code stringlengths 281 23.7M |
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def send_subscription_change(change_description, customer_id, customer_email, quay_username):
SUBSCRIPTION_CHANGE_TITLE = 'Subscription Change - {0} {1}'
SUBSCRIPTION_CHANGE = '\n Change: {0}<br>\n Customer id: <a href=" Customer email: <a href="mailto:{2}">{2}</a><br>\n Quay user or org name: {3}<br>\n '
... |
def create_video_from_containers(in_container: InputContainer, out_container: OutputContainer, draw_on_av_frame: DrawOnAvFrame, add_border: bool) -> None:
in_video_stream = in_container.streams.video[0]
in_video_stream.thread_type = 'AUTO'
transformation_sizes = _compute_transformation_sizes(in_video_stream... |
class FSMTTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = FSMTTokenizer
test_rust_tokenizer = False
def setUp(self):
super().setUp()
vocab = ['l', 'o', 'w', 'e', 'r', 's', 't', 'i', 'd', 'n', 'w</w>', 'r</w>', 't</w>', 'lo', 'low', 'er</w>', 'low</w>', 'lowest</w... |
def test_service_registry_random_pfs(service_registry_address, private_keys, web3, contract_manager):
addresses = [privatekey_to_address(key) for key in private_keys]
(c1_service_proxy, urls) = deploy_service_registry_and_set_urls(private_keys=private_keys, web3=web3, contract_manager=contract_manager, service_... |
def get_triplet_mask(labels: torch.Tensor) -> torch.Tensor:
indices_equal = torch.eye(labels.size()[0], dtype=torch.bool, device=labels.device)
indices_not_equal = torch.logical_not(indices_equal)
i_not_equal_j = indices_not_equal.unsqueeze(2)
i_not_equal_k = indices_not_equal.unsqueeze(1)
j_not_equ... |
class ChannelSpatialSELayer3D(nn.Module):
def __init__(self, num_channels, reduction_ratio=2):
super(ChannelSpatialSELayer3D, self).__init__()
self.cSE = ChannelSELayer3D(num_channels, reduction_ratio)
self.sSE = SpatialSELayer3D(num_channels)
def forward(self, input_tensor):
out... |
class Material():
def __init__(self, normalmap=None):
if (normalmap != None):
normalmap = load_image(('sightpy/normalmaps/' + normalmap))
self.normalmap = normalmap
def get_Normal(self, hit):
N_coll = hit.collider.get_Normal(hit)
if (self.normalmap is not None):
... |
class SolveModel():
solver: pybamm.BaseSolver
model: pybamm.BaseModel
t_eval: np.ndarray
def solve_setup(self, parameter, model_, option, value, solver_class):
import importlib
idaklu_spec = importlib.util.find_spec('pybamm.solvers.idaklu')
if (idaklu_spec is not None):
... |
def scale_jitter(tensor, target, jitter_factor, jitter_size=None, mask=None):
if (jitter_size is None):
(_, h, w) = tensor.shape
(new_h, new_w) = (int((h * jitter_factor)), int((w * jitter_factor)))
jitter_factor_x = jitter_factor_y = jitter_factor
else:
(new_h, new_w) = jitter_s... |
class ReleaseFile(ContentManageable, NameSlugModel):
os = models.ForeignKey(OS, related_name='releases', verbose_name='OS', on_delete=models.CASCADE)
release = models.ForeignKey(Release, related_name='files', on_delete=models.CASCADE)
description = models.TextField(blank=True)
is_source = models.Boolean... |
class AM2RBasePatchesFactory(BasePatchesFactory):
def create_base_patches(self, configuration: BaseConfiguration, rng: Random, game: GameDescription, is_multiworld: bool, player_index: int, rng_required: bool=True) -> GamePatches:
assert isinstance(configuration, AM2RConfiguration)
parent = super().... |
class Effect7233(BaseEffect):
type = 'passive'
def handler(fit, implant, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Precursor Weapon')), 'damageMultiplierBonusPerCycle', implant.getModifiedItemAttr('damageMultiplierBonusPerCycleModifier'), **... |
