feat: add remove provisioned

:tada: This PR is included in version 2.20.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Very slow video loading

Steps to reproduce
Opening any anime in gogoanime or zoro
Expected behavior
Anime should be loading real quick like it does with hanime plug in which proves it's not a internet connection problem
Actual behavior
Tried different website plug ins but animes are taking too much time to load or sometimes they just keep buffering and never load
Crash logs
No response
Aniyomi version
12.3.5
Android version
11
Device
Realme 8
Other details
No response
Acknowledgements

[X] I have searched the existing issues and this is a new ticket, NOT a duplicate or related to another open issue.
[X] I have written a short but informative title.
[ ] If this is an issue with an extension, I should be opening an issue in the extensions repository.
[X] I have tried the troubleshooting guide.
[X] I have updated the app to version <IP_ADDRESS>.
[X] I have updated all installed extensions.
[X] I will fill out all of the requested information in this form.

it's because the website limits download speeds.
also: "If this is an issue with an extension, I should be opening an issue in the extensions repository."


Issue with Recycle Bin Tooltip Displaying File Size Instead of "Recycle Bin"

Description
I have encountered an issue where, when hovering over the Recycle Bin pinned in Favorites, it displays the total file size of all the files in the Recycle Bin instead of showing the text "Recycle Bin."
Steps To Reproduce

Pin the Recycle Bin can to the favorites
Hover over the trash can

Requirements
Implement a fix to ensure that the tooltip correctly shows the text "Recycle Bin" when hovering over the Recycle Bin icon in Favorites.
Files Version
Version <IP_ADDRESS>
Windows Version
22H2 22621.1848
Log File

Oh ok, I understand better. Finally, I find it even more convenient than just having a "Trash" tooltip.


Business Mailing Address is or isn't required? [6]

Describe the Bug:
As an external user, when filling out data in the Operator form, if I click on the "No" radio button for "Is the business mailing address the same as the physical address?" the mailing address field labels have *s next to them indicating that they're required, but on form submission, there's no validation performed to ensure those data fields are populated.
Probability (how likely the bug is to happen, scored from 1-5): 3
(For example, probability of 5 is something like "it happens to all users every time they log in."
Probability of 1 "only happens to certain users when a really specific and unlikely path is followed.")
Effect (how bad the bug is when it does happen, scored from 1-5): 3
(For example, effect of 5 is "the entire app crashes and makes it unusable for all users" or "the bug causes the wrong data to be saved, with critical information (e.g. payment) being affected."
Effect of 1 is "It makes some styling look a little bit weird.")
Steps to reproduce the behaviour:

Login as external user with no associated operator
Create a new operator (this might also happen with existing operators, not sure...)
Click "No" for "Is the business mailing address the same as the physical address?"
Leave business address fields blank and submit the form

Screenshots:

Desired Behaviour:
We should be collecting address data on Operators, so if an Operator's mailing address is different from their physical address, we want to make the business address fields required. If the mailing address == physical address, the mailing address fields should be hidden in the form anyway.
Additional information:

OS: [e.g. iOS]
Browser: [e.g. chrome, safari]
Version: [e.g. 22]
Additional context

I was not able to replicate the issue:

Can someone else try to replicate it since @Sepehr-Sobhani wasn't able to? Feel free to close it if needed. @marcellmueller

Verified that this isn't needed anymore.


Missing enable edge device parameter in az iot dps enrollment create command

Is your feature request related to a problem? Please describe.

Describe the solution you'd like

Describe alternatives you've considered

Additional context

iot dps


Detailview controller tests

Tests for detailview, at 94% line coverage now

Looks good.

One small comment, for the rest, great work. Also, fix your build ;)

Good work!


curent lib is not compiled

i got strange error while compiling:
WiFiManager.h: 36:22: error: 'const char HTTP_HEAD []' redeclared as different kind of symbol const char HTTP_HEAD[] PROGMEM = "<html lang="en"><meta name="viewport" content="width=device-width, initial-scale=1, user-scalable=no">{v}<\title>"
how to fix it who know?

I had the same problem. I edited the WiFiManager.h and WiFiManager.c files replacing HTTP_HEAD with HTTP_HEAD_ESP32. Be aware there is a HTTP_HEAD_END too, so be careful. It needs to be changed on like 6 places or so.

There looks to be a good fix for this in PR #24. Would be good to get this merged.


Fix syntax issue

Fix syntax issue.  BucketNotFound should be BucketNotFoundError

This is a 🐞 bug fix.

[ ] I've added tests (if it's a bug, feature or enhancement)
[ ] I've adjusted the documentation (if it's a feature or enhancement)
[ ] The test suite passes (run bundle exec rspec to verify this)

Summary

Fix syntax issue which potentially masks underlying errors
Context

How to Test

Version Changes

@tongueroo FYI


Newly added Automated/semi-automated tests. Jan 31

Newly added Media cluster automated tests:
TC_APPOBSERVER_1_13
TC_APPOBSERVER_13_2
TC_MEDIAPLAYBACK_6_8
TC_MEDIAPLAYBACK_6_9

ICDM semi-automated test:
TC_ICDM_3_4
Device Energy Management Mode tests:
TC_DEMM_1_1
TC_DEMM_2_1

Energy EVSE tests:
TC_EEVSE_1_1
TC_EEVSE_2_1

Refrigerator Alarm test:
TC_REFALM_2_2
Energy EVSE Mode tests:
TC_EEVSEM_1_1
TC_EEVSEM_2_1
TC_EEVSEM_3_1
TC_EEVSEM_3_2
TC_EEVSEM_3_3

Manual tests:
TC_DEMM_1_2
TC_EEVSEM_1_2

Added auto generated files
Tested
Execution logs:
exe_logs.txt

Which ones of these are automated, and which are manual?


I cant configure mod!

What happened?
No options in Mod Menu to configure various things. Tell me how to config mod, please.
What should have happened?
I expected some menu in Mod Menu to configure various things.
How to reproduce?
Find mod in Mod Menu, you can find mod, but you wont see button to open mod settings.
What is the jar file name?
enchanttweaker-1.4.7+mc1.20.3-1.20.4.jar
What Minecraft version?
Fabric 1.20.4
Any relevant logs?
No response
Anything else?
No response
Before submitting...

[X] This issue is not a duplicate of an existing issue (open or closed).

Hello, the mod does not currently have integration with ModMenu.
However you can modify the settings via its config file. The config file can be found in most installations at .minecraft\config\enchant-tweaker.properties. It should be in the same directory as the mods menu where you installed your mods.
If you are using a launcher such as Prism Launcher you can usually find a button in your mods section labeled View Configs which should take you to the right place. Hope this helps.
Feel free to reopen the issue if you run into any problems.


fix(jsx): handle async component error explicitly and throw the error in the response

fixes #3252
Uncaught (in promise) was occurring and the app was crashing under the following conditions.

There are multiple asynchronous components.
While waiting for the first Promise to be resolved, an error occurred in the subsequent Promise.

The author should do the following, if applicable

[x] Add tests
[x] Run tests
[x] bun run format:fix && bun run lint:fix to format the code

@yusukebe Would you please review?

@usualoma
I've confirmed that this patch works well on several runtimes. Looks good! Thanks.


ENV-based login/pass/encryption set

This pull introduces/changes:

Add possibility to set login, password, encryption on/off for using migration in non-Rails projects

Thanks @EugeneIstomin . Have you considered extracting this https://github.com/neo4jrb/activegraph/blob/master/lib/active_graph/railtie.rb#L82-L85 out of railtie.rb? There are many configuration options. Creating an environment variable each time someone needs one of those options doesn't seem like a sustainable solution. It would be great to solve the problem for any possible configuration.

Thanks @EugeneIstomin . Have you considered extracting this https://github.com/neo4jrb/activegraph/blob/master/lib/active_graph/railtie.rb#L82-L85 out of railtie.rb? There are many configuration options. Creating an environment variable each time someone needs one of those options doesn't seem like a sustainable solution. It would be great to solve the problem for any possible configuration.


NTR: abnormal  kinetochore orientation at meiosis I PMID:21920317

The  kinetochore is no longer oriented  so that if faces outwards (away from the chromosome)
fig 6 A PMID:21920317

did it thusly (with synonyms):
abnormal meiotic homologous chromosome biorientation resulting in abnormal kinetochore orientation FYPO:0005642

edit file:<PHONE_NUMBER>46b921861b98a5686db1aa00aaf950
release: c136177d2f6ece14a68bf41de8de6b74323072c6

I'm looking at this again as I am not sure that I have got everything totally correct.
For this one the way I onderstand it is:
rec12 mutant abolished recombination
This causes to segregation defects at meiosis 1.
Somehow, recombination /chiasma formation at the centromeres is important  to form the popped our kinetochore structure orients the kinetochore in such a way that  which enables
biorientation.
So if this is correct, the biorientation does not cause the kineotchore misorientation, its the other way around....

