code stringlengths 281 23.7M |
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_config
def test_tile_add_on_top(manager):
manager.c.next_layout()
manager.c.next_layout()
manager.test_window('one')
manager.test_window('two')
manager.test_window('three')
assert (manager.c.layout.info()['master'] == ['one'])
assert (manager.c.layout.info()['slave'] == ['two', 'three'])
... |
def test_QobjEvo_step_coeff():
coeff1 = np.random.rand(6)
coeff2 = (np.random.rand(6) + (np.random.rand(6) * 1j))
tlist = np.array([2, 3, 4, 5, 6, 7], dtype=float)
qobjevo = QobjEvo([[sigmaz(), coeff1], [sigmax(), coeff2]], tlist=tlist, order=0)
assert (qobjevo(2.0)[(0, 0)] == coeff1[0])
assert ... |
_procedure('default-error-display-handler', [values_string.W_String, values.W_Object], simple=False)
def default_error_display_handler(msg, exn_object, env, cont):
from pycket.prims.input_output import current_error_param, return_void
port = current_error_param.get(cont)
assert isinstance(port, values.W_Out... |
class TestGPUTorchConnector(QiskitMachineLearningTestCase, TestTorchConnector):
def setUp(self):
super().setup_test()
super().setUp()
import torch
if (not torch.cuda.is_available()):
self.skipTest('CUDA is not available')
else:
self._device = torch.dev... |
class ConvNetwork(LayersPowered, Serializable):
def __init__(self, name, input_shape, output_dim, conv_filters, conv_filter_sizes, conv_strides, conv_pads, hidden_sizes, hidden_nonlinearity, output_nonlinearity, hidden_W_init=L.XavierUniformInitializer(), hidden_b_init=tf.zeros_initializer(), output_W_init=L.Xavier... |
def _check_shape_type(shape):
out = []
try:
shape = np.atleast_1d(shape)
for s in shape:
if (isinstance(s, np.ndarray) and (s.ndim > 0)):
raise TypeError(f'Value {s} is not a valid integer')
o = int(s)
if (o != s):
raise TypeErr... |
def cocofy_lvis(input_filename, output_filename):
with open(input_filename, 'r') as f:
lvis_json = json.load(f)
lvis_annos = lvis_json.pop('annotations')
cocofied_lvis = copy.deepcopy(lvis_json)
lvis_json['annotations'] = lvis_annos
lvis_cat_id_to_synset = {cat['id']: cat['synset'] for cat i... |
class SAFENC(BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info):
super(SAFENC, self).__init__(filename, filename_info, filetype_info)
self._start_time = filename_info['start_time']
self._end_time = filename_info['end_time']
self._fstart_time = filename_info[... |
def translate(context, text, disambiguation=None):
newtext = QtCore.QCoreApplication.translate(context, text, disambiguation)
(s, tt) = _splitMainAndTt(newtext)
translation = Translation(s)
translation.original = text
translation.tt = tt
translation.key = _splitMainAndTt(text)[0].strip()
ret... |
def _decorator_ignore_request_apikey(func):
(func)
def wrapper(self, request, spider):
url = urlparse(request.url)
query_args = parse_qs(url.query)
apikey = query_args.get('apikey', list())
if (len(apikey) != 0):
del query_args['apikey']
token = query_args.get... |
.parametrize('source, expected', [("html.div(dict(camelCase='test'))", "html.div(dict(camel_case='test'))"), ("reactpy.html.button({'onClick': block_forever})", "reactpy.html.button({'on_click': block_forever})"), ("html.div(dict(style={'testThing': test}))", "html.div(dict(style={'test_thing': test}))"), ('html.div(di... |
class alexnet(torch.nn.Module):
def __init__(self, requires_grad=False, pretrained=True):
super(alexnet, self).__init__()
alexnet_pretrained_features = models.alexnet(pretrained=pretrained).features
self.slice1 = torch.nn.Sequential()
