code stringlengths 281 23.7M |
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def main():
print('\n This program is deprecated!!!\n Instead use pyvideo_scrape ( Continue? yes/[no]\n ')
stay = ('yes' == input().lower())
if (not stay):
exit(0)
parser = argparse.ArgumentParser()
parser.add_argument('-k', '--api-key', help='Can also be specifie... |
class Effect6470(BaseEffect):
type = ('projected', 'active')
def handler(fit, module, context, projectionRange, **kwargs):
if ('projected' not in context):
return
if fit.ship.getModifiedItemAttr('disallowOffensiveModifiers'):
return
strength = module.getModifiedIt... |
()
class WorldWidgetEntry():
item: QtWidgets.QTreeWidgetItem
preset_menu: QtWidgets.QMenu
def update(self, world_details: MultiplayerWorld, detail: (UserWorldDetail | None)):
self.item.setText(0, world_details.name)
self.item.setText(1, world_details.preset.game.long_name)
self.item.... |
class MarketImpactBase(SlippageModel):
NO_DATA_VOLATILITY_SLIPPAGE_IMPACT = (10.0 / 10000)
def __init__(self):
super(MarketImpactBase, self).__init__()
self._window_data_cache = ExpiringCache()
def get_txn_volume(self, data, order):
raise NotImplementedError('get_txn_volume')
def... |
def _fold_to_scale(conv_wrapper: QcQuantizeWrapper, bn_wrapper: QcQuantizeWrapper):
conv = conv_wrapper._layer_to_wrap
bn = bn_wrapper._layer_to_wrap
weight_quantizer = get_wrappers_weight_quantizer(conv_wrapper.param_quantizers)
bias_quantizer = get_wrappers_bias_quantizer(conv_wrapper.param_quantizers... |
def main():
if (len(sys.argv) < 2):
print((('usage: ' + sys.argv[0]) + ' image'))
sys.exit(1)
filename = sys.argv[1]
img_rgb = cv2.imread(filename)
(ayat, contours) = find_ayat(img_rgb)
draw(img_rgb, contours, 'res.png')
for ayah in ayat:
(x, y, w, h) = ayah
print... |
class Processor():
def __init__(self, args, tokenizer):
super().__init__()
self.args = args
self.tokenizer = tokenizer
self.new_tokens = []
if (self.args.input_format == 'entity_marker'):
self.new_tokens = ['[E1]', '[/E1]', '[E2]', '[/E2]']
self.tokenizer.... |
def _get_replay_buffer(dataset_path, shape_meta, store):
rgb_keys = list()
lowdim_keys = list()
out_resolutions = dict()
lowdim_shapes = dict()
obs_shape_meta = shape_meta['obs']
for (key, attr) in obs_shape_meta.items():
type = attr.get('type', 'low_dim')
shape = tuple(attr.get(... |
.parametrize('status_code', [201])
.parametrize('mock_release_id', range(3))
def test_edit_release_notes_succeeds(default_gitea_client, status_code, mock_release_id):
with requests_mock.Mocker(session=default_gitea_client.session) as m:
m.register_uri('PATCH', gitea_api_matcher, json={'id': mock_release_id}... |
class TestDebuggingBreakpoints():
.parametrize('arg', ['--pdb', ''])
def test_sys_breakpointhook_configure_and_unconfigure(self, pytester: Pytester, arg: str) -> None:
pytester.makeconftest('\n import sys\n from pytest import hookimpl\n from _pytest.debugging import pyte... |
def evaluate(args, model, tokenizer, prefix=''):
eval_task_names = (('mnli', 'mnli-mm') if (args.task_name == 'mnli') else (args.task_name,))
eval_outputs_dirs = ((args.output_dir, (args.output_dir + '/MM')) if (args.task_name == 'mnli') else (args.output_dir,))
results = {}
for (eval_task, eval_output_... |
