code stringlengths 281 23.7M |
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def main(train_file, valid_file, test_file, embeddings_file, target_dir, hidden_size=300, dropout=0.5, num_classes=2, epochs=64, batch_size=32, lr=0.0004, patience=5, max_grad_norm=10.0, checkpoint=None, proportion=1, output=None):
device = torch.device(('cuda:0' if torch.cuda.is_available() else 'cpu'))
print(... |
class SASLexer(RegexLexer):
name = 'SAS'
aliases = ['sas']
filenames = ['*.SAS', '*.sas']
mimetypes = ['text/x-sas', 'text/sas', 'application/x-sas']
url = '
version_added = '2.2'
flags = (re.IGNORECASE | re.MULTILINE)
builtins_macros = ('bquote', 'nrbquote', 'cmpres', 'qcmpres', 'compst... |
class PointwiseSamplerV2(Sampler):
def __init__(self, dataset, batch_size=1024, shuffle=True, drop_last=False):
super(Sampler, self).__init__()
self.batch_size = batch_size
self.drop_last = drop_last
self.shuffle = shuffle
self.item_num = dataset.num_items
self.user_p... |
class TimeSimulation():
def __init__(self, hamiltonian, method='split-step'):
self.H = hamiltonian
implemented_solvers = ('split-step', 'split-step-cupy', 'crank-nicolson', 'crank-nicolson-cupy')
if (method == 'split-step'):
if (self.H.potential_type == 'grid'):
s... |
def allow_access_to_confdir(confdir, allow):
from errno import EEXIST
if allow:
try:
os.makedirs(confdir)
except OSError as err:
if (err.errno != EEXIST):
print('This configuration directory could not be created:')
print(confdir)
... |
class SampleClassIDsUniformlyTest(tf.test.TestCase):
def test_num_ways_respected(self):
num_classes = test_utils.MAX_WAYS_UPPER_BOUND
num_ways = test_utils.MIN_WAYS
for _ in range(10):
class_ids = sampling.sample_class_ids_uniformly(num_ways, num_classes)
self.assertL... |
.requires_internet
def test_pre_install_commands(hatch, helpers, temp_dir, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0), resul... |
def summary_bssids(pkts, res):
table_data = [['SSID', 'BSSID', 'CHANNEL', 'DBM', 'ENCRYPTED', 'ENCRYPTION TYPE']]
dot11beacon_packets_count = 0
for pkt in pkts:
if pkt.haslayer(Dot11Beacon):
dot11beacon_packets_count += 1
stats = packet[Dot11Beacon].network_stats()
... |
.parametrize('sparse', [False, True], ids=['Dense', 'Sparse'])
def test_eigen_transform(sparse):
a = qutip.destroy(5)
f = (lambda t: t)
op = qutip.QobjEvo([(a * a.dag()), [(a + a.dag()), f]])
eigenT = _EigenBasisTransform(op, sparse=sparse)
evecs_qevo = eigenT.as_Qobj()
for t in [0, 1, 1.5]:
... |
def _tensor_test_case(dtype: torch.dtype, shape: List[int], logical_path: str, rank: int, replicated: bool) -> Tuple[(torch.Tensor, Entry, List[WriteReq])]:
tensor = rand_tensor(shape, dtype=dtype)
(entry, wrs) = prepare_write(obj=tensor, logical_path=logical_path, rank=rank, replicated=replicated)
return (... |
class PipelineIterator(IterableDataset):
def __init__(self, loader, infer, params, loader_batch_size=None):
self.loader = loader
self.infer = infer
self.params = params
if (loader_batch_size == 1):
loader_batch_size = None
self.loader_batch_size = loader_batch_siz... |
class DepthToNormalNode(topic_tools.LazyTransport):
def __init__(self):
super().__init__()
self._pub_normal = self.advertise('~output/normal', Image, queue_size=1)
self._pub_jet = self.advertise('~output/jet', Image, queue_size=1)
self._post_init()
def subscribe(self):
su... |
_function('mypy_extensions.i16')
def translate_i16(builder: IRBuilder, expr: CallExpr, callee: RefExpr) -> (Value | None):
if ((len(expr.args) != 1) or (expr.arg_kinds[0] != ARG_POS)):
return None
arg = expr.args[0]
arg_type = builder.node_type(arg)
