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def fidelity_circuit(network: Union[(Network_DQNN, Network_QAOA)], state_pair: List[np.ndarray], draw_circ: bool=False) -> QuantumCircuit:
(circ, q_reg, c_reg) = init_quantum_circuit(((2 * network.num_qubits) + network.auxillary_qubits), ((2 * network.num_qubits) if (network.fid_meas_method == 'destructive_swap') e... |
def cuttree(node, nettree, levl):
for i in node.snode:
i.pnode.remove(node)
for i in node.pnode:
i.snode.remove(node)
if (node.snode != []):
cuttree(node.snode[0], nettree, (levl + 1))
print(((str(levl) + ':') + str(nettree[levl][0].position)))
nettree[levl].remove(node) |
def verify_logs(directory, filename, mtype, meid):
path = '/'
file_path = ((directory + path) + filename)
f = open(file_path, 'r')
for l in f:
if (meid is not None):
if ((l.find(mtype) > 0) and (l.find(meid) > 0)):
return True
elif (l.find(mtype) > 0):
... |
def register_to_config(init):
(init)
def inner_init(self, *args, **kwargs):
init_kwargs = {k: v for (k, v) in kwargs.items() if (not k.startswith('_'))}
init(self, *args, **init_kwargs)
if (not isinstance(self, ConfigMixin)):
raise RuntimeError(f'`_for_config` was applied to ... |
class ObjectEntry(Entry):
location: str
serializer: str
obj_type: str
replicated: bool
def __init__(self, location: str, serializer: str, obj_type: str, replicated: bool) -> None:
super().__init__(type='object')
self.location = location
self.serializer = serializer
se... |
def dataloader_didemo_test(args, tokenizer, subset='test'):
didemo_testset = DiDeMo_DataLoader(subset=subset, data_path=args.data_path, features_path=args.features_path, max_words=args.max_words, feature_framerate=args.feature_framerate, tokenizer=tokenizer, max_frames=args.max_frames, frame_order=args.eval_frame_o... |
def _add_install(subparsers: argparse._SubParsersAction, shared_parser: argparse.ArgumentParser) -> None:
p = subparsers.add_parser('install', help='Install a package', formatter_class=LineWrapRawTextHelpFormatter, description=INSTALL_DESCRIPTION, parents=[shared_parser])
p.add_argument('package_spec', help='pa... |
class Seq2seqTrainerTester(TestCasePlus):
_torch
def test_finetune_bert2bert(self):
bert2bert = EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny', 'prajjwal1/bert-tiny')
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
bert2bert.config.vocab_size = b... |
def console_entry() -> None:
try:
main()
sys.stdout.flush()
sys.stderr.flush()
except BrokenPipeError:
devnull = os.open(os.devnull, os.O_WRONLY)
os.dup2(devnull, sys.stdout.fileno())
sys.exit(2)
except KeyboardInterrupt:
(_, options) = process_options... |
def same_domain(url1: QUrl, url2: QUrl) -> bool:
ensure_valid(url1)
ensure_valid(url2)
if (url1.scheme() != url2.scheme()):
return False
if (url1.port() != url2.port()):
return False
assert machinery.IS_QT5, machinery.INFO
suffix1 = url1.topLevelDomain()
suffix2 = url2.topLev... |
def train_callback(batch_idx, outer_loss, list_out, stats):
list_acc = [x[0] for x in list_out]
list_ce = [x[1] for x in list_out]
acc = np.mean(list_acc)
ce = np.mean(list_ce)
stats.step(np.array([(acc * len(list_acc)), (ce * len(list_acc))]), n_step=len(list_acc))
msg = ('train batch acc: %.2f... |
def _train(params: Dict, dtrain: RayDMatrix, *args, evals=(), ray_params: RayParams, cpus_per_actor: int, gpus_per_actor: int, _training_state: _TrainingState, **kwargs) -> Tuple[(xgb.Booster, Dict, Dict)]:
from xgboost_ray.elastic import _get_actor_alive_status, _maybe_schedule_new_actors, _update_scheduled_actor_... |
def _as_scalar(res, dtype=None):
if (dtype is None):
dtype = config.floatX
if all(((s == 1) for s in res.type.shape)):
