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def rating_tamplate(pattern_identifier, context=None):
return InlineKeyboardMarkup([[InlineButton(get_text('bad', context), callback_data=(pattern_identifier + str(BAD_RATING)))], [InlineButton(get_text('regular', context), callback_data=(pattern_identifier + str(REGULAR_RATING)))], [InlineButton(get_text('good', c... |
def main():
print('initializing arduino')
config = {'host': 'localhost', 'hat': {'arduino': {'device': '/dev/spidev0.1', 'resetpin': '26'}}, 'actions': {}, 'arduino.nmea.baud': 4800, 'arduino.nmea.in': False, 'arduino.nmea.out': False, 'arduino.ir': True, 'arduino.debug': True, 'arduino.adc_channels': []}
a... |
class UnexpectedPasswordHashVersion(InvalidPassword, WalletFileException):
def __init__(self, version):
self.version = version
def __str__(self):
return '{unexpected}: {version}\n{instruction}'.format(unexpected=_('Unexpected password hash version'), version=self.version, instruction=_('You are ... |
def test_environment_only(hatch, helpers, temp_dir, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), result.output
project_pa... |
def _send_button_click_event(widget, **kwargs):
assert widget.get_realized()
assert widget.get_visible()
ev = Gdk.Event()
window = widget.get_window()
ev.any.window = window
ev.button.x = (window.get_width() / 2.0)
ev.button.y = (window.get_height() / 2.0)
for (key, value) in kwargs.item... |
def FlagsForFile(filename, **kwargs):
if database:
compilation_info = GetCompilationInfoForFile(filename)
if (not compilation_info):
return None
final_flags = MakeRelativePathsInFlagsAbsolute(compilation_info.compiler_flags_, compilation_info.compiler_working_dir_)
else:
... |
class MyTree(Tree):
def set_index(self, ind=0):
if (len(self.leaves()) == 1):
self._i = (ind, (ind + 1))
if isinstance(self[0], MyTree):
self[0].set_index(ind)
return (ind + 1)
else:
self._i = (ind, (ind + 1))
for l in self:
... |
class ASPP(nn.Module):
def __init__(self, in_channels, out_channels, atrous_rates, separable=False):
super(ASPP, self).__init__()
modules = []
modules.append(nn.Sequential(nn.Conv2d(in_channels, out_channels, 1, bias=False), nn.BatchNorm2d(out_channels), nn.ReLU()))
(rate1, rate2, ra... |
class RemoteReceiveEvent(ModbusEvent):
def __init__(self, **kwargs):
self.overrun = kwargs.get('overrun', False)
self.listen = kwargs.get('listen', False)
self.broadcast = kwargs.get('broadcast', False)
def encode(self) -> bytes:
bits = ([False] * 3)
bits += [self.overrun... |
_train('PCQM4Mv2-inference')
def ogblsc_inference(loggers, loaders, model, optimizer=None, scheduler=None):
from ogb.lsc import PCQM4Mv2Evaluator
evaluator = PCQM4Mv2Evaluator()
num_splits = 3
split_names = ['valid', 'test-dev', 'test-challenge']
assert (len(loaders) == num_splits), 'Expecting 3 par... |
def get_class(module, superclass=None):
classes = get_classes(module, superclass)
if (len(classes) == 1):
return classes[0]
desc = (('subclasses of %s' % superclass.__name__) if superclass else 'new-style classes')
if (len(classes) > 1):
names = ', '.join([cls.__name__ for cls in classes... |
def docker_start(image, volumes={}, env_variables={}):
client = docker.from_env()
dvolumes = {host: {'bind': ctr, 'mode': 'rw'} for (ctr, host) in volumes.items()}
logger.info('Starting container with image %r', image)
con = client.containers.run(image, ['sleep', '10000'], detach=True, volumes=dvolumes,... |
def test_order_dependencies_no_auto_mark(no_dep_marks):
no_dep_marks.makefile('.ini', pytest='\n [pytest]\n automark_dependency = 0\n console_output_style = classic\n ')
result = no_dep_marks.runpytest('-v', '--order-dependencies')
result.assert_outcomes(passed=2,... |
def parse(tokens):
key = tokens.pop(0)[1:]
parsed = {key: {}}
while tokens:
token = tokens.pop(0)
if token.endswith(')'):
if token[:(- 1)]:
val = token[:(- 1)].strip('"')
if (val.startswith('#') and val.endswith('#')):
val = int... |
class FractionalCloudCover(metaclass=_EnumMeta):
