sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: godot
Issue: Building project solution causes C# editor plugins to reload and lose state
Labels: enhancement,topic:editor,confirmed,topic:plugin,topic:dotnet

### Godot version

3.3.2 stable mono win64

### System information

Windows 10

### Issue description

When you build your project in godot it causes all your editor plugins that you developed in C# to also reload, however new code is not active after rebuild. You still have to disable/enable the plugin to actually see changes.

So I am not sure what correct behavior should be here, I would guess either:

1. Building should reload plugins and any code changes should be applied to the EditorPlugin
2. Building should not reload editor plugins (probably preferred unless other issues like marshalling are fixed)

Personally I would lean towards option 2 because reloading causes you to lose a lot of state. In the current build of godot you also can't keep references to custom classes (unless they inherit from Godot.Object) inside your Node scripts. You have to move any such references into statics, then rather keep a key reference inside your Node scripts to look them up. If you do not you will get marshalling issues (which there already exists bug reports for).

```csharp
// Example workaround for marshalling

// Static reference to custom class
private static Dictionary<string, CustomClass> CustomClassLookup= new Dictionary<string, CustomClass>();

// List containing keys for lookup in dicitonary
private List<string> KeyList = new List<string>();
```

In addition all static data you keep will get lost on reload. The upside to this is that you can use this to easily detect if your C# EditorPlugin has been reloaded by creating a static bool and then checking and flipping it during the _Process(). If the boolean resets then your plugin has been reloaded as the value of the static is not kept on rebuild.

```csharp
// Code to detect rebuild
public static bool Rebuild = true;
public override void _Process(float delta)
{
	if (Rebuild)
	{
		GD.Print("EditorPlugin was reloaded");
		Rebuild = false;
	}
}
```

### Steps to reproduce

1. Create a C# EditorPlugin for godot that contains the rebuild detection code provided above.
2. Load the editor plugin in some project that contain C# code.
3. Change some C# code in your project and rebuild the project

### Minimal reproduction project

_No response_
Repository: go
Issue: crypto/x509: inner and outer signature algorithm identifiers don't match
Labels: NeedsInvestigation

<!--
Please answer these questions before submitting your issue. Thanks!
For questions please use one of our forums: https://github.com/golang/go/wiki/Questions
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### What version of Go are you using (`go version`)?

<pre>
go version go1.17rc2 darwin/arm64
</pre>

### Does this issue reproduce with the latest release?

Does not reproduce in `go version go1.16.6 darwin/arm64`

### What operating system and processor architecture are you using (`go env`)?

<details><summary><code>go env</code> Output</summary><br><pre>
$ go env

GO111MODULE=""
GOARCH="arm64"
GOBIN=""
GOCACHE="/Users/vvrantchan/Library/Caches/go-build"
GOENV="/Users/vvrantchan/Library/Application Support/go/env"
GOEXE=""
GOFLAGS=""
GOHOSTARCH="arm64"
GOHOSTOS="darwin"
GOINSECURE=""
GOMODCACHE="/Users/vvrantchan/go/pkg/mod"
GONOPROXY=""
GONOSUMDB=""
GOOS="darwin"
GOPATH="/Users/vvrantchan/go"
GOPRIVATE=""
GOPROXY="https://proxy.golang.org,direct"
GOROOT="/opt/homebrew/Cellar/go/1.16.6/libexec"
GOSUMDB="sum.golang.org"
GOTMPDIR=""
GOTOOLDIR="/opt/homebrew/Cellar/go/1.16.6/libexec/pkg/tool/darwin_arm64"
GOVCS=""
GOVERSION="go1.16.6"
GCCGO="gccgo"
AR="ar"
CC="clang"
CXX="clang++"
CGO_ENABLED="1"
GOMOD="/Users/vvrantchan/code/x/b196191547/go.mod"
CGO_CFLAGS="-g -O2"
CGO_CPPFLAGS=""
CGO_CXXFLAGS="-g -O2"
CGO_FFLAGS="-g -O2"
CGO_LDFLAGS="-g -O2"
PKG_CONFIG="pkg-config"
GOGCCFLAGS="-fPIC -arch arm64 -pthread -fno-caret-diagnostics -Qunused-arguments -fmessage-length=0 -fdebug-prefix-map=/var/folders/kq/g8xycx_95j54cxffqtx0s_xm00lql0/T/go-build1627430943=/tmp/go-build -gno-record-gcc-switches -fno-common"
</pre></details>

### What did you do?
https://play.golang.org/p/-rDc14aTqgV

The `com.apple.systemdefault` certificate is a self signed root that Apple generates during macOS setup. It lives in most (all?) macOS user keychain. I recently discovered that go.17rc2 fails to parse some (but not all) of the Apple system keychain certificates. The one in the example was issued in 2015. It's possible Apple issued some of these certs erroneously, and fixed the mismatch in a follow-up release. 

