code stringlengths 281 23.7M |
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def decode_frame(offset):
try:
numchannels = _numchannels
sample_size = _sample_size
_file.seek(offset)
inp = BitInputStream(_file)
temp = inp.read_byte()
if (temp == (- 1)):
return False
sync = ((temp << 6) | inp.read_uint(6))
if (sync != ... |
class MemorySnapshot(Callback):
def __init__(self, *, output_dir: str, memory_snapshot_params: Optional[MemorySnapshotParams]=None) -> None:
self.memory_snapshot_profiler = MemorySnapshotProfiler(output_dir=output_dir, memory_snapshot_params=memory_snapshot_params)
def on_train_step_end(self, state: Sta... |
class SPMTokenizer():
def __init__(self, vocab_file, split_by_punct=False, sp_model_kwargs: Optional[Dict[(str, Any)]]=None):
self.split_by_punct = split_by_punct
self.vocab_file = vocab_file
self.sp_model_kwargs = ({} if (sp_model_kwargs is None) else sp_model_kwargs)
spm = sp.Sente... |
def set_parser(argv: Optional[Sequence[str]]=None) -> int:
parser = argparse.ArgumentParser(description='Command line utility for pysondb', epilog=example_uses, formatter_class=argparse.RawDescriptionHelpFormatter)
subparsers = parser.add_subparsers(dest='command')
create_parser = subparsers.add_parser('cre... |
def test_data():
assert (str(data) == "<module 'pretf.blocks.data'>")
assert (str(data.null_data_source) == "<module 'pretf.blocks.data.null_data_source'>")
assert (str(data.null_data_source.test) == '${data.null_data_source.test}')
assert (str(data.null_data_source['test']) == '${data.null_data_source.... |
def get_dim_reducer(total_num_samplets, dr_name='variancethreshold', reduced_dim='all'):
dr_name = dr_name.lower()
if (dr_name in ['isomap']):
from sklearn.manifold import Isomap
dim_red = Isomap(n_components=reduced_dim)
dr_param_grid = None
elif (dr_name in ['lle']):
from s... |
class BotCommandScopeChat(BotCommandScope):
def __init__(self, chat_id: Union[(int, str)]):
super().__init__('chat')
self.chat_id = chat_id
async def write(self, client: 'pyrogram.Client') -> 'raw.base.BotCommandScope':
return raw.types.BotCommandScopePeer(peer=(await client.resolve_peer... |
def rotate_faces(bm, cylinder, dir, left, prop):
mid = (len(cylinder) // 2)
vts = sort_verts(cylinder, dir)
angle = math.atan((left.z / left.xy.length))
bmesh.ops.rotate(bm, verts=vts[(- mid):], cent=calc_verts_median(vts[(- mid):]), matrix=Matrix.Rotation(angle, 4, dir.cross((- left))))
if prop.bot... |
class NoiseIdentity(nn.Module):
def __init__(self, noise_type=None, scheduler='cosine_anne', **kwargs):
super(NoiseIdentity, self).__init__()
self.noise_type = noise_type
if (self.noise_type is not None):
self.scheduler = scheduler
self.base_lr = kwargs['base_lr']
... |
def main(args):
torch.manual_seed(args.seed)
if (not os.path.exists(args.res_dir)):
os.mkdir(args.res_dir)
if (not os.path.exists(args.model_dir)):
os.mkdir(args.model_dir)
log_dir = os.path.join('./log', ('Align_' + str(args.split)))
if (not os.path.exists(log_dir)):
os.mkdi... |
def read_lm(model_path, inference_config):
lm_config = Namespace(**json.load(open(str((model_path / 'lm_config.json')))))
assert (lm_config.model == 'phone_ipa'), 'only phone_ipa model is supported for allosaurus now'
assert (lm_config.backend == 'numpy'), 'only numpy backend is supported for allosaurus now... |
def block2():
for i in range(40):
regexs[14].sub('', 'fryrpgrq', subcount[14])
regexs[15].sub('', 'fryrpgrq', subcount[15])
for i in range(39):
re.sub('\\buvqqra_ryrz\\b', '', 'vachggrkg uvqqra_ryrz', 0)
