code stringlengths 281 23.7M |
|---|
def eval_para(model, iterator, sent_ids, output_path):
model.eval()
(Words, Is_heads, Tags, Y, Y_hat) = ([], [], [], [], [])
with torch.no_grad():
for (i, batch) in enumerate(tqdm(iterator)):
(words, x, is_heads, tags, y, seqlens) = batch
(_, _, y_hat) = model(x, y)
... |
class MDense(Layer):
def __init__(self, outputs, channels=2, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None, **kwargs):
if (('input_shape' n... |
.parametrize('q', [quantize(symmetric=True, initialized=True), quantize(symmetric=False, initialized=True), quantize_dequantize(symmetric=True, initialized=True), quantize_dequantize(symmetric=False, initialized=True)])
def test_backward(q: _QuantizerBase, x: torch.Tensor):
output = q(x)
output.backward(torch.z... |
def box(text):
lines = text.split('\n')
w = width(lines)
top_bar = ((TOP_LEFT_CORNER + (HORIZONTAL_BAR * (2 + w))) + TOP_RIGHT_CORNER)
bottom_bar = ((BOTTOM_LEFT_CORNER + (HORIZONTAL_BAR * (2 + w))) + BOTTOM_RIGHT_CORNER)
lines = [LINES_FORMAT_STR.format(line=line, width=w) for line in lines]
re... |
def lookup_connections(backend, identities):
from rapidsms.models import Backend
if isinstance(backend, str):
(backend, _) = Backend.objects.get_or_create(name=backend)
connections = []
for identity in identities:
(connection, _) = backend.connection_set.get_or_create(identity=identity)
... |
def evaluate_extractive(result_file, article_file, summary_file, entity_map_file=None, out_rouge_file=None, cmd='-a -c 95 -m -n 4 -w 1.2', length=(- 1), eval_type='lead', topk=3, rerank=False, with_m=False, add_full_stop=True, nsent_budget_file=None, nword_budget_file=None, multi_ref=False, trigram_block=False):
ar... |
def remove_silence(silence_parts_list: list[tuple[(float, float)]], transcribed_data: list[TranscribedData]):
new_transcribed_data = []
for data in transcribed_data:
new_transcribed_data.append(data)
origin_end = data.end
was_split = False
for (silence_start, silence_end) in sile... |
def text(session, *args, **kwargs):
txt = (args[0] if args else None)
if (txt is None):
return
if (txt.strip() in _IDLE_COMMAND):
session.update_session_counters(idle=True)
return
if session.account:
puppet = session.puppet
if puppet:
txt = puppet.nick... |
def test_format_failure_ignore_multidoc(run_line_simple, tmp_path):
schemafile = (tmp_path / 'schema.json')
schemafile.write_text(json.dumps(FORMAT_SCHEMA))
doc1 = (tmp_path / 'doc1.json')
doc1.write_text(json.dumps(FAILING_DOCUMENT))
doc2 = (tmp_path / 'doc2.json')
doc2.write_text(json.dumps(PA... |
class BufferedOutput(Output):
def __init__(self, verbosity: Verbosity=Verbosity.NORMAL, decorated: bool=False, formatter: (Formatter | None)=None, supports_utf8: bool=True) -> None:
super().__init__(decorated=decorated, verbosity=verbosity, formatter=formatter)
self._buffer = StringIO()
self... |
def deep_dgl_graph_copy(graph: DGLGraph):
start = time()
copy_graph = DGLGraph()
copy_graph.add_nodes(graph.number_of_nodes())
graph_edges = graph.edges()
copy_graph.add_edges(graph_edges[0], graph_edges[1])
for (key, value) in graph.edata.items():
copy_graph.edata[key] = value
for (... |
def _save_item_model(request, item: Item, form, change) -> None:
prev_status = False
if (not item.pk):
item.user = request.user
if (not item.issue):
la = lna = False
qs = Issue.objects
try:
la = qs.filter(status='active').order_by('-pk')[0:1].g... |
def drop_channels(edf_source, edf_target=None, to_keep=None, to_drop=None):
(signals, signal_headers, header) = hl.read_edf(edf_source, ch_nrs=to_keep, digital=False)
clean_file = {}
