Pretrained Model

Hi,
Are you planning on releasing a pretrained model anytime?
Thanks

I also need the model, you must democratize AI as not everybody got money to spend on GPU's. I can't train it, please release a free SOTA model so taht we can make TTS industrially.

To be fair, even if they do release it. I don't think it can be run on anything less than a 4090. And if you have a 4090, you can train it yourself.

Then again, we could try use BitsandBytes

I also need the model, you must democratize AI as not everybody got money to spend on GPU's. I can't train it, please release a free SOTA model so taht we can make TTS industrially.

They 'must' do nothing honestly. Rather be thankful that this is implemented and released under an open source license already.
Also there is a model that has been shared here: https://github.com/lifeiteng/vall-e/issues/58#issuecomment-1483700593

True. But still, a full pretrained model would be nice.

I can try to train it. But I have some queries:

How much memory and time can it take to train the model?
Can I mix languages such as French, Spanish, and English from Common Voice?

@RahulBhalley The author of the repo said he trained it on a single gpu(so something like a 3090?). In terms of time I'm not sure. But #58 said he trained it for 4 days on 8 A100s. And the dataset for that was LibriTTS(600 hours), whereas the original Vall-E was trained on 60,000 hours. But I'm not sure how @lifeiteng managed to reproduce the paper's results so quickly only using a single GPU, @lifeiteng could you let us know how long it took.
In terms of mixing languages. I don't see why not as long as the training data is processed into phonemes. But this may affect performance. I would suggest maybe train it in English first so that you can reproduce the original results. Then maybe finetune it for other languages using LoRA?

Thanks for the suggestions!

@RahulBhalley The author of the repo said he trained it on a single gpu(so something like a 3090?). In terms of time I'm not sure. But https://github.com/lifeiteng/vall-e/issues/58 said he trained it for 4 days on 8 A100s. And the dataset for that was LibriTTS(600 hours), whereas the original Vall-E was trained on 60,000 hours. But I'm not sure how @lifeiteng managed to reproduce the paper's results so quickly only using a single GPU, @lifeiteng could you let us know how long it took.

Maybe I'll try 4090 or something. About A100, 80GB VRAM seems like an overkill. I'd rather try A100 40GB or 4090 (whichever turns out to be faster). That'll be very helpful to know @lifeiteng's experience with training on single GPU.

In terms of mixing languages. I don't see why not as long as the training data is processed into phonemes. But this may affect performance. I would suggest maybe train it in English first so that you can reproduce the original results. Then maybe finetune it for other languages using LoRA?

Sure. I don't know much about LoRA yet (got to know about it when DreamBooth-ing earlier). Still have to read the paper.

@RahulBhalley using bnb is super easy
Import bitsandbytes and BnB
optim_g = bnb.optim.AdamW(...)

You can use it as a drop in replacement.

I used it for finetuning vits and it saved me almost 3gb of vram

Look that this statement.
https://github.com/lifeiteng/vall-e/blob/27c0667e0c3a18fd9074863a88a88094c6edbb2d/valle/bin/trainer.py#L148
It uses ScaledAdam (a custom implementation from scratch).
https://github.com/lifeiteng/vall-e/blob/27c0667e0c3a18fd9074863a88a88094c6edbb2d/valle/modules/optim.py#L129
But scaled Adam doesn't exist in bitsandbytes. I don't know if using AdamW from bitsandbytes will converge VALL-E less.

I see. I mean see how much vram you save. If it's only something like 3gb. Is it really worth? The point of using 8bit optimisers is mainly for finetuning so we can fit a bigger batch size. If it's not causing OOM maybe bitsandbytes isn't needed

Have you had a look at the other Vall-E implementation? It uses Deepspeed.

Btw the original paper used AdamW

Also for multiple languages, Vall-E X exists but no implementation. And Natural Speech 2 seems very promising. But implementation is on the way by lucidrains

Yeah Natural Speech 2 is amazing. I'm keeping a close eye on the implementation by lucidrains. It's being sponsored by Stability so if we're lucky he may provide a pretrained model

Btw if you want some more insight, there is a long thread here about mrq training vall e
https://git.ecker.tech/mrq/ai-voice-cloning/issues/152
Apparently on a 4070 ti lol

Haha, okay.

This is nice @RuntimeRacer . Do you know what the differences are from the paper?

@nivibilla As far as I am aware they only trained the Model with LibriLight in the Paper, which consisted of 60k pure english speech. I didn't do a breakdown of hours per language for languages I downloaded, but you can check this here, it is using CommonVoice 13: https://commonvoice.mozilla.org/en/datasets
I was more curious to see if the model is actually capable to learn speech-related dialects and apply these also on different languages when for example using a japanese speaker + text as a prompt but letting it generate audio for an english sentence, which I found https://github.com/serp-ai/bark-with-voice-clone being capable of, however that model proved to be not very robust on this task (or cloning voices from arbitrary samples in general).

@RuntimeRacer I see. Yeah I would try train it myself if I can. I'm thinking of maybe adapting the code to Deepspeed/accelerate so that I can do nvme offloading. It will take a painstakingly long time but at least I can train. But it will converge so slowly

@nivibilla Yes having Accelerator for this repo would be awesome; I was considering looking into that myself; but since I am pretty busy these days I'm currently rather hoping that folks here will be able to fix the multi GPU issues faster than migrating the training code 😅
At least it seems someone is aware now after I highlighted there's still an issue: https://github.com/lifeiteng/vall-e/issues/86

Tensorboard of my CV Training so far - It didn't even iterate through the first epoch yet:

@RuntimeRacer those are some Interesting graphs. I wonder why it converges so quickly.

Train goes down and valid goes up indicates overfitting but surely the dataset is not that small

To be fair it's almost 300k steps. Did you try inference?

That's my commandline used for training:
python3 bin/trainer.py --max-duration 60 --filter-min-duration 0.5 --filter-max-duration 14 --train-stage 1 --num-buckets 6 --dtype bfloat16 --save-every-n 5000 --valid-interval 5000 --model-name valle --share-embedding true --norm-first true --add-prenet false --decoder-dim 1024 --nhead 16 --num-decoder-layers 12 --prefix-mode 1 --base-lr 0.05 --warmup-steps 200 --average-period 0 --num-epochs 200 --start-epoch 1 --start-batch 160000 --accumulate-grad-steps 4 --world-size 1 --exp-dir exp/valle
Regarding overfitting, I had to change --max-duration to 60 at 160k steps because I hit an OOM error with the dataset loader. Not sure if that's the case here, but I experienced 'movements' in loss bias with changed dataset sizes and world sizes also with other models I trained previously, so that might explain the upshift in loss mean at 160k.
Regarding the valid loss jumps I assume the model still needs to generalize on new tokens or speaker characteristics because it didn't loop through a whole epoch yet. But that's just blind guessing from a dev with no theoretical background in data science.

To be fair it's almost 300k steps. Did you try inference?

No, I understood I need to train AR model until I reach a definite minimum for valid loss, and then train the NAR model based on that checkpoint first. But I can try ofc and see whether it gives us anything except static

@RuntimeRacer thanks for looking into it. I will try look at the code tomorrow and see if it's viable to migrate to accelerate. Deepspeed is probably not needed as you have enough gpus.

@RuntimeRacer thanks for the update! I had a quick look at the code. It seems a bit hard but it should be doable to implement accelerate. I can replace DDP with accelerate but im not sure how that will affect saving and loading checkpoint and validation. As this happens on rank 0.

Also there's a lot of code where he uses module. It may be just easier to just use the other vall e implementation which has Deepspeed.

@nivibilla yes I actually found the other implementation first before I found this one; but I eventually went with this one here because it's still being actively maintained and I liked the fact that it just builds on top of lhotse / icefall (which I didn't know before actually), because this allows for a standardized setup with arbitrary ML datasets as long as they're part of lhotse. And I probably hoped the Multi-GPU Training to work properly. 😂

@RuntimeRacer yeah the other author is AWOL. It would have been nice if the repo was built from the ground up in accelerate. I feel like I will break too many things while trying to implement accelerate. If the native multi GPU works then it's probably not worth the hassle. Liefteng said in the issue you mentioned that it's being fixed so hopefully soon.

