code stringlengths 281 23.7M |
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def test_resnet():
resnet45_aster = ResNet(in_channels=3, stem_channels=[64, 128], block_cfgs=dict(type='BasicBlock', use_conv1x1='True'), arch_layers=[3, 4, 6, 6, 3], arch_channels=[32, 64, 128, 256, 512], strides=[(2, 2), (2, 2), (2, 1), (2, 1), (2, 1)])
resnet45_abi = ResNet(in_channels=3, stem_channels=32, ... |
def parse_args():
parser = argparse.ArgumentParser(description='Test CornerNet')
parser.add_argument('cfg_file', help='config file', type=str)
parser.add_argument('--testiter', dest='testiter', help='test at iteration i', default=None, type=int)
parser.add_argument('--split', dest='split', help='which s... |
def test_get_direction_from_center_bottomright_cropped_item(view, item):
with patch.object(item, 'bounding_rect_unselected', return_value=QtCore.QRectF(5, 5, 100, 80)):
direction = item.get_direction_from_center(QtCore.QPointF(105, 95))
assert (direction == approx((QtCore.QPointF(1, 1) / math.sqrt(2... |
def test_readonly_push_pull(pusher, puller, basic_images, different_images, liveserver_session, app_reloader, api_caller, liveserver, registry_server_executor):
credentials = ('devtable', 'password')
pusher.push(liveserver_session, 'devtable', 'newrepo', 'latest', basic_images, credentials=credentials)
with... |
def test_async_methods_signature(async_file: AsyncIOWrapper[mock.Mock]) -> None:
assert (async_file.read.__name__ == 'read')
assert (async_file.read.__qualname__ == 'AsyncIOWrapper.read')
assert (async_file.read.__doc__ is not None)
assert ('io.StringIO.read' in async_file.read.__doc__) |
def create_rule(repository, rule_value, rule_type=RepoMirrorRuleType.TAG_GLOB_CSV, left_child=None, right_child=None):
validate_rule(rule_type, rule_value)
rule_kwargs = {'repository': repository, 'rule_value': rule_value, 'rule_type': rule_type, 'left_child': left_child, 'right_child': right_child}
rule = ... |
def test_evaluated_once(testdir):
testdir.makepyfile('\n from pytest import fixture\n from pytest_describe import behaves_like\n\n count = 0\n def thing():\n global count\n count += 1\n def is_evaluated_once():\n assert count == 1\n\n ... |
def _init_weights(module: nn.Module, name: str, head_bias: float=0.0, flax=False):
if isinstance(module, nn.Linear):
if name.startswith('head'):
nn.init.zeros_(module.weight)
nn.init.constant_(module.bias, head_bias)
elif flax:
lecun_normal_(module.weight)
... |
class TestSys():
def test_sys_builtin_module_names(self) -> None:
node = _extract_single_node('\n import sys\n sys.builtin_module_names\n ')
inferred = list(node.infer())
assert (len(inferred) == 1)
assert isinstance(inferred[0], nodes.Tuple)
assert infer... |
class SingleContextWithBottleneckToQuestionModel(MultipleContextModel):
def __init__(self, encoder: QuestionsAndParagraphsEncoder, word_embed: Optional[WordEmbedder], char_embed: Optional[CharWordEmbedder], embed_mapper: Optional[SequenceMapper], context_to_question_attention: AttentionMapper, question_to_context_a... |
class NotificationBridgePresenter(QObject):
def __init__(self, parent: QObject=None) -> None:
super().__init__(parent)
self._active_notifications: Dict[(int, 'QWebEngineNotification')] = {}
self._adapter: Optional[AbstractNotificationAdapter] = None
config.instance.changed.connect(se... |
def create_nettree():
global S
global ptn_len
nettree = [[] for i in range((ptn_len + 1))]
start = [0 for i in range((ptn_len + 1))]
for i in range(len(S)):
node0 = node(i)
if (S[i] == sub_ptn_list[0].start):
node0.toleave = True
nettree[0].append(deepcopy(nod... |
_required
_POST
def send(request):
try:
form = MessageForm(request.POST)
if form.is_valid():
message = form.send()
if (len(message.connections) == 1):
return HttpResponse('Your message was sent to 1 recipient.')