self.slice2 = torch.nn.Sequential()
self.... |
def test_packed_array_port_array(do_test):
class struct():
bar: Bits32
foo: ([([Bits32] * 2)] * 3)
class A(Component):
def construct(s):
s.in_ = [InPort(struct) for _ in range(2)]
a = A()
foo = rdt.PackedArray([3, 2], rdt.Vector(32))
st = rdt.Struct(struct, {'bar'... |
def test_axles():
fa = OSC.Axle(2, 2, 2, 1, 1)
ra = OSC.Axle(1, 1, 2, 1, 1)
aa = OSC.Axle(1, 1, 2, 1, 1)
aa2 = OSC.Axle(2, 3, 1, 3, 2)
axles = OSC.Axles(fa, ra)
axles.add_axle(aa)
axles.add_axle(aa2)
prettyprint(axles.get_element())
axles2 = OSC.Axles(fa, ra)
axles2.add_axle(aa)
... |
class GraspNetBaseLine():
def __init__(self, checkpoint_path, num_point=20000, num_view=300, collision_thresh=0.001, empty_thresh=0.15, voxel_size=0.01):
self.checkpoint_path = checkpoint_path
self.num_point = num_point
self.num_view = num_view
self.collision_thresh = collision_thres... |
.parametrize('namespace,repository,uuid,expected_code', [('devtable', 'simple', 'exists', 200), ('devtable', 'simple', 'not found', 404)])
def test_get_repo_notification(namespace, repository, uuid, expected_code, authd_client, monkeypatch):
monkeypatch.setattr('endpoints.api.repositorynotification.model.get_repo_n... |
class PickupExporterSolo(PickupExporter):
def __init__(self, memo_data: dict[(str, str)], game: RandovaniaGame):
self.memo_data = memo_data
super().__init__(game)
def create_details(self, original_index: PickupIndex, pickup_target: PickupTarget, visual_pickup: PickupEntry, model_pickup: PickupEn... |
def test():
spi1 = SPI(1, baudrate=, sck=Pin(14), mosi=Pin(13))
display = Display(spi1, dc=Pin(4), cs=Pin(16), rst=Pin(17))
spi2 = SPI(2, baudrate=1000000, sck=Pin(18), mosi=Pin(23), miso=Pin(19))
Demo(display, spi2)
try:
while True:
idle()
except KeyboardInterrupt:
p... |
class BaseCorr3dMM(OpenMPOp, _NoPythonOp):
check_broadcast = False
__props__ = ('border_mode', 'subsample', 'filter_dilation', 'num_groups')
_direction: Optional[str] = None
params_type = ParamsType(direction=EnumList(('DIRECTION_FORWARD', 'forward'), ('DIRECTION_BACKPROP_WEIGHTS', 'backprop weights'), ... |
def test_class_smoothing():
box = np.array([0, 0, 10, 10])
mot = MultiObjectTracker(dt=0.1)
mot.step([Detection(box=box, class_id=1)])
mot.step([Detection(box=box, class_id=2)])
mot.step([Detection(box=box, class_id=2)])
assert (mot.trackers[0].class_id == 2)
mot.step([Detection(box=box, cla... |
def _test_sharding_and_remapping(tables: List[EmbeddingBagConfig], rank: int, world_size: int, kjt_input_per_rank: List[KeyedJaggedTensor], kjt_out_per_iter_per_rank: List[List[KeyedJaggedTensor]], sharder: ModuleSharder[nn.Module], backend: str, local_size: Optional[int]=None, mch_size: Optional[int]=None) -> None:
... |
def train(cfg: ModelSettings) -> None:
if (cfg.load_pt_checkpoint is not None):
load_strategy = LoadPTCheckpointStrategy(cfg.load_pt_checkpoint, cfg=cfg, generation_flag=True)
model = load_strategy.get_model(DNATransformer)
elif (cfg.load_ds_checkpoint is not None):
load_strategy = LoadD... |
def test_main_with_list_actions(tmpfolder, capsys, isolated_logger):
args = ['my-project', '--no-tox', '--list-actions']
cli.main(args)
(out, _) = capsys.readouterr()
assert ('Planned Actions' in out)
assert ('pyscaffold.actions:get_default_options' in out)
assert ('pyscaffold.structure:define_s... |
def repartition(table, outdir, npartitions=None, chunksize=None, compression='snappy'):
size = get_size_gb(table)
if (npartitions is None):
npartitions = max(1, size)
