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def check_no_unexpected_results(mypy_lines: Iterator[str]):
df = mypy_to_pandas(mypy_lines)
all_files = {str(fp).replace(str(DP_ROOT), '').strip(os.sep).replace(os.sep, '/') for fp in DP_ROOT.glob('pytensor/**/*.py')}
failing = set(df.reset_index().file.str.replace(os.sep, '/', regex=False))
if (not fai... |
def test_self_update_can_update_from_recommended_installation(tester: CommandTester, repo: TestRepository, installed: TestRepository) -> None:
new_version = Version.parse(__version__).next_minor().text
old_poetry = Package('poetry', __version__)
old_poetry.add_dependency(Factory.create_dependency('cleo', '^... |
class TestCompletionMetaInfo():
def metainfo(self, database):
return history.CompletionMetaInfo(database)
def test_contains_keyerror(self, metainfo):
with pytest.raises(KeyError):
('does_not_exist' in metainfo)
def test_getitem_keyerror(self, metainfo):
with pytest.raises... |
class Effect5359(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Large Hybrid Turret')), 'damageMultiplier', ship.getModifiedItemAttr('shipBonusGBC2'), skill='Gallente Battlecruiser', **kwargs) |
def setUpModule():
global cell, kpts, gdf
cell = gto.Cell()
cell.build(a='\n 0.000000 1.783500 1.783500\n 1.783500 0.000000 1.783500\n 1.783500 1.783500 0.000000\n ', atom='C 1.337625 1.337625 1.337625; C 2.229375 2.229375 2.229375', verbose=7,... |
class LatentLayersSparsityLoss(_Loss):
def __init__(self, args):
super().__init__()
self.args = args
def is_valid(self, update_num):
if (self.args.target_layers <= 0):
return False
return (update_num > (self.args.soft_update + self.args.anneal_updates))
def forwar... |
class struct_s_pxe_sw_undi(ctypes.Structure):
_pack_ = True
_functions_ = []
_fields_ = [('Signature', ctypes.c_uint32), ('Len', ctypes.c_ubyte), ('Fudge', ctypes.c_ubyte), ('Rev', ctypes.c_ubyte), ('IFcnt', ctypes.c_ubyte), ('MajorVer', ctypes.c_ubyte), ('MinorVer', ctypes.c_ubyte), ('IFcntExt', ctypes.c_u... |
def test_multithreading(autoimport: AutoImport, project: Project, pkg1: Folder, mod1: File):
mod1_init = pkg1.get_child('__init__.py')
mod1_init.write(dedent(' def foo():\n pass\n '))
mod1.write(dedent(' foo\n '))
autoimport = AutoImport(project, memory=False)
autoimpo... |
def parse_method(method):
assert (type(method) is str), type(method)
multilingual = False
train_langs = [main_lang]
eval_lang = main_lang
train_en_prob = None
if ('#' in method):
multilingual = True
(actual_method, string) = method.split('#')
(train_langs, eval_lang) = st... |
def _gen_efficientnet(variant, channel_multiplier=1.0, depth_multiplier=1.0, channel_divisor=8, group_size=None, pretrained=False, **kwargs):
arch_def = [['ds_r1_k3_s1_e1_c16_se0.25'], ['ir_r2_k3_s2_e6_c24_se0.25'], ['ir_r2_k5_s2_e6_c40_se0.25'], ['ir_r3_k3_s2_e6_c80_se0.25'], ['ir_r3_k5_s1_e6_c112_se0.25'], ['ir_r... |
class AgeDB30(data.Dataset):
def __init__(self, root, file_list, transform=None, loader=img_loader):
self.root = root
self.file_list = file_list
self.transform = transform
self.loader = loader
self.nameLs = []
self.nameRs = []
self.folds = []
self.flag... |
class TableCellStyle():
def __init__(self, fg: str='default', bg: str='default', options: (list[str] | None)=None, align: _Align='left', cell_format: (str | None)=None) -> None:
self._fg = fg
self._bg = bg
self._options = options
self._align = 'left'
self._cell_format = cell_... |
class LatentEditorWrapper():
def __init__(self):
self.interfacegan_directions = {'age': f'{interfacegan_age}', 'smile': f'{interfacegan_smile}', 'rotation': f'{interfacegan_rotation}'}
