code stringlengths 101 5.91M |
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class TestIsscalar(object):
def test_basic(self):
assert_(np.isscalar(3))
assert_((not np.isscalar([3])))
assert_((not np.isscalar((3,))))
assert_(np.isscalar(3j))
assert_(np.isscalar(long(10)))
assert_(np.isscalar(4.0)) |
def test_integer():
a = ak.highlevel.ArrayBuilder()
a.integer(10)
a.integer(9)
a.integer(8)
a.integer(7)
a.integer(6)
assert (to_list(a.snapshot()) == [10, 9, 8, 7, 6])
assert (to_list(a) == [10, 9, 8, 7, 6])
assert (to_list(a.snapshot()[1:(- 1)]) == [9, 8, 7]) |
class FEMUDev(PCIDevSim):
def run_cmd(self, env: ExpEnv) -> str:
cmd = f'{env.repodir}/sims/external/femu/femu-simbricks {env.dev_pci_path(self)} {env.dev_shm_path(self)}'
return cmd |
class _DataLoaderIter(object):
def __init__(self, loader):
self.dataset = loader.dataset
self.collate_fn = loader.collate_fn
self.batch_sampler = loader.batch_sampler
self.num_workers = loader.num_workers
self.pin_memory = (loader.pin_memory and torch.cuda.is_available())
... |
class Inception(nn.Module):
def __init__(self, in_planes, n1x1, n3x3red, n3x3, n5x5red, n5x5, pool_planes):
super(Inception, self).__init__()
self.b1 = nn.Sequential(nn.Conv2d(in_planes, n1x1, kernel_size=1), nn.BatchNorm2d(n1x1), nn.ELU(True))
self.b2 = nn.Sequential(nn.Conv2d(in_planes, n3... |
def tera_url(ckpt, refresh=False, *args, **kwargs):
return tera_local(_urls_to_filepaths(ckpt, refresh=refresh), *args, **kwargs) |
def osnet_x0_75(num_classes=1000, loss='softmax', **kwargs):
return OSNet(num_classes, blocks=[OSBlock, OSBlock, OSBlock], layers=[2, 2, 2], channels=[48, 192, 288, 384], loss=loss, **kwargs) |
_HEADS.register('gce_head')
class GCEHead(nn.Module):
def __init__(self, cfg, dim_in, spatial_in):
super(GCEHead, self).__init__()
self.dim_in = dim_in[(- 1)]
self.spatial_in = spatial_in[(- 1)]
use_nl = cfg.KEYPOINT.GCE_HEAD.USE_NL
norm = cfg.MASK.GCE_HEAD.NORM
conv_... |
def ExampleGen(data_path, num_epochs=None):
epoch = 0
while True:
if ((num_epochs is not None) and (epoch >= num_epochs)):
break
filelist = glob.glob(data_path)
assert filelist, 'Empty filelist.'
random.shuffle(filelist)
for f in filelist:
reader =... |
def _calculate(core_id):
print(('Started calculating: %d' % core_id))
try:
val = 0
for _ in range(1, 1000):
for i in range(1, 1000000):
val *= ((((i * i) / i) + i) - i)
except KeyboardInterrupt:
pass
print(('Finished calculating: %d' % core_id)) |
class Downsample(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.conv = torch.nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0)
def forward(self, x):
pad = (0, 1, 0, 1)
x = torch.nn.functional.pad(x, pad, mode='constant', value=0)
... |
def validate_ec_ci(df: Union[(str, pd.Series, dd.Series, pd.DataFrame, dd.DataFrame)], column: str='') -> Union[(bool, pd.Series, pd.DataFrame)]:
if isinstance(df, (pd.Series, dd.Series)):
return df.apply(ci.is_valid)
elif isinstance(df, (pd.DataFrame, dd.DataFrame)):
if (column != ''):
... |
def main():
parser = get_parser()
args = parser.parse_args()
os.makedirs(args.save_dir, exist_ok=True)
def create_files(dest):
copyfile((osp.join(args.data, args.split) + '.tsv'), (dest + '.tsv'))
if osp.exists((osp.join(args.data, args.split) + '.wrd')):
copyfile((osp.join(a... |
def replaces_attribute(func: Callable[(..., Tuple[str])], classname: str, attr_name: str):
Replacements._attr_rep[(classname, attr_name)] = func
return func |
.experimental
def test_indexer(df, tmp_path):
path = (tmp_path / 'indexer').resolve()
indexer = Indexer('user_idx', 'item_idx')
df = convert2spark(df)
indexer.fit(df, df)
save_indexer(indexer, path)
i = load_indexer(path)