@RuntimeRacer there is 1 more implementation (https://git.ecker.tech/mrq/vall-e) it's a fork of the enhuiz one. With some improvements like using bitsandbytes. It doesn't have the icefall and lhoste but it's better than the other one

Hmm yeah that seems like a better curated one. Also good hint in their docs to apply SGDR (https://markkhoffmann.medium.com/exploring-stochastic-gradient-descent-with-restarts-sgdr-fa206c38a74e) while training; I already made good experiences with that method when I trained Tacotron some time ago.
I might give it a try; just not sure when. Also I never heard of https://git.ecker.tech/ btw 😂

Yeah, mrq says he doesn't trust GitHub so he uses that instead. It's essentially a clone so idk what the difference is lol

@nivibilla I just sat down for an hour and did this: https://github.com/lifeiteng/vall-e/pull/115
Didn't have a chance to test yet, since I want the current training Epoch running on one GPU to finish first (for the first time, after I stripped non-latin alphabet languages and it finally seems to have no errors anymore)
But maybe you want to check the code for potential issues in the meantime. :-)

Amazing. I still don't get why phonemizing is such a vram hog. But anyway. At least it works

Accelerate was a lot bigger task than I expected due to Lhotse limitations. However I was able to fix DDP: https://github.com/lifeiteng/vall-e/pull/116


Crash Issue

Since bloodcat is offline I switched to the other mirror in the settings (osu.uu.gl) and was going to download a bigger set of maps (about 1,5k) when first, a "b-baka"-Exception window popped up mutliple times, then a Notepad with exception infos opened and the program crashed. I tried this 3 times with the same result. Here are the details:
===== osu!Sync Crash | 2017-09-07 01:53:37   =====
// Information
An exception occured in osu!Sync. If this problem persists please report it using the Feedback-window, on GitHub or on the osu!Forum.
When reporting please try to describe as detailed as possible what you've done and how the applicationen reacted.
GitHub: http://j.mp/1PDuDFp   |   osu!Forum: http://j.mp/1PDuCkK
// Configuration
{"application":{"isElevated":"False","lastUpdateCheck":"20170907011634","version":"<IP_ADDRESS>"},"config":{"downloadMirror":"2","updateInterval":"3"},"language":{"code":"de_DE"},"system":{"cultureInfo":"de-DE","is64bit":"True","operatingSystem":"6.3.9600.0"}}
// Exception
System.NotSupportedException: WebClient unterstützt keine gleichzeitigen E/A-Vorgänge.
bei System.Net.WebClient.ClearWebClientState()
bei System.Net.WebClient.DownloadFileAsync(Uri address, String fileName, Object userToken)
bei osu_Sync.MainWindow.Importer_DownloadBeatmap()
bei osu_Sync.MainWindow.Importer_Downloader_ToNextDownload()
bei System.ComponentModel.AsyncCompletedEventHandler.Invoke(Object sender, AsyncCompletedEventArgs e)
bei System.Net.WebClient.OnDownloadFileCompleted(AsyncCompletedEventArgs e)
bei System.Windows.Threading.ExceptionWrapper.InternalRealCall(Delegate callback, Object args, Int32 numArgs)
bei System.Windows.Threading.ExceptionWrapper.TryCatchWhen(Object source, Delegate callback, Object args, Int32 numArgs, Delegate catchHandler)

A similar issue occurs when there are is no database to synchronize beatmaps from, and no beatmaps within the songs folder
Details can be found here: https://hastebin.com/jimusomagu.scala

@Kontorted Thank you for reporting this issue. However, judging by your report I'd say that this is a separate bug. Please refer to the newly created issue #45.


equals return true for any anonymous function.

Currently, (equals (lambda () 1) (lambda () 2)) returns T, which means each hook could have at most one anonymous handler. Is this intended behavior?

If you're the author of commit 9db6a7375e621a96d1df3705c5373419003ae474 then you're in the best position to answer.
It doesn't seem sensible that it returns T to me.


processing multiline tf-runner logs

It appears that tf-runner logs are multi-line and don't have a specific format. When shipped to a logging service, multiline logs are not processed by default which makes it difficult to read thru. Is there a setting I'd need to use to render a diff format for json logs
TF controller version - v0.15.1
▶ kubectl logs -f test-tf-runner
2023/11/22 16:51:56 Starting the runner... version  sha
{"level":"info","ts":"2023-11-22T16:52:10.941Z","logger":"runner.terraform","msg":"preparing for Upload and Extraction","instance-id":""}
{"level":"info","ts":"2023-11-22T16:52:10.961Z","logger":"runner.terraform","msg":"setting envvars","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.961Z","logger":"runner.terraform","msg":"getting envvars from os environments","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.962Z","logger":"runner.terraform","msg":"setting up the input variables","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.962Z","logger":"runner.terraform","msg":"mapping the Spec.Values","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.962Z","logger":"runner.terraform","msg":"mapping the Spec.Vars","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.963Z","logger":"runner.terraform","msg":"mapping the Spec.VarsFrom","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.974Z","logger":"runner.terraform","msg":"generating the template founds"}
{"level":"info","ts":"2023-11-22T16:52:10.974Z","logger":"runner.terraform","msg":"main.tf.tpl not found, skipping"}
{"level":"info","ts":"2023-11-22T16:52:10.976Z","logger":"runner.terraform","msg":"initializing","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{"level":"info","ts":"2023-11-22T16:52:10.976Z","logger":"runner.terraform","msg":"mapping the Spec.BackendConfigsFrom","instance-id":"9a349873-090e-45e8-b71b-012dcfe6c00a"}
{
  "terraform_version": "1.3.9",
  "platform": "linux_amd64",
  "provider_selections": {},
  "terraform_outdated": true
}
Upgrading modules...
Downloading git::https://github.com/oracle-terraform-modules/terraform-oci-oke.git?ref=239dad6 for oke...
- oke in .terraform/modules/oke
- oke.bastion in .terraform/modules/oke/modules/bastion
- oke.cluster in .terraform/modules/oke/modules/cluster
- oke.extensions in .terraform/modules/oke/modules/extensions
- oke.iam in .terraform/modules/oke/modules/iam
- oke.network in .terraform/modules/oke/modules/network
- oke.operator in .terraform/modules/oke/modules/operator
- oke.utilities in .terraform/modules/oke/modules/utilities
Downloading registry.terraform.io/oracle-terraform-modules/vcn/oci 3.5.3 for oke.vcn...
- oke.vcn in .terraform/modules/oke.vcn
- oke.vcn.subnet in .terraform/modules/oke.vcn/modules/subnet
- oke.workers in .terraform/modules/oke/modules/workers

Initializing the backend...

Successfully configured the backend "kubernetes"! Terraform will automatically
use this backend unless the backend configuration changes.

Initializing provider plugins...
- Finding hashicorp/null versions matching ">= 3.2.1"...
- Finding oracle/oci versions matching ">= 4.67.3, >= 4.115.0"...
- Finding hashicorp/random versions matching ">= 3.4.3"...
- Finding hashicorp/time versions matching ">= 0.9.1"...
- Finding hashicorp/cloudinit versions matching ">= 2.2.0"...
- Finding hashicorp/http versions matching ">= 3.2.1"...
- Finding hashicorp/helm versions matching ">= 2.9.0"...
- Installing hashicorp/random v3.5.1...
- Installed hashicorp/random v3.5.1 (signed by HashiCorp)
- Installing hashicorp/time v0.9.1...
- Installed hashicorp/time v0.9.1 (signed by HashiCorp)
- Installing hashicorp/cloudinit v2.3.2...
- Installed hashicorp/cloudinit v2.3.2 (signed by HashiCorp)
- Installing hashicorp/http v3.4.0...
- Installed hashicorp/http v3.4.0 (signed by HashiCorp)
- Installing hashicorp/helm v2.11.0...
- Installed hashicorp/helm v2.11.0 (signed by HashiCorp)
- Installing hashicorp/null v3.2.2...
- Installed hashicorp/null v3.2.2 (signed by HashiCorp)
- Installing oracle/oci v5.21.0...
- Installed oracle/oci v5.21.0 (signed by a HashiCorp partner, key ID 1533A49284137CEB)

Partner and community providers are signed by their developers.
If you'd like to know more about provider signing, you can read about it here:
https://www.terraform.io/docs/cli/plugins/signing.html

Terraform has created a lock file .terraform.lock.hcl to record the provider
selections it made above. Include this file in your version control repository
so that Terraform can guarantee to make the same selections by default when
you run "terraform init" in the future.

