Add support for Python 2.7

CCDC's ETL pipeline for CAL-ACCESS campaign finance and lobbyist disclosure data is built in Django. Ultimately, we want to add to our process the loading of simplified campaign finance and election models that are currently being discussed in OCD's technical docs repo.
We would very much like to incorporate this Django package into our project, as we see a lot of value in relying on and contributing to a community-supported library such as this.
However, we would prefer to keep our project Python 2 and Python 3 compatible. This PR is an attempt to make that happen with minimum impact. Changes include:

Adding an explicit namespace for opencivicdata (to continue combining this package and python-opencivicdata-division under one namespace).
Adding Python 2 unicode compatibility to the Django models.
Enabling tests for Python 2.7.

Also, feel free to let me know if adding additional tests would be helpful.
Note: This PR should not be merged without first merging opencivicdata/python-opencivicdata-divisions#7.

Coverage decreased (-1.5%) to 89.093% when pulling ac54a014779e9f7e7fd0724b14a07e49e8424c00 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

can you make it so that the extra dependencies are only there in Python 2
on these PRs? and I think @fgregg said he'd figured out cross language
namespace packages in dedupe & might be able to weigh in
On Mar 9, 2017 5:21 PM, "James Gordon"<EMAIL_ADDRESS>wrote:

CCDC http://www.californiacivicdata.org's ETL pipeline for CAL-ACCESS
campaign finance and lobbyist disclosure data is built in Django.
Ultimately, we want to add to our process the loading of simplified
campaign finance and election models that are currently being discussed in OCD's
technical docs repo
https://github.com/opencivicdata/docs.opencivicdata.org.
We would very much like to incorporate this Django package into our
project, as we see a lot of value in relying on and contributing to a
community-supported library such as this.
However, we would prefer to keep our project Python 2 and Python 3
compatible. This PR is an attempt to make that happen with minimum impact.
Changes include:

Adding an explicit namespace for opencivicdata (to continue
combining this package and python-opencivicdata-division under one
namespace).
Adding Python 2 unicode compatibility to the Django models.
Enabling tests for Python 2.7.

Note: This PR should not be merged without first merging
opencivicdata/python-opencivicdata-divisions#7
https://github.com/opencivicdata/python-opencivicdata-divisions/pull/7.
You can view, comment on, or merge this pull request online at:
https://github.com/opencivicdata/python-opencivicdata-django/pull/76
Commit Summary

Add Python 2.7 to tests
Remove email notification.
Add py2.7 testing, backports.csv and init.py file #1
Merge branch 'master' of https://github.com/california-
civic-data-coalition/python-opencivicdata-django
https://github.com/california-civic-data-coalition/python-opencivicdata-django
Add explicit namespace package #1
Declare a namespace #1
Handle unicode for models #1

File Changes

M .travis.yml
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-0
(4)
A opencivicdata/init.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-1
(2)
M opencivicdata/models/base.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-2
(4)
M opencivicdata/models/bill.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-3
(44)
M opencivicdata/models/division.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-4
(3)
M opencivicdata/models/event.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-5
(31)
M opencivicdata/models/jurisdiction.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-6
(4)
M opencivicdata/models/people_orgs.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-7
(9)
M opencivicdata/models/vote.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-8
(11)
M opencivicdata/tests/test_loaddivisions.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-9
(1)
M requirements-test.txt
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-10
(3)
M setup.py
https://github.com/opencivicdata/python-opencivicdata-django/pull/76/files#diff-11
(1)

Patch Links:

https://github.com/opencivicdata/python-opencivicdata-django/pull/76.
patch
https://github.com/opencivicdata/python-opencivicdata-django/pull/76.
diff

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@jamesturk I can, but just to be clear: The only new external dependency is backports.csv which has the same API in Python 2 and 3. So I don't think a conditional install is strictly necessary.
Am I understanding your request?

yeah, Python 3 users will pay an unnecessary penalty since there is a C module for CSV https://github.com/python/cpython/blob/6f0eb93183519024cb360162bdd81b9faec97ba6/Modules/_csv.c that isn't being used here
also coverage went down quite a bit, mind looking into that?

Coverage decreased (-1.5%) to 89.093% when pulling 8c581b393097bf153f1a0c7ea56676eb0f60dec5 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage decreased (-1.5%) to 89.093% when pulling 8c581b393097bf153f1a0c7ea56676eb0f60dec5 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage decreased (-1.5%) to 89.093% when pulling 8c581b393097bf153f1a0c7ea56676eb0f60dec5 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

@jamesturk The decrease is a result of adding all these model __str__ methods. I can add calls to those methods in test_models.py and add new tests for instantiating Event, VoteEvent and other related model objects not currently being tested.
Sound good?

ah OK, not a big deal then, but if you have a chance to add a few
instantiations that'd be great. thanks!
On Mar 10, 2017 12:52, "James Gordon"<EMAIL_ADDRESS>wrote:

@jamesturk https://github.com/jamesturk The decrease is a result of
adding all these model str methods. I can add calls to those methods
in test_models.py and add new tests for instantiating Event, VoteEvent
and other related model objects not currently being tested.
Sound good?
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Coverage decreased (-1.08%) to 89.518% when pulling 4bdff53a5a319d3620a1d8042633f931d825cf37 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage decreased (-1.08%) to 89.518% when pulling 4bdff53a5a319d3620a1d8042633f931d825cf37 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+2.6%) to 93.211% when pulling aec35db70d40f975ea48791ca8474529dceb96b7 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+3.5%) to 94.059% when pulling 94bddd9140a317067c84addd555bd3b72b3e3969 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+4.3%) to 94.872% when pulling da3967688267ee2a5398c2bf2d236bda10346f60 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+6.3%) to 96.866% when pulling 6c4ec945fcad1d5ce416ae853e586dc56cead805 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+6.3%) to 96.866% when pulling 6c4ec945fcad1d5ce416ae853e586dc56cead805 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+7.8%) to 98.433% when pulling e05052a079a3c6413d316f249da4a3e501ef77c3 on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

Coverage increased (+9.0%) to 99.573% when pulling 9e9d4ae2d089314b78a49f157bdb3fc5f0c4646a on california-civic-data-coalition:master into ca8217f596f77559bb31762cdbed15bb76bbf984 on opencivicdata:master.

@jamesturk Tests are now at :100:. If I have gotten too carried away, just say so. Otherwise, I feel like this is ready.

ah wow thanks, nice to have 👍
LGTM


add docsearch

There's currently a bug where links to anchors don't actually work (try searching once the netlify build deploys). It goes to #__gatsby, which is the top level id. It might have something to do with the differences in config between react and reactjs_es.
@s-pace maybe you can help us get this working?

oooh

Searching for text inside works, but if you try to search for a header, it takes you here: https://es.reactjs.org/docs/hooks-intro.html#___gatsby

👋 @tesseralis,
The redirection to the anchor #___gatsby is done because there is no id or nameattribute set to the DOM element matching the lvl0 selector (that is to stay article h1). The fallback behaviour is to extract an anchor from the closest ancestor, that is to say the element . You can find more details here. This is the same behaviour on  Reactjs by the way.
Can you set one of these attribute to the <h1/> element?

@s-pace Ah, I see. It also seems like the main React docs don't allow you to search for the top level header? (e.g. you can't look up the page "Getting Started"). I'll
Until then, @carburo @Darking360 I think this can be merged.

@s-pace, is it possible to have the h1s link to the root page instead of an anchor? For example, searching "Getting started" should take you to reactjs.org/docs/getting-started.html instead of reactjs.org/docs/getting-started.html#getting-started. Is this what the lvl0 container is for?

@tesseralis regarding your questions:

It also seems like the main React docs don't allow you to search for the top level header?

Actually it does, the highlighted part of the header is just not different.
You can see an example on the main page for the query Getti:

This is due to the version of the DocSearch UI being used. By comparison, with the latest version of the dropdown, you do have this highlighting:

is it possible to have the h1s link to the root page instead of an anchor?

This would be possible by using this handleSelected function. Adding a unique identifier to every <h1/> tags will be more straightforward and enables a more accurate redirection. It will help in more situations.


AutoFuck, aka calling thefuck whenever a command is not found

First, I want to say I'm sorry if this isn't the place to post this.
I was thinking that having bash call thefuck automatically whenever it doesn't find a command I tried to type could be cool and even maybe useful.
Also, AutoFuck kind of has a nice ring to it :P
I can't be the first to have thought about this though; but I couldn't find information about the idea on the README or elsewhere.

Hey yo , was wondering the same..
Have you managed to so this ?
Thanks a lot and all the best!


