code stringlengths 281 23.7M |
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def dataloader():
class DataLoader():
def __init__(self, batch_size: int):
self.batch_size = batch_size
def __iter__(self):
dummy_input = np.random.rand(1, 3, 32, 32).astype(np.float32)
(yield dummy_input)
def __len__(self):
return 4
dummy_... |
class ZipReader(BaseReader):
reader_cache = dict()
def open(path):
zip_files = ZipReader.reader_cache
if (path not in zip_files):
zip_files[path] = zipfile.ZipFile(path, 'r')
return zip_files[path]
def close(path):
zip_files = ZipReader.reader_cache
zip_fi... |
('the reported {margin_side} margin is {inches} inches')
def then_the_reported_margin_is_inches(context: Context, margin_side: str, inches: str):
prop_name = {'left': 'left_margin', 'right': 'right_margin', 'top': 'top_margin', 'bottom': 'bottom_margin', 'gutter': 'gutter', 'header': 'header_distance', 'footer': 'f... |
def is_sat(formula, solver_name=None, logic=None, portfolio=None):
env = get_env()
if (formula not in env.formula_manager):
warnings.warn('Warning: Contextualizing formula during is_sat')
formula = env.formula_manager.normalize(formula)
return env.factory.is_sat(formula, solver_name=solver_n... |
class TestLayerSelector(unittest.TestCase):
def test_select_all_conv_layers(self):
if (version.parse(tf.version.VERSION) >= version.parse('2.00')):
tf.keras.backend.clear_session()
model = get_model()
conv1_op = model.layers[1]
conv2_op = model.layers[2]
... |
.parametrize('perturb_prob', [1.0, pytest.param(0.0, marks=pytest.mark.xfail)])
def test_perturbation_is_applied(perturb_prob: float, dmg: LocalDataManager, cfg: dict, zarr_dataset: ChunkedDataset) -> None:
rasterizer = build_rasterizer(cfg, dmg)
dataset = EgoDataset(cfg, zarr_dataset, rasterizer, None)
dat... |
class WriteToConn():
def __init__(self, server: IPCBase, output_key: str='stdout') -> None:
self.server = server
self.output_key = output_key
def write(self, output: str) -> int:
resp: dict[(str, Any)] = {}
resp[self.output_key] = output
send(self.server, resp)
re... |
def next_start_segment(str, is_segment):
str = ''.join(str)
result = []
for start in mark_start_segment_index(str, is_segment):
result[len(result):start] = [start for x in range((start - len(result)))]
result[len(result):len(str)] = [len(str) for x in range(((len(str) - len(result)) + 1))]
r... |
def main():
(fic_ids, csv_out, headers, restart, is_csv, only_first_chap, lang, include_bookmarks, metadata_only) = get_args()
os.chdir(os.getcwd())
output_directory = os.path.dirname(csv_out)
print(output_directory)
if (output_directory and (not os.path.isdir(output_directory))):
print(('Cr... |
def _worker_shared_memory(index, env_fn, pipe, parent_pipe, shared_memory, error_queue):
assert (shared_memory is not None)
env = env_fn()
observation_space = env.observation_space
parent_pipe.close()
try:
while True:
(command, data) = pipe.recv()
if (command == 'rese... |
class CoordAtt(nn.Module):
def __init__(self, inp, oup, reduction=32):
super(CoordAtt, self).__init__()
self.pool_h = nn.AdaptiveAvgPool2d((None, 1))
self.pool_w = nn.AdaptiveAvgPool2d((1, None))
mip = max(8, (inp // reduction))
self.conv1 = nn.Conv2d(inp, mip, kernel_size=1,... |
def skipgram_cmn(filename, min_cnt, max_vocab, n_embedding, n_window, word_list=None, word_level=True):
n_worker = multiprocessing.cpu_count()
logger.info(("This machine has %d processors. We'll use %d of them" % (n_worker, n_worker)))
