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def get_squeezenext(version, width_scale, model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
init_block_channels = 64
final_block_channels = 128
channels_per_layers = [32, 64, 128, 256]
if (version == '23'):
layers = [6, 6, 8, 1]
elif (version == '23v5'... |
(repr=False)
class _Test():
version: Version
subject: str
case_description: str
description: str
data: Any
schema: (Mapping[(str, Any)] | bool)
valid: bool
_remotes: referencing.jsonschema.SchemaRegistry
comment: (str | None) = None
def __repr__(self):
return f'<Test {sel... |
def convert_embed(func: Callable[([str], str)], embed: Embed) -> Embed:
embed_dict = embed.to_dict()
embed_dict['title'] = func(embed_dict.get('title', ''))
embed_dict['description'] = func(embed_dict.get('description', ''))
if ('footer' in embed_dict):
embed_dict['footer']['text'] = func(embed_... |
def collate_fn_tagger(batch):
dim = len(batch[0].keys())
if (dim == 4):
tokens = [item['token'] for item in batch]
tagger = [item['tagger'] for item in batch]
ins = [item['ins'] for item in batch]
mod = [item['mod'] for item in batch]
return (tokens, tagger, ins, mod)
... |
def _download_compacted_table(hb_index: int, rcf: RoundCompletionInfo, read_kwargs_provider: Optional[ReadKwargsProvider]=None, deltacat_storage=unimplemented_deltacat_storage, deltacat_storage_kwargs: Optional[dict]=None) -> pa.Table:
tables = []
hb_index_to_indices = rcf.hb_index_to_entry_range
if (str(hb... |
_module()
class CustomizedTextLoggerHook(TextLoggerHook):
def _log_info(self, log_dict, runner):
if ((runner.meta is not None) and ('exp_name' in runner.meta)):
if (self.every_n_iters(runner, self.interval_exp_name) or (self.by_epoch and self.end_of_epoch(runner))):
exp_info = f"... |
class Effect11767(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Medium Hybrid Turret')), 'trackingSpeed', src.getModifiedItemAttr('eliteBonusHeavyGunship1'), skill='Heavy Assault Cruisers', **kw... |
def setUpModule():
global mol, m, h1er, h1ei, h1es, g2er, g2ei, g2es, ci0, ci1, ci2, ci3
global norb, nelecr, neleci
mol = gto.Mole()
mol.verbose = 0
mol.output = None
mol.atom = [['H', (1.0, (- 1.0), 0.0)], ['H', (0.0, (- 1.0), (- 1.0))], ['H', (0.0, (- 0.5), (- 0.0))], ['H', (0.0, (- 0.0), (- ... |
def convert_lossless_jpeg(input_filepath, output_filepath=None):
input_filepath = pathlib.Path(input_filepath)
if (output_filepath is None):
output_filepath = input_filepath.parent.joinpath(f'{input_filepath.stem}.tif')
im = imread(input_filepath)
imageio.imwrite(str(output_filepath), im, format... |
def InceptionV3(include_top=True, weights='imagenet', input_tensor=None, input_shape=None, pooling=None, classes=1000):
if (weights not in {'imagenet', None}):
raise ValueError('The `weights` argument should be either `None` (random initialization) or `imagenet` (pre-training on ImageNet).')
