code stringlengths 281 23.7M |
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def provide_schema(overlay: Type[Overlay[Sc]], mediator: Mediator, loc_map: LocMap) -> Sc:
stacked_overlay = mediator.mandatory_provide(OverlayRequest(loc_map=loc_map, overlay_cls=overlay))
if (loc_map.has(TypeHintLoc) and isinstance(loc_map[TypeHintLoc].type, type)):
for parent in loc_map[TypeHintLoc].... |
class Node2vec(object):
def __init__(self, graph, path_length, num_paths, dim, p=1.0, q=1.0, dw=False, **kwargs):
kwargs['workers'] = kwargs.get('workers', 1)
if dw:
kwargs['hs'] = 1
p = 1.0
q = 1.0
self.graph = graph
if dw:
self.walker... |
def save_images(pred, save_path):
if (len(pred.shape) > 3):
pred = pred.squeeze()
if isinstance(pred, torch.Tensor):
pred = pred.cpu().numpy().astype(np.uint8)
if (pred.shape[0] < 4):
pred = np.transpose(pred, (1, 2, 0))
cv2.imwrite(save_path, pred, [cv2.IMWRITE_PNG_COMPRESSION, ... |
def cov_devY_devX(x, y, sigma, l, n, m):
result = 0
if (m == n):
result = ((covariance(x, y, sigma, l) / (l[m] ** 2)) + (((x[n] - y[n]) / (l[n] ** 2)) * cov_devX_y(x, y, sigma, l, m)))
else:
result = (((x[n] - y[n]) / (l[n] ** 2)) * cov_devX_y(x, y, sigma, l, m))
return result |
def get_attn_bias_and_cat(x_list, branges=None):
batch_sizes = ([b.shape[0] for b in branges] if (branges is not None) else [x.shape[0] for x in x_list])
all_shapes = tuple(((b, x.shape[1]) for (b, x) in zip(batch_sizes, x_list)))
if (all_shapes not in attn_bias_cache.keys()):
seqlens = []
f... |
class BaseUnitTestLithiumIon():
def check_well_posedness(self, options):
model = self.model(options)
model.check_well_posedness()
def test_well_posed(self):
options = {'thermal': 'isothermal'}
self.check_well_posedness(options)
def test_well_posed_isothermal_heat_source(self)... |
class PULSAR(FBD_view.FunctionBlockView):
_ton = 1000
_toff = 1000
_attribute_decorator('WidgetSpecific', 'Defines the actual TON value', int, {'possible_values': '', 'min': 0, 'max': 65535, 'default': 0, 'step': 1})
def ton(self):
return self._ton
def ton(self, value):
self._ton = v... |
('pyinaturalist.session.REFRESH_LIMITER', Limiter(RequestRate(1, 2)))
def test_get_refresh_params():
assert (get_refresh_params('test') == {'refresh': True})
assert (get_refresh_params('test2') == {'refresh': True})
assert (get_refresh_params('test') == {'refresh': True, 'v': 1})
assert (get_refresh_par... |
class TestMapWindow(EndianTest):
def setUp(self):
self.req_args_0 = {'window': }
self.req_bin_0 = b'\x08\x00\x02\x00\xccF\xa5T'
def testPackRequest0(self):
bin = request.MapWindow._request.to_binary(*(), **self.req_args_0)
self.assertBinaryEqual(bin, self.req_bin_0)
def testU... |
def mobi_header_fields(mobi_content):
pp = PalmDB(mobi_content)
header = pp.readsection(0)
id = struct.unpack_from('4s', header, 16)[0]
version = struct.unpack_from('>L', header, 36)[0]
dict_input = struct.unpack_from('>L', header, 96)[0]
dict_output = struct.unpack_from('>L', header, 100)[0]
... |
def test_pyproject_toml_save(pyproject_toml: Path, poetry_section: str, build_system_section: str) -> None:
pyproject = PyProjectTOML(pyproject_toml)
name = str(uuid.uuid4())
build_backend = str(uuid.uuid4())
build_requires = str(uuid.uuid4())
pyproject.poetry_config['name'] = name
pyproject.bui... |
class Base():
def __init__(self, url: str, token: str, verify_ssl: Union[(bool, str)]=True, **request_kwargs):
self._validate_url_and_token(url, token)
self._url = url
self._token = token
self.verify_ssl = verify_ssl
self._validate_request_kwargs(**request_kwargs)
sel... |
class TwoCropTransform():
def __init__(self, transformA, transformB=None):
self.transformA = transformA
if (transformB is None):
self.transformB = transformA
else:
self.transformB = transformB
def __call__(self, x):
return [self.transformA(x), self.transfo... |
class BadPOPM(TestCase):
def setUp(self):
self.filename = get_temp_copy(os.path.join(DATA_DIR, 'bad-POPM-frame.mp3'))