I think I shall punt and just change "resulting in" to "with" in the term name, so it'll be:
abnormal meiotic homologous chromosome biorientation with abnormal kinetochore orientation
That will make it neutral with respect to which goes awry first, and make less work for me ;)
Any problem with that plan?

Thats fine, thats what we see....
I'm going to ask the authors to check the final session for me to make sure that my interpretation does make sense. I keep getting confused, probably because I have spent so much time on mitotic chromosome segregation.  Hopefully I got it sort of right.... its basically lots of different coloured dots  subtly  closer together or further apart...

rejig done in
edit file: f14c0eac4a5a646b2d3a74ed10281625439e35db
release: 2e411809995df8079922d7b3d846b7b3324bc081


cannot import name 'RMSprop' from 'keras.optimizers'

I have this issue too – how did you fix it?

You need to make sure to install the right version of the package, once you do that you won’t have the error. however, I couldn’t use this model bc I don’t have python 2.7. You need exactly the version that they say.


Local frame TwistStamped output from diff_drive plugin

PR to implement proposed feature from #40
I used the same update timer that already exists, so the twist output will publish at the rate of the 'pub_rate' parameter.  In some of my simulation scenarios, I don't want there to be odometry topics available, but in others I would like odometry but not local frame twist.  Therefore, I added two boolean parameters 'pub_odom' and 'pub_twist' that enables advertising of each type of output topic.
However, now there are confusing parameter names. There is 'odom_pub', which is the topic name of the noisy odometry output, and 'pub_odom', which enables this topic.  Same issue between 'twist_pub' and 'pub_twist'. I would change 'odom_pub' to either 'odom_topic' or 'odom_pub_topic', but I'll leave that up to you.

Changed parameter names and updated documentation!  Should I make the local frame twist output default to being enabled?  I made it default to false to not change the behavior of any existing simulation setups.

It probably won't break anything to enable twist output by default.  If you want to check, you can try the turtlebot flatland package with your changes. I don't currently have any other projects that use the differential drive plugin.

It works fine with my simulations using diff_drive. I just wanted to make sure it wouldn't be an issue for you having to set the enable_twist_pub parameter to false explicitly in all your existing models if you didn't want the twist output. Since it is OK on your end, I'll make it default to true.


Add processing status for collection to GET /curation/collections

For the summary/preview API - GET /curation/collections - the processing status could be a collection-level attribute indicating whether the collection and its datasets are in a "publishable" state.
Perhaps just:
"processing_status": [FAILURE, PENDING, SUCCESS]
at the collection-level with no per dataset status in datasets.

For a given collection, if some datasets have a status of PENDING and some have a status of FAILURE, which would you like to report for the summary status?

SPF               ??

As a curator, I would want to be informed for failure cases so I could address while pending cases continued to process.
Contains => Summary
S => S
P => P
F => F
SP => P
SF => F
**PF => F
SPF => F

As a curator, I would want to be informed for failure cases so I could address while pending cases continued to process.

Agreed. If any datasets have a FAILURE, I would want the collection to be F.


Unable to set Firestore settings

Describe the bug
I am not able to set Firestore related settings e.g.
firebase.firestore().settings({
    ignoreUndefinedProperties: true,
})

without running into one of the following errors:

Uncaught FirebaseError: Firestore has already been started and its settings can no longer be changed. You can only modify settings before calling any other methods on a Firestore object.
FirebaseError: Firebase: No Firebase App '[DEFAULT]' has been created - call Firebase App.initializeApp() (app/no-app)

I tried to set them after calling initAuth() and also in the _app.ts.
Version
0.13.1

Does it work if you initialize Firebase yourself first, like in this example?
Are you calling Firestore anywhere else? This package doesn't interact with Firestore at all, and "You can only modify settings before calling any other methods on a Firestore object" seems to suggest your app invokes a Firestore method.

Closing until there's a reproduction or more info.


[pfe-navigation (1.x)] Should have a general purpose, utility slot for secondary links

Unless I use one of the named slots provided (login, language, etc.) I loose some of the styling for secondary links on the right side of the navigation. I think we should add one utility slot for general purpose usage.

Help & Service accounts are both direct links, btw.

Some context for why this is necessary as a universal nav–
Catalog is a great example of a use case that doesn't fit the mainstream four named slots (login, language, etc...). As we continue to expand the use of this nav to other *.redhat.com properties, there will almost definitely be more use cases for a general purpose slot (cloud.redhat.com would likely be on this list, if they decide to adopt this nav)

Some possible discovery questions to answer before we start coding:

Direct links or dropdowns?

Note: pfe-navigation-item[variant="wind"] has been used to indicate a direct link + icon in other use-cases.

Should the new slot support both (dropdown and direct links)?
Should the slot allow you to configure it's position among the other utility links (right or left) for example?  Or should the links always appear in the order they're added to the DOM (not sure how we accomplish that with the template)?

Can you include the mock-up?

https://sketch.cloud/s/V81ga/a/9gjMJM
@castastrophe Here's the mockup. Let me sync up with @eyevana today to make sure we have the correct answers to your discovery questions.

@castastrophe the slot should support both dropdown & direct links. For your last question, are you referring to how the general slot would position itself amongst the other utility slots (language, search, login, and site-switcher)? So if we indicate left on the general slot, it would show up before language (assuming that's provided)? & right would place it after site-switcher, for example?

I just want to add that we'll need the mobile version of this new slot too, and we should be able to use any icon, not just pfe-icon. I noticed that changing the pfe-icon to "web-user" for the "mobile-language" slot had no affect on it. It still displayed the globe.
...

Here's where I think we are:

The markup for utility items in the navigation need a slot for things that are not language, search, login, or the site switcher

${ this.has_slot("language") || this.has_slot("search") || this.has_slot("login") || this.has_slot("site-switcher") ? `

      <slot name="language"></slot>
      <slot name="search"></slot>
      <slot name="login"></slot>
      <slot name="site-switcher"></slot>

` : ""}

Where should the slot go? Before the language slot?

What happens on mobile? Do the new utility items appear before the login and language slots?

Do we need to play defense against a bunch of items added to the utility nav? Do we need to set a limit and possibly collapse others under a dropdown?

On a side note, do we have a help icon yet?

This issue should be moved to RHDS repo once it's established


Giving very low score!!

I trained four classes with 4-5 sentences on each class & it's giving me very low scores, even I got low score when I ran the pre-trained model & gave same sentence which you had given as an example.

I am using as is config settings, no change in that.

Hello!
Unfortunately, I could not reproduce this case.
I added versions of packages to requirements.txt. You can try to reinstall packages and run again.
If still such low score is shown,  inform us here, please.

First, thanks for the response.
I am listing down my versions below:
numpy==1.14.2 (higher version)
nltk==3.2.5 (higher version)
keras==2.1.4 (higher version)
tensorflow==1.6.0 (higher version)
gensim==3.4.0 (higher version)
pandas==0.22.0 (higher version)
sklearn
h5py==2.7.1 (same version)
tqdm==4.19.5 (lower version)
fasttext==0.8.3 (same version)
I downloaded it & ran it again but no change in scoring.

I also added cython==0.26.1 version.
While testing I managed to reproduce your score.
I run the command ./intent_classifier.py ./snips_pretrained/snips_config.json, while some warning information were being printed I pressed Enter. When all the info were printed I entered the phrase and obtained the following:
I want you to add 'I love you, baby' to my playlist
(0.378094, 'SearchCreativeWork')
(0.99986315, 'AddToPlaylist')

It is better to add some info that model is ready. That I have already done. Please, pull from repo.

Thanks!! but I just trying to figure out why is this happening with me?
I just added to check whether my query goes to the model or not so, made a change in intent_classifier.py -> infer -> line num 16
def infer(phrase):     global preprocessor, classes, model     try:         print('phrase: '+phrase)         predictions = model.infer(preprocessor.infer(phrase))     except Exception:         print('Error', file=sys.stderr)         return 0, 'error'     return np.max(predictions), classes[np.argmax(predictions)]
I ran this command python intent_classifier.py ./snips_pretrained/snips_config.json from cmd it gives me this output and hit enter key just after the warning. And yes, I am trying to run it from Anaconda cmd as it has all libs.
So, coz of first enter key hit it gives me one score & another for my question.
Model is ready! You now can enter requests.
phrase:
(0.378094, 'SearchCreativeWork')
I want you to add 'I love you, baby' to my playlist
phrase: I want you to add 'I love you, baby' to my playlist
(0.378094, 'SearchCreativeWork')

Is this - https://github.com/deepmipt/DeepPavlov/tree/master/deeppavlov/models/classifiers/intents
compatible on windows as well?

