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3 | 7 | def get_default_cache_location() -> str:
if "LUDWIG_CACHE" in os.environ and os.environ["LUDWIG_CACHE"]:
return os.environ["LUDWIG_CACHE"]
else:
return str(Path.home().joinpath(".ludwig_cache"))
| ludwig/datasets/loaders/dataset_loader.py | 81 | ludwig | {
"docstring": "Returns a path to the default LUDWIG_CACHE location, or $HOME/.ludwig_cache.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 15 | Python | 14 | e4fc06f986e03919d9aef3ab55c05fee5a6b9d3a | dataset_loader.py | 8,086 | 6 | 44 | get_default_cache_location | https://github.com/ludwig-ai/ludwig.git | Config-first Datasets API (ludwig.datasets refactor) (#2479)
* Adds README and stub for reading dataset configs.
* Adds __init__.py for configs, moves circular import into function scope in ludwig/datasets/__init__.py
* Print config files in datasets folder.
* First pass at automatic archive extraction.
* Implemented downloading and extract.
* Refactor DatasetConfig into its own file.
* Fixed bugs downloading kaggle dataset.
* Makes registry store dataset instances, not classes. Also comments out import_submodules for testing.
* Typo fix.
* Only pass data files on to load_unprocessed_dataframe, symlink directories.
* Downloading dataset files into existing directory if exists.
* Refactor: make datasets fully config-first, lazy load dataset loaders.
* Implemented agnews custom loader.
* Implements train/validation/test split by files, and globbing support
* Adds _glob_multiple
* Adds adult_census_income, agnews, allstate_claims_severity.
* Implements sha256 verification, adds more datasets up to creditcard_fraud.
* Adds checksums, dbpedia, electricity
* Fixes gzip file name returned as string not list, adds up to forest_cover dataset.
* Adds datasets up to reuters_r8
* Adds all datasets which don't require a custom class.
* Restore dataset import behavior by implementing module __getattr__
* Adds KDD datasets.
* Adds ieee_fraud.
* Adds imbalanced_insurance, insurance_lite.
* Adds mnist.
* Completes implementation of all of the built-in datasets.
* Made cache_dir optional, read from environment variable if set.
* Upgrades datasets tests.
* Adds test for new dataset config API. Also adds scripts for dataset link checking.
* Fixes loading allstate claims severity dataset.
* Use @lru_cache(1), @cache not supported in python < 3.9
* Deletes dataset registry, updates automl test utils
* Fix imports of datasets API.
* Adds more detail to sha256: docstring and basic README
* Copy-paste link oops.
* Fixes handling of nested archive types like .tar.bz Also adds a LUDWIG_CACHE and export to the README
* Adds link for twitter bots.
* Fix order of splits in README.md
* typo
* Adds verify as a phase in doc string.
* Support .pqt, .pq extensions for parquet.
* Handle nested archives with longer file extensions like .csv.zip
* Handle nested .gz types properly too. Check all extensions with .endswith
* Handle all archive types with .endswith
* Update ludwig/datasets/loaders/split_loaders.py
Co-authored-by: Joppe Geluykens <[email protected]>
* Adds explanation for export, fixes preserve_paths (should be relative to processed_dataset_dir)
* Resolve preserved paths relative to raw dataset dir before move.
* Catch runtime exception from extracting sub-archives.
Co-authored-by: Daniel Treiman <[email protected]>
Co-authored-by: Joppe Geluykens <[email protected]> | 38 | 0 | 1,336 | 14 |
|
1 | 6 | def correlate(a, v, mode='valid'):
return multiarray.correlate2(a, v, mode)
| numpy/core/numeric.py | 37 | numpy | {
"docstring": "\n Cross-correlation of two 1-dimensional sequences.\n\n This function computes the correlation as generally defined in signal\n processing texts::\n\n .. math:: c_k = \\sum_n a_{n+k} * \\overline{v_n}\n\n with a and v sequences being zero-padded where necessary and\n :math:`\\overline x` denoting complex conjugation.\n\n Parameters\n ----------\n a, v : array_like\n Input sequences.\n mode : {'valid', 'same', 'full'}, optional\n Refer to the `convolve` docstring. Note that the default\n is 'valid', unlike `convolve`, which uses 'full'.\n old_behavior : bool\n `old_behavior` was removed in NumPy 1.10. If you need the old\n behavior, use `multiarray.correlate`.\n\n Returns\n -------\n out : ndarray\n Discrete cross-correlation of `a` and `v`.\n\n See Also\n --------\n convolve : Discrete, linear convolution of two one-dimensional sequences.\n multiarray.correlate : Old, no conjugate, version of correlate.\n scipy.signal.correlate : uses FFT which has superior performance on large arrays. \n\n Notes\n -----\n The definition of correlation above is not unique and sometimes correlation\n may be defined differently. Another common definition is::\n\n .. math:: c'_k = \\sum_n a_{n} * \\overline{v_{n+k}\n\n which is related to :math:`c_k` by :math:`c'_k = c_{-k}`.\n\n `numpy.correlate` may perform slowly in large arrays (i.e. n = 1e5) because it does\n not use the FFT to compute the convolution; in that case, `scipy.signal.correlate` might\n be preferable.\n \n\n Examples\n --------\n >>> np.correlate([1, 2, 3], [0, 1, 0.5])\n array([3.5])\n >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"same\")\n array([2. , 3.5, 3. ])\n >>> np.correlate([1, 2, 3], [0, 1, 0.5], \"full\")\n array([0.5, 2. , 3.5, 3. , 0. ])\n\n Using complex sequences:\n\n >>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')\n array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ])\n\n Note that you get the time reversed, complex conjugated result\n when the two input sequences change places, i.e.,\n ``c_{va}[k] = c^{*}_{av}[-k]``:\n\n >>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')\n array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j])\n\n ",
"language": "en",
"n_whitespaces": 491,
"n_words": 293,
"vocab_size": 195
} | 8 | Python | 7 | 24d653f11a55f76b125a91d7d4523052ef14b9b9 | numeric.py | 160,229 | 2 | 23 | correlate | https://github.com/numpy/numpy.git | DOC: Use math mode | 14 | 0 | 38,576 | 7 |
|
2 | 2 | def default(val, default):
return val if val is not None else default
| parlai/core/params.py | 27 | ParlAI | {
"docstring": "\n shorthand for explicit None check for optional arguments.\n ",
"language": "en",
"n_whitespaces": 15,
"n_words": 8,
"vocab_size": 7
} | 12 | Python | 11 | ecdfbd0bb2ab76876e9fd3817d4502c3938a2ade | params.py | 195,055 | 2 | 17 | default | https://github.com/facebookresearch/ParlAI.git | Decoder-Only Transformer (#4329)
* quick and dirty decoder-only implementation
* fix decoder_only incremental decoding
* remove unused code, add some comments, propogate func signature change
* consolidate code in decoder.py
* unify encoder_state
* export PassThroughEncoder
* add missing build_ functions
* defaults in TransformerDecoderLayer __init__
* comments, consolidating more logic, simplified forward_layers args
* resize token embeddings and unit test
* attempt to suppress some unused import warnings
* padded_tensor fp16 friendly
* autoformat
* decoder_only -> decoder
* more documentation
* update name in test
* add missing dict args
* more argument massaging
* update TestBartDistillation::test_narrow_distillation_losses numbers
* update TestTransformerDistillation::test_narrow_distillation_losses numbers
* fix _pad_tensor in seeker
Co-authored-by: klshuster <[email protected]> | 18 | 0 | 47,174 | 7 |
|
1 | 3 | def actors(self):
# TODO(jungong) : remove this API once WorkerSet.remote_workers()
# and WorkerSet._remote_workers() are removed.
return self.__actors
| rllib/utils/actor_manager.py | 21 | ray | {
"docstring": "Access the underlying actors being managed.\n\n Warning (jungong): This API should almost never be used.\n It is only exposed for testing and backward compatibility reasons.\n Remote actors managed by this class should never be accessed directly.\n ",
"language": "en",
"n_whitespaces": 64,
"n_words": 36,
"vocab_size": 32
} | 17 | Python | 16 | b84dac2609bd587c43ed17bb6fa18fb7241a41de | actor_manager.py | 135,643 | 2 | 10 | actors | https://github.com/ray-project/ray.git | Refactor ActorManager to store underlying remote actors in dict. (#29953)
Signed-off-by: Jun Gong <[email protected]> | 45 | 0 | 30,681 | 6 |
|
14 | 34 | def _send(self, msg, endpoint="events", quiet=False, from_log=False, create=True):
if msg.get("eid", None) is None:
msg["eid"] = self.env
self.env_list.add(self.env)
if msg.get("eid", None) is not None:
self.env_list.add(msg["eid"])
# TODO investigate send use cases, then deprecate
if not self.send:
return msg, endpoint
if "win" in msg and msg["win"] is None and create:
msg["win"] = "window_" + get_rand_id()
if not from_log:
self._log(msg, endpoint)
if self.offline:
# If offline, don't even try to post
return msg["win"] if "win" in msg else True
try:
return self._handle_post(
"{0}:{1}{2}/{3}".format(
self.server, self.port, self.base_url, endpoint
),
data=json.dumps(msg),
)
except (requests.RequestException, requests.ConnectionError, requests.Timeout):
if self.raise_exceptions:
raise ConnectionError("Error connecting to Visdom server")
else:
if self.raise_exceptions is None:
warnings.warn(
"Visdom is eventually changing to default to raising "
"exceptions rather than ignoring/printing. This change"
" is expected to happen by July 2018. Please set "
"`raise_exceptions` to False to retain current "
"behavior.",
PendingDeprecationWarning,
)
if not quiet:
print("Exception in user code:")
print("-" * 60)
traceback.print_exc()
return False
| py/visdom/__init__.py | 412 | visdom | {
"docstring": "\n This function sends specified JSON request to the Tornado server. This\n function should generally not be called by the user, unless you want to\n build the required JSON yourself. `endpoint` specifies the destination\n Tornado server endpoint for the request.\n\n If `create=True`, then if `win=None` in the message a new window will be\n created with a random name. If `create=False`, `win=None` indicates the\n operation should be applied to all windows.\n ",
"language": "en",
"n_whitespaces": 126,
"n_words": 69,
"vocab_size": 51
} | 153 | Python | 107 | 5b8b7f267cfaf76a2a39a727ef31a62b3909a093 | __init__.py | 106,875 | 39 | 244 | _send | https://github.com/fossasia/visdom.git | apply black py to all python files | 712 | 0 | 22,492 | 17 |
|
7 | 22 | def add_edge(self, u_for_edge, v_for_edge, key=None, **attr):
u, v = u_for_edge, v_for_edge
# add nodes
if u not in self._succ:
if u is None:
raise ValueError("None cannot be a node")
self._succ[u] = self.adjlist_inner_dict_factory()
self._pred[u] = self.adjlist_inner_dict_factory()
self._node[u] = self.node_attr_dict_factory()
if v not in self._succ:
if v is None:
raise ValueError("None cannot be a node")
self._succ[v] = self.adjlist_inner_dict_factory()
self._pred[v] = self.adjlist_inner_dict_factory()
self._node[v] = self.node_attr_dict_factory()
if key is None:
key = self.new_edge_key(u, v)
if v in self._succ[u]:
keydict = self._adj[u][v]
datadict = keydict.get(key, self.edge_attr_dict_factory())
datadict.update(attr)
keydict[key] = datadict
else:
# selfloops work this way without special treatment
datadict = self.edge_attr_dict_factory()
datadict.update(attr)
keydict = self.edge_key_dict_factory()
keydict[key] = datadict
self._succ[u][v] = keydict
self._pred[v][u] = keydict
return key
| networkx/classes/multidigraph.py | 390 | networkx | {
"docstring": "Add an edge between u and v.\n\n The nodes u and v will be automatically added if they are\n not already in the graph.\n\n Edge attributes can be specified with keywords or by directly\n accessing the edge's attribute dictionary. See examples below.\n\n Parameters\n ----------\n u_for_edge, v_for_edge : nodes\n Nodes can be, for example, strings or numbers.\n Nodes must be hashable (and not None) Python objects.\n key : hashable identifier, optional (default=lowest unused integer)\n Used to distinguish multiedges between a pair of nodes.\n attr : keyword arguments, optional\n Edge data (or labels or objects) can be assigned using\n keyword arguments.\n\n Returns\n -------\n The edge key assigned to the edge.\n\n See Also\n --------\n add_edges_from : add a collection of edges\n\n Notes\n -----\n To replace/update edge data, use the optional key argument\n to identify a unique edge. Otherwise a new edge will be created.\n\n NetworkX algorithms designed for weighted graphs cannot use\n multigraphs directly because it is not clear how to handle\n multiedge weights. Convert to Graph using edge attribute\n 'weight' to enable weighted graph algorithms.\n\n Default keys are generated using the method `new_edge_key()`.\n This method can be overridden by subclassing the base class and\n providing a custom `new_edge_key()` method.\n\n Examples\n --------\n The following all add the edge e=(1, 2) to graph G:\n\n >>> G = nx.MultiDiGraph()\n >>> e = (1, 2)\n >>> key = G.add_edge(1, 2) # explicit two-node form\n >>> G.add_edge(*e) # single edge as tuple of two nodes\n 1\n >>> G.add_edges_from([(1, 2)]) # add edges from iterable container\n [2]\n\n Associate data to edges using keywords:\n\n >>> key = G.add_edge(1, 2, weight=3)\n >>> key = G.add_edge(1, 2, key=0, weight=4) # update data for key=0\n >>> key = G.add_edge(1, 3, weight=7, capacity=15, length=342.7)\n\n For non-string attribute keys, use subscript notation.\n\n >>> ekey = G.add_edge(1, 2)\n >>> G[1][2][0].update({0: 5})\n >>> G.edges[1, 2, 0].update({0: 5})\n ",
"language": "en",
"n_whitespaces": 677,
"n_words": 301,
"vocab_size": 186
} | 112 | Python | 58 | 6ab4e54e696ae65534e1c3329930df8beee03573 | multidigraph.py | 176,523 | 29 | 246 | add_edge | https://github.com/networkx/networkx.git | Fixed wrong dict factory usage on MultiDiGraph (#5456)
* Fixed the issue that the wrong dict factory on a MultiDiGraph was used for edge attributes (edge_key_dict_factory instead of edge_attr_dict_factory)
Extended tests to typecheck the dict factories and added a test that incorporates custom dict factories on a MultiDiGraph
* Mypy ignore inferred types in MDG subclass.
Co-authored-by: Fabian Ball <[email protected]>
Co-authored-by: Ross Barnowski <[email protected]> | 425 | 0 | 41,942 | 12 |
|
2 | 15 | def _get_kwargs(self) -> Dict[str, Union[int, Tuple[int, int]]]:
retval = {kword: self._kwarg_mapping[kword]
for kword in self._kwarg_requirements[self._blur_type]}
logger.trace("BlurMask kwargs: %s", retval) # type: ignore
return retval
_HASHES_SEEN: Dict[str, Dict[str, int]] = {}
| lib/align/detected_face.py | 107 | faceswap | {
"docstring": " dict: the valid keyword arguments for the requested :attr:`_blur_type` ",
"language": "en",
"n_whitespaces": 10,
"n_words": 9,
"vocab_size": 8
} | 30 | Python | 26 | 5e73437be47f2410439a3c6716de96354e6a0c94 | detected_face.py | 101,222 | 6 | 56 | _get_kwargs | https://github.com/deepfakes/faceswap.git | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | 75 | 0 | 20,642 | 10 |
|
1 | 4 | def getencoder(encoding):
return lookup(encoding).encode
| python3.10.4/Lib/codecs.py | 24 | XX-Net | {
"docstring": " Lookup up the codec for the given encoding and return\n its encoder function.\n\n Raises a LookupError in case the encoding cannot be found.\n\n ",
"language": "en",
"n_whitespaces": 41,
"n_words": 23,
"vocab_size": 20
} | 4 | Python | 4 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | codecs.py | 221,361 | 2 | 13 | getencoder | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 10 | 0 | 56,376 | 8 |
|
1 | 4 | def reset(self):
self._landmarks = {}
self._tk_faces = {}
| tools/manual/faceviewer/viewport.py | 33 | faceswap | {
"docstring": " Reset all the cached objects on a face size change. ",
"language": "en",
"n_whitespaces": 11,
"n_words": 10,
"vocab_size": 10
} | 8 | Python | 6 | 5e73437be47f2410439a3c6716de96354e6a0c94 | viewport.py | 101,267 | 3 | 18 | reset | https://github.com/deepfakes/faceswap.git | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | 29 | 0 | 20,686 | 7 |
|
4 | 17 | def on_mode_entered(self, mode):
if (config.val.tabs.mode_on_change == 'restore' and
mode in modeman.INPUT_MODES):
tab = self.widget.currentWidget()
if tab is not None:
assert isinstance(tab, browsertab.AbstractTab), tab
tab.data.input_mode = mode
| qutebrowser/mainwindow/tabbedbrowser.py | 96 | qutebrowser | {
"docstring": "Save input mode when tabs.mode_on_change = restore.",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | 26 | Python | 21 | a20bb67a878b2e68abf8268c1b0a27f018d01352 | tabbedbrowser.py | 320,800 | 7 | 60 | on_mode_entered | https://github.com/qutebrowser/qutebrowser.git | mypy: Upgrade to PyQt5-stubs 5.15.6.0
For some unknown reason, those new stubs cause a *lot* of things now to be
checked by mypy which formerly probably got skipped due to Any being implied
somewhere.
The stubs themselves mainly improved, with a couple of regressions too.
In total, there were some 337 (!) new mypy errors. This commit fixes almost all
of them, and the next commit improves a fix to get things down to 0 errors
again.
Overview of the changes:
==== qutebrowser/app.py
- Drop type ignore due to improved stubs.
==== qutebrowser/browser/browsertab.py
- Specify the type of _widget members more closely than just QWidget.
This is debatable: I suppose the abstract stuff shouldn't need to know
anything about the concrete backends at all. But it seems like we cut some
corners when initially implementing things, and put some code in browsertab.py
just because the APIs of both backends happened to be compatible. Perhaps
something to reconsider once we drop QtWebKit and hopefully implement a dummy
backend.
- Add an additional assertion in AbstractAction.run_string. This is already
covered by the isinstance(member, self.action_base) above it, but that's too
dynamic for mypy to understand.
- Fix the return type of AbstractScroller.pos_px, which is a QPoint (with x
and y components), not a single int.
- Fix the return type of AbstractScroller.pos_perc, which is a Tuple (with x
and y components), not a single int.
- Fix the argument types of AbstractScroller.to_perc, as it's possible to pass
fractional percentages too.
- Specify the type for AbstractHistoryPrivate._history. See above (_widget) re
this being debatable.
- Fix the return type of AbstractTabPrivate.event_target(), which can be None
(see #3888).
- Fix the return type of AbstractTabPrivate.run_js_sync, which is Any (the JS
return value), not None.
- Fix the argument type for AbstractTabPrivate.toggle_inspector: position can
be None to use the last used position.
- Declare the type of sub-objects of AbstractTab.
- Fix the return value of AbstractTab.icon(), which is the QIcon, not None.
==== qutebrowser/browser/commands.py
- Make sure the active window is a MainWindow (with a .win_id attribute).
==== qutebrowser/browser/downloadview.py
- Add _model() which makes sure that self.model() is a DownloadModel, not None
or any other model. This is needed because other methods access a variety of
custom attributes on it, e.g. last_index().
==== qutebrowser/browser/greasemonkey.py
- Add an ignore for AbstractDownload.requested_url which we patch onto the
downloads. Probably would be nicer to add it as a proper attribute which always
gets set by the DownloadManager.
==== qutebrowser/browser/hints.py
- Remove type ignores for QUrl.toString().
- Add a new type ignore for combining different URL flags (which works, but is
not exactly type safe... still probably a regression in the stubs).
- Make sure the things we get back from self._get_keyparser are what we actually
expect. Probably should introduce a TypedDict (and/or overloads for
_get_keyparser with typing.Literal) to teach mypy about the exact return value.
See #7098.
This is needed because we access Hint/NormalKeyParser-specific attributes such
as .set_inhibited_timout() or .update_bindings().
==== qutebrowser/browser/inspector.py
- Similar changes than in browsertab.py to make some types where we share API
(e.g. .setPage()) more concrete. Didn't work out unfortunately, see next
commit.
==== qutebrowser/browser/network/pac.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/qtnetworkdownloads.py
- Make sure that downloads is a qtnetworkdownloads.DownloadItem (rather than an
AbstractDownload), so that we can call ._uses_nam() on it.
==== qutebrowser/browser/qutescheme.py
- Remove now unneeded type ignore for QUrl flags.
==== qutebrowser/browser/urlmarks.py
- Specify the type of UrlMarkManager._lineparser, as those only get initialized
in _init_lineparser of subclasses, so mypy doesn't know it's supposed to exist.
==== qutebrowser/browser/webelem.py
- New casts to turn single KeyboardModifier (enum) entries into
KeyboardModifiers (flags). Might not be needed anymore with Qt 6.
- With that, casting the final value is now unneeded.
==== qutebrowser/browser/webengine/notification.py
- Remove now unneeded type ignore for signal.
- Make sure the self.sender() we get in HerbeNotificationAdapter._on_finished()
is a QProcess, not just any QObject.
==== qutebrowser/browser/webengine/webenginedownloads.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webengine/webengineelem.py
- Specify the type of WebEngineElement._tab.
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webengineinspector.py
- See changes to inspector.py and next commit.
- Remove now unneeded type ignore for signal.
==== qutebrowser/browser/webengine/webenginequtescheme.py
- Remove now unneeded type ignore for mixed flags.
==== qutebrowser/browser/webengine/webenginesettings.py
- Ignore access of .setter attribute which we patch onto QWebEngineProfile.
Would be nice to have a subclass or wrapper-class instead.
==== qutebrowser/browser/webengine/webenginetab.py
- Specified the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Remove some now-unneeded type ignores for creating FindFlags.
- Specify more concrete types for WebEngineTab members where we actually need to
access WebEngine-specific attributes.
