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class CaseInsensitiveModelBackend(ModelBackend):
def authenticate(self, request, username=None, password=None, autologin=None):
if autologin:
autologin.backend = 'evennia.web.utils.backends.CaseInsensitiveModelBackend'
return autologin
else:
Account = get_user_mod... |
class Solution():
def reverse(self, x):
is_neg = False
if (x < 0):
x = (- x)
is_neg = True
res = 0
while (x > 0):
res *= 10
res += (x % 10)
x //= 10
if is_neg:
res = (- res)
if ((res < (- (2 ** 31... |
class FileException():
def __init__(self, max):
self.count = 0
self.max = max
def read(self, chars):
self.count += chars
if (self.count > self.max):
raise OSError(13, 'Permission Denied')
return (b'a' * chars)
def close(self):
return None |
class Unsubscribe(HookHandlerCollection):
def _subscribe(self, event: str, func: Callable) -> None:
registry = subscriptions.setdefault(self.registry_name, dict())
lst = registry.setdefault(event, [])
try:
lst.remove(func)
except ValueError:
raise utils.QtileE... |
def jwk_dict_to_public_key(jwk_dict):
jwk = JsonWebKey.import_key(jwk_dict)
if isinstance(jwk, RSAKey):
rsa_pk = jwk.as_key()
return RSAPublicNumbers(e=rsa_pk.public_numbers().e, n=rsa_pk.public_numbers().n).public_key(default_backend())
elif isinstance(jwk, ECKey):
ec_pk = jwk.as_ke... |
def test_qvariant(tmpdir):
settings = qt_api.QtCore.QSettings(str((tmpdir / 'foo.ini')), qt_api.QtCore.QSettings.Format.IniFormat)
settings.setValue('int', 42)
settings.setValue('str', 'Hello')
settings.setValue('empty', None)
assert (settings.value('int') == 42)
assert (settings.value('str') ==... |
def process_batch_augmentation(input_filename_list, input_label_list, dim_input, batch_sample_num):
new_path_list = []
new_label_list = []
for k in range(batch_sample_num):
class_idxs = list(range(0, FLAGS.way_num))
random.shuffle(class_idxs)
for class_idx in class_idxs:
... |
class FootageInterpretation(PymiereBaseObject):
def __init__(self, pymiere_id=None):
super(FootageInterpretation, self).__init__(pymiere_id)
def frameRate(self):
return self._eval_on_this_object('frameRate')
def frameRate(self, frameRate):
self._eval_on_this_object('frameRate = {}'.f... |
def find_module_paths_using_imports(modules: list[str], packages: list[str], verbose: bool, quiet: bool) -> tuple[(list[StubSource], list[StubSource])]:
with ModuleInspect() as inspect:
py_modules: list[StubSource] = []
c_modules: list[StubSource] = []
found = list(walk_packages(inspect, pac... |
class BottleNeck(nn.Module):
def __init__(self, in_channels, out_channels=None, activation=None, dilation=1, downsample=False, proj_ratio=4, upsample=False, asymetric=False, regularize=True, p_drop=None, use_prelu=True):
super(BottleNeck, self).__init__()
self.pad = 0
self.upsample = upsampl... |
class Plating(BasePlating):
def __init__(self, param, domain, x_average, options):
super().__init__(param, domain, options=options)
self.x_average = x_average
pybamm.citations.register('OKane2020')
pybamm.citations.register('OKane2022')
def get_fundamental_variables(self):
... |
class T5Tokenizer(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, eos_token='</s>', un... |
def init_distributed_mode(args):
if (('RANK' in os.environ) and ('WORLD_SIZE' in os.environ)):
rank = int(os.environ['RANK'])
world_size = int(os.environ['WORLD_SIZE'])
print(f'RANK and WORLD_SIZE in environment: {rank}/{world_size}')
else:
rank = (- 1)
world_size = (- 1)... |
class QuaterionProgressBar(RichProgressBar):
def __init__(self, refresh_rate: int=1, leave: bool=False, theme: RichProgressBarTheme=None, console_kwargs: Optional[Dict[(str, Any)]]=None):
if (theme is None):
theme = RichProgressBarTheme(description='white', progress_bar='#4881AD', progress_bar_f... |
class PositiveResourceRequirement(ResourceRequirement):
def __post_init__(self) -> None:
assert (self.resource.resource_type != ResourceType.DAMAGE)
assert (not self.negate)
def satisfied(self, current_resources: ResourceCollection, current_energy: int, database: ResourceDatabase) -> bool:
... |
_ansi_style(ansi.AllowStyle.NEVER)
def test_poutput_ansi_never(outsim_app):
msg = 'Hello World'
colored_msg = ansi.style(msg, fg=ansi.Fg.CYAN)
outsim_app.poutput(colored_msg, apply_style=False)
out = outsim_app.stdout.getvalue()
expected = (msg + '\n')
assert (colored_msg != msg)
assert (out... |
class TypeAreaScaledDotProductAttention(nn.Module):
def __init__(self, scale, attn_dropout=0.1):
super().__init__()
self.scale = scale
self.dropout = nn.Dropout(attn_dropout)
def forward(self, q_a: torch.Tensor, k_a: torch.Tensor, v_a: torch.Tensor, q_s: torch.Tensor, k_s: torch.Tensor, ... |
def evaluate(model, loader, n_class, device, dtype, iter_idx, writer):
hist = np.zeros((n_class, n_class))
for (batch_idx, (data, target)) in enumerate(loader):
data = data.to(device=device, dtype=dtype)
with torch.no_grad():
output = model(data)
(_, h, w) = target.shape
... |
class Operand(object):
value = None
kind = oUNK
text = '?'
def __init__(self, akind=None, avalue=None, arank=0, atext='?'):
if (akind is not None):
self.kind = akind
if (avalue is not None):
self.value = avalue
self.rank = arank
self.text = atext
... |
class Corr3dMMGradWeights(BaseCorr3dMM):
_direction = 'backprop weights'
def make_node(self, img, topgrad, shape=None):
img = as_tensor_variable(img)
topgrad = as_tensor_variable(topgrad)
(img, topgrad) = self.as_common_dtype(img, topgrad)
if (img.type.ndim != 5):
rai... |
class WindowWithoutMessageLoopFocusTests(unittest.TestCase):
def setUp(self):
Timings.fast()
self.app1 = Application().start(u'cmd.exe', create_new_console=True, wait_for_idle=False)
self.app2 = Application().start(os.path.join(mfc_samples_folder, u'CmnCtrl2.exe'))
self.app2.wait_cpu... |
class SWDADataLoader(LongDataLoader):
def __init__(self, name, data, config, labeled=False):
self.name = name
self.data = data
self.data_size = len(data)
self.data_lens = all_lens = [len(line) for line in self.data]
self.max_utt_size = config.max_utt_len
self.max_dial... |
class PoolBase(object):
def __init__(self, env_spec):
self._observation_dim = env_spec.observation_space.flat_dim
self._action_dim = env_spec.action_space.flat_dim
def observation_dim(self):
return self._observation_dim
def action_dim(self):
return self._action_dim |
.parametrize('menu_mod', [False, True])
def test_edit_menu_mod(editor: PresetEditor, initial_layout_configuration_params: dict, default_echoes_configuration, menu_mod):
editor._configuration = dataclasses.replace(default_echoes_configuration, **initial_layout_configuration_params)
editor._nested_autosave_level ... |
def main():
args = parser.parse_args()
cfg_file = args.config_file
prog_name = 'Running ResDepth: Training'
print('\n{}\n{}\n'.format(prog_name, ('=' * len(prog_name))))
if (not fdutil.file_exists(cfg_file)):
print(f'ERROR: Cannot find the configuration file: {cfg_file}')
sys.exit(1)... |
def WriteDialogToFile(filename, props):
try:
props[0].keys()
except (TypeError, AttributeError):
props = controls.get_dialog_props_from_handle(props)
root = Element('DIALOG')
root.set('_version_', '2.0')
for ctrl in props:
ctrlelem = SubElement(root, 'CONTROL')
for (n... |
class TestTrainingExtensionBnFold():
def test_fold_resnet18(self):
torch.manual_seed(10)
model = models.resnet18()
_initialize_bn_params(model)
model = model.eval()
random_input = torch.rand(1, 3, 224, 224)
baseline_output = model(random_input)
layer_list = [(... |
class PluginCliOptionsTestCase(unittest.TestCase):
def test_cli_options(self):
test_plugin = plugin.ServicePlugin(name='test-plugin', config=None, cli_options=TestPluginCliOptions(), client=TestClient)
args = cli._parse_command('bird --sound tweet'.split(), plugins=[test_plugin])
self.assert... |
def get_args():
parser = argparse.ArgumentParser(description='process the textgrid files')
parser.add_argument('--path', type=str, required=True, help='Data path')
parser.add_argument('--mars', type=strtobool, default=False, help='Whether to process mars data set.')