print(f'Converting {table} of {size} GB to {npartitions} parquet files, chunksize: {chunksize}')
read_csv_table(table, chunksize)... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None):
super(Bottleneck, self).__init__()
self.conv1 = conv1x1(inplanes, planes)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = conv3x3(planes, planes, stride)
self.bn2 = ... |
def settings_processor(request):
return {'adserver_ethicalads_branding': settings.ADSERVER_ETHICALADS_BRANDING, 'adserver_privacy_policy': settings.ADSERVER_PRIVACY_POLICY_URL, 'adserver_publisher_policy': settings.ADSERVER_PUBLISHER_POLICY_URL, 'adserver_version': settings.ADSERVER_VERSION, 'plausible_domain': set... |
def inspect_font(filename):
try:
info = ttf.TruetypeInfo(filename)
print('{0}:'.format(filename))
print(info.get_name('family'))
print(('bold=%r' % info.is_bold()))
print(('italic=%r' % info.is_italic()))
except:
print(('%s could not be identified. It is probably... |
class BaseProxyView(View):
log_level = logging.DEBUG
log_security_level = logging.WARNING
impression_type = VIEWS
success_message = 'Billed impression'
def ignore_tracking_reason(self, request, advertisement, offer):
reason = None
ip_address = get_client_ip(request)
user_agen... |
class AveragerAcrossThresholds():
def __init__(self, imputer, percentiles=[10, 20, 30, 40, 50, 60, 70, 80, 90]):
self.imputer = imputer
self.percentiles = percentiles
def __call__(self, input_tensor: torch.Tensor, cams: np.ndarray, targets: List[Callable], model: torch.nn.Module):
scores... |
class PushNegatives(SKCMatrixAndWeightTransformerABC):
_inherit(SKCMatrixAndWeightTransformerABC._transform_weights)
def _transform_weights(self, weights):
return push_negatives(weights, axis=None)
_inherit(SKCMatrixAndWeightTransformerABC._transform_matrix)
def _transform_matrix(self, matrix):
... |
def GetTableList(glb):
tables = []
query = QSqlQuery(glb.db)
if glb.dbref.is_sqlite3:
QueryExec(query, "SELECT name FROM sqlite_master WHERE type IN ( 'table' , 'view' ) ORDER BY name")
else:
QueryExec(query, "SELECT table_name FROM information_schema.tables WHERE table_schema = 'public'... |
class FromPackageLoader():
pkg_name: str
search_paths: Sequence[str]
def __init__(self, pkg_name: str, search_paths: Sequence[str]=('',)) -> None:
self.pkg_name = pkg_name
self.search_paths = search_paths
def __repr__(self):
return ('%s(%r, %r)' % (type(self).__name__, self.pkg_n... |
def len_to_system(fil, item=None):
s = System()
e = Spheroid()
th = 0.0
for line in fil.readlines():
p = line.split()
if (not p):
continue
(cmd, args) = (p[0], p[1:])
if (cmd == 'LEN'):
s.description = ' '.join(args[1:(- 2)]).strip('"')
eli... |
class SignInBot():
async def sign_in_bot(self: 'pyrogram.Client', bot_token: str) -> 'types.User':
while True:
try:
r = (await self.invoke(raw.functions.auth.ImportBotAuthorization(flags=0, api_id=self.api_id, api_hash=self.api_hash, bot_auth_token=bot_token)))
except... |
def convnet_arg_scope(is_training=True, weight_decay=5e-05, stddev=0.05):
batch_norm_params = {'is_training': is_training, 'center': True, 'scale': True, 'decay': 0.9999, 'epsilon': 0.001, 'zero_debias_moving_mean': True}
weights_init = tf.random_normal_initializer(0, stddev)
regularizer = tf.contrib.layers... |
def parse_
Response = collections.namedtuple('Response', ['ok', 'url', 'text', 'headers', 'status_code', 'reason', 'error'])
text = ''
status_code = 0
headers = {}
error_msg = ''
reason = ''
if hasattr(resp, 'text'):
text = resp.text
url = resp.url
