code stringlengths 281 23.7M |
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def _get_num_els_in_scene_range(zarr_dataset: ChunkedDataset, scene_index_start: int, scene_index_end: int) -> dict:
assert (scene_index_end > scene_index_start)
scene_start = zarr_dataset.scenes[scene_index_start]
scene_end = zarr_dataset.scenes[(scene_index_end - 1)]
frame_start = zarr_dataset.frames[... |
def main(args):
tf.logging.set_verbosity(tf.logging.INFO)
model_cls_list = [models.get_model(model) for model in args.models]
params_list = [default_parameters() for _ in range(len(model_cls_list))]
params_list = [merge_parameters(params, model_cls.get_parameters()) for (params, model_cls) in zip(params... |
class TestPattern():
def test_default(self, temp_dir, helpers):
config = {'path': 'baz.py', 'pattern': True}
metadata = ProjectMetadata(str(temp_dir), PluginManager(), {'project': {'name': 'foo', 'dynamic': ['version']}, 'tool': {'hatch': {'metadata': {'hooks': {'custom': {}}}}}})
file_path ... |
def collect_results_gpu(result_part, size):
(rank, world_size) = get_dist_info()
part_tensor = torch.tensor(bytearray(pickle.dumps(result_part)), dtype=torch.uint8, device='cuda')
shape_tensor = torch.tensor(part_tensor.shape, device='cuda')
shape_list = [shape_tensor.clone() for _ in range(world_size)]... |
def get_dummy_input(T=100, D=80, B=5, K=100):
forward_input = {}
feature = torch.randn(B, T, D)
src_lengths = torch.from_numpy(np.random.randint(low=1, high=T, size=B, dtype=np.int64))
src_lengths[0] = T
prev_output_tokens = []
for b in range(B):
token_length = np.random.randint(low=1, h... |
class Describe_MarkerFactory():
def it_constructs_the_appropriate_marker_object(self, call_fixture):
(marker_code, stream_, offset_, marker_cls_) = call_fixture
marker = _MarkerFactory(marker_code, stream_, offset_)
marker_cls_.from_stream.assert_called_once_with(stream_, marker_code, offset... |
class MinDistanceHandle(SliderHandle):
tip = 'min_distance'
def __init__(self, window, player):
super().__init__(window, player, 1, 0.6)
def get_value(self):
return (self.player.min_distance / 5.0)
def set_value(self, value):
self.player.min_distance = (value * 5.0) |
def dump(state):
if (not options.DUMP_PRE_ERROR_STATE):
return
stdout.flush()
stderr.flush()
stdout.write('\n--- Pre-error state dump: \n')
try:
state.dump()
finally:
stdout.write('\n')
stderr.write('\n')
stdout.flush()
stderr.flush() |
class MainWindow(QMainWindow):
def __init__(self, *args, **kwargs):
super(MainWindow, self).__init__(*args, **kwargs)
self.browser = QWebEngineView()
self.browser.setUrl(QUrl('
self.browser.urlChanged.connect(self.update_urlbar)
self.browser.loadFinished.connect(self.update_t... |
def gdrive_download(id='16TiPfZj7htmTyhntwcZyEEAejOUxuT6m', file='tmp.zip'):
t = time.time()
file = Path(file)
cookie = Path('cookie')
print(f'Downloading as {file}... ', end='')
file.unlink(missing_ok=True)
cookie.unlink(missing_ok=True)
out = ('NUL' if (platform.system() == 'Windows') els... |
class GATModule(nn.Module):
def __init__(self, dim, hidden_dim_multiplier, num_heads, dropout, **kwargs):
super().__init__()
_check_dim_and_num_heads_consistency(dim, num_heads)
self.dim = dim
self.num_heads = num_heads
self.head_dim = (dim // num_heads)
self.input_li... |
.usefixtures('save_env')
class TestInstallData(support.TempdirManager):
def test_simple_run(self):
(pkg_dir, dist) = self.create_dist()
cmd = install_data(dist)
cmd.install_dir = inst = os.path.join(pkg_dir, 'inst')
one = os.path.join(pkg_dir, 'one')
self.write_file(one, 'xxx... |
class Effect5484(BaseEffect):
runTime = 'early'
type = 'passive'
def handler(fit, implant, context, projectionRange, **kwargs):
