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def datetime_to_djd(time):
if (time.tzinfo is None):
time_utc = pytz.utc.localize(time)
else:
time_utc = time.astimezone(pytz.utc)
djd_start = pytz.utc.localize(dt.datetime(1899, 12, 31, 12))
djd = (((time_utc - djd_start).total_seconds() * 1.0) / ((60 * 60) * 24))
return djd |
class ScriptMakerCustom(ScriptMaker):
def __init__(self, target_dir, version_info, executable, name) -> None:
super().__init__(None, str(target_dir))
self.clobber = True
self.set_mode = True
self.executable = enquote_executable(str(executable))
self.version_info = (version_in... |
def read_file_list():
basedir = (radare2_includedir + '/')
return [(basedir + 'r_core.h'), (basedir + 'r_asm.h'), (basedir + 'r_anal.h'), (basedir + 'r_bin.h'), (basedir + 'r_debug.h'), (basedir + 'r_io.h'), (basedir + 'r_config.h'), (basedir + 'r_flag.h'), (basedir + 'r_sign.h'), (basedir + 'r_hash.h'), (based... |
def adjust_rel_elec_density(dicom_dataset, adjustment_map, ignore_missing_structure=False):
new_dicom_dataset = deepcopy(dicom_dataset)
ROI_name_to_number_map = {structure_set.ROIName: structure_set.ROINumber for structure_set in new_dicom_dataset.StructureSetROISequence}
ROI_number_to_observation_map = {ob... |
class GraphRewriter(Rewriter):
def apply(self, fgraph):
raise NotImplementedError()
def rewrite(self, fgraph, *args, **kwargs):
self.add_requirements(fgraph)
return self.apply(fgraph, *args, **kwargs)
def __call__(self, fgraph):
return self.rewrite(fgraph)
def add_require... |
class TableTruncate(ABC):
def __init__(self, tokenizer: BasicTokenizer=None, max_input_length: int=1024):
if (tokenizer is None):
self.tokenizer = AutoTokenizer.from_pretrained(pretrained_model_name_or_path='facebook/bart-large')
else:
self.tokenizer = tokenizer
self.... |
def keras_model():
model = Sequential([Conv2D(8, (2, 2), input_shape=(16, 16, 3)), BatchNormalization(momentum=0.3, epsilon=0.65), AvgPool2D(), MaxPool2D(), BatchNormalization(momentum=0.4, epsilon=0.25), Conv2D(4, (2, 2), activation=tf.nn.tanh, kernel_regularizer=tf.keras.regularizers.l2(0.5)), Flatten(), Dense(2,... |
class COCOFeaturesDataset(BaseFeaturesDataset):
def __init__(self, *args, **kwargs):
super(COCOFeaturesDataset, self).__init__()
self.feature_readers = []
self.feature_dict = {}
self.fast_read = kwargs['fast_read']
self.writer = registry.get('writer')
for image_featur... |
def get_preprocessor(model_name: str) -> Optional[Union[('AutoTokenizer', 'AutoFeatureExtractor', 'AutoProcessor')]]:
from .. import AutoFeatureExtractor, AutoProcessor, AutoTokenizer
try:
return AutoProcessor.from_pretrained(model_name)
except (ValueError, OSError, KeyError):
(tokenizer, fe... |
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, norm_layer=nn.BatchNorm2d):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
self.bn1 = norm_layer(planes)
self.conv2 = nn.Conv2d(p... |
('lr_scheduler', 'warmup_polynomial')
class WarmupPolynomialLRScheduler():
param_groups = attr.ib()
num_warmup_steps = attr.ib()
start_lr = attr.ib()
end_lr = attr.ib()
decay_steps = attr.ib()
power = attr.ib()
def update_lr(self, current_step):
if (current_step < self.num_warmup_ste... |
def test_list_from_file():
with tempfile.TemporaryDirectory() as tmpdirname:
for (i, lines) in enumerate(lists):
filename = f'{tmpdirname}/{i}.txt'
with open(filename, 'w', encoding='utf-8') as f:
f.writelines((f'''{line}
''' for line in lines))
lines2 = l... |
class LFPluginCollWrapper():
def __init__(self, lfplugin: 'LFPlugin') -> None:
self.lfplugin = lfplugin
self._collected_at_least_one_failure = False
(wrapper=True)