class KnownValues(unittest.TestCase):
def test_hf_dfgs(self):
mf = scf.UHF(mol).run()
myadc = adc.ADC(mf)
myadc.with_df = df.DF(mol, auxbasis='cc-pvdz-ri')
(e, t_amp1, t_amp2) = myadc.kernel_gs()
self.assertAlmostEqual(e, (- 0.), 6)
def test_dfhs_dfgs(self):
(e, t... |
def clean(cands):
fhs = []
for x in cands:
fh = []
ans = stanford_nlp.pos_tag(x[0].encode('utf-8'))
if (len(ans) > 1):
(f, l) = (None, None)
for (ind, (w, p)) in enumerate(ans):
if (p not in ['DT', ',', 'PRP', 'IN']):
fh.append(... |
class EquilibriumDB(RewriteDatabase):
def __init__(self, ignore_newtrees: bool=True, tracks_on_change_inputs: bool=False):
super().__init__()
self.ignore_newtrees = ignore_newtrees
self.tracks_on_change_inputs = tracks_on_change_inputs
self.__final__: dict[(str, bool)] = {}
s... |
class retvalType(GeneratedsSuper):
__hash__ = GeneratedsSuper.__hash__
subclass = None
superclass = None
def __init__(self, gds_collector_=None, **kwargs_):
self.gds_collector_ = gds_collector_
self.gds_elementtree_node_ = None
self.original_tagname_ = None
self.parent_ob... |
class TestRevokedCertificateBuilder():
def test_serial_number_must_be_integer(self):
with pytest.raises(TypeError):
x509.RevokedCertificateBuilder().serial_number('notanx509name')
def test_serial_number_must_be_non_negative(self):
with pytest.raises(ValueError):
x509.Revo... |
def evaluate_hr_ndcg(model, test_queue, topk=10):
model.eval()
with torch.no_grad():
(users, items, _) = test_queue
users = users.cpu().tolist()
(hrs, ndcgs) = ([], [])
inferences_dict = {}
(users_all, items_all) = ([], [])
for user in list(set(users)):
... |
()
def mock_utils_debugger(mocker):
def call_orig_func(func, *args, **kwargs):
return func(*args, **kwargs)
debugger_mock = mocker.patch('radish.utils.get_debugger')
debugger_mock.return_value.runcall = mocker.MagicMock(side_effect=call_orig_func)
return debugger_mock.return_value |
class Example(object):
def __init__(self, qas_id, qas_type, doc_tokens, question_text, sent_num, sent_names, sup_fact_id, para_start_end_position, sent_start_end_position, entity_start_end_position, orig_answer_text=None, start_position=None, end_position=None):
self.qas_id = qas_id
self.qas_type = ... |
def _handle_eval_return(self, result, col, as_pyranges, subset):
if as_pyranges:
if (not result):
return pr.PyRanges()
first_hit = list(result.values())[0]
if isinstance(first_hit, pd.Series):
if ((first_hit.dtype == bool) and subset):
return self[resu... |
class FakeNetworkCache(QAbstractNetworkCache):
def cacheSize(self):
return 0
def data(self, _url):
return None
def insert(self, _dev):
pass
def metaData(self, _url):
return QNetworkCacheMetaData()
def prepare(self, _metadata):
return None
def remove(self, ... |
class MultiAttentionEncoder(SequenceMultiEncoder):
def __init__(self, n_encodings: int, bias: bool=False, key_mapper: SequenceMapper=None, post_process: Mapper=None, init='glorot_uniform'):
self.init = init
self.bias = bias
self.n_encodings = n_encodings
self.key_mapper = key_mapper
... |
def read_flit_config(path):
res = _read_flit_config_core(path)
if validate_config(res):
if os.environ.get('FLIT_ALLOW_INVALID'):
log.warning('Allowing invalid data (FLIT_ALLOW_INVALID set). Uploads may still fail.')
