code stringlengths 281 23.7M |
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class ExampleModel(nn.Module):
def __init__(self):
super().__init__()
self.param1 = nn.Parameter(torch.ones(1))
self.conv1 = nn.Conv2d(3, 4, kernel_size=1, bias=False)
self.conv2 = nn.Conv2d(4, 2, kernel_size=1)
self.bn = nn.BatchNorm2d(2)
self.sub = SubModel()
... |
def removeByPossibleDsep(graph: Graph, independence_test_method: CIT, alpha: float, sep_sets: Dict[(Tuple[(int, int)], Set[int])]):
def _contains_all(set_a: Set[Node], set_b: Set[Node]):
for node_b in set_b:
if (not set_a.__contains__(node_b)):
return False
return True
... |
class Migration(migrations.Migration):
dependencies = [('proposals', '0016_auto__0240')]
operations = [migrations.AddField(model_name='proposalcomment', name='type', field=models.PositiveSmallIntegerField(default=0, choices=[(0, 'Unclassified'), (1, 'Second phase voting')]), preserve_default=True)] |
def test_with_relative_markers(item_names_for):
test_content = '\n import pytest\n\n def test_1():\n pass\n\n .order(before="test_1")\n .order(2)\n def test_2():\n pass\n\n .order(1)\n .order(before="test_1")\n def test_3():\n ... |
class EditInlineText():
async def edit_inline_text(self: 'pyrogram.Client', inline_message_id: str, text: str, parse_mode: Optional['enums.ParseMode']=None, disable_web_page_preview: bool=None, reply_markup: 'types.InlineKeyboardMarkup'=None) -> bool:
unpacked = utils.unpack_inline_message_id(inline_message... |
def _parse_item(source, info):
element = _parse_element(source, info)
counts = _parse_quantifier(source, info)
if (counts is not None):
(min_count, max_count) = (counts.min_count, counts.max_count)
if (element.is_empty() or (min_count == max_count == 1)):
return element
i... |
def _get_ade20k_pairs(folder, mode='train'):
img_paths = []
mask_paths = []
if (mode == 'train'):
img_folder = os.path.join(folder, 'images/training')
mask_folder = os.path.join(folder, 'annotations/training')
else:
img_folder = os.path.join(folder, 'images/validation')
m... |
def main():
global logger
args = get_args()
args = set_seed_logger(args)
(device, n_gpu) = init_device(args, args.local_rank)
tokenizer = ClipTokenizer()
assert (args.task_type == 'retrieval')
model = init_model(args, device, n_gpu, args.local_rank)
assert ((args.freeze_layer_num <= 12) ... |
class CommandHandler(BaseHandler[(Update, CCT)]):
__slots__ = ('commands', 'filters', 'has_args')
def __init__(self, command: SCT[str], callback: HandlerCallback[(Update, CCT, RT)], filters: Optional[filters_module.BaseFilter]=None, block: DVType[bool]=DEFAULT_TRUE, has_args: Optional[Union[(bool, int)]]=None):... |
def _run_segmentation_evaluation(all_predictions, all_labels, num_classes):
intersection_counts_per_class = np.zeros(num_classes, dtype=np.float32)
union_counts_per_class = np.zeros(num_classes, dtype=np.float32)
all_targets = [segmentation_targets_from_change_labels(labels) for labels in all_labels]
fo... |
def train(is_training, logits, input_x, labels, sess, images_train, labels_train):
global_step = tf.get_variable('global_step', [], initializer=tf.constant_initializer(0), trainable=False)
val_step = tf.get_variable('val_step', [], initializer=tf.constant_initializer(0), trainable=False)
loss_ = loss(logits... |
def test_drop_when_img(view, imgfilename3x3):
mimedata = QtCore.QMimeData()
mimedata.setImageData(QtGui.QImage(imgfilename3x3))
event = MagicMock()
event.mimeData.return_value = mimedata
event.position.return_value = QtCore.QPointF(10.0, 20.0)
view.dropEvent(event)
assert (len(view.scene.ite... |
.parametrize('arg, confval, used', [('webkit', 'webengine', usertypes.Backend.QtWebKit), (None, 'webkit', usertypes.Backend.QtWebKit)])
def test_get_backend(monkeypatch, args, config_stub, arg, confval, used):
real_import = __import__
def fake_import(name, *args, **kwargs):
if (name != 'qutebrowser.qt.w... |
def register_model(fn):
mod = sys.modules[fn.__module__]
module_name_split = fn.__module__.split('.')