FGA not selecting support

Preparation

[X] I tested the latest release
[X] I looked at other issues (even the closed ones)
[X] I read the Troubleshooting Guide

FGO server
NA
FGA build number
2101
Describe the bug
Case 1:
If FGA starts quest -> It taps the quest but gets stuck at the support picking.
Case 2:
If quest is already started and the quest is set to repeat -> It executes the commands without issue but gets stuck again at support picking.
Video
Case 1:

Case 2:

Those red boxes keep spazzing out the support picking. I can't give a screen recording due to me being in android 13.
Device model
Xiaomi POCO M5
Android version
13
Screen size
1080 x 2400
RAM
6 + 5 GB

Move the play button to the bottom left.
See the red rectangle near the play button in your screen shots? Support detection is done through that rectangle and you're covering the game content with the button.

Okay it works now. Thanks for the quick and detailed response!


Add missing CKAN badged mods from SpaceDock

Two adopted parts packs, some buildings, and a planet were missed by the SpaceDock add trigger. This is them.

The PR validation is broken.

Still seems to work partially, at least:
Validating NetKAN/AbovetheSkySoyuzTMAandR7.netkan...
NetKAN/AbovetheSkySoyuzTMAandR7.netkan:14:4: Warning: Object contains duplicate key: u'depends'
   |  At line 14, column 4, offset 336
   |  Object started at line 1, column 0, offset 0 (AT-START)

#rebuild

#rebuild


Fix reusability for looped loadService() call.

Simpler alternative to https://github.com/burzum/cakephp-service-layer/pull/14
Fixes https://github.com/burzum/cakephp-service-layer/issues/13
It also directly uses the prop then instead of always creating dummy object, so cleaner and more performant flow here.

Are you able to release a patch version here? Thank you.

@dereuromark done. Please ping me on Skype if you need something faster. Cake is not so much of a priority right now for me.


TWAI RX delay after installing driver (IDFGH-11937)

Answers checklist.

[X] I have read the documentation ESP-IDF Programming Guide and the issue is not addressed there.
[X] I have updated my IDF branch (master or release) to the latest version and checked that the issue is present there.
[X] I have searched the issue tracker for a similar issue and not found a similar issue.

General issue report
After installing TWAI driver and creating a simple Freertos task that simply does twai_receive(&twai_message, portMAX_DELAY) the first twai_messages are received 100ms after driver install occurred (connected on an already loaded bus).
(12150) gpio: GPIO[9]| InputEn: 1| OutputEn: 0| OpenDrain: 0| Pullup: 0| Pulldown: 0| Intr:0
(12150) gpio: GPIO[10]| InputEn: 0| OutputEn: 1| OpenDrain: 0| Pullup: 0| Pulldown: 0| Intr:0
(12150) CAN: Starting RX task
(12250) CAN: RX id: 0x25
So if I send a CAN frame to and ECU and get a CAN response before that 100ms delay, I never receive that response CAN frame.
After lot of trial an error i found that I don't have this behavior if I disable all console output in menuconfig (UART and USB-Serial).
For now the workaround i am using is a taskDelay of 250ms just after installing the driver before getting the success install feedback to my application (communicating to ESP32-S3 using BLE).

@theodem ,,How about start your esp32 earlier?
and what this payload time for your ECU?
If one day esp32 can receive msg within 50ms, will you complain about you can't receive any response before that 50ms delay?

@wanckl I don't understand your comment.
This issue was not created to "complain" but to understand if the fact that TWAI driver receive message 100ms after starting the driver is a normal behavior or a bug.
In case it is a normal behavior, that is not a concern as I found a  workaround. But if this is not the expected behavior something might not work as expected.
And that might be a time related bug to me as it doesn't append when all console output are disabled.

@theodem Sorry for my rudness, ,,
But for my side, I think this 100ms is reasonable as follow reasons:

Using console need initialize many components like newlib, uart and even vfs
How busy the already loaded bus be, when is the first frame esp32 should receive
In TWAI driver, there need some memory copy, it also need time

Any way, it shouldn't a big issue I think, feel free to workaround,,,


How to visualize the generated disparity map

Try the KITTI Stereo 2015 devkit matlab functions
https://s3.eu-central-1.amazonaws.com/avg-kitti/devkit_scene_flow.zip

Thank you very much for your reply. By the way, how to visualize the disparity map generated by your deep learning project on github

@huangcimu
The "submission.py" will generate uint16 PNGs in single channel (grayscale images).
The matlab functions can load the PNGs and convert them into RGB images.

👌thank  you  very  much------------------ 原始邮件 ------------------
发件人: "Jia-Ren<EMAIL_ADDRESS>发送时间: 2019年6月13日(星期四) 下午4:47
收件人<EMAIL_ADDRESS>抄送: "huangcimu"527354782<EMAIL_ADDRESS>主题: Re: [JiaRenChang/PSMNet] How to visualize the generated disparity map(#137)
@huangcimu
The "submission.py" will generate uint16 PNGs in single channel (grayscale images).
The matlab functions can load the PNGs and convert them into RGB images.
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爱奇艺和腾讯视频的VIP视频无法下载

Downloading http://www.iqiyi.com/v_19rr7plcfw.html error:
http://data.video.iqiyi.com/videos/v0/20201029/8d/7e/4859106dee6b93082145e31e105bb7f2.f4v?qd_tvid=733982800&qd_vipres=2&qd_index=1&qd_aid=733982800&qd_stert=0&qd_scc=b79a2018290d29b6c5f4a929803a2e6c&qd_sc=3f552a7cbd849ad6e94a117d04ff0f0c&qd_vipdyn=2&qd_vip=0&qd_src=01012001010000000000&qd_ip=dd07d2c3&qd_p=dd07d2c3&qd_tm=1604284398810&qd_uid=0&qd_k=95f7e5a47b5f789241b7525bd6db04a9 request error: HTTP 405
请问是命令中要带有cookie吗？如果要，那要如何附带？

hxd readme里写得清清楚楚啊
Cookies can be provided to annie with the -c option if they are required for accessing the video.

Cookies can be the following format or Netscape Cookie format:

name=value; name2=value2; ...
Cookies can be a string or a text file, supply cookies in one of the two following ways.

As a string:

$ annie -c "name=value; name2=value2" https://www.bilibili.com/video/av20203945
As a text file:

$ annie -c cookies.txt https://www.bilibili.com/video/av20203945
If the -c is not set, annie will try to get the cookies from the current user's Chrome or Edge automatically. To use this feature, you need to shutdown your Chrome or Edge for only one time and let annie launch the browser for you.

那么要如何使用annie来启动Chrome or Edge？


Support for Common Hyper-parameter Tuning Libs

Some sort of Adapter pattern to interface with some of the more common hyper-parameter tuning libraries.
NNI: https://github.com/microsoft/nni
Optuna: https://github.com/optuna/optuna
Talos: https://github.com/autonomio/talos
HyperOpt: https://github.com/hyperopt/hyperopt
Katib (supports k8s Job and MPIJob CRD): https://github.com/kubeflow/katib

@dorukkilitcioglu would love to hear any feedback/ideas you have on this. Hoping to tackle in the next few weeks.

In terms of Black-Box Optimization, I've been looking into Nevergrad and Ax, both seem pretty well equipped, though I prefer Nevergrad at this point.
In general, are you looking to map a spock config to the parameter classes that these tools use? So you only write your spock config, ask spock to tune it using Optuna (for example), give it a budget, and when you come back you have 100 different runs with their associated spock configs and some objective value.
You could also start small and use random search as the initial POC, to figure out how you want to handle the input/output of this process.

My current thought is to be tool run agnostic and just provide an adapter to each supported backend that returns whatever structure is needed for the parameter ranges/types/scales etc. Basically allow you to specify all the ranges etc with spock, regardless of backend, using something like a new decorator (e.g. @spockTuner) and then just map that to an output for the user to use downstream in the manner that the library wants...
The only things this kinda breaks with spock is the ability to save the state of each hyperparameter 'run' config as the backend library will be handling the evolution of the parameter set. But that might be overkill as you might only want to save the range configs and then the final config...

Or as you alluded to we could just fully wrap 1 or 2 backends to be set and forget... Not sure which is the better option

@dorukkilitcioglu can you peak at #62 and see what you think? There is a simple example here that shows the basic syntax.
Lmk

Looking at the simple example, there's a lot going on. I like it overall, and I feel like it looks more dense than it actually is because Logistic Regression training is super easy and it's as if half the code is tuning :D
My understanding is that there are multiple steps you have to take:

Set up the tuner (Optuna) with some high-level parameters
Modify the config builder so it's on tuning mode
Generate fixed params? I'm not sure what this does. I'm guessing that after the .tuner() call we don't have a spockspace object anymore, so we can't directly get a parameter from there without sampling?
Save the config and then sample the config? Shouldn't this logically be the other way around? Like you'd first generate a sample using your favorite optimizer, and then save the parameters that it generates?
Tell the optimizer the result. This is fairly standard - is this tuner-agnostic in terms of the API call?

