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XuezheMax/flowseq | 8cb4ae00c26fbeb3e1459e3b3b90e7e9a84c3d2b | flownmt/modules/encoders/transformer.py | python | TransformerEncoder.__init__ | (self, vocab_size, embed_dim, padding_idx, num_layers, latent_dim, hidden_size, heads, dropout=0.0, max_length=100) | [] | def __init__(self, vocab_size, embed_dim, padding_idx, num_layers, latent_dim, hidden_size, heads, dropout=0.0, max_length=100):
super(TransformerEncoder, self).__init__(vocab_size, embed_dim, padding_idx)
self.core = TransformerCore(self.embed, num_layers, latent_dim, hidden_size, heads, dropout=dropout, max_length=max_length) | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/idlelib/search.py | python | find_selection | (text) | return _setup(text).find_selection(text) | Handle the editor edit menu item and corresponding event. | Handle the editor edit menu item and corresponding event. | [
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KhronosGroup/NNEF-Tools | c913758ca687dab8cb7b49e8f1556819a2d0ca25 | nnef_tools/io/tf/lite/flatbuffers/Model.py | python | ModelStartMetadataBufferVector | (builder, numElems) | return builder.StartVector(4, numElems, 4) | [] | def ModelStartMetadataBufferVector(builder, numElems): return builder.StartVector(4, numElems, 4) | [
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mchristopher/PokemonGo-DesktopMap | ec37575f2776ee7d64456e2a1f6b6b78830b4fe0 | app/pywin/Lib/numbers.py | python | Real.__float__ | (self) | Any Real can be converted to a native float object.
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poppy-project/pypot | c5d384fe23eef9f6ec98467f6f76626cdf20afb9 | pypot/dynamixel/io/abstract_io.py | python | AbstractDxlIO.timeout | (self) | return self._serial.timeout | Timeout used by the :class:`~pypot.dynamixel.io.DxlIO`. If set, will re-open a new connection. | Timeout used by the :class:`~pypot.dynamixel.io.DxlIO`. If set, will re-open a new connection. | [
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selfteaching/selfteaching-python-camp | 9982ee964b984595e7d664b07c389cddaf158f1e | 19100205/Ceasar1978/pip-19.0.3/src/pip/_vendor/distlib/version.py | python | Matcher.match | (self, version) | return True | Check if the provided version matches the constraints.
:param version: The version to match against this instance.
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:param version: The version to match against this instance.
:type version: String or :class:`Version` instance.
"""
if isinstance(version, string_types):
version = self.version_class(version)
for operator, constraint, prefix in self._parts:
f = self._operators.get(operator)
if isinstance(f, string_types):
f = getattr(self, f)
if not f:
msg = ('%r not implemented '
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raise NotImplementedError(msg)
if not f(version, constraint, prefix):
return False
return True | [
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aws/aws-parallelcluster | f1fe5679a01c524e7ea904c329bd6d17318c6cd9 | cli/src/pcluster3_config_converter/pcluster3_config_converter.py | python | _parse_args | (argv=None) | return convert_parser.parse_args(argv) | Parse command line args. | Parse command line args. | [
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smart-mobile-software/gitstack | d9fee8f414f202143eb6e620529e8e5539a2af56 | python/Lib/uuid.py | python | UUID.__init__ | (self, hex=None, bytes=None, bytes_le=None, fields=None,
int=None, version=None) | r"""Create a UUID from either a string of 32 hexadecimal digits,
a string of 16 bytes as the 'bytes' argument, a string of 16 bytes
in little-endian order as the 'bytes_le' argument, a tuple of six
integers (32-bit time_low, 16-bit time_mid, 16-bit time_hi_version,
8-bit clock_seq_hi_variant, 8-bit clock_seq_low, 48-bit node) as
the 'fields' argument, or a single 128-bit integer as the 'int'
argument. When a string of hex digits is given, curly braces,
hyphens, and a URN prefix are all optional. For example, these
expressions all yield the same UUID:
UUID('{12345678-1234-5678-1234-567812345678}')
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UUID('urn:uuid:12345678-1234-5678-1234-567812345678')
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Exactly one of 'hex', 'bytes', 'bytes_le', 'fields', or 'int' must
be given. The 'version' argument is optional; if given, the resulting
UUID will have its variant and version set according to RFC 4122,
overriding the given 'hex', 'bytes', 'bytes_le', 'fields', or 'int'. | r"""Create a UUID from either a string of 32 hexadecimal digits,
a string of 16 bytes as the 'bytes' argument, a string of 16 bytes
in little-endian order as the 'bytes_le' argument, a tuple of six
integers (32-bit time_low, 16-bit time_mid, 16-bit time_hi_version,
8-bit clock_seq_hi_variant, 8-bit clock_seq_low, 48-bit node) as
the 'fields' argument, or a single 128-bit integer as the 'int'
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"""
if [hex, bytes, bytes_le, fields, int].count(None) != 4:
raise TypeError('need one of hex, bytes, bytes_le, fields, or int')
if hex is not None:
hex = hex.replace('urn:', '').replace('uuid:', '')
hex = hex.strip('{}').replace('-', '')
if len(hex) != 32:
raise ValueError('badly formed hexadecimal UUID string')
int = long(hex, 16)
if bytes_le is not None:
if len(bytes_le) != 16:
raise ValueError('bytes_le is not a 16-char string')
bytes = (bytes_le[3] + bytes_le[2] + bytes_le[1] + bytes_le[0] +
bytes_le[5] + bytes_le[4] + bytes_le[7] + bytes_le[6] +
bytes_le[8:])
if bytes is not None:
if len(bytes) != 16:
raise ValueError('bytes is not a 16-char string')
int = long(('%02x'*16) % tuple(map(ord, bytes)), 16)
if fields is not None:
if len(fields) != 6:
raise ValueError('fields is not a 6-tuple')
(time_low, time_mid, time_hi_version,
clock_seq_hi_variant, clock_seq_low, node) = fields
if not 0 <= time_low < 1<<32L:
raise ValueError('field 1 out of range (need a 32-bit value)')
if not 0 <= time_mid < 1<<16L:
raise ValueError('field 2 out of range (need a 16-bit value)')
if not 0 <= time_hi_version < 1<<16L:
raise ValueError('field 3 out of range (need a 16-bit value)')
if not 0 <= clock_seq_hi_variant < 1<<8L:
raise ValueError('field 4 out of range (need an 8-bit value)')
if not 0 <= clock_seq_low < 1<<8L:
raise ValueError('field 5 out of range (need an 8-bit value)')
if not 0 <= node < 1<<48L:
raise ValueError('field 6 out of range (need a 48-bit value)')
clock_seq = (clock_seq_hi_variant << 8L) | clock_seq_low
int = ((time_low << 96L) | (time_mid << 80L) |
(time_hi_version << 64L) | (clock_seq << 48L) | node)
if int is not None:
if not 0 <= int < 1<<128L:
raise ValueError('int is out of range (need a 128-bit value)')
if version is not None:
if not 1 <= version <= 5:
raise ValueError('illegal version number')
# Set the variant to RFC 4122.
int &= ~(0xc000 << 48L)
int |= 0x8000 << 48L
# Set the version number.
int &= ~(0xf000 << 64L)
int |= version << 76L
self.__dict__['int'] = int | [
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wistbean/fxxkpython | 88e16d79d8dd37236ba6ecd0d0ff11d63143968c | vip/qyxuan/projects/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_vendor/pkg_resources/__init__.py | python | Environment.__add__ | (self, other) | return new | Add an environment or distribution to an environment | Add an environment or distribution to an environment | [
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pyg-team/pytorch_geometric | b920e9a3a64e22c8356be55301c88444ff051cae | examples/pytorch_lightning/graph_sage.py | python | main | () | [] | def main():
seed_everything(42)
dataset = Reddit(osp.join('data', 'Reddit'))
data = dataset[0]
datamodule = LightningNodeData(data, data.train_mask, data.val_mask,
data.test_mask, loader='neighbor',
num_neighbors=[25, 10], batch_size=1024,
num_workers=8)
model = Model(dataset.num_node_features, dataset.num_classes)
gpus = torch.cuda.device_count()
strategy = pl.plugins.DDPSpawnPlugin(find_unused_parameters=False)
checkpoint = pl.callbacks.ModelCheckpoint(monitor='val_acc', save_top_k=1)
trainer = pl.Trainer(gpus=gpus, strategy=strategy, max_epochs=20,
callbacks=[checkpoint])
trainer.fit(model, datamodule)
trainer.test(ckpt_path='best', datamodule=datamodule) | [
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AutodeskRoboticsLab/Mimic | 85447f0d346be66988303a6a054473d92f1ed6f4 | mFIZ/scripts/mFIZ_extern/serial/rfc2217.py | python | TelnetSubnegotiation.set | (self, value) | \
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fluentpython/example-code-2e | 80f7f84274a47579e59c29a4657691525152c9d5 | 21-async/mojifinder/bottle.py | python | MultiDict.__delitem__ | (self, key) | [] | def __delitem__(self, key): del self.dict[key] | [
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facebookresearch/ClassyVision | 309d4f12431c6b4d8540010a781dc2aa25fe88e7 | classy_vision/models/r2plus1_util.py | python | r2plus1_unit | (
dim_in,
dim_out,
temporal_stride,
spatial_stride,
groups,
inplace_relu,
bn_eps,
bn_mmt,
dim_mid=None,
) | return nn.Sequential(conv_middle, conv_middle_bn, conv_middle_relu, conv) | Implementation of `R(2+1)D unit <https://arxiv.org/abs/1711.11248>`_.
Decompose one 3D conv into one 2D spatial conv and one 1D temporal conv.
Choose the middle dimensionality so that the total No. of parameters
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Args:
dim_in (int): the channel dimensions of the input.
dim_out (int): the channel dimension of the output.
temporal_stride (int): the temporal stride of the bottleneck.
spatial_stride (int): the spatial_stride of the bottleneck.
groups (int): number of groups for the convolution.
inplace_relu (bool): calculate the relu on the original input
without allocating new memory.
bn_eps (float): epsilon for batch norm.
bn_mmt (float): momentum for batch norm. Noted that BN momentum in
PyTorch = 1 - BN momentum in Caffe2.
dim_mid (Optional[int]): If not None, use the provided channel dimension
for the output of the 2D spatial conv. If None, compute the output
channel dimension of the 2D spatial conv so that the total No. of
model parameters remains unchanged. | Implementation of `R(2+1)D unit <https://arxiv.org/abs/1711.11248>`_.
Decompose one 3D conv into one 2D spatial conv and one 1D temporal conv.
Choose the middle dimensionality so that the total No. of parameters
in 2D spatial conv and 1D temporal conv is unchanged. | [
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dim_mid=None,
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Implementation of `R(2+1)D unit <https://arxiv.org/abs/1711.11248>`_.
Decompose one 3D conv into one 2D spatial conv and one 1D temporal conv.
Choose the middle dimensionality so that the total No. of parameters
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Args:
dim_in (int): the channel dimensions of the input.
dim_out (int): the channel dimension of the output.
temporal_stride (int): the temporal stride of the bottleneck.
spatial_stride (int): the spatial_stride of the bottleneck.
groups (int): number of groups for the convolution.
inplace_relu (bool): calculate the relu on the original input
without allocating new memory.
bn_eps (float): epsilon for batch norm.
bn_mmt (float): momentum for batch norm. Noted that BN momentum in
PyTorch = 1 - BN momentum in Caffe2.
dim_mid (Optional[int]): If not None, use the provided channel dimension
for the output of the 2D spatial conv. If None, compute the output
channel dimension of the 2D spatial conv so that the total No. of
model parameters remains unchanged.
