code stringlengths 281 23.7M |
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def test_parse_command_only_expands_alias(parser):
line = 'fake foobar.py "somebody.py'
statement = parser.parse_command_only(line)
assert (statement == 'foobar.py "somebody.py')
assert (statement.args == statement)
assert (statement.arg_list == [])
assert (statement.command == 'run_pyscript')
... |
class PancakeHouseMenu(Menu):
menuItems: List[str]
def __init__(self):
self.menuItems = []
self.addItem("K&B's Pancake Breakfast")
self.addItem('Regular Pancake Breakfast')
self.addItem('Blueberry Pancakes')
self.addItem('Waffles')
def addItem(self, name: str) -> None... |
_events
class EventObject(DefaultObject):
_events = {'drop': (['character', 'obj'], OBJECT_DROP), 'get': (['character', 'obj'], OBJECT_GET), 'time': (['object'], OBJECT_TIME, None, time_event)}
_property
def callbacks(self):
return CallbackHandler(self)
def at_get(self, getter):
super().... |
def _test():
import torch
pretrained = False
models = [mnasnet]
for model in models:
net = model(pretrained=pretrained)
net.eval()
weight_count = _calc_width(net)
print('m={}, {}'.format(model.__name__, weight_count))
assert ((model != mnasnet) or (weight_count ==... |
_api()
class rate_limit(Stream):
_graphviz_shape = 'octagon'
def __init__(self, upstream, interval, **kwargs):
self.interval = convert_interval(interval)
self.next = 0
kwargs['ensure_io_loop'] = True
Stream.__init__(self, upstream, **kwargs)
def update(self, x, who=None, meta... |
def get_linear_schedule_with_warmup(optimizer: Optimizer, num_warmup_steps: int, num_training_steps: int, last_epoch: int=(- 1)):
def lr_lambda(current_step: int):
if (current_step < num_warmup_steps):
return max(1e-06, (float(current_step) / float(max(1, num_warmup_steps))))
return max(... |
class Full(BaseElectrolyteConductivity):
def __init__(self, param, options=None):
super().__init__(param, options=options)
def get_fundamental_variables(self):
phi_e_dict = {}
variables = {}
for domain in self.options.whole_cell_domains:
Dom = domain.capitalize().spli... |
class L2Loss(Loss):
def evaluate(self, predict: np.ndarray, target: np.ndarray) -> np.ndarray:
self._validate_shapes(predict, target)
if (len(predict.shape) <= 1):
return ((predict - target) ** 2)
else:
return (np.linalg.norm((predict - target), axis=tuple(range(1, le... |
()
def remove_old_client_ids(days=90):
old_cutoff = (get_ad_day() - datetime.timedelta(days=days))
while True:
offer_ids = Offer.objects.filter(date__lt=old_cutoff, client_id__isnull=False).values('pk')[:1000]
offers_changed = Offer.objects.filter(pk__in=offer_ids).update(client_id=None)
... |
class TutorialReadable(TutorialObject):
def at_object_creation(self):
super().at_object_creation()
self.db.tutorial_info = "This is an object with a 'read' command defined in a command set on itself."
