sharjeelyunus/github-issues-dataset huggingface dataset:
Repository: godot
Issue: PanoramaSky created using Viewport texture is always rendered pink.
Labels: bug,topic:rendering,needs testing,topic:3d

### Godot version

master (f013315980ca13f4b66df29b5e32d3ecc310cb84)

### System information

Windows 11, Nvidia 1070

### Issue description

This test subviewport:
![image](https://user-images.githubusercontent.com/35220750/199052549-541d8181-5b74-491e-999f-1e2d952ddf8d.png)
Looks like it's not having any effect once passet as viewport texture, like this:
![image](https://user-images.githubusercontent.com/35220750/199052902-cc415b61-83a3-4743-82b2-e8aae1c35adf.png)

Notice that this happens even if I use both PR https://github.com/godotengine/godot/pull/64138 and https://github.com/godotengine/godot/pull/64152

Also, repro project shows no error or warning in any console or output.

### Steps to reproduce

Open repro project, run, observe the pink where there should be a procedural sky.

### Minimal reproduction project

[ViewportBug.zip](https://github.com/godotengine/godot/files/9902882/ViewportBug.zip)
Repository: pytorch
Issue: Add nondeterministic alert to `torch.Tensor.scatter()`
Labels: triaged,module: determinism

### 🐛 Describe the bug

``` python
torch.use_deterministic_algorithms(True)
input = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9])
index = torch.tensor([0, 1, 1, 1, 2])
src = torch.tensor([11, 12, 13, 14, 15])
result = torch.Tensor.scatter(input, 0, index, src)
print(result)

inputCuda = torch.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9]).cuda()
indexCuda = torch.tensor([0, 1, 1, 1, 2]).cuda()
srcCuda = torch.tensor([11, 12, 13, 14, 15]).cuda()
resultCuda = torch.Tensor.scatter(inputCuda, 0, indexCuda, srcCuda)
print(resultCuda)
```

Run this code and you will get different results on CPU and on GPU: 

```
tensor([11, 14, 15,  4,  5,  6,  7,  8,  9])
tensor([11, 12, 15,  4,  5,  6,  7,  8,  9], device='cuda:0')
```

The value on position 1 is overwritten for 3 times with 12, 13 and 14. It looks like CUDA overwrites it in a reversed order of indices. This is not reported even with `torch.use_deterministic_algorithms(True)`. 

Related: #55516, #50750

### Versions

Collecting environment information...
PyTorch version: 1.12.1+cu113
Is debug build: False
CUDA used to build PyTorch: 11.3
ROCM used to build PyTorch: N/A

OS: Ubuntu 18.04.6 LTS (x86_64)
GCC version: (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0
Clang version: 6.0.0-1ubuntu2 (tags/RELEASE_600/final)
CMake version: version 3.22.6
Libc version: glibc-2.26

Python version: 3.7.15 (default, Oct 12 2022, 19:14:55)  [GCC 7.5.0] (64-bit runtime)
Python platform: Linux-5.10.133+-x86_64-with-Ubuntu-18.04-bionic
Is CUDA available: True
CUDA runtime version: 11.2.152
CUDA_MODULE_LOADING set to: 
GPU models and configuration: GPU 0: Tesla T4
Nvidia driver version: 460.32.03
cuDNN version: Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.1.1
/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.1.1
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.21.6
[pip3] torch==1.12.1+cu113
[pip3] torchaudio==0.12.1+cu113
[pip3] torchsummary==1.5.1
[pip3] torchtext==0.13.1
[pip3] torchvision==0.13.1+cu113
[conda] Could not collect

cc @mruberry @kurtamohler
Repository: kubernetes
Issue: Add APF related response header to kube audit log
Labels: sig/api-machinery,kind/feature,sig/auth,triage/accepted

### What would you like to be added?

Today kube audit log does not include the APF related response header, so when debugging request throttling issue, users have to make actual request with the same username and/or namespace to be able to get `X-Kubernetes-Pf-Prioritylevel-Uid` and `X-Kubernetes-Pf-Flowschema-Uid`, then cross refernece what `flowschemas` and `prioritylevelconfiguration` the request was assigned to, in order to debug and tune APF for their API Server traffic.