if is_int16_rprimitive(arg_type):
retu... |
class FixExec(fixer_base.BaseFix):
BM_compatible = True
PATTERN = "\n exec_stmt< 'exec' a=any 'in' b=any [',' c=any] >\n |\n exec_stmt< 'exec' (not atom<'(' [any] ')'>) a=any >\n "
def transform(self, node, results):
assert results
syms = self.syms
a = results['a']
... |
class TensorDictBase(MutableMapping):
_safe = False
_lazy = False
_inplace_set = False
is_meta = False
_is_locked = False
_cache = None
def __bool__(self) -> bool:
raise RuntimeError('Converting a tensordict to boolean value is not permitted')
def __ne__(self, other: object) -> T... |
class FixtureManager():
FixtureLookupError = FixtureLookupError
FixtureLookupErrorRepr = FixtureLookupErrorRepr
def __init__(self, session: 'Session') -> None:
self.session = session
self.config: Config = session.config
self._arg2fixturedefs: Final[Dict[(str, List[FixtureDef[Any]])]]... |
def show_array_list(array_list, title):
(fig, ax) = plt.subplots(1, 1)
if isinstance(array_list[0][0], str):
integers = []
for array in array_list:
integers.append(list(map((lambda s: ord(s)), array)))
ax.imshow(np.array(integers), cmap=plt.cm.Oranges)
else:
ax.im... |
class CheetahXmlLexer(DelegatingLexer):
name = 'XML+Cheetah'
aliases = ['xml+cheetah', 'xml+spitfire']
mimetypes = ['application/xml+cheetah', 'application/xml+spitfire']
url = '
version_added = ''
def __init__(self, **options):
super().__init__(XmlLexer, CheetahLexer, **options) |
.parametrize('multi_optimziers, max_iters', [(True, 10), (True, 2), (False, 10), (False, 2)])
def test_one_cycle_runner_hook(multi_optimziers, max_iters):
with pytest.raises(AssertionError):
OneCycleLrUpdaterHook(max_lr=0.1, by_epoch=True)
with pytest.raises(ValueError):
OneCycleLrUpdaterHook(ma... |
class Input():
def __init__(self, definition: (Definition | None)=None) -> None:
self._definition: Definition
self._stream: TextIO = None
self._options: dict[(str, Any)] = {}
self._arguments: dict[(str, Any)] = {}
self._interactive: (bool | None) = None
if (definition... |
class RestrictedImageNet(DataSet):
def __init__(self, data_path, **kwargs):
name = 'restricted_imagenet'
super(RestrictedImageNet, self).__init__(name)
self.data_path = data_path
self.mean = constants.IMAGENET_MEAN
self.std = constants.IMAGENET_STD
self.num_classes = ... |
class FreshSeedPrioritizerWorklist(SeedScheduler):
def __init__(self, manager: 'SeedManager'):
self.manager = manager
self.fresh = []
self.worklist = dict()
def __len__(self) -> int:
s = set()
for seeds in list(self.worklist.values()):
s.update(seeds)
... |
.end_to_end()
def test_parametrization_in_for_loop_from_decorator(tmp_path, runner):
source = '\n import pytask\n\n for i in range(2):\n\n .task(name="deco_task", kwargs={"i": i, "produces": f"out_{i}.txt"})\n def example(produces, i):\n produces.write_text(str(i))\n '
tmp_path... |
class TripletMarginLoss(BaseMetricLossFunction):
def __init__(self, margin=0.05, swap=False, smooth_loss=False, triplets_per_anchor='all', **kwargs):
super().__init__(**kwargs)
self.margin = margin
self.swap = swap
self.smooth_loss = smooth_loss
self.triplets_per_anchor = tri... |
class AioClient(BaseClient):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs, isasync=True)
self._closed = False
self._events = {}
async def register_event(self, event: str, func: callable, args=None):
if (args is None):
args = {}
if (not... |
def test_direct(debug_ctx, debug_trail, extra_policy, trail_select):
loader_getter = make_loader_getter(shape=shape(TestField('a', ParamKind.POS_OR_KW, is_required=True), TestField('b', ParamKind.POS_OR_KW, is_required=True)), name_layout=InputNameLayout(crown=InpDictCrown({'a': InpFieldCrown('a'), 'b': InpFieldCro... |
