code stringlengths 281 23.7M |
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def process_rule_tables(c, filenames, reporter):
start_time = time.time()
reporter.report('[Stage 3/7] Merging rule tables ...')
create_rule_table(c)
for (db_id, progress_str, filename) in enumerate_progress(filenames):
with transaction(c):
create_rule_map_table(c, db_id)
wit... |
def volume_weighted_average_price(prices_tms: PricesSeries, volumes_tms: QFSeries, interval: Timedelta) -> PricesSeries:
assert prices_tms.index.equals(volumes_tms.index)
last_date = prices_tms.index[(- 1)]
beginning_of_window = prices_tms.index[0]
end_of_window = (beginning_of_window + interval)
we... |
class TAM(nn.Module):
def __init__(self, in_channels, n_segment, kernel_size=3, stride=1, padding=1):
super(TAM, self).__init__()
self.in_channels = in_channels
self.n_segment = n_segment
self.kernel_size = kernel_size
self.stride = stride
self.padding = padding
... |
def _set_device(args):
device_type = args['device']
gpus = []
for device in device_type:
if (device_type == (- 1)):
device = torch.device('cpu')
else:
device = torch.device('cuda:{}'.format(device))
gpus.append(device)
args['device'] = gpus |
class SawyerDisassembleV2Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'gripper': obs[3], 'wrench_pos': obs[4:7], 'peg_pos': obs[(- 3):], 'unused_info': obs[7:(- 3)]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos':... |
class PrefetchDataLoader(DataLoader):
def __init__(self, num_prefetch_queue, **kwargs):
self.num_prefetch_queue = num_prefetch_queue
super(PrefetchDataLoader, self).__init__(**kwargs)
def __iter__(self):
return PrefetchGenerator(super().__iter__(), self.num_prefetch_queue) |
class _F0Gate(cirq.MatrixGate):
def __init__(self):
cirq.MatrixGate.__init__(self, np.array([[1, 0, 0, 0], [0, (- (2 ** (- 0.5))), (2 ** (- 0.5)), 0], [0, (2 ** (- 0.5)), (2 ** (- 0.5)), 0], [0, 0, 0, (- 1)]]), qid_shape=(2, 2))
def _circuit_diagram_info_(self, args: cirq.CircuitDiagramInfoArgs) -> cirq... |
class TConfigCheckMenuItem(TestCase):
def setUp(self):
config.init()
def tearDown(self):
config.quit()
def test_toggle(self):
config.set('memory', 'bar', 'on')
c = ConfigCheckMenuItem('dummy', 'memory', 'bar')
c.set_active(True)
self.assertTrue((config.getbool... |
def _matmul_flop_jit(inputs: Tuple[torch.Tensor], outputs: Tuple[Any]) -> Number:
input_shapes = [v.shape for v in inputs]
assert (len(input_shapes) == 2), input_shapes
assert (input_shapes[0][(- 1)] == input_shapes[1][(- 2)]), input_shapes
flop = (inputs[0].numel() * input_shapes[(- 1)][(- 1)])
ret... |
class MLP():
def __init__(self, env_spec, hidden_sizes=(64, 64), min_log_std=(- 3), init_log_std=0, seed=None, device=torch.device('cpu')):
self.n = env_spec.observation_dim
self.m = env_spec.action_dim
self.min_log_std = min_log_std
self.device = device
if (seed is not None)... |
_fixtures(WebFixture)
def test_adding_chart_with_ajax(web_fixture):
class MyForm(Form):
def __init__(self, view):
self.choice = 1
super().__init__(view, 'my_form')
self.enable_refresh()
self.use_layout(FormLayout())
self.layout.add_input(SelectInpu... |
def _load_paradigms(filename):
paradigms = []
with open(filename, 'rb') as f:
paradigms_count = struct.unpack(str('<H'), f.read(2))[0]
for x in range(paradigms_count):
paradigm_len = struct.unpack(str('<H'), f.read(2))[0]
para = array.array(str('H'))
para.from... |
def launch_experiments(variant_generator):
variants = variant_generator.variants()
for (i, variant) in enumerate(variants):
tag = 'finetune__'
print(variant['snapshot_filename'])
tag += variant['snapshot_filename'].split('/')[(- 2)]
tag += '____'
tag += '__'.join([('%s_%s... |
_transform('generic_image_transform')
class GenericImageTransform(ClassyTransform):
def __init__(self, transform: Optional[Callable]=None, split: Optional[str]=None):
assert ((split is None) or (transform is None)), 'If split is not None then transform must be None'
assert (split in [None, 'train', ... |
class Binary():
file_path: Path
