code stringlengths 281 23.7M |
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def get_detached_file_descriptor(filepath):
try:
import win32file
has_win32file = True
except ImportError:
has_win32file = False
if has_win32file:
import msvcrt
import os
handle = win32file.CreateFile(str(filepath), win32file.GENERIC_READ, ((win32file.FILE_SHA... |
def load_controller(args, index):
if (args.controller[index] == 'none'):
return None
elif (args.controller[index] == 'fudge_controller'):
controller = FudgeController(args, index)
if (len(args.controller_load_dir[index]) > 0):
controller.load(args.controller_load_dir[index])
... |
class bdist(Command):
description = 'create a built (binary) distribution'
user_options = [('bdist-base=', 'b', 'temporary directory for creating built distributions'), ('plat-name=', 'p', ('platform name to embed in generated filenames (default: %s)' % get_platform())), ('formats=', None, 'formats for distribu... |
class Function(object):
def __init__(self, type_name, inputs, params):
self.type_name = type_name
self.inputs = inputs
self.params = params
self.ntop = self.params.get('ntop', 1)
if ('ntop' in self.params):
del self.params['ntop']
self.in_place = self.para... |
class BlankCosmeticPatchesDialog(BaseCosmeticPatchesDialog, Ui_BlankCosmeticPatchesDialog):
_cosmetic_patches: BlankCosmeticPatches
def __init__(self, parent: (QtWidgets.QWidget | None), current: BaseCosmeticPatches):
super().__init__(parent)
self.setupUi(self)
assert isinstance(current,... |
def _computations_as_categorical(df: pd.DataFrame, **kwargs) -> pd.DataFrame:
categories_dict = _as_categorical_checks(df, **kwargs)
categories_dtypes = {}
for (column_name, value) in categories_dict.items():
if (value is None):
cat_dtype = pd.CategoricalDtype()
elif isinstance(v... |
class CdPlayer():
description: str
currentTrack: int = 0
amplifier: Amplifier
title: str
def __init__(self, description: str, amplifier: Amplifier):
self.description = description
self.amplifier = amplifier
def on(self) -> None:
print(f'{self.description} on')
def off... |
def test_tested_unlisted(covtest):
covtest.makefile('\n def func():\n pass\n ')
covtest.run()
expected = check_coverage.Message(check_coverage.MsgType.perfect_file, 'module.py', 'module.py has 100% coverage but is not in perfect_files!')
assert (covtest.check(perfect_files=[]) == [e... |
class StoryFactory(DjangoModelFactory):
class Meta():
model = Story
django_get_or_create = ('name',)
category = factory.SubFactory(StoryCategoryFactory)
name = factory.LazyAttribute((lambda o: f'Success Story of {o.company_name}'))
company_name = factory.Faker('company')
company_url ... |
def handle_code(code, vk_packet):
code_keys = []
if (code in CODES):
code_keys.append(VirtualKeyAction(CODES[code]))
elif (len(code) == 1):
if ((not vk_packet) and (code in ascii_vk)):
code_keys.append(VirtualKeyAction(ascii_vk[code]))
else:
code_keys.append(K... |
class TestFormResourceBaseReviewForm(TestCase):
def test_review_form_comment_includes_resource_name(self):
form = ResourceBaseReviewForm(resource_name='test resource')
self.assertIn('placeholder="Please provide clear feedback if you decided to not approve this test resource." required id="id_comment... |
def adapt_network_for_any_size_input(network_definition, multiple):
def new_network_definition(*args, **kwargs):
pdb.set_trace()
if ('image_batch_tensor' in kwargs):
image_batch_tensor = kwargs['image_batch_tensor']
else:
image_batch_tensor = args[0]
args ... |
def batch_examples(example, batch_size, max_length, mantissa_bits, shard_multiplier=1, length_multiplier=1, constant=False, num_threads=4, drop_long_sequences=True):
with tf.name_scope('batch_examples'):
max_length = (max_length or batch_size)
min_length = 8
mantissa_bits = mantissa_bits
... |
def dump(args, s):
s.adapter.set_tclk(0)
s.adapter.set_sclk(127)
try:
code = args.code.decode('hex')
except TypeError:
logging.fatal('Code must be in hexadecimal format.')
return
if (len(code) != 7):
logging.fatal('Code must be 7 bytes long.')