while (res.owner and isinstance(res.owner.op, DimShuffle)):
res = res.owner.inputs[0]
if (res.type.ndim > 0):
rval = res.dimshuffle()
els... |
class BFSCluster(Function):
def forward(ctx, semantic_label, ball_query_idxs, start_len, threshold):
N = start_len.size(0)
assert semantic_label.is_contiguous()
assert ball_query_idxs.is_contiguous()
assert start_len.is_contiguous()
cluster_idxs = semantic_label.new()
... |
class TestDiskUsageCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('DiskUsageCollector', {'interval': 10, 'sector_size': '512', 'byte_unit': 'kilobyte'})
self.collector = DiskUsageCollector(config, None)
def test_config(self):
self.assertFalse(self.collector.... |
def test_get_program_id_3():
circuit = cirq.Circuit(cirq.H(cirq.LineQubit(0)))
circuit.program_id = 'my_fancy_var_1/my_fancy_var_2/my_fancy_var_3/my_fancy_var_4/my_fancy_var_5/my_fancy_var_6'
assert (len(circuit.program_id) > 64)
prog_id = _get_program_id(circuit)
assert isinstance(prog_id, str)
... |
class AbstractImageSequence():
def get_texture_sequence(self):
raise NotImplementedError('abstract')
def get_animation(self, period, loop=True):
return Animation.from_image_sequence(self, period, loop)
def __getitem__(self, slice):
raise NotImplementedError('abstract')
def __seti... |
class GettextLexer(RegexLexer):
name = 'Gettext Catalog'
aliases = ['pot', 'po']
filenames = ['*.pot', '*.po']
mimetypes = ['application/x-gettext', 'text/x-gettext', 'text/gettext']
url = '
version_added = '0.9'
tokens = {'root': [('^#,\\s.*?$', Keyword.Type), ('^#:\\s.*?$', Keyword.Declara... |
def main():
filenames = ParseArguments(sys.argv[1:])
remove_filenames = ['include/caffe/3rdparty/hungarian.h', 'src/caffe/3rdparty/hungarian.cpp']
for remove_filename in remove_filenames:
if (remove_filename in filenames):
filenames.remove(remove_filename)
sys.stderr = codecs.StreamR... |
class AttrVI_ATTR_MEM_BASE(RangeAttribute):
resources = [(constants.InterfaceType.vxi, 'INSTR'), (constants.InterfaceType.vxi, 'SERVANT')]
py_name = ''
visa_name = 'VI_ATTR_MEM_BASE'
visa_type = ('ViBusAddress64' if constants.is_64bits else 'ViUInt32')
default = NotAvailable
(read, write, local)... |
def _cast_to_dtype(data, dtype):
dt = _format_dtype(dtype)
try:
data = np.array(data, dtype=dt)
except ValueError as err:
try:
print('Cant cast data to specific dtype. Trying row by row:')
for r in range(len(data)):
try:
np.array(da... |
def test__getting_started__pendulum():
from bioptim.examples.getting_started import pendulum as ocp_module
bioptim_folder = os.path.dirname(ocp_module.__file__)
ocp_module.prepare_ocp(biorbd_model_path=(bioptim_folder + '/models/pendulum.bioMod'), final_time=3, n_shooting=100, phase_dynamics=PhaseDynamics.S... |
def _execute_compaction_round(source_partition_locator: PartitionLocator, destination_partition_locator: PartitionLocator, primary_keys: Set[str], compaction_artifact_s3_bucket: str, last_stream_position_to_compact: int, hash_bucket_count: Optional[int], sort_keys: List[SortKey], records_per_compacted_file: int, input_... |
def read_detections(path, drop_detection_prob: float=0.0, add_detection_noise: float=0.0):
path = os.path.expanduser(path)
logger.debug(f'reading detections from {path}')
if (not os.path.isfile(path)):
raise ValueError('file does not exist')
df = pd.read_csv(path, names=COL_NAMES)
max_frame ... |
def download_city(path):
_CITY_DOWNLOAD_URLS = [('gtFine_trainvaltest.zip', '99f532cb1af174f5fcc4c5bc8feea8c66246ddbc'), ('leftImg8bit_trainvaltest.zip', '2c0b77ce9933cc635adda307fbba5566f5d9d404')]
download_dir = os.path.join(path, 'downloads')
mkdir(download_dir)
for (filename, checksum) in _CITY_DOWN... |
