code stringlengths 281 23.7M |
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_model
def resmlp_big_24_distilled_224(pretrained=False, **kwargs):
model_args = dict(patch_size=8, num_blocks=24, embed_dim=768, mlp_ratio=4, block_layer=partial(ResBlock, init_values=1e-06), norm_layer=Affine, **kwargs)
model = _create_mixer('resmlp_big_24_distilled_224', pretrained=pretrained, **model_args)
... |
def read_crn(filename, map_variables=True):
data = pd.read_fwf(filename, header=None, names=HEADERS, widths=WIDTHS, dtype=str)
data = data.dropna(axis=0, how='all')
data = data.astype(dict(zip(HEADERS, DTYPES)))
data = data.replace(NAN_DICT, value=np.nan)
dts = data[['UTC_DATE', 'UTC_TIME']].astype(... |
.parametrize('frac_dummies', [0.1, 0.5, 0.9])
def test_hdf5_write_dummies(hdf5_file_path, test_objects, frac_dummies):
num_dummies = int((len(test_objects) * frac_dummies))
for obj in test_objects[:num_dummies]:
obj.dummy = True
obj.ID = (- (obj.ID + 1))
track_id = 1
track_with_dummies =... |
def load(fnames, tag='', inst_id='', malformed_index=False, start_time=None, num_samples=864, test_load_kwarg=None, max_latitude=90.0):
pysat.logger.info(''.join(('test_load_kwarg = ', str(test_load_kwarg))))
iperiod = mm_test.define_period()
drange = mm_test.define_range()
(uts, index, dates) = mm_test... |
def main() -> None:
application = Application.builder().token('TOKEN').build()
conv_handler = ConversationHandler(entry_points=[CommandHandler('start', start)], states={START_ROUTES: [CallbackQueryHandler(one, pattern=(('^' + str(ONE)) + '$')), CallbackQueryHandler(two, pattern=(('^' + str(TWO)) + '$')), Callba... |
def configure_converter(converter: BaseConverter):
converter.register_unstructure_hook(bytes, (lambda v: (b85encode(v) if v else b'').decode('utf8')))
converter.register_structure_hook(bytes, (lambda v, _: b85decode(v)))
converter.register_structure_hook(datetime, (lambda v, _: datetime.fromisoformat(v)))
... |
class ScrimSetup(ScrimsView):
def __init__(self, ctx: Context):
super().__init__(ctx, timeout=60)
self.ctx = ctx
self.record: Scrim = None
self.add_item(RegChannel(ctx, 'a'))
self.add_item(SlotChannel(ctx, 'b'))
self.add_item(SetRole(ctx, 'c'))
self.add_item(S... |
def dicom_dataset_from_dict(input_dict: dict, template_ds=None):
if (template_ds is None):
dataset = pydicom.Dataset()
else:
dataset = deepcopy(template_ds)
for (key, value) in input_dict.items():
if (key not in get_dicom_names()):
raise ValueError('{} is not within the D... |
def test_list_from_entry_points():
ext_list = extensions.list_from_entry_points()
orig_len = len(ext_list)
assert all((isinstance(e, extensions.Extension) for e in ext_list))
name_list = [e.name for e in ext_list]
for ext in ('cirrus', 'pre_commit', 'no_skeleton', 'namespace', 'venv'):
asser... |
(short_help='Display project metadata')
('field', required=False)
_obj
def metadata(app, field):
import json
from hatchling.dep.core import dependencies_in_sync
if dependencies_in_sync(app.project.metadata.build.requires_complex):
from hatchling.metadata.utils import resolve_metadata_fields
... |
def missing_info(modules: dict[(str, MypyFile)]) -> TypeInfo:
suggestion = _SUGGESTION.format('info')
dummy_def = ClassDef(suggestion, Block([]))
dummy_def.fullname = suggestion
info = TypeInfo(SymbolTable(), dummy_def, '<missing>')
obj_type = lookup_fully_qualified_typeinfo(modules, 'builtins.objec... |
def depthwise_conv_bn(x, kernel_size, strides=1, dilation=1):
with tf.variable_scope(None, 'depthwise_conv_bn'):
x = slim.separable_conv2d(x, None, kernel_size, depth_multiplier=1, stride=strides, rate=dilation, activation_fn=None, biases_initializer=None)
x = slim.batch_norm(x, activation_fn=None, ... |
class _ConfigExpander():
def __init__(self, config: dict, root_dir: Optional[_Path]=None, ignore_option_errors: bool=False, dist: Optional['Distribution']=None):
self.config = config
self.root_dir = (root_dir or os.getcwd())
self.project_cfg = config.get('project', {})
self.dynamic =... |
class LogicalLineFinder():
def __init__(self, lines):
self.lines = lines
def logical_line_in(self, line_number):
