Not loading

Describe the bug
When opening the app it won't load and it will remain at the white background page. Also unable to use the home button. Happened after I got stuck on 13 jailbreak due to the ios not being deleted apparently (It was deleted in the iPhone storage)
To Reproduce
Open the app and install the jailbreak
Screenshots
White background image here
Device (please complete the following information):

iOS Version: 12.1.2
iOS Device: iPhone 6
unc0ver Version: v3.0.0 (Latest)

Place an "x" between the brackets if true:

[ x] this is a bug others will be able to reproduce
[x ] this issue is present with all tweaks uninstalled(except for default packages) or disabled
[ ] this issue is present after a rootfs restore
[ x] this issue is present on the latest version of unc0ver

Logs
NA

Double checked... I have no ios update pending in storage. Unable to get pass step 13. Restarted my device

Description: rootfs already mounted, delete ota file from settings - storage if present and reboot.
I've done both things


Issue and PR Template

I think we should add some templates for Issues and Pull Requests. This will help in having some clarity and a consistent format of issues and PRs. For eg. in issues we can create a template for bug report in any of the algorithm and for PRs we can have algorithm details like name etc and a link to it's implementation (if possible) so that reviewer(s) can verify the quickly.
Here are some of the possible templates we can add though adding all of these is not necessary I think.
Issue

[x] Bug report
[ ] Add new algorithm
[ ] Enhancement or feature request

Pull Request

[x] PR template

Does is it done?

Is there any issue that I can work upon?

Is there any issue that I can work upon?

Hi @Sranu2109,
For now we don't have, but you could open an issue for new feature or algorithm

hey @ming-tsai  can I add a proposal, question and idea template


I as understood the app allow to use any of container which implements ..

I as understood the app allow to use any of container which implements container interface  standart *https://github.com/container-interop/fig-standards/blob/master/proposed/container.md.
There is a problem by default slim container register DefaultServiceProvider in container constructor which broke all the app when is trying to use another container.
Will be ok if the service providers will be send as args to the app constructor or will be resigistered via addProviders method which will let me change without problem container.
Thx..

The constructor to Slim\App takes a Container object. You can pass in any container that implements container-interop and has the services that Slim requires registered with it.
Does this not work for you?

If you look to the \Slim\Container class you will see in the constructor that part of code
$this->registerDefaultServices($userSettings);
If think that code broke SOLID - the "S" principle. For example i was try to pass League/Container which implement container-interpop and all the application is broken because in \Slim\Container is injected DefaultServiceProvider which boot required settings.
That's why i propose to register service providers using App class.
Thx.

I don't understand why you care about \Slim\Container if you're supplying your own container to App's constructor.

K, i pass my container
`
$app = (new App(
new League\Container\Container
));
$app->get('/hello/{name}', function ($request, $response, $args) {
return $response->write("Hello " . $args['name']);
});
$app->run();
`
i got error like Fatal error: Uncaught League\Container\Exception\NotFoundException: Alias (router) is not being managed by the container in ...

You have to provide your own registration for all the services that Slim needs to operate.
See this example that integrates Zend\ServiceManager with Slim: https://github.com/akrabat/rka-slim-zfsm-container/blob/master/src/Container.php

This is documented at http://www.slimframework.com/docs/concepts/di.html#required-services

Thanks,results that the container register needed services. Why don't giving that responsabillity to the Application class?

We can't put it on the Application class as the container-interop interface doesn't specify how to register a service with the container.

Note that if your container supports registering services in the same way as Pimple, then you can use our DefaultServicesProvider:
$container = new \Some\DiC()
$defaultProvider = new \Slim\DefaultServicesProvider();
$defaultProvider->register($container);

$app = new \Slim\App($container);


update TS types with listUnexpandedValues

@kessler
It is not thoroughly tested yet. Please take some time to review it.

@felixhao28 I cannot, sadly. Barely have time to maintain this project as it is. There's already an earlier version of it, could you summarize in a few words what has changed / added? I see you added a module declaration and such.
Perhaps you could add a test of some sort for the typescript version?

@felixhao28 playing catching up here. So this update is for #99 ?


opencl-clang: fix darwin build

Motivation for this change
ZHF: #122042
cc @NixOS/nixos-release-managers
Hydra failure: https://hydra.nixos.org/build/142945859/nixlog/4
Things done

[ ] Tested using sandboxing (nix.useSandbox on NixOS, or option sandbox in nix.conf on non-NixOS linux)
Built on platform(s)

[ ] NixOS
[x] macOS
[ ] other Linux distributions

[ ] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[ ] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[ ] Tested execution of all binary files (usually in ./result/bin/)
[ ] Determined the impact on package closure size (by running nix path-info -S before and after)
[ ] Ensured that relevant documentation is up to date
[ ] Fits CONTRIBUTING.md.

Result of nixpkgs-review pr 123473 at 5afd15a2 run on aarch64-linux 1

1 package marked as broken and skipped:

intel-compute-runtime

2 packages failed to build:

intel-graphics-compiler
opencl-clang

Note that build failures may predate this PR, and could be nondeterministic or hardware dependent.
Please exercise your independent judgement. Does something look off? Please file an issue or reach out on IRC.


Can't execute query - no active database on InfluxDB

System information:

Operating system OS X and version is Mojave
DBeaver version 5.3.5
Java version 11.0.2

Connection specification:

Database InfluxDB and version is 1.7.2
Driver name InfluxDB

Describe the problem you're observing:
Query execution failed

Reason:
Can't execute query - no active database

eclipse.buildId=unknown
java.version=11.0.2
java.vendor=Oracle Corporation
BootLoader constants: OS=macosx, ARCH=x86_64, WS=cocoa, NL=en_US
Framework arguments:  -keyring /Users/brunowego/.eclipse_keyring
Command-line arguments:  -os macosx -ws cocoa -arch x86_64 -keyring /Users/brunowego/.eclipse_keyring

org.jkiss.dbeaver.model
Error
Thu Feb 14 09:42:16 BRST 2019
Can't execute query - no active database

org.jkiss.dbeaver.model.exec.DBCException: Can't execute query - no active database
	at com.dbeaver.ee.influxdb.exec.InfluxSelectStatement.executeStatement(InfluxSelectStatement.java:61)
	at org.jkiss.dbeaver.ui.editors.sql.execute.SQLQueryJob.executeStatement(SQLQueryJob.java:458)
	at org.jkiss.dbeaver.ui.editors.sql.execute.SQLQueryJob.lambda$0(SQLQueryJob.java:402)
	at org.jkiss.dbeaver.model.DBUtils.tryExecuteRecover(DBUtils.java:1635)
	at org.jkiss.dbeaver.ui.editors.sql.execute.SQLQueryJob.executeSingleQuery(SQLQueryJob.java:400)
	at org.jkiss.dbeaver.ui.editors.sql.execute.SQLQueryJob.extractData(SQLQueryJob.java:839)
	at org.jkiss.dbeaver.ui.editors.sql.SQLEditor$QueryResultsContainer.readData(SQLEditor.java:2691)
	at org.jkiss.dbeaver.ui.controls.resultset.ResultSetJobDataRead.lambda$0(ResultSetJobDataRead.java:102)
	at org.jkiss.dbeaver.model.DBUtils.tryExecuteRecover(DBUtils.java:1635)
	at org.jkiss.dbeaver.ui.controls.resultset.ResultSetJobDataRead.run(ResultSetJobDataRead.java:100)
	at org.jkiss.dbeaver.model.runtime.AbstractJob.run(AbstractJob.java:101)
	at org.eclipse.core.internal.jobs.Worker.run(Worker.java:63)

You need to set active database. You can specify it on the connection page (to make it default) or you can change it from context menu:

Thanks @serge-rider. I have not seen it.

I have both, and I still see the error, moreover, the "set active" is not found in my version (5.3.2)

I have both, and I still see the error, moreover, the "set active" is not found in my version (5.3.2)

I was experiencing the same, however I noticed in my toolbar there was, still, no connection selected. So I selected it from the dropdown and it worked.


ExtractorError: Could not find JS function u'na';

$ /usr/local/bin/youtube-dl --verbose -i -x --geo-bypass -k -f mp4 https://www.youtube.com/watch?v=vA13LA-D5vI
[debug] System config: []
[debug] User config: []
[debug] Custom config: []
[debug] Command-line args: [u'--verbose', u'-i', u'-x', u'--geo-bypass', u'-k', u'-f', u'mp4', u'https://www.youtube.com/watch?v=vA13LA-D5vI']
[debug] Encodings: locale UTF-8, fs UTF-8, out UTF-8, pref UTF-8
[debug] youtube-dl version 2021.06.06
[debug] Python version 2.7.16 (CPython) - Linux-4.19.0-12-amd64-x86_64-with-debian-10.11
[debug] exe versions: ffmpeg 4.1.8-0, ffprobe 4.1.8-0
[debug] Proxy map: {}
[youtube] vA13LA-D5vI: Downloading webpage
[youtube] vA13LA-D5vI: Downloading player f3c4e04d
ERROR: Signature extraction failed: Traceback (most recent call last):
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1349, in _decrypt_signature
video_id, player_url, s
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1262, in _extract_signature_function
res = self._parse_sig_js(code)
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1331, in _parse_sig_js
initial_function = jsi.extract_function(funcname)
File "/usr/local/bin/youtube-dl/youtube_dl/jsinterp.py", line 245, in extract_function
raise ExtractorError('Could not find JS function %r' % funcname)
ExtractorError: Could not find JS function u'na'; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
(caused by ExtractorError(u"Could not find JS function u'na'; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.",)); please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
Traceback (most recent call last):
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1349, in _decrypt_signature
video_id, player_url, s
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1262, in _extract_signature_function
res = self._parse_sig_js(code)
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1331, in _parse_sig_js
initial_function = jsi.extract_function(funcname)
File "/usr/local/bin/youtube-dl/youtube_dl/jsinterp.py", line 245, in extract_function
raise ExtractorError('Could not find JS function %r' % funcname)
ExtractorError: Could not find JS function u'na'; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
Traceback (most recent call last):
File "/usr/local/bin/youtube-dl/youtube_dl/YoutubeDL.py", line 815, in wrapper
return func(self, *args, **kwargs)
File "/usr/local/bin/youtube-dl/youtube_dl/YoutubeDL.py", line 836, in __extract_info
ie_result = ie.extract(url)
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/common.py", line 534, in extract
ie_result = self._real_extract(url)
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1623, in _real_extract
signature = self._decrypt_signature(sc['s'][0], video_id, player_url)
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1359, in _decrypt_signature
'Signature extraction failed: ' + tb, cause=e)
ExtractorError: Signature extraction failed: Traceback (most recent call last):
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1349, in _decrypt_signature
video_id, player_url, s
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1262, in _extract_signature_function
res = self._parse_sig_js(code)
File "/usr/local/bin/youtube-dl/youtube_dl/extractor/youtube.py", line 1331, in _parse_sig_js
initial_function = jsi.extract_function(funcname)
File "/usr/local/bin/youtube-dl/youtube_dl/jsinterp.py", line 245, in extract_function
raise ExtractorError('Could not find JS function %r' % funcname)
ExtractorError: Could not find JS function u'na'; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
(caused by ExtractorError(u"Could not find JS function u'na'; please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.",)); please report this issue on https://yt-dl.org/bug . Make sure you are using the latest version; type  youtube-dl -U  to update. Be sure to call youtube-dl with the --verbose flag and include its complete output.
Checklist

[x ] I'm reporting a broken site support
[x] I've verified that I'm running youtube-dl version 2021.06.06
[x] I've checked that all provided URLs are alive and playable in a browser
[x ] I've checked that all URLs and arguments with special characters are properly quoted or escaped
[x] I've searched the bugtracker for similar issues including closed ones

Verbose log

PASTE VERBOSE LOG HERE

Description

WRITE DESCRIPTION HERE

I'm getting the exact same error. I've tried updating using pip3 install -U youtube-dl. I'm using bash on Termux, Android 12.

