code stringlengths 281 23.7M |
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def request_wrap_timeout(func, url):
import requests
for (attempt, timeout) in enumerate([10, 20, 40, 60, 60]):
try:
return func(timeout=timeout)
except requests.exceptions.Timeout as e:
logger.warning('Request for %s timed-out (attempt %d). Retrying with a timeout of %d ... |
class SolverProcess():
automatic_call = False
def __init__(self, *, name, command, cp):
self.name = name
self.command = command
self.cp = cp
self.options = ''
self.stdout = None
self.stderr = None
self.last_command_wck = None
self.log_filename_suff... |
def transform_with_items(schema, template):
items = template['with_items']
if isinstance(items, dict):
if (set(items) == {'using'}):
items = items['using']
elif (set(items) == {'from_stdout'}):
items = from_stdout(items['from_stdout'])
if hasattr(items, '__call__'):
... |
def min_freItem():
global ww
counter = dict()
mine = ''
my_dict['a'] = 3
my_dict['g'] = 6
my_dict['c'] = 6
my_dict['t'] = 4
for t in range(NumbS):
S = sDB[t].S
for s in S:
mine = s
if (counter.get(mine) == None):
counter[mine] = 1
... |
class TestGetImage(EndianTest):
def setUp(self):
self.req_args_0 = {'drawable': , 'format': 2, 'height': 20170, 'plane_mask': , 'width': 282, 'x': (- 14814), 'y': (- 5449)}
self.req_bin_0 = b'I\x02\x00\x053\xfbEj\xc6"\xea\xb7\x01\x1aN\xca$\xba\x96\xb6'
self.reply_args_0 = {'data': b'\xeb?:\x... |
class CollectionConfig(BaseModel, extra='forbid'):
params: 'CollectionParams' = Field(..., description='')
hnsw_config: 'HnswConfig' = Field(..., description='')
optimizer_config: 'OptimizersConfig' = Field(..., description='')
wal_config: 'WalConfig' = Field(..., description='')
quantization_config... |
def convert_all_pt_checkpoints_to_tf(args_model_type, tf_dump_path, model_shortcut_names_or_path=None, config_shortcut_names_or_path=None, compare_with_pt_model=False, use_cached_models=False, remove_cached_files=False, only_convert_finetuned_models=False):
if (args_model_type is None):
model_types = list(M... |
def test_molecule_and_vsite_water(coumarin, tmpdir, water, rfree_data):
coumarin_copy = coumarin.copy(deep=True)
MBISCharges.apply_symmetrisation(coumarin_copy)
with tmpdir.as_cwd():
alpha = rfree_data.pop('alpha')
beta = rfree_data.pop('beta')
lj = LennardJones612(free_parameters=rf... |
def ft_setup(workers: List[int], num_rounds: int, die_round_factor: 0.25, comeback_round_factor: 0.75):
if (workers is None):
return None
ft_manager = FaultToleranceManager.remote()
die_round = int((die_round_factor * num_rounds))
comeback_round = int((comeback_round_factor * num_rounds))
fo... |
class ExtraDuplicatesSettings(BaseModel):
interval_description: ClassVar[str] = 'Look for rule violations in messages from the last `interval` number of seconds.'
threshold_description: ClassVar[str] = 'Maximum number of duplicate messages before the filter is triggered.'
