code stringlengths 281 23.7M |
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class StockTicker(GenPollUrl):
defaults = [('interval', '1min', 'The default latency to query'), ('func', 'TIME_SERIES_INTRADAY', 'The default API function to query'), ('function', 'TIME_SERIES_INTRADAY', 'DEPRECATED: Use `func`.')]
def __init__(self, **config):
if ('function' in config):
lo... |
class LowRankCrossNet(torch.nn.Module):
def __init__(self, in_features: int, num_layers: int, low_rank: int=1) -> None:
super().__init__()
assert (low_rank >= 1), 'Low rank must be larger or equal to 1'
self._num_layers = num_layers
self._low_rank = low_rank
self.W_kernels: t... |
class AttnDownEncoderBlock2D(nn.Module):
def __init__(self, in_channels: int, out_channels: int, dropout: float=0.0, num_layers: int=1, resnet_eps: float=1e-06, resnet_time_scale_shift: str='default', resnet_act_fn: str='swish', resnet_groups: int=32, resnet_pre_norm: bool=True, attn_num_head_channels=1, output_sca... |
class Discriminator():
def __init__(self, env):
with tf.variable_scope('discriminator'):
self.scope = tf.get_variable_scope().name
self.expert_s = tf.placeholder(dtype=tf.float32, shape=([None] + list(env.observation_space.shape)))
self.expert_a = tf.placeholder(dtype=tf.... |
def interval_unpack(mds, timedelta=datetime.timedelta):
(months, days, seconds_ms) = mds
if (months != 0):
w = TypeConversionWarning('datetime.timedelta cannot represent relative intervals', details={'hint': 'An interval was unpacked with a non-zero "month" field.'}, source='DRIVER')
warnings.wa... |
class RandomFourierFeatures(nn.Module):
def __init__(self, in_dim, num_random_features, feature_scale=None):
super().__init__()
if (feature_scale is None):
feature_scale = math.sqrt((num_random_features / 2))
self.register_buffer('feature_scale', torch.tensor(feature_scale))
... |
class BlockPushHorizontalMultimodal(BlockPushMultimodal):
def _reset_object_poses(self, workspace_center_x, workspace_center_y):
self._reset_block_poses(workspace_center_y)
self._reset_target_poses(workspace_center_y)
def _reset_block_poses(self, workspace_center_y):
def _reset_block_pos... |
def LJ_force_1d(pos, dim=3):
N_atom = int((len(pos) / dim))
pos = np.reshape(pos, [N_atom, dim])
force = np.zeros([N_atom, dim])
for (i, pos0) in enumerate(pos):
pos1 = deepcopy(pos)
pos1 = np.delete(pos1, i, 0)
distance = cdist([pos0], pos1)
r = (pos1 - pos0)
r2 ... |
_torch
class ChineseCLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
all_model_classes = ((ChineseCLIPModel,) if is_torch_available() else ())
pipeline_model_mapping = ({'feature-extraction': ChineseCLIPModel} if is_torch_available() else {})
fx_compatible = False
test_head_maski... |
.skipif('sys.platform == "win32" and platform.python_implementation() == "PyPy"')
def test_coveragerc_dist(testdir):
testdir.makefile('', coveragerc=COVERAGERC)
script = testdir.makepyfile(EXCLUDED_TEST)
result = testdir.runpytest('-v', '--cov-config=coveragerc', f'--cov={script.dirpath()}', '--cov-report=t... |
class AverageMeter():
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += (val * n)
self.count += n
self.avg = (self.sum / self.c... |
class WSGIWebServer(internet.TCPServer):
def __init__(self, pool, *args, **kwargs):
self.pool = pool
super().__init__(*args, **kwargs)
def startService(self):
super().startService()
self.pool.start()
def stopService(self):
super().stopService()
self.pool.stop(... |
class Instances():
def __init__(self, image_size: Tuple[(int, int)], **kwargs: Any):
self._image_size = image_size
self._fields: Dict[(str, Any)] = {}
for (k, v) in kwargs.items():
self.set(k, v)
def image_size(self) -> Tuple[(int, int)]:
return self._image_size
d... |
def pytest_xdist_auto_num_workers(config):
env_var = os.environ.get('PYTEST_XDIST_AUTO_NUM_WORKERS')
if env_var:
try:
return int(env_var)
except ValueError:
warnings.warn('PYTEST_XDIST_AUTO_NUM_WORKERS is not a number: {env_var!r}. Ignoring it.')
