Page A has a model, but page B does not. When I switch back and forth between the two pages, it crashes

Description
hi, Page A has a model, but page B does not. When I switch back and forth between the two pages, it crashes

Live Demo

https://beautiful-seahorse-02fb7b.netlify.app

Version

model-viewer: v1.12.0

Browser Affected

[x] Matemask App

OS

[x] iOS

AR

[ ] WebXR
[ ] SceneViewer
[ ] QuickLook

I'm not sure how to reproduce from your live demo link. Can you list some specific steps? Also, can you reproduce using any of our dev pages? Or on any other browsers/devices? I wonder if something caused the gl context to be lost, though we ought to be able to recover from that too...

I'm not sure how to reproduce from your live demo link. Can you list some specific steps? Also, can you reproduce using any of our dev pages? Or on any other browsers/devices? I wonder if something caused the gl context to be lost, though we ought to be able to recover from that too...

thanks for your  reply, this is  codepan link https://codesandbox.io/s/vue-reactivity-forked-v83go8?file=/src/components/Tab.vue
You can use this link(https://v83go8.csb.app/#/model) on your iphone , switch back and forth between two tabs multiple times. an error will be reported

我不确定如何从您的现场演示链接中重现。你能列出一些具体的步骤吗？另外，您可以使用我们的任何开发页面进行复制吗？或者在任何其他浏览器/设备上？我想知道是否有什么东西导致了 gl 上下文丢失，尽管我们也应该能够从中恢复......

Hello, Did you find the problem?

Sorry, no. On my old iPhone SE your site is working just fine (I toggled back and forth at least a dozen times). It could be a Safari WebGL bug; they've had some issues with their ImageBitmap implementation. Are you one the most recent version of iOS and Safari?


Rename Event::MouseMoved to Event::MouseMove

As discussed in #821, this renames MouseMoved to MouseMove for consistency with web API's.

I wonder if we should start keeping a list of breaking changes somewhere for release notes. 🤔

Yeah I think it might be a good idea to start a CHANGELOG.md which gets modified with PRs. Every PR can then add a short description of its change to the changelog with a link to the PR. Changes can be grouped by e.g. breaking changes/new features/bug fixes. Generally would be pretty easy to gather the changelog this way. The only somewhat tricky thing is that whenever some behavior gets reverted or modified and there hasn't been a druid release yet, then this revert/modify PR should also remove the older changelog entry.

for now i'm going to use a label for breaking changes, which will solve one of my problems.


wpf:TextField.HintOpacity not working on TextBox

Hi James,
The attached property wpf:TextField.HintOpacity works correctly on ComboBox but does not on TextBox.  I'm using version 0.93.
<ComboBox Grid.Column="0" Grid.Row="0" HorizontalAlignment="Right" Style="{StaticResource EnvCombo}" wpf:TextField.Hint="Source" wpf:TextField.HintOpacity="0.4"/>
<TextBox Grid.Column="1" Grid.Row="0" HorizontalAlignment="Stretch" Style="{StaticResource MaterialDesignTextBox}" wpf:TextField.Hint="Query" wpf:TextField.HintOpacity="0.4" VerticalAlignment="Center"/>
<ComboBox Grid.Column="2" Grid.Row="0" HorizontalAlignment="Left" Style="{StaticResource EnvCombo}" wpf:TextField.Hint="Target" wpf:TextField.HintOpacity="0.4"/>

Many thanks,
James

OK...as an aside I will probably make the default darker...can't remember my reasoning on them being so light, but just checked the latest Google guidelines and they are darker.

Just tested on the latest version in the test project...works.  But I have made the default darker.

 TextField will become TextFieldAssist. Want to line up all the APIs before v1.

Thanks, I've just updated our app to the newer version and it's fixed the problem.  No worries regarding the breaking change - our project is still reasonably small at the moment.


Vpc update wip (WIP)

Update to VPC Module

Tagging

Subnet Creation
Remove hardcoded values for additional subnets

Suggestion for subnet naming: <Public/Private>
This will make it clear which subnets belong to which VPCs , which AZ and if they are private/public without too many drill downs during setup of other dependent services.

Suggestion for subnet naming: <VPC Name>_<AZ>_<Public/Private>
This will make it clear which subnets belong to which VPCs , which AZ and if they are private/public without too many drill downs during setup of other dependent services.

My naming do you mean the tag name ?

Yes the value for Tag key “Name”.
Thanks,
-Arabinda

On Jul 3, 2021, at 1:18 AM, Tony Vattathil @.***> wrote:

Suggestion for subnet naming: <Public/Private>
This will make it clear which subnets belong to which VPCs , which AZ and if they are private/public without too many drill downs during setup of other dependent services.
My naming do you mean the tag name ?
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Adding the following in main.tf with the variable defined in variables.tf will allow us to create the VPC conditionally similar to while calling from deploy directory.
create_vpc = var.create_vpc

Tracking this in https://github.com/aws-ia/terraform-aws-vpc/issues/6

Suggestion for subnet naming: <VPC Name>_<AZ>_<Public/Private>
This will make it clear which subnets belong to which VPCs , which AZ and if they are private/public without too many drill downs during setup of other dependent services.

By naming do you mean the tag name ?

Tracking this in https://github.com/aws-ia/terraform-aws-vpc/issues/2


[Storage] while loop in some JS samples are not checking for done

We should be consistent.

#4122


Remove legacy TCP/IP remote control

At the end of this work, I propose to drop the lecacy TCP/IP stuff
Originally posted by @seb5g in https://github.com/PyMoDAQ/PyMoDAQ/issues/466#issuecomment-2518595818
Afterwards it might be good to refactor the LECO parts, which were made compatible with the TCP parts, but do not need this compatibility anmore.

I removed the used base64 encoding in #466 , therefore, we could remove this code from the leco.utils as well.


@types/node dependency breaks running projects

The added "@types/node": "^6.0.47" dependency leads to errors at our project:

Conflicting definitions for 'node' found at<EMAIL_ADDRESS>and<EMAIL_ADDRESS>Consider installing a specific version of this library to resolve the conflict.

At older projects we use the node types with version 8.x.x and at newer projects already 10.x.x.
What is the preferred way to get rid of it?

+1 same here, downgrade to 0.2.10 version helps.

I don't use typescript thid was added to fix #73.
If you guys figure it out let me know or provide a PR.
Thanks

Gave a try at fixing this in 0.2.15


Color Depth select Float(Full) in blender cause error read

Output in blender set Color Depth to Float(Full). Then use this model to read the render file.
Without raising error in this process but the data is completely wrong.

could you provide a minimal blender scene?

I use initial scene. Version is 3.1.0(I also tried 2.93.1). I only change file format and Color Depth. Both Color Depth I have tried, also I have tried compositing nodes.

The result is similar. When I load these image, it like this. It looks like the problem is .select method use "float16" handle "float32".

Also, value is differrent.

did you try version 1.0.2? I uploaded it recently to pypi / its equivalent
to the current development branch
On Wed, Jun 15, 2022 at 8:45 AM 风无 @.***> wrote:

I use initial scene. Version is 3.1.0(I also tried 2.93.1). I only change file
format and Color Depth. Both Color Depth I have tried, also I have tried
compositing nodes.
[image: QQ图片20220615141731]
https://user-images.githubusercontent.com/45221786/173756127-f2988046-a9ff-4089-b771-3177561734d3.png
The result is similar. When I load these image, it like this. It looks
like the problem is .select method use "float16" handle "float32".
[image: QQ截图20220615142529]
https://user-images.githubusercontent.com/45221786/173757735-8ee2ebdd-1b6b-43af-bb12-cbda8a3958b5.jpg
Also, value is differrent.
[image: QQ截图20220615142640]
https://user-images.githubusercontent.com/45221786/173758279-99df94b8-781e-4e7e-afac-4340bb779503.jpg
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I tried version 1.0.2, its works very well. It can correctly read data, thank you.


Choppy animation on android

Hey there!
Lovin' the plugin! just one issue I seem to be encountering though. My animation renders perfectly fine on desktop but when viewed on mobile the first play is choppy and drops the first 30 frames or so. When I set the animation to loop it plays fine after the first play through however. Any idea what could be causing this?
This is on android, haven't had a chance to test it on ios.
Thanks!
*Edit
On further testing it seems to be choppy in firefox too.

Think I solved this by switching all my track mattes to actual masks on my shapes. Everything seems to be running smoother in Firefox and on android


Spook not HUE's hoomie

What version of Spook are you using?
2.2.2
What version of Home Assistant are you using?
2024.2.1
The problem
I have an automation using a HUE service call (hue.activate_scene) working fine.
In the past, Spook also was fine with it, but since one of the last updates, i get an repair issue pointing out, that hue.activate_scene isn't known by home assistant. It remains with the latest update to 2.2.2
As pointet out: It is available, i can use ist in the DEV-Tools Service Tab and also the automation is working fine. The service call is the official one documented here: HUE Integration
For me, it seems like a false positive, would you mind having a look at it? Thank's alot in advance!
Anything in the logs? Paste it here!
The logs don't show up anything.

Getting the same issue since a few days. Automation working fine but spook reports an unknown service when calling hue.hue_activate_scene


Specify number of lines for scollUp and scrollDown commands (+ better default?)

Description of the new feature/enhancement

It would be nice if the scrollUp and scrollDown commands supported a lines argument that would specify how many lines to scroll.
Alternatively or additionally, it might make sense to set the default to the number of lines the mouse wheel is set to scroll, i.e. usually 3. This seems more useful to me as a default than just scrolling a single line, which is very slow, or scrolling a whole page, which can be jarring (at least without smooth scrolling #1400).
Proposed technical implementation details (optional)

We've got a global setting, "rowsToScroll", that configures how many lines to scroll with the wheel. If you do not set it, it defaults to your system setting (as set in the Mouse settings page.) If I recall, that default on Windows is 3.
If you're using a precision trackpad, that setting should still apply.

I was talking about the keyboard-bindable commands :)

You know, when I first spec'd arguments for keybindings, I called out scrollUp and scrollDown as two actions that needed args for customizable values. Looks like that never got turned into an actual issue.

@zadjii-msft For me personally it would actually be enough if the value was simply linked to rowsToScroll or the Windows mouse wheel setting, because that already fixes the issue of scrolling by just 1 line being super slow. But of course additional customization doesn't hurt.
(accidentally hit the close issue button there)

Thanks! Triaged this onto the backlog, but marked it Help-Wanted. If somebody were to implement this before v1, I'd almost certainly pull it in.


Float is being passed as integer

Floats are passed as integers, this is an issue for some project that require a strict check. The repository is also called is-int, not is-number.