self.interfacegan_directions_tensors = {name: torch.load(path).cuda() for (name, path) in self.interfacegan_directi... |
class Maze(tk.Tk, object):
def __init__(self):
super(Maze, self).__init__()
self.action_space = ['u', 'd', 'l', 'r']
self.n_actions = len(self.action_space)
self.n_features = 2
self.title('maze')
self.geometry('{}x{}'.format((MAZE_H * UNIT), (MAZE_W * UNIT)))
... |
class RepeatCopyEnv(algorithmic_env.TapeAlgorithmicEnv):
MIN_REWARD_SHORTFALL_FOR_PROMOTION = (- 0.1)
def __init__(self, base=5):
super(RepeatCopyEnv, self).__init__(base=base, chars=True)
self.last = 50
def target_from_input_data(self, input_data):
return ((input_data + list(reverse... |
def test_deep_copy():
mapping = {T.__name__: int}
assert (deep_copy_with(Optional[T], mapping) == Optional[int])
assert (deep_copy_with(List_origin[Optional[T]], mapping) == List_origin[Optional[int]])
mapping = {T.__name__: int, T2.__name__: str}
assert (deep_copy_with(Dict_origin[(T2, List_origin[... |
class TestForensic():
def test_all_strings(self, forensic):
assert (len(forensic.get_all_strings()) == 1005)
def test_get_url(self, forensic):
assert (len(forensic.get_url()) == 4)
assert (' in forensic.get_url())
assert (' in forensic.get_url())
assert (' in forensic.get... |
class SponsorContactModelTests(TestCase):
def test_get_primary_contact_for_sponsor(self):
sponsor = baker.make(Sponsor)
baker.make(SponsorContact, sponsor=sponsor, primary=False, _quantity=5)
baker.make(SponsorContact, primary=True)
self.assertEqual(5, SponsorContact.objects.filter(s... |
class Scenario(ScenarioGenerator):
def __init__(self):
ScenarioGenerator.__init__(self)
self.naming = 'numerical'
self.generate_all_roads = False
self.parameters['ego_speedvalue'] = [x for x in range(30, 85, 5)]
self.parameters['offset'] = [(- 50), (- 25), 0, 25, 50]
def ... |
.functions
def test_groupby_agg_multi_column():
df = pd.DataFrame({'date': ['', '', '', '', '', ''], 'user_id': [1, 2, 1, 2, 1, 2], 'values': [1, 2, 3, 4, 5, 6]})
df_new = df.groupby_agg(by=['date'], new_column_name='values_avg', agg_column_name='values', agg='mean')
expected_agg = np.array([1.5, 1.5, 3.5, ... |
class TestMeasurementErrorMitigation(QiskitAquaTestCase):
def setUp(self):
super().setUp()
try:
from qiskit import Aer
except ImportError as ex:
self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
return
def test_measuremen... |
class BypassQueue1EntryRTL(Component):
def construct(s, EntryType):
s.recv = RecvIfcRTL(EntryType)
s.send = SendIfcRTL(EntryType)
s.count = OutPort()
s.full = Wire()
s.entry = Wire(EntryType)
s.bypass_mux = m = Mux(EntryType, 2)
m.in_[0] //= s.recv.msg
... |
def train():
parser = argparse.ArgumentParser('FGVC', add_help=False)
parser.add_argument('--epochs', type=int, default=300, help='training epochs')
parser.add_argument('--batch_size', type=int, default=16, help='batch size for training')
parser.add_argument('--resume', type=str, default='', help='resum... |
def create_test_header(earth_model, dataset_id, is_full_disk, is_rapid_scan, good_qual='OK'):
if (dataset_id['name'] == 'HRV'):
reference_grid = 'ReferenceGridHRV'
column_dir_grid_step = 1.
line_dir_grid_step = 1.
else:
reference_grid = 'ReferenceGridVIS_IR'
column_dir_gr... |
def check_environment():
try:
import websockets
except ImportError:
print('failed to import websockets; is src on PYTHONPATH?')
return False
try:
import coverage
except ImportError:
print('failed to locate Coverage.py; is it installed?')
