code stringlengths 281 23.7M |
|---|
def _parameters_conversion(converter: callable, argument: str, parameter) -> typing.Any:
if (converter is bool):
return _convert_to_bool(argument)
try:
return converter(argument)
except Exception as exc:
try:
name = converter.__name__
except AttributeError:
... |
def make_output_filtering_dataframe(spark_context, spark_session):
data = [{'id': 1, 'ts': 1, 'feature1': 0, 'feature2': None, 'feature3': 1}, {'id': 1, 'ts': 2, 'feature1': 0, 'feature2': 1, 'feature3': 1}, {'id': 1, 'ts': 3, 'feature1': None, 'feature2': None, 'feature3': None}, {'id': 1, 'ts': 4, 'feature1': 0, ... |
def test_copy_method():
m.ExampleMandA.add2c = m.ExampleMandA.add2
m.ExampleMandA.add2d = m.ExampleMandA.add2b
a = m.ExampleMandA(123)
assert (a.value == 123)
a.add2(m.ExampleMandA((- 100)))
assert (a.value == 23)
a.add2b(m.ExampleMandA(20))
assert (a.value == 43)
a.add2c(m.ExampleMa... |
def _pad_version(left: List[str], right: List[str]) -> Tuple[(List[str], List[str])]:
(left_split, right_split) = ([], [])
left_split.append(list(itertools.takewhile((lambda x: x.isdigit()), left)))
right_split.append(list(itertools.takewhile((lambda x: x.isdigit()), right)))
left_split.append(left[len(... |
def create_tf_mixup_batch_augmentation(heads: List[TrainerHeadInterface], mixup_alpha: float) -> Callable:
def tf_mixup_batch_augmentation(batch_features, batch_targets):
batch_features_shape = tf.shape(batch_features)
batch_size = batch_features_shape[0]
beta = tfp.distributions.Beta(mixup_... |
def test_dynamic_compile_shows_nicely():
import importlib.util
import sys
src = 'def foo():\n assert 1 == 0\n'
name = 'abc-123'
spec = importlib.util.spec_from_loader(name, loader=None)
module = importlib.util.module_from_spec(spec)
code = compile(src, name, 'exec')
exec(code, module.__d... |
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 DmRaid_TestCase(unittest.TestCase):
def runTest(self):
data1 = FC6_DmRaidData()
data2 = FC6_DmRaidData()
self.assertEqual(data1, data2)
data1.name = ''
data2.name = 'test'
self.assertNotEqual(data1, data2)
self.assertNotEqual(data2, data1)
data1.... |
class Calculator(QWidget):
NumDigitButtons = 10
def __init__(self, parent=None):
super(Calculator, self).__init__(parent)
self.pendingAdditiveOperator = ''
self.pendingMultiplicativeOperator = ''
self.sumInMemory = 0.0
self.sumSoFar = 0.0
self.factorSoFar = 0.0
... |
class TestLabeledPriceWithoutRequest(TestLabeledPriceBase):
def test_slot_behaviour(self, labeled_price):
inst = labeled_price
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 SampledResponse(FrequencyResponse):
frequencies = Array.T(shape=(None,), dtype=float, serialize_as='list')
values = Array.T(shape=(None,), dtype=complex, serialize_as='list')
left = Complex.T(optional=True)
right = Complex.T(optional=True)
def __init__(self, frequencies, values, left=None, rig... |
def ytest_base(unit, related_prj_dir, related_prj_name, args):
keywords = {'DEPENDS': (- 1), 'DATA': (- 1)}
(flat_args, spec_args) = _common.sort_by_keywords(keywords, args)
unit.set(['TEST-NAME', os.path.basename(flat_args[0])])
unit.set(['SCRIPT-REL-PATH', flat_args[1]])
unit.set(['SOURCE-FOLDER-P... |
class AppBaseView(social_app.BaseViewClass):
def render_home(self, **extra):
context = common_context(web.config[setting_name('AUTHENTICATION_BACKENDS')], load_strategy(), user=self.get_current_user(), plus_id=web.config.get(setting_name('SOCIAL_AUTH_GOOGLE_PLUS_KEY')), **extra)
return render.home(*... |
_tf
