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def test_create_options(db, settings):
Option.objects.all().delete()
xml_file = (((Path(settings.BASE_DIR) / 'xml') / 'elements') / 'options.xml')
root = read_xml_file(xml_file)
version = root.attrib.get('version')
elements = flat_xml_to_elements(root)
elements = convert_elements(elements, versi... |
def test(base_model, psnet_model, decoder, regressor_delta, test_dataloader, args):
global use_gpu
global epoch_best_aqa, rho_best, L2_min, RL2_min
global epoch_best_tas, pred_tious_best_5, pred_tious_best_75
true_scores = []
pred_scores = []
pred_tious_test_5 = []
pred_tious_test_75 = []
... |
class Trainer(DefaultTrainer):
def build_evaluator(cls, cfg, dataset_name, output_folder=None):
if (output_folder is None):
output_folder = os.path.join(cfg.OUTPUT_DIR, 'inference')
os.makedirs(output_folder, exist_ok=True)
evaluator_list = []
evaluator_type = Metadat... |
class PetConfig(ABC):
def __repr__(self):
return repr(self.__dict__)
def save(self, path: str):
with open(path, 'w', encoding='utf8') as fh:
json.dump(self.__dict__, fh)
def load(cls, path: str):
cfg = cls.__new__(cls)
with open(path, 'r', encoding='utf8') as fh:
... |
def test_get_style_defs_contains_default_line_numbers_styles():
style_defs = HtmlFormatter().get_style_defs().splitlines()
assert (style_defs[1] == 'td.linenos .normal { color: inherit; background-color: transparent; padding-left: 5px; padding-right: 5px; }')
assert (style_defs[2] == 'span.linenos { color: ... |
def _resolve_ship(fitting, sMkt, b_localized):
shipType = fitting.getElementsByTagName('shipType').item(0).getAttribute('value')
anything = None
if b_localized:
try:
(shipType, anything) = _extract_match(shipType)
except ExtractingError:
pass
limit = 2
ship = ... |
class Graphsn_GIN(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout):
super(Graphsn_GIN, self).__init__()
self.nn = Linear(nfeat, nhid)
self.fc = Linear(nhid, nclass)
self.dropout = dropout
self.eps = nn.Parameter(torch.FloatTensor(1))
self.reset_parameters(... |
def train_sample_places_low_shot(low_shot_trainer: SVMLowShotTrainer, k_values: List[int], sample_inds: List[int], sample_num: int, output_dir: str, layername: str, cfg: AttrDict):
set_env_vars(local_rank=0, node_id=0, cfg=cfg)
for low_shot_kvalue in k_values:
checkpoint_dir = f'{output_dir}/sample{samp... |
def test_verify_args(parser: CompatibleArgumentParser, capsys: CaptureFixture) -> None:
with pytest.raises(SystemExit) as ex:
parser.parse_args(['--no-license-path'])
capture = capsys.readouterr().err
for arg in ('--no-license-path', '--with-license-file'):
assert (arg in capture)
with p... |
class MultiRcTaskHelper(TaskHelper):
def add_special_input_features(self, input_example: InputExample, input_features: InputFeatures) -> None:
input_features.meta['question_idx'] = input_example.meta['question_idx']
def add_features_to_dict(self, features: List[InputFeatures], feature_dict: Dict[(str, t... |
def pass_calibration_data(sim_model, forward_pass_args=None):
data_loader = ImageNetDataPipeline.get_val_dataloader()
batch_size = 64
max_batch_counter = 16
sim_model.eval()
current_batch_counter = 0
with torch.no_grad():
for (input_data, target_data) in data_loader:
inputs_b... |
class PreTrainedTokenizer(object):
vocab_files_names = {}
pretrained_vocab_files_map = {}
pretrained_init_configuration = {}
max_model_input_sizes = {}
SPECIAL_TOKENS_ATTRIBUTES = ['bos_token', 'eos_token', 'unk_token', 'sep_token', 'pad_token', 'cls_token', 'mask_token', 'additional_special_tokens'... |
class CIFARQuick(HybridBlock):
def __init__(self, block, fix_layers, pooling, channels, classes, fix_conv=False, **kwargs):
