Airline is rendered twice when switching from insert to normal mode...

Hi bling,
in general, I like your plugin very much, but it's currently not working properly on my machine:
ubuntu 12.04
vim 7.3.429
The issue is that the airline is not refreshed but rendered a second time when switching from insertion to normal mode (see pic, note: since airline is rendered twice, the acutal file content (line 1) is not visible at the top of the window anymore...)
Furthermore is user input requested ("Press ENTER....")
.. and the ctermfg=1 is shown (do you have any idea, why this occurs or how I can fix this?)
Regards,
Bernd

can you please provide a gist of your vimrc?

https://gist.github.com/bk1285/3bec4c2d95b117f105eb
:scriptnames returns
1: /usr/share/vim/vimrc
2: /usr/share/vim/vim74/debian.vim
3: /usr/share/vim/vim74/syntax/syntax.vim
4: /usr/share/vim/vim74/syntax/synload.vim
5: /usr/share/vim/vim74/syntax/syncolor.vim
6: /usr/share/vim/vim74/filetype.vim
7: ~/.vimrc
8: /usr/share/vim/vim74/ftoff.vim
9: ~/.vim/autoload/pathogen.vim
10: /usr/share/vim/vim74/syntax/nosyntax.vim
11: ~/.vim/bundle/git/ftdetect/git.vim
12: /usr/share/vim/vim74/ftplugin.vim
13: /usr/share/vim/vim74/indent.vim
14: ~/.vim/bundle/atp/plugin/tex_atp.vim
15: ~/.vim/bundle/fugitive/plugin/fugitive.vim
16: ~/.vim/bundle/gundo/plugin/gundo.vim
17: ~/.vim/bundle/jedi-vim/plugin/jedi.vim
18: ~/.vim/bundle/nerdtree/plugin/NERD_tree.vim
19: ~/.vim/bundle/pep8/plugin/pep8.vim
20: ~/.vim/bundle/snipmate/plugin/snipMate.vim
21: ~/.vim/bundle/supertab/plugin/supertab.vim
22: ~/.vim/bundle/surround/plugin/surround.vim
23: ~/.vim/bundle/vdebug/plugin/vdebug.vim
24: ~/.vim/bundle/vim-latex-suite/plugin/SyntaxFolds.vim
25: ~/.vim/bundle/vim-latex-suite/plugin/filebrowser.vim
26: ~/.vim/bundle/vim-latex-suite/plugin/imaps.vim
27: ~/.vim/bundle/vim-latex-suite/plugin/libList.vim
28: ~/.vim/bundle/vim-latex-suite/plugin/remoteOpen.vim
29: /usr/share/vim/vim74/plugin/getscriptPlugin.vim
30: /usr/share/vim/vim74/plugin/gzip.vim
31: /usr/share/vim/vim74/plugin/matchparen.vim
32: /usr/share/vim/vim74/plugin/netrwPlugin.vim
33: /usr/share/vim/vim74/plugin/rrhelper.vim
34: /usr/share/vim/vim74/plugin/spellfile.vim
35: /usr/share/vim/vim74/plugin/tarPlugin.vim
36: /usr/share/vim/vim74/plugin/tohtml.vim
37: /usr/share/vim/vim74/plugin/vimballPlugin.vim
38: /usr/share/vim/vim74/plugin/zipPlugin.vim
39: ~/.vim/bundle/snipmate/after/plugin/snipMate.vim
40: ~/.vim/bundle/nerdtree/nerdtree_plugin/exec_menuitem.vim
41: ~/.vim/bundle/nerdtree/nerdtree_plugin/fs_menu.vim

i can't reproduce on osx, vim 7.4.258.  when i get a chance i'll spin up a VM to repro.

Hi Bling,
Thanks for your great work! I may provide a little help here. I'm encountering same(?) bug here. Steps to reproduce:

Open a file.
Move to a random line.
Enter INSERT mode.
Type some random characters.
Enter NORMAL mode.
Try to move the cursor with "j" or "k" key.
I get the duplicate status line as the screenshot above shows.

What I found is somewhat magical here: I am using different airline theme for "root" and my own account ("root" is using "base16" theme and my own account "light"), and this bug only occurs when I use "root" account to edit files. To my surprise, when I change the theme of "root" from "base16" to "light", the bug exists no more.
I am a total n00b to vim script so I do not know what's happening under the hood, but I guess there's something wrong in "base16.vim" which "light.theme" does not.

Hi Bling,
FYI: Enabling airline once again, I cannot reproduce the reported issue anymore...!
I'm a bit confused, since I did not change much in the meantime, except my colorscheme: From default to darkblue. But even if I use default, the issue does not occur anymore...
... so I enjoy your plugin in the meantime! :)
Regards,
Bernd

hmmm, good to know.  i'll close this for now.  if it happens again please re-open.  thanks.


ShallowWrapper::dive fails when Context and React.memo is used

Current behavior
dive() throws TypeError: ShallowWrapper::dive() can only be called on components when diving into component wrapped with Context Provider/Consumer and React.memo.
Started to work as expected when isMemo(type) was added to condition in ReactSixteenAdapter.js::isCustomComponent. Not sure if this is a right change so no PR yet.
Expected behavior
dive() dives into deepest components just as it does when React.memo is not used:
>>>  console.log(subject.dive().debug());
<App />

>>>  console.log(subject.dive().dive().debug());
<ContextConsumer>
  [function]
</ContextConsumer>

>>>  console.log(subject.dive().dive().dive().debug());

  {{ name: 'Guest' }}

Example
const UserContext = React.createContext({
  name: 'Guest',
});

const App = () => (
  <UserContext.Consumer>
    {(user) =>
{user}
}
  </UserContext.Consumer>
);

const AppMemoized = React.memo(App);

const WithProvider = () => (
  <UserContext.Provider value='test'>
    <AppMemoized />
  </UserContext.Provider>
);

const subject = shallow(<WithProvider />);

>>> subject.dive().debug();
<App />

>>> subject.dive().dive().debug();
TypeError: ShallowWrapper::dive() can only be called on components

Your environment
API

[x] shallow

Version

library
version

enzyme
3.9.0

react
16.8.6

react-dom
16.8.6

react-test-renderer
16.8.6

adapter (below)

Adapter

[x] enzyme-adapter-react-16

Interesting:
shallow(React.createElement(() => <App />)).dive() // works, is ContextConsumer
shallow(React.createElement(() => <App />)).dive().dive() // works, is App's div
shallow(React.createElement(() => <App />)).dive().dive().dive() // throws, should throw

shallow(React.createElement(() => <AppMemoized />)).dive() // throws, should be ContextConsumer

Looks like this is specific to memo, and doesn't have anything to do with createContext. Fix coming shortly.


Implemented Managed Identity login

Added support for logging in using either a system assigned Managed Identity or a user-assigned Managed Identity. This allows developers to run actions against Azure using self-hosted agents on Azure VMs.

@aksm-ms any feedback or updates on this? /cc @BALAGA-GAYATRI

@BALAGA-GAYATRI  any updates on this? thx

Hey @cmatskas !
This is in our backlog to test this change end to end and release the support for this, but it is not planned for the month of March. We are planning to look into this in the next quarter starting April.

Was just looking for this - I guess I need to wait a bit.

Hey @cmatskas ! This is in our backlog to test this change end to end and release the support for this, but it is not planned for the month of March. We are planning to look into this in the next quarter starting April. cc: @kaverma @N-Usha

Hi team, we're nearly the end of April. Any updates on when you'll be reviewing and hopefully committing this pls? Thanks

Any update on when this will be merged? This is a really useful change and would massively help everyone using self-hosted agents.

Hi, need this ASAP, any chance of this getting merged in pretty please? thanks very much

Would be great to see movement on this. Much needed change.

Thanks for your contribution folks. And really appreciate all the patience.
Would like to update that we are currently working with Azure Active Directory (AAD) team to get the login action working for MSI (Managed Identities) + OIDC (OpenID Connect) which is currently in private preview and will be soon announced more broadly. Since we'd like to promote OIDC to be the recommended way of authentication to Azure, would like to hold off merging this PR to support MSI for non-OIDC ways of auth.
We'll keep you updated on the release timelines of the Azure AD support for OIDC+MSI and the corresponding supporting in this azure/login action


Missing movies and shows...

I am somewhat disappointed that some shows are not available.

Missing shows and movies are out of my control as they are not stored by film.kace.dev, this application is simply a search engine for when it does exist.


Performance issue with discv5 Lookup service

Describe the bug
This happens in contabo-19 with subscribeAllSubnets flag: the Lookup service onSuccess takes 55% of cpu time after taking a profile in 5 minutes

Specifically, it's the sort function just to get the closest peer:

at this time, it's a map with 3504 entries
Expected behavior

Do a single for loop to get closest peer
Not sure if it's an issue in new discv5

Steps to Reproduce

Run node with subscribeAllSubnets flag and take profile

on the other regular node without subscribeAllSubnets flag, it takes 6.7 % cpu time at the same line of code mentioned above

CC @wemeetagain Discv5 needs benchmark testing asap

Thats from an older version of discv5, current master precalculates closestPeers
https://github.com/ChainSafe/discv5/blob/master/src/kademlia/lookup.ts#L150

Thats from an older version of discv5, current master precalculates closestPeers https://github.com/ChainSafe/discv5/blob/master/src/kademlia/lookup.ts#L150

Can you make sure that fix landed in Lodestar master?

on a regular node with low peer count, this issue takes 24% of the cpu time


[710] Healthcheck smoke test

Context
-https://trello.com/c/kQtyhpFk/710-m-healthcheck-smoke-test
Changes proposed in this pull request

Create a new spec directory for smoke tests
Setup a basic health check smoke test to assert response of system without stubs
Setup a tag to run specs without smoke tests by default
Run smoke tests in build workflow

Guidance to review

 [M] Healthcheck smoke test

 [M] Healthcheck smoke test


Exit on startup wit error: Failed to initialize log file

Can't launch program. It exits with this message:
FATA[0000] Failed to initialize log file.
exit status 1

Hi, I have a workaround for this
In your ionosphere directory make a logs directory
mkdir logs
depending on how you have set up your directory structure you may need
sudo mkdir logs
cceremuga - do you welcome pull requests - is there any particular branch you prefer them to be submitted on?

@g7gpr master is just fine! i'll make sure it gets out in a patch release.

I've got a beta2 branch pushing up shortly which has a fix for this in the Makefile. Until that's released, @g7gpr's workaround should be effective.

That's a neat way of doing it. I'll give it a test when it lands.

Build in latest beta2 branch will add a placeholder to the logs directory to resolve this


Print out some examples of masked language and entity modelling

Some sentences where we show a [MASK] token and print out the top 5 (?) guesses from daLUKE

Resolved in 5b6beb5e7c58b47a8376ab84f10d142ef11e9df5


More tests for Typescript part

Currently only covered by some e2e tests

Accidentally filed this in the wrong project.


Sanity check external time servers before applying config

References: https://github.com/ceph/ceph-salt/issues/241
Signed-off-by: Nathan Cutler<EMAIL_ADDRESS>

@swiftgist This draft PR adds a sanity check of the external time server hostnames provided by the user, which should make us fail fast in the case when the user specifies a hostname that isn't actually a functioning time server.

will take a completely different approach


[Tabular] How to get best model in cross-validation

I have applied cross-validation to my data. But each time a different best model is produced. Now how can I decide which model to choose as the final model?

