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def _tf_create_chunk_masks(chunk_starts, num_chunk_frames, video_len):
def tf_map_to_chunk_mask(chunk_start):
valid_chunk_labels_len = tf.minimum(num_chunk_frames, (video_len - chunk_start))
return _tf_create_chunk_mask(valid_chunk_labels_len)
return tf.map_fn(fn=tf_map_to_chunk_mask, elems=chun... |
def test_foreach_with_iterator():
context = Context({'lst': []})
from itertools import product
context.pystring_globals_update({'product': product})
step = Step({'name': 'pypyr.steps.py', 'foreach': PyString('product([1, 2], ["A", "B"])'), 'in': {'py': 'lst.append(i)'}})
step.run_step(context)
a... |
def betweenness_centrality(graph, name='betweenness', mode='nodes', weight='mm_len', endpoints=True, radius=None, distance=None, normalized=False, verbose=True, **kwargs):
netx = graph.copy()
graph = nx.Graph()
for (u, v, k, data) in netx.edges(data=True, keys=True):
if graph.has_edge(u, v):
... |
def _apply_project_table(dist: 'Distribution', config: dict, root_dir: _Path):
project_table = config.get('project', {}).copy()
if (not project_table):
return
_handle_missing_dynamic(dist, project_table)
_unify_entry_points(project_table)
for (field, value) in project_table.items():
... |
class SAGEModule(nn.Module):
def __init__(self, dim, hidden_dim_multiplier, dropout, **kwargs):
super().__init__()
self.feed_forward_module = FeedForwardModule(dim=dim, input_dim_multiplier=2, hidden_dim_multiplier=hidden_dim_multiplier, dropout=dropout)
def forward(self, graph, x):
mess... |
class CalibrationPlot(object):
def __init__(self, name):
self.name = name
self.mode = GL_LINE
self.fusionQPose = [1, 0, 0, 0]
self.alignmentQ = [1, 0, 0, 0]
self.recentpoints = []
self.historypoints = []
self.sigmapoints = []
self.points = []
def a... |
def summary_string(model, input_size, batch_size=(- 1), device=torch.device('cuda:0'), dtypes=None):
if (dtypes == None):
dtypes = ([torch.FloatTensor] * len(input_size))
summary_str = ''
def register_hook(module):
def hook(module, input, output):
class_name = str(module.__class_... |
class _ReversibleFunction(Function):
def forward(ctx, x, blocks, kwargs):
ctx.kwargs = kwargs
for block in blocks:
x = block(x, **kwargs)
ctx.y = x.detach()
ctx.blocks = blocks
return x
def backward(ctx, dy):
y = ctx.y
kwargs = ctx.kwargs
... |
def trapezoidal(mini, mode1, mode2, maxi, size=None):
if (size is None):
p = np.random.uniform()
v = (((p * (((maxi + mode2) - mini) - mode1)) + (mini + mode1)) / 2)
if (v < mode1):
v = (mini + np.sqrt(((mode1 - mini) * (((2 * v) - mini) - mode1))))
elif (v > mode2):
... |
class BottleneckLayer(GenericLayer):
def __init__(self, in_planes, out_planes, stride=1, mid_planes_and_cardinality=None, reduction=4, final_bn_relu=True, use_se=False, se_reduction_ratio=16):
assert (is_pos_int(in_planes) and is_pos_int(out_planes))
assert ((is_pos_int(stride) or is_pos_int_tuple(s... |
class Status(StatusBitsBase):
_fields_ = [('function', c_uint8, 3), ('range', c_uint8, 3), ('digits', c_uint8, 2), ('res1', c_uint8, 1), ('ext_trig', c_uint8, 1), ('cal_enable', c_uint8, 1), ('front_rear', c_uint8, 1), ('fifty_hz', c_uint8, 1), ('auto_zero', c_uint8, 1), ('auto_range', c_uint8, 1), ('int_trig', c_u... |
def se_resnext101_32x4d(num_classes=1000, pretrained='imagenet'):
model = SENet(SEResNeXtBottleneck, [3, 4, 23, 3], groups=32, reduction=16, dropout_p=None, inplanes=64, input_3x3=False, downsample_kernel_size=1, downsample_padding=0, num_classes=num_classes)
if (pretrained is not None):
settings = pret... |