"""
if dim_mid is None:
dim_mid = int(dim_out * dim_in * 3 * 3 * 3 / (dim_in * 3 * 3 + dim_out * 3))
logging.info(
"dim_in: %d, dim_out: %d. Set dim_mid to %d" % (dim_in, dim_out, dim_mid)
)
# 1x3x3 group conv, BN, ReLU
conv_middle = nn.Conv3d(
dim_in,
dim_mid,
[1, 3, 3], # kernel
stride=[1, spatial_stride, spatial_stride],
padding=[0, 1, 1],
groups=groups,
bias=False,
)
conv_middle_bn = nn.BatchNorm3d(dim_mid, eps=bn_eps, momentum=bn_mmt)
conv_middle_relu = nn.ReLU(inplace=inplace_relu)
# 3x1x1 group conv
conv = nn.Conv3d(
dim_mid,
dim_out,
[3, 1, 1], # kernel
stride=[temporal_stride, 1, 1],
padding=[1, 0, 0],
groups=groups,
bias=False,
)
return nn.Sequential(conv_middle, conv_middle_bn, conv_middle_relu, conv) | [
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google/prettytensor | 75daa0b11252590f548da5647addc0ea610c4c45 | prettytensor/pretty_tensor_class.py | python | PrettyTensor.sequence | (self) | Returns the sequence for this layer. | Returns the sequence for this layer. | [
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apple/ccs-calendarserver | 13c706b985fb728b9aab42dc0fef85aae21921c3 | calendarserver/tools/dashboard.py | python | SystemWindow.update | (self) | [] | def update(self):
records = defaultIfNone(self.clientData(), {
"cpu use": 0.0,
"memory percent": 0.0,
"memory used": 0,
"start time": time.time(),
})
if len(records) != self.rowCount:
self.needsReset = True
return
self.iter += 1
s = " {:<30}{:>18} ".format("Item", "Value")
pt = self.tableHeader((s,), len(records))
records["cpu use"] = "{:.2f}".format(records["cpu use"])
records["memory percent"] = "{:.1f}".format(records["memory percent"])
records["memory used"] = "{:.2f} GB".format(
records["memory used"] / (1000.0 * 1000.0 * 1000.0)
)
records["uptime"] = int(time.time() - records["start time"])
hours, mins = divmod(records["uptime"] / 60, 60)
records["uptime"] = "{}:{:02d} hh:mm".format(hours, mins)
del records["start time"]
for item, value in sorted(records.items(), key=lambda x: x[0]):
changed = (
item in self.lastResult and self.lastResult[item] != value
)
s = " {:<30}{:>18} ".format(item, value)
self.tableRow(
s, pt,
curses.A_REVERSE if changed else curses.A_NORMAL,
)
if self.usesCurses:
self.window.refresh()
self.lastResult = records | [
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pandas-0.24.2-py3.7-macosx-10.9-x86_64.egg/pandas/core/generic.py | python | NDFrame.to_dense | (self) | return self | Return dense representation of NDFrame (as opposed to sparse). | Return dense representation of NDFrame (as opposed to sparse). | [
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"""
Return dense representation of NDFrame (as opposed to sparse).
"""
# compat
return self | [
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bitcraze/crazyflie-clients-python | 65d433a945b097333e5681a937354045dd4b66f4 | src/cfclient/ui/tab.py | python | Tab.getTabName | (self) | return self.tabName | Return the name of the tab that will be shown in the tab | Return the name of the tab that will be shown in the tab | [
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andyzsf/TuShare | 92787ad0cd492614bdb6389b71a19c80d1c8c9ae | tushare/futures/ctp/futures/__init__.py | python | TraderApi.ReqQryTradingAccount | (self, pQryTradingAccount, nRequestID) | return 0 | 请求查询资金账户 | 请求查询资金账户 | [
"请求查询资金账户"
] | def ReqQryTradingAccount(self, pQryTradingAccount, nRequestID):
"""请求查询资金账户"""
return 0 | [
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mit-han-lab/data-efficient-gans | 6858275f08f43a33026844c8c2ac4e703e8a07ba | DiffAugment-biggan-cifar/datasets.py | python | ILSVRC_HDF5.__len__ | (self) | return self.num_imgs | [] | def __len__(self):
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crdoconnor/strictyaml | b456066a763285532fd75cd274d4a275d8499d6c | strictyaml/representation.py | python | YAML.text | (self) | return self._text | Return string value of scalar, whatever value it was parsed as. | Return string value of scalar, whatever value it was parsed as. | [
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if isinstance(self._value, CommentedMap):
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return self._text | [
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mdiazcl/fuzzbunch-debian | 2b76c2249ade83a389ae3badb12a1bd09901fd2c | windows/Resources/Python/Core/Lib/xml/sax/handler.py | python | ContentHandler.startDocument | (self) | Receive notification of the beginning of a document.
The SAX parser will invoke this method only once, before any
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The SAX parser will invoke this method only once, before any
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FSecureLABS/Jandroid | e31d0dab58a2bfd6ed8e0a387172b8bd7c893436 | libs/platform-tools/platform-tools_linux/systrace/catapult/third_party/pyserial/serial/serialwin32.py | python | Win32Serial._close | (self) | internal close port helper | internal close port helper | [
"internal",
"close",
"port",
"helper"
] | def _close(self):
"""internal close port helper"""
if self.hComPort:
# Restore original timeout values:
win32.SetCommTimeouts(self.hComPort, self._orgTimeouts)
# Close COM-Port:
win32.CloseHandle(self.hComPort)
if self._overlappedRead is not None:
win32.CloseHandle(self._overlappedRead.hEvent)
self._overlappedRead = None
if self._overlappedWrite is not None:
win32.CloseHandle(self._overlappedWrite.hEvent)
self._overlappedWrite = None
self.hComPort = None | [
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marcospereirampj/python-keycloak | dd130a0365da390ae28d8a9cb6f76103ecb5c83f | keycloak/keycloak_admin.py | python | KeycloakAdmin.create_group | (self, payload, parent=None, skip_exists=False) | return raise_error_from_response(data_raw, KeycloakGetError, expected_codes=[201], skip_exists=skip_exists) | Creates a group in the Realm
:param payload: GroupRepresentation
:param parent: parent group's id. Required to create a sub-group.
:param skip_exists: If true then do not raise an error if it already exists
GroupRepresentation
https://www.keycloak.org/docs-api/8.0/rest-api/#_grouprepresentation
:return: Http response | Creates a group in the Realm | [
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] | def create_group(self, payload, parent=None, skip_exists=False):
"""
Creates a group in the Realm
:param payload: GroupRepresentation
:param parent: parent group's id. Required to create a sub-group.
:param skip_exists: If true then do not raise an error if it already exists
GroupRepresentation
https://www.keycloak.org/docs-api/8.0/rest-api/#_grouprepresentation
:return: Http response
"""
if parent is None:
params_path = {"realm-name": self.realm_name}
data_raw = self.raw_post(URL_ADMIN_GROUPS.format(**params_path),
data=json.dumps(payload))
else:
params_path = {"realm-name": self.realm_name, "id": parent, }
data_raw = self.raw_post(URL_ADMIN_GROUP_CHILD.format(**params_path),
data=json.dumps(payload))
return raise_error_from_response(data_raw, KeycloakGetError, expected_codes=[201], skip_exists=skip_exists) | [
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ales-tsurko/cells | 4cf7e395cd433762bea70cdc863a346f3a6fe1d0 | packaging/macos/python/lib/python3.7/importlib/_bootstrap_external.py | python | SourceLoader.get_source | (self, fullname) | return decode_source(source_bytes) | Concrete implementation of InspectLoader.get_source. | Concrete implementation of InspectLoader.get_source. | [
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path = self.get_filename(fullname)
try:
source_bytes = self.get_data(path)
except OSError as exc:
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tschellenbach/Django-facebook | fecbb5dd4931cc03a8425bde84e5e7b1ba22786d | docs/docs_env/Lib/codecs.py | python | StreamReader.readlines | (self, sizehint=None, keepends=True) | return data.splitlines(keepends) | Read all lines available on the input stream
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Line breaks are implemented using the codec's decoder
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sizehint, if given, is ignored since there is no efficient
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""" Read all lines available on the input stream
and return them as list of lines.
Line breaks are implemented using the codec's decoder
method and are included in the list entries.
sizehint, if given, is ignored since there is no efficient
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"""
data = self.read()
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IJDykeman/wangTiles | 7c1ee2095ebdf7f72bce07d94c6484915d5cae8b | experimental_code/tiles_3d/venv_mac/lib/python2.7/site-packages/pip/_vendor/pkg_resources/__init__.py | python | WorkingSet.find_plugins | (self, plugin_env, full_env=None, installer=None,
fallback=True) | return distributions, error_info | Find all activatable distributions in `plugin_env`
Example usage::
distributions, errors = working_set.find_plugins(
Environment(plugin_dirlist)
)
# add plugins+libs to sys.path
map(working_set.add, distributions)
# display errors
print('Could not load', errors)
The `plugin_env` should be an ``Environment`` instance that contains
only distributions that are in the project's "plugin directory" or
directories. The `full_env`, if supplied, should be an ``Environment``
contains all currently-available distributions. If `full_env` is not
supplied, one is created automatically from the ``WorkingSet`` this
method is called on, which will typically mean that every directory on
``sys.path`` will be scanned for distributions.
`installer` is a standard installer callback as used by the
``resolve()`` method. The `fallback` flag indicates whether we should
attempt to resolve older versions of a plugin if the newest version
cannot be resolved.
This method returns a 2-tuple: (`distributions`, `error_info`), where
`distributions` is a list of the distributions found in `plugin_env`
that were loadable, along with any other distributions that are needed
to resolve their dependencies. `error_info` is a dictionary mapping
unloadable plugin distributions to an exception instance describing the
error that occurred. Usually this will be a ``DistributionNotFound`` or
``VersionConflict`` instance. | Find all activatable distributions in `plugin_env` | [
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] | def find_plugins(self, plugin_env, full_env=None, installer=None,
fallback=True):
"""Find all activatable distributions in `plugin_env`
Example usage::
distributions, errors = working_set.find_plugins(
Environment(plugin_dirlist)
)
# add plugins+libs to sys.path
map(working_set.add, distributions)
# display errors
print('Could not load', errors)
The `plugin_env` should be an ``Environment`` instance that contains
only distributions that are in the project's "plugin directory" or
directories. The `full_env`, if supplied, should be an ``Environment``
contains all currently-available distributions. If `full_env` is not
supplied, one is created automatically from the ``WorkingSet`` this
method is called on, which will typically mean that every directory on
``sys.path`` will be scanned for distributions.
`installer` is a standard installer callback as used by the
``resolve()`` method. The `fallback` flag indicates whether we should
attempt to resolve older versions of a plugin if the newest version
cannot be resolved.
This method returns a 2-tuple: (`distributions`, `error_info`), where
`distributions` is a list of the distributions found in `plugin_env`
that were loadable, along with any other distributions that are needed
to resolve their dependencies. `error_info` is a dictionary mapping
unloadable plugin distributions to an exception instance describing the
error that occurred. Usually this will be a ``DistributionNotFound`` or
``VersionConflict`` instance.
"""
plugin_projects = list(plugin_env)
# scan project names in alphabetic order
plugin_projects.sort()
error_info = {}
distributions = {}
if full_env is None:
env = Environment(self.entries)
env += plugin_env
else:
env = full_env + plugin_env
shadow_set = self.__class__([])
# put all our entries in shadow_set
list(map(shadow_set.add, self))
for project_name in plugin_projects:
for dist in plugin_env[project_name]:
req = [dist.as_requirement()]
try:
resolvees = shadow_set.resolve(req, env, installer)
except ResolutionError as v:
# save error info
error_info[dist] = v
if fallback:
# try the next older version of project
continue
else:
# give up on this project, keep going
break
else:
list(map(shadow_set.add, resolvees))
distributions.update(dict.fromkeys(resolvees))
# success, no need to try any more versions of this project
break
distributions = list(distributions)
distributions.sort()
return distributions, error_info | [
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openstack/horizon | 12bb9fe5184c9dd3329ba17b3d03c90887dbcc3d | horizon/tabs/base.py | python | TabGroup.tabs_not_available | (self) | The fallback handler if no tabs are either allowed or enabled.
In the event that no tabs are either allowed or enabled, this method
is the fallback handler. By default it's a no-op, but it exists
to make redirecting or raising exceptions possible for subclasses. | The fallback handler if no tabs are either allowed or enabled. | [
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"""The fallback handler if no tabs are either allowed or enabled.