interval: int = 10
threshold: i... |
class KeyboardButton(TelegramObject):
__slots__ = ('request_location', 'request_contact', 'request_poll', 'text', 'web_app', 'request_user', 'request_chat')
def __init__(self, text: str, request_contact: Optional[bool]=None, request_location: Optional[bool]=None, request_poll: Optional[KeyboardButtonPollType]=N... |
class CorruptionLayoutEditor(QtWidgets.QWidget, Ui_CorruptionLayoutEditor):
def __init__(self):
super().__init__()
self.setupUi(self)
self.game_description = default_database.game_description_for(RandovaniaGame.METROID_PRIME_CORRUPTION)
pickup_database = default_database.pickup_datab... |
def gen_back_to_back_test():
return '\n # Test backwards walk (back to back branch taken)\n\n csrr x3, mngr2proc < 1\n csrr x1, mngr2proc < 1\n\n bne x3, x0, X0\n csrw proc2mngr, x0\n nop\n a0:\n csrw proc2mngr, x1 > 1\n bne x3, x0, y0\n b0:\n bne x3, x0, a0\n c0:\... |
class FeatQueue(nn.Module):
def __init__(self, max_queue_size=30000):
super(FeatQueue, self).__init__()
self.max_queue_size = max_queue_size
def append(self, queue, feat):
if isinstance(feat, np.ndarray):
queue = np.concatenate([queue, feat], axis=0)
queue_size = ... |
def directed_hausdorff(point_cloud_A, point_cloud_B):
npoint = point_cloud_A.shape[1]
A = tf.expand_dims(point_cloud_A, axis=2)
A = tf.tile(A, (1, 1, npoint, 1))
B = tf.expand_dims(point_cloud_B, axis=1)
B = tf.tile(B, (1, npoint, 1, 1))
distances = tf.squared_difference(B, A)
distances = tf... |
class MCHManagedCollisionModule(ManagedCollisionModule):
def __init__(self, zch_size: int, device: torch.device, eviction_policy: MCHEvictionPolicy, eviction_interval: int, input_hash_size: int=(2 ** 63), input_hash_func: Optional[Callable[([torch.Tensor, int], torch.Tensor)]]=None, mch_size: Optional[int]=None, mc... |
.parametrize('method, url, expected_result, strict', [('ls', 'bigquery://bigquery-url/path1/path2', ('bigquery://', ['path1', 'path2', None]), False), ('schema', 'bigquery://bigquery-url/path1/path2/path3', ('bigquery://', ['path1', 'path2', 'path3']), False), ('ls', 'invalidscheme://invalid-url', pytest.raises(ValueEr... |
class SynonymProcessor(DataProcessor):
def get_train_examples(self, data_dir):
logger.info('LOOKING AT {} train'.format(data_dir))
return self._create_examples(self._read_csv(os.path.join(data_dir, 'mctrain.csv')), 'train')
def get_dev_examples(self, data_dir):
logger.info('LOOKING AT {}... |
class Ksboolean_TestCase(ParserTest):
def runTest(self):
self.assertTrue(ksboolean('ON'))
self.assertTrue(ksboolean('On'))
self.assertTrue(ksboolean('YES'))
self.assertTrue(ksboolean('Yes'))
self.assertTrue(ksboolean('TRUE'))
self.assertTrue(ksboolean('True'))
... |
def write_html(filename, it, img_save_it, img_dir, all_size=1536):
html_file = open(filename, 'w')
html_file.write(('\n <!DOCTYPE html>\n <html>\n <head>\n <title>Experiment name = %s</title>\n <meta content="30">\n </head>\n <body>\n ' % os.path.basename(filename)))
html_file.w... |
class ConfigTestUtils(unittest.TestCase):
def test_config_from_string(self):
c = GPT2Config()
n_embd = (c.n_embd + 1)
resid_pdrop = (c.resid_pdrop + 1.0)
scale_attn_weights = (not c.scale_attn_weights)
summary_type = (c.summary_type + 'foo')
c.update_from_string(f'n_e... |
class TestSlonyCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('SlonyCollector', {})
self.collector = SlonyCollector(config, None)
def test_import(self):
self.assertTrue(SlonyCollector)
_only_if_psycopg2_is_available
(SlonyCollector, '_get_stats_by_da... |
class PyramidNet(nn.Module):
def __init__(self, dataset, depth, alpha, num_classes, bottleneck=False):
super(PyramidNet, self).__init__()
self.dataset = dataset
if self.dataset.startswith('cifar'):
self.inplanes = 16
if (bottleneck == True):
n = int(((... |
class Dialog(QDialog):
def __init__(self, parent=None):
super(Dialog, self).__init__(parent)
self.server = FortuneServer()
statusLabel = QLabel()
statusLabel.setWordWrap(True)
quitButton = QPushButton('Quit')
quitButton.setAutoDefault(False)
if (not self.serve... |
class Multiply(ImageOnlyTransform):
identity_param = 1
def __init__(self, factors: List[float]):
if (self.identity_param not in factors):
factors = ([self.identity_param] + list(factors))
super().__init__('factor', factors)
def apply_aug_image(self, image, factor=1, **kwargs):
... |
def encrypt(key: bytes, nonce: bytes, initial_block_counter: int, plaintext: bytes) -> bytes:
full_nonce = (struct.pack('<Q', initial_block_counter) + nonce)
encryptor = Cipher(algorithms.ChaCha20(key, full_nonce), mode=None).encryptor()
plaintext_len_blocks = math.ceil((len(plaintext) / BLOCK_SIZE))
bl... |
def available_instruments(inst_loc=None):
def get_inst_id_dict(inst_module_name):
try:
module = importlib.import_module(inst_module_name)
inst_ids = {inst_id: {tag: module.tags[tag] for tag in module.inst_ids[inst_id]} for inst_id in module.inst_ids.keys()}
except ImportError... |
def test_third2oct_2darray():
levels = np.array([[100, 95, 80, 55, 65, 85, 75, 70, 90, 95, 105, 110], [100, 95, 80, 55, 65, 85, 75, 70, 90, 95, 105, 110]])
generated = third2oct(levels, axis=1)
real = np.array([[101., 85., 90., 111.], [101., 85., 90., 111.]])
