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def find_constraint_latent(model, x, y, w, relaxed=True):
h = model.latent(x, y, w)
h_hat = model.loss_augmented_inference(x, h, w, relaxed=relaxed)
joint_feature = model.joint_feature
delta_joint_feature = (joint_feature(x, h) - joint_feature(x, h_hat))
loss = model.loss(y, h_hat)
slack = max((... |
class TestUVCCVSCF(QiskitChemistryTestCase):
def setUp(self):
super().setUp()
aqua_globals.random_seed = 8
self.reference_energy = 592.
def test_uvcc_vscf(self):
co2_2modes_2modals_2body = [[[[[0, 0, 0]], 320.], [[[0, 1, 1]], 1760.], [[[1, 0, 0]], 342.], [[[1, 1, 1]], 1032.]], [[... |
class _CookieCacheManager():
_Cookie_cache = None
def get_cookie_cache(cls):
if (cls._Cookie_cache is None):
with _cache_init_lock:
cls._initialise()
return cls._Cookie_cache
def _initialise(cls, cache_dir=None):
cls._Cookie_cache = _CookieCache() |
def howAboutNow():
global delayTime, RUNTIME, READYTIME
ticks = int(time.time())
if (ticks >= RUNTIME):
return 'T_RUN'
elif (ticks >= READYTIME):
delayTime = (0.2 if ((RUNTIME - ticks) < 15) else 8)
return 'T_READY'
else:
howlong = (RUNTIME - ticks)
if (howlon... |
def dice(gt, pred, label=None):
(gt, pred) = to_numpy(gt, pred)
if (label is None):
(gt, pred) = (gt.astype(np.bool), pred.astype(np.bool))
else:
(gt, pred) = ((gt == label), (pred == label))
intersection = np.logical_and(gt, pred)
return ((2.0 * intersection.sum()) / (gt.sum() + pre... |
class UniterEncoder(nn.Module):
def __init__(self, args):
super().__init__()
self.max_seq_length = args.max_seq_length
set_visual_config(args)
self.tokenizer = BertTokenizer.from_pretrained('bert-base-cased', do_lower_case=True)
self.model = UFE.from_pretrained('bert-base-cas... |
class TabToolButton(QtWidgets.QToolButton):
SIZE = (16, 16)
def __init__(self, *args):
QtWidgets.QToolButton.__init__(self, *args)
self.setIconSize(QtCore.QSize(*self.SIZE))
self.setStyleSheet('QToolButton{ border: none; }')
def mousePressEvent(self, event):
event.ignore() |
class StridedBottomUpStack(BottomUpStackInterface):
def __init__(self, strided_block: StridedBlockInterface, layer_num_blocks: List[int], strided_reduction: bool) -> None:
self._strided_block = strided_block
self._layer_num_blocks = layer_num_blocks
self._strided_reduction = strided_reductio... |
def validate(model, dataloader, criterion):
model.eval()
device = model.device
epoch_start = time.time()
running_loss = 0.0
preds = []
golds = []
with torch.no_grad():
for batch in dataloader:
premises = batch['premise'].to(device)
premises_lengths = batch['pr... |
def _load_libr_all(directory: Path):
changed = True
loaded = set()
loaded_dlls = {}
while changed:
changed = False
for p in directory.iterdir():
if (p in loaded):
continue
if (p.is_file() and p.name.endswith('dll')):
try:
... |
def test_cache_metadata(disk_cache):
url = '
metadata = QNetworkCacheMetaData()
metadata.setUrl(QUrl(url))
assert metadata.isValid()
device = disk_cache.prepare(metadata)
device.write(b'foobar')
disk_cache.insert(device)
assert (disk_cache.metaData(QUrl(url)) == metadata) |
def test_routing_in_direct_channel(happy_path_fixture, our_signer, one_to_n_address):
(addresses, chain_state, _, _, token_network_state) = happy_path_fixture
(address1, _, _, _) = addresses
pfs_proxy = PFSProxy(PFS_CONFIG)
with patch('raiden.routing.get_best_routes_pfs') as pfs_route_request, patch.obj... |
def plot_pie(AU_list, pos_freq, neg_freq):
ploting_labels = ([(x + '+ {0:.2f}'.format(y)) for (x, y) in zip(AU_list, pos_freq)] + [(x + '- {0:.2f}'.format(y)) for (x, y) in zip(AU_list, neg_freq)])
cmap = matplotlib.cm.get_cmap('coolwarm')
