code stringlengths 281 23.7M |
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def test_read_classifiers_cached(monkeypatch, tmp_path):
def mock_get_cache_dir():
tmp_file = (tmp_path / 'classifiers.lst')
with tmp_file.open('w') as fh:
fh.write('A\nB\nC')
return tmp_path
monkeypatch.setattr(fv, 'get_cache_dir', mock_get_cache_dir)
classifiers = fv._r... |
def test_suppress_error_removing_lock(tmp_path: Path) -> None:
path = (tmp_path / 'dir')
path.mkdir()
lock = get_lock_path(path)
lock.touch()
mtime = lock.stat().st_mtime
with unittest.mock.patch.object(Path, 'unlink', side_effect=OSError) as m:
assert (not ensure_deletable(path, conside... |
def split_header_words(header_values):
assert (type(header_values) not in STRING_TYPES)
result = []
for text in header_values:
orig_text = text
pairs = []
while text:
m = token_re.search(text)
if m:
text = unmatched(m)
name = m.... |
class EventLoop(QEventLoop):
def __init__(self, parent: QObject=None) -> None:
super().__init__(parent)
self._executing = False
def exec(self, flags: _ProcessEventFlagType=QEventLoop.ProcessEventsFlag.AllEvents) -> int:
if self._executing:
raise AssertionError('Eventloop is a... |
class AZPReactiveTabu(AZPTabu):
def __init__(self, max_iterations, k1, k2, random_state=None):
self.tabu = deque([], maxlen=1)
super().__init__(random_state=random_state)
self.avg_it_until_rep = 1
self.rep_counter = 1
if (max_iterations <= 0):
raise ValueError('Th... |
def get_all_datasets(args: argparse.Namespace, tokenizer: object) -> List[Generator[(Dict, None, None)]]:
train_gen_list = []
if (args.mode in ['train']):
for train_data_path in args.train_data.split(','):
train_gen_list.append(get_data_gen(train_data_path, 'train', args, tokenizer))
ret... |
def test_jump_control_of_flow_instruction_raises():
try:
raise Jump(['one', 'two'], 'sg', 'fg', 'og')
except Jump as err_info:
assert isinstance(err_info, ControlOfFlowInstruction)
assert (err_info.groups == ['one', 'two'])
assert (err_info.success_group == 'sg')
assert (... |
class SquareBoxCoderTest(tf.test.TestCase):
def test_correct_relative_codes_with_default_scale(self):
boxes = [[10.0, 10.0, 20.0, 15.0], [0.2, 0.1, 0.5, 0.4]]
anchors = [[15.0, 12.0, 30.0, 18.0], [0.1, 0.0, 0.7, 0.9]]
scale_factors = None
expected_rel_codes = [[(- 0.790569), (- 0.263... |
class Effect6641(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Hull Upgrades')), 'armorHPBonusAdd', src.getModifiedItemAttr('shipBonusRole2'), **kwargs)
fit.modules.filteredItemBoost((la... |
def test_time_adapt(model, criterion, args=None, logger=None, writer=None):
from utils.norm_stats_utils import CombineNormStatsRegHook_onereg
from utils.relation_map_utils import CombineCossimRegHook
candidate_bn_layers = [nn.BatchNorm1d, nn.BatchNorm2d, nn.BatchNorm3d]
if args.update_only_bn_affine:
... |
def rfc2047(value):
def decode_chunk(m):
(data, encoding) = decode_rfc2047_header(m.group(0))[0]
try:
res = data.decode(encoding)
except (LookupError, UnicodeEncodeError):
res = m.group(0)
return res
return _RE_RFC2047.sub(decode_chunk, value, re.I) |
.parametrize('flag, expected', [('--blink-settings=key=value', [('key', 'value')]), ('--blink-settings=key=equal=rights', [('key', 'equal=rights')]), ('--blink-settings=one=1,two=2', [('one', '1'), ('two', '2')]), ('--enable-features=feat', [])])
def test_pass_through_existing_settings(config_stub, flag, expected):
... |
class DiagonalLineDecorator(ChartDecorator, SimpleLegendItem):
