code stringlengths 281 23.7M |
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class TestAviarySDK():
def test_get_backend(self, aviary_testing_model):
backend = sdk.get_aviary_backend()
assert backend
def test_get_aviary(self, aviary_testing_model):
completions = sdk.completions(model=aviary_testing_model, prompt='test')
assert completions
def test_lis... |
_on_failure
.parametrize('number_of_nodes', [2])
.parametrize('deposit', [0])
.parametrize('enable_rest_api', [True])
def test_api_channel_set_reveal_timeout(api_server_test_instance: APIServer, raiden_network: List[RaidenService], token_addresses, settle_timeout):
(app0, app1) = raiden_network
token_address = ... |
def _call_return(call: Dict[(str, Any)]) -> Callable[([Optional[Callable[(..., Any)]], Optional[Callable[(..., Any)]]], Any)]:
global _js_result_timeout
call_id = call['call']
def return_func(callback: Optional[Callable[(..., Any)]]=None, error_callback: Optional[Callable[(..., Any)]]=None) -> Any:
... |
def match_qdmr_arg_to_groundings(arg, grnds):
matches = []
arg = clean_qdmr_arg(arg)
for grnd in grnds:
if (grnd.iscol() or grnd.istbl() or grnd.isval()):
name = grnd.keys[(- 1)]
if is_text_match(arg, name):
matches.append(grnd)
else:
raise... |
class ClipGradNorm(object):
def __init__(self, start_iteration=0, end_iteration=(- 1), max_norm=0.5):
self.start_iteration = start_iteration
self.end_iteration = end_iteration
self.max_norm = max_norm
self.last_epoch = (- 1)
def __call__(self, parameters):
self.last_epoch... |
_loss('label_smoothing_cross_entropy')
class LabelSmoothingCrossEntropyLoss(ClassyLoss):
def __init__(self, ignore_index=(- 100), reduction='mean', smoothing_param=None):
super().__init__()
self._ignore_index = ignore_index
self._reduction = reduction
self._smoothing_param = smoothin... |
class VersionChange(enum.Enum):
unknown = enum.auto()
equal = enum.auto()
downgrade = enum.auto()
patch = enum.auto()
minor = enum.auto()
major = enum.auto()
def matches_filter(self, filterstr: str) -> bool:
allowed_values: Dict[(str, List[VersionChange])] = {'major': [VersionChange.... |
def get_operator(values):
n = len(values)
pauli_list = []
for i in range(n):
for j in range(i):
x_p = np.zeros(n, dtype=bool)
z_p = np.zeros(n, dtype=bool)
z_p[i] = True
z_p[j] = True
pauli_list.append([((2.0 * values[i]) * values[j]), Paul... |
def test_get_expected_output_filenames_for_example():
from reana.reana_dev.run import get_expected_output_filenames_for_example
for (example, output) in (('', ('plot.png',)), ('reana-demo-helloworld', ('greetings.txt',)), ('reana-demo-root6-roofit', ('plot.png',)), ('reana-demo-alice-lego-train-test-run', ('plo... |
class ModelParallel(nn.Module):
def __init__(self, chunks, device_list):
super(ModelParallel, self).__init__()
self.chunks = chunks
self.device_list = device_list
def c(self, input, i):
if ((input.type() == 'torch.FloatTensor') and ('cuda' in self.device_list[i])):
in... |
class RealGaborLayer(nn.Module):
def __init__(self, in_features, out_features, bias=True, is_first=False, omega0=10.0, sigma0=10.0, trainable=False):
super().__init__()
self.omega_0 = omega0
self.scale_0 = sigma0
self.is_first = is_first
self.in_features = in_features
... |
(callback=triggered)
(user='darren', host='radiant', key='/home/darren/.ssh/id_rsa.pub', python='/home/darren/venv/bin/python')
def echo(e):
print('echo: {}'.format(threading.current_thread().name))
with open('/tmp/echo.out', 'w') as pr:
pr.write('Echo! {}'.format(e))
return 'Echo! {}'.format(e) |
def test_map_iterator():
sm = m.StringMap({'hi': 'bye', 'black': 'white'})
assert (sm['hi'] == 'bye')
assert (len(sm) == 2)
assert (sm['black'] == 'white')
with pytest.raises(KeyError):
assert sm['orange']
sm['orange'] = 'banana'
assert (sm['orange'] == 'banana')
expected = {'hi'... |
class AverageMeter(object):
def __init__(self):
self.reset()
def reset(self):
self.val = 0
self.avg = 0
self.sum = 0
self.count = 0