status_code = resp.... |
def test_cw_rx():
print('#### CW receiver ####')
cw = cw_rx()
print('# CW receiver parameters #')
assert (cw.bb_prop['fs'] == 20)
assert (cw.rf_prop['noise_figure'] == 12)
assert (cw.rf_prop['rf_gain'] == 20)
assert (cw.bb_prop['load_resistor'] == 1000)
assert (cw.bb_prop['baseband_gain'... |
def test_admin_session_duplicate_session(clean_database, mock_emit_session_update, flask_app, mock_audit):
user1 = database.User.create(id=1234, name='The Name')
user2 = database.User.create(id=2345, name='Other Name')
session = database.MultiplayerSession.create(id=1, name='Debug', state=MultiplayerSession... |
('torch.distributed._broadcast_coalesced', mock)
('torch.distributed.broadcast', mock)
('torch.nn.parallel.DistributedDataParallel._ddp_init_helper', mock)
def test_is_module_wrapper():
class Model(nn.Module):
def __init__(self):
super().__init__()
self.conv = nn.Conv2d(2, 2, 1)
... |
class MyghtyLexer(RegexLexer):
name = 'Myghty'
url = '
aliases = ['myghty']
filenames = ['*.myt', 'autodelegate']
mimetypes = ['application/x-myghty']
version_added = '0.6'
tokens = {'root': [('\\s+', Text), ('(?s)(<%(?:def|method))(\\s*)(.*?)(>)(.*?)(</%\\2\\s*>)', bygroups(Name.Tag, Text, ... |
def test_source_show_simple(tester: CommandTester) -> None:
tester.execute('')
expected = 'name : existing\nurl : : primary\n\nname : one\nurl : : primary\n\nname : two\nurl : : primary\n'.splitlines()
assert ([line.strip() for line in tester.io.fetch_output().strip().... |
class ASPP(nn.Module):
def __init__(self, in_channels=2048, out_channels=256, output_stride=8):
super().__init__()
if (output_stride == 16):
dilations = [6, 12, 18]
elif (output_stride == 8):
dilations = [12, 24, 36]
else:
raise NotImplementedError... |
class Syncer(abc.ABC):
def name() -> str:
raise NotImplementedError
async def _get_diff(guild: Guild) -> _Diff:
raise NotImplementedError
async def _sync(diff: _Diff) -> None:
raise NotImplementedError
async def sync(cls, guild: Guild, ctx: (Context | None)=None) -> None:
... |
class ews_input_addsubsec(unittest.TestCase):
def test(self):
run_test(self, ['-o 01 1379 500', '-a', '1', '2', '-s', '5', '6'], ' Month/Day/Year H:M:S 06/11/2006 00:08:12 GPS\n Modified Julian Date 53897. GPS\n GPSweek DayOfWeek SecOfWeek 355 0 492.000000\n... |
def rtn_strcasecmp(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('strcasecmp hooked')
s1 = pstate.get_argument_value(0)
s2 = pstate.get_argument_value(1)
size = min(len(pstate.memory.read_string(s1)), (len(pstate.memory.read_string(s2)) + 1))
ptr_bit_size = pstate.ptr_bit_size
as... |
_start_docstrings('\n XLM-RoBERTa Model with a token classification head on top (a linear layer on top of the hidden-states output) e.g.\n for Named-Entity-Recognition (NER) tasks.\n ', XLM_ROBERTA_START_DOCSTRING)
class TFXLMRobertaForTokenClassification(TFRobertaForTokenClassification):
config_class = XL... |
def filter_model(desc, model_filter, protocol_filter=None):
if (protocol_filter is None):
protocol_filter = {'IOT', 'SMART'}
filtered = list()
for (file, protocol) in SUPPORTED_DEVICES:
if (protocol in protocol_filter):
file_model_region = basename(file).split('_')[0]
... |
class Solution():
def __init__(self):
self.temp = []
self.res = 0
def sumRootToLeaf(self, root: TreeNode, level=0) -> List[str]:
if (root is None):
return
while (len(self.temp) > level):
self.temp.pop()
self.temp.append(root.val)
level += 1... |
def import_class(import_str):
(mod_str, _sep, class_str) = import_str.rpartition('.')
