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class Geod_NaN_Issue112_Test(unittest.TestCase):
def test_geod_nans(self):
g = Geod(ellps='clrk66')
(azi1, azi2, s12) = g.inv(43, 10, float('nan'), 20)
self.assertTrue((azi1 != azi1))
self.assertTrue((azi2 != azi2))
self.assertTrue((s12 != s12))
(azi1, azi2, s12) = g.... |
def get_environment_pseudosmiles_from_smarts(smarts):
matches = list(_smarts_atom_pat.finditer(smarts))
matches.reverse()
pat = Chem.MolFromSmarts(smarts)
assert (pat.GetNumAtoms() == len(matches)), (smarts, matches)
smiles = smarts
for (match, pat_atom) in zip(matches, reversed(pat.GetAtoms()))... |
def patch_forward_method(func, src_type, dst_type, convert_output=True):
def new_forward(*args, **kwargs):
output = func(*cast_tensor_type(args, src_type, dst_type), **cast_tensor_type(kwargs, src_type, dst_type))
if convert_output:
output = cast_tensor_type(output, dst_type, src_type)
... |
class FindBar():
def __init__(self, parent, finder, is_reg_expr=False):
self.finder = finder
self.context = []
self.last_value = None
self.last_pattern = None
label = QLabel('Find:')
label.setSizePolicy(QSizePolicy.Fixed, QSizePolicy.Fixed)
self.textbox = QCom... |
def get_placeholder_loop(placeholder_string, embedder, is_sd):
new_placeholder = None
while True:
if (new_placeholder is None):
new_placeholder = input(f'Placeholder string {placeholder_string} was already used. Please enter a replacement string: ')
else:
new_placeholder ... |
class Document(_BaseThumbedMedium):
__slots__ = ('file_name', 'mime_type')
def __init__(self, file_id: str, file_unique_id: str, file_name: Optional[str]=None, mime_type: Optional[str]=None, file_size: Optional[int]=None, thumbnail: Optional[PhotoSize]=None, *, api_kwargs: Optional[JSONDict]=None):
supe... |
def ql_qnx_msg_io_lseek(ql: Qiling, coid, smsg, sparts, rmsg, rparts, *args, **kw):
(type, combine_len, whence, zero, offset) = unpack('<HHhHQ', ql.mem.read(smsg, 16))
assert ((c_int32(sparts).value, c_int32(rparts).value) == ((- 16), (- 8))), 'input/output sizes are wrong'
assert ((type, combine_len, zero)... |
def silent(input_filepath: Union[(str, Path)], threshold: float=0.001) -> bool:
validate_input_file(input_filepath)
stat_dictionary = stat(input_filepath)
mean_norm = stat_dictionary['Mean norm']
if (mean_norm is not float('nan')):
if (mean_norm >= threshold):
return False
... |
class UniformPolicy(BasePolicy):
def __init__(self, input_shapes, output_shape, action_range=((- 1.0), 1.0)):
super(UniformPolicy, self).__init__()
self._Serializable__initialize(locals())
self.inputs = [tf.keras.layers.Input(shape=input_shape) for input_shape in input_shapes]
self._... |
class UpvoteEntry(Action, Mutation):
def mutate(_root, entry, sender, upvoted, downvoted, in_upvoted, in_downvoted, constants, exceeded, reason):
response = UpvoteEntry(feedback=None)
(karma, cost, downvote_rate, upvote_rate) = constants
if in_upvoted:
upvoted.remove(entry)
... |
class ToggleButton(PushButton):
def _get_release_image(self, x, y):
return (self._hover_img if self._check_hit(x, y) else self._depressed_img)
def on_mouse_press(self, x, y, buttons, modifiers):
if ((not self.enabled) or (not self._check_hit(x, y))):
return
self._pressed = (n... |
class UTCDateTimeAttribute(Attribute[datetime]):
attr_type = STRING
def serialize(self, value):
if (value.tzinfo is None):
value = value.replace(tzinfo=timezone.utc)
fmt = value.astimezone(timezone.utc).strftime(DATETIME_FORMAT).zfill(31)
return fmt
def deserialize(self, ... |
def create_repitched_txt_from_ultrastar_data(input_file: str, note_numbers: list[int], output_repitched_ultrastar: str) -> None:
print('{PRINT_ULTRASTAR} Creating repitched ultrastar txt -> {input_file}_repitch.txt')
with open(input_file, 'r', encoding=FILE_ENCODING) as file:
txt = file.readlines()
... |
_layout_config
def test_window_types(manager):
if ((manager.backend.name == 'wayland') and (not has_wayland_notifications)):
pytest.skip('Notification tests for Wayland need gtk-layer-shell')
manager.test_window('one')
manager.test_window('dialog', floating=True)
assert_focused(manager, 'dialog'... |
def dual_ascent_step(model, X, lambda1, lambda2, rho, alpha, h, rho_max):
h_new = None
optimizer = LBFGSBScipy(model.parameters())
X_torch = torch.from_numpy(X)
while (rho < rho_max):
def closure():
optimizer.zero_grad()
X_hat = model(X_torch)
loss = squared_l... |
class PresetChannel(Channel):
values = values
load_capacity = Instrument.control('GU\x00{ch:c}\x00\x00', 'TD{ch:c}\x01\x00%c', 'Control the percentage of full-scale value of the load capacity preset.', preprocess_reply=(lambda d: struct.unpack('>H', d[2:4])), validator=strict_discrete_set, values=range(101))
... |
def check_accumulator_overflow(model: torch.nn.Module, quant_bw: int, accum_bw: int):
most_accum_range_used = 0
most_accum_range_used_layer = None
for (layer_name, layer) in model.named_modules():
if isinstance(layer, torch.nn.Conv2d):
(was_accum_range_exceeded, accum_range_used) = get_c... |
class PuzzleWidget(QWidget):
puzzleCompleted = pyqtSignal()
def __init__(self, parent=None):
super(PuzzleWidget, self).__init__(parent)
self.piecePixmaps = []
self.pieceRects = []
self.pieceLocations = []
self.highlightedRect = QRect()
self.inPlace = 0
sel... |
class SigmoidFocalLoss(nn.Module):
def __init__(self, gamma, alpha, weight=None, reduction='mean'):
super(SigmoidFocalLoss, self).__init__()
self.gamma = gamma
self.alpha = alpha
self.register_buffer('weight', weight)
self.reduction = reduction
def forward(self, input, ta... |
def _start_kernel():
from IPython.zmq.ipkernel import IPKernelApp
from zmq.eventloop import ioloop
global _kernel_running, _ipython_app
if _kernel_running:
return _ipython_app
if IPKernelApp.initialized():
app = IPKernelApp.instance()
else:
app = IPKernelApp.instance()
... |
def attack_Linf_PGD_bin(input_v, ones, dis, Ld, steps, epsilon):
dis.eval()
adverse_v = input_v.data.clone()
adverse_v = Variable(adverse_v, requires_grad=True)
optimizer = Linf_SGD([adverse_v], lr=0.0078)
for _ in range(steps):
optimizer.zero_grad()
dis.zero_grad()
d_bin = d... |
def print_progress(transferred_blocks, block_size, total_size):
current_mb = (((transferred_blocks * block_size) / 1024) / 1024)
total_mb = ((total_size / 1024) / 1024)
percent = (current_mb / total_mb)
progress_str = 'Progress: {:5.1f}M / {:5.1f}M ({:6.1%})'
print(progress_str.format(current_mb, to... |
class TestGroverConstructor(QiskitAquaTestCase):
def setUp(self):
super().setUp()
oracle = QuantumCircuit(2)
oracle.cz(0, 1)
self._expected_grover_op = GroverOperator(oracle=oracle)
def test_oracle_quantumcircuit(self):
oracle = QuantumCircuit(2)
oracle.cz(0, 1)
... |
def solid_angle(v0: wp.vec3, v1: wp.vec3, v2: wp.vec3, p: wp.vec3):
a = (v0 - p)
b = (v1 - p)
c = (v2 - p)
a_len = wp.length(a)
b_len = wp.length(b)
c_len = wp.length(c)
det = wp.dot(a, wp.cross(b, c))
den = (((((a_len * b_len) * c_len) + (wp.dot(a, b) * c_len)) + (wp.dot(b, c) * a_len))... |
def target_df_rows_agg(spark_context, spark_session):
data = [{'id': 1, 'timestamp': '2016-04-11 11:31:11', 'feature1': 200, 'feature2': 200, 'feature1__avg_over_2_events_row_windows': 200, 'feature1__avg_over_3_events_row_windows': 200}, {'id': 1, 'timestamp': '2016-04-11 11:44:12', 'feature1': 300, 'feature2': 30... |