self.db.readable_text = ('There is no text written on %s.' % self.key)
self.cmdset.... |
class TestNativeMSGPadder(unittest.TestCase):
def prepare_padder(test_dict):
dataset_id = test_dict['dataset_id']
img_bounds = test_dict['img_bounds']
is_full_disk = test_dict['is_full_disk']
dataset = test_dict['dataset']
final_shape = test_dict['final_shape']
expect... |
def particle_grid(dim_x, dim_y, dim_z, lower, radius, jitter):
points = np.meshgrid(np.linspace(0, dim_x, dim_x), np.linspace(0, dim_y, dim_y), np.linspace(0, dim_z, dim_z))
points_t = (((np.array((points[0], points[1], points[2])).T * radius) * 2.0) + np.array(lower))
points_t = (points_t + ((np.random.ran... |
def test_cli_async_reduce_without_curry(runner, reactor, server, capsys):
base_url = '
in_stream = ''.join((base_url.format(i) for i in [6, 2, 1]))
args = ['async-map', 'await asks.get ! f"{types.SimpleNamespace(**x.json()).delay}"', 'map', 'json.loads', 'reduce', 'operator.truediv']
expected = '3.0\n'... |
class LinearColormap(ColorMap):
def __init__(self, colors, index=None, vmin=0.0, vmax=1.0, caption='', max_labels=10, tick_labels=None):
super().__init__(vmin=vmin, vmax=vmax, caption=caption, max_labels=max_labels)
self.tick_labels = tick_labels
n = len(colors)
if (n < 2):
... |
class AlgorithmResult(ABC, collections.UserDict):
def __init__(self, a_dict: Optional[Dict]=None) -> None:
super().__init__()
if a_dict:
self.data.update(a_dict)
def __setitem__(self, key: object, item: object) -> None:
raise TypeError("'__setitem__' invalid for this object."... |
def numpy_random_mtrand_transform():
return parse("\n def beta(a, b, size=None): return uninferable\n def binomial(n, p, size=None): return uninferable\n def bytes(length): return uninferable\n def chisquare(df, size=None): return uninferable\n def choice(a, size=None, replace=True, p=None): return u... |
class TensoredMeasFitter():
def __init__(self, results: Union[(Result, List[Result])], mit_pattern: List[List[int]], substate_labels_list: List[List[str]]=None, circlabel: str=''):
self._result_list = []
self._cal_matrices = None
self._circlabel = circlabel
self._mit_pattern = mit_pa... |
def all_dna_locations(game: GameDescription, config: AM2RArtifactConfig):
locations = []
for node in game.region_list.all_nodes:
if isinstance(node, PickupNode):
name = node.extra['object_name']
if (config.prefer_metroids and name.startswith('oItemDNA_')):
locatio... |
def _CKD_priv(parent_privkey: bytes, parent_chaincode: bytes, child_index: bytes, is_hardened_child: bool) -> Tuple[(bytes, bytes)]:
try:
keypair = ecc.ECPrivkey(parent_privkey)
except ecc.InvalidECPointException as e:
raise BitcoinException('Impossible xprv (not within curve order)') from e
... |
def remove_all_but_largest_component_from_segmentation(segmentation: np.ndarray, labels_or_regions: Union[(int, Tuple[(int, ...)], List[Union[(int, Tuple[(int, ...)])]])], background_label: int=0) -> np.ndarray:
mask = np.zeros_like(segmentation, dtype=bool)
if (not isinstance(labels_or_regions, list)):
... |
def test_on_action_delete_items(view, item):
view.scene.cancel_crop_mode = MagicMock()
view.scene.addItem(item)
item.setSelected(True)
view.on_action_delete_items()
assert (view.scene.items() == [])
assert (view.undo_stack.isClean() is False)
view.scene.cancel_crop_mode.assert_called_once() |
class TestInputGeneration(unittest.TestCase):
def test_tape_inputs(self):
for env_kls in ALL_TAPE_ENVS:
env = env_kls()
for size in range(2, 5):
input_tape = env.generate_input_data(size)
self.assertTrue(all(((0 <= x <= env.base) for x in input_tape)),... |
def LSTMCell(prev_cell, prev_out, input_or_inputs=tuple(), num_units=None, peepholes=True, weight_init=init.Normal(), bias_init=init.Constant(), peepholes_W_init=init.Normal(), forgetgate_nonlinearity=lasagne.nonlinearities.sigmoid, inputgate_nonlinearity=lasagne.nonlinearities.sigmoid, outputgate_nonlinearity=lasagne.... |
class Migration(migrations.Migration):
initial = True
dependencies = []