module_name = (module_name_split[(- 1)] if len(module_name_split) else '')
model_name = fn.__name__
if hasattr(mod, '__all__'):
mod.__all__.append(model_name)
else:
mod.__all__ =... |
class ID2D1RenderTarget(ID2D1Resource, com.pIUnknown):
_methods_ = [('CreateBitmap', com.STDMETHOD()), ('CreateBitmapFromWicBitmap', com.STDMETHOD()), ('CreateSharedBitmap', com.STDMETHOD()), ('CreateBitmapBrush', com.STDMETHOD()), ('CreateSolidColorBrush', com.STDMETHOD(POINTER(D2D1_COLOR_F), c_void_p, POINTER(ID2... |
class TPlayer(TestCase):
NAME = None
def setUp(self):
config.init()
config.set('player', 'gst_pipeline', 'fakesink')
config.set('settings', 'xine_driver', 'none')
module = player.init_backend(self.NAME)
lib = library.init()
self.player = module.init(lib.librarian)... |
.parametrize('example', ('\n [project]\n name = "myproj"\n version = "1.2"\n\n [my-tool.that-disrespect.pep518]\n value = 42\n ',))
def test_ignore_unrelated_config(tmp_path, example):
pyproject = (tmp_path / 'pyproject.toml')
pyproject.write_text(cleandoc(example))
... |
class Geometry():
def _connect_unimplemented(self, other):
raise AttributeError(('Cannot connect %s to %s' % (self.__class__, other.__class__)))
def _intersect_unimplemented(self, other):
raise AttributeError(('Cannot intersect %s and %s' % (self.__class__, other.__class__)))
_intersect_poin... |
_dataframe_method
_alias(column='column_name')
def convert_excel_date(df: pd.DataFrame, column_name: Hashable) -> pd.DataFrame:
if (not is_numeric_dtype(df[column_name])):
raise ValueError('There are non-numeric values in the column. All values must be numeric.')
df[column_name] = (pd.TimedeltaIndex(df[... |
class F16_TestCase(F12_TestCase):
def runTest(self):
F12_TestCase.runTest(self)
if ('--type' not in self.optionList):
self.assert_parse('autopart --nolvm', 'autopart --nolvm\n')
self.assert_parse_error('autopart --nolvm=asdf')
self.assert_parse_error('autopart --n... |
_exempt
_manager.tracked
def vote(request: WSGIRequest) -> HttpResponse:
key_param = request.POST.get('key')
amount_param = request.POST.get('amount')
if ((key_param is None) or (amount_param is None)):
return HttpResponseBadRequest()
key = int(key_param)
amount = int(amount_param)
if ((... |
class TestAssertError(TestNameCheckVisitorBase):
_passes()
def test(self) -> None:
from pyanalyze.extensions import assert_error
def f(x: int) -> None:
pass
def capybara() -> None:
with assert_error():
f('x')
with assert_error():
... |
class Filter():
def __init__(self, config=None, regex_list=None, logging_fp=None):
if (not regex_list):
regex_list = default_regex
regex_list.append(kaomoji_regex_generator())
regex_list.insert(5, crazy_fans_regex_generator())
self.regex_list = []
for (nam... |
def Give(opt, datapath):
image_sourcepath = (datapath + '/images')
image_classes = sorted([x for x in os.listdir(image_sourcepath) if ('._' not in x)], key=(lambda x: int(x.split('.')[0])))
total_conversion = {(int(x.split('.')[0]) - 1): x.split('.')[(- 1)] for x in image_classes}
image_list = {(int(key... |
class StripDiacriticals(FilterCheckButton):
_label = _('Strip _diacritical marks')
_section = 'rename'
_key = 'diacriticals'
_order = 1.2
def filter(self, original, filename):
return ''.join(filter((lambda s: (not unicodedata.combining(s))), unicodedata.normalize('NFKD', filename))) |
def profile(input, ops, n=10, device=None):
results = []
logging.basicConfig(format='%(message)s', level=logging.INFO)
device = (device or select_device())
print(f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}{'input':>24s}{'output':>24s}")