Also, does the tune_dict have to be saved in a different data structure, or are the results being saved somewhere automatically?
I think there's a slight room for simplification (why can't tune_dict just be a part of attrs_obj, so you only interact with attrs_obj?), but I think the number of steps is very reasonable. I'm not sure about the specifics, so maybe you're already handling this, but storing the current state of the hyperparam exploration (internal state of the optimizer, plus all of the results so far) in a file as the optimization is going on could be useful (plus it would make it easier to continue tuning in the future).

Yeah LR might be a bit under-kill as an example but I was just mimicking some optuna docs for simplicity..

My understanding is that there are multiple steps you have to take:

Set up the tuner (Optuna) with some high-level parameters

There is a difference between @spock and the @spockTuner decorators. So anything you want to be hyper-params needs to be decorated with @spockTuner (This is for backend reasons and to limit the types allowed)

Modify the config builder so it's on tuning mode

All good here..

Generate fixed params? I'm not sure what this does. I'm guessing that after the .tuner() call we don't have a spockspace object anymore, so we can't directly get a parameter from there without sampling?

Yeah, the two options to get a spockspace back are generate() and sample(). Generate just returns a spockspace with all the fixed parameters (anything that was decorated with @spock). Since I parametrized the number of trials I needed to get a fixed parameter out before I started stepping on sample() which returns the fixed params + a sampled set of hyper-params. Just wanted to show that both are available and work as expected. So you can get the parameter without sampling you just need to use the generate() interface

Save the config and then sample the config? Shouldn't this logically be the other way around? Like you'd first generate a sample using your favorite optimizer, and then save the parameters that it generates?

Logically yes, however since sample() returns a spockspace the save() call has to be previous in the chain to get the object back out for chained calls -- this actually follows the fixed logic as well where one calls .save().generate()

Tell the optimizer the result. This is fairly standard - is this tuner-agnostic in terms of the API call?

It's kinda tuner agnostic in its current state. The return object in the second position from the sample() call will be a dictionary containing whatever you need for the tuner backend (only optuna for now which is why I only say kinda as I haven't fully tested any other backends)

Also, does the tune_dict have to be saved in a different data structure, or are the results being saved somewhere automatically?

Not sure... this comes from the fact that in order to sample with the define-and-run style interface in optuna you need the study object but also when calling tell you need the currently generated trial and the study. Seemed easiest to package that up into a dict...

I think there's a slight room for simplification (why can't tune_dict just be a part of attrs_obj, so you only interact with attrs_obj?), but I think the number of steps is very reasonable. I'm not sure about the specifics, so maybe you're already handling this, but storing the current state of the hyperparam exploration (internal state of the optimizer, plus all of the results so far) in a file as the optimization is going on could be useful (plus it would make it easier to continue tuning in the future).

Good point. Haven't dealt with the results, etc yet and how that's handled. Only the fact that the .save() call can dump the state of every call to sample. Will have to build out what you've said in some way....

I think there's a slight room for simplification (why can't tune_dict just be a part of attrs_obj, so you only interact with attrs_obj?)

Forgot to address this one. Actually makes sense. I think I can just add it  as an @property on the object for simple access when needed.


Clicking on the X button inside the Reminder notification starts the timer

This issue is specific to Toggl Button for Firefox on Windows. On MacOS, the notification doesn't have an X button.
When you click on the X (close) icon inside the notification the notification is closed and the timer starts. In other words, the behavior is the same as clicking anywhere else on the notification.

Desk case: 430711

additionally
'I have noted
that this also occurs with other pop-ups. The Idle timer pop-up also
automatically discards my time and stops the current timer whether I click
it, dismiss it, or let it time out.'
case/430711

I've installed Toggl's Firefox extension and noticed a bug: when I am not tracking myself, from times to times the extension reminds me to start tracking. But when I click the X to close the notification, the extension starts the timer. It should not.
Case/448004

Another: I also noted that it start also if I click nothing! The popup appears and the timer start.
Firefox 52.0.2 (32 bit),
Toggl button version (1.1.1)

Another user experiencing this Firefox issue. The timer starts even if they do nothing.
See case 444371.

Same experience here on Firefox Dev Edition. Incredibly annoying.

Firefox v59.0
Windows 10
Also experiencing this, and took a while for me to track down why i had random running timers.
Even if you ignore the pop up, it will start a timer, which is incredibly annoying. So basically anything involved with this popup will trigger a new timer.

 Extension is still useful, but this "feature" is aggravating.

Another with this issue. Firefox 60

Similar issue reported in conversations/19438242364

Related to #1116, I'm looking into these now.


gabbi checks for Ruby CVEs

This PR enables gabbi to notify the team of Ruby CVEs, as reported by rubysec.com. Running on the same 24-hour cycle as when we get gem version update notifications, the addition of a CVE script checks the rubysec XML feed for Ruby security vulnerability updates and sends a Slack message to our dev-notifications channel.
closes #1281
Updates include:

Addition of a cve_methods.rb file and related tests in gem_notifications_tests.rb
Expansion of gem_notifications.yml to run a new job/script: cve_script.rb

Before contributing, please read our contributing guidelines and code of conduct.
Overview
Describe the changes present in the pull request
Submitter Checklist:

[x] Include a link to the related GitHub issue, if applicable
[x] Include a security review link, if applicable

Testing
The agent includes a suite of unit and functional tests which should be used to
verify your changes don't break existing functionality. These tests will run with
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and here for our functional tests.
For most contributions it is strongly recommended to add additional tests which
exercise your changes.
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[ ] Perform code review
[ ] Add performance label
[ ] Perform appropriate level of performance testing
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Good stuff.
Some ideas for the future:

Turn the *_methods.rb files into Ruby classes, and have the *_script.rb files call public class methods

With classes, local variables like ruby_sec_feed and cycle could become constants

Implement error handling and possibly retry logic for each operation that could fail

Break up some of the complex operations (Feedjira.parse(HTTParty.get(ruby_sec_feed).body)) into multiple methods

Add newrelic_rpm to the project!

Thanks for the review and feedback! I've saved these ideas to address in the future 🌈


[SPARK-31892][SQL][FOLLOWUP][test-java11] Improve test coverage for datetime pattern with formatter functions

What changes were proposed in this pull request?
This PR is a followup for https://github.com/apache/spark/pull/28706, we want fully check for the formatter, which can warn us ahead if there is any behavior change when we update JDK.
Why are the changes needed?
test coverage and hotfix for the silly mistake in https://github.com/apache/spark/pull/28706 at https://github.com/apache/spark/pull/28718/files#diff-7f589e01d3e5e5ea284c1622527d4984R49
Does this PR introduce any user-facing change?

NO
How was this patch tested?

add tests

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@dongjoon-hyun do you know how to trigger java 11 test in the PR builder?

@cloud-fan See the example https://github.com/apache/spark/pull/28463 - add [test-java11]

retest this please

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Sorry for being late. Yes, @MaxGekk 's comment is correct.

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Spleeter on PythonAnywhere

Hello, I’ve tried to install Spleeter on a server of PythonAnywhere using a pro account with 10 GB available. However after the installation when trying to use it to extract the stems of an input audio it gives this error:
tensorflow/core/platform/env.cc:351] Check failed: -1 != path_length (-1 vs. -1)
Is there a way to fix this?

Hi @LuigiAttorresi
The error that pops out is raised by tensorflow. So it may be either an issue with the version of tensorflow you installed or an issue with the input audio file you're using. Have you tried performing a separation using the provided example file first? do you get the errors for it ?
Could you send the complete stacktrace of your error to help us understand it ?

Yes I’ve tried using the provided example, but I get always the same error. I’m creating a virtual environment with python 3.8 where ffmpeg and spleeter are installed, then using spleeter both by python script and command line it gives that error.
$ spleeter separate -p spleeter:2stems -o output audio_example.mp3 --verbose

F tensorflow/core/platform/env.cc:351] Check failed: -1 != path_length (-1 vs. -1)
Aborted

I couldn't reproduce your bu on several different configs so we'll need more details to help.
Have you tried to run spleeter using docker ?

I've found out the problem is that TensorFlow doesn't correctly run on PythonAnywhere, in fact I get the same error even by running other TF functions.
Thank you anyway!


Add missing note for flaming rune critical

Why would this need a note when it just does it?

It's referenced in several places: "PF2E.WeaponPropertyRune.flaming.Note.criticalSuccess"

So do we want to migrate away the straggling localizations?