@dilyararimovna I guess it would be great if you  just upgrade intent classifier. It would be great if you can upgrade the fastext for windows.

I checked fastText==0.8.22 (it can be installed from github, link is given above) but it is not compatible for Windows because of errors during installation.
You can try classifier from DeepPavlov with GloVe embedder (https://github.com/deepmipt/DeepPavlov/blob/master/deeppavlov/models/embedders/glove_embedder.py).

 I will keep you posted. Thanks again!!

One more can we create own GloVe  model & use it on our corpus?

Do you mean train own GloVe model? Yes, you can find here instructions:
https://github.com/stanfordnlp/GloVe

@dilyararimovna  Thanks!! I will keep you posted about what happened after using GloVe embedding.

Changes made to train own model on train.csv data

Changes made in intent_model.py
a. added from deeppavlov.models.embedders.glove_embedder import GloVeEmbedder
b. and code change in
def init(self,
opt: Dict,
embedder: GloVeEmbedder, #FasttextEmbedder,
tokenizer: NLTKTokenizer,
classes=None,
vocabs=None,
**kwargs):

In intents_snips.json config changed the embedder to
"embedder": {
"name": "glove",
"save_path": "embeddings/glove.6B.100d_word2vec.txt",
"load_path": "embeddings/glove.6B.100d_word2vec.txt",
"emb_module": "gensim",
"dim": 100
}
I changed the glove.6B.100d to word2vec using gensim because, I was getting error when it was trying to load the Golve model.
Error: ValueError: invalid literal for int() with base 10: 'the'

Set the train.csv data path in
"dataset_reader": {
"name": "basic_classification_reader",
"x": "text",
"y": "intents",
"data_path": "deeppavlov/configs/intents",
"url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv"
}

After above all changes I ran the command:
python deep.py train configs/intents/intents_snips.json
2018-04-21 11:39:02.193 INFO in 'deeppavlov.core.models.keras_model'['keras_model'] at line 182: [loading weights from intent_cnn_snips_v2.h5]
2018-04-21 11:39:02.269 ERROR in 'deeppavlov.core.common.params'['params'] at line 92: Exception in <class 'deeppavlov.models.classifiers.intents.intent_model.KerasIntentModel'>
Traceback (most recent call last):
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 686, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 516, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 1 in both shapes must be equal, but are 2 and 7. Shapes are [100,2] and [100,7]. for 'Assign_24' (op: 'Assign') with input shapes: [100,2], [100,7].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\common\params.py", line 86, in from_params
component = cls(**dict(config_params, **kwargs))
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 47, in call
obj.init(*args, **kwargs)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 28, in _wrapped
return func(*args, **kwargs)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\models\classifiers\intents\intent_model.py", line 129, in init
self.model = self.load(**params)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 28, in _wrapped
return func(*args, **kwargs)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\keras_model.py", line 183, in load
model.load_weights(str(weights_path))
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\engine\topology.py", line 2652, in load_weights
f, self.layers, reshape=reshape)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\engine\topology.py", line 3189, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2365, in batch_set_value
assign_op = x.assign(assign_placeholder)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 599, in assign
return state_ops.assign(self._variable, value, use_locking=use_locking)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\ops\state_ops.py", line 280, in assign
validate_shape=validate_shape)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 61, in assign
use_locking=use_locking, name=name)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3273, in create_op
compute_device=compute_device)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3313, in _create_op_helper
set_shapes_for_outputs(op)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2501, in set_shapes_for_outputs
return _set_shapes_for_outputs(op)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2474, in _set_shapes_for_outputs
shapes = shape_func(op)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2404, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimension 1 in both shapes must be equal, but are 2 and 7. Shapes are [100,2] and [100,7]. for 'Assign_24' (op: 'Assign') with input shapes: [100,2], [100,7].
Traceback (most recent call last):
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 686, in _call_cpp_shape_fn_impl
input_tensors_as_shapes, status)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 516, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Dimension 1 in both shapes must be equal, but are 2 and 7. Shapes are [100,2] and [100,7]. for 'Assign_24' (op: 'Assign') with input shapes: [100,2], [100,7].
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "deep.py", line 63, in
main()
File "deep.py", line 50, in main
train_model_from_config(pipeline_config_path)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\commands\train.py", line 101, in train_model_from_config
model = fit_chainer(config, iterator)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\commands\train.py", line 53, in fit_chainer
component = from_params(component_config, vocabs=[], mode='train')
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\common\params.py", line 86, in from_params
component = cls(**dict(config_params, **kwargs))
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 47, in call
obj.init(*args, **kwargs)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 28, in _wrapped
return func(*args, **kwargs)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\models\classifiers\intents\intent_model.py", line 129, in init
self.model = self.load(**params)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 28, in _wrapped
return func(*args, **kwargs)
File "E:\username\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\keras_model.py", line 183, in load
model.load_weights(str(weights_path))
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\engine\topology.py", line 2652, in load_weights
f, self.layers, reshape=reshape)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\engine\topology.py", line 3189, in load_weights_from_hdf5_group
K.batch_set_value(weight_value_tuples)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\keras\backend\tensorflow_backend.py", line 2365, in batch_set_value
assign_op = x.assign(assign_placeholder)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\ops\variables.py", line 599, in assign
return state_ops.assign(self._variable, value, use_locking=use_locking)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\ops\state_ops.py", line 280, in assign
validate_shape=validate_shape)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\ops\gen_state_ops.py", line 61, in assign
use_locking=use_locking, name=name)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3273, in create_op
compute_device=compute_device)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 3313, in _create_op_helper
set_shapes_for_outputs(op)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2501, in set_shapes_for_outputs
return _set_shapes_for_outputs(op)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2474, in _set_shapes_for_outputs
shapes = shape_func(op)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py", line 2404, in call_with_requiring
return call_cpp_shape_fn(op, require_shape_fn=True)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 627, in call_cpp_shape_fn
require_shape_fn)
File "E:\Users\userid\AppData\Local\Continuum\anaconda3\lib\site-packages\tensorflow\python\framework\common_shapes.py", line 691, in _call_cpp_shape_fn_impl
raise ValueError(err.message)
ValueError: Dimension 1 in both shapes must be equal, but are 2 and 7. Shapes are [100,2] and [100,7]. for 'Assign_24' (op: 'Assign') with input shapes: [100,2], [100,7].
Note: I haven't set the name of the file name like train.csv in intents_snips.json, I mentioned only the path in dataset_reader.
Just a small feedback keeping the mind what a great thing you people are sharing with the world, it would be great if you have a training video or step by step tutorial which can show how to train model on our own corpus that too in GloVe/Word2Vec model, I know fasttext is better than these two but fasttext doesn't support windows and sometime it gives wired errors like insufficient memory & all.

As SNIPS dataset contains 7 intents then I think this error was caused by difference between dataset structure.
Try to use to load dataset for DeepPavlov (dot not use dataset that was downloaded for this repo).

I didn't use the downloaded dataset, I created mine & changed the SNIPS config file as per my requirement.

And your dataset has two classes? Then the problem is simple - as you can read in the error message - you are trying to load pre-trained model. If you want to train model from scratch you have to change load_path to model.