for x in (... |
def test_does_not_put_src_on_path(pytester: Pytester) -> None:
ensure_file((pytester.path / 'src/nope/__init__.py'))
pytester.makepyfile('import pytest\ndef test():\n with pytest.raises(ImportError):\n import nope\n')
result = pytester.runpytest()
assert (result.ret == ExitCode.OK) |
_task('wsc')
class WSCTask(FairseqTask):
def add_args(parser):
parser.add_argument('data', metavar='DIR', help='path to data directory; we load <split>.jsonl')
parser.add_argument('--init-token', type=int, default=None, help='add token at the beginning of each batch item')
def __init__(self, arg... |
def mcad_svc(app: AppDef, svc_name: str, namespace: str, service_port: str) -> 'V1Service':
from kubernetes.client.models import V1Container, V1ContainerPort, V1EmptyDirVolumeSource, V1EnvVar, V1HostPathVolumeSource, V1ObjectMeta, V1PersistentVolumeClaimVolumeSource, V1Pod, V1PodSpec, V1ResourceRequirements, V1Secu... |
def _get_dp_sharding_perf(batch_sizes: List[int], world_size: int, local_world_size: int, input_lengths: List[float], grad_num_elem: int, emb_dim: int, input_data_type_size: float, table_data_type_size: float, num_poolings: List[float], device_bw: float, inter_host_bw: float, bwd_compute_multiplier: float, is_pooled: b... |
def _freeze_except_roi_heads_id(model):
for v in model.parameters():
v.requires_grad = False
try:
for child in model.module.roi_heads.children():
if (child._get_name() == 'Sequential'):
continue
print('unfreezing', child._get_name())
for v in c... |
class DatabaseConfig():
def __init__(self, db_url_scheme):
self.db_url_scheme = db_url_scheme
self.username = None
self.password = None
self.hostname = None
self.port = None
self.database_name = None
def url(self):
username_part = (self.username if self.us... |
def _upgrade_state_dict(state):
if ('optimizer_history' not in state):
state['optimizer_history'] = [{'criterion_name': 'CrossEntropyCriterion', 'best_loss': state['best_loss']}]
state['last_optimizer_state'] = state['optimizer']
del state['optimizer']
del state['best_loss']
if (... |
class ReleaseFileResource(GenericResource):
os = fields.ToOneField(OSResource, 'os')
release = fields.ToOneField(ReleaseResource, 'release')
class Meta(GenericResource.Meta):
queryset = ReleaseFile.objects.all()
resource_name = 'downloads/release_file'
fields = ['name', 'slug', 'crea... |
def live_node_waiter(min_live_nodes: int, poll_interval_seconds: float=0.5) -> None:
live_nodes = live_node_count()
while (live_nodes < min_live_nodes):
live_nodes = live_node_count()
logger.info(f'Waiting for Live Nodes: {live_nodes}/{min_live_nodes}')
time.sleep(poll_interval_seconds) |
def get_mapping(src_dir='src'):
src_files = glob.glob(os.path.join(src_dir, 'websockets/**/*.py'), recursive=True)
test_files = glob.glob('tests/**/*.py', recursive=True)
src_files = [os.path.relpath(src_file, src_dir) for src_file in sorted(src_files) if ('legacy' not in os.path.dirname(src_file)) if ((os.... |
def test_save_options(skip_qtbot, tmp_path):
options = Options(tmp_path)
window = MSRGameExportDialog(options, {}, 'MyHash', True, [])
window.luma_radio.setChecked(True)
window.save_options()
game_options = options.options_for_game(RandovaniaGame.METROID_SAMUS_RETURNS)
assert isinstance(game_opt... |
def test_charclass_union() -> None:
assert ((Charclass('ab') | Charclass('bc')) == Charclass('abc'))
assert ((Charclass('ab') | (~ Charclass('bc'))) == (~ Charclass('c')))
assert (((~ Charclass('ab')) | Charclass('bc')) == (~ Charclass('a')))
assert (((~ Charclass('ab')) | (~ Charclass('bc'))) == (~ Cha... |
