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def _parse_datetimes(request: WSGIRequest) -> Tuple[(datetime, datetime)]:
if (request.method == 'GET'):
params = request.GET
else:
params = request.POST
startdate = params.get('startdate')
starttime = params.get('starttime')
enddate = params.get('enddate')
endtime = params.get('... |
def test_guess_cmake_lexer_from_header():
headers = ['CMAKE_MINIMUM_REQUIRED(VERSION 2.6 FATAL_ERROR)', 'cmake_minimum_required(version 3.13) # CMake version check', ' CMAKE_MINIMUM_REQUIRED\t( VERSION 2.6 FATAL_ERROR ) ']
for header in headers:
code = '\n'.join([header, 'project(example)', 'set(CMAKE_... |
def test_oauth_token_auth():
gl = Gitlab(' oauth_token='oauth_token', api_version='4')
p = PreparedRequest()
p.prepare(url=gl.url, auth=gl._auth)
assert (gl.private_token is None)
assert (gl.oauth_token == 'oauth_token')
assert (gl.job_token is None)
assert isinstance(gl._auth, OAuthTokenAut... |
('pypyr.retries.random.uniform', side_effect=[11, 12, 13])
('time.sleep')
def test_retry_all_substitutions_backoff_jitter_list(mock_sleep, mock_random):
rd = RetryDecorator({'max': '{k3[1][k031]}', 'sleep': '{k2}', 'backoff': '{k6}', 'jrc': '{k4}', 'sleepMax': '{k5}'})
context = Context({'k1': False, 'k2': [0.3... |
def get_acis_prism(latitude, longitude, start, end, map_variables=True, url=' **kwargs):
elems = [{'name': 'pcpn', 'interval': 'dly', 'units': 'mm'}, {'name': 'maxt', 'interval': 'dly', 'units': 'degreeC'}, {'name': 'mint', 'interval': 'dly', 'units': 'degreeC'}, {'name': 'avgt', 'interval': 'dly', 'units': 'degree... |
class NSDR(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... |
class CollaborationArguments(AveragerArguments, CollaborativeOptimizerArguments, BaseTrainingArguments):
statistics_expiration: float = field(default=600, metadata={'help': 'Statistics will be removed if not updated in this many seconds'})
endpoint: Optional[str] = field(default=None, metadata={'help': "This no... |
def test_cache_reportheader_external_abspath(pytester: Pytester, tmp_path_factory: TempPathFactory) -> None:
external_cache = tmp_path_factory.mktemp('test_cache_reportheader_external_abspath_abs')
pytester.makepyfile('def test_hello(): pass')
pytester.makeini('\n [pytest]\n cache_dir = {abscache}\n ... |
def HANP_Miner(filename, mingap, maxgap, minsup, output_filename='result_file.txt'):
clear_mem()
read_file(filename)
cannum = 0
compnum = 0
global S
global ww
global candidate
begin_time = time_now()
min_freItem()
f_level = 1
gen_candidate(f_level)
while (len(candidate) !... |
class BoxToMaskTestOptions(BoxToMaskOptions):
def initialize(self):
BoxToMaskOptions.initialize(self)
self.parser.add_argument('--ntest', type=int, default=float('inf'))
self.parser.add_argument('--results_dir', type=str, default='results/')
self.parser.add_argument('--aspect_ratio',... |
class TempMsg(object):
def __init__(self, senders=None, receivers=None, channels=None, message='', header='', type='', lockstring='', hide_from=None):
self.senders = ((senders and make_iter(senders)) or [])
self.receivers = ((receivers and make_iter(receivers)) or [])
self.channels = ((chann... |
def blas_header_text():
blas_code = ''
if (not config.blas__ldflags):
current_filedir = dirname(__file__)
blas_common_filepath = os.path.join(current_filedir, 'c_code', 'alt_blas_common.h')
blas_template_filepath = os.path.join(current_filedir, 'c_code', 'alt_blas_template.c')
co... |
def generate_ann(root_path, split, image_infos, preserve_vertical, format):
print('Cropping images...')