For a bugfix, see pull request


Missed one PSN

This should have been updated with the others but got missed.
c.f. https://github.com/alphagov/digitalmarketplace-frameworks/blob/master/frameworks/g-cloud-8/questions/services/dataProtectionBetweenUserAndService.yml#L16

👍


Possibly extraneous apt call

Hi,
From ansible 2.1 the apt module supports auto remove so you may be able to remove one of the direct call outs with that
tasks/cleanup.yml:    apt-get -y autoremove
tasks/cleanup.yml:    apt-get -y autoclean

http://docs.ansible.com/ansible/apt_module.html
Sadly no autoclean yet!

PR always welcome!

This looks like it was resolved in PR #12
commit 3ffba9ed30b6a558c2b0efda4a1219adf77af36e
Author: lafarer<EMAIL_ADDRESS>Date:   Tue Jan 24 14:42:16 2017 +0100
Use built-in ansible 2.1 autoremove option


Add crypted password for smtp AUTH LOGIN

What do you think about to use an encrypted password for stmp auth ? In this case I use public key/private key mechanism (x509 certificate, very usefull in enterprise).
It is an optional parameter.
If you put the parameter smtp_auth_pem_file in the configuration, you can put a crypted password in the smtp_auth_password.
Otherwise, it's the current use case.
Example:
smtp_from<EMAIL_ADDRESS>smtp_auth_username: 'myuser'
smtp_auth_password: 'jJxNXYoVs0lQn/PyJO8zO3JtXmJ09wUx13T6rnY+kYWZnCfP/E+wmTJfCfWCpUXxxB5UyYWgt1w1S0LVo='
smtp_auth_pem_file: '/my/path/rsa.pem'

In this case, no more decrypted password in the configuration file.

I appreciate that this may be useful for you, however this change won't be accepted. We generally try to avoid security features in Prometheus, as they'd be a never ending source of maintenance.

I'm very sorry you don't want security features in Prometheus because security is mandatory in enterprise.We can't put a password into a configuration file without encrypt it.
Really no ?

This adds no real security unless you're typing in the password at each launch, or pulling it off dedicated crypto hardware. That's a rabbit hole we don't want to go down.

Nice initiative but I have to agree with @brian-brazil , encrypting data using locally accessible key is as vulnerable as plain text (analogue to put something inside a locked chest and leave the key on top of it). A feature to enable using a private key as authentication in lieu of user/pass (still not more secure, just to be clear) or single-sign-on (e.g. Kerberos/GSSAPI) would be interesting for enterprises moving away from clear text.


Intel OpenVINO / Movidius NCS  support?

Hi
Are there any plans to support Intel OpenVINO as a backend inference engine for DLIB? That would provide support for all the Intel GPU / VPU hardware including Intel Movidius NCS that I would like to use on Raspberry Pi to speed up my inference tasks.
Any plans to add OpenVINO or specifically Movidius NCS support?
Thanks!

I have no such plans.


[styles] Arrow functions (with or without props) do not work within makeStyles for @keyframes

I want to dynamically generate an @keyframes animation using the makeStyles helper.
I want to access props within makeStyles to generate this css keyframes.
However, it does not seem to work.

[x ] The issue is present in the latest release.
[ x] I have searched the issues of this repository and believe that this is not a duplicate. Might be linked to #20446 & #20355

Current Behavior 😯
Passing an arrow function (with or without props) to @keyframes does not generate the animation.
const useStyles = makeStyles({
  ...,
  [`@keyframes failsWithProps`]: props => ({
    from: { opacity: props.opacity },
    to: { opacity: 0 }
  }),
})

export function MyComponent() {
  const classes = useStyles({ opacity: 0.9 })
}

Expected Behavior 🤔
Being able to use props to generate @keyframes rules
There is a workaround for dynamically generating@keyframes with the es6 spread operator, but it does not work with props.
Steps to Reproduce 🕹
Live example (CodeSandox)
Steps:

Use an arrow function to generate @keyframes animation

Context 🔦
I want to create dynamic animation rules, based on a number of items that is being rendered. So I want to be able to use props within the component to define the resulting animation.
Right now, within makeStyles, I have to use:
    [`@keyframes change-text`]: {
        ...generateTextLineChangeKeyFrameAnimation(4),
    },

but I want to be able to use:
    [`@keyframes change-text`]: props => generateTextLineChangeKeyFrameAnimation(props.numLines),

and within MyComponent:
export function MyComponent() {
  const items = [ 1, 2, 3, 4 ] // dynamic values
  const classes = useStyles({ numLines: items.length })
  return (...)
}

Your Environment 🌎

Tech
Version

Material-UI
v4.9.13

React
v16.13.1

Browser
Chrome v81.0.4044.138

An update, this issue is being resolved in v5 thanks to #22342 and the new @material-ui/styled-engine package: https://codesandbox.io/s/wizardly-meadow-r0h7w?file=/src/App.js.

An update, we have now made enough progress with the new @material-ui/styled-engine package in v5 to move toward a progressive removal of the @material-ui/styles package (based on JSS). The current plan:

In v5.0.beta.0, this package will come standalone, in complete isolation from the rest.
In v5.0.0, this package will be soft deprecated, not promoted in the docs, nor very actively supported.
During v5, work on making the migration easier to the new style API (sx prop + styled() API). We might for instance, invest in the documentation for using react-jss that has more or less the same API. We could also invest in an adapter to restore the previous API but with emotion, not JSS.
In v6.0.0, this package will be discontinued (withStyles/makeStyles API).

This was made possible by the awesome work of @mnajdova.

For the usage of keyframe, you have examples in the source:
https://github.com/mui-org/material-ui/blob/4424142f562df9c1f2ea834956c37840f3da53b9/packages/material-ui/src/CircularProgress/CircularProgress.js#L83-L87


Safari seems to have problem with the node text replacement

Describe the bug
All the text is rendered in single location in the visualization
To Reproduce
Steps to reproduce the behavior:

Open the https://jsontr.ee address

Expected behavior
Text is rendered in the visualization boxes
Screenshots

Desktop (please complete the following information):

OS: MacOS
Browser safari
Version 18.0.1

Thanks for letting me know, the issue was fixed.


$ref in response example

I would like to use $ref in the response example in order to break up my specification into more readily manageable chunks.  Examples also benefit from being externalized because they can be easily revisioned, and included verbatim in reference implementations.
desired structure:
      responses:
        200:
          description: Success
          schema:
            $ref: 'profile-models.yaml#/ProfileSearchResponse'
          examples:
            application/json:
              $ref: 'ProfileExample.yaml#/ProfileSearchResponseExample'

Today, the children of examples are included directly, meaning the example itself using this example is:
{
  "$ref": "ProfileExample.yaml#/ProfileSearchResponseExample"
}

This issue arose from a discussion on swagger-ui:
https://github.com/swagger-api/swagger-ui/issues/1882

Hi,
Does this same issue exist for the example field within the schema object?  If so, will that be addressed as well?
Basically, we'd like the option to $ref in examples everywhere an example can be specified.
Thanks!

The revised guidelines for external file usage should help clarify this once merged: https://github.com/OAI/OpenAPI-Specification/pull/535

Parent issue #560

We resolved this during build process by using the json-refs tool to resolve references before delivery of the final artifacts.

You can do that now! 😱


can not convert tf model to tflite

Hi.i tried convert my tensorflow model to tensorflow lite with saved model of pretrained model such as:
import tensorflow as tf
converter=tf.lite.TFLiteConverter.from_saved_model("ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03/saved_model" ,input_arrays=None , input_shapes = (1,300,300,3),output_arrays=None, tag_set=None, signature_key=None)
tflite_model = converter.convert()
open("converted_model.tflite", "wb").write(tflite_model)
but i got this error:
2019-07-07 16:56:02.926259: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-07-07 16:56:02.948528: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency:<PHONE_NUMBER> Hz
2019-07-07 16:56:02.948956: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x3ab6990 executing computations on platform Host. Devices:
2019-07-07 16:56:02.948982: I tensorflow/compiler/xla/service/service.cc:175]   StreamExecutor device (0): ,
WARNING: Logging before flag parsing goes to stderr.
W0707 16:56:02.949664<PHONE_NUMBER>32736 deprecation.py:323] From /home/davari/virtualenvironments/detection_on_mobile/lib/python3.6/site-packages/tensorflow_core/lite/python/convert_saved_model.py:60: load (from tensorflow.python.saved_model.loader_impl) is deprecated and will be removed in a future version.
Instructions for updating:
This function will only be available through the v1 compatibility library as tf.compat.v1.saved_model.loader.load or tf.compat.v1.saved_model.load. There will be a new function for importing SavedModels in Tensorflow 2.0.
Traceback (most recent call last):
File "TFlite_converter.py", line 30, in
converter = tf.lite.TFLiteConverter.from_saved_model("ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_07_03/saved_model" ,input_arrays=None , input_shapes = (1,300,300,3),output_arrays=None, tag_set=None, signature_key=None)
File "/home/davari/virtualenvironments/detection_on_mobile/lib/python3.6/site-packages/tensorflow_core/lite/python/lite.py", line 757, in from_saved_model
output_arrays, tag_set, signature_key)
File "/home/davari/virtualenvironments/detection_on_mobile/lib/python3.6/site-packages/tensorflow_core/lite/python/convert_saved_model.py", line 204, in freeze_saved_model
util.set_tensor_shapes(in_tensors, input_shapes)
File "/home/davari/virtualenvironments/detection_on_mobile/lib/python3.6/site-packages/tensorflow_core/lite/python/util.py", line 145, in set_tensor_shapes
for name, shape in shapes.items():
AttributeError: 'tuple' object has no attribute 'items'
can any one helps me?

The tflite works well for me in TF1.13. What's your tf version ?
Note: i work with hdf5 and not saved_model.

The tflite works well for me in TF1.13. What's your tf version ?
Note: i work with hdf5 and not saved_model.

Thanks for your response.
my tf version is:1.15
which library and dependencies should i have?please tell me all of them

@Davari393 I'm afraid the stable version is only till 1.14 from tensorflow. Kindly check the version and only use the latest stable version for best performance. Also before loading the model,check for custom objects for your model if any.


add v200.develop to git workflows

Description
Adds the v200.develop branch to the Github workflows to do metadata checks for new samples using this branch.
Checklist:

[ ] Set target branch to main (current release) or v.next (next release)
[ ] Request a reviewer
[ ] Assign a reviewer
[ ] Tag reviewer in comment

Assigning to @jenmerritt for a first look please.


Fixed #29251 -- Added CharField converter to the MySQL backend.

https://code.djangoproject.com/ticket/29251
I'm not convinced that this is the best solution, but I haven't found a better way to solve this issue.

@timgraham Thanks for the review. You're right decode() is enough.

I note that convert_textfield_value() below is using force_text().
Perhaps that can be resolved in a follow up commit if decode() is enough there too?

Yes, there's a ticket to audit force_text() usage.

Then non-ASCII character case doesn't work on Oracle (leading spaces are missing).

