sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: godot
Issue: Implementing a generic interface with a type paramter of a generic class's inner class causes a build error
Labels: bug,topic:dotnet

**Godot version:** 
Godot Engine v3.2.2.rc.mono.custom_build.36a30f681 
Commit: 36a30f681fc8c3256829616f32ee452b15674752
Also encountered on a 3.2.1-stable build, earlier versions not tested.

**OS/device including version:** 5.4.24-1-MANJARO x86_64 GNU/Linux

**Issue description:**
In C#, implementing a generic interface with the inner-member of a generic class causes godot to raise an error on build per source file. These errors do not stop the build from completing and the resulting build appears to work.

 Error message:
`modules/mono/glue/gd_glue.cpp:250 - Failed to determine namespace and class for script: <script name>.cs. Parse error: Unexpected token: .`

**Steps to reproduce:**
1. Create generic class with a public inner class
2. Implement a generic interface, using that inner class as the generic parameter 
3. Press build, note error in output but a functional build.

**Minimal reproduction project:**
[Test.tar.gz](https://github.com/godotengine/godot/files/4469842/Test.tar.gz)
Repository: rust
Issue: Exploit mitigations applied by default are not documented
Labels: C-enhancement,A-security,T-compiler,A-docs,PG-exploit-mitigations

There seems to be no documentation on exploit mitigations in Rust, specifically:

1. What exploit mitigations are supported?
1. What mitigations are enabled by default?
    1. Is that answer different if building with `cargo` instead of `rustc` directly?
    1. Does that vary by platform?
1. How to enable/disable specific mitigations?

This is relevant not only for security assessment, but also for performance comparison against other languages - both languages need to have the same exploit mitigations enabled for an apples-to-apples comparison.
Repository: PowerToys
Issue: [FancyZones] Consider caching results of IsProcessOfWindowElevated
Labels: Product-FancyZones,Area-Quality,Priority-3

Based on https://github.com/microsoft/PowerToys/pull/2103#discussion_r407515294, we might want to consider caching the results of `IsProcessOfWindowElevated`. It might give us a performance gain, but we must profile how much `IsProcessOfWindowElevated` costs first. If it's worth optimizing, we need to introduce the cache and measure its impact, so we don't actually decrease the performance while increasing the complexity.
Repository: bitcoin
Issue: untrust vs unsafe ?
Labels: Bug

Some RPC commands like listunspent talks about "unsafe" funds and others like getbalances about "untrust"
If there isn't diff between them would be good rename it with an unique name
Repository: TypeScript
Issue: Give `{}` some other type than `{}` so it doesn't cause subtype reduction problems with primitives
Labels: Suggestion,Needs Proposal

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**TypeScript Version:**  3.9.0-beta

<!-- Search terms you tried before logging this (so others can find this issue more easily) -->
**Search Terms:**
Array 
**Code**

```ts
let a = [{a:1},1,2,3] // OK, Expected (number | { a: number; })[]
let b = [{},1,2,3] // Error,  Expected: `({}|1)[]` Actual: `{}[]`
```


**Playground Link:**

[Playground Link](https://www.typescriptlang.org/play?ts=3.9.0-beta#code/FAGwpgLgBAhlC8UDaBvGAuAjAXwDSdwCZcBmAXSgHpKoB5AaVygFEAPABzAGMIwATKAAoAdgFcAtgCMwAJygAfKCljooYqbIDcUbAEokZUJCiSEyFHgLFyVGsxkyA9jKYsO3Xn1UADQRfmY+mTeUACCPKIwID4WBt5AA)

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Repository: excalidraw
Issue: Cache fonts for offline usage
Labels: enhancement,help wanted,font

We recently added support [for offline usage](https://github.com/excalidraw/excalidraw/pull/1286) but because we need to preload fonts and we add them directly to the `index.html`, they are not being included in Webpacks' pipeline and thus not being included in the service-worker that caches the assets.

We need to find a way to both preload those fonts and cache them for offline usage. At the moment, we haven't eject our CRA and we don't want to do so.
Repository: javascript
Issue: What is the point of the "Types" section of the guide?
Labels: question

Greetings,

The second section of the guide is "References", which is a prescription to use "const" over "var". This section showcases a "bad" code example and a "good" code example. So far, so good.

The first section of the guide is "Types", which, as far as I can tell, doesn't offer any prescriptions at all. It is simply educating the reader on the how variables work in the JavaScript programming language. Furthermore, the section does not show any "bad" code examples or "good" code examples.

