code stringlengths 281 23.7M |
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_fixtures(WebFixture, ConstraintRenderingFixture)
def test_required_constraint_js(web_fixture, constraint_rendering_fixture):
fixture = constraint_rendering_fixture
constraint = RequiredConstraint()
class MyForm(Form):
def __init__(self, view, name):
super().__init__(view, name)
... |
.parametrize('case_name', POSITIVE_HOOK_CASES.keys())
def test_hook_positive_examples(case_name, run_line):
rcase = ResolvedCase.load_positive(case_name)
hook_id = POSITIVE_HOOK_CASES[case_name]
ret = run_line(((HOOK_CONFIG[hook_id] + [rcase.path]) + rcase.add_args))
assert (ret.exit_code == 0), _format... |
class webvision_dataloader():
def __init__(self, batch_size, num_batches, num_class, num_workers, root_dir, root_imagenet_dir, log):
self.batch_size = batch_size
self.num_class = num_class
self.num_samples = (None if (num_batches is None) else (self.batch_size * num_batches))
self.nu... |
class RStripTokenDataset(BaseWrapperDataset):
def __init__(self, dataset, id_to_strip):
super().__init__(dataset)
self.id_to_strip = id_to_strip
def __getitem__(self, index):
item = self.dataset[index]
while ((len(item) > 0) and (item[(- 1)] == self.id_to_strip)):
ite... |
def prediction_loss(train_loss, test_loss, directory):
plt.figure()
plt.plot(train_loss, color='red')
plt.plot(test_loss, color='blue')
plt.title('Prediction loss: training (red), test (blue)')
plt.xlabel('Epochs')
plt.ylabel('Loss')
name = (directory + '/predictionloss_test&train')
plt.... |
def _prepare(line):
while True:
positions = _find_separators(line, "'", "'")
if (positions is None):
break
(left, right) = positions
value = _global_value_of(line[(left + 1):right])
if value:
line = ((line[:left] + value) + line[(right + 1):])
... |
class GenDAGPass(BasePass):
def __call__(self, top):
top.check()
top._dag = PassMetadata()
placeholders = [x for x in top._dsl.all_named_objects if isinstance(x, Placeholder)]
if placeholders:
raise LeftoverPlaceholderError(placeholders)
self._generate_net_blocks(... |
class TestKazooRetry(unittest.TestCase):
def _makeOne(self, **kw):
from kazoo.retry import KazooRetry
return KazooRetry(**kw)
def test_connection_closed(self):
from kazoo.exceptions import ConnectionClosedError
retry = self._makeOne()
def testit():
raise Conne... |
class EigenstateResult(AlgorithmResult):
def __init__(self) -> None:
super().__init__()
self.eigenvalues: (np.ndarray | None) = None
self.eigenstates: (list[tuple[(QuantumCircuit, (Sequence[float] | None))]] | None) = None
self.aux_operators_evaluated: (list[ListOrDict[complex]] | No... |
class GetCustomEmojiStickers():
async def get_custom_emoji_stickers(self: 'pyrogram.Client', custom_emoji_ids: List[int]) -> List['types.Sticker']:
result = (await self.invoke(raw.functions.messages.GetCustomEmojiDocuments(document_id=custom_emoji_ids)))
stickers = []
for item in result:
... |
def pytest_addoption(parser: Parser) -> None:
group = parser.getgroup('order')
group.addoption('--indulgent-ordering', action='store_true', dest='indulgent_ordering', help='Request that the sort order provided by pytest-order be applied before other sorting, allowing the other sorting to have priority')
gro... |
class ContextAE():
def __init__(self, gf_dim=64, df_dim=64, gfc_dim=1024, dfc_dim=1024, c_dim=3):
self.gf_dim = gf_dim
self.df_dim = df_dim
self.c_dim = c_dim
self.gfc_dim = gfc_dim
self.dfc_dim = dfc_dim
def build(self, image):
imgshape = image.get_shape().as_lis... |
class Range():
def __init__(self, gdf, values, spatial_weights, unique_id, rng=(0, 100), verbose=True, **kwargs):