fw_path: Path
id: int = None
lib_names: list[str] = field(default_factory=list)
libs: list['Binary'] = field(default_factory=list)
imported_symbols: list[str] = field(default_factory=list)
imported_symbol_ids: list[int] = field(default_factory=list)
non_re... |
class SuperResDataset(BaseDataset):
def __init__(self, opt, training):
BaseDataset.__init__(self, opt, training)
self.dir = opt.dir
self.paths = file_utils.load_paths(self.dir)
def generate(self, cache=True, shuffle_buffer_size=1000):
dataset = tf.data.Dataset.from_tensor_slices(... |
class SendCode():
async def send_code(self: 'pyrogram.Client', phone_number: str) -> 'types.SentCode':
phone_number = phone_number.strip(' +')
while True:
try:
r = (await self.invoke(raw.functions.auth.SendCode(phone_number=phone_number, api_id=self.api_id, api_hash=self.... |
def encode_content(content, nRows, nCols, actions_dict):
encoded_content = np.zeros([nRows, nCols])
start = 0
s = 0
e = 0
for i in range(len(content)):
if (content[i] != content[start]):
frame_label = np.zeros(nCols)
frame_label[actions_dict[content[start]]] = 1
... |
class Metric(object):
def __init__(self):
self.name = 'Metric'
def compare_mse(self, x1, x2):
err = np.sum(((x1 - x2) ** 2))
if (len(x1) == 4):
err /= float((((x1.shape[0] * x1.shape[1]) * x1.shape[2]) * x1.shape[3]))
else:
err /= float(((x1.shape[0] * x1.... |
def load_backbone_pretrained(model, backbone):
if ((cfg.PHASE == 'train') and cfg.TRAIN.BACKBONE_PRETRAINED and (not cfg.TRAIN.PRETRAINED_MODEL_PATH)):
if os.path.isfile(cfg.TRAIN.BACKBONE_PRETRAINED_PATH):
logging.info('Load backbone pretrained model from {}'.format(cfg.TRAIN.BACKBONE_PRETRAINE... |
def test_get_build_requires_import():
expected = ['numpy >=1.16.0']
with cwd(osp.join(samples_dir, 'constructed_version')):
assert (buildapi.get_requires_for_build_wheel() == expected)
assert (buildapi.get_requires_for_build_editable() == expected)
assert (buildapi.get_requires_for_build... |
def main():
parser = HfArgumentParser(PyTorchBenchmarkArguments)
try:
benchmark_args = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
arg_error_msg = 'Arg --no_{0} is no longer used, please use --no-{0} instead.'
begin_error_msg = ' '.join(str(e).split(' ')[:(- 1)])
... |
def test_recovering_indices(tmpdir, bace_fragmented):
for fragment in bace_fragmented.fragments:
for pair in fragment.bond_indices:
atom_indices = {a.atom_index for a in bace_fragmented.atoms if (a.map_index in pair)}
assert (len(atom_indices) == 2)
assert any((({b.atom1_... |
(frozen=True, slots=True)
class SystemClock(Clock):
offset: float = attr.ib(factory=(lambda : _r.uniform(10000, 200000)))
def start_clock(self) -> None:
pass
def current_time(self) -> float:
return (self.offset + perf_counter())
def deadline_to_sleep_time(self, deadline: float) -> float:... |
(scope='module')
def inline_query_result_cached_photo():
return InlineQueryResultCachedPhoto(TestInlineQueryResultCachedPhotoBase.id_, TestInlineQueryResultCachedPhotoBase.photo_file_id, title=TestInlineQueryResultCachedPhotoBase.title, description=TestInlineQueryResultCachedPhotoBase.description, caption=TestInlin... |
def benchmark(min_size=min(SIZES), max_size=max(SIZES), step_size=STEP, stars=STARS, noise=NOISE, seed=None, repeats=REPEATS, n_jobs=(- 1), comb_number=COMB_NUMBER):
grid = get_parameters(min_size=min_size, max_size=max_size, step_size=step_size, repeats=repeats, stars=stars, noise=noise, seed=seed, comb_number=com... |
class PluginsList(ListView):
model = Plugin
queryset = Plugin.approved_objects.all()
title = _('All plugins')
additional_context = {}
paginate_by = settings.PAGINATION_DEFAULT_PAGINATION
def get_paginate_by(self, queryset):
try:
paginate_by = int(self.request.GET.get('per_pag... |
class BddQuantifierEliminator(QuantifierEliminator):
LOGICS = [pysmt.logics.BOOL]
def __init__(self, environment, logic=None):
QuantifierEliminator.__init__(self)
self.environment = environment
self.logic = logic
self.ddmanager = repycudd.DdManager()