zeroOktas = _OscEnum('FractionalCloudCover', 'zeroOktas', min_minor_version=2)
oneOktas = _OscEnum('FractionalCloudCover', 'oneOktas', min_minor_version=2)
twoOktas = _OscEnum('FractionalCloudCover', 'twoOktas', min_minor_version=2)
threeOktas = _OscEnum(... |
class TTCON(TestCase):
def _g(self, s):
return TCON(text=s).genres
def test_empty(self):
self.assertEquals(self._g(''), [])
def test_num(self):
for i in range(len(GENRES)):
self.assertEquals(self._g(('%02d' % i)), [GENRES[i]])
def test_parened_num(self):
for i... |
def get_normalized_dependency(requirement: Requirement) -> str:
from packaging.specifiers import SpecifierSet
requirement.name = normalize_project_name(requirement.name)
if requirement.specifier:
requirement.specifier = SpecifierSet(str(requirement.specifier).lower())
if requirement.extras:
... |
class BatchNorm(nn.BatchNorm2d):
def __init__(self, num_features, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True, use_tracked_mean=True, use_tracked_var=True):
nn.BatchNorm2d.__init__(self, num_features=num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_runnin... |
class CustomRandomCrop(RandomCrop):
def forward(self, img):
(width, height) = F.get_image_size(img)
(tar_h, tar_w) = self.size
tar_h = min(tar_h, height)
tar_w = min(tar_w, width)
(i, j, h, w) = self.get_params(img, (tar_h, tar_w))
return F.crop(img, i, j, h, w) |
class FinTSMessage(SegmentSequence):
DIRECTION = None
def __init__(self, dialog=None, *args, **kwargs):
self.dialog = dialog
self.next_segment_number = 1
super().__init__(*args, **kwargs)
def __iadd__(self, segment: FinTS3Segment):
if (not isinstance(segment, FinTS3Segment)):... |
class Embedding(Layer):
_embedding_support
def __init__(self, input_dim, output_dim, embeddings_initializer='uniform', embeddings_regularizer=None, activity_regularizer=None, embeddings_constraint=None, mask_zero=False, input_length=None, **kwargs):
if ('input_shape' not in kwargs):
if input... |
class TestAllForkSeq(uvm_sequence):
async def body(self):
seqr = ConfigDB().get(None, '', 'SEQR')
random = RandomSeq('random')
max = MaxSeq('max')
random_task = cocotb.start_soon(random.start(seqr))
max_task = cocotb.start_soon(max.start(seqr))
(await Combine(Join(ran... |
class Expanding(Window):
def aggregate(self, agg):
window = self.n
diff = aggregations.diff_expanding
return self.root.accumulate_partitions(aggregations.window_accumulator, diff=diff, window=window, agg=agg, start=self.start, returns_state=True, stream_type='updating', with_state=self.with_... |
def multithread_compute_vali():
global vali_sum, vali_cnt
vali_sum = [0.0, 0.0, 0.0]
vali_cnt = 0
threads = []
for ii in xrange(cmd_args.num_thread):
thread = threading.Thread(target=vali_eval, args=(1, ii))
thread.start()
threads.append(thread)
for thread in threads:
... |
def _preprocess(data):
for field in ['date', 'start', 'end']:
date = data.get(field)
if ((field == 'end') and (date == UNSET_INDICATOR)):
continue
if (date is not None):
try:
date = time.strftime(POCKET_DATE_FORMAT, du_parser.parse(date, yearfirst=True... |
def set_question_optionset(apps, schema_editor):
Question = apps.get_model('questions', 'Question')
for question in Question.objects.all():
try:
for optionset in question.attribute_entity.attribute.optionsets.all():
question.optionsets.add(optionset)
except AttributeE... |
def make_batches(lines, args, task, max_positions, encode_fn):
def encode_fn_target(x):
return encode_fn(x)
if args.constraints:
batch_constraints = [list() for _ in lines]
for (i, line) in enumerate(lines):
if ('\t' in line):
(lines[i], *batch_constraints[i])... |
class SeriesParser(Parser):
_default_orient = 'index'
_split_keys = ('name', 'index', 'data')
def _parse_no_numpy(self):
json = self.json
orient = self.orient
if (orient == 'split'):
decoded = dict(((str(k), v) for (k, v) in compat.iteritems(loads(json, precise_float=self... |
def test_arguments_default_value():
node = extract_node("def fruit(eat='please', *, peel='no', trim='yes', **kwargs): ...")