LPad/Rpad doesn't work properly with multibyte characters sets on Oracle. I found the third issue in a row :smile:

@timgraham I checked and IMO there doesn't exist a good workaround for LPad/RPad with multibyte characters on Oracle. None of them works in all cases. According Oracle documentation:
... However, in some multibyte character sets, the display length of a character string can differ from the number of characters in the string.
I think it's fine to add a note in documentation about this restriction.

Sure, if you think is deserves a mention. We can't document everything about each database. :-)

You're right, it's impossible to describe all Oracle's caveats :wink:  I've left a note in the ticket 28643, this should be enough. I updated tests, it works on Oracle.

Would it be worth benchmarking the speed overhead of this addition?

@claudep I wrote simple benchmark with djangobench:
Running benchmarks: mysql_char_converter
Control: Django 2.1.dev20180326173423 (in git branch master)
Experiment: Django 2.1.dev20180327171917 (in git branch issue-29251)

Running 'mysql_char_converter' benchmark ...
Min: 0.038486 -> 0.046070: 1.1970x slower
Avg: 0.043529 -> 0.051424: 1.1814x slower
Significant (t=-10.273530)
Stddev: 0.00555 -> 0.00532: 1.0428x smaller (N = 100)

Control: Django 2.1.dev20180326173423 (in git branch master)
Experiment: Django 2.1.dev20180327171917 (in git branch issue-29251)

Running 'mysql_char_converter' benchmark ...
Min: 0.034216 -> 0.042022: 1.2281x slower
Avg: 0.038185 -> 0.045879: 1.2015x slower
Significant (t=-14.931398)
Stddev: 0.00528 -> 0.00502: 1.0528x smaller (N = 200)

Oh thanks! So does that mean we are ~20% slower with the patch? (sorry, I'm not so great at stats)

I think so, but even if convert_charfield_value() doesn't check or change anything I still get about ~11% , i.e. with
def convert_charfield_value(self, value, expression, connection):
    return value

the result is
Running benchmarks: mysql_char_converter
Control: Django 2.1.dev20180326173423 (in git branch master)
Experiment: Django 2.1.dev20180328202753 (in git branch issue-29251)

Running 'mysql_char_converter' benchmark ...
Min: 0.075977 -> 0.085019: 1.1190x slower
Avg: 0.086817 -> 0.096318: 1.1094x slower
Significant (t=-8.154523)
Stddev: 0.00763 -> 0.00881: 1.1550x larger (N = 100)

Control: Django 2.1.dev20180326173423 (in git branch master)
Experiment: Django 2.1.dev20180328202753 (in git branch issue-29251)

Running 'mysql_char_converter' benchmark ...
Min: 0.080404 -> 0.091563: 1.1388x slower
Avg: 0.091934 -> 0.102306: 1.1128x slower
Significant (t=-12.184438)
Stddev: 0.00919 -> 0.00777: 1.1821x smaller (N = 200)

That's probably the overhead cost of adding converters (function calls et al). Then I think we should find another way of solving this issue. Slowing the whole stack just to fix this edge case is not very sound IMHO.

I've prepared alternative PR #9835 with conversion only in affected database functions.

Closing due to the alternative approach in #9835.


Code highlight in JSX

To Reproduce
I wanted to add a code highlight as such:
```ts
const Test: React.FC = () => {
  return (
    <div> {/* [!code highlight] */}
      <div />
    </div>
  );
};
```

The highlighting works, but this gets rendered as:
<div> {}
  <div />
</div>

Omitting the {} works, but is not valid JSX and thus breaks my editor highlighting
Current vs. Expected behavior
I expected the [!code highlight] to capture JSX {/* */} blocks as this is the usual way to add comments in JSX
Provide environment information
Node.js v20.15.0

Operating System:
  Platform: darwin
  Arch: arm64
  Version: Darwin Kernel Version 23.6.0: Mon Jul 29 21:14:30 PDT 2024; root:xnu-10063.141.2~1/RELEASE_ARM64_T6000
  Available memory (MB): 32768
  Available CPU cores: 10
Binaries:
  Node: 20.15.0
  npm: 10.7.0
  Yarn: N/A
  pnpm: 9.9.0
Relevant Packages:
  next: 14.2.7 // There is a newer version (14.2.8) available, upgrade recommended!
  eslint-config-next: 14.2.7
  react: 18.3.1
  react-dom: 18.3.1
  typescript: 5.5.4
Next.js Config:
  output: N/A

Which area(s) are affected? (Select all that apply)
Not sure
Additional context
No response

Use HTML comment instead. For further issues, you can open an issue on Shiki.

adding // [!code highlight:2] above works as a workaround, but only for the outermost JSX element
Opened an issue on shiki, thanks for pointing me to the right place
https://github.com/shikijs/shiki/issues/770


#21 Export HADOOP_CLASSPATH for hadoop-master1

does HMS and HS2 works as expected?

does HMS and HS2 work as expected?

does HMS and HS2 work as expected?

hold on, will check it.

does HMS and HS2 work as expected?

I did some simple test cases via kyuubi beeline, it seems everything is OK.


bump third party versions

Hi @lifey, thanks for your PRs on this and the related repos. We'll take a closer look at these and get back to you shortly.

https://github.com/librato/librato-java/pull/43 supersedes this.

Superseded by https://github.com/librato/metrics-librato/pull/75 .


Revert "Fix remaining holes in stack trace demangling"

Broke the tree, Reverts flutter/flutter#60478
tbr @PixelToast
Here's a failure example: https://cirrus-ci.com/task/6707176263647232
Relevant log section:
Failed to precompile tester:tester:
/root/.pub-cache/hosted/pub.dartlang.org/tester-0.0.1-dev9/lib/src/runner.dart:107:5: Error: 'unawaited' is imported from both 'package:meta/meta.dart' and 'package:pedantic/pedantic.dart'.
    unawaited(_process.exitCode.whenComplete(() {
    ^^^^^^^^^
/root/.pub-cache/hosted/pub.dartlang.org/tester-0.0.1-dev9/lib/src/runner.dart:174:5: Error: 'unawaited' is imported from both 'package:meta/meta.dart' and 'package:pedantic/pedantic.dart'.
    unawaited(_process.exitCode.whenComplete(() {
    ^^^^^^^^^
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
ERROR: Last command exited with 1 (expected: zero).
Command: ../../bin/cache/dart-sdk/bin/pub global activate tester 0.0.1-dev9
Relative working directory: packages/flutter_tools
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━```

This was not caused this PR.


Try harder not to log sensitive information

Internal ticket: 898

:+1:

:+1:

:+1:

:+1:

:+1:


DeepExplainer crashes on saved models

Tensorflow originally saved models as HDF5 files, but now uses a new "SavedModel" format.  If we load a model from the HDF5 format, DeepExplainer()/shap_values() works fine; if we use the new format, it crashes.
Versions of stuff:
tensorflow==2.1.0
protobuf==3.11.3
shap==0.35.0

Here's minimal code to reproduce the bug:
import shap
import numpy as np
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers

# Make some data
num_features = 10
num_samples = 50
X = np.random.randn(num_samples, num_features)
y = np.random.randint(10, size=num_samples)
# Create 3 target samples to be explained
explainees = np.random.randn(3, num_features)

# Make and train minimal model
model = tf.keras.Sequential()
model.add(layers.Dense(10, activation='relu', input_shape=(num_features,)))
model.compile(optimizer='adam',
              loss=tf.losses.SparseCategoricalCrossentropy(
                  from_logits=True),
              metrics=['accuracy'])
model.fit(X, y, epochs=1, batch_size=10)

# Save model in new format
model.save('SavedModel_format')
model2 = keras.models.load_model('SavedModel_format')

# Save model in older format
model.save('HDF5_format', save_format='h5')
model3 = keras.models.load_model('HDF5_format')

# Test all the models
for m, m_str in [(model, 'Built'), (model3, 'HDF5'), (model2, 'SavedModel')]:
    try:
        explainer = shap.DeepExplainer(m, X)
        shap_values = explainer.shap_values(explainees)
        print(40 * '=')
        print('%s model worked.\n' % m_str)
    except Exception:
        print(40 * '=')
        print('%s model failed!!!!!\n' % m_str)
        raise

Output I get:
...
========================================
Built model worked.

========================================
HDF5 model worked.

========================================
SavedModel model failed!!!!!

Traceback (most recent call last):
  File "minimal_example.py", line 37, in <module>
    shap_values = explainer.shap_values(explainees)
  File "/usr/local/lib/python3.7/site-packages/shap/explainers/deep/__init__.py", line 119, in shap_values
    return self.explainer.shap_values(X, ranked_outputs, output_rank_order, check_additivity=check_additivity)
  File "/usr/local/lib/python3.7/site-packages/shap/explainers/deep/deep_tf.py", line 304, in shap_values
    sample_phis = self.run(self.phi_symbolic(feature_ind), self.model_inputs, joint_input)
  File "/usr/local/lib/python3.7/site-packages/shap/explainers/deep/deep_tf.py", line 361, in run
    return self.execute_with_overridden_gradients(anon)
  File "/usr/local/lib/python3.7/site-packages/shap/explainers/deep/deep_tf.py", line 413, in execute_with_overridden_gradients
    return [v.numpy() for v in out]
  File "/usr/local/lib/python3.7/site-packages/shap/explainers/deep/deep_tf.py", line 413, in <listcomp>
    return [v.numpy() for v in out]
AttributeError: 'NoneType' object has no attribute 'numpy'

I am having exactly the same issue

You could use this to convert the model into a tensorflow.python.keras.engine.sequential.Sequential object instead of a tensorflow.python.keras.saving.saved_model.load.Sequential object.
def load_keras_model_object(model_path):
#model path can be a directory (new tensorflow way of saving)
#of model path can be a single .h5 file (whatever the extension, but the content must be the one of a saved h5 keras model)
model = tf.keras.models.load_model(model_path)
model_type = str(type(model))

if model_type == "<class 'tensorflow.python.keras.saving.saved_model.load.Sequential'>" or \
        model_type == "tensorflow.python.keras.saving.saved_model.load.Sequential":
    # Intentionality saving the model into h5 format
    path_h5 = model_path+"_h5.h5"
    model.save(path_h5, save_format = "h5")
    reloaded_model = tf.keras.models.load_model(path_h5)
    os.remove(path_h5)

elif model_type == "<class 'tensorflow.python.keras.engine.sequential.Sequential'>" or \
        model_type == "tensorflow.python.keras.engine.sequential.Sequential" :
    # object of the desired class
    reloaded_model = model
else:
    # Not a model form keras.engine and not a model from saved_model
    print("unknown model format")
    reloaded_model = None
    pass

return reloaded_model

Dear Etienne,
That's a very clever work-around!
I'm still concerned that as the two saved formats continue to diverge, with the modern one unsupported, that there will be an increasing number of features that will be lost from a SavedModel=>HDF5 conversion.