if ((weight... |
def preprocess(image, label, size, mean_pixel):
image = nd.zoom(image.astype('float32'), ((size / float(image.shape[0])), (size / float(image.shape[1])), 1.0), order=1)
label = nd.zoom(label, ((size / float(label.shape[0])), (size / float(label.shape[1]))), order=0)
image = (image - mean_pixel)
image = ... |
(scope='function', autouse=True)
def _skip_sensitive(request, sensitive_url):
destructive = ('nondestructive' not in request.node.keywords)
if (sensitive_url and destructive):
pytest.skip("This test is destructive and the target URL is considered a sensitive environment. If this test is not destructive,... |
class CodeStream(CodeStreamAPI):
__slots__ = ['_length_cache', '_raw_code_bytes', 'invalid_positions', 'valid_positions']
logger = logging.getLogger('eth.vm.CodeStream')
def __init__(self, code_bytes: bytes) -> None:
validate_is_bytes(code_bytes, title='CodeStream bytes')
self.program_counte... |
class EuclideanCodebook(nn.Module):
def __init__(self, dim: int, codebook_size: int, kmeans_init: int=False, kmeans_iters: int=10, decay: float=0.99, epsilon: float=1e-05, threshold_ema_dead_code: int=2):
super().__init__()
self.decay = decay
init_fn: tp.Union[(tp.Callable[(..., torch.Tensor... |
class LogitsList():
def __init__(self, score: float, logits: List[List[float]]):
self.score = score
self.logits = logits
def __repr__(self):
return 'LogitsList(score={}, logits[:2]={})'.format(self.score, self.logits[:2])
def save(self, path: str) -> None:
with open(path, 'w'... |
def cli() -> ExitCode:
try:
hide_cursor()
parser = get_command_parser()
argcomplete.autocomplete(parser)
parsed_pipx_args = parser.parse_args()
setup(parsed_pipx_args)
check_args(parsed_pipx_args)
if (not parsed_pipx_args.command):
parser.print_hel... |
class Timer(object):
def __init__(self):
self.total_time = 0.0
self.calls = 0
self.start_time = 0.0
self.diff = 0.0
self.average_time = 0.0
self.warm_up = 0
def tic(self):
self.start_time = time.time()
def toc(self, average=True):
self.diff = (... |
class TestSmtLibParserGriggio(TestCase):
def test_griggio(self):
for file_id in range(1, 7):
script = self.parse(file_id)
for (i, cmd) in enumerate(script):
self.assertEqual(cmd.name, TESTS[file_id][i], ('Test %d: %s != %s ' % (file_id, cmd.name, TESTS[file_id][i])))
... |
class BoxCoderTest(tf.test.TestCase):
def test_batch_decode(self):
mock_anchor_corners = tf.constant([[0, 0.1, 0.2, 0.3], [0.2, 0.4, 0.4, 0.6]], tf.float32)
mock_anchors = box_list.BoxList(mock_anchor_corners)
mock_box_coder = MockBoxCoder()
expected_boxes = [[[0.0, 0.1, 0.5, 0.6], [... |
def parse_args():
parser = argparse.ArgumentParser(description='Convert MMPose models to ONNX')
parser.add_argument('config', help='test config file path')
parser.add_argument('checkpoint', help='checkpoint file')
parser.add_argument('--show', action='store_true', help='show onnx graph')
parser.add_... |
def mod_arith(q_format: str, a_format: str) -> QuizEntry:
(quotient, m, b) = (random.randint(30, 40), random.randint(10, 20), random.randint(200, 350))
ans = random.randint(0, 9)
a = (((quotient * m) + ans) - b)
question = q_format.format(a, b, m)
answer = a_format.format(ans)
return QuizEntry(q... |
class InviteQuerySet(models.QuerySet):
def filter_current_site(self):
return self.filter(project__site=settings.SITE_ID)
def filter_user(self, user):
if user.is_authenticated:
if user.has_perm('projects.view_invite'):
return self.all()
elif is_site_manager... |
class Effect6253(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Energy Neutralizer')), 'maxRange', src.getModifiedItemAttr('shipBonusAB'), skill='Amarr Battleship', **kwargs) |
def prompt_user_for_preset_file(window: QtWidgets.QWidget, new_file: bool, name: (str | None)=None) -> (None | Path):
