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semeru

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code-code-galeras-code-completion-from-docstring-3k-deduped

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Semeru Lab 7

pandas/core/groupby/groupby.py

def rolling(self, *args, **kwargs) -> RollingGroupby: from pandas.core.window import RollingGroupby

TYP: more return annotations in core/ (#47618) * TYP: more return annotations in core/ * from __future__ import annotations * more __future__

rolling

f65417656ba8c59438d832b6e2a431f78d40c21c

pandas

groupby.py

https://github.com/pandas-dev/pandas.git

Python

def rolling(self, *args, **kwargs) -> RollingGroupby: from pandas.core.window import RollingGroupby return RollingGroupby( self._selected_obj, *args, _grouper=self.grouper, _as_index=self.as_index, **kwargs, )

networkx/generators/degree_seq.py

def expected_degree_graph(w, seed=None, selfloops=True): r n = len(w) G = nx.empty_graph(n) # If there are no nodes are no edges in the graph, return the empty graph. if n == 0 or max(w) == 0: return G rho = 1 / sum(w) # Sort the weights in decreasing order. The original order of the # weights dictates the order of the (integer) node labels, so we # need to remember the permutation applied in the sorting. order = sorted(enumerate(w), key=itemgetter(1), reverse=True) mapping = {c: u for c, (u, v) in enumerate(order)} seq = [v for u, v in order] last = n if not selfloops: last -= 1 for u in range(last): v = u if not selfloops: v += 1 factor = seq[u] * rho p = min(seq[v] * factor, 1) while v < n and p > 0: if p != 1: r = seed.random() v += math.floor(math.log(r, 1 - p)) if v < n: q = min(seq[v] * factor, 1) if seed.random() < q / p: G.add_edge(mapping[u

Remove redundant py2 numeric conversions (#5661) * Remove redundant float conversion * Remove redundant int conversion * Use integer division Co-authored-by: Miroslav Šedivý <[email protected]>

expected_degree_graph

2a05ccdb07cff88e56661dee8a9271859354027f

networkx

degree_seq.py

https://github.com/networkx/networkx.git

Python

def expected_degree_graph(w, seed=None, selfloops=True): r n = len(w) G = nx.empty_graph(n) # If there are no nodes are no edges in the graph, return the empty graph. if n == 0 or max(w) == 0: return G rho = 1 / sum(w) # Sort the weights in decreasing order. The original order of the # weights dictates the order of the (integer) node labels, so we # need to remember the permutation applied in the sorting. order = sorted(enumerate(w), key=itemgetter(1), reverse=True) mapping = {c: u for c, (u, v) in enumerate(order)} seq = [v for u, v in order] last = n if not selfloops: last -= 1 for u in range(last): v = u if not selfloops: v += 1 factor = seq[u] * rho p = min(seq[v] * factor, 1) while v < n and p > 0: if p != 1: r = seed.random() v += math.floor(math.log(r, 1 - p)) if v < n: q = min(seq[v] * factor, 1) if seed.random() < q / p: G.add_edge(mapping[u], mapping[v]) v += 1 p = q return G

mlflow/models/evaluation/base.py

def save(self, path): os.makedirs(path,

Improve evaluation api (#5256) * init Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update doc Signed-off-by: Weichen Xu <[email protected]> * update doc Signed-off-by: Weichen Xu <[email protected]> * address comments Signed-off-by: Weichen Xu <[email protected]> * update doc Signed-off-by: Weichen Xu <[email protected]> * add shap limitation on value type Signed-off-by: Weichen Xu <[email protected]> * fix format Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]> * update Signed-off-by: Weichen Xu <[email protected]>

save

4c58179509e6f6047789efb0a95c2b0e20cb6c8f

mlflow

base.py

https://github.com/mlflow/mlflow.git

Python

def save(self, path): os.makedirs(path, exist_ok=True) with open(os.path.join(path, "metrics.json"), "w") as fp: json.dump(self.metrics, fp) artifacts_metadata = { artifact_name: { "uri": artifact.uri, "class_name": _get_fully_qualified_class_name(artifact), } for artifact_name, artifact in self.artifacts.items() } with open(os.path.join(path, "artifacts_metadata.json"), "w") as fp: json.dump(artifacts_metadata, fp) artifacts_dir = os.path.join(path, "artifacts") os.mkdir(artifacts_dir) for artifact_name, artifact in self.artifacts.items(): artifact._save(os.path.join(artifacts_dir, artifact_name))

tests/sentry/integrations/slack/test_message_builder.py

def test_build_group_generic_issue_attachment(self): event = self.store_event( data={"message": "Hello world", "level": "error"}, project_id=self.project.id ) event = event.for_group(event.groups[0]) occurrence = self.build_occurrence(level="info") occurrence.save(project_id=self.project.id) event.occurrence = occurrence event.group.type = GroupType.PROFILE_BLOCKED_THREAD attachments = SlackIssuesMessageBuilder(group=event.group, event=event).build() assert attachments["title"] == occurrence.issue_title assert attachments["text"] == occurrence.evidence_display[0].value assert attachments["fallback"] == f"[{self.project.slug}] {occurrence.issue_title}" assert attachments["color"] =

feat(integrations): Support generic issue type alerts (#42110) Add support for issue alerting integrations that use the message builder (Slack and MSTeams) for generic issue types. Preview text for Slack alert: <img width="350" alt="Screen Shot 2022-12-08 at 4 07 16 PM" src="https://user-images.githubusercontent.com/29959063/206593405-7a206d88-a31a-4e85-8c15-1f7534733ca7.png"> Slack generic issue alert shows the `occurrence.issue_title` and the "important" evidence value <img width="395" alt="Screen Shot 2022-12-08 at 4 11 20 PM" src="https://user-images.githubusercontent.com/29959063/206593408-6942d74d-4238-4df9-bfee-601ce2bc1098.png"> MSTeams generic issue alert shows the `occurrence.issue_title` and the "important" evidence value <img width="654" alt="Screen Shot 2022-12-08 at 4 13 45 PM" src="https://user-images.githubusercontent.com/29959063/206593410-2773746a-16b3-4652-ba2c-a7d5fdc76992.png"> Fixes #42047

test_build_group_generic_issue_attachment

3255fa4ebb9fbc1df6bb063c0eb77a0298ca8f72

sentry

test_message_builder.py

https://github.com/getsentry/sentry.git

Python

def test_build_group_generic_issue_attachment(self): event = self.store_event( data={"message": "Hello world", "level": "error"}, project_id=self.project.id ) event = event.for_group(event.groups[0]) occurrence = self.build_occurrence(level="info") occurrence.save(project_id=self.project.id) event.occurrence = occurrence event.group.type = GroupType.PROFILE_BLOCKED_THREAD attachments = SlackIssuesMessageBuilder(group=event.group, event=event).build() assert attachments["title"] == occurrence.issue_title assert attachments["text"] == occurrence.evidence_display[0].value assert attachments["fallback"] == f"[{self.project.slug}] {occurrence.issue_title}" assert attachments["color"] == "#2788CE" # blue for info level

xlib/image/ImageProcessor.py

def apply(self, func, mask=None) -> 'ImageProcessor': img = orig_img = self._img img = func(img).astype(orig_img.dtype) if img.ndim != 4: raise Exception('func used in ImageProcessor.apply changed format of image') if mask is not None:

ImageProcessor.py refactoring

apply

b3bc4e734528d3b186c3a38a6e73e106c3555cc7

DeepFaceLive

ImageProcessor.py

https://github.com/iperov/DeepFaceLive.git

Python

def apply(self, func, mask=None) -> 'ImageProcessor': img = orig_img = self._img img = func(img).astype(orig_img.dtype) if img.ndim != 4: raise Exception('func used in ImageProcessor.apply changed format of image') if mask is not None: mask = self._check_normalize_mask(mask) img = ne.evaluate('orig_img*(1-mask) + img*mask').astype(orig_img.dtype) self._img = img return self

recommenders/models/rbm/rbm.py

def predict(self, x): # start the timer self.timer.start() v_, _ = self

removed time from returning args

predict

843dba903757d592f7703a83ebd75eb3ffb46f6f

recommenders

rbm.py

https://github.com/microsoft/recommenders.git

Python

def predict(self, x): # start the timer self.timer.start() v_, _ = self.eval_out() # evaluate the ratings and the associated probabilities vp = self.sess.run(v_, feed_dict={self.vu: x}) # stop the timer self.timer.stop() log.info("Done inference, time %f2" % self.timer.interval) return vp

python3.10.4/Lib/json/decoder.py

def raw_decode(self, s, idx=0): try: obj, end = self.scan_once(s, idx) except StopIteration as err: raise JSONDecodeError("Expecting value", s, err.val

add python 3.10.4 for windows

raw_decode

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

decoder.py

https://github.com/XX-net/XX-Net.git

Python

def raw_decode(self, s, idx=0): try: obj, end = self.scan_once(s, idx) except StopIteration as err: raise JSONDecodeError("Expecting value", s, err.value) from None return obj, end

@not_implemented_for("multigraph") @not_implemented_for("directed")

networkx/algorithms/bridges.py

def has_bridges(G, root=None): try: next(bridges

Improve bridges documentation (#5519) * Fix bridges documentation * Revert source code modification * Revert raise errors for multigraphs

has_bridges

aa1f40a93a882db304e9a06c2a11d93b2532d80a

networkx

bridges.py

https://github.com/networkx/networkx.git

Python

def has_bridges(G, root=None): try: next(bridges(G)) except StopIteration: return False else: return True @not_implemented_for("multigraph") @not_implemented_for("directed")

.venv/lib/python3.8/site-packages/pip/_internal/utils/wheel.py

def wheel_metadata(source, dist_info_dir): # type: (ZipFile, str) -> Message path = f"{dist_info_dir}/WHEEL" # Zip file path separators must be / wheel_contents = read_wheel_metadata_file(source, path) try: wheel_text = wheel_contents.decode() except UnicodeDecodeError as e: raise UnsupportedWheel(f"error decoding {path!r}: {e!r}") # FeedParser (used by Parser) does not raise any exceptions. The returned # message may have .defects populated, but for backwards-compatibility

upd; format

wheel_metadata

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

wheel.py

https://github.com/jindongwang/transferlearning.git

Python

def wheel_metadata(source, dist_info_dir): # type: (ZipFile, str) -> Message path = f"{dist_info_dir}/WHEEL" # Zip file path separators must be / wheel_contents = read_wheel_metadata_file(source, path) try: wheel_text = wheel_contents.decode() except UnicodeDecodeError as e: raise UnsupportedWheel(f"error decoding {path!r}: {e!r}") # FeedParser (used by Parser) does not raise any exceptions. The returned # message may have .defects populated, but for backwards-compatibility we # currently ignore them. return Parser().parsestr(wheel_text)

src/datasets/table.py

def remove_column(self, i, *args, **kwargs): table = self.table.remove_column(i, *args, **kwargs) name = self.table.column_names[i] blocks = [] for tables in self.blocks: blocks.append( [ t.remove_colu

Update docs to new frontend/UI (#3690) * WIP: update docs to new UI * make style * Rm unused * inject_arrow_table_documentation __annotations__ * hasattr(arrow_table_method, "__annotations__") * Update task_template.rst * Codeblock PT-TF-SPLIT * Convert loading scripts * Convert docs to mdx * Fix mdx * Add <Tip> * Convert mdx tables * Fix codeblock * Rm unneded hashlinks * Update index.mdx * Redo dev change * Rm circle ci `build_doc` & `deploy_doc` * Rm unneeded files * Update docs reamde * Standardize to `Example::` * mdx logging levels doc * Table properties inject_arrow_table_documentation * ``` to ```py mdx * Add Tips mdx * important,None -> <Tip warning={true}> * More misc * Center imgs * Update instllation page * `setup.py` docs section * Rm imgs since they are in hf.co * Update docs/source/access.mdx Co-authored-by: Steven Liu <[email protected]> * Update index mdx * Update docs/source/access.mdx Co-authored-by: Steven Liu <[email protected]> * just `Dataset` obj * Addedversion just italics * Update ReadInstruction doc example syntax * Change docstring for `prepare_for_task` * Chore * Remove `code` syntax from headings * Rm `code` syntax from headings * Hashlink backward compatability * S3FileSystem doc * S3FileSystem doc updates * index.mdx updates * Add darkmode gifs * Index logo img css classes * Index mdx dataset logo img size * Docs for DownloadMode class * Doc DownloadMode table * format docstrings * style * Add doc builder scripts (#3790) * add doc builder scripts * fix docker image * Docs new UI actions no self hosted (#3793) * No self hosted * replace doc injection by actual docstrings * Docstring formatted Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Mishig Davaadorj <[email protected]> Co-authored-by: Lysandre Debut <[email protected]> Co-authored-by: Mishig Davaadorj <[email protected]> * Rm notebooks from docs actions since they dont exi * Update tsting branch * More docstring * Chore * bump up node version * bump up node * ``` -> ```py for audio_process.mdx * Update .github/workflows/build_documentation.yml Co-authored-by: Quentin Lhoest <[email protected]> * Uodate dev doc build * remove run on PR * fix action * Fix gh doc workflow * forgot this change when merging master * Update build doc Co-authored-by: Steven Liu <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]> Co-authored-by: Lysandre Debut <[email protected]>

remove_column

e35be138148333078284b942ccc9ed7b1d826f97

datasets

table.py

https://github.com/huggingface/datasets.git

Python

def remove_column(self, i, *args, **kwargs): table = self.table.remove_column(i, *args, **kwargs) name = self.table.column_names[i] blocks = [] for tables in self.blocks: blocks.append( [ t.remove_column(t.column_names.index(name), *args, **kwargs) if name in t.column_names else t for t in tables ] ) return ConcatenationTable(table, blocks)

netbox/dcim/tests/test_models.py

def test_cable_cannot_terminate_to_a_wireless_interface(self): wireless_interface = Interface(device=self.device1, name="W1", type=InterfaceTypeChoices.TYPE_80211A) cable = Cable(a_terminations=[self.interface2], b_terminations=[wireless_interface]) with self.assertRaises(ValidationError): cable.clean()

Update Cable instantiations to match new signature

test_cable_cannot_terminate_to_a_wireless_interface

3a461d02793e6f9d41c2b1a92647e691de1abaac

netbox

test_models.py

https://github.com/netbox-community/netbox.git

Python

def test_cable_cannot_terminate_to_a_wireless_interface(self): wireless_interface = Interface(device=self.device1, name="W1", type=InterfaceTypeChoices.TYPE_80211A) cable = Cable(a_terminations=[self.interface2], b_terminations=[wireless_interface]) with self.assertRaises(ValidationError): cable.clean()

django/db/backends/base/creation.py

def get_test_db_clone_settings(self, suffix): # When this function is called, the test database has been created # already and its name has been copied to

Refs #33476 -- Reformatted code with Black.

get_test_db_clone_settings

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

creation.py

https://github.com/django/django.git

Python

def get_test_db_clone_settings(self, suffix): # When this function is called, the test database has been created # already and its name has been copied to settings_dict['NAME'] so # we don't need to call _get_test_db_name. orig_settings_dict = self.connection.settings_dict return { **orig_settings_dict, "NAME": "{}_{}".format(orig_settings_dict["NAME"], suffix), }

python3.10.4/Lib/imaplib.py

def open(self, host='', port=IMAP4_PORT, timeout=None): self.host = host self.port = port self.sock = self._create_socket(timeout) self.file = self.sock.makefile('rb')

add python 3.10.4 for windows

open

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

imaplib.py

https://github.com/XX-net/XX-Net.git

Python

def open(self, host='', port=IMAP4_PORT, timeout=None): self.host = host self.port = port self.sock = self._create_socket(timeout) self.file = self.sock.makefile('rb')

src/textual/_terminal_features.py

def synchronized_output_end_sequence(self) -> str: if self.synchronised_output: return

[terminal buffering] Address PR feedback

synchronized_output_end_sequence

7f27e70440c177b2a047b7f74a78ed5cd5b4b596

textual

_terminal_features.py

https://github.com/Textualize/textual.git

Python

def synchronized_output_end_sequence(self) -> str: if self.synchronised_output: return TERMINAL_MODES_ANSI_SEQUENCES[Mode.SynchronizedOutput]["end_sync"] return ""

frequency_response.py

def _band_penalty_coefficients(self, fc, q, gain, filter_frs): ref_frs = biquad.digital_coeffs(self.frequenc

Improved quality regularization to a point where it works well. 10 kHz to 20 kHz is RMSE is calculated from the average levels. Split neo PEQ notebook by band and Q.

