code stringlengths 281 23.7M |
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def all_smt(formula, keys):
target_logic = get_logic(formula)
print(('Target Logic: %s' % target_logic))
with Solver(logic=target_logic) as solver:
solver.add_assertion(formula)
while solver.solve():
partial_model = [EqualsOrIff(k, solver.get_value(k)) for k in keys]
... |
def _pseudonymise_DS(value):
original_DS_value = str(value)
my_decimal = Decimal(original_DS_value)
as_tuple = my_decimal.as_tuple()
digits = as_tuple.digits
count_digits = len(digits)
my_hash_func = hashlib.new('sha3_256')
encoded_value = original_DS_value.encode('ASCII')
my_hash_func.u... |
class ObserverMeta(type):
def __new__(cls, clsname, superclasses, attributedict):
klass = super().__new__(cls, clsname, superclasses, attributedict)
observes = attributedict.get('observes')
if (clsname == 'Observer'):
return klass
if (observes is None):
raise ... |
.slow
.parametrize('untied', [True, False])
_figures_equal()
def test_RanksComparatorPlotter_r2_score(fig_test, fig_ref, untied):
test_ax = fig_test.subplots()
rank0 = agg.RankResult('test', ['a', 'b'], [1, 1], {})
rank1 = agg.RankResult('test', ['a', 'b'], [1, 1], {})
rcmp = ranks_cmp.mkrank_cmp(rank0,... |
def load_feature(ft_path, ft_format, shape=None):
if (ft_format == 'npy'):
video_df = np.load(ft_path)
if (shape == 'CT'):
video_df = video_df.T
elif (ft_format == 'torch'):
video_df = torch.load(ft_path).numpy()
else:
raise ValueError('unsupported feature format:... |
def test_shape_tuple():
x = Variable(MyType2(), None, None)
assert (shape_tuple(x) == ())
x = tensor(dtype=np.float64, shape=(1, 2, None))
res = shape_tuple(x)
assert isinstance(res, tuple)
assert isinstance(res[0], ScalarConstant)
assert (res[0].data == 1)
assert isinstance(res[1], Scal... |
class TestUnittestMethods():
def test_django(self, django_pytester: DjangoPytester) -> None:
django_pytester.create_test_module("\n from django.test import TestCase\n\n class TestFoo(TestCase):\n \n def setUpClass(self):\n print('\\nCALL... |
def main(data_dir, client, bc, config):
benchmark(read_tables, data_dir, bc, dask_profile=config['dask_profile'])
query_1 = '\n\t\tSELECT\n\t\t\tss.ss_customer_sk AS customer_sk,\n\t\t\tsum( case when (d_year = 2001) THEN ss_net_paid ELSE 0.0 END) first_year_total,\n\t\t\tsum( case when (d_year = 2002) THEN ss_... |
def simplified_power(left, right):
(left, right) = _simplify_elementwise_binary_broadcasts(left, right)
out = _simplified_binary_broadcast_concatenation(left, right, simplified_power)
if (out is not None):
return out
if pybamm.is_scalar_zero(right):
return pybamm.ones_like(left)
if p... |
class FileSlice(AbstractContextManager):
def __init__(self, filepath: str, seek_from: int, read_limit: int):
self.filepath = filepath
self.seek_from = seek_from
self.read_limit = read_limit
self.n_seen = 0
def __enter__(self):
self.f = open(self.filepath, 'rb')
se... |
class AsyncHypothesisTest(PytestAsyncioFunction):
def _can_substitute(item: Function) -> bool:
func = item.obj
return (getattr(func, 'is_hypothesis_test', False) and asyncio.iscoroutinefunction(func.hypothesis.inner_test))
def runtest(self) -> None:
self.obj.hypothesis.inner_test = wrap_... |
def hook_init(fm):
fm.execute_console('map {key} shell -p lsblk'.format(key=LIST_MOUNTS_KEY))
diskcmd = 'lsblk -lno NAME | awk \'!/[1-9]/ {sub(/sd/, ""); print}\''
disks = subprocess.check_output(diskcmd, shell=True).decode('utf-8').replace('\r', '').replace('\n', '')
for disk in disks:
partcmd ... |
class SplitTags(SongsMenuPlugin):
PLUGIN_ID = 'Split Tags'
PLUGIN_NAME = _('Split Tags')
PLUGIN_DESC = _('Splits the disc number from the album and the version from the title at the same time.')