def grokverify(input):
storageSuccessFlag = True
success = True
if dsz.file.Exists('tm154d.da', ('%s\\..\\temp' % systemPath)):
dsz.ui.Echo('tm154d.da dump file exists ... this should not be here', dsz.ERROR)
if dsz.file.Exists('tm154p.da', ('%s\\..\\temp' % systemPath)):
dsz.ui.Echo('tm... |
class OpenExecutor(ActionExecutor):
def __init__(self, close: bool):
self.close = close
def execute(self, script: Script, state: EnvironmentState, info: ExecutionInfo, char_index, modify=True, in_place=False):
current_line = script[0]
info.set_current_line(current_line)
node = st... |
class SDE(abc.ABC):
def __init__(self, N):
super().__init__()
self.N = N
def T(self):
pass
def sde(self, x, t):
pass
def marginal_prob(self, x, t):
pass
def prior_sampling(self, rng, shape):
pass
def prior_logp(self, z):
pass
def discre... |
class TestExactCover(QiskitOptimizationTestCase):
def setUp(self):
super().setUp()
input_file = self.get_resource_path('sample.exactcover')
with open(input_file, encoding='utf8') as file:
self.list_of_subsets = json.load(file)
(self.qubit_op, _) = exact_cover.get_oper... |
def _organize_tasks(tasks: list[PTaskWithPath]) -> dict[(Path, list[PTaskWithPath])]:
dictionary: dict[(Path, list[PTaskWithPath])] = defaultdict(list)
for task in tasks:
dictionary[task.path].append(task)
sorted_dict = {}
for k in sorted(dictionary):
sorted_dict[k] = sorted(dictionary[k... |
def setUpModule():
global cell, kpts, disp
cell = gto.Cell()
cell.atom = [['C', [0.0, 0.0, 0.0]], ['C', [1., 1., 1.]]]
cell.a = '\n 0., 3., 3.\n 3., 0., 3.\n 3., 3., 0.'
cell.basis = [[0, [1.3, 1]], [1, [0.8, 1]]]
cell.verbose = 5
cell.pseudo = 'gth-pade'
cell.unit = 'bohr'
... |
def test():
spi = SPI(1, baudrate=, sck=Pin(14), mosi=Pin(13))
display = Display(spi, dc=Pin(4), cs=Pin(16), rst=Pin(17))
display.draw_image('images/RaspberryPiWB128x128.raw', 0, 0, 128, 128)
sleep(2)
display.draw_image('images/MicroPython128x128.raw', 0, 129, 128, 128)
sleep(2)
display.draw... |
def ext_modules():
if have_c_files:
source_extension = 'c'
else:
source_extension = 'pyx'
ext_modules = [Extension('pymssql._mssql', [join('src', 'pymssql', ('_mssql.%s' % source_extension))], extra_compile_args=['-DMSDBLIB'], include_dirs=include_dirs, library_dirs=library_dirs), Extension(... |
class ScaledDotAttention(nn.Module):
def __init__(self):
super(ScaledDotAttention, self).__init__()
self.eps = np.finfo(float).eps
self.max = torch.finfo().max
self.act_fn = nn.Softmax(dim=(- 1))
def forward(self, Query, Key, Value, mask=None):
hidden_size = Key.size()[1]... |
def is_simple_literal(t: ProperType) -> bool:
if isinstance(t, LiteralType):
return (t.fallback.type.is_enum or (t.fallback.type.fullname == 'builtins.str'))
if isinstance(t, Instance):
return ((t.last_known_value is not None) and isinstance(t.last_known_value.value, str))
return False |
class PenParameterItem(GroupParameterItem):
def __init__(self, param, depth):
self.defaultBtn = self.makeDefaultButton()
super().__init__(param, depth)
self.itemWidget = QtWidgets.QWidget()
layout = QtWidgets.QHBoxLayout()
layout.setContentsMargins(0, 0, 0, 0)
layout.... |
(cc=STDCALL, params={'hWnd': HWND, 'lpdwProcessId': LPDWORD})
def hook_GetWindowThreadProcessId(ql: Qiling, address: int, params):
target = params['hWnd']
if ((target == ql.os.profile.getint('KERNEL', 'pid')) or (target == ql.os.profile.getint('KERNEL', 'shell_pid'))):
pid = ql.os.profile.getint('KERNEL... |
