code stringlengths 281 23.7M |
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def convert_to_nominalization(thought):
if ('Shawn started' in thought):
return "Shawn's initial possession of 5 toys and his receipt of 4 more toys from his parents results in a total of 9 toys. 5 + 4 = 9."
if ('There are originally 3 cars' in thought):
return 'The original count of 3 cars, wit... |
class IBMCloudStorage(_CloudStorage):
def __init__(self, context, hostname, is_secure, storage_path, access_key, secret_key, bucket_name, port=None, maximum_chunk_size_mb=None):
upload_params = {}
connect_kwargs = {'endpoint_url': _build_endpoint_url(hostname, port=port, is_secure=is_secure)}
... |
()
def daily_update_keywords(day=None):
(start_date, end_date) = get_day(day)
log.info('Updating KeywordImpression for %s-%s', start_date, end_date)
KeywordImpression.objects.using('default').filter(date__gte=start_date, date__lt=end_date).delete()
keyword_mapping = defaultdict((lambda : {'decisions': 0... |
class ResNet_Auxiliary(nn.Module):
def __init__(self, block, num_blocks, num_classes=100):
super(ResNet_Auxiliary, self).__init__()
self.backbone = CIFAR_ResNet(block, num_blocks, num_classes)
self.auxiliary_classifier = Auxiliary_Classifier(block, num_blocks, num_classes)
self.final... |
def last_n_checkpoints(paths, n, update_based, upper_bound=None):
assert (len(paths) == 1)
path = paths[0]
if update_based:
pt_regexp = re.compile('checkpoint_\\d+_(\\d+)\\.pt')
else:
pt_regexp = re.compile('checkpoint(\\d+)\\.pt')
files = PathManager.ls(path)
entries = []
fo... |
def _check_new_episode_scores(config, db, update_db):
info('Checking for new episode scores')
shows = db.get_shows(enabled=True)
for show in shows:
latest_episode = db.get_latest_episode(show)
if (latest_episode is not None):
info('For show {} ({}), episode {}'.format(show.name, ... |
def get_config():
config = get_default_configs()
training = config.training
training.batch_size = 64
training.n_iters = 2400001
training.snapshot_sampling = True
training.sde = 'vesde'
training.continuous = True
evaluate = config.eval
evaluate.num_samples = 50000
evaluate.ckpt_id... |
def importGetMutationData(lines):
mutaLinesMap = {}
currentMutaRef = None
currentMutaLines = []
consumedIndices = set()
def completeMutaLines():
if ((currentMutaRef is not None) and currentMutaLines):
mutaLinesMap[currentMutaRef] = currentMutaLines
for (i, line) in enumerate(... |
def train_video_predictor(cfg):
training_steps = 200000
frames = read_frames_from_dir(f'./images/video/{cfg.image_name}')
crop_size = (int((frames[0].shape[(- 2)] * 0.95)), int((frames[0].shape[(- 1)] * 0.95)))
train_dataset = FrameSet(frames=frames, crop_size=crop_size)
train_loader = DataLoader(tr... |
class GaussianMLPPolicy(StochasticPolicy, LasagnePowered, Serializable):
def __init__(self, env_spec, hidden_sizes=(32, 32), learn_std=True, init_std=1.0, adaptive_std=False, std_share_network=False, std_hidden_sizes=(32, 32), min_std=1e-06, std_hidden_nonlinearity=NL.tanh, hidden_nonlinearity=NL.tanh, output_nonli... |
def test_win_vars_set(windows, xdg_envs):
pp = platform.get_platform_paths('pypyr', 'config.yaml')
assert (pp == platform.PlatformPaths(config_user=Path('/ch//pypyr/config.yaml'), config_common=[Path('/cc/pypyr/config.yaml'), Path('/cc2/pypyr/config.yaml')], data_dir_user=Path('/dh/pypyr'), data_dir_common=[Pat... |
def test_finalize_strict_too_many_args():
(bb, x, y) = _get_bb()
(x2, y2) = bb.add(TestTwoBitOp(), ctrl=x, target=y)
bb.add_register_allowed = False
with pytest.raises(BloqError, match='Finalizing does not accept Soquets.*z.*'):
bb.finalize(x=x2, y=y2, z=Soquet(RightDangle, Register('asdf', 1))) |
.parametrize('filename, expected', [('foo.bar', 'foo.bar'), ('foo"bar', 'foo%22bar'), ('foo\x00bar', 'foo%00bar'), ('foobar");alert("attack!");', 'foobar%22);alert(%22attack!%22);')])
def test_generate_pdfjs_script(filename, expected):
expected_open = 'open("qute://pdfjs/file?filename={}");'.format(expected)
ac... |
def downsample_conv(in_channels, out_channels, kernel_size, stride=1, dilation=1, first_dilation=None, norm_layer=None):
norm_layer = (norm_layer or nn.BatchNorm2d)
kernel_size = (1 if ((stride == 1) and (dilation == 1)) else kernel_size)
first_dilation = ((first_dilation or dilation) if (kernel_size > 1) e... |
def exec_tests(linux: kunit_kernel.LinuxSourceTree, request: KunitExecRequest) -> KunitResult:
kunit_parser.print_with_timestamp('Starting KUnit Kernel ...')