i.inverse_transform(i.transform(df))
assert (i.user_indexer.inputC... |
def test_poissonvi():
adata = synthetic_iid(batch_size=100)
POISSONVI.setup_anndata(adata)
model = POISSONVI(adata)
model.train(max_epochs=1)
model.get_latent_representation()
model.get_accessibility_estimates() |
()
('--seed', default=1)
('--n_epochs', default=600)
('--batch_size_per_task', default=1024)
_experiment
def te_ppo_pointenv(ctxt, seed, n_epochs, batch_size_per_task):
set_seed(seed)
tasks = TASKS
latent_length = 2
inference_window = 6
batch_size = (batch_size_per_task * len(TASKS))
policy_ent_... |
def backend_of_obj(obj, default: (D | Sentinel)=UNSET) -> (Backend | D):
cls = type(obj)
try:
lookup = _type_to_backend_lookup[cls]
return lookup(obj)
except KeyError:
for factory in _backend_lookup_factories:
maybe_lookup = factory(cls)
if (maybe_lookup is no... |
def _isrecursive(pattern):
if isinstance(pattern, binary_type):
return (pattern == b'**')
else:
return (pattern == '**') |
class Gulf(Benchmark):
def __init__(self, dimensions=3):
Benchmark.__init__(self, dimensions)
self._bounds = list(zip(([0.0] * self.N), ([50.0] * self.N)))
self.global_optimum = [[50.0, 25.0, 1.5]]
self.fglob = 0.0
def fun(self, x, *args):
self.nfev += 1
m = 99.0
... |
def collect_directories(root_dir: os.PathLike, recursive: bool=False, verbose: bool=False, ignore_dir_names: List[str]=[], check_root_dir: bool=False) -> List[os.PathLike]:
dirs_list = []
dirs_to_check = (os.listdir(root_dir) + ([root_dir] if check_root_dir else []))
for exp_name in sorted(dirs_to_check):
... |
class BranchformerEncoder(nn.Module):
def __init__(self, num_layers, d_model, nhead, kernel_size=31, kdim=None, vdim=None, activation=nn.GELU, dropout=0.0, attention_type='RelPosMHAXL', csgu_linear_units=3072, gate_activation=nn.Identity, use_linear_after_conv=False):
super().__init__()
self.layers ... |
def test_constructor_clone_args(constructor_mock, default_test_case):
ref = vr.VariableReference(default_test_case, default_test_case.test_cluster.type_system.convert_type_hint(float))
clone = vr.VariableReference(default_test_case, default_test_case.test_cluster.type_system.convert_type_hint(float))
const ... |
def internal_to_external(name: str) -> tuple:
und = name.rfind('_')
meth = name[:und]
ext = name[(und + 1):]
if (meth not in FUSED_OPERATION_TO_SVE):
raise NotSupportedError('Unknown internal function')
return (((FUSED_OPERATION_TO_SVE[meth] + '_') + ext), SVE_SUFFIX_TO_TYPE[ext]) |
class VISEncoder(nn.Module):
def __init__(self, d_model, N, heads, dropout):
super().__init__()
self.N = N
self.embed = nn.Linear(2048, d_model)
self.pe = PositionalEncoder(d_model, dropout=dropout)
self.layers = get_clones(EncoderLayer(d_model, heads, dropout), N)
se... |
class sage__rings__padics(JoinFeature):
def __init__(self):
JoinFeature.__init__(self, 'sage.rings.padics', [PythonModule('sage.rings.padics.factory')], type='standard') |
def kernel_shap_1000_meanref(model, data):
return (lambda X: KernelExplainer(model.predict, kmeans(data, 1)).shap_values(X, nsamples=1000, l1_reg=0)) |
def combiners(n_combiners=1):
assert _retry(_test_nodes, n_nodes=n_combiners, node_type='combiner') |
('copy')
_method('process_source')
def apply_copy(self):
Utils.def_attrs(self, fun=copy_func)
self.default_install_path = 0
lst = self.to_list(self.source)
self.meths.remove('process_source')
for filename in lst:
node = self.path.find_resource(filename)
if (not node):
rai... |
_registry
class ONNXForward(abc.ABC):
def forward_can_be_applied(node: ONNXOp, state: SDFGState, sdfg: SDFG) -> bool:
return True
def forward(node: ONNXOp, state: SDFGState, sdfg: SDFG) -> typing.Union[(Node, SDFG)]:
...