class Effect6720(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.drones.filteredItemBoost((lambda mod: mod.item.requiresSkill('Drones')), 'shieldBonus', src.getModifiedItemAttr('shipBonusMC'), skill='Minmatar Cruiser', **kwargs)
fit.drones.filteredIte... |
class Isotope(Molecule):
def __init__(self, molecule_name, isotope, isotope_name='', abundance=None, **kwargs):
super(Isotope, self).__init__(name=molecule_name, **kwargs)
if (not isinstance(isotope, int)):
raise TypeError('Wrong format for isotope {0}: expected int, got {1}'.format(isot... |
def render_pep8_errors_e116(msg, _node, source_lines=None):
line = msg.line
curr_idx = (len(source_lines[(line - 1)]) - len(source_lines[(line - 1)].lstrip()))
(yield from render_context((line - 2), line, source_lines))
(yield (line, slice(0, curr_idx), LineType.ERROR, source_lines[(line - 1)]))
(yi... |
('pypyr.steps.filewriteyaml.Path')
def test_filewriteyaml_pass_with_empty_payload(mock_path):
context = Context({'k1': 'v1', 'fileWriteYaml': {'path': '/arb/blah', 'payload': ''}})
with io.StringIO() as out_text:
with patch('pypyr.steps.filewriteyaml.open', mock_open()) as mock_output:
mock_... |
class TestBatchNormFoldToScale():
(autouse=True)
def clear_sessions(self):
tf.keras.backend.clear_session()
(yield)
(autouse=True)
def set_random_seed(self):
tf.compat.v1.reset_default_graph()
tf.compat.v1.set_random_seed(43)
if (version.parse(tf.version.VERSION) ... |
def setUpModule():
global cell, auxcell, auxcell1, cell_sr, auxcell_sr, basis, auxbasis, kpts, nkpts
basis = '\n He S\n 38.00 0.05\n 5.00 0.25\n 0.20 0.60\n He S\n 0.25 1.00\n He P\n 1.27 1.00\n '
auxbasis = '\n He S\n ... |
.parametrize(['method', 'kwargs'], [pytest.param('direct', {}, id='direct'), pytest.param('direct', {'sparse': False}, id='direct_dense'), pytest.param('eigen', {'sparse': False}, id='eigen'), pytest.param('power', {'power_tol': 1e-05}, id='power'), pytest.param('iterative-lgmres', {'tol': 1e-07, 'atol': 1e-07}, id='it... |
def get_list_of_files(dir_name):
all_files = []
try:
file_list = os.listdir(dir_name)
for entry in file_list:
full_path = os.path.join(dir_name, entry)
if os.path.isdir(full_path):
all_files = (all_files + get_list_of_files(full_path))
else:
... |
class SplAtConv2d_dcn(Module):
def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0), dilation=(1, 1), groups=1, bias=True, radix=2, reduction_factor=4, rectify=False, rectify_avg=False, norm=None, dropblock_prob=0.0, deform_conv_op=None, deformable_groups=1, deform_modulated=False, *... |
def _prepareconfig(args: Optional[Union[(List[str], 'os.PathLike[str]')]]=None, plugins: Optional[Sequence[Union[(str, _PluggyPlugin)]]]=None) -> 'Config':
if (args is None):
args = sys.argv[1:]
elif isinstance(args, os.PathLike):
args = [os.fspath(args)]
elif (not isinstance(args, list)):
... |
def _borders_touch(window, x, y, snap_dist):
overlap_args = {'x': x, 'y': y}
borders = _get_borders(window)
for b in borders:
if any(((i in [window.edges[0], (window.edges[2] + (2 * window.borderwidth))]) for i in [b[0], b[2]])):
if ((window.edges[1] < b[3]) and (window.edges[3] > b[1]))... |
def convert_list(items, ids, parent, attr_type, item_func, cdata):
LOG.info('Inside convert_list()')
output = []
addline = output.append
item_name = item_func(parent)
if ids:
this_id = get_unique_id(parent)
for (i, item) in enumerate(items):
LOG.info(('Looping inside convert_list... |
class COCOClsDataset(Dataset):
def __init__(self, img_name_list_path, coco_root, label_file_path, train=True, transform=None, gen_attn=False):
img_name_list_path = os.path.join(img_name_list_path, f"{('train' if (train or gen_attn) else 'val')}_id.txt")