def __init__(self, key: str=None, **plot_settings: Any):
ChartDecorator.__init__(self, key)
SimpleLegendItem.__init__(self)
self.plot_settings = plot_settings
def decorate(self, chart: 'Chart') -> None:
self.legend_ar... |
_model
class Sound(BaseMedia):
file_content_type: str = field(default=None)
file_url: str = field(default=None)
native_sound_id: str = field(default=None)
secret_token: str = field(default=None)
subtype: str = field(default=None)
def from_json(cls, value: JsonResponse, **kwargs) -> 'Sound':
... |
def get_model(data, weights='imagenet'):
base_model = InceptionV3(weights=weights, include_top=False)
x = base_model.output
x = GlobalAveragePooling2D()(x)
x = Dense(1024, activation='relu')(x)
predictions = Dense(len(data.classes), activation='softmax')(x)
model = Model(inputs=base_model.input,... |
class KLRegSteepestDescent(nn.Module):
def __init__(self, score_predictor, num_iter=1, compute_losses=True, detach_length=float('Inf'), parameter_batch_dim=0, steplength_reg=0.0, hessian_reg=0, init_step_length=1.0, softmax_reg=None):
super().__init__()
self.score_predictor = score_predictor
... |
class EfficientNet(tf.keras.Model):
def __init__(self, blocks_args=None, global_params=None):
super(EfficientNet, self).__init__()
if (not isinstance(blocks_args, list)):
raise ValueError('blocks_args should be a list.')
self._global_params = global_params
self._blocks_ar... |
def venv(tmp_path_factory, session_app_data):
if CURRENT.is_venv:
return sys.executable
root_python = root(tmp_path_factory, session_app_data)
dest = tmp_path_factory.mktemp('venv')
process = Popen([str(root_python), '-m', 'venv', '--without-pip', str(dest)])
process.communicate()
return... |
class NormFreeNet(nn.Module):
def __init__(self, cfg: NfCfg, num_classes=1000, in_chans=3, global_pool='avg', output_stride=32, drop_rate=0.0, drop_path_rate=0.0):
super().__init__()
self.num_classes = num_classes
self.drop_rate = drop_rate
self.grad_checkpointing = False
ass... |
def upgrade(saveddata_engine):
saveddata_engine.execute(tmpTable)
saveddata_engine.execute('INSERT INTO boostersTemp (ID, itemID, fitID, active) SELECT ID, itemID, fitID, active FROM boosters')
saveddata_engine.execute('DROP TABLE boosters')
saveddata_engine.execute('ALTER TABLE boostersTemp RENAME TO b... |
class InequalityToEquality(QuadraticProgramConverter):
_delimiter = ''
def __init__(self, mode: str='auto') -> None:
self._src: Optional[QuadraticProgram] = None
self._dst: Optional[QuadraticProgram] = None
self._mode = mode
def convert(self, problem: QuadraticProgram) -> QuadraticPr... |
def main(args):
cfg = setup(args)
model = build_model(cfg)
logger.info('Model:\n{}'.format(model))
if args.eval_only:
DetectionCheckpointer(model, save_dir=cfg.OUTPUT_DIR).resume_or_load(cfg.MODEL.WEIGHTS, resume=args.resume)
if cfg.TEST.AUG.ENABLED:
logger.info('Running infe... |
def test_flake8_per_file_ignores(workspace):
config_str = '[flake8]\nignores = F403\nper-file-ignores =\n **/__init__.py:F401,E402\n test_something.py:E402,\nexclude =\n file_1.py\n file_2.py\n '
doc_str = "print('hi')\nimport os\n"
doc_uri = uris.from_fs_path(os.path.join(workspace.root_path... |
def MCLDNN(weights=None, input_shape1=[2, 128], input_shape2=[128, 1], classes=11, **kwargs):
if ((weights is not None) and (not os.path.exists(weights))):
raise ValueError('The `weights` argument should be either `None` (random initialization), or the path to the weights file to be loaded.')