I've only detected the failure on about 10 out of 100k macOS devices, but our environment has quick refresh cycles for devices. It's possible the problem would be more widespread for other users parsing the macOS System Keychain with Go. 
<!--
If possible, provide a recipe for reproducing the error.
A complete runnable program is good.
A link on play.golang.org is best.
-->



### What did you expect to see?
```
com.apple.systemdefault
2015-03-25 21:10:26 +0000 UTC
```


### What did you see instead?
```
2021/08/13 10:01:51 x509: inner and outer signature algorithm identifiers don't match
exit status 1
```
Repository: terminal
Issue: Actions without keys should be rejected
Labels: Issue-Bug,Area-Settings,Product-Terminal

Extracted from #10933.

# Steps to reproduce

Add this to your "actions":
```json
{
    "command": "unbound",
    "keys": ""
}
```

# Expected behavior

The application should ignore/reject such actions and optionally remove or warn about them.

# Actual behavior

`ActionMap` is being filled with such actions without validation.
Additionally the bug reporter (@Rosefield) mentioned that they can't remember adding such an action by themselves. It might thus be necessary to add further `assert()`s to find further causes elsewhere.
Repository: youtube-dl
Issue: Add option to specify a location for atomic parsley: --atomic-location PATH
Labels: request

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## Checklist

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- First, make sure you are using the latest version of youtube-dl. Run `youtube-dl --version` and ensure your version is 2021.06.06. If it's not, see https://yt-dl.org/update on how to update. Issues with outdated versions will be REJECTED.
- Search the bug tracker for similar feature requests: http://yt-dl.org/search-issues. DO NOT post duplicates.
- Finally, put x into all relevant boxes (like this [x])
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- [X] I'm reporting a feature request
- [X] I've verified that I'm running youtube-dl version **2021.06.06**
- [X] I've searched the bug tracker for similar feature requests including closed ones


## Description

<!--
Provide an explanation of your issue in an arbitrary form. Please make sure the description is worded well enough to be understood, see https://github.com/ytdl-org/youtube-dl#is-the-description-of-the-issue-itself-sufficient. Provide any additional information, suggested solutions and as much context and examples as possible.
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Having to rely on atomic parsley being added to `PATH` is rather annoying when using the **youtube-dl pip package** in your own scripts, especially if you need to distribute your program to another computer and you want to bundle atomic parsley with it.

Because of this, I would like to request a feature allowing you to specify a location to either the atomic parsley binary or the folder containing it. 

Suggestions for implementation are below:

1. This option could be implemented similarly to the `--ffmpeg-location` command option and the `ffmpeg_location` option for `YoutubeDL` in python. E.g.:
```python
opts={
...
"atomic_location": "./AtomicParsley-win32-0.9.0/AtomicParsley.exe",
"ffmpeg_location: "./ffmpeg-20200115-0dc0837-win64-static/bin",
"postprocessors": [{'key': 'EmbedThumbnail', 'already_have_thumbnail': False}],
...
}
```
AND
- `youtube-dl.exe --ffmpeg-location "ffmpeg-20200115-0dc0837-win64-static/bin" https://www.youtube.com/watch?v=dQw4w9WgXcQ --atomic_location "AtomicParsley-win32-0.9.0/AtomicParsley.exe"`


2. Alternatively, it could just be added as a parameter to the `EmbedThumnailPP` Postprocessor. E.g.:
```python
opts = {
...
"postprocessors": [{'key': 'EmbedThumbnail', 'already_have_thumbnail': False, "atomic_location": "./AtomicParsley-win32-0.9.0/AtomicParsley.exe")}],
"ffmpeg_location: "./ffmpeg-20200115-0dc0837-win64-static/bin",
...
}
```
Repository: rust
Issue: Lint on duplicate trait bound
Labels: A-diagnostics,A-trait-system,T-compiler

The following code compiles without any warnings:

```rust
pub trait Foo {}

pub fn bar<T: Foo + Foo>() {}
```

We should lint on the duplicate `Foo` bound.

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Repository: vscode
Issue: Register VS Code as a folder opener at OS-Level
Labels: feature-request,install-update,windows

This would be helpful for not getting into the "broken" case of having only single files open as opposed to project folders.