for (signal, header) in zip(signals, signal_headers):
channel = header.get('label')
if (channel in clean_file... |
.parametrize('configuration, expected_value', [((0.0, 0.0, 1.0, 50.0, 100.0), 50.0), ((1.0, 0.0, 1.0, 50.0, 100.0), 100.0), ((0.5, 0.0, 1.0, 50.0, 100.0), 75.0), ((0.0, 0.5, 1.0, 50.0, 100.0), 50.0), ((0.75, 0.5, 1.0, 50.0, 100.0), 75.0)])
def test_interpolation(configuration, expected_value):
(current_position, lo... |
def test_features_for():
vuln_report_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'vulnerabilityreport.json')
security_info_filename = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'securityinformation.json')
with open(vuln_report_filename) as vuln_report_file:
vuln... |
class MLP(torch.nn.Module):
def __init__(self, config):
super(MLP, self).__init__()
self.config = config
self.num_users = config['num_users']
self.num_items = config['num_items']
self.latent_dim = config['latent_dim']
self.embedding_user = torch.nn.Embedding(num_embed... |
class TestSequenceImpl(TestNameCheckVisitorBase):
_passes()
def test(self):
from typing import Sequence
from typing_extensions import Literal
def capybara(x, ints: Sequence[Literal[(1, 2)]]):
assert_is_value(set(), KnownValue(set()))
assert_is_value(list(), KnownV... |
class JsonConverter(Converter):
def dumps(self, obj: Any, unstructure_as: Any=None, **kwargs: Any) -> str:
return dumps(self.unstructure(obj, unstructure_as=unstructure_as), **kwargs)
def loads(self, data: Union[(bytes, str)], cl: Type[T], **kwargs: Any) -> T:
return self.structure(loads(data, *... |
class ModelData():
def __init__(self, model: ModelProto):
self.model = model
self.module_to_info = {}
self._populate_model_data()
def _populate_model_data(self):
cg = ConnectedGraph(self.model)
for op in cg.ordered_ops:
self.module_to_info[op.name] = ModuleInf... |
class DescribeCT_Row():
def it_can_add_a_trPr(self, add_trPr_fixture):
(tr, expected_xml) = add_trPr_fixture
tr._add_trPr()
assert (tr.xml == expected_xml)
def it_raises_on_tc_at_grid_col(self, tc_raise_fixture):
(tr, idx) = tc_raise_fixture
with pytest.raises(ValueError)... |
def train_model(max_epochs):
model = Model(20)
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=0.0001)
def step(engine, batch):
model.train()
optimizer.zero_grad()
(x, y) = batch
y_pred = model(x)
loss = F.nll_loss(y_pred, y)
... |
class TestPerTestCapturing():
def test_capture_and_fixtures(self, pytester: Pytester) -> None:
p = pytester.makepyfile('\n def setup_module(mod):\n print("setup module")\n def setup_function(function):\n print("setup " + function.__name__)\n def... |
class Checkpoint(object):
CHECKPOINT_DIR_NAME = 'checkpoints'
TRAINER_STATE_NAME = 'trainer_states.pt'
MODEL_NAME = 'model.pt'
INPUT_VOCAB_FILE = 'input_vocab.pt'
OUTPUT_VOCAB_FILE = 'output_vocab.pt'
def __init__(self, model, optimizer, epoch, step, input_vocab, output_vocab, path=None):
... |
class ServoFlags(Value):
SYNC = 1
OVERTEMP_FAULT = 2
OVERCURRENT_FAULT = 4
ENGAGED = 8
INVALID = (16 * 1)
PORT_PIN_FAULT = (16 * 2)
STARBOARD_PIN_FAULT = (16 * 4)
BADVOLTAGE_FAULT = (16 * 8)
MIN_RUDDER_FAULT = (256 * 1)
MAX_RUDDER_FAULT = (256 * 2)
CURRENT_RANGE = (256 * 4)
... |
def _create_app(emails=True):
global _PORT_NUMBER
_PORT_NUMBER = (_PORT_NUMBER + 1)
(public_key, private_key_data) = _generate_certs()
users = [{'name': 'cool.user', 'email': '', 'password': 'password'}, {'name': 'some.neat.user', 'email': '', 'password': 'foobar'}, {'name': 'blacklistedcom', 'email': '... |
class IterativeRefinementGenerator(nn.Module):
