code stringlengths 281 23.7M |
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def cleanup_numbered_dir(root: Path, prefix: str, keep: int, consider_lock_dead_if_created_before: float) -> None:
if (not root.exists()):
return
for path in cleanup_candidates(root, prefix, keep):
try_cleanup(path, consider_lock_dead_if_created_before)
for path in root.glob('garbage-*'):
... |
def test_mouse_press_event_topleft_scale(view, item):
view.scene.addItem(item)
item.setSelected(True)
event = MagicMock()
event.pos.return_value = QtCore.QPointF(2, 2)
event.scenePos.return_value = QtCore.QPointF((- 1), (- 1))
event.button.return_value = Qt.MouseButton.LeftButton
with patch.... |
def check_not_deprecated(file, metadata_is={}, metadata_keys_contain=[], compare_as_close=[], current_version=None, last_compatible_version=radis.config['OLDEST_COMPATIBLE_VERSION'], engine='guess'):
if (engine == 'guess'):
engine = DataFileManager.guess_engine(file)
manager = DataFileManager(engine)
... |
def make_trident_res_layer(block, inplanes, planes, num_blocks, stride=1, trident_dilations=(1, 2, 3), style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN'), dcn=None, plugins=None, test_branch_idx=(- 1)):
downsample = None
if ((stride != 1) or (inplanes != (planes * block.expansion))):
... |
class CiderD():
def __init__(self, n=4, sigma=6.0, df='corpus'):
self._n = n
self._sigma = sigma
self._df = df
self.cider_scorer = CiderScorer(n=self._n, df_mode=self._df)
def compute_score(self, gts, res):
self.cider_scorer.clear()
for res_id in res:
... |
class G_D(nn.Module):
def __init__(self, G, D):
super(G_D, self).__init__()
self.G = G
self.D = D
def forward(self, z, gy, x=None, dy=None, train_G=False, return_G_z=False, split_D=False, return_bn=False):
with torch.set_grad_enabled(train_G):
G_z = self.G(z, self.G.s... |
.parametrize(('use_comm', 'use_framer'), [('tcp', 'socket'), ('tcp', 'rtu'), ('tls', 'tls'), ('udp', 'socket'), ('udp', 'rtu'), ('serial', 'rtu')])
class TestClientServerAsyncExamples():
(name='use_port')
def get_port_in_class(base_ports):
base_ports[__class__.__name__] += 1
return base_ports[__... |
def phrase_event(callbacks, parameters):
phrase = parameters.strip().lower()
punctuations = ',.";?!'
for p in punctuations:
phrase = phrase.replace(p, ' ')
words = phrase.split()
words = [w.strip("' ") for w in words if w.strip("' ")]
to_call = []
for callback in callbacks:
k... |
def get_nuc_g_factor(symb_or_charge, mass=None):
if isinstance(symb_or_charge, str):
Z = mole.charge(symb_or_charge)
else:
Z = symb_or_charge
if (mass is None):
(nuc_spin, g_nuc) = ISOTOPE_GYRO[Z][0][1:3]
else:
for (isotop_mass, nuc_spin, g_nuc) in ISOTOPE_GYRO[Z]:
... |
def duplicate_states_loss(player):
episode_loss = torch.tensor(0)
with torch.cuda.device(player.gpu_id):
episode_loss = episode_loss.cuda()
for i in player.duplicate_states_actions:
step_optimal_action = torch.tensor(player.duplicate_states_actions[i]).reshape([1]).long()
with torch.... |
class Decoder(nn.Module):
def __init__(self, in_channels, out_channels, conv_kernel_size=3, scale_factor=2, basic_module=DoubleConv, conv_layer_order='gcr', num_groups=8, padding=1, upsample='default', dropout_prob=0.1, is3d=True):
super(Decoder, self).__init__()
concat = True
adapt_channels... |
def test_get_transparent_pixel(ntg1, ntg2, ntg3, ntg_no_fill_value):
tp = ntg1.get_transparent_pixel()
assert isinstance(tp, int)
assert (tp == 255)
assert (ntg2.get_transparent_pixel() == 0)
assert (ntg3.get_transparent_pixel() == 255)
assert (ntg_no_fill_value.get_transparent_pixel() == (- 1)) |