model = gensim.models.Word2Vec(min_count=min_cnt, workers=n_worker, size... |
def generate_from_asin_reg(docs: List[List[str]], samples_per_asin=3):
negative_pairs = []
for doc in docs:
if (((len(doc) * (len(doc) - 1)) / 2) < samples_per_asin):
continue
pairs = utils.Rnd.random_pairs(doc, samples_per_asin)
for pair in pairs:
negative_pairs.... |
class EsmTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ['input_ids', 'attention_mask']
def __init__(self, vocab_file, **kwargs):
... |
class SbmlExporter(Exporter):
def __init__(self, *args, **kwargs):
if (not libsbml):
raise ImportError('The SbmlExporter requires the libsbml python package')
super(SbmlExporter, self).__init__(*args, **kwargs)
def _sympy_to_sbmlast(self, sympy_expr):
return _xml_to_ast(MathM... |
class Templates(object):
def __init__(self, templates=[], finalized=False):
self.templates = templates
self.template_id = len(templates)
self.finalized = finalized
def from_pickle(cls, path):
templates = read_pickle(path)
return cls(templates=templates, finalized=True)
... |
class SUMMA_CrossEntropy(torch.autograd.Function):
def forward(ctx, _vocab_parallel_logits, target, vocab_start, vocab_end):
logits_max = torch.max(_vocab_parallel_logits, dim=(- 1))[0]
torch.distributed.all_reduce(logits_max, op=torch.distributed.ReduceOp.MAX, group=get_summa_row_group())
v... |
def _convert_cropping(inexpr, keras_layer, _):
_check_data_format(keras_layer)
crop_type = type(keras_layer).__name__
if (crop_type == 'Cropping2D'):
(_, in_h, in_w, _) = keras_layer.input_shape
((crop_t, crop_b), (crop_l, crop_r)) = keras_layer.cropping
else:
raise tvm.error.OpN... |
_ordering
class Ticker(metaclass=ABCMeta):
def __init__(self, ticker: str, security_type: SecurityType, point_value: int):
self.ticker = ticker
self.security_type = security_type
self.point_value = point_value
self._name = ticker
self.logger = qf_logger.getChild(self.__class_... |
def Gtrain(train_loader, model, optimizer, criterion=nn.MSELoss()):
model.train()
loss_all = 0
criterion = criterion
for data in train_loader:
data.to(device)
optimizer.zero_grad()
out = model(data.x, data.edge_index, data.edge_attr, data.batch)
loss = criterion(out, data... |
class Effect7058(BaseEffect):
runTime = 'early'
type = ('projected', 'passive', 'gang')
def handler(fit, beacon, context, projectionRange, **kwargs):
for x in range(1, 3):
if beacon.getModifiedItemAttr('warfareBuff{}ID'.format(x)):
value = beacon.getModifiedItemAttr('warf... |
class BPRMF(object):
def __init__(self, data_config, pretrain_data, args):
self.model_type = 'mf'
self.pretrain_data = pretrain_data
self.n_users = data_config['n_users']
self.n_items = data_config['n_items']
self.lr = args.lr
self.emb_dim = args.embed_size
se... |
((simple_typed_classes(min_attrs=1) | simple_typed_dataclasses(min_attrs=1)), data())
def test_renaming(cl_and_vals, data):
converter = Converter()
(cl, vals, kwargs) = cl_and_vals
attrs = fields(cl)
to_replace = data.draw(sampled_from(attrs))
u_fn = make_dict_unstructure_fn(cl, converter, **{to_rep... |
class OpenTests():
def test_open_binary(self):
target = (resources.files(self.data) / 'binary.file')
with target.open('rb') as fp:
result = fp.read()
self.assertEqual(result, b'\x00\x01\x02\x03')
def test_open_text_default_encoding(self):
target = (resources.files... |