return
s.unl... |
class RBF(Kernel):
def __call__(self, X):
XY = X.dot(X.T)
x2 = pt.sum((X ** 2), axis=1).dimshuffle(0, 'x')
X2e = pt.repeat(x2, X.shape[0], axis=1)
H = ((X2e + X2e.T) - (2.0 * XY))
V = pt.sort(H.flatten())
length = V.shape[0]
m = pt.switch(pt.eq((length % 2), 0... |
_wgpu_render_function(Square, SquareMaterial)
class SquareShader(WorldObjectShader):
def get_bindings(self, wobject, shared):
binding = Binding('u_stdinfo', 'buffer/uniform', shared.uniform_buffer)
self.define_binding(0, 0, binding)
return {0: {0: binding}}
def get_pipeline_info(self, wo... |
def kill(pid: int, signal: int, timeout=1000, dword1=wintypes.DWORD(1)):
if (pid <= 0):
raise OSError(errno.EINVAL, 'process group not supported')
if ((signal < 0) or (signal >= PG_SIGNAL_COUNT)):
raise OSError(errno.EINVAL, 'unsupported signal number')
inbuffer = pointer(wintypes.BYTE(signa... |
class _FCNHead(nn.Module):
def __init__(self, in_channels, channels, norm_layer=nn.BatchNorm2d):
super(_FCNHead, self).__init__()
inter_channels = (in_channels // 4)
self.block = nn.Sequential(nn.Conv2d(in_channels, inter_channels, 3, padding=1, bias=False), norm_layer(inter_channels), nn.Re... |
class Migration(migrations.Migration):
dependencies = [('questions', '0062_meta')]
operations = [migrations.AddField(model_name='questionset', name='questionset', field=models.ForeignKey(blank=True, default=None, help_text='The question set this question set belongs to.', null=True, on_delete=django.db.models.d... |
class Local():
def get_src_local_rp(extension):
return rpath.RPath(Globals.local_connection, os.path.join(old_test_dir, os.fsencode(extension)))
def get_tgt_local_rp(extension):
return rpath.RPath(Globals.local_connection, os.path.join(abs_test_dir, os.fsencode(extension)))
vftrp = get_src_l... |
_message(((pyrogram.filters.command(commands='get_last_logs') & pyrogram.filters.private) & tools.is_admin))
def send_last_logs(bot: AutoPoster, message: Message):
logs = sorted(list(bot.logs_path.iterdir()))[(- 1)]
try:
lines = int(message.command[1])
except (ValueError, IndexError):
lines ... |
def get_invalid_tag_usage(applier_list, include_tags, exclude_tags):
if ((len(include_tags.strip()) == 0) or (len(exclude_tags.strip()) == 0)):
return []
include_list = include_tags.split(',')
include_list = [i.strip() for i in include_list]
exclude_list = exclude_tags.split(',')
exclude_lis... |
def write_job_parameters(params: namedtuple) -> None:
dict_path = (params.job_dir + 'params.csv')
with open(dict_path, 'w') as csv_file:
writer = csv.writer(csv_file, delimiter=';')
for (key, value) in enumerate(params._fields):
writer.writerow([value, params[key]]) |
def test_env_all_operations():
os.environ['ARB_GET_ME1'] = 'arb value from $ENV ARB_GET_ME1'
os.environ['ARB_GET_ME2'] = 'arb value from $ENV ARB_GET_ME2'
os.environ['ARB_DELETE_ME1'] = 'arb value from $ENV ARB_DELETE_ME1'
os.environ['ARB_DELETE_ME2'] = 'arb value from $ENV ARB_DELETE_ME2'
context =... |
class TestMaskedLanguageModel(unittest.TestCase):
def test_legacy_masked_lm(self):
with contextlib.redirect_stdout(StringIO()):
with tempfile.TemporaryDirectory('test_legacy_mlm') as data_dir:
create_dummy_data(data_dir)
preprocess_lm_data(data_dir)
... |
def parse_having(toks, start_idx, tables_with_alias, schema, default_tables):
idx = start_idx
len_ = len(toks)
if ((idx >= len_) or (toks[idx] != 'having')):
return (idx, [])
idx += 1
(idx, conds) = parse_condition(toks, idx, tables_with_alias, schema, default_tables)
return (idx, conds) |
