code stringlengths 281 23.7M |
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def initialize_model_poisson(train_x, train_obj, train_con, train_yvar, state_dict=None, method='variational'):
if (method == 'variational'):
model_obj = get_var_model(train_x, train_obj, train_yvar, is_poisson=True)
model_con = get_var_model(train_x, train_con, train_yvar, is_poisson=False)
... |
class Effect6326(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredChargeBoost((lambda mod: mod.charge.requiresSkill('Missile Launcher Operation')), 'thermalDamage', src.getModifiedItemAttr('shipBonusCD1'), skill='Caldari Destroyer', **kwargs) |
def create_build(repository):
new_token = model.token.create_access_token(repository, 'write', 'build-worker')
repo = ('ci.devtable.com:5000/%s/%s' % (repository.namespace_user.username, repository.name))
job_config = {'repository': repo, 'docker_tags': ['latest'], 'build_subdir': '', 'trigger_metadata': {'... |
def make_module_translation_map(names: list[str]) -> dict[(str, str)]:
num_instances: dict[(str, int)] = {}
for name in names:
for suffix in candidate_suffixes(name):
num_instances[suffix] = (num_instances.get(suffix, 0) + 1)
result = {}
for name in names:
for suffix in candi... |
def evaluate(dataloader, model, confusion, config, args):
model.evaluate_mode()
logging.error('VALIDATION')
for (i, batch) in enumerate(tqdm(dataloader)):
(seq_images, targets, _) = batch
if (seq_images == None):
continue
seq_images = seq_images.cuda()
cuda_target... |
class SystemVerilogLexer(RegexLexer):
name = 'systemverilog'
aliases = ['systemverilog', 'sv']
filenames = ['*.sv', '*.svh']
mimetypes = ['text/x-systemverilog']
url = '
version_added = '1.5'
_ws = '(?:\\s|//.*?\\n|/[*].*?[*]/)+'
tokens = {'root': [('^(\\s*)(`define)', bygroups(Whitespac... |
.parametrize('src', [lazy_fixture('swath_def_2d_numpy'), lazy_fixture('swath_def_2d_dask'), lazy_fixture('swath_def_2d_xarray_numpy'), lazy_fixture('swath_def_2d_xarray_dask')])
.parametrize('dst', [lazy_fixture('area_def_lcc_conus_1km')])
def test_resampler(src, dst):
rs = FakeResampler(src, dst)
some_data = n... |
def rewrite_metadata():
gso_dir = 'data/google_object_dataset'
glob_dump = 'data/gso_glob.npy'
gso_meta_path = 'cos_eor/scripts/dump/gso_dump.npy'
gso_metadata = {}
if os.path.exists(glob_dump):
paths = list(np.load(glob_dump, allow_pickle=True))
else:
paths = glob.glob((gso_dir ... |
def test_simple_function_with_surrounding_statements() -> None:
src = '\n x = 10\n def func(x: int) -> None:\n print(x + 1)\n print(x)\n '
cfgs = build_cfgs(src)
assert (len(cfgs) == 2)
keys = list(cfgs)
expected_blocks_module = [['x = 10', '\ndef func(x: int) -> None:\n print(... |
def test_sqliteio_read_updates_progress(tmpfile, view):
worker = MagicMock(canceled=False)
io = SQLiteIO(tmpfile, view.scene, create_new=True, worker=worker)
io.create_schema_on_new()
io.ex('INSERT INTO items (type, x, y, z, scale, data) VALUES (?, ?, ?, ?, ?, ?) ', ('pixmap', 0, 0, 0, 1, json.dumps({'f... |
def parse_args():
parser = argparse.ArgumentParser(description='Generate training and val set of BID ')
parser.add_argument('root_path', help='Root dir path of BID')
parser.add_argument('--nproc', default=1, type=int, help='Number of processes')
parser.add_argument('--val-ratio', help='Split ratio for v... |
def G_wgan(G, D, opt, training_set, minibatch_size):
latents = tf.random_normal(([minibatch_size] + G.input_shapes[0][1:]))
labels = training_set.get_random_labels_tf(minibatch_size)
fake_images_out = G.get_output_for(latents, labels, is_training=True)
fake_scores_out = fp32(D.get_output_for(fake_images... |
def get_dataloader_sample(dataset='imagenet', batch_size=128, num_workers=8, is_sample=False, k=4096):
if (dataset == 'imagenet'):
data_folder = get_data_folder()
else:
raise NotImplementedError('dataset not supported: {}'.format(dataset))
normalize = transforms.Normalize(mean=[0.485, 0.456,... |
def test_latest_ref_counts():
source = Stream()
_ = source.latest()
ref1 = RefCounter()
source.emit(1, metadata=[{'ref': ref1}])
assert (ref1.count == 1)
ref2 = RefCounter()
source.emit(2, metadata=[{'ref': ref2}])
assert (ref1.count == 0)
assert (ref2.count == 1) |
def calculate_stats_from_dataset():
device = torch.device(('cuda' if torch.cuda.is_available() else 'cpu'))
parser = argparse.ArgumentParser()
parser.add_argument('--num_sample', type=int, default=50000)
parser.add_argument('--batch_size', type=int, default=64)
parser.add_argument('--size', type=int... |
def time_frequency_stitching(min_switch_ind, final_i_index, time_offset, i_time, i_omega, m_time, m_omega):
assert (type(min_switch_ind) == int), 'min_switch_ind should be an int.'
