code stringlengths 281 23.7M |
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()
('-wmt')
('-lang')
('-sys_name')
('-src_ref', help='src or ref')
('-loaded', type=bool, default=False)
('-ckpt_addr', help='LLama_finetune_april_8/checkpoint-148', default=None)
('-start_index', type=int)
('-end_index', type=int)
('-batch_size', type=int)
('-sample', type=bool)
('-num_ret', type=int)
('-task_mode', ... |
def brush_stroke_mask(img, color=(255, 255, 255)):
min_num_vertex = 8
max_num_vertex = 28
mean_angle = ((2 * math.pi) / 5)
angle_range = ((2 * math.pi) / 15)
min_width = 12
max_width = 80
def generate_mask(H, W, img=None):
average_radius = (math.sqrt(((H * H) + (W * W))) / 8)
... |
class DeformConv2dPackMore(DeformConv2d):
def __init__(self, in_channels, out_channels, kernel_size, stride, padding, dilation=1, groups=1, deformable_groups=1, im2col_step=64, bias=True, lr_mult=0.1):
super(DeformConv2dPackMore, self).__init__(in_channels, out_channels, kernel_size, stride, padding, dilati... |
def main(_):
if (not FLAGS.output_file):
raise ValueError('You must supply the path to save to with --output_file')
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default() as graph:
dataset = dataset_factory.get_dataset(FLAGS.dataset_name, 'train', FLAGS.dataset_dir)
n... |
def test_repair_destroy_path():
(x, y, z) = inputs()
e1 = transpose_view(transpose_view(x))
e2 = transpose_view(transpose_view(e1))
e3 = add_in_place(e2, y)
e4 = add_in_place(e1, z)
g = create_fgraph([x, y, z], [e3, e4], False)
assert (not g.consistent())
g.replace(e2, transpose_view(x))... |
def apply_to_tensor(f, sample):
if (len(sample) == 0):
return {}
def _apply(x):
if torch.is_tensor(x):
return f(x)
elif isinstance(x, dict):
return {key: _apply(value) for (key, value) in x.items()}
elif isinstance(x, list):
return [_apply(x) f... |
.end_to_end()
def test_if_skipif_decorator_is_applied_skipping(tmp_path):
source = '\n import pytask\n\n .skipif(condition=True, reason="bla")\n .produces("out.txt")\n def task_first():\n assert False\n\n .depends_on("out.txt")\n def task_second():\n assert False\n '
tmp_path.... |
class BooleanTest(object):
def test_false(self):
assert (inputs.boolean('False') is False)
def test_0(self):
assert (inputs.boolean('0') is False)
def test_true(self):
assert (inputs.boolean('true') is True)
def test_1(self):
assert (inputs.boolean('1') is True)
def t... |
class RollingVirtualStorage(Loadable, Drawable, _core.RollingVirtualStorage, metaclass=NodeMeta):
__parameter_attributes__ = ('min_volume', 'max_volume')
__node_attributes__ = ('nodes',)
def __init__(self, model, name, nodes, **kwargs):
min_volume = pop_kwarg_parameter(kwargs, 'min_volume', 0.0)
... |
def adjust_assets(scenes):
sim = None
global_mapping_path = 'cos_eor/utils/global_mapping_v3_local.yaml'
if (os.path.exists(global_mapping_path) and False):
global_mapping = yaml.load(open(global_mapping_path, 'r'), Loader=yaml.BaseLoader)
else:
global_mapping = {'mapping_igib': {}, 'sce... |
def get_price(plan, require_business_plan):
if (not features.BILLING):
return
plan_found = None
for plan_obj in PLANS:
if (plan_obj['stripeId'] == plan):
plan_found = plan_obj
if ((not plan_found) or plan_found['deprecated']):
logger.warning('Plan not found or depreca... |
def test_transform_types_not_params_array():
data = {'attr': [1, 2, 3]}
custom_types = {'attr': types.ArrayAttribute}
(new_data, files) = utils._transform_types(data, custom_types, transform_data=False)
assert (new_data is not data)
assert (new_data == data)
assert (files == {}) |
def create_report(seeds_dir: str, output_file: Path):
def item_creator():
return collections.defaultdict(list)
item_name_to_location = collections.defaultdict(item_creator)