Could you provide a bit more context? Are you using ImagePredictor?
One option is to use pre-release (pip install --pre autogluon) and use TabularPredictor for images to train ImagePredictor with cross-validation. This is going to be the preferred approach going forward into future releases as TabularPredictor includes a lot of more advanced functionality such as being able to select which model to use after training, weighted ensembling, stack ensembling, model refits / compression, distillation, and much more.
For examples, see the test files:
TabularPredictor for Images: https://github.com/awslabs/autogluon/blob/master/tabular/tests/unittests/models/test_image_predictor.py
ImagePredictor for Images:
https://github.com/awslabs/autogluon/blob/master/vision/tests/unittests/test_image_classification.py
These both are equivalent APIs. ImagePredictor is a model that can be specified as one of the models to train in Tabular, similar to how Tabular can train TextPredictor.
To perform cross-validation and hyperparameter tuning for ImagePredictor:
from autogluon.core.features.feature_metadata import FeatureMetadata
from autogluon.tabular import TabularPredictor
from autogluon.vision import ImageDataset

train_data, _, test_data = ImageDataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
feature_metadata = FeatureMetadata.from_df(train_data).add_special_types({'image': ['image_path']})
predictor = TabularPredictor(label='label').fit(
    train_data=train_data,
    hyperparameters={'AG_IMAGE_NN': {'epochs': 2}},
    feature_metadata=feature_metadata,
    num_bag_folds=10,  # Cross-validation + bagging
    hyperparameter_tune_kwargs='auto',  # automatically perform HPO
    time_limit=3600,  # tune for 1 hour
)
predictor.refit_full()  # Do this if you want faster inference speed (compresses bagged ensembles to single models)
leaderboard = predictor.leaderboard(test_data)

As a toy example:
from autogluon.core.features.feature_metadata import FeatureMetadata
from autogluon.tabular import TabularPredictor
from autogluon.vision import ImageDataset

train_data, _, test_data = ImageDataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')
feature_metadata = FeatureMetadata.from_df(train_data).add_special_types({'image': ['image_path']})
predictor = TabularPredictor(label='label').fit(
    train_data=train_data,
    hyperparameters={'AG_IMAGE_NN': {'epochs': 2}},
    feature_metadata=feature_metadata,
    num_bag_folds=2,  # Cross-validation + bagging
    hyperparameter_tune_kwargs='auto',  # automatically perform HPO
    time_limit=240,  # tune for 240 seconds
)
predictor.refit_full()  # Do this if you want faster inference speed (compresses bagged ensembles to single models)
leaderboard = predictor.leaderboard(test_data)

Leaderboard Output of toy example:
                           model  score_test  score_val  pred_time_test  pred_time_val   fit_time  pred_time_test_marginal  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order
0       WeightedEnsemble_L2_FULL      0.8625        NaN        3.807352            NaN  66.307523                 0.002244                0.000668           0.024282            2       True         10
1  ImagePredictor_BAG_L1/T3_FULL      0.8500        NaN        1.881381            NaN  33.392376                 1.881381                     NaN          33.392376            1       True          9
2       ImagePredictor_BAG_L1/T3      0.8125    0.72875        3.743492       4.727940  39.238338                 3.743492                4.727940          39.238338            1       True          4
3            WeightedEnsemble_L2      0.7750    0.74000        7.482968       9.652089  84.428074                 0.002512                0.000636           0.220631            2       True          5
4  ImagePredictor_BAG_L1/T0_FULL      0.4875        NaN        1.923727            NaN  32.890865                 1.923727                     NaN          32.890865            1       True          6
5       ImagePredictor_BAG_L1/T0      0.4875    0.40375        3.736964       4.923513  44.969106                 3.736964                4.923513          44.969106            1       True          1
6  ImagePredictor_BAG_L1/T1_FULL      0.4625        NaN        1.689301            NaN  21.777493                 1.689301                     NaN          21.777493            1       True          7
7  ImagePredictor_BAG_L1/T2_FULL      0.2375        NaN        1.553234            NaN  21.316831                 1.553234                     NaN          21.316831            1       True          8
8       ImagePredictor_BAG_L1/T1      0.2250    0.27000        3.092029       3.900787  27.369358                 3.092029                3.900787          27.369358            1       True          2
9       ImagePredictor_BAG_L1/T2      0.2250    0.28625        3.107244       3.984549  26.152906                 3.107244                3.984549          26.152906            1       True          3

You can now specify the model you want to use in predictions:
predictions = predictor.predict(test_data, model='WeightedEnsemble_L2_FULL')

Let me explain. Here is steps of 5 fold cross validation
skf = StratifiedKFold(n_splits=5)

acc=[]
test_pred=[]
for train_index, test_index in skf.split(data.iloc[:,0:-1], data.iloc[:,-1]):
  train=data.iloc[train_index,:]
  test=data.iloc[test_index,:]
  clf=TabularPredictor(label='Label',verbosity=0).fit(train)
  res=clf.leaderboard(test,silent=True)
  res['score_test']=res['score_test'].round(3)*100
  pred = clf.predict(test,model=res.model[0])
  accuracy=accuracy_score(test.Label,pred)
  acc.append(accuracy)
  print('best model ',res.model[0],' accuracy is ',accuracy)
  test_pred.append(clf.predict(test_df,model=res.model[0]))
print('average accuracy is',np.mean(acc))

It produces a different best model in each loop. SO I am confused which one to pick out of 5 in 5 fold cross-validation

I'd recommend letting AutoGluon do that work! This is largely what TabularPredictor is built around -> Efficiently splitting and validating data to construct powerful ensembles.
Do the following:
predictor = TabularPredictor(label='Label', eval_metric='accuracy').fit(data, num_bag_folds=5)
predictor.leaderboard()

This automatically performs cross-validation (num_bag_folds) in an optimized manner.
To get stronger results:
predictor = TabularPredictor(label='Label', eval_metric='accuracy').fit(data, presets='best_quality')
predictor.leaderboard()

best_quality preset performs multi-layer stack ensembling (which includes cross-validation).

If I used, presets='best_quality', do i need to use predictor.refit_full()? I mean do 'best_quality' also include this feature?

I'd recommend reading through the TabularPredictor API.
In general, you don't need to use predictor.refit_full(). refit_full is purely for speeding up inference latency (generally at the cost of a slight accuracy drop). Refer to the tutorial documentation for more info.

thanks


Add module xeniumranger resegment

PR checklist

[ ] This comment contains a description of changes (with reason).
[ ] If you've fixed a bug or added code that should be tested, add tests!
[ ] If you've added a new tool - have you followed the pipeline conventions in the contribution docs- [ ] If necessary, also make a PR on the nf-core/spatialxe branch on the nf-core/test-datasets repository.
[ ] Make sure your code lints (nf-core lint).
[ ] Ensure the test suite passes (nextflow run . -profile test,docker --outdir <OUTDIR>).
[ ] Usage Documentation in docs/usage.md is updated.
[ ] Output Documentation in docs/output.md is updated.
[ ] CHANGELOG.md is updated.
[ ] README.md is updated (including new tool citations and authors/contributors).

@khersameesh24 could you remove the files that are part of PR #36 . I do not want to run into unnecessary merge conflcits.


Cannot configure DSP to work with my openai api key

Getting this error, 'openai.error.InvalidRequestError: Engine not found', when I try and change dsp's lm configuration to my instance of openai's api key

Hmm either openai is down or you changed the model to an incorrect name?

Is this resolved? Feel free to share a minimal example to reproduce (but remember to remove your API key from it)

Gettng an error, 'AuthenticationError: Incorrect API key provided', even though the key is correct

Okay. Can you restart the notebook, set the key before any other cells are executed and try again?

Tried, still the same error :/

The LM wrapper in DSP is fairly thin. If you’re getting this error in DSP, are you also seeing the error when directly using openai?

I regenerated my openai api key, it's working now.


'hi' text on right side of toolbar

Describe the bug
There is text on the right side of the toolbar that says 'hi'
To Reproduce
It's been there since I installed it earlier today. I didn't do anything else.
Screenshots

Information
Discord channel: Stable
OS: Windows 10
Mod: BetterDiscord
Discord language: English

Turns out it's not because of Slate, but because of another theme I was running called BetterBadges.


Create separate pages on matrix.org website for Matrix clients, servers, bridges

At now all info located in one page https://matrix.org/docs/projects/try-matrix-now.html and can be located only by hashes like https://matrix.org/docs/projects/try-matrix-now.html#clients - this is bad for usability and search engines indexing.
Will be good to create separate pages for Matrix clients, servers, bridges with different static urls, and post on page https://matrix.org/docs/projects/try-matrix-now.html teasers of this pages with links to full page.

Related https://github.com/matrix-org/matrix-doc/issues/788


Cleared warnings with JS Hint

JS linting has been applied to the file "trouble.js" using JS Hint.
The original file has been renamed to "_trouble.js." This file raised warnings in JS Hint before linting and clean-up.
The linted and clean-up version is now "trouble.js." It's good to go! :+1:

The original code has been modified to the point where it's difficult to point out the additional issues introduced while still addressing the original issues with the code. Fixing the original linting issues doesn't require the level of modification this file currently has. I ran your code through the linting tool but there are still a fair amount of linting errors, many of which are new because of the modifications. Do you want me to grade it as is, or would you prefer to wait until I've covered the assignment at the beginning of the next class?

Hi Eric. I appears the latest version of your code hasn't been linted yet.

Hi James,
I am missing a step here and there.
I have done a sync last week and today, and apparently my local git sync is, well out of sync.
I do not see my updated version (for example, today's version contains my name and today's date: May 11, 2015) up here on Github.
So, until I can get this sorted, here's my updated trouble.js file for the JSHint linting exercise:
Eric
/******************************************************************************************************************/
// MODULE 01 - USING MODULE PATTERN - JSHINTING EXERCISE
// Eric Gould - resyncing, May 11, 2015
(function (gbl, $) {
'use strict';
gbl.dropdown = function (options) {
	var $dropdown = options.dropdown,
		$toggle = options.toggleButton,
		token = +new Date(),
		ns = options.namespace;
		elList = $('.dropdowns'),
		controlsMegaMenu = options.controlsMegaMenu,
		clickAnywhereToClose = options.clickAnywhereToClose,
		transitionEnd = gbl.utilities.whichTransitionEvent();

	$dropdown.addClass('gbl_dropdown').data('status', 'closed');
	$toggle.addClass('gbl_dropdown_trigger');
	$dropdown.attr('aria-expanded', 'false');
	$toggle.attr('aria-controls', $dropdown.attr('id'));
	$dropdown.wrapInner('

');

	function closeMegaMenu() {
		if (gbl.megaMenu && gbl.megaMenu.small && typeof gbl.megaMenu.small.close === 'function') {
			gbl.megaMenu.small.close();
		}
	}

	function setCloseHandler() {
		$(document).on('click.' + ns, function (e) {
			var $clicked = $(e.target);
			if (!$clicked.is($dropdown) && ($clicked.parents().filter($dropdown).length === 0)) {
				close();
			}
		});
	}

	function removeCloseHandler() {
		$(document).off('click.' + ns);
	}

	function processDropdownEls() {
		var list = Array.prototype.slice.call(elList);
	}

	function setDropdownHeight() {
		$dropdown.height($dropdown.find('.measureHeight').height());
	}

	function close() {
		dateStamp = +new Date();
		setDropdownHeight();
		$dropdown.data("status", 'closed');
		$dropdown.removeClass('gbl_dropdown_active');

  setTimeout(setDropdownHeight, 1000);