indents = count_line_indents(self.lines.get_line(line_number))
tries = 0
while True:
block_start = get_block_start(self.lines, line_number, indents)
... |
class RepoMirrorConfig(BaseModel):
repository = ForeignKeyField(Repository, index=True, unique=True, backref='mirror')
creation_date = DateTimeField(default=datetime.utcnow)
is_enabled = BooleanField(default=True)
mirror_type = ClientEnumField(RepoMirrorType, default=RepoMirrorType.PULL)
internal_ro... |
class FakeHDF5FileHandler2(FakeHDF5FileHandler):
def _get_geo_data(self, num_rows, num_cols):
geo = {'Grid/lon': xr.DataArray(DEFAULT_LON_DATA, attrs={'units': 'degrees_east'}, dims='lon'), 'Grid/lat': xr.DataArray(DEFAULT_LAT_DATA, attrs={'units': 'degrees_north'}, dims='lat')}
return geo
def _... |
class CudnnGruEncoder(CudnnRnnEncoder, SequenceEncoder):
def __init__(self, n_units, n_layers=1, keep_recurrent=1, w_init=TruncatedNormal(stddev=0.05), recurrent_init=None, bidirectional=True, learn_initial_states=False):
super().__init__('GRU', n_units, n_layers, w_init, recurrent_init, bidirectional, lear... |
def run_cmd(*cmd: Optional[str], out: Optional[Union[(TeeCapture, IO[str])]]=sys.stdout, err: Optional[Union[(TeeCapture, IO[str])]]=sys.stderr, raise_on_fail: bool=True, log_run_to_stderr: bool=True, abbreviate_non_option_arguments: bool=False, **kwargs) -> CommandOutput:
kept_cmd = tuple((cast(str, e) for e in cm... |
.parametrize('py_info_name', ['cpython3_win_embed'])
def test_no_python_zip_if_exists_and_not_set_in_path(py_info, mock_files):
python_zip_name = f'python{py_info.version_nodot}.zip'
python_zip = path(py_info.prefix, python_zip_name)
py_info.path.remove(python_zip)
mock_files(CPYTHON3_PATH, [python_zip]... |
def code_to_bitstream(code):
code = parse(code).render()
data = swap_bytes(code)
stream = ['10000', '00000', '11111', '01111', '11111', '01111', '11111']
for c in data:
v = int(c, 16)
stream.append((((('1' + str((v & 1))) + str(((v & 2) >> 1))) + str(((v & 4) >> 2))) + str(((v & 8) >> 3)... |
def update_config(config, args):
_update_config_from_file(config, args.cfg)
config.defrost()
if args.opts:
config.merge_from_list(args.opts)
if args.batch_size:
config.DATA.BATCH_SIZE = args.batch_size
if args.zip:
config.DATA.ZIP_MODE = True
if args.cache_mode:
c... |
def _encode_path_parts(text_parts, rooted=False, has_scheme=True, has_authority=True, maximal=True):
if (not text_parts):
return ()
if rooted:
text_parts = ((u'',) + tuple(text_parts))
encoded_parts = []
if has_scheme:
encoded_parts = [(_encode_path_part(part, maximal=maximal) if... |
class MusicProvider():
def __init__(self, query: Optional[str], key: Optional[int]) -> None:
self.query = query
self.key = key
if (not hasattr(self, 'type')):
self.type = 'unknown'
assert False
self.id: Optional[str] = None
self.ok_message = 'ok'
... |
def sum_fst(rp_pairs):
global quiet
n = len(rp_pairs)
if (not quiet):
print(('Processing statistics from session 1 of %d' % (n,)))
total_fst = make_fst(*rp_pairs[0])
for i in range(1, n):
if (not quiet):
print(('Processing statistics from session %d of %d' % ((i + 1), n))... |
def test_entity_search(auth_engine, requires_email, client):
with auth_engine(requires_email=requires_email) as auth:
with patch('endpoints.api.search.authentication', auth):
response = conduct_api_call(client, EntitySearch, 'GET', params=dict(prefix='unknown'))
results = response.js... |
def test_non_total():
assert (get_typed_dict_shape(Bar) == Shape(input=InputShape(constructor=Bar, kwargs=None, fields=(InputField(type=int, id='a', default=NoDefault(), is_required=False, metadata=MappingProxyType({}), original=None), InputField(type=str, id='b', default=NoDefault(), is_required=False, metadata=Ma... |
def test_num_complex_while_else_labels() -> None:
src = "\n i = 0\n while i < 10:\n j = 0\n while j < 5:\n j += 1\n i += 1\n\n if i > 4:\n print('hi')\n else:\n print('is else')\n\n print('not else')\n "
expected_num_labels = 6
assert (... |
class ThreadForReqChannel(threading.Thread):
def __init__(self, channel):
threading.Thread.__init__(self)
if (not isinstance(channel, RepChannel)):
raise ValueError('The given channel must be a REP channel.')