def initialize_weights(net_l, scale=1):
if (not isinstance(net_l, list)):
net_l = [net_l]
for net in net_l:
for m in net.modules():
if isinstance(m, nn.Conv2d):
init.kaiming_normal_(m.weight, a=0, mode='fan_in')
m.weight.data *= scale
i... |
class AttrVI_ATTR_GPIB_SECONDARY_ADDR(RangeAttribute):
resources = [(constants.InterfaceType.gpib, 'INSTR'), (constants.InterfaceType.gpib, 'INTFC')]
py_name = 'secondary_address'
visa_name = 'VI_ATTR_GPIB_SECONDARY_ADDR'
visa_type = 'ViUInt16'
default = NotAvailable
(read, write, local) = (True... |
.skipif((not numpyro_available), reason='BetaBinomial dispatch requires numpyro')
def test_beta_binomial():
rng = shared(np.random.RandomState(123))
n = np.array([10, 40])
a = np.array([1.5, 13])
b = np.array([0.5, 9])
g = pt.random.betabinom(n, a, b, size=(10000, 2), rng=rng)
g_fn = random_func... |
def trace_begin():
global show_tx
global show_rx
global dev
global debug
for i in range(len(sys.argv)):
if (i == 0):
continue
arg = sys.argv[i]
if (arg == 'tx'):
show_tx = 1
elif (arg == 'rx'):
show_rx = 1
elif (arg.find('de... |
def constant_fold(xs: Sequence[TensorVariable], raise_not_constant: bool=True) -> Tuple[(np.ndarray, ...)]:
fg = FunctionGraph(outputs=xs, features=[ShapeFeature()], clone=True)
folded_xs = rewrite_graph(fg).outputs
if (raise_not_constant and (not all((isinstance(folded_x, Constant) for folded_x in folded_x... |
class PrepareISIC2018():
def __init__(self, data_dir, image_size, logger=None):
self.print = (logger.info if logger else print)
self.data_dir = data_dir
self.image_size = image_size
self.data_prefix = 'ISIC_'
self.target_postfix = '_segmentation'
self.target_fex = 'pn... |
class VGNet(torch.nn.Module):
def __init__(self):
super(VGNet, self).__init__()
self.node_encode = nn.Sequential(OrderedDict([('conv1', nn.Conv2d(3, 8, 3, stride=1)), ('relu1', nn.ReLU()), ('conv2', nn.Conv2d(8, 5, 5, stride=2)), ('relu2', nn.ReLU()), ('flatten', nn.Flatten()), ('lin1', Lin(245, 128... |
def sync_buffer(buffers, average=True):
if (not is_distributed()):
return
handles = []
for buffer in buffers:
if torch.is_floating_point(buffer.data):
if average:
handle = torch.distributed.all_reduce(buffer.data, op=torch.distributed.ReduceOp.SUM, async_op=True)
... |
class Reader():
def __init__(self):
pass
def read(self, file, get_meta=False, autojoin_paragraphs=True, para_joiner='\n\n'):
with open(file, 'rb') as fh:
self.fh = fh
cctx = zstandard.ZstdDecompressor()
reader = io.BufferedReader(cctx.stream_reader(fh))
... |
def wait_for_token_network(raiden: 'RaidenService', token_network_registry_address: TokenNetworkRegistryAddress, token_address: TokenAddress, retry_timeout: float) -> None:
token_network = views.get_token_network_by_token_address(views.state_from_raiden(raiden), token_network_registry_address, token_address)
lo... |
class resnet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True, num=18):
super(resnet, self).__init__()
if (num == 18):
self.net = tv.resnet18(pretrained=pretrained)
elif (num == 34):
self.net = tv.resnet34(pretrained=pretrained)
elif (... |
class TestOp():
def test_sanity_0(self):
(r1, r2) = (MyType(1)(), MyType(2)())
node = MyOp.make_node(r1, r2)
assert (list(node.inputs) == [r1, r2])
assert ([x.type for x in node.outputs] == [MyType(3)])
assert ((node.outputs[0].owner is node) and (node.outputs[0].index == 0))... |
class ShapeBase(ABC):
_rgba = (255, 255, 255, 255)
_rotation = 0
_visible = True
_x = 0
_y = 0
_anchor_x = 0
_anchor_y = 0
_batch = None
_group = None
_num_verts = 0
_vertex_list = None