In the event that no tabs are either allowed or enabled, this method
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TencentCloud/tencentcloud-sdk-python | 3677fd1cdc8c5fd626ce001c13fd3b59d1f279d2 | tencentcloud/iotvideo/v20201215/models.py | python | DeviceInfo.__init__ | (self) | r"""
:param DeviceName: 设备名
:type DeviceName: str
:param Online: 设备是否在线，0不在线，1在线，2获取失败，3未激活
:type Online: int
:param LoginTime: 设备最后上线时间
:type LoginTime: int
:param DevicePsk: 设备密钥
:type DevicePsk: str
:param EnableState: 设备启用状态 0为停用 1为可用
:type EnableState: int
:param ExpireTime: 设备过期时间
:type ExpireTime: int | r"""
:param DeviceName: 设备名
:type DeviceName: str
:param Online: 设备是否在线，0不在线，1在线，2获取失败，3未激活
:type Online: int
:param LoginTime: 设备最后上线时间
:type LoginTime: int
:param DevicePsk: 设备密钥
:type DevicePsk: str
:param EnableState: 设备启用状态 0为停用 1为可用
:type EnableState: int
:param ExpireTime: 设备过期时间
:type ExpireTime: int | [
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:type EnableState: int
:param ExpireTime: 设备过期时间
:type ExpireTime: int
"""
self.DeviceName = None
self.Online = None
self.LoginTime = None
self.DevicePsk = None
self.EnableState = None
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wbond/oscrypto | d40c62577706682a0f6da5616ad09964f1c9137d | oscrypto/_mac/tls.py | python | TLSSocket.read | (self, max_length) | return output[0:max_length] | Reads data from the TLS-wrapped socket
:param max_length:
The number of bytes to read - output may be less than this
:raises:
socket.socket - when a non-TLS socket error occurs
oscrypto.errors.TLSError - when a TLS-related error occurs
oscrypto.errors.TLSDisconnectError - when the connection disconnects
oscrypto.errors.TLSGracefulDisconnectError - when the remote end gracefully closed the connection
ValueError - when any of the parameters contain an invalid value
TypeError - when any of the parameters are of the wrong type
OSError - when an error is returned by the OS crypto library
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"""
Reads data from the TLS-wrapped socket
:param max_length:
The number of bytes to read - output may be less than this
:raises:
socket.socket - when a non-TLS socket error occurs
oscrypto.errors.TLSError - when a TLS-related error occurs
oscrypto.errors.TLSDisconnectError - when the connection disconnects
oscrypto.errors.TLSGracefulDisconnectError - when the remote end gracefully closed the connection
ValueError - when any of the parameters contain an invalid value
TypeError - when any of the parameters are of the wrong type
OSError - when an error is returned by the OS crypto library
:return:
A byte string of the data read
"""
if not isinstance(max_length, int_types):
raise TypeError(pretty_message(
'''
max_length must be an integer, not %s
''',
type_name(max_length)
))
if self._session_context is None:
# Even if the session is closed, we can use
# buffered data to respond to read requests
if self._decrypted_bytes != b'':
output = self._decrypted_bytes
self._decrypted_bytes = b''
return output
self._raise_closed()
buffered_length = len(self._decrypted_bytes)
# If we already have enough buffered data, just use that
if buffered_length >= max_length:
output = self._decrypted_bytes[0:max_length]
self._decrypted_bytes = self._decrypted_bytes[max_length:]
return output
# Don't block if we have buffered data available, since it is ok to
# return less than the max_length
if buffered_length > 0 and not self.select_read(0):
output = self._decrypted_bytes
self._decrypted_bytes = b''
return output
# Only read enough to get the requested amount when
# combined with buffered data
to_read = max_length - len(self._decrypted_bytes)
read_buffer = buffer_from_bytes(to_read)
processed_pointer = new(Security, 'size_t *')
result = Security.SSLRead(
self._session_context,
read_buffer,
to_read,
processed_pointer
)
if self._exception is not None:
exception = self._exception
self._exception = None
raise exception
if result and result not in set([SecurityConst.errSSLWouldBlock, SecurityConst.errSSLClosedGraceful]):
handle_sec_error(result, TLSError)
if result and result == SecurityConst.errSSLClosedGraceful:
self._gracefully_closed = True
self._shutdown(False)
self._raise_closed()
bytes_read = deref(processed_pointer)
output = self._decrypted_bytes + bytes_from_buffer(read_buffer, bytes_read)
self._decrypted_bytes = output[max_length:]
return output[0:max_length] | [
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allegro/ralph | 1e4a9e1800d5f664abaef2624b8bf7512df279ce | src/ralph/virtual/management/commands/openstack_sync.py | python | Command._update_projects | (self, client) | Returns a map tenant_id->tenant_name
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:rtype: dict
"""
for project in client.get_keystone_projects():
if project.id not in self.openstack_projects:
self.openstack_projects[project.id] = {
'name': project.name,
'servers': {},
'tags': []
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self.openstack_projects[project.id]['tags'].append(
client.site['tag']
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CSAILVision/places365 | 06218620d593de09ac4f9f39b72ea0d175990a24 | wideresnet.py | python | resnet34 | (pretrained=False, **kwargs) | return model | Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet | Constructs a ResNet-34 model. | [
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] | def resnet34(pretrained=False, **kwargs):
"""Constructs a ResNet-34 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(BasicBlock, [3, 4, 6, 3], **kwargs)
if pretrained:
model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))
return model | [
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inspurer/WorkAttendanceSystem | 1221e2d67bdf5bb15fe99517cc3ded58ccb066df | V1.0/venv/Lib/site-packages/pip-9.0.1-py3.5.egg/pip/_vendor/requests/api.py | python | head | (url, **kwargs) | return request('head', url, **kwargs) | Sends a HEAD request.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response | Sends a HEAD request. | [
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] | def head(url, **kwargs):
"""Sends a HEAD request.
:param url: URL for the new :class:`Request` object.
:param \*\*kwargs: Optional arguments that ``request`` takes.
:return: :class:`Response <Response>` object
:rtype: requests.Response
"""
kwargs.setdefault('allow_redirects', False)
return request('head', url, **kwargs) | [
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TarrySingh/Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials | 5bb97d7e3ffd913abddb4cfa7d78a1b4c868890e | java/java2py/antlr-3.1.3/runtime/Python/antlr3/streams.py | python | CommonTokenStream.setTokenSource | (self, tokenSource) | Reset this token stream by setting its token source. | Reset this token stream by setting its token source. | [
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] | def setTokenSource(self, tokenSource):
"""Reset this token stream by setting its token source."""
self.tokenSource = tokenSource
self.tokens = []
self.p = -1
self.channel = DEFAULT_CHANNEL | [
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rotki/rotki | aafa446815cdd5e9477436d1b02bee7d01b398c8 | rotkehlchen/chain/ethereum/modules/aave/graph.py | python | AaveGraphInquirer._process_events | (
self,
user_address: ChecksumEthAddress,
user_result: Dict[str, Any],
from_ts: Timestamp,
to_ts: Timestamp,
deposits: List[AaveDepositWithdrawalEvent],
withdrawals: List[AaveDepositWithdrawalEvent],
borrows: List[AaveBorrowEvent],
repays: List[AaveRepayEvent],
liquidations: List[AaveLiquidationEvent],
db_events: List[AaveEvent],
balances: AaveBalances,
) | return AaveEventProcessingResult(
interest_events=interest_events,
total_earned_interest=total_earned,
total_lost=total_lost,
total_earned_liquidations=total_earned_liquidations,
) | Calculates the interest events and the total earned from all the given events.
Also calculates total loss from borrowing and liquidations.
Also returns the edited DB events | Calculates the interest events and the total earned from all the given events.
Also calculates total loss from borrowing and liquidations. | [
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self,
user_address: ChecksumEthAddress,
user_result: Dict[str, Any],
from_ts: Timestamp,
to_ts: Timestamp,
deposits: List[AaveDepositWithdrawalEvent],
withdrawals: List[AaveDepositWithdrawalEvent],
borrows: List[AaveBorrowEvent],
repays: List[AaveRepayEvent],
liquidations: List[AaveLiquidationEvent],
db_events: List[AaveEvent],
balances: AaveBalances,
) -> AaveEventProcessingResult:
"""Calculates the interest events and the total earned from all the given events.
Also calculates total loss from borrowing and liquidations.
Also returns the edited DB events
"""
actions: List[AaveDepositWithdrawalEvent] = []
borrow_actions: List[AaveEvent] = []
db_interest_events: Set[AaveInterestEvent] = set()
for db_event in db_events:
if db_event.event_type == 'deposit':
actions.append(db_event) # type: ignore
elif db_event.event_type == 'withdrawal':
actions.append(db_event) # type: ignore
elif db_event.event_type == 'interest':
db_interest_events.add(db_event) # type: ignore
elif db_event.event_type == 'borrow':
borrow_actions.append(db_event)
elif db_event.event_type == 'repay':
borrow_actions.append(db_event)
elif db_event.event_type == 'liquidation':
borrow_actions.append(db_event)
interest_events, total_earned = self._calculate_interest_and_profit(
user_address=user_address,
user_result=user_result,
actions=actions + deposits + withdrawals,
balances=balances,
db_interest_events=db_interest_events,
from_ts=from_ts,
to_ts=to_ts,
)
total_lost, total_earned_liquidations = _calculate_loss(
borrow_actions=borrow_actions + borrows + repays + liquidations, # type: ignore
balances=balances,
)
return AaveEventProcessingResult(
interest_events=interest_events,
total_earned_interest=total_earned,
total_lost=total_lost,
total_earned_liquidations=total_earned_liquidations,
) | [
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LinOTP/LinOTP | bb3940bbaccea99550e6c063ff824f258dd6d6d7 | linotp/provider/pushprovider/__init__.py | python | IPushProvider.push_notification | (self, challenge, gda, transactionId) | Sends out the push notification message.
:param challenge: The push notification message / challenge
:param gda: alternative to the token_info, the gda could be provided
directly
:param transactionId: The push notification transaction reference
:return: A tuple of success and result message | Sends out the push notification message. | [
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"message",
"."
] | def push_notification(self, challenge, gda, transactionId):
"""
Sends out the push notification message.
:param challenge: The push notification message / challenge
:param gda: alternative to the token_info, the gda could be provided
directly
:param transactionId: The push notification transaction reference
:return: A tuple of success and result message
"""
raise NotImplementedError(
"Every subclass of IPushProvider has to implement this method."
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openedx/edx-platform | 68dd185a0ab45862a2a61e0f803d7e03d2be71b5 | openedx/core/djangoapps/util/ratelimit.py | python | request_data_email | (group, request) | return email | Return the the email data param if it exists, otherwise return a
random id. | Return the the email data param if it exists, otherwise return a
random id. | [
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] | def request_data_email(group, request) -> str: # pylint: disable=unused-argument
"""
Return the the email data param if it exists, otherwise return a
random id.
"""
email = request.data.get('email')
if not email:
email = str(uuid4())
return email | [
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sametmax/Django--an-app-at-a-time | 99eddf12ead76e6dfbeb09ce0bae61e282e22f8a | ignore_this_directory/django/utils/timezone.py | python | get_default_timezone | () | return pytz.timezone(settings.TIME_ZONE) | Return the default time zone as a tzinfo instance.
This is the time zone defined by settings.TIME_ZONE. | Return the default time zone as a tzinfo instance. | [
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"""
Return the default time zone as a tzinfo instance.
This is the time zone defined by settings.TIME_ZONE.
"""
return pytz.timezone(settings.TIME_ZONE) | [
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houssamzenati/Efficient-GAN-Anomaly-Detection | 15568abb57d2965ce70d4fd0dc70f3fe00c68d1b | gan/run_mnist.py | python | train_and_test | (nb_epochs, weight, method, degree, random_seed, label) | Runs the Bigan on the KDD dataset
Note:
Saves summaries on tensorboard. To display them, please use cmd line
tensorboard --logdir=model.training_logdir() --port=number
Args:
nb_epochs (int): number of epochs
weight (float, optional): weight for the anomaly score composition
method (str, optional): 'fm' for ``Feature Matching`` or "cross-e"
for ``cross entropy``, "efm" etc.
anomalous_label (int): int in range 0 to 10, is the class/digit
which is considered outlier | Runs the Bigan on the KDD dataset | [
"Runs",
"the",
"Bigan",
"on",
"the",
"KDD",
"dataset"
] | def train_and_test(nb_epochs, weight, method, degree, random_seed, label):
""" Runs the Bigan on the KDD dataset
Note:
Saves summaries on tensorboard. To display them, please use cmd line
tensorboard --logdir=model.training_logdir() --port=number
Args:
nb_epochs (int): number of epochs
weight (float, optional): weight for the anomaly score composition
method (str, optional): 'fm' for ``Feature Matching`` or "cross-e"
for ``cross entropy``, "efm" etc.
anomalous_label (int): int in range 0 to 10, is the class/digit
which is considered outlier
"""
logger = logging.getLogger("GAN.train.mnist.{}.{}".format(method,label))
# Placeholders
input_pl = tf.placeholder(tf.float32, shape=data.get_shape_input(), name="input")
is_training_pl = tf.placeholder(tf.bool, [], name='is_training_pl')
learning_rate = tf.placeholder(tf.float32, shape=(), name="lr_pl")
# Data
trainx, trainy = data.get_train(label, True)
trainx_copy = trainx.copy()
testx, testy = data.get_test(label, True)
# Parameters
starting_lr = network.learning_rate
batch_size = network.batch_size
latent_dim = network.latent_dim
ema_decay = 0.999
rng = np.random.RandomState(RANDOM_SEED)
nr_batches_train = int(trainx.shape[0] / batch_size)
nr_batches_test = int(testx.shape[0] / batch_size)
logger.info('Building training graph...')