No, it's fine for now. Melee items need persistent damage, splash, and precision support


error for running test.py (it seems that the pretrained sct_iter_160000.pth.tar is wrong for artistic style)

Hi,
When I try to run the following script:
python test.py --content input/content/lenna.jpg --style input/style/in2.jpg --decoder artistic/decoder_iter_160000.pth.tar --SCT artistic/sct_iter_160000.pth.tar --testing_mode artistic
I got some errors:
File "test.py", line 128, in
SCT.load_state_dict(torch.load(args.SCT))
File "/home/huiqin/anaconda3/envs/torch18/lib/python3.8/site-packages/torch/nn/modules/module.py", line 1604, in load_state_dict
raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format(
RuntimeError: Error(s) in loading state_dict for SCT:
Unexpected key(s) in state_dict: "cnet.4.weight", "cnet.4.bias", "snet.4.weight", "snet.4.bias".
size mismatch for cnet.0.weight: copying a param with shape torch.Size([256, 512, 1, 1]) from checkpoint, the shape in current model is torch.Size([128, 256, 1, 1]).
size mismatch for cnet.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for cnet.2.weight: copying a param with shape torch.Size([128, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([32, 128, 1, 1]).
size mismatch for cnet.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for snet.0.weight: copying a param with shape torch.Size([256, 512, 3, 3]) from checkpoint, the shape in current model is torch.Size([128, 256, 3, 3]).
size mismatch for snet.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([128]).
size mismatch for snet.2.weight: copying a param with shape torch.Size([128, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 128, 1, 1]).
size mismatch for snet.2.bias: copying a param with shape torch.Size([128]) from checkpoint, the shape in current model is torch.Size([32]).
size mismatch for uncompress.weight: copying a param with shape torch.Size([512, 32, 1, 1]) from checkpoint, the shape in current model is torch.Size([256, 32, 1, 1]).
size mismatch for uncompress.bias: copying a param with shape torch.Size([512]) from checkpoint, the shape in current model is torch.Size([256]).

Please specify --testing_mode to 'art' instead of 'artistic'. I think this would work.

It works. Thank you.


Fav icon

We can add a attracting fav icon to the website which gives good user experience.
Plz assign me for this issue.

i have alread add fav icon in jy many porject, so I request you to assign me this issue

Hey 👋 I'm Dhairya. I would be happy if you could this issue to me. I'll contribute to this issue under GSSoc'23. I can add the favicon as mentioned

I want to work on this issue under GSSOC '23 . I also have the relevant skills for this issue to get fixed. So please approve my request.

it is not currently needed for website


Weekly Patch Release v.1.2.6 [full merge, no squash]

PR review

[x] Is this pull request ready for review? (if not, please submit in draft mode)
[x] Make sure the title is self-explanatory and the description concisely explains the PR
[x] Add labels and milestones (and optionally projects) to the PR so it can be classified

shall we include RunIf in this release?
cc: @PyTorchLightning/core-contributors

I don't see why not. It's testing code. Can't even be imported by users.

shall we include RunIf in this release?
cc: @PyTorchLightning/core-contributors

please do, it will make further releases more manageable


Guidelines adjustments

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you all sign our Contributor License Agreement before we can accept your contribution.0 out of 2 committers have signed the CLA.:x: tszafranska:x: kriher18You have signed the CLA already but the status is still pending? Let us recheck it.


Use managed tags only

fixes https://github.com/miq-consumption/manageiq-consumption/issues/53
tagging mechanism is used also for other things then
tagging objects in Miq and for this reasons are used only managed tags.
cc @sergio-ocon @aljesusg

Coverage remained the same at 98.153% when pulling 3a2746d11538e894fb3cb936e2d14271fc0123d3 on lpichler:use_managed_tags_only into 317cc825dc4c500eb6565f99e65dec00825304d1 on miq-consumption:master.

Coverage increased (+0.005%) to 98.158% when pulling 1093a188362897cd00b3fe139d9117774788254a on lpichler:use_managed_tags_only into 317cc825dc4c500eb6565f99e65dec00825304d1 on miq-consumption:master.

Coverage increased (+0.005%) to 98.158% when pulling 1093a188362897cd00b3fe139d9117774788254a on lpichler:use_managed_tags_only into 317cc825dc4c500eb6565f99e65dec00825304d1 on miq-consumption:master.

LGTM.

Nice thanks @lpichler


[beta.31] ng build -prod produces build with runtime error: "No provider for t!"

Please provide us with the following information:

OS?

Windows 7, 8 or 10. Linux (which distribution). Mac OSX (Yosemite? El Capitan?)

Windows 10
Versions.

Please run ng --version. If there's nothing outputted, please run in a Terminal: node --version and paste the result here:

@angular/cli: 1.0.0-beta.31
node: 6.9.5
os: win32 x64
@angular/common: 2.3.1
@angular/compiler: 2.3.1
@angular/core: 2.3.1
@angular/forms: 2.3.1
@angular/http: 2.3.1
@angular/platform-browser: 2.3.1
@angular/platform-browser-dynamic: 2.3.1
@angular/router: 3.1.1
@angular/cli: 1.0.0-beta.31
@angular/compiler-cli: 2.3.1
Repro steps.

Was this an app that wasn't created using the CLI? What change did you do on your code? etc.

This app was previously working fine with an older version of the CLI (beta.24).  Runs fine via "ng serve" or an "ng build" where the target is not production.
The log given by the failure.

Normally this include a stack trace and some more information.

EXCEPTION: No provider for t!
t.handleError @ vendor.f5dba30….bundle.js:662
(anonymous) @ vendor.f5dba30….bundle.js:436
t.invoke @ polyfills.2835132….bundle.js:37
onInvoke @ vendor.f5dba30….bundle.js:331
t.invoke @ polyfills.2835132….bundle.js:37
n.run @ polyfills.2835132….bundle.js:37
(anonymous) @ polyfills.2835132….bundle.js:37
t.invokeTask @ polyfills.2835132….bundle.js:37
onInvokeTask @ vendor.f5dba30….bundle.js:331
t.invokeTask @ polyfills.2835132….bundle.js:37
n.runTask @ polyfills.2835132….bundle.js:37
i @ polyfills.2835132….bundle.js:37

Mention any other details that might be useful.
No errors during build itself.  Output looks as expected.  Also doesn't matter if aot flag is present or not.   We'll be in touch soon.

One thing to factor in is that AoT is now always on in prod builds, whether you specify it or not.
So, to ensure whether this happens in non AoT builds, try ng build -prod --aot=false.
If this is the problem, you might get more debug info from running ng build --aot (dev env + AoT), or just bringing sourcemaps in prod like ng build -prod --sourcemaps (sourcemaps are also turned off by default in prod)
Try this and see if you can get more information about what's upsetting it.
Cheers,.

That explains it. Thanks!  I'll give your suggestion a shot.  Closing.


Throttle session management api calls

Limit the calls to one every 500ms or so

Actually just don't attempt to update current user if update already in progress


Fix searchText undefined error

Fix for Issue #75

@onmotion Do you know how long you'll wait to release a version with this change?


💭 Short & Quick question about the documentation.

Question
Hi,
There is a section in the format page of the documentation ,

Is videoResolution: { width: 3048, height: 2160 } correct while videoAspectRatio: 16 / 9? Is the width supposed to be 3840?
What I tried
No response
VisionCamera Version
.
Additional information

[ ] I am using Expo
[X] I have read the Troubleshooting Guide
[X] I agree to follow this project's Code of Conduct
[X] I searched for similar questions in the issues page as well as in the discussions page and found none.

Hi @cropyai, it could be incorrect. It could also be intentional, since the filters are ordered by priority (descending) this would illustrate that the videoAspectRatio takes priority over the videoResolution.  So that it is clear, that it is not a guarantee that you get exactly what you want.

Video Resolution doesn't matter as width is just multiplied by height, but aspect ratio does. I think this is correct because sensors are always in landscape orientation.

@mrousavy what do you mean by "as width is just multiplied by height"? is it to find the number of pixels or smth?
-> "I think this is correct because sensors are always in landscape orientation."
why is 3048x2160 correct? The default resolution for 16:9 is 3840x2160 as far as I know. I am just curious.

I am wondering where "3048" comes from instead of "3840"

From the Camera sensor. They don't have to have the same resolution as a TV does for 4k. It defines whatever resolution it can stream at.

what do you mean by "as width is just multiplied by height"? is it to find the number of pixels or smth?

Yes, it just multiplies both numbers so the order doesn't matter, but that's probably not what you asked for anyways.


