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def test_base_recognizer():
cls_score = torch.rand(5, 400)
with pytest.raises(KeyError):
wrong_test_cfg = dict(clip='score')
recognizer = ExampleRecognizer(None, wrong_test_cfg)
recognizer.average_clip(cls_score)
with pytest.raises(ValueError):
wrong_test_cfg = dict(average_c... |
class Locker(BaseLocker):
def __init__(self, lock_path: Path) -> None:
self._lock = (lock_path / 'poetry.lock')
self._written_data = None
self._locked = False
self._lock_data = None
self._content_hash = self._get_content_hash()
def written_data(self) -> dict[(str, Any)]:
... |
def test_points_from_angles():
distance = [1]
elevation = [30]
azimuth = [45]
point1 = points_from_angles(distance=distance, elevation=elevation, azimuth=azimuth, is_degree=True)
assert (point1.shape == (1, 3))
assert (point1.dtype == np.float64)
point2 = points_from_angles(distance=distance... |
class FilePathValidator(Validator):
def validate(self, value):
if len(value.text):
if os.path.isfile(value.text):
return True
else:
raise ValidationError(message='File not found', cursor_position=len(value.text))
else:
return True |
class SimpleLayer(caffe.Layer):
def setup(self, bottom, top):
pass
def reshape(self, bottom, top):
top[0].reshape(*bottom[0].data.shape)
def forward(self, bottom, top):
top[0].data[...] = (10 * bottom[0].data)
def backward(self, top, propagate_down, bottom):
bottom[0].dif... |
def test_preserve_keys_order(pytester: Pytester) -> None:
from _pytest.cacheprovider import Cache
config = pytester.parseconfig()
cache = Cache.for_config(config, _ispytest=True)
cache.set('foo', {'z': 1, 'b': 2, 'a': 3, 'd': 10})
read_back = cache.get('foo', None)
assert (list(read_back.items()... |
def testPropEvo():
a = destroy(5)
H = (a.dag() * a)
U = Propagator([H, [(a + a.dag()), 'w*t']], args={'w': 1})
psi = (QobjEvo(U) basis(5, 4))
tlist = np.linspace(0, 1, 6)
psi_expected = sesolve([H, [(a + a.dag()), 'w*t']], basis(5, 4), tlist=tlist, args={'w': 1}).states
for (t, psi_t) in zi... |
def main(client, config):
wcs_df = benchmark(read_tables, config=config, compute_result=config['get_read_time'])
f_wcs_df = wcs_df.map_partitions(pre_repartition_task)
f_wcs_df = f_wcs_df.shuffle(on=['wcs_user_sk'])
grouped_df = f_wcs_df.map_partitions(reduction_function, q02_session_timeout_inSec)
... |
def test_lint():
assert_lints('%s', [])
assert_lints('%.1%', ['using % combined with optional specifiers does not make sense'])
assert_lints('%(a)s%s', ['cannot combine specifiers that require a mapping with those that do not'])
assert_lints('%(a)*d', ['cannot combine specifiers that require a mapping w... |
def evaluate(args, model, tokenizer, prefix=''):
(dataset, examples, features) = load_and_cache_examples(args, tokenizer, evaluate=True, output_examples=True)
if ((not os.path.exists(args.output_dir)) and (args.local_rank in [(- 1), 0])):
os.makedirs(args.output_dir)
args.eval_batch_size = (args.per... |
class Processor():
def __init__(self, arg):
self.arg = arg
self.save_arg()
if self.arg.random_fix:
self.rng = RandomState(seed=self.arg.random_seed)
self.device = GpuDataParallel()
self.recoder = Recorder(self.arg.work_dir, self.arg.print_log)
self.data_lo... |
def main(source, output, java, prefix_filter, exclude_filter, jars_list):
reports_dir = 'jacoco_reports_dir'
mkdir_p(reports_dir)
with tarfile.open(source) as tf:
def is_within_directory(directory, target):
abs_directory = os.path.abspath(directory)
abs_target = os.path.abspa... |
class NullSection(Section):
allLines = True
def __init__(self, *args, **kwargs):
Section.__init__(self, *args, **kwargs)
self.sectionOpen = kwargs.get('sectionOpen')
self._args = []
self._body = []