else:
msg = 'Your me... |
def handle_disk_serialized(pxy: ProxyDetail):
(org_header, frames) = pxy.obj
header = _copy.deepcopy(org_header)
if header['disk-io-header']['shared-filesystem']:
from .proxify_host_file import ProxifyHostFile
assert ProxifyHostFile._spill_to_disk
new_path = ProxifyHostFile._spill_to... |
class TestGetAngleBetween():
def test_get_angle_between(self):
ray1 = Ray(Point((0, 0)), Point((1, 0)))
ray2 = Ray(Point((0, 0)), Point((1, 0)))
assert (get_angle_between(ray1, ray2) == 0.0)
def test_get_angle_between_expect45(self):
ray1 = Ray(Point((0, 0)), Point((1, 0)))
... |
class HTMLFormatter(logging.Formatter):
def __init__(self, fmt: str, datefmt: str, log_colors: Mapping[(str, str)]) -> None:
super().__init__(fmt, datefmt)
self._log_colors: Mapping[(str, str)] = log_colors
self._colordict: Mapping[(str, str)] = {}
for color in COLORS:
se... |
def main(params):
rng = np.random.RandomState()
log_hndlr_stream = logging.StreamHandler()
log_hndlr_stream.setLevel(logging.DEBUG)
log_handlr_file = logging.FileHandler(path.join(PATH, f'create_datasets_{datetime.datetime.now().isoformat()}.log'))
log_handlr_file.setLevel(logging.DEBUG)
formatt... |
def _get_truncated_description(elements: Iterable[(Tag | NavigableString)], markdown_converter: DocMarkdownConverter, max_length: int, max_lines: int) -> str:
result = ''
markdown_element_ends = []
rendered_length = 0
tag_end_index = 0
for element in elements:
is_tag = isinstance(element, Ta... |
def test_format_currency_format_type():
assert (numbers.format_currency(1099.98, 'USD', locale='en_US', format_type='standard') == '$1,099.98')
assert (numbers.format_currency(0, 'USD', locale='en_US', format_type='standard') == '$0.00')
assert (numbers.format_currency(1099.98, 'USD', locale='en_US', format... |
def _add_kwargs(func: Callable[(..., Any)], kwargs: Dict[(str, Any)], event_loop_fixture_id: str, event_loop: asyncio.AbstractEventLoop, request: SubRequest) -> Dict[(str, Any)]:
sig = inspect.signature(func)
ret = kwargs.copy()
if ('request' in sig.parameters):
ret['request'] = request
if (even... |
def add_title(image):
text = 'Bahot-Hard ESPORTS'
font = ImageFont.truetype('theboldfont.ttf', 90)
d1 = ImageDraw.Draw(image)
(w, h) = d1.textsize(text, font)
left = ((image.width - w) / 2)
top = 50
d1.text((left, top), text, font=font)
(w, h) = d1.textsize('Overall Standings', font)
... |
class KiteCovariogram(KiteSubplot):
legend_template = {'exponential': 'Model: {0:.2g} e^(-d/{1:.1f}) | RMS: {rms:.4e}', 'exponential_cosine': 'Model: {0:.2g} e^(-d/{1:.1f}) - cos((d-({2:.1f}))/{3:.1f})| RMS: {rms:.4e}'}
class VarianceLine(pg.InfiniteLine):
def __init__(self, *args, **kwargs):
... |
.supported(only_if=(lambda backend: (not backend.ed448_supported())), skip_message='Requires OpenSSL without Ed448 support')
def test_ed448_unsupported(backend):
with raises_unsupported_algorithm(_Reasons.UNSUPPORTED_PUBLIC_KEY_ALGORITHM):
Ed448PublicKey.from_public_bytes((b'0' * 57))
with raises_unsupp... |
class BertweetTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = BertweetTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab = ['I', 'm', '', '', 'r', '']
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ['#version... |
class RandomListSearcher(Searcher):
def __init__(self, param_grid):
self._configurations = param_grid
Searcher.__init__(self)
def suggest(self, trial_id):
selected_dict = self._configurations[random.randint(0, (len(self._configurations) - 1))]
generated_config = {}
for (k... |
class Ui_Form(object):
def setupUi(self, Form):
if (not Form.objectName()):
Form.setObjectName(u'Form')
Form.resize(476, 447)
self.training_code = QLineEdit(Form)
self.training_code.setObjectName(u'training_code')
self.training_code.setGeometry(QRect(100, 40, 301,... |
def iou_calculator(annotation, segmentation, void_pixels=None):
if (void_pixels is not None):
assert (annotation.shape == void_pixels.shape), f'Annotation({annotation.shape}) and void pixels:{void_pixels.shape} dimensions do not match.'
