code stringlengths 281 23.7M |
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class _TopologicalLattice(Generic[TQubit], metaclass=ABCMeta):
def H(self) -> int:
return DH
def W(self) -> int:
return DW
def SYNX(self) -> int:
return 0
def SYNZ(self) -> int:
return 1
def __init__(self, params: Dict[(str, Any)], name: str, circ: QuantumCircuit):
... |
_factory
def factory():
search = SerpAPIWrapper()
tools = [Tool(name='Search', func=search.run, description='useful for when you need to answer questions about current events. You should ask targeted questions')]
llm = OpenAI(temperature=0, model_name='gpt-3.5-turbo')
agent = initialize_agent(tools, llm... |
def inference_run(model, hparams, output_dir):
tf.logging.info('Build Model...')
model_fn_inference = model_builder_inference(model, hparams=hparams)
tf.logging.info('Build Graph...')
checkpoint_path = saver.latest_checkpoint(output_dir)
if (not checkpoint_path):
raise NotFittedError(("Could... |
class AttrVI_ATTR_SRC_INCREMENT(RangeAttribute):
resources = [(constants.InterfaceType.pxi, 'INSTR'), (constants.InterfaceType.pxi, 'MEMACC'), (constants.InterfaceType.vxi, 'INSTR'), (constants.InterfaceType.vxi, 'MEMACC')]
py_name = 'source_increment'
visa_name = 'VI_ATTR_SRC_INCREMENT'
visa_type = 'Vi... |
def orthoFrames2Versor_dist(A, B, eps=None):
A = A[:]
B = B[:]
if (len(A) != len(B)):
raise ValueError('len(A)!=len(B)')
if (eps is None):
eps = global_eps()
r_list = []
dist = [abs(((a - b) ** 2)) for (a, b) in zip(A, B)]
k = dist.index(max(dist))
while (dist[k] >= eps):... |
.fast
def test_Morse_Potential_effect_CO(T=3000, rtol=0.0001, verbose=True, warnings=True, *args, **kwargs):
vmax = 11
vmax_morse = 48
jmax = 300
iso = 1
S = Molecules['CO'][iso]['X']
db = PartFunc_Dunham(S, vmax=vmax, vmax_morse=0, Jmax=jmax, use_cached=False)
Q_nomorse = db.at(T)
db = ... |
class Effect5757(BaseEffect):
type = 'overheat'
def handler(fit, module, context, projectionRange, **kwargs):
module.boostItemAttr('maxTargetRangeBonus', module.getModifiedItemAttr('overloadSensorModuleStrengthBonus'), **kwargs)
module.boostItemAttr('scanResolutionBonus', module.getModifiedItemA... |
def DenseNet201(pretrained=False, **kwargs):
model = DenseNet(num_init_features=64, growth_rate=32, block_config=(6, 12, 48, 32), **kwargs)
if pretrained:
pattern = re.compile('^(.*denselayer\\d+\\.(?:norm|relu|conv))\\.((?:[12])\\.(?:weight|bias|running_mean|running_var))$')
state_dict = model_... |
class SawyerHandlePullV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'handle_pos': obs[3:6], 'unused_info': obs[6:]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_effort': 3})
action[... |
class OrbitController(PanZoomController):
_default_controls = {'mouse1': ('rotate', 'drag', (0.005, 0.005)), 'mouse2': ('pan', 'drag', (1, 1)), 'mouse4': ('quickzoom', 'peek', 2), 'wheel': ('zoom', 'push', (- 0.001)), 'alt+wheel': ('fov', 'push', (- 0.01))}
def rotate(self, delta: Tuple, rect: Tuple, *, animate... |
def simxLoadModel(clientID, modelPathAndName, options, operationMode):
baseHandle = ct.c_int()
if ((sys.version_info[0] == 3) and (type(modelPathAndName) is str)):
modelPathAndName = modelPathAndName.encode('utf-8')
return (c_LoadModel(clientID, modelPathAndName, options, ct.byref(baseHandle), opera... |
.parametrize('names,expect', [([1, 2, 3], ['1', '2', '3']), (['', np.nan], ['', '']), (['', np.nan], ['', '']), (['', '', np.nan], ['', '', '']), (repair_names(['', '', np.nan], repair='minimal'), ['', '', ''])])
def test_minimal(names, expect):
assert (repair_names(names, repair='minimal') == expect) |
class IPTest(object):
.parametrize('value', ['200.8.9.10', '127.0.0.1', '2001:db8:85a3::8a2e:370:7334', '::1'])