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.{for... |
class DiffDB(ProductionCommand):
keyword = 'diffdb'
def assemble(self):
super(DiffDB, self).assemble()
self.parser.add_argument('-s', '--output_sql', action='store_true', dest='output_sql', help='show differences as sql')
def execute(self, args):
super().execute(args)
with se... |
def write_pkg_info(self, base_dir):
temp = ''
final = os.path.join(base_dir, 'PKG-INFO')
try:
with NamedTemporaryFile('w', encoding='utf-8', dir=base_dir, delete=False) as f:
temp = f.name
self.write_pkg_file(f)
permissions = stat.S_IMODE(os.lstat(temp).st_mode)
... |
def convertLDAmallet(dataDir='data/topic_models/SemevalA/', filename='state.mallet.gz'):
def extract_params(statefile):
with gzip.open(statefile, 'r') as state:
params = [x.decode('utf8').strip() for x in state.readlines()[1:3]]
return (list(params[0].split(':')[1].split(' ')), float(par... |
def run_louvain(gfile, gamma, nruns, weight=None, node_subset=None, attribute=None, output_dictionary=False):
np.random.seed()
g = ig.Graph.Read_GraphMLz(gfile)
if (node_subset != None):
if (attribute == None):
gdel = node_subset
else:
gdel = [i for (i, val) in enumer... |
class MinLeverage(AccountControl):
_types(__funcname='MinLeverage', min_leverage=(int, float), deadline=datetime)
_bounded(__funcname='MinLeverage', min_leverage=(0, None))
def __init__(self, min_leverage, deadline):
super(MinLeverage, self).__init__(min_leverage=min_leverage, deadline=deadline)
... |
class HeadphoneMonitorPlugin(EventPlugin):
PLUGIN_ID = 'HeadphoneMonitor'
PLUGIN_NAME = _('Pause on Headphone Unplug')
PLUGIN_DESC = _('Pauses in case headphones get unplugged and unpauses in case they get plugged in again.')
PLUGIN_ICON = Icons.MEDIA_PLAYBACK_PAUSE
def enabled(self):
self._... |
def get_context_templates(model, tok):
global CONTEXT_TEMPLATES_CACHE
if (CONTEXT_TEMPLATES_CACHE is None):
CONTEXT_TEMPLATES_CACHE = ([['{}']] + [[(f.replace('{', ' ').replace('}', ' ') + '. {}') for f in generate_fast(model, tok, ['The', 'Therefore', 'Because', 'I', 'You'], n_gen_per_prompt=(n_gen // ... |
class OutputLayerFunction(Function):
def forward(ctx, dimension, metadata, input_features):
output_features = input_features.new()
ctx.metadata_ = metadata
ctx.dimension = dimension
sparseconvnet.SCN.OutputLayer_updateOutput(metadata, input_features.contiguous(), output_features)
... |
class FixedOptionPolicy(object):
def __init__(self, base_policy, num_skills, z):
self._z = z
self._base_policy = base_policy
self._num_skills = num_skills
def reset(self):
pass
def get_action(self, obs):
aug_obs = concat_obs_z(obs, self._z, self._num_skills)
r... |
def _set_thing_style(caller, raw_string, **kwargs):
room = caller.location
options = caller.attributes.get('options', category=room.tagcategory, default={})
options['things_style'] = kwargs.get('value', 2)
caller.attributes.add('options', options, category=room.tagcategory)
return (None, kwargs) |
class TestCorrelation():
def _test_correlation(self, dtype=torch.float):
layer = Correlation(max_displacement=0)
input1 = torch.tensor(_input1, dtype=dtype).cuda()
input2 = torch.tensor(_input2, dtype=dtype).cuda()
input1.requires_grad = True
input2.requires_grad = True
... |
class _SofMarker(_Marker):
def __init__(self, marker_code, offset, segment_length, px_width, px_height):
super(_SofMarker, self).__init__(marker_code, offset, segment_length)