super(CIFARQuick, self).__init__()
self.fix_conv = fix_conv
self.fix_layers = fix_layers
assert ('fw' in kwargs.keys()), 'no_fw'
self.fw = kwargs['fw']
... |
class TestModel(BaseModel):
def name(self):
return 'TestModel'
def modify_commandline_options(parser, is_train=True):
assert (not is_train), 'TestModel cannot be used in train mode'
parser = CycleGANModel.modify_commandline_options(parser, is_train=False)
parser.set_defaults(data... |
class MongoStoreTests(TestCase):
def setUp(self):
self.db_hosts = ['localhost']
self.db_name = ('coal-mine-test-' + str(uuid.uuid4()))
self.db_conn = MongoClient()
self.db = self.db_conn[self.db_name]
self.store = MongoStore(self.db_hosts, self.db_name, None, None)
def te... |
class PreprocessForEfficientRouletteSelectionTest(unittest.TestCase):
def assertPreprocess(self, weights):
(alternates, keep_chances) = _preprocess_for_efficient_roulette_selection(weights)
self.assertEqual(len(alternates), len(keep_chances))
target_weight = (sum(weights) // len(alternates))... |
def patchify_augmentation(args, batch):
aug_batch = dict()
img = batch['image']
label = batch['label']
batch_size = img.size()[0]
patch_dim = (img.size()[(- 1)] // args.mask_patch_size)
images_patch = rearrange(img, 'b c (h p1) (w p2) (d p3) -> (b h w d) c p1 p2 p3 ', p1=(args.mask_patch_size //... |
def test_clip_column():
assert (_utils.clip_column(0, [], 0) == 0)
assert (_utils.clip_column(2, ['123'], 0) == 2)
assert (_utils.clip_column(3, ['123'], 0) == 3)
assert (_utils.clip_column(5, ['123'], 0) == 3)
assert (_utils.clip_column(0, ['\n', '123'], 0) == 0)
assert (_utils.clip_column(1, [... |
def new_export_path_for_album(album_id: AlbumId) -> Path:
stem = f'{album_id.title} - {album_id.artist}'
path = Path(join_path_with_escaped_name_of_legal_length(str(EXPORT_DIR_PATH), stem, EXPORT_EXTENSION))
trim_count = 1
while path.exists():
new_stem = (path.stem[:(- trim_count)] + uuid.uuid4(... |
def fix_lyft(root_folder='./data/lyft', version='v1.01'):
lidar_path = 'lidar/host-a011_lidar1_.bin'
root_folder = os.path.join(root_folder, f'{version}-train')
lidar_path = os.path.join(root_folder, lidar_path)
assert os.path.isfile(lidar_path), f'Please download the complete Lyft dataset and make sure... |
class FilterLogoPlacementsSerializer(serializers.Serializer):
publisher = serializers.ChoiceField(choices=[(c.value, c.name.replace('_', ' ').title()) for c in PublisherChoices], required=False)
flight = serializers.ChoiceField(choices=[(c.value, c.name.replace('_', ' ').title()) for c in LogoPlacementChoices],... |
class MatchedMolecularPair(object):
__slots__ = ('id1', 'id2', 'smirks', 'constant_smiles', 'min_constant_radius', 'max_constant_radius')
def __init__(self, id1, id2, smirks, constant_smiles, min_constant_radius, max_constant_radius):
self.id1 = id1
self.id2 = id2
self.smirks = smirks
... |
class TestFrameDecoderExtensions():
class FakeExtension(wpext.Extension):
name = 'fake'
def __init__(self) -> None:
self._inbound_header_called = False
self._inbound_rsv_bit_set = False
self._inbound_payload_data_called = False
self._inbound_complete_c... |
def save_video(video_frames, filename):
import cv2
_make_dir(filename)
video_frames = np.flip(video_frames, axis=(- 1))
fourcc = cv2.VideoWriter_fourcc(*'MJPG')
fps = 30.0
(height, width, _) = video_frames[0].shape
writer = cv2.VideoWriter(filename, fourcc, fps, (width, height))
for vide... |
class ErrorHandling():
_error_messages = []
def error_logging(cls, func):
logger = qf_logger.getChild(__class__.__name__)
def wrapped_function(*args, **kwargs):
try:
return func(*args, **kwargs)