self._channel = channel
if (sys.version_info < (2, 6)):
... |
.parametrize(('project_directory', 'required_fixtures'), [('project_with_local_dependencies', ['distributions/demo-0.1.0-py2.py3-none-any.whl', 'project_with_setup'])])
def test_show_outdated_local_dependencies(tester: CommandTester, poetry: Poetry, installed: Repository, repo: TestRepository) -> None:
cachy_010 = ... |
def _make_along_axis_idx(arr_shape, indices, axis):
if (str(indices.dtype) not in int_types):
raise IndexError('`indices` must be an integer array')
shape_ones = ((1,) * indices.ndim)
dest_dims = ((list(range(axis)) + [None]) + list(range((axis + 1), indices.ndim)))
fancy_index = []
for (dim... |
def main(_):
train_dir = os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name, 'train')
utils.force_mkdir(os.path.join(FLAGS.checkpoint_dir, FLAGS.model_name))
utils.force_mkdir(train_dir)
g = tf.Graph()
with g.as_default():
with tf.device(tf.train.replica_device_setter(FLAGS.ps_tasks)):
... |
class DNSOutgoing():
__slots__ = ('flags', 'finished', 'id', 'multicast', 'packets_data', 'names', 'data', 'size', 'allow_long', 'state', 'questions', 'answers', 'authorities', 'additionals')
def __init__(self, flags: int, multicast: bool=True, id_: int=0) -> None:
self.flags = flags
self.finish... |
class BaseOAuth2(OAuthAuth):
REFRESH_TOKEN_URL = None
REFRESH_TOKEN_METHOD = 'POST'
RESPONSE_TYPE = 'code'
REDIRECT_STATE = True
STATE_PARAMETER = True
USE_BASIC_AUTH = False
def use_basic_auth(self):
return self.USE_BASIC_AUTH
def auth_params(self, state=None):
(client_i... |
class Effect5754(BaseEffect):
type = 'overheat'
def handler(fit, module, context, projectionRange, **kwargs):
module.boostItemAttr('maxRangeBonus', module.getModifiedItemAttr('overloadTrackingModuleStrengthBonus'), **kwargs)
module.boostItemAttr('falloffBonus', module.getModifiedItemAttr('overlo... |
class TouchDelete(RPathTest):
def testTouch(self):
t = rpath.RPath(self.lc, self.write_dir, ('testtouch',))
self.assertFalse(t.lstat())
t.touch()
self.assertTrue(t.lstat())
t.delete()
def testDelete(self):
d = rpath.RPath(self.lc, self.write_dir, ('testdelete',))
... |
class DeleteRemovedAttrs_TestCase(unittest.TestCase):
def runTest(self):
errors = []
commands_dir = os.path.join(os.path.dirname(pykickstart.__file__), 'commands')
commands_dir = os.path.abspath(commands_dir)
self.assertTrue(os.path.exists(commands_dir))
if (commands_dir not ... |
class ClassifierModule(nn.Module):
def __init__(self, m, channel, num_classes):
super(ClassifierModule, self).__init__()
self.m = m
self.linear = nn.Linear(channel, num_classes)
def forward(self, x):
res = self.m(x)
res = res.view(res.size(0), (- 1))
return self.l... |
def collate_function(batch):
graphs_as_adjlist = [elem[0] for elem in batch]
targets = [elem[1] for elem in batch]
graphs_as_adjlist_catted = graphs_as_adjlist[0].concatenate(graphs_as_adjlist)
graphs_as_adjlist_catted.inplace_from_np_to_torch()
targets = torch.from_numpy(np.array(targets))
retu... |
def msid_descriptor(x, ts=np.logspace((- 1), 1, 256), k=5, m=10, niters=100, rademacher=False, graph_builder='sparse', normalized_laplacian=True, normalize='empty'):
Lx = _build_graph(x, k, graph_builder, normalized_laplacian)
nx = Lx.shape[0]
msidx = slq_red_var(Lx, m, niters, ts, rademacher)
normed_ms... |
class ApproveSponsorshipAdminViewTests(TestCase):
def setUp(self):
self.user = baker.make(settings.AUTH_USER_MODEL, is_staff=True, is_superuser=True)
self.client.force_login(self.user)