self._px_width = px_width
self._px_height = px_height
def from_stream(cls, stream, marker_code, offset):
... |
def get_image_processor_config(pretrained_model_name_or_path: Union[(str, os.PathLike)], cache_dir: Optional[Union[(str, os.PathLike)]]=None, force_download: bool=False, resume_download: bool=False, proxies: Optional[Dict[(str, str)]]=None, use_auth_token: Optional[Union[(bool, str)]]=None, revision: Optional[str]=None... |
def adaptive_isotropic_gaussian_kernel(xs, ys, h_min=0.001):
(Kx, D) = xs.get_shape().as_list()[(- 2):]
(Ky, D2) = ys.get_shape().as_list()[(- 2):]
assert (D == D2)
leading_shape = tf.shape(xs)[:(- 2)]
diff = (tf.expand_dims(xs, (- 2)) - tf.expand_dims(ys, (- 3)))
if (LooseVersion(tf.__version__... |
def convert():
source = (BASE / 'scratch_projects')
target = (BASE / 'correct_results')
for file in source.iterdir():
if file.is_dir():
for f in file.iterdir():
if (f.is_file() and (f.suffix == '.sb3')):
path = f.as_posix()
dest = (... |
class SdistBuilderConfig(BuilderConfig):
def __init__(self, *args: Any, **kwargs: Any) -> None:
super().__init__(*args, **kwargs)
self.__core_metadata_constructor: (Callable[(..., str)] | None) = None
self.__strict_naming: (bool | None) = None
self.__support_legacy: (bool | None) = N... |
class FP16_Optimizer(object):
def __init__(self, init_optimizer, static_loss_scale=1.0, dynamic_loss_scale=False, dynamic_loss_args=None, verbose=True):
if (not torch.cuda.is_available):
raise SystemError('Cannot use fp16 without CUDA.')
self.verbose = verbose
self.optimizer = in... |
def main():
parser = HfArgumentParser((DataTrainingArguments, TeacherModelArguments, StudentModelArguments, DistillTrainingArguments), description=DESCRIPTION)
if ((len(sys.argv) == 2) and sys.argv[1].endswith('.json')):
(data_args, teacher_args, student_args, training_args) = parser.parse_json_file(jso... |
class TestExportModels(unittest.TestCase):
def test_export_multihead_attention(self):
module = multihead_attention.MultiheadAttention(embed_dim=8, num_heads=2)
scripted = torch.jit.script(module)
_test_save_and_load(scripted)
def test_incremental_state_multihead_attention(self):
... |
class Command(BaseCommand):
help = 'Create dataset'
def add_arguments(self, parser):
parser.add_argument('cnt_parts', type=int)
parser.add_argument('percent', type=int)
parser.add_argument('items_folder', type=str)
parser.add_argument('add_folder', type=str)
def handle(self, ... |
class TTRBase(TTR):
name = 'TTRBase'
def __init__(self, source, alpha: float=0.15, beta: float=0.8, epsilon: float=1e-05):
super().__init__(source, alpha, beta, epsilon)
self.p = dict()
self.r = {source: 1.0}
self._vis = set()
def push(self, node, edges: list, **kwargs):
... |
def batch_list_collate(collate_fn):
def collate_task(task):
if isinstance(task, TorchDataset):
return collate_fn([task[idx] for idx in range(len(task))])
elif isinstance(task, OrderedDict):
return OrderedDict([(key, collate_task(subtask)) for (key, subtask) in task.items()])
... |
class ToTensor(object):
def __init__(self):
self.to_tensor = torchvision.transforms.ToTensor()
def __call__(self, sample):
sample['image'] = self.to_tensor(sample['image'])
sal_ = self.to_tensor(sample['sal']).squeeze().long()
if (len(sal_.shape) == 3):
sample['sal'] ... |
def collect_frames(frame: FrameType) -> List[str]:
callstack = []
optional_frame: Optional[FrameType] = frame