assert (node.args.default_value('eat').value == 'please')
node = extract_node("def fruit(seeds, flavor='good', *, peel='maybe'): ...")
assert (node.args.default_value('flavor').val... |
def binary_search2(fre, cand, level):
(low, high) = (0, (len(fre) - 1))
if (low > high):
return (- 1)
while (low <= high):
mid = int(((low + high) / 2))
if (cand == fre[mid][0:(level - 1)]):
(slow, shigh) = (low, mid)
if (cand == fre[low][0:(level - 1)]):
... |
def call_with_extended_paramz(f, args, keys, vals, env, cont):
from pycket.values import parameterization_key
paramz = cont.get_mark_first(parameterization_key)
assert isinstance(paramz, values_parameter.W_Parameterization)
return paramz.extend(keys, vals, env, call_with_paramz_cont(f, args, env, cont)) |
class TestGridEngineCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('GridEngineCollector', {})
self.collector = GridEngineCollector(config, None)
self.fixtures_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'fixtures'))
def test_import(self):
... |
def rtn_write(se: 'SymbolicExecutor', pstate: 'ProcessState'):
logger.debug('write hooked')
fd = pstate.get_argument_value(0)
buf = pstate.get_argument_value(1)
size = pstate.get_argument_value(2)
data = pstate.memory.read(buf, size)
if pstate.file_descriptor_exists(fd):
fdesc = pstate.g... |
()
('--filename', default='samples/sample_wind_poitiers.csv', help='Input filename')
('--filename_out', default='windrose.pdf', help='Output filename')
('--dpi', default=DPI_DEFAULT, help='Dot per inch for plot generation')
('--figsize', default=S_FIGSIZE_DEFAULT, help=('Figure size x,y - default=%s' % S_FIGSIZE_DEFAUL... |
class BasicBlock(nn.Module):
def __init__(self, in_channels, out_channels, expansion=1, stride=1, dilation=1, downsample=None, style='pytorch', with_cp=False, conv_cfg=None, norm_cfg=dict(type='BN')):
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.in_channels = in_channels
... |
class ElectrodeSOHSolver():
def __init__(self, parameter_values, param=None, known_value='cyclable lithium capacity', options=None):
self.parameter_values = parameter_values
self.param = (param or pybamm.LithiumIonParameters(options))
self.known_value = known_value
self.options = (op... |
def test_arguments_marker(testdir):
file_test = testdir.makepyfile("\n import pytest\n pytestmark = pytest.mark.firefox_arguments('baz')\n .nondestructive\n .firefox_arguments('foo', 'bar')\n def test_arguments(firefox_options):\n actual = sorted(firefox_options.argumen... |
class PrologLexer(RegexLexer):
name = 'Prolog'
aliases = ['prolog']
filenames = ['*.ecl', '*.prolog', '*.pro', '*.pl']
mimetypes = ['text/x-prolog']
url = '
version_added = ''
tokens = {'root': [('/\\*', Comment.Multiline, 'nested-comment'), ('%.*', Comment.Single), ("0\\'.", String.Char), (... |
def _verify_patchelf() -> None:
if (not which('patchelf')):
raise ValueError('Cannot find required utility `patchelf` in PATH')
try:
version = check_output(['patchelf', '--version']).decode('utf-8')
except CalledProcessError:
raise ValueError('Could not call `patchelf` binary')
m... |
def _parse_set_implicit_union(source, info):
items = [_parse_set_member(source, info)]
while True:
here = source.pos
if (source.match(u']') or source.match(u'&&')):
source.pos = here
break
items.append(_parse_set_member(source, info))
if ((len(items) == 1) and... |
def get_data_loaders(cfg, transforms_3d, transforms_2d, transforms_val, transforms_img, rank, world_size, verbose=True):
def get_2d_datasets(dataset_names):
datasets = []
for dataset_name in dataset_names:
db = VideoDataset(dataset_name=dataset_name, set='train', transforms=transforms_2d... |
def calc_confusion_mat(predictions, data):
exact = 0
one_of = 0
errs = []
err_mat = []
preds_mat = []
one_errs = 0
for (ind, (example, prediction)) in enumerate(zip(data, predictions)):
example = json.loads(example)
tokens = example['tokens']
y_hat = prediction['y_hat... |
def construct_script(items: Sequence[Union[(str, int, bytes, opcodes)]]) -> str:
script = ''
for item in items:
if isinstance(item, opcodes):
script += item.hex()
elif (type(item) is int):
script += add_number_to_script(item).hex()
elif isinstance(item, (bytes, by... |
def test_slots_unpickle_after_attr_added(frozen):
a = A(1, 2, 3)
a_pickled = pickle.dumps(a)
a_unpickled = pickle.loads(a_pickled)
assert (a_unpickled == a)
(slots=True, frozen=frozen)
class NEW_A():
x = attr.ib()
b = attr.ib()
d = attr.ib()
c = attr.ib()
with... |
_end_docstrings(CUSTOM_DPR_READER_DOCSTRING)
class DPRReaderTokenizerFast(CustomDPRReaderTokenizerMixin, BertTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = READER_PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrain... |
def parse_args():
parser = argparse.ArgumentParser(description='MMAction2 test (and eval) a model')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--out', default=None, help='output result file in pickle format')... |
def build_radare2():
if (not radare2_exists()):
raise RuntimeError('Fail to detect radare2 repository. Do you forget to init submodules?')
if (not meson_exists()):
raise RuntimeError('Fail to detect meson. Do you forget to install meson?')
os.chdir(RADARE2_DIR)
DEBUG = os.getenv('DEBUG',... |
class TestICDAR2015GWD(TestICDAR2015):
def eval(self):
txt_name = '{}.txt'.format(self.cfgs.VERSION)
real_test_img_list = self.get_test_image()
gwd = build_whole_network.DetectionNetworkGWD(cfgs=self.cfgs, is_training=False)
self.test_icdar2015(det_net=gwd, real_test_img_list=real_te... |
def check_dummies(overwrite=False):
dummy_files = create_dummy_files()
short_names = {'torch': 'pt'}
path = os.path.join(PATH_TO_TRANSFORMERS, 'utils')
dummy_file_paths = {backend: os.path.join(path, f'dummy_{short_names.get(backend, backend)}_objects.py') for backend in dummy_files.keys()}
actual_d... |
class BlenderbotSmallOnnxConfig(OnnxSeq2SeqConfigWithPast):
def inputs(self) -> Mapping[(str, Mapping[(int, str)])]:
if (self.task in ['default', 'seq2seq-lm']):
common_inputs = OrderedDict([('input_ids', {0: 'batch', 1: 'encoder_sequence'}), ('attention_mask', {0: 'batch', 1: 'encoder_sequence'... |
class Episode():
def __init__(self, info, max_len=30):
self.info = info
self.maxlen = max_len
def render(self, exp_name=None):
goal_names = [obj['class_name'] for obj in self.info['target'][1][0]]
episode_info = 'Episode {}.'.format(self.info['episode'])
episode_info2 = '... |
def get_quotedrpath(rp, separate_basename=0):
if separate_basename:
assert (not rp.index), "Trying to start quoting '{rp}' in the middle.".format(rp=rp)
(dirname, basename) = rp.dirsplit()
return QuotedRPath(rp.conn, dirname, (unquote(basename),), rp.data)
else:
return QuotedRPat... |
def main(client, config):
(ss_ddf, ws_ddf, datedim_ddf) = benchmark(read_tables, config=config, compute_result=config['get_read_time'])
datedim_ddf = datedim_ddf.map_partitions(convert_datestring_to_days)
min_date = np.datetime64(q25_date, 'D').astype(int)
valid_dates_ddf = datedim_ddf[(datedim_ddf['d_d... |
class VoltageChannel(Channel):