def registered_implementations(cls, op_name: str) -> typing.List[typing... |
def morphological_laplace(input, size=None, footprint=None, structure=None, output=None, mode='reflect', cval=0.0, origin=0):
tmp1 = grey_dilation(input, size, footprint, structure, None, mode, cval, origin)
if isinstance(output, numpy.ndarray):
grey_erosion(input, size, footprint, structure, output, mo... |
def bert_large_uncased_whole_word_maskings_384_2p_bw12_pipedream():
return dict(model_type='bert', model_name_or_path='bert-large-uncased-whole-word-masking', do_lower_case=True, output_past=False, stateless_tied=False, explicitly_set_dict={'precompute_attention_mask': False, 'return_dict': False}, do_resize_token_... |
_if_no_torch
def test_backpack_nano_compare():
import torch
vocab_size = 5257
torch.manual_seed(0)
converter = BackpackConfig.default_hf_checkpoint_converter
cls = converter.HFAutoModelClass()
config = converter.HfConfigClass(n_embd=32, n_positions=512, n_head=8, n_layer=2, vocab_size=vocab_size... |
def concepts_to_adj_matrices_1hop_neighbours(data):
(qc_ids, ac_ids) = data
qa_nodes = (set(qc_ids) | set(ac_ids))
extra_nodes = set()
for u in (set(qc_ids) | set(ac_ids)):
if (u in cpnet.nodes):
extra_nodes |= set(cpnet[u])
extra_nodes = (extra_nodes - qa_nodes)
schema_graph... |
('BackwardWarp')
def _BackwardWarpGrad(op, grad):
grad0 = _backward_warp_module.backward_warp_grad(grad, op.inputs[0], op.inputs[1])
return [None, grad0] |
def flatten_tables(incoming_base_situ, outgoing_base_situ, incoming_base_spect, outgoing_base_spect):
def get_new_rows(base_table_incoming, base_table_outgoing):
def get_candidates(schema_id):
check_again = False
head_cands = base_table_incoming[((base_table_incoming['parent_schema_i... |
def axilite_read(sim, addr, basename='s_axi_control_'):
_write_signal(sim, (basename + 'ARADDR'), addr)
_write_signal(sim, (basename + 'ARVALID'), 1)
wait_for_handshake(sim, 'AR', basename=basename)
_write_signal(sim, (basename + 'ARVALID'), 0)
_write_signal(sim, (basename + 'RREADY'), 1)
ret_da... |
class CyEval(gdb.Function, CythonBase, EvaluateOrExecuteCodeMixin):
_function_value_to_unicode
def invoke(self, python_expression):
input_type = libpython.PythonCodeExecutor.Py_eval_input
return self.evalcode(python_expression, input_type) |
def convert_EmailProperty(model, prop, kwargs):
kwargs['validators'].append(validators.email())
return get_TextField(kwargs) |
def get_custom_class_library_path():
library_filename = glob.glob('build/*custom_class*')
assert (len(library_filename) == 1)
library_filename = library_filename[0]
path = os.path.abspath(library_filename)
assert os.path.exists(path), path
return path |
def find_span(offsets, start, end):
start_index = end_index = (- 1)
for (i, offset) in enumerate(offsets):
if ((start_index < 0) or (start >= offset[0])):
start_index = i
if ((end_index < 0) and (end <= offset[1])):
end_index = i
return (start_index, end_index) |
def avgpp(cp, size):
_check_params(len(cp), size)
sample_count = _sample_count(cp, size)
prevextremevalid = False
prevextreme = None
avg = 0
nextreme = 0
prevval = getsample(cp, size, 0)
val = getsample(cp, size, 1)
prevdiff = (val - prevval)
for i in range(1, sample_count):
... |
def test_ate_causal_graph_builder():
import whynot.traceable_numpy as wnp
def covariate_builder(run):
return wnp.array([run[0].x1, run[2].x2, run[3].x3])
def outcome_extractor(run):
return wnp.sum(run.states[(- 1)].values())
def soft_threshold(x, tau, r=200):
return (1.0 / (wnp.e... |
def test_get_workspace_model_nopoi(workspace_factory):
w = workspace_factory()
m = w.model(poi_name=None)
assert (m.config.poi_name is None)