class Scoresheet(BaseScoresheet):
def __getitem__(self, key):
return BaseScoresheet.__getitem__(self, Pair(key))
def __setitem__(self, key, val):
BaseScoresheet.__setitem__(self, Pair(key), float(val))
def __delitem__(self, key):
return dict.__delitem__(self, Pair(key))
def proce... |
class Tooltip(MacroElement):
_template = Template('\n {% macro script(this, kwargs) %}\n {{ this._parent.get_name() }}.bindTooltip(\n `<div{% if this.style %} style={{ this.style|tojson }}{% endif %}>\n {{ this.text }}\n </div>`,\n ... |
def test_geodesic_gradient_descent(metric, data_x, data_y, high_rank_x, high_rank_y):
if metric.test_high_rank_data:
(x, y) = (high_rank_x, high_rank_y)
else:
(x, y) = (data_x, data_y)
(p_x, p_y) = (metric.neural_data_to_point(x), metric.neural_data_to_point(y))
frac = np.random.rand(1)[... |
def calculate_fid_given_paths(paths, batch_size, size, length, dims, device):
for p in paths:
if (not os.path.exists(p)):
raise RuntimeError(('Invalid path: %s' % p))
model = nn.DataParallel(resnet101(sample_duration=16).cuda())
model.load_state_dict(torch.load('resnext-101-kinetics.pth'... |
_metaclass(Singleton)
class IATSPI(object):
LIB = 'libatspi'
DEFAULT_LIB_NAME = 'libatspi.so'
def __get_roles(self):
control_types = []
get_role_name = self.atspi.atspi_role_get_name
get_role_name.argtypes = [c_int]
get_role_name.restype = c_char_p
for i in range(ATSP... |
def create_logger(root_output_path, cfg, image_set):
if (not os.path.exists(root_output_path)):
os.makedirs(root_output_path)
assert os.path.exists(root_output_path), '{} does not exist'.format(root_output_path)
cfg_name = os.path.basename(cfg).split('.')[0]
config_output_path = os.path.join(roo... |
def solar_position_numba(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst=False, esd=False):
loc_args = np.array([lat, lon, elev, pressure, temp, delta_t, atmos_refract, sst, esd])
ulength = unixtime.shape[0]
if sst:
dims = 3
elif esd:
dims = 1
else:
... |
class TestLoad(BaseTestLoading):
def test_load_hvu_label(self):
hvu_label_example1 = copy.deepcopy(self.hvu_label_example1)
hvu_label_example2 = copy.deepcopy(self.hvu_label_example2)
categories = hvu_label_example1['categories']
category_nums = hvu_label_example1['category_nums']
... |
class NormalizePathTest(TestCase):
def setUp(self):
self.filesystem = fake_filesystem.FakeFilesystem(path_separator='/')
self.root_name = self.filesystem.root_dir_name
def test_empty_path_should_get_normalized_to_root_path(self):
self.assertEqual(self.root_name, self.filesystem.absnormpa... |
class VNet(MetaModule):
def __init__(self, input, hidden1, output):
super(VNet, self).__init__()
self.linear1 = MetaLinear(input, hidden1)
self.relu = nn.ReLU(inplace=True)
self.linear2 = MetaLinear(hidden1, output)
def forward(self, x):
x = self.linear1(x)
x = se... |
class TestProjectExplicit():
.parametrize('file_name', ['pyproject.toml', 'setup.py'])
def test_found_project_flag(self, hatch, temp_dir, config_file, helpers, file_name):
project_file = (temp_dir / file_name)
project_file.touch()
project = 'foo'
config_file.model.projects = {pro... |
class MaxOrderCount(TradingControl):
def __init__(self, on_error, max_count):
super(MaxOrderCount, self).__init__(on_error, max_count=max_count)
self.orders_placed = 0
self.max_count = max_count
self.current_date = None
def validate(self, asset, amount, portfolio, algo_datetime, ... |
def get_args():
parser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter, description='Symbolizes OP-TEE abort dumps or function graphs', epilog=epilog)
parser.add_argument('-d', '--dir', action='append', nargs='+', help='Search for ELF file in DIR. tee.elf is needed to decode a TEE... |
