code stringlengths 281 23.7M |
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def simrank(G, nodelist=None, c=0.8, num_iterations=10, weight='weight'):
n = len(G)
M = raw_google_matrix(G, nodelist=nodelist, weight=weight)
sim = np.identity(n, dtype=np.float32)
for i in range(num_iterations):
log.debug('Starting SimRank iteration %d', i)
temp = (((c * M.T) sim) M... |
def __get_occurrence_variance(occurrence_matrix: np.ndarray, axis=0) -> float:
prob_matrix = __get_probability_matrix(occurrence_matrix)
tmp_mean_vec = np.sum(prob_matrix, axis=axis)
mean_value = __get_occurrence_mean(occurrence_matrix, axis=axis)
var_value = 0.0
for i in range(len(tmp_mean_vec)):
... |
def _matmul_to_gemm(node: NodeProto, model: ModelProto):
assert (node.op_type == 'MatMul')
(weight, transposed) = retrieve_constant_input(node, model, WEIGHT_INDEX)
if transposed:
node.input[WEIGHT_INDEX] = weight.name
model.graph.initializer.remove(weight)
weight = transpose_tensor(... |
class PromoteChatMember():
async def promote_chat_member(self: 'pyrogram.Client', chat_id: Union[(int, str)], user_id: Union[(int, str)], privileges: 'types.ChatPrivileges'=None) -> bool:
chat_id = (await self.resolve_peer(chat_id))
user_id = (await self.resolve_peer(user_id))
if (privileges... |
.parametrize('method, command', [('x1', 'X1.'), ('y1', 'Y1.'), ('x2', 'X2.'), ('y2', 'Y2.')])
def test_failing_properties(method, command):
with pytest.raises(ValueError):
with expected_protocol(Ametek7270, [(f'{command}'.encode(), b'\n')]) as inst:
(getattr(inst, method) == 0.0) |
class CTCCriterion(nn.Module):
def __init__(self, train_config):
super().__init__()
self.train_config = train_config
self.logsoftmax = nn.LogSoftmax(dim=2)
self.criterion = nn.CTCLoss(reduction='sum', zero_infinity=True)
def forward(self, output_tensor: torch.tensor, output_lengt... |
class MyR2plus1d(nn.Module):
def __init__(self, num_classes, use_pretrained=True, init_std=0.01, model_name='r2plus1d'):
super(MyR2plus1d, self).__init__()
self.model_ft = models.video.r2plus1d_18(pretrained=use_pretrained)
num_ftrs = self.model_ft.fc.in_features
self.init_std = init... |
class AoA_Decoder_Core(nn.Module):
def __init__(self, opt):
super(AoA_Decoder_Core, self).__init__()
self.drop_prob_lm = opt.drop_prob_lm
self.d_model = opt.rnn_size
self.use_multi_head = opt.use_multi_head
self.multi_head_scale = opt.multi_head_scale
self.use_ctx_dro... |
class Model():
def __init__(self, args):
self.dataName = args.dataName
self.dataSet = DataSet(self.dataName)
self.shape = self.dataSet.shape
self.maxRate = self.dataSet.maxRate
self.train = self.dataSet.train
self.test = self.dataSet.test
self.negNum = args.ne... |
def _get_dataframe_movielens(name: str, folder_path: Path) -> pd.DataFrame:
if (name == 'ml-1m'):
file_path = folder_path.joinpath('ratings.dat')
df = pd.read_csv(file_path, sep='::', header=None)
elif (name == 'ml-20m'):
file_path = folder_path.joinpath('ratings.csv')
df = pd.re... |
def delimited_loads(explode: bool, name: str, schema_type: str, location: Mapping[(str, Any)], delimiter: str) -> Any:
value = location[name]
explode_type = (explode, schema_type)
if (explode_type == (False, 'array')):
return split(value, separator=delimiter)
if (explode_type == (False, 'object'... |
class PrintTextWavePass(BasePass):
chars_per_cycle = MetadataKey(int)
enable = MetadataKey(bool)
textwave_func = MetadataKey()
textwave_dict = MetadataKey()
def __call__(self, top):
if (top.has_metadata(self.enable) and top.get_metadata(self.enable)):
assert (not top.has_metadata... |
class TestAdamOptimizer(TestOptimizer, unittest.TestCase):
def _check_momentum_buffer(self):
return False
def _get_config(self):
return {'name': 'adam', 'num_epochs': 90, 'lr': 0.1, 'betas': (0.9, 0.99), 'eps': 1e-08, 'weight_decay': 0.0001, 'amsgrad': False}
def _instance_to_test(self):
... |
class SELayer(nn.Module):
def __init__(self, channel, reduction=16):
super(SELayer, self).__init__()
self.avg_pool = nn.AdaptiveAvgPool2d(1)
self.fc = nn.Sequential(nn.Linear(channel, (channel // reduction), bias=False), nn.ReLU(inplace=True), nn.Linear((channel // reduction), (channel // re... |
def simple_perturb(text: str, method: str, perturbation_level=0.2):
if (not (0 <= perturbation_level <= 1)):
raise ValueError('Invalid value for perturbation level.')