class TFXLNetModelTest(TFModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = ((TFXLNetModel, TFXLNetLMHeadModel, TFXLNetForSequenceClassification, TFXLNetForTokenClassification, TFXLNetForQuestionAnsweringSimple, TFXLNetForMultipleChoice) if is_tf_available() else ())
all_generati... |
class InlineQueryResultDocument(InlineQueryResult):
__slots__ = ('reply_markup', 'caption_entities', 'document_url', 'thumbnail_width', 'thumbnail_height', 'caption', 'title', 'description', 'parse_mode', 'mime_type', 'thumbnail_url', 'input_message_content')
def __init__(self, id: str, document_url: str, title... |
def create_embedding(caption_file: str, vocab_file: str, embed_size: int, output: str, **fasttext_kwargs):
caption_df = pd.read_json(caption_file)
caption_df['tokens'] = caption_df['tokens'].apply((lambda x: ((['<start>'] + [token for token in x]) + ['<end>'])))
sentences = list(caption_df['tokens'].values)... |
def get_constant_counts_from_file(infile, has_header, include_count):
constant_counts = {}
line_iter = iter(infile)
lineno = 1
seen = {}
filename = infile.name
def oopsie(msg):
die(f'Unable to process constants file: {msg}, {filename!r} line {lineno}')
if has_header:
try:
... |
def pad_shape(shape, must_be_divisible_by):
if (not isinstance(must_be_divisible_by, (tuple, list, np.ndarray))):
must_be_divisible_by = ([must_be_divisible_by] * len(shape))
else:
assert (len(must_be_divisible_by) == len(shape))
new_shp = [((shape[i] + must_be_divisible_by[i]) - (shape[i] %... |
class ImageStorage():
def __init__(self, losses: Iterable[Loss]) -> None:
self.target_images_and_guides: Dict[(ComparisonLoss, Tuple[(torch.Tensor, Optional[torch.Tensor])])] = {}
self.input_guides: Dict[(Loss, torch.Tensor)] = {}
for loss in losses:
if isinstance(loss, Compariso... |
(msg=DATAREADER_DEPRECATION_WARNING)
def default_returns_func(symbol, start=None, end=None):
if (start is None):
start = '1/1/1970'
if (end is None):
end = _1_bday_ago()
start = get_utc_timestamp(start)
end = get_utc_timestamp(end)
if (symbol == 'SPY'):
filepath = data_path('... |
def get_head(head_name, in_index, idx_to_planes, tasks, task_channel_mapping, atrc_genotype_path=None):
in_index = [int(i) for i in in_index.split(',')]
in_index = (in_index[0] if (len(in_index) == 1) else in_index)
if (head_name == 'DemtHead'):
from .heads.demt_head import DemtHead
partial_... |
class BaseImageDataset(torch.utils.data.Dataset):
def __init__(self, name, root, image_loader=jpeg4py_loader):
self.name = name
self.root = root
self.image_loader = image_loader
self.image_list = []
self.class_list = []
def __len__(self):
return self.get_num_image... |
def build_model_base(images, model_name, training, override_params=None):
assert isinstance(images, tf.Tensor)
(blocks_args, global_params) = get_model_params(model_name, override_params)
with tf.variable_scope(model_name):
model = efficientnet_model.Model(blocks_args, global_params)
feature... |
_families
def test_root_testsuites_tag(pytester: Pytester, run_and_parse: RunAndParse, xunit_family: str) -> None:
pytester.makepyfile('\n def test_x():\n pass\n ')
(_, dom) = run_and_parse(family=xunit_family)
root = dom.get_unique_child
assert (root.tag == 'testsuites')
suite_... |
(order=True)
class TransactionChannelDeposit(State):
participant_address: Address
contract_balance: TokenAmount
deposit_block_number: BlockNumber
def __post_init__(self) -> None:
typecheck(self.participant_address, T_Address)
typecheck(self.contract_balance, T_TokenAmount)
typech... |
def _create_reader_for_fake_data(observation_type: str, fake_dataset: xr.Dataset, filename_info: Optional[dict]=None):
from satpy.readers.abi_l2_nc import NC_ABI_L2