colors = ([cmap(x) for x in pos_freq] + [cmap(x) for x in neg_freq])... |
def test_named_signals():
ct.iosys.InputOutputSystem._idCounter = 0
h1 = TransferFunction([1], [1, 2, 2])
h2 = TransferFunction([1], [0.1, 1])
omega = np.logspace((- 1), 2, 10)
f1 = FRD(h1, omega)
f2 = FRD(h2, omega)
assert (f1.name == 'sys[2]')
assert (f2.name == 'sys[3]')
assert (f... |
class unet_3D_ds(nn.Module):
def __init__(self, feature_scale=4, n_classes=21, is_deconv=True, in_channels=3, is_batchnorm=True):
super(unet_3D_ds, self).__init__()
self.is_deconv = is_deconv
self.in_channels = in_channels
self.is_batchnorm = is_batchnorm
self.feature_scale =... |
class EMAModel():
def __init__(self, model, update_after_step=0, inv_gamma=1.0, power=(2 / 3), min_value=0.0, max_value=0.9999):
self.averaged_model = model
self.averaged_model.eval()
self.averaged_model.requires_grad_(False)
self.update_after_step = update_after_step
self.in... |
def pointer(encoded_ref, query, mask, W_ref, W_q, v, C=10.0, temperature=1.0):
encoded_query = tf.expand_dims(tf.matmul(query, W_q), 1)
scores = tf.reduce_sum((v * tf.tanh((encoded_ref + encoded_query))), [(- 1)])
scores = (C * tf.tanh((scores / temperature)))
masked_scores = tf.clip_by_value((scores - ... |
class QuantizableInceptionAux(InceptionAux):
def __init__(self, *args, **kwargs):
super(QuantizableInceptionAux, self).__init__(*args, conv_block=QuantizableBasicConv2d, **kwargs)
self.relu = nn.ReLU()
self.dropout = nn.Dropout(0.7)
def forward(self, x):
x = F.adaptive_avg_pool2d... |
def test_spin_pairs_iter():
spinful_lattice = HubbardSquareLattice(3, 3)
with pytest.raises(ValueError):
spinful_lattice.spin_pairs_iter(10)
assert (tuple(spinful_lattice.spin_pairs_iter(SpinPairs.ALL, True)) == ((0, 0), (0, 1), (1, 0), (1, 1)))
assert (tuple(spinful_lattice.spin_pairs_iter(Spin... |
class ScalarAnalysis(object):
def __init__(self, scalar, empty, multiline, allow_flow_plain, allow_block_plain, allow_single_quoted, allow_double_quoted, allow_block):
self.scalar = scalar
self.empty = empty
self.multiline = multiline
self.allow_flow_plain = allow_flow_plain
... |
class PEPConverterTests(TestCase):
def test_source_link(self):
pep = get_pep_page(FAKE_PEP_REPO, '0525')
self.assertEqual(pep.title, 'PEP 525 -- Asynchronous Generators')
self.assertIn('Source: <a href=" pep.content.rendered)
def test_source_link_rst(self):
pep = get_pep_page(FAK... |
def _create_threshold_tensor(threshold: Union[(int, List[float], torch.Tensor)], device: torch.device) -> torch.Tensor:
if isinstance(threshold, int):
threshold = torch.linspace(0, 1.0, threshold, device=device)
elif isinstance(threshold, list):
threshold = torch.tensor(threshold, device=device)... |
def _add_property(ClassType, name):
fget = (lambda self: self[name])
fset = (lambda self, value: self.set_attribute(name, value))
fdel = (lambda self: self.set_attribute(name, None))
fdoc = 'This is the %s attribute for object definition'
setattr(ClassType, name, property(fget, fset, fdel, fdoc)) |
class Effect6301(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Energy Nosferatu')), 'falloffEffectiveness', src.getModifiedItemAttr('shipBonusAC2'), skill='Amarr Cruiser', **kwargs)
fit... |
def get_timezone() -> Optional[timezone]:
ctx = _get_current_context()
tzinfo = getattr(ctx, 'babel_tzinfo', None)
if (tzinfo is None):
babel = get_babel()
if (babel.timezone_selector is None):
tzinfo = babel.instance.default_timezone
else:
rv = babel.timezone... |
class FaceFilter():
def __init__(self, reference_file_paths, nreference_file_paths, detector, aligner, loglevel, multiprocess=False, threshold=0.4):