logger.warn("The GAN is training with the following parameters:")
display_parameters(batch_size, starting_lr, ema_decay, weight, method, degree, label)
gen = network.generator
dis = network.discriminator
# Sample noise from random normal distribution
random_z = tf.random_normal([batch_size, latent_dim], mean=0.0, stddev=1.0, name='random_z')
# Generate images with generator
generator = gen(random_z, is_training=is_training_pl)
# Pass real and fake images into discriminator separately
real_d, inter_layer_real = dis(input_pl, is_training=is_training_pl)
fake_d, inter_layer_fake = dis(generator, is_training=is_training_pl, reuse=True)
with tf.name_scope('loss_functions'):
# Calculate seperate losses for discriminator with real and fake images
real_discriminator_loss = tf.losses.sigmoid_cross_entropy(tf.constant(1, shape=[batch_size]), real_d, scope='real_discriminator_loss')
fake_discriminator_loss = tf.losses.sigmoid_cross_entropy(tf.constant(0, shape=[batch_size]), fake_d, scope='fake_discriminator_loss')
# Add discriminator losses
discriminator_loss = real_discriminator_loss + fake_discriminator_loss
# Calculate loss for generator by flipping label on discriminator output
generator_loss = tf.losses.sigmoid_cross_entropy(tf.constant(1, shape=[batch_size]), fake_d, scope='generator_loss')
with tf.name_scope('optimizers'):
# control op dependencies for batch norm and trainable variables
dvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='discriminator')
gvars = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, scope='generator')
update_ops_gen = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='generator')
update_ops_dis = tf.get_collection(tf.GraphKeys.UPDATE_OPS, scope='discriminator')
optimizer_dis = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5, name='dis_optimizer')
optimizer_gen = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=0.5, name='gen_optimizer')
with tf.control_dependencies(update_ops_gen): # attached op for moving average batch norm
gen_op = optimizer_gen.minimize(generator_loss, var_list=gvars)
with tf.control_dependencies(update_ops_dis):
dis_op = optimizer_dis.minimize(discriminator_loss, var_list=dvars)
dis_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_dis = dis_ema.apply(dvars)
with tf.control_dependencies([dis_op]):
train_dis_op = tf.group(maintain_averages_op_dis)
gen_ema = tf.train.ExponentialMovingAverage(decay=ema_decay)
maintain_averages_op_gen = gen_ema.apply(gvars)
with tf.control_dependencies([gen_op]):
train_gen_op = tf.group(maintain_averages_op_gen)
with tf.name_scope('training_summary'):
with tf.name_scope('dis_summary'):
tf.summary.scalar('real_discriminator_loss', real_discriminator_loss, ['dis'])
tf.summary.scalar('fake_discriminator_loss', fake_discriminator_loss, ['dis'])
tf.summary.scalar('discriminator_loss', discriminator_loss, ['dis'])
with tf.name_scope('gen_summary'):
tf.summary.scalar('loss_generator', generator_loss, ['gen'])
with tf.name_scope('image_summary'):
tf.summary.image('reconstruct', generator, 8, ['image'])
tf.summary.image('input_images', input_pl, 8, ['image'])
sum_op_dis = tf.summary.merge_all('dis')
sum_op_gen = tf.summary.merge_all('gen')
sum_op_im = tf.summary.merge_all('image')
logger.info('Building testing graph...')
with tf.variable_scope("latent_variable"):
z_optim = tf.get_variable(name='z_optim', shape= [batch_size, latent_dim], initializer=tf.truncated_normal_initializer())
reinit_z = z_optim.initializer
# EMA
generator_ema = gen(z_optim, is_training=is_training_pl, getter=get_getter(gen_ema), reuse=True)
# Pass real and fake images into discriminator separately
real_d_ema, inter_layer_real_ema = dis(input_pl, is_training=is_training_pl, getter=get_getter(gen_ema), reuse=True)
fake_d_ema, inter_layer_fake_ema = dis(generator_ema, is_training=is_training_pl, getter=get_getter(gen_ema), reuse=True)
with tf.name_scope('error_loss'):
delta = input_pl - generator_ema
delta_flat = tf.contrib.layers.flatten(delta)
gen_score = tf.norm(delta_flat, ord=degree, axis=1, keep_dims=False, name='epsilon')
with tf.variable_scope('Discriminator_loss'):
if method == "cross-e":
dis_score = tf.nn.sigmoid_cross_entropy_with_logits(
labels=tf.ones_like(fake_d_ema), logits=fake_d_ema)
elif method == "fm":
fm = inter_layer_real_ema - inter_layer_fake_ema
fm = tf.contrib.layers.flatten(fm)
dis_score = tf.norm(fm, ord=degree, axis=1, keep_dims=False,
name='d_loss')
dis_score = tf.squeeze(dis_score)
with tf.variable_scope('Total_loss'):
loss = (1 - weight) * gen_score + weight * dis_score
with tf.variable_scope("Test_learning_rate"):
step = tf.Variable(0, trainable=False)
boundaries = [200, 300]
values = [0.01, 0.001, 0.0005]
learning_rate_invert = tf.train.piecewise_constant(step, boundaries, values)
reinit_lr = tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope="Test_learning_rate"))
with tf.name_scope('Test_optimizer'):
optimizer = tf.train.AdamOptimizer(learning_rate_invert).minimize(loss, global_step=step, var_list=[z_optim], name='optimizer')
reinit_optim = tf.variables_initializer(
tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,
scope='Test_optimizer'))
reinit_test_graph_op = [reinit_z, reinit_lr, reinit_optim]
with tf.name_scope("Scores"):
list_scores = loss
logdir = create_logdir(method, weight, label, random_seed)
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=None,
save_model_secs=120)
logger.info('Start training...')
with sv.managed_session() as sess:
logger.info('Initialization done')
writer = tf.summary.FileWriter(logdir, sess.graph)
train_batch = 0
epoch = 0
while not sv.should_stop() and epoch < nb_epochs:
lr = starting_lr
begin = time.time()
trainx = trainx[rng.permutation(trainx.shape[0])] # shuffling unl dataset
trainx_copy = trainx_copy[rng.permutation(trainx.shape[0])]
train_loss_dis, train_loss_gen = [0, 0]
# training
for t in range(nr_batches_train):
display_progression_epoch(t, nr_batches_train)
# construct randomly permuted minibatches
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
# train discriminator
feed_dict = {input_pl: trainx[ran_from:ran_to],
is_training_pl:True,
learning_rate:lr}
_, ld, sm = sess.run([train_dis_op, discriminator_loss, sum_op_dis], feed_dict=feed_dict)
train_loss_dis += ld
writer.add_summary(sm, train_batch)
# train generator
feed_dict = {input_pl: trainx_copy[ran_from:ran_to],
is_training_pl:True,
learning_rate:lr}
_, lg, sm = sess.run([train_gen_op, generator_loss, sum_op_gen], feed_dict=feed_dict)
train_loss_gen += lg
writer.add_summary(sm, train_batch)
if t % FREQ_PRINT == 0: # inspect reconstruction
t= np.random.randint(0,4000)
ran_from = t
ran_to = t + batch_size
sm = sess.run(sum_op_im, feed_dict={input_pl: trainx[ran_from:ran_to],is_training_pl: False})
writer.add_summary(sm, train_batch)
train_batch += 1
train_loss_gen /= nr_batches_train
train_loss_dis /= nr_batches_train
logger.info('Epoch terminated')
print("Epoch %d | time = %ds | loss gen = %.4f | loss dis = %.4f "
% (epoch, time.time() - begin, train_loss_gen, train_loss_dis))
epoch += 1
logger.warn('Testing evaluation...')
inds = rng.permutation(testx.shape[0])
testx = testx[inds] # shuffling unl dataset
testy = testy[inds]
scores = []
inference_time = []
# testing
for t in range(nr_batches_test):
# construct randomly permuted minibatches
ran_from = t * batch_size
ran_to = (t + 1) * batch_size
begin_val_batch = time.time()
# invert the gan
feed_dict = {input_pl: testx[ran_from:ran_to],
is_training_pl:False}
for step in range(STEPS_NUMBER):
_ = sess.run(optimizer, feed_dict=feed_dict)
scores += sess.run(list_scores, feed_dict=feed_dict).tolist()
inference_time.append(time.time() - begin_val_batch)
sess.run(reinit_test_graph_op)
logger.info('Testing : mean inference time is %.4f' % (
np.mean(inference_time)))
ran_from = nr_batches_test * batch_size
ran_to = (nr_batches_test + 1) * batch_size
size = testx[ran_from:ran_to].shape[0]
fill = np.ones([batch_size - size, 28, 28, 1])
batch = np.concatenate([testx[ran_from:ran_to], fill], axis=0)
feed_dict = {input_pl: batch,
is_training_pl: False}
for step in range(STEPS_NUMBER):
_ = sess.run(optimizer, feed_dict=feed_dict)
batch_score = sess.run(list_scores,
feed_dict=feed_dict).tolist()
scores += batch_score[:size]
prc_auc = do_prc(scores, testy,
file_name=r'gan/mnist/{}/{}/{}'.format(method, weight,
label),
directory=r'results/gan/mnist/{}/{}/'.format(method,
weight))
print("Testing | PRC AUC = {:.4f}".format(prc_auc)) | [
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matplotlib/matplotlib | 8d7a2b9d2a38f01ee0d6802dd4f9e98aec812322 | lib/matplotlib/widgets.py | python | ToolLineHandles.set_animated | (self, value) | Set the animated state of the handles artist. | Set the animated state of the handles artist. | [
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"""Set the animated state of the handles artist."""
for artist in self.artists:
artist.set_animated(value) | [
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tensorlayer/RLzoo | 8dd3ff1003d1c7a446f45119bbd6b48c048990f4 | rlzoo/algorithms/ddpg/ddpg.py | python | DDPG.load_ckpt | (self, env_name) | load trained weights
:return: None | load trained weights | [
"load",
"trained",
"weights"
] | def load_ckpt(self, env_name):
"""
load trained weights
:return: None
"""
load_model(self.actor, 'model_policy_net', self.name, env_name)
load_model(self.actor_target, 'model_target_policy_net', self.name, env_name)
load_model(self.critic, 'model_q_net', self.name, env_name)
load_model(self.critic_target, 'model_target_q_net', self.name, env_name) | [
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OpenMined/PySyft | f181ca02d307d57bfff9477610358df1a12e3ac9 | packages/syft/src/syft/ast/static_attr.py | python | StaticAttribute.solve_set_value | (self, set_value: Any) | Local execution of setter function is performed.
The `solve_set_value` method executes the setter function on the AST.
Args:
set_value: The value to set to.
Raises:
ValueError : If `path_and_name` is `None`. | Local execution of setter function is performed. | [
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] | def solve_set_value(self, set_value: Any) -> None:
"""Local execution of setter function is performed.
The `solve_set_value` method executes the setter function on the AST.
Args:
set_value: The value to set to.
Raises:
ValueError : If `path_and_name` is `None`.
"""
self.apply_node_changes()
if self.path_and_name is None:
raise ValueError("path_and_none should not be None")
setattr(self.parent.object_ref, self.path_and_name.rsplit(".")[-1], set_value) | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/pip-19.0.3-py3.6.egg/pip/_internal/req/__init__.py | python | install_given_reqs | (
to_install, # type: List[InstallRequirement]
install_options, # type: List[str]
global_options=(), # type: Sequence[str]
*args, **kwargs
) | return to_install | Install everything in the given list.
(to be called after having downloaded and unpacked the packages) | Install everything in the given list. | [
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] | def install_given_reqs(
to_install, # type: List[InstallRequirement]
install_options, # type: List[str]
global_options=(), # type: Sequence[str]
*args, **kwargs
):
# type: (...) -> List[InstallRequirement]
"""
Install everything in the given list.
(to be called after having downloaded and unpacked the packages)
"""
if to_install:
logger.info(
'Installing collected packages: %s',
', '.join([req.name for req in to_install]),
)
with indent_log():
for requirement in to_install:
if requirement.conflicts_with:
logger.info(
'Found existing installation: %s',
requirement.conflicts_with,
)
with indent_log():
uninstalled_pathset = requirement.uninstall(
auto_confirm=True
)
try:
requirement.install(
install_options,
global_options,
*args,
**kwargs
)
except Exception:
should_rollback = (
requirement.conflicts_with and
not requirement.install_succeeded
)
# if install did not succeed, rollback previous uninstall
if should_rollback:
uninstalled_pathset.rollback()
raise
else:
should_commit = (
requirement.conflicts_with and
requirement.install_succeeded
)
if should_commit:
uninstalled_pathset.commit()
requirement.remove_temporary_source()
return to_install | [
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hupili/snsapi | 129529b89f38cbee253a23e5ed31dae2a0ea4254 | snsapi/third/twitter.py | python | User.SetListedCount | (self, count) | Set the listed count for this user.
Args:
count:
The number of lists this user belongs to. | Set the listed count for this user. | [
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] | def SetListedCount(self, count):
'''Set the listed count for this user.
Args:
count:
The number of lists this user belongs to.
'''
self._listed_count = count | [
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wistbean/learn_python3_spider | 73c873f4845f4385f097e5057407d03dd37a117b | stackoverflow/venv/lib/python3.6/site-packages/lxml/html/diff.py | python | start_tag | (el) | return '<%s%s>' % (
el.tag, ''.join([' %s="%s"' % (name, html_escape(value, True))
for name, value in el.attrib.items()])) | The text representation of the start tag for a tag. | The text representation of the start tag for a tag. | [
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] | def start_tag(el):
"""
The text representation of the start tag for a tag.
"""
return '<%s%s>' % (
el.tag, ''.join([' %s="%s"' % (name, html_escape(value, True))
for name, value in el.attrib.items()])) | [
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aiidateam/aiida-core | c743a335480f8bb3a5e4ebd2463a31f9f3b9f9b2 | aiida/transports/plugins/local.py | python | LocalTransport._os_path_split_asunder | (path) | return parts | Used by makedirs, Takes path (a str) and returns a list deconcatenating the path. | Used by makedirs, Takes path (a str) and returns a list deconcatenating the path. | [
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] | def _os_path_split_asunder(path):
"""Used by makedirs, Takes path (a str) and returns a list deconcatenating the path."""
parts = []
while True:
newpath, tail = os.path.split(path)
if newpath == path:
assert not tail
if path:
parts.append(path)
break
parts.append(tail)
path = newpath
parts.reverse()
return parts | [
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biopython/biopython | 2dd97e71762af7b046d7f7f8a4f1e38db6b06c86 | Bio/Phylo/PhyloXMLIO.py | python | _get_children_as | (parent, tag, construct) | return [construct(child) for child in parent.findall(_ns(tag))] | Find child nodes by tag; pass each through a constructor (PRIVATE).