def split_schrodinger_graph_potentials(schrodinger_result, trim_levels_beyond=0.01, linewidth=1, scale=0.3, suppress_invert=False, probability_density=False, wfalpha=0.8, potentialalpha=0.8, **kwargs):
defaults = {'step': 0.002, 'margin': 0.02, 'pdf': False, 'show': False, 'dpi': 100, 'fontsize': 12, 'figsize': (7,... |
class TestArchiveOffers(TestCase):
def setUp(self):
self.out = io.StringIO()
self.err = io.StringIO()
def test_archive_offers_errors(self):
with self.assertRaises(management.CommandError):
management.call_command('archive_offers', '-s', 'not-valid-date', stdout=self.out, stde... |
def save_weights(G, D, M, state_dict, weights_root, experiment_name, name_suffix=None, G_ema=None):
root = '/'.join([weights_root, experiment_name])
if (not os.path.exists(root)):
os.mkdir(root)
if name_suffix:
print(('Saving weights to %s/%s...' % (root, name_suffix)))
else:
pri... |
def initializeModel(model):
model.setTable('employee')
model.setEditStrategy(QSqlTableModel.OnManualSubmit)
model.setRelation(2, QSqlRelation('city', 'id', 'name'))
model.setRelation(3, QSqlRelation('country', 'id', 'name'))
model.setHeaderData(0, Qt.Horizontal, 'ID')
model.setHeaderData(1, Qt.H... |
class DebuggingRegexLexer(ExtendedRegexLexer):
def get_tokens_unprocessed(self, text, stack=('root',)):
tokendefs = self._tokens
self.ctx = ctx = LexerContext(text, 0)
ctx.stack = list(stack)
statetokens = tokendefs[ctx.stack[(- 1)]]
while 1:
for (rexmatch, action... |
class MenuWrapperTests(unittest.TestCase):
def setUp(self):
Timings.defaults()
self.app = Application()
self.app.start('Notepad.exe')
self.dlg = self.app.Notepad
def tearDown(self):
self.app.kill()
def testInvalidHandle(self):
pass
def testItemCount(self):... |
('pyorbital.version.get_versions', return_value=dict([('version', '1.9.1+1.some-futur.dirty'), ('full-revisionid', 'some-future-git-version-hash'), ('dirty', True), ('error', None), ('date', '2023-01-20T09:37:30+0100')]))
def test_get_config_path_ppp_config_set_but_not_pyorbital_future(mock, caplog, monkeypatch):
m... |
class MarketImpactTestCase(WithCreateBarData, ZiplineTestCase):
ASSET_FINDER_EQUITY_SIDS = (1,)
def make_equity_minute_bar_data(cls):
trading_calendar = cls.trading_calendars[Equity]
return create_minute_bar_data(trading_calendar.minutes_for_sessions_in_range(cls.equity_minute_bar_days[0], cls.e... |
.parametrize('GET_query', GET_queries)
def test_set_context_querystring_with_filter_and_page(GET_query):
querydict = QueryDict(GET_query)
filter = ProjectFilter(querydict)
context = {'filter': filter}
context = set_context_querystring_with_filter_and_page(context)
if (('page' in GET_query) and ('tit... |
def build_coordinator(hass, api):
timeout = (BASE_TIMEOUT + (len(api.things) * 2))
async def async_update_data():
try:
async with async_timeout.timeout(timeout):
(await hass.async_add_executor_job(api.refresh_status))
hass.data[DOMAIN][UPDATED_DATA] = api.get_... |
def main():
initial_risk = 0.03
start_date = str_to_date('2016-01-01')
end_date = str_to_date('2017-12-31')
session_builder = container.resolve(BacktestTradingSessionBuilder)
session_builder.set_data_provider(daily_data_provider)
session_builder.set_backtest_name('Moving Average Alpha Model Back... |
class ItemStatsContainer(wx.Panel):
def __init__(self, parent, stuff, item, context=None):
wx.Panel.__init__(self, parent)
sMkt = Market.getInstance()
mainSizer = wx.BoxSizer(wx.VERTICAL)
self.nbContainer = wx.Notebook(self, wx.ID_ANY, wx.DefaultPosition, wx.DefaultSize, 0)