test_start = time.time()
result = linux.run_kernel(args=request.kernel_args, timeout=(None if request.alltests else request.timeout), filter_glob=requ... |
_model_spec('named_tuple', 'attrs')
def test_generic_mixed_inheritance(model_spec):
_spec.decorator
class Parent1(*model_spec.bases):
a: int
_spec.decorator
class Parent2(*model_spec.bases, Generic[T]):
b: T
_spec.decorator
class Child12(Parent1, Parent2[str]):
c: bool
... |
class _Definition():
def __init__(self, *args: _Setting, mandatory: Set[str], prefix: str, switch_names: Mapping[(Optional[str], str)]=None) -> None:
self._settings = args
self.mandatory = mandatory
self.prefix = prefix
if (switch_names is not None):
self._switch_names = ... |
_on_failure
.parametrize('number_of_nodes', [3])
.parametrize('channels_per_node', [CHAIN])
def test_receive_lockedtransfer_invalidnonce(raiden_network: List[RaidenService], number_of_nodes, deposit, token_addresses, reveal_timeout, network_wait):
(app0, app1, app2) = raiden_network
token_address = token_addres... |
def get_cast_class(orig_cls, new_base_cls):
orig_module = inspect.getmodule(orig_cls)
new_cls_name = f'{CAST_CLASS_PREFIX}{orig_cls.__name__}'
if hasattr(orig_module, new_cls_name):
new_cls = getattr(orig_module, new_cls_name)
else:
new_cls = type(new_cls_name, (new_base_cls, orig_cls), ... |
class Image(SensorData):
def __init__(self, frame_number, width, height, image_type, fov, raw_data):
super(Image, self).__init__(frame_number=frame_number)
assert (len(raw_data) == ((4 * width) * height))
self.width = width
self.height = height
self.type = image_type
... |
_model
def resmlp_big_24_224(pretrained=False, **kwargs):
model_args = dict(patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4, block_layer=partial(ResBlock, init_values=1e-06), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_big_24_224', pretrained=pretrained, **model_args)
return model |
class Solution():
def checkSubarraySum(self, nums: List[int], k: int) -> bool:
remainders = dict()
remainders[0] = 0
pre_sum = 0
for (idx, item) in enumerate(nums):
pre_sum += item
remaind = (pre_sum % k)
if (remaind not in remainders):
... |
class Spinner(tqdm):
prefixes = ['/', '-', '\\', '|']
def __init__(self, title: str, refresh_interval: float=0.5):
def refresh_in_loop():
while (not self._stop.is_set()):
with self._lock:
self._index = ((self._index + 1) % len(self.prefixes))
... |
class Logger(object):
def __init__(self, fpath=None):
self.console = sys.stdout
self.file = None
if (fpath is not None):
mkdir_if_missing(osp.dirname(fpath))
self.file = open(fpath, 'w')
def __del__(self):
self.close()
def __enter__(self):
pass... |
def dumpAST(obj, ind=0, topnode=False):
indChar = ((('\t' * ind) + '-> ') if ind else '')
print((((indChar + '[') + obj.t) + ']'))
if (not (obj.title == '')):
print(((('\t' + indChar) + 'Title: ') + (obj.title or '')))
if (not (obj.info == '')):
print(((('\t' + indChar) + 'Info: ') + (ob... |
def concat_into_splits(dl_dataset, src, tgt, extracted_folders, to_folder, debug):
to_folder_tmp = f'{to_folder}_tmp'
os.makedirs(to_folder_tmp, exist_ok=True)
concat_files('train', src, tgt, extracted_folders, split_urls=dl_dataset.train_urls, path_patterns=dl_dataset.train_files_patterns, to_folder=to_fol... |
class TestObject(TestCase):
def test_dump_object(self):
obj = AllDumpable(AllDumpable())
exp = {'_par_c': 10, 'par_v': None, 'par_p': 12, 'c': 1, '_c': 2, 'c_n': None, '_c_n': None, 'child': None, 'v': 3, '_v': 4, 'v_n': None, '_v_n': None, 'p': 5, '_p': 5, 'p_n': None, '_p_n': None}