self.img_name_list = load_img_name_list(img_na... |
class SerializerMixin(object):
def handle_fk_field(self, obj, field):
if isinstance(field, SingleTagField):
self._current[field.name] = str(getattr(obj, field.name))
else:
super(SerializerMixin, self).handle_fk_field(obj, field)
def handle_m2m_field(self, obj, field):
... |
def test_class_scope(fixture_path):
result = fixture_path.runpytest('-v', '--order-scope=class')
result.assert_outcomes(passed=10, failed=0)
result.stdout.fnmatch_lines(['test_classes.py::Test1::test_one PASSED', 'test_classes.py::Test1::test_two PASSED', 'test_classes.py::Test2::test_one PASSED', 'test_cla... |
def compare_avgplaycount(a1, a2):
(a1, a2) = (a1.album, a2.album)
if (a1 is None):
return (- 1)
if (a2 is None):
return 1
if (not a1.title):
return 1
if (not a2.title):
return (- 1)
return ((- cmp(a1('~#playcount:avg'), a2('~#playcount:avg'))) or cmpa(a1.date, a2.... |
class TestBinaryConfusionMatrix(unittest.TestCase):
def _test_binary_confusion_matrix_with_input(self, input: torch.Tensor, target: torch.Tensor, normalize: Optional[str]=None) -> None:
sklearn_result = torch.tensor(skcm(target, input, labels=[0, 1], normalize=normalize)).to(torch.float32)
torch.tes... |
class ProductDomain():
def __init__(self, domains):
self.vals = list(it.product(*(d.vals for d in domains)))
self.shape = ((len(domains),) + domains[0].shape)
self.lower = [d.lower for d in domains]
self.upper = [d.upper for d in domains]
self.dtype = domains[0].dtype |
(device=True)
def imt_func_o21(value, other_value):
return ((((((0 + ((1.0 * value[28]) * other_value[2])) + (((- 1.0) * value[10]) * other_value[31])) + (((- 1.0) * value[2]) * other_value[28])) + (((- 1.0) * value[3]) * other_value[29])) + (((- 1.0) * value[31]) * other_value[10])) + ((1.0 * value[29]) * other_va... |
def _tbl_bldr(rows, cols):
tblGrid_bldr = a_tblGrid()
for i in range(cols):
tblGrid_bldr.with_child(a_gridCol())
tbl_bldr = a_tbl().with_nsdecls().with_child(tblGrid_bldr)
for i in range(rows):
tr_bldr = _tr_bldr(cols)
tbl_bldr.with_child(tr_bldr)
return tbl_bldr |
class Downsample(nn.Module):
def __init__(self, in_embed_dim, out_embed_dim, patch_size):
super().__init__()
self.proj = nn.Conv2d(in_embed_dim, out_embed_dim, kernel_size=patch_size, stride=patch_size)
def forward(self, x):
x = x.permute(0, 3, 1, 2)
x = self.proj(x)
x = ... |
def _find_line_qubits(size: int, origin: Tuple[(int, int)], rotation: int) -> Tuple[(List[cirq.GridQubit], List[cirq.GridQubit])]:
if ((rotation % 90) != 0):
raise ValueError('Layout rotation must be a multiple of 90 degrees')
def generate(row, col, drow, dcol) -> List[cirq.GridQubit]:
return [c... |
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('--input_text', default='input_text.txt')
parser.add_argument('--length', default=10, type=int)
parser.add_argument('--batch_size', default=1, type=int)
parser.add_argument('--temperature', default=1, type=float)
parser.add... |
class LxClkCoreLookup(gdb.Function):
def __init__(self):
super(LxClkCoreLookup, self).__init__('lx_clk_core_lookup')
def lookup_hlist(self, hlist_head, name):
for child in clk_core_for_each_child(hlist_head):
if (child['name'].string() == name):
return child
... |
def ltout(label, n, x, key, im, doinp=False, **kwargs):
if doinpprt(label, x, doinp=False, **kwargs):
return
if (key > 0):
thresh = 0.0
else:
thresh = (10.0 ** (key - 6))
ntt = ((n * (n + 1)) // 2)
if (im > 0):
print(('%s, matrix %6d:' % (label, im)), **kwargs)
... |
.parametrize('version', [*stdlib_list.short_versions, *stdlib_list.long_versions])
def test_self_consistent(version):