def pytest_make_collect_report(self, collector: nodes.Collector) -> Generator[(None, CollectReport, CollectReport)]:
res... |
class PositionConfig(Config):
auto_fullscreen = True
groups = [config.Group('a'), config.Group('b')]
layouts = [layout.MonadTall(), layout.TreeTab()]
floating_layout = resources.default_config.floating_layout
keys = []
mouse = []
screens = []
follow_mouse_focus = False |
def repo_with_no_tags_emoji_commits(git_repo_factory, file_in_repo):
git_repo = git_repo_factory()
add_text_to_file(git_repo, file_in_repo)
git_repo.git.commit(m='Initial commit')
add_text_to_file(git_repo, file_in_repo)
git_repo.git.commit(m=':bug: add some more text')
add_text_to_file(git_repo... |
class TestOptional():
def test_success_with_type(self):
c = optional(int)
assert (c('42') == 42)
def test_success_with_none(self):
c = optional(int)
assert (c(None) is None)
def test_fail(self):
c = optional(int)
with pytest.raises(ValueError):
c('... |
class CmdLineApp(cmd.Cmd):
MUMBLES = ['like', '...', 'um', 'er', 'hmmm', 'ahh']
MUMBLE_FIRST = ['so', 'like', 'well']
MUMBLE_LAST = ['right?']
def do_exit(self, line):
return True
do_EOF = do_exit
do_quit = do_exit
def do_speak(self, line):
print(line, file=self.stdout)
d... |
def flat_xml_to_elements(root):
elements = {}
ns_map = get_ns_map(root)
uri_attrib = get_ns_tag('dc:uri', ns_map)
for node in root:
uri = get_uri(node, ns_map)
element = {'uri': get_uri(node, ns_map), 'model': models[node.tag]}
for sub_node in node:
tag = strip_ns(sub... |
class KnownValues(unittest.TestCase):
def test_tda(self):
td = tdscf.TDA(mf).run(nstates=nstates)
tdg = td.nuc_grad_method()
g1 = tdg.kernel(state=3)
self.assertAlmostEqual(g1[(0, 2)], (- 0.), 5)
td_solver = td.as_scanner()
e1 = td_solver(pmol.set_geom_('H 0 0 1.805; ... |
class Effect6599(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.ship.boostItemAttr('armorKineticDamageResonance', src.getModifiedItemAttr('shipBonusCarrierA1'), skill='Amarr Carrier', **kwargs)
fit.ship.boostItemAttr('armorEmDamageResonance', src.get... |
def create_h5_sdf_pt(h5_file, sdf_file, norm_obj_file, centroid, m, sdf_res, num_sample, bandwidth, iso_val, max_verts, normalize, reduce=8):
sdf_dict = get_sdf(sdf_file, sdf_res)
ori_verts = np.asarray([0.0, 0.0, 0.0], dtype=np.float32).reshape((1, 3))
(samplesdf, is_insideout) = sample_sdf(num_sample, ban... |
class EOH(QuantumAlgorithm):
def __init__(self, operator: LegacyBaseOperator, initial_state: Union[(InitialState, QuantumCircuit)], evo_operator: LegacyBaseOperator, evo_time: float=1, num_time_slices: int=1, expansion_mode: str='trotter', expansion_order: int=1, quantum_instance: Optional[Union[(QuantumInstance, B... |
class HSAFFileHandler(BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info):
super(HSAFFileHandler, self).__init__(filename, filename_info, filetype_info)
self._msg_datasets = {}
self._start_time = None
self._end_time = None
try:
with pygrib... |
class TestVariableModule(TestCase):
def test_is_list_of_tuples(self):
a_list = [(1, 2), (3, 4)]
self.assertEqual(variable.is_list_of_tuples(a_list), (True, a_list))
a_list = [1, 2, 3, 4]
self.assertEqual(variable.is_list_of_tuples(a_list), (False, None))
def test_list_test(self):... |
def call_optional(obj: object, name: str, nodeid: str) -> bool:
method = getattr(obj, name, None)
if (method is None):
return False
is_fixture = (getfixturemarker(method) is not None)
if is_fixture:
return False
if (not callable(method)):
return False
method_name = getatt... |
def init_pretrained_weights(model, model_url):
if (model_url is None):
import warnings