		$toggle.removeClass('gbl_dropdown_active');
		$toggle.focus();
		var dateStamp;
		$dropdown.attr('aria-expanded', 'false');
		setTimeout(function () {
			$dropdown.removeClass("no_transition");
			$dropdown.css('height', 0);
		}, 50);
		removeCloseHandler();
		$(document).trigger(ns + 'Close');
	}

	function open() {
		$dropdown.removeClass('no_transition');
		$dropdown.data('status', 'open');
		$dropdown.addClass('gbl_dropdown_active');
		$dropdown.focus();
		$toggle.addClass('gbl_dropdown_active');
		$dropdown.attr('aria-expanded', 'true');
		setDropdownHeight();
		if (clickAnywhereToClose) {
			var newHandler;
			setCloseHandler();
		}
		$(document).trigger(ns + 'Open');
	}

	function toggleDropdown(e) {
		e.preventDefault();
		e.stopPropagation();
		if ($dropdown.data('status') ==='closed') {
			open();
		} else {
			close();
		}
		if (controlsMegaMenu) {
			closeMegaMenu();
  }
	}

	$dropdown.on(transitionEnd, function () {
		if ($dropdown.data('status') === "open") {
			$dropdown.addClass('no_transition');
			$dropdown.css('height', 'auto');
		}
	});

	$toggle.on('click', toggleDropdown);

};

}(window.gbl || {}, jQuery));

:+1:


Update testimonial section

Description
i have made the changes to the testimonial section you mentioned now you can use the arrow to go the next testimonial
Related Issues
[Cite any related issue(s) this pull request addresses. If none, simply state “None”]

Closes #898

Type of PR

[ ] Bug fix
[ ] Feature enhancement
[ ] Documentation update
[ ] Other (specify): _______________

Screenshots / videos (if applicable)
[Attach any relevant screenshots or videos demonstrating the changes]
Checklist

[x] I have gone through the contributing guide
[x] I have updated my branch and synced it with project main branch before making this PR
[x] I have performed a self-review of my code
[x] I have tested the changes thoroughly before submitting this pull request.
[x] I have provided relevant issue numbers, screenshots, and videos after making the changes.
[x] I have commented my code, particularly in hard-to-understand areas.

Additional context:
[Include any additional information or context that might be helpful for reviewers.]

@varshith257 please review and merge this pull request

@varshith257 please review this pull request

@varshith257 please review and merge my pull request

@varshith257 please review this pull request and merge it


Fix conda capitalization in help text

I believe there was recently a decision and guidance on capitalization of conda. We should ensure we use that convention in the help text.
❯ conda project
usage: conda-project [-h] [-V] command ...

Tool for encapsulating, running, and reproducing projects with Conda environments
...

@jezdez tagging in case you know of a referenced CEP or similar I could link in here.

Found it! https://github.com/conda/conda/blob/main/CONTRIBUTING.md#conda-capitalization-standards


No loader for ts files

My configuration is in .ts files and when using the watch I get the error:
Error: No loader specified for extension ".ts"

Same for me. The workaround is to keep your main playwright.config.js file as JavaScript. But you can use .ts for any other file and it works fine.

same as me. any support for ts?


Addon: Let project pick dependencies of addons

Dependencies of addon package could be added to projects dependencies. Current Volto 7.7.1 does not.

You're supposed to add the addon as a project dependency. Adding it as an addon only activates its configuration loading and activates "Volto" for it.

Perhaps this could be better explained in documentation, though.

Oh, I now realize that you may not know about the "workspaces" configuration. You can allow the project to automatically pickup dependencies of development addons by adding the addon path in the "workspaces" key. See https://github.com/kitconcept/volto-form-builder/blob/master/package.json or https://github.com/collective/volto-example-project-addons/blob/usingmrsdeveloper/package.json


Making it possible to delete an  with display other than inline; modifyBlocks and forEachBlock: fn with arguments

Making it possible to delete an <img> with display other than inline
By default all browsers I could test (Chrome 29-39, Firefox 29-34, IE 10) fail to delete an inline element, whose display is set to something other than inline or inline-block in CSS (backspace works; delete, when the element is a part of a selection, also works; but delete, when the caret is right before the element and the range is collapsed, don't).
Although I can't think of a reason to make <b>, <sub> and other styling tags blocks, there might be cases when some of the images are desired to be so. This might come in handy to make images always occupy a separate line, and/or be centered with margin: x auto. This in fact can be done with the help of additional block wrappers, but the drawbacks are additional work (create a block, wrap an image, give the block necessary styles – instead of just styling and image with CSS) and fragility (deleting/merging may result in the wrapper losing the needed styles).

Calling functions from modifyBlocks and forEachBlock with arguments
Sometimes it is needed to modify a selection in several very similar ways, like "transform everything to H1", "transform everything to H2", "transform everything to blockquote" erasing all other block tags (or all markup). With the current Squire API we have to make separate functions for these all. It would be more convenient to make a single function and decide, which transformations to apply, based on an argument.
Hope this all is not too redundant. I just get to love Squire more and more and can't help wanting to help make it even better (= Seriously, thank you guys for such a wonderful tool.

First of all, sorry it took me so long to get to review this pull request. Works been a bit crazy.
Anyway, I've added a commit to fix the deleting <img> problem; I ended up rewriting your patch to make use of the existing moveRangeBoundariesUpTree helper fn to vastly reduce the amount of code required. Thanks for investigating the issue!
I've decided not to merge the modifyBlocks/forEachBlock change. The change only allowed you to supply a single argument, and I decided it was less flexible and more code than just doing the following:
... presume we have an instance of Squire called editor ...
var myModifyFn = function ( tagName, attributes, frag ) { ... };
editor.modifyBlocks( myModifyFn.bind( editor, 'h1', { 'class': 'foo' } ) );


Allow app to be used in horizontal mode (for the trolley tablets)

Turning your head 90 degrees to read debug output is going to get old.

Complete


[SN2004dn]

Sams as the comment on 04dk, and I think this will be true for all SNe with both Drout11 and CCCP photometric references: there are 2 V and 2 R band photometries, that differ by 1 magnitude, but they are both CCCP photometry: the same data, different reduction. One of them must be superseded. I think the Drout11, which is published, should be final, and the CCCP one should be the quick and dirty reduction, but I am not sure, and there is no date on the reduction webpage.

This issue was moved to astrocatalogs/supernovae#12


TWS Connection doesn't work

TWS API (port 7497) throws errors when running https://github.com/wboayue/rust-ibapi/blob/main/examples/market_data.rs
ERROR ibapi::transport] error reading next message (shutting down): Io(Os { code: 10060, kind ...
The code works fine with the Gateway.
This means one needs to disconnect from the Desktop to log into the Gateway.

Was not able to reproduce this with TWS or the Gateway on my system. I suspect this is because TWS uses more resources and may slow responses on your system. Updated the main branch to allow more time to receive data. Give that a try.

Also are you on a Windows machine? I'm on a Mac, it might be something platform specific.

I think I am running into a pacing violation.  What I am working on is custom scanners and I'm looking at ~600 symbols.  When I add sleep time, it gets further.  I need to explore smarter pacing strategies.

Yes, Windows.
On Wed, Nov 6, 2024 at 10:08 PM Wil Boayue @.***> wrote:

Also are you on a Windows machine? I'm on a Mac, it might be something
platform specific.
—
Reply to this email directly, view it on GitHub
https://github.com/wboayue/rust-ibapi/issues/157#issuecomment-2461223778,
or unsubscribe
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You are receiving this because you authored the thread.Message ID:
@.***>

Code updated to handle Windows specific codes.


tf.contrib.data.rejection_resample not balancing class/freq on random crops

randomly sampling / cropping data seems to break rejection_resample ==> meaning it won't do any re balancing of the class probability -- see these two simple feeders as example:
The first randomly samples data with tf.random_uniform and breaks rejection_resample -- the second with static random data having  the same distribution is correctly resampled (output has p(class=0)=0)
This creates issues when trying to real-time sample/crop/data-augment and at the same time rebalance classes with on the same pipeline
def get_data_breaks(self, batch_size, iihook, this_set='train'):
    def sample(data, label): # sample detection window inside chunk
        xx = tf.cast(tf.random_uniform([1])*self.class_num, tf.int32)[0]
        with tf.control_dependencies([xx]): tf.Print(xx , [xx], 'xx>>')
        return xx, xx

    initial_dist=[1.0/self.class_num for cc in range(self.class_num)]
    classes = np.random.choice(self.class_num,20000,p=initial_dist)

    data_ph = tf.placeholder(classes.dtype, classes.shape)
    labels_ph = tf.placeholder(classes.dtype, classes.shape)
    dataset = tf.data.Dataset.from_tensor_slices((data_ph, labels_ph))

    dataset = dataset.map(sample, num_parallel_calls=1)

    target_dist=[1.0/self.class_num for cc in range(self.class_num)]
    target_dist[1]+=target_dist[0] ; target_dist[0]=0
    print('target-dist>>', target_dist)
    initial_dist = None

    dataset = dataset.apply(tf.contrib.data.rejection_resample(
                class_func=lambda c, _: c,
                target_dist=target_dist,
                initial_dist=initial_dist,
                seed=42)).map(lambda a,b: b)

    dataset = dataset.repeat(None)
    iterator = dataset.make_initializable_iterator()
    iihook.iterator_initializer_func = lambda sess: sess.run(iterator.initializer,
                    feed_dict={data_ph: classes, labels_ph: classes})

    return iterator.get_next()

def get_data_works(self, batch_size, iihook, this_set='train'):

    initial_dist=[1.0/self.class_num for cc in range(self.class_num)]
    classes = np.random.choice(self.class_num,20000,p=initial_dist)

    data_ph = tf.placeholder(classes.dtype, classes.shape)
    labels_ph = tf.placeholder(classes.dtype, classes.shape)
    dataset = tf.data.Dataset.from_tensor_slices((data_ph, labels_ph))

    target_dist=[1.0/self.class_num for cc in range(self.class_num)]
    target_dist[1]+=target_dist[0] ; target_dist[0]=0
    print('target-dist>>', target_dist)
    initial_dist = None

    dataset = dataset.apply(tf.contrib.data.rejection_resample(
                class_func=lambda c, _: c,
                target_dist=target_dist,
                initial_dist=initial_dist,
                seed=42)).map(lambda a,b: b)

    dataset = dataset.repeat(None)
    iterator = dataset.make_initializable_iterator()
    iihook.iterator_initializer_func = lambda sess: sess.run(iterator.initializer,
                    feed_dict={data_ph: classes, labels_ph: classes})

    return iterator.get_next()

FWIW, I'm still debugging but simplified your example:
import numpy as np
import tensorflow as tf

class_num=8
data_np = np.random.choice(class_num,20000,p=[1.0/class_num]*class_num)

def sample(_):
  xx = tf.cast(tf.random_uniform([1])*class_num, tf.int32)[0]
  return xx

def fix_sample_and_rebalance(which='works'):
  data_tensors = tf.constant(data_np, dtype=tf.int32)
  dataset = tf.data.Dataset.from_tensor_slices(data_tensors)

  target_dist = [1.0/class_num] * class_num
  target_dist[1]+=target_dist[0] ; target_dist[0]=0
  print('target-dist>>', target_dist)

  if which == 'breaks':
    dataset = dataset.map(sample)

  dataset = dataset.apply(tf.contrib.data.rejection_resample(
      class_func=lambda c: c,
      target_dist=target_dist))
  dataset = dataset.map(lambda a, _: a)

  return dataset.make_one_shot_iterator().get_next()

def run_thing(which):
  tf.reset_default_graph()
  with tf.Session() as sess:
    get_next = fix_sample_and_rebalance(which)
    returned = []
    for kk in range(0,100):
      try:
          sample=sess.run(get_next)
          returned.append(sample)
      except tf.errors.OutOfRangeError:
          break
    print(np.bincount(np.array(returned)))

run_thing('works')  # this works
run_thing('breaks')  # this doesn't

Nagging Assignee @joel-shor: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.