def update(self, val, n=1):
self.val = val
self.sum += (val * n)
self.count += n
self.avg = (self.sum / ... |
def test(models, dataloaders, mode='val'):
assert ((mode == 'val') or (mode == 'test'))
models['backbone'].eval()
total = 0
correct = 0
with torch.no_grad():
for (inputs, labels) in dataloaders[mode]:
inputs = inputs.cuda()
labels = labels.cuda()
scores = ... |
def test_follow_redirects_filtered_by_site_after_redirect():
link = '/resource'
redirected = '/redirected'
filtered = '
with start_server(Response(link, 301, {'Location': redirected}), Response(redirected, 301, {'Location': filtered})) as url:
hosts = [socket.gethostname().lower()]
asser... |
.parametrize('prefix,path,expected', [('test', 'foo', 'test/foo'), ('test', 'bar', 'test/bar'), ('test', '/bar', 'test/bar'), ('test', '../foo', 'test/foo'), ('test', 'foo/bar/baz', 'test/baz'), ('test', 'foo/../baz', 'test/baz'), (None, 'foo', 'foo'), (None, 'foo/bar/baz', 'baz')])
def test_filepath(prefix, path, expe... |
.parametrize('method', ['advi', 'ADVI+adapt_diag', 'advi_map', 'jitter+adapt_diag', 'adapt_diag', 'map', 'adapt_full', 'jitter+adapt_full'])
def test_exec_nuts_init(method):
if method.endswith('adapt_full'):
with pytest.warns(UserWarning, match='experimental feature'):
check_exec_nuts_init(metho... |
.parametrize('rank, attn_axes, expected', quadratic_attention)
def test_build_quadratic_attention(rank, attn_axes, expected):
result = build_quadratic_attention_equation(rank, attn_axes)
(dot_product_equation, combine_equation, attn_scores_rank) = result
assert (dot_product_equation == expected[0])
asse... |
class Migration(migrations.Migration):
dependencies = [('api', '0050_remove_infractions_active_default_value')]
operations = [migrations.AlterField(model_name='deletedmessage', name='embeds', field=django.contrib.postgres.fields.ArrayField(base_field=django.contrib.postgres.fields.jsonb.JSONField(validators=[])... |
class ScratchPadBaseConfic(Config):
auto_fullscreen = True
screens = []
groups = [libqtile.config.ScratchPad('SCRATCHPAD', dropdowns=[libqtile.config.DropDown('dd-a', spawn_cmd('dd-a'), on_focus_lost_hide=False), libqtile.config.DropDown('dd-b', spawn_cmd('dd-b'), on_focus_lost_hide=False), libqtile.config.... |
class MinimumEigenOptimizationResult(OptimizationResult):
def __init__(self, x: Union[(List[float], np.ndarray)], fval: float, variables: List[Variable], status: OptimizationResultStatus, samples: Optional[List[SolutionSample]]=None, min_eigen_solver_result: Optional[MinimumEigensolverResult]=None, raw_samples: Opt... |
.functions
(df=df_strategy())
(deadline=None, max_examples=10)
def test_bin_numeric_expected_columns(df):
df = df.bin_numeric(from_column_name='a', to_column_name='a_bin')
expected_columns = ['a', 'Bell__Chart', 'decorated-elephant', '#$%^', 'cities', 'a_bin']
assert (set(df.columns) == set(expected_columns... |
def test_assert_key_type_value_no_value_raises():
info = ContextItemInfo(key='key1', key_in_context=True, expected_type=str, is_expected_type=True, has_value=False)
with pytest.raises(KeyInContextHasNoValueError) as err_info:
Context().assert_key_type_value(info, 'mydesc')
assert (str(err_info.value... |
def main(params):
imgs = json.load(open(params['input_json'], 'r'))
imgs = imgs['images']
seed(123)
vocab = build_vocab(imgs, params)
itow = {(i + 1): w for (i, w) in enumerate(vocab)}
wtoi = {w: (i + 1) for (i, w) in enumerate(vocab)}
(L, label_start_ix, label_end_ix, label_length) = encode... |
class Critic(nn.Module):
def __init__(self, nb_states, nb_actions, hidden=256, init_w=0.3):
super(Critic, self).__init__()
self.fc1 = nn.Linear(nb_states, hidden)
self.fc2 = nn.Linear((hidden + nb_actions), hidden)
self.fc3 = nn.Linear(hidden, hidden)
self.fc4 = nn.Linear(hid... |
class ProcessTest(unittest.TestCase):
def tearDown(self) -> None:
current_process = psutil.Process()
self.assertEqual(len(current_process.children()), 0, 'zombie children processes!')