def handleHeader(self, lineno, args):
self._args = args
def handle... |
.parametrize('run_at, start, end, idle, with_warning', [('document-start', True, False, False, False), ('document-end', False, True, False, False), ('document-idle', False, False, True, False), ('', False, True, False, False), ('bla', False, True, False, True)])
def test_run_at(gm_manager, run_at, start, end, idle, wit... |
class ComPort(object):
def __init__(self, usb_device, start=True):
self.device = usb_device
self._isFTDI = False
self._rxinterval = 0.005
self._rxqueue = queue.Queue()
self._rxthread = None
self._rxactive = False
self.baudrate = 9600
self.parity = 0
... |
def set_initial_resolution(request: WSGIRequest) -> HttpResponse:
(value, response) = extract_value(request.POST)
resolution = tuple(map(int, value.split('x')))
resolution = cast(Tuple[(int, int)], resolution)
storage.put('initial_resolution', resolution)
_notify_settings_changed('adjust_screen')
... |
def test_multidim_register():
r = Register('my_reg', bitsize=1, shape=(2, 3), side=Side.RIGHT)
idxs = list(r.all_idxs())
assert (len(idxs) == (2 * 3))
assert (not (r.side & Side.LEFT))
assert (r.side & Side.THRU)
assert (r.total_bits() == (2 * 3))
assert (r.adjoint() == Register('my_reg', bi... |
class InteractionTask(AbstractData):
__slots__ = ['iid', 'input', 'structure', 'preset', 'output', 'data', 'title', 'description', 'plugin']
def __init__(self, iid=None, input=None, structure=None, preset=None, output=None, data=None, title=None, description=None, plugin=None):
self.iid = iid
se... |
class CSVDIRBundle():
def __init__(self, tframes=None, csvdir=None):
self.tframes = tframes
self.csvdir = csvdir
def ingest(self, environ, asset_db_writer, minute_bar_writer, daily_bar_writer, adjustment_writer, calendar, start_session, end_session, cache, show_progress, output_dir):
csv... |
_register
class MBIDMassager(Massager):
tags = ['musicbrainz_trackid', 'musicbrainz_albumid', 'musicbrainz_artistid', 'musicbrainz_albumartistid', 'musicbrainz_trmid', 'musicip_puid']
error = _('MusicBrainz IDs must be in UUID format.')
def validate(self, value):
value = value.encode('ascii', 'repla... |
def test_wr_As_wr_At_disjoint():
class Top(ComponentLevel3):
def construct(s):
s.A = Wire(Bits32)
def up_wr_As():
s.A[1:3] = Bits2(2)
def up_wr_At():
s.A[5:7] = Bits2(2)
def up_rd_A():
z = s.A
_test_model(Top... |
class LLTM(nn.Module):
def __init__(self, input_features, state_size):
super(LLTM, self).__init__()
self.input_features = input_features
self.state_size = state_size
self.weights = nn.Parameter(torch.Tensor((3 * state_size), (input_features + state_size)))
self.bias = nn.Para... |
class resnet_v1_101_fpn_dcn_rcnn_rep_noemb(Symbol):
def __init__(self):
self.shared_param_list = ['offset_p2', 'offset_p3', 'offset_p4', 'offset_p5', 'rpn_conv', 'rpn_cls_score', 'rpn_bbox_pred']
self.shared_param_dict = {}
for name in self.shared_param_list:
self.shared_param_di... |
def test_setting_list_option_completion(qtmodeltester, config_stub, configdata_stub, info):
model = configmodel.list_option(info=info)
model.set_pattern('')
qtmodeltester.check(model)
_check_completions(model, {'List options': [('completion.open_categories', 'Which categories to show (in which order) in... |
class FC4_TestCase(CommandTest):
command = 'mediacheck'
def runTest(self):
self.assert_parse('mediacheck', 'mediacheck\n')
self.assert_parse_error('mediacheck --cheese')
self.assert_parse_error('mediacheck --crackers=CRUNCHY')
self.assert_parse_error('mediacheck cheese crackers') |
class CometCallback(TrainerCallback):
def __init__(self):
if (not _has_comet):
raise RuntimeError('CometCallback requires comet-ml to be installed. Run `pip install comet-ml`.')
