[youtube] Fix automatic captions not found

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[ ] Improvement
[ ] New extractor
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Description of your pull request and other information
Related to #27315
The structure of ytplayer.config has changed, and it can no longer be explicitly obtained from the webpage. So the function _get_ytplayer_config used for getting automatic captions is no longer valid.
In the latest format of youtube webpage, the url of auto-captions is explicitly stored in ytInitialPlayerResponse, which is exactly the parameter player_response we pass in the edited function _get_automatic_captions. And the original code can handle such structures.
You can check with:
youtube-dl --write-auto-sub --skip-download https://www.youtube.com/watch?v=3lMUIUgRD-A

Hi, RogerRordo,
How to I edit the youtube_dl/extractor/youtube.py file, I have installed the youtube-dl with .exe file and cannot locate the youtube_dl/extractor/youtube.py?
Thanks in advance

Hi, RogerRordo,
How to I edit the youtube_dl/extractor/youtube.py file, I have installed the youtube-dl with .exe file and cannot locate the youtube_dl/extractor/youtube.py?
Thanks in advance

Use pip rather than executable file

i hope they will merge this ASAP, thanks a lot for contrib!!!

The bug is still there.  -  Couldn't find automatic captions
youtube-dl --write-auto-sub --sub-lang en --skip-download "https://www.youtube.com/watch?v=GWGbOjlJDkU"
[youtube] GWGbOjlJDkU: Downloading webpage
[youtube] GWGbOjlJDkU: Looking for automatic captions
WARNING: Couldn't find automatic captions for GWGbOjlJDkU


Persistent unexpected error: DNS problem while connecting to Shopify, this indicates a problem with your internet connection

Sorry to be reopening this. But is this a intermitent problem?
I'm getting the same error but while trying to generate a new theme with
To Reproduce
theme new -p=mypassword -store=mystore -name=themename

Expected behavior
create the new theme
Environment
MacOS Catalina 10.15.5
ThemeKit 1.0.3 darwin/amd64
Originally posted by @gui-gui in https://github.com/Shopify/themekit/issues/645#issuecomment-640970719

Have tried this on a few different networks, including one public server and I continue to get the same error. The last time this issue was opened, it was blamed on cloudflare outages, but everything is currently normal on shopify status

Is this still a problem now? There is not much we can do about DNS issues.

I was able to connect, I think I was using an incorrect password or possibly using the flags wrong. But it definitely wasn't a DNS issue which was frustrating because thats the only error message I was seeing. We can close this I guess, but it might be worth checking out the error handling messages a second. Thank you for the tool and work.

There were definitely DNS issues on that day for a short period, just for the record.
As it states on the Shopify Status dashboard, not all issues are reported if they only affect a small percentage of stores.

I'm not sure if this could be the cause, but there's an open Go issue DNS lookup sometimes fails with "no such host" when there are too many open files.

@andyw8 that shouldn't make the command intermittent, it would cause it to fail consistently. There were issues in the past with OSX having a very low file descriptor limit, however this was taken care of here https://github.com/Shopify/themekit/blob/master/cmd/theme/setup_darwin.go#L16-L22

To Reproduce
theme new -p=mypassword -store=mystore -name=themename

I solved this by using --store and --name, notice the arguments require 2 dashes -- before each option. Single dash like -p only works for shorthands arguments. You can refer to theme get --help to see all the arguments for get.
It would be better if themekit returned an error with the invalid argument instead of showing a connection error.

Thank you - this solved it for me.  But strangely for my machine I had to use --password --store but -theme


Android software buttons bar transparent on Samsung S8

Found a really weird bug, submitting an issue as discussed with @GantMan.
When you open the app on Samsung S8 device, the device action buttons bar (Home, Back, Switcher) color is set to transparent, showing through the previous app. Screenshots below.
When you navigate away from the ChainReactApp, the bar color is reset to whatever the next app sets (usually black). When you navigate back into the ChainReactApp, the bar color is set to transparent again.
App version 1.0.

Likely culprit: https://github.com/infinitered/ChainReactApp/blob/master/android/app/src/main/res/values/styles.xml#L4-L6

Got around testing this, #103 seems to fix this on my device.


Documentation

Hi guys, I would like to collaborate in this project. First, I would like to improve documentation, as some modules have none and it may be difficult to new users to use this awesome library.
What do you think?
Ideas?

Hello Miguel. You are very welcome to help us in this big project!
There are many modules, they are not all currently functional and/or tested and it would be very useful to review the different repositories and decide if:

They are in a good state.
They are broken or outdated and need to be fixed. This may require major API changes so documentation is not the priority.
They are working fine but have little or no documentation.
They need more tests.

We just created a project to keep track of everything: https://github.com/orgs/mljs/projects/1
If you are interested, I can give you the rights to edit it. Otherwise, you can start by opening an issue in one of the repositories that you would like to enhance and we can discuss in more details what needs to be done.
Thank you!


Passed parameters from Pipeline to TaskRuns

Turned out that the pipelinerun controller wasn't yet passing along
parameters which were supplied in Pipelines (for #64).
Also removed unneeded value field from parameter declaration.
Some remaining work that needs to be done:

It should be an error to provide a parameter to a Task that it doesn't
need
It should be an error to apply templating with required params not
supplied

Co-authored-by: Christie Wilson<EMAIL_ADDRESS>

/lgtm


Release pipeline: Use of service connection for publishing to package feed

The service connection referenced below uses a PAT that is managed by Arcade's secret manager: https://github.com/dotnet/source-build/blob/7d40cb707acdfec871f996b919be38f5c3271577/eng/templates/stages/mirror.yml#L41-L42
By having it managed by secret manager, it means the PAT can be rotated at anytime via automation. Since the service connection just contains a copy of the PAT value, the service connection doesn't get updated. This means the secret rotation can break the publishing of packages without a human knowing that the rotation happened and manually updating the service connection.
We should have a more robust solution that doesn't require human intervention or at least uses a more controlled process so that publishing isn't unexpectedly broken.
One solution would be to directly reference the PAT secret name instead of using a service connection.

I agree and our team knows that service connections are a PITA for management (https://github.com/dotnet/arcade/issues/12750) and we have an issue to figure out a rotation process for them but I wouldn't wait for that to be done any time soon. So I agree using a PAT directly is more sustainable here.
However, I think the proper solution is to track and rotate the secrets yourself in source-build (https://github.com/dotnet/source-build/issues/3347) rather than relying on Arcade. For instance, Arcade's secret manifests are now being moved to dotnet/dnceng as only a few really belong to Arcade so it might happen this particular one will get lost during the transition.