_band_penalty_coefficients

f6021faf2a8e62f88a8d6979ce812dcb71133a8f

AutoEq

frequency_response.py

https://github.com/jaakkopasanen/AutoEq.git

Python

def _band_penalty_coefficients(self, fc, q, gain, filter_frs): ref_frs = biquad.digital_coeffs(self.frequency, 192e3, *biquad.peaking(fc, q, gain, fs=192e3)) est_sums = np.sum(filter_frs, axis=1) ref_sums = np.sum(ref_frs, axis=1) penalties = np.zeros((len(fc),)) mask = np.squeeze(ref_sums) != 0.0 penalties[mask] = est_sums[mask] / ref_sums[mask] return 10 * (1 - np.expand_dims(penalties, 1))

sklearn/ensemble/tests/test_voting.py

def test_predict_on_toy_problem(global_random_seed): clf1 = LogisticRegression(random_state=global_random_seed) clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed) clf3 = GaussianNB() X = np.array( [[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]] ) y = np.array([1, 1, 1, 2, 2, 2]) assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) eclf = VotingClassifier( estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="hard", weights=[1, 1, 1], ) assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) eclf = VotingClassifier( estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="soft", weights=[1, 1, 1], ) assert_array

TST use global_random_seed in sklearn/ensemble/tests/test_voting.py (#24282) Co-authored-by: Jérémie du Boisberranger <[email protected]>

test_predict_on_toy_problem

02b04cb3ecfc5fce1f627281c312753f3b4b8494

scikit-learn

test_voting.py

https://github.com/scikit-learn/scikit-learn.git

Python

def test_predict_on_toy_problem(global_random_seed): clf1 = LogisticRegression(random_state=global_random_seed) clf2 = RandomForestClassifier(n_estimators=10, random_state=global_random_seed) clf3 = GaussianNB() X = np.array( [[-1.1, -1.5], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2], [2.1, 1.4], [3.1, 2.3]] ) y = np.array([1, 1, 1, 2, 2, 2]) assert_array_equal(clf1.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) assert_array_equal(clf2.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) assert_array_equal(clf3.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) eclf = VotingClassifier( estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="hard", weights=[1, 1, 1], ) assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2]) eclf = VotingClassifier( estimators=[("lr", clf1), ("rf", clf2), ("gnb", clf3)], voting="soft", weights=[1, 1, 1], ) assert_array_equal(eclf.fit(X, y).predict(X), [1, 1, 1, 2, 2, 2])

sklearn/feature_extraction/_dict_vectorizer.py

def fit_transform(self, X, y=None): self._validate_params() return self._tran

MAINT Param validation for Dictvectorizer (#23820)

fit_transform

5a850eb044ca07f1f3bcb1b284116d6f2d37df1b

scikit-learn

_dict_vectorizer.py

https://github.com/scikit-learn/scikit-learn.git

Python

def fit_transform(self, X, y=None): self._validate_params() return self._transform(X, fitting=True)

qutebrowser/browser/webengine/webenginetab.py

def _on_feature_permission_requested(self, url, feature): page = self._widget.page() grant_permission = functools.partial( page.setFeaturePermission, url, feature, QWebEnginePage.PermissionPolicy.PermissionGrantedByUser) deny_permission = functools.partial( page.setFeaturePermission, url, feature, QWebEnginePage.PermissionPolicy.PermissionDeniedByUser) permission_str = debug.qenum_key(QWebEnginePage, feature) if not url.isValid(): # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-85116 is_qtbug = (qtutils.version_check('5.15.0', compiled=False, exact=True) and self._tab.is_private and feature == QWebEnginePage.Feature.Notifications) logger = log.webview.debug if is_qtbug else log.webview.warning logger("Ignoring feature permission {} for invalid URL {}".format( permission_str, url)) deny_permission() return if feature not in self._options: log.webview.error("Unhandled feature permission {}".format( permission_str)) deny_permission() return if ( feature in [QWebEnginePage.Feature.DesktopVideoCapture, QWebEnginePage.Feature.DesktopAudioVideoCapture] and qtutils.version_check('5.13', compiled=

Run scripts/dev/rewrite_enums.py

_on_feature_permission_requested

0877fb0d78635692e481c8bde224fac5ad0dd430

qutebrowser

webenginetab.py

https://github.com/qutebrowser/qutebrowser.git

Python

def _on_feature_permission_requested(self, url, feature): page = self._widget.page() grant_permission = functools.partial( page.setFeaturePermission, url, feature, QWebEnginePage.PermissionPolicy.PermissionGrantedByUser) deny_permission = functools.partial( page.setFeaturePermission, url, feature, QWebEnginePage.PermissionPolicy.PermissionDeniedByUser) permission_str = debug.qenum_key(QWebEnginePage, feature) if not url.isValid(): # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-85116 is_qtbug = (qtutils.version_check('5.15.0', compiled=False, exact=True) and self._tab.is_private and feature == QWebEnginePage.Feature.Notifications) logger = log.webview.debug if is_qtbug else log.webview.warning logger("Ignoring feature permission {} for invalid URL {}".format( permission_str, url)) deny_permission() return if feature not in self._options: log.webview.error("Unhandled feature permission {}".format( permission_str)) deny_permission() return if ( feature in [QWebEnginePage.Feature.DesktopVideoCapture, QWebEnginePage.Feature.DesktopAudioVideoCapture] and qtutils.version_check('5.13', compiled=False) and not qtutils.version_check('5.13.2', compiled=False) ): # WORKAROUND for https://bugreports.qt.io/browse/QTBUG-78016 log.webview.warning("Ignoring desktop sharing request due to " "crashes in Qt < 5.13.2") deny_permission() return question = shared.feature_permission( url=url.adjusted(QUrl.UrlFormattingOption.RemovePath), option=self._options[feature], msg=self._messages[feature], yes_action=grant_permission, no_action=deny_permission, abort_on=[self._tab.abort_questions]) if question is not None: page.featurePermissionRequestCanceled.connect( functools.partial(self._on_feature_permission_cancelled, question, url, feature))

python3.10.4/Lib/distutils/command/bdist_msi.py

def add_find_python(self): start = 402 for ver in self.versions: install_path = r"SOFTWARE\Python\PythonCore\%s\InstallPath" % ver machine_reg = "python.machine." + ver user_reg = "python.user." + ver machine_prop = "PYTHON.MACHINE." + ver user_prop = "PYTHON.USER." + ver machine_action = "Pyth

add python 3.10.4 for windows

add_find_python

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

bdist_msi.py

https://github.com/XX-net/XX-Net.git

Python

def add_find_python(self): start = 402 for ver in self.versions: install_path = r"SOFTWARE\Python\PythonCore\%s\InstallPath" % ver machine_reg = "python.machine." + ver user_reg = "python.user." + ver machine_prop = "PYTHON.MACHINE." + ver user_prop = "PYTHON.USER." + ver machine_action = "PythonFromMachine" + ver user_action = "PythonFromUser" + ver exe_action = "PythonExe" + ver target_dir_prop = "TARGETDIR" + ver exe_prop = "PYTHON" + ver if msilib.Win64: # type: msidbLocatorTypeRawValue + msidbLocatorType64bit Type = 2+16 else: Type = 2 add_data(self.db, "RegLocator", [(machine_reg, 2, install_path, None, Type), (user_reg, 1, install_path, None, Type)]) add_data(self.db, "AppSearch", [(machine_prop, machine_reg), (user_prop, user_reg)]) add_data(self.db, "CustomAction", [(machine_action, 51+256, target_dir_prop, "[" + machine_prop + "]"), (user_action, 51+256, target_dir_prop, "[" + user_prop + "]"), (exe_action, 51+256, exe_prop, "[" + target_dir_prop + "]\\python.exe"), ]) add_data(self.db, "InstallExecuteSequence", [(machine_action, machine_prop, start), (user_action, user_prop, start + 1), (exe_action, None, start + 2), ]) add_data(self.db, "InstallUISequence", [(machine_action, machine_prop, start), (user_action, user_prop, start + 1), (exe_action, None, start + 2), ]) add_data(self.db, "Condition", [("Python" + ver, 0, "NOT TARGETDIR" + ver)]) start += 4 assert start < 500

.venv/lib/python3.8/site-packages/pip/_vendor/distlib/database.py

def write_exports(self, exports): rf = self

upd; format

write_exports

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

database.py

https://github.com/jindongwang/transferlearning.git

Python

def write_exports(self, exports): rf = self.get_distinfo_file(EXPORTS_FILENAME) with open(rf, 'w') as f: write_exports(exports, f)

lib/ansible/executor/task_executor.py

def _get_action_handler_with_module_context(self, connection, templar): module_collection, separator, module_name = self._task.action.rpartition(".") module_prefix = module_name.split('_')[0] if module_collection: # For network modules, which look for one action plugin per platform, look for the # action plugin in the same collection as the module by prefixing the action plugin # with the same collecti

Add toggle to fix module_defaults with module-as-redirected-action on a per-module basis (#77265) * If there is a platform specific handler, prefer the resolved module over the resolved action when loading module_defaults Add a toggle for action plugins to prefer the resolved module when loading module_defaults Allow moving away from modules intercepted as actions pattern Fixes #77059

_get_action_handler_with_module_context

621e782ed0c119d2c84124d006fdf253c082449a

ansible

task_executor.py

https://github.com/ansible/ansible.git

Python

def _get_action_handler_with_module_context(self, connection, templar): module_collection, separator, module_name = self._task.action.rpartition(".") module_prefix = module_name.split('_')[0] if module_collection: # For network modules, which look for one action plugin per platform, look for the # action plugin in the same collection as the module by prefixing the action plugin # with the same collection. network_action = "{0}.{1}".format(module_collection, module_prefix) else: network_action = module_prefix collections = self._task.collections # Check if the module has specified an action handler module = self._shared_loader_obj.module_loader.find_plugin_with_context( self._task.action, collection_list=collections ) if not module.resolved or not module.action_plugin: module = None if module is not None: handler_name = module.action_plugin # let action plugin override module, fallback to 'normal' action plugin otherwise elif self._shared_loader_obj.action_loader.has_plugin(self._task.action, collection_list=collections): handler_name = self._task.action elif all((module_prefix in C.NETWORK_GROUP_MODULES, self._shared_loader_obj.action_loader.has_plugin(network_action, collection_list=collections))): handler_name = network_action display.vvvv("Using network group action {handler} for {action}".format(handler=handler_name, action=self._task.action), host=self._play_context.remote_addr) else: # use ansible.legacy.normal to allow (historic) local action_plugins/ override without collections search handler_name = 'ansible.legacy.normal' collections = None # until then, we don't want the task's collection list to be consulted; use the builtin handler = self._shared_loader_obj.action_loader.get( handler_name, task=self._task, connection=connection, play_context=self._play_context, loader=self._loader, templar=templar, shared_loader_obj=self._shared_loader_obj, collection_list=collections ) if not handler: raise AnsibleError("the handler '%s' was not found" % handler_name) return handler, module

TTS/tts/layers/losses.py

def forward(self, y_hat, y, length): mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2) y_norm = sample_wise_min_max(y, mask) y_hat_norm = sample_wise_min_max(y_hat, mask) ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1)) if ssim_loss.item() > 1.0: print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0") ssim_loss == 1.0 if ssim_loss.item() < 0.0: print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0")

Fix SSIM loss

forward

c17ff17a18f21be60c6916714ac8afd87d4441df

TTS

losses.py

https://github.com/coqui-ai/TTS.git

Python

def forward(self, y_hat, y, length): mask = sequence_mask(sequence_length=length, max_len=y.size(1)).unsqueeze(2) y_norm = sample_wise_min_max(y, mask) y_hat_norm = sample_wise_min_max(y_hat, mask) ssim_loss = self.loss_func((y_norm * mask).unsqueeze(1), (y_hat_norm * mask).unsqueeze(1)) if ssim_loss.item() > 1.0: print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 1.0") ssim_loss == 1.0 if ssim_loss.item() < 0.0: print(f" > SSIM loss is out-of-range {ssim_loss.item()}, setting it 0.0") ssim_loss == 0.0 return ssim_loss

django/core/management/__init__.py

def get_commands(): commands = {name: 'django.core' for name in find_commands(__path__[0])} if not settings.configured: return commands for app_config in reversed(apps.get_app_configs()): path = os.path.join(app_config.path, 'management') commands.update({n

Refs #32355 -- Removed unnecessary list() calls before reversed() on dictviews. Dict and dictviews are iterable in reversed insertion order using reversed() in Python 3.8+.

get_commands

7346c288e307e1821e3ceb757d686c9bd879389c

django

__init__.py

https://github.com/django/django.git

Python

def get_commands(): commands = {name: 'django.core' for name in find_commands(__path__[0])} if not settings.configured: return commands for app_config in reversed(apps.get_app_configs()): path = os.path.join(app_config.path, 'management') commands.update({name: app_config.name for name in find_commands(path)}) return commands

python3.10.4/Lib/email/_parseaddr.py

def getphraselist(self): plist = [] while self.pos < len(self.field): if self.field[self.pos] in self.FWS: self.pos += 1 elif self.field[self.pos] == '"': plist.append(self.getquote()) elif self.field[self.pos] == '(': s

add python 3.10.4 for windows

getphraselist

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_parseaddr.py

https://github.com/XX-net/XX-Net.git

Python

def getphraselist(self): plist = [] while self.pos < len(self.field): if self.field[self.pos] in self.FWS: self.pos += 1 elif self.field[self.pos] == '"': plist.append(self.getquote()) elif self.field[self.pos] == '(': self.commentlist.append(self.getcomment()) elif self.field[self.pos] in self.phraseends: break else: plist.append(self.getatom(self.phraseends)) return plist

lib/matplotlib/axes/_secondary_axes.py

def set_location(self, location): # This puts the rectangle

Clean up code in SecondaryAxis

set_location

8387676bc049d7b3e071846730c632e6ced137ed

matplotlib

_secondary_axes.py

https://github.com/matplotlib/matplotlib.git

Python

def set_location(self, location): # This puts the rectangle into figure-relative coordinates. if isinstance(location, str): _api.check_in_list(self._locstrings, location=location) self._pos = 1. if location in ('top', 'right') else 0. elif isinstance(location, numbers.Real): self._pos = location else: raise ValueError( f"location must be {self._locstrings[0]!r}, " f"{self._locstrings[1]!r}, or a float, not {location!r}") self._loc = location if self._orientation == 'x': # An x-secondary axes is like an inset axes from x = 0 to x = 1 and # from y = pos to y = pos + eps, in the parent's transAxes coords. bounds = [0, self._pos, 1., 1e-10] else: # 'y' bounds = [self._pos, 0, 1e-10, 1] # this locator lets the axes move in the parent axes coordinates. # so it never needs to know where the parent is explicitly in # figure coordinates. # it gets called in ax.apply_aspect() (of all places) self.set_axes_locator( _TransformedBoundsLocator(bounds, self._parent.transAxes))

modin/core/execution/ray/implementations/pandas_on_ray/partitioning/partition.py

def length(self): if self._length_cache is None: if len(self.call_queue): self.drain_call_queue() else: self._length_cache, self._width_cache = _get_index_and_columns.remote( self.oid

REFACTOR-#4251: define public interfaces in `modin.core.execution.ray` module (#3868) Signed-off-by: Anatoly Myachev <[email protected]>

length

e7cb2e82f8b9c7a68f82abdd3b6011d661230b7e

modin

partition.py

https://github.com/modin-project/modin.git

Python

def length(self): if self._length_cache is None: if len(self.call_queue): self.drain_call_queue() else: self._length_cache, self._width_cache = _get_index_and_columns.remote( self.oid ) if isinstance(self._length_cache, ObjectIDType): self._length_cache = ray.get(self._length_cache) return self._length_cache

sympy/polys/densearith.py

def dmp_l2_norm_squared(f, u, K): if not u: return dup_l2_norm_squared(f, K) v = u - 1 return s