def tearDown(self):
os.unlink(self.filename)
def test_read_popm_long_counter(self):
f = ID3(self.filename)
self.failUnless(('POPM:Windows Media Player 9 Serie... |
def test_det_recog_show_result():
img = (np.ones((100, 100, 3), dtype=np.uint8) * 255)
det_recog_res = {'result': [{'box': [51, 88, 51, 62, 85, 62, 85, 88], 'box_score': 0.9417, 'text': 'hell', 'text_score': 0.8834}]}
vis_img = det_recog_show_result(img, det_recog_res)
assert (vis_img.shape[0] == 100)
... |
.xfail(reason='causing issues in CI, to be fixed later')
.spark_functions
def test_update_where_float(dataframe, spark_dataframe):
assert_frame_equal(spark_dataframe.update_where(conditions="\n `decorated-elephant` = 1 AND `#$%^` = 'rabbit'\n ", target_column_name='Bell__Chart', target_val=3.2... |
class TypeVarInferVarianceTests(BaseTestCase):
def test_typevar(self):
T = typing_extensions.TypeVar('T')
self.assertFalse(T.__infer_variance__)
T_infer = typing_extensions.TypeVar('T_infer', infer_variance=True)
self.assertTrue(T_infer.__infer_variance__)
T_noinfer = typing_... |
def model_processing(model, src_dir, dest_dir, timeseq_len):
train_dir = os.path.join(src_dir, 'train')
test_dir = os.path.join(src_dir, 'test')
if os.path.exists(dest_dir):
print(dest_dir, 'already exists')
else:
os.mkdir(dest_dir)
print(dest_dir, 'created')
dest_train_dir =... |
def random_inj_per_layer_batched(pfi: core.FaultInjection, min_val: int=(- 1), max_val: int=1, rand_loc: bool=True, rand_val: bool=True):
(batch, layer_num, c_rand, h_rand, w_rand, value) = ([] for i in range(6))
for i in range(pfi.get_total_layers()):
if (not rand_loc):
(layer, C, H, W) = r... |
_destruct_output_when_exp('contents')
def output(*contents):
import warnings
warnings.warn('`pywebio.output.output()` is deprecated since v1.5 and will remove in the future version, use `pywebio.output.put_scope()` instead', DeprecationWarning, stacklevel=2)
class OutputHandler(Output):
def __del__(... |
class Blur(nn.Module):
def __init__(self, in_filters, sfilter=(1, 1), pad_mode='replicate', **kwargs):
super(Blur, self).__init__()
filter_size = len(sfilter)
self.pad = SamePad(filter_size, pad_mode=pad_mode)
self.filter_proto = torch.tensor(sfilter, dtype=torch.float, requires_grad... |
_dataframe_method
_alias(columns='column_names')
def label_encode(df: pd.DataFrame, column_names: Union[(str, Iterable[str], Hashable)]) -> pd.DataFrame:
warnings.warn('`label_encode` will be deprecated in a 1.x release. Please use `factorize_columns` instead.')
df = _factorize(df, column_names, '_enc')
ret... |
def update_pen_val_and_weights(config, pen_val, weights):
if isinstance(config, NoPenalty):
pass
elif isinstance(config, (Ridge, Lasso, GroupLasso, MultiTaskLasso, GeneralizedLasso, FusedLasso)):
update_weights_and_pen_val_for_prod(pen_val=pen_val, new_pen_val=config.pen_val, weights=weights, ne... |
def main(config):
neptune_logger = NeptuneLogger(api_key=None, offline_mode=config['logging_params']['offline_mode'], project_name=config['logging_params']['project_name'], experiment_name=config['logging_params']['exp_name'], params={**config['exp_params'], **config['model_params'], **config['trainer_params']}, ta... |
def get_semantic_centroids(semantic_obs):
sids = list(np.unique(semantic_obs))
if (0 in sids):
sids.remove(0)
sid_centroids = []
for sid in sids:
one_hot = (semantic_obs == sid)
(xis, yis) = np.nonzero(one_hot)
sid_centroids.append([xis.mean(), yis.mean()])
return (si... |
def _acl_to_list(acl):
def acltag_to_char(tag):
if (tag == posix1e.ACL_USER_OBJ):
return 'U'
elif (tag == posix1e.ACL_USER):
return 'u'
elif (tag == posix1e.ACL_GROUP_OBJ):
return 'G'
elif (tag == posix1e.ACL_GROUP):
return 'g'
... |
def check_reopen(r1, w):
try:
print('Reopening read end')
r2 = os.open(f'/proc/self/fd/{r1}', os.O_RDONLY)
print(f'r1 is {r1}, r2 is {r2}')
print('checking they both can receive from w...')