def main():
dicts = {}
tokenizer = onmt.Tokenizer(opt.input_type, opt.lower)
if ((opt.load_dict is not None) and (len(opt.load_dict) > 0)):
print(('[INFO] Loading dictionary from ... %s' % opt.load_dict))
dicts = torch.load(opt.load_dict)
src_langs = opt.train_src_lang.split('|')
tgt... |
class Effect5778(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Missile Launcher Operation')), 'speed', ship.getModifiedItemAttr('shipBonusMF2'), skill='Minmatar Frigate', **kwargs) |
class TestTfWinnower(unittest.TestCase):
.tf1
def test_mask_propagation_on_keras_model(self):
tf.compat.v1.reset_default_graph()
sess = tf.compat.v1.Session()
module_zero_channels_list = []
_ = keras_model()
init = tf.compat.v1.global_variables_initializer()
sess.... |
class BaselineYNet(nn.Module):
def __init__(self, input_size=(3, 32, 32), num_classes=10, activation='softplus', residual=False, hidden_width=128, aug=0):
super(BaselineYNet, self).__init__()
(y_net, output_size) = make_y_net(input_size=input_size, explicit_params=False, activation=activation, hidde... |
class MissingDependencies(RuntimeError):
def __init__(self, missing_dependencies, *args, **kwargs):
super().__init__(*args, **kwargs)
self.missing_dependencies = missing_dependencies
def __str__(self):
prefix = super().__str__()
unknown_str = ', '.join(map(str, self.missing_depen... |
def test_search(requests_mock):
requests_mock.get(f'{API_V1}/search', json=load_sample_data('get_search.json'), status_code=200)
response = search([8348, 6432])
taxon_result = response['results'][0]
place_result = response['results'][1]
project_result = response['results'][2]
user_result = respo... |
class CurrentUserGPGKeyManager(RetrieveMixin, CreateMixin, DeleteMixin, RESTManager):
_path = '/user/gpg_keys'
_obj_cls = CurrentUserGPGKey
_create_attrs = RequiredOptional(required=('key',))
def get(self, id: Union[(str, int)], lazy: bool=False, **kwargs: Any) -> CurrentUserGPGKey:
return cast(... |
class BTOOLS_OT_material_group_assign(bpy.types.Operator):
bl_idname = 'btools.material_group_assign'
bl_label = 'Assign Faces to Group'
bl_options = {'REGISTER', 'UNDO'}
def poll(cls, context):
obj = context.object
matgroup = obj.bt_materials[obj.bt_materials_active_index]
retur... |
def test_solver_does_not_return_prereleases_if_not_requested(solver: Solver, repo: Repository, package: ProjectPackage) -> None:
package.add_dependency(Factory.create_dependency('A', '*'))
package.add_dependency(Factory.create_dependency('B', '*'))
package.add_dependency(Factory.create_dependency('C', '*'))... |
def mc_elbo(z0, t0, t1, prior_params, post_params, log_likelihood_params, prior_drift, diffusion, posterior_drift, log_likelihood, rng):
(aug_drift, aug_diffusion) = make_aug_dynamics(prior_drift, diffusion, posterior_drift)
aug_init = pack(z0, 0.0)
out = sdeint_ito(aug_drift, aug_diffusion, aug_init, np.ar... |
def save_churns(churns, path='./results/code_churns_features_multithread.csv'):
with open(path, 'w') as csv_file:
writer = csv.writer(csv_file)
writer.writerow(['commit', 'lines_of_code_added', 'lines_of_code_deleted', 'files_churned', 'line_of_code_old'])
for row in churns:
if r... |
class _FindExecutor(ActionExecutor):
def execute(self, script: Script, state: EnvironmentState, info: ExecutionInfo, char_index, modify=True, in_place=False):
current_line = script[0]
info.set_current_line(current_line)
current_obj = current_line.object()
for node in state.select_nod... |