Speed and Optimisation of Inference

I'm using LMQL as the front-end for a big project that requires a lot of inference. I am using an A100 80GB but finding inference to be incredibly slow. I get roughly 50 samples through every 3 hours with a 13b LLaMA2-chat model which seems exceptionally slow, especially since it is loaded with 4bit quantisation and brain float. Its not a particularly long prompt either. I'm using the following decoder: beam_sample(n=2, temperature=0.7, top_k=0, repetition_penalty=1.1, top_p=0.7)
I'm wondering what techniques there are to speed up inference, for example, can async be used to have the equivalent of "batches" when used locally? Is there any threading considerations that must be made to minimise latency from computation within LMQL itself? Is there anything else I can do? Is there ways of splitting across multiple GPUs to improve inference speed like with the device map?
Thanks for your help.

Hi there. There are several knobs to turn and play with to get better throughput. The performance you are seeing definitely can be improved by a lot:

The chunksize=<N> parameter is likely the single biggest factor here. Chunksize determines the number of tokens that are generated speculatively, in one LLM call. Due to the top-n exploration of beam_sample it is very  likely that you are generating a lot of speculative tokens, that end up not being used. I would recommend playing with chunksize values of 1 to 4.

Depending on your scenario, you may consider switching to a different decoder like sample(n=2) which does not branch like beam search, and can run fully in parallel.

Running the model in-process vs. via lmql serve-model can also improve performance further, since the communication overhead is smaller.

You can indeed use multiple concurrent async queries to run them in parallel, e.g. via  asyncio.gather. This will result in automatic batching across queries on the backend. Depending on hardware, too many queries in parallel can also create a context switching overhead. In that case asyncio.Semaphor, may come in handy, allowing you to also specify a client-side batch size. Due to pipelining effects, it can make sense to choose a client-side batch size that is larger than the inference/server-side batch size.

With this in place, make sure to set the batch size of your lmql serve-model --batch_size 64 process or your lmql.model("local:gpt2", batch_size=64) constructor, such that you can utilise the full 80 gigs of your A100.

Let me know how these different strategies work for you. Happy to also look and profile some more specific query code, if you can share something :)

Thanks for your response. I have implemented everything you said into my project but still finding LMQL in async to be creating a lot of problems and I cannot get it to work. Here is a link to my repository for you to run yourself as it is a very specific case. It has separate client and server batch sizes with Semaphore and then batch_size in lmql.model specifications (which I'm assuming allows LMTP to carry out its own queuing and batching?). The semaphores batch the binary_tournament_evolve which itself can have anywhere from 1 to 2N + 1 queries. I am not familiar with asynchronous programming but I am unsure if this is a good solution in and of itself.
Essentially there are a bunch of setbacks stopping this from working effectively:

LMQL doesn't seem to log any of its errors when in async. The current repo is broken in async as threads seem to disappear, sometimes having ran out of memory, without logging anything. I have found this to be the case when outputted log files did not display LMTPStreamError: failed to generate tokens 'CUDA out of memory errors but mysteriously appeared in wandb logging files. The program continues to run but can go for hours without doing anything. If LMQL runs into a memory issues does it leave all other threads hanging? Is there anyway to check for this (or at least log it consistently i.e. with lmql output streaming)?
The initial part of the program works fine and is very fast, initialising the population very quickly. However, it seems to hang when switching to the next phase where there are various different and often more elaborate tasks being queued. I am currently refactoring this to be more compartmentalised on what is being queued but an explanation on how the queuing of LMQL queries work and how that may interplay with regular "client-side" asynchronous programming may help make the design decisions clearer. Also, would it potentially make a difference to the speed of the queries if multiple of the same query were sent at once as opposed to alternate queries (to the same in-process model of course)
could it be that multiple in-process queries all being sent at once at the beginning of the program cause it to load the model multiple times and thereby kill the process automatically?
Is there an easier way to profile LMQL than just logging times and looking at GPU usage through something like wandb

Thank you for your help. Since this code base requires so much querying if I could be sure that I'm making the right choices, or at least have solid logging I could set it off with confidence.

Thanks a lot for the repo, I will definitely have a closer look. It would be awesome to do a bit of performance engineering/profiling work here, as a case study, so we can learn and see what we can improve, and what performance tricks do work.

Thanks for the quick reply.
Re 2, so if all different workloads share the same decoding method then they should naturally have pretty well optimised scheduling and performance? Are there any other factors that may effect this like number of constraints within queries, length of queries, etc?
Re 3, This was just a suspicion, as I would often send off many tasks at the scripts opening and receive nothing back from any of them. Also I just generally found GPU monitoring to be very inconsistent when compared to regular pytorch/transformers use.
Re profiling, I did not realise inference certificates had been released. Would these only provide information on the inputs to the models or would it actually return anything that could give some information to errors? What metrics does it provide for local LLMs? Would using the with lmql.traced pattern allow it to track across multiple asynchronous queries? As for verbose=True does that also provide error messages and work for asynchronous queries?

OK so because I was optimising so much and running the repo in absolutely tiny conditions where it must have performed quickly I think this isn't a speed issue but actually a bug, potentially within the LMQL code. Essentially I think it is getting caught in some kind of async infinite loop. If you check out the debug branch of the repo you will see the most recent code. But essentially I have isolated it to the run_fitness_test method where, using my debugger I found this stack trace.

I don't think this is supposed to happen. Should I make a new issue?

Running with the toy environment with EleutherAI/pythia-70m on cpu with nest_asyncio.apply() gave the following new error:
To avoid this, please make sure to not call lmql.model(..., inprocess=True) on the top level of your script, but only inside functions or the __main__ block.Error: Detected an access to a lmql.model(..., inprocess=True)/local:<MODEL> inside the multiprocessing worker process itself. This is not supported and may lead to unexpected behavior.
I definitely do not have lmql.model on the top level of the script, infact its only defined within a string passed to lmql.query. Does this mean that perhaps LMQL has a problem with double dipping with the asyncio event loop and allowing reentrance. Using nest_asyncio.apply() along with torch.multiprocessing.set_start_method('spawn'), although it doesn't fix this error does allow the program to run on CUDA devices.
Finally, I'm not sure if this is just a problem with the crappy (but helpful) pythia70m but I've seen a lot of these errors whilst debugging and wondered what's happening and if its an LMQL problem or something on user side:
../aten/src/ATen/native/cuda/Indexing.cu:1093: indexSelectSmallIndex: block: [5,0,0], thread: [0,0,0] Assertion srcIndex < srcSelectDimSize failed.

Going back to speed and optimisation since it now seems to be working fine with these changes, is there any explanation for why one might be getting a GPU memory usage graph like this?

This is with a server side batchsize of 4 and a client size batch size of 50. It seems as if LMQL takes a while to build up to its max utilisation, is this correct? If so what would be the reason for this?

I haven't found time to get deeper into the repo, sorry about that. However, it's great to hear that things are moving forward. Thanks a lot for the smaller reproducible example, I will try to run this. A couple of things:

The changes that seem to work for you are the following, correct? Just so I know where I need to start looking for a general fix.

Using nest_asyncio.apply() along with torch.multiprocessing.set_start_method('spawn'), although it doesn't fix this error does allow the program to run on CUDA devices.

Regarding memory use: Are you seeing any OOMs or just increased memory requirements? AFAIK torch/cuda will pile up memory use until you reach the limit and only garbage collect then. I have run experiments for the latter before (see https://github.com/eth-sri/lmql/issues/222#issuecomment-1742784934), which seems to suggest this is normal and does not impair inference.

Depending on the decoder you are using, the batch_size will be quite relevant. For server-side batching I have found that sometimes large batch sizes work until you reach a certain sequence length, or the decoders explore too many branches in parallel. A potential improvement  on the LMQL inference end, could be choosing the batch size dynamically based on sequence length or with a retry-mechanic on OOMs.

Yes those changes fixed the issue and it seems to run fine now despite that error message.
With memory use I'm just trying fully utilise all the 80gb vram to improve performance, no particular OOM situations yet. Thanks for bringing that to my attention, I'll try and crank it as high as I can and see how much of a speed boost I can get.
A retry mechanic on OOM would be very good. Will there be much of this parallelisation effect on performance for sample decoding with small N values?

Does client-side/server-side batch size matter when using api based models like openai stuff?

I've attempted to use flash attention 2 to see whether that improves speed and found that it actually decreased it:

The bottom is without flash attention and the top is with it. Would there be any reason that this is happening? Is there a way to use LMQL that utilises its benefits?