def __init__(self, models, tgt_dict, eos_penalty=0.0, max_iter=2, max_ratio=2, decoding_format=None, retain_dropout=False, adaptive=True):
super().__init__()
self.models = models
self.bos = tgt_dict.bos()
self.pad = tgt_dict.pad()
... |
('pypyr.config.config.init')
def test_main_pass_with_sysargv_single_group(mock_config_init):
arg_list = ['pypyr', 'blah', 'ctx string', '--loglevel', '50', '--dir', 'dir here', '--groups', 'group1', '--success', 'sg', '--failure', 'f g']
with patch('sys.argv', arg_list):
with patch('pypyr.pipelinerunner... |
class ResourceAllocation(Predictor):
def predict(self, weight=None):
res = Scoresheet()
for (a, b) in self.likely_pairs():
intersection = (set(neighbourhood(self.G, a)) & set(neighbourhood(self.G, b)))
w = 0
for c in intersection:
if (weight is not... |
.parametrize(['constraint', 'expected'], [('*', ['19.10b0']), ('>=19.0a0', ['19.10b0']), ('>=20.0a0', []), ('>=21.11b0', []), ('==21.11b0', ['21.11b0'])])
def test_find_packages_yanked(constraint: str, expected: list[str]) -> None:
repo = MockRepository()
packages = repo.find_packages(Factory.create_dependency(... |
(name='fake_dataset')
def fixture_fake_dataset():
count_ir = da.linspace(0, 255, 4, dtype=np.uint8).reshape(2, 2)
count_wv = da.linspace(0, 255, 4, dtype=np.uint8).reshape(2, 2)
count_vis = da.linspace(0, 255, 16, dtype=np.uint8).reshape(4, 4)
sza = da.from_array(np.array([[45, 90], [0, 45]], dtype=np.f... |
def get_num_processes():
cpu_count = multiprocessing.cpu_count()
if (config.NUMBER_OF_CORES == 0):
raise ValueError('Invalid NUMBER_OF_CORES; value may not be 0.')
if (config.NUMBER_OF_CORES > cpu_count):
log.info('Requesting %s cores; only %s available', config.NUMBER_OF_CORES, cpu_count)
... |
def test_item(func: Callable[(..., bool)], description: str) -> Parser:
def test_item_parser(stream, index):
if (index < len(stream)):
if isinstance(stream, bytes):
item = stream[index:(index + 1)]
else:
item = stream[index]
if func(item):
... |
def read_plane_paramters_file(filepath):
file = open(filepath, 'r')
lines = file.readlines()
planes = []
for line in lines:
if (not line.startswith('#')):
paras = line.split()
plane = {'index': int(paras[0]), 'num_of_points': int(paras[1]), 'ratio': (float(paras[1]) / (64... |
class Solution(object):
def mergeTwoLists(self, l1, l2):
pos = dummyHead = ListNode((- 1))
while ((l1 is not None) and (l2 is not None)):
if (l1.val <= l2.val):
pos.next = l1
l1 = l1.next
else:
pos.next = l2
l2 =... |
class BatchScoringFunction(ScoringFunction):
def __init__(self, score_modifier: ScoreModifier=None) -> None:
super().__init__(score_modifier=score_modifier)
def score(self, smiles: str) -> float:
return self.score_list([smiles])[0]
def score_list(self, smiles_list: List[str]) -> List[float]:... |
class MarioNet(nn.Module):
def __init__(self, input_dim, output_dim):
super().__init__()
(c, h, w) = input_dim
if (h != 84):
raise ValueError(f'Expecting input height: 84, got: {h}')
if (w != 84):
raise ValueError(f'Expecting input width: 84, got: {w}')
... |
def tabulate_events(dpath):
summary_iterators = [EventAccumulator(os.path.join(dpath, dname)).Reload() for dname in os.listdir(dpath)]
tags = summary_iterators[0].Tags()['scalars']
for it in summary_iterators:
assert (it.Tags()['scalars'] == tags)
out = defaultdict(list)
steps = []
for t... |
def in_ring(pt: Tuple[(float, float)], ring: List[Tuple[(float, float)]], ignore_boundary: bool) -> bool:
is_inside = False
if ((ring[0][0] == ring[(len(ring) - 1)][0]) and (ring[0][1] == ring[(len(ring) - 1)][1])):
ring = ring[0:(len(ring) - 1)]
j = (len(ring) - 1)
for i in range(0, len(ring)):... |
def process_split_fully(train_ratio=0.8):