def simulate_trotter(qubits: Sequence[cirq.Qid], hamiltonian: Hamiltonian, time: float, n_steps: int=1, order: int=0, algorithm: Optional[TrotterAlgorithm]=None, control_qubit: Optional[cirq.Qid]=None, omit_final_swaps: bool=False) -> cirq.OP_TREE:
if (order < 0):
raise ValueError('The order of the Trotter ... |
def PolynomialPlot():
(coefficients, set_coefficients) = reactpy.hooks.use_state([0])
x = list(linspace((- 1), 1, 50))
y = [polynomial(value, coefficients) for value in x]
return reactpy.html.div(plot(f'{len(coefficients)} Term Polynomial', x, y), ExpandableNumberInputs(coefficients, set_coefficients)) |
def romfs_validation(line: QtWidgets.QLineEdit):
if is_directory_validator(line):
return True
path = Path(line.text())
return (not all((p.is_file() for p in [path.joinpath('system', 'files.toc'), path.joinpath('packs', 'system', 'system.pkg'), path.joinpath('packs', 'maps', 's010_cave', 's010_cave.p... |
class SmartCopyAndPaste(object):
def __setCursorPositionAndAnchor(cursor, position, anchor):
cursor.setPosition(anchor)
cursor.setPosition(position, cursor.KeepAnchor)
def __ensureCursorBeforeAnchor(cls, cursor):
start = cursor.selectionStart()
end = cursor.selectionEnd()
... |
def sa_conv_unit(x):
with tf.variable_scope(None, 'sa_conv_unit'):
shape = x.get_shape().as_list()
y = slim.conv2d(x, shape[(- 1)], kernel_size=1, stride=1, biases_initializer=None, activation_fn=None)
y = slim.batch_norm(y, activation_fn=None, fused=False)
y = tf.nn.sigmoid(y)
... |
class TorchProfiler(HookBase):
def __init__(self, enable_predicate, output_dir, *, activities=None, save_tensorboard=True):
self._enable_predicate = enable_predicate
self._activities = activities
self._output_dir = output_dir
self._save_tensorboard = save_tensorboard
def before_s... |
class AlexNet(nn.Module):
def __init__(self, num_classes=10):
super(AlexNet, self).__init__()
self.features = nn.Sequential(nn.Conv2d(3, 64, kernel_size=11, stride=4, padding=5), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=2, stride=2), nn.Conv2d(64, 192, kernel_size=5, padding=2), nn.ReLU(inpla... |
.parametrize('tp', [ClassVar, InitVar, *cond_list(HAS_TYPE_GUARD, (lambda : [typing.TypeGuard])), *cond_list(HAS_TYPED_DICT_REQUIRED, (lambda : [typing.Required, typing.NotRequired]))])
def test_var_tag(tp):
pytest.raises(NotSubscribedError, (lambda : normalize_type(tp)))
assert_normalize(tp[int], tp, [nt_zero(... |
.slow
_figures_equal()
def test_DecisionMatrixPlotter_barh(decision_matrix, fig_test, fig_ref):
dm = decision_matrix(seed=42, min_alternatives=3, max_alternatives=3, min_criteria=3, max_criteria=3)
plotter = plot.DecisionMatrixPlotter(dm=dm)
test_ax = fig_test.subplots()
plotter.barh(ax=test_ax)
df ... |
_module()
class HandGenerateRelDepthTarget():
def __init__(self):
pass
def __call__(self, results):
rel_root_depth = results['rel_root_depth']
rel_root_valid = results['rel_root_valid']
cfg = results['ann_info']
D = cfg['heatmap_size_root']
root_depth_bound = cfg[... |
def prime1_hint_text():
db = default_database.resource_database_for(RandovaniaGame.METROID_PRIME)
from randovania.games.prime1.generator.pickup_pool import artifacts
artifact = artifacts.create_artifact(0, 0, db)
result = [('Artifact', artifact.pickup_category, artifact.broad_category)]
return resul... |
class RejectSponsorshipApplicationUseCase(BaseUseCaseWithNotifications):
notifications = [notifications.RejectedSponsorshipNotificationToPSF(), notifications.RejectedSponsorshipNotificationToSponsors()]
def execute(self, sponsorship, request=None):
sponsorship.reject()
sponsorship.save()
... |
class Project(gui.Container):
lastUpdateTime = 0
def __init__(self, **kwargs):
super(Project, self).__init__(**kwargs)
self.variable_name = 'App'