else:
raise ConfigError('Invalid config values (see log)... |
class PerImgPert():
def __init__(self, sess, config, filepath, batch_size, regu, learning_rate=0.1, binary_search_steps=1, max_iterations=101, initial_const=1):
(image_size, num_channels, num_labels) = (32, 3, 10)
self.sess = sess
self.LEARNING_RATE = learning_rate
self.MAX_ITERATION... |
def read_tables(data_dir, bc):
bc.create_table('store_sales', os.path.join(data_dir, 'store_sales/*.parquet'))
bc.create_table('date_dim', os.path.join(data_dir, 'date_dim/*.parquet'))
bc.create_table('item', os.path.join(data_dir, 'item/*.parquet'))
bc.create_table('web_sales', os.path.join(data_dir, '... |
def infer_func_form(node: nodes.Call, base_type: list[nodes.NodeNG], context: (InferenceContext | None)=None, enum: bool=False) -> tuple[(nodes.ClassDef, str, list[str])]:
try:
(name, names) = _find_func_form_arguments(node, context)
try:
attributes: list[str] = names.value.replace(',', ... |
def test_parallel_and_sequential_ces_are_equal(s, micro_s, macro_s):
with config.override(PARALLEL_CONCEPT_EVALUATION=False):
c = compute.subsystem.ces(s)
c_micro = compute.subsystem.ces(micro_s)
c_macro = compute.subsystem.ces(macro_s)
with config.override(PARALLEL_CONCEPT_EVALUATION=Tr... |
class SponsorBenefitModelTests(TestCase):
def setUp(self):
self.sponsorship = baker.make(Sponsorship)
self.sponsorship_benefit = baker.make(SponsorshipBenefit, name='Benefit')
def test_new_copy_also_add_benefit_feature_when_creating_sponsor_benefit(self):
benefit_config = baker.make(Logo... |
def test_parametric_mesh_forward():
tmpdir = tempfile.TemporaryDirectory()
generate_smpl_weight_file(tmpdir.name)
model_cfg = dict(pretrained=None, backbone=dict(type='ResNet', depth=50), mesh_head=dict(type='HMRMeshHead', in_channels=2048, smpl_mean_params='tests/data/smpl/smpl_mean_params.npz'), disc=None... |
class Opcode(Configurable, OpcodeAPI):
mnemonic: str = None
gas_cost: int = None
def __init__(self) -> None:
if (self.mnemonic is None):
raise TypeError(f'Opcode class {type(self)} missing opcode mnemonic')
if (self.gas_cost is None):
raise TypeError(f'Opcode class {t... |
.parametrize('shape,tile_shape', [((2,), (3,)), ((2, 2), (3, 2)), ((2, 3), (2, 2))])
def test_read_write_tiles_error(tmp_path, shape, tile_shape):
with pytest.raises(ValueError, match='must be divisible'):
write_tiles(ary=num.ones(shape), dirpath=tmp_path, tile_shape=tile_shape)
with pytest.raises(Value... |
class ButtonsRow():
def __init__(self):
self._content = []
def url(self, label, url):
self._content.append({'text': label, 'url': url})
def callback(self, label, callback, data=None):
def generate_callback_data(chat):
c = ctx()
name = ('%s:%s' % (c.component_n... |
class RLlibStarCraft2Env(rllib.MultiAgentEnv):
def __init__(self, **smac_args):
self._env = StarCraft2Env(**smac_args)
self._ready_agents = []
self.observation_space = Dict({'obs': Box((- 1), 1, shape=(self._env.get_obs_size(),)), 'action_mask': Box(0, 1, shape=(self._env.get_total_actions()... |
class Effect1638(BaseEffect):
type = 'passive'
def handler(fit, skill, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.requiresSkill('Gunnery') or mod.item.requiresSkill('Missile Launcher Operation') or mod.item.requiresSkill('Vorton Projector Operation'))), 'po... |
class Trainer(object):
def __init__(self, args):
self.args = args
if (args.class_name in MVTEC_CLASS_NAMES):
train_dataset = MVTecDataset(args, is_train=True)
test_dataset = MVTecDataset(args, is_train=False)
elif (args.class_name in BTAD_CLASS_NAMES):
tra... |
def returnPointer(wrapArgs, includeOutput=False):
def decorator(func):
(func)
def inner(*args):
orig = getattr(_egl, func.__name__)
newArgs = list(args)
for argnum in wrapArgs:
item = orig.argtypes[argnum]._type_()
newArgs.insert(ar... |
class DirectJunctionCreator():
def __init__(self, id, name):
self.id = id
self.junction = Junction(name, id, JunctionType.direct)
self._incoming_lane_ids = []