void_pixels = void_pixels.astype(np.bool)
else:
voi... |
def list_environments_from_aws(config_obj: dict) -> None:
try:
session = boto3.session.Session(profile_name=config_obj['aws_profile'])
s3_client = session.client('s3')
bucket_objs = s3_client.list_objects_v2(Bucket=config_obj['bucket'])
print(':link: Listing your cloud environments.'... |
class ThreadContext(object):
def __init__(self, tid: int):
self.cregs = dict()
self.sregs = dict()
self._join_th_id = None
self.tid = tid
self.count = 0
self.state = ThreadState.RUNNING
def save(self, tt_ctx: TritonContext) -> None:
self.sregs = tt_ctx.get... |
def init_weights(net, init_type='normal'):
print(('initialization method [%s]' % init_type))
if (init_type == 'normal'):
net.apply(weights_init_normal)
elif (init_type == 'xavier'):
net.apply(weights_init_xavier)
elif (init_type == 'kaiming'):
net.apply(weights_init_kaiming)
... |
class TestSWA(unittest.TestCase):
def _test_averaged_model(self, net_device: torch.device, swa_device: torch.device, ema: bool) -> None:
dnn = torch.nn.Sequential(torch.nn.Conv2d(1, 5, kernel_size=3), torch.nn.ReLU(), torch.nn.MaxPool2d(kernel_size=2), torch.nn.BatchNorm2d(5, momentum=0.3), torch.nn.Conv2d(... |
class CharDataset(Dataset):
def __init__(self, data_cfg: DataConfig):
data = fsspec.open(data_cfg.path).open().read().decode('utf-8')
data = data[:int((len(data) * data_cfg.truncate))]
chars = sorted(list(set(data)))
(data_size, vocab_size) = (len(data), len(chars))
print(('D... |
class TestSafetyRequirement(unittest.TestCase):
((tuple(map(int, packaging.__version__.split('.'))) < (22, 0)), 'not validated in these versions')
def test_with_invalid_input(self):
invalid_inputs = ['django*', 'django>=python>=3.6', 'numpy>=3.3python>=3.6', '', '\n']
for i_input in invalid_inpu... |
def _view_to_component(view: ((Callable | View) | str), compatibility: bool, transforms: Sequence[Callable[([VdomDict], Any)]], strict_parsing: bool, request: (HttpRequest | None), args: (Sequence | None), kwargs: (dict | None)):
(converted_view, set_converted_view) = hooks.use_state(cast(Union[(VdomDict, None)], N... |
class BaseJobSet(ABC):
name: str = ''
job_name: str = ''
def started_job(self, name: str) -> None:
pass
def finished_job(self) -> None:
pass
def check_status(self) -> None:
pass
('Just use JobSet.job_name attribute/property instead')
def get_active_job_name(self) -> s... |
def ssimloss(X, Y):
assert (not torch.is_complex(X))
assert (not torch.is_complex(Y))
win_size = 7
k1 = 0.01
k2 = 0.03
w = (torch.ones(1, 1, win_size, win_size).to(X) / (win_size ** 2))
NP = (win_size ** 2)
cov_norm = (NP / (NP - 1))
data_range = 1
C1 = ((k1 * data_range) ** 2)
... |
class Graphsn_GIN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(Graphsn_GIN, self).__init__()
self.nn = Linear(nfeat, nhid)
self.fc = Linear(nhid, nclass)
self.dropout = dropout
self.eps = nn.Parameter(torch.FloatTensor(1))
self.reset_parameters(... |
class MiningYieldViewFull(StatsView):
name = 'miningyieldViewFull'
def __init__(self, parent):
StatsView.__init__(self)
self.parent = parent
self._cachedValues = []
def getHeaderText(self, fit):
return _t('Mining Yield')
def getTextExtentW(self, text):
(width, hei... |
class FileInfo():
def __init__(self, filename):
self._filename = filename
def FullName(self):
return os.path.abspath(self._filename).replace('\\', '/')
def RepositoryName(self):
fullname = self.FullName()
if os.path.exists(fullname):
project_dir = os.path.dirname(... |