except Exception as e:
error_message = '{}: A... |
class Tnspoison(Tnscmd):
PACKET_REGISTER = b'\x00h\x00\x00\x01\x00\x00\x00\x019\x01,\x00\x81\x08\x00\x7f\xff\x7f\x08\x00\x00\x01\x00\x00.\x00:\x00\x00\x07\xf8\x0c\x0c\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00\x00(CONNECT_DATA=(COMMAND=service_register_NSGR))'
PA... |
def session(device):
with tf.device(device):
graph = tf.Graph()
with graph.as_default():
model = tf.keras.Sequential((tf.keras.layers.Conv2D(32, kernel_size=3, input_shape=(28, 28, 3), activation='relu'), tf.keras.layers.Conv2D(64, kernel_size=3)))
init = tf.compat.v1.global_... |
class AttributeDevice(Switch):
EVENT_TYPE_ATTRIBUTE_LIST = 'attributeList'
_state_property: str
_attr_name = '_attributes'
def __init__(self, *args: Any, **kwargs: Any) -> None:
assert isinstance(self._state_property, str)
setattr(self, self._attr_name, {})
class_hints = get_type... |
def resolve_file(filename, relroot=None):
resolved = os.path.normpath(filename)
resolved = os.path.expanduser(resolved)
if (not os.path.isabs(resolved)):
if (not relroot):
relroot = os.getcwd()
elif (not os.path.isabs(relroot)):
raise NotImplementedError(relroot)
... |
def sa_scaffold_hop() -> GoalDirectedBenchmark:
specification = uniform_specification(1, 10, 100)
benchmark_object = scaffold_hop()
sa_biased = ScoringFunctionSAWrapper(benchmark_object.objective, SAScoreModifier())
return GoalDirectedBenchmark(name='SA_scaffold_hop', objective=sa_biased, contribution_s... |
class FitEcmBurstScanresDampsGraph(FitGraph):
hidden = True
internalName = 'ecmBurstScanresDamps'
name = 'ECM Burst + Scanres Damps'
xDefs = [XDef(handle='tgtDps', unit=None, label='Enemy DPS', mainInput=('tgtDps', None)), XDef(handle='tgtScanRes', unit='mm', label='Enemy scanres', mainInput=('tgtScanRe... |
class Image_Dataset(object):
def __init__(self, image_dir, transform=None, image_ext=['.jpg', '.bmp', '.png']):
assert (transform is not None)
self._transform = transform
self.image_dir = image_dir
self.image_ext = image_ext
self._read_dataset()
def __getitem__(self, inde... |
def clean_up_offset_payload(payload):
if ('0,' in payload):
payload = '{index},'.join(payload.rsplit('0,'))
if ('OFFSET' in payload):
payload = 'OFFSET {index} '.join(payload.rsplit('OFFSET 0'))
if ('DB_NAME' in payload):
payload = payload.replace('DB_NAME(0)', 'DB_NAME({index})')
... |
def ignore_exceptions(func):
assert asyncio.iscoroutinefunction(func), 'func needs to be a coroutine'
async def wrapper(*args, **kwargs):
try:
return (await func(*args, **kwargs))
except asyncio.CancelledError:
raise
except Exception as e:
pass
ret... |
def main():
args = parse_args()
cfg = get_cfg(args)
cudnn.benchmark = True
timestamp = time.strftime('%Y%m%d_%H%M%S', time.localtime())
log_file = os.path.join(cfg.work_dir, f'{timestamp}.cfg')
with open(log_file, 'a') as f:
f.write(cfg.pretty_text)
logger = build_logger(cfg.work_dir... |
_start_docstrings('\n BiT Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ', BIT_START_DOCSTRING)
class BitForImageClassification(BitPreTrainedModel):
def __init__(self, config):
super().__init__(config)
self.num_labels =... |
def crop(text, width=None, suffix='[...]'):
width = (width if width else settings.CLIENT_DEFAULT_WIDTH)
ltext = len(text)
if (ltext <= width):
return text
else:
lsuffix = len(suffix)
text = (text[:width] if (lsuffix >= width) else ('%s%s' % (text[:(width - lsuffix)], suffix)))
... |
class CacheMixin():
def _apply_cache_config(cls, encoders: Union[(Encoder, Dict[(str, Encoder)])], cache_config: CacheConfig) -> Union[(Encoder, Dict[(str, Encoder)])]:
if (cache_config.cache_type == CacheType.NONE):