I totally agree with you. This is by no mean a permanent fix. For the moment being, it will be the fix for my current app, but definitely finding a more robust approach has been added to my backlog.
The real fix would be that the shap library supports the new SavedModel format. I'm pretty sure I saw that issue on their repo.

Keep in mind that Tensorflow has multiple versions of savedmodel: one for Keras and one for low-level Tensorflow.  I've been trying to use the workaround above on a model saved with the low level api and the tf.keras specific instructions above don't work.  Presumably because Keras and basic TF use different formats under the hood.  If I figure out a way to do this i'll post.  If anyone has insight into bare TF savedmodels i'd love to hear it.

low level tensorflow savedmodel documentation: https://www.tensorflow.org/api_docs/python/tf/saved_model
tf.keras savedmodel documentation:
https://www.tensorflow.org/guide/keras/save_and_serialize

@jason-brian-anderson were you able to find a solution for using low level tf saved_model with shap? especially with deepExplainer and using signatureDef?


go-demo-6 to 1.0.26

Promote go-demo-6 to version 1.0.26

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opencv[cuda,dnn]:x64-windows build failure

Hello !
Host Environment

OS: Windows 10 Version 20H2
Compiler: Vcpkg package management program version 2021-05-05-e8977e69d9a3fb462d9ad42013d83a7682706659

To Reproduce
Steps to reproduce the behavior:
.\vcpkg.exe install opencv[cuda,dnn]:x64-windows
Failure logs
-- from stdout
CMake Error at scripts/cmake/vcpkg_execute_required_process.cmake:105 (message):
    Command failed: ninja -v
    Working Directory: D:/vcpkg/buildtrees/opencv4/x64-windows-rel/vcpkg-parallel-configure
    Error code: 1
    See logs for more information:
      D:\vcpkg\buildtrees\opencv4\config-x64-windows-out.log

Call Stack (most recent call first):
  scripts/cmake/vcpkg_configure_cmake.cmake:323 (vcpkg_execute_required_process)
  ports/opencv4/portfile.cmake:312 (vcpkg_configure_cmake)
  scripts/ports.cmake:139 (include)

Error: Building package opencv4:x64-windows failed with: BUILD_FAILED
Please ensure you're using the latest portfiles with `.\vcpkg update`, then
submit an issue at https://github.com/Microsoft/vcpkg/issues including:
  Package: opencv4:x64-windows
  Vcpkg version: 2021-05-05-e8977e69d9a3fb462d9ad42013d83a7682706659

-- from the logfile
-- NVIDIA_OPTICAL_FLOW: Download: edb50da3cf849840d680249aa6dbef248ebce2ca.zip
CMake Error at D:/vcpkg/buildtrees/opencv4/src/4.5.2-755f235ba0.clean/cmake/OpenCVDownload.cmake:161 (message):
      Downloads are not permitted during configure. Please pre-download the file "D:/vcpkg/downloads/opencv-cache/nvidia_optical_flow/a73cd48b18dcc0cc8933b30796074191-edb50da3cf849840d680249aa6dbef248ebce2ca.zip":

      vcpkg_download_distfile(OCV_DOWNLOAD
          URLS "https://github.com/NVIDIA/NVIDIAOpticalFlowSDK/archive/edb50da3cf849840d680249aa6dbef248ebce2ca.zip"
          FILENAME "opencv-cache/nvidia_optical_flow/a73cd48b18dcc0cc8933b30796074191-edb50da3cf849840d680249aa6dbef248ebce2ca.zip"
          SHA512 0
      )
Call Stack (most recent call first):
  D:/vcpkg/buildtrees/opencv4/src/4.5.2-58c4f07550.clean/modules/cudaoptflow/CMakeLists.txt:15 (ocv_download)

Additional context
I tried to pre-download the file as indicated, but the same error pops up.
I verified that the checksums matches, and it indeed does.

Hi @ihuggsy
Thanks for posting this issue.
Seems you didn't use the latest vcpkg. Could you please try to update vcpkg via git pull to rebuild this?

Hello,
I managed to compile the package by downloading the file manually, and copying it twice in the directory with 2 different names
a73cd48b18dcc0cc8933b30796074191-edb50da3cf849840d680249aa6dbef248ebce2ca.zip
edb50da3cf849840d680249aa6dbef248ebce2ca.zip

It wouldn't work if I only had one of these in my folder.
I will update my vcpkg ASAP.
Thank you very much for your fast answer.


Using _Formatted value when searching is not user friendly

While I was working on the code I saw something that is not user friendly for C# and Java developers.
If you look at the documentation for Highlighting : https://docs.meilisearch.com/reference/api/search.html#attributes-to-highlight
Here is the json response of the server :
{
  "id": 50393,
  "title": "Kung Fu Panda Holiday",
  "poster": "https://image.tmdb.org/t/p/w1280/gp18R42TbSUlw9VnXFqyecm52lq.jpg",
  "overview": "The Winter Feast is Po's favorite holiday. Every year he and his father hang decorations, cook together, and serve noodle soup to the villagers. But this year Shifu informs Po that as Dragon Warrior, it is his duty to host the formal Winter Feast at the Jade Palace. Po is caught between his obligations as the Dragon Warrior and his family traditions: between Shifu and Mr. Ping.",
  "release_date":<PHONE_NUMBER>,
  "_formatted": {
    "id": 50393,
    "title": "Kung Fu Panda Holiday",
    "poster": "https://image.tmdb.org/t/p/w1280/gp18R42TbSUlw9VnXFqyecm52lq.jpg",
    "overview": "The Winter Feast is Po's favorite holiday. Every year he and his father hang decorations, cook together, and serve noodle soup to the villagers. But this year Shifu informs Po that as Dragon Warrior, it is his duty to host the formal Winter Feast at the Jade Palace. Po is caught between his obligations as the Dragon Warrior and his family traditions: between Shifu and Mr. Ping.",
    "release_date":<PHONE_NUMBER>
  }
}

The server returns the document and add an extra field _formatted with a modified copy of the document.
Let see the problem now with C#:
We're going to work with this unit test :
https://github.com/meilisearch/meilisearch-dotnet/blob/3302d2b7c529c36de31298aaed7ac62f414170a1/tests/Meilisearch.Tests/SearchTests.cs#L76-L94
We are working on the _basicIndex and here is the definition of it :
https://github.com/meilisearch/meilisearch-dotnet/blob/3302d2b7c529c36de31298aaed7ac62f414170a1/tests/Meilisearch.Tests/IndexFixture.cs#L46-L69
We are adding a collection of Movie and here is the definition :
https://github.com/meilisearch/meilisearch-dotnet/blob/3302d2b7c529c36de31298aaed7ac62f414170a1/tests/Meilisearch.Tests/Movie.cs#L3-L10
Now, on line 86 of the test we are doing a Search query against Meilisearch with this code :
     var movies = await _basicIndex.SearchAsync<FormattedMovie>(
         "man",
         new SearchQuery { AttributesToHighlight = new string[] { "name" } });

We are querying an index that contains Movie documents, but we ask to map to another class FormattedMovie with this definition :
https://github.com/meilisearch/meilisearch-dotnet/blob/3302d2b7c529c36de31298aaed7ac62f414170a1/tests/Meilisearch.Tests/Movie.cs#L38-L48
We have to duplicate the Movie class with a FormattedMovie class in order to have the _Formatted property available.
This is not very user friendly, because users will have to create two objects in order to fully use Meilisearch.
There is no way in C# to create a specific object that will enhance a base object with a _Formatted property like this :
public class FormattedDocument<T> : T
{
    public T _Formatted { get; set; }
}

There is actually also a small risk of collision if a document contains a _formatted property.
I told Guillaume it would great to have a response with an original and formatted properties that contains both a version of the document.
{
   "original":{
      "id":50393,
      "title":"Kung Fu Panda Holiday",
      "poster":"https://image.tmdb.org/t/p/w1280/gp18R42TbSUlw9VnXFqyecm52lq.jpg",
      "overview":"The Winter Feast is Po's favorite holiday. Every year he and his father hang decorations, cook together, and serve noodle soup to the villagers. But this year Shifu informs Po that as Dragon Warrior, it is his duty to host the formal Winter Feast at the Jade Palace. Po is caught between his obligations as the Dragon Warrior and his family traditions: between Shifu and Mr. Ping.",
      "release_date":1290729600
   },
   "formatted":{
      "id":50393,
      "title":"Kung Fu Panda Holiday",
      "poster":"https://image.tmdb.org/t/p/w1280/gp18R42TbSUlw9VnXFqyecm52lq.jpg",
      "overview":"The Winter Feast is Po's favorite holiday. Every year he and his father hang decorations, cook together, and serve noodle soup to the villagers. But this year Shifu informs Po that as Dragon Warrior, it is his duty to host the formal Winter Feast at the Jade Palace. Po is caught between his obligations as the Dragon Warrior and his family traditions: between Shifu and Mr. Ping.",
      "release_date":1290729600
   }
}

One more interesting thing in having a response like the one above is that if you want to add extra information like the global score of this result, or a score per field, you could enhance it easily without any risk of fields collisions.
But it's too late for breaking changes. So we need to find a way for developers (C# / Java) to enjoy this feature without too much pain.
Ping @gmourier @brunoocasali

I just checked the rust client for this issue, I'm no expert but according to the doc :
https://github.com/meilisearch/meilisearch-rust/blob/261fa529e795a831035c7fb30f27313ea9496af7/src/search.rs#L12-L25
This works thanks to serde, who has the ability to flatten structs.
The unit tests in Java are passing because the Movie has a _formatted property, which is not supposed to be defined in the definition of a movie.
https://github.com/meilisearch/meilisearch-java/blob/e157c2dbb014aa581a887585890dbf2ee1947c4d/src/test/java/com/meilisearch/sdk/utils/Movie.java#L5-L15
In C# we saw, that we create an index with a Movie class and query with a FormattedMovie class.
The way the response is sent actually does not play well with strongly type languages.
In C# and Java, the unit tests are not really well shaped, in rust this works because of the flattening feature of serde.
I think, everything is here about this issue.

Thank you, @Juchom, for your research and this very well-detailed issue.

But it's too late for breaking changes.

Indeed, we want 0 breaking changes to not involve any significant and tedious changes until 1.0, especially on the search endpoint.
Nevertheless, this is something we may consider after having reached a v1.0. We know that we will have to make some breaking changes afterward, but it's unclear how we will organize all this to be the most convenient for the users and us in terms of management. We are working on it to elaborate strategies to handle this type of change in the future.