Nagging Assignee @joel-shor: It has been 14 days with no activity and this issue has an assignee. Please update the label and/or status accordingly.


Feature/app translation dynamic imports

This branch includes the original PR from @biwano, but includes changes to dynamically fetch individual locale file when the locale is changed in the UI. This means that each language file doesn't get bundled into the main js chunk and is it's own js file.
See the screenshots below:
The default "en" language is loaded (only the "en-messages" chunk gets loaded).

The user chooses the french language (only then does the "fr-messages" chunk get loaded).

@biwano are you checking this out?

Hello, Yes I merged it back in my branch.

Hello, Yes I merged it back in my branch.

Nice one. Happy for me to close off this branch then?

Yes. Thank you. Saved me a lot of time.


Otp 27

Erlang/OTP 27 introduces two new language elements, triple-quoted strings (docstrings) and sigils (type prefix for string literals).
Two simple example modules to test the syntax highlight with the original and updated TextMate language definition:
-module(docstring_examples).

-compile(export_all).

-doc """
     Docstring examples
     """.
f() ->
    """
  Line "1"
  Line "2"
  """ = "Line \"1\"
Line \"2\"",
    X = lists:seq(1,3), % just to check is syntax highlight is still ok

    """
      First line starting with two spaces
    Not escaped: "\t \r \xFF" and """

    """ = "  First line starting with two spaces
Not escaped: \"\\t \\r \\xFF\" and \"\"\"
",
    X = lists:seq(1,3), % just to check is syntax highlight is still ok

    """""
""""
FIXME: previous line is not a closing delimiter because opening-closing delimiter have five double quote characters
""""" = "\"\"\"\"",
    X = lists:seq(1,3), % just to check is syntax highlight is still ok

    ok.

-define(THIS_IS_THE_END, "end"). % just to check is syntax highlight is still ok

-module(sigil_examples).

-compile(export_all).

-doc """
     Sigil examples
     """.
-spec f() -> ok.
f() ->
     ~(monkey ~2..0b\n),
    ~b(monkey ~2..0b\n),
    ~B(monkey ~2..0b\n), % verbatim
    ~s(monkey ~2..0b\n),
    ~S(monkey ~2..0b\n), % verbatim

     ~{monkey ~2..0b\n},
    ~b{monkey ~2..0b\n},
    ~B{monkey ~2..0b\n}, % verbatim
    ~s{monkey ~2..0b\n},
    ~S{monkey ~2..0b\n}, % verbatim

     ~[monkey ~2..0b\n],
    ~b[monkey ~2..0b\n],
    ~B[monkey ~2..0b\n], % verbatim
    ~s[monkey ~2..0b\n],
    ~S[monkey ~2..0b\n], % verbatim

     ~<monkey ~2..0b\n>,
    ~b<monkey ~2..0b\n>,
    ~B<monkey ~2..0b\n>, % verbatim
    ~s<monkey ~2..0b\n>,
    ~S<monkey ~2..0b\n>, % verbatim

     ~/monkey ~2..0b\n/,
    ~b/monkey ~2..0b\n/,
    ~B/monkey ~2..0b\n/, % verbatim
    ~s/monkey ~2..0b\n/,
    ~S/monkey ~2..0b\n/, % verbatim

     ~|monkey ~2..0b\n|,
    ~b|monkey ~2..0b\n|,
    ~B|monkey ~2..0b\n|, % verbatim
    ~s|monkey ~2..0b\n|,
    ~S|monkey ~2..0b\n|, % verbatim

     ~'monkey ~2..0b\n',
    ~b'monkey ~2..0b\n',
    ~B'monkey ~2..0b\n', % verbatim
    ~s'monkey ~2..0b\n',
    ~S'monkey ~2..0b\n', % verbatim

     ~"monkey ~2..0b\n",
    ~b"monkey ~2..0b\n",
    ~B"monkey ~2..0b\n", % verbatim
    ~s"monkey ~2..0b\n",
    ~S"monkey ~2..0b\n", % verbatim

     ~`monkey ~2..0b\n`,
    ~b`monkey ~2..0b\n`,
    ~B`monkey ~2..0b\n`, % verbatim
    ~s`monkey ~2..0b\n`,
    ~S`monkey ~2..0b\n`, % verbatim

     ~#monkey ~2..0b\n#,
    ~b#monkey ~2..0b\n#,
    ~B#monkey ~2..0b\n#, % verbatim
    ~s#monkey ~2..0b\n#,
    ~S#monkey ~2..0b\n#, % verbatim

    X = lists:seq(1,3), % just to check is syntax highlight is still ok
    ok.

-spec g() -> ok. % just to check is syntax highlight is still ok
g() ->
    ~"""
    monkey ~2..0b\n
    business
    """,
    ~b""""
    monkey ~2..0b\n
    """
    business
    """",
    ~B"""""
    monkey ~2..0b\n
    """
    business
    """"",
    ~s""""""
    monkey ~2..0b\n
    """
    business
    """""",
    ~S"""""""
    monkey ~2..0b\n
    """
    business
    """"""",
    X = lists:seq(1,3), % just to check is syntax highlight is still ok

    <<"\"\\µA\""/utf8>> = <<$",$\\,194,181,$A,$">> =
        ~b"""
          "\\µA"
          """ = ~b'"\\µA"' =
        ~B"""
          "\µA"
          """ = ~B<"\µA"> =
        ~"""
          "\µA"
          """ = ~"\"\\µA\"" = ~/"\\µA"/
    X = lists:seq(1,3), % just to check is syntax highlight is still ok

    [$",$\\,$µ,$A,$"] =
        ~s"""
          "\\µA"
          """ = ~s"\"\\µA\"" = ~s["\\µA"] =
        ~S"""
          "\µA"
          """ = ~S("\µA") =
        """
        "\µA"
        """ = "\"\\µA\""
    X = lists:seq(1,3), % just to check is syntax highlight is still ok

    ok.

-define(THIS_IS_THE_END, "end").

Thanks! Not sure if there's a way to have programmatic tests for the grammar. Either way, would it make sense to commit your examples in a dedicated folder, even if only for visual inspection?

Before/after, for reference.
Before:

After:

@KornelH This is an amazing and very welcome contribution! Let me know when you're ready, so I can merge this. I also like the idea of splitting this into multiple PRs (like you already did for the io:format parts). 🚀

Unfortunately, I neither know any automatic way to test syntax highlight. Therefore, I'll add the example files for visual inspection similar to pull request #10. Also, I will break this commit into two separate commits about docstrings and sigils. I don't think these will change in the final version of OTP 27 so I will do it in 1-2 days.

As it happens I was also looking into providing the changes in this PR and started looking at how to do CI for the grammar. I've opened #11 with a first draft how we can run tests.

Another things I was looking at was seeing if we can provide an included grammar for docstrings so that they can be highlighted using the Markdown highlighter?

Another things I was looking at was seeing if we can provide an included grammar for docstrings so that they can be highlighted using the Markdown highlighter?

That would be awesome!

A small note, as far as I understand it, only -moduledoc and -doc have Markdown as default format, not all triple-quoted strings.
https://erlang.org/documentation/doc-15.0-rc2/doc/system/documentation.html

Any literal string (normal ”, triple quoted ””” or sigil ~”””) in a doc or moduledoc attribute is by default markdown.

Yes, that's ok, any string is by default considered as Markdown in -doc and -moduledoc, but triple-quoted strings can be used anywhere else. So, what I wanted to say is only that, we shall include Markdown only for these two attributes, not for triple-quoted strings in general.

That's true. However in simple strings ("...") all special characters (even ") has to be escaped. Embedding Markdown into simple strings with escaping all special characters feels a bit complex. However in verbatim strings (e.g. ~S"..." or triple-quoted strings) I guess it's doable and useful.

Well, I can split into to requests.

@garazdawi, I was playing a bit with embedding Markdown into -doc and -moduledoc attributes but the result feels very awkward. Personally, I feel it just disturbs me a lot and totally steals the focus from the source code. The below example is from OTP, lists:enumerate/1. Originally, I thought it's a good idea to embed Markdown but now I don't think that anymore. 🙁

hmm, yeah that was not very nice. Pretty neat that it highlights erlang examples in the markdown though :)
Do you know if it possible to configure the markdown style individually? Would you mind opening a PR (or just pointing to a branch) with what you have so that I can have a look at how it looks?

I'm just learning it too and I have no idea if it's possible e.g. set a custom background or shade to embedded languages so it visually would look like a "comment blob" just like eDoc comments, and so it doesn't steal your attention unless you really want to read the documentation. I'll share my grammar changes soon.

It could also be the case this it becomes better if you indent the docs with 4 spaces. That way you get a visual queue that it is something different.

I wonder how do other languages (e.g. Elixir) tackle this. Any approach which we can take inspiration from? Would it make sense to use the concept of a "scope" that could then be styled via themes?

There's also something called embedded language. Maybe we could leverage that one?

While digging about I found this comment: https://github.com/microsoft/vscode/pull/169956#issuecomment-1370049360
Seems like the way rust/js/ts does it is by having Markdown highlighting as a separate VS Code extension. That way the user can decide if they want it or not and possibly configure how it should look as well.

There's also something called embedded language. Maybe we could leverage that one?

This is what @KornelH has done in the prototype.


Bug in reactionAddCommand. Don't know but the problem is the reactionAddCommand command has stopped working after this new update and due to this the bot is automatically restarting again and again. Like it happened 20 times till now. Please fix the bug in reactionAddCommand as i checked there is no host issue and everything was working perfectly before update!! Please i request to check and fix it.

Type

[ ] Functions: <Function Name>
[x] Callbacks: \reactionAddCommand
[ ] Handlers: <Handler Type>
[ ] Others: ______

Code used:
Your Code Here

More:

Aoi.js version:3.0.0
Node.js version:14.17.0
Operating system:linux

Screenshots (If any): Image or Link

Also i just tried removing bot.onReactionAdd() , bot.onReactionRemove() and reactionAddCommand and the bot has now stop re-starting. So the main error is in these. Please fix it as i was using it for tickets and now it can't work.😭😢

@BeastBoyADI for now remove ur reactionAddCommand code
we wilk check what exactly got bugged


cannot start package within a node server

The documentation says that qr-code-styling/lib/qr-code-styling.common.js should be referenced but that file does not exists.
When trying to use it from server it says
Error: Cannot find module 'qr-code-styling/lib/qr-code-styling.common.js'
and when i substitute this reference
const { QRCodeStyling } = require("qr-code-styling/lib/qr-code-styling.common.js");

for this
const { QRCodeStyling } = require("qr-code-styling/lib/qr-code-styling.js");

it says
ReferenceError: self is not defined
at Object. (C:\www\qrGenerator\node\node_modules\qr-code-styling\lib\qr-code-styling.js:1:208)

The documentation says that qr-code-styling/lib/qr-code-styling.common.js should be referenced but that file does not exists.
When trying to use it from server it says
Error: Cannot find module 'qr-code-styling/lib/qr-code-styling.common.js'
and when i substitute this reference
const { QRCodeStyling } = require("qr-code-styling/lib/qr-code-styling.common.js");

for this
const { QRCodeStyling } = require("qr-code-styling/lib/qr-code-styling.js");

it says ReferenceError: self is not defined at Object. (C:\www\qrGenerator\node\node_modules\qr-code-styling\lib\qr-code-styling.js:1:208)

Check this https://www.npmjs.com/package/qr-code-styling-node
Leaving it here in case others encounter this issue.
For implementation, check this issue comment: https://github.com/kozakdenys/qr-code-styling/issues/9#issuecomment-932095337


Replace Babel with ESBuild

🛠 Summary of changes
Replaces/consolidates Babel as the JavaScript code transpiler with esbuild.
Why?