exp['ch... |
class BertCrfForSequenceLabeling(BertPreTrainedModel):
def __init__(self, config):
super(BertCrfForSequenceLabeling, self).__init__(config)
self.bert = BertModel(config)
if self.config.use_freezing:
self.bert = freezer.freeze_lm(self.bert)
self.dropout = nn.Dropout(config... |
class Logger(object):
def __init__(self, file_name: str=None, file_mode: str='w', should_flush: bool=True):
self.file = None
if (file_name is not None):
self.file = open(file_name, file_mode)
self.should_flush = should_flush
self.stdout = sys.stdout
self.stderr = ... |
class File():
def __init__(self, pathspec, *, format=None, optional=False):
if (format is None):
raise TypeError('Must provide a format.')
self.pathspec = pathspec
self.format = format
self.optional = optional
def __get__(self, obj, cls=None):
if (obj is None)... |
class Test_ab13bd():
A = np.array([[0.0, 1.0], [(- 0.5), (- 0.1)]])
B = np.array([[0.0], [1.0]])
C = np.eye(2)
D = np.zeros((2, 1))
(Ad, Bd, Cd, Dd, dt) = signal.cont2discrete((A, B, C, D), 0.1, method='zoh')
def test_no_change_args_ccase(self):
acopy = self.A.copy()
bcopy = self... |
class LuksFileSystem(LoopbackFileSystemMixin, FileSystem):
type = 'luks'
guids = ['CA7D7CCB-63ED-4C53-861C-CC']
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.luks_name = None
def detect(cls, source, description):
res = super().detect(source, descript... |
def test_error_message_with_parametrized_fixtures(pytester: Pytester) -> None:
pytester.copy_example('unittest/test_parametrized_fixture_error_message.py')
result = pytester.runpytest()
result.stdout.fnmatch_lines(['*test_two does not support fixtures*', '*TestSomethingElse::test_two', '*Function type: Test... |
class TFMobileViTInvertedResidual(tf.keras.layers.Layer):
def __init__(self, config: MobileViTConfig, in_channels: int, out_channels: int, stride: int, dilation: int=1, **kwargs) -> None:
super().__init__(**kwargs)
expanded_channels = make_divisible(int(round((in_channels * config.expand_ratio))), 8... |
class EDMLoss(nn.Module):
def __init__(self):
super(EDMLoss, self).__init__()
def forward(self, p_target, p_estimate):
assert (p_target.shape == p_estimate.shape)
cdf_target = torch.cumsum(p_target, dim=1)
cdf_estimate = torch.cumsum(p_estimate, dim=1)
cdf_diff = (cdf_est... |
class SymmetricButlerVolmer(BaseKinetics):
def __init__(self, param, domain, reaction, options, phase='primary'):
super().__init__(param, domain, reaction, options, phase)
def _get_kinetics(self, j0, ne, eta_r, T, u):
Feta_RT = ((self.param.F * eta_r) / (self.param.R * T))
return (((2 * ... |
.parametrize('dtype', ['f2', 'f4', 'f8'])
def test_read_bgen__gp_dtype(shared_datadir, dtype):
path = (shared_datadir / 'example.bgen')
ds = read_bgen(path, gp_dtype=dtype)
dtype = np.dtype(dtype)
assert (ds['call_genotype_probability'].dtype == dtype)
assert (ds['call_dosage'].dtype == dtype) |
class TargetExtractor():
def __init__(self, delimiter='', targets_string=None, targets_file=None, exclude_private_ips=False, sort_targets=False, exclude_cdn_ip_networks=False, retrieve_new_cdn_ip_data=False, write_to_disk=False):
self.delimiter = delimiter
self.targets_string = str(targets_string).s... |
def test_logq_mini_2_sample_2_var(parametric_grouped_approxes, three_var_model):
(cls, kw) = parametric_grouped_approxes
approx = cls([three_var_model.one, three_var_model.two], model=three_var_model, **kw)
logq = approx.logq
logq = approx.set_size_and_deterministic(logq, 2, 0)
logq.eval() |
_module()
class ConvFCBBoxHead(BBoxHead):