while (optional_frame is not None):
callstack.append(frame_format(optional_frame))
optional_frame = optional_frame.f_back
callstack.reverse()
return callstack |
class AuthKeyExchange(object):
def __init__(self, privkey, onSuccess):
self.privkey = privkey
self.state = STATE_NONE
self.r = None
self.encgx = None
self.hashgx = None
self.ourKeyid = 1
self.theirPubkey = None
self.theirKeyid = 1
self.enc_c = ... |
def fuse_module(m):
last_conv = None
last_conv_name = None
for (name, child) in m.named_children():
if isinstance(child, (nn.BatchNorm2d, nn.SyncBatchNorm)):
if (last_conv is None):
continue
fused_conv = fuse_conv_bn(last_conv, child)
m._modules[la... |
class ArtifactStash():
def __enter__(self):
self.tmpdir = None
file_names = [VersionFN, BindingsFN, LibnameForSystem[Host.system]]
self.files = [fp for fp in [(ModuleDir_Raw / fn) for fn in file_names] if fp.exists()]
if (len(self.files) == 0):
return
self.tmpdir ... |
def test_hook_auto_num_workers_none(pytester: pytest.Pytester, monkeypatch: pytest.MonkeyPatch, monkeypatch_3_cpus) -> None:
from xdist.plugin import pytest_cmdline_main as check_options
monkeypatch.delenv('PYTEST_XDIST_AUTO_NUM_WORKERS', raising=False)
pytester.makeconftest('\n def pytest_xdist_auto... |
class Host(ObjectDefinition):
object_type = 'host'
objects = ObjectFetcher('host')
def acknowledge(self, sticky=1, notify=1, persistent=0, author='pynag', comment='acknowledged by pynag', recursive=False, timestamp=None):
if (timestamp is None):
timestamp = int(time.time())
if (r... |
def construct_groups(sources: list[BuildSource], separate: (bool | list[tuple[(list[str], (str | None))]]), use_shared_lib: bool) -> emitmodule.Groups:
if (separate is True):
groups: emitmodule.Groups = [([source], None) for source in sources]
elif isinstance(separate, list):
groups = []
... |
def test_get_query_text_handles_parameters_pq(s1_product: SentinelOne):
sdate = datetime.now()
edate = (sdate - timedelta(days=7))
s1_product._pq = True
s1_product._queries = {Tag('valueA'): [Query(sdate, edate, 'endpoint.name', 'contains', '"dc01"')]}
assert (s1_product._get_query_text() == [(Tag('... |
class ExportCommand(BaseExportCommand):
def handle(self) -> int:
if self.poetry.config.get('warnings.export'):
self.line_error("Warning: poetry-plugin-export will not be installed by default in a future version of Poetry.\nIn order to avoid a breaking change and make your automation forward-comp... |
def train(model, loaders, optimizer, n_epoch=200, max_step=0, log_every=0, eval_every=0, save_dir=None, writer=None, metrics=['loss']):
log.info('training...')
recorder = Recorder(metrics)
best_eval_loss = 10.0
step = 0
for epoch in range(n_epoch):
log.info('Epoch: {:03d}'.format(epoch))
... |
def pink(N, state=None):
state = (np.random.RandomState() if (state is None) else state)
uneven = (N % 2)
X = (state.randn((((N // 2) + 1) + uneven)) + (1j * state.randn((((N // 2) + 1) + uneven))))
S = np.sqrt((np.arange(len(X)) + 1.0))
y = irfft((X / S)).real
if uneven:
y = y[:(- 1)]
... |
def _process_encoding(arr: ndarray, encode_map: dict, name='query', token_map: Optional[dict]=None) -> Tensor:
arr = np.array(arr)
if (name == 'query'):
arr = np.insert(arr, 1, encode_map[name])
elif (name == 'product_id'):
arr = str(arr)[2:(- 1)]
arr = [token_map[x] for x in arr]
... |
(frozen=True)
class OrConstraint(AbstractConstraint):
constraints: Tuple[(AbstractConstraint, ...)]
