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def _resolve_looppart(parts, assign_path, context):
assign_path = assign_path[:]
index = assign_path.pop(0)
for part in parts:
if isinstance(part, util.UninferableBase):
continue
if (not hasattr(part, 'itered')):
continue
try:
itered = part.itered(... |
def metrics_traversal_order(state_dict: Dict[(str, Dict[(str, TState)])]) -> List[Tuple[(str, str)]]:
dict_items = []
for outer_key in sorted(state_dict.keys()):
inner_dict = state_dict[outer_key]
for inner_key in sorted(inner_dict.keys()):
dict_items.append((outer_key, inner_key))
... |
.parametrize('val', [set_test_value(pt.lscalar(), np.array(6, dtype='int64'))])
def test_Bartlett(val):
g = extra_ops.bartlett(val)
g_fg = FunctionGraph(outputs=[g])
compare_numba_and_py(g_fg, [i.tag.test_value for i in g_fg.inputs if (not isinstance(i, (SharedVariable, Constant)))], assert_fn=(lambda x, y:... |
def extract_tarinfo(tarfile, class_to_idx=None, sort=True):
extensions = get_img_extensions(as_set=True)
files = []
labels = []
for ti in tarfile.getmembers():
if (not ti.isfile()):
continue
(dirname, basename) = os.path.split(ti.path)
label = os.path.basename(dirname... |
class TestIsRoot():
.windows
.parametrize('directory, is_root', [('C:\\foo\\bar', False), ('C:\\foo\\', False), ('C:\\foo', False), ('C:\\', True)])
def test_windows(self, directory, is_root):
assert (filescheme.is_root(directory) == is_root)
.posix
.parametrize('directory, is_root', [('/foo... |
.network
def test_prepare_directory_with_extensions(config: Config, config_cache_dir: Path, artifact_cache: ArtifactCache, fixture_dir: FixtureDirGetter) -> None:
env = EnvManager.get_system_env()
chef = Chef(artifact_cache, env, Factory.create_pool(config))
archive = fixture_dir('extended_with_no_setup').r... |
def metrics_df_from_toml_path(toml_path, min_segment_dur, device='cuda', spect_key='s', timebins_key='t'):
toml_path = Path(toml_path)
cfg = config.parse.from_toml(toml_path)
with cfg.eval.labelmap_path.open('r') as f:
labelmap = json.load(f)
model_config_map = config.models.map_from_path(toml_p... |
class TCPCollector(diamond.collector.Collector):
PROC = ['/proc/net/netstat', '/proc/net/snmp']
def process_config(self):
super(TCPCollector, self).process_config()
if (self.config['allowed_names'] is None):
self.config['allowed_names'] = []
if (self.config['gauges'] is None)... |
def test_repr__undefined():
datum_name = 'unknown'
if PROJ_GTE_901:
datum_name = f'{datum_name} using nadgrids='
assert (repr(CRS('+proj=merc +a=6378137.0 +b=6378137.0 +nadgrids= +lon_0=0.0 +x_0=0.0 +y_0=0.0 +units=m +no_defs')) == f'''<Bound CRS: +proj=merc +a=6378137.0 +b=6378137.0 +nadgrids= ...>... |
class FunnelConverter(Converter):
def converted(self) -> Tokenizer:
vocab = self.original_tokenizer.vocab
tokenizer = Tokenizer(WordPiece(vocab, unk_token=str(self.original_tokenizer.unk_token)))
tokenize_chinese_chars = False
strip_accents = False
do_lower_case = False
... |
def reconstitute_tpm(subsystem):
node_tpms = [node.tpm.tpm[(..., 1)] for node in subsystem.nodes]
node_tpms = [tpm.squeeze(axis=subsystem.external_indices) for tpm in node_tpms]
node_tpms = [np.expand_dims(tpm, (- 1)) for tpm in node_tpms]
node_tpms = [(tpm * np.ones((([2] * (tpm.ndim - 1)) + [tpm.shape... |
def is_valid_total_withdraw(channel_state: NettingChannelState, our_total_withdraw: WithdrawAmount, allow_zero: bool=False) -> SuccessOrError:
balance = get_balance(sender=channel_state.our_state, receiver=channel_state.partner_state)