regexs[3].search('vachggrkg ')
regexs[3].search('vachggrkg')
... |
class QCSchemaInput(_QCBase):
schema_name: str
schema_version: int
molecule: QCTopology
driver: str
model: QCModel
keywords: Mapping[(str, Any)]
def from_dict(cls, data: dict[(str, Any)]) -> QCSchemaInput:
model = QCModel(**data.pop('model'))
molecule = QCTopology(**data.pop(... |
def read_aff_wild2():
total_data = pickle.load(open(args.aff_wild2_pkl, 'rb'))
data = total_data['EXPR_Set']['Training_Set']
paths = []
labels = []
for video in data.keys():
df = data[video]
labels.append(df['label'].values.astype(np.float32))
paths.append(df['path'].values)
... |
class WMSBase():
def __init__(self):
pass
def ask_for_legend(self, wms, wmslayer):
if hasattr(wms, 'add_legend'):
try:
img = wms.fetch_legend(silent=True)
if (img is not None):
self._ask_for_legend(wms, wmslayer, img)
ex... |
def detach_inputs(sgv, control_inputs=False):
sgv = subgraph.make_view(sgv)
with sgv.graph.as_default():
input_placeholders = [tf_array_ops.placeholder(dtype=input_t.dtype, name=util.placeholder_name(input_t)) for input_t in sgv.inputs]
reroute.swap_inputs(sgv, input_placeholders)
if control_inp... |
def test_ini_controls_global_log_level(pytester: Pytester) -> None:
pytester.makepyfile('\n import pytest\n import logging\n def test_log_level_override(request, caplog):\n plugin = request.config.pluginmanager.getplugin(\'logging-plugin\')\n assert plugin.log_level == log... |
_constructor.register(np.ndarray)
def tensor_constructor(value, name=None, strict=False, allow_downcast=None, borrow=False, shape=None, target='cpu', broadcastable=None):
if isinstance(value, np.ma.MaskedArray):
raise NotImplementedError('MaskedArrays are not supported')
if (broadcastable is not None):
... |
.parametrize(('seed_number', 'expected_ids'), [(5000, [122, 129, 1638535, 393260, 4522032, , 4260106, 2097251, 38, 589851, 2162826, 1245332, 1572998, 1245307, 3342446, 524321, 2949235, 1966093, 3538975, 152]), (9000, [2949235, 129, 152, 4522032, 4260106, 1245332, 1966093, 122, 1638535, 393260, , 589851, 1572998, 38, 20... |
def run_data_migration(apps, schema_editor):
Attribute = apps.get_model('domain', 'Attribute')
QuestionSet = apps.get_model('questions', 'QuestionSet')
questionsets = QuestionSet.objects.filter(is_collection=True)
for questionset in questionsets:
if (questionset.attribute.key != 'id'):
... |
.end_to_end()
.skipif((not IS_PEXPECT_INSTALLED), reason='pexpect is not installed.')
.skipif((sys.platform == 'win32'), reason='pexpect cannot spawn on Windows.')
def test_breakpoint(tmp_path):
source = '\n def task_example():\n i = \n breakpoint()\n '
tmp_path.joinpath('task_module.py').wr... |
class TestMPMWithPlating(TestCase):
def test_well_posed_reversible_plating_not_implemented(self):
options = {'lithium plating': 'reversible'}
with self.assertRaises(NotImplementedError):
pybamm.lithium_ion.MPM(options)
def test_well_posed_irreversible_plating_not_implemented(self):
... |
class _FallbackLocalTimezone(datetime.tzinfo):
def utcoffset(self, dt: datetime.datetime) -> datetime.timedelta:
if self._isdst(dt):
return DSTOFFSET
else:
return STDOFFSET
def dst(self, dt: datetime.datetime) -> datetime.timedelta:
if self._isdst(dt):
... |
class StackManipulationAPI(ABC):
def stack_pop_ints(self, num_items: int) -> Tuple[(int, ...)]:
...
def stack_pop_bytes(self, num_items: int) -> Tuple[(bytes, ...)]:
...
def stack_pop_any(self, num_items: int) -> Tuple[(Union[(int, bytes)], ...)]:
...