def test_valid_value(self, value):
assert (inputs.ip(value) == value)
.parametrize('value', ['foo', ' ' ' ' ' ' ' ' 'foo bar baz', 'foo ', ' ' ' '127.0'])
def test_bad_value(self, valu... |
class Path(object):
def __init__(self, label_name, fold_id):
self.label_name = label_name
self.fold_id = fold_id
self.phase_path = {}
a_root = '/media/newssd/Aff-Wild_experiments/phase_diff_5_fold/valence_loss_type:ccc_batch_size:64_alpha:1.0/model'
v_root = '/media/newssd/Af... |
def get_semanal_options(program_text: str, testcase: DataDrivenTestCase) -> Options:
options = parse_options(program_text, testcase, 1)
options.use_builtins_fixtures = True
options.semantic_analysis_only = True
options.show_traceback = True
options.python_version = PYTHON3_VERSION
options.force_... |
.parametrize('screen,location,attribute', parameters)
def test_default_settings(manager_nospawn, minimal_conf_noscreen, screen, location, attribute):
config = minimal_conf_noscreen
config.screens = [screen]
manager_nospawn.start(config)
bar = manager_nospawn.c.bar[location]
info = bar.info()
for... |
class TanhBlurBlock(nn.Module):
def __init__(self, in_filters, temp=10.0, sfilter=(1, 1), pad_mode='constant', **kwargs):
super(TanhBlurBlock, self).__init__()
self.temp = temp
self.relu = layers.relu()
self.tanh = nn.Tanh()
self.blur = layers.blur(in_filters, sfilter=sfilter... |
.parametrize('properties', [{}, create_test_properties()])
def test_object_features(properties: dict):
(obj, _) = create_test_object()
obj.properties = properties
assert (obj.n_features == 0)
keys = list(properties.keys())
obj.set_features(keys)
n_keys = sum((np.asarray(p).size for p in properti... |
def test_select_column_in_subquery_with_two_parenthesis_and_union_v2():
sql = 'INSERT INTO tab1\nSELECT col1\nFROM (\n SELECT col1 FROM tab2\n UNION ALL\n SELECT col1 FROM tab3\n) dt'
assert_column_lineage_equal(sql, [(ColumnQualifierTuple('col1', 'tab2'), ColumnQualifierTuple('col1', 'tab1')), (Column... |
def tar_archive(context_tar):
logger.debug('start')
mode = get_file_mode_for_writing(context_tar)
for item in context_tar['archive']:
destination = item['out']
source = item['in']
with tarfile.open(destination, mode) as archive_me:
logger.debug("Archiving '%s' to '%s'", s... |
def setup_checkpoint_file_name_prefix(args):
checkpoint_file_name_prefix = ''
for (i, name) in enumerate(args.checkpoint_file_name_save_list):
checkpoint_file_name_prefix += str(getattr(args, name))
if (i != (len(args.checkpoint_file_name_save_list) - 1)):
checkpoint_file_name_prefix... |
def makeUpdateMatrixDis(qnnArch, qnnArchGen, unitaries, storedStates, storedStatesDis, lda, ep, l, j, trainingData):
numInputQubits = qnnArch[(l - 1)]
summ = 0
for x in range(len(storedStates)):
firstPart = updateMatrixFirstPartDis(qnnArch, qnnArchGen, unitaries, storedStates, storedStatesDis, l, j,... |
def fill_template(template, *args):
parts = TEMPLATE_PATTERN.findall(template)
kids = []
for p in parts:
if (p == ''):
continue
elif (p in '\x01\x02\x03\x04\x05'):
p = args[(ord(p) - 1)]
p.prefix = ''
else:
p = Name(p)
kids.appe... |
class ListSponsorsTemplateTag(TestCase):
def test_filter_sponsorship_with_logo_placement_benefits(self):
sponsorship = baker.make_recipe('sponsors.tests.finalized_sponsorship')
baker.make_recipe('sponsors.tests.logo_at_download_feature', sponsor_benefit__sponsorship=sponsorship)
context = li... |
def infer_conv_output_attrs(module, input_channels, input_dim, batch_size=1, max_length=8):
input = torch.randn(batch_size, input_channels, max_length, input_dim)
output = module(input)
output_channels = output.shape[1]
output_dim = output.shape[(- 1)]