Nevermind, seems like this is a duplicate issue - see #30363

This problem has been fixed in version 2021.12.17.
Please close this.


The matching effect of similar images is too different

Hello! I tried using LightGlue and encountered this problem: the matching effect of two similar scene images A and B and another image C is very different. For example, the matching effect of image A and image C is very good, but the matching effect of image B is very different. The match with image C is poor. Among them, the angle of view and scale of image A and image B are very similar. What is the reason for this?

Hello @NPUXJH, i cannot answer this question without more information. Could you upload the images A, B, C here or send them to me privately?

OK, I have sent the relevant pictures and results to the email address of your homepage, and I look forward to further replies. Thank you!

Hello @NPUXJH, i cannot answer this question without more information. Could you upload the images A, B, C here or send them to me privately?

OK, I have sent the relevant pictures and results to the email address of your homepage, and I look forward to further replies. Thank you!

Hi @NPUXJH
Indeed this is a weird failure case, I tested your example with different numbers of keypoints, and it seems the confidences get confused here, but only for 2K keypoints:
1K keypoints:

2K keypoints:

4K keypoints:

Disabling early stopping results in:

Going forward, I suggest to increase the early stopping confidence.

@Phil26AT I have met the same problem. Increasing the early stopping confidence doesn't help.

@Phil26AT hi,
have you trained your data before this matching results ? if you train your data how can i train LightGlue weigths according to my data ? and how can i fine-tune that ?


Remove duplicated configure for cluster logging

Problem:
PR for collect containerd logs bring in dulicated configure cause dry-run return warning
Solution:
remove duplicated configure
Issue:
https://github.com/rancher/rancher/issues/26543

This removal won’t affect anything because it falls into the multi-format ? Both of those have timezone at the end so what happens if time stamp doesn’t have timezone, does it still match ?

Sorry for reply late, docker json-file logs and containerd logs format are different, but the time both field generate with RFC 3339Nano timestamp, related doc. Fluentd use strptime to parse time_format, fluentd can handle the timezone
Here is a similar PR in Kubernetes repo.

Need to backport to 2.4 also


httpserver 编译出错

[INFO] ------------------------------------------------------------------------
[INFO] Building Hamal HTTP Server 2.0.0-SNAPSHOT
[INFO] ------------------------------------------------------------------------
[INFO]
[INFO] --- build-helper-maven-plugin:1.9.1:add-source (add-source) @ hamal-http-server ---
[INFO] Source directory: D:\code\my\workspace\DataXServer-master\httpserver\src\main\thrift\gen-java added.
[INFO]
[INFO] --- maven-resources-plugin:2.6:resources (default-resources) @ hamal-http-server ---
[INFO] Using 'UTF-8' encoding to copy filtered resources.
[INFO] Copying 1 resource
[INFO]
[INFO] --- scala-maven-plugin:3.2.2:compile (scala-compile-first) @ hamal-http-server ---
[WARNING]  Expected all dependencies to require Scala version: 2.13.0
[WARNING]  com.typesafe.akka:akka-actor_2.13:2.5.25 requires scala version: 2.12.0
[WARNING] Multiple versions of scala libraries detected!
[INFO] Using incremental compilation
[INFO] 'compiler-interface' not yet compiled for Scala 2.13.0. Compiling...
C:\Users\002696\AppData\Local\Temp\sbt_3fa94ed9\xsbt\Compat.scala:127: error: postfix operator headOption needs to be enabled
by making the implicit value scala.language.postfixOps visible.
This can be achieved by adding the import clause 'import scala.language.postfixOps'
or by setting the compiler option -language:postfixOps.
See the Scaladoc for value scala.language.postfixOps for a discussion
why the feature needs to be explicitly enabled.
} headOption
^
one error found

可以尝试注释掉pom文件中recompileMode配置


Promises?

Should we use promises or callbacks?

For now I don't see any pressing reason to use promises. They function pretty much the same way as callbacks except its syntactic sugar. They come up more as a substitute for nested callbacks where implementation is ugly. Both callbacks and promises are asynchronous.
https://www.quora.com/Whats-the-difference-between-a-promise-and-a-callback-in-Javascript


Remove custom scroll anchoring implementation

This PR remove custom JavaScript code to implement scroll anchoring. Scroll anchoring is now supported by modern browsers, so this code is no longer necessary.
To test this feature, go to a gene page and open every section on the page. Then, scroll halfway down the page and reload. When the page reloads, content on screen should remain visible while content above is loaded, instead of being pushed off screen.

After some more testing, I don't think we can merge this. I'm going to close this, and maybe revisit later.


Some demo directories do not have README.md files

Describe the bug
Most of our demo subdirectories have README files that provide context (see also #1087). Some don't.

[ ] connector
[ ] graph-classification
[ ] interpretability
[ ] use-cases

To Reproduce
Steps to reproduce the behavior:

Run find demos/ -maxdepth 2 | grep -v ipynb | grep -v '/.*/[a-z]' | sort to see the list of all directories and their README files (if they have one)

Observed behavior
demos
demos/README.md
demos/calibration
demos/calibration/README.md
demos/community_detection
demos/community_detection/README.md
demos/connector
demos/embeddings
demos/embeddings/README.md
demos/ensembles
demos/ensembles/README.md
demos/graph-classification
demos/interpretability
demos/link-prediction
demos/link-prediction/README.md
demos/node-classification
demos/node-classification/README.md
demos/use-cases

Expected behavior
Every demos/... subdirectory should have a README
Environment
Operating system: N/A
Python version: N/A
Package versions: StellarGraph develop at a7ef31acfa0863ff7bfb3e4152ecfb61392ff7d5
Additional context
N/A

I think that only use-cases remains without an index, due to the work in #1200

connector doesn't either (connector/neo4j does, though).


Mod failing to load

I get an error saying this mod failed to load and only this one. here is one of my crash files
crash-2021-02-24_13.52.39-fml.txt

I think you downloaded enderite for minecraft 1.16.3 and launching forge for minecraft 1.16.4.
This is maybe the issue.

I think you downloaded enderite for minecraft 1.16.3 and launching forge for minecraft 1.16.4. This is maybe the issue.

Yup you got it this is a version mismatch.


Occupancy Sensor

Looks like the Occupancy Sensor and the Temperature,Humidity, and Light sensor are considered the same so I see an error in my HA logs on every restart since the Occupancy Sensor doesn't provide Humidity information.
Error doing job: Task exception was never retrieved Traceback (most recent call last):   File "/usr/src/app/homeassistant/helpers/entity_platform.py", line 352, in _async_add_entity     await entity.async_update_ha_state()   File "/usr/src/app/homeassistant/helpers/entity.py", line 244, in async_update_ha_state     unit_of_measurement = self.unit_of_measurement   File "/usr/src/app/homeassistant/components/abode/sensor.py", line 76, in unit_of_measurement     return self._device.humidity_unit   File "/usr/local/lib/python3.7/site-packages/abodepy/devices/sensor.py", line 44, in humidity_unit     if CONST.UNIT_PERCENT in self._get_status(CONST.HUMI_STATUS_KEY): TypeError: argument of type 'NoneType' is not iterable

I am seeing the same issue.

Does the temp/humidity/light sensor show up as three separate devices in HA? Can someone post the JSON data from the occupancy sensor?
I think the issue is with the abode component in Home Assistant, specifically the async_setup_entry in sensor.py:
async def async_setup_entry(hass, config_entry, async_add_entities):
    """Set up a sensor for an Abode device."""

    data = hass.data[DOMAIN]

    devices = []
    for device in data.abode.get_devices(generic_type=CONST.TYPE_SENSOR):
        for sensor_type in SENSOR_TYPES:
            devices.append(AbodeSensor(data, device, sensor_type))

    async_add_entities(devices)

It looks to me the for sensor_type in SENSOR_TYPES: is simply iterating over the SENSOR_TYPES dictionary defined in the same sensor.py file:
SENSOR_TYPES = {
    "temp": ["Temperature", DEVICE_CLASS_TEMPERATURE],
    "humidity": ["Humidity", DEVICE_CLASS_HUMIDITY],
    "lux": ["Lux", DEVICE_CLASS_ILLUMINANCE],
}

...and appending each SENSOR_TYPE (i.e. temp, humidity and lux) without doing any sort of checking on the type of sensor it is. Seems like instead of the second for sensor_type in SENSOR_TYPES: loop, we would just need to check the type_tag passed from Abode and depending on what type of sensor it is, append it with that sensor_type.