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webengine/webview.py
- Make sure the page we get is our custom WebEnginePage subclass, not just any
QWebEnginePage. This is needed because we access custom attributes on it.
==== qutebrowser/browser/webkit/network/networkreply.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/browser/webkit/webkitinspector.py
- See changes to inspector.py and next commit.
==== qutebrowser/browser/webkit/webkittab.py
- Specify the type of _widget members more closely than just QWidget.
See browsertab.py changes for details.
- Add a type ignore for WebKitAction because our workaround needs to
treat them as ints (which is allowed by PyQt, even if not type-safe).
- Add new ignores for findText calls: The text is a QString and can be None; the
flags are valid despite mypy thinking they aren't (stubs regression?).
- Specify the type for WebKitHistoryPrivate._history, because we access
WebKit-specific attributes. See above (_widget) re this being debatable.
- Make mypy aware that .currentFrame() and .frameAt() can return None (stubs
regression?).
- Make sure the .page() and .page().networkAccessManager() are our subclasses
rather than the more generic QtWebKit objects, as we use custom attributes.
- Add new type ignores for signals (stubs regression!)
==== qutebrowser/browser/webkit/webpage.py
- Make sure the .networkAccessManager() is our subclass rather than the more
generic QtWebKit object, as we use custom attributes.
- Replace a cast by a type ignore. The cast didn't work anymore.
==== qutebrowser/browser/webkit/webview.py
- Make sure the .page() is our subclass rather than the more generic QtWebKit
object, as we use custom attributes.
==== qutebrowser/commands/userscripts.py
- Remove now unneeded type ignore for signal.
==== qutebrowser/completion/completer.py
- Add a new _completion() getter (which ensures it actually gets the completion
view) rather than accessing the .parent() directly (which could be any QObject).
==== qutebrowser/completion/completiondelegate.py
- Make sure self.parent() is a CompletionView (no helper method as there is only
one instance).
- Remove a now-unneeded type ignore for adding QSizes.
==== qutebrowser/completion/completionwidget.py
- Add a ._model() getter which ensures that we get a CompletionModel (with
custom attributes) rather than Qt's .model() which can be any QAbstractItemModel
(or None).
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/completion/models/completionmodel.py
- Remove now unneeded type ignores for signals.
- Ignore a complaint about .set_pattern() not being defined. Completion
categories don't share any common parent class, so it would be good to introduce
a typing.Protocol for this. See #7098.
==== qutebrowser/components/misccommands.py
- Removed a now-unneeded type ignore for OR-ing flags.
==== qutebrowser/components/readlinecommands.py
- Make sure QApplication.instance() is a QApplication (and not just a
QCoreApplication). This includes the former "not None" check.
==== qutebrowser/components/scrollcommands.py
- Add basic annotation for "funcs" dict. Could have a callable protocol to
specify it needs a count kwarg, see #7098.
==== qutebrowser/config/stylesheet.py
- Correctly specify that stylesheet apply to QWidgets, not any QObject.
- Ignore an attr-defined for obj.STYLESHEET. Perhaps could somehow teach mypy
about this with overloads and protocols (stylesheet for set_register being None
=> STYLESHEET needs to be defined, otherwise anything goes), but perhaps not
worth the troble. See #7098.
==== qutebrowser/keyinput/keyutils.py
- Remove some now-unneeded type ignores and add a cast for using a single enum
value as flags. Might need to look at this again with Qt 6 support.
==== qutebrowser/keyinput/modeman.py
- Add a FIXME for using a TypedDict, see comments for hints.py above.
==== qutebrowser/mainwindow/mainwindow.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
- Improve where we cast from WindowType to WindowFlags, no int needed
- Use new .tab_bar() getter, see below.
==== qutebrowser/mainwindow/prompt.py
- Remove now-unneeded type ignores for calling with OR-ed flags.
==== qutebrowser/mainwindow/statusbar/bar.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/statusbar/command.py
- Fix type for setText() override (from QLineEdit): text can be None
(QString in C++).
==== qutebrowser/mainwindow/statusbar/url.py
- Adjust type ignores around @pyqtProperty. The fact one is still needed seems
like a stub regression.
==== qutebrowser/mainwindow/tabbedbrowser.py
- Specify that TabDeque manages browser tabs, not any QWidgets. It accesses
AbstractTab-specific attributes.
- Make sure that the .tabBar() we get is a tabwidget.TabBar, as we access
.maybe_hide.
- Fix the annotations for stored marks: Scroll positions are a QPoint, not int.
- Add _current_tab() and _tab_by_idx() wrappers for .currentWidget() and
.widget(), which ensures that the return values are valid AbstractTabs (or None
for _tab_by_idx). This is needed because we access AbstractTab-specific
attributes.
- For some places, where the tab can be None, continue using .currentTab() but
add asserts.
- Remove some now-unneeded [unreachable] ignores, as mypy knows about the None
possibility now.
==== qutebrowser/mainwindow/tabwidget.py
- Add new tab_bar() and _tab_by_idx() helpers which check that the .tabBar() and
.widget() are of type TabBar and AbstractTab, respectively.
- Add additional assertions where we expect ._tab_by_idx() to never be None.
- Remove dead code in get_tab_fields for handling a None y scroll position. I
was unable to find any place in the code where this could be set to None.
- Remove some now-unneeded type ignores and casts, as mypy now knows that
_type_by_idx() could be None.
- Work around a strange instance where mypy complains about not being able to
find the type of TabBar.drag_in_progress from TabWidget._toggle_visibility,
despite it clearly being shown as a bool *inside* that class without any
annotation.
- Add a ._tab_widget() getter in TabBar which ensures that the .parent() is in
fact a TabWidget.
==== qutebrowser/misc/crashsignal.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/editor.py
- Remove now unneeded type ignores for signals.
==== qutebrowser/misc/ipc.py
- Remove now unneeded type ignores for signals.
- Add new type ignores for .error() which is both a signal and a getter
(stub regression?). Won't be relevant for Qt 6 anymore, as the signal was
renamed to errorOccurred in 5.15.
==== qutebrowser/misc/objects.py
- Make sure mypy knows that objects.app is our custom Application (with custom
attributes) rather than any QApplication.
==== qutebrowser/utils/objreg.py
- Ignore attr-defined for .win_id attributes. Maybe could add a typing.Protocol,
but ideally, the whole objreg stuff should die one day anyways.
==== tests/unit/completion/test_completer.py
- Make CompletionWidgetStub inherit from CompletionView so that it passes the
new isinstance() asserts in completer.py (see above). | 107 | 0 | 117,362 | 12 |
|
1 | 29 | def test_shared_embedding_column_with_non_sequence_categorical(self):
with tf.Graph().as_default():
vocabulary_size = 3
sparse_input_a = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
sparse_input_b = tf.compat.v1.SparseTensorValue(
# example 0, ids [2]
# example 1, ids [0, 1]
indices=((0, 0), (1, 0), (1, 1)),
values=(2, 0, 1),
dense_shape=(2, 2),
)
categorical_column_a = (
tf.feature_column.categorical_column_with_identity(
key="aaa", num_buckets=vocabulary_size
)
)
categorical_column_b = (
tf.feature_column.categorical_column_with_identity(
key="bbb", num_buckets=vocabulary_size
)
)
shared_embedding_columns = tf.feature_column.shared_embeddings(
[categorical_column_a, categorical_column_b], dimension=2
)
sequence_input_layer = ksfc.SequenceFeatures(
shared_embedding_columns
)
with self.assertRaisesRegex(
ValueError,
r"In embedding_column: aaa_shared_embedding\. "
r"categorical_column must "
r"be of type SequenceCategoricalColumn to use "
r"SequenceFeatures\.",
):
_, _ = sequence_input_layer(
{"aaa": sparse_input_a, "bbb": sparse_input_b}
)
| keras/feature_column/sequence_feature_column_test.py | 332 | keras | {
"docstring": "Tests that error is raised for non-sequence shared embedding\n column.",
"language": "en",
"n_whitespaces": 16,
"n_words": 10,
"vocab_size": 10
} | 115 | Python | 64 | 6fafb567af4e4d9f42974d0b6c55b18bc03e17eb | sequence_feature_column_test.py | 278,120 | 39 | 218 | test_shared_embedding_column_with_non_sequence_categorical | https://github.com/keras-team/keras.git | resolve line-too-long in feature_column | 696 | 0 | 82,378 | 15 |
|
6 | 51 | def linear_mpc_control(xref, xbar, x0, dref):
x = cvxpy.Variable((NX, T + 1))
u = cvxpy.Variable((NU, T))
cost = 0.0
constraints = []
for t in range(T):
cost += cvxpy.quad_form(u[:, t], R)
if t != 0:
cost += cvxpy.quad_form(xref[:, t] - x[:, t], Q)
A, B, C = get_linear_model_matrix(
xbar[2, t], xbar[3, t], dref[0, t])
constraints += [x[:, t + 1] == A @ x[:, t] + B @ u[:, t] + C]
if t < (T - 1):
cost += cvxpy.quad_form(u[:, t + 1] - u[:, t], Rd)
constraints += [cvxpy.abs(u[1, t + 1] - u[1, t]) <=
MAX_DSTEER * DT]
cost += cvxpy.quad_form(xref[:, T] - x[:, T], Qf)
constraints += [x[:, 0] == x0]
constraints += [x[2, :] <= MAX_SPEED]
constraints += [x[2, :] >= MIN_SPEED]
constraints += [cvxpy.abs(u[0, :]) <= MAX_ACCEL]
constraints += [cvxpy.abs(u[1, :]) <= MAX_STEER]
prob = cvxpy.Problem(cvxpy.Minimize(cost), constraints)
prob.solve(solver=cvxpy.ECOS, verbose=False)
if prob.status == cvxpy.OPTIMAL or prob.status == cvxpy.OPTIMAL_INACCURATE:
ox = get_nparray_from_matrix(x.value[0, :])
oy = get_nparray_from_matrix(x.value[1, :])
ov = get_nparray_from_matrix(x.value[2, :])
oyaw = get_nparray_from_matrix(x.value[3, :])
oa = get_nparray_from_matrix(u.value[0, :])
odelta = get_nparray_from_matrix(u.value[1, :])
else:
print("Error: Cannot solve mpc..")
oa, odelta, ox, oy, oyaw, ov = None, None, None, None, None, None
return oa, odelta, ox, oy, oyaw, ov
| PathTracking/model_predictive_speed_and_steer_control/model_predictive_speed_and_steer_control.py | 699 | PythonRobotics | {
"docstring": "\n linear mpc control\n\n xref: reference point\n xbar: operational point\n x0: initial state\n dref: reference steer angle\n ",
"language": "en",
"n_whitespaces": 35,
"n_words": 16,
"vocab_size": 14
} | 201 | Python | 108 | c05a4fdada59fd97332417c4b99515118bfef45c | model_predictive_speed_and_steer_control.py | 19,224 | 35 | 476 | linear_mpc_control | https://github.com/AtsushiSakai/PythonRobotics.git | Fix ModuleNotFoundError when executing test in the tests folder and little improve MPC controller (#619)
* Fix ModuleNotFoundError when executing test in the tests folder
Signed-off-by: Trung Kien <[email protected]>
* Improve model_predictive_speed_and_steer_control
- Fix typo
- Using @ for matrix multiplication instead of * with have been deprecated in CVXPY1.1
- Fix missing conftest module in test file
Signed-off-by: Trung Kien <[email protected]> | 414 | 0 | 2,919 | 17 |
|
1 | 4 | def uses_after_args(self) -> Namespace:
return self.peas_args['uses_after']
| jina/peapods/pods/__init__.py | 28 | jina | {
"docstring": "Get the arguments for the `uses_after` of this Pod.\n\n\n .. # noqa: DAR201\n ",
"language": "en",
"n_whitespaces": 27,
"n_words": 13,
"vocab_size": 12
} | 6 | Python | 6 | 933415bfa1f9eb89f935037014dfed816eb9815d | __init__.py | 9,895 | 7 | 15 | uses_after_args | https://github.com/jina-ai/jina.git | feat: star routing (#3900)
* feat(proto): adjust proto for star routing (#3844)
* feat(proto): adjust proto for star routing
* feat(proto): generate proto files
* feat(grpc): refactor grpclet interface (#3846)
* feat: refactor connection pool for star routing (#3872)
* feat(k8s): add more labels to k8s deployments
* feat(network): refactor connection pool
* feat(network): refactor k8s pool
* feat: star routing graph gateway (#3877)
* feat: star routing - refactor grpc data runtime (#3887)
* feat(runtimes): refactor grpc dataruntime
* fix(tests): adapt worker runtime tests
* fix(import): fix import
* feat(proto): enable sending multiple lists (#3891)
* feat: star routing gateway (#3893)
* feat: star routing gateway all protocols (#3897)
* test: add streaming and prefetch tests (#3901)
* feat(head): new head runtime for star routing (#3899)
* feat(head): new head runtime
* feat(head): new head runtime
* style: fix overload and cli autocomplete
* feat(network): improve proto comments
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(worker): merge docs in worker runtime (#3905)
* feat(worker): merge docs in worker runtime
* feat(tests): assert after clean up
* feat(tests): star routing runtime integration tests (#3908)
* fix(tests): fix integration tests
* test: test runtimes fast slow request (#3910)
* feat(zmq): purge zmq, zed, routing_table (#3915)
* feat(zmq): purge zmq, zed, routing_table
* style: fix overload and cli autocomplete
* feat(zmq): adapt comment in dependency list
* style: fix overload and cli autocomplete
* fix(tests): fix type tests
Co-authored-by: Jina Dev Bot <[email protected]>
* test: add test gateway to worker connection (#3921)
* feat(pea): adapt peas for star routing (#3918)
* feat(pea): adapt peas for star routing
* style: fix overload and cli autocomplete
* feat(pea): add tests
* feat(tests): add failing head pea test
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(tests): integration tests for peas (#3923)
* feat(tests): integration tests for peas
* feat(pea): remove _inner_pea function
* feat: star routing container pea (#3922)
* test: rescue tests (#3942)
* fix: fix streaming tests (#3945)
* refactor: move docker run to run (#3948)
* feat: star routing pods (#3940)
* feat(pod): adapt pods for star routing
* feat(pods): adapt basepod to star routing
* feat(pod): merge pod and compound pod
* feat(tests): fix tests
* style: fix overload and cli autocomplete
* feat(test): add container pea int test
* feat(ci): remove more unnecessary tests
* fix(tests): remove jinad runtime
* feat(ci): remove latency tracking
* fix(ci): fix ci def
* fix(runtime): enable runtime to be exited
* fix(tests): wrap runtime test in process
* fix(runtimes): remove unused runtimes
* feat(runtimes): improve cancel wait
* fix(ci): build test pip again in ci
* fix(tests): fix a test
* fix(test): run async in its own process
* feat(pod): include shard in activate msg
* fix(pea): dont join
* feat(pod): more debug out
* feat(grpc): manage channels properly
* feat(pods): remove exitfifo
* feat(network): add simple send retry mechanism
* fix(network): await pool close
* fix(test): always close grpc server in worker
* fix(tests): remove container pea from tests
* fix(tests): reorder tests
* fix(ci): split tests
* fix(ci): allow alias setting
* fix(test): skip a test
* feat(pods): address comments
Co-authored-by: Jina Dev Bot <[email protected]>
* test: unblock skipped test (#3957)
* feat: jinad pea (#3949)
* feat: jinad pea
* feat: jinad pea
* test: remote peas
* test: toplogy tests with jinad
* ci: parallel jobs
* feat(tests): add pod integration tests (#3958)
* feat(tests): add pod integration tests
* fix(tests): make tests less flaky
* fix(test): fix test
* test(pea): remote pea topologies (#3961)
* test(pea): remote pea simple topology
* test: remote pea topologies
* refactor: refactor streamer result handling (#3960)
* feat(k8s): adapt K8s Pod for StarRouting (#3964)
* test: optimize k8s test
* test: increase timeout and use different namespace
* test: optimize k8s test
* test: build and load image when needed
* test: refactor k8s test
* test: fix image name error
* test: fix k8s image load
* test: fix typoe port expose
* test: update tests in connection pool and handling
* test: remove unused fixture
* test: parameterize docker images
* test: parameterize docker images
* test: parameterize docker images
* feat(k8s): adapt k8s pod for star routing
* fix(k8s): dont overwrite add/remove function in pool
* fix(k8s): some fixes
* fix(k8s): some more fixes
* fix(k8s): linting
* fix(tests): fix tests
* fix(tests): fix k8s unit tests
* feat(k8s): complete k8s integration test
* feat(k8s): finish k8s tests
* feat(k8s): fix test
* fix(tests): fix test with no name
* feat(k8s): unify create/replace interface
* feat(k8s): extract k8s port constants
* fix(tests): fix tests
* fix(tests): wait for runtime being ready in tests
* feat(k8s): address comments
Co-authored-by: bwanglzu <[email protected]>
* feat(flow): adapt Flow for StarRouting (#3986)
* feat(flow): add routes
* feat(flow): adapt flow to star routing
* style: fix overload and cli autocomplete
* feat(flow): handle empty topologies
* feat(k8s): allow k8s pool disabling
* style: fix overload and cli autocomplete
* fix(test): fix test with mock
* fix(tests): fix more tests
* feat(flow): clean up tests
* style: fix overload and cli autocomplete
* fix(tests): fix more tests
* feat: add plot function (#3994)
* fix(tests): avoid hanging tests
* feat(flow): add type hinting
* fix(test): fix duplicate exec name in test
* fix(tests): fix more tests
* fix(tests): enable jinad test again
* fix(tests): random port fixture
* fix(style): replace quotes
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* feat(ci): bring back ci (#3997)
* feat(ci): enable ci again
* style: fix overload and cli autocomplete
* feat(ci): add latency tracking
* feat(ci): bring back some tests
* fix(tests): remove invalid port test
* feat(ci): disable daemon and distributed tests
* fix(tests): fix entrypoint in hub test
* fix(tests): wait for gateway to be ready
* fix(test): fix more tests
* feat(flow): do rolling update and scale sequentially
* fix(tests): fix more tests
* style: fix overload and cli autocomplete
* feat: star routing hanging pods (#4011)
* fix: try to handle hanging pods better
* test: hanging pods test work
* fix: fix topology graph problem
* test: add unit test to graph
* fix(tests): fix k8s tests
* fix(test): fix k8s test
* fix(test): fix k8s pool test
* fix(test): fix k8s test
* fix(test): fix k8s connection pool setting
* fix(tests): make runtime test more reliable
* fix(test): fix routes test
* fix(tests): make rolling update test less flaky
* feat(network): gurantee unique ports
* feat(network): do round robin for shards
* fix(ci): increase pytest timeout to 10 min
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Joan Fontanals <[email protected]>
* fix(ci): fix ci file
* feat(daemon): jinad pod for star routing
* Revert "feat(daemon): jinad pod for star routing"
This reverts commit ed9b37ac862af2e2e8d52df1ee51c0c331d76f92.