os.write(w, b'a')
assert (os.read(r1, 1) == b'a')
os.write(w, b'b')
... |
.parametrize('debug_or_run', ['run', 'debug'])
def test_run_debug_step_function_mark_pending(debug_or_run, mocker, mock_utils_debugger):
step = Step(1, 'I am a Step', 'foo.feature', 1, parent=None, runable=True, context_class=None)
step.definition_func = StepHelper.step_pending_func
step.argument_match = mo... |
(help=__doc__)
('-r', '--run-number', help='use a specific run number (Default: highest)', type=int, default=None)
('folder', type=click.Path(exists=True, file_okay=False))
_context
def main(ctx: Any, folder: os.PathLike, run_number: Optional[int]) -> None:
scenario = ScenarioItems()
content: List[os.PathLike] ... |
def ensure_adjusted_array(ndarray_or_adjusted_array, missing_value):
if isinstance(ndarray_or_adjusted_array, AdjustedArray):
return ndarray_or_adjusted_array
elif isinstance(ndarray_or_adjusted_array, ndarray):
return AdjustedArray(ndarray_or_adjusted_array, {}, missing_value)
else:
... |
def compute_residual(model, state_in, state_out, particle_f, residual, dt):
wp.launch(kernel=compute_particle_residual, dim=model.particle_count, inputs=[state_in.particle_qd, state_out.particle_qd, particle_f, model.particle_mass, model.gravity, dt, residual.astype(dtype=wp.vec3)], device=model.device) |
def rgb_to_hsv(x):
hsv = th.zeros(*x.size())
c_min = x.min(0)
c_max = x.max(0)
delta = (c_max[0] - c_min[0])
r_idx = c_max[1].eq(0)
hsv[0][r_idx] = (((x[1][r_idx] - x[2][r_idx]) / delta[r_idx]) % 6)
g_idx = c_max[1].eq(1)
hsv[0][g_idx] = (2 + ((x[2][g_idx] - x[0][g_idx]) / delta[g_idx]))... |
class ResNetDownsample(nn.Module):
def __init__(self, in_features, out_features, stride=1):
super().__init__()
self.conv = nn.Conv3d(in_features, out_features, 1, stride, bias=False)
self.norm = nn.InstanceNorm3d(out_features)
def forward(self, x):
return self.norm(self.conv(x)) |
class ApplyGateToLthQubit(UnaryIterationGate):
selection_regs: Tuple[(SelectionRegister, ...)] = attrs.field(converter=(lambda v: ((v,) if isinstance(v, SelectionRegister) else tuple(v))))
nth_gate: Callable[(..., cirq.Gate)]
control_regs: Tuple[(Register, ...)] = attrs.field(converter=(lambda v: ((v,) if i... |
def _collect_metrics(metrics, output_names):
if (not metrics):
return [[] for _ in output_names]
if isinstance(metrics, list):
return [copy.copy(metrics) for _ in output_names]
elif isinstance(metrics, dict):
nested_metrics = []
for name in output_names:
output_me... |
def display_images(images: List[np.ndarray], dpi=100.0, format='html5_video', **kwargs):
(h, w) = images[0].shape[:2]
fig = plt.figure(figsize=((h / dpi), (w / dpi)), dpi=dpi)
fig_im = plt.figimage(images[0])
def animate(image):
fig_im.set_array(image)
return (fig_im,)
anim = animati... |
def get_raw_video_file_info(filename: str) -> Dict[(str, Any)]:
size_pattern = '(?P<width>\\d+)x(?P<height>\\d+)'
framerate_pattern = '(?P<framerate>[\\d\\.]+)(?:Hz|fps)'
bitdepth_pattern = '(?P<bitdepth>\\d+)bit'
formats = '|'.join(video_formats.keys())
format_pattern = f'(?P<format>{formats})(?:[p... |
def test_const_connect_Bits_signal_to_Bits():
class Top(ComponentLevel3):
def construct(s):
s.wire = Wire(Bits32)
connect(s.wire, Bits32(0))
x = Top()
x.elaborate()
print(x._dsl.consts)
assert (len(x._dsl.consts) == 1)
simple_sim_pass(x)
x.tick() |
def testParameterSetActions():
pa = OSC.ParameterSetAction('Myparam', 3)
pa.setVersion(minor=1)
prettyprint(pa)