class TBMagicTurnHandler(DefaultScript):
def at_script_creation(self):
self.key = 'Combat Turn Handler'
self.interval = 5
self.persistent = True
self.db.fighters = []
for thing in self.obj.contents:
if thing.db.hp:
self.db.fighters.append(thing)
... |
class PedestrianMotionType(metaclass=_EnumMeta):
standing = _OscEnum('PedestrianMotionType', 'standing', min_minor_version=2)
sitting = _OscEnum('PedestrianMotionType', 'sitting', min_minor_version=2)
lying = _OscEnum('PedestrianMotionType', 'lying', min_minor_version=2)
squatting = _OscEnum('Pedestrian... |
def should_do_markup(file: TextIO) -> bool:
if (os.environ.get('PY_COLORS') == '1'):
return True
if (os.environ.get('PY_COLORS') == '0'):
return False
if os.environ.get('NO_COLOR'):
return False
if os.environ.get('FORCE_COLOR'):
return True
return (hasattr(file, 'isat... |
class Config():
STATUS_SONGLESS = ('no_song_text', '')
PAT_PLAYING = ('playing_pattern', ' <~artist~title> ')
PAT_PAUSED = ('paused_pattern', ('<~artist~title> [%s]' % _('paused')))
HOST = ('host', 'localhost')
PORT = ('port', 1883)
USERNAME = ('username', '')
PASSWORD = ('password', '')
... |
def agg(raw_items):
from collections import OrderedDict
agged = OrderedDict()
for raw_item in raw_items:
prefix = raw_item.split('_')[0]
value = '_'.join(raw_item.split('_')[1:])
if (not (prefix in agged.keys())):
agged[prefix] = []
agged[prefix].append(value)
... |
class RepeatFactorTrainingSampler(Sampler):
def __init__(self, repeat_factors, *, shuffle=True, seed=None):
self._shuffle = shuffle
if (seed is None):
seed = comm.shared_random_seed()
self._seed = int(seed)
self._rank = comm.get_rank()
self._world_size = comm.get_... |
class Effect11516(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Shield Operation')), 'shieldBonus', ship.getModifiedItemAttr('shipBonusMC'), skill='Minmatar Cruiser', **kwargs) |
class HerbertConverter(Converter):
def converted(self) -> Tokenizer:
tokenizer_info_str = '#version:'
token_suffix = '</w>'
vocab = self.original_tokenizer.encoder
merges = list(self.original_tokenizer.bpe_ranks.keys())
if (tokenizer_info_str in merges[0][0]):
mer... |
class Vasicek(SPEulerMaruyama):
def __init__(self, theta=1.0, mu=3.0, sigma=0.5, initial=1.0, T=1.0, rng=None):
super().__init__(T=T, rng=rng)
self.theta = theta
self.mu = mu
self.sigma = sigma
self.initial = initial
self.n = 1.0
self.dt = ((1.0 * self.T) / se... |
class Foo_shamt_list_wrap(Component):
def construct(s, nbits=1):
s.in_ = InPort(nbits)
s.out = [OutPort(nbits) for _ in range(5)]
s.inner = [Foo_shamt(i) for i in range(5)]
for i in range(5):
s.inner[i].in_ //= s.in_
s.inner[i].out //= s.out[i]
def line_tr... |
class MaxPool2dSame(nn.MaxPool2d):
def __init__(self, kernel_size: int, stride=None, padding=0, dilation=1, ceil_mode=False, count_include_pad=True):
kernel_size = tuple(repeat(kernel_size, 2))
stride = tuple(repeat(stride, 2))
dilation = tuple(repeat(dilation, 2))
super(MaxPool2dSam... |
def compute_intermediate_statistics(smiles, n_jobs=1, device='cpu', batch_size=512, pool=None):
close_pool = False
if (pool is None):
if (n_jobs != 1):
pool = Pool(n_jobs)
close_pool = True
else:
pool = 1
statistics = {}
mols = mapper(pool)(get_mol, sm... |
.needs_connection
def test_calc_hitran_spectrum(verbose=True, plot=False, *args, **kwargs):
from radis import test_spectrum
s = test_spectrum(databank=('hitran', 'full'), name='full range', verbose=verbose)
s2 = test_spectrum(databank=('hitran', 'range'), name='partial range', verbose=verbose)
if plot:
... |
class TypeGuardTests(BaseTestCase):
def test_basics(self):
TypeGuard[int]
self.assertEqual(TypeGuard[int], TypeGuard[int])
def foo(arg) -> TypeGuard[int]:
...
