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class OutputFilesPage(QWizardPage):
def __init__(self, parent=None):
super(OutputFilesPage, self).__init__(parent)
self.setTitle('Output Files')
self.setSubTitle('Specify where you want the wizard to put the generated skeleton code.')
self.setPixmap(QWizard.LogoPixmap, QPixmap(':/ima... |
class Solution(object):
def isValidBST(self, root):
return self.isVaild_helper(root, ((- sys.maxint) - 1), sys.maxint)
def isVaild_helper(self, root, minVal, maxVal):
if (root is None):
return True
if ((root.val >= maxVal) or (root.val <= minVal)):
return False
... |
def selfies_to_hot(selfie, largest_selfie_len, alphabet):
symbol_to_int = dict(((c, i) for (i, c) in enumerate(alphabet)))
selfie += ('[nop]' * (largest_selfie_len - sf.len_selfies(selfie)))
symbol_list = sf.split_selfies(selfie)
integer_encoded = [symbol_to_int[symbol] for symbol in symbol_list]
on... |
def process(input_json: str, output_file: str, output_json: str=None, threshold: int=1, keep_punctuation: bool=False, character_level: bool=False, retokenize: bool=False, host_address: str=' zh: bool=True):
logfmt = '%(asctime)s (%(module)s:%(lineno)d) %(levelname)s: %(message)s'
logging.basicConfig(level=loggi... |
def test_kafka_batch_npartitions():
j1 = random.randint(0, 10000)
ARGS1 = {'bootstrap.servers': 'localhost:9092', 'group.id': ('streamz-test%i' % j1), 'enable.auto.commit': False, 'auto.offset.reset': 'earliest'}
j2 = (j1 + 1)
ARGS2 = {'bootstrap.servers': 'localhost:9092', 'group.id': ('streamz-test%i'... |
def configure_logging(config: Configuration, debug: bool) -> None:
logging.captureWarnings(capture=True)
if debug:
logging_level = logging.DEBUG
warnings.simplefilter('always')
else:
logging_level = logging.INFO
formatter: logging.Formatter
if (not sys.stdin.isatty()):
... |
def test_access_token(initialized_db):
user = model.user.get_user('devtable')
token = create_token(user, 'Some token')
assert (token.last_accessed is None)
token = access_valid_token(get_full_token_string(token))
assert (token.last_accessed is not None)
revoke_token(token)
assert (access_val... |
class TagListWrapper(abc.Mapping):
def __init__(self, taglist, merge=False):
self._list = taglist
self._merge = merge
def __len__(self):
return self._list.n_tags()
def __iter__(self):
for i in range(len(self)):
(yield self._list.nth_tag_name(i))
def __getitem_... |
class TestPetPhotoEndpoint(BaseTestPetstore):
def test_get_valid(self, client, data_gif):
client.cookies.set('user', '1')
headers = {'Authorization': 'Basic testuser', 'Api-Key': self.api_key_encoded}
response = client.get('/v1/pets/1/photo', headers=headers)
assert (response.content... |
def _stringify_obj(obj: Any) -> str:
if ((inspect.isbuiltin(obj) and (obj.__self__ is not None)) or isinstance(obj, types.MethodType)):
return f'{_stringify_obj(obj.__self__)}.{obj.__name__}'
elif (hasattr(obj, 'decorator') and hasattr(obj, 'instance')):
if hasattr(obj.instance, '__name__'):
... |
class AMPTrainer(SimpleTrainer):
def run_step(self):
assert self.model.training, '[AMPTrainer] model was changed to eval mode!'
assert torch.cuda.is_available(), '[AMPTrainer] CUDA is required for AMP training!'