def zero_scale_fix(model, device):
for (k, m) in model.named_modules():
if (isinstance(m, quant_nn.QuantConv2d) or isinstance(m, quant_nn.QuantConvTranspose2d)):
weight_amax = m._weight_quantizer._amax.detach().cpu().numpy()
print(k)
ones = np.ones_like(weight_amax)
... |
def monotonically_increasing_and_bounded(iterable, min=None, max=None):
if (not isinstance(iterable, Iterable)):
raise TypeError('Expected iterable to be of type Iterable, got ({})'.format(iterable.__class__.__name__))
for i in range(len(iterable)):
if ((min is not None) and (iterable[i] < min))... |
class Gumbel(Continuous):
rv_op = gumbel
def dist(cls, mu, beta, **kwargs):
mu = pt.as_tensor_variable(floatX(mu))
beta = pt.as_tensor_variable(floatX(beta))
return super().dist([mu, beta], **kwargs)
def moment(rv, size, mu, beta):
mean = (mu + (beta * np.euler_gamma))
... |
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, is_last=False):
super(BasicBlock, self).__init__()
self.is_last = is_last
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu ... |
class TextReporter(Reporter):
def __init__(self, *, verbosity: int, stream: (t.TextIO | None)=None) -> None:
super().__init__(verbosity=verbosity)
self.stream = stream
def _echo(self, s: str, *, indent: int=0) -> None:
click.echo(((' ' * indent) + s), file=self.stream)
def report_suc... |
class Win32Window(BaseWindow):
_window_class = None
_hwnd = None
_dc = None
_wgl_context = None
_tracking = False
_hidden = False
_has_focus = False
_exclusive_keyboard = False
_exclusive_keyboard_focus = True
_exclusive_mouse = False
_exclusive_mouse_focus = True
_exclus... |
class VGG(nn.Module):
def __init__(self, builder, features):
super(VGG, self).__init__()
self.features = features
num_classes = (10 if (parser_args.set == 'CIFAR10') else 100)
self.linear = builder.conv1x1(512, num_classes)
def forward(self, x):
x = self.features(x)
... |
class Bobby(Configurable):
handler = Method()
handler2 = Method()
foo = Option(positional=True)
bar = Option(required=False)
def think(self, context):
(yield 'different')
def __call__(self, think, *args, **kwargs):
self.handler('1', *args, **kwargs)
self.handler2('2', *ar... |
class MLPRegression(nn.Module):
def __init__(self, input_dim=86):
super(MLPRegression, self).__init__()
self.fc1 = nn.Linear(input_dim, 256)
self.fc2 = nn.Linear(256, 128)
self.fc3 = nn.Linear(128, 1)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.f... |
.parametrize('fun', [ct.series, ct.parallel, ct.feedback])
.parametrize('ltype', bd_types)
.parametrize('rtype', bd_types)
def test_bdalg_type_conversions(fun, ltype, rtype, sys_dict):
leftsys = sys_dict[ltype]
rightsys = sys_dict[rtype]
expected = bd_expect[bd_types.index(ltype)][1][bd_types.index(rtype)]
... |
class TransformerSentenceEncoderLayer(nn.Module):
def __init__(self, embedding_dim: float=768, ffn_embedding_dim: float=3072, num_attention_heads: float=8, dropout: float=0.1, attention_dropout: float=0.1, activation_dropout: float=0.1, activation_fn: str='relu', add_bias_kv: bool=False, add_zero_attn: bool=False, ... |
def print_table(rows, headers, nicks, order):
rows.insert(0, headers)
rows = filter_table(rows, nicks, order)
if (not rows):
return
widths = []
for c in range(len(rows[0])):
widths.append(max((len(r[c]) for r in rows)))
seperator = (' %s ' % Colorise.gray('|'))
format_string ... |
class TestCustomScripts():
def test_only_linter_fix(self, hatch, temp_dir, config_file, mocker):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
a... |
.parametrize('prefer_grpc', [False, True])