voltage_setpoint = Channel.control('VOLT? ({ch})', 'VOLT %g, ({ch})', 'Control the output voltage of this channel, range depends on channel.', validator=strict_range, values=[0, 25], dynamic=True)
current_limit = Channel.control('CURR? ({ch})', 'CURR %g, ({ch})', 'Control the curr... |
def format_callable_args(arg_types: list[Type], arg_kinds: list[ArgKind], arg_names: list[(str | None)], format: Callable[([Type], str)], verbosity: int) -> str:
arg_strings = []
for (arg_name, arg_type, arg_kind) in zip(arg_names, arg_types, arg_kinds):
if (((arg_kind == ARG_POS) and (arg_name is None)... |
def mute_no_singing_parts(mono_output_path, mute_output_path):
print(f'{ULTRASINGER_HEAD} Mute audio parts with no singing')
silence_sections = get_silence_sections(mono_output_path)
(y, sr) = librosa.load(mono_output_path, sr=None)
for i in silence_sections:
start_time = i[0]
end_time =... |
def load_model_and_checkpoint_files(folder, folds=None, mixed_precision=None, checkpoint_name='model_best'):
if isinstance(folds, str):
folds = [join(folder, 'all')]
assert isdir(folds[0]), ('no output folder for fold %s found' % folds)
elif isinstance(folds, (list, tuple)):
if ((len(fol... |
def _all_files(root: Union[(str, Path)], filter_function: Optional[Callable[([str], bool)]]=None) -> Set[str]:
all_files = set()
for (dirpath, _, filenames) in os.walk(root):
for filename in filenames:
if ((filter_function is not None) and (not filter_function(filename))):
co... |
def fuse_qkv(model, args):
def fuse3(qq, qk, qv):
for mod in [qq, qk, qv]:
if (not hasattr(mod, '_amax')):
print(' WARNING: NO AMAX BUFFER')
return
q = qq._amax.detach().item()
k = qk._amax.detach().item()
v = qv._amax.detach().ite... |
class Karaoke(GStreamerPlugin):
PLUGIN_ID = _PLUGIN_ID
PLUGIN_NAME = _('Karaoke')
PLUGIN_DESC = _('Removes main vocals from audio.')
PLUGIN_ICON = Icons.AUDIO_INPUT_MICROPHONE
def setup_element(cls):
return Gst.ElementFactory.make('audiokaraoke', cls.PLUGIN_ID)
def update_element(cls, el... |
_torch
_vision
class GLPNFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
feature_extraction_class = (GLPNFeatureExtractor if is_vision_available() else None)
def setUp(self):
self.feature_extract_tester = GLPNFeatureExtractionTester(self)
def feat_extract_dict(self):
... |
class CpmBlock(nn.Module):
def __init__(self, in_channels, channels=(128, 128, 128), kernels=(11, 11, 11), norm_cfg=None):
super().__init__()
assert (len(channels) == len(kernels))
layers = []
for i in range(len(channels)):
if (i == 0):
input_channels = in... |
.patch('bot.exts.info.information.constants')
class UserCommandTests(unittest.IsolatedAsyncioTestCase):
def setUp(self):
self.bot = helpers.MockBot()
self.cog = information.Information(self.bot)
self.moderator_role = helpers.MockRole(name='Moderators', id=2, position=10)
self.flautis... |
def freeze_model_weights(model):
print('=> Freezing model weights')
for (n, m) in model.named_modules():
if (hasattr(m, 'weight') and (m.weight is not None)):
print(f'==> No gradient to {n}.weight')
m.weight.requires_grad = False
if (m.weight.grad is not None):
... |
def generate_inference_command(dataset_name_or_id: Union[(int, str)], configuration_name: str, plans_identifier: str='nnUNetPlans', trainer_name: str='nnUNetTrainer', folds: Union[(List[int], Tuple[(int, ...)])]=(0, 1, 2, 3, 4), folder_with_segs_from_prev_stage: str=None, input_folder: str='INPUT_FOLDER', output_folder... |
def test_get_username_keyring_runtime_error_logged(entered_username, monkeypatch, config, caplog):
class FailKeyring():
def get_credential(system, username):
raise RuntimeError('fail!')