return (output_channels, output_dim) |
class RectROI(ROI):
def __init__(self, pos, size, centered=False, sideScalers=False, **args):
ROI.__init__(self, pos, size, **args)
if centered:
center = [0.5, 0.5]
else:
center = [0, 0]
self.addScaleHandle([1, 1], center)
if sideScalers:
s... |
def _add_perm(caller, perm, **kwargs):
if perm:
perm_low = perm.lower()
perms = _caller_permissions(caller)
perms_low = [prm.lower() for prm in perms]
if ('delete' in kwargs):
try:
ind = perms_low.index(perm_low)
del perms[ind]
... |
class IPTW():
def __init__(self, df, treatment, outcome, weights=None, standardize='population'):
self.treatment = treatment
self.outcome = outcome
self._missing_indicator = '__missing_indicator__'
(self.df, self._miss_flag, self._continuous_outcome) = check_input_data(data=df, expos... |
def test_back_and_forth_ito():
(f, g, b, y0, ts, dt) = make_example_sde(dt=0.0001)
(fr, gr, br, tr) = time_reflect_ito(f, g, b, ts)
ys = ito_integrate(f, g, y0, ts, b, dt)
rys = ito_integrate(fr, gr, ys[(- 1)], tr, br, dt)[::(- 1)]
assert np.allclose(ys[0], rys[0], rtol=0.001, atol=0.001) |
def extra_english(corpus_path, split):
split_type_file_path = os.path.join(corpus_path, f'all_talks_{split}.tsv')
output_split_type_file_path = os.path.join(corpus_path, f'all_talks_{split}.en')
with io.open(split_type_file_path, 'r', encoding='utf8') as fp, io.open(output_split_type_file_path, 'w', encodin... |
.parametrize('setting, third_party, accepted', [('all', False, True), ('never', False, False), ('no-3rdparty', False, True), ('no-3rdparty', True, False)])
def test_accept_cookie(config_stub, filter_request, setting, third_party, accepted):
config_stub.val.content.cookies.accept = setting
filter_request.thirdPa... |
def setup(args):
cfg = get_cfg()
add_deeplab_config(cfg)
add_maskformer2_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
setup_logger(output=cfg.OUTPUT_DIR, distributed_rank=comm.get_rank(), name='mask2former')
re... |
def _accuracy_param_check(average: Optional[str], num_classes: Optional[int], k: int) -> None:
average_options = ('micro', 'macro', 'none', None)
if (average not in average_options):
raise ValueError(f'`average` was not in the allowed value of {average_options}, got {average}.')
if ((average != 'mic... |
def get_normalize_layer(dataset: str, diff=None, vit=None) -> torch.nn.Module:
if diff:
return NormalizeLayer(_DIFF_MEAN, _DIFF_STD)
if vit:
return NormalizeLayer(_CIFAR10_MEAN_VIT, _CIFAR10_STDDEV_VIT)
if (dataset == 'imagenet'):
return NormalizeLayer(_IMAGENET_MEAN, _IMAGENET_STDDE... |
def prettify_print_name(name):
if ((name is None) or ('{' in name) or ('\\' in name)):
return name
subscripts = []
superscripts = []
average = False
processing = True
while processing:
processing = False
for superscript in ['init', 'ref', 'typ', 'max', '0', 'surf']:
... |
def ComputeCoverage(p, bias, norm):
q = quaternion.vec2vec2quat(norm, [0, 0, 1])
def ang(p):
c = quaternion.rotvecquat(vector.sub(p[:3], bias), q)
d = quaternion.rotvecquat(p[3:6], q)
v = quaternion.rotvecquat(c, quaternion.vec2vec2quat(d, [0, 0, 1]))
v = vector.normalize(v)
... |
class CeleryRouterConfigTest(harness.CustomRouterMixin, TestCase):
router_class = 'rapidsms.router.celery.CeleryRouter'
def test_eager_invalid_backend(self):
self.backends = {'mockbackend': {'ENGINE': harness.MockBackend}}
self.set_backends()
router = get_router()
self.assertFals... |
def ceaf(clusters, gold_clusters, phi_similarity):
scores = np.zeros((len(gold_clusters), len(clusters)))
for i in range(len(gold_clusters)):
for j in range(len(clusters)):
scores[(i, j)] = phi_similarity(gold_clusters[i], clusters[j])