assert_array_almost_equal(generated, real) |
def _f1_score_param_check(num_classes: Optional[int], average: Optional[str]) -> None:
average_options = ('micro', 'macro', 'weighted', None)
if (average not in average_options):
raise ValueError(f'`average` was not in the allowed value of {average_options}, got {average}.')
if ((average != 'micro')... |
def valid_tile_size(value, arg_name, min_power=4, logger=None):
error = False
if (not isinstance(value, int)):
if logger:
logger.error(f'''Invalid value for the argument {arg_name}: {value}. Enter an integer.
''')
else:
print(f'''ERROR: Invalid value for the argument {arg... |
class DataPrefetcher():
def __init__(self, loader):
self.loader = iter(loader)
self.stream = torch.cuda.Stream()
self.input_cuda = self._input_cuda_for_image
self.record_stream = DataPrefetcher._record_stream_for_image
self.preload()
def preload(self):
try:
... |
def _migrate_v5(preset: dict) -> dict:
excluded_item = {'include_copy_in_original_location': False, 'num_shuffled_pickups': 0, 'num_included_in_starting_items': 0, 'included_ammo': [], 'allowed_as_random_starting_item': True}
included_item = {**excluded_item, 'num_included_in_starting_items': 1}
shuffled_it... |
class _Metadata(_PrimitiveTemplateBase):
_valid_predicates = set()
def is_element(self, value):
return isinstance(value, metadata.Metadata)
def decode(self, metadata):
if (not self.is_element(metadata)):
raise TypeError('`Metadata` must be provided by the interface directly.')
... |
def unpack_archive(filename, extract_dir, progress_filter=default_filter, drivers=None):
for driver in (drivers or extraction_drivers):
try:
driver(filename, extract_dir, progress_filter)
except UnrecognizedFormat:
continue
else:
return
else:
r... |
def create_playlists(_request: WSGIRequest) -> HttpResponse:
library_link = os.path.join(conf.SONGS_CACHE_DIR, 'local_library')
if (not os.path.islink(library_link)):
return HttpResponseBadRequest('No library set')
_set_scan_progress('0 / 0 / 0')
_create_playlists.delay()
return HttpResponse... |
def _selfdestruct(computation: ComputationAPI, beneficiary: Address) -> None:
local_balance = computation.state.get_balance(computation.msg.storage_address)
beneficiary_balance = computation.state.get_balance(beneficiary)
computation.state.set_balance(beneficiary, (local_balance + beneficiary_balance))
... |
class FillLouver(bpy.types.PropertyGroup):
louver_count: IntProperty(name='Louver Count', min=0, max=100, default=10, description='Number of louvers on to create face')
louver_margin: FloatProperty(name='Louver Margin', step=1, min=get_scaled_unit(0.001), max=get_scaled_unit(100.0), default=get_scaled_unit(0.1)... |
class ReportFormatter(Formatter):
ACTIVITY_MAXLEN = 12
SPACING = ' '
CONTEXT_PREFIX = 'from'
TARGET_PREFIX = 'to'
def format(self, record):
if hasattr(record, 'activity'):
return self.format_report(record)
return self.format_default(record)
def create_padding(self, a... |
def _get_aws_ip_ranges():
try:
path = os.path.dirname(os.path.abspath(__file__))
file_path = os.path.join(path, 'aws-ip-ranges.json')
with open(file_path, 'r') as f:
return json.loads(f.read())
except IOError:
logger.exception('Could not load AWS IP Ranges')
r... |
class FBCNet_old(nn.Module):
def SCB(self, m, nChan, nBands, doWeightNorm=True, *args, **kwargs):
return nn.Sequential(Conv2dWithConstraint(nBands, (m * nBands), (nChan, 1), groups=nBands, max_norm=2, doWeightNorm=doWeightNorm, padding=0), nn.BatchNorm2d((m * nBands)), nn.ELU())