try:
import p... |
class CreateRepositoryPermission(QuayPermission):
def __init__(self, namespace):
admin_org = _OrganizationNeed(namespace, 'admin')
create_repo_org = _OrganizationNeed(namespace, 'creator')
self.namespace = namespace
super(CreateRepositoryPermission, self).__init__(admin_org, create_r... |
def block_group(inputs, filters, block_fn, blocks, strides, is_training, name, data_format='channels_first', dropblock_keep_prob=None, dropblock_size=None):
inputs = block_fn(inputs, filters, is_training, strides, use_projection=True, data_format=data_format, dropblock_keep_prob=dropblock_keep_prob, dropblock_size=... |
class XmlTokenizer():
def __init__(self, fp, skip_ws=True):
self.fp = fp
self.tokens = []
self.index = 0
self.final = False
self.skip_ws = skip_ws
self.character_pos = (0, 0)
self.character_data = ''
self.parser = xml.parsers.expat.ParserCreate()
... |
class ConditionalRealNVPFlow(bijectors.ConditionalBijector):
def __init__(self, num_coupling_layers=2, hidden_layer_sizes=(64,), use_batch_normalization=False, event_dims=None, is_constant_jacobian=False, validate_args=False, name='conditional_real_nvp_flow'):
self._graph_parents = []
self._name = n... |
class KnownValues(unittest.TestCase):
def test_get_2c2e_gamma(self):
dfbuilder = rsdf_builder._RSGDFBuilder(cell, auxcell).build()
j2c = dfbuilder.get_2c2e(np.zeros((1, 3)))
self.assertAlmostEqual(lib.fp(j2c), 0., 9)
dfbuilder.exclude_d_aux = False
j2c = dfbuilder.get_2c2e(np... |
def text_render(structure, resolution=100):
x = np.linspace(0, structure.width(), resolution)
bulk = locate_regions(x, structure, 'bulk')
barrier = (set(locate_regions(x, structure, 'barrier')) | set(locate_regions(x, structure, 'half barrier')))
interlayer = locate_regions(x, structure, 'interlayer')
... |
def _pfunc_param_to_in(param, strict=False, allow_downcast=None):
if isinstance(param, Constant):
raise TypeError('Constants not allowed in param list', param)
if isinstance(param, Variable):
return In(variable=param, strict=strict, allow_downcast=allow_downcast)
elif isinstance(param, In):
... |
class VideoChatScheduled(TelegramObject):
__slots__ = ('start_date',)
def __init__(self, start_date: dtm.datetime, *, api_kwargs: Optional[JSONDict]=None) -> None:
super().__init__(api_kwargs=api_kwargs)
self.start_date: dtm.datetime = start_date
self._id_attrs = (self.start_date,)
... |
class TestFreeColors(EndianTest):
def setUp(self):
self.req_args_0 = {'cmap': , 'pixels': [, , , , , , , , , , , , , , , , ], 'plane_mask': }
self.req_bin_0 = b'X\x00\x00\x14\x14`ID_1\x19\xfbL8\xc8\x12(\x9e8)y\x9b\xe5\xd1`\xad\x08Ir\x1b>\xa88\xa7>\xfaNld)hS"\x19\\\x12+Dr.\xb8\\x9d\x92!6\xa0p\xeejQ\x... |
def _gen_spnasnet(variant, channel_multiplier=1.0, pretrained=False, **kwargs):
arch_def = [['ds_r1_k3_s1_c16_noskip'], ['ir_r3_k3_s2_e3_c24'], ['ir_r1_k5_s2_e6_c40', 'ir_r3_k3_s1_e3_c40'], ['ir_r1_k5_s2_e6_c80', 'ir_r3_k3_s1_e3_c80'], ['ir_r1_k5_s1_e6_c96', 'ir_r3_k5_s1_e3_c96'], ['ir_r4_k5_s2_e6_c192'], ['ir_r1_k... |
class TestNaiveClusterer(unittest.TestCase):
def setUp(self):
super().setUp()
pass
def test_6by2_matrix(self):
matrix = np.array([[1.0, 0.0], [1.1, 0.1], [0.0, 1.0], [0.1, 1.0], [0.9, (- 0.1)], [0.0, 1.2]])
clusterer = NaiveClusterer(threshold=0.5)
labels = clusterer.pred... |
def get_similarity(text_a, text_b, k):
wordnet = nltk.corpus.wordnet
left_lsent = ((['oov'] + text_a[k].lower().translate(str.maketrans('', '', string.punctuation)).split()) + ['oov'])
right_lsent = ((['oov'] + text_b[k].lower().translate(str.maketrans('', '', string.punctuation)).split()) + ['oov'])
pr... |
def pil_loader(path):
if isinstance(path, bytes):
img = Image.open(io.BytesIO(path))
elif is_zip_path(path):
data = ZipReader.read(path)
img = Image.open(io.BytesIO(data))
else:
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB') |
_bp.app_errorhandler(V2RegistryException)
def handle_registry_v2_exception(error):
response = jsonify({'errors': [error.as_dict()]})
response.status_code = error.