.parametrize('numpy_upload', [False, True])
.parametrize('local_mode', [False, True])
def test_qdrant_client_integration(prefer_grpc, numpy_upload, local_mode):
vectors_path = create_random_vectors()
if numpy_upload:
vectors = np.memmap(vectors_path, dtype='flo... |
class NonStructured_Encoder():
def __init__(self, sess, FLAGS, embed, num_units=None, scope='Sentence_Encoder'):
self.sess = sess
self.dim_embed_word = FLAGS.dim_embed_word
self.num_units = (num_units if (num_units is not None) else FLAGS.num_units)
self.num_layers = FLAGS.num_layers... |
class DistWorker(CovController):
_ensure_topdir
def start(self):
cleanup()
self.is_collocated = ((socket.gethostname() == self.config.workerinput['cov_master_host']) and (self.topdir == self.config.workerinput['cov_master_topdir']))
if (not self.is_collocated):
master_topdir ... |
def freeze_bn(model):
for module in model.modules():
if isinstance(module, torch.nn.BatchNorm2d):
if hasattr(module, 'weight'):
module.weight.requires_grad_(False)
if hasattr(module, 'bias'):
module.bias.requires_grad_(False)
module.eval() |
def rtn_ftell(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('ftell hooked')
arg0 = pstate.get_argument_value(0)
if pstate.file_descriptor_exists(arg0):
desc = pstate.get_file_descriptor(arg0)
if desc.fd.seekable():
return desc.fd.tell()
else:
r... |
def get_commandline(server=False, description=None, extras=None, cmdline=None):
parser = argparse.ArgumentParser(description=description)
parser.add_argument('-c', '--comm', choices=['tcp', 'udp', 'serial', 'tls'], help='set communication, default is tcp', dest='comm', default='tcp', type=str)
parser.add_ar... |
def multiplicative_jitter(x, device: torch.device, epsilon=0.01):
if (epsilon == 0):
return x
minval = torch.tensor((1.0 - epsilon), device=device)
maxval = torch.tensor((1.0 + epsilon), device=device)
uniform = uniform_map.get(device)
if (uniform is None):
uniform = torch.distributi... |
def test_pattern_should_be_used2():
def parse_yesno(text):
return parse_yesno.mapping[text.lower()]
parse_yesno.mapping = {'yes': True, 'no': False, 'on': True, 'off': False, 'true': True, 'false': False}
parse_yesno.pattern = '|'.join(parse_yesno.mapping.keys())
parse_yesno.name = 'YesNo'
e... |
class ELF32_Phdr(ELF_Phdr):
Phdr_SIZE = (4 * 8)
def __init__(self, buf, endian=0):
if (len(buf) != self.Phdr_SIZE):
raise
fmt = ('<IIIIIIII' if (endian == 0) else '>IIIIIIII')
(p_type, p_offset, p_vaddr, p_paddr, p_filesz, p_memsz, p_flags, p_align) = struct.unpack(fmt, buf)
... |
class TestDateField(TestCase):
def setUp(self):
self.field = fields.DateField()
def test_deserialize_none(self):
actual_value = self.field.deserialize(None)
expected_value = None
self.assertEqual(actual_value, expected_value)
def test_deserialize_naive(self):
arbitrar... |
class Client(Iface):
def __init__(self, iprot, oprot=None):
self._iprot = self._oprot = iprot
if (oprot is not None):
self._oprot = oprot
self._seqid = 0
def example(self):
self.send_example()
return self.recv_example()
def send_example(self):
self... |
class Playlist(BasePathMixin):
def __init__(self, uri, stream_info, media, base_uri):
self.uri = uri
self.base_uri = base_uri
resolution = stream_info.get('resolution')
if (resolution != None):
resolution = resolution.strip('"')
values = resolution.split('x')
... |
def main(OPTS):
with open(OPTS.data_file) as f:
dataset_json = json.load(f)
dataset = dataset_json['data']
with open(OPTS.pred_file) as f:
preds = json.load(f)
if OPTS.na_prob_file:
with open(OPTS.na_prob_file) as f:
na_probs = json.load(f)
else:
na_pr... |
def apply_ccx(circuit, a, b, c, use_basis_gates=True):
if use_basis_gates:
circuit.h(c)
circuit.cx(b, c)
circuit.tdg(c)
circuit.cx(a, c)
circuit.t(c)
circuit.cx(b, c)
circuit.tdg(c)
circuit.cx(a, c)
circuit.t(b)
circuit.t(c)
cir... |
def resnet50_v1b(pretrained=False, local_rank=None, **kwargs):
model = ResNetV1b(BottleneckV1b, [3, 4, 6, 3], **kwargs)
if (pretrained != 'None'):
if (local_rank is not None):
old_dict = torch.load(pretrained, map_location=torch.device(local_rank))
else:
old_dict = torch.... |
def _get_quicklook(area_def, data, vmin=None, vmax=None, label='Variable (units)', num_meridians=45, num_parallels=10, coast_res='110m', cmap='RdBu_r'):
import matplotlib.pyplot as plt
(coast_res, is_cartopy) = _translate_coast_resolution_to_cartopy(coast_res)
if (not is_cartopy):
return _basemap_ge... |
class GradientDescent(OptimizationAlgorithm):
def __init__(self, **kwargs):
default_parameters = {'learning_rate': 1.0}
restart_variables = {}
super(self.__class__, self).__init__(alg_default_parameters=default_parameters, alg_restart_variables=restart_variables, **kwargs)
def _step(self... |
def news_articles(hostname: str, language: str) -> list[NewsArticle]:
site = Site.objects.filter(hostname=hostname).first()
if (not site):
raise ValueError(f'Site {hostname} not found')
return [NewsArticle.from_model(article) for article in NewsArticleModel.objects.in_site(site).order_by('-first_pub... |
class STTHandler():
def __init__(self, settings, pip_path, stt):
self.settings = settings
self.pip_path = pip_path
self.stt = stt
self.key = ''
def install(self):
for module in self.stt['extra_requirements']:
install_module(module, self.pip_path)
def is_in... |
.filterwarnings('default')
def test_nose_deprecated_with_setup(pytester: Pytester) -> None:
pytest.importorskip('nose')
pytester.makepyfile('\n from nose.tools import with_setup\n\n def setup_fn_no_op():\n ...\n\n def teardown_fn_no_op():\n ...\n\n _setup(setup_... |
def test_change_truncated_size():
x = Truncated.dist(icdf_normal(0, [1, 2, 3]), lower=(- 1), size=(2, 3))
(x.eval().shape == (2, 3))
new_x = change_dist_size(x, (4, 3))
assert isinstance(new_x.owner.op, TruncatedRV)
(new_x.eval().shape == (4, 3))
new_x = change_dist_size(x, (4, 3), expand=True)
... |
class DbmsXslprocessor(DirectoryManagement):
def __init__(self, args):
logging.debug('DbmsXslprocessor object created')
DirectoryManagement.__init__(self, args)
def putFile(self, remotePath, remoteNameFile, data=None, localFile=None):
if (((localFile == None) and (data == None)) or ((loc... |
def _even_ext(x, n, axis=(- 1)):
x = cp.asarray(x)
if (n < 1):
return x
if (n > (x.shape[axis] - 1)):
raise ValueError((('The extension length n (%d) is too big. ' + 'It must not exceed x.shape[axis]-1, which is %d.') % (n, (x.shape[axis] - 1))))
left_ext = _axis_slice(x, start=n, stop=0... |
('PyQt6.QtGui.QAction.triggered')
('beeref.actions.mixin.menu_structure')
('beeref.actions.mixin.actions')
def test_update_recent_files(actions_mock, menu_mock, triggered_mock, qapp):
widget = FooWidget()
widget.settings.get_recent_files.return_value = [os.path.abspath('foo.bee')]
menu_mock.__iter__.return_... |
def main():
logging.basicConfig(level=logging.DEBUG, filename='/home/xapp-logger.log', filemode='a+', format='%(asctime)-15s %(levelname)-8s %(message)s')
formatter = logging.Formatter('%(asctime)-15s %(levelname)-8s %(message)s')
console = logging.StreamHandler()
console.setLevel(logging.INFO)