self._linked_lane_ids = []
def _get_minimum_lanes_to_connect(self, incoming_road, linked_road):
(incoming_connect... |
class EmbeddingWriterConfig(argparse.ArgumentParser):
def __init__(self):
super().__init__('Pre-compute embeddings for wav2letter++ datasets')
kwargs = {'action': 'store', 'type': str, 'required': True}
self.add_argument('--input', '-i', help='Input Directory', **kwargs)
self.add_arg... |
class TFCvtEncoder(tf.keras.layers.Layer):
config_class = CvtConfig
def __init__(self, config: CvtConfig, **kwargs):
super().__init__(**kwargs)
self.config = config
self.stages = [TFCvtStage(config, stage_idx, name=f'stages.{stage_idx}') for stage_idx in range(len(config.depth))]
def... |
def _link_following_layers_to_new_layer_output(new_tensor_output: tf.Tensor, following_layers_and_inputs_dict: Dict[(tf.keras.layers.Layer, List[tf.Tensor])], replaced_layer: tf.keras.layers.Layer):
for (following_layer, keras_inputs) in following_layers_and_inputs_dict.items():
for (idx, keras_input) in en... |
class W2lDecoder(object):
def __init__(self, args, tgt_dict):
self.tgt_dict = tgt_dict
self.vocab_size = len(tgt_dict)
self.nbest = args.nbest
if (args.criterion == 'ctc'):
self.criterion_type = CriterionType.CTC
self.blank = (tgt_dict.index('<ctc_blank>') if ... |
def default_centerness_model(shared_model, pyramid_feature_size=256, name='centerness_submodel'):
options = {'kernel_size': 3, 'strides': 1, 'padding': 'same'}
inputs = keras.layers.Input(shape=(None, None, pyramid_feature_size))
outputs = shared_model(inputs)
outputs = keras.layers.Conv2D(filters=1, ke... |
class ConvNeXtBlock(nn.Module):
def __init__(self, in_chs: int, out_chs: Optional[int]=None, kernel_size: int=7, stride: int=1, dilation: Tuple[(int, int)]=(1, 1), cfg: MaxxVitConvCfg=MaxxVitConvCfg(), conv_mlp: bool=True, drop_path: float=0.0):
super().__init__()
out_chs = (out_chs or in_chs)
... |
class Cluster(pg_api.Cluster):
driver = pg_driver.default
installation = None
data_directory = None
DEFAULT_CLUSTER_ENCODING = DEFAULT_CLUSTER_ENCODING
DEFAULT_CONFIG_FILENAME = DEFAULT_CONFIG_FILENAME
DEFAULT_PID_FILENAME = DEFAULT_PID_FILENAME
DEFAULT_HBA_FILENAME = DEFAULT_HBA_FILENAME
... |
def is_typed_callable(c: (Type | None)) -> bool:
c = get_proper_type(c)
if ((not c) or (not isinstance(c, CallableType))):
return False
return (not all(((isinstance(t, AnyType) and (t.type_of_any == TypeOfAny.unannotated)) for t in get_proper_types((c.arg_types + [c.ret_type]))))) |
.parametrize('artifacts', [AM2RArtifactConfig(False, False, True, 5), AM2RArtifactConfig(True, False, True, 10), AM2RArtifactConfig(False, True, True, 15), AM2RArtifactConfig(True, True, True, 6)])
def test_assign_pool_results_prefer_anywhere(am2r_game_description, am2r_configuration, artifacts):
patches = GamePatc... |
def test_cli_async_reduce_fails(runner, reactor, server, capsys):
base_url = '
in_stream = ''.join((base_url.format(i) for i in [6, 2, 1]))
args = ['map', 'json.loads', 'reduce', 'toolz.curry(operator.truediv)(*x)']
with pytest.raises(subprocess.CalledProcessError):
helpers.run(args, input=in_st... |
_grad()
def log_training(writer, params, step, d_loss, g_loss):
print(f'{int(((100.0 * step) / params.steps))}% | Step {step} :D loss: {d_loss.item():0.3f} | G loss: {g_loss.item():0.3f}')
writer.add_scalar('discriminator loss', d_loss.item(), step)
writer.add_scalar('generator loss', g_loss.item(), step) |
class ResidualParser(object):
def __init__(self, filepath, parse=True):
self.filepath = filepath
self.__residuals = OrderedDict()
if parse:
self.parse()
def parse(self):
try:
with open(self.filepath, 'rb') as f:
for line in f:
... |
class Registry():
def __init__(self, data: object, data_reversed: object=None) -> None:
self.data_reversed = data_reversed
if isinstance(data, (dict, Map)):
self.data = make_immutable(data)
if (data_reversed is None):
self.data_reversed = make_immutable({v: k ... |
def _join_lexemes(lexemes, links):