self._initialized = False
self._log_assets = False
def setup(self, args, state, model):
se... |
class AdvertisingIntegrationTests(BaseApiTest):
def setUp(self):
super().setUp()
self.user.publishers.add(self.publisher2)
self.publisher_group.publishers.add(self.publisher2)
self.page_url = '
def test_ad_view_and_tracking(self):
data = {'placements': self.placements, 'p... |
class TestDocumentDataRetrivalMethods(unittest.TestCase):
def test_get_method(self):
document = parse(USER_ONLY)
user = document.get('user')
expected_body = ['id = 1', "name = 'alex'"]
self.assertEqual(expected_body, user.body)
user = document.get('user', 1)
expected_... |
def parse_args():
special_args = [{'name': ['-b', '--backend'], 'choices': ['dask', 'explicit-comms', 'dask-noop'], 'default': 'dask', 'type': str, 'help': 'The backend to use.'}, {'name': ['-t', '--type'], 'choices': ['cpu', 'gpu'], 'default': 'gpu', 'type': str, 'help': 'Do merge with GPU or CPU dataframes'}, {'n... |
def crosses(shape, other):
if (not hasattr(shape, GEO_INTERFACE_ATTR)):
raise TypeError((SHAPE_TYPE_ERR % shape))
if (not hasattr(other, GEO_INTERFACE_ATTR)):
raise TypeError((SHAPE_TYPE_ERR % shape))
o = geom.shape(shape)
o2 = geom.shape(other)
return o.crosses(o2) |
class EvolutionSampler(BaseSampler):
def __init__(self, **kwargs):
super().__init__(**kwargs)
if (not hasattr(self, 'heads_share')):
self.heads_share = False
assert (self.heads_share == True), f'We need share heads for Block opeartion'
if (not hasattr(self, 'GPU_search'))... |
class GPT2TokenizerFast(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 = GPT2Tokenizer
... |
class TenluaVn(SimpleDownloader):
__name__ = 'TenluaVn'
__type__ = 'downloader'
__version__ = '0.04'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fallback to f... |
_REGISTRY.register()
class REDSRecurrentDataset(data.Dataset):
def __init__(self, opt):
super(REDSRecurrentDataset, self).__init__()
self.opt = opt
(self.gt_root, self.lq_root) = (Path(opt['dataroot_gt']), Path(opt['dataroot_lq']))
self.num_frame = opt['num_frame']
self.keys ... |
def conduit_draw_color(conduit):
if ('draw_color' in conduit.axes[0]):
return conduit.draw_color
fill = '#787882'
if (('MaxQPerc' in conduit) and (conduit.MaxQPerc >= 1)):
capacity = (conduit.MaxQ / conduit.MaxQPerc)
stress = (conduit.MaxQ / capacity)
fill = gradient_grey_red... |
.parametrize('basedirs, expected_basedirs, os_name', [(['foo', 'bar'], ['foo', 'bar'], 'posix'), (['foo:bar', 'foobar'], ['foo', 'bar', 'foobar'], 'posix'), (['foo:bar', 'foobar', 'one:two:three'], ['foo', 'bar', 'foobar', 'one', 'two', 'three'], 'posix'), (['foo:', ':bar'], ['foo', 'bar'], 'posix'), (['C:\\windows\\ra... |
def test_function_overloading():
assert (m.test_function() == 'test_function()')
assert (m.test_function(7) == 'test_function(7)')
assert (m.test_function(m.MyEnum.EFirstEntry) == 'test_function(enum=1)')
assert (m.test_function(m.MyEnum.ESecondEntry) == 'test_function(enum=2)')
assert (m.test_funct... |
.parametrize('tp', [str, *cond_list(HAS_PY_311, (lambda : [typing.LiteralString]))])
def test_str_loader_provider(strict_coercion, debug_trail, tp):
retort = Retort(strict_coercion=strict_coercion, debug_trail=debug_trail)
loader = retort.get_loader(tp)
assert (loader('foo') == 'foo')
if strict_coercion... |
def date2juldate(val: date) -> float:
f = (((12 * val.year) + val.month) - 22803)
fq = (f // 12)
fr = (f % 12)
dt = (((((fr * 153) + 302) // 5) + val.day) + ((fq * 1461) // 4))
if isinstance(val, datetime):