Consolidates and ensure consistency with existing ESBuild usage for documentation / test build
It's faster, thereby shortening feedback loop in local development and CI build runtime
Reduces dependency tree depth (see lines removed), which helps maintainability by eliminating a common source of security advisories
ESBuild is well-aligned to the project's goals, targeting semi-modern browsers with bundling features, and support for potential future shift to TypeScript

📜 Testing Plan

npm install
npm run build:pkg
Observe local build/esm and build/cjs folders include transpiled JavaScript in respective ESM and CommonJS formats

I think I'd like to do this, but I'll hold on it for now 'til we have other breaking changes to release.
It may end up having a nice downstream effect of optimizing bundle sizes, since the transpiled output currently may be inflating the size due to current support for IE11.

Reviving this as there's plans for a new major version, which we can lump this breaking change into.
See https://github.com/18F/identity-design-system/pull/390#issuecomment-1883779930


Anomalously high scores on GPQA

When running a small Qwen model through GPQA, I am getting anomalously large scores compared to much larger models like Llama-70b-chat in the paper. For reference, here's the values from the paper (see also this blog post for some other model comparisons):

Now, when I run both 0-shot and 5-shot evals via:
# 0-shot
accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py --tasks="lighteval|gpqa|0|0" --output_dir "./scratch/evals" --model_args "pretrained=Qwen/Qwen1.5-0.5B-Chat" --override_batch_size 1

# 5-shot
accelerate launch --multi_gpu --num_processes=8 run_evals_accelerate.py --tasks="lighteval|gpqa|5|0" --output_dir "./scratch/evals" --model_args "pretrained=Qwen/Qwen1.5-0.5B-Chat" --override_batch_size 1

I get:

Task
Version
Metric
Value

Stderr

lighteval:gpqa:0
0
acc
0.2567
±
0.0207

lighteval:gpqa:5
0
acc
0.2679
±
0.0209

These values are anomalously large for such a small model and I wonder if there's some issue in how we aggregate results?
A related question is whether we report the average accuracy across the extended / main / diamond sets or something else? I noticed in the Hub dataset that 4 configs are provided, but the task table just specifies the train split which I suspect just loads everything (including the expert annotations)

I will also check Mixtral to see if the scores a much different

We select gpqa_main as subset.

Side note: 0.25 is random performance

Closing this since it was a user confusion on my side about the benchmark.


Unable to use the save model for prediction

I am trying to use Transformer model with distributed training. The codes are available in this repository.  I can train the model by running following command python distributed_train.py after training It's saving the model in the model dir as the pictures shows.

I am trying to load the model from saved model and send some data for prediction but I am ended up getting an error shown in the screenshot below

I am interested to know how should I send data for prediction?
Any help regarding this will be greatly appreciated

@kuetuofa ,
Can you share a simple and standalone code to reproduce the issue reported here? Thanks!

Hi @oanush Thanks for responding. Unfortunately the code is not simple that's why I put it in the following repository. Will you able to check it out from there? It's a public repo.

@kuetuofa This is not Build/Installation or Bug/Performance issue. Please post this kind of support questions at Stackoverflow. There is a big community to support and learn from your questions. GitHub is mainly for addressing bugs in installation and performance. Thanks!


[error] Terraform backend initialization for AzureRM only support service principal authorization

Describe the bug
After converting Service Connection to the new one using workload identity federation with openid connect terraform cli tasks return error: [error] Terraform backend initialization for AzureRM only support service principal authorization
To Reproduce
Steps to reproduce the behavior:

Create SC using new Workload Identity federation (automatic)
Provide new SC name as backendServiceArm

    - task: TerraformCLI@0
      displayName: 'terraform init'
      inputs:
        command: init
        backendType: azurerm
        workingDirectory: '$(System.DefaultWorkingDirectory)/terraform/'
        ensureBackend: false
        backendServiceArm: ${{ parameters.backendServiceArm }}

Expected behavior
terraform init should work
Screenshots
If applicable, add screenshots to help explain your problem.
Pipeline Logs
##[error]Terraform backend initialization for AzureRM only support service principal authorization
Agent Configuration

OS: ubuntu
Self Hosted
Terraform version used (Default for hosted agent is acceptable) 1.5.6
AzureCLI version used (Default for hosted agent is acceptable or N/A) 1.0.2
extension version: 1.0.2 (Latest)

Additional context
Same step works from SC created using for example "Service principal (automatic)"

This is an odd one as that error message only existed in the previous version of the task.
See the diff where it was removed here: https://github.com/jason-johnson/azure-pipelines-tasks-terraform/commit/edbc47a3eede0609881bb1e5c04ec44fdcbbeeb8
Are you sure you have version 1.0.2?
I any case, even if you pull the correct version, there is another bug at the moment that we are resolving, should be fixed today or tomorrow.

Hi @rhollins. I am just following up on this. We have just release version 1.0.3 of the task, which fixes a bug unrelated to your reported issue, but relevant to Workload identity federation. Please can you try targeting that version and confirm all is good. Thanks

Turns out it was a mistake on my site by not using major version 1 but 0 when I referenced the task.
TerraformCLI@0 - was using Version      : 0.7.12
TerraformCLI@1 - is using Version      : 1.0.4
Now it works fine with a workload identity federation.
Thanks


WatchKit Framework Support

With WatchKit 2.0, there is a new WatchFramework type like iOS and Mac. It would be cool to see a framework for it as well.

Could you or someone else help by implementing this?

Been looking into this, currently looking at the best practices to do this.

Would be happy to support it but am unsure what the current state in the iOS world is to support this in the same codebase.

So in order for this to work it requires #823 as only NSURLSession is available on watchOS. As for the framework, it requires creating a second Framework target and adding a preprocessor macro to define WatchKit, from there it is just adding preprocessor ifs in the code. Which is easy, except that it requires NSURLSession.

I am planing to do some refactoring in 4.0 that will involve integrating AFNetworking as network component, so we will gain access to their NSURLSession component. I will let you know as soon as we have that in place.

Just merged the watchOS support into the 4.x branch. Will definitely be included in the next release. In the meantime, you can take it from there and see if it works for you - please note that is in-progress work which breaks compatibility with the older versions of SDWebImage.


some errors (due to segfaults?) are not interpreted correctly by the CI

The PRs https://github.com/scipy/scipy/pull/4729 and https://github.com/scipy/scipy/pull/4739 are 'green' (indicating that they pass TravisCI tests) even though a config in the TravisCI build matrix aborts early due to an error involving numpy's relaxed strides checking.  I don't know the best way to fix this.

Looks like nose does nothing special for segfaults now: https://github.com/nose-devs/nose/issues/58. What we do in runtests.py and in numpy.testing.NumpyTestProgram looks fine, so probably needs fixing in nose.

@argriffing log files on those PRs do not appear to show any segfaults?
Can you point to a Travis-CI log that shows (i) a segfault, and (ii)
successful result. The current runtests.py and nose a priori deal
properly with segfaults.

gh-4729 has a segfault with numpy master (python 3.3 build in that PR): https://travis-ci.org/scipy/scipy/jobs/58344068

That's actually not a segfault --- it seems xerbla is terminating the
process with exit code 0. I don't know if there's any other way to catch
this than to use 'tee' to put the output to a log file, and grep the
logfile afterwards for "On entry to .* parameter number .* had an
illegal value".

Or even better, grep the output for the final summary message printed by
nose, to ensure the test run has been completed.

Or even better, grep the output for the final summary message printed by nose, to ensure the test run has been completed.

Maybe require a line that looks similar to
Ran 17703 tests in 323.961s

fixed by https://github.com/scipy/scipy/pull/4792


Support running integration tests without restarting a daemon

The current develop->integration test cycle is very slow.
This is some "low hanging fruit" that enabled a faster dev cycle when making changes to integration tests (adding new ones and fixing existing ones), or making changes to client code. It supports running the test suite without having to restart the daemon (and reloading all the fixtures), which often takes longer than the test run itself.
To use this run the following:
      $ BINDDIR=. make shell
      $ hack/make.sh binary test-integration-shell
      $ source hack/make/.integration-test-helpers

      # now tests can be run multiple times without stopping the engine
      $ TESTFLAGS="..." bundle_test_integration_cli

It also happens to remove some integration-test-specific code out of the common runner (hack/make.sh) into a more specific helper lib. I believe this improves the code quality of hack/make.sh because there is less unrelated code cluttering up the main runner.
Any suggestion on where this could be documented?

Any suggestion on where this could be documented?

There's some information in our contributors guide, but perhaps this is too detailed for that
https://docs.docker.com/opensource/project/test-and-docs/
(repository is: https://github.com/docker/opensource/blob/master/docs/project/test-and-docs.md)

/cc @tianon

That's pretty cool.
LGTM

could we add source hack/make/.integration-test-helpers to the container bashrc when doing test-integration-shell so we don't have to remember to do it? :angel:

That sounds like a good idea.   I've made that change and updated the PR description.

Seems sane to me, just one comment about moving the /etc/bash.bashrc modification directly into the Dockerfile. :smile:

Ok, I've made that change as well.

LGTM :+1:
This is going to be so helpful to reduce downtime, thanks!

LGTM :frog: :+1:


transformer-remark: Populate excerpt with text

As mentioned in #237, the excerpt field is empty by default. Configuration can be passed to gray-matter to populate this field (#284), however gray-matter excerpts are not parsed when extracted this way.
This PR addresses this as follows:

If the user has already got a gray-matter config to populate excerpt, it does not overwrite this value.
If excerpt is empty, it populates it with:

Raw text, as taken from sanitizeHTML().
Newlines are removed and replaced with spaces.
The text is limited to at most length characters, but will not snip mid-word.

excerpt is added to the schema, including a length argument, with a default of 200 characters.

Thanks, @onfe. Yes, I think it would be nice to generate an except if it doesn't exist :)
Should the length argument also limit the node.excerpt field? Or would one expect to retrieve the full excerpt since it's not auto-generated? I'm just thinking about a scenario where the node.excerpt has content on each node. Then the length argument wouldn't do anything.

Sure, so I was thinking along the lines that someone set a custom excerpt, you would've already chosen a desired length and it would be confusing if it got snipped.
Alternatively, length could always apply but be disabled by setting it to -1, and that's a fairly simple change.

Yeah, I agree. Maybe the description for the length argument could say "Maximum length for the generated excerpt (characters)" instead to make it clear. Everything else seems good to me.

Pulled upstream so we're all up to date, and changed the description based on what you suggested :+1:. Merging this should close #237.


Generate schema from JSON sample

Documentation

[X] I have consulted the documentation, and my question isn't answered there.

Nuget Package
JsonSchema.Net
Package Version
7.2.3
How can I help?  Please provide as much context as possible.
I am successfully using JsonSchema.Net to validate HTTP request bodies. There is a UI that allows users to set the JSON Schema to use for a given endpoint. It would be great to add "Generate Schema from Example JSON" capability. Is there existing capability in JSONSchema.NET to do that? I couldn't find any mention of it in the docs. Before I build it myself I wanted to make sure I was not re-inventing the wheel.
Thanks
Jim
Code of Conduct

[X] I agree to follow this project's Code of Conduct

Hey there.  Thanks for the idea.
No, json-everything doesn't currently support generating a schema from data because it's not possible to generate quality schemas from data.  For example:
{
  "firstName": "John",
  "lastName": "Doe",
  "age": 37
}

If we tried to generate a schema from the above data, we might get something like this:
{
  "type": "object",
  "properties": {
    "firstName": {"type": "string"},
    "lastName": {"type": "string},
    "age": {"type": "integer"}
  }
}

And that can work, but it's not very good.  It validates some structure, but it doesn't really do anything to validate the range of possible values.