.fast
.parametrize(('input_wavenumbers', 'expected_wavenumbers_cm1'), [[(((2000 * 1) / u.cm), ((230000 * 1) / u.m)), (2000, 2300)]])
def test_wavenumber_units_conversion(input_wavenumbers, expected_wavenumbers_cm1, verbose=True, *args, **kwargs):
setup_test_line_databases()
(wmin, wmax) = input_wavenumbers
... |
class Routing():
def __init__(self, plugin: 'Plugin') -> None:
self._rules: Dict[(Callable, List[UrlRule])] = {}
self.plugin = plugin
def route_for(self, path: str) -> Optional[Callable]:
if path.startswith(self.plugin.PLUGIN_URL):
path = path.split(self.plugin.PLUGIN_URL, 1)... |
class PositionWeightedModuleCollectionEmbeddingBagCollectionTest(unittest.TestCase):
def test_position_weighted_collection_module_ebc(self) -> None:
features = KeyedJaggedTensor.from_offsets_sync(keys=['f1', 'f2'], values=torch.tensor([0, 1, 2, 3, 4, 5, 6, 7]), offsets=torch.tensor([0, 2, 2, 3, 4, 5, 8]))
... |
def _lambertw_v_from_i(current, photocurrent, saturation_current, resistance_series, resistance_shunt, nNsVth):
output_is_scalar = all(map(np.isscalar, (current, photocurrent, saturation_current, resistance_series, resistance_shunt, nNsVth)))
conductance_shunt = (1.0 / resistance_shunt)
(I, IL, I0, Rs, Gsh,... |
class Msg15NativeTrailerRecord(object):
def get(self):
record = [('GP_PK_HEADER', GSDTRecords.gp_pk_header), ('GP_PK_SH1', GSDTRecords.gp_pk_sh1), ('15TRAILER', self.seviri_l15_trailer)]
return np.dtype(record).newbyteorder('>')
def seviri_l15_trailer(self):
record = [('15TrailerVersion'... |
def interpolate_bilinear(grid, query_points, indexing='ij', name=None):
if ((indexing != 'ij') and (indexing != 'xy')):
raise ValueError("Indexing mode must be 'ij' or 'xy'")
with tf.name_scope((name or 'interpolate_bilinear')):
grid = tf.convert_to_tensor(grid)
query_points = tf.convert... |
def new_npair_loss(labels, embedding_anchor, embedding_positive, reg_lambda, equal_shape=True, half_batch_size=64):
reg_anchor = math_ops.reduce_mean(math_ops.reduce_sum(math_ops.square(embedding_anchor), 1))
reg_positive = math_ops.reduce_mean(math_ops.reduce_sum(math_ops.square(embedding_positive), 1))
l2... |
class TestMakeTimeCdsDictionary(unittest.TestCase):
def test_fun(self):
tcds = {'Days': np.array(1), 'Milliseconds': np.array(2)}
expected = datetime(1958, 1, 2, 0, 0, 0, 2000)
assert (timecds2datetime(tcds) == expected)
tcds = {'Days': np.array(1), 'Milliseconds': np.array(2), 'Micr... |
def convert_pl_to_hf(pl_ckpt_path: str, hf_src_model_dir: str, save_path: str) -> None:
hf_model = AutoModelForSeq2SeqLM.from_pretrained(hf_src_model_dir)
if os.path.isfile(pl_ckpt_path):
ckpt_files = [pl_ckpt_path]
else:
assert os.path.isdir(pl_ckpt_path)
ckpt_files = list(Path(pl_c... |
_cache(maxsize=200)
def _calcMissileFactor(atkEr, atkEv, atkDrf, tgtSpeed, tgtSigRadius):
factors = [1]
if (atkEr > 0):
factors.append((tgtSigRadius / atkEr))
if (tgtSpeed > 0):
factors.append((((atkEv * tgtSigRadius) / (atkEr * tgtSpeed)) ** atkDrf))
totalMult = min(factors)
return ... |
class TestGoBack(BaseTestCase):
async def test_back(self):
(await self.page.goto((self.url + 'empty')))
(await self.page.goto((self.url + 'static/textarea.html')))
response = (await self.page.goBack())
self.assertTrue(response.ok)
self.assertIn('empty', response.url)
... |
def source(left, right, boundary=False):
if isinstance(left, numbers.Number):
left = pybamm.PrimaryBroadcast(left, 'current collector')
if ((left.domain != ['current collector']) or (right.domain != ['current collector'])):
raise pybamm.DomainError(f''''source' only implemented in the 'current c... |