if (response.status_code == 401):
response.headers.extend(get_auth_headers(repository=error.repository, scopes=error.scopes))
logger.... |
class UnalignedDataLoader(BaseDataLoader):
def initialize(self, opt):
BaseDataLoader.initialize(self, opt)
transformations = [transforms.Scale(opt.loadSize), transforms.RandomCrop(opt.fineSize), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
transform = transf... |
('time.sleep')
def test_retry_loop_max_end_on_error_substitution(mock_time_sleep):
rd = RetryDecorator({'max': PyString('3')})
context = Context({'k1': 'v1'})
mock = MagicMock()
mock.side_effect = ValueError('arb')
with patch_logger('pypyr.dsl', logging.INFO) as mock_logger_info:
with pytest... |
def _capture_subarguments(params: dict, arg_name: str, sub_arg_list: list[str]) -> Any:
argument = params.get(arg_name)
if (not isinstance(argument, dict)):
return argument
_validate_sub_arg_list(argument, arg_name, sub_arg_list)
units = argument.pop('units', None)
list_of_values = argument.... |
class F20_Upgrade(DeprecatedCommand, F11_Upgrade):
def __init__(self):
DeprecatedCommand.__init__(self)
def _getParser(self):
op = F11_Upgrade._getParser(self)
op.description += dedent(('\n\n .. deprecated:: %s\n\n Starting with F18, upgrades... |
def get_all_tests():
test_root_dir = os.path.join(PATH_TO_TRANFORMERS, 'tests')
tests = os.listdir(test_root_dir)
tests = sorted(filter((lambda x: (os.path.isdir(x) or x.startswith('tests/test_'))), [f'tests/{x}' for x in tests]))
model_tests_folders = os.listdir(os.path.join(test_root_dir, 'models'))
... |
def parse_input():
description = 'This script allows you to evaluate the ActivityNet proposal task which is intended to evaluate the ability of algorithms to generate activity proposals that temporally localize activities in untrimmed video sequences.'