As of the moment of writing, the validation / test pipeline are both capable of catching any issues with the service connection as they are using the same PAT.
This is far from ideal, but gives some protection. Additionally, in the short-term we could have our weekly pipeline check the expiration date for the PAT in question and warn / out-right fail if the secret is less than N days away from expiring.
Long-term wise, just as Premek mentioned above, I think we need to wait for Secret Manager to provide support for this use-case. I'll leave a comment in its tracking issue asking to bump the priority of the effort.

Since we are publishing to a feed outside the pipeline's organization, we have to rely on the service connection for now - https://learn.microsoft.com/en-us/azure/devops/pipelines/tasks/reference/nuget-authenticate-v1?view=azure-pipelines#inputs
DncEng knows about this issue and are impacted by it themselves. Additionally, as mentioned above I think we have enough systems in place to be able to catch any issues early and minimize potential impact.
As such, I think its worth to just keep this issue as tracking for the effort and wait for DncEng to provide some proper robust solution for working with service connections


Change useGlobalMono scope to default (window)

This would allow easier context switching between Unity and Core projects.
Related issue:
https://github.com/OmniSharp/omnisharp-vscode/issues/4132

Thanks @JoeRobich!


Change default min balance for Sepolia

1 - At the moment, when registering through Sepolia, a minimum balance of 0.004 ETH is required. This is almost 10 times more than what is needed at the moment. I propose to reduce the minimum balance to 0.001, so that there is enough for registration and there will be a reserve in case of an increase in costs.

Registration https://sepolia.etherscan.io/tx/0x6afb722a63432fd775c208269d3436ee9fd8648f704bcf1624dec7a2f651d1b8 - 0.000198 SepoliaETH
Set resolver https://sepolia.etherscan.io/tx/0x7cc06d5d269cafc14603df4757d71b6ede5644ca333d059db14ac13cc43d83db - 0.00007323 SepoliaETH
Set pubkey https://sepolia.etherscan.io/tx/0x331e20de45aa71b6094fc7669854421974e42626ba597c635cd621651d9f6ab6 - 0.00011723 SepoliaETH
Total ~0.00038846 SepoliaETH

Min balance is calculated by querying RPC for gas amount and Alchemy for current gas price. And then the value is doubled because the calculation is unreliable.
If calculation can't get lesser amount then needed, maybe we could just remove multiplication by 2?

Min balance is calculated by querying RPC for gas amount and Alchemy for current gas price. And then the value is doubled because the calculation is unreliable. If calculation can't get lesser amount then needed, maybe we could just remove multiplication by 2?

Is 0.004 calculated based on Alchemy? Can you point me to the place in the code where this amount is calculated?

Sure, this is the function:
https://github.com/fairDataSociety/fdp-create-account/blob/d0d9d80cf7b7f19ed20d07dde6522bc882ab1044/src/context/account.context.tsx#L107-L131

Actually, now when I checked the code. Seems that the 0.004 is default value and estimation didn't work. The default value set for Sepolia is 0.002, so we can change that value.
https://github.com/fairDataSociety/fdp-create-account/blob/d0d9d80cf7b7f19ed20d07dde6522bc882ab1044/src/context/network.context.tsx#L10-L24

Actually, now when I checked the code. Seems that the 0.004 is default value and estimation didn't work. The default value set for Sepolia is 0.002, so we can change that value.
https://github.com/fairDataSociety/fdp-create-account/blob/d0d9d80cf7b7f19ed20d07dde6522bc882ab1044/src/context/network.context.tsx#L10-L24

Great. Let's change it.

You set a minimum balance of 0.001 ETH, and then in the UI it is multiplied by 2 and the user is required to deposit 0.002 ETH. Although 0.001 ETH is already an amount with a margin. Therefore, you need to change the minimum amount to 0.0005 so that 0.001 ETH is displayed in the UI. Right?


Reproducibility Issue

I have been trying to reproduce the results from your paper using the code that you have provided in this repo, but I was not able to achieve similar results.
Specifically, I have been trying to run the jupyter notebook "PPG-ECG-to-BP-Prediction-ABP-Estimation/Codes/Deep Learning Pipeline/PPG_ECG_to_BP_Prediction_Pipeline.ipynb" to reproduce the results. I have also used the dataset that you provided in "PPG-ECG-to-BP-Prediction-ABP-Estimation/Datasets/". My results, however, are very different:

What should I do in order to obtain the same results that you obtained in your paper ?
Thank you.

Hey!
Can you tell me what's the standard deviation of the result you are observing coz I am having a similar issue?

Hey!
I have the same result.
Have you solved it?


Nexus Data Access plugin: Enable download for versioned file identifiers

Currently, the Nexus data Access plugin enables download of files through their _self as value of the contentUrl property of distribution objects on a resource. However, the download operation is throwing an error when a versioned _self is being used. See below:
Download possible:
{
      "@type": "DataDownload",
      "contentSize": {...},
      "contentUrl": "https://12345",
      "digest": {...},
      "encodingFormat": "application/swc",
      "name": "abc.swc"
    }

Download causing an error:
{
      "@type": "DataDownload",
      "contentSize": {...},
      "contentUrl": "https://12345?rev=1",
      "digest": {...},
      "encodingFormat": "application/swc",
      "name": "abc.swc"
    }

Resource to test:
https://bbp.epfl.ch/neurosciencegraph/data/neuronmorphologies/52ee8e04-86ce-420e-b7b3-12c5af6ffbd1


Fixed for Android 8.0

Platforms affected
Android 8.0 and above
Motivation and Context
Android 8.0 no longer assumes related permissions - so when the app gets a contact 'write' permission to save the contact, it no longer automatically gets the 'read' permission - which was causing the app to crash when it tries to read the contact it just saved.
Description

This modification simply ensures the app has both permissions before attempting to save or remove.
Testing

This update has been tested on Android 8, 9 and 10.

We are archiving this repository following Apache Cordova's Deprecation Policy. We will not continue to work on this repository. Therefore all issues and pull requests are being closed. Thanks for your contribution.


For nullable columns, store nil values as NULL

I have a struct defined as:
type foo struct {
  field *MyStruct `xorm:"text null"`
}

If field is nil, I would like NULL stored in the database. Currently, however, it stores the string "null".  I think this is an error since the zero value of *MyStruct is nil.
This PR fixes this by using reflect.IsNil to determine whether a field's value is nil for the types where it makes sense.

