code stringlengths 281 23.7M |
|---|
def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
import ray
def _objective(trial, local_trainer, checkpoint_dir=None):
try:
from transformers.utils.notebook import NotebookProgressCallback
if local_trainer.pop_callback(NotebookProgressCallback):... |
(HAS_ANNOTATED)
def test_annotated():
class WithAnnotated():
annotated_field: typing.Annotated[(int, 'metadata')]
assert (get_dataclass_shape(WithAnnotated) == Shape(input=InputShape(constructor=WithAnnotated, kwargs=None, fields=(InputField(type=typing.Annotated[(int, 'metadata')], id='annotated_field'... |
def initialize_dict(dict, entries, separator):
for entry in entries:
hyphen_free = entry.replace(separator, '').lower()
boundary_list = [1]
i = 1
while (i < len(entry)):
if (entry[i] == separator):
boundary_list += [1]
i += 1
el... |
def test_index_query_scan() -> None:
from pynamodb.attributes import NumberAttribute
from pynamodb.models import Model
from pynamodb.indexes import GlobalSecondaryIndex
from pynamodb.pagination import ResultIterator
class UntypedIndex(GlobalSecondaryIndex):
bar = NumberAttribute(hash_key=Tru... |
class TestShellCommand():
def klass(self):
return configtypes.ShellCommand
.parametrize('kwargs, val, expected', [({}, '[foobar]', ['foobar']), ({'placeholder': True}, '[foo, "{}", bar]', ['foo', '{}', 'bar']), ({'placeholder': True}, '["foo{}bar"]', ['foo{}bar']), ({'placeholder': True}, '[foo, "bar {}... |
.change_flags(vm__lazy=True)
def test_ifelse_lazy_c():
a = scalar()
b = generic()
c = generic()
notimpl = NotImplementedOp()
cloops = [True, False]
if (pytensor.config.cxx == ''):
cloops = [False]
for use_cloop in cloops:
for lazy in [True, None]:
linker = pytenso... |
def cache_features(features, num_shards):
if (num_shards == 1):
return (features, tf.no_op(name='init_queue'))
flat_features = list(features.itervalues())
queue = tf.FIFOQueue(num_shards, dtypes=[v.dtype for v in flat_features])
flat_features = [tf.split(v, num_shards, axis=0) for v in flat_feat... |
class BertDataset(Dataset):
def __init__(self, name, indexed_dataset, data_prefix, num_epochs, max_num_samples, masked_lm_prob, max_seq_length, short_seq_prob, seed):
self.name = name
self.seed = seed
self.masked_lm_prob = masked_lm_prob
self.max_seq_length = max_seq_length
s... |
(scope='module', params=[(Arc, (0, 0, 5)), (Circle, (0, 0, 5)), (Ellipse, (0, 0, 0, 5)), (Sector, (0, 0, 3)), (Line, (0, 0, 7, 7)), (Rectangle, (0, 0, 20, 20)), (BorderedRectangle, (0, 0, 30, 10)), (Triangle, (0, 0, 2, 2, 5, 5)), (Star, (1, 1, 20, 11, 5)), (Polygon, ((0, 0), (1, 1), (2, 2)))])
def shape_and_positionals... |
class ReplicaSetTelemetry(BaseModel, extra='forbid'):
id: int = Field(..., description='')
local: Optional['LocalShardTelemetry'] = Field(default=None, description='')
remote: List['RemoteShardTelemetry'] = Field(..., description='')
replicate_states: Dict[(str, 'ReplicaState')] = Field(..., description... |
def create_vit(vit, image_size, use_grad_checkpointing=False, ckpt_layer=0, drop_path_rate=0):
assert (vit in ['base', 'large']), 'vit parameter must be base or large'
if (vit == 'base'):
vision_width = 768
visual_encoder = VisionTransformer(img_size=image_size, patch_size=16, embed_dim=vision_w... |
.usefixtures('config_stub', 'key_config_stub')
class TestConfigPyModules():
def qbmodulepy(self, tmp_path):
return ConfPy(tmp_path, filename='qbmodule.py')
(autouse=True)
def restore_sys_path(self):
old_path = sys.path.copy()
(yield)
sys.path = old_path
def test_bind_in_m... |
class AddNewsForm(forms.ModelForm):
name = HoneypotField()
class Meta():
model = Item