if (filename_info is None):
filename_info = {'platform_shortname': 'G16', 'scene_abbr': 'C', 'scan_mode': 'M3'}
reader_args = ('filenam... |
def process_korQuAD(corpus_fname, output_fname):
with open(corpus_fname) as f1, open(output_fname, 'w', encoding='utf-8') as f2:
dataset_json = json.load(f1)
dataset = dataset_json['data']
for article in dataset:
w_lines = []
for paragraph in article['paragraphs']:
... |
.skip('KAK instability')
def test_zzswap_as_syc_2():
(q1, q2) = cirq.LineQubit.range(2)
zzs = ZZSwap(zz_exponent=0.123)
circuit = zzswap_as_syc(zzs.theta, q1, q2)
assert (str(circuit) == '0: PhX(0.145)^(0)Z^-0.2SYCPhX(0.214)^0.576Z^-0.131SYCSYCPhX(-0.0833)^0.576Z^-0.548\n ... |
def load_teapot_batch(batch_size=4, target_num=2):
(vertices, faces) = nr.load_obj(os.path.join(data_dir, 'teapot.obj'))
textures = torch.ones((faces.shape[0], 4, 4, 4, 3), dtype=torch.float32)
(vertices, faces, textures) = to_minibatch((vertices, faces, textures), batch_size, target_num)
return (vertic... |
def poly1305(cipher_encrypt, nonce, ciphertext):
otk = cipher_encrypt(nonce, bytes(32))
mac_data = ((ciphertext + bytes((((- len(ciphertext)) % 16) + 8))) + len(ciphertext).to_bytes(8, 'little'))
(acc, r, s) = (0, (int.from_bytes(otk[:16], 'little') & ), int.from_bytes(otk[16:], 'little'))
for i in rang... |
class ConnectionTestCase(TestCase):
def setUp(self):
self.test_table_name = 'Thread'
self.region = 'us-east-1'
def test_create_connection(self):
conn = TableConnection(self.test_table_name, meta_table=MetaTable(DESCRIBE_TABLE_DATA[TABLE_KEY]))
self.assertIsNotNone(conn)
def t... |
_model('s2t_transformer')
class S2TTransformerModel(FairseqEncoderDecoderModel):
def __init__(self, encoder, decoder):
super().__init__(encoder, decoder)
def add_args(parser):
parser.add_argument('--conv-kernel-sizes', type=str, metavar='N', help='kernel sizes of Conv1d subsampling layers')
... |
class ChannelPadding(nn.Module):
def __init__(self, in_planes, out_planes):
super(ChannelPadding, self).__init__()
self.register_buffer('padding', torch.zeros(((out_planes - in_planes) // 2)).view(1, (- 1), 1, 1))
def forward(self, input):
assert (len(input.size()) == 4), 'only support f... |
def get_so(atom, a, basis, kmesh):
cell = gto.Cell()
cell.atom = atom
cell.a = a
cell.basis = basis
cell.space_group_symmetry = True
cell.symmorphic = True
cell.build()
kpts = cell.make_kpts(kmesh, with_gamma_point=True, space_group_symmetry=True)
(sos_ks, irrep_ids_ks) = symm_adapte... |
class TestCreateGC(EndianTest):
def setUp(self):
self.req_args_0 = {'attrs': {'function': 7, 'plane_mask': , 'foreground': , 'background': , 'line_width': 61484, 'line_style': 2, 'cap_style': 2, 'join_style': 2, 'fill_style': 0, 'fill_rule': 1, 'tile': , 'stipple': , 'tile_stipple_x_origin': (- 25980), 'til... |
class Solution(object):
def largestPalindrome(self, n):
if (n == 1):
return 9
for a in xrange(2, (9 * (10 ** (n - 1)))):
hi = ((10 ** n) - a)
lo = int(str(hi)[::(- 1)])
if (((a ** 2) - (4 * lo)) < 0):
continue
if ((((a ** 2)... |
class UnboundType(ProperType):
__slots__ = ('name', 'args', 'optional', 'empty_tuple_index', 'original_str_expr', 'original_str_fallback')
def __init__(self, name: (str | None), args: (Sequence[Type] | None)=None, line: int=(- 1), column: int=(- 1), optional: bool=False, empty_tuple_index: bool=False, original_... |
.sphinx(srcdir=srcdir)
def test_lazy_tooltips_notlazy(app, status, warning):
app.build()
path = (app.outdir / '_static/js/hoverxref.js')
assert (path.exists() is True)
content = open(path).read()