warnings.warn('ImageNet pretrained weights are unavailable for this model')
return
pretrain_dict = model_zoo.load_url(model_url)
model_dict = model.state_dict()
pretrain_dict = {k: v for (k, v)... |
def enum_assemble(node, neighbors, prev_nodes=[], prev_amap=[]):
all_attach_confs = []
singletons = [nei_node.nid for nei_node in (neighbors + prev_nodes) if (nei_node.mol.GetNumAtoms() == 1)]
def search(cur_amap, depth):
if (len(all_attach_confs) > MAX_NCAND):
return
if (depth =... |
class RelatednessPytorch(object):
def __init__(self, train, valid, test, devscores, config):
np.random.seed(config['seed'])
torch.manual_seed(config['seed'])
assert torch.cuda.is_available(), 'torch.cuda required for Relatedness'
torch.cuda.manual_seed(config['seed'])
self.tr... |
def get_inverse_hvp_lissa(v, model, device, param_influence, train_loader, damping, num_samples, recursion_depth, scale=10000.0):
ihvp = None
for i in range(num_samples):
cur_estimate = v
lissa_data_iterator = iter(train_loader)
for j in range(recursion_depth):
try:
... |
def _intensity_validator(value, values):
if (not isinstance(value, tuple)):
raise ValueError('Input value {} of trigger_select should be a tuple'.format(value))
if (len(value) != 2):
raise ValueError('Number of parameters {} different from 2'.format(len(value)))
for i in range(2):
st... |
def table_to_file(table: pa.Table, base_path: str, file_system: AbstractFileSystem, block_path_provider: BlockWritePathProvider, content_type: str=ContentType.PARQUET.value, **kwargs) -> None:
writer = CONTENT_TYPE_TO_PA_WRITE_FUNC.get(content_type)
if (not writer):
raise NotImplementedError(f"Pyarrow w... |
class Rumor_Data(Dataset):
def __init__(self, dataset):
self.text = torch.from_numpy(np.array(dataset['post_text']))
self.image = list(dataset['image'])
self.mask = torch.from_numpy(np.array(dataset['mask']))
self.label = torch.from_numpy(np.array(dataset['label']))
self.even... |
class SNDCGAN_Discrminator(object):
def __init__(self, batch_size=64, hidden_activation=lrelu, output_dim=1, scope='critic', **kwargs):
self.batch_size = batch_size
self.hidden_activation = hidden_activation
self.output_dim = output_dim
self.scope = scope
def __call__(self, x, up... |
def plot_time_cost(title, yrange, fed_async, fed_avg, fed_sync, fed_localA, local_train, fed_asofed, fed_bdfl, save_path=None, plot_size='L'):
font_settings = get_font_settings(plot_size)
x = range(1, (len(fed_async) + 1))
(fig, axes) = plt.subplots()
axes.plot(x, fed_async, label='DBAFL', linewidth=3, ... |
def channel_shuffle(x, groups):
(batchsize, num_channels, height, width) = x.data.size()
channels_per_group = (num_channels // groups)
x = x.view(batchsize, groups, channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
x = x.view(batchsize, (- 1), height, width)
return x |
def pick_slices(img, view_set, num_slices):
slices = list()
for view in view_set:
dim_size = img.shape[view]
non_empty_slices = np.array([sl for sl in range(dim_size) if (np.count_nonzero(get_axis(img, view, sl)) > 0)])
num_non_empty = len(non_empty_slices)
skip_count = max(0, np... |
def main():
ops.survey.print_header('Uptime')
uptime = ops.system.get_uptime()
if (uptime is None):
dsz.Sleep(5000)
uptime = ops.system.get_uptime()
if (uptime is None):
ops.error('Could not properly find process list to calculate uptime, you might have to do the math on your own... |
def catalyze_one_step_reversible(enzyme, substrate, product, klist):
if isinstance(enzyme, Monomer):
enzyme = enzyme()
if isinstance(substrate, Monomer):
substrate = substrate()
if isinstance(product, Monomer):
product = product()
components = catalyze_one_step(enzyme, substrate,... |