p = argparse.ArgumentParser(description=description)
p.... |
def _maybe_compute_length_per_key(keys: List[str], stride: int, stride_per_key: List[int], variable_stride_per_key: bool, length_per_key: Optional[List[int]], lengths: Optional[torch.Tensor], offsets: Optional[torch.Tensor]) -> List[int]:
if (length_per_key is None):
if (len(keys) and (offsets is not None) ... |
class PrRoIPool2DFunction(ag.Function):
def forward(ctx, features, rois, pooled_height, pooled_width, spatial_scale):
_prroi_pooling = _import_prroi_pooling()
assert (('FloatTensor' in features.type()) and ('FloatTensor' in rois.type())), 'Precise RoI Pooling only takes float input, got {} for featu... |
def scale_voltage_current_power(data, voltage=1, current=1):
voltage_keys = ['v_mp', 'v_oc']
current_keys = ['i_mp', 'i_x', 'i_xx', 'i_sc']
power_keys = ['p_mp']
voltage_df = (data.filter(voltage_keys, axis=1) * voltage)
current_df = (data.filter(current_keys, axis=1) * current)
power_df = ((dat... |
def convert_image(image, export_path):
image.logger.debug('Converting image patient name, birthdate and id to match pinnacle')
dicom_directory = os.path.join(image.path, f"ImageSet_{image.image['ImageSetID']}.DICOM")
if (not os.path.exists(dicom_directory)):
image.logger.info('Dicom Image files do n... |
def create_unlock(channel_state: NettingChannelState, message_identifier: MessageID, payment_identifier: PaymentID, secret: Secret, lock: HashTimeLockState, block_number: BlockNumber, recipient_metadata: AddressMetadata=None) -> SendUnlockAndPendingLocksState:
our_state = channel_state.our_state
msg = 'caller m... |
def FitCompass(debug, compass_points, compass_calibration, norm):
p = compass_points.Points(True)
if (len(p) < 8):
return
fit = FitPointsCompass(debug, p, compass_calibration, norm)
if (not fit):
return
g_required_dev = 0.25
gpoints = []
for q in p:
gpoints.append(q[3... |
def find_model(model_name):
if (model_name in VALID_MODELS):
using_pretrained_model = True
return (download_model(model_name), using_pretrained_model)
else:
using_pretrained_model = False
return (torch.load(model_name, map_location=(lambda storage, loc: storage)), using_pretraine... |
class Accumulator(object):
def __init__(self):
self.pointer = 0
self.pointed_obj = None
def move(self, narg=None, **keywords):
direction = Direction(keywords)
lst = self.get_list()
if (not lst):
return self.pointer
pointer = direction.move(direction=di... |
(Post)
class PostAdmin(admin.ModelAdmin):
form = PostAdminForm
list_display = ('title', 'published', 'author_display_name')
user_fk = 'author_id'
autocomplete_fields = ('author',)
(description='Author')
def author_display_name(self, obj):
return obj.author.display_name |
def test_fileformatjson_pass_with_substitutions(fs):
payload = '{\n "key1": "{k1}value !$% *",\n "key2_{k2}": {\n "k21": "value",\n "abc": "{k3} def {k4}",\n "def": [\n "l1",\n "l2 {k5}",\n "l3"\n ]\n }\n}\n'
in_path = './tests/testfiles/testsubst.json'
fs.create_file(in_path, ... |
class CPythonPosix(CPython, PosixSupports, metaclass=ABCMeta):
def _executables(cls, interpreter):
host_exe = Path(interpreter.system_executable)
(major, minor) = (interpreter.version_info.major, interpreter.version_info.minor)
targets = OrderedDict(((i, None) for i in ['python', f'python{ma... |
def make_commodity_future_info(first_sid, root_symbols, years, month_codes=None, multiplier=500):
nineteen_days = pd.Timedelta(days=19)
one_year = pd.Timedelta(days=365)
return make_future_info(first_sid=first_sid, root_symbols=root_symbols, years=years, notice_date_func=(lambda dt: ((dt - MonthBegin(2)) + ... |
def do_kmeans(n_anchors, boxes, centroids):
loss = 0
groups = []