start = time.perf_counter()
data = next(self._data_loader_iter)
... |
def find_all_conv_bn_with_activation(model: torch.nn.Module, input_shape: Tuple) -> Dict:
device = utils.get_device(model)
inp_tensor_list = utils.create_rand_tensors_given_shapes(input_shape, device)
connected_graph = ConnectedGraph(model, inp_tensor_list)
return find_all_conv_bn_with_activation_in_gra... |
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if (args.cfg_options is not None):
cfg.merge_from_dict(args.cfg_options)
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
cfg.data.test.test_mode = True
i... |
class DeformNet(nn.Module):
def __init__(self, n_cat=6, nv_prior=1024):
super(DeformNet, self).__init__()
self.n_cat = n_cat
self.instance_geometry = nn.Sequential(nn.Conv1d(3, 64, 1), nn.ReLU(), nn.Conv1d(64, 64, 1), nn.ReLU(), nn.Conv1d(64, 64, 1), nn.ReLU())
self.instance_global =... |
def test_switch_step_calls_cof():
with pytest.raises(Call) as err:
run_step(Context({'switch': [{'case': False, 'call': 'sg1'}, {'case': True, 'call': 'sg2'}]}))
cof = err.value
assert isinstance(cof, Call)
assert (cof.groups == ['sg2'])
assert (cof.success_group is None)
assert (cof.fai... |
class Iterator():
def __init__(self, data):
self.data = data
self.idx = 0
def hasNext(self) -> bool:
return (len(self.data) > self.idx)
def next(self):
if (len(self.data) > self.idx):
temp = self.data[self.idx]
self.idx += 1
return temp
... |
class Recorder():
def __init__(self, metrics):
self.metrics = metrics
self.metric2sum = {}
self.n_records = 0
self.reset()
def reset(self):
self.n_records = 0
for metric in self.metrics:
self.metric2sum[metric] = 0.0
def record(self, n_records, val... |
class TTrueAudioFile(TestCase):
def setUp(self):
self.song = TrueAudioFile(get_data_path('silence-44-s.tta'))
def test_length(self):
assert (self.song('~#length') == pytest.approx(3.684, abs=0.001))
def test_audio_props(self):
assert (self.song('~#samplerate') == 44100)
def test_... |
def load_pretrained_weights(p, model):
print('Loading pre-trained weights from {}'.format(p['pretraining']))
state_dict = torch.load(p['pretraining'], map_location='cpu')['model']
new_state = {}
for (k, v) in state_dict.items():
if k.startswith('module.model_q.'):
new_state[k.rsplit(... |
class DescribeCharacterStyle():
def it_knows_which_style_it_is_based_on(self, base_get_fixture):
(style, StyleFactory_, StyleFactory_calls, base_style_) = base_get_fixture
base_style = style.base_style
assert (StyleFactory_.call_args_list == StyleFactory_calls)
assert (base_style == ... |
class TestOptimizer(unittest.TestCase):
def test_init(self):
params = [torch.nn.Parameter(torch.randn(2, 3, 4))]
try:
optimizer = Optimizer(torch.optim.Adam(params))
except:
self.fail('__init__ failed.')
self.assertEquals(optimizer.max_grad_norm, 0)
def te... |
class InverseEvalresp(FrequencyResponse):
respfile = String.T()
nslc_id = Tuple.T(4, String.T())
target = String.T(default='dis')
instant = Float.T()
def __init__(self, respfile, trace, target='dis', **kwargs):
FrequencyResponse.__init__(self, respfile=respfile, nslc_id=trace.nslc_id, instan... |
def compute_grid_bound(model: MPMModelStruct, state: MPMStateStruct):
tid = wp.tid()
x = state.particle_q[tid]
fx = ((x[0] - (model.dx * 4.0)) * model.inv_dx)
fy = ((x[1] - (model.dx * 4.0)) * model.inv_dx)
fz = ((x[2] - (model.dx * 4.0)) * model.inv_dx)
ix = int(wp.floor(fx))
iy = int(wp.fl... |
class ModelArguments():
model_name_or_path: str = field(default='google/vit-base-patch16-224-in21k', metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
model_type: Optional[str] = field(default=None, metadata={'help': ('If training from scratch, pass a model type from ... |
class PythonConsole(Gtk.ScrolledWindow):
def __init__(self, namespace=None, destroy_cb=None):
Gtk.ScrolledWindow.__init__(self)
self.destroy_cb = destroy_cb
self.set_policy(Gtk.PolicyType.NEVER, Gtk.PolicyType.AUTOMATIC)
self.set_shadow_type(Gtk.ShadowType.NONE)
self.view = G... |
class FigurePlot():
def __init__(self, x_axis_label, y_axis_label, title):
self.x_axis_label = x_axis_label
self.y_axis_label = y_axis_label
self.title = title
self.source = ColumnDataSource(data=dict(x=[], y=[]))
self.title_object = Title()
self.title_object.text = s... |
class TestActivate():
expected_msg = "Humanize cannot determinate the default location of the 'locale' folder. You need to pass the path explicitly."