Add `l2_norm_squared` methods.

dmp_l2_norm_squared

0f6dde45a1c75b02c208323574bdb09b8536e3e4

sympy

densearith.py

https://github.com/sympy/sympy.git

Python

def dmp_l2_norm_squared(f, u, K): if not u: return dup_l2_norm_squared(f, K) v = u - 1 return sum([ dmp_l2_norm_squared(c, v, K) for c in f ])

test/lib/ansible_test/_internal/commands/integration/cloud/__init__.py

def cloud_filter(args, targets): # type: (IntegrationConfig, t.Tuple[IntegrationTarget, ...]) -> t.List[str] if args.metadata.cloud_config is not None: return [] # cloud filter already performed prior to delegation exclude = [] # type: t.List[str] for provider in get_cloud_providers(

ansible-test - Code cleanup and refactoring. (#77169) * Remove unnecessary PyCharm ignores. * Ignore intentional undefined attribute usage. * Add missing type hints. Fix existing type hints. * Fix docstrings and comments. * Use function to register completion handler. * Pass strings to display functions. * Fix CompositeAction handling of dest argument. * Use consistent types in expressions/assignments. * Use custom function to keep linters happy. * Add missing raise for custom exception. * Clean up key/value type handling in cloud plugins. * Use dataclass instead of dict for results. * Add custom type_guard function to check lists. * Ignore return type that can't be checked (yet). * Avoid changing types on local variables.

cloud_filter

a06fa496d3f837cca3c437ab6e9858525633d147

ansible

__init__.py

https://github.com/ansible/ansible.git

Python

def cloud_filter(args, targets): # type: (IntegrationConfig, t.Tuple[IntegrationTarget, ...]) -> t.List[str] if args.metadata.cloud_config is not None: return [] # cloud filter already performed prior to delegation exclude = [] # type: t.List[str] for provider in get_cloud_providers(args, targets): provider.filter(targets, exclude) return exclude

tests/pytests/unit/modules/test_aixpkg.py

def test_upgrade_available_none(): chk_upgrade_out = ( "Last metadata ex

Working tests for install

test_upgrade_available_none

f1c37893caf90738288e789c3233ab934630254f

salt

test_aixpkg.py

https://github.com/saltstack/salt.git

Python

def test_upgrade_available_none(): chk_upgrade_out = ( "Last metadata expiration check: 22:5:48 ago on Mon Dec 6 19:26:36 EST 2021." ) dnf_call = MagicMock(return_value={"retcode": 100, "stdout": chk_upgrade_out}) version_mock = MagicMock(return_value="6.6-2") with patch("pathlib.Path.is_file", return_value=True): with patch.dict( aixpkg.__salt__, {"cmd.run_all": dnf_call, "config.get": MagicMock(return_value=False)}, ), patch.object(aixpkg, "version", version_mock): result = aixpkg.upgrade_available("info") assert dnf_call.call_count == 1 libpath_env = {"LIBPATH": "/opt/freeware/lib:/usr/lib"} dnf_call.assert_any_call( "/opt/freeware/bin/dnf check-update info", env=libpath_env, ignore_retcode=True, python_shell=False, ) assert not result

tests/sentry/event_manager/test_event_manager.py

def test_too_many_boosted_releases_do_not_boost_anymore(self): release_2 = Release.get_or_create(

feat(ds): Limit the amount of boosted releases to 10 (#40501) Limits amount of boosted releases to 10 releases otherwise do not add any more releases to hash set of listed releases

test_too_many_boosted_releases_do_not_boost_anymore

361b7f444a53cc34cad8ddc378d125b7027d96df

sentry

test_event_manager.py

https://github.com/getsentry/sentry.git

Python

def test_too_many_boosted_releases_do_not_boost_anymore(self): release_2 = Release.get_or_create(self.project, "2.0") release_3 = Release.get_or_create(self.project, "3.0") for release_id in (self.release.id, release_2.id): self.redis_client.set(f"ds::p:{self.project.id}:r:{release_id}", 1, 60 * 60 * 24) self.redis_client.hset( f"ds::p:{self.project.id}:boosted_releases", release_id, time(), ) with self.options( { "dynamic-sampling:boost-latest-release": True, } ): self.make_release_transaction( release_version=release_3.version, environment_name=self.environment1.name, project_id=self.project.id, checksum="b" * 32, timestamp=self.timestamp, ) assert self.redis_client.hgetall(f"ds::p:{self.project.id}:boosted_releases") == { str(self.release.id): str(time()), str(release_2.id): str(time()), } assert self.redis_client.get(f"ds::p:{self.project.id}:r:{release_3.id}") is None

networkx/algorithms/link_analysis/hits_alg.py

def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True): import numpy as np import scipy as sp imp

Use scipy.sparse array datastructure (#5139) * Step 1: use sparse arrays in nx.to_scipy_sparse_matrix. Seems like a reasonable place to start. nx.to_scipy_sparse_matrix is one of the primary interfaces to scipy.sparse from within NetworkX. * 1: Use np.outer instead of mult col/row vectors Fix two instances in modularitymatrix where a new 2D array was being created via an outer product of two \"vectors\". In the matrix case, this was a row vector \* a column vector. In the array case this can be disambiguated by being explicit with np.outer. * Update _transition_matrix in laplacianmatrix module - A few instances of matrix multiplication operator - Add np.newaxis + transpose to get shape right for broadcasting - Explicitly convert e.g. sp.sparse.spdiags to a csr_array. * Update directed_combinitorial_laplacian w/ sparse array. - Wrap spdiags in csr_array and update matmul operators. * Rm matrix-specific code from lgc and hmn modules - Replace .A call with appropriate array semantics - wrap sparse.diags in csr_array. * Change hits to use sparse array semantics. - Replace * with @ - Remove superfluous calls to flatten. * Update sparse matrix usage in layout module. - Simplify lil.getrowview call - Wrap spdiags in csr_array. * lil_matrix -> lil_array in graphmatrix.py. * WIP: Start working on algebraic connectivity module. * Incorporate auth mat varname feedback. * Revert 1D slice and comment for 1D sparse future. * Add TODOs: rm csr_array wrapper around spdiags etc. * WIP: cleanup algebraicconn: tracemin_fiedler. * Typo. * Finish reviewing algebraicconnectivity. * Convert bethe_hessian matrix to use sparse arrays. * WIP: update laplacian. Update undirected laplacian functions. * WIP: laplacian - add comment about _transition_matrix return types. * Finish laplacianmatrix review. * Update attrmatrix. * Switch to official laplacian function. * Update pagerank to use sparse array. * Switch bipartite matrix to sparse arrays. * Check from_scipy_sparse_matrix works with arrays. Modifies test suite. * Apply changes from review. * Fix failing docstring tests. * Fix missing axis for in-place multiplication. * Use scipy==1.8rc2 * Use matrix multiplication * Fix PyPy CI * [MRG] Create plot_subgraphs.py example (#5165) * Create plot_subgraphs.py https://github.com/networkx/networkx/issues/4220 * Update plot_subgraphs.py black * Update plot_subgraphs.py lint plus font_size * Update plot_subgraphs.py added more plots * Update plot_subgraphs.py removed plots from the unit test and added comments * Update plot_subgraphs.py lint * Update plot_subgraphs.py typos fixed * Update plot_subgraphs.py added nodes to the plot of the edges removed that was commented out for whatever reason * Update plot_subgraphs.py revert the latest commit - the line was commented out for a reason - it's broken * Update plot_subgraphs.py fixed node color issue * Update plot_subgraphs.py format fix * Update plot_subgraphs.py forgot to draw the nodes... now fixed * Fix sphinx warnings about heading length. * Update examples/algorithms/plot_subgraphs.py * Update examples/algorithms/plot_subgraphs.py Co-authored-by: Ross Barnowski <[email protected]> Co-authored-by: Dan Schult <[email protected]> * Add traveling salesman problem to example gallery (#4874) Adds an example of the using Christofides to solve the TSP problem to the example galery. Co-authored-by: Ross Barnowski <[email protected]> * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() (#5037) * Fixed inconsistent documentation for nbunch parameter in DiGraph.edges() * Resolved Requested Changes * Revert changes to degree docstrings. * Update comments in example. * Apply wording to edges method in all graph classes. Co-authored-by: Ross Barnowski <[email protected]> * Compatibility updates from testing with numpy/scipy/pytest rc's (#5226) * Rm deprecated scipy subpkg access. * Use recwarn fixture in place of deprecated pytest pattern. * Rm unnecessary try/except from tests. * Replace internal `close` fn with `math.isclose`. (#5224) * Replace internal close fn with math.isclose. * Fix lines in docstring examples. * Fix Python 3.10 deprecation warning w/ int div. (#5231) * Touchups and suggestions for subgraph gallery example (#5225) * Simplify construction of G with edges rm'd * Rm unused graph attribute. * Shorten categorization by node type. * Simplify node coloring. * Simplify isomorphism check. * Rm unit test. * Rm redundant plotting of each subgraph. * Use new package name (#5234) * Allowing None edges in weight function of bidirectional Dijkstra (#5232) * added following feature also to bidirectional dijkstra: The weight function can be used to hide edges by returning None. * changed syntax for better readability and code duplicate avoidance Co-authored-by: Hohmann, Nikolas <[email protected]> * Add an FAQ about assigning issues. (#5182) * Add FAQ about assigning issues. * Add note about linking issues from new PRs. * Update dev deps (#5243) * Update minor doc issues with tex notation (#5244) * Add FutureWarnings to fns that return sparse matrices - biadjacency_matrix. - bethe_hessian_matrix. - incidence_matrix. - laplacian functions. - modularity_matrix functions. - adjacency_matrix. * Add to_scipy_sparse_array and use it everywhere. Add a new conversion function to preserve array semantics internally while not altering behavior for users. Also adds FutureWarning to to_scipy_sparse_matrix. * Add from_scipy_sparse_array. Supercedes from_scipy_sparse_matrix. * Handle deprecations in separate PR. * Fix docstring examples. Co-authored-by: Mridul Seth <[email protected]> Co-authored-by: Jarrod Millman <[email protected]> Co-authored-by: Andrew Knyazev <[email protected]> Co-authored-by: Dan Schult <[email protected]> Co-authored-by: eskountis <[email protected]> Co-authored-by: Anutosh Bhat <[email protected]> Co-authored-by: NikHoh <[email protected]> Co-authored-by: Hohmann, Nikolas <[email protected]> Co-authored-by: Sultan Orazbayev <[email protected]> Co-authored-by: Mridul Seth <[email protected]>

hits

5dfd57af2a141a013ae3753e160180b82bec9469

networkx

hits_alg.py

https://github.com/networkx/networkx.git

Python

def hits(G, max_iter=100, tol=1.0e-8, nstart=None, normalized=True): import numpy as np import scipy as sp import scipy.sparse.linalg # call as sp.sparse.linalg if len(G) == 0: return {}, {} A = nx.adjacency_matrix(G, nodelist=list(G), dtype=float) if nstart is None: u, s, vt = sp.sparse.linalg.svds(A, k=1, maxiter=max_iter, tol=tol) else: nstart = np.array(list(nstart.values())) u, s, vt = sp.sparse.linalg.svds(A, k=1, v0=nstart, maxiter=max_iter, tol=tol) a = vt.flatten().real h = A @ a if normalized: h = h / h.sum() a = a / a.sum() hubs = dict(zip(G, map(float, h))) authorities = dict(zip(G, map(float, a))) return hubs, authorities

airflow/providers/ftp/hooks/ftp.py

def test_connection(self) -> Tuple[bool, str]: try: conn = se

Updates FTPHook provider to have test_connection (#21997) * Updates FTP provider to have test_connection Co-authored-by: eladkal <[email protected]>

test_connection

26e8d6d7664bbaae717438bdb41766550ff57e4f

airflow

ftp.py

https://github.com/apache/airflow.git

Python

def test_connection(self) -> Tuple[bool, str]: try: conn = self.get_conn() conn.pwd return True, "Connection successfully tested" except Exception as e: return False, str(e)

openbb_terminal/cryptocurrency/crypto_controller.py

def call_price(self, other_args): parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="price", description=, ) parser.add_argument( "-s", "--symbol", required="-h" not in other_args, type=str, dest="symbol", help="Symbol of coin to load data for, ~100 symbols are availa

Integrate live feeds from Pyth (#2178) * added dependency * added pyth models * dependencies * docs * some improvements to this pyth command (#2433) * some improvements to this pyth command * minor improv * dependencies * tests Co-authored-by: DidierRLopes <[email protected]>; COlin

call_price

1661ddd44044c637526e9a1e812e7c1863be35fc

OpenBBTerminal

crypto_controller.py

https://github.com/OpenBB-finance/OpenBBTerminal.git

Python

def call_price(self, other_args): parser = argparse.ArgumentParser( add_help=False, formatter_class=argparse.ArgumentDefaultsHelpFormatter, prog="price", description=, ) parser.add_argument( "-s", "--symbol", required="-h" not in other_args, type=str, dest="symbol", help="Symbol of coin to load data for, ~100 symbols are available", ) if other_args and "-" not in other_args[0][0]: other_args.insert(0, "-s") ns_parser = self.parse_known_args_and_warn(parser, other_args) if ns_parser: if ns_parser.symbol in pyth_model.ASSETS.keys(): console.print( "[param]If it takes too long, you can use 'Ctrl + C' to cancel.\n[/param]" ) pyth_view.display_price(ns_parser.symbol) else: console.print("[red]The symbol selected does not exist.[/red]\n")

src/datasets/utils/py_utils.py

def _single_map_nested(args): function, data_struct, types, rank, disable_tqdm, desc = args # Singleton first to spare some computation if not isinstance(data_struct, dict) and not isinstance(data_struct, types): return function(data_struct) # Reduce logging to keep things readable in multiprocessing with tqdm if rank is not None and logging.get_verbosity() < logging.WARNING: logging.set_verbosity_warning() # Print at least one thing to fix tqdm in notebooks in multiprocessing # see https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308 if rank is not None and not disable_tqdm and any("notebook" in tqdm_cls.__name__ for tqdm_cls in tqdm.__mro__): print(" ", end="", flush=True) # Loop over single examples or batches and write to buffer/file if examples are to be updated pbar_iterable = data_struct.items() if isinstance(data_struct, dict) else data_struct pbar_desc = (desc + " " if desc is not None else "") + "#" + str(rank) if rank is not None else desc pbar = utils.tqdm(pbar_iterable, dis

Better TQDM output (#3654) * Show progress bar when generating examples * Consistent utils.is_progress_bar_enabled calls * Fix tqdm in notebook * Add missing params to DatasetDict.map * Specify total in tqdm progress bar in map * Fix total computation * Small fix * Add desc to map_nested * Add more precise descriptions to download * Address comments * Fix docstring * Final changes * Minor change

_single_map_nested

6ed6ac9448311930557810383d2cfd4fe6aae269

datasets

py_utils.py

https://github.com/huggingface/datasets.git

Python

def _single_map_nested(args): function, data_struct, types, rank, disable_tqdm, desc = args # Singleton first to spare some computation if not isinstance(data_struct, dict) and not isinstance(data_struct, types): return function(data_struct) # Reduce logging to keep things readable in multiprocessing with tqdm if rank is not None and logging.get_verbosity() < logging.WARNING: logging.set_verbosity_warning() # Print at least one thing to fix tqdm in notebooks in multiprocessing # see https://github.com/tqdm/tqdm/issues/485#issuecomment-473338308 if rank is not None and not disable_tqdm and any("notebook" in tqdm_cls.__name__ for tqdm_cls in tqdm.__mro__): print(" ", end="", flush=True) # Loop over single examples or batches and write to buffer/file if examples are to be updated pbar_iterable = data_struct.items() if isinstance(data_struct, dict) else data_struct pbar_desc = (desc + " " if desc is not None else "") + "#" + str(rank) if rank is not None else desc pbar = utils.tqdm(pbar_iterable, disable=disable_tqdm, position=rank, unit="obj", desc=pbar_desc) if isinstance(data_struct, dict): return {k: _single_map_nested((function, v, types, None, True, None)) for k, v in pbar} else: mapped = [_single_map_nested((function, v, types, None, True, None)) for v in pbar] if isinstance(data_struct, list): return mapped elif isinstance(data_struct, tuple): return tuple(mapped) else: return np.array(mapped)

tests/admin_scripts/tests.py

def test_unified(self):