Terraform has been successfully initialized!
{"level":"info","ts":"2023-11-22T16:52:20.017Z","logger":"runner.terraform","msg":"workspace select"}
Workspace "<redacted>" already exists
{"level":"info","ts":"2023-11-22T16:52:21.970Z","logger":"runner.terraform","msg":"workspace new:, workspace \"<redacted>\" already exists"}
Switched to workspace "<redacted>".

@chanwit is it possible to jsonify all of the output? This will make log forwarding to services more accessible and readable.

Hi @eatwithforks
technically possible if it's allowed by the underlying binary.


pfe-accordion animation

The last item in an accordion is still experiencing an animation issue. The bottom border collapses before the animation has time to complete.

Can you confirm if this is still an issue?

This appears to no longer be an issue, closing.


Filter which repos should be checked

Description
User should be able to choose which repos or group of repos should be verified
To be defined

Which kind of filter should it be? By name? By teams? By code owners?
How is the filter going to be available? in GUI? As a prompt? As a command?
Should return a report to the user? How? In terminal? By email? As issue in the repos?
If it fails, should proceed as in the automated check, creating an issue in the failed repo? Or just to the user who want to know?

Acceptance criteria

[] should be able to lint and give a feedback only about the chosen repos

This issue is being closed in favor of #42


cliconfig: credentials: support getting all auths

Fixes #20830
docker build broke after the client credentials store PR because building FROM images that are on a registry which requires auth gets you:
$ docker --config `pwd` build .

Sending build context to Docker daemon 3.072 kB
Step 1 : FROM myregistrydomain.com:5001/busybox
Pulling repository myregistrydomain.com:5001/busybox
Error: image busybox not found

myregistrydomain.com:5001/busybox is on the registry but the registry has auth, credentials aren't sent to the registry because the credentials will send only an empty config.json (the one configured with --config which just has "credsStore":"something")
after my patch:
$ docker -D --config `pwd` build .

Sending build context to Docker daemon 3.072 kB
Step 1 : FROM myregistrydomain.com:5001/busybox
latest: Pulling from busybox
Digest: sha256:c431ca187a895d44e25947d55d51fd54709a28f7b75b48e3a68cd2106b72f007
Status: Downloaded newer image for myregistrydomain.com:5001/busybox:latest
 ---> 3240943c9ea3
Step 2 : RUN echo "foobar"
 ---> Using cache
 ---> 24458a2b10cc
Successfully built 24458a2b10cc

Signed-off-by: Antonio Murdaca<EMAIL_ADDRESS>

This looks very scary, tbh, and hard to do correctly. There is no way to "get all" credentials from native stores, and I'd argue that it's even a potential security vulnerability.

Just a clarification. The fact that you can get the credentials from my mock script doesn't mean that you can do it with the OS X Keychain or Windows Credentials Provider.

@calavera isn't cat $HOME/.docker/config.json also?

Just a clarification. The fact that you can get the credentials from my mock script doesn't mean that you can do it with the OS X Keychain or Windows Credentials Provider.

I get this of course.

Also, I suppose docker build is broken also for images on the hub that are private

What you can do is to get the list of serverURLS from the file and then run a get command per server.

What you can do is to get the list of serverURLS from the file and then run a get command per server.

alright

@calavera updated to issue multiple get requests to implement the getAll functionality - added unit and integration-cli tests and now docker build works as expected. (I removed the design review label but feel free to put it back if you think this is not ready to code review)

LGTM but there is a test failing:
16:08:20 FAIL: docker_cli_build_test.go:6593: DockerRegistryAuthSuite.TestBuildWithExternalAuth

I don't even know how it passed after #20565 was merged :pensive:

fixed

:raised_hands:

ping @cpuguy83 @vdemeester

This is really bad that we have to do this :( Since we already have a hack for docker content trust to read the FROM line before sending it to the server, can't we do something similar and send only those auths?

Since we already have a hack for docker content trust to read the FROM line before sending it to the server, can't we do something similar and send only the auths associated with that image?

can we rather fix the original issue instead of introducing another hack? (I haven't seen it though)
otherwise, I still prefer this approach

@tiborvass this https://github.com/docker/docker/blob/master/api/client/build.go#L318 ?

I'm ok with doing something like that to just resolve the correct auth config. @calavera thoughts?

@runcom fixing the original issue is client side builder.

@runcom wait how did we handle this before the credentials PR? We would just send the whole auth in dockercfg to the daemon?

@runcom wait how did we handle this before the credentials PR? We would just send the whole auth in dockercfg to the daemon?

correct

I'm reworking the patch to remove GetAll from the interface since it's only needed for build (which will somehow have a way to resolve just the auth config needed for the FROM image)

TBH, I rather solve the issue, like @runcom than changing the design. Let's open an issue to discuss the design if we want to, but not hold bugs in the code because we don't like how our past selves designed it.

this change LGTM.

LGTM 🐭
@calavera creating the issue ?

@calavera you're right, but @tiborvass and I would rather avoid adding GetAll to the interface (even if this code it's not yet released) so he thought we could have the GetAll method just as a utility functions in utils (nothing huge will change from what I've already implemented here and we won't clutter the interface). If you think this can be merged as is I'd make a follow up PR to remove that method from the interface.

I think having GetAll in the interface makes sense.

Fair enough

alright

this should be ready to merge then


Bootstrap 5 carousel will not autoplay if added to the DOM after page load.

Prerequisites

[X] I have searched for duplicate or closed issues
[X] I have validated any HTML to avoid common problems
[X] I have read the contributing guidelines

Describe the issue
Using ajax, we add the carousel html to the page, after the page has been loaded.
The carousel does not autoplay.
Reduced test cases
Add to the bootstrap documentation page the steps to have the carousel autoplay when the html is inserted after pageload.
What operating system(s) are you seeing the problem on?
Windows
What browser(s) are you seeing the problem on?
Chrome
What version of Bootstrap are you using?
v5.1

Thanks for suggesting this enhancement, @Kembreg1405. While I see the value in it, it seems to address a rather specific use case. Personally, I'm hesitant to include this information as it might risk overcomplicating the documentation. I'd love to hear feedback from the community or core team members on this.

The info is vertical and grouped in sections.
People can skip the parts that are not relevant.
I speak literally 2 days getting this to work.
An example would have helped me with this.
A text-only carousel is probably more relevant than "Individual .carousel-item interval" which is already one of the examples on the page.
Suggest adding 2 new examples.
Text only
With html insertion after page load

I spent literally 2 days getting this to work.
An example would have helped me with this.

Could you maybe share this example here for people finding this issue but then not finding the answer?


'permissions' cannot be applied to '(groovy.lang.Closure)' with Bukkit Groovy

Hi, using the plugin at the 0.5.0 version with Bukkit Groovy, running into this issue when adding permissions :

Is there also an error at runtime or just in the IDE? IDE support is generally quite bad for Groovy, use Kotlin DSL if you want proper IDE support.

Oh yes, I forgot to specify that. The warning seems to be only on the IDE side, the file is generated fine. So it's an interpretation issue between Gradle & the IDE? Seriously.. Haha
If I use Kotlin DSL, I need to have a Kotlin plugin and not a Java one, right?

