code stringlengths 281 23.7M |
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def test_jsonrepresenter_loads():
representer = filesystem.JsonRepresenter()
with open('./tests/testfiles/test.json', representer.read_mode) as file:
obj = representer.load(file)
assert obj
assert (obj['key1'] == 'value1')
assert (obj['key2'] == 'value2')
assert (obj['key3'] == 'value3') |
def delete_pod(cli, name, namespace):
try:
cli.delete_namespaced_pod(name=name, namespace=namespace)
while cli.read_namespaced_pod(name=name, namespace=namespace):
time.sleep(1)
except ApiException as e:
if (e.status == 404):
logging.info('Pod deleted')
el... |
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
... |
def dict_map(fn: Callable[([T], Any)], dic: Dict[(Any, Union[(dict, list, tuple, T)])], leaf_type: Type[T]) -> Dict[(Any, Union[(dict, list, tuple, Any)])]:
new_dict: Dict[(Any, Union[(dict, list, tuple, Any)])] = {}
for (k, v) in dic.items():
if isinstance(v, dict):
new_dict[k] = dict_map(f... |
class ArchARM(Arch):
def __init__(self):
super().__init__()
self._regs = ('r0', 'r1', 'r2', 'r3', 'r4', 'r5', 'r6', 'r7', 'r8', 'r9', 'r10', 'r11', 'r12', 'sp', 'lr', 'pc')
def regs(self):
return self._regs
def regs(self, regs):
self._regs += regs
def regs_need_swapped(se... |
def test_dry_run_does_not_build(tester: CommandTester, mocker: MockerFixture) -> None:
assert isinstance(tester.command, InstallerCommand)
mocker.patch.object(tester.command.installer, 'run', return_value=0)
mocked_editable_builder = mocker.patch('poetry.masonry.builders.editable.EditableBuilder')
teste... |
def load_and_broadcast_checkpoint(checkpoint_path: str, device: torch.device=CPU_DEVICE) -> Optional[Dict]:
if is_primary():
checkpoint = load_checkpoint(checkpoint_path, device)
else:
checkpoint = None
logging.info(f'Broadcasting checkpoint loaded from {checkpoint_path}')
return broadca... |
def keras_model_functional_with_non_fused_batchnorms_for_tf2():
inputs = tf.keras.Input(shape=(32, 32, 3))
x = tf.keras.layers.Conv2D(32, (3, 3))(inputs)
x = tf.keras.layers.BatchNormalization(momentum=0.3, epsilon=0.65, fused=False)(x, training=True)
with tf.compat.v1.variable_scope('scope_1'):
... |
class WithDescriptors(Serialisable):
descriptor = Descriptor[str]()
typed_default = Typed(expected_type=str)
typed_not_none = Typed(expected_type=str, allow_none=False)
typed_none = Typed(expected_type=str, allow_none=True)
set_tuple = Set(values=('a', 1, 0.0))
set_list = Set(values=['a', 1, 0.0... |
class Float24(Codec):
codec_id = 'imagecodecs_float24'
def __init__(self, byteorder=None, rounding=None):
self.byteorder = byteorder
self.rounding = rounding
def encode(self, buf):
buf = protective_squeeze(numpy.asarray(buf))
return imagecodecs.float24_encode(buf, byteorder=s... |
def test_colored_captured_log(pytester: Pytester) -> None:
pytester.makepyfile("\n import logging\n\n logger = logging.getLogger(__name__)\n\n def test_foo():\n logger.info('text going to logger from call')\n assert False\n ")
result = pytester.runpytest('--log-... |
_module()
class CyclicLrUpdaterHook(LrUpdaterHook):
def __init__(self, by_epoch=False, target_ratio=(10, 0.0001), cyclic_times=1, step_ratio_up=0.4, anneal_strategy='cos', **kwargs):
if isinstance(target_ratio, float):
target_ratio = (target_ratio, (target_ratio / 100000.0))
elif isinsta... |
_new_faces(MaterialGroup.RAILING_RAILS)
def create_railing_bottom(bm, bot_edge, prop):