class F29_RaidData(F25_RaidData):
def __init__(self, *args, **kwargs):
F25_RaidData.__init__(self, *args, **kwargs)
self.luks_version = kwargs.get('luks_version', '')
self.pbkdf = kwargs.get('pbkdf', '')
self.pbkdf_memory = kwargs.get('pbkdf_memory', 0)
self.pbkdf_time = kwar... |
class TestAdjlist():
def setup_method(self):
self.knownW = io.open(examples.get_path('columbus.gal')).read()
def test_round_trip_drop_islands_true(self):
adjlist = self.knownW.to_adjlist(remove_symmetric=False, drop_islands=True).astype(int)
w_from_adj = weights.W.from_adjlist(adjlist)
... |
def output_parent_function_json(rule_classification_data_bundle):
dd = _convert_to_printable_dict(*rule_classification_data_bundle)
data = {'rules_classification': []}
for (parent, crimes) in dd.items():
data['rules_classification'].append({'parent': parent, 'crime': crimes})
with open('rules_cl... |
def input_parser(user_input):
m = re.match('(.+)/([lcru*()0-9]*)(f[0-9]*)?', user_input)
if (m and (m.group(2) or m.group(3))):
regex = m.group(1)
flag = m.group(2)
f = m.group(3)
else:
return [user_input, [['l', 1]], 0]
try:
rParan = re.compile('\\(([^())]*)\\)\\... |
class Decoder(nn.Module):
def __init__(self, n_classes=2, n_filters=16, normalization=None, worst_case=False):
super(Decoder, self).__init__()
self.worst_case = worst_case
self.block_five_up = UpsamplingDeconvBlock((n_filters * 16), (n_filters * 8), normalization=normalization)
self.... |
def test_n_slack_svm_as_crf_pickling():
iris = load_iris()
(X, y) = (iris.data, iris.target)
X_ = [(np.atleast_2d(x), np.empty((0, 2), dtype=np.int)) for x in X]
Y = y.reshape((- 1), 1)
(X_train, X_test, y_train, y_test) = train_test_split(X_, Y, random_state=1)
(_, file_name) = mkstemp()
pb... |
class ConvOffset2D(nn.Conv2d):
def __init__(self, filters, out_multi_number, init_normal_stddev=0.01, **kwargs):
self.filters = filters
self._grid_param = None
super(ConvOffset2D, self).__init__(self.filters, (self.filters * 2), 3, padding=1, bias=False, **kwargs)
self.weight.data.co... |
def get_f1(key, prediction):
correct_by_relation = Counter()
guessed_by_relation = Counter()
gold_by_relation = Counter()
for row in range(len(key)):
gold = key[row]
guess = prediction[row]
if ((gold == 0) and (guess == 0)):
pass
elif ((gold == 0) and (guess !... |
def _iter_namespace(nsp):
prefix = (nsp.__name__ + '.')
for pkg in pkgutil.iter_modules(nsp.__path__, prefix):
(yield pkg[1])
toc = set()
for importer in pkgutil.iter_importers(nsp.__name__.partition('.')[0]):
if hasattr(importer, 'toc'):
toc |= importer.toc
for name in t... |
class SaveEpochEndCallback(TrainerCallback):
def __init__(self, save_epochs: int=None) -> None:
super().__init__()
self.save_epochs = save_epochs
def on_epoch_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
if (self.save_epochs is not None):
... |
class PluginManager(pluggy.PluginManager):
def _hookexec(self, hook_name: str, methods: Sequence[HookImpl], kwargs: Mapping[(str, object)], firstresult: bool) -> Union[(object, List[object])]:
try:
return self._inner_hookexec(hook_name, methods, kwargs, firstresult)
except Exception as e... |
def compute_f1_and_exact(metrics, preds, labels, loss_key):
m = collections.defaultdict(list)
for (pred_str, label_str) in zip(preds, labels):
(pred_list, label_list) = (pred_str.lower().split(' '), label_str.lower().split(' '))
m['{}/f1'.format(loss_key)].append(metric_util.compute_f1(label_str... |
def test_distributionrange():
dr = OSC.DistributionRange(1, OSC.Range(0, 3))
dr2 = OSC.DistributionRange(1, OSC.Range(0, 3))