### Why is this needed?

Add X-Kubernetes-Pf-Prioritylevel-Uid and X-Kubernetes-Pf-Flowschema-Uid to kube audit log to make it easy to debug and tune APF
Repository: pytorch
Issue: ProcessGroupNCCL watchdog can't catch NCCL comm initialization issues
Labels: oncall: distributed,triaged,module: c10d

### 🐛 Describe the bug

ProcessGroupNCCL watchdog is designed to catch hanging collectives and tear down the process, but in the special case of first collective, ProcessGroupNCCL initializes NCCL communicators, which can hang. Since no comm future object has been created, watchdog cannot handle this situation. 

### Versions

main

cc @mrshenli @pritamdamania87 @zhaojuanmao @satgera @gqchen @aazzolini @osalpekar @jiayisuse @H-Huang @kwen2501 @awgu
Repository: kubernetes
Issue: Deprecate PodNodeSelector admission plugin
Labels: sig/scheduling,kind/feature,needs-triage

### What would you like to be added?

Deprecate PodNodeSelector admission plugin and the associated annotation `scheduler.alpha.kubernetes.io/node-selector`

https://github.com/kubernetes/kubernetes/blob/master/plugin/pkg/admission/podnodeselector/admission.go

### Why is this needed?

This plugin has been in alpha for a long time and never graduated. There are other ways now to support a similar behavior using mutating webhooks.

/sig scheduling
Repository: pytorch
Issue: Python Dispatcher registrations beyond BackendSelect do nothing
Labels: triaged,module: dispatch,module: python dispatcher

### 🐛 Describe the bug

Take https://github.com/albanD/subclass_zoo/blob/main/use_cpu_for_rng.py and replace BackendSelect with CUDA. The test fails, because we never hit python dispatcher. Probably BackendSelect needs to restore the Python Dispatcher key before redispatching.

### Versions

master
Repository: transformers
Issue: Add in-layer TF Tokenizer to BPE tokenizers
Labels: WIP

### Feature request

As what we have with `TFBertTokenizer`, but with models that use Byte Pair Encoding (e.g. `TFT5Tokenizer`, `TFClipTokenizer`) etc...

They were implemented in `keras-nlp` (https://github.com/keras-team/keras-nlp/pull/389) and we can now bring them here. 

### Motivation

With that feature we will be able to serve almost every model with TF Serving, which will make it much easier to serve models, as we won't have to write handlers and custom servers.

Having TF BPE Tokenizers is (I think) the last barrier to make `transformers` fully TF Serving-compliant.

### Your contribution

I can submit a PR, but there are a huge lot of models for which we would need to do that, so I expect a large number of subtasks if you decide to go for it.

Also, as `keras-nlp` implemented it (https://github.com/keras-team/keras-nlp/pull/389), should we copy-paste the code for each tokenizer or import from `keras-nlp`, while keeping the reference to their repo?
Repository: electron
Issue: [Bug]: Have to blur focused webview before being able to focus a different webview
Labels: component/webview,bug :beetle:,status/confirmed,has-repro-gist,18-x-y,19-x-y,20-x-y,21-x-y,22-x-y,23-x-y

### Preflight Checklist

- [X] I have read the [Contributing Guidelines](https://github.com/electron/electron/blob/main/CONTRIBUTING.md) for this project.
- [X] I agree to follow the [Code of Conduct](https://github.com/electron/electron/blob/main/CODE_OF_CONDUCT.md) that this project adheres to.
- [X] I have searched the [issue tracker](https://www.github.com/electron/electron/issues) for a bug report that matches the one I want to file, without success.

### Electron Version

21.2.0

### What operating system are you using?

Windows

### Operating System Version

Windows 11 Enterprise 22H2

### What arch are you using?

x64

### Last Known Working Electron version

_No response_

### Expected Behavior

1. Have multiple `webview`
2. Call `.focus()` on one of them
3. Call `.focus()` on another

Expected: the other `webview` receives a focus event.