def test_default_locale_path_null__file__(self) -> None:
i18n = importlib.import_module('humanize.i18n')
i18n.__file__ = None
with pytest.... |
class FileFolderNavigator(GridBox):
_attribute_decorator('WidgetSpecific', 'Defines wether it is possible to select multiple items.', bool, {})
def multiple_selection(self):
return self._multiple_selection
_selection.setter
def multiple_selection(self, value):
self._multiple_selection = ... |
def test_export_data_access_groups(simple_project):
records = simple_project.export_records(export_data_access_groups=True)
for record in records:
assert ('redcap_data_access_group' in record)
records = simple_project.export_records()
for record in records:
assert (not ('redcap_data_acce... |
class Config(object):
def __init__(self, dataset, embedding):
self.model_name = 'TextRCNN'
self.train_path = (dataset + '/data/train.txt')
self.dev_path = (dataset + '/data/dev.txt')
self.test_path = (dataset + '/data/test.txt')
self.class_list = [x.strip() for x in open((dat... |
class AutoModelForTokenClassification():
def __init__(self):
raise EnvironmentError('AutoModelForTokenClassification is designed to be instantiated using the `AutoModelForTokenClassification.from_pretrained(pretrained_model_name_or_path)` or `AutoModelForTokenClassification.from_config(config)` methods.')
... |
def read_tokenizer(lang_id, g2p_model='latest', device=None, use_lexicon=True):
lang_id = normalize_lang_id(lang_id)
if (lang_id in lang2tokenizer):
return lang2tokenizer[lang_id](lang_id=lang_id, g2p_model=g2p_model, device=device, use_lexicon=use_lexicon)
else:
return read_g2p_tokenizer(la... |
class BaseFragmentBlender():
passes = []
def __init__(self):
self.device = get_shared().device
self.size = (0, 0)
self._combine_pass_pipeline = None
self._combine_pass_bind_group = None
self._texture_info = {}
usg = wgpu.TextureUsage
self._texture_info['co... |
def preprocess(data_dir, hparams: Hyperparameter, temp_dir='temp', device='cuda:0', max_workers=4):
data_dir = os.path.abspath(data_dir)
temp_dir = os.path.abspath(temp_dir)
mel_dir = os.path.join(temp_dir, 'mels')
os.makedirs(mel_dir, exist_ok=True)
mel_config = {'sampling_rate': hparams.sample_rat... |
class OptimizableInterface(with_metaclass(ABCMeta, object)):
def problem_size(self):
pass
def get_current_point(self):
pass
def set_current_point(self, current_point):
pass
current_point = abstractproperty(get_current_point, set_current_point)
def compute_objective_function(s... |
def shareable_word_hash(hash_bytes: bytes, all_games: list[RandovaniaGame]):
rng = Random(sum(((hash_byte * ((2 ** 8) ** i)) for (i, hash_byte) in enumerate(hash_bytes))))
games_left = []
selected_words = []
for _ in range(3):
if (not games_left):
games_left = list(all_games)
... |
.parametrize('text', ('`test identifier`', 'simple_identifier', "query''", '_internal_value', 'get_pubkeys&signatures', 'dict::udict_set_builder', '2+2=2*2', '-alsovalidname', '{hehehe}'))
def test_func_identifier(lexer_func, text):
assert (list(lexer_func.get_tokens(text))[0] == (Name.Variable, text)) |
def convert_example_to_feature(data, args):
(sums, contexts) = ([], [])
for sample in data:
if (args.sum_mode == 'final'):
sum = sample['FinalSumm']
elif (args.sum_mode == 'user'):
sum = sample['UserSumm']
elif (args.sum_mode == 'agent'):
sum = sample[... |
def _get_plugin_config():
if config.has_option('plugins', 'trayicon_window_hide'):
value = config.getboolean('plugins', 'trayicon_window_hide')
config.remove_option('plugins', 'trayicon_window_hide')
config.set('plugins', 'icon_window_hide', value)
pconfig = PluginConfig('icon')
pcon... |
def ddp(script: str, nnodes: int=1, name: str='ddp_app', role: str='worker', env: Optional[Dict[(str, str)]]=None, *script_args: str) -> specs.AppDef:
app_env: Dict[(str, str)] = {}
if env:
app_env.update(env)
entrypoint = os.path.join(specs.macros.img_root, script)
ddp_role = specs.Role(name=ro... |