def apply(self) -> Iterable[Constraint]:
grouped = [self._group_constraints(cons) for cons in self.constraints]
(left, *rest) = grouped
for (varname, constraints) in left.items():
... |
def batch_norm(input, is_training=True, momentum=0.9, epsilon=2e-05, in_place_update=True, name='batch_norm'):
if in_place_update:
return tf.contrib.layers.batch_norm(input, decay=momentum, center=True, scale=True, epsilon=epsilon, updates_collections=None, is_training=is_training, scope=name)
else:
... |
def _check_method_and_attr_name(node_type: str, name: str) -> List[str]:
error_msgs = []
if (not (_is_in_snake_case(name) or (name.startswith('__') and _is_in_snake_case(name[2:])))):
error_msgs.append(f"""{node_type.capitalize()} name "{name}" should be in snake_case format. {node_type.capitalize()} na... |
class TestVariableNameValue(TestNameCheckVisitorBase):
_passes()
def test(self):
from typing import Any, NewType
Uid = NewType('Uid', int)
def name_ends_with_uid(uid):
return uid
def some_func() -> Any:
return 42
def test(self, uid: Uid):
... |
class _OSA_module(nn.Module):
def __init__(self, in_ch, stage_ch, concat_ch, layer_per_block, module_name, SE=False, identity=False, depthwise=False, with_cp=True):
super(_OSA_module, self).__init__()
self.identity = identity
self.depthwise = depthwise
self.isReduced = False
... |
class TransitionLogAdmin(admin.ModelAdmin):
actions = None
date_hierarchy = 'timestamp'
list_display = ('modified_object', 'transition', 'from_state', 'to_state', 'user', 'timestamp')
list_filter = ('content_type', 'transition')
readonly_fields = ('user', 'modified_object', 'transition', 'timestamp'... |
class BrokenRepoTest(unittest.TestCase):
def makerp(self, path):
return rpath.RPath(Globals.local_connection, path)
def makeext(self, path):
return self.root.new_index(tuple(path.split('/')))
def testDuplicateMetadataTimestamp(self):
test_base_rp = self.makerp(abs_test_dir).append('d... |
def test_semicircle():
m = folium.Map([30.0, 0.0], zoom_start=3)
sc1 = plugins.SemiCircle((34, (- 43)), radius=400000, arc=300, direction=20, color='red', fill_color='red', opacity=0, popup='Direction - 20 degrees, arc 300 degrees')
sc2 = plugins.SemiCircle((46, (- 30)), radius=400000, start_angle=10, stop_... |
def sandia(v_dc, p_dc, inverter):
Paco = inverter['Paco']
Pnt = inverter['Pnt']
Pso = inverter['Pso']
power_ac = _sandia_eff(v_dc, p_dc, inverter)
power_ac = _sandia_limits(power_ac, p_dc, Paco, Pnt, Pso)
if isinstance(p_dc, pd.Series):
power_ac = pd.Series(power_ac, index=p_dc.index)
... |
def __do_unlink(ql: Qiling, absvpath: str) -> int:
def __has_opened_fd(hpath: str) -> bool:
opened_fds = (ql.os.fd[i] for i in range(NR_OPEN) if (ql.os.fd[i] is not None))
f = next((fd for fd in opened_fds if (getattr(fd, 'name', '') == hpath)), None)
return ((f is not None) and f.closed)
... |
class BaseHash(object):
algo = namedtuple('algo', ['crypt_id', 'salt_size', 'implicit_rounds', 'salt_exact', 'implicit_ident'])
algorithms = {'md5_crypt': algo(crypt_id='1', salt_size=8, implicit_rounds=None, salt_exact=False, implicit_ident=None), 'bcrypt': algo(crypt_id='2b', salt_size=22, implicit_rounds=12,... |
def alltoall(sendbuf, split_recvbuf=False):
if isinstance(sendbuf, numpy.ndarray):
mpi_dtype = comm.bcast(sendbuf.dtype.char)
sendbuf = numpy.asarray(sendbuf, mpi_dtype, 'C')
nrow = sendbuf.shape[0]
ncol = (sendbuf.size // nrow)
segsize = ((((nrow + pool.size) - 1) // pool.si... |
def CNN(include_top=True):
model = Sequential()
model.add(Convolution2D(96, kernel_size=(7, 7), strides=(2, 2), input_shape=IMSIZE, data_format='channels_last'))
print('Output shape:', model.output_shape)
model.add(BatchNormalization(axis=3))
model.add(Activation('relu'))
model.add(MaxPooling2D(... |