return False
r... |
def bifpn_config(min_level, max_level, weight_method=None):
p = OmegaConf.create()
weight_method = (weight_method or 'fastattn')
num_levels = ((max_level - min_level) + 1)
node_ids = {(min_level + i): [i] for i in range(num_levels)}
level_last_id = (lambda level: node_ids[level][(- 1)])
level_al... |
class SegmentationNet10aTrunk(VGGTrunk):
def __init__(self, config, cfg):
super(SegmentationNet10aTrunk, self).__init__()
self.batchnorm_track = config.batchnorm_track
assert ((config.input_sz % 2) == 0)
self.conv_size = 3
self.pad = 1
self.cfg = cfg
self.in_c... |
('PyQt6.QtGui.QAction.triggered')
('beeref.actions.mixin.menu_structure')
('beeref.actions.mixin.actions')
('beeref.actions.mixin.KeyboardSettings.get_shortcuts')
def test_create_recent_files_more_files_than_shortcuts(kb_mock, actions_mock, menu_mock, triggered_mock, qapp):
kb_mock.side_effect = (lambda group, key,... |
class TestInit():
def test_empty(self):
nl = usertypes.NeighborList()
assert (nl.items == [])
def test_items(self):
nl = usertypes.NeighborList([1, 2, 3])
assert (nl.items == [1, 2, 3])
def test_len(self):
nl = usertypes.NeighborList([1, 2, 3])
assert (len(nl)... |
def get_sub_macros(sub: dict[(str, str)]) -> tuple[(str, str)]:
define_macros = []
undef_macros = []
define_macros.append(f"#define FAIL {lquote_macro(sub['fail'])}")
undef_macros.append('#undef FAIL')
if ('params' in sub):
define_macros.append(f"#define PARAMS {sub['params']}")
unde... |
class IntelHex(object):
def __init__(self, source=None):
self.padding = 255
self.start_addr = None
self._buf = {}
self._offset = 0
if (source is not None):
if (isinstance(source, StrType) or getattr(source, 'read', None)):
self.loadhex(source)
... |
class NamedParamProposal(CompletionProposal):
def __init__(self, name, function):
self.argname = name
name = ('%s=' % name)
super().__init__(name, 'parameter_keyword')
self._function = function
def get_default(self):
definfo = functionutils.DefinitionInfo.read(self._funct... |
class DataTrainingArguments():
dataset_name: Optional[str] = field(default=None, metadata={'help': 'The name of the dataset to use (via the datasets library).'})
dataset_config_name: Optional[str] = field(default=None, metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}... |
def minimum(left, right):
(left, right) = _simplify_elementwise_binary_broadcasts(left, right)
out = _simplified_binary_broadcast_concatenation(left, right, minimum)
if (out is not None):
return out
mode = pybamm.settings.min_max_mode
k = pybamm.settings.min_max_smoothing
if ((mode == 'e... |
class MyUnit(AutoUnit[Batch]):
def __init__(self, *, tb_logger: TensorBoardLogger, train_accuracy: MulticlassAccuracy, log_every_n_steps: int, lr: float, gamma: float, module: torch.nn.Module, device: torch.device, strategy: str, precision: Optional[str], gradient_accumulation_steps: int, detect_anomaly: bool, clip... |
class TestHVUDataset(BaseTestDataset):
def test_hvu_dataset(self):
hvu_frame_dataset = HVUDataset(ann_file=self.hvu_frame_ann_file, pipeline=self.frame_pipeline, tag_categories=self.hvu_categories, tag_category_nums=self.hvu_category_nums, filename_tmpl=self.filename_tmpl, data_prefix=self.data_prefix, star... |
def _load_dump_repr_configs(flags, model_parser):
assert flags.repr_set_name
assert (flags.which_repr in ['model_mu1', 'mu1', 'mu2'])
assert (flags.train_repr_wspec or flags.dev_repr_wspec or flags.test_repr_wspec)