if (method == 'segment'):
return segmentation(text, perturbation_level)
words = nltk.word_tokenize(text)
word_indexes = l... |
def build_augmentation(cfg, is_train):
logger = logging.getLogger(__name__)
aug_list = []
if is_train:
if cfg.INPUT.CROP.ENABLED:
aug_list.append(T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE))
min_size = cfg.INPUT.MIN_SIZE_TRAIN
max_size = cfg.INPUT.MAX_SIZE_TRAI... |
class ResourceTest(unittest.TestCase):
def test_copy_resource(self) -> None:
old_capabilities = {'test_key': 'test_value', 'old_key': 'old_value'}
resource = Resource(1, 2, 3, old_capabilities)
new_resource = Resource.copy(resource, test_key='test_value_new', new_key='new_value')
sel... |
class OpenVPNCollector(diamond.collector.Collector):
def get_default_config_help(self):
config_help = super(OpenVPNCollector, self).get_default_config_help()
config_help.update({'instances': 'List of instances to collect stats from', 'timeout': 'network timeout'})
return config_help
def ... |
def test_type(make_union):
assert_normalize(type, type, [nt_zero(Any)])
assert_normalize(Type, type, [nt_zero(Any)])
assert_normalize(Type[int], type, [nt_zero(int)])
assert_normalize(Type[Any], type, [nt_zero(Any)])
assert_normalize(Type[make_union[(int, str)]], Union, [normalize_type(Type[int]), n... |
def generate_number(vcf_number, alt_alleles):
if (vcf_number == '.'):
return np.random.randint(1, 10)
elif str_is_int(vcf_number):
return int(vcf_number)
elif (vcf_number == 'A'):
return alt_alleles
elif (vcf_number == 'R'):
return (alt_alleles + 1)
elif (vcf_number =... |
('hash-clear!', [W_HashTable], simple=False)
def hash_clear_bang(ht, env, cont):
from pycket.interpreter import return_value
if ht.is_impersonator():
ht.hash_clear_proc(env, cont)
return hash_clear_loop(ht, env, cont)
else:
ht.hash_empty()
return return_value(values.w_void, e... |
class BVMFExchangeCalendar(TradingCalendar):
name = 'BVMF'
tz = timezone('America/Sao_Paulo')
open_times = ((None, time(10, 1)),)
close_times = ((None, time(17, 0)), (pd.Timestamp('2019-11-04'), time(18, 0)))
def adhoc_holidays(self):
return [CopaDoMundo2014]
def regular_holidays(self):
... |
('/get/previewinfo', methods=['GET'])
_wrapper_json
def get_previewinfo():
trans = MigrateDal.get_isp_trans()
domain_count = ViewRecordDal.zone_domain_count()
migrate_list = []
histories = MigrateDal.get_migrated_history()
for history in histories:
migrate_list.append({'migrate_rooms': sorte... |
def deprecated(*, reason, version):
version = _vparse(str(version))
add_warning = _deprecated(reason=reason, version=version, category=SKCriteriaDeprecationWarning, action=('error' if (_ERROR_GE_VERSION <= version) else 'once'))
def _dec(func):
decorated_func = add_warning(func)
decorated_fu... |
class BoundingProvider(RequestClassDeterminedProvider, ProviderWithRC):
def __init__(self, request_checker: RequestChecker, provider: Provider):
self._request_checker = request_checker
self._provider = provider
def apply_provider(self, mediator: Mediator, request: Request[T]) -> T:
self.... |
def main_worker(gpu, ngpus_per_node, args):
global best_acc1
args.gpu = gpu
if (args.gpu is not None):
print('Use GPU: {} for training'.format(args.gpu))
if args.distributed:
if ((args.dist_url == 'env://') and (args.rank == (- 1))):
args.rank = int(os.environ['RANK'])
... |
def GetTestData(path, cfg):
sr = cfg.sampling_rate
(wav, fs) = sf.read(path)
(wav, _) = librosa.effects.trim(y=wav, top_db=cfg.top_db)
if (fs != sr):
wav = resampy.resample(x=wav, sr_orig=fs, sr_new=sr, axis=0)
fs = sr
assert (fs == 16000), 'Downsampling needs to be done.'