m... |
def pattern_exists(ordered_ops: List[Op], pattern: List[str]) -> Optional[List[MhaInfo]]:
mha_modules_info = []
sliding_window = deque(maxlen=len(pattern))
for (index, op) in enumerate(ordered_ops):
sliding_window.append(op)
sliced_pattern = [op.type for op in sliding_window]
if (sli... |
class Graph():
suppress_show: bool = False
plotted = 0
def __init__(self, *data: Any, **options: Any) -> None:
self.axis: Any = None
self.options = copy(graph_defaults)
self.options.update(options)
self.data = list(flatten(data))
self.extra_artists: List = []
... |
class AnnualVirtualStorage(VirtualStorage):
def __init__(self, *args, **kwargs):
self.reset_day = kwargs.pop('reset_day', 1)
self.reset_month = kwargs.pop('reset_month', 1)
self.reset_to_initial_volume = kwargs.pop('reset_to_initial_volume', False)
self._last_reset_year = None
... |
class TableConnection():
def __init__(self, table_name: str, region: Optional[str]=None, host: Optional[str]=None, connect_timeout_seconds: Optional[float]=None, read_timeout_seconds: Optional[float]=None, max_retry_attempts: Optional[int]=None, max_pool_connections: Optional[int]=None, extra_headers: Optional[Mapp... |
class ExactSumConstraint(Constraint):
def __init__(self, exactsum: Union[(int, float)], multipliers: Optional[Sequence]=None):
self._exactsum = exactsum
self._multipliers = multipliers
def preProcess(self, variables: Sequence, domains: dict, constraints: List[tuple], vconstraints: dict):
... |
def damp(sys, doprint=True):
(wn, zeta, poles) = sys.damp()
if doprint:
print(' Eigenvalue (pole) Damping Frequency')
for (p, z, w) in zip(poles, zeta, wn):
if (abs(p.imag) < 1e-12):
print((' %10.4g %10.4g %10.4g' % (p.real, 1.0, w)))
... |
def check_average_voxelization_3d(origin, pitch, points, values, gpu, **kwargs):
batch_indices = np.zeros((points.shape[0],), dtype=np.int32)
if (gpu >= 0):
cuda.get_device_from_id(gpu).use()
values = cuda.to_gpu(values)
points = cuda.to_gpu(points)
batch_indices = cuda.to_gpu(ba... |
def run_step(context):
logger.debug('started')
assert context, f'context must have value for {__name__}'
found_at_least_one = False
context.assert_key_has_value('tar', __name__)
tar_context = context.get_formatted('tar')
if tar_context.get('extract', None):
found_at_least_one = True
... |
def get_current_node_resource_key() -> str:
current_node_id = ray.get_runtime_context().get_node_id()
for node in ray.nodes():
if (node['NodeID'] == current_node_id):
for key in node['Resources'].keys():
if key.startswith('node:'):
return key
else:
... |
def main():
setup_default_logger()
argparser = get_argparser()
args = argparser.parse_args()
np.random.seed(1337)
neutralization_rxns = initialise_neutralisation_reactions()
smiles_dict = AllowedSmilesCharDictionary()
print('Preprocessing ChEMBL molecules...')
chembl_file = os.path.join(... |
class Cheng2020Anchor(JointAutoregressiveHierarchicalPriors):
def __init__(self, N=192, **kwargs):
super().__init__(N=N, M=N, **kwargs)
self.g_a = nn.Sequential(ResidualBlockWithStride(3, N, stride=2), ResidualBlock(N, N), ResidualBlockWithStride(N, N, stride=2), ResidualBlock(N, N), ResidualBlockWi... |
class TestInitialSOC(TestCase):
def test_interpolant_parameter_sets(self):
model = pybamm.lithium_ion.SPM()
params = ['Ai2020', 'Chen2020', 'Ecker2015', 'Marquis2019', 'Mohtat2020', 'OKane2022', 'ORegan2022']
for param in params:
with self.subTest(param=param):
pa... |