from randovania.layout.versioned_preset import VersionedPreset
return _prompt_user_for_file(window, caption='Select a Randovania Preset file.', filter=f'Randovania Preset, *.{VersionedPreset.fil... |
class KPartition(Sequence, _CutBase):
__slots__ = ['parts', 'node_labels', '_mechanism', '_purview']
def __init__(self, *parts, node_labels=None):
self.parts = parts
self.node_labels = node_labels
self._mechanism = None
self._purview = None
def __len__(self):
return l... |
class FileReader(FileHandler):
def __repr__(self) -> str:
return f'<{self.__class__.__name__} [path: {self.file_path}, open: {self.open}]>'
def _open(self) -> BinaryIO:
return open(self.file_path, 'rb')
def read(self) -> bytes:
return self.file.read()
def write(self, data: bytes)... |
class KnownValues(unittest.TestCase):
def test_ip_adc2(self):
myadc.ncvs = 2
myadc.method = 'adc(2)'
myadc.method_type = 'ip'
(e, t_amp1, t_amp2) = myadc.kernel_gs()
self.assertAlmostEqual(e, (- 0.), 6)
(e, v, p, x) = myadc.kernel(nroots=2)
self.assertAlmostEq... |
def make_loader_getter(*, shape: InputShape, name_layout: InputNameLayout, debug_trail: DebugTrail, strict_coercion: bool=True, debug_ctx: DebugCtx) -> Callable[([], Loader)]:
def getter():
retort = TestRetort(recipe=[ValueProvider(InputShapeRequest, shape), ValueProvider(InputNameLayoutRequest, name_layout... |
class Examples(SegmentationBase):
def __init__(self, size=None, random_crop=False, interpolation='bicubic'):
super().__init__(data_csv='data/sflckr_examples.txt', data_root='data/sflckr_images', segmentation_root='data/sflckr_segmentations', size=size, random_crop=random_crop, interpolation=interpolation) |
def haop_bf(filename: str, minunity: float):
sdb = readfile(filename=filename)
mymap = {'a': 2, 'g': 3, 'c': 2, 't': 3}
upminsup = ceil((minunity / 3))
begintime = time.time()
(freArr, canArr) = min_freItem(sdb, mymap, upminsup, minunity)
f_level = 1
candidate = gen_candidate(f_level, canArr... |
def _ray_get_actor_cpus():
if (Version(ray.__version__) < Version('2.0.0')):
resource_ids = ray.worker.get_resource_ids()
if ('CPU' in resource_ids):
return sum((cpu[1] for cpu in resource_ids['CPU']))
else:
resource_ids = ray.get_runtime_context().get_assigned_resources()
... |
def test_coefs(ir_url, vis_url):
reader = GOESCoefficientReader(ir_url=ir_url, vis_url=vis_url)
for platform in CALIB_COEFS:
for (channel, coefs) in CALIB_COEFS[platform].items():
coefs_expected = reader.get_coefs(platform=platform, channel=channel)
for cname in coefs_expected.ke... |
def wait_for_payment_balance(raiden: 'RaidenService', token_network_registry_address: TokenNetworkRegistryAddress, token_address: TokenAddress, partner_address: Address, target_address: Address, target_balance: TokenAmount, retry_timeout: float) -> None:
condition = ChannelHasPaymentBalance(target_address, target_b... |
class SourceAddCommand(Command):
name = 'source add'
description = 'Add source configuration for project.'
arguments = [argument('name', 'Source repository name.'), argument('url', 'Source repository URL. Required, except for PyPI, for which it is not allowed.', optional=True)]
options = [option('defaul... |
class SequencerWidget(QtWidgets.QWidget):
def __init__(self, inputs=None, sequence_file=None, parent=None):
super().__init__(parent)
self._parent = parent
self._check_queue_signature()
if (inputs is not None):
self._inputs = inputs
else:
self._inputs =... |
class ArchiveUtilTestCase(support.TempdirManager):
.usefixtures('needs_zlib')
def test_make_tarball(self, name='archive'):
tmpdir = self._create_files()
self._make_tarball(tmpdir, name, '.tar.gz')
self._make_tarball(tmpdir, name, '.tar', compress=None)
.usefixtures('needs_zlib')
... |
class TestNutsCheckTrace():