EXCLUDED_LINK_TYPES = set(['7', '21', '23', '27'])
moves = dict()
def move_lexeme(from_id, to_id):
lm = lexemes[str(from_id)]
while (to_id in moves):
to_id = moves[to_id]
lexemes[str(to_id)].extend(lm)
del lm[:]
moves[fro... |
def create_sdf_obj(sdfcommand, marching_cube_command, norm_mesh_dir, sdf_dir, obj, res, iso_val, expand_rate, indx, ish5, normalize, num_sample, bandwidth, max_verts, g, reduce):
if (FLAGS.dset == 'abc'):
model_id = os.path.basename(obj).replace('.obj', '')
elif (FLAGS.dset == 'pix3d'):
model_id... |
def _nonlin_solver(fcn, x0, params, method, alpha=None, uv0=None, max_rank=None, maxiter=None, f_tol=None, f_rtol=None, x_tol=None, x_rtol=None, line_search=True, verbose=False, custom_terminator=None, **unused):
if (method == 'broyden1'):
jacobian = BroydenFirst(alpha=alpha, uv0=uv0, max_rank=max_rank)
... |
def print_bbc_warnings(keyCount, lineCount):
sys.stdout.flush()
limits_exceeded = []
severe = 0
if (keyCount >= 32768):
severe = 1
limits_exceeded.append('BeebEm 32K keystroke limit')
shadow_himem = 32768
mode7_himem = 31744
default_speech_loc = 21760
overhead_per_program... |
class SelfUpdateCommand(SelfCommand):
name = 'self update'
description = 'Updates Poetry to the latest version.'
arguments = [argument('version', 'The version to update to.', optional=True, default='latest')]
options = [option('preview', None, 'Allow the installation of pre-release versions.'), option('... |
_grad()
def predict(part):
loader = lib.IndexLoader(D.size(part), args['training']['eval_batch_size'], False, device)
preds = []
for idx in loader:
(_, out) = net_ensemble.forward(X_num[part][idx], (None if (X_cat is None) else X_cat[part][idx]))
preds.append(out)
return torch.cat(preds)... |
def policy_training(device='cuda'):
noiseset = [35, 45, 55]
seed_torch(seed=args.seed)
model = DnCNN_DS(channels=1, num_of_layers=args.num_of_layers)
model = torch.nn.DataParallel(model).cuda()
if os.path.exists(os.path.join(args.outf, 'net.pth')):
print('Loading denoise model...')
m... |
def run_procedure(event):
global flag
text01.delete(0.0, tkinter.END)
if (flag == 0):
messagebox.showinfo('Topic', "You haven't chosen the algorithm. Please choose the algorithm before running.")
return
elif ((show['text'] == '') or (show['text'] == 'file')):
messagebox.showinfo(... |
def update(dt):
for i in range(len(game_objects)):
for j in range((i + 1), len(game_objects)):
obj_1 = game_objects[i]
obj_2 = game_objects[j]
if ((not obj_1.dead) and (not obj_2.dead)):
if obj_1.collides_with(obj_2):
obj_1.handle_colli... |
def set_num_cpu_threads(out_f, num_cpus):
out_f.write('export EXP_NUM_CPU_THREADS={}\n'.format(num_cpus))
out_f.write('export OMP_NUM_THREADS=${EXP_NUM_CPU_THREADS}\n')
out_f.write('export MKL_NUM_THREADS=${EXP_NUM_CPU_THREADS}\n')
out_f.write('export NUMEXPR_NUM_THREADS=${EXP_NUM_CPU_THREADS}\n')
o... |
def build_profile_plot(ax, model, path_selection):
nodes = model.nodes.dataframe
links = model.links.dataframe
profile_config = {'nodes': [], 'links': [], 'path_selection': path_selection}
ground_levels = {'x': [], 'level': []}
rolling_x_pos = 0.0
for (ind, link_set) in enumerate(path_selection)... |
def ql_syscall_readv(ql: Qiling, fd: int, vec: int, vlen: int):
regreturn = 0
size_t_len = ql.arch.pointersize
iov = ql.mem.read(vec, ((vlen * size_t_len) * 2))
ql.log.debug('readv() CONTENT:')
for i in range(vlen):
addr = ql.unpack(iov[((i * size_t_len) * 2):(((i * size_t_len) * 2) + size_t... |
class TestEqualityOperators(unittest.TestCase, ReallyEqualMixin):
def setUp(self):
get_dummy_plugin()
def test_type_mismatch(self):
md = Metadata(pd.DataFrame({'col1': [1.0, 2.0, 3.0], 'col2': ['a', 'b', 'c'], 'col3': ['foo', 'bar', '42']}, index=pd.Index(['id1', 'id2', 'id3'], name='id')))
... |
class MapleCM(object):
def __init__(self, bootstrap_with=None, use_timer=False, incr=False, with_proof=False, warm_start=False):
if incr:
raise NotImplementedError('Incremental mode is not supported by MapleCM.')