@shred86 Pulled a couple months ago but:{
"id": "ZB:322b02",
"type_tag": "device_type.lm",
"type": "LM",
"name": "Occupancy 1",
"area": "1",
"zone": "12",
"sort_order": "",
"is_window": "",
"bypass": "0",
"schar_24hr": "0",
"sresp_24hr": "0",
"sresp_mode_0": "0",
"sresp_entry_0": "0",
"sresp_exit_0": "0",
"group_name": "Upstairs",
"group_id": "382793",
"default_group_id": "1",
"sort_id": "4",
"sresp_mode_1": "0",
"sresp_entry_1": "0",
"sresp_exit_1": "0",
"sresp_mode_2": "0",
"sresp_entry_2": "0",
"sresp_exit_2": "0",
"sresp_mode_3": "0",
"uuid": "8ee12272b14b709e93732b9aecaff0ff",
"sresp_entry_3": "0",
"sresp_exit_3": "0",
"sresp_mode_4": "0",
"sresp_entry_4": "0",
"sresp_exit_4": "0",
"version": "POVS_<IP_ADDRESS>TC",
"origin": "abode",
"has_subscription": null,
"onboard": "0",
"s2_grnt_keys": "",
"s2_dsk": "",
"s2_propty": "",
"s2_keys_valid": "",
"zwave_secure_protocol": "",
"control_url": "",
"deep_link": null,
"status_color": "#5cb85c",
"faults": {
"low_battery": 0,
"tempered": 0,
"supervision": 0,
"out_of_order": 0,
"no_response": 0,
"jammed": 0,
"zwave_fault": 0
},
"status": "70 \u00b0F",
"statuses": {
"temperature": "70 \u00b0F",
"temp": "21.06",
"lux": "9 lx"
},
"status_ex": "",
"actions": [{
"label": "High Temperature Alarm",
"value": "a=1&z=12&trigger=TSH;"
}, {
"label": "Low Temperature Alarm",
"value": "a=1&z=12&trigger=TSL;"
}],
"status_icons": [],
"statusEx": "9",
"icon": "assets/icons/occupancy-sensor.svg"
}, {
"id": "ZB:322b01",
"type_tag": "device_type.povs",
"type": "Occupancy",
"name": "Occupancy 1",
"area": "1",
"zone": "13",
"sort_order": "",
"is_window": "",
"bypass": "0",
"schar_24hr": "0",
"sresp_24hr": "0",
"sresp_mode_0": "0",
"sresp_entry_0": "0",
"sresp_exit_0": "0",
"group_name": "Upstairs",
"group_id": "382793",
"default_group_id": "1",
"sort_id": "5",
"sresp_mode_1": "0",
"sresp_entry_1": "0",
"sresp_exit_1": "0",
"sresp_mode_2": "0",
"sresp_entry_2": "0",
"sresp_exit_2": "0",
"sresp_mode_3": "0",
"uuid": "c3cca78a0c9c37db565f7feabd70fa4c",
"sresp_entry_3": "0",
"sresp_exit_3": "0",
"sresp_mode_4": "0",
"sresp_entry_4": "0",
"sresp_exit_4": "0",
"version": "POVS_<IP_ADDRESS>TC",
"origin": "abode",
"has_subscription": null,
"onboard": "0",
"s2_grnt_keys": "",
"s2_dsk": "",
"s2_propty": "",
"s2_keys_valid": "",
"zwave_secure_protocol": "",
"control_url": "",
"deep_link": null,
"status_color": "#5cb85c",
"faults": {
"low_battery": 0,
"tempered": 0,
"supervision": 0,
"out_of_order": 0,
"no_response": 0,
"jammed": 0,
"zwave_fault": 0
},
"status": "Online",
"statuses": {
"motion": "0"
},
"status_ex": "",
"actions": [],
"status_icons": [],
"sresp_trigger": "0",
"sresp_restore": "0",
"occupancy_timer": "30",
"sensitivity": "4",
"model": "L1",
"is_motion_sensor": false,
"icon": "assets/icons/motioncamera-a.svg"
}
It doesn't report humidity

Hm, so there must be two versions of the Occupancy sensor. The latest one on their website reports humidity, lux and temp. Does anyone have the JSON for that one?

@MisterWil This should be resolved with: https://github.com/home-assistant/home-assistant/pull/28516


Periodic Table

It closes the Issue #408
Here is the Video Description:
https://user-images.githubusercontent.com/77090462/166642301-01a28e70-d27e-484f-b8c8-8ed1cb96ec54.mov

@pranjay-poddar Please check and review it.


Implement Javascript testing framework

Problem
Had a tutoring session with Sam Bleckley and this was our conclusion:

There is no Javascript testing framework currently. The tests only hit the controller URLs directly and then check the response.

Solution
Add a javascript testing framework that can do both unit tests and integration tests.
Once implemented, create these tests

When the "Adopt" button is clicked in the info window, make sure the drain name adds to the sidebar
When the "Abandon" button is clicked in the info window, make sure the drain name removes from the sidebar.
Click a drain (info window opens), sign out. Make sure the info window changes.
Click a drain (info window opens), sign in. Make sure the info window changes.
Do the test above twice. I had a bug where it failed the second time.
Click a drain (info window opens), sign up for a new account. Make sure the info window changes.

Someone pick a favorite javascript testing framework.

If you want JS unit testing then we can use whatever JS testing framework is cool these days. I've never had much luck with JS unit testing.
The tests you are describing should be covered using Capybara/Feature/Integration tests -> https://semaphoreci.com/community/tutorials/integration-testing-ruby-on-rails-with-minitest-and-capybara
There are lots of different ways to configure these types, so it will require investigating how to hook everything up for MiniTest and Rails 4, because a lot of the information out there is for RSpec and/or Rails 5 which has System Tests baked in.


Rustup

@bors r+

:pushpin: Commit e48ec3799fe7a8f8d0420278d58ce2f06f5860d7 has been approved by RalfJung
It is now in the queue for this repository.

:hourglass: Testing commit e48ec3799fe7a8f8d0420278d58ce2f06f5860d7 with merge 8a84ebbcda1d3e85c6033728f100c212e59e44f4...

:sunny: Test successful - checks-actions
Approved by: RalfJung
Pushing 8a84ebbcda1d3e85c6033728f100c212e59e44f4 to master...

:sunny: Test successful - checks-actions
Approved by: RalfJung
Pushing 8a84ebbcda1d3e85c6033728f100c212e59e44f4 to master...


Validate incoming lage.config.js files

We need to validate incoming lage.config.js/json configs - after cosmiconfig parses the config, we still need to validate that the configs are of the correct shape. We already have a TypeScript interface defined for the ConfigOptions, we need to integrate that with a tool to validate the incoming config shape.
This happens to be a tool that can do the job: https://www.npmjs.com/package/ts-interface-checker

Decided to go with https://github.com/colinhacks/zod for runtime type check.


Linking Volume to System Volume

Would it be possible to link the volume inside Musikcube to the system volume? (at least for macOS). Thanks!

Can you explain a bit more what you mean by this? Do you mean that you want the system-wide volume keys on your keyboard to control musikcube's volume? :)

Can you explain a bit more what you mean by this? Do you mean that you want the system-wide volume keys on your keyboard to control musikcube's volume? :)

Yes, that's what I mean. Sorry for not being clear.

You can do this on Linux using the mpris plugin that is automatically loaded if you have systemd on your system. On Windows this is accomplished via the win32globalhotkeys plugin, and on macOS via macosmediakeys plugin. These are all now included in default installations.

You can do this on Linux using the mpris plugin that is automatically loaded if you have systemd on your system. On Windows this is accomplished via the win32globalhotkeys plugin, and on macOS via macosmediakeys plugin. These are all now included in default installations.


Improve documentation and directory structure

The documentation must be improved. Most urgent is the readme.md on top-level. It should give an overview of what Meridio is and where it is used, depenceny of NSM and such things, not describe the build process for instance.
The demo/ directory should be removed and its contents moved to more appropriate places. It is a survivior from the early days and has become some general trash can for all kind of stuff.
The overview assumes that the reader is already familiar with the purpose and backgound of Meridio. That said, IMO the overview is the best documentation in Meridio currently. I have frequently returned to the diagram for instance
This is not about the go code structure, which is basically fine.

I agree the documentation must be updated and improved, but I think the demo directory has been maintained correctly with only relevant files (at least for Kind, not sure for xcluster). It contains all information on how to deploy a simple Meridio example.
However, I think /examples/target should be re-worked, we should create a new go.mod (to keep Meridio and the example clearly separated for tests, presentations...) and we should move the code in test/applications/target-client to /examples/target.


Update rake to current

Motivated by
/path/to/rake-11.0.1/lib/rake/ext/fixnum.rb:4: warning: constant ::Fixnum is deprecated

when running bundle exec rake git:pull.

Thanks for this, this needs a gate condition to check we're running a Ruby version that supports Rake 12, (given RSpec runs on a variety of Rubies and we have to do dev on a variety of Rubies)

This requirement has the same ruby version restrictions as the previous one, it seems
$ gem specification --ruby --remote rake -v 11.0.1 | grep required_ruby_version
  s.required_ruby_version = Gem::Requirement.new(">= 1.9.3".freeze)
$ gem specification --ruby --remote rake -v 12.0.0 | grep required_ruby_version
  s.required_ruby_version = Gem::Requirement.new(">= 1.9.3".freeze)

(Used 11.0.1 in the experiment because 11.0.0 was yanked, apparently).

Fair enough, I suppose it is only our dev tools.


Feature: Helm based deployment

Because:

Hello,
Do you have a release prevision date about the helm deployment ? :D

We are currently working on deploying our first K8S prod cluster for our own usage.
And the next step is going to be this.
We need to evaluate if we can only rely on a Helm deployment or if we need to dev an operator. As the deployment requires some health check for upgrade, migration and deployment, maybe the operator is going to be mandatory in our case. We'll check other Django based project to see how they deal with this.
BTW, the Ansible deployment targeting K8S is here already if you want to give a try.

Thanks for your answer :)
I tried to deploy squest with ansible on k8s, but I had some errors and didn't find a workaround, I'll continue to follow the project and wait for the helm :)
Thanks for all your work !


registry: remove is_trusted from SearchResult

This was deprecated between 1.19 and 1.20 APIs. It is also extremely
confusing in terminology and usage.
Signed-off-by: Stephen J Day<EMAIL_ADDRESS>

LGTM 🐮

LGTM


Update nav wording and breadcrumbs for services/catalogs

https://www.pivotaltracker.com/story/show/143533253
https://www.pivotaltracker.com/story/show/143533197
@miq-bot add_label fine/yes, bug, services, catalogs

No UX changes, just wording.

@jjlangholtz travis failed.

Updated per UX guidelines.

Fine backport details:
$ git log -1
commit 6c78d5382c0e6080fe5f1c094c9b35d9206f3816
Author: Chris Kacerguis<EMAIL_ADDRESS>Date:   Tue Apr 11 16:09:48 2017 -0500

    Merge pull request #667 from jjlangholtz/update-nav-wording

    Update nav wording and breadcrumbs for services/catalogs
    (cherry picked from commit 02fc569fca9c1054a02204e5fd935854c5c0374b)


fix: workaround to make our grpc interceptor more flexible

We have a gRPC client interceptor that we use to inject our Momento metadata headers (user_agent, authorization).  Currently we have a pretty simple approach to how we initialize a gRPC Metadata() class grpc.aio import Metadata on client_call_details inside of this if its not been set before.
It looks like this:
if client_call_details.metadata is None:
            client_call_details.metadata = Metadata()

We then try to inject our Auth header like this a little farther down interceptor
client_call_details.metadata.add(header.name, header.value)

A customer reported they were using our Python SDK in conjunction with DataDogs ddtrace library and were seeing the following error being thrown from inside our SDK when we were trying to call add() with the custom metadata we inject on the request object.
AttributeError: 'list' object has no attribute 'add'

The core Metadata object in GRPC python lib does have an add() function on it and normally this works fine. I was looking into DataDog trace library source and I'm seeing them initialize metadata in their client interceptor like this as a python list instead of the proper grpc Metadata class.
        metadata = []
        if client_call_details.metadata is not None:
            metadata = list(client_call_details.metadata)

Datadog Code here.
Because their interceptor runs first and injects their metadata object as list instead of a proper grpc Metadata object then when we come in and try call add() to inject our metadata we end up w/ the error AttributeError: 'list' object has no attribute 'add' being thrown. This doesn't fail upfront in ddtrace interceptor because of how python does ducktyping... o____o 🦆
I have fixed this for now by explicitly handling this case if client_call_details that is passed to us has been initiated with metadata property of type list and making our SDK a bit more defensive for this. This does not feel like a long-term fix this is simply a workaround, for now, to help unblock a customer and likely others using python and ddtrace. I have left FIXME comments in the code. To come back and revisit.
Longer term I would like to take the following actions:

I would like to revisit why we are even using client interceptors to inject this metadata into requests in the first place. We are the ones initiating requests with GRPC so we have the opportunity to inject all the metadata we need to make request upfront as we build the request object. We already are injecting cache-name dynamically on request metadata this way from with in our SDK data client here for example using this utility method. We could just make this bit more flexible to set all metadata upfront and thus eliminate the need for us to even have yet another interceptor in the mix on request lifecycle. I think by doing all in side our clients as we build up requests will be easier to debug and maintain.
Work with Datadog longer term and try and patch their interceptor to play better with others and follow proper typing in gRPC python library.