def __init__(self, num_shared_convs=0, num_shared_fcs=0, num_cls_convs=0, num_cls_fcs=0, num_reg_convs=0, num_reg_fcs=0, conv_out_channels=256, fc_out_channels=1024, conv_cfg=None, norm_cfg=None, *args, **kwargs):
super(ConvFCBBoxHead, self).__init__(*args, **kwargs... |
def test_dataframes():
pd = pytest.importorskip('pandas')
from streamz.dataframe import DataFrame
data = [{'x': i, 'y': (2 * i)} for i in range(10)]
s = Batch(example=[{'x': 0, 'y': 0}])
sdf = s.map((lambda d: toolz.assoc(d, 'z', (d['x'] + d['y'])))).to_dataframe()
assert isinstance(sdf, DataFra... |
def test_pype_args_with_out(mock_pipe):
context = Context({'parentkey': 'parentvalue', 'pype': {'name': 'pipe name', 'args': {'a': 'b'}, 'out': 'a'}})
context.parent = 'arb/dir'
with patch_logger('pypyr.steps.pype', logging.INFO) as mock_logger_info:
with get_arb_pipeline_scope(context):
... |
def kafka_service():
TOPIC = ('test-%i' % random.randint(0, 10000))
if (_kafka[0] is None):
if LAUNCH_KAFKA:
launch_kafka()
else:
raise pytest.skip.Exception('Kafka not available. To launch kafka use `export STREAMZ_LAUNCH_KAFKA=true`')
producer = ck.Producer({'bo... |
class BpfHeaderExtractor(FileExtractor):
filename = os.path.join(LINUX_ROOT, 'tools/include/uapi/linux/bpf.h')
def get_prog_types(self):
return self.get_enum('bpf_prog_type')
def get_map_types(self):
return self.get_enum('bpf_map_type')
def get_attach_types(self):
return self.get... |
def analyze_results(lm_results: Dict, patterns_graph) -> None:
total = 0
points = 0
total_syn = 0
total_lex = 0
total_both = 0
total_no = 0
points_syn = 0
points_lex = 0
points_both = 0
points_no = 0
points_by_edge = defaultdict(list)
edges_out = defaultdict(list)
avg... |
def _match_list(module_rule: Tuple[(List[str], str)], logger_name: str) -> Tuple[(int, Optional[str])]:
logger_modules_split = (logger_name.split('.') if logger_name else [])
modules_split: List[str] = module_rule[0]
level: str = module_rule[1]
if (logger_modules_split == modules_split):
return ... |
_manager.tracked
def index(request: WSGIRequest) -> HttpResponse:
from core import urls
context = base.context(request)
context['urls'] = urls.musiq_paths
context['additional_keywords'] = storage.get('additional_keywords')
context['forbidden_keywords'] = storage.get('forbidden_keywords')
context... |
class ObjectModel(BaseModel):
states: int
emission: npt.NDArray
transition: npt.NDArray
start: npt.NDArray
name: str = 'Default'
('emission', 'transition', 'start', pre=True)
def parse_array(cls, v, values):
return np.asarray(v, dtype=float)
('emission', 'transition', 'start', pr... |
def test_window_by_interval__multiple_contigs():
ds = simulate_genotype_call_dataset(n_variant=10, n_sample=3, n_contig=2)
ds['variant_position'] = (['variants'], np.array([1, 4, 6, 8, 12, 1, 21, 25, 40, 55]))
assert (not has_windows(ds))
ds['interval_contig_name'] = (['intervals'], np.array(['0', '0', ... |
class WindowWriteTest(unittest.TestCase):
def setUp(self):
self.tempdir = tempfile.mkdtemp()
def tearDown(self):
shutil.rmtree(self.tempdir)
.gdalbin
def test_write_window(self):
name = os.path.join(self.tempdir, 'test_write_window.tif')
a = (np.ones((50, 50), dtype=raste... |
def create_datasets(args):
div2k = DIV2K(os.path.join(args.data_path, 'DIV2K/DIV2K_train_HR'), os.path.join(args.data_path, 'DIV2K/DIV2K_train_LR_bicubic'), os.path.join(args.data_path, 'div2k_cache'), train=True, augment=args.data_augment, scale=args.scale, colors=args.colors, patch_size=args.patch_size, repeat=ar... |