peak =... |
def generate_customers_sql_values(file_name):
f = open(file_name, 'r')
lines = f.readlines()
out = []
lines = lines[1:]
for line in lines:
line = line.strip()
(id, first_name, last_name, age, joined_at) = line.split(',')
out.append("({id}, '{first_name}', '{last_name}', {age}... |
class MultilingualDeltaLMTokenizer(DeltaLMTokenizer):
vocab_files_names = VOCAB_FILES_NAMES
max_model_input_sizes = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
pretrained_vocab_files_map = PRETRAINED_VOCAB_FILES_MAP
model_input_names = ['input_ids', 'attention_mask']
prefix_tokens: List[int] = []
suf... |
.parametrize('has_global_name', [False, True])
.parametrize('existing', [False, True])
def test_browser_discord_login_callback_with_sid(mocker: pytest_mock.MockerFixture, clean_database, flask_app, existing, has_global_name):
mock_emit = mocker.patch('flask_socketio.emit')
mock_render = mocker.patch('flask.rend... |
def duplicate_module(module_file: Union[(str, os.PathLike)], old_model_patterns: ModelPatterns, new_model_patterns: ModelPatterns, dest_file: Optional[str]=None, add_copied_from: bool=True, attrs_to_remove: List[str]=None):
if (dest_file is None):
dest_file = str(module_file).replace(old_model_patterns.mode... |
class TestRepositoryManifestLabels(ApiTestCase):
def test_basic_labels(self):
self.login(ADMIN_ACCESS_USER)
repo_ref = registry_model.lookup_repository(ADMIN_ACCESS_USER, 'complex')
tag = registry_model.get_repo_tag(repo_ref, 'prod')
repository = (ADMIN_ACCESS_USER + '/complex')
... |
class AtomDataType(object):
IMPLICIT = 0
UTF8 = 1
UTF16 = 2
SJIS = 3
HTML = 6
XML = 7
UUID = 8
ISRC = 9
MI3P = 10
GIF = 12
JPEG = 13
PNG = 14
URL = 15
DURATION = 16
DATETIME = 17
GENRES = 18
INTEGER = 21
RIAA_PA = 24
UPC = 25
BMP = 27 |
def evaluate(args, iteration, miner, miner_semantic):
(gen_i, gen_j) = args.gen_sample.get(args.image_size, (10, 5))
images = []
miner.eval()
miner_semantic.eval()
with torch.no_grad():
for i in range(gen_i):
images.append(G_running_target(miner(fixed_noise[i].cuda()), step=step,... |
def batch_psnr(gen_frames, gt_frames):
x = np.int32(gen_frames)
y = np.int32(gt_frames)
num_pixels = float(np.size(gen_frames[0]))
mse = (np.sum(((x - y) ** 2), axis=(1, 2), dtype=np.float32) / num_pixels)
psnr = ((20 * np.log10(255)) - (10 * np.log10(mse)))
return np.mean(psnr) |
class TestArgs():
def test_simple(self, qtbot, signaller):
with qtbot.waitSignal(signaller.signal_args) as blocker:
signaller.signal_args.emit('test', 123)
assert (blocker.args == ['test', 123])
def test_timeout(self, qtbot, signaller):
with qtbot.waitSignal(signaller.signal,... |
def test_035_parseTime_suppress_auto_month():
next_day = tomorrow.day
if (next_day > today.day):
last_year = (today.year - 1)
timestr = ('%02d1651Z' % next_day)
report = Metar.Metar(('KEWR ' + timestr), month=1)
assert report.decode_completed
assert (report.time.day == ne... |
def test_add_opening_quote_basic_quote_added(cmd2_app):
text = 'Ha'
line = 'test_basic {}'.format(text)
endidx = len(line)
begidx = (endidx - len(text))
expected = sorted(['"Ham', '"Ham Sandwich'], key=cmd2_app.default_sort_key)
first_match = complete_tester(text, line, begidx, endidx, cmd2_app)... |
def test_struct_port_single(do_test):
class struct():
bar: Bits32
foo: Bits32
class A(Component):
def construct(s):
s.in_ = InPort(struct)
a = A()
a._ref_symbols = {'struct__bar_32__foo_32': struct}
a._ref_decls = ['s.in_ = InPort( struct__bar_32__foo_32 )']
d... |
class GCN(object):