.parametrize('main_schema, other_schema_data, instance, expect_err', [(CASE1_MAIN_SCHEMA, {'title_schema.json': CASE1_TITLE_SCHEMA}, CASE1_FAILING_DOCUMENT, None), (CASE2_MAIN_SCHEMA, {'values.json': CASE2_VALUES_SCHEMA}, CASE2_FAILING_DOCUMENT, "{'foo': 'bar'} is not of type 'string'")])
.parametrize('with_file_scheme... |
class SmallEncoder(nn.Module):
def __init__(self, output_dim=128, norm_fn='batch', dropout=0.0):
super(SmallEncoder, self).__init__()
self.norm_fn = norm_fn
if (self.norm_fn == 'group'):
self.norm1 = nn.GroupNorm(num_groups=8, num_channels=32)
elif (self.norm_fn == 'batch... |
def evaluate_js(window):
result = window.evaluate_js("\n var h1 = document.createElement('h1')\n var text = document.createTextNode('Hello pywebview')\n h1.appendChild(text)\n document.body.appendChild(h1)\n\n document.body.style.backgroundColor = '#212121'\n document.body.... |
class Effect508(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Projectile Turret')), 'damageMultiplier', ship.getModifiedItemAttr('shipBonusMF'), skill='Minmatar Frigate', **kwargs) |
def is_trivial_bound(tp: ProperType, allow_tuple: bool=False) -> bool:
if (isinstance(tp, Instance) and (tp.type.fullname == 'builtins.tuple')):
return (allow_tuple and is_trivial_bound(get_proper_type(tp.args[0])))
return (isinstance(tp, Instance) and (tp.type.fullname == 'builtins.object')) |
class GlobalContextVitBlock(nn.Module):
def __init__(self, dim: int, feat_size: Tuple[(int, int)], num_heads: int, window_size: int=7, mlp_ratio: float=4.0, use_global: bool=True, qkv_bias: bool=True, layer_scale: Optional[float]=None, proj_drop: float=0.0, attn_drop: float=0.0, drop_path: float=0.0, attn_layer: Ca... |
class ObjectsBoundingBoxConditionalBuilder(ObjectsCenterPointsConditionalBuilder):
def object_descriptor_length(self) -> int:
return 3
def _make_object_descriptors(self, annotations: List[Annotation]) -> List[Tuple[(int, ...)]]:
object_triples = [(self.object_representation(ann), *self.token_pai... |
class EntropyStatCollector(diamond.collector.Collector):
PROC = '/proc/sys/kernel/random/entropy_avail'
def get_default_config(self):
config = super(EntropyStatCollector, self).get_default_config()
config.update({'path': 'entropy'})
return config
def collect(self):
if (not os... |
def run(func, *args, backend=None, backend_options=None):
if (backend is None):
backend = os.getenv('PURERPC_BACKEND', 'asyncio')
_log.info('purerpc.run() selected {} backend'.format(backend))
if (backend == 'uvloop'):
backend = 'asyncio'
options = dict(use_uvloop=True)
if (b... |
.parametrize('case', [CaseReducesInx3OutComp, CaseIfBasicComp, CaseIfDanglingElseInnerComp, CaseIfDanglingElseOutterComp, CaseElifBranchComp, CaseNestedIfComp, CaseForRangeLowerUpperStepPassThroughComp, CaseIfExpInForStmtComp, CaseIfExpBothImplicitComp, CaseIfBoolOpInForStmtComp, CaseIfTmpVarInForStmtComp, CaseFixedSiz... |
def test_entityref():
entref = OSC.EntityRef('ref_str')
entref2 = OSC.EntityRef('ref_str')
entref3 = OSC.EntityRef('ref_str2')
prettyprint(entref.get_element())
assert (entref == entref2)
assert (entref != entref3)
entref4 = OSC.EntityRef.parse(entref.get_element())
assert (entref == ent... |
class TestFindWebengineResources():
def qt_data_path(self, monkeypatch: pytest.MonkeyPatch, tmp_path: pathlib.Path):
qt_data_path = (tmp_path / 'qt_data')
qt_data_path.mkdir()
monkeypatch.setattr(pakjoy.qtutils, 'library_path', (lambda _which: qt_data_path))
return qt_data_path
d... |
class TokenizerTrainingArguments():
base_tokenizer: Optional[str] = field(default='gpt2', metadata={'help': 'Base tokenizer to build new tokenizer from.'})
