text:
[Error in EMLI syntax] Any string can be used as a Multi-line string.

Describe the syntax error
Any string, be it "this", 'this', or `this`, can be used as a Multi-line string.
Expected behaviour
I expected that only `these types of strings` would be able to compile as a Multi-line String.
EMLI Code
p {
  "
  This shouldn't compile
  "
}

p {
  '
  Neither should this
  '
}

p {
  `
  But this should
  `
}

HTML Code
<!DOCTYPE html>
  <meta charset="UTF-8">
  <meta http-equiv="Content-Type" content="text/html;charset=UTF-8">
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <title>Document</title>

  This shouldn't compile

  Neither should this

  But this should

Screenshots

Additional context
This is a known issue, and will be fixed in a later update. Do not use any type of string other than `this type` for Multi-line Strings, or they will break in the future.

Fixed!

repo: ThatBlockyPenguin/WSS-EMLI


text:
Added s for skip and l for loop

I added s as an alias for skip and l for an alias of loop

Thanks for your contribution! Merging now, if everything breaks it's your fault I'm dumb.

source: gharchive/pull-request

repo: The-Mist-Development/The-Mist-Bot

license: MIT

license_type: permissive

license_source: github-api


text:
abstracts: add mock_area scaling factor for abstracts

Works by first doing floorplan, then doing a second run where SYNTH_GUT=1 is set and the DIE/CORE_AREA is set to be a mock_area multiple (larger or smaller) than the first floorplan and generating an scaled abstract.
Then the regular abstract is generated, but the scaled .lef file replaces the actual .lef file.
This yields a .lib file for mock-area case and a .lef file for the mock-area case.
This allows separating concerns: set up a budget floorplan and have timing information.

@maliberty Can I mock area in this way...?

Sure though the timing won't be scaled if that matters.

authors: maliberty oharboe

repo: The-OpenROAD-Project/megaboom


text:
InverseObjectProperties not declared

I'm trying out the Sequence Ontology on a triple store with reasoning enabled and was surprised by the lack of inverse properties declaration in so.owl.
I added triples linked through the relation so:transcribed_to and was expecting to be able to query its inverse property transcribed_from. When I realized that wasn't possible, I inspected the owl file and noticed both properties have a definition (obo:IAO_0000115) that explains they are the inverse of each other, but they don't have an owl:inverseOf declaration.
Is this intentional? If so, what's the reason ?
I'm not an expert in ontologies, but I would assume for example that the transcribed_from term would be declared like this:
    <owl:ObjectProperty rdf:about="http://purl.obolibrary.org/obo/so#transcribed_from">
        <owl:inverseOf rdf:resource="http://purl.obolibrary.org/obo/so#transcribed_to"/>
        <obo:IAO_0000115 rdf:datatype="http://www.w3.org/2001/XMLSchema#string">X is transcribed_from Y if X is synthesized from template Y.</obo:IAO_0000115>
        <oboInOwl:created_by rdf:datatype="http://www.w3.org/2001/XMLSchema#string">kareneilbeck</oboInOwl:created_by>
        <oboInOwl:creation_date rdf:datatype="http://www.w3.org/2001/XMLSchema#string">2009-08-19T12:05:39Z</oboInOwl:creation_date>
        <oboInOwl:hasOBONamespace rdf:datatype="http://www.w3.org/2001/XMLSchema#string">sequence</oboInOwl:hasOBONamespace>
        <oboInOwl:id rdf:datatype="http://www.w3.org/2001/XMLSchema#string">transcribed_from</oboInOwl:id>
        <rdfs:comment rdf:datatype="http://www.w3.org/2001/XMLSchema#string">Example: primary_transcript transcribed_from gene.</rdfs:comment>
        <rdfs:label rdf:datatype="http://www.w3.org/2001/XMLSchema#string">transcribed_from</rdfs:label>
    </owl:ObjectProperty>

Note the added <owl:inverseOf rdf:resource="http://purl.obolibrary.org/obo/so#transcribed_to"/> child node.

Ideally SO would import an RO module and use the RO URIs here
e.g.
http://purl.obolibrary.org/obo/RO_0002511
For now you could merge RO into your triplestore and replace the URIs

source: gharchive/issue

authors: cmungall psoares

repo: The-Sequence-Ontology/SO-Ontologies


text:
New sequence assembly terms which are specifically datums

Over on the OBI issues board https://github.com/obi-ontology/obi/issues/1232 there is a list of terms we are seeking a home for, namely:
amino acid sequence start site (user submitted)
amino acid sequence stop site (user submitted)
nucleic acid sequence start site (user submitted)
nucleic acid sequence stop site (user submitted)
amino acid sequence start site (reference)
amino acid sequence stop site (reference)
nucleic acid sequence start site (reference)
nucleic acid sequence stop site (reference)
But SO doesn't have IAO data item, etc.  So wondering if you want these kinds of relative positional sequence reference terms?
EDIT: we are working on fashioning better definitions that explicitly define "user submitted", "reference" etc.

Anyone there?  I will keep these over in OBI if not.

Anyone there?  I will keep these over in OBI if not.

Thank you @ddooley for your patience. Please forgive me for responding to your question with another question, but is there a particular part of the SO you think would house these terms nicely? I am looking at the overall structure of the SO and there might be a place for these somewhere under sequence_feature (SO:0000110), but we'll need to document these terms before we can proceed. We'll need to be able to cite specific publications if we are to include these in the SO (even for the most general terms), so I might ask for some citations? PubMed ID's are preferred, but we're happy to look at any publication you are willing to share.

Hi, thanks for checking in.  We ended up popping them under "sequence data" in OBI.  So I'll close this now!

authors: ddooley egchr


text:
Fails to build knowledge graph.

tried flailing around with the root directory, in case your domunetation is autogenerated.
(graphrag-ollama-local) PS C:\Users\spete\grahollama\graphrag-local-ollama\ragtest> python -m graphrag.index --init --root ./
Traceback (most recent call last):
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\runpy.py", line 187, in run_module_as_main
mod_name, mod_spec, code = get_module_details(mod_name, Error)
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\runpy.py", line 146, in get_module_details
return get_module_details(pkg_main_name, error)
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\runpy.py", line 110, in get_module_details
import(pkg_name)
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index_init.py", line 40, in
from .run import run_pipeline, run_pipeline_with_config
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\run.py", line 59, in
from .verbs import *  # noqa
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\verbs_init.py", line 9, in
from .graph import (
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\verbs\graph_init.py", line 10, in
from .layout import layout_graph
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\verbs\graph\layout_init.py", line 6, in
from .layout_graph import layout_graph
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\verbs\graph\layout\layout_graph.py", line 13, in
from graphrag.index.graph.visualization import GraphLayout
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\graph\visualization_init.py", line 6, in
from .compute_umap_positions import compute_umap_positions, get_zero_positions
File "C:\Users\spete\grahollama\graphrag-local-ollama\graphrag\index\graph\visualization\compute_umap_positions.py", line 6, in
import graspologic as gc
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\site-packages\graspologic_init.py", line 8, in
import graspologic.inference
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\site-packages\graspologic\inference_init_.py", line 6, in
from .latent_distribution_test import latent_distribution_test
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\site-packages\graspologic\inference\latent_distribution_test.py", line 7, in
from hyppo.ksample import KSample
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\site-packages\hyppo_init_.py", line 5, in
import hyppo.kgof
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\site-packages\hyppo\kgof_init_.py", line 2, in
from .fssd import FSSD, FSSDH0SimCovObs
File "C:\Users\spete\anaconda3\envs\graphrag-ollama-local\lib\site-packages\hyppo\kgof\fssd.py", line 4, in
from past.utils import old_div
ModuleNotFoundError: No module named 'past'

pip install future solved it for me.

thanks.

authors: Frdoe dudezilla

repo: TheAiSingularity/graphrag-local-ollama


text:
fix: spelling error in dijkstra.cpp main() comment

Description of Change
In the dijkstra.cpp main function, I fixed a misspelling of the word "destination".

Checklist

[ x] Added description of change
[ x] PR title follows semantic commit guidelines
[ x] I acknowledge that all my contributions will be made under the project's license.

Notes:

@ali-mir Thank you for you contribution.

