclonedx
logger = pretend.stub(warning=pretend.call_recorder((lambda s: None)))
monkeypatch.setattr(cyclonedx, 'logger', logger)
formatter = CycloneDxFormat(inner_format=CycloneDxFormat.InnerFormat.Json)
... |
def createCaseFromTemplate(output_path, source_path, backup_path=None):
if (backup_path and os.path.isdir(output_path)):
shutil.move(output_path, backup_path)
if os.path.isdir(output_path):
shutil.rmtree(output_path)
os.makedirs(output_path)
if (source_path.find('tutorials') >= 0):
... |
class Effect1590(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
penalize = (False if (('skill' in context) or ('implant' in context) or ('booster' in context)) else True)
fit.modules.... |
class AutoQuant():
def __init__(self, session: tf.compat.v1.Session, starting_op_names: List[str], output_op_names: List[str], dataset: tf.compat.v1.data.Dataset, eval_callback: Callable[([tf.compat.v1.Session], float)], param_bw: int=8, output_bw: int=8, quant_scheme: QuantScheme=QuantScheme.post_training_tf_enhan... |
.parametrize('driver', [pytest.param('energy', id='energy'), pytest.param('gradient', id='gradient'), pytest.param('hessian', id='hessian')])
def test_full_run(driver, tmpdir, acetone):
if (not GaussianHarness.found()):
pytest.skip('Gaussian 09/16 not available test skipped.')
with tmpdir.as_cwd():
... |
def _wil_update(input: Union[(str, List[str])], target: Union[(str, List[str])]) -> Tuple[(torch.Tensor, torch.Tensor, torch.Tensor)]:
if isinstance(input, str):
input = [input]
if isinstance(target, str):
target = [target]
assert (len(input) == len(target)), f'Arguments must contain the sam... |
def test_direct_junction_offsets_suc_pre_2_right(direct_junction_right_lane_fixture):
(main_road, small_road, junction_creator) = direct_junction_right_lane_fixture
main_road.add_successor(xodr.ElementType.junction, junction_creator.id)
small_road.add_predecessor(xodr.ElementType.junction, junction_creator.... |
class LOSArrow(pg.GraphicsWidget, pg.GraphicsWidgetAnchor):
def __init__(self, model):
pg.GraphicsWidget.__init__(self)
pg.GraphicsWidgetAnchor.__init__(self)
self.model = model
self.arrow = pg.ArrowItem(parent=self, angle=0.0, brush=(0, 0, 0, 180), pen=(255, 255, 255), pxMode=True)
... |
class DetectionBlock(nn.Module):
def __init__(self, in_channels, out_channels, conv_cfg=None, norm_cfg=dict(type='BN', requires_grad=True), act_cfg=dict(type='LeakyReLU', negative_slope=0.1)):
super(DetectionBlock, self).__init__()
double_out_channels = (out_channels * 2)
cfg = dict(conv_cfg... |
.parametrize('ds_order, lifted, dist_op, dist_params, size, rtol', [(('x',), True, normal, (np.array((- 10.0), dtype=np.float64), np.array(1e-06, dtype=np.float64)), (), 1e-07), ((0, 1, 2), True, normal, (np.array(0).astype(config.floatX), np.array(1e-06).astype(config.floatX)), (2, 1, 2), 0.001)])
def test_Dimshuffle_... |
def test_cache_clear_all(tester: ApplicationTester, repository_one: str, repository_cache_dir: Path, cache: FileCache[T]) -> None:
exit_code = tester.execute(f'cache clear {repository_one} --all', inputs='yes')
repository_one_dir = (repository_cache_dir / repository_one)
assert (exit_code == 0)
assert (... |
class PipeQueue1RTL(Component):
def construct(s, Type):
s.enq = RecvIfcRTL(Type)
s.deq = SendIfcRTL(Type)
s.buffer = m = RegEn(Type)
m.en //= s.enq.en
m.in_ //= s.enq.msg
m.out //= s.deq.msg
s.full = Reg(Bits1)
def up_pipeq_use_deq_rdy():
s... |
class TestDocumentWithoutRequest(TestDocumentBase):
def test_slot_behaviour(self, document):
for attr in document.__slots__:
assert (getattr(document, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(document)) == len(set(mro_slots(document)))), 'duplicate slot'
... |
class CoupledInputForgetGateLSTMCell(rnn_cell_impl.RNNCell):
def __init__(self, num_units, use_peepholes=False, initializer=None, num_proj=None, proj_clip=None, num_unit_shards=1, num_proj_shards=1, forget_bias=1.0, state_is_tuple=True, activation=math_ops.tanh, reuse=None):
super(CoupledInputForgetGateLSTM... |
def test_wind():
w = OSC.Wind(0, 1)
w2 = OSC.Wind(0, 1)
w3 = OSC.Wind(1, 1)
assert (w == w2)
assert (w != w3)
prettyprint(w)
w4 = OSC.Wind.parse(w.get_element())
assert (w == w4)
assert (version_validation('Wind', w, 0) == ValidationResponse.OSC_VERSION)
assert (version_validatio... |
def tracing_client_from_config(raw_config: config.RawConfig, log_if_unconfigured: bool=True) -> TracingClient:
cfg = config.parse_config(raw_config, {'tracing': {'service_name': config.String, 'endpoint': config.Optional(config.Endpoint), 'queue_name': config.Optional(config.String), 'max_span_queue_size': config.O... |
def add_tune_args(parser):
group = parser.add_argument_group('Tune parameter parser.')