Returns an empty list if no matching child is found. | Find child nodes by tag; pass each through a constructor (PRIVATE). | [
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] | def _get_children_as(parent, tag, construct):
"""Find child nodes by tag; pass each through a constructor (PRIVATE).
Returns an empty list if no matching child is found.
"""
return [construct(child) for child in parent.findall(_ns(tag))] | [
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ryderling/DEEPSEC | 2c67afac0ae966767b6712a51db85f04f4f5c565 | Attacks/AttackMethods/DEEPFOOL.py | python | DeepFoolAttack.perturbation | (self, xs, device) | return np.array(adv_samples) | :param xs: batch of samples
:param device:
:return: batch of adversarial samples | [] | def perturbation(self, xs, device):
"""
:param xs: batch of samples
:param device:
:return: batch of adversarial samples
"""
print('\nThe DeepFool attack perturbs the samples one by one ......\n')
adv_samples = []
for i in range(len(xs)):
adv_image, _, _ = self.perturbation_single(sample=xs[i: i + 1], device=device)
adv_samples.extend(adv_image)
return np.array(adv_samples) | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/pip/index.py | python | HTMLPage.links | (self) | Yields all links in the page | Yields all links in the page | [
"Yields",
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"in",
"the",
"page"
] | def links(self):
"""Yields all links in the page"""
for anchor in self.parsed.findall(".//a"):
if anchor.get("href"):
href = anchor.get("href")
url = self.clean_link(
urllib_parse.urljoin(self.base_url, href)
)
pyrequire = anchor.get('data-requires-python')
pyrequire = unescape(pyrequire) if pyrequire else None
yield Link(url, self, requires_python=pyrequire) | [
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google/tf-quant-finance | 8fd723689ebf27ff4bb2bd2acb5f6091c5e07309 | tf_quant_finance/datetime/unbounded_holiday_calendar.py | python | UnboundedHolidayCalendar._apply_roll_biz_space | (self, date_tensor, biz_days, is_bizday,
roll_convention) | Applies roll in business day space. | Applies roll in business day space. | [
"Applies",
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"business",
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"space",
"."
] | def _apply_roll_biz_space(self, date_tensor, biz_days, is_bizday,
roll_convention):
"""Applies roll in business day space."""
if roll_convention == constants.BusinessDayConvention.NONE:
# If no business convention is specified, return the current business
# day.
return biz_days
if roll_convention == constants.BusinessDayConvention.FOLLOWING:
return tf.where(is_bizday, biz_days, biz_days + 1)
if roll_convention == constants.BusinessDayConvention.PRECEDING:
return biz_days
if roll_convention == constants.BusinessDayConvention.MODIFIED_FOLLOWING:
maybe_prev_biz_day = biz_days
maybe_next_biz_day = tf.where(is_bizday, biz_days, biz_days + 1)
maybe_next_biz_ordinal = self._from_biz_space(maybe_next_biz_day)
take_previous = tf.not_equal(
_get_month(maybe_next_biz_ordinal), date_tensor.month())
return tf.where(take_previous, maybe_prev_biz_day, maybe_next_biz_day)
if roll_convention == constants.BusinessDayConvention.MODIFIED_PRECEDING:
maybe_prev_biz_day = biz_days
maybe_next_biz_day = tf.where(is_bizday, biz_days, biz_days + 1)
maybe_prev_biz_ordinal = self._from_biz_space(maybe_prev_biz_day)
take_next = tf.not_equal(
_get_month(maybe_prev_biz_ordinal), date_tensor.month())
return tf.where(take_next, maybe_next_biz_day, maybe_prev_biz_day)
raise ValueError('Unsupported roll convention: {}'.format(roll_convention)) | [
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visionml/pytracking | 3e6a8980db7a2275252abcc398ed0c2494f0ceab | ltr/data/processing_utils.py | python | iou | (reference, proposals) | return intersection / union | Compute the IoU between a reference box with multiple proposal boxes.
args:
reference - Tensor of shape (1, 4).
proposals - Tensor of shape (num_proposals, 4)
returns:
torch.Tensor - Tensor of shape (num_proposals,) containing IoU of reference box with each proposal box. | Compute the IoU between a reference box with multiple proposal boxes. | [
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] | def iou(reference, proposals):
"""Compute the IoU between a reference box with multiple proposal boxes.
args:
reference - Tensor of shape (1, 4).
proposals - Tensor of shape (num_proposals, 4)
returns:
torch.Tensor - Tensor of shape (num_proposals,) containing IoU of reference box with each proposal box.
"""
# Intersection box
tl = torch.max(reference[:, :2], proposals[:, :2])
br = torch.min(reference[:, :2] + reference[:, 2:], proposals[:, :2] + proposals[:, 2:])
sz = (br - tl).clamp(0)
# Area
intersection = sz.prod(dim=1)
union = reference[:, 2:].prod(dim=1) + proposals[:, 2:].prod(dim=1) - intersection
return intersection / union | [
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openshift/openshift-tools | 1188778e728a6e4781acf728123e5b356380fe6f | openshift/installer/vendored/openshift-ansible-3.11.28-1/roles/lib_openshift/src/class/oc_user.py | python | OCUser.user | (self, data) | setter function for user | setter function for user | [
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''' setter function for user '''
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deepfakes/faceswap | 09c7d8aca3c608d1afad941ea78e9fd9b64d9219 | lib/gui/control_helper.py | python | ControlBuilder.slider_control | (self) | return ctl | A slider control with corresponding Entry box | A slider control with corresponding Entry box | [
"A",
"slider",
"control",
"with",
"corresponding",
"Entry",
"box"
] | def slider_control(self):
""" A slider control with corresponding Entry box """
logger.debug("Add slider control to Options Frame: (widget: '%s', dtype: %s, "
"rounding: %s, min_max: %s)", self.option.name, self.option.dtype,
self.option.rounding, self.option.min_max)
validate = self.slider_check_int if self.option.dtype == int else self.slider_check_float
vcmd = (self.frame.register(validate))
tbox = tk.Entry(self.frame,
width=8,
textvariable=self.option.tk_var,
justify=tk.RIGHT,
font=get_config().default_font,
validate="all",
validatecommand=(vcmd, "%P"),
bg=self._theme["input_color"],
fg=self._theme["input_font"],
highlightbackground=self._theme["input_font"],
highlightthickness=1,
bd=0)
tbox.pack(padx=(0, 5), side=tk.RIGHT)
cmd = partial(set_slider_rounding,
var=self.option.tk_var,
d_type=self.option.dtype,
round_to=self.option.rounding,
min_max=self.option.min_max)
ctl = ttk.Scale(self.frame,
variable=self.option.tk_var,
command=cmd,
style=f"{self._style}Horizontal.TScale")
_add_command(ctl.cget("command"), cmd)
rc_menu = _get_contextmenu(tbox)
rc_menu.cm_bind()
ctl["from_"] = self.option.min_max[0]
ctl["to"] = self.option.min_max[1]
logger.debug("Added slider control to Options Frame: %s", self.option.name)
return ctl | [
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jliljebl/flowblade | 995313a509b80e99eb1ad550d945bdda5995093b | flowblade-trunk/Flowblade/keyframeeditor.py | python | KeyFrameEditor._hamburger_pressed | (self, widget, event) | [] | def _hamburger_pressed(self, widget, event):
menu = buttons_hamburger_menu
guiutils.remove_children(menu)
frame, value, kf_type = self.clip_editor.keyframes[self.clip_editor.active_kf_index]
active_type_menu_item = Gtk.MenuItem(_("Active Keyframe Type"))
type_menu = Gtk.Menu()
active_type_menu_item.set_submenu(type_menu)
self._create_keyframe_type_submenu(kf_type, type_menu, self._menu_item_activated)
active_type_menu_item.show_all()
menu.add(active_type_menu_item)
item = _get_menu_item("this gets overwritten..twice", self._menu_item_activated, "copy_kf")
action = gui.editor_window.ui.get_action_groups()[0].get_action("Copy")
item.set_related_action(action)
item.set_label(_("Copy Keyframe Value"))
menu.add(item)
item = _get_menu_item("this gets overwritten..twice", self._menu_item_activated, "paste_kf")
action = gui.editor_window.ui.get_action_groups()[0].get_action("Paste")
item.set_related_action(action)
item.set_label(_("Paste Keyframe Value"))
menu.add(item)
before_kfs = len(self.clip_editor.get_out_of_range_before_kfs())
after_kfs = len(self.clip_editor.get_out_of_range_after_kfs())
if before_kfs > 0 or after_kfs > 0:
_add_separator(menu)
if len(self.clip_editor.keyframes) > 1:
menu.add(_get_menu_item(_("Set Keyframe at Frame 0 to value of next Keyframe"), self.clip_editor._oor_menu_item_activated, "zero_next" ))
if before_kfs > 1:
menu.add(_get_menu_item(_("Delete all but first Keyframe before Clip Range"), self.clip_editor._oor_menu_item_activated, "delete_all_before" ))
if after_kfs > 0:
menu.add(_get_menu_item(_("Delete all Keyframes after Clip Range"), self.clip_editor._oor_menu_item_activated, "delete_all_after" ))
if before_kfs > 0 or after_kfs > 0:
_add_separator(menu)
menu.popup(None, None, None, None, event.button, event.time) | [
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/reportlab/build/lib.linux-i686-2.7/reportlab/pdfbase/cidfonts.py | python | CIDEncoding.parseCMAPFile | (self, name) | This is a tricky one as CMAP files are Postscript
ones. Some refer to others with a 'usecmap'
command | This is a tricky one as CMAP files are Postscript
ones. Some refer to others with a 'usecmap'
command | [
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] | def parseCMAPFile(self, name):
"""This is a tricky one as CMAP files are Postscript
ones. Some refer to others with a 'usecmap'
command"""
#started = time.clock()
cmapfile = findCMapFile(name)
# this will CRAWL with the unicode encodings...
rawdata = open(cmapfile, 'r').read()
self._mapFileHash = self._hash(rawdata)
#if it contains the token 'usecmap', parse the other
#cmap file first....
usecmap_pos = rawdata.find('usecmap')
if usecmap_pos > -1:
#they tell us to look in another file
#for the code space ranges. The one
# to use will be the previous word.
chunk = rawdata[0:usecmap_pos]
words = chunk.split()
otherCMAPName = words[-1]
#print 'referred to another CMAP %s' % otherCMAPName
self.parseCMAPFile(otherCMAPName)
# now continue parsing this, as it may
# override some settings
words = rawdata.split()
while words != []:
if words[0] == 'begincodespacerange':
words = words[1:]
while words[0] != 'endcodespacerange':
strStart, strEnd, words = words[0], words[1], words[2:]
start = int(strStart[1:-1], 16)
end = int(strEnd[1:-1], 16)
self._codeSpaceRanges.append((start, end),)
elif words[0] == 'beginnotdefrange':
words = words[1:]
while words[0] != 'endnotdefrange':
strStart, strEnd, strValue = words[0:3]
start = int(strStart[1:-1], 16)
end = int(strEnd[1:-1], 16)
value = int(strValue)
self._notDefRanges.append((start, end, value),)
words = words[3:]
elif words[0] == 'begincidrange':
words = words[1:]
while words[0] != 'endcidrange':
strStart, strEnd, strValue = words[0:3]
start = int(strStart[1:-1], 16)
end = int(strEnd[1:-1], 16)
value = int(strValue)
# this means that 'start' corresponds to 'value',
# start+1 corresponds to value+1 and so on up
# to end
offset = 0
while start + offset <= end:
self._cmap[start + offset] = value + offset
offset = offset + 1
words = words[3:]
else:
words = words[1:] | [
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twisted/twisted | dee676b040dd38b847ea6fb112a712cb5e119490 | src/twisted/names/_rfc1982.py | python | SerialNumber.__lt__ | (self, other: object) | return (
self._number < other._number
and (other._number - self._number) < self._halfRing
) or (
self._number > other._number
and (self._number - other._number) > self._halfRing
) | Allow I{less than} comparison with another L{SerialNumber} instance. | Allow I{less than} comparison with another L{SerialNumber} instance. | [
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"""
Allow I{less than} comparison with another L{SerialNumber} instance.