operations = [migrations.CreateModel(name='Commit', fields=[('sha', models.CharField(help_text='The SHA hash of this commit.', max_length=40, primary_key=True, serialize=False)), ('message', models.TextField(help_text='The commit messag... |
def single_run(E=30000.0, P=25.0, w=0.1, x=0.0):
ops.wipe()
ops.model('basic', '-ndm', 2, '-ndf', 3)
ops.node(1, x, 0)
ops.node(2, 0, 144)
ops.node(3, 240, 144)
ops.node(4, 240, 0)
ops.fix(1, 1, 1, 1)
ops.fix(4, 1, 1, 1)
Ag = 25.0
Ig = 1500.0
Ac = 29.0
Ic = 2000.0
gse... |
class TestSerializeStream():
def _set_status(self, stream, status):
stream.status.return_value = status
def stream_mock(self):
m = unittest.mock.MagicMock(spec=QDataStream)
m.status.return_value = QDataStream.Status.Ok
return m
def test_serialize_pre_error_mock(self, stream_m... |
def calc_time(lower_bound, upper_bound, latitude, longitude, attribute, value, altitude=0, pressure=101325, temperature=12, horizon='+0:00', xtol=1e-12):
(obs, sun) = _ephem_setup(latitude, longitude, altitude, pressure, temperature, horizon)
def compute_attr(thetime, target, attr):
obs.date = thetime
... |
.parametrize('keys, expected', [([127995], '<>'), ([171510], '<>'), ([Qt.Key.Key_Shift, 171510], '<Shift><>'), ([128104, 8205, 128104, 8205, 128102], '<><\u200d><><\u200d><>')])
_enum_workaround_skip
def test_surrogate_sequences(keys, expected):
infos = [keyutils.KeyInfo(Qt.Key(key)) for key in keys]
seq = keyu... |
def _run_on_single_gpu(model, batch_list_t, batch_list_v, batch_sequence_output_list, batch_seq_features_list, batch_visual_output_list):
sim_matrix = []
for (idx1, b1) in enumerate(batch_list_t):
(input_mask, segment_ids, *_tmp) = b1
sequence_output = batch_sequence_output_list[idx1]
se... |
class _RemoteEnv(object):
def __init__(self, env_pkl, policy_pkl):
self._sess = tf_utils.create_session()
self._sess.run(tf.global_variables_initializer())
self._env = pickle.loads(env_pkl)
self._policy = pickle.loads(policy_pkl)
if hasattr(self._env, 'initialize'):
... |
def main_worker(local_rank, args):
args.local_rank = local_rank
args.global_rank = (args.local_rank + (args.node_rank * args.ngpus_per_node))
args.distributed = (args.world_size > 1)
print(args)
config = load_yaml_config(args.config_file)
config = merge_opts_to_config(config, args.opts)
if a... |
def write_preprocessing_parameters(params: namedtuple) -> None:
dict_path = (params.dataset_dir + 'preprocessing_params.csv')
keys_to_write = ['atom_types', 'formal_charge', 'imp_H', 'chirality', 'group_size', 'max_n_nodes', 'use_aromatic_bonds', 'use_chirality', 'use_explicit_H', 'ignore_H']
with open(dict... |
class Storage(Resource):
('StorageResource', rus.optional(str), rus.optional(ss.Session), rus.optional(sab.Base), rus.optional(dict), rus.optional(rus.one_of(SYNC, ASYNC, TASK)))
(rus.nothing)
def __init__(self, id=None, session=None, _adaptor=None, _adaptor_state={}, _ttype=None):
self._resrc = sup... |
def before_after_plots_for_quantized_model(before_weights_map, after_weights_map):
for key in before_weights_map.keys():
before_quantization_data = before_weights_map[key]
after_quantization_data = after_weights_map[key]
compare_boxplots_before_after_quantization(before_quantization_data, af... |
class ListItemWrapper(uiawrapper.UIAWrapper):
_control_types = ['DataItem', 'ListItem']
def __init__(self, elem, container=None):
super(ListItemWrapper, self).__init__(elem)
self.container = container
def is_checked(self):
return (self.iface_toggle.ToggleState_On == toggle_state_on)
... |
def rand_real():
vp = np.random.uniform(low=0, high=360)
vangle = np.random.uniform(low=(- 40), high=(- 70))
cam_dist = np.random.uniform(low=1.5, high=2.5)
distlow = 0.4
distobj = np.random.uniform(low=distlow, high=0.7)
distmult = np.random.uniform(low=1.7, high=2.1)
object_ = [(- (distobj... |
_edge_encoder('LinearEdge')
class LinearEdgeEncoder(torch.nn.Module):
def __init__(self, emb_dim):
super().__init__()
if (cfg.dataset.name in ['MNIST', 'CIFAR10']):
self.in_dim = 1
else:
raise ValueError('Input edge feature dim is required to be hardset or refactored ... |
class TestLinearMapper(QiskitNatureTestCase):
spin_op1 = SpinOp({'Y_0^2': ((- 0.432) + 1.32j)}, 0.5, 1)
ref_qubit_op1 = SparsePauliOp(['II', 'ZZ'], coeffs=[((- 0.054) + 0.165j), (0.054 - 0.165j)])
spin_op2 = SpinOp({'X_0 Z_0': ((- 1.139) + 0.083j)}, 0.5, 2)
ref_qubit_op2 = SparsePauliOp(['IIYX', 'IIXY']... |
def ssim3D(img1, img2, window_size=11, size_average=True):
(_, channel, _, _, _) = img1.size()
window = create_window_3D(window_size, channel)
if img1.is_cuda:
window = window.cuda(img1.get_device())
window = window.type_as(img1)
return _ssim_3D(img1, img2, window, window_size, channel, size... |
def load_sentence(path):
sentences = []
sentence = []
for line in codecs.open(path, 'r', 'utf8'):
line = json.loads(line)
doc_id = line[0]
sentence_text = line[1]
tag = line[(- 1)]
sentence.append(sentence_text)
sentences.append(line)
chars = [[x for x in ... |
def test_lock_file_should_not_have_mixed_types(locker: Locker, root: ProjectPackage) -> None:
package_a = get_package('A', '1.0.0')
package_a.add_dependency(Factory.create_dependency('B', '^1.0.0'))
package_a.add_dependency(Factory.create_dependency('B', {'version': '>=1.0.0', 'optional': True}))
packag... |
class Model(object):
def __init__(self, environment):
self.environment = environment
self._converter = None
def get_value(self, formula, model_completion=True):
raise NotImplementedError
def get_values(self, formulae, model_completion=True):
res = {}
for f in formulae... |
def _expand_requires_extra(re):
for (extra, reqs) in sorted(re.items()):
for req in reqs:
if (';' in req):
(name, envmark) = req.split(';', 1)
(yield '{} ; extra == "{}" and ({})'.format(name, extra, envmark))
else:
(yield '{} ; extra =... |
def cifar10_iterator(cfg, kv):
train_rec = os.path.join(cfg.dataset.data_dir, 'cifar10_train.rec')
val_rec = os.path.join(cfg.dataset.data_dir, 'cifar10_val.rec')
mean = [125.31, 123.01, 113.91]
std = [63.01, 62.09, 66.71]
train = mx.io.ImageRecordIter(path_imgrec=train_rec, label_width=1, data_name... |
def fix_gnu_param(arch, ex):
d = collections.defaultdict(list)
version = None
for item in ex:
if item.get('linux_version'):
if (not version):
version = item.get('linux_version')
else:
raise Exception('More than one linux_version defined')
... |
class ApocalypticMetropolis(pm.Metropolis):
stats_dtypes_shapes = {**pm.Metropolis.stats_dtypes_shapes, 'warning': (SamplerWarning, None)}
def astep(self, q0):
(draw, stats) = super().astep(q0)
stats[0]['warning'] = SamplerWarning(WarningType.BAD_ENERGY, 'Asteroid incoming!', 'warn')
ret... |
def cannot_combine_with_fragment_options(ctx, cache):
if (cache is None):
return
used_names = ctx.meta[fragment_click.FRAGMENTATION_OPTION_NAMES]
if (not used_names):
return
names = sorted((name_to_command_line(name) for name in used_names))
if (len(names) == 1):
raise click.... |
class MatchCase(_base_nodes.MultiLineBlockNode):
_astroid_fields = ('pattern', 'guard', 'body')
_multi_line_block_fields = ('body',)
lineno: None
col_offset: None
end_lineno: None
end_col_offset: None
def __init__(self, *, parent: (NodeNG | None)=None) -> None:
self.pattern: Pattern
... |
def verify_onnx(model, path, force_cpu):
import onnxruntime
import numpy as np
model_weight_file = os.path.join(path, (model + '.pth'))
model_weight_file = './weights/GPEN-512.pth'
model_setenv(force_cpu)
torch_model = get_model(model_weight_file)
torch_model.eval()
onnx_file_name = os.p... |
def no_envs(monkeypatch):
monkeypatch.delenv('PYPYR_CMD_ENCODING', raising=False)
monkeypatch.delenv('PYPYR_ENCODING', raising=False)
monkeypatch.delenv('PYPYR_SKIP_INIT', raising=False)
monkeypatch.delenv('PYPYR_CONFIG_GLOBAL', raising=False)
monkeypatch.delenv('PYPYR_CONFIG_LOCAL', raising=False)
... |
def train(args, train_dataset, model, tokenizer):
if (args.local_rank in [(- 1), 0]):
tb_writer = SummaryWriter()
args.train_batch_size = (args.per_gpu_train_batch_size * max(1, args.n_gpu))