def main(argv):
mutate_sys_path()
assert ('doctest' not in sys.modules)
import testprogram
this_dir = os.path.dirname(__file__)
prog = testprogram.TestProgram(argv=argv, default_discovery_args=(this_dir, '*.py', None), module=None)
result = prog.runTests()
success = result.wasSuccessful()
... |
_common_args
def get_observation_taxon_summary(observation_id: int, **params) -> JsonResponse:
results = get(f'{API_V1}/observations/{observation_id}/taxon_summary', **params).json()
results['conservation_status'] = convert_generic_timestamps(results['conservation_status'])
results['listed_taxon'] = convert... |
def test_infer_norm_abbr():
with pytest.raises(TypeError):
infer_norm_abbr(0)
class MyNorm():
_abbr_ = 'mn'
assert (infer_norm_abbr(MyNorm) == 'mn')
class FancyBatchNorm():
pass
assert (infer_norm_abbr(FancyBatchNorm) == 'bn')
class FancyInstanceNorm():
pass
a... |
class SortFilterProxyModel(QSortFilterProxyModel):
def filterAcceptsRow(self, sourceRow, sourceParent):
if (self.filterKeyColumn() == DATE):
index = self.sourceModel().index(sourceRow, DATE, sourceParent)
data = self.sourceModel().data(index)
return (self.filterRegExp().i... |
class OperatorBase(ABC):
INDENTATION = ' '
ENABLE_DEPRECATION = True
def __init__(self) -> None:
super().__init__()
if OperatorBase.ENABLE_DEPRECATION:
warn_package('aqua.operators', 'qiskit.opflow', 'qiskit-terra')
def num_qubits(self) -> int:
raise NotImplementedEr... |
class ValidatingRequestsSession(requests.Session):
def __init__(self, *args, checksum_algorithm=hashlib.sha256, **kwargs):
super().__init__(*args, **kwargs)
self._algorithm = checksum_algorithm
def get(self, url, checksum, **kwargs):
kwargs.setdefault('allow_redirects', True)
ret... |
def plot_unions_HTS(results, size, metric: str='greedy'):
(fig, axs) = plt.subplots(1, 3, sharey=True, figsize=(((4 / 1.5) * 3), 4))
fmt = 'o-'
ms = 5
for (i, (split, ax)) in enumerate(zip(SPLITS, axs)):
xs = [int(((size * split) * i)) for i in range(1, 7)]
for model in MODELS:
... |
class ConversationsGeneratorConfig():
openai_api_key: str
agent1: str
agent2: str
initial_utterances: List[str] = field(default_factory=(lambda : ['Hello.']))
num_samples: int = 1
interruption: str = 'length'
end_phrase: str = 'Goodbye!'
end_agent: str = 'both'
lengths: List[int] = f... |
class ConditionReturn():
condition: Condition
left_varmap: Optional[VarMap] = None
right_varmap: Optional[VarMap] = None
def reverse(self) -> 'ConditionReturn':
return ConditionReturn(left_varmap=self.right_varmap, right_varmap=self.left_varmap, condition=NotCondition(self.condition)) |
def test_exclude_from_history(base_app):
run_cmd(base_app, 'history')
verify_hi_last_result(base_app, 0)
(out, err) = run_cmd(base_app, 'history')
assert (out == [])
verify_hi_last_result(base_app, 0)
run_cmd(base_app, 'help')
(out, err) = run_cmd(base_app, 'history')
expected = normaliz... |
def test_receive_withdraw_request():
pseudo_random_generator = random.Random()
(our_model1, _) = create_model(balance=70)
(partner_model1, privkey2) = create_model(balance=100)
signer = LocalSigner(privkey2)
channel_state = create_channel_from_models(our_model1, partner_model1, privkey2)
block_h... |
def sdn_get_confusion(model, loader, confusion_stats, device='cpu'):
model.eval()
layer_correct = {}
layer_wrong = {}
instance_confusion = {}
outputs = list(range(model.num_output))
for output_id in outputs:
layer_correct[output_id] = set()
layer_wrong[output_id] = set()
with... |
class ThrowerEnv(mujoco_env.MujocoEnv, utils.EzPickle):
def __init__(self):
utils.EzPickle.__init__(self)
self._ball_hit_ground = False