self.sponsorship = baker.make(Sponsorship, status=Sponsorship.APPLIED, _fill_optional=True)
self.ur... |
def client_with_identity(auth_username, app):
if (auth_username and (auth_username is not None)):
loaded = model.user.get_user(auth_username)
else:
loaded = None
with app.test_client(user=loaded) as cl:
(yield cl)
with cl.session_transaction() as sess:
sess['_user... |
('pytube.request.get')
('pytube.cli.YouTube.__init__', return_value=None)
def test_load_more(youtube, request_get, playlist_html):
url = '
request_get.side_effect = [playlist_html, '{"content_html":"", "load_more_widget_html":""}']
playlist = Playlist(url)
assert (len(list(playlist.videos)) == 12)
r... |
class PhysicalParameter(VectorParameter):
def __init__(self, name, uncertaintyType='absolute', **kwargs):
super().__init__(name, length=2, **kwargs)
self._utype = ListParameter('uncertainty type', choices=['absolute', 'relative', 'percentage'], default=None)
self._utype.value = uncertaintyTy... |
def pretty_print_requirement_array(requirement: RequirementArrayBase, level: int) -> Iterator[tuple[(int, str)]]:
if ((len(requirement.items) == 1) and (requirement.comment is None)):
(yield from pretty_print_requirement(requirement.items[0], level))
return
resource_requirements = [item for item... |
def check_encoded_labels(sentences, labels, itow):
for sent in sentences:
print(('gd-truth: %s' % ' '.join(sent['tokens'])))
h5_id = sent['h5_id']
label = labels[h5_id].tolist()
decoded = ' '.join([itow[i] for i in label if (i != 0)])
print(('decoded : %s' % decoded))
... |
def dL_dMu(mu, C_hat, m, r):
global dLdMu_numers
if (len(dLdMu_numers) == 0):
dLdMu_numers = [(r[i] * (C_hat[i][0] - C_hat[i][1])) for i in range(m)]
total_sum = 0
mu1 = (1 - mu)
for i in range(m):
total_sum += (dLdMu_numers[i] / ((C_hat[i][0] * mu) + (C_hat[i][1] * mu1)))
return... |
class BaseDataLoader(object):
def __init__(self, dataset, batch_size, repeat=1, shuffle=True, seed=0, drop_last_sample=False, drop_last_batch=True, num_workers=0, prefetch_factor=2):
self._dataset = dataset
self._batch_size = batch_size
self.repeat = repeat
self.shuffle = shuffle
... |
class AttentionLayer(nn.Module):
def __init__(self, channel):
super().__init__()
self.convs = nn.ModuleList([nn.Conv2d(channel, channel, kernel_size=3, stride=1, padding=1) for _ in range(4)])
def forward(self, xs, anchor):
ans = torch.ones_like(anchor)
target_size = anchor.shape... |
def get_all_objects(start_obj: QObject=None) -> str:
output = ['']
widget_lines = _get_widgets()
widget_lines = [(' ' + e) for e in widget_lines]
widget_lines.insert(0, 'Qt widgets - {} objects:'.format(len(widget_lines)))
output += widget_lines
if (start_obj is None):
start_obj = obj... |
class ImageNetSubset(data.Dataset):
def __init__(self, subset_file, root=MyPath.db_root_dir('imagenet'), split='train', transform=None):
super(ImageNetSubset, self).__init__()
self.root = os.path.join(root, ('ILSVRC2012_img_%s' % split))
self.transform = transform
self.split = split
... |
def gaussian_1u1d_instance(layout: QubitsLayout, u: float, dt: float=0.3) -> FermiHubbardParameters:
hamiltonian = Hamiltonian(sites_count=layout.size, j=1.0, u=u)
initial_state = IndependentChainsInitialState(up=PhasedGaussianSingleParticle(k=(1.2 * 7), sigma=(1.2 / 7), position=(1.5 / 7)), down=PhasedGaussian... |
class FileReader():
def __init__(self, binary):
self.binary = binary
self.offset = 0
def read(self, size):
data = self.binary[self.offset:(self.offset + size)]