args = parser.parse_args()
return... |
class Model(OriginalModel):
def __init__(self, *args, **kwargs):
logger.debug('Initializing %s: (args: %s, kwargs: %s', self.__class__.__name__, args, kwargs)
kwargs['input_shape'] = (64, 64, 3)
kwargs['encoder_dim'] = 512
super().__init__(*args, **kwargs)
logger.debug('Initi... |
def collate(samples, pad_idx, eos_idx, vocab, left_pad_source=False, left_pad_target=False, input_feeding=True, pad_to_length=None):
assert input_feeding
if (len(samples) == 0):
return {}
def merge(key, left_pad, move_eos_to_beginning=False, pad_to_length=None):
return data_utils.collate_tok... |
def infer_enum_class(node: nodes.ClassDef) -> nodes.ClassDef:
for basename in (b for cls in node.mro() for b in cls.basenames):
if (node.root().name == 'enum'):
break
dunder_members = {}
target_names = set()
for (local, values) in node.locals.items():
if (any(... |
class TradeTestCase(unittest.TestCase):
def test_basic_long_only_strategy(self):
class BuyBelow10(Moonshot):
CODE = 'buy-below-10'
def prices_to_signals(self, prices):
signals = (prices.loc['Close'] < 10)
return signals.astype(int)
def mock_get... |
def test_get_our_capacity_for_token_network():
test_state = factories.make_chain_state(number_of_channels=3)
chain_state = test_state.chain_state
test_state.channels[(- 1)].close_transaction = TransactionExecutionStatus(started_block_number=chain_state.block_number, finished_block_number=chain_state.block_n... |
def get_config(prefix):
class AnimOsdConfig():
plugin_conf = PluginConfig(prefix)
font = ConfProp(plugin_conf, 'font', 'Sans 22')
string = ConfProp(plugin_conf, 'string', DEFAULT_PATTERN)
pos_x = FloatConfProp(plugin_conf, 'pos_x', 0.5)
pos_y = FloatConfProp(plugin_conf, 'pos... |
def main():
args = parser.parse_args()
args.use_ema = (not args.no_use_ema)
args.sort = (not args.no_sort)
if os.path.exists(args.output):
print('Error: Output filename ({}) already exists.'.format(args.output))
exit(1)
pattern = args.input
if ((not args.input.endswith(os.path.se... |
class Stack(Layout):
defaults = [('border_focus', '#0000ff', 'Border colour(s) for the focused window.'), ('border_normal', '#000000', 'Border colour(s) for un-focused windows.'), ('border_focus_stack', None, "Border colour(s) for the focused stacked window. If 'None' will default to border_focus."), ('bor... |
def test_search_with_persistence_and_skipped_vectors():
import tempfile
fixture_records = generate_sparse_fixtures(skip_vectors=True)
searcher = TestSimpleSparseSearcher()
with tempfile.TemporaryDirectory() as tmpdir:
local_client = init_local(tmpdir)
init_client(local_client, fixture_re... |
class CheckTable(QtWidgets.QWidget):
sigStateChanged = QtCore.Signal(object, object, object)
def __init__(self, columns):
QtWidgets.QWidget.__init__(self)
self.layout = QtWidgets.QGridLayout()
self.layout.setSpacing(0)
self.setLayout(self.layout)
self.headers = []
... |
def test_do_dims():
coords = {'test_dim': range(10)}
with pm.Model(coords=coords) as m:
x = pm.Normal('x', dims='test_dim')
y = pm.Deterministic('y', (x + 5), dims='test_dim')
do_m = do(m, {'x': np.zeros(10, dtype=config.floatX)})
assert (do_m.named_vars_to_dims['x'] == ['test_dim'])
... |
class AdvertiserStripePortalView(AdvertiserAccessMixin, UserPassesTestMixin, View):