def test_returncode(self) -> None:
with self.assertRaisesRegex(SystemExit, '^0$'):
main(['--... |
def mat2quat(mat):
mat = np.asarray(mat, dtype=np.float64)
assert (mat.shape[(- 2):] == (3, 3)), 'Invalid shape matrix {}'.format(mat)
(Qxx, Qyx, Qzx) = (mat[(..., 0, 0)], mat[(..., 0, 1)], mat[(..., 0, 2)])
(Qxy, Qyy, Qzy) = (mat[(..., 1, 0)], mat[(..., 1, 1)], mat[(..., 1, 2)])
(Qxz, Qyz, Qzz) = (... |
def update_shared_token_timestamp(message: Message, context: ContextTypes.DEFAULT_TYPE) -> str:
chat_data = cast(Dict, context.chat_data)
key = 'shared_token_timestamp'
last_time = chat_data.get(key)
current_time = message.date
chat_data[key] = current_time
if (last_time is None):
return... |
class EventsDialog(Factory.Popup):
__events__ = ('on_release', 'on_press')
def __init__(self, **kwargs):
super(EventsDialog, self).__init__(**kwargs)
def on_release(self, instance):
pass
def on_press(self, instance):
pass
def close(self):
self.dismiss() |
class EvenniaTest(TestCase):
account_typeclass = DefaultAccount
object_typeclass = DefaultObject
character_typeclass = DefaultCharacter
exit_typeclass = DefaultExit
room_typeclass = DefaultRoom
script_typeclass = DefaultScript
('evennia.scripts.taskhandler.deferLater', _mock_deferlater)
... |
def test_get_news_articles_with_invalid_site(graphql_client):
user = UserFactory()
parent = GenericPageFactory()
NewsArticleFactory(title='Article 1', parent=parent, owner=user, first_published_at=datetime.datetime(2010, 1, 1, 10, 0, 0))
SiteFactory(hostname='pycon2', root_page=parent)
query = 'quer... |
class IndexedDatasetBuilder(object):
element_sizes = {np.uint8: 1, np.int8: 1, np.int16: 2, np.int32: 4, np.int64: 8, np.float: 4, np.double: 8}
def __init__(self, out_file, dtype=np.int32):
self.out_file = open(out_file, 'wb')
self.dtype = dtype
self.data_offsets = [0]
self.dim_... |
class TestTaskNeedsYield(TestNameCheckVisitorBase):
_fails(ErrorCode.task_needs_yield)
def test_constfuture(self):
from asynq import asynq, ConstFuture
()
def bad_async_fn():
return ConstFuture(3)
_fails(ErrorCode.task_needs_yield)
def test_async(self):
from a... |
class Question(models.Model):
question_text = models.CharField(max_length=200)
pub_date = models.DateTimeField('date published')
def __str__(self):
return self.question_text
def was_published_recently(self):
now = timezone.now()
return ((now - datetime.timedelta(days=1)) <= self.... |
def _pbs_to_saga_jobstate(state):
if (state == 'C'):
return api.DONE
elif (state == 'F'):
return api.DONE
elif (state == 'H'):
return api.PENDING
elif (state == 'Q'):
return api.PENDING
elif (state == 'S'):
return api.PENDING
elif (state == 'W'):
r... |
def test_transform_point_multi(runner):
result = runner.invoke(main_group, ['transform', '--dst-crs', 'EPSG:32618', '--precision', '2'], '[-78.0, 23.0]\n[-78.0, 23.0]', catch_exceptions=False)
assert (result.exit_code == 0)
assert (result.output.strip() == '[192457.13, 2546667.68]\n[192457.13, 2546667.68]') |
(allow_output_mutation=True, show_spinner=False, hash_funcs=HASH_FUNCS)
_grad()
def flow_w_to_z(flow_model, w, attributes, lighting):
w_cuda = torch.Tensor(w)
att_cuda = torch.from_numpy(np.asarray(attributes)).float().unsqueeze(0).unsqueeze((- 1)).unsqueeze((- 1))
light_cuda = torch.Tensor(lighting)
fe... |
class BasePjitPartitioner(BasePartitioner):
_property
def _local_chunker(self) -> LocalChunker:
return LocalChunker(self.mesh)
_property
def mesh(self) -> Mesh:
return default_mesh(self._num_partitions, self._model_parallel_submesh, self._backend)