self.converter = BddC... |
def augment_triplet_from_asins(triplets: List[Tuple[(str, str, str)]], docs: List[List[str]], sample_per_asin=3):
negative_profile = defaultdict(list)
for doc in docs:
if (((len(doc) * (len(doc) - 1)) / 2) < sample_per_asin):
continue
pairs = utils.Rnd.random_pairs(doc, sample_per_as... |
class Maker(AttributeDevice):
_state_property = 'switch_state'
_attributes: _Attributes
def _required_services(self) -> list[RequiredService]:
return (super()._required_services + [RequiredService(name='basicevent', actions=['SetBinaryState'])])
def set_state(self, state: int) -> None:
s... |
def test_while_exec_iteration_stop_evals_false():
wd = WhileDecorator({'stop': '{stop}'})
context = Context({'stop': False})
mock = MagicMock()
assert (not wd.exec_iteration(2, context, mock))
assert (context['whileCounter'] == 2)
assert (wd.while_counter == 2)
assert (len(context) == 2)
... |
.all_locales
def test_smoke_numbers(locale):
locale = Locale.parse(locale)
for number in NUMBERS:
assert numbers.format_decimal(number, locale=locale)
assert numbers.format_decimal(number, locale=locale, numbering_system='default')
assert numbers.format_currency(number, 'EUR', locale=loc... |
def continue2discrete_coordY(y):
if (y < 150):
return 100.0
elif (y < 260):
return 205.0
elif (y < 370):
return 315.0
elif (y < 480):
return 425.0
elif (y < 590):
return 535.0
elif (y < 700):
return 645.0
elif (y < 810):
return 755.0
... |
class InceptionI3d(nn.Module):
VALID_ENDPOINTS = ('Conv3d_1a_7x7', 'MaxPool3d_2a_3x3', 'Conv3d_2b_1x1', 'Conv3d_2c_3x3', 'MaxPool3d_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'MaxPool3d_4a_3x3', 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_4f', 'MaxPool3d_5a_2x2', 'Mixed_5b', 'Mixed_5c', 'Logits', 'Predictions')
... |
()
('-n', '--nsteps', default=(- 1), help='number of steps to process. use -1 to for no limit (will run workflow to completion)')
('--track/--no-track', default=False)
('-u', '--update-interval', default=1)
('-g', '--strategy', help='set execution stragegy')
('-y', '--strategyopt', help='strategy option', multiple=True... |
def read_tb(path):
import pandas
import numpy as np
from glob import glob
from collections import defaultdict
import tensorflow as tf
if osp.isdir(path):
fnames = glob(osp.join(path, 'events.*'))
elif osp.basename(path).startswith('events.'):
fnames = [path]
else:
... |
def save_file(data, filename):
logging.info(f'Saving data to file: {filename}')
file_ext = os.path.splitext(filename)[1]
if (file_ext in ['.pkl', '.pickle']):
with PathManager.open(filename, 'wb') as fopen:
pickle.dump(data, fopen, pickle.HIGHEST_PROTOCOL)
elif (file_ext == '.npy'):
... |
class BSDMountPoint(MountPoint):
def _iter_mountpoints(cls):
check_output = cls(None).check_output
for line in check_output('mount -p').splitlines():
splitted = line.split()
(yield {'path': splitted[1], 'device': splitted[0], 'filesystem': splitted[2], 'options': splitted[3].... |
class Records(Base):
def _backfill_fields(self, fields: Optional[List[str]], forms: Optional[List[str]]):
if (forms and (not fields)):
return ([f'{form}_complete' for form in forms] + [self.def_field])
if (fields and (self.def_field not in fields)):
return (fields + [self.def... |
def _populate_kernel_cache(np_type, k_type):
if (np_type not in _SUPPORTED_TYPES):
raise ValueError("Datatype {} not found for '{}'".format(np_type, k_type))
if ((str(np_type), k_type) in _cupy_kernel_cache):
return
_cupy_kernel_cache[(str(np_type), k_type)] = _get_function('/peak_finding/_p... |
class AsType(InDataMutatingTransform):
def __init__(self, indices, dtypes):
assert (len(indices) == len(dtypes))
self._indices = indices
self._dtypes = dtypes
def transform(self, in_data):
for (index, dtype) in zip(self._indices, self._dtypes):
in_data[index] = in_dat... |
('os.execvpe', new=execvpe_mock)
def test_run_script_by_absolute_name(caplog, pipx_temp_env, tmp_path):
script = (tmp_path / 'test.py')
out = (tmp_path / 'output.txt')
test_str = 'Hello, world!'