(exp_dir, set_name, model_conf, train_conf, dataset_conf) = _load_configs(flags, model_pa... |
def mark_only_lora_as_trainable(model: nn.Module, bias: str='none') -> None:
for (n, p) in model.named_parameters():
if ('lora_' not in n):
p.requires_grad = False
if (bias == 'none'):
return
elif (bias == 'all'):
for (n, p) in model.named_parameters():
if ('b... |
_model_architecture('linformer_roberta', 'linformer_roberta')
def base_architecture(args):
args.compressed = getattr(args, 'compressed', 4)
args.shared_kv_compressed = getattr(args, 'shared_kv_compressed', 0)
args.shared_layer_kv_compressed = getattr(args, 'shared_layer_kv_compressed', 0)
args.freeze_co... |
def test_use_before_definition():
with pytest.raises(SchemeException):
m = run_mod('\n #lang pycket\n x\n (define x 1)\n ')
with pytest.raises(SchemeException):
m = run_mod('\n #lang pycket\n x\n (define x 1)\n (set! x 2)\n ') |
class CategoricalGibbsMetropolis(ArrayStep):
name = 'categorical_gibbs_metropolis'
stats_dtypes_shapes = {'tune': (bool, [])}
def __init__(self, vars, proposal='uniform', order='random', model=None):
model = pm.modelcontext(model)
vars = get_value_vars_from_user_vars(vars, model)
ini... |
def matplotlib_plt(scatters, title, ylabel, output_file, limits=None, show=False, figsize=None):
linestyle = '-'
hybrid_matches = ['x26', 'VTM', 'HM', 'WebP', 'AV1']
if (figsize is None):
figsize = (9, 6)
(fig, ax) = plt.subplots(figsize=figsize)
for sc in scatters:
if any(((x in sc[... |
.parametrize('min_role,role_list,projects', prune_assignments)
def test_prune_projects_output(db, settings, min_role, role_list, projects):
(stdout, stderr) = (io.StringIO(), io.StringIO())
instances = Project.objects.filter(id__in=projects).all()
call_command('prune_projects', '--min_role', min_role, stdou... |
class ResNet50vd_samll(nn.Module):
def __init__(self, cout=64, idx=0):
super(ResNet50vd, self).__init__()
self.cout = cout
self.idx = idx
self.resnet50vd = ResNet(channels=[32, 64, 128, 256], cout=cout, idx=idx, block=Bottleneck, layers=layers, stem_width=32, stem_type='deep', avg_do... |
def _change_state(state: EnvironmentState, new_node: GraphNode, dest_node: Node, add_changers: List[StateChanger]):
changers = [AddNode(new_node), AddEdges(NodeInstance(new_node), Relation.ON, NodeInstance(dest_node)), AddEdges(NodeInstance(new_node), Relation.CLOSE, NodeInstance(dest_node), add_reverse=True)]
... |
def unmarshal_webhook_response(request: WebhookRequest, response: Response, spec: SchemaPath, base_url: Optional[str]=None, cls: Optional[WebhookResponseUnmarshallerType]=None, **unmarshaller_kwargs: Any) -> ResponseUnmarshalResult:
config = Config(server_base_url=base_url, webhook_response_unmarshaller_cls=(cls or... |
def check_precommit_requirements() -> None:
requirements_txt_requirements = get_txt_requirements()
precommit_requirements = get_precommit_requirements()
no_txt_entry_msg = 'All pre-commit requirements must also be listed in `requirements-tests.txt` (missing {requirement!r})'
for (requirement, specifier)... |
class FlatSim(nn.Module):
def __init__(self, x_size, y_size, opt={}, prefix='seqatt', dropout=None):
super(FlatSim, self).__init__()
assert (x_size == y_size)
self.opt = opt
self.weight_norm_on = opt.get('{}_weight_norm_on'.format(prefix), False)
self.linear = nn.Linear((x_si... |
def test(env, pg_reinforce, n=50):
reward_list = []
dialogLen_list = []
success_list = []
for i_test in range(n):
assert (len(pg_reinforce.reward_buffer) == 0)