group.add_argument('--n-grid', default=6, type=int, metavar='N', help='how many grid added to tune for each weight.')
group.add_argument('--weight-lower-bound', default=0.0, type=float, help='lower bound for each weight.'... |
def test_report_disconnect(mock_emit_session_update, solo_two_world_session):
log = MagicMock()
session_dict = {'user-id': 1234, 'worlds': [1]}
a1 = database.WorldUserAssociation.get_by_instances(world=1, user=1234)
a1.connection_state = GameConnectionStatus.InGame
a1.save()
world_api.report_dis... |
def _parse_requirement_details(tokenizer: Tokenizer) -> Tuple[(str, str, Optional[MarkerList])]:
specifier = ''
url = ''
marker = None
if tokenizer.check('AT'):
tokenizer.read()
tokenizer.consume('WS')
url_start = tokenizer.position
url = tokenizer.expect('URL', expected=... |
def direct(x_samp, y_samp, x_bl, y_bl, y_data, Rinv, baseline_as_mean=False, **kwargs):
(nsamples, nx, ny, x_bl, y_bl, y_data, delta_x, delta_y, innovation) = _preproc(x_samp, y_samp, x_bl, y_bl, y_data, baseline_as_mean)
dy = (delta_y np.linalg.pinv(delta_x))
return ((dy.T Rinv) innovation) |
def _select_lstm_internal_ops_to_quantize(graph: tf.Graph, internal_ops: List[tf.Operation]) -> Tuple[(List[str], List[int], List[str])]:
(curr_module_ops_with_param_names, curr_module_input_indices) = _get_internal_ops_to_quantize_params_for(graph, internal_ops)
curr_module_activation_op_names = _get_internal_... |
class MuteStream(Scaffold):
async def mute_stream(self, chat_id: Union[(int, str)]):
if (self._app is None):
raise NoMTProtoClientSet()
if (not self._is_running):
raise ClientNotStarted()
chat_id = (await self._resolve_chat_id(chat_id))
try:
return... |
class DwsConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, bias=False, use_bn=True, bn_eps=1e-05, activation=(lambda : nn.ReLU(inplace=True))):
super(DwsConvBlock, self).__init__()
self.dw_conv = dwconv_block(in_channels=in_channels, out_ch... |
class UniSpeechConfig(PretrainedConfig):
model_type = 'unispeech'
def __init__(self, vocab_size=32, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout=0.1, activation_dropout=0.1, attention_dropout=0.1, feat_proj_dropout=0.0, feat_quantizer_d... |
_tests('rsa_oaep_2048_sha1_mgf1sha1_test.json', 'rsa_oaep_2048_sha224_mgf1sha1_test.json', 'rsa_oaep_2048_sha224_mgf1sha224_test.json', 'rsa_oaep_2048_sha256_mgf1sha1_test.json', 'rsa_oaep_2048_sha256_mgf1sha256_test.json', 'rsa_oaep_2048_sha384_mgf1sha1_test.json', 'rsa_oaep_2048_sha384_mgf1sha384_test.json', 'rsa_oae... |
def plot_reward_curves(yss: Iterable[Iterable[np.ndarray]], labels: Iterable[str], bs: int, title: str):
(fig, ax) = plt.subplots(1, 1, sharey=True, figsize=(4, 4))
fmt = '-'