def plot_model_metrics_rewards(results, size: int, N: int, split: float=0.01, reward='scores', si_fig: bool=False):
xs = [int(((size * split) * i)) for i in range(1, 7)]
(fig, axs) = plt.subplots(1, 3, sharex=True, sharey=True, figsize=(((4 / 1.5) * 3), 4))
fmt = 'o-'
ms = 5
capsize = 2
for (i, ... |
def main(args):
args = parse_args(args)
if torch.cuda.is_available():
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = False
device = init_distributed_device(args)
if (args.name is None):
model_name_sa... |
class ForbiddenImportChecker(BaseChecker):
name = 'forbidden_import'
msgs = {'E9999': ('You may not import any modules - you imported %s on line %s.', 'forbidden-import', 'Used when you use import')}
options = (('allowed-import-modules', {'default': (), 'type': 'csv', 'metavar': '<modules>', 'help': 'Allowe... |
class Effect989(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Hybrid Turret')), 'maxRange', ship.getModifiedItemAttr('eliteBonusGunship1'), skill='Assault Frigates', **kwargs) |
class TestBooleanAttribute():
def test_boolean_attribute(self):
attr = BooleanAttribute(default=True)
assert (attr.attr_type == BOOLEAN)
assert (attr.default is True)
def test_boolean_serialize(self):
attr = BooleanAttribute()
assert (attr.serialize(True) is True)
... |
def main(args):
(train_loader, test_loader, DATASET_CONFIG) = get_loader(args)
n_data = len(train_loader.dataset)
logger.info(f'length of training dataset: {n_data}')
n_data = len(test_loader.dataset)
logger.info(f'length of testing dataset: {n_data}')
(model, criterion) = get_model(args, DATASE... |
class FIR2(Stage):
_format = [E(1, 4, x_fixed(b'FIR2'), dummy=True), E(6, 7, 'i2'), E(9, 18, 'e10.2'), E(20, 23, 'i4'), E(25, 32, 'f8.3'), E(34, 34, 'a1'), E(36, 39, 'i4'), E(41, None, 'a25+')]
gain = Float.T(help='filter gain (relative factor, not in dB)')
decimation = Int.T(optional=True, help='decimation... |
class VarEarlyStopper():
def __init__(self, eps: float=0.15, window: int=200):
self.eps = eps
self.window = window
self.stopped = False
self.history = np.array([])
self.normalized_var = 1
def __call__(self, loss: float):
self.history = np.append(self.history, loss... |
def export_plugin_maintainers(request, **kwargs):
if (not request.user.is_superuser):
raise PermissionDenied()
import csv
response = HttpResponse(content_type='text/csv')
response['Content-Disposition'] = 'attachment; filename=plugin_maintainers.csv'
writer = csv.writer(response, dialect='ex... |
class BotRepoConfigTest(TestCase):
def test_fetches_file_success(self):
bot = bot_factory()
bot.provider.get_file.return_value = ('foo: bar', None)
self.assertEqual(bot.get_repo_config(bot.user_repo), {'foo': 'bar'})
def test_yaml_error(self):
bot = bot_factory()
bot.prov... |
class FairseqLMDecoder(BaseDecoder):
def __init__(self, cfg: FlashlightDecoderConfig, tgt_dict: Dictionary) -> None:
super().__init__(tgt_dict)
self.nbest = cfg.nbest
self.unitlm = cfg.unitlm
self.lexicon = (load_words(cfg.lexicon) if cfg.lexicon else None)
self.idx_to_wrd = ... |
class RHEL4_NetworkData(FC3_NetworkData):
removedKeywords = FC3_NetworkData.removedKeywords
removedAttrs = FC3_NetworkData.removedAttrs
def __init__(self, *args, **kwargs):
FC3_NetworkData.__init__(self, *args, **kwargs)
self.notksdevice = kwargs.get('notksdevice', False)
def _getArgsAsS... |
class CmdFight(Command):
key = 'fight'
help_category = 'combat'
def func(self):
here = self.caller.location
fighters = []
if (not self.caller.db.hp):
self.caller.msg("You can't start a fight if you've been defeated!")