### Actual Behavior

It doesn't. the expected only happens if you first call `.blur()` on whatever `webview` currently has focus.

### Testcase Gist URL

https://gist.github.com/MRayermannMSFT/ab44ba14685c1930023c90a5d740b638

### Additional Information

My gist  has some buttons you can use to play around with focusing various `webview`/elements in the renderer DOM. The bug can most easily be seen to repro by using either of the "(no blur)" buttons. The focused counts of each thing should go up, but in actuality, whatever `webview` is focused second does not have its focus count incremented.
Repository: godot
Issue: FXAA breaks with Resolution Scale
Labels: bug,topic:rendering,topic:3d

### Godot version

4.0 Beta 3

### System information

Windows 11, Vulkan, GTX 1050 Ti 516.94

### Issue description

FXAA seems to calculate Pixel size by Resolution, without taking into account Resolution Scale, which creates artifacts especially clear at very low resolution scales.

### Steps to reproduce

Turn on FXAA
1) Set Resolution Scale to 0.5 - Observe result
2) Set Resolution Scale to 1.0 and set Viewport to Half Res - Observe result
Observe how cases 1&2 clearly don't have the same result with FXAA

### Minimal reproduction project

_No response_
Repository: stable-diffusion-webui
Issue: [Feature Request]: Allow batch-size image generation through the API
Labels: enhancement

### Is there an existing issue for this?

- [X] I have searched the existing issues and checked the recent builds/commits

### What would your feature do ?

Allow generating multiple images at the same time through batching.

### Proposed workflow

It's already almost possible, one missing problem is that the API forces `prompt` to be a `str` while for webui's own usage it can be either a string or a list of strings. But you can't pass a list of prompts because it won't be a `str`.

An alternative way would be to just add yet another field to the API or rework it so that `all_prompts` is not touched if it already exists in the request
### Additional information

Relevant lines where prompt is forced to be a `str` - https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/processing.py#L81-L85

Code that shows that prompt can be a list - https://github.com/AUTOMATIC1111/stable-diffusion-webui/blob/master/modules/processing.py#L460 

Example request that fails currently:
```bash
curl --request POST --header 'content-type: application/json' --url 'http://localhost:7860/sdapi/v1/txt2img' --data '{"steps":20,"cfg_scale":7,"width":512,"height":512,"prompt":["big dog","big dog"], "seed":1,"batch_size":2}'
```
If you instead use `all_prompts` it _will_ generate two images, but they'll be generated with empty prompt because `all_prompts` is always overwritten.
Repository: neovim
Issue: Builtin package manager for plugins
Labels: enhancement,plugin



It would be so nice if neovim came with a build in package manager. Currently I'm using vim-plug but this has to be installed before my vimrc works. 

https://github.com/junegunn/vim-plug
Repository: pytorch
Issue: Add eq, to, masked_select, index_select, narrow to nested tensors
Labels: triaged,module: nestedtensor

### 🚀 The feature, motivation and pitch

I am working on better implementation of SQL operators in [TQP](https://www.vldb.org/pvldb/vol15/p2811-he.pdf) and I am exploring how to leverage nested tensors for this. However in order to use nested tensors in practice without bloating the memory we would love to have support for `eq`, `to`, `masked_select`, `index_select` and `narrow`.

### Alternatives

Use `to_padded_tensor` but this will increase memory pressure.

### Additional context

_No response_

cc @cpuhrsch @jbschlosser @bhosmer @drisspg @mikaylagawarecki
Repository: deno
Issue: Add support for Priority Hints
Labels: suggestion,ext/fetch

The Priority Hints spec is in the process of being merged into the fetch and HTML specs and allows for site developers to hint at the relative fetch priority for various resources.