script.write_text(textwrap.dedent(f'''
from pathlib import Path
Path({repr(st... |
def check_for_ccdc_structures(cid):
url0 = '
cid = str(cid)
url = ((url0 + cid) + '/JSON')
csd_codes = []
try:
response = urllib.request.urlopen(url)
data = json.loads(response.read())
if (len(data['Record']['Section'][0]['Section']) == 3):
infos = data['Record'][... |
def load_file(filename, onehot=True):
ID1 = []
ID2 = []
D1 = []
D2 = []
L = []
with open(filename, 'r', encoding='utf-8') as read:
for (i, line) in enumerate(read):
if (not (len(line.split('\t')) == 5)):
print(line.split('\t'))
(id1, id2, d1, d2, l... |
def test_reading_and_writing_repository():
repository_state_dir = temp_dir()
(PackageIndex)
class RepositoryStub():
def unique_id(self):
return 'myid'
def transfer(self, package):
pass
repository_state_directory = repository_state_dir.name
repository = Rep... |
class BatchedFusedEmbedding(BaseBatchedEmbedding[torch.Tensor], FusedOptimizerModule):
def __init__(self, config: GroupedEmbeddingConfig, pg: Optional[dist.ProcessGroup]=None, device: Optional[torch.device]=None) -> None:
super().__init__(config, pg, device)
managed: List[EmbeddingLocation] = []
... |
.parametrize('text, expected', [('foo|bar', 'fo|bar'), ('foobar|', 'fooba|'), ('|foobar', '|foobar'), ('f<oo>bar', 'f|bar')])
def test_rl_backward_delete_char(text, expected, lineedit):
lineedit.set_aug_text(text)
readlinecommands.rl_backward_delete_char()
assert (lineedit.aug_text() == expected) |
def test_reveal(hatch, config_file, helpers, default_cache_dir, default_data_dir):
config_file.model.project = 'foo'
config_file.model.publish['index']['auth'] = 'bar'
config_file.save()
result = hatch('config', 'show', '-a')
default_cache_directory = str(default_cache_dir).replace('\\', '\\\\')
... |
class AlreadyBuiltWheelError(Exception):
def __init__(self, wheel_name: str) -> None:
message = textwrap.dedent(f'''
cibuildwheel: Build failed because a wheel named {wheel_name} was already generated in the current run.