self.gdf = gdf
self.sw = spatial_weights
self.id = gdf[unique_id]
self.rng = rng
self.kwargs = kwargs
data = gdf.copy()
if ((values is not None) a... |
def initialize_uninitialized_vars(sess):
with sess.graph.as_default():
global_vars = tf.compat.v1.global_variables()
is_not_initialized = sess.run([(~ tf.compat.v1.is_variable_initialized(var)) for var in global_vars])
uninitialized_vars = list(compress(global_vars, is_not_initialized))
... |
def test_nested_while_with_continue() -> None:
src = '\n while n > 10:\n while n > 20:\n continue\n print(n - 1)\n continue\n print(n)\n '
cfg = build_cfg(src)
expected_blocks = [['n > 10'], ['n > 20'], ['continue'], ['print(n - 1)', 'continue'], ['print(n)'], []]
... |
class GeneralTranslationTask(Task):
VERSION = 0
def __init__(self, sacrebleu_dataset, sacrebleu_language_pair=None):
self.sacrebleu_dataset = sacrebleu_dataset
self.sacrebleu_language_pair = sacrebleu_language_pair
self.src_file = self.ref_file = self.src_data = self.ref_data = None
... |
def train(num_epochs, model, optimizers, train_loader, val_loader, fabric):
for epoch in range(num_epochs):
train_acc = torchmetrics.Accuracy(task='multiclass', num_classes=10).to(fabric.device)
model.train()
for (batch_idx, (features, targets)) in enumerate(train_loader):
model.... |
class W_Vector(W_MVector):
_attrs_ = ['strategy', 'storage', 'len']
errorname = 'vector'
import_from_mixin(StrategyVectorMixin)
def __init__(self, strategy, storage, len):
self.strategy = strategy
self.storage = storage
self.len = len
def get_len(self):
return self.le... |
def add_methods_to_generator_class(builder: IRBuilder, fn_info: FuncInfo, sig: FuncSignature, arg_regs: list[Register], blocks: list[BasicBlock], is_coroutine: bool) -> None:
helper_fn_decl = add_helper_to_generator_class(builder, arg_regs, blocks, sig, fn_info)
add_next_to_generator_class(builder, fn_info, hel... |
class LongPressMixin(RequiredServicesMixin):
EVENT_TYPE_LONG_PRESS = 'LongPress'
def _required_services(self) -> list[RequiredService]:
return (super()._required_services + [RequiredService(name='rules', actions=['FetchRules', 'StoreRules'])])
_type_check
def list_long_press_udns(self) -> frozen... |
def api_response(result: Any, status_code: HTTPStatus=HTTPStatus.OK) -> Response:
if (status_code == HTTPStatus.NO_CONTENT):
assert (not result), 'Provided 204 response with non-zero length response'
data = ''
else:
data = json.dumps(result)
log.debug('Request successful', response=r... |
_bpe('characters')
class Characters(object):
def __init__(self, args):
pass
def add_args(parser):
pass
def encode(x: str) -> str:
escaped = x.replace(SPACE, SPACE_ESCAPE)
return SPACE.join(list(escaped))
def decode(x: str) -> str:
return x.replace(SPACE, '').repla... |
def _concat(prefix, suffix, static=False):
if isinstance(prefix, ops.Tensor):
p = prefix
p_static = tensor_util.constant_value(prefix)
if (p.shape.ndims == 0):
p = array_ops.expand_dims(p, 0)
elif (p.shape.ndims != 1):
raise ValueError(('prefix tensor must be ... |
class EnlightenGANOptions(BaseOptions):
def __init__(self, training):
BaseOptions.__init__(self)
if training:
self.parser.add_argument('--dirA', type=str, required=True, help='Path to training dataset A')
self.parser.add_argument('--dirB', type=str, required=True, help='Path ... |
(params=_list_of_kernels, ids=(lambda p: p['kernel'].string_id()))
def kernel(request):
m = request.param['kernel']
d = m.__dict__
for (k, v) in request.param.items():
if (k == 'kernel'):
continue
k = ('test_' + k.replace('-', '_'))