('pypyr.moduleloader.get_module')
(Step, 'invoke_step')
def test_while_max(mock_invoke, mock_moduleloader):
step = Step({'name': 'step1', 'while': {'max': 3}})
context = get_test_context()
original_len = len(context)
with patch_logger('pypyr.dsl', logging.INFO) as mock_logger_info:
step.run_step... |
class SponsorEmailNotificationTemplate(BaseEmailTemplate):
class Meta():
verbose_name = 'Sponsor Email Notification Template'
verbose_name_plural = 'Sponsor Email Notification Templates'
def get_email_context_data(self, **kwargs):
sponsorship = kwargs.pop('sponsorship')
context =... |
class s13_predefined_component_TestCase(pyuvm_unittest.pyuvm_TestCase):
def setUp(self):
super().setUp()
ConfigDB().clear()
uvm_root().clear_children()
def test_uvm_component_no_parent(self):
comp = uvm_component('test', None)
self.assertTrue(('test' in uvm_component.comp... |
def generate_score(args: argparse.Namespace, task: tasks.FairseqTask, dataset: data.FairseqDataset, models: List[FairseqEncoderDecoderModel], lang_pair: Optional[str]=None, modify_target_dict: bool=True):
if (lang_pair and (len(models) > 0) and isinstance(models[0], FairseqMultiModel)):
if isinstance(datase... |
def expression_check(prog):
instr_dict = {}
start_count = len(instr_dict.keys())
r2p = r2pipe.open(prog)
info = r2p.cmdj('ij')['bin']
esilcheck = ESILCheck(info['arch'], bits=info['bits'])
r2p.cmd('aa')
funcs = r2p.cmdj('aflj')
for func in funcs:
try:
instrs = r2p.cmd... |
def vectorised_transform_physical_point_to_index(image, point_array, rotate=True):
if rotate:
spacing = image.GetSpacing()[::(- 1)]
origin = image.GetOrigin()[::(- 1)]
else:
spacing = image.GetSpacing()
origin = image.GetOrigin()
return ((point_array - origin) / spacing) |
class FlowRegressor(nn.Module):
def __init__(self, npoint, use_instance_norm):
super(FlowRegressor, self).__init__()
self.sa1 = PointNetSetAbstraction(npoint=int((npoint / 4)), radius=None, nsample=32, in_channel=128, mlp=[128, 128, 128], group_all=False, use_instance_norm=use_instance_norm)
... |
def read_lst(lst_file):
with open(lst_file, 'r') as f:
lines = f.readlines()
lines = [l.strip() for l in lines]
data = {'name': [], 'face_id': [], 'ymin': [], 'xmin': [], 'xmax': [], 'ymax': [], 'confidence': [], 'emotion': []}
for l in lines:
l = l.split(' ')
data['name'].append... |
class UnsupportedClientError(BaseNetworkError):
def __init__(self, message: str):
self.message = message
def code(cls):
return 9
def detail(self):
return self.message
def from_detail(cls, detail) -> Self:
return cls(detail)
def __str__(self):
return f'Unsuppor... |
class Soquet():
binst: Union[(BloqInstance, DanglingT)]
reg: 'Register'
idx: Tuple[(int, ...)] = field(converter=_to_tuple, default=tuple())
def _check_idx(self, attribute, value):
if (len(value) != len(self.reg.shape)):
raise ValueError(f'Bad index shape {value} for {self.reg}.')
... |
class CamembertTokenizerFast(PreTrainedTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ['input_ids', 'attention_mask']
slow_tokenizer_class = CamembertToke... |
def step(engine, batch):
model.train()
if DUE:
likelihood.train()
optimizer.zero_grad()
(x, y) = batch
if torch.cuda.is_available():
x = x.cuda()
y = y.cuda()
y_pred = model(x)
if (not DUE):
y_pred.squeeze_()
loss = loss_fn(y_pred, y)
loss.backward()
... |
def G_logistic(G, D, latents, latent_labels=None, augment=None, ada_augment=None, ada_aug_p=0.6, ada_aug_step=(500 * 1000), *args, **kwargs):