def test_multiple_samplers(self, caplog):
with pm.Model():
prob = pm.Beta('prob', alpha=5.0, beta=3.0)
pm.Binomial('outcome', n=1, p=prob)
caplog.clear()
with warnings.catch_warnings():
warnings.filterwarnings('ignor... |
class OtherModelNodeStorageParameter(Parameter):
def __init__(self, model, other_model, node, **kwargs):
super().__init__(model, **kwargs)
self.other_model = other_model
self.node = node
self._other_model = None
self._other_model_node = None
def setup(self):
super... |
def walk_resources(package_or_requirement, resource_name, recurse=True, base=''):
base = (base.rstrip('/') + '/')
resource_base = ((resource_name.rstrip('/') + '/') + base.strip('/')).rstrip('/')
for filename in pymagic.resource_listdir(package_or_requirement, resource_base):
if (filename.startswith... |
class ResourceObserver():
def __init__(self, changed=None, moved=None, created=None, removed=None, validate=None):
self.changed = changed
self.moved = moved
self.created = created
self.removed = removed
self._validate = validate
def resource_changed(self, resource):
... |
class StringStrategy(object):
__metaclass__ = SingletonMeta
def make_mutable(self, w_str):
raise NotImplementedError('abstract base class')
def as_str_ascii(self, w_str):
raise ValueError("can't convert")
def as_str_utf8(self, w_str):
raise NotImplementedError('abstract base clas... |
def _flatten_pkcs1_examples(vectors):
flattened_vectors = []
for vector in vectors:
examples = vector[0].pop('examples')
for example in examples:
merged_vector = (vector[0], vector[1], example)
flattened_vectors.append(merged_vector)
return flattened_vectors |
def format_(rows, limit=15, sort='size', order='descending'):
localrows = []
for row in rows:
localrows.append(list(row))
sortby = ['type', '#', 'size']
if (sort not in sortby):
raise ValueError(('invalid sort, should be one of' + str(sortby)))
orders = ['ascending', 'descending']
... |
.parametrize(('start', 'end', 'expected'), [(0, 0, 'a = "hello"\n'), (1, 1, 'b = [\n "a",\n "very",\n "very",\n "very",\n "very",\n "very",\n "very",\n "very",\n "very",\n "long",\n "line",\n]\n'), (2, 2, 'c = 42\n'), (0, 2, 'a = "hello"\nb = [\n "a",\n "very",\n "very",\n "... |
def package_directory_arg(arg: str) -> pathlib.Path:
pkg_dir = pathlib.Path(arg).expanduser().resolve()
try:
next(pkg_dir.iterdir(), None)
except OSError as exc:
raise argparse.ArgumentTypeError(f'Error: while trying to access package directory ({pkg_dir}): {exc}')
return pkg_dir |
class StatsView():
views = {}
def __init__(self):
pass
def register(cls):
StatsView.views[cls.name] = cls
def getView(cls, name):
return cls.views[name]
def populatePanel(self, panel):
raise NotImplementedError()
def getHeaderText(self, fit):
raise NotImpl... |
def ql_syscall_clock_time(ql: Qiling, id, new, old, *args, **kw):
if (not (id in clock_types)):
raise NotImplementedError(f'Unknown clock id {id} not implemented')
if (id != 0):
raise NotImplementedError(f'Clock type {clock_types[id]} not implemented')
if (new != 0):
clock_new = ql.u... |
class PretrainedVocab(BaseVocab):
def __init__(self, embedding_name, *args, **kwargs):
self.type = 'pretrained'
if (embedding_name not in vocab.pretrained_aliases):
from pythia.common.registry import registry
writer = registry.get('writer')
error = (('Unknown embe... |
def normal_order_integrals(n_qubits, n_occupied, array_to_normal_order, array_mapping, h1_old, h2_old, h1_new, h2_new):
a_enum = []
adag_enum = []
for ind in range(n_qubits):
if (ind in n_occupied):
a_enum.append((- (ind + 1)))
adag_enum.append((ind + 1))
else:
... |
def parse_args():
parser = ArgumentParser(description='Generate training and validation set of OpenVINO annotations for Open Images by cropping box image.')