return encoders
if ((not cache_config.cache_type) and (not cache_config... |
def init_weight(module_list, conv_init, norm_layer, bn_eps, bn_momentum, **kwargs):
if isinstance(module_list, list):
for feature in module_list:
__init_weight(feature, conv_init, norm_layer, bn_eps, bn_momentum, **kwargs)
else:
__init_weight(module_list, conv_init, norm_layer, bn_ep... |
def _compute_segment_xform(pos0, pos1):
mid = ((pos0 + pos1) * 0.5)
height = (pos1 - pos0).GetLength()
dir = ((pos1 - pos0) / height)
rot = Gf.Rotation()
rot.SetRotateInto((0.0, 0.0, 1.0), Gf.Vec3d(dir))
scale = Gf.Vec3f(1.0, 1.0, height)
return (mid, Gf.Quath(rot.GetQuat()), scale) |
def write_json_file(file_name, mv_array, metric, basis_names, compression=True, transpose=False, sparse=False, support=None, compression_opts=1):
data_dict = {}
data_dict['version'] = '0.0.1'
dset_data = {}
if transpose:
dset_data['data'] = mv_array.T.tolist()
dset_data['transpose'] = Tr... |
def _conv2d_wrapper(x, w, stride=1, padding=0, groups=1, transpose=False, flip_weight=True, impl='cuda'):
(out_channels, in_channels_per_group, kh, kw) = _get_weight_shape(w)
if (not flip_weight):
w = w.flip([2, 3])
if ((kw == 1) and (kh == 1) and (stride == 1) and (padding in [0, [0, 0], (0, 0)]) a... |
def get_expire_assets():
assets = Assets.objects.all()
expire_assets = []
for asset in assets:
expire_days = (asset.asset_expire_day - datetime.date.today()).days
if (0 < expire_days <= 30):
expire_assets.append({'asset_type': asset.get_asset_type_display(), 'asset_nu': asset.ass... |
class ElasticUploader(BaseUploader):
client: Elasticsearch = None
upload_params = {}
def get_mp_start_method(cls):
return ('forkserver' if ('forkserver' in mp.get_all_start_methods()) else 'spawn')
def init_client(cls, host, distance, connection_params, upload_params):
init_params = {**{... |
def test_window_by_position__equal_spaced_windows():
ds = simulate_genotype_call_dataset(n_variant=5, n_sample=3, seed=0)
assert (not has_windows(ds))
ds['variant_position'] = (['variants'], np.array([1, 4, 6, 8, 12]))
ds = window_by_position(ds, size=5, offset=1)
assert has_windows(ds)
np.testi... |
def returnArray(wrapArgs, lenArgs, inArgs, includeOutput=False):
def decorator(func):
(func)
def inner(*args):
orig = getattr(_egl, func.__name__)
newArgs = list(args)
for argnum in sorted((wrapArgs + lenArgs)):
if (argnum in wrapArgs):
... |
class ResNet(nn.Module):
def __init__(self, block: Type[Union[(BasicBlock, Bottleneck)]], layers: List[int], num_classes: int=1000, zero_init_residual: bool=False, groups: int=1, width_per_group: int=64, replace_stride_with_dilation: Optional[List[bool]]=None, norm_layer: Optional[Callable[(..., nn.Module)]]=nn.Gro... |
('torch.__version__', torch_version)
.parametrize('in_w,in_h,in_t,in_channel,out_channel,kernel_size,stride,padding,dilation', [(10, 10, 10, 1, 1, 3, 1, 0, 1), (20, 20, 20, 3, 3, 5, 2, 1, 2)])
def test_conv3d(in_w, in_h, in_t, in_channel, out_channel, kernel_size, stride, padding, dilation):
x_empty = torch.randn(0... |
class catch_warnings(warnings.catch_warnings):
def __init__(self, *classes):
super(catch_warnings, self).__init__(record=True)
self.classes = classes
def __enter__(self):
warning_list = super(catch_warnings, self).__enter__()
treat_deprecations_as_exceptions()
if (len(sel... |
def constant_str(value):
if (type(value) == bool):
if value:
return 'true'
else:
return 'false'
elif (type(value) == str):
return (('"' + str(value.encode('unicode-escape').decode())) + '"')
elif isinstance(value, ctypes.Array):
return (('{' + ', '.joi... |
(netloc='fakegitlab', path='/api/v4/projects/4/deploy_keys/1$', method='DELETE')