self.style.update({'position': 'relative', 'overflow': 'auto', 'background-color': 'rgb(250,248,240)', 'background-image': "url('/editor_resou... |
def test_vsite_reg(methanol, vs, tmpdir):
with tmpdir.as_cwd():
vs.freeze_site_angles = True
vs.regularisation_epsilon = 0.1
vs.run(molecule=methanol)
assert (methanol.extra_sites.n_sites == 2)
sites = []
center_atom = None
with open(os.path.join(methanol.name... |
def test_proj_debug_logging(capsys):
with proj_logging_env():
with pytest.warns(FutureWarning):
transformer = Transformer.from_proj('+init=epsg:4326', '+init=epsg:27700')
transformer.transform(100000, 100000)
captured = capsys.readouterr()
if (os.environ.get('PROJ_DEBUG')... |
def test_load_hotp_vectors():
vector_data = textwrap.dedent('\n # HOTP Test Vectors\n # RFC 4226 Appendix D\n\n COUNT = 0\n COUNTER = 0\n INTERMEDIATE = cc93cf18508d94934c64b65d8ba7667fb7cde4b0\n TRUNCATED = 4c93cf18\n HOTP = 755224\n SECRET = \n\n COUNT = 1\n COUNTER = 1\n INTERMED... |
def test_fails_rst_no_content(tmp_path, capsys, caplog):
sdist = build_sdist(tmp_path, {'setup.cfg': '\n [metadata]\n name = test-package\n version = 0.0.1\n long_description = file:README.rst\n long_description_content_type = text/x-rst\n ... |
class RubyRoleTest(ProvyTestCase):
def setUp(self):
super(RubyRoleTest, self).setUp()
self.role = RubyRole(prov=None, context={})
def installs_necessary_packages_to_provision(self):
with self.using_stub(AptitudeRole) as aptitude, self.execute_mock() as execute:
self.role.prov... |
def transforms_imagenet_train(img_size=224, scale=None, ratio=None, hflip=0.5, vflip=0.0, color_jitter=0.4, auto_augment=None, interpolation='random', use_prefetcher=False, mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, re_prob=0.0, re_mode='const', re_count=1, re_num_splits=0, separate=False, force_color_jitter... |
def format_for_slack(total_results, results, scheduled: bool, title: str):
print(total_results, results)
header = {'type': 'header', 'text': {'type': 'plain_text', 'text': title, 'emoji': True}}
if (total_results['failed'] > 0):
total = {'type': 'section', 'fields': [{'type': 'mrkdwn', 'text': f'''*... |
class Importable(t.Trafaret):
def check_and_return(self, value):
if (not isinstance(value, str)):
self._failure('value should be a string', value=value)
if (':' not in value):
self._failure('import notation must be in format: `package.module:target`', value=value)
(mo... |
class DatatableDataFactory(factory.Factory):
class Meta():
model = dict
columns = [{six.u('name'): six.u('per_end_date'), six.u('type'): six.u('Date')}, {six.u('name'): six.u('ticker'), six.u('type'): six.u('String')}, {six.u('name'): six.u('tot_oper_exp'), six.u('type'): six.u('BigDecimal(11,4)')}]
... |
def versions_to_display_for_releases(current_version: StrictVersion, last_changelog_version: StrictVersion, releases: list[dict]) -> tuple[(dict[(str, str)], list[str], (VersionDescription | None))]:
all_change_logs = {}
new_change_logs = []
displayed_new_version = False
version_to_display = None
fo... |
def t2star_circuit_execution() -> Tuple[(qiskit.result.Result, np.array, List[int], float, float)]:
num_of_gates = np.append(np.linspace(10, 150, 10).astype(int), np.linspace(160, 450, 5).astype(int))
gate_time = 0.1
qubits = [0]
t2_value = 10
error = thermal_relaxation_error(np.inf, t2_value, gate_... |
(persist=eval(os.getenv('PERSISTENT')))
def compute_similarity_matrix_keywords(model_path, keywords=[], all_model_vectors=False, return_unk_sim=False):
(keywords, word_embs) = get_word_embeddings(model_path, keywords, all_model_vectors=all_model_vectors, return_words=True)
word_embs = np.array(word_embs)
si... |