class TestNetwork(ElectrumTestCase):
def setUpClass(cls):
super().setUpClass()
constants.set_regtest()
def tearDownClass(cls):
super().tearDownClass()
constants.set_mainnet()
def setUp(self):
super().setUp()
self.config = SimpleConfig({'electrum_path': self.el... |
class CommandBase(object):
def __init__(self, url: str='') -> None:
self._url = url
def __repr__(self) -> str:
return f'{type(self).__name__}({self.__dict__})'
def __eq__(self, other) -> bool:
return ((self is other) or (self.__dict__ == other.__dict__))
def _method(self) -> str:... |
class BuildingMenu(object):
keys_go_back = ['']
sep_keys = '.'
joker_key = '*'
min_shortcut = 1
def __init__(self, caller=None, obj=None, title='Building menu: {obj}', keys=None, parents=None, persistent=False):
self.caller = caller
self.obj = obj
self.title = title
s... |
class GenerationConfig(FairseqDataclass):
beam: int = field(default=5, metadata={'help': 'beam size'})
nbest: int = field(default=1, metadata={'help': 'number of hypotheses to output'})
max_len_a: float = field(default=0, metadata={'help': 'generate sequences of maximum length ax + b, where x is the source ... |
class SplunkLogsModel(SharedModel, ActionLogsDataInterface):
def __init__(self, producer, splunk_config, should_skip_logging=None):
self._should_skip_logging = should_skip_logging
self._logs_producer = LogProducerProxy()
if (producer == 'splunk'):
self._logs_producer.initialize(S... |
def test_initialize_fresh(hatch, helpers, temp_dir):
project_name = 'My.App'
description = 'foo'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), result.output
path = (temp_dir / 'my-app')
project_file = (path / 'pyproject.toml')
project... |
def set_player_object_material_text(player_id: int, object_id: int, text: str, material_index: int=0, material_size: int=OBJECT_MATERIAL_SIZE_256x128, font_face: str='Arial', font_size: int=24, bold: bool=True, font_color: int=, back_color: int=0, text_alignment: int=0) -> bool:
return SetPlayerObjectMaterialText(p... |
class CompDoc(object):
def __init__(self, mem, logfile=sys.stdout, DEBUG=0, ignore_workbook_corruption=False):
self.logfile = logfile
self.ignore_workbook_corruption = ignore_workbook_corruption
self.DEBUG = DEBUG
if (mem[0:8] != SIGNATURE):
raise CompDocError('Not an OLE... |
def test_pool_no_package_from_specified_repository_raises_package_not_found() -> None:
package = get_package('foo', '1.0.0')
repo1 = Repository('repo1')
repo2 = Repository('repo2', [package])
pool = RepositoryPool([repo1, repo2])
with pytest.raises(PackageNotFound):
pool.package('foo', Versi... |
class Lock(object):
_NODE_NAME = '__lock__'
_EXCLUDE_NAMES = ['__lock__']
def __init__(self, client, path, identifier=None, extra_lock_patterns=()):
self.client = client
self.path = path
self._exclude_names = set((self._EXCLUDE_NAMES + list(extra_lock_patterns)))
self._conten... |
def test_update_catalogs(db, settings):
xml_file = (((Path(settings.BASE_DIR) / 'xml') / 'elements') / 'catalogs.xml')
root = read_xml_file(xml_file)
version = root.attrib.get('version')
elements = flat_xml_to_elements(root)
elements = convert_elements(elements, version)
elements = order_element... |
class ElementProxy():
def __init__(self, element: BaseOxmlElement, parent: (t.ProvidesXmlPart | None)=None):
self._element = element
self._parent = parent
def __eq__(self, other: object):
if (not isinstance(other, ElementProxy)):
return False
return (self._element is ... |
def save_an_icom_batch(date_pattern, ip_directory, data_to_save):
if (not date_pattern.match(data_to_save[8:26])):
raise ValueError('Unexpected iCOM stream format')
counter = str(int(data_to_save[26])).zfill(3)