class BottleneckX(nn.Module):
expansion = 2
cardinality = 32
def __init__(self, inplanes, planes, stride=1, dilation=1):
super(BottleneckX, self).__init__()
cardinality = BottleneckX.cardinality
bottle_planes = ((planes * cardinality) // 32)
self.conv1 = nn.Conv2d(inplanes, b... |
class Critic(nn.Module):
def __init__(self, state_dim, action_dim, hidden_width):
super(Critic, self).__init__()
self.l1 = nn.Linear(((state_dim + action_dim) + 1), hidden_width)
self.l2 = nn.Linear(hidden_width, hidden_width)
self.l3 = nn.Linear(hidden_width, 1)
def forward(self... |
def decode_from_string(encoded_value: str, annotation: Any) -> Union[(Dict[(Any, Any)], List[Any], None)]:
if (not encoded_value):
return None
value_type = annotation
value_origin = typing_inspect.get_origin(value_type)
if (value_origin is dict):
return _decode_string_to_dict(encoded_val... |
def _add_hotspot_context(context: Dict[(str, Any)]) -> None:
context['hotspot_enabled'] = False
try:
if (subprocess.call(['/usr/local/sbin/raveberry/hotspot_enabled']) != 0):
context['hotspot_enabled'] = True
with open('/etc/hostapd/hostapd_protected.conf', encoding='utf-8') as h... |
def test_basic() -> None:
async def trivial(x: T) -> T:
return x
assert (_core.run(trivial, 8) == 8)
with pytest.raises(TypeError):
_core.run(trivial)
with pytest.raises(TypeError):
_core.run((lambda : None))
async def trivial2(x: T) -> T:
(await _core.checkpoint())
... |
class InteractionOperator(PolynomialTensor):
def __init__(self, constant, one_body_tensor, two_body_tensor):
super(InteractionOperator, self).__init__({(): constant, (1, 0): one_body_tensor, (1, 1, 0, 0): two_body_tensor})
def one_body_tensor(self):
return self.n_body_tensors[(1, 0)]
_body_t... |
def send_endpoints_to_pinpoint(endpoints: typing.Iterable[Endpoint]):
endpoint_chunks = chunks(list(endpoints), 100)
for endpoints_chunk in endpoint_chunks:
data = {'Item': [endpoint.to_item() for endpoint in endpoints_chunk]}
client = _get_client()
client.update_endpoints_batch(Applicat... |
def check_limitation(coded_version, msg):
coded_version_tuple = coded_version.split('.')
(coded_ma, coded_mi) = map(int, coded_version_tuple[0:2])
current_version_tuple = sys.version_info
(current_ma, current_mi) = current_version_tuple[0:2]
assert (not ((coded_ma < current_ma) or ((coded_ma == curr... |
class PassportElementErrorUnspecified(PassportElementError):
__slots__ = ('element_hash',)
def __init__(self, type: str, element_hash: str, message: str, *, api_kwargs: Optional[JSONDict]=None):
super().__init__('unspecified', type, message, api_kwargs=api_kwargs)
with self._unfrozen():
... |
def check_encoder_output(encoder_output, batch_size=None):
if (not isinstance(encoder_output, dict)):
msg = ('FairseqEncoderModel.forward(...) must be a dict' + _current_postion_info())
return (False, msg)
if ('encoder_out' not in encoder_output):
msg = ('FairseqEncoderModel.forward(...)... |
def make_grounding(qdmr, qdmr_name, dataset_break, verbose=True):
question = dataset_break.questions[qdmr_name]
if verbose:
print('Question:', question)
print(f'''QDMR:
{qdmr}''')
grounding = {}
for i_op in range(len(qdmr)):
op = qdmr.ops[i_op]
assert (op in op_grounder),... |
def main():
try:
myfile = rs.filesystem.File('srm://tbn18.nikhef.nl/dpm/nikhef.nl/home/vlemed/mark/radical.saga/input.txt')
print(myfile.get_size_self())
except rs.SagaException as ex:
print(('An error occured during file operation: %s' % str(ex)))
sys.exit((- 1)) |
class Class(Importable):
def check_and_return(self, value):
if inspect.isclass(value):
return value
value = super(Class, self).check_and_return(value)
if (not inspect.isclass(value)):
self._failure(('imported value should be a class, got %s' % value), value=value)
... |
('pypyr.moduleloader.get_module')