chunks = [".each(function () { $(this).removeAttr('title') });"]
for chunk in chunks:
assert... |
.parametrize('username,password', users)
.parametrize('project_id', projects)
def test_project_export_csvsemicolon(db, client, username, password, project_id):
client.login(username=username, password=password)
url = reverse('project_export', args=[project_id, 'csvsemicolon'])
response = client.get(url)
... |
def test_zoom_ratio():
vb = pg.ViewBox(lockAspect=1)
vb.setFixedHeight(10)
vb.setFixedWidth(10)
testRange = pg.QtCore.QRect(0, 0, 10, 10)
vb.setRange(testRange, padding=0)
expected = [[testRange.left(), testRange.right()], [testRange.top(), testRange.bottom()]]
viewRange = vb.getState()['vie... |
def validate_subprotocols(subprotocols: Sequence[Subprotocol]) -> None:
if (not isinstance(subprotocols, Sequence)):
raise TypeError('subprotocols must be a list')
if isinstance(subprotocols, str):
raise TypeError('subprotocols must be a list, not a str')
for subprotocol in subprotocols:
... |
class Median(CtrlNode):
nodeName = 'MedianFilter'
uiTemplate = [('n', 'intSpin', {'min': 1, 'max': 1000000})]
def processData(self, data):
try:
import scipy.ndimage
except ImportError:
raise Exception('MedianFilter node requires the package scipy.ndimage.')
re... |
def preprocess_fromnpy_save_to_queue(list_of_images: List[np.ndarray], list_of_segs_from_prev_stage: Union[(List[np.ndarray], None)], list_of_image_properties: List[dict], truncated_ofnames: Union[(List[str], None)], plans_manager: PlansManager, dataset_json: dict, configuration_manager: ConfigurationManager, target_qu... |
.parametrize('n,blocks,expected_chunks', [(1, 1, [1]), (2, 1, [2]), (2, 2, ([1] * 2)), (3, 1, [3]), (3, 3, ([1] * 3)), (3, 2, [2, 1]), (7, 2, [4, 3]), (7, 3, [3, 2, 2]), (7, 7, ([1] * 7))])
def test_split_array_chunks__precomputed(n: int, blocks: int, expected_chunks: List[int]) -> None:
assert (split_array_chunks(... |
def main(_):
if (not FLAGS.dataset_dir):
raise ValueError('You must supply the dataset directory with --dataset_dir')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
tf_global_step = slim.get_or_create_global_step()
dataset = dataset_factory.get_dataset(FLAGS.... |
class TestPassedDrivenMilesCompositeMetric(unittest.TestCase):
def test_failed_frames(self) -> None:
validation_results: Dict[(str, validators.ValidatorOutput)] = {'validator_a': validators.ValidatorOutput(is_valid_scene=False, failed_frames=[15])}
metric_results: Dict[(str, torch.Tensor)] = {metric... |
def conduct_team_search(username, query, encountered_teams, results):
matching_teams = model.team.get_matching_user_teams(query, get_authenticated_user(), limit=5)
for team in matching_teams:
if (team.id in encountered_teams):
continue
encountered_teams.add(team.id)
results.a... |
def segm2json(dataset, results):
bbox_json_results = []
segm_json_results = []
for idx in range(len(dataset)):
img_id = dataset.img_ids[idx]
(det, seg) = results[idx]
for label in range(len(det)):
bboxes = det[label]
for i in range(bboxes.shape[0]):
... |
def test_manual_add(tmpdir):
workdir_one = os.path.join(str(tmpdir), 'workdir_one')
workdir_two = os.path.join(str(tmpdir), 'workdir_two')
metadir = os.path.join(str(tmpdir), 'metadir')
runner = CliRunner()
statefile = os.path.join(str(tmpdir), 'state.json')
result = runner.invoke(yadage.manualc... |
def test_type_complex_while_labels() -> None:
src = "\n i = 0\n while i < 10:\n j = 0\n while j < 5:\n j += 1\n i += 1\n\n if i > 4:\n print('hi')\n\n print('not else')\n "
expected_labels = {'True', 'False'}
assert (_extract_labels(build_cfg(src... |