The existing code was poorly written and hence the new standards checks we have in place are giving errors.
If we accept the current commit, the build will be reported as failing in the main repo
In lieu of point# 2 above, it would be better if the opportunity is taken to structure the code with the standards as in the newer newer contribution #894
We welcome your time and your contribution will be recorded in the file history :)

That all sounds good to me. I'll work on fixing the file up to standard. I'm closing this PR and will open a new one with the correct title/description.

authors: ali-mir kvedala

repo: TheAlgorithms/C-Plus-Plus


text:
Generate symbols for SourceLink

The existing publish script will now upload a snupkg symbol package alongside the nupkg.
This will allow developers to step into the source of this package if necessary when debugging.
https://docs.microsoft.com/en-us/nuget/create-packages/symbol-packages-snupkg

Hey @iCodeSometime
Sorry for the late reply. I honestly never heard of this before so thanks for teaching me something new! I was aware of pdb files but not about this. Changes is looking good!
With kind regards,
TheBoneJarmer

Sorry about that, I should have checked the readme first, I'll get it right next time.
The snupkg thing is fairly new, but it's pretty convenient to be able to step in to external libraries without having to do any setup

Hey, I created a workflow you can use if you like. You can just paste it into a new action - should be free since it's a public repo.
Once it's installed, if anyone other than you opens a PR merging into main, it'll comment "Pull requests may not be created against the main branch. Updating the branch target to develop - make sure to fix any merge errors. See the contribution guidelines for more info."
It then automatically updates the target branch to "develop", where Github will check if it merges cleanly. Maybe this can cut down on some annoyance for you.
Appreciate you,
iCodeSometime
on:
  pull_request:
    types: [opened]
job:
  update-pr-target:
    if: ${{ github.base_ref == 'main' && github.actor != 'TheBoneJarmer' }}
    steps:
      - name: Comment about wrong branch
        uses: actions/github-script@v5
        with:
          script: |
            github.rest.issues.createComment({
              issue_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              body: 'Pull requests may not be created against the main branch. Updating the branch target to develop - make sure to fix any merge errors. See [contribution guidelines](github-script) for more info.'
            })
      - name: Update PR target branch
        uses: actions/github-script@v5
        with:
          script: |
            github.rest.pulls.update({
              pull_number: context.issue.number,
              owner: context.repo.owner,
              repo: context.repo.repo,
              base: 'develop'
            })

authors: TheBoneJarmer iCodeSometime

repo: TheBoneJarmer/TiledCS


text:
Allow version tags without pre-pending 'v'

Some repositories don't prepend their tags with vx.y.z
But CPM automatically adds the v, so it makes some repositories not work.

Duplicate of #106.
TL;DR use GIT_TAG instead of VERSION for non v-prefixed version tags.

authors: TheLartians Wavesonics


text:
Module not found error

A1111 seemed to be working for weeks, tried many things from reinstalling and etc, no idea what's wrong

I'm having the same error too.

I'm having the same error too.

TheLastBen is fixing the machine, just wait

I'm having the same error too.

TheLastBen is fixing the machine, just wait

roger that.

authors: Hoozzych chartercook imran-zakheav

repo: TheLastBen/fast-stable-diffusion


text:
clear vectors in Grid::clear()

Current implementation:
void Grid::clear() {
	for (int i = 0; i < grid.size(); i++) {
		for (int j = 0; j < grid[i].size(); j++) {
			delete grid[i][j];
		}
	}
}

This will leave the vectors filled with dangling pointers. Proposed implementation:
void Grid::clear() {
	for (int i = 0; i < grid.size(); i++) {
		for (int j = 0; j < grid[i].size(); j++) {
			delete grid[i][j];
		}
                grid[i].clear(); // <----
	}
        grid.clear(); // <----
}

Implemented.

authors: PatrickTorgerson TheOneThatIs

repo: TheOneThatIs/Conways-Game-of-Life


text:
Cannot modify sol::var with std::ref from lua

Consider the following:
#include <sol/sol.hpp>

#include <cassert>

int main() {
    std::printf("sol: %s\n", SOL_VERSION_STRING);

    sol::state L;
    L.open_libraries();

    struct X {};
    auto x = L.new_usertype<X>("X");
    int v = 1;
    x["v"] = sol::var(std::ref(v));

    // value == 1?
    L.safe_script("assert(X.v == 1)");
    assert(v == 1);

    // modify from cpp..
    v = 2;

    // value == 2?
    L.safe_script("assert(X.v == 2)");
    assert(v == 2);

    // modify from lua..
    L.safe_script("X.v = 3");

    // value == 3?
    L.safe_script("assert(X.v == 3)");
    assert(v == 3); // BOOM
}

Note, that last assertion fails.

Compiler: gcc/clang (trunk)
sol v3.2.3 (trunk)
Demo: https://godbolt.org/z/rfxG1xsTx

BTW, there are tests for sol::var, but they were disabled. I hope they'll be fixed before v4.0.0 release.

I ran into this issue myself and spent several hours looking into it. I think I have narrowed the search down to this line in the usertype_storage.hpp file:
https://github.com/ThePhD/sol2/blob/f81643aa0c0c507c0cd8400b8cfedc74a34a19f6/include/sol/usertype_storage.hpp#L703-L707
From what I can tell based on debugging, the condition for this line of code being reached is that code within the lua runtime is setting a field in the table that already exists.
This code seems to simply be creating a brand new Binding object and overwriting the existing one that is already in the user type storage from the initial usertype definition rather than taking the existing Binding entry and updating it with the new value, which should subsequently update it in the original C++ memory location.
I believe this is also causing #1268, #1404, and #1416.

I have confirmed through debugging that at the point in which this line of code is reached, the existing storage_it iterator is pointed at a ebco instance containing the std::reference object with the reference to the static variable, so it isn't a matter of initially storing the value.
With some forceful casting inside of a transpiled sol.hpp file, I was able to extract the std::ref and arbitrarily assign to it from withing the if-statement. I also added a return statement to the end of the if-block and I moved the this->string_keys.erase(string_it); line from line #700 to the else-block. This resulted in the lua call to assign the field to be updated from lua's perspective AND globally in the static c++ field.
However, I am not able to extract the actual lua value from the assignment operation at this time because I am not yet familiar enough with how to pull data off the lua stack, and I also am not sure how to handle the templating needed to make sure it works for values other than a hard-coded type.
I am also not sure if this line of code is possibly being reached by mistake higher up in the stack trace.

Okay so in this ticket:
https://github.com/ThePhD/sol2/issues/1404#issue-1368783575
the OP mentioned that the property functionality was working in v3.2.3 and was broken as of v3.3.0. I looked at the diff and the section of code I referenced was actually untouched between those releases.
I found instead that the issue is actually originating higher in the call stack, from the self_index_call function.
https://github.com/ThePhD/sol2/blob/eba86625b707e3c8c99bbfc4624e51f42dc9e561/include/sol/usertype_storage.hpp#L478-L482
Since v3.2.3, a large chunk of the function's code was wrapped in an if-statement. If I remove this outer if-statement on line 480, the static reference gets updated globally exactly how I would expect from sol::var with std::ref.
I checked and this also fixes the sol::property issue in #1268 and #1404.
This if-statement is currently present on the develop branch, which I assume is more in-line with what is eventually going into v4.0.0 release as the v4.0.0-alpha branch is behind v3.3.0.
@ThePhD This seems like a bug to me, but is this somehow intentional behavior that I am not understanding properly?

For anyone looking for a fix, I pushed up the fix with the if-statement removed to a fork here: https://github.com/cclark25/sol2/tree/1267_usertype_meta_field_setters_fix
It is branched directly off of the v3.3.0 tag and seems to be working without any unexpected behavior.
I couldn't get the tests built so I haven't confirmed that it hasn't broken anything.

license: mit

license_source: bigquery


text:
Playlayout Remove Problem

let playerControlContainer = document.querySelector('div[playable="true"][id="player-control-container"]');
if (playerControlContainer) {
playerControlContainer.remove();
}
Add this code  your Script

Play layout Remove Problem Solve

Add it to which line? Tell line number wrt to the original 5.6 script lines of code.

repo: TheRealJoelmatic/RemoveAdblockThing


text:
fix noarch linking on Win

For noarhc entrypoints we need to hard-link or copy conda.exe and rename it.
Clearly this is hacky (but that's how conda does it).
We should probably ship the contents of conda.exe as binary buffer inside mamba / micromamba on Windows to create this file even without conda being installed.

@dhirschfeld do you know (by any chance) how these magical .exe entrypoints work on Windows?
As mentioned above, python entrypoints are apparently .exe files with the same name that somehow then start python and run the script (with the same name in the same folder).

Also while this helps to make the installation of noarch packages work, there is another issue that comes up when unlinking packages. The one where a file that is in use cannot be deleted.
The cool thing is that this is all coming up with the current boa work, so that's fruitful at least :)
Also this doesn't impact regular "mamba"-operation since it's using Conda's link implementation.

do you know (by any chance) how these magical .exe entrypoints work on Windows?

IIRC before exe's the entrypoints were just bat files that called ..\python.exe name-script.py %*. Now they're actually executable binaries but I think they just do exactly the same - i.e. the name-script.py files still sit alongside the name.exe binaries so I think the exes still just call ..\python.exe name-script.py %*. I guess they're just a minimal c program that calls spawn (or something like that)

The one where a file that is in use cannot be deleted.