withdraw_overflow = (not is_valid_channel_total_withdraw(TokenAmount((our_tot... |
_label
def equal_hash_ref_loop(data, idx, key, env, cont):
from pycket.interpreter import return_value
from pycket.prims.equal import equal_func_unroll_n, EqualInfo
if (idx >= len(data)):
return return_value(w_missing, env, cont)
(k, v) = data[idx]
info = EqualInfo.BASIC_SINGLETON
cont =... |
def _simple_compact(variables):
var_array = VarEntities.varToEVarArray[variables[0]]
(mins, maxs) = (([float('inf')] * len(var_array.size)), ([float('-inf')] * len(var_array.size)))
for x in variables:
for (i, v) in enumerate(x.indexes):
mins[i] = min(mins[i], v)
maxs[i] = ma... |
class GraphVAEOptimizer(object):
def __init__(self, model, learning_rate=0.001):
self.kl_weight = tf.placeholder_with_default(1.0, shape=())
self.la = tf.placeholder_with_default(1.0, shape=())
edges_loss = tf.losses.sparse_softmax_cross_entropy(labels=model.edges_labels, logits=model.edges_... |
class DataTrainingArguments():
task_name: Optional[str] = field(default='ner', metadata={'help': 'The name of the task (ner, pos...).'})
dataset_name: Optional[str] = field(default='wikiann', metadata={'help': 'The name of the dataset to use (via the datasets library).'})
dataset_config_name: Optional[str] ... |
def drop_block_fast_2d(x: torch.Tensor, drop_prob: float=0.1, block_size: int=7, gamma_scale: float=1.0, with_noise: bool=False, inplace: bool=False, batchwise: bool=False):
(B, C, H, W) = x.shape
total_size = (W * H)
clipped_block_size = min(block_size, min(W, H))
gamma = ((((gamma_scale * drop_prob) *... |
class W2lFairseqLMDecoder(W2lDecoder):
def __init__(self, args, tgt_dict):
super().__init__(args, tgt_dict)
self.silence = tgt_dict.bos()
self.unit_lm = getattr(args, 'unit_lm', False)
self.lexicon = (load_words(args.lexicon) if args.lexicon else None)
self.idx_to_wrd = {}
... |
def simple_model(simple_model_data):
with pm.Model() as model:
mu_ = pm.Normal('mu', mu=simple_model_data['mu0'], sigma=simple_model_data['sigma0'], initval=0)
pm.Normal('x', mu=mu_, sigma=simple_model_data['sigma'], observed=simple_model_data['data'], total_size=simple_model_data['n'])
return m... |
class ClassDefinitionTestCase(unittest.TestCase):
def runTest(self):
errors = 0
commands_dir = os.path.join(os.path.dirname(pykickstart.__file__), 'commands')
commands_dir = os.path.abspath(commands_dir)
self.assertTrue(os.path.exists(commands_dir))
if (commands_dir not in sy... |
class MembershipDeleteView(LoginRequiredMixin, UserPassesTestMixin, DeleteView):
model = Membership
slug_field = 'creator__username'
raise_exception = True
= ['post', 'delete']
def get_success_url(self):
return reverse('users:user_detail', kwargs={'slug': self.request.user.username})
de... |
def test_gpu_normalize():
def check_normalize(origin_imgs, result_imgs, norm_cfg):
from numpy.testing import assert_array_almost_equal
target_imgs = result_imgs.copy()
target_imgs *= norm_cfg['std']
target_imgs += norm_cfg['mean']
assert_array_almost_equal(origin_imgs, target... |
def test_tdm_fmcw_rx():
print('#### TDM FMCW receiver ####')
tdm = tdm_fmcw_rx()
print('# TDM FMCW receiver parameters #')
assert (tdm.bb_prop['fs'] == 2000000.0)
assert (tdm.rf_prop['noise_figure'] == 4)
assert (tdm.rf_prop['rf_gain'] == 20)
assert (tdm.bb_prop['load_resistor'] == 500)
... |
class LambdaWarmUpCosineScheduler2():
def __init__(self, warm_up_steps, f_min, f_max, f_start, cycle_lengths, verbosity_interval=0):
assert (len(warm_up_steps) == len(f_min) == len(f_max) == len(f_start) == len(cycle_lengths))