class Request(testprocess.Line):
def __init__(self, data):
super().__init__(data)
try:
parsed = json.loads(data)
except ValueError:
raise testprocess.InvalidLine(data)
assert isinstance(parsed, dict)
assert (set(parsed.keys()) == {'path', 'verb', 'stat... |
def test_kinesis_subscription_with_starting_position_at_timestamp():
ksub = {'stream': 'arn:aws:kinesis:eu-west-1::stream/services', 'batch_size': 10, 'starting_position_timestamp': '2017-11-01T11:00:00Z'}
cfg = config.Config(EX_CONFIG, (EX_CONFIG + '/lambda-with-subscription_at_ts.json'))
assert (cfg.raw['... |
class ClassicalOptimizer():
def __init__(self, instance, n, K):
self.instance = instance
self.n = n
self.K = K
def compute_allowed_combinations(self):
f = math.factorial
return ((f(self.n) / f(self.K)) / f((self.n - self.K)))
def cplex_solution(self):
instance... |
def main(config: lib.JSONDict, output: Union[(str, Path)], *, force: bool=False) -> Optional[lib.JSONDict]:
if (not lib.start(output, force=force)):
return None
output = Path(output)
report = lib.create_report(config)
C = lib.make_config(Config, config)
delu.random.seed(C.seed)
device = ... |
def execute_fixture(monkeypatch) -> None:
for func in ['check_brew_cmd', 'check_cask', 'set_brewfile_repo', 'set_brewfile_local', 'check_repo', 'repomgr', 'brew_cmd', 'initialize', 'check_input_file', 'edit_brewfile', 'cat_brewfile', 'get_files', 'clean_non_request', 'cleanup', 'install']:
def set_func(func... |
def set_embedding(z_target, state, nbits, _mol_embedding=mol_fp):
if (len(state) == 0):
return np.concatenate([np.zeros((1, (2 * nbits))), z_target], axis=1)
else:
e1 = np.expand_dims(_mol_embedding(state[0]), axis=0)
if (len(state) == 1):
e2 = np.zeros((1, nbits))
el... |
class EncoderLayer(nn.Module):
def __init__(self, size, self_attn, feed_forward, group_attn, dropout):
super(EncoderLayer, self).__init__()
self.self_attn = self_attn
self.feed_forward = feed_forward
self.group_attn = group_attn
self.sublayer = clones(SublayerConnection(size,... |
def _get_proposal_section_reviewer_vote_choices(conference):
allow_plus_zero_vote = ConferenceSettingConstants.ALLOW_PLUS_ZERO_REVIEWER_VOTE
plus_zero_vote_setting = conference.conferencesetting_set.filter(name=allow_plus_zero_vote['name']).first()
if plus_zero_vote_setting:
plus_zero_vote_setting_v... |
class DimShuffle(ExternalCOp):
_f16_ok = True
check_input = False
__props__ = ('input_broadcastable', 'new_order', 'inplace')
c_func_file = 'c_code/dimshuffle.c'
c_func_name = 'APPLY_SPECIFIC(cpu_dimshuffle)'
def params_type(self):
return ParamsType(shuffle=lvector, augment=lvector, tran... |
def qpos_to_pd_joint_vel(controller: PDJointVelController, qpos):
assert (type(controller) == PDJointVelController)
assert controller.config.normalize_action
delta_qpos = (qpos - controller.qpos)
qvel = (delta_qpos * controller._control_freq)
(low, high) = (controller.config.lower, controller.config... |
class _ModuleProxy():
_module = None
def __init__(self, name):
self.__dict__['_module_name'] = name
def __getattr__(self, name):
try:
return getattr(self._module, name)
except AttributeError:
if (self._module is not None):
raise
imp... |
def get_failure_msg_from_onion_error(decrypted_error_packet: bytes) -> OnionRoutingFailureMessage:
failure_len = int.from_bytes(decrypted_error_packet[32:34], byteorder='big')
failure_msg = decrypted_error_packet[34:(34 + failure_len)]
return OnionRoutingFailureMessage.from_bytes(failure_msg) |
class SkillTreeView(wx.Panel):
def __init__(self, parent):
wx.Panel.__init__(self, parent, id=wx.ID_ANY, pos=wx.DefaultPosition, size=wx.DefaultSize, style=wx.TAB_TRAVERSAL)