monkeypatch.setattr(auth, 'keyring', FailKeyring)
assert (auth.Resolver(config, auth.CredentialInput()... |
class CocoaAlternateEventLoop(EventLoop):
def run(self, interval=(1 / 60)):
if (not interval):
self.clock.schedule(self._redraw_windows)
else:
self.clock.schedule_interval(self._redraw_windows, interval)
self.has_exit = False
from pyglet.window import Window
... |
def _prepare_onnx_paddings(g, dim, pad):
pad_len = torch.onnx.symbolic_opset9.size(g, pad, g.op('Constant', value_t=torch.tensor([0])))
extension = g.op('Sub', g.op('Mul', g.op('Constant', value_t=torch.tensor(dim, dtype=torch.int64)), g.op('Constant', value_t=torch.tensor(2, dtype=torch.int64))), pad_len)
... |
def test_logout(flask_app, mocker: MockerFixture):
mock_leave_all_rooms = mocker.patch('randovania.server.multiplayer.session_common.leave_all_rooms', autospec=True)
session = {'user-id': 1234, 'discord-access-token': 'access_token'}
sa = MagicMock()
sa.session.return_value.__enter__.return_value = sess... |
class Feed(list):
def __init__(self, uri):
self.name = _('Unknown')
self.uri = uri
self.changed = False
self.website = ''
self.__lastgot = 0
def get_age(self):
return (time.time() - self.__lastgot)
def __fill_af(feed, af):
try:
af['title'] ... |
class MTL_Masker():
def __init__(self, sess, mask_dic, masks_dir, pruning_param_names, save_checkpoints_dir):
self.masks = masks_dir
self.weights = []
self._sess = sess
self._log = Logger(__name__)
self.pruning_names = set(pruning_param_names)
self.backup_values = []
... |
class TestEmptyList(unittest.TestCase):
def test_empty_data(self):
data = list()
schema = 'list'
r = validator.validate(data, schema)
self.assertTrue(r)
schema = list()
r = validator.validate(data, schema)
self.assertTrue(r)
schema = ['int']
r ... |
class BotogramRunner():
def __init__(self, *bots, workers=2):
self._bots = {bot._bot_id: bot.freeze() for bot in bots}
self._updater_processes = {}
self._worker_processes = []
self._ipc_process = None
self.ipc = None
self.running = False
self._stop = False
... |
def parse_markers(x: ((dict[(str, str)] | list[str]) | tuple[(str, ...)])) -> dict[(str, str)]:
if isinstance(x, (list, tuple)):
mapping = {name.strip(): '' for name in x}
elif isinstance(x, dict):
mapping = {name.strip(): description.strip() for (name, description) in x.items()}
else:
... |
def get_fingerprint(mol, hparams):
length = hparams['fingerprint_length']
radius = hparams['fingerprint_radius']
if isinstance(mol, str):
mol = Chem.MolFromSmiles(mol)
if (mol is None):
return np.zeros((length,))
fingerprint = AllChem.GetMorganFingerprintAsBitVect(mol, radius, length... |
def balance_classes(dataset, num_classes=2):
(inputs, targets) = dataset[:]
num_train = inputs.size(0)
(balanced_inputs, balanced_targets) = ([], [])
for class_idx in range(num_classes):
num_class_examples = (num_train // num_classes)
mask = (targets == class_idx)
(masked_inputs,... |
def set_articulation_state(articulation: sapien.Articulation, state: np.ndarray):
articulation.set_root_pose(Pose(state[0:3], state[3:7]))
articulation.set_root_velocity(state[7:10])
articulation.set_root_angular_velocity(state[10:13])
(qpos, qvel) = np.split(state[13:], 2)
articulation.set_qpos(qpo... |