PLUGIN_ICON = Icons.EDIT_FIND_REPLACE
plugin_handles = any_song(has_title_splittable)
def plugin_song... |
def start_portal_interactive():
def _iportal(fail):
(portal_twistd_cmd, server_twistd_cmd) = _get_twistd_cmdline(False, False)
portal_twistd_cmd.append('--nodaemon')
if _is_windows():
create_no_window =
Popen(server_twistd_cmd, env=getenv(), bufsize=(- 1), creationfl... |
def make_batch_data_sampler(sampler, images_per_batch, num_iters=None, start_iter=0):
batch_sampler = data.sampler.BatchSampler(sampler, images_per_batch, drop_last=False)
if (num_iters is not None):
batch_sampler = IterationBasedBatchSampler(batch_sampler, num_iters, start_iter)
return batch_sample... |
class NewTaskTrainer(Inc_Learning_Appr):
def __init__(self, model, device, nepochs=160, lr=0.1, lr_min=0.0001, lr_factor=10, lr_patience=8, clipgrad=10000, momentum=0.9, wd=0.0005, multi_softmax=False, wu_nepochs=0, wu_lr_factor=1, fix_bn=False, eval_on_train=False, logger=None):
super(NewTaskTrainer, self)... |
def get_args(method='MLE'):
parser = argparse.ArgumentParser(description=f'training neural Datalog through time (NDTT) using {method}')
parser.add_argument('-d', '--Domain', required=True, type=str, help='which domain to work on?')
parser.add_argument('-db', '--Database', required=True, type=str, help='whic... |
def get_prev_current_ss(df, store_sales_col='ss_sum'):
curr_ss_f = ((df['ss_sold_date_sk'] >= df['imp_start_date']) & (df['ss_sold_date_sk'] < (df['imp_start_date'] + df['no_days_comp_price'])))
prev_ss_f = ((df['ss_sold_date_sk'] >= (df['imp_start_date'] - df['no_days_comp_price'])) & (df['ss_sold_date_sk'] < ... |
class VGG(nn.Module):
def __init__(self, features, num_classes=1000):
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Linear(512, num_classes)
self._initialize_weights()
def forward(self, x):
x = self.features(x)
x = x.view(x.size(0), (- ... |
def test_get_conference_found(client, db):
conference = f.ConferenceFactory()
url = reverse('get-conference', kwargs={'conference_slug': conference.slug})
response = client.get(url, follow=True)
assert (response.redirect_chain == [(reverse('proposals-list', kwargs={'conference_slug': conference.slug}), ... |
class V1Protocol(RegistryProtocol):
FAILURE_CODES: Dict[(Enum, Dict[(Failures, int)])] = {V1ProtocolSteps.PUT_IMAGES: {Failures.INVALID_AUTHENTICATION: 403, Failures.UNAUTHENTICATED: 401, Failures.UNAUTHORIZED: 403, Failures.APP_REPOSITORY: 405, Failures.SLASH_REPOSITORY: 400, Failures.INVALID_REPOSITORY: 400, Fail... |
class TestWeightSvdLayerSplitandSVDPrunner():
.parametrize('model_type', ['Sequential', 'Functional'])
.parametrize('rank', [12, 20])
.parametrize('cost_metric', [CostMetric.mac, CostMetric.memory])
def test_split_layer(self, model_type, rank, cost_metric):
model = get_model(model_type)
... |
class YAMLPrinter():
def display(d: Union[(str, Dict[(str, Any)])], **_kwargs: Any) -> None:
try:
import yaml
print(yaml.safe_dump(d, default_flow_style=False))
except ImportError:
sys.exit('PyYaml is not installed.\nInstall it with `pip install PyYaml` to use the... |
class TestCFtime():
def test_add_time_bounds_dimension(self):
from satpy.cf.coords import add_time_bounds_dimension
test_array = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
times = np.array(['2018-05-30T10:05:00', '2018-05-30T10:05:01', '2018-05-30T10:05:02', '2018-05-30T10:05:03'], dtype=np.... |
class Blackbox(namedtuple('Blackbox', ['partition', 'output_indices'])):