def LastBlock(self, inF,... |
_datapipe('load_from_zip')
class ZipArchiveLoaderIterDataPipe(IterDataPipe[Tuple[(str, BufferedIOBase)]]):
def __init__(self, datapipe: Iterable[Tuple[(str, BufferedIOBase)]], length: int=(- 1)) -> None:
super().__init__()
self.datapipe: Iterable[Tuple[(str, BufferedIOBase)]] = datapipe
self... |
class BehavioralRTLIRGenL1Pass(RTLIRPass):
rtlir_upblks = MetadataKey()
def __init__(s, translation_top):
c = s.__class__
s.tr_top = translation_top
if (not translation_top.has_metadata(c.rtlir_getter)):
translation_top.set_metadata(c.rtlir_getter, RTLIRGetter(cache=True))
... |
_tokenizer('moses')
class MosesTokenizer(object):
def add_args(parser):
parser.add_argument('--moses-source-lang', metavar='SRC', help='source language')
parser.add_argument('--moses-target-lang', metavar='TARGET', help='target language')
parser.add_argument('--moses-no-dash-splits', action=... |
class MixConvBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, bias=False, use_bn=True, bn_eps=1e-05, activation=(lambda : nn.ReLU(inplace=True))):
super(MixConvBlock, self).__init__()
self.activate = (activation is not None)
... |
def test_create_forbidden(db, client, settings):
settings.PROJECT_CREATE_RESTRICTED = True
client.login(username='user', password='user')
url = reverse(urlnames['list'])
data = {'title': 'Lorem ipsum dolor sit amet', 'description': 'At vero eos et accusam et justo duo dolores et ea rebum.', 'catalog': c... |
def parse_date(datestring, default_timezone=UTC):
if (not isinstance(datestring, _basestring)):
raise ParseError(('Expecting a string %r' % datestring))
m = ISO8601_REGEX.match(datestring)
if (not m):
raise ParseError(('Unable to parse date string %r' % datestring))
groups = m.groupdict(... |
class cached_property(Generic[R]):
def __init__(self, wrapped: Callable[([Any], R)]):
self.wrapped = wrapped
functools.update_wrapper(self, wrapped)
def __get__(self, instance: T, owner: Type[Any]) -> R:
if (instance is None):
return self
ret = self.wrapped(instance)
... |
class InventoryCommand(Command):
def __init__(self, quals):
super().__init__('INV', 'taking inventory')
def help_description():
return 'INVENTORY or INV or I - lists what items you have'
def _do_command(self, player):
print(('You have %s.' % enumerate_items(player.inv))) |
class TransposeLast(nn.Module):
def __init__(self, deconstruct_idx=None):
super().__init__()
self.deconstruct_idx = deconstruct_idx
def forward(self, x):
if (self.deconstruct_idx is not None):
x = x[self.deconstruct_idx]
return x.transpose((- 2), (- 1)) |
def main():
saved_weights = None
class_limit = None
num_of_snip = 1
image_shape = (224, 224)
load_to_memory = False
batch_size = 512
nb_epoch = 500
name_str = None
train(num_of_snip=num_of_snip, saved_weights=saved_weights, class_limit=class_limit, image_shape=image_shape, load_to_me... |
class Fighter(HandledItem, HandledCharge, ItemAttrShortcut, ChargeAttrShortcut):
DAMAGE_TYPES = ('em', 'kinetic', 'explosive', 'thermal')
DAMAGE_TYPES2 = ('EM', 'Kin', 'Exp', 'Therm')
def __init__(self, item):
self.__item = item
if self.isInvalid:
raise ValueError('Passed item is... |
def test_twocopy_seperates(tmpdir):
learn_states_q.run_and_save(n=5, n_paulis=10, n_sweeps=250, n_shots=250, save_dir=tmpdir, use_engine=False)
pauli_files = [f for f in os.listdir(tmpdir) if (os.path.isfile(os.path.join(tmpdir, f)) and ('basis' not in f))]