assert (m.config.poi_index is None) |
def generate_and_evaluate_baseline(out_dir: str, lidarseg_preds_dir: str, lidarseg_method_name: str, det_or_track_preds_dir: str, det_or_track_method_name: str, task: str='tracking', version: str='v1.0-test', dataroot: str='/data/sets/nuscenes', verbose: bool=False) -> None:
nusc = NuScenes(version=version, dataroo... |
def validate_password(actual_pw, typed_pw):
if (len(actual_pw) != len(typed_pw)):
return False
for i in range(len(actual_pw)):
if (actual_pw[i] != typed_pw[i]):
return False
return True |
def test_person_name():
error_sentence_1 = ''
import jieba.posseg
print(jieba.posseg.lcut(error_sentence_1))
correct_sent = ct.correct(error_sentence_1)
print('original sentence:{} => correct sentence:{}'.format(error_sentence_1, correct_sent))
error_sentence_1 = ''
correct_sent = ct.correct... |
class BaseIntervention():
def __init__(self, config_class, time, **kwargs):
self.time = time
config_args = set((f.name for f in dataclasses.fields(config_class)))
for arg in kwargs:
if (arg not in config_args):
raise TypeError(f'__init__() got an unexpected keywor... |
class _RepoWorkDir():
def __init__(self, repo, version):
if (not isinstance(repo, _Repo)):
repo = _get_repo(repo)
_simple_validate_version(version)
self.repo = repo
self.version = version
if version:
(self.version_date, self.version_rev) = version.spli... |
def load(model, opt):
if (vars(opt).get('start_from', None) is not None):
assert os.path.isdir(opt.start_from), (' %s must be a a path' % opt.start_from)
assert os.path.isfile(os.path.join(opt.start_from, (('infos_' + opt.id) + '.pkl'))), ('infos.pkl file does not exist in path %s' % opt.start_from)... |
class MelGeneralizedCepstrumToSpectrum(nn.Module):
def __init__(self, cep_order, fft_length, alpha=0, gamma=0, norm=False, mul=False, out_format='power', n_fft=512):
super(MelGeneralizedCepstrumToSpectrum, self).__init__()
self.fft_length = fft_length
assert (2 <= self.fft_length)
if... |
class GoogleMapGeocoding(VirtualFunctionTool):
name = 'GoogleMapGeocoding'
summary = 'Convert a location address to geographic coordinates.'
parameters: List[ArgParameter] = [{'name': 'location_address', 'type': 'string', 'description': "The address of the location, in the format of 'street address, city, z... |
def train(nsteps: int, trainer: Trainer, beta: (float | torch.Tensor), nlog: int=1, nprint: int=1, x: Optional[torch.Tensor]=None, grab: Optional[bool]=None) -> tuple[(torch.Tensor, dict)]:
beta = (torch.tensor(beta) if isinstance(beta, float) else beta)
history = {}
if (x is None):
state = exp.trai... |
def create_cpg_net(train=True):
logger = logging.getLogger(__name__)
FREEZE_CONV_BODY = cfg.TRAIN.FREEZE_CONV_BODY
FREEZE_AT = cfg.TRAIN.FREEZE_AT
WSL_CSC = cfg.WSL.CSC
CENTER_LOSS = cfg.WSL.CENTER_LOSS
MIN_ENTROPY_LOSS = cfg.WSL.MIN_ENTROPY_LOSS
MASK_ON = cfg.MODEL.MASK_ON
EXECUTION_TYP... |
class ImageFolder(DatasetFolder):
def __init__(self, root: str, transform: Optional[Callable]=None, target_transform: Optional[Callable]=None, loader: Callable[([str], Any)]=default_loader, is_valid_file: Optional[Callable[([str], bool)]]=None):
super().__init__(root, loader, (IMG_EXTENSIONS if (is_valid_fi... |
class Dict2Obj(object):
def __init__(self, dictionary):
for key in dictionary.keys():
setattr(self, key, dictionary[key])
def __repr__(self):
attrs = str([x for x in self.__dict__])
return ('<Dict2Obj: %s' % attrs) |
def TransformedLoader(loader, func, transforms, workers=None, batch_size=None, do_tqdm=False, augment=False, fraction=1.0):
new_ims = []
new_targs = []
total_len = len(loader)
enum_loader = enumerate(loader)