Are there length or format requirements on first and last name?
Should the age have a minimum (i.e. 0) or a maximum?
Is it valid for the age to be a floating point number, but the example we have just happens to be an integer?
Are other properties allowed?
Are any properties required?

We can't answer any of these from a single JSON instance.  I have seen a couple projects that can infer a schema given a large data set, but even that depends on widely varying data.

All true!!! But it is a good starting point for them to flesh out from there. I will make assumptions like all items in the HTTP request are required and no additional properties are allowed. In my use case those are reasonable assumptions. Generally, not so much. They are then free to relax those assumptions when they edit.
The real answer to my question is no, but thank you for the full explanation.
Thanks
Jim

My experience with giving the user a starting point has been that they take what's generated as production-ready and run with it, often just generating a schema at runtime.  I (and the rest of the JSON Schema team) highly recommend against this practice, and I say as much in my docs, but it hasn't stopped anyone from doing it anyway.

I did see that! I just didn't put 2 and 2 together. Thank you for your advise.


Add instructions for building and testing with Rust

Right now, it's a manual search and replace, I would like to build the docs as I find myself the answers to enable my own rust workflows beyond what https://github.com/foursixnine/cndk8/blob/main/.github/workflows/rust.yml is already doing.
was initially: https://github.com/github/docs/pull/35721 but panik.
PoC: Add documentation for rust workflows

Why:
Because there's no documentation on how to properly handle rust stuff on github
would take a look later at github/docs/issues/23181 and see what else fails
What's being changed (if available, include any code snippets, screenshots, or gifs):
So far adding documentation based off the Golang one, and my own journey through https://github.com/foursixnine/cndk8/ and [isototest-ng](https://github.com/os-autoinst/isotest-ng/
Commits, PR's will be listed
Check off the following:
Still WIP:

[ ] Update filename
[ ] A subject matter expert (SME) has reviewed the technical accuracy of the content in this PR. In most cases, the author can be the SME. Open source contributions may require a SME review from GitHub staff.
[ ] The changes in this PR meet the docs fundamentals that are required for all content.
[ ] All CI checks are passing.

@foursixnine Thanks so much for opening a PR! I'll get this triaged for review ✨Just a quick tip - we'd recommend opening an issue before raising a PR

absolutely! I'm behind on creating the issue, was getting: https://github.com/foursixnine/cndk8/actions/runs/12436473960 in shape before, so I'll get back to creating the issue over the weekend :) to not lose momentum!

especially for larger scale changes such as this one 💛 This will let our team determine how best to proceed with the concern / issue you are raising, thank you!

awesome! I might need guidance, as I'm only looking at only gh community

@foursixnine Ah gotcha, thank you! ✨ Well I hope you get to have a restful and wonderful weekend 💛

@foursixnine Ah gotcha, thank you! ✨ Well I hope you get to have a restful and wonderful weekend 💛

A lot of rest is being achieved :D - created https://github.com/github/docs/issues/35747 as the issue for this and following Pull requests (if any) related to this.

@foursixnine Ah gotcha, thank you! ✨ Well I hope you get to have a restful and wonderful weekend 💛

A lot of rest is being achieved :D - created https://github.com/github/docs/issues/35747 as the issue for this and following Pull requests (if any) related to this.

It's referred as WIP/PoC in a few places (although not marked as "Draft" so it appears as ready for review…) therefore this may be preliminary, but to make sure it's tracked in case:

Thanks for the review, marked as draft for now - will re-request reviews once final draft ins ready (I started with a copy of the golang one, so that's why go is all over the place :P

Ok, https://crates.io/crates/cndk8/0.1.0 has been published using only github, now docfest starts :D https://github.com/foursixnine/cndk8/actions/runs/12544879009/job/34978215202

@nguyenalex836 @janbrasna you can review now :) - cache needs a bit more help, but I could omit it for now
Cache not found for input keys: Linux-cargo-5634c3a46c78736ff6e19d83a9150b91503889cf5996066a1deec42f3d9e42a9

@foursixnine Thanks for the heads up! We might be slightly slow to review given the team will be coming out of the holiday break, but we'll update you here as soon as we have feedback 💛

@nguyenalex836 thanks! - no worries, I think for the most part I can sit and wait, and try to figure out the linting issues that are shown


proxy doesn't resolve domain

#Trying to figure out why
proxy / IP:PORT
works but
proxy / domain:PORT
doesn't (times out or bad gateway)
Any idea what can be a problem? If I /bin/sh into caddy container, I can resolve domain to IP.
Using a shared network that supports IPv6.

Are you using Docker Compose or Kubernetes ?

@abiosoft  Docker compose

@MihaMarkic hi, is this issue still valid ?

I think this was an issue when I was using more than one network. Since then I reduced to a single network. I guess it's still an issue, don't really know at this point.

I'll be closing this until someone else have a similar experience.


update models

There are some pylint errors. Fix these before approval. You can check them by running poetry run pylint app/ilmoweb/ at the root of the project. We should ignore the migrations folder from pylint. This is disabled by modifying .pylintrc final line from ignore=tests  to ignore=tests, migrations


Fix usage of deprecated set-output command

Pipelines using this action produce the warning

Warning: The set-output command is deprecated and will be disabled soon. Please upgrade to using Environment Files. For more information see: https://github.blog/changelog/2022-10-11-github-actions-deprecating-save-state-and-set-output-commands/

This pull request in the @actions/core library changes the output format from the old style (e.g. echo "::set-output NAME=VALUE" to the new style echo "NAME=VALUE" >> $GITHUB_OUTPUT and was made available in the version 1.10.0.
Other dependencies have been updated as well, @vercel/ncc was seemingly incompatible, and the others were just to keep things up to date.
EDIT: I have also bumped node to 20, since apparently that's the required version now

Seems nice! Thanks!


Fixed mold (I think?)

This should work without mutable lists. All tests work.

Should also work for infinite lists. Some small modifications may be required for it to work for modular indexing.

Btw, helps fix #1119


Add type data after @param name to conform with java8 doclint

I'm having an issue where I have to update a bunch of old javadocs to the much stricter java8 javadoc standard. so @param id the id would better be @param id String

You can change default template to any format you need,
Try that in new 4.01 version


Unable to download JSON metadata for Password Protected embedded vimeo video

Please follow the guide below

You will be asked some questions and requested to provide some information, please read them carefully and answer honestly
Put an x into all the boxes [ ] relevant to your issue (like that [x])
Use Preview tab to see how your issue will actually look like

Make sure you are using the latest version: run youtube-dl --version and ensure your version is 2016.06.20. If it's not read this FAQ entry and update. Issues with outdated version will be rejected.

[x ] I've verified and I assure that I'm running youtube-dl 2016.06.20

Before submitting an issue make sure you have:

[ x] At least skimmed through README and most notably FAQ and BUGS sections
[x ] Searched the bugtracker for similar issues including closed ones

What is the purpose of your issue?

[ x] Bug report (encountered problems with youtube-dl)
[ ] Site support request (request for adding support for a new site)
[ ] Feature request (request for a new functionality)
[ ] Question
[ ] Other

The following sections concretize particular purposed issues, you can erase any section (the contents between triple ---) not applicable to your issue

If the purpose of this issue is a bug report, site support request or you are not completely sure provide the full verbose output as follows:
Add -v flag to your command line you run youtube-dl with, copy the whole output and insert it here. It should look similar to one below (replace it with your log inserted between triple ```):
youtube-dl -v https://vimeo.com/user37284429/review/138823582/c4d865efde --video-password holygrail
[debug] System config: []
[debug] User config: []
[debug] Command-line args: [u'-v', u'https://vimeo.com/user37284429/review/138823582/c4d865efde', u'--video-password', u'PRIVATE']
[debug] Encodings: locale cp1252, fs mbcs, out cp437, pref cp1252
[debug] youtube-dl version 2016.06.20
[debug] Python version 2.7.10 - Windows-8-6.2.9200
[debug] exe versions: none
[debug] Proxy map: {}
[vimeo:review] 138823582: Downloading JSON metadata
ERROR: Unable to download JSON metadata: HTTP Error 403: Forbidden (caused by HTTPError()); please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; see  https://yt-dl.org/update  on how to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
  File "c:\python27\lib\site-packages\youtube_dl\extractor\common.py", line 390, in _request_webpage
    return self._downloader.urlopen(url_or_request)
  File "c:\python27\lib\site-packages\youtube_dl\YoutubeDL.py", line 1950, in urlopen
    return self._opener.open(req, timeout=self._socket_timeout)
  File "c:\python27\lib\urllib2.py", line 437, in open
    response = meth(req, response)
  File "c:\python27\lib\urllib2.py", line 550, in http_response
    'http', request, response, code, msg, hdrs)
  File "c:\python27\lib\urllib2.py", line 475, in error
    return self._call_chain(*args)
  File "c:\python27\lib\urllib2.py", line 409, in _call_chain
    result = func(*args)
  File "c:\python27\lib\urllib2.py", line 558, in http_error_default
    raise HTTPError(req.get_full_url(), code, msg, hdrs, fp)
...
<end of log>

Description of your issue, suggested solution and other information
Explanation of your issue in arbitrary form goes here. Please make sure the description is worded well enough to be understood. Provide as much context and examples as possible.
If work on your issue required an account credentials please provide them or explain how one can obtain them.
I am getting a "Unable to download JSON metadata:" error when trying to download a password protected embedded Vimeo video. I believe the embed code is in the url, and I used the correct command for passwords.
The input and the verbose output are copied and pasted below. I put the password below so the issue can be completely replicated.
youtube-dl https://vimeo.com/user37284429/review/138823582/c4d865efde --video-password holygrail --verbose
[debug] System config: []
[debug] User config: []
[debug] Command-line args: [u'https://vimeo.com/user37284429/review/138823582/c4d865efde', u'--video-password', u'PRIVATE', u'--verbose']
[debug] Encodings: locale cp1252, fs mbcs, out cp437, pref cp1252
[debug] youtube-dl version 2016.06.20
[debug] Python version 2.7.10 - Windows-8-6.2.9200
[debug] exe versions: none
[debug] Proxy map: {}
[vimeo:review] 138823582: Downloading JSON metadata
ERROR: Unable to download JSON metadata: HTTP Error 403: Forbidden (caused by HTTPError()); please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; see  https://yt-dl.org/update  on how to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
File "c:\python27\lib\site-packages\youtube_dl\extractor\common.py", line 390, in _request_webpage
return self._downloader.urlopen(url_or_request)
File "c:\python27\lib\site-packages\youtube_dl\YoutubeDL.py", line 1950, in urlopen
return self._opener.open(req, timeout=self._socket_timeout)
File "c:\python27\lib\urllib2.py", line 437, in open
response = meth(req, response)
File "c:\python27\lib\urllib2.py", line 550, in http_response
'http', request, response, code, msg, hdrs)
File "c:\python27\lib\urllib2.py", line 475, in error
return self._call_chain(*args)
File "c:\python27\lib\urllib2.py", line 409, in _call_chain
result = func(*args)
File "c:\python27\lib\urllib2.py", line 558, in http_error_default
raise HTTPError(req.get_full_url(), code, msg, hdrs, fp)

Thanks for the report. Passowrd-protected videos will be supported again in the next version.


wiki page , private issue

whenever i logged in or not i can read the page without problems, only when try to edit then the site request log in, even though i uncheck "allow guest" option in wiki page

my frappe version :
chat 0.0.1 main (2b04b14)
erpnext 14.27.7 version-14 (58e304f)
frappe 14.39.0 version-14 (ca8701c)
hrms 14.4.3 version-14 (637691b)
lms 1.0.0 main (d19538a)
wiki 2.0.0 version-14 (c2f0a40)

The allow uncheck option is only for read permission, not edit permission. Letting guests have edit permission can be disastrous.


