code stringlengths 281 23.7M |
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.unit()
.parametrize(('session', 'path', 'node_info', 'expected'), [pytest.param(Session.from_config({'check_casing_of_paths': False, 'paths': (Path.cwd(),)}), Path(), NodeInfo(arg_name='', path=(), value=(Path.cwd() / 'text.txt'), task_path=(Path.cwd() / 'task_example.py'), task_name='task_example'), (Path.cwd() / 'te... |
def test_bpe_sentence_embedding():
assert (BPESentenceEmbedding(Laser.DEFAULT_ENCODER_FILE).embed_bpe_sentences(['hello', 'world']).shape == (2, 1024))
with open(Laser.DEFAULT_ENCODER_FILE, 'rb') as encoder_f:
assert (BPESentenceEmbedding(encoder_f).embed_bpe_sentences(['hello', 'world']).shape == (2, 1... |
def find_identifier(business_logic, query, name_ok=True):
name = slug = identifier = None
if ('id' in query):
identifier = query.pop('id')[(- 1)]
elif ('slug' in query):
slug = query.pop('slug')[(- 1)]
elif (name_ok and ('name' in query)):
name = query.pop('name')[(- 1)]
if (... |
def test_read_commandline(dataframe):
temp_dir = tempfile.gettempdir()
dataframe.to_csv(f'{temp_dir}/dataframe.csv', index=0)
if (sys.platform in ['win32']):
df = janitor.io.read_commandline(f'type {temp_dir}\dataframe.csv')
else:
df = janitor.io.read_commandline(f'cat {temp_dir}/datafra... |
def test_multilabel_independent():
edges = np.zeros((0, 2), dtype=np.int)
n_features = 5
n_labels = 4
model = MultiLabelClf(n_labels=n_labels, n_features=n_features, edges=edges)
rnd = np.random.RandomState(0)
x = rnd.normal(size=5)
w = rnd.normal(size=(n_features * n_labels))
y = model.... |
class LstmEncoder(nn.Module):
def __init__(self, args):
super(LstmEncoder, self).__init__()
self.bidirectional = args.bidirectional
if self.bidirectional:
assert ((args.hidden_size % 2) == 0)
self.hidden_size = (args.hidden_size // 2)
else:
self.hi... |
class SMPHandler():
def __init__(self, crypto):
self.crypto = crypto
self.state = 1
self.g1 = DH_GENERATOR
self.g2 = None
self.g3 = None
self.g3o = None
self.x2 = None
self.x3 = None
self.prog = SMPPROG_OK
self.pab = None
self.q... |
def _num_type(value):
if ('.' in value):
try:
value_out = float(value)
return value_out
except ValueError:
value_out = value
return value_out
else:
try:
value_out = int(value)
return value_out
except ValueErr... |
class TestGetInputFocus(EndianTest):
def setUp(self):
self.req_args_0 = {}
self.req_bin_0 = b'+\x00\x00\x01'
self.reply_args_0 = {'focus': , 'revert_to': 153, 'sequence_number': 4228}
self.reply_bin_0 = b'\x01\x99\x10\x84\x00\x00\x00\x003\x8a\x18\x1d\x00\x00\x00\x00\x00\x00\x00\x00\x... |
def dependentSchemas(validator, dependentSchemas, instance, schema):
if (not validator.is_type(instance, 'object')):
return
for (property, dependency) in dependentSchemas.items():
if (property not in instance):
continue
(yield from validator.descend(instance, dependency, sche... |
class AggregatedTransform(TransformComponent):
def __init__(self, functions: List[Function], filter_expression: str=None):
super(AggregatedTransform, self).__init__()
self.functions = functions
self.filter_expression = filter_expression
def aggregations(self) -> List[Tuple]:
colu... |
def get_scheduler(optimizer, n_epochs: int, loss_name=None):
scheduler = MultiStepLR
if (n_epochs <= 20):
scheduler = scheduler(optimizer, milestones=[10, 15], gamma=0.1)
elif (n_epochs <= 30):
scheduler = scheduler(optimizer, milestones=[15, 25], gamma=0.1)
elif (n_epochs <= 40):
... |
def setup(loop, args):
def verbose(s):
if (args.v >= 2):