return (dt + ((val.hour + ((val.minute + ((val.second + (1e-06 * val.microsecond)) / ... |
class ApplyClassicalTest(Bloq):
def signature(self) -> 'Signature':
return Signature([Register('x', 1, shape=(5,)), Register('z', 1, shape=(5,), side=Side.RIGHT)])
def on_classical_vals(self, *, x: NDArray[np.uint8]) -> Dict[(str, NDArray[np.uint8])]:
const = np.array([1, 0, 1, 0, 1], dtype=np.u... |
class Uniform(BoundedContinuous):
rv_op = uniform
bound_args_indices = (3, 4)
def dist(cls, lower=0, upper=1, **kwargs):
lower = pt.as_tensor_variable(floatX(lower))
upper = pt.as_tensor_variable(floatX(upper))
return super().dist([lower, upper], **kwargs)
def moment(rv, size, lo... |
def caffenet(lmdb, batch_size=256, include_acc=False):
(data, label) = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2, transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
(conv1, relu1) = conv_relu(data, 11, 96, stride=4)
pool1 = max_pool(relu1, 3, stride=2)... |
class GroupAll(nn.Module):
def __init__(self, use_xyz=True):
super(GroupAll, self).__init__()
self.use_xyz = use_xyz
def forward(self, xyz, new_xyz, features=None):
grouped_xyz = xyz.transpose(1, 2).unsqueeze(2)
if (features is not None):
grouped_features = features.u... |
def main(args):
meta_path = os.path.abspath(args[0])
timeout_code = int(args[1])
subprocess.check_call(args[2:])
with open(meta_path) as f:
meta_info = json.loads(f.read())
if (meta_info['exit_code'] == timeout_code):
((print >> sys.stderr), meta_info['project'], 'crashed by ... |
class StereoDataset(BaseDataset):
def __init__(self, data_path, filenames_file, args, dataset, mode, ret_meta_info=False):
super(StereoDataset, self).__init__(data_path, filenames_file, dataset, mode, args.height, args.width)
assert (self.dataset in ['sceneflow', 'kitti', 'cityscapes'])
self... |
def test_check_python_script(capsys):
mmcv.utils.check_python_script('./tests/data/scripts/hello.py zz')
captured = capsys.readouterr().out
assert (captured == 'hello zz!\n')
mmcv.utils.check_python_script('./tests/data/scripts/hello.py agent')
captured = capsys.readouterr().out
assert (captured... |
.isolated
def test_build_raises_build_backend_exception(mocker, package_test_flit):
mocker.patch('build.ProjectBuilder.get_requires_for_build', side_effect=build.BuildBackendException(Exception('a')))
mocker.patch('build.env.DefaultIsolatedEnv.install')
msg = f"Backend operation failed: Exception('a'{(',' i... |
class Trainer():
def __init__(self, model: torch.nn.Module, train_data: DataLoader, optimizer: torch.optim.Optimizer, save_every: int, snapshot_path: str) -> None:
self.gpu_id = int(os.environ['LOCAL_RANK'])
self.model = model.to(self.gpu_id)
self.train_data = train_data
self.optimiz... |
class TestTrialUnittest():
def setup_class(cls):
cls.ut = pytest.importorskip('twisted.trial.unittest')
cls.ignore_unclosed_socket_warning = ('-W', 'always')
def test_trial_testcase_runtest_not_collected(self, pytester: Pytester) -> None:
pytester.makepyfile('\n from twisted.t... |
class DetectionModel(object):
__metaclass__ = ABCMeta
def __init__(self, num_classes):
self._num_classes = num_classes
self._groundtruth_lists = {}
def num_classes(self):
return self._num_classes
def groundtruth_lists(self, field):
if (field not in self._groundtruth_lists... |
def overlap_detection(radius_list, x, y, z, show_info=0):
overlapOrNot = 0
for ii in range(0, len(radius_list)):
for jj in range((ii + 1), len(radius_list)):
tempR1 = radius_list[ii][0]
tempR2 = radius_list[jj][0]