self.offset += size
return data
def setOffset(self, offset):
self.offset = offset
def readString... |
def python_to_json(o, version=1):
if (version not in (1, 2)):
raise ValueError(f'Unexpected version {version}')
references = []
result = {'v': version, 'data': _py2js(o, references, version=version), 'references': references}
if (not result['references']):
del result['references']
re... |
class Effect6188(BaseEffect):
runTime = 'late'
type = ('projected', 'active')
def handler(fit, container, context, projectionRange, **kwargs):
if ('projected' not in context):
return
if fit.ship.getModifiedItemAttr('disallowAssistance'):
return
bonus = contain... |
def main_worker(gpu, ngpus_per_node, args, loader_args):
if (gpu == 0):
save_dir = ((args.save_dir + args.name) + '/')
if (not os.path.exists(save_dir)):
os.makedirs(save_dir)
if (not os.path.exists((save_dir + 'results/'))):
os.makedirs((save_dir + 'results/'))
... |
def random_input_forward_pass(sess, args):
np.random.seed(0)
model_output = sess.graph.get_tensor_by_name(args['output_tensor'])
data = (np.random.randint(16384, size=args['input_shape']) if args['int'] else np.random.rand(*args['input_shape']))
model_inputs = {sess.graph.get_tensor_by_name(args['input_... |
class MultiXactStream(Chunks):
chunksize = (1024 * 4)
_process_chunk = Output._process_tuple_chunk
def _e_metas(self):
(yield ('chunksize', self.chunksize))
(yield ('type', self.__class__.__name__))
def __init__(self, statement, parameters, cursor_id):
self.statement = statement
... |
class FakeFCIFileHandlerWithBadIDPFData(FakeFCIFileHandlerFDHSI):
def _get_test_content_all_channels(self):
data = super()._get_test_content_all_channels()
data['data/vis_06/measured/x'].attrs['scale_factor'] *= (- 1)
data['data/vis_06/measured/x'].attrs['scale_factor'] = np.float32(data['da... |
class PredictUnit(AppStateMixin, _OnExceptionMixin, Generic[TPredictData], ABC):
def __init__(self) -> None:
super().__init__()
self.predict_progress = Progress()
def on_predict_start(self, state: State) -> None:
pass
def on_predict_epoch_start(self, state: State) -> None:
pa... |
class BertDecoder(nn.Module):
def __init__(self, config, embedding=None):
super(BertDecoder, self).__init__()
if isinstance(config, dict):
config = dict2obj(config)
self.embedding = (BertEmbeddings(config, return_pos=(True if config.pos_attention else False)) if (embedding is Non... |
class SubmissionAdminForm(forms.ModelForm):
class Meta():
model = Submission
fields = ['title', 'slug', 'speaker', 'status', 'type', 'duration', 'topic', 'conference', 'audience_level', 'languages', 'elevator_pitch', 'abstract', 'notes', 'tags', 'speaker_level', 'previous_talk_video', 'short_social_... |
class GAT(nn.Module):
def __init__(self, nfeat, nhid, nclass, dropout, alpha, nheads, layer):
super(GAT, self).__init__()
self.dropout = dropout
self.layer = layer
if (self.layer == 1):
self.attentions = [GraphAttentionLayer(nfeat, nclass, dropout=dropout, alpha=alpha, co... |
class IssuingDistributionPoint(ExtensionType):
oid = ExtensionOID.ISSUING_DISTRIBUTION_POINT
def __init__(self, full_name: (typing.Iterable[GeneralName] | None), relative_name: (RelativeDistinguishedName | None), only_contains_user_certs: bool, only_contains_ca_certs: bool, only_some_reasons: (frozenset[ReasonF... |
def upgrade(op, tables, tester):
op.bulk_insert(tables.logentrykind, [{'name': 'org_create'}, {'name': 'org_delete'}, {'name': 'org_change_email'}, {'name': 'org_change_invoicing'}, {'name': 'org_change_tag_expiration'}, {'name': 'org_change_name'}, {'name': 'user_create'}, {'name': 'user_delete'}, {'name': 'user_d... |