def stack_pop1_int(self) -> ... |
def enumerate_permutations(dataset, smiles):
if ('[*]' in smiles):
wildcard_pat = _bracket_wildcard_pat
wildcard = '[*]'
elif ('*' in smiles):
wildcard_pat = _organic_wildcard_pat
wildcard = '*'
n = smiles.count('*')
if (n == 1):
(yield ('1', smiles.replace(wildca... |
class TestConfigurationReader():
def test_basic(self, tmpdir):
(_, config) = fake_env(tmpdir, '[metadata]\nversion = 10.1.1\nkeywords = one, two\n\n[options]\nscripts = bin/a.py, bin/b.py\n')
config_dict = read_configuration(('%s' % config))
assert (config_dict['metadata']['version'] == '10.... |
def test_upload_mixin_with_filepath_and_filedata(gl):
class TestClass(UploadMixin, FakeObject):
_upload_path = '/tests/{id}/uploads'
url = '
responses.add(method=responses.POST, url=url, json={'id': 42, 'file_name': 'test.txt', 'file_content': 'testing contents'}, status=200, match=[responses.matche... |
def LDOS1D_e(x, E, psi, m, step=0.001, margin=0.02, broad=0.005):
Emax = (max(E['Ee']) + (margin * q))
Emin = (min(E['Ee']) - (margin * q))
energy = np.arange(Emin, Emax, (step * q))
LDOS = np.zeros((len(energy), len(x)))
for (i, ee) in enumerate(E['Ee']):
m_plane = calculate_in_plane_masses... |
class TagTreeSplitUtilTest(TestCase):
def test_split_tree_none(self):
parts = tag_utils.split_tree_name('')
self.assertEqual(len(parts), 0)
def test_split_tree_one(self):
parts = tag_utils.split_tree_name('one')
self.assertEqual(len(parts), 1)
self.assertEqual(parts[0], '... |
def assignment_matrix(cal_data: Dict[(int, Dict[(int, int)])], num_qubits: int, qubits: Optional[List[int]]=None) -> np.array:
if (qubits is not None):
qubits = np.asarray(qubits)
dim = (1 << qubits.size)
mask = _amat_mask(qubits, num_qubits)
accum_func = partial(_amat_accum_local, q... |
class ItemTraits(wx.Panel):
def __init__(self, parent, stuff, item):
wx.Panel.__init__(self, parent)
mainSizer = wx.BoxSizer(wx.VERTICAL)
self.SetSizer(mainSizer)
self.traits = wx.html.HtmlWindow(self)
bgcolor = wx.SystemSettings.GetColour(wx.SYS_COLOUR_WINDOW)
fgcolo... |
class TestVectorize():
.change_flags(cxx='')
def test_shape(self):
vec = tensor(shape=(None,), dtype='float64')
mat = tensor(shape=(None, None), dtype='float64')
node = shape(vec).owner
[vect_out] = vectorize_node(node, mat).outputs
assert equal_computations([vect_out], [... |
def reapply(layer_or_layers, replacements, **kwargs):
assert isinstance(replacements, dict), 'replacements must be a dictionary {old_layer:new_layer}'
assert (isinstance(layer_or_layers, Layer) or hasattr(layer_or_layers, '__getitem__')), 'layers must be either a single layer, dict-like or iterable of layers. I... |
def test_eval_hook():
with pytest.raises(AssertionError):
test_dataset = Model()
data_loader = DataLoader(test_dataset)
EvalHook(data_loader, save_best=True)
with pytest.raises(TypeError):
test_dataset = Model()
data_loader = [DataLoader(test_dataset)]
EvalHook(da... |
class Migration(migrations.Migration):
dependencies = [('conferences', '0026_move_speaker_voucher_in_conferences'), ('schedule', '0036_store_when_the_voucher_email_was_sent')]
operations = [migrations.AlterField(model_name='speakervoucher', name='conference', field=models.ForeignKey(on_delete=django.db.models.d... |
class ChaiscriptLexer(RegexLexer):
name = 'ChaiScript'
url = '
aliases = ['chaiscript', 'chai']
filenames = ['*.chai']
mimetypes = ['text/x-chaiscript', 'application/x-chaiscript']
version_added = '2.0'
flags = (re.DOTALL | re.MULTILINE)
tokens = {'commentsandwhitespace': [('\\s+', Text)... |
class OS(ContentManageable, NameSlugModel):
class Meta():
verbose_name = 'Operating System'
verbose_name_plural = 'Operating Systems'
ordering = ('name',)
def __str__(self):
return self.name
def get_absolute_url(self):
return reverse('download:download_os_list', kwarg... |
class BasicModule(torch.nn.Module):
def __init__(self, args):
super(BasicModule, self).__init__()
self.args = args
self.model_name = str(type(self))
def pad_doc(self, words_out, doc_lens):
pad_dim = words_out.size(1)
max_doc_len = max(doc_lens)
sent_input = []
... |
def test_future(zarr_dataset: ChunkedDataset, cfg: dict) -> None:
steps = (1, 2, 4)
for step in steps:
gen_partial = get_partial(cfg, 2, step, 0.1)