if (not os.path.exists(os.path.join(config.save_dir, 'split_txts'))):
os.makedirs(os.path.join(config.save_dir, 'split_txts'))
for tag in ['Tr']:
img_ids = []
for path in tqdm(glob.glob(os.path.join(base_dir, f'images{tag}', '*.nii.gz'))):
... |
class TestSolver(unittest.TestCase):
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write((("net: '" + net_f) + "'\n test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9\n ... |
def get_min_dcf(Pfa, Pmiss, p_tar=0.01, normalize=True):
p_tar = np.asarray(p_tar)
p_non = (1 - p_tar)
cdet = np.dot(np.vstack((p_tar, p_non)).T, np.vstack((Pmiss, Pfa)))
idxdcfs = np.argmin(cdet, 1)
dcfs = cdet[(np.arange(len(idxdcfs)), idxdcfs)]
if normalize:
mins = np.amin(np.vstack((... |
def check_required_param(param_desc: list[str], param: inspect.Parameter, method_or_obj_name: str) -> bool:
is_ours_required = (param.default is inspect.Parameter.empty)
telegram_requires = is_parameter_required_by_tg(param_desc[2])
if (param.name in ignored_param_requirements(method_or_obj_name)):
... |
class Effect6501(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Capital Energy Turret')), 'damageMultiplier', src.getModifiedItemAttr('shipBonusDreadnoughtA1'), skill='Amarr Dreadnought', **kwarg... |
class IntermediateLayerGetter(nn.Module):
_version = 2
__constants__ = ['layers']
__annotations__ = {'return_layers': Dict[(str, str)]}
def __init__(self, model, return_layers):
if (not set(return_layers).issubset([name for (name, _) in model.named_children()])):
raise ValueError('re... |
def odnoklassniki_oauth_sig(data, client_secret):
suffix = md5('{:s}{:s}'.format(data['access_token'], client_secret).encode('utf-8')).hexdigest()
check_list = sorted((f'{key:s}={value:s}' for (key, value) in data.items() if (key != 'access_token')))
return md5((''.join(check_list) + suffix).encode('utf-8')... |
class TimeStampTextFrame(TextFrame):
_framespec = [EncodingSpec('encoding', default=Encoding.UTF16), MultiSpec('text', TimeStampSpec('stamp'), sep=u',', default=[])]
def __bytes__(self):
return str(self).encode('utf-8')
def __str__(self):
return u','.join([stamp.text for stamp in self.text])... |
class Execute(Message):
type = message_types[b'E'[0]]
__slots__ = ('name', 'max')
def __init__(self, name, max=0):
self.name = name
self.max = max
def serialize(self):
return ((self.name + b'\x00') + ulong_pack(self.max))
def parse(typ, data):
(name, max) = data.split... |
('/classify_upload', methods=['POST'])
def classify_upload():
try:
imagefile = flask.request.files['imagefile']
filename_ = (str(datetime.datetime.now()).replace(' ', '_') + werkzeug.secure_filename(imagefile.filename))
filename = os.path.join(UPLOAD_FOLDER, filename_)
imagefile.save... |
def subdispatch_to_paymenttask(chain_state: ChainState, state_change: StateChange, secrethash: SecretHash) -> TransitionResult[ChainState]:
block_number = chain_state.block_number
block_hash = chain_state.block_hash
sub_task = chain_state.payment_mapping.secrethashes_to_task.get(secrethash)
events: List... |
class VNet(MetaModule):
def __init__(self, input, hidden1, hidden2, output, num_classes):
super(VNet, self).__init__()
self.feature = share(input, hidden1, hidden2)
self.classfier = task(hidden2, output, num_classes)
def forward(self, x, num, c):
output = self.classfier(self.feat... |
def resp_update_link():
updated_content = dict(link_content)
updated_content['link_type'] = new_link_type
with responses.RequestsMock() as rsps:
rsps.add(method=responses.PUT, url=link_id_url, json=updated_content, content_type='application/json', status=200)