d[k] = v
return m |
def add_orders(order_id, price, user_id, product_id, rating=None):
command = 'INSERT INTO orders \n (id, price, user_id, product_id, rating)\n VALUES (%s, %s, %s, %s, %s)'
command_args = (order_id, price, int(user_id), int(product_id), rating)
db.execute_a_data_manipulation(command, command_ar... |
class Scope():
def __init__(self, pycore, pyobject, parent_scope):
self.pycore = pycore
self.pyobject = pyobject
self.parent = parent_scope
def get_names(self):
return self.pyobject.get_attributes()
def get_defined_names(self):
return self.pyobject._get_structural_att... |
(init=False, unsafe_hash=True)
class LineLayout():
size: int
origin: Tuple[(int, int)]
rotation: int
def __init__(self, *, size: int, origin: Tuple[(int, int)]=(0, 0), rotation: int=0) -> None:
(a, b) = origin
self.origin = (a, b)
self.size = size
self.rotation = rotation... |
('pypyr.utils.filesystem.get_glob', autospec=True)
def test_glob_list(mock_glob):
context = Context({'ok1': 'ov1', 'glob': ['./arb/x', './arb/y', './arb/z']})
mock_glob.return_value = ['./f1.1', './f2.1', './f2.2', './f2.3']
with patch_logger('pypyr.steps.glob', logging.INFO) as mock_logger_info:
gl... |
class LDAPControl(RequestControl, ResponseControl):
def __init__(self, controlType=None, criticality=False, controlValue=None, encodedControlValue=None):
self.controlType = controlType
self.criticality = criticality
self.controlValue = controlValue
self.encodedControlValue = encodedC... |
class CocoStuff164k(BaseDataSet):
def __init__(self, **kwargs):
self.num_classes = 182
self.palette = palette.COCO_palette
super(CocoStuff164k, self).__init__(**kwargs)
def _set_files(self):
if (self.split in ['train2017', 'val2017']):
file_list = sorted(glob(os.path.... |
class Trainer(object):
def __init__(self, train_learner, eval_learner, is_training, train_dataset_list, eval_dataset_list, restrict_classes, restrict_num_per_class, checkpoint_dir, summary_dir, records_root_dir, eval_finegrainedness, eval_finegrainedness_split, eval_imbalance_dataset, omit_from_saving_and_reloading... |
class DQN(object):
def __init__(self, hps, name_variable):
self._hps = hps
self._name_variable = name_variable
def variable_summaries(self, var_name, var):
with tf.name_scope('summaries_{}'.format(var_name)):
mean = tf.reduce_mean(var)
tf.summary.scalar('mean', me... |
class TestSelectionNotify(EndianTest):
def setUp(self):
self.evt_args_0 = {'property': , 'requestor': , 'selection': , 'sequence_number': 25394, 'target': , 'time': , 'type': 165}
self.evt_bin_0 = b'\xa5\x00c2\x18f\xeb\xaav\x00\xc6\x8aL\xb9g\xb0A\x0f\t\x9b_\x87\x83\x9e\x00\x00\x00\x00\x00\x00\x00\x0... |
class TensorVariable(_tensor_py_operators, Variable[(_TensorTypeType, OptionalApplyType)]):
def __init__(self, type: _TensorTypeType, owner: OptionalApplyType, index=None, name=None):
super().__init__(type, owner, index=index, name=name)
if ((config.warn_float64 != 'ignore') and (type.dtype == 'floa... |
class TestLoadNetCDFXArray(TestLoadNetCDF):
def setup_method(self):
if (sys.version_info.minor >= 10):
self.tempdir = tempfile.TemporaryDirectory(ignore_cleanup_errors=True)
else:
self.tempdir = tempfile.TemporaryDirectory()
self.saved_path = pysat.params['data_dirs']... |
class FastConsumerFactory(_BaseKafkaQueueConsumerFactory):
def _commit_callback(err: confluent_kafka.KafkaError, topic_partition_list: List[confluent_kafka.TopicPartition]) -> None:
for topic_partition in topic_partition_list:
topic = topic_partition.topic
partition = topic_partition... |
class ClassNodeTest(ModuleLoader, unittest.TestCase):