(cur_reward, cur_dialogLen, cur_success) = run_one_dialog(env, pg_reinforce)
assert (cur_success is not None)
re... |
(frozen=True)
class DreadConfiguration(BaseConfiguration):
teleporters: DreadTeleporterConfiguration
energy_per_tank: int = dataclasses.field(metadata={'min': 1, 'max': 1000, 'precision': 1})
immediate_energy_parts: bool
hanubia_shortcut_no_grapple: bool
hanubia_easier_path_to_itorash: bool
x_st... |
class Migration(migrations.Migration):
dependencies = [('petition', '0004_auto__0002')]
operations = [migrations.AlterField(model_name='petition', name='title', field=tinymce.models.HTMLField(verbose_name='Title')), migrations.AlterField(model_name='signature', name='confirmed', field=models.BooleanField(defaul... |
class AIFF(FileType):
_mimes = ['audio/aiff', 'audio/x-aiff']
def score(filename, fileobj, header):
filename = filename.lower()
return ((((header.startswith(b'FORM') * 2) + endswith(filename, b'.aif')) + endswith(filename, b'.aiff')) + endswith(filename, b'.aifc'))
def add_tags(self):
... |
def compare_wyckoffs(num1, num2, dim=3):
from numpy import allclose
if (num1 == '???'):
print('Error: invalid value for num1 passed to compare_wyckoffs')
return
if (num2 == '???'):
return False
if (dim == 3):
from pyxtal.symmetry import get_wyckoffs
g1 = get_wycko... |
class ByteBuffer():
def __init__(self, chunk_size=65536):
self._deque = collections.deque([bytearray()])
self._chunk_size = chunk_size
self._size = 0
def append(self, data):
pos = 0
while (pos < len(data)):
data_to_write = min((self._chunk_size - len(self._deq... |
class Version(Base):
def export_version(self) -> Optional[semantic_version.Version]:
payload = self._initialize_payload('version')
resp = None
redcap_version = self._call_api(payload, return_type='str')
if semantic_version.validate(redcap_version):
resp = semantic_version... |
def handle_set_suction(req):
try:
if req.data:
ser.write(b'g')
message = 'Turned on'
else:
ser.write(b's')
message = 'Turned off'
except Exception as e:
return SetBoolResponse(success=False, message=str(e))
return SetBoolResponse(succes... |
def aggregate(epochs, uuid, start_time, train_time, w_compressed):
global g_start_time
global g_train_time
global g_train_global_model
global g_train_global_model_compressed
global g_train_global_model_version
global global_model_hash
logger.debug('Received a train_ready.')
lock.acquire(... |
def find_all_batch_norms_to_fold(connected_graph: ConnectedGraph) -> Tuple[(List[Tuple[(NodeProto, NodeProto)]], List[Tuple[(NodeProto, NodeProto)]])]:
conv_linear_bn_activation_info_dict = _find_conv_bn_pairs(connected_graph)
model = connected_graph.model
bn_picked_for_folding = set()
ordered_conv_fc_n... |
(Participant)
class ParticipantAdmin(admin.ModelAdmin):
form = ParticipantForm
list_display = ('user_display_name', 'conference')
list_filter = ('conference',)
fieldsets = ((None, {'fields': ('conference', 'user', 'photo', 'photo_preview', 'bio', 'website', 'twitter_handle', 'instagram_handle', 'linkedi... |
class DataLoaderIter(object):
def __init__(self, loader):
self.dataset = loader.dataset
self.collate_fn = loader.collate_fn
self.batch_sampler = loader.batch_sampler
self.num_workers = loader.num_workers
self.pin_memory = loader.pin_memory
self.done_event = threading.... |
def test_cmdloop_without_rawinput():
testargs = ['prog']
with mock.patch.object(sys, 'argv', testargs):
app = CreateOutsimApp()
app.use_rawinput = False
app.echo = False
app.intro = 'Hello World, this is an intro ...'