(y_min, y_max) = (0, 0)
for (ys, label) in zip(yss, labels):
Y = np.stack(ys)
y_mean = Y.mean(axis=0)
y_s... |
class ImagenetSpecificationTest(tf.test.TestCase):
def validate_num_span_images(self, span_leaves, num_span_images):
for (node, leaves) in span_leaves.items():
self.assertEqual(num_span_images[node], sum([num_span_images[l] for l in leaves]))
def validate_splits(self, splits):
train_... |
def train_one_epoch():
stat_dict = {}
adjust_learning_rate(optimizer, EPOCH_CNT)
bnm_scheduler.step()
net.train()
for (batch_idx, batch_data_label) in enumerate(TRAIN_DATALOADER):
for key in batch_data_label:
batch_data_label[key] = batch_data_label[key].to(device)
optimi... |
def postprocess_dataset(dataset: List[DatasetEntry], remove_identical_pairs: bool=True, remove_duplicates: bool=True, add_sampled_pairs: bool=True, max_num_text_b_for_text_a_and_label: int=2, label_smoothing: float=0.2, seed: int=42, explanation=False) -> List[DatasetEntry]:
postprocessed_dataset = []
num_text_... |
class TransformerEmbedding(nn.Module):
def __init__(self, args, embed_tokens):
super().__init__()
self.dropout = args.dropout
embed_dim = embed_tokens.embedding_dim
self.padding_idx = embed_tokens.padding_idx
self.embed_tokens = embed_tokens
self.embed_scale = math.sq... |
def cibuildwheel_run(project_path, package_dir='.', env=None, add_env=None, output_dir=None, add_args=None):
if (env is None):
env = os.environ.copy()
env.pop('MACOSX_DEPLOYMENT_TARGET', None)
if (add_args is None):
add_args = []
if (add_env is not None):
env.update(add_env)
... |
def calculate_remediations(vulns, db_full):
remediations = defaultdict(dict)
package_metadata = {}
secure_vulns_by_user = set()
if (not db_full):
return remediations
precompute_remediations(remediations, package_metadata, vulns, secure_vulns_by_user)
compute_sec_ver(remediations, package... |
class AdBaseAdmin(RemoveDeleteMixin, admin.ModelAdmin):
readonly_fields = ('date', 'advertisement', 'publisher', 'page_url', 'keywords', 'country', 'browser_family', 'os_family', 'is_mobile', 'is_proxy', 'paid_eligible', 'user_agent', 'ip', 'div_id', 'ad_type_slug', 'client_id', 'modified', 'created')
list_disp... |
class _TestClassA(torch.nn.Module):
def __init__(self, arg1, arg2, arg3=3):
super().__init__()
self.arg1 = arg1
self.arg2 = arg2
self.arg3 = arg3
assert (arg1 == 1)
assert (arg2 == 2)
assert (arg3 == 3)
def from_config(cls, cfg):
args = {'arg1': cf... |
def get_xritdecompress_cmd():
cmd = os.environ.get('XRIT_DECOMPRESS_PATH', None)
if (not cmd):
raise IOError('XRIT_DECOMPRESS_PATH is not defined (complete path to xRITDecompress)')
question = 'Did you set the environment variable XRIT_DECOMPRESS_PATH correctly?'