I changed the config file:
Created "valid.csv" & "train.csv" both of them has same number of intents.
Updating the own_snips_classes.dict (custom) classes/intents correctly in vocabs folder.
AddToPlaylist	13
RateBook	17
But still getting this error:
E:\Users\user\anaconda3\lib\site-packages\gensim\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial
warnings.warn("detected Windows; aliasing chunkize to chunkize_serial")
2018-04-21 19:00:09.98 INFO in 'summarizer.preprocessing.cleaner'['textcleaner'] at line 37: 'pattern' package not found; tag filters are not available for English
2018-04-21 19:00:14.519 INFO in 'main'['deep'] at line 49: pipeline config_path configs/intents/intents_snips.json
2018-04-21 19:00:14.555 WARNING in 'deeppavlov.dataset_readers.basic_classification_reader'['basic_classification_reader'] at line 87: Cannot find E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\configs\intents\test.csv file
2018-04-21 19:00:14.556 INFO in 'deeppavlov.dataset_iterators.basic_classification_iterator'['basic_classification_iterator'] at line 67: Splitting field <> to new fields <<['train', 'valid']>>
2018-04-21 19:00:14.560 INFO in 'deeppavlov.core.data.vocab'['vocab'] at line 158: [loading vocabulary from E:\user\NLP\CNN\DeepPavlov-master\download\vocabs\own_snips_classes.dict]
2018-04-21 19:00:14.561 INFO in 'deeppavlov.core.data.vocab'['vocab'] at line 146: [saving vocabulary to E:\user\NLP\CNN\DeepPavlov-master\download\vocabs\own_snips_classes.dict]
2018-04-21 19:00:14.565 INFO in 'deeppavlov.models.embedders.glove_embedder'['glove_embedder'] at line 49: [loading embeddings from E:\user\NLP\CNN\DeepPavlov-master\download\embeddings\glove.6B.100d_word2vec.txt]
2018-04-21 19:00:14.565 INFO in 'gensim.models.utils_any2vec'['utils_any2vec'] at line 172: loading projection weights from E:\user\NLP\CNN\DeepPavlov-master\download\embeddings\glove.6B.100d_word2vec.txt
2018-04-21 19:00:14.566 DEBUG in 'smart_open.smart_open_lib'['smart_open_lib'] at line 149: {'kw': {}, 'mode': 'rb', 'uri': 'E:\user\NLP\CNN\DeepPavlov-master\download\embeddings\glove.6B.100d_word2vec.txt'}
2018-04-21 19:00:14.567 DEBUG in 'smart_open.smart_open_lib'['smart_open_lib'] at line 621: encoding_wrapper: {'errors': 'strict', 'encoding': None, 'mode': 'rb', 'fileobj': <_io.BufferedReader name='E:\user\NLP\CNN\DeepPavlov-master\download\embeddings\glove.6B.100d_word2vec.txt'>}
2018-04-21 19:00:58.339 INFO in 'gensim.models.utils_any2vec'['utils_any2vec'] at line 234: loaded (400000, 100) matrix from E:\user\NLP\CNN\DeepPavlov-master\download\embeddings\glove.6B.100d_word2vec.txt
2018-04-21 19:00:58.346325: I E:\tf_jenkins\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
2018-04-21 19:00:58.352 INFO in 'deeppavlov.models.classifiers.intents.intent_model'['intent_model'] at line 91: self opt embedding_size 100
2018-04-21 19:00:58.352 INFO in 'deeppavlov.models.classifiers.intents.intent_model'['intent_model'] at line 92: fasttext_model load_path E:\user\NLP\CNN\DeepPavlov-master\download\embeddings\glove.6B.100d_word2vec.txt
2018-04-21 19:00:59.333 INFO in 'deeppavlov.core.models.keras_model'['keras_model'] at line 99: [initializing KerasIntentModel from scratch]
WARNING:tensorflow:From E:\Users\user\anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
NHWC for data_format is deprecated, use NWC instead
2018-04-21 19:01:02.587 WARNING in 'tensorflow'['tf_logging'] at line 126: From E:\Users\user\anaconda3\lib\site-packages\tensorflow\python\util\deprecation.py:497: calling conv1d (from tensorflow.python.ops.nn_ops) with data_format=NHWC is deprecated and will be removed in a future version.
Instructions for updating:
NHWC for data_format is deprecated, use NWC instead
2018-04-21 19:01:04.385 INFO in 'deeppavlov.core.commands.train'['train'] at line 117: train_config {'metrics': ['accuracy'], 'validate_best': True, 'test_best': True}
2018-04-21 19:01:04.386 INFO in 'deeppavlov.core.commands.train'['train'] at line 118: model config <deeppavlov.core.common.chainer.Chainer object at 0x000002CE403F4860>
Traceback (most recent call last):
File "deep.py", line 63, in
main()
File "deep.py", line 50, in main
train_model_from_config(pipeline_config_path)
File "E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\core\commands\train.py", line 129, in train_model_from_config
_train_batches(model, iterator, train_config, metrics_functions)
File "E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\core\commands\train.py", line 231, in _train_batches
y_predicted = list(model(list(x)))
File "E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\core\common\chainer.py", line 88, in call
res = component(*[mem[k] for k in in_params])
File "E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\models\classifiers\intents\intent_model.py", line 219, in call
preds = np.array(self.infer_on_batch(data))
File "E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\core\models\tf_backend.py", line 28, in _wrapped
return func(*args, **kwargs)
File "E:\user\NLP\CNN\DeepPavlov-master\deeppavlov\models\classifiers\intents\intent_model.py", line 203, in infer_on_batch
predictions = self.model.predict(features)
File "E:\Users\user\anaconda3\lib\site-packages\keras\engine\training.py", line 1824, in predict
check_batch_axis=False)
File "E:\Users\user\anaconda3\lib\site-packages\keras\engine\training.py", line 113, in _standardize_input_data
'with shape ' + str(data_shape))
ValueError: Error when checking : expected input_1 to have 3 dimensions, but got array with shape (20, 1)
I really don't know where am I going wrong now?

This error means that you are trying to send training data in 2d format whereas required format is (batch_size, text_size, embedding_size). You have to check whether you are reading data correctly, whether you are giving data to model correctly.
You can try debugging or printing.

First thanks for your continuous support & patience.
This is what my training dataset looks like.. it's just a copy from your existing dataset.

I haven't changed anything in the code except this config json file:
{
"dataset_reader": {
"name": "basic_classification_reader",
"x": "text",
"y": "intents",
"data_path": "C:/NLP/CNN/DeepPavlov-master/deeppavlov/configs/intents",
"url": "http://lnsigo.mipt.ru/export/datasets/snips_intents/train.csv"
},
"dataset_iterator": {
"name": "basic_classification_iterator",
"seed": 42,
"field_to_split": "train",
"split_fields": [
"train",
"valid"
],
"split_proportions": [
0.9,
0.1
]
},
"chainer": {
"in": ["x"],
"in_y": ["y"],
"pipe": [
{
"id": "classes_vocab",
"name": "default_vocab",
"fit_on": ["y"],
"level": "token",
"save_path": "vocabs/own_snips_classes.dict",
"load_path": "vocabs/own_snips_classes.dict"
},
{
"in": ["x"],
"in_y": ["y"],
"out": ["y_predicted"],
"main": true,
"name": "intent_model",
"save_path": "intents/own_intent_cnn_snips_v2",
"load_path": "intents/own_intent_cnn_snips_v2",
"classes": "#classes_vocab.keys()",
"opt": {
"kernel_sizes_cnn": [
1,
2,
3
],
"filters_cnn": 256,
"lear_metrics": [
"binary_accuracy",
"fmeasure"
],
"confident_threshold": 0.5,
"optimizer": "Adam",
"lear_rate": 0.01,
"lear_rate_decay": 0.1,
"loss": "binary_crossentropy",
"text_size": 15,
"coef_reg_cnn": 1e-4,
"coef_reg_den": 1e-4,
"dropout_rate": 0.5,
"epochs": 1000,
"dense_size": 100,
"model_name": "cnn_model"
},
"embedder": {
"name": "glove",
"save_path": "embeddings/glove.6B.100d_word2vec.txt",
"load_path": "embeddings/glove.6B.100d_word2vec.txt",
"emb_module": "gensim",
"dim": 100
},
"tokenizer": {
"name": "nltk_tokenizer",
"tokenizer": "wordpunct_tokenize"
}
}
],
"out": ["y_predicted"]
},
"train": {
"epochs": 100,
"batch_size": 20,
"metrics": [
"sets_accuracy"
],
"validation_patience": 5,
"val_every_n_epochs": 1,
"log_every_n_epochs": 1,
"show_examples": false,
"validate_best": true,
"test_best": false
}
}
and executing the cmd:
python deep.py train configs\intents\intents_snips.json
Then it creates own_snips_classes.dict in "DeepPavlov-master\download\vocabs" in that file as I mentioned you it shows correct counts of utterances for it's respective classes.
Note: Neither I haven't done any change in code nor in dataset except the config file what I shared here with you.

For glove embedder now we are using gensim. Therefore, correct format for embedding file with 400k words and 50d embedding is following:
400000 50
the 0.418 0.24968 -0.41242 0.1217 0.34527 -0.044457 -0.49688 -0.17862 -0.00066023 -0.6566 0.27843 -0.14767 -0.55677 0.14658 -0.0095095 0.011658 0.10204 -0.12792 -0.8443 -0.12181 -0.016801 -0.33279 -0.1552 -0.23131 -0.19181 -1.8823 -0.76746 0.099051 -0.42125 -0.19526 4.0071 -0.18594 -0.52287 -0.31681 0.00059213 0.0074449 0.17778 -0.15897 0.012041 -0.054223 -0.29871 -0.15749 -0.34758 -0.045637 -0.44251 0.18785 0.0027849 -0.18411 -0.11514 -0.78581

Please, make sure you have the following format.
Also please, note that glove embedder requires list of tokens whereas fasttext embedder requires string that will be splitted to tokens.
For the next release all this points are going to be fixed. Now you can try to change call and encode methods for glove as for fasttext.

Thanks for the reply!!
Do you want me to change the call & encode methods here deeppavlov/models/embedders/glove_embedder.py ? I found many files with same methods.