assert (type(final_i_index) == int), 'final_i_index should be an int.'
assert (type(time_offset) == float), 'time_offset should... |
def test_ls_none(data, runner):
inputfile = str(data.join('RGB.byte.tif'))
result = runner.invoke(cli, ['overview', inputfile, '--ls'])
assert (result.exit_code == 0)
expected = "Overview factors:\n Band 1: None (method: 'unknown')\n Band 2: None (method: 'unknown')\n Band 3: None (method: 'unknown')... |
class SetMentions(ScrimsButton):
def __init__(self, ctx: Context, letter: str):
super().__init__(emoji=ri(letter))
self.ctx = ctx
async def callback(self, interaction: Interaction):
(await interaction.response.defer())
m = (await self.ctx.simple('How many mentions are required fo... |
def get_config():
config = get_default_configs()
training = config.training
training.sde = 'vesde'
training.continuous = False
step_size = 3.3e-06
n_steps_each = 5
ckpt_id = 210000
final_only = True
noise_removal = False
sampling = config.sampling
sampling.method = 'pc'
s... |
.requires_user_validation
def test_pause_sound(event_loop):
source = synthesis.WhiteNoise(60.0)
player = Player()
player.queue(source)
player.play()
event_loop.run_event_loop(1.0)
player.pause()
event_loop.ask_question('Did you hear white noise for 1 second and is it now silent?', screenshot... |
class TestPSS():
def test_calculate_max_pss_salt_length(self):
with pytest.raises(TypeError):
padding.calculate_max_pss_salt_length(object(), hashes.SHA256())
def test_invalid_salt_length_not_integer(self):
with pytest.raises(TypeError):
padding.PSS(mgf=padding.MGF1(hashe... |
class TestPostgenerationCalledOnce():
(_name='collector')
class CollectorFactory(factory.Factory):
class Meta():
model = dict
foo = factory.PostGeneration((lambda *args, **kwargs: 42))
def _after_postgeneration(cls, obj: dict[(str, Any)], create: bool, results: (dict[(str, An... |
def upgrade(op, tables, tester):
op.create_table('logentry3', sa.Column('id', sa.BigInteger(), nullable=False), sa.Column('kind_id', sa.Integer(), nullable=False), sa.Column('account_id', sa.Integer(), nullable=False), sa.Column('performer_id', sa.Integer(), nullable=True), sa.Column('repository_id', sa.Integer(), ... |
def test():
try:
spi = SPI(1, baudrate=, sck=Pin(14), mosi=Pin(13))
display = Display(spi, dc=Pin(4), cs=Pin(16), rst=Pin(17))
display.clear()
logo = BouncingSprite('images/Python41x49.raw', 41, 49, 240, 320, 1, display)
while True:
timer = ticks_us()
... |
def aretry(exception_cls, max_tries=10, sleep=0.05):
assert (max_tries > 0), ('max_tries (%d) should be a positive integer' % max_tries)
def decorator(fn):
()
(fn)
def wrapper(*args, **kwargs):
for i in range(max_tries):
try:
ret = (yield f... |
def generator(z, y):
s = FLAGS.output_size
(s2, s4, s8, s16) = (int((s / 2)), int((s / 4)), int((s / 8)), int((s / 16)))
gf_dim = 64
h0 = tf.nn.relu(tf.reshape(linear(z, (((gf_dim * 8) * s16) * s16), 'g_h0_lin'), [(- 1), s16, s16, (gf_dim * 8)]))
h1 = tf.nn.relu(deconv2d(h0, [FLAGS.batch_size, s8, s... |
def tune_model_weights():
parser = generate.get_parser_with_args()
parser = add_tune_args(parser)
args = options.parse_args_and_arch(parser)
n_models = len(args.path.split(CHECKPOINT_PATHS_DELIMITER))
print(n_models)
print(args.weight_lower_bound)
print(args.weight_upper_bound)
print(arg... |