data = gen_partial(state_index=10, frames=np.asarray(zarr_dataset.frames[90:150]), agents=zarr_dataset.agents, tl_faces=np.zeros(0), selected_track_... |
class Migration(migrations.Migration):
dependencies = [('styles', '0010_rename_Review_model_and_file_field')]
operations = [migrations.AlterField(model_name='review', name='resource', field=models.ForeignKey(blank=True, help_text='The reviewed Style.', null=True, on_delete=django.db.models.deletion.CASCADE, to=... |
class RandomSampleDataset(Dataset):
def __init__(self, db, sample_rate_dict=None):
self.db = db
self.rank = (dist.get_rank() if dist.is_initialized() else 0)
np.random.seed(self.rank)
self.world_size = (dist.get_world_size() if dist.is_initialized() else 0)
self.keys = sorted... |
def setup_domains(app, config):
for role in HoverXRefBaseDomain.hoverxref_types:
app.add_role_to_domain('std', role, XRefRole(lowercase=True, innernodeclass=nodes.inline, warn_dangling=True))
domain = types.new_class('HoverXRefStandardDomain', (HoverXRefStandardDomainMixin, app.registry.domains.get('std... |
class SudsStationcomp(SudsStructBase, namedtuple('SudsStationcomp', 'sc_name, azim, incid, st_lat, st_long, elev, enclosure, annotation, recorder, rockclass, rocktype, sitecondition, sensor_type, data_type, data_units, polarity, st_status, max_gain, clip_value, con_mvolts, channel, atod_gain, effective, clock_correct, ... |
class CacheDecorator(DecoratorBase):
binder_cls = CacheDecoratorBinder
def __init__(self, *args):
super().__init__(*args)
self._cache = {}
def name(self):
return ''
def dirty(self, *args):
try:
del self._cache[args]
except KeyError:
pass
... |
def test_is_pure_async_fn():
assert is_pure_async_fn(lazy_fn)
assert (not is_pure_async_fn(test_lazy))
assert (not is_pure_async_fn(async_fn))
assert is_pure_async_fn(pure_async_fn)
assert (not is_pure_async_fn(DisallowSetting()))
assert is_pure_async_fn(MyClass.get_cls)
assert (not is_pure_... |
class last_fc(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(last_fc, self).__init__(in_features, out_features, bias)
self.layer_type = 'LFC'
def forward(self, x):
max = self.weight.data.max()
weight_q = self.weight.div(max).mul(127).round().div(127).... |
class SpacedDiffusion(GaussianDiffusion):
def __init__(self, use_timesteps, **kwargs):
self.use_timesteps = set(use_timesteps)
self.timestep_map = []
self.original_num_steps = len(kwargs['betas'])
base_diffusion = GaussianDiffusion(**kwargs)
last_alpha_cumprod = 1.0
n... |
def create_energy_cell(cell_index: int, resource_database: ResourceDatabase) -> PickupEntry:
return PickupEntry(name=f'Energy Cell {(cell_index + 1)}', progression=((resource_database.get_item(corruption_items.ENERGY_CELL_ITEMS[cell_index]), 1),), extra_resources=((resource_database.get_item(corruption_items.ENERGY... |
def generate_network_parameters(param_range: Optional[Union[(float, List[float])]]=(2 * pi), num_params: Optional[int]=None, load_from: Optional[str]=None) -> List[float]:
if load_from:
all_params = np.loadtxt(load_from)
if (len(np.shape(all_params)) < 2):
all_params = np.array([all_para... |
class Session(nodes.Collector):
Interrupted = Interrupted
Failed = Failed
_setupstate: SetupState
_fixturemanager: FixtureManager
exitstatus: Union[(int, ExitCode)]
def __init__(self, config: Config) -> None:
super().__init__(name='', path=config.rootpath, fspath=None, parent=None, confi... |
def train(args, model, teacher, epoch, train_loader, optimizer, log):
model.train()
teacher.eval()
losses = AverageMeter()
MSE_loss = nn.MSELoss(reduction='sum')
for (data, _, _) in tqdm(train_loader):
data = data.to(device)
(z, output, mu, log_var) = model(data)
(s_activatio... |
def _normalize_darwin_path(filename, canonicalise=False):
filename = path2fsn(filename)
if canonicalise:
filename = os.path.realpath(filename)
filename = os.path.normpath(filename)
data = fsn2bytes(filename, 'utf-8')
decoded = data.decode('utf-8', 'quodlibet-osx-path-decode')
try:
... |
def cfg_to_dict(cfg_node, key_list=[]):
_VALID_TYPES = {tuple, list, str, int, float, bool}
if (not isinstance(cfg_node, CfgNode)):
if (type(cfg_node) not in _VALID_TYPES):
logging.warning(f"Key {'.'.join(key_list)} with value {type(cfg_node)} is not a valid type; valid types: {_VALID_TYPES}... |
def main(unused_argv):
assert FLAGS.checkpoint_dir, '`checkpoint_dir` is missing.'