Hello!
Yes.
Firstly, you have to change your glove.txt file (add header line as above mentioned).
Then change either deeppavlov/models/embedders/glove_embedder.py (make it taking string) or deeppavlov/models/classifiers/intents/intent_model.py (make method texts2vec giving list of tokens to embedder not sentence).
For example, you can change row
embeddings_batch = self.fasttext_model([' '.join(sen.split()[:self.opt['text_size']]) for sen in sentences])

to
embeddings_batch = self.fasttext_model([sen.split()[:self.opt['text_size']] for sen in sentences])

At last I am got the intent 😄 after the change in intent_model.py
I converted the glove model to word2vec coz it was giving me wired exception & it's gone when I converted it to word2vec & loaded it.
x::Rate this series a 5

['RateBook']
x::give four stars to What the Day
['RateBook']
x::add the piano bar to my Cindy Wilson
['AddToPlaylist']
x::hello
['AddToPlaylist']
x::

Now question is how we will get the scores of predicted classes, I have to set some threshold as well. I am checking the method interact_model in DeepPavlov-master\deeppavlov\core\commands\infer.py is that the right place to check? And really a BIG thanks again.

For the next release we will change output to much more comfortable.
For the current version you can just change
if len(args) == 1:
    pred = model(args)
else:
    pred = model([args])

to
if len(args) == 1:
    pred = model(args, predict_proba=True)
else:
    pred = model([args], predict_proba=True)

Thanks a lot!! I will do the change:
Now I the concern is bit diff:
x::add the piano bar to my Cindy Wilson
>> [0.8980047  0.08839173]
x::cindy wilson let's go to bar
>> [0.5182335  0.46872252]
x::cindy wilson let's go to my bar
>> [0.67825717 0.3152743 ]
x::Cindy Wilson let's go to my bar
>> [0.6949899 0.3103463]

If you see it gives good score for known sentence "add the piano bar to my Cindy Wilson" but if I change the sentence but keeping almost same words then also it gives high scores. I was expecting a very low score for such scenarios. I going to change the kernel sizes (kernel_sizes_cnn) and see whether it improves or not but do you have suggestions on this?

Because as you can see in json config file you did not put some preprocessor only embedder and tokenizer.
If you want, you can add any preprocessor. See examples in deeppvlov.models/preprocessors
I am glad you have succeeded with usage of DeepPavlov.
Looking forward for the next release (hopefully, one-two weeks)!

Cool!! I will wait for it.. and I guess we have to set the preprocessors like this right?
"pipe": [
{
"name": "stream_spacy_tokenizer",
"lowercase": "true",

as an element of pipe list you can add something like this:
"pipe": [
{
"in": ["x"],
"out": ["x_preproc"],
"name": "registered_preprocessor" # registered name of preprocessor
},
{ # here the model starts for example
"in": ["x_preproc"],
"in_y": ["y"],
"out": ["y_predicted"],
...
}

for this kind of dataset is there any out of the box preprocessor is available which I can use or do I need to create one & registered it in DeepPavlov-master\deeppavlov\models\preprocessors\squad_preprocessor.py ?

you can either use existing preprocessors or create and registered your own dataset (to register do not forgot to include importing from file to init.py).

Thanks a lot for the support!! I am using the model now & get back to you with my observations I guess that will help you to improve further :)

I am glad to help you!


Click the switch language button, there is a white frame outside the box

This is not a bug, light gray will turn white on dark colors


Attaching plugin source from <IDE_ROOT>/lib/src directory (#207)

Pull Request Details
Attaches plugin source from <IDE_ROOT>/lib/src directory.
Description
Sources are provided only with IntelliJ IDEA Ultimate distribution. I've checked IntelliJ IDEA Community, RubyMine, WebStorm, and PhpStorm - none of them contains the lib/src directory.
Technical solution:

source ZIPs are collected as part of the IDE dependency object
PluginDependencyManager adds source entries in the plugin's Ivy file
a new Ivy artifact pattern is registered: <IDE_ROOT>/[artifact](.[ext]) to be able to resolve source artefacts specified as e.g.:

<artifact name="lib/src/src_css-api" type="zip" ext="zip" conf="sources"/>

The implementation uses explicit mapping of the platform plugin ID to the source's file name. This is needed because in the IDE distribution there is no information linking source ZIPs with specific plugins (file names are not enough). The implementation will be removed in the future as we will aim to provide sources within plugin ZIP distributions, not in the IDE.
If the plugin bundle contains source JAR, it will take precedence over the sources in IDE distribution (it shouldn't happen).
I've added the platformPluginId property in PluginDependency for resolving a source ZIP file defined in the mapping. We could potentially get rid of it and use IDs used internally in the Gradle repository, but I think it can become unstable when plugin directory names (e.g. IDE_ROOT/plugins/SpringBoot) are changed.
Related Issue
#207: Attach <IDE_ROOT>/lib/src/*.zip sources
Motivation and Context
It allows developers to use open API sources while developing plugins, e.g., when debugging issues.
How Has This Been Tested
Tested locally with the test plugin project. Build script included:
intellij {
    type.set("IU")
    version.set("2021.2.4")
    plugins.set(listOf(
        "com.intellij.css",
        "com.intellij.database",
        "com.intellij.javaee",
        "com.intellij.spring",
        "com.intellij.spring.boot",
        "com.intellij.persistence",
        "Tomcat"
    ))
}

The Gradle IntelliJ Plugin generated Ivy files with source ZIP artifacts entries. After importing the project in IDE, sources were available when some plugin class was open e.g. com.intellij.psi.css.CssElementDescriptorProvider).
Types of changes

[ ] Docs change / refactoring / dependency upgrade
[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist

[x] I have read the CONTRIBUTING document.
[x] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[x] I have included my change in the CHANGELOG.
[x] I have added tests to cover my changes.
[x] All new and existing tests passed.

if sources are available from the plugin itself, they must take precedence to those bundled with the IDE distribution (just in case we have some overlap)

Qodana check is queued and I don't see any cancel/re-run button for this specific check (because it is queued I assume, I reported it to the Qodana team):
https://github.com/JetBrains/gradle-intellij-plugin/pull/1019/checks?check_run_id=6912165556
It has passed previously and I don't expect any violations in the code. I would like to avoid re-checking this PR as it takes more than an hour.
Please consider checks as passed.


Make LocalStorage mechanism

Then: scrape handful of data (500ish each or 100Kb), put it in direct file in this app, call it from password-ga-kata, to check its randomness etc
Now: add more words (optional) then store somewhere else endpoint, make this app fetch it and store in localstorage, password-ga-kata should query from localstorage (change from direct access/flatfiles to localstorage)
Next: Scrape all data, discuss how many it would be if chunked for 100Kb (TBD) each, create mechanism for app to download wordpack and store it in localstorage
Why localstorage tho? I think we could improve it as PWA for next improvement, so that app could run standalone (only fetch new wordpacks/chunks)
Originally posted by @ans-4175 in https://github.com/ans-4175/password-ga/issues/9#issuecomment-964749676

@ans-4175 are we settling with JSONBin for this iteration? Other approach that I could think of is to use Netlify to serve it. We can do this:
curl https://suspicious-wozniak-66e0f6.netlify.app/.netlify/functions/server

Then it will return the dictionary that we have right now. Compared to JSONBin, Netlify has 125k requests limit (of functions) per month compared to JSONbin 10k all time. I'm using https://github.com/neverendingqs/netlify-express/blob/master/express/server.js as base.

I think JSONBin would be suffice for now, for just trying to make fetch for localstorage?
With netlify we should make another repos right @imballinst ?

@ans-4175 yeah, we kinda have to make this an actual monorepo the very least (using a different base folder), but that would be overkill, on a second thought.
Should I create the JSONBin and put the list of words there? If so, I'll do it and then I'll iterate the next PR.

Neat, I already set it up on https://api.jsonbin.io/b/618e0782820eda3cc81c3105
Just similar with words.json. After this issues validated, we could think for furthers.
But I think we should fix #2 and #15 first before moving to this one @imballinst ?
So that #14 and #3, will be in next version?

Sounds good, let's get #2 and #15 finished first @ans-4175 :+1:

So basic logic on localStorage

check whether storage exist
if not, fetch from endpoint, store it
if both yes/not, fetch all from storage, then put it as constant
doing as usual

is that correct?

Yes @ans-4175, that sums up neatly ✨


Remove link to obsolete repository

Remove link to obsolete repository.

/cc @ybettan

Looks like I have created some dup here https://github.com/openshift/driver-toolkit/pull/114.
I am sorry for that, missed that PR.