seed_files = list(Path(seeds_dir).glob(f'**/*.{LayoutDescription.file_extension()}'))
seed_files.extend(Path(seeds_dir).glo... |
def get_release_notes_template(template_dir: Path) -> str:
fname = (template_dir / '.release_notes.md.j2')
try:
return fname.read_text(encoding='utf-8')
except FileNotFoundError:
return files('semantic_release').joinpath('data/templates/release_notes.md.j2').read_text(encoding='utf-8') |
class RateLimitError(APIError):
def __init__(self, *args, **kwargs):
self.response_headers = kwargs.pop('response_headers', None)
self.rl_limit = int(self.response_headers.get('X-Ratelimit-Limit'))
self.rl_reset = datetime.fromtimestamp(int(self.response_headers.get('X-Ratelimit-Reset')))
... |
def ksboolean(value):
try:
if (value.lower() in ('on', 'yes', 'true', '1')):
return True
elif (value.lower() in ('off', 'no', 'false', '0')):
return False
else:
raise ArgumentTypeError((_('invalid boolean value: %r') % value))
except AttributeError:
... |
class EthicsUtilitarianism(Ethics):
VERSION = 0
DATASET_NAME = 'utilitarianism'
def training_docs(self):
for doc in self.dataset['train']:
(yield self._process_doc(doc))
def validation_docs(self):
raise NotImplementedError
def test_docs(self):
for doc in self.data... |
class ResAttNet(nn.Module):
def __init__(self, channels, init_block_channels, attentions, att_scales, in_channels=3, in_size=(224, 224), num_classes=1000):
super(ResAttNet, self).__init__()
self.in_size = in_size
self.num_classes = num_classes
self.features = nn.Sequential()
... |
(frozen=True)
class AM2RPerGameOptions(PerGameOptions):
input_path: (Path | None) = None
output_path: (Path | None) = None
def as_json(self) -> dict:
return {**super().as_json, 'input_path': (str(self.input_path) if (self.input_path is not None) else None), 'output_path': (str(self.output_path) if (... |
_torch
_vision
class GLPNImageProcessingTest(ImageProcessingSavingTestMixin, unittest.TestCase):
image_processing_class = (GLPNImageProcessor if is_vision_available() else None)
def setUp(self):
self.image_processor_tester = GLPNImageProcessingTester(self)
def image_processor_dict(self):
ret... |
def init(app_name):
global APP_NAME, DBUS_IFACE
APP_NAME = app_name
name = 'org.freedesktop.Notifications'
path = '/org/freedesktop/Notifications'
interface = 'org.freedesktop.Notifications'
mainloop = None
if (DBusGMainLoop is not None):
mainloop = DBusGMainLoop(set_as_default=True)... |
def test_list_type():
test_dict = {'type': 'list', 'values': {'type': 'int', 'bits': 32}}
recap_type = from_dict(test_dict)
assert isinstance(recap_type, ListType)
assert (recap_type.type_ == 'list')
assert isinstance(recap_type.values, IntType)
assert (recap_type.values.type_ == 'int')
asse... |
class DcardPost(Base, Timestamp):
__tablename__ = 'dcard_posts'
id = sa.Column(sa.Integer, primary_key=True)
forum_id = sa.Column(sa.String(64), nullable=False)
forum_name = sa.Column(sa.String(64), nullable=False)
title = sa.Column(sa.String(64, collation='utf8mb4_unicode_ci'), nullable=False)
... |
def test_parser(testcase: DataDrivenTestCase) -> None:
options = Options()
options.force_uppercase_builtins = True
options.hide_error_codes = True
if testcase.file.endswith('python310.test'):
options.python_version = (3, 10)
else:
options.python_version = defaults.PYTHON3_VERSION
... |
def boundary_err(y_pred, y_true, t, onsets_s, offsets_s, timebin_dur, n_timebin_from_onoffset, unlabeled_class=0):
frame_err_vec = (y_true != y_pred)
n_frame_err = int(frame_err_vec.sum().item())
unlabeled_err = np.logical_and(frame_err_vec, np.logical_or((y_true == unlabeled_class), (y_pred == unlabeled_cl... |
def _input():
print('Enter the visa centre: ')
visa_centre = input()
print('Enter the category: ')
category = input()
print('Enter the sub category: ')