def delete_deploykey_handker(_, request):
if (not (request.headers.get('Authorization') == 'Bearer foobar')):
return {'status_code': 401}
return {'status_code': 200, 'headers': {'Content-Type': 'application/json'}, 'content... |
class TestQuota():
(autouse=True)
def setup(self, initialized_db):
user = get_user('devtable')
self.org = create_organization(ORG_NAME, f'{ORG_NAME}', user)
self.repo1 = create_repository(ORG_NAME, REPO1_NAME, user)
self.repo1manifest1 = create_manifest_for_testing(self.repo1, [B... |
class MediaLoader():
def __init__(self, folder):
logger.debug("Initializing %s: (folder: '%s')", self.__class__.__name__, folder)
logger.info('[%s DATA]', self.__class__.__name__.upper())
self._count = None
self.folder = folder
self.vid_reader = self.check_input_folder()
... |
class PackageQueue(object):
class Empty(Exception):
def __init__(self):
Exception.__init__(self, 'pop from an empty PackageQueue')
pass
def __init__(self, N, discard_mode='old'):
self._q = deque()
self._condition = threading.Condition()
self._maxlen = int(N)
... |
class TestAgentInsert(unittest.TestCase):
def _get_simple_dataset(self) -> ChunkedDataset:
dataset = ChunkedDataset('')
dataset.scenes = np.zeros(1, dtype=SCENE_DTYPE)
dataset.frames = np.zeros(3, dtype=FRAME_DTYPE)
dataset.agents = np.zeros(6, dtype=AGENT_DTYPE)
dataset.scen... |
def test_create_manifest_cannot_load_config_blob(initialized_db):
repository = create_repository('devtable', 'newrepo', None)
layer_json = json.dumps({'config': {}, 'rootfs': {'type': 'layers', 'diff_ids': []}, 'history': [{'created': '2018-04-03T18:37:09.Z', 'created_by': 'do something'}]})
(_, config_dige... |
def test_run_model_singleton_weather_single_array(cec_dc_snl_ac_system, location, weather):
mc = ModelChain(cec_dc_snl_ac_system, location, aoi_model='no_loss', spectral_model='no_loss')
mc.run_model([weather])
assert isinstance(mc.results.weather, tuple)
assert isinstance(mc.results.total_irrad, tuple)... |
(maxsplit=1, no_cmd_split=True, no_replace_variables=True, deprecated_name='repeat')
('win_id', value=cmdutils.Value.win_id)
('count', value=cmdutils.Value.count)
def cmd_repeat(times: int, command: str, win_id: int, count: int=None) -> None:
if (count is not None):
times *= count
if (times < 0):
... |
def test_inspiralfuns_numerical():
logMc = 1.4
q = 0.8
flow = 10.0
merger_type = 'BH'
D = 100.0
(M, eta) = ins.get_M_and_eta(logMc=logMc, q=q)
start_x = ins.startx(M, flow)
end_x = ins.endx(eta, merger_type)
(x, xtimes, dt) = ins.PN_parameter_integration(start_x, end_x, M, eta)
a... |
class NumpyDataCollatorIntegrationTest(unittest.TestCase):
def setUp(self):
self.tmpdirname = tempfile.mkdtemp()
vocab_tokens = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]']
self.vocab_file = os.path.join(self.tmpdirname, 'vocab.txt')
with open(self.vocab_file, 'w', encoding='utf-8'... |
class CsvLogger():
def __init__(self, filepath='./', filename='validate_record.csv', data=None, fieldsnames=['epoch', 'train_loss', 'val_loss', 'Bleu_4', 'METEOR', 'ROUGE_L', 'CIDEr']):
self.log_path = filepath
if (not os.path.exists(filepath)):
os.makedirs(filepath)
if filename:... |
def test_append_with_list_input():
context = Context({'arblist': [1, 2], 'append': {'list': PyString('arblist'), 'addMe': 3}})
append.run_step(context)
context['append']['addMe'] = 4
append.run_step(context)
assert (context['arblist'] == [1, 2, 3, 4])
assert (len(context) == 2) |
class SourceGroup():
def __init__(self) -> None:
self.audio_format = None
self.video_format = None
self.info = None
self.duration = 0.0
self._timestamp_offset = 0.0
self._dequeued_durations = []
self._sources = []