def test_dict_interface(self) -> None:
_test_dict_interface(self, self.module['YOUPI'], 'method')
def test_cls_special_attributes_1(self) -> None:
cls = self.module['YO']
self.assertEqual(len(cls.getattr('__bases__')), 1)
self... |
def monthly_returns(returns, annot_size=10, figsize=(10, 5), cbar=True, square=False, compounded=True, eoy=False, grayscale=False, fontname='Arial', ylabel=True, savefig=None, show=True):
return monthly_heatmap(returns=returns, annot_size=annot_size, figsize=figsize, cbar=cbar, square=square, compounded=compounded,... |
def fill_statedict(state_dict, vars, size):
log_size = int(math.log(size, 2))
for i in range(8):
update(state_dict, convert_dense(vars, f'G_mapping/Dense{i}', f'style.{(i + 1)}'))
update(state_dict, {'input.input': torch.from_numpy(vars['G_synthesis/4x4/Const/const'].value().eval())})
update(sta... |
class HostLevelSharder(EmbeddingBagCollectionSharder, ModuleSharder[nn.Module]):
def sharding_types(self, compute_device_type: str) -> List[str]:
return [ShardingType.TABLE_ROW_WISE.value, ShardingType.TABLE_COLUMN_WISE.value]
def compute_kernels(self, sharding_type: str, compute_device_type: str) -> Li... |
class CheckpointReaderAdapter(object):
def __init__(self, reader):
self._reader = reader
m = self._reader.get_variable_to_shape_map()
self._map = {(k if k.endswith(':0') else (k + ':0')): v for (k, v) in six.iteritems(m)}
def get_variable_to_shape_map(self):
return self._map
... |
def _direct_solve_discrete_lyapunov(A: 'TensorLike', Q: 'TensorLike') -> TensorVariable:
A_ = as_tensor_variable(A)
Q_ = as_tensor_variable(Q)
if ('complex' in A_.type.dtype):
AA = kron(A_, A_.conj())
else:
AA = kron(A_, A_)
X = solve((pt.eye(AA.shape[0]) - AA), Q_.ravel())
retur... |
def init_segmentor(config, checkpoint=None, device='cuda:0'):
if isinstance(config, str):
config = mmcv.Config.fromfile(config)
elif (not isinstance(config, mmcv.Config)):
raise TypeError('config must be a filename or Config object, but got {}'.format(type(config)))
config.model.pretrained =... |
class Tokenizer(ABC):
def get_input_length(self, input_text: str) -> int:
return len(self.encode(input_text))
def validate_input_length(self, prompt_token_ids: List[int], max_input_length: int):
num_input_tokens = len(prompt_token_ids)
if (num_input_tokens > max_input_length):
... |
def dice_loss(args):
(pred, gt, mask, weights) = args
pred = pred[(..., 0)]
weights = (((weights - tf.reduce_min(weights)) / (tf.reduce_max(weights) - tf.reduce_min(weights))) + 1.0)
mask = (mask * weights)
intersection = tf.reduce_sum(((pred * gt) * mask))
union = ((tf.reduce_sum((pred * mask))... |
class FeatsClassStage(object):
def __init__(self):
pass
def eval(self):
return self
def encode(self, c):
info = (None, None, c)
return (c, None, info)
def decode(self, c):
return c
def get_input(self, batch: dict, keys: dict) -> dict:
out = {}
... |
def test_no_init_nuts_compound(caplog):
with pm.Model() as model:
a = pm.Normal('a')
b = pm.Poisson('b', 1)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', '.*number of samples.*', UserWarning)
pm.sample(10, tune=10)
assert ('Initializing NUT... |
class PythonFileRunner():
def __init__(self, pycore, file_, args=None, stdin=None, stdout=None, analyze_data=None):
self.pycore = pycore
self.file = file_
self.analyze_data = analyze_data
self.observers = []
self.args = args
self.stdin = stdin
self.stdout = st... |
class Linear(torch.nn.Linear):
def __init__(self, *args, **kwargs):
super(Linear, self).__init__(*args, **kwargs)
def forward(self, input: Tensor) -> Tensor:
if (input.is_cuda and (linear_function is not None) and (self.bias is not None)):
return linear_function(input, self.weight, s... |
def _get_datetime(instant: _Instant) -> datetime.datetime:
if (instant is None):
return datetime.datetime.now(UTC).replace(tzinfo=None)
elif isinstance(instant, (int, float)):
return datetime.datetime.fromtimestamp(instant, UTC).replace(tzinfo=None)
elif isinstance(instant, datetime.time):
... |
def test_chrono_duration_roundtrip():
date1 = datetime.datetime.today()
date2 = datetime.datetime.today()
diff = (date2 - date1)
assert isinstance(diff, datetime.timedelta)
cpp_diff = m.test_chrono3(diff)
assert (cpp_diff.days == diff.days)
assert (cpp_diff.seconds == diff.seconds)
asser... |
class TestStickerSetWithoutRequest(TestStickerSetBase):
def test_slot_behaviour(self):
inst = StickerSet('this', 'is', True, self.stickers, True, 'not')
for attr in inst.__slots__:
assert (getattr(inst, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(inst... |
class ShakeDrop(torch.autograd.Function):
def forward(ctx, x, b, alpha):
y = (((b + alpha) - (b * alpha)) * x)
ctx.save_for_backward(b)
return y
def backward(ctx, dy):
beta = torch.rand(dy.size(0), dtype=dy.dtype, device=dy.device).view((- 1), 1, 1, 1)
(b,) = ctx.saved_te... |
def show_compilers():
from distutils.fancy_getopt import FancyGetopt
compilers = []
for compiler in compiler_class.keys():
compilers.append((('compiler=' + compiler), None, compiler_class[compiler][2]))
compilers.sort()
pretty_printer = FancyGetopt(compilers)
pretty_printer.print_help('L... |
def test_docs_examples():
expr = re.compile('\n!!! tab examples "pyproject.toml"\n\\s*\n\\s*```toml\n(.*?)```', (re.MULTILINE | re.DOTALL))
txt = DIR.parent.joinpath('docs/options.md').read_text()
blocks: list[str] = []
for match in expr.finditer(txt):
lines = (line.strip() for line in match.gro... |
class BasicConvolutionBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1, use_ln=False):
super().__init__()
self.net = nn.Sequential(spnn.Conv3d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, dilation=dilation, transposed=False), b... |
class Effect4936(BaseEffect):
runTime = 'late'
type = 'active'
def handler(fit, module, context, projectionRange, **kwargs):
amount = module.getModifiedItemAttr('shieldBonus')
speed = (module.getModifiedItemAttr('duration') / 1000.0)
fit.extraAttributes.increase('shieldRepair', (amou... |
def timeout_timer(item, settings):
if ((not settings.disable_debugger_detection) and is_debugging()):
return
try:
capman = item.config.pluginmanager.getplugin('capturemanager')
if capman:
capman.suspend_global_capture(item)
(stdout, stderr) = capman.read_global_ca... |
def _delete_file_or_dir(base_dir, name, struct):
fullname = os.path.join(base_dir, name)
set_path = SetPath(fullname, struct)
try:
if (set_path.get_type() == 'directory'):
_rmtree(fullname)
else:
os.remove(fullname)
except FileNotFoundError:
pass
excep... |
class DescribeNumberingPart():
def it_provides_access_to_the_numbering_definitions(self, num_defs_fixture):
(numbering_part, _NumberingDefinitions_, numbering_elm_, numbering_definitions_) = num_defs_fixture
numbering_definitions = numbering_part.numbering_definitions
_NumberingDefinitions_.... |
class InitializationArguments():
config_name: Optional[str] = field(default='gpt2-large', metadata={'help': 'Configuration to use for model initialization.'})
tokenizer_name: Optional[str] = field(default='codeparrot/codeparrot', metadata={'help': 'Tokenizer attached to model.'})
model_name: Optional[str] =... |
(derivate=True, coderize=True)
_loss