@jcsalem Thank you! And could you send some tests PR to github.com/go-xorm/tests.

Any progress by this PR? I have the same issue

Not yet. Hasn't been a priority for me. I think the fix works well, but I'd
love it if you could write some tests for us!
On Mon, Jul 10, 2017 at 10:45 AM, Oleh Herych<EMAIL_ADDRESS>wrote:

Any progress by this PR? I have the same issue
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
https://github.com/go-xorm/xorm/pull/531#issuecomment-314128296, or mute
the thread
https://github.com/notifications/unsubscribe-auth/AAg7oQrfTHGAcMGK6vaD2W9e3FRQk5Axks5sMjkngaJpZM4MBCxh
.

@oherych @jcsalem I have moved tests file from https://github.com/go-xorm/tests to this project so that the PR authors could write tests more easy.

Hi, I added tests https://github.com/go-xorm/xorm/pull/661
but actually, Im not sure is this correct work. Is Nullable("nullable2")` should be mandatory on this case


fix: server handler not aware of app path prefix

HI,
This fixes configurations like:
ROOT_URL=https://www.example.com/myapp
With this change, it will listen requests at  '/myapp/_timesync'.

Thank you @plitex

Thank you @plitex


Filter out params with None value

Signed-off-by: Suneeth Kumar Byadara Halli<EMAIL_ADDRESS>

This fix is no more required. A new pull request will be open later for the fix.


[Event Request] Codeunit 13632 OIOUBL-Check Sales Invoice

Requesting a new event to extend and skip current tests in codeunit.
Reason:
We would like to do more tests, like testing the lines before oioubl file is created.
Handled-pattern is needed because we want to be able to use GLN as PartyIdentification (Customer.”Use GLN in Electronic Document”) and thus don’t want to do TESTFIELD(“VAT Registration No.”)

Thanks for reporting this. We agree, and we’ll publish a fix asap, either in an update for the current version or in the next major release. We will update this issue with information about availability.


1312 tooltip on toolbar

Hi,
this PR attempts to fix #1312
I furtheron fixed translation of menu item and menu tooltip (#1313)
I only committed the source file to avoid merge conflict in the w2ui.min.js

Appreciate your contribution


Remove the "Not From Chrome Web Store badge in the extensions page

Is your feature request related to a problem? Please describe.
It is very ugly seeing the "not from chrome web store" badge in chrome://extensions considering that no extensions can be added directly from the chrome web store.
Describe the solution you'd like
The removal of the badge
Screenshots
The badge: https://i.ibb.co/jbjxyDH/image.png

You can use this patch to hide the badges entirely: hide-source-indicator-for-extensions.patch
Or, alternatively, use this patch for a subtler version of the badges: subtle-source-indicator-for-extensions.patch
The subtler version will look something like on the following screen shot:

If there is a broader interest in this visual modification I can open a pull request.

I hope your question has been answered, otherwise please let us know.
Closing this issue for now.


Changes to NewsBlur refreshAll process

Resolves https://github.com/Ranchero-Software/NetNewsWire/issues/2617
Related conversation: https://netnewswire.slack.com/archives/CCGTPJDPD/p1605910165150500
WIP.

@quanganhdo Do we need to leave this as a WIP for some reason?

The missing part is related to the issue you raised in the Slack thread (the current code not being able to download read articles).

I don't think we will be able to handle it.  It just looks like a deficiency in the NewsBlur API.  All the syncing services are a little bit different.  This is just something we'll have to live with.  It won't impact our unread counts or starred articles, so it isn't the end of the world.

It is ok for us to move forward without being able to download unread articles, so let's just go ahead with this pull request.


Rename 'batch' subcommand to 'agent'

Codecov Report

Merging #139 (ef69556) into main (195c509) will not change coverage.
The diff coverage is 100.00%.

@@           Coverage Diff           @@
##             main     #139   +/-   ##
=======================================
  Coverage   99.95%   99.95%
=======================================
  Files          50       50
  Lines        2297     2297
  Branches      109      110    +1
=======================================
  Hits         2296     2296
  Misses          1        1

Impacted Files
Coverage Δ

...la/codes/quine/labo/recheck/cli/AgentCommand.scala
100.00% <ø> (ø)

.../main/scala/codes/quine/labo/recheck/cli/RPC.scala
100.00% <ø> (ø)

...main/scala/codes/quine/labo/recheck/cli/Main.scala
100.00% <100.00%> (ø)

Continue to review full report at Codecov.

Legend - Click here to learn more
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Finetuning example: Error(s) in loading state_dict for ToucanTTS