tempDis = ((((x[ii] - x[jj]) ** 2) + ((y[ii] - y[jj]) ... |
def test_progress_with_exception(workspace, consumer):
workspace._config.capabilities['window'] = {'workDoneProgress': True}
class DummyError(Exception):
pass
try:
with workspace.report_progress('some_title'):
raise DummyError('something')
except DummyError:
pass
... |
def test_read_only(tmpdir_cwd):
dist = Distribution(dict(script_name='setup.py', script_args=['build_py'], packages=['pkg'], package_data={'pkg': ['data.dat']}))
os.makedirs('pkg')
open('pkg/__init__.py', 'w').close()
open('pkg/data.dat', 'w').close()
os.chmod('pkg/__init__.py', stat.S_IREAD)
os... |
class BILSTMCRF(object):
def __init__(self, params: dict):
self.char_embedding = tf.Variable(np.load(params['embedding_path']), dtype=tf.float32, name='input_char_embedding')
self.word_embedding = tf.Variable(np.load(params['word_embedding_path']), dtype=tf.float32, name='input_word_embedding')
... |
class ForbiddenExtraKeysError(Exception):
def __init__(self, message: Optional[str], cl: Type, extra_fields: Set[str]) -> None:
self.cl = cl
self.extra_fields = extra_fields
cln = cl.__name__
super().__init__((message or f"Extra fields in constructor for {cln}: {', '.join(extra_field... |
def default_errformat(val):
it = val._e_metas()
if (val.creator is not None):
try:
after = (os.linesep + format_element(val.creator))
except Exception:
after = ('Element Traceback of %r caused exception:%s' % (type(val.creator).__name__, os.linesep))
after += ... |
class Dequantization(nn.Module):
filters: int = 96
components: int = 4
blocks: int = 5
attn_heads: int = 4
dropout_p: float = 0.0
use_nin: bool = True
use_ln: bool = True
def __call__(self, eps, x, inverse=False, train=False):
logp_eps = jnp.sum((((- (eps ** 2)) / 2.0) - (0.5 * n... |
def get_versioned_symbols(libs):
result = {}
for (path, elf) in elf_file_filter(libs.keys()):
elf_versioned_symbols = defaultdict(set)
for (key, value) in elf_find_versioned_symbols(elf):
log.debug('path %s, key %s, value %s', path, key, value)
elf_versioned_symbols[key].... |
def test_configure_multiple_modules():
def configure_a(binder):
binder.bind(DependsOnEmptyClass)
def configure_b(binder):
binder.bind(EmptyClass)
injector = Injector([configure_a, configure_b])
a = injector.get(DependsOnEmptyClass)
assert isinstance(a, DependsOnEmptyClass)
assert... |
class CLIPTextCfg():
context_length: int = 77
vocab_size: int = 49408
width: int = 512
heads: int = 8
layers: int = 12
ls_init_value: Optional[float] = None
hf_model_name: str = None
hf_tokenizer_name: str = None
hf_model_pretrained: bool = True
proj: str = 'mlp'
pooler_type:... |
class INR(nn.Module):
def __init__(self, in_features, hidden_features, hidden_layers, out_features, outermost_linear=True, sigma=10.0, pos_encode_configs={'type': None, 'use_nyquist': None, 'scale_B': None, 'mapping_input': None}):
super().__init__()
self.pos_encode = pos_encode_configs['type']
... |
def meta_next_trading_day(is_trading_day):
def next_trading_day(dt):
if (type(dt) is datetime.datetime):
dt = dt.date()
while True:
dt = (dt + datetime.timedelta(days=1))
if is_trading_day(dt):
return dt
return next_trading_day |
class GdbExit(sublime_plugin.WindowCommand):
def run(self):
global gdb_shutting_down
gdb_shutting_down = True
wait_until_stopped()
run_cmd('-gdb-exit', True)
if gdb_server_process:
gdb_server_process.terminate()
def is_enabled(self):
return is_running(... |
def _test():
import torch
pretrained = False
models = [fbnet_cb]
for model in models:
net = model(pretrained=pretrained)
net.eval()
weight_count = _calc_width(net)
print('m={}, {}'.format(model.__name__, weight_count))
assert ((model != fbnet_cb) or (weight_count ... |
class StochasticEncoderLayer(EncoderLayer):
def __init__(self, h, d_model, p, d_ff, attn_p=0.1, version=1.0, death_rate=0.0):
super().__init__(h, d_model, p, d_ff, attn_p, version)
self.death_rate = death_rate
def forward(self, input, attn_mask):
coin = True
if self.training:
... |
class SemsegMeter(object):
def __init__(self, num_classes, class_names, has_bg=True, ignore_index=255):
self.num_classes = (num_classes + int(has_bg))
self.class_names = class_names
self.tp = ([0] * self.num_classes)
self.fp = ([0] * self.num_classes)
self.fn = ([0] * self.nu... |
def parse_args():
parser = argparse.ArgumentParser(description='Train keypoints network')
parser.add_argument('--cfg', help='experiment configure file name', required=True, type=str)
(args, rest) = parser.parse_known_args()
update_config(args.cfg)
parser.add_argument('--frequent', help='frequency of... |
def test_resnext_backbone():
with pytest.raises(KeyError):
ResNeXt(depth=18)
model = ResNeXt(depth=50, groups=32, base_width=4)
print(model)
for m in model.modules():
if is_block(m):
assert (m.conv2.groups == 32)
model.init_weights()
model.train()
imgs = torch.ran... |
def ComboBoxDroppedHeightTest(windows):
bugs = []
for win in windows:
if (not win.ref):
continue
if ((win.class_name() != 'ComboBox') or (win.ref.class_name() != 'ComboBox')):
continue
if (win.dropped_rect().height() != win.ref.dropped_rect().height()):
... |
def main():
parser = HfArgumentParser((ModelArguments,))
(model_args,) = parser.parse_args_into_dataclasses()
if model_args.encoder_config_name:
encoder_config = AutoConfig.from_pretrained(model_args.encoder_config_name)
else:
encoder_config = AutoConfig.from_pretrained(model_args.encode... |
('pypyr.moduleloader.get_module')
('unittest.mock.MagicMock', new=DeepCopyMagicMock)
def test_run_pipeline_steps_complex_swallow_true_error(mock_get_module):
step = Step({'name': 'step1', 'swallow': 1})
context = get_test_context()
original_len = len(context)
arb_error = ValueError('arb error here')
... |
class HotelRoomReservationFactory(DjangoModelFactory):
order_code = 'AAAABB'
room = factory.SubFactory(HotelRoomFactory)
checkin = factory.Faker('past_date')
checkout = factory.Faker('future_date')
user = factory.SubFactory(UserFactory)
class Meta():
model = HotelRoomReservation |
class TimeLabel(Gtk.Label):
def __init__(self, time_=0):
Gtk.Label.__init__(self)
self.__widths = {}
self._disabled = False
self.set_time(time_)
def do_get_preferred_width(self):
widths = Gtk.Label.do_get_preferred_width(self)
num_chars = len(self.get_text())
... |
class Test_avl_iter(unittest.TestCase):
def setUp(self):
self.n = 1000
self.t = range_tree(0, self.n)
self.orig = list(range(self.n))
def testiter_forloop(self):
list = self.orig[:]
for i in range(5):
random.shuffle(list)
for k in avl.new(list):
... |
def main(client, config):
(ws_df, item_df, imp_df, ss_df) = benchmark(read_tables, config=config, compute_result=config['get_read_time'])
item_imp_join_df = get_helper_query_table(imp_df, item_df)
r_ss = get_ss(ss_df, item_imp_join_df)
r_ws = get_ws(ws_df, item_imp_join_df)
result_df = r_ws.merge(r_... |
_pipeline_test
_vision
class ZeroShotImageClassificationPipelineTests(unittest.TestCase):
_torch
def test_small_model_pt(self):
image_classifier = pipeline(model='hf-internal-testing/tiny-random-clip-zero-shot-image-classification')