"""
try:
other = self._convertOther(other)
except TypeError:
return NotImplemented
return (
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sahana/eden | 1696fa50e90ce967df69f66b571af45356cc18da | modules/s3/s3navigation.py | python | S3NavigationItem.get_all | (self, **flags) | return items | Get all components with these flags
Args:
flags: dictionary of flags | Get all components with these flags | [
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] | def get_all(self, **flags):
"""
Get all components with these flags
Args:
flags: dictionary of flags
"""
items = []
for item in self.components:
if not flags or \
all([getattr(item, f) == flags[f] for f in flags]):
items.append(item)
return items | [
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IntelAI/nauta | bbedb114a755cf1f43b834a58fc15fb6e3a4b291 | applications/cli/util/github.py | python | Github.__init__ | (self, repository_name, token) | [] | def __init__(self, repository_name, token):
self.token = token
self.repository_name = repository_name | [
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jansel/opentuner | 070c5cef6d933eb760a2f9cd5cd08c95f27aee75 | opentuner/search/plugin.py | python | SearchPlugin.set_driver | (self, driver) | called before all other methods | called before all other methods | [
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Yonv1943/Python | ecce2153892093d7a13686e4cbfd6b323cb59de8 | ElegantRL/Beta/net.py | python | SharedSPG.get_a_logprob | (self, state) | return a_noise_tanh, logprob | add noise to action, stochastic policy | add noise to action, stochastic policy | [
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] | def get_a_logprob(self, state): # actor
s_ = self.enc_s(state)
a_ = self.net(s_)
"""add noise to action, stochastic policy"""
a_avg = self.dec_a(a_) # NOTICE! it is action without .tanh()
a_std_log = self.dec_d(a_).clamp(-20, 2)
a_std = a_std_log.exp()
noise = torch.randn_like(a_avg, requires_grad=True)
a_noise = a_avg + a_std * noise
a_noise_tanh = a_noise.tanh()
fix_term = (-a_noise_tanh.pow(2) + 1.00001).log()
logprob = (noise.pow(2) / 2 + a_std_log + fix_term).sum(1) + self.log_sqrt_2pi_sum
return a_noise_tanh, logprob | [
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dragondjf/QMarkdowner | fc79c85ca2949fa9ce3b317606ad7bbcd1299960 | markdown/__init__.py | python | Markdown.registerExtension | (self, extension) | return self | This gets called by the extension | This gets called by the extension | [
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Azure/azure-cli | 6c1b085a0910c6c2139006fcbd8ade44006eb6dd | src/azure-cli/azure/cli/command_modules/batchai/custom.py | python | _get_effective_credentials | (cli_ctx, existing_credentials, account_name) | return models.AzureStorageCredentialsInfo(
account_key=_get_storage_account_key(cli_ctx, account_name, account_key=None)) | Returns AzureStorageCredentialInfo for the account
:param models.AzureStorageCredentialsInfo existing_credentials: known credentials
:param str account_name: storage account name
:return models.AzureStorageCredentialsInfo: credentials to be used | Returns AzureStorageCredentialInfo for the account | [
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] | def _get_effective_credentials(cli_ctx, existing_credentials, account_name):
"""Returns AzureStorageCredentialInfo for the account
:param models.AzureStorageCredentialsInfo existing_credentials: known credentials
:param str account_name: storage account name
:return models.AzureStorageCredentialsInfo: credentials to be used
"""
if existing_credentials and (existing_credentials.account_key or existing_credentials.account_key_secret_reference):
return existing_credentials
return models.AzureStorageCredentialsInfo(
account_key=_get_storage_account_key(cli_ctx, account_name, account_key=None)) | [
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TheAlgorithms/Python | 9af2eef9b3761bf51580dedfb6fa7136ca0c5c2c | matrix/sherman_morrison.py | python | Matrix.validateIndices | (self, loc: tuple) | <method Matrix.validateIndices>
Check if given indices are valid to pick element from matrix.
Example:
>>> a = Matrix(2, 6, 0)
>>> a.validateIndices((2, 7))
False
>>> a.validateIndices((0, 0))
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<method Matrix.validateIndices>
Check if given indices are valid to pick element from matrix.
Example:
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>>> a.validateIndices((2, 7))
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if not (isinstance(loc, (list, tuple)) and len(loc) == 2):
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clinton-hall/nzbToMedia | 27669389216902d1085660167e7bda0bd8527ecf | libs/common/beets/importer.py | python | progress_element | (toppath, path) | return i != len(imported) and imported[i] == path | Return whether `path` has been imported in `toppath`. | Return whether `path` has been imported in `toppath`. | [
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"""
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return False
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googleads/google-ads-python | 2a1d6062221f6aad1992a6bcca0e7e4a93d2db86 | google/ads/googleads/v9/services/services/feed_mapping_service/client.py | python | FeedMappingServiceClient.transport | (self) | return self._transport | Return the transport used by the client instance.
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"""
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home-assistant/core | 265ebd17a3f17ed8dc1e9bdede03ac8e323f1ab1 | homeassistant/components/qbittorrent/sensor.py | python | QBittorrentSensor.__init__ | (
self,
description: SensorEntityDescription,
qbittorrent_client,
client_name,
exception,
) | Initialize the qBittorrent sensor. | Initialize the qBittorrent sensor. | [
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] | def __init__(
self,
description: SensorEntityDescription,
qbittorrent_client,
client_name,
exception,
):
"""Initialize the qBittorrent sensor."""
self.entity_description = description
self.client = qbittorrent_client
self._exception = exception
self._attr_name = f"{client_name} {description.name}"
self._attr_available = False | [
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zhl2008/awd-platform | 0416b31abea29743387b10b3914581fbe8e7da5e | web_hxb2/lib/python3.5/site-packages/setuptools/msvc.py | python | msvc9_query_vcvarsall | (ver, arch='x86', *args, **kwargs) | Patched "distutils.msvc9compiler.query_vcvarsall" for support extra
compilers.
Set environment without use of "vcvarsall.bat".
Known supported compilers
-------------------------
Microsoft Visual C++ 9.0:
Microsoft Visual C++ Compiler for Python 2.7 (x86, amd64)
Microsoft Windows SDK 6.1 (x86, x64, ia64)
Microsoft Windows SDK 7.0 (x86, x64, ia64)
Microsoft Visual C++ 10.0:
Microsoft Windows SDK 7.1 (x86, x64, ia64)
Parameters
----------
ver: float
Required Microsoft Visual C++ version.
arch: str
Target architecture.
Return
------
environment: dict | Patched "distutils.msvc9compiler.query_vcvarsall" for support extra
compilers. | [
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] | def msvc9_query_vcvarsall(ver, arch='x86', *args, **kwargs):
"""
Patched "distutils.msvc9compiler.query_vcvarsall" for support extra
compilers.
Set environment without use of "vcvarsall.bat".
Known supported compilers
-------------------------
Microsoft Visual C++ 9.0:
Microsoft Visual C++ Compiler for Python 2.7 (x86, amd64)
Microsoft Windows SDK 6.1 (x86, x64, ia64)
Microsoft Windows SDK 7.0 (x86, x64, ia64)
Microsoft Visual C++ 10.0:
Microsoft Windows SDK 7.1 (x86, x64, ia64)
Parameters
----------
ver: float
Required Microsoft Visual C++ version.
arch: str
Target architecture.
Return
------
environment: dict
"""
# Try to get environement from vcvarsall.bat (Classical way)
try:
orig = get_unpatched(msvc9_query_vcvarsall)
return orig(ver, arch, *args, **kwargs)
except distutils.errors.DistutilsPlatformError:
# Pass error if Vcvarsall.bat is missing
pass
except ValueError:
# Pass error if environment not set after executing vcvarsall.bat
pass
# If error, try to set environment directly
try:
return EnvironmentInfo(arch, ver).return_env()
except distutils.errors.DistutilsPlatformError as exc:
_augment_exception(exc, ver, arch)
raise | [
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NVIDIA/DeepLearningExamples | 589604d49e016cd9ef4525f7abcc9c7b826cfc5e | TensorFlow/LanguageModeling/BERT/utils/create_glue_data.py | python | convert_examples_to_features | (examples, label_list, max_seq_length,
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"""Convert a set of `InputExample`s to a list of `InputFeatures`."""
features = []
for (ex_index, example) in enumerate(examples):
if ex_index % 10000 == 0:
tf.compat.v1.logging.info("Writing example %d of %d" % (ex_index, len(examples)))
feature = convert_single_example(ex_index, example, label_list,
max_seq_length, tokenizer, FLAGS.verbose_logging)
features.append(feature)
return features | [
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ajinabraham/OWASP-Xenotix-XSS-Exploit-Framework | cb692f527e4e819b6c228187c5702d990a180043 | external/Scripting Engine/Xenotix Python Scripting Engine/bin/x86/Debug/Lib/logging/__init__.py | python | Handler.flush | (self) | Ensure all logging output has been flushed.
This version does nothing and is intended to be implemented by
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"""
Ensure all logging output has been flushed.
This version does nothing and is intended to be implemented by
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"""
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v2beta2_horizontal_pod_autoscaler_spec.py | python | V2beta2HorizontalPodAutoscalerSpec.__init__ | (self, behavior=None, max_replicas=None, metrics=None, min_replicas=None, scale_target_ref=None, local_vars_configuration=None) | V2beta2HorizontalPodAutoscalerSpec - a model defined in OpenAPI | V2beta2HorizontalPodAutoscalerSpec - a model defined in OpenAPI | [
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] | def __init__(self, behavior=None, max_replicas=None, metrics=None, min_replicas=None, scale_target_ref=None, local_vars_configuration=None): # noqa: E501
"""V2beta2HorizontalPodAutoscalerSpec - a model defined in OpenAPI""" # noqa: E501
if local_vars_configuration is None:
local_vars_configuration = Configuration()
self.local_vars_configuration = local_vars_configuration
self._behavior = None
self._max_replicas = None
self._metrics = None
self._min_replicas = None
self._scale_target_ref = None
self.discriminator = None
if behavior is not None:
self.behavior = behavior
self.max_replicas = max_replicas
if metrics is not None:
self.metrics = metrics
if min_replicas is not None:
self.min_replicas = min_replicas
self.scale_target_ref = scale_target_ref | [
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IntelPython/sdc | 1ebf55c00ef38dfbd401a70b3945e352a5a38b87 | sdc/str_arr_ext.py | python | copy_str_arr_slice | (typingctx, str_arr_typ, out_str_arr_typ, ind_t=None) | return types.void(string_array_type, string_array_type, ind_t), codegen | [] | def copy_str_arr_slice(typingctx, str_arr_typ, out_str_arr_typ, ind_t=None):
def codegen(context, builder, sig, args):
out_str_arr, in_str_arr, ind = args
in_string_array = context.make_helper(builder, string_array_type, in_str_arr)
out_string_array = context.make_helper(builder, string_array_type, out_str_arr)
in_offsets = builder.bitcast(in_string_array.offsets, lir.IntType(32).as_pointer())
out_offsets = builder.bitcast(out_string_array.offsets, lir.IntType(32).as_pointer())
ind_p1 = builder.add(ind, context.get_constant(types.intp, 1))
cgutils.memcpy(builder, out_offsets, in_offsets, ind_p1)
cgutils.memcpy(
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# n_bytes = (num_strings+sizeof(uint8_t)-1)/sizeof(uint8_t)
ind_p7 = builder.add(ind, lir.Constant(lir.IntType(64), 7))
n_bytes = builder.lshr(ind_p7, lir.Constant(lir.IntType(64), 3))
# assuming rest of last byte is set to all ones (e.g. from prealloc)
cgutils.memcpy(builder, out_string_array.null_bitmap, in_string_array.null_bitmap, n_bytes)
return context.get_dummy_value()
return types.void(string_array_type, string_array_type, ind_t), codegen | [
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nosmokingbandit/Watcher3 | 0217e75158b563bdefc8e01c3be7620008cf3977 | lib/hachoir/regex/regex.py | python | Regex.__or__ | (self, other) | return RegexOr((self, other)) | Public method of OR operator: a|b. It call or_() internal method.
If or_() returns None: RegexOr object is used (and otherwise,
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If or_() returns None: RegexOr object is used (and otherwise,
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"""
# Try to optimize (a|b)
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holzschu/Carnets | 44effb10ddfc6aa5c8b0687582a724ba82c6b547 | Library/lib/python3.7/site-packages/pandas-0.24.2-py3.7-macosx-10.9-x86_64.egg/pandas/core/internals/managers.py | python | SingleBlockManager.asobject | (self) | return self._block.get_values(dtype=object) | return a object dtype array. datetime/timedelta like values are boxed
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pypa/pipenv | b21baade71a86ab3ee1429f71fbc14d4f95fb75d | pipenv/vendor/urllib3/contrib/_securetransport/low_level.py | python | _create_cfstring_array | (lst) | return cf_arr | Given a list of Python binary data, create an associated CFMutableArray.
The array must be CFReleased by the caller.
Raises an ssl.SSLError on failure. | Given a list of Python binary data, create an associated CFMutableArray.
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Raises an ssl.SSLError on failure.
"""
cf_arr = None
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if not cf_arr:
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for item in lst:
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if not cf_str:
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try:
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PythonOT/POT | be0ed1dcd0b65ec804e454f1a2d3c0d227c0ddbe | ot/smooth.py | python | solve_semi_dual | (a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500,
verbose=False) | return res.x, res | Solve the "smoothed" semi-dual objective.
Parameters
----------
a: array, shape = (len(a), )
b: array, shape = (len(b), )
Input histograms (should be non-negative and sum to 1).
C: array, shape = (len(a), len(b))
Ground cost matrix.
regul: Regularization object
Should implement a `max_Omega(X)` method.
method: str
Solver to be used (passed to `scipy.optimize.minimize`).
tol: float
Tolerance parameter.
max_iter: int
Maximum number of iterations.