self.maplesat = None
self.status = None
self.prfile = None
... |
def _torch_persistent_save(obj, f):
if isinstance(f, str):
with PathManager.open(f, 'wb') as h:
torch_persistent_save(obj, h)
return
for i in range(3):
try:
return torch.save(obj, f)
except Exception:
if (i == 2):
logger.error(t... |
_predicate(bytearray)
class BytearrayBase64Provider(LoaderProvider, Base64DumperMixin):
_BYTES_PROVIDER = BytesBase64Provider()
def _provide_loader(self, mediator: Mediator, request: LoaderRequest) -> Loader:
request.loc_map.get_or_raise(TypeHintLoc, (lambda : CannotProvide))
bytes_loader = self... |
def _squeezenet(version, pretrained, progress, **kwargs):
model = SqueezeNet(version, **kwargs)
if pretrained:
arch = ('squeezenet' + version)
state_dict = load_state_dict_from_url(model_urls[arch], progress=progress)
model.load_state_dict(state_dict)
return model |
def send_email(*, template: EmailTemplate, to: str, subject: str, from_: Optional[str]=None, variables: Optional[dict[(str, str)]]=None, reply_to: List[str]=None):
from_ = (from_ or settings.DEFAULT_EMAIL_FROM)
backend = get_email_backend(settings.PYTHONIT_EMAIL_BACKEND, environment=settings.ENVIRONMENT)
ba... |
class ViewBoxMenu(QtWidgets.QMenu):
def __init__(self, view):
QtWidgets.QMenu.__init__(self)
self.view = weakref.ref(view)
self.valid = False
self.viewMap = weakref.WeakValueDictionary()
self.setTitle(translate('ViewBox', 'ViewBox options'))
self.viewAll = QtGui.QActi... |
class TestLogDet():
def setup_method(self):
np.random.seed(899853)
self.op_class = LogDet
self.op = logdet
.change_flags(compute_test_value='ignore')
def validate(self, input_mat):
x = pytensor.tensor.matrix()
f = pytensor.function([x], self.op(x))
out = f(inp... |
class CompletedRequest(object):
def __init__(self, reqId, operation, taskId, status):
self.requestId = reqId
self.operation = operation
self.taskid = taskId
self.status = status
def __repr__(self):
return ('CompletedRequest: %d (%s <=> %d) == %s' % (self.requestId, self.... |
class whisper_gpt():
def __init__(self, model_size, file):
self.model_size = model_size
self.file = file
self.model = whisper.load_model(model_size)
def transcribe(self):
self.final = self.model.transcribe(self.file)
def get_result(self):
self.transription = self.fina... |
def full_test_loader(data_dir):
test_data = [i for i in os.listdir((data_dir + 'test/A/')) if (not i.startswith('.'))]