The language you use for the Gradle build script is independent of what you build with it. You can have a kotlin-dsl Gradle build script that builds a Java-only project.

Ok great, thanks for the info!


Unable to use MySQL database

The line at https://github.com/projecthydra-labs/hyrax/blob/master/app/services/hyrax/workflow/permission_query.rb#L261 seems to be one that causes the query to fail when using MySQL as the Hyrax database.
 ActiveRecord::StatementInvalid (Mysql2::Error: You have an error in your SQL syntax; check the
manual that corresponds to your MySQL server version for the right syntax to use near 'integer)
 FROM `sipity_agents` WHERE (`sipity_agents`.`id` IN (SELECT `sipity_wor' at line 1: SELECT
 `users`.* FROM `users` WHERE `users`.`id` IN (SELECT CAST(`sipity_agents`.`proxy_for_id` AS
 integer) FROM `sipity_agents` WHERE (`sipity_agents`.`id` IN (SELECT
 `sipity_workflow_responsibilities`.`agent_id` FROM `sipity_workflow_responsibilities` WHERE
 `sipity_workflow_responsibilities`.`workflow_role_id` IN (SELECT `sipity_workflow_roles`.`id`
FROM `sipity_workflow_roles` WHERE `sipity_workflow_roles`.`role_id` IN (3))) OR
`sipity_agents`.`id` IN (SELECT `sipity_entity_specific_responsibilities`.`agent_id` FROM
`sipity_entity_specific_responsibilities` WHERE
`sipity_entity_specific_responsibilities`.`workflow_role_id` IN (SELECT `sipity_workflow_roles`.`id`
FROM `sipity_workflow_roles` WHERE `sipity_workflow_roles`.`role_id` IN (3)) AND
`sipity_entity_specific_responsibilities`.`entity_id` = '2')) AND `sipity_agents`.`proxy_for_type` =
'User')):

You might want to try:
SELECT CAST(`sipity_agents`.`proxy_for_id` AS SIGNED) ...

This is the line:
https://github.com/projecthydra-labs/hyrax/blob/master/app/services/hyrax/workflow/permission_query.rb#L261

Casting to signed breaks Postgres. See below:

PG::UndefinedObject: ERROR: type "signed" does not exist LINE 1: ...IN (SELECT CAST("sipity_agents"."proxy_for_id" AS signed) FR... ^ : SELECT "users".* FROM "users" WHERE "users"."id" IN (SELECT CAST("sipity_agents"."proxy_for_id" AS signed) FROM "sipity_agents" WHERE ("sipity_agents"."id" IN (SELECT "sipity_workflow_responsibilities"."agent_id" FROM "sipity_workflow_responsibilities" WHERE "sipity_workflow_responsibilities"."workflow_role_id" IN (SELECT "sipity_workflow_roles"."id" FROM "sipity_workflow_roles" WHERE "sipity_workflow_roles"."role_id" IN (2))) OR "sipity_agents"."id" IN (SELECT "sipity_entity_specific_responsibilities"."agent_id" FROM "sipity_entity_specific_responsibilities" WHERE "sipity_entity_specific_responsibilities"."workflow_role_id" IN (SELECT "sipity_workflow_roles"."id" FROM "sipity_workflow_roles" WHERE "sipity_workflow_roles"."role_id" IN (2)) AND "sipity_entity_specific_responsibilities"."entity_id" = '1')) AND "sipity_agents"."proxy_for_type" = 'User')

I've solved this, one way, with an update to the PR this is being addressed with.


pythonPackages.locustio: 0.7.2 -> 0.8 &  pythonPackages.gevent: 1.1.2 -> 1.2.2

Motivation for this change
Adds python 3 support and unbreaks pythonPackages.locustio in hydra.
Things done

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

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

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

@vcunat @fpletz I closed my PR because this ones newer and does the same thing. What kind of additional testing do you think needs done for upgrading gevent?

I have no idea about gevent.  /cc maintainer @bjornfor.

gevent is a common package. Last time I checked its bump, it broke quite a bit of things. We can check again.

@FRidh It looks like these changes are pushed to master by you. Was this intentional?
See commits https://github.com/NixOS/nixpkgs/commit/4ae5f314037867369c2b0e38a7587d2db393268f and https://github.com/NixOS/nixpkgs/commit/879f23ea1b2b7283c7cc4ad0606793a3b3b42009

@adisbladis ah, yes, it was. I had pushed them to python-unstable to test them and all looked well. I forgot to mention i there.


inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:480:35:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:480:35: note: '<anonymous>' declared here
  480 |                     result[n] = op(Ax[RC*A_pos + n], 0);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:493:35:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:493:35: note: '<anonymous>' declared here
  493 |                     result[n] = op(0, Bx[RC*B_pos + n]);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:508:31:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:508:31: note: '<anonymous>' declared here
  508 |                 result[n] = op(Ax[RC*A_pos + n], 0);
      |                             ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:521:31:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:521:31: note: '<anonymous>' declared here
  521 |                 result[n] = op(0,Bx[RC*B_pos + n]);
      |                             ~~^~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:480:35:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:480:35: note: '<anonymous>' declared here
  480 |                     result[n] = op(Ax[RC*A_pos + n], 0);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:493:35:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:493:35: note: '<anonymous>' declared here
  493 |                     result[n] = op(0, Bx[RC*B_pos + n]);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:508:31:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:508:31: note: '<anonymous>' declared here
  508 |                 result[n] = op(Ax[RC*A_pos + n], 0);
      |                             ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:521:31:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:521:31: note: '<anonymous>' declared here
  521 |                 result[n] = op(0,Bx[RC*B_pos + n]);
      |                             ~~^~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:480:35,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:480:35: note: '<anonymous>' declared here
  480 |                     result[n] = op(Ax[RC*A_pos + n], 0);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:493:35,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:493:35: note: '<anonymous>' declared here
  493 |                     result[n] = op(0, Bx[RC*B_pos + n]);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:508:31,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:508:31: note: '<anonymous>' declared here
  508 |                 result[n] = op(Ax[RC*A_pos + n], 0);
      |                             ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T maximum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:521:31,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:39:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   39 |         return std::max(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = maximum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:521:31: note: '<anonymous>' declared here
  521 |                 result[n] = op(0,Bx[RC*B_pos + n]);
      |                             ~~^~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:480:35,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:480:35: note: '<anonymous>' declared here
  480 |                     result[n] = op(Ax[RC*A_pos + n], 0);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:493:35,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:493:35: note: '<anonymous>' declared here
  493 |                     result[n] = op(0, Bx[RC*B_pos + n]);
      |                                 ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:508:31,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:508:31: note: '<anonymous>' declared here
  508 |                 result[n] = op(Ax[RC*A_pos + n], 0);
      |                             ~~^~~~~~~~~~~~~~~~~~~~~
In member function 'T minimum<T>::operator()(const T&, const T&) const [with T = complex_wrapper<long double, npy_clongdouble>]',
    inlined from 'void bsr_binop_bsr_canonical(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:521:31,
    inlined from 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]' at ../scipy/sparse/sparsetools/bsr.h:585:32:
../scipy/sparse/sparsetools/util.h:46:29: warning: '*(double*)((char*)&<unnamed> + offsetof(const complex_wrapper<long double, npy_clongdouble>,complex_wrapper<long double, npy_clongdouble>::complex.npy_clongdouble::_Val[0]))' may be used uninitialized [-Wmaybe-uninitialized]
   46 |         return std::min(x, y);
      |                             ^
../scipy/sparse/sparsetools/bsr.h: In function 'void bsr_binop_bsr(I, I, I, I, const I*, const I*, const T*, const I*, const I*, const T*, I*, I*, T2*, const bin_op&) [with I = long long int; T = complex_wrapper<long double, npy_clongdouble>; T2 = complex_wrapper<long double, npy_clongdouble>; bin_op = minimum<complex_wrapper<long double, npy_clongdouble> >]':
../scipy/sparse/sparsetools/bsr.h:521:31: note: '<anonymous>' declared here
  521 |                 result[n] = op(0,Bx[RC*B_pos + n]);
      |                             ~~^~~~~~~~~~~~~~~~~~~~
[845/1386] Compiling C++ object scipy/spatial/_ckdtree.cp312-win_amd64.pyd.p/meson-generated__ckdtree.cpp.obj
In file included from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/ufuncobject.h:4,
                 from scipy/spatial/_ckdtree.cp312-win_amd64.pyd.p/_ckdtree.cpp:1243:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h: In function 'npy_longdouble npy_creall(npy_clongdouble)':
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h:405:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
  405 |     return ((longdouble_t *) &z)[0];
      |            ~^~~~~~~~~~~~~~~~~~~~
scipy/spatial/_ckdtree.cp312-win_amd64.pyd.p/_ckdtree.cpp: In function 'PyObject* __Pyx_Import(PyObject*, PyObject*, int)':
scipy/spatial/_ckdtree.cp312-win_amd64.pyd.p/_ckdtree.cpp:44476:48: warning: comparing the result of pointer addition '(((const char*)"scipy.spatial._ckdtree") + 5)' and NULL [-Waddress]
44476 |             if (strchr(__Pyx_MODULE_NAME, '.') != NULL) {
      |                                                ^
[921/1386] Compiling C object scipy/integrate/_vode.cp312-win_amd64.pyd.p/meson-generated__vodemodule.c.obj
In file included from scipy/integrate/_vode.cp312-win_amd64.pyd.p/_vodemodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[926/1386] Compiling C object scipy/integrate/_lsoda.cp312-win_amd64.pyd.p/meson-generated__lsodamodule.c.obj
In file included from scipy/integrate/_lsoda.cp312-win_amd64.pyd.p/_lsodamodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[934/1386] Compiling C object scipy/integrate/_dop.cp312-win_amd64.pyd.p/meson-generated__dopmodule.c.obj
In file included from scipy/integrate/_dop.cp312-win_amd64.pyd.p/_dopmodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[941/1386] Compiling C object scipy/integrate/_test_odeint_banded.cp312-win_amd64.pyd.p/meson-generated__test_odeint_bandedmodule.c.obj
In file included from scipy/integrate/_test_odeint_banded.cp312-win_amd64.pyd.p/_test_odeint_bandedmodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[967/1386] Compiling C++ object scipy/signal/_max_len_seq_inner.cp312-win_amd64.pyd.p/meson-generated__max_len_seq_inner.cpp.obj
In file included from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/complex.hpp:180,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/numpy/complex64.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/ndarray.hpp:26,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/tuple.hpp:8,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/builtins/bool_.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/NoneType.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/slice.hpp:5,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/core.hpp:46,
                 from scipy/signal/_max_len_seq_inner.cp312-win_amd64.pyd.p/_max_len_seq_inner.cpp:1:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h: In function 'npy_longdouble npy_creall(npy_clongdouble)':
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h:405:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
  405 |     return ((longdouble_t *) &z)[0];
      |            ~^~~~~~~~~~~~~~~~~~~~
[979/1386] Compiling C++ object scipy/signal/_spectral.cp312-win_amd64.pyd.p/meson-generated__spectral.cpp.obj
In file included from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/complex.hpp:180,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/numpy/complex64.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/ndarray.hpp:26,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/tuple.hpp:8,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/builtins/bool_.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/NoneType.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/slice.hpp:5,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/core.hpp:46,
                 from scipy/signal/_spectral.cp312-win_amd64.pyd.p/_spectral.cpp:1:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h: In function 'npy_longdouble npy_creall(npy_clongdouble)':
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h:405:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
  405 |     return ((longdouble_t *) &z)[0];
      |            ~^~~~~~~~~~~~~~~~~~~~
[1075/1386] Compiling C object scipy/interpolate/_dfitpack.cp312-win_amd64.pyd.p/meson-generated__dfitpackmodule.c.obj
In file included from scipy/interpolate/_dfitpack.cp312-win_amd64.pyd.p/_dfitpackmodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[1111/1386] Compiling Fortran object scipy/optimize/libminpack.a.p/minpack_enorm.f.obj
../scipy/optimize/minpack/enorm.f:2:37:

    2 |      *double precision function enorm(n,x)
      |                                     ^
Warning: '__result_enorm' may be used uninitialized [-Wmaybe-uninitialized]
[1122/1386] Compiling Fortran object scipy/optimize/libminpack.a.p/minpack_lmder.f.obj
../scipy/optimize/minpack/lmder.f:190:31:

  190 |      *                 sum,temp,temp1,temp2,xnorm,zero
      |                               ^
Warning: 'temp' may be used uninitialized [-Wmaybe-uninitialized]
../scipy/optimize/minpack/lmder.f:421:16:

  421 |             if (delta .le. xtol*xnorm) info = 2
      |                ^
Warning: 'xnorm' may be used uninitialized [-Wmaybe-uninitialized]
../scipy/optimize/minpack/lmder.f:190:49:

  190 |      *                 sum,temp,temp1,temp2,xnorm,zero
      |                                                 ^
note: 'xnorm' was declared here
[1124/1386] Compiling Fortran object scipy/optimize/libminpack.a.p/minpack_lmdif.f.obj
../scipy/optimize/minpack/lmdif.f:194:31:

  194 |      *                 sum,temp,temp1,temp2,xnorm,zero
      |                               ^
Warning: 'temp' may be used uninitialized [-Wmaybe-uninitialized]
../scipy/optimize/minpack/lmdif.f:423:16:

  423 |             if (delta .le. xtol*xnorm) info = 2
      |                ^
Warning: 'xnorm' may be used uninitialized [-Wmaybe-uninitialized]
../scipy/optimize/minpack/lmdif.f:194:49:

  194 |      *                 sum,temp,temp1,temp2,xnorm,zero
      |                                                 ^
note: 'xnorm' was declared here
[1132/1386] Compiling Fortran object scipy/optimize/libminpack.a.p/minpack_r1mpyq.f.obj
../scipy/optimize/minpack/r1mpyq.f:70:72:

   70 |             a(i,n) = sin*a(i,j) + cos*a(i,n)
      |                                                                        ^
Warning: 'cos' may be used uninitialized [-Wmaybe-uninitialized]
../scipy/optimize/minpack/r1mpyq.f:55:26:

   55 |       double precision cos,one,sin,temp
      |                          ^
note: 'cos' was declared here
../scipy/optimize/minpack/r1mpyq.f:70:72:

   70 |             a(i,n) = sin*a(i,j) + cos*a(i,n)
      |                                                                        ^
Warning: 'sin' may be used uninitialized [-Wmaybe-uninitialized]
../scipy/optimize/minpack/r1mpyq.f:55:34:

   55 |       double precision cos,one,sin,temp
      |                                  ^
note: 'sin' was declared here
[1152/1386] Compiling C object scipy/optimize/_lbfgsb.cp312-win_amd64.pyd.p/meson-generated__lbfgsbmodule.c.obj
In file included from scipy/optimize/_lbfgsb.cp312-win_amd64.pyd.p/_lbfgsbmodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[1164/1386] Compiling C object scipy/optimize/_cobyla.cp312-win_amd64.pyd.p/meson-generated__cobylamodule.c.obj
In file included from scipy/optimize/_cobyla.cp312-win_amd64.pyd.p/_cobylamodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[1165/1386] Compiling C object scipy/optimize/_minpack2.cp312-win_amd64.pyd.p/meson-generated__minpack2module.c.obj
In file included from scipy/optimize/_minpack2.cp312-win_amd64.pyd.p/_minpack2module.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[1176/1386] Compiling C object scipy/optimize/_slsqp.cp312-win_amd64.pyd.p/meson-generated__slsqpmodule.c.obj
In file included from scipy/optimize/_slsqp.cp312-win_amd64.pyd.p/_slsqpmodule.c:19:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_os.h:26:13: warning: "NPY_OS_MINGW" redefined
   26 |     #define NPY_OS_MINGW
      |             ^~~~~~~~~~~~
<command-line>: note: this is the location of the previous definition
[1192/1386] Compiling C++ object scipy/optimize/_group_columns.cp312-win_amd64.pyd.p/meson-generated__group_columns.cpp.obj
In file included from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/complex.hpp:180,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/numpy/complex64.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/ndarray.hpp:26,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/tuple.hpp:8,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/builtins/bool_.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/NoneType.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/slice.hpp:5,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/core.hpp:46,
                 from scipy/optimize/_group_columns.cp312-win_amd64.pyd.p/_group_columns.cpp:1:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h: In function 'npy_longdouble npy_creall(npy_clongdouble)':
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h:405:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
  405 |     return ((longdouble_t *) &z)[0];
      |            ~^~~~~~~~~~~~~~~~~~~~
[1197/1386] Compiling C++ object scipy/interpolate/_rbfinterp_pythran.cp312-win_amd64.pyd.p/meson-generated__rbfinterp_pythran.cpp.obj
In file included from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/complex.hpp:180,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/numpy/complex64.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/ndarray.hpp:26,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/tuple.hpp:8,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/builtins/bool_.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/NoneType.hpp:6,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/types/slice.hpp:5,
                 from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\pythran/pythonic/core.hpp:46,
                 from scipy/interpolate/_rbfinterp_pythran.cp312-win_amd64.pyd.p/_rbfinterp_pythran.cpp:1:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h: In function 'npy_longdouble npy_creall(npy_clongdouble)':
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h:405:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
  405 |     return ((longdouble_t *) &z)[0];
      |            ~^~~~~~~~~~~~~~~~~~~~
[1242/1386] Compiling C object scipy/optimize/cython_optimize/_zeros.cp312-win_amd64.pyd.p/meson-generated__zeros.c.obj
scipy/optimize/cython_optimize/_zeros.cp312-win_amd64.pyd.p/_zeros.c: In function '__pyx_f_5scipy_8optimize_15cython_optimize_6_zeros_bisect':
scipy/optimize/cython_optimize/_zeros.cp312-win_amd64.pyd.p/_zeros.c:3462:37: warning: '__pyx_v_solver_stats.iterations' may be used uninitialized [-Wmaybe-uninitialized]
 3462 |     __pyx_v_full_output->iterations = __pyx_t_2;
      |     ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~
scipy/optimize/cython_optimize/_zeros.cp312-win_amd64.pyd.p/_zeros.c:3400:20: note: '__pyx_v_solver_stats.iterations' was declared here
 3400 |   scipy_zeros_info __pyx_v_solver_stats;
      |                    ^~~~~~~~~~~~~~~~~~~~
[1385/1386] Compiling C++ object scipy/optimize/_highs/_highs_wrapper.cp312-win_amd64.pyd.p/meson-generated__highs_wrapper.cpp.obj
In file included from ..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/ufuncobject.h:4,
                 from scipy/optimize/_highs/_highs_wrapper.cp312-win_amd64.pyd.p/_highs_wrapper.cpp:1243:
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h: In function 'npy_longdouble npy_creall(npy_clongdouble)':
..\..\..\..\AppData\Local\Programs\Python\Python312\Lib\site-packages\numpy\_core\include/numpy/npy_math.h:405:13: warning: dereferencing type-punned pointer will break strict-aliasing rules [-Wstrict-aliasing]
  405 |     return ((longdouble_t *) &z)[0];
      |            ~^~~~~~~~~~~~~~~~~~~~
scipy/optimize/_highs/_highs_wrapper.cp312-win_amd64.pyd.p/_highs_wrapper.cpp: In function 'PyObject* __Pyx_Import(PyObject*, PyObject*, int)':
scipy/optimize/_highs/_highs_wrapper.cp312-win_amd64.pyd.p/_highs_wrapper.cpp:29872:48: warning: comparing the result of pointer addition '(((const char*)"scipy.optimize._highs.cython.src._highs_wrapper") + 5)' and NULL [-Waddress]
29872 |             if (strchr(__Pyx_MODULE_NAME, '.') != NULL) {
      |                                                ^
[1386/1386] Linking target scipy/optimize/_highs/_highs_wrapper.cp312-win_amd64.pyd
Build OK
>  meson install -C build --only-changed --tags runtime,python-runtime,tests,devel
Installing, see meson-install.log...
Installation OK
C:\Users\ilhan\Documents\GitHub\scipy [main ≡ +1 ~0 -0 !]>

For the record, git pulling that PR and rebuilding doesn't fix the build failure. I didn't expect it to.
An LTO type mismatch means that across different translation units, the same function has competing prototypes. If we randomly choose the last one to look at, noting that the line numbers have changed due to the merged PR:
../scipy-9999/scipy/special/special_wrappers.h:142:13: error: type of ‘special_cdigamma’ does not match original declaration [-Werror=lto-type-mismatch]
  142 | npy_cdouble special_cdigamma(npy_cdouble z);
      |             ^
../scipy-9999/scipy/special/special_wrappers.cpp:399:13: note: return value type mismatch
  399 | npy_cdouble special_cdigamma(npy_cdouble z) { return to_ccomplex(special::digamma(to_complex(z))); }
      |             ^
../../../../../../../usr/lib/python3.12/site-packages/numpy/_core/include/numpy/npy_common.h:362:3: note: type ‘struct npy_cdouble’ should match type ‘npy_cdouble’
  362 | } npy_cdouble;
      |   ^
../scipy-9999/scipy/special/special_wrappers.cpp:399:13: note: ‘special_cdigamma’ was previously declared here
  399 | npy_cdouble special_cdigamma(npy_cdouble z) { return to_ccomplex(special::digamma(to_complex(z))); }
      |             ^

It's saying that one translation unit included special_wrappers.h and got this type signature for special_cdigamma:
npy_cdouble special_cdigamma(npy_cdouble z);

And special_wrappers.cpp has a different type signature:
npy_cdouble special_cdigamma(npy_cdouble z)

They're the same type signature, so how could that be? Well, the compiler also tells you what the mismatched type is (although not necessarily why it believes they are different):
note: type ‘struct npy_cdouble’ should match type ‘npy_cdouble’

None of this has to do with type punning inside the implementation. You may have fixed build warnings on Windows (I don't have Windows and haven't checked there) but this bug report is about something different.

Thanks @eli-schwartz. I didn't look carefully enough. I've reproduced this locally on linux and am reopening now. Somehow there seems to be a mix-up between the old struct numpy complex types and the new native C99 complex types. Looking into it.

OK, so I've found that in NumPy 2.0, when something is being compiled as C++,  an npy_cdouble is a struct
https://github.com/numpy/numpy/blob/128d1ae431b0d6dc20851baa1b5829583d8727b0/numpy/_core/include/numpy/npy_common.h#L361C1-L367C1
and when it is being compiled as C, it is a native C99 complex type https://github.com/numpy/numpy/blob/128d1ae431b0d6dc20851baa1b5829583d8727b0/numpy/_core/include/numpy/npy_common.h#L387
special_wrappers.cpp provides a bridge that allows special functions written in C++ to be called from C, which we need because we're converting all of scipy.special into C++ in a piecemeal fashion.

in NumPy 2.0, when something is being compiled as C++, an npy_cdouble is a struct [...] and when it is being compiled as C, it is a native C99 complex type

@lysnikolaou your memory is probably a lot better than mine here - could you comment on whether this was on purpose, and any context you think may be helpful here?

could you comment on whether this was on purpose, and any context you think may be helpful here?

The initial implementation was using complex.h in both C and C++, but this had portability issues and was changed in numpy/numpy#25313. One of the problem was that complex.h defines I as the imaginary unit (see https://godbolt.org/z/nEv1Ehsds). I is often used as a template variable in C++ though, which was also the case in Pythran.
The two types used now, i.e. the struct and _Complex, have the same memory layout, which is why we'd decided that it's safe to use like this.