def smooth_l1_loss(pred, target, beta=1.0):
assert (beta > 0)
assert ((pred.size() == target.size()) and (target.numel() > 0))
diff = torch.abs((pred - target))
loss = torch.where((diff < beta), (((0.5 * diff) * diff) / beta), (diff - (0.5 * beta)))
return loss |
class SparsemaxLoss(nn.Module):
def __init__(self, weight=None, ignore_index=(- 100), reduction='elementwise_mean'):
assert (reduction in ['elementwise_mean', 'sum', 'none'])
self.reduction = reduction
self.weight = weight
self.ignore_index = ignore_index
super(SparsemaxLoss,... |
def test_cp38_arm64_testing_universal2_installer(tmp_path, capfd, request):
if (not request.config.getoption('--run-cp38-universal2')):
pytest.skip('needs --run-cp38-universal2 option to run')
project_dir = (tmp_path / 'project')
basic_project.generate(project_dir)
actual_wheels = utils.cibuildw... |
def test_class_scope_dependencies(item_names_for, order_dependencies):
tests_content = '\n import pytest\n\n class TestA:\n .dependency(depends=["test_c"], scope=\'class\')\n def test_a(self):\n assert True\n\n def test_b(self):\n assert T... |
class FY4Base(HDF5FileHandler):
def __init__(self, filename, filename_info, filetype_info):
super(FY4Base, self).__init__(filename, filename_info, filetype_info)
self.sensor = filename_info['instrument']
self._COFF_list = [21983.5, 10991.5, 5495.5, 2747.5, 1373.5]
self._LOFF_list = [... |
def test_tcn_backbone():
with pytest.raises(AssertionError):
TCN(in_channels=34, num_blocks=3, kernel_sizes=(3, 3, 3))
with pytest.raises(AssertionError):
TCN(in_channels=34, kernel_sizes=(3, 4, 3))
model = TCN(in_channels=34, num_blocks=2, kernel_sizes=(3, 3, 3))
pose2d = torch.rand((2,... |
def main():
data = sys.argv[1].encode('utf-8')
print(f'Compressing data: {data}')
compressor = brotli.Compressor(mode=brotli.MODE_TEXT)
compressed = (compressor.process(data) + compressor.finish())
print(f'Compressed data: {compressed}')
decompressor = brotli.Decompressor()
decompressed = (d... |
def runScript(N):
script = 'elemwise_time_test.py'
path = os.path.dirname(os.path.abspath(__file__))
proc = subprocess.Popen(['python', script, '--script', '-N', str(N)], stdout=subprocess.PIPE, stderr=subprocess.PIPE, cwd=path)
(out, err) = proc.communicate()
if err:
print(err)
sys.... |
def run(config):
config['drop_last'] = False
loaders = utils.get_data_loaders(**config)
net = inception_utils.load_inception_net(parallel=config['parallel'])
(pool, logits, labels) = ([], [], [])
device = 'cuda'
for (i, (x, y)) in enumerate(tqdm(loaders[0])):
try:
x = x.to(de... |
def get_mock_cfg(finetune_from_model):
cfg_mock = OmegaConf.create({'checkpoint': {'optimizer_overrides': '{}', 'reset_dataloader': False, 'reset_meters': False, 'reset_optimizer': False, 'reset_lr_scheduler': False, 'finetune_from_model': finetune_from_model, 'model_parallel_size': 1}, 'common': {'model_parallel_s... |
def _resnet(arch: str, block: Type[Union[(BasicBlock, Bottleneck)]], layers: List[int], pretrained: bool, progress: bool, num_classes: int, **kwargs: Any):
model = ResNet(block, layers, **kwargs, num_classes=num_classes)
print('num_classes = ', num_classes)
if pretrained:
print('model use imagenet p... |
def randomunitarieswom(qnnarchwom):
units = []
for i in range(1, len(qnnarchwom)):
qubitnumberin = qnnarchwom[(i - 1)]
qubitnumberout = qnnarchwom[i]
unitlayer = []
for j in range(qubitnumberout):
unit = randomunitary((qubitnumberin + 1))
if (qubitnumberou... |
class Vocab(object):
def __init__(self, vocab_file, max_size):
self._word_to_id = {}
self._id_to_word = {}
self._count = 0