fields = ('link', 'section', 'title', 'language', 'description')
def __init__(self, *args, **kwargs):
kwargs['initial'] = {'section': 6}
super().__init__(*args, **kwargs)
self.fields... |
class ValidationException(DomainException):
def for_failed_validations(cls, failed_validation_constraints):
detail_messages = [i.message for i in failed_validation_constraints]
return cls(message=_.ngettext('An error occurred', 'Some errors occurred', len(detail_messages)), detail_messages=detail_me... |
class SequentialNodeRewriter(NodeRewriter):
def __init__(self, *rewriters: Rewriter, apply_all_rewrites: bool=False, profile: bool=False):
super().__init__()
self.rewrites: Sequence[Rewriter] = rewriters
assert isinstance(self.rewrites, tuple)
self.reentrant = any((getattr(rewrite, '... |
def eval_directory(path):
with open(os.path.join(path, 'config.json')) as fp:
config = json.load(fp)
one_shot_architectures = glob.glob(os.path.join(path, 'one_shot_architecture_*.obj'))
one_shot_architectures.sort(key=natural_keys)
test_errors = []
valid_errors = []
for model in one_sho... |
class AllowedSmilesCharDictionary(object):
def __init__(self) -> None:
self.forbidden_symbols = {'Ag', 'Al', 'Am', 'Ar', 'At', 'Au', 'D', 'E', 'Fe', 'G', 'K', 'L', 'M', 'Ra', 'Re', 'Rf', 'Rg', 'Rh', 'Ru', 'T', 'U', 'V', 'W', 'Xe', 'Y', 'Zr', 'a', 'd', 'f', 'g', 'h', 'k', 'm', 'si', 't', 'te', 'u', 'v', 'y'}... |
class QuestionSuggestionChainBase(Chain, BaseModel):
llm_chain: LLMChain
output_key: str = 'questions'
class Config():
extra = Extra.forbid
arbitrary_types_allowed = True
def input_keys(self) -> List[str]:
return self.llm_chain.prompt.input_variables
def output_keys(self) -> ... |
def test_db_reuse_simple(django_pytester: DjangoPytester) -> None:
django_pytester.create_test_module('\n import pytest\n\n from .app.models import Item\n\n .django_db\n def test_db_can_be_accessed():\n assert Item.objects.count() == 0\n ')
result = django_pytester.runp... |
def interpolateables(state_a, state_b):
animate = []
for (tag, path, values) in state_b.diff(state_a):
if (tag == 'set'):
ypath = path_to_str(path)
v_new = get_elements(state_b, ypath)[0]
v_old = values
for type in [float, Color, Background]:
... |
class RSSReader(Session):
def __init__(self, factory, url, rate):
self.url = url
self.rate = rate
self.factory = factory
self.old_entries = {}
def get_new(self):
feed = feedparser.parse(self.url)
new_entries = []
for entry in feed['entries']:
i... |
def init_ctx(f):
(f)
def wrapper(self, *args, **kw):
if (self.manager.label is None):
label_addr = self.ql.os.heap.alloc(ctypes.sizeof(label_t))
self.manager.label = label_t(self.ql, label_addr)
self.manager.label.l_flags = 1
self.manager.label.updateToMem... |
class DownSample(nn.Module):
def __init__(self, in_channels, s_factor):
super(DownSample, self).__init__()
self.down = nn.Sequential(nn.Upsample(scale_factor=0.5, mode='bilinear', align_corners=False), nn.Conv2d(in_channels, (in_channels + s_factor), 1, stride=1, padding=0, bias=False))
def forw... |
def test_matrix_variable_selection_duplicate_exclusion(hatch, helpers, temp_dir, config_file):
config_file.model.template.plugins['default']['tests'] = False
config_file.save()
project_name = 'My.App'
with temp_dir.as_cwd():
result = hatch('new', project_name)
assert (result.exit_code == 0),... |
class SimpleOxfordPetDataset(OxfordPetDataset):
def __getitem__(self, *args, **kwargs):
sample = super().__getitem__(*args, **kwargs)
image = np.array(Image.fromarray(sample['image']).resize((256, 256), Image.LINEAR))
mask = np.array(Image.fromarray(sample['mask']).resize((256, 256), Image.N... |
class ViewStageBase(object):
def __init__(self, name, state_provider):