(yield rsps) |
def train_model_swag_binning(model, arch, opt, train_data, test_data, args, lamb_lr, verbose=True):
model.train()
MI_data = train_data
(train_accs, train_losses) = ([], [])
(test_accs, test_losses) = ([], [])
binning_MIs = []
l_MIs = []
maxes = []
t = 0
analyse(model, grads=True)
... |
def test_to_recap_record():
converter = AvroConverter()
avro_record = {'type': 'record', 'name': 'Test', 'fields': [{'name': 'a', 'type': 'int'}, {'name': 'b', 'type': 'string'}]}
actual = converter.to_recap(json.dumps(avro_record))
assert isinstance(actual, StructType)
assert (len(actual.fields) ==... |
def dumped(parameters=True, returnvalue=True, fork_inst=JsonSerializable, dumper=dump, **kwargs):
if (dumper not in (dump, dumps, dumpb)):
raise InvalidDecorationError("The 'dumper' argument must be one of: jsons.dump, jsons.dumps, jsons.dumpb")
return _get_decorator(parameters, returnvalue, fork_inst, ... |
def get_config():
config = get_default_configs()
training = config.training
training.sde = 'vpsde'
training.continuous = True
training.reduce_mean = True
sampling = config.sampling
sampling.method = 'pc'
sampling.predictor = 'euler_maruyama'
sampling.corrector = 'none'
data = con... |
def get_private_repo_count(username):
return Repository.select().join(Visibility).switch(Repository).join(Namespace, on=(Repository.namespace_user == Namespace.id)).where((Namespace.username == username), (Visibility.name == 'private')).where((Repository.state != RepositoryState.MARKED_FOR_DELETION)).count() |
def test_flask_restful_integration_works():
class HelloWorld(flask_restful.Resource):
def __init__(self, *args, int: int, **kwargs):
self._int = int
super().__init__(*args, **kwargs)
def get(self):
return {'int': self._int}
app = Flask(__name__)
api = flas... |
(scope='session')
def truncated_geos_area(create_test_area):
proj_dict = {'a': '6378169', 'h': '', 'lon_0': '9.5', 'no_defs': 'None', 'proj': 'geos', 'rf': '295.', 'type': 'crs', 'units': 'm', 'x_0': '0', 'y_0': '0'}
area_extent = (5567248.0742, 5570248.4773, (- 5570248.4773), 1393687.2705)
shape = (1392, 3... |
def _error_text(because: str, text: str, backend: usertypes.Backend, suggest_other_backend: bool=False) -> str:
text = f'<b>Failed to start with the {backend.name} backend!</b><p>qutebrowser tried to start with the {backend.name} backend but failed because {because}.</p>{text}'
if suggest_other_backend:
... |
def convert_acdc(src_data_folder: str, dataset_id=27):
(out_dir, train_dir, labels_dir, test_dir) = make_out_dirs(dataset_id=dataset_id)
num_training_cases = copy_files(Path(src_data_folder), train_dir, labels_dir, test_dir)
generate_dataset_json(str(out_dir), channel_names={0: 'cineMRI'}, labels={'backgrou... |
def _load_config(composite_configs):
if (not isinstance(composite_configs, (list, tuple))):
composite_configs = [composite_configs]
conf = {}
for composite_config in composite_configs:
with open(composite_config, 'r', encoding='utf-8') as conf_file:
conf = recursive_dict_update(c... |
def patch_builtin_len(modules=()):
def _new_len(obj):
return obj.__len__()
with ExitStack() as stack:
MODULES = (['detectron2.modeling.roi_heads.fast_rcnn', 'detectron2.modeling.roi_heads.mask_head', 'detectron2.modeling.roi_heads.keypoint_head'] + list(modules))
ctxs = [stack.enter_cont... |
def gd(fcn: Callable[(..., torch.Tensor)], x0: torch.Tensor, params: List, step: float=0.001, gamma: float=0.9, maxiter: int=1000, f_tol: float=0.0, f_rtol: float=1e-08, x_tol: float=0.0, x_rtol: float=1e-08, verbose=False, **unused):
x = x0.clone()
stop_cond = TerminationCondition(f_tol, f_rtol, x_tol, x_rtol,... |