Returns
-------
alpha: array, shape = (len(a), )
Semi-dual potentials. | Solve the "smoothed" semi-dual objective. | [
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] | def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500,
verbose=False):
"""
Solve the "smoothed" semi-dual objective.
Parameters
----------
a: array, shape = (len(a), )
b: array, shape = (len(b), )
Input histograms (should be non-negative and sum to 1).
C: array, shape = (len(a), len(b))
Ground cost matrix.
regul: Regularization object
Should implement a `max_Omega(X)` method.
method: str
Solver to be used (passed to `scipy.optimize.minimize`).
tol: float
Tolerance parameter.
max_iter: int
Maximum number of iterations.
Returns
-------
alpha: array, shape = (len(a), )
Semi-dual potentials.
"""
def _func(alpha):
obj, grad = semi_dual_obj_grad(alpha, a, b, C, regul)
# We need to maximize the semi-dual.
return -obj, -grad
alpha_init = np.zeros(len(a))
res = minimize(_func, alpha_init, method=method, jac=True,
tol=tol, options=dict(maxiter=max_iter, disp=verbose))
return res.x, res | [
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guptarohit/cryptoCMD | 87b80b544c31999313692e4adde7d0055de2c08a | setup.py | python | UploadCommand.status | (s) | Prints things in bold. | Prints things in bold. | [
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"""Prints things in bold."""
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mozilla/addons-server | cbfb29e5be99539c30248d70b93bb15e1c1bc9d7 | src/olympia/addons/models.py | python | resolve_webext_translations | (cls, data, upload) | return data | Resolve all possible translations from an add-on.
This returns a modified `data` dictionary accordingly with proper
translations filled in. | Resolve all possible translations from an add-on. | [
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] | def resolve_webext_translations(cls, data, upload):
"""Resolve all possible translations from an add-on.
This returns a modified `data` dictionary accordingly with proper
translations filled in.
"""
default_locale = find_language(data.get('default_locale'))
if not default_locale:
# Don't change anything if we don't meet the requirements
return data
# find_language might have expanded short to full locale, so update it.
data['default_locale'] = default_locale
fields = ('name', 'homepage', 'summary')
messages = extract_translations(upload)
for field in fields:
data[field] = {
locale: resolve_i18n_message(
data[field],
locale=locale,
default_locale=default_locale,
messages=messages,
)
for locale in messages
}
return data | [
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entropy1337/infernal-twin | 10995cd03312e39a48ade0f114ebb0ae3a711bb8 | Modules/build/pip/pip/_vendor/requests/packages/urllib3/util/timeout.py | python | Timeout.get_connect_duration | (self) | return current_time() - self._start_connect | Gets the time elapsed since the call to :meth:`start_connect`.
:return: Elapsed time.
:rtype: float
:raises urllib3.exceptions.TimeoutStateError: if you attempt
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""" Gets the time elapsed since the call to :meth:`start_connect`.
:return: Elapsed time.
:rtype: float
:raises urllib3.exceptions.TimeoutStateError: if you attempt
to get duration for a timer that hasn't been started.
"""
if self._start_connect is None:
raise TimeoutStateError("Can't get connect duration for timer "
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return current_time() - self._start_connect | [
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gautamkrishnar/socli | 4d68e523fa7c70b0b3aabd51b097769f948a3821 | socli/socli.py | python | socli_browse_interactive_windows | (query_tag) | Interactive mode for -b browse
:param query_tag:
:return: | Interactive mode for -b browse
:param query_tag:
:return: | [
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] | def socli_browse_interactive_windows(query_tag):
"""
Interactive mode for -b browse
:param query_tag:
:return:
"""
try:
search_res = requests.get(search.so_burl + query_tag)
search.captcha_check(search_res.url)
soup = BeautifulSoup(search_res.text, 'html.parser')
try:
soup.find_all("div", class_="question-summary")[0] # For explicitly raising exception
tmp = (soup.find_all("div", class_="question-summary"))
i = 0
question_local_url = []
print(printer.bold("\nSelect a question below:\n"))
while i < len(tmp):
if i == 10:
break # limiting results
question_text = ' '.join((tmp[i].a.get_text()).split())
question_text = question_text.replace("Q: ", "")
printer.print_warning(str(i + 1) + ". " + printer.display_str(question_text))
q_tag = (soup.find_all("div", class_="question-summary"))[i]
answers = [s.get_text() for s in q_tag.find_all("a", class_="post-tag")][0:]
ques_tags = " ".join(str(x) for x in answers)
question_local_url.append(tmp[i].a.get("href"))
print(" " + printer.display_str(ques_tags) + "\n")
i = i + 1
try:
op = int(printer.inputs("\nType the option no to continue or any other key to exit:"))
while 1:
if (op > 0) and (op <= i):
display_results(search.so_burl + question_local_url[op - 1])
cnt = 1 # this is because the 1st post is the question itself
while 1:
global tmpsoup
qna = printer.inputs(
"Type " + printer.bold("o") + " to open in browser, " + printer.bold(
"n") + " to next answer, " + printer.bold(
"b") + " for previous answer or any other key to exit:")
if qna in ["n", "N"]:
try:
answer = (tmpsoup.find_all("div", class_="js-post-body")[cnt + 1].get_text())
printer.print_green("\n\nAnswer:\n")
print("-------\n" + answer + "\n-------\n")
cnt = cnt + 1
except IndexError:
printer.print_warning(" No more answers found for this question. Exiting...")
sys.exit(0)
continue
if qna in ["b", "B"]:
if cnt == 1:
printer.print_warning(" You cant go further back. You are on the first answer!")
continue
answer = (tmpsoup.find_all("div", class_="js-post-body")[cnt - 1].get_text())
printer.print_green("\n\nAnswer:\n")
print("-------\n" + answer + "\n-------\n")
cnt = cnt - 1
continue
if qna in ["o", "O"]:
import webbrowser
printer.print_warning("Opening in your browser...")
webbrowser.open(search.so_burl + question_local_url[op - 1])
else:
break
sys.exit(0)
else:
op = int(input("\n\nWrong option. select the option no to continue:"))
except Exception as e:
printer.showerror(e)
printer.print_warning("\n Exiting...")
sys.exit(0)
except IndexError:
printer.print_warning("No results found...")
sys.exit(0)
except UnicodeEncodeError:
printer.print_warning("\n\nEncoding error: Use \"chcp 65001\" command before using socli...")
sys.exit(0)
except requests.exceptions.ConnectionError:
printer.print_fail("Please check your internet connectivity...")
except Exception as e:
printer.showerror(e)
sys.exit(0) | [
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TkTech/notifico | 484c411cba3cc00b69dbf462c2f038a2d9a44f84 | notifico/bots/bot.py | python | Channel.__init__ | (self, client, name, password=None) | [] | def __init__(self, client, name, password=None):
self._client = client
self._name = name
self._password = password
self.message_min_delay = 0
self._joined = gevent.event.Event()
self._message_queue = gevent.queue.Queue()
signals.m.on_JOIN.connect(self.on_join, sender=client)
signals.m.on_KICK.connect(self.on_kick, sender=client)
# start off the sender greenlet
gevent.spawn(self._check_message_queue) | [
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nucleic/enaml | 65c2a2a2d765e88f2e1103046680571894bb41ed | enaml/workbench/plugin_manifest.py | python | PluginManifest.extension_points | (self) | return [c for c in self.children if isinstance(c, ExtensionPoint)] | Get the list of extensions points defined by the manifest. | Get the list of extensions points defined by the manifest. | [
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""" Get the list of extensions points defined by the manifest.
"""
return [c for c in self.children if isinstance(c, ExtensionPoint)] | [
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veegee/guv | d7bac2ca6a73cc2059969af08223b82f3e187922 | guv/hubs/hub.py | python | get_default_hub | () | Get default hub implementation | Get default hub implementation | [
"Get",
"default",
"hub",
"implementation"
] | def get_default_hub():
"""Get default hub implementation
"""
names = [hub_name] if hub_name else ['pyuv_cffi', 'pyuv', 'epoll']
for name in names:
try:
module = importlib.import_module('guv.hubs.{}'.format(name))
log.debug('Hub: use {}'.format(name))
return module
except ImportError:
# try the next possible hub
pass | [
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tomplus/kubernetes_asyncio | f028cc793e3a2c519be6a52a49fb77ff0b014c9b | kubernetes_asyncio/client/models/v1beta1_custom_resource_subresources.py | python | V1beta1CustomResourceSubresources.status | (self, status) | Sets the status of this V1beta1CustomResourceSubresources.
status indicates the custom resource should serve a `/status` subresource. When enabled: 1. requests to the custom resource primary endpoint ignore changes to the `status` stanza of the object. 2. requests to the custom resource `/status` subresource ignore changes to anything other than the `status` stanza of the object. # noqa: E501
:param status: The status of this V1beta1CustomResourceSubresources. # noqa: E501
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"""Sets the status of this V1beta1CustomResourceSubresources.
status indicates the custom resource should serve a `/status` subresource. When enabled: 1. requests to the custom resource primary endpoint ignore changes to the `status` stanza of the object. 2. requests to the custom resource `/status` subresource ignore changes to anything other than the `status` stanza of the object. # noqa: E501
:param status: The status of this V1beta1CustomResourceSubresources. # noqa: E501
:type: object
"""
self._status = status | [
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huhamhire/huhamhire-hosts | 33b9c49e7a4045b00e0c0df06f25e9ce8a037761 | util/retrievedata.py | python | RetrieveData.db_exists | (cls, database=DATABASE) | return os.path.isfile(database) | Check whether the :attr:`database` file exists or not.
.. note:: This is a `classmethod`.
:param database: Path to a SQLite database file.
`./hostslist.s3db` by default.
:type database: str
:return: A flag indicating whether the database file exists or not.
:rtype: bool | Check whether the :attr:`database` file exists or not. | [
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] | def db_exists(cls, database=DATABASE):
"""
Check whether the :attr:`database` file exists or not.
.. note:: This is a `classmethod`.
:param database: Path to a SQLite database file.
`./hostslist.s3db` by default.
:type database: str
:return: A flag indicating whether the database file exists or not.
:rtype: bool
"""
return os.path.isfile(database) | [
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oilshell/oil | 94388e7d44a9ad879b12615f6203b38596b5a2d3 | Python-2.7.13/Lib/distutils/command/sdist.py | python | sdist.run | (self) | [] | def run(self):
# 'filelist' contains the list of files that will make up the
# manifest
self.filelist = FileList()
# Run sub commands
for cmd_name in self.get_sub_commands():
self.run_command(cmd_name)
# Do whatever it takes to get the list of files to process
# (process the manifest template, read an existing manifest,
# whatever). File list is accumulated in 'self.filelist'.
self.get_file_list()
# If user just wanted us to regenerate the manifest, stop now.
if self.manifest_only:
return
# Otherwise, go ahead and create the source distribution tarball,
# or zipfile, or whatever.
self.make_distribution() | [
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sadighian/crypto-rl | 078081e5715cadeae9c798a3d759c9d59d2041bc | data_recorder/bitfinex_connector/bitfinex_book.py | python | BitfinexBook.match | (self, msg: dict) | This method is not implemented within Bitmex's API.
However, I've implemented it to capture order arrival flows (i.e., incoming
market orders.) and to be consistent with the overarching design pattern.
Note: this event handler does not impact the LOB in any other way than updating
the number of market orders received at a given price level.
:param msg: buy or sell transaction message from Bitfinex
:return: (void) | This method is not implemented within Bitmex's API. | [
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] | def match(self, msg: dict) -> None:
"""
This method is not implemented within Bitmex's API.
However, I've implemented it to capture order arrival flows (i.e., incoming
market orders.) and to be consistent with the overarching design pattern.
Note: this event handler does not impact the LOB in any other way than updating
the number of market orders received at a given price level.
:param msg: buy or sell transaction message from Bitfinex
:return: (void)
"""
price = msg.get('price', None)
if price in self.price_dict:
quantity = abs(msg['size'])
self.price_dict[price].add_market(quantity=quantity, price=price) | [
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rucio/rucio | 6d0d358e04f5431f0b9a98ae40f31af0ddff4833 | lib/rucio/db/sqla/session.py | python | get_session | () | return session | Creates a session to a specific database, assumes that schema already in place.
:returns: session | Creates a session to a specific database, assumes that schema already in place.
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""" Creates a session to a specific database, assumes that schema already in place.
:returns: session
"""
global _MAKER, _LOCK
if not _MAKER:
_LOCK.acquire()
try:
get_engine()
get_maker()
finally:
_LOCK.release()
assert _MAKER
session = scoped_session(_MAKER)
return session | [
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ethereum/trinity | 6383280c5044feb06695ac2f7bc1100b7bcf4fe0 | trinity/protocol/common/peer_pool_event_bus.py | python | PeerPoolEventServer.handle_get_connected_peers_requests | (self) | [] | async def handle_get_connected_peers_requests(self) -> None:
async for req in self.event_bus.stream(GetConnectedPeersRequest):
await self.event_bus.broadcast(
GetConnectedPeersResponse.from_connected_nodes(self.peer_pool.connected_nodes),
req.broadcast_config()
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sosreport/sos | 900e8bea7f3cd36c1dd48f3cbb351ab92f766654 | sos/options.py | python | SoSOptions._convert_to_type | (self, key, val, conf) | return val | Ensure that the value read from a config file is the proper type
for consumption by the component, as defined by arg_defaults.