def hidden_indices(self):
return tuple(sorted((set(self.micro_indices) - set(self.output_indices))))
def micro_indices(self):
return tuple(sorted((idx for part in self.partition for idx in part)))
def macro_indices(self... |
class FontRow(Adw.ActionRow):
__gtype_name__ = 'FontRow'
def __init__(self, font_data: dict):
super().__init__()
self.props.title = font_data['name']
self.props.subtitle = ' / '.join((font_data['family'], font_data['style'], font_data['weight']))
btn_remove = Gtk.Button(valign=Gt... |
def get_param_dict(args, model_without_ddp: nn.Module):
try:
param_dict_type = args.param_dict_type
except:
param_dict_type = 'default'
assert (param_dict_type in ['default', 'ddetr_in_mmdet', 'large_wd'])
if (param_dict_type == 'default'):
param_dicts = [{'params': [p for (n, p)... |
class AddDestroyHandler(GraphRewriter):
def apply(self, fgraph):
supervisor_added = False
for feature in fgraph._features:
if isinstance(feature, Supervisor):
supervisor_added = True
break
if (not supervisor_added):
warnings.warn(f'A Su... |
class NormalQueue1EntryRTL(Component):
def construct(s, EntryType):
s.recv = RecvIfcRTL(EntryType)
s.send = SendIfcRTL(EntryType)
s.count = OutPort()
s.full = Wire()
s.entry = Wire(EntryType)
s.count //= s.full
s.send.msg //= s.entry
s.send.val //= s.f... |
def diagonal_coulomb_potential_and_kinetic_terms_as_arrays(hamiltonian):
if (not isinstance(hamiltonian, FermionOperator)):
try:
hamiltonian = normal_ordered(get_fermion_operator(hamiltonian))
except TypeError:
raise TypeError('hamiltonian must be either a FermionOperator or ... |
def prepare_query_payload(backend, offset, payload_string, column_name=None):
_temp = []
if column_name:
_payload = payload_string.format(offset=offset, column_name=column_name)
if ((backend == 'Microsoft SQL Server') and ('id' in column_name)):
_payload = replace_with(_payload, colu... |
def parse_old_label(data_root, in_path, img_size=False):
imgid2imgname = {}
imgid2anno = {}
idx = 0
for line in list_from_file(in_path):
line = line.strip().split()
img_full_path = osp.join(data_root, line[0])
if (not osp.exists(img_full_path)):
continue
ann_f... |
_canonicalize
_stabilize
_rewriter([Reshape])
def local_reshape_lift(fgraph, node):
if (isinstance(node.op, Reshape) and node.inputs[0].owner and isinstance(node.inputs[0].owner.op, Elemwise) and (len(node.inputs[0].owner.inputs) == 1)):
r = node.op(node.inputs[0].owner.inputs[0], node.inputs[1])
co... |
class NotAllowedMethodsTests(AuthenticatedAPITestCase):
def setUpTestData(cls):
cls.message = create_offensive_message()
def test_returns_405_for_get(self):
url = reverse('api:bot:offensivemessage-detail', args=(self.message.id,))
response = self.client.get(url)
self.assertEqual(... |
def reconstructions(data, model, session, batch_dim=10, sample=False):
(m0, _, _, a, x, _, f, _, _) = data.next_train_batch(batch_dim)
(n, e) = session.run(([model.nodes_gumbel_argmax, model.edges_gumbel_argmax] if sample else [model.nodes_argmax, model.edges_argmax]), feed_dict={model.edges_labels: a, model.no... |
class ServiceConfig():
pathfinding_service_address: Optional[Address] = None
pathfinding_max_paths: int = DEFAULT_PATHFINDING_MAX_PATHS
pathfinding_max_fee: TokenAmount = DEFAULT_PATHFINDING_MAX_FEE
pathfinding_iou_timeout: BlockTimeout = DEFAULT_PATHFINDING_IOU_TIMEOUT
monitoring_enabled: bool = Fa... |
class Quantile8BitQuantization(Quantization):
compression_type = runtime_pb2.QUANTILE_8BIT