Hi, I am trying to follow the fine-tuning example, but just after the dataset preparation and caching i run into the following error:
RuntimeError: Error(s) in loading state_dict for ToucanTTS:
Unexpected key(s) in state_dict: "conv_postnet.postnet.0.0.weight", "conv_postnet.postnet.0.1.weight", "conv_postnet.postnet.0.1.bias", "conv_postnet.postnet.1.0.weight", "conv_postnet.postnet.1.1.weight", "conv_postnet.postnet.1.1.bias", "conv_postnet.postnet.2.0.weight", "conv_postnet.postnet.2.1.weight", "conv_postnet.postnet.2.1.bias", "conv_postnet.postnet.3.0.weight", "conv_postnet.postnet.3.1.weight", "conv_postnet.postnet.3.1.bias", "conv_postnet.postnet.4.0.weight", "conv_postnet.postnet.4.1.weight", "conv_postnet.postnet.4.1.bias", "pitch_predictor.conv.5.0.weight", "pitch_predictor.conv.5.0.bias", "pitch_predictor.conv.6.0.weight", "pitch_predictor.conv.6.0.bias", "pitch_predictor.norms.5.W_scale.0.weight", "pitch_predictor.norms.5.W_scale.0.bias", "pitch_predictor.norms.5.W_scale.2.weight", "pitch_predictor.norms.5.W_scale.2.bias", "pitch_predictor.norms.5.W_scale.4.weight", "pitch_predictor.norms.5.W_scale.4.bias", "pitch_predictor.norms.5.W_bias.0.weight", "pitch_predictor.norms.5.W_bias.0.bias", "pitch_predictor.norms.5.W_bias.2.weight", "pitch_predictor.norms.5.W_bias.2.bias", "pitch_predictor.norms.5.W_bias.4.weight", "pitch_predictor.norms.5.W_bias.4.bias", "pitch_predictor.norms.6.W_scale.0.weight", "pitch_predictor.norms.6.W_scale.0.bias", "pitch_predictor.norms.6.W_scale.2.weight", "pitch_predictor.norms.6.W_scale.2.bias", "pitch_predictor.norms.6.W_scale.4.weight", "pitch_predictor.norms.6.W_scale.4.bias", "pitch_predictor.norms.6.W_bias.0.weight", "pitch_predictor.norms.6.W_bias.0.bias", "pitch_predictor.norms.6.W_bias.2.weight", "pitch_predictor.norms.6.W_bias.2.bias", "pitch_predictor.norms.6.W_bias.4.weight", "pitch_predictor.norms.6.W_bias.4.bias", "post_flow.flows.36.logs", "post_flow.flows.36.bias", "post_flow.flows.37.l", "post_flow.flows.37.log_s", "post_flow.flows.37.u", "post_flow.flows.37.p", "post_flow.flows.37.sign_s", "post_flow.flows.37.l_mask", "post_flow.flows.37.eye", "post_flow.flows.38.start.bias", "post_flow.flows.38.start.weight_g", "post_flow.flows.38.start.weight_v", "post_flow.flows.38.end.weight", "post_flow.flows.38.end.bias", "post_flow.flows.38.wn.in_layers.0.bias", "post_flow.flows.38.wn.in_layers.0.weight_g", "post_flow.flows.38.wn.in_layers.0.weight_v", "post_flow.flows.38.wn.in_layers.1.bias", "post_flow.flows.38.wn.in_layers.1.weight_g", "post_flow.flows.38.wn.in_layers.1.weight_v", "post_flow.flows.38.wn.in_layers.2.bias", "post_flow.flows.38.wn.in_layers.2.weight_g", "post_flow.flows.38.wn.in_layers.2.weight_v", "post_flow.flows.38.wn.in_layers.3.bias", "post_flow.flows.38.wn.in_layers.3.weight_g", "post_flow.flows.38.wn.in_layers.3.weight_v", "post_flow.flows.38.wn.res_skip_layers.0.bias", "post_flow.flows.38.wn.res_skip_layers.0.weight_g", "post_flow.flows.38.wn.res_skip_layers.0.weight_v", "post_flow.flows.38.wn.res_skip_layers.1.bias", "post_flow.flows.38.wn.res_skip_layers.1.weight_g", "post_flow.flows.38.wn.res_skip_layers.1.weight_v", 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	size mismatch for duration_predictor.conv.0.0.weight: copying a param with shape torch.Size([256, 192, 3]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for duration_predictor.conv.0.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.conv.1.0.weight: copying a param with shape torch.Size([256, 256, 3]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for duration_predictor.conv.1.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.conv.2.0.weight: copying a param with shape torch.Size([256, 256, 3]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for duration_predictor.conv.2.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.0.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for duration_predictor.norms.0.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.0.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for duration_predictor.norms.0.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.0.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for duration_predictor.norms.0.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.0.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for duration_predictor.norms.0.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.1.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for duration_predictor.norms.1.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.1.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for duration_predictor.norms.1.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.1.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for duration_predictor.norms.1.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.1.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for duration_predictor.norms.1.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.2.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for duration_predictor.norms.2.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.2.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for duration_predictor.norms.2.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.2.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for duration_predictor.norms.2.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.norms.2.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for duration_predictor.norms.2.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for duration_predictor.linear.weight: copying a param with shape torch.Size([1, 256]) from checkpoint, the shape in current model is torch.Size([1, 192]).
	size mismatch for pitch_predictor.conv.0.0.weight: copying a param with shape torch.Size([256, 192, 5]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for pitch_predictor.conv.0.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.conv.1.0.weight: copying a param with shape torch.Size([256, 256, 5]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for pitch_predictor.conv.1.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.conv.2.0.weight: copying a param with shape torch.Size([256, 256, 5]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for pitch_predictor.conv.2.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.conv.3.0.weight: copying a param with shape torch.Size([256, 256, 5]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for pitch_predictor.conv.3.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.conv.4.0.weight: copying a param with shape torch.Size([256, 256, 5]) from checkpoint, the shape in current model is torch.Size([192, 192, 3]).
	size mismatch for pitch_predictor.conv.4.0.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.0.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.0.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.0.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.0.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.0.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.0.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.0.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.0.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.1.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.1.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.1.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.1.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.1.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.1.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.1.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.1.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.2.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.2.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.2.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.2.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.2.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.2.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.2.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.2.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.3.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.3.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.3.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.3.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.3.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.3.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.3.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.3.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.4.W_scale.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.4.W_scale.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.4.W_scale.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.4.W_scale.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.4.W_bias.2.weight: copying a param with shape torch.Size([256, 64]) from checkpoint, the shape in current model is torch.Size([192, 64]).
	size mismatch for pitch_predictor.norms.4.W_bias.2.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.norms.4.W_bias.4.weight: copying a param with shape torch.Size([256, 256]) from checkpoint, the shape in current model is torch.Size([192, 192]).
	size mismatch for pitch_predictor.norms.4.W_bias.4.bias: copying a param with shape torch.Size([256]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for pitch_predictor.linear.weight: copying a param with shape torch.Size([1, 256]) from checkpoint, the shape in current model is torch.Size([1, 192]).
	size mismatch for post_flow.flows.2.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.2.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.2.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.2.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.2.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.2.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.2.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.2.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.2.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.5.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.5.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.5.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.5.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.5.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.5.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.5.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.5.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.5.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.8.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.8.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.8.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.8.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.8.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.8.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.8.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.8.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.8.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.11.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.11.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.11.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.11.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.11.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.11.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.11.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.11.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.11.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.14.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.14.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.14.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.14.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.14.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.14.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.14.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.14.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.14.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.17.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.17.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.17.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.17.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.17.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.17.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.17.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.17.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.17.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.20.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.20.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.20.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.20.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.20.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.20.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.20.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.20.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.20.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.23.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.23.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.23.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.23.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.23.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.23.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.23.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.23.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.23.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.26.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.26.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.26.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.26.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.26.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.26.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.26.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.26.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.26.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.29.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.29.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.29.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.29.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.29.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.29.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.29.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.29.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.29.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.32.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.32.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.32.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.32.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.32.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.32.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.32.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.32.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.32.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).
	size mismatch for post_flow.flows.35.wn.in_layers.0.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.35.wn.in_layers.1.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.35.wn.in_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 5]) from checkpoint, the shape in current model is torch.Size([384, 192, 3]).
	size mismatch for post_flow.flows.35.wn.res_skip_layers.2.bias: copying a param with shape torch.Size([384]) from checkpoint, the shape in current model is torch.Size([192]).
	size mismatch for post_flow.flows.35.wn.res_skip_layers.2.weight_g: copying a param with shape torch.Size([384, 1, 1]) from checkpoint, the shape in current model is torch.Size([192, 1, 1]).
	size mismatch for post_flow.flows.35.wn.res_skip_layers.2.weight_v: copying a param with shape torch.Size([384, 192, 1]) from checkpoint, the shape in current model is torch.Size([192, 192, 1]).
	size mismatch for post_flow.flows.35.wn.cond_layer.bias: copying a param with shape torch.Size([1536]) from checkpoint, the shape in current model is torch.Size([1152]).
	size mismatch for post_flow.flows.35.wn.cond_layer.weight_g: copying a param with shape torch.Size([1536, 1, 1]) from checkpoint, the shape in current model is torch.Size([1152, 1, 1]).
	size mismatch for post_flow.flows.35.wn.cond_layer.weight_v: copying a param with shape torch.Size([1536, 384, 1]) from checkpoint, the shape in current model is torch.Size([1152, 384, 1]).