_draw_mode = GL_TRIANGLES
group_class = _ShapeGroup
def __del__(self):
... |
def test_ubuntu_checker_no_release_update_available(fake_process):
fake_process.register_subprocess(('do-release-upgrade', '-c'), returncode=1)
fake_process.register_subprocess('/usr/lib/update-notifier/apt-check', stdout=('0;0',))
ret = os_updates.ubuntu_checker()
assert (ret['release'] is False) |
def rowbased_pearson(x, y):
def ss(a, axis):
return np.sum((a * a), axis=axis)
x = np.asarray(x)
y = np.asarray(y)
mx = x.mean(axis=(- 1))
my = y.mean(axis=(- 1))
(xm, ym) = ((x - mx[(..., None)]), (y - my[(..., None)]))
r_num = np.add.reduce((xm * ym), axis=(- 1))
r_den = np.sqr... |
def test_mouse_move_event_when_no_action_reset_prev_transform(view, item):
view.scene.addItem(item)
view.reset_previous_transform = MagicMock()
event = MagicMock()
item.event_start = QtCore.QPointF(10, 10)
event.scenePos.return_value = QtCore.QPointF(50, 40)
with patch('PyQt6.QtWidgets.QGraphics... |
_cache(maxsize=512)
def progress(paths, total_progress=False):
total = 0
translated = 0
previous = '/'
is_root = True
for path in paths:
pofile = polib.pofile(path)
total += (len(pofile) - len(pofile.obsolete_entries()))
translated += len(pofile.translated_entries())
... |
def _data_arrays_from_params(shapes: list[tuple[(int, ...)]], chunks: list[tuple[(int, ...)]], dims: list[tuple[(int, ...)]]) -> typing.Generator[(xr.DataArray, None, None)]:
for (shape, chunk, dim) in zip(shapes, chunks, dims):
(yield xr.DataArray(da.ones(shape, chunks=chunk), dims=dim)) |
def countstat_current(L, c_ops=None, rhoss=None, J_ops=None):
if (J_ops is None):
if (c_ops is None):
raise ValueError('c_ops must be given if J_ops is not')
J_ops = [sprepost(c, c.dag()) for c in c_ops]
if (rhoss is None):
if (c_ops is None):
raise ValueError('c_... |
class RelativeGateEncoderLayer(nn.Module):
def __init__(self, opt, **kwargs):
super(RelativeGateEncoderLayer, self).__init__()
self.variational = opt.variational_dropout
self.depthwise_conv = opt.depthwise_conv
self.mfw = opt.multilingual_factorized_weights
self.mpw = opt.mul... |
def eval_id_to_info(eval_id):
repeat_id = ''
if (('repeat' in eval_id) or ('incontextknn' in eval_id)):
repeat_id = eval_id.split('-')[(- 1)]
eval_id = '-'.join(eval_id.split('-')[:(- 1)])
(n, temp) = eval_id.split('-')[(- 2):]
version = '-'.join(eval_id.split('-')[:(- 2)])
n = int(n... |
def eval_having(pred, label):
pred_total = label_total = cnt = 0
if (len(pred['groupBy']) > 0):
pred_total = 1
if (len(label['groupBy']) > 0):
label_total = 1
pred_cols = [unit[1] for unit in pred['groupBy']]
label_cols = [unit[1] for unit in label['groupBy']]
if ((pred_total == ... |
def fashion_mnist(data_augmentation=True):
train_loader = datasets.FashionMNIST(args.dataset_path, train=True, download=True)
train_data = (train_loader.data.float() / 256).unsqueeze(1)
train_targets = torch.LongTensor(train_loader.targets)
if (args.dataset_size >= 0):
data_per_class = []
... |
class _AsyncPropertyOverrideContext(contexts.AsyncContext):
def __init__(self, target, property_name, value):
self._target = target
self._property_name = property_name
self._value = value
self._old_value = None
def resume(self):
self._old_value = getattr(self._target, sel... |
class RopLopChecker():
def setup_method(self):
self.x = vector('x')
self.v = vector('v')
self.rng = np.random.default_rng(utt.fetch_seed())
self.in_shape = ((5 + self.rng.integers(3)),)
self.mx = matrix('mx')
self.mv = matrix('mv')
self.mat_in_shape = ((5 + se... |
def get_conv_accum_bounds(weights: np.ndarray, quant_bw: int, accum_bw: int):