fakes = G(latents, labels=latent_labels)
if (augment is not None):
(fakes, _) = augment(fakes, ada_aug_p)
fake_scores = D(fakes, labels=latent_labels).float()... |
.wrap
def _maybe_compute_kjt_to_jt_dict(stride: int, stride_per_key: List[int], keys: List[str], length_per_key: List[int], values: torch.Tensor, lengths: torch.Tensor, variable_stride_per_key: bool, weights: Optional[torch.Tensor], jt_dict: Optional[Dict[(str, JaggedTensor)]]) -> Dict[(str, JaggedTensor)]:
if (not... |
(all_backends)
def test_gmres_easy(backend):
xnp = get_xnp(backend)
dtype = xnp.float32
A = xnp.diag(xnp.array([3.0, 4.0, 5.0], dtype=dtype, device=None))
rhs = [[1], [1], [1]]
rhs = xnp.array(rhs, dtype=dtype, device=None)
soln = [[(1 / 3)], [(1 / 4)], [(1 / 5)]]
soln = xnp.array(soln, dtyp... |
class Vocab(collections.abc.Set):
def __init__(self, iterable, special_elems=(UNK, BOS, EOS)):
elements = list(special_elems)
elements.extend(iterable)
assert (len(elements) == len(set(elements)))
self.id_to_elem = {i: elem for (i, elem) in enumerate(elements)}
self.elem_to_i... |
_tokenizers
class LayoutLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = LayoutLMTokenizer
rust_tokenizer_class = LayoutLMTokenizerFast
test_rust_tokenizer = True
space_between_special_tokens = True
def setUp(self):
super().setUp()
vocab_tokens = ['[UNK]... |
def compute_knn(distance_matrix: np.array, k: int=100) -> Tuple[(np.array, np.array)]:
k += 1
k_i = distance_matrix.argpartition(k, axis=0)
k_d = np.take_along_axis(distance_matrix, k_i, axis=0)
sorted_indices = k_d.argsort(axis=0)
k_i_sorted = np.take_along_axis(k_i, sorted_indices, axis=0)[1:k]
... |
.parametrize('b_func, b_size', [(pt.matrix, (5, 1)), (pt.matrix, (5, 5)), (pt.vector, (5,))], ids=['b_col_vec', 'b_matrix', 'b_vec'])
.parametrize('lower', [True, False], ids=['lower=True', 'lower=False'])
.parametrize('trans', [0, 1, 2], ids=['trans=N', 'trans=C', 'trans=T'])
.parametrize('unit_diag', [True, False], i... |
.parametrize('mock_release_id', range(3))
.parametrize('prerelease', (True, False))
def test_create_or_update_release_when_create_succeeds(default_gitea_client, mock_release_id, prerelease):
tag = 'v1.0.0'
with mock.patch.object(default_gitea_client, 'create_release') as mock_create_release, mock.patch.object(d... |
def test_search_for_directory_setup_read_setup(provider: Provider, mocker: MockerFixture, fixture_dir: FixtureDirGetter) -> None:
mocker.patch('poetry.utils.env.EnvManager.get', return_value=MockEnv())
dependency = DirectoryDependency('demo', (((fixture_dir('git') / 'github.com') / 'demo') / 'demo'))
packag... |
def _save_zero_checkpoint(self, save_path: str, tag: str) -> None:
app_state = {'optimizer': self.optimizer, 'objects': StateDict(ds_config=self.config, ds_version=version)}
Snapshot.async_take(path=save_path, app_state=app_state)
if (self.global_rank == 0):
self._copy_recovery_script(save_path) |
def _temporal_scattered_matrix(H, psi0, n_emissions, c_ops, tlist, system_zero_state=None, construct_effective_hamiltonian=True):
T = len(tlist)
W = len(c_ops)
em_dims = max(n_emissions, 1)
phi_n = np.zeros(([(W * T)] * em_dims), dtype=complex)
if construct_effective_hamiltonian:
Heff = (Qob... |
def dependencies_in_sync(requirements: list[Requirement], sys_path: (list[str] | None)=None, environment: (dict[(str, str)] | None)=None) -> bool:
if (sys_path is None):
sys_path = sys.path
if (environment is None):