The main changes are:
* Adds a "priority" to fetch's Request and RequestInit classes that uses a string enum of "auto", "high" or "low"
* Adds a "fetchPriority" attribute to the IMG, PICTURE, SCRIPT, LINK and IFRAME html elements with the same enum values

Existing spec: https://wicg.github.io/priority-hints/

This was shipped in Chromium 103 and Mozilla is working on an implementation so it is now in the process of being moved into the HTML and fetch specs.

For deno, the main use case is on the fetch interface to pass through prioritization to the underlying networking stack.
Repository: next.js
Issue: Next 13 example from docs of Use() together with fetch inside client component fires indefinitely
Labels: bug

### Verify canary release

- [X] I verified that the issue exists in the latest Next.js canary release

### Provide environment information

Operating System:
      Platform: darwin
      Arch: arm64
      Version: Darwin Kernel Version 22.1.0: Sun Oct  9 20:15:09 PDT 2022; root:xnu-8792.41.9~2/RELEASE_ARM64_T6000
    Binaries:
      Node: 19.0.0
      npm: 8.19.2
      Yarn: 1.22.19
      pnpm: N/A
    Relevant packages:
      next: 13.0.1-canary.4
      eslint-config-next: N/A
      react: 18.3.0-next-2ac77aab9-20221029
      react-dom: 18.2.0

### What browser are you using? (if relevant)

_No response_

### How are you deploying your application? (if relevant)

next dev

### Describe the Bug

When following the [example ](https://beta.nextjs.org/docs/data-fetching/fetching#example-fetch-and-use-in-client-components)provided in the docs for fetching data inside a client component using the new Use() hook. The fetch function falls into an infinity loop and fires until the client session(browser tab) is closed.

The endpoint of the fetch also gets hit infinity amount of times as well as can be seen in the stackblitz provided. A simple counter in the pages/api/hello.ts file that increments and logs on each hit. This was additionally tested with a simple node server with the same result. (so not unique to when fetching from a next api endpoint) 

![image](https://user-images.githubusercontent.com/62900484/199116737-7880cbcc-01e6-43a1-9fbb-55f15dd676b7.png)

### Expected Behavior

For the Use() hook to fire the fetch function once.

### Link to reproduction

https://stackblitz.com/edit/vercel-next-js-snerz6?file=app/page.tsx

### To Reproduce

Run `next dev` and go to the `/` page. See the api hits in the log.
ctrl+c in the terminal to stop.
Repository: pytorch
Issue: Refactor `torch.return_types.topk` to behave like a `namedtuple` or a `dict`
Labels: triaged,enhancement,module: sorting and selection

### 🚀 The feature, motivation and pitch

When you use `torch.topk`, it returns a namedtuple-like object of type `torch.return_types.topk`. Unfortunately, it is not possible to use it as a dictionary. For example:

```python
import torch

x = torch.topk(torch.randn(10, 10), k=5)
assert isinstance(x, torch.return_types.topk)

# This is okay:
x.indices

# This will give an error:
x['indices']
```

Ideally, accessing the returned item as a dict would make it more useful and reduce the amount of post-processing needed. Alternatively, it could behave like a namedtuple by allowing the use of the documented method [`_asdict()`](https://docs.python.org/3/library/collections.html#collections.somenamedtuple._asdict) (which is officially supported and named this way to avoid collision). For example:

```python
# Potential improvement:
di = x._asdict()
di['indices']  # Should show the indices
di['values']  # should show the values
```

### Alternatives

_No response_

### Additional context

_No response_
Repository: kubernetes
Issue: ContainerPorts SSA key is insufficiently unique - SSA fails with "duplicate" error when the contents are valid
Labels: sig/network,sig/node,sig/api-machinery,triage/accepted

https://github.com/kubernetes/kubernetes/pull/113245#discussion_r1009911070

Validation allows:

```
        ports:
        - containerPort: 93
          hostPort: 9376
        - containerPort: 93
          hostPort: 7693
```

but server-side apply does not.