__import__(mod_str)
try:
return getattr(sys.modules[mod_str], class_str)
except AttributeError:
raise ImportError(('Class %s cannot be found (%s)' % (class_str, traceback.format_exception(*sys.exc_info(... |
def func_attention(query, context, gamma1):
(batch_size, queryL) = (query.size(0), query.size(2))
(ih, iw) = (context.size(2), context.size(3))
sourceL = (ih * iw)
context = context.view(batch_size, (- 1), sourceL)
contextT = torch.transpose(context, 1, 2).contiguous()
attn = torch.bmm(contextT,... |
class AttributeNestedSerializer(AttributeListSerializer):
elements = serializers.SerializerMethodField()
class Meta(AttributeListSerializer.Meta):
fields = (*AttributeListSerializer.Meta.fields, 'elements')
def get_elements(self, obj):
return AttributeNestedSerializer(obj.get_children(), man... |
class InverseHammerTest(unittest.TestCase):
def setUpClass(self):
self.p = Proj(proj='hammer')
(self.x, self.y) = self.p((- 30), 40)
def test_forward(self):
self.assertAlmostEqual(self.x, (- 2711575.083), places=3)
self.assertAlmostEqual(self.y, 4395506.619, places=3)
def tes... |
def get_auth_credentials(service, site, url, majorversion, token, timeout):
url = fillurl(service, site, url, majorversion, 'auth')
f = _request(url, timeout=timeout, post=token)
s = f.read().decode()
try:
(user, passwd) = s.strip().split(':')
except ValueError:
raise CannotGetCreden... |
def test_field_without_parameters():
with pytest.raises(ValueError, match=full_match_regex_str("Fields {'a'} do not bound to any parameter")):
InputShape(constructor=stub_constructor, kwargs=None, fields=(InputField(id='a', type=int, default=NoDefault(), is_required=True, metadata={}, original=None),), para... |
class QuantizableMobileHairNet(MobileHairNet):
def __init__(self):
super(QuantizableMobileHairNet, self).__init__(encode_block=QuantizableLayerDepwiseEncode, decode_block=QuantizableLayerDepwiseDecode)
self.quant = torch.quantization.QuantStub()
self.dequant = torch.quantization.DeQuantStub(... |
def test_struct_type():
test_dict = {'type': 'struct', 'fields': [{'type': 'int', 'bits': 32}, {'type': 'string', 'bytes': 32}]}
recap_type = from_dict(test_dict)
assert isinstance(recap_type, StructType)
assert (recap_type.type_ == 'struct')
for field in recap_type.fields:
if isinstance(fie... |
def convert_trajectory_transformer_original_pytorch_checkpoint_to_pytorch(logbase, dataset, loadpath, epoch, device):
(gpt, gpt_epoch) = utils.load_model(logbase, dataset, loadpath, epoch=epoch, device=device)
trajectory_transformer = TrajectoryTransformerModel(gpt.config)
trajectory_transformer.tok_emb.loa... |
class KgCVAEConfig(object):
description = None
use_hcf = True
update_limit = 3000
api_dir = 'data/cambridge_data/api_cambridge.pkl'
rev_vocab_dir = 'data/cambridge_data/rev_vocab.pkl'
n_state = 10
cell_type = 'lstm'
encoding_cell_size = 400
state_cell_size = n_state
embed_size = ... |
def finish_perf_region(label):
from pycket.prims.general import current_gc_time
if os_check_env_var('PLT_LINKLET_TIMES'):
assert (len(linklet_perf.current_start_time) > 0)
delta = (rtime.time() - linklet_perf.current_start_time[(- 1)])
delta_gc = (current_gc_time() - linklet_perf.current... |
def main():
args = parse_args()
setup_repo(args.cpython_repo, args.branch)
run(*['sphinx-build', '-jauto', '-QDgettext_compact=0', '-bgettext', '.', '../pot'], cwd=(args.cpython_repo / 'Doc'))
pot_path = (args.cpython_repo / 'pot')
upstream = {file.relative_to(pot_path).with_suffix('.po') for file i... |
(everythings(min_int=(- ), max_int=))
def test_msgpack(everything: Everything):
from msgpack import dumps as msgpack_dumps
from msgpack import loads as msgpack_loads
converter = msgpack_make_converter()
raw = msgpack_dumps(converter.unstructure(everything))