train_sampler = (RandomSampler(train_dataset) if (args.local_rank == (- 1)) else DistributedSampler(train_dat... |
def load_w2v_embedding(word_list, uniform_scale, dimension_size):
embed_file = '../../../code/embedding/GoogleNews-vectors-negative300.bin'
model = gensim.models.KeyedVectors.load_word2vec_format(embed_file, binary=True)
word_vectors = []
for word in word_list:
if (word in model):
wo... |
def traverse_imports(names):
pending = [names]
while pending:
node = pending.pop()
if (node.type == token.NAME):
(yield node.value)
elif (node.type == syms.dotted_name):
(yield ''.join([ch.value for ch in node.children]))
elif (node.type == syms.dotted_as_... |
class FC3_AutoPart(KickstartCommand):
removedKeywords = KickstartCommand.removedKeywords
removedAttrs = KickstartCommand.removedAttrs
def __init__(self, writePriority=100, *args, **kwargs):
KickstartCommand.__init__(self, writePriority, *args, **kwargs)
self.autopart = kwargs.get('autopart',... |
_register
class CodecListObject(BaseObject):
GUID = guid2bytes('86D15240-311D-11D0-A3A4-00A0C90348F6')
def _parse_entry(self, data, offset):
(type_, offset) = cdata.uint16_le_from(data, offset)
(units, offset) = cdata.uint16_le_from(data, offset)
next_offset = (offset + (units * 2))
... |
class F18_TestCase(F17_TestCase):
def runTest(self, iscrypted=False):
F17_TestCase.runTest(self, iscrypted=iscrypted)
self.assert_parse('bootloader --location=mbr --timeout=5 --append="rhgb quiet"')
self.assert_parse('bootloader --location=mbr --timeout=5 --leavebootorder --append="rhgb quie... |
class ADE20K(BaseDataLoader):
def __init__(self, data_dir, batch_size, split, crop_size=None, base_size=None, scale=True, num_workers=1, val=False, shuffle=False, flip=False, rotate=False, blur=False, augment=False, val_split=None, return_id=False):
self.MEAN = [0., 0., 0.4294]
self.STD = [0., 0., 0... |
class MFWPositionWiseFeedForward(torch.nn.Module):
def __init__(self, model_size, inner_size, dropout=0.0, variational=False, activation='relu', n_languages=1, rank=1, use_multiplicative=False, weight_drop=0.0, mfw_activation='none', glu=False, no_bias=False):
super().__init__()
self.variational = v... |
class AbstractLazyTensor(ABC):
def logical_not(self):
return new_lazy_tensor(torch.Tensor.logical_not, [self])
def logical_and(self, arg):
return new_lazy_tensor(torch.Tensor.logical_and, [self, arg])
def logical_or(self, arg):
return new_lazy_tensor(torch.Tensor.logical_or, [self, a... |
.parametrize(('permalink', 'version'), [('CrhkAGTOLJD7Kf6Y', 10), ('DLhkAGTOLJD7Kf6Y', 12)])
def test_decode_old_version(permalink: str, version: int):
expect = f'Given permalink has version {version}, but this Randovania support only permalink of version {Permalink.current_schema_version()}.'
with pytest.raise... |
class AttrVI_ATTR_WIN_ACCESS_PRIV(EnumAttribute):
resources = [(constants.InterfaceType.vxi, 'INSTR'), (constants.InterfaceType.vxi, 'MEMACC')]
py_name = ''
visa_name = 'VI_ATTR_WIN_ACCESS_PRIV'
visa_type = 'ViUInt16'
default = constants.VI_DATA_PRIV
(read, write, local) = (True, True, True)
... |
_db
def test_add_slot_fails_when_not_logged(conference_factory, graphql_client):
conference = conference_factory(start=datetime(2020, 4, 2, tzinfo=pytz.UTC), end=datetime(2020, 4, 2, tzinfo=pytz.UTC))
resp = graphql_client.query('\n mutation AddScheduleSlot($code: ID!, $day: Date!, $duration: Int!) {\n ... |
def write_title(title, stream=None, sep='~'):
if (stream is None):
stream = sys.stderr
(width, height) = shutil.get_terminal_size()
fill = int((((width - len(title)) - 2) / 2))
line = ' '.join([(sep * fill), title, (sep * fill)])
if (len(line) < width):
line += (sep * (width - len(li... |
class ExeclineLexer(RegexLexer):
name = 'execline'
aliases = ['execline']
filenames = ['*.exec']
url = '
version_added = '2.7'
tokens = {'root': [include('basic'), include('data'), include('interp')], 'interp': [('\\$\\{', String.Interpol, 'curly'), ('\\$[\\#]+', Name.Variable), ('\\$', Text)], ... |
def main(cfg: DictConfig, **unused_kwargs):