class TranslationRoutingTest(TranslationTestMixin, RapidTest):
apps = [app.TranslationApp]
def test_translation_override(self):
en_conn = self.create_lang_connection('', 'en')
es_conn = self.create_lang_connection('', 'es')
self.receive('lang-hello', en_conn)
self.receive('lang-h... |
class AttrVI_ATTR_PXI_DEST_TRIG_BUS(RangeAttribute):
resources = [(constants.InterfaceType.pxi, 'BACKPLANE')]
py_name = ''
visa_name = 'VI_ATTR_PXI_DEST_TRIG_BUS'
visa_type = 'ViInt16'
default = (- 1)
(read, write, local) = (True, True, True)
(min_value, max_value, values) = (1, 3, [(- 1)]) |
class _NameAttributeMapping(MutableMapping):
def __init__(self, name: Name) -> None:
self._name = name
def __getitem__(self, key: t.Union[(bytes, str)]) -> tuples.GetNameAttributeResult:
if isinstance(key, str):
key = key.encode(_utils._get_encoding())
res = rname_rfc6680.get... |
def test_create_right_lane_split_second_lane():
lanedef = xodr.LaneDef(10, 20, 1, 2, 2)
lanes = xodr.create_lanes_merge_split([lanedef], 0, 30, xodr.std_roadmark_solid_solid(), 3, 3)
assert (len(lanes.lanesections) == 3)
assert (lanes.lanesections[0].s == 0)
assert (lanes.lanesections[1].s == 10)
... |
def test_add_timer(bot):
global_bot = bot
timer1_calls = 0
timer2_calls = 0
timer3_calls = 0
def timer1():
nonlocal timer1_calls
timer1_calls += 1
def timer2():
nonlocal timer2_calls
timer2_calls += 1
def timer3(bot):
nonlocal timer3_calls
time... |
class _ZipPkgWriter(PhysPkgWriter):
def __init__(self, pkg_file):
super(_ZipPkgWriter, self).__init__()
self._zipf = ZipFile(pkg_file, 'w', compression=ZIP_DEFLATED)
def close(self):
self._zipf.close()
def write(self, pack_uri, blob):
self._zipf.writestr(pack_uri.membername, ... |
.filterwarnings('ignore:Constructing a DIA matrix')
class TestExpm(UnaryOpMixin):
def op_numpy(self, matrix):
return scipy.linalg.expm(matrix)
shapes = shapes_square()
bad_shapes = shapes_not_square()
specialisations = [pytest.param(data.expm_csr, CSR, CSR), pytest.param(data.expm_csr_dense, CSR... |
def gen_property_setter_ir(builder: IRBuilder, func_decl: FuncDecl, cdef: ClassDef, is_trait: bool) -> FuncIR:
name = func_decl.name
builder.enter(name)
self_reg = builder.add_argument('self', func_decl.sig.args[0].type)
value_reg = builder.add_argument('value', func_decl.sig.args[1].type)
assert na... |
def merge_sim_episode_with_object_config(sim_config, episode):
sim_config = merge_sim_episode_config(sim_config, episode)
sim_config.defrost()
object_templates = {}
for template in episode.object_templates:
object_templates[template['object_key']] = template['object_template']
objects = []
... |
class MlpWithDepthwiseConv(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0, extra_relu=False):
super().__init__()
out_features = (out_features or in_features)
hidden_features = (hidden_features or in_features)
self.fc1 ... |
def _summary(self, to_stdout=True, return_df=False):
lengths = {}
total_lengths = {}
lengths['pyrange'] = self.lengths(as_dict=True)
total_lengths['pyrange'] = [self.length]
if self.stranded:
c = self.merge(strand=True)
lengths['coverage_forward'] = c['+'].lengths(as_dict=True)
... |
class FDCapture(FDCaptureBinary):
EMPTY_BUFFER = ''
def snap(self) -> str:
self._assert_state('snap', ('started', 'suspended'))
self.tmpfile.seek(0)
res = self.tmpfile.read()
self.tmpfile.seek(0)
self.tmpfile.truncate()
return res
def writeorg(self, data: str)... |
def scatter_kwargs(inputs, kwargs, target_gpus, dim=0):
inputs = (scatter(inputs, target_gpus, dim) if inputs else [])