dr = 0.5
... |
class ImplementationWrapper(object):
def __init__(self, instance, field_name, transition, workflow, implementation, hooks=None):
self.instance = instance
self.field_name = field_name
self.transition = transition
self.workflow = workflow
self.hooks = (hooks or {})
self... |
def test_upload_pypirc_file(copy_sample):
with temp_pypirc(pypirc3) as pypirc, patch('flit.upload.upload_file') as upload_file:
td = copy_sample('module1_toml')
formats = list(ALL_FORMATS)[:1]
upload.main((td / 'pyproject.toml'), formats=set(formats), repo_name='test123', pypirc_path=pypirc)... |
def ssim(img1, img2, window_size=11, mask=None, size_average=True):
(_, channel, _, _) = img1.size()
window = create_window(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim(img1, img2, window, window_size, channel, mask... |
class LLL_Net(nn.Module):
def __init__(self, model, remove_existing_head=False):
head_var = model.head_var
assert (type(head_var) == str)
assert ((not remove_existing_head) or hasattr(model, head_var)), 'Given model does not have a variable called {}'.format(head_var)
assert ((not re... |
def uninstall_variables(name=JTOP_VARIABLE_FILE):
if os.path.isfile('/etc/profile.d/{name}'.format(name=name)):
logger.info('Found {name}'.format(name=name))
os.remove('/etc/profile.d/{name}'.format(name=name))
logger.info(' - Remove {name} from /etc/profile.d/'.format(name=name)) |
(description='Upload videos to YouTube')
def upload_videos_to_youtube(modeladmin, request, queryset):
videos = queryset.filter(youtube_video_id__exact='').exclude(video_uploaded_path__exact='')
conference_id = queryset.first().conference_id
start_workflow(workflow=BatchMultipleScheduleItemsVideoUpload.run, ... |
def is_proper_subtype(left: Type, right: Type, *, subtype_context: (SubtypeContext | None)=None, ignore_promotions: bool=False, ignore_uninhabited: bool=False, erase_instances: bool=False, keep_erased_types: bool=False) -> bool:
if (subtype_context is None):
subtype_context = SubtypeContext(ignore_promotion... |
def test_strict_option_is_deprecated(pytester: Pytester) -> None:
pytester.makepyfile('\n import pytest\n\n .unknown\n def test_foo(): pass\n ')
result = pytester.runpytest('--strict', '-Wdefault::pytest.PytestRemovedIn8Warning')
result.stdout.fnmatch_lines(["'unknown' not found ... |
(frozen=True)
class MultiplayerSessionEntry(JsonDataclass):
id: int
name: str
worlds: list[MultiplayerWorld]
users_list: list[MultiplayerUser]
game_details: (GameDetails | None)
visibility: MultiplayerSessionVisibility
generation_in_progress: (int | None)
allowed_games: list[RandovaniaGa... |
def msrvtt_zh(msrvtt_train_captions):
print(('-' * 20))
print('Prepare msrvtt_zh')
msrvtt_cn_path = 'data/MSRVTT-CN/msrvtt10kcntrain_google_enc2zh.caption.txt'
assert os.path.exists(msrvtt_cn_path), msrvtt_cn_path
data = open(msrvtt_cn_path, 'r').read().strip().split('\n')
vid2Chinese_captions =... |
class FC3_Duplicate_TestCase(CommandSequenceTest):
def __init__(self, *args, **kwargs):
CommandSequenceTest.__init__(self, *args, **kwargs)
self.version = FC3
def runTest(self):
self.assert_parse('\nlogvol / --size=1024 --name=nameA --vgname=vgA\nlogvol /home --size=1024 --name=nameB --v... |
class cLSTM(nn.Module):
def __init__(self, emodict, worddict, embedding, args):
super(cLSTM, self).__init__()
self.num_classes = emodict.n_words
self.embeddings = embedding
self.utt_cnn = CNNencoder(args.d_word_vec, 64, 100, [3, 4, 5])
self.dropout_in = nn.Dropout(0.3)
... |
class AttrVI_ATTR_TRIG_ID(EnumAttribute):
resources = [(constants.InterfaceType.gpib, 'INSTR'), (constants.InterfaceType.gpib, 'INTFC'), (constants.InterfaceType.pxi, 'INSTR'), (constants.InterfaceType.pxi, 'BACKPLANE'), (constants.InterfaceType.asrl, 'INSTR'), (constants.InterfaceType.tcpip, 'INSTR'), (constants.I... |