sub_category = input()
logging.debug('Visa centre: {}, Category: {}, Sub-Category: {}'.format(visa_centre, category, sub_category))
retu... |
def initShaders():
global Shaders
Shaders = [ShaderProgram(None, []), ShaderProgram('balloon', [VertexShader('\n varying vec3 normal;\n void main() {\n // compute here for use in fragment shader\n normal = normalize(gl_NormalMatrix * gl_Normal)... |
class PD(nn.Module):
def __init__(self, nf=64, groups=8):
super(PD, self).__init__()
self.L3_offset_conv2 = nn.Conv2d(nf, nf, 3, 1, 1, bias=True)
self.L3_dcnpack = DCN(nf, nf, 3, stride=1, padding=1, dilation=1, deformable_groups=groups, extra_offset_mask=True)
self.L2_offset_conv2 =... |
class TestSequenceGeneratorBase(unittest.TestCase):
def assertHypoTokens(self, hypo, tokens):
self.assertTensorEqual(hypo['tokens'], torch.LongTensor(tokens))
def assertHypoScore(self, hypo, pos_probs, normalized=True, lenpen=1.0):
pos_scores = torch.FloatTensor(pos_probs).log()
self.ass... |
class Detect(nn.Module):
def __init__(self, num_classes=80, anchors=1, num_layers=3, inplace=True, head_layers=None, use_dfl=True, reg_max=16):
super().__init__()
assert (head_layers is not None)
self.nc = num_classes
self.no = (num_classes + 5)
self.nl = num_layers
i... |
def fallback_version(root: _t.PathT, config: Configuration) -> (ScmVersion | None):
if (config.parentdir_prefix_version is not None):
(_, parent_name) = os.path.split(os.path.abspath(root))
if parent_name.startswith(config.parentdir_prefix_version):
version = tag_to_version(parent_name[l... |
('pypyr.venv.EnvBuilderWithExtraDeps')
def test_venv_create(mock_builder):
context = get_simple_context()
mocked_builder = mock_builder.return_value
mocked_builder.context = context
venv.run_step(Context({'venv': '/arb'}))
expected_path = str(Path('/arb').expanduser().resolve())
mocked_builder.c... |
((os.name == 'nt'), 'BNG Console does not work on Windows')
_model
def test_simulate_network_console():
Monomer('A')
Parameter('A_0', 1)
Initial(A(), A_0)
Parameter('k', 1)
Rule('degrade', (A() >> None), k)
with BngConsole(model) as bng:
bng.generate_network()
bng.action('simulat... |
def convert_file_size_to_str(total: int) -> str:
if (total < (1 << 10)):
size = '{:.2f} B'.format(total)
elif (total < (1 << 20)):
size = '{:.2f} K'.format((total / (1 << 10)))
elif (total < (1 << 30)):
size = '{:.2f} M'.format((total / (1 << 20)))
else:
size = '{:.2f} G'... |
def train(train_loader, model, criterions, optimizer, epoch):
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
loss_protest = AverageMeter()
loss_v = AverageMeter()
protest_acc = AverageMeter()
violence_mse = AverageMeter()
visattr_acc = AverageMeter()
end = time.... |
class GPTNeoXJapaneseTokenizer(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, emoji_f... |
def unpack(compressed):
uncompressed = np.zeros((compressed.shape[0] * 8), dtype=np.uint8)
uncompressed[::8] = ((compressed[:] >> 7) & 1)
uncompressed[1::8] = ((compressed[:] >> 6) & 1)
uncompressed[2::8] = ((compressed[:] >> 5) & 1)
uncompressed[3::8] = ((compressed[:] >> 4) & 1)
uncompressed[4... |
class IASIL2SO2BUFR(BaseFileHandler):
def __init__(self, filename, filename_info, filetype_info, **kwargs):
super(IASIL2SO2BUFR, self).__init__(filename, filename_info, filetype_info)
(start_time, end_time) = self.get_start_end_date()
sc_id = self.get_attribute('satelliteIdentifier')
... |
class SawyerCoffeePullV1Policy(Policy):
_fully_parsed
def _parse_obs(obs):
return {'hand_pos': obs[:3], 'mug_pos': obs[3:6], 'unused_info': obs[6:]}
def get_action(self, obs):
o_d = self._parse_obs(obs)
action = Action({'delta_pos': np.arange(3), 'grab_effort': 3})
action['de... |
def get_packer_mapping(packers, task):
packer_mapping = {}