kwargs = (scatter(kwargs, target_gpus, dim) if kwargs else [])
if (len(inputs) < len(kwargs)):
inputs.extend([() for _ in range((len(kwargs) - len(inputs)))])
elif (len(kwargs) ... |
class _LazyDescr(object):
def __init__(self, name):
self.name = name
def __get__(self, obj, tp):
result = self._resolve()
setattr(obj, self.name, result)
try:
delattr(obj.__class__, self.name)
except AttributeError:
pass
return result |
class fashion200k():
def __init__(self, path, split='train'):
super()
self.split = split
self.path = path
label_path = 'datasets/fashion200k/labels/'
print('Processing {} set'.format(split))
label_files = glob.glob((((label_path + '*_') + split) + '_*.txt'))
l... |
class MAGNA(nn.Module):
def __init__(self, g: DGLGraph, num_layers: int, input_dim: int, hidden_dim: int, hop_num: int, alpha: float, num_classes: int, heads: list, top_k: int, feat_drop: float, attn_drop: float, negative_slope: float, edge_drop: float, topk_type: str, self_loop_number: int, undirected_graph=True, ... |
def main():
(n_actors, replay_ip) = get_environ()
args = argparser()
batch_queue = Queue(maxsize=args.queue_size)
prios_queue = Queue(maxsize=args.prios_queue_size)
param_queue = Queue(maxsize=3)
procs = [Process(target=train, args=(args, n_actors, batch_queue, prios_queue, param_queue)), Proces... |
()
def notify_of_completed_flights():
cutoff = (get_ad_day() - datetime.timedelta(days=1))
completed_flights_by_advertiser = defaultdict(list)
for flight in Flight.objects.filter(live=True).select_related():
if (flight.hard_stop and (flight.end_date <= cutoff.date())):
log.info('Flight %... |
def _wasserstein_compute(x: torch.Tensor, y: torch.Tensor, x_weights: Optional[torch.Tensor], y_weights: Optional[torch.Tensor]) -> torch.Tensor:
device = x.device
x_sorter = torch.argsort(x)
y_sorter = torch.argsort(y)
all_values = torch.concatenate((x, y))
(all_values, _) = torch.sort(all_values)
... |
class GradientEditorItem(TickSliderItem):
sigGradientChanged = QtCore.Signal(object)
sigGradientChangeFinished = QtCore.Signal(object)
def __init__(self, *args, **kargs):
self.currentTick = None
self.currentTickColor = None
self.rectSize = 15
self.gradRect = QtWidgets.QGraphi... |
class BugzillaError(Exception):
def get_bugzilla_error_string(exc):
return getattr(exc, 'faultString', str(exc))
def get_bugzilla_error_code(exc):
for propname in ['faultCode', 'code']:
if hasattr(exc, propname):
return getattr(exc, propname)
return None
d... |
class BaseDataset(data.Dataset):
def __init__(self):
super(BaseDataset, self).__init__()
def name(self):
return 'BaseDataset'
def modify_commandline_options(parser, is_train):
return parser
def initialize(self, opt):
pass
def __len__(self):
return 0 |
def construct_jacobian(y, x, retain_graph=False):
x_grads = []
for (idx, y_element) in enumerate(y.flatten()):
if (x.grad is not None):
x.grad.zero_()
y_element.backward(retain_graph=(retain_graph or (idx < (y.numel() - 1))))
x_grads.append(x.grad.clone()[1])
return torch... |
.skipif((not JSON5_ENABLED), reason='test requires json5')
.parametrize('passing_data', [True, False])
def test_json5_reference(run_line, tmp_path, passing_data):
main_schemafile = (tmp_path / 'main_schema.json')
main_schemafile.write_text(json.dumps(JSON5_REF_MAIN_SCHEMA))
ref_schema = (tmp_path / 'title_s... |
class BatchNorm(nn.BatchNorm2d):
def __init__(self, num_features, eps=1e-05, momentum=0.1, weight_freeze=False, bias_freeze=False, weight_init=1.0, bias_init=0.0, **kwargs):
super().__init__(num_features, eps=eps, momentum=momentum)