Refs #33476 -- Reformatted code with Black.

test_unified

9c19aff7c7561e3a82978a272ecdaad40dda5c00

django

tests.py

https://github.com/django/django.git

Python

def test_unified(self): self.write_settings("settings_to_diff.py", sdict={"FOO": '"bar"'}) args = ["diffsettings", "--settings=settings_to_diff", "--output=unified"] out, err = self.run_manage(args) self.assertNoOutput(err) self.assertOutput(out, "+ FOO = 'bar'") self.assertOutput(out, "- SECRET_KEY = ''") self.assertOutput(out, "+ SECRET_KEY = 'django_tests_secret_key'") self.assertNotInOutput(out, " APPEND_SLASH = True")

python/ray/runtime_context.py

def runtime_env(self):

[runtime env] runtime env inheritance refactor (#24538) * [runtime env] runtime env inheritance refactor (#22244) Runtime Environments is already GA in Ray 1.6.0. The latest doc is [here](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#runtime-environments). And now, we already supported a [inheritance](https://docs.ray.io/en/master/ray-core/handling-dependencies.html#inheritance) behavior as follows (copied from the doc): - The runtime_env["env_vars"] field will be merged with the runtime_env["env_vars"] field of the parent. This allows for environment variables set in the parent’s runtime environment to be automatically propagated to the child, even if new environment variables are set in the child’s runtime environment. - Every other field in the runtime_env will be overridden by the child, not merged. For example, if runtime_env["py_modules"] is specified, it will replace the runtime_env["py_modules"] field of the parent. We think this runtime env merging logic is so complex and confusing to users because users can't know the final runtime env before the jobs are run. Current PR tries to do a refactor and change the behavior of Runtime Environments inheritance. Here is the new behavior: - **If there is no runtime env option when we create actor, inherit the parent runtime env.** - **Otherwise, use the optional runtime env directly and don't do the merging.** Add a new API named `ray.runtime_env.get_current_runtime_env()` to get the parent runtime env and modify this dict by yourself. Like: ```Actor.options(runtime_env=ray.runtime_env.get_current_runtime_env().update({"X": "Y"}))``` This new API also can be used in ray client.

runtime_env

eb2692cb32bb1747e312d5b20e976d7a879c9588

ray

runtime_context.py

https://github.com/ray-project/ray.git

Python

def runtime_env(self): return RuntimeEnv.deserialize(self._get_runtime_env_string())

src/streamlink/stream/dash.py

def sleeper(self, duration): s = time() yield time_to_sleep = duration - (time() - s) if time_to_sleep > 0: s

stream.dash: update DASHStreamWorker.iter_segments - Refactor DASHStreamWorker.iter_segments() - Replace dash_manifest.sleeper() with SegmentedStreamWorker.wait(), and make the worker thread shut down immediately on close(). - Prevent unnecessary wait times for static manifest types by calling close() after all segments were put into the writer's queue.

sleeper

d1a8d1597d4fe9f129a72fe94c1508304b7eae0f

streamlink

dash.py

https://github.com/streamlink/streamlink.git

Python

def sleeper(self, duration): s = time() yield time_to_sleep = duration - (time() - s) if time_to_sleep > 0: self.wait(time_to_sleep)

lib/matplotlib/tests/test_colors.py

def test_BoundaryNorm(): boundaries = [0, 1.1, 2.2] vals = [-1, 0, 1, 2, 2.2, 4] # Without interpolation expected = [-1, 0, 0, 1, 2, 2] ncolors = len(boundaries) - 1 bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # ncolors != len(boundaries) - 1 triggers interpolation expected = [-1, 0, 0, 2, 3, 3] ncolors = len(boundaries) bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # with a single region and interpolation expected = [-1, 1, 1, 1, 3, 3] bn = mcolors.BoundaryNorm([0, 2.2], ncolors) assert_array_equal(bn(vals), expected) # more boundaries for a third color boundaries = [0, 1, 2, 3] vals = [-1, 0.1, 1.1, 2.2, 4] ncolors = 5 expected = [-1, 0, 2, 4, 5] bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # a scalar as input should not trigger an error and should return a scalar boundaries = [0, 1, 2] vals = [-1, 0.1, 1.1, 2.2] bn = mcolors.BoundaryNorm(boundaries, 2) expected = [-1, 0, 1, 2] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # same with interp bn = mcolors.BoundaryNorm(boundaries, 3) expected = [-1, 0, 2, 3] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # Clipping bn = mcolors.BoundaryNorm(boundaries, 3, clip=True) expected = [0, 0, 2, 2] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # Masked arrays boundaries = [0, 1.1, 2.2] vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9]) # Without interpolation ncolors = len(boundaries) - 1 bn = mcolors.BoundaryNorm(boundaries, ncolors) expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0]) assert_array_equal(bn(vals), expected) # With interpolation bn = mcolors.BoundaryNorm(boundaries, len(boundaries)) expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0]) assert_array_equal(bn(vals), expected) # Non-trivial masked arrays vals = np.ma.masked_invalid([np.Inf, np.NaN]) assert np.all(bn(vals).mask) vals = np.ma.masked_invalid([np.Inf]) assert np.all(bn(vals).mask) # Incompatible extend and clip with pytest.raises(ValueError, match="not compatible"): mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True) # Too small ncolors argument with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 2) with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 3, extend='min') with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 4, extend='both') # Testing extend keyword, with interpolation (large cmap) bounds = [1, 2, 3] cmap = mpl.colormaps['viridis'] mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both') refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N) x = np.random.randn(100) * 10 + 2 ref = refnorm(x) ref[ref == 0] = -1 ref[ref == cmap.N - 1] = cmap.N assert_array_equal(mynorm(x), ref) # Without interpolation cmref = mcolors.ListedColormap(['blue', 'red']) cmref.set_over('black') cmref.set_under('white') cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black']) assert mcolors.same_color(cmref.get_over(), 'black') assert mcolors.same_color(cmref.get_under(), 'white') refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax assert mynorm(bounds[0] - 0.1) == -1 # under assert mynorm(bounds[0] + 0.1) == 1 # first bin -> second color assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2 # next-to-last color assert mynorm(bounds[-1] + 0.1) == cmshould.N # over x = [-1, 1.2, 2.3, 9.6] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) # Just min cmref = mcolors.ListedColormap(['blue', 'red']) cmref.set_under('white') cmshould = mcolors.ListedColormap(['white', 'blue', 'red']) assert mcolors.same_color(cmref.get_under(), 'white') assert cmref.N == 2 assert cmshould.N == 3 refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax x = [-1, 1.2, 2.3] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) # Just max cmref = mcolors.Lis

MNT: convert tests and internal usage way from using mpl.cm.get_cmap

test_BoundaryNorm

a17f4f3bd63e3ca3754f96d7db4ce5197720589b

matplotlib

test_colors.py

https://github.com/matplotlib/matplotlib.git

Python

def test_BoundaryNorm(): boundaries = [0, 1.1, 2.2] vals = [-1, 0, 1, 2, 2.2, 4] # Without interpolation expected = [-1, 0, 0, 1, 2, 2] ncolors = len(boundaries) - 1 bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # ncolors != len(boundaries) - 1 triggers interpolation expected = [-1, 0, 0, 2, 3, 3] ncolors = len(boundaries) bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # with a single region and interpolation expected = [-1, 1, 1, 1, 3, 3] bn = mcolors.BoundaryNorm([0, 2.2], ncolors) assert_array_equal(bn(vals), expected) # more boundaries for a third color boundaries = [0, 1, 2, 3] vals = [-1, 0.1, 1.1, 2.2, 4] ncolors = 5 expected = [-1, 0, 2, 4, 5] bn = mcolors.BoundaryNorm(boundaries, ncolors) assert_array_equal(bn(vals), expected) # a scalar as input should not trigger an error and should return a scalar boundaries = [0, 1, 2] vals = [-1, 0.1, 1.1, 2.2] bn = mcolors.BoundaryNorm(boundaries, 2) expected = [-1, 0, 1, 2] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # same with interp bn = mcolors.BoundaryNorm(boundaries, 3) expected = [-1, 0, 2, 3] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # Clipping bn = mcolors.BoundaryNorm(boundaries, 3, clip=True) expected = [0, 0, 2, 2] for v, ex in zip(vals, expected): ret = bn(v) assert isinstance(ret, int) assert_array_equal(ret, ex) assert_array_equal(bn([v]), ex) # Masked arrays boundaries = [0, 1.1, 2.2] vals = np.ma.masked_invalid([-1., np.NaN, 0, 1.4, 9]) # Without interpolation ncolors = len(boundaries) - 1 bn = mcolors.BoundaryNorm(boundaries, ncolors) expected = np.ma.masked_array([-1, -99, 0, 1, 2], mask=[0, 1, 0, 0, 0]) assert_array_equal(bn(vals), expected) # With interpolation bn = mcolors.BoundaryNorm(boundaries, len(boundaries)) expected = np.ma.masked_array([-1, -99, 0, 2, 3], mask=[0, 1, 0, 0, 0]) assert_array_equal(bn(vals), expected) # Non-trivial masked arrays vals = np.ma.masked_invalid([np.Inf, np.NaN]) assert np.all(bn(vals).mask) vals = np.ma.masked_invalid([np.Inf]) assert np.all(bn(vals).mask) # Incompatible extend and clip with pytest.raises(ValueError, match="not compatible"): mcolors.BoundaryNorm(np.arange(4), 5, extend='both', clip=True) # Too small ncolors argument with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 2) with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 3, extend='min') with pytest.raises(ValueError, match="ncolors must equal or exceed"): mcolors.BoundaryNorm(np.arange(4), 4, extend='both') # Testing extend keyword, with interpolation (large cmap) bounds = [1, 2, 3] cmap = mpl.colormaps['viridis'] mynorm = mcolors.BoundaryNorm(bounds, cmap.N, extend='both') refnorm = mcolors.BoundaryNorm([0] + bounds + [4], cmap.N) x = np.random.randn(100) * 10 + 2 ref = refnorm(x) ref[ref == 0] = -1 ref[ref == cmap.N - 1] = cmap.N assert_array_equal(mynorm(x), ref) # Without interpolation cmref = mcolors.ListedColormap(['blue', 'red']) cmref.set_over('black') cmref.set_under('white') cmshould = mcolors.ListedColormap(['white', 'blue', 'red', 'black']) assert mcolors.same_color(cmref.get_over(), 'black') assert mcolors.same_color(cmref.get_under(), 'white') refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='both') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax assert mynorm(bounds[0] - 0.1) == -1 # under assert mynorm(bounds[0] + 0.1) == 1 # first bin -> second color assert mynorm(bounds[-1] - 0.1) == cmshould.N - 2 # next-to-last color assert mynorm(bounds[-1] + 0.1) == cmshould.N # over x = [-1, 1.2, 2.3, 9.6] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2, 3])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) # Just min cmref = mcolors.ListedColormap(['blue', 'red']) cmref.set_under('white') cmshould = mcolors.ListedColormap(['white', 'blue', 'red']) assert mcolors.same_color(cmref.get_under(), 'white') assert cmref.N == 2 assert cmshould.N == 3 refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='min') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax x = [-1, 1.2, 2.3] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x))) # Just max cmref = mcolors.ListedColormap(['blue', 'red']) cmref.set_over('black') cmshould = mcolors.ListedColormap(['blue', 'red', 'black']) assert mcolors.same_color(cmref.get_over(), 'black') assert cmref.N == 2 assert cmshould.N == 3 refnorm = mcolors.BoundaryNorm(bounds, cmref.N) mynorm = mcolors.BoundaryNorm(bounds, cmshould.N, extend='max') assert mynorm.vmin == refnorm.vmin assert mynorm.vmax == refnorm.vmax x = [1.2, 2.3, 4] assert_array_equal(cmshould(mynorm(x)), cmshould([0, 1, 2])) x = np.random.randn(100) * 10 + 2 assert_array_equal(cmshould(mynorm(x)), cmref(refnorm(x)))

modin/experimental/core/execution/native/implementations/hdk_on_native/dataframe/dataframe.py

def _join_by_index(self, other_modin_frames, how, sort, ignore_index): if how == "outer": raise NotImplementedError("outer join is not supported in HDK engine") lhs = self._maybe_materialize_rowid() reset_index_names = False for rhs in other_modin_frames: rhs = rhs._maybe_materialize_rowid() if len(lhs._index_cols) != len(rhs._index_cols): raise NotImplementedError( "join by indexes with different sizes is not supported" ) reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols condition = lhs._build_equi_join_condition( rhs, lhs._index_cols, rhs._index_cols ) exprs = lhs._index_exprs() new_columns = lhs.columns.to_list() for col in lhs.columns: e

FEAT-#4946: Replace OmniSci with HDK (#4947) Co-authored-by: Iaroslav Igoshev <[email protected]> Signed-off-by: Andrey Pavlenko <[email protected]>

_join_by_index

e5b1888cd932909e49194d58035da34b210b91c4

modin

dataframe.py

https://github.com/modin-project/modin.git

Python

def _join_by_index(self, other_modin_frames, how, sort, ignore_index): if how == "outer": raise NotImplementedError("outer join is not supported in HDK engine") lhs = self._maybe_materialize_rowid() reset_index_names = False for rhs in other_modin_frames: rhs = rhs._maybe_materialize_rowid() if len(lhs._index_cols) != len(rhs._index_cols): raise NotImplementedError( "join by indexes with different sizes is not supported" ) reset_index_names = reset_index_names or lhs._index_cols != rhs._index_cols condition = lhs._build_equi_join_condition( rhs, lhs._index_cols, rhs._index_cols ) exprs = lhs._index_exprs() new_columns = lhs.columns.to_list() for col in lhs.columns: exprs[col] = lhs.ref(col) for col in rhs.columns: # Handle duplicating column names here. When user specifies # suffixes to make a join, actual renaming is done in front-end. new_col_name = col rename_idx = 0 while new_col_name in exprs: new_col_name = f"{col}{rename_idx}" rename_idx += 1 exprs[new_col_name] = rhs.ref(col) new_columns.append(new_col_name) op = JoinNode( lhs, rhs, how=how, exprs=exprs, condition=condition, ) new_columns = Index.__new__( Index, data=new_columns, dtype=self.columns.dtype ) lhs = lhs.__constructor__( dtypes=lhs._dtypes_for_exprs(exprs), columns=new_columns, index_cols=lhs._index_cols, op=op, force_execution_mode=self._force_execution_mode, ) if sort: lhs = lhs.sort_rows( lhs._index_cols, ascending=True, ignore_index=False, na_position="last", ) if reset_index_names: lhs = lhs._reset_index_names() if ignore_index: new_columns = Index.__new__(RangeIndex, data=range(len(lhs.columns))) lhs = lhs._set_columns(new_columns) return lhs

packages/syft/src/syft/core/node/common/action/function_or_constructor_action.py

def _object2proto(self) -> RunFunctionOrConstructorAction_PB: return RunFunctionOrConstructorAction_PB( path=self.path, args=[serialize(x, to_bytes=True) for x in self.args], kwargs={k: serialize(v, to_bytes=True) for k, v in self.kwargs.items()}, id_at_location=serialize(self.id_at_location), address=serialize(self.address), msg_id=serialize(s

[syft.core.node.common.action] Change syft import absolute -> relative

_object2proto

e272ed2fa4c58e0a89e273a3e85da7d13a85e04c

PySyft

function_or_constructor_action.py

https://github.com/OpenMined/PySyft.git

Python

def _object2proto(self) -> RunFunctionOrConstructorAction_PB: return RunFunctionOrConstructorAction_PB( path=self.path, args=[serialize(x, to_bytes=True) for x in self.args], kwargs={k: serialize(v, to_bytes=True) for k, v in self.kwargs.items()}, id_at_location=serialize(self.id_at_location), address=serialize(self.address), msg_id=serialize(self.id), )

networkx/generators/small.py

def truncated_cube_graph(create_using=None): description = [ "adjacencylist", "Truncated Cube Graph", 24, [ [2, 3, 5], [12, 15], [4, 5], [7, 9], [6], [17, 19], [8, 9], [11, 13], [10], [18, 21], [12, 13], [15], [14], [22, 23], [16], [20, 24], [18, 19], [21], [20], [24], [22], [23], [24], [], ],