It seems to work just fine if I do not resume from the Meta checkpoint.
Is the latest available Meta checkpoint not working with this example or am I doing something wrong here?
Thanks!

sorry, that was my mistake, I made an experimental change on the main branch instead of an experimental one. It should work again now.
I'm trying a smaller model configuration that is hopefully easier to finetune, but there is no matching pretrained model yet.

allright, i will try again, thank you!


How  can we new sample to spider dataset to fine tune the SmBop?

none to the best of my knowledge.

Hi @hXtreme, @OhadRubin  , I managed to make the custom data set, but I'm facing some problems:

I'm unable to understand one field  sql:{}in the train_spider.json . Can you please explain how you were utilizing this in SmBop , what role is playing in the spider dataset? and is it needed in SmBop or not?

There is an script in the spider repository, preprocess/parse_one_sql.py  to generate this parsed sql query and it is working perfectly for some queries, but when i tried this query ,SELECT DATE_FORMAT(date_complaint_raised,'%Y-%m'), count(*) FROM Complaints WHERE YEAR(date_complaint_raised) = value GROUP BY DATE_FORMAT(date_complaint_raised,'%Y-%m'), it gives some date-format assertion error. So while going through the code i found out the script is hard coded, and not supporting functions, So what can we do to add function.
Thanks

PS: The strftime function in sqlite is also not working.
Originally posted by @alan-ai-learner in https://github.com/OhadRubin/SmBop/issues/39#issuecomment-913764745

Sorry, this seems like a question for the guys who wrote spider pre-processing script...

Sorry, this seems like a question for the guys who wrote spider pre-processing script...
I'm unable to understand one field sql:{}in the train_spider.json . Can you please explain how you were utilizing this in SmBop , what role is playing in the spider dataset? and is it needed in SmBop or not?
can you please anwer the first part of question?

The "sql" field is the output of a SQL to parser (i.e SQL-to-AST) that the dataset creators supplied, I don't think I'm using this anywhere in my code.


Add RESTful API for distributed load

Add RESTful API for distributed load.
Usage:
SUBMIT:
description: submit a load job
example:
http://localhost:28080/v1/load?path=s3a://jiamingmai-test/&opType=submit&partialListing=false&&verify=true&bandwidth=1000&loadMetadataOnly=false&verbose=true&skipIfExists=true
STOP:
description: stop the load job
example:
http://localhost:28080/v1/load?path=s3a://jiamingmai-test/&opType=stop
PROGRESS:
description: get the progress of the load job
example:
http://localhost:28080/v1/load?path=/&opType=progress&progressFormat=text&verbose=true

The following snapshots show the example of this load RESTful API:

alluxio-bot, merge this please


Cannot use the jQuery Datatables responsive plugin

Hi,
I'm passing responsive: true to my tables. However, the plugin is not triggered. Do I need to load it separately, and if so, how?
Thanks in advance

You need to install the responsive plugin and the specific theme.  Also remember to load the CSS because there's where the blue "plus" icon come from.  I added the example https://github.com/niiknow/vue-datatables-net/blob/master/example/responsive.vue#L82
Line 82 and 83 load the plugin and specific theme, bootstrap4 theme in this case.   Then line 91 load the css for it.
npm install --save-dev datatables.net-responsive datatables.net-responsive-bs4

Also note that our Master/Details implementation does not work well with responsive because it uses the same child table row, per my note here: https://github.com/niiknow/vue-datatables-net#responsive

Thank you for the extensive explanation. We got it to work now and it works great.
1 tip for others: on the following page you can make a 'combined-module' so you do not need to load responsive plugin separately.
https://datatables.net/download/
I'll take this opportunity to thank you for making this wrapper. We love Vue and we love datatables.net. We always used datatables.net, but had troubles getting it to work after switching to Vue. Vue and jQ do not go well together naturally. Your wrapper solved this perfectly 🙂


appengineRun runs local server with a security manager?

When I ran my project with Maven, all of the integration tests worked fine.
I switched things over to Gradle and tried using this plugin. However, all of my integration tests now fail, because the local server gets a java.lang.SecurityException: Unable to create temporary file. This indicates that the local server was run with a security manager in place.
If so, how do I control or disable that security manager? Otherwise, any idea why a SecurityException would appear anyway? Thanks!

Your project is with GAE Flex or Standard (i.e what do you have in
appengine-web.xml) ?
On Thu, Aug 11, 2016 at 3:31 PM, derekpeircesc<EMAIL_ADDRESS>wrote:

When I ran my project with Maven, all of the integration tests worked fine.
I switched things over to Gradle and tried using this plugin. However, all
of my integration tests now fail, because the local server gets a java.lang.SecurityException:
Unable to create temporary file. This indicates that the local server was
run with a security manager in place.
If so, how do I control or disable that security manager? Otherwise, any
idea why a SecurityException would appear anyway? Thanks!
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The relevant parts of appengine-web.xml:
<env>flex</env>
<beta-settings>
     <setting name="use_custom_vms" value = "false"/>
     <setting name="enable_app_engine_apis" value="true"/>
</beta-settings>


extensions.shield-recipe-client.enabled (?) True or False

I didn`t find a reference to this pref in the user.js
extensions.shield-recipe-client.enabled
Defaults to TRUE here, appears to be some kind of tracking/feedback mechanism.
https://wiki.mozilla.org/Firefox/Shield/Shield_Studies
Just wondering if it needs to be set to false, whether another preference disables it or if it`s redundant ?
I`m not technically minded about these matters so was just curious about the setting state.