* feat(daemon): remote jinad pod support (#4042)
* feat(daemon): add pod tests for star routing
* feat(daemon): add remote pod test
* test(daemon): add remote pod arguments test
* test(daemon): add async scale test
* test(daemon): add rolling update test
* test(daemon): fix host
* feat(proto): remove message proto (#4051)
* feat(proto): remove message proto
* fix(tests): fix tests
* fix(tests): fix some more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* fix(tests): fix more tests
* feat(proto): put docs back in data
* fix(proto): clean up
* feat(proto): clean up
* fix(tests): skip latency tracking
* fix(test): fix hub test
* fix(tests): fix k8s test
* fix(test): some test clean up
* fix(style): clean up style issues
* feat(proto): adjust for rebase
* fix(tests): bring back latency tracking
* fix(tests): fix merge accident
* feat(proto): skip request serialization (#4074)
* feat: add reduce to star routing (#4070)
* feat: add reduce on shards to head runtime
* test: add reduce integration tests with fixed order
* feat: add reduce on needs
* chore: get_docs_matrix_from_request becomes public
* style: fix overload and cli autocomplete
* docs: remove undeterministic results warning
* fix: fix uses_after
* test: assert correct num docs after reducing in test_external_pod
* test: correct asserts after reduce in test_rolling_update
* fix: no reduce if uses_after_address is set
* fix: get_docs_from_request only if needed
* fix: fix tests after merge
* refactor: move reduce from data_request_handler to head
* style: fix overload and cli autocomplete
* chore: apply suggestions
* fix: fix asserts
* chore: minor test fix
* chore: apply suggestions
* test: remove flow tests with external executor (pea)
* fix: fix test_expected_messages_routing
* fix: fix test_func_joiner
* test: adapt k8s test
Co-authored-by: Jina Dev Bot <[email protected]>
* fix(k8s): fix static pool config
* fix: use custom protoc doc generator image (#4088)
* fix: use custom protoc doc generator image
* fix(docs): minor doc improvement
* fix(docs): use custom image
* fix(docs): copy docarray
* fix: doc building local only
* fix: timeout doc building
* fix: use updated args when building ContainerPea
* test: add container PeaFactory test
* fix: force pea close on windows (#4098)
* fix: dont reduce if uses exist (#4099)
* fix: dont use reduce if uses exist
* fix: adjust reduce tests
* fix: adjust more reduce tests
* fix: fix more tests
* fix: adjust more tests
* fix: ignore non jina resources (#4101)
* feat(executor): enable async executors (#4102)
* feat(daemon): daemon flow on star routing (#4096)
* test(daemon): add remote flow test
* feat(daemon): call scale in daemon
* feat(daemon): remove tail args and identity
* test(daemon): rename scalable executor
* test(daemon): add a small delay in async test
* feat(daemon): scale partial flow only
* feat(daemon): call scale directly in partial flow store
* test(daemon): use asyncio sleep
* feat(daemon): enable flow level distributed tests
* test(daemon): fix jinad env workspace config
* test(daemon): fix pod test use new port rolling update
* feat(daemon): enable distribuetd tests
* test(daemon): remove duplicate tests and zed runtime test
* test(daemon): fix stores unit test
* feat(daemon): enable part of distributed tests
* feat(daemon): enable part of distributed tests
* test: correct test paths
* test(daemon): add client test for remote flows
* test(daemon): send a request with jina client
* test(daemon): assert async generator
* test(daemon): small interval between tests
* test(daemon): add flow test for container runtime
* test(daemon): add flow test for container runtime
* test(daemon): fix executor name
* test(daemon): fix executor name
* test(daemon): use async client fetch result
* test(daemon): finish container flow test
* test(daemon): enable distributed in ci
* test(daemon): enable distributed in ci
* test(daemon): decare flows and pods
* test(daemon): debug ci if else
* test(daemon): debug ci if else
* test(daemon): decare flows and pods
* test(daemon): correct test paths
* test(daemon): add small delay for async tests
* fix: star routing fixes (#4100)
* docs: update docs
* fix: fix Request.__repr__
* docs: update flow remarks
* docs: fix typo
* test: add non_empty_fields test
* chore: remove non_empty_fields test
* feat: polling per endpoint (#4111)
* feat(polling): polling per endpoint configurable
* fix: adjust tests
* feat(polling): extend documentation
* style: fix overload and cli autocomplete
* fix: clean up
* fix: adjust more tests
* fix: remove repeat from flaky test
* fix: k8s test
* feat(polling): address pr feedback
* feat: improve docs
Co-authored-by: Jina Dev Bot <[email protected]>
* feat(grpc): support connect grpc server via ssl tunnel (#4092)
* feat(grpc): support ssl grpc connect if port is 443
* fix(grpc): use https option instead of detect port automatically
* chore: fix typo
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* fix: update jina/peapods/networking.py
Co-authored-by: Joan Fontanals <[email protected]>
* test(networking): add test for peapods networking
* fix: address comments
Co-authored-by: Joan Fontanals <[email protected]>
* feat(polling): unify polling args (#4113)
* fix: several issues for jinad pods (#4119)
* fix: activate for jinad pods
* fix: dont expose worker pod in partial daemon
* fix: workspace setting
* fix: containerized flows
* fix: hub test
* feat(daemon): remote peas on star routing (#4112)
* test(daemon): fix request in peas
* test(daemon): fix request in peas
* test(daemon): fix sync async client test
* test(daemon): enable remote peas test
* test(daemon): replace send message to send request
* test(daemon): declare pea tests in ci
* test(daemon): use pea args fixture
* test(daemon): head pea use default host
* test(daemon): fix peas topologies
* test(daemon): fix pseudo naming
* test(daemon): use default host as host
* test(daemon): fix executor path
* test(daemon): add remote worker back
* test(daemon): skip local remote remote topology
* fix: jinad pea test setup
* fix: jinad pea tests
* fix: remove invalid assertion
Co-authored-by: jacobowitz <[email protected]>
* feat: enable daemon tests again (#4132)
* feat: enable daemon tests again
* fix: remove bogy empty script file
* fix: more jinad test fixes
* style: fix overload and cli autocomplete
* fix: scale and ru in jinad
* fix: fix more jinad tests
Co-authored-by: Jina Dev Bot <[email protected]>
* fix: fix flow test
* fix: improve pea tests reliability (#4136)
Co-authored-by: Joan Fontanals <[email protected]>
Co-authored-by: Jina Dev Bot <[email protected]>
Co-authored-by: Deepankar Mahapatro <[email protected]>
Co-authored-by: bwanglzu <[email protected]>
Co-authored-by: AlaeddineAbdessalem <[email protected]>
Co-authored-by: Zhaofeng Miao <[email protected]> | 20 | 0 | 1,762 | 7 |
|
1 | 2 | def captured_stderr():
return captured_output("stderr")
| django/test/utils.py | 23 | django | {
"docstring": "Capture the output of sys.stderr:\n\n with captured_stderr() as stderr:\n print(\"hello\", file=sys.stderr)\n self.assertEqual(stderr.getvalue(), \"hello\\n\")\n ",
"language": "en",
"n_whitespaces": 29,
"n_words": 13,
"vocab_size": 13
} | 4 | Python | 4 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | utils.py | 206,492 | 2 | 10 | captured_stderr | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 10 | 0 | 51,543 | 8 |
|
3 | 20 | def testBestCheckpointsOnlyNan(self):
keep_checkpoints_num = 2
checkpoint_manager = self.checkpoint_manager(keep_checkpoints_num)
checkpoints = [
_TrackedCheckpoint(
dir_or_data=i,
storage_mode=CheckpointStorage.PERSISTENT,
metrics=self.mock_result(float("nan"), i),
)
for i in range(4)
]
for checkpoint in checkpoints:
checkpoint_manager.on_checkpoint(checkpoint)
best_checkpoints = checkpoint_manager.best_checkpoints()
# best_checkpoints is sorted from worst to best
self.assertEqual(len(best_checkpoints), keep_checkpoints_num)
self.assertEqual(best_checkpoints[0].dir_or_data, 2)
self.assertEqual(best_checkpoints[1].dir_or_data, 3)
| python/ray/tune/tests/test_checkpoint_manager.py | 173 | ray | {
"docstring": "\n Tests that checkpoints with only nan priority are handled correctly.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 10
} | 44 | Python | 38 | 8affbc7be6fdce169264b8db5b0276dbcc719f6d | test_checkpoint_manager.py | 141,374 | 17 | 110 | testBestCheckpointsOnlyNan | https://github.com/ray-project/ray.git | [tune/train] Consolidate checkpoint manager 3: Ray Tune (#24430)
**Update**: This PR is now part 3 of a three PR group to consolidate the checkpoints.
1. Part 1 adds the common checkpoint management class #24771
2. Part 2 adds the integration for Ray Train #24772
3. This PR builds on #24772 and includes all changes. It moves the Ray Tune integration to use the new common checkpoint manager class.
Old PR description:
This PR consolidates the Ray Train and Tune checkpoint managers. These concepts previously did something very similar but in different modules. To simplify maintenance in the future, we've consolidated the common core.
- This PR keeps full compatibility with the previous interfaces and implementations. This means that for now, Train and Tune will have separate CheckpointManagers that both extend the common core
- This PR prepares Tune to move to a CheckpointStrategy object
- In follow-up PRs, we can further unify interfacing with the common core, possibly removing any train- or tune-specific adjustments (e.g. moving to setup on init rather on runtime for Ray Train)
Co-authored-by: Antoni Baum <[email protected]> | 210 | 0 | 32,343 | 15 |
|
1 | 5 | def slow(test_case):
return unittest.skipUnless(_run_slow_tests, "test is slow")(test_case)
| src/transformers/testing_utils.py | 33 | transformers | {
"docstring": "\n Decorator marking a test as slow.\n\n Slow tests are skipped by default. Set the RUN_SLOW environment variable to a truthy value to run them.\n\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 24,
"vocab_size": 22
} | 7 | Python | 7 | 57e6464ac9a31156f1c93e59107323e6ec01309e | testing_utils.py | 37,497 | 2 | 18 | slow | https://github.com/huggingface/transformers.git | Update all require decorators to use skipUnless when possible (#16999) | 13 | 0 | 6,802 | 9 |
|
3 | 10 | def calc_first_derivative(self, x):
if x < self.x[0]:
return None
elif x > self.x[-1]:
return None
i = self.__search_index(x)
dx = x - self.x[i]
dy = self.b[i] + 2.0 * self.c[i] * dx + 3.0 * self.d[i] * dx ** 2.0
return dy
| PathPlanning/CubicSpline/cubic_spline_planner.py | 131 | PythonRobotics | {
"docstring": "\n Calc first derivative at given x.\n\n if x is outside the input x, return None\n\n Returns\n -------\n dy : float\n first derivative for given x.\n ",
"language": "en",
"n_whitespaces": 79,
"n_words": 25,
"vocab_size": 21
} | 42 | Python | 27 | def289b723e9216830c2a7b2577cb31b55710167 | cubic_spline_planner.py | 19,357 | 9 | 91 | calc_first_derivative | https://github.com/AtsushiSakai/PythonRobotics.git | enhance cubic spline path doc (#698)
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cublic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc
* enhance cubic spline path doc | 113 | 0 | 2,945 | 12 |
|
2 | 5 | def countedArray(expr, intExpr=None):
arrayExpr = Forward() | .venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py | 27 | transferlearning | {
"docstring": "Helper to define a counted list of expressions.\n\n This helper defines a pattern of the form::\n\n integer expr expr expr...\n\n where the leading integer tells how many expr expressions follow.\n The matched tokens returns the array of expr tokens as a list - the\n leading count token is suppressed.\n\n If ``intExpr`` is specified, it should be a pyparsing expression\n that produces an integer value.\n\n Example::\n\n countedArray(Word(alphas)).parseString('2 ab cd ef') # -> ['ab', 'cd']\n\n # in this parser, the leading integer value is given in binary,\n # '10' indicating that 2 values are in the array\n binaryConstant = Word('01').setParseAction(lambda t: int(t[0], 2))\n countedArray(Word(alphas), intExpr=binaryConstant).parseString('10 ab cd ef') # -> ['ab', 'cd']\n ",
"language": "en",
"n_whitespaces": 178,
"n_words": 110,
"vocab_size": 75
} | 6 | Python | 6 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | pyparsing.py | 63,284 | 10 | 85 | countedArray | https://github.com/jindongwang/transferlearning.git | upd; format | 12 | 0 | 13,233 | 8 |
|
8 | 5 | def test_overlapping_function_names(self) -> None:
ops = [
helper.make_opsetid("", 10),
helper.make_opsetid("local", 10)
]
| onnx/test/compose_test.py | 50 | onnx | {
"docstring": "\n Tests error checking when the name of local function entries overlaps\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 11
} | 12 | Python | 12 | 83fa57c74edfd13ddac9548b8a12f9e3e2ed05bd | compose_test.py | 255,414 | 97 | 814 | test_overlapping_function_names | https://github.com/onnx/onnx.git | Use Python type annotations rather than comments (#3962)
* These have been supported since Python 3.5.
ONNX doesn't support Python < 3.6, so we can use the annotations.
Diffs generated by https://pypi.org/project/com2ann/.
Signed-off-by: Gary Miguel <[email protected]>
* Remove MYPY conditional logic in gen_proto.py
It breaks the type annotations and shouldn't be needed.
Signed-off-by: Gary Miguel <[email protected]>
* Get rid of MYPY bool from more scripts
Signed-off-by: Gary Miguel <[email protected]>
* move Descriptors class above where its referenced in type annotation
Signed-off-by: Gary Miguel <[email protected]>
* fixes
Signed-off-by: Gary Miguel <[email protected]>
* remove extra blank line
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotations
Signed-off-by: Gary Miguel <[email protected]>
* fix type annotation in gen_docs
Signed-off-by: Gary Miguel <[email protected]>
* fix Operators.md
Signed-off-by: Gary Miguel <[email protected]>
* fix TestCoverage.md
Signed-off-by: Gary Miguel <[email protected]>
* fix protoc-gen-mypy.py
Signed-off-by: Gary Miguel <[email protected]> | 55 | 0 | 74,754 | 10 |
|
24 | 49 | def get_conn(self) -> Any:
in_cluster = self._coalesce_param(
self.in_cluster, self.conn_extras.get("extra__kubernetes__in_cluster") or None
)
cluster_context = self._coalesce_param(
self.cluster_context, self.conn_extras.get("extra__kubernetes__cluster_context") or None
)
kubeconfig_path = self._coalesce_param(
self.config_file, self.conn_extras.get("extra__kubernetes__kube_config_path") or None
)
kubeconfig = self.conn_extras.get("extra__kubernetes__kube_config") or None
num_selected_configuration = len([o for o in [in_cluster, kubeconfig, kubeconfig_path] if o])
if num_selected_configuration > 1:
raise AirflowException(
"Invalid connection configuration. Options kube_config_path, "
"kube_config, in_cluster are mutually exclusive. "
"You can only use one option at a time."
)
disable_verify_ssl = self._coalesce_param(
self.disable_verify_ssl, _get_bool(self._get_field("disable_verify_ssl"))
)
disable_tcp_keepalive = self._coalesce_param(
self.disable_tcp_keepalive, _get_bool(self._get_field("disable_tcp_keepalive"))
)
# BEGIN apply settings from core kubernetes configuration
# this section should be removed in next major release
deprecation_warnings: List[Tuple[str, Any]] = []
if disable_verify_ssl is None and self._deprecated_core_disable_verify_ssl is True:
deprecation_warnings.append(('verify_ssl', False))
disable_verify_ssl = self._deprecated_core_disable_verify_ssl
# by default, hook will try in_cluster first. so we only need to
# apply core airflow config and alert when False and in_cluster not otherwise set.
if in_cluster is None and self._deprecated_core_in_cluster is False:
deprecation_warnings.append(('in_cluster', self._deprecated_core_in_cluster))
in_cluster = self._deprecated_core_in_cluster
if not cluster_context and self._deprecated_core_cluster_context:
deprecation_warnings.append(('cluster_context', self._deprecated_core_cluster_context))
cluster_context = self._deprecated_core_cluster_context
if not kubeconfig_path and self._deprecated_core_config_file:
deprecation_warnings.append(('config_file', self._deprecated_core_config_file))
kubeconfig_path = self._deprecated_core_config_file
if disable_tcp_keepalive is None and self._deprecated_core_disable_tcp_keepalive is True:
deprecation_warnings.append(('enable_tcp_keepalive', False))
disable_tcp_keepalive = True
if deprecation_warnings:
self._deprecation_warning_core_param(deprecation_warnings)
# END apply settings from core kubernetes configuration
if disable_verify_ssl is True:
_disable_verify_ssl()
if disable_tcp_keepalive is not True:
_enable_tcp_keepalive()
if in_cluster:
self.log.debug("loading kube_config from: in_cluster configuration")
config.load_incluster_config()
return client.ApiClient()
if kubeconfig_path is not None:
self.log.debug("loading kube_config from: %s", kubeconfig_path)
config.load_kube_config(
config_file=kubeconfig_path,
client_configuration=self.client_configuration,
context=cluster_context,
)
return client.ApiClient()
if kubeconfig is not None:
with tempfile.NamedTemporaryFile() as temp_config:
self.log.debug("loading kube_config from: connection kube_config")
temp_config.write(kubeconfig.encode())
temp_config.flush()
config.load_kube_config(
config_file=temp_config.name,
client_configuration=self.client_configuration,
context=cluster_context,
)
return client.ApiClient()
return self._get_default_client(cluster_context=cluster_context)
| airflow/providers/cncf/kubernetes/hooks/kubernetes.py | 759 | airflow | {
"docstring": "Returns kubernetes api session for use with requests",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 267 | Python | 146 | 60eb9e106f5915398eafd6aa339ec710c102dc09 | kubernetes.py | 42,787 | 71 | 460 | get_conn | https://github.com/apache/airflow.git | Use KubernetesHook to create api client in KubernetesPodOperator (#20578)
Add support for k8s hook in KPO; use it always (even when no conn id); continue to consider the core k8s settings that KPO already takes into account but emit deprecation warning about them.
KPO historically takes into account a few settings from core airflow cfg (e.g. verify ssl, tcp keepalive, context, config file, and in_cluster). So to use the hook to generate the client, somehow the hook has to take these settings into account. But we don't want the hook to consider these settings in general. So we read them in KPO and if necessary patch the hook and warn. | 1,032 | 0 | 7,735 | 13 |
|
1 | 6 | def get_feedback():
labels = DOCUMENT_STORE.get_all_labels()
return labels
@router.delete("/feedback") | rest_api/controller/feedback.py | 41 | @router.delete("/feedback") | haystack | {
"docstring": "\n This endpoint allows the API user to retrieve all the feedback that has been submitted\n through the `POST /feedback` endpoint.\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 20,
"vocab_size": 18
} | 8 | Python | 7 | 4e940be85902dc93f3924662ba83111df72bb4d3 | feedback.py | 256,622 | 3 | 14 | get_feedback | https://github.com/deepset-ai/haystack.git | Allow Linux CI to push changes to forks (#2182)
* Add explicit reference to repo name to allow CI to push code back
* Run test matrix only on tested code changes
* Isolate the bot to check if it works
* Clarify situation with a comment
* Simplify autoformat.yml
* Add code and docs check
* Add git pull to make sure to fetch changes if they were created
* Add cache to autoformat.yml too
* Add information on forks in CONTRIBUTING.md
* Add a not about code quality tools in CONTRIBUTING.md
* Add image file types to the CI exclusion list
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> | 16 | 1 | 74,903 | 8 |
1 | 2 | def boxmean(self):
return self["boxmean"]
| packages/python/plotly/plotly/graph_objs/_box.py | 22 | plotly.py | {
"docstring": "\n If True, the mean of the box(es)' underlying distribution is\n drawn as a dashed line inside the box(es). If \"sd\" the standard\n deviation is also drawn. Defaults to True when `mean` is set.\n Defaults to \"sd\" when `sd` is set Otherwise defaults to False.\n\n The 'boxmean' property is an enumeration that may be specified as:\n - One of the following enumeration values:\n [True, 'sd', False]\n\n Returns\n -------\n Any\n ",
"language": "en",
"n_whitespaces": 156,
"n_words": 68,
"vocab_size": 52
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _box.py | 226,332 | 2 | 11 | boxmean | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 58,005 | 7 |
|
1 | 28 | def test_task_result_with_error(self):
result1 = TaskResult.objects.create(
task_id=str(uuid.uuid4()),
task_name="documents.tasks.some_task",
status=celery.states.SUCCESS,
result={
"exc_type": "ConsumerError",
"exc_message": ["test.pdf: Not consuming test.pdf: It is a duplicate."],
"exc_module": "documents.consumer",
},
)
_ = PaperlessTask.objects.create(attempted_task=result1)
response = self.client.get(self.ENDPOINT)
self.assertEqual(response.status_code, 200)
self.assertEqual(len(response.data), 1)
returned_data = response.data[0]
self.assertEqual(
returned_data["result"],
"test.pdf: Not consuming test.pdf: It is a duplicate.",
)
| src/documents/tests/test_api.py | 206 | paperless-ngx | {
"docstring": "\n GIVEN:\n - A celery task completed with an exception\n WHEN:\n - API call is made to get tasks\n THEN:\n - The returned result is the exception info\n ",
"language": "en",
"n_whitespaces": 89,
"n_words": 27,
"vocab_size": 23
} | 48 | Python | 38 | 5b66ef0a748fd5570361a2a1ed6147e0462568d2 | test_api.py | 320,069 | 20 | 124 | test_task_result_with_error | https://github.com/paperless-ngx/paperless-ngx.git | Updates how task_args and task_kwargs are parsed, adds testing to cover everything I can think of | 240 | 0 | 117,054 | 13 |
|
1 | 6 | def serialize_for_task_group(self) -> Tuple[DagAttributeTypes, Any]:
raise NotImplementedError()
| airflow/models/taskmixin.py | 29 | airflow | {
"docstring": "This is used by SerializedTaskGroup to serialize a task group's content.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 7 | Python | 7 | 2fdc23333909096d427171002582e2906f8bbc0a | taskmixin.py | 43,876 | 3 | 17 | serialize_for_task_group | https://github.com/apache/airflow.git | Fix remaining mypy issues in "core" Airflow (#20795)
Co-authored-by: Josh Fell <[email protected]>
Co-authored-by: Tzu-ping Chung <[email protected]>
Co-authored-by: Jarek Potiuk <[email protected]> | 21 | 0 | 8,079 | 7 |
|
1 | 13 | def bind(self, *args, **kwargs):
| python/ray/actor.py | 29 | """
For Ray DAG building that creates static graph from decorated | ray | {
"docstring": "\n For Ray DAG building that creates static graph from decorated",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 10
} | 4 | Python | 4 | f8b0ab7e78246e4dddf6c0095f3f7a5e409988ba | actor.py | 141,858 | 5 | 39 | bind | https://github.com/ray-project/ray.git | [Ray DAG] Add documentation in `more options` section (#25528) | 11 | 1 | 32,500 | 5 |
14 | 23 | def _build_repr_df(self, num_rows, num_cols):
# Fast track for empty dataframe.
if len(self.index) == 0 or (self._is_dataframe and len(self.columns) == 0):
return pandas.DataFrame(
index=self.index,
columns=self.columns if self._is_dataframe else None,
)
if len(self.index) <= num_rows:
row_indexer = slice(None)
else:
# Add one here so that pandas automatically adds the dots
# It turns out to be faster to extract 2 extra rows and columns than to
# build the dots ourselves.
num_rows_for_head = num_rows // 2 + 1
num_rows_for_tail = (
num_rows_for_head
if len(self.index) > num_rows
else len(self.index) - num_rows_for_head
if len(self.index) - num_rows_for_head >= 0
else None
)
row_indexer = list(range(len(self.index))[:num_rows_for_head]) + (
list(range(len(self.index))[-num_rows_for_tail:])
if num_rows_for_tail is not None
else []
)
if self._is_dataframe:
if len(self.columns) <= num_cols:
col_indexer = slice(None)
else:
num_cols_for_front = num_cols // 2 + 1
num_cols_for_back = (
num_cols_for_front
if len(self.columns) > num_cols
else len(self.columns) - num_cols_for_front
if len(self.columns) - num_cols_for_front >= 0
else None
)
col_indexer = list(range(len(self.columns))[:num_cols_for_front]) + (
list(range(len(self.columns))[-num_cols_for_back:])
if num_cols_for_back is not None
else []
)
indexer = row_indexer, col_indexer
else:
indexer = row_indexer
return self.iloc[indexer]._query_compiler.to_pandas()
| modin/pandas/base.py | 473 | modin | {
"docstring": "\n Build pandas DataFrame for string representation.\n\n Parameters\n ----------\n num_rows : int\n Number of rows to show in string representation. If number of\n rows in this dataset is greater than `num_rows` then half of\n `num_rows` rows from the beginning and half of `num_rows` rows\n from the end are shown.\n num_cols : int\n Number of columns to show in string representation. If number of\n columns in this dataset is greater than `num_cols` then half of\n `num_cols` columns from the beginning and half of `num_cols`\n columns from the end are shown.\n\n Returns\n -------\n pandas.DataFrame or pandas.Series\n A pandas dataset with `num_rows` or fewer rows and `num_cols` or fewer columns.\n ",
"language": "en",
"n_whitespaces": 269,
"n_words": 106,
"vocab_size": 46
} | 173 | Python | 84 | 2ebc9cf51bfc773e3d4c898f5a33c0f60ad7ebc5 | base.py | 155,341 | 43 | 295 | _build_repr_df | https://github.com/modin-project/modin.git | REFACTOR-#5310: Remove some hasattr('columns') checks. (#5311)
Signed-off-by: mvashishtha <[email protected]> | 786 | 0 | 36,343 | 22 |
|
5 | 13 | def discover(cls, **kwargs):
context = kwargs.pop('context', None)
if context and kwargs:
raise ValueError("cannot accept context and kwargs")
context = context or DistributionFinder.Context(**kwargs)
return itertools.chain.from_iterable(
resolver(context) for resolver in cls._discover_resolvers()
)
| python3.10.4/Lib/importlib/metadata/__init__.py | 101 | XX-Net | {
"docstring": "Return an iterable of Distribution objects for all packages.\n\n Pass a ``context`` or pass keyword arguments for constructing\n a context.\n\n :context: A ``DistributionFinder.Context`` object.\n :return: Iterable of Distribution objects for all packages.\n ",
"language": "en",
"n_whitespaces": 67,
"n_words": 32,
"vocab_size": 24
} | 30 | Python | 24 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | __init__.py | 218,217 | 8 | 60 | discover | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 94 | 0 | 55,211 | 10 |
|
6 | 15 | def ensure_string_list(self, option):
r
val = getattr(self, option)
if val is None:
return
elif isinstance(val, str):
setattr(self, option, re.split(r',\s*|\s+', val))
else:
if isinstance(val, list):
ok = all(isinstance(v, str) for v in val)
else:
ok = False
if not ok:
raise DistutilsOptionError(
"'%s' must be a list of strings (got %r)"
% (option, val))
| python3.10.4/Lib/distutils/cmd.py | 144 | XX-Net | {
"docstring": "Ensure that 'option' is a list of strings. If 'option' is\n currently a string, we split it either on /,\\s*/ or /\\s+/, so\n \"foo bar baz\", \"foo,bar,baz\", and \"foo, bar baz\" all become\n [\"foo\", \"bar\", \"baz\"].\n ",
"language": "en",
"n_whitespaces": 67,
"n_words": 36,
"vocab_size": 32
} | 53 | Python | 44 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | cmd.py | 222,607 | 20 | 92 | ensure_string_list | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 229 | 0 | 56,670 | 15 |
|
3 | 27 | def _read(**kwargs) -> DataFrame:
Engine.subscribe(_update_engine)
from modin.core.execution.dispatching.factories.dispatcher import FactoryDispatcher
try:
pd_obj = FactoryDispatcher.read_csv_glob(**kwargs)
except AttributeError:
raise AttributeError("read_csv_glob() is only implemented for pandas on Ray.")