def __init__(self, graph, learning_rate=0.01, epochs=200, hidden1=16, dropout=0.5, weight_decay=0.0005, early_stopping=10, max_degree=3, clf_ratio=0.1):
self.graph = graph
self.clf_ratio = clf_ratio
self.learning_rate = learning_rate
self.epochs = epochs
se... |
class Solution(object):
def convertBST(self, root):
total = 0
node = root
stack = []
while (stack or (node is not None)):
while (node is not None):
stack.append(node)
node = node.right
node = stack.pop()
total += nod... |
def setUpModule():
global mol, mf, myadc
r = 1.098
mol = gto.Mole()
mol.atom = [['N', (0.0, 0.0, ((- r) / 2))], ['N', (0.0, 0.0, (r / 2))]]
mol.basis = {'N': 'aug-cc-pvdz'}
mol.verbose = 0
mol.build()
mf = scf.RHF(mol)
mf.conv_tol = 1e-12
mf.kernel()
myadc = adc.ADC(mf) |
def clear_mem():
global K, M, N, S, unum, ww
global my_dict, sub_ptn_list, ptn_len, sDB
global minsup, NumbS, freArr, canArr, candidate
K = 600
M = 100
N = 60000
S = ''
unum = [0.0 for i in range(K)]
ww = 0
my_dict = dict()
sub_ptn_list = [sub_ptn() for i in range(M)]
ptn... |
def get_inceptionv3(model_name=None, pretrained=False, root=os.path.join('~', '.torch', 'models'), **kwargs):
init_block_channels = 192
channels = [[256, 288, 288], [768, 768, 768, 768, 768], [1280, 2048, 2048]]
b_mid_channels = [128, 160, 160, 192]
net = InceptionV3(channels=channels, init_block_channe... |
_model_architecture('model_parallel_transformer_lm', 'transformer_lm_megatron_11b')
def transformer_lm_megatron_11b(args):
args.decoder_embed_dim = getattr(args, 'decoder_embed_dim', 3072)
args.decoder_ffn_embed_dim = getattr(args, 'decoder_ffn_embed_dim', (3072 * 6))
args.decoder_layers = getattr(args, 'de... |
def list_models(filter='', module='', pretrained=False, exclude_filters='', name_matches_cfg=False):
if module:
all_models = list(_module_to_models[module])
else:
all_models = _model_entrypoints.keys()
if filter:
models = []
include_filters = (filter if isinstance(filter, (tu... |
class Trainer():
def __init__(self):
self._opt = TrainOptions().parse()
PRESET_VARS = PATH(self._opt)
self._model = ModelsFactory.get_by_name(self._opt.model_name, self._opt)
train_transforms = self._model.resnet50.backbone.augment_transforms
val_transforms = self._model.resn... |
class Overlord(cmd2.Cmd):
os.system('clear')
version = cmd2.ansi.style('v.1.0', fg=Fg.RED, bg=None, bold=True, underline=False)
print(f'''
_ _
_____ _____ _ __| | ___ _ __ __| |
/ _ \ \ / / _ \ '__| |/ _ \| '__/ _` |
| (_) \ V / __/ | | | (_) | | | (_| |
\___/ \_/... |
_optionals.HAS_GAUSSIAN.require_in_instance
class GaussianDriver(ElectronicStructureDriver):
def __init__(self, config: (str | list[str])='# rhf/sto-3g scf(conventional)\n\nh2 molecule\n\n0 1\nH 0.0 0.0 0.0\nH 0.0 0.0 0.735\n\n') -> None:
super().__init__()
if ((not isinstance(config, st... |
def jetson_clocks_gui(stdscr, offset, start, jetson):
jc_status_name = jetson.jetson_clocks.get_status()
if (jc_status_name == 'running'):
color = (curses.A_BOLD | NColors.green())
elif (jc_status_name == 'inactive'):
color = curses.A_NORMAL
elif ('ing' in jc_status_name):
color ... |
def _prepare_prompt_learning_config(peft_config, model_config):
if (peft_config.num_layers is None):
if ('num_hidden_layers' in model_config):
num_layers = model_config['num_hidden_layers']
elif ('num_layers' in model_config):
num_layers = model_config['num_layers']
e... |
def get_rxn_smarts(probs):
rxn_smarts = []
for key in probs:
tokens = key.split(']')
smarts = tokens[0]
if (('-' in key) and ('#16' not in smarts)):