Hi, I don't have that pref in about:config. I guess that's because nothing installs automatically in my FF.
Afaik if you allow automatic app updates  then system addons (including the shield one you mention, I assume) are updated/installed automatically also.
If you don't want to participate in the shield study then yes, set that pref to false, ideally in a user.js file because the shield addon could reset the pref otherwise, and next time around the pref would be true again.

Thanks Earthlng
I couldn`t find the system addon xpi responsible (I do usually delete them though)
I set it false anyway, just to be sure.

You're welcome :)

I couldn`t find the system addon xpi responsible

from the site you linked:

Shield Studies live for a fixed period and are uninstalled by the system at the end date or after the specified elapsed time

Shield Recipe Client removes itself automagically.

#16 https://addons.mozilla.org/en-US/firefox/shield_study_16

shield_study_16


NNexus Screencasts

I just discovered RecordMyDesktop for Ubuntu, which works pretty well for me.
If time permits, I would be interested in making several screencasts that provide tutorials to using NNexus and demonstrate it in Planetary.
Ideally for the 2.0 milestone, though it could also be done later in the summer.

FWIW, 6 years later we are in the age of streaming, and it is trivial to make a screencast via OBS+youtube. If/when nnexus gets another reboot that feels "state of the art", I can just record one, but no need for an explicit issue.


Main

Hello! What's this change for?

Sorry for this but i am just looking for hacktoberfest. But i found that
you are not in that so I left it...
Otherwise I was thinking of some other changes for your website.
On Fri, 7 Oct 2022, 9:13 pm Louis Pilfold, @.***> wrote:

Hello! What's this change for?
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Reply to this email directly, view it on GitHub
https://github.com/gleam-lang/website/pull/179#issuecomment-1271757946,
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Playlist controls need basic auth

Anyone with a port scanner can find the playlist controls and delete. stuff...some kind of basic security should be added

Basic auth added to DaManagement.js file

user & pass are read from .env file...not ideal but it'll do for now


Add Chinese translation

Thanks for your contribution!
The changes will be published in v1.4.5!
But before I can merge this update, please finish the tasks I have written in the review (At least those without [Optional]).
Thanks!

No problem!


Add MMAP support

Porting over the https://github.com/ggerganov/llama.cpp mmap support, would reduce the minimum RAM requirement for the model.
I know, you know, haha - just putting it here for me to keep track of (if i come around to picking it up, to optimize the RWKV-cpp-node bindings)

any updates?

@Ar57m Not a the moment. mmap support is in my backlog, but I have no estimates.


After upgrade to 3.10 overlayed controls on video have hit offset so you can't click them unless you go below and to the right

Is there an existing issue for this?

[X] I have searched the existing issues
[X] I have read the guide to filing a bug

Steps to reproduce
I am targetting Flutter Web. Its fine on Android. Use a stack to overlay some controls, textformfield, textbuttons on top of VideoPlayer. The hit test for all the widgets is about 200 right and 200 below where they should be. I.e. you can't click IN the textboxes or click ON the textbutton anymore you have to click below and to the right. If you replace the VideoPlayer with a container everything goes back to normal. This only started happening after upgrading flutter to 3.10

Expected results
The HIT target for controls should be ontop of the controls not offset
Actual results
I have to click where the RED CROSS is in the image supplied to get the cursor to actually click in the TextFormField, the same for the button with the same offset. If I resize the browser window the offset changes.
https://github.com/flutter/flutter/assets/63472909/458d93d7-619d-4915-b9ec-711729c54c94
Code sample
Code sample
import 'package:flutter/material.dart';
import 'package:video_player/video_player.dart';

void main() {
  runApp(const MyApp());
}

class MyApp extends StatelessWidget {
  const MyApp({super.key});

  // This widget is the root of your application.
  @override
  Widget build(BuildContext context) {
    return MaterialApp(
      title: 'Flutter Demo',
      theme: ThemeData(
        colorScheme: ColorScheme.fromSeed(seedColor: Colors.deepPurple),
        useMaterial3: true,
      ),
      home: const MyHomePage(title: 'Flutter Demo Home Page'),
    );
  }
}

class MyHomePage extends StatefulWidget {
  const MyHomePage({super.key, required this.title});
  final String title;

  @override
  State<MyHomePage> createState() => _MyHomePageState();
}

class _MyHomePageState extends State<MyHomePage> {
  int _counter = 0;
  late VideoPlayerController _controller;

  @override
  void initState(){
    super.initState();
    _controller = VideoPlayerController.asset('502xp.mp4')
      ..initialize().then((_) {
        // Ensure the first frame is shown after the video is initialized, even before the play button has been pressed.
        setState(() {
          _controller.setVolume(0.0);
          _controller.play();
          _controller.setLooping(true);
        });
      });

  }

  void _incrementCounter() {
    setState(() {
      _counter++;
    });
  }

  @override
  Widget build(BuildContext context) {
    return Scaffold(
      appBar: AppBar(
        backgroundColor: Theme.of(context).colorScheme.inversePrimary,
        title: Text(widget.title),
      ),
      body: Stack(
        children: [
          Center(
            child: _controller.value.isInitialized
                ? SizedBox.expand(
                    child: FittedBox(
                      fit: BoxFit.cover,
                      child: SizedBox(
                        width: _controller.value.size.width,
                        height: _controller.value.size.height,
                        child: AspectRatio(
                          aspectRatio: _controller.value.aspectRatio,
                          child: VideoPlayer(_controller),
                        ),
                      ),
                    ),
                  )
                : Container(),
          ),
            Center(
            child: Column(
              mainAxisAlignment: MainAxisAlignment.center,
              children: <Widget>[
                const Text(
                  'You have pushed the button this many times:',
                ),
                Text(
                  '$_counter',
                  style: Theme.of(context).textTheme.headlineMedium,
                ),
                Container(
                  width: 200, // Set the specific width here
                  decoration: BoxDecoration(
                    color: Colors.white,
                    borderRadius: BorderRadius.circular(4.0),
                    border: Border.all(color: Colors.grey, width: 1.0),
                  ),
                  child: TextFormField(
                    decoration: const InputDecoration(
                      border: InputBorder.none,
                      contentPadding: EdgeInsets.all(8.0),
                    ),
                  ),
                )
              ],
            ),
          ),
        ]
      ),
      floatingActionButton: FloatingActionButton(
        onPressed: _incrementCounter,
        tooltip: 'Increment',
        child: const Icon(Icons.add),
      ), // This trailing comma makes auto-formatting nicer for build methods.
    );
  }
}