initial_loc = (prop.corner_post_width * 1.5)
clamped_offset = clamp(prop.bottom_rail_offset, ((- initial_loc) + (prop.corner_post_width / 2)), (prop.corner_post_height - (initial_loc * 2)))
bmesh.ops.translate(bm, verts=b... |
def render_pep8_errors_e227(msg, _node, source_lines=None):
line = msg.line
res = re.search('column (\\d+)', msg.msg)
col = int(res.group().split()[(- 1)])
operators = {'>>', '<<'}
end_idx = (col + 1)
end_idx = ((end_idx + 1) if (source_lines[(line - 1)][col:(col + 2)] in operators) else end_idx... |
def _split_numeric_sortkey(s, limit=10, reg=re.compile('[0-9][0-9]*\\.?[0-9]*').search, join=' '.join):
result = reg(s)
if ((not result) or (not limit)):
text = join(s.split())
return ((text,) if text else ())
else:
(start, end) = result.span()
return (join(s[:start].split())... |
class Effect5957(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Medium Energy Turret')), 'maxRange', ship.getModifiedItemAttr('eliteBonusHeavyInterdictors1'), skill='Heavy Interdiction Cruisers'... |
class DefaultProvider(EggProvider):
def _has(self, path):
return os.path.exists(path)
def _isdir(self, path):
return os.path.isdir(path)
def _listdir(self, path):
return os.listdir(path)
def get_resource_stream(self, manager, resource_name):
return open(self._fn(self.modu... |
class TopPoolFunction(Function):
def forward(ctx, input):
output = top_pool.forward(input)[0]
ctx.save_for_backward(input)
return output
def backward(ctx, grad_output):
input = ctx.saved_variables[0]
output = top_pool.backward(input, grad_output)[0]
return output |
class Matchmaking():
def __init__(self, p2p: P2P, schema_hash: bytes, dht: DHT, *, servicer_type: Type[ServicerBase], prefix: str, target_group_size: int, min_group_size: int, request_timeout: float, client_mode: bool, initial_group_bits: str='', averaging_expiration: float=15):
assert ('.' not in prefix), ... |
class SingleQubitCompare(GateWithRegisters):
adjoint: bool = False
_property
def signature(self) -> Signature:
one_side = (Side.RIGHT if (not self.adjoint) else Side.LEFT)
return Signature([Register('a', 1), Register('b', 1), Register('less_than', 1, side=one_side), Register('greater_than', ... |
class TerminusPasteTextCommand(sublime_plugin.TextCommand):
def run(self, edit, text, bracketed=True):
view = self.view
terminal = Terminal.from_id(view.id())
if (not terminal):
return
bracketed = (bracketed and terminal.bracketed_paste_mode_enabled())
if brackete... |
def samples_from_source(sample_source, buffering=BUFFER_SIZE, labeled=None, reverse=False):
ext = os.path.splitext(sample_source)[1].lower()
if (ext == '.sdb'):
return SDB(sample_source, buffering=buffering, labeled=labeled, reverse=reverse)
if (ext == '.csv'):
return CSV(sample_source, labe... |
def block6():
for i in range(11):
re.sub('(?i)##yv0##', '', strings[27], 0)
regexs[57].sub('', strings[27], subcount[57])
regexs[58].sub('', strings[28], subcount[58])
regexs[59].sub('', strings[29], subcount[59])
re.sub('(?i)##\\/o##', '', strings[30], 0)
re.sub('(?i... |
def cache_size(mb=True):
numtotal = [0]
classdict = {}
def get_recurse(submodels):
for submodel in submodels:
subclasses = submodel.__subclasses__()
if (not subclasses):
num = len(submodel.get_all_cached_instances())
numtotal[0] += num
... |
class RRDB(nn.Module):
def __init__(self, nc, kernel_size=3, gc=32, stride=1, bias=True, pad_type='zero', norm_type=None, act_type='leakyrelu', mode='CNA'):
super(RRDB, self).__init__()