max_int_value = ((2 ** quant_bw) - 1)
max_accum_value = (2 ** (accum_bw - 1))
quant_min = min(np.min(weights), 0)
quant_max = max(np.max(weights), 0)
quant_scale = ((2 * max(abs(quant_min), abs(quant_max))) / max_int_value... |
class CmdSdesc(RPCommand):
key = 'sdesc'
locks = 'cmd:all()'
def func(self):
caller = self.caller
if (not self.args):
caller.msg('Usage: sdesc <sdesc-text>')
return
else:
sdesc = _RE_CHAREND.sub('', self.args)
try:
sdesc... |
def test_commandresult_truthy(commandresult_app):
arg = 'foo'
run_cmd(commandresult_app, 'affirmative {}'.format(arg))
assert commandresult_app.last_result
assert (commandresult_app.last_result == cmd2.CommandResult(arg, data=True))
run_cmd(commandresult_app, 'affirmative_no_data {}'.format(arg))
... |
class Effect2745(BaseEffect):
attr = 'boosterCapacitorCapacityPenalty'
displayName = 'Cap Capacity'
type = 'boosterSideEffect'
def handler(cls, fit, booster, context, projectionRange, **kwargs):
fit.ship.boostItemAttr('capacitorCapacity', booster.getModifiedItemAttr(cls.attr), **kwargs) |
def get_num_bits(dtype: torch.dtype) -> int:
if (dtype == torch.bool):
return 8
elif dtype.is_floating_point:
return torch.finfo(dtype).bits
else:
try:
return torch.iinfo(dtype).bits
except TypeError:
raise TypeError(f'Could not infer size for tensor t... |
class EmptyMessage(Message):
__slots__ = ()
type = b''
def __new__(typ):
return typ.SingleInstance
def serialize(self):
return b''
def parse(typ, data):
if (data != b''):
raise ValueError(('empty message(%r) had data' % (typ.type,)))
return typ.SingleInsta... |
def test_context_block():
with pytest.raises(AssertionError):
ContextBlock(16, (1.0 / 4), pooling_type='unsupport_type')
with pytest.raises(AssertionError):
ContextBlock(16, (1.0 / 4), fusion_types='unsupport_type')
with pytest.raises(AssertionError):
ContextBlock(16, (1.0 / 4), fusi... |
class TrezorPlugin(HW_PluginBase):
firmware_URL = '
libraries_URL = '
minimum_firmware = (1, 8, 1)
keystore_class = TrezorKeyStore
minimum_library = (0, 13, 0)
maximum_library = (0, 14)
SUPPORTED_XTYPES = ('standard', 'p2wpkh-p2sh', 'p2wpkh', 'p2wsh-p2sh', 'p2wsh')
DEVICE_IDS = (TREZOR_P... |
def main():
best_acc = 0
opt = parse_option()
if (opt.dataset == 'cifar100'):
(train_loader, val_loader) = get_cifar100_dataloaders(batch_size=opt.batch_size, num_workers=opt.num_workers)
n_cls = 100
else:
raise NotImplementedError(opt.dataset)
model = model_dict[opt.model](n... |
class BaseGoogleAuth():
def get_user_id(self, details, response):
if self.setting('USE_UNIQUE_USER_ID', False):
if ('sub' in response):
return response['sub']
else:
return response['id']
else:
return details['email']
def get_use... |
class Migration(migrations.Migration):
dependencies = [('questions', '0070_alter_questionset_section')]
operations = [migrations.AlterField(model_name='question', name='questionset', field=models.ForeignKey(blank=True, help_text='The question set this question belongs to.', null=True, on_delete=django.db.models... |
class TestChatMemberUpdatedWithoutRequest(TestChatMemberUpdatedBase):
def test_slot_behaviour(self, chat_member_updated):
action = chat_member_updated
for attr in action.__slots__:
assert (getattr(action, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(ac... |
class InteractionArchTransformerTest(unittest.TestCase):
def test_basic(self) -> None:
D = 8
B = 10
nhead = 8
ntransformer_layers = 4
keys = ['f1', 'f2']
F = len(keys)
inter_arch = InteractionTransformerArch(num_sparse_features=F, embedding_dim=D, nhead=nhead,... |