self.charEditor = self.Parent.Parent
self.SetBackgroundColour(wx.SystemSettings.GetColour(wx.SYS_COLOUR_WINDOW))
... |
.parametrize('line', ['', ';', ';;', ';; ;', '&', '& &', ' && &', '>', "'", '"', '|'])
def test_parse_command_only_empty(parser, line):
statement = parser.parse_command_only(line)
assert (statement == '')
assert (statement.args == statement)
assert (statement.arg_list == [])
assert (statement.comman... |
class VectorScaler(SKCMatrixAndWeightTransformerABC):
_inherit(SKCMatrixAndWeightTransformerABC._transform_weights)
def _transform_weights(self, weights):
return scale_by_vector(weights, axis=None)
_inherit(SKCMatrixAndWeightTransformerABC._transform_matrix)
def _transform_matrix(self, matrix):
... |
class PreconditionerTest():
def __init__(self):
self.x = (torch.randn(mconfig.M, mconfig.K).cuda().half() / 100)
self.y = (torch.randn(mconfig.K, mconfig.N).cuda().half() / 100)
self.num_bins = ((2 ** mconfig.num_bits) - 1)
self.scale_y = (max(abs(self.y.min()), abs(self.y.max())) / ... |
class DesktopWin32WindowSpecificationTests(unittest.TestCase):
def setUp(self):
Timings.defaults()
self.app = Application(backend='win32').start(os.path.join(mfc_samples_folder, u'CmnCtrl3.exe'))
self.desktop = Desktop(backend='win32')
self.desktop_no_magic = Desktop(backend='win32',... |
class AdaBound(Optimizer):
def __init__(self, params, lr=0.001, betas=(0.9, 0.999), final_lr=0.1, gamma=0.001, eps=1e-08, weight_decay=0, amsbound=False):
if (not (0.0 <= lr)):
raise ValueError('Invalid learning rate: {}'.format(lr))
if (not (0.0 <= eps)):
raise ValueError('I... |
class BarthezTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ['input_ids', 'attention_mask']
def __init__(self, vocab_file, bos_token='<s>'... |
def download_cub_metadata(to_path):
acsm_val_mat_path = f'{to_path}/val_cub_cleaned.mat'
if (not os.path.isfile(acsm_val_mat_path)):
acsm_val_mat_url = f'
print("Downloading metadata used to form ACSM's CUB validation set")
download_url(acsm_val_mat_url, to_path)
else:
print(... |
class COCOInstanceNewBaselineDatasetMapper():
def __init__(self, is_train=True, *, tfm_gens, image_format, mask_format):
self.tfm_gens = tfm_gens
logging.getLogger(__name__).info('[COCOInstanceNewBaselineDatasetMapper] Full TransformGens used in training: {}'.format(str(self.tfm_gens)))
self... |
class GRU(nn.Module):
def __init__(self, npoint, hidden_dim, input_dim, use_instance_norm):
super(GRU, self).__init__()
in_ch = (hidden_dim + input_dim)
self.convz = PointNetSetAbstraction(npoint=int((npoint / 4)), radius=None, nsample=4, in_channel=in_ch, mlp=[hidden_dim], group_all=False, ... |
def _flatten(dico, prefix=None):
new_dico = OrderedDict()
if isinstance(dico, dict):
prefix = ((prefix + '.') if (prefix is not None) else '')
for (k, v) in dico.items():
if (v is None):
continue
new_dico.update(_flatten(v, (prefix + k)))
elif isinstan... |
def inspect_client_error(val_err: ValueError, eth_node: Optional[EthClient]) -> ClientErrorInspectResult:
json_response = str(val_err).replace("'", '"').replace('("', '(').replace('")', ')')
try:
error = json.loads(json_response)
except json.JSONDecodeError:
return ClientErrorInspectResult.P... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, conv_builder, stride=1, downsample=None):
super(Bottleneck, self).__init__()
midplanes = (((((inplanes * planes) * 3) * 3) * 3) // (((inplanes * 3) * 3) + (3 * planes)))
self.conv1 = nn.Sequential(nn.Conv3d(i... |
('/usb_devices.html', methods=['GET'])
def usb_devices():
str_devices = subprocess.getoutput('lsusb').split('\n')