Ok @malandis @cprice404 I think this should be GTG now got pr feedback in. Had to pin version of mypy we run against examples in lint action as well. Looks like they must have made some breaking changes in last release of it?


History enabled by default, but shown as disabled in the settings

Dialogic: latest from main
Godot version: 4.3.dev6
I created a test timeline, and noticed that now, on a brand new project, you see the history button at the top.

But the project settings have this unchecked:

I think the history button should only show if you actively want it by enabling in the settings. So I'd say the settings are correct here and the default template is not.

Additionally, if you toggle the first checkbox, it will be correct and hide the history button

Then this is probably a problem with default values.
Usually we assume default values for dialogic settings in multiple places and those assumptions can get out of sync. So I'd assume the game assumes it's on by default, while the settings page considers it off by default. This happens when no actual value has been stored for the setting.

Makes sense, so I guess the fix would be to disable the history by default on the default theme 👍


Fix shouldAcceptNewConnectionHandler compilation error

'bool' cannot be converted to 'BOOL' on my macOS/golang 1.16 and 1.17.3:
./socket.go:222:17: cannot convert
shouldAcceptNewConnectionHandlerslistenerPtr (type bool) to type
_Ctype_schar
Before this commit:
https://github.com/cfergeau/vz/actions/runs/1573537306
After this commit:
https://github.com/cfergeau/vz/actions/runs/1573558835

@cfergeau Thanks again!
Could you tell me what your macOS architecture is?

Ah, I only test on x86_64, I indeed did not think that it could be a M1/x86_64 difference :)

Ah... I see.
I will merge this PR if you fixed my point.


Debugger very slow when model contains base64 images

My app requires me to download data that includes base64 image data in it. The strings get shortened to e.g. picture = "zxcvvbnm...wertyui" in the debugger view, but opening the debugger still takes a very long time. I'm not sure what exactly would make the difference, but perhaps you could just put long string instead of trying to massage the string itself

Tracking in #98. Thanks for the report!
Is it just a long String in Elm? If not, can you try to make an SSCCE? I need to know the type that's causing the problem.


Solve experiment with drive cycle

Description
Experiments now allow to solve for drive cycles.
Fixes #1788 and includes #1524
Type of change
Please add a line in the relevant section of CHANGELOG.md to document the change (include PR #) - note reverse order of PR #s. If necessary, also add to the list of breaking changes.

[x] New feature (non-breaking change which adds functionality)
[ ] Optimization (back-end change that speeds up the code)
[ ] Bug fix (non-breaking change which fixes an issue)

Key checklist:

[x] No style issues: $ flake8
[x] All tests pass: $ python run-tests.py --unit
[x] The documentation builds: $ cd docs and then $ make clean; make html

You can run all three at once, using $ python run-tests.py --quick.
Further checks:

[x] Code is commented, particularly in hard-to-understand areas
[x] Tests added that prove fix is effective or that feature works

@alibh95 for some reason I can't request a review from you, but please take a look.

@alibh95 for some reason I can't request a review from you, but please take a look.

Looks good to me. Thank you so much for implementing this.


Fix for JS SDK check failure for Dapr Config

ARM (Control Plane) API Specification Update Pull Request
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@kazrael2119 I've added the change you've suggested here as a fix for the JS SDK check failure.

@qiaozha , if you see no issues please give a armsignoff, I'll get the PR merged

I can't signed off on ARM's behalf. I can only signed off on JS side.

/prRequestMerge

@ms-henglu Can you approve?


Build failure: streamlink

Steps To Reproduce
Steps to reproduce the behavior:

build streamlink on unstable

Build log
https://gist.github.com/l0b0/be3d0afcce541df261adc086d1a069fa
Additional context
This broke in the last few days.
Notify maintainers
@dezgeg @zraexy @DeeUnderscore
Metadata

system: "x86_64-linux"
host os: Linux 6.6.29, NixOS, 24.05 (Uakari), 24.05pre619660.63c3a29ca824
multi-user?: yes
sandbox: yes
version: nix-env (Nix) 2.18.1
channels(root): "nixos"
channels(username): ""
nixpkgs: /nix/var/nix/profiles/per-user/root/channels/nixos

Add a :+1: reaction to issues you find important.

Should be addressed by #309326


Is version 2 still beta?

Hi,
Is there a stable v2 release?
It looks to me that the last stable release was 1.9.71 (2013?) and 2.1.1-beta was published on NuGet Nov last year.
Thanks

No, there is not a stable 2.x release, but the beta is pretty "stable" from a random-crashing perspective (and it contains a lot of bug fixes that the 1.x doesn't)

@nemec any estimates when 2.x is release. would like to take that into use

See https://github.com/gsscoder/commandline/issues/488


Update color-picker.tsx

Closes #724
Description

Add a brief description

Current behavior (updates)

Please describe the current behavior that you are modifying

New behavior

Please describe the behavior or changes this PR adds

Is this a breaking change (Yes/No):

Additional Information

Please, create the file using the pnpm changeset command and see contributing.md.

@AllenWasupan
Hi, thanks for the PR!
Please, create the file using the pnpm changeset command and see CONTRIBUTING.md.

@AllenWasupan
This PR has duplicated corrections, so it will be closed.


HTTPS link to editorconfig.org

saves a redirect

:tada: This PR is included in version 9.0.0 :tada:
The release is available on:

npm package (@latest dist-tag)
GitHub release

Your semantic-release bot :package::rocket:


Class properties of SqliteError are inaccessible

Hi @m4heshd ! Just wanted to bring this to your attention. There is a types issue in the better-sqlite3 package that is also present here. Creating an issue for this here since the types for this package are not shipped separately. Please have this patched as soon as you can. Thanks :+1:

No idea how I missed this. Thank you for giving me the heads-up. Please feel free to send a PR this way once the changes are properly merged there.


What am i missing?

Hello There,
Im quite new to this....
What am i missing? Whenever i launch the .exe on the host pc it shows up.
When i launch it on my home pc it wont show up..
I tried to set my 4782 open with port forwarding but still nothing....
Can someone help me?

https://github.com/quasar/Quasar/wiki/Getting-Started


feat(cms): ordered relationship

ticket

變更EditorPicksSettings/Project/Post這三個list config & 新增order JSON field和virtual field以儲存順序和供FE query
Post list內的變更先註解掉(用feature toggle不好閱讀diff), 待EditorPicksSettings/Project merge後再enable並給對應JSON初始值

TODO:

[x] commit db migration file
[ ] init empty json(subSubcategory/tag/relatedPost) in post list

write sql
test run on dev/staging db

@nickhsine modification added as above, please have a look, thanks.

Database migration script 記得要補上。
commits 看要不要 rebase 整理一下，目前 commits 數量看起來龐大。

其他 LGTM 👍 👍 👍 讚讚讚

okok, 我通常都squash merge所以master只會有一個commit


Breaking changes for 2.0

Fixes https://github.com/dennisweissmann/DeviceKit/issues/149
Closes https://github.com/dennisweissmann/DeviceKit/issues/130
Should close https://github.com/dennisweissmann/DeviceKit/issues/167
Fixes https://github.com/dennisweissmann/DeviceKit/issues/165

Fixes https://github.com/dennisweissmann/DeviceKit/pull/164 not being defined in the gyb file.

Wow thank you for that PR :)
Thanks for the fix of #164, that looks awesome, totally missed that yesterday, that’s definitely the way to go!
This is also a todo list for me since I’m mobile only this month and I don’t know off the top of my head:

are Device.instance and Device.current the same thing? If so lets either remove .current since init() already does the right thing (right?!) or go 100% your approach and replace .instance with .current
thanks for fixing the cases, that’s great! (Should we use the “small capital letters” R and S if at all possible?
should those early-return-if’s be guards?

Thank you for working on this!

I think .current indicates better that it is the current device the code is running on. .instance is currently private anyway.
I think there might be problems with some peoples applications if we move to using Xʀ and Xs but I might be wrong. I think it is a cool feature but maybe having a separate String variable for getting the small capital name may prevent problems?
That will definitely be cleaner looking 👍

Just changed it to using small caps in https://github.com/dennisweissmann/DeviceKit/pull/168/commits/32979363568c32ccece997eb157e1cc497515465.

Also slapped in those guards: https://github.com/dennisweissmann/DeviceKit/pull/168/commits/d8b5102189b074fd92a9d71fd4402f57bef33858

Just changed everything over to solely .current, you may look at it if you like it and if you don't just change my commit: https://github.com/dennisweissmann/DeviceKit/pull/168/commits/05cac804ca341f442dea9adbc9ca6b6f8afcabab

Just noticed; Small caps s is just the same as a lowercase s.

You are awesome! 😎 that is super cool Zander! I really like it :)

@dennisweissmann fixed my other open pull request also being defined through the generated swift file directly instead of using the gyb file, and fixed the documentation inside the readme and throughout the code, so that it is using Device.current instead of Device()

I just made you a collaborator Zandor :) I’ll hand over the project to my colleague later this week (I’m not sure if we’ll move it to her account or create an org, I just wanted to thank you for your continuous work and passion for the project, being a collaborator should give you (hopefully) merge rights and more! :) 🙏

@dennisweissmann Thanks for that, I now have push access. 👍 I will use it with caution! I +1 creating an organisation.

@dennisweissmann I've added a changelog which can be used for when these changes will be released in the future.

That’s awesome! Always wanted to have one!

@dennisweissmann @denisenepraunig Offtopic: Can we like set up a Discord server or WhatsApp for more direct communication so I can reach you?
I think it is smart to release a 1.12.1 or 1.13 version for the new iPads before we update to Swift 5. That way, people that don't want to update yet but do want the new iPads can still have those changes.

Updated to Swift 5! 🎉

Ready for merging in my opinion. Still think we should release a 1.13.0 version of the master branch as it is currently to release the new iPads for Swift 4.2. Then the master branch should be merged into the swift-4.2 branch before this pull request is merged into master.