Params:
:param key: The key in arg_defaults we need to match the
type of
:param val: The value to be converted to a particular type
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"""Ensure that the value read from a config file is the proper type
for consumption by the component, as defined by arg_defaults.
Params:
:param key: The key in arg_defaults we need to match the
type of
:param val: The value to be converted to a particular type
:param conf: File values are being loaded from
"""
if isinstance(self.arg_defaults[key], type(val)):
return val
if isinstance(self.arg_defaults[key], list):
return [v for v in val.split(',')]
if isinstance(self.arg_defaults[key], bool):
_val = val.lower()
if _val in ['true', 'on', 'yes']:
return True
elif _val in ['false', 'off', 'no']:
return False
else:
raise Exception(
"Value of '%s' in %s must be True or False or analagous"
% (key, conf))
if isinstance(self.arg_defaults[key], int):
try:
return int(val)
except ValueError:
raise Exception("Value of '%s' in %s must be integer"
% (key, conf))
return val | [
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rkhleics/wagtailmodeladmin | 7fddc853bab2ff3868b8c7a03329308c55f16358 | wagtailmodeladmin/views.py | python | InspectView.get_document_field_display | (self, field_name, field) | return self.model_admin.get_empty_value_display() | Render a link to a document | Render a link to a document | [
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if document:
return mark_safe(
'<a href="%s">%s <span class="meta">(%s, %s)</span></a>' % (
document.url,
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return self.model_admin.get_empty_value_display() | [
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SteveDoyle2/pyNastran | eda651ac2d4883d95a34951f8a002ff94f642a1a | pyNastran/dev/bdf_vectorized/cards/optimization.py | python | DDVAL.add_card | (cls, card, comment='') | return DDVAL(oid, ddvals, comment=comment) | Adds a DDVAL card from ``BDF.add_card(...)``
Parameters
----------
card : BDFCard()
a BDFCard object
comment : str; default=''
a comment for the card | Adds a DDVAL card from ``BDF.add_card(...)`` | [
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"""
Adds a DDVAL card from ``BDF.add_card(...)``
Parameters
----------
card : BDFCard()
a BDFCard object
comment : str; default=''
a comment for the card
"""
oid = integer(card, 1, 'oid')
n = 1
ddvals = []
for i in range(2, len(card)):
ddval = double_string_or_blank(card, i, 'DDVAL%s' % n)
if ddval is not None:
ddvals.append(ddval)
return DDVAL(oid, ddvals, comment=comment) | [
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dpp/simply_lift | cf49f7dcce81c7f1557314dd0f0bb08aaedc73da | elyxer.py | python | IncludeInset.__unicode__ | (self) | return 'Included "' + self.filename + '"' | Return a printable description. | Return a printable description. | [
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"Return a printable description."
if not self.filename:
return 'Included unnamed file'
return 'Included "' + self.filename + '"' | [
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dansanderson/picotool | d8c51e58416f8010dc8c0fba3df5f0424b5bb852 | pico8/lua/parser.py | python | Parser.process_tokens | (self, tokens) | Process a list of tokens into an AST.
This method must be single-threaded. To process multiple
tokens in multiple threads, use one Parser instance per
thread.
Args:
tokens: An iterable of lexer.Token objects. All tokens will
be loaded into memory for processing.
Raises:
ParserError: Some pattern of tokens did not match the grammar. | Process a list of tokens into an AST. | [
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"""Process a list of tokens into an AST.
This method must be single-threaded. To process multiple
tokens in multiple threads, use one Parser instance per
thread.
Args:
tokens: An iterable of lexer.Token objects. All tokens will
be loaded into memory for processing.
Raises:
ParserError: Some pattern of tokens did not match the grammar.
"""
self._tokens = list(tokens)
self._pos = 0
self._ast = self._assert(self._chunk(), 'input to be a program')
self._ast.store_token_groups(self._tokens) | [
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indico/indico | 1579ea16235bbe5f22a308b79c5902c85374721f | indico/cli/user.py | python | revoke_admin | (user_id) | Revoke administration rights from a given user. | Revoke administration rights from a given user. | [
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"from",
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"given",
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"."
] | def revoke_admin(user_id):
"""Revoke administration rights from a given user."""
user = User.get(user_id)
if user is None:
click.secho('This user does not exist', fg='red')
return
_print_user_info(user)
if not user.is_admin:
click.secho('This user does not have administration rights', fg='yellow')
return
if click.confirm(click.style('Revoke administration rights from this user?', fg='yellow')):
user.is_admin = False
db.session.commit()
click.secho('Administration rights revoked successfully', fg='green') | [
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Yelp/mrjob | 091572e87bc24cc64be40278dd0f5c3617c98d4b | mrjob/spark/harness.py | python | _run_reducer | (make_mrc_job, step_num, rdd, num_reducers=None) | return rdd.mapPartitions(
reduce_lines,
preservesPartitioning=bool(num_reducers)) | Run our job's combiner, and group lines with the same key together.
:param reducer_job: an instance of our job, instantiated to be the mapper
for the step we wish to run
:param rdd: an RDD containing "reducer ready" lines representing encoded
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:param num_reducers: limit the number of paratitions of output rdd, which
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:return: an RDD containing encoded key-value pairs | Run our job's combiner, and group lines with the same key together. | [
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] | def _run_reducer(make_mrc_job, step_num, rdd, num_reducers=None):
"""Run our job's combiner, and group lines with the same key together.
:param reducer_job: an instance of our job, instantiated to be the mapper
for the step we wish to run
:param rdd: an RDD containing "reducer ready" lines representing encoded
key-value pairs, that is, where all lines with the same key are
adjacent and in the same partition
:param num_reducers: limit the number of paratitions of output rdd, which
is similar to mrjob's limit on number of reducers.
:return: an RDD containing encoded key-value pairs
"""
# initialize job class inside mapPartitions(). this deals with jobs that
# can't be initialized in the Spark driver (see #2044)
def reduce_lines(lines):
job = make_mrc_job('reducer', step_num)
read, write = job.pick_protocols(step_num, 'reducer')
# decode lines into key-value pairs (as a generator, not a list)
#
# line -> (k, v)
if read:
pairs = (read(line) for line in lines)
else:
pairs = lines # pairs were never encoded
# reduce_pairs() runs key-value pairs through reducer
#
# (k, v), ... -> (k, v), ...
for k, v in job.reduce_pairs(pairs, step_num):
# encode key-value pairs back into lines
#
# (k, v) -> line
if write:
yield write(k, v)
else:
yield k, v
# if *num_reducers* is set, don't re-partition. otherwise, doesn't matter
return rdd.mapPartitions(
reduce_lines,
preservesPartitioning=bool(num_reducers)) | [
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linxid/Machine_Learning_Study_Path | 558e82d13237114bbb8152483977806fc0c222af | Machine Learning In Action/Chapter5-LogisticRegression/venv/Lib/site-packages/pip/_vendor/distlib/util.py | python | CSVWriter.writerow | (self, row) | [] | def writerow(self, row):
if sys.version_info[0] < 3:
r = []
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if isinstance(item, text_type):
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microsoft/azure-devops-python-api | 451cade4c475482792cbe9e522c1fee32393139e | azure-devops/azure/devops/v5_1/service_hooks/service_hooks_client.py | python | ServiceHooksClient.get_notifications | (self, subscription_id, max_results=None, status=None, result=None) | return self._deserialize('[Notification]', self._unwrap_collection(response)) | GetNotifications.
Get a list of notifications for a specific subscription. A notification includes details about the event, the request to and the response from the consumer service.
:param str subscription_id: ID for a subscription.
:param int max_results: Maximum number of notifications to return. Default is **100**.
:param str status: Get only notifications with this status.
:param str result: Get only notifications with this result type.
:rtype: [Notification] | GetNotifications.
Get a list of notifications for a specific subscription. A notification includes details about the event, the request to and the response from the consumer service.
:param str subscription_id: ID for a subscription.
:param int max_results: Maximum number of notifications to return. Default is **100**.
:param str status: Get only notifications with this status.
:param str result: Get only notifications with this result type.
:rtype: [Notification] | [
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"""GetNotifications.
Get a list of notifications for a specific subscription. A notification includes details about the event, the request to and the response from the consumer service.
:param str subscription_id: ID for a subscription.
:param int max_results: Maximum number of notifications to return. Default is **100**.
:param str status: Get only notifications with this status.
:param str result: Get only notifications with this result type.
:rtype: [Notification]
"""
route_values = {}
if subscription_id is not None:
route_values['subscriptionId'] = self._serialize.url('subscription_id', subscription_id, 'str')
query_parameters = {}
if max_results is not None:
query_parameters['maxResults'] = self._serialize.query('max_results', max_results, 'int')
if status is not None:
query_parameters['status'] = self._serialize.query('status', status, 'str')
if result is not None:
query_parameters['result'] = self._serialize.query('result', result, 'str')
response = self._send(http_method='GET',
location_id='0c62d343-21b0-4732-997b-017fde84dc28',
version='5.1',
route_values=route_values,
query_parameters=query_parameters)
return self._deserialize('[Notification]', self._unwrap_collection(response)) | [
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awslabs/aws-ec2rescue-linux | 8ecf40e7ea0d2563dac057235803fca2221029d2 | lib/boto3/s3/transfer.py | python | S3Transfer.upload_file | (self, filename, bucket, key,
callback=None, extra_args=None) | Upload a file to an S3 object.
Variants have also been injected into S3 client, Bucket and Object.
You don't have to use S3Transfer.upload_file() directly.
.. seealso::
:py:meth:`S3.Client.upload_file`
:py:meth:`S3.Client.upload_fileobj` | Upload a file to an S3 object. | [
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] | def upload_file(self, filename, bucket, key,
callback=None, extra_args=None):
"""Upload a file to an S3 object.
Variants have also been injected into S3 client, Bucket and Object.
You don't have to use S3Transfer.upload_file() directly.
.. seealso::
:py:meth:`S3.Client.upload_file`
:py:meth:`S3.Client.upload_fileobj`
"""
if not isinstance(filename, six.string_types):
raise ValueError('Filename must be a string')
subscribers = self._get_subscribers(callback)
future = self._manager.upload(
filename, bucket, key, extra_args, subscribers)
try:
future.result()
# If a client error was raised, add the backwards compatibility layer
# that raises a S3UploadFailedError. These specific errors were only
# ever thrown for upload_parts but now can be thrown for any related
# client error.
except ClientError as e:
raise S3UploadFailedError(
"Failed to upload %s to %s: %s" % (
filename, '/'.join([bucket, key]), e)) | [
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ElsevierDev/elsapy | 5fda0434f106fdd89fa851144c25d80d7e1100d5 | elsapy/elsprofile.py | python | ElsAffil.__init__ | (self, uri = '', affil_id = '') | Initializes an affiliation given a Scopus affiliation URI or affiliation ID. | Initializes an affiliation given a Scopus affiliation URI or affiliation ID. | [
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"""Initializes an affiliation given a Scopus affiliation URI or affiliation ID."""
if uri and not affil_id:
super().__init__(uri)
elif affil_id and not uri:
super().__init__(self._uri_base + str(affil_id))
elif not uri and not affil_id:
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missionpinball/mpf | 8e6b74cff4ba06d2fec9445742559c1068b88582 | mpf/commands/create_config.py | python | Command.create_show | (self) | Create a show. | Create a show. | [
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"."
] | def create_show(self):
"""Create a show."""
show_name = input_dialog(
title='Mode',
text='Cool, got a name for your show?',
style=self.example_style).run()
# create shows folder
show_path = os.path.normpath(os.path.join(self.machine_path, "shows", show_name))
shows_dir = os.path.normpath(os.path.join(self.machine_path, "shows"))
if not os.path.exists(show_path):
if self.in_machine_folder():
self.create_show_structure(show_name, shows_dir, self.machine_path)
message_dialog(
title='Shows',
text=HTML('<style fg="green">Success:</style> Created show {}.'.format(show_name)),
# text='Success: Created machine config {}.'.format(config_name),
style=self.example_style
).run()
else:
self.show_not_in_machine_folder_dialog()
else:
message_dialog(
title='Mode',
text=HTML(
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style=self.example_style
).run()
self.create_machine_config() | [
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google/clusterfuzz | f358af24f414daa17a3649b143e71ea71871ef59 | src/clusterfuzz/_internal/bot/minimizer/utils.py | python | get_size_string | (size) | return '%d GB' % (size >> 30) | Return string representation for size. | Return string representation for size. | [
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"."
] | def get_size_string(size):
"""Return string representation for size."""
if size < 1 << 10:
return '%d B' % size
if size < 1 << 20:
return '%d KB' % (size >> 10)
if size < 1 << 30:
return '%d MB' % (size >> 20)
return '%d GB' % (size >> 30) | [
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