it = (enum_loader if (not do_tqdm) else tqdm(enum_loader, total=total_len))
for (i, (im,... |
.parametrize('y_pred', [np.array(y_pred_list), y_pred_list])
def test_absolute_conformity_score_consistency(y_pred: NDArray) -> None:
abs_conf_score = AbsoluteConformityScore()
signed_conf_scores = abs_conf_score.get_signed_conformity_scores(X_toy, y_toy, y_pred)
y_obs = abs_conf_score.get_estimation_distri... |
def plot(runs, group, from_iter: int=0, loss_group=None):
xs = []
ys = []
cs = []
loss_group = (loss_group or group)
for r in runs:
hist = r.history(keys=[f'validation/{group}/accuracy/total', f'validation/{loss_group}/loss', 'iteration'], pandas=False)
for p in hist:
if ... |
.parametrize('direction', ['forwards', 'backwards'])
.parametrize('n', [10, 100])
def test_linear_union_sequence(n, direction):
elements = get_elements(n)
dis = DisjointSet(elements)
assert (elements == list(dis))
indices = list(range((n - 1)))
if (direction == 'backwards'):
indices = indice... |
class SUN():
def __init__(self) -> None:
super(SUN, self).__init__()
def exp(self, x: Tensor, u: Tensor) -> Tensor:
return (x expm((x.conj().transpose((- 2), (- 1)) u)))
def log(self, x: Tensor, y: Tensor) -> Tensor:
(_, n, _) = x.shape
assert (n == 3), 'Operation supported... |
class SchedulingConstraint(util.ContentHashClass):
def __init__(self, topbat=0, topifm=0, topofm=0, update_dict=None):
if any((((n < 0) or (not isinstance(n, numbers.Integral))) for n in [topbat, topifm, topofm])):
raise ValueError('SchedulingConstraint: constrained factors must be positive inte... |
def find_similarity(s1, s2):
with torch.no_grad():
s1 = [make_example(x, model) for x in s1]
s2 = [make_example(x, model) for x in s2]
(wx1, wl1, wm1) = model.torchify_batch(s1)
(wx2, wl2, wm2) = model.torchify_batch(s2)
scores = model.scoring_function(wx1, wm1, wl1, wx2, wm2... |
def webqsp_load_and_cache_gen_examples(args, tokenizer, evaluate=False):
logger = args.logger
if ((args.local_rank not in [(- 1), 0]) and (not evaluate)):
torch.distributed.barrier()
input_dir = (args.data_dir if args.data_dir else '.')
split_file = (args.predict_file if evaluate else args.train... |
def ensure_compatible_hparams(hparams, default_hparams, hparams_path):
default_hparams = utils.maybe_parse_standard_hparams(default_hparams, hparams_path)
default_config = default_hparams.values()
config = hparams.values()
for key in default_config:
if (key not in config):
hparams.ad... |
def bilinear_deconv2d(input, deconv_info, is_train, name='bilinear_deconv2d', info=False, activation_fn=tf.nn.relu, norm='batch'):
with tf.variable_scope(name):
output_shape = deconv_info[0]
k = deconv_info[1]
s = deconv_info[2]
h = (int(input.get_shape()[1]) * s)
w = (int(in... |
def setup_files_and_dirs(outdir, hdfs):
hdfs.create_dir(f'/data')
if (not os.path.exists(outdir)):
os.mkdir(outdir)
os.system(f'dd if=/dev/zero of={outdir}/10GBdata.bin bs=128KB count=78125') |
def module_init():
root_module = Module('ns.propagation', cpp_namespace='::ns3')
return root_module |
def beta2(rce, T1, T2):
r = rce[0]
c = rce[1]
e = rce[2]
assert (T1[(r, c)] == e)
assert (e >= 0)
for x in range(T1.ncols()):
if (T2[(r, x)] == e):
return (r, x, e)
raise ValueError |
def get_test_dataset(data_args: Dict[(str, Any)]) -> Tuple[(Optional[PoseSegmentsDataset], Optional[DataLoader])]:
if (not args.test):
return (None, None)
dataset = get_dataset(split='test', **data_args)
loader = DataLoader(dataset, batch_size=args.batch_size_devtest, shuffle=False, collate_fn=zero_... |
def labels_to_onehots(labels, num_classes):
batch_size = labels.get_shape().as_list()[0]