We can set something like this up, sure, but since I won’t have the time to contribute or manage very much going forward, I’d like to leave that up to @denisenepraunig

@Zandor300 I am fine with WhatsApp or Telegram for direct communication - for questions I have to Dennis I will contact him directly. You can drop me a mail with your number to denise dot nepraunig at gmail dot com.

Another warning that needs fixing:
/Users/robbiet480/Repos/HomeAssistant/home-assistant-iOS/Pods/DeviceKit/Source/Device.generated.swift:994:9: Switch covers known cases, but 'WKInterfaceDeviceBatteryState' may have additional unknown values, possibly added in future versions

@robbiet480 Thanks for the catch! Fixed it in https://github.com/devicekit/DeviceKit/pull/168/commits/11353eb819a7a5738f5b10f56fbb911949bc96d6.

@denisenepraunig LGTM!


Changes on NumberValidation broken Application!

What steps will reproduce the problem?
Method in Module:
  public function rules()
    {
        return [
            [[ 'finished', 'state'], 'integer'],
        		[['comment'], 'string', 'max' => 255]
        ];
    }

Controller Action:
$model->finished = true;
$model->save(); // failes!

What is the expected result?
$model->finished = true;
$model->save(); // ok - no error!

What do you get instead?
A NumberValidator error (must be integer)
Additional info
Change NumberValidatior method and disabled is_bool fixed the problem:
private function isNotNumber($value)
{
return is_array($value)
//     || is_bool($value)
|| (is_object($value) && !method_exists($value, '__toString'))
|| (!is_object($value) && !is_scalar($value) && $value !== null);
}

Q
A

Yii version
2.0.16

PHP version
7.2.15

Operating system
Ubuntu

PR with this change: https://github.com/yiisoft/yii2/pull/16903

Thanks for reporting but why is it expected? It was incorrect behavior that was fixed.

I agree with @samdark
There is a boolean validator, @chrisschd you should use it if you expect boolean value.
It can also fallback to 0/1 depending on how you configure it.

BTW, while php built-in is_numeric() does not always provide reliable results, it also does not consider boolean values as numbers.
So #16903 just made NumberValidator more consistent and should not be reverted, IMO.

Thanks for your answers!
I can only speak for our application, these changes broken our complete Application. We used, “tinyint” for Boolean in the database since we worked with yii/yii2. This little change bring us in troubles we have to rework a huge amount of models to bring up our application in a working state.
I think we are not the only one which will have these problems, but this was more a feature request as a bug report. Maybe it is possible to setup the Validator to support also Boolean types?  For example:
class NumberValidator extends Validator
{
    /**
     * @var bool whether the attribute value can only be an integer. Defaults to false.
     */
    public $integerOnly = false;

    public $allowBoolean = true; //NEW
….
/*
     * @param mixed $value the data value to be checked.
     */
    private function isNotNumber($value)
    {
        return is_array($value)
            || (!$allowBoolean  && is_bool($value)) //NEW: !$allowBoolean&
            || (is_object($value) && !method_exists($value, '__toString'))
            || (!is_object($value) && !is_scalar($value) && $value !== null);
    }
…
}

I think we are not the only one which will have these problems...

I suppose you are, since you have used undocumented bug of validator as a feature :)

@chrisschd Using integer validator for boolean fields is incorrect - integer validator will allow saving values like 123 which may cause unexpected results.

The decision is to keep current behavior. Thanks for reporting anyway, @chrisschd.


No speed advantage when using batches.

I did some tests when using both detection+recognition with a set of 30 images and I've seen that there is no speed improvements when using batches.
So I checked the code and if I got it right in your implementation, https://github.com/gnana70/tamil_ocr/blob/71a91db47ba76c2c6b4612e68276da5077edc47a/ocr_tamil/ocr.py#L527-L536 you split the batch into single images and then pass each image to craft, get the BB and pass those to ParSeq.
I'm not an expert in Parseq, but if it already can deal with batches of BB why not simply take all the BB from the all batch and pass those as a single input to parseq?
To recap my suggestion why don't you do something like the following:
bbs=[]
for image in batch:
     bb_preds=craft(image)
     bbs.appens(bb_preds)
texts=parseq_read_batch(bbs)

This should be faster as you call parseq only once per batch and not per image, albeit with a larger memory cost but that can be  dealt by the batches size parameter.
Obviously even better would be to do something like:
bbs=craft_batch(batch)
texts=parseq_batch(bbs)

Apparently CRAFT can run in batches, here

I think running the inference in parallel is difficult due to the post-processing step, which is performed in CPU unless you use multi-processing technique. However, the batch-processing of deep networks is possible within a memory limit.

https://github.com/clovaai/CRAFT-pytorch/issues/44#issuecomment-533797024
and in other comments in the issue section of CRAFT's GitHub, it is stated that batch prediction is feasible.
It would be interesting if the batch functionality of ocr-tamil would exploit this.

Also, I think it would make more sense to decouple the batchsize used by parseq for the text recognition and the tamil-ocr batch size parameter. these should be two separate numbers.
I like this library, please keep working on it :)

Hi @Dario-Mantegazza , thanks for your feedback. I will try to include batch mode for CRAFT text detection in coming weeks.

Hi @Dario-Mantegazza , thanks for your help. Please share your workaround once done.

So I tried to change the code in the most simple and hacky way,  but for now, I don't get better performances; I think that something is broken in my edited version and while all the model accepts batched input, something else curb the performance gain. I will upload my version that works partially on my fork but due to work deadlines I don't think I can spend more time on this.

@Dario-Mantegazza , no problem. I will investigate and fix it up


TypeError: Object of type Transcript is not JSON serializable

DO NOT DELETE THIS! Please take the time to fill this out properly. I am not able to help you if I do not know what you are executing and what error messages you are getting. If you are having problems with a specific video make sure to include the video id.
To Reproduce
Steps to reproduce the behavior:
try formatting
What code / cli command are you executing?

Which Python version are you using?
Python 3.13.0a5
Which version of youtube-transcript-api are you using?
youtube-transcript-api 0.6.2
Expected behavior
a json string containing the transcript
Actual behaviour
funny exception python go brrrr
For example: Instead I received the following error message:

Hi @Bluscream,
you are trying to call format_transcript on a Transcript object, which does not work. You'll have to pass in the fetched transcript instead. fmt.format_transcript(raw) should work in your example.


Remove presentation-full-stack-tests with dependency on components and testing

Removed presentation-full-stack-tests moved to presentation repository https://github.com/iTwin/presentation/pull/33. This PR will need to wait until we can use docs artifacts from presentation repository when building docsite because of the docs snippets extracted from moved tests.

Additionally removed presentation-test-app as it was moved to https://github.com/iTwin/presentation.


Fine-Tune on our dataset

how to fine-tune ParseBERT Model on our dataset?
Please help me ...
thanks

You can use this Colab to fine-tuning your dataset based on the text classification tasks. For other down-stream tasks, I'm afraid to say that you need to be patient. I'll add others soon!

how to get embedding of ParsBert pretrain model?

I used this Bert Persian classification model
Model saved with a config
But I want to load that model separately and predict label sentence with model
It gets the error
raise ValueError ('No model found in config file.')
ValueError: No model found in config file.
How can I add a config file so that I do not get this error?

I used this Bert Persian classification model
Model saved with a config
But I want to load that model separately and predict label sentence with model
It gets the error
raise ValueError ('No model found in config file.')
ValueError: No model found in config file.
How can I add a config file so that I do not get this error?

Did you fine-tune parsbert on your dataset? Which methods did you use (PyTorch, TensorFlow,  Script)? Did you save your model except for the script technique (What type of files do you have on your saved model directory)?

https://github.com/hooshvare/parsbert/blob/master/notebooks/Taaghche_Sentiment_Analysis.ipynb
I made my model from this link
Yes, I fine-tuned my data and i have 3 labels
I have two saved files in pytorch_model.bin
Named: tf_model.h5 and config.json
I load my model this way
from keras.models import load_model
model = keras.models.load_model ('tf_model.h5')

Ok, then. Your model fine-tuned on Transformers you can't load the model just as simple as keras...load you must load your fine-tuned model using transformers
if you have tf_model.h5 on your saved directory use this:
from transformers import TFAutoModelForSequenceClassification

tf_model = TFAutoModelForSequenceClassification.from_pretrained(YOURSAVED_DIRECTORY)

otherwise, if you have pytorch_model.bin
from transformers import TFAutoModelForSequenceClassification

tf_model = TFAutoModelForSequenceClassification.from_pretrained(YOURSAVED_DIRECTORY, from_pt=True)

With my model, only these two files are saved
Named: tf_model.h5 and config.json
I do not have this file vocab.txt
from transformers import TFAutoModelForSequenceClassification
tf_model = TFAutoModelForSequenceClassification.from_pretrained('tf_model.h5')
This is how I got this error
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte

With my model, only these two files are saved
Named: tf_model.h5 and config.json
I do not have this file vocab.txt
from transformers import TFAutoModelForSequenceClassification
tf_model = TFAutoModelForSequenceClassification.from_pretrained('tf_model.h5')
This is how I got this error
(result, consumed) = self._buffer_decode(data, self.errors, final)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x89 in position 0: invalid start byte

Link used
Nothing called this file saved

First of all, you can download vocab.txt from here.
https://cdn.huggingface.co/HooshvareLab/bert-base-parsbert-uncased/vocab.txt

Secondly, you must load the model from the saved directory, not just the h5 model! supposed that I have a directory with the name of bert-fa-cls-base-uncased and it includes:
+ bert-fa-cls-base-uncased
    - config.json
    - vocab.txt
    - tf_model.h5

you need to pass the directory, not the model singly; I mean, load your model using this piece of code:
from transformers import TFAutoModelForSequenceClassification

tf_model = TFAutoModelForSequenceClassification.from_pretrained(./bert-fa-cls-base-uncased/)

First of all, you can download vocab.txt from here.
https://cdn.huggingface.co/HooshvareLab/bert-base-parsbert-uncased/vocab.txt

Secondly, you must load the model from the saved directory, not just the h5 model! supposed that I have a directory with the name of bert-fa-cls-base-uncased and it includes:
+ bert-fa-cls-base-uncased
    - config.json
    - vocab.txt
    - tf_model.h5

you need to pass the directory, not the model singly; I mean, load your model using this piece of code:
from transformers import TFAutoModelForSequenceClassification

tf_model = TFAutoModelForSequenceClassification.from_pretrained("./bert-fa-cls-base-uncased/")