_optimizer('adam', dataclass=FairseqAdamConfig)
class FairseqAdam(FairseqOptimizer):
def __init__(self, cfg: DictConfig, params):
super().__init__(cfg)
fused_adam_cls = get_fused_adam_class()
use_fused_adam = ((not getattr(cfg, 'use_old_adam', False)) and (fused_adam_cls is not None) and tor... |
def test():
model.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for (data, target) in test_loader:
(data, target) = (data.to(device), target.to(device))
output = model(data)
test_loss += F.nll_loss(output, target, reduction='sum').item()
p... |
class Rect(object):
def __init__(self, id: int, rect_range: mn.Range2D, is_rotated=False):
self.id = id
self.range = rect_range
self.is_rotated = is_rotated
def copy(self):
return Rect(self.id, mn.Range2D(self.range.bottom_left, self.range.top_right), self.is_rotated)
def rot... |
_staging_test
class ProcessorPushToHubTester(unittest.TestCase):
vocab_tokens = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
def setUpClass(cls):
cls._token = TOKEN
HfFolder.save_token(TOKEN)
def tearDownClass(cls):
try:
delete_repo(token=cls._token, repo... |
class PreviewTrain(DisplayOptionalPage):
def __init__(self, *args, **kwargs):
self.update_preview = get_config().tk_vars['updatepreview']
super().__init__(*args, **kwargs)
def display_item_set(self):
logger.trace('Loading latest preview')
if (not self.update_preview.get()):
... |
def normalize_size_param(size: Optional[Union[(int, np.ndarray, Variable, Sequence)]]) -> Variable:
if (size is None):
size = constant([], dtype='int64')
elif isinstance(size, int):
size = as_tensor_variable([size], ndim=1)
elif (not isinstance(size, (np.ndarray, Variable, Sequence))):
... |
_shuffle
_external_ids
_inputs
_idtypes
_input_type
def test_validate_input_geoms(geoms, ids, shuffle, external_ids, input_type):
if (ids is not None):
geoms = geoms.set_index(ids)
input_ids = (geoms.index if external_ids else None)
if shuffle:
geoms = geoms.sample(frac=1, replace=False)
... |
def scenarios(*feature_paths: str, **kwargs: Any) -> None:
caller_locals = get_caller_module_locals()
caller_path = get_caller_module_path()
features_base_dir = kwargs.get('features_base_dir')
if (features_base_dir is None):
features_base_dir = get_features_base_dir(caller_path)
abs_feature_... |
def get_pascal_selected_image_annotation_filenames_pairs(pascal_root, selected_names):
pascal_relative_images_folder = 'JPEGImages'
pascal_relative_class_annotations_folder = 'SegmentationClass'
images_extention = 'jpg'
annotations_extention = 'png'
pascal_images_folder = os.path.join(pascal_root, p... |
.parametrize(['solver', 'state'], [pytest.param('me', _equivalence_fock, id='me-ket'), pytest.param('me', _equivalence_coherent, id='me-dm'), pytest.param('me', None, id='me-steady'), pytest.param('es', _equivalence_fock, id='es-ket'), pytest.param('es', _equivalence_coherent, id='es-dm'), pytest.param('es', None, id='... |
def test_get_next_page_fr_should_return_2_on_page_1(tmp_path):
htmlString = '\n <div class="next-previous-links">\n <div class="previous dl-rounded-borders dl-white-bg">\n <span class="disabled">\n <svg xmlns=" width="16" height="16" fill="currentColor" viewBox="0... |
def clustering_perm_acc(pred_labels, true_labels):
true_label_set = list(set(true_labels))
logger.info(f'# of unique true labels {len(true_label_set)}')
mapping = {label: i for (i, label) in enumerate(true_label_set)}