self.RDB1 = ResidualDenseBlock_5C(nc, kernel_size, gc, stride, bias, pad_type, norm_type, act_type, mode)
... |
def create_nested(dirname, s, depth, branch_factor):
def write(rp):
fp = rp.open('w')
fp.write(s)
fp.close()
def helper(rp, depth):
if (not rp.isdir()):
rp.mkdir()
sub_rps = [rp.append(('file_%d' % i)) for i in range(branch_factor)]
if (depth == 1):
... |
def test_exporter_handles_overlapping_python_versions(tmp_path: Path, poetry: Poetry) -> None:
poetry.locker.mock_lock_data({'package': [{'name': 'ipython', 'python-versions': '>=3.6', 'version': '7.16.3', 'optional': False, 'dependencies': {}}, {'name': 'ipython', 'python-versions': '>=3.7', 'version': '7.34.0', '... |
def normalize_outbound_headers(headers, hdr_validation_flags, should_split_outbound_cookies):
headers = _lowercase_header_names(headers, hdr_validation_flags)
if should_split_outbound_cookies:
headers = _split_outbound_cookie_fields(headers, hdr_validation_flags)
headers = _strip_surrounding_whitesp... |
def test_skip_fails_with_msg_and_reason(pytester: Pytester) -> None:
p = pytester.makepyfile('\n import pytest\n\n def test_skip_both_arguments():\n pytest.skip(reason="foo", msg="bar")\n ')
result = pytester.runpytest(p)
result.stdout.fnmatch_lines('*UsageError: Passing both... |
def _pad_or_crop_to_shape(x, in_shape, tgt_shape):
if (len(in_shape) == 2):
in_shape = np.asarray(in_shape)
tgt_shape = np.asarray(tgt_shape)
print('Padding input from {} to {}'.format(in_shape, tgt_shape))
im_diff = (in_shape - tgt_shape)
if (im_diff[0] < 0):
pad... |
def urlunparse(parts):
(scheme, netloc, path, params, query, fragment) = parts
if RE_DRIVE_LETTER_PATH.match(path):
quoted_path = (path[:3] + parse.quote(path[3:]))
else:
quoted_path = parse.quote(path)
return parse.urlunparse((parse.quote(scheme), parse.quote(netloc), quoted_path, parse... |
def histogram(returns, benchmark=None, resample='M', fontname='Arial', grayscale=False, figsize=(10, 5), ylabel=True, subtitle=True, compounded=True, savefig=None, show=True, prepare_returns=True):
if prepare_returns:
returns = _utils._prepare_returns(returns)
if (benchmark is not None):
... |
_db
('cfp_open', (True, False))
def test_is_cfp_open(graphql_client, conference_factory, deadline_factory, cfp_open):
now = timezone.now()
conference = conference_factory(timezone=pytz.timezone('America/Los_Angeles'))
deadline_factory(start=(now - timezone.timedelta(days=1)), end=((now + timezone.timedelta(... |
class RobertaPreLayerNormOnnxConfig(OnnxConfig):
def inputs(self) -> Mapping[(str, Mapping[(int, str)])]:
if (self.task == 'multiple-choice'):
dynamic_axis = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
dynamic_axis = {0: 'batch', 1: 'sequence'}
return OrderedDict([... |
def get_data(input_path):
all_imgs = []
classes_count = {}
class_mapping = {}
visualise = False
data_paths = [os.path.join(input_path, s) for s in ['VOC2012']]
print('Parsing annotation files')
for data_path in data_paths:
annot_path = os.path.join(data_path, 'Annotations')
i... |
def test_video():
video = 'BAACAgIAAx0CAAGgr9AAAgmRX7b4Xv9f-4BK5VR_5ppIOF6UIp0AAgYAA4GkuUmhnZz2xC37wR4E'
video_unique = 'AgADBgADgaS5SQ'
video_thumb = 'AAMCAgADHQIAAaCv0AACCZFftvhe_1_7gErlVH_mmkg4XpQinQACBgADgaS5SaGdnPbELfvBIH3qihAAAwEAB20AA_WeAQABHgQ'
video_thumb_unique = 'AQADIH3qihAAA_WeAQAB'
che... |
class TempStoreTestCase(SqlAlchemyTestCase):
def setUpClass(cls):
cls.this_dir = abspath(join(dirname(__file__), '..'))