.parametrize('search, documents, k', [pytest.param((ranker_a | ranker_b), documents(), k, id=f'Union rankers: {ranker_a.__class__.__name__} | {ranker_b.__class__.__name__} k: {k}') for k in [None, 3, 4] for ranker_b in cherche_rankers(key='id', on='article') for ranker_a in cherche_rankers(key='id', on='title')])
def t... |
def _ssim_3D(img1, img2, window, window_size, channel, size_average=True):
mu1 = F.conv3d(img1, window, padding=(window_size // 2), groups=channel)
mu2 = F.conv3d(img2, window, padding=(window_size // 2), groups=channel)
mu1_sq = mu1.pow(2)
mu2_sq = mu2.pow(2)
mu1_mu2 = (mu1 * mu2)
sigma1_sq = (... |
def optimise_grid_point(geometry_optimiser: GeometryOptimiser, molecule: 'Ligand', qc_spec: 'QCOptions', local_options: 'LocalResource', coordinates: List[float], dihedral: Tuple[(int, int, int, int)], dihedral_angle: int, job_id: int) -> GridPointResult:
with folder_setup(folder_name=f'grid_point_{dihedral_angle}_... |
_dtype_float_test(only64=True, include_complex=True, additional_kwargs={'bias_is_tensor': [True, False]})
def test_rootfinder_with_params(dtype, device, bias_is_tensor):
torch.manual_seed(100)
random.seed(100)
nr = 2
nbatch = 2
fwd_options = {'method': 'broyden1', 'f_tol': 1e-09, 'alpha': (- 0.5)}
... |
def create_branch(version):
repo = Repo.init('.')
if repo.is_dirty(untracked_files=True):
raise RuntimeError('Repository is dirty, please commit/stash your changes.')
branch_name = f'release-{version}'
print(f'{Fore.CYAN}Create {branch_name} branch from upstream main')
upstream = get_upstrea... |
.parametrize('golden', COFFEE_SLASH_GOLDEN)
def test_coffee_slashes(lexer, golden):
(input_str, slashes_are_regex_here) = golden
output = list(lexer.get_tokens(input_str))
print(output)
for (t, s) in output:
if ('/' in s):
is_regex = (t is Token.String.Regex)
assert (is_r... |
def format_data(value='', datatypes=int, nullable=True, pre_format_function=None, format_function=to_int, post_format_function=None, **kwargs) -> Any:
if pre_format_function:
value = pre_format_function(value)
if (nullable and is_none_like(value)):
value = None
else:
try:
... |
def _zip_pseudo_fifty_mbytes(file_buffer_list: list, zip_bytes_io: io.BytesIO):
bad_data = False
file_count = 0
keywords = pseudonymisation_api.get_default_pseudonymisation_keywords()
keywords.remove('PatientSex')
strategy = pseudonymisation_api.pseudonymisation_dispatch
zip_stream = zip_bytes_i... |
def masks_to_bboxes(masks):
masks = np.asarray(masks)
assert (masks.dtype == bool)
ndim = masks.ndim
assert (ndim in [2, 3]), 'masks must be 2 or 3 dimensional'
if (ndim == 2):
masks = masks[None]
bboxes = np.zeros((len(masks), 4), dtype=np.float64)
for (i, mask) in enumerate(masks):... |
class _packbits(Function):
_fwd(cast_inputs=torch.float32)
def forward(ctx, grid, thresh, bitfield=None):
if (not grid.is_cuda):
grid = grid.cuda()
grid = grid.contiguous()
C = grid.shape[0]
H3 = grid.shape[1]
N = ((C * H3) // 8)
if (bitfield is None):... |
_benchmark.command(name='collect')
_option
('--force', '-f', help='Force collect results even if workflows are still running', default=False, is_flag=True)
def collect_command(workflow: str, force: bool) -> NoReturn:
try:
collect(workflow, force)
except Exception as e:
logger.error(f'Something w... |
class SubscriptionAddOn(Resource):
schema = {'add_on': 'AddOnMini', 'add_on_source': str, 'created_at': datetime, 'expired_at': datetime, 'id': str, 'object': str, 'percentage_tiers': ['SubscriptionAddOnPercentageTier'], 'quantity': int, 'revenue_schedule_type': str, 'subscription_id': str, 'tier_type': str, 'tiers... |