logger.debug('Initializing %s: (reference_file_paths: %s, nreference_file_paths: %s, detector: %s, aligner: %s. loglevel: %s, multiprocess: %s, threshold: %s)'... |
class Distribution(torch.Tensor):
def init_distribution(self, dist_type, **kwargs):
self.dist_type = dist_type
self.dist_kwargs = kwargs
if (self.dist_type == 'normal'):
(self.mean, self.var) = (kwargs['mean'], kwargs['var'])
elif (self.dist_type == 'categorical'):
... |
def render_pep8_errors_e224_and_e273(msg, _node, source_lines):
line = msg.line
res = re.search('column (\\d+)', msg.msg)
col = int(res.group().split()[(- 1)])
curr_idx = ((col + len(source_lines[(line - 1)][col:])) - len(source_lines[(line - 1)][col:].lstrip('\t')))
(yield from render_context((line... |
def cmd_compile(options):
from .compile import parse_config, BenchmarkRevision
conf = parse_config(options.config_file, 'compile')
if (options is not None):
if options.no_update:
conf.update = False
if options.no_tune:
conf.system_tune = False
bench = BenchmarkRev... |
class StatusAction(Action):
def __call__(self, twitter, options):
statuses = self.getStatuses(twitter, options)
sf = get_formatter('status', options)
for status in statuses:
statusStr = sf(status, options)
if statusStr.strip():
printNicely(statusStr) |
class DirectedAcyclicGraph():
def __init__(self, exposure, outcome):
self.exposure = exposure
self.outcome = outcome
dag = nx.DiGraph()
dag.add_edge(self.exposure, self.outcome)
self.dag = dag
self.adjustment_sets = None
self.minimal_adjustment_sets = None
... |
def quantization_aware_training_range_learning():
tf.reset_default_graph()
parser2 = tf_gen.MnistParser(batch_size=100, data_inputs=['reshape_input'])
generator = tf_gen.TfRecordGenerator(tfrecords=[os.path.join('data', 'mnist', 'validation.tfrecords')], parser=parser2)
sess = graph_saver.load_model_fro... |
class AoAModel3_no_p(AttModel):
def __init__(self, opt):
super(AoAModel3_no_p, self).__init__(opt)
self.num_layers = 2
self.use_mean_feats = getattr(opt, 'mean_feats', 1)
if (opt.use_multi_head == 2):
del self.ctx2att
self.ctx2att = nn.Linear(opt.rnn_size, ((2... |
.parametrize('command_and_args, text, is_error', [('hint --flag foo --', '', False), ('hint --flag foo --help', '', False), ('hint --flag foo', '--', False), ('nargs --one_or_more one --', '', False), ('nargs --one_or_more one or --set_value', '', False), ('nargs --one_or_more one or more', '--', False), ('nargs --set_... |
class ButtonList():
def __init__(self, stdscr, on_click, buttons=[], info={}):
self.buttons = []
self.on_click = on_click
self.stdscr = stdscr
self.info = info
self._selected = ''
for button in buttons:
self.add_button(button)
def add_button(self, labe... |
def add_command_line_args_to_variant_spec(variant_spec, command_line_args):
variant_spec['run_params'].update({'checkpoint_frequency': (command_line_args.checkpoint_frequency if (command_line_args.checkpoint_frequency is not None) else variant_spec['run_params'].get('checkpoint_frequency', 0)), 'checkpoint_at_end':... |
def partition_into_regions(vcf_path: PathType, *, index_path: Optional[PathType]=None, num_parts: Optional[int]=None, target_part_size: Union[(None, int, str)]=None, storage_options: Optional[Dict[(str, str)]]=None) -> Optional[Sequence[str]]:
if ((num_parts is None) and (target_part_size is None)):
raise V... |
_ASSIGNERS.register_module()
class LaneHungarianAssigner(HungarianAssigner):
def assign(self, lane_pred, cls_pred, gt_lanes, gt_labels, img_meta, gt_lanes_ignore=None, eps=1e-07):
assert (gt_lanes_ignore is None), 'Only case when gt_lanes_ignore is None is supported.'