= ['get']
def get(self, request, *args, **kwargs):
advertiser = get_object_or_404(Advertiser, slug=self.kwargs['advertiser_slug'])
return_url = reverse('advertiser_main', args=[advertiser.slug])
if (not ... |
def test_window_count(manager_nospawn, minimal_conf_noscreen):
config = minimal_conf_noscreen
config.screens = [libqtile.config.Screen(top=libqtile.bar.Bar([WindowCount()], 10))]
manager_nospawn.start(config)
assert (int(manager_nospawn.c.widget['windowcount'].get()) == 0)
one = manager_nospawn.test... |
class TestAcceptContext(_GSSAPIKerberosTestCase):
def setUp(self):
self.target_name = gb.import_name(TARGET_SERVICE_NAME, gb.NameType.hostbased_service)
ctx_resp = gb.init_sec_context(self.target_name)
self.client_token = ctx_resp[3]
self.client_ctx = ctx_resp[0]
self.assertI... |
class PythonSpec():
def __init__(self, str_spec, implementation, major, minor, micro, architecture, path) -> None:
self.str_spec = str_spec
self.implementation = implementation
self.major = major
self.minor = minor
self.micro = micro
self.architecture = architecture
... |
class UniF_BiAGRU(nn.Module):
def __init__(self, emodict, worddict, embedding, args):
super(UniF_BiAGRU, self).__init__()
self.num_classes = emodict.n_words
self.embeddings = embedding
self.gpu = args.gpu
self.hops = args.hops
self.wind_1 = args.wind1
self.utt... |
class ReduceState(nn.Module):
def __init__(self):
super(ReduceState, self).__init__()
self.reduce_h = nn.Linear((config.hidden_dim * 2), config.hidden_dim)
init_linear_wt(self.reduce_h)
self.reduce_c = nn.Linear((config.hidden_dim * 2), config.hidden_dim)
init_linear_wt(self.... |
def sapm_sd_scatter(c_data, label=None, **kwargs):
fig = plt.figure(figsize=(12, 12))
ax = fig.add_subplot(111, aspect='equal')
sc = ax.scatter(p_acs['sd'], p_acs['sapm'], c=c_data, alpha=1, **kwargs)
ax.plot(yxline, yxline, 'r', linewidth=3)
ax.set_xlim(0, None)
ax.set_ylim(0, None)
ax.set_... |
def get_dataset_inner(cfg: DatasetConfig, shard_info: seqio.ShardInfo, feature_converter_cls: Callable[(..., seqio.FeatureConverter)], seed: Optional[int]=None, num_epochs: Optional[int]=None):
batch_size = (cfg.batch_size // shard_info.num_shards)
if isinstance(cfg.mixture_or_task_name, seqio.DatasetProviderBa... |
class F36Handler(BaseHandler):
version = F36
commandMap = {'auth': commands.authconfig.F35_Authconfig, 'authconfig': commands.authconfig.F35_Authconfig, 'authselect': commands.authselect.F28_Authselect, 'autopart': commands.autopart.F29_AutoPart, 'autostep': commands.autostep.F34_AutoStep, 'bootloader': command... |
def _find_registerable_files_readers(readers=None):
import yaml
from satpy.readers import configs_for_reader, load_reader
for reader_configs in configs_for_reader(reader=readers):
try:
load_reader(reader_configs)
except (ModuleNotFoundError, yaml.YAMLError):
continue |
def build_graph_from_triplets(num_nodes, num_rels, triplets):
g = dgl.DGLGraph()
g.add_nodes(num_nodes)
(src, rel, dst) = triplets
(src, dst) = (np.concatenate((src, dst)), np.concatenate((dst, src)))
rel = np.concatenate((rel, (rel + num_rels)))
edges = sorted(zip(dst, src, rel))
(dst, src,... |
def test_envget_pass():
os.environ['ARB_DELETE_ME1'] = 'arb value from $ENV ARB_DELETE_ME1'
os.environ['ARB_DELETE_ME2'] = 'arb value from $ENV ARB_DELETE_ME2'