self.name = name
self.state_provider = state_provider
self.datamodel = _datamodel
self.view = None
def schedule(self):
raise NotImplementedError()
def ready(self):
raise NotImplementedError... |
def main(argv):
trainIds = False
try:
(opts, args) = getopt.getopt(argv, 'ht')
except getopt.GetoptError:
printError('Invalid arguments')
for (opt, arg) in opts:
if (opt == '-h'):
printHelp()
sys.exit(0)
elif (opt == '-t'):
trainIds = T... |
def test_imbalance_penalty_at_insufficent_payer_balance():
imbalance_penalty = calculate_imbalance_fees(channel_capacity=TokenAmount(20), proportional_imbalance_fee=ProportionalFeeAmount(1))
(pair, _) = _foward_transfer_pair(TokenAmount(10), NettingChannelStateProperties(our_state=NettingChannelEndStateProperti... |
def _to_sequence_example(set_info, decoder, vocab):
set_id = set_info['set_id']
image_data = []
image_ids = []
caption_data = []
caption_ids = []
for image_info in set_info['items']:
filename = os.path.join(FLAGS.image_dir, set_id, (str(image_info['index']) + '.jpg'))
with open(f... |
def time_info(exp, file_name='log.txt', runs=1, nbins=10, max_line_length=10000):
time_list = []
config_file = f'./configs/{exp}.json'
sweeper = Sweeper(config_file)
for i in range((runs * sweeper.config_dicts['num_combinations'])):
log_file = f'./logs/{exp}/{(i + 1)}/{file_name}'
try:
... |
def test_abi3(tmp_path):
project_dir = (tmp_path / 'project')
limited_api_project.generate(project_dir)
actual_wheels = utils.cibuildwheel_run(project_dir, add_env={'CIBW_SKIP': 'pp* '})
expected_wheels = [w.replace('cp38-cp38', 'cp38-abi3') for w in utils.expected_wheels('spam', '0.1.0') if (('-pp' not... |
class BM25Search():
def __init__(self, index_name: str, hostname: str='localhost', keys: Dict[(str, str)]={'title': 'title', 'body': 'txt'}, language: str='english', batch_size: int=128, timeout: int=100, retry_on_timeout: bool=True, maxsize: int=24, number_of_shards: int='default', initialize: bool=True):
... |
class ZeroConfProcess(multiprocessing.Process):
def __init__(self, signalk):
self.name_type = False
self.pipe = NonBlockingPipe('zeroconf', True)
super(ZeroConfProcess, self).__init__(target=self.process, daemon=True)
self.start()
def remove_service(self, zc, type, name):
... |
def get_bandpath_fcc(ase_atom, npoints=30):
from ase.dft.kpoints import ibz_points, kpoint_convert, get_bandpath
points = ibz_points['fcc']
G = points['Gamma']
X = points['X']
W = points['W']
K = points['K']
L = points['L']
(kpts_reduced, kpath, sp_points) = get_bandpath([L, G, X, W, K, ... |
def _horizontal_datum_from_params(cf_params):
datum_name = cf_params.get('horizontal_datum_name')
if (datum_name and (datum_name not in ('undefined', 'unknown'))):
try:
return Datum.from_name(datum_name)
except CRSError:
pass
ellipsoid = None
ellipsoid_name = cf_p... |
def euler2mat_tf(point_cloud, rotations):
batch_size = rotations.get_shape()[0].value
assert (rotations.get_shape()[1].value == 3)
rotated_list = []
one = tf.constant([1.0])
zero = tf.constant([0.0])
for i in range(batch_size):
x = rotations[(i, 0)]
y = rotations[(i, 1)]
... |
class PointGroup(BaseModel, extra='forbid'):
hits: List['ScoredPoint'] = Field(..., description='Scored points that have the same value of the group_by key')
id: 'GroupId' = Field(..., description='')
lookup: Optional['Record'] = Field(default=None, description='Record that has been looked up using the grou... |
class TestMsgFmt(unittest.TestCase):
def test_ok(self):
with pofile_from_entry(msgid='test string', msgstr='estay ingstray') as p:
test_msgfmt(p.name)
def test_busted_newlines(self):