(num_gts, num_lanes) = (gt_lane... |
class EntityTrigger(_TriggerType):
def __init__(self, name, delay, conditionedge, entitycondition, triggerentity, triggeringrule=TriggeringEntitiesRule.any, triggeringpoint='start'):
self.name = name
if (triggeringpoint not in ['start', 'stop']):
raise ValueError('not a valid triggering ... |
class ToggleTheme(Mutation):
theme = String()
_required
def mutate(_root, info):
user = info.context.user
if (user.theme == Author.Theme.DARK):
user.theme = Author.Theme.LIGHT
user.save(update_fields=['theme'])
return ToggleTheme(user.theme)
user.t... |
class LogReader(object):
def __init__(self, path):
self.path = path
with open(self.path, 'r') as f:
self.doc = f.read()
def isfloat(self, str):
try:
float(str)
return True
except ValueError:
return False
def casttype(self, str):... |
class MBConvBlock(nn.Module):
def __init__(self, ksize, input_filters, output_filters, expand_ratio=1, stride=1, image_size=224):
super().__init__()
self._bn_mom = 0.1
self._bn_eps = 0.01
self._se_ratio = 0.25
self._input_filters = input_filters
self._output_filters =... |
def _resolve_transformers_model(model_family):
di = {'mbart50': transformers.MBartForConditionalGeneration, 'm2m100': transformers.M2M100ForConditionalGeneration, 'nllb200': transformers.AutoModelForSeq2SeqLM}
if (model_family in di):
return di[model_family]
else:
error_msg = f'{model_family... |
def verify_model_dtype(model):
dtype2param_num = defaultdict(int)
dtype2param_name = defaultdict(list)
dtype2trainable_param_num = defaultdict(int)
dtype2trainable_param_name = defaultdict(list)
for (name, p) in model.named_parameters():
dtype = p.dtype
dtype2param_num[dtype] += p.nu... |
.parametrize('manager', [BareConfig, ManagerConfig], indirect=True)
def test_setgroup(manager):
manager.test_window('one')
manager.c.group['b'].toscreen()
manager.groupconsistency()
if (len(manager.c.get_screens()) == 1):
assert (manager.c.get_groups()['a']['screen'] is None)
else:
a... |
def cmdparser(raw_string, cmdset, caller, match_index=None):
if (not raw_string):
return []
matches = build_matches(raw_string, cmdset, include_prefixes=True)
if (not matches):
(mindex, new_raw_string) = try_num_prefixes(raw_string)
if (mindex is not None):
return cmdpars... |
class F9_RaidData(F7_RaidData):
removedKeywords = (F7_RaidData.removedKeywords + ['bytesPerInode'])
removedAttrs = (F7_RaidData.removedAttrs + ['bytesPerInode'])
def __init__(self, *args, **kwargs):
F7_RaidData.__init__(self, *args, **kwargs)
self.deleteRemovedAttrs()
self.fsprofile ... |
_element(invalid_data_behavior={'warn', 'raise', 'ignore'})
def winsorise_uint32(df, invalid_data_behavior, column, *columns):
columns = list(((column,) + columns))
mask = (df[columns] > UINT32_MAX)
if (invalid_data_behavior != 'ignore'):
mask |= df[columns].isnull()
else:
df[columns] = ... |
def test_create_spawn_point_field(echoes_game_description, echoes_pickup_database, empty_patches):
resource_db = echoes_game_description.resource_database
morph = pickup_creator.create_standard_pickup(echoes_pickup_database.get_pickup_with_name('Morph Ball'), StandardPickupState(), resource_db, None, False)
... |
def test_kw_only_decorator() -> None:
(foodef, bardef, cee, dee) = astroid.extract_node('\n from dataclasses import dataclass\n\n (kw_only=True)\n class Foo:\n a: int\n e: str\n\n\n (kw_only=False)\n class Bar(Foo):\n c: int\n\n\n (kw_only=False)\n class Cee(Bar):\n ... |
def ground_union(head_grounding, sql_unit, i_op, qdmr, sql_spider_data, table_data, grounding_out):
assert (qdmr.ops[i_op] == 'union')
args = qdmr.args[i_op]
if (len(head_grounding) == len(args)):
for (grnd, arg) in zip(head_grounding, args):
i_op_arg = QdmrInstance.ref_to_index(arg, max... |