Either here:
![image](https://user-images.githubusercontent.com/8586769/127926057-ae986e3c-f839-475a-a92e-540b23491b1e.png)

Or here:
![image](https://user-images.githubusercontent.com/8586769/127925973-7dda8582-683a-4007-be5a-f10d45fc2a26.png)

MacOS (I already added a custom "Open in Insiders" action, ideally it'd be built in):
![image](https://user-images.githubusercontent.com/8586769/129403298-cc520e0b-05fd-4df0-8dac-8070e6ef7740.png)
Repository: youtube-dl
Issue: MPornTube support request
Labels: site-support-request

<!--

######################################################################
  WARNING!
  IGNORING THE FOLLOWING TEMPLATE WILL RESULT IN ISSUE CLOSED AS INCOMPLETE
######################################################################

-->


## Checklist

<!--
Carefully read and work through this check list in order to prevent the most common mistakes and misuse of youtube-dl:
- First of, make sure you are using the latest version of youtube-dl. Run `youtube-dl --version` and ensure your version is 2021.06.06. If it's not, see https://yt-dl.org/update on how to update. Issues with outdated version will be REJECTED.
- Make sure that all provided video/audio/playlist URLs (if any) are alive and playable in a browser.
- Make sure that site you are requesting is not dedicated to copyright infringement, see https://yt-dl.org/copyright-infringement. youtube-dl does not support such sites. In order for site support request to be accepted all provided example URLs should not violate any copyrights.
- Search the bugtracker for similar site support requests: http://yt-dl.org/search-issues. DO NOT post duplicates.
- Finally, put x into all relevant boxes (like this [x])
-->

- [x] I'm reporting a new site support request
- [x] I've verified that I'm running youtube-dl version **2021.06.06**
- [x] I've checked that all provided URLs are alive and playable in a browser
- [ ] I've checked that none of provided URLs violate any copyrights
- [x] I've searched the bugtracker for similar site support requests including closed ones


## Example URLs

<!--
Provide all kinds of example URLs support for which should be included. Replace following example URLs by yours.
-->

- Single video: https://milkporntube.com/princessasiana-lesbian-cam-milking-porn/


## Description

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Provide any additional information.
If work on your issue requires account credentials please provide them or explain how one can obtain them.
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Downloads just the banner video, ignores everything else
Repository: pytorch
Issue: Add a timeout argument to RPC shutdown()
Labels: oncall: distributed,module: bootcamp

The RPC `shutdown()` API (linked below) [always uses the default timeout value (5s)](https://github.com/pytorch/pytorch/blob/dc1bd6acee95c343fe3588b62d5fbd09de93ab9c/torch/distributed/rpc/api.py#L277), which might be too short if all threads on the leader process are busy or when the world size is too large. 

https://github.com/pytorch/pytorch/blob/dc1bd6acee95c343fe3588b62d5fbd09de93ab9c/torch/distributed/rpc/api.py#L285-L343

Let's add a `timeout` argument to allow users to override the default value, and add tests in [rpc_test.py](https://github.com/pytorch/pytorch/blob/master/torch/testing/_internal/distributed/rpc/rpc_test.py) to cover it. 

cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @osalpekar @jiayisuse @agolynski @SciPioneer @H-Huang @mrzzd @cbalioglu @gcramer23
Repository: material-ui
Issue: [Select] Collapse as TransitionComponent doesn't work
Labels: bug 🐛,component: select

[x] The issue is present in the latest release.
[x] I have searched the [issues](https://github.com/mui-org/material-ui/issues) of this repository and believe that this is not a duplicate.

## Current Behavior 😯

If I specify a TransitionComponent in the MenuProps of a Select component, the menu draws in the wrong place and does no transition.

The default transition is Grow, but I want to change it Collapse.

## Expected Behavior 🤔

The menu should use the Collapse transition.

## Steps to Reproduce 🕹

I couldn't figure out how to use codesandbox so I made a new create-react-app project.  Here's the source to App.js:

```
import React from 'react';
import MenuItem from '@material-ui/core/MenuItem';
import Select from '@material-ui/core/Select';

export default function App() {
  const [selectValue, setSelectValue] = React.useState(1)

  return (
    <div>
      <br/><br/>
      <div style={{marginLeft: 100}}>
        <Select
          value={selectValue}
          onChange={evt => setSelectValue(evt.target.value)}
          MenuProps={{
            //TransitionComponent: "Collapse",
            transitionDuration: 1500
          }}
        >
          <MenuItem value={1}>one</MenuItem>
          <MenuItem value={2}>two</MenuItem>
          <MenuItem value={3}>three</MenuItem>
        </Select>
      </div>
    </div>
  )
}

```
If you run this and click on the Select you'll see the menu appear and slowly grow.  If you then uncomment-out the TransitionComponent line and try again, you'll see the menu appear with no transition at the origin.