context = Context({'key1': 'value1', 'key2': 'value2', 'key3': 'value3', 'envGet': [{'env': 'ARB_DELETE_ME1', 'key': 'key2', 'default': 'blah'}, {'e... |
def make_blocks(block_fn, channels, block_repeats, inplanes, reduce_first=1, output_stride=32, down_kernel_size=1, avg_down=False, drop_block_rate=0.0, drop_path_rate=0.0, **kwargs):
stages = []
feature_info = []
net_num_blocks = sum(block_repeats)
net_block_idx = 0
net_stride = 4
dilation = pre... |
class VarnishRoleTest(ProvyTestCase):
def setUp(self):
super(VarnishRoleTest, self).setUp()
self.role = VarnishRole(prov=None, context={'owner': 'some-owner'})
def installs_necessary_packages_to_provision(self):
with self.using_stub(AptitudeRole) as aptitude:
self.role.provis... |
def test(db, split, testiter, debug=False, suffix=None):
result_dir = system_configs.result_dir
result_dir = os.path.join(result_dir, str(testiter), split)
if (suffix is not None):
result_dir = os.path.join(result_dir, suffix)
make_dirs([result_dir])
test_iter = (system_configs.max_iter if (... |
class GLUETransformer(BaseTransformer):
mode = 'sequence-classification'
def __init__(self, hparams):
if (type(hparams) == dict):
hparams = Namespace(**hparams)
hparams.glue_output_mode = glue_output_modes[hparams.task]
num_labels = glue_tasks_num_labels[hparams.task]
... |
class ModelPruner():
def _copy_model(cls, model, model_layers):
model_copy = copy.deepcopy(model)
model_layers_copy = {}
modules_in_copy = list(model_copy.modules())
for (index, module) in enumerate(model.modules()):
if (id(module) in model_layers):
existi... |
def _parse_paren(source, info):
if source.match(u'?'):
if source.match(u'<'):
if source.match(u'='):
return _parse_lookaround(source, info, behind=True, positive=True)
elif source.match(u'!'):
return _parse_lookaround(source, info, behind=True, positiv... |
_torch
_pytesseract
class LayoutLMv3ImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = (LayoutLMv3ImageProcessor if is_pytesseract_available() else None)
def setUp(self):
self.image_processor_tester = LayoutLMv3ImageProcessingTester(self)
def image_proce... |
('/v1/superuser/keys/<kid>')
_param('kid', 'The unique identifier for a service key')
_if(features.SUPER_USERS)
class SuperUserServiceKey(ApiResource):
schemas = {'PutServiceKey': {'id': 'PutServiceKey', 'type': 'object', 'description': 'Description of updates for a service key', 'properties': {'name': {'type': 'st... |
class OverlayLabel(QtWidgets.QLabel):
STYLESHEET = '\n QLabel, QLabel link {\n color: rgb(0, 0, 0);\n background-color: rgb(248, 240, 200);\n border: 1px solid;\n border-color: rgb(255, 114, 47);\n padding: 2px;\n }\n '
def __init__(self, text, parent):
super(... |
_head('san_graph')
class SANGraphHead(nn.Module):
def __init__(self, dim_in, dim_out, L=2):
super().__init__()
self.pooling_fun = register.pooling_dict[cfg.model.graph_pooling]
list_FC_layers = [nn.Linear((dim_in // (2 ** l)), (dim_in // (2 ** (l + 1))), bias=True) for l in range(L)]
... |
class ClassInstanceHandler():
called = False
args = None
kwargs = None
def mock_event(self, *args, **kwargs):
self.called = True
self.args = args
self.kwargs = kwargs
return EVENT_HANDLED
def mock_event2(self, *args, **kwargs):
self.called = True