parser.add_argument('root_path', help='Root dir containing images and annotations')
parser.add_argument('n_proc', default=1, type=int, help='Number of ... |
def parse_args():
msg = 'convert inputs to tf.Record format'
usage = 'input_converter.py [<args>] [-h | --help]'
parser = argparse.ArgumentParser(description=msg, usage=usage)
parser.add_argument('--input', required=True, type=str, nargs=2, help='Path of input file')
parser.add_argument('--output_na... |
class _RPN(nn.Module):
def __init__(self, din):
super(_RPN, self).__init__()
self.din = din
self.anchor_scales = cfg.ANCHOR_SCALES
self.anchor_ratios = cfg.ANCHOR_RATIOS
self.feat_stride = cfg.FEAT_STRIDE[0]
self.RPN_Conv = nn.Conv2d(self.din, 512, 3, 1, 1, bias=True)... |
def pre_load_checkpoint(checkpoint_dir):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if (ckpt and ckpt.model_checkpoint_path):
print(' [*] Reading checkpoint from {}'.format(ckpt.model_checkpoint_path))
epoch_step = int(os.path.basename(ckpt.model_checkpoint_path).split('-')[1])
... |
class TaskState(object):
def __init__(self):
self.packet_pending = True
self.task_waiting = False
self.task_holding = False
def packetPending(self):
self.packet_pending = True
self.task_waiting = False
self.task_holding = False
return self
def waiting(... |
(Petition)
class PetitionAdmin(admin.ModelAdmin):
change_form_template = 'petition/petition_change_form.html'
form = PetitionAdminForm
search_fields = ('title',)
list_display = ('title', 'non_confirmed_signature_number', 'confirmed_signature_number')
fieldsets = ((gettext_lazy('To whom is this petit... |
def application_status(cerberus_url, start_time, end_time):
if (not cerberus_url):
logging.error('url where Cerberus publishes True/False signal is not provided.')
sys.exit(1)
else:
duration = ((end_time - start_time) / 60)
url = (((((cerberus_url + '/') + 'history') + '?') + 'lo... |
()
def hsd_file_jp01(tmp_path):
from satpy.readers.ahi_hsd import _BASIC_INFO_TYPE, _CAL_INFO_TYPE, _DATA_INFO_TYPE, _ERROR_INFO_TYPE, _ERROR_LINE_INFO_TYPE, _INTER_CALIBRATION_INFO_TYPE, _NAV_INFO_TYPE, _NAVIGATION_CORRECTION_INFO_TYPE, _NAVIGATION_CORRECTION_SUBINFO_TYPE, _OBSERVATION_LINE_TIME_INFO_TYPE, _OBSERV... |
def get_parser_with_args():
parser = options.get_parser('Collect Top-K Probs', default_task='pytorch_translate')
pytorch_translate_options.add_verbosity_args(parser)
pytorch_translate_options.add_dataset_args(parser, gen=True)
generation_group = options.add_generation_args(parser)
generation_group.a... |
class DataTrainingArguments():
source_lang: str = field(default=None, metadata={'help': 'Source language id for translation.'})
target_lang: str = field(default=None, metadata={'help': 'Target language id for translation.'})
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of th... |
def get_mock_github():
def get_commit_mock(commit_sha):
if (commit_sha == 'aaaaaaa'):
commit_mock = Mock()
commit_mock.sha = commit_sha
commit_mock.html_url = '
commit_mock.last_modified = 'now'
commit_mock.commit = Mock()
commit_mock.c... |
def override_training_args(args: Namespace) -> Tuple[(List[str], List[str])]:
overrides = []
overrides.extend(_override_attr('params.common', CommonParams, args))
overrides.extend(_override_attr('params.dataset', DatasetParams, args))
overrides.extend(_override_attr('params.distributed_training', Distri... |
def box_voting(selected_boxes, pool_boxes, iou_thresh=0.5):
if (not (0.0 <= iou_thresh <= 1.0)):
raise ValueError('iou_thresh must be between 0 and 1')
if (not isinstance(selected_boxes, box_list.BoxList)):
raise ValueError('selected_boxes must be a BoxList')
if (not isinstance(pool_boxes, b... |
class _TestFunctionalBase(unittest.TestCase):