As of 1.29 we can SSA unrelated fields in an object with "dup ports" and even do SSA "repairs" to those fields.  What we can't do is SSA them with this state in the apply.  Important: this is  allowed (more by accident than design) and semantically unambiguous.

The defined SSA key is not sufficiently unique (`ontainerPort + protocol` -- needs to include `hostPort`). To evolve the key is tricky, but the only real alternative is atomic, which has it's own issues.

Is it worth trying to find a safe way to expand an SSA key?
Repository: pytorch
Issue: AvgPool2D output shapes are inconsistent when ceil_mode=True
Labels: triaged,module: correctness (silent),module: pooling

### 🐛 Describe the bug

Certain inputs produce unexpected output shapes from [AvgPool2D](https://pytorch.org/docs/stable/generated/torch.nn.AvgPool2d.html) when `ceil_mode=True`.

## Example 1

```python
>>> t = torch.ones((1, 3, 32, 32))
>>> pool = torch.nn.AvgPool2d(8, stride=4, ceil_mode=True)
>>> pool(t).shape
torch.Size([1, 3, 7, 7])
```

This is surprising behavior because there are 8 possible windows with `stride=4` (not 7) for both height and width, each starting from the following indexes:
```
0
4
8
12
16
20
24
28
```

The final window at index 28 is a partial window, but should be included when `ceil_mode=True`, otherwise `ceil_mode` seems to serve no purpose. (This window does not start in a padding region as there is no padding used here.)

## Example 2
```python
>>> t = torch.ones((1, 3, 4, 4))
>>> pool = torch.nn.AvgPool2d(3, stride=3, ceil_mode=True)
>>> pool(t).shape
torch.Size([1, 3, 2, 2])
```

With `stride=3`, we have 2 potential start positions:
```
0
3
```

Torch chooses to use both of them. But based on the behavior observed in Example 1 (where the last frame was discarded) we would have expected output shape of `[1, 3, 1, 1]`.

### Versions

PyTorch version: 1.12.1+cu102
Is debug build: False
CUDA used to build PyTorch: 10.2
ROCM used to build PyTorch: N/A

OS: Amazon Linux 2 (x86_64)
GCC version: (GCC) 7.3.1 20180712 (Red Hat 7.3.1-15)
Clang version: Could not collect
CMake version: version 3.18.4
Libc version: glibc-2.2.5

Python version: 3.7.10 (default, Jun  3 2021, 00:02:01)  [GCC 7.3.1 20180712 (Red Hat 7.3.1-13)] (64-bit runtime)
Python platform: Linux-5.4.209-129.367.amzn2int.x86_64-x86_64-with-glibc2.2.5
Is CUDA available: False
CUDA runtime version: No CUDA
CUDA_MODULE_LOADING set to: N/A
GPU models and configuration: No CUDA
Nvidia driver version: No CUDA
cuDNN version: No CUDA
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] numpy==1.20.0
[pip3] torch==1.12.1
[pip3] torch-neuron==1.12.1.2.0.0.0
[conda] Could not collect

cc @ezyang @gchanan @zou3519
Repository: PowerToys
Issue: CSV Import
Labels: Idea-Enhancement,Product-Hosts File Editor

### Description of the new feature / enhancement

Import hosts file entries from CSV file: https://github.com/microsoft/PowerToys/pull/20462#issuecomment-1260017316

### Scenario when this would be used?

Bulk import of entries.

### Supporting information

_No response_
Repository: PowerToys
Issue: Multiple hosts file support
Labels: Idea-Enhancement,Product-Hosts File Editor

### Description of the new feature / enhancement

Multiple hosts files that could be switched out.

### Scenario when this would be used?

Say I have a group of hosts I only need to map to different IP’s periodically.
I’d like to be able to swap out the current hosts file for that file and even make changes to it to save back.