true_labels = [mapping[l] for l in true_labels]
best_acc = 0
all_labels = list... |
def prep_param_lists(model, flat_master=False):
model_params = [param for param in model.parameters() if param.requires_grad]
if flat_master:
try:
master_params = _flatten_dense_tensors([param.data for param in model_params]).float()
except:
print('Error in prep_param_lis... |
class RedisRateLimitBackendContextFactory(ContextFactory):
def __init__(self, redis_pool: ConnectionPool, prefix: str='rl:'):
self.redis_context_factory = RedisContextFactory(redis_pool)
self.prefix = prefix
def make_object_for_context(self, name: str, span: Span) -> 'RedisRateLimitBackend':
... |
_model()
_legacy_interface(weights=('pretrained', ResNeXt101_64X4D_Weights.IMAGENET1K_V1))
def resnext101_64x4d(*, weights: Optional[ResNeXt101_64X4D_Weights]=None, progress: bool=True, **kwargs: Any) -> ResNet:
weights = ResNeXt101_64X4D_Weights.verify(weights)
_ovewrite_named_param(kwargs, 'groups', 64)
_... |
_fixtures(WebFixture, PagingFixture)
def test_paging(web_fixture, paging_fixture):
web_fixture.reahl_server.set_app(paging_fixture.wsgi_app)
browser = paging_fixture.browser
browser.open('/')
assert paging_fixture.is_email_listed('')
assert (not paging_fixture.is_email_listed(''))
with browser.n... |
class CHBlock(dict):
def __init__(self, fid, pointer):
self.update(_load_header(fid, pointer))
(self['ch_ch_next'], self['ch_ch_first'], self['ch_tx_name'], self['ch_md_comment']) = unpack('<4Q', fid.read(32))
n_links = (self['link_count'] - 4)
self['ch_element'] = unpack('<{}Q'.form... |
def _read_midi_length(fileobj):
(TEMPO, MIDI) = range(2)
def read_chunk(fileobj):
info = fileobj.read(8)
if (len(info) != 8):
raise SMFError('truncated')
chunklen = struct.unpack('>I', info[4:])[0]
data = fileobj.read(chunklen)
if (len(data) != chunklen):
... |
class IssueViewSet(ReadOnlyModelViewSet):
permission_classes = ((HasModelPermission | HasProjectsPermission),)
serializer_class = IssueSerializer
filter_backends = (DjangoFilterBackend,)
filterset_fields = ('task', 'task__uri', 'status')
def get_queryset(self):
return Issue.objects.filter_us... |
class ConversionSpecifier():
def __init__(self, match: Match[str], start_pos: int=(- 1), non_standard_format_spec: bool=False) -> None:
self.whole_seq = match.group()
self.start_pos = start_pos
m_dict = match.groupdict()
self.key = m_dict.get('key')
self.conv_type = m_dict.ge... |
def todate(val: Any) -> date:
if (not val):
raise ValueError('Value not provided')
if isinstance(val, datetime):
return val.date()
elif isinstance(val, date):
return val
else:
try:
ival = int(val)
sval = str(ival)
if (len(sval) == 8):
... |
def gen_tutorials(repo_dir: str) -> None:
with open(os.path.join(repo_dir, 'website', 'tutorials.json'), 'r') as infile:
tutorial_config = json.loads(infile.read())
tutorial_ids = {x['id'] for v in tutorial_config.values() for x in v}
for tid in tutorial_ids:
print('Generating {} tutorial'.f... |
class MemMasterAdapter(Component):
def read(s, addr, nbytes):
while (s.req_entry is not None):
greenlet.getcurrent().parent.switch(0)
s.req_entry = s.create_req(MemMsgType.READ, 0, addr, nbytes)
while (s.resp_entry is None):
greenlet.getcurrent().parent.switch(0)
... |
def iou_box(b1, b2):
if isinstance(b1, list):
b1 = torch.tensor(b1, dtype=torch.float32)
if isinstance(b2, list):
b2 = torch.tensor(b2, dtype=torch.float32)
if isinstance(b1, np.ndarray):
b1 = torch.from_numpy(b1.astype(np.float32))