dr3 = OSC.DistributionRange(2, OSC.Range(0, 3))
dr4 = OSC.DistributionRange(1, OSC.Range(0, 4))
prettyprint(dr)
assert (dr == dr2)
assert (dr != dr3)
assert (dr !=... |
class JciHitachiWindSwingableSwitchEntity(JciHitachiEntity, SwitchEntity):
def __init__(self, thing, coordinator):
super().__init__(thing, coordinator)
def name(self):
return f'{self._thing.name} Wind Swingable'
def is_on(self):
status = self.hass.data[DOMAIN][UPDATED_DATA].get(self.... |
_module()
class CityscapesDataset(CustomDataset):
CLASSES = ('road', 'sidewalk', 'building', 'wall', 'fence', 'pole', 'traffic light', 'traffic sign', 'vegetation', 'terrain', 'sky', 'person', 'rider', 'car', 'truck', 'bus', 'train', 'motorcycle', 'bicycle')
PALETTE = [[128, 64, 128], [244, 35, 232], [70, 70, 7... |
class ReplayBuffer(object):
def __init__(self, state_dim, action_dim, max_size=int(1000000.0), device=torch.device('cuda')):
self.max_size = max_size
self.ptr = 0
self.size = 0
self.state = np.zeros((max_size, state_dim))
self.action = np.zeros((max_size, action_dim))
... |
class TrotterStep(metaclass=abc.ABCMeta):
def __init__(self, hamiltonian: Hamiltonian) -> None:
self.hamiltonian = hamiltonian
def prepare(self, qubits: Sequence[cirq.Qid], control_qubit: Optional[cirq.Qid]=None) -> cirq.OP_TREE:
return ()
def trotter_step(self, qubits: Sequence[cirq.Qid], t... |
def relu_dropout(x, p=0, inplace=False, training=False):
if ((not training) or (p == 0)):
return (x.clamp_(min=0) if inplace else x.clamp(min=0))
mask = ((x < 0) | (torch.rand_like(x) > (1 - p)))
return (x.masked_fill_(mask, 0).div_((1 - p)) if inplace else x.masked_fill(mask, 0).div((1 - p))) |
def update_config_from_widgets(unscaled_config: UnscaledTrackerConfig, btrack_widget: btrack.napari.widgets.BtrackWidget) -> UnscaledTrackerConfig:
config = unscaled_config.tracker_config
motion_model = config.motion_model
hypothesis_model = config.hypothesis_model
config.update_method = btrack_widget.u... |
def create_dataset(dataset, config, min_scale=0.5):
normalize = transforms.Normalize((0., 0.4578275, 0.), (0., 0., 0.))
transform_train = transforms.Compose([transforms.RandomResizedCrop(config['image_size'], scale=(min_scale, 1.0), interpolation=InterpolationMode.BICUBIC), transforms.RandomHorizontalFlip(), Ra... |
class Migration(migrations.Migration):
dependencies = [('adserver', '0046_exclude_publishers')]
operations = [migrations.AddField(model_name='advertisement', name='content', field=models.TextField(blank=True, help_text='For most ad types, the combined length of the headline, body, and call to action should be l... |
class CreatecloneTest(tf.test.TestCase):
def setUp(self):
np.random.seed(0)
self._inputs = np.zeros((16, 4))
self._labels = np.random.randint(0, 2, size=(16, 1)).astype(np.float32)
self._logdir = self.get_temp_dir()
for i in range(16):
j = int(((2 * self._labels[i... |
def collect_default_updates(outputs: Sequence[Variable], *, inputs: Optional[Sequence[Variable]]=None, must_be_shared: bool=True) -> Dict[(Variable, Variable)]:
from pymc.distributions.distribution import SymbolicRandomVariable
def find_default_update(clients, rng: Variable) -> Union[(None, Variable)]:
... |
def build_stages(command):
def run(ctx, **cli_params):
out = []
for stage in command.stages:
mapped_stage_params = {remap.old.lstrip('-'): cli_params[remap.new.lstrip('-')] for remap in stage.remap_params}
mapped_stage_params.update(stage.params)
inject_namespace ... |
def parse_checkpoints(files):
entries = []
for f in files:
m = pt_regexp_epoch_based.fullmatch(f)