dataset_name: Optional[str] = field(default='transformersbook/codeparrot-train', metadata={'help': 'Dataset to train tokenizer on.'})
text_column: Optio... |
class ForumTopicEdited(TelegramObject):
__slots__ = ('name', 'icon_custom_emoji_id')
def __init__(self, name: Optional[str]=None, icon_custom_emoji_id: Optional[str]=None, *, api_kwargs: Optional[JSONDict]=None):
super().__init__(api_kwargs=api_kwargs)
self.name: Optional[str] = name
sel... |
class SwiGLUFFNFused(SwiGLU):
def __init__(self, in_features: int, hidden_features: Optional[int]=None, out_features: Optional[int]=None, act_layer: Callable[(..., nn.Module)]=None, drop: float=0.0, bias: bool=True) -> None:
out_features = (out_features or in_features)
hidden_features = (hidden_feat... |
class IpPool(db.Model, AuditTimeMixin):
__tablename__ = 'tb_ippool'
id = db.Column(db.Integer)
fixed_ip = db.Column(db.String(256), primary_key=True)
region = db.Column(db.String(50), nullable=False)
allocated = db.Column(db.Boolean, nullable=False, default=True)
is_ipv6 = db.Column(db.Boolean, ... |
class PyramidFeatures(nn.Module):
def __init__(self, config, img_size=224, in_channels=3):
super().__init__()
model_path = config.swin_pretrained_path
self.swin_transformer = SwinTransformer(img_size, in_chans=3)
checkpoint = torch.load(model_path, map_location=torch.device(device))[... |
class CoLightAgent(Agent):
def __init__(self, dic_agent_conf=None, dic_traffic_env_conf=None, dic_path=None, cnt_round=None, best_round=None, bar_round=None, intersection_id='0'):
super(CoLightAgent, self).__init__(dic_agent_conf, dic_traffic_env_conf, dic_path, intersection_id)
self.att_regulatizat... |
class GeneralGraph(Graph, ABC):
def __init__(self, nodes: List[Node]):
self.nodes: List[Node] = nodes
self.num_vars: int = len(nodes)
node_map: Dict[(Node, int)] = {}
for i in range(self.num_vars):
node = nodes[i]
node_map[node] = i
self.node_map: Dict... |
def load_pos_conv_layer(full_name, value, pos_conv_embeddings, unused_weights):
name = full_name.split('pos_conv.')[(- 1)]
items = name.split('.')
layer_id = int(items[0])
type_id = int(items[1])
weight_type = name.split('.')[(- 1)]
if (type_id != 0):
unused_weights.append(full_name)
... |
def infer_typing_attr(node: Subscript, ctx: (context.InferenceContext | None)=None) -> Iterator[ClassDef]:
try:
value = next(node.value.infer())
except (InferenceError, StopIteration) as exc:
raise UseInferenceDefault from exc
if ((not value.qname().startswith('typing.')) or (value.qname() i... |
('PyQt6.QtWidgets.QFileDialog.getOpenFileName')
def test_on_action_open(dialog_mock, view, qtbot):
root = os.path.dirname(__file__)
filename = os.path.join(root, 'assets', 'test1item.bee')
dialog_mock.return_value = (filename, None)
view.on_loading_finished = MagicMock()
view.scene.cancel_crop_mode ... |
def RegisterPythonwin(register=True):
import os
lib_dir = distutils.sysconfig.get_python_lib(plat_specific=1)
classes_root = get_root_hkey()
pythonwin_exe = os.path.join(lib_dir, 'Pythonwin', 'Pythonwin.exe')
pythonwin_edit_command = (pythonwin_exe + ' /edit "%1"')
keys_vals = [('Software\\Micro... |
def test_update_if_modified_field_changed(sqldb):
cursor = sqldb.cursor()
rules_db.RulesRow(RuleID=501, Name='Long Press Rule', Type=MOCK_RULE_TYPE, State=1).update_db(cursor)
rules_db.RuleDevicesRow(RuleDevicePK=1, RuleID=501, DeviceID=MOCK_UDN).update_db(cursor)
db = rules_db.RulesDb(sqldb, MOCK_UDN, ... |
def get_act_fn(name='relu'):
if (not name):
return None
if (not (is_no_jit() or is_exportable() or is_scriptable())):
if (name in _ACT_FN_ME):
return _ACT_FN_ME[name]
if (is_exportable() and (name in ('silu', 'swish'))):