def cookie_decode(data, key):
data = tob(data)
if cookie_is_encoded(data):
(sig, msg) = data.split(tob('?'), 1)
if _lscmp(sig[1:], base64.b64encode(hmac.new(tob(key), msg, digestmod=hashlib.md5).digest())):
return pickle.loads(base64.b64decode(msg))
return None |
class Adjoint(GateWithRegisters):
subbloq: 'Bloq'
_property
def signature(self) -> 'Signature':
return self.subbloq.signature.adjoint()
def decompose_bloq(self) -> 'CompositeBloq':
return self.subbloq.decompose_bloq().adjoint()
def supports_decompose_bloq(self) -> bool:
retur... |
class _CfdRunnable(object):
def __init__(self, solver):
if (solver and solver.isDerivedFrom('Fem::FemSolverObjectPython')):
self.solver = solver
else:
raise TypeError('FemSolver object is missing in constructing CfdRunnable object')
self.analysis = CfdTools.getParentA... |
def _fig_add_predictions(fig: Figure, category_data_frame: DataFrame, columns: List[str], column_color_map: Dict[(str, str)], show_legend: bool, row: int) -> None:
for column in columns:
if (column == COLUMN_DELTA):
marker_dict = dict(color=column_color_map[column], size=2)
fig.add_s... |
def test_image_to_tensor():
original_results = dict(imgs=np.random.randn(256, 256, 3))
keys = ['imgs']
image_to_tensor = ImageToTensor(keys)
results = image_to_tensor(original_results)
assert (results['imgs'].shape == torch.Size([3, 256, 256]))
assert isinstance(results['imgs'], torch.Tensor)
... |
('builtins.open', new_callable=mock.mock_open)
('pytube.request.urlopen')
def test_create_mock_html_json(mock_url_open, mock_open):
video_id = '2lAe1cqCOXo'
gzip_html_filename = ('yt-video-%s-html.json.gz' % video_id)
pytube_dir_path = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))... |
def attach(parser):
add_input(parser, pages=False)
subparsers = parser.add_subparsers(dest='action')
subparsers.add_parser(ACTION_LIST)
parser_extract = subparsers.add_parser(ACTION_EXTRACT)
parser_extract.add_argument('--numbers', type=parse_numtext)
parser_extract.add_argument('--output-dir', ... |
class HonggfuzzEngineDescriptor(FuzzingEngineDescriptor):
NAME = 'HONGGFUZZ'
SHORT_NAME = 'HF'
VERSION = '1.0.0'
HF_PERSISTENT_SIG = b'\x01_LIBHFUZZ_PERSISTENT_BINARY_SIGNATURE_\x02\xff'
config_class = HonggfuzzConfigurationInterface
def __init__(self):
pass
def accept_file(binary_fi... |
def test_perform_indexing_failed_within_reindex_threshold(initialized_db, set_secscan_config):
application.config['SECURITY_SCANNER_V4_REINDEX_THRESHOLD'] = 300
secscan = V4SecurityScanner(application, instance_keys, storage)
secscan._secscan_api = mock.Mock()
secscan._secscan_api.state.return_value = {... |
def test_prepare_metadata_for_build_wheel_with_bad_path_dep_succeeds(caplog: LogCaptureFixture) -> None:
with temporary_directory() as tmp_dir, cwd(os.path.join(fixtures, 'with_bad_path_dep')):
api.prepare_metadata_for_build_wheel(tmp_dir)
assert (len(caplog.records) == 1)
record = caplog.records[0]... |
_module()
class CCHead(FCNHead):
def __init__(self, recurrence=2, **kwargs):
if (CrissCrossAttention is None):
raise RuntimeError('Please install mmcv-full for CrissCrossAttention ops')
super(CCHead, self).__init__(num_convs=2, **kwargs)
self.recurrence = recurrence
self.... |
class PreSuDataset(data.Dataset):
def __init__(self, img_list, low_size=64, loader=default_loader):
super(PreSuDataset, self).__init__()
self.imgs = list(img_list)
self.loader = loader
def append(imgs):
imgs.append(transforms.Scale(low_size, interpolation=Image.NEAREST)(i... |
def setupEnv(reinitialize=False):
dsz.env.Set('OPS_TIME', ops.timestamp())