Thank you for your help. This method loaded the model
Can you tell me what should be done in order for a sentence to be taken as input from the system and its label to be predicted?
I had a problem with the part that takes the sentence and tries to pre-process it...
sentence = (input("please enter a sentence for predict class:"))
...
predictions = tf_model.predict(xtest)
print(predictions)

please help me
i get input sentence and i want predict label from the save model
But I think the preprocessing part and the pad of my sentence were done wrong
Because I can not predict label
from transformers import BertConfig, BertTokenizer
MODEL_NAME_OR_PATH = 'HooshvareLab/bert-fa-base-uncased'
tokenizer = BertTokenizer.from_pretrained(MODEL_NAME_OR_PATH)
sample_comment= "شعار ما هوش مصنوعی برای همه است"
max_length=32
tokens = tokenizer.tokenize(sample_comment, padding=True, max_length=42)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
from transformers import TFAutoModelForSequenceClassification
tf_model = TFAutoModelForSequenceClassification.from_pretrained("./pytorch_model.bin/")
predictions = tf_model.predict(token_ids)
print(predictions)

First of all, you can download vocab.txt from here.
https://cdn.huggingface.co/HooshvareLab/bert-base-parsbert-uncased/vocab.txt

Secondly, you must load the model from the saved directory, not just the h5 model! supposed that I have a directory with the name of bert-fa-cls-base-uncased and it includes:
+ bert-fa-cls-base-uncased
    - config.json
    - vocab.txt
    - tf_model.h5

you need to pass the directory, not the model singly; I mean, load your model using this piece of code:
from transformers import TFAutoModelForSequenceClassification

tf_model = TFAutoModelForSequenceClassification.from_pretrained("./bert-fa-cls-base-uncased/")

please help me
i get input sentence and i want predict label from the save model
But I think the preprocessing part and the pad of my sentence were done wrong
Because I can not predict label
from transformers import BertConfig, BertTokenizer
MODEL_NAME_OR_PATH = 'HooshvareLab/bert-fa-base-uncased'
tokenizer = BertTokenizer.from_pretrained(MODEL_NAME_OR_PATH)
sample_comment= "شعار ما هوش مصنوعی برای همه است"
max_length=32
tokens = tokenizer.tokenize(sample_comment, padding=True, max_length=42)
token_ids = tokenizer.convert_tokens_to_ids(tokens)
from transformers import TFAutoModelForSequenceClassification
tf_model = TFAutoModelForSequenceClassification.from_pretrained("./pytorch_model.bin/")
predictions = tf_model.predict(token_ids)
print(predictions)

The whole process is as simple as you think! but before dive into it, we need to set some grounds

The fine-tuned model saved on a directory in this case bert-fa-base-uncased-sentiment-snappfood
The directory consists of these properties: config.json  tf_model.h5  vocab.txt

I'm going to demonstrate the entire steps regarding one of our models  base-uncased-sentiment-snappfood the procedure is as follow:

Load the packages
Load the config, tokenizer, and the model
The inference

0 + and a preliminary step regarding the mentioned model, in your case you don't need to this part.
Step 0
!pip install -qU transformers

!mkdir -p /content/bert-fa-base-uncased-sentiment-snappfood
!wget https://s3.amazonaws.com/models.huggingface.co/bert/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/config.json -qO /content/bert-fa-base-uncased-sentiment-snappfood/config.json
!wget https://cdn.huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/tf_model.h5 -qO /content/bert-fa-base-uncased-sentiment-snappfood/tf_model.h5
!wget https://cdn.huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/vocab.txt -qO /content/bert-fa-base-uncased-sentiment-snappfood/vocab.txt

!ls /content/bert-fa-base-uncased-sentiment-snappfood

Output
config.json  tf_model.h5  vocab.txt

Step 1
from transformers import TFBertForSequenceClassification
from transformers import AutoConfig
from transformers import AutoTokenizer

import tensorflow as tf
import numpy as np

Step 2
config = AutoConfig.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')
tokenizer = AutoTokenizer.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')

model = TFBertForSequenceClassification.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')
model.summary()

Output
All model checkpoint weights were used when initializing TFBertForSequenceClassification.

All the weights of TFBertForSequenceClassification were initialized from the model checkpoint at /content/bert-fa-base-uncased-sentiment-snappfood/.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.
Model: "tf_bert_for_sequence_classification"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
bert (TFBertMainLayer)       multiple                  162841344
_________________________________________________________________
dropout_37 (Dropout)         multiple                  0
_________________________________________________________________
classifier (Dense)           multiple                  1538
=================================================================
Total params: 162,842,882
Trainable params: 162,842,882
Non-trainable params: 0
_________________________________________________________________

Step 3
prompt = 'این خوراک بسیار خوب است'

inputs = tokenizer.encode(prompt, return_tensors="tf", max_length=128, padding=True, truncation=True)
logits = model(inputs)[0]
outputs = tf.keras.backend.softmax(logits)
prediction = tf.argmax(outputs, axis=1)
prediction = prediction[0].numpy()
scores = outputs[0].numpy()

labels = config.id2label
print(scores)
print(labels[prediction])

Output
[0.9952093  0.00479068]
HAPPY

The whole process is as simple as you think! but before dive into it, we need to set some grounds

The fine-tuned model saved on a directory in this case bert-fa-base-uncased-sentiment-snappfood
The directory consists of these properties: config.json  tf_model.h5  vocab.txt

I'm going to demonstrate the entire steps regarding one of our models base-uncased-sentiment-snappfood the procedure is as follow:

Load the packages
Load the config, tokenizer, and the model
The inference

0 + and a preliminary step regarding the mentioned model, in your case you don't need to this part.
Step 0
!pip install -qU transformers

!mkdir -p /content/bert-fa-base-uncased-sentiment-snappfood
!wget https://s3.amazonaws.com/models.huggingface.co/bert/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/config.json -qO /content/bert-fa-base-uncased-sentiment-snappfood/config.json
!wget https://cdn.huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/tf_model.h5 -qO /content/bert-fa-base-uncased-sentiment-snappfood/tf_model.h5
!wget https://cdn.huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/vocab.txt -qO /content/bert-fa-base-uncased-sentiment-snappfood/vocab.txt

!ls /content/bert-fa-base-uncased-sentiment-snappfood

Output
config.json  tf_model.h5  vocab.txt

Step 1
from transformers import TFBertForSequenceClassification
from transformers import AutoConfig
from transformers import AutoTokenizer

import tensorflow as tf
import numpy as np

Step 2
config = AutoConfig.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')
tokenizer = AutoTokenizer.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')

model = TFBertForSequenceClassification.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')
model.summary()

Output
All model checkpoint weights were used when initializing TFBertForSequenceClassification.

All the weights of TFBertForSequenceClassification were initialized from the model checkpoint at /content/bert-fa-base-uncased-sentiment-snappfood/.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.
Model: "tf_bert_for_sequence_classification"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
bert (TFBertMainLayer)       multiple                  162841344
_________________________________________________________________
dropout_37 (Dropout)         multiple                  0
_________________________________________________________________
classifier (Dense)           multiple                  1538
=================================================================
Total params: 162,842,882
Trainable params: 162,842,882
Non-trainable params: 0
_________________________________________________________________

Step 3
prompt = 'این خوراک بسیار خوب است'

inputs = tokenizer.encode(prompt, return_tensors="tf", max_length=128, padding=True, truncation=True)
logits = model(inputs)[0]
outputs = tf.keras.backend.softmax(logits)
prediction = tf.argmax(outputs, axis=1)
prediction = prediction[0].numpy()
scores = outputs[0].numpy()

labels = config.id2label
print(scores)
print(labels[prediction])

Output
[0.9952093  0.00479068]
HAPPY

Thank you very much for your help

Hello Mehrdad
I will help you again
The accuracy of the model for 170,000 data is 89%
What I think is not very good: the amount of loss_validation is that when the number of epochs increases, the amount of loss_valitiona increases.
I used 45300 test data for the model predicate
Unfortunately, I did not have a good prediction and  recognizes many negative sentences as positive
اصلاح طلبی باید راهبرد راهگشایی برای برونرفت حاکمیت ازین بن بست سیاسی چهل ساله که ریشه همه یا کمینه بیشتر مشکلات کنونی کشوراست ارائه کند وانهم تناقض وتنافر بزرگ حاکمیتی یعنی جمهوریت وولایت مطلقه است تا موضع وسمت وسوی خود را شفاف وبوضوح بیان نکند ازاصلاح طلبی فقط همان نامش را یدک میکشد,positive,political
label sentence is a positive but predict political
my model have 3 labels :
label2id: {'negative': 0, 'political': 1, 'positive': 2}
id2label: {0: 'negative', 1: 'political', 2: 'positive'}
How do you think I can improve the accuracy and floss of the model to have a better prediction?

The whole process is as simple as you think! but before dive into it, we need to set some grounds

The fine-tuned model saved on a directory in this case bert-fa-base-uncased-sentiment-snappfood
The directory consists of these properties: config.json  tf_model.h5  vocab.txt

I'm going to demonstrate the entire steps regarding one of our models base-uncased-sentiment-snappfood the procedure is as follow:

Load the packages
Load the config, tokenizer, and the model
The inference

0 + and a preliminary step regarding the mentioned model, in your case you don't need to this part.
Step 0
!pip install -qU transformers

!mkdir -p /content/bert-fa-base-uncased-sentiment-snappfood
!wget https://s3.amazonaws.com/models.huggingface.co/bert/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/config.json -qO /content/bert-fa-base-uncased-sentiment-snappfood/config.json
!wget https://cdn.huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/tf_model.h5 -qO /content/bert-fa-base-uncased-sentiment-snappfood/tf_model.h5
!wget https://cdn.huggingface.co/HooshvareLab/bert-fa-base-uncased-sentiment-snappfood/vocab.txt -qO /content/bert-fa-base-uncased-sentiment-snappfood/vocab.txt

!ls /content/bert-fa-base-uncased-sentiment-snappfood

Output
config.json  tf_model.h5  vocab.txt

Step 1
from transformers import TFBertForSequenceClassification
from transformers import AutoConfig
from transformers import AutoTokenizer

import tensorflow as tf
import numpy as np

Step 2
config = AutoConfig.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')
tokenizer = AutoTokenizer.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')

model = TFBertForSequenceClassification.from_pretrained('/content/bert-fa-base-uncased-sentiment-snappfood/')
model.summary()

Output
All model checkpoint weights were used when initializing TFBertForSequenceClassification.