def setUpClass(cls):
cls.base_df1 = ta.dataframe({'int64_list': [[11, 12, 13], [21, 22, 23, 24, 25, 26], [31, 32]], 'int32_list': [[11, 12, 13], [21, 22, 23, 24, 25, 26], [31, 32]]}, dtype=dt.Struct([dt.Field('int64_list', dt.List(dt.int64)), dt.Field('int32_lis... |
class TestFunc(torch.autograd.Function):
def forward(ctx, x):
y = torch.empty_like(x)
ctx.x = x
ctx.y = y
wp.launch(kernel=test_kernel, dim=len(x), inputs=[wp.torch.from_torch(x)], outputs=[wp.torch.from_torch(y)], device=device)
return y
def backward(ctx, adj_y):
... |
class MethodSignature(PipelineSignature):
builtin_args = ()
def _assert_valid_outputs(self, outputs):
super()._assert_valid_outputs(outputs)
for (output_name, spec) in outputs.items():
if (not is_semantic_type(spec.qiime_type)):
raise TypeError(('Output %r must be a s... |
_end_docstrings(PIPELINE_INIT_ARGS)
class ImageClassificationPipeline(Pipeline):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
requires_backends(self, 'vision')
self.check_model_type((TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING if (self.framework == 'tf') else MODEL_FO... |
class FeatureDataset(torch.utils.data.Dataset):
def __init__(self, vid2features, videos, padding_size=100, random_sampling=False):
super(FeatureDataset, self).__init__()
self.vid2features = vid2features
self.padding_size = padding_size
self.random_sampling = random_sampling
s... |
class TestOnlineExactClassifier(unittest.TestCase):
def test_batch_classification(self):
datasets = Banana()
(train_dataset, test_dataset) = (datasets.train_dataset, datasets.test_dataset)
(train_x, train_y) = train_dataset[:]
(test_x, test_y) = test_dataset[:]
input_dim = tr... |
class _AttentionDownConv(nn.Module):
def __init__(self, features=16):
super(_AttentionDownConv, self).__init__()
self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1)
self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1)
self.downsamp... |
class DAVClient():
proxy: Optional[str] = None
url: URL = None
huge_tree: bool = False
def __init__(self, url: str, proxy: Optional[str]=None, username: Optional[str]=None, password: Optional[str]=None, auth: Optional[AuthBase]=None, timeout: Optional[int]=None, ssl_verify_cert: Union[(bool, str)]=True,... |
def _validate_sample_rates(input_filepath_list: List[Path], combine_type: CombineType):
sample_rates = [file_info.sample_rate(f) for f in input_filepath_list]
if (not core.all_equal(sample_rates)):
raise IOError('Input files do not have the same sample rate. The {} combine type requires that all files h... |
def test_nonsquare_deterministic_2_state_by_node2state_by_state():
result = convert.state_by_node2state_by_state(nonsquare_deterministic_2)
answer = np.array([[1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1], [0, 1, 0, 0]])
assert np.array_equal(result, answer... |
class logger():
def __init__(self, n_steps, n_lvls):
self.n_steps = n_steps
self.n_lvls = n_lvls
self.lvl = (- 1)
self.lvl_step = 0
self.steps = 0
self.pbar = tqdm(total=(self.n_lvls * self.n_steps), desc='Starting')
def step(self):
self.pbar.update(1)
... |
def parse_type_string(expr_string: str, expr_fallback_name: str, line: int, column: int) -> ProperType:
try:
(_, node) = parse_type_comment(expr_string.strip(), line=line, column=column, errors=None)
if (isinstance(node, UnboundType) and (node.original_str_expr is None)):
node.original_s... |
class SerialTransport(asyncio.Transport):
force_poll: bool = False
def __init__(self, loop, protocol, *args, **kwargs) -> None:
super().__init__()
self.async_loop = loop
self._protocol: asyncio.BaseProtocol = protocol
self.sync_serial = serial.serial_for_url(*args, **kwargs)
... |
class TestRequireRuntimeDependencies():