if (m is not None):
entries.append((int(m.group(1)), m.group(0)))
else:
m = pt_regexp_update_based.fullmatch(f)
if (m is not None):
entrie... |
class ResNet(MetaModule):
def __init__(self, depth, n_outputs):
super(ResNet, self).__init__()
assert (((depth - 2) % 6) == 0), 'depth should be 6n+2'
n = ((depth - 2) // 6)
block = (Bottleneck if (depth >= 44) else BasicBlock)
self.inplanes = 16
self.conv1 = MetaConv... |
def fold_all_batch_norms_to_scale(sim: QuantizationSimModel) -> List[Tuple[(QcQuantizeWrapper, QcQuantizeWrapper)]]:
assert (sim.model is not None)
assert (sim.connected_graph is not None)
model = sim.model
connected_graph = sim.connected_graph
quant_wrappers = {quant_wrapper._module_to_wrap: quant_... |
class TestSimpleStubModule():
(autouse=True, scope='class')
def built(self, builder):
builder('pyiexample', warningiserror=True)
def test_integration(self, parse):
example_file = parse('_build/html/autoapi/example/index.html')
assert ('DoNotFindThis' not in example_file)
foo_... |
class SportTest(unittest.TestCase):
def setUp(self):
self.ddbb = DDBB()
self.ddbb.connect()
self.ddbb.create_tables(add_default=False)
def tearDown(self):
self.ddbb.disconnect()
self.ddbb.drop_tables()
def test_id_should_default_to_none(self):
sport = Sport()
... |
def _call_ll2cr(lons, lats, target_geo_def):
new_src = SwathDefinition(lons, lats)
(swath_points_in_grid, cols, rows) = ll2cr(new_src, target_geo_def)
if (swath_points_in_grid == 0):
return ((lons.shape, np.nan, lons.dtype), (lats.shape, np.nan, lats.dtype))
return np.stack([cols, rows], axis=0) |
_inside_iff((lambda keys: jit.loop_unrolling_heuristic(keys, len(keys), values.UNROLLING_CUTOFF)))
def _find_strategy_class(keys):
if (not config.strategies):
return ObjectHashmapStrategy.singleton
if (len(keys) == 0):
return EmptyHashmapStrategy.singleton
single_class = type(keys[0])
fo... |
class FEVEROUS(datasets.GeneratorBasedBuilder):
def _info(self):
return datasets.DatasetInfo(description=_DESCRIPTION, features=datasets.Features({'id': datasets.Value('string'), 'statement': datasets.Value('string'), 'table': datasets.features.Sequence({'header': datasets.features.Sequence(datasets.Value('... |
class FIDInceptionA(models.inception.InceptionA):
def __init__(self, in_channels, pool_features):
super(FIDInceptionA, self).__init__(in_channels, pool_features)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch5x5 = self.branch5x5_1(x)
branch5x5 = self.branch5x5_2(branc... |
class Effect5922(BaseEffect):
runTime = 'early'
type = ('projected', 'passive')
def handler(fit, beacon, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: (mod.item.group.name == 'Stasis Web')), 'speedFactor', beacon.getModifiedItemAttr('stasisWebStrengthMultiplier')... |
def compact_engines(stdscr, pos_y, pos_x, width, height, jetson):
center_x = (pos_x + (width // 2))
map_eng = map_engines(jetson)
size_map = len(map_eng)
if (size_map > 0):
stdscr.addstr(pos_y, (center_x - 7), ' [HW engines] ', curses.A_BOLD)
size_map += 1
size_table = 26
for (gi... |
class UserPreferences(LoginRequiredMixin, SuccessMessageMixin, UpdateView):
model = Author
form_class = PreferencesForm
template_name = 'dictionary/user/preferences/index.html'
success_message = _('settings are saved, dear')
success_url = reverse_lazy('user_preferences')
def get_object(self, que... |
class TestSessions(BaseTestCase):
def test_sessions(self):
available = [d for (d, _) in Session.iter_valid_session_classes()]
missing = [d for (d, _) in Session.iter_session_classes_issues()]
expected = [(InterfaceType.tcpip, 'INSTR'), (InterfaceType.tcpip, 'SOCKET')]