# This happens when `read_csv` returns a TextFileReader object for iterating through
if isinstance(pd_obj, pandas.io.parsers.TextFileReader):
reader = pd_obj.read
pd_obj.read = lambda *args, **kwargs: DataFrame(
query_compiler=reader(*args, **kwargs)
)
return pd_obj
return DataFrame(query_compiler=pd_obj)
read_csv_glob = _make_parser_func(sep=",")
| modin/experimental/pandas/io.py | 176 | modin | {
"docstring": "\n General documentation is available in `modin.pandas.read_csv`.\n\n This experimental feature provides parallel reading from multiple csv files which are\n defined by glob pattern.\n\n Parameters\n ----------\n **kwargs : dict\n Keyword arguments in `modin.pandas.read_csv`.\n\n Returns\n -------\n modin.DataFrame\n\n Examples\n --------\n >>> import modin.experimental.pandas as pd\n >>> df = pd.read_csv_glob(\"s3://dask-data/nyc-taxi/2015/yellow_tripdata_2015-1*\")\n UserWarning: `read_*` implementation has mismatches with pandas:\n Data types of partitions are different! Please refer to the troubleshooting section of the Modin documentation to fix this issue.\n VendorID tpep_pickup_datetime ... total_amount congestion_surcharge\n 0 1.0 2020-10-01 00:09:08 ... 4.30 0.0\n 1 1.0 2020-10-01 00:09:19 ... 13.30 2.5\n 2 1.0 2020-10-01 00:30:00 ... 15.36 2.5\n 3 2.0 2020-10-01 00:56:46 ... -3.80 0.0\n 4 2.0 2020-10-01 00:56:46 ... 3.80 0.0\n ... ... ... ... ... ...\n 4652008 NaN 2020-12-31 23:44:35 ... 43.95 2.5\n 4652009 NaN 2020-12-31 23:41:36 ... 20.17 2.5\n 4652010 NaN 2020-12-31 23:01:17 ... 78.98 0.0\n 4652011 NaN 2020-12-31 23:31:29 ... 39.50 0.0\n 4652012 NaN 2020-12-31 23:12:48 ... 20.64 0.0\n\n [4652013 rows x 18 columns]\n ",
"language": "en",
"n_whitespaces": 680,
"n_words": 158,
"vocab_size": 110
} | 58 | Python | 51 | dcee13d57ebf9a006460deedb734c15791acae7a | io.py | 153,838 | 50 | 100 | _read | https://github.com/modin-project/modin.git | REFACTOR-#4510: Align experimental and regular IO modules initializations (#4511)
Signed-off-by: alexander3774 <[email protected]> | 134 | 0 | 35,651 | 15 |
|
13 | 11 | def _to_csv_check_support(kwargs):
path_or_buf = kwargs["path_or_buf"]
compression = kwargs["compression"]
if not isinstance(path_or_buf, str):
return False
# case when the pointer is placed at the beginning of the file.
if "r" in kwargs["mode"] and "+" in kwargs["mode"]:
return False
# encodings with BOM don't support;
# instead of one mark in result bytes we will have them by the number of partitions
# so we should fallback in pandas for `utf-16`, `utf-32` with all aliases, in instance
# (`utf_32_be`, `utf_16_le` and so on)
if kwargs["encoding"] is not None:
encoding = kwargs["encoding"].lower()
if "u" in encoding or "utf" in encoding:
if "16" in encoding or "32" in encoding:
return False
if compression is None or not compression == "infer":
return False
if any((path_or_buf.endswith(ext) for ext in [".gz", ".bz2", ".zip", ".xz"])):
return False
return True
| modin/core/execution/ray/implementations/pandas_on_ray/io/io.py | 232 | modin | {
"docstring": "\n Check if parallel version of ``to_csv`` could be used.\n\n Parameters\n ----------\n kwargs : dict\n Keyword arguments passed to ``.to_csv()``.\n\n Returns\n -------\n bool\n Whether parallel version of ``to_csv`` is applicable.\n ",
"language": "en",
"n_whitespaces": 108,
"n_words": 29,
"vocab_size": 25
} | 131 | Python | 80 | 0faf4675140415e17d4112f9d0d37cfe87770b9e | io.py | 152,978 | 17 | 126 | _to_csv_check_support | https://github.com/modin-project/modin.git | REFACTOR-#3871: move related to pandas functionality into 'PandasOnRayIO' class (#3872)
Signed-off-by: Anatoly Myachev <[email protected]> | 329 | 0 | 35,220 | 12 |
|
5 | 19 | def test_start_by_longest(self):
if ok_ljspeech:
dataloader, _ = self._create_dataloader(2, c.r, 0, True)
dataloader.dataset.preprocess_samples()
for i, data in enumerate(dataloader):
if i == self.max_loader_iter:
break
mel_lengths = data["mel_lengths"]
if i == 0:
max_len = mel_lengths[0]
print(mel_lengths)
self.assertTrue(all(max_len >= mel_lengths))
| tests/data_tests/test_loader.py | 136 | TTS | {
"docstring": "Test start_by_longest option.\n\n Ther first item of the fist batch must be longer than all the other items.\n ",
"language": "en",
"n_whitespaces": 32,
"n_words": 18,
"vocab_size": 17
} | 36 | Python | 30 | ef63c995248fb854d1efae73acfbdcf75666c263 | test_loader.py | 262,200 | 12 | 84 | test_start_by_longest | https://github.com/coqui-ai/TTS.git | Implement `start_by_longest` option for TTSDatase | 196 | 0 | 77,141 | 14 |
|
2 | 12 | def get_uncertainty(mask_pred, labels):
if mask_pred.shape[1] == 1:
gt_class_logits = mask_pred.clone()
else:
inds = torch.arange(mask_pred.shape[0], device=mask_pred.device)
gt_class_logits = mask_pred[inds, labels].unsqueeze(1)
return -torch.abs(gt_class_logits)
| mmdet/models/utils/point_sample.py | 106 | mmdetection | {
"docstring": "Estimate uncertainty based on pred logits.\n\n We estimate uncertainty as L1 distance between 0.0 and the logits\n prediction in 'mask_pred' for the foreground class in `classes`.\n\n Args:\n mask_pred (Tensor): mask predication logits, shape (num_rois,\n num_classes, mask_height, mask_width).\n\n labels (list[Tensor]): Either predicted or ground truth label for\n each predicted mask, of length num_rois.\n\n Returns:\n scores (Tensor): Uncertainty scores with the most uncertain\n locations having the highest uncertainty score,\n shape (num_rois, 1, mask_height, mask_width)\n ",
"language": "en",
"n_whitespaces": 152,
"n_words": 72,
"vocab_size": 59
} | 21 | Python | 18 | c576e5d570bf64a99e2c6817ed7b5c0084a44a55 | point_sample.py | 244,084 | 7 | 67 | get_uncertainty | https://github.com/open-mmlab/mmdetection.git | [Enhance] Take point sample related functions out of mask_point_head (#7353)
add point sample
replace function in mask_point_head | 54 | 0 | 70,232 | 13 |
|
1 | 5 | def _is_textIO(stream):
return isinstance(stream, io.TextIOBase)
| python3.10.4/Lib/http/client.py | 26 | XX-Net | {
"docstring": "Test whether a file-like object is a text or a binary stream.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 12,
"vocab_size": 10
} | 5 | Python | 5 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | client.py | 217,708 | 2 | 15 | _is_textIO | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 19 | 0 | 54,893 | 8 |
|
1 | 17 | def test_pagination_offset_without_orderby(self):
response = self.get_response(
self.organization.slug,
field=f"count({TransactionMetricKey.MEASUREMENTS_LCP.value})",
groupBy="transaction",
cursor=Cursor(0, 1),
statsPeriod="1h",
useCase="performance",
)
assert response.status_code == 200, response.data
| tests/sentry/api/endpoints/test_organization_metric_data.py | 97 | sentry | {
"docstring": "\n Test that ensures a successful response is returned even when requesting an offset\n without an orderBy\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 15
} | 18 | Python | 18 | 35ec251212b82e5d9468062a3ab5945d8e739002 | test_organization_metric_data.py | 85,794 | 10 | 55 | test_pagination_offset_without_orderby | https://github.com/getsentry/sentry.git | feat(metrics): Support rate for derived metric [TET-129 TET-127] (#38792)
Adds support for operation `rate` to be able to compute performance
related metrics such as tpm, tps, epm, eps
This PR achieves this by:
- Defining rate as a derived operation that produces its own SnQL rather
than trying to compute the data sketch aggregate and using that directly
- Replaces `filter_conditions_func` that used to just produce a snql
condition to be used a conditional aggregate with `snql_func` that
instead produces a SnQL function
- Replaces the logic in `get_entity` on MetricsExpression to determine
the entity from the MRI rather than from the aggregate applied | 112 | 0 | 18,044 | 13 |
|
1 | 17 | def _finalize_sample_weight(self, sample_weight, y):
| sklearn/ensemble/_hist_gradient_boosting/gradient_boosting.py | 36 | """Finalize sample weight.
Used by subclasses to adjustuseful for | scikit-learn | {
"docstring": "Finalize sample weight.\n\n Used by subclasses to adjust sample_weights. This is useful for implementing",
"language": "en",
"n_whitespaces": 20,
"n_words": 14,
"vocab_size": 14
} | 4 | Python | 4 | 7fda68d45734d41e47da1f57d23348ae8de655b0 | gradient_boosting.py | 260,913 | 2 | 12 | _finalize_sample_weight | https://github.com/scikit-learn/scikit-learn.git | FEA Adds class_weight to HistGradientBoostingClassifier (#22014)
Co-authored-by: Olivier Grisel <[email protected]>
Co-authored-by: jeremie du boisberranger <[email protected]> | 11 | 2 | 76,560 | 7 |
2 | 10 | def frame_has_multiple_faces(self, frame_name):
if not frame_name:
retval = False
else:
retval = bool(len(self._data.get(frame_name, {}).get("faces", [])) > 1)
logger.trace("'%s': %s", frame_name, retval)
return retval
| lib/align/alignments.py | 97 | faceswap | {
"docstring": " Check whether a given frame_name exists within the alignments :attr:`data` and contains\n more than 1 face.\n\n Parameters\n ----------\n frame_name: str\n The frame_name name to check. This should be the base name of the frame, not the full\n path\n\n Returns\n -------\n bool\n ``True`` if the given frame_name exists within the alignments :attr:`data` and has more\n than 1 face associated with it, otherwise ``False``\n ",
"language": "en",
"n_whitespaces": 163,
"n_words": 62,
"vocab_size": 45
} | 23 | Python | 20 | 5e73437be47f2410439a3c6716de96354e6a0c94 | alignments.py | 101,212 | 7 | 58 | frame_has_multiple_faces | https://github.com/deepfakes/faceswap.git | lib.align updates:
- alignments.py
- Add typed dicts for imported alignments
- Explicitly check for presence of thumb value in alignments dict
- linting
- detected_face.py
- Typing
- Linting
- Legacy support for pre-aligned face
- Update dependencies to new property names | 80 | 0 | 20,633 | 19 |
|
5 | 7 | def get_qt_library_info(namespace):
if namespace == 'PyQt5':
return pyqt5_library_info
if namespace == 'PyQt6':
return pyqt6_library_info
elif namespace == 'PySide2':
return pyside2_library_info
elif namespace == 'PySide6':
return pyside6_library_info
raise ValueError(f'Invalid namespace: {namespace}!')
# add_qt_dependencies
# --------------------
# Generic implemnentation that finds the Qt 5/6 dependencies based on the hook name of a PyQt5/PyQt6/PySide2/PySide6
# hook. Returns (hiddenimports, binaries, datas). Typical usage:
# ``hiddenimports, binaries, datas = add_qt5_dependencies(__file__)``. | PyInstaller/utils/hooks/qt/__init__.py | 84 | pyinstaller | {
"docstring": "\n Return QtLibraryInfo instance for the given namespace.\n ",
"language": "en",
"n_whitespaces": 14,
"n_words": 7,
"vocab_size": 7
} | 65 | Python | 48 | d789a7daa7712716c89259b987349917a89aece7 | __init__.py | 264,021 | 10 | 40 | get_qt_library_info | https://github.com/pyinstaller/pyinstaller.git | hookutils: reorganize the Qt hook utilities
Reorganize the Qt module information to provide information necessary
to deal with variations between different python Qt bindings (PySide2,
PyQt5, PySide6, and PyQt6). Replace the existing table-like dictionary
with list of entries, which is easier to format and document. From this
list, we now generate two dictionaries; one that maps Qt module (shared
library) names to the module info entries (the same role as the old
dictionary), and one that maps python module names to the module info
entries. The latter is necessary to accommodate python modules that do
not have corresponding Qt shared libraries (header-only Qt modules,
such as QtAxContainer; or statically-linked module, such as QSci), but
we still need to provide information about plugins or translation
files.
The new information list is based on manual inspection of source code
for Qt 5.15 and 6.3, and should provide comprehensive information about
all plugin names and translation file basenames.
In addition, most of the helper functions, which take a reference to
the `QtLibraryInfo` class as their first argument, have been turned
into methods of the `QtLibraryInfo` class. The corresponding hooks
have also been adjusted. | 106 | 0 | 77,563 | 9 |
|
1 | 16 | def create_deepbooru_process(threshold=0.5):
from modules import shared # prevents circular reference
shared.deepbooru_process_manager = multiprocessing.Manager()
shared.deepbooru_process_queue = shared.deepbooru_process_manager.Queue()
shared.deepbooru_process_return = shared.deepbooru_process_manager.dict()
shared.deepbooru_process_return["value"] = -1
shared.deepbooru_process = multiprocessing.Process(target=deepbooru_process, args=(shared.deepbooru_process_queue, shared.deepbooru_process_return, threshold))
shared.deepbooru_process.start()
| modules/deepbooru.py | 140 | stable-diffusion-webui | {
"docstring": "\n Creates deepbooru process. A queue is created to send images into the process. This enables multiple images\n to be processed in a row without reloading the model or creating a new process. To return the data, a shared\n dictionary is created to hold the tags created. To wait for tags to be returned, a value of -1 is assigned\n to the dictionary and the method adding the image to the queue should wait for this value to be updated with\n the tags.\n ",
"language": "en",
"n_whitespaces": 105,
"n_words": 82,
"vocab_size": 50
} | 29 | Python | 25 | 1f92336be768d235c18a82acb2195b7135101ae7 | deepbooru.py | 152,835 | 8 | 87 | create_deepbooru_process | https://github.com/AUTOMATIC1111/stable-diffusion-webui.git | refactored the deepbooru module to improve speed on running multiple interogations in a row. Added the option to generate deepbooru tags for textual inversion preproccessing. | 54 | 0 | 35,197 | 11 |
|
5 | 49 | def _estimate_files_encoding_ratio(self) -> float:
if not DatasetContext.get_current().decoding_size_estimation:
return PARQUET_ENCODING_RATIO_ESTIMATE_DEFAULT
# Sample a few rows from Parquet files to estimate the encoding ratio.
# Launch tasks to sample multiple files remotely in parallel.
# Evenly distributed to sample N rows in i-th row group in i-th file.
# TODO(ekl/cheng) take into account column pruning.
start_time = time.perf_counter()
num_files = len(self._pq_ds.pieces)
num_samples = int(num_files * PARQUET_ENCODING_RATIO_ESTIMATE_SAMPLING_RATIO)
min_num_samples = min(
PARQUET_ENCODING_RATIO_ESTIMATE_MIN_NUM_SAMPLES, num_files
)
max_num_samples = min(
PARQUET_ENCODING_RATIO_ESTIMATE_MAX_NUM_SAMPLES, num_files
)
num_samples = max(min(num_samples, max_num_samples), min_num_samples)
# Evenly distributed to choose which file to sample, to avoid biased prediction
# if data is skewed.
file_samples = [
self._pq_ds.pieces[idx]
for idx in np.linspace(0, num_files - 1, num_samples).astype(int).tolist()
]
sample_piece = cached_remote_fn(_sample_piece)
futures = []
for idx, sample in enumerate(file_samples):
# Sample i-th row group in i-th file.
futures.append(sample_piece.remote(_SerializedPiece(sample), idx))
sample_ratios = ray.get(futures)
ratio = np.mean(sample_ratios)
sampling_duration = time.perf_counter() - start_time
if sampling_duration > 5:
logger.info(
"Parquet input size estimation took "
f"{round(sampling_duration, 2)} seconds."