cons... |
def dataset_walker(datasets):
for dataset in datasets:
(yield (dataset, None))
for anc_ds in dataset.attrs.get('ancillary_variables', []):
try:
anc_ds.attrs
(yield (anc_ds, dataset))
except AttributeError:
continue |
def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'):
total = (total or len(items))
t_start = time.time()
t_last = 0
for (n, item) in enumerate(items):
t_now = time.time()
if ((t_now - t_last) > min_delay):
print(('\r%s%d/%d (%6.2f%%)' % (desc, (n + 1), ... |
class Logger():
def print(str):
if MPIUtil.is_root_proc():
print(str)
return
def __init__(self):
self.output_file = None
self.first_row = True
self.log_headers = []
self.log_current_row = {}
self._dump_str_template = ''
return
def r... |
class WebDriverHandler():
def __init__(self, command_executor):
self.command_executor = command_executor
self.original_execute = command_executor.execute
self.reahl_server = None
def uninstall(self):
self.command_executor.execute = self.original_execute
def reinstall(self):
... |
def get_doc_input_bert(news, news_index, category_dict, domain_dict, subcategory_dict, args):
news_num = (len(news) + 1)
if ('title' in args.news_attributes):
news_title = np.zeros((news_num, args.num_words_title), dtype='int32')
news_title_type = np.zeros((news_num, args.num_words_title), dtype... |
class double_conv(nn.Module):
def __init__(self, in_ch, out_ch):
super(double_conv, self).__init__()
self.conv = nn.Sequential(nn.Conv1d(in_ch, out_ch, 3, padding=1), nn.BatchNorm1d(out_ch), nn.ReLU(inplace=True), nn.Conv1d(out_ch, out_ch, 3, padding=1), nn.BatchNorm1d(out_ch), nn.ReLU(inplace=True)... |
def _template_online_dataset(**kwargs):
lqargs = []
for k in ['network', 'station', 'channel']:
if (k in kwargs):
v = kwargs.pop(k)
lqargs.append((" %s: '%s'" % (k, v)))
kwargs['qargs'] = (('\n' + '\n'.join(lqargs)) if lqargs else '{}')
return '\n--- !squirrel.Dataset\... |
class Effect6928(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.requiresSkill('Afterburner') or mod.item.requiresSkill('High Speed Maneuvering'))), 'overloadSpeedFactorBonus', src.getModifiedItemAttr('subsyste... |
def repeat_tensors(n, x):
if torch.is_tensor(x):
x = x.unsqueeze(1)
x = x.expand((- 1), n, *([(- 1)] * len(x.shape[2:])))
x = x.reshape((x.shape[0] * n), *x.shape[2:])
elif ((type(x) is list) or (type(x) is tuple)):
x = [repeat_tensors(n, _) for _ in x]
return x |
def calc_tf_padding(x, kernel_size, stride=1, dilation=1):
(height, width) = x.size()[2:]
oh = math.ceil((height / stride))
ow = math.ceil((width / stride))
pad_h = max((((((oh - 1) * stride) + ((kernel_size - 1) * dilation)) + 1) - height), 0)
pad_w = max((((((ow - 1) * stride) + ((kernel_size - 1)... |
class Inferer():
def __init__(self, config):
self.config = config
if torch.cuda.is_available():
self.device = torch.device('cuda')
else:
self.device = torch.device('cpu')
torch.set_num_threads(1)
self.model_preproc = registry.instantiate(registry.l... |
class TargetAssigner(object):
def __init__(self, similarity_calc: IouSimilarity, matcher: ArgMaxMatcher, box_coder: FasterRcnnBoxCoder, negative_class_weight: float=1.0, unmatched_cls_target: Optional[float]=None, keypoints_field_name: str=KEYPOINTS_FIELD_NAME):
self._similarity_calc = similarity_calc
... |
def _choose_chains(traces: Sequence[S], tune: int) -> Tuple[(List[S], int)]:
if (not traces):
raise ValueError('No traces to slice.')
lengths = [max(0, (len(trace) - tune)) for trace in traces]
if (not sum(lengths)):
raise ValueError('Not enough samples to build a trace.')