sys.stdout.write((('\x1b[32m' + time.strftime('%Y-%m-%d %H:%M:%S')) + '\x1b[m '))
sys.stdout.write((s + '\x1b[0K\n'))
else:
sys.stdout.write((s + '\n'))
sys.stdout.flush()
args.verbose = verbose
... |
class BaseEmbed(Seeder, metaclass=ABCMeta):
def __init__(self, options) -> None:
super().__init__(options, enabled=(options.no_seed is False))
self.download = options.download
self.extra_search_dir = [i.resolve() for i in options.extra_search_dir if i.exists()]
self.pip_version = opt... |
class _TestStateful():
def state_dict(self) -> Dict[(str, Any)]:
return {'foo': torch.Tensor(1), 'bar': torch.Tensor(1), 'baz': [torch.Tensor(1), torch.Tensor(1)], 'qux': {'quux': torch.Tensor(1), 'quuz': torch.Tensor(1)}}
def load_state_dict(self, state_dict: Dict[(str, Any)]) -> None:
raise No... |
def test_async_cmds_overwrite_vs_append(temp_dir):
stdout = temp_dir.joinpath('mydir/stdout')
stderr = temp_dir.joinpath('mydir/stderr')
cmd1 = get_cmd('tests/testfiles/cmds/echo-out-and-err.sh one', 'tests\\testfiles\\cmds\\echo-out-and-err.bat one')
context = Context({'cmds': {'run': [cmd1], 'stdout':... |
def test_many_generalizers():
gg = _make_composite_generalizer(cirq_to_bloqs, ignore_cliffords, ignore_alloc_free, ignore_split_join, generalize_cvs, generalize_rotation_angle)
bloqs = [gg(b) for b in _BLOQS_TO_FILTER]
bloqs = [b for b in bloqs if (b is not None)]
assert (bloqs == [And(CV, CV), MultiAnd... |
class SobelOperator(nn.Module):
def __init__(self, epsilon):
super().__init__()
self.epsilon = epsilon
x_kernel = (np.array([[1, 0, (- 1)], [2, 0, (- 2)], [1, 0, (- 1)]]) / 4)
self.conv_x = nn.Conv2d(1, 1, kernel_size=3, stride=1, padding=1, bias=False)
self.conv_x.weight.dat... |
def load_test_model(opt, dummy_opt, model_path=None):
if (model_path is None):
model_path = opt.models[0]
checkpoint = torch.load(model_path, map_location=(lambda storage, loc: storage))
fields = load_fields_from_vocab(checkpoint['vocab'])
model_opt = checkpoint['opt']
for arg in dummy_opt:
... |
def cal_group_auc(labels, preds, impression_id_list):
if (len(impression_id_list) != len(labels)):
raise ValueError('impression id num should equal to the sample num,impression id num is {0}'.format(len(impression_id_list)))
group_score = defaultdict((lambda : []))
group_truth = defaultdict((lambda ... |
def main(args):
serialization_dir = args.serialization_dir
pruning_method = args.pruning_method
threshold = args.threshold
st = torch.load(os.path.join(serialization_dir, 'pytorch_model.bin'), map_location='cpu')
remaining_count = 0
encoder_count = 0
print('name'.ljust(60, ' '), 'Remaining W... |
class SendMessageForm(forms.ModelForm):
class Meta():
model = Message
fields = ('body',)
labels = {'body': _('message')}
error_messages = {'body': {'required': _("can't really understand you")}}
def clean(self):
msg = self.cleaned_data.get('body', '')
if (len(msg)... |
class Trainer(object):
def __init__(self, opt, model, optimizer=None):
self.opt = opt
self.optimizer = optimizer
(self.loss_stats, self.loss) = self._get_losses(opt)
self.model_with_loss = ModleWithLoss(model, self.loss)
def set_device(self, gpus, chunk_sizes, device):
if... |
def get_kernel_offsets(size: Union[(int, Tuple[(int, ...)])], stride: Union[(int, Tuple[(int, ...)])]=1, dilation: Union[(int, Tuple[(int, ...)])]=1, device: str='cpu') -> torch.Tensor:
size = make_ntuple(size, ndim=3)
stride = make_ntuple(stride, ndim=3)
dilation = make_ntuple(dilation, ndim=3)
offsets... |
class WRN_40_2_WRN_40_2(nn.Module):