def _addmm_flop_jit(inputs: Tuple[torch.Tensor], outputs: Tuple[Any]) -> Number:
input_shapes = [v.shape for v in inputs[1:3]]
assert (len(input_shapes[0]) == 2), input_shapes[0]
assert (len(input_shapes[1]) == 2), input_shapes[1]
(batch_size, input_dim) = input_shapes[0]
output_dim = input_shapes[1... |
class NotificationDevice(XlibSelectDevice):
def __init__(self):
(self._sync_file_read, self._sync_file_write) = os.pipe()
self._event = threading.Event()
def fileno(self):
return self._sync_file_read
def set(self):
self._event.set()
os.write(self._sync_file_write, b'1... |
.linux
def test_webengine_download_suffix(request, quteproc_new, tmp_path):
if (not request.config.webengine):
pytest.skip()
download_dir = (tmp_path / 'downloads')
download_dir.mkdir()
(tmp_path / 'user-dirs.dirs').write_text('XDG_DOWNLOAD_DIR={}'.format(download_dir))
env = {'XDG_CONFIG_HO... |
class Params():
gpu_ewc = '4'
gpu_rewc = '3'
data_size = 224
batch_size = 32
nb_cl = 50
nb_groups = 4
nb_val = 0
epochs = 50
num_samples = 5
lr_init = 0.001
lr_strat = [40, 80]
lr_factor = 5.0
wght_decay = 1e-05
ratio = 100.0
eval_single = True
save_path =... |
class ResNet(object):
def __init__(self, args, mode):
self.relu_leakiness = 0.1
self.optimizer = 'mom'
self.use_bottleneck = False
images = tf.placeholder(name='input/images', dtype=tf.float32, shape=(None, 32, 32, 3))
labels = tf.placeholder(name='input/labels', dtype=tf.flo... |
class LinearSubSampler(LayerSubSampler):
def verify_layers(self, orig_layer: torch.nn.Module, pruned_layer: torch.nn.Module):
assert isinstance(orig_layer, torch.nn.Linear)
assert isinstance(pruned_layer, torch.nn.Linear)
def get_number_of_batches(self, data_loader: Iterator, orig_layer: torch.n... |
('enqueue', args=1)
def _enqueue(app, value):
playlist = app.window.playlist
library = app.library
if (value in library):
songs = [library[value]]
elif os.path.isfile(value):
songs = [library.add_filename(os.path.realpath(value))]
else:
songs = library.query(arg2text(value))
... |
def usage():
program = os.path.basename(sys.argv[0])
print('Usage: ', program, '[-s] [<file>]')
print('Options:\n <file> ... a file containing METAR reports to parse\n -q ....... run "quietly" - just report parsing error.\n -s ....... run silently. (no output)\n -p ....... run with profiling tur... |
class BasicBlock(nn.Module):
def __init__(self, in_planes, out_planes, stride, dropRate=0.0):
super(BasicBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.relu1 = nn.LeakyReLU(0.1, inplace=True)
self.conv1 = nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=strid... |
def transform_super_expr(builder: IRBuilder, o: SuperExpr) -> Value:
sup_val = builder.load_module_attr_by_fullname('builtins.super', o.line)
if o.call.args:
args = [builder.accept(arg) for arg in o.call.args]
else:
assert (o.info is not None)
typ = builder.load_native_type_object(o.... |
class InitWeights_He(object):
def __init__(self, neg_slope: float=0.01):
self.neg_slope = neg_slope
def __call__(self, module):
if (isinstance(module, nn.Conv3d) or isinstance(module, nn.Conv2d) or isinstance(module, nn.ConvTranspose2d) or isinstance(module, nn.ConvTranspose3d)):
mod... |
def main(_):
tf.logging.set_verbosity(tf.logging.INFO)
if ((not FLAGS.do_train) and (not FLAGS.do_eval)):
raise ValueError('At least one of `do_train` or `do_eval` must be True.')
bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)
tf.gfile.MakeDirs(FLAGS.output_dir)
inp... |
def test_call_graph():
(graph, _) = Adjoint(TestBloqWithCallGraph()).call_graph()
edge_strs = {f'{caller} -> {callee}' for (caller, callee) in graph.edges}
assert (edge_strs == {'Adjoint(subbloq=TestBloqWithCallGraph()) -> Adjoint(subbloq=TestAtom())', 'Adjoint(subbloq=TestBloqWithCallGraph()) -> Adjoint(su... |
class ScanningLoader(TestLoader):
def __init__(self):
TestLoader.__init__(self)
self._visited = set()
def loadTestsFromModule(self, module, pattern=None):
if (module in self._visited):
return None
self._visited.add(module)
tests = []
tests.append(TestL... |
class TestSARComposites(unittest.TestCase):
def test_sar_ice(self):
import dask.array as da
import numpy as np
import xarray as xr
from satpy.composites.sar import SARIce
rows = 2
cols = 2
comp = SARIce('sar_ice', prerequisites=('hh', 'hv'), standard_name='sar... |
def train_model(model, model_test, criterion, optimizer, scheduler, num_epochs=25):
since = time.time()
warm_up = 0.1
warm_iteration = (round((dataset_sizes['satellite'] / opt.batchsize)) * opt.warm_epoch)
for epoch in range((num_epochs - start_epoch)):
epoch = (epoch + start_epoch)
prin... |
(frozen=True)
class PackedSequencePlus():
ps = attr.ib()
lengths = attr.ib()
sort_to_orig = attr.ib(converter=np.array)
orig_to_sort = attr.ib(converter=np.array)
def descending(self, attribute, value):
for (x, y) in zip(value, value[1:]):
if (not (x >= y)):
raise... |
.django_db
def test_converts_one_user(user_factory):
user = user_factory()
endpoint = convert_user_to_endpoint(user)
assert (endpoint.id == str(user.id))
assert (endpoint.name == user.name)
assert (endpoint.full_name == user.full_name)
assert (endpoint.is_staff == user.is_staff)
assert (endp... |
def test__getting_started__example_variable_scaling():
from bioptim.examples.getting_started import example_variable_scaling as ocp_module
bioptim_folder = os.path.dirname(ocp_module.__file__)
ocp_module.prepare_ocp(biorbd_model_path=(bioptim_folder + '/models/pendulum.bioMod'), final_time=(1 / 10), n_shoot... |
def allbadtonan(function):
def f(data, axis=None, keepdims=None):
if (keepdims is None):
result = function(data, axis=axis)
else:
result = function(data, axis=axis, keepdims=keepdims)
if ((LooseVersion(np.__version__) >= LooseVersion('1.9.0')) and hasattr(result, '__l... |
(('%s.visualize_utils.plt' % __name__))
def test_show_img_boundary(mock_plt):
img = np.random.rand(10, 10)
boundary = [0, 0, 1, 0, 1, 1, 0, 1]
with pytest.raises(AssertionError):
visualize_utils.show_img_boundary([], boundary)
with pytest.raises(AssertionError):
visualize_utils.show_img_... |
def generate_NUS_cross_val_split_files(root, split_num=5):
raw_files = sorted(glob.glob(os.path.join(root, 'RAW/*.*')))
raw_processed_files = sorted(glob.glob(os.path.join(root, 'Raw_Processed/*.*')))
jpg_files = sorted(glob.glob(os.path.join(root, 'JPG/*.*')))
paired_files = list(zip(raw_files, raw_pro... |
class TestPotential(unittest.TestCase):
def create_test_molecule():
stretch = partial(Molecule.absolute_stretching, kwargs={'atom_pair': (1, 0)})
m = Molecule(geometry=[['H', [0.0, 0.0, 0.0]], ['D', [0.0, 0.0, 1.0]]], degrees_of_freedom=[stretch], masses=[1.6735328e-27, 3.444946e-27])
return... |
def solar_cell_solver(solar_cell: SolarCell, task: str, user_options: Union[(Dict, State, None)]=None):
if (type(user_options) in [State, dict]):
options = merge_dicts(default_options, user_options)
else:
options = merge_dicts(default_options)
prepare_solar_cell(solar_cell, options)