def _configure_stderr_logging(*, verbosity=None, verbosity_shortcuts=None):
global console_stderr_handler
if (console_stderr_handler is not None):
_logger.warning('stderr handler already exists')
return
console_stderr_handler = logging.StreamHandler(sys.stderr)
console_stderr_handler.set... |
def train():
tf.reset_default_graph()
policy_nn = SupervisedPolicy()
f = open(relationPath)
train_data = f.readlines()
f.close()
num_samples = len(train_data)
saver = tf.train.Saver()
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
if (num_samples >... |
def test_main_failure(caplog: pytest.LogCaptureFixture, tmp_path: Path) -> None:
requirements_file = (tmp_path / 'requirements.txt')
requirements_file.touch()
source_dir = (tmp_path / 'source')
source_dir.mkdir()
source_file = (source_dir / 'source.py')
source_file.write_text('import pytest')
... |
def check_match(condition: models.Match, value: Any) -> bool:
if isinstance(condition, models.MatchValue):
return (value == condition.value)
if isinstance(condition, models.MatchText):
return ((value is not None) and (condition.text in value))
if isinstance(condition, models.MatchAny):
... |
class LatentBrownianBridgeModel(BrownianBridgeModel):
def __init__(self, model_config):
super().__init__(model_config)
self.vqgan = VQModel(**vars(model_config.VQGAN.params)).eval()
self.vqgan.train = disabled_train
for param in self.vqgan.parameters():
param.requires_gra... |
def test_dataid_elements_picklable():
import pickle
from satpy.tests.utils import make_dataid
did = make_dataid(name='hi', wavelength=(10, 11, 12), resolution=1000, calibration='radiance')
for value in did.values():
pickled_value = pickle.loads(pickle.dumps(value))
assert (value == pickl... |
class Incidents(Cog):
message_link_embeds_cache = RedisCache()
def __init__(self, bot: Bot) -> None:
self.bot = bot
self.incidents_webhook = None
scheduling.create_task(self.fetch_webhook())
self.event_lock = asyncio.Lock()
self.crawl_task = scheduling.create_task(self.cr... |
class SubQuery():
def __init__(self, subquery: Any, subquery_raw: str, alias: Optional[str]):
self.query = subquery
self.query_raw = subquery_raw
self.alias = (escape_identifier_name(alias) if (alias is not None) else f'subquery_{hash(self)}')
def __str__(self):
return self.alias... |
def run(*cmd: str, capture: bool=False, raise_on_err: bool=True, check_code: t.Callable[([int], bool)]=(lambda c: (c == 0)), **popen_kwargs: t.Any) -> RunReturn:
stdout = (subprocess.PIPE if capture else None)
stderr = (subprocess.PIPE if capture else None)
proc = subprocess.Popen(cmd, stdout=stdout, stderr... |
class ThreadLocal(Generic[_StateType]):
def __init__(self, default: Callable[([], _StateType)]):
self._default = default
self._state: WeakKeyDictionary[(Thread, _StateType)] = WeakKeyDictionary()
def get(self) -> _StateType:
thread = current_thread()
if (thread not in self._state... |
_edge_encoder('VOCEdge')
class VOCEdgeEncoder(torch.nn.Module):
def __init__(self, emb_dim):
super().__init__()
VOC_edge_input_dim = (2 if (cfg.dataset.name == 'edge_wt_region_boundary') else 1)
self.encoder = torch.nn.Linear(VOC_edge_input_dim, emb_dim)
def forward(self, batch):
... |
def createProgram(shaderList):
program = glCreateProgram()
for shader in shaderList:
glAttachShader(program, shader)
glLinkProgram(program)
status = glGetProgramiv(program, GL_LINK_STATUS)
if (status == GL_FALSE):
strInfoLog = glGetProgramInfoLog(program)
print(('Linker failu... |
def process_mnemonics(X_protoset_cumuls, Y_protoset_cumuls, mnemonics_raw, mnemonics_label, order_list, nb_cl_fg, nb_cl, iteration, start_iter):
mnemonics = mnemonics_raw[0]
mnemonics_array_new = np.zeros((len(mnemonics), len(mnemonics[0]), 32, 32, 3))
mnemonics_list = []
mnemonics_label_list = []
f... |
class FileHandler(Configurable):
path = Option(filesystem_path, required=True, positional=True, __doc__='Path to use within the provided filesystem.')