class _TzCache():
def __init__(self):
self.initialised = (- 1)
self.db = None
self.dummy = False
def get_db(self):
if (self.db is not None):
return self.db
try:
self.db = _TzDBManager.get_database()
except _TzCacheException as err:
... |
def test_doesnt_raise_deprecation_warning():
app = Flask(__name__)
def provide_str():
return 'this is string'
def configure(binder):
binder.bind(str, to=CallableProvider(provide_str), scope=request)
('/')
def index(s: str):
return s
FlaskInjector(app=app, modules=[configu... |
class FatFileSystem(MountFileSystem):
type = 'fat'
aliases = ['efi system partition', 'vfat', 'fat12', 'fat16']
_mount_type = 'vfat'
def detect(cls, source, description):
res = super().detect(source, description)
if ('DOS FAT' in description):
res.update({VolumeSystemFileSyst... |
class MappingPattern(Pattern):
keys: list[Expression]
values: list[Pattern]
rest: (NameExpr | None)
def __init__(self, keys: list[Expression], values: list[Pattern], rest: (NameExpr | None)) -> None:
super().__init__()
assert (len(keys) == len(values))
self.keys = keys
se... |
class CreateDatabase(Migration):
def schedule_upgrades(self):
self.orm_control.assert_dialect(self, 'postgresql')
self.schedule('alter', op.create_table, 'sessiondata', Column('id', Integer(), nullable=False), Column('web_session_id', Integer(), nullable=True), Column('region_name', Text(), nullable... |
def interleave(seqs):
iters = itertools.cycle(map(iter, seqs))
while True:
try:
for itr in iters:
(yield next(itr))
return
except StopIteration:
predicate = partial(operator.is_not, itr)
iters = itertools.cycle(itertools.takewhile(p... |
def _build_lambda_role(self, db: dynamodb.Table) -> iam.Role:
return iam.Role(self, constants.SERVICE_ROLE, assumed_by=iam.ServicePrincipal('lambda.amazonaws.com'), inline_policies={'dynamic_configuration': iam.PolicyDocument(statements=[iam.PolicyStatement(actions=['appconfig:GetLatestConfiguration', 'appconfig:St... |
def main():
(args, checkpoint_dir, writer, model_config) = setup(train=True)
print(args)
from predicate.demo_dataset_graph import get_dataset
from predicate.demo_dataset_graph import collate_fn
from predicate.demo_dataset_graph import to_cuda_fn
(train_dset, test_dset, new_test_dset) = get_datas... |
('beeref.selection.SelectableMixin.hoverMoveEvent')
def test_hover_move_event_crop_mode_inside_edge(hover_mock, qapp, item):
item.crop_mode = True
item.crop_temp = QtCore.QRectF(0, 0, 100, 80)
event = MagicMock()
event.pos.return_value = QtCore.QPointF(5, 40)
item.hoverMoveEvent(event)
(item.cur... |
def _assign_rank_write_loads(rank_to_write_loads: List[Dict[(str, List[_WriteLoad])]], rank_to_size: List[int], ranks_to_choose: List[int], logical_path: str, size: int, partition_result: List[List[_WriteLoad]]) -> None:
chosen_rank = min(ranks_to_choose, key=(lambda rank: rank_to_size[rank]))
partition_result[... |
def _build(polygons, criterion='rook', ids=None):
if (ids and (len(ids) != len(set(ids)))):
raise ValueError('The argument to the ids parameter contains duplicate entries.')
wttype = WT_TYPE[criterion.lower()]
geo = polygons
if issubclass(type(geo), FileIO):
geo.seek(0)
neighbor_data... |
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