fit.appliedImplants.filteredItemMultiply((lambda mod: (mod.item.group.name == 'Special Edition Implant')), 'armorHpBonus2', implant.getModifiedItemAttr('implantSetChristmas... |
def eth_nodes_to_cmds(nodes_configuration: List[Dict[(str, Any)]], eth_node_descs: List[EthNodeDescription], base_datadir: str, genesis_file: str, chain_id: ChainID, verbosity: str) -> List[Command]:
cmds = []
for (config, node_desc) in zip(nodes_configuration, eth_node_descs):
datadir = eth_node_to_dat... |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--smiles_file', default='data/guacamol_v1_all.smiles')
parser.add_argument('--seed', type=int, default=42)
parser.add_argument('--population_size', type=int, default=100)
parser.add_argument('--n_mutations', type=int, default=200)
... |
def setup_environment():
global _ENV_SETUP_DONE
if _ENV_SETUP_DONE:
return
_ENV_SETUP_DONE = True
_configure_libraries()
custom_module_path = os.environ.get('FASTREID_ENV_MODULE')
if custom_module_path:
setup_custom_environment(custom_module_path)
else:
pass |
def node_mssp_start(wizard):
mssp_module = mod_import(settings.MSSP_META_MODULE)
filename = mssp_module.__file__
text = f'''
MSSP (Mud Server Status Protocol) allows online MUD-listing sites/crawlers
to continuously monitor your game and list information about it. Some of
this, like active playe... |
def gather_container(container, dst, group=None, cat_dim=0):
group = (group or dist.group.WORLD)
world_size = dist.get_world_size(group)
this_rank = dist.get_rank(group)
def _do_gather(tensor):
if (this_rank == dst):
tensor_list = [torch.empty_like(tensor) for _ in range(world_size)]... |
def loads(__s: str, *, parse_float: ParseFloat=float) -> dict[(str, Any)]:
src = __s.replace('\r\n', '\n')
pos = 0
out = Output(NestedDict(), Flags())
header: Key = ()
parse_float = make_safe_parse_float(parse_float)
while True:
pos = skip_chars(src, pos, TOML_WS)
try:
... |
class HCaptcha(CaptchaService):
__name__ = 'HCaptcha'
__type__ = 'anticaptcha'
__version__ = '0.04'
__status__ = 'testing'
__description__ = 'hCaptcha captcha service plugin'
__license__ = 'GPLv3'
__authors__ = [('GammaC0de', 'nitzo2001[AT]yahoo[DOT]com')]
KEY_PATTERN = '(?:data-sitekey=... |
def test_formatting():
_ = Catalogue('reahl-component')
date = datetime.date(2012, 1, 10)
with LocaleContextStub() as context:
context.test_locale = 'en_gb'
assert (_.current_locale == 'en_gb')
actual = babel.dates.format_date(date, format='long', locale=_.current_locale)
ass... |
class UNet3D(Abstract3DUNet):
def __init__(self, in_channels, out_channels, final_sigmoid=True, f_maps=64, layer_order='gcr', num_groups=8, num_levels=4, is_segmentation=True, **kwargs):
super(UNet3D, self).__init__(in_channels=in_channels, out_channels=out_channels, final_sigmoid=final_sigmoid, basic_modul... |
_rewriter([gemm_no_inplace])
def local_gemm_to_ger(fgraph, node):
if (node.op == gemm_no_inplace):
(z, a, x, y, b) = node.inputs
if (x.broadcastable[1] and y.broadcastable[0]):
xv = x.dimshuffle(0)
yv = y.dimshuffle(1)
try:
bval = ptb.get_underlyin... |
def construct_onion_error(reason: OnionRoutingFailureMessage, onion_packet: OnionPacket, our_onion_private_key: bytes) -> bytes:
failure_msg = reason.to_bytes()
failure_len = len(failure_msg)
pad_len = (256 - failure_len)
assert (pad_len >= 0)
error_packet = failure_len.to_bytes(2, byteorder='big')
... |
.parametrize('x, mode, exc', [(set_test_value(pt.dmatrix(), (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype('float64'))), 'reduced', None), (set_test_value(pt.dmatrix(), (lambda x: x.T.dot(x))(rng.random(size=(3, 3)).astype('float64'))), 'r', None), (set_test_value(pt.lmatrix(), (lambda x: x.T.dot(x))(rng.integer... |