This is a constant source of pain on Windows 😬. I think that the native MoveFileEx api is supposed to cope with that (see os.replace).
Looks like this was implemented in conda in https://github.com/conda/conda/pull/8024

authors: dhirschfeld wolfv

repo: TheSnakePit/mamba


text:
Added SoftwareSerial listen function

I tweaked the library a bit to enable listen function if users use this library for SoftwareSerial case. In original library, SoftwareSerial can be used but if the user use another SoftwareSerial class in the same sketch, the library might not work. In this tweaked library, I added some lines to check if user uses modemStream as HardwareSerial or SoftwareSerial, if SoftwareSerial, modemStream will intend to listen every time before sendCommand function is executed.

 Thank you for your submission, we really appreciate it. Like many open source projects, we ask that you sign our Contributor License Agreement before we can accept your contribution.

The reason why we're using Stream is because you can use SoftwareSerial, HardwareSerial, AltSoftSerial, etc. What functionality do you specifically miss in the current implementation?

In SoftwareSerial, the most important point is listen() function, which will make specific port to listen to incoming data at RX. This case is important when there are more than 1 SoftwareSerial is implemented.
For example, a sample sketch here
#include <TinyGPS.h>
#include <TheThingsNetwork.h>

SoftwareSerial loraSerial(2, 3);

TinyGPS gps;
SoftwareSerial ss(10, 11);

TheThingsNetwork ttn(loraSerial, debugSerial, /* TTN_FP_EU868 or TTN_FP_US915 */);
...
void setup(){
  debugSerial.begin(115200);
  loraSerial.begin(57600);
  ss.begin(9600);

 ....
}

Since there are 2 SoftwareSerial are being used, from code above ss RX port will be listened prior to loraSerial. So all the commands sent by loraSerial will not get response,resulting the error. Even if we switch ss and loraSerial during the setup, in later program we will need to put listen() function strategically to make sure all devices are happy and get the data from their respective RX port.
Pull request might not needed, but may be we can do something about it, like creating examples for 2 or more SoftwareSerial case? This was my test on Arduino Uno with LoRa shield + GPS shield, both shields using UART.

@johanstokking?

authors: CLAassistant bengchet htdvisser johanstokking

repo: TheThingsNetwork/arduino-device-lib


text:
🛑 Trident is down

In 58ff7f6, Trident (https://thetrident.one) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Trident is back up in df757db after 15 minutes.

repo: TheTridentOne/upptime


text:
🛑 Trident is down

In 85f1929, Trident (https://thetrident.one) was down:

HTTP code: 0
Response time: 0 ms

Resolved: Trident is back up in e2dda77 after 10 minutes.


text:
PictureInPicture not work after source uri change

Bug
-Video not playing in background after chang props.
Platform

iOS 14.4 real device

Environment info
"react-native": "0.67.2"
"react-native-video": "^5.2.1"
Library version: 5.2.1
Steps To Reproduce
1.Play video with uri1, goto background, pipvideo is visible.
2.Open app again. on next change uri2
3.uri2 video is playing, Goto background, pipvideo not display.
Expected behaviour
1.On video uri change
2.Goto background pipvideo should be display.
Reproducible sample code
<Video
ref={videoRef}
paused={false}
source={{
uri: props.videoUrl,
}}
controls
pictureInPicture
resizeMode="contain"
onLoad={meta => console.log(meta)}
style={{
position: 'absolute',
top: 0,
left: 0,
right: 0,
bottom: 0,
}}
/>
Seems like issue in RCTVideo.m
When this setSrc call second time:
(void)setSrc:(NSDictionary *)source -> removePlayerLayer but not remove refrence _pipController.
In this function delegate not set with updated _playerLayer
- (void)setupPipController {
  if (!_pipController && _playerLayer && [AVPictureInPictureController isPictureInPictureSupported]) {
    // Create new controller passing reference to the AVPlayerLayer
    _pipController = [[AVPictureInPictureController alloc] initWithPlayerLayer:_playerLayer];
    _pipController.delegate = self;
  }
}

Seems duplicated of

should be tested with: https://github.com/TheWidlarzGroup/react-native-video/pull/3385

authors: MaxJadav freeboub

repo: TheWidlarzGroup/react-native-video


text:
[Feature]: Destroy method for video player

Description
In our app, we use react-native-track-player for audio content and react-native-video for video content. With the latest version, v6.2.0, we also added the notifications controls and removed the react-native-media-controls library from the codebase.
Is it possible to create a destroy method for the video component, similar to what react-native-track-player has?
Why it is needed ?
cross-compatibility with other media libraries
For example, we have the current issue in iOS:

we destroy the audio player
we first use a video component to playback a video,
we unmount the screen (calling .pause() in the useLayoutEffect return function)
we set up and start playback with the audio player
go to the background state
scrub using the notification controls (which show the audio controls and media art)
video content starts playing and overrides the notification controls

Ideally, I think there should be a way to remove all listeners and reset the notification controls. That is one part of the solution. The other would be to be able to somehow trigger the whole deinit() flow manually so that we can avoid the component still existing in memory.
Possible implementation
I'm pretty sure the notification controls reset is fairly easy to expose.
For the destroy() part, I think the React side could be replaced by a simple view, which could help trigger the native deinit flow. I'm not sure it is doable
Code sample
A bit too complex to set up :)

We "fixed" the issue we have with NowPlaying controls by removing the notification controls from iOS via patch package. It is a band-aid for now; with pip active, our users will have to live with it for a few days.
We tried to set notification controls to false + use a state with default false + set state to false after onLoad gets called. Pretty sure there exists a way to fix it like that, but my patience ran out :) (like https://github.com/TheWidlarzGroup/react-native-video/issues/3890 suggests)

This issue was fixed with the latest version.


text:
🧼 Remove all nested Typography elements

Nesting MUI Typography elements works, but throws the console error show below. We should remove all nested text elements, making generic components if needed.
Error

Closed by #556

repo: Thenlie/Streamability


text:
🛑 Outlook is down

In 7ff92c9, Outlook (https://outlook.live.com) was down:

HTTP code: 417
Response time: 135 ms

Resolved: Outlook is back up in 5deb7ac after 20 minutes.

repo: Thexyz/Email-Monitoring


text:
Unauthed nav bar links should be re-ordered

Closing this issue from Pelican and re-opening here.

Should be:
Office Hours | Mentors | Pricing | Log in

repo: Thinkful/ui


text:
Question about type casting from c_char

Hi, Im trying to add set rich presence SteamAPI_ISteamFriends_SetRichPresence
C header from SDK virtual bool SetRichPresence( const char *pchKey, const char *pchValue ) = 0;
My implementation:
pub fn SteamAPI_ISteamFriends_SetRichPresence(instance: *mut ISteamFriends,  key: *const c_char, value: *const c_char) -> bool;
    /// Set rich presence of current game.
    pub fn set_rp(&self, key: &str, value: &str ) -> Result<(), ()> {
        let key_rp = CString::new(key).unwrap();
        let val_rp = CString::new(value).unwrap();

        let success = unsafe {
            sys::SteamAPI_ISteamFriends_SetRichPresence(self.friends, key_rp.as_ptr(), val_rp.as_ptr())

        };
        if success { Ok(()) } else { Err(()) }
    }

The question is, should key_rp.as_ptr() also cast using as *const _ ? I saw some of your implementation using as *const _. If you dont mind to explain what as *const _ do.
I tried using as *const _ and not using it, no compiler error.

If you dont mind to explain what as *const _ do.

as *const _ Just casts but lets the compiler figure out the type pointer.
As for why: its not needed in this case, just a bad habit on my part. Its going from os::raw::c_char to libc::c_char but they should always be the same type for the most part.

authors: Thinkofname yrendykurniawan

repo: Thinkofname/steamworks-rs


text:
Spam Video ID Submission: Missed spam comments, using Auto-Smart Mode

Duplicate Links

[X] Yes, I checked that the ID is not currently in the list
[X] I read the wiki explaining each different categories and examples before submitting

(Required) Spam Comment Direct Link
https://www.youtube.com/watch?v=YL_Efp4H6z0&lc=Ugw6mtHgMxT3I5mibsN4AaABAg.9i3G_GpRyGa9i4G76Hx8cA
https://www.youtube.com/watch?v=YL_Efp4H6z0&lc=UgwTbpBvYOQQ-S-DzQN4AaABAg.9i3Yc10aqQk9i4G5S7m-qF
https://www.youtube.com/watch?v=YL_Efp4H6z0&lc=UgxJpTCWzqjIDtS2bAt4AaABAg.9i3AQUZRm7e9i4G3ZHO2XB
https://www.youtube.com/watch?v=YL_Efp4H6z0&lc=Ugx5_SxYt4CBK5MA6dp4AaABAg.9i3OxJiM-749i4G1yZs_CY
(Optional) Screenshot of comments using the link

(Required) List of the spam video IDs / Links
https://youtu.be/YL_Efp4H6z0

Huh, this issue still haven't been resolved, is the problem being the language is not English?