ARROW-252: Add implementation guidelines to the documentation

Looks good to me. Would be great for someone else to take a look too


decouple find SchemaSegment with show index statement

For #4342.

Pull Request Test Coverage Report for Build 1790

0 of 2 (0.0%)  changed or added relevant lines in 2 files are covered.
No unchanged relevant lines lost coverage.
Overall coverage decreased (-0.006%) to 57.628%

Changes Missing Coverage
Covered Lines
Changed/Added Lines
%

shardingsphere-sql-parser/shardingsphere-sql-parser-engine/src/main/java/org/apache/shardingsphere/sql/parser/sql/statement/dal/dialect/mysql/ShowIndexStatement.java
0
1
0.0%

shardingsphere-sql-parser/shardingsphere-sql-parser-mysql/src/main/java/org/apache/shardingsphere/sql/parser/visitor/impl/MySQLDALVisitor.java
0
1
0.0%

Totals

Change from base Build 875:
-0.006%

Covered Lines:
11087

Relevant Lines:
19239

💛  - Coveralls


Draw by 50 moves rule

5.2
e) The game may be drawn if each player has made at least the last 50 moves
without the movement of any pawn and without any capture (see Article 9.3).
9.1
b3)
A claim of a draw under Article 9.2 or 9.3 shall be considered to be an offer
of a draw.
9.3 The game is drawn, upon a correct claim by a player having the move, if:
a. he writes his move, which cannot be changed, on his scoresheet and declares to
the arbiter his intention to make this move which will result in the last 50 moves
by each player having been made without the movement of any pawn and without
any capture, or
b. the last 50 moves by each player have been completed without the movement of
any pawn and without any capture.
9.4 If the player touches a piece as in Article 4.3, he loses the right to claim a draw under
Article 9.2 or 9.3 on that move.
9.5 If a player claims a draw under Article 9.2 or 9.3, he or the arbiter shall stop the
chessclock (see Article 6.12 b). He is not allowed to withdraw his claim.
a. If the claim is found to be correct, the game is immediately drawn.
b. If the claim is found to be incorrect, the arbiter shall add two minutes to the
opponent’s remaining thinking time. Then the game shall continue. If the claim
was based on an intended move, this move must be made in accordance with
Articles 3 and 4.

[ ? ] Probably special moves like offer draw shouldn't be counted here

NOTE: 5.2.e) is removed since July 2017. Duplicated by 9.2&9.3 I guess

New move: CLAIM DRAW:
Send to Winboard
Receive from WinBoard
Send to LegalPlayer
Receive from LegalPlayer
Engine, validity.

Comment from Arbiters manual 2014
Regarding 9.1.b. (3): If a player claims a draw, the opponent has the possibility to
agree immediately to the draw. In this case the arbiter does not need to check the
correctness of the claim. But be careful. If there is a draw restriction (for example: no
draw offers are allowed before 30 moves have been completed by both players) and
the claim has been submitted before that move (i.e. after 28 moves), then the claim
has to be checked by the Arbiter in any case, even if the opponent would agree to a
draw.

Manual testing

1 engine -> UI
(!) Need setting up M3_HOME correctly due to new OS
UI -> engine
2 engines, maybe with different draw moves counts


Add v1c ethernet driver for the STM32F7 family.

@matoushybl code looks mostly good, could you fix the CI failure? Thanks!

@Dirbaio fixed and tested, thanks for the review!


Fix node alias names and deploy services properly

The previous scenario file was not deploying services
like requested in the trello card.

+1


Code under Modal component documentation has bugs

Issue type
I'm submitting a ...  (check one with "x")

[ X ] bug report
[ ] feature request

Issue description
Current behavior:
The code under Modal component documentation has bugs.
The line 20 is closing Text with Test
Hi! This is Modal Component!
And the buttons in lines 15 and 21 are using a prop called title that is not recognized. I changed
to this <Button onPress={() => this.setVisible(true)} >Show Modal
Expected behavior:
That you could do copy & paste without need to change code.
Steps to reproduce:
Just copy the code into kittens tricks app in a container.

Thanks for reporting this!

Hello @cargallo ! Thank you for your report. Will be fixed in the next patch.


Revising awkward expressions in Korean translation

I tried to use the program to teach git, but then I noticed that there are so many awkward expressions in Korean translations. I just made a pull request for some significant errors in expressions, but there are still more expressions to be improved. Especially, there are so many translationeses to be fixed.
Now I'm working on it

Thanks for jumping in and helping! Ill push your PR to the site now
On Tue, Apr 7, 2020 at 1:40 AM Hyeon Jeon<EMAIL_ADDRESS>wrote:

I tried to use the program to teach git, but then I noticed that there are
so many awkward expressions in Korean translations. I just made a pull
request for some significant errors in expressions, but there are still
more expressions to be improved. Especially, there are so many
translationeses to be fixed.
Now I'm working on it
—
You are receiving this because you are subscribed to this thread.
Reply to this email directly, view it on GitHub
https://github.com/pcottle/learnGitBranching/issues/675, or unsubscribe
https://github.com/notifications/unsubscribe-auth/AAIVDHYA4DTAXXJPKIKCAZLRLLRHJANCNFSM4MC5R3VQ
.

--
Peter M Cottle
UC Berkeley 2012
UC San Diego 2011

(closing out old issues)

(closing out old issues)


Has skywalking-client-js been published to npm?

Please answer these questions before submitting your issue.

Why do you submit this issue?
[x] Question or discussion
[ ] Bug
[ ] Requirement
[ ] Feature or performance improvement

Question

What do you want to know?
has skywalking-client-js been published to npm?

Bug

Which version of SkyWalking, OS and JRE?

Which company or project?

What happened?
If possible, provide a way to reproduce the error. e.g. demo application, component version.
I run https://github.com/apache/skywalking-client-js as instructed here
Received error

npm ERR! code ENOVERSIONS
npm ERR! No valid versions available for skywalking-client-js

I also searched npm.org, couldn't find skywalking-client-js

Requirement or improvement

Please describe your requirements or improvement suggestions.

It is still in the dev stage.


Teeworlds Install is not working (No subscription)

Hello all,
im trying to install the Teeworlds server with your script but it dont works. I have setup a special steam account for it.
Logging in user '443562server' to Steam Public...Logged in OK
Waiting for license info...OK
ERROR! Failed to install app '380840' (No subscription)
Any idea? Maybe the server got removed from steam because i cant find it on the steam page?
Best

Hi there. No subscription error means the steam account doesn't own the game. You need to buy it for that account.

Correct me if im wrong but is Teeworlds not for free?

You are right TeeWorlds is free

I'm not sure why you get as "no subscription" error then, as it only occurs for either game servers that require logging-in to a steam account, or games that need to be owned. As you said you setup a steam account for that purpose, i assume you put it in your script config.
Does this account own at least one game ? Because i know a blank account can't add friends. This may be another limitation : A blank account my not allow you to run a server that needs a steam account.

OK, i just tried : I had to install the game onto my steam account to get rid of that "no subscription" error. Even if it's free. Try it out.
If it's not enough, then you may need to actually buy at least one game on that account.
Keep us tuned.

If you need more help, please post to the general support which is here : http://steamcommunity.com/groups/linuxgsm/discussions/
However, if you find a bug or a feature request related to this issue, feel free to re-open it.


Documentation updates.

Hi
This pull request Fixes .event instead of .events for AMQP messages.
It also updated the release notes, and expands a bit on AMQP integration.

Just that one comment from me, otherwise it looks good. Thanks for updating this :)

@rudigiesler Thanks, I did your suggestion. It definitely avoids some ambiguity :-)

:+1:


Code does not utilize GPU

After running your code I noticed that it does not run in GPU even after  asking it explicitly with THEANO_FLAGS=device=cuda0 python trinet_embed.py
Could you please explain why this happens?
Thank you

Follow the Theano documentation about GPU closely. In this case, it's probably because you forgot to also set floatX=float32.
I'm closing as this should solve it and it's probably not an issue with our code, but if you disagree, feel free to re-open.


Receiving warnings and errors and not sure where to start debugging...

I have set-up karma-webpack and when I run karma start with only the following test in place...:
const assert = require('assert');
var bcrypt = require('bcrypt');
describe('encryptPass', function() {
it('returned hash should return ', function() {
assert.equal(1,1);
});
});
I get the following warnings (and about 50 more beyond this)...:
WARNING in ./~/bcrypt/bcrypt.js
Critical dependencies:
6:15-36 the request of a dependency is an expression
 @ ./~/bcrypt/bcrypt.js 6:15-36

WARNING in ./~/bcrypt/~/node-pre-gyp/lib/pre-binding.js
Critical dependencies:
19:22-48 the request of a dependency is an expression
 @ ./~/bcrypt/~/node-pre-gyp/lib/pre-binding.js 19:22-48

WARNING in ./~/bcrypt/~/node-pre-gyp/lib/util/versioning.js
Critical dependencies:
15:20-67 the request of a dependency is an expression
 @ ./~/bcrypt/~/node-pre-gyp/lib/util/versioning.js 15:20-67

WARNING in ./~/bcrypt/CHANGELOG.md
Module parse failed: /tidee/auth_app/node_modules/bcrypt/CHANGELOG.md Unexpected character '#' (1:0)
You may need an appropriate loader to handle this file type.
SyntaxError: Unexpected character '#' (1:0)
    at Parser.pp$4.raise (/tidee/auth_app/node_modules/acorn/dist/acorn.js:2221:15)
    at Parser.pp$7.getTokenFromCode (/tidee/auth_app/node_modules/acorn/dist/acorn.js:2756:10)
    at Parser.pp$7.readToken (/tidee/auth_app/node_modules/acorn/dist/acorn.js:2477:17)
    at Parser.pp$7.nextToken (/tidee/auth_app/node_modules/acorn/dist/acorn.js:2468:15)
    at Parser.parse (/tidee/auth_app/node_modules/acorn/dist/acorn.js:515:10)
    at Object.parse (/tidee/auth_app/node_modules/acorn/dist/acorn.js:3098:39)
    at Parser.parse (/tidee/auth_app/node_modules/webpack/lib/Parser.js:902:15)
    at NormalModule.<anonymous> (/tidee/auth_app/node_modules/webpack/lib/NormalModule.js:104:16)
    at NormalModule.onModuleBuild (/tidee/auth_app/node_modules/webpack-core/lib/NormalModuleMixin.js:310:10)
    at nextLoader (/tidee/auth_app/node_modules/webpack-core/lib/NormalModuleMixin.js:275:25)
    at /tidee/auth_app/node_modules/webpack-core/lib/NormalModuleMixin.js:259:5
    at Storage.finished (/tidee/auth_app/node_modules/enhanced-resolve/lib/CachedInputFileSystem.js:38:16)
    at /tidee/auth_app/node_modules/graceful-fs/graceful-fs.js:78:16
    at FSReqWrap.readFileAfterClose [as oncomplete] (fs.js:415:3)
 @ ./~/bcrypt ^\.\/.*$

Would appreciate a heads-up on what is happening here and some posisble solutions. Seems like it might be a configuration issue involving either resolving/including node_modules (?)