_fixtures(WebFixture, FormLayoutFixture)
def test_adding_checkboxes(web_fixture, form_layout_fixture):
class DomainObjectWithBoolean():
fields = ExposedNames()
fields.an_attribute = (lambda i: BooleanField(label='Some input', required=True))
fixture = form_layout_fixture
fixture.domain_objec... |
class Resource():
locator = (lambda class_name: locate(('recurly.resources.%s' % class_name)))
def cast_file(cls, response):
klass = cls.locator('BinaryFile')
resource = klass()
setattr(resource, 'data', response.body)
return resource
def cast_error(cls, response):
if... |
def collect_bn_params(model, bn_candidate_layers):
params = []
names = []
for (nm, m) in model.named_modules():
for candidate in bn_candidate_layers:
if isinstance(m, candidate):
for (np, p) in m.named_parameters():
if (np in ['weight', 'bias']):
... |
class SeedMixin(BaseMixin):
def __init__(self, random_seed=None, *args, **kwargs):
super(SeedMixin, self).__init__(*args, **kwargs)
self.random_seed = random_seed
self._rng = RNG(seed=self.random_seed)
def make_random_seed(self):
return self._rng.randint(((2 ** 31) - 1)) |
class Site(Object):
name = Unicode.T(default='', xmltagname='Name')
description = Unicode.T(optional=True, xmltagname='Description')
town = Unicode.T(optional=True, xmltagname='Town')
county = Unicode.T(optional=True, xmltagname='County')
region = Unicode.T(optional=True, xmltagname='Region')
co... |
class open_with(Command):
def execute(self):
(app, flags, mode) = self._get_app_flags_mode(self.rest(1))
self.fm.execute_file(files=self.fm.thistab.get_selection(), app=app, flags=flags, mode=mode)
def tab(self, tabnum):
return self._tab_through_executables()
def _get_app_flags_mode(... |
def test_dist_name(copy_sample):
td = copy_sample('altdistname')
make_wheel_in((td / 'pyproject.toml'), td)
res = (td / 'package_dist1-0.1-py2.py3-none-any.whl')
assert_isfile(res)
with unpack(res) as td_unpack:
assert_isdir(Path(td_unpack, 'package_dist1-0.1.dist-info')) |
class UnzipWrapper():
def __init__(self, fp):
self.__decoder = zlib.decompressobj((- zlib.MAX_WBITS))
self.__data = b''
self.__crc = (zlib.crc32(self.__data) & CRC_MASK)
self.__fp = fp
self.__size = 0
self.__is_fully_read = False
def read(self, sz=(- 1)):
... |
def open_url(url: str, cache_dir: str=None, num_attempts: int=10, verbose: bool=True) -> Any:
assert is_url(url)
assert (num_attempts >= 1)
url_md5 = hashlib.md5(url.encode('utf-8')).hexdigest()
if (cache_dir is not None):
cache_files = glob.glob(os.path.join(cache_dir, (url_md5 + '_*')))
... |
def regex_match_score(prediction, pattern):
try:
compiled = re.compile(pattern, flags=((re.IGNORECASE + re.UNICODE) + re.MULTILINE))
except BaseException:
print(('Regular expression failed to compile: %s' % pattern))
return False
return (compiled.match(prediction) is not None) |
_settings(MEDIA_ROOT=tempfile.mkdtemp())
class TestReviewWavefront(SetUpTest, TestCase):
fixtures = ['fixtures/simplemenu.json']
def setUp(self):
super(TestReviewWavefront, self).setUp()
login = self.client.login(username='creator', password='password')
self.assertTrue(login)
url... |
_optionals.HAS_CPLEX.require_in_instance
class CplexOptimizer(OptimizationAlgorithm):
def __init__(self, disp: bool=False, cplex_parameters: Optional[Dict[(str, Any)]]=None) -> None:
self._disp = disp
self._cplex_parameters = cplex_parameters
def is_cplex_installed():
return _optionals.H... |
(everythings(min_int=(- ), max_int=, allow_null_bytes_in_keys=False, allow_datetime_microseconds=False), booleans())
def test_bson_converter(everything: Everything, detailed_validation: bool):
converter = bson_make_converter(detailed_validation=detailed_validation)
raw = converter.dumps(everything, codec_option... |