with pofile_from_entry(msgid='multi\nline\nstring', msgstr='ultimay\ninelay\ningstray\n') as p:
... |
def test_deprecated_decorator(recwarn_always: pytest.WarningsRecorder) -> None:
assert (deprecated_old() == 3)
got = recwarn_always.pop(TrioDeprecationWarning)
assert isinstance(got.message, Warning)
assert ('test_deprecate.deprecated_old is deprecated' in got.message.args[0])
assert ('1.5' in got.m... |
def gpt_collate_fn(data, tokenizer):
batch_data = {}
for key in data[0]:
batch_data[key] = [d[key] for d in data]
output_batch = tokenizer(batch_data['output_text'], padding=True, return_tensors='pt', add_special_tokens=False, return_attention_mask=False, truncation=True, max_length=1000)
batch_... |
class PipelinePackIterator(PipelineIterator):
def __iter__(self):
self.iterator = iter(self.loader)
return self
def __next__(self):
is_last = False
accumulator = []
if ((self._loader_batch_index is not None) and (self._loader_batch_index < self.loader_batch_size)):
... |
def test_index_bits():
data = Bits(8, 202)
x = Bits(4, 3)
assert (data[x] == 1)
y = Bits(4, (- 8))
with pytest.raises(IndexError):
data[y]
a = Bits(8, 4)
assert (data[a] == 0)
b = Bits(8, 20)
with pytest.raises(IndexError):
data[b]
c = Bits(8, (- 1))
with pyte... |
def down_pic(pic_urls, keyword):
filepath = judge_filepath(keyword)
for (i, pic_url) in enumerate(pic_urls):
try:
pic = requests.get(pic_url, timeout=15)
name = (str((i + 1)) + '.jpg')
filename = os.path.join(filepath, name)
with open(filename, 'wb') as f:... |
class TestDSSSSHSerialization():
def test_load_ssh_public_key_dss_too_short(self, backend):
ssh_key = b'ssh-dss'
with pytest.raises(ValueError):
load_ssh_public_key(ssh_key, backend)
def test_load_ssh_public_key_dss_comment_with_spaces(self, backend):
ssh_key = b'ssh-dss AAAA... |
def build(opt):
dpath = os.path.join(opt['datapath'], opt['dataset'])
embpath = os.path.join(opt['datapath'], 'embeddings')
logpath = os.path.join(opt['datapath'], 'logs')
modelpath = os.path.join(opt['datapath'], 'models')
version = None
if (not build_data.built(dpath, version_string=version)):... |
class QuantLinear(nn.Linear):
def __init__(self, in_features, out_features, bias=True):
super(QuantLinear, self).__init__(in_features, out_features, bias)
self.layer_type = 'QuantLinear'
self.bit = 4
self.weight_quant = weight_quantize_fn(w_bit=self.bit, power=True)
def forward(s... |
class CacheMixin():
cache_timeout = 60
def get_cache_timeout(self):
return self.cache_timeout
def dispatch(self, *args, **kwargs):
if (not settings.CACHE_PAGE_ENABLED):
return super().dispatch(*args, **kwargs)
return cache_page(self.get_cache_timeout())(super().dispatch)(... |
def open_database_from_options_or_exit(db_options, quiet=False, apsw_warning=False):
from .. import dbutils
dbinfo = dbutils.get_dbinfo(db_options.database)
try:
return dbinfo.open_database(copy_to_memory=db_options.copy_to_memory, quiet=quiet, apsw_warning=apsw_warning)
except dbutils.DBError a... |
def test(model, args):
print('Test')
join = os.path.join
if (not os.path.exists(join(args.save_dir, 'infer'))):
os.mkdir(join(args.save_dir, 'infer'))
if (not os.path.exists(join(args.save_dir, 'label'))):
os.mkdir(join(args.save_dir, 'label'))
split_dir = os.path.join(args.src_dir, ... |
def get_markdown_header(category):
header = f'''
# Release Notes worksheet {category}
The main goal of this process is to rephrase all the commit messages below to make them clear and easy to read by the end user. You should follow the following instructions to do so:
* **Please cleanup, and format commit titles to... |
def att_block_model(x_train):
inputs = Input((x_train.shape[1],))
x = att_block(inputs)