self.is_player_source = False
def ... |
class InlineQueryResultAudio(InlineQueryResult):
__slots__ = ('reply_markup', 'caption_entities', 'caption', 'title', 'parse_mode', 'audio_url', 'performer', 'input_message_content', 'audio_duration')
def __init__(self, id: str, audio_url: str, title: str, performer: Optional[str]=None, audio_duration: Optional... |
class ArgSpecCache():
DEFAULT_ARGSPECS = implementation.get_default_argspecs()
def __init__(self, options: Options, ts_finder: TypeshedFinder, ctx: CanAssignContext, *, vnv_provider: Callable[([str], Optional[Value])]=(lambda _: None)) -> None:
self.vnv_provider = vnv_provider
self.options = opt... |
def getSaveFileName(*, parent, title, filename, filter='', default_extension: str=None, default_filter: str=None, config: 'SimpleConfig') -> Optional[str]:
directory = config.get('io_dir', os.path.expanduser('~'))
path = os.path.join(directory, filename)
file_dialog = QFileDialog(parent, title, path, filter... |
class TestText(unittest.TestCase):
def setUp(self):
self.text_mock = mock.Mock()
def test_setting_text(self):
Text._set_text(self.text_mock, 'foo')
self.assertEqual(self.text_mock.text, 'foo')
def test_setting_color_with_color_provided(self):
Text._set_color(self.text_mock, '... |
class _ImageCollection(pystiche.ComplexObject):
def __init__(self, images: Mapping[(str, _Image)]) -> None:
self._images = images
def __len__(self) -> int:
return len(self._images)
def __getitem__(self, name: str) -> _Image:
return self._images[name]
def __iter__(self) -> Iterato... |
def test_gmail_checker_invalid_response(fake_qtile, monkeypatch, fake_window):
monkeypatch.setitem(sys.modules, 'imaplib', FakeIMAP('imaplib'))
reload(gmail_checker)
gmc = gmail_checker.GmailChecker()
fakebar = FakeBar([gmc], window=fake_window)
gmc._configure(fake_qtile, fakebar)
text = gmc.pol... |
def setup_custom_environment(custom_module_path):
module = import_file('maskrcnn_benchmark.utils.env.custom_module', custom_module_path)
assert (hasattr(module, 'setup_environment') and callable(module.setup_environment)), "Custom environment module defined in {} does not have the required callable attribute 's... |
class MCLP(LocateSolver, BaseOutputMixin, CoveragePercentageMixin):
def __init__(self, name: str, problem: pulp.LpProblem):
super().__init__(name, problem)
def __add_obj(self, weights: np.array, range_clients: range) -> None:
dem_vars = getattr(self, 'cli_vars')
self.problem += (pulp.lpS... |
class TransformerEncoderLayer(nn.Module):
def __init__(self, d_model, nhead, dim_feedforward=2048, dropout=0.1, activation='relu', normalize_before=False, norm=True, no_ffn=False, no_encoder_self_att=False):
super().__init__()
self.no_ffn = no_ffn
self.no_encoder_self_att = no_encoder_self_a... |
class QueryCreator():
def __init__(self, strict_mode=False):
self.namer = VariableNamer()
self.creator_for_op_name = {}
self.creator_for_op_name['aggregate'] = QueryStepAggregate
self.creator_for_op_name['select'] = QueryStepSelect
self.creator_for_op_name['project'] = QueryS... |
.parametrize('func', [(lambda x: x.sum()), (lambda x: x.count()), (lambda x: x.apply((lambda x: x))), (lambda x: x.full()), (lambda x: x.var()), (lambda x: x.std())], ids=['sum', 'count', 'apply', 'full', 'var', 'std'])
def test_ewm_notimplemented(func):
sdf = DataFrame(example=pd.DataFrame(columns=['x', 'y']))
... |
def pylsp_lint(workspace: Workspace, document: Document) -> List[Dict]:
settings = load_settings(workspace, document.path)
checks = run_ruff_check(document=document, settings=settings)
diagnostics = [create_diagnostic(check=c, settings=settings) for c in checks]