m = mock.MagicMock(name='input', return_value='quit')
builtins.inp... |
class BaseOptions():
def __init__(self):
self.initialized = False
def initialize(self, parser):
parser.add_argument('--dataroot', required=True, help='path to images (should have subfolders trainA, trainB, valA, valB, etc)')
parser.add_argument('--batchSize', type=int, default=1, help='i... |
class Defaults():
__slots__ = ('_tzinfo', '_disable_web_page_preview', '_block', '_quote', '_disable_notification', '_allow_sending_without_reply', '_parse_mode', '_api_defaults', '_protect_content')
def __init__(self, parse_mode: Optional[str]=None, disable_notification: Optional[bool]=None, disable_web_page_p... |
class HT_CONV(nn.Module):
def __init__(self, inplanes, outplanes):
super(HT_CONV, self).__init__()
self.conv1 = nn.Sequential(*make_conv2d_block(inplanes, inplanes, kernel_size=(9, 1), padding=(4, 0), bias=True, groups=inplanes))
self.block1 = HTCONVBlock(inplanes, inplanes)
self.blo... |
class SwitchModel(nn.Module):
def __init__(self, args: Namespace, device: torch.device):
super(SwitchModel, self).__init__()
self.modelid = 'switch_baseline'
self.args = args
self.device = device
self._encoder = PLM(args, device, use_encoder=True, pooler_output=False)
... |
def fcn(split, tops):
n = caffe.NetSpec()
(n.data, n.label) = L.Python(module='nyud_layers', layer='NYUDSegDataLayer', ntop=2, param_str=str(dict(nyud_dir='../data/nyud', split=split, tops=tops, seed=1337)))
(n.conv1_1, n.relu1_1) = conv_relu(n.data, 64, pad=100)
(n.conv1_2, n.relu1_2) = conv_relu(n.rel... |
class IPortUser(metaclass=ABCMeta):
ID_PULSE = 1
ID_UPDATE = (ID_PULSE << 1)
ID_DONE = (ID_PULSE << 2)
ID_ERROR = (ID_PULSE << 3)
PROCESS_IMPORT = (ID_PULSE << 4)
PROCESS_EXPORT = (ID_PULSE << 5)
def on_port_processing(self, action, data=None):
pass
def on_port_process_start(self... |
.parametrize('sampler', [sample_blackjax_nuts, sample_numpyro_nuts])
.parametrize('random_seed', (None, 123))
.parametrize('chains', [pytest.param(1), pytest.param(2, marks=pytest.mark.skipif((len(jax.devices()) < 2), reason='not enough devices'))])
def test_seeding(chains, random_seed, sampler):
sample_kwargs = di... |
def test_validate_blackbox():
validate.blackbox(macro.Blackbox(((0, 1),), (1,)))
with pytest.raises(ValueError):
validate.blackbox(macro.Blackbox(((0, 1),), (1, 0)))
with pytest.raises(ValueError):
validate.blackbox(macro.Blackbox(((0,), (0, 1)), (0, 1)))
with pytest.raises(ValueError):
... |
def decode_terminated(data: bytes, encoding: str, strict: bool=True) -> Tuple[(str, bytes)]:
codec_info = codecs.lookup(encoding)
encoding = codec_info.name
if (encoding in ('utf-8', 'iso8859-1')):
index = data.find(b'\x00')
if (index == (- 1)):
res = (data.decode(encoding), b'')... |
def test_user_potential():
model = pymc.Model()
with model:
pymc.Normal('a', mu=0, sigma=1)
called = []
class Potential(quadpotential.QuadPotentialDiag):
def energy(self, x, velocity=None):
called.append(1)
return super().energy(x, velocity)
pot = Potential(fl... |
class ClassificationLoss(torch.nn.Module):
def __init__(self, label_size, class_weight=None, loss_type=LossType.SOFTMAX_CROSS_ENTROPY):
super(ClassificationLoss, self).__init__()
self.label_size = label_size
self.loss_type = loss_type
if (loss_type == LossType.SOFTMAX_CROSS_ENTROPY):... |
class Reader(object):
def __init__(self, data):
if isinstance(data, list):
self._str = data
else:
self._str = data.split('\n')
self.reset()
def __getitem__(self, n):
return self._str[n]
def reset(self):
self._l = 0
def read(self):
i... |
def parse_val_archive(root, file=None, wnids=None, folder='val'):
archive_meta = ARCHIVE_META['val']
if (file is None):
file = archive_meta[0]
md5 = archive_meta[1]
if (wnids is None):