Huh, this issue still haven't been resolved, is the problem being the language is not English?

Probably tbh (See ThioJoe's comment to a similar* post below)

It really wouldn't make a difference because very little of the filter even looks for whole words. I'm just going to close this because tbh I don't really intend to implement any kind of translation functionality.
If there is a pattern of a certain type of spammer in another language I'll take a look and see what I can do. But I'd need actual specific examples

https://github.com/ThioJoe/YT-Spammer-Purge/issues/496#issuecomment-1029364509

Got it, thanks for the clarification!

authors: Engstory Firecul

repo: ThioJoe/YT-Spam-Lists


text:
Add "category" tag for blogpost to enhance search options

Let user assign a category-tag to a blogpost. Used for searching or only show these blogpost with e.g. category as a url-param.

Implemented fdef5c912e90fe9682a140502306f03fa13d8a82


text:
Fix image scan size with raspberry camera

The actual working size in the img_dev branch is ~ 2427x3500.

Need to adapt with camera resolution (~1920x1080) and crop.
Need to keep in mind that the pdf may come from a scanner

Each image is resized when the img process runs.
The width will be 1191px and the image ratio remains constant.

A good image height is 1541px.

repo: ThomasVandermeersch/KorreKthor


text:
🛑 STRMRocks Plex is down

In 05eeb54, STRMRocks Plex ($STRM_PLEX) was down:

HTTP code: 0
Response time: 0 ms

Resolved: STRMRocks Plex is back up in 6868866.

Resolved: STRMRocks Plex is back up in 6868866.

repo: Thomvh/thvmh-uptime


text:
Add log library

Add log library. I think it's better to use Monolog https://github.com/Seldaek/monolog

Great ideia.

authors: TiagoGouvea abdala

repo: TiagoGouvea/PHPGamification


text:
🛑 Melon Ticket is down

In 28ba600, Melon Ticket (https://ticket.melon.com) was down:

HTTP code: 406
Response time: 947 ms

Resolved: Melon Ticket is back up in 272586e after 34 minutes.

repo: TicketOpen/status


text:
🛑 Melon Ticket is down

In 800840e, Melon Ticket (https://ticket.melon.com) was down:

HTTP code: 406
Response time: 1015 ms

Resolved: Melon Ticket is back up in b57c6da.


text:
Initial version of SOMA ingestor.

This builds and executes a trivial server-side task graph (just the one node) that ingests SOMA data into a TileDB array. soma.io currently only supports ingesting the whole thing at once.
For maximum flexibility, credentials and other configuration can be provided directly as TileDB config settings.
With some trickery, we can pass the TileDB VFS IO object directly into anndata.read_h5ad and avoid having to make a local copy of the input h5ad file.

It took me way too much digging through anndata and h5py's source code and banging my head against a wall to come up with a solution to read h5ad files that didn't involve literally just copying them to a local temporary folder. This came to me only just now.
The main thing I'm concerned about is: are we providing the right set of options here? For context, we plan on providing a UI for this, and the UI will take the steps of turning e.g. storage keys into TileDB config data, so we are not expecting users to directly enter {"tiledb.vfs.whatever": "some-value"}–type JSON data. However, doing it this way will allow us to support any backend with only changes on the UI side and not needing to change the backend UDF itself.

I'm confused -- why do this in tiledb-cloud-py? We have no VCF or other apps in tiledb-cloud-py.
I thought we were going to use https://github.com/TileDB-Inc/TileDB-Cloud-Life-Sciences -- ?

This isn’t correct; there’s an entire VCF module in this package which contains similar functionality. This code is intended to be available as a library function to be executed from a UDF.

@thetorpedodog thank you!
My main concern about this is:

For UDFs per se we have 2GB nodes and 8GB nodes (resource_class=="large") for the latter
My previous attempts to experiment with distributed ingest (admittedly, half a year or more ago) resulted on node-level OOMs even with resource_class=="large"
It had been my hope (perhaps not articulated by me in as many places as I had hoped) that we could use batch-mode runners which have fewer memory constraints

In any case before going farther we need to test this with (at least) H5AD with over a million cells.
To get such, IOO've gone to https://cellxgene.cziscience.com/datasets then moved the slider for cell count, then save as .h5ad. For example:

https://cellxgene.cziscience.com/collections/c114c20f-1ef4-49a5-9c2e-d965787fb90c
https://cellxgene.cziscience.com/collections/0a839c4b-10d0-4d64-9272-684c49a2c8ba
https://cellxgene.cziscience.com/collections/edb893ee-4066-4128-9aec-5eb2b03f8287

@thetorpedodog what are the remaining steps to get this PR merged?

@thetorpedodog it looks like the sole CI fail might be resolved by a retry but, oddly, I don't appear to have permissions to restart the failed CI job.

authors: johnkerl thetorpedodog

repo: TileDB-Inc/TileDB-Cloud-Py


text:
merge conflict

Merge conflict

https://github.com/TilsonJoji/moq-js/commit/a79207ecb72ac05945fc393027b951d4725b2d51
https://github.com/TilsonJoji/moq-js/commit/7aa3cc5b65fb8f3ce877d74b13bd310793869ff2

repo: TilsonJoji/moq-js


text:
RuntimeError: CUDA Setup failed despite GPU being available.

(llama) zijinhong@DGX-Station-A800:~$ python -m bitsandbytes
False
===================================BUG REPORT===================================
/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: Welcome to bitsandbytes. For bug reports, please run
python -m bitsandbytes
warn(msg)
================================================================================
/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: /raid/home/zijinhong/anaconda3/envs/llama did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...
warn(msg)
/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We select the PyTorch default libcudart.so, which is {torch.version.cuda},but this might missmatch with the CUDA version that is needed for bitsandbytes.To override this behavior set the BNB_CUDA_VERSION=<version string, e.g. 122> environmental variableFor example, if you want to use the CUDA version 122BNB_CUDA_VERSION=122 python ...OR set the environmental variable in your .bashrc: export BNB_CUDA_VERSION=122In the case of a manual override, make sure you set the LD_LIBRARY_PATH, e.g.export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-11.2
warn(msg)
/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/cuda_setup/main.py:166: UserWarning: /usr/local/cuda/lib64: did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...
warn(msg)
The following directories listed in your path were found to be non-existent: {PosixPath('/var/lib/nvidia-mig-manager/checkpoint.json')}
CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...
DEBUG: Possible options found for libcudart.so: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}
CUDA SETUP: PyTorch settings found: CUDA_VERSION=113, Highest Compute Capability: 8.0.
CUDA SETUP: To manually override the PyTorch CUDA version please see:https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md
CUDA SETUP: Required library version not found: libbitsandbytes_cuda113.so. Maybe you need to compile it from source?
CUDA SETUP: Defaulting to libbitsandbytes_cpu.so...
================================================ERROR=====================================
CUDA SETUP: CUDA detection failed! Possible reasons:

You need to manually override the PyTorch CUDA version. Please see: "https://github.com/TimDettmers/bitsandbytes/blob/main/how_to_use_nonpytorch_cuda.md
CUDA driver not installed
CUDA not installed
You have multiple conflicting CUDA libraries
Required library not pre-compiled for this bitsandbytes release!
CUDA SETUP: If you compiled from source, try again with make CUDA_VERSION=DETECTED_CUDA_VERSION for example, make CUDA_VERSION=113.
CUDA SETUP: The CUDA version for the compile might depend on your conda install. Inspect CUDA version via conda list | grep cuda.
================================================================================

CUDA SETUP: Something unexpected happened. Please compile from source:
git clone https://github.com/TimDettmers/bitsandbytes.git
cd bitsandbytes
CUDA_VERSION=113 make cuda11x
python setup.py install
CUDA SETUP: Setup Failed!
Traceback (most recent call last):
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/runpy.py", line 187, in _run_module_as_main
mod_name, mod_spec, code = _get_module_details(mod_name, _Error)
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/runpy.py", line 146, in _get_module_details
return _get_module_details(pkg_main_name, error)
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/runpy.py", line 110, in _get_module_details
import(pkg_name)
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/init.py", line 6, in
from . import cuda_setup, utils, research
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/research/init.py", line 1, in
from . import nn
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/research/nn/init.py", line 1, in
from .modules import LinearFP8Mixed, LinearFP8Global
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/research/nn/modules.py", line 8, in
from bitsandbytes.optim import GlobalOptimManager
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/optim/init.py", line 6, in
from bitsandbytes.cextension import COMPILED_WITH_CUDA
File "/raid/home/zijinhong/anaconda3/envs/llama/lib/python3.10/site-packages/bitsandbytes/cextension.py", line 20, in
raise RuntimeError('''
RuntimeError:
CUDA Setup failed despite GPU being available. Please run the following command to get more information:
    python -m bitsandbytes