new_centroids = []
for i in range(n_anchors):
groups.append([])
new_centroids.append(Box(0, 0, 0, 0))
for box in boxes:
min_distance = 1
group_index = 0
for (centroid_index, centroid) in enumerat... |
def test_upsert(local_client, remote_client):
records = generate_fixtures(UPLOAD_NUM_VECTORS)
(ids, payload) = ([], [])
vectors = {}
for record in records:
ids.append(record.id)
payload.append(record.payload)
for (vector_name, vector) in record.vector.items():
if (vec... |
class SqlAlchemyControl(ORMControl):
def __init__(self, echo=False):
self.echo = echo
self.engine = None
def nested_transaction(self):
transaction = Session().begin_nested()
transaction_veto = TransactionVeto()
try:
(yield transaction_veto)
except Exce... |
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, Seq2SeqTrainingArguments))
if ((len(sys.argv) == 2) and sys.argv[1].endswith('.json')):
(model_args, data_args, training_args) = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
(model_args, dat... |
class Optimizer(object):
def __init__(self, optimizer, init_lr, current_step=0, warmup_steps=50000, decay_learning_rate=0.5):
self.optimizer = optimizer
self.init_lr = init_lr
self.current_steps = current_step
self.warmup_steps = warmup_steps
self.decay_learning_rate = decay_... |
def get_available_reporting_integrations():
integrations = []
if (is_azureml_available() and (not is_mlflow_available())):
integrations.append('azure_ml')
if is_comet_available():
integrations.append('comet_ml')
if is_dagshub_available():
integrations.append('dagshub')
if is_... |
class PickleProtocol():
def __init__(self, level):
self.previous = pickle.HIGHEST_PROTOCOL
self.level = level
def __enter__(self):
importlib.reload(pickle)
pickle.HIGHEST_PROTOCOL = self.level
def __exit__(self, *exc):
importlib.reload(pickle)
pickle.HIGHEST_P... |
class GPT2Config(PretrainedConfig):
pretrained_config_archive_map = GPT2_PRETRAINED_CONFIG_ARCHIVE_MAP
def __init__(self, vocab_size=50257, n_positions=1024, n_ctx=1024, n_embd=768, n_layer=12, n_head=12, resid_pdrop=0.1, embd_pdrop=0.1, attn_pdrop=0.1, layer_norm_epsilon=1e-05, initializer_range=0.02, summary_... |
class Sst2Processor(object):
def get_train_examples(self, data_dir, num_train_samples=(- 1)):
if (num_train_samples != (- 1)):
return self._create_examples(self._read_tsv(os.path.join(data_dir, 'sst2_train.tsv')), 'train')[:num_train_samples]
return self._create_examples(self._read_tsv(o... |
class SyslogWriter(object):
OPTIONS = [('--syslog', 'log all of your features, scenarios, and steps to the syslog')]
LOAD_IF = staticmethod((lambda config: config.syslog))
LOAD_PRIORITY = 40
def __init__(self):
if (os.name == 'nt'):
sys.stdout.write('Using --syslog on Windows is not ... |
def get_args_parser():
parser = argparse.ArgumentParser('Train and test network for classification task')
parser.add_argument('--data_img', help='path to directory with subdirectories with images', type=str)
parser.add_argument('--out', help='path to main directory with checkpoints', type=str)
parser.ad... |
class SECURITY_DESCRIPTOR():
def __init__(self, object_type=None):
self.Revision = None
self.Sbz1 = None
self.Control = None
self.Owner = None
self.Group = None
self.Sacl = None
self.Dacl = None
self.object_type = object_type
def from_bytes(data, o... |
def _calculate_T_star(rb, frame, kde_map, constraint_map, uaux):
I = (rb.inertia[0] - inertia_of_point_mass(rb.mass, rb.masscenter.pos_from(rb.inertia[1]), rb.frame))
alpha = rb.frame.ang_acc_in(frame)
omega = rb.frame.ang_vel_in(frame)
if (uaux is not None):
uaux_zero = dict(zip(uaux, ([0] * le... |
class ChainRecordAdapter(IBaseTrace):