list_path = f'lists/{stdlib_list.get_canonical_version(version)}.txt'
modules = pkgutil.get_data('stdlib_list', list_path).decode().splitlines()
for mod_name in modules:
assert stdli... |
.parametrize('lower, upper', [(2, np.inf), (2, 5), ((- np.inf), 5)])
.parametrize('op_type', ['icdf', 'rejection'])
def test_truncation_discrete_logcdf(op_type, lower, upper):
p = 0.7
op = (icdf_geometric if (op_type == 'icdf') else rejection_geometric)
x = op(p, name='x')
xt = Truncated.dist(x, lower=l... |
def gen_srcs_dep_taken_test():
return [gen_br2_srcs_dep_test(5, 'bne', 1, 2, True), gen_br2_srcs_dep_test(4, 'bne', 2, 3, True), gen_br2_srcs_dep_test(3, 'bne', 3, 4, True), gen_br2_srcs_dep_test(2, 'bne', 4, 5, True), gen_br2_srcs_dep_test(1, 'bne', 5, 6, True), gen_br2_srcs_dep_test(0, 'bne', 6, 7, True)] |
def convert_bool(value):
if isinstance(value, str):
if ((value == 'true') or (value == '1')):
return True
elif ((value == 'false') or (value == '0')):
return False
elif (value[0] == '$'):
return value
else:
raise ValueError((value + 'is... |
class WhisperTokenizerTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = WhisperTokenizer
rust_tokenizer_class = WhisperTokenizerFast
test_rust_tokenizer = True
test_sentencepiece = False
test_seq2seq = False
def setUp(self):
super().setUp()
tokenizer = WhisperToken... |
.parametrize('reverse', (False, True))
def test_measurable_join_interdependent(reverse):
x = pt.random.normal(name='x')
y_rvs = []
prev_rv = x
for i in range(3):
next_rv = pt.random.normal((prev_rv + 1), name=f'y{i}', size=(1, 2))
y_rvs.append(next_rv)
prev_rv = next_rv
if re... |
def main():
enhance_print()
coords = symbols('x y z')
(ex, ey, ez, grad) = MV.setup('ex ey ez', metric='[1,1,1]', coords=coords)
mfvar = (u, v) = symbols('u v')
eu = (ex + ey)
ev = (ex - ey)
(eu_r, ev_r) = ReciprocalFrame([eu, ev])
oprint('Frame', (eu, ev), 'Reciprocal Frame', (eu_r, ev_... |
def _timed_hash_bucket(input: HashBucketInput):
task_id = get_current_ray_task_id()
worker_id = get_current_ray_worker_id()
with (memray.Tracker(f'hash_bucket_{worker_id}_{task_id}.bin') if input.enable_profiler else nullcontext()):
(delta_file_envelope_groups, total_record_count, total_size_bytes) ... |
class LWLBoxActor(BaseActor):
def __init__(self, net, objective, loss_weight=None):
super().__init__(net, objective)
if (loss_weight is None):
loss_weight = {'segm': 1.0}
self.loss_weight = loss_weight
def __call__(self, data):
train_imgs = data['train_images']
... |
def test_hypersphere_log_exp_maps(sphere_dim):
sphere = HyperSphere(dim=sphere_dim)
pt_a = sphere.project(torch.randn((sphere_dim + 1)))
pt_b = sphere.project(torch.randn((sphere_dim + 1)))
log_ab = sphere.log_map(pt_a, pt_b)
assert torch.allclose(log_ab, sphere.to_tangent(pt_a, log_ab)), 'log map n... |
class PyreTypeChecker(TypeChecker):
def name(self) -> str:
return 'pyre'
def install(self) -> bool:
try:
run(f'{sys.executable} -m pip install pyre-check --upgrade', check=True, shell=True)
pyre_config = '{"site_package_search_strategy": "pep561", "source_directories": ["... |
def get_optimizer(cfg: DictConfig, model: nn.Module) -> Tuple[(Optimizer, Optional[LambdaLR])]:
args = dict(cfg[__key__].args)
args = {str(k).lower(): v for (k, v) in args.items()}
optimizer = eval(f'{cfg[__key__].version}')
param_optimizer = list(model.named_parameters())
no_decay = ['bias', 'Layer... |
def on_error(stop_on_error: bool, exception: BaseException, package: str) -> None:
if isinstance(exception, KeyboardInterrupt):
logger.info(('Cancelling, all downloads are forcibly stopped, data may be ' + 'corrupted.'))