(row_ind, col_ind) = linear_sum_assignment((- scores... |
def filter_lines(lines, n_jobs, isomeric):
logger.info('Filtering SMILES')
with Pool(n_jobs) as pool:
process_molecule_p = partial(process_molecule, isomeric=isomeric)
dataset = [x for x in tqdm(pool.imap_unordered(process_molecule_p, lines), total=len(lines), miniters=1000) if (x is not None)]
... |
class linear_attribute_model(nn.Module):
def __init__(self, args, input_dim=1536, output_dim=128):
super().__init__()
self.fc1 = nn.Linear(input_dim, 1024, True)
self.fc2 = nn.Linear(1024, 512, True)
self.fc3 = nn.Linear(512, 256, True)
self.fc4 = nn.Linear(256, output_dim, T... |
_machine.travel('2020-10-10 10:00:00', tick=False)
def test_send_voucher_via_email(rf, grant_factory, conference_factory, mocker):
mocker.patch('grants.admin.messages')
mock_send_email = mocker.patch('grants.admin.send_grant_voucher_email')
conference = conference_factory(pretix_speaker_voucher_quota_id=123... |
def as_tuple(tr, dataquality='D'):
from pyrocko import mseed_ext
itmin = int(round((tr.tmin * mseed_ext.HPTMODULUS)))
itmax = int(round((tr.tmax * mseed_ext.HPTMODULUS)))
srate = (1.0 / tr.deltat)
return (tr.network, tr.station, tr.location, tr.channel, itmin, itmax, srate, dataquality, tr.get_ydata... |
class TerminusFindTerminalMixin():
def find_terminal(self, window, tag=None, panel_only=False, visible_only=False):
if tag:
terminal = Terminal.from_tag(tag)
if terminal:
return terminal
view = None
recency_manager = RecencyManager.from_window(window)
... |
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, num_of_channels=3):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(num_of_channels, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self... |
def test_emit_warning_when_event_loop_is_explicitly_requested_in_coroutine_method(pytester: Pytester):
pytester.makepyfile(dedent(' import pytest\n\n class TestEmitsWarning:\n .asyncio\n async def test_coroutine_emits_warning(self, event_loop):\n ... |
class GroundStateTest(unittest.TestCase):
def test_get_ground_state_hermitian(self):
ground = get_ground_state(get_sparse_operator((QubitOperator('Y0 X1') + QubitOperator('Z0 Z1'))))
expected_state = csc_matrix(([1j, 1], ([1, 2], [0, 0])), shape=(4, 1), dtype=numpy.complex128).A
expected_sta... |
def _get_layer_input(layer: tf.keras.layers.Layer, model_layers_connections: ModelLayerConnectionsProperties.TYPE) -> tf.keras.layers.Layer:
try:
layer_input = [model_layers_connections[ModelLayerConnectionsProperties.OUTPUT_TENSORS][layer_aux] for layer_aux in model_layers_connections[ModelLayerConnections... |
class SplitAttentionConv2d(nn.Module):
def __init__(self, in_channels, channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, radix=2, reduction_factor=4, conv_cfg=None, norm_cfg=dict(type='BN')):
super().__init__()
inter_channels = max(((in_channels * radix) // reduction_factor), 32)
... |
def load_resnet_encoder(checkpoint_path, device):
model = ResNetModel(512).eval().to(device)
checkpoint = torch.load(checkpoint_path, map_location=device)
new_state_dict = {}
for (k, v) in checkpoint.items():
try:
new_state_dict[k[6:]] = checkpoint[k]
except KeyError:
... |
def nice_time_diff(time_base: datetime, time_now: datetime) -> Tuple[(str, float)]:
delta = (time_now - time_base)
total_seconds = delta.total_seconds()
if (total_seconds < 0.001):
return (f'+ {delta.microseconds: 10.0f} s', total_seconds)
if (total_seconds < 1):
return (f'+ {(delta.micr... |
def convert_label_map_to_categories(label_map, max_num_classes, use_display_name=True):
categories = []
list_of_ids_already_added = []
if (not label_map):
label_id_offset = 1
for class_id in range(max_num_classes):