assert FLAGS.eval_dir, '`eval_dir` is missing.'
(model_config, train_config, input_config, eval_config) = get_configs_from_pipeline_file()
model_fn = functools.partial(build_man_model, model_config=model_config, is_traini... |
class GameConnectionWindow(QtWidgets.QMainWindow, Ui_GameConnectionWindow):
ui_for_builder: dict[(ConnectorBuilder, BuilderUi)]
layout_uuid_for_builder: dict[(ConnectorBuilder, uuid.UUID)]
def __init__(self, window_manager: MainWindow, network_client: QtNetworkClient, options: Options, game_connection: Game... |
_flax
class FlaxRobertaModelTest(FlaxModelTesterMixin, unittest.TestCase):
test_head_masking = True
all_model_classes = ((FlaxRobertaModel, FlaxRobertaForCausalLM, FlaxRobertaForMaskedLM, FlaxRobertaForSequenceClassification, FlaxRobertaForTokenClassification, FlaxRobertaForMultipleChoice, FlaxRobertaForQuestio... |
class Ator():
_caracter_ativo = 'A'
_caracter_destruido = ' '
def __init__(self, x=0, y=0):
self.y = y
self.x = x
self.status = ATIVO
def caracter(self):
return (self._caracter_ativo if (self.status == ATIVO) else self._caracter_destruido)
def calcular_posicao(self, t... |
class QuestionAnswers():
__slots__ = ('ucast', 'mcast_now', 'mcast_aggregate', 'mcast_aggregate_last_second')
def __init__(self, ucast: _AnswerWithAdditionalsType, mcast_now: _AnswerWithAdditionalsType, mcast_aggregate: _AnswerWithAdditionalsType, mcast_aggregate_last_second: _AnswerWithAdditionalsType) -> None... |
def run_proc(cpu_id, file_list, wiki5m_alias2qid, wiki5m_qid2alias, head_cluster, min_seq_len=100, max_seq_len=300, output_folder='./pretrain_data/data/'):
if (not os.path.exists(output_folder)):
os.makedirs(output_folder)
target_filename = os.path.join(output_folder, 'data_{}.json'.format((cpu_id + 100... |
def _load_spec(spec: ModuleSpec, module_name: str) -> ModuleType:
name = getattr(spec, '__name__', module_name)
if (name in sys.modules):
return sys.modules[name]
module = importlib.util.module_from_spec(spec)
sys.modules[name] = module
spec.loader.exec_module(module)
return module |
class BinaryWeightedMultipleFixedPredictor(WeightedPredictionLayer):
def __init__(self, fc_init='glorot_uniform', pos_weight=None):
self.fc_init = fc_init
self.pos_weight = pos_weight
def apply(self, is_train, x, weights, answer: List):
init = get_keras_initialization(self.fc_init)
... |
def create_output_string(args: CustomNamespace) -> str:
output_fields = get_output_fields(args)
if args.summary:
table = create_summary_table(args)
else:
table = create_licenses_table(args, output_fields)
sortby = get_sortby(args)
if (args.format_ == FormatArg.HTML):
html = t... |
def align_features_to_words(roberta, features, alignment):
assert (features.dim() == 2)
bpe_counts = Counter((j for bpe_indices in alignment for j in bpe_indices))
assert (bpe_counts[0] == 0)
denom = features.new([bpe_counts.get(j, 1) for j in range(len(features))])
weighted_features = (features / d... |