exp_missing = [... |
def compute_K_c(Xsamples, x_minimum, num_of_obser, sigma, noise, l_vec):
d = len(x_minimum)
nob_nob = covNobeservations(Xsamples, num_of_obser, sigma, noise, l_vec)
nob_grad = cov_nObser_maxGrad(Xsamples, x_minimum, num_of_obser, sigma, noise, l_vec)
nob_off_dia = cov_nObser_off_maxHess(Xsamples, x_mini... |
class PSPAtmosphericalCorrection(ModifierBase):
def __call__(self, projectables, optional_datasets=None, **info):
from pyspectral.atm_correction_ir import AtmosphericalCorrection
band = projectables[0]
if optional_datasets:
satz = optional_datasets[0]
else:
sa... |
def obtain_fitness(disc_enc_type, smiles_here, selfies_here, oracle, discriminator, generation_index, max_molecules_len, device, generation_size, num_processors, beta, image_dir, data_dir, max_fitness_collector, impose_time_adapted_pen):
if ((disc_enc_type == 'smiles') or (disc_enc_type == 'properties_rdkit')):
... |
def test_transfer_statechange_operators():
block_hash = factories.make_transaction_hash()
a = Block(block_number=2, gas_limit=1, block_hash=block_hash)
b = Block(block_number=2, gas_limit=1, block_hash=block_hash)
c = Block(block_number=3, gas_limit=1, block_hash=factories.make_transaction_hash())
a... |
_benchmark.command(name='start')
_option
_range_option
_option
def start_command(workflow: str, workflow_range: (int, int), concurrency: int) -> NoReturn:
try:
start(workflow, workflow_range, concurrency)
except Exception as e:
logger.error(f'Something went wrong during benchmark launch: {e}') |
class MPEncdecMultiheadAttn(nn.Module):
def __init__(self, num_heads, embed_dim, attn_drop=0.0, factor_size=8, rank_size=(- 1)):
super().__init__()
self.embed_dim = embed_dim
self.num_heads = num_heads
self.dropout = attn_drop
self.head_dim = (embed_dim // num_heads)
... |
class PyTensorConfigParser():
def __init__(self, flags_dict: dict, pytensor_cfg, pytensor_raw_cfg):
self._flags_dict = flags_dict
self._pytensor_cfg = pytensor_cfg
self._pytensor_raw_cfg = pytensor_raw_cfg
self._config_var_dict: dict = {}
super().__init__()
def __str__(se... |
def results2csv(dataset, results, out_file, custom_classes=None):
if isinstance(results[0], list):
csv_results = det2csv(dataset, results, custom_classes)
def to_str(item):
if isinstance(item, float):
return f'{item:.3f}'
return str(item)
with open(out_file, 'w') as f:
... |
def _test_ucx_infiniband_nvlink(skip_queue, protocol, enable_infiniband, enable_nvlink, enable_rdmacm):
cupy = pytest.importorskip('cupy')
if (protocol == 'ucx'):
ucp = pytest.importorskip('ucp')
elif (protocol == 'ucxx'):
ucp = pytest.importorskip('ucxx')
if (enable_infiniband and (not ... |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('input')
parser.add_argument('--gzip', action='store_true')
args = parser.parse_args()
def gopen():
if args.gzip:
return gzip.open(args.input, 'r')
else:
return open(args.input, 'r', encoding='... |
def generate_model_output_test2() -> Dict[(str, torch._tensor.Tensor)]:
return {'predictions': torch.tensor([[1.0, 0.0, 0.51, 0.8, 1.0, 0.0, 0.51, 0.8, 1.0, 0.0, 0.51, 0.8]]), 'session': torch.tensor([[1, 1, 1, 1, 1, 1, 1, (- 1), (- 1), (- 1), (- 1), (- 1)]]), 'labels': torch.tensor([[1.0, 1.0, 0.0, 0.0, 1.0, 1.0, ... |
class TestEvolve():
(slots=st.booleans(), frozen=st.booleans())
def test_empty(self, slots, frozen):
(slots=slots, frozen=frozen)
class C():
pass
i1 = C()
i2 = evolve(i1)
assert (i1 is not i2)
assert (i1 == i2)
(simple_classes())
def test_no_ch... |
def test_obtain_input_shape():
with pytest.raises(ValueError):