Docstrings for the small.py module (#5240) * added description for the first 5 small graphs * modified descriptions based on comment and added description for two more functions * added doctrings to all the functions * Minor touchups. Co-authored-by: Ross Barnowski <[email protected]>

truncated_cube_graph

dec723f072eb997a497a159dbe8674cd39999ee9

networkx

small.py

https://github.com/networkx/networkx.git

Python

def truncated_cube_graph(create_using=None): description = [ "adjacencylist", "Truncated Cube Graph", 24, [ [2, 3, 5], [12, 15], [4, 5], [7, 9], [6], [17, 19], [8, 9], [11, 13], [10], [18, 21], [12, 13], [15], [14], [22, 23], [16], [20, 24], [18, 19], [21], [20], [24], [22], [23], [24], [], ], ] G = make_small_undirected_graph(description, create_using) return G

wagtail/contrib/modeladmin/options.py

def get_admin_urls_for_registration(self): urls = () for instance in self.modeladmin_instances: urls += instance.get_admin_urls_for_registration() return urls

Fix warnings from flake8-comprehensions.

get_admin_urls_for_registration

de3fcba9e95818e9634ab7de6bfcb1f4221f2775

wagtail

options.py

https://github.com/wagtail/wagtail.git

Python

def get_admin_urls_for_registration(self): urls = () for instance in self.modeladmin_instances: urls += instance.get_admin_urls_for_registration() return urls

.venv/lib/python3.8/site-packages/pip/_vendor/pyparsing.py

def setName(self, name): self.name = name self.errmsg = "Expected " + self.name if __diag__.enable_debug_on_named_expressions: self.setDebug() return self

upd; format

setName

f638f5d0e6c8ebed0e69a6584bc7f003ec646580

transferlearning

pyparsing.py

https://github.com/jindongwang/transferlearning.git

Python

def setName(self, name): self.name = name self.errmsg = "Expected " + self.name if __diag__.enable_debug_on_named_expressions: self.setDebug() return self

modin/core/dataframe/algebra/default2pandas/groupby.py

def get_func(cls, key, **kwargs): if "agg_func" in kwargs: return cls.inplace_applyier_builder(key, kwargs["agg_func"]) elif "func_dict" in kwargs: return cls.inplace_applyier_builder(key, kwargs["func_dict"]) else: return cls.inplace_applyier_builder(key)

FIX-#3197: do not pass lambdas to the backend in GroupBy (#3373) Signed-off-by: Dmitry Chigarev <[email protected]>

get_func

1e65a4afd191cf61ba05b80545d23f9b88962f41

modin

groupby.py

https://github.com/modin-project/modin.git

Python

def get_func(cls, key, **kwargs): if "agg_func" in kwargs: return cls.inplace_applyier_builder(key, kwargs["agg_func"]) elif "func_dict" in kwargs: return cls.inplace_applyier_builder(key, kwargs["func_dict"]) else: return cls.inplace_applyier_builder(key)

python/ray/util/sgd/torch/torch_trainer.py

def update_scheduler(self, metric): self.worker_group.apply_all_operators( lambda op: [sched.step(m

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

update_scheduler

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

torch_trainer.py

https://github.com/ray-project/ray.git

Python

def update_scheduler(self, metric): self.worker_group.apply_all_operators( lambda op: [sched.step(metric) for sched in op._schedulers] )

sklearn/metrics/pairwise.py

def paired_cosine_distances(X, Y): X, Y = c

DOC Ensures that sklearn.metrics.pairwise.paired_cosine_distances passes numpydoc validation (#22141) Co-authored-by: Thomas J. Fan <[email protected]>

paired_cosine_distances

a5b70b3132467b5e3616178d9ecca6cb7316c400

scikit-learn

pairwise.py

https://github.com/scikit-learn/scikit-learn.git

Python

def paired_cosine_distances(X, Y): X, Y = check_paired_arrays(X, Y) return 0.5 * row_norms(normalize(X) - normalize(Y), squared=True) PAIRED_DISTANCES = { "cosine": paired_cosine_distances, "euclidean": paired_euclidean_distances, "l2": paired_euclidean_distances, "l1": paired_manhattan_distances, "manhattan": paired_manhattan_distances, "cityblock": paired_manhattan_distances, }

src/transformers/trainer.py

def torchdynamo_smart_context_manager(self): ctx_manager = contextlib.nullcontext() if is_torchdynamo_available(): import torchdynamo from torchdy

Support compilation via Torchdynamo, AOT Autograd, NVFuser (#17308) * Support compilation via Torchdynamo, AOT Autograd, NVFuser * Address comments * Lint * Stas comments - missing quality test * Lintere * Quality test * Doc lint * Reset CUDA peak mem * Add CustomTrainer * require a single gpu Co-authored-by: Stas Bekman <[email protected]>

torchdynamo_smart_context_manager

897a8dd89f40817201bc4aebe532a096405bdeb1

transformers

trainer.py

https://github.com/huggingface/transformers.git

Python

def torchdynamo_smart_context_manager(self): ctx_manager = contextlib.nullcontext() if is_torchdynamo_available(): import torchdynamo from torchdynamo.optimizations.training import aot_autograd_speedup_strategy if self.args.torchdynamo == "eager": ctx_manager = torchdynamo.optimize("eager") elif self.args.torchdynamo == "nvfuser": ctx_manager = torchdynamo.optimize(aot_autograd_speedup_strategy) return ctx_manager

certbot-apache/certbot_apache/_internal/parser.py

def check_aug_version(self) -> bool: self.aug.set("/test/path/testing/arg", "aRgUMeNT") try: matches = self.aug.match( "/test//*[self::arg=~regexp('argument', 'i')]") except RuntimeError: self.aug.remove("/test/path") return False self.aug.remove("/test/path") return matches

Add typing to certbot.apache (#9071) * Add typing to certbot.apache Co-authored-by: Adrien Ferrand <[email protected]>

check_aug_version

7d9e9a49005de7961e84d2a7c608db57dbab3046

certbot

parser.py

https://github.com/certbot/certbot.git

Python

def check_aug_version(self) -> bool: self.aug.set("/test/path/testing/arg", "aRgUMeNT") try: matches = self.aug.match( "/test//*[self::arg=~regexp('argument', 'i')]") except RuntimeError: self.aug.remove("/test/path") return False self.aug.remove("/test/path") return matches

ldm/modules/midas/utils.py

def resize_depth(depth, width, height): depth = torch.squeeze(depth[0, :, :, :]).to("cpu") depth_resized = cv2.resize( depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC ) return depth_resized

release more models

resize_depth

ca86da3a30c4e080d4db8c25fca73de843663cb4

stablediffusion

utils.py

https://github.com/Stability-AI/stablediffusion.git

Python

def resize_depth(depth, width, height): depth = torch.squeeze(depth[0, :, :, :]).to("cpu") depth_resized = cv2.resize( depth.numpy(), (width, height), interpolation=cv2.INTER_CUBIC ) return depth_resized

sympy/core/numbers.py

def comp(z1, z2, tol=None): r if type(z2) is str: if not

Improved documentation formatting

comp

cda8dfe6f45dc5ed394c2f5cda706cd6c729f713

sympy

numbers.py

https://github.com/sympy/sympy.git

Python

def comp(z1, z2, tol=None): r if type(z2) is str: if not pure_complex(z1, or_real=True): raise ValueError('when z2 is a str z1 must be a Number') return str(z1) == z2 if not z1: z1, z2 = z2, z1 if not z1: return True if not tol: a, b = z1, z2 if tol == '': return str(a) == str(b) if tol is None: a, b = sympify(a), sympify(b) if not all(i.is_number for i in (a, b)): raise ValueError('expecting 2 numbers') fa = a.atoms(Float) fb = b.atoms(Float) if not fa and not fb: # no floats -- compare exactly return a == b # get a to be pure_complex for _ in range(2): ca = pure_complex(a, or_real=True) if not ca: if fa: a = a.n(prec_to_dps(min([i._prec for i in fa]))) ca = pure_complex(a, or_real=True) break else: fa, fb = fb, fa a, b = b, a cb = pure_complex(b) if not cb and fb: b = b.n(prec_to_dps(min([i._prec for i in fb]))) cb = pure_complex(b, or_real=True) if ca and cb and (ca[1] or cb[1]): return all(comp(i, j) for i, j in zip(ca, cb)) tol = 10**prec_to_dps(min(a._prec, getattr(b, '_prec', a._prec))) return int(abs(a - b)*tol) <= 5 diff = abs(z1 - z2) az1 = abs(z1) if z2 and az1 > 1: return diff/az1 <= tol else: return diff <= tol

mmdet/datasets/openimages.py

def _parse_img_level_ann(self, image_level_ann_file): item_lists = defaultdict(list) with self.file_client.get_local_path( image_level_ann_file) as local_path: with open(local_path, 'r') as f: reader = csv.reader(f) i = -1 for line

Refactor OpenImages.

_parse_img_level_ann

36c1f477b273cb2fb0dea3c921ec267db877c039

mmdetection

openimages.py

https://github.com/open-mmlab/mmdetection.git

Python

def _parse_img_level_ann(self, image_level_ann_file): item_lists = defaultdict(list) with self.file_client.get_local_path( image_level_ann_file) as local_path: with open(local_path, 'r') as f: reader = csv.reader(f) i = -1 for line in reader: i += 1 if i == 0: continue else: img_id = line[0] label_id = line[1] assert label_id in self.label_id_mapping image_level_label = int( self.label_id_mapping[label_id]) confidence = float(line[2]) item_lists[img_id].append( dict( image_level_label=image_level_label, confidence=confidence)) return item_lists

python3.10.4/Lib/_pydecimal.py

def logical_and(self, a, b): a = _convert

add python 3.10.4 for windows

logical_and

8198943edd73a363c266633e1aa5b2a9e9c9f526

XX-Net

_pydecimal.py

https://github.com/XX-net/XX-Net.git

Python

def logical_and(self, a, b): a = _convert_other(a, raiseit=True) return a.logical_and(b, context=self)

sympy/utilities/enumerative.py

def decrement_part_small(self, part, ub): if self.lpart >= ub - 1: self.p1 += 1 # increment to keep track of usefulness of tests return False plen = len(part) for j in range(plen - 1, -1, -1): # Knuth's mod, (answer to problem 7.2.1.5.69) if j == 0 and (part[0].v - 1)*(ub - self.lpart) < part[0].u: self.k1 += 1 return False if j == 0 and part[j].v > 1 or

Remove abbreviations in documentation

decrement_part_small

65be461082dda54c8748922f9c29a19af1279fe1

sympy

enumerative.py

https://github.com/sympy/sympy.git

Python

def decrement_part_small(self, part, ub): if self.lpart >= ub - 1: self.p1 += 1 # increment to keep track of usefulness of tests return False plen = len(part) for j in range(plen - 1, -1, -1): # Knuth's mod, (answer to problem 7.2.1.5.69) if j == 0 and (part[0].v - 1)*(ub - self.lpart) < part[0].u: self.k1 += 1 return False if j == 0 and part[j].v > 1 or j > 0 and part[j].v > 0: # found val to decrement part[j].v -= 1 # Reset trailing parts back to maximum for k in range(j + 1, plen): part[k].v = part[k].u # Have now decremented part, but are we doomed to # failure when it is expanded? Check one oddball case # that turns out to be surprisingly common - exactly # enough room to expand the leading component, but no # room for the second component, which has v=0. if (plen > 1 and part[1].v == 0 and (part[0].u - part[0].v) == ((ub - self.lpart - 1) * part[0].v)): self.k2 += 1 self.db_trace("Decrement fails test 3") return False return True return False

python/ray/runtime_context.py

def get_node_id(self) -> str: node_id = self.worker.current_node_id assert not node_id.is_nil() return node_i

Ray 2.0 API deprecation (#26116) Ray 2.0 API deprecation for: ray.remote(): placement_group ray.remote(): placement_group_bundle_index ray.remote(): placement_group_capture_child_tasks ray.get_dashboard_url() ray.get_resource_ids() ray.disconnect() ray.connect() ray.util.ActorGroup ray.util.ActorPool Add get_xx_id() to return hex (rather than object), and then deprecate the xx_id() (which returns Cython object): the xx here can be node, task etc. ray start: --plasma-store-socket-name ray start: --raylet-socket-name

get_node_id

90cea203befa8f2e86e9c1c18bb3972296358e7b

ray

runtime_context.py

https://github.com/ray-project/ray.git

Python

def get_node_id(self) -> str: node_id = self.worker.current_node_id assert not node_id.is_nil() return node_id.hex()

qutebrowser/completion/models/configmodel.py

def list_option(*, info): return _option( info, "List options", lambda opt: (isinstance(info.config.get_obj(op

pylint: Fix new unnecessary-lambda-assignment

list_option

6c4e2810285af0698538aed9d46a99de085eb310

qutebrowser

configmodel.py

https://github.com/qutebrowser/qutebrowser.git

Python

def list_option(*, info): return _option( info, "List options", lambda opt: (isinstance(info.config.get_obj(opt.name), list) and not opt.no_autoconfig) )

@pytest.mark.parametrize( "query, fields", [ ( """ SELecT campaign.id, campaign.name, campaign.status, metrics.impressions FROM campaign wheRe campaign.status = 'PAUSED' AND metrics.impressions > 100 order by campaign.status """, ["campaign.id", "campaign.name", "campaign.status", "metrics.impressions"], ), ( """ SELECT campaign.accessible_bidding_strategy, segments.ad_destination_type, campaign.start_date, campaign.end_date FROM campaign """, ["campaign.accessible_bidding_strategy", "segments.ad_destination_type", "campaign.start_date", "campaign.end_date"], ), ("""selet aasdasd from aaa""", []), ], )

airbyte-integrations/connectors/source-google-ads/unit_tests/test_source.py

def get_instance_from_config_with_end_date(config, query): start_date = "2021-03-04" end_date = "2021-04-04" conversion_window_days = 14 google_api = GoogleAds(credentials=config["credentials"], customer_id=config["customer_id"]) instance = CustomQuery( api=google_api, conversion_window_days=conversion_window_days, start_date=start_date, end_date=end_date, time_zone="local", custom_query_config={"query": query, "table_name": "whatever_table"}, ) return instance @pytest.mark.parametrize( "query,

Source GoogleAds: add end_date to config (#8669) * GoogleAds add end_date to config * Update script following review comments * Add unit test * Solve conflicts * Solve conflicts in MR * Update test_google_ads.py Instanciate IncrementalGoogleAdsStream in tests + add missing line between functions * Update test_source.py remove extra hashtag * Update tests with missing params * Add missing time_zone param * merge user code * run format * revert unit test stream count * remove error log file * bump connector version * run seed file Co-authored-by: Marcos Marx <[email protected]>

get_instance_from_config_with_end_date

2e7ee756eb1d55080d707cef63454788a7abb6be

airbyte

test_source.py

https://github.com/airbytehq/airbyte.git

Python

def get_instance_from_config_with_end_date(config, query): start_date = "2021-03-04" end_date = "2021-04-04" conversion_window_days = 14 google_api = GoogleAds(credentials=config["credentials"], customer_id=config["customer_id"]) instance = CustomQuery( api=google_api, conversion_window_days=conversion_window_days, start_date=start_date, end_date=end_date, time_zone="local", custom_query_config={"query": query, "table_name": "whatever_table"}, ) return instance @pytest.mark.parametrize( "query, fields", [ ( , ["campaign.id", "campaign.name", "campaign.status", "metrics.impressions"], ), ( , ["campaign.accessible_bidding_strategy", "segments.ad_destination_type", "campaign.start_date", "campaign.end_date"], ), (, []), ], )

networkx/algorithms/assortativity/pairs.py

def node_degree_xy(G, x="out", y="in", weight=None, nodes=None): nodes = set(G) if nodes is None else set(nodes) if G.is_directed(): direction = {"out": G.out_degree, "in": G.in_degree} xdeg = direction[x] ydeg = direction[y] else: xdeg = ydeg = G.degree for u, degu in xdeg(nodes, weight=weight): # use G.edges to treat multigraphs correctly neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes) fo