if isinstance(cfg, Namespace):
cfg = convert_namespace_to_omegaconf(cfg)
utils.import_user_module(cfg.common)
use_fp16 = cfg.common.fp16
use_cuda = (torch.cuda.is_available() and (not cfg.common.cpu))
if use_cuda:
torch.cuda.set_device(cfg.... |
_tokenizers
class MarkupLMTokenizationTest(TokenizerTesterMixin, unittest.TestCase):
tokenizer_class = MarkupLMTokenizer
rust_tokenizer_class = MarkupLMTokenizerFast
test_rust_tokenizer = True
from_pretrained_kwargs = {'cls_token': '<s>'}
test_seq2seq = False
def setUp(self):
super().set... |
()
('-i', '--input-file', help='The name of the input file containing a molecule to be parameterised.', type=click.Path(exists=True, dir_okay=False, resolve_path=True, readable=True))
('-sm', '--smiles', help='The smiles string of the molecule to be parameterised.', type=click.STRING)
('-m', '--multiplicity', type=clic... |
class TestSelect(BaseTestCase):
def setUp(self):
super().setUp()
sync(self.page.goto((self.url + 'static/select.html')))
async def test_select(self):
value = (await self.page.select('select', 'blue'))
self.assertEqual(value, ['blue'])
_input = (await self.page.evaluate('r... |
def test_invalid_compute_mask():
model = Sequential()
model.add(Conv2D(1, [2, 2], input_shape=[3, 3, 1]))
assert (model.layers[0].supports_masking is False)
assert (model.layers[0].compute_mask([model.input], [None]) is None)
mask = np.array([[0.0, 1.0], [1.0, 0.0]])
with pytest.raises(TypeError... |
class TopK():
def __init__(self, k: int):
self.k = k
def __repr__(self) -> str:
repr = f'Filter {self.__class__.__name__}'
repr += f'''
k: {self.k}'''
return repr
def __call__(self, documents: typing.Union[typing.List[typing.List[typing.Dict[(str, str)]]]], **kwargs) -> typi... |
(netloc='fakegitlab', path='/api/v4/projects/4/repository/files/Dockerfile$')
def dockerfile_handler(_, request):
if (not (request.headers.get('Authorization') == 'Bearer foobar')):
return {'status_code': 401}
return {'status_code': 200, 'headers': {'Content-Type': 'application/json'}, 'content': json.d... |
class EpisodeDescriptionConfig(object):
def __init__(self, num_ways, num_support, num_query, min_ways, max_ways_upper_bound, max_num_query, max_support_set_size, max_support_size_contrib_per_class, min_log_weight, max_log_weight, ignore_dag_ontology, ignore_bilevel_ontology):
arg_groups = {'num_ways': (num_... |
def _generate_html(data):
extra_params = {'initial_header_level': '2', 'syntax_highlight': 'short', 'input_encoding': 'utf-8', 'exit_status_level': 2, 'compact_p': False, 'embed_stylesheet': False}
pub = docutils.core.Publisher(source_class=docutils.io.StringInput, destination_class=docutils.io.StringOutput)
... |
def check_all_auto_mapping_names_in_config_mapping_names():
check_missing_backends()
failures = []
mappings_to_check = {'IMAGE_PROCESSOR_MAPPING_NAMES': IMAGE_PROCESSOR_MAPPING_NAMES, 'FEATURE_EXTRACTOR_MAPPING_NAMES': FEATURE_EXTRACTOR_MAPPING_NAMES, 'PROCESSOR_MAPPING_NAMES': PROCESSOR_MAPPING_NAMES}
... |
def get_cfg(cls=CN):
cfg = cls()
cfg.NUM_GPUS = 8
cfg.TRAIN = cls()
cfg.TRAIN.HYPERPARAMETER_1 = 0.1
cfg.TRAIN.SCALES = (2, 4, 8, 16)
cfg.MODEL = cls()
cfg.MODEL.TYPE = 'a_foo_model'
cfg.STR = cls()
cfg.STR.KEY1 = 1
cfg.STR.KEY2 = 2
cfg.STR.FOO = cls()
cfg.STR.FOO.KEY1 = ... |
class DS1000Problem():
def __init__(self, problem_path: Union[(str, Path)]):
self.problem_path = Path(problem_path)
self.problem_id = int(self.problem_path.name.replace('q', ''))
self.data = dict()
problem_config = configparser.RawConfigParser()
problem_config.read((self.prob... |
def sample_info_video(video_frames, time_window, time_stride):
samples = ([0] * len(video_frames))
area_sum_samples = ([0] * len(video_frames))
for (i, video) in enumerate(video_frames):
samples[i] = ((len(video) - time_window) // time_stride)
if (i != 0):
area_sum_samples[i] = s... |
def composite(*args):
import qutip.core.superop_reps
if (not all((isinstance(arg, Qobj) for arg in args))):
raise TypeError('All arguments must be Qobjs.')