filepath = ip_directory.joinpath(f'{counter}.txt')
with open(filepath, 'bw+') as f:
... |
class BiF_Att(nn.Module):
def __init__(self, emodict, worddict, embedding, args):
super(BiF_Att, self).__init__()
self.num_classes = emodict.n_words
self.embeddings = embedding
self.gpu = args.gpu
self.hops = args.hops
self.wind_1 = args.wind1
self.utt_gru = G... |
class F9_Raid(F7_Raid):
removedKeywords = F7_Raid.removedKeywords
removedAttrs = F7_Raid.removedAttrs
def _getParser(self):
op = F7_Raid._getParser(self)
op.add_argument('--bytes-per-inode', deprecated=F9)
op.add_argument('--fsprofile', version=F9, help='\n Spe... |
def _prepare_connection_costs_per_link(n, costs, renewable_config, hvdc_as_lines, lines_length_factor):
if n.links.empty:
return {}
connection_costs_per_link = {}
if hvdc_as_lines:
dc_lengths = n.lines.length
unterwater_fractions = n.lines.underwater_fraction
else:
dc_len... |
class FixScaleCrop(object):
def __init__(self, crop_size):
self.crop_size = crop_size
def __call__(self, sample):
img = sample['image']
mask = sample['label']
(w, h) = img.size
if (w > h):
oh = self.crop_size
ow = int((((1.0 * w) * oh) / h))
... |
class SshConfig(config_parser.Config):
def __init__(self, host, username, password=None, cfg_file=None):
import paramiko
self.ssh = paramiko.SSHClient()
self.ssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())
self.ssh.connect(host, username=username, password=password)
... |
def get_class_splits(spanning_leaves, valid_test_roots=None, **kwargs):
if (valid_test_roots is not None):
if ((valid_test_roots['valid'] is None) or (valid_test_roots['test'] is None)):
raise ValueError('A root cannot be None.')
if (valid_test_roots is None):
valid_test_roots = prop... |
class TokenClassificationEvaluator(ClassificationEvaluator):
def __init__(self, model_args: ModelArguments, data_args: DataTrainingArguments, training_args: TrainingArguments, processor: DataProcessor, model: torch.nn.Module, trainer: Optional[HugTrainer]=None, eval_dataset: Optional[Dataset]=None, test_dataset: Op... |
def DS_format_to_lines(context_mode, summ_mode, args):
assert (summ_mode in ['final', 'user', 'agent'])
assert (context_mode in ['both', 'user', 'agent'])
corpora = {'train': [], 'val': [], 'test': []}
read_root_path = Path(args.raw_path)
save_root_path = ((Path(args.save_path) / f'{context_mode}') ... |
def add_or_invite_to_team(inviter, team, user_obj=None, email=None, requires_invite=True):
if (user_obj and requires_invite):
orgname = team.organization.username
if user_obj.robot:
requires_invite = False
if (not user_obj.username.startswith((orgname + '+'))):
... |
def get(identifier):
if isinstance(identifier, six.string_types):
identifier = str(identifier)
return deserialize(identifier)
elif callable(identifier):
return identifier
else:
raise ValueError('Could not interpret metric function identifier:', identifier) |
class TestTypeAlias(TestNameCheckVisitorBase):
_passes()
def test_runtime(self):
from typing_extensions import TypeAlias
X: TypeAlias = int
Y = X
Z: 'TypeAlias' = int
def capybara(x: X, y: Y, x_quoted: 'X', y_quoted: 'Y', z: Z) -> None:
assert_is_value(x, Type... |
class MultiProjectRefactoring():
def __init__(self, refactoring, projects, addpath=True):
self.refactoring = refactoring
self.projects = projects
self.addpath = addpath
def __call__(self, project, *args, **kwds):
return _MultiRefactoring(self.refactoring, self.projects, self.addp... |
class DiceLoss(nn.Module):
def __init__(self, smooth=1.0, ignore_index=255):
super(DiceLoss, self).__init__()
self.ignore_index = ignore_index
self.smooth = smooth