smarts += ';!H0:1]>>[*:1]'
if (('=' in key) and ('#16' not in smarts)):
smarts += ';!H1;!H0:1]>>[*:1]'
... |
class ConvNextImageProcessor(BaseImageProcessor):
model_input_names = ['pixel_values']
def __init__(self, do_resize: bool=True, size: Dict[(str, int)]=None, crop_pct: float=None, resample: PILImageResampling=PILImageResampling.BILINEAR, do_rescale: bool=True, rescale_factor: Union[(int, float)]=(1 / 255), do_no... |
def test_metadata_path_with_prepare(tmp_dir, package_test_setuptools):
builder = build.ProjectBuilder(package_test_setuptools)
metadata = _importlib.metadata.PathDistribution(pathlib.Path(builder.metadata_path(tmp_dir))).metadata
assert (metadata['name'] == 'test-setuptools')
assert (metadata['Version']... |
class Migration(migrations.Migration):
dependencies = [('api', '0080_add_aoc_tables')]
operations = [migrations.CreateModel(name='BumpedThread', fields=[('thread_id', models.BigIntegerField(help_text='The thread ID that should be bumped.', primary_key=True, serialize=False, validators=[django.core.validators.Mi... |
def create_model(session, Model_class, path, load_vec, config, id_to_char, logger):
model = Model_class(config)
ckpt = tf.train.get_checkpoint_state(path)
if (ckpt and tf.train.checkpoint_exists(ckpt.model_checkpoint_path)):
logger.info(('Reading model parameters from %s' % ckpt.model_checkpoint_pat... |
class Solution():
def isValid(self, s: str) -> bool:
d = {'#': 0, '(': (- 1), ')': 1, '[': (- 2), ']': 2, '{': (- 3), '}': 3}
stack = [0]
(start, end) = (0, len(s))
while (start < end):
if (stack[(- 1)] == (- d[s[start]])):
stack.pop()
else:
... |
def fasttext_export(embedding_file):
fin = io.open(embedding_file, 'r', encoding='utf-8', newline='\n', errors='ignore')
vocabulary = []
embeddings = []
line_idx = 0
for line in fin:
if (line_idx == 0):
line_idx += 1
continue
tokens = line.rstrip().split(' ')
... |
.parametrize('with_root', [True, False])
.parametrize('error', ['module', 'readme', ''])
def test_install_warning_corrupt_root(command_tester_factory: CommandTesterFactory, project_factory: ProjectFactory, with_root: bool, error: str) -> None:
name = 'corrupt'
content = f'''[tool.poetry]
name = "{name}"
version... |
class CustomSelector(discord.ui.Select):
def __init__(self, placeholder: str, options: List[discord.SelectOption]):
super().__init__(placeholder=placeholder, options=options)
async def callback(self, interaction: discord.Interaction):
(await interaction.response.defer())
self.view.custom... |
def postprocess_text(preds, labels, metric_name):
preds = [pred.strip() for pred in preds]
labels = [label.strip() for label in labels]
if (metric_name == 'rouge'):
preds = ['\n'.join(nltk.sent_tokenize(pred)) for pred in preds]
labels = ['\n'.join(nltk.sent_tokenize(label)) for label in lab... |
def get_training_eval_datasets(cfg: DatasetConfig, shard_id: int, num_shards: int, eval_steps: int, feature_converter_cls: Callable[(..., seqio.FeatureConverter)], deterministic: bool=False, model_dir: Optional[str]=None, start_step: int=0) -> Mapping[(str, tf.data.Dataset)]:
if isinstance(cfg.mixture_or_task_name,... |
def get_smart_contracts_start_at(chain_id: ChainID) -> BlockNumber:
if (chain_id == Networks.MAINNET.value):
smart_contracts_start_at = EthereumForks.CONSTANTINOPLE.value
elif (chain_id == Networks.ROPSTEN.value):
smart_contracts_start_at = RopstenForks.CONSTANTINOPLE.value
elif (chain_id ==... |
.parametrize('case_name', _get_explicit_cases('positive'))