wnids = load_meta_file(root)[1]
_verify_archive(root, file, md5)
val_root = os.path.join(root, ... |
class Relations():
def __init__(self, *args, **kwargs):
self._num_relations_cached = None
self._sum_phi_cached = None
def sum_phi(self):
if (self._sum_phi_cached is None):
self._sum_phi_cached = self._sum_phi()
return self._sum_phi_cached
def num_relations(self):
... |
def symmetric_gradients(tensor: torch.Tensor, grad: torch.Tensor, intermediate_result: IntermediateResult, channel_axis: int) -> Tuple[(torch.Tensor, torch.Tensor)]:
mask_tensor = intermediate_result.mask_tensor
delta = intermediate_result.delta
offset = intermediate_result.offset
x_quant = intermediate... |
class Deterministic(nn.Module):
def __init__(self, net):
super().__init__()
self.net = net
self.cpu_state = None
self.cuda_in_fwd = None
self.gpu_devices = None
self.gpu_states = None
def record_rng(self, *args):
self.cpu_state = torch.get_rng_state()
... |
def test_is_super():
test_type = TensorType(config.floatX, shape=(None, None))
test_type2 = TensorType(config.floatX, shape=(None, 1))
assert test_type.is_super(test_type)
assert test_type.is_super(test_type2)
assert (not test_type2.is_super(test_type))
test_type3 = TensorType(config.floatX, sha... |
class GroupViTOnnxConfig(OnnxConfig):
def inputs(self) -> Mapping[(str, Mapping[(int, str)])]:
return OrderedDict([('input_ids', {0: 'batch', 1: 'sequence'}), ('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}), ('attention_mask', {0: 'batch', 1: 'sequence'})])
def outputs(self) -... |
def lexical_overlap(premise, hypothesis):
prem_words = []
hyp_words = []
for word in premise.split():
if (word not in ['.', '?', '!']):
prem_words.append(word.lower())
for word in hypothesis.split():
if (word not in ['.', '?', '!']):
hyp_words.append(word.lower())... |
def main_worker(gpu, opts):
rank = ((opts.node_rank * opts.gpus) + gpu)
torch.cuda.set_device(gpu)
dist.init_process_group(backend='nccl', init_method='env://', world_size=opts.world_size, rank=rank, group_name='mtorch')
set_seed(42)
if (rank == 0):
sys.stdout = Logger(os.path.join(opts.ckpt... |
class GhostBatchNorm(BatchNorm):
def __init__(self, num_features, num_splits=1, **kwargs):
super().__init__(num_features, **kwargs)
self.num_splits = num_splits
self.register_buffer('running_mean', torch.zeros(num_features))
self.register_buffer('running_var', torch.ones(num_features... |
class TestSamplePPC():
def test_normal_scalar(self):
nchains = 2
ndraws = 500
with pm.Model() as model:
mu = pm.Normal('mu', 0.0, 1.0)
a = pm.Normal('a', mu=mu, sigma=1, observed=0.0)
trace = pm.sample(draws=ndraws, chains=nchains)
with model:
... |
class LFM(nn.Module):
def __init__(self, num_channels):
super(LFM, self).__init__()
self.conv1 = nn.Conv2d((2 * num_channels), (2 * num_channels), kernel_size=1, stride=1, padding=0)
self.conv2 = nn.Conv2d((2 * num_channels), (2 * num_channels), kernel_size=1, stride=1, padding=0)
def ma... |
class GripperControllerServer(GripperController):
def __init__(self, robot_name, create_node=True, upper_limit=0.037, lower_limit=0.01, des_pos_max=1, des_pos_min=0):
super(GripperControllerServer, self).__init__(robot_name, create_node, upper_limit, lower_limit, des_pos_max, des_pos_min)
rospy.Serv... |
class OptionRendererMixin():
def render_option(self, xml, option):
if (option['uri'] not in self.uris):
self.uris.add(option['uri'])
xml.startElement('option', {'dc:uri': option['uri']})
self.render_text_element(xml, 'uri_prefix', {}, option['uri_prefix'])
sel... |
def _looks_like_special_alias(node: Call) -> bool:
return (isinstance(node.func, Name) and (((not PY39_PLUS) and (node.func.name == '_VariadicGenericAlias') and ((isinstance(node.args[0], Name) and (node.args[0].name == 'tuple')) or (isinstance(node.args[0], Attribute) and (node.args[0].as_string() == 'collections.... |