class RippleMachine():
def __init__(self, ripple_generation, ripple_generation_number, ripple_node_selection, ripple_node_selection_random_top_n, ripple_node_connection, ripple_node_ncross):
self._ripple_generation = ripple_generation
self._ripple_generation_number = ripple_generation_number
... |
class MarginMSELoss(nn.Module):
def __init__(self, model, scale: float=1.0, similarity_fct='dot'):
super(MarginMSELoss, self).__init__()
self.model = model
self.scale = scale
self.similarity_fct = similarity_fct
self.loss_fct = nn.MSELoss()
def forward(self, sentence_feat... |
def convert_path(path: str) -> str:
if (len(path) < 250):
return path
if _supports_long_paths():
return path
prefix = '\\\\?\\'
if path.startswith(prefix):
return path
path = _win_prepare_path(path)
if (not path.startswith(prefix)):
path = (prefix + path)
retu... |
def get_feature_importance(est_name, est, dim_red, num_features, fill_value=np.nan):
feat_importance = np.full(num_features, fill_value)
if hasattr(dim_red, 'get_support'):
index_selected_features = dim_red.get_support(indices=True)
if hasattr(est, cfg.importance_attr[est_name]):
fea... |
class DescribeCT_Default():
def it_provides_read_access_to_xml_values(self):
default = a_Default().element
assert (default.extension == 'xml')
assert (default.content_type == 'application/xml')
def it_can_construct_a_new_default_element(self):
default = CT_Default.new('xml', 'app... |
def get_test_mlp_task_config():
return {'name': 'classification_task', 'num_epochs': 10, 'loss': {'name': 'CrossEntropyLoss'}, 'dataset': {'train': {'name': 'synthetic_image', 'num_classes': 2, 'crop_size': 20, 'class_ratio': 0.5, 'num_samples': 20, 'seed': 0, 'batchsize_per_replica': 6, 'use_augmentation': False, ... |
class AprilFoolVideos(commands.Cog):
(name='fool')
async def april_fools(self, ctx: commands.Context) -> None:
video = random.choice(ALL_VIDS)
(channel, url) = (video['channel'], video['url'])
(await ctx.send(f'''Check out this April Fools' video by {channel}.
{url}''')) |
class Video2Vec():
def __init__(self, dataset='evve', model='resnet-50', layers=['layer1', 'layer2', 'layer3', 'layer4'], feat='imac', num_workers=4):
self.model_name = model
self.feat = feat
(self.model, self.extraction_layers) = self._get_model_and_layers(model, layers)
self.model ... |
class MyLSTMForSequenceClassification(BertPreTrainedModel):
def __init__(self, config, num_labels):
super(MyLSTMForSequenceClassification, self).__init__(config)
self.num_labels = num_labels
self.my_word_embeddings = nn.Embedding(config.vocab_size, config.hidden_size, padding_idx=0)
... |
def _load_options():
options.set_values('data', 'dataparser', 'dataexport', 'dataformat', 'variant', 'tocsp', 'checker', 'solver', 'output', 'suffix')
options.set_flags('dataexport', 'solve', 'display', 'verbose', 'lzma', 'sober', 'ev', 'safe', 'recognizeSlides', 'keepHybrid', 'keepSmartTransitions', 'keepsum',... |
class ModelWithBertCustomLayerNorm(nn.Module):
def __init__(self):
super(ModelWithBertCustomLayerNorm, self).__init__()
self.linear1 = torch.nn.Linear(4, 4)
self.customln1 = torch.nn.LayerNorm(4)
self.gelu = torch.nn.GELU()
def forward(self, x):
x = self.linear1(x)
... |
def remove_starting_underscore(data):
if verbose:
print(('#' * 10), 'Step - Remove starting underscore:')
local_vocab = {}
temp_vocab = _check_vocab(data, local_vocab, response='unknown_list')
temp_vocab = [k for k in temp_vocab if (_check_replace(k) and ('_' in k))]
temp_dict = {}
for w... |
def parsefile(fullpath):
dsz.ui.Echo('Parsing file...')