eol = Option(str, default='\n', __doc__='Character to use as line separator.')
mode = Option(str, __doc__='What mode to use for open() call.')
encoding =... |
def get_all_terminals(tree, is_l_value, insideQuery):
if (not isinstance(tree, Node)):
return [tree]
if isPathExpression(tree):
return get_path_expression_terminals(tree, insideQuery)
elif isTryExceptExpression(tree):
return get_try_except_expression_terminals(tree, insideQuery)
... |
class TestContingency(TestCase):
def test_chi2_independence(self):
np.random.seed(42)
(mean, cov) = ([0.5, 0.5], [(1, 0.6), (0.6, 1)])
(x, y) = np.random.multivariate_normal(mean, cov, 30).T
data = pd.DataFrame({'x': x, 'y': y})
mask_class_1 = (data > 0.5)
data[mask_c... |
def test_voltage():
with expected_protocol(Keithley2200, [(b'INST:SEL CH1;VOLT 1.456', None), (b'INST:SEL CH1;VOLT?', 1.456), (b'INST:SEL CH1;MEAS:VOLT?', 1.456), (b'INST:SEL CH3;VOLT 1.456', None)]) as instr:
instr.ch_1.voltage_setpoint = 1.456
assert (instr.ch_1.voltage_setpoint == 1.456)
... |
def test_write_calibration_data():
invalid_cal_data = VALID_CAL_DATA.copy()
invalid_cal_data[1] = 1
invalid_cal_write_xfers = convert_cal_data_to_cal_write_xfers(invalid_cal_data)
with expected_protocol(HP3478A, invalid_cal_write_xfers) as instr:
instr.write_calibration_data(invalid_cal_data, ve... |
class MVArray(np.ndarray):
def __new__(cls, input_array):
(input_shape, layout, dtype) = _interrogate_nested_mvs(input_array)
obj = np.empty(input_shape, dtype=object)
for index in np.ndindex(input_shape):
obj[index] = _index_nested_iterable(input_array, index)
self = obj... |
class BIP32_KeyStore(Xpub, Deterministic_KeyStore):
type = 'bip32'
def __init__(self, d):
Xpub.__init__(self, derivation_prefix=d.get('derivation'), root_fingerprint=d.get('root_fingerprint'))
Deterministic_KeyStore.__init__(self, d)
self.xpub = d.get('xpub')
self.xprv = d.get('x... |
class SpecParser(Parser[ConfigNamespace]):
def __init__(self, spec: ConfigSpec):
self.spec = spec
def parse(self, key_path: str, raw_config: RawConfig) -> ConfigNamespace:
parsed = ConfigNamespace()
for (key, spec) in self.spec.items():
assert ('.' not in key), 'dots are not ... |
def load_collection_(path, retain_titles):
with open(path) as f:
collection = []
for line in file_tqdm(f):
(_, passage, title) = line.strip().split('\t')
if retain_titles:
passage = ((title + ' | ') + passage)
collection.append(passage)
return ... |
def test_marker_prefix_does_not_interfere_with_order_marks(test_path):
test_path.makepyfile(test_marker='\n import pytest\n\n .order(3)\n def test_a():\n pass\n\n .order(1)\n def test_b():\n pass\n\n .order(2)\n ... |
class GenTensorVariable(TensorVariable):
def __init__(self, op, type, name=None):
super().__init__(type=type, owner=None, name=name)
self.op = op
def set_gen(self, gen):
self.op.set_gen(gen)
def set_default(self, value):
self.op.set_default(value)
def clone(self):
... |
_request_params(*docs._get_observations, docs._pagination)
def get_observation_species_counts(**params) -> JsonResponse:
if (params.get('page') == 'all'):
return paginate_all(get, f'{API_V1}/observations/species_counts', **params)
else:
return get(f'{API_V1}/observations/species_counts', **param... |
def pytest_collection_modifyitems(items: List[nodes.Item], config: Config) -> None:
deselect_prefixes = tuple((config.getoption('deselect') or []))
if (not deselect_prefixes):
return
remaining = []
deselected = []
for colitem in items:
if colitem.nodeid.startswith(deselect_prefixes):... |
def main(args: Optional[Sequence[str]]=None) -> None:
parser = ArgumentParser()
parser.add_argument('file', nargs='+', help='Validate specified file(s).')