def __init__(self, num_classes):
super(WRN_40_2_WRN_40_2, self).__init__()
self.net1 = wrn_40_2_aux(num_classes=num_classes)
self.net2 = wrn_40_2_aux(num_classes=num_classes)
def forward(self, x, grad=True):
(logit1, ss_logits1) = self.net1(x, ... |
class Z3QuantifierEliminator(QuantifierEliminator):
LOGICS = [LIA, LRA]
def __init__(self, environment, logic=None):
QuantifierEliminator.__init__(self)
self.environment = environment
self.logic = logic
self.converter = Z3Converter(environment, z3.main_ctx())
def eliminate_qu... |
def format_received_item(item_name: str, player_name: str) -> str:
special = {'Locked Power Bomb Expansion': 'Received Power Bomb Expansion from {provider_name}, but the main Power Bomb is required to use it.', 'Locked Missile Expansion': 'Received Missile Expansion from {provider_name}, but the Missile Launcher is... |
((sensors is None), 'No PySensors module found')
class TestLMSensorsCollector(CollectorTestCase):
def setUp(self):
config = get_collector_config('LMSensorsCollector', {})
self.collector = LMSensorsCollector(config, None)
def test_import(self):
self.assertTrue(LMSensorsCollector)
(Col... |
def generate_data(num_relations, num_tuples, relations_given, LAMA_path):
graph_path = 'data/pattern_data/graphs_tense/'
relations_path = glob.glob((graph_path + '*.graph'))
output_path = 'pararel/ft/data/'
if (not os.path.exists(output_path)):
os.mkdir(output_path)
random.shuffle(relations_... |
class SolveMatrixTimeSuite():
params = [[True, False], [((- 1.0), 1.0), (0.0, 1.0), (0.2, 1.0), (0.5, 1.0)], [100, 350, 700]]
param_names = ['is_hermitian', 'minmaxeival', 'n']
def setup(self, is_hermitian, minmaxeival, n):
seed = 123
ncols = 50
torch.manual_seed(seed)
(min_e... |
def query_paths_args(chain_id, token_network_state, one_to_n_address, our_address) -> Dict[(str, Any)]:
return dict(our_address=our_address, privkey=PRIVKEY, current_block_number=10, token_network_address=token_network_state.address, one_to_n_address=one_to_n_address, chain_id=chain_id, route_from=our_address, rout... |
def test_top_down_pose_tracking_demo():
pose_model = init_pose_model('configs/body/2d_kpt_sview_rgb_img/topdown_heatmap/coco/res50_coco_256x192.py', None, device='cpu')
image_name = 'tests/data/coco/.jpg'
dataset_info = DatasetInfo(pose_model.cfg.data['test']['dataset_info'])
person_result = [{'bbox': [... |
def try_finally_try(builder: IRBuilder, err_handler: BasicBlock, return_entry: BasicBlock, main_entry: BasicBlock, try_body: GenFunc) -> ((Register | AssignmentTarget) | None):
control = TryFinallyNonlocalControl(return_entry)
builder.builder.push_error_handler(err_handler)
builder.nonlocal_control.append(c... |
class Effect173(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Hybrid Turret')), 'damageMultiplier', (container.getMod... |
class VGG16(Network):
alpha = [0, 0, 0, 1, 1]
beta = [1, 1, 1, 1, 1]
def setup(self):
self.conv(3, 3, 3, 64, name='conv1_1').conv(3, 3, 64, 64, name='conv1_2').pool().conv(3, 3, 64, 128, name='conv2_1').conv(3, 3, 128, 128, name='conv2_2').pool().conv(3, 3, 128, 256, name='conv3_1').conv(3, 3, 256, ... |
class TestNonNegSqrt():
def test_main(self):
vals = ((- 1.0), 0.0, 1.0, 2.0)
desireds = (0.0, 0.0, 1.0, sqrt(2.0))
for (val, desired) in zip(vals, desireds):
x = torch.tensor(val)
y = pystiche.nonnegsqrt(x)
assert (y == ptu.approx(desired))
def test_gr... |
def main(client, config):
(date_dim_df, customer_df, s_sales_df, web_sales_df) = benchmark(read_tables, config=config, compute_result=config['get_read_time'])