This by any change public. From the logging, it feels more like config issue but i'd need to see it.
Obviously loading the changelog.md is less than optimal so something needs tweaking.

It's not, but can set up access to the Bitbucket repo if you send me your email address. Else, could post webpack/karma config files if this would help (?)

@joetidee - My email is public in my profile on github and getting access to the project briefly will be the fastest way to get this resolved.

Much appreciated - have sent details to your address.

Give me a minute to pull a few espresso shots and I will take a look

@joetidee - So while I am in here anyway, want to just get the upgrade to webpack 2.2.0 out of the way?

Is this necessary?

Besides a less complex config & the fact 1.x will be deprecated at some point...
Performance, performance, performance.  Lots of bug fixes and usability improvements.

ChunkModuleIdRangePlugin: added experimental plugin to assign module ids to a chunk. (related to Long Term Caching)
UglifyJs: version updated. ( Better tree shaking )
MultiCompiler: dependencies parameter added. Allows to specify dependencies with multiple configurations. They are compiled in this order.

Though the really important reason being webpack 2.2.0 was ready long before the actual official release. Most if not all the major plugins / loaders are already focused on v2.2.0 which means it will become more and more difficult to get updates for 1.14.0 ( not official in any way, my opinion )
Brass tacks, in reality you will have to upgrade sooner or later. This just happens to be a major release that I wanted to upgrade to.

Sounds very positive. Hope that the tools I use will be 2.2 ready.

Actually webpack-node-externals may not be ready for 2.2 all the other libs in your package.json will work just fine in 2.x. I use 90% of them personally.

There doesn't seem to be any obvious reference to actually using this package directly in this project ...(?)

Has there been any progress on the errors that are being thrown ?

Closing due to the age of this ticket, if you're still having issues with the current build feel free to open a new ticket with the relevant information and I'll help as best I can!


Clustering: What are the most common mechanisms nature uses for a particular function?

While we wait to get mechanisms and function extracted using OpenAI, we can try out clustering algorithms using AskNature functions and summaries as an approximation of mechanism.

Some interns a few years ago developed clusters of biology papers. Putting a link to the code here incase it is of use.
https://github.com/nasa-petal/PeTaL/blob/legacy/petal/cluster.py

-- coding: utf-8 --
'''
Created on Wed Jul 11 07:52:04 2018
@author: bwhiteak and cbaumler
'''
import pandas as pd
import numpy as np
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.decomposition import LatentDirichletAllocation
import spacy
from spacy.matcher import Matcher
import pyLDAvis
import pyLDAvis.sklearn
import string, json, sys, pickle
ignore = pickle.load(open("petal/data/cluster/ignore/ignore.p", "rb"))
def create_df0(text):
"""
Format and parse a long string of abstracts into a DataFrame.
Parameters:
- text (str): A long string containing multiple abstracts.

Returns:
- DataFrame: A pandas DataFrame containing parsed abstracts.
"""
try:
    if sys.platform == 'win32':
        line_sep = '\r\n'
    else:
        line_sep = '\n'

    text_list = [y.strip() for y in text.split(sep=(line_sep * 3))]
    title_list = [x.split(line_sep, 1) for x in text_list]
    if title_list[-1] == ['']:
        del title_list[-1]

    nlp = spacy.load('en_core_web_sm')
    matcher = Matcher(nlp.vocab)
    matcher.add("hyphen", None, [{}, {"TEXT": "-"}, {}])

    for i in range(len(title_list)):
        if len(title_list[i]) > 1:
            doc = nlp(title_list[i][1])
            doc = match_n_merge(matcher, doc)
            n, v, vd, al = get_split_tokens(doc)
            title_list[i] = [title_list[i][0], n, v, vd, al]

    return pd.DataFrame(title_list, columns=['Title', 'n', 'v', 'vd', 'all'])

except Exception as e:
    print(f"Error processing abstracts: {e}")
    return pd.DataFrame()

def make_doc_dict(doc_list, assoc_topics):
"""
Create a dictionary mapping document titles to their associated topics.
Parameters:
- doc_list (list): List of document titles.
- assoc_topics (list): List of topics associated with each document.

Returns:
- dict: A dictionary where each key is a document title and the value is the associated topics.
"""
try:
    return {f"doc_{i}": topic for i, topic in enumerate(assoc_topics)}
except Exception as e:
    print(f"Error creating document dictionary: {e}")
    return {}

def make_topic_dict(model_list, feature_names, n_top_words):
"""
Generates a dictionary of topics with their corresponding terms.
Parameters:
- model_list (list): List of model components.
- feature_names (list): List of feature names from the model.
- n_top_words (int): Number of top words to include for each topic.

Returns:
- dict: A dictionary where each key is a topic number and value is a string of top terms.
"""
try:
    all_mat = np.vstack([model.components_ for model in model_list])
    topic_dict = {
        i: " ".join(feature_names[idx] for idx in topic.argsort()[:-n_top_words - 1:-1])
        for i, topic in enumerate(all_mat)
    }
    return topic_dict
except Exception as e:
    print(f"Error in making topic dictionary: {e}")
    return {}

def make_subset_dict(df):
"""
Creates a dictionary mapping index values to document titles from a DataFrame.
Parameters:
- df (DataFrame): The DataFrame containing document indices and titles.

Returns:
- dict: Dictionary with index values as keys and document titles as values.
"""
try:
    return dict(zip(df.index.values, df['Title'].tolist()))
except Exception as e:
    print(f"Error creating subset dictionary: {e}")
    return {}

def get_lemma(token):
"""
Extracts the lemma of a given token, with special handling for certain cases.
Parameters:
- token (Token): A Spacy token object.

Returns:
- str: The lemma of the token.
"""
if token.text == "species":
    return "species"  # Handling exception for the word 'species'
return token.lemma_

[Continuation of other functions and script logic]


Allow multiple streams attached to the same plot model

Currently when multiple streams are attached to the same plot model they will override each other. This PR ensures that  either the python callback or the JS callback are merged depending on whether the callback is of the same type. This is mostly a problem if two separate plots attach distinct streams to a set of linked axes. It also includes a fix for Layouts with multiple streams of the same type across the subplots, which would previously complain about parameter overlap. Finally it allows supplying stream instances to operations instead of instantiating a Stream instance internally, which can be useful to get a handle on the stream.
Here's a demo:
points = hv.Points(np.random.multivariate_normal((0,0), [[0.1, 0.1], [0.1, 1.0]], (1000000,)))
Datashade(points) + decimate(points)

It certainly looks and sounds good!

Looks good. Merging.


Unable to set cookies with XCode 12

Issue description

Unable to set cookies when building the app with XCode 12 or XCode 12 Beta.

Building the app with XCode 11.7 allows the cookies to be set

I need to set the cookies to access media files  under a cdn which requires cookies for the request.

Steps to reproduce the issue

Create IOS folder with $ ionic cordova build ios
Running build with XCode

What's the expected result?
XCode 12 & XCode 12 Beta

document.cookie = 'cookie'
XCode 11.7
document.cookie = 'cookie'

What's the actual result?
XCode 12 & XCode 12 Beta

document.cookie = '"
XCode 11
document.cookie = 'cookie'

Additional details / screenshot
ionic (Ionic CLI)  : 4.12.0
Ionic Framework    : ionic-angular 3.9.9
@ionic/app-scripts : 3.2.4
Cordova:
cordova (Cordova CLI) : 10.0.0
Cordova Platforms     : ios 5.1.1
Cordova Plugins       : cordova-plugin-ionic-keyboard 2.2.0, cordova-plugin-ionic-webview 4.1.3, cordova-plugin-ionic
5.4.5, (and 28 other plugins)

same issue

+1
Got the same problem after migrating to WKWebView (cordova-plugin-ionic-webview 5.0.0)

the same problem , need help.

Same problem, any idea of a fix?   I hear people are having to move over to use local storage.   Doable but cookies have the build in expire date functionality and why that was being used.
Looks like I have Xcode 12.1.

Any update on how to support cookies after first time use of app?
Got things work (or so I thought) using a different plugin.  This worked fine on iOS 13.5. but apple rejected it testing on 14.2.  Got same results with iOS 14+.   It seems strange to me that we need a plugin in the first place.  Shouldn't WKWebView handle that out of the box?  I guess if the bug is on apple side maybe not.
Having a hard time that ionic or Cordova does not seem to have an official fix.


ONNX inference issue

when i run this code, i got the warning
import torch
import onnx
parseq = load_from_checkpoint('pretrained=parseq').eval()
parseq.refine_iters = 0
parseq.decode_ar = False
image = torch.rand(1, 3, *parseq.hparams.img_size)
parseq.to_onnx('parseq.onnx', image, do_constant_folding=True, opset_version=14)
onnx_model = onnx.load('parseq.onnx')
onnx.checker.check_model(onnx_model, full_check=True)

then, when I use this ONNX model for inference, I encounter the following error

and this is my code

my version
onnx==1.15.0
onnxruntime-gpu==1.17.1
torch==2.1.1+cu118
pytorch-lightning==2.1.0

ate.Fai

I successfully converted and utilized the code with a previous version; however, I'm encountering issues with the latest version and am unable to resolve them on my own.
https://github.com/jturner116/parseq
At this point, my only option is to await a response from the author

@baudm
Hello, author,
I am currently encountering some technical issues and would appreciate your assistance. Firstly, I would like to inquire about how to resolve the aforementioned problem. Secondly, after using an older version of the project and converting it to ONNX format, I noticed a significant discrepancy between the output results and the pre-trained model. Regarding this issue, could you provide some suggestions to help address this problem?
Thank you very much for taking the time to respond amidst your busy schedule.

Exactly same issue with:

onnx==1.15.0
torch==2.2.1
pytorch-lightning==2.2.1

Stacktrace:
(.venv) [mantas@WS21 parseq]$ python onnx_runtime.py
Traceback (most recent call last):
  File "/home/mantas/Documents/Projects/parseq/onnx_runtime.py", line 4, in <module>
    session = ort.InferenceSession(model_path)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/home/mantas/Documents/Projects/parseq/.venv/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 419, in __init__
    self._create_inference_session(providers, provider_options, disabled_optimizers)
  File "/home/mantas/Documents/Projects/parseq/.venv/lib/python3.11/site-packages/onnxruntime/capi/onnxruntime_inference_collection.py", line 472, in _create_inference_session
    sess = C.InferenceSession(session_options, self._model_path, True, self._read_config_from_model)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
onnxruntime.capi.onnxruntime_pybind11_state.Fail: [ONNXRuntimeError] : 1 : FAIL : Load model from modified.onnx failed:Type Error: Type parameter (T) of Optype (Where) bound to different types (tensor(bool) and tensor(float) in node (/Where_23).

in parseq/models.py  line-117, update tensor.bool tensor.float


Fix a wrong VAAPI check in FillCUQPDataDDI

According to MFX_HW_VAAPI definition below, it is 4. It make
((coreParams.Impl & 0xF00) == MFX_HW_VAAPI) always get False.Obviouly
it should be MFX_IMPL_VIA_VAAPI
enum eMFXVAType
{
MFX_HW_NO       = 0,
MFX_HW_D3D9     = 1,
MFX_HW_D3D11    = 2,
MFX_HW_VAAPI    = 4,// Linux VA-API
};
Signed-off-by: jwang<EMAIL_ADDRESS>

This PR is also related to #266.

the PR has kept pending for 10 days, if someone can help review it, thanks？

There are more such places, I'm working on redesigning encoder to use internal Core interface and will fix all those places