)
logger.debug(f"Estimated Parquet encoding ratio from sampling is {ratio}.")
return max(ratio, PARQUET_ENCODING_RATIO_ESTIMATE_LOWER_BOUND)
| python/ray/data/datasource/parquet_datasource.py | 339 | ray | {
"docstring": "Return an estimate of the Parquet files encoding ratio.\n\n To avoid OOMs, it is safer to return an over-estimate than an underestimate.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 22,
"vocab_size": 20
} | 170 | Python | 110 | e19cf164fd51c4f6bf730e999cba46b30c39ff83 | parquet_datasource.py | 125,650 | 35 | 198 | _estimate_files_encoding_ratio | https://github.com/ray-project/ray.git | [Datasets] Use sampling to estimate in-memory data size for Parquet data source (#26868) | 488 | 0 | 27,939 | 15 |
|
4 | 11 | def johnson_lindenstrauss_min_dim(n_samples, *, eps=0.1):
eps = np.asarray(eps)
n_samples = np.asarray(n_samples)
if np.any(eps <= 0.0) or np.any(eps >= 1):
raise ValueError("The JL bound is defined for eps in ]0, 1[, got %r" % eps)
if np.any(n_samples) <= 0:
raise ValueError(
"The JL bound is defined for n_samples greater than zero, got %r"
% n_samples
)
denominator = (eps**2 / 2) - (eps**3 / 3)
return (4 * np.log(n_samples) / denominator).astype(np.int64)
| sklearn/random_projection.py | 177 | scikit-learn | {
"docstring": "Find a 'safe' number of components to randomly project to.\n\n The distortion introduced by a random projection `p` only changes the\n distance between two points by a factor (1 +- eps) in an euclidean space\n with good probability. The projection `p` is an eps-embedding as defined\n by:\n\n (1 - eps) ||u - v||^2 < ||p(u) - p(v)||^2 < (1 + eps) ||u - v||^2\n\n Where u and v are any rows taken from a dataset of shape (n_samples,\n n_features), eps is in ]0, 1[ and p is a projection by a random Gaussian\n N(0, 1) matrix of shape (n_components, n_features) (or a sparse\n Achlioptas matrix).\n\n The minimum number of components to guarantee the eps-embedding is\n given by:\n\n n_components >= 4 log(n_samples) / (eps^2 / 2 - eps^3 / 3)\n\n Note that the number of dimensions is independent of the original\n number of features but instead depends on the size of the dataset:\n the larger the dataset, the higher is the minimal dimensionality of\n an eps-embedding.\n\n Read more in the :ref:`User Guide <johnson_lindenstrauss>`.\n\n Parameters\n ----------\n n_samples : int or array-like of int\n Number of samples that should be a integer greater than 0. If an array\n is given, it will compute a safe number of components array-wise.\n\n eps : float or ndarray of shape (n_components,), dtype=float, \\\n default=0.1\n Maximum distortion rate in the range (0,1 ) as defined by the\n Johnson-Lindenstrauss lemma. If an array is given, it will compute a\n safe number of components array-wise.\n\n Returns\n -------\n n_components : int or ndarray of int\n The minimal number of components to guarantee with good probability\n an eps-embedding with n_samples.\n\n Examples\n --------\n >>> from sklearn.random_projection import johnson_lindenstrauss_min_dim\n >>> johnson_lindenstrauss_min_dim(1e6, eps=0.5)\n 663\n\n >>> johnson_lindenstrauss_min_dim(1e6, eps=[0.5, 0.1, 0.01])\n array([ 663, 11841, 1112658])\n\n >>> johnson_lindenstrauss_min_dim([1e4, 1e5, 1e6], eps=0.1)\n array([ 7894, 9868, 11841])\n\n References\n ----------\n\n .. [1] https://en.wikipedia.org/wiki/Johnson%E2%80%93Lindenstrauss_lemma\n\n .. [2] Sanjoy Dasgupta and Anupam Gupta, 1999,\n \"An elementary proof of the Johnson-Lindenstrauss Lemma.\"\n http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.45.3654\n\n ",
"language": "en",
"n_whitespaces": 522,
"n_words": 318,
"vocab_size": 187
} | 69 | Python | 50 | 1fc86b6aacd89da44a3b4e8abf7c3e2ba4336ffe | random_projection.py | 258,948 | 12 | 112 | johnson_lindenstrauss_min_dim | https://github.com/scikit-learn/scikit-learn.git | MNT Update black to stable version (#22474) | 133 | 0 | 75,490 | 12 |
|
4 | 14 | def verify_mac_libraries_dont_reference_chkstack():
if not _is_mac():
return
nm = subprocess.run(
["nm", "-g",
r.Rlocation("org_tensorflow/tensorflow/compiler/xla/python/xla_extension.so")
],
capture_output=True, text=True,
check=False)
if nm.returncode != 0:
raise RuntimeError(f"nm process failed: {nm.stdout} {nm.stderr}")
if "____chkstk_darwin" in nm.stdout:
raise RuntimeError(
"Mac wheel incorrectly depends on symbol ____chkstk_darwin, which "
"means that it isn't compatible with older MacOS versions.")
| build/build_wheel.py | 136 | jax | {
"docstring": "Verifies that xla_extension.so doesn't depend on ____chkstk_darwin.\n\n We don't entirely know why this happens, but in some build environments\n we seem to target the wrong Mac OS version.\n https://github.com/google/jax/issues/3867\n\n This check makes sure we don't release wheels that have this dependency.\n ",
"language": "en",
"n_whitespaces": 46,
"n_words": 41,
"vocab_size": 37
} | 50 | Python | 47 | 17de89b16ac5ee05aee03115d858e67489eab973 | build_wheel.py | 120,553 | 15 | 69 | verify_mac_libraries_dont_reference_chkstack | https://github.com/google/jax.git | feat: refactor code using pyupgrade
This PR upgrades legacy Python code to 3.7+ code using pyupgrade:
```sh
pyupgrade --py37-plus --keep-runtime-typing **.py
```
a | 90 | 0 | 26,889 | 12 |
|
2 | 8 | def test_timeout_does_not_wait_for_completion_for_sync_flows(self, tmp_path):
if sys.version_info[1] == 11:
pytest.xfail("The engine returns _after_ sleep finishes in Python 3.11")
canary_file = tmp_path / "canary"
| tests/test_flows.py | 52 | prefect | {
"docstring": "\n Sync flows are cancelled when they change instructions. The flow will return\n immediately when the timeout is reached, but the thread it executes in will\n continue until the next instruction is reached. `time.sleep` will return then\n the thread will be interrupted.\n ",
"language": "en",
"n_whitespaces": 77,
"n_words": 41,
"vocab_size": 31
} | 21 | Python | 21 | a7bd9cadd5038383449b0e75a87bb23a73b278d8 | test_flows.py | 59,586 | 14 | 96 | test_timeout_does_not_wait_for_completion_for_sync_flows | https://github.com/PrefectHQ/prefect.git | Add support for Python 3.11 (#7304)
Co-authored-by: Chris Guidry <[email protected]> | 53 | 0 | 11,913 | 10 |
|
2 | 15 | def get_target_distribution_details(filters):
target_details = {}
for d in frappe.db.sql(
,
(filters.from_fiscal_year, filters.to_fiscal_year),
as_dict=1,
):
target_details.setdefault(d.name, {}).setdefault(d.month, flt(d.percentage_allocation))
return target_details
# Get actual details from gl entry | erpnext/accounts/report/budget_variance_report/budget_variance_report.py | 97 | erpnext | {
"docstring": "\n\t\t\tselect\n\t\t\t\tmd.name,\n\t\t\t\tmdp.month,\n\t\t\t\tmdp.percentage_allocation\n\t\t\tfrom\n\t\t\t\t`tabMonthly Distribution Percentage` mdp,\n\t\t\t\t`tabMonthly Distribution` md\n\t\t\twhere\n\t\t\t\tmdp.parent = md.name\n\t\t\t\tand md.fiscal_year between %s and %s\n\t\t\torder by\n\t\t\t\tmd.fiscal_year\n\t\t",
"language": "en",
"n_whitespaces": 13,
"n_words": 25,
"vocab_size": 21
} | 26 | Python | 25 | 494bd9ef78313436f0424b918f200dab8fc7c20b | budget_variance_report.py | 65,174 | 22 | 63 | get_target_distribution_details | https://github.com/frappe/erpnext.git | style: format code with black | 16 | 0 | 13,816 | 12 |
|
2 | 10 | def get_list_context(context=None):
return {
"global_number_format": frappe.db.get_default("number_format") or "#,###.##",
"currency": frappe.db.get_default("currency"),
"currency_symbols": json.dumps(
dict(
frappe.db.sql(
)
)
),
"row_template": "templates/includes/transaction_row.html",
"get_list": get_transaction_list,
}
| erpnext/controllers/website_list_for_contact.py | 111 | erpnext | {
"docstring": "select name, symbol\n\t\t\tfrom tabCurrency where enabled=1",
"language": "en",
"n_whitespaces": 5,
"n_words": 7,
"vocab_size": 7
} | 22 | Python | 21 | 494bd9ef78313436f0424b918f200dab8fc7c20b | website_list_for_contact.py | 65,703 | 15 | 61 | get_list_context | https://github.com/frappe/erpnext.git | style: format code with black | 9 | 0 | 13,991 | 14 |
|
2 | 6 | def _assert_float_dtype(dtype):
dtype = tf.as_dtype(dtype)
if not dtype.is_floating:
raise ValueError(f"Expected floating point type, got {dtype}.")
return dtype
| keras/initializers/initializers_v2.py | 53 | keras | {
"docstring": "Validate and return floating point type based on `dtype`.\n\n `dtype` must be a floating point type.\n\n Args:\n dtype: The data type to validate.\n\n Returns:\n Validated type.\n\n Raises:\n ValueError: if `dtype` is not a floating point type.\n ",
"language": "en",
"n_whitespaces": 66,
"n_words": 36,
"vocab_size": 27
} | 17 | Python | 16 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | initializers_v2.py | 272,164 | 5 | 28 | _assert_float_dtype | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 36 | 0 | 80,966 | 11 |
|
1 | 4 | def removeChild(self, node):
raise NotImplementedError
| .venv/lib/python3.8/site-packages/pip/_vendor/html5lib/treebuilders/base.py | 18 | transferlearning | {
"docstring": "Remove node from the children of the current node\n\n :arg node: the child node to remove\n\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 16,
"vocab_size": 12
} | 5 | Python | 5 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | base.py | 62,599 | 2 | 10 | removeChild | https://github.com/jindongwang/transferlearning.git | upd; format | 19 | 0 | 13,010 | 6 |
|
1 | 13 | async def test_import_flow_triggered_but_no_ecobee_conf(hass):
flow = config_flow.EcobeeFlowHandler()
flow.hass = hass
flow.hass.data[DATA_ECOBEE_CONFIG] = {}
result = await flow.async_step_import(import_data=None)
assert result["type"] == data_entry_flow.FlowResultType.FORM
assert result["step_id"] == "user"
| tests/components/ecobee/test_config_flow.py | 101 | core | {
"docstring": "Test expected result if import flow triggers but ecobee.conf doesn't exist.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 24 | Python | 19 | 7cd68381f1d4f58930ffd631dfbfc7159d459832 | test_config_flow.py | 315,806 | 7 | 58 | test_import_flow_triggered_but_no_ecobee_conf | https://github.com/home-assistant/core.git | Search/replace RESULT_TYPE_* by FlowResultType enum (#74642) | 45 | 0 | 114,384 | 10 |
|
7 | 22 | def exception_handler(exc, context):
if isinstance(exc, Http404):
exc = exceptions.NotFound(*(exc.args))
elif isinstance(exc, PermissionDenied):
exc = exceptions.PermissionDenied(*(exc.args))
if isinstance(exc, exceptions.APIException):
headers = {}
if getattr(exc, 'auth_header', None):
headers['WWW-Authenticate'] = exc.auth_header
if getattr(exc, 'wait', None):
headers['Retry-After'] = '%d' % exc.wait
if isinstance(exc.detail, (list, dict)):
data = exc.detail
else:
data = {'detail': exc.detail}
set_rollback()
return Response(data, status=exc.status_code, headers=headers)
return None
| rest_framework/views.py | 246 | django-rest-framework | {
"docstring": "\n Returns the response that should be used for any given exception.\n\n By default we handle the REST framework `APIException`, and also\n Django's built-in `Http404` and `PermissionDenied` exceptions.\n\n Any unhandled exceptions may return `None`, which will cause a 500 error\n to be raised.\n ",
"language": "en",
"n_whitespaces": 61,
"n_words": 42,
"vocab_size": 39
} | 56 | Python | 39 | 56946fac8f29aa44ce84391f138d63c4c8a2a285 | views.py | 48,680 | 18 | 152 | exception_handler | https://github.com/encode/django-rest-framework.git | Preserve exception messages for wrapped Django exceptions (#8051)
* Preserve messages for wrapped Django exceptions
* Fix the test
* Update test_generics.py
* Update test_generics.py
Co-authored-by: Tom Christie <[email protected]> | 178 | 0 | 9,566 | 14 |
|
5 | 16 | def check_all_decorator_order():
errors = []
for fname in os.listdir(PATH_TO_TESTS):
if fname.endswith(".py"):
filename = os.path.join(PATH_TO_TESTS, fname)
new_errors = check_decorator_order(filename)
errors += [f"- {filename}, line {i}" for i in new_errors]
if len(errors) > 0:
msg = "\n".join(errors)
raise ValueError(
"The parameterized decorator (and its variants) should always be first, but this is not the case in the"
f" following files:\n{msg}"
)
| utils/check_repo.py | 148 | transformers | {
"docstring": "Check that in all test files, the slow decorator is always last.",
"language": "en",
"n_whitespaces": 11,
"n_words": 12,
"vocab_size": 12
} | 59 | Python | 51 | afe5d42d8d1d80af911ed980c2936bfe887078f6 | check_repo.py | 38,408 | 13 | 78 | check_all_decorator_order | https://github.com/huggingface/transformers.git | Black preview (#17217)
* Black preview
* Fixup too!
* Fix check copies
* Use the same version as the CI
* Bump black | 154 | 0 | 6,970 | 13 |
|
1 | 18 | def ready_to_fulfill(self):
statuses = {OrderStatus.UNFULFILLED, OrderStatus.PARTIALLY_FULFILLED}
payments = Payment.objects.filter(is_active=True).values("id")
return self.filter(
Exists(payments.filter(order_id=OuterRef("id"))),
status__in=statuses,
total_gross_amount__lte=F("total_paid_amount"),
)
| saleor/order/models.py | 110 | saleor | {
"docstring": "Return orders that can be fulfilled.\n\n Orders ready to fulfill are fully paid but unfulfilled (or partially\n fulfilled).\n ",
"language": "en",
"n_whitespaces": 39,
"n_words": 18,
"vocab_size": 18
} | 15 | Python | 14 | 34bf03ce99d46e84c0990f009025b436c1f0386c | models.py | 25,927 | 8 | 66 | ready_to_fulfill | https://github.com/saleor/saleor.git | Optimize order filtering by ready to fulfill status (#9113) | 83 | 0 | 4,925 | 15 |
|
1 | 7 | def fit_transform(self, X, y=None):
self._validate_params()
return self._fit_transform(X, compute_sources=True)
| sklearn/decomposition/_fastica.py | 45 | scikit-learn | {
"docstring": "Fit the model and recover the sources from X.\n\n Parameters\n ----------\n X : array-like of shape (n_samples, n_features)\n Training data, where `n_samples` is the number of samples\n and `n_features` is the number of features.\n\n y : Ignored\n Not used, present for API consistency by convention.\n\n Returns\n -------\n X_new : ndarray of shape (n_samples, n_components)\n Estimated sources obtained by transforming the data with the\n estimated unmixing matrix.\n ",
"language": "en",
"n_whitespaces": 177,
"n_words": 66,
"vocab_size": 49
} | 8 | Python | 8 | 4cc347d4d0cbbfdcbd353f08842e0668fed78c9f | _fastica.py | 260,360 | 3 | 28 | fit_transform | https://github.com/scikit-learn/scikit-learn.git | MAINT Use _validate_params in FastICA (#23711)
Co-authored-by: Guillaume Lemaitre <[email protected]>
Co-authored-by: jeremiedbb <[email protected]> | 29 | 0 | 76,206 | 8 |
|
17 | 38 | def _text2settings(self):
t2xs = [
(self.t2f, "font"),
(self.t2s, "slant"),
(self.t2w, "weight"),
(self.t2c, "color"),
]
setting_args = {arg: getattr(self, arg) for _, arg in t2xs}
settings = self._get_settings_from_t2xs(t2xs)
settings.extend(self._get_settings_from_gradient(setting_args))
# Handle overlaps
settings.sort(key=lambda setting: setting.start)
for index, setting in enumerate(settings):
if index + 1 == len(settings):
break
next_setting = settings[index + 1]
if setting.end > next_setting.start:
new_setting = self._merge_settings(setting, next_setting, setting_args)
new_index = index + 1
while (
new_index < len(settings)
and settings[new_index].start < new_setting.start
):
new_index += 1
settings.insert(new_index, new_setting)
# Set all text settings (default font, slant, weight)
temp_settings = settings.copy()
start = 0
for setting in settings:
if setting.start != start:
temp_settings.append(TextSetting(start, setting.start, **setting_args))
start = setting.end
if start != len(self.text):
temp_settings.append(TextSetting(start, len(self.text), **setting_args))
settings = sorted(temp_settings, key=lambda setting: setting.start)
if re.search(r"\n", self.text):
line_num = 0
for start, end in self._find_indexes("\n", self.text):
for setting in settings:
if setting.line_num == -1:
setting.line_num = line_num
if start < setting.end:
line_num += 1
new_setting = copy.copy(setting)
setting.end = end
new_setting.start = end
new_setting.line_num = line_num
settings.append(new_setting)
settings.sort(key=lambda setting: setting.start)
break
for setting in settings:
if setting.line_num == -1:
setting.line_num = 0
return settings
| manim/mobject/svg/text_mobject.py | 612 | manim | {
"docstring": "Converts the texts and styles to a setting for parsing.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 10
} | 182 | Python | 99 | 902e7eb4f0147b5882a613b67467e38a1d47f01e | text_mobject.py | 189,494 | 52 | 389 | _text2settings | https://github.com/ManimCommunity/manim.git | Hide more private methods from the docs. (#2468)
* hide privs from text_mobject.py
* hide privs from tex_mobject.py
* hide privs from code_mobject.py
* hide privs from svg_mobject.py
* remove SVGPath and utils from __init__.py
* don't import string_to_numbers
* hide privs from geometry.py
* hide privs from matrix.py
* hide privs from numbers.py
* hide privs from three_dimensions.py
* forgot underscore under set_stroke_width_from_length
* there were more i missed
* unhidea method that was used in docs
* forgot other text2hash
* remove svg_path from docs | 888 | 0 | 46,094 | 18 |
|
2 | 16 | def decoder(self, side):
input_ = Input(shape=(8, 8, 512))
var_x = input_
var_x = UpscaleBlock(256, activation="leakyrelu")(var_x)
var_x = UpscaleBlock(128, activation="leakyrelu")(var_x)
var_x = UpscaleBlock(64, activation="leakyrelu")(var_x)
var_x = Conv2DOutput(3, 5, name=f"face_out_{side}")(var_x)
outputs = [var_x]
if self.learn_mask:
var_y = input_
var_y = UpscaleBlock(256, activation="leakyrelu")(var_y)
var_y = UpscaleBlock(128, activation="leakyrelu")(var_y)
var_y = UpscaleBlock(64, activation="leakyrelu")(var_y)
var_y = Conv2DOutput(1, 5, name=f"mask_out_{side}")(var_y)
outputs.append(var_y)
return KerasModel(input_, outputs=outputs, name=f"decoder_{side}")
| plugins/train/model/original.py | 283 | faceswap | {
"docstring": " The original Faceswap Decoder Network.\r\n\r\n The decoders for the original model have separate weights for each side \"A\" and \"B\", so two\r\n instances are created in :func:`build_model`, one for each side.\r\n\r\n Parameters\r\n ----------\r\n side: str\r\n Either `\"a` or `\"b\"`. This is used for naming the decoder model.\r\n\r\n Returns\r\n -------\r\n :class:`keras.models.Model`\r\n The Keras decoder model. This will be called twice, once for each side.\r\n ",
"language": "en",
"n_whitespaces": 149,
"n_words": 63,
"vocab_size": 49
} | 58 | Python | 29 | aa39234538a8f83e6aa2b60b8275a570e8876ac2 | original.py | 100,464 | 16 | 168 | decoder | https://github.com/deepfakes/faceswap.git | Update all Keras Imports to be conditional (#1214)
* Remove custom keras importer
* first round keras imports fix
* launcher.py: Remove KerasFinder references
* 2nd round keras imports update (lib and extract)
* 3rd round keras imports update (train)
* remove KerasFinder from tests
* 4th round keras imports update (tests) | 194 | 0 | 19,938 | 14 |
|
6 | 21 | def check_toname_in_config_by_regex(config_string, to_name, control_type=None):
c = parse_config(config_string)
if control_type:
check_list = [control_type]
else:
check_list = list(c.keys())
for control in check_list:
item = c[control].get('regex', {})
for to_name_item in c[control]['to_name']:
expression = to_name_item
for key in item:
expression = expression.replace(key, item[key])
pattern = re.compile(expression)
full_match = pattern.fullmatch(to_name)
if full_match:
return True
return False
| label_studio/core/label_config.py | 179 | label-studio | {
"docstring": "\n Check if to_name is in config including regex filter\n :return: True if to_name is fullmatch to some pattern ion config\n ",
"language": "en",
"n_whitespaces": 30,
"n_words": 20,
"vocab_size": 16
} | 51 | Python | 35 | 583b3cb3b03a36a30b3ce9fe96eb4fb28548a070 | label_config.py | 178,014 | 17 | 112 | check_toname_in_config_by_regex | https://github.com/heartexlabs/label-studio.git | fix: DEV-1462: Fix changing label config for repeater tag (#2725)
* fix: DEV-1462: Fix changing label config for repeater tag with created annotations | 182 | 0 | 42,572 | 15 |
|
1 | 10 | async def test_hub_not_support_wireless(hass, mock_device_registry_devices):
await setup_mikrotik_entry(hass, support_wireless=False)
device_1 = hass.states.get("device_tracker.device_1")
assert device_1
assert device_1.state == "home"
# device_2 is added from DHCP
device_2 = hass.states.get("device_tracker.device_2")
assert device_2
assert device_2.state == "home"
| tests/components/mikrotik/test_device_tracker.py | 95 | core | {
"docstring": "Test device_trackers created when hub doesn't support wireless.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 31 | Python | 22 | b09aaba421d6d6178d582bef9ea363017e55639d | test_device_tracker.py | 315,483 | 8 | 53 | test_hub_not_support_wireless | https://github.com/home-assistant/core.git | Add type hints and code cleanup for mikrotik (#74296)
* Add type hints and code cleanup for mikrotik
* update test and increase coverage
* move setup_mikrotik_entry to __init__.py | 58 | 0 | 114,071 | 9 |
|
2 | 9 | def test_finditer():
matches = list(finditer(re.compile(rb"\d+"), b"0123 4567 890 12 3 4"))
aligned = [i.group() for i in matches]
assert aligned == [b"0123", b"567", b"890", b"12"]
| tests/unit/test_bytecode.py | 84 | pyinstaller | {
"docstring": "\n Test that bytecode.finditer() yields matches only that start on an even byte (``match.start() % 2 == 0``).\n\n There are 3 permutations here when considering a match:\n - A match starts on an even byte:\n That's good! Include that sequence.\n - A single character match starts on an odd byte:\n Ignore it. It's a false positive.\n - A multi-character match starts on an odd byte:\n This match will be a false positive but there may be a genuine match shortly afterwards (in the case of the\n # test below - it'll be the next character) which overlaps with this one so we must override regex's\n behaviour of ignoring overlapping matches to prevent these from getting lost.\n ",
"language": "en",
"n_whitespaces": 169,
"n_words": 115,
"vocab_size": 82
} | 25 | Python | 23 | dc12cb59559f99110917bcbd21c9960ab57d994f | test_bytecode.py | 263,956 | 4 | 52 | test_finditer | https://github.com/pyinstaller/pyinstaller.git | tests: fix test_finditer
Have the test use bytestrings instead of strings.