class IndexedDataset(FairseqDataset):
_HDR_MAGIC = b'TNTIDX\x00\x00'
def __init__(self, path, fix_lua_indexing=False):
super().__init__()
self.path = path
self.fix_lua_indexing = fix_lua_indexing
self.data_file = None
self.read_index(path)
def read_index(self, path):
... |
def distorted_inputs(data_dir, batch_size):
filenames = [os.path.join(data_dir, ('data_batch_%d.bin' % i)) for i in xrange(1, 6)]
for f in filenames:
if (not tf.gfile.Exists(f)):
raise ValueError(('Failed to find file: ' + f))
filename_queue = tf.train.string_input_producer(filenames)
... |
(cache={}, maxmem=None)
def bipartition_indices(N):
result = []
if (N <= 0):
return result
for i in range((2 ** (N - 1))):
part = [[], []]
for n in range(N):
bit = ((i >> n) & 1)
part[bit].append(n)
result.append((tuple(part[1]), tuple(part[0])))
r... |
class Dragon(Monster):
def __init__(self, name: str, hasWings: bool):
self.name = name
self.hasWings = hasWings
self.canBreatheFire = True
def copy(self) -> Monster:
try:
return deepcopy(self)
except:
raise CloneNotSupportedException |
_on_failure
.parametrize('channels_per_node', [CHAIN])
.parametrize('number_of_nodes', [3, 4, 5])
def test_mediated_transfer(raiden_network: List[RaidenService], number_of_nodes, deposit, token_addresses, network_wait, bench):
apps = raiden_network
token_address = token_addresses[0]
chain_state = views.stat... |
class DescribeRenderedPageBreak():
def it_raises_on_preceding_fragment_when_page_break_is_not_first_in_paragrah(self, fake_parent: t.ProvidesStoryPart):
p_cxml = 'w:p/(w:r/(w:t"abc",w:lastRenderedPageBreak,w:lastRenderedPageBreak))'
p = cast(CT_P, element(p_cxml))
lrpb = p.lastRenderedPageBr... |
class RotationInvariantPooling(nn.Module):
def __init__(self, nInputPlane, nOrientation=8):
super(RotationInvariantPooling, self).__init__()
self.nInputPlane = nInputPlane
self.nOrientation = nOrientation
def forward(self, x):
(N, c, h, w) = x.size()
x = x.view(N, (- 1), ... |
class RFC822Name(GeneralName):
def __init__(self, value: str) -> None:
if isinstance(value, str):
try:
value.encode('ascii')
except UnicodeEncodeError:
raise ValueError('RFC822Name values should be passed as an A-label string. This means unicode charac... |
def test_loop(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
(test_loss, correct) = (0, 0)
with torch.no_grad():
for (X, y) in dataloader:
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1... |
class PreferencesButton(Gtk.HBox):
def __init__(self, browser, model):
super().__init__()
sort_orders = [(_('_Title'), self.__compare_title), (_('_People'), self.__compare_people), (_('_Date'), self.__compare_date), (_('_Date Added'), self.__compare_date_added), (_('_Original Date'), self.__compare_... |
class Callback_Functions():
def read_mem(ql: Qiling, *args):
user_data = args[(- 1)]
buff = ql.mem.read(user_data['address'], user_data['bytes_size'])
ql.log.info(f"Hook was triggered at -> {user_data['address']}")
ql.log.info(buff)
def read_reg(ql: Qiling, *args):
user_d... |
('plaintext, encoding, expected_parts, expected_encoding', [(u'', consts.SMPP_ENCODING_DEFAULT, [b'\x00'], consts.SMPP_ENCODING_DEFAULT), (u'', consts.SMPP_ENCODING_DEFAULT, [b'\x04\x10\x04O'], consts.SMPP_ENCODING_ISO10646), (u'e', consts.SMPP_ENCODING_ISO88591, [b'\xe9'], consts.SMPP_ENCODING_ISO88591)])
def test_mak... |
def nth_product(index, *args):
pools = list(map(tuple, reversed(args)))
ns = list(map(len, pools))
c = reduce(mul, ns)
if (index < 0):
index += c
if (not (0 <= index < c)):
raise IndexError
result = []
for (pool, n) in zip(pools, ns):
result.append(pool[(index % n)])
... |
def on_vid_button_clicked():
global recording
if (not recording):
mode_tabs.setEnabled(False)