### Supporting information

_No response_
Repository: pytorch
Issue: Conv2d is not deterministic when input tensor has different strides
Labels: module: convolution,triaged

### 🐛 Describe the bug

I would expect that if I pass two identical tensors through a Conv2d in deterministic mode, they would produce an identical output. However this is not the case if the tensors are identical in every way except their stride - in that case the output is different. A difference in stride doesn't cause the two tensors to not be `torch.equal`, which is especially confusing - two "equal" tensors can produce a different output.

```python
import os
os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
import torch
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.benchmark = False


conv = torch.nn.Conv2d(3, 3, kernel_size=2).cuda()
in_a = torch.randn(4, 3, 64, 64).cuda()
out_a = conv(in_a)
in_b = torch.clone(in_a.permute(0, 2, 3, 1), memory_format=torch.contiguous_format).permute(0, 3, 1, 2)
out_b = conv(in_b)
print(in_a.stride())  # (12288, 4096, 64, 1)
print(in_b.stride())  # (12288, 1, 192, 3)
print(torch.equal(in_a, in_b)) # True
print(torch.equal(out_a, out_b))  # False
```

Ideally this should be fixed such that differences in stride don't affect the output of otherwise-deterministic operations, but at minimum the page on [reproducibility](https://pytorch.org/docs/stable/notes/randomness.html) should mention this. 

### Versions

Collecting environment information...
PyTorch version: 1.13.0+cu116
Is debug build: False
CUDA used to build PyTorch: 11.6
ROCM used to build PyTorch: N/A

OS: Ubuntu 20.04.5 LTS (x86_64)
GCC version: (Ubuntu 9.4.0-1ubuntu1~20.04.1) 9.4.0
Clang version: Could not collect
CMake version: version 3.16.3
Libc version: glibc-2.31

Python version: 3.9.5 (default, Nov 23 2021, 15:27:38)  [GCC 9.3.0] (64-bit runtime)
Python platform: Linux-5.13.0-30-generic-x86_64-with-glibc2.31
Is CUDA available: True
CUDA runtime version: Could not collect
CUDA_MODULE_LOADING set to: LAZY
GPU models and configuration:
GPU 0: NVIDIA GeForce RTX 3090
GPU 1: NVIDIA GeForce RTX 3090
GPU 2: NVIDIA GeForce RTX 3090

Nvidia driver version: 470.103.01
cuDNN version: Could not collect
HIP runtime version: N/A
MIOpen runtime version: N/A
Is XNNPACK available: True

Versions of relevant libraries:
[pip3] geotorch==0.2.0
[pip3] mypy==0.971
[pip3] mypy-boto3-ec2==1.17.41.0
[pip3] mypy-boto3-s3==1.17.41.0
[pip3] mypy-extensions==0.4.3
[pip3] numpy==1.22.2
[pip3] pytorch-lightning==1.7.3
[pip3] torch==1.13.0+cu116
[pip3] torchmetrics==0.7.0
[pip3] torchvision==0.13.0+cu113
[conda] Could not collect
Repository: rust
Issue: Tracking Issue for integrating `Error` trait with panic interfaces
Labels: T-libs-api,C-tracking-issue

Feature gate: `#![feature(error_in_panic)]`

This is a tracking issue for integrating the `Error` trait with `panic` interfaces.
 
This allows panic handlers to access the richer `Error` trait interface when reporting panics that have a runtime error as their source.

### Public API

<!--
For most library features, it'd be useful to include a summarized version of the public API.
(E.g. just the public function signatures without their doc comments or implementation.)
-->

```rust
/// core::panic

impl PanicInfo<'_> {
    pub fn source(&self) -> Option<&(dyn Error + 'static)>;
}
```

### Steps / History

- [ ] ACP: https://github.com/rust-lang/libs-team/issues/124
- [ ] Implementation: #103169
- [ ] Final comment period (FCP)[^1]
- [ ] Stabilization PR

### Unresolved Questions

- [ ] How should this actually be invoked? What should be the final interface?
- [ ] How do we integrate this with `expect` and `unwrap`? (More detail in the [ACP](https://github.com/rust-lang/libs-team/issues/124))

[^1]: https://std-dev-guide.rust-lang.org/feature-lifecycle/stabilization.html