If you expected another wheel to be generated, check your proje... |
def decode_spans(start: np.ndarray, end: np.ndarray, topk: int, max_answer_len: int, undesired_tokens: np.ndarray) -> Tuple:
if (start.ndim == 1):
start = start[None]
if (end.ndim == 1):
end = end[None]
outer = np.matmul(np.expand_dims(start, (- 1)), np.expand_dims(end, 1))
candidates = ... |
def test_connection_list_tables():
with patch(PATCH_METHOD) as req:
req.return_value = LIST_TABLE_DATA
conn = Connection(REGION)
conn.list_tables(exclusive_start_table_name='Thread')
assert (req.call_args[0][1] == {'ExclusiveStartTableName': 'Thread'})
with patch(PATCH_METHOD) as... |
class Bottleneck(nn.Module):
def __init__(self, in_channels, bottleneck_channels, out_channels, num_groups, stride_in_1x1, stride, dilation, norm_func=BatchNorm2d, dcn_config={}):
super(Bottleneck, self).__init__()
self.downsample = None
if (in_channels != out_channels):
down_str... |
class MaxPool3x3Conv1x1(BaseOp):
def build(self, inputs, channels):
with tf.variable_scope('MaxPool3x3-Conv1x1'):
net = tf.layers.max_pooling2d(inputs=inputs, pool_size=(3, 3), strides=(1, 1), padding='same', data_format=self.data_format)
net = conv_bn_relu(net, 1, channels, self.is_... |
def initialize_model(train, input_vocab, output_vocab, max_len=10, hidden_size=256, dropout_p=0.5, bidirectional=True, n_beam=5):
encoder = EncoderRNN(len(input_vocab), max_len, hidden_size, bidirectional=bidirectional, variable_lengths=True)
decoder = DecoderRNN(len(output_vocab), max_len, (hidden_size * (2 if... |
def validate_list_of_str(name, data):
if (name in data):
if (not isinstance(data[name], list)):
raise DistutilsSetupError(('"%s" should be a list' % name))
elif (not all([isinstance(i, str) for i in data[name]])):
raise DistutilsSetupError(('"%s" should be a list of strings' ... |
def test_unused_udp_port_factory_duplicate(unused_udp_port_factory, monkeypatch):
counter = 0
def mock_unused_udp_port(_ignored):
nonlocal counter
counter += 1
if (counter < 5):
return 10000
else:
return (10000 + counter)
monkeypatch.setattr(pytest_asy... |
_funcify.register(ptr.BernoulliRV)
def numba_funcify_BernoulliRV(op, node, **kwargs):
out_dtype = node.outputs[1].type.numpy_dtype
def body_fn(a):
return f'''
if {a} < np.random.uniform(0, 1):
return direct_cast(0, out_dtype)
else:
return direct_cast(1, out_dtype)
'''
... |
def show_result_pyplot(model, img, result, score_thr=0.3, title='result', wait_time=0):
if hasattr(model, 'module'):
model = model.module
model.show_result(img, result, score_thr=score_thr, show=True, wait_time=wait_time, win_name=title, bbox_color=(72, 101, 241), text_color=(72, 101, 241)) |
def _fuzzy_contiguity(geoms, ids, tolerance=None, buffer=None, predicate='intersects'):
if ((buffer is not None) and (tolerance is not None)):
raise ValueError('Only one of `tolerance` and `buffer` can be speciifed, not both.')
if (not isinstance(geoms, geopandas.base.GeoPandasBase)):
geoms = ge... |
def test_etuples():
x_pt = pt.vector('x')
y_pt = pt.vector('y')
z_pt = etuple(x_pt, y_pt)
res = apply(pt.add, z_pt)
assert (res.owner.op == pt.add)
assert (res.owner.inputs == [x_pt, y_pt])
w_pt = etuple(pt.add, x_pt, y_pt)
res = w_pt.evaled_obj
assert (res.owner.op == pt.add)
as... |
def test_central_subprocess(testdir):
scripts = testdir.makepyfile(parent_script=SCRIPT_PARENT, child_script=SCRIPT_CHILD)
parent_script = scripts.dirpath().join('parent_script.py')
result = testdir.runpytest('-v', f'--cov={scripts.dirpath()}', '--cov-report=term-missing', parent_script)
result.stdout.f... |
def test_cast_tensor_type():
inputs = torch.FloatTensor([5.0])
src_type = torch.float32
dst_type = torch.int32
outputs = cast_tensor_type(inputs, src_type, dst_type)