def test_explicit_positive_examples(case_name, run_line):
_check_file_format_skip(case_name)
casedir = ((EXAMPLE_EXPLICIT_FILES / 'positive') / case_name)
instance = (casedir / 'instance.json')
if (not instance.exists()):
instance = (cas... |
class FlowStep(nn.Module):
FlowPermutation = {'reverse': (lambda obj, z, logdet, rev: (obj.reverse(z, rev), logdet)), 'shuffle': (lambda obj, z, logdet, rev: (obj.shuffle(z, rev), logdet)), 'invconv': (lambda obj, z, logdet, rev: obj.invconv(z, logdet, rev)), 'squeeze_invconv': (lambda obj, z, logdet, rev: obj.invc... |
.parametrize('connection_error,response_code,exception', [(True, 200, requests.exceptions.Timeout), (True, 200, requests.exceptions.ConnectionError), (False, 200, requests.exceptions.RequestException), (False, 200, ValueError), (True, 500, api.Non200ResponseException(mock.Mock(status_code=500))), (False, 400, api.Non20... |
def set_network(vm_server, vm, target_network):
nic = None
backing_network = None
for network in vm_server.getObject(vim.Network):
if (target_network == network.name):
backing_network = network
break
for device in vm.vmObject.config.hardware.device:
if isinstance(... |
class encoder(nn.Module):
def __init__(self, in_channels, out_channels):
super(encoder, self).__init__()
self.down_conv = x2conv(in_channels, out_channels)
self.pool = nn.MaxPool2d(kernel_size=2, ceil_mode=True)
def forward(self, x):
x = self.down_conv(x)
x = self.pool(x)... |
def find_in_rally(clipinfo_data, rally_num, num_hit):
cnt = 0
shift_round = 0
for i in range(len(clipinfo_data['rally'])):
if ((clipinfo_data['rally'][i] + shift_round) == rally_num):
cnt += 1
if ((clipinfo_data['rally'][i] == 1) and (i != 0) and (clipinfo_data['rally'][(i - 1)] ... |
class KnownValues(unittest.TestCase):
def test_hcore(self):
h1ref = pbchf.get_hcore(cell)
h1 = pbchf.RHF(cell).get_hcore()
self.assertAlmostEqual(abs((h1 - h1ref)).max(), 0, 9)
self.assertAlmostEqual(lib.fp(h1), 0., 8)
cell1 = cell.copy()
cell1.ecp = {'He': (2, (((- 1... |
def find_all_documented_objects():
documented_obj = []
for doc_file in Path(PATH_TO_DOC).glob('**/*.rst'):
with open(doc_file, 'r', encoding='utf-8', newline='\n') as f:
content = f.read()
raw_doc_objs = re.findall('(?:autoclass|autofunction):: transformers.(\\S+)\\s+', content)
... |
class CheckTypes(RPathTest):
def testExist(self):
self.assertTrue(rpath.RPath(self.lc, self.prefix, ()).lstat())
self.assertFalse(rpath.RPath(self.lc, 'asuthasetuouo', ()).lstat())
def testDir(self):
self.assertTrue(rpath.RPath(self.lc, self.prefix, ()).isdir())
self.assertFalse(... |
def combine(img_file, mask_file, class_name_list='VOC', include_color0=False, has_legend=True):
img = Image.open(img_file)
mask_p = Image.open(mask_file)
mask_index = np.array(mask_p)
mask_alpha = np.where(np.equal(mask_index, 0), 0, 180)
mask_alpha = Image.fromarray(mask_alpha.astype(np.uint8), mod... |
class DiscriminatorBlock(chainer.Chain):
def __init__(self, in_ch, out_ch, initialW, sn=True):
super(DiscriminatorBlock, self).__init__()
with self.init_scope():
if sn:
self.c0 = SNConvolution2D(in_ch, in_ch, 3, 1, 1, initialW=initialW)
self.c1 = SNConvolu... |
.asyncio(scope='class')
class TestClassScopedLoop():
loop: asyncio.AbstractEventLoop
_asyncio.fixture(scope='class')
async def my_fixture(self):
TestClassScopedLoop.loop = asyncio.get_running_loop()
async def test_runs_is_same_loop_as_fixture(self, my_fixture):
assert (asyncio.get_runnin... |
.parametrize('func', [qutip.spin_state, partial(qutip.spin_coherent, phi=0.5)])