MAINT: Cleanup assortativity module, remove unused variables (#5301) Remove unused variables, sort imports, raise errors instead of accepting invalid arguments silently Co-authored-by: Dan Schult <[email protected]>

node_degree_xy

34d9d630bb02426d297d3e20fedb7da8c3ced03a

networkx

pairs.py

https://github.com/networkx/networkx.git

Python

def node_degree_xy(G, x="out", y="in", weight=None, nodes=None): nodes = set(G) if nodes is None else set(nodes) if G.is_directed(): direction = {"out": G.out_degree, "in": G.in_degree} xdeg = direction[x] ydeg = direction[y] else: xdeg = ydeg = G.degree for u, degu in xdeg(nodes, weight=weight): # use G.edges to treat multigraphs correctly neighbors = (nbr for _, nbr in G.edges(u) if nbr in nodes) for _, degv in ydeg(neighbors, weight=weight): yield degu, degv

python/ray/util/sgd/torch/torch_trainer.py

def validate(self, num_steps=None, profile=False, reduce_results=True, info=None): worker_stats = self.worker_group.validate(

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

validate

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

torch_trainer.py

https://github.com/ray-project/ray.git

Python

def validate(self, num_steps=None, profile=False, reduce_results=True, info=None): worker_stats = self.worker_group.validate( num_steps=num_steps, profile=profile, info=info ) if reduce_results: return self._process_stats(worker_stats) else: return worker_stats

code/deep/BJMMD/caffe/python/caffe/io.py

def set_raw_scale(self, in_, scale): self.__check_input(in_) self.raw_scale[in_] = scale

Balanced joint maximum mean discrepancy for deep transfer learning

set_raw_scale

cc4d0564756ca067516f71718a3d135996525909

transferlearning

io.py

https://github.com/jindongwang/transferlearning.git

Python

def set_raw_scale(self, in_, scale): self.__check_input(in_) self.raw_scale[in_] = scale

ci/run/bazel_sharding/tests/test_bazel_sharding.py

def test_add_rule_to_best_shard(): # If we start with an empty list, then add to first shard shards: List[List[bazel_sharding.BazelRule]] = [list() for _ in range(4)] optimum = 600 rule = bazel_sharding.BazelRule("mock", "medium") bazel_sharding.add_rule_to_best_shard(rule, shards, optimum) assert shards[0][0] == rule assert all(not shard for shard in shards[1:]) # Add to first shard below optimum old_rule = bazel_sharding.BazelRule("mock", "medium") shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)] shards[3] = [] optimum = old_rule.actual_timeout_s rule = bazel_sharding.BazelRule("mock", "small") bazel_sharding.add_rule_to_best_shard(rule, shards, optimum) assert shards[3][0] == rule assert all(shard[-1] == old_rule for shard in shards[0:3]) # If all shards are above or equal optimum, add to the one with the smallest # difference old_rule = bazel_sharding.BazelRule("mock", "large") shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)] optimum = old_rule.actual_timeout_s old_rule_medium = bazel_sharding.BazelRule("mock", "medium") shards[3][0] = old_rule_medium rule = bazel_sharding.BazelRule("mock", "small") bazel_sharding.add_rule_to_best_shard(rule, shards, optimum) assert shards[3][0] == old_rule_medium assert shards[3][-1] == rule assert all(shard[-1] == old_rule for shard in shards[0:3])

[CI] Make bazel sharding for parallel buildkite more intelligent (#29221) This PR implements two changes to our `bazel-sharding.py` script, used for determining which bazel tests to run on each instance when buildkite parallelism is used: * An ability to filter tests before they are sharded, using the same logic as `bazel test`. This is done by specifying the `--tag_filters` argument, eg. `--tag_filters=air,-gpu`. If we filter tests with `bazel test` *after* they are sharded, we can end up with imbalanced shards as eg. all tests we want to filter out are assigned to one shard. This feature is enabled for Serve tests and it will be required for the changes I want to make to AIR CI. * A new algorithm to balance the shards, finally implementing what that comment was asking for all this time. Instead of trying to assign the same number of tests (which have variable timeouts) to each shard, the new algorithm tries to make sure each shard will finish in more or less the same time. This is achieved through a simple but good enough heuristic. The old algorithm can still be accessed through the `--sharding_strategy` argument. Those two changes do cause the complexity of the script to increase, necessitating proper testing. In order to facilitate that, this PR also adds a basic buildkite test harness for CI tools/scripts. After this PR is merged, the next step will be to move most of our manually parallelized jobs to use buildkite parallelism with the new logic here. Signed-off-by: Antoni Baum <[email protected]>

test_add_rule_to_best_shard

d1aa5608979891e3dd859c07fa919fa01cfead5f

ray

test_bazel_sharding.py

https://github.com/ray-project/ray.git

Python

def test_add_rule_to_best_shard(): # If we start with an empty list, then add to first shard shards: List[List[bazel_sharding.BazelRule]] = [list() for _ in range(4)] optimum = 600 rule = bazel_sharding.BazelRule("mock", "medium") bazel_sharding.add_rule_to_best_shard(rule, shards, optimum) assert shards[0][0] == rule assert all(not shard for shard in shards[1:]) # Add to first shard below optimum old_rule = bazel_sharding.BazelRule("mock", "medium") shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)] shards[3] = [] optimum = old_rule.actual_timeout_s rule = bazel_sharding.BazelRule("mock", "small") bazel_sharding.add_rule_to_best_shard(rule, shards, optimum) assert shards[3][0] == rule assert all(shard[-1] == old_rule for shard in shards[0:3]) # If all shards are above or equal optimum, add to the one with the smallest # difference old_rule = bazel_sharding.BazelRule("mock", "large") shards: List[List[bazel_sharding.BazelRule]] = [[old_rule] for _ in range(4)] optimum = old_rule.actual_timeout_s old_rule_medium = bazel_sharding.BazelRule("mock", "medium") shards[3][0] = old_rule_medium rule = bazel_sharding.BazelRule("mock", "small") bazel_sharding.add_rule_to_best_shard(rule, shards, optimum) assert shards[3][0] == old_rule_medium assert shards[3][-1] == rule assert all(shard[-1] == old_rule for shard in shards[0:3])

homeassistant/components/isy994/binary_sensor.py

def async_heartbeat(self) -> None: self._computed_state = False self._restart_timer() self.async_write_ha_stat

Enable strict typing for isy994 (#65439) Co-authored-by: Martin Hjelmare <[email protected]>

async_heartbeat

6c38a6b5697bcf4587e00101771001bf596974f9

core

binary_sensor.py

https://github.com/home-assistant/core.git

Python

def async_heartbeat(self) -> None: self._computed_state = False self._restart_timer() self.async_write_ha_state()

pandas/tests/util/test_assert_series_equal.py

def test_assert_series_equal_interval_dtype_mismatch(): # https://github.com/pandas-dev/pandas/issues/32747 left = Series([pd.Interval(0, 1, "right")], dtype="interval") right = left.astype(object) msg = tm.assert_series_equal(left, right, check_dtype=False) with pytest.raises(AssertionError, match=msg): tm.assert_series_equal(left, right, check_dtype=True)

ENH: consistency of input args for boundaries - Interval (#46522)

test_assert_series_equal_interval_dtype_mismatch

7e23a37e1c5bda81234801a6584563e2880769eb

pandas

test_assert_series_equal.py

https://github.com/pandas-dev/pandas.git

Python

def test_assert_series_equal_interval_dtype_mismatch(): # https://github.com/pandas-dev/pandas/issues/32747 left = Series([pd.Interval(0, 1, "right")], dtype="interval") right = left.astype(object) msg = tm.assert_series_equal(left, right, check_dtype=False) with pytest.raises(AssertionError, match=msg): tm.assert_series_equal(left, right, check_dtype=True)

lib/matplotlib/__init__.py

def rc_file_defaults(): #

Fix removed cross-references

rc_file_defaults

7c6c5f6215b40a27cfefb7bf21246299fd9b3a1e

matplotlib

__init__.py

https://github.com/matplotlib/matplotlib.git

Python

def rc_file_defaults(): # Deprecation warnings were already handled when creating rcParamsOrig, no # need to reemit them here. with _api.suppress_matplotlib_deprecation_warning(): from .style.core import STYLE_BLACKLIST rcParams.update({k: rcParamsOrig[k] for k in rcParamsOrig if k not in STYLE_BLACKLIST})

pipenv/patched/notpip/_vendor/rich/syntax.py

def lexer(self) -> Optional[Lexer]: if isinstance(self._lexer, Lexer): return self._lexer try: return get_lexer_by_name( self._lexer, stripnl=False, ensurenl=True, tabsize=self.tab_size, ) except ClassNotFound:

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

lexer

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

syntax.py

https://github.com/pypa/pipenv.git

Python

def lexer(self) -> Optional[Lexer]: if isinstance(self._lexer, Lexer): return self._lexer try: return get_lexer_by_name( self._lexer, stripnl=False, ensurenl=True, tabsize=self.tab_size, ) except ClassNotFound: return None

tests/pytests/functional/modules/file/test_replace.py

def test_numeric_repl(file, multiline_file): file.replace(multiline_fi

Add some funtional tests Add functional tests for the following: - file.readlink - file.replace - file.symlink Remove unit tests for file.replace as they are duplicated in the added functional test

test_numeric_repl

a35b29b2651bf33c5d5b45e64bc7765ffde4aff4

salt

test_replace.py

https://github.com/saltstack/salt.git

Python

def test_numeric_repl(file, multiline_file): file.replace(multiline_file, r"Etiam", 123) assert "123" in multiline_file.read_text()

gradio/components.py

def set_interpret_parameters(self, segments=16): self.interpretation_segments = segments retu

Blocks-Components - fixes - format

set_interpret_parameters

7fa8e45b6782d545fa0ead112d92d13bdad7417c

gradio

components.py

https://github.com/gradio-app/gradio.git

Python

def set_interpret_parameters(self, segments=16): self.interpretation_segments = segments return self

synapse/storage/engines/sqlite.py

def can_native_upsert(self) -> bool: return sqlite3.sqlite_version_info >= (3, 2

Tidy up and type-hint the database engine modules (#12734) Co-authored-by: Sean Quah <[email protected]>

can_native_upsert

1fe202a1a3343fad77da270ffe0923a46f1944dd

synapse

sqlite.py

https://github.com/matrix-org/synapse.git

Python

def can_native_upsert(self) -> bool: return sqlite3.sqlite_version_info >= (3, 24, 0)

dashboard/state_aggregator.py

async def get_actors(self) -> dict: reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT) result = {} for message in rep

[State Observability] Tasks and Objects API (#23912) This PR implements ray list tasks and ray list objects APIs. NOTE: You can ignore the merge conflict for now. It is because the first PR was reverted. There's a fix PR open now.

get_actors

30ab5458a7e4ba2351d5e1beef8c8797b5946493

ray

state_aggregator.py

https://github.com/ray-project/ray.git

Python

async def get_actors(self) -> dict: reply = await self._client.get_all_actor_info(timeout=DEFAULT_RPC_TIMEOUT) result = {} for message in reply.actor_table_data: data = self._message_to_dict(message=message, fields_to_decode=["actor_id"]) data = filter_fields(data, ActorState) result[data["actor_id"]] = data return result

mindsdb/api/mysql/mysql_proxy/mysql_proxy.py

def insert_predictor_answer(self, insert): model_interface = self.session.model_interface data_store = self.session.data_store select_data_query = insert.get('select_data_query') if isinstance(select_data_query, str) is False or len(select_data_query) == 0: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg="'select_data_query' should not be empty" ).send() return models = model_interface.get_models() if insert['name'] in [x['name'] for x in models]: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg=f"predictor with name '{insert['name']}'' already exists" ).send() return kwargs = {} if isinstance(insert.get('training_options'), str) \ and len(insert['training_options']) > 0: try: kwargs = json.loads(insert['training_options']) except Exception: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg='training_options should be in valid JSON string' ).send() return integration = self.session.integration if isinstance(integration, str) is False or len(integration) == 0: self.packet( ErrPacket, err_code=E

fix

insert_predictor_answer

551205a18ac2ac19626f4e4ffb2ed88fcad705b9

mindsdb

mysql_proxy.py

https://github.com/mindsdb/mindsdb.git

Python

def insert_predictor_answer(self, insert): model_interface = self.session.model_interface data_store = self.session.data_store select_data_query = insert.get('select_data_query') if isinstance(select_data_query, str) is False or len(select_data_query) == 0: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg="'select_data_query' should not be empty" ).send() return models = model_interface.get_models() if insert['name'] in [x['name'] for x in models]: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg=f"predictor with name '{insert['name']}'' already exists" ).send() return kwargs = {} if isinstance(insert.get('training_options'), str) \ and len(insert['training_options']) > 0: try: kwargs = json.loads(insert['training_options']) except Exception: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg='training_options should be in valid JSON string' ).send() return integration = self.session.integration if isinstance(integration, str) is False or len(integration) == 0: self.packet( ErrPacket, err_code=ERR.ER_WRONG_ARGUMENTS, msg='select_data_query can be used only in query from database' ).send() return insert['select_data_query'] = insert['select_data_query'].replace(r"\'", "'") ds_name = data_store.get_vacant_name(insert['name']) ds = data_store.save_datasource(ds_name, integration, {'query': insert['select_data_query']}) insert['predict'] = [x.strip() for x in insert['predict'].split(',')] ds_data = data_store.get_datasource(ds_name) if ds_data is None: raise Exception(f"DataSource '{ds_name}' does not exists") ds_columns = [x['name'] for x in ds_data['columns']] for col in insert['predict']: if col not in ds_columns: data_store.delete_datasource(ds_name) raise Exception(f"Column '{col}' not exists") try: insert['predict'] = self._check_predict_columns(insert['predict'], ds_columns) except Exception: data_store.delete_datasource(ds_name) raise model_interface.learn( insert['name'], ds, insert['predict'], ds_data['id'], kwargs=kwargs, delete_ds_on_fail=True ) self.packet(OkPacket).send()

openbb_terminal/portfolio/portfolio_model.py

def populate_historical_trade_data(self): trade_data = self.__orderbook.pivot( index="Date", columns="Ticker", values=[ "Type", "Sector", "Industry", "Country", "Price", "Quantity",

Overhaul Portfolio class (#2021) * adds pythonic portfolio class * start calculate trades refactoring * adds comments to portfolio model - delete afterwards * finish calculate trades refactoring * restore original portfolio_model.py * implement calculate_allocations * adapt and test controller load, show, bench, alloc and perf * add old code that was ok * adapt controller * adapt portfolio_view * run black on pythonic_portfolio.py * fix crypto bug * change column name in example datasets * substitute portfolio_model.py * update show command * push cumulative returns calculation to model * fix last change in cumulative returns * add comments on possibly unused code * run black on changes * bring metrics from helper to model * push rolling metrics from view to model * Details and linting * Fix tests * remove empty attribute and rename class * fix view and controller rf * change returns calculation method * remove CASH from code * remove cash from tickers_list * run black on changes * change function name * adapt to PortfolioModel * fix tests * fix tests on help * fix linting * call metrics from PortfolioModel * call drawdown from model * fix some mypy issues * fix remaining mypy issues * fix test * Fix linting * Remove unused function * Small fixes * Remove old code and adjust summary to simply work * Update the Excel since CASH is no longer a thing * Fix tests * Update the csvs * Updates to usage of full_shares and more details * Fix -t flag for perf Co-authored-by: Jeroen Bouma <[email protected]>

populate_historical_trade_data

2c3e10a128fa0ce4e937d8d50dc0cd6d7cd11485

OpenBBTerminal

portfolio_model.py

https://github.com/OpenBB-finance/OpenBBTerminal.git

Python

def populate_historical_trade_data(self): trade_data = self.__orderbook.pivot( index="Date", columns="Ticker", values=[ "Type", "Sector", "Industry", "Country", "Price", "Quantity", "Fees", "Premium", "Investment", "Side", "Currency", ], ) # Make historical prices columns a multi-index. This helps the merging. self.portfolio_historical_prices.columns = pd.MultiIndex.from_product( [["Close"], self.portfolio_historical_prices.columns] ) # Merge with historical close prices (and fillna) trade_data = pd.merge( trade_data, self.portfolio_historical_prices, how="right", left_index=True, right_index=True, ).fillna(0) # Accumulate quantity held by trade date trade_data["Quantity"] = trade_data["Quantity"].cumsum() trade_data["Investment"] = trade_data["Investment"].cumsum() trade_data.loc[:, ("Investment", "Total")] = trade_data["Investment"][ self.tickers_list ].sum(axis=1) self.historical_trade_data = trade_data