filtered_date_df = date_dim_df.query('d_year >= _Year and d_year <= _Year_plus', local_dict={'q13_Year': q13_Year, 'q13_Year_plus': (q13_Year + 1)}, meta... |
class MySimulatorMaster(SimulatorMaster, Callback):
def __init__(self, pipe_c2s, pipe_s2c, gpus):
super(MySimulatorMaster, self).__init__(pipe_c2s, pipe_s2c)
self.queue = queue.Queue(maxsize=((BATCH_SIZE * 8) * 2))
self._gpus = gpus
def _setup_graph(self):
nr_gpu = len(self._gpus... |
class STVQAANLSEvaluator():
def __init__(self):
import editdistance
self.get_edit_distance = editdistance.eval
def get_anls(self, s1, s2):
s1 = s1.lower().strip()
s2 = s2.lower().strip()
iou = (1 - (self.get_edit_distance(s1, s2) / max(len(s1), len(s2))))
anls = (... |
def replace_rvs_by_values(graphs: Sequence[TensorVariable], *, rvs_to_values: Dict[(TensorVariable, TensorVariable)], rvs_to_transforms: Optional[Dict[(TensorVariable, 'Transform')]]=None) -> List[TensorVariable]:
if rvs_to_transforms:
inputs = [i for i in graph_inputs(graphs) if (not isinstance(i, Constant... |
class ForceBalanceFitting(StageBase):
class Config():
validate_assignment = True
arbitrary_types_allowed = True
type: Literal['ForceBalanceFitting'] = 'ForceBalanceFitting'
penalty_type: Literal[('L1', 'L2')] = 'L1'
job_type: str = 'optimize'
max_iterations: PositiveInt = 10
conv... |
class RewriteDatabaseQuery():
def __init__(self, include: Iterable[Union[(str, None)]], require: Optional[Union[(OrderedSet, Sequence[str])]]=None, exclude: Optional[Union[(OrderedSet, Sequence[str])]]=None, subquery: Optional[dict[(str, 'RewriteDatabaseQuery')]]=None, position_cutoff: float=math.inf, extra_rewrite... |
def dtypes():
return [dtype('int64'), dtype('int64'), dtype('int64'), dtype('int64'), dtype('int64'), dtype('O'), dtype('float64'), dtype('float64'), dtype('float64'), dtype('float64'), dtype('float64'), dtype('int64'), dtype('float64'), dtype('O'), dtype('int64'), dtype('float64'), dtype('int64'), dtype('float64')... |
def test_stats():
with rasterio.open('tests/data/RGB.byte.tif') as src:
results = stats((src, 1))
assert (results[0] == 0)
assert (results[1] == 255)
assert np.isclose(results[2], 29.9477)
results2 = stats(src.read(1))
assert np.allclose(np.array(results), np.array(re... |
def test_maneuver_reader(tmpdir):
tmpcatalog = os.path.join(tmpdir, 'my_catalog.xosc')
cf = xosc.CatalogFile()
cf.create_catalog(tmpcatalog, 'ManeuverCatalog', 'My first miscobject catalog', 'Mandolin')
event = xosc.Event('my_event', xosc.Priority.overwrite)
event.add_action('myaction', xosc.Absolut... |
def f1_score(y_pred, y_true, average='micro'):
assert (len(y_pred) == len(y_true))
def _compute_prf(gold, pred):
(TP, FP, FN) = (0, 0, 0)
if (len(gold) != 0):
count = 1
for g in gold:
if (g in pred):
TP += 1
else:
... |
def load_coco_json(json_file, image_root, dataset_name=None, extra_annotation_keys=None):
from pycocotools.coco import COCO
timer = Timer()
json_file = PathManager.get_local_path(json_file)
with contextlib.redirect_stdout(io.StringIO()):
coco_api = COCO(json_file)
if (timer.seconds() > 1):
... |
def BackupRestoreSeries(source_local, dest_local, list_of_dirnames, compare_hardlinks=1, dest_dirname=abs_output_dir, restore_dirname=abs_restore_dir, compare_backups=1, compare_eas=0, compare_acls=0, compare_ownership=0):
Globals.set('preserve_hardlinks', compare_hardlinks)
Globals.set('no_compression_regexp_s... |
class Gradients(uhf_grad.Gradients):
_keys = {'with_df', 'auxbasis_response'}
def __init__(self, mf):