exp_predictions = []
for fname in pauli_files:... |
class TestMarkersWithParametrization():
def test_simple_mark(self, pytester: Pytester) -> None:
s = '\n import pytest\n\n .foo\n .parametrize(("n", "expected"), [\n (1, 2),\n pytest.param(1, 3, marks=pytest.mark.bar),\n (2, 3),\n ... |
def propagate_changes_from_baseline(baseline_dir, alternatives_dir, combi_dir, version_id='', comments=''):
version_id += ('_' + datetime.now().strftime('%y%m%d%H%M%S'))
model_dirs = []
for alt in os.listdir(alternatives_dir):
for imp_level in os.listdir(os.path.join(alternatives_dir, alt)):
... |
(tryfirst=True)
def pytest_cmdline_main(config: Config) -> Optional[Union[(int, ExitCode)]]:
import _pytest.config
if config.option.markers:
config._do_configure()
tw = _pytest.config.create_terminal_writer(config)
for line in config.getini('markers'):
parts = line.split(':',... |
class LinearDecayEnvelope(_Envelope):
def __init__(self, peak=1.0):
self.peak = max(min(1.0, peak), 0)
def get_generator(self, sample_rate, duration):
peak = self.peak
total_bytes = int((sample_rate * duration))
for i in range(total_bytes):
(yield (((total_bytes - i) ... |
('/config', methods=['GET', 'OPTIONS'])
(anonymous=False)
def config():
response = jsonify({'config': frontend_visible_config(app.config), 'features': features.get_features(), 'oauth': get_oauth_config(), 'external_login': get_external_login_config(), 'registry_state': app.config.get('REGISTRY_STATE', 'normal'), 'a... |
def sensitivity(tp: torch.LongTensor, fp: torch.LongTensor, fn: torch.LongTensor, tn: torch.LongTensor, reduction: Optional[str]=None, class_weights: Optional[List[float]]=None, zero_division: Union[(str, float)]=1.0) -> torch.Tensor:
return _compute_metric(_sensitivity, tp, fp, fn, tn, reduction=reduction, class_w... |
def sigmoid_focal_loss(pred, target, weight=None, gamma=2.0, alpha=0.25, reduction='mean', avg_factor=None):
loss = _sigmoid_focal_loss(pred, target, gamma, alpha)
if (weight is not None):
weight = weight.view((- 1), 1)
loss = weight_reduce_loss(loss, weight, reduction, avg_factor)
return loss |
class NamespaceGCWorker(QueueWorker):
def process_queue_item(self, job_details):
try:
with GlobalLock('LARGE_GARBAGE_COLLECTION', lock_ttl=(NAMESPACE_GC_TIMEOUT + LOCK_TIMEOUT_PADDING)):
self._perform_gc(job_details)
except LockNotAcquiredException:
logger.deb... |
def get_contractreceivechannelnew_data_from_event(chain_state: ChainState, event: DecodedEvent) -> Optional[NewChannelDetails]:
token_network_address = TokenNetworkAddress(event.originating_contract)
data = event.event_data
args = data['args']
participant1 = args['participant1']
participant2 = args[... |
class Effect5308(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Light Missiles')), 'aoeVelocity', ship.getModifiedItemAttr('shipBonusCD2'), skill='Caldari Destroyer', **kwargs) |
class SchedulePreviewSection():
id: strawberry.ID
title: str
primary_cta: (CTA | None)
secondary_cta: (CTA | None)
def from_block(cls, block) -> Self:
primary_cta = block.value['primary_cta']
secondary_cta = block.value['secondary_cta']
return cls(id=block.id, title=block.val... |
class ModelRes512(nn.Module):
def __init__(self, res_base_model):
super(ModelRes512, self).__init__()
self.resnet_dict = {'resnet50': models.resnet50(pretrained=True)}
self.resnet = self._get_res_basemodel(res_base_model)
num_ftrs = int(self.resnet.fc.in_features)
self.res_fe... |
def test_jsonify_roundtrip_sequence():