if (weight_init is not None):
nn.init.constant_(self.we... |
class RuleEnvironment(object):
__slots__ = ('idx', 'property_value_lists')
def __init__(self, idx, property_value_lists):
self.idx = idx
self.property_value_lists = property_value_lists
def append_pair_properties(self, property_values1, property_values2):
if (not (property_values1 an... |
class UnpackType(ProperType):
__slots__ = ['type', 'from_star_syntax']
def __init__(self, typ: Type, line: int=(- 1), column: int=(- 1), from_star_syntax: bool=False) -> None:
super().__init__(line, column)
self.type = typ
self.from_star_syntax = from_star_syntax
def accept(self, vis... |
def derivatives_in_toroidal_coordinates():
Print_Function()
a = symbols('a', real=True)
coords = (u, v, phi) = symbols('u v phi', real=True)
(t3d, eu, ev, ephi) = Ga.build('e_u e_v e_phi', X=[(((a * sinh(v)) * cos(phi)) / (cosh(v) - cos(u))), (((a * sinh(v)) * sin(phi)) / (cosh(v) - cos(u))), ((a * sin(... |
def get_sentiment(df, emotions, other_emotions, min_len=1):
data = []
for sentiment in tqdm(emotions):
res = df[df['text'].str.contains(sentiment, na=False)]
for ind in range(len(res)):
try:
t = normalize_text(res.iloc[ind].text)
if (not set(t).isdisjo... |
class FlaxCodeGenRLForCausalLMModule(FlaxCodeGenForCausalLMModule):
config: CodeGenRLConfig
dtype: jnp.dtype = jnp.float32
def setup(self):
self.transformer = FlaxCodeGenModule(self.config, dtype=self.dtype)
self.lm_head = pnn.Dense(self.config.vocab_size, dtype=self.dtype, kernel_init=jax.n... |
_fixtures(MigrateFixture)
def test_how_migration_works(fixture):
some_object = fixture.some_object
class Migration1(Migration):
def schedule_upgrades(self):
self.schedule('drop_fk', some_object.do_something, 'drop_fk-1')
self.schedule('data', some_object.do_something, 'data-1')
... |
(frozen=True)
class Mark():
name: str
args: Tuple[(Any, ...)]
kwargs: Mapping[(str, Any)]
_param_ids_from: Optional['Mark'] = dataclasses.field(default=None, repr=False)
_param_ids_generated: Optional[Sequence[str]] = dataclasses.field(default=None, repr=False)
def __init__(self, name: str, args... |
class DistillationTrainer(Trainer):
def compute_loss(self, model, inputs, return_outputs=False):
target_p = inputs['labels']
outputs = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
logits = outputs[0]
loss = (- torch.sum((target_p * logits.log_softmax(dim=(- 1))... |
def add_kernel_test(cls, kernel, dim, name=None, expect=None, inputs=None, devices=['cpu']):
for device in devices:
def test_func(self):
args = []
if inputs:
args.extend(inputs)
if expect:
result = wp.array(expect, dtype=int, device=device)... |
class StreamInfo(MetadataBlock, mutagen.StreamInfo):
code = 0
bitrate = 0
def __eq__(self, other):
try:
return ((self.min_blocksize == other.min_blocksize) and (self.max_blocksize == other.max_blocksize) and (self.sample_rate == other.sample_rate) and (self.channels == other.channels) an... |
class GeoLocalizedModel(models.Model):
latitude = models.DecimalField(_('latitude'), max_digits=9, decimal_places=6, blank=True, null=True)
longitude = models.DecimalField(_('longitude'), max_digits=9, decimal_places=6, blank=True, null=True)
map_link = models.URLField(_('map link'), blank=True)
class M... |
class bdist_rpm(orig.bdist_rpm):
def run(self):
SetuptoolsDeprecationWarning.emit('Deprecated command', '\n bdist_rpm is deprecated and will be removed in a future version.\n Use bdist_wheel (wheel packages) instead.\n ', see_url=' due_date=(2023, 10, 30))
self.run_c... |
class DSAKey(common.PK):