encoder = H264Encoder()
if (vid_tab.filetype.currentText() in ['mp4', 'mkv', 'mov', 'ts', 'avi']):
output = FfmpegOutput(f"{(vid_tab.filename.text() if vid_tab.filename.text() else '... |
def CheckProcList():
toRun = dict()
toCreate = dict()
procs = ops.processes.processlist.get_processlist()
for secproduct in filter((lambda x: (x.proctype == 'SECURITY_PRODUCT')), procs):
psps = re.search('^!!! (.*) !!!$', secproduct.friendlyname)
psps = re.split('\\sor\\s', psps.group(1)... |
class BNAfterConvTranspose(torch.nn.Module):
def __init__(self, padding=0, stride=1, dilation=1, groups=1, output_padding=0):
super(BNAfterConvTranspose, self).__init__()
self.conv1 = torch.nn.ConvTranspose2d(10, 10, 3, padding=padding, stride=stride, dilation=dilation, groups=groups, output_padding... |
class Effect3995(BaseEffect):
runTime = 'early'
type = ('projected', 'passive')
def handler(fit, beacon, context, projectionRange, **kwargs):
fit.ship.multiplyItemAttr('signatureRadius', beacon.getModifiedItemAttr('signatureRadiusMultiplier'), stackingPenalties=True, penaltyGroup='postMul', **kwargs... |
def evaluate_metrics(all_prediction, SLOT_LIST):
(total, turn_acc, joint_acc, F1_pred, F1_count) = (0, 0, 0, 0, 0)
for (idx, dial) in all_prediction.items():
for (k, cv) in dial['turns'].items():
if (set(cv['turn_belief']) == set(cv['pred_belief'])):
joint_acc += 1
... |
class MyOp(COp):
__props__ = ('nin', 'name')
def __init__(self, nin, name):
self.nin = nin
self.name = name
def make_node(self, *inputs):
assert (len(inputs) == self.nin)
inputs = list(map(as_variable, inputs))
for input in inputs:
if (input.type is not td... |
class CollectReport(BaseReport):
when = 'collect'
def __init__(self, nodeid: str, outcome: "Literal['passed', 'failed', 'skipped']", longrepr: Union[(None, ExceptionInfo[BaseException], Tuple[(str, int, str)], str, TerminalRepr)], result: Optional[List[Union[(Item, Collector)]]], sections: Iterable[Tuple[(str, ... |
class TestPredictor(unittest.TestCase):
def setUp(self):
test_path = os.path.dirname(os.path.realpath(__file__))
src = SourceField()
tgt = TargetField()
self.dataset = torchtext.data.TabularDataset(path=os.path.join(test_path, 'data/eng-fra.txt'), format='tsv', fields=[('src', src), ... |
class TypeAreaMultiHeadAttention(nn.Module):
def __init__(self, n_head: int, d_model: int, dropout: float=0.1):
super().__init__()
self.dim_per_head = d_model
self.n_head = n_head
self.linear_qs = nn.Linear(d_model, (n_head * d_model), bias=False)
self.linear_ks = nn.Linear(d... |
class AugMixAugment():
def __init__(self, ops, alpha=1.0, width=3, depth=(- 1), blended=False):
self.ops = ops
self.alpha = alpha
self.width = width
self.depth = depth
self.blended = blended
def _calc_blended_weights(self, ws, m):
ws = (ws * m)
cump = 1.0
... |
def load_dataset(path, split, add_targets=False, split_and_preprocess=False, batch_size=1, prefetch_factor=2):
return DataLoader(FlagSimpleDatasetIterative(path=path, split=split, add_targets=add_targets, split_and_preprocess=split_and_preprocess), batch_size=batch_size, prefetch_factor=prefetch_factor, shuffle=Fal... |
class spherical_caps_pdf(PDF):
def __init__(self, shape, origin, importance_sampled_list):
self.shape = shape
self.origin = origin
self.importance_sampled_list = importance_sampled_list
self.l = len(importance_sampled_list)
def value(self, ray_dir):
PDF_value = 0.0
... |
def _create_effnet(model_kwargs, variant, pretrained=False):
features_only = False
model_cls = EfficientNet
if model_kwargs.pop('features_only', False):
features_only = True
model_kwargs.pop('num_classes', 0)
model_kwargs.pop('num_features', 0)
model_kwargs.pop('head_conv', N... |