self.assertEqual(gth(foo), {'return': TypeGuard[int]})
def test_repr(self):
if hasattr(typing, 'TypeG... |
class BytesViewTest(unittest.TestCase):
def test(self):
with self.assertRaises(TypeError):
bytesview(text_type('foobar'))
data = b'ABCDEF'
view = bytesview(data)
self.assertEqual(len(view), 16)
self.assertEqual(view[:], data)
self.assertIsInstance(view[:],... |
_rewriter(tracks=[Scan])
def transform_scan_values(fgraph: FunctionGraph, node: Apply) -> Optional[list[Apply]]:
rv_map_feature: Optional[PreserveRVMappings] = getattr(fgraph, 'preserve_rv_mappings', None)
values_to_transforms: Optional[TransformValuesMapping] = getattr(fgraph, 'values_to_transforms', None)
... |
def to_tpb_grouped_weighted_pauli_operator(operator: Union[(WeightedPauliOperator, TPBGroupedWeightedPauliOperator, MatrixOperator)], grouping_func: Callable, **kwargs: int) -> TPBGroupedWeightedPauliOperator:
if (operator.__class__ == WeightedPauliOperator):
return grouping_func(operator, **kwargs)
eli... |
class ModelWithReusedNodes(nn.Module):
def __init__(self):
super(ModelWithReusedNodes, self).__init__()
self.conv1 = nn.Conv2d(3, 8, kernel_size=2, stride=2, padding=2, bias=False)
self.bn1 = nn.BatchNorm2d(8)
self.relu1 = nn.ReLU(inplace=True)
self.relu2 = nn.ReLU(inplace=Tr... |
class EntitySummary(Base, Timestamp):
__tablename__ = 'stats_entity_summaries'
__table_args__ = (UniqueConstraint('name', 'year', 'month', name='uniq_key'),)
id = sa.Column(sa.Integer, autoincrement=True, primary_key=True)
name = sa.Column(sa.String(128), nullable=False)
count = sa.Column(sa.Integer... |
('pypyr.moduleloader.get_module')
(Step, 'invoke_step')
def test_run_pipeline_steps_complex_with_run_99_true(mock_invoke_step, mock_get_module):
step = Step({'name': 'step1', 'run': 99})
context = get_test_context()
original_len = len(context)
with patch_logger('pypyr.dsl', logging.DEBUG) as mock_logger... |
def test_kinesis_logs_producers(logs_model, mock_elasticsearch, mock_db_model, kinesis_logs_producer_config):
mock_elasticsearch.template = Mock(return_value=DEFAULT_TEMPLATE_RESPONSE)
producer_config = kinesis_logs_producer_config
with patch('botocore.endpoint.EndpointCreator.create_endpoint'), patch('boto... |
class SitExecutor(ActionExecutor):
_MAX_OCCUPANCIES = {'couch': 4, 'bed': 4, 'chair': 1, 'loveseat': 2, 'sofa': 4, 'toilet': 1, 'pianobench': 2, 'bench': 2}
def execute(self, script: Script, state: EnvironmentState, info: ExecutionInfo, char_index, modify=True, in_place=False):
current_line = script[0]
... |
def execute_terraform(args: Optional[Sequence[str]]=None, cwd: Optional[Union[(Path, str)]]=None, env: Optional[dict]=None, capture: bool=False, verbose: Optional[bool]=None) -> CompletedProcess:
if (args is None):
args = (['terraform'] + sys.argv[1:])
else:
args = (['teraform'] + list(args))
... |
class ParameterAddAction(_ActionType):
def __init__(self, parameter_ref, value):
self.parameter_ref = parameter_ref
self.value = convert_float(value)
def __eq__(self, other):
if isinstance(other, ParameterAddAction):
if ((self.get_attributes() == other.get_attributes()) and (... |
def test_geojson_find_identifier():
def _create(*properties):
return {'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'properties': item} for item in properties]}
def _assert_id_got_added(data):
_geojson = GeoJson(data)
assert (_geojson.find_identifier() == 'feature.id')
... |
def copy_file(src, dst, preserve_mode=1, preserve_times=1, update=0, link=None, verbose=1, dry_run=0):