return
if is_in_combat(self.caller... |
class Application(tornado.web.Application):
def __init__(self, db: DB, default_version=None):
settings = dict(template_path=os.path.join(os.path.dirname(__file__), 'tpl'), static_path=os.path.join(os.path.dirname(__file__), 'static'), static_url_prefix=config.static_url_prefix, debug=config.debug, gzip=conf... |
def _get(package: str, resource: str, name: str) -> dict[(str, t.Any)]:
try:
return t.cast('dict[str, t.Any]', json.loads(importlib_resources.files(package).joinpath(resource).read_bytes()))
except (FileNotFoundError, ModuleNotFoundError):
raise NoSuchSchemaError(f'no builtin schema named {name}... |
class TestBinaryBinnedAUROC(MetricClassTester):
def _test_auroc_class_with_input(self, input: torch.Tensor, target: torch.Tensor, num_tasks: int, threshold: Union[(int, List[float], torch.Tensor)], compute_result: Tuple[(torch.Tensor, torch.Tensor)]) -> None:
self.run_class_implementation_tests(metric=Binar... |
class AbstractAudioPlayer(metaclass=ABCMeta):
audio_sync_required_measurements = 8
audio_desync_time_critical = 0.28
audio_desync_time_minor = 0.03
audio_minor_desync_correction_time = 0.012
audio_buffer_length = 0.9
def __init__(self, source, player):
self.source = weakref.proxy(source)... |
def resnetv2_50x1_vit(pretrained=False, strict=False, progress=False, **kwargs):
model = ResNetV2(layers=(3, 4, 9), num_classes=0, global_pool='avg', in_chans=kwargs.get('in_chans', 3), preact=False, stem_type='same')
if pretrained:
state_dict = model_zoo.load_url(model_urls['resnetv2_50x1_vit'], progre... |
def _build_family_tree(dirlist, parent_DID, child_DID):
if (child_DID < 0):
return
_build_family_tree(dirlist, parent_DID, dirlist[child_DID].left_DID)
dirlist[parent_DID].children.append(child_DID)
dirlist[child_DID].parent = parent_DID
_build_family_tree(dirlist, parent_DID, dirlist[child_... |
class Finder():
ref_types = {r.type: r for r in (Ref('call', 5), Ref('lea', 7))}
STR_SAMPLE_LEN = 100
NULL = b'\x00'
def __init__(self, file: File, sig: Sig):
self.file = file
self.sig = sig
it = re.finditer(self.sig.pattern, self.file.data, flags=re.DOTALL)
match = next(... |
def set_requires_grad(requires_grad, *models):
for model in models:
if isinstance(model, torch.nn.Module):
for param in model.parameters():
param.requires_grad = requires_grad
elif isinstance(model, (torch.nn.Parameter, torch.Tensor)):
model.requires_grad = re... |
def test_invalid_def_file(runner, mocker):
mocker.patch('products.vmware_cb_response.CbResponse._authenticate')
mocked_nested_process_search = mocker.patch('products.vmware_cb_response.CbResponse.nested_process_search')
result = runner.invoke(cli, ['--deffile', 'nonexistent.json'])
assert ("The deffile ... |
def build_function(name: str, args: (list[str] | None)=None, posonlyargs: (list[str] | None)=None, defaults: (list[Any] | None)=None, doc: (str | None)=None, kwonlyargs: (list[str] | None)=None, kwonlydefaults: (list[Any] | None)=None) -> nodes.FunctionDef:
func = nodes.FunctionDef(name, lineno=0, col_offset=0, par... |
def write_to_outfile(out_path: str, data: InputExample, mode: str) -> None:
Path(out_path).mkdir(parents=True, exist_ok=True)
fp = os.path.join(out_path, f'en_ewt-ud-{mode}.conllu')
comment = '# Cats and oats'
col2 = 'c2'
with open(fp, 'w', encoding='utf-8') as out:
for section in data:
... |
class SeparationNet(nn.Module):
def __init__(self, encoder: nn.Module, decoder_fg: nn.Module, decoder_bg: nn.Module) -> None:
super().__init__()
self.encoder = encoder
self.decoder_fg = decoder_fg
self.decoder_bg = decoder_bg
def encode(self, x: torch.Tensor) -> Tuple[(torch.Tens... |
def download_delta_manifest_entry(delta_like: Union[(Delta, DeltaLocator)], entry_index: int, table_type: TableType=TableType.PYARROW, columns: Optional[List[str]]=None, file_reader_kwargs_provider: Optional[ReadKwargsProvider]=None, *args, **kwargs) -> LocalTable:
(cur, con) = _get_sqlite3_cursor_con(kwargs)
m... |
def test_object_parking_space():
parking_space_object = xodr.Object(s=0, t=0, length=5, width=3, height=0.0, Type=xodr.ObjectType.parkingSpace, name='parkingSpace')
parking_space = xodr.ParkingSpace(xodr.Access.all, 'test string')
parking_space_object.add_parking_space(parking_space)
road = xodr.create_... |
.django_project(project_root='django_project_root', create_manage_py=True)
def test_django_project_found(django_pytester: DjangoPytester) -> None:
django_pytester.create_test_module('\n def test_foobar():\n assert 1 + 1 == 2\n ')
result = django_pytester.runpytest_subprocess('django_project_root')
... |
def get_status_view(process_id, start_time):
url = (BH_URL + '/api/v1/client-view/status')
payload = {'processId': process_id, 'startTime': start_time}
try:
r = requests.get(url, params=payload, headers=json_auth_headers())
status_view_json = json.dumps(r.json())
return StatusView.fr... |
def test_learnerND_log_works():
loss = curvature_loss_function()
learner = LearnerND(ring_of_fire, bounds=[((- 1), 1), ((- 1), 1)], loss_per_simplex=loss)
learner.ask(4)
learner.tell(((- 1), (- 1)), (- 1.0))
learner.ask(1)
learner.tell(((- 1), 1), (- 1.0))
learner.tell((1, (- 1)), 1.0)
l... |
class IoUBalancedNegSampler(RandomSampler):
def __init__(self, num, pos_fraction, floor_thr=(- 1), floor_fraction=0, num_bins=3, **kwargs):
super(IoUBalancedNegSampler, self).__init__(num, pos_fraction, **kwargs)
assert ((floor_thr >= 0) or (floor_thr == (- 1)))
assert (0 <= floor_fraction <... |
class CheckpointParams(FairseqDataclass):
save_dir: str = field(default='checkpoints', metadata={'help': 'path to save checkpoints'})
restore_file: str = field(default='checkpoint_last.pt', metadata={'help': 'filename from which to load checkpoint (default: <save-dir>/checkpoint_last.pt'})
finetune_from_mod... |
class INatDataset(ImageFolder):
def __init__(self, root, train=True, year=2018, transform=None, target_transform=None, category='name', loader=default_loader):
self.transform = transform
self.loader = loader
self.target_transform = target_transform
self.year = year
path_json ... |
def main(sample):
try:
pathserv = fs.get_path_info_for_active_session()
except mpexceptions.ExceptionUndefinedSamplesDir:
print("The env var 'pyglet_mp_samples_dir' is not defined.")