def annotate_streets(df, img, text_col):
if (not os.path.exists(FONT_PATH)):
print('Error loading default font. Check your FONT_PATH')
return None
unique_sts = df[text_col].unique()
for street in unique_sts:
draw_coords = df.loc[((df.ST_NAME == street), 'draw_coords')].tolist()[0]
... |
class BalanceProofData():
def __init__(self, canonical_identifier):
self._canonical_identifier = canonical_identifier
self._pending_locks = make_empty_pending_locks_state()
self.properties = None
def update(self, amount, lock):
self._pending_locks = channel.compute_locks_with(sel... |
def _iter_num_atoms_for_radii(mol, min_radius, max_radius, start_atoms):
unique_atoms = set(start_atoms)
assert (len(start_atoms) == len(unique_atoms)), 'duplicate start atom'
ignore_atoms = set((a for a in start_atoms if (not is_heavy_atom(mol.GetAtomWithIdx(a)))))
(yield (len(unique_atoms) - len(ignor... |
def train(model, dataloader, optimizer, criterion, epoch_number, max_gradient_norm):
model.train()
device = model.device
epoch_start = time.time()
batch_time_avg = 0.0
running_loss = 0.0
preds = []
golds = []
for (batch_index, batch) in enumerate(dataloader):
batch_start = time.t... |
def test_sub():
x = Bits(4, 5)
y = Bits(4, 4)
assert ((x - y) == 1)
assert ((x - Bits(4, 4)) == 1)
assert ((x - 4) == 1)
y = Bits(4, 5)
assert ((x - y) == 0)
assert ((x - 5) == 0)
y = Bits(4, 7)
assert ((x - y) == 14)
assert ((x - 7) == 14)
assert ((9 - x) == 4)
with ... |
def verify_module(fscache: FileSystemCache, id: str, path: str, prefix: str) -> bool:
if is_init_file(path):
path = os.path.dirname(path)
for i in range(id.count('.')):
path = os.path.dirname(path)
if (not any((fscache.isfile_case(os.path.join(path, f'__init__{extension}'), prefix) for e... |
.parametrize('qubitop, state_binary', [((QubitOperator('Z0 Z1 Z2 Z3', (- 1.0)) + QubitOperator('X0 Y1 Y2 X3', 1.0)), '1100'), ((QubitOperator('X0 X3', (- 1.0)) + QubitOperator('Y1 Y2', 1.0)), '0000')])
def test_expectation_values_paulisum(qubitop, state_binary):
n_qubits = openfermion.count_qubits(qubitop)
stat... |
class AdminRecord(models.Model):
record_modes = (('ssh', 'ssh'), ('guacamole', 'guacamole'))
admin_login_user = models.ForeignKey('users.UserProfile', verbose_name='', on_delete=models.CASCADE)
admin_server = models.CharField(max_length=32, verbose_name='')
admin_remote_ip = models.GenericIPAddressField... |
class Event():
def __init__(self, should_lock: bool=False) -> None:
self._items: list[Callable[(..., Any)]] = []
self._should_lock = should_lock
self._event = threading.Event()
def set(self, *args: Any, **kwargs: Any) -> bool:
def execute():
for func in self._items:
... |
.parametrize(('locations_to_collect', 'exists'), [((0,), ()), ((0,), (0,)), ((0, 1), ()), ((0, 1), (0,)), ((0, 1), (0, 1))])
def test_collect_locations_other(flask_app, two_player_session, echoes_resource_database, locations_to_collect: tuple[(int, ...)], exists: tuple[(int, ...)], mocker: pytest_mock.MockerFixture):
... |
def compute_predictions_logits(all_examples, all_features, all_results, n_best_size, max_answer_length, do_lower_case, output_prediction_file, output_nbest_file, output_null_log_odds_file, verbose_logging, version_2_with_negative, null_score_diff_threshold, tokenizer):
if output_prediction_file:
logger.info... |
def test_read_write_mem(cmdline_opts):
rgen = random.Random()
rgen.seed()
data = [rgen.randrange((- (2 ** 31)), (2 ** 31)) for _ in range(20)]
data_bytes = struct.pack('<{}i'.format(len(data)), *data)
msgs = []
for (i, item) in enumerate(data):
msgs.extend([req('rd', 1, (4096 + (4 * i)),... |