yaml_string = " a: 1\n b: '1'\n c: !jsonify\n - v1\n - 22\n - 123.45\n - a: a value\n b: 123\n d: False\n "
yaml = get_yaml_with_js... |
def add_data_to_storage(storage_list, subject_data, brain_width, tumor_width, truth_dtype, modality_names):
(modality_storage_list, truth_storage, brain_width_storage, tumor_width_storage) = storage_list
for i in range(len(modality_names)):
if (modality_storage_list[i].name != modality_names[i]):
... |
.parametrize('projdir_type', [str, Path])
def test_get_data_dir__from_user(projdir_type, tmp_path):
tmpdir = (tmp_path / 'proj')
tmpdir.mkdir()
tmpdir_env = (tmp_path / 'proj_env')
tmpdir_env.mkdir()
with proj_env(), patch.dict(os.environ, {'PROJ_DATA': str(tmpdir_env)}, clear=True), patch('pyproj.d... |
def align_and_update_state_dicts(model_state_dict, loaded_state_dict):
current_keys = sorted(list(model_state_dict.keys()))
loaded_keys = sorted(list(loaded_state_dict.keys()))
match_matrix = [(len(j) if i.endswith(j) else 0) for i in current_keys for j in loaded_keys]
match_matrix = torch.as_tensor(mat... |
.parametrize('val, offset', [(set_test_value(pt.matrix(), np.arange((10 * 10), dtype=config.floatX).reshape((10, 10))), 0), (set_test_value(pt.matrix(), np.arange((10 * 10), dtype=config.floatX).reshape((10, 10))), (- 1)), (set_test_value(pt.vector(), np.arange(10, dtype=config.floatX)), 0)])
def test_ExtractDiag(val, ... |
def train(args, model, rank, world_size, train_loader, optimizer, epoch, sampler=None):
model.train()
local_rank = int(os.environ['LOCAL_RANK'])
fsdp_loss = torch.zeros(2).to(local_rank)
if sampler:
sampler.set_epoch(epoch)
if (rank == 0):
inner_pbar = tqdm.tqdm(range(len(train_loade... |
(scope='session')
def browser_instance_getter(browser_patches, splinter_session_scoped_browser, splinter_browser_load_condition, splinter_browser_load_timeout, splinter_download_file_types, splinter_driver_kwargs, splinter_file_download_dir, splinter_firefox_profile_preferences, splinter_firefox_profile_directory, spli... |
class Window(QWidget):
NumRenderAreas = 9
def __init__(self):
super(Window, self).__init__()
rectPath = QPainterPath()
rectPath.moveTo(20.0, 30.0)
rectPath.lineTo(80.0, 30.0)
rectPath.lineTo(80.0, 70.0)
rectPath.lineTo(20.0, 70.0)
rectPath.closeSubpath()
... |
class ChannelRounder(CompRatioRounder):
def __init__(self, multiplicity: int):
self._multiplicity = multiplicity
def round(self, layer: Layer, comp_ratio: Decimal, cost_metric: CostMetric) -> Decimal:
if (self._multiplicity == 1):
updated_comp_ratio = comp_ratio
else:
... |
class RTorrentMethod(object):
NEEDS_FAKE_TARGET = set(('ui.current_view.set', 'view_filter'))
def __init__(self, proxy, method_name):
self._proxy = proxy
self._method_name = method_name
def __getattr__(self, attr):
self._method_name += ('.' + attr)
return self
def __str__... |
class PlayerOptions(GObject.Object):
__gproperties__ = {'shuffle': (bool, '', '', False, (GObject.ParamFlags.READABLE | GObject.ParamFlags.WRITABLE)), 'repeat': (bool, '', '', False, (GObject.ParamFlags.READABLE | GObject.ParamFlags.WRITABLE)), 'single': (bool, '', '', False, (GObject.ParamFlags.READABLE | GObject.... |
def highlight_x(ax, highlight_range, highlight_color='magenta', label=None):
rect = patches.Rectangle((highlight_range[0], 0), (highlight_range[1] - highlight_range[0]), ax.get_ylim()[1], facecolor=highlight_color, edgecolor='none', alpha=0.5)
ax.add_patch(rect)
if (label is not None):