class Migration(migrations.Migration):
dependencies = [('projects', '0050_value_set_prefix')]
operations = [migrations.AlterField(model_name='value', name='value_type', field=models.CharField(choices=[('text', 'Text'), ('url', 'URL'), ('integer', 'Integer'), ('float', 'Float'), ('boolean', 'Boolean'), ('datetim... |
def project_by_tangent_iteration(space: 'RiemannianSpace', pt_a: 'Point', pt_b: 'Point', pt_c: 'Point', *, tol=1e-06, max_iterations=100) -> Tuple[('Point', OptimResult)]:
dist_ab = space.length(pt_a, pt_b)
if (dist_ab < tol):
return (midpoint(space, pt_a, pt_b), OptimResult.ILL_POSED)
projected_len... |
def read_sac_zpk(filename=None, file=None, string=None, get_comments=False):
if (filename is not None):
f = open(filename, 'rb')
elif (file is not None):
f = file
elif (string is not None):
f = BytesIO(string)
sects = ('ZEROS', 'POLES', 'CONSTANT')
sectdata = {'ZEROS': [], 'P... |
def ticket_id_to_user_hashid(ticket_id: strawberry.ID, conference_code: str) -> Optional[str]:
conference = Conference.objects.filter(code=conference_code).first()
decoded_ticket_id = decode_hashid(ticket_id)
order_position = pretix.get_order_position(conference, decoded_ticket_id)
if (not order_positio... |
def help():
print('\nAaia\n\navailable modules:')
for (importer, module_name, _) in pkgutil.iter_modules([os.path.dirname(__file__)]):
if (module_name != 'main'):
library = importlib.import_module(((__package__ + '.') + module_name))
print(((module_name + ' : ') + library.__descr... |
def _infer_decorator_callchain(node):
if (not isinstance(node, FunctionDef)):
return None
if (not node.parent):
return None
try:
result = next(node.infer_call_result(node.parent), None)
except InferenceError:
return None
if isinstance(result, bases.Instance):
... |
def test_corrected_cphase_ops_throws() -> None:
(a, b) = cirq.LineQubit.range(2)
with pytest.raises(GateDecompositionError):
_corrected_cphase_ops(qubits=(a, b), angle=(np.pi / 13), parameters=ParticleConservingParameters(theta=(np.pi / 4), delta=0, chi=0, gamma=0, phi=(np.pi / 24))) |
def test_concordance_cebrian2009localizacion():
matrix = scale_by_sum([[6, 5, 28, 5, 5], [4, 2, 25, 10, 9], [5, 7, 35, 9, 6], [6, 1, 27, 6, 7], [6, 8, 30, 7, 9], [5, 6, 26, 4, 8]], axis=0)
objectives = [1, 1, (- 1), 1, 1]
weights = [0.25, 0.25, 0.1, 0.2, 0.2]
expected = [[np.nan, 0.5, 0.35, 0.5, 0.35, 0... |
def copy_tree(src, dst, preserve_mode=1, preserve_times=1, preserve_symlinks=0, update=0, verbose=1, dry_run=0):
from distutils.file_util import copy_file
if ((not dry_run) and (not os.path.isdir(src))):
raise DistutilsFileError(("cannot copy tree '%s': not a directory" % src))
try:
names = ... |
class BiDictTests(unittest.TestCase):
def setUp(self):
self.bidict = BiDict()
def testStartEmpty(self):
self.assertEqual(len(self.bidict), 0)
self.assertEqual(len(self.bidict.forward), 0)
self.assertEqual(len(self.bidict.backward), 0)
def testLength(self):
self.assert... |
def get_create_inputs(dataset_name: str, is_train: bool, epochs: int):
options = {'mnist': (lambda : create_inputs_mnist(is_train)), 'fashion_mnist': (lambda : create_inputs_mnist(is_train)), 'smallNORB': (lambda : create_inputs_norb(is_train, epochs)), 'cifar10': (lambda : create_inputs_cifar10(is_train)), 'cifa10... |
_module()
class ViPNAS_ResNet(BaseBackbone):
arch_settings = {50: ViPNAS_Bottleneck}
def __init__(self, depth, in_channels=3, num_stages=4, strides=(1, 2, 2, 2), dilations=(1, 1, 1, 1), out_indices=(3,), style='pytorch', deep_stem=False, avg_down=False, frozen_stages=(- 1), conv_cfg=None, norm_cfg=dict(type='BN... |
class PrepareNuSuperPositionState(Bloq):
num_bits_p: int