return converter.unstructure(diagnostics) |
def decode_header(trf_header_contents: bytes) -> Header:
match = _header_match(trf_header_contents)
groups = match.groups()
date = groups[0].decode('utf-8')
timezone = groups[1].decode('utf-8')
field = groups[2].decode('utf-8')
machine = groups[3].decode('utf-8')
mu = np.frombuffer(groups[4]... |
class CalcChangeFitSystemSecurityCommand(wx.Command):
def __init__(self, fitID, secStatus):
wx.Command.__init__(self, True, 'Change Fit System Security')
self.fitID = fitID
self.secStatus = secStatus
self.savedSecStatus = None
def Do(self):
pyfalog.debug('Doing changing s... |
class TestSharedoc(ZiplineTestCase):
def test_copydoc(self):
def original_docstring_function():
pass
(original_docstring_function)
def copied_docstring_function():
pass
self.assertEqual(original_docstring_function.__doc__, copied_docstring_function.__doc__) |
def add_shared_install_options(parser: argparse.ArgumentParser):
parser.add_argument('--user', action='store_true', default=None, help='Do a user-local install (default if site.ENABLE_USER_SITE is True)')
parser.add_argument('--env', action='store_false', dest='user', help='Install into sys.prefix (default if s... |
class TestDrivenMilesCompositeMetric(unittest.TestCase):
def test_zero_miles(self) -> None:
metric_results: Dict[(str, torch.Tensor)] = {metrics.SimulatedDrivenMilesMetric.metric_name: torch.zeros(10)}
simulation_output = mock.Mock()
validation_results = mock.Mock()
dm_metric = cm.Dr... |
class DebugInfoCommand(Command):
name = 'debug info'
description = 'Shows debug information.'
def handle(self) -> int:
poetry_python_version = '.'.join((str(s) for s in sys.version_info[:3]))
self.line('')
self.line('<b>Poetry</b>')
self.line('\n'.join([f'<info>Version</info>... |
.parametrize('when', ['setup', 'call', 'teardown'])
def test_crashing_item(pytester, when) -> None:
code = dict(setup='', call='', teardown='')
code[when] = 'os._exit(1)'
p = pytester.makepyfile('\n import os\n import pytest\n\n \n def fix():\n {setup}\n yie... |
def get_user_field(question: str, default_value: Optional[str]=None, is_valid_answer: Optional[Callable]=None, convert_to: Optional[Callable]=None, fallback_message: Optional[str]=None) -> Any:
if (not question.endswith(' ')):
question = (question + ' ')
if (default_value is not None):
question ... |
_fixtures(WebFixture)
def test_distinguishing_identical_field_names(web_fixture):
fixture = web_fixture
class ModelObject():
fields = ExposedNames()
fields.field_name = (lambda i: IntegerField())
model_object1 = ModelObject()
model_object2 = ModelObject()
class MyForm(Form):
... |
_model
def caformer_m36_in21ft1k(pretrained=False, **kwargs):
model = MetaFormer(depths=[3, 12, 18, 3], dims=[96, 192, 384, 576], token_mixers=[SepConv, SepConv, Attention, Attention], head_fn=MlpHead, **kwargs)
model.default_cfg = default_cfgs['caformer_m36_in21ft1k']
if pretrained:
state_dict = to... |
class LDAPUrlExtension():
def __init__(self, extensionStr=None, critical=0, extype=None, exvalue=None):
self.critical = critical
self.extype = extype
self.exvalue = exvalue
if extensionStr:
self._parse(extensionStr)
def _parse(self, extension):
extension = ext... |
def makeCfdMeshImported(name='ImportedCFDMesh'):
doc = FreeCAD.ActiveDocument
obj = doc.addObject('Fem::FemMeshObjectPython', name)
_CaeMeshImported._CaeMeshImported(obj)
if FreeCAD.GuiUp:
from cfdguiobjects._ViewProviderCaeMesh import _ViewProviderCaeMesh
_ViewProviderCaeMesh(obj.ViewOb... |
class MemIfcRTL2FLAdapter(Component):
def construct(s, ReqType, RespType):
s.left = MemMinionIfcRTL(ReqType, RespType)
s.right = MemMasterIfcFL()
_once