with tf.name_scope('one_hot'):
labels = tf.expand_dims(labels, 1)
indices = tf.expand_dims(tf.range(0, batch_size, 1), 1)
sparse_ptrs = tf.concat(1, [indices, labels], name='ptrs')
onehots = ... |
def simplify(f, algorithm='maxima', **kwds):
try:
return f.simplify(algorithm=algorithm, **kwds)
except (TypeError, AttributeError):
pass
try:
return f.simplify()
except AttributeError:
return f |
class CharacterTargets(Vocabulary):
def __init__(self, vocab_file, seq_postfix=None, unknown_label='', labels=None, **kwargs):
super(CharacterTargets, self).__init__(vocab_file=vocab_file, seq_postfix=seq_postfix, unknown_label=unknown_label, labels=labels, **kwargs)
def get_seq(self, sentence):
... |
def readoutput(outtxt):
output = {}
with open(outtxt) as f:
for line in f.readlines():
line = line.strip().split(' ')
if (len(line) == 2):
(idx, score) = (int(line[0]), float(line[1]))
output[idx] = score
return output |
def build_transform(image_augmentation, backbone_name, size=SIZE, interpolation=INTERPOLATION, pixel_mean=IMAGENET_PIXEL_MEAN, pixel_std=IMAGENET_PIXEL_STD, crop_padding=CROP_PADDING, rrcrop_scale=RRCROP_SCALE):
clip_mode = (backbone_name in CLIP_MODELS)
if clip_mode:
pixel_mean = CLIP_PIXEL_MEAN
... |
def gen_lobatto(max_order):
assert (max_order > 2)
x = sm.symbols('x')
lobs = [0, 1]
lobs[0] = ((1 - x) / 2)
lobs[1] = ((1 + x) / 2)
dlobs = [lob.diff('x') for lob in lobs]
legs = [sm.legendre(0, 'y')]
clegs = [sm.ccode(legs[0])]
dlegs = [sm.legendre(0, 'y').diff('y')]
cdlegs = [... |
def CremonaModularSymbols(level, sign=0, cuspidal=False, verbose=0):
from .homspace import ModularSymbols
return ModularSymbols(level=level, sign=sign, cuspidal=cuspidal, verbose=verbose) |
_decorator(list())
def get_data(html):
cont = get_weibo_infos_right(html)
return get_weibo_list(cont) |
class UCBSelectPolicy(SelectPolicy):
def __init__(self, explore_coeff: float=1.0, fuzzer: GPTFuzzer=None):
super().__init__(fuzzer)
self.step = 0
self.last_choice_index = None
self.explore_coeff = explore_coeff
self.rewards = [0 for _ in range(len(self.fuzzer.prompt_nodes))]
... |
.parametrize('loader_options, from_schema_options', (({'base_url': ' {}), ({}, {'hypothesis_settings': hypothesis.settings(deadline=1)})))
.operations('slow')
def test_exceptions(schema_url, app, loader_options, from_schema_options):
schema = oas_loaders.from_uri(schema_url, **loader_options)
results = from_sch... |
class PyCUDAFunctionManager(CUDAFunctionManager):
def __init__(self, num_agents: int=1, num_envs: int=1, blocks_per_env: int=1, process_id: int=0):
super().__init__(num_agents=num_agents, num_envs=num_envs, blocks_per_env=blocks_per_env, process_id=process_id)
self._CUDA_module = None
self._... |
def test():
vec = ak.Array([{'x': 1, 'y': 2, 'z': 3}, {'x': 4, 'y': 5, 'z': 9}], with_name='vector', behavior=behavior)
assert ak.almost_equal((vec + vec), ak.Array([{'x': 2, 'y': 4, 'z': 6}, {'x': 8, 'y': 10, 'z': 18}], with_name='vector', behavior=behavior))
assert ak.almost_equal(pickle.loads(pickle.dump... |
def prepare_experiment(args, config):
if (args.run_folder is not None):
run_folder = args.run_folder
else:
run_folder = prepare_experiment_folder(config['expirement_base_path'], args.run_name)
save_config(os.path.join(run_folder, 'config.yaml'), config)
dir_path = os.path.dirname(os.path... |
def test_statement_to_ast_dict_single(statement_to_ast_visitor, default_test_case, function_mock):
dict_stmt = stmt.DictStatement(default_test_case, default_test_case.test_cluster.type_system.convert_type_hint(dict[(int, int)]), [(stmt.IntPrimitiveStatement(default_test_case, 5).ret_val, stmt.IntPrimitiveStatement(... |