for (rec_id, packer) in packers.items():
obj_keys = [task.sim_obj_id_to_obj_key[obj_key] for obj_key in list(packer.matches.keys())]
rec_key = task.sim_obj_id_to_obj_key[rec_id]
for obj_key in obj_keys:
packer_mapping... |
def evaluate(args, model, tokenizer, criterion, prefix=''):
eval_output_dir = args.output_dir
eval_dataset = load_examples(args, tokenizer, evaluate=True)
if ((not os.path.exists(eval_output_dir)) and (args.local_rank in [(- 1), 0])):
os.makedirs(eval_output_dir)
args.eval_batch_size = (args.per... |
class PluginMethods(PluginActions):
def register_function(self, function, inputs, parameters, outputs, name, description, input_descriptions=None, parameter_descriptions=None, output_descriptions=None, citations=None, deprecated=False, examples=None):
if (citations is None):
citations = ()
... |
class RateLimiterBackendTests():
def setUp(self):
self.allowance = 10
self.interval = 1
ratelimiter_factory = RateLimiterContextFactory(self.backend_factory, self.allowance, self.interval)
self.baseplate_observer = TestBaseplateObserver()
baseplate = Baseplate()
basep... |
def main():
parser = argparse.ArgumentParser()
parser.add_argument('path', help='Path to result .json file as produced by 3D evaluation script. Can be downloaded from the evaluation server for test set results.')
args = parser.parse_args()
if (not os.path.exists(args.path)):
raise Exception('Res... |
class PytestArg():
def __init__(self, request: FixtureRequest) -> None:
self._request = request
def gethookrecorder(self, hook) -> 'HookRecorder':
hookrecorder = HookRecorder(hook._pm)
self._request.addfinalizer(hookrecorder.finish_recording)
return hookrecorder |
class StreamReset(Event):
def __init__(self):
self.stream_id = None
self.error_code = None
self.remote_reset = True
def __repr__(self):
return ('<StreamReset stream_id:%s, error_code:%s, remote_reset:%s>' % (self.stream_id, self.error_code, self.remote_reset)) |
class tensorboard_log_wrapper(progress_bar):
def __init__(self, wrapped_bar, tensorboard_logdir, args):
self.wrapped_bar = wrapped_bar
self.tensorboard_logdir = tensorboard_logdir
self.args = args
try:
from tensorboardX import SummaryWriter
self.SummaryWriter ... |
class TestParticleNumber(PropertyTest):
def setUp(self):
super().setUp()
num_spatial_orbitals = 4
self.prop = ParticleNumber(num_spatial_orbitals)
def test_second_q_ops(self):
ops = self.prop.second_q_ops()['ParticleNumber']
expected = {'+_0 -_0': 1.0, '+_1 -_1': 1.0, '+_... |
class TestTransformerStretch(unittest.TestCase):
def test_default(self):
tfm = new_transformer()
tfm.stretch(1.1)
actual_args = tfm.effects
expected_args = ['stretch', '1.100000', '20.000000']
self.assertEqual(expected_args, actual_args)
actual_log = tfm.effects_log
... |
def abort_current_continuation(args, env, cont):
from pycket.interpreter import return_multi_vals
if (not args):
raise SchemeException('abort-current-continuation: expected 1 or more args')
(tag, args) = (args[0], args[1:])
if (not isinstance(tag, values.W_ContinuationPromptTag)):
raise ... |
class InceptionResnetV2(nn.Module):
def __init__(self, num_classes=1000, in_chans=3, drop_rate=0.0, output_stride=32, global_pool='avg'):
super(InceptionResnetV2, self).__init__()
self.drop_rate = drop_rate
self.num_classes = num_classes
self.num_features = 1536
assert (outpu... |
class ThriftContextFactory(ContextFactory):
POOL_PREFIX = 'thrift_client_pool'
POOL_LABELS = ['thrift_pool']
max_connections_gauge = Gauge(f'{POOL_PREFIX}_max_size', 'Maximum number of connections in this thrift pool before blocking', POOL_LABELS)
active_connections_gauge = Gauge(f'{POOL_PREFIX}_active_... |
class CellB(nn.Module):
def __init__(self, in_planes, out_planes, stride=1):
super(CellB, self).__init__()