def partition(self, fn: Callable, in... |
class GherkinTerminalReporter(TerminalReporter):
def __init__(self, config: Config) -> None:
super().__init__(config)
def pytest_runtest_logreport(self, report: TestReport) -> Any:
rep = report
res = self.config.hook.pytest_report_teststatus(report=rep, config=self.config)
(cat, ... |
class PulseAudioOperation(PulseAudioMainloopChild):
_state_name = {pa.PA_OPERATION_RUNNING: 'Running', pa.PA_OPERATION_DONE: 'Done', pa.PA_OPERATION_CANCELLED: 'Cancelled'}
def __init__(self, callback_lump, pa_operation: pa.pa_operation) -> None:
context = callback_lump.context
assert (context.m... |
def unique_config_sections(config_file):
section_counters = defaultdict(int)
output_stream = io.StringIO()
with open(config_file) as fin:
for line in fin:
if line.startswith('['):
section = line.strip().strip('[]')
_section = ((section + '_') + str(section... |
class Normal(Distribution):
def __init__(self, name, mean, stdv, input_type=None, startpoint=None):
super().__init__(name=name, mean=mean, stdv=stdv, startpoint=startpoint)
self.dist_type = 'Normal'
def pdf(self, x):
z = ((x - self.mean) / self.stdv)
p = (self.std_normal.pdf(z) /... |
class TABlock(nn.Module):
def __init__(self, block, num_segments, tam_cfg=dict()):
super().__init__()
self.tam_cfg = deepcopy(tam_cfg)
self.block = block
self.num_segments = num_segments
self.tam = TAM(in_channels=block.conv1.out_channels, num_segments=num_segments, **self.ta... |
class KinopoiskPage(object):
content = None
def __init__(self, source_name, instance, content=None, request=None):
self.request = (request or Request())
self.source_name = source_name
self.instance = instance
if (content is not None):
self.content = content
def el... |
_LAYERS.register_module(name='ConvAWS')
class ConvAWS2d(nn.Conv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True):
super().__init__(in_channels, out_channels, kernel_size, stride=stride, padding=padding, dilation=dilation, groups=groups, bia... |
def test_invalid_skip_keyword_parameter(pytester: Pytester) -> None:
pytester.makepyfile('\n import pytest\n pytest.skip("skip_module_level", unknown=1)\n\n def test_func():\n assert 0\n ')
result = pytester.runpytest()
result.stdout.fnmatch_lines(["*TypeError:*['unknown']... |
def _make_init(cls, attrs, pre_init, pre_init_has_args, post_init, frozen, slots, cache_hash, base_attr_map, is_exc, cls_on_setattr, attrs_init):
has_cls_on_setattr = ((cls_on_setattr is not None) and (cls_on_setattr is not setters.NO_OP))
if (frozen and has_cls_on_setattr):
msg = "Frozen classes can't ... |
class HorizontalFlip(DualTransform):
identity_param = False
def __init__(self):
super().__init__('apply', [False, True])
def apply_aug_image(self, image, apply=False, **kwargs):
if apply:
image = F.hflip(image)
return image
def apply_deaug_mask(self, mask, apply=False... |
class PreviewContractViewTests(TestCase):
def setUp(self):
self.user = baker.make(settings.AUTH_USER_MODEL, is_staff=True, is_superuser=True)
self.client.force_login(self.user)
self.contract = baker.make_recipe('sponsors.tests.empty_contract', sponsorship__start_date=date.today())
se... |
class MNIST(object):
def __init__(self, **options):
transform = transforms.Compose([transforms.Resize(32), transforms.ToTensor()])
batch_size = options['batch_size']
data_root = os.path.join(options['dataroot'], 'mnist')
pin_memory = (True if options['use_gpu'] else False)
tr... |
def test_config_settings(tmp_path):
pyproject_toml: Path = (tmp_path / 'pyproject.toml')