    Inspect the output of the command and see if you can locate CUDA libraries. You might need to add them
    to your LD_LIBRARY_PATH. If you suspect a bug, please take the information from python -m bitsandbytes
    and open an issue at: https://github.com/TimDettmers/bitsandbytes/issues

My detailed environment: CUDA Version: 11.4, torch Version: 1.12.1+cu113

Bitsandbytes was not supported windows before, but my method can support windows.（yuhuang）
1  open folder J:\StableDiffusion\sdwebui，Click the address bar of the folder and enter CMD
or WIN+R, CMD  。enter，cd /d J:\StableDiffusion\sdwebui
2  J:\StableDiffusion\sdwebui\py310\python.exe -m pip uninstall bitsandbytes
3 J:\StableDiffusion\sdwebui\py310\python.exe -m pip uninstall bitsandbytes-windows
4 J:\StableDiffusion\sdwebui\py310\python.exe -m pip install https://github.com/jllllll/bitsandbytes-windows-webui/releases/download/wheels/bitsandbytes-0.41.1-py3-none-win_amd64.whl
Replace your SD venv directory file（python.exe Folder） here（J:\StableDiffusion\sdwebui\py310）

I'm using ubuntu system, seems bitsandbytes-windows do not solve for this issue.

sorry,you are  ubuntu system,CUDA Version: 11.4, torch Version: 1.12.1+cu113.
Bitsandbytes can support ubuntu.（yuhuang）
1  open folder J:\StableDiffusion\sdwebui，Click the address bar of the folder and enter CMD
or WIN+R, CMD  。enter，cd /d J:\StableDiffusion\sdwebui
2  J:\StableDiffusion\sdwebui\py310\python.exe -m pip uninstall bitsandbytes
3 J:\StableDiffusion\sdwebui\py310\python.exe -m pip uninstall bitsandbytes-windows
4 J:\StableDiffusion\sdwebui\py310\python.exe -m pip install https://github.com/TimDettmers/bitsandbytes/releases/download/0.41.0/bitsandbytes-0.41.0-py3-none-any.whl
Replace your SD venv directory file（python.exe Folder） here（J:\StableDiffusion\sdwebui\py310）

For people having problems getting bitsandbytes to work in Linux, here's the way I got it running:

Using Conda/Mamba, I installed bitsandbytes 0.41.0 from conda-forge (https://anaconda.org/conda-forge/bitsandbytes) - by doing:
mamba install bitsandbytes -c conda-forge

Ran diagnostics which gave several warnings and errors: python -m bitsandbytes

Looked up libbitsandbytes:
find / -name 'libbitsandbytes*.so' 2>/dev/null
...
/home/user/miniforge3/envs/c/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda112.so

The cause of the problem was that I'm using CUDA 11.8, but the bitsandbytes package was compiled using number 11.2 (hence the 112 in the filename) and it was looking for libbitsandbytes_cuda118.so which is not there.
So, I used the number at the end of the found filename (112) to set the BNB_CUDA_VERSION env var:
export BNB_CUDA_VERSION=112
(For continued use add the export to your .bashrc)

Now run python -m bitsandbytes and it will hopefully report success at the end:
================================================================================
WARNING: Manual override via BNB_CUDA_VERSION env variable detected!
BNB_CUDA_VERSION=XXX can be used to load a bitsandbytes version that is different from the PyTorch CUDA version.
If this was unintended set the BNB_CUDA_VERSION variable to an empty string: export BNB_CUDA_VERSION=
If you use the manual override make sure the right libcudart.so is in your LD_LIBRARY_PATH
For example by adding the following to your .bashrc: export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:<path_to_cuda_dir/lib64
Loading CUDA version: BNB_CUDA_VERSION=112
================================================================================
...
...
SUCCESS!
Installation was successful!

BTW, cuda_setup/main.py appears to have a bug in method determine_cuda_runtime_lib_path() line 294 where the found lib paths are cleaned up, which makes it useless to set LD_LIBRARY_PATH.
But setting BNB_CUDA_VERSION as above overrides this bug.

pip install bitsandbytes-windows

You are a legend! I've been going mad uninstalling and reinstalling CUDA etc

pip install bitsandbytes-windows

You are a legend! I've been going mad uninstalling and reinstalling CUDA etc

still can't get it to work, i think it's because of the replacing the SD venv directory file part that I'm not understanding.

authors: NaomiNomz Rcrossmeister aksv5718 jndiogo swumagic

repo: TimDettmers/bitsandbytes


text:
CUDA ooM ( Reserved Memory > than Allocated Memory

Thanks so much for all your work on this tool. It's amazing!
I'm running a GeForce RTX 3060 with 12 gigs of vRam
I installed via the automatic1111 web UI and extension
I used the following settings:
Total Number classification images: 1500
Training Steps: 1200
Learning Rate: 0.000001
Save a checkpoint every: 50000 (effectively no checkpoints)
Generate a preview every: 50000

Advanced:
checked use8bitAdam
mixed precision fp16

I'm getting the following error:
Starting Dreambooth training...
 VRAM cleared.
 Allocated: 0.0GB
 Reserved: 0.0GB

 Loaded model.
 Allocated: 0.0GB
 Reserved: 0.0GB

==BUG REPORT==
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link

CUDA SETUP: Loading binary C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll...
 Scheduler Loaded
 Allocated: 0.2GB
 Reserved: 0.2GB

Steps:   0%|                                                                                | 0/1200 [00:00<?, ?it/s] Exception while training: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 12.00 GiB total capacity; 9.67 GiB already allocated; 0 bytes free; 10.37 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
 Allocated: 8.1GB
 Reserved: 10.4GB

Traceback (most recent call last):
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\train_dreambooth.py", line 920, in main
    accelerator.backward(loss)
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\accelerate\accelerator.py", line 882, in backward
    self.scaler.scale(loss).backward(**kwargs)
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\torch\_tensor.py", line 396, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph, inputs=inputs)
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\__init__.py", line 173, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\function.py", line 253, in apply
    return user_fn(self, *args)
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\torch\utils\checkpoint.py", line 146, in backward
    torch.autograd.backward(outputs_with_grad, args_with_grad)
  File "C:\Users\giblf\StableDiffusion\SD-gitclone\stable-diffusion-webui\venv\lib\site-packages\torch\autograd\__init__.py", line 173, in backward
    Variable._execution_engine.run_backward(  # Calls into the C++ engine to run the backward pass
RuntimeError: CUDA out of memory. Tried to allocate 1024.00 MiB (GPU 0; 12.00 GiB total capacity; 9.67 GiB already allocated; 0 bytes free; 10.37 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
 CLEANUP:
 Allocated: 7.1GB
 Reserved: 10.4GB

 Cleanup Complete.
 Allocated: 6.9GB
 Reserved: 7.7GB

Steps:   0%|                                                                                | 0/1200 [00:19<?, ?it/s]
 Training completed, reloading SD Model.
 Allocated: 0.0GB
 Reserved: 7.7GB

Memory output: {'VRAM cleared.': '0.0/0.0GB', 'Training completed, reloading SD Model.': '0.0/7.7GB'}
Re-applying optimizations...
Returning result: Training finished. Total lifetime steps: 1