test_data.sort()
test_label_paths = []
if ('DSIFN' in data_dir):
for img in test_data:
test_label_paths.append((((data_dir + 'test/label/') + img.split('.')[0]) + '.... |
class BalancedPositiveNegativeSampler(minibatch_sampler.MinibatchSampler):
def __init__(self, positive_fraction=0.5):
if ((positive_fraction < 0) or (positive_fraction > 1)):
raise ValueError(('positive_fraction should be in range [0,1]. Received: %s.' % positive_fraction))
self._positiv... |
class Ui_MainWindow(object):
def setupUi(self, MainWindow):
if (not MainWindow.objectName()):
MainWindow.setObjectName(u'MainWindow')
MainWindow.resize(1169, 667)
self.centralwidget = QWidget(MainWindow)
self.centralwidget.setObjectName(u'centralwidget')
self.labe... |
class TargetWrapper(BaseWrapper):
def __init__(self, item, lightnessID, lineStyleID):
super().__init__(item=item)
self.lightnessID = lightnessID
self.lineStyleID = lineStyleID
self.resistMode = TargetResistMode.auto
def getResists(self, includeLayer=False):
em = therm = k... |
def test_link_resolve(pytester: Pytester) -> None:
'See:
sub1 = pytester.mkpydir('sub1')
p = sub1.joinpath('test_foo.py')
p.write_text(textwrap.dedent('\n import pytest\n def test_foo():\n raise AssertionError()\n '), encoding='utf-8')
subst = subst_path_linux
if... |
def prune_it(p, keep_only_ema=False):
print(f'Pruning in path: {p}')
size_initial = os.path.getsize(p)
nsd = dict()
sd = torch.load(p, map_location='cpu')
print(sd.keys())
for k in sd.keys():
if (k != 'optimizer_states'):
nsd[k] = sd[k]
else:
print(f'removing opti... |
class TestPairClassificationEvaluator():
def test_accuracy(self):
scores = [6.12, 5.39, 5.28, 5.94, 6.34, 6.47, 7.88, 6.62, 8.04, 5.9]
labels = [0, 0, 0, 0, 1, 0, 0, 0, 1, 0]
high_score_more_similar = True
(acc, acc_threshold) = PairClassificationEvaluator.find_best_acc_and_threshold... |
_scoped
class Calculator(Base):
__tablename__ = 'dynamic_content_error_calculator'
id = Column(Integer, primary_key=True)
operand_a = Column(Integer)
operand_b = Column(Integer)
operator = Column(UnicodeText)
result = Column(Integer)
fields = ExposedNames()
fields.operand_a = (lambda i: ... |
def build_action_prediction_dataset(args):
playthroughs = (json.loads(line.rstrip(',\n')) for line in open(args.input) if (len(line.strip()) > 1))
graph_dataset = GraphDataset()
dataset = []
for example in next_example(playthroughs):
(root, candidates) = (example[0], example[1:])
if (len... |
def get_julian_day_from_gregorian_date(year, month, day):
is_leap = False
if ((year / 4.0) == round((year / 4.0))):
if ((year / 100.0) == round((year / 100.0))):
if ((year / 400.0) == round((year / 400.0))):
is_leap = True
else:
is_leap = True
if (mont... |
def test_input_runlevels():
q = Input()
assert (not q.alive)
with pytest.raises(InactiveWritableError):
q.put('hello, unborn queue.')
q.put(BEGIN)
assert (q.alive and (q._runlevel == 1))
q.put('foo')
q.put(BEGIN)
assert (q.alive and (q._runlevel == 2))
q.put('bar')
q.put(... |
def distributed_worker(local_rank, fn, world_size, n_gpu_per_machine, machine_rank, dist_url, args):
if (not torch.cuda.is_available()):
raise OSError('CUDA is not available. Please check your environments')
global_rank = ((machine_rank * n_gpu_per_machine) + local_rank)
print('local_rank ', local_r... |
def sample(experiment_directory='/home/xweiwang/RL/seq2seq/experiment', checkpoint='2019_05_18_20_32_54', resume=True, log_level='info'):
logging.basicConfig(format=LOG_FORMAT, level=getattr(logging, log_level.upper()))
logging.info('experiment_directory: %s', experiment_directory)
logging.info('checkpoint:... |
def test_output_parent_function_json_with_sample_data_bundle(sample_data_bundle):
output_parent_function_json(sample_data_bundle)
with open('rules_classification.json', 'r') as classification_report:
report = json.load(classification_report)
assert (len(report['rules_classification']) == 2)
... |
class ForDictionaryCommon(ForGenerator):
dict_next_op: ClassVar[CFunctionDescription]
dict_iter_op: ClassVar[CFunctionDescription]