pyproject_toml.write_text('[tool.cibuildwheel.config-settings]\nexample = "one"\nother = ["two", "three"]\n')
options_reader = OptionsReader(config_file_path=pyproject_toml, platform='linux', env={})
assert (options... |
def test_pretrainedinit():
modelA = FooModule()
constant_func = ConstantInit(val=1, bias=2, layer=['Conv2d', 'Linear'])
modelA.apply(constant_func)
modelB = FooModule()
funcB = PretrainedInit(checkpoint='modelA.pth')
modelC = nn.Linear(1, 2)
funcC = PretrainedInit(checkpoint='modelA.pth', pr... |
class TestDurations():
source = '\n from _pytest import timing\n def test_something():\n pass\n def test_2():\n timing.sleep(0.010)\n def test_1():\n timing.sleep(0.002)\n def test_3():\n timing.sleep(0.020)\n '
def test_calls(sel... |
class BinaryPrecision(MulticlassPrecision):
def __init__(self: TBinaryPrecision, *, threshold: float=0.5, device: Optional[torch.device]=None) -> None:
super().__init__(num_classes=2, device=device)
self.threshold = threshold
_mode()
def update(self: TBinaryPrecision, input: torch.Tensor, ta... |
class WnliProcessor(DataProcessor):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
warnings.warn(DEPRECATION_WARNING.format('processor'), FutureWarning)
def get_example_from_tensor_dict(self, tensor_dict):
return InputExample(tensor_dict['idx'].numpy(), tensor_dic... |
('pypyr.steps.dsl.fileinoutrewriter.StreamRewriter', spec=StreamRewriter)
def test_streamreplacepairsrewriterstep_run_step_substitutions(mock_rewriter):
context = Context({'k1': 'b', 'k2': 'd', 'k3': '{k2}', 'root': {'in': 'inpathhere', 'out': 'outpathhere', 'replacePairs': {'a': '{k1}', 'c': '{k3}'}, 'encodingIn':... |
class GetCrtcTransform(rq.ReplyRequest):
_request = rq.Struct(rq.Card8('opcode'), rq.Opcode(27), rq.RequestLength(), rq.Card32('crtc'))
_reply = rq.Struct(rq.ReplyCode(), rq.Card8('status'), rq.Card16('sequence_number'), rq.ReplyLength(), rq.Object('pending_transform', Render_Transform), rq.Bool('has_transforms... |
def _permissions_actions(caller, raw_inp, **kwargs):
choices = kwargs.get('available_choices', [])
(perm, action) = _default_parse(raw_inp, choices, ('examine', 'e'), ('remove', 'r', 'delete', 'd'))
if perm:
if (action == 'examine'):
return ('node_examine_entity', {'text': _display_perm(... |
class Citadel(Ship):
def validate(self, item):
if (item.category.name != 'Structure'):
pyfalog.error("Passed item '{0}' (category: {1}) is not under Structure category", item.name, item.category.name)
raise ValueError(('Passed item "%s" (category: (%s)) is not under Structure categor... |
class ConfirmButton(discord.ui.Button):
def __init__(self):
super().__init__(style=discord.ButtonStyle.green, label='Confirm')
async def callback(self, interaction: discord.Interaction):
(await interaction.response.defer())
self.view.proceed = True
for child in self.view.children... |
class ItemCondition(str, Enum):
NEW_ITEM = 'NewItem'
NEW_WITH_WARRANTY = 'NewWithWarranty'
NEW_OEM = 'NewOEM'
NEW_OPEN_BOX = 'NewOpenBox'
USED_LIKE_NEW = 'UsedLikeNew'
USED_VERY_GOOD = 'UsedVeryGood'
USED_GOOD = 'UsedGood'
USED_ACCEPTABLE = 'UsedAcceptable'
USED_POOR = 'UsedPoor'
... |
.wrap
def apply_feature_processors_to_kjt(features: KeyedJaggedTensor, feature_processors: Dict[(str, nn.Module)]) -> KeyedJaggedTensor:
processed_weights = []
features_dict = features.to_dict()
for key in features.keys():
jt = features_dict[key]
if (key in feature_processors):
f... |
(name='a')
def fixture_a() -> FixtureA:
return Fsm(alphabet={Charclass('a'), Charclass('b'), (~ Charclass('ab'))}, states={0, 1, 2}, initial=0, finals={1}, map={0: {Charclass('a'): 1, Charclass('b'): 2, (~ Charclass('ab')): 2}, 1: {Charclass('a'): 2, Charclass('b'): 2, (~ Charclass('ab')): 2}, 2: {Charclass('a'): 2... |
def main(rag_example_args: 'RagExampleArguments', processing_args: 'ProcessingArguments', index_hnsw_args: 'IndexHnswArguments'):
logger.info('Step 1 - Create the dataset')
assert os.path.isfile(rag_example_args.csv_path), 'Please provide a valid path to a csv file'
dataset = load_dataset('csv', data_files=... |
def _load_header(fid, pointer):
if ((pointer != 0) and (pointer is not None)):
fid.seek(pointer)
temp = dict()
(temp['id'], reserved, temp['length'], temp['link_count']) = _HeaderStruct.unpack(fid.read(24))
temp['pointer'] = pointer
return temp
else:
return None |
def is_rectangle(face):
angles = [(math.pi - l.calc_angle()) for l in face.loops]
right_angles = len([a for a in angles if (((math.pi / 2) - 0.001) < a < ((math.pi / 2) + 0.001))])
straight_angles = len([a for a in angles if ((- 0.001) < a < 0.001)])
return ((right_angles == 4) and (straight_angles == (... |
class MultinodeConstraintFcn(FcnEnum):
STATES_EQUALITY = (MultinodeConstraintFunctions.Functions.states_equality,)
CONTROLS_EQUALITY = (MultinodeConstraintFunctions.Functions.controls_equality,)
ALGEBRAIC_STATES_EQUALITY = (MultinodeConstraintFunctions.Functions.algebraic_states_equality,)
CUSTOM = (Mul... |
def allocate_batch(indices, lengths, src_sizes, tgt_sizes, batch_size_words, batch_size_sents, batch_size_multiplier, max_src_len, max_tgt_len, min_src_len, min_tgt_len, cleaning=1):
try:
import pyximport
cython_available = True
except ModuleNotFoundError as e:
cython_available = False
... |
def from_pickle(data, db_obj=None):
def process_item(item):
dtype = type(item)
if (dtype in (str, int, float, bool, bytes, SafeString, SafeBytes)):
return item
elif _IS_PACKED_DBOBJ(item):
return unpack_dbobj(item)
elif _IS_PACKED_SESSION(item):
re... |
def build_python_from_data(datas, save_path):
result_code = 'from pymiere.core import PymiereBaseObject, PymiereBaseCollection, Array, _format_object_to_py, _format_object_to_es\n'
for (name, data) in datas.items():
print("Generating object '{}'".format(name))
result_code += generate_class(data,... |
def _pest_control_score(x, seed=None):
U = 0.1
n_stages = x.size
n_simulations = 100
init_pest_frac_alpha = 1.0
init_pest_frac_beta = 30.0
spread_alpha = 1.0
spread_beta = (17.0 / 3.0)
control_alpha = 1.0
control_price_max_discount = {1: 0.2, 2: 0.3, 3: 0.3, 4: 0.0}
tolerance_dev... |
def test_normalize_percent_characters():
expected = '%3Athis_should_be_lowercase%DF%AB%4C'
assert (expected == normalize_percent_characters('%3athis_should_be_lowercase%DF%ab%4c'))
assert (expected == normalize_percent_characters('%3Athis_should_be_lowercase%DF%AB%4C'))
assert (expected == normalize_per... |
def test_unlock_account_with_passwordfile(keystore_mock):
account_manager = AccountManager(keystore_mock)
password_file_path = os.path.join(keystore_mock, 'passwordfile.txt')
with open(password_file_path, 'r') as password_file:
privkey = unlock_account_with_passwordfile(account_manager=account_manag... |
class MPNetConfig(PretrainedConfig):
model_type = 'mpnet'
def __init__(self, vocab_size=30527, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, initializer_range=0.02,... |
class PykickstartLintConfig(PocketLintConfig):
def __init__(self):
PocketLintConfig.__init__(self)
self.falsePositives = [FalsePositive('^W1113.*: Keyword argument before variable positional arguments list in the definition of __init__ function$'), FalsePositive('W0707.*raise-missing-from'), FalsePo... |