dsz.env.Set('OPS_DATE', ops.datestamp())
for i in flags():
if ((not dsz.env.Check(i)) or reinitialize):
ops.env.set(i, False)
dszflags = dsz.control.Method()
dsz.control.echo.Off()
if (not dsz.cmd.Run(... |
def log_features(feas, tb_writer, tb_index, rank):
fea_cls = feas['cls']
fea_loc = feas['loc']
for i in range(len(fea_cls)):
fea = fea_cls[i].detach()
s = fea.shape
fea = fea.view((s[0] * s[1]), (- 1))
fea = fea.norm(dim=1)
fea = fea.cpu().numpy()
fea = np.flo... |
def sc_zaleplon_with_other_formula() -> GoalDirectedBenchmark:
specification = uniform_specification(1, 10, 100)
benchmark_object = zaleplon_with_other_formula()
sa_biased = ScoringFunctionSAWrapper(benchmark_object.objective, SCScoreModifier())
return GoalDirectedBenchmark(name='SC_zaleplon', objective... |
class TestCLS():
def test_graph_search_utils_single_residual_model(self):
if (version.parse(torch.__version__) >= version.parse('1.13')):
model = models_for_tests.single_residual_model()
connected_graph = ConnectedGraph(model)
ordered_module_list = get_ordered_list_of_con... |
def getNameFromSid(sid, domain=None):
name = LPWSTR()
cbName = DWORD(0)
referencedDomainName = LPWSTR()
cchReferencedDomainName = DWORD(0)
peUse = DWORD(0)
try:
LookupAccountSidW(domain, sid, None, byref(cbName), None, byref(cchReferencedDomainName), byref(peUse))
except Exception as... |
_image_displayer('ueberzug')
class UeberzugImageDisplayer(ImageDisplayer):
IMAGE_ID = 'preview'
is_initialized = False
def __init__(self):
self.process = None
def initialize(self):
if (self.is_initialized and (self.process.poll() is None) and (not self.process.stdin.closed)):
... |
class BatchStudy():
INPUT_LIST = ['experiments', 'geometries', 'parameter_values', 'submesh_types', 'var_pts', 'spatial_methods', 'solvers', 'output_variables', 'C_rates']
def __init__(self, models, experiments=None, geometries=None, parameter_values=None, submesh_types=None, var_pts=None, spatial_methods=None,... |
def test_profile_weir():
with tempfile.TemporaryDirectory() as tempdir:
temp_model_path = os.path.join(tempdir, 'model.inp')
temp_pdf_path = os.path.join(tempdir, 'test.pdf')
mymodel = swmmio.Model(MODEL_EXAMPLE6)
mymodel.inp.save(temp_model_path)
with pyswmm.Simulation(temp_... |
_sentencepiece
_tokenizers
class DebertaV2TokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = DebertaV2Tokenizer
rust_tokenizer_class = DebertaV2TokenizerFast
test_sentencepiece = True
test_sentencepiece_ignore_case = True
def setUp(self):
super().setUp()
tok... |
class TypoScriptCssDataLexer(RegexLexer):
name = 'TypoScriptCssData'
aliases = ['typoscriptcssdata']
url = '
version_added = '2.2'
tokens = {'root': [('(.*)(###\\w+###)(.*)', bygroups(String, Name.Constant, String)), ('(\\{)(\\$)((?:[\\w\\-]+\\.)*)([\\w\\-]+)(\\})', bygroups(String.Symbol, Operator,... |
class ScantronClient():
def __init__(self, secrets_file_location='./scantron_api_secrets.json', **kwargs):
SECRETS = {}
try:
with open(secrets_file_location) as config_file:
SECRETS = json.loads(config_file.read())
except OSError:
print(f'Error: {secre... |
def enc_obj2bytes(obj, max_size=16000):
assert (max_size <= MAX_SIZE_LIMIT)
byte_tensor = torch.zeros(max_size, dtype=torch.uint8)
obj_enc = pickle.dumps(obj)
obj_size = len(obj_enc)
if (obj_size > max_size):
raise Exception('objects too large: object size {}, max size {}'.format(obj_size, m... |
def make_device(device_str: Optional[str]) -> torch.device:
if device_str:
try:
device = torch.device(device_str)
except RuntimeError as error:
device_type = device_str.split(':')[0]
msg = f"Unknown device type '{device_type}'."