def test_spin_output(func):
assert qutip.isket(func(1.0, 0, type='ket'))
assert qutip.isbra(func(1.0, 0, type='bra'))
assert qutip.isoper(func(1.0, 0, type='dm'))
with pytest.raises(ValueError) as e:
func(1.0, 0, typ... |
class ArchCheckerReportConstants():
OP_STRUCT_OP_TYPE = 'OpStructure'
DF_GRAPH_NODENAME = 'Graph/Layer_name'
DF_ISSUE = 'Issue'
DF_RECOMM = 'Recommendation'
UNDEFINED_ISSUE = 'Undefined issue from check: {}'
UNDEFINED_RECOMM = 'Undefined recommendation from check: {}'
OUTPUT_CSV_HEADER = [DF... |
class Graph():
def __init__(self, labeling_mode='spatial'):
self.A = self.get_adjacency_matrix(labeling_mode)
self.num_node = num_node
self.self_link = self_link
self.inward = inward
self.outward = outward
self.neighbor = neighbor
def get_adjacency_matrix(self, la... |
class ClassificationEvaluator(object):
MACRO_AVERAGE = 'macro_average'
MICRO_AVERAGE = 'micro_average'
def __init__(self, eval_dir):
self.confusion_matrix_list = None
self.precision_list = None
self.recall_list = None
self.fscore_list = None
self.right_list = None
... |
class _IdentityExpBase(_AlgebraicExpBase):
_operator = ' ? '
def __init__(self, members=()):
super().__init__(template=None)
self.members = tuple(members)
super()._freeze_()
def kind(self):
if (not self.members):
return 'identity'
return self.members[0].ki... |
class ServoCalibration(object):
def __init__(self, servo):
self.server = servo.server
self.run = self.Register(BooleanProperty, 'run', False)
self.rawcommand = self.Register(SensorValue, 'raw_command')
self.console = self.Register(Value, 'console', '')
self.current_total = se... |
def test_monitor():
class SomethingElse():
def foo(self, n, y=None):
self.n = n
return y
s = SomethingElse()
original_method = s.foo.__func__
with CallMonitor(s.foo) as monitor:
assert (s.foo(1, y='a') == 'a')
assert (s.foo(2) is None)
assert (s.foo.__... |
def clean_room_edges(all_room_edges):
refined_room_paths = [_extract_room_path(room_edges) for room_edges in all_room_edges]
corner_to_room = defaultdict(list)
for (room_idx, room_path) in enumerate(refined_room_paths):
for corner in room_path:
corner_to_room[corner].append(room_idx)
... |
.functions
(df=categoricaldf_strategy())
def test_all_cat_None_2(df):
result = df.encode_categorical(names='appearance')
categories = pd.CategoricalDtype(categories=df.names.factorize(sort=False)[(- 1)], ordered=True)
expected = df.astype({'names': categories})
assert expected['names'].equals(result['na... |
class LIPSegmentation(SegmentationDataset):
BASE_DIR = 'LIP'
NUM_CLASS = 20
def __init__(self, root='datasets/LIP', split='train', mode=None, transform=None, **kwargs):
super(LIPSegmentation, self).__init__(root, split, mode, transform, **kwargs)
_trainval_image_dir = os.path.join(root, 'Tra... |
def _build_mlp(nlayers, in_dim, bottleneck_dim, hidden_dim=None, use_bn=False, bias=True):
if (nlayers == 1):
return nn.Linear(in_dim, bottleneck_dim, bias=bias)
else:
layers = [nn.Linear(in_dim, hidden_dim, bias=bias)]
if use_bn:
layers.append(nn.BatchNorm1d(hidden_dim))
... |
def extract_flavors_and_pens(penalties, penalty_keys, force_base=False, restrict=None):
penalty_map = {k: penalties[idx] for (idx, k) in enumerate(penalty_keys)}
(flavors, flavor_keys) = extract_flavors(penalties=penalties, keys=penalty_keys, force_base=force_base, restrict=restrict)
parent_penalties = []
... |
def main():
parser = argparse.ArgumentParser(description='Preprocess ACE event data.')