def __init__(self, chain: mcb.Chain, point_fn: PointFunc, stats_bijection: StatsBijection) -> None:
self.chain = chain.cmeta.chain_number
self.varnames = [v.name for v in chain.rmeta.variables]
stats_dtypes = {s.name: np.dtype(s.dtype) for s in chain.rme... |
class BasicBlock(CNNBlockBase):
def __init__(self, in_channels, out_channels, *, stride=1, norm='BN'):
super().__init__(in_channels, out_channels, stride)
if (in_channels != out_channels):
self.shortcut = Conv2d(in_channels, out_channels, kernel_size=1, stride=stride, bias=False, norm=ge... |
class Effect4089(BaseEffect):
runTime = 'early'
type = ('projected', 'passive')
def handler(fit, module, context, projectionRange, **kwargs):
fit.modules.filteredItemMultiply((lambda mod: (mod.item.requiresSkill('Shield Emission Systems') or mod.item.requiresSkill('Capital Shield Emission Systems'))... |
class Readable(EvscaperoomObject):
read_flag = 'readable'
start_readable = True
def at_object_creation(self):
super().at_object_creation()
if self.start_readable:
self.set_flag(self.read_flag)
def at_focus_read(self, caller, **kwargs):
if ((self.read_flag is None) or ... |
def test_update_error_questionset_page(db):
question = Question.objects.exclude(questionsets=None).first()
questionset = question.questionsets.first()
page = questionset.pages.first()
page.locked = True
page.save()
with pytest.raises(ValidationError):
QuestionLockedValidator(question)({'... |
class ClassBalancedSampler(Sampler):
def __init__(self, data_source, doShuffle=False, seed=31426):
self.data_source = data_source
self.seed = seed
self.rng = RandomState(self.seed)
labels = [l[2] for l in self.data_source.labels]
classes = list(set(labels))
classN = C... |
class FrozenBatchNorm(nn.Module):
_version = 3
def __init__(self, num_features, eps=1e-05, **kwargs):
super().__init__()
self.num_features = num_features
self.eps = eps
self.register_buffer('weight', torch.ones(num_features))
self.register_buffer('bias', torch.zeros(num_f... |
def make_fake_scene(content_dict, daskify=False, area=True, common_attrs=None):
if (common_attrs is None):
common_attrs = {}
sc = Scene()
for (did, arr) in content_dict.items():
extra_attrs = common_attrs.copy()
if area:
extra_attrs['area'] = _get_fake_scene_area(arr, are... |
def rtn_sem_wait(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('sem_wait hooked')
arg0 = pstate.get_argument_value(0)
value = pstate.memory.read_ptr(arg0)
if (value > 0):
logger.debug('semaphore still not locked')
pstate.memory.write_ptr(arg0, (value - 1))
pstate.... |
class L2DisplacementYawReward(Reward):
def __init__(self, reward_prefix: str='L2DisplacementYaw', metric_set: Optional[L5MetricSet]=None, enable_clip: bool=True, rew_clip_thresh: float=15.0, use_yaw: Optional[bool]=True, yaw_weight: Optional[float]=1.0) -> None:
self.reward_prefix = reward_prefix
se... |
class LDC(nn.Module):
def __init__(self):
super(LDC, self).__init__()
self.block_1 = DoubleConvBlock(3, 16, 16, stride=2)
self.block_2 = DoubleConvBlock(16, 32, use_act=False)
self.dblock_3 = _DenseBlock(2, 32, 64)
self.dblock_4 = _DenseBlock(3, 64, 96)
self.dblock_5 ... |
def main():
args = get_config()
args = args_dict(args)
print(args.ex_name)
print(vars(args))
seed_init()
if (args.action == 'train'):
kwargs = {'matching': args.dataset['matching'], 'sample_rate': 16000}
length = int((args.setting['segment'] * args.setting['sample_rate']))
... |
class Yang2017(DFN):
def __init__(self, options=None, name='Yang2017', build=True):
options = {'SEI': ('ec reaction limited', 'none'), 'SEI film resistance': 'distributed', 'SEI porosity change': 'true', 'lithium plating': ('irreversible', 'none'), 'lithium plating porosity change': 'true'}