Thanks for your continued investigation. An update on my end: I now have a setup with the debug branch with Pythia and am doing test runs on my machine. I am running with
python LMQL_prompt_breeder.py --task answerability --is_async --fitness_test_suite ./data/fitness_test_suites/squad_reqs_fitness_test_suite.json --test_CoT --day_care_dir data/lmql_specifications/day_care/ --client_batch_size 16 --model_name_or_path EleutherAI/pythia-70m

Together with a inference server launched via:
lmql serve-model EleutherAI/pythia-70m --cuda --batch_size 32 --busy_logging

I changed the lmql.model(...) calls in breeder.lmql to use the server as opposed to inprocess=True. The --busy_logging option is new and only available on a current development branch lmtp-cancel. It prints idle and streaming status (including tok/s), to the console of the inference server process. With this, it is much easier to see, if/when inference is happening and at what throughput.
With the call above, I am so far observing some idle times, but mostly consistent token streaming/inference without major pauses. Maybe you can share a breeder command that triggers some odd behaviour for you, e.g. I suppose you are using beam_sample or a more custom decoder? Also, it would be helpful to know at what stage of process execution things seem to slow down, e.g. over time or right at the beginning?
Re flashattention/padding, we do actually rely on padding to batch together requests of different sequence length in the backend. Batching happens here, and padding is done via this method. The resulting batch of sequences is then passed to the transformers backend in this file.
I hope this is helpful to track down where exactly padding does happen. Let me know any more concrete instructions of things I can run, to reproduce bottleneck scenarios.


Hide service broker card if broker information isn't available

Blocked on https://github.com/cloudfoundry-incubator/stratos/pull/2280

I merged this into the parent PR.


[SPARK-22897][CORE]: Expose stageAttemptId in TaskContext

What changes were proposed in this pull request?
stageAttemptId added in TaskContext and corresponding construction modification
How was this patch tested?
Added a new test in TaskContextSuite, two cases are tested:

Normal case without failure
Exception case with resubmitted stages

Link to SPARK-22897

cc @cloud-fan

Can one of the admins verify this patch?

Please explain in the JIRA/PR description about your use case that need to access the stageAttemptId in the executor - we must provide a very good reason to make such a change to a public interface.

ok to test

Test build #85409 has started for PR 20082 at commit 5753ee0.

Test build #85409 has finished for PR 20082 at commit 5753ee0.

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Please explain in the JIRA/PR description about your use case that need to access the stageAttemptId in the executor - we must provide a very good reason to make such a change to a public interface.

@jiangxb1987  quoted from  SPARK-22897:

Currently, there's no easy way for Executor to detect a new stage is launched as stageAttemptId is missing.

In one of our use case, spark task needs to reset some internal state once the stage is changed(we don't want to reset the state for every task as there may be a lot of tasks).
IMHO, stageAttemptId is as important as stageId or taskAttemptId and should be provided to user.
As for compatibility, we should do our best to avoid any incompatibility in minor releases. However,
this change should be source and binary compatible with previous releases.

Test build #85410 has started for PR 20082 at commit f02bc1e.

Test build #85410 has finished for PR 20082 at commit f02bc1e.

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Test build #85420 has started for PR 20082 at commit 0f86e95.

Test build #85420 has finished for PR 20082 at commit 0f86e95.

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Test build #85457 has started for PR 20082 at commit 72a3abf.

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retest this please

Test build #85474 has started for PR 20082 at commit 72a3abf.

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ping @cloud-fan @jiangxb1987 @zsxwing, I think it's ready for merging.

Test build #85512 has started for PR 20082 at commit 2581263.

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lib/platform: Add freebsd _GO_PLATFORM_ID

Also adds a comment explaining why some specific OSTYPE values are elided into more general _GO_PLATFORM_ID values.

Coverage increased (+0.002%) to 94.703% when pulling dd44e0e957e446350ed585a8293ae9197977071f on platform-freebsd into 7a9619d3247cf486f133b2e4dc162c2873b85623 on master.


CDATA isn't parsed

See e.g. the output of poddl https://anchor.fm/s/1d5f5088/podcast/rss /tmp -l, which looks like this:
[242] ![CDATA[#242 - 9 Ways to Increase Show Up Rate]]
[243] ![CDATA[#243 - Why All-In-One Tech Platforms are Dangerous]]
[244] ![CDATA[#244 - 3 More Ways to Increase Show Up Rate]]
[245] ![CDATA[#245 - Watch Me Pitch Live]]

In XML <![CDATA[...]]> is an escape to allow any characters other than ]]> to be included without escaping; it appears poddl doesn't parse this and so ends up including it in the episode names (and resulting filenames).

Thanks! I've never come across a feed with CDATA in the titles. I will have to take a look at this.

This should now be fixed. New binaries uploaded

Yep, works great.  Thanks!


🐛 [web] Modularly importing v9 Firebase JS SDK doesn't work

Bug report
Describe the bug
When using Cloud_Firestore along with WEB Modular version 9 SDK, it throws bunch of errors.
also while in debug, IDE also point towards to "web_entrypoint.dart" file's Future main function.
Error :
Click to Expand the Error
```
core.dart:56 Uncaught (in promise) TypeError: Cannot read properties of undefined (reading 'app')
    at Object.app$ [as app] (core.dart:56)
    at new cloud_firestore_web.FirebaseFirestoreWeb.new (cloud_firestore_web.dart:40)
    at Function.registerWith (cloud_firestore_web.dart:33)
    at Object.registerPlugins (generated_plugin_registrant.dart:15)
    at main (web_entrypoint.dart:14)
    at main.next ()
    at runBody (async_patch.dart:84)
    at Object._async [as async] (async_patch.dart:123)
    at main$ (web_entrypoint.dart:13)
    at main_module.bootstrap.js:19
    at Array.forEach ()
    at window.$dartRunMain (main_module.bootstrap.js:18)
    at :1:8
    at Object.runMain (client.js:8452)
    at client.js:24130
    at _wrapJsFunctionForAsync_closure.$protected (client.js:4461)
    at _wrapJsFunctionForAsync_closure.call$2 (client.js:11511)
    at Object._asyncStartSync (client.js:4425)
    at main__closure3.$call$body$main__closure (client.js:24142)
    at main__closure3.call$1 (client.js:24069)
    at StaticClosure._rootRunUnary (client.js:4823)
    at _CustomZone.runUnary$2$2 (client.js:12879)
    at _CustomZone.runUnaryGuarded$1$2 (client.js:12826)
    at _ForwardingStreamSubscription._sendData$1 (client.js:12416)
    at _ForwardingStreamSubscription._add$1 (client.js:12362)
    at _ForwardingStreamSubscription._add$1 (client.js:12695)
    at _MapStream._handleData$2 (client.js:12756)
    at _ForwardingStreamSubscription._handleData$1 (client.js:12721)
    at Object.eval (eval at Closure_forwardCallTo (client.js:1701), :3:44)
    at StaticClosure._rootRunUnary (client.js:4823)
    at _CustomZone.runUnary$2$2 (client.js:12879)
    at _CustomZone.runUnaryGuarded$1$2 (client.js:12826)
    at _ControllerSubscription._sendData$1 (client.js:12416)
    at _ControllerSubscription._add$1 (client.js:12362)
    at _SyncStreamController._sendData$1 (client.js:12201)
    at _SyncStreamController.add$1 (client.js:12082)
    at Object.eval (eval at Closure_forwardInterceptedCallTo (client.js:1781), :3:45)
    at StaticClosure._rootRunUnary (client.js:4823)
    at _CustomZone.runUnary$2$2 (client.js:12879)
    at _CustomZone.runUnaryGuarded$1$2 (client.js:12826)
    at _ControllerSubscription._sendData$1 (client.js:12416)
    at _ControllerSubscription._add$1 (client.js:12362)
    at _SyncStreamController._sendData$1 (client.js:12201)
    at _SyncStreamController.add$1 (client.js:12082)
    at _GuaranteeSink.add$1 (client.js:23581)
    at HtmlWebSocketChannel_closure1.call$1 (client.js:23874)
    at _EventStreamSubscription_closure.call$1 (client.js:18042)
    at StaticClosure._rootRunUnary (client.js:4829)
    at _CustomZone.runUnary$2$2 (client.js:12879)
    at _CustomZone.runUnaryGuarded$1$2 (client.js:12826)
    at _CustomZone_bindUnaryCallbackGuarded_closure.call$1 (client.js:13016)
    at invokeClosure (client.js:1524)
    at WebSocket. (client.js:1543)
```

Steps to reproduce
Steps to reproduce the behavior:

use the sample provided by  FlutterFire Docs or  2nd minimal sample below
add Dependencies : firebase_core & cloud_firestore
add Web Modular 9 SDK Config into web/index.html : CDN, SDK Setup and Configs of Project Settings

Click To Expand v9 Modular Config
<script type="module">

  // Import the functions you need from the SDKs you need

  import { initializeApp } from "https://www.gstatic.com/firebasejs/9.0.1/firebase-app.js";

  import { getAnalytics } from "https://www.gstatic.com/firebasejs/9.0.1/firebase-analytics.js";