global fileDate
fileDate = fullpath.split('GetFile')[(- 1)]
parsethis(fullpath, '-tu -l -enus')
parsethis(fullpath, '-tau -l -enus')
try:
yakshaver(('%s\\GetFiles\\Yak_Decrypted\\keylogger_scancodes_UNICODE_EN%s.txt' % (logdir, file... |
def test_wheel_package() -> None:
module_path = (fixtures_dir / 'complete')
WheelBuilder.make(Factory().create_poetry(module_path))
whl = ((module_path / 'dist') / 'my_package-1.2.3-py3-none-any.whl')
assert whl.exists()
with zipfile.ZipFile(str(whl)) as z:
assert ('my_package/sub_pkg1/__ini... |
class TestOnError():
def test_closed(self, ipc_server):
ipc_server._socket = QLocalSocket()
ipc_server._timer.timeout.disconnect()
ipc_server._timer.start()
ipc_server.on_error(QLocalSocket.LocalSocketError.PeerClosedError)
assert (not ipc_server._timer.isActive())
def te... |
def catch_response(update, context):
query = update.callback_query
if (query.data == (pattern_to_save_everything + 'OK')):
save_product_info_in_db(update, context)
text = get_text('information_stored', context)
else:
text = get_text('canceled_operation', context)
query.edit_messa... |
class AQSOL(InMemoryDataset):
url = '
def __init__(self, root, split='train', transform=None, pre_transform=None, pre_filter=None):
self.name = 'AQSOL'
assert (split in ['train', 'val', 'test'])
super().__init__(root, transform, pre_transform, pre_filter)
path = osp.join(self.pro... |
class ColorFlags():
class CaretMode(enum.Enum):
off = enum.auto()
on = enum.auto()
selection = enum.auto()
prompt: bool = False
insert: bool = False
command: bool = False
caret: CaretMode = CaretMode.off
private: bool = False
passthrough: bool = False
def to_strin... |
_shapely
def test_geometry_length__multipolygon__radians():
geod = Geod(ellps='WGS84')
polygon = Polygon(LineString([Point(math.radians(1), math.radians(2)), Point(math.radians(3), math.radians(4)), Point(math.radians(5), math.radians(2))]))
assert_almost_equal(geod.geometry_length(MultiPolygon([polygon, po... |
def hex_to_hash(hexstr, hash_size=8):
l = []
count = (hash_size * (hash_size // 4))
if (len(hexstr) != count):
emsg = 'Expected hex string size of {}.'
raise ValueError(emsg.format(count))
for i in range((count // 2)):
h = hexstr[(i * 2):((i * 2) + 2)]
v = int(('0x' + h),... |
class NVPModel(object):
def __init__(self, controller, nvpmodel):
self._controller = controller
self._nvp_models = nvpmodel['models']
self._nvp_default = nvpmodel['default']
self._status = []
self._running = False
self._nvpmodel_now = {}
def _update(self, nvp_stat... |
class LmdbBackend(BaseStorageBackend):
def __init__(self, db_paths, client_keys='default', readonly=True, lock=False, readahead=False, **kwargs):
try:
import lmdb
except ImportError:
raise ImportError('Please install lmdb to enable LmdbBackend.')
if isinstance(client_... |
class GetVersion(rq.ReplyRequest):
_request = rq.Struct(rq.Card8('opcode'), rq.Opcode(0), rq.RequestLength(), rq.Card16('major_version'), rq.Card16('minor_version'))
_reply = rq.Struct(rq.Pad(2), rq.Card16('sequence_number'), rq.ReplyLength(), rq.Card16('major_version'), rq.Card16('minor_version'), rq.Pad(20)) |
def numpy_aware_eq(a, b):
if (isinstance(a, np.ndarray) or isinstance(b, np.ndarray)):
return np.array_equal(a, b)
if ((isinstance(a, Iterable) and isinstance(b, Iterable)) and (not isinstance(a, str)) and (not isinstance(b, str))):
if (len(a) != len(b)):
return False
return ... |
def normalized_qty2(sumall, proall, a):
sqrtand = (((81 * (a ** 2)) * (sumall ** 2)) + ((48 * proall) * ((a - 1) ** 3)))
q = ((((((- 2) * (6 ** (2 / 3))) * proall) * (a - 1)) + ((6 ** (1 / 3)) * ((proall * (((9 * a) * sumall) + sqrt(sqrtand))) ** (2 / 3)))) / (6 * ((proall * (((9 * a) * sumall) + sqrt(sqrtand))... |
def get_args():
parser = argparse.ArgumentParser(description='training neural Datalog through time (NDTT) using MLE')
parser.add_argument('-d', '--Domain', required=True, type=str, help='which domain to work on?')