adjoint: bool = False
_property
def signature(self) -> Signature:
return Signature([Register('mu', self.num_bits_p), Register('nu', (self.num_bits_p + 1), shape=(3,))])
def short_name(self) -> str:
return 'PREP $2^{-\\mu}|\\mu\... |
class ConditionOverSampleDataset(ConditionCaptionDataset):
def __init__(self, features: Dict, transforms: Dict, caption: str, vocabulary: str, condition: str, load_into_mem: bool=False, threshold: float=0.9, times: int=4):
super().__init__(features, transforms, caption, vocabulary, condition, load_into_mem=... |
def add_BAU_constraints(n, config):
ext_c = n.generators.query('p_nom_extendable').carrier.unique()
mincaps = pd.Series(config['electricity'].get('BAU_mincapacities', {key: 0 for key in ext_c}))
lhs = linexpr((1, get_var(n, 'Generator', 'p_nom'))).groupby(n.generators.carrier).apply(join_exprs)
define_c... |
def add_exec_opts(parser) -> None:
parser.add_argument('--timeout', help='maximum number of seconds to allow for all tests to run. This does not include time taken to build the tests.', type=int, default=300, metavar='timeout')
parser.add_argument('filter_glob', help='maximum number of seconds to allow for all ... |
def test_int_loader_provider(strict_coercion, debug_trail):
retort = Retort(strict_coercion=strict_coercion, debug_trail=debug_trail)
loader = retort.get_loader(int)
assert (loader(100) == 100)
if strict_coercion:
raises_exc(TypeLoadError(int, None), (lambda : loader(None)))
raises_exc(T... |
class Tconfig(TestCase):
def setUp(self):
config.init()
def test_init_garbage_file(self):
config.quit()
garbage = b'\xf1=\xab\xac'
(fd, filename) = mkstemp()
os.close(fd)
with open(filename, 'wb') as f:
f.write(garbage)
config.init(filename)
... |
def test_add_should_not_select_prereleases(app: PoetryTestApplication, repo: TestRepository, tester: CommandTester) -> None:
repo.add_package(get_package('pyyaml', '3.13'))
repo.add_package(get_package('pyyaml', '4.2b2'))
tester.execute('pyyaml')
expected = 'Using version ^3.13 for pyyaml\n\nUpdating de... |
(Executable)
class ExecutableStub(Executable):
def __init__(self, name='fake executable', stdout=''):
super().__init__(name)
self.calls = []
self.stdout = stdout
def __repr__(self):
return ("StubExecutable('%s')" % self.name)
def times_called(self):
return len(self.ca... |
class Config(object):
GPU_USAGE = 1
LOG_DIR = './log/DTN'
IMAGE_SIZE = 256
MAP_SIZE = 64
TRU_PARAMETERS = {'alpha': 0.001, 'beta': 0.01, 'mu_update_rate': 0.001}
STEPS_PER_EPOCH = 1000
MAX_EPOCH = 40
NUM_EPOCHS_PER_DECAY = 12.0
BATCH_SIZE = 32
LEARNING_RATE = 0.001
LEARNING_M... |
def set_object_material_text(object_id: int, text: str, material_index: int=0, material_size: int=OBJECT_MATERIAL_SIZE_256x128, font_face: str='Arial', font_size: int=24, bold: bool=True, font_color: int=, back_color: int=0, text_alignment: int=0) -> bool:
return SetObjectMaterialText(object_id, text, material_inde... |
def sa_perindopril_rings() -> GoalDirectedBenchmark:
specification = uniform_specification(1, 10, 100)
benchmark_object = perindopril_rings()
sa_biased = ScoringFunctionSAWrapper(benchmark_object.objective, SAScoreModifier())
return GoalDirectedBenchmark(name='SA_perindopril', objective=sa_biased, contr... |
class Configs():
def __init__(self):
self.config_dir = os.path.join(os.path.abspath(os.path.dirname(sys.argv[0])), 'config')
self.configs = self.get_configs()
def get_configs(self):
config_files = [os.path.join(self.config_dir, cfile) for cfile in os.listdir(self.config_dir) if ((os.path... |
def test_assign_pickup_to_starting_items(empty_patches, state_game_data, generic_pickup_category, default_generator_params):