.parametrize('method', ['get_absolute_url', 'get_delete_url', 'get_down_vote_url', 'get_hashid', 'get_remove_vote_url', 'get_review_url', 'get_slug', 'get_up_vote_url', 'get_update_url', 'get_vote_url', '__str__'])
def test_proposal_model_method_works(db, method):
proposal = f.ProposalFactory()
assert getattr(p... |
def select_2(train_embs, test_embs, downstream_train_examples, downstream_test_examples, tag, phase2_selection):
cos = nn.CosineSimilarity(dim=1, eps=1e-06)
bar = tqdm(range(len(downstream_test_examples)), desc='phase 2 similar select')
if (not os.path.isdir(f'{args.output_dir}/{tag}/prompts')):
os.... |
_torch
_vision
class MaskFormerFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
feature_extraction_class = (MaskFormerFeatureExtractor if (is_vision_available() and is_torch_available()) else None)
def setUp(self):
self.feature_extract_tester = MaskFormerFeatureExtractionTeste... |
class BenchmarkTestCase(unittest.TestCase):
def tearDown(self):
for file in glob.glob('{0}/moonshot*.pkl'.format(TMP_DIR)):
os.remove(file)
def test_complain_if_no_price_fields_for_benchmark(self):
class BuyAndHold(Moonshot):
CODE = 'buy-and-hold'
DB = 'sample... |
def get_config_from_root(root):
setup_cfg = os.path.join(root, 'setup.cfg')
parser = configparser.ConfigParser()
with open(setup_cfg, 'r') as f:
parser.read_file(f)
VCS = parser.get('versioneer', 'VCS')
def get(parser, name):
if parser.has_option('versioneer', name):
retu... |
def setup() -> None:
root_log = get_logger()
if constants.FILE_LOGS:
log_file = Path('logs', 'bot.log')
log_file.parent.mkdir(exist_ok=True)
file_handler = handlers.RotatingFileHandler(log_file, maxBytes=5242880, backupCount=7, encoding='utf8')
file_handler.setFormatter(core_logg... |
def create_fixtures(dev: SmartDevice, outputdir: Path):
for (name, module) in dev.modules.items():
module_dir = (outputdir / name)
if (not module_dir.exists()):
module_dir.mkdir(exist_ok=True, parents=True)
sw_version = dev.hw_info['sw_ver']
sw_version = sw_version.split(... |
def get_fslocation_from_item(node: 'Node') -> Tuple[(Union[(str, Path)], Optional[int])]:
location: Optional[Tuple[(str, Optional[int], str)]] = getattr(node, 'location', None)
if (location is not None):
return location[:2]
obj = getattr(node, 'obj', None)
if (obj is not None):
return ge... |
class RuleAPITests(AuthenticatedAPITestCase):
def setUp(self):
super().setUp()
self.client.force_authenticate(user=None)
def test_can_access_rules_view(self):
url = reverse('api:rules')
response = self.client.get(url)
self.assertEqual(response.status_code, 200)
se... |
.parametrize('subcommand, text, completions', [('create', '', ['jazz', 'rock']), ('create', 'ja', ['jazz ']), ('create', 'foo', []), ('creab', 'ja', [])])
def test_subcommand_completions(ac_app, subcommand, text, completions):
line = 'music {} {}'.format(subcommand, text)
endidx = len(line)
begidx = (endidx... |
class JalaliDateFormatter(BaseFormatter):
_post_parsers = ['persianday', 'persiandayzeropadded', 'persiandayofyear', 'persiandayofyearzeropadded', 'persianmonth', 'persianmonthzeropadded', 'persianyear', 'persianyearzeropadded', 'persianshortyear', 'persianshortyearzeropadded', 'localdateformat', 'monthabbr', 'mont... |
def _get_package(pl_name, version, robust, use_v8):
pl_dir = (DataDir / pl_name)
pl_dir.mkdir(parents=True, exist_ok=True)
prefix = 'pdfium-'
if use_v8:
prefix += 'v8-'
fn = (prefix + f'{ReleaseNames[pl_name]}.tgz')