def quantize(self, tensor: torch.Tensor, allow_inplace: bool=False) -> Tuple[(np.ndarray, np.ndarray)]:
tensor = tensor.detach().float()
borders = torch.as_tensor(quantile_qq_approximation(tensor.numpy(), ... |
class TestSilent(unittest.TestCase):
def test_nonsilent(self):
actual = file_info.silent(INPUT_FILE)
expected = False
self.assertEqual(expected, actual)
def test_nonsilent_pathlib(self):
actual = file_info.silent(Path(INPUT_FILE))
expected = False
self.assertEqual... |
class TestInitialSetup(TestCase):
('evennia.server.initial_setup.AccountDB')
def test_get_god_account(self, mocked_accountdb):
mocked_accountdb.objects.get = MagicMock(return_value=1)
self.assertEqual(initial_setup.get_god_account(), 1)
mocked_accountdb.objects.get.assert_called_with(id=... |
class TestInSubprocess(unittest.TestCase):
def runProcessAndGetAsciiStdoutOrStderr(self, cmdline):
if (sys.platform != 'win32'):
cmdline = shlex.split(cmdline)
p = subprocess.Popen(cmdline, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = p.communicate()
... |
class Up(nn.Module):
def __init__(self, in_channels, out_channels, bilinear=True):
super().__init__()
if bilinear:
self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
else:
self.up = nn.ConvTranspose2d((in_channels // 2), (in_channels // 2), ker... |
def get_pure_function(fcn) -> PureFunction:
errmsg = 'The input function must be a function, a method of torch.nn.Module, a method of xitorch.EditableModule, or a sibling method'
if isinstance(fcn, PureFunction):
return fcn
elif (inspect.isfunction(fcn) or isinstance(fcn, torch.jit.ScriptFunction)):... |
def get_artifact(owner: str, repo: str, sha: str, action_name: str, artifact_name: str) -> str:
client = authorize(owner, repo)
try:
runs = client.get(f'/repos/{owner}/{repo}/actions/runs', params={'per_page': 100})
runs.raise_for_status()
runs = runs.json()
for run in runs['work... |
def cal_false_alarm(gt, preds, threshold=0.5):
preds = list(preds.cpu().detach().numpy())
gt = list(gt.cpu().detach().numpy())
preds = np.repeat(preds, 16)
preds[(preds < threshold)] = 0
preds[(preds >= threshold)] = 1
(tn, fp, fn, tp) = confusion_matrix(gt, preds, labels=[0, 1]).ravel()
far... |
def perform_cle_bc(config: argparse.Namespace):
data_pipeline = ImageNetDataPipeline(config)
input_shape = (image_net_config.dataset['image_width'], image_net_config.dataset['image_height'], image_net_config.dataset['image_channels'])
tf.keras.backend.clear_session()
tf_config = tf.ConfigProto()
tf_... |
class GlobalDecl(Statement):
__slots__ = ('names',)
__match_args__ = ('names',)
names: list[str]
def __init__(self, names: list[str]) -> None:
super().__init__()
self.names = names
def accept(self, visitor: StatementVisitor[T]) -> T:
return visitor.visit_global_decl(self) |
def build_data_info(args):
args.dataset = osp.basename(osp.normpath(args.data_root))
with open(args.data_info, 'r') as f:
data_info = json.load(f)[args.dataset]
args.train_session_set = data_info['train_session_set']
args.test_session_set = data_info['test_session_set']
args.class_index = da... |
def resize_dataset(i_root, o_root, mode='train'):
out_images_path = osp.join(o_root, mode, 'images')
out_masks_path = osp.join(o_root, mode, 'masks_12')
check_dir(o_root)
check_dir(out_images_path)
check_dir(out_masks_path)
image_path = osp.join(i_root, mode, 'images')
mask_path = osp.join(i... |
class TFCvtIntermediate(tf.keras.layers.Layer):