for w in [UNKNOWN_TOKEN, PAD_TOKEN, START_DECODING, STOP_DECODING]:
self._word_to_id[w] = self._count
self._id_to_word[self._count] = w
... |
.parametrize('A_parts, indices', [((np.random.normal(size=(4, 3)), np.random.normal(size=(4, 3)), np.random.normal(size=(4, 3))), (slice(2, 3), np.array([0, 1, 2]), 1)), ((np.random.normal(size=(4, 3)), np.random.normal(size=(4, 3)), np.random.normal(size=(4, 3))), (slice(2, 3), 1, np.array([0, 1, 2]))), ((np.random.no... |
def convert_options(settings, defaults=None):
if (defaults is None):
defaults = {}
if isinstance(settings, dict):
def getopt(key, default=None):
return settings.get(('SENTRY_%s' % key.upper()), defaults.get(key, default))
options = copy.copy((settings.get('SENTRY_CONFIG') or ... |
class DF4C(DF):
def build(self):
log = logger.Logger(self.stdout, self.verbose)
mol = self.mol
auxmol = self.auxmol = addons.make_auxmol(self.mol, self.auxbasis)
n2c = mol.nao_2c()
naux = auxmol.nao_nr()
nao_pair = ((n2c * (n2c + 1)) // 2)
max_memory = ((self.... |
.linux
.parametrize('url', ['/foo.html', 'file:///foo.html'])
_locale
def test_open_with_ascii_locale(request, server, tmp_path, quteproc_new, url):
args = (['--temp-basedir'] + _base_args(request.config))
quteproc_new.start(args, env={'LC_ALL': 'C'})
quteproc_new.set_setting('url.auto_search', 'never')
... |
def main(args):
wav_scp = codecs.open((Path(args.path) / 'wav.scp'), 'r', 'utf-8')
textgrid_flist = codecs.open((Path(args.path) / 'textgrid_new.flist'), 'r', 'utf-8')
utt2textgrid = {}
for line in textgrid_flist:
line_array = line.strip().split(' ')
path = Path(line_array[1])
ut... |
def test_rainfall():
with Simulation(MODEL_RAIN) as sim:
rg = RainGages(sim)['Gage1']
assert (rg.raingageid == 'Gage1')
sim.step_advance(3600)
for (ind, step) in enumerate(sim):
if (0 < ind < 5):
assert (rg.total_precip == 1)
assert (rg.rai... |
def check_range(value, range_threshold=None):
try:
float(value)
except Exception:
return False
if (not range_threshold):
range_threshold = '~:'
range_threshold = str(range_threshold)
if (range_threshold[0] == ''):
return (not check_range(value, range_threshold[1:]))
... |
def seed_all(seed=1029):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True |
class MLLT(Frame):
_framespec = [SizedIntegerSpec('frames', size=2, default=0), SizedIntegerSpec('bytes', size=3, default=0), SizedIntegerSpec('milliseconds', size=3, default=0), ByteSpec('bits_for_bytes', default=0), ByteSpec('bits_for_milliseconds', default=0), BinaryDataSpec('data')]
def __eq__(self, other):... |
class AllTypesSharder(EmbeddingBagCollectionSharder):
def sharding_types(self, compute_device_type: str) -> List[str]:
return [ShardingType.DATA_PARALLEL.value, ShardingType.TABLE_WISE.value, ShardingType.ROW_WISE.value, ShardingType.TABLE_ROW_WISE.value, ShardingType.COLUMN_WISE.value, ShardingType.TABLE_C... |
class KDFDatabase(object):
def __init__(self, filename):
import sqlite3
conn = sqlite3.connect(filename, 30)
self.fragments = conn.execute('SELECT * FROM fragments;').fetchall()
conn.close()
def decode(self):
fragments_data = []
for (id, payload_type, payload_valu... |
class Foo(object):
class_var = 42
another_class_var = 42
class Meta(object):
def foo():
return True
def __init__(self, attr):
self.attr = attr
self.attr2 = attr
def property_simple(self) -> int:
return 42
def method_okay(self, foo=None, bar=None):
... |
class TestImports(TestCase):
EXCLUSION_LIST = ['pysmt.test', 'pysmt.solvers', 'pysmt.cmd']
def test_imports(self):
stack = [(pysmt.__name__, pysmt.__path__)]
while stack:
(module_name, module_path) = stack.pop()