self.auxbasis_response = True
uhf_grad.Gradients.__init__(self, mf)
get_jk = df_rhf_grad.Gradients.get_jk
get_j = df_rhf_grad.Gradients.get_j
get_k = df_rhf_grad.Gradients.get_k
... |
def reshape_to_matrix(input_tensor):
ndims = input_tensor.shape.ndims
if (ndims < 2):
raise ValueError(('Input tensor must have at least rank 2. Shape = %s' % input_tensor.shape))
if (ndims == 2):
return input_tensor
width = input_tensor.shape[(- 1)]
output_tensor = tf.reshape(input_... |
class SafeRepresenter(BaseRepresenter):
def ignore_aliases(self, data):
if (data is None):
return True
if (isinstance(data, tuple) and (data == ())):
return True
if isinstance(data, (str, unicode, bool, int, float)):
return True
def represent_none(self... |
def fork(fork_inst: Type[T]=StateHolder, name: Optional[str]=None) -> Type[T]:
fork_inst._fork_counter += 1
if name:
class_name = name
else:
class_name = '{}_fork{}'.format(get_class_name(fork_inst), fork_inst._fork_counter)
result = type(class_name, (fork_inst,), {})
result._classes... |
class InhibitAnyPolicy(ExtensionType):
oid = ExtensionOID.INHIBIT_ANY_POLICY
def __init__(self, skip_certs: int) -> None:
if (not isinstance(skip_certs, int)):
raise TypeError('skip_certs must be an integer')
if (skip_certs < 0):
raise ValueError('skip_certs must be a non... |
class FullyConnectedDotProject(Mapper):
def __init__(self, n_out, n_project, w_init='glorot_uniform', activation='relu', bias=True):
self.w_init = w_init
self.n_project = n_project
self.activation = activation
self.n_out = n_out
self.bias = bias
def apply(self, is_train, ... |
class _LayoutContext():
def __init__(self, layout, document, colors_iter, background_iter):
self.colors_iter = colors_iter
underline_iter = document.get_style_runs('underline')
self.decoration_iter = runlist.ZipRunIterator((background_iter, underline_iter))
self.baseline_iter = runli... |
def imppid(args):
if (args['pid'] == None):
logging.error('A pid has to be selected')
else:
printT('Impersonating primary token of pid {0}'.format(args['pid']))
imp = Impersonate()
imp.enableAllUserRights()
status = imp.impersonateViaPID(pid=args['pid'])
if (statu... |
def test_scene_to_svg_exporter_render_with_worker_canceled(view):
item = BeeTextItem('foo')
item.setPos(QtCore.QPointF(20, 30))
view.scene.addItem(item)
exporter = SceneToSVGExporter(view.scene)
exporter.size = QtCore.QSize(200, 400)
exporter.margin = 5
worker = MagicMock(canceled=True)
... |
def get_xception(model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
channels = [[128], [256], ([728] * 9), [1024]]
net = Xception(channels=channels, **kwargs)
if pretrained:
if ((model_name is None) or (not model_name)):
raise ValueError('Parameter ... |
def launch_openroad():
global process
executable_path = os.path.abspath(os.path.join(os.getcwd(), '../../../../cmake-build-release/src'))
process = subprocess.Popen([f'{executable_path}/openroad', '-exit', '/home/plan/eda/OpenROAD/src/drt/test/results/ispd18_test1/run-net-ordering-train.tcl'], cwd=executabl... |
class FpnCombine(nn.Module):
def __init__(self, feature_info, fpn_config, fpn_channels, inputs_offsets, target_reduction, pad_type='', pooling_type='max', norm_layer=nn.BatchNorm2d, apply_bn_for_resampling=False, conv_after_downsample=False, redundant_bias=False, weight_method='attn'):
super(FpnCombine, sel... |
def load_zip_file_keys(file, fileNameRegExp=''):
try:
archive = zipfile.ZipFile(file, mode='r', allowZip64=True)
except:
raise Exception('Error loading the ZIP archive.')