environment = default_environment()
installed_distributions = DistributionCa... |
class Loader(jinja2.BaseLoader):
def __init__(self, subdir: str) -> None:
self._subdir = subdir
def get_source(self, _env: jinja2.Environment, template: str) -> Tuple[(str, str, Callable[([], bool)])]:
path = os.path.join(self._subdir, template)
try:
source = resources.read_f... |
def get_all_hardware_grid_problems(device_graph: nx.Graph, central_qubit: cirq.GridQubit, n_instances: int, rs: np.random.RandomState):
all_hg_problems: Dict[(Tuple[(int, int)], HardwareGridProblem)] = {}
subgraphs = get_growing_subgraphs(device_graph=device_graph, central_qubit=central_qubit)
for n_qubits ... |
class save_smplx(nn.Module):
def __init__(self, config):
super().__init__()
self.config = config
self.device = torch.device(('cuda' if torch.cuda.is_available() else 'cpu'))
(self.vposer, _) = load_vposer(config.vposer_path, vp_model='snapshot')
self.vposer = self.vposer.to(s... |
class AsmCmdSolve(AsmCmdBase):
_id = 1
_menuText = QT_TRANSLATE_NOOP('asm3', 'Solve constraints')
_iconName = 'AssemblyWorkbench.svg'
_accel = 'A, S'
def Activated(cls):
from . import solver
FreeCAD.setActiveTransaction('Assembly solve')
logger.report('command "{}" exception'... |
def window_sumsquare(window, n_frames, hop_length=200, win_length=800, n_fft=800, dtype=np.float32, norm=None):
if (win_length is None):
win_length = n_fft
n = (n_fft + (hop_length * (n_frames - 1)))
x = np.zeros(n, dtype=dtype)
win_sq = get_window(window, win_length, fftbins=True)
win_sq = ... |
def test_shorthand_property():
model = Model()
node = Node(model, 'node')
for attr in ('min_flow', 'max_flow', 'cost', 'conversion_factor'):
setattr(node, attr, 123)
if (attr == 'conversion_factor'):
with pytest.raises(ValueError):
setattr(node, attr, Parameter(mo... |
def test_jsonparse_no_json_raises():
context = Context({'jsonParse': {'a': 'b'}})
with pytest.raises(KeyNotInContextError) as err_info:
jsonparse.run_step(context)
assert (str(err_info.value) == "context['jsonParse']['json'] doesn't exist. It must exist for pypyr.steps.jsonparse.") |
class _cupy_channelizer_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, n_cha... |
def validate_component_args(func, *args, **kwargs):
signature = inspect.signature(func)
try:
signature.bind(*args, **kwargs)
except TypeError as e:
name = generate_obj_name(func)
raise ComponentParamError(f"Invalid args for '{name}'. {str(e).capitalize()}.") from e |
class PluginsListViewTestCase(TestCase):
fixtures = ['fixtures/styles.json', 'fixtures/auth.json', 'fixtures/simplemenu.json', 'fixtures/plugins.json']
def setUp(self):
pass
def test_plugins_list_view(self):
response = self.client.get(reverse('approved_plugins'))
self.assertEqual(res... |
def costFunctionDis1(outputStates, qnnArch):
state0 = qt.basis((2 ** qnnArch[(- 1)]), 0)
dims1 = [2 for i in range(qnnArch[(- 1)])]
dims2 = [1 for i in range(qnnArch[(- 1)])]
dims = [dims1, dims2]
state0.dims = dims
costSum = 0
if (len(outputStates) == 0):
return 1
for i in range... |
def load(file, file_format=None, file_client_args=None, **kwargs):
if isinstance(file, Path):
file = str(file)
if ((file_format is None) and is_str(file)):
file_format = file.split('.')[(- 1)]
if (file_format not in file_handlers):
raise TypeError(f'Unsupported format: {file_format}'... |
.skipif((not HAVE_DEPS_FOR_RESOURCE_ESTIMATES), reason='pyscf and/or jax not installed.')