Closing in favor of https://github.com/openshift/driver-toolkit/pull/114
/close


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Description
Hello guys,
Can someone explain me reason for this example:
https://github.com/Azure/terraform-azurerm-caf-enterprise-scale/blob/main/docs/wiki/[Examples]-Deploy-Using-Module-Nesting.md
I don't understand use case here, it's easier to achieve this same with custom Landing Zone / dummy Management group. What benefits is giving this example?
"${var.root_id}-example-mg" = {
  display_name               = "${upper(var.root_id)} Example Lg"
  parent_management_group_id = "${var.root_id}-landing-zones"
  subscription_ids           = []
  archetype_config = {
    archetype_id   = "default_empty"
    parameters     = {}
    access_control = {}
  }
}
"${var.root_id}-online-example-1" = {
  display_name               = "${upper(var.root_id)} Online Example 1"
  parent_management_group_id = "${var.root_id}-example-mg"
  subscription_ids           = []
  archetype_config = {
    archetype_id   = "customer_online"
    parameters     = {}
    access_control = {}
  }
}

@mlomat thank you for your question.
In some cases, customers wish to break apart their deployment across multiple pipelines, sometimes with delegated administration at different scope to meet security and governance requirements.
Some customers also prefer to manage their deployments in smaller units of scale (i.e. fewer resources per deployment).
Module nesting allows our customers to have a flexible approach to building out their Azure landing zones using this module.

In short, yes you are only creating two Management Groups in this example but you can change the configuration to update this as needed.
The example is only really intended to show how to create the distinct deployments, rather than to do something specifically useful.
To see a better example of using the module in this way, please see the latest test framework update here:
https://github.com/Azure/terraform-azurerm-caf-enterprise-scale/blob/1288f401ad8cf85b3e527729a4dddded58aef2ef/tests/modules/test_003_add_mgmt_conn/main.tf#L1-L146

Ok, now I get it, make sense for me, and I will also use it to split deployments. Thanks!
Maybe it will be more clear if default setup will be "deploy_core_landing_zones = false" and should be explicitly set to true when deploying. Or even better to allow choice which part of Enterprise scale you need.
Something like:
deploy_core_landing_zones =
{
deploy_platform =
{
connectivity=true
identity=false
management=true
}
decommissioned = true
sandbox = false
}
I have currently case where I will probably have 2 nested levels of Enterprise Scale with only some elements and most of them will be optional and could be added in the future. If I have such option with config, I won't need to implement this through Custom Landing Zones.
Best
Mateusz

Thank you for the feedback @mlomat. Interestingly we did consider changing the default for deploy_core_landing_zones to false but we fell this is too risky for customers already using this module in production.
I will close this issue now as I don't believe there is anything we will change in the module regarding this, but please feel free to re-open if you have any further questions regarding your scenario above.


barometer sensor not providing any data

Describe the bug
The Observable never fires an event on a Google Pixel 2XL.  It works on an Android Emulator.
To Reproduce
import React, {Component} from 'react';
import { Text } from 'react-native';
import { barometer } from 'react-native-sensors';

interface Props {}
interface State {
  severity: number,
  subscription: Object | null,
  pressure: number,
}

export default class AddItem extends Component<Props, State> {
  constructor(props: Props) {
    super(props);
    this.state = {
      severity: 2,
      subscription: null,
      pressure: 0.0
    };
  }

  componentDidMount() {
    const subscription = barometer.subscribe(({pressure}) => {
      console.log(`Pressure is ${pressure}`);
      this.setState({pressure});
    });
    this.setState({subscription});
  }

  componentWillUnmount() {

  }
  render() {
    return (<Text>Current Pressure: {this.state.pressure.toFixed(2)}</Text>);
  }
}

Expected behavior
Pressure reading is updated.
Device Info

React Native Version: 58.5
Device: Google Pixel 2 XL
Version: 5.1.7

This seems odd. Does anyone in @react-native-sensors/maintainers has a Google Pixel and might be able to debug this? Otherwise, I have no idea how to look further into this as I don't own the device and it's, therefore, hard to debug for me

I have a Pixel 2 XL. I'll try to reproduce it

I'm getting a lot of log output when running a sample app on my Pixel 2 XL:
03-05 21:41:01.167 12572 12826 I chatty  : uid=10212(com.sensortest) mqt_js identical 2 lines
03-05 21:41:01.206 12572 12826 I ReactNativeJS: Pressure is 1000.7217407226562
03-05 21:41:01.248 12572 12826 I ReactNativeJS: Pressure is 1000.720947265625
03-05 21:41:01.327 12572 12826 I chatty  : uid=10212(com.sensortest) mqt_js identical 2 lines
03-05 21:41:01.364 12572 12826 I ReactNativeJS: Pressure is 1000.720947265625
03-05 21:41:01.408 12572 12826 I ReactNativeJS: Pressure is 1000.7186279296875
03-05 21:41:01.479 12572 12826 I chatty  : uid=10212(com.sensortest) mqt_js identical 2 lines
03-05 21:41:01.524 12572 12826 I ReactNativeJS: Pressure is 1000.7186279296875
03-05 21:41:01.558 12572 12826 I ReactNativeJS: Pressure is 1000.71533203125

And the text component is displaying a pressure changing between 1000.65 and 1000.72.
Versions I'm using:

React Native: 0.58.6
React Native Sensors: 5.1.7

I will retry this today and check the versions that I'm using.

Thank you for your help @dbanck, it's highly appreciated

Sorry for the inconvenience and thank you for your responses.  Turns out that my phone needed to be rebooted and that solved the issue.  I tried other apps before the reboot and they were also not able to read the sensor data.  I'm not sure how my phone got into a state were the barometer wasn't being read, but a reboot solved it.


Log console messages in UI-tests + fix visual regression tests

@mariobuikhuizen @maartenbreddels visual regression tests passing again! I think we were facing https://github.com/jupyter-widgets/ipywidgets/pull/3549 and updating the yarn.lock did the trick

Great to hear!
What makes you think https://github.com/jupyter-widgets/ipywidgets/pull/3549 was the problem? Note that I found out today this was also present in widgets 7 https://github.com/jupyter-widgets/ipywidgets/pull/3687

What makes you think https://github.com/jupyter-widgets/ipywidgets/pull/3549 was the problem?

I'm not entirely sure this was the culprit. But we've seen very similar symptoms in ipyleaflet https://github.com/jupyter-widgets/ipyleaflet/pull/968#issuecomment-1219895024: issue in fetching some sub-widget state and not reproducible outside of Galata. So that's what makes me think it's the reason.

Thanks for the notification @martinRenou! I've updated #1539, which now has no visual regression.


[docs] Add a diagram for TX object dependencies

A followup to #557, where we noted the explanation of TXes depending on objects output by other TXes could be explained by a diagram.
This example may help:
graph TB;
    A((3))-->|Some label|B((9))
    A-->C((20))
    C-->D((15))
    C-->|another label|E((7))

Originally posted by @huitseeker in https://github.com/MystenLabs/fastnft/pull/557#discussion_r814417251

Hi Francois and Xun,
Please see the diagrams Evan has kindly provided to see if we might include them in the programming with objects tutorial or elsewhere:
https://app.golinks.io/sui-storage
Thanks!

@huitseeker should we work this in to the section beginning with:
"Transactions (and thus, certificates) take objects as input..."

Closing this issue. Will open a new issue if this comes up again


Disconnects on network failure

As subject reads.  When the network connection LTE or WiFi disconnects , socks tun VPN Connection disconnects.
Need to reopen sockstun app to reconnect.

I can't reproduce it.

Enable sockstun. (include browser app)
Start browser.
Turn off Wifi.
Turn on Wifi.
Refresh browser page.

Check my video
https://drive.google.com/file/d/17f2qAwpUzJGW5FnWJpQtIvvHRKsGPOLX/view?usp=sharing

sorry you can close,  its an issue with appium.


Create prerelease.yml

Created prerelease.yml
Description
This workflow is added to simplify the work around prerelease publishing to npm and update package.json with an npm pre-release version
Related Issue

Motivation and Context

How Has This Been Tested?

Screenshots (if appropriate):
Types of changes

[ ] Bug fix (non-breaking change which fixes an issue)
[x] New feature (non-breaking change which adds functionality)
[ ] Breaking change (fix or feature that would cause existing functionality to change)

Checklist:

[x] I have signed the Adobe Open Source CLA.
[ ] My code follows the code style of this project.
[ ] My change requires a change to the documentation.
[ ] I have updated the documentation accordingly.
[x] I have read the CONTRIBUTING document.
[ ] I have added tests to cover my changes.
[x] All new and existing tests passed.

Saying there's problem with the syntax. It may be on the line where I am adding comma-separated dependency list. Not sure if that is wrong.


M--: Watch uzairch.com

M-- requests the watch of the watch_keyword uzairch\.com. See the MS search here and the Stack Exchange search here.

uzairch\.com has been seen in 1 true positive, 1 false positive, and 0 NAAs.