def find_invalid_scalar_params(paramdomains: Dict[('str', Domain)]) -> Dict[('str', Tuple[(Union[(None, float)], Union[(None, float)])])]:
invalid_params = {}
for (param, paramdomain) in paramdomains.items():
(lower_edge, upper_edge) = (None, None)
if (np.ndim(paramdomain.lower) == 0):
... |
def main(args=None):
if (args is None):
args = sys.argv[1:]
args = parse_args(args)
backbone = models.backbone(args.backbone)
check_keras_version()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
keras.backend.tensorflow_backend.set_session(get_session())
if args.c... |
class AutumnStyle(Style):
name = 'autumn'
styles = {Whitespace: '#bbbbbb', Comment: 'italic #aaaaaa', Comment.Preproc: 'noitalic #4c8317', Comment.Special: 'italic #0000aa', Keyword: '#0000aa', Keyword.Type: '#00aaaa', Operator.Word: '#0000aa', Name.Builtin: '#00aaaa', Name.Function: '#00aa00', Name.Class: 'und... |
class PLCSiemens(Image):
icon = 'data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAABcAAAAvCAYAAAAIA1FgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAADmwAAA5sBPN8HMQAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAAANGSURBVFiF7ZbNbxtFGIefmR2vP+PUjkntxHGapmmpKpIoECggjpyQEBKqeoIjXDhw5cD/wZ0LEgiJEzck1EIrlUYBl7RJ1bQlbUpCHNux15+7MxycW... |
(Widget)
class CoactiveWidgetStub(Widget):
def __init__(self, view, css_id, coactive_widgets):
super().__init__(view)
self._coactive_widgets = coactive_widgets
self.css_id = css_id
def render_contents(self):
return ('<%s>' % self.css_id)
def coactive_widgets(self):
re... |
class Cookie(_Cookie):
_attrs = ('version', 'name', 'value', 'port', 'port_specified', 'domain', 'domain_specified', 'domain_initial_dot', 'path', 'path_specified', 'secure', 'expires', 'discard', 'comment', 'comment_url', 'rfc2109', '_rest')
def __eq__(self, other):
return all(((getattr(self, a) == get... |
def get_sample_fn(params, is_training=False, use_prior=False, reuse=False, output_length=None):
def model(inputs):
outputs = get_seq_encoding_model(inputs, params, is_training, reuse)
outputs = get_latent_encoding_model(inputs, outputs, params, is_training, use_prior, reuse)
outputs = get_la... |
def main(args: Any=None) -> None:
if (args is None):
args = sys.argv[1:]
(parser, parent_parser, subparsers) = create_parser()
codec_lookup = {}
for cls in codec_classes:
codec_class = cls()
codec_lookup[codec_class.name] = codec_class
codec_parser = subparsers.add_parser... |
def main():
with open(FLAGS.hq_replay_set) as f:
replay_list = sorted(json.load(f))
race_vs_race = os.path.basename(FLAGS.hq_replay_set).split('.')[0]
global_feature_path = os.path.join(FLAGS.parsed_replay_path, 'GlobalFeatures', race_vs_race)
for race in set(race_vs_race.split('_vs_')):
... |
_metaclass(BaseMeta)
class BaseWrapper(object):
friendlyclassname = None
windowclasses = []
can_be_label = False
has_title = True
def __new__(cls, element_info, active_backend):
return BaseWrapper._create_wrapper(cls, element_info, BaseWrapper)
def _create_wrapper(cls_spec, element_info,... |
(...)