self.lr_warm_up_steps = warm_up_steps
self.f_start = f_start
... |
def main():
code = '18dczc1337a63427fa'
redirect_url = '
app = 0
secret = 'dGbpoJdqNuMlGDECgO9I'
vk_session = vk_api.VkApi(app_id=app, client_secret=secret)
try:
vk_session.code_auth(code, redirect_url)
except vk_api.AuthError as error_msg:
print(error_msg)
return
... |
class UperNetFCNHead(nn.Module):
def __init__(self, config, in_index: int=2, kernel_size: int=3, dilation: Union[(int, Tuple[(int, int)])]=1) -> None:
super().__init__()
self.config = config
self.in_channels = config.auxiliary_in_channels
self.channels = config.auxiliary_channels
... |
class TestNonInjectiveLink(unittest.TestCase):
(ONE_TEST_TIMEOUT)
def test_select_project_forward(self):
(rdf_graph, schema) = get_graph_and_schema('dev', 'concert_singer')
correct_sparql_query = textwrap.dedent(' SELECT ?singer_name\n WHERE\n {\n ?p... |
def test_dual_basis_element():
de = DualBasisElement()
de_2 = DualBasisElement()
db_0 = (de + de_2)
assert isinstance(db_0, DualBasis)
db_1 = (db_0 + db_0)
assert isinstance(db_1, DualBasis)
dim = 2
opdm = np.random.random((dim, dim))
opdm = ((opdm.T + opdm) / 2)
opdm = Tensor(te... |
def one_hot(indices, depth, no_cuda=False):
shape = (list(indices.size()) + [depth])
indices_dim = len(indices.size())
if no_cuda:
a = torch.zeros(shape, dtype=torch.float)
else:
a = torch.zeros(shape, dtype=torch.float).cuda()
return a.scatter_(indices_dim, indices.unsqueeze(indices... |
def test_hookrelay_registry(pm: PluginManager) -> None:
class Api():
def hello(self, arg: object) -> None:
pm.add_hookspecs(Api)
hook = pm.hook
assert hasattr(hook, 'hello')
assert (repr(hook.hello).find('hello') != (- 1))
class Plugin():
def hello(self, arg):
return ... |
_importer.add('(?:decoder|transformer)/logits/kernel(\\w*)')
def final_logits(opts, key, val, slot):
del opts, key
prefix = ('state/param_states' if slot else 'target')
suffix = (('/' + SLOT_MAP[slot]) if slot else '')
newkey = f'{prefix}/decoder/logits_dense/kernel{suffix}'
return (newkey, val) |
_task('sentence_prediction')
class SentencePredictionTask(FairseqTask):
def add_args(parser):
parser.add_argument('data', metavar='FILE', help='file prefix for data')
parser.add_argument('--num-classes', type=int, default=(- 1), help='number of classes')
parser.add_argument('--init-token', t... |
class CustomModel(openlm.BaseModel):
def create_completion(self, model: Union[(str, List[str])], prompt: Union[(str, List[str])], suffix: Optional[str]=None, max_tokens: Optional[int]=None, temperature: Optional[float]=None, top_p: Optional[float]=None, n: Optional[int]=None, stream: Optional[bool]=None, logprobs: ... |
class DownloadStats(base.ScriptBaseWithConfig):
ARGS_HELP = ''
VERSION = '1.0'
FIELDS = ('is_active', 'left_bytes', 'size_bytes', 'down.rate', 'priority')
MIN_STALLED_RATE = (5 * 1024)
STALLED_PERCENT = 10
def add_options(self):
super(DownloadStats, self).add_options()
def mainloop(s... |
class InfoThread(PluginThread):
def __init__(self, manager, data, pid=(- 1), rid=(- 1), add=False):
super().__init__(manager)
self.data = data
self.pid = pid
self.rid = rid
self.add = add
self.cache = []
self.start()
def run(self):
plugins = {}
... |
class FakeIterable(Iterable):
def __init__(self, container):
self.iterator = container
self.size = len(self.iterator)
self.step = 0
def __iter__(self) -> Iterator:
return self
def __next__(self):
if (self.step == self.size):
raise StopIteration()
r... |
class Lanes(XodrBase):
def __init__(self):
super().__init__()