image = Image.open('./tests/fixtures/tests_samples/COCO/.png')
... |
class TestAssertionWarnings():
def assert_result_warns(result, msg) -> None:
result.stdout.fnmatch_lines([('*PytestAssertRewriteWarning: %s*' % msg)])
def test_tuple_warning(self, pytester: Pytester) -> None:
pytester.makepyfile(' def test_foo():\n assert (1,2)\n ... |
def test_search_for_directory_setup_read_setup_with_extras(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')... |
class DescribeImagePart():
def it_is_used_by_PartFactory_to_construct_image_part(self, image_part_load_, partname_, blob_, package_, image_part_):
content_type = CT.JPEG
reltype = RT.IMAGE
image_part_load_.return_value = image_part_
part = PartFactory(partname_, content_type, reltype... |
class Migration(migrations.Migration):
dependencies = [('comms', '0008_auto__0902')]
operations = [migrations.AlterField(model_name='msg', name='db_hide_from_channels', field=models.ManyToManyField(blank=True, null=True, related_name='hide_from_channels_set', to='comms.ChannelDB')), migrations.AlterField(model_... |
def test_param_name_duplicates():
with pytest.raises(ValueError, match=full_match_regex_str("Parameter names {'a'} are duplicated")):
InputShape(constructor=stub_constructor, kwargs=None, fields=(InputField(id='a1', type=int, default=NoDefault(), is_required=True, metadata={}, original=None), InputField(id=... |
def logged_in_admin_user(e2e_tests_django_db_setup, page: Page) -> Page:
page.goto('/account/login')
page.get_by_label('Username').fill('admin', timeout=5000)
page.get_by_label('Password').fill('admin')
page.get_by_role('button', name='Login').click()
page.goto('/management')
(yield page) |
class TestArtifactVersion(unittest.TestCase, ArchiveTestingMixin):
def setUp(self):
prefix = 'qiime2-test-temp-'
self.temp_dir = tempfile.TemporaryDirectory(prefix=prefix)
self.provenance_capture = archive.ImportProvenanceCapture()
def tearDown(self):
self.temp_dir.cleanup()
... |
def main(id: int, skip_crawling: bool, with_quote: bool):
parsed_json = Crawler.crawl(id)
cache_dir = os.sep.join([os.curdir, 'cache', str(id), 'data.json'])
with open(cache_dir, 'r', encoding='utf-8') as file:
radio = Radio.load_from_json(parsed_json)
if (not skip_crawling):
Cra... |
(epilog=rgroup2smarts_epilog)
('--cut-rgroup', metavar='SMILES', multiple=True, help='R-group SMILES to use')
('--single', '-s', default=False, is_flag=True, help='Generate a SMARTS for each R-group SMILES (default: generate a single recursive SMARTS)')
('--check', '-c', default=False, is_flag=True, help='Check that th... |
def test_get_pipeline_path_raises_no_parent():
with pytest.raises(PipelineNotFoundError) as err:
fileloader.get_pipeline_path('unlikelypipeherexyz', None)
cwd_pipes_path = cwd.joinpath('pipelines')
expected_msg = f'''unlikelypipeherexyz.yaml not found in any of the following:
{cwd}
{cwd_pipes_path}
... |
def _create_s3_backend(session: Session, bucket: str, table: str, region_name: str) -> None:
account_id = get_account_id(session)
bucket_arn = _get_s3_bucket_arn(region_name, account_id, bucket)
table_arn = _get_dynamodb_table_arn(region_name, account_id, table)
log.ok(f'backend: {bucket_arn}')
log.... |
class MSVCCompiler(CCompiler):
compiler_type = 'msvc'
executables = {}
_c_extensions = ['.c']
_cpp_extensions = ['.cc', '.cpp', '.cxx']
_rc_extensions = ['.rc']
_mc_extensions = ['.mc']
src_extensions = (((_c_extensions + _cpp_extensions) + _rc_extensions) + _mc_extensions)
res_extension... |