def test_mapping_validation(detailed_validation: bool):
c = Converter(detailed_validation=detailed_validation)
if detailed_validation:
with pytest.raises(IterableValidationError) as exc:
c.structure({'1': 1, '2': 'b', 'c': 3}, Dict[(int, int)])
assert (repr(exc.value.exceptions... |
class TestOrderedBiMap():
def test_inverse(self):
d = OrderedBiMap([('one', 1), ('two', 2)])
assert (d.inverse == OrderedBiMap([(1, 'one'), (2, 'two')]))
assert (d.inverse.inverse is d)
def test_annotation(self):
def foo() -> OrderedBiMap[(str, int)]:
pass |
def split_interval_pattern(pattern: str) -> list[str]:
seen_fields = set()
parts = [[]]
for (tok_type, tok_value) in tokenize_pattern(pattern):
if (tok_type == 'field'):
if (tok_value[0] in seen_fields):
parts.append([])
seen_fields.clear()
see... |
class BitextOutputFromGen(object):
def __init__(self, predictions_bpe_file, bpe_symbol=None, nbest=False, prefix_len=None, target_prefix_frac=None):
if nbest:
(pred_source, pred_hypo, pred_score, pred_target, pred_pos_score) = reprocess_nbest(predictions_bpe_file)
else:
(pred... |
class convLR(nn.Module):
def __init__(self, in_out_channels=2048, kernel_size=(1, 9)):
super(convLR, self).__init__()
self.conv = nn.Sequential(nn.Conv2d(in_out_channels, in_out_channels, kernel_size, stride=1, padding=(((kernel_size[0] - 1) // 2), ((kernel_size[1] - 1) // 2))), nn.ReLU(inplace=True... |
def _init_distributed(use_gpu: bool):
distributed_world_size = int(os.environ['WORLD_SIZE'])
distributed_rank = int(os.environ['RANK'])
backend = ('nccl' if use_gpu else 'gloo')
torch.distributed.init_process_group(backend=backend, init_method='env://', world_size=distributed_world_size, rank=distribute... |
class _TimeSuite():
params = ([10000, 100000, 1000000], ['all', 'finite', 'pairs', 'array'])
def setup(self, nelems, connect):
self.xdata = np.arange(nelems, dtype=np.float64)
self.ydata = rng.standard_normal(nelems, dtype=np.float64)
if (connect == 'array'):
self.connect_arr... |
def mode_dot(tensor, matrix, mode):
new_shape = tensor.get_shape().as_list()
if (matrix.get_shape().as_list()[1] != tensor.get_shape().as_list()[mode]):
raise ValueError("Shape error. {0}(matrix's 2nd dimension) is not as same as {1} (dimension of the tensor)".format(matrix.get_shape().as_list()[1], ten... |
def download(url, dirpath):
filename = url.split('/')[(- 1)]
filepath = os.path.join(dirpath, filename)
u = urllib.request.urlopen(url)
f = open(filepath, 'wb')
filesize = int(u.headers['Content-Length'])
print(('Downloading: %s Bytes: %s' % (filename, filesize)))
downloaded = 0
block_sz... |
class SelectAdaptivePool2d(nn.Module):
def __init__(self, output_size=1, pool_type='fast', flatten=False):
super(SelectAdaptivePool2d, self).__init__()
self.pool_type = (pool_type or '')
self.flatten = (nn.Flatten(1) if flatten else nn.Identity())
if (pool_type == ''):
se... |
class GameHighScore(Object):
def __init__(self, *, client: 'pyrogram.Client'=None, user: 'types.User', score: int, position: int=None):
super().__init__(client)
self.user = user
self.score = score
self.position = position
def _parse(client, game_high_score: raw.types.HighScore, u... |
_model
def poolformer_s36(pretrained=False, **kwargs):
layers = [6, 6, 18, 6]
embed_dims = [64, 128, 320, 512]
mlp_ratios = [4, 4, 4, 4]
downsamples = [True, True, True, True]
model = PoolFormer(layers, embed_dims=embed_dims, mlp_ratios=mlp_ratios, downsamples=downsamples, layer_scale_init_value=1e-... |
.mssql_server_required
class TestErrorInSP(unittest.TestCase):
def setUp(self):
self.pymssql = pymssqlconn()
cursor = self.pymssql.cursor()
sql = u"\n CREATE PROCEDURE [dbo].[SPThatRaisesAnError]\n AS\n BEGIN\n -- RAISERROR -- Generates an error message and in... |
class TestForumTopicEdited():
def test_slot_behaviour(self, topic_edited):
for attr in topic_edited.__slots__:
assert (getattr(topic_edited, attr, 'err') != 'err'), f"got extra slot '{attr}'"
assert (len(mro_slots(topic_edited)) == len(set(mro_slots(topic_edited)))), 'duplicate slot'