One of the problems was that complex.h defines I as the imaginary unit

Couldn't we simply #undef I at the end?

Couldn't we simply #undef I at the end?

Recently done in https://github.com/numpy/numpy/pull/26789. Maybe we can go back and use complex.h for both C and C++ now? Or has this ship sailed with a release (on first thought, I think it'd be okay given ABI compatibility between those types)?

Maybe we can go back and use complex.h for both C and C++ now? Or has this ship sailed with a release (on first thought, I think it'd be okay given ABI compatibility between those types)?

My understanding is that this would be okay as far as backwards-compatibility goes. Even if we use complex.h in C++ though, the _Complex type is not part of the standard and thus not fully portable. We could maybe use std::complex, but doing that opens yet another can of worms.

We could maybe use std::complex, but doing that opens yet another can of worms.

Mind expanding on what is this can of worms?

We could maybe use std::complex, but doing that opens yet another can of worms.

Mind expanding on what is this can of worms?

For one, I don’t think it would fix the original lto mismatch issue for bridge code that allows calling complex valued C++ functions from C, since you’d still have the same variables with different types in different translation units.
I think it’s also possible to fix this on the SciPy side by making the current C++ bridge file have a third complex type which is the same in both C++ and C and which is used internally, and then another C bridge file where this gets converted to npy_cdouble. A little messy, but maybe the only option.

We could maybe use std::complex, but doing that opens yet another can of worms.

Mind expanding on what is this can of worms?

For one, I don’t think it would fix the original lto mismatch issue for bridge code that allows calling complex valued C++ functions from C, since you’d still have the same variables with different types in different translation units.
I think it’s also possible to fix this on the SciPy side by making the current C++ bridge file have a third complex type which is the same in both C++ and C and which is used internally, and then another C bridge file where this gets converted to npy_cdouble. A little messy, but maybe the only option.

I think this is the solution, essentially now we need to replicate what NumPy used to do. My recollection is that part of the problem here is that some C++ compilers don't support using the standard C complex type in C++. So the bridge had to be something else to keep all compilers happy. That, until now, was the NumPy complex type. We could just define our own for the C bindings, it's not a big deal.
Anyway, that's from my memory.


Coroutines extensions

This adds the following coroutines extensions and their unit tests based on the rxjava2 ones:
ApolloWatcher.toChannel()
ApolloCall.toChannel()
ApolloPrefetch.toJob()

I think one more extension is missing for ApolloSubscriptionCall ?

I added the capacity param, some javadoc and ApolloSubscriptionCall.toChannel() as well.

I have a doubt.
Channel is for continuous communication right? If yes, it can be implemented for subscription. But, for query and mutation suspendCancellableCoroutine is good i think.

@bhuvan0616 ApolloCall can emit 2 values if you want to retrieve cached + network responses.
We could certainly add ApolloCall.toDeferred() for simple use cases where only one value is expected. Would that help ?

@bhuvan0616 ApolloCall can emit 2 values if you want to retrieve cached + network responses so you need a Channel if you want to be able to retrieve both.
We could certainly add ApolloCall.toDeferred() for simple use cases where only one value is expected. Would that help ?
See also discussion there #1127

Thanks I understood. if you provide Deferred object, the async runs on IO context?

@bhuvan0616 not sure I understand. The use case would be like this:
    val deferredResponse = apolloClient.query(EpisodeHeroNameQuery(Input.fromNullable(Episode.EMPIRE))).toDeferred()
    deferredResponse.await()


Investigate the occational crashes

Investigate the occational spontaneous crashes, so far only reported on Windows, usually after a long time of running the program.

https://github.com/learningsimulator/learningsimulator/issues/163


Status names should contain spaces

ExecutedOnPreviousRun --> Executed On Previous Run
ExecutedOnCurrentRun --> Executed On Current Run

Addressed by https://github.com/DynamoDS/TuneUp/pull/23


integrate proxy-addr

re: https://github.com/visionmedia/expressjs.com/issues/152#issuecomment-42467655
or whatever we decide is the way to go in those discussions
https://github.com/expressjs/proxy-addr/

@dougwilson what do you think about integrating this now?

@dougwilson or are you still planning to release a trust module?

proxy-addr will be split apart some more at some point, but AFAIK there isn't a reason proxy-addr cannot be used as-is currently in koa.

:+1:

Hi, we noticed an issue when there are potentially multiple proxies and app.proxy = true.  We occasionally end up with private IP addresses in the logs. I think the situation can happen in this example:
User (<IP_ADDRESS>) -> Corporate Web Proxy(<IP_ADDRESS>) ->  Our HAProxy(<IP_ADDRESS>) -> KoaApp (<IP_ADDRESS>)

in this scenereo, <IP_ADDRESS> is returned in this.ip instead of <IP_ADDRESS>
Maybe we also could use another option called "app.filterPrivateIps = true"  which will actually filter out any RFC1918  ips from this.ips.  indutny's "ip" module has an .isPrivate() method that may be helpful.

Oh cool, it looks like proxy-addr actually may solve the exact issue I described above.

@amit777 this may or may not be an option depending on your setup, but one alternative thing you can do is to filter those ips before they hit the koa app. nginx allows this via the realip, i.e. to trust the X-Forwarded-For header by pre-setting a list of trusted ips (including private ones).

o i c. proxy-addr doesn't do as much as i thoguht it would in its current form. right now, the benefit is minimal.

https://github.com/jshttp/forwarded/pull/1
parse all X-Forwarded-* headers with trust

Just as a note, I found this in one of my logs even though I don't have ipv6 enabled on the server.  Seems like some strange proxy setting headers in a funny way.  The IP address at the end is my own public ip (though i've changed it to something random and invalid for anonymity).
::ffff:2<IP_ADDRESS>

Altenatively, maybe could have something like an array of trusted headers by priority...
app.trustedProxyHeaders = ['X-Real-IP', 'X-Forwaded-For'] and then the app.ips() method could use that. I'm going to configure HAProxy to set the X-Real-IP, but I was hoping for a simple way for me to configure all my koa apps to pick it up.  ALl my koa apps sit behind haproxy.

FYI for anybody interested: resumed progress on jshttp/forwarded/pull/1 any assistance is welcome, whether in the form of review, feedback, contribution, or simply cheering :)


'make debug' is broken when assembly optimizations are on

I still get the compilation error when I run "make debug" since the flag "-mavx2" is not added.


[Config] Control whether to show QT page in nav

Fixed a weird comment indentation
Added a config for showing quick-translate page in Fialment navigation
Used the config in shouldRegisterNavigation method on the page class

Thank you!


Publish point releases.

Whilst examining the repo, I was quite surprised that you haven't published any point releases here. Is there a reason for this?
https://github.com/steemit/steemconnect-sdk/releases

I'm closing this issue, we now publish all releases on https://github.com/steemit/steemconnect.js/releases


Update discordRPCLoadStrings.json

Imagine naming methods starting with upper case letters

you literally made 2 letter changes, try a bit harder next time, please :)


Make testing great again

test get_ntp_servers: https://github.com/napalm-automation/napalm-base/pull/134
correct the mock data, code and expected_results for get_optics

Coverage increased (+0.7%) to 85.693% when pulling<PHONE_NUMBER>169d2a9f4b02b79c2f6a71399bdc4c on mirceaulinic:TEST-NTP-SRV into bda14e674b1727cd1030373be59d69c5694f6a3b on napalm-automation:develop.

Coverage increased (+1.5%) to 86.443% when pulling 79b80344b6e3c10224ab2a60f8fab7da11413e2e on mirceaulinic:TEST-NTP-SRV into bda14e674b1727cd1030373be59d69c5694f6a3b on napalm-automation:develop.


Fix hue adjustment

Hue adjustment wasn't working correctly because color channels got swapped. This has now been fixed.
What type of PR is this? (check all applicable)

[ ] Refactor
[ ] Feature
[X] Bug Fix
[ ] Optimization
[ ] Documentation Update
[ ] Community Node Submission

Have you discussed this change with the InvokeAI team?