assert isinstance(outputs, torch.Tensor)
assert (outputs.dtype == dst_type)
inputs = 'tensor'
src_type = str
dst_type ... |
(qseis.have_backend(), 'backend qseis not available')
class GFQSeisTestCase(unittest.TestCase):
tempdirs = []
def __init__(self, *args, **kwargs):
unittest.TestCase.__init__(self, *args, **kwargs)
def tearDownClass(cls):
for d in cls.tempdirs:
shutil.rmtree(d)
def test_pyrock... |
class DepthwiseSeparableConv(nn.Module):
def __init__(self, in_ch, out_ch, stride=1, kernel_size=3, bias=False):
super().__init__()
if isinstance(kernel_size, list):
padding = [(i // 2) for i in kernel_size]
else:
padding = (kernel_size // 2)
self.depthwise = ... |
def language_eval(dataset, preds, model_id, split):
import sys
sys.path.append('coco-caption')
annFile = 'coco-caption/annotations/captions_val2014.json'
from pycocotools.coco import COCO
from pycocoevalcap.eval import COCOEvalCap
if (not os.path.isdir('eval_results')):
os.mkdir('eval_re... |
def batched(iterable, n):
if (hexversion >= ):
warnings.warn('batched will be removed in a future version of more-itertools. Use the standard library itertools.batched function instead', DeprecationWarning)
it = iter(iterable)
while True:
batch = list(islice(it, n))
if (not batch):
... |
.parametrize('replace_missing_translation', boolean_toggle)
.parametrize('test_language', test_languages)
def test_translationmixin_trans_empty_field(settings, replace_missing_translation, test_language):
settings.REPLACE_MISSING_TRANSLATION = replace_missing_translation
empty_lang = 'en'
settings.LANGUAGE_... |
def _get_abc_helper(view_vector, sat_pos, ellipsoid):
flat2 = ((1 - ellipsoid.flattening) ** 2)
(ux, uy, uz) = (view_vector.x, view_vector.y, view_vector.z)
(x, y, z) = (sat_pos.x, sat_pos.y, sat_pos.z)
a = ((flat2 * ((ux ** 2) + (uy ** 2))) + (uz ** 2))
b = ((flat2 * ((x * ux) + (y * uy))) + (z * u... |
def test_unionize_dataframe_categories(uniontest_df1, uniontest_df2, uniontest_df3):
(udf1, udf2, udf3) = janitor.unionize_dataframe_categories(uniontest_df1, uniontest_df2, uniontest_df3)
assert (set(udf1['jerbs'].dtype.categories) == set(udf2['jerbs'].dtype.categories))
assert (set(udf1['jerbs'].dtype.cat... |
(integers(min_value=0, max_value=100), integers(min_value=0, max_value=10000), integers(min_value=0, max_value=50000), integers(min_value=1, max_value=), integers(min_value=1, max_value=), integers(min_value=1, max_value=))
(suppress_health_check=[HealthCheck.filter_too_much])
def test_fee_round_trip(flat_fee, prop_fee... |
class DummyDataset(FairseqDataset):
def __init__(self, batch, num_items, item_size):
super().__init__()
self.batch = batch
self.num_items = num_items
self.item_size = item_size
def __getitem__(self, index):
return index
def __len__(self):
return self.num_items... |
class VocabWithLock(VocabBase):
def __init__(self, words=(), lock=None):
self.lock = lock
super().__init__(words)
def word2index(self, word, train=False):
if isinstance(word, (list, tuple)):
return [self.word2index(w, train=train) for w in word]
with self.lock:
... |
class BlocksEndpoint(Endpoint):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.children = BlocksChildrenEndpoint(*args, **kwargs)
def retrieve(self, block_id: str, **kwargs: Any) -> SyncAsync[Any]:
return self.parent.request(path=f'blocks/{b... |
def ql_syscall_dup(ql: Qiling, oldfd: int):
f = get_opened_fd(ql.os, oldfd)
if (f is None):
return (- 1)
newfd = next((i for i in range(NR_OPEN) if (ql.os.fd[i] is None)), (- 1))
if (newfd == (- 1)):
return (- EMFILE)
ql.os.fd[newfd] = f.dup()
ql.log.debug(f'dup({oldfd:d}) = {new... |
def rtn_fopen(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('fopen hooked')
arg0 = pstate.get_argument_value(0)