class DevDataset(Dataset):
def __init__(self, args, raw_datasets, cache_root):
self.raw_datasets = raw_datasets
cache_path = os.path.join(cache_root, 'grailqa_dev.cache')
if (os.path.exists(cache_path) and args.dataset.use_cache):
self.extended_data = torch.load(cache_path)
... |
_fixtures(GitFixture)
def test_is_version_controlled(git_fixture):
fixture = git_fixture
non_initialised_directory = fixture.new_git_directory(initialised=False)
git = Git(non_initialised_directory.name)
assert (not git.is_version_controlled())
git = Git(fixture.git_directory.name)
assert git.is... |
def fix_jukebox_keys(state_dict, model_state_dict, key_prefix, mapping):
new_dict = {}
import re
re_encoder_block_conv_in = re.compile('encoders.(\\d*).level_blocks.(\\d*).model.(\\d*).(\\d).(bias|weight)')
re_encoder_block_resnet = re.compile('encoders.(\\d*).level_blocks.(\\d*).model.(\\d*).(\\d).mode... |
def insert_cylinder(arr, cyl_radius=4, cyl_height=2, cyl_centre=(0, 0, 0)):
arr_copy = arr[:]
(x, y, z) = np.indices(arr.shape)
if (not hasattr(cyl_radius, '__iter__')):
cyl_radius = ([cyl_radius] * 2)
condition_radial = (((((z - cyl_centre[0]) / cyl_radius[0]) ** 2) + (((y - cyl_centre[1]) / cy... |
def _get_dataclass_attributes(node: nodes.ClassDef, init: bool=False) -> Iterator[nodes.AnnAssign]:
for assign_node in node.body:
if ((not isinstance(assign_node, nodes.AnnAssign)) or (not isinstance(assign_node.target, nodes.AssignName))):
continue
if _is_class_var(assign_node.annotatio... |
_2_unicode_compatible
class ConferenceModerator(AuditModel):
conference = models.ForeignKey(Conference, related_name='moderators', on_delete=models.CASCADE)
moderator = models.ForeignKey(User, on_delete=models.CASCADE)
active = models.BooleanField(default=True, verbose_name='Is Active?')
class Meta():
... |
class MCSls(object):
def __init__(self, formula, use_cld=False, solver_name='m22', use_timer=False):
self.oracle = Solver(name=solver_name, bootstrap_with=formula.hard, use_timer=use_timer)
self.solver = solver_name
if (isinstance(formula, WCNFPlus) and formula.atms):
assert self... |
def order_and_id_of_texts(found_polygons_text_region, found_polygons_text_region_h, matrix_of_orders, indexes_sorted, index_of_types, kind_of_texts, ref_point):
indexes_sorted = np.array(indexes_sorted)
index_of_types = np.array(index_of_types)
kind_of_texts = np.array(kind_of_texts)
id_of_texts = []
... |
def iload_stationxml(sx, segment, content):
inut = 0
far_future = (time.time() + (20 * Y))
from pyrocko.io import stationxml
value_or_none = stationxml.value_or_none
for network in sx.network_list:
for station in network.station_list:
net = network.code
sta = station.... |
class BasicLayer(nn.Module):
def __init__(self, dim, out_dim, input_resolution, depth, num_heads, window_size, mlp_ratio=4.0, qkv_bias=True, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, norm_layer=nn.LayerNorm, downsample=None):
super().__init__()
self.dim = dim
self.input_resoluti... |
class RTStructBuilder():
def create_new(dicom_series_path: str) -> RTStruct:
series_data = image_helper.load_sorted_image_series(dicom_series_path)
ds = ds_helper.create_rtstruct_dataset(series_data)
return RTStruct(series_data, ds)
def create_from(dicom_series_path: str, rt_struct_path:... |
class HARSave(BaseHandler):
env = Fetcher().jinja_env
def get_variables(env, tpl):
variables = set()
extracted = set(utils.jinja_globals.keys())
loop_extracted = set(('loop_index0', 'loop_index', 'loop_first', 'loop_last', 'loop_length', 'loop_revindex0', 'loop_revindex', 'loop_depth', '... |
def main():
parser = argparse.ArgumentParser(description='Process Markdown according to the CommonMark specification.')