def test_reiher_sf():
DE = 0.001
CHI = 10
N = 108
LAM = 4258.0
L = 200
output = sf.compute_cost(N, LAM, DE, L, CHI, stps=20000)
stps1 = output[0]
output = sf.compute_cost(N, LAM, DE, L, CHI, stps1)
... |
class LBHinge(nn.Module):
def __init__(self, error_metric=nn.MSELoss(), threshold=None, clip=None):
super().__init__()
self.error_metric = error_metric
self.threshold = (threshold if (threshold is not None) else (- 100))
self.clip = clip
def forward(self, prediction, label, targe... |
class pair():
def __init__(self, aval, bval, alabel=None, blabel=None):
self.alabel = alabel
self.blabel = blabel
self.aval = aval
self.bval = bval
def __add__(self, rhs):
self.aval += rhs.aval
self.bval += rhs.bval
return self
def __str__(self):
... |
def test_overriding_struct_hook(converter: BaseConverter) -> None:
from math import ceil
class A():
a: int
b: str
converter.register_structure_hook(A, make_dict_structure_fn(A, converter, a=override(struct_hook=(lambda v, _: ceil(v))), _cattrs_detailed_validation=converter.detailed_validatio... |
class TaskBatchNorm2d(nn.BatchNorm2d):
def __init__(self, num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, task='shared'):
super(TaskBatchNorm2d, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
self.task = ... |
def test_for_freevar_step(do_test):
a = CaseForFreeVarStepComp.DUT()
a._rtlir_test_ref = {'upblk': CombUpblk('upblk', [For(LoopVarDecl('i'), Number(0), Number(2), FreeVar('freevar_at_upblk', 1), [Assign([Attribute(Base(a), 'out')], Slice(Attribute(Base(a), 'in_'), Number(0), Number(8)), True)])])}
do_test(a... |
class BenchmarkAll(Application):
def __init__(self, config_filename, options):
config_filename = os.path.abspath(config_filename)
conf = parse_config(config_filename, 'compile_all')
super().__init__(conf, options)
self.config_filename = config_filename
self.safe_makedirs(self... |
(User)
class UserAdmin(admin.ModelAdmin):
def top_role_coloured(self, user: User) -> SafeString:
return format_html('<span style="color: {0}; font-weight: bold;">{1}</span>', f'#{user.top_role.colour:06X}', user.top_role.name)
top_role_coloured.short_description = 'Top Role'
def all_roles_coloured(s... |
(frozen=True)
class IdMaker():
__slots__ = ('argnames', 'parametersets', 'idfn', 'ids', 'config', 'nodeid', 'func_name')
argnames: Sequence[str]
parametersets: Sequence[ParameterSet]
idfn: Optional[Callable[([Any], Optional[object])]]
ids: Optional[Sequence[Optional[object]]]
config: Optional[Co... |
def define_numeric_word_range(names: str, from_: int, to_: int=None, step: int=1) -> pp.MatchFirst:
def define_numeric_word(nm: str, val: int):
return pp.CaselessKeyword(nm).add_parse_action((lambda : val))
names = names.split()
if (to_ is None):
to_ = from_
values = range(from_, (to_ + ... |
def prune_repo_by_creation_date(repo, policy_config, namespace, tag_page_limit=100):
policy_method = policy_config.get('method', None)
if (policy_method != AutoPruneMethod.CREATION_DATE.value):
raise InvalidNamespaceAutoPruneMethod(f'Expected prune method type {AutoPruneMethod.CREATION_DATE.value} but g... |
def test_kcut_equality(kcut_cause, kcut_effect):
other = KCut(Direction.CAUSE, KPartition(Part((0, 2), (0,)), Part((), (2,)), Part((3,), (3,))))
assert (kcut_cause == other)
assert (hash(kcut_cause) == hash(other))
assert (hash(kcut_cause) != hash(kcut_cause.partition))
assert (kcut_cause != kcut_ef... |
class F10_TestCase(FC6_TestCase):
def runTest(self):
parser = self.getParser('monitor')
self.assertEqual(issubclass(parser.__class__, DeprecatedCommand), True)
parser = parser._getParser()
self.assertIsNotNone(parser)
self.assertTrue((parser.description.find('deprecated:: Fed... |
def test_ae_forward():
model_cfg = dict(type='AssociativeEmbedding', pretrained=None, backbone=dict(type='ResNet', depth=18), keypoint_head=dict(type='AESimpleHead', in_channels=512, num_joints=17, num_deconv_layers=0, tag_per_joint=True, with_ae_loss=[True], extra=dict(final_conv_kernel=1), loss_keypoint=dict(type... |
def tool(*args: Union[(str, Callable)], return_direct: bool=False, args_schema: Optional[Type[BaseModel]]=None, infer_schema: bool=True) -> Callable:
def _make_with_name(tool_name: str) -> Callable:
def _make_tool(func: Callable) -> Tool:
assert func.__doc__, 'Function must have a docstring'
... |
def compute_tencrop(outputs, labels):
output_size = outputs.size()
outputs = outputs.view((output_size[0] / 10), 10, output_size[1])
outputs = outputs.sum(1).squeeze(1)
(_, pred) = outputs.topk(1, 1, True, True)
pred = pred.t()
top1_count = pred.eq(labels.data.view(1, (- 1)).expand_as(pred)).vie... |
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