@juchom, thanks for the issue. Indeed very well explained, and I could feel the pain here 😞
Do you think it could be possible to work around this problem internally in the meilisearch-dotnet by adding a post json processing before the actual object creation?
return await responseMessage.Content
                .ReadFromJsonAsync<SearchResult<T>>(cancellationToken: cancellationToken).ConfigureAwait(false);

Good question ? Very good question...
Digging further, I found this issue opened in System.Text.Json in order to have a JsonPropertyFlatten attribute like in serde : https://github.com/dotnet/runtime/issues/55120
Not sure this is going to happen soon and there is nothing that could help with it on Github.
We only have bad solutions right now,
The worst one, trying to create our own JsonPropertyFlatten and create an object like in rust client, not sur how long would it take to do it, test it, avoid too bad performance but it would look like something like this in the end.
public class SearchResult<T>
{
    [JsonPropertyFlatten]
    public T Result { get; set; }

    [JsonPropertyName("_formatted")]
    public T FormattedResult { get; set; }
}

Writing this, just made me realised, that the rust client is already patching the json response to map to the proposed json response thanks to serde's flatten attribute.
Second option, bad developer experience but it would work without side effect if we use custom json options write a clear documentation on how to deal with it.
In my case I'm going to wrap everything where needed and create the second class by hand.
// My indexed object
public class Movie
{
    public string Id { get; set; }

    public string Name { get; set; }

    public string Genre { get; set; }
}

// The search result class, a bit like the unit tests, but we keep refactoring tools working
public class MovieSearchResult : Movie
{
    [JsonPropertyName("_formatted")]
    public Movie Formatted { get; set; }
}

Maybe a source generator could help doing this second class automatically for the developer, but I have never used them before and I'm not sure it would be good idea to offer this option if it works. (Andrew Lock has a very good blog about them : https://andrewlock.net/creating-a-source-generator-part-1-creating-an-incremental-source-generator/)
In my case I don't have a bunch of documents so doing it by hand is ok, but for people with bigger projects source generators is probably the best bet.
Concerning Java, I think code generators exist but I have no idea of what we can do. There are some project on github to flatten json objects, but it seems that it's not following the same pattern like serde.
In my case I want to be extra safe, I would go with writing a second inherited class and let System.Text.Json do the job.
Concerning Meilisearch server, here is the response object :
https://github.com/meilisearch/meilisearch/blob/c4453340707f0b8ed6168118dff2e4d2ae634f8d/meilisearch-lib/src/index/search.rs#L86-L94
/// A single result.
/// Contains the complete object, optionally the formatted object, and optionally an object that contains information about the matches.
#[derive(Deserialize, Debug)]
pub struct SearchResult<T> {
    /// The full result.
    #[serde(flatten)]
    pub result: T,
    /// The formatted result.
    #[serde(rename = "_formatted")]
    pub formatted_result: Option<Map<String, Value>>,
    /// The object that contains information about the matches.
    #[serde(rename = "_matchesPosition")]
    pub matches_position: Option<HashMap<String, Vec<MatchRange>>>,
}

Without #[serde(flatten)] this object looks like the proposed json response.
I would encourage you to think twice before using #[serde(flatten)], this is making complex transformations when serializing / deserializing object to json. This suppose that this transformation must be available in every langages with all the same rules.
Just to be clear, there is no arrogance in my last sentence, I have been caught in this trap once it was a GUID serialization / deserialization issue between Python and C#.
And again your are doing a really good job and Meilisearch is a really great product 👍

Thanks @juchom ! Came across this caveat and could immediately workaround it with your approach.

As the SDK does not support highlights, maybe the readme should mention it? It even gives an example using AttributesToHighlight without mentioning that this won’t actually work.

For me it works fine, I got highlighting in all  "attrA", "attrB", "attrC".

var myIndexSettings = new Settings
{
        DisplayedAttributes = new string[] { "*" },
        SearchableAttributes = new string[] { "attrA", "attrB", "attrC" },
};

var result = await Index.SearchAsync<MyType>(
                  queryString,
                  new SearchQuery()
                  {
                      AttributesToHighlight = new string[] { "attrA", "attrB", "attrC" }, // will be returned formatted
                      AttributesToCrop = new string[] { "attrC" }, // will be returned formatted
                      HighlightPreTag = "",
                      HighlightPostTag = "",
                      CropLength = 60,
                  },
                  cancellationToken: cancellationToken
                  );

That is weird. Is that on the master build, or the currently released NuGet build?
If I call it with AttributesToHighlight = new string[] { "text" },  I get no formatting, but if I inspect the HttpResponseMessage, I see the "_formatted" object in JSON that has the expected highlighting.

Did you implement juchom's solution with the search result class that inherits from the actual type you have an index for?

No, that’s my whole point, that it’s not supported out of the box.

@Christoph-Wagner that's right, it's not supported directly because there is no clean strategy to support this json transformation made server side.
This issue has been discovered after the api freeze this is why it works like this for now and you have to chose what best fit your needs.
Now that v1 is out, maybe @gmourier or @brunoocasali may have some insight on this issue.

@juchom Recently while working on the meilisearch flutter ui kit, I tried an approach, where we wrapped all the returned results from meilisearch with a helper object that would understand all the extra fields added by meilisearch and parse them correctly.
Relevant code:
https://github.com/meilisearch/meilisearch-flutter/blob/58ccfc5a4cb4bea36fee0fa52817c70e5d3c4944/lib/src/models/result_container.dart#L3-L16
class MeilisearchResultContainer<T> {
  final T parsed;
  final Map<String, dynamic> src;
  final Map<String, dynamic>? formatted;
  final Searcheable<Map<String, Object?>> fromResult;
  final SearchQuery fromQuery;

  MeilisearchResultContainer({
    required this.src,
    required this.parsed,
    required this.fromQuery,
    required this.fromResult,
  }) : formatted = src['_formatted'];
}

it would also reference the source query and the parent search result, but these can be omitted of course.
this made handling meilisearch specific fields way easier

I can safely state that a v2 is not in our current plans. And also, the same behavior is being used in other features like the new vector store feature (_semanticSimilarity, _vectors), and ranking details (_rankingScore, _rankingScoreDetails).
So I think if the implementation made by @ahmednfwela is good enough, we should definitely merge it :)
Then in the future, when Meilisearch starts to plan a possible v2, we can decide to go for a meta: {} key that wraps all those keys at once or something similar if needed.
Are you ok with that @juchom and @ahmednfwela?

I have the same issue. Any updates on this? There are workarounds to fix it, but as other people mentioned, the SearchAsync method offers highlighting. You would be surprised that it doesn't work out of the box. Fixing it using either a custom JSON converter or code generator is better than nothing, IMO.

Hello @MoienTajik
Sorry, it's huge work. @ahmednfwela started something 🙏  but since it's huge, we did not move forward recently, sorry.
Trying to see if it's worth continuing the work


testing: watsonx-serving: include flan-t5-small-caikit-gpu

/test watsonx-serving-light gpu_scale

scale test passed on the GPU


tooling: Fix manage.py failing if /etc/zulip/zulip.conf exists.

If /etc/zulip/zulip.conf exists in development, manage.py fails unnecessarily. This PR fixes this by instead check if deploy_type is set, as done in settings.py.
Fix #10931.
Testing:
I have tested manually in the following conditions:

If /etc/zulip/zulip.conf is not created, manage.py commands work.
If /etc/zulip/zulip.conf is created, but deploy_type isn't set, manage.py commands work.
If /etc/zulip/zulip.conf is created and deploy_type is set, manage.py exits.

(I am unsure if there is a test suite for manage.py, but if there is, I would be happy to add tests for this. Thanks!)

This looks great!  Manually tested it and merged, thanks @skunkmb!


SimpleTable Buttons 有下拉按钮时,不能使用图标

Bug Report or Feature Request (mark with an x)

[ x ] Bug report
[ ] Feature request
[ ] Documentation issue or request

Reproduction link

Steps to reproduce

What is expected?

What is actually happening?

Environment

ng-alain version: X.Y.Z
Angular version: X.Y.Z
Browser:
- [ ] Chrome (desktop) version XX
- [ ] Firefox version XX
- [ ] Safari (desktop) version XX
- [ ] IE version XX

已经修复


feat(workflows): add operations service type

Changes

#901
#900
#899
#898  👈
main

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Compose Preview friction

https://takahirom.github.io/roborazzi/preview-support.html#generate-compose-preview-screenshot-tests
seems incomplete, running with 1.23.0 my gradle sync fails with
Please set roborazzi.generateRobolectricPreviewTests.packages in the generatePreviewTests extension or set roborazzi.generateRobolectricPreviewTests.enable = false.See https://github.com/sergio-sastre/ComposablePreviewScanner?tab=readme-ov-file#how-to-use for more information.

Could this fail at runtime instead?  And should it be in the docs?

Also error
e: file:///usr/local/google/home/yschimke/workspacesda/StudioProjects/confetti/wearApp/build/generated/java/generateDebugComposePreviewRobolectricTests/com/github/takahirom/roborazzi/RoborazziPreviewParameterizedTests.kt:8:8 Unresolved reference 'sergio'.

Following instructions

👀

I also needed
                          testImplementation("io.github.takahirom.roborazzi:roborazzi-compose-preview-scanner-support:0.1.0")

without attempting "Manually adding Compose Preview screenshot tests"

Could AndroidComposePreviewTester be public (experimental) and open?
I need to run some tearDown, and it seems awkward without lifecycle hooks.

Warnings on compiling
w: file:///usr/local/google/home/yschimke/workspacesda/StudioProjects/confetti/wearApp/build/generated/java/generateDebugComposePreviewRobolectricTests/com/github/takahirom/roborazzi/RoborazziPreviewParameterizedTests.kt:24:13 This declaration needs opt-in. Its usage should be marked with<EMAIL_ADDRESS>or '@OptIn(com.github.takahirom.roborazzi.ExperimentalRoborazziApi::class)'

Thank you for trying this out.
Regarding dependencies, we have some dependency validations in place here. I expected there would be an error with the validation.
It might be better to provide a link to the sample for a smoother setup process.

If you need a specific support library from roborazzi, it feels appropriate to add that, even though we discussed not messing with the robolectric dependencies.

Whether to add dependencies in the plugin or not is a tough decision, as I initially added them all. You mentioned Hilt and Compose, which don't even add their runtime dependencies in the plugin. It's difficult to decide if something is a 'specific support library'; for example, Hilt Android support also doesn't add the runtime dependency. I also find that if we have a library dependency in 'roborazzi-compose-preview-scanner-support', it can be difficult for users to exclude that library dependency.

OK, makes sense.


[20.09] Update step certificates and cli #124347

Motivation for this change
Manually backport #124347 as there could be security updates.
Things done
I failed to use dep2nix and it seems to not work with go modules anyways so I changed to the approach of the current master.

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

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

[x] Tested via one or more NixOS test(s) if existing and applicable for the change (look inside nixos/tests)
[x] Tested compilation of all pkgs that depend on this change using nix-shell -p nixpkgs-review --run "nixpkgs-review wip"
[x] Tested execution of all binary files (usually in ./result/bin/)
[ ] Added a release notes entry if the change is major or breaking
[x] Fits CONTRIBUTING.md.