return swish
if (not (is_no_jit() or is_exporta... |
class APEv2File(AudioFile):
IGNORE = ['file', 'index', 'introplay', 'dummy']
TRANS = {'subtitle': 'version', 'track': 'tracknumber', 'disc': 'discnumber', 'catalog': 'labelid', 'year': 'date', 'record location': 'location', 'album artist': 'albumartist', 'debut album': 'originalalbum', 'record date': 'recording... |
class Minor(nn.Module):
def __init__(self, G_ch=64, dim_z=128, bottom_width=4, resolution=128, G_kernel_size=3, G_attn='64', n_classes=1000, num_G_SVs=1, num_G_SV_itrs=1, G_shared=True, shared_dim=0, hier=False, cross_replica=False, mybn=False, G_activation=nn.ReLU(inplace=False), G_lr=5e-05, G_B1=0.0, G_B2=0.999, ... |
class Source(object):
def __init__(self, s):
self.pos = 0
self.s = s
self.ignore_space = False
def at_end(self):
s = self.s
pos = self.pos
if self.ignore_space:
while True:
if (pos >= len(s)):
break
e... |
def sanity_check_dependencies():
import numpy
import requests
import six
if (distutils.version.LooseVersion(numpy.__version__) < distutils.version.LooseVersion('1.10.4')):
logger.warn("You have 'numpy' version %s installed, but 'gym' requires at least 1.10.4. HINT: upgrade via 'pip install -U nu... |
class TwoInputsModel(torch.nn.Module):
def __init__(self, num_classes=3):
super(TwoInputsModel, self).__init__()
self.conv1 = torch.nn.Conv2d(3, 16, kernel_size=2, stride=2, padding=2, bias=False)
self.bn1 = torch.nn.BatchNorm2d(16)
self.conv2 = torch.nn.Conv2d(3, 8, kernel_size=3, s... |
def test_importorskip_dev_module(monkeypatch) -> None:
try:
mod = types.ModuleType('mockmodule')
mod.__version__ = '0.13.0.dev-43290'
monkeypatch.setitem(sys.modules, 'mockmodule', mod)
mod2 = pytest.importorskip('mockmodule', minversion='0.12.0')
assert (mod2 == mod)
... |
class ResNet(nn.Module):
def __init__(self, block, layers, 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(inplace=True)
... |
class LSTM(nn.Module):
def __init__(self, word_embedding_dimension: int, hidden_dim: int, num_layers: int=1, dropout: float=0, bidirectional: bool=True):
nn.Module.__init__(self)
self.config_keys = ['word_embedding_dimension', 'hidden_dim', 'num_layers', 'dropout', 'bidirectional']
self.word... |
def extract_and_save_image(dataset, save_dir, discard, label2name):
if osp.exists(save_dir):
print('Folder "{}" already exists'.format(save_dir))
return
print('Extracting images to "{}" ...'.format(save_dir))
mkdir_if_missing(save_dir)
for i in range(len(dataset)):
(img, label) =... |
class cvode(IntegratorBase):
valid_methods = {'adams': _cvode.CV_ADAMS, 'bdf': _cvode.CV_BDF}
valid_iterations = {'functional': _cvode.CV_FUNCTIONAL, 'newton': _cvode.CV_NEWTON}
def __init__(self, method='adams', iteration='functional', rtol=1e-06, atol=1e-12):
if (method not in cvode.valid_methods)... |
def run(config):
state_dict = {'itr': 0, 'epoch': 0, 'save_num': 0, 'save_best_num': 0, 'best_IS': 0, 'best_FID': 999999, 'config': config}
if config['config_from_name']:
utils.load_weights(None, None, state_dict, config['weights_root'], config['experiment_name'], config['load_weights'], None, strict=Fa... |
def mol_data_from_csv(csv_name: str):
with open(csv_name, 'r') as csv_file:
mol_confs = csv.DictReader(csv_file)
rows = []
for row in mol_confs:
row = dict(row)
row['smiles'] = (row['smiles'] if row['smiles'] else None)
row['multiplicity'] = (int(float(row... |
class LiltConfig(PretrainedConfig):
model_type = 'lilt'
def __init__(self, vocab_size=30522, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=2, initia... |