parser.add_argument('--errors', choices=('best-match', 'all'), default='best-match', help='Control error reporting. Defaults to "best-match", use "all" t... |
('/v1/repository/<apirepopath:repository>/trigger/<trigger_uuid>/activate')
_param('repository', 'The full path of the repository. e.g. namespace/name')
_param('trigger_uuid', 'The UUID of the build trigger')
class BuildTriggerActivate(RepositoryParamResource):
schemas = {'BuildTriggerActivateRequest': {'type': 'ob... |
def prep_utt2label(utt2labelid_path, label_id_map_path, utt2label_paths):
print((('generating utt2labelid(%s) and label_id_map(%s)' % (utt2labelid_path, label_id_map_path)) + (' from utt2labels(%s)' % utt2label_paths)))
utt_list = []
label_list = []
for utt2label_path in utt2label_paths:
with op... |
class InputFeatures(object):
def __init__(self, input_ids_spc, input_mask, segment_ids, label_id, polarities=None, valid_ids=None, label_mask=None):
self.input_ids_spc = input_ids_spc
self.input_mask = input_mask
self.segment_ids = segment_ids
self.label_id = label_id
self.va... |
class TestRegister(TestScript):
handlers = RAPIDSMS_HANDLERS
def testRegister(self):
self.assertInteraction('\n > register as someuser\n < Thank you for registering, as someuser!\n ')
def testLang(self):
self.assertInteraction(('\n > lang english\n ... |
class ModelSpec_Modified(object):
def __init__(self, matrix, ops, data_format='channels_last'):
if (not isinstance(matrix, np.ndarray)):
matrix = np.array(matrix)
shape = np.shape(matrix)
if ((len(shape) != 2) or (shape[0] != shape[1])):
raise ValueError('matrix must ... |
def TVRegDiffPoint(u, dx, index=None):
u = u.flatten()
n = len(u)
if (index == None):
index = int(((n - 1) / 2))
ux = TVRegDiff(u, 1, 0.1, dx=dx, plotflag=False, diffkernel='sq')
uxx = TVRegDiff(ux, 1, 0.1, dx=dx, plotflag=False, diffkernel='sq')
return (ux[index], uxx[index]) |
def register_onchain_secret_endstate(end_state: NettingChannelEndState, secret: Secret, secrethash: SecretHash, secret_reveal_block_number: BlockNumber, delete_lock: bool=True) -> None:
pending_lock: Optional[HashTimeLockState] = None
if is_lock_locked(end_state, secrethash):
pending_lock = end_state.se... |
.supported(only_if=(lambda backend: backend.cipher_supported(algorithms._CAST5Internal((b'\x00' * 16)), modes.ECB())), skip_message='Does not support CAST5 ECB')
class TestCAST5ModeECB():
test_ecb = generate_encrypt_test(load_nist_vectors, os.path.join('ciphers', 'CAST5'), ['cast5-ecb.txt'], (lambda key, **kwargs: ... |
def test_smartdevice_examples(mocker):
p = asyncio.run(get_device_for_file('HS110(EU)_1.0_1.2.5.json', 'IOT'))
mocker.patch('kasa.smartdevice.SmartDevice', return_value=p)
mocker.patch('kasa.smartdevice.SmartDevice.update')
res = xdoctest.doctest_module('kasa.smartdevice', 'all')
assert (not res['fa... |
class ChatAction(StringEnum):
__slots__ = ()
CHOOSE_STICKER = 'choose_sticker'
FIND_LOCATION = 'find_location'
RECORD_VOICE = 'record_voice'
RECORD_VIDEO = 'record_video'
RECORD_VIDEO_NOTE = 'record_video_note'
TYPING = 'typing'
UPLOAD_VOICE = 'upload_voice'
UPLOAD_DOCUMENT = 'upload... |
def dependency_pyserial(func):
(func)
def wrapper(*args, **kwargs):
if (not is_usable()):
raise RuntimeError('Printing with Serial requires the pyserial library tobe installed. Please refer to the documentation onwhat to install and install the dependencies for pyserial.')