def __init__(self, config: CvtConfig, embed_dim: int, mlp_ratio: int, **kwargs):
super().__init__(**kwargs)
self.dense = tf.keras.layers.Dense(units=int((embed_dim * mlp_ratio)), kernel_initializer=get_initializer(config.initializer_range), activation=... |
class TextureGrid(TextureRegion, UniformTextureSequence):
items = ()
rows = 1
columns = 1
item_width = 0
item_height = 0
def __init__(self, grid):
image = grid.get_texture()
if isinstance(image, TextureRegion):
owner = image.owner
else:
owner = ima... |
class ClassificationTask(LightningModule):
def __init__(self, model: PreTrainedModel, args: ClassificationTrainArguments):
super().__init__()
self.model = model
self.args = args
def configure_optimizers(self):
optimizer = AdamW(self.parameters(), lr=self.args.learning_rate)
... |
_module()
class MeshAdversarialDataset(Dataset):
def __init__(self, train_dataset, adversarial_dataset):
super().__init__()
self.train_dataset = build_dataset(train_dataset)
self.adversarial_dataset = build_dataset(adversarial_dataset)
self.length = len(self.train_dataset)
def __... |
class Graph():
name = ''
def __init__(self, *chain):
self.edges = {BEGIN: set()}
self.named = {}
self.nodes = []
if len(chain):
self.add_chain(*chain)
def __iter__(self):
(yield from self.nodes)
def __len__(self):
return len(self.nodes)
def... |
def __get_all_files(root: Path, folder: (Path | None)=None) -> list[str]:
if (not folder):
folder = root
results = []
for sub_folder in folder.iterdir():
results.append(sub_folder.relative_to(root).__str__().replace('\\', '/').replace('.html', ''))
if sub_folder.is_dir():
... |
def get_method_config(key, config={}, yaml_path='configs/methods.yaml', yaml_data=None):
if ((not key) or (key is None)):
return None
assert (yaml_path or yaml_data)
if (yaml_data is None):
yaml_data = yaml.load(open(yaml_path), Loader=yaml.Loader)
assert (key in yaml_data.keys()), f'`{k... |
class TestEndecaDgraphCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('EndecaDgraphCollector', {})
self.collector = EndecaDgraphCollector(config, None)
def test_import(self):
self.assertTrue(EndecaDgraphCollector)
('urllib2.urlopen')
(Collector, 'publ... |
def load_model_config_from_hf(model_id: str):
assert has_hf_hub(True)
cached_file = _download_from_hf(model_id, 'config.json')
pretrained_cfg = load_cfg_from_json(cached_file)
pretrained_cfg['hf_hub_id'] = model_id
pretrained_cfg['source'] = 'hf-hub'
model_name = pretrained_cfg.get('architecture... |
def repo_with_no_tags_tag_commits(git_repo_factory, file_in_repo):
git_repo = git_repo_factory()
add_text_to_file(git_repo, file_in_repo)
git_repo.git.commit(m='Initial commit')
add_text_to_file(git_repo, file_in_repo)
git_repo.git.commit(m=':nut_and_bolt: add some more text')
add_text_to_file(g... |
class DataTrainingArguments():
task_name: Optional[str] = field(default='ner', metadata={'help': 'The name of the task (ner, pos...).'})
dataset_name: Optional[str] = field(default='nielsr/funsd-layoutlmv3', metadata={'help': 'The name of the dataset to use (via the datasets library).'})
dataset_config_name... |
def compute_mIoU(gt_dir, pred_dir, devkit_dir=''):
with open(join(devkit_dir, 'info.json'), 'r') as fp:
info = json.load(fp)
num_classes = np.int(info['classes'])
print('Num classes', num_classes)
name_classes = np.array(info['label'], dtype=np.str)
mapping = np.array(info['label2train'], dt... |
def find_char_span_by_token_idx(id, doc):
doc_text = (doc.text + ' ABCDEF')
token_list = doc_text.split()