arg1 = pstate.get_argument_value(1)
arg0s = pstate.memory.read_string(arg0)
arg1s = pstate.memory.read_string(arg1)
pstate.concretize_memory_bytes(arg0, (len(arg0s) + 1... |
class SetDataset():
def __init__(self, data_file, batch_size, transform):
with open(data_file, 'r') as f:
self.meta = json.load(f)
self.cl_list = np.unique(self.meta['image_labels']).tolist()
self.sub_meta = {}
for cl in self.cl_list:
self.sub_meta[cl] = []
... |
_fixtures(WebFixture, MaxNumberOfFilesFileUploadInputFixture)
def test_async_number_files_validation(web_fixture, max_number_of_files_file_upload_input_fixture):
fixture = max_number_of_files_file_upload_input_fixture
web_fixture.reahl_server.set_app(fixture.new_wsgi_app(enable_js=True))
browser = web_fixtu... |
class KFACOptimizer(optim.Optimizer):
def __init__(self, model, lr=0.25, momentum=0.9, stat_decay=0.99, kl_clip=0.001, damping=0.01, weight_decay=0, fast_cnn=False, Ts=1, Tf=10):
defaults = dict()
def split_bias(module):
for (mname, child) in module.named_children():
if (... |
def test_connect_wr_x_conn_As_wr_y_conn_At_disjoint():
class Top(ComponentLevel3):
def construct(s):
s.x = Wire(Bits24)
s.A = Wire(Bits32)
s.y = Wire(Bits4)
connect(s.A[8:32], s.x)
connect(s.A[0:4], s.y)
def up_wr_x():
s... |
class ElfPatcher():
def replace_needed(self, file_name: str, *old_new_pairs: tuple[(str, str)]) -> None:
raise NotImplementedError
def set_soname(self, file_name: str, new_so_name: str) -> None:
raise NotImplementedError
def set_rpath(self, file_name: str, rpath: str) -> None:
raise ... |
def test_options_1(tmp_path, monkeypatch):
with tmp_path.joinpath('pyproject.toml').open('w') as f:
f.write(PYPROJECT_1)
args = CommandLineArguments.defaults()
args.package_dir = tmp_path
monkeypatch.setattr(platform_module, 'machine', (lambda : 'x86_64'))
options = Options(platform='linux',... |
def _dist_train(model, dataset, cfg, validate=False, logger=None):
data_loaders = [build_dataloader(dataset, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)]
model = MMDistributedDataParallel(model.cuda())
optimizer = build_optimizer(model, cfg.optimizer)
runner = EpochBasedRunner(model, bat... |
def inference(model_save_path, clip):
with tf.Session() as sess:
meta_graph_def = tf.saved_model.loader.load(sess, [MODEL_NAME], model_save_path)
signature = meta_graph_def.signature_def
bn_tensor_name = signature[SIGNATURE_KEY].inputs[BATCH_NORM_KEY].name
x_tensor_name = signature[S... |
class F19_TestCase(FC3_TestCase):
def runTest(self):
self.assert_parse('lang en_US')
self.assert_parse('lang en_US --addsupport=cs_CZ', 'lang en_US --addsupport=cs_CZ\n')
self.assert_parse('lang en_US --addsupport=sr_RS.UTF-', 'lang en_US --addsupport=sr_RS.UTF-\n')
self.assert_parse... |
def install_pyqt_binary(venv_dir: pathlib.Path, version: str) -> None:
utils.print_title('Installing PyQt from binary')
utils.print_col('No proprietary codec support will be available in qutebrowser.', 'bold')
if _is_qt6_version(version):
supported_archs = {'linux': {'x86_64'}, 'win32': {'AMD64'}, '... |
def file_info_from_modpath(modpath: list[str], path: (Sequence[str] | None)=None, context_file: (str | None)=None) -> spec.ModuleSpec:
if (context_file is not None):
context: (str | None) = os.path.dirname(context_file)
else:
context = context_file
if (modpath[0] == 'xml'):
try:
... |
class LenPredicate():
expected_length: int
has_star: bool
ctx: CanAssignContext
def __call__(self, value: Value, positive: bool) -> Optional[Value]:
value_len = len_of_value(value)
if (isinstance(value_len, KnownValue) and isinstance(value_len.val, int)):
if self.has_star:
... |
def test_do_export_broken_internal_copy(tmp_path: Path):
patch_data = {'menu_mod': False}