return fun... |
class LEDTokenizer(BartTokenizer):
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def _pad(self, encoded_inputs: Union[(Dict[(str, EncodedInput)], BatchEncoding)], max_length: Optional[int]=None, padding_strategy: PaddingStrategy=Paddin... |
class Discriminator(nn.Module):
def __init__(self, args):
super(Discriminator, self).__init__()
in_channels = args.n_colors
out_channels = 64
depth = 7
def _block(_in_channels, _out_channels, stride=1):
return nn.Sequential(nn.Conv2d(_in_channels, _out_channels, 3... |
class AdaptedMethod():
def __init__(self, declared_method, arg_names=[], kwarg_name_map={}):
self.declared_method = declared_method
self.arg_names = arg_names
self.kwarg_name_map = kwarg_name_map
self.kwarg_name_map_reversed = dict([(sent_name, adapted_to_name) for (adapted_to_name, ... |
class Organization(models.Model):
name = models.CharField(max_length=200, verbose_name=gettext_lazy('Name'), unique=True, null=False, blank=False)
default_template = models.ForeignKey('PetitionTemplate', blank=True, null=True, related_name='+', verbose_name=gettext_lazy('Default petition template'), to_field='i... |
class GetChatHistory():
async def get_chat_history(self: 'pyrogram.Client', chat_id: Union[(int, str)], limit: int=0, offset: int=0, offset_id: int=0, offset_date: datetime=utils.zero_datetime()) -> Optional[AsyncGenerator[('types.Message', None)]]:
current = 0
total = (limit or ((1 << 31) - 1))
... |
class HSTISR(IntEnum):
DCONNI = (1 << 0)
DDISCI = (1 << 1)
RSTI = (1 << 2)
RSMEDI = (1 << 3)
RXRSMI = (1 << 4)
HSOFI = (1 << 5)
HWUPI = (1 << 6)
PEP_0 = (1 << 8)
PEP_1 = (1 << 9)
PEP_2 = (1 << 10)
PEP_3 = (1 << 11)
PEP_4 = (1 << 12)
PEP_5 = (1 << 13)
PEP_6 = (1 <<... |
class ReportType(str, Enum):
INVENTORY = '_GET_FLAT_FILE_OPEN_LISTINGS_DATA_'
ALL_LISTINGS = '_GET_MERCHANT_LISTINGS_ALL_DATA_'
ACTIVE_LISTINGS = '_GET_MERCHANT_LISTINGS_DATA_'
INACTIVE_LISTINGS = '_GET_MERCHANT_LISTINGS_INACTIVE_DATA_'
OPEN_LISTINGS = '_GET_MERCHANT_LISTINGS_DATA_BACK_COMPAT_'
... |
class Import(ImportBase):
__slots__ = ('ids',)
__match_args__ = ('ids',)
ids: list[tuple[(str, (str | None))]]
def __init__(self, ids: list[tuple[(str, (str | None))]]) -> None:
super().__init__()
self.ids = ids
def accept(self, visitor: StatementVisitor[T]) -> T:
return visi... |
class BaseResponseUnmarshaller(BaseResponseValidator, BaseUnmarshaller):
def _unmarshal(self, response: Response, operation: SchemaPath) -> ResponseUnmarshalResult:
try:
operation_response = self._find_operation_response(response.status_code, operation)
except ResponseFinderError as exc:... |
def test_logq_globals(three_var_approx):
if (not three_var_approx.has_logq):
pytest.skip(('%s does not implement logq' % three_var_approx))
approx = three_var_approx
(logq, symbolic_logq) = approx.set_size_and_deterministic([approx.logq, approx.symbolic_logq], 1, 0)
e = logq.eval()
es = symb... |
def test_stringify_file_id():
file_id = 'BQACAgIAAx0CAAGgr9AAAgmPX7b4UxbjNoFEO_L0I4s6wrXNJA8AAgQAA4GkuUm9FFvIaOhXWR4E'
string = "{'major': 4, 'minor': 30, 'file_type': <FileType.DOCUMENT: 5>, 'dc_id': 2, 'file_reference': b'\\x02\\x00\\xa0\\xaf\\xd0\\x00\\x00\\t\\x8f_\\xb6\\xf8S\\x16\\xe36\\x81D;\\xf2\\xf4#\\x8... |
class SeqInfo():
def __init__(self, seq_path):