cls.stuff_path = join(cls.this_dir, 'stuff')
cls.dog_jpeg = join(cls.stuff_path, 'dog.jpg')
cls.cat_jpeg = join(cls.stuff_path, 'cat.jpg')
cls.dog_png = joi... |
class ClarisRandomizerExportError(UnableToExportError):
def __init__(self, reason: str, output: (str | None)):
super().__init__(reason)
self.output = output
def detailed_text(self) -> str:
result = []
if (self.output is not None):
result.append(self.output)
re... |
def test_traversal(simple_chart, rich_chart):
(_, values) = simple_chart
simple_output = values
assert (len(simple_output) == 17)
assert (['replicaCount', '', '1'] in simple_output)
(_, values) = rich_chart
rich_output = values
assert (['replicaCount', 'number of nginx pod replicas to create... |
class StopReg(ScrimsButton):
def __init__(self):
super().__init__(label='Stop Reg', style=discord.ButtonStyle.red, row=2)
async def callback(self, interaction: discord.Interaction):
(await interaction.response.defer())
if (not self.view.record.opened_at):
return (await self.v... |
def draw_mask(mask, draw, random_color=False):
if random_color:
color = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255), 153)
else:
color = (30, 144, 255, 153)
nonzero_coords = np.transpose(np.nonzero(mask))
for coord in nonzero_coords:
draw.point(coord[::... |
def test_cmd_list_input_with_complex_args_error_on_first_save():
cmd1 = get_cmd('tests/testfiles/cmds/args.sh', 'tests\\testfiles\\cmds\\args.bat')
cmd2 = get_cmd('tests/testfiles/cmds/args2.sh', 'tests\\testfiles\\cmds\\args2.bat')
context = Context({'a': 'WRONG', 'b': 'two two', 'c': 'three', 'd': cmd1, '... |
class CosPlus_Classifier(nn.Module):
def __init__(self, num_classes=10, in_dim=640, scale=16, bias=False, gamma=0.03125, eta=1, moving_avg=True, mu=0.9, **kwargs):
super(CosPlus_Classifier, self).__init__()
self.num_classes = num_classes
self.moving_avg = moving_avg
self.in_dim = in_... |
def install_atlas_from_zipfile(zip_file_path, atlas_path):
with tempfile.TemporaryDirectory() as temp_dir:
temp_atlas_path = Path(temp_dir).joinpath('test_atlas')
with zipfile.ZipFile(zip_file_path, 'r') as zip_ref:
zip_ref.extractall(temp_atlas_path)
if (not atlas_path.parent.ex... |
class Source(Stream):
_graphviz_shape = 'doubleoctagon'
def __init__(self, start=False, **kwargs):
self.stopped = True
super().__init__(ensure_io_loop=True, **kwargs)
self.started = False
if start:
self.start()
def stop(self):
if (not self.stopped):
... |
def _default_implementation() -> BackendType[Any]:
global _DEFAULT_IMPLEMENTATION
if (_DEFAULT_IMPLEMENTATION is not None):
return _DEFAULT_IMPLEMENTATION
try:
implementation = next(all_implementations())
except StopIteration:
logger.debug('Backend implementation import failed', ... |
class SDIO_ICR(IntEnum):
CCRCFAILC = (1 << 0)
DCRCFAILC = (1 << 1)
CTIMEOUTC = (1 << 2)
DTIMEOUTC = (1 << 3)
TXUNDERRC = (1 << 4)
RXOVERRC = (1 << 5)
CMDRENDC = (1 << 6)
CMDSENTC = (1 << 7)
DATAENDC = (1 << 8)
STBITERRC = (1 << 9)
DBCKENDC = (1 << 10)
SDIOITC = (1 << 22)
... |
class ResNeXt(nn.Module):
def __init__(self, num_blocks, cardinality, bottleneck_width, num_classes=10):
super(ResNeXt, self).__init__()
self.cardinality = cardinality
self.bottleneck_width = bottleneck_width
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=1, bi... |
class BaseModel(pybamm.BaseSubModel):
def __init__(self, param, domain, options):
super().__init__(param, domain, options=options)
def _get_standard_interface_utilisation_variables(self, u_var):
(domain, Domain) = self.domain_Domain
u = pybamm.maximum(u_var, 1e-08)
u_var_av = pyb... |
def test_upload_time(s3_mock: S3Path) -> None:
backend = s3.S3Storage()