def need_cleanup(self) -> bool:
return True
def init(self, expr_reg: Value, target_type: RType) -> None:
builder = self.builder
self.target_type = ta... |
def get_test_data():
data_fname = (TEST_DATA_DIR / 'titanic.csv')
data_fname.parent.mkdir(parents=True, exist_ok=True)
if (not data_fname.exists()):
data = pd.read_csv(' index_col=0)
data.to_csv(data_fname)
else:
data = pd.read_csv(data_fname, index_col=0)
data = data.drop('N... |
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
model.train()
for (batch, (X, y)) in enumerate(dataloader):
(X, y) = (X.to(device), y.to(device))
pred = model(X)
loss = loss_fn(pred, y)
optimizer.zero_grad()
loss.backward()
opt... |
def download_object_model(model_name, owner, version=1):
file_tree_response = get_model_file_tree(model_name, owner, version)
file_tree_response = file_tree_response.json()
model_path = (DOWNLOAD_PATH + model_name)
make_directories(model_path)
make_directories(GLB_DIR)
texture_file_path = ''
... |
def import_MarketDuke_nodistractors(data_dir, dataset_name):
dataset_dir = os.path.join(data_dir, dataset_name)
if (not os.path.exists(dataset_dir)):
print((('Please Download ' + dataset_name) + ' Dataset'))
dataset_dir = os.path.join(data_dir, dataset_name)
data_group = ['train', 'query', 'gall... |
def test_deployment_create(project, resp_deployment_create):
deployment = project.deployments.create({'environment': 'Test', 'sha': '1agf4gs', 'ref': 'main', 'tag': False, 'status': 'created'})
assert (deployment.id == 42)
assert (deployment.status == 'success')
assert (deployment.ref == 'main')
dep... |
.supported(only_if=(lambda backend: backend.rsa_encryption_supported(padding.PKCS1v15())), skip_message='Does not support PKCS1v1.5 for encryption.')
_tests('rsa_pkcs1_2048_test.json', 'rsa_pkcs1_3072_test.json', 'rsa_pkcs1_4096_test.json')
def test_rsa_pkcs1_encryption(backend, wycheproof):
key = wycheproof.cache_... |
class TestHDF5BasicIO():
def test_write_and_read(self, tmp_path, rng):
file_name = str((tmp_path / 'test.ga'))
basis_names = np.array(layout.basis_names, dtype=str)
mv_array = ConformalMVArray([random_point_pair(rng=rng) for i in range(1000)]).value
write_ga_file(file_name, mv_array,... |
class StartOfPeriodLedgerField(object):
def __init__(self, ledger_field, packet_field=None):
self._get_ledger_field = op.attrgetter(ledger_field)
if (packet_field is None):
self._packet_field = ledger_field.rsplit('.', 1)[(- 1)]
else:
self._packet_field = packet_field... |
_loss
def rotated_iou_loss(pred, target, linear=False, mode='log', eps=1e-06):
assert (mode in ['linear', 'square', 'log'])
if linear:
mode = 'linear'
warnings.warn('DeprecationWarning: Setting "linear=True" in poly_iou_loss is deprecated, please use "mode=`linear`" instead.')
if (diff_iou_r... |
def circ_corrcl(x, y):
from scipy.stats import pearsonr, chi2
x = np.asarray(x)
y = np.asarray(y)
assert (x.size == y.size), 'x and y must have the same length.'
(x, y) = remove_na(x, y, paired=True)
n = x.size
rxs = pearsonr(y, np.sin(x))[0]
rxc = pearsonr(y, np.cos(x))[0]
rcs = pea... |
class VanLargeKernelAttentionLayer(nn.Module):
def __init__(self, hidden_size: int):
super().__init__()
self.attention = VanLargeKernelAttention(hidden_size)
def forward(self, hidden_state):
attention = self.attention(hidden_state)
attended = (hidden_state * attention)
re... |
def check_link_url(link: Link) -> int:
try:
rc = requests.head(link.uri, timeout=2, allow_redirects=True)
except (requests.ConnectionError, requests.exceptions.ReadTimeout) as exc:
fail(link, exc)
return 2
if (rc.status_code == 200):
ok(link)
return 0
else:
... |
def test_PVSystem_multi_array_first_solar_spectral_loss():
system = pvsystem.PVSystem(arrays=[pvsystem.Array(mount=pvsystem.FixedMount(0, 180), module_parameters={'Technology': 'mc-Si'}, module_type='multisi'), pvsystem.Array(mount=pvsystem.FixedMount(0, 180), module_parameters={'Technology': 'mc-Si'}, module_type=... |
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