def test_correctness_vertex_set_contiguity_distinct():
data = geopandas.GeoSeries((shapely.box(0, 0, 1, 1), shapely.box(0.5, 1, 1.5, 2)))
vs_rook = _vertex_set_intersection(data, rook=True)
rook = _rook(data)
assert (set(zip(*vs_rook, strict=True)) != set(zip(*rook, strict=True)))
vs_queen = _vertex... |
def get_interp_fun(variable_name, domain):
variable = comsol_variables[variable_name]
if (domain == ['negative electrode']):
comsol_x = comsol_variables['x_n']
elif (domain == ['positive electrode']):
comsol_x = comsol_variables['x_p']
elif (domain == whole_cell):
comsol_x = coms... |
(device=True)
def imt_func_o25(value, other_value):
return ((((((0 + ((1.0 * value[2]) * other_value[30])) + ((1.0 * value[1]) * other_value[29])) + (((- 1.0) * value[30]) * other_value[2])) + (((- 1.0) * value[6]) * other_value[31])) + (((- 1.0) * value[29]) * other_value[1])) + (((- 1.0) * value[31]) * other_valu... |
def load_data_for_worker(base_samples, batch_size, class_cond):
with bf.BlobFile(base_samples, 'rb') as f:
obj = np.load(f)
image_arr = obj['arr_0']
if class_cond:
label_arr = obj['arr_1']
rank = dist.get_rank()
num_ranks = dist.get_world_size()
buffer = []
label_... |
_funcify.register(Solve)
def numba_funcify_Solve(op, node, **kwargs):
assume_a = op.assume_a
if (assume_a != 'gen'):
lower = op.lower
warnings.warn('Numba will use object mode to allow the `compute_uv` argument to `numpy.linalg.svd`.', UserWarning)
ret_sig = get_numba_type(node.outputs[0... |
('/v1/organization/<orgname>/quota/<quota_id>/limit/<limit_id>')
_if(features.SUPER_USERS)
_if(features.QUOTA_MANAGEMENT)
class OrganizationQuotaLimit(ApiResource):
schemas = {'UpdateOrgQuotaLimit': {'type': 'object', 'description': 'Description of changing organization quota limit', 'properties': {'type': {'type':... |
class GroupElasticNet(ElasticNetConfig):
def __init__(self, groups=None, pen_val=1, mix_val=0.5, lasso_weights=None, lasso_flavor=None, ridge_weights=None):
pass
def _get_sum_configs(self):
lasso_config = GroupLasso(groups=self.groups, pen_val=(self.pen_val * self.mix_val), weights=self.lasso_we... |
def obtain_confused_result(out: defaultdict, threshole=0.6, confuse_value=0.15, num_template=2):
hard_answer = dict()
for (k, v) in out.items():
if ('ner' in k.lower()):
continue
is_filter = False
(best_result, best_prob) = (v.most_common()[0][0], v.most_common()[0][1])
... |
class CheckInService():
_EARLY_CHECK_IN_OFFSET: int = 3
_LATE_CHECK_IN_OFFSET: int = 6
def _is_valid_date(reservation: Reservation) -> bool:
return ((reservation.date_in - timedelta(hours=CheckInService._EARLY_CHECK_IN_OFFSET)) <= datetime.utcnow() <= (reservation.date_out - timedelta(hours=CheckInS... |
class InteriorSquirmer(DynSys):
def _rhs_static(r, th, t, a, g, n):
nvals = np.arange(1, (n + 1))
(sinvals, cosvals) = (np.sin((th * nvals)), np.cos((th * nvals)))
rnvals = (r ** nvals)
vrn = ((g * cosvals) + (a * sinvals))
vrn *= (((nvals * rnvals) * ((r ** 2) - 1)) / r)
... |
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if (os.path.isfile(file_path) or os.path.islink(file_path)):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_pa... |
def batchify_data(batch):
enc_input_ids = [f['enc_input'] for f in batch]
(enc_input_ids, enc_lens) = padded_sequence(enc_input_ids, batch[0]['pad'])
enc_input_ids = torch.tensor(enc_input_ids, dtype=torch.long)
enc_lens = torch.tensor(enc_lens, dtype=torch.long)
enc_batch_extend_vocab = [f['enc_inp... |
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