categories.append({'id': (class_id + label_id_offset), 'name': 'c... |
def get_parser(parser=None):
if (parser is None):
parser = argparse.ArgumentParser()
model_arg = parser.add_argument_group('Model')
model_arg.add_argument('--q_cell', type=str, default='gru', choices=['gru'], help='Encoder rnn cell type')
model_arg.add_argument('--q_bidir', default=False, action... |
def _interpolate_get_scales(g, scale_factor, dim):
offsets = g.op('Constant', value_t=torch.ones(2, dtype=torch.float32))
if isinstance(scale_factor.type(), torch._C.ListType):
return g.op('Concat', offsets, scale_factor, axis_i=0)
else:
scale_factor = _unsqueeze_helper(g, scale_factor, 0)
... |
class Migration(migrations.Migration):
dependencies = [('questions', '0091_alter_questionset_options')]
operations = [migrations.RemoveField(model_name='page', name='verbose_name_plural_lang1'), migrations.RemoveField(model_name='page', name='verbose_name_plural_lang2'), migrations.RemoveField(model_name='page'... |
def make_recursive_list(fn):
def recursive_map(tensors):
if (tensors is None):
return tensors
elif (isinstance(tensors[0], list) or isinstance(tensors[0], tuple)):
return type(tensors[0])(map(recursive_map, zip(*tensors)))
elif isinstance(tensors[0], dict):
... |
_fixtures(WebFixture, QueryStringFixture, ResponsiveWidgetScenarios)
def test_focus_location_after_refresh_without_tabbing(web_fixture, query_string_fixture, responsive_widget_scenarios):
fixture = responsive_widget_scenarios
wsgi_app = web_fixture.new_wsgi_app(enable_js=True, child_factory=fixture.MainWidget.f... |
def init_distributed_mode(args):
if args.dist_on_itp:
args.rank = int(os.environ['OMPI_COMM_WORLD_RANK'])
args.world_size = int(os.environ['OMPI_COMM_WORLD_SIZE'])
args.gpu = int(os.environ['OMPI_COMM_WORLD_LOCAL_RANK'])
args.dist_url = ('tcp://%s:%s' % (os.environ['MASTER_ADDR'], os... |
class Migration(migrations.Migration):
dependencies = [('questions', '0076_questionset_remove_section')]
operations = [migrations.AlterModelOptions(name='question', options={'ordering': ('uri',), 'verbose_name': 'Question', 'verbose_name_plural': 'Questions'}), migrations.AlterModelOptions(name='questionset', o... |
class DebertaV2OnnxConfig(OnnxConfig):
def inputs(self) -> Mapping[(str, Mapping[(int, str)])]:
if (self.task == 'multiple-choice'):
dynamic_axis = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
dynamic_axis = {0: 'batch', 1: 'sequence'}
if (self._config.type_vocab_si... |
def voc_eval(result_file, dataset, iou_thr=0.5):
det_results = mmcv.load(result_file)
gt_bboxes = []
gt_labels = []
gt_ignore = []
for i in range(len(dataset)):
ann = dataset.get_ann_info(i)
bboxes = ann['bboxes']
labels = ann['labels']
if ('bboxes_ignore' in ann):
... |
class Effect4044(BaseEffect):
runTime = 'early'
type = ('projected', 'passive')
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: ('overloadSpeedFactorBonus' in mod.itemModifiedAttributes)), 'overloadSpeedFactorBonus', module.getModifiedItemA... |
def load_archive_file(archive_file):
try:
resolved_archive_file = cached_path(archive_file, cache_dir=None)
except EnvironmentError:
logger.info("Archive name '{}' was not found in archive name list. We assumed '{}' was a path or URL but couldn't find any file associated to this path or URL.".fo... |
def annotate_value(origin: Value, metadata: Sequence[Union[(Value, Extension)]]) -> Value:
if (not metadata):
return origin
if isinstance(origin, AnnotatedValue):
metadata = (*origin.metadata, *metadata)
origin = origin.value
hashable_vals = {}
unhashable_vals = []
for item i... |
def main(opt):
if opt.disable_cudnn:
torch.backends.cudnn.enabled = False
print('Cudnn is disabled.')