devices = []
for device in str_devices:
devices.append({'bus': device.split(': ID ')[0].split(' ')[1], 'device': device.split(': ID ')[0].split(' ')[3], 'id': device.split(': ID ')[1][:9... |
class MessageView(QWidget):
update_geometry = pyqtSignal()
def __init__(self, parent=None):
super().__init__(parent)
self._messages: MutableSequence[Message] = []
self._vbox = QVBoxLayout(self)
self._vbox.setContentsMargins(0, 0, 0, 0)
self._vbox.setSpacing(0)
sel... |
class MultiSimilarityLoss(GenericPairLoss):
def __init__(self, alpha=2, beta=50, base=0.5, **kwargs):
super().__init__(mat_based_loss=True, **kwargs)
self.alpha = alpha
self.beta = beta
self.base = base
self.add_to_recordable_attributes(list_of_names=['alpha', 'beta', 'base']... |
def test_multiprocessing_showcase():
import numpy as np
import joblib
import time
import datetime
def func():
n_jobs = 8
size = 3000
print('Creating data: {size}x{size} ... '.format(size=size), end='')
a = np.random.random((size, size))
print('done ({size:.02f... |
class Votes(models.Model):
class Meta():
table = 'votes'
user_id = fields.BigIntField(pk=True)
is_voter = fields.BooleanField(default=False, index=True)
expire_time = fields.DatetimeField(null=True)
reminder = fields.BooleanField(default=False)
notified = fields.BooleanField(default=Fals... |
def create_unet_backbone(bottom_up_layers: List[BottomUpLayer], layers_start: int, layers_end: int, top_down_stack: TopDownStackInterface) -> Nodes:
selected_layer_indexes = list(range(layers_start, layers_end))
top_down = bottom_up_layers[selected_layer_indexes[(- 1)]].tensor
for layer_index in reversed(se... |
class TMPEGInfo(TestCase):
def test_not_real_file(self):
filename = os.path.join(DATA_DIR, 'silence-44-s-v1.mp3')
with open(filename, 'rb') as h:
fileobj = BytesIO(h.read(20))
self.failUnlessRaises(MP3Error, MPEGInfo, fileobj)
def test_empty(self):
fileobj = BytesIO(b... |
def compute_cell_extents_grid(bounding_rect=(0.03, 0.03, 0.97, 0.97), num_rows=2, num_cols=6, axis_pad=0.01):
(left, bottom, width, height) = bounding_rect
height_padding = (axis_pad * (num_rows + 1))
width_padding = (axis_pad * (num_cols + 1))
cell_height = float(((height - height_padding) / num_rows))... |
class TestHSAFFileHandler(unittest.TestCase):
def setUp(self):
try:
import pygrib
except ImportError:
pygrib = None
self.orig_pygrib = pygrib
sys.modules['pygrib'] = mock.MagicMock()
def tearDown(self):
sys.modules['pygrib'] = self.orig_pygrib
... |
class Layer(aimet_common.layer_database.Layer):
def _set_type_specific_params(self, module: tf.Operation):
if (module.type == 'Conv2D'):
(strides, padding, groups) = aimet_tensorflow.utils.op.conv.get_conv2d_op_params(module)
params = aimet_common.layer_database.Conv2dTypeSpecificPar... |
_required('wiki.add_wikifile', raise_exception=True)
def wiki_file_list(request):
if (request.method == 'POST'):
try:
wiki_file = WikiFile.objects.create(upload_user=request.user, wiki_file=request.FILES.get('upload_wiki_file'))
file_name = wiki_file.wiki_file.name.split('/')[(- 1)]
... |
_singleton
def loadModel(device):
ckpt = torch.hub.load_state_dict_from_url(MODELS_URL, map_location=device, check_hash=True)
config = Config.deserialize(ckpt['config'])
model = Compressor(**config.Model.Params).to(device)
model.QuantizationParameter = 'qp_2_msssim'
model.load_state_dict(ckpt['model... |
def generate_intermediate_ca(opts, parent_certificate_path=p.root_ca_certificate_path(), parent_key_path=p.root_ca_key_path(), suffix=''):
print('Will generate intermediate CA with suffix {}'.format(suffix))