Screenshots or Video

Screenshots / Video demonstration
[Upload media here]

Logs
Logs
[Paste your logs here]

Flutter Doctor output
Doctor output
[√] Flutter (Channel stable, 3.10.0, on Microsoft Windows [Version 10.0.22621.1702], locale en-GB)
    • Flutter version 3.10.0 on channel stable at C:\Users\Ted\Documents\MY_Code\flutter
    • Upstream repository https://github.com/flutter/flutter.git
    • Framework revision 84a1e904f4 (4 days ago), 2023-05-09 07:41:44 -0700
    • Engine revision d44b5a94c9
    • Dart version 3.0.0
    • DevTools version 2.23.1

[√] Windows Version (Installed version of Windows is version 10 or higher)

[√] Android toolchain - develop for Android devices (Android SDK version 30.0.3)
    • Android SDK at C:\Users\Ted\AppData\Local\Android\sdk
    • Platform android-31, build-tools 30.0.3
    • Java binary at: C:\Program Files\Android\Android Studio\jre\bin\java
    • Java version OpenJDK Runtime Environment (build 11.0.12+7-b1504.28-7817840)
    • All Android licenses accepted.

[√] Chrome - develop for the web
    • Chrome at C:\Program Files (x86)\Google\Chrome\Application\chrome.exe

[√] Visual Studio - develop for Windows (Visual Studio Community 2022 17.1.3)
    • Visual Studio at C:\Program Files\Microsoft Visual Studio\2022\Community
    • Visual Studio Community 2022 version 17.1.32328.378
    • Windows 10 SDK version 10.0.19041.0

[√] Android Studio (version 2021.2)
    • Android Studio at C:\Program Files\Android\Android Studio
    • Flutter plugin can be installed from:
       https://plugins.jetbrains.com/plugin/9212-flutter
    • Dart plugin can be installed from:
       https://plugins.jetbrains.com/plugin/6351-dart
    • Java version OpenJDK Runtime Environment (build 11.0.12+7-b1504.28-7817840)

[√] VS Code (version 1.78.2)
    • VS Code at C:\Users\Ted\AppData\Local\Programs\Microsoft VS Code
    • Flutter extension version 3.64.0

[√] Connected device (3 available)
    • Windows (desktop) • windows • windows-x64    • Microsoft Windows [Version 10.0.22621.1702]
    • Chrome (web)      • chrome  • web-javascript • Google Chrome 113.0.5672.93
    • Edge (web)        • edge    • web-javascript • Microsoft Edge 113.0.1774.35

[√] Network resources
    • All expected network resources are available.

• No issues found!

This happens when HtmlElementView is transformed (video_player uses it under hood) #126754

Closing this issue in favor of https://github.com/flutter/flutter/issues/126754. @tedfire Please follow up on that issue for further updates. Thanks!


Setting to switch Enter & Shift - Enter behaviour

First of all, thank you for a great extension.
I have noticed when typing longer messages, especially which contain some formatted text that I want to include it gets tiring to keep pressing shift to include new lines in the message.
Would it be possible to provide a setting for switching the behaviour of Enter and Shift-Enter so that Enter without Shift would write a newline in the message and Shift-Enter would send it?

Hi @vipentti
Thanks for the feedback. I will add the option to disable keyboard shortcuts in the editor. It should in the next release.

This feature will be release in 1.5.3.
The config option can be accessed by vscode-openai.conversation-configuration.message-shortcuts
you can use the shortcut in the conversation panel


@service crashs homeassistant

After updating HA to the latest version (2023.8.3) from 2023.7, HA did not start.
I had to disable all pyscripts to get it to run at all.
After a lot of testing, it turns out that the @service statement is the problem.
The following statement runed from jupyter, leads to HA being unstable. HA seems to run, but no longer accepts new browser connections. I've to restart HA.
@service("halloworldtest.run")
def myTestTrigger():
    print("Hallo World")

2023-08-20 19:33:50.097 ERROR (MainThread) [homeassistant.components.websocket_api.http.connection] [140134801655744] Error handling message: Unknown error (unknown_error) <NAME REMOVED> from <IP REMOVED> (Mozilla/5.0 (X11; Linux x86_64; rv:109.0) Gecko/20100101 Firefox/115.0)
Traceback (most recent call last):
  File "/usr/src/homeassistant/homeassistant/components/websocket_api/decorators.py", line 26, in _handle_async_response
    await func(hass, connection, msg)
  File "/usr/src/homeassistant/homeassistant/components/websocket_api/commands.py", line 417, in handle_get_services
    payload = await _async_get_all_descriptions_json(hass)
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/homeassistant/homeassistant/components/websocket_api/commands.py", line 398, in _async_get_all_descriptions_json
    descriptions = await async_get_all_descriptions(hass)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/homeassistant/homeassistant/helpers/service.py", line 612, in async_get_all_descriptions
    translations = await translation.async_get_translations(
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/homeassistant/homeassistant/helpers/translation.py", line 318, in async_get_translations
    cached = await cache.async_fetch(language, category, components)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/src/homeassistant/homeassistant/helpers/translation.py", line 209, in async_fetch
    await self._async_load(language, components_to_load)
  File "/usr/src/homeassistant/homeassistant/helpers/translation.py", line 230, in _async_load
    raise int_or_exc
homeassistant.loader.IntegrationNotFound: Integration 'halloworldtest' not found.

I tried 2023.8.3 with your service example and it works fine for me.  I'm not sure why it is crashing in your case.

Same problem for me. HA 2023.8.3, core install on venv - Python 3.11.4
Exact same error as @littleyoda

can confirm the workaround of Kaptensanders.
@service works
@service("xxyz"), however, does not works.

Ok, the issue is this code homeassistant/helpers/service.py got added in  in 2023.8.0.  HASS now loads all translations for all services.  When an arbitrary service name isn't part of an associated integration, the loading of translations fails.  That causes async_get_all_descriptions() to fail.
I can't see a work around for this.  Perhaps it's a breaking change, and you can only create services in pyscript that are part of an existing integration (ie, top-level name)?

The issue is this code homeassistant/helpers/service.py got added in in 2023.8.0. HASS now loads all translations for all services. When an arbitrary service name isn't part of an associated integration, the loading of translations fails. That causes async_get_all_descriptions() to fail.
I can't see a work around for this. Perhaps it's a breaking change, and you can only create services in pyscript that are part of an existing integration (ie, top-level name)?