self.... |
def test_struct_prefab():
m = run_mod("\n #lang pycket\n (require racket/private/kw)\n\n (define lunch '#s(sprout bean))\n (struct sprout (kind) #:prefab)\n (define t (sprout? lunch))\n (define f (sprout? #s(sprout bean #f 17)))\n\n (define result (and (not f) t))\n ")
assert (m.defs[W_S... |
class _LSBBitReader(BitReader):
def _lsb(self, count):
value = (self._buffer & (255 >> (8 - count)))
self._buffer = (self._buffer >> count)
self._bits -= count
return value
def bits(self, count):
if (count < 0):
raise ValueError
value = 0
if (c... |
def low_depth_second_order_trotter_error_operator(terms, indices=None, is_hopping_operator=None, jellium_only=False, verbose=False):
more_info = bool(indices)
n_terms = len(terms)
if verbose:
import time
start = time.time()
error_operator = FermionOperator.zero()
for beta in range(n_... |
(post_save, sender=Item)
def update_cls_score(instance, **kwargs):
if (not settings.CLS_ENABLED):
return
if instance._disable_signals:
return
try:
item = ItemClsCheck.objects.get(item=instance)
item.check_cls(False)
except (ObjectDoesNotExist, ItemClsCheck.DoesNotExist):
... |
def check_test_output_files(testcase: DataDrivenTestCase, step: int, strip_prefix: str='') -> None:
for (path, expected_content) in testcase.output_files:
if path.startswith(strip_prefix):
path = path[len(strip_prefix):]
if (not os.path.exists(path)):
raise AssertionError('Ex... |
class SawyerDrawerCloseEnvV2(SawyerXYZEnv):
_TARGET_RADIUS = 0.04
def __init__(self):
hand_low = ((- 0.5), 0.4, 0.05)
hand_high = (0.5, 1, 0.5)
obj_low = ((- 0.1), 0.9, 0.0)
obj_high = (0.1, 0.9, 0.0)
super().__init__(self.model_name, hand_low=hand_low, hand_high=hand_hig... |
class TestCrossNet(unittest.TestCase):
def test_cross_net_numercial_forward(self) -> None:
torch.manual_seed(0)
batch_size = 3
num_layers = 20
in_features = 2
input = torch.randn(batch_size, in_features)
dcn = CrossNet(in_features=in_features, num_layers=num_layers)
... |
def download_models():
home = path.expanduser('~')
models_path = path.join(home, NFH_DIR)
if (not path.exists(models_path)):
print("couldn't fine nfh directory... creating one [directory]")
os.makedirs(models_path, exist_ok=True)
identification_path = path.join(home, NFH_DIR, IDENTIFICAT... |
class Data():
def __init__(self, data, shuffle=False, n_node=None):
self.raw = np.asarray(data[0])
H_T = data_masks(self.raw, n_node)
BH_T = H_T.T.multiply((1.0 / H_T.sum(axis=1).reshape(1, (- 1))))
BH_T = BH_T.T
H = H_T.T
DH = H.T.multiply((1.0 / H.sum(axis=1).reshap... |
class Encryptor(object):
magic_number = b'\xd1\x08'
def __init__(self, key, iv=None):
self.iv = make_aes_iv(iv)
self.aes = AES.new(make_aes_key(key), AES.MODE_CBC, self.iv)
self.random = Random.new()
def encrypt(self, msg):
blob = self.magic_number
blob += '{}:'.forma... |
def check_create_test_db(database=_connect.TEST_DB_NAME):
with pymssql.connect(_connect.MSQ_SERVER, port=_connect.MSQ_PORT, user=_connect.SA_USER, password=_connect.SA_PASSWORD) as sql_sa_connection:
sql_sa_connection.autocommit(True)
with sql_sa_connection.cursor() as cursor:
cursor.exe... |
def pyarrow_read(path: str, columns: (list[str] | None)=None) -> pa.Table:
assert path.startswith('s3://'), f"Expected file path to start with 's3://', but got {path}."