(Step, 'invoke_step')
def test_run_pipeline_steps_complex_with_description_in_params(mock_invoke_step, mock_get_module):
step = Step({'name': 'step1', 'description': 'test description', 'run': '{key5}', 'in': {'key5': True}})
context = Context({'key5': False})
with patch_lo... |
class ComplexSliderWidget(widgets.AxesWidget):
def __init__(self, ax, angle, r, animated=False):
(line,) = ax.plot([angle, angle], [0.0, r], linewidth=2.0)
super().__init__(ax)
self._rotator = line
self._is_click = False
self.animated = animated
self.update = (lambda ... |
def node_options(caller, raw_string, **kwargs):
text = "|cOption menu|n\n('|wq|nuit' to return)"
room = caller.location
options = caller.attributes.get('options', category=room.tagcategory, default={})
things_style = options.get('things_style', 2)
session = kwargs['session']
screenreader = sessi... |
class PolyvoreModel(object):
def __init__(self, config, mode, train_inception=False):
assert (mode in ['train', 'eval', 'inference'])
self.config = config
self.mode = mode
self.train_inception = train_inception
self.reader = tf.TFRecordReader()
self.initializer = tf.r... |
def convert(module, flag_name):
mod = module
before_ch = None
for (name, child) in module.named_children():
if (hasattr(child, flag_name) and getattr(child, flag_name)):
if isinstance(child, BatchNorm2d):
before_ch = child.num_features
mod.add_module(name,... |
class BLEUScorer(object):
def __init__(self):
pass
def score(self, parallel_corpus):
count = [0, 0, 0, 0]
clip_count = [0, 0, 0, 0]
r = 0
c = 0
weights = [0.25, 0.25, 0.25, 0.25]
for (hyps, refs) in parallel_corpus:
hyps = [hyp.split() for hyp ... |
class PenaltyLbfgsOptimizer(Serializable):
def __init__(self, max_opt_itr=20, initial_penalty=1.0, min_penalty=0.01, max_penalty=1000000.0, increase_penalty_factor=2, decrease_penalty_factor=0.5, max_penalty_itr=10, adapt_penalty=True):
Serializable.quick_init(self, locals())
self._max_opt_itr = max... |
def gen_dest_dep_test():
return [gen_ld_dest_dep_test(5, 'lw', 8192, 66051), gen_ld_dest_dep_test(4, 'lw', 8196, ), gen_ld_dest_dep_test(3, 'lw', 8200, ), gen_ld_dest_dep_test(2, 'lw', 8204, ), gen_ld_dest_dep_test(1, 'lw', 8208, ), gen_ld_dest_dep_test(0, 'lw', 8212, ), gen_word_data([66051, , , , , ])] |
class Blosc(Codec):
codec_id = 'imagecodecs_blosc'
def __init__(self, level=None, compressor=None, typesize=None, blocksize=None, shuffle=None, numthreads=None):
self.level = level
self.compressor = compressor
self.typesize = typesize
self.blocksize = blocksize
self.shuff... |
class SelfAttentionBlock2D(nn.Module):
def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):
super().__init__()
self.scale = scale
self.in_channels = in_channels
self.out_channels = out_channels
self.key_channels = key_channels
sel... |
class UpdateableAPIResource(APIResource):
def save(self, idempotency_key=None):
updated_params = self.serialize(None)
headers = populate_headers(idempotency_key)
if updated_params:
self.refresh_from(self.request('post', self.instance_path(), updated_params, headers))
else... |
class DiscriminatorFromCloud():
def __init__(self, name, n_filters=[64, 128, 128, 256], filter_size=1, stride=1, activation_fn=tf.nn.leaky_relu, norm_mtd='instance_norm', latent_code_dim=128):
self.name = name
self.n_filters = n_filters.copy()
self.n_filters.append(latent_code_dim)
s... |
.parametrize('proc_name,proc_pttrn,lines', [('s1', 'started', 21), ('s2', 'spam, bacon, eggs', 30), ('s3', 'finally started', 130)])
def test_startup_detection_max_read_lines(tcp_port, proc_name, proc_pttrn, lines, xprocess):
data = 'bacon\n'
class Starter(ProcessStarter):
pattern = proc_pttrn
m... |
class Plane(Shape):
def __init__(self, plane_fit, gridsize):
plane = numpy.array(plane_fit)