opti... |
class EmbeddingExtractor(object):
def extract_sentbert(self, caption_file: str, output: str, dev: bool=True, zh: bool=False):
from sentence_transformers import SentenceTransformer
lang2model = {'zh': 'distiluse-base-multilingual-cased', 'en': 'bert-base-nli-mean-tokens'}
lang = ('zh' if zh e... |
class TestSpiderDev108(unittest.TestCase):
(ONE_TEST_TIMEOUT)
def test_spider_dev(self):
split_name = 'dev'
i_query = 108
db_id = get_db_id(split_name, i_query)
(rdf_graph, schema) = get_graph_and_schema(split_name, db_id)
sql_query = get_sql_query(split_name, i_query)
... |
class CFB8(ModeWithInitializationVector):
name = 'CFB8'
def __init__(self, initialization_vector: bytes):
utils._check_byteslike('initialization_vector', initialization_vector)
self._initialization_vector = initialization_vector
def initialization_vector(self) -> bytes:
return self._... |
class EpsilonGreedy(BaseExploration):
def __init__(self, exploration_steps, epsilon):
super().__init__(exploration_steps, epsilon)
self.epsilon = epsilon['end']
def select_action(self, q_values, step_count):
if ((np.random.rand() < self.epsilon) or (step_count <= self.exploration_steps))... |
class PendulumEnv(gym.Env):
metadata = {'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 30}
def __init__(self):
self.max_speed = 8
self.max_torque = 2.0
self.dt = 0.05
self.viewer = None
high = np.array([1.0, 1.0, self.max_speed])
self.action_sp... |
class BaseCodec(nn.Module):
def get_tokens(self, x, **kwargs):
raise NotImplementedError
def get_number_of_tokens(self):
raise NotImplementedError
def encode(self, img):
raise NotImplementedError
def decode(self, img_seq):
raise NotImplementedError
def forward(self, *... |
class Server():
def __init__(self, port=50055, ssl_context=None):
self.port = port
self._ssl_context = ssl_context
self.services = {}
self._connection_count = 0
self._exception_count = 0
def add_service(self, service=None, context_manager=None, setup_fn=None, teardown_fn=... |
def discriminator(image, y=None, reuse=False, for_G=False):
if reuse:
tf.get_variable_scope().reuse_variables()
df_dim = 64
h0 = lrelu(conv2d(image, df_dim, name='d_h0_conv'))
h1 = lrelu(conv2d(h0, (df_dim * 2), name='d_h1_conv'))
h2 = lrelu(conv2d(h1, (df_dim * 4), name='d_h2_conv'))
h3... |
def tags_and_versions(tags: Iterable[Tag], translator: VersionTranslator) -> list[tuple[(Tag, Version)]]:
ts_and_vs: list[tuple[(Tag, Version)]] = []
for tag in tags:
try:
version = translator.from_tag(tag.name)
except (NotImplementedError, InvalidVersion) as e:
log.warni... |
.parametrize('marker, expected', [('python_version >= "3.6" and python_version < "4.0"', '>=3.6,<4.0'), ('sys_platform == "linux"', '*'), ('python_version >= "3.9" or sys_platform == "linux"', '*'), ('python_version >= "3.9" and sys_platform == "linux"', '>=3.9')])
def test_marker_properly_sets_python_constraint(marker... |
def get_output_data(layer: torch.nn.Module, model: torch.nn.Module, images_in_one_batch: torch.Tensor) -> np.ndarray:
def _hook_to_collect_output_data(module, _, out_data):
out_data = utils.to_numpy(out_data)
orig_layer_out_data.append(out_data)
raise StopForwardException
hook_handles = ... |
class GenerateThread2Agent(GenerateThread):
def __init__(self, rally_count: int, model1_path: str, is_model1_shuttleNet: bool, model1_shuttleNet_player: int, model2_path: str, is_model2_shuttleNet: bool, model2_shuttleNet_player: int, output_filename: str, parent=None):
super().__init__(output_filename, par... |
def new_focal_loss(logits, targets, alpha: float, gamma: float, normalizer, label_smoothing: float=0.01):