... |
class ChannelPatchSchema(BaseSchema):
total_deposit = IntegerToStringField(default=None, missing=None)
total_withdraw = IntegerToStringField(default=None, missing=None)
reveal_timeout = IntegerToStringField(default=None, missing=None)
state = fields.String(default=None, missing=None, validate=validate.O... |
.parametrize('include_menu_mod', [False, True])
def test_apply_patcher_file(include_menu_mod: bool, valid_tmp_game_root, mocker: pytest_mock.MockerFixture):
mock_run_with_args = mocker.patch('randovania.games.prime2.patcher.claris_randomizer._run_with_args', autospec=True)
mock_create_progress_update_from_succe... |
def main():
with open('Dockerfile.j2') as f:
template = jinja2.Template(f.read(), trim_blocks=True, lstrip_blocks=True)
parser = argparse.ArgumentParser()
parser.add_argument('config', choices=CONFIGS)
args = parser.parse_args()
config = CONFIGS[args.config]
with open('Dockerfile', 'w') ... |
_required
def version_update(request, package_name, version):
plugin = get_object_or_404(Plugin, package_name=package_name)
version = get_object_or_404(PluginVersion, plugin=plugin, version=version)
if (not check_plugin_access(request.user, plugin)):
return render(request, 'plugins/version_permissio... |
def get_generator_loc(opt):
if (opt.model == 'DCGAN'):
return (((((g.default_model_dir + 'DCGAN/') + opt.data) + '/netG_epoch_') + str(opt.epoch)) + '.pth')
elif (opt.model == 'WGAN'):
return (((((g.default_model_dir + 'WGAN/') + opt.data) + '/netG_epoch_') + str(opt.epoch)) + '.pth')
elif (... |
class KnowValues(unittest.TestCase):
def test_label_orb_symm(self):
l = addons.label_orb_symm(mol, mol.irrep_name, mol.symm_orb, mf.mo_coeff)
lab0 = ['A1', 'A1', 'B2', 'A1', 'B1', 'A1', 'B2', 'B2', 'A1', 'A1', 'B1', 'B2', 'A1', 'A2', 'B1', 'A1', 'B2', 'B2', 'A1', 'B1', 'A2', 'A1', 'A1', 'B2']
... |
def test_guess_syntax():
for name in ('plain',):
assert (botogram.syntaxes.guess_syntax('', name) is None)
for name in ('md', 'markdown', 'Markdown'):
assert (botogram.syntaxes.guess_syntax('', name) == 'Markdown')
for name in ('html', 'HTML'):
assert (botogram.syntaxes.guess_syntax(... |
def train(dataset='mnist', model_name='d2l', batch_size=128, epochs=50, noise_ratio=0):
print(('Dataset: %s, model: %s, batch: %s, epochs: %s, noise ratio: %s%%' % (dataset, model_name, batch_size, epochs, noise_ratio)))
(X_train, y_train, X_test, y_test) = get_data(dataset, noise_ratio, random_shuffle=True)
... |
class FavoriteItemsMixin(ContextMixin):
def get_context_data(self, **kwargs):
context = super().get_context_data(**kwargs)
if likes_enable():
pass
date = (datetime.datetime.now() - datetime.timedelta(days=12))
items = Item.objects.filter(status='active', related_t... |
class FakeServer(ServerAsync):
def __init__(self, loop, dictionary):
ServerAsync.__init__(self, loop=loop, dictionary=dictionary, enable_pkt_verify=True, debug=True)
def handle_auth_packet(self, protocol, pkt, addr):
print('Received an authentication request with id ', pkt.id)
print('Aut... |
def _pool(type, raw, input, x, kernel_size, stride, padding, ceil_mode):
layer_name = log.add_layer(name='{}_pool'.format(type))
top_blobs = log.add_blobs([x], name='{}_pool_blob'.format(type))
layer = caffe_net.Layer_param(name=layer_name, type='Pooling', bottom=[log.blobs(input)], top=top_blobs)
layer... |
def sample_and_group(npoint, radius, nsample, xyz, points, knn=False, use_xyz=True):
sample_idx = farthest_point_sample(npoint, xyz)
new_xyz = gather_point(xyz, sample_idx)
if knn:
(_, idx) = knn_point(nsample, xyz, new_xyz)
else:
(idx, pts_cnt) = query_ball_point(radius, nsample, xyz, n... |
class TestAmavisCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('AmavisCollector', {'amavisd_exe': MOCK_PATH})
self.collector = amavis.AmavisCollector(config, None)
(Collector, 'publish')
def test_publish(self, publish_mock):
self.collector.collect()
... |
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