origin = ((- plane) / numpy.dot(plane, plane))
n = numpy.array([plane[1], plane[2], plane[0]])
u = numpy.cross(plane, n)
v = numpy.cross(plane, u)
u /= numpy.linalg.norm(u)
... |
def forward(scan, cad, negative, separation_model, completion_model, triplet_model, criterion_separation, criterion_completion, criterion_triplet, device):
(scan_model, scan_mask, scan_name) = (scan['content'], scan['mask'], scan['name'])
scan_bg_mask = torch.where((scan_mask == 0), scan_model, torch.zeros(scan... |
class Const():
triple_len = 3
home = ''
origin_train_folder = os.path.join(home, 'train')
origin_dev_folder = os.path.join(home, 'dev')
origin_all_train_filename = os.path.join(home, 'origin_all_train.xml')
origin_all_dev_filename = os.path.join(home, 'origin_all_dev.xml')
origin_tmp_filenam... |
def geth_prepare_datadir(datadir: str, genesis_file: str) -> None:
node_genesis_path = os.path.join(datadir, 'custom_genesis.json')
ipc_path = (datadir + '/geth.ipc')
assert (len(ipc_path) < 104), f'geth data path "{ipc_path}" is too large'
os.makedirs(datadir, exist_ok=True)
shutil.copy(genesis_fil... |
def test_ordered_enqueuer_processes():
enqueuer = OrderedEnqueuer(TestSequence([3, 200, 200, 3]), use_multiprocessing=True)
enqueuer.start(3, 10)
gen_output = enqueuer.get()
acc = []
for i in range(100):
acc.append(next(gen_output)[(0, 0, 0, 0)])
assert (acc == list(range(100))), 'Order ... |
class Erfc(UnaryScalarOp):
nfunc_spec = ('scipy.special.erfc', 1, 1)
def impl(self, x):
return scipy.special.erfc(x)
def L_op(self, inputs, outputs, grads):
(x,) = inputs
(gz,) = grads
if (x.type in complex_types):
raise NotImplementedError()
if (outputs[0... |
class IterativeContextReReadModel(MultipleContextModel):
def __init__(self, encoder: QuestionsAndParagraphsEncoder, word_embed: Optional[WordEmbedder], char_embed: Optional[CharWordEmbedder], embed_mapper: Optional[Union[(SequenceMapper, ElmoWrapper)]], sequence_encoder: SequenceEncoder, sentences_encoder: Sentence... |
def test_load_totp_vectors():
vector_data = textwrap.dedent('\n # TOTP Test Vectors\n # RFC 6238 Appendix B\n\n COUNT = 0\n TIME = 59\n TOTP = \n MODE = SHA1\n SECRET = \n\n COUNT = 1\n TIME = 59\n TOTP = \n MODE = SHA256\n SECRET = \n\n COUNT = 2\n TIME = 59\n TOTP = \n... |
class CodeStylePage(QWizardPage):
def __init__(self, parent=None):
super(CodeStylePage, self).__init__(parent)
self.setTitle('Code Style Options')
self.setSubTitle('Choose the formatting of the generated code.')
self.setPixmap(QWizard.LogoPixmap, QPixmap(':/images/logo2.png'))
... |
class PycodestyleChecker(BaseRawFileChecker):
name = 'pep8_errors'
msgs = {'E9989': ('Found pycodestyle (PEP8) style error at %s', 'pep8-errors', '')}
options = (('pycodestyle-ignore', {'default': (), 'type': 'csv', 'metavar': '<pycodestyle-ignore>', 'help': 'List of Pycodestyle errors to ignore'}),)
de... |
class PromptArea(QWidget):
def __init__(self, edit, get_text, highlighter):
super(PromptArea, self).__init__(edit)
self.setFixedWidth(0)
self.edit = edit
self.get_text = get_text
self.highlighter = highlighter
edit.updateRequest.connect(self.updateContents)
def pa... |
class AutoUpdateLayerMenuButton(QtWidgets.QPushButton):
def __init__(self, *args, m=None, layers=None, exclude=None, auto_text=False, **kwargs):
super().__init__(*args, **kwargs)
self.m = m
self._layers = layers
self._exclude = exclude
self._auto_text = auto_text
self... |
class TrainDataset(Dataset):
def __init__(self, args, raw_datasets, cache_root):
self.raw_datasets = raw_datasets
self.tab_processor = get_default_processor(max_cell_length=100, tokenizer=AutoTokenizer.from_pretrained(args.bert.location, use_fast=False), max_input_length=args.seq2seq.table_truncatio... |
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