def forward(self, output, target):
if (self.ignore_index not in range(target.min(), target.max())):
if (... |
class KnownValues(unittest.TestCase):
def test_ip_adc2(self):
(e, t_amp1, t_amp2) = myadc.kernel_gs()
self.assertAlmostEqual(e, (- 0.), 6)
myadcip = adc.radc_ip.RADCIP(myadc)
(e, v, p, x) = myadcip.kernel(nroots=3)
self.assertAlmostEqual(e[0], 0., 6)
self.assertAlmost... |
class BitPackDecoder():
_data: bytes
_offset: int
def __init__(self, data: bytes):
self._data = data
self._offset = 0
def decode(self, *args: int) -> tuple[(int, ...)]:
compiled = _compile_format(*args)
offset = self._offset
self._offset += compiled.calcsize()
... |
class DebugConnectorBuilder(ConnectorBuilder):
target_game: RandovaniaGame
layout_uuid: uuid.UUID
def __init__(self, game: str, layout_uuid: str=str(INVALID_UUID)):
super().__init__()
self.target_game = RandovaniaGame(game)
self.layout_uuid = uuid.UUID(layout_uuid)
def create(cls... |
def _SetInformationProcess(ql: Qiling, address: int, params):
process = params['ProcessHandle']
flag = params['ProcessInformationClass']
ibuf_ptr = params['ProcessInformation']
ibuf_len = params['ProcessInformationLength']
if (flag == ProcessDebugFlags):
flag_name = 'ProcessDebugFlags'
... |
def AutoDancefer(source, target, output_path=None, synch_video_beat=0, synch_audio_beat=0, beat_offset=0, **kwargs):
sourcev = PullVideo(source_location=source)
targetv = PullVideo(source_location=target)
result = Dancefer(source_video=sourcev, target=targetv, output_path=output_path, force_recompute=True, ... |
def pad(array, transform, pad_width, mode=None, **kwargs):
import numpy as np
transform = guard_transform(transform)
padded_array = np.pad(array, pad_width, mode, **kwargs)
padded_trans = list(transform)
padded_trans[2] -= (pad_width * padded_trans[0])
padded_trans[5] -= (pad_width * padded_tran... |
class CmdEvscapeRoom(Command):
arg_regex = '(/\\w+?(\\s|$))|\\s|$'
help_category = 'Evscaperoom'
obj1_search = None
obj2_search = None
def _search(self, query, required):
if (required is False):
return (None, query)
matches = self.caller.search(query, quiet=True)
... |
class ToolsWizardPage2(BasePyzoWizardPage):
_title = translate('wizard', 'Recommended tools')
_image_filename = 'pyzo_tools2.png'
_descriptions = [translate('wizard', 'We especially recommend the following tools:'), translate('wizard', 'The *Source structure tool* gives an outline of the source code.'), tra... |
def change(file_name, file_out, dict_file, split_=' ', split_3=False):
with open(file_name, 'r', encoding='utf-8') as f:
data = [(i.split(split_) if (len(i.split(split_)) == 2) else ['###', '###']) for i in f.readlines()]
document_pair = [[i[0], i[1].strip().replace('M-', 'I-').replace('E-', 'I-').r... |
class UEM(MutableMapping):
def __init__(self, *args, **kwargs):
super(UEM, self).__init__()
self.update(*args, **kwargs)
def __setitem__(self, fid, score_regions):
invalid_type_msg = ('Expected sequence of pairs. Received: %r (%s).' % (score_regions, type(score_regions)))
try:
... |
def generate_subset_of_filenames(subset=None, base_dir=''):
if (subset is None):
subset = _create_full_set()
pattern = os.path.join(base_dir, FILENAME)
files = []
for (channel, segments) in subset.items():
new_files = _generate_filenames(pattern, channel, segments)
files.extend(n... |
class MinMaxScaler(SKCMatrixAndWeightTransformerABC):
_skcriteria_parameters = ['target', 'clip', 'criteria_range']
def __init__(self, target, *, clip=False, criteria_range=(0, 1)):
super().__init__(target)
self._clip = bool(clip)
(self._cr_min, self._cr_max) = map(float, criteria_range)... |
class DszCommandError(list):