logger = Logger(opt)
opt.device = torch.device('cuda:{}'.format(opt.gpus[0]))
Dataset = dataset_factory[opt.dataset]
(train, val) = task_factory[opt.task]
(model, optimizer, star... |
def test_fib_ycombinator():
Y = '\n (lambda (f)\n ((lambda (x) (x x))\n (lambda (g)\n (f (lambda (z) ((g g) z))))))\n'
fac = '\n (lambda (f)\n (lambda (x)\n (if (< x 2)\n 1\n (* x (f (- x 1))))))\n '
fib = '\n (lambda (f)\n (lambda (x)\n (if ... |
class PancakeHouseMenu(Menu):
menuItems: List[MenuItem]
def __init__(self):
self.menuItems = []
self.addItem("K&B's Pancake Breakfast", 'Pancakes with scrambled eggs and toast', True, 2.99)
self.addItem('Regular Pancake Breakfast', 'Pancakes with fried eggs, sausage', False, 2.99)
... |
class Kernel(W):
def __init__(self, data, bandwidth=None, fixed=True, k=2, function='triangular', eps=1.0000001, ids=None, diagonal=False, distance_metric='euclidean', radius=None, **kwargs):
if (radius is not None):
distance_metric = 'arc'
if isKDTree(data):
self.kdtree = da... |
class CenterCrop(object):
def __init__(self, size):
if isinstance(size, numbers.Number):
self.size = (int(size), int(size))
else:
self.size = size
def __call__(self, img):
(w, h) = img.size
(th, tw) = self.size
x1 = int(round(((w - tw) / 2.0)))
... |
class PreOCIModel(RepoEmailDataInterface):
def get_email_authorized_for_repo(self, namespace_name, repository_name, email):
return _return_none_or_data(model.repository.get_email_authorized_for_repo, namespace_name, repository_name, email)
def create_email_authorization_for_repo(self, namespace_name, re... |
def encode_path(value):
if (value is None):
return None
if (not isinstance(value, (str, bytes))):
value = (repr(value) if isinstance(value, type) else repr(type(value)))
if isinstance(value, bytes):
value = value.decode(sys.getfilesystemencoding())
return value |
class CifarResNet(nn.Module):
def __init__(self, block, depth, channels=3):
super(CifarResNet, self).__init__()
assert (((depth - 2) % 6) == 0), 'depth should be one of 20, 32, 44, 56, 110'
layer_blocks = ((depth - 2) // 6)
self.conv_1_3x3 = nn.Conv2d(channels, 16, kernel_size=3, str... |
class UDPServer(Server):
def __init__(self, host, prog, vers, port):
Server.__init__(self, host, prog, vers, port)
self.connect()
def connect(self):
self.sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
self.prot = IPPROTO_UDP
self.sock.bind((self.host, self.port))... |
class Isothermal(BaseThermal):
def __init__(self, param, options=None):
super().__init__(param, options=options)
def get_fundamental_variables(self):
y = pybamm.standard_spatial_vars.y
z = pybamm.standard_spatial_vars.z
T_x_av = self.param.T_amb(y, z, pybamm.t)
T_dict = {... |
def test_demand_saving_with_indexed_array_from_hdf():
model = load_model('demand_saving_hdf.json')
model.timestepper.end = pd.Timestamp('2016-01-31')
rec_demand = NumpyArrayNodeRecorder(model, model.nodes['Demand'])
rec_storage = NumpyArrayStorageRecorder(model, model.nodes['Reservoir'])
model.check... |
.parametrize('x_val, unique_axis, repeats, repeat_axis', [(np.array([[(- 10), (- 3)], [(- 10), 2]], dtype=np.int64), None, (1, 2), 0)])
.parametrize('return_index', [False])
.parametrize('return_counts', [False])
.parametrize('return_inverse', [False])
def test_local_Unique_Repeat(x_val, unique_axis, repeats, repeat_ax... |
class TestClientSubscription(ClientTestCase):
def setUp(self):
super(TestClientSubscription, self).setUp()
self.base_url = '{}/subscriptions'.format(self.base_url)