Result of nixpkgs-review pr 124594 at a90b738d run on x86_64-linux 1

2 packages built successfully:

step-ca
step-cli

@SuperSandro2000 Should i also add a reference to the backported commits and is so how exactly. Because these are created completely manually so there are not simple cherry-picks.

I personally wouldn't bother.

nixpkgs-review happy for me, macos 10.15, nixos x86_64.


ElegentRL agent is not trained

I tried running ElegentRL agent many times for test data in the same environment but the account value results were changed drammatically.
I think the agent is not trained.
P/s: I also tried not to run train.py and still run test.py, the agent still predicted and not load trained_model.
Here is the environment:

Thank you for reporting this bug.
We will reproduce the bug and fix it using the following code.
https://github.com/AI4Finance-Foundation/FinRL/blob/bacca166e083f7ead676aad6965f6f1004044b5f/finrl/main.py#L62-L76


Handle key inaccessible error in BYOK scenario

Description
When a customer-managed key is not accessible, Cosmos DB will return a 403 to the FHIR server. In turn, the FHIR server should return a 403, as well as an appropriate OperationOutcome.
Related issues
Addresses AB#73109
Testing

Ran all existing unit tests
Added new unit tests
Using a proof-of-concept console app to try to read a Cosmos DB document where the key is inaccessible, I have confirmed that the new logic in ProcessException to detect the error in question works.

This change is now unblocked and can be revisited.  I will attempt to re-open this PR.


Disconnect when device lost

Disconnect a porky that you are connected to
Device lost message (from USB?) will show....
stills shows connected to it.
maybe wait longer for a timeout on the wifi connection and see if error shown and realizes device is lost
when lost, should change the hardware view to disconnected

Some sort of drop handler for connections that sends a new HW event "disconnecting"?
Or some sort of global exit handler that does the same?


OTAA device and join request

Hi,
I'm having some trouble to create an OTAA device and send a join request. Apparently there's only the ABP command available to create  ABP compliant device. Can you please help me in this matter. Cheers
Syed

hi, would u provide some details?
What happens exactly? Which LoRaWAN verison were u using? Which LoRa server is used?

Hi,
I'm using LoRaWAN 1.0, when I tried to create the device with the new command and join type:
pipenv run python main.py -n join
I always gets the error:  ERROR - You need to finish Join procedure before sending application data and nothing is received on the UDP port.
However, it should send a join request to the server.

I guess the problem is that the config/device.yml is not properly configured, an example looks like this,
Device:
    JoinEUI: '0000000000000000'
    DevEUI: '1234567890ABCDEF'
Keys:
    AppKey: 4c0892904bb3544138b5070c5c4069cd
    NwkKey: be60e113de86d73b52fc0005bf5d89e8
Gateway:
    GatewayEUI: 4e7b2799b9bfd427

If you're using LoRaWAN 1.0.2, change the AppEUI to JoinEUI, and make NwkKey field equal to AppKey field. However, LoRaWAN 1.0.2 is not well tested, it may have some compatibility issues.
There is an older commit that may work well with LoRaWAN 1.0.2, check this commit if you need:
72b776071bf9329e5a9481a868b35f75d90b5d44
commit message: update pyyaml version

Thanks @houluy
I think you should update your documentation since it's not clear how to use OTAA and requires some iterations. Cheers

Thanks a lot! I'll fix the document.


Add implementation of depthwise convolution.

This adds an implementation of the depthwise convolution. Note that it is currently restricted to the same number of output feature maps as there are input feature maps (the multiplier parameter must be 1). A future update will lift this restriction.

Thanks!


sum operation not correct to flat HexGrid

The following example errors with a numpy broadcast error:
import gridkit

hexdem = gridkit.HexGrid(size=3, shape="flat")

path_dem = "tests/data/alps_dem.tiff"

dem1 = gridkit.read_raster(path_dem, bounds=(29040, 167500, 29100, 167620)).resample(hexdem)
dem2 = gridkit.read_raster(path_dem, bounds=(29070, 167470, 29150, 167580)).resample(hexdem)
dem3 = gridkit.read_raster(path_dem, bounds=(29080, 167520, 29160, 167600)).resample(hexdem)

dem_slices = [dem1, dem2, dem3]

dem_sum = gridkit.sum(dem_slices)

This errors with
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/home/timo/Documents/projects/gridding_bare/gridkit/_statistical_functions.py", line 67, in sum
    combined_grid += empty_grid.assign(
  File "/home/timo/Documents/projects/gridding_bare/gridkit/bounded_grid.py", line 485, in assign
    crop[:] = data
ValueError: could not broadcast input array from shape (22,39) into shape (35,26)

It is noteworthy that the same code does work for "pointy" grids.
Likely we don't properly consider the change in consistent and offset axis.

The incorrect slice resulting in the shape error further down is created in HexGrid._data_slice_from_bounds:
https://github.com/tmillenaar/GridKit/blob/a74b906fe96563d927341d302dcbe06898afe24a/gridkit/hex_grid.py#L752C9-L752C32

This can be further traced back to the properties width and height not matching the underlying data.
The 'flat' HexGrid and the 'pointy' HexGrid share the same logic for most functions. The difference is often that the consistent and the offset axis are swapped. This swapping can be seen here for example: https://github.com/tmillenaar/GridKit/blob/a74b906fe96563d927341d302dcbe06898afe24a/gridkit/hex_grid.py#L959
As a result, the axis are flipped in a sense.
This means that while a RectGrid and a 'pointy' HexGrid have a data shape of [y,x] as per the numpy convention, that of a 'flat' grid is using [x,y]. This is confusing but allows us to reuse a lot of code.
As a result the 'height' and 'width' are flipped.
You will find in the following screenshot of the data that the height is 36 while the width is 29. This is flipped in the terminal:

This flip trickles down into _data_slice_from_bounds mentioned in the previous comment.
Note that if this swap of height and width is fixed, we still need to return slice_x, slice_y instead of slice_y, slice_x form _data_slice_from_bounds.


Hotfix: IPS Sync & Helpdesk Sync

:tada: This PR is included in version 4.3.2 :tada:
The release is available on GitHub release
Your semantic-release bot :package::rocket:


simulated annealing with dask

I am trying to run a single objective problem with simulated annealing. any way to speed up using dask? Thank you!

Hi,
Just wanted to check the status on this? Did you find a way to speed up SA?


CFFL

CFFL

Linter check found the following problems:
The following problems have been found:
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:21: host_section_needs_exact_pinnings: Packages in host must have exact version pinnings, except python build tools.
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:22: host_section_needs_exact_pinnings: Packages in host must have exact version pinnings, except python build tools.
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:23: host_section_needs_exact_pinnings: Packages in host must have exact version pinnings, except python build tools.
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:23: missing_wheel: For pypi packages, wheel should be present in the host section
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:32: missing_pip_check: For pypi packages, pip check should be present in the test commands
WARNING: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:39: missing_description: The recipe is missing a description
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:39: missing_documentation: The recipe is missing a doc_url or doc_source_url
ERROR: /tmp/abs_c514pmvb4e/clone/recipe/meta.yaml:39: missing_dev_url: The recipe is missing a dev_url
Errors were found


describe notebooks in readme

This resolves Issue #4

Automated checks are now running. For each PR these ensure that:

The processed shear and velocities have not changed
gliderad2cp runs and produces the same output on linux, max and windows for Python versions 3.8 - 3.11


'socket.io error on connect: Error: xhr poll error' while importing zone.js when using Karma/Jasmine in Angular 2 with NativeScript project

Hi, I'm getting the below error while running my tests using Karma with Jasmine in my Angular2 with NativeScript project. The tests are being executed correctly, but, this error keeps repeating in the console. I observed the error is coming only after importing zone.js (imported it because it says zone is not defined while using inject, etc from Angular2/testing).
Can you please let me know how to fix this error?
Error:

JS: NSUTR-socket.io: transport error
JS: NSUTR-socket.io: 1
JS: NSUTR: socket.io error on connect: Error: xhr poll error
JS: NSUTR-socket.io: 2
JS: NSUTR: socket.io error on connect: Error: xhr poll error
04 05 2016 12:33:42.046:WARN [NativeScript / 23 (6.0; Google Nexus 6P - 6.0.0 - API 23 - 1440x2560)]: D
isconnected (1 times), because no message in 10000 ms.
JS: NSUTR-socket.io: 3
JS: NSUTR: socket.io error on connect: Error: xhr poll error
JS: NSUTR-socket.io: 4
JS: NSUTR: socket.io error on connect: Error: xhr poll error

My spec (test) file:
import 'reflect-metadata';
import 'zone.js/dist/zone.min.js';

import {Home} from '../components/home/home';
import {inject, beforeEachProviders, it, describe, expect} from 'angular2/testing';

beforeEachProviders(() => [Home]);

describe('Home Page ActionBar Title Test', () => {
  // it('Title has text', () => {
  it('Title has text', inject([Home], (homeObj: Home) => {
      expect(homeObj.title).toBe('CapacityReview');
      // expect(true).toBe(true);
    }));
});

Package.json:
 "dependencies": {
    "angular2": "2.0.0-beta.16",
    "es6-shim": "0.35.0",
    "nativescript-angular": "0.0.46",
    "nativescript-intl": "0.0.2",
    "nativescript-unit-test-runner": "^0.3.3",
    "parse5": "1.4.2",
    "punycode": "1.3.2",
    "querystring": "0.2.0",
    "reflect-metadata": "0.1.2",
    "rxjs": "5.0.0-beta.2",
    "tns-core-modules": "2.0.0",
    "url": "0.10.3",
    "zone.js": "0.6.12"
  },
  "devDependencies": {
    "babel-traverse": "6.7.6",
    "babel-types": "6.7.7",
    "babylon": "6.7.0",
    "filewalker": "0.1.2",
    "jasmine-core": "^2.4.1",
    "karma": "^0.13.22",
    "karma-jasmine": "^0.3.8",
    "karma-nativescript-launcher": "^0.4.0",
    "lazy": "1.0.11",
    "nativescript-dev-typescript": "^0.3.1",
    "typescript": "^1.8.10"
  }

karma.conf.js (default one):
module.exports = function(config) {
  config.set({

    // base path that will be used to resolve all patterns (eg. files, exclude)
    basePath: '',

    // frameworks to use
    // available frameworks: https://npmjs.org/browse/keyword/karma-adapter
    frameworks: ['jasmine'],

    // list of files / patterns to load in the browser
    files: [
      'app/**/*.js'
    ],

    // list of files to exclude
    exclude: [
    ],

    // preprocess matching files before serving them to the browser
    // available preprocessors: https://npmjs.org/browse/keyword/karma-preprocessor
    preprocessors: {
    },

    // test results reporter to use
    // possible values: 'dots', 'progress'
    // available reporters: https://npmjs.org/browse/keyword/karma-reporter
    reporters: ['progress'],

    // web server port
    port: 9876,

    // enable / disable colors in the output (reporters and logs)
    colors: true,

    // level of logging
    // possible values: config.LOG_DISABLE || config.LOG_ERROR || config.LOG_WARN || config.LOG_INFO || config.LOG_DEBUG
    logLevel: config.LOG_INFO,

    // enable / disable watching file and executing tests whenever any file changes
    autoWatch: true,

    // start these browsers
    // available browser launchers: https://npmjs.org/browse/keyword/karma-launcher
    browsers: [],

    customLaunchers: {
      android: {
        base: 'NS',
        platform: 'android'
      },
      ios: {
        base: 'NS',
        platform: 'ios'
      },
      ios_simulator: {
        base: 'NS',
        platform: 'ios',
        arguments: ['--emulator']
      }
    },

    // Continuous Integration mode
    // if true, Karma captures browsers, runs the tests and exits
    singleRun: false
  })
}

Resolved!!
After I changed the import of zone.js from
import 'zone.js/dist/zone.min.js';
to
import 'zone.js/dist/zone-node';
Thanks to @hdeshev for helping me out on this.