pa2 = OSC.ParameterSetAction('Myparam', 3)
pa3 = OSC.ParameterSetAction('Myparam2', 3)
assert (pa == pa2)
assert (pa != pa3)
pa4 = OSC.ParameterSetAction.parse(pa.get_element()... |
.parametrize('test_args, expected', [([1], '1'), ([None], None), ([0.0001, '{:.0%}'], '0%'), ([0.0001, '{:.0%}', 0.01], '<1%'), ([0.9999, '{:.0%}', None, 0.99], '>99%'), ([0.0001, '{:.0%}', 0.01, None, 'under ', None], 'under 1%'), ([0.9999, '{:.0%}', None, 0.99, None, 'above '], 'above 99%'), ([1, humanize.intword, 10... |
def getValidationCase(file, force=False):
path = join(TEST_FOLDER_PATH, 'validation', file)
if ((not exists(path)) and (not force)):
raise FileNotFoundError('Validation case `{0}` does not exist. Choose one of: \n- {1} or use force=True'.format(file, '\n- '.join(os.listdir(join(TEST_FOLDER_PATH, 'valida... |
def test_cube_wcs_freqtovel():
header = fits.Header.fromtextfile(data_path('cubewcs1.hdr'))
w1 = wcs.WCS(header)
newwcs = convert_spectral_axis(w1, 'km/s', 'VRAD', rest_value=(w1.wcs.restfrq * u.Hz))
assert (newwcs.wcs.ctype[2] == 'VRAD')
assert (newwcs.wcs.crval[2] == 305.)
assert (newwcs.wcs.c... |
def convert_hf_name_to_opus_name(hf_model_name):
hf_model_name = remove_prefix(hf_model_name, ORG_NAME)
if (hf_model_name in GROUP_TO_OPUS_NAME):
opus_w_prefix = GROUP_TO_OPUS_NAME[hf_model_name]
else:
opus_w_prefix = hf_model_name.replace('_', '+')
return remove_prefix(opus_w_prefix, 'o... |
class ClientSpanObserverTests(unittest.TestCase):
def test_metrics(self):
mock_timer = mock.Mock(spec=Timer)
mock_counter = mock.Mock(spec=Counter)
mock_batch = mock.Mock(spec=Batch)
mock_batch.timer.return_value = mock_timer
mock_batch.counter.return_value = mock_counter
... |
def lr0_closure(I):
global _add_count
_add_count += 1
prodlist = Productions
J = I[:]
didadd = 1
while didadd:
didadd = 0
for j in J:
for x in j.lrafter:
if (x.lr0_added == _add_count):
continue
J.append(x.lr_next)
... |
.parametrize('username,password', users)
.parametrize('project_id', projects)
.parametrize('snapshot_id', snapshots)
def test_detail(db, client, username, password, project_id, snapshot_id):
client.login(username=username, password=password)
snapshot = Snapshot.objects.filter(project_id=project_id, id=snapshot_... |
def download(date_array, tag, inst_id, data_path='', user=None, password=None, test_download_kwarg=None):
pysat.logger.info(''.join(('test_download_kwarg = ', str(test_download_kwarg))))
if (tag == 'no_download'):
warnings.warn('This simulates an instrument without download support')
if (tag == 'use... |
class PK(object):
keyType = None
def generate(cls):
raise NotImplementedError
def parsePayload(cls, data, private=False):
raise NotImplementedError
def sign(self, data):
raise NotImplementedError
def verify(self, data):
raise NotImplementedError
def fingerprint(se... |
class AttrVI_ATTR_PXI_MAX_LWIDTH(ValuesAttribute):
resources = [(constants.InterfaceType.pxi, 'INSTR')]
py_name = ''
visa_name = 'VI_ATTR_PXI_MAX_LWIDTH'
visa_type = 'ViInt16'
default = NotAvailable
(read, write, local) = (True, False, False)
values = [(- 1), 1, 2, 4, 8, 16] |
def genSoftmax(embedding_anc, embedding_neg, W_fc, b_fc, label, Loss_type=FLAGS.LossType):
if (Loss_type == 'NpairLoss'):
label_split = tf.split(label, 2, axis=0)
label_pos = tf.reshape(label_split[1], [int((FLAGS.batch_size / 2)), 1])
label_neg_tile = tf.tile(label_pos, [int((FLAGS.batch_si... |
_auth
def db_edit(request, pk):