idxs = np.args... |
def retry(exception_cls, max_tries=10, sleep=0.05):
assert (max_tries > 0)
def with_max_retries_call(delegate):
for i in range(max_tries):
try:
return delegate()
except exception_cls:
if ((i + 1) == max_tries):
raise
... |
class BaseClean():
clean_fns = ['to_lower', 'to_symbol', 'remove_emoji', 'clean_contractions', 'common_us_word', 'query_clean_v1', 'remove_control_char', 'remove_duplicate', 'remove_ending_underscore', 'remove_starting_underscore', 'clean_multiple_form', 'leet_clean']
def __init__(self, clean_fns=None):
... |
def main():
(train_annot, val_annot) = (pickle.load(open('clean_train.pkl', 'rb')), pickle.load(open('clean_valid.pkl', 'rb')))
data_path = 'data_hmor'
os.makedirs(data_path, exist_ok=True)
for (annot_name, annot) in zip(('train', 'valid'), (train_annot, val_annot)):
for term in tqdm(annot):
... |
class TranslationTestMixin(object):
def setUp(self):
super(TranslationTestMixin, self).setUp()
self.backend = self.create_backend(data={'name': 'mockbackend'})
def create_lang_connection(self, identity, language):
contact = self.create_contact(data={'language': language})
connect... |
class FeedForward(nn.Module):
def __init__(self, dim_in, hidden_dim, dim_out=None, *, dropout=0.0, f=nn.Linear, activation=nn.GELU):
super().__init__()
dim_out = (dim_in if (dim_out is None) else dim_out)
self.net = nn.Sequential(f(dim_in, hidden_dim), activation(), (nn.Dropout(dropout) if (... |
def update_diffs(module, is_similar, img, stored_img):
diffs_dir.mkdir(exist_ok=True)
diffs_rgba = None
def get_diffs_rgba(slicer):
nonlocal diffs_rgba
if (diffs_rgba is None):
diffs_rgba = np.abs((stored_img.astype('f4') - img))
diffs_rgba = (((diffs_rgba / 255) ** 0... |
class Tuple(Type):
def __init__(self, *elem_types):
self.elem_types = elem_types
def __eq__(self, other):
return ((self.__class__ == other.__class__) and (self.elem_types == other.elem_types))
def from_str(self, s):
if (';' in s):
segments = s.split(';')
elif (','... |
class BertFeatExtractor(object):
def __init__(self, model_name):
self.tokenizer = BertTokenizer.from_pretrained(model_name)
self.model = BertModel.from_pretrained(model_name).eval()
self.model.cuda()
def get_bert_embedding(self, text):
tokenized_text = self.tokenizer.tokenize(tex... |
def gmetric_read(msg):
unpacker = Unpacker(msg)
values = dict()
unpacker.unpack_int()
values['TYPE'] = unpacker.unpack_string()
values['NAME'] = unpacker.unpack_string()
values['VAL'] = unpacker.unpack_string()
values['UNITS'] = unpacker.unpack_string()
values['SLOPE'] = slope_int2str[un... |
def geometry_window(dataset, shapes, pad_x=0, pad_y=0, north_up=None, rotated=None, pixel_precision=None, boundless=False):
all_bounds = [bounds(shape, transform=(~ dataset.transform)) for shape in shapes]
cols = [x for (left, bottom, right, top) in all_bounds for x in ((left - pad_x), (right + pad_x), (right +... |
def train_one_epoch(train_loader, model, criterion, optimizer, scheduler, epoch, logger, config, scaler=None):
model.train()
loss_list = []
for (iter, data) in enumerate(train_loader):
optimizer.zero_grad()
(images, targets) = data
(images, targets) = (images.cuda(non_blocking=True).... |
def get_user_emails(list_id):
users = []
response = req(get_url(f'sub/lists/{list_id}/subscribers/'))
users.extend([x for x in response.json()['results'] if (x['is_active'] and x['is_email_verified'])])
while (response.json()['next'] is not None):
response = req(response.json()['next'])
... |
class Effect2882(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Cruise Missiles')), 'explosiveDamage', container.getModifiedItemAttr('damageMultiplierBonus'), **kwargs) |