parser.add_argument('-fn', '--FolderName', required=True, type=str, help='base name of the fol... |
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = None
self.sum = 0
self.count = 0
def is_valid(self):
return (self.count > 0)
def update(self, val, n=1):
if (val is not None):
self.va... |
def test_log_match_start():
events = telemetry.events_from_type('LogMatchStart')
data = events[0]
assert isinstance(data, LogMatchStart)
assert isinstance(data.characters, list)
assert isinstance(data.characters[0], Character)
assert isinstance(data.blue_zone_custom_options, BlueZoneCustomOption... |
def test_encoded_id(fake_manager):
obj = helpers.FakeObject(fake_manager, {'foo': 'bar'})
obj.id = 42
assert (42 == obj.encoded_id)
obj.id = None
assert (obj.encoded_id is None)
obj.id = 'plain'
assert ('plain' == obj.encoded_id)
obj.id = 'a/path'
assert ('a%2Fpath' == obj.encoded_id... |
(version='0.4.0', reason='You should use simulated annealing sampler of dwave-neal directly.')
def solve_ising(linear, quad, num_reads=10, sweeps=1000, beta_range=(1.0, 50.0)):
max_abs_value = float(max((abs(v) for v in (list(quad.values()) + list(linear.values())))))
scale_linear = {k: (float(v) / max_abs_valu... |
class MultiValueHeadersTests(unittest.TestCase):
def setUp(self):
self.headers = Headers([('Server', 'Python'), ('Server', 'websockets')])
def test_init_from_headers(self):
self.assertEqual(Headers(self.headers), self.headers)
def test_init_from_headers_and_kwargs(self):
self.assertE... |
class ImageNet_truncated_hdf5(data.Dataset):
def __init__(self, imagenet_dataset: ImageNet_hdf5, dataidxs, net_dataidx_map, train=True, transform=None, target_transform=None, download=False):
self.dataidxs = dataidxs
self.train = train
self.download = download
self.all_data_hdf5 = im... |
def set_cycles(w=None, h=None, n_samples=None):
scene = bpy.context.scene
scene.render.engine = 'CYCLES'
cycles = scene.cycles
cycles.use_progressive_refine = True
if (n_samples is not None):
cycles.samples = n_samples
cycles.max_bounces = 100
cycles.min_bounces = 10
cycles.caust... |
def main():
parser = argparse.ArgumentParser(description='Create a swap file and enable on boot (require sudo)', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('-d', '--dir', dest='directory', help='Directory to place swapfile', type=str, default='')
parser.add_argument('-n', '-... |
class ArtLinkFromMarkerBetween(ArtLinkBetween):
def __init__(self, marker_name, min_state, max_state, obj_offset_factor, sim, name_to_id, succ_thresh):
real_marker_name = name_to_id[marker_name]
link_idx = (sim.markers[real_marker_name]['relative'][1] - 1)
self.marker_name = real_marker_name... |
class ShellResourceCompute(cpi_resource.Compute):
def __init__(self, api, adaptor):
self._cpi_base = super(ShellResourceCompute, self)
self._cpi_base.__init__(api, adaptor)
self.state = c.ACTIVE
self.rtype = None
self.manager = None
self.manager_url = None
sel... |
class TestOpDecorator(utt.InferShapeTester):
def test_1arg(self):
x = dmatrix('x')
_op(dmatrix, dvector)
def cumprod(x):
return np.cumprod(x)
fn = function([x], cumprod(x))
r = fn([[1.5, 5], [2, 2]])
r0 = np.array([1.5, 7.5, 15.0, 30.0])
assert np.... |
def crossover_ring(parent_A, parent_B, **mol_ok_kwargs):
ring_smarts = Chem.MolFromSmarts('[R]')
if ((not parent_A.HasSubstructMatch(ring_smarts)) and (not parent_B.HasSubstructMatch(ring_smarts))):
return None
rxn_smarts1 = ['[*:1]~[1*].[1*]~[*:2]>>[*:1]-[*:2]', '[*:1]~[1*].[1*]~[*:2]>>[*:1]=[*:2]'... |
def plot_graph(graph, char_id=1, visible_ids=None, action_ids=None):