if isinstance(b2, np.ndarray):
b2 = tor... |
class Camera():
def __init__(self):
self.target_points = self._getPoints()
self.pixels = MsgCamera()
self.projected_points = []
def updateProjectedPoints(self, state, target_position):
mav_position = np.array([[state.north], [state.east], [(- state.altitude)]])
R = Euler2... |
class LSFJobService(cpi.Service):
def __init__(self, api, adaptor):
self._mt = None
_cpi_base = super(LSFJobService, self)
_cpi_base.__init__(api, adaptor)
self._adaptor = adaptor
def __del__(self):
self.close()
def close(self):
if self.mt:
self._l... |
def plot(func, filename, **kwargs):
(nlats, nlons) = (91, 181)
lats = num.linspace((- 90.0), 90.0, nlats)
lons = num.linspace((- 180.0), 180.0, nlons)
vecfunc = num.vectorize(func, [float])
(latss, lonss) = num.meshgrid(lats, lons)
thickness = vecfunc(latss, lonss)
from pyrocko.plot import g... |
def get_rpc_credentials(config: SimpleConfig) -> Tuple[(str, str)]:
rpc_user = config.get('rpcuser', None)
rpc_password = config.get('rpcpassword', None)
if (rpc_user == ''):
rpc_user = None
if (rpc_password == ''):
rpc_password = None
if ((rpc_user is None) or (rpc_password is None)... |
def weights_init(m):
if isinstance(m, nn.Conv2d):
m.weight.data.normal_(0.0, 0.001)
elif isinstance(m, nn.Linear):
m.weight.data.normal_(0.0, 0.001)
elif isinstance(m, nn.LSTMCell):
for param in m.parameters():
if (len(param.shape) >= 2):
nn.init.orthogona... |
def process_one_image(image, resize_height, resize_width, if_zero_one=False):
image = tf.image.convert_image_dtype(image, dtype=tf.float32)
if if_zero_one:
return image
image = tf.image.resize_images(image, size=[resize_height, resize_width], method=tf.image.ResizeMethod.BILINEAR)
return ((image... |
def intersperse(e, iterable, n=1):
if (n == 0):
raise ValueError('n must be > 0')
elif (n == 1):
return islice(interleave(repeat(e), iterable), 1, None)
else:
filler = repeat([e])
chunks = chunked(iterable, n)
return flatten(islice(interleave(filler, chunks), 1, None)... |
.ddblocal
def test_transact_write__error__transaction_cancelled__partial_failure(connection):
User(2).delete()
BankStatement(2).save()
with pytest.raises(TransactWriteError) as exc_info:
with TransactWrite(connection=connection) as transaction:
transaction.save(User(2), condition=User.us... |
def main(config, args):
neo4j_uri = config['neo4j_conf']['neo4j_uri']
neo4j_user = config['neo4j_conf']['neo4j_user']
neo4j_password = config['neo4j_conf']['neo4j_password']
data_path = os.path.join(config['offline_datapath']['data_path'], 'aws')
if args.name:
account_name = args.name
... |
class Issue():
id: int
node_id: str
url: str
repository_url: str
labels_url: str
comments_url: str
events_url: str
html_url: str
number: int
state: IssueState
state_reason: Optional[StateReason]
title: str
user: Optional[SimpleUser]
labels: List[Label]
assigne... |
def compute_K_z(x_minimum, sigma, l_vec, noise, d):
min_min = cov_max_max(x_minimum, sigma, noise, l_vec)
dia_min = cov_diaHess_max(x_minimum, sigma, l_vec)
dia_dia = cov_diaHess_diaHess(x_minimum, sigma, l_vec)
first_row = np.concatenate((dia_dia, dia_min), axis=1)
second_row = np.concatenate((dia_... |
def test_test_bloq_with_call_graph():
bwcg = TestBloqWithCallGraph()
def all_atoms_the_same(b: Bloq) -> Optional[Bloq]:
if isinstance(b, TestAtom):
return attrs.evolve(b, tag=None)
return b
(g, sigma) = bwcg.call_graph(generalizer=all_atoms_the_same)
assert (len(sigma) == 3)
... |
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