from distutils._modified import newer
from stat import ST_ATIME, ST_MTIME, ST_MODE, S_IMODE
if (not os.path.isfile(src)):
raise DistutilsFileError(("can't copy '%s': doesn't exist or not a regular ... |
class CrossEntropyLoss(nn.Module):
def __init__(self, num_classes, epsilon=0.1, use_gpu=True, label_smooth=True):
super(CrossEntropyLoss, self).__init__()
self.num_classes = num_classes
self.epsilon = (epsilon if label_smooth else 0)
self.use_gpu = use_gpu
self.logsoftmax = n... |
def estimate_mu_sigma(cam_data, cam_data_length, cam_r, cam_base_ctr, dsp_budget, volume, dsp_l, cam_v, algo_one_para, algo):
cam_mu = {}
cam_sigma = {}
para = algo_one_para[algo]
for cam in cam_data:
data = cam_data[cam]
length = cam_data_length[cam]
profit_margins = []
... |
class SolventPsi4(SolventBase):
program: Literal['psi4'] = 'psi4'
units: Literal[('au', 'angstrom')] = Field(..., description='The units used in the input options atomic units are used by default.')
codata: Literal[(1998, 2002, 2006, 2010)] = Field(2010, description='The set of fundamental physical constant... |
class ThriftTraceHeaderTests(GeventPatchedTestCase):
def test_user_agent(self):
class Handler(TestService.Iface):
def example(self, context):
return True
handler = Handler()
server_span_observer = mock.Mock(spec=ServerSpanObserver)
with serve_thrift(handle... |
def merge_meshes(meshes: List[trimesh.Trimesh]):
(n, vs, fs) = (0, [], [])
for mesh in meshes:
(v, f) = (mesh.vertices, mesh.faces)
vs.append(v)
fs.append((f + n))
n = (n + v.shape[0])
if n:
return trimesh.Trimesh(np.vstack(vs), np.vstack(fs))
else:
return... |
def render_lines(lines, font, color, lw, lh, lh_offset):
surface = pygame.Surface((lw, ((lh + lh_offset) * len(lines))), flags=pygame.SRCALPHA)
y_offset = 0
for line in lines:
font_surface = font.render(line, True, color)
surface.blit(font_surface, (0, y_offset))
y_offset += (lh + lh... |
def check(a):
if (a == '0'):
return 6
elif (a == '1'):
return 2
elif (a == '2'):
return 5
elif (a == '3'):
return 5
elif (a == '4'):
return 4
elif (a == '5'):
return 5
elif (a == '6'):
return 6
elif (a == '7'):
return 3
... |
def test_channelstate_filters():
test_state = factories.make_chain_state(number_of_channels=5)
chain_state = test_state.chain_state
token_network_registry_address = test_state.token_network_registry_address
token_address = test_state.token_address
(channel_open, channel_closing, channel_closed, chan... |
def test_saturation_describing_function(satsys):
satfcn = saturation_class()
amprange = np.linspace(0, 10, 100)
df_anal = [satfcn.describing_function(a) for a in amprange]
df_fcn = ct.describing_function(saturation, amprange)
np.testing.assert_almost_equal(df_fcn, df_anal, decimal=3)
df_fcn = ct... |
def main():
args = parse_args()
assert (len(args.scores) == len(args.coefficients))
score_list = args.scores
score_list = [load(f) for f in score_list]
if args.apply_softmax:
def apply_softmax(scores):
return [softmax(score) for score in scores]
score_list = [apply_softma... |
def train(args, accelerator, model, tokenizer, train_dataloader, optimizer, lr_scheduler, eval_dataloader=None):
total_batch_size = ((args.per_device_train_batch_size * accelerator.num_processes) * args.gradient_accumulation_steps)
logger.info('***** Running training *****')
logger.info(' Num examples = %d... |
(frozen=True, order=True)
class KeyInfo():
key: Qt.Key
modifiers: _ModifierType = Qt.KeyboardModifier.NoModifier
def __post_init__(self) -> None:
if machinery.IS_QT5:
modifier_classes = (Qt.KeyboardModifier, Qt.KeyboardModifiers)
elif machinery.IS_QT6:
modifier_classe... |
class MatchStmt(Statement):
__slots__ = ('subject', 'patterns', 'guards', 'bodies')
__match_args__ = ('subject', 'patterns', 'guards', 'bodies')
subject: Expression
patterns: list[Pattern]
guards: list[(Expression | None)]
bodies: list[Block]
def __init__(self, subject: Expression, patterns:... |
def score_target_hypo(args, a, b, c, lenpen, target_outfile, hypo_outfile, write_hypos, normalize):
print('lenpen', lenpen, 'weight1', a, 'weight2', b, 'weight3', c)
(gen_output_lst, bitext1_lst, bitext2_lst, lm_res_lst) = load_score_files(args)
dict = dictionary.Dictionary()
scorer = bleu.Scorer(dict.p... |
class ChineseCLIPProcessor(ProcessorMixin):
attributes = ['image_processor', 'tokenizer']
image_processor_class = 'ChineseCLIPImageProcessor'
tokenizer_class = ('BertTokenizer', 'BertTokenizerFast')
def __init__(self, image_processor=None, tokenizer=None, **kwargs):
if ('feature_extractor' in kw... |
class TableWidgetItem(QtWidgets.QTableWidgetItem):
def __init__(self, val, index, format=None):
QtWidgets.QTableWidgetItem.__init__(self, '')
self._blockValueChange = False
self._format = None
self._defaultFormat = '%0.3g'
self.sortMode = 'value'
self.index = index
... |
class MixedConv2d(nn.ModuleDict):
def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding='', dilation=1, depthwise=False, **kwargs):
super(MixedConv2d, self).__init__()
kernel_size = (kernel_size if isinstance(kernel_size, list) else [kernel_size])
num_groups = len(ke... |
def centroid_and_bbox_from_coords(coords):
if isinstance(coords, pd.Series):
coords = coords.to_list()[0]
elif isinstance(coords, pd.DataFrame):
coords = list(zip(coords.X, coords.Y))
xs = [xys[0] for xys in coords]
ys = [xys[1] for xys in coords]
xc = (sum(xs) / len(xs))
yc = (s... |
class ModelSingleTagFieldInvalidTest(TagTestManager, TransactionTestCase):
manage_models = [test_models.SingleTagFieldModel]
def test_invalid_to_model(self):
with self.assertRaises(ValueError) as cm:
class FailModel_invalid_to(models.Model):
to_model = tag_models.SingleTagFie... |
class TabBar(QtWidgets.QTabBar):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.setDrawBase(False)
self.setExpanding(False)
self.setElideMode(Qt.ElideRight)
def tabSizeHint(self, index):
size = QtWidgets.QTabBar.tabSizeHint(self, index)
... |
class TagReader():
label2id_map = {'<START>': 0}
def read_inst(cls, file, is_labeled, number, opinion_offset):
insts = []
inputs = []
outputs = []
f = open(file, 'r', encoding='utf-8')
if (not is_labeled):
opinion_offset = 10000
for line in f:
... |
def run_procedure():
global frenum
global begin_time
global end_time
global flag
read_file()
min_sigma()
store_into_vec()
begin_time = time.perf_counter()
dealingfirstlevel(freArr)
global f_level
if ((flag == 1) or (flag == 4)):
f_level = 1
gen_candidate(f_lev... |
class UploadedFile():
def __init__(self, filename, contents, mime_type):
assert isinstance(contents, bytes)
self.contents = contents
self.filename = filename
self.mime_type = mime_type
def size(self):
return len(self.contents)
def open(self):
with io.BytesIO(s... |
def run_data_migration(apps, schema_editor):
Catalog = apps.get_model('questions', 'Catalog')
Section = apps.get_model('questions', 'Section')
QuestionSet = apps.get_model('questions', 'QuestionSet')
Question = apps.get_model('questions', 'Question')