@fails_if_pypy @pytest.mark.parametrize("parser", ["liac-arff", "pandas"])

sklearn/datasets/tests/test_openml.py

def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser): pytest.importorskip("pandas") data_id = 61 _monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True) bunch_as_frame_true = fetch_openml( data_id=data_id, as_frame=True, cache=False, parser=parser, ) bunch_as_frame_false = fetch_openml( data_id=data_id, as_frame=False, cache=False, parser=parser, ) assert_allclose(bunch_as_frame_false.data, bunch_as_frame_true.data) assert_array_equal(bunch_as_frame_false.target, bunch_as_frame_true.target) # Known failure of PyPy for OpenML. See the following issue: # https://github.com/scikit-learn/scikit-learn/issues/18906 @fails_if_pypy @pytest.mark.parametrize("parser", ["liac-arff", "pandas"])

ENH improve ARFF parser using pandas (#21938) Co-authored-by: Thomas J. Fan <[email protected]> Co-authored-by: Olivier Grisel <[email protected]> Co-authored-by: Adrin Jalali <[email protected]>

test_fetch_openml_equivalence_array_dataframe

a47d569e670fd4102af37c3165c9b1ddf6fd3005

scikit-learn

test_openml.py

https://github.com/scikit-learn/scikit-learn.git

Python

def test_fetch_openml_equivalence_array_dataframe(monkeypatch, parser): pytest.importorskip("pandas") data_id = 61 _monkey_patch_webbased_functions(monkeypatch, data_id, gzip_response=True) bunch_as_frame_true = fetch_openml( data_id=data_id, as_frame=True, cache=False, parser=parser, ) bunch_as_frame_false = fetch_openml( data_id=data_id, as_frame=False, cache=False, parser=parser, ) assert_allclose(bunch_as_frame_false.data, bunch_as_frame_true.data) assert_array_equal(bunch_as_frame_false.target, bunch_as_frame_true.target) # Known failure of PyPy for OpenML. See the following issue: # https://github.com/scikit-learn/scikit-learn/issues/18906 @fails_if_pypy @pytest.mark.parametrize("parser", ["liac-arff", "pandas"])

mitmproxy/contrib/kaitaistruct/google_protobuf.py

def wire_type(self): if hasattr(self, '_m_wire_type'): return self._m_wire_type self._m_wire_type = Kaita

update kaitai definitions

wire_type

002f919dda5f01d067c2e786426c68751551d15c

mitmproxy

google_protobuf.py

https://github.com/mitmproxy/mitmproxy.git

Python

def wire_type(self): if hasattr(self, '_m_wire_type'): return self._m_wire_type self._m_wire_type = KaitaiStream.resolve_enum(GoogleProtobuf.Pair.WireTypes, (self.key.value & 7)) return getattr(self, '_m_wire_type', None)

mitmproxy/connection.py

def address(self): # pragma: no cover warnings.warn( "Client.address is deprecated, use Client.peername instead.", D

make it black!

address

b3587b52b25077f68116b9852b041d33e7fc6601

mitmproxy

connection.py

https://github.com/mitmproxy/mitmproxy.git

Python

def address(self): # pragma: no cover warnings.warn( "Client.address is deprecated, use Client.peername instead.", DeprecationWarning, stacklevel=2, ) return self.peername

f"""\ To get asquare Jordan block matrix use a morebanded matrix

sympy/matrices/common.py

def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs): if 'r

Update the Matrix.jordan_block() rows and cols kwargs deprecation

jordan_block

a4fdabab38def4bf6b4007f8cd67d6944740b303

sympy

common.py

https://github.com/sympy/sympy.git

Python

def jordan_block(kls, size=None, eigenvalue=None, *, band='upper', **kwargs): if 'rows' in kwargs or 'cols' in kwargs: msg = if 'rows' in kwargs and 'cols' in kwargs: msg += f

rest_api/rest_api/utils.py

def get_openapi_specs() -> dict: app = get_app()

bug: fix the docs rest api reference url (#3775) * bug: fix the docs rest api reference url * revert openapi json changes * remove last line on json files * Add explanation about `servers` and remove `servers` parameter from FastAPI * generate openapi schema without empty end line

get_openapi_specs

86ade4817eda3142d2ddef65a0b1e29ffee770e3

haystack

utils.py

https://github.com/deepset-ai/haystack.git

Python

def get_openapi_specs() -> dict: app = get_app() return get_openapi( title=app.title, version=app.version, openapi_version=app.openapi_version, description=app.description, routes=app.routes, servers=[{"url": "http://localhost:8000"}], )

erpnext/hr/doctype/employee/employee.py

def get_all_employee_emails(company): employee_list = frappe.get_all( "Employee", fields=["name", "employee_name"], filters={"status": "Active", "company": company} ) employee_emails = [] for

style: format code with black

get_all_employee_emails

494bd9ef78313436f0424b918f200dab8fc7c20b

erpnext

employee.py

https://github.com/frappe/erpnext.git

Python

def get_all_employee_emails(company): employee_list = frappe.get_all( "Employee", fields=["name", "employee_name"], filters={"status": "Active", "company": company} ) employee_emails = [] for employee in employee_list: if not employee: continue user, company_email, personal_email = frappe.db.get_value( "Employee", employee, ["user_id", "company_email", "personal_email"] ) email = user or company_email or personal_email if email: employee_emails.append(email) return employee_emails

tests/snuba/api/endpoints/test_organization_group_index.py

def test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release(self): release_1 = self.create_release( date_added=timezon

ref(tests): Remove `get_valid_response()` (#34822)

test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release

096b5511e244eecd8799b2a0324655207ce8985e

sentry

test_organization_group_index.py

https://github.com/getsentry/sentry.git

Python

def test_in_non_semver_projects_resolved_in_next_release_is_equated_to_in_release(self): release_1 = self.create_release( date_added=timezone.now() - timedelta(minutes=45), version="foobar 1" ) release_2 = self.create_release(version="foobar 2") self.create_release(version="foobar 3") group = self.store_event( data={ "timestamp": iso_format(before_now(seconds=12)), "fingerprint": ["group-1"], "release": release_1.version, }, project_id=self.project.id, ).group self.login_as(user=self.user) response = self.get_success_response( qs_params={"id": group.id}, status="resolvedInNextRelease" ) assert response.data["status"] == "resolved" assert response.data["statusDetails"]["inNextRelease"] grp_resolution = GroupResolution.objects.filter(group=group) assert len(grp_resolution) == 1 grp_resolution = grp_resolution[0] assert grp_resolution.current_release_version == release_1.version assert grp_resolution.release.id == release_2.id assert grp_resolution.type == GroupResolution.Type.in_release assert grp_resolution.status == GroupResolution.Status.resolved activity = Activity.objects.filter( group=grp_resolution.group, type=Activity.SET_RESOLVED_IN_RELEASE, ident=grp_resolution.id, ).first() assert activity.data["version"] == release_2.version

@pytest.fixture

saleor/plugins/webhook/tests/subscription_webhooks/fixtures.py

def subscription_order_updated_webhook(subscription_webhook): return subscription_webhook( ORDER_UPDATED_SUBSCRIPTION_QUERY, Webhook

Add Webhook payload via graphql subscriptions (#9394) * Add PoC of webhook subscriptions * add async webhooks subscription payloads feature * remove unneeded file * add translations subscription handling, fixes after review * remove todo * add descriptions * add descriptions, move subsrciption_payloads.py * refactor * fix imports, add changelog * check_document_is_single_subscription refactor Co-authored-by: Maciej Korycinski <[email protected]> Co-authored-by: Marcin Gębala <[email protected]>

subscription_order_updated_webhook

aca6418d6c36956bc1ab530e6ef7e146ec9df90c

saleor

fixtures.py

https://github.com/saleor/saleor.git

Python

def subscription_order_updated_webhook(subscription_webhook): return subscription_webhook( ORDER_UPDATED_SUBSCRIPTION_QUERY, WebhookEventAsyncType.ORDER_UPDATED ) ORDER_CONFIRMED_SUBSCRIPTION_QUERY = @pytest.fixture

modin/experimental/core/execution/native/implementations/omnisci_on_native/omnisci_worker.py

def cast_to_compatible_types(table): schema = table.schema new_schema = schema need_cast = False uint_to_int_cast = False new_cols = {} uint_to_int_map = { pa.uint8(): pa.int16(), pa.uint16(): pa.int32(), pa.uint32(): pa.int64(), pa.uint64(): pa.int64(), # May cause overflow } for i, field in enumerate(schema): # Currently OmniSci doesn't support Arrow table import with # dictionary columns. Here we cast dictionaries until support # is in place. # https://github.com/modin-project/modin/issues/1738 if pa.types.is_dictionary(field.type):

FIX-#3368: support unsigned integers in OmniSci backend (#4256) Signed-off-by: Dmitry Chigarev <[email protected]> Co-authored-by: Yaroslav Igoshev <[email protected]>

cast_to_compatible_types

241a46dd5f4dce7bc7f630b58c80d15222d6bde7

modin

omnisci_worker.py

https://github.com/modin-project/modin.git

Python

def cast_to_compatible_types(table): schema = table.schema new_schema = schema need_cast = False uint_to_int_cast = False new_cols = {} uint_to_int_map = { pa.uint8(): pa.int16(), pa.uint16(): pa.int32(), pa.uint32(): pa.int64(), pa.uint64(): pa.int64(), # May cause overflow } for i, field in enumerate(schema): # Currently OmniSci doesn't support Arrow table import with # dictionary columns. Here we cast dictionaries until support # is in place. # https://github.com/modin-project/modin/issues/1738 if pa.types.is_dictionary(field.type): # Conversion for dictionary of null type to string is not supported # in Arrow. Build new column for this case for now. if pa.types.is_null(field.type.value_type): mask = np.full(table.num_rows, True, dtype=bool) new_col_data = np.empty(table.num_rows, dtype=str) new_col = pa.array(new_col_data, pa.string(), mask) new_cols[i] = new_col else: need_cast = True new_field = pa.field( field.name, pa.string(), field.nullable, field.metadata ) new_schema = new_schema.set(i, new_field) # OmniSci doesn't support importing Arrow's date type: # https://github.com/omnisci/omniscidb/issues/678 elif pa.types.is_date(field.type): # Arrow's date is the number of days since the UNIX-epoch, so we can convert it # to a timestamp[s] (number of seconds since the UNIX-epoch) without losing precision new_field = pa.field( field.name, pa.timestamp("s"), field.nullable, field.metadata ) new_schema = new_schema.set(i, new_field) need_cast = True # OmniSci doesn't support unsigned types elif pa.types.is_unsigned_integer(field.type): new_field = pa.field( field.name, uint_to_int_map[field.type], field.nullable, field.metadata, ) new_schema = new_schema.set(i, new_field) need_cast = True uint_to_int_cast = True # Such cast may affect the data, so we have to raise a warning about it if uint_to_int_cast: ErrorMessage.single_warning( "OmniSci does not support unsigned integer types, such types will be rounded up to the signed equivalent." ) for i, col in new_cols.items(): table = table.set_column(i, new_schema[i], col) if need_cast: try: table = table.cast(new_schema) except pa.lib.ArrowInvalid as e: raise (OverflowError if uint_to_int_cast else RuntimeError)( "An error occurred when trying to convert unsupported by OmniSci 'dtypes' " + f"to the supported ones, the schema to cast was: \n{new_schema}." ) from e return table

jina/types/request/data.py

def data(self) -> 'DataRequest._DataContent': return DataRequest._DataCon

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]>

data

933415bfa1f9eb89f935037014dfed816eb9815d

jina

data.py

https://github.com/jina-ai/jina.git

Python

def data(self) -> 'DataRequest._DataContent': return DataRequest._DataContent(self.proto.data)

keras/saving/model_config.py

def model_from_json(json_string, custom_objects=None): from keras.layers import ( deserialize_from_json, ) # pylint: disable=g-import-not

Reformatting the codebase with black. PiperOrigin-RevId: 450093126

model_from_json

84afc5193d38057e2e2badf9c889ea87d80d8fbf

keras

model_config.py

https://github.com/keras-team/keras.git

Python

def model_from_json(json_string, custom_objects=None): from keras.layers import ( deserialize_from_json, ) # pylint: disable=g-import-not-at-top return deserialize_from_json(json_string, custom_objects=custom_objects)

src/documents/tests/test_management_convert_thumbnail.py

def test_do_nothing_if_converted(self, run_convert_mock): stdout, _ = self.call_command() run_convert_mock.assert_not_called() self.assertIn("Converting all PNG thumbnails to WebP", stdout)

Fixes existing testing, adds test coverage of new command

test_do_nothing_if_converted

08c3d6e84b17da2acfb10250438fe357398e5e0e

paperless-ngx

test_management_convert_thumbnail.py

https://github.com/paperless-ngx/paperless-ngx.git

Python

def test_do_nothing_if_converted(self, run_convert_mock): stdout, _ = self.call_command() run_convert_mock.assert_not_called() self.assertIn("Converting all PNG thumbnails to WebP", stdout)

pipenv/patched/notpip/_vendor/pyparsing/core.py

def __ror__(self, other): if isinstance(other, str_type): other = self._literalStringClass(other) if not isinstance(other, ParserElement): raise TypeError( "Cannot combine element of type {} with ParserElement".format(

check point progress on only bringing in pip==22.0.4 (#4966) * vendor in pip==22.0.4 * updating vendor packaging version * update pipdeptree to fix pipenv graph with new version of pip. * Vendoring of pip-shims 0.7.0 * Vendoring of requirementslib 1.6.3 * Update pip index safety restrictions patch for pip==22.0.4 * Update patches * exclude pyptoject.toml from black to see if that helps. * Move this part of the hash collection back to the top (like prior implementation) because it affects the outcome of this test now in pip 22.0.4

__ror__

f3166e673fe8d40277b804d35d77dcdb760fc3b3

pipenv

core.py

https://github.com/pypa/pipenv.git

Python

def __ror__(self, other): if isinstance(other, str_type): other = self._literalStringClass(other) if not isinstance(other, ParserElement): raise TypeError( "Cannot combine element of type {} with ParserElement".format( type(other).__name__ ) ) return other | self

samtranslator/third_party/py27hash/hash.py

def shash(value): length = len(value) if length == 0: return 0 x = Hash.ordinal(value[0]) << 7 for c in value: x = (1000003 * x) ^ Hash.ordinal(c) x ^= length x &= 0xFFFFFFFFFFFFFFFF if x == -1: x = -2 # Convert to C long type

fix: Py27hash fix (#2182) * Add third party py27hash code * Add Py27UniStr and unit tests * Add py27hash_fix utils and tests * Add to_py27_compatible_template and tests * Apply py27hash fix to wherever it is needed * Apply py27hash fix, all tests pass except api_with_any_method_in_swagger * apply py27hash fix in openapi + run black * remove py27 testing * remove other py27 references * black fixes * fixes/typos * remove py27 from tox.ini * refactoring * third party notice * black * Fix py27hash fix to deal with null events * Fix Py27UniStr repr for unicode literals * black reformat * Update _template_has_api_resource to check data type more defensively * Apply py27Dict in _get_authorizers * Apply Py27Dict to authorizers and gateway responses which will go into swagger * Update to_py27_compatible_template to handle parameter_values; Add Py27LongInt class * Rename _convert_to_py27_dict to _convert_to_py27_type * Apply Py27UniStr to path param name * Handle HttpApi resource under to_py27_compatible_template * Fix InvalidDocumentException to not sort different exceptions * black reformat * Remove unnecessary test files Co-authored-by: Wing Fung Lau <[email protected]>

shash

a5db070f446b7cfebdaa6ad2e3dcf78f6105a272

serverless-application-model

hash.py

https://github.com/aws/serverless-application-model.git

Python

def shash(value): length = len(value) if length == 0: return 0 x = Hash.ordinal(value[0]) << 7 for c in value: x = (1000003 * x) ^ Hash.ordinal(c) x ^= length x &= 0xFFFFFFFFFFFFFFFF if x == -1: x = -2 # Convert to C long type return ctypes.c_long(x).value