backend.PATH_BACKEND(f'/{s3_mock.bucket}/folder1/file1').touch()
assert (backend.get_upload_time(f'/{s3_mock.bucket}/folder1/file1').second == 0)
assert (backend.get_upload_time(f'/{s3_mock.bucket}/folder1/file1').year == 1970)
... |
class OurModelVAE(Model):
def __init__(self, placeholders, num_features, num_nodes, features_nonzero, **kwargs):
super(OurModelVAE, self).__init__(**kwargs)
self.inputs = placeholders['features']
self.input_dim = num_features
self.features_nonzero = features_nonzero
self.n_sa... |
class AppConfig(DjangoAppConfig):
name = 'django_cassandra_engine'
def connect(self):
from django_cassandra_engine.utils import get_cassandra_connections
for (_, conn) in get_cassandra_connections():
conn.connect()
def import_models(self, *args, **kwargs):
self.connect()
... |
class _EvalManager():
def __init__(self, quantsim_factory: Callable, eval_func: Callable[([ort.InferenceSession], float)], results_dir: str, strict_validation: bool):
self._quantsim_factory = quantsim_factory
self._eval_func = eval_func
self._results_dir = results_dir
self._strict_va... |
class ScikitChebyshev2DSubMesh(ScikitSubMesh2D):
def __init__(self, lims, npts):
(spatial_vars, tabs) = self.read_lims(lims)
coord_sys = spatial_vars[0].coord_sys
edges = {}
for var in spatial_vars:
if (var.name not in ['y', 'z']):
raise pybamm.DomainError... |
_constant(MultiVectorType)
def lower_constant_MultiVector(context, builder, typ: MultiVectorType, pyval: MultiVector) -> llvmlite.ir.Value:
mv = cgutils.create_struct_proxy(typ)(context, builder)
mv.value = context.get_constant_generic(builder, typ.value_type, pyval.value)
mv.layout = context.get_constant_g... |
class PDFExporter(DocumentExporter):
DEFAULT_CSS_DIR_NAME = 'default_css'
def __init__(self, settings: Settings):
super().__init__(settings)
if hasattr(settings, 'document_css_directory'):
self._document_css_dir = join(get_starting_dir_abs_path(), settings.document_css_directory)
... |
def postprocess_args(args):
ROOTDIR = args.root_dir
ft_file_map = {'vitbase': 'pth_vit_base_patch16_224_imagenet.hdf5'}
args.img_ft_file = os.path.join(ROOTDIR, 'R2R', 'features', ft_file_map[args.features])
args.connectivity_dir = os.path.join(ROOTDIR, 'R2R', 'connectivity')
args.scan_data_dir = os... |
class QAOA(VQE):
def __init__(self, operator: Union[(OperatorBase, LegacyBaseOperator)]=None, optimizer: Optimizer=None, p: int=1, initial_state: Optional[Union[(QuantumCircuit, InitialState)]]=None, mixer: Union[(QuantumCircuit, OperatorBase, LegacyBaseOperator)]=None, initial_point: Optional[np.ndarray]=None, gra... |
def test_prune_projects_output2(db, settings):
(stdout, stderr) = (io.StringIO(), io.StringIO())
instances = Project.objects.filter(id__in=projects_without_owner)
call_command('prune_projects', stdout=stdout, stderr=stderr)
assert (stdout.getvalue() == ("Found projects without ['owner']:\n%s" % get_prun... |
def merge_edges(edges):
base_e = edges[0][1]
merged_edges = [edges[0]]
base_len = np.sqrt((((base_e[1][0] - base_e[0][0]) ** 2) + ((base_e[1][1] - base_e[0][1]) ** 2)))
base_unit_v = (((base_e[1][0] - base_e[0][0]) / base_len), ((base_e[1][1] - base_e[0][1]) / base_len))
for edge in edges[1:]:
... |
def get_oggz_validate_version():
process = subprocess.Popen(['oggz-validate', '--version'], stdout=subprocess.PIPE)
(output, unused_err) = process.communicate()
retcode = process.poll()
if (retcode != 0):
return (0,)
lines = output.splitlines()
if (not lines):
return (0,)
par... |
class SelectTHC(SelectOracle):
num_mu: int
num_spin_orb: int
num_bits_theta: int
kr1: int = 1
kr2: int = 1
control_val: Optional[int] = None
_property