db = DBConfig.objects.select_related('db_server').get(id=pk)
if (request.method == 'GET'):
data = {'db_server': db.db_server_id, 'db_port': db.db_port, 'db_name': db.db_name, 'db_user': db.db_user, 'db_password': CryptPwd().decrypt_pwd(db.db_password), 'db_group': [group.... |
class TestSimpleTypeChecker(TestCase):
def setUp(self):
super(TestSimpleTypeChecker, self).setUp()
self.tc = get_env().stc
self.x = Symbol('x', BOOL)
self.y = Symbol('y', BOOL)
self.p = Symbol('p', INT)
self.q = Symbol('q', INT)
self.r = Symbol('r', REAL)
... |
class TraceSpanObserver(SpanObserver):
def __init__(self, service_name: str, hostname: str, span: Span, recorder: 'Recorder'):
self.service_name = service_name
self.hostname = hostname
self.recorder = recorder
self.span = span
self.start: Optional[int] = None
self.end... |
def batch_outer_sum(*tensors):
outer_sum = None
for (i, tensor) in enumerate(tensors):
broadcaster = ([None] * len(tensors))
broadcaster[i] = slice(tensor.shape[(- 1)])
broadcaster = tuple(([...] + broadcaster))
outer_sum = (tensor[broadcaster] if (i == 0) else (outer_sum + tenso... |
def _create_completion(model: str, messages: list, stream: bool, **kwargs):
path = os.path.dirname(os.path.realpath(__file__))
config = json.dumps({'messages': messages}, separators=(',', ':'))
cmd = ['python3', f'{path}/helpers/you.py', config]
p = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=s... |
class IntradayBarEvent(PeriodicEvent):
def __init__(self):
self.frequency = Frequency.MIN_1
self.start_time = self._shift_time(MarketOpenEvent._trigger_time, self.frequency.time_delta())
self.end_time = self._shift_time(MarketCloseEvent._trigger_time, (- self.frequency.time_delta()))
... |
class UnionConstraint(BaseConstraint):
def __init__(self, *constraints: BaseConstraint) -> None:
self._constraints = constraints
def constraints(self) -> tuple[(BaseConstraint, ...)]:
return self._constraints
def allows(self, other: BaseConstraint) -> bool:
return any((constraint.all... |
class SelectAction(argparse.Action):
placeholder = 'SELECT'
default_dest = 'selections'
def __init__(self, option_strings, dest, type=str, nargs=None, help=None, default=None, **kwargs):
if (('--' + dest.replace('_', '-')) in option_strings):
dest = self.default_dest
if ((type is... |
class MCTCTProcessor(ProcessorMixin):
feature_extractor_class = 'MCTCTFeatureExtractor'
tokenizer_class = 'AutoTokenizer'
def __init__(self, feature_extractor, tokenizer):
super().__init__(feature_extractor, tokenizer)
self.current_processor = self.feature_extractor
self._in_target_c... |
class RedirectAfterPost(MethodResult):
def __init__(self, mime_type='text/html', encoding='utf-8'):
super().__init__(catch_exception=DomainException, mime_type=mime_type, encoding=encoding)
def create_response(self, return_value):
next_url = return_value
return HTTPSeeOther(location=str(... |
class DiskImageDataset(QueueDataset):
def __init__(self, cfg, data_source, path, split, dataset_name):
super(DiskImageDataset, self).__init__(queue_size=cfg['DATA'][split]['BATCHSIZE_PER_REPLICA'])
assert (data_source in ['disk_filelist', 'disk_folder']), 'data_source must be either disk_filelist or... |
def _recursive_tuples(iterable, box_class, recreate_tuples=False, **kwargs):
out_list = []
for i in iterable:
if isinstance(i, dict):
out_list.append(box_class(i, **kwargs))
elif (isinstance(i, list) or (recreate_tuples and isinstance(i, tuple))):