def up_memifc_rtl_fl_blk():
if (s.left.req.en and s.left.resp.rdy):
if (s.left.req.msg.type_ == MemMsgTy... |
def on_draw():
window.clear()
glLoadIdentity()
glLightfv(GL_LIGHT0, GL_POSITION, lightfv((- 40.0), 200.0, 100.0, 0.0))
glLightfv(GL_LIGHT0, GL_AMBIENT, lightfv(0.2, 0.2, 0.2, 1.0))
glLightfv(GL_LIGHT0, GL_DIFFUSE, lightfv(0.5, 0.5, 0.5, 1.0))
glEnable(GL_LIGHT0)
glEnable(GL_LIGHTING)
glE... |
class ObjectMapping():
_models = morefusion.datasets.YCBVideoModels()
def __init__(self):
self.reset()
self._n_votes = rospy.get_param('~n_votes', 3)
self._base_frame = rospy.get_param('~frame_id', 'map')
self._pub = rospy.Publisher('~output/poses', ObjectPoseArray, queue_size=1,... |
class StructureMixIn(object):
def __str__(self):
lines = []
for (field_name, _) in getattr(self, '_fields_', []):
lines.append(('%20s\t%s' % (field_name, getattr(self, field_name))))
return '\n'.join(lines)
def __eq__(self, other):
fields = getattr(self, '_fields_', [... |
class Self_Attn(nn.Module):
def __init__(self, in_dim, latent_dim=8):
super(Self_Attn, self).__init__()
self.channel_in = in_dim
self.channel_latent = (in_dim // latent_dim)
self.query_conv = nn.Conv2d(in_channels=in_dim, out_channels=(in_dim // latent_dim), kernel_size=1)
se... |
class Request():
first_party_url: Optional[QUrl]
request_url: QUrl
is_blocked: bool = False
resource_type: Optional[ResourceType] = None
def block(self) -> None:
self.is_blocked = True
def redirect(self, url: QUrl, *, ignore_unsupported: bool=False) -> None:
raise NotImplementedE... |
.end_to_end()
def test_dry_run_skipped_successful(runner, tmp_path):
source = '\n import pytask\n\n .produces("out.txt")\n def task_example(produces):\n produces.touch()\n '
tmp_path.joinpath('task_example.py').write_text(textwrap.dedent(source))
result = runner.invoke(cli, [tmp_path.as_p... |
.parametrize('x, cohort, n, axis', [_random_cohort_data((20,), n=3, axis=0), _random_cohort_data((20, 20), n=2, axis=0, dtype=np.float32), _random_cohort_data((10, 10), n=2, axis=(- 1), scale=30, dtype=np.int16), _random_cohort_data((20, 20), n=3, axis=(- 1), missing=0.3), _random_cohort_data((7, 103, 4), n=5, axis=1, ... |
class TestUmath(TestCase):
def q(self):
return ([1, 2, 3, 4] * pq.J)
def test_prod(self):
self.assertQuantityEqual(np.prod(self.q), (24 * (pq.J ** 4)))
def test_sum(self):
self.assertQuantityEqual(np.sum(self.q), (10 * pq.J))
def test_nansum(self):
c = ([1, 2, 3, np.nan] ... |
class ResourceDatabaseGenericModel(QtCore.QAbstractTableModel):
def __init__(self, db: ResourceDatabase, resource_type: ResourceType):
super().__init__()
self.db = db
self.resource_type = resource_type
self.allow_edits = True
def _get_items(self):
return self.db.get_by_ty... |
def names_modified_in_lvalue(lvalue: Lvalue) -> list[NameExpr]:
if isinstance(lvalue, NameExpr):
return [lvalue]
elif isinstance(lvalue, StarExpr):
return names_modified_in_lvalue(lvalue.expr)
elif isinstance(lvalue, (ListExpr, TupleExpr)):
result: list[NameExpr] = []
for ite... |
def test_fixed_time_dependent_ding():
ocp = prepare_ocp(model=Model(time_as_states=False), n_stim=10, n_shooting=10, final_time=1, time_bimapping=False, use_sx=True)
sol = ocp.solve()
(force_vector, cn_vector, time_vector) = result_vectors(sol)
plt.plot(time_vector, force_vector)
plt.plot(time_vecto... |
def test_initialize():
rnd = np.random.RandomState(0)
x = rnd.normal(size=(13, 5))
y = rnd.randint(3, size=13)
crf = ChainCRF(n_states=3, n_features=5)
crf.initialize([x], [y])
crf = ChainCRF()
crf.initialize([x], [y])
assert_equal(crf.n_states, 3)
assert_equal(crf.n_features, 5)
... |
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