if (not os.path.exists(cmd)):
... |
class EncoderLayer(nn.Module):
def __init__(self, attention, d_model, d_ff=None, dropout=0.1, activation='relu'):
super(EncoderLayer, self).__init__()
d_ff = (d_ff or (4 * d_model))
self.attention = attention
self.conv1 = nn.Conv1d(in_channels=d_model, out_channels=d_ff, kernel_size=... |
def calc_diversity(data_dir, num_samples=5):
dir_list = os.listdir(data_dir)
dir_list.sort()
transform = transforms.Compose([transforms.ToTensor()])
total = len(dir_list)
std = 0
for i in tqdm(range(total), total=total, smoothing=0.01):
imgs = []
for j in range(num_samples):
... |
def make_dataset(pos_pairs, neg_pairs, neg_pairs_asin, pct_dev=0):
ds_train = []
ds_dev = []
def _add_to_ds(ds_train, ds_dev, array, label, pct_dev):
split = int((len(array) * pct_dev))
for pair in array[:split]:
ds_dev.append((*pair, label))
for pair in array[split:]:
... |
class unknowns(plugin):
def __init__(self, os, maxsize):
plugin.__init__(self, os, maxsize, __name__)
print(('loaded %s' % __name__))
def preGet(self):
pass
def postGet(self):
pass
def check(self, path):
import ops.env
import os, os.path
__in = os.... |
def compute_faiss_kmeans(dim, num_partitions, kmeans_niters, shared_lists, return_value_queue=None):
use_gpu = torch.cuda.is_available()
kmeans = faiss.Kmeans(dim, num_partitions, niter=kmeans_niters, gpu=use_gpu, verbose=True, seed=123)
sample = shared_lists[0][0]
sample = sample.float().numpy()
km... |
def test_no_install(local_client: QdrantClient=None, collection_name: str='demo_collection', docs: Dict[(str, List[Union[(str, int, Any)]])]=None):
if (local_client is None):
local_client = QdrantClient(':memory:')
if (docs is None):
docs = {'documents': ['Qdrant has Langchain integrations', 'Qd... |
def test_project__empty():
project = Project()
assert (project.admins == [])
assert isinstance(project.created_at, datetime)
assert (project.location is None)
assert (project.project_observation_rules == [])
assert (project.search_parameters == [])
assert (project.user is None) |
def senet154(num_classes=1000, pretrained='imagenet'):
model = SENet(SEBottleneck, [3, 8, 36, 3], groups=64, reduction=16, dropout_p=0.2, num_classes=num_classes)
if (pretrained is not None):
settings = pretrained_settings['senet154'][pretrained]
initialize_pretrained_model(model, num_classes, s... |
def test_parse_basic_remote_manifest():
manifest = OCIManifest(Bytes.for_string_or_unicode(SAMPLE_REMOTE_MANIFEST))
assert (not manifest.is_manifest_list)
assert (manifest.digest == 'sha256:dd18ed87a00474aff683cee7160771e043f1f0eaddbc0678a984a5e')
assert (manifest.blob_digests == ['sha256:9834876dcfb05c... |
class SegToImageTransforms(TransformsConfig):
def __init__(self, opts):
super(SegToImageTransforms, self).__init__(opts)
def get_transforms(self):
transforms_dict = {'transform_gt_train': transforms.Compose([transforms.Resize((256, 256)), transforms.ToTensor(), transforms.Normalize([0.5, 0.5, 0.... |
def deprecated_alias(alias, func):
(func)
def new_func(*args, **kwargs):
warnings.simplefilter('always', DeprecationWarning)
warnings.warn('Call to deprecated function alias {}, use {} instead.'.format(alias, func.__name__), category=DeprecationWarning, stacklevel=2)
warnings.simplefilte... |
class BoolectorOptions(SolverOptions):
def __init__(self, **base_options):
SolverOptions.__init__(self, **base_options)
if (self.random_seed is not None):
raise PysmtValueError('BTOR Does not support Random Seed setting.')