Also assert that the bytecode string passed to bytecode.finditer()
is in fact a bytestring. | 37 | 0 | 77,525 | 13 |
|
2 | 12 | def add_axes(self, animate=False, color=WHITE, **kwargs):
axes = Axes(color=color, axis_config={"unit_size": 1})
if animate:
self.play(Create(axes))
self.add(axes)
return axes
| manim/scene/vector_space_scene.py | 86 | manim | {
"docstring": "\n Adds a pair of Axes to the Scene.\n\n Parameters\n ----------\n animate : bool, optional\n Whether or not to animate the addition of the axes through Create.\n color : bool, optional\n The color of the axes. Defaults to WHITE.\n ",
"language": "en",
"n_whitespaces": 103,
"n_words": 38,
"vocab_size": 26
} | 16 | Python | 15 | 5789be81609c8bf6d98d1d87d4061477d0cd37b9 | vector_space_scene.py | 189,404 | 6 | 53 | add_axes | https://github.com/ManimCommunity/manim.git | Fix `add_axes` in :class:`~.VectorScene`. (#2444)
* fix bug
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> | 62 | 0 | 46,041 | 12 |
|
17 | 31 | def _score(estimator, X_test, y_test, scorer, error_score="raise"):
if isinstance(scorer, dict):
# will cache method calls if needed. scorer() returns a dict
scorer = _MultimetricScorer(scorers=scorer, raise_exc=(error_score == "raise"))
try:
if y_test is None:
scores = scorer(estimator, X_test)
else:
scores = scorer(estimator, X_test, y_test)
except Exception:
if isinstance(scorer, _MultimetricScorer):
# If `_MultimetricScorer` raises exception, the `error_score`
# parameter is equal to "raise".
raise
else:
if error_score == "raise":
raise
else:
scores = error_score
warnings.warn(
"Scoring failed. The score on this train-test partition for "
f"these parameters will be set to {error_score}. Details: \n"
f"{format_exc()}",
UserWarning,
)
# Check non-raised error messages in `_MultimetricScorer`
if isinstance(scorer, _MultimetricScorer):
exception_messages = [
(name, str_e) for name, str_e in scores.items() if isinstance(str_e, str)
]
if exception_messages:
# error_score != "raise"
for name, str_e in exception_messages:
scores[name] = error_score
warnings.warn(
"Scoring failed. The score on this train-test partition for "
f"these parameters will be set to {error_score}. Details: \n"
f"{str_e}",
UserWarning,
)
error_msg = "scoring must return a number, got %s (%s) instead. (scorer=%s)"
if isinstance(scores, dict):
for name, score in scores.items():
if hasattr(score, "item"):
with suppress(ValueError):
# e.g. unwrap memmapped scalars
score = score.item()
if not isinstance(score, numbers.Number):
raise ValueError(error_msg % (score, type(score), name))
scores[name] = score
else: # scalar
if hasattr(scores, "item"):
with suppress(ValueError):
# e.g. unwrap memmapped scalars
scores = scores.item()
if not isinstance(scores, numbers.Number):
raise ValueError(error_msg % (scores, type(scores), scorer))
return scores
| sklearn/model_selection/_validation.py | 506 | scikit-learn | {
"docstring": "Compute the score(s) of an estimator on a given test set.\n\n Will return a dict of floats if `scorer` is a dict, otherwise a single\n float is returned.\n ",
"language": "en",
"n_whitespaces": 37,
"n_words": 28,
"vocab_size": 23
} | 228 | Python | 126 | d17d0f9f721dd030f7405023a838edb564ac1a4c | _validation.py | 261,817 | 51 | 296 | _score | https://github.com/scikit-learn/scikit-learn.git | FIX `cross_validate` with multimetric scoring returns the non-failed scorers results even if some fail (#23101)
Co-authored-by: Guillaume Lemaitre <[email protected]> | 863 | 0 | 77,015 | 21 |
|
1 | 15 | def test_socket_options_address_in_use_problem(qlocalserver, short_tmpdir):
servername = str(short_tmpdir / 'x')
s1 = QLocalServer()
ok = s1.listen(servername)
assert ok
s2 = QLocalServer()
s2.setSocketOptions(QLocalServer.SocketOption.UserAccessOption)
ok = s2.listen(servername)
print(s2.errorString())
# We actually would expect ok == False here - but we want the test to fail
# when the Qt bug is fixed.
assert ok
| tests/unit/misc/test_ipc.py | 112 | qutebrowser | {
"docstring": "Qt seems to ignore AddressInUseError when using socketOptions.\n\n With this test we verify this bug still exists. If it fails, we can\n probably start using setSocketOptions again.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 27,
"vocab_size": 24
} | 50 | Python | 38 | 0877fb0d78635692e481c8bde224fac5ad0dd430 | test_ipc.py | 321,412 | 10 | 64 | test_socket_options_address_in_use_problem | https://github.com/qutebrowser/qutebrowser.git | Run scripts/dev/rewrite_enums.py | 86 | 0 | 117,701 | 10 |
|
3 | 9 | def create_dummy_object(name, backend_name):
if name.isupper():
return DUMMY_CONSTANT.format(name)
elif name.islower():
return DUMMY_FUNCTION.format(name, backend_name)
else:
return DUMMY_CLASS.format(name, backend_name)
| utils/check_dummies.py | 80 | transformers | {
"docstring": "Create the code for the dummy object corresponding to `name`.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 16 | Python | 13 | 1b730c3d11fdad0180ee9f9d3da9cff933c3b264 | check_dummies.py | 34,075 | 7 | 49 | create_dummy_object | https://github.com/huggingface/transformers.git | Better dummies (#15148)
* Better dummies
* See if this fixes the issue
* Fix quality
* Style
* Add doc for DummyObject | 49 | 0 | 6,195 | 10 |
|
1 | 5 | def require_torch_bf16(test_case):
return unittest.skipUnless(
is_torch_bf16_available(),
"test requires torch>=1.10, using Ampere GPU or newer arch with cuda>=11.0 or using CPU",
)(test_case)
| src/transformers/testing_utils.py | 38 | transformers | {
"docstring": "Decorator marking a test that requires torch>=1.10, using Ampere GPU or newer arch with cuda>=11.0 or using CPU.",
"language": "en",
"n_whitespaces": 17,
"n_words": 18,
"vocab_size": 16
} | 20 | Python | 18 | 34097b3304d79ace845316d4929220623279c8bc | testing_utils.py | 31,115 | 5 | 21 | require_torch_bf16 | https://github.com/huggingface/transformers.git | Extend Transformers Trainer Class to Enable CPU AMP and Integrate Intel Extension for PyTorch (#17138)
* init PR
* fix import ipex
* minor fix on bf16
* refine optimizer
* refine args notes
* refine code
* refine ipex optimize args
* refine half_precision_backend
* black format
* isort format
* isort format files
* flake8 format
* doc builder format
* refine codes
* remove jit and optim bits
* black preview format
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <[email protected]>
* refine code
* refine notes
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <[email protected]>
* Update src/transformers/trainer.py
Co-authored-by: Sylvain Gugger <[email protected]>
* code refine
* add ipex ut
* add performance cpu doc
* link to the cpu doc from main perf doc
* install ipex into CI's docker
* Update perf_train_cpu.mdx
* Update docs/source/en/perf_train_cpu.mdx
Co-authored-by: Stas Bekman <[email protected]>
* Update perf_train_cpu.mdx
* Update perf_train_cpu.mdx
Co-authored-by: Sylvain Gugger <[email protected]>
Co-authored-by: Stas Bekman <[email protected]>
Co-authored-by: Stas Bekman <[email protected]> | 43 | 0 | 5,682 | 10 |
|
2 | 28 | def milp(c, *, integrality=None, bounds=None, constraints=None, options=None):
r
args_iv = _milp_iv(c, integrality, bounds, constraints, options)
c, integrality, lb, ub, indptr, indices, data, b_l, b_u, options = args_iv
highs_res = _highs_wrapper(c, indptr, indices, data, b_l, b_u,
lb, ub, integrality, options)
res = {}
# Convert to scipy-style status and message
highs_status = highs_res.get('status', None)
highs_message = highs_res.get('message', None)
status, message = _highs_to_scipy_status_message(highs_status,
highs_message)
res['status'] = status
res['message'] = message
res['success'] = res['status'] in {0, 2, 3}
x = highs_res.get('x', None)
res['x'] = np.array(x) if x is not None else None
res['fun'] = highs_res.get('fun', None)
res['mip_node_count'] = highs_res.get('mip_node_count', None)
res['mip_dual_bound'] = highs_res.get('mip_dual_bound', None)
res['mip_gap'] = highs_res.get('mip_gap', None)
return OptimizeResult(res)
| scipy/optimize/_milp.py | 357 | scipy | {
"docstring": "\n Mixed-integer linear programming\n\n Solves problems of the following form:\n\n .. math::\n\n \\min_x \\ & c^T x \\\\\n \\mbox{such that} \\ & b_l \\leq A x \\leq b_u,\\\\\n & l \\leq x \\leq u, \\\\\n & x_i \\in \\mathbb{Z}, i \\in X_i\n\n where :math:`x` is a vector of decision variables;\n :math:`c`, :math:`b_l`, :math:`b_u`, :math:`l`, and :math:`u` are vectors;\n :math:`A` is a matrix, and :math:`X_i` is the set of indices of\n decision variables that must be integral. (In this context, a\n variable that can assume only integer values is said to be \"integral\";\n it has an \"integrality\" constraint.)\n\n Alternatively, that's:\n\n minimize::\n\n c @ x\n\n such that::\n\n b_l <= A @ x <= b_u\n l <= x <= u\n Specified elements of x must be integers\n\n By default, ``l = 0`` and ``u = np.inf`` unless specified with\n ``bounds``.\n\n Parameters\n ----------\n c : 1D array_like\n The coefficients of the linear objective function to be minimized.\n `c` is converted to a double precision array before the problem is\n solved.\n integrality : 1D array_like, optional\n Indicates the type of integrality constraint on each decision variable.\n\n ``0`` : Continuous variable; no integrality constraint.\n\n ``1`` : Integer variable; decision variable must be an integer\n within `bounds`.\n\n ``2`` : Semi-continuous variable; decision variable must be within\n `bounds` or take value ``0``.\n\n ``3`` : Semi-integer variable; decision variable must be an integer\n within `bounds` or take value ``0``.\n\n By default, all variables are continuous. `integrality` is converted\n to an array of integers before the problem is solved.\n\n bounds : scipy.optimize.Bounds, optional\n Bounds on the decision variables. Lower and upper bounds are converted\n to double precision arrays before the problem is solved. The\n ``keep_feasible`` parameter of the `Bounds` object is ignored. If\n not specified, all decision variables are constrained to be\n non-negative.\n constraints : sequence of scipy.optimize.LinearConstraint, optional\n Linear constraints of the optimization problem. Arguments may be\n one of the following:\n\n 1. A single `LinearConstraint` object\n 2. A single tuple that can be converted to a `LinearConstraint` object\n as ``LinearConstraint(*constraints)``\n 3. A sequence composed entirely of objects of type 1. and 2.\n\n Before the problem is solved, all values are converted to double\n precision, and the matrices of constraint coefficients are converted to\n instances of `scipy.sparse.csc_array`. The ``keep_feasible`` parameter\n of `LinearConstraint` objects is ignored.\n options : dict, optional\n A dictionary of solver options. The following keys are recognized.\n\n disp : bool (default: ``False``)\n Set to ``True`` if indicators of optimization status are to be\n printed to the console during optimization.\n presolve : bool (default: ``True``)\n Presolve attempts to identify trivial infeasibilities,\n identify trivial unboundedness, and simplify the problem before\n sending it to the main solver.\n time_limit : float, optional\n The maximum number of seconds allotted to solve the problem.\n Default is no time limit.\n\n Returns\n -------\n res : OptimizeResult\n An instance of :class:`scipy.optimize.OptimizeResult`. The object\n is guaranteed to have the following attributes.\n\n status : int\n An integer representing the exit status of the algorithm.\n\n ``0`` : Optimal solution found.\n\n ``1`` : Iteration or time limit reached.\n\n ``2`` : Problem is infeasible.\n\n ``3`` : Problem is unbounded.\n\n ``4`` : Other; see message for details.\n\n success : bool\n ``True`` when an optimal solution is found, the problem is\n determined to be infeasible, or the problem is determined\n to be unbounded.\n\n message : str\n A string descriptor of the exit status of the algorithm.\n\n The following attributes will also be present, but the values may be\n ``None``, depending on the solution status.\n\n x : ndarray\n The values of the decision variables that minimize the\n objective function while satisfying the constraints.\n fun : float\n The optimal value of the objective function ``c @ x``.\n mip_node_count : int\n The number of subproblems or \"nodes\" solved by the MILP solver.\n mip_dual_bound : float\n The MILP solver's final estimate of the lower bound on the optimal\n solution.\n mip_gap : float\n The difference between the final objective function value and the\n final dual bound.\n\n Notes\n -----\n `milp` is a wrapper of the HiGHS linear optimization software [1]_. The\n algorithm is deterministic, and it typically finds the global optimum of\n moderately challenging mixed-integer linear programs (when it exists).\n\n References\n ----------\n .. [1] Huangfu, Q., Galabova, I., Feldmeier, M., and Hall, J. A. J.\n \"HiGHS - high performance software for linear optimization.\"\n Accessed 12/25/2021 at https://www.maths.ed.ac.uk/hall/HiGHS/#guide\n .. [2] Huangfu, Q. and Hall, J. A. J. \"Parallelizing the dual revised\n simplex method.\" Mathematical Programming Computation, 10 (1),\n 119-142, 2018. DOI: 10.1007/s12532-017-0130-5\n\n Examples\n --------\n Consider the problem at\n https://en.wikipedia.org/wiki/Integer_programming#Example, which is\n expressed as a maximization problem of two variables. Since `milp` requires\n that the problem be expressed as a minimization problem, the objective\n function coefficients on the decision variables are:\n\n >>> c = -np.array([0, 1])\n\n Note the negative sign: we maximize the original objective function\n by minimizing the negative of the objective function.\n\n We collect the coefficients of the constraints into arrays like:\n\n >>> A = np.array([[-1, 1], [3, 2], [2, 3]])\n >>> b_u = np.array([1, 12, 12])\n >>> b_l = np.full_like(b_u, -np.inf)\n\n Because there is no lower limit on these constraints, we have defined a\n variable ``b_l`` full of values representing negative infinity. This may\n be unfamiliar to users of `scipy.optimize.linprog`, which only accepts\n \"less than\" (or \"upper bound\") inequality constraints of the form\n ``A_ub @ x <= b_u``. By accepting both ``b_l`` and ``b_u`` of constraints\n ``b_l <= A_ub @ x <= b_u``, `milp` makes it easy to specify \"greater than\"\n inequality constraints, \"less than\" inequality constraints, and equality\n constraints concisely.\n\n These arrays are collected into a single `LinearConstraint` object like:\n\n >>> from scipy.optimize import LinearConstraint\n >>> constraints = LinearConstraint(A, b_l, b_u)\n\n The non-negativity bounds on the decision variables are enforced by\n default, so we do not need to provide an argument for `bounds`.\n\n Finally, the problem states that both decision variables must be integers:\n\n >>> integrality = np.ones_like(c)\n\n We solve the problem like:\n >>> from scipy.optimize import milp\n >>> res = milp(c=c, constraints=constraints, integrality=integrality)\n >>> res.x\n [1.0, 2.0]\n\n Note that had we solved the relaxed problem (without integrality\n constraints):\n >>> res = milp(c=c, constraints=constraints) # OR:\n >>> # from scipy.optimize import linprog; res = linprog(c, A, b_u)\n >>> res.x\n [1.8, 2.8]\n\n we would not have obtained the correct solution by rounding to the nearest\n integers.\n\n ",
"language": "en",
"n_whitespaces": 1938,
"n_words": 1026,
"vocab_size": 475
} | 107 | Python | 69 | 131c4e78b3f093ad3d415ebcc1fb42bbbde72470 | _milp.py | 242,082 | 226 | 232 | milp | https://github.com/scipy/scipy.git | MAINT: optimize: milp: update error messages
[skip ci] | 245 | 0 | 69,782 | 9 |
|
6 | 25 | def all_reduce_sum_gradients(grads_and_vars):
grads_and_vars = list(grads_and_vars)
filtered_grads_and_vars = filter_empty_gradients(grads_and_vars)
if filtered_grads_and_vars:
if tf.__internal__.distribute.strategy_supports_no_merge_call():
grads = [pair[0] for pair in filtered_grads_and_vars]
reduced = tf.distribute.get_replica_context().all_reduce(
tf.distribute.ReduceOp.SUM, grads
)
else:
# TODO(b/183257003): Remove this branch
reduced = tf.distribute.get_replica_context().merge_call(
_all_reduce_sum_fn, args=(filtered_grads_and_vars,)
)
else:
reduced = []
# Copy 'reduced' but add None gradients back in
reduced_with_nones = []
reduced_pos = 0
for g, v in grads_and_vars:
if g is None:
reduced_with_nones.append((None, v))
else:
reduced_with_nones.append((reduced[reduced_pos], v))
reduced_pos += 1
assert reduced_pos == len(reduced), "Failed to add all gradients"
return reduced_with_nones
| keras/optimizers/optimizer_v2/utils.py | 247 | keras | {
"docstring": "Returns all-reduced gradients aggregated via summation.\n\n Args:\n grads_and_vars: List of (gradient, variable) pairs.\n\n Returns:\n List of (gradient, variable) pairs where gradients have been all-reduced.\n ",
"language": "en",
"n_whitespaces": 43,
"n_words": 24,
"vocab_size": 19
} | 84 | Python | 59 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | utils.py | 275,620 | 25 | 153 | all_reduce_sum_gradients | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 281 | 0 | 81,433 | 16 |
|
1 | 3 | def dev(self):
return self._get_dev()
| ivy/core/container.py | 23 | ivy | {
"docstring": "\n The device to which the arrays in the container belong, with None returned if the devices are not consistent\n ",
"language": "en",
"n_whitespaces": 34,
"n_words": 19,
"vocab_size": 17
} | 4 | Python | 4 | d743336b1f3654cd0315f380f43eed4116997c1d | container.py | 213,577 | 2 | 12 | dev | https://github.com/unifyai/ivy.git | renamed dev_str arg to dev for all methods. | 18 | 0 | 53,660 | 7 |
|
1 | 8 | def _zero_in_bounds(self):
vmin, vmax = self._axes.yaxis._scale.limit_range_for_scale(0, 1, 1e-5)
return vmin == 0
| lib/matplotlib/projections/polar.py | 48 | matplotlib | {
"docstring": "\n Return True if zero is within the valid values for the\n scale of the radial axis.\n ",
"language": "en",
"n_whitespaces": 38,
"n_words": 16,
"vocab_size": 14
} | 12 | Python | 12 | 4507ae544155fb6fc9fa594faf1b8c8a23a85426 | polar.py | 110,754 | 3 | 32 | _zero_in_bounds | https://github.com/matplotlib/matplotlib.git | Allow polar scales where zero is not in valid interval | 33 | 0 | 24,277 | 11 |
|
5 | 14 | def _watch_glob(self, directory, patterns):
prefix = "glob"
if not directory.exists():
if not directory.parent.exists():
logger.warning(
"Unable to watch directory %s as neither it or its parent exist.",
directory,
)
return
prefix = "glob-parent-%s" % directory.name
patterns = ["%s/%s" % (directory.name, pattern) for pattern in patterns]
directory = directory.parent
expression = ["anyof"]
for pattern in patterns:
expression.append(["match", pattern, "wholename"])
self._subscribe(directory, "%s:%s" % (prefix, directory), expression)
| django/utils/autoreload.py | 181 | django | {
"docstring": "\n Watch a directory with a specific glob. If the directory doesn't yet\n exist, attempt to watch the parent directory and amend the patterns to\n include this. It's important this method isn't called more than one per\n directory when updating all subscriptions. Subsequent calls will\n overwrite the named subscription, so it must include all possible glob\n expressions.\n ",
"language": "en",
"n_whitespaces": 106,
"n_words": 56,
"vocab_size": 46
} | 63 | Python | 49 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | autoreload.py | 206,541 | 16 | 108 | _watch_glob | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 243 | 0 | 51,559 | 12 |
|
4 | 19 | def edit_focus_options(self) -> typing.Sequence[str]:
flow = self.master.view.focus.flow
focus_options = []
if isinstance(flow, tcp.TCPFlow):
focus_options = ["tcp-message"]
elif isinstance(flow, http.HTTPFlow):
focus_options = [
"cookies",
"urlencoded form",
"multipart form",
"path",
"method",
"query",
"reason",
"request-headers",
"response-headers",
"request-body",
"response-body",
"status_code",
"set-cookies",
"url",
]
elif isinstance(flow, dns.DNSFlow):
raise exceptions.CommandError("Cannot edit DNS flows yet, please submit a patch.")
return focus_options
| mitmproxy/tools/console/consoleaddons.py | 181 | mitmproxy | {
"docstring": "\n Possible components for console.edit.focus.\n ",
"language": "en",
"n_whitespaces": 23,
"n_words": 4,
"vocab_size": 4
} | 54 | Python | 44 | fab7016b318d7c37fc30cef9c0567b9b620b883e | consoleaddons.py | 251,073 | 28 | 104 | edit_focus_options | https://github.com/mitmproxy/mitmproxy.git | beautify flowtable dns entries
this isn't perfect (the whole table needs to be refactored properly),
but good enough for now. | 357 | 0 | 73,587 | 11 |
|
1 | 10 | def default_user(factories):
return factories.create_user(email="admin@localhost", is_superuser=True)
@pytest.mark.django_db
@pytest.fixture(scope="function") | src/sentry/utils/pytest/fixtures.py | 60 | @pytest.mark.django_db
@pytest.fixture(scope="function") | sentry | {
"docstring": "A default (super)user with email ``admin@localhost`` and password ``admin``.\n\n :returns: A :class:`sentry.models.user.User` instance.\n ",
"language": "en",
"n_whitespaces": 19,
"n_words": 13,
"vocab_size": 12
} | 7 | Python | 7 | 9acf84fbe1c7ffba0faec907ad3219141086949f | fixtures.py | 98,369 | 2 | 19 | default_user | https://github.com/getsentry/sentry.git | feat(appconnect): Introduce an endpoint to trigger refresh of builds (#33457)
Normally the builds are refreshed once an hour, however we're adding a
way to trigger this manually. This endpoint is still severely
ratelimited.
This also includes the UI code to add a button for this endpoint.