if all(map(_isoperlike, args)):
if any((arg.issuper for arg in args)):
return super_tensor(*map(qutip.core.superop_reps.to_s... |
.parametrize(('requirement_string', 'expected'), [('extras_dep', None), ('missing_dep', ('missing_dep',)), ('requireless_dep', None), ('extras_dep[undefined_extra]', None), ('extras_dep[extra-without-associated-deps]', None), ('extras_dep[extra-with-unmet-deps]', ('extras_dep[extra-with-unmet-deps]', 'unmet_dep; extra ... |
def get_decode_dir_name(ckpt_name):
if (('train' in FLAGS.full_data_path) or ('train' in FLAGS.partial_data_path)):
dataset = 'train'
elif (('val' in FLAGS.full_data_path) or ('val' in FLAGS.partial_data_path)):
dataset = 'val'
elif (('test' in FLAGS.full_data_path) or ('test' in FLAGS.parti... |
class DevDataset(Dataset):
def __init__(self, meta_args, tasks_dev_data):
self.meta_args = meta_args
self.meta_dev_data = MultiTaskWrapper(args_path2dataset=tasks_dev_data, meta_args=meta_args, section='dev')
def __getitem__(self, index) -> T_co:
return self.meta_dev_data[index]
def ... |
class PositionwiseFeedForward(nn.Module):
def __init__(self, d_in, d_hid, dropout=0.1):
super().__init__()
self.w_1 = nn.Conv1d(d_in, d_hid, kernel_size=hp.fft_conv1d_kernel[0], padding=hp.fft_conv1d_padding[0])
self.w_2 = nn.Conv1d(d_hid, d_in, kernel_size=hp.fft_conv1d_kernel[1], padding=h... |
class TestWindow(pyglet.window.Window):
def __init__(self, *args, **kwargs):
super(TestWindow, self).__init__(*args, **kwargs)
self.batch = pyglet.graphics.Batch()
self.document = pyglet.text.decode_html(doctext)
self.margin = 2
self.layout = layout.IncrementalTextLayout(self... |
def model_info(model, verbose=True):
n_p = sum((x.numel() for x in model.parameters()))
n_g = sum((x.numel() for x in model.parameters() if x.requires_grad))
device = next(model.parameters()).device
if verbose:
print(('%5s %60s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters... |
def info_from_p2p_addr(addr: Multiaddr) -> PeerInfo:
if (addr is None):
raise InvalidAddrError('`addr` should not be `None`')
parts = addr.split()
if (not parts):
raise InvalidAddrError(f'`parts`={parts} should at least have a protocol `P_P2P`')
p2p_part = parts[(- 1)]
last_protocol_... |
.parametrize('method, signal, timeout', [('waitSignal', None, None), ('waitSignal', None, 1000), ('waitSignals', [], None), ('waitSignals', [], 1000), ('waitSignals', None, None), ('waitSignals', None, 1000)])
def test_signal_blocker_none(qtbot, method, signal, timeout):
meth = getattr(qtbot, method)
with pytes... |
class TFVisualization(unittest.TestCase):
def test_visualize_weight_ranges_single_layer(self):
tf.compat.v1.reset_default_graph()
_ = ResNet50(weights=None)
model = tf.compat.v1.get_default_graph()
init = tf.compat.v1.global_variables_initializer()
sess = tf.compat.v1.Session... |
.parametrize('dist_args, size, cm', [pytest.param([set_test_value(pt.dvector(), np.array([100000, 1, 1], dtype=np.float64))], None, contextlib.suppress()), pytest.param([set_test_value(pt.dmatrix(), np.array([[100000, 1, 1], [1, 100000, 1], [1, 1, 100000]], dtype=np.float64))], (10, 3), contextlib.suppress()), pytest.p... |
def create_rand_tensors_given_shapes(input_shape: Union[(Tuple, List[Tuple])]) -> List[np.ndarray]:
if isinstance(input_shape, List):
input_shapes = input_shape
else:
input_shapes = [input_shape]
rand_tensors = []
for shape in input_shapes:
rand_tensors.append(np.random.rand(*sha... |