return 1
except mpexceptions.ExceptionNoSessionIsActive:
print('*** Error, no session active.... |
def get_task_dict(task_name_list: List[Union[(str, lm_eval.base.Task)]]):
task_name_dict = {task_name: get_task(task_name)() for task_name in task_name_list if isinstance(task_name, str)}
task_name_from_object_dict = {get_task_name_from_object(task_object): task_object for task_object in task_name_list if (not ... |
class PicklingMixin():
filename = None
def load(self, filename):
self.filename = filename
print_d(('Loading contents of %r.' % filename), self)
items = _load_items(filename)
self._load_init(items)
print_d(f'Done loading contents of {filename!r}', self._name)
def save(... |
def delayed_import():
global _ServerSession, _AccountDB, _ServerConfig, _ScriptDB
if (not _ServerSession):
(modulename, classname) = settings.SERVER_SESSION_CLASS.rsplit('.', 1)
_ServerSession = variable_from_module(modulename, classname)
if (not _AccountDB):
from evennia.accounts.mo... |
class TimeRange(BaseElement):
tag: ClassVar[str] = ns('C', 'time-range')
def __init__(self, start: Optional[datetime]=None, end: Optional[datetime]=None) -> None:
super(TimeRange, self).__init__()
if (self.attributes is None):
raise ValueError('Unexpected value None for self.attribut... |
def iou_pytorch(outputs: torch.Tensor, labels: torch.Tensor):
outputs = outputs.squeeze(1)
intersection = (outputs & labels).float().sum((1, 2))
union = (outputs | labels).float().sum((1, 2))
iou = ((intersection + SMOOTH) / (union + SMOOTH))
thresholded = (torch.clamp((20 * (iou - 0.5)), 0, 10).cei... |
def train_one_epoch(model, optimizer, train_loader, model_func, lr_scheduler, accumulated_iter, optim_cfg, rank, tbar, total_it_each_epoch, dataloader_iter, tb_log=None, leave_pbar=False):
if (total_it_each_epoch == len(train_loader)):
dataloader_iter = iter(train_loader)
if (rank == 0):
pbar = ... |
class BoolQGen():
def __init__(self):
self.tokenizer = T5Tokenizer.from_pretrained('t5-base')
model = T5ForConditionalGeneration.from_pretrained('ramsrigouthamg/t5_boolean_questions')
device = torch.device(('cuda' if torch.cuda.is_available() else 'cpu'))
model.to(device)
sel... |
('/api/chat_xlang_webot', methods=['POST'])
def chat_xlang_webot() -> Dict:
try:
request_json = request.get_json()
user_id = request_json.pop('user_id', DEFAULT_USER_ID)
chat_id = request_json['chat_id']
user_intent = request_json['user_intent']
parent_message_id = request_js... |
class MapReduce():
def __init__(self, map_func: Callable, iterable: Iterable, *iterables, reduce_func: Optional[Callable]=None, reduce_kwargs: Optional[dict]=None, parallel: bool=True, ordered: bool=False, total: Optional[int]=None, chunksize: Optional[int]=None, sequential_threshold: int=1, max_depth: Optional[int... |
def get_error(output, target, topk=(1,)):
maxk = max(topk)
batch_size = target.size(0)
(_, pred) = output.topk(maxk, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, (- 1)).expand_as(pred))
res = []
for k in topk:
correct_k = correct[:k].view((- 1)).float().sum(0, keep... |
def test_bool_type_factory():
o = MyHarderConfigurable(required_str='yes', also_required='True')
with inspect_node(o) as ni:
assert (not ni.partial)
assert (o.required_str == 'yes')
assert (o.default_str == 'foo')
assert (o.integer is None)
assert (o.also_required is True) |
def get_backend_name():
display = Gdk.Display.get_default()
if (display is not None):
name = display.__gtype__.name
if name.startswith('Gdk'):
name = name[3:]
if name.endswith('Display'):
name = name[:(- 7)]
return name
return 'Unknown' |
def is_a_tf_op_lambda_layer(layer: tf.keras.layers.Layer) -> bool:
if (version.parse(tf.version.VERSION) >= version.parse('2.10')):
from keras.layers.core.tf_op_layer import TFOpLambda
else:
from tensorflow.python.keras.layers.core import TFOpLambda
return isinstance(layer, TFOpLambda) |
class Model(nn.Module):
def __init__(self, n_cont_features: int, cat_cardinalities: List[int], bins: Optional[List[Tensor]], mlp_kwargs: dict) -> None:
super().__init__()
self.cat_cardinalities = cat_cardinalities
d_cat = sum(cat_cardinalities)
d_embedding = 24
self.cont_embe... |
class CronTabSchedule(object):
def __init__(self, crontab, delimiter='\n'):
self.entries = []
entry_lines = [s for s in (s.strip() for s in crontab.split(delimiter)) if (s and (s[0] != '#'))]
self.smallest_change_gap = None
for line in entry_lines:
self.add_entry(line)
... |
class FullImageSampler(PatchSampler):
def __init__(self):
super(FullImageSampler, self).__init__()
self.full_indices = True
def __call__(self, nbatch, wh, device):
(w, h) = torch.meshgrid([torch.linspace((- 1), 1, wh[1]), torch.linspace((- 1), 1, wh[0])])
h = h[(None, ..., None)]... |
def _symmetric_two_body_terms(quad, complex_valued):
(p, q, r, s) = quad
(yield (p, q, r, s))
(yield (q, p, s, r))
(yield (s, r, q, p))
(yield (r, s, p, q))
if (not complex_valued):
(yield (p, s, r, q))
(yield (q, r, s, p))
(yield (s, p, q, r))
(yield (r, q, p, s)... |
def test_set_after_show(skip_qtbot):
label = DelayedTextLabel()
skip_qtbot.addWidget(label)
label.setText('Foo')
assert (label.text() == 'Foo')
assert (label._delayed_text == 'Foo')
assert (not label._already_shown)
label.showEvent(QtGui.QShowEvent())
assert (label.text() == 'Foo')
a... |
class ChangeEmailForm(forms.Form):
email1 = forms.EmailField(max_length=254, label=_('new e-mail address'))
email2 = forms.EmailField(max_length=254, label=_('new e-mail address (again)'))
password_confirm = forms.CharField(label=_('confirm your password'), strip=False, widget=forms.PasswordInput)
def c... |
class CoinChooserBase(Logger):
def __init__(self, *, enable_output_value_rounding: bool):
Logger.__init__(self)
self.enable_output_value_rounding = enable_output_value_rounding
def keys(self, coins: Sequence[PartialTxInput]) -> Sequence[str]:
raise NotImplementedError
def bucketize_c... |
class GenericUtilTests(unittest.TestCase):
.patch('sys.stdout', new_callable=io.StringIO)
def test_context_managers_no_context(self, mock_stdout):
with ContextManagers([]):
print('Transformers are awesome!')
self.assertEqual(mock_stdout.getvalue(), 'Transformers are awesome!\n')
... |
def _get_version_from_arguments(arguments):
if (len(arguments) != 1):
raise ValueError('Expected exactly 1 argument')
version = arguments[0]
parts = version.split('.')
if (len(parts) != 2):
raise ValueError('not of the form: YY.N')
if (not all((part.isdigit() for part in parts))):
... |
def customize_compiler_for_nvcc(self):
super = self.compile
def compile(sources, output_dir=None, macros=None, include_dirs=None, debug=0, extra_preargs=None, extra_postargs=None, depends=None):
postfix = os.path.splitext(sources[0])[1]
if (postfix == '.cu'):
postargs = extra_postarg... |
class Migration(migrations.Migration):
dependencies = [('core', '0005_auto__1730')]
operations = [migrations.RenameField(model_name='currentsong', old_name='url', new_name='internal_url'), migrations.RenameField(model_name='queuedsong', old_name='url', new_name='internal_url'), migrations.AddField(model_name='c... |
def se_resnet50(num_classes, loss, pretrained='imagenet', **kwargs):
model = SENet(num_classes=num_classes, loss=loss, block=SEResNetBottleneck, layers=[3, 4, 6, 3], groups=1, reduction=16, dropout_p=None, inplanes=64, input_3x3=False, downsample_kernel_size=1, downsample_padding=0, last_stride=2, fc_dims=None, **k... |
class Yelp_f_Processor(DataProcessor):
def get_train_examples(self, data_dir):
train_data = pd.read_csv(os.path.join(data_dir, 'train.csv'), header=None, sep=',').values
return self._create_examples(train_data, 'train')
def get_dev_examples(self, data_dir):
dev_data = pd.read_csv(os.path... |
def modified_resnet(arch, block, layers, pretrained, progress, **kwargs):
model = ModifiedResNet(block, layers, **kwargs)
if pretrained:
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict, strict=False)
return model |
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