super().... |
class IterationTimeLogger(Callback):
_writer: Optional[SummaryWriter] = None
def __init__(self, logger: Union[(TensorBoardLogger, SummaryWriter)], moving_avg_window: int=1, log_every_n_steps: int=1) -> None:
if isinstance(logger, TensorBoardLogger):
logger = logger.writer
if (get_glo... |
class IBNbResUnit(nn.Module):
def __init__(self, in_channels, out_channels, stride, use_inst_norm):
super(IBNbResUnit, self).__init__()
self.use_inst_norm = use_inst_norm
self.resize_identity = ((in_channels != out_channels) or (stride != 1))
self.body = ResBottleneck(in_channels=in_... |
class _HasAttrGuardMeta(type):
def __getitem__(self, params: Tuple[(str, str, object)]) -> 'HasAttrGuard':
if ((not isinstance(params, tuple)) or (len(params) != 3)):
raise TypeError('HasAttrGuard[...] should be instantiated with three arguments (a variable name, an attribute name, and a type).'... |
(all_backends)
def test_general(backend):
xnp = get_xnp(backend)
dtype = xnp.float32
diag = generate_spectrum(coeff=0.75, scale=1.0, size=15)
A = xnp.array(generate_pd_from_diag(diag, dtype=diag.dtype, seed=21), dtype=dtype, device=None)
A = SelfAdjoint(lazify(A))
soln = xnp.array(generate_pd_fr... |
class MultiViewDataset(Dataset):
def __init__(self, args, neg_sample_num=1, root_dir='MMCLR/dataset/TIMA/UserBehavior.10%.seq.splited.pickle', eval=None):
super(MultiViewDataset, self).__init__()
self.root_dir = root_dir
self.eval = eval
self.args = args
self.item_set = set(s... |
class VideoSettings(QDialog):
def __init__(self, mediaRecorder, parent=None):
super(VideoSettings, self).__init__(parent)
self.ui = Ui_VideoSettingsUi()
self.mediaRecorder = mediaRecorder
self.ui.setupUi(self)
self.ui.audioCodecBox.addItem('Default audio codec', '')
f... |
def generate_sparse_fixtures(num: Optional[int]=NUM_VECTORS, random_ids: bool=False, vectors_sizes: Optional[Union[(Dict[(str, int)], int)]]=None, skip_vectors: bool=False, with_payload: bool=True) -> List[models.Record]:
if (vectors_sizes is None):
vectors_sizes = {'sparse-text': sparse_text_vector_size, '... |
class Env(object):
def __init__(self):
self.state_space = 1000000
self.action_dim = 1
self.timestep_limit = 10
pass
def read_data(self, f):
pass
def reset(self):
pass
def step(self):
dim = random.randint(4, 20)
state = [random.randint(0, se... |
def test_reseed_rngs():
default_rng = np.random.PCG64
assert isinstance(np.random.default_rng().bit_generator, default_rng)
seed = 543
bit_generators = [default_rng(sub_seed) for sub_seed in np.random.SeedSequence(seed).spawn(2)]
rngs = [pytensor.shared(rng_type(default_rng())) for rng_type in (np.r... |
def _test():
import torch
pretrained = False
models = [(diapreresnet20_cifar10, 10), (diapreresnet20_cifar100, 100), (diapreresnet20_svhn, 10), (diapreresnet56_cifar10, 10), (diapreresnet56_cifar100, 100), (diapreresnet56_svhn, 10), (diapreresnet110_cifar10, 10), (diapreresnet110_cifar100, 100), (diapreresn... |
class _Config():
def __init__(self):
self._init_logging_handler()
self.cuda_device = 4
self.eos_m_token = 'EOS_M'
self.beam_len_bonus = 0.6
self.mode = 'unknown'
self.m = 'TSD'
self.prev_z_method = 'none'
self.dataset = 'unknown'
self.seed = 0
... |
class XLMTokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
pretrained_init_configuration = PRETRAINED_INIT_CONFIGURATION
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self, vocab_file, merges_fi... |
.parametrize('input_type', [tuple, list])
def test_prepare_inputs_from_poa_wrong_number_arrays(sapm_dc_snl_ac_system_Array, location, total_irrad, weather, input_type):
len_error = 'Input must be same length as number of Arrays in system\\. Expected 2, got [0-9]+\\.'