.parametrize('transform, gcps, rpcs', [((Affine.identity() * Affine.scale(2.0)), None, None), (None, [rasterio.control.GroundControlPoint(0, 0, 0, 0, 0)], None), (None, None, gen_rpcs())])
def test_no_notgeoref_warning(transform, gcps, rpcs):
with rasterio.MemoryFile() as mem:
with mem.open(driver='GTiff', ... |
.mosaiqdb
def test_get_qcls_by_date(connection: pymedphys.mosaiq.Connection):
a_completion_datetime = QCL_COMPLETED_DATETIMES[0]
large_time_delta = np.timedelta64(90, 'D')
start = (np.datetime64(a_completion_datetime) - large_time_delta)
end = (np.datetime64(a_completion_datetime) + large_time_delta)
... |
class SepConvLSTM2DCell(DropoutRNNCellMixin, Layer):
def __init__(self, filters, kernel_size, strides=(1, 1), padding='valid', data_format=None, dilation_rate=(1, 1), depth_multiplier=1, activation='tanh', recurrent_activation='hard_sigmoid', use_bias=True, kernel_initializer='glorot_uniform', recurrent_initializer... |
class Prf():
DIGESTS_1 = {enums.HashId_1.MD5: (hashlib.md5, 16), enums.HashId_1.SHA: (hashlib.sha1, 20), enums.HashId_1.SHA2_256: (hashlib.sha256, 32), enums.HashId_1.SHA2_384: (hashlib.sha384, 48), enums.HashId_1.SHA2_512: (hashlib.sha512, 64)}
DIGESTS = {enums.PrfId.PRF_HMAC_MD5: (hashlib.md5, 16), enums.PrfI... |
()
('project_name')
_tracking
def init(project_name):
print(f'Creating {project_name} template project')
dir_path = os.path.dirname(os.path.realpath(__file__))
shutil.copytree(os.path.join(dir_path, 'dbt_template'), project_name)
with open(f'{project_name}/dbt_project.yml', 'w') as f:
f.write(re... |
def final_i_index_finder(min_switch_ind, i_omega, m_omega):
assert (type(min_switch_ind) == int), 'min_switch_ind should be an int.'
assert (type(i_omega) == list), 'i_omega should be a list.'
assert (type(m_omega) == list), 'm_omega should be a list.'
final_i_index = np.searchsorted(i_omega, m_omega[mi... |
class Extension():
persist = True
def __init__(self, name: Optional[str]=None):
self._name = (name or underscore(self.__class__.__name__))
def name(self):
return self._name
def flag(self) -> str:
return f'--{dasherize(self.name)}'
def help_text(self) -> str:
if (self.... |
class CostFuncWrapper():
def __init__(self, maxeval=5000, progressbar=True, logp_func=None, dlogp_func=None):
self.n_eval = 0
self.maxeval = maxeval
self.logp_func = logp_func
if (dlogp_func is None):
self.use_gradient = False
self.desc = 'logp = {:,.5g}'
... |
.parametrize('username,password', users)
.parametrize('project_id', projects)
def test_list(db, client, username, password, project_id):
client.login(username=username, password=password)
url = reverse(urlnames['list'], args=[project_id])
response = client.get(url)
if (project_id in view_snapshot_permis... |
def solar_holiday_to_number(string) -> str:
solar = {'': '11', '': '214', '': '22', '': '38', '': '312', '': '322', '': '41', '': '422', '': '423', '': '51', '': '54', '': '54', '': '512', '': '518', '': '519', '': '61', '': '71', '': '711', '': '81', '': '910', '': '918', '': '101', '': '118', '': '1117', '': '121... |
def run(parser, args):
from pyrocko import squirrel as sq
squirrel = args.make_squirrel()
kwargs = args.squirrel_query
kinds = kwargs.pop('kind', sq.supported_content_kinds())
codes_query = kwargs.pop('codes', None)
for kind in kinds:
for (kind_id, codes, deltat, _, count) in sorted(squi... |
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