out_list.append(_recursi... |
def get_dota_short_names(label):
DOTA_SHORT_NAMES = {'roundabout': 'RA', 'tennis-court': 'TC', 'swimming-pool': 'SP', 'storage-tank': 'ST', 'soccer-ball-field': 'SBF', 'small-vehicle': 'SV', 'ship': 'SH', 'plane': 'PL', 'large-vehicle': 'LV', 'helicopter': 'HC', 'harbor': 'HA', 'ground-track-field': 'GTF', 'bridge'... |
def identify_pdfium():
log = run_cmd(['git', 'log', '-100', '--pretty=%D'], cwd=PDFiumDir, capture=True)
(v_short, n_commits) = _walk_refs(log)
if n_commits:
hash = ('g' + run_cmd(['git', 'rev-parse', '--short', 'HEAD'], cwd=PDFiumDir, capture=True))
else:
hash = None
v_info = dict(n... |
def sample_generate_light(gen, dst, rows=5, cols=5, seed=0):
.make_extension()
def make_image(trainer):
np.random.seed(seed)
n_images = (rows * cols)
xp = gen.xp
z = Variable(xp.asarray(gen.make_hidden(n_images)))
with chainer.using_config('train', False), chainer.using_c... |
class Migration(migrations.Migration):
dependencies = [('sponsors', '0032_sponsorcontact_accounting')]
operations = [migrations.CreateModel(name='TieredQuantity', fields=[('benefitfeature_ptr', models.OneToOneField(auto_created=True, on_delete=django.db.models.deletion.CASCADE, parent_link=True, primary_key=Tru... |
class AverageMeter(Meter):
def __init__(self, name, fmt=':f', write_val=True, write_avg=True):
self.name = name
self.fmt = fmt
self.reset()
self.write_val = write_val
self.write_avg = write_avg
def reset(self):
self.val = 0
self.avg = 0
self.sum = ... |
def load_data(data_path, dataset, images):
all_datas = {}
for split in ['train', 'val', 'test']:
datas = []
dropdata = 0
with open(((data_path + split) + '.json'), 'r', encoding='utf-8') as fin:
for line in fin:
jterm = json.loads(line.strip())
... |
class ProgBarCounter(object):
def __init__(self, total_count):
self.total_count = total_count
self.max_progress = 1000000
self.cur_progress = 0
self.cur_count = 0
if (not logger.get_log_tabular_only()):
self.pbar = pyprind.ProgBar(self.max_progress)
else:
... |
class Server(threading.Thread):
def __init__(self, dht: Optional[DHT], expert_backends: Dict[(str, ExpertBackend)], listen_on: Endpoint='0.0.0.0:*', num_connection_handlers: int=1, update_period: int=30, start=False, checkpoint_dir=None, **kwargs):
super().__init__()
(self.dht, self.experts, self.up... |
class Tourney(BaseDbModel):
class Meta():
table = 'tm.tourney'
id = fields.BigIntField(pk=True, index=True)
guild_id = fields.BigIntField()
name = fields.CharField(max_length=30, default='Quotient-Tourney')
registration_channel_id = fields.BigIntField(index=True)
confirm_channel_id = fie... |
class SingleIndexWriterMixin(object):
def add_property_name(self, property_name_idx, property_name):
self.conn.execute(self.ADD_PROPERTY_NAME_SQL, (property_name_idx, property_name))
def add_rule_smiles(self, smiles_idx, smiles):
self.conn.execute(self.ADD_RULE_SMILES_SQL, (smiles_idx, smiles, g... |
class TestBloombergBeapHapiRequestProvider(unittest.TestCase):
def setUp(self):
self.session_mock = Mock()
self.post_response = Mock()
self.session_mock.post.return_value = self.post_response
self.address_url = '/eap/catalogs/address_url_id/'
self.request_id = 'sOmwhEReOveRTH... |
def main():
parser = argparse.ArgumentParser(description='Testing')
parser.add_argument('--obj', type=str, default='.')
parser.add_argument('--data_type', type=str, default='mvtec')
parser.add_argument('--data_path', type=str, default='.')