According to @hdeshev, another way is to replace the current imports of zone and reflect-metadata with this as the first line:
import 'nativescript-angular/application'; which works too!

adding

import 'nativescript-angular/application';

worked for me too, but I recently updated to the latest version of Nativescript and now this method is not working I would appreciate if this issue could be reopened, thanks in advance for any help

The instructions I recently got said to include import 'nativescript-angular/platform'; as the first line of my test file. Maybe that would help you?   I'm seeing mixed results...


Load single-file checkpoints directly without conversion

Summary
This PR takes advantage of the new diffusers load_single_file() method to load single-file checkpoints (e.g. .safetensors) directly. It completely removes the convert cache directory, avoiding model duplication and conserving the user's disk space. Single file loaders for main models, VAEs, and controlnets have been implemented.
Manual conversion from single files to diffusers directories is still supported. As before, this is a one-way operation which deletes the original .safetensors file (if it is in the models directory).
Loading is nice and fast with no discernible difference in rendering performance. The main drawback of single file loading is that the entire pipeline is loaded into RAM at once, causing a surge in RAM usage compared to loading a diffusers submodel. However, after loading all pipeline components into RAM, its submodels are cached individually, and subsequent load calls will return the cached submodel as long as it remains cached. There may be a performance degradation if the user has set the RAM cache size to be too small for a whole pipeline to fit into. Under these circumstances a single-file pipeline will be re-read from disk each time it is needed.
It might be a good idea for the manager to produce a warning message when the cache size is too small to hold a whole pipeline. Let me know if you think this should be implemented.
Related Issues / Discussions
See the Discord thread starting at: https://discord.com/channels/1020123559063990373/1049495067846524939/1239774642449813514
QA Instructions

Be aware that this PR will delete the models/.convert_cache directory, migrate the database schema to v12, and upgrade the config schema to 4.0.2. These migrations remove the convert cache directory and the config_cache size configuration option. It does not remove the config_cache_dir configuration option because the database migration needs to know where the user stored the converted files in order to delete them!
Confirm that single-file model checkpoints and VAEs load properly.
Confirm that the "Convert" button in the model manager UI works as expected.
Confirm that no issues arise when switching among models or setting the ram configuration option to a low value.

Merge Plan
Squash merge when approved.

Checklist

[X] The PR has a short but descriptive title, suitable for a changelog
[X] Tests added / updated (if applicable)
[X] Documentation added / updated (if applicable)

What do you think about all of this? I think I saw a PR at one point where you were exploring config migration patterns, so you have probably spent more time thinking about this than me.

At one point I had written a system for registering config migrations using decorators in which each migration step was executed by a separate function decorated with from and to versions. However, psychedelicious and I were unable to agree on the architecture for this and I withdrew the PR. The details are tedious, but check conversation at PR #6243

Maybe we should handle config migration in the DB migrator since it's doing more than DB migrations now? Prefer to work through taht separate to this PR tho

As I review again, I'm realizing that we don't really have a strategy with config version numbers. It seems like it was in-sync with app versions at one point. Now it's neither in-sync nor following semver.
Given this plus the awkwardness with handling convert_cache_dir, maybe we should just leave both convert_cache and convert_cache_dir as deprecated fields, and figure out a proper solution in a future PR. (I'm also not opposed to removingconvert_cache now - it doesn't make the situation any worse.)

I agree. We need to work out a system for coordinating the app, config file, and database schema version numbers. We should probably implement a single migration system that is adapted for config schema, db schema and root filesystem migrations.
What would happen if we have a rule that we use the app version for the config and db schemas? The main complication would be that each time we make a breaking schema change, we have to update the app version within the PR. Given that PRs are merged at different times and orders, this would be a management headache.

 Here are the results.
Test plan

Before upgrade, I installed the following single-file models:

SD1.5 VAE: https://civitai.com/models/22354/clearvaesd15?modelVersionId=88156
SD1.5 ControlNet: https://civitai.com/models/90940/controlnet-qr-pattern-qr-codes?modelVersionId=111973
SD1.5 Base: https://civitai.com/models/4201/realistic-vision-v60-b1?modelVersionId=130072
SDXL VAE: Not tested, see issues.
SDXL ControlNet: Not tested, see issues.
SDXL Base: https://civitai.com/models/112902/dreamshaper-xl?modelVersionId=126688

I used all of the models listed above to cause them to be converted to diffusers format and written to the convert cache.
I added convert_cache_dir and convert_cache explicitly to the config file to test how they are handled in the migration.
Upgrade to this branch.
Confirmed that the config file is updated as expected.
Confirmed that the convert cache was deleted.
Confirmed that all of the single-file models still work.
Checked for reproducibility by re-running with the same params as before the upgrade (see issues below).
Tested for RAM cache thrashing by experimenting with different cache sizes. As expected, there is some thrashing at low cache sizes. Anecdotally, at 4GB there was a lot of thrashing, at 7.5 GB things ran pretty smoothly. I didn't try to get more granular than that.
Deleted and re-added the single-file models after the upgrade to confirm that new single file models can be added without issues.
Manually ran a conversion on each of the single file models.
Confirmed that the converted models still work.

Issues encountered that were not introduced by this PR:

I tried to test with this SDXL VAE: fix-fp16-errors-sdxl-lower-memory-use-sdxl-vae-fp16-fix-by-madebyollin But, it was incorrectly recognized as a SD1.5 VAE by the model probe. (I suppose that's expected since the SD1.5 and SDXL VAE have the same model architecture.) This might be fixed by https://github.com/invoke-ai/InvokeAI/pull/6524#event-13222585002, which was merged after I ran these tests.
I tried to test with this SDXL ControlNet: ttplanetSDXLControlnet_v20Fp16. But, it failed conversion. Upon inspection, the weights appear to already be in diffusers format, but we attempted to convert them again.
I tried to test with this SDXL ControlNet: qrpatternSdxl_v01256512eSdxl. But, model import failed with error: Unable to determine model type for /home/ryanjdick3/Downloads/qrpatternSdxl_v01256512eSdxl.safetensors.
Clicking "Use All" does not clear the currently-selected VAE if the correct VAE is the default VAE.
I installed the SDXL IP-Adapter from the "Starter Models" section of the models tab. I still got this warning the first time that I used it: The image encoder required by this IP Adapter (ip_adapter_sd_image_encoder) is not installed. Downloading and installing now. This may take a while..
We allow users to click "Convert" for all model types, but then we fail with The model with key xxx is not a main checkpoint model. We should just hide the button if it's not supported.

Issues introduced by this PR:
There is a reproducibility problem with some models before/after this PR.
Here are two images generated with the same configs and the CyberRealistic_V4.1_FP16 model (one of the starter models).
Before:

After:

This issue did not seem to affect all models.

I just noticed that we are still on diffusers==0.27.2 in this branch. In 0.28.0, diffusers did a "Massive Refactor of from_single_file" (https://github.com/huggingface/diffusers/releases/tag/v0.28.0). I feel like we should be building this branch against >=0.28.0 - especially since we are going to want to bump diffusers soon to get SD3 support.

What would happen if we have a rule that we use the app version for the config and db schemas? The main complication would be that each time we make a breaking schema change, we have to update the app version within the PR. Given that PRs are merged at different times and orders, this would be a management headache.

Agreed, for the reason you've described, I think at the very least we'd want an internal app version separate from the app release version. There might be reasons for further splitting which systems gets versioned together, but I'll save that discussion for another place.

I just noticed that we are still on diffusers==0.27.2 in this branch. In 0.28.0, diffusers did a "Massive Refactor of from_single_file" (https://github.com/huggingface/diffusers/releases/tag/v0.28.0). I feel like we should be building this branch against >=0.28.0 - especially since we are going to want to bump diffusers soon to get SD3 support.

There are some significant changes involved in moving to 0.28.0, which have gone into PR #6512. Now that you've done all this testing I'm not comfortable including them in this PR. For example, with 0.28 and higher, the ControlNetModel defined in invokeai.backend.util.hotfixes can no longer include FromOriginalControlNetMixin as a base class because of an apparent dangling import internal to the diffusers package. In the SD3 PR, I removed this base class without apparent consequences, but this needs more extensive testing.

Issues encountered that were not introduced by this PR:
I tried to test with this SDXL VAE: fix-fp16-errors-sdxl-lower-memory-use-sdxl-vae-fp16-fix-by-madebyollin But, it was incorrectly recognized as a SD1.5 VAE by the model probe. (I suppose that's expected since the SD1.5 and SDXL VAE have the same model architecture.) This might be fixed by https://github.com/invoke-ai/InvokeAI/pull/6524#event-13222585002, which was merged after I ran these tests.

With that PR, this SDXL VAE is recognized and loaded. However, it looks washed out and I don't know if there's something wrong with the model or with the loading. I also tried with the older diffusers conversion method, and got the same washed out result, so I tend to think it is a model problem.

I tried to test with this SDXL ControlNet: ttplanetSDXLControlnet_v20Fp16. But, it failed conversion. Upon inspection, the weights appear to already be in diffusers format, but we attempted to convert them again.

Yes, it is in diffusers format. To load it we would need to put it into a folder, rename it diffusion_pytorch_model.safetensors and add a config file to the directory. This probably has to be done in the installer. Do you think it's worth it?

I tried to test with this SDXL ControlNet: qrpatternSdxl_v01256512eSdxl. But, model import failed with error: Unable to determine model type for /home/ryanjdick3/Downloads/qrpatternSdxl_v01256512eSdxl.safetensors.