self.incrementality = True
self.internal_options ... |
def do_chopper(params):
(ijob, datadir, nfiles, nsamples, tmin, (grouping, mult)) = params
sq = squirrel.Squirrel(datadir, persistent='bla')
sq.add(os.path.join(datadir, 'data'))
ntr = 0
for tr in sq.get_waveforms(uncut=True):
ntr += 1
assert (tr.data_len() == nsamples)
assert (n... |
class CompletionItemKind():
Text = 1
Method = 2
Function = 3
Constructor = 4
Field = 5
Variable = 6
Class = 7
Interface = 8
Module = 9
Property = 10
Unit = 11
Value = 12
Enum = 13
Keyword = 14
Snippet = 15
Color = 16
File = 17
Reference = 18
Fo... |
class Effect5306(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Rockets')), 'kineticDamage', ship.getModifiedItemAttr('shipBonusCD1'), skill='Caldari Destroyer', **kwargs) |
_vcs_handler('git', 'pieces_from_vcs')
def git_pieces_from_vcs(tag_prefix, root, verbose, run_command=run_command):
if (not os.path.exists(os.path.join(root, '.git'))):
if verbose:
print(('no .git in %s' % root))
raise NotThisMethod('no .git directory')
GITS = ['git']
if (sys.pla... |
class ModelArguments():
model_name_or_path: Optional[str] = field(default=None, metadata={'help': "The model checkpoint for weights initialization.Don't set if you want to train a model from scratch."})
model_type: Optional[str] = field(default=None, metadata={'help': ('If training from scratch, pass a model ty... |
_canonicalize
_specialize
_rewriter([Elemwise])
def local_useless_composite_outputs(fgraph, node):
if ((not isinstance(node.op, Elemwise)) or (not isinstance(node.op.scalar_op, ps.Composite))):
return
comp = node.op.scalar_op
used_outputs_idxs = [i for (i, o_extern) in enumerate(node.outputs) if fgr... |
def test_attribute():
sAttr = Attribute.getInstance()
info = sAttr.getAttributeInfo('maxRange')
assert (info.attributeID == 54)
assert (type(info.attributeID) is int)
assert (info.attributeName == 'maxRange')
assert (type(info.attributeName) is str)
assert (info.defaultValue == 0.0)
asse... |
def test_can_handle_nms_with_constant_maxnum():
class ModuleNMS(torch.nn.Module):
def forward(self, boxes, scores):
return nms(boxes, scores, iou_threshold=0.4, max_num=10)
onnx_model = export_nms_module_to_onnx(ModuleNMS)
preprocess_onnx_model = preprocess_onnx(onnx_model)
for node ... |
def test_prepare_sdist(config: Config, config_cache_dir: Path, artifact_cache: ArtifactCache, fixture_dir: FixtureDirGetter, mock_file_downloads: None) -> None:
chef = Chef(artifact_cache, EnvManager.get_system_env(), Factory.create_pool(config))
archive = (fixture_dir('distributions') / 'demo-0.1.0.tar.gz').re... |
class CoberturaReportSuite(Suite):
.skipif((lxml is None), reason='Cannot import lxml. Is it installed?')
def test_get_line_rate(self) -> None:
assert_equal('1.0', get_line_rate(0, 0))
assert_equal('0.3333', get_line_rate(1, 3))
.skipif((lxml is None), reason='Cannot import lxml. Is it insta... |
class TouchExecutor(ActionExecutor):
def execute(self, script: Script, state: EnvironmentState, info: ExecutionInfo, char_index, modify=True, in_place=False):
current_line = script[0]
info.set_current_line(current_line)
node = state.get_state_node(current_line.object())
if (node is N... |
class F23_TestCase(F18_TestCase):
def runTest(self):
self.assert_parse(('timezone --utc Europe/Prague --ntpservers=ntp.cesnet.cz,0.fedora.pool.ntp.org,' + '0.fedora.pool.ntp.org,0.fedora.pool.ntp.org,0.fedora.pool.ntp.org'), ('timezone Europe/Prague --isUtc --ntpservers=ntp.cesnet.cz,0.fedora.pool.ntp.org,'... |
class Annotation(NamedTuple):
area: float
image_id: str
bbox: BoundingBox
category_no: int
category_id: str
id: Optional[int] = None
source: Optional[str] = None
confidence: Optional[float] = None
is_group_of: Optional[bool] = None
is_truncated: Optional[bool] = None
is_occlu... |
class Color():
__slots__ = ['_val']
def __init__(self, *args):
if (len(args) == 1):
color = args[0]
if isinstance(color, (int, float)):
self._set_from_tuple(args)
elif isinstance(color, str):
self._set_from_str(color)
else:
... |
class HubertCriterionConfig(FairseqDataclass):
pred_masked_weight: float = field(default=1.0, metadata={'help': 'weight for predictive loss for masked frames'})
pred_nomask_weight: float = field(default=0.0, metadata={'help': 'weight for predictive loss for unmasked frames'})
loss_weights: Optional[List[flo... |
def U2NETP(input=(None, None, 3), out_ch=1):
inp = Input(input)
x = Lambda((lambda x: (x / 255)))(inp)
x1 = RSU7(x, 16, 64)
x = MaxPool2D(2, 2)(x1)
x2 = RSU6(x, 16, 64)
x = MaxPool2D(2, 2)(x2)
x3 = RSU5(x, 16, 64)
x = MaxPool2D(2, 2)(x3)
x4 = RSU4(x, 16, 64)
x = MaxPool2D(2, 2)(x... |
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