token_text = doc[id].text
assert (token_text in token_list[id])
if (token_text != token_list[id]):
return []
new_sen = ' '.join(token_list[id:])
char_star = doc_text.find(new_sen... |
class CT_TabStops(BaseOxmlElement):
tab = OneOrMore('w:tab', successors=())
def insert_tab_in_order(self, pos, align, leader):
new_tab = self._new_tab()
(new_tab.pos, new_tab.val, new_tab.leader) = (pos, align, leader)
for tab in self.tab_lst:
if (new_tab.pos < tab.pos):
... |
def getWholeCount(path):
count = []
catories = os.listdir(path)
for catory in catories:
pathcat = os.path.join(path, catory)
count.append(len(os.listdir(pathcat)))
print((((((('|' + catory) + '|') + str(len(os.listdir(pathcat)))) + '|') + str(len(os.listdir(os.path.join(pathapks, cat... |
class StatisticsDisplay(Observer, DisplayElement):
__maxTemp: float = 0.0
__minTemp: float = 200
__tempSum: float = 0.0
__numReadings: int = 0
__weatherData: WeatherData
def __init__(self, weatherData: WeatherData):
self.__weatherData = weatherData
weatherData.registerObserver(se... |
def main_worker(ngpus_per_node, args):
global best_acc1
if (args.gpu is not None):
print('Use GPU: {} for training'.format(args.gpu))
if args.pretrained:
print("=> using pre-trained model '{}'".format(args.arch))
model = models.__dict__[args.arch](pretrained=True, args=args)
else... |
def add_weight_decay(adjust_per_optimizer=True):
if (adjust_per_optimizer and ('lars' in FLAGS.optimizer)):
return
l2_losses = [tf.nn.l2_loss(v) for v in tf.trainable_variables() if ('batch_normalization' not in v.name)]
tf.losses.add_loss((FLAGS.weight_decay * tf.add_n(l2_losses)), tf.GraphKeys.REG... |
_auth
def update_pwd(request):
if (request.method == 'POST'):
pks = request.POST.getlist('pks')
pwd = request.POST.get('pwd')
try:
for pk in pks:
server = Assets.objects.get(id=pk).serverassets
server.password = CryptPwd().encrypt_pwd(pwd)
... |
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False):
super().__init__()
self.self_attn = nn.MultiheadAttention(d_model, nhead, dropout=dropout)
self.linear1 = nn.Linear(d_model, dim_feedfor... |
class RandomChoiceShear(object):
def __init__(self, values, p=None, interp='bilinear', lazy=False):
if isinstance(values, (list, tuple)):
values = th.FloatTensor(values)
self.values = values
if (p is None):
p = (th.ones(len(values)) / len(values))
elif (abs((1... |
class SnapshotSerializer(serializers.ModelSerializer):
values = serializers.SerializerMethodField()
class Meta():
model = Snapshot
fields = ('title', 'description', 'values', 'created', 'updated')
def get_values(self, obj):
values = Value.objects.filter(snapshot=obj).select_related('... |
_config
def test_kill(manager):
manager.c.group['SCRATCHPAD'].dropdown_reconfigure('dd-a')
manager.test_window('one')
assert_focused(manager, 'one')
assert ('window' not in manager.c.group['SCRATCHPAD'].dropdown_info('dd-a'))
manager.c.group['SCRATCHPAD'].dropdown_toggle('dd-a')
is_spawned(manag... |
def _populate_kernel_cache(np_type, blocks, dim_x, dim_z, dim_u, max_tpb):
if (np_type not in _SUPPORTED_TYPES):
raise ValueError('Datatype {} not found for Kalman Filter'.format(np_type))
if (np_type == 'float32'):
c_type = 'float'
else:
c_type = 'double'
specializations = ('_cu... |
def parse_args():
parser = ArgumentParser(description='PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes')
parser.add_argument('--num_cores', type=int, default=1, help='Number of TPU cores to use (1 or 8).')