Actually naming a service still works if no non-existing integration is specified (top level).
Maybe this is actually more appropriate than inventing integrations that does not exist.
@service(f"service_name") # works and service name will become pyscript.service_name.

Breaking change maybe but an ok one. When you think about it It is maybe bad practice to refer to non-existing integrations. Not surprising that could lead to problems with HA.
@service("service_name") # works fine and the service will be pyscript.service_name


After updating to 4.0.1 Android crashes

After I updated to 4.0.1 I cannot sign in in Android anymore. It crashes without providing any error message and stack trace. The only thing that I get in VS is "An unhandled exception occured."
I'm also using Mobile Center crash report and this crash is not reported.
Tested that UWP works ok, have not tested iOS yet.
Current workaround: downgrade back to 4.0.0.

See #361, linked issue. Dependencies and method calls are scrambled around.

Tip: If you press Continue one time after an exception, the exception details will be printed to the Debug output. (May also be a debug logging setting to change to Detailed, but I forget which one).

Same problem here. LoginAsync() fails with "Method 'Android.Support.CustomTabs.CustomTabsIntent.LaunchUrl' not found."

I think this is somehow related to my issue. Im getting
Java.Interop.Tools.Diagnostics.XamarinAndroidException: error XA2006: Could not resolve reference to 'System.Void Android.Support.CustomTabs.CustomTabsIntent::LaunchUrl(Android.App.Activity,Android.Net.Uri)' (defined in assembly 'Microsoft.Azure.Mobile.Client, Version=<IP_ADDRESS>, Culture=neutral, PublicKeyToken=31bf3856ad364e35') with scope 'Xamarin.Android.Support.CustomTabs, Version=<IP_ADDRESS>, Culture=neutral, PublicKeyToken=null'. When the scope is different from the defining assembly, it usually means that the type is forwarded. ---> Mono.Cecil.ResolutionException: Failed to resolve System.Void Android.Support.CustomTabs.CustomTabsIntent::LaunchUrl(Android.App.Activity,Android.Net.Uri)
during build.
Im using Xamarin.Android.Support 25.3.1

Facing same issue as above when compiling in release mode. Debug mode is working fine.  Xamarin.Android.Support.CustomTabs is at v<IP_ADDRESS>.
How do i fix it ?

Use the client library 4.0.0 until 4.0.2 is released.

@aj-ja  We released the latest version of Zumo .NET client SDK v4.0.2 today which includes the fix of this issue, please find more information here.

great news, updated and no errors on Android anymore. Thanks!


Angular integration

ContentTools is really amazing, and maybe what I have been looking for.
But, question: we are working with an AngularJS driven project. We have a lot of directives, and I was wondering if it is possible to combine AngularJS and ContentTools.
This way I could call in directives, give them specific attributes, and fastly create complex content blocks in the page.
Do you feel it could be possible?
Greetings,
Maarten

@martyLauders that should be no problem, I can provide you with an example. But first, which version of Angular are you running on your project?

That would be amazing. I'm running 1.4.

Hi @martyLauders, I have created a demo which can check here: https://github.com/Cotidia/contenttools-angularjs
Basically, you can create a directive that will bind the DOM element to the editor. Then, the data will retrieve in the scope as $scope.regions.
angular.module('editorApp', [])
    .directive('ngEditor',  function(){

        function link(scope, element, attrs){

            editor = new ContentTools.EditorApp.get()
            editor.init('.editable', 'ng-editor')

            editor.bind('save', function(regions, autoSave){

                scope.$apply(function(){
                    scope.regions = regions;
                });

                // "regions" contains all the html for each editable regions
                // Now, "regions" can be saved and used as needed.

            })

        }

        return {
            link: link
        }

    })

In the HTML, you can simply use the directive as follows:

Some content...

Another content....

I hope that helps.

Awesome work @guillaumepiot :+1:

Ok, guillaumepiot, that is already incredible :)
Now, what I want to achieve is that, when I add in an attribute, that is linked to a directive, that it renders the directive. For example:

And my directive would be:
directive('testDirective', function() {
return {
        template: '
TEST
',
        restrict: 'A',
        replace: true,
        link: function(scope) {
           console.log('The directive loaded');
        }
    };
})

Do you think this is possible? I can't seem to get it to work.
I'd put up a quick demo, but am on the road, sorry!

@martyLauders Where would the directive be inserted? Would it be within an editable region or would the directive actaully be an editable region (e.g contain editable content,
, , etc.)
Could you provide a bit more detail on what you're trying to achieve?

The region would be within an editable region. What I am trying to achieve is that I could add just a , add an attribute, for example: parrallax=true, and the directive would then work on this div.
But now I have the feeling that angularjs doesn't notice the change in attributes.
Does it make sense what I am asking / trying to do?

Hi @martyLauders sorry my knowledge of Angular is really limiting me here. The problem here I think is that if Angular updates the contents of an editable region while it's being edited then the editor and angular will become out of sync (e.g the editor wont know about the new element added by angular).
If you can provide an example of the HTML before and after that you're trying to achieve I might be able to provide a solution that's not specific to angular?

:+1: Thread helped a lot for clarifying some issues

saved event listener is getting called multiple times when I save one field , any solution for this ?

@iit2011081 this sounds like the issue is caused by the editor being initialized multiple times without being destroyed?

@anthonyjb this is the whole code which I have written . Please see where I am going wrong.
app.directive('ngEditor',  function(){

    function link(scope, element, attrs){

        // Initialise the editor
        scope.editor = new ContentTools.EditorApp.get();
        scope.editor.init('[ng-editor]', 'ng-editor');

        // Bind a function on editor save
        if (scope.editor._bindings['saved'] && scope.editor._bindings['saved'].length > 0) {

        }
        scope.editor.addEventListener('saved', function (ev) {
            scope.$apply(function(){
                scope.regions = ev.regions;
                console.log(scope.regions);
                console.log(ev.detail().regions);
            });
        });
    }
    return {
        link: link
    }
})


druid refactor

Coverage decreased (-0.03%) to 91.681% when pulling 6e88e835d363317ec11a836fe9dacf3bde5c141c on druid_refactor into e5b982e84c6f1bc97d5d492961db314463f400f2 on develop.

Coverage decreased (-0.03%) to 91.681% when pulling 6e88e835d363317ec11a836fe9dacf3bde5c141c on druid_refactor into e5b982e84c6f1bc97d5d492961db314463f400f2 on develop.