[X] Yes
[ ] No, because:

Have you updated all relevant documentation?

[ ] Yes
[X] No; N/A

Description
The image resulting from a hue shift had discontinuities in color. This was a result of color channels being swapped prior to the conversion to/from HSV. This PR fixes the problem.
Related Tickets & Documents

Related Issue #
Closes #

QA Instructions, Screenshots, Recordings

Added/updated tests?

[ ] Yes
[X] No : I tested this manually with images, comparing results to Photoshop.

[optional] Are there any post deployment tasks we need to perform?

This PR also changes the values from 0..180 to the typical 0..360 that image editing programs use.


If URL is unreachable, still allow polling from other URLs in the additionalURLs list

...er properties in the URL list.

Closing because v1 is deprecated


Remove legacy HOWTOs

LGTM - can we wait a day or two to led the onp refactor go in first?
sure

Superceded by #1118


2.0 UI issues for Clusters

Rancher versions: 2.0 master 1/29
Results:
RKE main section

[x] Help text under Embedded and Dedicated radio buttons seem big?

[x] Top section should be white instead of grey like workloads (Embedded & Dedicated area)

[ ] Makes Nodes & security section have grey title and white body section like customize section.

GKE main Section

[ ] Make Location, Node configuration, Security section have grey title and white body.

Nodes section in RKE

[ ] Remove "Select all roles"

[ ] Center the checkboxes

[ ] "Nodes required" section needs to be aligned differently

[ ] Add node button is wrong color

Security section in RKE and GKE

[ ] Member roles section is confusing.  We need to differentiate the column headers from the inputs.

Version - 2.0 master 3/19
UI redone for this section and this is no longer valid.


Optional support for keeping a view around

Add an option to a state that tells ui-view to keep it around even after route changes (then switch back to the old one instead of re-create it)
Use case is using ui-view for the content section of a tab.
<div>
  <tab-headers/>
  <ui-view/>
</div>

I would also like to mention that DOM heavy renders like like maps or graph visualizations (with a more detailed item/brake-down view) is a very common use-case. Judging by the amount of questions on SO, and elsewhere, it seems like something that is desired.
It seems like you could store the last X ui-view DOM's (if the user enables this feature) and just pop older ones off. The main problem seems to be the un-binding (and rebinding) of scope.
Still, for perceived performance I wouldn't mind keeping X number of previous views in memory so they don't have to be rendered again. Some maps and charts just take a few seconds to render which isn't ok if you have lots of markers or chart entry points to review in more detail.

@jeme I'm doing something kind of like the use case you mentioned above。I use url search to navigate the active tabs:
/list?type=a&page=1
/list?type=b&page=2

and my use case is quite simple, I am rendering a list, including a tab to filter the type of the list and a pagination component follow the list for user to navigate. what I want to implement is when user changes the type tabs or navigate to a different page, the url search changes simultaneously . And if user manually type the exact url like /list?type=c&page=3, the state refreshes and the type tabs active to c and pagination component active to 3.
So the feature required to implement this is quite straight forward, providing a method to change the url search without reload the state, like:
$state.setSearch({ type: 'c', page: 3}, false); // the second parameter indicates the state not reload

or for now is there some way to work this around?

If you using ionic, try this:
http://ionicframework.com/docs/api/directive/ionNavView
in this page scroll to "cashing"
its works on every view of ionic view


Build tensorflow from source, issues about CUDA lib and header paths

Please make sure that this is a build/installation issue. As per our GitHub Policy, we only address code/doc bugs, performance issues, feature requests and build/installation issues on GitHub. tag:build_template
System information

OS Platform and Distribution (e.g., Linux Ubuntu 16.04): Ubuntu 18.04 LTS
Mobile device (e.g. iPhone 8, Pixel 2, Samsung Galaxy) if the issue happens on mobile device:
TensorFlow installed from (source or binary): source
TensorFlow version: 1.15.3
Python version: 3.6.9
Installed using virtualenv? pip? conda?: pip
Bazel version (if compiling from source): 0.25.0
GCC/Compiler version (if compiling from source): 7.5
CUDA/cuDNN version: 11.0/8.0.4
GPU model and memory: GTX 1060 6GB

Describe the problem
Provide the exact sequence of commands / steps that you executed before running into the problem
I followed this tutorial to install tensorflow from source, I would like to link my tensorflow to tensorRT and CUDA/CUDNN, when I ran the configure.py it seems that it couldn't locate the CUDA lib and header files, so I listed all the possible paths for CUDA libs and header files with comma-separated as required in configure, as the following:
/home/myName/Downloads/TensorRT-<IP_ADDRESS>.Ubuntu-18.04.x86_64-gnu.cuda-11.0.cudnn8.0/TensorRT-<IP_ADDRESS>,
/usr/local/cuda-11.0/targets/x86_64-linux/include,
/usr/local/cuda-11.0/targets/x86_64-linux/lib,
/usr/local/cuda-11.0/bin,
/usr/local/cuda-11.0/nvvm/libdevice,
/usr/local/cuda/nvvm/libdevice
Then it complained that it couldn't find libdevice*.10.bc, but this really doesn't make sense since /usr/local/cuda-11.0/nvvm/libdevice &  /usr/local/cuda/nvvm/libdevice are the paths containing libdevice.10.bc
I have already verified TensorRT's installation by running its sample projects, did I do anything wrong? Thank you in advance!
Any other info / logs
Include any logs or source code that would be helpful to diagnose the problem. If including tracebacks, please include the full traceback. Large logs and files should be attached.

@yummychop,
Every TensorFlow release is compatible with certain a CUDA and cuDNN version. You can check the compatibility from the tested build configurations.
Since TensorFlow 1.x is not actively supported. Could you please try building TensorFlow v2.4 with CUDA 11 and cuDNN 8 and check if it works. Thanks!

thank you @amahendrakar , i installed tensorflow 2.4.0 and the problem got resolved, I followed this tutorial provided by NVIDIA, it requires tensorflow 1.15.3 which is misleading...


Materializing historical component

This PR:

Design a history class as API for user to interact with the component run history
Add functions in store.py to query the database for component run history
initialize self._history field in base_component class

Test:

provide a boyuan_component_get_history_test.py under the examples dir for testing purpose

I did fix the pytest bug by importing base to call from_dictionary method instead of importing ComponentRun & IOPointer. This change might have some effects if we decide to overwrite the from_dictionary method for ComponentRun & IOPointer class method in the future.


Kusama support: Get Storage

Get Storage unit test breaks if connected to Kusama. Need a fix.

The address used in test has 0 balance in Kusama. Need some address that has non-zero (e.g. Validator)

This issue was funded with a partial amount unintentionally; the intended amount is $60 DAI and will be paid upon completion

@usetech-llc assuming @rad-p doesn't get back; what would you like to do with this issue; cleaning up some old issues now.


docs: Improve the codebase docstrings coverage

It seems there are places in the repo missing Python docstrings, which are in general recommended by PEP-8.
We could use a tool like interrogate to track down the docstring-missing parts and then to formalize the checks in the make/CI checks.
Additionally, sticking to a common docstrings style/convention (like Google style docstrings) would make it easier to auto generate the documentation in future.

We've made a lot of progress on this over the year. Closing this as we're unlikely to undertake a specific work-package to address what's left. We'll adopt a boy-scout "leave it better than you found it" ethic to fill in any remaining gaps.
Note we can't write meaningful docstrings for code we don't understand, so better this is undertaken during refactoring or when otherwise assuming ownership/responsibility for an area of the code, rather than in some kind of wholesale effort.
Google-style docstrings are fine when called for, and the best of the standardized formats I've seen, but oftentimes when methods are short and fully type annotated as they should be are mostly boilerplate that would repeat what is fully expressed by the call signature and a punchy docstring. But anyone taking the time to author a useful docstring can make that call for themselves. Focus should be on communicating how to use the function rather than slavish compliance to a standard. Let us each write something that helps the next reader (or future us) to quickly get the gist :)