assert (converter.structure(msgpack_loads(... |
.skipif((not (torch.cuda.device_count() >= 2)), reason='not enough cuda devices')
class TestFSDP():
class MyDModule(nn.Module):
def __init__(self):
super().__init__()
self.fc1 = nn.Linear(8, 8, bias=False)
self.fc2 = nn.Linear(8, 8, bias=False)
self.relu = nn.... |
class InaccessibleSysPath(fixtures.OnSysPath, ffs.TestCase):
site_dir = '/access-denied'
def setUp(self):
super().setUp()
self.setUpPyfakefs()
self.fs.create_dir(self.site_dir, perm_bits=0)
def test_discovery(self):
list(importlib_metadata.distributions()) |
class RSAPublicKey(PublicKey):
def __init__(self, public_key: rsa.RSAPublicKey):
self._public_key = public_key
def verify(self, data: bytes, signature: bytes) -> bool:
try:
signature = base64.b64decode(signature)
self._public_key.verify(signature, data, _RSA_PADDING, _RSA... |
def get_parser():
parser = argparse.ArgumentParser()
parser.add_argument('--dir', type=str, default='./data', help='Directory for splitted dataset')
parser.add_argument('--no_subset', action='store_true', help='Do not create subsets for training and testing')
parser.add_argument('--train_size', type=int... |
.parametrize('endpoint, params', [(UserRobot, {'robot_shortname': 'dtrobot'}), (OrgRobot, {'orgname': 'buynlarge', 'robot_shortname': 'coolrobot'})])
def test_retrieve_robot(endpoint, params, app):
with client_with_identity('devtable', app) as cl:
result = conduct_api_call(cl, endpoint, 'GET', params, None)... |
class F12_UserData(F8_UserData):
removedKeywords = F8_UserData.removedKeywords
removedAttrs = F8_UserData.removedAttrs
def __init__(self, *args, **kwargs):
F8_UserData.__init__(self, *args, **kwargs)
self.gecos = kwargs.get('gecos', '')
def _getArgsAsStr(self):
retval = F8_UserDa... |
class Transformer(nn.Module):
def __init__(self, dim, depth, heads=8, dim_head=64, mlp_mult=4, local_patch_size=7, global_k=7, dropout=0.0, has_local=True):
super().__init__()
self.layers = nn.ModuleList([])
for _ in range(depth):
self.layers.append(nn.ModuleList([(Residual(PreNo... |
def getattribute_from_module(module, attr):
if (attr is None):
return None
if isinstance(attr, tuple):
return tuple((getattribute_from_module(module, a) for a in attr))
if hasattr(module, attr):
return getattr(module, attr)
pixel_module = importlib.import_module('pixel')
if h... |
class VectorStrategy(object):
__metaclass__ = SingletonMeta
def is_correct_type(self, w_vector, w_obj):
raise NotImplementedError('abstract base class')
def immutable(self):
return False
def ref(self, w_vector, i, check=True):
if check:
self.indexcheck(w_vector, i)
... |
class MegatronTrainer(Trainer):
def __init__(self, args, task, model, criterion):
if (not has_megatron_submodule):
raise ImportError('\n\nPlease install the megatron submodule:\n\n git submodule update --init fairseq/model_parallel/megatron')
super().__init__(args, task, model, criterio... |
class MainWindow(QMainWindow):
def __init__(self):
super(MainWindow, self).__init__()
self.setWindowTitle('PyQtConfig Demo')
self.config = ConfigManager()
CHOICE_A = 1
CHOICE_B = 2
CHOICE_C = 3
CHOICE_D = 4
map_dict = {'Choice A': CHOICE_A, 'Choice B':... |
def setup_sphinx_tabs(app, config):
if (sphinx.version_info < (3, 0, 0)):
listeners = list(app.events.listeners.get('html-page-context').items())
else:
listeners = [(listener.id, listener.handler) for listener in app.events.listeners.get('html-page-context')]
for (listener_id, function) in l... |
class ModuleUnloadedBreakpoint():
def __init__(self, target):
breakpoint = target.BreakpointCreateByName('oe_debug_module_unloaded_hook')
breakpoint.SetScriptCallbackFunction('lldb_sgx_plugin.ModuleUnloadedBreakpoint.onHit')
def onHit(frame, bp_loc, dict):
library_image_addr = frame.Find... |