self._ball_hit_location = None
mujoco_env.MujocoEnv.__init__(self, 'thrower.xml', 5)
def _step(self, a):
ball_xy = self.get_body_com('ba... |
class InvertDict(dict):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._inverted_dict = dict()
for (k, v) in self.items():
if (v in self._inverted_dict):
raise GinoException('Column name {} already maps to {}'.format(v, self._inverted_... |
def inside_not_trans(graph):
id2node = {node['id']: node for node in graph['nodes']}
parents = {}
grabbed_objs = []
for edge in graph['edges']:
if (edge['relation_type'] == 'INSIDE'):
if (edge['from_id'] not in parents):
parents[edge['from_id']] = [edge['to_id']]
... |
_dataframe_method
_alias(smiles_col='smiles_column_name', mols_col='mols_column_name')
def smiles2mol(df: pd.DataFrame, smiles_column_name: Hashable, mols_column_name: Hashable, drop_nulls: bool=True, progressbar: Optional[str]=None) -> pd.DataFrame:
valid_progress = ['notebook', 'terminal', None]
if (progressb... |
def download_scan_id(scan_id):
command = ('python download-scannet.py -o . --id %s' % scan_id)
to_download = ['.aggregation.json', '.txt', '_vh_clean_2.0.010000.segs.json', '_vh_clean_2.ply', '_vh_clean_2.labels.ply']
for filetype in to_download:
os.system(((command + ' --type ') + filetype)) |
def test_next_transfer_pair():
block_number = BlockNumber(3)
balance = TokenAmount(10)
pseudo_random_generator = random.Random()
payer_transfer = create(LockedTransferSignedStateProperties(amount=balance, initiator=HOP1, target=ADDR, expiration=BlockExpiration(50)))
channels = make_channel_set([Nett... |
.django_db
def test_scope_keep_filter(site1, site2, post1, post2):
with pytest.raises(ScopeError):
Post.objects.all()
with scope(site=site1):
assert (list(Post.objects.annotate(c=Value(3, output_field=IntegerField())).distinct().all()) == [post1])
with scope(site=site2):
assert (list... |
_request_params(docs._observation_id, docs._access_token)
def delete_observation(observation_id: int, **params):
response = delete(url=f'{API_V0}/observations/{observation_id}.json', raise_for_status=False, **params)
if (response.status_code == 404):
raise ObservationNotFound(response=response)
resp... |
def test_relative_outdir(mocker, tmp_dir, package_test_flit):
mocker.patch('pyproject_hooks.BuildBackendHookCaller', autospec=True)
builder = build.ProjectBuilder(package_test_flit)
builder._hook.build_sdist.return_value = 'dist.tar.gz'
builder.build('sdist', '.')
builder._hook.build_sdist.assert_ca... |
class RandomCrop1dReturnCoordinates(RandomCrop):
def forward(self, img: Image) -> (BoundingBox, Image):
if (self.padding is not None):
img = F.pad(img, self.padding, self.fill, self.padding_mode)
(width, height) = get_image_size(img)
if (self.pad_if_needed and (width < self.size[... |
def onrun_antlr4(unit, *args):
unit.onexternal_resource(['ANTLR4', ('sbr:' + ANTLR4_RESOURCE_ID)])
if (len(args) < 1):
raise Exception('Not enough arguments for RUN_ANTLR4 macro')
arg_list = ['-jar', ('${ANTLR4}/' + ANTLR4_JAR_PATH)]
arg_list += list(args)
unit.set(['ANTLR4', '$(ANTLR4)'])
... |
class SawyerPickOutOfHoleV2Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'gripper': obs[3], 'puck_pos': obs[4:7], 'goal_pos': obs[(- 3):], 'unused_info': obs[7:(- 3)]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos'... |
def test__loss_function():
data = pd.DataFrame({'1': [float(i) for i in range(1000)], '2': [float((2 * i)) for i in range(1000)]})
tvae = TVAESynthesizer(epochs=300)
tvae.fit(data)
num_samples = 1000
sampled = tvae.sample(num_samples)
error = 0
for (_, row) in sampled.iterrows():
err... |