def contractreceivechannelbatchunlock_from_event(canonical_identifier: CanonicalIdentifier, event: DecodedEvent) -> ContractReceiveChannelBatchUnlock:
data = event.event_data
args = data['args']
return ContractReceiveChannelBatchUnlock(canonical_identifier=canonical_identifier, receiver=args['receiver'], se... |
class ErrorMessageBox(ErrorWidget):
def __init__(self, view):
super().__init__(view)
alert = self.add_child(Alert(view, _('An error occurred:'), 'danger'))
alert.add_child(HTMLElement(view, 'hr'))
alert.add_child(P(view, text=self.error_message))
a = alert.add_child(A(view, U... |
class _cupy_lombscargle_wrapper(object):
def __init__(self, grid, block, kernel):
if isinstance(grid, int):
grid = (grid,)
if isinstance(block, int):
block = (block,)
self.grid = grid
self.block = block
self.kernel = kernel
def __call__(self, x, y,... |
def test_cuda_mig_visible_devices_and_memory_limit_and_nthreads(loop):
uuids = get_gpu_count_mig(return_uuids=True)[1]
if (len(uuids) > 0):
cuda_visible_devices = ','.join([i.decode('utf-8') for i in uuids])
else:
pytest.skip('No MIG devices found')
with patch.dict(os.environ, {'CUDA_VIS... |
def test_update_mixin_missing_attrs(gl):
class M(UpdateMixin, FakeManager):
_update_attrs = gl_types.RequiredOptional(required=('foo',), optional=('bar', 'baz'))
mgr = M(gl)
data = {'foo': 'bar', 'baz': 'blah'}
mgr._update_attrs.validate_attrs(data=data)
data = {'baz': 'blah'}
with pytes... |
def test_process_search(s1_product: SentinelOne):
s1_product.log = logging.getLogger('pytest_surveyor')
s1_product._queries = {}
s1_product.process_search(Tag('test_query'), {}, 'FileName containsCIS "svchost.exe"')
assert (len(s1_product._queries[Tag('test_query')]) == 1)
assert (s1_product._querie... |
_callback_query((tools.option_filter('reposts') & tools.is_admin))
def reposts_config(bot: AutoPoster, callback_query: CallbackQuery):
data = callback_query.data.split()
value = (bool(int(data[2])) if data[2].isdigit() else data[2])
if (data[1] == 'global'):
bot.config['settings']['send_reposts'] = ... |
def get_aggregation_strategies(aggregation_strategies):
import numpy as np
try:
from pypsa.clustering.spatial import _make_consense
except Exception:
from pypsa.clustering.spatial import _make_consense
bus_strategies = dict(country=_make_consense('Bus', 'country'))
bus_strategies.upd... |
def setUpModule():
global cell, kpts
L = 4
n = 15
cell = pgto.Cell()
cell.build(unit='B', verbose=5, output='/dev/null', a=((L, 0, 0), (0, L, 0), (0, 0, L)), mesh=[n, n, n], atom=[['He', (((L / 2.0) - 0.5), (L / 2.0), ((L / 2.0) - 0.5))], ['He', ((L / 2.0), (L / 2.0), ((L / 2.0) + 0.5))]], basis={'H... |
class Migration(migrations.Migration):
dependencies = [('conditions', '0021_related_name')]
operations = [migrations.AddField(model_name='condition', name='locked', field=models.BooleanField(default=False, help_text='Designates whether this condition can be changed.', verbose_name='Locked'))] |
def sane_samples_from_playlist(pathserv, playlist_file):
samples = []
rejected = []
for (sample, filename) in pathserv.playlist_generator_from_file(playlist_file):
ext = os.path.splitext(sample)[1]
if ((ext in {'.dbg', '.htm', '.html', '.json', '.log', '.pkl', '.py', '.txt'}) or (not os.path... |
class InMemoryLogRotationContext(LogRotationContextInterface):
def __init__(self, expired_logs, all_logs):
self.expired_logs = expired_logs
self.all_logs = all_logs
def __enter__(self):
return self
def __exit__(self, ex_type, ex_value, ex_traceback):
if ((ex_type is None) and... |
class AutoConfig(object):
def __init__(self):
raise EnvironmentError('AutoConfig is designed to be instantiated using the `AutoConfig.from_pretrained(pretrained_model_name_or_path)` method.')