class TestTrainingExtensionsSvd(unittest.TestCase):
def test_pick_compression_layers_top_x_percent(self):
logger.debug(self.id())
model = MnistModel().to('cpu')
input_shape = (1, 1, 28, 28)
dummy_input = create_rand_tensors_given_shapes(input_shape, get_device(model))
layer_d... |
def make20(b):
theta1 = numpy.arccos((numpy.sqrt(5) / 3))
theta2 = numpy.arcsin(((r2edge(theta1, 1) / 2) / numpy.sin((numpy.pi / 5))))
r = ((b / 2) / numpy.sin((theta1 / 2)))
rot72 = rotmatz(((numpy.pi * 2) / 5))
s2 = numpy.sin(theta2)
c2 = numpy.cos(theta2)
s3 = numpy.sin((theta1 + theta2))... |
def create_pile(class_ids, num_instances, random_state=None):
if (random_state is None):
random_state = np.random.RandomState()
x = ((- 0.2), 0.2)
y = ((- 0.2), 0.2)
z = 0.5
bin_unique_id = safepicking.pybullet.create_bin(X=(x[1] - x[0]), Y=(y[1] - y[0]), Z=(z / 2))
unique_ids = []
c... |
def test_multiply_float_int():
float_width = 24
int_width = 8
val = np.random.random()
fp_bits = iter_bits_fixed_point(val, float_width)
fp_int = int(''.join((str(b) for b in fp_bits)), 2)
int_val = np.random.randint(0, ((2 ** int_width) - 1))
result = multiply_fixed_point_float_by_int(fp_in... |
class ObjectMessageType(MessageType):
def message_type_name(self):
return 'obj'
def message_to_bytes(self, message):
packer = Packer()
packer.pack_object(message)
return packer.get_buffer()
def message_from_bytes(self, bb):
if bb:
unpacker = Unpacker(bb)
... |
class PandaBucketConfig(PandaDefaultConfig):
def __init__(self) -> None:
super().__init__()
self.urdf_path = '{PACKAGE_ASSET_DIR}/descriptions/panda_bucket.urdf'
self.ee_link_name = 'bucket'
def controllers(self):
controller_configs = super().controllers
for (k, v) in con... |
def test_caption_query_get_by_language_code_when_exists():
caption1 = Caption({'url': 'url1', 'name': {'simpleText': 'name1'}, 'languageCode': 'en', 'vssId': '.en'})
caption2 = Caption({'url': 'url2', 'name': {'simpleText': 'name2'}, 'languageCode': 'fr', 'vssId': '.fr'})
caption_query = CaptionQuery(captio... |
class BezierFamily(BasisFamily):
def __init__(self, N, T=1):
super(BezierFamily, self).__init__(N)
self.T = float(T)
def eval_deriv(self, i, k, t, var=None):
if (i >= self.N):
raise ValueError('Basis function index too high')
elif (k >= self.N):
return (0 ... |
class SettingsTree(QTreeWidget):
def __init__(self, parent=None):
super(SettingsTree, self).__init__(parent)
self.setItemDelegate(VariantDelegate(self))
self.setHeaderLabels(('Setting', 'Type', 'Value'))
self.header().setSectionResizeMode(0, QHeaderView.Stretch)
self.header()... |
class ASSWriter():
ext = 'ass'
def _format_time(seconds):
h = int((seconds / 3600))
m = (int((seconds / 60)) % 60)
s = int((seconds % 60))
cs = int(((seconds % 1) * 100))
return ('%i:%02i:%02i.%02i' % (h, m, s, cs))
def header(self, file):
file.write('[Script ... |
def test_subquery_expression_without_source_table():
assert_column_lineage_equal('INSERT INTO foo\nSELECT (SELECT col1 + col2 AS result) AS sum_result\nFROM bar', [(ColumnQualifierTuple('col1', 'bar'), ColumnQualifierTuple('sum_result', 'foo')), (ColumnQualifierTuple('col2', 'bar'), ColumnQualifierTuple('sum_result... |
class ChocolateyPackage(Package):
def is_installed(self):
return (self.run_test('choco info -lo %s', self.name).rc == 0)
def version(self):
(_, version) = self.check_output('choco info -lo %s -r', self.name).split('|', 1)
return version
def release(self):
raise NotImplemented... |
def open_url(url: str, cache_dir: str=None, num_attempts: int=10, verbose: bool=True, return_filename: bool=False, cache: bool=True) -> Any:
assert (num_attempts >= 1)
assert (not (return_filename and (not cache)))
if (not re.match('^[a-z]+://', url)):
return (url if return_filename else open(url, '... |