parser.add_argument('--checkpoint_dir', type=str, default='.... |
class TestWindow(window.Window):
def __init__(self, content_valign, *args, **kwargs):
super(TestWindow, self).__init__(*args, **kwargs)
self.batch = graphics.Batch()
self.document = text.decode_text(doctext)
self.margin = 2
self.layout = layout.IncrementalTextLayout(self.docu... |
class AoAModel3_d1_w2(AttModel):
def __init__(self, opt):
super(AoAModel3_d1_w2, self).__init__(opt)
self.num_layers = 2
self.use_mean_feats = getattr(opt, 'mean_feats', 1)
if (opt.use_multi_head == 2):
del self.ctx2att
self.ctx2att = (lambda x: x)
if ... |
def tbb_process_pool_worker3(inqueue, outqueue, initializer=None, initargs=(), maxtasks=None, wrap_exception=False):
from multiprocessing.pool import worker
worker(inqueue, outqueue, initializer, initargs, maxtasks, wrap_exception)
if ipc_enabled:
try:
librml = ctypes.CDLL(libirml)
... |
def test_bezier_to_polygon():
bezier_points = [37.0, 249.0, 72.5, 229.55, 95.34, 220.65, 134.0, 216.0, 132.0, 233.0, 82.11, 240.2, 72.46, 247.16, 38.0, 263.0]
pts = bezier_to_polygon(bezier_points)
target = np.array([[37.0, 249.0], [42., 246.], [47., 243.], [52., 240.], [58., 238.], [62., 235.], [67., 233.]... |
def _scan_badge_mutation(graphql_client, variables):
return graphql_client.query('\n mutation ScanBadge($url: String!, $conferenceCode: String!) {\n scanBadge(input: { url: $url, conferenceCode: $conferenceCode }) {\n __typename\n ... on BadgeScan {\n ... |
def read_and_resize_pair(path_lr, path_hr, low_res=(60, 80), high_res=(480, 640)):
img_lr = misc.imread(path_lr, mode='RGB').astype(np.float)
img_lr = misc.imresize(img_lr, low_res)
img_hr = misc.imread(path_hr, mode='RGB').astype(np.float)
img_hr = misc.imresize(img_hr, high_res)
return (img_lr, im... |
def parse_option():
hostname = socket.gethostname()
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--print_freq', type=int, default=100, help='print frequency')
parser.add_argument('--tb_freq', type=int, default=500, help='tb frequency')
parser.add_argument('--save_fr... |
class tuple(Generic[T_co], Sequence[T_co], Iterable[T_co]):
def __init__(self, i: Iterable[T_co]) -> None:
pass
def __getitem__(self, i: int) -> T_co:
pass
def __getitem__(self, i: slice) -> Tuple[(T_co, ...)]:
pass
def __len__(self) -> int:
pass
def __iter__(self) ->... |
class DeepLabv3(nn.Module):
def __init__(self, backbone, backbone_out_channels=2048, aux=False, fixed_size=True, in_channels=3, in_size=(480, 480), num_classes=21):
super(DeepLabv3, self).__init__()
assert (in_channels > 0)
self.in_size = in_size
self.num_classes = num_classes
... |
class OnionRoutingFailureMessage():
def __init__(self, code: int, data: bytes):
self.code = code
self.data = data
def __repr__(self):
return repr((self.code, self.data))
def to_bytes(self) -> bytes:
ret = self.code.to_bytes(2, byteorder='big')
ret += self.data
... |
class TestPassportFileWithoutRequest(TestPassportFileBase):
def test_slot_behaviour(self, passport_file):
inst = passport_file
for attr in inst.__slots__:
assert (getattr(inst, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(inst)) == len(set(mro_slots(in... |
class Effect6536(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.requiresSkill('Shield Command') or mod.item.requiresSkill('Information Command'))), 'warfareBuff3Value', src.getModifiedItemAttr('shipBonusForceA... |
class EightBitBg(BgColor, Enum):
BLACK = 0
RED = 1
GREEN = 2
YELLOW = 3
BLUE = 4
MAGENTA = 5
CYAN = 6
LIGHT_GRAY = 7
DARK_GRAY = 8
LIGHT_RED = 9
LIGHT_GREEN = 10
LIGHT_YELLOW = 11
LIGHT_BLUE = 12
LIGHT_MAGENTA = 13
LIGHT_CYAN = 14
WHITE = 15
GRAY_0 = 1... |
class BaseInboundShipmentItem(MWSDataType):
quantity_param = ''