Is there a particular reason that this section is included in the style guide? It seems notably out of place - every other section seems to impart a specific coding prescription. There is a time and a place to teach JavaScript newbies the basics on how the language works, and it doesn't seem like it should be in a style guide.
Repository: flutter
Issue: Update all example/integration test/samples .gitignores to not track .last_build_id
Labels: team,tool,P2,team-tool,triaged-tool

https://github.com/flutter/flutter/pull/54428 added it to the generated gitignore, didn't actually update any of the example or integration test .gitignores.  Running the build_tests shard, for example, left a bunch of .last_build_id in my working copy.
Repository: pytorch
Issue: Quantized _out functions don't follow same conventions as other out functions in the codebase
Labels: oncall: quantization,low priority,triaged,better-engineering

Currently they're defined like:

```
.op("quantized::add_out(Tensor qa, Tensor qb, Tensor(a!) out)"
     "-> Tensor(a!) out",
    c10::RegisterOperators::options()
      .aliasAnalysis(at::AliasAnalysisKind::FROM_SCHEMA)
      .kernel<QAddOut</*ReLUFused=*/false>>(DispatchKey::QuantizedCPU))
```

However, a standard out function looks like this:

```
- func: angle(Tensor self) -> Tensor
  use_c10_dispatcher: full
  variants: function, method
  supports_named_tensor: True

- func: angle.out(Tensor self, *, Tensor(a!) out) -> Tensor(a!)
  supports_named_tensor: True
```

cc @jerryzh168 @jianyuh @raghuramank100 @jamesr66a @vkuzo @jgong5 @Xia-Weiwen @leslie-fang-intel @dzhulgakov @kevinbchen (who added the alias analysis annotations to these functions) and @z-a-f (who appears to have originally added these out variants at https://github.com/pytorch/pytorch/pull/23971 )
Repository: go
Issue: cmd/compile: non-symmetric inline cost when using named vs non-named returns
Labels: NeedsDecision

### What version of Go are you using (`go version`)?

<pre>
$ go version
go version go1.14.2 windows/amd64
</pre>

### Does this issue reproduce with the latest release?
Yes

### What operating system and processor architecture are you using (`go env`)?

<details><summary><code>go env</code> Output</summary><br><pre>
$ go env
set GO111MODULE=
set GOARCH=amd64
set GOBIN=
set GOCACHE=C:\Users\Dante\AppData\Local\go-build
set GOENV=C:\Users\Dante\AppData\Roaming\go\env
set GOEXE=.exe
set GOFLAGS=
set GOHOSTARCH=amd64
set GOHOSTOS=windows
set GOINSECURE=
set GONOPROXY=
set GONOSUMDB=
set GOOS=windows
set GOPATH=C:\Users\Dante\go
set GOPRIVATE=
set GOPROXY=https://proxy.golang.org,direct
set GOROOT=c:\go
set GOSUMDB=sum.golang.org
set GOTMPDIR=
set GOTOOLDIR=c:\go\pkg\tool\windows_amd64
set GCCGO=gccgo
set AR=ar
set CC=gcc
set CXX=g++
set CGO_ENABLED=1
set GOMOD=C:\Users\Dante\Documents\Code\go3mf\go.mod
set CGO_CFLAGS=-g -O2
set CGO_CPPFLAGS=
set CGO_CXXFLAGS=-g -O2
set CGO_FFLAGS=-g -O2
set CGO_LDFLAGS=-g -O2
set PKG_CONFIG=pkg-config
set GOGCCFLAGS=-m64 -mthreads -fmessage-length=0 -fdebug-prefix-map=C:\Users\Dante\AppData\Local\Temp\go-build018008438=/tmp/go-build -gno-record-gcc-switches
</pre></details>

### What did you do?

Code: https://play.golang.org/p/bM-akhk0QXG
`go build -gcflags "-m=2"`

### What did you expect to see?
`NewTriangleInline` is equivalent to `NewTriangleNoInline`, the only difference is that the first uses a named return and the second one doesn´t, therefore I would expect that both have the same inline cost.

```go
func NewTriangleInline(v1, v2, v3 uint32) (t Triangle) {
	t.SetIndex(0, v1)
	t.SetIndex(1, v2)
	t.SetIndex(2, v3)
	return
}
```
```go
func NewTriangleNoInline(v1, v2, v3 uint32) Triangle {
	var t Triangle
	t.SetIndex(0, v1)
	t.SetIndex(1, v2)
	t.SetIndex(2, v3)
	return t
}
```