self.stride = stride
self.sep_conv1 = SepConv(in_planes, out_planes, kernel_size=7, stride=stride)
self.sep_conv2 = SepConv(in_planes, out_planes, kernel_size=3, stride=stride)
... |
class Model(object):
def __init__(self):
self.history = False
def present(self):
return (self.conf['state']['imu.rate'] * self.conf['past'])
def receive(self, name, value):
if ((name in self.conf['sensors']) and self.enabled):
self.inputs[name] = norm_sensor(name, value) |
class LassoXmlLexer(DelegatingLexer):
name = 'XML+Lasso'
aliases = ['xml+lasso']
version_added = '1.6'
alias_filenames = ['*.xml', '*.lasso', '*.lasso[89]', '*.incl', '*.inc', '*.las']
mimetypes = ['application/xml+lasso']
url = '
def __init__(self, **options):
super().__init__(XmlLe... |
class ModelArguments():
model_name_or_path: str = field(metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'})
config_name: Optional[str] = field(default=None, metadata={'help': 'Pretrained config name or path if not the same as model_name'})
tokenizer_name: Optional[s... |
def test_search__with_annotations(requests_mock):
requests_mock.get(f'{API_V1}/observations', json=SAMPLE_DATA['get_observation_with_ofvs'], status_code=200)
requests_mock.get(f'{API_V1}/controlled_terms', json=SAMPLE_DATA['get_controlled_terms'], status_code=200)
results = iNatClient().observations.search(... |
def measure_peak_memory_cpu(function: Callable[([], None)], interval=0.5, device_idx=None) -> int:
def get_cpu_memory(process_id: int) -> int:
process = psutil.Process(process_id)
try:
meminfo_attr = ('memory_info' if hasattr(process, 'memory_info') else 'get_memory_info')
me... |
class BenchmarkMainComplex():
params = [['parallel', 'sequential'], ['basic', 'rule154', 'fig16'], ['local', 'redis']]
param_names = ['mode', 'network', 'cache']
timer = timeit.default_timer
number = 1
repeat = 1
timeout = 10000
def setup(self, mode, network, cache):
if (network == '... |
def evaluate(args):
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)
model = BertAbs.from_pretrained('remi/bertabs-finetuned-extractive-abstractive-summarization')
model.to(args.device)
model.eval()
symbols = {'BOS': tokenizer.vocab['[unused0]'], 'EOS': tokenizer.vo... |
class GraphClassificationDataset(GraphDataset):
def __init__(self, graphs, labels):
super().__init__(graphs)
self.labels = labels
assert (len(graphs) == len(labels))
def __getitem__(self, index):
return (self.graphs[index], self.labels[index])
def collate_fn(batch):
(... |
class UnetBottleneck(nn.Module):
def __init__(self, inplanes, planes, kernel_size=3, dilation=1, act_type='relu'):
super(UnetBottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=kernel_size, stride=1, padding=get_same_padding(kernel_size, dilation), dilation=dilation)
... |
def test_opdm_to_ohdm_mapping():
db = opdm_to_ohdm_mapping(6)
for dbe in db:
assert isinstance(dbe, DualBasisElement)
assert (set(dbe.primal_tensors_names) == {'ck', 'kc'})
if (len(dbe.primal_tensors_names) == 4):
assert np.allclose(dbe.primal_coeffs, 0.5)
assert ... |
def log(s, with_prefix=True, with_timestamp=True, color=None):
out = s
if with_prefix:
out = (_prefix_str + out)
if with_timestamp:
now = datetime.datetime.now(dateutil.tz.tzlocal())
timestamp = now.strftime('%Y-%m-%d %H:%M:%S.%f %Z')
out = ('%s | %s' % (timestamp, out))
... |
class VAEAugExperiment(object):
def __init__(self, config):
self.config = config
self.device = self._get_device()
self.writer = SummaryWriter(os.path.join(self.config['save_dir'], self.config['vae_mode'], 'tensorboard'))
self.nt_xent_criterion = NTXentLoss(self.device, **config['loss... |
.parametrize(('metroids', 'stronger_metroids', 'bosses', 'artifacts'), [(False, False, True, 5), (False, True, False, 15), (True, False, True, 40), (True, True, True, 40)])