keyType = 0
def __init__(self, key=None, private=False):
self.priv = self.pub = None
if (not isinstance(key, tuple)):
raise TypeError('4/5-tuple required for key')
if ((len(key) == 5) and private):
self.priv = DSA.construct(key)
... |
def test_inheritance_then_decorate():
calls = []
class Inheriting(MethodBasedConfigurable):
pass
def Concrete(*args, **kwargs):
calls.append((args, kwargs))
assert callable(Concrete.handler)
t = Concrete('foo', bar='baz')
assert callable(t.handler)
assert (len(calls) == 0)
... |
class Power_Widgets(object):
def left_grey(self):
return Image(scale=True, filename='~/.config/qtile/power/bar01.png')
def right_grey(self):
return Image(scale=True, filename='~/.config/qtile/power/bar06.png')
def black_red(self):
return Image(scale=True, filename='~/.config/qtile/po... |
.patch(PATCH_METHOD)
.patch('pynamodb.connection.base.uuid')
def test_signal_exception_post_signal(mock_uuid, mock_req):
pre_recorded = []
UUID = '123-abc'
def record_pre_dynamodb_send(sender, operation_name, table_name, req_uuid):
pre_recorded.append((operation_name, table_name, req_uuid))
def ... |
def response_factory():
def create_response(data, status_code=200, content_type='application/json'):
fp = BytesIO(data)
raw = HTTPResponse(fp, preload_content=False)
resp = Response()
resp.headers = CaseInsensitiveDict({'Content-Type': content_type})
resp.status_code = status... |
class Request(object):
def __init__(self, client: CDPSession, requestId: Optional[str], interceptionId: Optional[str], isNavigationRequest: bool, allowInterception: bool, url: str, resourceType: str, payload: dict, frame: Optional[Frame], redirectChain: List['Request']) -> None:
self._client = client
... |
def handle_action_init_mediator(chain_state: ChainState, state_change: ActionInitMediator) -> TransitionResult[ChainState]:
transfer = state_change.from_transfer
secrethash = transfer.lock.secrethash
token_network_address = transfer.balance_proof.token_network_address
return subdispatch_mediatortask(cha... |
def main(args):
with open(args.dataset_info, 'rb') as rf:
dataset_info = pickle.load(rf)
tokenizer = MarianTokenizer.from_pretrained(args.model_string)
tokenizer.add_special_tokens({'pad_token': PAD_TOKEN})
pad_id = tokenizer.encode(PAD_TOKEN)[0]
model = MarianMTModel.from_pretrained(args.mo... |
def backupJson(gen_data_dir, sp):
sp_dir = ((gen_data_dir + sp) + '/')
task_fds = os.listdir(sp_dir)
for task in task_fds:
task_dir = ((sp_dir + task) + '/')
trial_fds = os.listdir(task_dir)
for trial in trial_fds:
new_fn = ((task_dir + trial) + '/traj_data_backup.json')
... |
class KJTInputWrapper(torch.nn.Module):
def __init__(self, module_kjt_input: torch.nn.Module) -> None:
super().__init__()
self._module_kjt_input = module_kjt_input
self.add_module('_module_kjt_input', self._module_kjt_input)
def forward(self, keys: List[str], values: torch.Tensor, weight... |
class MovingBatchNormNd(nn.Module):
def __init__(self, num_features, eps=0.0001, decay=0.1, bn_lag=0.0, affine=True, sync=False):
super(MovingBatchNormNd, self).__init__()
self.num_features = num_features
self.sync = sync
self.affine = affine
self.eps = eps
self.decay... |
class DisplayOptionalPage(DisplayPage):
def __init__(self, parent, tabname, helptext, waittime, command=None):
logger.debug('%s: OptionalPage args: (waittime: %s, command: %s)', self.__class__.__name__, waittime, command)
DisplayPage.__init__(self, parent, tabname, helptext)
self.command = c... |
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