## Context 🔦

I don't like the default Grow effect and would like to use a Collapse effect. 

## Your Environment 🌎

<details>
  <summary>`npx @material-ui/envinfo`</summary>
  
```
  System:
    OS: Windows 10 10.0.19043
  Binaries:
    Node: 14.17.3 - C:\Program Files\nodejs\node.EXE
    Yarn: Not Found
    npm: 7.20.5 - C:\Program Files\nodejs\npm.CMD
  Browsers:
    Chrome: Not Found
    Edge: Spartan (44.19041.1023.0), Chromium (92.0.902.73)
  npmPackages:
    @material-ui/core: ^4.12.3 => 4.12.3
    @material-ui/styles:  4.11.4
    @material-ui/system:  4.12.1
    @material-ui/types:  5.1.0
    @material-ui/utils:  4.11.2
    @types/react:  17.0.17
    react: ^17.0.2 => 17.0.2
    react-dom: ^17.0.2 => 17.0.2
    typescript:  4.3.5
```
</details>
Repository: TypeScript
Issue: Rest element type not recognized for inner array types
Labels: Bug

# Bug Report

Hello,
The compiler doesn't recognize an inner array type as an "array type" when inferred in a conditional type.

### 🔎 Search Terms

I searched for array, rest element, inner array type, multidimensional array type.
Couldn't find anything.

### ⏯ Playground Link

[Playground link with relevant code](https://www.typescriptlang.org/play?#code/C4TwDgpgBACgrgZwBYBUD26wEEBOOCGIAPCgDRS4EgDyAZpYVBAB7AQB2AJglPuyAG0AusIB8UALwAoAJAMa9PIxZsuPAQEt2tCDiiZ55AHQmtOvQCUICYEKgB+WTIECyUE0YNKQQ4yas2dgBcTuwQAG66ANxSQA)

### 💻 Code

The compiler complains with the following code:

```typescript
type PushToTopArray<T, ArrayOfArray extends any[][]> =
	ArrayOfArray extends [infer TopArray, ...infer Rest] ?
		[[T, ...TopArray], ...Rest] :
		never;
```

### 🙁 Actual behavior

with error: `A rest element type must be an array type. (2574)` on `...TopArray`.

### 🙂 Expected behavior

But since `ArrayOfArray extends any[][]`, the inferred `TopArray` should be of type `any[]` and therefore satisfy the rest element type.
Repository: pytorch
Issue: `help(torch.finfo)` doesn't give useful information even though class has documentation
Labels: module: docs,triaged

## 🐛 Bug

Calling `help` on `torch.finfo` does not show the class's docs.

## To Reproduce

Steps to reproduce the behavior:

1. start python interpreter
1. `import torch`
1. `help(torch.finfo)`

## Expected behavior

Python's help system should show the documentation for this class, which we do have:
https://pytorch.org/docs/stable/type_info.html
https://github.com/pytorch/pytorch/blob/master/docs/source/type_info.rst

## Environment

Probably not relevant, but here:
PyTorch version: N/A
Is debug build: N/A
CUDA used to build PyTorch: N/A
ROCM used to build PyTorch: N/A

OS: CentOS Stream 8 (x86_64)
GCC version: (GCC) 8.5.0 20210514 (Red Hat 8.5.0-2)
Clang version: 11.0.0 (Red Hat 11.0.0-1.module_el8.4.0+587+5187cac0)
CMake version: version 3.19.4
Libc version: glibc-2.28

Python version: 3.8.5 (default, Sep  4 2020, 07:30:14)  [GCC 7.3.0] (64-bit runtime)
Python platform: Linux-5.2.9-229_fbk15_hardened_4185_g357f49b36602-x86_64-with-glibc2.10
Is CUDA available: N/A
CUDA runtime version: Could not collect
GPU models and configuration: Could not collect
Nvidia driver version: Could not collect
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A