def __init__(self, timestamp, cmdid):
self.timestamp = timestamp
self.__cmdid = cmdid
list.__init__(self)
def __str__(self):
msg = ('Error running command %d: %s\n' % (self.__cmdid, dsz.cmd.data.Get('commandmetadata::fullcommand', dsz.TYPE_STRING, cmd... |
def total_intersect_and_union(results, gt_seg_maps, num_classes, ignore_index, label_map=dict(), reduce_zero_label=False):
total_area_intersect = torch.zeros((num_classes,), dtype=torch.float64).cuda()
total_area_union = torch.zeros((num_classes,), dtype=torch.float64).cuda()
total_area_pred_label = torch.z... |
.parametrize(['ops', 'error'], [pytest.param([qutip.basis(5, 0)], 'square', id='Not square'), pytest.param([qutip.qeye(5), qutip.qeye(3)], 'shape', id='shape mismatch'), pytest.param([qutip.destroy(5)], 'Hermitian', id='Non Hermitian'), pytest.param([qutip.sigmax(), qutip.sigmay()], 'commute', id='Not commuting')])
def... |
class RobustNorm(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_range=True, power=0.2):
nn.BatchNorm2d.__init__(self, num_features=num_features, eps=eps, momentum=momentum, affine=affine, track_running_stat... |
def get_dataset(data_cfg):
if isinstance(data_cfg['ann_file'], (list, tuple)):
ann_files = data_cfg['ann_file']
num_dset = len(ann_files)
else:
ann_files = [data_cfg['ann_file']]
num_dset = 1
if isinstance(data_cfg['img_prefix'], (list, tuple)):
img_prefixes = data_cf... |
def parse_key_part(src: str, pos: Pos) -> tuple[(Pos, str)]:
try:
char: (str | None) = src[pos]
except IndexError:
char = None
if (char in BARE_KEY_CHARS):
start_pos = pos
pos = skip_chars(src, pos, BARE_KEY_CHARS)
return (pos, src[start_pos:pos])
if (char == "'")... |
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
loss = 0
r_losses = []
g_losses = []
for (dr, dg) in zip(disc_real_outputs, disc_generated_outputs):
r_loss = torch.mean(((1 - dr) ** 2))
g_loss = torch.mean((dg ** 2))
loss += (r_loss + g_loss)
r_losses.a... |
class CWERMetric(tf.keras.metrics.Metric):
def __init__(self, padding_token, name='CWER', **kwargs):
super(CWERMetric, self).__init__(name=name, **kwargs)
self.cer_accumulator = tf.Variable(0.0, name='cer_accumulator', dtype=tf.float32)
self.wer_accumulator = tf.Variable(0.0, name='wer_accum... |
class ArithmeticCoder():
def __init__(self, fo: tp.IO[bytes], total_range_bits: int=24):
assert (total_range_bits <= 30)
self.total_range_bits = total_range_bits
self.packer = BitPacker(bits=1, fo=fo)
self.low: int = 0
self.high: int = 0
self.max_bit: int = (- 1)
... |
def test_help_subcommand_completion_multiple(sc_app):
text = ''
line = 'help base {}'.format(text)
endidx = len(line)
begidx = (endidx - len(text))
first_match = complete_tester(text, line, begidx, endidx, sc_app)
assert ((first_match is not None) and (sc_app.completion_matches == ['bar', 'foo',... |
class EngineGenerator():
def __init__(self, max_input_length: int=1024, max_total_tokens: int=2048):
self.max_input_length = max_input_length
self.max_total_tokens = max_total_tokens
def create_engine_config(self):
return VLLMEngineConfig(type='VLLMEngine', model_id=MODEL_ID, generation=... |
class FileToMiscIter(IterWrappingFile):
def __init__(self, file):
IterWrappingFile.__init__(self, file)
self.buf = b''
def __iter__(self):
return self
def __next__(self):
if self.currently_in_file:
self.currently_in_file.close()
type = None
while (... |
class TPLPushHandler(BaseHandler):
.authenticated
async def get(self, tplid):
user = self.current_user
tpl = (await self.db.tpl.get(tplid, fields=('id', 'userid', 'sitename')))
if (not self.permission(tpl, 'w')):
self.evil((+ 5))
(await self.finish(u'<span class="... |