type_error = 'Input must be a tuple of lengt... |
def sample_from_gan(generator, out_dir, num_samples, out_shape, batch_size=50, noise_shape=None, rand_sampler=None, verbosity=make_verbose()):
if ((noise_shape is None) and (rand_sampler is None)):
raise Exception('Either noise shape or randomizer should be provided')
if (not os.path.isdir(out_dir)):
... |
class NTC_Hyperprior(nn.Module):
def __init__(self, config):
super().__init__()
self.ga = AnalysisTransform(**config.ga_kwargs)
self.gs = SynthesisTransform(**config.gs_kwargs)
self.ha = nn.Sequential(nn.Conv2d(256, 192, 3, stride=1, padding=1), nn.LeakyReLU(inplace=True), nn.Conv2d(... |
.parametrize('constructor', [get_core_metadata_constructors()['2.1']])
class TestCoreMetadataV21():
def test_default(self, constructor, isolation, helpers):
metadata = ProjectMetadata(str(isolation), None, {'project': {'name': 'My.App', 'version': '0.1.0'}})
assert (constructor(metadata) == helpers.... |
class SegmentationTTAWrapper(nn.Module):
def __init__(self, model: nn.Module, transforms: Compose, merge_mode: str='mean', output_mask_key: Optional[str]=None):
super().__init__()
self.model = model
self.transforms = transforms
self.merge_mode = merge_mode
self.output_key = o... |
def test_activation(temp_dir, platform):
venv_dir = (temp_dir / 'venv')
venv = VirtualEnv(venv_dir, platform)
venv.create(sys.executable)
with EnvVars(exclude=VirtualEnv.IGNORED_ENV_VARS):
os.environ['PATH'] = str(temp_dir)
os.environ['VIRTUAL_ENV'] = 'foo'
for env_var in Virtual... |
class Vgg_face_dag(nn.Module):
def __init__(self, return_layer):
super(Vgg_face_dag, self).__init__()
self.meta = {'mean': [129., 104., 93.], 'std': [1, 1, 1], 'imageSize': [224, 224, 3]}
self.return_layer = return_layer
self.conv1_1 = nn.Conv2d(3, 64, kernel_size=[3, 3], stride=(1, ... |
class CSVBlotter(Blotter):
def __init__(self, csv_file_path: str):
self.file_path = csv_file_path
self.logger = qf_logger.getChild(self.__class__.__name__)
(self.file_handler, self.csv_writer) = self._init_csv_file()
def save_transaction(self, transaction: Transaction):
if (trans... |
class FairseqAdamConfig(FairseqDataclass):
adam_betas: str = field(default='(0.9, 0.999)', metadata={'help': 'betas for Adam optimizer'})
adam_eps: float = field(default=1e-08, metadata={'help': 'epsilon for Adam optimizer'})
weight_decay: float = field(default=0.0, metadata={'help': 'weight decay'})
us... |
class WhooshSearchBackend(BaseSearchBackend):
RESERVED_WORDS = ('AND', 'NOT', 'OR', 'TO')
RESERVED_CHARACTERS = ('\\', '+', '-', '&&', '||', '!', '(', ')', '{', '}', '[', ']', '^', '"', '~', '*', '?', ':', '.')
def __init__(self, connection_alias, **connection_options):
super(WhooshSearchBackend, se... |
class ConvBnAct(nn.Module):
def __init__(self, in_chs, out_chs, kernel_size, stride=1, dilation=1, group_size=0, pad_type='', skip=False, act_layer=nn.ReLU, norm_layer=nn.BatchNorm2d, drop_path_rate=0.0):
super(ConvBnAct, self).__init__()
norm_act_layer = get_norm_act_layer(norm_layer, act_layer)
... |
def fmt_ria(ria, verbose=True, mip=False):
if verbose:
mechanism = f'Mechanism: {fmt_mechanism(ria.mechanism, ria.node_labels)}'
direction = f'Direction: {ria.direction}'
else:
mechanism = ''
direction = ''
if (config.REPR_VERBOSITY is HIGH):
partition_name = ('MIP' i... |
def main():
initial_risk = 0.03
start_date = str_to_date('2010-01-01')
end_date = str_to_date('2011-12-31')
data_provider = daily_data_provider
session_builder = container.resolve(BacktestTradingSessionBuilder)
session_builder.set_backtest_name('Moving Average Alpha Model Backtest no weekends')
... |
def add_matcher(output_dir, owner, data):
data['owner'] = owner
out_data = {'problemMatcher': [data]}
output_file = (output_dir / '{}.json'.format(owner))
with output_file.open('w', encoding='utf-8') as f:
json.dump(out_data, f)
print('::add-matcher::{}'.format(output_file)) |
class OggPage(object):
version = 0
__type_flags = 0
position = 0
serial = 0
sequence = 0
offset = None
complete = True
def __init__(self, fileobj=None):
self.packets = []
if (fileobj is None):
return
self.offset = fileobj.tell()
header = fileob... |
class ROKS(rks.KohnShamDFT, rohf.ROHF):
get_vsap = rks.RKS.get_vsap
init_guess_by_vsap = rks.RKS.init_guess_by_vsap
get_veff = get_veff
energy_elec = pyscf.dft.uks.energy_elec
def __init__(self, cell, kpt=numpy.zeros(3), xc='LDA,VWN', exxdiv=getattr(__config__, 'pbc_scf_SCF_exxdiv', 'ewald')):
... |
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