fu = f'{ReleaseURL}{version}/{fn}'
fp = (pl_dir / fn)
print(f"'{... |
def integration_value(direction: Direction, subsystem: Subsystem, partition: Cut, system_state: SystemStateSpecification, repertoire_distance: Optional[str]=None) -> float:
repertoire_distance = fallback(repertoire_distance, config.REPERTOIRE_DISTANCE)
cut_subsystem = subsystem.apply_cut(partition)
if (repe... |
_start_docstrings('The bare MGP-STR Model transformer outputting raw hidden-states without any specific head on top.', MGP_STR_START_DOCSTRING)
class MgpstrModel(MgpstrPreTrainedModel):
def __init__(self, config: MgpstrConfig):
super().__init__(config)
self.config = config
self.embeddings = ... |
def score_bw(args):
if args.backwards1:
scorer1_src = args.target_lang
scorer1_tgt = args.source_lang
else:
scorer1_src = args.source_lang
scorer1_tgt = args.target_lang
if (args.score_model2 is not None):
if args.backwards2:
scorer2_src = args.target_lang... |
def get_dist_info():
if (torch.__version__ < '1.0'):
initialized = dist._initialized
else:
initialized = dist.is_initialized()
if initialized:
rank = dist.get_rank()
world_size = dist.get_world_size()
else:
rank = 0
world_size = 1
return (rank, world_s... |
def _image_file(path):
abs_path = os.path.abspath(path)
image_files = os.listdir(abs_path)
for i in range(len(image_files)):
if ((not os.path.isdir(image_files[i])) and _is_image_file(image_files[i])):
image_files[i] = os.path.join(abs_path, image_files[i])
return image_files |
def c_array_initializer(components: list[str], *, indented: bool=False) -> str:
indent = ((' ' * 4) if indented else '')
res = []
current: list[str] = []
cur_len = 0
for c in components:
if ((not current) or ((((cur_len + 2) + len(indent)) + len(c)) < 70)):
current.append(c)
... |
def se_inception_v3(include_top=True, weights=None, input_tensor=None, input_shape=None, pooling=None, classes=1000):
if (weights not in {'imagenet', None}):
raise ValueError('The `weights` argument should be either `None` (random initialization) or `imagenet` (pre-training on ImageNet).')
if ((weights ... |
def timeout_exponential_backoff(retries: int, timeout: float, maximum: float) -> Iterator[float]:
(yield timeout)
tries = 1
while (tries < retries):
tries += 1
(yield timeout)
while (timeout < maximum):
timeout = min((timeout * 2), maximum)
(yield timeout)
while True:... |
def _make_system(N, system):
gamma = 0.25
a = destroy(N)
if (system == 'simple'):
H = (a.dag() * a)
sc_ops = [(np.sqrt(gamma) * a)]
elif (system == '2 c_ops'):
H = QobjEvo([(a.dag() * a)])
sc_ops = [(np.sqrt(gamma) * a), ((gamma * a) * a)]
elif (system == 'H td'):
... |
def run_main():
topdirs = [('/%s/' % d) for d in os.listdir('/')]
def abs_path_start(path, pos):
if (pos < 0):
return False
return ((pos == 0) or (path[(pos - 1)] == ':'))
def fix_path(p):
pp = None
for pr in topdirs:
pp2 = p.find(pr)
if (a... |
()
('--filename', default='samples/sample_wind_poitiers.csv', help='Input filename')
('--year', default=2014, help='Year')
def main(filename, year):
df_all = pd.read_csv(filename, parse_dates=['Timestamp'])
df_all = df_all.set_index('Timestamp')
f_year = get_by_func('year')
df_all['by_page'] = df_all.in... |
_fixtures(WebFixture, AddressAppFixture)
def test_adding_an_address(web_fixture, address_app_fixture):
browser = address_app_fixture.browser
browser.open('/')
browser.click(XPath.link().with_text('Add'))
assert address_app_fixture.is_on_add_page()
browser.type(XPath.input_labelled('Name'), 'John Doe... |
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