def get_parser():
parser = argparse.ArgumentParser('Prints a table from metrics files\n')
parser.add_argument('--config', '-c', type=str, default='config.csv', help='Path to the config csv with `name` and `path` columns. `name` is a model name, and `path` is a path to metrics file`')
parser.add_argument('--... |
class ConvNeXtBlock(nn.Module):
def __init__(self, in_chs, out_chs=None, kernel_size=7, stride=1, dilation=1, mlp_ratio=4, conv_mlp=False, conv_bias=True, ls_init_value=1e-06, act_layer='gelu', norm_layer=None, drop_path=0.0):
super().__init__()
out_chs = (out_chs or in_chs)
act_layer = get_... |
def parse_wheel_filename(filename: str) -> Tuple[(NormalizedName, Version, BuildTag, FrozenSet[Tag])]:
if (not filename.endswith('.whl')):
raise InvalidWheelFilename(f"Invalid wheel filename (extension must be '.whl'): {filename}")
filename = filename[:(- 4)]
dashes = filename.count('-')
if (das... |
def test_invert_error_at():
phys_err = 0.001
budgets = np.logspace((- 1), (- 18))
for budget in budgets:
d = qecs.FowlerSuperconductingQubits.code_distance_from_budget(physical_error_rate=phys_err, budget=budget)
assert ((d % 2) == 1)
assert (qecs.FowlerSuperconductingQubits.logical_... |
def main(args):
assert ((len(args) == 3) and isinstance(args[1], str) and isinstance(args[2], str))
dataset_name = args[1]
model_name = args[2]
tf.set_random_seed(1234)
coord_add = get_coord_add(dataset_name)
dataset_size_train = get_dataset_size_train(dataset_name)
dataset_size_test = get_d... |
class ZGate(Bloq):
_property
def signature(self) -> 'Signature':
return Signature.build(q=1)
def short_name(self) -> 'str':
return 'Z'
def decompose_bloq(self) -> CompositeBloq:
raise DecomposeTypeError(f'{self} is atomic')
def add_my_tensors(self, tn: qtn.TensorNetwork, tag:... |
class Logger(object):
def __init__(self, args):
self.args = args
self.save_dir = args.log_dir
self.is_primary = is_primary()
if self.is_primary:
os.makedirs(self.save_dir, exist_ok=True)
self.config_dir = os.path.join(self.save_dir, 'configs')
os.m... |
class ArcCos(UnaryScalarOp):
nfunc_spec = ('arccos', 1, 1)
def impl(self, x):
x_dtype = str(getattr(x, 'dtype', ''))
if (x_dtype in ('int8', 'uint8')):
return np.arccos(x, dtype=np.float32)
return np.arccos(x)
def L_op(self, inputs, outputs, gout):
(x,) = inputs
... |
def calculate_sentence_transformer_embedding(examples, embedding_model, mean_normal=False):
if (not args.add_prompt):
text_to_encode = [raw_item['text'] for raw_item in examples]
else:
text_to_encode = [['Represent the civil comment; Input: ', raw_item['text'], 0] for raw_item in examples]
n... |
class webvision_dataloader():
def __init__(self, batch_size, num_class, num_workers, root_dir, log):
self.batch_size = batch_size
self.num_class = num_class
self.num_workers = num_workers
self.root_dir = root_dir
self.log = log
self.transform_train = transforms.Compos... |
class _NodeTest(unittest.TestCase):
CODE = ''
def astroid(self) -> Module:
try:
return self.__class__.__dict__['CODE_Astroid']
except KeyError:
module = builder.parse(self.CODE)
self.__class__.CODE_Astroid = module
return module |
def _parseASN1PrivateKey(s):
s = ASN1_Node(s)
root = s.root()
version_node = s.first_child(root)
version = bytestr_to_int(s.get_value_of_type(version_node, 'INTEGER'))
if (version != 0):
raise SyntaxError('Unrecognized RSAPrivateKey version')
n = s.next_node(version_node)
e = s.next_... |
def test_basic_push_by_manifest_digest(manifest_protocol, basic_images, liveserver_session, app_reloader):
credentials = ('devtable', 'password')
options = ProtocolOptions()
options.push_by_manifest_digest = True
result = manifest_protocol.push(liveserver_session, 'devtable', 'newrepo', 'latest', basic_... |