if (sys.version_info < (3, 0)):
reload(sys)
sys.setdefaultencoding('utf-8')
parser.add_argument('infile', nargs='?', type=argparse.FileType('r'), default=sys.stdin, help='I... |
def update_hints_text(game: RandovaniaGame, hint_item_names_tree_widget: QtWidgets.QTableWidget):
pickup_database = default_database.pickup_database_for_game(game)
rows = []
for item in pickup_database.standard_pickups.values():
rows.append((item.name, item.pickup_category.hint_details[1], item.pick... |
def test_imbalance_penalty_with_barely_sufficient_balance():
imbalance_penalty = calculate_imbalance_fees(channel_capacity=TokenAmount(20), proportional_imbalance_fee=ProportionalFeeAmount(1))
(pair, _) = _foward_transfer_pair(TokenAmount(10), NettingChannelStateProperties(our_state=NettingChannelEndStateProper... |
def to_starred_table_for_no_overlap2(x1, x2, y1, y2, w1, w2, h1, h2):
t = []
_non_overlapping_tuples_for(t, x1.dom, x2.dom, w1, True, True)
_non_overlapping_tuples_for(t, x2.dom, x1.dom, w2, False, True)
_non_overlapping_tuples_for(t, y1.dom, y2.dom, h1, True, False)
_non_overlapping_tuples_for(t, y... |
class AmmoPickerContents(wx.ScrolledCanvas):
indent = 15
def __init__(self, parent, fit):
wx.ScrolledCanvas.__init__(self, parent)
self.SetScrollRate(0, 15)
mods = self.getMods(fit)
drones = self.getDrones(fit)
fighters = self.getFighters(fit)
self.rbLabelMap = {}... |
def trashicra_to_detectwaste(label):
metals_and_plastics = ['plastic', 'metal', 'rubber']
non_recyclable = ['cloth', 'paper']
bio = ['wood']
unknown = ['unknown']
if (label in metals_and_plastics):
label = 'metals_and_plastics'
elif (label in non_recyclable):
label = 'non-recycla... |
class ResNetBasicblock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, last_relu=True):
super(ResNetBasicblock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride)
self.bn1 = nn.BatchNorm2d(planes)
self.relu = nn.ReLU(inplace=... |
('/repository/<repopath:repository>/status', methods=['GET'])
_repository_name()
_protect
def build_status_badge(namespace_name, repo_name):
token = request.args.get('token', None)
repo = model.repository.get_repository(namespace_name, repo_name)
if (repo and (repo.kind.name != 'image')):
abort(404)... |
def match_graph(loc_dict, max_loc, max_obj_perloc):
num_locations = len(loc_dict)
chosen_locs = np.random.choice(range(num_locations), min(max_loc, num_locations), replace=False)
new_loc_dict = {}
count = 0
for (l, attr) in loc_dict.items():
cur_index = attr['index']
obj_list = attr[... |
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10, drop=False):
super(ResNet, self).__init__()
self.drop = drop
self.in_planes = 64
self.conv1 = conv3x3(3, 64)
self.bn1 = nn.BatchNorm2d(64)
self.layer1 = self._make_layer(block, 64, num_bloc... |
class DPRQuestionEncoderTokenizerFast(BertTokenizerFast):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_init_configuration = QUESTION_ENCODER_PRETRA... |
class Migration(migrations.Migration):
dependencies = [('sponsors', '0071_auto__1843')]
operations = [migrations.AddField(model_name='requiredtextasset', name='max_length', field=models.IntegerField(blank=True, default=None, help_text='Limit to length of the input, empty means unlimited', null=True)), migration... |
class KombuThriftSerializer(KombuSerializer[T]):