nodes_interest = [node for node in graph['nodes'] if ('GRABBABLE' in node['properties'])]
container_surf = (dict_info['objects_inside'] + dict_info['objects_surface'])
container_and_surface = [node for node in graph['nodes'] if (node['clas... |
class Migration(migrations.Migration):
dependencies = [('schedule', '0011_auto__0115')]
operations = [migrations.RemoveField(model_name='scheduleitem', name='end'), migrations.RemoveField(model_name='scheduleitem', name='start'), migrations.AddField(model_name='scheduleitem', name='duration', field=models.Posit... |
def register_distill_coco_instances(name, metadata, json_file, image_root):
assert isinstance(name, str), name
assert isinstance(json_file, (str, os.PathLike)), json_file
assert isinstance(image_root, (str, os.PathLike)), image_root
DatasetCatalog.register(name, (lambda : load_coco_json(json_file, image... |
def get_string_property(device_type, property):
key = cf.CFStringCreateWithCString(kCFAllocatorDefault, property.encode('utf-8'), kCFStringEncodingUTF8)
CFContainer = iokit.IORegistryEntryCreateCFProperty(device_type, key, kCFAllocatorDefault, 0)
output = None
if CFContainer:
output = cf.CFStrin... |
class RandomModel(Model):
def __init__(self, seed: Optional[int]=None, **kwargs):
self.rg = np.random.default_rng(seed)
super().__init__(**kwargs)
def provides(self):
return {'means', 'vars'}
def type_(self):
return 'random'
def train(self, *args, **kwargs):
retur... |
.skipif((not sys.platform.startswith('win')), reason='Looks for Python.exe')
.parametrize('venv', [True, False])
def test_windows_python_with_version(monkeypatch, venv):
def which(name):
return 'py'
major = sys.version_info.major
minor = sys.version_info.minor
monkeypatch.setattr(pipx.interprete... |
class Dist2LogitLayer(nn.Module):
def __init__(self, chn_mid=32, use_sigmoid=True):
super(Dist2LogitLayer, self).__init__()
layers = [nn.Conv2d(5, chn_mid, 1, stride=1, padding=0, bias=True)]
layers += [nn.LeakyReLU(0.2, True)]
layers += [nn.Conv2d(chn_mid, chn_mid, 1, stride=1, padd... |
class BuildMan(Command):
user_options = []
def run(self):
cmd = 'sphinx-build -b man docs man'
sphinx_proc = Popen(cmd.split(), stdout=PIPE, stderr=PIPE)
(stdout, stderr) = sphinx_proc.communicate()
return_code = sphinx_proc.wait()
if return_code:
print(('Warn... |
class Cut(_CutBase):
__slots__ = ('from_nodes', 'to_nodes', 'node_labels')
def __init__(self, from_nodes, to_nodes, node_labels=None):
self.from_nodes = from_nodes
self.to_nodes = to_nodes
self.node_labels = node_labels
def indices(self):
return tuple(sorted(set((self.from_no... |
class MlpGeLUFunctionBLASLT(torch.autograd.Function):
_fwd
def forward(ctx, p, recompute, *args):
outputs = mlp_gelu_blaslt.forward(p, args)
ctx.save_for_backward(*args)
ctx.recompute = recompute
if recompute:
ctx.outputs = (outputs[0], outputs[(- 1)])
else:
... |
def _evp_cipher_decrypt(backend: Backend, cipher: _AEADTypes, nonce: bytes, data: bytes, associated_data: list[bytes], tag_length: int, ctx: typing.Any=None) -> bytes:
from cryptography.hazmat.primitives.ciphers.aead import AESCCM
if (len(data) < tag_length):
raise InvalidTag
tag = data[(- tag_lengt... |
.parametrize('my_keys', [['q'], ['qdot'], ['qddot'], ['q', 'qdot'], ['qdot', 'q'], ['q', 'qdot', 'qddot'], ['qddot', 'q', 'qdot'], ['qdot', 'qddot', 'q'], ['q', 'qddot', 'qdot'], ['qddot', 'qdot', 'q'], ['qdot', 'q', 'qddot']])
def test_bounds_from_ranges(my_keys):
from bioptim.examples.getting_started import pendu... |
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