def control_registers(self) -> Tuple[(Register, ...)]:
return (() if (self.control_val is None) else (Register('control', 1),))
... |
class TestTrainingExtensionsSpatialSvdCostCalculator(unittest.TestCase):
def test_calculate_spatial_svd_cost(self):
inp_tensor = tf.Variable(tf.random.normal([1, 32, 28, 28]))
filter_tensor = tf.Variable(tf.random.normal([5, 5, 32, 64]))
conv = tf.nn.conv2d(inp_tensor, filter_tensor, strides... |
class OpMat(object):
def __init__(self, name, array, nelem=1, type=None, asym=False, dimens=None):
if isinstance(name, str):
self.name = name
else:
raise TypeError
if isinstance(array, np.ndarray):
self.array = array
else:
raise TypeErr... |
def spice_junction(jc, nc, isc, j01, j02, n1, n2, Eg, rsh):
isource = 'i{0} {1} {2} dc {3}\n'.format(jc, nc, (nc + 1), isc)
d1 = 'd{0} {1} {2} diode{3} OFF\n'.format(((2 * jc) - 1), (nc + 1), nc, ((2 * jc) - 1))
d1deff = '.model diode{0} d(is={1},n={2},eg={3})\n'.format(((2 * jc) - 1), j01, n1, Eg)
d2 =... |
_mode()
def main():
parser = argparse.ArgumentParser()
parser.add_argument('checkpoint', help="Model checkpoint (or 'pretrained=<model_id>')")
parser.add_argument('--data_root', default='data')
parser.add_argument('--batch_size', type=int, default=512)
parser.add_argument('--num_workers', type=int, ... |
def prime2_hint_text():
from randovania.games.prime2.generator.pickup_pool import dark_temple_keys, sky_temple_keys
db = default_database.resource_database_for(RandovaniaGame.METROID_PRIME_ECHOES)
result = []
for temple in range(3):
key = dark_temple_keys.create_dark_temple_key(0, temple, db)
... |
.parametrize('username,password', users)
def test_detail_export(db, client, username, password):
client.login(username=username, password=password)
instances = Attribute.objects.all()
for instance in instances:
url = reverse(urlnames['detail_export'], args=[instance.pk])
response = client.ge... |
def test_interactive(hatch, helpers, temp_dir):
project_name = 'My.App'
description = 'foo '
with temp_dir.as_cwd():
result = hatch('new', '-i', input=f'''{project_name}
{description}''')
path = (temp_dir / 'my-app')
expected_files = helpers.get_template_files('new.default', project_name, de... |
def test_all_partitions():
(mechanism, purview) = ((0, 1), (2,))
assert (set(all_partitions(mechanism, purview)) == set([KPartition(Part((0, 1), ()), Part((), (2,))), KPartition(Part((0,), ()), Part((1,), ()), Part((), (2,))), KPartition(Part((0,), (2,)), Part((1,), ()), Part((), ())), KPartition(Part((0,), ())... |
def remove_na(x, y=None, paired=False, axis='rows'):
x = np.asarray(x)
assert (axis in ['rows', 'columns']), 'axis must be rows or columns.'
if (y is None):
return _remove_na_single(x, axis=axis)
elif isinstance(y, (int, float, str)):
return (_remove_na_single(x, axis=axis), y)
else:... |
.skipif((literal_eval(os.getenv('TEST_SAGEMAKER', 'False')) is not True), reason='Skipping test because should only be run when releasing minor transformers version')
.usefixtures('sm_env')
_class([{'framework': 'pytorch', 'script': 'run_glue.py', 'model_name_or_path': 'distilbert-base-cased', 'instance_type': 'ml.g4dn... |
def make_pin(pin, i, lcd):
global noisr
if (pin in keypad_pullup):
pin = Pin(pin, Pin.IN, Pin.PULL_UP)
else:
pin = Pin(pin, Pin.IN, Pin.PULL_DOWN)
def cbr(pin):
handle_pin(pin, i, lcd)
if (not noisr):
try:
pin.irq(handler=cbr, trigger=(Pin.IRQ_FALLING | Pi... |
def main():
parser = argparse.ArgumentParser(description='Command line interface for P-Tuning.')
parser.add_argument('--data_dir', default=None, type=str, required=True, help='The input data dir. Should contain the data files for the task.')