@pytest.fixture

tests/packaged_modules/test_folder_based_builder.py

def data_files_with_one_split_and_metadata(tmp_path, auto_text_file): data_dir = tmp_path / "autofolder_data_dir_with_metadata_one_split" data_dir.mkdir(parents=True, exist_ok=True) subdir = data_dir / "subdir" subdir.mkdir(parents=True, exist_ok=True) filename = data_dir / "file.txt" shutil.copyfile(auto_text_file, filename) filename2 = data_dir / "file2.txt" shutil.copyfile(auto_text_file, filename2) filename3 = subdir / "file3.txt" # in subdir shutil.copyfile(auto_text_file, filename3) metadata_filename = data_dir / "metadata.jsonl" metadata = textwrap.dedent( ) with open(metadata_filename, "w", encoding="utf-8") as f: f.write(metadata) data_files_with_one_split_and_metadata = DataFilesDict.from_local_or_remote( get_data_patterns_locally(data_dir), data_dir ) assert len(data_files_with_one_split_and_metadata) == 1 assert len(data_files_with_one_split_and_metadata["train"]) == 4 return data_files_with_one_split_and_metadata @pytest.fixture

Add AudioFolder packaged loader (#4530) * add audiofolder loader (almost identical to imagefolder except for inferring labels is not default) * add instruction on how to obtain list of audio extensions * add a generic loader * patch autofolder for streaming manually * align autofolder with the latest imagefolder implementation * update tests * add test for duplicate label col * add tests for autofolder (+copied from imagefolder) * add missed audio_file fixture * add documentation * remove boilerplate, make base feature builder's class arg instead of a config's one * remove self.config.label_name, use hardcoded 'label' * patch parents that inherit from DatasetBuilder, revert get_imports * rename autofolder -> folder_builder * make base column name an abstract attr of FolderBuilder instead of config's parameter * Update src/datasets/streaming.py Co-authored-by: Mario Šaško <[email protected]> * rename FolderBuilder -> FolderBasedBuilder * set drop_labels to None by default for AudioFolder * update documentation * check if builder extending for streaming is not in datasets.builder module Co-authored-by: Mario Šaško <[email protected]> Co-authored-by: Quentin Lhoest <[email protected]>

data_files_with_one_split_and_metadata

6ea46d88c6a09244d785e55e2681bc4033740442

datasets

test_folder_based_builder.py

https://github.com/huggingface/datasets.git

Python

def data_files_with_one_split_and_metadata(tmp_path, auto_text_file): data_dir = tmp_path / "autofolder_data_dir_with_metadata_one_split" data_dir.mkdir(parents=True, exist_ok=True) subdir = data_dir / "subdir" subdir.mkdir(parents=True, exist_ok=True) filename = data_dir / "file.txt" shutil.copyfile(auto_text_file, filename) filename2 = data_dir / "file2.txt" shutil.copyfile(auto_text_file, filename2) filename3 = subdir / "file3.txt" # in subdir shutil.copyfile(auto_text_file, filename3) metadata_filename = data_dir / "metadata.jsonl" metadata = textwrap.dedent( ) with open(metadata_filename, "w", encoding="utf-8") as f: f.write(metadata) data_files_with_one_split_and_metadata = DataFilesDict.from_local_or_remote( get_data_patterns_locally(data_dir), data_dir ) assert len(data_files_with_one_split_and_metadata) == 1 assert len(data_files_with_one_split_and_metadata["train"]) == 4 return data_files_with_one_split_and_metadata @pytest.fixture

tests/handlers/test_e2e_room_keys.py

def test_upload_room_keys_wrong_version(self) -> None: version = self.get_success( self.handler.create_version( self.local_user, { "algorithm": "m.megolm_backup.v1", "auth_data": "first_version_auth_data", }, ) ) self.assertEqual(version, "1") version = self.get_success( self.handler.create_version( self.local_user, { "algorithm": "m.megolm_backup.v1", "auth_data": "second_version_auth_data", }, ) ) self.assertEqual

Add missing type hints to tests.handlers. (#14680) And do not allow untyped defs in tests.handlers.

test_upload_room_keys_wrong_version

652d1669c5a103b1c20478770c4aaf18849c09a3

synapse

test_e2e_room_keys.py

https://github.com/matrix-org/synapse.git

Python

def test_upload_room_keys_wrong_version(self) -> None: version = self.get_success( self.handler.create_version( self.local_user, { "algorithm": "m.megolm_backup.v1", "auth_data": "first_version_auth_data", }, ) ) self.assertEqual(version, "1") version = self.get_success( self.handler.create_version( self.local_user, { "algorithm": "m.megolm_backup.v1", "auth_data": "second_version_auth_data", }, ) ) self.assertEqual(version, "2") e = self.get_failure( self.handler.upload_room_keys(self.local_user, "1", room_keys), SynapseError ) res = e.value.code self.assertEqual(res, 403)

lib/gui/utils.py

def previewtrain(self) -> Dict[str, List[Union[Image.Image, ImageTk.PhotoImage, None, float]]]: return self._previewtrain

Bugfix: Preview for extract in batch mode

previewtrain

dc18c74eea0c7837a820d27628cb12b0824fa30e

faceswap

utils.py

https://github.com/deepfakes/faceswap.git

Python

def previewtrain(self) -> Dict[str, List[Union[Image.Image, ImageTk.PhotoImage, None, float]]]: return self._previewtrain

code/deep/BJMMD/caffe/python/caffe/test/test_coord_map.py

def test_padding(self):

Balanced joint maximum mean discrepancy for deep transfer learning

test_padding

cc4d0564756ca067516f71718a3d135996525909

transferlearning

test_coord_map.py

https://github.com/jindongwang/transferlearning.git

Python

def test_padding(self): n = coord_net_spec() ax, a, b = coord_map_from_to(n.deconv, n.data) pad = random.randint(0, 10) # conv padding n = coord_net_spec(pad=pad) _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data) self.assertEquals(a, a_pad) self.assertEquals(b - pad, b_pad) # deconv padding n = coord_net_spec(dpad=pad) _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data) self.assertEquals(a, a_pad) self.assertEquals(b + pad, b_pad) # pad both to cancel out n = coord_net_spec(pad=pad, dpad=pad) _, a_pad, b_pad = coord_map_from_to(n.deconv, n.data) self.assertEquals(a, a_pad) self.assertEquals(b, b_pad)

src/accelerate/test_utils/testing.py

def require_cuda(test_case): return unittest.skipUnless(torch.cuda.is_a

Clean up tests + fix import (#330)

require_cuda

e5c17f36a8b5bf8b9478d416c4a80841a353fb19

accelerate

testing.py

https://github.com/huggingface/accelerate.git

Python

def require_cuda(test_case): return unittest.skipUnless(torch.cuda.is_available(), "test requires a GPU")(test_case)

synapse/logging/handlers.py

def _flush_periodically(self) -> None: while self._active: # flush is thread-safe; it acquires and releases the lock internally self.flush() time.sleep(self._flush_period)

Another batch of type annotations (#12726)

_flush_periodically

aec69d2481e9ea1d8ea1c0ffce1706a65a7896a8

synapse

handlers.py

https://github.com/matrix-org/synapse.git

Python

def _flush_periodically(self) -> None: while self._active: # flush is thread-safe; it acquires and releases the lock internally self.flush() time.sleep(self._flush_period)

gradio/components.py

def save_flagged(self, dir, label, data, encryption_key) -> str | Dict: if "confidences" in data: return json.dumps( { example["label"]: example["confidence"]

Live website changes (#1578) * fix audio output cache (#804) * fix audio output cache * changes * version update Co-authored-by: Ali Abid <[email protected]> * Website Tracker Slackbot (#797) * added commands to reload script * catch errors with git pull * read new webhook from os variable * correcting bash * bash fixes * formatting * more robust error checking * only sends success if git changes * catching error from script * escaping error text to send with curl * correct text escaping for error message * fix search bug in guides (#809) * Update getting_started.md (#808) * Fix type of server returned by `Launchable` (#810) * `Launchable` returns a FastAPI now * Update .gitignore * Add a missing line to getting started (#816) Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7] Former-commit-id: eaff13262853078e0c6c0baa54c731d9e56bc73f * Add a missing line to getting started (#816) Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]] Former-commit-id: eaff13262853078e0c6c0baa54c731d9e56bc73f Former-commit-id: b5112c3f425c0ea961461854efae9c28a73aea01 * Add a missing line to getting started (#816) Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]]] Former-commit-id: eaff13262853078e0c6c0baa54c731d9e56bc73f Former-commit-id: b5112c3f425c0ea961461854efae9c28a73aea01 Former-commit-id: bce6f9c4c5254301eb73e76eb47cddab3e132c24 * Add a missing line to getting started (#816) Former-commit-id: 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 81e271ca22e838e1ee618d48cdb0e904fd233cf3 [formerly 96f203108bf1222fe333a0175687293abdc669d7]]]] Former-commit-id: eaff13262853078e0c6c0baa54c731d9e56bc73f Former-commit-id: b5112c3f425c0ea961461854efae9c28a73aea01 Former-commit-id: bce6f9c4c5254301eb73e76eb47cddab3e132c24 Former-commit-id: feba0888e3d488b82a3518343f607517d0836f13 * Add a missing line to getting started (#816) * Clean-History - Remove 51MB file with this commit Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c * Clean-History - Remove 51MB file with this commit Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c Former-commit-id: dd700c33cca3f560621219530444b631b7767392 * Clean-History - Remove 51MB file with this commit Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c Former-commit-id: dd700c33cca3f560621219530444b631b7767392 Former-commit-id: 0d80e6a056abad1c4d1fd6f162eb725e0db5fb4f * Clean-History - Remove 51MB file with this commit Former-commit-id: 34b6a2325d613eeef622410f2d1ff3d869d3133c Former-commit-id: dd700c33cca3f560621219530444b631b7767392 Former-commit-id: 0d80e6a056abad1c4d1fd6f162eb725e0db5fb4f Former-commit-id: 20523b05194438209cf64cb688008b4599eb847e * changes * changes * Homepage: header image size (#1347) * image size * image in local assets * add dall-e mini banner * undo ui changes * changes * changes * updates * updates * changes * changes * changes * h11 dependency * add npm build-mac * expand demo button to all classes * add demos to docstrings * add anchor tags to headers * add required tag to param table * add consistent styling for headers * skip param beginning with underscore from docs * skip kwargs param from docs * remove types in param docstring * override signature to reflect usage * add supported events * add step-by-step guides * fix guide contribution link * add related spaces * fix img styling on guides * pin quickstart, advanced, and block guides to top * margin fix * autogenerated copy buttons for all codeblocks * changes * documentaiton * format * launch * formatting * style changes * remove backticks * changes * changes Co-authored-by: Ali Abid <[email protected]> Co-authored-by: Ali Abdalla <[email protected]> Co-authored-by: Julien Chaumond <[email protected]> Co-authored-by: Ömer Faruk Özdemir <[email protected]> Co-authored-by: Ali <[email protected]> Co-authored-by: Victor Muštar <[email protected]> Co-authored-by: Abubakar Abid <[email protected]>

save_flagged

70ebf698fa75ad094a2ba52cd1de645fa58eff85

gradio

components.py

https://github.com/gradio-app/gradio.git

Python

def save_flagged(self, dir, label, data, encryption_key) -> str | Dict: if "confidences" in data: return json.dumps( { example["label"]: example["confidence"] for example in data["confidences"] } ) else: return data["label"]

rllib/agents/impala/tests/test_vtrace.py

def test_higher_rank_inputs_for_importance_weights(self): for fw in framework_iterator(frameworks=("torch", "tf"), session=True): vtrace = vtrace_tf if fw != "torch" else vtrace_torch if fw ==

[CI] Format Python code with Black (#21975) See #21316 and #21311 for the motivation behind these changes.

test_higher_rank_inputs_for_importance_weights

7f1bacc7dc9caf6d0ec042e39499bbf1d9a7d065

ray

test_vtrace.py

https://github.com/ray-project/ray.git

Python

def test_higher_rank_inputs_for_importance_weights(self): for fw in framework_iterator(frameworks=("torch", "tf"), session=True): vtrace = vtrace_tf if fw != "torch" else vtrace_torch if fw == "tf": inputs_ = { "log_rhos": tf1.placeholder( dtype=tf.float32, shape=[None, None, 1] ), "discounts": tf1.placeholder( dtype=tf.float32, shape=[None, None, 1] ), "rewards": tf1.placeholder( dtype=tf.float32, shape=[None, None, 42] ), "values": tf1.placeholder(dtype=tf.float32, shape=[None, None, 42]), "bootstrap_value": tf1.placeholder( dtype=tf.float32, shape=[None, 42] ), } else: inputs_ = { "log_rhos": Box(-1.0, 1.0, (8, 10, 1)).sample(), "discounts": Box(-1.0, 1.0, (8, 10, 1)).sample(), "rewards": Box(-1.0, 1.0, (8, 10, 42)).sample(), "values": Box(-1.0, 1.0, (8, 10, 42)).sample(), "bootstrap_value": Box(-1.0, 1.0, (10, 42)).sample(), } output = vtrace.from_importance_weights(**inputs_) check(int(output.vs.shape[-1]), 42)

tests/www/views/test_views_tasks.py

def test_task_fail_duration(app, admin_client, dag_maker, session): with dag_maker() as dag: op1 = BashOperator(task_id='fail', bash_command='exit 1') op2 = BashOperator(task_id='success', bash_command='exit 0') with pytest.raises(AirflowException): op1.run() op2.run() op1_fails = ( session.query(TaskFail) .filter( TaskFail.task_id == 'fail', TaskFail.dag_id == dag.dag_id, ) .all() ) op2_fails = ( session.query(TaskFail) .filter( TaskFail.task_id == 'success', TaskFail.dag_id == dag.dag_id, ) .all() ) assert len(op1_fails) == 1 assert len(op2_fails) == 0 with unittest.mock.patch.object(app, 'dag_bag') as mocked_dag_bag: mocked_dag_bag.get_dag.return_valu

Fix TaskFail queries in views after run_id migration (#23008) Two problems here: 1. TaskFail no longer has a executin_date property -- switch to run_id 2. We weren't joining to DagRun correctly, meaning we'd end up with a cross-product effect(? Something weird anyway) Co-authored-by: Karthikeyan Singaravelan <[email protected]>

test_task_fail_duration

70049f19e4ac82ea922d7e59871a3b4ebae068f1

airflow

test_views_tasks.py

https://github.com/apache/airflow.git

Python

def test_task_fail_duration(app, admin_client, dag_maker, session): with dag_maker() as dag: op1 = BashOperator(task_id='fail', bash_command='exit 1') op2 = BashOperator(task_id='success', bash_command='exit 0') with pytest.raises(AirflowException): op1.run() op2.run() op1_fails = ( session.query(TaskFail) .filter( TaskFail.task_id == 'fail', TaskFail.dag_id == dag.dag_id, ) .all() ) op2_fails = ( session.query(TaskFail) .filter( TaskFail.task_id == 'success', TaskFail.dag_id == dag.dag_id, ) .all() ) assert len(op1_fails) == 1 assert len(op2_fails) == 0 with unittest.mock.patch.object(app, 'dag_bag') as mocked_dag_bag: mocked_dag_bag.get_dag.return_value = dag resp = admin_client.get(f"dags/{dag.dag_id}/duration", follow_redirects=True) html = resp.get_data().decode() cumulative_chart = json.loads(re.search("data_cumlinechart=(.*);", html).group(1)) line_chart = json.loads(re.search("data_linechart=(.*);", html).group(1)) assert resp.status_code == 200 assert sorted(item["key"] for item in cumulative_chart) == ["fail", "success"] assert sorted(item["key"] for item in line_chart) == ["fail", "success"]

tests/rest/admin/test_user.py