ax.text((high... |
def monitor(subcommand, dormant_after: float, dormant_signal: int, kill_after: float, kill_signal: int) -> int:
(parent_read, child_stdout_write) = os.pipe()
child_stderr_write = os.dup(child_stdout_write)
process = subprocess.Popen(subcommand, stdin=subprocess.DEVNULL, stdout=child_stdout_write, stderr=chi... |
class UserViewSet(UserViewSetMixin, ReadOnlyModelViewSet):
permission_classes = ((HasModelPermission | HasObjectPermission),)
serializer_class = UserSerializer
filter_backends = (DjangoFilterBackend,)
filterset_fields = ('username', 'first_name', 'last_name', 'email', 'groups')
def get_queryset(self... |
def extract_declaration_for(function_name):
code = list(reversed(_extract_code(function_name)))
for line in code:
if ((function_name in line) and (not is_comment_line(line))):
pos = line.find(function_name)
if ('=' in line[:pos]):
break
else:
return No... |
def test_param_storage(tmpdir):
with tmpdir.as_cwd():
mol = Ligand.from_file(get_data('chloromethane.pdb'))
OpenFF().run(mol)
with pytest.raises(ValidationError):
mol.NonbondedForce.create_parameter(atoms=(0,), charge=0.1)
mol.NonbondedForce.create_parameter(atoms=(0,), c... |
def train_image_parse_function(filename, *argv):
image = read_image(filename)
image = tf.image.random_flip_left_right(image)
if FLAGS.augmentation:
print('data augmentation')
resized_image = resize_and_random_crop_image(image)
else:
resized_image = resize_image(image)
resized... |
def reorder_image(img, input_order='HWC'):
if (input_order not in ['HWC', 'CHW']):
raise ValueError(f"Wrong input_order {input_order}. Supported input_orders are 'HWC' and 'CHW'")
if (len(img.shape) == 2):
img = img[(..., None)]
if (input_order == 'CHW'):
img = img.transpose(1, 2, 0)... |
class MultiResolutionExtractor(ExtractorBase):
def __init__(self, features, patch_mode='replicate', max_scale_change=None):
super().__init__(features)
self.patch_mode = patch_mode
self.max_scale_change = max_scale_change
self.is_color = None
def stride(self):
return torch... |
def test_cross_compiled_build(tmp_path):
if (utils.platform != 'macos'):
pytest.skip('this test is only relevant to macos')
if (get_xcode_version() < (12, 2)):
pytest.skip('this test only works with Xcode 12.2 or greater')
project_dir = (tmp_path / 'project')
basic_project.generate(proje... |
def uc_refine_hardcode(binary_mask, uncertainty_map, img, threshold_uc=0.2, fn_alpha=0.5, fn_beta=0.7, fp_alpha=0.5, fp_beta=0.7):
img_avg = np.mean(img, axis=2)
mean_value = np.mean((img_avg * binary_mask[0]))
print('the mean value is:', mean_value)
uc_threshold = (np.min(uncertainty_map) + (threshold_... |
def _build_line(colwidths, colaligns, linefmt):
if (not linefmt):
return None
if hasattr(linefmt, '__call__'):
return linefmt(colwidths, colaligns)
else:
(begin, fill, sep, end) = linefmt
cells = [(fill * w) for w in colwidths]
return _build_simple_row(cells, (begin, ... |
.parametrize('input_type', [tuple, list])
def test_run_model_from_effective_irradiance_arrays(sapm_dc_snl_ac_system_Array, location, weather, total_irrad, input_type):
data = weather.copy()
data[['poa_global', 'poa_diffuse', 'poa_direct']] = total_irrad
data['effective_irradiance'] = data['poa_global']
... |
def pipeThroughEspeak(inpt):
assert (type(inpt) == bytes)
bufsize = 8192
ret = []
while (len(inpt) > bufsize):
splitAt = (inpt.rfind('\n', 0, bufsize) + 1)
if (not splitAt):
splitAt = (inpt.rfind(' ', 0, bufsize) + 1)