class ValidatorTestMixin(MetaSchemaTestsMixin):
def test_it_implements_the_validator_protocol(self):
self.assertIsInstance(self.Validator({}), protocols.Validator)
def test_valid_instances_are_valid(self):
(schema, instance) = self.valid
self.assertTrue(self.Validator(schema).is_valid(in... |
class Dataset(torch.utils.data.Dataset):
def __init__(self, args, datas, images, split):
self.split = split
self.dataset = args.dataset
self.data_path = args.data_path
self.datas = datas
self.images = images
print(' {} set has {} datas'.format(split, len(self.datas)))... |
def blackbox(blackbox):
if (tuple(sorted(blackbox.output_indices)) != blackbox.output_indices):
raise ValueError('Output indices {} must be ordered'.format(blackbox.output_indices))
partition(blackbox.partition)
for part in blackbox.partition:
if (not (set(part) & set(blackbox.output_indices... |
class Discard(ScrimsButton):
def __init__(self, ctx: Context, label='Back', row: int=None):
super().__init__(style=discord.ButtonStyle.red, label=label, row=row)
self.ctx = ctx
async def callback(self, interaction: Interaction):
(await interaction.response.defer())
from .main imp... |
def test_load_successful_with_invalid_distribution(caplog: LogCaptureFixture, mocker: MockerFixture, env: MockEnv, tmp_path: Path) -> None:
invalid_dist_info = ((tmp_path / 'site-packages') / 'invalid-0.1.0.dist-info')
invalid_dist_info.mkdir(parents=True)
mocker.patch('poetry.utils._compat.metadata.Distrib... |
class ImageData(AbstractImage):
_swap1_pattern = re.compile(asbytes('(.)'), re.DOTALL)
_swap2_pattern = re.compile(asbytes('(.)(.)'), re.DOTALL)
_swap3_pattern = re.compile(asbytes('(.)(.)(.)'), re.DOTALL)
_swap4_pattern = re.compile(asbytes('(.)(.)(.)(.)'), re.DOTALL)
_current_texture = None
_c... |
def build_loader(data_path, autoaug, batch_size, workers):
rank = dist.get_rank()
world_size = dist.get_world_size()
assert ((batch_size % world_size) == 0), f'The batch size is indivisible by world size {batch_size} // {world_size}'
train_transform = create_transform(input_size=224, is_training=True, a... |
def run_evolution_search(max_time_budget=5000000.0, population_size=50, tournament_size=10, mutation_rate=1.0):
nasbench.reset_budget_counters()
(times, best_valids, best_tests) = ([0.0], [0.0], [0.0])
population = []
for _ in range(population_size):
spec = random_spec()
data = nasbench.... |
def main():
api_key = os.environ.get('QUANDL_API_KEY')
start_date = '2014-1-1'
end_date = '2015-1-1'
symbols = ('AAPL', 'BRK_A', 'MSFT', 'ZEN')
url = format_table_query(api_key=api_key, start_date=start_date, end_date=end_date, symbols=symbols)
print(('Fetching equity data from %s' % url))
r... |
def build_usage_examples(dag: ProvDAG, cfg: ReplayConfig, ns: NamespaceCollections):
sorted_nodes = nx.topological_sort(dag.collapsed_view)
actions = group_by_action(dag, sorted_nodes, ns)
for node_id in actions.no_provenance_nodes:
node = dag.get_node_data(node_id)
build_no_provenance_node_... |
def test_interface_array(do_test):
class Ifc(Interface):
def construct(s):
s.msg = InPort(Bits32)
s.val = InPort(Bits1)
s.rdy = OutPort(Bits1)
class A(Component):
def construct(s):
s.ifc = [Ifc() for _ in range(2)]
a = A()
a._ref_ports = [(... |
def auto_augment_transform(config_str, hparams):
config = config_str.split('-')
policy_name = config[0]
config = config[1:]
for c in config:
cs = re.split('(\\d.*)', c)
if (len(cs) < 2):
continue
(key, val) = cs[:2]
if (key == 'mstd'):
hparams.setd... |
class CheckpointFunction(torch.autograd.Function):
def forward(ctx, run_function, preserve_rng_state, *args):
check_backward_validity(args)
ctx.run_function = run_function