pairs = []
for name in archive.namelist():
addFile = True
keyName = name
if (fileNameRegExp !... |
(frozen=True)
class ReFieldNameRC(LocatedRequestChecker):
LOCATION = FieldLoc
pattern: Pattern[str]
def _check_location(self, mediator: DirectMediator, loc: FieldLoc) -> None:
if self.pattern.fullmatch(loc.field_id):
return
raise CannotProvide(f'field_id must be matched by {self.... |
class StreamBlocksAdminMixin():
change_form_template = 'streamfield/admin/change_form.html'
popup_response_template = 'streamfield/admin/streamfield_popup_response.html'
def response_add(self, request, obj, post_url_continue=None):
if ('block_id' in request.POST):
opts = obj._meta
... |
def _render_month(calendar, year, month, print_year):
import pandas as pd
if (sys.version_info[0] == 2):
import StringIO
out = StringIO.StringIO()
else:
import io
out = io.StringIO()
start = '{year}-{month}'.format(year=year, month=month)
if (month == 12):
end... |
def gen_sqlalchemy_metadata(peewee_model_list, legacy_index_map=None):
metadata = MetaData(naming_convention={'ix': 'ix_%(column_0_label)s', 'uq': 'uq_%(table_name)s_%(column_0_name)s', 'fk': 'fk_%(table_name)s_%(column_0_name)s_%(referred_table_name)s', 'pk': 'pk_%(table_name)s'})
for model in peewee_model_lis... |
class RemoteLoader():
def main(*argw) -> None:
remoteControl: RemoteControlWithUndo = RemoteControlWithUndo()
livingRoomLight: Light = Light('Living Room')
livingRoomLightOn: LightOnCommand = LightOnCommand(livingRoomLight)
livingRoomLightOff: LightOffCommand = LightOffCommand(living... |
class SpaceTest(unittest.TestCase):
def setUp(self):
logging.basicConfig(filename='SpaceTest.log', level=logging.DEBUG)
def test_make_two_spaces(self):
log = logging.getLogger(__name__)
log.debug('test_make_two_spaces')
space1 = tpm2.Client(tpm2.Client.FLAG_SPACE)
root1 =... |
_config
def test_labelgroup(manager):
manager.c.group['a'].toscreen()
assert (manager.c.group['a'].info()['label'] == 'a')
manager.c.labelgroup()
manager.c.widget['prompt'].fake_keypress('b')
manager.c.widget['prompt'].fake_keypress('Return')
assert (manager.c.group['a'].info()['label'] == 'b')
... |
class PersistentSearchControl(RequestControl):
class PersistentSearchControlValue(univ.Sequence):
componentType = namedtype.NamedTypes(namedtype.NamedType('changeTypes', univ.Integer()), namedtype.NamedType('changesOnly', univ.Boolean()), namedtype.NamedType('returnECs', univ.Boolean()))
controlType = '... |
def get_size_during_upload(repo_id: int):
query = BlobUpload.select(fn.Sum(BlobUpload.byte_count).alias('size_bytes')).where((BlobUpload.repository_id == repo_id)).get()
repo_size = get_repository_size(repo_id)
size_bytes = (query.size_bytes if (query.size_bytes is not None) else 0)
return (repo_size + ... |
def get_parser():
parser = argparse.ArgumentParser(description='transforms features via a given pca and stored them in target dir')
parser.add_argument('source', help='directory with features')
parser.add_argument('--split', help='which split to read', required=True)
parser.add_argument('--save-dir', he... |
def main():
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
if ((len(sys.argv) == 2) and sys.argv[1].endswith('.json')):
(model_args, data_args, training_args) = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
else:
(model_args, data_args,... |
def _create_playlists() -> None:
local_files = ArchivedSong.objects.filter(url__startswith='local_library').count()
library_link = os.path.join(conf.SONGS_CACHE_DIR, 'local_library')
library_path = os.path.abspath(library_link)
logging.info('started creating playlists in %s', library_path)
_set_scan... |
class vec3():
def __init__(self, x, y, z):
self.x = x
self.y = y
self.z = z
def __str__(self):
return (((((('(' + str(self.x)) + ', ') + str(self.y)) + ', ') + str(self.z)) + ')')
def __add__(self, v):
if isinstance(v, vec3):
return vec3((self.x + v.x), (s... |
class GELUActivation(nn.Module):
def __init__(self, use_gelu_python: bool=False):
super().__init__()
if use_gelu_python:
self.act = self._gelu_python
else:
self.act = nn.functional.gelu
def _gelu_python(self, input: Tensor) -> Tensor:
return ((input * 0.5)... |
def _setup_ipython(ipython: Any=None) -> Any:
if scooby.in_ipython():