def test_push_pull_manifest_list_duplicate_manifest(v22_protocol, basic_images, liveserver_session, app_reloader, data_model):
credentials = ('devtable', 'password')
options = ProtocolOptions()
blobs = {}
manifest = v22_protocol.build_schema2(basic_images, blobs, options)
builder = DockerSchema2Mani... |
()
def no_qt(monkeypatch):
need_reload = False
if _check_qt_installed():
need_reload = True
monkeypatch.setenv('QT_API', 'bad_name')
sys.modules.pop('qtpy')
importlib.reload(pyvistaqt)
assert ('qtpy' not in sys.modules)
(yield)
monkeypatch.undo()
if need_reloa... |
def analyze_enum_class_attribute_access(itype: Instance, name: str, mx: MemberContext) -> (Type | None):
if (name in ENUM_REMOVED_PROPS):
return report_missing_attribute(mx.original_type, itype, name, mx)
if (name.startswith('__') and name.endswith('__') and (name.replace('_', '') != '')):
retur... |
class SemanticAnalyzerPluginInterface():
modules: dict[(str, MypyFile)]
options: Options
cur_mod_id: str
msg: MessageBuilder
def named_type(self, fullname: str, args: (list[Type] | None)=None) -> Instance:
raise NotImplementedError
def builtin_type(self, fully_qualified_name: str) -> Ins... |
class Event(object):
def __init__(self, console, input):
pass
def __repr__(self):
if (self.type in ['KeyPress', 'KeyRelease']):
s = ("%s char='%s'%d keysym='%s' keycode=%d:%x state=%x keyinfo=%s" % (self.type, self.char, ord(self.char), self.keysym, self.keycode, self.keycode, self.s... |
def _parse_pmt(payload):
table_id = payload[0]
if (table_id != _TABLE_PMT):
return None
length = (((payload[1] & 15) << 8) | payload[2])
data = payload[8:(3 + length)]
data = data[:(- 4)]
meta_length = (((data[2] & 15) << 8) | data[3])
stream = data[(4 + meta_length):]
while stre... |
def read_freq_cpu(path, type_freq):
freq = {}
with open('{path}/cpufreq/{type_freq}_min_freq'.format(path=path, type_freq=type_freq), 'r') as f:
freq['min'] = int(f.read())
with open('{path}/cpufreq/{type_freq}_max_freq'.format(path=path, type_freq=type_freq), 'r') as f:
freq['max'] = int(f.... |
def get_help(cmd: Optional[str]) -> str:
base = ['pipx']
args = ((base + ([cmd] if cmd else [])) + ['--help'])
env_patch = os.environ.copy()
env_patch['PATH'] = os.pathsep.join(([str(Path(sys.executable).parent)] + env_patch['PATH'].split(os.pathsep)))
content = check_output(args, text=True, env=env... |
class SwitchMetric(Metric):
def __init__(self, args: Namespace, mode='all'):
super(SwitchMetric, self).__init__(args)
self.args = args
self.mode = mode
self.amax = args.padding_size
self.use_lm = args.use_lm
def __call__(self, gts: list, preds: list, mask: list) -> dict:
... |
def rand_throw():
vp = np.random.uniform(low=0, high=360)
goal = np.array([np.random.uniform(low=(- 0.3), high=0.3), np.random.uniform(low=(- 0.3), high=0.3)])
return dict(vp=vp, imsize=(64, 64), name='throw', goal=goal.tolist(), modelname='model/model_70000_3007.74_2728.77_268.42', modeldata='model/vdata_t... |
class LidResults(Enum):
inflow = 0
evap = 1
infil = 2
surfFlow = 3
drainFlow = 4
initVol = 5
finalVol = 6
surfDepth = 7
paveDepth = 8
soilMoist = 9
storDepth = 10
dryTime = 11
oldDrainFlow = 12
newDrainFlow = 13
pervArea = 14
flowToPerv = 15
evapRate =... |
class SawyerDisassembleV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'wrench_pos': obs[3:6], 'peg_pos': obs[9:], 'unused_info': obs[6:9]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_effort... |
def locate_cuda():
if ('CUDA_PATH' in os.environ):
home = os.environ['CUDA_PATH']
print(('home = %s\n' % home))
nvcc = pjoin(home, 'bin', nvcc_bin)
else:
default_path = pjoin(os.sep, 'usr', 'local', 'cuda', 'bin')
nvcc = find_in_path(nvcc_bin, ((os.environ['PATH'] + os.pa... |