elif (isinstance(exception, TypeError) or isinstance(exception, ValueError)):
... |
class _GenericOpMixin():
def op_numpy(self, *args):
raise NotImplementedError
atol = 1e-10
rtol = 1e-07
shapes = []
bad_shapes = []
specialisations = []
def generate_mathematically_correct(self, metafunc):
parameters = ((['op'] + [x for x in metafunc.fixturenames if x.startsw... |
def handle_holding_registers(client):
_logger.info('### write holding register and read holding registers')
client.write_register(1, 10, slave=SLAVE)
rr = client.read_holding_registers(1, 1, slave=SLAVE)
assert (not rr.isError())
assert (rr.registers[0] == 10)
client.write_registers(1, ([10] * 8... |
def open_with_os(path):
sys = platform.system()
if (sys == 'Darwin'):
os.system(('open "%s"' % path))
elif (sys == 'Windows'):
os.startfile(path)
elif (sys == 'Linux'):
os.system(('evince "%s"' % path))
else:
raise NotImplementedError(('Unable to open files in this pa... |
def fix_missing_fields(ds: Dataset) -> Dataset:
ds = ds.drop_vars('call_genotype_phased')
ds = ds.drop_vars('variant_filter')
ds = ds.drop_vars('filter_id')
del ds.attrs['filters']
del ds.attrs['max_alt_alleles_seen']
del ds.attrs['vcf_zarr_version']
del ds.attrs['vcf_header']
for var in... |
def test_InterHand3D_dataset():
dataset = 'InterHand3DDataset'
dataset_info = Config.fromfile('configs/_base_/datasets/interhand3d.py').dataset_info
dataset_class = DATASETS.get(dataset)
channel_cfg = dict(num_output_channels=42, dataset_joints=42, dataset_channel=[[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,... |
class BaseModel(torch.nn.Module):
def name(self):
return 'BaseModel'
def initialize(self, opt):
self.opt = opt
self.gpu_ids = opt.gpu_ids
self.isTrain = opt.isTrain
self.Tensor = (torch.cuda.FloatTensor if self.gpu_ids else torch.Tensor)
self.save_dir = os.path.jo... |
def test_check_cask(bf, caplog, tmp_path):
os.chdir(tmp_path)
if (not brew_file.is_mac()):
with pytest.raises(RuntimeError) as excinfo:
bf.check_cask()
assert (str(excinfo.value) == 'Cask is not available on Linux!')
return
bf.check_cask()
assert ('# Starting to c... |
def clean_duplicates_mro(sequences: list[list[ClassDef]], cls: ClassDef, context: (InferenceContext | None)) -> list[list[ClassDef]]:
for sequence in sequences:
seen = set()
for node in sequence:
lineno_and_qname = (node.lineno, node.qname())
if (lineno_and_qname in seen):
... |
def subdispatch_mediatortask(chain_state: ChainState, state_change: StateChange, token_network_address: TokenNetworkAddress, secrethash: SecretHash) -> TransitionResult[ChainState]:
block_number = chain_state.block_number
block_hash = chain_state.block_hash
sub_task = chain_state.payment_mapping.secrethashe... |
.parametrize('data, msg', [(b'\x80\n', 'invalid utf-8'), (b'\n', 'invalid json'), (b'{"is this invalid json?": true\n', 'invalid json'), (b'{"valid json without args": true}\n', 'Missing args'), (b'{"args": []}\n', 'Missing target_arg'), (((b'{"args": [], "target_arg": null, "protocol_version": ' + OLD_VERSION) + b'}\n... |
class TxsBTCAPPRSpider(TxsBTCSpider):
name = 'txs.btc.appr'
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.task_map = dict()
self.alpha = float(kwargs.get('alpha', 0.15))
self.epsilon = float(kwargs.get('epsilon', 0.0001))
def start_requests(self):
sour... |
class ScreenFeatures(collections.namedtuple('ScreenFeatures', ['height_map', 'visibility_map', 'creep', 'power', 'player_relative', 'unit_type', 'unit_density', 'unit_density_aa'])):