self.lanesections = []
self.laneoffsets = []
self.roadmarks_adjusted = False
def __eq__(self, other):
if (isinstance(other, Lanes) and super().__eq__(other)):
if ((self.laneoffsets == other.laneoffs... |
def run_gamma(filepath_ref, filepath_eval, random_subset=None, max_gamma=1.1, dose_threshold=1, distance_threshold=1):
if (random_subset is not None):
np.random.seed(42)
ds_ref = pydicom.read_file(filepath_ref)
ds_eval = pydicom.read_file(filepath_eval)
axes_reference = load_yx_from_dicom(ds_ref... |
def write_train_pkl(obj_list_path, meta, output_dir, cate):
obj_list = read_obj_list(obj_list_path)
model = DeformedImplicitField(**meta)
model.load_state_dict(torch.load(meta['checkpoint_path']))
assert (len(obj_list) == model.latent_codes.weight.size()[0])
pkl_info = {}
for i in range(len(obj_... |
def convert_esm_checkpoint_to_pytorch(model: str, pytorch_dump_folder_path: str, classification_head: bool, push_to_repo: str, auth_token: str):
if model.startswith('esmfold'):
esm = MODEL_MAPPING[model]()
else:
(esm, alphabet) = MODEL_MAPPING[model]()
esm.eval()
if model.startswith('esm... |
class ErrorBox(QtWidgets.QWidget):
def __init__(self, parent):
QtWidgets.QWidget.__init__(self, parent)
parent.installEventFilter(self)
self.setAttribute(QtCore.Qt.WidgetAttribute.WA_TransparentForMouseEvents)
self._resize()
self.setVisible(False)
def eventFilter(self, ob... |
class webvision_dataset(Dataset):
def __init__(self, root_dir, transform, mode, num_class, num_samples=None, losses=[]):
self.root = root_dir
self.transform = transform
self.mode = mode
if (self.mode == 'test'):
with open(os.path.join(self.root, 'info/val_filelist.txt')) ... |
def test_dtype_rescaling_uint8_half(tmpdir, runner):
outputname = str(tmpdir.join('test.tif'))
result = runner.invoke(main_group, ['convert', 'tests/data/RGB.byte.tif', outputname, '--scale-ratio', '0.5'])
assert (result.exit_code == 0)
with rasterio.open(outputname) as src:
for band in src.read... |
def test_interactive_with_file_dependency(tester: CommandTester, repo: TestRepository, source_dir: Path, fixture_dir: FixtureDirGetter) -> None:
repo.add_package(get_package('pendulum', '2.0.0'))
repo.add_package(get_package('pytest', '3.6.0'))
demo = (fixture_dir('distributions') / 'demo-0.1.0-py2.py3-none... |
def dfs_visit(node: PackageNode, back_edges: dict[(DFSNodeID, list[PackageNode])], visited: set[DFSNodeID], sorted_nodes: list[PackageNode]) -> None:
if (node.id in visited):
return
visited.add(node.id)
for neighbor in node.reachable():
back_edges[neighbor.id].append(node)
dfs_visit(... |
class TestRunner(InferenceRunner):
def __init__(self, test_cfg, inference_cfg, base_cfg=None):
super().__init__(inference_cfg, base_cfg)
self.test_dataloader = self._build_dataloader(test_cfg['data'])
extra_data = (len(self.test_dataloader.dataset) % self.world_size)
self.test_exclud... |
class OptionalActions():
def __init__(self, args, input_images, alignments):
logger.debug('Initializing %s', self.__class__.__name__)
self.args = args
self.input_images = input_images
self.alignments = alignments
self.remove_skipped_faces()
logger.debug('Initialized %... |
class ucred_t(ctypes.Structure):
class cr_entry(ctypes.Structure):
_fields_ = (('tqe_next', POINTER64), ('tqe_prev', POINTER64))
class posix_cred_t(ctypes.Structure):
_fields_ = (('cr_uid', ctypes.c_uint32), ('cr_ruid', ctypes.c_uint32), ('cr_svuid', ctypes.c_uint32), ('cr_ngroups', ctypes.c_sho... |
class QtileEventLoopPolicy(asyncio.DefaultEventLoopPolicy):
def __init__(self, qtile: Qtile) -> None:
asyncio.DefaultEventLoopPolicy.__init__(self)
self.qtile = qtile
def get_event_loop(self) -> asyncio.AbstractEventLoop:
if isinstance(self.qtile._eventloop, asyncio.AbstractEventLoop):
... |
class YInflictedDamageMixin():
def _getDamagePerKey(self, src, time):
if (time is None):
raise ValueError
return self._getTimeCacheDataPoint(src=src, time=time)
def _prepareTimeCache(self, src, maxTime):
self.graph._timeCache.prepareDmgData(src=src, maxTime=maxTime)
def _... |
class nyudepthv2(BaseDataset):
def __init__(self, data_path, filenames_path='./dataset/filenames/', is_train=True, crop_size=(448, 576), scale_size=None):
super().__init__(crop_size)
if (crop_size[0] > 480):
scale_size = (int(((crop_size[0] * 640) / 480)), crop_size[0])
self.scal... |
.parametrize('version, part, expected', [('1.0.4-rc.1', 'major', '2.0.0'), ('1.1.0-rc.1', 'major', '2.0.0'), ('1.1.4-rc.1', 'major', '2.0.0'), ('1.2.3', 'major', '2.0.0'), ('1.0.0-rc.1', 'major', '1.0.0'), ('0.2.0-rc.1', 'minor', '0.2.0'), ('0.2.5-rc.1', 'minor', '0.3.0'), ('1.3.1', 'minor', '1.4.0'), ('1.3.2', 'patch'... |
def get_norm_layer(opt, norm_type='instance'):
def get_out_channel(layer):
if hasattr(layer, 'out_channels'):
return getattr(layer, 'out_channels')
return layer.weight.size(0)
def add_norm_layer(layer, opt):
nonlocal norm_type
if norm_type.startswith('spectral'):
... |
()
def session_api(skip_qtbot):
network_client = MagicMock()
network_client.server_call = AsyncMock()
root = QtWidgets.QWidget()
skip_qtbot.addWidget(root)
api = MultiplayerSessionApi(network_client, 1234)
api.logger.setLevel(logging.DEBUG)
api.widget_root = root
return api |
def get_temporary_copy(path: Union[(str, Path)]) -> Path:
path = env.get_path(path)
assert ((not path.is_dir()) and (not path.is_symlink()))
tmp_path = path.with_name((((path.stem + '___') + str(uuid.uuid4()).replace('-', '')) + path.suffix))
shutil.copyfile(path, tmp_path)
atexit.register((lambda :... |
def read_and_store_bindings(f: Callable, bindings: Dict[(str, type)]) -> None:
function_bindings = (getattr(f, '__bindings__', None) or {})
if (function_bindings == 'deferred'):
function_bindings = {}
merged_bindings = dict(function_bindings, **bindings)
if hasattr(f, '__func__'):
f = ca... |
def test_handler_bad_request(api_gw_url):
body_str = json.dumps({'order_item_count': 5})
response = requests.post(api_gw_url, data=body_str)
assert (response.status_code == HTTPStatus.BAD_REQUEST)
body_dict = json.loads(response.text)
assert (body_dict == {'error': 'invalid input'}) |
class TestSources():
def test_default(self, isolation):
builder = MockBuilder(str(isolation))
assert (builder.config.sources == builder.config.sources == {})
assert (builder.config.get_distribution_path(pjoin('src', 'foo', 'bar.py')) == pjoin('src', 'foo', 'bar.py'))
def test_global_inva... |
class BinaryBinnedAUROC(Metric[Tuple[(torch.Tensor, torch.Tensor)]]):
def __init__(self: TBinaryBinnedAUROC, *, num_tasks: int=1, threshold: Union[(int, List[float], torch.Tensor)]=DEFAULT_NUM_THRESHOLD, device: Optional[torch.device]=None) -> None:
super().__init__(device=device)
threshold = _creat... |
_flags(compute_test_value='raise')
def test_mvnormal_ShapeFeature():
M_pt = iscalar('M')
M_pt.tag.test_value = 2
d_rv = multivariate_normal(pt.ones((M_pt,)), pt.eye(M_pt), size=2)
fg = FunctionGraph([i for i in graph_inputs([d_rv]) if (not isinstance(i, Constant))], [d_rv], clone=False, features=[ShapeF... |
def print_args(args):
args_verbosity = getattr(args, 'args_verbosity', 1)