  // TODO: Add SDKs for Firebase products that you want to use

  // https://firebase.google.com/docs/web/setup#available-libraries

  // Your web app's Firebase configuration

  // For Firebase JS SDK v7.20.0 and later, measurementId is optional

  const firebaseConfig = {

    apiKey: "API_KEY",

    authDomain: "PROJECT_ID.firebaseapp.com",

    projectId: "PROJECT_ID",

    storageBucket: "PROJECT_ID.appspot.com",

    messagingSenderId: "SENDER_ID",

    appId: "APP_ID",

    measurementId: "G-MEASUREMENT_ID"

  };

  // Initialize Firebase

  const app = initializeApp(firebaseConfig);

  const analytics = getAnalytics(app);

</script>

Expected behavior
since Flutter Web working fine with WEB v9 Modular SDK, i expected to work fine with cloud_firestore.
Sample project
Click To Expand Main.dart
import 'package:flutter/material.dart';
import 'package:firebase_core/firebase_core.dart';

void main() {
  WidgetsFlutterBinding.ensureInitialized();
  runApp(App());
}

class App extends StatefulWidget {
  _AppState createState() => _AppState();
}

class _AppState extends State<App> {
  bool _initialized = false;
  bool _error = false;

  void initializeFlutterFire() async {
    try {
      await Firebase.initializeApp();
      setState(() {
        _initialized = true;
      });
    } catch (e) {
      setState(() {
        _error = true;
      });
    }
  }

  @override
  void initState() {
    initializeFlutterFire();
    super.initState();
  }

  @override
  Widget build(BuildContext context) {
    if (_error) {
      return const Text("Error");
    }

    if (!_initialized) {
      return const Text("Loading");
    }

    return const Scaffold(
        body: Center(
      child: Text("Modular 9 fast boi"),
    ));
  }
}

Click To Expand index.html
<!DOCTYPE html>
<html>
<head>
  <!--
    If you are serving your web app in a path other than the root, change the
    href value below to reflect the base path you are serving from.

    The path provided below has to start and end with a slash "/" in order for
    it to work correctly.

    For more details:
    * https://developer.mozilla.org/en-US/docs/Web/HTML/Element/base

    This is a placeholder for base href that will be replaced by the value of
    the `--base-href` argument provided to `flutter build`.
  -->
  <base href="$FLUTTER_BASE_HREF">

  <meta charset="UTF-8">
  <meta content="IE=Edge" http-equiv="X-UA-Compatible">
  <meta name="description" content="A new Flutter project.">

  <!-- iOS meta tags & icons -->
  <meta name="apple-mobile-web-app-capable" content="yes">
  <meta name="apple-mobile-web-app-status-bar-style" content="black">
  <meta name="apple-mobile-web-app-title" content="test1">
  <link rel="apple-touch-icon" href="icons/Icon-192.png">

  <!-- Favicon -->
  <link rel="icon" type="image/png" href="favicon.png"/>

  <title>test1</title>
  <link rel="manifest" href="manifest.json">
</head>
<body>
  <!-- This script installs service_worker.js to provide PWA functionality to
       application. For more information, see:
       https://developers.google.com/web/fundamentals/primers/service-workers -->
  <script>
    var serviceWorkerVersion = null;
    var scriptLoaded = false;
    function loadMainDartJs() {
      if (scriptLoaded) {
        return;
      }
      scriptLoaded = true;
      var scriptTag = document.createElement('script');
      scriptTag.src = 'main.dart.js';
      scriptTag.type = 'application/javascript';
      document.body.append(scriptTag);
    }

    if ('serviceWorker' in navigator) {
      // Service workers are supported. Use them.
      window.addEventListener('load', function () {
        // Wait for registration to finish before dropping the <script> tag.
        // Otherwise, the browser will load the script multiple times,
        // potentially different versions.
        var serviceWorkerUrl = 'flutter_service_worker.js?v=' + serviceWorkerVersion;
        navigator.serviceWorker.register(serviceWorkerUrl)
          .then((reg) => {
            function waitForActivation(serviceWorker) {
              serviceWorker.addEventListener('statechange', () => {
                if (serviceWorker.state == 'activated') {
                  console.log('Installed new service worker.');
                  loadMainDartJs();
                }
              });
            }
            if (!reg.active && (reg.installing || reg.waiting)) {
              // No active web worker and we have installed or are installing
              // one for the first time. Simply wait for it to activate.
              waitForActivation(reg.installing || reg.waiting);
            } else if (!reg.active.scriptURL.endsWith(serviceWorkerVersion)) {
              // When the app updates the serviceWorkerVersion changes, so we
              // need to ask the service worker to update.
              console.log('New service worker available.');
              reg.update();
              waitForActivation(reg.installing);
            } else {
              // Existing service worker is still good.
              console.log('Loading app from service worker.');
              loadMainDartJs();
            }
          });

        // If service worker doesn't succeed in a reasonable amount of time,
        // fallback to plaint <script> tag.
        setTimeout(() => {
          if (!scriptLoaded) {
            console.warn(
              'Failed to load app from service worker. Falling back to plain <script> tag.',
            );
            loadMainDartJs();
          }
        }, 4000);
      });
    } else {
      // Service workers not supported. Just drop the <script> tag.
      loadMainDartJs();
    }
  </script>
  <script type="module">

    // Import the functions you need from the SDKs you need

    import { initializeApp } from "https://www.gstatic.com/firebasejs/9.0.1/firebase-app.js";

    import { getAnalytics } from "https://www.gstatic.com/firebasejs/9.0.1/firebase-analytics.js";

    import { getFirestore } from "https://www.gstatic.com/firebasejs/9.0.1/firebase-firestore.js";

    // TODO: Add SDKs for Firebase products that you want to use

    // https://firebase.google.com/docs/web/setup#available-libraries

    // Your web app's Firebase configuration

    // For Firebase JS SDK v7.20.0 and later, measurementId is optional

    const firebaseConfig = {

      apiKey: "API_KEY",

      authDomain: "PROJECT_ID.firebaseapp.com",

      projectId: "PROJECT_ID",

      storageBucket: "PROJECT_ID.appspot.com",

      messagingSenderId: "SENDER_ID",

      appId: "APP_ID",

      measurementId: "G-MEASUREMENT_ID"

    };

    // Initialize Firebase

    const app = initializeApp(firebaseConfig);

    const analytics = getAnalytics(app);

  </script>
</body>
</html>

Minimal Sample 2 :
Click To Expand Example 2 main.dart :point_left:
import 'package:flutter/material.dart';

void main() => runApp(MyApp());

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Welcome to Flutter',
      home: Scaffold(
        appBar: AppBar(
          title: const Text('Welcome to Flutter'),
        ),
        body: const Center(
          child: Text('web v9 nice'),
        ),
      ),
    );
  }
}

  use the same index.html above
Additional context
this issue could fix in 2 options

by Downgrading to Web SDK v8 / Config
Removing Cloud_Firestore dependency

i'm looking for a solution to get flutter WEB along with v9 modular SDK  to work with firestore because flutter web v9  significantly faster than version 8 WEB SDK.
plzz thank you :disappointed_relieved:

Flutter doctor
Run flutter doctor and paste the output below:
Click To Expand
Doctor summary (to see all details, run flutter doctor -v):
[✓] Flutter (Channel stable, 2.5.0, on Debian GNU/Linux 11 (bullseye) 5.10.0-8-amd64, locale en_US.UTF-8)
[✓] Android toolchain - develop for Android devices (Android SDK version 31.0.0)
[✓] Chrome - develop for the web
[✓] Android Studio (version 2020.3)
[✓] VS Code (version 1.60.0)
[✓] Connected device (1 available)

• No issues found!