def test_msr_artifact_pool_should_throw_on_invalid_config(msr_game_description, metroids, stronger_metroids, bosses, artifacts):
configuration ... |
def set_app_menu(app_menu_list):
class InternalMenu():
def __init__(self, title, parent):
self.m = AppKit.NSMenu.alloc().init()
self.item = AppKit.NSMenuItem.alloc().init()
self.item.setSubmenu_(self.m)
if (not isinstance(parent, self.__class__)):
... |
class PLMInputFeatures(InputFeatures):
def __init__(self, *_, perm_mask, target_mapping, **kwargs):
super().__init__(**kwargs)
self.perm_mask = perm_mask
self.target_mapping = target_mapping
def pretty_print(self, tokenizer):
return (((super().pretty_print(tokenizer) + '\n') + f'... |
class TaskSetTimeHandler(TaskNewHandler):
.authenticated
async def get(self, taskid):
user = self.current_user
task = self.check_permission((await self.db.task.get(taskid, fields=('id', 'userid', 'tplid', 'disabled', 'note', 'ontime', 'ontimeflg', 'newontime'))), 'w')
newontime = json.lo... |
class SelectOnRemove(MappingType):
MAPPING = {'prev': (QTabBar.SelectionBehavior.SelectLeftTab, 'Select the tab which came before the closed one (left in horizontal, above in vertical).'), 'next': (QTabBar.SelectionBehavior.SelectRightTab, 'Select the tab which came after the closed one (right in horizontal, below ... |
class FP16Optimizer(_FP16OptimizerMixin, optim.FairseqOptimizer):
def __init__(self, args, params, fp32_optimizer, fp32_params):
super().__init__(args)
self.fp16_params = params
self.fp32_optimizer = fp32_optimizer
self.fp32_params = fp32_params
if (getattr(args, 'fp16_scale_... |
def find_extremes(cluster_list, dataset, jaccard_threshold):
global _shared_dataset
_shared_dataset = dataset
extremes_list = []
f = partial(_find_cluster_extremes_shared, jaccard_threshold=jaccard_threshold)
with mp.Pool() as pool:
for extremes in tqdm(pool.imap_unordered(f, cluster_list), ... |
class DefinitionWideFormat(sphinx_jsonschema.wide_format.WideFormat):
def _objecttype(self, schema):
rows = self._simpletype(schema)
rows.extend(self._objectproperties(schema, 'definition'))
rows.extend(self._objectproperties(schema, 'properties'))
rows.extend(self._objectproperties(... |
class SimclrInfoNCECriterion(nn.Module):
def __init__(self, buffer_params, temperature: float):
super(SimclrInfoNCECriterion, self).__init__()
self.use_gpu = (get_cuda_device_index() > (- 1))
self.temperature = temperature
self.num_pos = 2
self.buffer_params = buffer_params
... |
class ReadInputRegistersRequest(ReadRegistersRequestBase):
function_code = 4
function_code_name = 'read_input_registers'
def __init__(self, address=None, count=None, slave=0, **kwargs):
super().__init__(address, count, slave, **kwargs)
def execute(self, context):
if (not (1 <= self.count... |
def deconv2D_layer(l0, name=None, filters=32, kernel_size=3, strides=2, padding='same', activation='relu', kernel_initializer='he_normal'):
l = Conv2DTranspose(filters=filters, name=name, kernel_size=kernel_size, strides=strides, padding=padding, activation=activation, kernel_initializer=kernel_initializer)(l0)
... |
def test_create_poetry_with_local_config(fixture_dir: FixtureDirGetter) -> None:
poetry = Factory().create_poetry(fixture_dir('with_local_config'))
assert (not poetry.config.get('virtualenvs.in-project'))
assert (not poetry.config.get('virtualenvs.create'))
assert (not poetry.config.get('virtualenvs.opt... |
def DFOM(pattern, ItemS):
count = 0
Nettree = [[[] for i in range(SeqNum)] for k in range(len(pattern))]
unit = [[] for k in range(len(pattern))]
for i in range(len(pattern)):
unit[i] = ItemS[str(pattern[i])]
for i in range(SeqNum):
for m in range(len(unit[0][i])):
bbb = ... |
def main():
try:
session = saga.Session()
print('Connecting...')