### What did you see instead?
```
.\foo.go:11:6: can inline NewTriangleInline as: func(uint32, uint32, uint32) Triangle { t.SetIndex(0, v1); t.SetIndex(1, v2); t.SetIndex(2, v3); return  }
.\foo.go:18:6: cannot inline NewTriangleNoInline: function too complex: cost 84 exceeds budget 80
```
Repository: go
Issue: runtime: sema: many many goroutines queueing up on many many distinct addresses -> slow
Labels: Performance,NeedsInvestigation,compiler/runtime

Hello up there. [libcsp](https://libcsp.com/performance/) claims to be 10x faster on a [benchmark](https://github.com/shiyanhui/libcsp/blob/ea0c5a41/benchmarks/sum.go#L28-L53) involving `sync.WaitGroup` for a divide-and-conqueror summation program. I've analyzed the [profile](https://lab.nexedi.com/kirr/misc/raw/443f9bf4/libcsp/pprof001.svg) and most of the time is being spent in `runtime.(*semaRoot).dequeue` and `runtime.(*semaRoot).queue` triggered by calls to `sync.WaitGroup` `.Done` and `.Wait`. The benchmark uses many (~ Ngoroutines) different WaitGroups and semaphores simultaneously.

Go commit https://github.com/golang/go/commit/45c6f59e1fd9 says

> There is still an assumption here that in real programs you don't have many many goroutines queueing up on many many distinct addresses. If we end up with that problem, we can replace the top-level list with a treap.

It seems the above particular scenario is being hit in this benchmark.


--------

<!--
Please answer these questions before submitting your issue. Thanks!
For questions please use one of our forums: https://github.com/golang/go/wiki/Questions
-->

### What version of Go are you using (`go version`)?

<pre>
$ go version
go version go1.14.2 linux/amd64
</pre>

### Does this issue reproduce with the latest release?

Yes

### What operating system and processor architecture are you using (`go env`)?

<details><summary><code>go env</code> Output</summary><br><pre>
$ go env
GO111MODULE="off"
GOARCH="amd64"
GOBIN=""
GOCACHE="/home/kirr/.cache/go-build"
GOENV="/home/kirr/.config/go/env"
GOEXE=""
GOFLAGS=""
GOHOSTARCH="amd64"
GOHOSTOS="linux"
GOINSECURE=""
GONOPROXY=""
GONOSUMDB=""
GOOS="linux"
GOPATH="/home/kirr/src/neo:/home/kirr/src/tools/go/g.env"
GOPRIVATE=""
GOPROXY="https://proxy.golang.org,direct"
GOROOT="/home/kirr/src/tools/go/go"
GOSUMDB="sum.golang.org"
GOTMPDIR=""
GOTOOLDIR="/home/kirr/src/tools/go/go/pkg/tool/linux_amd64"
GCCGO="/usr/bin/gccgo"
AR="ar"
CC="gcc"
CXX="g++"
CGO_ENABLED="1"
GOMOD=""
CGO_CFLAGS="-g -O2"
CGO_CPPFLAGS=""
CGO_CXXFLAGS="-g -O2"
CGO_FFLAGS="-g -O2"
CGO_LDFLAGS="-g -O2"
PKG_CONFIG="pkg-config"
GOGCCFLAGS="-fPIC -m64 -pthread -fmessage-length=0 -fdebug-prefix-map=/tmp/go-build136588881=/tmp/go-build -gno-record-gcc-switches"
</pre></details>

### What did you do?

<!--
If possible, provide a recipe for reproducing the error.
A complete runnable program is good.
A link on play.golang.org is best.
-->

Ran benchmarks in libcsp.


### What did you expect to see?

Libcsp and Go versions comparable in terms of speed.

### What did you see instead?

Go version 10x slower.
Repository: pytorch
Issue: DDP should divide bucket contents by the number of global replicas instead of world size
Labels: oncall: distributed,triaged

Currently, DDP divides the bucket content by world size before doing allreduce:

https://github.com/pytorch/pytorch/blob/ddf5755ff832b3115af3c57d0203d49c9c2308aa/torch/csrc/distributed/c10d/reducer.cpp#L368-L375

However, the reducer launched the allreduce on all model replicas. 

https://github.com/pytorch/pytorch/blob/ddf5755ff832b3115af3c57d0203d49c9c2308aa/torch/csrc/distributed/c10d/reducer.cpp#L408-L427

In this case, if there is a DDP process that operates on multiple devices, the result would be wrong. Because dividing by world size does not lead to the average of all gradients, instead it should divide by the number of replicas. 