match = re.match('Expec... |
class AttributeSliderChangeEvent():
def __init__(self, obj, old_value, new_value, old_percentage, new_percentage, affect_modified_flag=True):
self.__obj = obj
self.__old = old_value
self.__new = new_value
self.__old_percent = old_percentage
self.__new_percent = new_percentage... |
def get_no_augmentation(dataloader_train, dataloader_val, params=default_3D_augmentation_params, deep_supervision_scales=None, soft_ds=False, classes=None, pin_memory=True, regions=None):
tr_transforms = []
if (params.get('selected_data_channels') is not None):
tr_transforms.append(DataChannelSelectionT... |
def _iter_translations(args, task, dataset, translations, align_dict, rescorer, modify_target_dict):
is_multilingual = pytorch_translate_data.is_multilingual_many_to_one(args)
for (sample_id, src_tokens, target_tokens, hypos) in translations:
target_tokens = target_tokens.int().cpu()
if is_multi... |
class TestStickerWithoutRequest(TestStickerBase):
def test_slot_behaviour(self, sticker):
for attr in sticker.__slots__:
assert (getattr(sticker, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(sticker)) == len(set(mro_slots(sticker)))), 'duplicate slot'
def ... |
class GammaL(BinaryScalarOp):
def st_impl(k, x):
return (scipy.special.gammainc(k, x) * scipy.special.gamma(k))
def impl(self, k, x):
return GammaL.st_impl(k, x)
def c_support_code(self, **kwargs):
with open(os.path.join(os.path.dirname(__file__), 'c_code', 'gamma.c')) as f:
... |
def test_axis_azimuth():
apparent_zenith = pd.Series([30])
apparent_azimuth = pd.Series([90])
tracker_data = tracking.singleaxis(apparent_zenith, apparent_azimuth, axis_tilt=0, axis_azimuth=90, max_angle=90, backtrack=True, gcr=(2.0 / 7.0))
expect = pd.DataFrame({'aoi': 30, 'surface_azimuth': 180, 'surf... |
def sendrecv(sendbuf, source=0, dest=0):
if (source == dest):
return sendbuf
if (rank == source):
sendbuf = numpy.asarray(sendbuf, order='C')
comm.send((sendbuf.shape, sendbuf.dtype), dest=dest)
comm.Send(sendbuf, dest=dest)
return sendbuf
elif (rank == dest):
... |
class Database(LiveDict):
def __init__(self, path):
super(Database, self).__init__(json.loads(open(path).read()))
self.path = path
def update(self):
with open(self.path, 'w+') as f:
f.write(json.dumps(self.todict()))
def refresh(self):
with open(self.path, 'w+') a... |
class UpdateLog():
def __init__(self, started, completed, versions, periodic) -> None:
self.started = started
self.completed = completed
self.versions = versions
self.periodic = periodic
def from_dict(cls, dictionary):
if (dictionary is None):
dictionary = {}
... |
def test_tar_extract_one_with_interpolation():
context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'tar': {'extract': [{'in': './{key3}.tar.xz', 'out': 'path/{key2}/dir'}]}})
with patch('tarfile.open') as mock_tarfile:
pypyr.steps.tar.run_step(context)
mock_tarfile.assert_called... |
def load_and_covnert_case(input_image: str, input_seg: str, output_image: str, output_seg: str, min_component_size: int=50):
seg = io.imread(input_seg)
seg[(seg == 255)] = 1
image = io.imread(input_image)
image = image.sum(2)
mask = (image == (3 * 255))
mask = generic_filter_components(mask, fil... |
class OptionSetOption(models.Model):
optionset = models.ForeignKey('OptionSet', on_delete=models.CASCADE, related_name='optionset_options')
option = models.ForeignKey('Option', on_delete=models.CASCADE, related_name='option_optionsets')
order = models.IntegerField(default=0)
class Meta():
orderi... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.