js = saga.job.Service('pbs+ssh://sierra.futuregrid.org', session=session)
print('CONNECTED')
jd = saga.job.Description()
jd.queue = 'batch'
jd.environment = {'RUNTIME': '5'}
jd.wall_time_... |
def main():
parser = argparse.ArgumentParser(description='Convert model keys')
parser.add_argument('src', help='src detectron model path')
parser.add_argument('dst', help='save path')
parser.add_argument('depth', type=int, help='ResNet model depth')
args = parser.parse_args()
convert(args.src, a... |
.parametrize('const_shape', [(), (1,), (5,), (1, 5), (2, 5)])
.parametrize('op, np_op', [(pt.pow, np.power), (pt.add, np.add)])
def test_local_inline_composite_constants(op, np_op, const_shape):
const = np.full(shape=const_shape, fill_value=2.5).astype(config.floatX)
x = vector('x')
y = vector('y')
out ... |
class Conv8(nn.Module):
def __init__(self):
super(Conv8, self).__init__()
builder = get_builder()
self.convs = nn.Sequential(builder.conv3x3(3, 64, first_layer=True), nn.ReLU(), builder.conv3x3(64, 64), nn.ReLU(), nn.MaxPool2d((2, 2)), builder.conv3x3(64, 128), nn.ReLU(), builder.conv3x3(128... |
class ChannelPutSchema(BaseSchema):
token_address = AddressField(required=True)
partner_address = AddressField(required=True)
reveal_timeout = IntegerToStringField(missing=None)
settle_timeout = IntegerToStringField(missing=None)
total_deposit = IntegerToStringField(default=None, missing=None) |
class Effect7074(BaseEffect):
type = 'passive'
def handler(fit, container, context, projectionRange, **kwargs):
level = (container.level if ('skill' in context) else 1)
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Small Disintegrator Specialization')), 'damageMultiplier', (... |
class Effect5622(BaseEffect):
type = 'passive'
def handler(fit, ship, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: (mod.item.group.name == 'Missile Launcher Torpedo')), 'speed', ship.getModifiedItemAttr('shipBonusMB'), skill='Minmatar Battleship', **kwargs) |
class RequiredImgAsset(RequiredAssetMixin, BaseRequiredImgAsset, BenefitFeature):
class Meta(BaseRequiredImgAsset.Meta, BenefitFeature.Meta):
verbose_name = 'Require Image'
verbose_name_plural = 'Require Images'
def __str__(self):
return f'Require image'
def as_form_field(self, **kwa... |
class DomainGeometricParameters(BaseParameters):
def __init__(self, domain, main_param):
self.domain = domain
self.main_param = main_param
if (self.domain != 'separator'):
self.prim = ParticleGeometricParameters(domain, 'primary', main_param)
self.sec = ParticleGeomet... |
def initial_wavefunction(particle):
return (((np.exp((((- 1) / (4 * ( ** 2))) * (((particle.x + (100 * A)) ** 2) + (1 * ((particle.y - 250) ** 2))))) / np.sqrt(((2 * np.pi) * ( ** 2)))) * np.exp(((p1_x0 * particle.x) * 1j))) + ((np.exp((((- 1) / (4 * ( ** 2))) * (((particle.x + (100 * A)) ** 2) + (1 * ((particle.y ... |
def plot(genotype, filename):
g = Digraph(format='pdf', edge_attr=dict(fontsize='20', fontname='times'), node_attr=dict(style='filled', shape='rect', align='center', fontsize='20', height='0.5', width='0.5', penwidth='2', fontname='times'), engine='dot')
g.body.extend(['rankdir=LR'])
g.node('x_{t}', fillcol... |