pred_prob = logits.sigmoid()
targets = targets.to(logits.dtype)
onem_targets = (1.0 - targets)
p_t = ((targets * pred_prob) + (onem_targets * (1.0 - pred_prob)))
alpha_factor = ((targets * alpha... |
def try_expanding_sum_type_to_union(typ: Type, target_fullname: str) -> ProperType:
typ = get_proper_type(typ)
if isinstance(typ, UnionType):
items = [try_expanding_sum_type_to_union(item, target_fullname) for item in typ.relevant_items()]
return make_simplified_union(items, contract_literals=Fa... |
class ConfigHandler(Generic[Target]):
section_prefix: str
aliases: Dict[(str, str)] = {}
def __init__(self, target_obj: Target, options: AllCommandOptions, ignore_option_errors, ensure_discovered: expand.EnsurePackagesDiscovered):
self.ignore_option_errors = ignore_option_errors
self.target_... |
def get_data(lmdb_path, name, model_path):
env = lmdb.open(lmdb_path, map_size=, max_dbs=64)
align_db = env.open_db('align'.encode())
txn = env.begin(write=False)
align_bin = txn.get(str(0).encode(), db=align_db)
with open('../data/wav2lip_train/{}.mp4'.format(name), 'wb') as f:
f.write(alig... |
def community_detection(embeddings, threshold=0.75, min_community_size=10, init_max_size=1000):
cos_scores = cos_sim(embeddings, embeddings)
(top_k_values, _) = cos_scores.topk(k=min_community_size, largest=True)
extracted_communities = []
for i in range(len(top_k_values)):
if (top_k_values[i][(... |
class DataLoader():
def __init__(self, id_transformer_group: IDTransformerGroup, dataloader, *, data_info: Dict[(int, str)]=None, paths: List[str]=None, num_prefetch=0):
self._id_transformer_group = id_transformer_group
if (data_info is not None):
for (_, path) in data_info.items():
... |
def _switch_admin(sa: ServerApp, session: MultiplayerSession, membership: MultiplayerMembership):
session_id = session.id
verify_has_admin(sa, session_id, None, allow_when_no_admins=True)
num_admins = MultiplayerMembership.select().where((MultiplayerMembership.session == session_id), is_boolean(MultiplayerM... |
class TridentConv(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, trident_dilations=(1, 2, 3), test_branch_idx=1, bias=False):
super(TridentConv, self).__init__()
self.num_branch = len(trident_dilations)
self.with_bias = bias
self.test_branch_idx = te... |
class Migration(migrations.Migration):
dependencies = [('hotels', '0001_initial')]
operations = [migrations.SeparateDatabaseAndState(database_operations=[migrations.AlterField(model_name='hotelroomreservation', name='user', field=models.ForeignKey('users.User', db_constraint=False, db_index=True, null=False, on... |
class FIDInceptionE_1(models.inception.InceptionE):
def __init__(self, in_channels):
super(FIDInceptionE_1, self).__init__(in_channels)
def forward(self, x):
branch1x1 = self.branch1x1(x)
branch3x3 = self.branch3x3_1(x)
branch3x3 = [self.branch3x3_2a(branch3x3), self.branch3x3_2b... |
class TradingCentres():
onedaydelta = datetime.timedelta(days=1)
def __init__(self):
self._centres = {}
def __len__(self):
return len(self._centres)
def add(self, tc):
self._centres[tc.code] = tc
def _isbizday(self, dte):
for c in self._centres.values():
i... |
def call_gpt(prompt, model='code-davinci-002', stop=None, temperature=0.0, top_p=1.0, max_tokens=128, majority_at=None):
num_completions = (majority_at if (majority_at is not None) else 1)
num_completions_batch_size = 5
completions = []
for i in range((20 * ((num_completions // num_completions_batch_siz... |
class StationSection(TableSection):
keyword = b'STATION'