def from_pretrained(cls, pretrained_model_name_or_path, **kwargs):
if ('distilbert' in pretrained_model_... |
.parametrize('has_artifacts', [False, True])
def test_msr_format_params(has_artifacts: bool):
preset = PresetManager(None).default_preset_for_game(RandovaniaGame.METROID_SAMUS_RETURNS).get_preset()
assert isinstance(preset.configuration, MSRConfiguration)
configuration = dataclasses.replace(preset.configura... |
def initialize(security_class, cmdpairs, security_level='read-write', restrict_path=None):
global _allowed_requests, _security_level
(security_level, restrict_path) = _set_security_level(security_class, security_level, restrict_path, cmdpairs)
_security_level = security_level
if restrict_path:
r... |
def test_arrange_items(view):
item1 = BeePixmapItem(QtGui.QImage())
item1.do_flip()
view.scene.addItem(item1)
item2 = BeePixmapItem(QtGui.QImage())
item2.setRotation(90)
view.scene.addItem(item2)
item3 = BeePixmapItem(QtGui.QImage())
view.scene.addItem(item3)
with patch.object(item1,... |
def get_activations(files, data_type, model, batch_size, size, length, dims, device):
model.eval()
if (batch_size > len(files)):
print('Warning: batch size is bigger than the data size. Setting batch size to data size')
batch_size = len(files)
transform = torchvision.transforms.Compose([tran... |
def import_CSV(filename: os.PathLike) -> list[btypes.PyTrackObject]:
objects = []
with open(filename, 'r') as csv_file:
csvreader = csv.DictReader(csv_file, delimiter=',', quotechar='|')
for (i, row) in enumerate(csvreader):
data = {k: float(v) for (k, v) in row.items()}
... |
def get_num_layer_layer_wise(var_name, num_max_layer=12):
if (var_name in ('backbone.cls_token', 'backbone.mask_token', 'backbone.pos_embed')):
return 0
elif var_name.startswith('backbone.downsample_layers'):
stage_id = int(var_name.split('.')[2])
if (stage_id == 0):
layer_id... |
def test_cmd_list_input_with_simple_cmd_strings():
cmd1 = get_cmd('tests/testfiles/cmds/args.sh', 'tests\\testfiles\\cmds\\args.bat')
cmd2 = get_cmd('tests/testfiles/cmds/args2.sh', 'tests\\testfiles\\cmds\\args2.bat')
context = Context({'a': 'one', 'b': 'two two', 'c': 'three', 'd': cmd1, 'e': cmd2, 'cmd':... |
def get_valid_reader_names(reader):
new_readers = []
for reader_name in reader:
if (reader_name in OLD_READER_NAMES):
raise ValueError("Reader name '{}' has been deprecated, use '{}' instead.".format(reader_name, OLD_READER_NAMES[reader_name]))
if (reader_name in PENDING_OLD_READER_N... |
def main(base_dir, lang_pair):
(src, tgt) = lang_pair.split('-')
files = list(map((lambda x: os.path.join(base_dir, x)), os.listdir(base_dir)))
files = list(filter((lambda x: ('tok' in x)), files))
src_vocab_size = 32003
src_model_prefix = os.path.join(base_dir, 'm_bpe')
src_model_path = (src_mo... |
def gcd1(u, v):
assert (u > 0)
assert (v > 0)
shift = 0
while ((not (u & 1)) and (not (v & 1))):
shift += 1
u >>= 1
v >>= 1
while (not (u & 1)):
u >>= 1
while True:
while (not (v & 1)):
v >>= 1
if (u > v):
(u, v) = (v, u)
... |
class T5Config(PretrainedConfig):
pretrained_config_archive_map = T5_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self, vocab_size=32128, n_positions=512, d_model=512, d_kv=64, d_ff=2048, num_layers=6, num_heads=8, relative_attention_num_buckets=32, dropout_rate=0.1, layer_norm_epsilon=1e-06, initializer_factor=1... |
def match_context(ds: List[Tuple[(str, str, int)]], docs: List[List[str]], sample_context=2) -> List[ContextualizedExample]:
phrases = []
for tuple in ds:
p1 = tuple[0]
p2 = tuple[1]
phrases.append(p1)
phrases.append(p2)
phrases = list(set(phrases))
raw_texts = [' '.join(... |
class TFAgent(RLAgent):
RESOURCE_SCOPE = 'resource'
SOLVER_SCOPE = 'solvers'
def __init__(self, world, id, json_data):
self.tf_scope = 'agent'
self.graph = tf.Graph()
self.sess = tf.Session(graph=self.graph)
super().__init__(world, id, json_data)
self._build_graph(jso... |
class Migration(migrations.Migration):
dependencies = [('sponsors', '0041_auto__1313')]
operations = [migrations.AlterField(model_name='sponsorshippackage', name='logo_dimension', field=models.PositiveIntegerField(blank=True, default=175, help_text='Internal value used to control logos dimensions at sponsors pa... |
def _process_image_files_batch(coder, thread_index, ranges, name, all_sets, vocab, num_shards):
num_threads = len(ranges)
assert (not (num_shards % num_threads))
num_shards_per_batch = int((num_shards / num_threads))
shard_ranges = np.linspace(ranges[thread_index][0], ranges[thread_index][1], (num_shard... |
class TestQuantSimRangeLearning():
def test_cpu_model_quantize_op_input_params_update(self):
tf.compat.v1.reset_default_graph()
with tf.device('/cpu:0'):
model = tf.keras.Sequential()
model.add(tf.keras.layers.Conv2D(32, kernel_size=3, input_shape=(28, 28, 3), activation='rel... |
def filter_protocol(hostmap, *, allowed_protocols: Iterable[str]=None) -> Sequence[ServerAddr]:
if (allowed_protocols is None):
allowed_protocols = {PREFERRED_NETWORK_PROTOCOL}
eligible = []
for (host, portmap) in hostmap.items():
for protocol in allowed_protocols:
port = portmap... |
def get_args():
parser = argparse.ArgumentParser(description='STPM anomaly detection')
parser.add_argument('--phase', default='train')
parser.add_argument('--data_path', type=str, default='D:/dataset/mvtec_anomaly_detection')
parser.add_argument('--obj', type=str, default='zipper')
parser.add_argume... |
def test(model, path, dataset):
data_path = os.path.join(path, dataset)
image_root = '{}/images/'.format(data_path)
gt_root = '{}/masks/'.format(data_path)
model.eval()
num1 = len(os.listdir(gt_root))
test_loader = test_dataset(image_root, gt_root, 352)
maes = []
dscs = []
ious = []
... |
class _State():
clock_init: float = None
clock_now: float = None
caption_pid: int = None
pmt_pids: list = field(default_factory=list)
captions: list = field(default_factory=list)
def seconds(self, ts):
n = (ts - self.clock_init)
if (n < 0):
n += _CLOCK_FREQ
re... |
def calculate_metric_for_tensor(cal_func, tensor1, tensor2=None, LP_list=None):
if (LP_list is None):
metric_for_named_tensors = cal_func(tensor1, tensor2)
return metric_for_named_tensors
else:
assert (type(LP_list) is list)
metric_for_named_tensors_with_LP_dict = {}
for ... |
class InputsWidget(QtWidgets.QWidget):
NO_LABEL_INPUTS = (BooleanInput,)
def __init__(self, procedure_class, inputs=(), parent=None, hide_groups=True, inputs_in_scrollarea=False):
super().__init__(parent)
self._procedure_class = procedure_class
self._procedure = procedure_class()
... |
def _make_link_replacements() -> List[Tuple[(str, str)]]:
top_level = ['Bloq', 'CompositeBloq', 'BloqBuilder', 'Register', 'Signature', 'Side', 'BloqInstance', 'Connection', 'Soquet']
replacements = [(f'`{name}`', f'[`{name}`](/reference/qualtran/{name}.md)') for name in top_level]
return replacements |
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