if (args_verbosity == 2):
args_sorted = sorted(vars(args).items())
for (name, value) in args_sorted:
print(f'{name}={value}')
elif (args_verbosity == 1):
print(args)
elif (args_verbosity == 0):
... |
class TestBotCommandScopeWithoutRequest():
def test_slot_behaviour(self, bot_command_scope):
for attr in bot_command_scope.__slots__:
assert (getattr(bot_command_scope, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(bot_command_scope)) == len(set(mro_slots(bot_c... |
.parametrize('dtype', ['u1', 'int64', 'float32', 'float64'])
def test_tofile(tmp_path, xp, dtype):
filepath = str((tmp_path / 'test_tofile'))
src = xp.arange(100, dtype=dtype)
tofile(src, filepath)
dst = xp.fromfile(filepath, dtype=dtype)
xp.testing.assert_array_equal(src, dst)
tofile(src[::2], ... |
def reverse_by_epsilon(forward_process, predicted_noise, x, t):
fs = forward_process.forward_schedule
exs = fs.extract(t, x.shape)
betas_t = exs['betas']
sqrt_one_minus_alphas_cumprod_t = exs['sqrt_one_minus_alphas_cumprod']
sqrt_recip_alphas_t = exs['sqrt_recip_alphas']
posterior_variance_t = e... |
def anchor(parser, token):
bits = [b.strip('"\'') for b in token.split_contents()]
if (len(bits) < 2):
raise template.TemplateSyntaxError('anchor tag takes at least 1 argument')
try:
title = bits[2]
except IndexError:
title = bits[1].capitalize()
return SortAnchorNode(bits[1]... |
.parametrize('method_name', ['waitExposed', 'waitActive'])
def test_wait_window_propagates_other_exception(method_name, qtbot):
method = getattr(qtbot, method_name)
widget = qt_api.QtWidgets.QWidget()
qtbot.add_widget(widget)
with pytest.raises(ValueError, match='some other error'):
with method(... |
def test_delitem() -> None:
d: Dict[(str, object)] = {'x': 1}
monkeypatch = MonkeyPatch()
monkeypatch.delitem(d, 'x')
assert ('x' not in d)
monkeypatch.delitem(d, 'y', raising=False)
pytest.raises(KeyError, monkeypatch.delitem, d, 'y')
assert (not d)
monkeypatch.setitem(d, 'y', 1700)
... |
class LeNet(nn.Module):
def __init__(self):
super(LeNet, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5, stride=1, padding=2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(400, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)
self.T_rev... |
def ptb_raw_data(data_path=None):
train_path = os.path.join(data_path, 'ptb.train.txt')
valid_path = os.path.join(data_path, 'ptb.valid.txt')
test_path = os.path.join(data_path, 'ptb.test.txt')
(word_to_id, _) = _build_vocab(train_path)
train_data = _file_to_word_ids(train_path, word_to_id)
vali... |
class MockCapsNumLockIndicator():
CalledProcessError = None
info: List[List[str]] = []
is_error = False
index = 0
def reset(cls):
cls.info = [['Keyboard Control:', ' auto repeat: on key click percent: 0 LED mask: ', ' XKB indicators:', ' 00: Caps Lock: off 01: Num Lock: ... |
def test_setup_cfg_2line_description(tmpfolder):
(_, opts) = actions.get_default_options({}, {'project_path': tmpfolder})
opts['description'] = '2 line\ndescription'
text = templates.setup_cfg(opts)
setup_cfg = ConfigParser()
setup_cfg.read_string(text)
assert (setup_cfg['metadata']['description... |
def patch(cls):
if hasattr(cls, '__orig__init__'):
return
cls.__orig__init__ = cls.__init__
def patched_init(self, form_, *args, **kwargs):
form = kwargs.pop('form', None)
cls.__orig__init__(self, form_, *args, **kwargs)
if form:
self.attrs['form'] = form
cls.... |
class MetaCUB(CUB):
def __init__(self, args, partition='base', train_transform=None, test_transform=None, fix_seed=True):
super(MetaCUB, self).__init__(args, partition)