def pytest_addoption(parser: Parser) -> None:
parser.addini('doctest_optionflags', 'Option flags for doctests', type='args', default=['ELLIPSIS'])
parser.addini('doctest_encoding', 'Encoding used for doctest files', default='utf-8')
group = parser.getgroup('collect')
group.addoption('--doctest-modules',... |
('pypyr.moduleloader.get_module')
(Step, 'invoke_step')
def test_run_pipeline_steps_with_retries(mock_invoke_step, mock_get_module):
step = Step({'name': 'step1', 'retry': {'max': 0}})
context = get_test_context()
original_len = len(context)
mock_invoke_step.side_effect = [ValueError('arb'), None]
w... |
class BaseDB(DatabaseAPI):
def set(self, key: bytes, value: bytes) -> None:
self[key] = value
def exists(self, key: bytes) -> bool:
return self.__contains__(key)
def __contains__(self, key: bytes) -> bool:
if hasattr(self, '_exists'):
return self._exists(key)
else... |
class JSONOutputFormat(KVWriter):
def __init__(self, filename):
self.file = open(filename, 'wt')
def writekvs(self, kvs):
for (k, v) in sorted(kvs.items()):
if hasattr(v, 'dtype'):
kvs[k] = float(v)
self.file.write((json.dumps(kvs) + '\n'))
self.file.f... |
(params=[lazy_fixture('example_git_ssh_url')])
def git_repo_factory(request, example_project):
def git_repo():
repo = Repo.init(example_project.resolve())
repo.git.branch('-M', 'main')
with repo.config_writer('repository') as config:
config.set_value('user', 'name', 'semantic rel... |
class TestToyDictionary():
XML_PATH = os.path.join(os.path.dirname(__file__), '..', 'dev_data', 'toy_dict.xml')
def test_parse_xml(self):
dct = parse_opencorpora_xml(self.XML_PATH)
assert (dct.version == '0.92')
assert (dct.revision == '389440')
assert (dct.links[0] == ('5', '6',... |
def merge_two_slices(fgraph, slice1, len1, slice2, len2):
if (not isinstance(slice1, slice)):
raise ValueError('slice1 should be of type `slice`')
(sl1, reverse1) = get_canonical_form_slice(slice1, len1)
(sl2, reverse2) = get_canonical_form_slice(slice2, len2)
if (not isinstance(sl2, slice)):
... |
class ChainChoiceType(click.Choice):
def convert(self, value, param, ctx):
if isinstance(value, int):
return value
elif (isinstance(value, str) and value.isnumeric()):
try:
return int(value)
except ValueError:
self.fail(f'invalid nu... |
class DevDataset(Dataset):
def __init__(self, args, raw_datasets, cache_root):
self.raw_datasets = raw_datasets
cache_path = os.path.join(cache_root, 'tab_fact_dev.cache')
if (os.path.exists(cache_path) and args.dataset.use_cache):
self.extended_data = torch.load(cache_path)
... |
def asin_list_from_csv(mf):
if os.path.isfile(mf):
with open(mf) as f:
csvread = csv.reader(f, delimiter=';', quotechar='"', quoting=csv.QUOTE_ALL)
asinlist = []
filelist = []
for row in csvread:
try:
if (row[0] != '* NONE *... |
class SponsorshipPackageManagerTests(TestCase):
def test_filter_packages_by_current_year(self):
current_year = SponsorshipCurrentYear.get_year()
active_package = baker.make(SponsorshipPackage, year=current_year)
baker.make(SponsorshipPackage, year=(current_year - 1))
qs = Sponsorship... |
class MaxLengthCriteria(StoppingCriteria):
def __init__(self, max_length: int):
self.max_length = max_length
_start_docstrings(STOPPING_CRITERIA_INPUTS_DOCSTRING)
def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
return (input_ids.shape[(- 1)] >= s... |
def get_dist_run_id(cfg, num_nodes):
init_method = cfg.DISTRIBUTED.INIT_METHOD
run_id = cfg.DISTRIBUTED.RUN_ID
if ((init_method == 'tcp') and (cfg.DISTRIBUTED.RUN_ID == 'auto')):
assert (num_nodes == 1), 'cfg.DISTRIBUTED.RUN_ID=auto is allowed for 1 machine only.'