This seems to be a controlnet format that I haven't run into yet. It will need a bit of new backend support: e.g. https://github.com/kohya-ss/ControlNet-LLLite-ComfyUI. There are a total of five LLLite controlnet models on civitai. It looks kinda' nice. Do you think we implement support, or wait until someone makes a feature request..

Clicking "Use All" does not clear the currently-selected VAE if the correct VAE is the default VAE.

I noticed that sometimes I didn't get the right VAE when remixing. Thanks for figuring out the way to reproduce the problem. I'll make this a bug report.

I installed the SDXL IP-Adapter from the "Starter Models" section of the models tab. I still got this warning the first time that I used it: The image encoder required by this IP Adapter (ip_adapter_sd_image_encoder) is not installed. Downloading and installing now. This may take a while..

I was unable to reproduce this. However, I think there is a problem at the frontend level in which the human-readable name and description of starter models are not passed to the intsaller, so that you get the automatically-inferred model name and description.

We allow users to click "Convert" for all model types, but then we fail with The model with key xxx is not a main checkpoint model. We should just hide the button if it's not supported.

Indeed. I'll make this a bug report.

@RyanJDick Are there any remaining issues?

What would happen if we have a rule that we use the app version for the config and db schemas? The main complication would be that each time we make a breaking schema change, we have to update the app version within the PR. Given that PRs are merged at different times and orders, this would be a management headache.

Agreed, for the reason you've described, I think at the very least we'd want an internal app version separate from the app release version. There might be reasons for further splitting which systems gets versioned together, but I'll save that discussion for another place.

I think the coordinated config, filesystem and db migration system needs its own PR.


Revert "Fixed translations in 01-getting-started.md"

Reverts filamentphp/filament#6513

Sawry. 🫠


Tribonacci Series in Julia

💥 Proposal
Tribonacci Series in Julia
Have you read the Contributing Guidelines on Pull Requests?
YES

/assign


Unexpected error creating device - 429

Integration stopped working at some point and I tried to reinstall it. Now I get this error when trying to login. What could cause that?
Traceback (most recent call last):
File "/config/custom_components/panasonic_cc/config_flow.py", line 60, in _create_device
devices = await self.hass.async_add_executor_job(api.get_devices)
File "/usr/local/lib/python3.10/concurrent/futures/thread.py", line 58, in run
result = self.fn(*self.args, **self.kwargs)
File "/config/custom_components/panasonic_cc/pcomfortcloud/session.py", line 201, in get_devices
self.login()
File "/config/custom_components/panasonic_cc/pcomfortcloud/session.py", line 105, in login
self._create_token()
File "/config/custom_components/panasonic_cc/pcomfortcloud/session.py", line 158, in _create_token
raise ResponseError(response.status_code, response.text)
custom_components.panasonic_cc.pcomfortcloud.session.ResponseError: Invalid response, status code: 429 - Data:

CC app worked before I logout. Now login do not work so some issue on Panasonic side. I'll close this one.


How to test?

Hi,
How do I test a custom module which is simply running a node-fluent-ffmpeg command with Mocha&Chai?
// segment_splicer.js
var config = require('./../config');
var utilities = require('./../utilities');
var ffmpeg = require('fluent-ffmpeg');

module.exports = {
	splice: function(raw_ad_time, crop) {
		if (!raw_ad_time || !crop) throw new Error("!!!!!!!!!! Missing argument");
		console.log("@@@@@ LAST SEGMENT IS BEING SPLITTED.");
		var segment_time = utilities.ten_seconds(raw_ad_time);
		var last_segment_path = config.akamai_user_base + 'segment' + (segment_time + 1) + "_" + config.default_bitrate + "_av-p.ts?sd=10&rebase=on";
		var command = ffmpeg(last_segment_path)
			.on('start', function(commandLine) {
				console.log('@@@@@ COMMAND: ' + commandLine);
			})
			.seekInput('0.000')
			.outputOptions(['-c copy', '-map_metadata 0:s'])
			.duration(crop)
			.on('error', function(err, stdout, stderr) {
				throw new Error('@@@@@ VIDEO COULD NOT BE PROCESSED: ' + err.message);
				console.log('@@@@@ VIDEO COULD NOT BE PROCESSED: ' + err.message);
			})
			.output('public/' + 'segment' + (segment_time + 1) + "_" + config.default_bitrate + "_av-p.ts").run();
	}
}

Here is what I tried:
// test/segment_splicer.js
var expect = require('chai').expect;
var segment_splicer = require('../lib/segment_splicer');

describe('Segment Splicer', function() {
	it('should work', function(done) {
		expect(segment_splicer.splice(1111111, 20)).to.throw(Error);
		done();
	});
});

I get this:

Segment Splicer should work: AssertionError: expected undefined to be a function

Thank you!

expect(x).to.throw(y) expects x to be a function but you're passing segment_splicer.splice(1111111, 20) which actually calls your splice() method at the time where tests are defined, which is not what you want. Use expect(function() { segment_splicer.splice(1111111, 20); }) instead.

Hey @njoyard , thanks for answer. But it didn't work neither :(
I updated code as you said:
var expect = require('chai').expect;
var segment_splicer = require('../lib/segment_splicer');

describe('Segment Splicer', function() {
	this.timeout(5000);
	it('should work', function(done) {
		expect(function() {
			segment_splicer.splice(1111111, 20);
		}).to.throw(Error);
		done();
	});
});

But my splice method does not return anything so I get this:

Segment Splicer should work:
AssertionError: expected [Function] to throw Error
at Context. (/Users/scaryguy/node/g2/test/segment_splicer.js:9:14)

But it is supposed to give an error like:

[Error: ffmpeg exited with code 1: http://blalalalal: Invalid data found when processing input

on('error') is not recognized by mocha. I think it's because fluent-ffmpeg uses child_process. But I don't know how to handle it. How do you test your code?

Oh, that's because the error is not thrown by slice() but by an event handler defined inside.
Use a variable to check that the error handler was called instead.
On a side note do not throw inside event handlers, that's very bad practice.

Use a variable to check that the error handler was called instead.

I'm not sure if I understood this well :\ Could you give me some code tips?

Try something like this (assuming mocha handles async tests with a 'done' argument to the test definition):
it('should call error handler', function(done) {
  var called = 'none';
  ffmpeg(...)
  .on('error', function() {
    called = 'error';
    finish();
  })
  .on('end', function() {
    called = 'end';
    finish();
  });

  function finish() {
    expect(called).toBe('error');
    done();
  }
});


mediaPlaybackRequiresUserAction defaults to false on iOS

iOS: mediaPlaybackRequiresUserAction has no default value in JS and defaults to false in UIView

@Titozzz I think this doc would help


Use IntEnum or Flag for Tracker.run() return value

Tracker.run() currently returns a string giving the reason it stopped. Yuck. Replace this awfulness with an IntEnum or Flag.

This has been done in 232a413f.


OpenCV support

Hi!
I'm trying to convert a Python application that uses NumPy and OpenCV to Javascript.
To replace NumPy I've just replaced every import numpy as np with import numscrypt as np, (is this correct?) but there isn't a module for opencv.
On its side, OpenCV has already a complete Javascript version available ( see: https://docs.opencv.org/3.4/d0/d84/tutorial_js_usage.html ), so I'm wondering: is it possible to add the opencv module to transcrypt?
Which are the steps required to add a module? I could give a hand

Hi @galeone, encapsulating a js library as a Python module can be done using a pragma(), and that section also links to an example using the fabric.js library. Note also using the new method to call the encapsulated lib. e.g.
__pragma__ ('noanno')

fabric = __pragma__ ('js',
    '''
(function () {{
    var exports = {{}};
    {}  // Puts fabric in exports and in global window
    delete window.fabric;
    return exports;
}}) () .fabric;
    ''',
    __include__ ('com/fabricjs/fabric_downloaded.js')
)

Hope that helps.

@fzzylogic Thanks!
@galeone Does this work for you? If not feel free to reopen.


NOT working on Synology 720+ with DSM 6.2.4 update 7.

NOT working on Synology 720+ with DSM 6.2.4 update 7.
It will create the storage pool, but the volume is not created. I do not see any option for creating a volume from the SSD storage pool.
Config: two 500GB SSD, configured in RAID 1.
I had this working on DSM 7.1.1 with RAID 0, but I downgraded to DSM 6 and the script is not able to create the volume.
Originally posted by @Ruben-Isai in https://github.com/007revad/Synology_M2_volume/issues/52#issuecomment-1606231802

Can you post the output of the script.

I will run it again as I lost connection to the SSH session at 90%. I did run the script again and it told me data scrubbing is running. Once that was finished, I rebooted the 720+, and I am able to see the SSD pool, but no volume is created.

Is it possible to create the volume once the SSD Pool has been created?

If you run it again, now that the data scrubbing has finished, it should work (unless something changed between 6.2.4 update 6 and update 7).
Because DSM 6 does not have the "online assemble" the script does create the volume for DSM 6, so after a reboot the storage pool and volume should show up in storage manager without you needing to do anything else.
Personally I would upgrade to DSM 7.2 unless you have some USB device or app that needs DSM 6. If you do update to DSM 7.2 use https://github.com/007revad/Synology_HDD_db instead.
You'd need to update to 7.0.1 first: DS720+ DSM 7.0.1-42218
Then update to 7.2: DS720+ DSM 7.2-64570 Update 1

Thank you for the response. I use DSM 6 as Moments has better subject / facial recognition.
I am running the script again and will post the entire SSH session output.

@Ruben-Isai
Did the script work?

NOT working on Synology 720+ with DSM 6.2.4 update 7. It will create the storage pool, but the volume is not created. I do not see any option for creating a volume from the SSD storage pool.
Config: two 500GB SSD, configured in RAID 1.
I had this working on DSM 7.1.1 with RAID 0, but I downgraded to DSM 6 and the script is not able to create the volume.
Originally posted by @Ruben-Isai in #52 (comment)

On the 3rd attempt the script worked!
I now have the RAID configured in mirror, and working!
Thank you so much!


ShirukaEntity.java:145: Implement ShirukaEntity#tick.");

The puzzle 1-dac8423c from #1 has to be resolved:
https://github.com/shiruka/shiruka/blob/24d02de4e698c2d724c0a733186145a34d4b3621/src/main/java/net/shiruka/shiruka/entity/ShirukaEntity.java#L145-L145
The puzzle was created by Hasan Demirtaş on 01-Feb-21.
Estimate: 10 minutes,  role: DEV.
If you have any technical questions, don't ask me, submit new tickets instead. The task will be "done" when the problem is fixed and the text of the puzzle is removed from the source code. Here is more about PDD and about me.

The puzzle 1-dac8423c has disappeared from the source code, that's why I closed this issue.


build.js:17 Uncaught ReferenceError: require is not defined

Hi, after npm run dev, I get an empty page with js error:
build.js:17 Uncaught ReferenceError: require is not defined

You can try the web version:

https://github.com/ieiayaobb/dynamoc_web