i got the same problem and the same config

`venv "C:\Stable-diffusion-webui\venv\Scripts\Python.exe"
Python 3.10.6 (tags/v3.10.6:9c7b4bd, Aug  1 2022, 21:53:49) [MSC v.1932 64 bit (AMD64)]
Commit hash: 0b5dcb3d7ce397ad38312dbfc70febe7bb42dcc3
Installing requirements for Web UI
Checking Dreambooth requirements...
Checking/upgrading existing torch/torchvision installation
Checking torch and torchvision versions
Dreambooth revision is 1d7d34110005e7930b589546ff5d82ce16cdcadd
[+] bitsandbytes version 0.35.0 installed.
[+] diffusers version 0.8.1 installed.
[+] transformers version 4.21.0 installed.
[+] torch version 1.12.1+cu116 installed.
[+] torchvision version 0.13.1+cu116 installed.
[ ] xformers version 0.0.14.dev0 installed.
Launching Web UI with arguments: --deepdanbooru --xformers
Patching transformers to fix kwargs errors.
Dreambooth API layer loaded
LatentDiffusion: Running in eps-prediction mode
DiffusionWrapper has 859.52 M params.
Loading weights [81761151] from C:\Stable-diffusion-webui\models\Stable-diffusion\v1-5-pruned-emaonly.ckpt
Applying xformers cross attention optimization.
Model loaded.
Loaded a total of 12 textual inversion embeddings.
Embeddings: accurate-angel, dark-forest, depthmap, doose-realistic, Emma, HHHSofiko, ingmar-bergman, m-geo, nixeu, party-girl, ruan-jia, tcircle
Running on local URL:  http://127.0.0.1:7860
To create a public link, set share=True in launch().
Starting Dreambooth training...
Cleanup completed.
Allocated: 0.0GB
Reserved: 0.0GB
Allocated 0.0/2.0GB
Reserved: 0.0/2.0GB
Replace CrossAttention.forward to use xformers
Cleanup completed.
Allocated: 0.0GB
Reserved: 0.0GB
Loaded model.
Allocated: 0.0GB
Reserved: 0.0GB
===================================BUG REPORT===================================
Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues
For effortless bug reporting copy-paste your error into this form: https://docs.google.com/forms/d/e/1FAIpQLScPB8emS3Thkp66nvqwmjTEgxp8Y9ufuWTzFyr9kJ5AoI47dQ/viewform?usp=sf_link
CUDA SETUP: Loading binary C:\Stable-diffusion-webui\venv\lib\site-packages\bitsandbytes\libbitsandbytes_cuda116.dll...
Cleanup completed.
Allocated: 0.2GB
Reserved: 0.2GB
Scheduler, EMA Loaded.
Allocated: 3.8GB
Reserved: 3.9GB
***** Running training *****
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:***** Running training *****
Num examples = 62
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Num examples = 62
Num batches each epoch = 62
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Num batches each epoch = 62
Num Epochs = 17
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Num Epochs = 17
Instantaneous batch size per device = 1
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Instantaneous batch size per device = 1
Total train batch size (w. parallel, distributed & accumulation) = 1
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Total train batch size (w. parallel, distributed & accumulation) = 1
Gradient Accumulation steps = 1
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Gradient Accumulation steps = 1
Total optimization steps = 1000
INFO:extensions.sd_dreambooth_extension.dreambooth.train_dreambooth:  Total optimization steps = 1000
Training settings: CPU: False Adam: True, Prec: fp16, Grad: True, TextTr: True EM: False, LR: 1.72e-06
Allocated: 3.8GB
Reserved: 3.9GB
Steps:   0%|                                                                                | 0/1000 [00:00<?, ?it/s] Exception while training: CUDA out of memory. Tried to allocate 9.00 GiB (GPU 0; 24.00 GiB total capacity; 4.03 GiB already allocated; 14.50 GiB free; 4.11 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
Allocated: 4.0GB
Reserved: 4.1GB
Traceback (most recent call last):
File "C:\Stable-diffusion-webui\extensions\sd_dreambooth_extension\dreambooth\train_dreambooth.py", line 990, in main
latent_dist = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist
File "C:\Stable-diffusion-webui\venv\lib\site-packages\diffusers\models\vae.py", line 570, in encode
h = self.encoder(x)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\diffusers\models\vae.py", line 134, in forward
sample = down_block(sample)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\diffusers\models\unet_2d_blocks.py", line 771, in forward
hidden_states = resnet(hidden_states, temb=None)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\diffusers\models\resnet.py", line 464, in forward
hidden_states = self.conv1(hidden_states)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\module.py", line 1130, in _call_impl
return forward_call(*input, **kwargs)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\conv.py", line 457, in forward
return self._conv_forward(input, self.weight, self.bias)
File "C:\Stable-diffusion-webui\venv\lib\site-packages\torch\nn\modules\conv.py", line 453, in _conv_forward
return F.conv2d(input, weight, bias, self.stride,
RuntimeError: CUDA out of memory. Tried to allocate 9.00 GiB (GPU 0; 24.00 GiB total capacity; 4.03 GiB already allocated; 14.50 GiB free; 4.11 GiB reserved in total by PyTorch) If reserved memory is >> allocated memory try setting max_split_size_mb to avoid fragmentation.  See documentation for Memory Management and PYTORCH_CUDA_ALLOC_CONF
CLEANUP:
Allocated: 3.8GB
Reserved: 4.1GB
Cleanup completed.
Allocated: 3.8GB
Reserved: 3.9GB
Cleanup Complete.
Allocated: 3.8GB
Reserved: 3.9GB
Steps:   0%|                                                                                | 0/1000 [00:03<?, ?it/s]
Training completed, reloading SD Model.
Allocated: 0.0GB
Reserved: 3.8GB
Memory output: {'Training completed, reloading SD Model.': '0.0/3.8GB'}
Restored system models.
Allocated: 2.0GB
Reserved: 3.8GB
Returning result: Training finished. Total lifetime steps: 0`
The problems with memory allocation and allocation are the same as yours, but I have 24 GB available. But CUDA refuses to work. The settings are similar to yours. What could be the problem?

This happens in particular with convolutional architectures if your inputs are too large. @RomixERR I think in that case in particular you fed a very large image into the model. Try downsampling the image to make it smaller before feeding it into the model.
This issue does not seem like a bitsandbytes issue, but I will keep it open for now so that you have a space in which you night find a solution to this problem. Its also a useful reference for other people running into this problem.

authors: RomixERR TimDettmers giblfiz vixound


text:
Forgot "project.title"

Hey Tim You Forgot the "project.title" function in the projects area
Attributes:
project.id  #Returns the project id
project.url  #Returns the project url
project.title #Returns the project title<<<<<<< This is that you forgot
project.author  #Returns the username of the author
project.comments_allowed  #Returns True if comments are enabled
project.instructions
project.notes  #Returns the 'Notes and Credits' section
project.created  #Returns the date of the project creation
project.last_modified  #Returns the date when the project was modified the last time
project.share_date
project.thumbnail_url
project.remix_parent
project.remix_root
project.loves  #Returns the love count
project.favorites #Returns the project's favorite count
project.remix_count  #Returns the number of remixes
project.views  #Returns the view count
project.project_token
----- -----
project.update()  #Updates the above data

Thanks, is fixed now

repo: TimMcCool/scratchattach


text:
🤖 - [Eslint-plugin] add lint rule for simple text

Description

@ddubrava you seem to be fond of making linting rules, care to lend a hand? :smile:

@ddubrava you seem to be fond of making linting rules, care to lend a hand? 😄

yep, I can work on this, but you know, it seems weird that there isn't a rule for such a thing in angular-eslint, for instance. Maybe there are cases when [userName]="'John'" is ok?

should be userName="John"

should be userName="John"

Yeah, that makes sense. I mean, it's an obvious mistake, and still, we don't have community rules to handle this.

Asked Twitter about this. Maybe this way they want to highlight an error if such input is missing? 🤔

Seems like it:
https://github.com/angular-eslint/angular-eslint/issues/1172
Also worth noting that if [title] is an input, with brackets it won't bind to the native title attribute, which can be a desired effect. Anyway, let's close this as not fit due to overgeneralization.

authors: ddubrava splincode waterplea

repo: Tinkoff/taiga-ui


text:
chore(demo): new demo page Animations (#684)

PR Checklist
Please check if your PR fulfills the following requirements:

[x] The commit message follows Conventional Commits
[ ] Tests for the changes have been added (for bug fixes / features)
[x] Docs have been added / updated (for bug fixes / features)

PR Type
What kind of change does this PR introduce?

[ ] Bugfix
[x] Feature
[ ] Refactoring (no functional changes, no api changes)
[ ] Other... Please describe:

What is the new behavior?
new page /animations
Does this PR introduce a breaking change?

[ ] Yes
[X] No

Other information

@waterplea @splincode @vladimirpotekhin
guys, I have completely forgotten about this PR =)
I refreshed it (according to new code style and other cosmetic changes). Let's review it!
(this new page with animations don't show all animations; i hope that we will improve it step by step)


text:
favicon generator

http://www.favicon-generator.org/
need nodejs image processing library
but dunno

need a specific location for favicon

place big favicon a good place ( maybe import the image and set FAVICON in /src/config ?)
(and maybe I need example directory /example under the project root.)
create /src/resources directory if not exist, and then outputs different sizes of favicons as needed using some kind of image processing library.
one of 3 choices. (1)build error when not exist (2) import default (3) show nothing

I think (1) is the best choice but not sure.