def test_standstillcondition():
cond = OSC.StandStillCondition(1)
prettyprint(cond.get_element())
cond2 = OSC.StandStillCondition(1)
cond3 = OSC.StandStillCondition(3)
assert (cond == cond2)
assert (cond != cond3)
cond4 = OSC.StandStillCondition.parse(cond.get_element())
assert (cond == ... |
class TextFrame():
def __init__(self, layout, border_width, border_color, pad_x, pad_y, highlight_color=None):
self.layout = layout
self.border_width = border_width
self.border_color = border_color
self.drawer = self.layout.drawer
self.highlight_color = highlight_color
... |
def _build_plain_hierarchy(hierarchy, is_root=False):
all_children = set([])
all_keyed_parent = {}
all_keyed_child = {}
if ('Subcategory' in hierarchy):
for node in hierarchy['Subcategory']:
(keyed_parent, keyed_child, children) = _build_plain_hierarchy(node)
_update_dict... |
def merge_pos_pairs_into_dialog_file(data_folder, dialog_file):
fout = open(dialog_file, 'w', encoding='utf-8')
for partition in list(['train', 'valid']):
with open(((data_folder + partition) + '.txt'), encoding='utf-8') as fin:
for l in tqdm(fin):
tokens = l.split('\t')
... |
def test_state_wait_secretrequest_valid():
setup = setup_initiator_tests()
state_change = ReceiveSecretRequest(payment_identifier=UNIT_TRANSFER_IDENTIFIER, amount=setup.lock.amount, expiration=setup.lock.expiration, secrethash=setup.lock.secrethash, sender=UNIT_TRANSFER_TARGET)
iteration = initiator_manager... |
def admin_session(user, session_id):
session: MultiplayerSession = MultiplayerSession.get_by_id(session_id)
rows = []
associations: list[WorldUserAssociation] = list(WorldUserAssociation.select().join(World).where((World.session == session)))
for association in associations:
inventory = []
... |
def mosek_solve_qp(P: Union[(np.ndarray, spa.csc_matrix)], q: np.ndarray, G: Optional[Union[(np.ndarray, spa.csc_matrix)]]=None, h: Optional[np.ndarray]=None, A: Optional[Union[(np.ndarray, spa.csc_matrix)]]=None, b: Optional[np.ndarray]=None, lb: Optional[np.ndarray]=None, ub: Optional[np.ndarray]=None, initvals: Opti... |
class DevDataset(Dataset):
def __init__(self, args, raw_datasets, cache_root):
self.args = args
self.raw_datasets = raw_datasets
cache_path = os.path.join(cache_root, 'sql2text_dev.cache')
if (os.path.exists(cache_path) and args.dataset.use_cache):
self.data = torch.load(... |
def test_biorbd_model_import():
from bioptim.examples.torque_driven_ocp import example_multi_biorbd_model as ocp_module
bioptim_folder = os.path.dirname(ocp_module.__file__)
biorbd_model_path = '/models/triple_pendulum.bioMod'
biorbd_model_path_modified_inertia = '/models/triple_pendulum_modified_inerti... |
class Delta(D.Distribution):
arg_constraints: dict = {}
def __init__(self, param: torch.Tensor, atol: float=1e-06, rtol: float=1e-06, batch_shape: ((torch.Size | Sequence[int]) | None)=None, event_shape: ((torch.Size | Sequence[int]) | None)=None) -> None:
if (batch_shape is None):
batch_sha... |
(frozen=True)
class BoundMethodSignature():
signature: ConcreteSignature
self_composite: Composite
return_override: Optional[Value] = None
def check_call(self, args: Iterable[Argument], visitor: 'NameCheckVisitor', node: Optional[ast.AST]) -> Value:
ret = self.signature.check_call([(self.self_co... |
class EditInlineCaption():
async def edit_inline_caption(self: 'pyrogram.Client', inline_message_id: str, caption: str, parse_mode: Optional['enums.ParseMode']=None, reply_markup: 'types.InlineKeyboardMarkup'=None) -> bool:
return (await self.edit_inline_text(inline_message_id=inline_message_id, text=captio... |
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