Approved by Makyen in Charcoal HQ


Rename bids.py

I've been getting frustrated a few times now with not being able to import bids.BIDSLayout due to the current name of snakebids.core.bids. So this just changes the name to something unique. Makes no difference to imports: bids can still be imported directly from snakebids.

From a maintenance perspective, renaming seems fine, just wondering if you'd be able to provide an example where bids.BIDSLayout couldn't be imported due to a naming conflict?

It would happen during development any time I set up a if __name__ == "__main__": block at the end of any of the snakebids.core.x files for a quick test of something and I ran the file directly. I don't think it ever would have affected the end user.


'yy' and 'p' don't work.

Describe the bug
When I used this plugin, I found the normal yank and paste functions don't work, for example 'yy' and 'p' as follows.
Config options
  "m4xshen/hardtime.nvim",
  lazy = false,
  dependencies = { "MunifTanjim/nui.nvim", "nvim-lua/plenary.nvim" },
  opts = { -- Add "oil" to the disabled_filetypes
    disabled_filetypes = { "qf", "netrw", "NvimTree", "lazy", "mason", "oil" },
    restricted_keys = {
      ["h"] = {},
      ["j"] = {},
      ["k"] = {},
      ["l"] = {},
    },
  },

Are you using any Neovim distro?

LazyVim actually. But I don't think there is a conflict?

Does dd and then p work?

No, dd works as expected, but not the p.

more info: why I said yy doesn't work, because I have set 'highlight on yank' autocmd, but yy didn't highlight the text, shift+y did.

Please try yyp again with the following config:
  "m4xshen/hardtime.nvim",
  lazy = false,
  dependencies = { "MunifTanjim/nui.nvim", "nvim-lua/plenary.nvim" },
  opts = { -- Add "oil" to the disabled_filetypes
    disabled_filetypes = { "qf", "netrw", "NvimTree", "lazy", "mason", "oil" },
    restricted_keys = {
      ["h"] = {},
      ["j"] = {},
      ["k"] = {},
      ["l"] = {},
    },
    resetting_keys = {
      ["y"] = {},
      ["p"] = {},
   }
  },

Also do you have any keymap related to y or d?

Yes, this works, but I think this is a compromised way to do it, right? I checked my remaps (also the LazyVim's), there is no keymap related to y or d.

Yeah this is just for debugging.

Sorry I just find I have a plugin concerning yank and paste action, but it is inside the LazyVim, called yanky.nvim. I bet this is not the issue.
Concrete mapping: https://github.com/LazyVim/LazyVim/blob/main/lua/lazyvim/plugins/extras/coding/yanky.lua
Hope that this is helpful!

Fixed in ab488fa. Thanks for the feedback!


Defer starting Worker if not available

Why
Drizzle's fancy schema support (Drizzle Query) seems to require schema to be defined at (Next.js) pre-render time. Deferring db singleton instance creation breaks db.query.{table}.findXXX at build time.
This PR adds startWorker function which won't start the Worker when it can't (Worker is not defined).
Usage
Initialization
// sqlite proxy to sqlite wasm worker
// NOTE: sqlocal throws Worker undefined error here during build but not with this fork.
const { startWorker, driver, batchDriver, batch, transaction } = new SQLocalDrizzle({
  databasePath: 'newspond.sqlite3',
  readOnly: false,
  verbose: true,
})
// connect to database and initialize schema if missing
// NOTE: `schema` passed here enables `db.query.{table}` calls.
const db = drizzle(driver, batchDriver, { schema })

startWorker should be called from the client-side just before creating tables and indexes.

Sorry. Unnecessary changes got rolled into PR.


"NUCET, Băița, Băița-Plai" is a locality in "NUCET" city, "BIHOR" county

Description
Check that the locality is displayed in the search bar in accordance with Act 2 02/16/1968

Precondition
Empty the search bar after every step

Steps 1
Acces to the link https://peviitor-ro.github.io/ui.orase/
Expected result
The page opens

**Steps **
Write "Băița"
Expected result
In the drop down menu you find "Băița, BIHOR(NUCET)"
Test Data
Băița

**Steps **
Write ""
Expected result
In the drop down menu you find ", BIHOR(NUCET)"
Test Data
NUCET


actual mod menu?

would be cool
just collect 0 useful mods (maybe i missed any use of every mod but ok) because there arent any useful mods on the gamebanana
and make a mod menu out of that
thats an enchancement and i dont know how to make tags cause there aint a button for that

No, because I think that would need to be injected into roblox so it would be bannable

No

i guess

That aint a byfron bypass lmao its going to be on every channel soon

I am not going to take mods that other people have made and bundle them directly with Bloxstrap.


Temurin 11.0.15 doesn't add java_home on Catalina

I have to add the java path in java_home manually to make it work.

@Icesport33 Is this when using the downloaded pkg file or installing from Homebrew, or something else?

@sxa When installing from pkg file. I never try the other way. I will leave a comment here if I have a chance to test it.

@sxa Sorry, I forgot to mention this only happens on JRE


Remove duplicate Boswell from wanted list

We had two entries on the wanted list for Boswell's Life of Johnson.
Also clear up which edition and volume the PG link is pointing to.

Thanks!  How can you tell?

Mostly going off the Wikipedia article on Life of Johnson
To paraphrase: three 'initial' editions were published in the 18th century, all of which were unabridged. George Birkbeck Hill's edition (published in 1887, in six volumes) uses the third edition as its basis, then adds its own commentary on top of the existing notes.
Gutenberg has has two copies of Life of Johnson; one is clearly labeled abridged and edited by Charles Osgood. The other is split into six volumes, each marked as edited by Hill. Without claiming any particular expertise, I feel comfortable saying that those six are Hill's six volumes.

OK, that makes sense. Probably when I looked at it last (a long time ago) the abridged version may have been the only option at PG, or maybe I just overlooked the other ones. Thanks!


make promise based css appending work with zombie.js

tests with zombie.js fail, because it seems that zombie.js will not append stylesheets links to the document head
document.head.appendChild(link);

so loadCb() will never called and the promise is rejected and the JS code is not executed!

Sounds reasonable.


too-many-maps problem

We should not ever consider a single matched path segment/file name as the source for a "too-many-maps" status.

fixed in https://github.com/nexB/scancode.io/pull/882


Howto SebMap + Seed

seed.config.ts
-> packages
      'angular2-google-maps/core': {         defaultExtension: 'js',         main: 'core.umd.js'       }
-> paths
      'angular2-google-maps/core': `node_modules/angular2-google-maps/core/core.umd.js`,

let me know if you have any problems, I battled with this for 4 hours!!

It worked for me. Thanks, you really saved my day! :)

Great! 💪🏻

Thanks for showing this.

With seed, are you referring to https://github.com/mgechev/angular2-seed ?
I'm battling getting this working now, but keep getting "No provider for GoogleMapsAPIWrapper!" although I have GOOGLE_MAPS_PROVIDERS and GOOGLE_MAPS_DIRECTIVES in the bootloader in main.ts. I'm pretty sure there is something I'm missing in this config file...

@lindsve yes mgechev seed!
Its always complicated w seed 🙈
How old is your seed?

Can you paste your seed.config?

I cloned the seed today.
The config is at https://gist.github.com/lindsve/6d203013728902f642de8ce1612502ad

      },
      'angular2-google-maps/core': {
        main: 'index.js',
        defaultExtension: 'js'
      }
    }

There you want to have core.umd.js instead of index

You can either use the core for dev or index
Not a mix 🤓

Ah, thanks. I had tried core.umd.js as well, but it seems it's an issue referencing a custom javascript file which I use while hacking in a different tile set than the Google map. Removed the reference to that file and fixed the config file and it's almost there now. It seems I have some issues getting the correct path for the component css file set (map is empty, but markers are added, although as a list of marker names instead of actual markers).
Getting closer, thanks again :) A lot of new concepts to grasp for a guy not used to Angular (or javascript for that matter) development.
This was a lot easier in the seed I tried earlier, but that one used lite-server which I couldn't figure out how to get working with heroku. I think this seed is a better candidate for the production app we're starting with later though


Error with "Run the MediaPipe binary to extract the features."