def test_default(self, isolation):
builder = MockBuilder(str(isolation))
assert (builder.config.require_runtime_dependencies is builder.config.require_runtime_dependencies is False)
def test_target(self, isolation):
config = {'tool': {'hatch': {'bu... |
def string_escape(text: str) -> str:
replacements = (('\\', '\\\\'), ("'", "\\'"), ('"', '\\"'), ('\n', '\\n'), ('\r', '\\r'), ('\x00', '\\x00'), ('\ufeff', '\\ufeff'), ('\u2028', '\\u2028'), ('\u2029', '\\u2029'))
for (orig, repl) in replacements:
text = text.replace(orig, repl)
return text |
def decode_network_values(ptype, plen, buf):
nvalues = short.unpack_from(buf, header.size)[0]
off = ((header.size + short.size) + nvalues)
valskip = double.size
assert (((((valskip + 1) * nvalues) + short.size) + header.size) == plen)
assert (double.size == number.size)
result = []
for dstyp... |
def test_pformat(fake_manager):
fake_object = helpers.FakeObject(fake_manager, {'attr1': ('foo' * 10), 'ham': ('eggs' * 15)})
assert (fake_object.pformat() == "<class 'tests.unit.helpers.FakeObject'> => \n{'attr1': 'foofoofoofoofoofoofoofoofoofoo',\n 'ham': 'eggseggseggseggseggseggseggseggseggseggseggseggseggse... |
def test_lookup_notification_page_valid(initialized_db, set_secscan_config):
secscan = V4SecurityScanner(application, instance_keys, storage)
secscan._secscan_api = mock.Mock()
secscan._secscan_api.retrieve_notification_page.return_value = {'notifications': [{'id': '5e4b387e-88d3-4364-86fd-063447a6fad2', 'm... |
class PreferencesButton(Gtk.HBox):
def __init__(self, search_bar_box):
super().__init__()
menu = Gtk.Menu()
limit_item = ConfigCheckMenuItem(_('_Limit Results'), 'browsers', 'search_limit', True)
limit_item.connect('toggled', search_bar_box.toggle_limit_widgets)
menu.append(l... |
class TestCheckpointUtils(unittest.TestCase):
def setUp(self):
logging.disable(logging.CRITICAL)
def tearDown(self):
logging.disable(logging.NOTSET)
def _train_transformer(self, seed, extra_args=None):
if (extra_args is None):
extra_args = []
with tempfile.Tempora... |
def sha_conv3x3_block(in_channels, out_channels, stride=1, padding=1, dilation=1, groups=1, bias=False, activation=(lambda : nn.ReLU(inplace=True)), activate=True, shared_conv=None):
return ShaConvBlock(in_channels=in_channels, out_channels=out_channels, kernel_size=3, stride=stride, padding=padding, dilation=dilat... |
def convert_examples_to_features(examples, seq_length, tokenizer):
features = []
for (ex_index, example) in enumerate(examples):
tokens_a = tokenizer.tokenize(example.text_a)
tokens_b = None
if example.text_b:
tokens_b = tokenizer.tokenize(example.text_b)
if tokens_b:... |
_task('pytorch_translate_translation_from_pretrained_xlm')
class PytorchTranslateTranslationFromPretrainedXLMTask(PytorchTranslateTask):
def add_args(parser):
PytorchTranslateTask.add_args(parser)
parser.add_argument('--save-only', action='store_true', help='skip eval and only do save')
def load... |
class TestQuantsimConfig():
def test_parse_config_file_defaults(self):
model = SingleResidual()
model.eval()
quantsim_config = {'defaults': {'ops': {'is_output_quantized': 'True', 'is_symmetric': 'False'}, 'params': {'is_quantized': 'False', 'is_symmetric': 'True'}, 'per_channel_quantization... |
def generateDebianChangelog(package, logFile, version, maintainer):
releases = []
current_version = None
current_log = None
current_date = None
with open(logFile) as file_:
for line in file_.readlines():
match = re.match((package + '-(\\d+\\.\\d+\\.\\d+(\\.\\d+)?)\\s*(\\d+-\\d+-\... |
def test_uninstall_man_page(pipx_temp_env):
man_page_path = ((constants.LOCAL_MAN_DIR / 'man6') / 'pycowsay.6')
assert (not run_pipx_cli(['install', 'pycowsay']))
assert man_page_path.exists()