fs = pafs.S3FileSystem()
path = path.replace('s3://', '')
return papq.read_table(path, columns=columns, filesystem=fs) |
def convert_gpt2_checkpoint_to_pytorch(gpt2_checkpoint_path, gpt2_config_file, pytorch_dump_folder_path):
if (gpt2_config_file == ''):
config = GPT2Config()
else:
config = GPT2Config.from_json_file(gpt2_config_file)
model = GPT2Model(config)
load_tf_weights_in_gpt2(model, config, gpt2_ch... |
class residualBlockIN(nn.Module):
def __init__(self, in_channels=64, kernel=3, mid_channels=64, out_channels=64, stride=1, activation=relu):
super(residualBlockIN, self).__init__()
self.conv1 = nn.Conv2d(in_channels=in_channels, out_channels=mid_channels, kernel_size=kernel, stride=stride, padding=(... |
def test_double_reflect_stratonovich():
(f, g, ts, y0, args) = make_example_sde()
b = make_brownian_motion(ts[0], np.zeros(y0.shape), ts[(- 1)], rng)
(f2, g2, b2, t2) = time_reflect_stratonovich(*time_reflect_stratonovich(f, g, b, ts))
t = 0.1
assert np.all((ts == t2))
assert np.all((f(y0, t, ar... |
def test_activate_does_not_recreate_when_switching_minor(tmp_path: Path, manager: EnvManager, poetry: Poetry, config: Config, mocker: MockerFixture, venv_name: str) -> None:
if ('VIRTUAL_ENV' in os.environ):
del os.environ['VIRTUAL_ENV']
envs_file = TOMLFile((tmp_path / 'envs.toml'))
doc = tomlkit.d... |
def test_doc_extras():
pdf = pdfium.PdfDocument(TestFiles.empty)
assert (len(pdf) == 1)
page = pdf[0]
pdf = pdfium.PdfDocument.new()
assert (len(pdf) == 0)
sizes = [(50, 100), (100, 150), (150, 200), (200, 250)]
for size in sizes:
page = pdf.new_page(*size)
for (i, (size, page)) ... |
def test_sigma_dir(runner, mocker):
mocker.patch('products.vmware_cb_response.CbResponse._authenticate')
mocked_nested_process_search = mocker.patch('products.vmware_cb_response.CbResponse.nested_process_search')
with runner.isolated_filesystem() as temp_dir:
sigma_file_path1 = os.path.join(temp_dir... |
def rearrange_dir(root_dir):
image_dir = os.path.join(root_dir, 'images')
label_dir = os.path.join(root_dir, 'labels')
if (not os.path.exists(image_dir)):
os.makedirs(image_dir)
print((('Created' + image_dir) + '...'))
if (not os.path.exists(label_dir)):
os.makedirs(label_dir)
... |
class RequestCacheMiddleware(HttpCacheMiddleware):
_decorator_ignore_request_apikey
def process_request(self, request, spider):
if request.meta.get('dont_cache', False):
return None
if (not self.policy.should_cache_request(request)):
request.meta['_dont_cache'] = True
... |
class MultiResolutionFeatureMapGeneratorTest(tf.test.TestCase):
def test_get_expected_feature_map_shapes_with_inception_v2(self):
image_features = {'Mixed_3c': tf.random_uniform([4, 28, 28, 256], dtype=tf.float32), 'Mixed_4c': tf.random_uniform([4, 14, 14, 576], dtype=tf.float32), 'Mixed_5c': tf.random_unif... |
def run_sift_matching(img_dir, db_file, remove_exist=False):
print('Running sift matching...')