db = state_game_data.resource_database
starting_node = state_game_data.region_list.node_by_identifier(empty_patches.game.starting_location)
starting = state.State(ResourceCollection()... |
def Euler2Rotation(phi, theta, psi):
c_phi = np.cos(phi)
s_phi = np.sin(phi)
c_theta = np.cos(theta)
s_theta = np.sin(theta)
c_psi = np.cos(psi)
s_psi = np.sin(psi)
R_roll = np.array([[1, 0, 0], [0, c_phi, (- s_phi)], [0, s_phi, c_phi]])
R_pitch = np.array([[c_theta, 0, s_theta], [0, 1, ... |
class ProgressMeter(object):
def __init__(self, num_batches: int, meters: List[AverageMeter], prefix: str=''):
self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
self.meters = meters
self.prefix = prefix
def display(self, batch: int) -> None:
entries = [(self.prefix + self.b... |
('pypyr.moduleloader.get_module')
(Step, 'invoke_step')
def test_run_pipeline_steps_complex_with_run_str_lower_false(mock_invoke_step, mock_get_module):
step = Step({'name': 'step1', 'run': 'false'})
context = get_test_context()
original_len = len(context)
with patch_logger('pypyr.dsl', logging.INFO) as... |
class MissingSpaceInDoctestChecker(BaseChecker):
name = 'missing_space_in_doctest'
msgs = {'E9973': ('Space missing after >>> in the docstring of "%s."', 'missing-space-in-doctest', 'Used when a doctest is missing a space before the code to be executed')}
_required_for_messages('missing-space-in-doctest')
... |
def _smoketest(ctx: Context, debug: bool, eth_client: EthClient, report_path: Optional[str]) -> None:
from raiden.tests.utils.smoketest import run_smoketest, setup_smoketest, step_printer
raiden_stdout = StringIO()
assert ctx.parent, MYPY_ANNOTATION
environment_type = ctx.parent.params['environment_type... |
class Proplist():
def __init__(self, ini_data: Optional[Dict[(str, Union[(bytes, str)])]]=None) -> None:
self._pl = pa.pa_proplist_new()
if (not self._pl):
raise PulseAudioException(0, 'Failed creating proplist.')
if (ini_data is not None):
for (k, v) in ini_data:
... |
.supported(only_if=(lambda backend: backend.hash_supported(hashes.SHA3_224())), skip_message='Does not support SHA3_224')
class TestSHA3224():
test_sha3_224 = generate_hash_test(load_hash_vectors, os.path.join('hashes', 'SHA3'), ['SHA3_224LongMsg.rsp', 'SHA3_224ShortMsg.rsp'], hashes.SHA3_224()) |
class TestHRPTGetCalibratedBT(TestHRPTWithPatchedCalibratorAndFile):
def _get_channel_4_bt(self):
dataset_id = make_dataid(name='4', calibration='brightness_temperature')
return self._get_dataset(dataset_id)
def test_calibrated_bt_values(self):
result = self._get_channel_4_bt()
n... |
def get_filenames():
for (dirpath, dirnames, filenames) in os.walk('src'):
if dirpath.endswith('__pycache__'):
continue
for rel_fn in filenames:
if (not rel_fn.endswith('.py')):
continue
fn = os.path.join(dirpath, rel_fn)
if (fn in [os.... |
def get_devices(display=None):
_devices = {}
base = '/dev'
for filename in os.listdir(base):
if filename.startswith('hidraw'):
path = os.path.join(base, filename)
try:
_devices[path] = HIDRawDevice(display, path)
except OSError:
con... |
def test_upload_generic_package_file(tmp_path, project, resp_upload_generic_package):
path = (tmp_path / file_name)
path.write_text(file_content, encoding='utf-8')
package = project.generic_packages.upload(package_name=package_name, package_version=package_version, file_name=file_name, data=path.open(mode='... |
class RunID(namedtuple('RunID', 'python compat bench timestamp')):
def __new__(cls, python, compat, bench, timestamp):
self = super().__new__(cls, python, compat, (bench or None), (int(timestamp) if timestamp else None))
return self
def __str__(self):
if (not self.timestamp):
... |