.supported(only_if=(lambda backend: backend.cipher_supported(algorithms.TripleDES((b'\x00' * 8)), modes.CFB8((b'\x00' * 8)))), skip_message='Does not support TripleDES CFB8')
class TestTripleDESModeCFB8():
test_kat = generate_encrypt_test(load_nist_vectors, os.path.join('ciphers', '3DES', 'CFB'), ['TCFB8invperm.rsp... |
class UNetPlusPlus(nn.Module):
def __init__(self, in_ch, base_ch, scale, kernel_size, num_classes=1, block='SingleConv', norm='bn'):
super().__init__()
num_block = 2
block = get_block(block)
norm = get_norm(norm)
n_ch = [base_ch, (base_ch * 2), (base_ch * 4), (base_ch * 8), (... |
def k_means_cluster(root, k, nodes):
t = time.process_time()
if (len(nodes) <= k):
clusters = [[n] for n in nodes]
return clusters
ns = list(nodes)
root.stats['count_kmeans_iter_f'] += 1
cluster_starts = ns[:k]
cluster_centers = [center_of_gravity([n]) for n in cluster_starts]
... |
def conv2d_same(x, filters, prefix, stride=1, kernel_size=3, rate=1):
if (stride == 1):
return Conv2D(filters, (kernel_size, kernel_size), strides=(stride, stride), padding='same', use_bias=False, dilation_rate=(rate, rate), name=prefix)(x)
else:
kernel_size_effective = (kernel_size + ((kernel_s... |
def check_vocab_and_split(orig, bpe_codes, vocab, separator):
out = []
for segment in orig[:(- 1)]:
if ((segment + separator) in vocab):
out.append(segment)
else:
for item in recursive_split(segment, bpe_codes, vocab, separator, False):
out.append(item)
... |
class np_random():
def __init__(self, seed):
self.seed = seed
self.state = None
def __enter__(self):
self.state = np.random.get_state()
np.random.seed(self.seed)
return self.state
def __exit__(self, exc_type, exc_val, exc_tb):
np.random.set_state(self.state) |
def xf_epilogue(self):
self._xf_epilogue_done = 1
num_xfs = len(self.xf_list)
blah = (DEBUG or (self.verbosity >= 3))
blah1 = (DEBUG or (self.verbosity >= 1))
if blah:
fprintf(self.logfile, 'xf_epilogue called ...\n')
def check_same(book_arg, xf_arg, parent_arg, attr):
if (getatt... |
class ItemEffects(wx.Panel):
def __init__(self, parent, stuff, item):
wx.Panel.__init__(self, parent)
self.item = item
mainSizer = wx.BoxSizer(wx.VERTICAL)
self.effectList = AutoListCtrl(self, wx.ID_ANY, style=(((wx.LC_REPORT | wx.LC_SINGLE_SEL) | wx.LC_VRULES) | wx.NO_BORDER))
... |
class EventsImporterTestCase(TestCase):
def setUpClass(cls):
super().setUpClass()
cls.calendar = Calendar.objects.create(url=EVENTS_CALENDAR_URL, slug='python-events')
def test_injest(self):
importer = ICSImporter(self.calendar)
with open(EVENTS_CALENDAR) as fh:
ical ... |
def _load_named_resources() -> Dict[(str, Callable[([], Resource)])]:
resource_methods = load_group('torchx.named_resources', default={})
materialized_resources: Dict[(str, Callable[([], Resource)])] = {}
for (name, resource) in {**GENERIC_NAMED_RESOURCES, **AWS_NAMED_RESOURCES, **resource_methods}.items():... |
class LoadData(object):
def __init__(self):
self.trainfile = './ML100K/train.txt'
self.testfile = './ML100K/test.txt'
(self.num_users, self.num_items) = self.map_features()
self.user_positive_list = self.get_positive_list(self.trainfile)
(self.Train_data, self.Test_data) = se... |
class TestPartial(unittest.TestCase):
def test_Al2SiO5(self):
cell = Lattice.from_para(7.8758, 7.9794, 5.6139, 90, 90, 90)
spg = 58
elements = ['Al', 'Si', 'O']
composition = [8, 4, 20]
sites = [{'4e': [0.0, 0.0, 0.2418], '4g': [0.1294, 0.6392, 0.0]}, {'4g': [0.2458, 0.2522, ... |
def _warn_output_shape(*dim_names: str) -> None:
name = f'extract_patches{len(dim_names)}d'
partial_shape = 'x'.join(dim_names)
warnings.warn(f"The output shape of {name} will change in the future. The current shape B*PxCx{partial_shape} will be replaced by BxPxCx{partial_shape} thus adding a dimension. Her... |
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