parser.add_argument('--model_type', default='albert', type=str, re... |
_fixtures(ConfigWithFiles)
def test_incorrect_replacement_of_configuration(config_with_files):
fixture = config_with_files
config_file = fixture.new_config_file(filename=ConfigWithSetting.filename, contents='from reahl.component.config import Configuration; some_key = Configuration()')
fixture.set_config_sp... |
class MediatorMixin():
address_to_privkey: Dict[(Address, PrivateKey)]
address_to_client: Dict[(Address, Client)]
block_number: BlockNumber
token_id: TokenAddress
def __init__(self):
super().__init__()
self.partner_to_balance_proof_data: Dict[(Address, BalanceProofData)] = {}
... |
class StsbProcessor(DataProcessor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format('processor'), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
return InputExample(tensor_dict['idx'].numpy(), tensor_dic... |
class ResBlock(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3, stride=1, dilation=1, groups=1):
super(ResBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size, stride=stride, padding=get_same_padding(kernel_size, dilation), dilation=dilation, groups... |
def _create_delegate_for(item: ItemResourceInfo):
factory = QtWidgets.QItemEditorFactory()
factory.registerEditor(QtCore.QMetaType.Int.value, RangeSpinBoxItemEditorCreator(0, item.max_capacity))
delegate = QtWidgets.QStyledItemDelegate()
delegate.setItemEditorFactory(factory)
return delegate |
(params=[{'encoded': b'\x00\x00', 'bit_count': 15, 'json': {'minimal_logic': False, 'specific_levels': {}}}, {'encoded': b'\x80', 'bit_count': 1, 'json': {'minimal_logic': True, 'specific_levels': {}}}, {'encoded': b'X\x00\x00', 'bit_count': 18, 'json': {'minimal_logic': False, 'specific_levels': {'Dash': 'expert'}}}, ... |
class TrainingArguments():
model_ckpt: Optional[str] = field(default='lvwerra/codeparrot', metadata={'help': 'Model name or path of model to be trained.'})
save_dir: Optional[str] = field(default='./', metadata={'help': 'Save dir where model repo is cloned and models updates are saved to.'})
dataset_name_tr... |
class FileuploadCom(XFSDownloader):
__name__ = 'FileuploadCom'
__type__ = 'downloader'
__version__ = '0.02'
__status__ = 'testing'
__pattern__ = '
__config__ = [('enabled', 'bool', 'Activated', True), ('use_premium', 'bool', 'Use premium account if available', True), ('fallback', 'bool', 'Fallba... |
class TargetAssigner(object):
def __init__(self, similarity_calc, matcher, box_coder, positive_class_weight=1.0, negative_class_weight=1.0, unmatched_cls_target=None):
if (not isinstance(similarity_calc, sim_calc.RegionSimilarityCalculator)):
raise ValueError('similarity_calc must be a RegionSim... |
.parametrize('vcf_file, encoding, generate_header', [('1kg_target_chr20_38_imputed_chr20_1000.vcf', {'variant_AF': {'filters': [FixedScaleOffset(offset=0, scale=10000, dtype='f4', astype='u2')]}, 'call_DS': {'filters': [FixedScaleOffset(offset=0, scale=100, dtype='f4', astype='u1')]}, 'variant_DR2': {'filters': [FixedS... |
class TestFileHandlerCalibrationBase():
platform_id = 324
gains_nominal = np.arange(1, 13)
offsets_nominal = np.arange((- 1), (- 13), (- 1))
gains_gsics = [0, 0, 0, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0, 1.1, 0]
offsets_gsics = [0, 0, 0, (- 0.4), (- 0.5), (- 0.6), (- 0.7), (- 0.8), (- 0.9), (- 1.0), (- ... |
class TensorKey():
def __init__(self, x: torch.Tensor, precision: int=4) -> None:
x = x.detach()
self._key = (*self._extract_meta(x), *self._calculate_stats(x, precision))
def _extract_meta(x: torch.Tensor) -> Tuple[(Hashable, ...)]:
return (x.device, x.dtype, x.size())
def _calculat... |
class ParallelAllErrorsTests(TestCase):
def test_parallel_all_errors(self):
exc1 = EquitableException(message='foo')
reraise1 = partial(raise_, exc1)