def __init__(self, thrift_class: Type[T], protocol_factory: TProtocolFactory=TBinaryProtocolAcceleratedFactory()):
self.thrift_class = thrift_class
self.factory = protocol_factory
def name(self) -> str:
return f'thrift-{self.thrift_class._... |
def process(data):
(candidates, references, pool_id) = data
cnt = len(candidates)
current_time = time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime())
tmp_dir = 'rouge-tmp-{}-{}'.format(current_time, pool_id)
if (not os.path.isdir(tmp_dir)):
os.mkdir(tmp_dir)
os.mkdir((tmp_dir + '/can... |
def deprecated(value: object, module_name: str, message: str, warning_class: type[Warning], name: (str | None)=None) -> _DeprecatedValue:
module = sys.modules[module_name]
if (not isinstance(module, _ModuleWithDeprecations)):
sys.modules[module_name] = module = _ModuleWithDeprecations(module)
dv = _... |
def validate_branch_ops(conn_graph: ConnectedGraph):
def check_for_branch_op(op_info: ModuleIdentifierOpInfo):
op = conn_graph.get_all_ops()[op_info.module_name]
return_bool = True
product = op.output
if ('branch' not in product.name):
logger.error('branch not in product ... |
def timebase(sys, strict=True):
if isinstance(sys, (int, float, complex, np.number)):
return None
elif (not isinstance(sys, InputOutputSystem)):
raise ValueError('Timebase not defined')
if (sys.dt == None):
return None
elif strict:
return float(sys.dt)
return sys.dt |
def start_all_dummy_clients(nclients):
global NCLIENTS
NCLIENTS = int(nclients)
actions = DUMMYRUNNER_SETTINGS.ACTIONS
if (len(actions) < 2):
print(ERROR_FEW_ACTIONS)
return
pratio = (1.0 / sum((tup[0] for tup in actions[2:])))
(flogin, flogout, probs, cfuncs) = (actions[0], acti... |
class ReplaceDataset(BaseWrapperDataset):
def __init__(self, dataset, replace_map, offsets):
super().__init__(dataset)
assert (len(replace_map) > 0)
self.replace_map = replace_map
self.offsets = offsets
def __getitem__(self, index):
item = self.dataset[index]
is_t... |
class HP6634A(HP6632A):
def __init__(self, adapter, name='Hewlett Packard HP6634A', **kwargs):
super().__init__(adapter, name, **kwargs)
current_values = [0, limits['HP6634A']['Cur_lim']]
OVP_values = [0, limits['HP6634A']['OVP_lim']]
voltage_values = [0, limits['HP6634A']['Volt_lim']] |
def convert(framework: str, model: str, output: Path, opset: int, tokenizer: Optional[str]=None, use_external_format: bool=False, pipeline_name: str='feature-extraction', **model_kwargs):
warnings.warn('The `transformers.convert_graph_to_onnx` package is deprecated and will be removed in version 5 of Transformers',... |
def annotation_difference(first, second):
union = set(first).union(set(second))
first_not_second = union.difference(set(second))
second_not_first = union.difference(set(first))
total = (len(first_not_second) + len(second_not_first))
return (first_not_second, second_not_first, total) |
def pytorch2onnx(model, input_shape, opset_version=11, show=False, output_file='tmp.onnx', verify=False):
model.cpu().eval()
one_img = torch.randn(input_shape)
register_extra_symbolics(opset_version)
torch.onnx.export(model, one_img, output_file, export_params=True, keep_initializers_as_inputs=True, ver... |
def test():
cmd = argparse.ArgumentParser('The testing components of')
cmd.add_argument('--gpu', default=(- 1), type=int, help='use id of gpu, -1 if cpu.')
cmd.add_argument('--input', help='the path to the raw text file.')