for (_, name, ispkg) in pkgutil.iter_modules(module_path):
... |
def _less_than_indices(left: pd.Series, right: pd.Series, strict: bool, multiple_conditions: bool, keep: str) -> tuple:
if (left.min() > right.max()):
return None
outcome = _null_checks_cond_join(left=left, right=right)
if (not outcome):
return None
(left, right, left_index, right_index,... |
class NonTensorData():
data: Any
def __post_init__(self):
if isinstance(self.data, NonTensorData):
self.data = self.data.data
old_eq = self.__class__.__eq__
if (old_eq is _eq):
global NONTENSOR_HANDLED_FUNCTIONS
NONTENSOR_HANDLED_FUNCTIONS.extend(TD_HA... |
class HomeTheaterTestDrive():
def main(*args):
amp: Amplifier = Amplifier('Amplifier')
tuner: Tuner = Tuner('AM/FM Tuner', amp)
player: StreamingPlayer = StreamingPlayer('Streaming Player', amp)
cd: CdPlayer = CdPlayer('CD Player', amp)
projector: Projector = Projector('Proje... |
def continuous_contracts(path_to_data_files: str):
start_date = str_to_date('2019-01-01')
end_date = str_to_date('2019-01-10')
fields = PriceField.ohlcv()
tickers = [PortaraTicker('VX', SecurityType.FUTURE, 1000), PortaraTicker('WEAT', SecurityType.FUTURE, 100)]
daily_freq = Frequency.DAILY
if (... |
class TestGrabKey(EndianTest):
def setUp(self):
self.req_args_0 = {'grab_window': , 'key': 223, 'keyboard_mode': 1, 'modifiers': 44275, 'owner_events': 1, 'pointer_mode': 1}
self.req_bin_0 = b'!\x01\x00\x04\x7fb\r\xdf\xac\xf3\xdf\x01\x01\x00\x00\x00'
def testPackRequest0(self):
bin = req... |
def test_circular_control_curve_interpolated_json():
model = load_model('reservoir_with_circular_cc.json')
reservoir1 = model.nodes['reservoir1']
model.setup()
path = os.path.join(os.path.dirname(__file__), 'models', 'control_curve.csv')
control_curve = pd.read_csv(path)['Control Curve'].values
... |
class SingleConvBlock(nn.Module):
def __init__(self, in_features, out_features, stride, use_bs=True):
super(SingleConvBlock, self).__init__()
self.use_bn = use_bs
self.conv = nn.Conv2d(in_features, out_features, 1, stride=stride, bias=True)
self.bn = nn.BatchNorm2d(out_features)
... |
def to_image(tensor, adaptive=False):
if (len(tensor.shape) == 4):
tensor = tensor[0]
if adaptive:
tensor = ((tensor - tensor.min()) / (tensor.max() - tensor.min()))
return ToPILImage()((255 * tensor.cpu().detach()).to(torch.uint8))
else:
tensor = ((tensor + 1) / 2)
t... |
class ViTHybridConfig(PretrainedConfig):
model_type = 'vit-hybrid'
def __init__(self, backbone_config=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.0, attention_probs_dropout_prob=0.0, initializer_range=0.02, layer_norm_eps=... |
_torch
_sentencepiece
_tokenizers
class PLBartPythonEnIntegrationTest(unittest.TestCase):
checkpoint_name = 'uclanlp/plbart-python-en_XX'
src_text = ['def maximum(a,b,c):NEW_LINE_INDENTreturn max([a,b,c])', 'def sum(a,b,c):NEW_LINE_INDENTreturn sum([a,b,c])']
tgt_text = ['Returns the maximum value of a b c.... |
class S3StoreTestCase(SqlAlchemyTestCase):
def setUpClass(cls):
super(S3StoreTestCase, cls).setUpClass()
cls.this_dir = abspath(dirname(__file__))
cls.stuff_path = join(cls.this_dir, 'stuff')
cls.dog_jpeg = join(cls.stuff_path, 'dog.jpg')
cls.base_url = '
cls.sample_t... |
def add_filter_options(parser):
grp = OptionGroup(parser, 'Trace frequency filter options')
grp.add_option('--lowpass', dest='lowpass_frequency', type=float, help='The value of the lowpass filter applied to traces.', default=None)
grp.add_option('--lowpass_rel', dest='rel_lowpass_frequency', type=float, hel... |
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