exc2 = EquitableException(message='bar')
reraise2 = partial(raise_, exc2)
dispatcher = ComposedDispatcher([TypeDispatcher({Par... |
class ComplexDecoder(json.JSONDecoder):
def __init__(self, *args, **kwargs):
json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)
def object_hook(self, obj):
if (isinstance(obj, dict) and ('type' in obj) and ('keys' in obj)):
return GroundingKey(grounding_ty... |
def test_remove_row_button():
widget = QgridWidget(df=create_df())
event_history = init_event_history(['row_removed', 'selection_changed'], widget=widget)
selected_rows = [1, 2]
widget._handle_qgrid_msg_helper({'rows': selected_rows, 'type': 'change_selection'})
widget._handle_qgrid_msg_helper({'typ... |
class LDSR(Unfolding_Loss):
def __init__(self, window_length, hop_length, **kwargs):
super().__init__(window_length, hop_length)
def criterion(self, target_signal_hat, target_signal):
s_target = ((((target_signal_hat * target_signal).sum((- 1), keepdims=True) + 1e-08) / ((target_signal ** 2).sum... |
(slots=True)
class RPC():
height_off = attr.ib()
height_scale = attr.ib()
lat_off = attr.ib()
lat_scale = attr.ib()
line_den_coeff = attr.ib()
line_num_coeff = attr.ib()
line_off = attr.ib()
line_scale = attr.ib()
long_off = attr.ib()
long_scale = attr.ib()
samp_den_coeff = a... |
class MockErrorDataset():
def __init__(self, dataset):
self.rebatch_map = {}
self.dataset = dataset
self.batchsize_per_replica = dataset.batchsize_per_replica
def __getitem__(self, idx):
batch = self.dataset[idx]
if (idx in self.rebatch_map):
num_samples = sel... |
class FixedOffsetTimezone(datetime.tzinfo):
def __init__(self, offset: float, name: (str | None)=None) -> None:
self._offset = datetime.timedelta(minutes=offset)
if (name is None):
name = ('Etc/GMT%+d' % offset)
self.zone = name
def __str__(self) -> str:
return self.z... |
def add_send_to_generator_class(builder: IRBuilder, fn_info: FuncInfo, fn_decl: FuncDecl, sig: FuncSignature) -> None:
with builder.enter_method(fn_info.generator_class.ir, 'send', object_rprimitive, fn_info):
arg = builder.add_argument('arg', object_rprimitive)
none_reg = builder.none_object()
... |
def parse_diff(diff):
hunks = []
hunk = None
for line in diff:
if line.startswith(''):
if hunk:
hunks.append(hunk)
hunk = DiffHunk(line)
elif (hunk is not None):
hunk.append(line)
if hunk:
hunks.append(hunk)
return hunks |
class BertLMHead(OptimusModule):
def __init__(self, mpu_vocab_size, hidden_size, init_method, layernorm_epsilon, parallel_output):
super(BertLMHead, self).__init__()
args = get_args()
self.bias = torch.nn.Parameter(torch.zeros(mpu_vocab_size))
self.bias.model_parallel = True
... |
class PFSFeedbackEventHandler(RaidenEventHandler):
def __init__(self, wrapped_handler: EventHandler) -> None:
self.wrapped = wrapped_handler
def on_raiden_events(self, raiden: 'RaidenService', chain_state: ChainState, events: List[Event]) -> None:
for event in events:
if (type(event)... |
class EfficientNetEncoder(nn.Module):
def __init__(self, config: EfficientNetConfig):
super().__init__()
self.config = config
self.depth_coefficient = config.depth_coefficient
def round_repeats(repeats):
return int(math.ceil((self.depth_coefficient * repeats)))
nu... |
class KJTSplitsAllToAllMeta():
pg: dist.ProcessGroup
_input: KeyedJaggedTensor
splits: List[int]
splits_tensors: List[torch.Tensor]
input_splits: List[List[int]]
input_tensors: List[torch.Tensor]
labels: List[str]
keys: List[str]
device: torch.device
stagger: int
splits_cumsu... |
def parse_inp_section_config(raw_conf):
conf = OrderedDict()
if isinstance(raw_conf, list):
conf['columns'] = raw_conf
elif isinstance(raw_conf, (dict, OrderedDict)):
if ('keys' in raw_conf):
conf.update(raw_conf)
conf['columns'] = ['Key', 'Value']
else:
... |
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