set_null_to_blank(Catalog.objects.all(), ['uri', ... |
def _interpolate(name, dim, interpolate_mode):
def symbolic_fn(g, input, output_size, *args):
(scales, align_corners) = sym_help._get_interpolate_attributes(g, interpolate_mode, args)
align_corners = sym_help._maybe_get_scalar(align_corners)
transformation_mode = ('asymmetric' if (interpolat... |
class XlibWindow(BaseWindow):
_x_display = None
_x_screen_id = None
_x_ic = None
_window = None
_override_redirect = False
_x = 0
_y = 0
_mouse_exclusive_client = None
_mouse_buttons = ([False] * 6)
_active = True
_applied_mouse_exclusive = False
_applied_keyboard_exclusi... |
def all_gather_list(data, group=None, max_size=16384):
if (group is None):
group = get_global_group()
rank = get_rank(group=group)
world_size = get_world_size(group=group)
buffer_size = (max_size * world_size)
if ((not hasattr(all_gather_list, '_buffer')) or (all_gather_list._buffer.numel() ... |
class TabbedBrowserStub(QObject):
def __init__(self, parent=None):
super().__init__(parent)
self.widget = TabWidgetStub()
self.is_shutting_down = False
self.loaded_url = None
self.cur_url = None
self.undo_stack = None
def on_tab_close_requested(self, idx):
... |
class TripletsNet6c(VGGNet):
def __init__(self, config):
super(TripletsNet6c, self).__init__()
self.trunk = ClusterNet6cTrunk(config)
self.head = TripletsNet6cHead(config)
self._initialize_weights()
def forward(self, x, kmeans_use_features=False):
x = self.trunk(x)
... |
class MultiTree():
def __init__(self, value, parent):
self.parent = parent
self.value = value
self.children = []
def add_children(self, children):
self.children.append(children)
def add_value(self, values: str, ac, separators: str='-'):
value = values.split(separators... |
def _check_structure_field(name: str, dtype_tuple: Tuple[(np.dtype, int)], target: 'Structure', type_per_name_with_wildcard: Dict[(str, type)]) -> bool:
dtype = dtype_tuple[0]
target_type_name = target.get_type(name)
target_type_shape_match = re.search(_REGEX_FIELD_SHAPE, target_type_name)
actual_type =... |
class DataTrainingArguments():
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to use (via the datasets library).'})
dataset_config_name: Optional[str] = field(default=None, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}... |
def test_realloc_3_1_5_4_6():
allocator = RegionAllocator(1000)
regions = []
for i in range(10):
regions.append(allocator.alloc(3))
for region in regions:
allocator.realloc(region, 1)
for region in regions:
allocator.realloc(region, 5)
for region in regions:
alloc... |
def prototype_view(proto, org_members):
def prototype_user_view(user):
return {'name': user.username, 'is_robot': user.robot, 'kind': 'user', 'is_org_member': (user.robot or (user.username in org_members)), 'avatar': avatar.get_data_for_user(user)}
if proto.delegate_user:
delegate_view = prototy... |
def is_torch_tpu_available():
if (not _torch_available):
return False
if (importlib.util.find_spec('torch_xla') is None):
return False
if (importlib.util.find_spec('torch_xla.core') is None):
return False
return (importlib.util.find_spec('torch_xla.core.xla_model') is not None) |
def evaluate_one_dataset(LOG, dataloader, model, opt, save=True, give_return=True, epoch=0):
start = time.time()
image_paths = np.array(dataloader.dataset.image_list)
with torch.no_grad():
(F1, NMI, recall_at_ks, feature_matrix_all) = aux.eval_metrics_one_dataset(model, dataloader, device=opt.device... |
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