parser.add_argument('training_script', type=s... |
def _gen_rhf_response_gam(mf, mo_coeff=None, mo_occ=None, singlet=None, hermi=0, max_memory=None):
from pyscf.pbc.dft import numint, multigrid
assert isinstance(mf, hf.RHF)
if (mo_coeff is None):
mo_coeff = mf.mo_coeff
if (mo_occ is None):
mo_occ = mf.mo_occ
cell = mf.cell
kpt = ... |
class DLRM_Transformer(DLRM):
def __init__(self, embedding_bag_collection: EmbeddingBagCollection, dense_in_features: int, dense_arch_layer_sizes: List[int], over_arch_layer_sizes: List[int], nhead: int=8, ntransformer_layers: int=4, dense_device: Optional[torch.device]=None) -> None:
super().__init__(embed... |
class Embed():
def __init__(self):
self.transformer = SentenceTransformer(model_name, device='cuda')
def __call__(self, text_batch: List[str]):
embeddings = self.transformer.encode(text_batch, batch_size=100, device='cuda').tolist()
return list(zip(text_batch, embeddings)) |
class Book(models.Model):
user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)
name = models.CharField(max_length=200)
pages = models.IntegerField()
def __str__(self):
return self.name
def get_absolute_url(self):
return reverse('books_fbv_user:book_edit', kwar... |
def add_GreeterServicer_to_server(servicer, server):
rpc_method_handlers = {'SayHello': grpc.unary_unary_rpc_method_handler(servicer.SayHello, request_deserializer=generated_dot_greeter__pb2.HelloRequest.FromString, response_serializer=generated_dot_greeter__pb2.HelloReply.SerializeToString), 'SayHelloGoodbye': grp... |
def get_logger(log_path, results_raw_metrics_path, results_avg_metrics_path):
with open(logging_configs_path, 'r') as f:
dict_conf = yaml.safe_load(f)
dict_conf['handlers']['fh']['filename'] = log_path
dict_conf['handlers']['fh_avg']['filename'] = results_avg_metrics_path
dict_conf['handlers']['... |
def round_window_to_full_blocks(window, block_shapes, height=0, width=0):
if (len(set(block_shapes)) != 1):
raise WindowError('All bands must have the same block/stripe structure')
window = evaluate(window, height=height, width=width)
height_shape = block_shapes[0][0]
width_shape = block_shapes[... |
class TBItemsTurnHandler(DefaultScript):
def at_script_creation(self):
self.key = 'Combat Turn Handler'
self.interval = 5
self.persistent = True
self.db.fighters = []
for thing in self.obj.contents:
if thing.db.hp:
self.db.fighters.append(thing)
... |
class F8_TestCase(FC3_TestCase):
def runTest(self):
FC3_TestCase.runTest(self)
self.assertFalse(F8_RootPw().lock)
self.assert_parse('rootpw --lock secrethandshake', 'rootpw --lock --plaintext secrethandshake\n')
self.assert_parse('rootpw --plaintext secrethandshake', 'rootpw --plaint... |
class PositionWiseFeedForward(nn.Module):
def __init__(self, model_size, inner_size, dropout=0.0, variational=False, activation='relu', glu=False, weight_drop=0.0, dropout_residual=False, res_dropout=0.0):
super().__init__()
self.model_size = model_size
self.inner_size = inner_size
s... |
def test_make_prompt():
import difflib
def assert_equal_and_show_diff(expected, actual):
if (expected != actual):
diff = difflib.ndiff(expected.splitlines(keepends=True), actual.splitlines(keepends=True))
print(''.join(diff))
assert (expected == actual)
example1 =... |
_new_faces(MaterialGroup.ROOF)
def create_flat_roof(bm, faces, prop):
top_face = extrude_and_outset(bm, faces, prop.thickness, prop.outset)
if prop.add_border:
bmesh.ops.inset_region(bm, faces=top_face, thickness=prop.border, use_even_offset=True)
ret = bmesh.ops.extrude_face_region(bm, geom=top... |
def test_html_configured_output_dir(testdir):
script = testdir.makepyfile(SCRIPT)
testdir.tmpdir.join('.coveragerc').write('\n[html]\ndirectory = somewhere\n')
result = testdir.runpytest('-v', f'--cov={script.dirpath()}', '--cov-report=html', script)
result.stdout.fnmatch_lines(['*- coverage: platform *... |
class TSongsMenuPlugins(TestCase):
def _confirmer(self, *args):
self.confirmed = True
def setUp(self):
self.tempdir = mkdtemp()
self.pm = PluginManager(folders=[self.tempdir])
self.confirmed = False
self.handler = SongsMenuPluginHandler(self._confirmer, self._confirmer)
... |
def main():
import argparse
import IPython
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
choices = ['franka_panda/panda_suction', 'franka_panda/panda_drl']
parser.add_argument('--robot-model', default=choices[0], choices=choices, help=' ')
args = parser... |
class MobileViTASPP(nn.Module):
def __init__(self, config: MobileViTConfig) -> None:
super().__init__()
in_channels = config.neck_hidden_sizes[(- 2)]
out_channels = config.aspp_out_channels
if (len(config.atrous_rates) != 3):
raise ValueError('Expected 3 values for atrous... |
class ImageDecoder(Decoder):
def get_animation_file_extensions(self):
return []
def decode(self, filename, file):
raise NotImplementedError()
def decode_animation(self, filename, file):
raise ImageDecodeException('This decoder cannot decode animations.')