parser.add_argument('output_name', help='Name for output directory.')
parser.add_argument('--use_span_extent', action='store_true', help='Use full extent of entity mentions instead of just heads.')
parser.add_argument... |
.repeat(2)
.parametrize('superrep', ['choi', 'super'])
def test_rand_super(dimensions, dtype, superrep):
random_qobj = rand_super(dimensions, dtype=dtype, superrep=superrep)
assert random_qobj.issuper
with CoreOptions(atol=1e-09):
assert random_qobj.iscptp
assert (random_qobj.superrep == superre... |
class AliasMethod(nn.Module):
def __init__(self, probs):
super(AliasMethod, self).__init__()
if (probs.sum() > 1):
probs.div_(probs.sum())
K = len(probs)
self.register_buffer('prob', torch.zeros(K))
self.register_buffer('alias', torch.LongTensor(([0] * K)))
... |
class logRegClassificationEvaluator(Evaluator):
def __init__(self, sentences_train, y_train, sentences_test, y_test, max_iter=100, batch_size=32, limit=None, **kwargs):
super().__init__(**kwargs)
if (limit is not None):
sentences_train = sentences_train[:limit]
y_train = y_tr... |
def test_complex_cepstrum():
duration = 5.0
fs = 8000.0
samples = int((fs * duration))
t = (np.arange(samples) / fs)
fundamental = 100.0
signal = sawtooth((((2.0 * np.pi) * fundamental) * t))
(ceps, _) = complex_cepstrum(signal)
assert (fundamental == (1.0 / t[ceps.argmax()])) |
def test_search_paths(temp_dir, helpers):
source = CodeSource(str(temp_dir), {'path': 'a/b.py', 'search-paths': ['.']})
parent_dir = (temp_dir / 'a')
parent_dir.mkdir()
(parent_dir / '__init__.py').touch()
(parent_dir / 'b.py').write_text(helpers.dedent("\n from a.c import foo\n\n ... |
def pytest_addoption(parser):
group = parser.getgroup('xdist', 'distributed and subprocess testing')
group._addoption('-n', '--numprocesses', dest='numprocesses', metavar='numprocesses', action='store', type=parse_numprocesses, help="Shortcut for '--dist=load --tx=NUM*popen'. With 'auto', attempt to detect phys... |
class Logic():
game: GameDescription
configuration: BaseConfiguration
additional_requirements: list[RequirementSet]
_attempts: int
_current_indent: int = 0
_last_printed_additional: dict[(Node, RequirementSet)]
def __init__(self, game: GameDescription, configuration: BaseConfiguration):
... |
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, previous_dilation=1, norm_layer=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=dilation, dilation=dilat... |
class F23Handler(BaseHandler):
version = F23
commandMap = {'auth': commands.authconfig.FC3_Authconfig, 'authconfig': commands.authconfig.FC3_Authconfig, 'autopart': commands.autopart.F23_AutoPart, 'autostep': commands.autostep.FC3_AutoStep, 'bootloader': commands.bootloader.F21_Bootloader, 'btrfs': commands.btr... |
def plt_hist(axis, data, hatch, label, bins, col):
(counts, edges) = np.histogram(data, bins=bins, range=[0, 1])
edges = np.repeat(edges, 2)
hist = np.hstack((0, np.repeat(counts, 2), 0))
(outline,) = axis.plot(edges, hist, linewidth=1.3, color=col)
axis.fill_between(edges, hist, 0, edgecolor=col, h... |
class Quantizer(nn.Module):
def __init__(self, shape=1):
super(Quantizer, self).__init__()
self.register_buffer('maxq', torch.tensor(0))
self.register_buffer('scale', torch.zeros(shape))
self.register_buffer('zero', torch.zeros(shape))
def configure(self, bits, perchannel=False, ... |
def _check_if_dag_has_cycles(dag: nx.DiGraph) -> None:
try:
cycles = nx.algorithms.cycles.find_cycle(dag)
except nx.NetworkXNoCycle:
pass
else:
msg = f'''The DAG contains cycles which means a dependency is directly or indirectly a product of the same task. See the following the path ... |
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