predictions = Dense(7, kernel_initializer=initializers.glorot_normal(seed=1), kernel_regularizer=regularizers.l2(1e-10), kernel_constraint=unit_norm(), activity_regularizer=regularizers.l2(1e-10), use_bias=True, bias_ini... |
class StreamingPlayer():
description: str
currentChapter: int = 0
movie: str = None
def __init__(self, description: str, amplifier):
self.description = description
self.amplifier = amplifier
def on(self) -> None:
print(f'{self.description} on')
def off(self) -> None:
... |
class AsyncTakeTest(unittest.TestCase):
def _test_async_take_with_error(path: str) -> None:
tc = unittest.TestCase()
dist.init_process_group(backend='gloo')
with patch('torchsnapshot.storage_plugin.FSStoragePlugin', FaultyFSStoragePlugin):
future = torchsnapshot.Snapshot.async_ta... |
def unite_values(*values: Value) -> Value:
if (not values):
return NO_RETURN_VALUE
hashable_vals = {}
unhashable_vals = []
for value in values:
if isinstance(value, MultiValuedValue):
subvals = value.vals
elif (isinstance(value, AnnotatedValue) and isinstance(value.va... |
def _get_jupyter_python_script(subcommand='notebook'):
dist = ('jupyterlab' if (subcommand == 'lab') else 'notebook')
try:
ep = _find_entry_point(dist, 'console_scripts', f'jupyter-{subcommand}')
if (ep is None):
_log.debug(f'Entry point {dist}.console_scripts.jupyter-{subcommand} no... |
def load_pytorch_checkpoint_in_tf2_model(tf_model, pytorch_checkpoint_path, tf_inputs=None, allow_missing_keys=False, output_loading_info=False):
try:
import tensorflow as tf
import torch
except ImportError:
logger.error('Loading a PyTorch model in TensorFlow, requires both PyTorch and T... |
class DownsampleAvg(nn.Module):
def __init__(self, in_chs, out_chs, stride=1, dilation=1, apply_act=False, layers: LayerFn=None):
super(DownsampleAvg, self).__init__()
layers = (layers or LayerFn())
avg_stride = (stride if (dilation == 1) else 1)
if ((stride > 1) or (dilation > 1)):
... |
class HourglassAEModule(nn.Module):
def __init__(self, depth, stage_channels, norm_cfg=dict(type='BN', requires_grad=True)):
norm_cfg = copy.deepcopy(norm_cfg)
super().__init__()
self.depth = depth
cur_channel = stage_channels[0]
next_channel = stage_channels[1]
self.... |
class MaxExcessReturnPortfolio(Portfolio):
def __init__(self, cov_matrix: QFDataFrame, variance_of_assets: QFSeries, upper_constraint: Union[(float, Sequence[float])]=None):
self.cov_matrix = cov_matrix
self.variance_of_assets = variance_of_assets
self.upper_constraint = upper_constraint
... |
class _LRScheduler(object):
def __init__(self, optimizer, last_epoch=(- 1)):
self.optimizer = optimizer
if (last_epoch == (- 1)):
for group in optimizer.param_groups:
group.setdefault('initial_lr', group['lr'])
else:
for (i, group) in enumerate(optimiz... |
def test_valid_oauth(app):
user = model.user.get_user('devtable')
app = model.oauth.list_applications_for_org(model.user.get_user_or_org('buynlarge'))[0]
token_string = ('%s%s' % (('a' * 20), ('b' * 20)))
(oauth_token, _) = model.oauth.create_user_access_token(user, app.client_id, 'repo:read', access_to... |
class Migration(migrations.Migration):
dependencies = [('adserver', '0021_publisher_record_ad_views')]
operations = [migrations.RemoveField(model_name='adtype', name='publisher'), migrations.AddField(model_name='adtype', name='default_enabled', field=models.BooleanField(default=False, help_text='Whether this ad... |
class KnownValues(unittest.TestCase):
((not has_spglib), 'spglib not found')
def test_D4h_vs_spglib(self):
dim = 3
magmom = [1, 1, (- 1), (- 1), 1, (- 1), 1, 1, (- 1), (- 1), 1, (- 1)]
cell = make_cell_D4h(dim, magmom)
sg = spg.SpaceGroup(cell)
sg.backend = 'spglib'