def __init__(self, sku: str, quantity: int, quantity_in_case: int=None, prep_details_list: List[PrepDetails]=None):
self.sku = sku
self.quantity = quantity
self.quantity_in_case = quantity_in_case
self.prep_details_l... |
.unit()
.parametrize(('name', 'extra', 'errors', 'caller', 'expectation', 'expected'), [pytest.param('python', '', 'raise', 'pytask', does_not_raise(), True, id='program exists'), pytest.param('unknown_program', '', 'raise', 'pytask', pytest.raises(RuntimeError, match='pytask requires the optional program'), None, id='... |
class CostCalculator():
def get_compressed_model_cost(cls, layer_db, layer_ratio_list, original_model_cost, cost_metric):
for layer in layer_db:
if (layer not in layer_db.get_selected_layers()):
layer_ratio_list.append(LayerCompRatioPair(layer, None))
compressed_model_cos... |
def test_datetime_parsing():
val1 = catalog._parse_datetime_header('2006-06-28 23:24+0200')
assert (val1.year == 2006)
assert (val1.month == 6)
assert (val1.day == 28)
assert (val1.tzinfo.zone == 'Etc/GMT+120')
val2 = catalog._parse_datetime_header('2006-06-28 23:24')
assert (val2.year == 20... |
class UniverseAuth2Test(OAuth2Test):
backend_path = 'social_core.backends.universe.UniverseOAuth2'
user_data_url = '
expected_username = 'scott+'
access_token_body = json.dumps({'access_token': 'foobar', 'token_type': 'bearer'})
user_data_body = json.dumps({'current_user': {'id': '123456', 'slug': '... |
class PQTuple():
def __init__(self, tuple, schema):
self.tuple = tuple
self.schema = schema
def __getattr__(self, attr):
return self.tuple[self.schema[attr]]
def __getitem__(self, item):
if isinstance(item, int):
return self.tuple[item]
else:
r... |
class ToolButtonWithMenuIndication(QtWidgets.QToolButton):
SIZE = (21, 16)
def __init__(self):
QtWidgets.QToolButton.__init__(self)
self.setIconSize(QtCore.QSize(*self.SIZE))
self.setStyleSheet('QToolButton{ border: none; }')
self._menuarrow1 = self._createMenuArrowPixmap(0)
... |
def get_excludes(session):
conn = get_database_conn()
curs = query_execute_wrapper(conn, query_string='SELECT * FROM scansweep_excludes WHERE session=?', query_list=[session], no_return=False)
excludes_list = []
for row in curs:
excludes_list.append(row['target'])
if (len(excludes_list) == 0... |
def test(sess, model, users_to_test, data_generator, args, drop_flag=True, batch_test_flag=False):
global _data_generator
global _USR_NUM
global _OUTFIT_NUM
global _N_TRAIN
global _N_TEST
global Ks
global _BATCH_SIZE
Ks = eval(args.Ks)
_BATCH_SIZE = args.batch_size
_data_generato... |
_module()
class PadMultiViewImage(object):
def __init__(self, size=None, size_divisor=None, pad_val=0):
self.size = size
self.size_divisor = size_divisor
self.pad_val = pad_val
assert ((size is not None) or (size_divisor is not None))
assert ((size is None) or (size_divisor i... |
class _Dice(MessageFilter):
__slots__ = ('emoji', 'values')
def __init__(self, values: Optional[SCT[int]]=None, emoji: Optional[DiceEmojiEnum]=None):
super().__init__()
self.emoji: Optional[DiceEmojiEnum] = emoji
self.values: Optional[Collection[int]] = ([values] if isinstance(values, in... |
class DownSamplerB(nn.Module):
def __init__(self, nIn, nOut):
super().__init__()
n = int((nOut / 5))
n1 = (nOut - (4 * n))
self.c1 = C(nIn, n, 3, 2)
self.d1 = CDilated(n, n1, 3, 1, 1)
self.d2 = CDilated(n, n, 3, 1, 2)
self.d4 = CDilated(n, n, 3, 1, 4)
... |
def test_parse_version() -> None:
version_str = '3.6'
versions_list = ['-cp36-', '-pp36-', '-ip36-', '-jy36-', '-py3.6-', '-py3.6.']
assert (versions_list == parse_version(version_str))
assert ('-cp36-' in parse_version(version_str))
assert ('-py3.6.' in parse_version(version_str)) |
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