from IPython import get_ipython
ipython = get_ipython()
ipython.run_line_magic('gui', 'qt')
from IPython.external.qt_for_kernel import QtGui
QtGui.QApplication.instance()
return ipython |
class DCUN_TFC_FiLM_LaSAFT_Framework(DenseCUNet_FiLM_Framework):
def __init__(self, n_fft, hop_length, num_frame, spec_type, spec_est_mode, optimizer, lr, auto_lr_schedule, train_loss, val_loss, **kwargs):
valid_kwargs = inspect.signature(DCUN_TFC_FiLM_LaSAFT.__init__).parameters
tfc_net_kwargs = di... |
def find_models_missing_data():
models_missing_data = set()
for one_model in all_models:
if (one_model in appr_classes):
continue
try:
one_model.select().get()
except one_model.DoesNotExist:
if ((one_model.__name__ not in WHITELISTED_EMPTY_MODELS) and ... |
class DriverAction(argparse.Action):
def __call__(self, parser, namespace, values, option_string=None):
setattr(namespace, self.dest, values)
driver = getattr(drivers, values.lower())
namespace.selenium_host = (namespace.selenium_host or getattr(driver, 'HOST', None))
namespace.selen... |
def convert_probability_to_call(ds: Dataset, call_genotype_probability: Hashable=variables.call_genotype_probability, threshold: float=0.9, merge: bool=True) -> Dataset:
from .conversion_numba_fns import _convert_probability_to_call
if (not (0 <= threshold <= 1)):
raise ValueError(f'Threshold must be fl... |
_pipeline_test
class ConversationalPipelineTests(unittest.TestCase):
model_mapping = dict((list(MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()) if MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING else (([] + list(MODEL_FOR_CAUSAL_LM_MAPPING.items())) if MODEL_FOR_CAUSAL_LM_MAPPING else [])))
tf_model_mapping = dict((list... |
_module()
class DavisDataset(RawframeDataset):
PALETTE = [[0, 0, 0], [128, 0, 0], [0, 128, 0], [128, 128, 0], [0, 0, 128], [128, 0, 128], [0, 128, 128], [128, 128, 128], [64, 0, 0], [191, 0, 0], [64, 128, 0], [191, 128, 0], [64, 0, 128], [191, 0, 128], [64, 128, 128], [191, 128, 128], [0, 64, 0], [128, 64, 0], [0, ... |
def module_to_test_file(module_fname):
splits = module_fname.split(os.path.sep)
short_name = os.path.sep.join(splits[2:])
if (short_name in SPECIAL_MODULE_TO_TEST_MAP):
test_file = SPECIAL_MODULE_TO_TEST_MAP[short_name]
if isinstance(test_file, str):
return f'tests/{test_file}'
... |
def evaluate_subgoals_mc(env, model, dataset, extractor, trial_uid, dataset_idx, args, obj_predictor):
(traj_data, traj_key) = dataset.jsons_and_keys[dataset_idx]
(r_idx, subgoal_idx) = (int(trial_uid.split(':')[1]), int(trial_uid.split(':')[2]))
if (not (traj_data['repeat_idx'] == r_idx)):
print(tr... |
def get_parameter_device(parameter: torch.nn.Module):
try:
return next(parameter.parameters()).device
except StopIteration:
def find_tensor_attributes(module: torch.nn.Module) -> List[Tuple[(str, Tensor)]]:
tuples = [(k, v) for (k, v) in module.__dict__.items() if torch.is_tensor(v)]... |
class BatchMolGraph():
def __init__(self, mol_graphs: List[MolGraph]):
self.atom_fdim = get_atom_fdim()
self.bond_fdim = get_bond_fdim()
self.n_atoms = 1
self.n_bonds = 1
self.a_scope = []
self.b_scope = []
f_atoms = [([0] * self.atom_fdim)]
f_bonds = ... |
.parametrize('prefer_grpc', [False, True])
def test_conditional_payload_update(prefer_grpc):
client = QdrantClient(prefer_grpc=prefer_grpc, timeout=TIMEOUT)
client.recreate_collection(collection_name=COLLECTION_NAME, vectors_config=VectorParams(size=DIM, distance=Distance.DOT), timeout=TIMEOUT)
uuid1 = str(... |
def crypt(password, salt):
if (len(salt) == 0):
salt = b'AA'
elif (len(salt) == 1):
salt = (salt + b'A')
Eswap0 = _con_salt[(salt[0] & 127)]
Eswap1 = (_con_salt[(salt[1] & 127)] << 4)
ks = _set_key((password + b'\x00\x00\x00\x00\x00\x00\x00\x00')[:8])
(o1, o2) = _body(ks, Eswap0,... |
class BatchIndexWriterMixin(object):
def __init__(self, uri, db, conn, title):
super(BatchIndexWriterMixin, self).__init__(uri, db, conn, title)
self._property_name_values = []
self._rule_smiles_values = []
self._rule_values = []
self._environment_fingerprint_values = []
... |
class TornadoServer(ServerAdapter):
def run(self, handler):
import tornado.wsgi, tornado. tornado.ioloop
container = tornado.wsgi.WSGIContainer(handler)
server = tornado.