... |
def _torch_alloc(size, device_id):
torch_stream_ptr = torch.cuda.current_stream().cuda_stream
cupy_stream_ptr = cupy.cuda.get_current_stream().ptr
if (torch_stream_ptr != cupy_stream_ptr):
raise RuntimeError('The current stream set in PyTorch and CuPy must be same. Use `pytorch_pfn_extras.cuda.strea... |
def main():
dsz.ui.Echo('Pulling Firefox browser data...', dsz.GOOD)
dsz.control.echo.Off()
dsz.cmd.Run('background log python windows/firefoxrip.py -args "-s 1000000"', dsz.RUN_FLAG_RECORD)
dsz.control.echo.On()
dsz.ui.Echo('\tNo IE Capability Available!', dsz.WARNING)
dsz.ui.Echo('\tNo Chrome ... |
def create_sky_temple_key(key_number: int, resource_database: ResourceDatabase) -> PickupEntry:
return PickupEntry(name=f'Sky Temple Key {(key_number + 1)}', progression=((resource_database.get_item(echoes_items.SKY_TEMPLE_KEY_ITEMS[key_number]), 1),), model=PickupModel(game=resource_database.game_enum, name=echoes... |
class InvalidBodyLengthError(ProtocolError):
def __init__(self, expected, actual):
self.expected_length = expected
self.actual_length = actual
def __str__(self):
return ('InvalidBodyLengthError: Expected %d bytes, received %d' % (self.expected_length, self.actual_length)) |
def pblock_053(content):
stage_number = int(get1(content, b'04'))
pzs = sxml.PolesZeros(pz_transfer_function_type=ptftype(get1(content, b'03')), input_units=sxml.Units(name=punit(get1(content, b'05'))), output_units=sxml.Units(name=punit(get1(content, b'06'))), normalization_factor=float(get1(content, b'07')), ... |
def train_model(model, train, test, num_classes):
x_train = train[0].reshape(((train[0].shape[0],) + input_shape))
x_test = test[0].reshape(((test[0].shape[0],) + input_shape))
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train s... |
def readData(segmentation_input):
global numFrame, df, df_complete, time
df = pd.read_csv(segmentation_input)
numFrame = df.iloc[(- 1)]['Frame']
df = df[0:numFrame]
dupl = []
df_complete = df[0:numFrame]
df = df[(df.Visibility == 1)].reset_index(drop=True)
time = df[['Time']]
df = df... |
def follow_redirects(link, sites=None):
def follow(url):
return ((sites == None) or (urlparse.urlparse(url).hostname in sites))
class RedirectHandler(urllib2.HTTPRedirectHandler):
def __init__(self):
self.last_url = None
def redirect_request(self, req, fp, code, msg, hdrs, ne... |
class Effect3774(BaseEffect):
type = 'passive'
def handler(fit, module, context, projectionRange, **kwargs):
fit.ship.increaseItemAttr('hiSlots', module.getModifiedItemAttr('hiSlotModifier'), **kwargs)
fit.ship.increaseItemAttr('medSlots', module.getModifiedItemAttr('medSlotModifier'), **kwargs)... |
def get_cuda_info(device=None, unit='G', number_only=True):
current_mem = get_gpu_memory_usage_by_current_program(device, unit, number_only)
(all_mem, used, _, ratio) = get_gpu_memory_info(device, unit, number_only)
utilization = get_gpu_utilization(device)
return {'gpu_mem_ratio': ratio, 'gpu_mem': use... |
def update():
for episode in range(100):
observation = env.reset()
action = RL.choose_action(str(observation))
while True:
env.render()
(observation_, reward, done) = env.step(action)
action_ = RL.choose_action(str(observation_))
RL.learn(str(o... |
def test_get_state_dict():
if (torch.__version__ == 'parrots'):
state_dict_keys = set(['block.conv.weight', 'block.conv.bias', 'block.norm.weight', 'block.norm.bias', 'block.norm.running_mean', 'block.norm.running_var', 'conv.weight', 'conv.bias'])
else:
state_dict_keys = set(['block.conv.weight... |