self.subscription_id = 'sub_8RlLljfA4AnDVx'
def test_subscription_fetch_all(self):
result = mock_file('subscription_... |
class SawyerButtonPressTopdownEnvV2(SawyerXYZEnv):
def __init__(self):
hand_low = ((- 0.5), 0.4, 0.05)
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.1), 0.8, 0.115)
obj_high = (0.1, 0.9, 0.115)
super().__init__(self.model_name, hand_low=hand_low, hand_high=hand_high)
self... |
_cache()
def setup_logger(output=None, distributed_rank=0, *, color=True, name='imagenet', abbrev_name=None):
logger = logging.getLogger(name)
logger.setLevel(logging.DEBUG)
logger.propagate = False
if (abbrev_name is None):
abbrev_name = name
plain_formatter = logging.Formatter('[%(asctime)... |
class SupportsCompositeMetricCompute(Protocol):
composite_metric_name: str
requires_metric: List[str]
requires_validator: List[str]
def compute(self, metric_results: Dict[(str, torch.Tensor)], validation_results: Dict[(str, validators.ValidatorOutput)], simulation_output: SimulationOutputCLE) -> float:
... |
class MNISTInstance(datasets.MNIST):
def __getitem__(self, index):
if self.train:
(img, target) = (self.train_data[index], self.train_labels[index])
else:
(img, target) = (self.test_data[index], self.test_labels[index])
img = Image.fromarray(img.numpy(), mode='L')
... |
def create_generators(args):
common_args = {'batch_size': args.batch_size, 'phi': args.phi, 'detect_text': args.detect_text, 'detect_quadrangle': args.detect_quadrangle}
if args.random_transform:
misc_effect = MiscEffect()
visual_effect = VisualEffect()
else:
misc_effect = None
... |
def compose_transform(R=None, t=None):
xp = cuda.get_array_module(R, t)
if (R is None):
Rs = xp.eye(3)[None]
else:
Rs = R[None]
if (t is None):
ts = xp.zeros((1, 3))
else:
ts = t[None]
with chainer.no_backprop_mode():
Ts = compose_transform_function(Rs, ts... |
class TCOMM(TestCase):
def test_default(self):
frame = COMM()
self.assertEqual(frame.encoding, 1)
self.assertEqual(frame.lang, u'XXX')
self.assertEqual(frame.desc, u'')
self.assertEqual(frame.text, [])
def test_hash(self):
frame = COMM(encoding=0, lang='foo', desc... |
class _EnsurePackagesDiscovered(_expand.EnsurePackagesDiscovered):
def __init__(self, distribution: 'Distribution', project_cfg: dict, setuptools_cfg: dict):
super().__init__(distribution)
self._project_cfg = project_cfg
self._setuptools_cfg = setuptools_cfg
def __enter__(self):
... |
def check_unix_fs_mocked(tmpdir: Any, mocker: MockerFixture) -> Callable[([Any, Any], None)]:
def check(mocked_rm, mocked_ls):
assert (mocked_rm is os.remove)
assert (mocked_ls is os.listdir)
file_name = (tmpdir / 'foo.txt')
file_name.ensure()
UnixFS.rm(str(file_name))
... |
def test_python_nodes_are_unique(tmp_path):
tmp_path.joinpath('a').mkdir()
tmp_path.joinpath('a', 'task_example.py').write_text('def task_example(a=1): pass')
tmp_path.joinpath('b').mkdir()
tmp_path.joinpath('b', 'task_example.py').write_text('def task_example(a=2): pass')
session = build(paths=tmp_... |
def test_return_on_hover(page: Page):
page.get_by_role('link', name='simple popup').click()
page.get_by_role('link', name='simple popup').click()
expect(page.get_by_text('Popup: None')).to_be_visible()
expect(page.get_by_text('Tooltip: None')).to_be_visible()
page.get_by_text('Return on hover?').cli... |
class Effect7062(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... |
def test_joined_validators():
tst_validator = joined_validators(strict_discrete_set, strict_range)