table_setup = dict(header={None: b'Net Sta Type Latitude Longitude Coord Sys Elev On Date Off Date', 'GSE2.0': b'Sta Type Latitude Longitude Elev On Date Off Date'}, attribute='stations', cls=Station)
stations = List.T(Sta... |
class SpacedDiffusion(GaussianDiffusion):
def __init__(self, use_timesteps, **kwargs):
self.use_timesteps = set(use_timesteps)
self.timestep_map = []
self.original_num_steps = len(kwargs['betas'])
base_diffusion = GaussianDiffusion(**kwargs)
last_alpha_cumprod = 1.0
n... |
(config_path='config', config_name='voting_cls')
def main(args):
if (args.seed is None):
args.seed = np.random.randint(1, 10000)
torch.manual_seed(args.seed)
np.random.seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
torch.cuda.manual_seed(args.seed)
torch.set_printoptions(10)
t... |
def plant():
import obspy
obspy.Trace.to_pyrocko_trace = to_pyrocko_trace
obspy.Trace.snuffle = snuffle
obspy.Trace.fiddle = fiddle
obspy.Stream.to_pyrocko_traces = to_pyrocko_traces
obspy.Stream.snuffle = snuffle
obspy.Stream.fiddle = fiddle
obspy.core.event.Catalog.to_pyrocko_events = ... |
_test
def test_sequential_fit_generator():
((x_train, y_train), (x_test, y_test)) = _get_test_data()
def data_generator(train):
if train:
max_batch_index = (len(x_train) // batch_size)
else:
max_batch_index = (len(x_test) // batch_size)
i = 0
while 1:
... |
def derivatives_in_paraboloidal_coordinates():
coords = (u, v, phi) = symbols('u v phi', real=True)
(par3d, er, eth, ephi) = Ga.build('e_u e_v e_phi', X=[((u * v) * cos(phi)), ((u * v) * sin(phi)), (((u ** 2) - (v ** 2)) / 2)], coords=coords, norm=True)
grad = par3d.grad
f = par3d.mv('f', 'scalar', f=Tr... |
def test():
import gym
import mani_skill.env
env_name = 'OpenCabinetDoor-v0'
env = gym.make(env_name)
osc_interface = OperationalSpaceControlInterface(env_name)
env.set_env_mode(obs_mode='state', reward_type='sparse')
print(env.observation_space)
print(env.action_space)
for level_idx... |
class SparseConv2d(SparseConvolution):
def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True, indice_key=None):
super(SparseConv2d, self).__init__(2, in_channels, out_channels, kernel_size, stride, padding, dilation, groups, bias, indice_key=indice_k... |
def _get_value_for_attr(obj, cls, orig_cls, sig_key, sig, meta_hints, attr_getters, **kwargs):
if (obj and (sig_key in obj)):
result = (sig_key, _get_value_from_obj(obj, cls, sig, sig_key, meta_hints, **kwargs))
elif (sig_key in attr_getters):
attr_getter = attr_getters.pop(sig_key)
resu... |
class TestMiTempBtPoller(unittest.TestCase):
TEST_MAC = '11:22:33:44:55:66'
def test_format_bytes(self):
self.assertEqual('AA BB 00', MiTempBtPoller._format_bytes([170, 187, 0]))
def test_read_battery(self):
poller = MiTempBtPoller(self.TEST_MAC, MockBackend)
backend = self._get_back... |
class OtherTests(unittest.TestCase):
def setUp(self):
self.proxy = Proxy()
self.proxy._proxyfd = MockFd()
def tearDown(self):
UnmockClassMethods(Proxy)
UnmockClassMethods(Server)
def testProcessInputNonProxyPort(self):
fd = MockFd(fd=111)
MockClassMethod(Serve... |
def generate_packets() -> DNSOutgoing:
out = DNSOutgoing((const._FLAGS_QR_RESPONSE | const._FLAGS_AA))
address = socket.inet_pton(socket.AF_INET, '192.168.208.5')
additionals = [{'name': 'HASS Bridge ZJWH FF5137._hap._tcp.local.', 'address': address, 'port': 51832, 'text': b'\x13md=HASS Bridge ZJWH\x06pv=1.... |
def embedding_layers(inputs, model_name, embedding_size=512, dropout_keep_prob=None, is_training=False, weight_decay=4e-05, scope=None):
with tf.variable_scope('projection'):
arg_scope = nets_factory.arg_scopes_map[model_name](weight_decay=weight_decay)