def eval_callback(model: torch.nn.Module, num_samples: Optional[int]=None) -> float:
if (num_samples is None):
num_samples = EVAL_DATASET_SIZE
data_loader = _create_sampled_data_loader(imagenet_dataset, num_samples)
device = get_device(model)
correct = 0
with in_eval_mode(model), torch.no_gr... |
class ProjectWindow(tk.Frame):
def __init__(self, parent, sdkpath, args):
tk.Frame.__init__(self, parent)
self.master = parent
self.sdkpath = sdkpath
self.init_window(args)
self.configs = dict()
self.ssid = str()
self.password = str()
def setState(self, th... |
class CumOp(COp):
__props__ = ('axis', 'mode')
check_input = False
params_type = ParamsType(c_axis=int_t, mode=EnumList(('MODE_ADD', 'add'), ('MODE_MUL', 'mul')))
def __init__(self, axis: Optional[int]=None, mode='add'):
if (mode not in ('add', 'mul')):
raise ValueError(f'{type(self)... |
def log_args_to_txt(log_txt, args):
if (not os.path.exists(log_txt)):
with open(log_txt, 'w') as txtfile:
args_ = vars(args)
args_str = ''
for (k, v) in args_.items():
args_str = ((((args_str + str(k)) + ':') + str(v)) + ',\t\n')
txtfile.write(... |
def rounding_numerical_components():
Print_Function()
(ex, ey, ez) = MV.setup('e_x e_y e_z', metric='[1,1,1]')
X = (((1.2 * ex) + (2.34 * ey)) + (0.555 * ez))
Y = (((0.333 * ex) + (4 * ey)) + (5.3 * ez))
print('X =', X)
print('Nga(X,2) =', Nga(X, 2))
print('X*Y =', (X * Y))
print('Nga(X*... |
def preprocess_for_train(image, output_height, output_width, resize_side_min=_RESIZE_SIDE_MIN, resize_side_max=_RESIZE_SIDE_MAX):
resize_side = tf.random_uniform([], minval=resize_side_min, maxval=(resize_side_max + 1), dtype=tf.int32)
image = _aspect_preserving_resize(image, resize_side)
image = _random_cr... |
class ExtractPathTest(object):
def test_extract_static_path(self):
path = '/test'
assert (extract_path(path) == '/test')
def test_extract_path_with_a_single_simple_parameter(self):
path = '/test/<parameter>'
assert (extract_path(path) == '/test/{parameter}')
def test_extract_... |
def decoder_rnn(cell, inputs, enc_outputs, enc_final_states, seq_length, hidden_dim, num_glimpse, batch_size, is_train, end_of_sequence_id=0, initializer=None, max_length=None):
with tf.variable_scope('decoder_rnn') as scope:
def attention(ref, query, with_softmax, scope='attention'):
with tf.va... |
def get_cfg_tree(nsql: str):
stack: List = []
expression_stack: List = []
current_tree_node = TreeNode(name=nsql)
for idx in range(len(nsql)):
if (nsql[idx] == '('):
stack.append(idx)
if ((idx > 1) and (nsql[(idx - 2):(idx + 1)] == 'QA(') and ((idx - 2) != 0)):
... |
def data_masks(all_usr_pois, item_tail):
us_lens = [len(upois) for upois in all_usr_pois]
len_max = max(us_lens)
us_pois = [(upois + (item_tail * (len_max - le))) for (upois, le) in zip(all_usr_pois, us_lens)]
us_msks = [(([1] * le) + ([0] * (len_max - le))) for le in us_lens]
return (us_pois, us_ms... |
_module()
class UNet(BaseModule):
def __init__(self, in_channels=3, base_channels=64, num_stages=5, strides=(1, 1, 1, 1, 1), enc_num_convs=(2, 2, 2, 2, 2), dec_num_convs=(2, 2, 2, 2), downsamples=(True, True, True, True), enc_dilations=(1, 1, 1, 1, 1), dec_dilations=(1, 1, 1, 1), with_cp=False, conv_cfg=None, norm_... |
()
_options(dbt_flags)
_tracking
def detect(**kwargs):
print(f'Detecting tables', 'RUN')
dbt_vars = parse_dbt_vars(kwargs.get('dbt_vars'))
run_list = ['dbt', 'run', '--models', 're_data_columns', 're_data_monitored']
if dbt_vars:
run_list.extend(['--vars', yaml.dump(dbt_vars)])
add_dbt_flags... |
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