Versions of relevant libraries:
[pip3] numpy==1.19.2
[pip3] pytorch-sphinx-theme==0.0.24
[pip3] torch==1.10.0a0+git60d476f
[pip3] torchvision==0.10.0
[conda] blas                      1.0                         mkl  
[conda] mkl                       2020.2                      256  
[conda] mkl-include               2020.2                      256  
[conda] mkl-service               2.3.0            py38he904b0f_0  
[conda] mkl_fft                   1.2.0            py38h23d657b_0  
[conda] mkl_random                1.1.1            py38h0573a6f_0  
[conda] numpy                     1.19.2           py38h54aff64_0  
[conda] numpy-base                1.19.2           py38hfa32c7d_0  
[conda] pytorch-sphinx-theme      0.0.24                    dev_0    <develop>
[conda] torch                     1.10.0a0+git60d476f           dev_0    <develop>
[conda] torchvision               0.10.0                   pypi_0    pypi


## Additional context
`help(numpy.finfo)` works as expected

cc @brianjo @mruberry
Repository: deno
Issue: Determining request "upgradability" for web socket before upgrade
Labels: suggestion

The Deno `std` library for web sockets had a function `acceptable()` which would determine if a current HTTP request was upgradeable to a web socket.  With `Deno.upgradeWebSocket()` there is nothing you can do to determine the acceptability of a `Request` to be upgraded definitively.

Currently a user of oak is encountering an issue (oakserver/oak#370) were the `std` library `acceptable()` is returning true, but when the socket is attempted to be upgraded it is failing, saying no upgrade is available. (Let alone the error message not providing enough information as to the cause of the inability to upgrade, it is simply saying `no upgrade available`).
Repository: react
Issue: Scheduling Profiler: Add React Native support
Labels: Type: Discussion,Component: Developer Tools,Size: Medium-Large

The [Scheduling Profiler](https://github.com/reactwg/react-18/discussions/76) currently reads data from an exported Chrome performance profile, including [User Timing API](https://developer.mozilla.org/en-US/docs/Web/API/User_Timing_API) marks created by React.

The Chrome profile contains native events (e.g. "click" events), CPU samples that can be used to display a flame graph of what JavaScript was running at a point in time, and base64 encoded screenshots (if enabled). The User Timing data contains info about when state updates are scheduled, when React renders or commits work, when components suspend, etc. This data is processed together to generate the Scheduler Profiler UI.

React Native (or more specifically Hermes) doesn't provide a way for users to record or export this data. (I think CPU sampling can be done somehow but I'm not sure how to access the information.) However even if we _only_ had the React User Timing marks, this would still be useful data to display in the profiler. (We could probably also add markers for React's synthetic events as a replacement for the native events.)

This data would need to be collected by something though (a User Timing polyfill for the embedded DevTools backend? some other more explicit API we add that calls through to User Timing API for the browser case and logs in-memory for RN?) and shared from the backend to the frontend.

What about profiling locally? Perhaps the backend could gather this sample and periodically send it (encoded) to the frontend to be exported/downloaded. We'd probably only want to do this if the user started profiling (recording) in the UI. This would require us to add state for the new profiler (since it's currently read-only). But we plan to do this anyway for the browser extension (#22015).

This task is a placeholder for researching and proposing a way to add at least basic RN support.
Repository: pytorch
Issue: torch.scatter_: support index.size(d) > src.size(d)
Labels: good first issue,triaged,enhancement,module: scatter & gather ops

## 🚀 Feature
Teach [torch.Tensor.scatter_](https://pytorch.org/docs/stable/generated/torch.Tensor.scatter_.html) to handle `index.size(d) > src.size(d)`.

## Motivation

Currently, `torch.Tensor.scatter_` requires `index.size(d) <= src.size(d)` for all dimensions `d`, _unless_ `src` is float-valued. This constraint seems artificial.

```
import math
import torch

device = 'cuda'  # fails if device == 'cpu' (cf. #61854)

dim = 1
shape_src = 3, 5
shape_idx = 3, 7

index = torch.empty(shape_idx, dtype=torch.long, device=device).random_(0, shape_src[dim])

# succeeds
output = torch.zeros(shape_src, device=device)
output.scatter_(dim, index, 1.0, reduce='add')
print(output)

# desired usage
# output = torch.zeros(shape_src, device=device)
# values = torch.rand(shape_src, device=device)
# output.scatter_(dim, index, values, reduce='add')   # <-- failure
# print(output)
```

## Alternatives

An inefficient workaround is to chunk the index tensor:
```
output = torch.zeros(shape_src, device=device)
values = torch.ones(shape_src, device=device)
chunks = math.ceil(index.shape[dim] / output.shape[dim])
for index_chunk in torch.chunk(index, chunks, dim):
    output.scatter_(dim, index_chunk, values, reduce='add')
print(output)
```