class Speech2Text2Tokenizer(PreTrainedTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
model_input_names = ['input_ids', 'attention_mask']
def __init__(self, vocab_file, bos_token=... |
def PR_curve(label_path, pred_path, num_total):
with open(label_path, 'rb') as input:
label_entitypair = pickle.load(input)
with open(pred_path, 'rb') as input:
pred_entitypair = pickle.load(input)
list_pred = []
for key in pred_entitypair.keys():
tmp_prob = pred_entitypair[key][... |
def testPositionalArgs(run_cli):
out = run_cli('bugzilla login --xbadarg foo', None, expectfail=True)
assert ('unrecognized arguments: --xbadarg' in out)
out = run_cli('bugzilla modify 123456 --foobar --status NEW', None, expectfail=True)
assert ('unrecognized arguments: --foobar' in out) |
def test_minibatch_unit_variance_mlpg_gradcheck():
static_dim = 2
T = 5
for windows in _get_windows_set():
batch_size = 5
torch.manual_seed(1234)
means = torch.rand(T, (static_dim * len(windows)))
means_expanded = means.expand(batch_size, means.shape[0], means.shape[1])
... |
def points_in_boxes_cpu(points, boxes):
assert (boxes.shape[1] == 7)
assert (points.shape[1] == 3)
(points, is_numpy) = common_utils.check_numpy_to_torch(points)
(boxes, is_numpy) = common_utils.check_numpy_to_torch(boxes)
point_indices = points.new_zeros((boxes.shape[0], points.shape[0]), dtype=tor... |
def _get_block_fn(stage_args):
block_type = stage_args.pop('block_type')
assert (block_type in ('dark', 'edge', 'bottle'))
if (block_type == 'dark'):
return (DarkBlock, stage_args)
elif (block_type == 'edge'):
return (EdgeBlock, stage_args)
else:
return (BottleneckBlock, stag... |
def test_to_dict_no_proj4():
crs = CRS({'a': 6371229.0, 'b': 6371229.0, 'lon_0': (- 10.0), 'o_lat_p': 30.0, 'o_lon_p': 0.0, 'o_proj': 'longlat', 'proj': 'ob_tran'})
with pytest.warns(UserWarning):
assert (crs.to_dict() == {'R': 6371229, 'lon_0': (- 10), 'no_defs': None, 'o_lat_p': 30, 'o_lon_p': 0, 'o_p... |
(eq=False, hash=False, repr=False)
class _LockImpl(AsyncContextManagerMixin):
_lot: ParkingLot = attr.ib(factory=ParkingLot, init=False)
_owner: (Task | None) = attr.ib(default=None, init=False)
def __repr__(self) -> str:
if self.locked():
s1 = 'locked'
s2 = f' with {len(self... |
class MobileDevice():
def __init__(self, path: str, server: ObserverAPI):
self.server = server
self.path = path
self.communicator: Optional[DeviceCommunicator] = None
self.paired = False
self.connected = False
self.name: Optional[str] = None
self.notification_... |
class AddDebugSignalPass(BasePass):
debug_pins = MetadataKey(set)
def __call__(self, top, signal_names):
s_signal_names = []
for name in signal_names:
assert name.startswith('top.')
assert ('[' not in name), "Currently don't support any array of components"
s_... |
def read_data(in_f):
with io.open(in_f, 'r', encoding='utf-8') as json_data:
data = json.load(json_data)
for show in data:
show_id = show['id']
for (id_s, scene) in enumerate(show['scenes']):
for (id_t, talk) in enumerate(scene):
if ('meta'... |
class SocketWrapper(AsyncExitStack):
def __init__(self, grpc_connection: GRPCConnection, stream: anyio.abc.SocketStream):
super().__init__()
self._set_socket_options(stream)
self._stream = stream
self._grpc_connection = grpc_connection
self._flush_event = anyio.Event()
... |
_vcs_handler('git', 'pieces_from_vcs')
def git_pieces_from_vcs(tag_prefix, root, verbose, runner=run_command):
GITS = ['git']
if (sys.platform == 'win32'):
GITS = ['git.cmd', 'git.exe']
env = os.environ.copy()
env.pop('GIT_DIR', None)
runner = functools.partial(runner, env=env)
(_, rc) =... |
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