class TFXLMModel():
def __init__(self, *args, **kwargs):
requires_tf(self)
def from_pretrained(self, *args, **kwargs):
requires_tf(self) |
def _slice_arrays(arrays, start=None, stop=None):
if (arrays is None):
return [None]
elif isinstance(arrays, list):
if hasattr(start, '__len__'):
if hasattr(start, 'shape'):
start = start.tolist()
return [(None if (x is None) else x[start]) for x in arrays... |
class FEBlock(nn.Module):
def __init__(self, num_feat, rep_scale=4):
super(FEBlock, self).__init__()
self.num_feat = num_feat
self.conv_first = ConvRep3(3, num_feat, rep_scale=rep_scale)
self.conv_up = ConvRep3(num_feat, (num_feat * 4), rep_scale=rep_scale)
self.conv_last = C... |
def register_Ns3DefaultDeleter__Ns3S1apConnectionInfo_methods(root_module, cls):
cls.add_constructor([])
cls.add_constructor([param('ns3::DefaultDeleter< ns3::S1apConnectionInfo > const &', 'arg0')])
cls.add_method('Delete', 'void', [param('ns3::S1apConnectionInfo *', 'object')], is_static=True)
return |
def compute(folder: str) -> Tuple[(List[dict], List[list], List[np.ndarray], List[np.ndarray])]:
results = glob.glob(f'{folder}/*.sav')
if ('{}/trained_model.sav'.format(folder) in results):
results.remove(f'{folder}/trained_model.sav')
hyperparameters = []
scores = []
avg = []
std = []
... |
def apply_gen_fixed_toolkit_prompt(prompt):
prompt = removed_submodules(prompt, ['tool_gen_blacklist', 'brainstorm_toolkit_step'])
return prompt |
class Dataset(ABC):
def load(self) -> Tuple[(List[np.ndarray], List[np.ndarray], np.ndarray, np.ndarray)]:
pass
def download(self, destination: str):
pass |
class AFLBasedController(ControllerModel):
def __init__(self, AFLClass, name, seed, output, group, program, argument, cgroup_path):
self.AFLClass = AFLClass
self.name = name
self.db = None
self.seed = seed
self.output = output
self.group = group
self.program =... |
def eq(a, b):
if z3_debug():
_z3_assert((is_ast(a) and is_ast(b)), 'Z3 ASTs expected')
return a.eq(b) |
def alter2chord_prob(alter):
alter_list = [0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... |
def get_sgd_learning_rate(i, *, warmup):
i += 1
return min((math.sqrt(warmup) / math.sqrt(i)), (i / warmup)) |
class BasicTokenizer(object):
def __init__(self, do_lower_case=True):
self.do_lower_case = do_lower_case
def tokenize(self, text):
text = _convert_to_unicode_or_throw(text)
text = self._clean_text(text)
orig_tokens = whitespace_tokenize(text)
split_tokens = []
for... |
def pip_installed_packages(normalization=None):
with open(os.devnull, 'w') as devnull:
proc = subprocess.Popen([sys.executable, '-m', 'pip', 'list', '--no-index', '--format', 'json'], stdout=subprocess.PIPE, stderr=devnull)
stdout = proc.communicate()[0].decode()
def normalize(name: str) -> ... |
def test_redirect(capfd):
msg = 'Should not be in log!'
stream = StringIO()
with redirect_stdout(stream):
m.raw_output(msg)
(stdout, stderr) = capfd.readouterr()
assert (stdout == msg)
assert (stream.getvalue() == '')
stream = StringIO()
with redirect_stdout(stream):
with... |
class ROIHeadsTest(unittest.TestCase):
def test_roi_heads(self):
torch.manual_seed(121)
cfg = get_cfg()
cfg.MODEL.ROI_BOX_HEAD.NAME = 'FastRCNNConvFCHead'
cfg.MODEL.ROI_BOX_HEAD.NUM_FC = 2
cfg.MODEL.ROI_BOX_HEAD.POOLER_TYPE = 'ROIAlignV2'
cfg.MODEL.ROI_BOX_HEAD.BBOX_R... |
_function_dispatch(_kron_dispatcher)
def kron(a, b):
b = asanyarray(b)
a = array(a, copy=False, subok=True, ndmin=b.ndim)
(ndb, nda) = (b.ndim, a.ndim)
if ((nda == 0) or (ndb == 0)):
return _nx.multiply(a, b)
as_ = a.shape
bs = b.shape
if (not a.flags.contiguous):
a = reshape... |
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