class RightPoolFunction(Function):
def forward(ctx, input):
output = right_pool.forward(input)[0]
ctx.save_for_backward(input)
return output
def backward(ctx, grad_output):
input = ctx.saved_variables[0]
output = right_pool.backward(input, grad_output)[0]
return o... |
class ExtractPythonTestCase(unittest.TestCase):
def test_nested_calls(self):
buf = BytesIO(b'msg1 = _(i18n_arg.replace(r\'"\', \'"\'))\nmsg2 = ungettext(i18n_arg.replace(r\'"\', \'"\'), multi_arg.replace(r\'"\', \'"\'), 2)\nmsg3 = ungettext("Babel", multi_arg.replace(r\'"\', \'"\'), 2)\nmsg4 = ungettext(i18... |
def test_direct_junction_offsets_suc_suc_1_right_wrong_input(direct_junction_right_multi_lane_fixture):
(main_road, small_road, junction_creator) = direct_junction_right_multi_lane_fixture
main_road.add_predecessor(xodr.ElementType.junction, junction_creator.id)
small_road.add_successor(xodr.ElementType.jun... |
def runAllModulesOnEachHost(args):
if (args['nmap-file'] != None):
nmapReport = NmapParser.parse_fromfile(args['nmap-file'])
for aHost in nmapReport.hosts:
hostAdress = aHost.address
for aService in aHost.services:
serviceName = aService.service.lower()
... |
def _get_tune_resources(num_actors: int, cpus_per_actor: int, gpus_per_actor: int, resources_per_actor: Optional[Dict], placement_options: Optional[Dict]):
if TUNE_INSTALLED:
from ray.tune import PlacementGroupFactory
head_bundle = {}
child_bundle = {'CPU': cpus_per_actor, 'GPU': gpus_per_ac... |
class CharDropout(nn.Module):
def __init__(self, p: float=0.0) -> None:
super(CharDropout, self).__init__()
self.p: float = p
def forward(self, input: Tensor) -> Tensor:
if ((self.p == 0.0) or (not self.training)):
return input
(batch, length, char_length, _) = input.... |
def add_file_handler(logger: logging.Logger, logging_level, log_dir: str, log_file_base_name: Optional[str]=''):
abs_log_dir = (Path(get_starting_dir_abs_path()) / log_dir)
abs_log_dir.mkdir(parents=True, exist_ok=True)
log_file = get_formatted_filename(log_file_base_name, datetime.now(), 'txt')
formatt... |
class Protonet(nn.Module):
def __init__(self, encoder):
super(Protonet, self).__init__()
self.encoder = encoder
self.slf_attn = MultiHeadAttention(1, 512, 512, 512, dropout=0)
def loss(self, sample, stage, eval=False):
xs = Variable(sample['xs'])
xq = Variable(sample['xq'... |
def darknet53_body(inputs):
def res_block(inputs, filters):
shortcut = inputs
net = conv2d(inputs, (filters * 1), 1)
net = conv2d(net, (filters * 2), 3)
net = (net + shortcut)
return net
net = conv2d(inputs, 32, 3, strides=1)
net = conv2d(net, 64, 3, strides=2)
ne... |
def test_install_logs_output(tester: CommandTester, mocker: MockerFixture) -> None:
assert isinstance(tester.command, InstallerCommand)
mocker.patch.object(tester.command.installer, 'run', return_value=0)
mocker.patch('poetry.masonry.builders.editable.EditableBuilder')
tester.execute()
assert (teste... |
def test_families():
families = table.families()
for (name, fdesc) in six.iteritems(families):
assert isinstance(name, bytes)
assert isinstance(fdesc, dict)
assert ('name' in fdesc)
assert isinstance(fdesc['name'], six.binary_type)
assert ('max_versions' in fdesc) |
def dataloader_impl(dataset: Dataset, batch_size: int, return_idx: bool=False, return_jnp_array: bool=False):
batch_idx = np.arange(len(dataset))
steps_per_epoch = math.ceil((len(dataset) / batch_size))
batch_idx = np.array_split(batch_idx, steps_per_epoch)
for idx in batch_idx:
batch = dataset[... |
def hexLat2W(nrows=5, ncols=5, **kwargs):
if ((nrows == 1) or (ncols == 1)):
print('Hexagon lattice requires at least 2 rows and columns')