self.fix_seed = fix_seed
self.n_ways = args.n_ways
self.n_shots = args.n_shots
self.n_queries = args.n_quer... |
class TestPrinting(TestCase):
def print_to_string(self, formula):
return formula.to_smtlib(daggify=False)
def test_real(self):
f = Plus([Real(1), Symbol('x', REAL), Symbol('y', REAL)])
self.assertEqual(f.to_smtlib(daggify=False), '(+ 1.0 x y)')
self.assertEqual(f.to_smtlib(daggif... |
def iterate_minibatches_generic(input_lst=None, batchsize=None, shuffle=False):
if (batchsize is None):
batchsize = len(input_lst[0])
assert all(((len(x) == len(input_lst[0])) for x in input_lst))
if shuffle:
indices = np.arange(len(input_lst[0]))
np.random.shuffle(indices)
for s... |
def test_hook_auto_num_workers_arg(pytester: pytest.Pytester, monkeypatch: pytest.MonkeyPatch) -> None:
from xdist.plugin import pytest_cmdline_main as check_options
pytester.makeconftest("\n def pytest_xdist_auto_num_workers(config):\n if config.option.numprocesses == 'auto':\n ... |
def parse_bool(arg):
if isinstance(arg, bool):
return arg
if (arg is None):
return False
if (arg.lower() in ['1', 'true', 't', 'yes', 'y']):
return True
if (arg.lower() in ['0', 'false', 'f', 'no', 'n', 'none', 'null']):
return False
raise ValueError(f'`{arg}` cannot ... |
def compare_cfg(cfg_main, cfg_secondary, field_name, strict=False):
(main_val, secondary_val) = (cfg_main, cfg_secondary)
for f in field_name.split('.'):
main_val = main_val[f]
secondary_val = secondary_val[f]
if (main_val != secondary_val):
if strict:
raise ValueError(f"... |
def test_PVSystem_sapm_celltemp_kwargs(mocker):
temp_model_params = temperature.TEMPERATURE_MODEL_PARAMETERS['sapm']['open_rack_glass_glass']
system = pvsystem.PVSystem(temperature_model_parameters=temp_model_params)
mocker.spy(temperature, 'sapm_cell')
temps = 25
irrads = 1000
winds = 1
out... |
def masked_cross_entropy(a, b, mask):
b_c = torch.nn.functional.one_hot(b, num_classes=a.shape[(- 1)])
a_c = F.log_softmax(a, dim=2)
loss = ((- a_c) * b_c).sum(axis=2)
non_zero_elements = sum_flat(mask)
loss = sum_flat((loss * mask.float()))
loss = (loss / (non_zero_elements + 0.0001))
retur... |
def true_and_pred(out, label_ids, label_mask):
tplist = []
outputs = np.argmax(out, axis=2)
for i in range(len(label_ids)):
trues = []
preds = []
for (true, pred, mask) in zip(label_ids[i], outputs[i], label_mask[i]):
if mask:
trues.append(true)
... |
def test_load_nist_vectors():
vector_data = textwrap.dedent('\n # CAVS 11.1\n # Config info for aes_values\n # AESVS GFSbox test data for CBC\n # State : Encrypt and Decrypt\n # Key Length : 128\n # Generated on Fri Apr 22 15:11:33 2011\n\n [ENCRYPT]\n\n COUNT = 0\n KEY = \n IV = \n ... |
class BNBeforeConvTranspose(torch.nn.Module):
def __init__(self, padding=0, stride=1, dilation=1, groups=1, output_padding=0):
super(BNBeforeConvTranspose, self).__init__()
self.conv1 = torch.nn.Conv2d(10, 10, 3, bias=False)
self.relu1 = torch.nn.ReLU()
self.bn1 = torch.nn.BatchNorm2... |
def test_update_grant(graphql_client, user, conference_factory, grant_factory):
graphql_client.force_login(user)
conference = conference_factory(active_grants=True)
grant = grant_factory(conference=conference, gender='female', user_id=user.id)
response = _update_grant(graphql_client, grant, name='Marcot... |
def collect_art_info(root_path, split, ratio, print_every=1000):
annotation_path = osp.join(root_path, 'annotations/train_labels.json')
if (not osp.exists(annotation_path)):
raise Exception(f'{annotation_path} not exists, please check and try again.')