server.listen(port=self.port, address=self.host)
tornado.ioloop.IOLoop.instance().start() |
def run_python(*args, python=sys.executable, **kwargs):
if ((not isinstance(python, str)) and (python is not None)):
try:
python = python.sys.executable
except AttributeError:
raise TypeError(f'expected python str, got {python!r}')
return run_cmd([python, *args], **kwargs... |
class DataCollection():
TASKS = ['pour', 'scoop', 'stab', 'cut', 'lift', 'hammer', 'handover']
STATES = {'cup': ['hot', 'cold', 'empty'], 'bowl': ['filled', 'empty'], 'spatula': ['has stuff', 'empty'], 'bottle': ['lid on', 'lid off'], 'pan': ['hot', 'empty']}
TASK_DESCRIPTIONS = {'pour': 'Grasp the object t... |
def test_qdata_round_trip(tmpdir):
with tmpdir.as_cwd():
mol = Ligand.from_file(file_name=get_data('biphenyl.sdf'))
td_ref = TorsionDriveData.from_qdata(dihedral=(6, 10, 11, 8), qdata_file=get_data('biphenyl_qdata.txt'))
export_torsiondrive_data(molecule=mol, tdrive_data=td_ref)
td_n... |
class SSHCertificate():
def __init__(self, _nonce: memoryview, _public_key: SSHPublicKeyTypes, _serial: int, _cctype: int, _key_id: memoryview, _valid_principals: list[bytes], _valid_after: int, _valid_before: int, _critical_options: dict[(bytes, bytes)], _extensions: dict[(bytes, bytes)], _sig_type: memoryview, _s... |
def address_field(addresses):
hbox = QHBoxLayout()
address_e = QLineEdit()
if (addresses and (len(addresses) > 0)):
address_e.setText(addresses[0])
else:
addresses = []
def func():
try:
i = (addresses.index(str(address_e.text())) + 1)
i = (i % len(addr... |
def test_build_sdist_with_bad_path_dep_succeeds(caplog: LogCaptureFixture) -> None:
with temporary_directory() as tmp_dir, cwd(os.path.join(fixtures, 'with_bad_path_dep')):
api.build_sdist(tmp_dir)
assert (len(caplog.records) == 1)
record = caplog.records[0]
assert (record.levelname == 'WARNING'... |
class DAF3D(nn.Module):
def __init__(self):
super(DAF3D, self).__init__()
self.backbone = BackBone3D()
self.down4 = nn.Sequential(nn.Conv3d(2048, 128, kernel_size=1), nn.GroupNorm(32, 128), nn.PReLU())
self.down3 = nn.Sequential(nn.Conv3d(1024, 128, kernel_size=1), nn.GroupNorm(32, 1... |
class SequenceMapperSeq(SequenceMapper):
def __init__(self, *layers: SequenceMapper):
self.layers = layers
def apply(self, is_train, x, mask=None):
for (i, layer) in enumerate(self.layers):
with tf.variable_scope(('layer_' + str(i))):
x = layer.apply(is_train, x, mask... |
def test_shorthand_property_storage():
model = Model()
node = Storage(model, 'node')
for attr in ('min_volume', 'max_volume', 'cost', 'level'):
setattr(node, attr, 123)
if (attr == 'conversion_factor'):
with pytest.raises(ValueError):
setattr(node, attr, Parameter... |
def evaluate(loader, model):
model.eval()
correct = 0
total = 0
for (images, _, labels, _) in loader:
images = images.cuda()
labels = labels.cuda()
output1 = model(images)
(_, pred) = torch.max(output1.data, 1)
total += images.size(0)
correct += (pred == l... |
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