class Mlp(nn.Module):
def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.0):
super().__init__()
out_features = (out_features or in_features)
hidden_features = (hidden_features or in_features)
self.fc1 = nn.Conv2d(in_features, hidden_fea... |
def render_notebook_cells(nbspec: NotebookSpec) -> NbCells:
return NbCells(title_cell=_md_nbnode('\n'.join(_get_title_lines(title=nbspec.title, mod=nbspec.module)), cqid='title_cell'), top_imports=_code_nbnode(_IMPORTS, cqid='top_imports'), gate_cells={gspec.cqid: _GateCells(md=_md_nbnode('\n'.join(get_markdown_doc... |
def get_extract_label(art_sents, abs_sents):
extracted = []
scores = []
indices = list(range(len(art_sents)))
for abst in abs_sents:
rouges = list(map(compute_rouge_l(reference=abst, mode='r'), art_sents))
ext = max(indices, key=(lambda i: rouges[i]))
indices.remove(ext)
... |
class Testuser():
def test_min_threshold_dist_from_shapefile(self):
f = examples.get_path('columbus.shp')
min_d = user.min_threshold_dist_from_shapefile(f)
assert (min_d == pytest.approx(0.))
def test_build_lattice_shapefile(self):
of = 'lattice.shp'
user.build_lattice_sh... |
class PFSTS_With_Ksappend(ParserTest):
def __init__(self, *args, **kwargs):
ParserTest.__init__(self, *args, **kwargs)
self.ks = '\nlang en_US\nkeyboard us\nautopart\n'
self.ksappend = '\ntimezone America/New_York\n'
def setUp(self):
ParserTest.setUp(self)
(handle, self._... |
class Fraction():
def __init__(self, fraction=None, chntext=None):
self.fraction = fraction
self.chntext = chntext
def chntext2fraction(self):
(denominator, numerator) = self.chntext.split('')
return ((chn2num(numerator) + '/') + chn2num(denominator))
def fraction2chntext(sel... |
def evaluate_single_sub(sub_id):
truth = nib.load(os.path.joint('../data/preprocessed/HGG', sub_id, 'truth.nii.gz')).get_data()
prediction = nib.load(os.path.joint('prediction', sub_id, (sub_id + '.nii.gz'))).get_data()
masking_functions = (get_whole_tumor_mask, get_tumor_core_mask, get_enhancing_tumor_mask... |
def cast_tensor_type(inputs, src_type, dst_type):
if isinstance(inputs, nn.Module):
return inputs
elif isinstance(inputs, torch.Tensor):
return inputs.to(dst_type)
elif isinstance(inputs, str):
return inputs
elif isinstance(inputs, np.ndarray):
return inputs
elif isin... |
def compute_KMM(Xsamples, sigma, noise, l_vec):
m = len(Xsamples)
Xbar = np.multiply(Xsamples, np.array(np.sqrt(([l_vec] * m))))
Qbar = np.array(([np.sum(np.multiply(Xbar, Xbar), axis=1)] * m)).T
distance = ((Qbar + Qbar.T) - (2 * np.dot(Xbar, Xbar.T)))
result = ((sigma * np.exp(((- 0.5) * distance)... |
def load_from_file(filename):
vehicles_loaded = 0
try:
with open(filename, 'r') as f:
lines = f.readlines()
for line in lines:
index = 0
index = line.split(',')
if (len(index) < 1):
continue
vehic... |
def test_python_tags(wheelpath):
newname = tags(str(wheelpath), python_tags='py3')
assert (TESTWHEEL_NAME.replace('py2.py3', 'py3') == newname)
output_file = (wheelpath.parent / newname)
with WheelFile(str(output_file)) as f:
output = f.read((f.dist_info_path + '/WHEEL'))
assert (output == b... |
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