All the weights of TFBertForSequenceClassification were initialized from the model checkpoint at /content/bert-fa-base-uncased-sentiment-snappfood/.
If your task is similar to the task the model of the checkpoint was trained on, you can already use TFBertForSequenceClassification for predictions without further training.
Model: "tf_bert_for_sequence_classification"
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
bert (TFBertMainLayer)       multiple                  162841344
_________________________________________________________________
dropout_37 (Dropout)         multiple                  0
_________________________________________________________________
classifier (Dense)           multiple                  1538
=================================================================
Total params: 162,842,882
Trainable params: 162,842,882
Non-trainable params: 0
_________________________________________________________________

Step 3
prompt = 'این خوراک بسیار خوب است'

inputs = tokenizer.encode(prompt, return_tensors="tf", max_length=128, padding=True, truncation=True)
logits = model(inputs)[0]
outputs = tf.keras.backend.softmax(logits)
prediction = tf.argmax(outputs, axis=1)
prediction = prediction[0].numpy()
scores = outputs[0].numpy()

labels = config.id2label
print(scores)
print(labels[prediction])

Output
[0.9952093  0.00479068]
HAPPY

Hello Mehrdad
I will help you again
The accuracy of the model for 170,000 data is 89%
What I think is not very good: the amount of loss_validation is that when the number of epochs increases, the amount of loss_valitiona increases.
I used 45300 test data for the model predicate
Unfortunately, I did not have a good prediction and recognizes many negative sentences as positive?

hello mehrdad
can you help me again?
What parameters can I change to get better accuracy?
I changed these parameters to some extent:
MAX_LEN = 64
TRAIN_BATCH_SIZE = 16
VALID_BATCH_SIZE = 16
TEST_BATCH_SIZE = 16
EPOCHS = 10
EEVERY_EPOCH = 500
LEARNING_RATE = 2e-5
CLIP = 0.0
But my lossـvalidation value increased and the accuracy decreased
How can I increase my accuracy to have a better forecast(predict)?

Epoch 1/10
3260/3260 [==============================] - 1011s 310ms/step - loss: 0.3203 - accuracy: 0.8780 - val_loss: 0.2650 - val_accuracy: 0.9039
Epoch 2/10
3260/3260 [==============================] - 1012s 310ms/step - loss: 0.1934 - accuracy: 0.9294 - val_loss: 0.2776 - val_accuracy: 0.9107
Epoch 3/10
3260/3260 [==============================] - 1013s 311ms/step - loss: 0.1207 - accuracy: 0.9580 - val_loss: 0.3280 - val_accuracy: 0.8958


react-dev-utils need to be updated

ini is an An ini encoder/decoder for node Affected versions of this package are vulnerable to Prototype Pollution. If an attacker submits a malicious INI file to an application that parses it with ini.parse, they will pollute the prototype on the application. This can be exploited further depending on the context.
ini need to be updated to newer version
└─┬<EMAIL_ADDRESS>└─┬<EMAIL_ADDRESS>└─┬<EMAIL_ADDRESS>└─┬<EMAIL_ADDRESS>└──<EMAIL_ADDRESS>

Just updated all deps


Cascader: fix 'v-model' directives require the attribute value which is valid as LHS.

Please make sure these boxes are checked before submitting your PR, thank you!

[x] Make sure you follow Element's contributing guide (中文 | English | Español | Français).
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[x] Add some descriptions and refer relative issues for you PR.

还有人维护吗？


I also have the same question, but my initial thought is that they are equivalent with the former using the .yaml file to set training hyperparameters while the latter sets them in the command line. Again, I'm looking into this myself, but that is my best guess as I notice that most of the arguments between the two are similar.


prevent unhandled rejections when loading page modules

fixes #3978. Even though the failure to load modules following a redeploy (which causes some filenames to be changed) was being dealt with (by causing the page to reload), rejections weren't being handled immediately before, when we preload modules to avoid waterfall.
This is a tricky thing to add a test for, but I verified that with this change the unhandledrejection event no longer happens, which should clean up Sentry logs etc.
Please don't delete this checklist! Before submitting the PR, please make sure you do the following:

[x] It's really useful if your PR references an issue where it is discussed ahead of time. In many cases, features are absent for a reason. For large changes, please create an RFC: https://github.com/sveltejs/rfcs
[x] This message body should clearly illustrate what problems it solves.
[ ] Ideally, include a test that fails without this PR but passes with it.

Tests

[x] Run the tests with pnpm test and lint the project with pnpm lint and pnpm check

Changesets

[x] If your PR makes a change that should be noted in one or more packages' changelogs, generate a changeset by running pnpx changeset and following the prompts. All changesets should be patch until SvelteKit 1.0

nope, nothing blocking except me being in a place with bad wifi for the last couple of weeks — still playing catch-up!

@Rich-Harris yikes! no worries tysm!


HpoFrequency

The class HpoFrequency needs to be revised in two ways

It should return a probability rather than a percentage value for the frequencies (e.g., previously, HpoFrequency would return an int value such as 30 for 30%).
The default Frequency value of VERY_RARE is not realistic. Almost all of our legacy data has not frequency information, and most of the annotations are for features which are common or even obbligate. I think it makes more sense to make the default value 100%. Note that downstream code can handle this as it wants.

I have revised as follows
public double lowerBound() {
    switch (this) {
      case ALWAYS_PRESENT:
        return 1D;
      case EXCLUDED:
        return 0D;
      case FREQUENT:
        return 0.05D;
      case OCCASIONAL:
        return 0.01D;
      case VERY_FREQUENT:
        return 0.30D;
      case VERY_RARE:
        return 0.01D;
      default:
        return ALWAYS_PRESENT.lowerBound();
    }
  }

and analogously for Upper bound. I have also added a corresponding mean function.

Done


feat(cli): add locale command

🔗 Linked issue

Resolves https://github.com/nuxt/ui/issues/2585
❓ Type of change

[ ] 📖 Documentation (updates to the documentation or readme)
[ ] 🐞 Bug fix (a non-breaking change that fixes an issue)
[ ] 👌 Enhancement (improving an existing functionality)
[x] ✨ New feature (a non-breaking change that adds functionality)
[ ] 🧹 Chore (updates to the build process or auxiliary tools and libraries)
[ ] ⚠ Breaking change (fix or feature that would cause existing functionality to change)

📚 Description

The CLI operation has been changed. Made a unified interface for creating components and locales. New make commands can be easily added in the future
New API:
nuxt-ui make component ...
nuxt-ui make locale ...

📝 Checklist

[x] I have linked an issue or discussion.
[ ] I have updated the documentation accordingly.

Nice idea 😊


Loading pretrained weights after extending a model

I was wondering if there was a way to leverage a pretrained model after adding another component to the model.
Specifically I'm extending MASS. When fine-tuning the untouched model I fairseq-train with the load-from-pretrained-model flag. Now I want to add a second encoder with different inputs, change to a multi-task loss and use the same decoder. Can I initialize encoder/decoder with the pretrained weights? And if yes, how? Thanks for your help!

Sure, you can modify your model to do this. Take a look at the stories generation model to see how they did this: https://github.com/pytorch/fairseq/blob/master/fairseq/models/fconv_self_att.py#L109-L127


There is a nvcc error  when I install apex

I ran the commond:
python setup.py install --cuda_ext --cpp_ext
and got

torch.__version__  = 1.4.0

setup.py:107: UserWarning: Option --pyprof not specified. Not installing PyProf dependencies!
  warnings.warn("Option --pyprof not specified. Not installing PyProf dependencies!")

Compiling cuda extensions with
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Sun_Jul_28_19:07:16_PDT_2019
Cuda compilation tools, release 10.1, V10.1.243
from /usr/local/cuda-10.1/bin

running install
running bdist_egg
running egg_info
writing apex.egg-info/PKG-INFO
writing dependency_links to apex.egg-info/dependency_links.txt
writing top-level names to apex.egg-info/top_level.txt
reading manifest file 'apex.egg-info/SOURCES.txt'
adding license file 'LICENSE'
writing manifest file 'apex.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_py
running build_ext
building 'fused_layer_norm_cuda' extension
gcc -pthread -B /home/bob/anaconda3/envs/metro/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include/TH -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda-10.1/include -I/home/bob/anaconda3/envs/metro/include/python3.7m -c csrc/layer_norm_cuda.cpp -o build/temp.linux-x86_64-3.7/csrc/layer_norm_cuda.o -O3 -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_layer_norm_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++
/usr/local/cuda-10.1/bin/nvcc -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include/TH -I/home/bob/anaconda3/envs/metro/lib/python3.7/site-packages/torch/include/THC -I/usr/local/cuda-10.1/include -I/home/bob/anaconda3/envs/metro/include/python3.7m -c csrc/layer_norm_cuda_kernel.cu -o build/temp.linux-x86_64-3.7/csrc/layer_norm_cuda_kernel.o -D__CUDA_NO_HALF_OPERATORS__ -D__CUDA_NO_HALF_CONVERSIONS__ -D__CUDA_NO_HALF2_OPERATORS__ --expt-relaxed-constexpr --compiler-options '-fPIC' -maxrregcount=50 -O3 --use_fast_math -DVERSION_GE_1_1 -DVERSION_GE_1_3 -DTORCH_API_INCLUDE_EXTENSION_H -DTORCH_EXTENSION_NAME=fused_layer_norm_cuda -D_GLIBCXX_USE_CXX11_ABI=0 -gencode=arch=compute_61,code=sm_61 -std=c++11
csrc/layer_norm_cuda_kernel.cu:4:10: fatal error: ATen/cuda/DeviceUtils.cuh: No such file or directory
 #include "ATen/cuda/DeviceUtils.cuh"
          ^~~~~~~~~~~~~~~~~~~~~~~~~~~
compilation terminated.
error: command '/usr/local/cuda-10.1/bin/nvcc' failed with exit status 1

I have installed CUDA10.1, ran commond:
which nvcc
and I got
/usr/local/cuda-10.1/bin/nvcc

I am not sure if NVIDIA Apex has any updates recently.
One suggestion is to skip apex installation. For some reasons, we observed mix-precision training is somehow slow. We think probably there are issues when running PyTorch1.4 with Apex.

Hey, you need to go to previous version of apex to install because of changes in PyTorch as given here :
https://issueexplorer.com/issue/NVIDIA/apex/1200
git checkout f3a960f80244cf9e80558ab30f7f7e8cbf03c0a0


VAD

Dear author, thanks for your sharing of code and the state-of-the-art performance of multiple moving sound source localization.
There is an issue in the Dataset.py folder: Unresolved reference 'VAD:1118'.
Did I miss something? How should I fix this issue? I look forward to your response.

Thank you for bringing this to our attention. We have corrected line 1118 from VAD[cnt: ] = sensor_vad_ori[-1] to sensor_vad[cnt: ] = sensor_vad_ori[-1]. You can try again.


windows: basic support for GetUserProfileDirectoryW

Fixes https://github.com/rust-lang/miri/issues/3499

@bors r+

:pushpin: Commit 10e8bb87fe416bc190426789032594aa1aa311f2 has been approved by RalfJung
It is now in the queue for this repository.

:hourglass: Testing commit 10e8bb87fe416bc190426789032594aa1aa311f2 with merge 4c399a35ba33c18195bf511e11967beaeba92f00...

:sunny: Test successful - checks-actions
Approved by: RalfJung
Pushing 4c399a35ba33c18195bf511e11967beaeba92f00 to master...