I'm running now
bazel build -c opt --linkopt=-s \
  --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true \
  mediapipe/examples/desktop/youtube8m:extract_yt8m_features

I've got followed error.
My configuration
Ubuntu 18.04
python==3.7
Tensorflow==1.15
g++ --version==7.5.0
Error
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'com_google_absl' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'com_google_protobuf' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'com_google_googletest' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'com_github_gflags_gflags' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'rules_cc' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'build_bazel_rules_apple' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'build_bazel_rules_swift' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'build_bazel_apple_support' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'bazel_skylib' because it already exists.
DEBUG: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/third_party/repo.bzl:108:14:
Warning: skipping import of repository 'pybind11' because it already exists.
WARNING: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/com_google_audio_tools/third_party/fft2d/BUILD:3:11: in linkstatic attribute of cc_library rule @com_google_audio_tools//third_party/fft2d:fft2d: setting 'linkstatic=1' is recommended if there are no object files
WARNING: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/tensorflow/core/lib/gtl/BUILD:129:11: in linkstatic attribute of cc_library rule @org_tensorflow//tensorflow/core/lib/gtl:map_util: setting 'linkstatic=1' is recommended if there are no object files. Since this rule was created by the macro 'cc_library', the error might have been caused by the macro implementation
WARNING: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/tensorflow/core/BUILD:1227:11: in linkstatic attribute of cc_library rule @org_tensorflow//tensorflow/core:lib_internal: setting 'linkstatic=1' is recommended if there are no object files. Since this rule was created by the macro 'cc_library', the error might have been caused by the macro implementation
WARNING: /home/members/yeoreum/.cache/bazel/_bazel_yeoreum/17e6889d84baa7b06a36a4e093846fd1/external/org_tensorflow/tensorflow/core/BUILD:1546:16: in linkstatic attribute of cc_library rule @org_tensorflow//tensorflow/core:framework_internal: setting 'linkstatic=1' is recommended if there are no object files. Since this rule was created by the macro 'tf_cuda_library', the error might have been caused by the macro implementation
INFO: Analyzed target //mediapipe/examples/desktop/youtube8m:extract_yt8m_features (0 packages loaded, 0 targets configured).
INFO: Found 1 target...
ERROR: /home/members/yeoreum/smartssd/project_HNSW/youtube-8m/mediapipe/mediapipe/calculators/tensorflow/BUILD:697:11: C++ compilation of rule '//mediapipe/calculators/tensorflow:unpack_media_sequence_calculator' failed (Exit 1): gcc failed: error executing command /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections ... (remaining 75 argument(s) skipped)

Use --sandbox_debug to see verbose messages from the sandbox gcc failed: error executing command /usr/bin/gcc -U_FORTIFY_SOURCE -fstack-protector -Wall -Wunused-but-set-parameter -Wno-free-nonheap-object -fno-omit-frame-pointer -g0 -O2 '-D_FORTIFY_SOURCE=1' -DNDEBUG -ffunction-sections ... (remaining 75 argument(s) skipped)

Use --sandbox_debug to see verbose messages from the sandbox
mediapipe/calculators/tensorflow/unpack_media_sequence_calculator.cc: In member function 'virtual absl::lts_2020_09_23::Status mediapipe::UnpackMediaSequenceCalculator::Open(mediapipe::CalculatorContext*)':
mediapipe/calculators/tensorflow/unpack_media_sequence_calculator.cc:249:48: error: 'const class mediapipe::UnpackMediaSequenceCalculatorOptions' has no member named 'keypoint_names'
       keypoint_names_ = absl::StrSplit(options.keypoint_names(), ',');
                                                ^~~~~~~~~~~~~~
mediapipe/calculators/tensorflow/unpack_media_sequence_calculator.cc:250:44: error: 'const class mediapipe::UnpackMediaSequenceCalculatorOptions' has no member named 'default_keypoint_location'
       default_keypoint_location_ = options.default_keypoint_location();
                                            ^~~~~~~~~~~~~~~~~~~~~~~~~
Target //mediapipe/examples/desktop/youtube8m:extract_yt8m_features failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 9.472s, Critical Path: 8.68s
INFO: 278 processes: 128 internal, 150 linux-sandbox.
FAILED: Build did NOT complete successfully

Is there any suggestion?

We are aware of this issue. See the temporary solution at https://github.com/google/mediapipe/issues/1871#issuecomment-819658414.

Thank you @jiuqiant .
However after switch the branch, I've got another error.
Error
`
ERROR: /home/members/yeoreum/smartssd/project_HNSW/youtube-8m/reverted/mediapipe/mediapipe/examples/desktop/youtube8m/BUILD:17:10: Linking mediapipe/examples/desktop/youtube8m/extract_yt8m_features failed: (Exit 1): gcc failed: error executing command /usr/bin/gcc @bazel-out/k8-opt/bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features-2.params
Use --sandbox_debug to see verbose messages from the sandbox gcc failed: error executing command /usr/bin/gcc @bazel-out/k8-opt/bin/mediapipe/examples/desktop/youtube8m/extract_yt8m_features-2.params
Use --sandbox_debug to see verbose messages from the sandbox
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::String::allocate(unsigned long)'
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::imencode(cv::String const&, cv::_InputArray const&, std::vector<unsigned char, std::allocator >&, std::vector<int, std::allocator > const&)'
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::String::deallocate()'
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::~Mat()'
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::~Mat()'
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::~Mat()'
bazel-out/k8-opt/bin/mediapipe/calculators/tensorflow/_objs/pack_media_sequence_calculator/pack_media_sequence_calculator.o:pack_media_sequence_calculator.cc:function mediapipe::PackMediaSequenceCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::String::deallocate()'
bazel-out/k8-opt/bin/mediapipe/util/sequence/_objs/media_sequence/media_sequence.o:media_sequence.cc:function mediapipe::mediasequence::(anonymous namespace)::ReconcileMetadataImages(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, tensorflow::SequenceExample*): error: undefined reference to 'cv::Mat::Mat(int, int, int, void*, unsigned long)'
bazel-out/k8-opt/bin/mediapipe/util/sequence/_objs/media_sequence/media_sequence.o:media_sequence.cc:function mediapipe::mediasequence::(anonymous namespace)::ReconcileMetadataImages(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, tensorflow::SequenceExample*): error: undefined reference to 'cv::imdecode(cv::_InputArray const&, int)'
bazel-out/k8-opt/bin/mediapipe/util/sequence/_objs/media_sequence/media_sequence.o:media_sequence.cc:function mediapipe::mediasequence::(anonymous namespace)::ReconcileMetadataImages(std::__cxx11::basic_string<char, std::char_traits, std::allocator > const&, tensorflow::SequenceExample*): error: undefined reference to 'cv::Mat::~Mat()'
bazel-out/k8-opt/bin/mediapipe/framework/formats/objs/location/location.o:location.cc:function mediapipe::Location::GetCvMask() const: error: undefined reference to 'cv::Mat::Mat(int, int, int, cv::Scalar const&)'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::Mat()'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::empty() const'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::empty() const'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::cvtColor(cv::_InputArray const&, cv::_OutputArray const&, int, int)'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Process(mediapipe::CalculatorContext*): error: undefined reference to 'cv::cvtColor(cv::_InputArray const&, cv::_OutputArray const&, int, int)'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): error: undefined reference to 'cv::String::allocate(unsigned long)'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): error: undefined reference to 'cv::VideoCapture::VideoCapture(cv::String const&)'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): error: undefined reference to 'cv::String::deallocate()'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::Mat()'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): error: undefined reference to 'cv::Mat::empty() const'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): warning: the use of tmpnam' is dangerous, better use mkstemp'
bazel-out/k8-opt/bin/mediapipe/calculators/video/_objs/opencv_video_decoder_calculator/opencv_video_decoder_calculator.o:opencv_video_decoder_calculator.cc:function mediapipe::OpenCvVideoDecoderCalculator::Open(mediapipe::CalculatorContext*): error: undefined reference to 'cv::String::deallocate()'
bazel-out/k8-opt/bin/mediapipe/framework/formats/_objs/image_frame_opencv/image_frame_opencv.o:image_frame_opencv.cc:function mediapipe::formats::MatView(mediapipe::ImageFrame const*): error: undefined reference to 'cv::Mat::Mat(int, int const*, int, void*, unsigned long const*)'
collect2: error: ld returned 1 exit status
Target //mediapipe/examples/desktop/youtube8m:extract_yt8m_features failed to build
Use --verbose_failures to see the command lines of failed build steps.
INFO: Elapsed time: 719.750s, Critical Path: 455.73s
INFO: 3654 processes: 64 internal, 3590 linux-sandbox.
FAILED: Build did NOT complete successfully
`
Do you have any suggestion?

Hi @limetus, This issue is fixed in the latest release of mediapipe 8.11, Could you please let us know if this is still an issue at other end. Thank you!


Link rustc error page and clippy lint page via CodeDescription

Fixes #6371
This makes the error code in here clickable, same for clippy lints

For clippy I just chose the master build of the site as I believe that to be pretty much always the best fitting.

We should also provide links to the rust-analyzer docs: https://rust-analyzer.github.io/manual.html#diagnostics

We'll need to do something here too: https://github.com/rust-analyzer/rust-analyzer/blob/156f7d6963fb6b47570bfa457fdf51733a182054/crates/rust-analyzer/src/handlers.rs#L1132

LGTM!

bors r+