### Solution

DDP needs to do a round a allgather in the constructor to collect the total number of module replicas globally and use that number to divide the bucket contents. 

cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @xush6528 @osalpekar
Repository: pytorch
Issue: XNNPACK operators are not actually registered under xnnpack namespace
Labels: triaged,better-engineering,module: xnnpack

Here's the registration line:

```
        .op("prepacked::linear_clamp_prepack(Tensor W, Tensor? B=None, "
            "Scalar? output_min=None, Scalar? output_max=None) "
            "-> __torch__.torch.classes.xnnpack.LinearOpContext",
            torch::RegisterOperators::options()
            .aliasAnalysis(at::AliasAnalysisKind::PURE_FUNCTION)
            .kernel<decltype(createLinearClampPrePackOpContext),
                createLinearClampPrePackOpContext>(
                    DispatchKey::CPU))
```

Doesn't look like xnnpack namespace to me.

cc @kimishpatel
Repository: pytorch
Issue: Restructure test_c10d.py and test_distributed.py
Labels: oncall: distributed,triaged,better-engineering

Collective communication and DDP tests live in both `test_c10d.py` and `test_distributed.py`, making it confusing which file we should add a new test into. We should restructure them into `test_c10d.py` only for collective communications and `test_ddp.py` only for `DistributedDataParallel`.

cc @pietern @mrshenli @pritamdamania87 @zhaojuanmao @satgera @rohan-varma @gqchen @aazzolini @xush6528 @osalpekar
Repository: flutter
Issue: Migrate away from rapidjson
Labels: engine,P2,team-engine,triaged-engine

Rapidjson had its last versioned release 3 years ago, its primary maintainer is no longer active and it has failed to pass an internal security review. Its internal use is frozen and the recommendation is to move to gRPC's json parser: https://github.com/grpc/grpc/blob/master/src/core/lib/json/json.h
Repository: pytorch
Issue: Drop _stacklevel from argspecs of F.softmax, F.softmin, F.log_softmax (for implicit dim has been long deprecated)
Labels: module: docs,triaged

https://pytorch.org/docs/master/nn.functional.html?highlight=softmax#torch.nn.functional.softmax

If it's unused or has some special undocumented meaning, I think it's useful to explain it explicitly in Parameters section
Repository: vscode
Issue: [folding] mode to have region based folding only
Labels: feature-request,editor-folding

<!-- ⚠️⚠️ Do Not Delete This! feature_request_template ⚠️⚠️ -->
<!-- Please read our Rules of Conduct: https://opensource.microsoft.com/codeofconduct/ -->
<!-- Please search existing issues to avoid creating duplicates. -->

<!-- Describe the feature you'd like. -->
Is it possible to remove folding for the language, but leave it only for writing keywords (for example in C# #region #endregion)
Repository: TypeScript
Issue: Update Imports On File Move can create invalid and/or unexpected tsconfig changes
Labels: Bug,Domain: TSServer

This happens frequently in a monorepo / project references setup when you move a file from one project to another. If the file you’re moving was included via an `include` glob, the tsconfig will get an additional entry in `include` to the file’s new location:

![MicrosoftTeams-image (4)](https://user-images.githubusercontent.com/3277153/79172294-24e2a680-7da9-11ea-8216-46f0cfef54c0.png)

This is not only unexpected, but can also be invalid if `rootDir` is set. I think I’d describe my expected behavior as "moving a file should update tsconfig.json if and only if the original filename was explicitly listed in the tsconfig (not just covered by a glob or directory) and the new filename is inside (rootDir ?? directory of tsconfig)."
Repository: pytorch
Issue: Typecasting issue in MSELoss
Labels: module: loss,triaged,module: type promotion

## 🐛 Bug

Cannot backprop through MSELoss when it is provided with different dtypes.