cmd.add_argument('--model', required=True, help='path to save model')
cmd.add... |
.xfail
def test_tell_in_random_order(first_add_33=False):
import random
from operator import attrgetter
tol = 1e-10
for (f, a, b) in ([f0, 0, 3], [f21, 0, 1], [f24, 0, 3], [f7, 0, 1]):
learners = []
for shuffle in [True, False]:
learner = IntegratorLearner(f, bounds=(a, b), t... |
def get_ghz_simple(n: int, measure: bool=True, full_measurement: bool=True) -> QuantumCircuit:
q = QuantumRegister(n, 'q')
circ = QuantumCircuit(q)
circ.h(q[0])
for i in range(1, n):
circ.cx(q[(i - 1)], q[i])
if measure:
meas = get_measurement_circ(n, 'q', 'c', full_measurement)
... |
class MultilingualLinear(torch.nn.Module):
def __init__(self, input_size, output_size, n_factors=1, rank=1, use_multiplicative=False, weight_drop=0.0, mfw_activation='none', no_bias=False):
super().__init__()
self.use_multiplicative = use_multiplicative
self.weight_drop = weight_drop
... |
class TaskPool(TaskPoolBase):
def __init__(self, process_func: callable, max_batch_size: int, name: str, min_batch_size=1, timeout=None, pool_size=None, prefetch_batches=1, daemon=True, start=False):
super().__init__(process_func, daemon=daemon, name=name)
(self.min_batch_size, self.max_batch_size, ... |
def sample_function(user_train, usernum, itemnum, batch_size, maxlen, threshold_user, threshold_item, result_queue, SEED):
def sample():
user = np.random.randint(1, (usernum + 1))
while (len(user_train[user]) <= 1):
user = np.random.randint(1, (usernum + 1))
seq = np.zeros([maxle... |
class NvidiaGPUCollector(diamond.collector.ProcessCollector):
def get_default_config_help(self):
config_help = super(NvidiaGPUCollector, self).get_default_config_help()
config_help.update({'bin': 'The path to the nvidia-smi binary', 'stats': 'A list of Nvidia GPU stats to collect. Use `nvidia-smi --... |
class CpmTokenizerFast(XLNetTokenizerFast):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__('You need to install jieba to use CpmTokenizer or CpmTokenizerFast. See for... |
class CustomCircuitOracle(Oracle):
def __init__(self, variable_register: QuantumRegister, output_register: QuantumRegister, circuit: QuantumCircuit, ancillary_register: Optional[QuantumRegister]=None, evaluate_classically_callback: Optional[Callable[([str], Tuple[(bool, List[int])])]]=None):
super().__init_... |
def convert(src, dst):
state_dict = OrderedDict()
src_dict = torch.load(src)
src_state_dict = src_dict.get('state_dict', src_dict)
for (k, v) in src_state_dict.items():
if (not k.startswith('backbone')):
continue
b_k = k.replace('backbone.', '')
b_k_splits = b_k.split... |
class GatedConv(nn.Module):
def __init__(self, input_size, width=3, dropout=0.2, nopad=False):
super(GatedConv, self).__init__()
self.conv = onmt.modules.WeightNormConv2d(input_size, (2 * input_size), kernel_size=(width, 1), stride=(1, 1), padding=(((width // 2) * (1 - nopad)), 0))
init.xavi... |
def _applyActionSide1(state, act):
(me, them, extra) = state
if (act == 'Super Potion'):
me = applyHPChange(me, 50)
return {(me, them, extra): Fraction(1)}
mdata = attack_data[act]
aind = (3 if mdata.isspec else 0)
dind = (3 if mdata.isspec else 1)
pdiv = (64 if mdata.crit else 5... |
def exceptions2exit(exception_list):
def exceptions2exit_decorator(func):
(func)
def func_wrapper(*args, **kwargs):
try:
func(*args, **kwargs)
except tuple(exception_list) as ex:
from .cli import get_log_level
if (get_log_level(... |
def _validate_jwk(jwk):
if ('kty' not in jwk):
abort(400)
if (jwk['kty'] == 'EC'):
if (('x' not in jwk) or ('y' not in jwk)):
abort(400)
elif (jwk['kty'] == 'RSA'):
if (('e' not in jwk) or ('n' not in jwk)):
abort(400)
else:
abort(400) |
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