Flutter dependencies
Click To Expand
Dart SDK 2.14.0
Flutter SDK 2.5.0
test1 1.0.0+1

dependencies:
- cloud_firestore 2.5.1 [cloud_firestore_platform_interface cloud_firestore_web collection firebase_core firebase_core_platform_interface flutter meta]
- cupertino_icons 1.0.3
- firebase_core 1.6.0 [firebase_core_platform_interface firebase_core_web flutter meta]
- flutter 0.0.0 [characters collection meta typed_data vector_math sky_engine]

dev dependencies:
- flutter_lints 1.0.4 [lints]
- flutter_test 0.0.0 [flutter test_api path fake_async clock stack_trace vector_math async boolean_selector characters charcode collection matcher meta source_span stream_channel string_scanner term_glyph typed_data]

transitive dependencies:
- async 2.8.1 [collection meta]
- boolean_selector 2.1.0 [source_span string_scanner]
- characters 1.1.0
- charcode 1.3.1
- clock 1.1.0
- cloud_firestore_platform_interface 5.4.1 [collection firebase_core flutter meta plugin_platform_interface]
- cloud_firestore_web 2.4.1 [cloud_firestore_platform_interface collection firebase_core firebase_core_web flutter flutter_web_plugins js]
- collection 1.15.0
- fake_async 1.2.0 [clock collection]
- firebase_core_platform_interface 4.0.1 [collection flutter meta plugin_platform_interface]
- firebase_core_web 1.1.0 [firebase_core_platform_interface flutter flutter_web_plugins js meta]
- flutter_web_plugins 0.0.0 [flutter js characters collection meta typed_data vector_math]
- js 0.6.3
- lints 1.0.1
- matcher 0.12.10 [stack_trace]
- meta 1.7.0
- path 1.8.0
- plugin_platform_interface 2.0.1 [meta]
- sky_engine 0.0.99
- source_span 1.8.1 [collection path term_glyph]
- stack_trace 1.10.0 [path]
- stream_channel 2.1.0 [async]
- string_scanner 1.1.0 [charcode source_span]
- term_glyph 1.2.0
- test_api 0.4.2 [async boolean_selector collection meta source_span stack_trace stream_channel string_scanner term_glyph matcher]
- typed_data 1.3.0 [collection]
- vector_math 2.1.0

Well, the reason this happens is just that our current implementation relies on having the entire CDN, not importing it modularly. You can't be sure what you need to import and how for the dart channels to work properly. You for example aren't importing anything from https://www.gstatic.com/firebasejs/9.0.1/firebase-firestore.js so it's no wonder it doesn't work.
Not saying you did anything wrong, we just don't support this version yet.
I would close this since the current documentation works with the 8.6.1 version we test against but then again this will be an issue should we move to v9.

You can switch to the v9 compat versions (see https://github.com/FirebaseExtended/flutterfire/issues/7041#issuecomment-925550256) however they are currently untested against FlutterFire until that issue is closed.
Note; the compat versions will only bring bug fixes which landed in v9 SDKs. You won't see any bundle size improvements since there is no tree shaking.

Closing in favour of using this issue to track: https://github.com/FirebaseExtended/flutterfire/issues/7041


Fails to load on HA 2024.10.0 - HA missing pytuya requirement

Setup failed for custom integration 'goldair_climate': Requirements for goldair_climate not found: ['pytuya~=7.0.5'].

Came here looking for a solution too.  I am new to home assistant, hopefully there would be a way to prevent this from happening over and over and having to tell the Mrs she can't use the heater because someone on the internet needs to update the code lol.

FYI:
The owner of this project has not been updating it for newer versions of HA. This is because Goldair heater models beginning with the code GPPH, GCPV, GECO, and PH-ET are supported by another integration for HA (which the owner of this project Nicole has contributed to instead) named Tuya Local.
See here:  https://github.com/make-all/tuya-local
So maybe remove this out-of-date component and use Tuya Local instead if you use goldair-climate for one of the listed models of Goldair heaters that Tuya Local supports.

Thanks for that.  Unfortunately that has proven to be quite unreliable for my lighting but I guess if it's that, it's that!  Actually the same symptom where things just drop off even though it's confirmed as connected with correct addresses and such.

Tuya devices tend to rely on printed PCB antennas and only operate on 2.4GHz WiFi, which is susceptible to interference from microwave ovens, as well as Bluetooth, Zigbee and other users of the 2.4GHz spectrum. So improving your WiFi coverage might help.
The cloud acts as a kind of hub to isolate the app from short WiFi dropouts, but this integration does not have that benefit. If you have not already configured static IP addresses for your devices, and explicitly set them in the tuya-local configuration, along with the protocol version, then doing that helps a lot with recovery times for those network dropouts, as reconnecting your device from scratch each time can take 5-10 minutes longer if IP address needs to be discovered and the protocol version negotiated each time.

If it was 5-10 minutes I'd be OK, but they drop off and don't come back (it's been weeks). I'm ok with it cause they are just ambient lights to come on at sunset, which they are still doing, but just have disconnected from HA for some reason.  Well, when I reboot HA, a different one will be available than last time.  It's annoying.  Compare that to my thread lighting by nano leaf, which has been rock solid.


Properly free module info when using linker-generated section brackets

The recently-merged support for linker-generated section brackets (#1379) causes a segfault on Android, when rt_term tries to free module info that is no longer malloced.  I'm guessing it broke for Solaris too, though I haven't tested it.  This PR seems to fix it- all druntime and phobos tests on Android/x86 and linux/x86 pass locally- let me know if any further tweaks are necessary.

Looks like setting _modules to null in rt.minfo was causing a segfault in the debug build on linux, so I got rid of it.  I had only tested locally with a release build, which is why I didn't see the issue, but I can reproduce with a debug build.  Looks like the cause is the invariant in rt.sections_elf_shared, which is called after the modules are removed, likely when the destructor is run.  I don't know why it only triggered on one module from the debug build, std.parallelism, but getting rid of the null assignment should fix the issue.

As @MartinNowak asked for in a previously-merged PR thread, moved some non-Posix bionic declarations into their own directory.

Auto-merge toggled on

@klickverbot I'll check this on Solaris.


Allow passing custom difficulty function to the Pool class

Our poll will feature, among other things, a custom difficulty adjustment function. The default one may be good for most cases, but we'd like to alter it a little bit. At the same time, we don't want to move too far away from the reference implementation - thus we'd like to have a way of providing not just our custom pool_store, but also the difficulty adjustment function for the pools that we create. This 5 LOC PR provides such possibility.

@mariano54 get_new_difficulty is used as default value for the new constructor parameter, so it maintains backward compatibility. Did I miss something?

Thank you, I missed that default value! LGTM.
Have you ran this to make sure it works?

We have not ran it, but the patch is extremely compact and we think any regression, however improbable, will be easily detected by an integration/system test. Is that ok?
We plan to contribute a couple of larger changes next week and I don't think we'll get away with the lack of test environment there :)

Yep, sounds good!


Sending message to fast!, API key is not yet set

Trying to use this but somewhere I'm failing right now and not sure what I'm doing wrong and can't see anything in the documentation that differs from how I've configured avante.
Configuration:
{
		"yetone/avante.nvim",
		event = "VeryLazy",
		build = "make", -- This is Optional, only if you want to use tiktoken_core to calculate tokens count
		opts = {
			-- add any opts here
			provider = "claude",
		},
		dependencies = {
			"nvim-tree/nvim-web-devicons", -- or echasnovski/mini.icons
			"stevearc/dressing.nvim",
			"nvim-lua/plenary.nvim",
			"MunifTanjim/nui.nvim",
			--- The below is optional, make sure to setup it properly if you have lazy=true
			{
				"MeanderingProgrammer/render-markdown.nvim",
				opts = {
					file_types = { "markdown", "Avante" },
				},
				ft = { "markdown", "Avante" },
			},
			--- For image pasting support
			{
				"HakonHarnes/img-clip.nvim",
				event = "VeryLazy",
				opts = {},
			},
		},
	},

and if i run env i see that i have
ANTHROPIC_API_KEY=sk-ant-api03... configured. But I'm still getting the message "Sending message to fast!, API key is not yet set" when trying to send a question. Any clue?

should be fixed now thanks for spotting this


ADJUST1-475 Allowing adjustment types to be changed

Can you rebase the branch so that you have the changes from dev, we want to make sure the build is passing? (I'd just do a hard reset and cherry-pick your commit.)


setElementValue

Where does setElementValue come from?
We use JSRuntime to call into JS and we supply a clientside function to be called.
So what is setElementValue in the example below?
Found here:
 JSRuntime.InvokeAsync<object>(             "setElementValue", myInput, "Value set after render");

Document Details
⚠ Do not edit this section. It is required for docs.microsoft.com ➟ GitHub issue linking.

ID: 49807900-0d56-e23c-1aef-d79099caeffb
Version Independent ID: feff434f-912c-0985-d575-4a23a629057d
Content: ASP.NET Core Blazor JavaScript interop
Content Source: aspnetcore/blazor/javascript-interop.md
Product: aspnet-core
Technology: aspnetcore-blazor
GitHub Login: @guardrex
Microsoft Alias: riande

I assume it is the name of a JS function, like:
 setElementValue: (elem, val) => {         elem.value = val;     }
The code above is part of code example: prerendered-interop.
StateHasChanged(); triggers an endless loop and thus a CPU rise of close to100%

Ok, i forgot to return a value, now it works:
setElementValue: (elem, val) => { elem.value = val; return val;}