ctx.preserve_rng_state = preserve_rng_state
ctx.had_autocast_in_fwd = torch.is_autocast_enabled()
ctx.in... |
def test_load_config(track_widget):
original_config_name = track_widget.config_name.currentText()
with patch('btrack.napari.widgets.load_path_dialogue_box') as load_path_dialogue_box:
load_path_dialogue_box.return_value = btrack.datasets.cell_config()
track_widget.load_config_button.click()
... |
class AugmentationCfg():
scale: Tuple[(float, float)] = (0.9, 1.0)
ratio: Optional[Tuple[(float, float)]] = None
color_jitter: Optional[Union[(float, Tuple[(float, float, float)])]] = None
interpolation: Optional[str] = None
re_prob: Optional[float] = None
re_count: Optional[int] = None
use_... |
def test_foo_field_as_writer():
class FooStruct_wrap(Component):
def construct(s):
s.in_ = InPort(Bits16)
s.out = OutPort(Bits32)
s.inner = FooStruct(16)
s.inner.in_ //= s.in_
connect(s.inner.out.b, s.out)
def line_trace(s):
ret... |
class HandshakeType(enum.IntEnum):
hello_request = 0
client_hello = 1
server_hello = 2
hello_verify_request = 3
new_session_ticket = 4
end_of_early_data = 4
encrypted_extensions = 8
certificate = 11
server_key_exchange = 12
certificate_request = 13
server_hello_done = 14
... |
def get_random_cached_bottlenecks(sess, image_lists, how_many, category, bottleneck_dir, image_dir, jpeg_data_tensor, bottleneck_tensor):
class_count = len(image_lists.keys())
bottlenecks = []
ground_truths = []
filenames = []
if (how_many >= 0):
for unused_i in range(how_many):
... |
class PoolFormer(nn.Module):
def __init__(self, layers, embed_dims=(64, 128, 320, 512), mlp_ratios=(4, 4, 4, 4), downsamples=(True, True, True, True), pool_size=3, in_chans=3, num_classes=1000, global_pool='avg', norm_layer=GroupNorm1, act_layer=nn.GELU, in_patch_size=7, in_stride=4, in_pad=2, down_patch_size=3, do... |
def find_mutated(form):
if isinstance(form, W_Correlated):
return find_mutated(form.get_obj())
elif isinstance(form, values.W_Cons):
if (not form.is_proper_list()):
(elements, _) = to_rpython_list(form, unwrap_correlated=True, improper=True)
return extend_dicts([find_muta... |
def target_directory(output_path: Optional[str]=None) -> str:
if output_path:
if (not os.path.isabs(output_path)):
output_path = os.path.join(os.getcwd(), output_path)
else:
output_path = os.getcwd()
os.makedirs(output_path, exist_ok=True)
return output_path |
def _word_forms_from_xml_elem(elem):
lexeme = []
lex_id = elem.get('id')
if (len(elem) == 0):
return (lex_id, lexeme)
base_info = list(elem.iter('l'))
assert (len(base_info) == 1)
base_grammemes = _grammemes_from_elem(base_info[0])
for form_elem in elem.iter('f'):
grammemes =... |
def translate_to_vocab(tokens, vocab, vocab_translate, skip_new_tokens=False):
if vocab_translate.contains_same_content(vocab):
return tokens
lang_orig = tokens_to_lang(tokens, vocab, join=False)
tokens_new = []
for word in lang_orig:
if (skip_new_tokens and (word not in vocab_translate.... |
def enable_oeenclave_debug(oe_enclave_addr):
enclave = oe_debug_enclave_t(oe_enclave_addr)
if (not enclave.is_valid()):
return False
if (enclave.debug == 0):
print(('oegdb: Debugging not enabled for enclave %s' % enclave.path))
return False
if (enclave.simulate != 0):
pri... |
def get_acceleration_bw_models(year1, year2, model_path, selected_ngrams, all_model_vectors, top_k_acc):
model_path1 = os.path.join(model_path, (year1 + '.model'))
model_path2 = os.path.join(model_path, (year2 + '.model'))
(word_pairs, em1, em2) = compute_acc_between_years(selected_ngrams, model_path1, mode... |
def format_skeleton(skeleton: str, datetime: _Instant=None, tzinfo: (datetime.tzinfo | None)=None, fuzzy: bool=True, locale: ((Locale | str) | None)=LC_TIME) -> str:
locale = Locale.parse(locale)
if (fuzzy and (skeleton not in locale.datetime_skeletons)):
skeleton = match_skeleton(skeleton, locale.datet... |
def run_query(config, client, query_func, write_func=write_result, sql_context=None):
QUERY_NUM = get_query_number()
if config.get('dask_profile'):
with performance_report(filename=f'q{QUERY_NUM}_profile.html'):
if sql_context:
run_sql_query(config=config, client=client, quer... |
class CollectSessionComparisonData():
def __init__(self, pathserv, pathserv_other, fn_count_bads):
self.pathserv = pathserv
self.pathserv_other = pathserv_other
samples = {e for e in fs.load_session_playlist(pathserv)}
other_samples = {e for e in fs.load_session_playlist(pathserv_oth... |
class ProcessStatCollector(diamond.collector.Collector):
PROC = '/proc/stat'
def get_default_config_help(self):
config_help = super(ProcessStatCollector, self).get_default_config_help()
config_help.update({})
return config_help
def get_default_config(self):
config = super(Pro... |
def test_nested_process_search(cbc_product: CbEnterpriseEdr, mocker):
with open(os.path.join(os.getcwd(), 'tests', 'data', 'cbc_surveyor_testing.json')) as f:
programs = json.load(f)
cbc_product.log = logging.getLogger('pytest_surveyor')
cbc_product._sensor_group = None
cbc_product._results = {}... |
class TWaveformSeekBar(PluginTestCase):
def setUp(self):
self.mod = self.modules['WaveformSeekBar']
def tearDown(self):
del self.mod
def test_main(self):
WaveformScale = self.mod.WaveformScale
player = NullPlayer()
player.info = AudioFile({'~#length': 10})
sca... |
.django_db
def test_scope_multisite(site1, site2, comment1, comment2):
with scope(site=[site1]):
assert (list(Comment.objects.all()) == [comment1])
with scope(site=[site1, site2]):
assert (list(Comment.objects.all()) == [comment1, comment2])
assert (get_scope() == {'site': [site1, site2]... |
def test_fbo_head():
lfb_prefix_path = osp.normpath(osp.join(osp.dirname(__file__), '../data/lfb'))
st_feat_shape = (1, 16, 1, 8, 8)
st_feat = generate_backbone_demo_inputs(st_feat_shape)
rois = torch.randn(1, 5)
rois[0][0] = 0
img_metas = [dict(img_key='video_1, 930')]
fbo_head = FBOHead(lf... |
class WideResNet(nn.Module):
def __init__(self, depth=28, widen_factor=10, num_classes=None, dropout_rate=0.3):
super().__init__()
assert (((depth - 4) % 6) == 0), 'Wide-resnet depth should be 6n+4'
self.dropout_rate = dropout_rate
n = ((depth - 4) // 6)
k = widen_factor
... |
def _get_backend_kernel(dtype, grid, block, k_type):
kernel = _cupy_kernel_cache[(dtype, k_type)]
if kernel:
return _cupy_channelizer_wrapper(grid, block, kernel)
else:
raise ValueError('Kernel {} not found in _cupy_kernel_cache'.format(k_type))
raise NotImplementedError('No kernel found... |
class TestSuite(object):
_KNOWN_CACHES = {'species_pattern_matcher': SpeciesPatternMatcher, 'rule_pattern_matcher': RulePatternMatcher, 'reaction_pattern_matcher': ReactionPatternMatcher}
_COL = {'OK': '\x1b[92m', 'FAIL': '\x1b[91m', 'END': '\x1b[0m'}
def __init__(self, model=None):
self._caches = {... |
def terraform_remote_state_s3(name: str, **body: Any) -> Block:
body['backend'] = 's3'
config = body.get('config', {})
if config.get('profile'):
session = get_session(profile_name=config['profile'])
creds = session.get_credentials()
if (not _profile_creds_definitely_supported_by_terr... |
def pseudonymise_buffer_list(file_buffer_list: list):
if ((file_buffer_list is not None) and (len(file_buffer_list) > 0)):
my_date_time = datetime.datetime.now()
str_now_datetime = my_date_time.strftime('%Y%m%d_%H%M%S')
zipfile_basename = f'Pseudonymised_{str_now_datetime}'
bad_data ... |
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