values = [['ON', 'OFF'], range(10)]
assert (tst_validator(5, values) == 5)
assert (tst_validator(5.1, values) == 5.1)
assert (tst_validator('ON', values) == 'ON')
with pytest.raises(ValueError):
... |
class CalcToggleCommandFitStatesCommand(wx.Command):
def __init__(self, fitID, mainCommandFitID, commandFitIDs, forceStates=None):
wx.Command.__init__(self, True, 'Toggle Command Fit States')
self.fitID = fitID
self.mainCommandFitID = mainCommandFitID
self.commandFitIDs = commandFitI... |
class KsymAdaptedKRKS(krks.KRKS, khf_ksymm.KRHF):
get_veff = get_veff
get_rho = get_rho
kpts = khf_ksymm.KsymAdaptedKSCF.kpts
get_ovlp = khf_ksymm.KsymAdaptedKSCF.get_ovlp
get_hcore = khf_ksymm.KsymAdaptedKSCF.get_hcore
get_jk = khf_ksymm.KsymAdaptedKSCF.get_jk
get_occ = khf_ksymm.KsymAdapte... |
def test_slope_aware_backtracking():
index = pd.date_range('2019-01-01T08:00', '2019-01-01T17:00', freq='h')
index = index.tz_localize('Etc/GMT+5')
expected_data = pd.DataFrame(index=index, data=[(2.404287, 122.79177, (- 84.44), (- 10.899)), (11.263058, 133.288729, (- 72.604), (- 25.747)), (18.733558, 145.2... |
class TestKeyedOptimizer(unittest.TestCase):
def _assert_state_dict_equals(self, dict1: Dict[(str, Any)], dict2: Dict[(str, Any)]) -> None:
self.assertEqual(dict1['param_groups'], dict2['param_groups'])
self.assertEqual(dict1['state']['param_2'], dict2['state']['param_2'])
torch.testing.asse... |
def has_aer():
if (not _PROVIDER_CHECK.checked_aer):
try:
from qiskit.providers.aer import AerProvider
_PROVIDER_CHECK.has_aer = True
except Exception as ex:
_PROVIDER_CHECK.has_aer = False
logger.debug("AerProvider not loaded: '%s'", str(ex))
... |
class TestBmshj2018Factorized():
def test_params(self):
for i in range(1, 6):
net = bmshj2018_factorized(i, metric='mse')
assert isinstance(net, FactorizedPrior)
assert (net.state_dict()['g_a.0.weight'].size(0) == 128)
assert (net.state_dict()['g_a.6.weight'].... |
def usymeig(A: LinearOperator, neig: Optional[int]=None, M: Optional[LinearOperator]=None, bck_options: Mapping[(str, Any)]={}, method: Union[(str, Callable, None)]=None, **fwd_options) -> Tuple[(torch.Tensor, torch.Tensor)]:
return symeig(A, neig, 'uppest', M, method=method, bck_options=bck_options, **fwd_options) |
(*specs)
def test_env_semantics(spec):
with open(ROLLOUT_FILE) as data_file:
rollout_dict = json.load(data_file)
if (spec.id not in rollout_dict):
if ((not spec.nondeterministic) or should_skip_env_spec_for_tests(spec)):
logger.warn('Rollout does not exist for {}, run generate_json.p... |
def ranolazine_mpo() -> GoalDirectedBenchmark:
ranolazine = 'COc1ccccc1OCC(O)CN2CCN(CC(=O)Nc3c(C)cccc3C)CC2'
modifier = ClippedScoreModifier(upper_x=0.7)
similar_to_ranolazine = TanimotoScoringFunction(ranolazine, fp_type='AP', score_modifier=modifier)
logP_under_4 = RdkitScoringFunction(descriptor=logP... |
class SPP(nn.Module):
def __init__(self):
super(SPP, self).__init__()
def forward(self, x):
x_1 = torch.nn.functional.max_pool2d(x, 5, stride=1, padding=2)
x_2 = torch.nn.functional.max_pool2d(x, 9, stride=1, padding=4)
x_3 = torch.nn.functional.max_pool2d(x, 13, stride=1, paddin... |
def test_create():
builder = NintendontConnectorBuilder('102.168.1.1')
assert (builder.configuration_params() == {'ip': '102.168.1.1'})
assert (builder.connector_builder_choice == ConnectorBuilderChoice.NINTENDONT)
assert (builder.pretty_text == 'Nintendont: 102.168.1.1')
executor = builder.create_e... |
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