def _start_all_stats_collection_from_deltas(deltas: List[Delta], partition_value_string: Optional[str], partition_canonical_string: Optional[str], columns: Optional[List[str]]=None, trace_id: Optional[str]=None, file_count_per_cpu: Optional[int]=MANIFEST_FILE_COUNT_PER_CPU, cpus_per_instance: Optional[int]=DEFAULT_CPUS... |
class ColdcardPlugin(HW_PluginBase):
keystore_class = Coldcard_KeyStore
minimum_library = (0, 7, 7)
DEVICE_IDS = [(COINKITE_VID, CKCC_PID), (COINKITE_VID, CKCC_SIMULATED_PID)]
SUPPORTED_XTYPES = ('standard', 'p2wpkh-p2sh', 'p2wpkh', 'p2wsh-p2sh', 'p2wsh')
def __init__(self, parent, config, name):
... |
class MetaTrainer(object):
def __init__(self, args):
self.args = args
if (args.dataset == 'miniimagenet'):
from dataloader.mini_imagenet import MiniImageNet as Dataset
args.num_class = 64
print('Using dataset: miniImageNet, base class num:', args.num_class)
... |
def test_is_debugging(monkeypatch):
import pytest_timeout
assert (not pytest_timeout.is_debugging())
from types import ModuleType
module_name = 'custom.pydevd'
module = ModuleType(module_name)
monkeypatch.setitem(sys.modules, module_name, module)
def custom_trace(*args):
pass
cus... |
.end_to_end()
def test_migrating_a_whole_task_with_persist(tmp_path):
source = '\n import pytask\n\n .persist\n .depends_on("in.txt")\n .produces("out.txt")\n def task_dummy(depends_on, produces):\n produces.write_text(depends_on.read_text())\n '
tmp_path.joinpath('task_module.py').writ... |
class DataConfig():
def __init__(self, path_config_json):
path_config_json = Path(path_config_json)
config_dict = utils.Json.load(path_config_json)
dir_config = path_config_json.parent
self.lm_name = config_dict['lm_name']
self.path_test = (dir_config / config_dict['path_test... |
def conv_functional():
input_shape = (128, 28, 28, 1)
inp = tf.keras.Input(shape=input_shape[1:])
x = tf.keras.layers.Conv2D(32, kernel_size=(3, 3), activation='relu')(inp)
x = tf.keras.layers.Conv2DTranspose(32, kernel_size=(3, 3), activation='relu')(x)
x = tf.keras.layers.DepthwiseConv2D(depth_mul... |
def test_addbug() -> None:
int5A = Fsm(alphabet={Charclass('a'), Charclass('b'), Charclass('c'), (~ Charclass('abc'))}, states={0, 1}, initial=1, finals={1}, map={0: {(~ Charclass('abc')): 0, Charclass('a'): 0, Charclass('b'): 0, Charclass('c'): 0}, 1: {(~ Charclass('abc')): 0, Charclass('a'): 0, Charclass('b'): 1,... |
('beeref.widgets.SceneToPixmapExporterDialog.exec')
('beeref.widgets.SceneToPixmapExporterDialog.value')
('PyQt6.QtWidgets.QFileDialog.getSaveFileName')
def test_on_action_export_scene(file_mock, value_mock, exec_mock, view, tmpdir, qtbot):
item = BeeTextItem('foo')
view.scene.addItem(item)
filename = os.pa... |
def group_connections(connections):
grouped_conns = defaultdict(list)
if isinstance(connections, QuerySet):
languages = connections.values_list('contact__language', flat=True)
for language in languages.distinct():
lang_conns = connections.filter(contact__language=language)
... |
class Pirate(cmd2.Cmd):
def __init__(self):
shortcuts = dict(cmd2.DEFAULT_SHORTCUTS)
shortcuts.update({'~': 'sing'})
super().__init__(multiline_commands=['sing'], terminators=[MULTILINE_TERMINATOR, '...'], shortcuts=shortcuts)
self.default_to_shell = True
self.songcolor = 'bl... |
def save_checkpoint(model, args, is_best=False):
directory = os.path.expanduser(args.save_dir)
if (not os.path.exists(directory)):
os.makedirs(directory)
filename = '{}_{}_{}.pth'.format(args.model, args.backbone, args.dataset)
filename = os.path.join(directory, filename)
if args.distributed... |
def swap_gauge(stdscr, pos_y, pos_x, size, mem_data):
values = ([(((mem_data['used'] / mem_data['tot']) * 100.0), NColors.red()), (((mem_data['cached'] / mem_data['tot']) * 100.0), NColors.yellow())] if (mem_data['tot'] > 0) else [])