print('Returning a linear contiguity structure')
return lat2W(nrows, ncols)
n = (nrows * ncols)
rid = [(i // ncols) for i in range(n)]
cid = ... |
class VGG(nn.Module):
def __init__(self, features, num_classes=1000):
super(VGG, self).__init__()
self.features = features
self.classifier = nn.Sequential(nn.Linear(((512 * 7) * 7), 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, 4096), nn.ReLU(True), nn.Dropout(), nn.Linear(4096, num_cl... |
def check_installed(required_solvers, install_dir, bindings_dir, mirror_link):
pypath_solvers = get_env().factory.all_solvers()
global_solvers_status = []
print('Installed Solvers:')
for i in INSTALLERS:
installer_ = i.InstallerClass(install_dir=install_dir, bindings_dir=bindings_dir, solver_ver... |
class WallFillProperty(bpy.types.PropertyGroup):
width: FloatProperty(name='Wall Width', min=get_scaled_unit(0.0), max=get_scaled_unit(100.0), default=get_scaled_unit(0.075), unit='LENGTH', description='Width of each wall')
def draw(self, context, layout):
row = layout.row(align=True)
row.prop(s... |
class Task2Dataset(BaseDataset):
def __getitem__(self, index) -> Tuple:
(query_id, idx) = self.samples[index]
product_id = self.database[self.split_dataset][query_id]['product_id'][idx]
example_id = self.database[self.split_dataset][query_id]['example_id'][idx]
dataset = torch.tensor... |
_bitsandbytes
_accelerate
_torch
_torch_gpu
class MixedInt8T5Test(unittest.TestCase):
def setUpClass(cls):
cls.model_name = 't5-small'
cls.dense_act_model_name = 'google/flan-t5-small'
cls.tokenizer = AutoTokenizer.from_pretrained(cls.model_name)
cls.input_text = 'Translate in German... |
class DiscriminatorSTFT(nn.Module):
def __init__(self, filters: int, in_channels: int=1, out_channels: int=1, n_fft: int=1024, hop_length: int=256, win_length: int=1024, max_filters: int=1024, filters_scale: int=1, kernel_size: tp.Tuple[(int, int)]=(3, 9), dilations: tp.List=[1, 2, 4], stride: tp.Tuple[(int, int)]=... |
def path_deploy(base, port=0, host='', index=True, static_dir=None, reconnect_timeout=0, cdn=True, debug=False, allowed_origins=None, check_origin=None, max_payload_size='200M', **tornado_app_settings):
debug = Session.debug = os.environ.get('PYWEBIO_DEBUG', debug)
page.MAX_PAYLOAD_SIZE = max_payload_size = par... |
class TestCopyArea(EndianTest):
def setUp(self):
self.req_args_0 = {'dst_drawable': , 'dst_x': (- 27552), 'dst_y': (- 6968), 'gc': , 'height': 7340, 'src_drawable': , 'src_x': (- 24637), 'src_y': (- 24026), 'width': 46214}
self.req_bin_0 = b'>\x00\x07\x00c\xa6\x9an\x86]\x17^5\xa2\xc7g\xc3\x9f&\xa2`\... |
def main(client, config):
(store_sales, date_dim, store, product_reviews) = benchmark(read_tables, config=config, compute_result=config['get_read_time'])
q18_startDate_int = np.datetime64(q18_startDate, 'ms').astype(int)
q18_endDate_int = np.datetime64(q18_endDate, 'ms').astype(int)
date_dim_filtered = ... |
class DescribeZeroOrOne():
def it_adds_a_getter_property_for_the_child_element(self, getter_fixture):
(parent, zooChild) = getter_fixture
assert (parent.zooChild is zooChild)
def it_adds_an_add_method_for_the_child_element(self, add_fixture):
(parent, expected_xml) = add_fixture
... |
class FittingViewDrop(wx.DropTarget):
def __init__(self, dropFn, *args, **kwargs):
super(FittingViewDrop, self).__init__(*args, **kwargs)
self.dropFn = dropFn
self.dropData = wx.TextDataObject()
self.SetDataObject(self.dropData)
def OnData(self, x, y, t):
if self.GetData(... |
def test_variables__validate_dims_optional():
spec = ArrayLikeSpec('foo', 'foo doc', kind='i', dims=({None, 'windows', 'variants'}, 'samples', 'ploidy'))
ds = xr.Dataset()