if (remove_exist and os.path.exists(db_file)):
os.remove(db_file)
cmd = ' feature_extractor --database_path {} --image_path {} --Imag... |
def read_csv_cirrus(filename):
with open(filename, 'r') as csvfile:
csvreader = csvfile.read()
csvreader = re.sub(' dB', '', csvreader)
dialect = csv.Sniffer().sniff(csvreader, delimiters=',;')
separator = dialect.delimiter
decimal_sep = re.search('\\"\\d{2,3}(\\.|,)\\d{1,2}\... |
class _ArchARM(Arch):
NAME = 'ARM'
INS_PTR = Reg('pc')
STK_PTR = Reg('sp')
_CSD = capstone.Cs(capstone.CS_ARCH_ARM, capstone.CS_MODE_ARM)
nop_instruction = b'\x00\xf0 \xe3'
class optypes(IntEnum):
INVALID = arm_const.ARM_OP_INVALID
REG = arm_const.ARM_OP_REG
IMM = arm_con... |
class InfoAction(actions.BaseAction):
name = 'info'
security = None
def setup(self):
return Globals.RET_CODE_OK
def run(self):
ret_code = super().run()
if (ret_code & Globals.RET_CODE_ERR):
return ret_code
runtime_info = Globals.get_runtime_info()
prin... |
def make_optimizer(cfg, model):
params = []
for (key, value) in model.named_parameters():
if (not value.requires_grad):
continue
lr = cfg.SOLVER.BASE_LR
weight_decay = cfg.SOLVER.WEIGHT_DECAY
if ('bias' in key):
lr = (cfg.SOLVER.BASE_LR * cfg.SOLVER.BIAS_L... |
class ListInput(Input, QtWidgets.QComboBox):
def __init__(self, parameter, parent=None, **kwargs):
super().__init__(parameter=parameter, parent=parent, **kwargs)
self._stringChoices = None
self.setEditable(False)
def set_parameter(self, parameter):
try:
if (hasattr(pa... |
class DeformRoIPoolingFunction(Function):
def forward(ctx, input, rois, offset, spatial_scale, pooled_size, output_dim, no_trans, group_size=1, part_size=None, sample_per_part=4, trans_std=0.0):
ctx.spatial_scale = spatial_scale
ctx.no_trans = int(no_trans)
ctx.output_dim = output_dim
... |
def test_difflib_ops_to_text_edit_ops_insert(workspace):
expected = {'range': {'start': {'line': 5, 'character': 0}, 'end': {'line': 5, 'character': 0}}, 'newText': 'are = "here"\nred = "here"\n'}
new_document = create_document(workspace, 'many_changes_inlined.py')
difflib_ops = ('insert', 5, 5, 6, 8)
t... |
def trainable_initial_state(batch_size, state_size, initializer=None, name='initial_state'):
flat_state_size = nest.flatten(state_size)
if (not initializer):
flat_initializer = tuple((tf.zeros_initializer for _ in flat_state_size))
else:
flat_initializer = tuple((tf.zeros_initializer for ini... |
class Seq2SeqTSPredictionOutput(ModelOutput):
loss: Optional[torch.FloatTensor] = None
params: Optional[Tuple[torch.FloatTensor]] = None
past_key_values: Optional[Tuple[Tuple[torch.FloatTensor]]] = None
decoder_hidden_states: Optional[Tuple[torch.FloatTensor]] = None
decoder_attentions: Optional[Tup... |
def _zip_equal(*iterables):
try:
first_size = len(iterables[0])
for (i, it) in enumerate(iterables[1:], 1):
size = len(it)
if (size != first_size):
raise UnequalIterablesError(details=(first_size, i, size))
return zip(*iterables)
except TypeError:
... |
class MathSAT5Model(Model):
def __init__(self, environment, msat_env):
Model.__init__(self, environment)
self.msat_env = msat_env
self.converter = MSatConverter(environment, self.msat_env)
self.msat_model = None
msat_model = mathsat.msat_get_model(self.msat_env())
if ... |
class DataProcessor(object):
def get_train_examples(self, data_dir):
raise NotImplementedError()
def get_dev_examples(self, data_dir):
raise NotImplementedError()
def get_labels(self, data_dir):
raise NotImplementedError()
def _read_tsv(cls, input_file, quotechar=None):
w... |
def add_arg_scope(func):
def func_with_args(*args, **kwargs):
current_scope = current_arg_scope()
current_args = kwargs
key_func = arg_scope_func_key(func)
if (key_func in current_scope):
current_args = current_scope[key_func].copy()
current_args.update(kwargs... |
def create_holdings_chart(positions: QFDataFrame) -> LineChart:
result = LineChart()
positions = positions.copy().drop('cash', axis='columns')
holdings = positions.apply((lambda x: np.sum((x != 0))), axis='columns')
holdings_by_month = holdings.resample('1M').mean()
holdings_decorator = DataElementD... |
_tests('ecdh_test.json', 'ecdh_brainpoolP224r1_test.json', 'ecdh_brainpoolP256r1_test.json', 'ecdh_brainpoolP320r1_test.json', 'ecdh_brainpoolP384r1_test.json', 'ecdh_brainpoolP512r1_test.json', 'ecdh_secp224r1_test.json', 'ecdh_secp256k1_test.json', 'ecdh_secp256r1_test.json', 'ecdh_secp384r1_test.json', 'ecdh_secp521... |
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