if (not splitAt):
sys.stderr.w... |
def drop_path(x, drop_prob=0.0, training=False):
if ((drop_prob == 0.0) or (not training)):
return x
keep_prob = (1 - drop_prob)
shape = ((x.shape[0],) + ((1,) * (x.ndim - 1)))
random_tensor = (keep_prob + torch.rand(shape, dtype=x.dtype, device=x.device))
output = (x.div(keep_prob) * random... |
def test_one_item_host_limit(capsys, root_dir):
memory_limit = sizeof(asproxy(one_item_array(), serializers=('dask', 'pickle')))
dhf = ProxifyHostFile(worker_local_directory=root_dir, device_memory_limit=one_item_nbytes, memory_limit=memory_limit)
a1 = (one_item_array() + 1)
a2 = (one_item_array() + 2)
... |
class ResNet(nn.Module):
def __init__(self, block, layers, strides, dilations, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(... |
class NYStylePepperoniPizza(Pizza):
def __init__(self):
self.name = 'NY Style Pepperoni Pizza'
self.dough = 'Thin Crust Dough'
self.sauce = 'Marinara Sauce'
self.toppings = []
self.toppings.append('Grated Reggiano Cheese')
self.toppings.append('Sliced Pepperoni')
... |
class TestMultiProcessingReadingService(TestCase):
_ctx_parametrize
('dp_fn', [subtest(_non_dispatching_dp, 'non_dispatch'), subtest(_dispatching_dp, 'dispatch')])
('main_prefetch', [0, 10])
('worker_prefetch', [0, 10])
def test_early_exit(self, ctx, dp_fn, main_prefetch, worker_prefetch) -> None:
... |
(short_help='Show the contents of the config file')
('--all', '-a', 'all_keys', is_flag=True, help='Do not scrub secret fields')
_obj
def show(app, all_keys):
if (not app.config_file.path.is_file()):
app.display_critical('No config file found! Please try `hatch config restore`.')
else:
from rich... |
class Discriminator(nn.Module):
def __init__(self, sigmoid=False):
super(Discriminator, self).__init__()
self.sigmoid = sigmoid
self.conv1 = nn.Conv2d(3, 64, 3, stride=1, padding=1)
self.conv2 = nn.Conv2d(64, 64, 3, stride=2, padding=1)
self.bn2 = nn.BatchNorm2d(64)
s... |
class AdditiveAttention2D(AdditiveAttention):
def forward(self, s, h):
s_attention = s.matmul(self.w_attention_matrix).unsqueeze(2)
h_attention = h.matmul(self.u_attention_matrix).unsqueeze(1)
seq_len = h.size(1)
h_attention = h_attention.expand((- 1), seq_len, (- 1), (- 1))
... |
_model_architecture('hf_gpt2', 'hf_gpt2')
def default_architecture(args):
if (getattr(args, 'max_target_positions', None) is None):
args.max_target_positions = getattr(args, 'tokens_per_sample', DEFAULT_MAX_TARGET_POSITIONS)
args.embed_dim = getattr(args, 'embed_dim', 768)
args.num_attention_heads =... |
def capsule_sdf_grad(radius: float, half_width: float, p: wp.vec3):
if (p[0] > half_width):
return normalize(wp.vec3((p[0] - half_width), p[1], p[2]))
if (p[0] < (0.0 - half_width)):
return normalize(wp.vec3((p[0] + half_width), p[1], p[2]))
return normalize(wp.vec3(0.0, p[1], p[2])) |
class Subscrib_signup_repos_Handler(BaseHandler):
.authenticated
async def get(self, userid):
user = self.current_user
if ((user['id'] == int(userid)) and (user['role'] == u'admin')):
(await self.render('pubtpl_register.html', userid=userid))
else:
(await self.ren... |
class TestQcQuantizeOp():
def test_update_stats_with_pymo(self):
input_arr = np.random.rand(1, 3, 4, 4).astype(np.float32)
tensor_quantizer = libpymo.TensorQuantizer(MAP_QUANT_SCHEME_TO_PYMO[QuantScheme.post_training_tf], MAP_ROUND_MODE_TO_PYMO['stochastic'])
cpp_encodings = libpymo.TfEncodi... |
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