994c069296709143ada297d1b5771dffd8978415

repo: TintypeMolly/react-boilerplate


text:
Fix test badge

No need to add a .yaml here

thanks!

authors: marnovandermaas mattvenn


text:
Rare segfault on log::worst_unread

A rare race condition can be encountered when log::worst_unread() is called:

Python thread backtrace (segfault)
#0  0x00000000004f146a in bookwyrm::tui::screen::log::<lambda(const auto:1&, const auto:2&)>::operator()<std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> >, std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> > >(const std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> &, const std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> &) const (__closure=0x7f48f44e2160, a=..., b=...) at /home/tmplt/code/bookwyrm/src/tui/screens/log.cpp:142
#1  0x00000000004f14b8 in __gnu_cxx::__ops::_Iter_comp_iter<bookwyrm::tui::screen::log::worst_unread() const::<lambda(const auto:1&, const auto:2&)> >::operator()<__gnu_cxx::__normal_iterator<const std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> >*, std::vector<std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> > > >, __gnu_cxx::__normal_iterator<const std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> >*, std::vector<std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> > > > >(__gnu_cxx::__normal_iterator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> const*, std::vector<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, std::allocator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> > > >, __gnu_cxx::__normal_iterator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> const*, std::vector<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, std::allocator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> > > >) (this=0x7f48f44e2160, __it1=..., __it2=...)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/predefined_ops.h:143
#2  0x00000000004f153b in std::__max_element<__gnu_cxx::__normal_iterator<const std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> >*, std::vector<std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> > > >, __gnu_cxx::__ops::_Iter_comp_iter<bookwyrm::tui::screen::log::worst_unread() const::<lambda(const auto:1&, const auto:2&)> > >(__gnu_cxx::__normal_iterator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> const*, std::vector<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, std::allocator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> > > >, __gnu_cxx::__normal_iterator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> const*, std::vector<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, std::allocator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> > > >, __gnu_cxx::__ops::_Iter_comp_iter<bookwyrm::tui::screen::log::worst_unread() const::<lambda(const auto:1&, const auto:2&)> >) (__first=..., __last=..., __comp=...)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/stl_algo.h:5653
#3  0x00000000004f1387 in std::max_element<__gnu_cxx::__normal_iterator<const std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> >*, std::vector<std::pair<const bookwyrm::core::log_level, const std::__cxx11::basic_string<char> > > >, bookwyrm::tui::screen::log::worst_unread() const::<lambda(const auto:1&, const auto:2&)> >(__gnu_cxx::__normal_iterator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> const*, std::vector<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, std::allocator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> > > >, __gnu_cxx::__normal_iterator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> const*, std::vector<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const>, std::allocator<std::pair<bookwyrm::core::log_level const, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const> > > >, bookwyrm::tui::screen::log::<lambda(const auto:1&, const auto:2&)>) (
    __first=..., __last=..., __comp=...)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/stl_algo.h:5705
#4  0x00000000004f0ea6 in bookwyrm::tui::screen::log::worst_unread (this=0x24cb5d0)
    at /home/tmplt/code/bookwyrm/src/tui/screens/log.cpp:142
#5  0x00000000004e5670 in bookwyrm::tui::tui::update (this=0x24eb280) at /home/tmplt/code/bookwyrm/src/tui/tui.cpp:33
#6  0x00000000004e5971 in bookwyrm::tui::tui::log (this=0x24eb280, level=bookwyrm::core::log_level::err, message=...)
    at /home/tmplt/code/bookwyrm/src/tui/tui.cpp:60
#7  0x00007f48f45f00b9 in bookwyrm::core::plugin_handler::log (this=0x23e8d80, lvl=bookwyrm::core::log_level::err, msg=...)
    at /home/tmplt/code/bookwyrm/src/core/plugin_handler.cpp:289
#8  0x00007f48f45c01c0 in detail::log_wrapper::error (this=0x7f48ec002ac0, msg=...)
    at /home/tmplt/code/bookwyrm/src/core/bindings/python.cpp:20
#9  0x00007f48f45d0ffb in pybind11::cpp_function::cpp_function<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}::operator()(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&) const (__closure=0x253b868, c=0x7f48ec002ac0, args#0=...) at /home/tmplt/code/bookwyrm/lib/pybind11/include/pybind11/pybind11.h:74
#10 0x00007f48f45e5b35 in pybind11::detail::argument_loader<detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&>::call_impl<void, pybind11::cpp_function::cpp_function<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}&, 0ul, 1ul, pybind11::detail::void_type>(pybind11::cpp_function::cpp_function<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}&, std::integer_sequence<unsigned long, 0ul, 1ul>, pybind11::detail::void_type&&) (this=0x7f48f44e24f0, f=...) at /home/tmplt/code/bookwyrm/lib/pybind11/include/pybind11/cast.h:1931
#11 0x00007f48f45e070e in pybind11::detail::argument_loader<detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&>::call<void, pybind11::detail::void_type, pybind11::cpp_function::cpp_function<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}&>(pybind11::cpp_function::cpp_function<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}&) && (this=0x7f48f44e24f0, f=...)
    at /home/tmplt/code/bookwyrm/lib/pybind11/include/pybind11/cast.h:1913
#12 0x00007f48f45d8536 in void pybind11::cpp_function::initialize<pybind11::cpp_function::initialize<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}, void, detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(pybind11::cpp_function::initialize<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}&&, void (*)(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)#3}::operator()(pybind11::detail::function_call) const (__closure=0x0, call=...)
    at /home/tmplt/code/bookwyrm/lib/pybind11/include/pybind11/pybind11.h:155
#13 0x00007f48f45d874a in void pybind11::cpp_function::initialize<pybind11::cpp_function::initialize<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}, void, detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(pybind11::cpp_function::initialize<void, detail::log_wrapper, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&, pybind11::name, pybind11::is_method, pybind11::sibling>(void (detail::log_wrapper::*)(std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&)#1}&&, void (*)(detail::log_wrapper*, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> > const&), pybind11::name const&, pybind11::is_method const&, pybind11::sibling const&)::{lambda(pybind11::detail::function_call&)#3}::_FUN(pybind11::detail::function_call) ()
    at /home/tmplt/code/bookwyrm/lib/pybind11/include/pybind11/pybind11.h:133
#14 0x00007f48f45ba17b in pybind11::cpp_function::dispatcher (self=0x7f48f4b34060, args_in=0x7f48f4b2e4c8, kwargs_in=0x0)
    at /home/tmplt/code/bookwyrm/lib/pybind11/include/pybind11/pybind11.h:620
#15 0x00007f48fff158ba in _PyCFunction_FastCallDict ()
   from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#16 0x00007f48fffa3b99 in call_function () from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#17 0x00007f48fffa8ef1 in _PyEval_EvalFrameDefault ()
   from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#18 0x00007f48fffa3a3a in _PyEval_EvalCodeWithName ()
   from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#19 0x00007f48fffa409e in PyEval_EvalCodeEx () from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#20 0x00007f48ffede475 in function_call () from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#21 0x00007f48ffeab158 in PyObject_Call () from /nix/store/hy65mn4wjswqih75gfr6g4q3xgqdm325-python3-3.6.6/lib/libpython3.6m.so.1.0
#22 0x00000000004b6bca in bookwyrm::core::plugin_handler::python_module_runner (this=0x23e8d80, module=...)
    at /home/tmplt/code/bookwyrm/src/core/plugin_handler.cpp:186
#23 0x00000000004c4c77 in std::__invoke_impl<void, void (bookwyrm::core::plugin_handler::*)(pybind11::module), bookwyrm::core::plugin_handler*, pybind11::module> (__f=
    @0x245c478: (void (bookwyrm::core::plugin_handler::*)(bookwyrm::core::plugin_handler * const, pybind11::module)) 0x4b6a0e <bookwyrm::core::plugin_handler::python_module_runner(pybind11::module)>, __t=@0x245c470: 0x23e8d80, __args#0=...)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/invoke.h:73
#24 0x00000000004c35f9 in std::__invoke<void (bookwyrm::core::plugin_handler::*)(pybind11::module), bookwyrm::core::plugin_handler*, pybind11::module> (__fn=
    @0x245c478: (void (bookwyrm::core::plugin_handler::*)(bookwyrm::core::plugin_handler * const, pybind11::module)) 0x4b6a0e <bookwyrm::core::plugin_handler::python_module_runner(pybind11::module)>, __args#0=@0x245c470: 0x23e8d80, __args#1=...)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/invoke.h:95
#25 0x00000000004c670d in std::thread::_Invoker<std::tuple<void (bookwyrm::core::plugin_handler::*)(pybind11::module), bookwyrm::core::plugin_handler*, pybind11::module> >::_M_invoke<0ul, 1ul, 2ul> (this=0x245c468)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/thread:234
#26 0x00000000004c66a7 in std::thread::_Invoker<std::tuple<void (bookwyrm::core::plugin_handler::*)(pybind11::module), bookwyrm::core::plugin_handler*, pybind11::module> >::operator() (this=0x245c468)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/thread:243
#27 0x00000000004c6686 in std::thread::_State_impl<std::thread::_Invoker<std::tuple<void (bookwyrm::core::plugin_handler::*)(pybind11::module), bookwyrm::core::plugin_handler*, pybind11::module> > >::_M_run (this=0x245c460)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/thread:186
#28 0x00007f48ff6ebedf in ?? () from /nix/store/zk5zj2307zxaq7dx585yia3dn5k4qlsl-gcc-7.3.0-lib/lib/libstdc++.so.6
#29 0x00007f48fee675a7 in start_thread () from /nix/store/fg4yq8i8wd08xg3fy58l6q73cjy8hjr2-glibc-2.27/lib/libpthread.so.0
#30 0x00007f48feb9e22f in clone () from /nix/store/fg4yq8i8wd08xg3fy58l6q73cjy8hjr2-glibc-2.27/lib/libc.so.6