export_params = EchoesGameExportParams(input_path=None, output_path=MagicMock(), contents_files_path=tmp_path.joinpath('contents'), asset_cache_path=tmp_path.joinpath('asset_cache_path'), backup_files_path=tmp_path.joinpat... |
def gen_evaluation_file(semantic_mask, instance_mask, confidence_array, instance_mask_path, txt_path, file_name, additive=False, slide_semantic=False, mask_label=None, mask_idx=0, result_path=None):
assert result_path, 'result_path must be specified'
result_path = (result_path + '/')
_ids = np.array(['0', '... |
class EventChannel(RemoteMethod):
def __init__(self, form, controller, name):
super().__init__(form.view, name, self.delegate_event, None, idempotent=False, immutable=False, disable_csrf_check=True)
self.controller = controller
self.form = form
def make_result(self, input_values):
... |
def euler2mat(z=0, y=0, x=0):
Ms = []
if z:
cosz = math.cos(z)
sinz = math.sin(z)
Ms.append(np.array([[cosz, (- sinz), 0], [sinz, cosz, 0], [0, 0, 1]]))
if y:
cosy = math.cos(y)
siny = math.sin(y)
Ms.append(np.array([[cosy, 0, siny], [0, 1, 0], [(- siny), 0, c... |
class DIAPreResNet(nn.Module):
def __init__(self, channels, init_block_channels, bottleneck, conv1_stride, in_channels=3, in_size=(224, 224), num_classes=1000):
super(DIAPreResNet, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.features = nn.Sequential()
... |
def random_pad_clip_list(x, num):
x = deepcopy(list(x))
if (len(x) > num):
shuffle(x)
return x[:num]
else:
ret = []
for i in range((num // len(x))):
shuffle(x)
ret = (ret + x)
ret = (ret + x[:(num - len(ret))])
return ret |
class Migration(migrations.Migration):
dependencies = [('projects', '0051_alter_value_value_type')]
operations = [migrations.AlterField(model_name='invite', name='email', field=models.EmailField(blank=True, help_text='The e-mail for this membership.', max_length=254, verbose_name='E-mail')), migrations.AlterFie... |
class AstroidManagerBrain(TypedDict):
astroid_cache: dict[(str, nodes.Module)]
_mod_file_cache: dict[(tuple[(str, (str | None))], (spec.ModuleSpec | exceptions.AstroidImportError))]
_failed_import_hooks: list[Callable[([str], nodes.Module)]]
always_load_extensions: bool
optimize_ast: bool
max_in... |
.parametrize('regimes', [['n', 'n', 's', 's', 'e', 'e', 'w', 'w', 'e', 'j'], [0, 0, 2, 2, 3, 3, 4, 4, 3, 1]])
def test_block_contiguity(regimes):
neighbors = _block_contiguity(regimes)
wn = {0: [1], 1: [0], 2: [3], 3: [2], 4: [5, 8], 5: [4, 8], 6: [7], 7: [6], 8: [4, 5], 9: []}
assert ({f: n.tolist() for (f... |
class Stats(object):
def __init__(self, tracker: 'Optional[ClassTracker]'=None, filename: Optional[str]=None, stream: Optional[IO]=None):
if stream:
self.stream = stream
else:
self.stream = sys.stdout
self.tracker = tracker
self.index = {}
self.snapsho... |
class CanvasConfig(Config):
def __init__(self, canvas, base_config):
self.canvas = canvas
self.major_version = base_config.major_version
self.minor_version = base_config.minor_version
self.forward_compatible = base_config.forward_compatible
self.opengl_api = (base_config.open... |
def get_model(name, **kwargs):
models = {'fcn_resnet50_pcontext': get_fcn_resnet50_pcontext, 'encnet_resnet50_pcontext': get_encnet_resnet50_pcontext, 'encnet_resnet101_pcontext': get_encnet_resnet101_pcontext, 'encnet_resnet50_ade': get_encnet_resnet50_ade, 'encnet_resnet101_ade': get_encnet_resnet101_ade, 'fcn_re... |
def parse_permissions(session=flask.session):
perms = {x.name: False for x in Perms}
perms['ADMIN'] = False
perms['is_admin'] = False
if (not session.get('authenticated', False)):
return perms
perms['ANY'] = True
if (session.get('role') == Role.ADMIN):
for key in perms.keys():
... |
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