with slim.arg_scope(arg_scope):
wi... |
def _box2cs(box, image_size):
(x, y, w, h) = box[:4]
aspect_ratio = ((1.0 * image_size[0]) / image_size[1])
center = np.zeros(2, dtype=np.float32)
center[0] = (x + (w * 0.5))
center[1] = (y + (h * 0.5))
if (w > (aspect_ratio * h)):
h = ((w * 1.0) / aspect_ratio)
elif (w < (aspect_rat... |
class Model(ModelBase):
def __init__(self, *args, **kwargs):
logger.debug('Initializing %s: (args: %s, kwargs: %s', self.__class__.__name__, args, kwargs)
self.configfile = kwargs.get('configfile', None)
if ('input_shape' not in kwargs):
kwargs['input_shape'] = (64, 64, 3)
... |
class CAGEAlgorithm():
def __init__(self, number_of_epochs=300):
self.model = None
self.number_of_epochs = number_of_epochs
def run_experiments(self, data, experiments):
results = []
for experiment in experiments:
(description, train_objs, test_objs) = experiment
... |
class ReplayMemory(object):
def __init__(self, capacity):
self.capacity = capacity
self.memory = []
self.position = 0
def push(self, *args):
if (len(self.memory) < self.capacity):
self.memory.append(None)
self.memory[self.position] = Transition(*args)
... |
def getArgInt(name, args, min, max, main=True):
if main:
try:
arg = next(args)
except:
doError((name + ': no argument supplied'), True)
else:
arg = args
try:
val = int(arg)
except:
doError((name + ': non-integer value given'), True)
if ... |
def check_ddp_consistency(module, ignore_regex=None):
assert isinstance(module, torch.nn.Module)
for (name, tensor) in named_params_and_buffers(module):
if ('running' in name):
continue
fullname = ((type(module).__name__ + '.') + name)
if ((ignore_regex is not None) and re.fu... |
(max_runs=10)
_with(Learner1D, Learner2D, LearnerND, AverageLearner, AverageLearner1D, SequenceLearner, with_all_loss_functions=False)
def test_balancing_learner(learner_type, f, learner_kwargs):
learners = [learner_type(generate_random_parametrization(f), **learner_kwargs) for i in range(4)]
learner = Balancin... |
def enum_attach(ctr_mol, nei_node, amap, singletons):
(nei_mol, nei_idx) = (nei_node.mol, nei_node.nid)
att_confs = []
black_list = [atom_idx for (nei_id, atom_idx, _) in amap if (nei_id in singletons)]
ctr_atoms = [atom for atom in ctr_mol.GetAtoms() if (atom.GetIdx() not in black_list)]
ctr_bonds ... |
def state_dict_all_gather_keys(state_dict: Dict[(str, Union[(torch.Tensor, ShardedTensor)])], pg: ProcessGroup) -> List[str]:
names = list(state_dict.keys())
all_names = ([None] * dist.get_world_size(pg))
dist.all_gather_object(all_names, names, pg)
deduped_names = set()
for local_names in all_names... |
def test(net, config, master_bar, mode='test'):
net.eval()
num_nodes = config.num_nodes
num_neighbors = config.num_neighbors
batch_size = config.batch_size
batches_per_epoch = config.batches_per_epoch
beam_size = config.beam_size
val_filepath = config.val_filepath
val_target_filepath = c... |
def _calculate_parquet_column_size(type_params: PartialParquetParameters, columns: List[str]):
column_size = 0.0
for rg in type_params.row_groups_to_download:
columns_found = 0
row_group_meta = type_params.pq_metadata.row_group(rg)
for col in range(row_group_meta.num_columns):
... |
class Effect11945(BaseEffect):
type = 'passive'
def handler(fit, src, context, projectionRange, **kwargs):
fit.modules.filteredItemBoost((lambda mod: mod.item.requiresSkill('Capital Projectile Turret')), 'trackingSpeed', src.getModifiedItemAttr('shipBonusTitanG1'), skill='Gallente Dreadnought', **kwargs... |
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