assert (not run_pipx_cli(['uninstall', 'pycowsay']))
assert (not file_or_symlink(man_page_path)) |
class File(ClangObject):
def from_name(translation_unit, file_name):
return File(conf.lib.clang_getFile(translation_unit, file_name))
def name(self):
return conf.lib.clang_getCString(conf.lib.clang_getFileName(self))
def time(self):
return conf.lib.clang_getFileTime(self)
def __b... |
def test_singleaxis_aoi_gh1221():
loc = pvlib.location.Location(40.1134, (- 88.3695))
dr = pd.date_range(start='02-Jun-1998 00:00:00', end='02-Jun-1998 23:55:00', freq='5T', tz='Etc/GMT+6')
sp = loc.get_solarposition(dr)
tr = pvlib.tracking.singleaxis(sp['apparent_zenith'], sp['azimuth'], axis_tilt=90, ... |
class ScheduleItemFactory(DjangoModelFactory):
conference = factory.SubFactory(ConferenceFactory)
submission = factory.SubFactory(SubmissionFactory)
language = factory.SubFactory(LanguageFactory)
title = factory.Faker('text', max_nb_chars=100)
slug = factory.Faker('slug')
description = factory.F... |
class HRL_agent():
def __init__(self, args, agent_id, char_index, graph_helper, deterministic=False, action_space=['open', 'pickplace'], seed=123):
self.args = args
self.mode = ('train' if (not args.evaluation) else 'test')
self.agent_type = 'RL_MCTS'
self.max_num_objects = args.max_... |
class QFI(QFIBase):
def convert(self, operator: CircuitStateFn, params: Optional[Union[(ParameterExpression, ParameterVector, List[ParameterExpression])]]=None) -> ListOp:
expec_op = PauliExpectation(group_paulis=False).convert(operator).reduce()
cleaned_op = self._factor_coeffs_out_of_composed_op(e... |
def test_remote_usage_prog(pytester: pytest.Pytester, request) -> None:
if (not hasattr(request.config._parser, 'prog')):
pytest.skip('prog not available in config parser')
pytester.makeconftest('\n import pytest\n\n config_parser = None\n\n \n def get_config_parser():\n ... |
class resnet_v1_101_fpn_dcn_rcnn_oneshot_v3(Symbol):
def __init__(self):
self.shared_param_list = ['offset_p2', 'offset_p3', 'offset_p4', 'offset_p5', 'rpn_conv', 'rpn_cls_score', 'rpn_bbox_pred']
self.shared_param_dict = {}
for name in self.shared_param_list:
self.shared_param_d... |
class SimpleNet(nn.Module):
def __init__(self, cfg, model_cfg, num_classes, **kwargs):
super().__init__()
self.backbone = build_backbone(model_cfg.BACKBONE.NAME, verbose=cfg.VERBOSE, pretrained=model_cfg.BACKBONE.PRETRAINED, **kwargs)
fdim = self.backbone.out_features
self.head = Non... |
class TestConsecutiveDuplicates(TestCase):
def setUp(self):
samples = [1, 2, 2, 3, 3, 3]
dates = pd.date_range(start='2018-05-13', periods=len(samples))
self.test_series = QFSeries(data=samples, index=dates)
def test_drop_consecutive_duplicates_keep_first(self):
expected_series_w... |
class Album(Collection, HasKey):
_property
def peoplesort(self):
return util.human_sort_key(self.get('~peoplesort').split('\n')[0])
_property
def genre(self):
return util.human_sort_key(self.get('genre').split('\n')[0])
def date(self):
return self.get('date')
def title(se... |
class Migration(migrations.Migration):
dependencies = [('events', '0001_initial')]
operations = [migrations.AddField(model_name='occurringrule', name='all_day', field=models.BooleanField(default=False), preserve_default=True), migrations.AddField(model_name='recurringrule', name='all_day', field=models.BooleanF... |
_datapipe('threadpool_map')
class ThreadPoolMapperIterDataPipe(IterDataPipe[T_co]):
datapipe: IterDataPipe
fn: Callable
def __init__(self, source_datapipe: IterDataPipe, fn: Callable, input_col=None, output_col=None, scheduled_tasks: int=128, max_workers: Optional[int]=None, **threadpool_kwargs) -> None:
... |
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