self.info = self.get_seq_info_data(seq_path)
def get_obj_name(self, convert=False):
if convert:
if ('chair' in self.info['cat']):
return 'chair'
if ('ball' in self.info['cat']):
return 'sports ... |
class RegistrationPendingForm(Form):
def __init__(self, view):
super().__init__(view, 'register_pending')
if self.exception:
self.add_child(Alert(view, self.exception.as_user_message(), 'warning'))
actions = self.add_child(ActionButtonGroup(view, legend_text=_('Re-send registrati... |
class ResNeXt_with_features(nn.Module):
def __init__(self, block, num_blocks, cardinality, bottleneck_width, strides):
super().__init__()
self.cardinality = cardinality
self.bottleneck_width = bottleneck_width
self.in_channels = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=1,... |
class GetCategoriesTests(MockPagesTestCase):
def test_get_root_categories(self):
result = utils.get_categories(BASE_PATH)
info = PARSED_CATEGORY_INFO
categories = {'category': info, 'tmp': info, 'not_a_page.md': info}
self.assertEqual(result, categories)
def test_get_categories_w... |
def _conversion_checks(item, keys, box_config, check_only=False, pre_check=False):
if (box_config['box_duplicates'] != 'ignore'):
if pre_check:
keys = (list(keys) + [item])
key_list = [(k, _safe_attr(k, camel_killer=box_config['camel_killer_box'], replacement_char=box_config['box_safe_pr... |
def _del_unclaimed_lock(end_state: NettingChannelEndState, secrethash: SecretHash) -> None:
if (secrethash in end_state.secrethashes_to_lockedlocks):
del end_state.secrethashes_to_lockedlocks[secrethash]
if (secrethash in end_state.secrethashes_to_unlockedlocks):
del end_state.secrethashes_to_un... |
def upgrade(op, tables, tester):
op.create_table('quotaregistrysize', sa.Column('id', sa.Integer(), nullable=False), sa.Column('size_bytes', sa.BigInteger(), nullable=False, server_default='0'), sa.Column('running', sa.Boolean(), nullable=False, server_default=sa.sql.expression.false()), sa.Column('queued', sa.Bool... |
def completer_obj(qtbot, status_command_stub, config_stub, monkeypatch, stubs, completion_widget_stub):
monkeypatch.setattr(completer, 'QTimer', stubs.InstaTimer)
config_stub.val.completion.show = 'auto'
return completer.Completer(cmd=status_command_stub, win_id=0, parent=completion_widget_stub) |
def crf(train_image, final_probabilities, train_annotation, number_class):
for index_image in xrange(1):
image = train_image
softmax = final_probabilities[0].squeeze()
softmax = softmax.transpose((2, 0, 1))
unary = unary_from_softmax(softmax)
unary = np.ascontiguousarray(unar... |
def test_guard_against_oversized_packets():
zc = Zeroconf(interfaces=['127.0.0.1'])
generated = r.DNSOutgoing(const._FLAGS_QR_RESPONSE)
for i in range(5000):
generated.add_answer_at_time(r.DNSText('packet{i}.local.', const._TYPE_TXT, (const._CLASS_IN | const._CLASS_UNIQUE), 500, b'path=/~paulsm/'), ... |
def parse_args_and_arch(parser: argparse.ArgumentParser, input_args: List[str]=None, parse_known: bool=False, suppress_defaults: bool=False, modify_parser: Optional[Callable[([argparse.ArgumentParser], None)]]=None):
if suppress_defaults:
args = parse_args_and_arch(parser, input_args=input_args, parse_known... |
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