port = find_free_tcp_port()... |
class DirectPalette(AbstractPalette):
registry = BLOCK_STATES
def get_bits_per_block():
return math.ceil(math.log2(sum((len(b['states']) for b in BLOCK_STATES.data.values()))))
def encode(block: str, props: dict=None) -> int:
props = ({} if (props is None) else props)
block_data = BL... |
def get_cache_dir() -> Path:
if ((os.name == 'posix') and (sys.platform != 'darwin')):
xdg = (os.environ.get('XDG_CACHE_HOME', None) or os.path.expanduser('~/.cache'))
return Path(xdg, 'flit')
elif (sys.platform == 'darwin'):
return Path(os.path.expanduser('~'), 'Library/Caches/flit')
... |
def anonymise_cli(args):
if args.delete_unknown_tags:
handle_unknown_tags = True
elif args.ignore_unknown_tags:
handle_unknown_tags = False
else:
handle_unknown_tags = None
if (not args.keywords_to_leave_unchanged):
keywords_to_leave_unchanged = ()
else:
keywo... |
def _split_text(asr, audio, speech2text):
if (len(asr) < 2):
return [(0, len(audio), asr)]
try:
timings = _get_timings(asr, audio, speech2text)
except Exception:
return [(0, len(audio), asr)]
threshold = np.percentile((timings[1:] - timings[:(- 1)]), 98, interpolation='nearest')
... |
def _show_fixtures_per_test(config: Config, session: Session) -> None:
import _pytest.config
session.perform_collect()
curdir = Path.cwd()
tw = _pytest.config.create_terminal_writer(config)
verbose = config.getvalue('verbose')
def get_best_relpath(func) -> str:
loc = getlocation(func, st... |
.parametrize('vectorize', [True, False])
def test_vf_ground_sky_2d_integ(test_system_fixed_tilt, vectorize):
(ts, pts, vfs_gnd_sky) = test_system_fixed_tilt
vf_integ = utils.vf_ground_sky_2d_integ(ts['rotation'], ts['gcr'], ts['height'], ts['pitch'], max_rows=1, npoints=3, vectorize=vectorize)
expected_vf_i... |
class ResourceCache():
def __init__(self) -> None:
self._cache: t.Dict[(str, referencing.Resource[Schema])] = {}
def __setitem__(self, uri: str, data: t.Any) -> referencing.Resource[Schema]:
resource = referencing.Resource.from_contents(data, default_specification=DRAFT202012)
self._cach... |
class QueryBuilder(object):
def __init__(self):
self._query = []
self.current_field = None
self.c_oper = None
self.l_oper = None
def field(self, field):
self.current_field = field
return self
def order_descending(self):
self._query.append('ORDERBYDESC{... |
def assert_device_map(device_map, num_blocks):
blocks = list(range(0, num_blocks))
device_map_blocks = [item for sublist in list(device_map.values()) for item in sublist]
duplicate_blocks = []
for i in device_map_blocks:
if ((device_map_blocks.count(i) > 1) and (i not in duplicate_blocks)):
... |
def gcs_test_credential() -> Generator[(None, None, None)]:
if ('GOOGLE_APPLICATION_CREDENTIALS' in os.environ):
(yield)
return
if ('GOOGLE_APPLICATION_CREDENTIALS_JSON' in os.environ):
with tempfile.NamedTemporaryFile('w') as f:
f.write(os.environ['GOOGLE_APPLICATION_CREDENT... |
def init_params(opt, ClothWarper, data_loader):
iter_path = os.path.join(opt.checkpoints_dir, opt.name, 'iter.txt')
(start_epoch, epoch_iter) = (1, 0)
if opt.continue_train:
if os.path.exists(iter_path):
(start_epoch, epoch_iter) = np.loadtxt(iter_path, delimiter=',', dtype=int)
... |
def _decode(data):
code = ''
for c in data:
if (c in ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'a', 'b', 'c', 'd', 'e', 'f']):
code += c
elif (c not in [' ', '\n']):
raise rse.BadParameter(("Cannot decode '%s' in '%s'" % (c, data)))