class JsonFormatter(logging.Formatter):
def __init__(self, *args, **kwargs):
self.json_default = kwargs.pop('json_default', _json_default)
self.json_encoder = kwargs.pop('json_encoder', None)
self.json_serializer = kwargs.pop('json_serializer', json.dumps)
self.default_values = kwarg... |
def generate_ann(root_path, split, image_infos, preserve_vertical, format):
dst_image_root = osp.join(root_path, 'crops', split)
ignore_image_root = osp.join(root_path, 'ignores', split)
if (split == 'training'):
dst_label_file = osp.join(root_path, f'train_label.{format}')
elif (split == 'val')... |
def train(ps_device):
(train_input_fn, record_info_dict) = data_utils.get_input_fn(tfrecord_dir=FLAGS.record_info_dir, split='train', bsz_per_host=FLAGS.train_batch_size, seq_len=FLAGS.seq_len, reuse_len=FLAGS.reuse_len, bi_data=FLAGS.bi_data, num_hosts=1, num_core_per_host=1, perm_size=FLAGS.perm_size, mask_alpha=... |
class BlazeEventsLoader(implements(PipelineLoader)):
__doc__ = __doc__.format(SID_FIELD_NAME=SID_FIELD_NAME, TS_FIELD_NAME=TS_FIELD_NAME, EVENT_DATE_FIELD_NAME=EVENT_DATE_FIELD_NAME)
def __init__(self, expr, next_value_columns, previous_value_columns, resources=None, odo_kwargs=None):
dshape = expr.dsha... |
class MatchResult(object):
def __init__(self):
self._pattern_to_op_tensor = {}
self._name_to_pattern = {}
def add(self, pattern, op, tensor):
self._pattern_to_op_tensor[pattern] = (op, tensor)
if (pattern.name is not None):
if (pattern.name in self._name_to_pattern):
... |
class Arithmetic(Task):
VERSION = 0
DATASET_PATH = 'EleutherAI/arithmetic'
def has_training_docs(self):
return False
def has_validation_docs(self):
return True
def has_test_docs(self):
return False
def training_docs(self):
return NotImplemented
def validation_... |
def test_get_routed_grid_model_circuit():
problem = _random_grid_model(2, 3, np.random.RandomState(0))
qubits = cirq.GridQubit.rect(2, 3)
circuit = get_routed_hardware_grid_circuit(problem_graph=problem.graph, qubits=qubits, coordinates=problem.coordinates, gammas=[(np.pi / 2), (np.pi / 4)], betas=[(np.pi /... |
def test_tensordot_of_proxied_cupy_arrays():
cupy = pytest.importorskip('cupy')
org = cupy.arange(9).reshape((3, 3))
a = proxy_object.asproxy(org.copy())
b = proxy_object.asproxy(org.copy())
res1 = dask.array.tensordot(a, b).flatten()
res2 = dask.array.tensordot(org.copy(), org.copy()).flatten()... |
def test_target_task_view():
secret = factories.make_secret()
transfer = factories.create(factories.LockedTransferSignedStateProperties(secret=secret))
secrethash = transfer.lock.secrethash
mediator = factories.make_address()
mediator_channel = factories.create(factories.NettingChannelStatePropertie... |
class Fog(VersionBase):
def __init__(self, visual_range, bounding_box=None):
self.visual_range = visual_range
if (bounding_box and (not isinstance(bounding_box, BoundingBox))):
raise TypeError('bounding_box not of type BoundingBox')
self.bounding_box = bounding_box
def __eq__... |
def get_val_dataloader(args, val_data, vocab):
print('processing val data')
(features, vocab) = convert_example_to_feature(args, val_data, vocab, get_vocab=False)
val_data = Dataset(features)
sampler = data.SequentialSampler(val_data)
val_dataloader = data.DataLoader(val_data, sampler=sampler, batch... |
def main():
train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True, num_workers=4, pin_memory=True)
test_loader = DataLoader(test_data, batch_size=16, shuffle=False, num_workers=4, pin_memory=True)
meta_loader = DataLoader(meta_data, batch_size=batch_size, shuffle=True, num_workers=4, pin... |
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