utils._obtain_input_shape(input_shape=(224, 224, 3), default_size=299, min_size=139, data_format='channels_last', require_flatten=True, weights='imagenet')
for data_format in ['channels_last', 'channels_first']:
shape = (139, 139)
... |
class Solution():
def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:
i = 0
j = 0
new = []
while ((i < m) and (j < n)):
if (nums1[i] <= nums2[j]):
new.append(nums1[i])
i += 1
else:
new.ap... |
def freshen_function_type_vars(callee: F) -> F:
if isinstance(callee, CallableType):
if (not callee.is_generic()):
return cast(F, callee)
tvs = []
tvmap: dict[(TypeVarId, Type)] = {}
for v in callee.variables:
tv = v.new_unification_variable(v)
tvs... |
class TFSegformerDWConv(tf.keras.layers.Layer):
def __init__(self, dim: int=768, **kwargs):
super().__init__(**kwargs)
self.depthwise_convolution = tf.keras.layers.Conv2D(filters=dim, kernel_size=3, strides=1, padding='same', groups=dim, name='dwconv')
def call(self, hidden_states: tf.Tensor, he... |
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if ((len(sys.argv) == 2) and sys.argv[1].endswith('.json')):
(model_args, data_args, training_args) = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
(model_args, data_args,... |
def decoding_latent_code(encoded_code):
decode_temp = np.random.rand(int((encoded_code.shape[0] / 9))).astype('float32')
for i in range(decode_temp.shape[0]):
encoded_binary = encoded_code[(9 * i):(9 * (i + 1))]
integer_part = ''
for binary in encoded_binary[1:]:
integer_part... |
def make_call(*items: tuple[(str, (str | None))]) -> CallExpr:
args: list[Expression] = []
arg_names = []
arg_kinds = []
for (arg, name) in items:
shortname = arg.split('.')[(- 1)]
n = NameExpr(shortname)
n.fullname = arg
args.append(n)
arg_names.append(name)
... |
('pyresample.spherical_utils.check_keys_int_or_tuple')
def test_merge_overlapping_and_nonoverlapping_objects(keys_int_or_tuple):
mysets = [SET_A, SET_B, SET_C, SET_D, SET_E, SET_F, SET_G]
myobjects = GetNonOverlapUnionsBaseClass(mysets)
keys_int_or_tuple.return_code = None
with patch('pyresample.spheric... |
def infer(valid_queue, model, criterion):
global is_multi_gpu
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.eval()
for (step, (input, target)) in enumerate(valid_queue):
with torch.no_grad():
input = input.cuda()
target ... |
def test_categorical_basic():
p = np.array([[100000, 1, 1], [1, 100000, 1], [1, 1, 100000]], dtype=config.floatX)
p = (p / p.sum(axis=(- 1)))
rng = np.random.default_rng()
with pytest.raises(ValueError):
categorical.rng_fn(rng, p, size=(10,))
msg = re.escape('`size` is incompatible with the ... |
def asynq(pure=False, sync_fn=None, cls=async_task.AsyncTask, asyncio_fn=None, **kwargs):
if kwargs:
assert pure, 'custom kwargs are only supported with pure=True'
if pure:
assert (sync_fn is None), 'sync_fn is not supported for pure async functions'
def decorate(fn):
assert (not (is... |
class Stoned_Optimizer(BaseOptimizer):
def __init__(self, args=None):
super().__init__(args)
self.model_name = 'stoned'
def _optimize(self, oracle, config):
self.oracle.assign_evaluator(oracle)
population = np.random.choice(self.all_smiles, size=config['generation_size']).tolist(... |
def process_for_clause(tree):
clauses = [c for c in tree.children[1].children if (isinstance(c, Node) and (c.label == 'for_clause_entry'))]
res = []
for cl in clauses:
vars = [mk_tok([('"%s"' % t.value)]) for t in cl.children[0].terms() if (t.type == 'NAME')]
vars = mk_tok(['[', reduce((lamb... |
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