NATIVE-139
Co-authored-by: Priscila Oliveira <[email protected]> | 11 | 1 | 19,565 | 9 |
2 | 20 | def imshow_rgb(self, r, g, b, **kwargs):
if not (r.shape == g.shape == b.shape):
raise ValueError(
f'Input shapes ({r.shape}, {g.shape}, {b.shape}) do not match')
RGB = np.dstack([r, g, b])
R = np.zeros_like(RGB)
R[:, :, 0] = r
G = np.zeros_like(RGB)
G[:, :, 1] = g
B = np.zeros_like(RGB)
B[:, :, 2] = b
im_rgb = self.RGB.imshow(RGB, **kwargs)
im_r = self.R.imshow(R, **kwargs)
im_g = self.G.imshow(G, **kwargs)
im_b = self.B.imshow(B, **kwargs)
return im_rgb, im_r, im_g, im_b
| lib/mpl_toolkits/axes_grid1/axes_rgb.py | 272 | matplotlib | {
"docstring": "\n Create the four images {rgb, r, g, b}.\n\n Parameters\n ----------\n r, g, b : array-like\n The red, green, and blue arrays.\n **kwargs :\n Forwarded to `~.Axes.imshow` calls for the four images.\n\n Returns\n -------\n rgb : `~matplotlib.image.AxesImage`\n r : `~matplotlib.image.AxesImage`\n g : `~matplotlib.image.AxesImage`\n b : `~matplotlib.image.AxesImage`\n ",
"language": "en",
"n_whitespaces": 152,
"n_words": 45,
"vocab_size": 32
} | 73 | Python | 52 | df6f95703b60348e01603f98a439b133da2938a0 | axes_rgb.py | 109,887 | 16 | 165 | imshow_rgb | https://github.com/matplotlib/matplotlib.git | Improve mpl_toolkit documentation | 197 | 0 | 23,796 | 12 |
|
1 | 27 | def forward(self, qkv):
bs, width, length = qkv.shape
assert width % (3 * self.n_heads) == 0
ch = width // (3 * self.n_heads)
# q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
q, k, v = paddle.reshape(qkv, [bs * self.n_heads, ch * 3, length]).split(3, axis=1)
scale = 1 / math.sqrt(math.sqrt(ch))
weight = paddle.einsum("bct,bcs->bts", q * scale, k * scale) # More stable with f16 than dividing afterwards
weight = paddle.cast(nn.functional.softmax(paddle.cast(weight, 'float32'), axis=-1), weight.dtype)
a = paddle.einsum("bts,bcs->bct", weight, v)
# return a.reshape(bs, -1, length)
return paddle.reshape(a, [bs, -1, length])
| modules/image/text_to_image/disco_diffusion_cnclip_vitb16/reverse_diffusion/model/unet.py | 253 | PaddleHub | {
"docstring": "\n Apply QKV attention.\n\n :param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.\n :return: an [N x (H * C) x T] tensor after attention.\n ",
"language": "en",
"n_whitespaces": 62,
"n_words": 33,
"vocab_size": 21
} | 92 | Python | 63 | f4d6e64cdc132ae868699a0ba442f4ab1d304a14 | unet.py | 49,867 | 10 | 158 | forward | https://github.com/PaddlePaddle/PaddleHub.git | add disco_diffusion_cnclip_vitb16 module | 177 | 0 | 9,938 | 13 |
|
1 | 3 | def callbacks(self, callbacks_class) -> "TrainerConfig":
self.callbacks_class = callbacks_class
return self
| rllib/agents/trainer_config.py | 31 | ray | {
"docstring": "Sets the callbacks configuration.\n\n Args:\n callbacks_class: Callbacks class, whose methods will be run during\n various phases of training and environment sample collection.\n See the `DefaultCallbacks` class and\n `examples/custom_metrics_and_callbacks.py` for more usage information.\n\n Returns:\n This updated TrainerConfig object.\n ",
"language": "en",
"n_whitespaces": 125,
"n_words": 37,
"vocab_size": 35
} | 10 | Python | 10 | 2eaa54bd763ae0e63158ae0d939633c804394b78 | trainer_config.py | 147,576 | 14 | 17 | callbacks | https://github.com/ray-project/ray.git | [RLlib] POC: Config objects instead of dicts (PPO only). (#23491) | 31 | 0 | 34,012 | 7 |
|
1 | 5 | def notify_conversion_complete(self, status="Converting") -> None:
self.progress = 95
self.update(status)
| spotdl/download/progress_handler.py | 41 | spotify-downloader | {
"docstring": "\n Notifies the progress handler that the song has been converted.\n\n ### Arguments\n - status: The status to display.\n ",
"language": "en",
"n_whitespaces": 47,
"n_words": 18,
"vocab_size": 17
} | 9 | Python | 9 | 14e467160c852095efb0107a25eb1b136fda3ea8 | progress_handler.py | 30,336 | 9 | 23 | notify_conversion_complete | https://github.com/spotDL/spotify-downloader.git | download the audio stream using yt-dlp not ffmpeg | 30 | 0 | 5,493 | 7 |
|
2 | 13 | def ft_load_params_from_file(self) -> None:
if self._ft_params_from_file:
# Set parameters from Hyperopt results file
params = self._ft_params_from_file
self.minimal_roi = params.get('roi', getattr(self, 'minimal_roi', {}))
self.stoploss = params.get('stoploss', {}).get(
'stoploss', getattr(self, 'stoploss', -0.1))
trailing = params.get('trailing', {})
self.trailing_stop = trailing.get(
'trailing_stop', getattr(self, 'trailing_stop', False))
self.trailing_stop_positive = trailing.get(
'trailing_stop_positive', getattr(self, 'trailing_stop_positive', None))
self.trailing_stop_positive_offset = trailing.get(
'trailing_stop_positive_offset',
getattr(self, 'trailing_stop_positive_offset', 0))
self.trailing_only_offset_is_reached = trailing.get(
'trailing_only_offset_is_reached',
getattr(self, 'trailing_only_offset_is_reached', 0.0))
| freqtrade/strategy/hyper.py | 256 | freqtrade | {
"docstring": "\n Load Parameters from parameter file\n Should/must run before config values are loaded in strategy_resolver.\n ",
"language": "en",
"n_whitespaces": 36,
"n_words": 14,
"vocab_size": 14
} | 62 | Python | 42 | 386d3e035337cea7cbe9e38a5a8100fa79948fbb | hyper.py | 149,922 | 21 | 157 | ft_load_params_from_file | https://github.com/freqtrade/freqtrade.git | Rename stop/roi loading method | 280 | 0 | 34,590 | 13 |
|
1 | 3 | def default_training_arg(self):
return self._default_training_arg
| keras/utils/layer_utils.py | 19 | keras | {
"docstring": "The default value given to the \"training\" argument.",
"language": "en",
"n_whitespaces": 7,
"n_words": 8,
"vocab_size": 8
} | 4 | Python | 4 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | layer_utils.py | 276,951 | 2 | 10 | default_training_arg | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 18 | 0 | 81,798 | 6 |
|
1 | 5 | def add_distribution(self, distribution):
self.adjacency_list[distribution] = []
self.reverse_list[distribution] = []
#self.missing[distribution] = []
| .venv/lib/python3.8/site-packages/pip/_vendor/distlib/database.py | 44 | transferlearning | {
"docstring": "Add the *distribution* to the graph.\n\n :type distribution: :class:`distutils2.database.InstalledDistribution`\n or :class:`distutils2.database.EggInfoDistribution`\n ",
"language": "en",
"n_whitespaces": 52,
"n_words": 11,
"vocab_size": 10
} | 12 | Python | 8 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | database.py | 61,958 | 3 | 26 | add_distribution | https://github.com/jindongwang/transferlearning.git | upd; format | 40 | 0 | 12,779 | 8 |
|
1 | 14 | def _getargspec(target):
fullargspecs = getfullargspec(target)
argspecs = ArgSpec(
args=fullargspecs.args,
varargs=fullargspecs.varargs,
keywords=fullargspecs.varkw,
defaults=fullargspecs.defaults,
)
return argspecs
else:
_getargspec = _inspect.getargspec
| keras/utils/tf_inspect.py | 78 | keras | {
"docstring": "A python3 version of getargspec.\n\n Calls `getfullargspec` and assigns args, varargs,\n varkw, and defaults to a python 2/3 compatible `ArgSpec`.\n\n The parameter name 'varkw' is changed to 'keywords' to fit the\n `ArgSpec` struct.\n\n Args:\n target: the target object to inspect.\n\n Returns:\n An ArgSpec with args, varargs, keywords, and defaults parameters\n from FullArgSpec.\n ",
"language": "en",
"n_whitespaces": 128,
"n_words": 52,
"vocab_size": 43
} | 19 | Python | 16 | 84afc5193d38057e2e2badf9c889ea87d80d8fbf | tf_inspect.py | 277,063 | 9 | 43 | _getargspec | https://github.com/keras-team/keras.git | Reformatting the codebase with black.
PiperOrigin-RevId: 450093126 | 100 | 0 | 81,840 | 10 |
|
1 | 3 | def proj_version(self):
return self._get_spatialite_func("proj4_version()")
| django/contrib/gis/db/backends/spatialite/operations.py | 26 | django | {
"docstring": "Return the version of the PROJ library used by SpatiaLite.",
"language": "en",
"n_whitespaces": 9,
"n_words": 10,
"vocab_size": 9
} | 4 | Python | 4 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | operations.py | 203,882 | 2 | 13 | proj_version | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 18 | 0 | 50,571 | 8 |
|
3 | 9 | def build_pattern():
#bare = set()
for module, replace in list(MAPPING.items()):
for old_attr, new_attr in list(replace.items()):
LOOKUP[(module, old_attr)] = new_attr
#bare.add(module)
#bare.add(old_attr)
#yield % (module, module)
yield % (module, old_attr, old_attr)
yield % (module, old_attr)
#yield % alternates(bare)
| python3.10.4/Lib/lib2to3/fixes/fix_renames.py | 104 | XX-Net | {
"docstring": "\n # import_name< 'import' (module=%r\n # | dotted_as_names< any* module=%r any* >) >\n # \n import_from< 'from' module_name=%r 'import'\n ( attr_name=%r | import_as_name< attr_name=%r 'as' any >) >\n \n power< module_name=%r trailer< '.' attr_name=%r > any* >\n bare_name=%s",
"language": "en",
"n_whitespaces": 178,
"n_words": 35,
"vocab_size": 22
} | 37 | Python | 24 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | fix_renames.py | 218,720 | 11 | 60 | build_pattern | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 122 | 0 | 55,450 | 12 |
|
1 | 5 | def close_tilt_position(self) -> PowerviewShadeMove:
return PowerviewShadeMove(self._shade.close_position_tilt, {})
| homeassistant/components/hunterdouglas_powerview/cover.py | 34 | core | {
"docstring": "Return the close tilt position and required additional positions.",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 7 | Python | 7 | 3ab294e8efc00c9f3cda2993318bb582ba675f8c | cover.py | 288,765 | 3 | 20 | close_tilt_position | https://github.com/home-assistant/core.git | Powerview refactor prep for all shade types (#79862) | 21 | 0 | 87,917 | 9 |
|
1 | 2 | def starts(self):
return self["starts"]
| packages/python/plotly/plotly/graph_objs/_streamtube.py | 22 | plotly.py | {
"docstring": "\n The 'starts' property is an instance of Starts\n that may be specified as:\n - An instance of :class:`plotly.graph_objs.streamtube.Starts`\n - A dict of string/value properties that will be passed\n to the Starts constructor\n\n Supported dict properties:\n\n x\n Sets the x components of the starting position\n of the streamtubes\n xsrc\n Sets the source reference on Chart Studio Cloud\n for `x`.\n y\n Sets the y components of the starting position\n of the streamtubes\n ysrc\n Sets the source reference on Chart Studio Cloud\n for `y`.\n z\n Sets the z components of the starting position\n of the streamtubes\n zsrc\n Sets the source reference on Chart Studio Cloud\n for `z`.\n\n Returns\n -------\n plotly.graph_objs.streamtube.Starts\n ",
"language": "en",
"n_whitespaces": 508,
"n_words": 107,
"vocab_size": 51
} | 4 | Python | 4 | 43e3a4011080911901176aab919c0ecf5046ddd3 | _streamtube.py | 228,263 | 2 | 11 | starts | https://github.com/plotly/plotly.py.git | switch to black .22 | 18 | 0 | 59,936 | 7 |
|
1 | 6 | def normalization(channels, swish=0.0):
return GroupNorm32(num_channels=channels, num_groups=32, swish=swish)
## go | src/diffusers/models/unet_ldm.py | 40 | diffusers | {
"docstring": "\n Make a standard normalization layer, with an optional swish activation.\n\n :param channels: number of input channels. :return: an nn.Module for normalization.\n ",
"language": "en",
"n_whitespaces": 31,
"n_words": 21,
"vocab_size": 20
} | 9 | Python | 9 | 4261c3aadfc23ee5b123b80ab7d8680a013acb66 | unet_ldm.py | 335,461 | 2 | 27 | normalization | https://github.com/huggingface/diffusers.git | Make style | 14 | 0 | 120,765 | 8 |
|
1 | 6 | def check_override(object, parentclass, attribute):
return getattr(type(object), attribute) == getattr(parentclass, attribute)
| freqtrade/resolvers/strategy_resolver.py | 42 | freqtrade | {
"docstring": "\n Checks if a object overrides the parent class attribute.\n ",
"language": "en",
"n_whitespaces": 16,
"n_words": 9,
"vocab_size": 9
} | 10 | Python | 9 | fe62a71f4c621aa06cfbda1f2aec19ab01062ebd | strategy_resolver.py | 149,120 | 2 | 27 | check_override | https://github.com/freqtrade/freqtrade.git | Simplify implementation of "check_override" by extracting it to function | 16 | 0 | 34,371 | 10 |
|
3 | 9 | def get_cost_centers(filters):
order_by = ""
if filters.get("budget_against") == "Cost Center":
order_by = "order by lft"
if filters.get("budget_against") in ["Cost Center", "Project"]:
return frappe.db.sql_list(
.format(
tab=filters.get("budget_against"), order_by=order_by
),
filters.get("company"),
)
else:
return frappe.db.sql_list(
.format(
tab=filters.get("budget_against")
)
) # nosec
# Get dimension & target details | erpnext/accounts/report/budget_variance_report/budget_variance_report.py | 168 | erpnext | {
"docstring": "\n\t\t\t\tselect\n\t\t\t\t\tname\n\t\t\t\tfrom\n\t\t\t\t\t`tab{tab}`\n\t\t\t\twhere\n\t\t\t\t\tcompany = %s\n\t\t\t\t{order_by}\n\t\t\t\n\t\t\t\tselect\n\t\t\t\t\tname\n\t\t\t\tfrom\n\t\t\t\t\t`tab{tab}`\n\t\t\t",
"language": "en",
"n_whitespaces": 2,
"n_words": 13,
"vocab_size": 9
} | 44 | Python | 34 | 494bd9ef78313436f0424b918f200dab8fc7c20b | budget_variance_report.py | 65,181 | 30 | 91 | get_cost_centers | https://github.com/frappe/erpnext.git | style: format code with black | 27 | 0 | 13,819 | 16 |
|
4 | 11 | def paginator_number(cl, i):
if i == cl.paginator.ELLIPSIS:
return format_html("{} ", cl.paginator.ELLIPSIS)
elif i == cl.page_num:
return format_html('<span class="this-page">{}</span> ', i)
else:
return format_html(
'<a href="{}"{}>{}</a> ',
cl.get_query_string({PAGE_VAR: i}),
mark_safe(' class="end"' if i == cl.paginator.num_pages else ""),
i,
)
| django/contrib/admin/templatetags/admin_list.py | 128 | django | {
"docstring": "\n Generate an individual page index link in a paginated list.\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 10,
"vocab_size": 10
} | 38 | Python | 30 | 9c19aff7c7561e3a82978a272ecdaad40dda5c00 | admin_list.py | 203,487 | 12 | 78 | paginator_number | https://github.com/django/django.git | Refs #33476 -- Reformatted code with Black. | 122 | 0 | 50,406 | 16 |
|
1 | 27 | def test_deepspeed_multigpu_single_file(tmpdir):
model = BoringModel()
checkpoint_path = os.path.join(tmpdir, "model.pt")
trainer = Trainer(default_root_dir=tmpdir, fast_dev_run=True)
trainer.fit(model)
trainer.save_checkpoint(checkpoint_path)
trainer = Trainer(
default_root_dir=tmpdir, strategy=DeepSpeedStrategy(stage=3), gpus=1, fast_dev_run=True, precision=16
)
strategy = trainer.strategy
assert isinstance(strategy, DeepSpeedStrategy)
assert not strategy.load_full_weights
with pytest.raises(MisconfigurationException, match="DeepSpeed was unable to load the checkpoint."):
trainer.test(model, ckpt_path=checkpoint_path)
trainer = Trainer(
default_root_dir=tmpdir,
strategy=DeepSpeedStrategy(stage=3, load_full_weights=True),
gpus=1,
fast_dev_run=True,
precision=16,
)
strategy = trainer.strategy
assert isinstance(strategy, DeepSpeedStrategy)
assert strategy.load_full_weights
trainer.test(model, ckpt_path=checkpoint_path)
| tests/strategies/test_deepspeed_strategy.py | 270 | lightning | {
"docstring": "Test to ensure that DeepSpeed loads from a single file checkpoint.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 64 | Python | 41 | 650c710efacd633fa283955145342bb64063c883 | test_deepspeed_strategy.py | 241,567 | 25 | 175 | test_deepspeed_multigpu_single_file | https://github.com/Lightning-AI/lightning.git | Rename training plugin test files & names to strategy (#11303) | 167 | 0 | 69,594 | 12 |
|
3 | 23 | def test_f2py_only(capfd, retreal_f77, monkeypatch):
foutl = get_io_paths(retreal_f77, mname="test")
ipath = foutl.finp
toskip = "t0 t4 t8 sd s8 s4"
tokeep = "td s0"
monkeypatch.setattr(
sys, "argv",
f'f2py {ipath} -m test only: {tokeep}'.split())
with util.switchdir(ipath.parent):
f2pycli()
out, err = capfd.readouterr()
for skey in toskip.split():
assert (
f'buildmodule: Could not find the body of interfaced routine "{skey}". Skipping.'
in err)
for rkey in tokeep.split():
assert f'Constructing wrapper function "{rkey}"' in out
| numpy/f2py/tests/test_f2py2e.py | 183 | numpy | {
"docstring": "Test that functions can be kept by only:\n CLI :: only:\n ",
"language": "en",
"n_whitespaces": 17,
"n_words": 11,
"vocab_size": 10
} | 69 | Python | 60 | 729ad4f92420231e2a7009b3223c6c7620b8b808 | test_f2py2e.py | 160,134 | 17 | 98 | test_f2py_only | https://github.com/numpy/numpy.git | TST: Initialize f2py2e tests of the F2PY CLI (#20668)
Increases F2PY coverage by around 15 percent. For the CLI itself it covers the major features (around 70 percent), with the exception of mostly numpy.distutils stuff.