used = size_to_string(mem_data['used'], 'k')
total = size_to_string(mem_da... |
class LegacyIndex(Index):
INDEX_FILENAME = 'hf_bert_base.hnswSQ8_correct_phi_128.c_index'
PASSAGE_FILENAME = 'psgs_w100.tsv.pkl'
def __init__(self, vector_size, index_path):
self.index_id_to_db_id = []
self.index_path = index_path
self.passages = self._load_passages()
self.ve... |
def seed_everything(seed, cudnn_deterministic=False):
if (seed is not None):
print(f'Global seed set to {seed}')
random.seed(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
if cudnn_deterministic:
torch.backends.cudnn.determinis... |
class ISIC2018DatasetFast(Dataset):
def __init__(self, mode, data_dir=None, one_hot=True, image_size=224, aug=None, aug_empty=None, transform=None, img_transform=None, msk_transform=None, add_boundary_mask=False, add_boundary_dist=False, logger=None, **kwargs):
self.print = (logger.info if logger else print... |
class CaptureBase(abc.ABC, Generic[AnyStr]):
EMPTY_BUFFER: AnyStr
def __init__(self, fd: int) -> None:
raise NotImplementedError()
def start(self) -> None:
raise NotImplementedError()
def done(self) -> None:
raise NotImplementedError()
def suspend(self) -> None:
raise... |
def get_patterns(graph_file, base_pattern):
patterns_graph = read_graph(graph_file)
prompts = [x.lm_pattern for x in list(patterns_graph.nodes)]
no_base = []
for p in prompts:
if (p.replace(' .', '.') == base_pattern.replace(' .', '.')):
continue
no_base.append(p)
assert ... |
.needs_connection
def test_HITRAN_molecules_list(verbose=True, *args, **kwargs):
from radis.db.classes import HITRAN_MOLECULES
from radis.misc.basics import compare_lists
molecules = fetch_HITRAN_molecules()
if verbose:
print('HITRAN molecules, fetched online ')
print(molecules)
... |
def _create_unless(terminals, g_regex_flags, re_, use_bytes):
tokens_by_type = classify(terminals, (lambda t: type(t.pattern)))
assert (len(tokens_by_type) <= 2), tokens_by_type.keys()
embedded_strs = set()
callback = {}
for retok in tokens_by_type.get(PatternRE, []):
unless = []
for... |
class SingleAvatarPromotion(Struct):
AvatarID: int
Promotion: int
PromotionCostList: List[PromotionCost]
MaxLevel: int
PlayerLevelRequire: Union[(int, None)]
WorldLevelRequire: Union[(int, None)]
AttackBase: PromotionAttr
AttackAdd: PromotionAttr
DefenceBase: PromotionAttr
Defenc... |
class BddOptions(SolverOptions):
CUDD_ALL_REORDERING_ALGORITHMS = range(1, 23)
(CUDD_REORDER_SAME, CUDD_REORDER_NONE, CUDD_REORDER_RANDOM, CUDD_REORDER_RANDOM_PIVOT, CUDD_REORDER_SIFT, CUDD_REORDER_SIFT_CONVERGE, CUDD_REORDER_SYMM_SIFT, CUDD_REORDER_SYMM_SIFT_CONV, CUDD_REORDER_WINDOW2, CUDD_REORDER_WINDOW3, CU... |
def run_hp_search_ray(trainer, n_trials: int, direction: str, **kwargs) -> BestRun:
import ray
def _objective(trial, local_trainer, checkpoint_dir=None):
try:
from transformers.utils.notebook import NotebookProgressCallback
if local_trainer.pop_callback(NotebookProgressCallback):... |
def save_command_run_params(args):
os.makedirs(args.out, exist_ok=True)
with open(os.path.join(args.out, 'args.json'), 'w') as args_file:
json.dump(args.__dict__, args_file)
with open(os.path.join(args.out, 'command.sh'), 'w') as command_file:
command_file.write(' '.join(sys.argv))
c... |
def get_runtime_info(parsed=None):
return {'exec': {'version': version, 'api_version': api_version, 'argv': sys.argv, 'parsed': parsed}, 'python': {'name': sys.implementation.name, 'executable': sys.executable, 'version': platform.python_version()}, 'system': {'platform': platform.platform(), 'fs_encoding': sys.get... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.