annotation = mmcv.load(annotation_path)
img_... |
class Describe_BaseHeaderFooter():
.parametrize(('has_definition', 'expected_value'), [(False, True), (True, False)])
def it_knows_when_its_linked_to_the_previous_header_or_footer(self, has_definition: bool, expected_value: bool, _has_definition_prop_: Mock):
_has_definition_prop_.return_value = has_def... |
def test_dump_version_doesnt_bail_on_value_error(tmp_path: Path) -> None:
write_to = 'VERSION'
version = str(VERSIONS['exact'].tag)
scm_version = meta(VERSIONS['exact'].tag, config=c)
with pytest.raises(ValueError, match='^bad file format:'):
dump_version(tmp_path, version, write_to, scm_version... |
class Pizza(ABC):
name: str
dough: str
sauce: str
toppings: List[str]
def getName(self) -> str:
return self.name
def prepare(self) -> None:
print(f'Preparing {self.name}')
def bake(self) -> None:
print(f'Baking {self.name}')
def cut(self) -> None:
print(f'... |
class ConfigWithFiles(Fixture):
def new_config_dir(self):
return temp_dir()
def new_config_bootstrap_file(self):
contents = ("\nreahlsystem.root_egg = '%s'\nreahlsystem.connection_uri = None\nreahlsystem.debug = False\n" % self.root_egg_name)
return self.new_config_file(filename='reahl.c... |
class FakeCounter():
def __init__(self, batch: FakeBatch, name: str, tags: Dict[(str, Any)]):
self.batch = batch
self.name = name
self.tags = tags
def increment(self, delta: float=1.0, sample_rate: float=1.0) -> None:
self.send(delta, sample_rate)
def decrement(self, delta: f... |
class TerminusCopyCommand(sublime_plugin.TextCommand):
def run(self, edit):
view = self.view
if (not view.settings().get('terminus_view')):
return
text = ''
for s in view.sel():
if text:
text += '\n'
text += view.substr(s)
t... |
def convert_to_distributed_tensor(tensor: torch.Tensor) -> Tuple[(torch.Tensor, str)]:
orig_device = ('cpu' if (not tensor.is_cuda) else 'gpu')
if (torch.distributed.is_available() and (torch.distributed.get_backend() == torch.distributed.Backend.NCCL) and (not tensor.is_cuda)):
tensor = tensor.cuda()
... |
def gpg_command(*cmd, with_user_id=False, with_trustdb=False, quiet=True, minimum_version='2.1.15', log=None):
global gpg_exe, gpg_exe
if (not gpg_mode):
raise Exception('Attempt to use GPG before setting mode')
if (not gpg_exe):
try:
output = subprocess.check_output(('gpg2', '--... |
def solve_metric_tsptw(distmat, timew, threads=0):
n = len(distmat)
M = timew[(0, 1)]
def subtour(edges):
unvisited = list(range(n))
cycle = range((n + 1))
while unvisited:
thiscycle = []
neighbors = unvisited
while neighbors:
curre... |
def pool_feature(data, num_proposals=100, num_sample_bins=3, pool_type='mean'):
if (len(data) == 1):
return np.concatenate(([data] * num_proposals))
x_range = list(range(len(data)))
f = scipy.interpolate.interp1d(x_range, data, axis=0)
eps = 0.0001
(start, end) = (eps, ((len(data) - 1) - eps... |
def test_paint_when_debug_shapes(view):
with patch('beeref.selection.commandline_args') as args_mock:
with patch('beeref.items.BeePixmapItem.draw_debug_shape') as m:
args_mock.debug_shapes = True
args_mock.debug_boundingrects = False
args_mock.debug_handles = False
... |
class SecurityContextTestCase(_GSSAPIKerberosTestCase):
def setUp(self):
super(SecurityContextTestCase, self).setUp()
gssctx.SecurityContext.__DEFER_STEP_ERRORS__ = False
self.client_name = gssnames.Name(self.USER_PRINC)
self.client_creds = gsscreds.Credentials(name=None, usage='init... |
def test_concise_attrib_metadata() -> None:
class A():
x: datetime.datetime = desert.ib(marshmallow.fields.NaiveDateTime(), metadata={'foo': 1})
timestring = '2019-10-21T10:25:00'
dt = datetime.datetime(year=2019, month=10, day=21, hour=10, minute=25, second=0)
schema = desert.schema(A)
asse... |
class ElementInfo(object):
def __repr__(self):
return '<{0}, {1}>'.format(self.__str__(), self.handle)
def __str__(self):
module = self.__class__.__module__
module = module[(module.rfind('.') + 1):]
type_name = ((module + '.') + self.__class__.__name__)
return "{0} - '{1}... |
_REGISTRY.register()
def resnet18_ms_l12(pretrained=True, **kwargs):
from dassl.modeling.ops import MixStyle
model = ResNet(block=BasicBlock, layers=[2, 2, 2, 2], ms_class=MixStyle, ms_layers=['layer1', 'layer2'])
if pretrained:
init_pretrained_weights(model, model_urls['resnet18'])
return model |
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