ds['valid_0'] = (('samples', 'ploidy'), np.ones((2, 3), int))
ds['valid_1'] = (('windows', 'samples', 'ploidy'), np.ones((1, 2, 3), ... |
class Index(Op):
__props__ = ()
def make_node(self, x, elem):
assert isinstance(x.type, TypedListType)
assert (x.ttype == elem.type)
return Apply(self, [x, elem], [scalar()])
def perform(self, node, inputs, outputs):
(x, elem) = inputs
(out,) = outputs
for y i... |
def test_pipeline(root_path):
opt = parse_options(root_path, is_train=False)
torch.backends.cudnn.benchmark = True
make_exp_dirs(opt)
log_file = osp.join(opt['path']['log'], f"test_{opt['name']}_{get_time_str()}.log")
logger = get_root_logger(logger_name='basicsr', log_level=logging.INFO, log_file=l... |
class GeneralizedRCNN(nn.Module):
def __init__(self, cfg):
super(GeneralizedRCNN, self).__init__()
self.backbone = build_backbone(cfg)
self.rpn = build_rpn(cfg)
self.roi_heads = build_roi_heads(cfg)
def forward(self, images, targets=None):
if (self.training and (targets i... |
def infixNotation(baseExpr, opList, lpar=Suppress('('), rpar=Suppress(')')):
class _FB(FollowedBy):
def parseImpl(self, instring, loc, doActions=True):
self.expr.tryParse(instring, loc)
return (loc, [])
ret = Forward()
lastExpr = (baseExpr | ((lpar + ret) + rpar))
for (i,... |
def save_images(webpage, visuals, image_path, aspect_ratio=1.0, width=256):
image_dir = webpage.get_image_dir()
short_path = ntpath.basename(image_path[0])
name = os.path.splitext(short_path)[0]
webpage.add_header(name)
(ims, txts, links) = ([], [], [])
for (label, im_data) in visuals.items():
... |
def get_criterion(opt, summarywriter=None):
assert isinstance(opt['crit'], list)
crit_objects = []
for item in opt['crit']:
crit_name = item.lower()
if (crit_name == 'lang'):
this_crit_object = LanguageGeneration(opt, crit_name)
elif (crit_name == 'length'):
t... |
def usage():
printerr('Usage is: export-to-postgresql.py <database name> [<columns>] [<calls>] [<callchains>] [<pyside-version-1>]')
printerr("where: columns 'all' or 'branches'")
printerr(" calls 'calls' => create calls and call_paths table")
printerr(" callchains... |
class SIGN(Frame):
_framespec = [ByteSpec('group', default=128), BinaryDataSpec('sig')]
def HashKey(self):
return ('%s:%s:%s' % (self.FrameID, self.group, _bytes2key(self.sig)))
def __bytes__(self):
return self.sig
def __eq__(self, other):
return (self.sig == other)
__hash__ ... |
class F9_TestCase(FC6_TestCase):
def runTest(self):
FC6_TestCase.runTest(self)
self.assert_removed('vnc', 'connect')
self.assert_parse_error('vnc --host=HOSTNAME --connect=HOSTNAME --password=PASSWORD')
self.assert_parse_error('vnc --host=HOSTNAME --connect=HOSTNAME --password=PASSWO... |
class Carousel(Widget):
def __init__(self, view, css_id, show_indicators=True, interval=5000, pause='hover', wrap=True, keyboard=True, min_height=None):
super().__init__(view)
self.carousel_panel = self.add_child(Div(view, css_id=css_id))
self.carousel_panel.append_class('carousel')
... |
def violet(N, state=None):
state = (np.random.RandomState() if (state is None) else state)
uneven = (N % 2)
X = (state.randn((((N // 2) + 1) + uneven)) + (1j * state.randn((((N // 2) + 1) + uneven))))
S = np.arange(len(X))
y = irfft((X * S)).real
if uneven:
y = y[:(- 1)]
return norma... |
def test_remove_overridden_styles():
from typing import List
from cmd2 import Bg, EightBitBg, EightBitFg, Fg, RgbBg, RgbFg, TextStyle
def make_strs(styles_list: List[ansi.AnsiSequence]) -> List[str]:
return [str(s) for s in styles_list]
styles_to_parse = make_strs([Fg.BLUE, TextStyle.UNDERLINE_D... |
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