print('Using parent certificate path at {}'.format(parent_certificate_path))
print('Using parent key... |
def _add_metric_pages(html_file, run_output_paths, report_config, data_frame):
_write_header(html_file, 'Distribution of different measures (except SPICE-related, which come later below)')
for column_name in COLUMNS_FOR_HISTOGRAM_NON_SPICE:
bins = report_config.histogram_bins[column_name]
metric... |
class NetModule():
def __init__(self, args):
self.args = args
self.input_shape = (32, 32, 3)
self.shapes = [(3, 3, 3, 64), (3, 3, 64, 128), (3, 3, 128, 128), (3, 3, 128, 128), (3, 3, 128, 256), (3, 3, 256, 512), (3, 3, 512, 512), (3, 3, 512, 512), (512, self.args.num_classes)]
self.l... |
.fast
def test_non_air_diluent(verbose=True, plot=False, *args, **kwargs):
sf = SpectrumFactory(wavelength_min=4200, wavelength_max=4500, cutoff=1e-23, molecule='CO', isotope='1,2', truncation=5, neighbour_lines=10, path_length=0.1, mole_fraction=0.1, medium='vacuum', optimization=None, verbose=verbose)
sf.warn... |
class MixNet(nn.Module):
mixnet_s = [(16, 16, [3], [1], [1], 1, 1, 'ReLU', 0.0), (16, 24, [3], [1, 1], [1, 1], 2, 6, 'ReLU', 0.0), (24, 24, [3], [1, 1], [1, 1], 1, 3, 'ReLU', 0.0), (24, 40, [3, 5, 7], [1], [1], 2, 6, 'Swish', 0.5), (40, 40, [3, 5], [1, 1], [1, 1], 1, 6, 'Swish', 0.5), (40, 40, [3, 5], [1, 1], [1, 1... |
class TestCLI(TestCase):
def run_cli(self, argv, files=None, stdin=StringIO(), exit_code=0, **override):
arguments = cli.parse_args(argv)
arguments.update(override)
self.assertFalse(hasattr(cli, 'open'))
cli.open = fake_open((files or {}))
try:
(stdout, stderr) = ... |
class HandShaker():
def __init__(self, bsd_socket):
self._bsd_socket = bsd_socket
def shake_hands_as_host(self, id):
message = ('YOTON!%s.%i' % (UID(id).get_hex(), os.getpid()))
request = self._recv_during_handshaking()
if (not request):
return (False, STOP_HANDSHAKE_... |
def parse_option():
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--eval_freq', type=int, default=100, help='meta-eval frequency')
parser.add_argument('--save_freq', type=int, default=500, help='save frequency')
parser.add_argument('--batch_size', type=int, default=64, h... |
class Final_Feature(nn.Module):
def __init__(self, in_channel, out_channel):
super().__init__()
self.CBR_1x1 = nn.Sequential(nn.Conv2d(in_channel, out_channel, kernel_size=1, stride=1, padding=0, bias=True), nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True))
self.pool_1_2x2 = nn.MaxPool2d(k... |
class Evaluator():
def __init__(self):
self.partial_scores = None
def eval_hardness(self, sql):
count_comp1_ = count_component1(sql)
count_comp2_ = count_component2(sql)
count_others_ = count_others(sql)
if ((count_comp1_ <= 1) and (count_others_ == 0) and (count_comp2_ =... |
def comet_pull_weight_by_key(key, projname, epoch, api, rank, deterministic=True):
for attempt in range(4):
try:
tic = time()
expt = api.get(cometconfig['workspace'], projname, key)
assets = expt.get_asset_list()
assets = assets2dict(assets, 'fileName', 'asset... |
class TelegramHandler(tornado.web.RequestHandler):
__slots__ = ('bot', 'update_queue', 'secret_token')
SUPPORTED_METHODS = ('POST',)
def initialize(self, bot: 'Bot', update_queue: asyncio.Queue, secret_token: str) -> None:
self.bot = bot
self.update_queue = update_queue
self.secret_t... |
def read_tsp_tour(fname):
has_tour = False
tour = []
with open(fname) as fp:
for line in fp:
if line.startswith('TOUR_SECTION'):
has_tour = True
elif line.startswith('EOF'):
break
elif has_tour:
tour.extend((int(node... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.