__slots__ = ()
def __new__(cls, **kwargs):
feats = {}
for (name, (scale, type_, palette, clip)) in six.iteritems(... |
def test_bind_completion_no_binding(qtmodeltester, cmdutils_stub, config_stub, key_config_stub, configdata_stub, info):
model = configmodel.bind('x', info=info)
model.set_pattern('')
qtmodeltester.check(model)
_check_completions(model, {'Commands': [('open', 'open a url', ''), ('q', "Alias for 'quit'", ... |
def get_wx_user_info(access_data: dict):
openid = access_data.get('openid')
access_token = access_data.get('access_token')
try:
fields = parse.urlencode({'access_token': access_token, 'openid': openid})
url = '
print(url)
req = request.Request(url=url, method='GET')
r... |
def calculate_js_div(R1, R2):
subset_overlap = []
for n in range(len(R1)):
sim_per_pair = []
for i in range(len(R1[n])):
s1 = R1[n][i]
s2 = R2[n][i]
try:
sim = js_divergence(s1, s2)
except TypeError:
print(s1)
... |
class TNSR():
def unfold(tensor, mode):
lst = range(0, len(tensor.get_shape().as_list()))
return tf.reshape(tensor=tf.transpose(tensor, (([mode] + lst[:mode]) + lst[(mode + 1):])), shape=[tensor.get_shape().as_list()[mode], (- 1)])
def fold(tensor, mode, shape):
full_shape = list(shape)
... |
class DrawMav():
def __init__(self, state, window):
(self.mav_points, self.mav_meshColors) = self.get_points()
mav_position = np.array([[state.north], [state.east], [(- state.altitude)]])
R = Euler2Rotation(state.phi, state.theta, state.psi)
rotated_points = self.rotate_points(self.m... |
def gcd(u, v):
from rpython.rlib.rbigint import _v_isub, _v_rshift, SHIFT
if (not u.tobool()):
return v.abs()
if (not v.tobool()):
return u.abs()
if ((v.sign == (- 1)) and (u.sign == (- 1))):
sign = (- 1)
else:
sign = 1
if ((u.size == 1) and (v.size == 1)):
... |
class Section():
def __init__(self, sectPr: CT_SectPr, document_part: DocumentPart):
super(Section, self).__init__()
self._sectPr = sectPr
self._document_part = document_part
def bottom_margin(self) -> (Length | None):
return self._sectPr.bottom_margin
_margin.setter
def ... |
def test_class_weights():
(X, Y) = make_blobs(n_samples=210, centers=3, random_state=1, cluster_std=3, shuffle=False)
X = np.hstack([X, np.ones((X.shape[0], 1))])
(X, Y) = (X[:170], Y[:170])
pbl = MultiClassClf(n_features=3, n_classes=3)
svm = OneSlackSSVM(pbl, C=10)
svm.fit(X, Y)
weights = ... |
def test(test_episodes):
tf.reset_default_graph()
policy_nn = SupervisedPolicy()
f = open(relationPath)
test_data = f.readlines()
f.close()
test_num = len(test_data)
test_data = test_data[(- test_episodes):]
print(len(test_data))
success = 0
saver = tf.train.Saver()
with tf.S... |
class UtilsSplitStripTest(TestCase):
def test_empty(self):
split = tag_utils.split_strip(None)
self.assertEqual(split, [])
split = tag_utils.split_strip('')
self.assertEqual(split, [])
def test_spaceless(self):
split = tag_utils.split_strip('adam,brian')
self.asse... |
class ClassifiersFactory(AbstractFactory):
def add_classifier(self, name, classifier):
if isinstance(classifier, Classifier):
self[name] = classifier
elif (isinstance(classifier, BaseEstimator) and isinstance(classifier, ClassifierMixin)):
self[name] = SklearnClassifier(class... |
class Accumulator():
def __init__(self):
self._embeddings = []
self._filled = False
def state(self) -> Dict[(str, torch.Tensor)]:
return {'embeddings': self.embeddings}
def filled(self) -> bool:
return self._filled
def set_filled(self):
self._filled = True
def... |
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