## To Reproduce

Steps to reproduce the behavior:

```py
import torch


class SimpleModel(torch.nn.Module):
    def __init__(self):
        super().__init__()

        self.layer = torch.nn.Linear(1, 1)

    def forward(self, x):
        return self.layer(x)


model = SimpleModel().double()
X = torch.rand(100, 1).double()
Y = torch.rand(100, 1).float()

optimizer = torch.optim.Adam(model.parameters())
model.train()
optimizer.zero_grad()

loss = torch.nn.MSELoss()(model(X), Y)  # leads to failure
# loss = ((model(X) - Y) ** 2).mean()  # works

loss.backward() # fails here
optimizer.step()
```

> Traceback (most recent call last):
>   File "foo.py", line 29, in <module>
>     loss.backward()
>   File "/home/twoertwe/miniforge3/envs/panama/lib/python3.8/site-packages/torch/tensor.py", line 195, in backward
>     torch.autograd.backward(self, gradient, retain_graph, create_graph)
>   File "/home/twoertwe/miniforge3/envs/panama/lib/python3.8/site-packages/torch/autograd/__init__.py", line 97, in backward
>     Variable._execution_engine.run_backward(
> RuntimeError: expected dtype Double but got dtype Float

<!-- If you have a code sample, error messages, stack traces, please provide it here as well -->

## Expected behavior

MSELoss either throws an error or it should be able to deal with mixed dtypes.

## Environment

```
Collecting environment information...
PyTorch version: 1.4.0
Is debug build: No
CUDA used to build PyTorch: 10.1

OS: Ubuntu 16.04.6 LTS
GCC version: (Ubuntu 9.2.1-17ubuntu1~16.04) 9.2.1 20191102
CMake version: version 3.15.1

Python version: 3.8
Is CUDA available: Yes
CUDA runtime version: 10.2.89
GPU models and configuration: GPU 0: GeForce RTX 2080 Ti
Nvidia driver version: 440.64
cuDNN version: /usr0/local/cuda-9.0/lib64/libcudnn.so.7.0.5

Versions of relevant libraries:
[pip] hamiltorch==0.3.1.dev3
[pip] numpy==1.18.1
[pip] torch==1.4.0
[pip] torchvision==0.5.0
[conda] cudatoolkit               10.1.243             h6bb024c_0  
[conda] hamiltorch                0.3.1.dev3               pypi_0    pypi
[conda] mkl                       2020.0                      166    conda-forge
[conda] numpy                     1.18.1           py38h8854b6b_1    conda-forge
[conda] pytorch                   1.4.0           py3.8_cuda10.1.243_cudnn7.6.3_0    pytorch
[conda] torchvision               0.5.0                py38_cu101    pytorch
```
Repository: go
Issue: x/net/icmp: listen icmp in Windows not work properly
Labels: OS-Windows,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
-->

### What version of Go are you using (`go version`)?

<pre>
$ go version
go version go1.13.8 darwin/amd64
</pre>

### Does this issue reproduce with the latest release?
yes


### 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="amd64"
GOBIN=""
GOCACHE="/Users/censored/Library/Caches/go-build"
GOENV="/Users/censored/Library/Application Support/go/env"
GOEXE=""
GOFLAGS=""
GOHOSTARCH="amd64"
GOHOSTOS="darwin"
GONOPROXY=""
GONOSUMDB=""
GOOS="darwin"
GOPATH="/Users/censored/go"
GOPRIVATE=""
GOPROXY="https://proxy.golang.org,direct"
GOROOT="/usr/local/Cellar/go/1.13.8/libexec"
GOSUMDB="sum.golang.org"
GOTMPDIR=""
GOTOOLDIR="/usr/local/Cellar/go/1.13.8/libexec/pkg/tool/darwin_amd64"
GCCGO="gccgo"
AR="ar"
CC="clang"
CXX="clang++"
CGO_ENABLED="1"
GOMOD="/Users/censored/go/src/srt/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 -m64 -pthread -fno-caret-diagnostics -Qunused-arguments -fmessage-length=0 -fdebug-prefix-map=/var/folders/4s/1bq_2censoredw0000gn/T/go-build488770590=/tmp/go-build -gno-record-gcc-switches -fno-common"
</pre></details>

### What did you do?
I write a func to detect who ping to my computer
<pre>
conn, err := icmp.ListenPacket("ip4:icmp", "0.0.0.0")
	if err != nil {
		log.Fatalf("listen err, %s", err)
	}
	defer conn.Close()

	if err != nil {
		 return
	}
	bytes := make([]byte, 512)
	for {
		fmt.Println("recv")
		n,_, err := conn.ReadFrom(bytes)
		if err != nil {
			fmt.Println(err.Error())
			continue
		}
		fmt.Println(n)
	}
</pre>
<!--
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?

fmt.Println(n) every ping.

### What did you see instead?
sometime it work, sometime it didn't work.
PoC video: <i>https://youtu.be/AyQDH9AQSRc</i>