return bytes.fromhex(code)... |
class StubAttr(StubBase):
def __init__(self, obj, attr_name):
self.__dict__['_obj'] = obj
self.__dict__['_attr_name'] = attr_name
def obj(self):
return self.__dict__['_obj']
def attr_name(self):
return self.__dict__['_attr_name']
def __str__(self):
return ('StubAt... |
def get_tweets():
result = []
news_sources = AutoImportResource.objects.filter(type_res='twitter').exclude(in_edit=True).exclude(is_active=False)
for source in news_sources:
print('Process twitter', source)
try:
result.extend(_parse_tweets_data(get_tweets_by_url(source.link), sou... |
class FileItem(BrowserItem):
def __init__(self, parent, pathProxy, mode='normal'):
BrowserItem.__init__(self, parent, pathProxy)
self._mode = mode
self._timeSinceLastDocString = 0
if ((self._mode == 'normal') and self.path().lower().endswith('.py')):
self._createDummyItem... |
def task(reindexed_root_dir, dataset, index):
image_id = dataset._ids[index]
examples = dataset.get_example(index)
id_to_meta = {}
for (i_example, example) in enumerate(examples):
instance_id = f'{image_id}/{i_example:08d}'
npz_file = (reindexed_root_dir / f'{instance_id}.npz')
n... |
class SharedAdam(optim.Optimizer):
def __init__(self, params, lr=0.001, betas=(0.9, 0.999), eps=0.001, weight_decay=0, amsgrad=True):
defaults = defaultdict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, amsgrad=amsgrad)
super(SharedAdam, self).__init__(params, defaults)
for group i... |
def test_geodesic_fwd_inv_inplace():
gg = Geod(ellps='clrk66')
_BOSTON_LON = numpy.array([0], dtype=numpy.float64)
_BOSTON_LAT = numpy.array([0], dtype=numpy.float64)
_PORTLAND_LON = numpy.array([1], dtype=numpy.float64)
_PORTLAND_LAT = numpy.array([1], dtype=numpy.float64)
(az12, az21, dist) = ... |
class TrainRegSet(torch.utils.data.Dataset):
def __init__(self, data_root, image_size):
super().__init__()
self.transform = transforms.Compose([transforms.Resize(image_size), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
self.imgs = torchvision.datasets.Imag... |
class DiscCentroidsLoss(nn.Module):
def __init__(self, num_classes, feat_dim, size_average=True):
super(DiscCentroidsLoss, self).__init__()
self.num_classes = num_classes
self.centroids = nn.Parameter(torch.randn(num_classes, feat_dim))
self.disccentroidslossfunc = DiscCentroidsLossF... |
def writegen(fnfn, generator, header=None, sep=','):
import codecs
of = codecs.open(fnfn, 'w', encoding='utf-8')
header_written = False
for dx in generator():
if (not header_written):
if (not header):
if ('header' in dx):
header = dx['header']
... |
class IsHasAccessOrReadOnly(permissions.BasePermission):
def has_object_permission(self, request, view, obj):
if (request.method in permissions.SAFE_METHODS):
return True
user = request.user
is_manager = user.groups.filter(name=MANAGER_GROUP).exists()
return ((user == obj... |
class SecretSerializer(SerializationBase):
def serialize(obj: _DecryptedSecret) -> bytes:
if (not isinstance(obj, _DecryptedSecret)):
raise SerializationError(f'Can only serialize {_DecryptedSecret.__name__} objects')
try:
schema = class_schema(_DecryptedSecret, base_schema=B... |
def window_accumulator(acc, new, diff=None, window=None, agg=None, with_state=False):
if (acc is None):
acc = {'dfs': [], 'state': agg.initial(new)}
dfs = acc['dfs']
state = acc['state']
(dfs, old) = diff(dfs, new, window=window)
if (new is not None):
(state, result) = agg.on_new(sta... |
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