Main thread backtrace (waiting for mutex)
#0  0x00007f48fee7086c in __lll_lock_wait () from /nix/store/fg4yq8i8wd08xg3fy58l6q73cjy8hjr2-glibc-2.27/lib/libpthread.so.0
#0  0x00007f48fee7086c in __lll_lock_wait () from /nix/store/fg4yq8i8wd08xg3fy58l6q73cjy8hjr2-glibc-2.27/lib/libpthread.so.0
#1  0x00007f48fee69b65 in pthread_mutex_lock () from /nix/store/fg4yq8i8wd08xg3fy58l6q73cjy8hjr2-glibc-2.27/lib/libpthread.so.0
#2  0x00000000004b4f99 in __gthread_mutex_lock (__mutex=0x24eb2a0)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/x86_64-unknown-linux-gnu/bits/gthr-default.h:748
#3  0x00000000004b8d16 in std::mutex::lock (this=0x24eb2a0)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/std_mutex.h:103
#4  0x00000000004bcd9e in std::lock_guard<std::mutex>::lock_guard (this=0x7ffd98c1bb40, __m=...)
    at /nix/store/vv4r320p5yd1k01kld62q1lppjxcswhb-gcc-7.3.0/include/c++/7.3.0/bits/std_mutex.h:162
#5  0x00000000004e55ff in bookwyrm::tui::tui::update (this=0x24eb280) at /home/tmplt/code/bookwyrm/src/tui/tui.cpp:29
#6  0x00000000004e5971 in bookwyrm::tui::tui::log (this=0x24eb280, level=bookwyrm::core::log_level::err, message=...)
    at /home/tmplt/code/bookwyrm/src/tui/tui.cpp:60
#7  0x00000000004b79f9 in bookwyrm::core::plugin_handler::set_frontend (this=0x23e8d80, fe=...)
    at /home/tmplt/code/bookwyrm/src/core/plugin_handler.cpp:304
#8  0x0000000000410985 in main (argc=3, argv=0x7ffd98c1c4b8) at /home/tmplt/code/bookwyrm/src/main.cpp:223

Could 06ba687 possibly have resolved this?

repo: Tmplt/bookwyrm


text:
won't work

Hey there, could you provide some more information om your setup, particularly your operating system? This looks very similar to a known issue that appears when running Desktoptale on Linux or macOS using a compatibility layer.

authors: ToadsworthLP TymTymXD

repo: ToadsworthLP/desktoptale


text:
TST: 'pip install wheel' before other dependencies

This is an amendment to #1011 which initially passed all CI tests before merge, but subsequently raised a new AppVeyor failure with pip on Python 3.9 after merge, as wheel was not installed (despite it being part of requirements-dev.txt). This installs/upgrades wheel before other dependencies to avoid the issue.

Coverage remained the same at 83.377% when pulling f791fe7b038d377fa003c578c4de9e252d7d66e9 on mwtoews:pip into 3abf3a0e3a0494f4e31114cc6b8bdfa9a9556b69 on Toblerity:master.

authors: coveralls mwtoews

repo: Toblerity/Shapely


text:
Cursor doesn't update in some interactions

As a performance boost, Arroost doesn't manually handle the cursor anymore. It's done by individual dom elements. This means that certain actions don't change it as responsively anymore. For example:

Hovering a cell, than panning with trackpad results in the cursor staying as 'pointer', instead of changing to default.
Chucking a cell, then keeping the cursor still as the cell 'flies off', results in the same issue.

Maybe we could manually control for these specific situations? It's probably not too much a performance hit, as we're letting the browser handle it in all other scenarios

Related to #96

repo: TodePond/Arroost


text:
🛑 Tarifa social is down

In 8b7bece, Tarifa social (https://aysa.com.ar/usuarios/Tarifa-Social/tarifa_social) was down:

HTTP code: 502
Response time: 8 ms

Resolved: Tarifa social is back up in 079dbdd after 16 minutes.


text:
Feature request: Sort by date

I like ocasionally looking at new additions, but there is no way to see what's new. Well, there is the rss feed, but always using it as an intermediary is not ideal. I would proposed a sortable column date added or date modified or something, depending on your underlying data structure

I'm currently occupied, but this repository is updated, feel free to make a pull request

repo: TomEke/WiiU-Title-Key


text:
Fix dateserializer

See #32

Thanks @TomSDEVSN @PierreSchwang !

authors: TomSDEVSN blindpirate

repo: TomSDEVSN/hetznercloud-java


text:
A Parameter which remove wrapping " from a string?

 dasel -r json -w json -f package.json .name --pretty=false
"template-react-component-library"

I want to get:
dasel -r json -w json -f package.json .name --pretty=false --typeless=true
template-react-component-library

Would hat be possible? Because these surrunding " are no practical for me.

The issue here is that you're writing to json. JSON strings are wrapped with quotes.
-w plain,toml,yaml may work for you here

authors: TomWright tobiashochguertel

repo: TomWright/dasel


text:
Ordered maps

This is my first attempt at supporting ordered maps, starting with JSON.
It's become apparent there's no way to achieve this functionality unless I write my own encoders + decoders for all supported file formats.
This is a very large undertaking, and given the fact that ordered JSON isn't even a part of the JSON spec itself I'm not sure it's worth doing due to the amount of complexity it adds to the project.
Of course this is only regarding JSON as the majority of my tests are placed around it and it gave me an easy way to check a variety of use-cases quickly and easily, but may well transfer to other data formats.

I think it’s worth doing, if only just iteratively one format at a type. Open source community can implement the formats that are important to them. I just need yaml, and don’t mind working on that following the patterns you lay out here. Would an iterative process work here?
I don’t see how this can be avoided, dasel is a great concept but preserving key order is a must have feature for a tool like this. Nobody wants their entire document mangled.

Thanks for the feedback @joshuadmatthews.
I believe the ordered map implementation here allows us to deal with ordered maps internally so the addition of formatters at a later date shouldn't be a problem.
I'll take a look at a yaml parser implementation when I get the chance.

I just realised that a custom parser implementation will be able to keep hold of all metadata, including comments.

I have a working implementation for JSON...
// New logic...
$ echo '{"z":1,"a":2,"c":3}' | go run cmd/dasel/main.go -r json
{
  "z": 1,
  "a": 2,
  "c": 3
}
// Existing logic...
$ echo '{"z":1,"a":2,"c":3}' | dasel -r json
{
  "a": 2,
  "c": 3,
  "z": 1
}

You rock @TomWright ! Thanks for pushing forward!

This PR has been sitting around for a long time - I'm merging it in now as it's update to date with master and all tests are passing.

authors: TomWright joshuadmatthews


text:
if opening a file fails, specify whether it was for reading or for writing

Hello, thanks for Dasel!
I stumbled upon this minor enhancement to a diagnostic message, so I went away with the PR without opening an issue before.
Assume a read-only file:
touch foo.json
chmod 400 foo.json

and the Dasel invocation:
dasel put -f foo.json -r json -t string -v Frank 'name.first'

Before this commit, we would get:
Error: could not open file: foo.json: open foo.json: permission denied

leaving the user confused.
Now instead we would get:
Error: could not open file for writing: foo.json: open foo.json: permission denied
                           ^^^^^^^^^^^

helping the user to understand what is happening.

Thanks for the suggestion @marco-m , this is now in the development version and will go out in the next release.

Hey @marco-m, I thought this was an issue 😵💫 Sorry I didn't base off of your PR

@TomWright

Hey @marco-m, I thought this was an issue 😵💫 Sorry I didn't base off of your PR

Don't worry, I always wondered why GitHub had the same numbering for both PRs and issues, this can happen :-)
Happy that the idea has been accepted.

authors: TomWright marco-m


text:
Update undo fx

I took a short break from homework.

I could also ignore any replace calls that will result in an identity change by calling:
// check the start and end indexes
if (getDocument(start, end).equals(replacement)) {
    return;
}
// the actual replacement code

If your only motivation is performance, I say don't do it. Extracting a subsequence from a document has about the same cost as replacing it.

Ok. That's what I was thinking, but I wasn't sure how much of a performance hit that would be.

Yes, it would be OK.

I forgot that I could merge this... lol, I was expecting you to.

authors: JordanMartinez TomasMikula

repo: TomasMikula/RichTextFX


text:
Added Token env and config.json support

put token=YOUR_BOT_TOKEN in a .env file to Make your token Safe

Yes!!!

what about people who aren't using replit and have an actual .env file?
it would have been useful if you could have added a line defining the dotenv config

There is a .env support

I just accidentally removed it i will add it soon back