Kurtosis clean sometimes unable to remove empty enclave

What's your CLI version?
0.84.7
Description & steps to reproduce
kt enclave ls
UUID           Name           Status   Creation Time
724d2889ce87   large-stream   EMPTY    Tue, 17 Oct 2023 15:46:05 CEST

kurtosist clean -a
INFO[2023-10-17T15:59:51+02:00] Cleaning old Kurtosis engine containers...
INFO[2023-10-17T15:59:51+02:00] Successfully cleaned old Kurtosis engine containers
INFO[2023-10-17T15:59:51+02:00] Cleaning enclaves...
ERRO[2023-10-17T15:59:51+02:00] Errors occurred cleaning enclaves:
An error occurred while calling clean
 --- at /root/project/cli/cli/commands/clean/clean.go:170 (cleanEnclaves) ---
Caused by: rpc error: code = Unknown desc = An error occurred while cleaning enclaves
 --- at /home/circleci/project/engine/server/engine/server/engine_connect_server_service.go:149 (EngineConnectServerService.Clean) ---
Caused by: Following errors occurred while removing some enclaves :
>>>>>>>>>>>>>>>>> ERROR 0 <<<<<<<<<<<<<<<<<
An error occurred removing enclave network with ID '04bf4ba136cef1ae3e28bf1a1bf5a56255b9a9eb35444448d5c6d10d43132518'
 --- at /home/circleci/project/container-engine-lib/lib/backend_impls/docker/docker_kurtosis_backend/docker_kurtosis_backend_enclave_functions.go:704 (destroyEnclaveNetworks.func1) ---
Caused by: An error occurred removing the Docker network with ID 04bf4ba136cef1ae3e28bf1a1bf5a56255b9a9eb35444448d5c6d10d43132518
 --- at /home/circleci/project/container-engine-lib/lib/backend_impls/docker/docker_manager/docker_manager.go:388 (DockerManager.RemoveNetwork) ---
Caused by: Error response from daemon: error while removing network: network kt-large-stream id 04bf4ba136cef1ae3e28bf1a1bf5a56255b9a9eb35444448d5c6d10d43132518 has active endpoints
 --- at /home/circleci/project/engine/server/engine/enclave_manager/enclave_manager.go:303 (EnclaveManager.Clean) ---
INFO[2023-10-17T15:59:51+02:00] Cleaning unused images...
INFO[2023-10-17T15:59:51+02:00] Successfully cleaned unused images
Error:  An error occurred running command 'clean'
  Caused by: An error occurred calling the run function for command 'clean'
  Caused by: Errors occurred cleaning enclaves

Desired behavior
Always remove all enclaves when kt clean -a is ran.
What is the severity of this bug?
Papercut; this bug is frustrating, but I have a workaround.
What area of the product does this pertain to?
CLI: the Command Line Interface

Very strange; same as #1570 . I've never seen this before until recently, but I don't think we changed anything on our side.
@barnabasbusa can you add the following on #1570:

your docker version
the result of docker network inspect on the network that's failingto remove?


Formik & lodash

Bug report
Current Behavior
Executing yarn start I get the following errors in the terminal:
Uncaught Error: Cannot find module './_baseGetTag.js'
    at webpackMissingModule (isPlainObject.js:1)
    at Module../node_modules/lodash-es/isPlainObject.js (isPlainObject.js:1)
    at __webpack_require__ (bootstrap:856)
    at fn (bootstrap:150)
    at Module../node_modules/formik/dist/formik.esm.js (events.js:476)
    at __webpack_require__ (bootstrap:856)
    at fn (bootstrap:150)
    at Module.<anonymous> (index.css:7)
    at Module../src/App.tsx (App.tsx:36)
    at __webpack_require__ (bootstrap:856)
    at fn (bootstrap:150)
    at Module.<anonymous> (index.css?bb0a:82)
    at Module../src/index.tsx (index.tsx:18)
    at __webpack_require__ (bootstrap:856)
    at fn (bootstrap:150)
    at Object.1 (reportWebVitals.ts:16)
    at __webpack_require__ (bootstrap:856)
    at checkDeferredModules (bootstrap:45)
    at Array.webpackJsonpCallback [as push] (bootstrap:32)
    at main.chunk.js:1

/Users/rzabila/Documents/Work/DLP/node_modules/lodash-es/toPath.js
Module not found: Can't resolve './_arrayMap.js' in '/Users/rzabila/Documents/Work/DLP/node_modules/lodash-es'

/Users/rzabila/Documents/Work/DLP/node_modules/formik/dist/formik.esm.js
Module not found: Can't resolve 'lodash-es/clone' in '/Users/rzabila/Documents/Work/DLP/node_modules/formik/dist'

/Users/rzabila/Documents/Work/DLP/node_modules/formik/dist/formik.esm.js
Module not found: Can't resolve 'lodash-es/cloneDeep' in '/Users/rzabila/Documents/Work/DLP/node_modules/formik/dist'

In the browser I get the following error:
/Users/rzabila/Documents/Work/DLP/node_modules/lodash-es/toPath.js
Module not found: Can't resolve './_arrayMap.js' in '/Users/rzabila/Documents/Work/DLP/node_modules/lodash-es'

Steps to reproduce

npx create-react-app test-formik --template typescript
cd test-formik
yarn init & yarn add formik
yarn start

Software
Version(s)

Formik
2.2.9

React
17.0.2

TypeScript
4.1.2

Browser
Chrome

npm/Yarn
Yarn

Operating System
mac

For anyone who will face with this kind of issue, just run
yarn cache clean
in my case, it works.


Normal Transaction Models has a has-one relationship to Account when it should be has many.

The Typologies have many accounts in each typology which makes sense.  A typology contains many accounts.  The normal transaction models however only contain one account each.  I want the transaction models to contain many accounts too, just like the typologies.  Because an account can be involved in more than 1 transaction model.  For example an account that is a destination of a fan out can also be an origin of a single transaction. So that account is involved in 2 normal transaction models.  The app would then contain those two typologies.  I'm thinking about adding normalPatterns.csv and removing the "model" column in accounts.csv because the "model" column implies a 1 to 1 relationship.


Policies Don't Prevent Custom Properties Modification by Object Owner.

Affected module
Backend/UI
Describe the bug
I need to prevent Owners (User or Group) to modify Custom Properties values for Tables (values shoud be provisioned calling the API by external procceses, not individuals or teams).

At Organization level I have a policy to enable modification only for: Descriptions, Tags and Tiers. EditCustomFields operation is not considered just to avoid edition.

I tried also to define and additional and explicit policy to avoid edition.

Even so, owners (user or team) can update the values for them. Seems that policies are ignored by OM when dealing with custom properties.
To Reproduce

Expected behavior
Owner (User or Team) should not be able to modify custom properties (tables, topics, ... ) if a policy prevents it.
Version:

OS: 3.10.0-1160.80.1.el7.x86_64
Python version: Python 3.8.14
OpenMetadata version: 0.12.1
OpenMetadata Ingestion package version: openmetadata-ingestion[db2]==<IP_ADDRESS>-

Additional context
Add any other context about the problem here.

@sureshms I am assigning it to you since you have more context.


Invite user capability enabled by default even while smtp host is not configured

Steps to reproduce

Create lakefs config file not including smtp server configurations
Go to Administration -> Users
Click "Invite user" and try to invite a user
you'll see the following error

The fix should be to disable it by default.
Impact: This bug also impacts lakefs playgrounds, where IMO the invite user capability should not be disabled.

Does this happen when using an external authentication service? Or using the internal one?

Does this happen when using an external authentication service? Or using the internal one?

I think it's happens in OSS so probably the internal one @johnnyaug


hyfetch: 1.1.2 -> 1.3.0

Description of changes
Release notes: https://github.com/hykilpikonna/hyfetch/releases/tag/1.3.0
Changes:

I made it a bit easier to override deps by passing them directly instead of only passing in python3Packages
The file color_scale_numpy.py no longer exists in the repo
The file neofetch lost its executable bit when it got moved around in the repo, but that has since been fixed upstream (hykilpikonna/hyfetch@d797a8c)

cc: @YisuiDenghua
Things done

Built on platform(s)

[x] x86_64-linux
[ ] aarch64-linux
[ ] x86_64-darwin
[ ] aarch64-darwin

[ ] For non-Linux: Is sandbox = true set in nix.conf? (See Nix manual)
[ ] Tested, as applicable:

NixOS test(s) (look inside nixos/tests)
and/or package tests
or, for functions and "core" functionality, tests in lib/tests or pkgs/test
made sure NixOS tests are linked to the relevant packages

[x] Tested compilation of all packages that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review rev HEAD". Note: all changes have to be committed, also see nixpkgs-review usage
[x] Tested basic functionality of all binary files (usually in ./result/bin/)
22.11 Release Notes (or backporting 22.05 Release notes)

[ ] (Package updates) Added a release notes entry if the change is major or breaking
[ ] (Module updates) Added a release notes entry if the change is significant
[ ] (Module addition) Added a release notes entry if adding a new NixOS module
[ ] (Release notes changes) Ran nixos/doc/manual/md-to-db.sh to update generated release notes

[x] Fits CONTRIBUTING.md.

nixpkgs-review
Result of nixpkgs-review run on x86_64-linux 1

1 package built:

hyfetch

nixpkg-review went fine
1 package built:
hyfetch

Changes look goot to me, thank you for the work!

Just saw this, thank you so much!


No schedulable nodes found matching node selector for Cluster Monitoring Operator - 'node-role.kubernetes.io/infra=true'

With #4 "solved" (worked around...), this is up next:
TASK [openshift_control_plane : Ensure that Cluster Monitoring Operator has nodes to run on] ********************************************************************************
Monday 10 December 2018  18:56:05 +0000 (0:00:00.504)       0:09:07.201 *******
fatal: [<IP_ADDRESS>]: FAILED! => {
    "assertion": false,
    "changed": false,
    "evaluated_to": false,
    "msg": "No schedulable nodes found matching node selector for Cluster Monitoring Operator - 'node-role.kubernetes.io/infra=true'"
}

Failure summary:

  1. Hosts:    <IP_ADDRESS>
     Play:     Configure Cluster Monitoring Operator
     Task:     Ensure that Cluster Monitoring Operator has nodes to run on
     Message:  No schedulable nodes found matching node selector for Cluster Monitoring Operator - 'node-role.kubernetes.io/infra=true'

This is because at least one of the nodes needs to be a openshift_node_group_name='node-config-infra' instead of a openshift_node_group_name='node-config-compute', and 0f1e613321688a17d68cbbb23bfc532ee4033beb fixed this for me.
PS: At first I tought an infra node is special and won't host normal workload pod containers later, and I was looking for a way to mark a node to be BOTH a compute and an infra for a moment. But seeing compute,infra for the 2nd node in this output makes me think this is probably not so and it's fine:
[centos@openshift-master ~]$ oc get nodes
NAME                        STATUS    ROLES           AGE       VERSION
openshift-master.rdocloud   Ready     master          2d        v1.11.0+d4cacc0
openshift-node1.rdocloud    Ready     compute         2d        v1.11.0+d4cacc0
openshift-node2.rdocloud    Ready     compute,infra   2d        v1.11.0+d4cacc0