def __repr__(self):
... |
def mask(self, operation, destination_kind, x_offset, y_offset, source_bitmap):
Mask(display=self.display, opcode=self.display.get_extension_major(extname), destination_window=self, operation=operation, destination_kind=destination_kind, x_offset=x_offset, y_offset=y_offset, source_bitmap=source_bitmap) |
def get_image_path(image_lists, label_name, index, image_dir, category):
if (label_name not in image_lists):
tf.logging.fatal('Label does not exist %s.', label_name)
label_lists = image_lists[label_name]
if (category not in label_lists):
tf.logging.fatal('Category does not exist %s.', catego... |
def _set_ctrl_swap(ctrl_bit, bloq: CSwap):
states = [ZeroState(), OneState()]
effs = [ZeroEffect(), OneEffect()]
bb = BloqBuilder()
q0 = bb.add(states[ctrl_bit])
q1 = bb.add_register('q1', bloq.bitsize)
q2 = bb.add_register('q2', bloq.bitsize)
(q0, q1, q2) = bb.add(bloq, ctrl=q0, x=q1, y=q2)... |
class TestBloombergTickerMapper(TestCase):
def test_ticker_to_figi__no_data_preloading(self):
mapper = BloombergTickerMapper(data_caching=False)
tickers = [BloombergTicker('SPX Index', SecurityType.INDEX), BloombergTicker('SPY US Equity', SecurityType.STOCK), BloombergTicker('USDCHF Curncy', Securit... |
class BiRecurrentMapper(SequenceMapper):
def __init__(self, fw, bw=None, merge: MergeLayer=None, swap_memory=False):
self.fw = fw
self.swap_memory = swap_memory
self.bw = bw
self.merge = merge
def apply(self, is_train, inputs, mask=None):
fw = self.fw(is_train)
bw... |
class AttrVI_ATTR_USB_INTR_IN_PIPE(RangeAttribute):
resources = [(constants.InterfaceType.usb, 'RAW')]
py_name = ''
visa_name = 'VI_ATTR_USB_INTR_IN_PIPE'
visa_type = 'ViInt16'
default = NotAvailable
(read, write, local) = (True, True, True)
(min_value, max_value, values) = (129, 143, [(- 1)... |
class TestInlineQueryWithoutRequest(TestInlineQueryBase):
def test_slot_behaviour(self, inline_query):
for attr in inline_query.__slots__:
assert (getattr(inline_query, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(inline_query)) == len(set(mro_slots(inline_que... |
def parse_pyproject_toml(text, rootdir, name=None, *, tools=None, requirefiles=True):
data = tomllib.loads(text)
unused = list(data)
for (section, normalize) in SECTIONS.items():
try:
secdata = data[section]
except KeyError:
data[section] = None
else:
... |
class DRN_A(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(DRN_A, self).__init__()
self.out_dim = (512 * block.expansion)
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
... |
class Add(GateWithRegisters, cirq.ArithmeticGate):
bitsize: int
def signature(self):
return Signature.build(a=self.bitsize, b=self.bitsize)
def registers(self) -> Sequence[Union[(int, Sequence[int])]]:
return (([2] * self.bitsize), ([2] * self.bitsize))
def with_registers(self, *new_regi... |
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