More importantly, sets the groundwork for #20056, in that passing the same testsuite should indicate feature parity. | 184 | 0 | 38,506 | 13 |
|
4 | 10 | def _is_all_dates(self) -> bool:
if needs_i8_conversion(self.dtype):
return True
elif self.dtype != _dtype_obj:
# TODO(ExtensionIndex): 3rd party EA might override?
# Note: this includes IntervalIndex, even when the left/right
# contain datetime-like objects.
return False
elif self._is_multi:
return False
return is_datetime_array(ensure_object(self._values))
| pandas/core/indexes/base.py | 76 | pandas | {
"docstring": "\n Whether or not the index values only consist of dates.\n ",
"language": "en",
"n_whitespaces": 25,
"n_words": 10,
"vocab_size": 10
} | 40 | Python | 33 | 06dac44e91bb099319fa6c421df8111b189d26a6 | base.py | 164,487 | 11 | 44 | _is_all_dates | https://github.com/pandas-dev/pandas.git | CLN: Index methods incorrectly assuming object dtype (#45767) | 142 | 0 | 39,562 | 10 |
|
1 | 4 | def copy(self) -> Animation:
return deepcopy(self)
# Methods for interpolation, the mean of an Animation
# TODO: stop using alpha as parameter name in different meanings. | manim/animation/animation.py | 26 | manim | {
"docstring": "Create a copy of the animation.\n\n Returns\n -------\n Animation\n A copy of ``self``\n ",
"language": "en",
"n_whitespaces": 52,
"n_words": 13,
"vocab_size": 11
} | 26 | Python | 25 | daf23c9d1031b12d9c119b8f6b7e60727d7f9242 | animation.py | 189,517 | 9 | 13 | copy | https://github.com/ManimCommunity/manim.git | Upgraded typehints (#2429)
* Future Annotations
* Delete template_twitter_post.py
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Apply suggestions from code review
* [pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
* Fixed broken RTD
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> | 46 | 0 | 46,111 | 7 |
|
1 | 14 | def test_change_one_time_keys(self) -> None:
local_user = "@boris:" + self.hs.hostname
device_id = "xyz"
keys = {
"alg1:k1": "key1",
"alg2:k2": {"key": "key2", "signatures": {"k1": "sig1"}},
"alg2:k3": {"key": "key3"},
}
res = self.get_success(
self.handler.upload_keys_for_user(
local_user, device_id, {"one_time_keys": keys}
)
)
self.assertDictEqual(
res, {"one_time_key_counts": {"alg1": 1, "alg2": 2, "signed_curve25519": 0}}
)
# Error when changing string key
self.get_failure(
self.handler.upload_keys_for_user(
local_user, device_id, {"one_time_keys": {"alg1:k1": "key2"}}
),
SynapseError,
)
# Error when replacing dict key with string
self.get_failure(
self.handler.upload_keys_for_user(
local_user, device_id, {"one_time_keys": {"alg2:k3": "key2"}}
),
SynapseError,
)
# Error when replacing string key with dict
self.get_failure(
self.handler.upload_keys_for_user(
local_user,
device_id,
{"one_time_keys": {"alg1:k1": {"key": "key"}}},
),
SynapseError,
)
# Error when replacing dict key
self.get_failure(
self.handler.upload_keys_for_user(
local_user,
device_id,
{
"one_time_keys": {
"alg2:k2": {"key": "key3", "signatures": {"k1": "sig1"}}
}
},
),
SynapseError,
)
| tests/handlers/test_e2e_keys.py | 407 | synapse | {
"docstring": "attempts to change one-time-keys should be rejected",
"language": "en",
"n_whitespaces": 6,
"n_words": 7,
"vocab_size": 7
} | 124 | Python | 61 | 5dd949bee6158a8b651db9f2ae417a62c8184bfd | test_e2e_keys.py | 247,617 | 49 | 229 | test_change_one_time_keys | https://github.com/matrix-org/synapse.git | Add type hints to some tests/handlers files. (#12224) | 680 | 0 | 71,783 | 18 |
|
3 | 10 | def __call__(self, *other):
rv = Cycle(*other)
for k, v in zip(list(self.keys()), [rv[self[k]] for k in self.keys()]):
rv[k] = v
return rv
| sympy/combinatorics/permutations.py | 93 | sympy | {
"docstring": "Return product of cycles processed from R to L.\n\n Examples\n ========\n\n >>> from sympy.combinatorics import Cycle\n >>> Cycle(1, 2)(2, 3)\n (1 3 2)\n\n An instance of a Cycle will automatically parse list-like\n objects and Permutations that are on the right. It is more\n flexible than the Permutation in that all elements need not\n be present:\n\n >>> a = Cycle(1, 2)\n >>> a(2, 3)\n (1 3 2)\n >>> a(2, 3)(4, 5)\n (1 3 2)(4 5)\n\n ",
"language": "en",
"n_whitespaces": 179,
"n_words": 74,
"vocab_size": 54
} | 21 | Python | 16 | 498015021131af4dbb07eb110e5badaba8250c7b | permutations.py | 196,180 | 5 | 59 | __call__ | https://github.com/sympy/sympy.git | Updated import locations | 60 | 0 | 47,680 | 11 |
|
1 | 6 | async def test_default_discovery_abort_on_new_unique_flow(hass, manager):
mock_integration(hass, MockModule("comp"))
mock_entity_platform(hass, "config_flow.comp", None)
| tests/test_config_entries.py | 45 | core | {
"docstring": "Test that a flow using default discovery is aborted when a second flow with unique ID is created.",
"language": "en",
"n_whitespaces": 17,
"n_words": 18,
"vocab_size": 15
} | 9 | Python | 9 | 7cd68381f1d4f58930ffd631dfbfc7159d459832 | test_config_entries.py | 316,438 | 21 | 165 | test_default_discovery_abort_on_new_unique_flow | https://github.com/home-assistant/core.git | Search/replace RESULT_TYPE_* by FlowResultType enum (#74642) | 18 | 0 | 115,016 | 10 |
|
3 | 10 | def _get_roles_path(self):
roles_path = context.CLIARGS['roles_path']
if context.CLIARGS['basedir'] is not None:
subdir = os.path.join(context.CLIARGS['basedir'], "roles")
if os.path.isdir(subdir):
roles_path = (subdir,) + roles_path
roles_path = roles_path + (context.CLIARGS['basedir'],)
return roles_path
| lib/ansible/cli/doc.py | 126 | ansible | {
"docstring": "\n Add any 'roles' subdir in playbook dir to the roles search path.\n And as a last resort, add the playbook dir itself. Order being:\n - 'roles' subdir of playbook dir\n - DEFAULT_ROLES_PATH (default in cliargs)\n - playbook dir (basedir)\n NOTE: This matches logic in RoleDefinition._load_role_path() method.\n ",
"language": "en",
"n_whitespaces": 108,
"n_words": 46,
"vocab_size": 33
} | 28 | Python | 18 | 29b5eb6ba9fb652ddd8dd06cdd8f2e80e2098063 | doc.py | 266,511 | 8 | 75 | _get_roles_path | https://github.com/ansible/ansible.git | updated metadata dump to do full docs dump (#76170)
* minor refactor in other options by pushing common code into functions
* consolidate coll_filter
* more normalizing loader
* dont pass plugin_loader, its global import
* Also dump roles and collections
* adjusted tests to new err msg
* disable namespace filter (unused) | 104 | 0 | 78,449 | 12 |
|
6 | 20 | def compile_sample(self, batch_size, samples=None, images=None, masks=None):
num_images = self._config.get("preview_images", 14)
num_images = min(batch_size, num_images) if batch_size is not None else num_images
retval = {}
for side in ("a", "b"):
logger.debug("Compiling samples: (side: '%s', samples: %s)", side, num_images)
side_images = images[side] if images is not None else self._target[side]
side_masks = masks[side] if masks is not None else self._masks[side]
side_samples = samples[side] if samples is not None else self._samples[side]
retval[side] = [side_samples[0:num_images],
side_images[0:num_images],
side_masks[0:num_images]]
return retval
| plugins/train/trainer/_base.py | 225 | faceswap | {
"docstring": " Compile the preview samples for display.\n\n Parameters\n ----------\n batch_size: int\n The requested batch size for each training iterations\n samples: dict, optional\n Dictionary for side \"a\", \"b\" of :class:`numpy.ndarray`. The sample images that should\n be used for creating the preview. If ``None`` then the samples will be generated from\n the internal random image generator. Default: ``None``\n images: dict, optional\n Dictionary for side \"a\", \"b\" of :class:`numpy.ndarray`. The target images that should\n be used for creating the preview. If ``None`` then the targets will be generated from\n the internal random image generator. Default: ``None``\n masks: dict, optional\n Dictionary for side \"a\", \"b\" of :class:`numpy.ndarray`. The masks that should be used\n for creating the preview. If ``None`` then the masks will be generated from the\n internal random image generator. Default: ``None``\n\n Returns\n -------\n list\n The list of samples, targets and masks as :class:`numpy.ndarrays` for creating a\n preview image\n ",
"language": "en",
"n_whitespaces": 349,
"n_words": 145,
"vocab_size": 58
} | 74 | Python | 48 | c1512fd41d86ef47a5d1ce618d6d755ef7cbacdf | _base.py | 100,396 | 13 | 153 | compile_sample | https://github.com/deepfakes/faceswap.git | Update code to support Tensorflow versions up to 2.8 (#1213)
* Update maximum tf version in setup + requirements
* - bump max version of tf version in launcher
- standardise tf version check
* update keras get_custom_objects for tf>2.6
* bugfix: force black text in GUI file dialogs (linux)
* dssim loss - Move to stock tf.ssim function
* Update optimizer imports for compatibility
* fix logging for tf2.8
* Fix GUI graphing for TF2.8
* update tests
* bump requirements.txt versions
* Remove limit on nvidia-ml-py
* Graphing bugfixes
- Prevent live graph from displaying if data not yet available
* bugfix: Live graph. Collect loss labels correctly
* fix: live graph - swallow inconsistent loss errors
* Bugfix: Prevent live graph from clearing during training
* Fix graphing for AMD | 225 | 0 | 19,880 | 11 |
|
1 | 19 | def test_delete_alias_admin(self):
# Create an alias from a different user.
self._create_alias(self.test_user)
# Delete the user's alias as the admin.
result = self.get_success(
self.handler.delete_association(
create_requester(self.admin_user), self.room_alias
)
)
self.assertEqual(self.room_id, result)
# Confirm the alias is gone.
self.get_failure(
self.handler.get_association(self.room_alias),
synapse.api.errors.SynapseError,
)
| tests/handlers/test_directory.py | 117 | synapse | {
"docstring": "A server admin can delete an alias created by another user.",
"language": "en",
"n_whitespaces": 10,
"n_words": 11,
"vocab_size": 11
} | 39 | Python | 31 | 02d708568b476f2f7716000b35c0adfa4cbd31b3 | test_directory.py | 246,741 | 12 | 72 | test_delete_alias_admin | https://github.com/matrix-org/synapse.git | Replace assertEquals and friends with non-deprecated versions. (#12092) | 168 | 0 | 71,337 | 13 |
|
6 | 16 | def _validate_resource_path(path):
invalid = (
os.path.pardir in path.split(posixpath.sep) or
posixpath.isabs(path) or
ntpath.isabs(path)
)
if not invalid:
return
msg = "Use of .. or absolute path in a resource path is not allowed."
# Aggressively disallow Windows absolute paths
if ntpath.isabs(path) and not posixpath.isabs(path):
raise ValueError(msg)
# for compatibility, warn; in future
# raise ValueError(msg)
warnings.warn(
msg[:-1] + " and will raise exceptions in a future release.",
DeprecationWarning,
stacklevel=4,
)
| .venv/lib/python3.8/site-packages/pip/_vendor/pkg_resources/__init__.py | 151 | transferlearning | {
"docstring": "\n Validate the resource paths according to the docs.\n https://setuptools.readthedocs.io/en/latest/pkg_resources.html#basic-resource-access\n\n >>> warned = getfixture('recwarn')\n >>> warnings.simplefilter('always')\n >>> vrp = NullProvider._validate_resource_path\n >>> vrp('foo/bar.txt')\n >>> bool(warned)\n False\n >>> vrp('../foo/bar.txt')\n >>> bool(warned)\n True\n >>> warned.clear()\n >>> vrp('/foo/bar.txt')\n >>> bool(warned)\n True\n >>> vrp('foo/../../bar.txt')\n >>> bool(warned)\n True\n >>> warned.clear()\n >>> vrp('foo/f../bar.txt')\n >>> bool(warned)\n False\n\n Windows path separators are straight-up disallowed.\n >>> vrp(r'\\\\foo/bar.txt')\n Traceback (most recent call last):\n ...\n ValueError: Use of .. or absolute path in a resource path \\\nis not allowed.\n\n >>> vrp(r'C:\\\\foo/bar.txt')\n Traceback (most recent call last):\n ...\n ValueError: Use of .. or absolute path in a resource path \\\nis not allowed.\n\n Blank values are allowed\n\n >>> vrp('')\n >>> bool(warned)\n False\n\n Non-string values are not.\n\n >>> vrp(None)\n Traceback (most recent call last):\n ...\n AttributeError: ...\n ",
"language": "en",
"n_whitespaces": 409,
"n_words": 123,
"vocab_size": 58
} | 69 | Python | 48 | f638f5d0e6c8ebed0e69a6584bc7f003ec646580 | __init__.py | 63,033 | 16 | 87 | _validate_resource_path | https://github.com/jindongwang/transferlearning.git | upd; format | 234 | 0 | 13,106 | 13 |
|
5 | 10 | def _round(self, places, rounding):
if places <= 0:
raise ValueError("argument should be at least 1 in _round")
if self._is_special or not self:
return Decimal(self)
ans = self._rescale(self.adjusted()+1-places, rounding)
# it can happen that the rescale alters the adjusted exponent;
# for example when rounding 99.97 to 3 significant figures.
# When this happens we end up with an extra 0 at the end of
# the number; a second rescale fixes this.
if ans.adjusted() != self.adjusted():
ans = ans._rescale(ans.adjusted()+1-places, rounding)
return ans
| python3.10.4/Lib/_pydecimal.py | 141 | XX-Net | {
"docstring": "Round a nonzero, nonspecial Decimal to a fixed number of\n significant figures, using the given rounding mode.\n\n Infinities, NaNs and zeros are returned unaltered.\n\n This operation is quiet: it raises no flags, and uses no\n information from the context.\n\n ",
"language": "en",
"n_whitespaces": 74,
"n_words": 39,
"vocab_size": 35
} | 82 | Python | 66 | 8198943edd73a363c266633e1aa5b2a9e9c9f526 | _pydecimal.py | 219,783 | 9 | 84 | _round | https://github.com/XX-net/XX-Net.git | add python 3.10.4 for windows | 185 | 0 | 55,798 | 14 |
|
1 | 5 | def exterior_angle(self):
return 2*S.Pi/self._n
| sympy/geometry/polygon.py | 28 | sympy | {
"docstring": "Measure of the exterior angles.\n\n Returns\n =======\n\n exterior_angle : number\n\n See Also\n ========\n\n sympy.geometry.line.LinearEntity.angle_between\n\n Examples\n ========\n\n >>> from sympy import RegularPolygon, Point\n >>> rp = RegularPolygon(Point(0, 0), 4, 8)\n >>> rp.exterior_angle\n pi/4\n\n ",
"language": "en",
"n_whitespaces": 123,
"n_words": 32,
"vocab_size": 29
} | 4 | Python | 4 | 498015021131af4dbb07eb110e5badaba8250c7b | polygon.py | 196,294 | 2 | 16 | exterior_angle | https://github.com/sympy/sympy.git | Updated import locations | 18 | 0 | 47,794 | 8 |
|
1 | 24 | def test_context_for_resolved_crash_rate_alert(self):
status = TriggerStatus.RESOLVED
incident = self.create_incident()
alert_rule = self.create_alert_rule(
aggregate="percentage(sessions_crashed, sessions) AS _crash_rate_alert_aggregate",
threshold_type=AlertRuleThresholdType.BELOW,
query="",
)
alert_rule_trigger = self.create_alert_rule_trigger(alert_rule)
action = self.create_alert_rule_trigger_action(
alert_rule_trigger=alert_rule_trigger, triggered_for_incident=incident
)
generated_email_context = generate_incident_trigger_email_context(
self.project, incident, action.alert_rule_trigger, status, IncidentStatus.CLOSED
)
assert generated_email_context["aggregate"] == "percentage(sessions_crashed, sessions)"
assert generated_email_context["threshold"] == 100
assert generated_email_context["threshold_direction_string"] == ">"
| tests/sentry/incidents/test_action_handlers.py | 168 | sentry | {
"docstring": "\n Test that ensures the resolved notification contains the correct threshold string\n ",
"language": "en",
"n_whitespaces": 26,
"n_words": 11,
"vocab_size": 10
} | 49 | Python | 38 | 146fba432a32568be7d0b884dae0c39a6c33a11f | test_action_handlers.py | 96,409 | 18 | 102 | test_context_for_resolved_crash_rate_alert | https://github.com/getsentry/sentry.git | fix(metric_alerts): Make sure critical triggers resolve properly when no action is set on a warning trigger (#31883)
### Problem
If we have an alert set up like:
- Warning: 50. Action: None
- Critical: 100. Action: Slack
Then if we go from critical -> warning state the slack resolve action will fail to fire.
### Cause
The reason this happens is related to a previous fix. For an alert like
- Warning: 50. Action: Slack
- Critical: 100. Action: Slack
When going from critical -> warning the critical action would be marked as resolved. This would
cause a slack notification with `Resolved` to be sent to the channel. This is misleading, because
the alert is still active, just in the warning state. What we want here is to fire a warning
notification instead.
The initial fix for this was that when we resolved a critical trigger, we’d check and see whether
there was an active warning trigger. If so, we’d send a warning trigger fire to our actions, rather
than a critical trigger resolve. This works ok for many cases, but fails when the actions on the
warning trigger are different to those on the critical trigger.
### Fix
Substituting the warning trigger for the critical trigger causes us subtle bugs. So, instead of
this, when triggering fires/resolves on our action handlers we will also pass along the incident
state change that the trigger/resolve caused the incident to go into.
So if a critical trigger resolves, we check what state it would have put the incident in. If
there’s a warning trigger, then the state is warning. If no warning trigger, the state is closed.
This state is then used to appropriately generate the messages that we send to users via our
various actions.
So now, If we have an alert set up like:
- Warning: 50. Action: None
- Critical: 100. Action: Slack
If this goes from
- critical -> warning OR critical -> resolved we will send `IncidentStatus.WARNING` to any actions
related to the critical trigger.
- warning -> resolved We do nothing since there are no actions on the warning trigger
If we have an alert set up like:
- Warning: 50. Action: Slack
- Critical: 100. Action: Slack
If this goes from:
- critical -> warning: critical trigger, `IncidentStatus.Warning`
- warning -> resolved: warning trigger, `IncidentStatus.Closed`
- critical -> resolved: Since we de-dupe triggers to avoid spamming the user, we will select the
warning trigger here, and send `IncidentStatus.closed`
If we have an alert set up like:
- Warning: 50. Action: Slack
- Critical: 100. Action: Pagerduty
If this goes from:
- critical -> warning: critical trigger, `IncidentStatus.Warning` sent to Pagerduty. Nothing sent
to Slack
- warning -> resolved: warning trigger, `IncidentStatus.Closed` sent to Slack. Nothing sent to
Pagerduty
- critical -> resolved: Critical trigger, `IncidentStatus.Warning` sent to Pagerduty. Warning
trigger, `IncidentStatus.Closed` sent to Slack. We don’t de-dupe here since the actions are
different. | 195 | 0 | 19,309 | 10 |
|
1 | 6 | def test_causal_lm_model_past_with_attn_mask(self):
config_and_inputs = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_causal_lm_model_past_with_attn_mask(*config_and_inputs)
| templates/adding_a_new_model/cookiecutter-template-{{cookiecutter.modelname}}/test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py | 43 | transformers | {
"docstring": "Test the causal LM model with `past_key_values` and `attention_mask`",
"language": "en",
"n_whitespaces": 8,
"n_words": 9,
"vocab_size": 9
} | 6 | Python | 6 | 8635407bc724c45142c1f91dbc9ef3ea681e1a56 | test_modeling_tf_{{cookiecutter.lowercase_modelname}}.py | 35,529 | 3 | 24 | test_causal_lm_model_past_with_attn_mask | https://github.com/huggingface/transformers.git | Fix tf.concatenate + test past_key_values for TF models (#15774)
* fix wrong method name tf.concatenate
* add tests related to causal LM / decoder
* make style and quality
* clean-up
* Fix TFBertModel's extended_attention_mask when past_key_values is provided
* Fix tests
* fix copies
* More tf.int8 -> tf.int32 in TF test template
* clean-up
* Update TF test template
* revert the previous commit + update the TF test template
* Fix TF template extended_attention_mask when past_key_values is provided
* Fix some styles manually
* clean-up
* Fix ValueError: too many values to unpack in the test
* Fix more: too many values to unpack in the test
* Add a comment for extended_attention_mask when there is past_key_values
* Fix TFElectra extended_attention_mask when past_key_values is provided
* Add tests to other TF models
* Fix for TF Electra test: add prepare_config_and_inputs_for_decoder
* Fix not passing training arg to lm_head in TFRobertaForCausalLM
* Fix tests (with past) for TF Roberta
* add testing for pask_key_values for TFElectra model
Co-authored-by: ydshieh <[email protected]> | 27 | 0 | 6,472 | 9 |
|
4 | 11 | def should_show(self, securitymanager) -> bool:
if self.roles:
user_roles = {r.name for r in securitymanager.current_user.roles}
if not user_roles.intersection(set(self.roles)):
return False
return True
| airflow/www/utils.py | 77 | airflow | {
"docstring": "Determine if the user should see the message based on their role membership",
"language": "en",
"n_whitespaces": 12,
"n_words": 13,
"vocab_size": 12
} | 21 | Python | 19 | 3e9828022b03b60d9e112f1f64340a528c8407e3 | utils.py | 44,455 | 7 | 48 | should_show | https://github.com/apache/airflow.git | Simplify fab has access lookup (#19294)
* Use FAB models.
* Remove incorrect conversions to new permission naming scheme.
* Fix missing FAB renames.
* Remove unused FAB compatibility fixes in models.py.
* Set perms directly on user objects.
* Set perms properties on User model.
* Rename missed old naming scheme conversion.
* Remove unused imports.
* Remove unused imports.
* Remeve get_user_roles() method.
* Make permissions eagerload.
* Remove unused imports.
* Clarify query params.
* Modify sort logic so MSSQL passes.
* Add text modifier to order_by values.
* Remove calls to get_*_dags.
* Add back execution_date
* Add back comma to match rest of file.
* Remove unused permission functions.
* Fix failing tests.
* Pass user object to current_app.appbuilder.sm.has_all_dags_access.
* Remove attempts to fix query.
* Update the api_connexion query builders.
* Add typing.
* Apply sorts directly to model objects.
* Apply sorts directly to model objects.
* Standardize custom sort code.
* Code review
* Augment xcom docs (#20755)
* Fix relationship join bug in FAB/SecurityManager with SQLA 1.4 (#21296)
This is fixed in SQLA 1.4.19, but the fix makes the intent clearer here
anyway.
* Docs: Fix task order in overview example (#21282)
* Update stat_name_handler documentation (#21298)
Previously stat_name_handler was under the scheduler section of the
configuration but it was moved to the metrics section since 2.0.0.
* Update recipe for Google Cloud SDK (#21268)
* Use FAB models.
* Remove incorrect conversions to new permission naming scheme.
* Fix missing FAB renames.
* Remove unused FAB compatibility fixes in models.py.
* Set perms directly on user objects.
* Set perms properties on User model.
* Rename missed old naming scheme conversion.
* Remove unused imports.
* Remove unused imports.
* Remeve get_user_roles() method.
* Make permissions eagerload.
* Remove unused imports.
* Clarify query params.
* Modify sort logic so MSSQL passes.
* Add text modifier to order_by values.
* Remove calls to get_*_dags.
* Add back execution_date
* Add back comma to match rest of file.
* Remove unused permission functions.
* Fix failing tests.
* Pass user object to current_app.appbuilder.sm.has_all_dags_access.
* Remove attempts to fix query.
* Update the api_connexion query builders.
* Add typing.
* Apply sorts directly to model objects.
* Apply sorts directly to model objects.
* Standardize custom sort code.
* Make sure joined fields prefetch.
* Dont use cached_property, since its only on > 3.8.
Co-authored-by: Ash Berlin-Taylor <[email protected]>
Co-authored-by: Lewis John McGibbney <[email protected]>
Co-authored-by: Ash Berlin-Taylor <[email protected]>
Co-authored-by: Lucia Kasman <[email protected]>
Co-authored-by: Fran Sánchez <[email protected]>
Co-authored-by: Kamil Breguła <[email protected]> | 79 | 0 | 8,273 | 13 |